diff --git a/0.download_data/README.md b/0.download_data/README.md index 1c5f71cf..0ebe9ce3 100644 --- a/0.download_data/README.md +++ b/0.download_data/README.md @@ -2,16 +2,29 @@ In this module, we present our method for downloading nucleus morphology data. -## Download/Process Data +### Download/Preprocess Data -Complete instructions for data download and processing can be found at: https://github.com/WayScience/mitocheck_data +Complete instructions for data download and preprocessing can be found at: https://github.com/WayScience/mitocheck_data -## Usage +### Usage -In this repository, all training data is compiled from version controlled data from [mitocheck_data](https://github.com/WayScience/mitocheck_data) and used to create [training_data.csv.gz](../1.format_data/data/training_data.csv.gz). +In this repository, all training data is downloaded from a version controlled [mitocheck_data](https://github.com/WayScience/mitocheck_data). The version of mitocheck_data used is specified by the hash corresponding to a current commit. +The current hash being used is `19bfa5b0959d6b7536f83e7bb85745ba3edf7ff9` which corresponds to [mitocheck_data/19bfa5b](https://github.com/WayScience/mitocheck_data/tree/19bfa5b0959d6b7536f83e7bb85745ba3edf7ff9). +The `hash` variable can be set in [download_data.ipynb](download_data.ipynb) to change which version of mitocheck_data is being accessed. -The current hash being used is `de21b9c3201ba4298db2b1704f3ae510a5dc47e2` which corresponds to [mitocheck_data/de21b9c](https://github.com/WayScience/mitocheck_data/tree/de21b9c3201ba4298db2b1704f3ae510a5dc47e2). +## Step 1: Download Data -The `hash` variable can be set in [format_training_data.ipynb](../1.format_data/format_training_data.ipynb) to changed which version of mitocheck_data is being accessed. \ No newline at end of file +Use the commands below to download labeled training dataset: + +```sh +# Make sure you are located in 0.download_data +cd 0.download_data + +# Activate phenotypic_profiling conda environment +conda activate phenotypic_profiling + +# Download data +bash download_data.sh +``` diff --git a/0.download_data/data/training_data.csv.gz b/0.download_data/data/training_data.csv.gz new file mode 100644 index 00000000..5659ec31 Binary files /dev/null and b/0.download_data/data/training_data.csv.gz differ diff --git a/0.download_data/download_data.ipynb b/0.download_data/download_data.ipynb new file mode 100644 index 00000000..3dfb48fb --- /dev/null +++ b/0.download_data/download_data.ipynb @@ -0,0 +1,525 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import pathlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Specify version of mitocheck_data to download from" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://raw.github.com/WayScience/mitocheck_data/19bfa5b0959d6b7536f83e7bb85745ba3edf7ff9/3.normalize_data/normalized_data/training_data.csv.gz\n" + ] + } + ], + "source": [ + "hash = \"19bfa5b0959d6b7536f83e7bb85745ba3edf7ff9\"\n", + "file_url = f\"https://raw.github.com/WayScience/mitocheck_data/{hash}/3.normalize_data/normalized_data/training_data.csv.gz\"\n", + "print(file_url)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load training data from github" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"... ... ... ... ... \n", + "4648 ... -0.691697 0.809051 -0.522286 \n", + "4649 ... 0.014888 2.423067 -0.530521 \n", + "4650 ... 1.127832 0.492408 -0.531921 \n", + "4651 ... 0.410533 1.964066 -0.833740 \n", + "4652 ... -0.041231 0.998568 0.006131 \n", + "\n", + " efficientnet_1273 efficientnet_1274 efficientnet_1275 \\\n", + "0 -1.377590 0.454974 0.188488 \n", + "1 -1.431541 -0.063308 -0.412793 \n", + "2 -1.427502 -0.901764 -0.355080 \n", + "3 -1.365543 -0.276932 0.023856 \n", + "4 -1.584454 0.891666 1.223252 \n", + "... ... ... ... \n", + "4648 -0.956816 0.112946 -0.087137 \n", + "4649 -1.026853 0.021895 -0.550902 \n", + "4650 -0.766331 0.286463 0.493081 \n", + "4651 -0.246026 0.984373 0.755903 \n", + "4652 -0.857846 1.163148 0.904470 \n", + "\n", + " efficientnet_1276 efficientnet_1277 efficientnet_1278 \\\n", + "0 0.141427 -1.553405 2.346107 \n", + "1 0.452684 -1.906647 1.962141 \n", + "2 0.418053 -2.298449 1.098266 \n", + "3 0.376514 -1.700348 1.833686 \n", + "4 -0.359166 -0.826366 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pathlib.Path(\"data/\")\n", + "training_data_save_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "training_data_save_path = pathlib.Path(f\"{training_data_save_dir}/training_data.csv.gz\")\n", + "training_data.to_csv(training_data_save_path, compression=\"gzip\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/0.download_data/download_data.sh b/0.download_data/download_data.sh new file mode 100644 index 00000000..51e9875f --- /dev/null +++ b/0.download_data/download_data.sh @@ -0,0 +1,5 @@ +#!/bin/bash +# Convert notebook to python file and execute +jupyter nbconvert --to python \ + --FilesWriter.build_directory=scripts/nbconverted \ + --execute download_data.ipynb diff --git a/0.download_data/scripts/nbconverted/download_data.py b/0.download_data/scripts/nbconverted/download_data.py new file mode 100644 index 00000000..e45c8813 --- /dev/null +++ b/0.download_data/scripts/nbconverted/download_data.py @@ -0,0 +1,42 @@ +#!/usr/bin/env python +# coding: utf-8 + +# ### Import libraries + +# In[1]: + + +import pandas as pd +import pathlib + + +# ### Specify version of mitocheck_data to download from + +# In[2]: + + +hash = "19bfa5b0959d6b7536f83e7bb85745ba3edf7ff9" +file_url = f"https://raw.github.com/WayScience/mitocheck_data/{hash}/3.normalize_data/normalized_data/training_data.csv.gz" +print(file_url) + + +# ### Load training data from github + +# In[3]: + + +training_data = pd.read_csv(file_url, compression="gzip", index_col=0) +training_data + + +# ### Save training data + +# In[4]: + + +training_data_save_dir = pathlib.Path("data/") +training_data_save_dir.mkdir(parents=True, exist_ok=True) + +training_data_save_path = pathlib.Path(f"{training_data_save_dir}/training_data.csv.gz") +training_data.to_csv(training_data_save_path, compression="gzip") + diff --git a/1.format_data/1.format_env.yml b/1.format_data/1.format_env.yml deleted file mode 100644 index bf99a6de..00000000 --- a/1.format_data/1.format_env.yml +++ /dev/null @@ -1,7 +0,0 @@ -name: 1.format_training_data -channels: - - conda-forge -dependencies: - - conda-forge::python=3.8.13 - - conda-forge::jupyter=1.0.0 - - conda-forge::pandas=1.4.2 diff --git a/1.format_data/1.format_training_data.sh b/1.format_data/1.format_training_data.sh deleted file mode 100644 index a82fa98f..00000000 --- a/1.format_data/1.format_training_data.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash -jupyter nbconvert --to python format_training_data.ipynb -python format_training_data.py diff --git a/1.format_data/README.md b/1.format_data/README.md deleted file mode 100644 index 3e6b8e0c..00000000 --- a/1.format_data/README.md +++ /dev/null @@ -1,31 +0,0 @@ -# 1. Format Data - -In this module, we use data from a specific version of [mitocheck_data](https://github.com/WayScience/mitocheck_data) to compile a [training dataframe file](data/training_data.csv.gz). - -The [format training data script](format_training_data.ipynb) accesses the version of mitocheck_data to retrieve the preprocessed feature data, segmentation data, and trainingset.dat file. -With these data, the script is able to compile a dataframe with all labeled single-nuclei embeddings, their metadata, and their MitoCheck-assigned object ID/phenotypic class. - -**Note**: The version of mitocheck_data used to compile training data can be changed by setting the `hash` variable in the [format training data script](format_training_data.ipynb) to the desired commit of mitocheck_data. - -## Step 1: Setup Download Environment - -### Step 1a: Create Download Environment - -```sh -# Run this command to create the conda environment for downloading data -conda env create -f 1.format_env.yml -``` - -### Step 1b: Activate Download Environment - -```sh -# Run this command to activate the conda environment for downloading data -conda activate 1.format_training_data -``` - -## Step 2: Execute Training Data Preprocessing - -```bash -# Run this script to preprocess training movies -bash 1.format_training_data.sh -``` \ No newline at end of file diff --git a/1.format_data/data/training_data.csv.gz b/1.format_data/data/training_data.csv.gz deleted file mode 100644 index 39c5e63b..00000000 Binary files a/1.format_data/data/training_data.csv.gz and /dev/null differ diff --git a/1.format_data/format_training_data.ipynb b/1.format_data/format_training_data.ipynb deleted file mode 100644 index 077d83c4..00000000 --- a/1.format_data/format_training_data.ipynb +++ /dev/null @@ -1,4816 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Format Training Data\n", - "\n", - "### Access training data from specific commit of [mitocheck_data](https://github.com/WayScience/mitocheck_data) and format this data into a single CSV file\n", - "\n", - "### Import libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import urllib\n", - "import pathlib" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define functions for formatting training data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def get_single_cell_metadata(single_cell_data: pd.DataFrame):\n", - " \"\"\"get plate, well, frame information from single cell data\n", - "\n", - " Args:\n", - " single_cell_data (pd.Dataframe): dataframe with single cell data\n", - "\n", - " Returns:\n", - " str, str, str: plate, well, frame metadata as strings\n", - " \"\"\"\n", - " # Metadata_DNA is in format plate/well/frame/filename.tif so get plate, well, frame info from this\n", - " single_cell_info = single_cell_data[\"Metadata_DNA\"].split(\"/\")\n", - " plate = single_cell_info[0]\n", - " well = single_cell_info[1]\n", - " frame = single_cell_info[2]\n", - " return plate, well, frame\n", - "\n", - "\n", - "def get_cell_class(\n", - " single_cell_data: pd.DataFrame,\n", - " trainingset_file_url: str,\n", - " plate: str,\n", - " well: str,\n", - " frame: str,\n", - ") -> str:\n", - " \"\"\"get phenotypic class of cell from trainingset.dat file, as labeled by MitoCheck\n", - "\n", - " Args:\n", - " single_cell_data (pd.DataFrame): dataframe with single cell data\n", - " trainingset_file_url (str): url location of raw traininset.dat file\n", - " plate (str): plate cell is from\n", - " well (str): well cell is from\n", - " frame (str): frame cell is from\n", - "\n", - " Returns:\n", - " str: phenotypic class of nucleus, as labeled by MitoCheck\n", - " \"\"\"\n", - " well_string = f\"W{str(well).zfill(5)}\"\n", - " frame_time = (int(frame) - 1) * 30\n", - " frame_time_string = f\"T{str(frame_time).zfill(5)}\"\n", - " frame_file_details = [plate, well_string, frame_time_string]\n", - " obj_id = int(single_cell_data[\"Mitocheck_Object_ID\"].item())\n", - " obj_id_prefix = f\"{obj_id}: \"\n", - "\n", - " append = False\n", - " # need to open trainingset file each time\n", - " trainingset_file = urllib.request.urlopen(trainingset_file_url)\n", - " for line in trainingset_file:\n", - " decoded_line = line.decode(\"utf-8\").strip()\n", - " # match plate, well, frame to starting line for movie labels\n", - " if all(detail in decoded_line for detail in frame_file_details):\n", - " append = True\n", - " if append and decoded_line.startswith(obj_id_prefix):\n", - " return decoded_line.split(\": \")[1]\n", - " return None\n", - "\n", - "\n", - "def get_cell_control(\n", - " plate: str,\n", - " well: str,\n", - " idr_metadata: pd.DataFrame,\n", - "):\n", - "\n", - " cell_annotations = idr_metadata.loc[\n", - " (plate == idr_metadata[\"Plate\"]) & (idr_metadata[\"Well Number\"].astype(int) == int(well))\n", - " ]\n", - " control_type = cell_annotations.iloc[0][\"Control Type\"]\n", - " \n", - " if control_type == \"positive control\":\n", - " return \"positive\"\n", - " elif control_type == \"negative control\":\n", - " return \"negative\"\n", - " else:\n", - " return \"none\"\n", - "\n", - "\n", - "def complete_single_cell(\n", - " single_cell_data: pd.DataFrame,\n", - " trainingset_file_url: str,\n", - " segmentation_data_dir: str,\n", - " idr_metadata: pd.DataFrame,\n", - ") -> pd.DataFrame:\n", - " \"\"\"Add Mitocheck_Object_ID and Mitocheck_Phenotypic_Class fields to single cell data by matching cell object ID to phenotypic class given in traininset.dat\n", - "\n", - " Args:\n", - " single_cell_data (pd.DataFrame): single cell data\n", - " trainingset_file_url (str): url location of raw traininset.dat file\n", - " segmentation_data_dir (str): url location of the raw segmentation data directory\n", - "\n", - " Returns:\n", - " pd.DataFrame: completed single cell data\n", - " \"\"\"\n", - " plate, well, frame = get_single_cell_metadata(single_cell_data)\n", - " segmentation_data_url = (\n", - " f\"{segmentation_data_dir}/{plate}/{well}/{frame}/{plate}_{well}_{frame}.tsv\"\n", - " )\n", - " full_segmentation_data = pd.read_csv(segmentation_data_url, delimiter=\"\\t\").round(0)\n", - " cell_x_y = (\n", - " round(single_cell_data[\"Location_Center_X\"]),\n", - " round(single_cell_data[\"Location_Center_Y\"]),\n", - " )\n", - " cell_segmentation_data = full_segmentation_data.loc[\n", - " (full_segmentation_data[\"Location_Center_X\"] == cell_x_y[0])\n", - " & (full_segmentation_data[\"Location_Center_Y\"] == cell_x_y[1])\n", - " ]\n", - " print(f\"Processed cell at: {plate}/{well}/{frame}, location: {cell_x_y}\")\n", - " if cell_segmentation_data.empty:\n", - " print(\"No segmentation data match found for this cell!\")\n", - " else:\n", - " single_cell_data = single_cell_data.to_frame().transpose()\n", - " single_cell_data.insert(\n", - " 0,\n", - " \"Mitocheck_Object_ID\",\n", - " cell_segmentation_data[\"Mitocheck_Object_ID\"].item(),\n", - " )\n", - " # get class and append to single cell data\n", - " cell_phenotypic_class = get_cell_class(\n", - " single_cell_data, trainingset_file_url, plate, well, frame\n", - " )\n", - " if cell_phenotypic_class == None:\n", - " print(\"This cell was not found in trainingset.dat!\")\n", - " single_cell_data.insert(0, \"Mitocheck_Phenotypic_Class\", cell_phenotypic_class)\n", - "\n", - " cell_control_type = get_cell_control(plate, well, idr_metadata)\n", - " single_cell_data.insert(1, \"Control_Type\", cell_control_type)\n", - "\n", - " return single_cell_data\n", - "\n", - "\n", - "def format_training_data(mitocheck_data_version_url: str) -> pd.DataFrame:\n", - " \"\"\"Add Mitocheck_Object_ID and Mitocheck_Phenotypic_Class fields to each single cell and compile all the cells into a single training data dataframe\n", - "\n", - " Args:\n", - " mitocheck_data_version_url (str): url with path to desired version of raw mitocheck_data\n", - "\n", - " Returns:\n", - " pd.DataFrame: completed training data with Mitocheck_Object_ID and Mitocheck_Phenotypic_Class for each cell\n", - " \"\"\"\n", - " trainingset_file_url = (\n", - " f\"{mitocheck_data_version_url}/0.download_data/trainingset.dat\"\n", - " )\n", - " segmentation_data_dir = f\"{mitocheck_data_version_url}/2.segment_nuclei/segmented/\"\n", - " preprocessed_features_url = f\"{mitocheck_data_version_url}/4.preprocess_features/data/normalized_training_data.csv.gz\"\n", - " idr_metadata_url = f\"{mitocheck_data_version_url}/3.extract_features/idr0013-screenA-annotation.csv.gz\"\n", - "\n", - " preprocessed_features = pd.read_csv(preprocessed_features_url, compression=\"gzip\")\n", - " print(\"Loaded preprocessed features!\")\n", - "\n", - " idr_metadata = pd.read_csv(idr_metadata_url, dtype=object, compression=\"gzip\")\n", - " print(\"Loaded idr metadata!\")\n", - "\n", - " training_data = []\n", - " for index, row in preprocessed_features.iterrows():\n", - " single_cell = row\n", - " completed_single_cell = complete_single_cell(\n", - " single_cell, trainingset_file_url, segmentation_data_dir, idr_metadata\n", - " )\n", - " training_data.append(completed_single_cell)\n", - "\n", - " training_data = pd.concat(training_data)\n", - " return training_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Format training data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded preprocessed features!\n", - "Loaded idr metadata!\n", - "Processed cell at: LT0043_48/166/48, location: (263, 20)\n", - "Processed cell at: LT0043_48/166/48, location: (240, 28)\n", - "Processed cell at: LT0043_48/166/48, location: (253, 36)\n", - "Processed cell at: LT0043_48/166/48, location: (258, 46)\n", - "Processed cell at: LT0043_48/166/55, location: (1213, 21)\n", - "Processed cell at: LT0043_48/166/55, location: (70, 105)\n", - "Processed cell at: LT0043_48/166/55, location: (517, 159)\n", - "Processed cell at: LT0043_48/166/55, location: (1156, 191)\n", - "Processed cell at: LT0043_48/166/55, location: (748, 221)\n", - "Processed cell at: LT0043_48/166/55, location: (795, 243)\n", - "Processed cell at: LT0043_48/166/55, location: (744, 236)\n", - "Processed cell at: LT0043_48/166/55, location: (719, 246)\n", - "Processed cell at: LT0043_48/166/55, location: (668, 253)\n", - "Processed cell at: LT0043_48/166/55, location: (522, 252)\n", - "Processed cell at: LT0043_48/166/55, location: (644, 256)\n", - "Processed cell at: LT0043_48/166/55, location: (234, 256)\n", - "Processed cell at: LT0043_48/166/55, location: (856, 257)\n", - "Processed cell at: LT0043_48/166/55, location: (553, 271)\n", - "Processed cell at: LT0043_48/166/55, location: (783, 265)\n", - "Processed cell at: LT0043_48/166/55, location: (785, 283)\n", - "Processed cell at: LT0043_48/166/55, location: (809, 276)\n", - "Processed cell at: LT0043_48/166/55, location: (825, 285)\n", - "Processed cell at: LT0043_48/166/55, location: (294, 298)\n", - "Processed cell at: LT0043_48/166/55, location: (844, 306)\n", - "Processed cell at: LT0043_48/166/55, location: (312, 307)\n", - "Processed cell at: LT0043_48/166/55, location: (301, 318)\n", - "Processed cell at: LT0043_48/166/55, location: (267, 309)\n", - "Processed cell at: LT0043_48/166/55, location: (277, 322)\n", - "Processed cell at: LT0043_48/166/55, location: (580, 335)\n", - "Processed cell at: LT0043_48/166/55, location: (449, 334)\n", - "Processed cell at: LT0043_48/166/55, location: (467, 355)\n", - "Processed cell at: LT0043_48/166/55, location: (436, 376)\n", - "Processed cell at: LT0043_48/166/55, location: (464, 379)\n", - "Processed cell at: LT0043_48/166/55, location: (708, 390)\n", - "Processed cell at: LT0043_48/166/55, location: (642, 459)\n", - "Processed cell at: LT0043_48/166/55, location: (653, 491)\n", - "Processed cell at: LT0043_48/166/55, location: (989, 502)\n", - "Processed cell at: LT0043_48/166/55, location: (901, 511)\n", - "Processed cell at: LT0043_48/166/55, location: (1006, 517)\n", - "Processed cell at: LT0043_48/166/55, location: (464, 596)\n", - "Processed cell at: LT0043_48/166/55, location: (475, 607)\n", - "Processed cell at: LT0043_48/166/55, location: (474, 625)\n", - "Processed cell at: LT0043_48/166/55, location: (804, 738)\n", - "Processed cell at: LT0043_48/166/55, location: (250, 916)\n", - "Processed cell at: LT0043_48/166/36, location: (1208, 8)\n", - "Processed cell at: LT0043_48/166/36, location: (1211, 37)\n", - "Processed cell at: LT0043_48/166/36, location: (1109, 156)\n", - "Processed cell at: LT0043_48/166/36, location: (1095, 185)\n", - "Processed cell at: LT0043_48/166/36, location: (297, 317)\n", - "Processed cell at: LT0043_48/166/36, location: (834, 718)\n", - "Processed cell at: LT0043_48/166/36, location: (626, 725)\n", - "Processed cell at: LT0043_48/166/36, location: (811, 725)\n", - "Processed cell at: LT0043_48/166/36, location: (615, 743)\n", - "Processed cell at: LT0043_48/166/74, location: (339, 63)\n", - "Processed cell at: LT0043_48/166/74, location: (366, 83)\n", - "Processed cell at: LT0043_48/166/74, location: (337, 80)\n", - "Processed cell at: LT0043_48/166/74, location: (388, 98)\n", - "Processed cell at: LT0043_48/166/74, location: (754, 123)\n", - "Processed cell at: LT0043_48/166/74, location: (777, 138)\n", - "Processed cell at: LT0043_48/166/74, location: (667, 158)\n", - "Processed cell at: LT0043_48/166/74, location: (672, 186)\n", - "Processed cell at: LT0043_48/166/74, location: (812, 192)\n", - "Processed cell at: LT0043_48/166/74, location: (855, 298)\n", - "Processed cell at: LT0043_48/166/74, location: (507, 313)\n", - "Processed cell at: LT0043_48/166/74, location: (830, 314)\n", - "Processed cell at: LT0043_48/166/74, location: (879, 317)\n", - "Processed cell at: LT0043_48/166/74, location: (469, 327)\n", - "Processed cell at: LT0043_48/166/74, location: (452, 325)\n", - "Processed cell at: LT0043_48/166/74, location: (494, 331)\n", - "Processed cell at: LT0043_48/166/74, location: (859, 345)\n", - "Processed cell at: LT0043_48/166/74, location: (882, 343)\n", - "Processed cell at: LT0043_48/166/74, location: (487, 351)\n", - "Processed cell at: LT0043_48/166/74, location: (454, 414)\n", - "Processed cell at: LT0043_48/166/74, location: (176, 422)\n", - "Processed cell at: LT0043_48/166/74, location: (560, 425)\n", - "Processed cell at: LT0043_48/166/74, location: (489, 430)\n", - "Processed cell at: LT0043_48/166/74, location: (467, 433)\n", - "Processed cell at: LT0043_48/166/74, location: (507, 466)\n", - "Processed cell at: LT0043_48/166/74, location: (976, 485)\n", - "Processed cell at: LT0043_48/166/74, location: (1008, 490)\n", - "Processed cell at: LT0043_48/166/74, location: (984, 504)\n", - "Processed cell at: LT0043_48/166/74, location: (39, 562)\n", - "Processed cell at: LT0043_48/166/74, location: (18, 567)\n", - "Processed cell at: LT0043_48/166/74, location: (534, 939)\n", - "Processed cell at: LT0043_48/166/74, location: (530, 953)\n", - "Processed cell at: LT0043_48/166/44, location: (887, 64)\n", - "Processed cell at: LT0043_48/166/44, location: (771, 109)\n", - "Processed cell at: LT0043_48/166/44, location: (556, 121)\n", - "Processed cell at: LT0043_48/166/44, location: (276, 255)\n", - "Processed cell at: LT0043_48/166/44, location: (299, 307)\n", - "Processed cell at: LT0043_48/166/44, location: (283, 321)\n", - "Processed cell at: LT0043_48/166/44, location: (315, 316)\n", - "Processed cell at: LT0043_48/166/44, location: (307, 329)\n", - "Processed cell at: LT0043_48/166/44, location: (307, 385)\n", - "Processed cell at: LT0043_48/166/44, location: (378, 383)\n", - "Processed cell at: LT0043_48/166/44, location: (144, 393)\n", - "Processed cell at: LT0043_48/166/44, location: (144, 407)\n", - "Processed cell at: LT0043_48/166/44, location: (138, 423)\n", - "Processed cell at: LT0043_48/166/44, location: (363, 433)\n", - "Processed cell at: LT0043_48/166/44, location: (132, 432)\n", - "Processed cell at: LT0043_48/166/44, location: (764, 485)\n", - "Processed cell at: LT0043_48/166/44, location: (795, 494)\n", - "Processed cell at: LT0043_48/166/44, location: (496, 599)\n", - "Processed cell at: LT0043_48/166/44, location: (1153, 709)\n", - "Processed cell at: LT0043_48/166/44, location: (1175, 712)\n", - "Processed cell at: LT0043_48/166/44, location: (61, 744)\n", - "Processed cell at: LT0043_48/166/44, location: (46, 749)\n", - "Processed cell at: LT0043_48/166/71, location: (727, 56)\n", - "Processed cell at: LT0043_48/166/71, location: (693, 57)\n", - "Processed cell at: LT0043_48/166/71, location: (1069, 67)\n", - "Processed cell at: LT0043_48/166/71, location: (1080, 84)\n", - "Processed cell at: LT0043_48/166/71, location: (69, 86)\n", - "Processed cell at: LT0043_48/166/71, location: (54, 110)\n", - "Processed cell at: LT0043_48/166/71, location: (76, 123)\n", - "Processed cell at: LT0043_48/166/71, location: (1122, 144)\n", - "Processed cell at: LT0043_48/166/71, location: (1021, 160)\n", - "Processed cell at: LT0043_48/166/71, location: (1081, 164)\n", - "Processed cell at: LT0043_48/166/71, location: (1047, 198)\n", - "Processed cell at: LT0043_48/166/71, location: (1055, 213)\n", - "Processed cell at: LT0043_48/166/71, location: (265, 255)\n", - "Processed cell at: LT0043_48/166/71, location: (302, 274)\n", - "Processed cell at: LT0043_48/166/71, location: (269, 276)\n", - "Processed cell at: LT0043_48/166/71, location: (862, 292)\n", - "Processed cell at: LT0043_48/166/71, location: (268, 289)\n", - "Processed cell at: LT0043_48/166/71, location: (292, 314)\n", - "Processed cell at: LT0043_48/166/71, location: (881, 314)\n", - "Processed cell at: LT0043_48/166/71, location: (263, 313)\n", - "Processed cell at: LT0043_48/166/71, location: (896, 312)\n", - "Processed cell at: LT0043_48/166/71, location: (876, 341)\n", - "Processed cell at: LT0043_48/166/71, location: (449, 408)\n", - "Processed cell at: LT0043_48/166/71, location: (558, 423)\n", - "Processed cell at: LT0043_48/166/71, location: (464, 425)\n", - "Processed cell at: LT0043_48/166/71, location: (509, 466)\n", - "Processed cell at: LT0043_48/166/71, location: (464, 593)\n", - "Processed cell at: LT0043_48/166/71, location: (470, 618)\n", - "Processed cell at: LT0043_48/166/71, location: (68, 660)\n", - "Processed cell at: LT0043_48/166/71, location: (213, 678)\n", - "Processed cell at: LT0043_48/166/71, location: (60, 684)\n", - "Processed cell at: LT0043_48/166/71, location: (84, 676)\n", - "Processed cell at: LT0043_48/166/71, location: (80, 689)\n", - "Processed cell at: LT0043_48/166/71, location: (233, 703)\n", - "Processed cell at: LT0043_48/166/71, location: (311, 836)\n", - "Processed cell at: LT0043_48/166/71, location: (302, 853)\n", - "Processed cell at: LT0043_48/166/71, location: (311, 869)\n", - "Processed cell at: LT0043_48/166/71, location: (301, 881)\n", - "Processed cell at: LT0043_48/166/56, location: (664, 155)\n", - "Processed cell at: LT0043_48/166/56, location: (679, 179)\n", - "Processed cell at: LT0043_48/166/56, location: (806, 188)\n", - "Processed cell at: LT0043_48/166/56, location: (830, 204)\n", - "Processed cell at: LT0043_48/166/56, location: (854, 204)\n", - "Processed cell at: LT0043_48/166/56, location: (712, 228)\n", - "Processed cell at: LT0043_48/166/56, location: (799, 241)\n", - "Processed cell at: LT0043_48/166/56, location: (860, 258)\n", - "Processed cell at: LT0043_48/166/56, location: (794, 260)\n", - "Processed cell at: LT0043_48/166/56, location: (803, 273)\n", - 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"Processed cell at: LT0043_48/166/56, location: (572, 692)\n", - "Processed cell at: LT0043_48/166/56, location: (792, 730)\n", - "Processed cell at: LT0043_48/166/56, location: (811, 738)\n", - "Processed cell at: LT0043_48/166/56, location: (793, 747)\n", - "Processed cell at: LT0043_48/166/56, location: (310, 855)\n", - "Processed cell at: LT0043_48/166/56, location: (254, 915)\n", - "Processed cell at: LT0043_48/166/56, location: (334, 913)\n", - "Processed cell at: LT0043_48/166/56, location: (342, 926)\n", - "Processed cell at: LT0043_48/166/45, location: (280, 256)\n", - "Processed cell at: LT0043_48/166/45, location: (296, 303)\n", - "Processed cell at: LT0043_48/166/45, location: (280, 318)\n", - "Processed cell at: LT0043_48/166/45, location: (314, 312)\n", - "Processed cell at: LT0043_48/166/45, location: (306, 326)\n", - "Processed cell at: LT0043_48/166/45, location: (478, 602)\n", - "Processed cell at: LT0043_48/166/45, location: (509, 600)\n", - "Processed cell at: LT0043_48/166/47, location: (257, 35)\n", - 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"Processed cell at: LT0017_19/365/65, location: (1046, 447)\n", - "Processed cell at: LT0017_19/365/65, location: (326, 463)\n", - "Processed cell at: LT0017_19/365/65, location: (204, 460)\n", - "Processed cell at: LT0017_19/365/65, location: (1050, 482)\n", - "Processed cell at: LT0017_19/365/65, location: (190, 482)\n", - "Processed cell at: LT0017_19/365/65, location: (836, 654)\n", - "Processed cell at: LT0017_19/365/65, location: (816, 659)\n", - "Processed cell at: LT0017_19/365/65, location: (483, 730)\n", - "Processed cell at: LT0017_19/365/65, location: (507, 735)\n", - "Processed cell at: LT0017_19/365/65, location: (527, 792)\n", - "Processed cell at: LT0017_19/365/65, location: (516, 811)\n", - "Processed cell at: LT0017_19/365/65, location: (495, 815)\n", - "Processed cell at: LT0017_19/365/65, location: (753, 823)\n", - "Processed cell at: LT0017_19/365/65, location: (728, 849)\n", - "Processed cell at: LT0017_19/365/65, location: (760, 854)\n", - "Processed cell at: LT0017_19/365/65, location: (596, 873)\n", - "Processed cell at: LT0017_19/365/65, location: (739, 872)\n", - "Processed cell at: LT0017_19/365/65, location: (92, 888)\n", - "Processed cell at: LT0017_19/365/65, location: (577, 896)\n", - "Processed cell at: LT0017_19/365/65, location: (787, 929)\n", - "Processed cell at: LT0017_19/365/65, location: (827, 944)\n", - "Processed cell at: LT0064_14/003/22, location: (690, 395)\n", - "Processed cell at: LT0064_14/003/22, location: (593, 686)\n", - "Processed cell at: LT0064_14/003/22, location: (541, 692)\n", - "Processed cell at: LT0064_14/003/22, location: (483, 698)\n", - "Processed cell at: LT0064_14/003/22, location: (608, 721)\n", - "Processed cell at: LT0064_14/003/22, location: (524, 725)\n", - "Processed cell at: LT0064_14/003/22, location: (554, 765)\n", - "Processed cell at: LT0064_14/003/22, location: (407, 960)\n", - "Processed cell at: LT0064_14/003/22, location: (480, 981)\n", - "Processed cell at: LT0064_14/003/54, location: (593, 291)\n", - "Processed cell at: LT0064_14/003/54, location: (581, 315)\n", - "Processed cell at: LT0064_14/003/54, location: (694, 418)\n", - "Processed cell at: LT0064_14/003/54, location: (700, 427)\n", - "Processed cell at: LT0064_14/003/54, location: (701, 436)\n", - "Processed cell at: LT0064_14/003/54, location: (684, 453)\n", - "Processed cell at: LT0064_14/003/54, location: (928, 591)\n", - "Processed cell at: LT0064_14/003/54, location: (902, 596)\n", - "Processed cell at: LT0064_14/003/54, location: (949, 604)\n", - "Processed cell at: LT0064_14/003/54, location: (911, 608)\n", - "Processed cell at: LT0064_14/003/54, location: (1138, 613)\n", - "Processed cell at: LT0064_14/003/54, location: (1133, 637)\n", - "Processed cell at: LT0064_14/003/54, location: (595, 688)\n", - "Processed cell at: LT0064_14/003/54, location: (545, 688)\n", - "Processed cell at: LT0064_14/003/54, location: (484, 702)\n", - "Processed cell at: LT0064_14/003/54, location: (606, 724)\n", - "Processed cell at: LT0064_14/003/54, location: (78, 798)\n", - "Processed cell at: LT0064_14/003/54, location: (467, 983)\n", - "Processed cell at: LT0064_14/003/27, location: (30, 161)\n", - "Processed cell at: LT0064_14/003/27, location: (752, 303)\n", - "Processed cell at: LT0064_14/003/27, location: (746, 361)\n", - "Processed cell at: LT0064_14/003/27, location: (687, 391)\n", - "Processed cell at: LT0064_14/003/27, location: (559, 645)\n", - "Processed cell at: LT0064_14/003/27, location: (593, 687)\n", - "Processed cell at: LT0064_14/003/27, location: (542, 690)\n", - "Processed cell at: LT0064_14/003/27, location: (484, 697)\n", - "Processed cell at: LT0064_14/003/27, location: (523, 724)\n", - "Processed cell at: LT0064_14/003/27, location: (554, 761)\n", - "Processed cell at: LT0064_14/003/27, location: (745, 937)\n", - "Processed cell at: LT0042_10/044/28, location: (905, 200)\n", - "Processed cell at: LT0042_10/044/28, location: (944, 258)\n", - "Processed cell at: LT0042_10/044/28, location: (657, 260)\n", - "Processed cell at: LT0042_10/044/28, location: (862, 270)\n", - "Processed cell at: LT0042_10/044/28, location: (637, 276)\n", - "Processed cell at: LT0042_10/044/28, location: (888, 341)\n", - "Processed cell at: LT0042_10/144/88, location: (212, 87)\n", - "Processed cell at: LT0042_10/144/88, location: (224, 102)\n", - "Processed cell at: LT0042_10/144/88, location: (200, 116)\n", - "Processed cell at: LT0042_10/144/88, location: (228, 413)\n", - "Processed cell at: LT0042_10/144/88, location: (253, 423)\n", - "Processed cell at: LT0042_10/144/88, location: (207, 424)\n", - "Processed cell at: LT0042_10/144/88, location: (211, 440)\n", - "Processed cell at: LT0042_10/144/88, location: (237, 446)\n", - "Processed cell at: LT0042_10/144/88, location: (221, 452)\n", - "Processed cell at: LT0042_10/144/88, location: (256, 841)\n", - "Processed cell at: LT0042_10/144/88, location: (273, 861)\n", - "Processed cell at: LT0042_10/144/88, location: (213, 881)\n", - "Processed cell at: LT0042_10/144/88, location: (239, 895)\n", - "Processed cell at: LT0042_10/144/88, location: (222, 906)\n", - "Processed cell at: LT0042_10/144/88, location: (269, 935)\n", - "Processed cell at: LT0042_10/144/88, location: (240, 941)\n", - "Processed cell at: LT0042_10/144/88, location: (251, 952)\n", - "Processed cell at: LT0042_10/144/88, location: (276, 960)\n", - "Processed cell at: LT0042_10/144/88, location: (258, 973)\n", - "Processed cell at: LT0042_10/144/36, location: (987, 232)\n", - "Processed cell at: LT0042_10/144/36, location: (674, 422)\n", - "Processed cell at: LT0042_10/144/36, location: (240, 429)\n", - "Processed cell at: LT0042_10/144/36, location: (219, 440)\n", - "Processed cell at: LT0042_10/144/36, location: (238, 457)\n", - "Processed cell at: LT0042_10/144/36, location: (214, 462)\n", - "Processed cell at: LT0042_10/144/36, location: (230, 472)\n", - "Processed cell at: LT0042_10/144/36, location: (413, 853)\n", - "Processed cell at: LT0042_10/144/50, location: (110, 49)\n", - "Processed cell at: LT0042_10/144/50, location: (807, 168)\n", - "Processed cell at: LT0042_10/144/50, location: (996, 218)\n", - "Processed cell at: LT0042_10/144/50, location: (973, 234)\n", - "Processed cell at: LT0042_10/144/50, location: (665, 417)\n", - "Processed cell at: LT0042_10/144/50, location: (508, 617)\n", - "Processed cell at: LT0042_10/144/50, location: (938, 665)\n", - "Processed cell at: LT0042_10/144/50, location: (184, 696)\n", - "Processed cell at: LT0042_10/144/50, location: (1308, 747)\n", - "Processed cell at: LT0042_10/144/50, location: (399, 773)\n", - "Processed cell at: LT0042_10/144/50, location: (97, 782)\n", - "Processed cell at: LT0042_10/144/50, location: (354, 783)\n", - "Processed cell at: LT0042_10/144/50, location: (138, 796)\n", - "Processed cell at: LT0042_10/144/50, location: (362, 807)\n", - "Processed cell at: LT0042_10/144/50, location: (166, 850)\n", - "Processed cell at: LT0042_10/144/50, location: (255, 861)\n", - "Processed cell at: LT0042_10/144/50, location: (231, 888)\n", - "Processed cell at: LT0042_10/144/50, location: (253, 957)\n", - "Processed cell at: LT0096_33/255/49, location: (446, 277)\n", - "Processed cell at: LT0096_33/255/38, location: (929, 450)\n", - "Processed cell at: LT0096_33/255/52, location: (389, 735)\n", - "Processed cell at: LT0030_17/184/41, location: (602, 139)\n", - "Processed cell at: LT0030_17/184/41, location: (350, 150)\n", - "Processed cell at: LT0030_17/184/41, location: (631, 201)\n", - "Processed cell at: LT0030_17/184/41, location: (164, 205)\n", - "Processed cell at: LT0030_17/184/41, location: (480, 210)\n", - "Processed cell at: LT0030_17/184/41, location: (380, 272)\n", - "Processed cell at: LT0030_17/184/41, location: (1052, 448)\n", - "Processed cell at: LT0030_17/184/41, location: (734, 475)\n", - "Processed cell at: LT0030_17/184/41, location: (1107, 505)\n", - "Processed cell at: LT0030_17/184/41, location: (350, 510)\n", - "Processed cell at: LT0030_17/184/41, location: (1069, 556)\n", - "Processed cell at: LT0030_17/184/41, location: (530, 651)\n", - "Processed cell at: LT0030_17/184/41, location: (408, 664)\n", - "Processed cell at: LT0030_17/184/41, location: (370, 921)\n", - "Processed cell at: LT0030_17/184/36, location: (395, 34)\n", - "Processed cell at: LT0030_17/184/36, location: (597, 145)\n", - "Processed cell at: LT0030_17/184/36, location: (614, 146)\n", - "Processed cell at: LT0030_17/184/36, location: (124, 162)\n", - "Processed cell at: LT0030_17/184/36, location: (590, 197)\n", - "Processed cell at: LT0030_17/184/36, location: (630, 206)\n", - "Processed cell at: LT0030_17/184/36, location: (490, 210)\n", - "Processed cell at: LT0030_17/184/36, location: (377, 274)\n", - "Processed cell at: LT0030_17/184/36, location: (631, 282)\n", - "Processed cell at: LT0030_17/184/36, location: (695, 335)\n", - "Processed cell at: LT0030_17/184/36, location: (665, 368)\n", - "Processed cell at: LT0030_17/184/36, location: (707, 380)\n", - "Processed cell at: LT0030_17/184/36, location: (1014, 425)\n", - "Processed cell at: LT0030_17/184/36, location: (814, 467)\n", - "Processed cell at: LT0030_17/184/36, location: (83, 484)\n", - "Processed cell at: LT0030_17/184/36, location: (86, 500)\n", - "Processed cell at: LT0030_17/184/36, location: (799, 497)\n", - "Processed cell at: LT0030_17/184/36, location: (1113, 503)\n", - "Processed cell at: LT0030_17/184/36, location: (1036, 525)\n", - "Processed cell at: LT0030_17/184/36, location: (1047, 523)\n", - "Processed cell at: LT0030_17/184/36, location: (1108, 539)\n", - "Processed cell at: LT0030_17/184/36, location: (1095, 544)\n", - "Processed cell at: LT0030_17/184/36, location: (1079, 555)\n", - "Processed cell at: LT0030_17/184/36, location: (409, 669)\n", - "Processed cell at: LT0030_17/184/36, location: (424, 872)\n", - "Processed cell at: LT0030_17/184/59, location: (399, 36)\n", - "Processed cell at: LT0030_17/184/59, location: (395, 84)\n", - "Processed cell at: LT0030_17/184/59, location: (395, 105)\n", - "Processed cell at: LT0030_17/184/59, location: (600, 128)\n", - "Processed cell at: LT0030_17/184/59, location: (354, 141)\n", - "Processed cell at: LT0030_17/184/59, location: (593, 144)\n", - "Processed cell at: LT0030_17/184/59, location: (126, 147)\n", - "Processed cell at: LT0030_17/184/59, location: (606, 152)\n", - "Processed cell at: LT0030_17/184/59, location: (131, 161)\n", - "Processed cell at: LT0030_17/184/59, location: (347, 158)\n", - "Processed cell at: LT0030_17/184/59, location: (112, 165)\n", - "Processed cell at: LT0030_17/184/59, location: (594, 194)\n", - "Processed cell at: LT0030_17/184/59, location: (579, 204)\n", - "Processed cell at: LT0030_17/184/59, location: (969, 296)\n", - "Processed cell at: LT0030_17/184/59, location: (429, 687)\n", - "Processed cell at: LT0030_17/184/59, location: (242, 889)\n", - "Processed cell at: LT0030_17/184/59, location: (435, 925)\n", - "Processed cell at: LT0030_17/184/46, location: (602, 130)\n", - "Processed cell at: LT0030_17/184/46, location: (603, 147)\n", - "Processed cell at: LT0030_17/184/46, location: (120, 158)\n", - "Processed cell at: LT0030_17/184/46, location: (355, 153)\n", - "Processed cell at: LT0030_17/184/46, location: (352, 168)\n", - "Processed cell at: LT0030_17/184/46, location: (384, 271)\n", - "Processed cell at: LT0030_17/184/46, location: (391, 283)\n", - "Processed cell at: LT0030_17/184/46, location: (1106, 583)\n", - "Processed cell at: LT0030_17/184/46, location: (1056, 593)\n", - "Processed cell at: LT0030_17/184/46, location: (438, 592)\n", - "Processed cell at: LT0030_17/184/46, location: (457, 619)\n", - "Processed cell at: LT0030_17/184/46, location: (650, 836)\n", - "Processed cell at: LT0030_17/184/87, location: (143, 227)\n", - "Processed cell at: LT0030_17/184/87, location: (130, 238)\n", - "Processed cell at: LT0030_17/184/87, location: (500, 256)\n", - "Processed cell at: LT0030_17/184/87, location: (138, 251)\n", - "Processed cell at: LT0030_17/184/87, location: (487, 263)\n", - "Processed cell at: LT0030_17/184/87, location: (145, 349)\n", - "Processed cell at: LT0030_17/184/87, location: (148, 363)\n", - "Processed cell at: LT0030_17/184/87, location: (210, 653)\n", - "Processed cell at: LT0030_17/184/87, location: (216, 664)\n", - "Processed cell at: LT0030_17/184/87, location: (68, 664)\n", - "Processed cell at: LT0030_17/184/87, location: (57, 675)\n", - "Processed cell at: LT0030_17/184/87, location: (256, 677)\n", - "Processed cell at: LT0030_17/184/87, location: (253, 691)\n", - "Processed cell at: LT0030_17/184/87, location: (80, 720)\n", - "Processed cell at: LT0030_17/184/87, location: (95, 721)\n", - "Processed cell at: LT0030_17/184/87, location: (81, 734)\n", - "Processed cell at: LT0030_17/184/50, location: (124, 159)\n", - "Processed cell at: LT0030_17/184/50, location: (166, 204)\n", - "Processed cell at: LT0030_17/184/50, location: (636, 199)\n", - "Processed cell at: LT0030_17/184/50, location: (249, 277)\n", - "Processed cell at: LT0030_17/184/50, location: (1159, 502)\n", - "Processed cell at: LT0030_17/184/50, location: (1096, 513)\n", - "Processed cell at: LT0030_17/184/50, location: (1065, 568)\n", - "Processed cell at: LT0030_17/184/50, location: (412, 660)\n", - "Processed cell at: LT0030_17/184/50, location: (440, 679)\n", - "Processed cell at: LT0030_17/184/50, location: (709, 729)\n", - "Processed cell at: LT0030_17/184/39, location: (490, 49)\n", - "Processed cell at: LT0030_17/184/39, location: (463, 67)\n", - "Processed cell at: LT0030_17/184/39, location: (488, 72)\n", - "Processed cell at: LT0030_17/184/39, location: (513, 90)\n", - "Processed cell at: LT0030_17/184/39, location: (608, 142)\n", - "Processed cell at: LT0030_17/184/39, location: (591, 143)\n", - "Processed cell at: LT0030_17/184/39, location: (121, 159)\n", - "Processed cell at: LT0030_17/184/39, location: (166, 200)\n", - "Processed cell at: LT0030_17/184/39, location: (631, 202)\n", - "Processed cell at: LT0030_17/184/39, location: (183, 205)\n", - "Processed cell at: LT0030_17/184/39, location: (167, 219)\n", - "Processed cell at: LT0030_17/184/39, location: (369, 268)\n", - "Processed cell at: LT0030_17/184/39, location: (384, 273)\n", - "Processed cell at: LT0030_17/184/39, location: (744, 336)\n", - "Processed cell at: LT0030_17/184/39, location: (771, 380)\n", - "Processed cell at: LT0030_17/184/39, location: (983, 400)\n", - "Processed cell at: LT0030_17/184/39, location: (794, 441)\n", - "Processed cell at: LT0030_17/184/39, location: (800, 495)\n", - "Processed cell at: LT0030_17/184/39, location: (1112, 502)\n", - "Processed cell at: LT0030_17/184/39, location: (1095, 530)\n", - "Processed cell at: LT0030_17/184/39, location: (1106, 536)\n", - "Processed cell at: LT0030_17/184/39, location: (1082, 553)\n", - "Processed cell at: LT0030_17/184/39, location: (1066, 570)\n", - "Processed cell at: LT0030_17/184/39, location: (1096, 581)\n", - "Processed cell at: LT0030_17/184/39, location: (323, 623)\n", - "Processed cell at: LT0030_17/184/39, location: (368, 894)\n", - "Processed cell at: LT0030_17/184/39, location: (348, 934)\n", - "Processed cell at: LT0030_17/184/39, location: (438, 931)\n", - "Processed cell at: LT0101_01/277/74, location: (829, 498)\n", - "Processed cell at: LT0101_01/277/74, location: (789, 590)\n", - "Processed cell at: LT0101_01/277/74, location: (789, 648)\n", - "Processed cell at: LT0101_01/277/74, location: (722, 686)\n", - "Processed cell at: LT0101_01/277/74, location: (739, 723)\n", - "Processed cell at: LT0101_01/277/74, location: (1069, 822)\n", - "Processed cell at: LT0101_01/277/74, location: (1058, 952)\n", - "Processed cell at: LT0101_01/277/79, location: (1182, 110)\n", - "Processed cell at: LT0101_01/277/79, location: (826, 500)\n", - "Processed cell at: LT0101_01/277/79, location: (705, 548)\n", - "Processed cell at: LT0101_01/277/79, location: (730, 557)\n", - "Processed cell at: LT0101_01/277/79, location: (811, 557)\n", - "Processed cell at: LT0101_01/277/79, location: (792, 591)\n", - "Processed cell at: LT0101_01/277/79, location: (792, 644)\n", - "Processed cell at: LT0101_01/277/79, location: (294, 760)\n", - "Processed cell at: LT0101_01/277/79, location: (1069, 822)\n", - "Processed cell at: LT0101_01/277/79, location: (1145, 822)\n", - "Processed cell at: LT0101_01/277/66, location: (620, 88)\n", - "Processed cell at: LT0101_01/277/66, location: (647, 107)\n", - "Processed cell at: LT0101_01/277/66, location: (657, 179)\n", - "Processed cell at: LT0101_01/277/66, location: (699, 180)\n", - "Processed cell at: LT0101_01/277/66, location: (540, 180)\n", - "Processed cell at: LT0101_01/277/66, location: (595, 183)\n", - "Processed cell at: LT0101_01/277/66, location: (497, 225)\n", - "Processed cell at: LT0101_01/277/66, location: (467, 246)\n", - "Processed cell at: LT0101_01/277/66, location: (498, 281)\n", - "Processed cell at: LT0101_01/277/66, location: (806, 371)\n", - "Processed cell at: LT0101_01/277/66, location: (963, 427)\n", - "Processed cell at: LT0101_01/277/66, location: (969, 439)\n", - "Processed cell at: LT0101_01/277/66, location: (968, 455)\n", - "Processed cell at: LT0101_01/277/66, location: (980, 463)\n", - "Processed cell at: LT0101_01/277/66, location: (753, 479)\n", - "Processed cell at: LT0101_01/277/66, location: (751, 493)\n", - "Processed cell at: LT0101_01/277/66, location: (826, 503)\n", - "Processed cell at: LT0101_01/277/66, location: (1260, 512)\n", - "Processed cell at: LT0101_01/277/66, location: (1260, 531)\n", - "Processed cell at: LT0101_01/277/66, location: (813, 557)\n", - "Processed cell at: LT0101_01/277/66, location: (1237, 581)\n", - "Processed cell at: LT0101_01/277/66, location: (1262, 594)\n", - "Processed cell at: LT0101_01/277/66, location: (785, 592)\n", - "Processed cell at: LT0101_01/277/66, location: (1070, 648)\n", - "Processed cell at: LT0101_01/277/66, location: (1050, 664)\n", - "Processed cell at: LT0101_01/277/66, location: (716, 687)\n", - "Processed cell at: LT0101_01/277/66, location: (737, 721)\n", - "Processed cell at: LT0101_01/277/66, location: (588, 728)\n", - "Processed cell at: LT0101_01/277/66, location: (583, 746)\n", - "Processed cell at: LT0101_01/277/66, location: (1099, 782)\n", - "Processed cell at: LT0101_01/277/66, location: (1143, 823)\n", - "Processed cell at: LT0101_01/277/66, location: (1199, 865)\n", - "Processed cell at: LT0101_01/277/66, location: (1246, 891)\n", - "Processed cell at: LT0101_01/277/66, location: (1153, 916)\n", - "Processed cell at: LT0144_01/166/27, location: (835, 343)\n", - "Processed cell at: LT0144_01/166/27, location: (901, 374)\n", - "Processed cell at: LT0144_01/166/27, location: (874, 378)\n", - "Processed cell at: LT0144_01/166/27, location: (853, 458)\n", - "Processed cell at: LT0144_01/166/27, location: (504, 476)\n", - "Processed cell at: LT0144_01/166/27, location: (641, 485)\n", - "Processed cell at: LT0144_01/166/27, location: (624, 508)\n", - "Processed cell at: LT0144_01/166/27, location: (663, 555)\n", - "Processed cell at: LT0144_01/166/27, location: (635, 583)\n", - "Processed cell at: LT0144_01/166/27, location: (668, 587)\n", - "Processed cell at: LT0144_01/166/27, location: (970, 820)\n", - "Processed cell at: LT0144_01/166/34, location: (852, 454)\n", - "Processed cell at: LT0144_01/166/34, location: (918, 521)\n", - "Processed cell at: LT0144_01/166/34, location: (912, 620)\n", - "Processed cell at: LT0144_01/166/34, location: (881, 619)\n", - "Processed cell at: LT0144_01/166/34, location: (949, 854)\n", - "Processed cell at: LT0144_01/166/50, location: (984, 268)\n", - "Processed cell at: LT0144_01/166/50, location: (1062, 379)\n", - "Processed cell at: LT0144_01/166/50, location: (833, 485)\n", - "Processed cell at: LT0094_44/319/74, location: (662, 12)\n", - "Processed cell at: LT0094_44/319/74, location: (639, 22)\n", - "Processed cell at: LT0094_44/319/74, location: (626, 52)\n", - "Processed cell at: LT0094_44/319/74, location: (642, 75)\n", - "Processed cell at: LT0094_44/319/74, location: (1000, 459)\n", - "Processed cell at: LT0094_44/319/74, location: (822, 748)\n", - "Processed cell at: LT0094_44/319/57, location: (611, 734)\n", - "Processed cell at: LT0094_44/319/57, location: (789, 737)\n", - "Processed cell at: LT0094_44/319/57, location: (620, 745)\n", - "Processed cell at: LT0094_44/319/57, location: (809, 899)\n", - "Processed cell at: LT0094_44/319/57, location: (800, 924)\n", - "Processed cell at: LT0094_44/319/57, location: (792, 932)\n", - "Processed cell at: LT0023_04/005/72, location: (291, 93)\n", - "Processed cell at: LT0023_04/005/72, location: (298, 123)\n", - "Processed cell at: LT0023_04/005/72, location: (904, 208)\n", - "Processed cell at: LT0023_04/005/72, location: (878, 213)\n", - "Processed cell at: LT0023_04/005/72, location: (83, 253)\n", - "Processed cell at: LT0023_04/005/72, location: (112, 251)\n", - "Processed cell at: LT0023_04/005/72, location: (133, 267)\n", - "Processed cell at: LT0023_04/005/72, location: (685, 388)\n", - "Processed cell at: LT0023_04/005/72, location: (325, 611)\n", - "Processed cell at: LT0023_04/005/72, location: (518, 621)\n", - "Processed cell at: LT0023_04/005/72, location: (334, 625)\n", - "Processed cell at: LT0023_04/005/72, location: (517, 638)\n", - "Processed cell at: LT0023_04/005/72, location: (338, 646)\n", - "Processed cell at: LT0023_04/005/72, location: (540, 648)\n", - "Processed cell at: LT0023_04/005/72, location: (572, 666)\n", - "Processed cell at: LT0023_04/005/72, location: (546, 673)\n", - "Processed cell at: LT0023_04/005/72, location: (341, 678)\n", - "Processed cell at: LT0023_04/005/72, location: (576, 683)\n", - "Processed cell at: LT0023_04/005/72, location: (567, 692)\n", - "Processed cell at: LT0023_04/005/8, location: (872, 460)\n", - "Processed cell at: LT0023_04/005/8, location: (762, 515)\n", - "Processed cell at: LT0023_04/005/30, location: (87, 229)\n", - "Processed cell at: LT0023_04/005/30, location: (61, 242)\n", - "Processed cell at: LT0023_04/005/30, location: (1062, 269)\n", - "Processed cell at: LT0023_04/005/30, location: (666, 308)\n", - "Processed cell at: LT0023_04/005/30, location: (664, 332)\n", - "Processed cell at: LT0023_04/005/30, location: (215, 461)\n", - "Processed cell at: LT0023_04/005/30, location: (716, 471)\n", - "Processed cell at: LT0023_04/005/30, location: (711, 496)\n", - "Processed cell at: LT0023_04/005/30, location: (369, 541)\n", - "Processed cell at: LT0023_04/005/30, location: (118, 596)\n", - "Processed cell at: LT0023_04/005/30, location: (61, 597)\n", - "Processed cell at: LT0023_04/005/30, location: (310, 639)\n", - "Processed cell at: LT0023_04/005/30, location: (332, 653)\n", - "Processed cell at: LT0023_04/005/30, location: (1178, 684)\n", - "Processed cell at: LT0023_04/005/30, location: (545, 858)\n", - "Processed cell at: LT0023_04/005/30, location: (1115, 884)\n", - "Processed cell at: LT0023_04/005/30, location: (1128, 892)\n", - "Processed cell at: LT0023_04/005/7, location: (412, 224)\n", - "Processed cell at: LT0023_04/005/7, location: (721, 471)\n", - "Processed cell at: LT0023_04/005/7, location: (721, 500)\n", - "Processed cell at: LT0023_04/005/7, location: (458, 630)\n", - "Processed cell at: LT0023_04/005/51, location: (695, 483)\n", - "Processed cell at: LT0023_04/005/51, location: (476, 584)\n", - "Processed cell at: LT0023_04/005/51, location: (499, 603)\n", - "Processed cell at: LT0023_04/005/51, location: (312, 631)\n", - "Processed cell at: LT0023_04/005/51, location: (327, 647)\n", - "Processed cell at: LT0023_04/005/51, location: (502, 663)\n", - "Processed cell at: LT0023_04/005/51, location: (320, 672)\n", - "Processed cell at: LT0023_04/005/64, location: (671, 457)\n", - "Processed cell at: LT0023_04/005/64, location: (699, 475)\n", - "Processed cell at: LT0023_04/005/64, location: (1054, 477)\n", - "Processed cell at: LT0023_04/005/64, location: (665, 480)\n", - "Processed cell at: LT0023_04/005/64, location: (696, 490)\n", - "Processed cell at: LT0023_04/005/64, location: (684, 497)\n", - "Processed cell at: LT0023_04/005/64, location: (502, 582)\n", - "Processed cell at: LT0023_04/005/64, location: (309, 599)\n", - "Processed cell at: LT0023_04/005/64, location: (331, 619)\n", - "Processed cell at: LT0023_04/005/64, location: (266, 632)\n", - "Processed cell at: LT0023_04/005/64, location: (334, 639)\n", - "Processed cell at: LT0023_04/005/64, location: (240, 644)\n", - "Processed cell at: LT0023_04/005/64, location: (528, 661)\n", - "Processed cell at: LT0023_04/005/64, location: (329, 659)\n", - "Processed cell at: LT0023_04/005/64, location: (331, 673)\n", - "Processed cell at: LT0023_04/005/64, location: (554, 686)\n", - "Processed cell at: LT0023_04/005/64, location: (1291, 975)\n", - "Processed cell at: LT0023_04/005/87, location: (291, 84)\n", - "Processed cell at: LT0023_04/005/87, location: (296, 103)\n", - "Processed cell at: LT0023_04/005/87, location: (304, 115)\n", - "Processed cell at: LT0023_04/005/87, location: (292, 127)\n", - "Processed cell at: LT0023_04/005/87, location: (394, 253)\n", - "Processed cell at: LT0023_04/005/87, location: (99, 262)\n", - "Processed cell at: LT0023_04/005/87, location: (122, 265)\n", - "Processed cell at: LT0023_04/005/87, location: (405, 280)\n", - "Processed cell at: LT0023_04/005/87, location: (138, 280)\n", - "Processed cell at: LT0023_04/005/87, location: (343, 282)\n", - "Processed cell at: LT0023_04/005/87, location: (328, 305)\n", - "Processed cell at: LT0023_04/005/87, location: (158, 337)\n", - "Processed cell at: LT0023_04/005/87, location: (345, 358)\n", - "Processed cell at: LT0023_04/005/87, location: (368, 357)\n", - "Processed cell at: LT0023_04/005/87, location: (392, 362)\n", - "Processed cell at: LT0023_04/005/87, location: (155, 497)\n", - "Processed cell at: LT0023_04/005/87, location: (193, 563)\n", - "Processed cell at: LT0023_04/005/87, location: (216, 581)\n", - "Processed cell at: LT0023_04/005/87, location: (877, 591)\n", - "Processed cell at: LT0023_04/005/87, location: (901, 593)\n", - "Processed cell at: LT0023_04/005/87, location: (270, 603)\n", - "Processed cell at: LT0023_04/005/87, location: (902, 611)\n", - "Processed cell at: LT0023_04/005/87, location: (326, 624)\n", - "Processed cell at: LT0023_04/005/87, location: (331, 645)\n", - "Processed cell at: LT0023_04/005/87, location: (799, 661)\n", - "Processed cell at: LT0023_04/005/87, location: (333, 670)\n", - "Processed cell at: LT0023_04/005/87, location: (997, 675)\n", - "Processed cell at: LT0023_04/005/87, location: (977, 686)\n", - "Processed cell at: LT0023_04/005/87, location: (333, 684)\n", - "Processed cell at: LT0023_04/005/87, location: (989, 710)\n", - "Processed cell at: LT0023_04/005/87, location: (967, 726)\n", - "Processed cell at: LT0023_04/005/87, location: (487, 770)\n", - "Processed cell at: LT0023_04/005/87, location: (1196, 818)\n", - "Processed cell at: LT0023_04/005/87, location: (1196, 837)\n", - "Processed cell at: LT0023_04/005/87, location: (1183, 847)\n", - "Processed cell at: LT0023_04/005/87, location: (69, 854)\n", - "Processed cell at: LT0023_04/005/87, location: (42, 864)\n", - "Processed cell at: LT0023_04/005/87, location: (37, 890)\n", - "Processed cell at: LT0023_04/005/87, location: (853, 936)\n", - "Processed cell at: LT0023_04/005/87, location: (839, 962)\n", - "Processed cell at: LT0023_04/005/87, location: (522, 969)\n", - "Processed cell at: LT0023_04/005/87, location: (366, 969)\n", - "Processed cell at: LT0023_04/005/87, location: (529, 995)\n", - "Processed cell at: LT0023_04/005/50, location: (705, 211)\n", - "Processed cell at: LT0023_04/005/50, location: (702, 482)\n", - "Processed cell at: LT0023_04/005/50, location: (476, 587)\n", - "Processed cell at: LT0023_04/005/50, location: (500, 596)\n", - "Processed cell at: LT0023_04/005/50, location: (497, 611)\n", - "Processed cell at: LT0023_04/005/50, location: (312, 631)\n", - "Processed cell at: LT0023_04/005/50, location: (328, 645)\n", - "Processed cell at: LT0023_04/005/50, location: (323, 662)\n", - "Processed cell at: LT0023_04/005/50, location: (321, 676)\n", - "Processed cell at: LT0023_04/005/50, location: (1141, 872)\n", - "Processed cell at: LT0023_04/005/50, location: (1138, 891)\n", - "Processed cell at: LT0038_01/245/72, location: (180, 57)\n", - "Processed cell at: LT0038_01/245/72, location: (165, 127)\n", - "Processed cell at: LT0038_01/245/72, location: (701, 177)\n", - "Processed cell at: LT0038_01/245/72, location: (557, 218)\n", - "Processed cell at: LT0038_01/245/72, location: (523, 241)\n", - "Processed cell at: LT0038_01/245/72, location: (798, 271)\n", - "Processed cell at: LT0038_01/245/72, location: (445, 324)\n", - "Processed cell at: LT0038_01/245/72, location: (781, 362)\n", - "Processed cell at: LT0038_01/245/72, location: (676, 432)\n", - "Processed cell at: LT0038_01/245/72, location: (59, 463)\n", - "Processed cell at: LT0038_01/245/72, location: (1307, 609)\n", - "Processed cell at: LT0038_01/245/72, location: (621, 677)\n", - "Processed cell at: LT0038_01/245/72, location: (632, 725)\n", - "Processed cell at: LT0038_01/245/22, location: (767, 506)\n", - "Processed cell at: LT0038_01/245/40, location: (191, 158)\n", - "Processed cell at: LT0038_01/245/40, location: (245, 164)\n", - "Processed cell at: LT0038_01/245/40, location: (564, 210)\n", - "Processed cell at: LT0038_01/245/40, location: (760, 503)\n", - "Processed cell at: LT0038_01/245/40, location: (492, 551)\n", - "Processed cell at: LT0038_01/245/40, location: (696, 647)\n", - "Processed cell at: LT0038_01/245/40, location: (593, 739)\n", - "Processed cell at: LT0038_01/245/46, location: (247, 171)\n", - "Processed cell at: LT0038_01/245/46, location: (844, 263)\n", - "Processed cell at: LT0038_01/245/46, location: (803, 279)\n", - "Processed cell at: LT0038_01/245/46, location: (59, 465)\n", - "Processed cell at: LT0038_01/245/46, location: (515, 514)\n", - "Processed cell at: LT0038_01/245/46, location: (528, 543)\n", - "Processed cell at: LT0038_01/245/46, location: (493, 554)\n", - "Processed cell at: LT0038_01/245/46, location: (697, 647)\n", - "Processed cell at: LT0038_01/245/46, location: (583, 788)\n", - "Processed cell at: LT0038_01/245/81, location: (690, 68)\n", - "Processed cell at: LT0038_01/245/81, location: (213, 116)\n", - "Processed cell at: LT0038_01/245/81, location: (161, 125)\n", - "Processed cell at: LT0038_01/245/81, location: (699, 172)\n", - "Processed cell at: LT0038_01/245/81, location: (520, 236)\n", - "Processed cell at: LT0038_01/245/81, location: (798, 269)\n", - "Processed cell at: LT0038_01/245/81, location: (763, 446)\n", - "Processed cell at: LT0038_01/245/81, location: (60, 461)\n", - "Processed cell at: LT0038_01/245/81, location: (64, 515)\n", - "Processed cell at: LT0038_01/245/81, location: (452, 522)\n", - "Processed cell at: LT0038_01/245/81, location: (718, 540)\n", - "Processed cell at: LT0038_01/245/81, location: (633, 721)\n", - "Processed cell at: LT0014_12/159/93, location: (725, 176)\n", - "Processed cell at: LT0014_12/159/93, location: (730, 196)\n", - "Processed cell at: LT0014_12/159/93, location: (715, 210)\n", - "Processed cell at: LT0014_12/159/93, location: (252, 376)\n", - "Processed cell at: LT0014_12/159/93, location: (692, 398)\n", - "Processed cell at: LT0014_12/159/93, location: (172, 401)\n", - "Processed cell at: LT0014_12/159/93, location: (641, 515)\n", - "Processed cell at: LT0014_12/159/93, location: (546, 522)\n", - "Processed cell at: LT0014_12/159/93, location: (577, 536)\n", - "Processed cell at: LT0014_12/159/93, location: (179, 709)\n", - "Processed cell at: LT0014_12/159/93, location: (88, 716)\n", - "Processed cell at: LT0014_12/159/93, location: (132, 805)\n", - "Processed cell at: LT0014_12/159/93, location: (846, 828)\n", - "Processed cell at: LT0014_12/159/93, location: (1202, 858)\n", - "Processed cell at: LT0014_12/159/93, location: (1186, 901)\n", - "Processed cell at: LT0014_12/159/93, location: (42, 907)\n", - "Processed cell at: LT0014_12/159/93, location: (1263, 937)\n", - "Processed cell at: LT0014_12/159/93, location: (1307, 962)\n", - "Processed cell at: LT0014_12/159/93, location: (1266, 974)\n", - "Processed cell at: LT0014_12/159/93, location: (1295, 998)\n", - "Processed cell at: LT0014_12/159/70, location: (728, 177)\n", - "Processed cell at: LT0014_12/159/70, location: (734, 190)\n", - "Processed cell at: LT0014_12/159/70, location: (1019, 196)\n", - "Processed cell at: LT0014_12/159/70, location: (724, 205)\n", - "Processed cell at: LT0014_12/159/70, location: (1080, 213)\n", - "Processed cell at: LT0014_12/159/70, location: (1053, 219)\n", - "Processed cell at: LT0014_12/159/70, location: (1067, 225)\n", - "Processed cell at: LT0014_12/159/70, location: (1075, 237)\n", - "Processed cell at: LT0014_12/159/70, location: (1081, 331)\n", - "Processed cell at: LT0014_12/159/70, location: (256, 374)\n", - "Processed cell at: LT0014_12/159/70, location: (708, 382)\n", - "Processed cell at: LT0014_12/159/70, location: (256, 405)\n", - "Processed cell at: LT0014_12/159/70, location: (721, 418)\n", - "Processed cell at: LT0014_12/159/70, location: (229, 423)\n", - "Processed cell at: LT0014_12/159/70, location: (205, 427)\n", - "Processed cell at: LT0014_12/159/70, location: (112, 470)\n", - "Processed cell at: LT0014_12/159/70, location: (567, 528)\n", - "Processed cell at: LT0014_12/159/70, location: (910, 657)\n", - "Processed cell at: LT0014_12/159/70, location: (901, 667)\n", - "Processed cell at: LT0014_12/159/70, location: (919, 670)\n", - "Processed cell at: LT0014_12/159/70, location: (895, 674)\n", - "Processed cell at: LT0014_12/159/70, location: (928, 679)\n", - "Processed cell at: LT0014_12/159/70, location: (845, 682)\n", - "Processed cell at: LT0014_12/159/70, location: (906, 690)\n", - "Processed cell at: LT0014_12/159/70, location: (920, 687)\n", - "Processed cell at: LT0014_12/159/70, location: (854, 690)\n", - "Processed cell at: LT0014_12/159/70, location: (838, 703)\n", - "Processed cell at: LT0014_12/159/70, location: (923, 702)\n", - "Processed cell at: LT0014_12/159/70, location: (23, 718)\n", - "Processed cell at: LT0014_12/159/70, location: (777, 733)\n", - "Processed cell at: LT0014_12/159/70, location: (775, 751)\n", - "Processed cell at: LT0014_12/159/70, location: (765, 770)\n", - "Processed cell at: LT0014_12/159/70, location: (356, 799)\n", - "Processed cell at: LT0014_12/159/70, location: (779, 798)\n", - "Processed cell at: LT0014_12/159/70, location: (794, 805)\n", - "Processed cell at: LT0014_12/159/70, location: (787, 822)\n", - "Processed cell at: LT0014_12/159/70, location: (363, 825)\n", - "Processed cell at: LT0014_12/159/70, location: (362, 847)\n", - "Processed cell at: LT0014_12/159/70, location: (1270, 854)\n", - "Processed cell at: LT0014_12/159/70, location: (311, 864)\n", - "Processed cell at: LT0014_12/159/70, location: (1252, 869)\n", - "Processed cell at: LT0014_12/159/70, location: (1273, 878)\n", - "Processed cell at: LT0014_12/159/70, location: (1208, 878)\n", - "Processed cell at: LT0014_12/159/70, location: (118, 883)\n", - "Processed cell at: LT0014_12/159/70, location: (308, 881)\n", - "Processed cell at: LT0014_12/159/70, location: (322, 897)\n", - "Processed cell at: LT0014_12/159/70, location: (1288, 917)\n", - "Processed cell at: LT0014_12/159/70, location: (723, 932)\n", - "Processed cell at: LT0014_12/159/70, location: (389, 937)\n", - "Processed cell at: LT0014_12/159/70, location: (688, 937)\n", - "Processed cell at: LT0014_12/159/70, location: (684, 972)\n", - "Processed cell at: LT0014_12/159/70, location: (711, 979)\n", - "Processed cell at: LT0014_12/159/70, location: (336, 996)\n", - "Processed cell at: LT0014_12/159/77, location: (1080, 270)\n", - "Processed cell at: LT0014_12/159/77, location: (1086, 299)\n", - "Processed cell at: LT0014_12/159/77, location: (1082, 328)\n", - "Processed cell at: LT0014_12/159/77, location: (1007, 336)\n", - "Processed cell at: LT0014_12/159/77, location: (746, 390)\n", - "Processed cell at: LT0014_12/159/77, location: (208, 394)\n", - "Processed cell at: LT0014_12/159/77, location: (392, 447)\n", - "Processed cell at: LT0014_12/159/77, location: (568, 533)\n", - "Processed cell at: LT0014_12/159/77, location: (586, 568)\n", - "Processed cell at: LT0014_12/159/77, location: (630, 585)\n", - "Processed cell at: LT0014_12/159/77, location: (87, 721)\n", - "Processed cell at: LT0014_12/159/77, location: (142, 728)\n", - "Processed cell at: LT0014_12/159/77, location: (108, 736)\n", - "Processed cell at: LT0014_12/159/77, location: (113, 794)\n", - "Processed cell at: LT0014_12/159/77, location: (1204, 870)\n", - "Processed cell at: LT0014_12/159/77, location: (122, 885)\n", - "Processed cell at: LT0014_12/159/77, location: (475, 909)\n", - "Processed cell at: LT0014_12/159/77, location: (1284, 912)\n", - "Processed cell at: LT0014_12/159/77, location: (1263, 928)\n", - "Processed cell at: LT0014_12/159/77, location: (723, 928)\n", - "Processed cell at: LT0014_12/159/77, location: (687, 936)\n", - "Processed cell at: LT0014_12/159/77, location: (415, 950)\n", - "Processed cell at: LT0014_12/159/77, location: (1286, 973)\n", - "Processed cell at: LT0014_12/159/59, location: (982, 136)\n", - "Processed cell at: LT0014_12/159/59, location: (956, 153)\n", - "Processed cell at: LT0014_12/159/59, location: (1017, 195)\n", - "Processed cell at: LT0014_12/159/59, location: (33, 508)\n", - "Processed cell at: LT0014_12/159/59, location: (242, 685)\n", - "Processed cell at: LT0014_12/159/59, location: (1285, 870)\n", - "Processed cell at: LT0014_12/159/59, location: (1232, 869)\n", - "Processed cell at: LT0014_12/159/59, location: (376, 875)\n", - "Processed cell at: LT0014_12/159/59, location: (108, 880)\n", - "Processed cell at: LT0014_12/159/59, location: (90, 911)\n", - "Processed cell at: LT0014_12/159/59, location: (552, 1002)\n", - "Processed cell at: LT0028_14/129/55, location: (695, 71)\n", - "Processed cell at: LT0028_14/129/55, location: (716, 84)\n", - "Processed cell at: LT0028_14/129/55, location: (695, 110)\n", - "Processed cell at: LT0028_14/129/55, location: (719, 122)\n", - "Processed cell at: LT0028_14/129/55, location: (814, 212)\n", - "Processed cell at: LT0028_14/129/55, location: (797, 231)\n", - "Processed cell at: LT0028_14/129/55, location: (674, 242)\n", - "Processed cell at: LT0028_14/129/55, location: (809, 248)\n", - "Processed cell at: LT0028_14/129/55, location: (792, 251)\n", - "Processed cell at: LT0028_14/129/55, location: (689, 254)\n", - "Processed cell at: LT0028_14/129/55, location: (659, 258)\n", - "Processed cell at: LT0028_14/129/55, location: (694, 268)\n", - "Processed cell at: LT0028_14/129/55, location: (681, 279)\n", - "Processed cell at: LT0028_14/129/55, location: (1121, 436)\n", - "Processed cell at: LT0028_14/129/55, location: (1089, 458)\n", - "Processed cell at: LT0028_14/129/55, location: (1115, 452)\n", - "Processed cell at: LT0028_14/129/55, location: (726, 637)\n", - "Processed cell at: LT0028_14/129/91, location: (353, 79)\n", - "Processed cell at: LT0028_14/129/91, location: (305, 85)\n", - "Processed cell at: LT0028_14/129/91, location: (326, 85)\n", - "Processed cell at: LT0028_14/129/91, location: (345, 95)\n", - "Processed cell at: LT0028_14/129/91, location: (1073, 95)\n", - "Processed cell at: LT0028_14/129/91, location: (1045, 98)\n", - "Processed cell at: LT0028_14/129/91, location: (294, 98)\n", - "Processed cell at: LT0028_14/129/91, location: (1226, 131)\n", - "Processed cell at: LT0028_14/129/91, location: (805, 136)\n", - "Processed cell at: LT0028_14/129/91, location: (776, 152)\n", - "Processed cell at: LT0028_14/129/91, location: (1230, 152)\n", - "Processed cell at: LT0028_14/129/91, location: (1050, 169)\n", - "Processed cell at: LT0028_14/129/91, location: (1235, 173)\n", - "Processed cell at: LT0028_14/129/91, location: (1205, 177)\n", - "Processed cell at: LT0028_14/129/91, location: (718, 185)\n", - "Processed cell at: LT0028_14/129/91, location: (1024, 190)\n", - "Processed cell at: LT0028_14/129/91, location: (620, 207)\n", - "Processed cell at: LT0028_14/129/91, location: (633, 196)\n", - "Processed cell at: LT0028_14/129/91, location: (708, 206)\n", - "Processed cell at: LT0028_14/129/91, location: (647, 217)\n", - "Processed cell at: LT0028_14/129/91, location: (647, 242)\n", - "Processed cell at: LT0028_14/129/91, location: (275, 246)\n", - "Processed cell at: LT0028_14/129/91, location: (295, 260)\n", - "Processed cell at: LT0028_14/129/91, location: (257, 265)\n", - "Processed cell at: LT0028_14/129/91, location: (815, 271)\n", - "Processed cell at: LT0028_14/129/91, location: (273, 267)\n", - "Processed cell at: LT0028_14/129/91, location: (574, 270)\n", - "Processed cell at: LT0028_14/129/91, location: (594, 273)\n", - "Processed cell at: LT0028_14/129/91, location: (816, 295)\n", - "Processed cell at: LT0028_14/129/91, location: (1055, 299)\n", - "Processed cell at: LT0028_14/129/91, location: (816, 319)\n", - "Processed cell at: LT0028_14/129/91, location: (1060, 311)\n", - "Processed cell at: LT0028_14/129/91, location: (582, 323)\n", - "Processed cell at: LT0028_14/129/91, location: (1046, 326)\n", - "Processed cell at: LT0028_14/129/91, location: (606, 342)\n", - "Processed cell at: LT0028_14/129/91, location: (1034, 348)\n", - "Processed cell at: LT0028_14/129/91, location: (546, 452)\n", - "Processed cell at: LT0028_14/129/91, location: (526, 475)\n", - "Processed cell at: LT0028_14/129/91, location: (817, 534)\n", - "Processed cell at: LT0028_14/129/91, location: (844, 547)\n", - "Processed cell at: LT0028_14/129/91, location: (906, 649)\n", - "Processed cell at: LT0028_14/129/91, location: (924, 682)\n", - "Processed cell at: LT0028_14/129/91, location: (824, 774)\n", - "Processed cell at: LT0028_14/129/91, location: (1121, 775)\n", - "Processed cell at: LT0028_14/129/91, location: (1146, 780)\n", - "Processed cell at: LT0028_14/129/91, location: (840, 795)\n", - "Processed cell at: LT0028_14/129/91, location: (1183, 818)\n", - "Processed cell at: LT0028_14/129/91, location: (1158, 833)\n", - "Processed cell at: LT0028_14/129/66, location: (906, 33)\n", - "Processed cell at: LT0028_14/129/66, location: (934, 32)\n", - "Processed cell at: LT0028_14/129/66, location: (891, 41)\n", - "Processed cell at: LT0028_14/129/66, location: (930, 44)\n", - "Processed cell at: LT0028_14/129/66, location: (916, 54)\n", - "Processed cell at: LT0028_14/129/66, location: (690, 74)\n", - "Processed cell at: LT0028_14/129/66, location: (723, 77)\n", - "Processed cell at: LT0028_14/129/66, location: (703, 103)\n", - "Processed cell at: LT0028_14/129/66, location: (721, 124)\n", - "Processed cell at: LT0028_14/129/66, location: (706, 152)\n", - "Processed cell at: LT0028_14/129/66, location: (1025, 163)\n", - "Processed cell at: LT0028_14/129/66, location: (1000, 179)\n", - "Processed cell at: LT0028_14/129/66, location: (1220, 183)\n", - "Processed cell at: LT0028_14/129/66, location: (1202, 196)\n", - "Processed cell at: LT0028_14/129/66, location: (678, 244)\n", - "Processed cell at: LT0028_14/129/66, location: (652, 244)\n", - "Processed cell at: LT0028_14/129/66, location: (816, 260)\n", - "Processed cell at: LT0028_14/129/66, location: (680, 275)\n", - "Processed cell at: LT0028_14/129/66, location: (812, 281)\n", - "Processed cell at: LT0028_14/129/66, location: (817, 302)\n", - "Processed cell at: LT0028_14/129/66, location: (1317, 310)\n", - "Processed cell at: LT0028_14/129/66, location: (858, 327)\n", - "Processed cell at: LT0028_14/129/66, location: (1326, 334)\n", - "Processed cell at: LT0028_14/129/66, location: (858, 345)\n", - "Processed cell at: LT0028_14/129/66, location: (823, 365)\n", - "Processed cell at: LT0028_14/129/66, location: (843, 372)\n", - "Processed cell at: LT0028_14/129/66, location: (555, 390)\n", - "Processed cell at: LT0028_14/129/66, location: (578, 387)\n", - "Processed cell at: LT0028_14/129/66, location: (817, 435)\n", - "Processed cell at: LT0028_14/129/66, location: (1114, 444)\n", - "Processed cell at: LT0028_14/129/66, location: (826, 460)\n", - "Processed cell at: LT0028_14/129/66, location: (1089, 465)\n", - "Processed cell at: LT0028_14/129/66, location: (1116, 461)\n", - "Processed cell at: LT0028_14/129/66, location: (1026, 474)\n", - "Processed cell at: LT0028_14/129/66, location: (1001, 472)\n", - "Processed cell at: LT0028_14/129/66, location: (824, 487)\n", - "Processed cell at: LT0028_14/129/66, location: (1018, 499)\n", - "Processed cell at: LT0028_14/129/66, location: (318, 500)\n", - "Processed cell at: LT0028_14/129/66, location: (322, 527)\n", - "Processed cell at: LT0084_46/003/80, location: (396, 24)\n", - "Processed cell at: LT0084_46/003/80, location: (379, 35)\n", - "Processed cell at: LT0084_46/003/80, location: (362, 36)\n", - "Processed cell at: LT0084_46/003/80, location: (405, 114)\n", - "Processed cell at: LT0084_46/003/80, location: (216, 125)\n", - "Processed cell at: LT0084_46/003/80, location: (315, 124)\n", - "Processed cell at: LT0084_46/003/80, location: (386, 130)\n", - "Processed cell at: LT0084_46/003/80, location: (292, 136)\n", - "Processed cell at: LT0084_46/003/80, location: (232, 142)\n", - "Processed cell at: LT0084_46/003/80, location: (378, 140)\n", - "Processed cell at: LT0084_46/003/80, location: (210, 149)\n", - "Processed cell at: LT0084_46/003/80, location: (360, 153)\n", - "Processed cell at: LT0084_46/003/80, location: (294, 154)\n", - "Processed cell at: LT0084_46/003/80, location: (272, 163)\n", - "Processed cell at: LT0084_46/003/80, location: (177, 244)\n", - "Processed cell at: LT0084_46/003/80, location: (159, 244)\n", - "Processed cell at: LT0084_46/003/80, location: (143, 242)\n", - "Processed cell at: LT0084_46/003/80, location: (126, 255)\n", - "Processed cell at: LT0084_46/003/80, location: (159, 257)\n", - "Processed cell at: LT0084_46/003/80, location: (202, 291)\n", - "Processed cell at: LT0084_46/003/80, location: (191, 312)\n", - "Processed cell at: LT0084_46/003/80, location: (936, 319)\n", - "Processed cell at: LT0084_46/003/80, location: (190, 325)\n", - "Processed cell at: LT0084_46/003/80, location: (168, 337)\n", - "Processed cell at: LT0084_46/003/80, location: (930, 338)\n", - "Processed cell at: LT0084_46/003/80, location: (127, 343)\n", - "Processed cell at: LT0084_46/003/80, location: (107, 339)\n", - "Processed cell at: LT0084_46/003/80, location: (104, 360)\n", - "Processed cell at: LT0084_46/003/80, location: (915, 361)\n", - "Processed cell at: LT0084_46/003/80, location: (128, 368)\n", - "Processed cell at: LT0084_46/003/80, location: (941, 457)\n", - "Processed cell at: LT0084_46/003/80, location: (959, 470)\n", - "Processed cell at: LT0084_46/003/80, location: (926, 472)\n", - "Processed cell at: LT0084_46/003/80, location: (935, 484)\n", - "Processed cell at: LT0084_46/003/80, location: (1055, 522)\n", - "Processed cell at: LT0084_46/003/80, location: (1036, 535)\n", - "Processed cell at: LT0084_46/003/80, location: (152, 550)\n", - "Processed cell at: LT0084_46/003/80, location: (1048, 545)\n", - "Processed cell at: LT0084_46/003/80, location: (1030, 556)\n", - "Processed cell at: LT0084_46/003/80, location: (187, 573)\n", - "Processed cell at: LT0084_46/003/80, location: (164, 575)\n", - "Processed cell at: LT0084_46/003/80, location: (813, 612)\n", - "Processed cell at: LT0084_46/003/80, location: (448, 630)\n", - "Processed cell at: LT0084_46/003/80, location: (783, 631)\n", - "Processed cell at: LT0084_46/003/80, location: (804, 635)\n", - "Processed cell at: LT0084_46/003/80, location: (775, 655)\n", - "Processed cell at: LT0084_46/003/80, location: (450, 656)\n", - "Processed cell at: LT0084_46/003/80, location: (647, 662)\n", - "Processed cell at: LT0084_46/003/80, location: (450, 675)\n", - "Processed cell at: LT0084_46/003/80, location: (654, 680)\n", - "Processed cell at: LT0084_46/003/80, location: (549, 682)\n", - "Processed cell at: LT0084_46/003/80, location: (454, 696)\n", - "Processed cell at: LT0084_46/003/80, location: (670, 705)\n", - "Processed cell at: LT0084_46/003/80, location: (549, 708)\n", - "Processed cell at: LT0084_46/003/80, location: (574, 711)\n", - "Processed cell at: LT0084_46/003/80, location: (583, 725)\n", - "Processed cell at: LT0084_46/003/80, location: (668, 866)\n", - "Processed cell at: LT0084_46/003/80, location: (634, 884)\n", - "Processed cell at: LT0084_46/003/56, location: (381, 19)\n", - "Processed cell at: LT0084_46/003/56, location: (417, 20)\n", - "Processed cell at: LT0084_46/003/56, location: (398, 17)\n", - "Processed cell at: LT0084_46/003/56, location: (406, 26)\n", - "Processed cell at: LT0084_46/003/56, location: (392, 34)\n", - "Processed cell at: LT0084_46/003/56, location: (218, 94)\n", - "Processed cell at: LT0084_46/003/56, location: (222, 122)\n", - "Processed cell at: LT0084_46/003/56, location: (226, 139)\n", - "Processed cell at: LT0084_46/003/56, location: (229, 153)\n", - "Processed cell at: LT0084_46/003/56, location: (128, 167)\n", - "Processed cell at: LT0084_46/003/56, location: (153, 178)\n", - "Processed cell at: LT0084_46/003/56, location: (100, 379)\n", - "Processed cell at: LT0084_46/003/56, location: (99, 398)\n", - "Processed cell at: LT0084_46/003/56, location: (147, 536)\n", - "Processed cell at: LT0084_46/003/56, location: (165, 560)\n", - "Processed cell at: LT0084_46/003/56, location: (187, 567)\n", - "Processed cell at: LT0084_46/003/56, location: (1260, 622)\n", - "Processed cell at: LT0084_46/003/56, location: (1236, 628)\n", - "Processed cell at: LT0084_46/003/56, location: (1249, 633)\n", - "Processed cell at: LT0084_46/003/56, location: (1228, 639)\n", - "Processed cell at: LT0084_46/003/56, location: (1240, 649)\n", - "Processed cell at: LT0084_46/003/56, location: (537, 683)\n", - "Processed cell at: LT0084_46/003/56, location: (540, 706)\n", - "Processed cell at: LT0084_46/003/56, location: (573, 720)\n", - "Processed cell at: LT0084_46/003/56, location: (559, 712)\n", - "Processed cell at: LT0084_46/003/56, location: (774, 756)\n", - "Processed cell at: LT0084_46/003/56, location: (793, 783)\n", - "Processed cell at: LT0084_46/003/56, location: (774, 774)\n", - "Processed cell at: LT0084_46/003/56, location: (658, 828)\n", - "Processed cell at: LT0084_46/003/56, location: (1007, 854)\n", - "Processed cell at: LT0084_46/003/56, location: (1022, 863)\n", - "Processed cell at: LT0084_46/003/91, location: (403, 115)\n", - "Processed cell at: LT0084_46/003/91, location: (338, 121)\n", - "Processed cell at: LT0084_46/003/91, location: (167, 122)\n", - "Processed cell at: LT0084_46/003/91, location: (148, 137)\n", - "Processed cell at: LT0084_46/003/91, location: (319, 133)\n", - 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"Processed cell at: LT0066_19/287/1, location: (1223, 910)\n", - "Processed cell at: LT0066_19/287/14, location: (908, 89)\n", - "Processed cell at: LT0066_19/287/14, location: (739, 104)\n", - "Processed cell at: LT0066_19/287/14, location: (774, 120)\n", - "Processed cell at: LT0066_19/287/14, location: (936, 122)\n", - "Processed cell at: LT0066_19/287/14, location: (837, 193)\n", - "Processed cell at: LT0066_19/287/14, location: (1067, 241)\n", - "Processed cell at: LT0066_19/287/14, location: (694, 249)\n", - "Processed cell at: LT0066_19/287/14, location: (383, 271)\n", - "Processed cell at: LT0066_19/287/14, location: (1015, 273)\n", - "Processed cell at: LT0066_19/287/14, location: (645, 288)\n", - "Processed cell at: LT0066_19/287/14, location: (707, 314)\n", - "Processed cell at: LT0066_19/287/14, location: (749, 337)\n", - "Processed cell at: LT0066_19/287/14, location: (757, 348)\n", - "Processed cell at: LT0066_19/287/14, location: (1318, 444)\n", - "Processed cell at: LT0066_19/287/14, location: (1061, 558)\n", - "Processed cell at: LT0066_19/287/14, location: (292, 572)\n", - "Processed cell at: LT0066_19/287/14, location: (1052, 570)\n", - "Processed cell at: LT0066_19/287/14, location: (1090, 596)\n", - "Processed cell at: LT0066_19/287/14, location: (545, 601)\n", - "Processed cell at: LT0066_19/287/14, location: (594, 610)\n", - "Processed cell at: LT0066_19/287/14, location: (1123, 641)\n", - "Processed cell at: LT0066_19/287/14, location: (1285, 696)\n", - "Processed cell at: LT0066_19/287/14, location: (1280, 744)\n", - "Processed cell at: LT0066_19/287/14, location: (1204, 800)\n", - "Processed cell at: LT0066_19/287/14, location: (1241, 855)\n", - "Processed cell at: LT0066_19/287/75, location: (699, 144)\n", - "Processed cell at: LT0066_19/287/75, location: (715, 147)\n", - "Processed cell at: LT0066_19/287/75, location: (685, 159)\n", - "Processed cell at: LT0066_19/287/75, location: (710, 159)\n", - "Processed cell at: LT0066_19/287/75, location: (721, 165)\n", - "Processed cell at: LT0066_19/287/75, location: (696, 171)\n", - "Processed cell at: LT0066_19/287/75, location: (1324, 252)\n", - "Processed cell at: LT0066_19/287/75, location: (744, 255)\n", - "Processed cell at: LT0066_19/287/75, location: (761, 264)\n", - "Processed cell at: LT0066_19/287/75, location: (1319, 264)\n", - "Processed cell at: LT0066_19/287/75, location: (1200, 266)\n", - "Processed cell at: LT0066_19/287/75, location: (738, 270)\n", - "Processed cell at: LT0066_19/287/75, location: (1308, 271)\n", - "Processed cell at: LT0066_19/287/75, location: (1215, 277)\n", - "Processed cell at: LT0066_19/287/75, location: (753, 282)\n", - "Processed cell at: LT0066_19/287/75, location: (1321, 280)\n", - "Processed cell at: LT0066_19/287/75, location: (774, 288)\n", - "Processed cell at: LT0066_19/287/75, location: (1195, 279)\n", - "Processed cell at: LT0066_19/287/75, location: (1239, 290)\n", - "Processed cell at: LT0066_19/287/75, location: (1133, 291)\n", - "Processed cell at: LT0066_19/287/75, location: (1126, 303)\n", - "Processed cell at: LT0066_19/287/75, location: (1156, 307)\n", - "Processed cell at: LT0066_19/287/75, location: (1105, 306)\n", - "Processed cell at: LT0066_19/287/75, location: (1105, 320)\n", - "Processed cell at: LT0066_19/287/75, location: (1122, 317)\n", - "Processed cell at: LT0066_19/287/75, location: (727, 330)\n", - "Processed cell at: LT0066_19/287/75, location: (718, 337)\n", - "Processed cell at: LT0066_19/287/75, location: (706, 344)\n", - "Processed cell at: LT0066_19/287/75, location: (723, 353)\n", - "Processed cell at: LT0066_19/287/75, location: (1238, 364)\n", - "Processed cell at: LT0066_19/287/75, location: (731, 364)\n", - "Processed cell at: LT0066_19/287/75, location: (721, 370)\n", - "Processed cell at: LT0066_19/287/75, location: (1222, 373)\n", - "Processed cell at: LT0066_19/287/75, location: (1241, 380)\n", - "Processed cell at: LT0066_19/287/75, location: (1212, 381)\n", - "Processed cell at: LT0066_19/287/75, location: (1229, 387)\n", - "Processed cell at: LT0066_19/287/75, location: (1216, 391)\n", - "Processed cell at: LT0066_19/287/75, location: (1226, 400)\n", - "Processed cell at: LT0066_19/287/75, location: (1211, 398)\n", - "Processed cell at: LT0066_19/287/75, location: (503, 399)\n", - "Processed cell at: LT0066_19/287/75, location: (488, 410)\n", - "Processed cell at: LT0066_19/287/75, location: (507, 412)\n", - "Processed cell at: LT0066_19/287/75, location: (1217, 408)\n", - "Processed cell at: LT0066_19/287/75, location: (471, 417)\n", - "Processed cell at: LT0066_19/287/75, location: (1151, 419)\n", - "Processed cell at: LT0066_19/287/75, location: (1137, 420)\n", - "Processed cell at: LT0066_19/287/75, location: (1124, 424)\n", - "Processed cell at: LT0066_19/287/75, location: (1110, 427)\n", - "Processed cell at: LT0066_19/287/75, location: (462, 429)\n", - "Processed cell at: LT0066_19/287/75, location: (1154, 430)\n", - "Processed cell at: LT0066_19/287/75, location: (1138, 439)\n", - "Processed cell at: LT0066_19/287/75, location: (1124, 444)\n", - "Processed cell at: LT0066_19/287/75, location: (1001, 453)\n", - "Processed cell at: LT0066_19/287/75, location: (1104, 453)\n", - "Processed cell at: LT0066_19/287/75, location: (213, 467)\n", - "Processed cell at: LT0066_19/287/75, location: (195, 480)\n", - "Processed cell at: LT0066_19/287/75, location: (219, 479)\n", - "Processed cell at: LT0066_19/287/75, location: (208, 486)\n", - "Processed cell at: LT0066_19/287/75, location: (225, 492)\n", - "Processed cell at: LT0066_19/287/75, location: (764, 504)\n", - "Processed cell at: LT0066_19/287/75, location: (199, 496)\n", - "Processed cell at: LT0066_19/287/75, location: (215, 499)\n", - "Processed cell at: LT0066_19/287/75, location: (229, 504)\n", - "Processed cell at: LT0066_19/287/75, location: (1167, 510)\n", - "Processed cell at: LT0066_19/287/75, location: (208, 509)\n", - "Processed cell at: LT0066_19/287/75, location: (218, 510)\n", - "Processed cell at: LT0066_19/287/75, location: (1180, 515)\n", - "Processed cell at: LT0066_19/287/75, location: (1159, 521)\n", - "Processed cell at: LT0066_19/287/75, location: (1190, 519)\n", - "Processed cell at: LT0066_19/287/75, location: (1188, 532)\n", - 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"Processed cell at: LT0066_19/287/75, location: (479, 786)\n", - "Processed cell at: LT0066_19/287/75, location: (408, 792)\n", - "Processed cell at: LT0066_19/287/75, location: (473, 795)\n", - "Processed cell at: LT0066_19/287/75, location: (427, 799)\n", - "Processed cell at: LT0066_19/287/75, location: (533, 800)\n", - "Processed cell at: LT0066_19/287/75, location: (410, 804)\n", - "Processed cell at: LT0066_19/287/75, location: (552, 804)\n", - "Processed cell at: LT0066_19/287/75, location: (425, 813)\n", - "Processed cell at: LT0066_19/287/75, location: (528, 810)\n", - "Processed cell at: LT0066_19/287/75, location: (545, 813)\n", - "Processed cell at: LT0066_19/287/75, location: (539, 826)\n", - "Processed cell at: LT0066_19/287/75, location: (450, 897)\n", - "Processed cell at: LT0066_19/287/75, location: (512, 894)\n", - "Processed cell at: LT0066_19/287/75, location: (455, 910)\n", - "Processed cell at: LT0066_19/287/75, location: (478, 917)\n", - "Processed cell at: LT0066_19/287/75, location: (504, 929)\n", - 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"Processed cell at: LT0066_19/287/87, location: (570, 601)\n", - "Processed cell at: LT0066_19/287/87, location: (607, 604)\n", - "Processed cell at: LT0066_19/287/87, location: (1301, 604)\n", - "Processed cell at: LT0066_19/287/87, location: (1284, 605)\n", - "Processed cell at: LT0066_19/287/87, location: (584, 613)\n", - "Processed cell at: LT0066_19/287/87, location: (597, 616)\n", - "Processed cell at: LT0066_19/287/87, location: (1288, 613)\n", - "Processed cell at: LT0066_19/287/87, location: (1284, 628)\n", - "Processed cell at: LT0066_19/287/87, location: (576, 619)\n", - "Processed cell at: LT0066_19/287/87, location: (1190, 732)\n", - "Processed cell at: LT0066_19/287/87, location: (1193, 755)\n", - "Processed cell at: LT0066_19/287/87, location: (1178, 756)\n", - "Processed cell at: LT0066_19/287/87, location: (421, 778)\n", - "Processed cell at: LT0066_19/287/87, location: (431, 788)\n", - "Processed cell at: LT0066_19/287/87, location: (413, 794)\n", - "Processed cell at: LT0066_19/287/87, location: (1240, 796)\n", - 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"Processed cell at: LT0027_44/030/74, location: (273, 392)\n", - "Processed cell at: LT0027_44/030/74, location: (815, 396)\n", - "Processed cell at: LT0027_44/030/74, location: (794, 406)\n", - "Processed cell at: LT0027_44/030/74, location: (284, 416)\n", - "Processed cell at: LT0027_44/030/74, location: (265, 428)\n", - "Processed cell at: LT0027_44/030/74, location: (269, 456)\n", - "Processed cell at: LT0027_44/030/74, location: (823, 493)\n", - "Processed cell at: LT0027_44/030/74, location: (306, 551)\n", - "Processed cell at: LT0027_44/030/74, location: (281, 548)\n", - "Processed cell at: LT0027_44/030/74, location: (238, 555)\n", - "Processed cell at: LT0027_44/030/74, location: (752, 558)\n", - "Processed cell at: LT0027_44/030/74, location: (255, 579)\n", - "Processed cell at: LT0027_44/030/74, location: (791, 576)\n", - "Processed cell at: LT0027_44/030/74, location: (761, 591)\n", - "Processed cell at: LT0027_44/030/74, location: (193, 610)\n", - "Processed cell at: LT0027_44/030/74, location: (232, 623)\n", - "Processed cell at: LT0027_44/030/74, location: (178, 623)\n", - "Processed cell at: LT0027_44/030/74, location: (254, 636)\n", - "Processed cell at: LT0027_44/030/74, location: (193, 631)\n", - "Processed cell at: LT0027_44/030/74, location: (229, 644)\n", - "Processed cell at: LT0027_44/030/74, location: (208, 647)\n", - "Processed cell at: LT0027_44/030/74, location: (185, 642)\n", - "Processed cell at: LT0027_44/030/74, location: (752, 671)\n", - "Processed cell at: LT0027_44/030/74, location: (727, 677)\n", - "Processed cell at: LT0027_44/030/74, location: (750, 703)\n", - "Processed cell at: LT0027_44/030/74, location: (732, 706)\n", - "Processed cell at: LT0027_44/030/74, location: (831, 790)\n", - "Processed cell at: LT0027_44/030/74, location: (845, 808)\n", - "Processed cell at: LT0027_44/030/74, location: (826, 812)\n", - "Processed cell at: LT0027_44/030/74, location: (868, 821)\n", - "Processed cell at: LT0027_44/030/74, location: (842, 825)\n", - "Processed cell at: LT0027_44/030/74, location: (1006, 876)\n", - "Processed cell at: LT0027_44/030/74, location: (980, 887)\n", - "Processed cell at: LT0027_44/030/74, location: (997, 904)\n", - "Processed cell at: LT0027_44/030/74, location: (944, 908)\n", - "Processed cell at: LT0027_44/030/74, location: (968, 916)\n", - "Processed cell at: LT0027_44/030/58, location: (817, 148)\n", - "Processed cell at: LT0027_44/030/58, location: (840, 153)\n", - "Processed cell at: LT0027_44/030/58, location: (850, 169)\n", - "Processed cell at: LT0027_44/030/58, location: (870, 183)\n", - "Processed cell at: LT0027_44/030/58, location: (246, 195)\n", - "Processed cell at: LT0027_44/030/58, location: (270, 198)\n", - "Processed cell at: LT0027_44/030/58, location: (241, 230)\n", - "Processed cell at: LT0027_44/030/58, location: (264, 238)\n", - "Processed cell at: LT0027_44/030/58, location: (949, 239)\n", - "Processed cell at: LT0027_44/030/58, location: (966, 251)\n", - "Processed cell at: LT0027_44/030/58, location: (939, 257)\n", - "Processed cell at: LT0027_44/030/58, location: (945, 316)\n", - "Processed cell at: LT0027_44/030/58, location: (806, 333)\n", - "Processed cell at: LT0027_44/030/58, location: (776, 341)\n", - "Processed cell at: LT0027_44/030/58, location: (822, 359)\n", - "Processed cell at: LT0027_44/030/58, location: (792, 357)\n", - "Processed cell at: LT0027_44/030/58, location: (880, 365)\n", - "Processed cell at: LT0027_44/030/58, location: (894, 372)\n", - "Processed cell at: LT0027_44/030/58, location: (884, 391)\n", - "Processed cell at: LT0027_44/030/58, location: (1220, 405)\n", - "Processed cell at: LT0027_44/030/58, location: (838, 481)\n", - "Processed cell at: LT0027_44/030/58, location: (802, 487)\n", - "Processed cell at: LT0027_44/030/58, location: (810, 504)\n", - "Processed cell at: LT0027_44/030/58, location: (217, 576)\n", - "Processed cell at: LT0027_44/030/58, location: (191, 578)\n", - "Processed cell at: LT0027_44/030/58, location: (219, 604)\n", - "Processed cell at: LT0027_44/030/58, location: (184, 596)\n", - "Processed cell at: LT0027_44/030/58, location: (196, 606)\n", - "Processed cell at: LT0027_44/030/58, location: (742, 624)\n", - "Processed cell at: LT0027_44/030/58, location: (755, 669)\n", - "Processed cell at: LT0027_44/030/58, location: (737, 687)\n", - "Processed cell at: LT0027_44/030/58, location: (749, 710)\n", - "Processed cell at: LT0027_44/030/58, location: (736, 722)\n", - "Processed cell at: LT0027_44/030/58, location: (844, 798)\n", - "Processed cell at: LT0027_44/030/58, location: (819, 800)\n", - "Processed cell at: LT0027_44/030/58, location: (1190, 807)\n", - "Processed cell at: LT0027_44/030/58, location: (1221, 808)\n", - "Processed cell at: LT0027_44/030/58, location: (938, 840)\n", - "Processed cell at: LT0027_44/030/58, location: (935, 873)\n", - "Processed cell at: LT0027_44/030/58, location: (61, 989)\n", - "Processed cell at: LT0027_44/030/58, location: (99, 999)\n", - "Processed cell at: LT0027_44/030/86, location: (767, 159)\n", - "Processed cell at: LT0027_44/030/86, location: (735, 160)\n", - "Processed cell at: LT0027_44/030/86, location: (892, 182)\n", - "Processed cell at: LT0027_44/030/86, location: (806, 180)\n", - "Processed cell at: LT0027_44/030/86, location: (778, 180)\n", - "Processed cell at: LT0027_44/030/86, location: (734, 179)\n", - "Processed cell at: LT0027_44/030/86, location: (751, 185)\n", - "Processed cell at: LT0027_44/030/86, location: (253, 194)\n", - "Processed cell at: LT0027_44/030/86, location: (838, 201)\n", - "Processed cell at: LT0027_44/030/86, location: (276, 209)\n", - "Processed cell at: LT0027_44/030/86, location: (212, 229)\n", - "Processed cell at: LT0027_44/030/86, location: (251, 230)\n", - "Processed cell at: LT0027_44/030/86, location: (230, 238)\n", - "Processed cell at: LT0027_44/030/86, location: (957, 252)\n", - "Processed cell at: LT0027_44/030/86, location: (255, 248)\n", - "Processed cell at: LT0027_44/030/86, location: (977, 254)\n", - "Processed cell at: LT0027_44/030/86, location: (979, 270)\n", - "Processed cell at: LT0027_44/030/86, location: (954, 277)\n", - "Processed cell at: LT0027_44/030/86, location: (959, 293)\n", - "Processed cell at: LT0027_44/030/86, location: (942, 307)\n", - "Processed cell at: LT0027_44/030/86, location: (911, 326)\n", - "Processed cell at: LT0027_44/030/86, location: (946, 327)\n", - "Processed cell at: LT0027_44/030/86, location: (893, 347)\n", - "Processed cell at: LT0027_44/030/86, location: (917, 340)\n", - "Processed cell at: LT0027_44/030/86, location: (717, 367)\n", - "Processed cell at: LT0027_44/030/86, location: (851, 378)\n", - "Processed cell at: LT0027_44/030/86, location: (721, 382)\n", - "Processed cell at: LT0027_44/030/86, location: (877, 385)\n", - "Processed cell at: LT0027_44/030/86, location: (259, 393)\n", - "Processed cell at: LT0027_44/030/86, location: (746, 394)\n", - "Processed cell at: LT0027_44/030/86, location: (967, 396)\n", - "Processed cell at: LT0027_44/030/86, location: (850, 391)\n", - "Processed cell at: LT0027_44/030/86, location: (895, 400)\n", - "Processed cell at: LT0027_44/030/86, location: (725, 404)\n", - "Processed cell at: LT0027_44/030/86, location: (891, 413)\n", - "Processed cell at: LT0027_44/030/86, location: (265, 415)\n", - "Processed cell at: LT0027_44/030/86, location: (122, 418)\n", - "Processed cell at: LT0027_44/030/86, location: (976, 416)\n", - "Processed cell at: LT0027_44/030/86, location: (171, 424)\n", - "Processed cell at: LT0027_44/030/86, location: (840, 434)\n", - "Processed cell at: LT0027_44/030/86, location: (137, 428)\n", - "Processed cell at: LT0027_44/030/86, location: (252, 435)\n", - "Processed cell at: LT0027_44/030/86, location: (977, 428)\n", - "Processed cell at: LT0027_44/030/86, location: (998, 443)\n", - "Processed cell at: LT0027_44/030/86, location: (261, 459)\n", - "Processed cell at: LT0027_44/030/86, location: (838, 460)\n", - "Processed cell at: LT0027_44/030/86, location: (998, 458)\n", - "Processed cell at: LT0027_44/030/86, location: (290, 554)\n", - "Processed cell at: LT0027_44/030/86, location: (286, 574)\n", - "Processed cell at: LT0027_44/030/86, location: (275, 593)\n", - "Processed cell at: LT0027_44/030/86, location: (681, 590)\n", - "Processed cell at: LT0027_44/030/86, location: (714, 605)\n", - "Processed cell at: LT0027_44/030/86, location: (659, 602)\n", - "Processed cell at: LT0027_44/030/86, location: (686, 612)\n", - "Processed cell at: LT0027_44/030/86, location: (170, 619)\n", - "Processed cell at: LT0027_44/030/86, location: (824, 625)\n", - "Processed cell at: LT0027_44/030/86, location: (626, 633)\n", - "Processed cell at: LT0027_44/030/86, location: (176, 640)\n", - "Processed cell at: LT0027_44/030/86, location: (823, 644)\n", - "Processed cell at: LT0027_44/030/86, location: (642, 650)\n", - "Processed cell at: LT0027_44/030/86, location: (620, 652)\n", - "Processed cell at: LT0027_44/030/86, location: (184, 654)\n", - "Processed cell at: LT0027_44/030/86, location: (223, 662)\n", - "Processed cell at: LT0027_44/030/86, location: (824, 675)\n", - "Processed cell at: LT0027_44/030/86, location: (194, 669)\n", - "Processed cell at: LT0027_44/030/86, location: (739, 676)\n", - "Processed cell at: LT0027_44/030/86, location: (249, 673)\n", - "Processed cell at: LT0027_44/030/86, location: (645, 674)\n", - "Processed cell at: LT0027_44/030/86, location: (716, 683)\n", - "Processed cell at: LT0027_44/030/86, location: (214, 682)\n", - "Processed cell at: LT0027_44/030/86, location: (732, 709)\n", - "Processed cell at: LT0027_44/030/86, location: (751, 712)\n", - "Processed cell at: LT0027_44/030/86, location: (839, 797)\n", - "Processed cell at: LT0027_44/030/86, location: (873, 820)\n", - "Processed cell at: LT0027_44/030/86, location: (850, 814)\n", - "Processed cell at: LT0027_44/030/86, location: (846, 830)\n", - "Processed cell at: LT0027_44/030/86, location: (951, 848)\n", - "Processed cell at: LT0027_44/030/86, location: (972, 870)\n", - "Processed cell at: LT0027_44/030/86, location: (926, 877)\n", - "Processed cell at: LT0027_44/030/86, location: (950, 875)\n", - "Processed cell at: LT0027_44/030/86, location: (970, 900)\n", - "Processed cell at: LT0027_44/030/86, location: (948, 887)\n", - "Processed cell at: LT0027_44/030/86, location: (943, 904)\n", - "Processed cell at: LT0027_44/030/86, location: (90, 932)\n", - "Processed cell at: LT0027_44/030/86, location: (68, 947)\n", - "Processed cell at: LT0027_44/030/86, location: (101, 972)\n", - "Processed cell at: LT0027_44/030/86, location: (71, 978)\n", - "Processed cell at: LT0027_44/292/80, location: (1040, 57)\n", - "Processed cell at: LT0027_44/292/80, location: (930, 79)\n", - "Processed cell at: LT0027_44/292/80, location: (397, 87)\n", - "Processed cell at: LT0027_44/292/80, location: (425, 98)\n", - "Processed cell at: LT0027_44/292/80, location: (414, 124)\n", - "Processed cell at: LT0027_44/292/80, location: (671, 218)\n", - "Processed cell at: LT0027_44/292/80, location: (431, 222)\n", - "Processed cell at: LT0027_44/292/80, location: (637, 234)\n", - "Processed cell at: LT0027_44/292/80, location: (395, 291)\n", - "Processed cell at: LT0027_44/292/80, location: (194, 339)\n", - "Processed cell at: LT0027_44/292/80, location: (176, 359)\n", - "Processed cell at: LT0027_44/292/80, location: (163, 355)\n", - "Processed cell at: LT0027_44/292/80, location: (154, 373)\n", - "Processed cell at: LT0027_44/292/80, location: (1054, 564)\n", - "Processed cell at: LT0027_44/292/80, location: (1000, 605)\n", - "Processed cell at: LT0027_44/292/80, location: (407, 606)\n", - "Processed cell at: LT0027_44/292/80, location: (723, 663)\n", - "Processed cell at: LT0027_44/292/80, location: (474, 661)\n", - "Processed cell at: LT0027_44/292/80, location: (474, 687)\n", - "Processed cell at: LT0027_44/292/80, location: (486, 677)\n", - "Processed cell at: LT0027_44/292/80, location: (534, 732)\n", - "Processed cell at: LT0027_44/292/88, location: (399, 87)\n", - "Processed cell at: LT0027_44/292/88, location: (425, 98)\n", - "Processed cell at: LT0027_44/292/88, location: (838, 91)\n", - "Processed cell at: LT0027_44/292/88, location: (403, 120)\n", - "Processed cell at: LT0027_44/292/88, location: (898, 132)\n", - "Processed cell at: LT0027_44/292/88, location: (673, 217)\n", - "Processed cell at: LT0027_44/292/88, location: (200, 330)\n", - "Processed cell at: LT0027_44/292/88, location: (1074, 335)\n", - "Processed cell at: LT0027_44/292/88, location: (186, 344)\n", - "Processed cell at: LT0027_44/292/88, location: (169, 349)\n", - "Processed cell at: LT0027_44/292/88, location: (1081, 361)\n", - "Processed cell at: LT0027_44/292/88, location: (181, 357)\n", - "Processed cell at: LT0027_44/292/88, location: (160, 368)\n", - "Processed cell at: LT0027_44/292/88, location: (210, 371)\n", - "Processed cell at: LT0027_44/292/88, location: (422, 444)\n", - "Processed cell at: LT0027_44/292/88, location: (564, 499)\n", - "Processed cell at: LT0027_44/292/88, location: (1054, 568)\n", - "Processed cell at: LT0027_44/292/88, location: (995, 605)\n", - "Processed cell at: LT0027_44/292/88, location: (715, 640)\n", - "Processed cell at: LT0027_44/292/88, location: (534, 654)\n", - "Processed cell at: LT0027_44/292/88, location: (474, 658)\n", - "Processed cell at: LT0027_44/292/88, location: (482, 672)\n", - "Processed cell at: LT0027_44/292/88, location: (472, 687)\n", - "Processed cell at: LT0027_44/292/88, location: (264, 772)\n", - "Processed cell at: LT0027_44/292/88, location: (288, 790)\n", - "Processed cell at: LT0027_44/292/88, location: (287, 811)\n", - "Processed cell at: LT0027_44/292/88, location: (301, 829)\n", - "Processed cell at: LT0027_44/292/47, location: (882, 69)\n", - "Processed cell at: LT0027_44/292/47, location: (396, 86)\n", - "Processed cell at: LT0027_44/292/47, location: (404, 112)\n", - "Processed cell at: LT0027_44/292/47, location: (689, 185)\n", - "Processed cell at: LT0027_44/292/47, location: (677, 221)\n", - "Processed cell at: LT0027_44/292/47, location: (1140, 379)\n", - "Processed cell at: LT0027_44/292/47, location: (376, 431)\n", - "Processed cell at: LT0027_44/292/47, location: (910, 443)\n", - "Processed cell at: LT0027_44/292/47, location: (332, 446)\n", - "Processed cell at: LT0027_44/292/47, location: (979, 478)\n", - "Processed cell at: LT0027_44/292/47, location: (564, 524)\n", - "Processed cell at: LT0027_44/292/47, location: (1058, 565)\n", - "Processed cell at: LT0027_44/292/47, location: (1042, 604)\n", - "Processed cell at: LT0027_44/292/47, location: (417, 619)\n", - "Processed cell at: LT0027_44/292/47, location: (994, 637)\n", - "Processed cell at: LT0027_44/292/47, location: (387, 666)\n", - "Processed cell at: LT0027_44/292/47, location: (487, 684)\n", - "Processed cell at: LT0027_44/292/47, location: (472, 692)\n", - "Processed cell at: LT0027_44/292/47, location: (563, 750)\n", - "Processed cell at: LT0027_44/292/47, location: (536, 763)\n", - "Processed cell at: LT0027_44/292/65, location: (396, 82)\n", - "Processed cell at: LT0027_44/292/65, location: (422, 91)\n", - "Processed cell at: LT0027_44/292/65, location: (854, 94)\n", - "Processed cell at: LT0027_44/292/65, location: (420, 116)\n", - "Processed cell at: LT0027_44/292/65, location: (670, 218)\n", - "Processed cell at: LT0027_44/292/65, location: (644, 228)\n", - "Processed cell at: LT0027_44/292/65, location: (214, 235)\n", - "Processed cell at: LT0027_44/292/65, location: (395, 290)\n", - 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"Processed cell at: LT0027_44/292/92, location: (828, 332)\n", - "Processed cell at: LT0027_44/292/92, location: (183, 347)\n", - "Processed cell at: LT0027_44/292/92, location: (165, 356)\n", - "Processed cell at: LT0027_44/292/92, location: (1070, 346)\n", - "Processed cell at: LT0027_44/292/92, location: (1086, 358)\n", - "Processed cell at: LT0027_44/292/92, location: (989, 607)\n", - "Processed cell at: LT0027_44/292/92, location: (717, 638)\n", - "Processed cell at: LT0027_44/292/92, location: (512, 678)\n", - "Processed cell at: LT0027_44/292/92, location: (503, 702)\n", - "Processed cell at: LT0027_44/292/92, location: (268, 767)\n", - "Processed cell at: LT0027_44/292/92, location: (221, 773)\n", - "Processed cell at: LT0027_44/292/92, location: (290, 787)\n", - "Processed cell at: LT0027_44/292/92, location: (281, 807)\n", - "Processed cell at: LT0027_44/292/92, location: (301, 817)\n", - "Processed cell at: LT0067_02/099/19, location: (430, 27)\n", - "Processed cell at: LT0067_02/099/19, location: (727, 197)\n", - "Processed cell at: LT0067_02/099/19, location: (733, 224)\n", - "Processed cell at: LT0067_02/099/19, location: (775, 368)\n", - "Processed cell at: LT0067_02/099/19, location: (751, 383)\n", - "Processed cell at: LT0067_02/099/19, location: (589, 403)\n", - "Processed cell at: LT0067_02/099/19, location: (598, 429)\n", - "Processed cell at: LT0067_02/099/19, location: (597, 651)\n", - "Processed cell at: LT0067_02/099/19, location: (615, 668)\n", - "Processed cell at: LT0067_02/099/19, location: (604, 749)\n", - "Processed cell at: LT0067_02/099/19, location: (546, 753)\n", - "Processed cell at: LT0067_02/099/19, location: (627, 763)\n", - "Processed cell at: LT0067_02/099/19, location: (636, 784)\n", - "Processed cell at: LT0067_02/099/19, location: (553, 780)\n", - "Processed cell at: LT0067_02/099/19, location: (555, 795)\n", - "Processed cell at: LT0067_02/099/19, location: (1160, 811)\n", - "Processed cell at: LT0067_02/099/19, location: (1136, 821)\n", - "Processed cell at: LT0067_02/099/19, location: (299, 968)\n", - "Processed cell at: LT0067_02/099/19, location: (288, 989)\n", - "Processed cell at: LT0067_02/099/77, location: (368, 74)\n", - "Processed cell at: LT0067_02/099/77, location: (364, 103)\n", - "Processed cell at: LT0067_02/099/77, location: (241, 110)\n", - "Processed cell at: LT0067_02/099/77, location: (269, 111)\n", - "Processed cell at: LT0067_02/099/77, location: (360, 128)\n", - "Processed cell at: LT0067_02/099/77, location: (251, 131)\n", - "Processed cell at: LT0067_02/099/77, location: (1210, 148)\n", - "Processed cell at: LT0067_02/099/77, location: (1222, 164)\n", - "Processed cell at: LT0067_02/099/77, location: (1228, 181)\n", - "Processed cell at: LT0067_02/099/77, location: (1207, 188)\n", - "Processed cell at: LT0067_02/099/77, location: (337, 210)\n", - "Processed cell at: LT0067_02/099/77, location: (365, 215)\n", - "Processed cell at: LT0067_02/099/77, location: (354, 235)\n", - "Processed cell at: LT0067_02/099/77, location: (335, 248)\n", - "Processed cell at: LT0067_02/099/77, location: (1148, 388)\n", - "Processed cell at: LT0067_02/099/77, location: (789, 401)\n", - "Processed cell at: LT0067_02/099/77, location: (1134, 404)\n", - "Processed cell at: LT0067_02/099/77, location: (767, 415)\n", - "Processed cell at: LT0067_02/099/77, location: (1154, 421)\n", - "Processed cell at: LT0067_02/099/77, location: (780, 431)\n", - "Processed cell at: LT0067_02/099/77, location: (764, 454)\n", - "Processed cell at: LT0067_02/099/77, location: (614, 682)\n", - "Processed cell at: LT0067_02/099/77, location: (594, 692)\n", - "Processed cell at: LT0067_02/099/77, location: (807, 695)\n", - "Processed cell at: LT0067_02/099/77, location: (794, 712)\n", - "Processed cell at: LT0067_02/099/77, location: (486, 803)\n", - "Processed cell at: LT0067_02/099/77, location: (1323, 808)\n", - "Processed cell at: LT0067_02/099/77, location: (1132, 812)\n", - "Processed cell at: LT0067_02/099/77, location: (1280, 808)\n", - "Processed cell at: LT0067_02/099/77, location: (511, 819)\n", - "Processed cell at: LT0067_02/099/77, location: (1307, 817)\n", - "Processed cell at: LT0067_02/099/77, location: (1281, 823)\n", - "Processed cell at: LT0067_02/099/77, location: (532, 830)\n", - "Processed cell at: LT0067_02/099/77, location: (1137, 827)\n", - "Processed cell at: LT0067_02/099/77, location: (1118, 834)\n", - "Processed cell at: LT0067_02/099/77, location: (1309, 829)\n", - "Processed cell at: LT0067_02/099/77, location: (558, 938)\n", - "Processed cell at: LT0067_02/099/77, location: (558, 976)\n", - "Processed cell at: LT0067_02/099/92, location: (1150, 36)\n", - "Processed cell at: LT0067_02/099/92, location: (1166, 58)\n", - "Processed cell at: LT0067_02/099/92, location: (374, 71)\n", - "Processed cell at: LT0067_02/099/92, location: (1154, 73)\n", - "Processed cell at: LT0067_02/099/92, location: (365, 98)\n", - "Processed cell at: LT0067_02/099/92, location: (282, 104)\n", - "Processed cell at: LT0067_02/099/92, location: (248, 114)\n", - "Processed cell at: LT0067_02/099/92, location: (1259, 117)\n", - "Processed cell at: LT0067_02/099/92, location: (1283, 120)\n", - "Processed cell at: LT0067_02/099/92, location: (358, 121)\n", - "Processed cell at: LT0067_02/099/92, location: (267, 128)\n", - "Processed cell at: LT0067_02/099/92, location: (1274, 140)\n", - "Processed cell at: LT0067_02/099/92, location: (1225, 187)\n", - "Processed cell at: LT0067_02/099/92, location: (533, 201)\n", - "Processed cell at: LT0067_02/099/92, location: (1186, 214)\n", - "Processed cell at: LT0067_02/099/92, location: (562, 213)\n", - "Processed cell at: LT0067_02/099/92, location: (1223, 214)\n", - "Processed cell at: LT0067_02/099/92, location: (1205, 230)\n", - "Processed cell at: LT0067_02/099/92, location: (545, 234)\n", - "Processed cell at: LT0067_02/099/92, location: (1159, 399)\n", - "Processed cell at: LT0067_02/099/92, location: (1144, 414)\n", - "Processed cell at: LT0067_02/099/92, location: (1160, 435)\n", - "Processed cell at: LT0067_02/099/92, location: (593, 684)\n", - "Processed cell at: LT0067_02/099/92, location: (613, 698)\n", - "Processed cell at: LT0067_02/099/92, location: (767, 705)\n", - "Processed cell at: LT0067_02/099/92, location: (728, 701)\n", - "Processed cell at: LT0067_02/099/92, location: (736, 717)\n", - "Processed cell at: LT0067_02/099/92, location: (806, 755)\n", - "Processed cell at: LT0067_02/099/92, location: (773, 759)\n", - "Processed cell at: LT0067_02/099/92, location: (748, 760)\n", - "Processed cell at: LT0067_02/099/37, location: (584, 25)\n", - "Processed cell at: LT0067_02/099/37, location: (642, 28)\n", - "Processed cell at: LT0067_02/099/37, location: (616, 42)\n", - "Processed cell at: LT0067_02/099/37, location: (562, 45)\n", - "Processed cell at: LT0067_02/099/37, location: (1206, 111)\n", - "Processed cell at: LT0067_02/099/37, location: (276, 122)\n", - "Processed cell at: LT0067_02/099/37, location: (1208, 136)\n", - "Processed cell at: LT0067_02/099/37, location: (202, 136)\n", - "Processed cell at: LT0067_02/099/37, location: (267, 144)\n", - "Processed cell at: LT0067_02/099/37, location: (352, 147)\n", - "Processed cell at: LT0067_02/099/37, location: (383, 158)\n", - "Processed cell at: LT0067_02/099/37, location: (212, 158)\n", - "Processed cell at: LT0067_02/099/37, location: (1257, 176)\n", - "Processed cell at: LT0067_02/099/37, location: (1274, 196)\n", - "Processed cell at: LT0067_02/099/37, location: (904, 200)\n", - "Processed cell at: LT0067_02/099/37, location: (746, 199)\n", - "Processed cell at: LT0067_02/099/37, location: (937, 205)\n", - "Processed cell at: LT0067_02/099/37, location: (319, 209)\n", - "Processed cell at: LT0067_02/099/37, location: (888, 224)\n", - "Processed cell at: LT0067_02/099/37, location: (734, 225)\n", - "Processed cell at: LT0067_02/099/37, location: (923, 232)\n", - "Processed cell at: LT0067_02/099/37, location: (1025, 239)\n", - "Processed cell at: LT0067_02/099/37, location: (326, 243)\n", - "Processed cell at: LT0067_02/099/37, location: (1037, 262)\n", - "Processed cell at: LT0067_02/099/37, location: (1135, 375)\n", - "Processed cell at: LT0067_02/099/37, location: (1126, 401)\n", - "Processed cell at: LT0067_02/099/37, location: (1151, 546)\n", - "Processed cell at: LT0067_02/099/37, location: (136, 555)\n", - "Processed cell at: LT0067_02/099/37, location: (1168, 561)\n", - "Processed cell at: LT0067_02/099/37, location: (130, 581)\n", - "Processed cell at: LT0067_02/099/37, location: (51, 632)\n", - "Processed cell at: LT0067_02/099/37, location: (26, 639)\n", - "Processed cell at: LT0067_02/099/37, location: (621, 710)\n", - "Processed cell at: LT0067_02/099/37, location: (544, 732)\n", - "Processed cell at: LT0067_02/099/37, location: (645, 742)\n", - "Processed cell at: LT0067_02/099/37, location: (556, 761)\n", - "Processed cell at: LT0067_02/099/37, location: (602, 763)\n", - "Processed cell at: LT0067_02/099/37, location: (468, 796)\n", - "Processed cell at: LT0067_02/099/37, location: (25, 796)\n", - "Processed cell at: LT0067_02/099/37, location: (754, 798)\n", - "Processed cell at: LT0067_02/099/37, location: (499, 803)\n", - "Processed cell at: LT0067_02/099/37, location: (66, 806)\n", - "Processed cell at: LT0067_02/099/37, location: (728, 807)\n", - "Processed cell at: LT0067_02/099/37, location: (1158, 812)\n", - "Processed cell at: LT0067_02/099/37, location: (521, 819)\n", - "Processed cell at: LT0067_02/099/37, location: (24, 823)\n", - "Processed cell at: LT0067_02/099/37, location: (1138, 828)\n", - "Processed cell at: LT0067_02/099/37, location: (65, 830)\n", - "Processed cell at: LT0067_02/099/37, location: (604, 843)\n", - "Processed cell at: LT0067_02/099/37, location: (631, 848)\n", - "Processed cell at: LT0067_02/099/37, location: (607, 857)\n", - "Processed cell at: LT0067_02/099/37, location: (1135, 864)\n", - "Processed cell at: LT0067_02/099/37, location: (1160, 875)\n", - "Processed cell at: LT0067_02/099/37, location: (734, 885)\n", - "Processed cell at: LT0067_02/099/37, location: (759, 904)\n", - "Processed cell at: LT0067_02/099/37, location: (274, 938)\n", - "Processed cell at: LT0067_02/099/37, location: (256, 958)\n", - "Processed cell at: LT0067_02/099/37, location: (341, 963)\n", - "Processed cell at: LT0067_02/099/37, location: (360, 983)\n", - "Processed cell at: LT0067_02/099/37, location: (709, 995)\n", - "Processed cell at: LT0067_02/099/37, location: (735, 999)\n", - "Processed cell at: LT0093_17/114/72, location: (929, 314)\n", - "Processed cell at: LT0093_17/114/88, location: (732, 387)\n", - "Processed cell at: LT0093_17/114/88, location: (764, 389)\n", - "Processed cell at: LT0093_17/114/88, location: (756, 401)\n", - "Processed cell at: LT0093_17/114/88, location: (743, 401)\n", - "Processed cell at: LT0093_17/114/88, location: (966, 498)\n", - "Processed cell at: LT0093_17/114/88, location: (619, 690)\n", - "Processed cell at: LT0093_17/114/88, location: (602, 682)\n", - "Processed cell at: LT0093_17/114/88, location: (587, 699)\n", - "Processed cell at: LT0093_17/114/88, location: (119, 714)\n", - "Processed cell at: LT0093_17/114/88, location: (143, 707)\n", - "Processed cell at: LT0093_17/114/88, location: (610, 713)\n", - "Processed cell at: LT0093_17/114/88, location: (153, 727)\n", - "Processed cell at: LT0093_17/114/88, location: (789, 750)\n", - "Processed cell at: LT0093_17/114/88, location: (812, 751)\n", - "Processed cell at: LT0093_17/114/88, location: (829, 751)\n", - "Processed cell at: LT0093_17/114/88, location: (763, 751)\n", - "Processed cell at: LT0093_17/114/45, location: (153, 652)\n", - "Processed cell at: LT0093_17/114/83, location: (1022, 469)\n", - "Processed cell at: LT0093_17/114/79, location: (350, 113)\n", - "Processed cell at: LT0093_17/114/46, location: (153, 650)\n", - "Processed cell at: LT0093_17/114/87, location: (1081, 619)\n", - "Processed cell at: LT0038_27/250/8, location: (1039, 365)\n", - "Processed cell at: LT0038_27/250/8, location: (903, 377)\n", - "Processed cell at: LT0038_27/250/8, location: (697, 379)\n", - "Processed cell at: LT0038_27/250/8, location: (920, 483)\n", - "Processed cell at: LT0038_27/250/8, location: (386, 511)\n", - "Processed cell at: LT0038_27/250/8, location: (948, 524)\n", - "Processed cell at: LT0038_27/250/8, location: (801, 604)\n", - "Processed cell at: LT0038_27/250/8, location: (859, 630)\n", - "Processed cell at: LT0038_27/250/8, location: (1133, 658)\n", - "Processed cell at: LT0038_27/250/8, location: (955, 756)\n", - "Processed cell at: LT0038_27/250/8, location: (290, 790)\n", - "Processed cell at: LT0038_27/250/8, location: (294, 810)\n", - "Processed cell at: LT0038_27/250/8, location: (921, 894)\n", - "Processed cell at: LT0065_06/054/93, location: (928, 64)\n", - "Processed cell at: LT0065_06/054/93, location: (896, 82)\n", - "Processed cell at: LT0065_06/054/93, location: (880, 88)\n", - "Processed cell at: LT0065_06/054/93, location: (996, 143)\n", - "Processed cell at: LT0065_06/054/93, location: (973, 148)\n", - "Processed cell at: LT0065_06/054/93, location: (1019, 163)\n", - "Processed cell at: LT0065_06/054/93, location: (977, 163)\n", - "Processed cell at: LT0065_06/054/93, location: (1024, 175)\n", - "Processed cell at: LT0065_06/054/93, location: (134, 244)\n", - "Processed cell at: LT0065_06/054/93, location: (153, 270)\n", - "Processed cell at: LT0065_06/054/93, location: (168, 295)\n", - "Processed cell at: LT0065_06/054/93, location: (1239, 302)\n", - "Processed cell at: LT0065_06/054/93, location: (1263, 317)\n", - "Processed cell at: LT0065_06/054/93, location: (1235, 326)\n", - "Processed cell at: LT0065_06/054/93, location: (1255, 350)\n", - "Processed cell at: LT0065_06/054/93, location: (624, 445)\n", - "Processed cell at: LT0065_06/054/93, location: (600, 447)\n", - "Processed cell at: LT0065_06/054/93, location: (780, 971)\n", - "Processed cell at: LT0065_06/054/93, location: (763, 966)\n", - "Processed cell at: LT0065_06/054/93, location: (777, 989)\n", - "Processed cell at: LT0026_22/258/67, location: (1139, 54)\n", - "Processed cell at: LT0026_22/258/67, location: (1158, 63)\n", - "Processed cell at: LT0026_22/258/67, location: (1137, 75)\n", - "Processed cell at: LT0026_22/258/67, location: (1161, 81)\n", - "Processed cell at: LT0026_22/258/67, location: (933, 90)\n", - "Processed cell at: LT0026_22/258/67, location: (948, 108)\n", - "Processed cell at: LT0026_22/258/67, location: (1062, 113)\n", - "Processed cell at: LT0026_22/258/67, location: (1087, 108)\n", - "Processed cell at: LT0026_22/258/67, location: (1078, 130)\n", - "Processed cell at: LT0026_22/258/67, location: (1095, 149)\n", - "Processed cell at: LT0026_22/258/67, location: (619, 289)\n", - "Processed cell at: LT0026_22/258/67, location: (621, 312)\n", - "Processed cell at: LT0026_22/258/67, location: (975, 386)\n", - "Processed cell at: LT0026_22/258/67, location: (910, 397)\n", - "Processed cell at: LT0026_22/258/67, location: (957, 387)\n", - "Processed cell at: LT0026_22/258/67, location: (868, 408)\n", - "Processed cell at: LT0026_22/258/67, location: (975, 407)\n", - "Processed cell at: LT0026_22/258/67, location: (889, 420)\n", - "Processed cell at: LT0026_22/258/67, location: (1006, 435)\n", - "Processed cell at: LT0026_22/258/67, location: (1013, 450)\n", - "Processed cell at: LT0026_22/258/67, location: (1096, 589)\n", - "Processed cell at: LT0026_22/258/67, location: (1111, 713)\n", - "Processed cell at: LT0026_22/258/67, location: (1102, 738)\n", - "Processed cell at: LT0026_22/258/67, location: (1046, 761)\n", - "Processed cell at: LT0026_22/258/67, location: (1061, 772)\n", - "Processed cell at: LT0026_22/258/67, location: (1172, 777)\n", - "Processed cell at: LT0026_22/258/67, location: (1036, 780)\n", - "Processed cell at: LT0026_22/258/67, location: (1053, 795)\n", - "Processed cell at: LT0026_22/258/67, location: (1175, 796)\n", - "Processed cell at: LT0026_22/258/67, location: (1187, 816)\n", - "Processed cell at: LT0026_22/258/67, location: (496, 905)\n", - "Processed cell at: LT0026_22/258/67, location: (492, 928)\n", - "Processed cell at: LT0026_22/258/85, location: (1131, 63)\n", - "Processed cell at: LT0026_22/258/85, location: (1099, 61)\n", - "Processed cell at: LT0026_22/258/85, location: (1183, 63)\n", - "Processed cell at: LT0026_22/258/85, location: (1134, 88)\n", - "Processed cell at: LT0026_22/258/85, location: (1167, 92)\n", - "Processed cell at: LT0026_22/258/85, location: (923, 96)\n", - "Processed cell at: LT0026_22/258/85, location: (944, 108)\n", - 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"Processed cell at: LT0026_22/258/85, location: (948, 657)\n", - "Processed cell at: LT0026_22/258/85, location: (939, 680)\n", - "Processed cell at: LT0026_22/258/85, location: (871, 696)\n", - "Processed cell at: LT0026_22/258/85, location: (856, 711)\n", - "Processed cell at: LT0026_22/258/85, location: (884, 714)\n", - "Processed cell at: LT0026_22/258/85, location: (1115, 717)\n", - "Processed cell at: LT0026_22/258/85, location: (1094, 720)\n", - "Processed cell at: LT0026_22/258/85, location: (867, 727)\n", - "Processed cell at: LT0026_22/258/85, location: (1106, 742)\n", - "Processed cell at: LT0026_22/258/85, location: (1227, 776)\n", - "Processed cell at: LT0026_22/258/85, location: (1246, 786)\n", - "Processed cell at: LT0026_22/258/85, location: (961, 789)\n", - "Processed cell at: LT0026_22/258/85, location: (1189, 794)\n", - "Processed cell at: LT0026_22/258/85, location: (1172, 792)\n", - "Processed cell at: LT0026_22/258/85, location: (1159, 802)\n", - "Processed cell at: LT0026_22/258/85, location: (1176, 804)\n", - "Processed cell at: LT0026_22/258/85, location: (1192, 818)\n", - "Processed cell at: LT0026_22/258/85, location: (500, 897)\n", - "Processed cell at: LT0026_22/258/85, location: (496, 921)\n", - "Processed cell at: LT0026_22/258/85, location: (917, 998)\n", - "Processed cell at: LT0026_22/258/37, location: (1139, 49)\n", - "Processed cell at: LT0026_22/258/37, location: (1158, 65)\n", - "Processed cell at: LT0026_22/258/37, location: (1096, 121)\n", - "Processed cell at: LT0026_22/258/37, location: (1080, 121)\n", - "Processed cell at: LT0026_22/258/37, location: (1115, 138)\n", - "Processed cell at: LT0026_22/258/37, location: (1077, 140)\n", - "Processed cell at: LT0026_22/258/37, location: (1100, 154)\n", - "Processed cell at: LT0026_22/258/37, location: (623, 283)\n", - "Processed cell at: LT0026_22/258/37, location: (621, 312)\n", - "Processed cell at: LT0026_22/258/37, location: (909, 386)\n", - "Processed cell at: LT0026_22/258/37, location: (882, 401)\n", - "Processed cell at: LT0026_22/258/37, location: (865, 517)\n", - "Processed cell at: LT0026_22/258/37, location: (888, 531)\n", - "Processed cell at: LT0026_22/258/37, location: (1064, 592)\n", - "Processed cell at: LT0026_22/258/37, location: (1085, 603)\n", - "Processed cell at: LT0026_22/258/37, location: (972, 608)\n", - "Processed cell at: LT0026_22/258/37, location: (1094, 707)\n", - "Processed cell at: LT0026_22/258/37, location: (1095, 733)\n", - "Processed cell at: LT0026_22/258/37, location: (532, 755)\n", - "Processed cell at: LT0026_22/258/37, location: (513, 762)\n", - "Processed cell at: LT0026_22/258/37, location: (544, 845)\n", - "Processed cell at: LT0026_22/258/37, location: (550, 866)\n", - "Processed cell at: LT0026_22/258/37, location: (489, 939)\n", - "Processed cell at: LT0026_22/258/37, location: (473, 945)\n", - "Processed cell at: LT0109_38/349/88, location: (1141, 79)\n", - "Processed cell at: LT0109_38/349/88, location: (1181, 79)\n", - "Processed cell at: LT0109_38/349/88, location: (1152, 112)\n", - "Processed cell at: LT0109_38/349/88, location: (1166, 141)\n", - "Processed cell at: LT0109_38/349/88, location: (369, 147)\n", - "Processed cell at: LT0109_38/349/88, location: (226, 486)\n", - "Processed cell at: LT0109_38/349/88, location: (630, 511)\n", - "Processed cell at: LT0109_38/349/88, location: (238, 501)\n", - "Processed cell at: LT0109_38/349/88, location: (877, 510)\n", - "Processed cell at: LT0109_38/349/88, location: (1065, 581)\n", - "Processed cell at: LT0109_38/349/88, location: (252, 587)\n", - "Processed cell at: LT0109_38/349/88, location: (1009, 589)\n", - "Processed cell at: LT0109_38/349/88, location: (1183, 657)\n", - "Processed cell at: LT0109_38/349/88, location: (1102, 671)\n", - "Processed cell at: LT0109_38/349/88, location: (1139, 677)\n", - "Processed cell at: LT0109_38/349/88, location: (964, 899)\n", - "Processed cell at: LT0109_38/349/88, location: (998, 936)\n", - "Processed cell at: LT0109_38/349/88, location: (1039, 951)\n", - "Processed cell at: LT0109_38/349/88, location: (1283, 955)\n", - "Processed cell at: LT0109_38/349/88, location: (1302, 967)\n", - "Processed cell at: LT0109_38/349/69, location: (348, 93)\n", - "Processed cell at: LT0109_38/349/69, location: (236, 98)\n", - "Processed cell at: LT0109_38/349/69, location: (227, 120)\n", - "Processed cell at: LT0109_38/349/69, location: (368, 152)\n", - "Processed cell at: LT0109_38/349/69, location: (394, 172)\n", - "Processed cell at: LT0109_38/349/69, location: (221, 190)\n", - "Processed cell at: LT0109_38/349/69, location: (211, 203)\n", - "Processed cell at: LT0109_38/349/69, location: (197, 201)\n", - "Processed cell at: LT0109_38/349/69, location: (990, 210)\n", - "Processed cell at: LT0109_38/349/69, location: (1211, 297)\n", - "Processed cell at: LT0109_38/349/69, location: (1054, 293)\n", - "Processed cell at: LT0109_38/349/69, location: (632, 507)\n", - "Processed cell at: LT0109_38/349/69, location: (643, 521)\n", - "Processed cell at: LT0109_38/349/69, location: (634, 539)\n", - "Processed cell at: LT0109_38/349/69, location: (258, 577)\n", - "Processed cell at: LT0109_38/349/69, location: (657, 614)\n", - "Processed cell at: LT0109_38/349/69, location: (478, 646)\n", - "Processed cell at: LT0109_38/349/69, location: (651, 636)\n", - "Processed cell at: LT0109_38/349/69, location: (19, 647)\n", - "Processed cell at: LT0109_38/349/69, location: (1259, 686)\n", - "Processed cell at: LT0109_38/349/69, location: (1017, 741)\n", - "Processed cell at: LT0109_38/349/69, location: (1233, 761)\n", - "Processed cell at: LT0109_38/349/69, location: (307, 767)\n", - "Processed cell at: LT0109_38/349/69, location: (462, 837)\n", - "Processed cell at: LT0109_38/349/69, location: (507, 843)\n", - "Processed cell at: LT0109_38/349/69, location: (355, 855)\n", - "Processed cell at: LT0109_38/349/69, location: (257, 904)\n", - "Processed cell at: LT0109_38/349/69, location: (217, 962)\n", - "Processed cell at: LT0109_38/338/80, location: (1050, 81)\n", - "Processed cell at: LT0109_38/338/80, location: (1112, 120)\n", - "Processed cell at: LT0109_38/338/80, location: (1208, 125)\n", - "Processed cell at: LT0109_38/338/80, location: (1114, 160)\n", - "Processed cell at: LT0109_38/338/80, location: (758, 178)\n", - "Processed cell at: LT0109_38/338/80, location: (807, 190)\n", - "Processed cell at: LT0109_38/338/80, location: (896, 396)\n", - "Processed cell at: LT0109_38/338/80, location: (1050, 557)\n", - "Processed cell at: LT0109_38/338/80, location: (964, 555)\n", - "Processed cell at: LT0109_38/338/80, location: (1045, 570)\n", - "Processed cell at: LT0109_38/338/80, location: (964, 574)\n", - "Processed cell at: LT0109_38/338/80, location: (392, 814)\n", - "Processed cell at: LT0109_38/338/80, location: (1024, 828)\n", - "Processed cell at: LT0109_38/338/80, location: (952, 849)\n", - "Processed cell at: LT0109_38/338/80, location: (936, 859)\n", - "Processed cell at: LT0109_38/338/80, location: (948, 869)\n", - "Processed cell at: LT0109_38/338/80, location: (932, 873)\n", - "Processed cell at: LT0109_38/338/80, location: (482, 904)\n", - "Processed cell at: LT0109_38/338/80, location: (558, 915)\n", - "Processed cell at: LT0109_38/338/7, location: (907, 50)\n", - "Processed cell at: LT0109_38/338/7, location: (1226, 114)\n", - "Processed cell at: LT0109_38/338/7, location: (21, 142)\n", - "Processed cell at: LT0109_38/338/7, location: (1219, 156)\n", - "Processed cell at: LT0109_38/338/7, location: (765, 190)\n", - "Processed cell at: LT0109_38/338/7, location: (807, 194)\n", - "Processed cell at: LT0109_38/338/7, location: (85, 351)\n", - "Processed cell at: LT0109_38/338/7, location: (67, 382)\n", - "Processed cell at: LT0109_38/338/7, location: (938, 387)\n", - "Processed cell at: LT0109_38/338/7, location: (953, 454)\n", - "Processed cell at: LT0109_38/338/7, location: (875, 465)\n", - "Processed cell at: LT0109_38/338/7, location: (253, 466)\n", - "Processed cell at: LT0109_38/338/7, location: (1046, 573)\n", - "Processed cell at: LT0109_38/338/7, location: (1008, 588)\n", - "Processed cell at: LT0109_38/338/7, location: (1204, 665)\n", - "Processed cell at: LT0109_38/338/7, location: (380, 752)\n", - "Processed cell at: LT0109_38/338/7, location: (290, 800)\n", - "Processed cell at: LT0109_38/338/7, location: (356, 808)\n", - "Processed cell at: LT0109_38/338/7, location: (145, 858)\n", - "Processed cell at: LT0109_38/338/7, location: (921, 874)\n", - "Processed cell at: LT0109_38/338/7, location: (160, 909)\n", - "Processed cell at: LT0109_38/338/7, location: (44, 919)\n", - "Processed cell at: LT0109_38/338/7, location: (1007, 927)\n", - "Processed cell at: LT0109_38/338/7, location: (217, 941)\n", - "Processed cell at: LT0109_38/338/27, location: (908, 41)\n", - "Processed cell at: LT0109_38/338/27, location: (1156, 105)\n", - "Processed cell at: LT0109_38/338/27, location: (1209, 119)\n", - "Processed cell at: LT0109_38/338/27, location: (1105, 124)\n", - "Processed cell at: LT0109_38/338/27, location: (756, 179)\n", - "Processed cell at: LT0109_38/338/27, location: (810, 186)\n", - "Processed cell at: LT0109_38/338/27, location: (829, 391)\n", - "Processed cell at: LT0109_38/338/27, location: (870, 404)\n", - "Processed cell at: LT0109_38/338/27, location: (858, 447)\n", - "Processed cell at: LT0109_38/338/27, location: (970, 450)\n", - "Processed cell at: LT0109_38/338/27, location: (261, 464)\n", - "Processed cell at: LT0109_38/338/27, location: (1268, 527)\n", - "Processed cell at: LT0109_38/338/27, location: (1158, 548)\n", - "Processed cell at: LT0109_38/338/27, location: (15, 560)\n", - "Processed cell at: LT0109_38/338/27, location: (1043, 572)\n", - "Processed cell at: LT0109_38/338/27, location: (66, 590)\n", - "Processed cell at: LT0109_38/338/27, location: (1001, 596)\n", - "Processed cell at: LT0109_38/338/27, location: (383, 603)\n", - "Processed cell at: LT0109_38/338/27, location: (439, 614)\n", - "Processed cell at: LT0109_38/338/27, location: (58, 655)\n", - "Processed cell at: LT0109_38/338/27, location: (401, 706)\n", - "Processed cell at: LT0109_38/338/27, location: (363, 748)\n", - "Processed cell at: LT0109_38/338/27, location: (441, 750)\n", - "Processed cell at: LT0109_38/338/27, location: (299, 783)\n", - "Processed cell at: LT0109_38/338/27, location: (281, 788)\n", - "Processed cell at: LT0109_38/338/27, location: (1121, 837)\n", - "Processed cell at: LT0109_38/338/27, location: (964, 847)\n", - "Processed cell at: LT0109_38/338/27, location: (918, 872)\n", - "Processed cell at: LT0109_38/338/27, location: (495, 891)\n", - "Processed cell at: LT0109_38/338/27, location: (541, 894)\n", - "Processed cell at: LT0109_38/338/27, location: (156, 910)\n", - "Processed cell at: LT0109_38/338/27, location: (1067, 916)\n", - "Processed cell at: LT0109_38/338/27, location: (1003, 923)\n", - "Processed cell at: LT0109_38/338/27, location: (75, 930)\n", - "Processed cell at: LT0109_38/338/27, location: (210, 941)\n", - "Processed cell at: LT0109_38/338/27, location: (22, 973)\n", - "Processed cell at: LT0109_38/338/27, location: (237, 976)\n", - "Processed cell at: LT0109_38/338/31, location: (1203, 120)\n", - "Processed cell at: LT0109_38/338/31, location: (752, 179)\n", - "Processed cell at: LT0109_38/338/31, location: (808, 187)\n", - "Processed cell at: LT0109_38/338/31, location: (88, 346)\n", - "Processed cell at: LT0109_38/338/31, location: (77, 382)\n", - "Processed cell at: LT0109_38/338/31, location: (855, 447)\n", - "Processed cell at: LT0109_38/338/31, location: (970, 455)\n", - "Processed cell at: LT0109_38/338/31, location: (1043, 574)\n", - "Processed cell at: LT0109_38/338/31, location: (74, 594)\n", - "Processed cell at: LT0109_38/338/31, location: (1002, 595)\n", - "Processed cell at: LT0109_38/338/31, location: (58, 655)\n", - "Processed cell at: LT0109_38/338/31, location: (395, 708)\n", - "Processed cell at: LT0109_38/338/31, location: (349, 748)\n", - "Processed cell at: LT0109_38/338/31, location: (917, 873)\n", - "Processed cell at: LT0109_38/338/31, location: (492, 895)\n", - "Processed cell at: LT0109_38/338/31, location: (537, 892)\n", - "Processed cell at: LT0109_38/338/31, location: (1001, 925)\n", - "Processed cell at: LT0109_38/381/87, location: (222, 28)\n", - "Processed cell at: LT0109_38/381/87, location: (173, 42)\n", - "Processed cell at: LT0109_38/381/87, location: (178, 73)\n", - "Processed cell at: LT0109_38/381/87, location: (242, 76)\n", - "Processed cell at: LT0109_38/381/87, location: (247, 225)\n", - "Processed cell at: LT0109_38/381/87, location: (308, 233)\n", - "Processed cell at: LT0109_38/381/87, location: (264, 231)\n", - "Processed cell at: LT0109_38/381/87, location: (285, 250)\n", - "Processed cell at: LT0109_38/381/87, location: (881, 317)\n", - "Processed cell at: LT0109_38/381/87, location: (868, 325)\n", - "Processed cell at: LT0109_38/381/87, location: (916, 368)\n", - "Processed cell at: LT0109_38/381/87, location: (910, 385)\n", - "Processed cell at: LT0109_38/381/87, location: (935, 537)\n", - "Processed cell at: LT0109_38/381/87, location: (1135, 551)\n", - "Processed cell at: LT0109_38/381/87, location: (1190, 552)\n", - "Processed cell at: LT0109_38/381/87, location: (912, 551)\n", - "Processed cell at: LT0109_38/381/87, location: (1235, 556)\n", - "Processed cell at: LT0109_38/381/87, location: (404, 551)\n", - "Processed cell at: LT0109_38/381/87, location: (396, 564)\n", - "Processed cell at: LT0109_38/381/87, location: (1116, 596)\n", - "Processed cell at: LT0109_38/381/87, location: (1043, 601)\n", - "Processed cell at: LT0109_38/381/87, location: (1117, 632)\n", - "Processed cell at: LT0109_38/381/87, location: (1143, 633)\n", - "Processed cell at: LT0109_38/381/87, location: (1280, 643)\n", - "Processed cell at: LT0109_38/381/87, location: (763, 644)\n", - "Processed cell at: LT0109_38/381/87, location: (1225, 662)\n", - "Processed cell at: LT0109_38/381/87, location: (761, 673)\n", - "Processed cell at: LT0109_38/381/87, location: (380, 681)\n", - "Processed cell at: LT0109_38/381/87, location: (599, 731)\n", - "Processed cell at: LT0109_38/381/87, location: (409, 769)\n", - "Processed cell at: LT0109_38/381/87, location: (592, 801)\n", - "Processed cell at: LT0109_38/381/87, location: (1036, 811)\n", - "Processed cell at: LT0109_38/381/87, location: (1043, 830)\n", - "Processed cell at: LT0109_38/381/66, location: (1033, 163)\n", - "Processed cell at: LT0109_38/381/66, location: (1018, 179)\n", - "Processed cell at: LT0109_38/381/66, location: (1283, 189)\n", - "Processed cell at: LT0109_38/381/66, location: (804, 352)\n", - "Processed cell at: LT0109_38/381/66, location: (1059, 511)\n", - "Processed cell at: LT0109_38/381/66, location: (1107, 557)\n", - "Processed cell at: LT0109_38/381/66, location: (1209, 582)\n", - "Processed cell at: LT0109_38/381/66, location: (1164, 585)\n", - "Processed cell at: LT0109_38/381/66, location: (877, 576)\n", - "Processed cell at: LT0109_38/381/66, location: (1091, 585)\n", - "Processed cell at: LT0109_38/381/66, location: (863, 590)\n", - "Processed cell at: LT0109_38/381/66, location: (850, 601)\n", - "Processed cell at: LT0109_38/381/66, location: (1080, 619)\n", - "Processed cell at: LT0109_38/381/66, location: (1128, 629)\n", - "Processed cell at: LT0109_38/381/66, location: (861, 629)\n", - "Processed cell at: LT0109_38/381/66, location: (327, 633)\n", - "Processed cell at: LT0109_38/381/66, location: (876, 639)\n", - "Processed cell at: LT0109_38/381/66, location: (764, 639)\n", - 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"Processed cell at: LT0089_01/175/93, location: (895, 376)\n", - "Processed cell at: LT0089_01/175/93, location: (487, 374)\n", - "Processed cell at: LT0089_01/175/93, location: (264, 398)\n", - "Processed cell at: LT0089_01/175/93, location: (283, 394)\n", - "Processed cell at: LT0089_01/175/93, location: (690, 400)\n", - "Processed cell at: LT0089_01/175/93, location: (291, 410)\n", - "Processed cell at: LT0089_01/175/93, location: (168, 422)\n", - "Processed cell at: LT0089_01/175/93, location: (673, 420)\n", - "Processed cell at: LT0089_01/175/93, location: (145, 423)\n", - "Processed cell at: LT0089_01/175/93, location: (175, 440)\n", - "Processed cell at: LT0089_01/175/93, location: (160, 450)\n", - "Processed cell at: LT0089_01/175/93, location: (180, 460)\n", - "Processed cell at: LT0089_01/175/93, location: (241, 479)\n", - "Processed cell at: LT0089_01/175/93, location: (167, 506)\n", - "Processed cell at: LT0089_01/175/93, location: (933, 589)\n", - "Processed cell at: LT0089_01/175/93, location: (429, 584)\n", - "Processed cell at: LT0089_01/175/93, location: (413, 591)\n", - "Processed cell at: LT0089_01/175/93, location: (419, 598)\n", - "Processed cell at: LT0089_01/175/93, location: (404, 598)\n", - "Processed cell at: LT0089_01/175/93, location: (224, 611)\n", - "Processed cell at: LT0089_01/175/93, location: (1146, 617)\n", - "Processed cell at: LT0089_01/175/93, location: (1149, 631)\n", - "Processed cell at: LT0089_01/175/93, location: (1076, 677)\n", - "Processed cell at: LT0089_01/175/93, location: (1008, 758)\n", - "Processed cell at: LT0089_01/175/93, location: (1042, 765)\n", - "Processed cell at: LT0089_01/175/93, location: (1012, 770)\n", - "Processed cell at: LT0089_01/175/44, location: (341, 18)\n", - "Processed cell at: LT0089_01/175/44, location: (327, 22)\n", - "Processed cell at: LT0089_01/175/44, location: (354, 32)\n", - "Processed cell at: LT0089_01/175/44, location: (318, 36)\n", - "Processed cell at: LT0089_01/175/44, location: (347, 51)\n", - "Processed cell at: LT0089_01/175/44, location: (317, 62)\n", - "Processed cell at: LT0089_01/175/44, location: (778, 134)\n", - "Processed cell at: LT0089_01/175/44, location: (795, 167)\n", - "Processed cell at: LT0089_01/175/44, location: (742, 232)\n", - "Processed cell at: LT0089_01/175/44, location: (715, 235)\n", - "Processed cell at: LT0089_01/175/44, location: (1195, 287)\n", - "Processed cell at: LT0089_01/175/44, location: (1187, 297)\n", - "Processed cell at: LT0089_01/175/44, location: (1207, 303)\n", - "Processed cell at: LT0089_01/175/44, location: (1222, 301)\n", - "Processed cell at: LT0089_01/175/44, location: (401, 559)\n", - "Processed cell at: LT0089_01/175/44, location: (380, 574)\n", - "Processed cell at: LT0089_01/175/44, location: (402, 585)\n", - "Processed cell at: LT0089_01/175/44, location: (1113, 593)\n", - "Processed cell at: LT0089_01/175/44, location: (1129, 609)\n", - "Processed cell at: LT0089_01/175/44, location: (868, 633)\n", - "Processed cell at: LT0089_01/175/44, location: (895, 636)\n", - "Processed cell at: LT0089_01/175/44, location: (1023, 740)\n", - "Processed cell at: LT0089_01/175/44, location: (992, 738)\n", - "Processed cell at: LT0089_01/175/44, location: (1003, 755)\n", - "Processed cell at: LT0089_01/175/44, location: (1114, 885)\n", - "Processed cell at: LT0089_01/175/44, location: (1126, 894)\n", - "Processed cell at: LT0089_01/175/44, location: (1119, 907)\n", - "Processed cell at: LT0089_01/175/71, location: (327, 26)\n", - "Processed cell at: LT0089_01/175/71, location: (344, 33)\n", - "Processed cell at: LT0089_01/175/71, location: (313, 37)\n", - "Processed cell at: LT0089_01/175/71, location: (356, 50)\n", - "Processed cell at: LT0089_01/175/71, location: (324, 58)\n", - "Processed cell at: LT0089_01/175/71, location: (344, 66)\n", - "Processed cell at: LT0089_01/175/71, location: (712, 229)\n", - "Processed cell at: LT0089_01/175/71, location: (333, 264)\n", - "Processed cell at: LT0089_01/175/71, location: (1184, 294)\n", - "Processed cell at: LT0089_01/175/71, location: (1222, 294)\n", - "Processed cell at: LT0089_01/175/71, location: (1206, 293)\n", - "Processed cell at: LT0089_01/175/71, location: (797, 330)\n", - "Processed cell at: LT0089_01/175/71, location: (719, 337)\n", - "Processed cell at: LT0089_01/175/71, location: (526, 349)\n", - "Processed cell at: LT0089_01/175/71, location: (274, 387)\n", - "Processed cell at: LT0089_01/175/71, location: (691, 394)\n", - "Processed cell at: LT0089_01/175/71, location: (266, 406)\n", - "Processed cell at: LT0089_01/175/71, location: (290, 400)\n", - "Processed cell at: LT0089_01/175/71, location: (690, 417)\n", - "Processed cell at: LT0089_01/175/71, location: (241, 468)\n", - "Processed cell at: LT0089_01/175/71, location: (226, 481)\n", - "Processed cell at: LT0089_01/175/71, location: (403, 561)\n", - "Processed cell at: LT0089_01/175/71, location: (379, 569)\n", - "Processed cell at: LT0089_01/175/71, location: (405, 580)\n", - "Processed cell at: LT0089_01/175/71, location: (379, 589)\n", - "Processed cell at: LT0089_01/175/71, location: (1050, 718)\n", - "Processed cell at: LT0089_01/175/71, location: (1036, 753)\n", - "Processed cell at: LT0089_01/175/71, location: (993, 752)\n", - "Processed cell at: LT0089_01/175/71, location: (1005, 759)\n", - "Processed cell at: LT0089_01/175/71, location: (1206, 792)\n", - "Processed cell at: LT0089_01/175/71, location: (1219, 807)\n", - "Processed cell at: LT0089_01/175/71, location: (756, 870)\n", - "Processed cell at: LT0089_01/175/71, location: (777, 874)\n", - "Processed cell at: LT0089_01/175/71, location: (766, 889)\n", - "Processed cell at: LT0089_01/175/71, location: (1168, 893)\n", - "Processed cell at: LT0089_01/175/60, location: (328, 23)\n", - "Processed cell at: LT0089_01/175/60, location: (345, 32)\n", - "Processed cell at: LT0089_01/175/60, location: (314, 34)\n", - "Processed cell at: LT0089_01/175/60, location: (352, 50)\n", - "Processed cell at: LT0089_01/175/60, location: (319, 52)\n", - "Processed cell at: LT0089_01/175/60, location: (717, 232)\n", - "Processed cell at: LT0089_01/175/60, location: (888, 341)\n", - "Processed cell at: LT0089_01/175/60, location: (550, 345)\n", - "Processed cell at: LT0089_01/175/60, location: (866, 347)\n", - "Processed cell at: LT0089_01/175/60, location: (904, 364)\n", - "Processed cell at: LT0089_01/175/60, location: (689, 390)\n", - "Processed cell at: LT0089_01/175/60, location: (694, 405)\n", - "Processed cell at: LT0089_01/175/60, location: (683, 422)\n", - "Processed cell at: LT0089_01/175/60, location: (233, 470)\n", - "Processed cell at: LT0089_01/175/60, location: (404, 555)\n", - "Processed cell at: LT0089_01/175/60, location: (382, 565)\n", - "Processed cell at: LT0089_01/175/60, location: (399, 579)\n", - "Processed cell at: LT0089_01/175/60, location: (373, 590)\n", - "Processed cell at: LT0089_01/175/60, location: (1029, 747)\n", - "Processed cell at: LT0089_01/175/60, location: (1003, 752)\n", - "Processed cell at: LT0089_01/175/60, location: (988, 751)\n", - "Processed cell at: LT0089_01/175/60, location: (980, 770)\n", - "Processed cell at: LT0089_01/175/60, location: (775, 863)\n", - "Processed cell at: LT0089_01/175/60, location: (761, 874)\n", - "Processed cell at: LT0089_01/175/60, location: (781, 875)\n", - "Processed cell at: LT0089_01/175/60, location: (1155, 886)\n", - "Processed cell at: LT0089_01/175/60, location: (780, 891)\n", - "Processed cell at: LT0089_01/175/60, location: (1184, 900)\n", - "Processed cell at: LT0089_01/175/60, location: (1149, 910)\n", - "Processed cell at: LT0089_01/175/60, location: (1175, 924)\n", - "Processed cell at: LT0089_01/175/60, location: (1157, 932)\n", - "Processed cell at: LT0089_01/175/39, location: (350, 27)\n", - "Processed cell at: LT0089_01/175/39, location: (335, 24)\n", - "Processed cell at: LT0089_01/175/39, location: (321, 33)\n", - "Processed cell at: LT0089_01/175/39, location: (354, 43)\n", - "Processed cell at: LT0089_01/175/39, location: (339, 58)\n", - "Processed cell at: LT0089_01/175/39, location: (311, 58)\n", - "Processed cell at: LT0089_01/175/39, location: (126, 279)\n", - "Processed cell at: LT0089_01/175/39, location: (44, 286)\n", - "Processed cell at: LT0089_01/175/39, location: (28, 306)\n", - "Processed cell at: LT0089_01/175/39, location: (133, 306)\n", - "Processed cell at: LT0089_01/175/39, location: (911, 352)\n", - "Processed cell at: LT0089_01/175/39, location: (927, 377)\n", - "Processed cell at: LT0089_01/175/39, location: (428, 585)\n", - "Processed cell at: LT0089_01/175/39, location: (403, 591)\n", - "Processed cell at: LT0089_01/175/39, location: (1116, 596)\n", - "Processed cell at: LT0089_01/175/39, location: (1134, 612)\n", - "Processed cell at: LT0089_01/175/39, location: (1120, 615)\n", - "Processed cell at: LT0089_01/175/39, location: (1134, 879)\n", - "Processed cell at: LT0089_01/175/39, location: (1160, 890)\n", - "Processed cell at: LT0089_01/175/39, location: (1110, 883)\n", - "Processed cell at: LT0089_01/175/39, location: (1121, 894)\n", - "Processed cell at: LT0089_01/175/39, location: (1113, 906)\n", - "Processed cell at: LT0089_01/175/39, location: (1128, 920)\n", - "Processed cell at: LT0105_04/144/93, location: (479, 171)\n", - "Processed cell at: LT0105_04/144/93, location: (699, 428)\n", - "Processed cell at: LT0105_04/144/93, location: (405, 565)\n", - "Processed cell at: LT0105_04/144/93, location: (674, 745)\n", - "Processed cell at: LT0132_31/053/51, location: (501, 129)\n", - "Processed cell at: LT0132_31/053/51, location: (518, 140)\n", - "Processed cell at: LT0132_31/053/51, location: (505, 153)\n", - "Processed cell at: LT0132_31/053/51, location: (492, 165)\n", - "Processed cell at: LT0132_31/053/51, location: (528, 175)\n", - "Processed cell at: LT0132_31/053/51, location: (209, 190)\n", - "Processed cell at: LT0132_31/053/51, location: (196, 193)\n", - "Processed cell at: LT0132_31/053/51, location: (217, 201)\n", - "Processed cell at: LT0132_31/053/51, location: (191, 209)\n", - "Processed cell at: LT0132_31/053/51, location: (207, 210)\n", - "Processed cell at: LT0132_31/053/51, location: (605, 266)\n", - "Processed cell at: LT0132_31/053/51, location: (497, 277)\n", - "Processed cell at: LT0132_31/053/51, location: (607, 316)\n", - "Processed cell at: LT0132_31/053/51, location: (622, 353)\n", - "Processed cell at: LT0132_31/053/51, location: (537, 398)\n", - "Processed cell at: LT0132_31/053/51, location: (1299, 658)\n", - "Processed cell at: LT0132_31/053/51, location: (1313, 659)\n", - "Processed cell at: LT0132_31/053/51, location: (1288, 669)\n", - "Processed cell at: LT0132_31/053/51, location: (1319, 676)\n", - "Processed cell at: LT0132_31/053/51, location: (1303, 671)\n", - "Processed cell at: LT0132_31/053/51, location: (777, 694)\n", - "Processed cell at: LT0132_31/053/51, location: (766, 705)\n", - "Processed cell at: LT0132_31/053/51, location: (789, 707)\n", - "Processed cell at: LT0132_31/053/51, location: (775, 718)\n", - "Processed cell at: LT0132_31/053/51, location: (1083, 725)\n", - "Processed cell at: LT0132_31/053/51, location: (1103, 733)\n", - "Processed cell at: LT0132_31/053/51, location: (1073, 740)\n", - "Processed cell at: LT0132_31/053/51, location: (489, 761)\n", - "Processed cell at: LT0132_31/053/51, location: (502, 806)\n", - "Processed cell at: LT0132_31/053/51, location: (281, 912)\n", - "Processed cell at: LT0132_31/053/42, location: (664, 280)\n", - "Processed cell at: LT0132_31/053/42, location: (606, 315)\n", - "Processed cell at: LT0132_31/053/42, location: (629, 356)\n", - "Processed cell at: LT0132_31/053/42, location: (265, 450)\n", - "Processed cell at: LT0132_31/053/42, location: (288, 899)\n", - "Processed cell at: LT0132_31/053/42, location: (703, 941)\n", - "Processed cell at: LT0132_31/053/42, location: (713, 991)\n", - "Processed cell at: LT0132_31/053/75, location: (496, 146)\n", - "Processed cell at: LT0132_31/053/75, location: (516, 155)\n", - "Processed cell at: LT0132_31/053/75, location: (501, 170)\n", - "Processed cell at: LT0132_31/053/75, location: (484, 178)\n", - "Processed cell at: LT0132_31/053/75, location: (183, 207)\n", - "Processed cell at: LT0132_31/053/75, location: (198, 210)\n", - "Processed cell at: LT0132_31/053/75, location: (170, 218)\n", - "Processed cell at: LT0132_31/053/75, location: (190, 227)\n", - "Processed cell at: LT0132_31/053/75, location: (1300, 654)\n", - "Processed cell at: LT0132_31/053/75, location: (1315, 658)\n", - "Processed cell at: LT0132_31/053/75, location: (1325, 671)\n", - "Processed cell at: LT0132_31/053/75, location: (1303, 673)\n", - "Processed cell at: LT0132_31/053/75, location: (776, 690)\n", - "Processed cell at: LT0132_31/053/75, location: (791, 700)\n", - "Processed cell at: LT0132_31/053/75, location: (769, 704)\n", - "Processed cell at: LT0132_31/053/75, location: (779, 716)\n", - "Processed cell at: LT0132_31/053/63, location: (1195, 226)\n", - "Processed cell at: LT0132_31/053/63, location: (597, 272)\n", - "Processed cell at: LT0132_31/053/63, location: (1246, 273)\n", - "Processed cell at: LT0132_31/053/63, location: (487, 278)\n", - "Processed cell at: LT0132_31/053/63, location: (1197, 276)\n", - "Processed cell at: LT0132_31/053/63, location: (606, 315)\n", - "Processed cell at: LT0132_31/053/63, location: (518, 360)\n", - "Processed cell at: LT0132_31/053/63, location: (632, 363)\n", - "Processed cell at: LT0132_31/053/63, location: (546, 402)\n", - "Processed cell at: LT0132_31/053/63, location: (774, 446)\n", - "Processed cell at: LT0132_31/053/63, location: (223, 574)\n", - "Processed cell at: LT0132_31/053/63, location: (256, 614)\n", - "Processed cell at: LT0132_31/053/63, location: (489, 763)\n", - "Processed cell at: LT0132_31/053/63, location: (500, 805)\n", - "Processed cell at: LT0132_31/053/63, location: (242, 905)\n", - "Processed cell at: LT0132_31/053/63, location: (153, 922)\n", - "Processed cell at: LT0132_31/053/63, location: (576, 949)\n", - "Processed cell at: LT0132_31/053/63, location: (706, 1006)\n", - "Processed cell at: LT0132_31/053/33, location: (638, 276)\n", - "Processed cell at: LT0132_31/053/33, location: (678, 286)\n", - "Processed cell at: LT0132_31/053/33, location: (351, 426)\n", - "Processed cell at: LT0132_31/053/33, location: (347, 474)\n", - "Processed cell at: LT0132_31/053/37, location: (673, 281)\n", - "Processed cell at: LT0132_31/053/37, location: (607, 314)\n", - "Processed cell at: LT0132_31/053/37, location: (638, 355)\n", - "Processed cell at: LT0132_31/053/37, location: (348, 424)\n", - "Processed cell at: LT0132_31/053/37, location: (506, 806)\n", - "Processed cell at: LT0066_23/163/51, location: (400, 581)\n", - "Processed cell at: LT0066_23/163/51, location: (376, 577)\n", - "Processed cell at: LT0066_23/163/51, location: (377, 591)\n", - "Processed cell at: LT0066_23/163/51, location: (357, 598)\n", - "Processed cell at: LT0066_23/163/51, location: (784, 623)\n", - "Processed cell at: LT0066_23/163/51, location: (787, 637)\n", - "Processed cell at: LT0066_23/163/51, location: (782, 659)\n", - "Processed cell at: LT0048_14/335/1, location: (939, 175)\n", - "Processed cell at: LT0048_14/335/1, location: (373, 405)\n", - "Processed cell at: LT0048_14/335/1, location: (171, 421)\n", - "Processed cell at: LT0048_14/335/1, location: (301, 477)\n", - "Processed cell at: LT0048_14/335/1, location: (790, 503)\n", - "Processed cell at: LT0048_14/335/1, location: (479, 550)\n", - "Processed cell at: LT0048_14/335/1, location: (1067, 605)\n", - "Processed cell at: LT0048_14/335/1, location: (1043, 610)\n", - "Processed cell at: LT0048_14/335/1, location: (779, 622)\n", - "Processed cell at: LT0048_14/335/1, location: (729, 694)\n", - "Processed cell at: LT0048_14/335/1, location: (609, 746)\n", - "Processed cell at: LT0048_14/335/1, location: (241, 809)\n", - "Processed cell at: LT0048_14/335/1, location: (284, 823)\n", - "Processed cell at: LT0048_14/335/1, location: (419, 875)\n", - "Processed cell at: LT0048_14/335/1, location: (264, 903)\n", - "Processed cell at: LT0048_14/335/1, location: (593, 902)\n", - "Processed cell at: LT0048_14/335/1, location: (500, 931)\n", - "Processed cell at: LT0048_14/335/1, location: (271, 940)\n", - "Processed cell at: LT0048_14/335/1, location: (454, 951)\n", - "Processed cell at: LT0048_14/335/1, location: (490, 974)\n", - "Processed cell at: LT0048_14/335/56, location: (918, 48)\n", - "Processed cell at: LT0048_14/335/56, location: (1227, 77)\n", - "Processed cell at: LT0048_14/335/56, location: (1169, 90)\n", - "Processed cell at: LT0048_14/335/56, location: (1250, 95)\n", - "Processed cell at: LT0048_14/335/56, location: (528, 123)\n", - "Processed cell at: LT0048_14/335/56, location: (598, 126)\n", - "Processed cell at: LT0048_14/335/56, location: (151, 134)\n", - "Processed cell at: LT0048_14/335/56, location: (534, 198)\n", - "Processed cell at: LT0048_14/335/56, location: (715, 203)\n", - "Processed cell at: LT0048_14/335/56, location: (210, 206)\n", - "Processed cell at: LT0048_14/335/56, location: (724, 258)\n", - "Processed cell at: LT0048_14/335/56, location: (262, 329)\n", - "Processed cell at: LT0048_14/335/56, location: (760, 324)\n", - "Processed cell at: LT0048_14/335/56, location: (278, 384)\n", - "Processed cell at: LT0048_14/335/56, location: (189, 401)\n", - "Processed cell at: LT0048_14/335/56, location: (796, 419)\n", - "Processed cell at: LT0048_14/335/56, location: (184, 417)\n", - "Processed cell at: LT0048_14/335/56, location: (197, 426)\n", - "Processed cell at: LT0048_14/335/56, location: (483, 558)\n", - "Processed cell at: LT0048_14/335/56, location: (757, 563)\n", - "Processed cell at: LT0048_14/335/56, location: (815, 574)\n", - "Processed cell at: LT0048_14/335/56, location: (715, 574)\n", - "Processed cell at: LT0048_14/335/56, location: (380, 610)\n", - "Processed cell at: LT0048_14/335/56, location: (765, 620)\n", - "Processed cell at: LT0048_14/335/56, location: (331, 636)\n", - "Processed cell at: LT0048_14/335/56, location: (413, 646)\n", - "Processed cell at: LT0048_14/335/56, location: (242, 804)\n", - "Processed cell at: LT0048_14/335/56, location: (216, 806)\n", - "Processed cell at: LT0048_14/335/56, location: (259, 825)\n", - "Processed cell at: LT0048_14/335/56, location: (550, 856)\n", - "Processed cell at: LT0048_14/335/56, location: (235, 899)\n", - "Processed cell at: LT0048_14/335/56, location: (261, 908)\n", - "Processed cell at: LT0048_14/335/56, location: (288, 921)\n", - "Processed cell at: LT0048_14/335/56, location: (300, 944)\n", - "Processed cell at: LT0048_14/335/56, location: (315, 949)\n", - "Processed cell at: LT0048_14/335/13, location: (264, 164)\n", - "Processed cell at: LT0048_14/335/13, location: (943, 172)\n", - "Processed cell at: LT0048_14/335/13, location: (771, 232)\n", - "Processed cell at: LT0048_14/335/13, location: (1207, 300)\n", - "Processed cell at: LT0048_14/335/13, location: (33, 314)\n", - "Processed cell at: LT0048_14/335/13, location: (1132, 335)\n", - "Processed cell at: LT0048_14/335/13, location: (1260, 349)\n", - "Processed cell at: LT0048_14/335/13, location: (746, 384)\n", - "Processed cell at: LT0048_14/335/13, location: (368, 401)\n", - "Processed cell at: LT0048_14/335/13, location: (165, 422)\n", - "Processed cell at: LT0048_14/335/13, location: (296, 474)\n", - "Processed cell at: LT0048_14/335/13, location: (790, 502)\n", - "Processed cell at: LT0048_14/335/13, location: (911, 522)\n", - "Processed cell at: LT0048_14/335/13, location: (1242, 566)\n", - "Processed cell at: LT0048_14/335/13, location: (812, 581)\n", - "Processed cell at: LT0048_14/335/13, location: (1236, 592)\n", - "Processed cell at: LT0048_14/335/13, location: (778, 620)\n", - "Processed cell at: LT0048_14/335/13, location: (344, 639)\n", - "Processed cell at: LT0048_14/335/13, location: (725, 689)\n", - "Processed cell at: LT0048_14/335/13, location: (614, 745)\n", - "Processed cell at: LT0048_14/335/13, location: (719, 782)\n", - "Processed cell at: LT0048_14/335/13, location: (241, 800)\n", - "Processed cell at: LT0048_14/335/13, location: (283, 821)\n", - "Processed cell at: LT0048_14/335/13, location: (418, 875)\n", - "Processed cell at: LT0048_14/335/13, location: (255, 903)\n", - "Processed cell at: LT0048_14/335/13, location: (595, 901)\n", - "Processed cell at: LT0048_14/335/13, location: (405, 907)\n", - "Processed cell at: LT0048_14/335/13, location: (1283, 927)\n", - "Processed cell at: LT0048_14/335/13, location: (498, 933)\n", - "Processed cell at: LT0048_14/335/13, location: (276, 937)\n", - "Processed cell at: LT0048_14/335/13, location: (487, 968)\n", - "Processed cell at: LT0048_14/335/29, location: (880, 19)\n", - "Processed cell at: LT0048_14/335/29, location: (920, 44)\n", - "Processed cell at: LT0048_14/335/29, location: (371, 58)\n", - "Processed cell at: LT0048_14/335/29, location: (560, 83)\n", - "Processed cell at: LT0048_14/335/29, location: (1246, 86)\n", - "Processed cell at: LT0048_14/335/29, location: (595, 126)\n", - "Processed cell at: LT0048_14/335/29, location: (149, 140)\n", - "Processed cell at: LT0048_14/335/29, location: (265, 173)\n", - "Processed cell at: LT0048_14/335/29, location: (539, 197)\n", - "Processed cell at: LT0048_14/335/29, location: (207, 204)\n", - "Processed cell at: LT0048_14/335/29, location: (720, 207)\n", - "Processed cell at: LT0048_14/335/29, location: (21, 326)\n", - "Processed cell at: LT0048_14/335/29, location: (1245, 337)\n", - "Processed cell at: LT0048_14/335/29, location: (1295, 366)\n", - "Processed cell at: LT0048_14/335/29, location: (742, 389)\n", - "Processed cell at: LT0048_14/335/29, location: (796, 416)\n", - "Processed cell at: LT0048_14/335/29, location: (171, 431)\n", - "Processed cell at: LT0048_14/335/29, location: (789, 500)\n", - "Processed cell at: LT0048_14/335/29, location: (482, 543)\n", - "Processed cell at: LT0048_14/335/29, location: (478, 548)\n", - "Processed cell at: LT0048_14/335/29, location: (813, 579)\n", - "Processed cell at: LT0048_14/335/29, location: (476, 566)\n", - "Processed cell at: LT0048_14/335/29, location: (458, 612)\n", - "Processed cell at: LT0048_14/335/29, location: (383, 617)\n", - "Processed cell at: LT0048_14/335/29, location: (334, 641)\n", - "Processed cell at: LT0048_14/335/29, location: (733, 685)\n", - "Processed cell at: LT0048_14/335/29, location: (726, 699)\n", - "Processed cell at: LT0048_14/335/29, location: (617, 740)\n", - "Processed cell at: LT0048_14/335/29, location: (248, 792)\n", - "Processed cell at: LT0048_14/335/29, location: (284, 827)\n", - "Processed cell at: LT0048_14/335/29, location: (536, 849)\n", - "Processed cell at: LT0048_14/335/29, location: (224, 874)\n", - "Processed cell at: LT0048_14/335/29, location: (273, 876)\n", - "Processed cell at: LT0048_14/335/29, location: (233, 897)\n", - "Processed cell at: LT0048_14/335/29, location: (253, 911)\n", - "Processed cell at: LT0048_14/335/29, location: (277, 938)\n", - "Processed cell at: LT0048_14/335/29, location: (485, 973)\n", - "Processed cell at: LT0098_13/021/21, location: (225, 51)\n", - "Processed cell at: LT0098_13/021/21, location: (723, 54)\n", - "Processed cell at: LT0098_13/021/21, location: (949, 69)\n", - "Processed cell at: LT0098_13/021/21, location: (411, 103)\n", - "Processed cell at: LT0098_13/021/21, location: (1294, 109)\n", - "Processed cell at: LT0098_13/021/21, location: (342, 136)\n", - "Processed cell at: LT0098_13/021/21, location: (767, 147)\n", - "Processed cell at: LT0098_13/021/21, location: (548, 259)\n", - "Processed cell at: LT0098_13/021/21, location: (520, 271)\n", - "Processed cell at: LT0098_13/021/21, location: (285, 380)\n", - "Processed cell at: LT0098_13/021/21, location: (618, 377)\n", - "Processed cell at: LT0098_13/021/21, location: (629, 383)\n", - "Processed cell at: LT0098_13/021/21, location: (245, 414)\n", - "Processed cell at: LT0098_13/021/21, location: (335, 418)\n", - "Processed cell at: LT0098_13/021/21, location: (824, 502)\n", - "Processed cell at: LT0098_13/021/21, location: (838, 517)\n", - "Processed cell at: LT0098_13/021/21, location: (865, 522)\n", - "Processed cell at: LT0098_13/021/21, location: (301, 555)\n", - "Processed cell at: LT0098_13/021/21, location: (401, 563)\n", - "Processed cell at: LT0098_13/021/21, location: (1240, 608)\n", - "Processed cell at: LT0098_13/021/21, location: (1011, 654)\n", - "Processed cell at: LT0098_13/021/21, location: (138, 687)\n", - "Processed cell at: LT0098_13/021/21, location: (540, 892)\n", - "Processed cell at: LT0098_13/021/21, location: (539, 929)\n", - "Processed cell at: LT0098_13/021/21, location: (824, 948)\n", - "Processed cell at: LT0098_13/021/21, location: (368, 960)\n", - "Processed cell at: LT0098_13/021/27, location: (550, 262)\n", - "Processed cell at: LT0098_13/021/27, location: (511, 262)\n", - "Processed cell at: LT0098_13/021/27, location: (511, 276)\n", - "Processed cell at: LT0098_13/021/27, location: (555, 280)\n", - "Processed cell at: LT0098_13/021/27, location: (618, 377)\n", - "Processed cell at: LT0098_13/021/27, location: (638, 379)\n", - "Processed cell at: LT0098_13/021/27, location: (1249, 603)\n", - "Processed cell at: LT0098_13/021/77, location: (891, 52)\n", - "Processed cell at: LT0098_13/021/77, location: (1009, 77)\n", - "Processed cell at: LT0098_13/021/77, location: (938, 85)\n", - "Processed cell at: LT0098_13/021/77, location: (884, 98)\n", - "Processed cell at: LT0098_13/021/77, location: (1033, 95)\n", - "Processed cell at: LT0098_13/021/77, location: (910, 99)\n", - "Processed cell at: LT0098_13/021/77, location: (736, 173)\n", - "Processed cell at: LT0098_13/021/77, location: (755, 185)\n", - "Processed cell at: LT0098_13/021/77, location: (536, 279)\n", - "Processed cell at: LT0098_13/021/77, location: (524, 293)\n", - "Processed cell at: LT0098_13/021/77, location: (771, 315)\n", - "Processed cell at: LT0098_13/021/77, location: (776, 323)\n", - "Processed cell at: LT0098_13/021/77, location: (91, 332)\n", - "Processed cell at: LT0098_13/021/77, location: (791, 335)\n", - "Processed cell at: LT0098_13/021/77, location: (774, 339)\n", - "Processed cell at: LT0098_13/021/77, location: (395, 389)\n", - "Processed cell at: LT0098_13/021/77, location: (869, 416)\n", - "Processed cell at: LT0098_13/021/77, location: (394, 428)\n", - "Processed cell at: LT0098_13/021/77, location: (440, 502)\n", - "Processed cell at: LT0098_13/021/77, location: (420, 527)\n", - "Processed cell at: LT0098_13/021/77, location: (937, 538)\n", - "Processed cell at: LT0098_13/021/77, location: (954, 564)\n", - "Processed cell at: LT0098_13/021/77, location: (276, 566)\n", - "Processed cell at: LT0098_13/021/77, location: (299, 570)\n", - "Processed cell at: LT0098_13/021/77, location: (314, 579)\n", - "Processed cell at: LT0098_13/021/77, location: (522, 594)\n", - "Processed cell at: LT0098_13/021/77, location: (1013, 595)\n", - "Processed cell at: LT0098_13/021/77, location: (1015, 624)\n", - "Processed cell at: LT0098_13/021/77, location: (383, 639)\n", - "Processed cell at: LT0098_13/021/77, location: (32, 664)\n", - "Processed cell at: LT0098_13/021/77, location: (375, 671)\n", - "Processed cell at: LT0098_13/021/77, location: (668, 879)\n", - "Processed cell at: LT0098_13/021/77, location: (691, 903)\n", - "Processed cell at: LT0098_13/021/33, location: (555, 265)\n", - "Processed cell at: LT0098_13/021/33, location: (515, 265)\n", - "Processed cell at: LT0098_13/021/33, location: (511, 279)\n", - "Processed cell at: LT0098_13/021/33, location: (522, 283)\n", - "Processed cell at: LT0098_13/021/33, location: (561, 281)\n", - "Processed cell at: LT0098_13/021/33, location: (671, 317)\n", - "Processed cell at: LT0098_13/021/33, location: (679, 334)\n", - "Processed cell at: LT0098_13/021/33, location: (665, 331)\n", - "Processed cell at: LT0098_13/021/33, location: (621, 378)\n", - "Processed cell at: LT0098_13/021/33, location: (642, 379)\n", - "Processed cell at: LT0098_13/021/33, location: (638, 398)\n", - "Processed cell at: LT0098_13/021/33, location: (408, 553)\n", - "Processed cell at: LT0098_13/021/33, location: (386, 563)\n", - "Processed cell at: LT0098_13/021/33, location: (977, 579)\n", - "Processed cell at: LT0098_13/021/33, location: (994, 589)\n", - "Processed cell at: LT0098_13/021/33, location: (1016, 614)\n", - "Processed cell at: LT0098_13/021/33, location: (987, 619)\n", - "Processed cell at: LT0098_13/021/33, location: (970, 631)\n", - "Processed cell at: LT0039_45/136/62, location: (1261, 334)\n", - "Processed cell at: LT0039_45/136/62, location: (467, 599)\n", - "Processed cell at: LT0039_45/136/62, location: (499, 623)\n", - "Processed cell at: LT0039_45/136/53, location: (880, 146)\n", - "Processed cell at: LT0039_45/136/53, location: (513, 237)\n", - "Processed cell at: LT0039_45/136/53, location: (455, 249)\n", - "Processed cell at: LT0039_45/136/53, location: (1176, 429)\n", - "Processed cell at: LT0039_45/136/53, location: (1158, 461)\n", - "Processed cell at: LT0039_45/136/53, location: (1072, 464)\n", - "Processed cell at: LT0039_45/136/53, location: (529, 626)\n", - "Processed cell at: LT0039_45/136/53, location: (533, 639)\n", - "Processed cell at: LT0013_38/042/47, location: (484, 161)\n", - "Processed cell at: LT0013_38/042/47, location: (506, 168)\n", - "Processed cell at: LT0013_38/042/47, location: (1110, 179)\n", - "Processed cell at: LT0013_38/042/47, location: (994, 193)\n", - "Processed cell at: LT0013_38/042/47, location: (1124, 197)\n", - "Processed cell at: LT0013_38/042/47, location: (985, 221)\n", - "Processed cell at: LT0013_38/042/47, location: (1121, 352)\n", - "Processed cell at: LT0013_38/042/47, location: (1267, 365)\n", - "Processed cell at: LT0013_38/042/47, location: (1288, 364)\n", - "Processed cell at: LT0013_38/042/47, location: (1301, 371)\n", - "Processed cell at: LT0013_38/042/47, location: (1110, 373)\n", - "Processed cell at: LT0013_38/042/47, location: (1268, 382)\n", - "Processed cell at: LT0013_38/042/47, location: (1292, 385)\n", - "Processed cell at: LT0013_38/042/47, location: (1043, 696)\n", - "Processed cell at: LT0013_38/042/47, location: (1044, 720)\n", - "Processed cell at: LT0013_38/042/47, location: (1047, 788)\n", - "Processed cell at: LT0013_38/042/47, location: (1171, 804)\n", - "Processed cell at: LT0013_38/042/47, location: (1150, 805)\n", - "Processed cell at: LT0013_38/042/47, location: (1059, 810)\n", - "Processed cell at: LT0013_38/042/47, location: (1139, 824)\n", - "Processed cell at: LT0013_38/042/47, location: (1163, 837)\n", - "Processed cell at: LT0013_38/042/47, location: (1144, 846)\n", - "Processed cell at: LT0013_38/042/47, location: (390, 853)\n", - "Processed cell at: LT0013_38/042/47, location: (576, 874)\n", - "Processed cell at: LT0013_38/042/47, location: (391, 879)\n", - "Processed cell at: LT0013_38/042/47, location: (595, 893)\n", - "Processed cell at: LT0013_38/042/75, location: (480, 125)\n", - "Processed cell at: LT0013_38/042/75, location: (543, 113)\n", - "Processed cell at: LT0013_38/042/75, location: (541, 138)\n", - "Processed cell at: LT0013_38/042/75, location: (400, 152)\n", - "Processed cell at: LT0013_38/042/75, location: (376, 167)\n", - "Processed cell at: LT0013_38/042/75, location: (97, 158)\n", - "Processed cell at: LT0013_38/042/75, location: (70, 166)\n", - "Processed cell at: LT0013_38/042/75, location: (401, 182)\n", - "Processed cell at: LT0013_38/042/75, location: (94, 178)\n", - "Processed cell at: LT0013_38/042/75, location: (1152, 186)\n", - "Processed cell at: LT0013_38/042/75, location: (72, 182)\n", - "Processed cell at: LT0013_38/042/75, location: (1172, 197)\n", - "Processed cell at: LT0013_38/042/75, location: (1235, 248)\n", - "Processed cell at: LT0013_38/042/75, location: (1054, 258)\n", - "Processed cell at: LT0013_38/042/75, location: (1038, 264)\n", - "Processed cell at: LT0013_38/042/75, location: (1240, 269)\n", - "Processed cell at: LT0013_38/042/75, location: (1057, 286)\n", - "Processed cell at: LT0013_38/042/75, location: (150, 352)\n", - "Processed cell at: LT0013_38/042/75, location: (1254, 366)\n", - "Processed cell at: LT0013_38/042/75, location: (1241, 379)\n", - "Processed cell at: LT0013_38/042/75, location: (337, 386)\n", - "Processed cell at: LT0013_38/042/75, location: (137, 387)\n", - "Processed cell at: LT0013_38/042/75, location: (1270, 385)\n", - "Processed cell at: LT0013_38/042/75, location: (215, 391)\n", - "Processed cell at: LT0013_38/042/75, location: (1248, 394)\n", - "Processed cell at: LT0013_38/042/75, location: (318, 403)\n", - "Processed cell at: LT0013_38/042/75, location: (1263, 403)\n", - "Processed cell at: LT0013_38/042/75, location: (200, 411)\n", - "Processed cell at: LT0013_38/042/75, location: (395, 408)\n", - "Processed cell at: LT0013_38/042/75, location: (384, 426)\n", - "Processed cell at: LT0013_38/042/75, location: (342, 430)\n", - "Processed cell at: LT0013_38/042/75, location: (326, 448)\n", - "Processed cell at: LT0013_38/042/75, location: (300, 472)\n", - "Processed cell at: LT0013_38/042/75, location: (282, 490)\n", - "Processed cell at: LT0013_38/042/75, location: (303, 493)\n", - "Processed cell at: LT0013_38/042/75, location: (346, 500)\n", - "Processed cell at: LT0013_38/042/75, location: (1109, 498)\n", - "Processed cell at: LT0013_38/042/75, location: (1096, 511)\n", - "Processed cell at: LT0013_38/042/75, location: (336, 513)\n", - "Processed cell at: LT0013_38/042/75, location: (1115, 516)\n", - "Processed cell at: LT0013_38/042/75, location: (737, 521)\n", - "Processed cell at: LT0013_38/042/75, location: (1138, 529)\n", - "Processed cell at: LT0013_38/042/75, location: (933, 546)\n", - "Processed cell at: LT0013_38/042/75, location: (740, 544)\n", - "Processed cell at: LT0013_38/042/75, location: (486, 559)\n", - "Processed cell at: LT0013_38/042/75, location: (808, 588)\n", - "Processed cell at: LT0013_38/042/75, location: (827, 604)\n", - "Processed cell at: LT0013_38/042/75, location: (706, 717)\n", - "Processed cell at: LT0013_38/042/75, location: (725, 723)\n", - "Processed cell at: LT0013_38/042/75, location: (700, 745)\n", - "Processed cell at: LT0013_38/042/75, location: (389, 760)\n", - "Processed cell at: LT0013_38/042/75, location: (414, 763)\n", - "Processed cell at: LT0013_38/042/75, location: (1132, 829)\n", - "Processed cell at: LT0013_38/042/75, location: (1151, 841)\n", - "Processed cell at: LT0013_38/042/75, location: (1120, 844)\n", - "Processed cell at: LT0013_38/042/75, location: (1136, 866)\n", - "Processed cell at: LT0013_38/042/75, location: (384, 864)\n", - "Processed cell at: LT0013_38/042/75, location: (1117, 865)\n", - "Processed cell at: LT0013_38/042/75, location: (1292, 871)\n", - "Processed cell at: LT0013_38/042/75, location: (1306, 888)\n", - "Processed cell at: LT0013_38/042/75, location: (381, 887)\n", - "Processed cell at: LT0013_38/042/75, location: (1218, 909)\n", - "Processed cell at: LT0013_38/042/75, location: (1196, 919)\n", - "Processed cell at: LT0013_38/042/75, location: (742, 945)\n", - "Processed cell at: LT0013_38/042/75, location: (769, 954)\n", - "Processed cell at: LT0013_38/042/75, location: (670, 958)\n", - "Processed cell at: LT0013_38/042/75, location: (682, 978)\n", - "Processed cell at: LT0041_32/132/68, location: (704, 195)\n", - "Processed cell at: LT0041_32/132/68, location: (692, 281)\n", - "Processed cell at: LT0041_32/132/68, location: (646, 299)\n", - "Processed cell at: LT0041_32/132/68, location: (999, 799)\n", - "Processed cell at: LT0041_32/132/68, location: (447, 846)\n", - "Processed cell at: LT0041_32/132/74, location: (989, 123)\n", - "Processed cell at: LT0041_32/132/74, location: (902, 158)\n", - "Processed cell at: LT0041_32/132/74, location: (970, 175)\n", - "Processed cell at: LT0041_32/132/74, location: (703, 191)\n", - "Processed cell at: LT0041_32/132/74, location: (957, 184)\n", - "Processed cell at: LT0041_32/132/74, location: (627, 243)\n", - "Processed cell at: LT0041_32/132/74, location: (694, 270)\n", - "Processed cell at: LT0041_32/132/74, location: (656, 429)\n", - "Processed cell at: LT0041_32/132/74, location: (974, 544)\n", - "Processed cell at: LT0041_32/132/74, location: (959, 551)\n", - "Processed cell at: LT0041_32/132/74, location: (150, 616)\n", - "Processed cell at: LT0041_32/132/74, location: (1030, 663)\n", - "Processed cell at: LT0041_32/132/65, location: (994, 133)\n", - "Processed cell at: LT0041_32/132/65, location: (700, 199)\n", - "Processed cell at: LT0041_32/132/65, location: (690, 279)\n", - "Processed cell at: LT0038_08/250/80, location: (769, 312)\n", - "Processed cell at: LT0038_08/250/80, location: (780, 323)\n", - "Processed cell at: LT0038_08/250/80, location: (789, 330)\n", - "Processed cell at: LT0038_08/250/80, location: (772, 340)\n", - "Processed cell at: LT0038_08/250/54, location: (931, 17)\n", - "Processed cell at: LT0038_08/250/54, location: (875, 96)\n", - "Processed cell at: LT0038_08/250/54, location: (802, 105)\n", - "Processed cell at: LT0038_08/250/54, location: (787, 129)\n", - "Processed cell at: LT0038_08/250/54, location: (435, 176)\n", - "Processed cell at: LT0038_08/250/54, location: (402, 180)\n", - "Processed cell at: LT0038_08/250/54, location: (967, 228)\n", - "Processed cell at: LT0038_08/250/54, location: (158, 477)\n", - "Processed cell at: LT0038_08/250/54, location: (314, 514)\n", - "Processed cell at: LT0038_08/250/54, location: (1047, 589)\n", - "Processed cell at: LT0038_08/250/54, location: (548, 597)\n", - "Processed cell at: LT0038_08/250/54, location: (778, 654)\n", - "Processed cell at: LT0038_08/250/54, location: (1232, 882)\n", - "Processed cell at: LT0038_08/250/54, location: (1206, 890)\n", - "Processed cell at: LT0038_08/250/54, location: (1231, 909)\n", - "Processed cell at: LT0157_04/005/2, location: (1248, 309)\n", - "Processed cell at: LT0157_04/005/2, location: (725, 518)\n", - "Processed cell at: LT0157_04/005/2, location: (444, 546)\n", - "Processed cell at: LT0157_04/005/2, location: (709, 565)\n", - "Processed cell at: LT0157_04/005/2, location: (1081, 673)\n", - "Processed cell at: LT0157_04/005/2, location: (1110, 681)\n", - "Processed cell at: LT0157_04/005/2, location: (1119, 700)\n", - "Processed cell at: LT0157_04/005/2, location: (631, 727)\n", - "Processed cell at: LT0157_04/005/2, location: (368, 724)\n", - "Processed cell at: LT0157_04/005/2, location: (356, 722)\n", - "Processed cell at: LT0157_04/005/2, location: (1253, 766)\n", - "Processed cell at: LT0157_04/005/2, location: (351, 813)\n", - "Processed cell at: LT0157_04/005/2, location: (436, 920)\n", - "Processed cell at: LT0157_04/005/47, location: (1035, 21)\n", - "Processed cell at: LT0157_04/005/47, location: (1059, 34)\n", - "Processed cell at: LT0157_04/005/47, location: (330, 357)\n", - "Processed cell at: LT0157_04/005/47, location: (963, 360)\n", - "Processed cell at: LT0157_04/005/47, location: (313, 374)\n", - "Processed cell at: LT0157_04/005/47, location: (968, 385)\n", - "Processed cell at: LT0157_04/005/47, location: (843, 404)\n", - "Processed cell at: LT0157_04/005/47, location: (975, 410)\n", - "Processed cell at: LT0157_04/005/47, location: (856, 417)\n", - "Processed cell at: LT0157_04/005/47, location: (851, 433)\n", - "Processed cell at: LT0157_04/005/47, location: (653, 509)\n", - "Processed cell at: LT0157_04/005/47, location: (634, 527)\n", - "Processed cell at: LT0157_04/005/47, location: (618, 518)\n", - "Processed cell at: LT0157_04/005/47, location: (411, 576)\n", - "Processed cell at: LT0157_04/005/47, location: (435, 583)\n", - "Processed cell at: LT0157_04/005/47, location: (451, 586)\n", - "Processed cell at: LT0157_04/005/47, location: (281, 598)\n", - "Processed cell at: LT0157_04/005/47, location: (400, 598)\n", - "Processed cell at: LT0157_04/005/47, location: (418, 600)\n", - "Processed cell at: LT0157_04/005/47, location: (300, 609)\n", - "Processed cell at: LT0157_04/005/47, location: (386, 616)\n", - "Processed cell at: LT0157_04/005/47, location: (313, 615)\n", - "Processed cell at: LT0157_04/005/47, location: (404, 615)\n", - "Processed cell at: LT0157_04/005/47, location: (338, 917)\n", - "Processed cell at: LT0157_04/005/47, location: (353, 928)\n", - "Processed cell at: LT0157_04/005/47, location: (310, 933)\n", - "Processed cell at: LT0157_04/005/47, location: (49, 942)\n", - "Processed cell at: LT0157_04/005/47, location: (321, 940)\n", - "Processed cell at: LT0157_04/005/47, location: (352, 941)\n", - "Processed cell at: LT0157_04/005/47, location: (334, 948)\n", - "Processed cell at: LT0157_04/005/47, location: (1279, 941)\n", - "Processed cell at: LT0157_04/005/47, location: (1265, 952)\n", - "Processed cell at: LT0157_04/005/47, location: (1294, 948)\n", - "Processed cell at: LT0157_04/005/47, location: (27, 959)\n", - "Processed cell at: LT0157_04/005/47, location: (344, 961)\n", - "Processed cell at: LT0157_04/005/47, location: (1286, 958)\n", - "Processed cell at: LT0157_04/005/47, location: (1302, 959)\n", - "Processed cell at: LT0157_04/005/47, location: (50, 958)\n", - "Processed cell at: LT0157_04/005/47, location: (36, 965)\n", - "Processed cell at: LT0157_04/005/47, location: (311, 967)\n", - "Processed cell at: LT0157_04/005/47, location: (31, 976)\n", - "Processed cell at: LT0157_04/005/47, location: (18, 981)\n", - "Processed cell at: LT0157_04/005/78, location: (753, 203)\n", - "Processed cell at: LT0157_04/005/78, location: (780, 214)\n", - "Processed cell at: LT0157_04/005/78, location: (758, 224)\n", - "Processed cell at: LT0157_04/005/78, location: (925, 328)\n", - "Processed cell at: LT0157_04/005/78, location: (938, 338)\n", - "Processed cell at: LT0157_04/005/78, location: (925, 343)\n", - "Processed cell at: LT0157_04/005/78, location: (951, 355)\n", - "Processed cell at: LT0157_04/005/78, location: (934, 357)\n", - "Processed cell at: LT0157_04/005/78, location: (944, 373)\n", - "Processed cell at: LT0157_04/005/78, location: (824, 405)\n", - "Processed cell at: LT0157_04/005/78, location: (837, 400)\n", - "Processed cell at: LT0157_04/005/78, location: (843, 414)\n", - "Processed cell at: LT0157_04/005/78, location: (844, 426)\n", - "Processed cell at: LT0157_04/005/78, location: (824, 433)\n", - "Processed cell at: LT0157_04/005/78, location: (841, 443)\n", - "Processed cell at: LT0157_04/005/78, location: (829, 450)\n", - "Processed cell at: LT0157_04/005/78, location: (613, 492)\n", - "Processed cell at: LT0157_04/005/78, location: (626, 490)\n", - "Processed cell at: LT0157_04/005/78, location: (632, 505)\n", - "Processed cell at: LT0157_04/005/78, location: (615, 511)\n", - "Processed cell at: LT0157_04/005/78, location: (633, 518)\n", - "Processed cell at: LT0157_04/005/78, location: (619, 521)\n", - "Processed cell at: LT0157_04/005/78, location: (416, 564)\n", - "Processed cell at: LT0157_04/005/78, location: (398, 573)\n", - "Processed cell at: LT0157_04/005/78, location: (374, 574)\n", - "Processed cell at: LT0157_04/005/78, location: (552, 690)\n", - "Processed cell at: LT0157_04/005/78, location: (549, 710)\n", - "Processed cell at: LT0157_04/005/78, location: (1280, 903)\n", - "Processed cell at: LT0157_04/005/78, location: (346, 908)\n", - "Processed cell at: LT0157_04/005/78, location: (1292, 911)\n", - "Processed cell at: LT0157_04/005/78, location: (1212, 920)\n", - "Processed cell at: LT0157_04/005/78, location: (304, 917)\n", - "Processed cell at: LT0157_04/005/78, location: (629, 916)\n", - "Processed cell at: LT0157_04/005/78, location: (315, 923)\n", - "Processed cell at: LT0157_04/005/78, location: (645, 926)\n", - "Processed cell at: LT0157_04/005/78, location: (751, 931)\n", - "Processed cell at: LT0157_04/005/78, location: (1289, 920)\n", - "Processed cell at: LT0157_04/005/78, location: (1310, 924)\n", - "Processed cell at: LT0157_04/005/78, location: (1193, 931)\n", - "Processed cell at: LT0157_04/005/78, location: (330, 935)\n", - "Processed cell at: LT0157_04/005/78, location: (1319, 934)\n", - "Processed cell at: LT0157_04/005/78, location: (1305, 936)\n", - "Processed cell at: LT0157_04/005/78, location: (316, 941)\n", - "Processed cell at: LT0157_04/005/78, location: (765, 945)\n", - "Processed cell at: LT0157_04/005/78, location: (656, 946)\n", - "Processed cell at: LT0157_04/005/78, location: (1181, 951)\n", - "Processed cell at: LT0157_04/005/78, location: (335, 954)\n", - "Processed cell at: LT0157_04/005/61, location: (764, 202)\n", - "Processed cell at: LT0157_04/005/61, location: (753, 219)\n", - "Processed cell at: LT0157_04/005/61, location: (1031, 237)\n", - "Processed cell at: LT0157_04/005/61, location: (816, 244)\n", - "Processed cell at: LT0157_04/005/61, location: (811, 261)\n", - "Processed cell at: LT0157_04/005/61, location: (1022, 255)\n", - "Processed cell at: LT0157_04/005/61, location: (833, 267)\n", - "Processed cell at: LT0157_04/005/61, location: (505, 291)\n", - "Processed cell at: LT0157_04/005/61, location: (529, 293)\n", - "Processed cell at: LT0157_04/005/61, location: (941, 347)\n", - "Processed cell at: LT0157_04/005/61, location: (324, 349)\n", - "Processed cell at: LT0157_04/005/61, location: (303, 369)\n", - "Processed cell at: LT0157_04/005/61, location: (939, 360)\n", - "Processed cell at: LT0157_04/005/61, location: (951, 374)\n", - "Processed cell at: LT0157_04/005/61, location: (837, 401)\n", - "Processed cell at: LT0157_04/005/61, location: (959, 405)\n", - "Processed cell at: LT0157_04/005/61, location: (844, 415)\n", - "Processed cell at: LT0157_04/005/61, location: (846, 427)\n", - "Processed cell at: LT0157_04/005/61, location: (832, 435)\n", - "Processed cell at: LT0157_04/005/61, location: (842, 446)\n", - "Processed cell at: LT0157_04/005/61, location: (611, 487)\n", - "Processed cell at: LT0157_04/005/61, location: (626, 493)\n", - "Processed cell at: LT0157_04/005/61, location: (612, 503)\n", - "Processed cell at: LT0157_04/005/61, location: (631, 505)\n", - "Processed cell at: LT0157_04/005/61, location: (618, 516)\n", - "Processed cell at: LT0157_04/005/61, location: (417, 565)\n", - "Processed cell at: LT0157_04/005/61, location: (401, 578)\n", - "Processed cell at: LT0157_04/005/61, location: (376, 580)\n", - "Processed cell at: LT0157_04/005/61, location: (397, 658)\n", - "Processed cell at: LT0157_04/005/61, location: (415, 668)\n", - "Processed cell at: LT0157_04/005/61, location: (429, 680)\n", - "Processed cell at: LT0157_04/005/61, location: (408, 684)\n", - "Processed cell at: LT0157_04/005/61, location: (374, 887)\n", - "Processed cell at: LT0157_04/005/61, location: (372, 902)\n", - "Processed cell at: LT0157_04/005/61, location: (400, 910)\n", - "Processed cell at: LT0157_04/005/61, location: (377, 917)\n", - "Processed cell at: LT0157_04/005/61, location: (414, 925)\n", - "Processed cell at: LT0157_04/005/61, location: (38, 934)\n", - "Processed cell at: LT0157_04/005/61, location: (374, 929)\n", - "Processed cell at: LT0157_04/005/61, location: (402, 938)\n", - "Processed cell at: LT0157_04/005/61, location: (373, 941)\n", - "Processed cell at: LT0157_04/005/61, location: (31, 957)\n", - "Processed cell at: LT0157_04/005/61, location: (37, 970)\n", - "Processed cell at: LT0157_04/005/61, location: (23, 978)\n", - "Processed cell at: LT0157_04/005/17, location: (835, 84)\n", - "Processed cell at: LT0157_04/005/17, location: (76, 420)\n", - "Processed cell at: LT0157_04/005/17, location: (98, 425)\n", - "Processed cell at: LT0157_04/005/17, location: (289, 549)\n", - "Processed cell at: LT0157_04/005/17, location: (297, 568)\n", - "Processed cell at: LT0157_04/005/17, location: (280, 591)\n", - "Processed cell at: LT0157_04/005/17, location: (276, 673)\n", - "Processed cell at: LT0157_04/005/17, location: (279, 690)\n", - "Processed cell at: LT0157_04/005/17, location: (265, 702)\n", - "Processed cell at: LT0157_04/005/17, location: (542, 847)\n", - "Processed cell at: LT0157_04/005/17, location: (498, 903)\n", - "Processed cell at: LT0065_04/020/72, location: (377, 118)\n", - "Processed cell at: LT0065_04/020/72, location: (357, 131)\n", - "Processed cell at: LT0065_04/020/72, location: (867, 176)\n", - "Processed cell at: LT0065_04/020/72, location: (850, 188)\n", - "Processed cell at: LT0065_04/020/72, location: (871, 190)\n", - "Processed cell at: LT0065_04/020/72, location: (856, 203)\n", - "Processed cell at: LT0065_04/020/72, location: (243, 292)\n", - "Processed cell at: LT0065_04/020/72, location: (226, 312)\n", - "Processed cell at: LT0065_04/020/72, location: (817, 347)\n", - "Processed cell at: LT0065_04/020/72, location: (797, 361)\n", - "Processed cell at: LT0065_04/020/72, location: (835, 368)\n", - "Processed cell at: LT0065_04/020/72, location: (819, 382)\n", - "Processed cell at: LT0065_04/020/72, location: (782, 396)\n", - "Processed cell at: LT0065_04/020/72, location: (811, 391)\n", - "Processed cell at: LT0065_04/020/72, location: (830, 404)\n", - "Processed cell at: LT0065_04/020/72, location: (800, 468)\n", - "Processed cell at: LT0065_04/020/72, location: (172, 475)\n", - "Processed cell at: LT0065_04/020/72, location: (123, 475)\n", - "Processed cell at: LT0065_04/020/72, location: (140, 485)\n", - "Processed cell at: LT0065_04/020/72, location: (747, 485)\n", - "Processed cell at: LT0065_04/020/72, location: (764, 483)\n", - "Processed cell at: LT0065_04/020/72, location: (781, 496)\n", - "Processed cell at: LT0065_04/020/72, location: (796, 501)\n", - "Processed cell at: LT0065_04/020/72, location: (162, 503)\n", - "Processed cell at: LT0065_04/020/72, location: (750, 507)\n", - "Processed cell at: LT0065_04/020/72, location: (766, 524)\n", - "Processed cell at: LT0065_04/020/72, location: (291, 548)\n", - "Processed cell at: LT0065_04/020/72, location: (309, 569)\n", - "Processed cell at: LT0065_04/020/72, location: (216, 623)\n", - "Processed cell at: LT0065_04/020/72, location: (651, 623)\n", - "Processed cell at: LT0065_04/020/72, location: (216, 655)\n", - "Processed cell at: LT0065_04/020/72, location: (536, 861)\n", - "Processed cell at: LT0065_04/020/72, location: (553, 890)\n", - "Processed cell at: LT0065_04/020/72, location: (98, 969)\n", - "Processed cell at: LT0065_04/020/49, location: (238, 288)\n", - "Processed cell at: LT0065_04/020/49, location: (215, 290)\n", - "Processed cell at: LT0065_04/020/49, location: (794, 468)\n", - "Processed cell at: LT0065_04/020/49, location: (772, 468)\n", - "Processed cell at: LT0065_04/020/49, location: (758, 477)\n", - "Processed cell at: LT0065_04/020/49, location: (772, 486)\n", - "Processed cell at: LT0065_04/020/49, location: (797, 493)\n", - "Processed cell at: LT0065_04/020/49, location: (775, 501)\n", - "Processed cell at: LT0065_04/020/49, location: (992, 775)\n", - "Processed cell at: LT0065_04/020/49, location: (1017, 795)\n", - "Processed cell at: LT0065_04/020/49, location: (977, 805)\n", - "Processed cell at: LT0065_04/020/49, location: (997, 824)\n", - "Processed cell at: LT0065_04/020/49, location: (569, 861)\n", - "Processed cell at: LT0065_04/020/49, location: (590, 888)\n", - "Processed cell at: LT0065_04/020/49, location: (557, 895)\n", - "Processed cell at: LT0065_04/020/49, location: (119, 952)\n", - "Processed cell at: LT0065_04/020/57, location: (883, 223)\n", - "Processed cell at: LT0065_04/020/57, location: (900, 244)\n", - "Processed cell at: LT0065_04/020/57, location: (985, 371)\n", - "Processed cell at: LT0065_04/020/57, location: (783, 376)\n", - "Processed cell at: LT0065_04/020/57, location: (799, 390)\n", - "Processed cell at: LT0065_04/020/57, location: (764, 391)\n", - "Processed cell at: LT0065_04/020/57, location: (770, 402)\n", - "Processed cell at: LT0065_04/020/57, location: (358, 417)\n", - "Processed cell at: LT0065_04/020/57, location: (776, 416)\n", - "Processed cell at: LT0065_04/020/57, location: (319, 420)\n", - "Processed cell at: LT0065_04/020/57, location: (336, 419)\n", - "Processed cell at: LT0065_04/020/57, location: (757, 434)\n", - "Processed cell at: LT0065_04/020/57, location: (779, 463)\n", - "Processed cell at: LT0065_04/020/57, location: (800, 468)\n", - "Processed cell at: LT0065_04/020/57, location: (1073, 469)\n", - "Processed cell at: LT0065_04/020/57, location: (792, 490)\n", - "Processed cell at: LT0065_04/020/57, location: (1086, 493)\n", - "Processed cell at: LT0065_04/020/57, location: (773, 486)\n", - "Processed cell at: LT0065_04/020/57, location: (996, 777)\n", - "Processed cell at: LT0065_04/020/57, location: (1016, 798)\n", - "Processed cell at: LT0065_04/020/57, location: (977, 801)\n", - "Processed cell at: LT0065_04/020/57, location: (993, 824)\n", - "Processed cell at: LT0065_04/020/57, location: (571, 859)\n", - "Processed cell at: LT0065_04/020/57, location: (587, 880)\n", - "Processed cell at: LT0065_04/020/57, location: (559, 898)\n", - "Processed cell at: LT0065_04/020/57, location: (597, 893)\n", - "Processed cell at: LT0065_04/020/92, location: (904, 119)\n", - "Processed cell at: LT0065_04/020/92, location: (881, 125)\n", - "Processed cell at: LT0065_04/020/92, location: (894, 137)\n", - "Processed cell at: LT0065_04/020/92, location: (820, 324)\n", - "Processed cell at: LT0065_04/020/92, location: (789, 325)\n", - "Processed cell at: LT0065_04/020/92, location: (819, 344)\n", - "Processed cell at: LT0065_04/020/92, location: (795, 342)\n", - "Processed cell at: LT0065_04/020/92, location: (980, 347)\n", - "Processed cell at: LT0065_04/020/92, location: (854, 350)\n", - "Processed cell at: LT0065_04/020/92, location: (1001, 357)\n", - "Processed cell at: LT0065_04/020/92, location: (969, 358)\n", - "Processed cell at: LT0065_04/020/92, location: (830, 364)\n", - "Processed cell at: LT0065_04/020/92, location: (979, 372)\n", - "Processed cell at: LT0065_04/020/92, location: (849, 380)\n", - "Processed cell at: LT0065_04/020/92, location: (827, 377)\n", - "Processed cell at: LT0065_04/020/92, location: (843, 448)\n", - "Processed cell at: LT0065_04/020/92, location: (860, 444)\n", - "Processed cell at: LT0065_04/020/92, location: (854, 465)\n", - "Processed cell at: LT0065_04/020/92, location: (759, 475)\n", - "Processed cell at: LT0065_04/020/92, location: (777, 466)\n", - "Processed cell at: LT0065_04/020/92, location: (783, 477)\n", - "Processed cell at: LT0065_04/020/92, location: (199, 481)\n", - "Processed cell at: LT0065_04/020/92, location: (176, 488)\n", - "Processed cell at: LT0065_04/020/92, location: (158, 492)\n", - "Processed cell at: LT0065_04/020/92, location: (789, 491)\n", - "Processed cell at: LT0065_04/020/92, location: (742, 515)\n", - "Processed cell at: LT0065_04/020/92, location: (183, 510)\n", - "Processed cell at: LT0065_04/020/92, location: (766, 515)\n", - "Processed cell at: LT0065_04/020/92, location: (1022, 810)\n", - "Processed cell at: LT0065_04/020/92, location: (1037, 823)\n", - "Processed cell at: LT0065_04/020/92, location: (1002, 823)\n", - "Processed cell at: LT0065_04/020/92, location: (1005, 843)\n", - "Processed cell at: LT0013_42/107/23, location: (893, 89)\n", - "Processed cell at: LT0013_42/107/23, location: (850, 131)\n", - "Processed cell at: LT0013_42/107/23, location: (909, 132)\n", - "Processed cell at: LT0013_42/107/23, location: (1264, 204)\n", - "Processed cell at: LT0013_42/107/23, location: (676, 247)\n", - "Processed cell at: LT0013_42/107/23, location: (623, 269)\n", - "Processed cell at: LT0013_42/107/23, location: (265, 413)\n", - "Processed cell at: LT0013_42/107/23, location: (1056, 429)\n", - "Processed cell at: LT0013_42/107/23, location: (249, 481)\n", - "Processed cell at: LT0013_42/107/23, location: (184, 481)\n", - "Processed cell at: LT0013_42/107/23, location: (747, 546)\n", - "Processed cell at: LT0013_42/107/23, location: (410, 581)\n", - "Processed cell at: LT0013_42/107/23, location: (872, 587)\n", - "Processed cell at: LT0013_42/107/23, location: (406, 599)\n", - "Processed cell at: LT0013_42/107/23, location: (831, 797)\n", - "Processed cell at: LT0013_42/107/23, location: (850, 821)\n", - "Processed cell at: LT0013_42/107/52, location: (297, 69)\n", - "Processed cell at: LT0013_42/107/52, location: (269, 87)\n", - "Processed cell at: LT0013_42/107/52, location: (895, 162)\n", - "Processed cell at: LT0013_42/107/52, location: (954, 163)\n", - "Processed cell at: LT0013_42/107/52, location: (1251, 169)\n", - "Processed cell at: LT0013_42/107/52, location: (824, 173)\n", - "Processed cell at: LT0013_42/107/52, location: (980, 196)\n", - "Processed cell at: LT0013_42/107/52, location: (617, 230)\n", - "Processed cell at: LT0013_42/107/52, location: (607, 244)\n", - "Processed cell at: LT0013_42/107/52, location: (594, 256)\n", - "Processed cell at: LT0013_42/107/52, location: (982, 249)\n", - "Processed cell at: LT0013_42/107/52, location: (982, 259)\n", - "Processed cell at: LT0013_42/107/52, location: (64, 286)\n", - "Processed cell at: LT0013_42/107/52, location: (81, 303)\n", - "Processed cell at: LT0013_42/107/52, location: (834, 430)\n", - "Processed cell at: LT0013_42/107/52, location: (823, 452)\n", - "Processed cell at: LT0013_42/107/52, location: (1277, 481)\n", - "Processed cell at: LT0013_42/107/52, location: (45, 519)\n", - "Processed cell at: LT0013_42/107/52, location: (1060, 549)\n", - "Processed cell at: LT0013_42/107/52, location: (1076, 565)\n", - "Processed cell at: LT0013_42/107/52, location: (1055, 568)\n", - "Processed cell at: LT0013_42/107/52, location: (398, 582)\n", - "Processed cell at: LT0013_42/107/52, location: (421, 593)\n", - "Processed cell at: LT0013_42/107/52, location: (167, 626)\n", - "Processed cell at: LT0013_42/107/52, location: (1178, 671)\n", - "Processed cell at: LT0013_42/107/52, location: (857, 978)\n", - "Processed cell at: LT0013_42/107/52, location: (842, 995)\n", - "Processed cell at: LT0013_42/107/63, location: (1244, 200)\n", - "Processed cell at: LT0013_42/107/63, location: (1253, 219)\n", - "Processed cell at: LT0013_42/107/63, location: (977, 266)\n", - "Processed cell at: LT0013_42/107/63, location: (1019, 274)\n", - "Processed cell at: LT0013_42/107/63, location: (124, 359)\n", - "Processed cell at: LT0013_42/107/63, location: (164, 360)\n", - "Processed cell at: LT0013_42/107/63, location: (283, 414)\n", - "Processed cell at: LT0013_42/107/63, location: (101, 459)\n", - "Processed cell at: LT0013_42/107/63, location: (425, 511)\n", - "Processed cell at: LT0013_42/107/63, location: (1060, 550)\n", - "Processed cell at: LT0013_42/107/63, location: (430, 558)\n", - "Processed cell at: LT0013_42/107/63, location: (1071, 569)\n", - "Processed cell at: LT0013_42/107/63, location: (1052, 567)\n", - "Processed cell at: LT0013_42/107/63, location: (197, 592)\n", - "Processed cell at: LT0013_42/107/63, location: (1178, 671)\n", - "Processed cell at: LT0013_42/107/63, location: (192, 744)\n", - "Processed cell at: LT0013_42/107/63, location: (792, 762)\n", - "Processed cell at: LT0013_42/107/63, location: (179, 800)\n", - "Processed cell at: LT0013_42/107/63, location: (789, 822)\n", - "Processed cell at: LT0013_42/107/63, location: (812, 860)\n", - "Processed cell at: LT0013_42/107/63, location: (857, 974)\n", - "Processed cell at: LT0013_42/107/63, location: (846, 996)\n", - "Processed cell at: LT0013_42/107/76, location: (1236, 186)\n", - "Processed cell at: LT0013_42/107/76, location: (979, 192)\n", - "Processed cell at: LT0013_42/107/76, location: (671, 197)\n", - "Processed cell at: LT0013_42/107/76, location: (1295, 195)\n", - "Processed cell at: LT0013_42/107/76, location: (1315, 198)\n", - "Processed cell at: LT0013_42/107/76, location: (692, 200)\n", - "Processed cell at: LT0013_42/107/76, location: (1252, 213)\n", - "Processed cell at: LT0013_42/107/76, location: (1327, 216)\n", - "Processed cell at: LT0013_42/107/76, location: (1303, 213)\n", - "Processed cell at: LT0013_42/107/76, location: (676, 217)\n", - "Processed cell at: LT0013_42/107/76, location: (608, 227)\n", - "Processed cell at: LT0013_42/107/76, location: (1314, 234)\n", - "Processed cell at: LT0013_42/107/76, location: (592, 246)\n", - "Processed cell at: LT0013_42/107/76, location: (621, 255)\n", - "Processed cell at: LT0013_42/107/76, location: (989, 257)\n", - "Processed cell at: LT0013_42/107/76, location: (986, 277)\n", - "Processed cell at: LT0013_42/107/76, location: (65, 292)\n", - "Processed cell at: LT0013_42/107/76, location: (88, 312)\n", - "Processed cell at: LT0013_42/107/76, location: (124, 365)\n", - "Processed cell at: LT0013_42/107/76, location: (172, 367)\n", - "Processed cell at: LT0013_42/107/76, location: (1032, 435)\n", - "Processed cell at: LT0013_42/107/76, location: (100, 458)\n", - "Processed cell at: LT0013_42/107/76, location: (1261, 467)\n", - "Processed cell at: LT0013_42/107/76, location: (1283, 480)\n", - "Processed cell at: LT0013_42/107/76, location: (960, 508)\n", - "Processed cell at: LT0013_42/107/76, location: (49, 520)\n", - "Processed cell at: LT0013_42/107/76, location: (153, 521)\n", - "Processed cell at: LT0013_42/107/76, location: (1043, 564)\n", - "Processed cell at: LT0013_42/107/76, location: (1066, 576)\n", - "Processed cell at: LT0013_42/107/76, location: (188, 579)\n", - "Processed cell at: LT0013_42/107/76, location: (1045, 584)\n", - "Processed cell at: LT0013_42/107/76, location: (493, 691)\n", - "Processed cell at: LT0013_42/107/76, location: (272, 700)\n", - "Processed cell at: LT0013_42/107/76, location: (291, 708)\n", - "Processed cell at: LT0013_42/107/76, location: (279, 728)\n", - "Processed cell at: LT0013_42/107/76, location: (501, 804)\n", - "Processed cell at: LT0013_42/107/76, location: (1251, 889)\n", - "Processed cell at: LT0013_42/107/76, location: (266, 934)\n", - "Processed cell at: LT0013_42/107/76, location: (1302, 936)\n", - "Processed cell at: LT0013_42/107/76, location: (285, 939)\n", - "Processed cell at: LT0013_42/107/76, location: (276, 957)\n", - "Processed cell at: LT0013_42/107/76, location: (316, 1002)\n", - "Processed cell at: LT0013_42/107/76, location: (338, 1003)\n", - "Processed cell at: LT0013_42/107/28, location: (959, 130)\n", - "Processed cell at: LT0013_42/107/28, location: (848, 130)\n", - "Processed cell at: LT0013_42/107/28, location: (982, 138)\n", - "Processed cell at: LT0013_42/107/28, location: (912, 133)\n", - "Processed cell at: LT0013_42/107/28, location: (1257, 149)\n", - "Processed cell at: LT0013_42/107/28, location: (457, 155)\n", - "Processed cell at: LT0013_42/107/28, location: (822, 175)\n", - "Processed cell at: LT0013_42/107/28, location: (652, 197)\n", - "Processed cell at: LT0013_42/107/28, location: (1260, 204)\n", - "Processed cell at: LT0013_42/107/28, location: (640, 215)\n", - "Processed cell at: LT0013_42/107/28, location: (657, 220)\n", - "Processed cell at: LT0013_42/107/28, location: (605, 231)\n", - "Processed cell at: LT0013_42/107/28, location: (643, 229)\n", - "Processed cell at: LT0013_42/107/28, location: (591, 237)\n", - "Processed cell at: LT0013_42/107/28, location: (598, 248)\n", - "Processed cell at: LT0013_42/107/28, location: (587, 258)\n", - "Processed cell at: LT0013_42/107/28, location: (1060, 424)\n", - "Processed cell at: LT0013_42/107/28, location: (173, 519)\n", - "Processed cell at: LT0013_42/107/28, location: (201, 536)\n", - "Processed cell at: LT0013_42/107/28, location: (189, 533)\n", - "Processed cell at: LT0013_42/107/28, location: (1057, 549)\n", - "Processed cell at: LT0013_42/107/28, location: (750, 548)\n", - "Processed cell at: LT0013_42/107/28, location: (893, 566)\n", - "Processed cell at: LT0013_42/107/28, location: (401, 576)\n", - "Processed cell at: LT0013_42/107/28, location: (411, 596)\n", - "Processed cell at: LT0013_42/107/28, location: (893, 591)\n", - "Processed cell at: LT0013_42/107/28, location: (152, 628)\n", - "Processed cell at: LT0013_42/107/28, location: (1177, 671)\n", - "Processed cell at: LT0013_42/107/28, location: (190, 762)\n", - "Processed cell at: LT0013_42/107/28, location: (165, 767)\n", - "Processed cell at: LT0013_42/107/28, location: (118, 987)\n", - "Processed cell at: LT0013_42/107/28, location: (107, 1004)\n", - "Processed cell at: LT0094_01/319/36, location: (971, 478)\n", - "Processed cell at: LT0094_01/319/36, location: (629, 487)\n", - "Processed cell at: LT0094_01/319/36, location: (686, 499)\n", - "Processed cell at: LT0094_01/319/36, location: (1008, 580)\n", - "Processed cell at: LT0094_01/319/36, location: (448, 734)\n", - "Processed cell at: LT0094_01/319/36, location: (315, 801)\n", - "Processed cell at: LT0094_01/319/36, location: (882, 918)\n", - "Processed cell at: LT0094_01/319/38, location: (1068, 25)\n", - "Processed cell at: LT0094_01/319/38, location: (485, 107)\n", - "Processed cell at: LT0094_01/319/38, location: (981, 558)\n", - "Processed cell at: LT0094_01/319/38, location: (951, 957)\n", - "Processed cell at: LT0094_01/319/60, location: (1036, 54)\n", - "Processed cell at: LT0094_01/319/60, location: (135, 258)\n", - "Processed cell at: LT0094_01/319/60, location: (1048, 473)\n", - "Processed cell at: LT0094_01/319/60, location: (979, 553)\n", - "Processed cell at: LT0094_01/319/60, location: (1037, 580)\n", - "Processed cell at: LT0094_01/319/60, location: (1009, 583)\n", - "Processed cell at: LT0094_01/319/60, location: (1015, 658)\n", - "Processed cell at: LT0094_01/319/60, location: (213, 986)\n", - "Processed cell at: LT0094_01/319/34, location: (511, 113)\n", - "Processed cell at: LT0094_01/319/34, location: (690, 504)\n", - "Processed cell at: LT0094_01/319/34, location: (361, 656)\n", - "Processed cell at: LT0094_01/319/34, location: (501, 667)\n", - "Processed cell at: LT0094_01/319/34, location: (447, 732)\n", - "Processed cell at: LT0094_01/319/34, location: (466, 909)\n", - "Processed cell at: LT0094_01/319/85, location: (1089, 88)\n", - "Processed cell at: LT0094_01/319/85, location: (568, 339)\n", - "Processed cell at: LT0094_01/319/85, location: (754, 370)\n", - "Processed cell at: LT0094_01/319/85, location: (290, 408)\n", - "Processed cell at: LT0094_01/319/85, location: (276, 422)\n", - "Processed cell at: LT0094_01/319/85, location: (206, 444)\n", - "Processed cell at: LT0094_01/319/85, location: (226, 442)\n", - "Processed cell at: LT0094_01/319/85, location: (221, 454)\n", - "Processed cell at: LT0094_01/319/85, location: (1058, 505)\n", - "Processed cell at: LT0094_01/319/85, location: (1017, 531)\n", - "Processed cell at: LT0094_01/319/85, location: (955, 587)\n", - "Processed cell at: LT0094_01/319/85, location: (508, 671)\n", - "Processed cell at: LT0094_01/319/85, location: (1210, 686)\n", - "Processed cell at: LT0094_01/319/85, location: (513, 698)\n", - "Processed cell at: LT0094_01/319/46, location: (1068, 25)\n", - "Processed cell at: LT0094_01/319/46, location: (376, 110)\n", - "Processed cell at: LT0094_01/319/46, location: (970, 473)\n", - "Processed cell at: LT0094_01/319/46, location: (1047, 474)\n", - "Processed cell at: LT0094_01/319/46, location: (628, 485)\n", - "Processed cell at: LT0094_01/319/66, location: (1067, 19)\n", - "Processed cell at: LT0094_01/319/66, location: (1038, 52)\n", - "Processed cell at: LT0094_01/319/66, location: (569, 346)\n", - "Processed cell at: LT0094_01/319/66, location: (624, 476)\n", - "Processed cell at: LT0094_01/319/66, location: (978, 553)\n", - "Processed cell at: LT0094_01/319/66, location: (1009, 581)\n", - "Processed cell at: LT0094_01/319/66, location: (1015, 655)\n", - "Processed cell at: LT0094_01/319/66, location: (234, 687)\n", - "Processed cell at: LT0094_01/319/66, location: (281, 712)\n", - "Processed cell at: LT0094_01/319/66, location: (1145, 765)\n", - "Processed cell at: LT0094_01/319/66, location: (502, 920)\n", - "Processed cell at: LT0100_03/093/37, location: (208, 131)\n", - "Processed cell at: LT0100_03/093/37, location: (188, 150)\n", - "Processed cell at: LT0100_03/093/37, location: (519, 149)\n", - "Processed cell at: LT0100_03/093/37, location: (654, 174)\n", - "Processed cell at: LT0100_03/093/37, location: (586, 200)\n", - "Processed cell at: LT0100_03/093/37, location: (572, 216)\n", - "Processed cell at: LT0100_03/093/37, location: (584, 226)\n", - "Processed cell at: LT0100_03/093/37, location: (1164, 233)\n", - "Processed cell at: LT0100_03/093/37, location: (1040, 240)\n", - "Processed cell at: LT0100_03/093/37, location: (62, 250)\n", - "Processed cell at: LT0100_03/093/37, location: (108, 254)\n", - "Processed cell at: LT0100_03/093/37, location: (1064, 250)\n", - "Processed cell at: LT0100_03/093/37, location: (1041, 254)\n", - "Processed cell at: LT0100_03/093/37, location: (115, 283)\n", - "Processed cell at: LT0100_03/093/37, location: (1161, 309)\n", - "Processed cell at: LT0100_03/093/37, location: (789, 422)\n", - "Processed cell at: LT0100_03/093/37, location: (49, 477)\n", - "Processed cell at: LT0100_03/093/37, location: (53, 515)\n", - "Processed cell at: LT0100_03/093/37, location: (47, 534)\n", - "Processed cell at: LT0100_03/093/37, location: (1221, 550)\n", - "Processed cell at: LT0100_03/093/37, location: (1279, 547)\n", - "Processed cell at: LT0100_03/093/37, location: (790, 558)\n", - "Processed cell at: LT0100_03/093/37, location: (779, 571)\n", - "Processed cell at: LT0100_03/093/37, location: (1065, 627)\n", - "Processed cell at: LT0100_03/093/37, location: (1065, 644)\n", - "Processed cell at: LT0100_03/093/37, location: (1089, 649)\n", - "Processed cell at: LT0100_03/093/37, location: (193, 669)\n", - "Processed cell at: LT0100_03/093/37, location: (1079, 665)\n", - "Processed cell at: LT0100_03/093/37, location: (740, 672)\n", - "Processed cell at: LT0100_03/093/37, location: (760, 680)\n", - "Processed cell at: LT0100_03/093/37, location: (1316, 680)\n", - "Processed cell at: LT0100_03/093/37, location: (1301, 691)\n", - "Processed cell at: LT0100_03/093/37, location: (1315, 700)\n", - "Processed cell at: LT0100_03/093/37, location: (277, 828)\n", - "Processed cell at: LT0100_03/093/37, location: (339, 857)\n", - "Processed cell at: LT0044_36/249/61, location: (1161, 51)\n", - "Processed cell at: LT0044_36/249/61, location: (801, 420)\n", - "Processed cell at: LT0044_36/249/61, location: (795, 435)\n", - "Processed cell at: LT0044_36/249/46, location: (1151, 88)\n", - "Processed cell at: LT0044_36/249/46, location: (158, 245)\n", - "Processed cell at: LT0044_36/249/46, location: (133, 251)\n", - "Processed cell at: LT0044_36/249/46, location: (1129, 269)\n", - "Processed cell at: LT0044_36/249/46, location: (1123, 301)\n", - "Processed cell at: LT0044_36/249/46, location: (451, 394)\n", - "Processed cell at: LT0044_36/249/46, location: (1321, 646)\n", - "Processed cell at: LT0044_36/249/46, location: (547, 767)\n", - "Processed cell at: LT0044_36/249/46, location: (963, 851)\n", - "Processed cell at: LT0044_36/249/46, location: (676, 898)\n", - "Processed cell at: LT0044_36/249/46, location: (941, 909)\n", - "Processed cell at: LT0044_36/249/46, location: (1315, 948)\n", - "Processed cell at: LT0044_36/249/46, location: (1276, 977)\n", - "Processed cell at: LT0044_36/249/46, location: (1257, 991)\n", - "Processed cell at: LT0035_06/274/21, location: (1067, 245)\n", - "Processed cell at: LT0035_06/274/21, location: (1046, 285)\n", - "Processed cell at: LT0035_06/274/21, location: (109, 327)\n", - "Processed cell at: LT0035_06/274/21, location: (312, 339)\n", - "Processed cell at: LT0035_06/274/21, location: (223, 362)\n", - "Processed cell at: LT0035_06/274/21, location: (96, 365)\n", - "Processed cell at: LT0035_06/274/21, location: (449, 368)\n", - "Processed cell at: LT0035_06/274/21, location: (269, 372)\n", - "Processed cell at: LT0035_06/274/21, location: (501, 393)\n", - "Processed cell at: LT0035_06/274/21, location: (781, 432)\n", - "Processed cell at: LT0035_06/274/21, location: (310, 433)\n", - "Processed cell at: LT0035_06/274/21, location: (807, 481)\n", - "Processed cell at: LT0035_06/274/21, location: (685, 614)\n", - "Processed cell at: LT0035_06/274/21, location: (742, 637)\n", - "Processed cell at: LT0035_06/274/4, location: (556, 366)\n", - "Processed cell at: LT0035_06/274/4, location: (392, 420)\n", - "Processed cell at: LT0035_06/274/4, location: (458, 572)\n", - "Processed cell at: LT0035_06/274/4, location: (493, 587)\n", - "Processed cell at: LT0035_06/274/4, location: (770, 604)\n", - "Processed cell at: LT0035_06/274/4, location: (455, 869)\n", - "Processed cell at: LT0035_06/274/40, location: (772, 135)\n", - "Processed cell at: LT0035_06/274/40, location: (539, 144)\n", - "Processed cell at: LT0035_06/274/40, location: (746, 182)\n", - "Processed cell at: LT0035_06/274/40, location: (1069, 243)\n", - "Processed cell at: LT0035_06/274/40, location: (1051, 282)\n", - "Processed cell at: LT0035_06/274/40, location: (311, 335)\n", - "Processed cell at: LT0035_06/274/40, location: (226, 347)\n", - "Processed cell at: LT0035_06/274/40, location: (267, 372)\n", - "Processed cell at: LT0035_06/274/40, location: (1316, 401)\n", - "Processed cell at: LT0035_06/274/40, location: (305, 428)\n", - "Processed cell at: LT0035_06/274/40, location: (776, 442)\n", - "Processed cell at: LT0035_06/274/40, location: (952, 465)\n", - "Processed cell at: LT0035_06/274/40, location: (812, 474)\n", - "Processed cell at: LT0035_06/274/40, location: (212, 497)\n", - "Processed cell at: LT0047_27/140/68, location: (381, 57)\n", - "Processed cell at: LT0047_27/140/68, location: (734, 287)\n", - "Processed cell at: LT0047_27/140/68, location: (252, 307)\n", - "Processed cell at: LT0047_27/140/68, location: (783, 307)\n", - "Processed cell at: LT0047_27/140/68, location: (240, 321)\n", - "Processed cell at: LT0047_27/140/68, location: (208, 337)\n", - "Processed cell at: LT0047_27/140/68, location: (230, 339)\n", - "Processed cell at: LT0047_27/140/68, location: (1206, 359)\n", - "Processed cell at: LT0047_27/140/68, location: (1256, 405)\n", - "Processed cell at: LT0047_27/140/68, location: (581, 426)\n", - "Processed cell at: LT0047_27/140/68, location: (559, 437)\n", - "Processed cell at: LT0047_27/140/68, location: (690, 533)\n", - "Processed cell at: LT0047_27/140/68, location: (712, 550)\n", - "Processed cell at: LT0047_27/140/68, location: (832, 591)\n", - "Processed cell at: LT0047_27/140/68, location: (624, 633)\n", - "Processed cell at: LT0047_27/140/68, location: (683, 733)\n", - "Processed cell at: LT0047_27/140/68, location: (750, 740)\n", - "Processed cell at: LT0047_27/140/68, location: (267, 776)\n", - "Processed cell at: LT0047_27/140/68, location: (104, 849)\n", - "Processed cell at: LT0047_27/140/68, location: (1043, 849)\n", - "Processed cell at: LT0047_27/140/68, location: (998, 856)\n", - "Processed cell at: LT0047_27/140/68, location: (126, 861)\n", - "Processed cell at: LT0047_27/140/68, location: (799, 886)\n", - "Processed cell at: LT0047_27/140/68, location: (213, 898)\n", - "Processed cell at: LT0047_27/140/68, location: (817, 901)\n", - "Processed cell at: LT0047_27/140/68, location: (292, 903)\n", - "Processed cell at: LT0047_27/140/68, location: (368, 904)\n", - "Processed cell at: LT0047_27/140/68, location: (1003, 907)\n", - "Processed cell at: LT0047_27/140/68, location: (388, 918)\n", - "Processed cell at: LT0047_27/140/68, location: (611, 930)\n", - "Processed cell at: LT0047_27/140/68, location: (354, 938)\n", - "Processed cell at: LT0047_27/140/68, location: (988, 950)\n", - "Processed cell at: LT0047_27/140/68, location: (678, 979)\n", - "Processed cell at: LT0047_27/140/68, location: (846, 988)\n", - "Processed cell at: LT0047_27/140/94, location: (768, 286)\n", - "Processed cell at: LT0047_27/140/94, location: (646, 319)\n", - "Processed cell at: LT0047_27/140/94, location: (478, 378)\n", - "Processed cell at: LT0047_27/140/94, location: (565, 380)\n", - "Processed cell at: LT0047_27/140/94, location: (365, 384)\n", - "Processed cell at: LT0047_27/140/94, location: (750, 446)\n", - "Processed cell at: LT0047_27/140/94, location: (743, 503)\n", - "Processed cell at: LT0047_27/140/94, location: (441, 513)\n", - "Processed cell at: LT0047_27/140/94, location: (509, 573)\n", - "Processed cell at: LT0047_27/140/94, location: (691, 625)\n", - "Processed cell at: LT0047_27/140/94, location: (623, 633)\n", - "Processed cell at: LT0047_27/140/94, location: (421, 637)\n", - "Processed cell at: LT0047_27/140/94, location: (537, 653)\n", - "Processed cell at: LT0047_27/140/94, location: (477, 662)\n", - "Processed cell at: LT0047_27/140/94, location: (873, 711)\n", - "Processed cell at: LT0047_27/140/94, location: (392, 724)\n", - "Processed cell at: LT0047_27/140/94, location: (670, 747)\n", - "Processed cell at: LT0047_27/140/94, location: (495, 785)\n", - "Processed cell at: LT0047_27/140/94, location: (379, 817)\n", - "Processed cell at: LT0047_27/140/94, location: (836, 856)\n", - "Processed cell at: LT0047_27/140/94, location: (356, 860)\n", - "Processed cell at: LT0047_27/140/94, location: (723, 925)\n", - "Processed cell at: LT0047_27/140/89, location: (480, 375)\n", - "Processed cell at: LT0047_27/140/89, location: (576, 448)\n", - "Processed cell at: LT0047_27/140/89, location: (376, 452)\n", - "Processed cell at: LT0047_27/140/89, location: (491, 487)\n", - "Processed cell at: LT0047_27/140/89, location: (538, 507)\n", - "Processed cell at: LT0047_27/140/89, location: (440, 514)\n", - "Processed cell at: LT0047_27/140/89, location: (513, 571)\n", - "Processed cell at: LT0047_27/140/89, location: (538, 651)\n", - "Processed cell at: LT0047_27/140/89, location: (471, 664)\n", - "Processed cell at: LT0047_27/140/89, location: (467, 888)\n", - "Processed cell at: LT0047_27/140/89, location: (264, 905)\n", - "Processed cell at: LT0047_27/140/89, location: (279, 915)\n", - "Processed cell at: LT0047_27/140/89, location: (602, 999)\n", - "Processed cell at: LT0047_27/140/75, location: (1231, 316)\n", - "Processed cell at: LT0047_27/140/75, location: (251, 316)\n", - "Processed cell at: LT0047_27/140/75, location: (237, 328)\n", - "Processed cell at: LT0047_27/140/75, location: (229, 347)\n", - "Processed cell at: LT0047_27/140/75, location: (204, 352)\n", - "Processed cell at: LT0047_27/140/75, location: (568, 396)\n", - "Processed cell at: LT0047_27/140/75, location: (474, 398)\n", - "Processed cell at: LT0047_27/140/75, location: (549, 406)\n", - "Processed cell at: LT0047_27/140/75, location: (1257, 405)\n", - "Processed cell at: LT0047_27/140/75, location: (430, 433)\n", - "Processed cell at: LT0047_27/140/75, location: (582, 431)\n", - "Processed cell at: LT0047_27/140/75, location: (559, 441)\n", - "Processed cell at: LT0047_27/140/75, location: (447, 501)\n", - "Processed cell at: LT0047_27/140/75, location: (440, 552)\n", - "Processed cell at: LT0047_27/140/75, location: (281, 561)\n", - "Processed cell at: LT0047_27/140/75, location: (439, 599)\n", - "Processed cell at: LT0047_27/140/75, location: (230, 610)\n", - "Processed cell at: LT0047_27/140/75, location: (427, 637)\n", - "Processed cell at: LT0047_27/140/75, location: (469, 655)\n", - "Processed cell at: LT0047_27/140/75, location: (763, 723)\n", - "Processed cell at: LT0047_27/140/75, location: (953, 741)\n", - "Processed cell at: LT0047_27/140/75, location: (715, 742)\n", - "Processed cell at: LT0047_27/140/75, location: (486, 766)\n", - "Processed cell at: LT0047_27/140/75, location: (176, 777)\n", - "Processed cell at: LT0047_27/140/75, location: (483, 780)\n", - "Processed cell at: LT0047_27/140/75, location: (215, 791)\n", - "Processed cell at: LT0047_27/140/75, location: (481, 801)\n", - "Processed cell at: LT0047_27/140/75, location: (180, 812)\n", - "Processed cell at: LT0047_27/140/75, location: (202, 815)\n", - "Processed cell at: LT0047_27/140/75, location: (826, 850)\n", - "Processed cell at: LT0047_27/140/75, location: (842, 866)\n", - "Processed cell at: LT0047_27/140/75, location: (215, 898)\n", - "Processed cell at: LT0047_27/140/75, location: (281, 908)\n", - "Processed cell at: LT0047_27/140/75, location: (614, 925)\n", - "Processed cell at: LT0047_27/140/75, location: (982, 955)\n", - "Processed cell at: LT0047_27/140/75, location: (863, 978)\n", - "Processed cell at: LT0047_27/140/75, location: (680, 979)\n", - "Processed cell at: LT0047_27/140/84, location: (457, 199)\n", - "Processed cell at: LT0047_27/140/84, location: (475, 217)\n", - "Processed cell at: LT0047_27/140/84, location: (559, 374)\n", - "Processed cell at: LT0047_27/140/84, location: (480, 375)\n", - "Processed cell at: LT0047_27/140/84, location: (480, 437)\n", - "Processed cell at: LT0047_27/140/84, location: (575, 452)\n", - "Processed cell at: LT0047_27/140/84, location: (540, 507)\n", - "Processed cell at: LT0047_27/140/84, location: (435, 587)\n", - "Processed cell at: LT0047_27/140/84, location: (690, 622)\n", - "Processed cell at: LT0047_27/140/84, location: (431, 643)\n", - "Processed cell at: LT0047_27/140/84, location: (172, 758)\n", - "Processed cell at: LT0047_27/140/84, location: (162, 772)\n", - "Processed cell at: LT0047_27/140/84, location: (128, 774)\n", - "Processed cell at: LT0047_27/140/84, location: (177, 780)\n", - "Processed cell at: LT0047_27/140/84, location: (341, 790)\n", - "Processed cell at: LT0047_27/140/84, location: (131, 788)\n", - "Processed cell at: LT0047_27/140/84, location: (122, 802)\n", - "Processed cell at: LT0047_27/140/84, location: (140, 810)\n", - "Processed cell at: LT0047_27/140/84, location: (753, 815)\n", - "Processed cell at: LT0046_19/356/67, location: (169, 42)\n", - "Processed cell at: LT0046_19/356/67, location: (350, 43)\n", - "Processed cell at: LT0046_19/356/67, location: (349, 57)\n", - "Processed cell at: LT0046_19/356/67, location: (171, 67)\n", - "Processed cell at: LT0046_19/356/67, location: (359, 81)\n", - "Processed cell at: LT0046_19/356/67, location: (394, 131)\n", - "Processed cell at: LT0046_19/356/67, location: (389, 153)\n", - "Processed cell at: LT0046_19/356/67, location: (815, 180)\n", - "Processed cell at: LT0046_19/356/67, location: (854, 188)\n", - "Processed cell at: LT0046_19/356/67, location: (878, 192)\n", - "Processed cell at: LT0046_19/356/67, location: (800, 200)\n", - "Processed cell at: LT0046_19/356/67, location: (660, 221)\n", - "Processed cell at: LT0046_19/356/67, location: (635, 233)\n", - "Processed cell at: LT0046_19/356/67, location: (711, 273)\n", - "Processed cell at: LT0046_19/356/67, location: (282, 273)\n", - "Processed cell at: LT0046_19/356/67, location: (734, 282)\n", - "Processed cell at: LT0046_19/356/67, location: (277, 297)\n", - "Processed cell at: LT0046_19/356/67, location: (299, 294)\n", - "Processed cell at: LT0046_19/356/67, location: (1108, 384)\n", - "Processed cell at: LT0046_19/356/67, location: (1124, 410)\n", - "Processed cell at: LT0046_19/356/67, location: (1084, 416)\n", - "Processed cell at: LT0046_19/356/67, location: (787, 429)\n", - "Processed cell at: LT0046_19/356/67, location: (640, 430)\n", - "Processed cell at: LT0046_19/356/67, location: (761, 438)\n", - "Processed cell at: LT0046_19/356/67, location: (408, 437)\n", - "Processed cell at: LT0046_19/356/67, location: (681, 438)\n", - "Processed cell at: LT0046_19/356/67, location: (1093, 438)\n", - "Processed cell at: LT0046_19/356/67, location: (618, 441)\n", - "Processed cell at: LT0046_19/356/67, location: (400, 458)\n", - "Processed cell at: LT0046_19/356/67, location: (667, 461)\n", - "Processed cell at: LT0046_19/356/67, location: (286, 494)\n", - "Processed cell at: LT0046_19/356/67, location: (616, 497)\n", - "Processed cell at: LT0046_19/356/67, location: (309, 500)\n", - "Processed cell at: LT0046_19/356/67, location: (189, 512)\n", - "Processed cell at: LT0046_19/356/67, location: (630, 526)\n", - "Processed cell at: LT0046_19/356/67, location: (176, 526)\n", - "Processed cell at: LT0046_19/356/67, location: (206, 527)\n", - "Processed cell at: LT0046_19/356/67, location: (201, 543)\n", - "Processed cell at: LT0046_19/356/67, location: (116, 593)\n", - "Processed cell at: LT0046_19/356/67, location: (640, 591)\n", - "Processed cell at: LT0046_19/356/67, location: (146, 596)\n", - "Processed cell at: LT0046_19/356/67, location: (169, 594)\n", - "Processed cell at: LT0046_19/356/67, location: (97, 609)\n", - "Processed cell at: LT0046_19/356/67, location: (653, 608)\n", - "Processed cell at: LT0046_19/356/57, location: (353, 49)\n", - "Processed cell at: LT0046_19/356/57, location: (350, 80)\n", - "Processed cell at: LT0046_19/356/57, location: (521, 77)\n", - "Processed cell at: LT0046_19/356/57, location: (544, 88)\n", - "Processed cell at: LT0046_19/356/57, location: (612, 101)\n", - "Processed cell at: LT0046_19/356/57, location: (959, 113)\n", - "Processed cell at: LT0046_19/356/57, location: (638, 117)\n", - "Processed cell at: LT0046_19/356/57, location: (980, 124)\n", - "Processed cell at: LT0046_19/356/57, location: (523, 127)\n", - "Processed cell at: LT0046_19/356/57, location: (548, 130)\n", - "Processed cell at: LT0046_19/356/57, location: (668, 216)\n", - "Processed cell at: LT0046_19/356/57, location: (644, 230)\n", - "Processed cell at: LT0046_19/356/57, location: (571, 248)\n", - "Processed cell at: LT0046_19/356/57, location: (120, 246)\n", - "Processed cell at: LT0046_19/356/57, location: (215, 249)\n", - "Processed cell at: LT0046_19/356/57, location: (115, 265)\n", - "Processed cell at: LT0046_19/356/57, location: (277, 268)\n", - "Processed cell at: LT0046_19/356/57, location: (192, 270)\n", - "Processed cell at: LT0046_19/356/57, location: (991, 269)\n", - "Processed cell at: LT0046_19/356/57, location: (564, 278)\n", - "Processed cell at: LT0046_19/356/57, location: (301, 282)\n", - "Processed cell at: LT0046_19/356/57, location: (1005, 288)\n", - "Processed cell at: LT0046_19/356/57, location: (278, 288)\n", - "Processed cell at: LT0046_19/356/57, location: (279, 301)\n", - "Processed cell at: LT0046_19/356/57, location: (1019, 308)\n", - "Processed cell at: LT0046_19/356/57, location: (60, 387)\n", - "Processed cell at: LT0046_19/356/57, location: (69, 407)\n", - "Processed cell at: LT0046_19/356/57, location: (645, 415)\n", - "Processed cell at: LT0046_19/356/57, location: (631, 431)\n", - "Processed cell at: LT0046_19/356/57, location: (682, 441)\n", - "Processed cell at: LT0046_19/356/57, location: (671, 464)\n", - "Processed cell at: LT0046_19/356/57, location: (570, 494)\n", - "Processed cell at: LT0046_19/356/57, location: (623, 498)\n", - "Processed cell at: LT0046_19/356/57, location: (581, 520)\n", - "Processed cell at: LT0046_19/356/57, location: (936, 523)\n", - "Processed cell at: LT0046_19/356/57, location: (634, 524)\n", - "Processed cell at: LT0046_19/356/57, location: (542, 530)\n", - "Processed cell at: LT0046_19/356/57, location: (636, 536)\n", - "Processed cell at: LT0046_19/356/57, location: (1005, 546)\n", - "Processed cell at: LT0046_19/356/57, location: (561, 554)\n", - "Processed cell at: LT0046_19/356/57, location: (881, 560)\n", - "Processed cell at: LT0046_19/356/57, location: (1060, 570)\n", - "Processed cell at: LT0046_19/356/57, location: (971, 573)\n", - "Processed cell at: LT0046_19/356/57, location: (607, 635)\n", - "Processed cell at: LT0046_19/356/57, location: (374, 652)\n", - "Processed cell at: LT0046_19/356/57, location: (605, 659)\n", - "Processed cell at: LT0046_19/356/57, location: (388, 674)\n", - "Processed cell at: LT0046_19/356/57, location: (122, 890)\n", - "Processed cell at: LT0046_19/356/57, location: (112, 906)\n", - "Processed cell at: LT0046_19/356/57, location: (95, 914)\n", - "Processed cell at: LT0046_19/356/92, location: (811, 77)\n", - "Processed cell at: LT0046_19/356/92, location: (795, 87)\n", - "Processed cell at: LT0046_19/356/92, location: (821, 96)\n", - "Processed cell at: LT0046_19/356/92, location: (482, 179)\n", - "Processed cell at: LT0046_19/356/92, location: (701, 192)\n", - "Processed cell at: LT0046_19/356/92, location: (737, 200)\n", - "Processed cell at: LT0046_19/356/92, location: (475, 205)\n", - "Processed cell at: LT0046_19/356/92, location: (719, 207)\n", - "Processed cell at: LT0046_19/356/92, location: (855, 211)\n", - "Processed cell at: LT0046_19/356/92, location: (498, 210)\n", - "Processed cell at: LT0046_19/356/92, location: (755, 217)\n", - "Processed cell at: LT0046_19/356/92, location: (646, 229)\n", - "Processed cell at: LT0046_19/356/92, location: (626, 239)\n", - "Processed cell at: LT0046_19/356/92, location: (604, 247)\n", - "Processed cell at: LT0046_19/356/92, location: (280, 275)\n", - "Processed cell at: LT0046_19/356/92, location: (285, 296)\n", - "Processed cell at: LT0046_19/356/92, location: (280, 314)\n", - "Processed cell at: LT0046_19/356/92, location: (167, 423)\n", - "Processed cell at: LT0046_19/356/92, location: (614, 487)\n", - "Processed cell at: LT0046_19/356/92, location: (1079, 606)\n", - "Processed cell at: LT0046_19/356/92, location: (1123, 614)\n", - "Processed cell at: LT0046_19/356/92, location: (172, 618)\n", - "Processed cell at: LT0046_19/356/92, location: (226, 618)\n", - "Processed cell at: LT0046_19/356/92, location: (1095, 624)\n", - "Processed cell at: LT0046_19/356/92, location: (194, 622)\n", - "Processed cell at: LT0046_19/356/92, location: (1056, 631)\n", - "Processed cell at: LT0046_19/356/92, location: (209, 635)\n", - "Processed cell at: LT0046_19/356/92, location: (180, 637)\n", - "Processed cell at: LT0046_19/356/92, location: (1087, 647)\n", - "Processed cell at: LT0046_19/356/92, location: (1125, 657)\n", - "Processed cell at: LT0093_13/147/69, location: (1125, 118)\n", - "Processed cell at: LT0093_13/147/69, location: (305, 213)\n", - "Processed cell at: LT0093_13/147/69, location: (280, 229)\n", - "Processed cell at: LT0093_13/147/69, location: (297, 232)\n", - "Processed cell at: LT0093_13/147/69, location: (125, 246)\n", - "Processed cell at: LT0093_13/147/69, location: (96, 243)\n", - "Processed cell at: LT0093_13/147/69, location: (278, 246)\n", - "Processed cell at: LT0093_13/147/69, location: (148, 251)\n", - "Processed cell at: LT0093_13/147/69, location: (1116, 388)\n", - "Processed cell at: LT0093_13/147/69, location: (57, 782)\n", - "Processed cell at: LT0093_13/147/69, location: (64, 796)\n", - "Processed cell at: LT0093_13/147/69, location: (49, 806)\n", - "Processed cell at: LT0093_13/147/69, location: (34, 821)\n", - "Processed cell at: LT0093_13/147/69, location: (85, 836)\n", - "Processed cell at: LT0093_13/147/69, location: (385, 840)\n", - "Processed cell at: LT0093_13/147/69, location: (382, 854)\n", - "Processed cell at: LT0093_13/147/69, location: (54, 866)\n", - "Processed cell at: LT0093_13/147/69, location: (80, 863)\n", - "Processed cell at: LT0093_13/147/69, location: (398, 870)\n", - "Processed cell at: LT0093_13/147/69, location: (519, 875)\n", - "Processed cell at: LT0093_13/147/69, location: (386, 882)\n", - "Processed cell at: LT0093_13/147/77, location: (937, 108)\n", - "Processed cell at: LT0093_13/147/77, location: (922, 140)\n", - "Processed cell at: LT0093_13/147/77, location: (1114, 385)\n", - "Processed cell at: LT0093_13/147/77, location: (1079, 555)\n", - "Processed cell at: LT0093_13/147/77, location: (1097, 810)\n", - "Processed cell at: LT0093_13/147/77, location: (657, 830)\n", - "Processed cell at: LT0093_13/147/77, location: (688, 835)\n", - "Processed cell at: LT0093_13/147/77, location: (1113, 869)\n", - "Processed cell at: LT0093_13/147/77, location: (1065, 879)\n", - "Processed cell at: LT0093_13/147/77, location: (744, 899)\n", - "Processed cell at: LT0093_13/147/77, location: (1089, 910)\n", - "Processed cell at: LT0093_13/147/77, location: (748, 912)\n", - "Processed cell at: LT0093_13/147/77, location: (738, 930)\n", - "Processed cell at: LT0093_13/147/77, location: (836, 966)\n", - "Processed cell at: LT0093_13/147/77, location: (848, 995)\n", - "Processed cell at: LT0093_13/147/59, location: (1121, 103)\n", - "Processed cell at: LT0093_13/147/59, location: (1267, 359)\n", - "Processed cell at: LT0093_13/147/59, location: (896, 541)\n", - "Processed cell at: LT0093_13/147/75, location: (1070, 90)\n", - "Processed cell at: LT0093_13/147/75, location: (984, 234)\n", - "Processed cell at: LT0093_13/147/75, location: (616, 327)\n", - "Processed cell at: LT0093_13/147/75, location: (1102, 440)\n", - "Processed cell at: LT0093_13/147/75, location: (997, 574)\n", - "Processed cell at: LT0093_13/147/75, location: (523, 871)\n", - "Processed cell at: LT0138_03/127/44, location: (682, 222)\n", - "Processed cell at: LT0138_03/127/44, location: (625, 436)\n", - "Processed cell at: LT0138_03/127/44, location: (638, 498)\n", - "Processed cell at: LT0138_03/127/44, location: (900, 521)\n", - "Processed cell at: LT0138_03/127/44, location: (844, 528)\n", - "Processed cell at: LT0138_03/127/44, location: (643, 672)\n", - "Processed cell at: LT0138_03/127/44, location: (621, 698)\n", - "Processed cell at: LT0138_03/127/44, location: (182, 844)\n", - "Processed cell at: LT0138_03/127/44, location: (1139, 920)\n", - "Processed cell at: LT0138_03/127/51, location: (554, 152)\n", - "Processed cell at: LT0138_03/127/51, location: (698, 162)\n", - "Processed cell at: LT0138_03/127/51, location: (542, 173)\n", - "Processed cell at: LT0138_03/127/51, location: (739, 186)\n", - "Processed cell at: LT0138_03/127/51, location: (641, 497)\n", - "Processed cell at: LT0138_03/127/51, location: (900, 523)\n", - "Processed cell at: LT0138_03/127/51, location: (139, 629)\n", - "Processed cell at: LT0138_03/127/51, location: (633, 661)\n", - "Processed cell at: LT0138_03/127/51, location: (646, 671)\n", - "Processed cell at: LT0138_03/127/51, location: (74, 692)\n", - "Processed cell at: LT0138_03/127/51, location: (530, 840)\n", - "Processed cell at: LT0138_03/127/62, location: (602, 198)\n", - "Processed cell at: LT0138_03/127/62, location: (600, 209)\n", - "Processed cell at: LT0138_03/127/62, location: (175, 360)\n", - "Processed cell at: LT0138_03/127/62, location: (201, 369)\n", - "Processed cell at: LT0138_03/127/62, location: (900, 525)\n", - "Processed cell at: LT0138_03/127/62, location: (843, 529)\n", - "Processed cell at: LT0138_03/127/62, location: (966, 589)\n", - "Processed cell at: LT0138_03/127/62, location: (697, 805)\n", - "Processed cell at: LT0138_03/127/62, location: (521, 811)\n", - "Processed cell at: LT0138_03/127/62, location: (537, 847)\n", - "Processed cell at: LT0138_03/127/35, location: (868, 464)\n", - "Processed cell at: LT0138_03/127/35, location: (631, 497)\n", - "Processed cell at: LT0138_03/127/35, location: (638, 672)\n", - "Processed cell at: LT0138_03/127/35, location: (706, 680)\n", - "Processed cell at: LT0138_03/127/35, location: (25, 693)\n", - "Processed cell at: LT0138_03/127/35, location: (818, 692)\n", - "Processed cell at: LT0138_03/127/35, location: (618, 699)\n", - "Processed cell at: LT0138_03/127/35, location: (707, 802)\n", - "Processed cell at: LT0138_03/127/35, location: (707, 836)\n", - "Processed cell at: LT0138_03/127/31, location: (561, 141)\n", - "Processed cell at: LT0138_03/127/31, location: (555, 151)\n", - "Processed cell at: LT0138_03/127/31, location: (724, 228)\n", - "Processed cell at: LT0138_03/127/31, location: (971, 269)\n", - "Processed cell at: LT0138_03/127/31, location: (870, 467)\n", - "Processed cell at: LT0138_03/127/31, location: (633, 498)\n", - "Processed cell at: LT0138_03/127/31, location: (707, 676)\n", - "Processed cell at: LT0138_03/127/31, location: (1134, 914)\n", - "Processed cell at: LT0094_04/319/73, location: (694, 159)\n", - "Processed cell at: LT0094_04/319/73, location: (680, 217)\n", - "Processed cell at: LT0094_04/319/73, location: (695, 222)\n", - "Processed cell at: LT0094_04/319/73, location: (1213, 573)\n", - "Processed cell at: LT0094_04/319/73, location: (502, 671)\n", - "Processed cell at: LT0094_04/319/73, location: (257, 898)\n", - "Processed cell at: LT0094_04/319/93, location: (838, 366)\n", - "Processed cell at: LT0094_04/319/93, location: (982, 391)\n", - "Processed cell at: LT0094_04/319/93, location: (241, 395)\n", - "Processed cell at: LT0094_04/319/93, location: (963, 400)\n", - "Processed cell at: LT0094_04/319/93, location: (901, 406)\n", - "Processed cell at: LT0094_04/319/93, location: (875, 416)\n", - "Processed cell at: LT0094_04/319/93, location: (918, 420)\n", - "Processed cell at: LT0094_04/319/93, location: (891, 415)\n", - "Processed cell at: LT0094_04/319/93, location: (900, 421)\n", - "Processed cell at: LT0094_04/319/93, location: (891, 437)\n", - "Processed cell at: LT0094_04/319/93, location: (947, 460)\n", - "Processed cell at: LT0094_04/319/93, location: (967, 467)\n", - "Processed cell at: LT0094_04/319/93, location: (979, 485)\n", - "Processed cell at: LT0094_04/319/93, location: (1212, 571)\n", - "Processed cell at: LT0094_04/319/93, location: (1145, 775)\n", - "Processed cell at: LT0094_04/319/93, location: (1121, 782)\n", - "Processed cell at: LT0094_04/319/93, location: (534, 865)\n", - "Processed cell at: LT0094_04/319/93, location: (485, 918)\n", - "Processed cell at: LT0094_04/319/93, location: (521, 936)\n", - "Processed cell at: LT0094_04/319/30, location: (986, 816)\n", - "Processed cell at: LT0094_04/319/30, location: (334, 870)\n", - "Processed cell at: LT0094_04/319/7, location: (900, 310)\n", - "Processed cell at: LT0094_04/319/7, location: (750, 827)\n", - "Processed cell at: LT0094_04/319/7, location: (750, 843)\n", - "Processed cell at: LT0094_04/319/89, location: (1211, 571)\n", - "Processed cell at: LT0094_04/319/89, location: (976, 817)\n", - "Processed cell at: LT0094_04/319/89, location: (531, 863)\n", - "Processed cell at: LT0094_04/319/89, location: (434, 864)\n", - "Processed cell at: LT0094_04/319/89, location: (567, 874)\n", - "Processed cell at: LT0094_04/319/89, location: (487, 915)\n", - "Processed cell at: LT0094_04/319/89, location: (522, 932)\n", - "Processed cell at: LT0094_04/319/61, location: (813, 298)\n", - "Processed cell at: LT0094_04/319/61, location: (963, 360)\n", - "Processed cell at: LT0094_04/319/61, location: (958, 440)\n", - "Processed cell at: LT0094_04/319/61, location: (702, 519)\n", - "Processed cell at: LT0094_04/319/61, location: (1248, 894)\n", - "Processed cell at: LT0094_04/319/61, location: (258, 896)\n", - "Processed cell at: LT0094_04/319/61, location: (528, 904)\n", - "Processed cell at: LT0094_04/319/61, location: (493, 910)\n", - "Processed cell at: LT0094_04/319/61, location: (1109, 926)\n", - "Processed cell at: LT0094_04/319/61, location: (1093, 932)\n", - "Processed cell at: LT0094_04/319/5, location: (612, 361)\n", - "Processed cell at: LT0094_04/319/5, location: (639, 403)\n", - "Processed cell at: LT0094_04/319/5, location: (620, 479)\n", - "Processed cell at: LT0094_04/319/5, location: (750, 827)\n", - "Processed cell at: LT0094_04/319/5, location: (750, 843)\n", - "Processed cell at: LT0094_04/319/5, location: (1041, 891)\n", - "Processed cell at: LT0094_04/319/5, location: (518, 899)\n", - "Processed cell at: LT0094_04/319/66, location: (897, 399)\n", - "Processed cell at: LT0094_04/319/66, location: (900, 411)\n", - "Processed cell at: LT0094_04/319/66, location: (918, 421)\n", - "Processed cell at: LT0094_04/319/66, location: (882, 421)\n", - "Processed cell at: LT0094_04/319/66, location: (1218, 574)\n", - "Processed cell at: LT0094_04/319/66, location: (563, 868)\n", - "Processed cell at: LT0094_04/319/66, location: (258, 897)\n", - "Processed cell at: LT0094_04/319/66, location: (494, 909)\n", - "Processed cell at: LT0094_04/319/66, location: (530, 912)\n", - "Processed cell at: LT0094_04/319/66, location: (1092, 927)\n", - "Processed cell at: LT0094_04/319/66, location: (1096, 946)\n", - "Processed cell at: LT0094_04/319/66, location: (1077, 949)\n", - "Processed cell at: LT0090_33/383/72, location: (1193, 95)\n", - "Processed cell at: LT0090_33/383/72, location: (1189, 118)\n", - "Processed cell at: LT0090_33/383/72, location: (1056, 124)\n", - "Processed cell at: LT0090_33/383/72, location: (1184, 133)\n", - "Processed cell at: LT0090_33/383/72, location: (1051, 138)\n", - "Processed cell at: LT0090_33/383/72, location: (255, 154)\n", - "Processed cell at: LT0090_33/383/72, location: (1091, 163)\n", - "Processed cell at: LT0090_33/383/72, location: (547, 198)\n", - "Processed cell at: LT0090_33/383/72, location: (1026, 316)\n", - "Processed cell at: LT0090_33/383/72, location: (663, 332)\n", - "Processed cell at: LT0090_33/383/72, location: (1019, 331)\n", - "Processed cell at: LT0090_33/383/72, location: (647, 338)\n", - "Processed cell at: LT0090_33/383/72, location: (520, 349)\n", - "Processed cell at: LT0090_33/383/72, location: (1080, 367)\n", - "Processed cell at: LT0090_33/383/72, location: (1026, 367)\n", - "Processed cell at: LT0090_33/383/72, location: (517, 405)\n", - "Processed cell at: LT0090_33/383/72, location: (457, 410)\n", - "Processed cell at: LT0090_33/383/72, location: (518, 419)\n", - "Processed cell at: LT0090_33/383/72, location: (710, 607)\n", - "Processed cell at: LT0090_33/383/72, location: (570, 611)\n", - "Processed cell at: LT0090_33/383/72, location: (667, 619)\n", - "Processed cell at: LT0090_33/383/72, location: (1309, 626)\n", - "Processed cell at: LT0090_33/383/72, location: (740, 668)\n", - "Processed cell at: LT0090_33/383/72, location: (780, 670)\n", - "Processed cell at: LT0090_33/383/72, location: (724, 670)\n", - "Processed cell at: LT0090_33/383/72, location: (739, 684)\n", - "Processed cell at: LT0090_33/383/60, location: (1073, 119)\n", - "Processed cell at: LT0090_33/383/60, location: (1058, 125)\n", - "Processed cell at: LT0090_33/383/60, location: (1106, 165)\n", - "Processed cell at: LT0090_33/383/60, location: (1089, 161)\n", - "Processed cell at: LT0090_33/383/60, location: (1037, 188)\n", - "Processed cell at: LT0090_33/383/60, location: (343, 210)\n", - "Processed cell at: LT0090_33/383/60, location: (344, 228)\n", - "Processed cell at: LT0090_33/383/60, location: (1264, 586)\n", - "Processed cell at: LT0090_33/383/60, location: (601, 593)\n", - "Processed cell at: LT0090_33/383/60, location: (719, 667)\n", - "Processed cell at: LT0090_33/383/60, location: (734, 671)\n", - "Processed cell at: LT0090_33/383/60, location: (735, 683)\n", - "Processed cell at: LT0090_33/383/84, location: (1173, 53)\n", - "Processed cell at: LT0090_33/383/84, location: (1031, 56)\n", - "Processed cell at: LT0090_33/383/84, location: (1157, 57)\n", - "Processed cell at: LT0090_33/383/84, location: (1169, 67)\n", - "Processed cell at: LT0090_33/383/84, location: (1011, 88)\n", - "Processed cell at: LT0090_33/383/84, location: (552, 155)\n", - "Processed cell at: LT0090_33/383/84, location: (485, 227)\n", - "Processed cell at: LT0090_33/383/84, location: (1053, 257)\n", - "Processed cell at: LT0090_33/383/84, location: (1062, 269)\n", - "Processed cell at: LT0090_33/383/84, location: (1069, 279)\n", - "Processed cell at: LT0090_33/383/84, location: (389, 294)\n", - "Processed cell at: LT0090_33/383/84, location: (406, 297)\n", - "Processed cell at: LT0090_33/383/84, location: (876, 307)\n", - "Processed cell at: LT0090_33/383/84, location: (1024, 305)\n", - "Processed cell at: LT0090_33/383/84, location: (410, 309)\n", - "Processed cell at: LT0090_33/383/84, location: (1072, 316)\n", - "Processed cell at: LT0090_33/383/84, location: (568, 320)\n", - "Processed cell at: LT0090_33/383/84, location: (807, 320)\n", - "Processed cell at: LT0090_33/383/84, location: (1017, 319)\n", - "Processed cell at: LT0090_33/383/84, location: (658, 332)\n", - "Processed cell at: LT0090_33/383/84, location: (1032, 324)\n", - "Processed cell at: LT0090_33/383/84, location: (1080, 330)\n", - "Processed cell at: LT0090_33/383/84, location: (490, 345)\n", - "Processed cell at: LT0090_33/383/84, location: (1025, 362)\n", - "Processed cell at: LT0090_33/383/84, location: (1086, 360)\n", - "Processed cell at: LT0090_33/383/84, location: (781, 378)\n", - "Processed cell at: LT0090_33/383/84, location: (667, 391)\n", - "Processed cell at: LT0090_33/383/84, location: (516, 398)\n", - "Processed cell at: LT0090_33/383/84, location: (681, 400)\n", - "Processed cell at: LT0090_33/383/84, location: (285, 436)\n", - "Processed cell at: LT0090_33/383/84, location: (299, 482)\n", - "Processed cell at: LT0090_33/383/84, location: (674, 607)\n", - "Processed cell at: LT0090_33/383/84, location: (664, 618)\n", - "Processed cell at: LT0090_33/383/84, location: (414, 623)\n", - "Processed cell at: LT0090_33/383/84, location: (414, 639)\n", - "Processed cell at: LT0090_33/383/84, location: (315, 646)\n", - "Processed cell at: LT0090_33/383/84, location: (733, 659)\n", - "Processed cell at: LT0090_33/383/84, location: (601, 658)\n", - "Processed cell at: LT0090_33/383/84, location: (729, 673)\n", - "Processed cell at: LT0090_33/383/84, location: (606, 673)\n", - "Processed cell at: LT0090_33/383/84, location: (549, 697)\n", - "Processed cell at: LT0090_33/383/84, location: (562, 708)\n", - 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"Processed cell at: LT0090_33/383/92, location: (1025, 381)\n", - "Processed cell at: LT0090_33/383/92, location: (668, 394)\n", - "Processed cell at: LT0090_33/383/92, location: (451, 398)\n", - "Processed cell at: LT0090_33/383/92, location: (914, 403)\n", - "Processed cell at: LT0090_33/383/92, location: (628, 396)\n", - "Processed cell at: LT0090_33/383/92, location: (514, 404)\n", - "Processed cell at: LT0090_33/383/92, location: (939, 410)\n", - "Processed cell at: LT0090_33/383/92, location: (456, 415)\n", - "Processed cell at: LT0090_33/383/92, location: (518, 446)\n", - "Processed cell at: LT0090_33/383/92, location: (1263, 447)\n", - "Processed cell at: LT0090_33/383/92, location: (606, 580)\n", - "Processed cell at: LT0090_33/383/92, location: (524, 590)\n", - "Processed cell at: LT0090_33/383/92, location: (601, 595)\n", - "Processed cell at: LT0090_33/383/92, location: (483, 612)\n", - "Processed cell at: LT0090_33/383/92, location: (530, 603)\n", - "Processed cell at: LT0090_33/383/92, location: (752, 612)\n", - 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"Processed cell at: LT0106_02/287/6, location: (574, 98)\n", - "Processed cell at: LT0106_02/287/6, location: (855, 324)\n", - "Processed cell at: LT0106_02/287/6, location: (816, 408)\n", - "Processed cell at: LT0106_02/287/6, location: (645, 665)\n", - "Processed cell at: LT0106_02/287/33, location: (828, 338)\n", - "Processed cell at: LT0106_02/287/33, location: (63, 384)\n", - "Processed cell at: LT0106_02/287/33, location: (800, 408)\n", - "Processed cell at: LT0106_02/287/33, location: (105, 429)\n", - "Processed cell at: LT0106_02/287/33, location: (94, 469)\n" - ] - } - ], - "source": [ - "base_url = \"https://raw.github.com/WayScience/mitocheck_data/\"\n", - "# hash changes depending on desired version of mitocheck_data being used\n", - "hash = \"de21b9c3201ba4298db2b1704f3ae510a5dc47e2\"\n", - "mitocheck_data_version_url = f\"{base_url}/{hash}\"\n", - "\n", - "output_dir = pathlib.Path(\"data/\")\n", - "output_dir.mkdir(parents=True, exist_ok=True)\n", - "save_path = pathlib.Path(f\"{output_dir}/training_data.csv.gz\")\n", - "\n", - "training_data = format_training_data(mitocheck_data_version_url)\n", - "training_data.to_csv(save_path, compression=\"gzip\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(4308, 1293)\n" - ] - }, - { - "data": { - "text/html": [ - "
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"4 LT0043_48/166/55/LT0043_48_166_55.tif ... 1.764085 \n", - "\n", - " efficientnet_1271 efficientnet_1272 efficientnet_1273 efficientnet_1274 \\\n", - "0 -0.736547 0.010863 0.290715 -0.508518 \n", - "1 -0.562691 -0.044208 -0.159093 -0.605761 \n", - "2 -0.519065 0.584269 0.860831 -0.446671 \n", - "3 -0.587031 0.838506 1.16317 -0.083327 \n", - "4 -0.364659 -0.623983 0.087524 -0.678471 \n", - "\n", - " efficientnet_1275 efficientnet_1276 efficientnet_1277 efficientnet_1278 \\\n", - "0 -0.666912 0.527043 -0.216474 0.659347 \n", - "1 -0.605434 0.3765 -0.496571 0.028506 \n", - "2 -0.409693 0.383752 -0.343047 -0.370232 \n", - "3 -0.20665 0.253444 -0.084782 0.073759 \n", - "4 -1.04743 0.1197 0.254014 0.080685 \n", - "\n", - " efficientnet_1279 \n", - "0 -0.692728 \n", - "1 -0.152331 \n", - "2 0.267983 \n", - "3 -0.251357 \n", - "4 -0.808582 \n", - "\n", - "[5 rows x 1293 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(training_data.shape)\n", - "training_data.head()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.13 ('2.ML_phenotypic_classification')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "4cc408a06ad49ae0c78cd765de22f61d31a0f8b0861ec15e52107dd82d811e52" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/1.format_data/format_training_data.py b/1.format_data/format_training_data.py deleted file mode 100644 index 18f4af6d..00000000 --- a/1.format_data/format_training_data.py +++ /dev/null @@ -1,208 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # Format Training Data -# -# ### Access training data from specific commit of [mitocheck_data](https://github.com/WayScience/mitocheck_data) and format this data into a single CSV file -# -# ### Import libraries - -# In[1]: - - -import pandas as pd -import urllib -import pathlib - - -# ### Define functions for formatting training data - -# In[2]: - - -def get_single_cell_metadata(single_cell_data: pd.DataFrame): - """get plate, well, frame information from single cell data - - Args: - single_cell_data (pd.Dataframe): dataframe with single cell data - - Returns: - str, str, str: plate, well, frame metadata as strings - """ - # Metadata_DNA is in format plate/well/frame/filename.tif so get plate, well, frame info from this - single_cell_info = single_cell_data["Metadata_DNA"].split("/") - plate = single_cell_info[0] - well = single_cell_info[1] - frame = single_cell_info[2] - return plate, well, frame - - -def get_cell_class( - single_cell_data: pd.DataFrame, - trainingset_file_url: str, - plate: str, - well: str, - frame: str, -) -> str: - """get phenotypic class of cell from trainingset.dat file, as labeled by MitoCheck - - Args: - single_cell_data (pd.DataFrame): dataframe with single cell data - trainingset_file_url (str): url location of raw traininset.dat file - plate (str): plate cell is from - well (str): well cell is from - frame (str): frame cell is from - - Returns: - str: phenotypic class of nucleus, as labeled by MitoCheck - """ - well_string = f"W{str(well).zfill(5)}" - frame_time = (int(frame) - 1) * 30 - frame_time_string = f"T{str(frame_time).zfill(5)}" - frame_file_details = [plate, well_string, frame_time_string] - obj_id = int(single_cell_data["Mitocheck_Object_ID"].item()) - obj_id_prefix = f"{obj_id}: " - - append = False - # need to open trainingset file each time - trainingset_file = urllib.request.urlopen(trainingset_file_url) - for line in trainingset_file: - decoded_line = line.decode("utf-8").strip() - # match plate, well, frame to starting line for movie labels - if all(detail in decoded_line for detail in frame_file_details): - append = True - if append and decoded_line.startswith(obj_id_prefix): - return decoded_line.split(": ")[1] - return None - - -def get_cell_control( - plate: str, - well: str, - idr_metadata: pd.DataFrame, -): - - cell_annotations = idr_metadata.loc[ - (plate == idr_metadata["Plate"]) & (idr_metadata["Well Number"].astype(int) == int(well)) - ] - control_type = cell_annotations.iloc[0]["Control Type"] - - if control_type == "positive control": - return "positive" - elif control_type == "negative control": - return "negative" - else: - return "none" - - -def complete_single_cell( - single_cell_data: pd.DataFrame, - trainingset_file_url: str, - segmentation_data_dir: str, - idr_metadata: pd.DataFrame, -) -> pd.DataFrame: - """Add Mitocheck_Object_ID and Mitocheck_Phenotypic_Class fields to single cell data by matching cell object ID to phenotypic class given in traininset.dat - - Args: - single_cell_data (pd.DataFrame): single cell data - trainingset_file_url (str): url location of raw traininset.dat file - segmentation_data_dir (str): url location of the raw segmentation data directory - - Returns: - pd.DataFrame: completed single cell data - """ - plate, well, frame = get_single_cell_metadata(single_cell_data) - segmentation_data_url = ( - f"{segmentation_data_dir}/{plate}/{well}/{frame}/{plate}_{well}_{frame}.tsv" - ) - full_segmentation_data = pd.read_csv(segmentation_data_url, delimiter="\t").round(0) - cell_x_y = ( - round(single_cell_data["Location_Center_X"]), - round(single_cell_data["Location_Center_Y"]), - ) - cell_segmentation_data = full_segmentation_data.loc[ - (full_segmentation_data["Location_Center_X"] == cell_x_y[0]) - & (full_segmentation_data["Location_Center_Y"] == cell_x_y[1]) - ] - print(f"Processed cell at: {plate}/{well}/{frame}, location: {cell_x_y}") - if cell_segmentation_data.empty: - print("No segmentation data match found for this cell!") - else: - single_cell_data = single_cell_data.to_frame().transpose() - single_cell_data.insert( - 0, - "Mitocheck_Object_ID", - cell_segmentation_data["Mitocheck_Object_ID"].item(), - ) - # get class and append to single cell data - cell_phenotypic_class = get_cell_class( - single_cell_data, trainingset_file_url, plate, well, frame - ) - if cell_phenotypic_class == None: - print("This cell was not found in trainingset.dat!") - single_cell_data.insert(0, "Mitocheck_Phenotypic_Class", cell_phenotypic_class) - - cell_control_type = get_cell_control(plate, well, idr_metadata) - single_cell_data.insert(1, "Control_Type", cell_control_type) - - return single_cell_data - - -def format_training_data(mitocheck_data_version_url: str) -> pd.DataFrame: - """Add Mitocheck_Object_ID and Mitocheck_Phenotypic_Class fields to each single cell and compile all the cells into a single training data dataframe - - Args: - mitocheck_data_version_url (str): url with path to desired version of raw mitocheck_data - - Returns: - pd.DataFrame: completed training data with Mitocheck_Object_ID and Mitocheck_Phenotypic_Class for each cell - """ - trainingset_file_url = ( - f"{mitocheck_data_version_url}/0.download_data/trainingset.dat" - ) - segmentation_data_dir = f"{mitocheck_data_version_url}/2.segment_nuclei/segmented/" - preprocessed_features_url = f"{mitocheck_data_version_url}/4.preprocess_features/data/normalized_training_data.csv.gz" - idr_metadata_url = f"{mitocheck_data_version_url}/3.extract_features/idr0013-screenA-annotation.csv.gz" - - preprocessed_features = pd.read_csv(preprocessed_features_url, compression="gzip") - print("Loaded preprocessed features!") - - idr_metadata = pd.read_csv(idr_metadata_url, dtype=object, compression="gzip") - print("Loaded idr metadata!") - - training_data = [] - for index, row in preprocessed_features.iterrows(): - single_cell = row - completed_single_cell = complete_single_cell( - single_cell, trainingset_file_url, segmentation_data_dir, idr_metadata - ) - training_data.append(completed_single_cell) - - training_data = pd.concat(training_data) - return training_data - - -# ### Format training data - -# In[3]: - - -base_url = "https://raw.github.com/WayScience/mitocheck_data/" -# hash changes depending on desired version of mitocheck_data being used -hash = "de21b9c3201ba4298db2b1704f3ae510a5dc47e2" -mitocheck_data_version_url = f"{base_url}/{hash}" - -output_dir = pathlib.Path("data/") -output_dir.mkdir(parents=True, exist_ok=True) -save_path = pathlib.Path(f"{output_dir}/training_data.csv.gz") - -training_data = format_training_data(mitocheck_data_version_url) -training_data.to_csv(save_path, compression="gzip") - - -# In[4]: - - -print(training_data.shape) -training_data.head() - diff --git a/1.split_data/README.md b/1.split_data/README.md new file mode 100644 index 00000000..c924bccc --- /dev/null +++ b/1.split_data/README.md @@ -0,0 +1,25 @@ +# 1. Split Data + +In this module, we split the training data into training, testing, and holdout datasets. + +First, we split the data into training, test, and holdout subsets in [split_data.ipynb](split_data.ipynb). +The `get_representative_images()` function used to create the holdout dataset determines which images to holdout such that all phenotypic classes can be represented in these holdout images. +The test dataset is determined by taking a random number of samples (stratified by phenotypic class) from the dataset after the holdout images are removed. +The training dataset is the subset remaining after holdout/test samples are removed. +Sample indexes associated with training, test, and holdout subsets are stored in [data_split_indexes.tsv](indexes/data_split_indexes.tsv). +Sample indexes are later used to load subsets from [training_data.csv.gz](../0.download_data/data/training_data.csv.gz). + +## Step 1: Split Data + +Use the commands below to create indexes for training, testing, and holdout data subsets: + +```sh +# Make sure you are located in 1.split_data +cd 1.split_data + +# Activate phenotypic_profiling conda environment +conda activate phenotypic_profiling + +# Split data +bash split_data.sh +``` diff --git a/1.split_data/indexes/data_split_indexes.tsv b/1.split_data/indexes/data_split_indexes.tsv new file mode 100644 index 00000000..bb155d57 --- /dev/null +++ b/1.split_data/indexes/data_split_indexes.tsv @@ -0,0 +1,4475 @@ + label index +0 holdout 3291 +1 holdout 3292 +2 holdout 3293 +3 holdout 3294 +4 holdout 3295 +5 holdout 3296 +6 holdout 3297 +7 holdout 3298 +8 holdout 3299 +9 holdout 3300 +10 holdout 3301 +11 holdout 3302 +12 holdout 3303 +13 holdout 3304 +14 holdout 3305 +15 holdout 3306 +16 holdout 3307 +17 holdout 3308 +18 holdout 3309 +19 holdout 3310 +20 holdout 3311 +21 holdout 3312 +22 holdout 3313 +23 holdout 3314 +24 holdout 3315 +25 holdout 3316 +26 holdout 3317 +27 holdout 3318 +28 holdout 3319 +29 holdout 3320 +30 holdout 3321 +31 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3.ML_model/scripts/0.split_data.py rename to 1.split_data/scripts/nbconverted/split_data.py index 7e887469..c6bde7e8 100644 --- a/3.ML_model/scripts/0.split_data.py +++ b/1.split_data/scripts/nbconverted/split_data.py @@ -11,14 +11,12 @@ import pandas as pd import numpy as np import pathlib -from typing import Tuple, Any, List, Union from sklearn.utils import shuffle import sys -# adding utils to system path -sys.path.insert(0, '../utils') -from MlPipelineUtils import get_features_data, get_random_images_indexes, get_representative_images, get_image_indexes +sys.path.append("../utils") +from split_utils import get_features_data, get_random_images_indexes, get_representative_images, get_image_indexes # ### Load data and set holdout/test parameters @@ -26,11 +24,8 @@ # In[2]: -# set numpy seed to make random operations reproduceable -np.random.seed(0) - # load x (features) and y (labels) dataframes -load_path = pathlib.Path("../../1.format_data/data/training_data.csv.gz") +load_path = pathlib.Path("../0.download_data/data/training_data.csv.gz") training_data = get_features_data(load_path) print(training_data.shape) @@ -80,12 +75,14 @@ index_data +# ### Save indexes + # In[6]: # make results dir for saving -results_dir = pathlib.Path("../results/") +results_dir = pathlib.Path("indexes/") results_dir.mkdir(parents=True, exist_ok=True) # save indexes as tsv file -index_data.to_csv(f"{results_dir}/0.data_split_indexes.tsv", sep="\t") +index_data.to_csv(f"{results_dir}/data_split_indexes.tsv", sep="\t") diff --git a/3.ML_model/notebooks/0.split_data.ipynb b/1.split_data/split_data.ipynb similarity index 77% rename from 3.ML_model/notebooks/0.split_data.ipynb rename to 1.split_data/split_data.ipynb index 8b6a332b..6ddef5dd 100644 --- a/3.ML_model/notebooks/0.split_data.ipynb +++ b/1.split_data/split_data.ipynb @@ -18,14 +18,12 @@ "import pandas as pd\n", "import numpy as np\n", "import pathlib\n", - "from typing import Tuple, Any, List, Union\n", "\n", "from sklearn.utils import shuffle\n", "\n", "import sys\n", - "# adding utils to system path\n", - "sys.path.insert(0, '../utils')\n", - "from MlPipelineUtils import get_features_data, get_random_images_indexes, get_representative_images, get_image_indexes" + "sys.path.append(\"../utils\")\n", + "from split_utils import get_features_data, get_random_images_indexes, get_representative_images, get_image_indexes" ] }, { @@ -44,16 +42,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "(4123, 1292)\n" + "(4474, 1293)\n" ] } ], "source": [ - "# set numpy seed to make random operations reproduceable\n", - "np.random.seed(0)\n", - "\n", "# load x (features) and y (labels) dataframes\n", - "load_path = pathlib.Path(\"../../1.format_data/data/training_data.csv.gz\")\n", + "load_path = pathlib.Path(\"../0.download_data/data/training_data.csv.gz\")\n", "training_data = get_features_data(load_path)\n", "print(training_data.shape)\n", "\n", @@ -72,7 +67,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "(3926, 1292)\n" + "(3996, 1293)\n" ] } ], @@ -93,7 +88,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "(3338, 1292)\n" + "(3398, 1293)\n" ] } ], @@ -144,27 +139,27 @@ " \n", " 0\n", " holdout\n", - " 107\n", + " 3291\n", " \n", " \n", " 1\n", " holdout\n", - " 108\n", + " 3292\n", " \n", " \n", " 2\n", " holdout\n", - " 109\n", + " 3293\n", " \n", " \n", " 3\n", " holdout\n", - " 110\n", + " 3294\n", " \n", " \n", " 4\n", " holdout\n", - " 111\n", + " 3295\n", " \n", " \n", " ...\n", @@ -172,50 +167,50 @@ " ...\n", " \n", " \n", - " 4118\n", + " 4469\n", " train\n", - " 4302\n", + " 4646\n", " \n", " \n", - " 4119\n", + " 4470\n", " train\n", - " 4303\n", + " 4647\n", " \n", " \n", - " 4120\n", + " 4471\n", " train\n", - " 4304\n", + " 4648\n", " \n", " \n", - " 4121\n", + " 4472\n", " train\n", - " 4306\n", + " 4650\n", " \n", " \n", - " 4122\n", + " 4473\n", " train\n", - " 4307\n", + " 4652\n", " \n", " \n", "\n", - "

4123 rows × 2 columns

\n", + "

4474 rows × 2 columns

\n", "" ], "text/plain": [ " label index\n", - "0 holdout 107\n", - "1 holdout 108\n", - "2 holdout 109\n", - "3 holdout 110\n", - "4 holdout 111\n", + "0 holdout 3291\n", + "1 holdout 3292\n", + "2 holdout 3293\n", + "3 holdout 3294\n", + "4 holdout 3295\n", "... ... ...\n", - "4118 train 4302\n", - "4119 train 4303\n", - "4120 train 4304\n", - "4121 train 4306\n", - "4122 train 4307\n", + "4469 train 4646\n", + "4470 train 4647\n", + "4471 train 4648\n", + "4472 train 4650\n", + "4473 train 4652\n", "\n", - "[4123 rows x 2 columns]" + "[4474 rows x 2 columns]" ] }, "execution_count": 5, @@ -236,6 +231,13 @@ "index_data" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Save indexes" + ] + }, { "cell_type": "code", "execution_count": 6, @@ -243,16 +245,16 @@ "outputs": [], "source": [ "# make results dir for saving\n", - "results_dir = pathlib.Path(\"../results/\")\n", + "results_dir = pathlib.Path(\"indexes/\")\n", "results_dir.mkdir(parents=True, exist_ok=True)\n", "# save indexes as tsv file\n", - "index_data.to_csv(f\"{results_dir}/0.data_split_indexes.tsv\", sep=\"\\t\")" + "index_data.to_csv(f\"{results_dir}/data_split_indexes.tsv\", sep=\"\\t\")" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.13 ('2.ML_phenotypic_classification')", + "display_name": "Python 3.8.13 ('phenotypic_profiling')", "language": "python", "name": "python3" }, @@ -271,7 +273,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "4cc408a06ad49ae0c78cd765de22f61d31a0f8b0861ec15e52107dd82d811e52" + "hash": "f9df586d1764dbc68785000a153dad1832127ac564b5e2e4c94e83fc43160b30" } } }, diff --git a/1.split_data/split_data.sh b/1.split_data/split_data.sh new file mode 100644 index 00000000..0b5c5626 --- /dev/null +++ b/1.split_data/split_data.sh @@ -0,0 +1,5 @@ +#!/bin/bash +# Convert notebook to python file and execute +jupyter nbconvert --to python \ + --FilesWriter.build_directory=scripts/nbconverted \ + --execute split_data.ipynb diff --git a/2.analyze_data/2.analyze_data.sh b/2.analyze_data/2.analyze_data.sh deleted file mode 100644 index f3405308..00000000 --- a/2.analyze_data/2.analyze_data.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash -# Step 0: Convert notebook to script -jupyter nbconvert --to=script analyze_training_data.ipynb - -# Step 1: Execute jupyter notebook -jupyter nbconvert --to=html \ - --ExecutePreprocessor.kernel_name=python3 \ - --ExecutePreprocessor.timeout=10000000 \ - --execute analyze_training_data.ipynb diff --git a/2.analyze_data/2.analyze_data_env.yml b/2.analyze_data/2.analyze_data_env.yml deleted file mode 100644 index 8a6a1e02..00000000 --- a/2.analyze_data/2.analyze_data_env.yml +++ /dev/null @@ -1,11 +0,0 @@ -name: 2.analyze_training_data -channels: - - conda-forge -dependencies: - - conda-forge::python=3.8.13 - - conda-forge::jupyter=1.0.0 - - conda-forge::pandas=1.4.2 - - conda-forge::scikit-learn=1.1.1 - - conda-forge::matplotlib=3.5.2 - - conda-forge::seaborn=0.11.2 - - conda-forge::umap-learn=0.5.3 diff --git a/2.analyze_data/README.md b/2.analyze_data/README.md deleted file mode 100644 index a4c2ae91..00000000 --- a/2.analyze_data/README.md +++ /dev/null @@ -1,38 +0,0 @@ -# 2. Analyze Features - -In this module, we present our pipeline for analyzing features. - -### Feature Analysis - -We use [UMAP](https://github.com/lmcinnes/umap) for analyis of features. -UMAP was introduced in [McInnes, L, Healy, J, 2018](https://arxiv.org/abs/1802.03426) as a manifold learning technique for dimension reduction. -We use UMAP to reduce the feature data from 1280 features to 1, 2, and 3 dimensions. - -We use [Matplotlib](https://matplotlib.org/) and [seaborn](https://seaborn.pydata.org/) for data visualization. - -**Note:** Phenotypic classes used for analysis can be changed with the `classes_to_keep` variable in [2.analyze_training_data.ipynb](2.analyze_training_data.ipynb). - -## Step 1: Setup Feature Analysis Environment - -### Step 1a: Create Feature Analysis Environment - -```sh -# Run this command to create the conda environment for feature analysis -conda env create -f 2.analyze_data_env.yml -``` - -### Step 1b: Activate Feature Analysis Environment - -```sh -# Run this command to activate the conda environment for feature analysis -conda activate 2.analyze_training_data -``` - -## Step 2: Execute Feature Analysis Pipeline - -```bash -# Run this script to analyze features -bash 2.analyze_data.sh -``` -**Note:** Running pipeline will produce all intermediate files (located in [results](results/)). -Analysis jupyter notebook ([2.analyze_training_data.ipynb](2.analyze_training_data.ipynb)) will not be updated but the executed notebook ([2.analyze_training_data.html](2.analyze_training_data.html)) will be updated. \ No newline at end of file diff --git a/2.analyze_data/analyze_training_data.html b/2.analyze_data/analyze_training_data.html deleted file mode 100644 index 8407706e..00000000 --- a/2.analyze_data/analyze_training_data.html +++ /dev/null @@ -1,15340 +0,0 @@ - - - - - -<p>analyze_training_data</p> - - - - - - - - - - - - - - - - - - - - - -
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- - - - - - - - - diff --git a/2.analyze_data/analyze_training_data.ipynb b/2.analyze_data/analyze_training_data.ipynb deleted file mode 100644 index 70e33103..00000000 --- a/2.analyze_data/analyze_training_data.ipynb +++ /dev/null @@ -1,690 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analyze all feature data\n", - "\n", - "### Import libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pathlib\n", - "from sklearn.datasets import load_digits\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import StandardScaler\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.colors import ListedColormap, rgb2hex\n", - "from pylab import cm\n", - "import seaborn as sns\n", - "import pandas as pd\n", - "import umap\n", - "\n", - "from utils.analysisUtils import get_features_data, show_1D_umap, show_2D_umap, show_3D_umap " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initalize analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# make random numpy operations consistent\n", - "np.random.seed(0)\n", - "\n", - "# create results dir for saving results\n", - "results_dir = pathlib.Path(\"results/\")\n", - "results_dir.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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efficientnet_0efficientnet_1efficientnet_2efficientnet_3efficientnet_4efficientnet_5efficientnet_6efficientnet_7efficientnet_8efficientnet_9...efficientnet_1270efficientnet_1271efficientnet_1272efficientnet_1273efficientnet_1274efficientnet_1275efficientnet_1276efficientnet_1277efficientnet_1278efficientnet_1279
40.0062050.1195460.905428-0.672271-0.0689631.7572870.1243360.5936950.274280-1.274982...1.764085-0.364659-0.6239830.087524-0.678471-1.0474300.1197000.2540140.080685-0.808582
52.378942-0.955787-0.691866-0.397104-0.6169750.648150-0.670943-0.592767-0.990411-0.380712...-0.030402-0.3061050.4713121.111647-0.3955800.2655790.337486-0.7287580.5192631.143726
6-0.9762262.157527-0.278376-0.6805611.744093-0.456953-0.296961-0.7094880.2494111.771207...-2.070584-0.419038-0.7161602.525790-0.3004070.2437620.2705430.473745-1.024547-0.401801
7-1.3788841.122315-0.569486-0.3687860.201950-0.4910150.692530-0.391879-0.4717180.897925...-1.264048-0.6783960.0769163.1426200.2021740.3312710.5677000.072269-1.7156321.303155
8-1.909268-0.839781-0.552060-0.5295060.837143-0.428041-0.459263-0.8926070.1321911.081521...-0.834010-0.4042910.8395590.230029-0.322646-0.254167-0.602655-0.273222-0.7220490.554533
..................................................................
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" - ], - "text/plain": [ - " efficientnet_0 efficientnet_1 efficientnet_2 efficientnet_3 \\\n", - "4 0.006205 0.119546 0.905428 -0.672271 \n", - "5 2.378942 -0.955787 -0.691866 -0.397104 \n", - "6 -0.976226 2.157527 -0.278376 -0.680561 \n", - "7 -1.378884 1.122315 -0.569486 -0.368786 \n", - "8 -1.909268 -0.839781 -0.552060 -0.529506 \n", - "... ... ... ... ... \n", - "4303 -0.116541 -0.629463 1.401698 -0.489478 \n", - "4304 1.059086 -0.224794 -0.530644 0.240305 \n", - "4305 1.071799 -0.186700 0.053190 -0.546116 \n", - "4306 0.689590 0.097733 -0.615206 1.272017 \n", - "4307 0.807782 -0.023426 -0.958250 0.650229 \n", - "\n", - " efficientnet_4 efficientnet_5 efficientnet_6 efficientnet_7 \\\n", - "4 -0.068963 1.757287 0.124336 0.593695 \n", - "5 -0.616975 0.648150 -0.670943 -0.592767 \n", - "6 1.744093 -0.456953 -0.296961 -0.709488 \n", - "7 0.201950 -0.491015 0.692530 -0.391879 \n", - "8 0.837143 -0.428041 -0.459263 -0.892607 \n", - "... ... ... ... ... \n", - "4303 -2.831115 -0.642692 0.988942 -0.719675 \n", - "4304 -3.130908 -0.178424 0.143485 1.416218 \n", - "4305 -0.483472 1.296396 -0.615709 -0.928396 \n", - "4306 1.094201 0.710853 0.436329 1.444259 \n", - "4307 0.604586 0.538085 -0.179938 -0.469140 \n", - "\n", - " efficientnet_8 efficientnet_9 ... efficientnet_1270 \\\n", - "4 0.274280 -1.274982 ... 1.764085 \n", - "5 -0.990411 -0.380712 ... -0.030402 \n", - "6 0.249411 1.771207 ... -2.070584 \n", - "7 -0.471718 0.897925 ... -1.264048 \n", - "8 0.132191 1.081521 ... -0.834010 \n", - "... ... ... ... ... \n", - "4303 -0.754563 -1.002060 ... -0.010054 \n", - "4304 -0.976807 0.024085 ... 0.828838 \n", - "4305 -0.879711 -0.864035 ... 0.342158 \n", - "4306 -0.527824 0.413573 ... -0.890952 \n", - "4307 -0.636907 -0.601189 ... 0.116183 \n", - "\n", - " efficientnet_1271 efficientnet_1272 efficientnet_1273 \\\n", - "4 -0.364659 -0.623983 0.087524 \n", - "5 -0.306105 0.471312 1.111647 \n", - "6 -0.419038 -0.716160 2.525790 \n", - "7 -0.678396 0.076916 3.142620 \n", - "8 -0.404291 0.839559 0.230029 \n", - "... ... ... ... \n", - "4303 2.490791 0.112932 -0.448705 \n", - "4304 2.328690 2.365700 -1.219878 \n", - "4305 1.118108 2.618269 -1.146326 \n", - "4306 0.301522 0.345463 0.594489 \n", - "4307 0.073442 -0.035741 -0.020786 \n", - "\n", - " efficientnet_1274 efficientnet_1275 efficientnet_1276 \\\n", - "4 -0.678471 -1.047430 0.119700 \n", - "5 -0.395580 0.265579 0.337486 \n", - "6 -0.300407 0.243762 0.270543 \n", - "7 0.202174 0.331271 0.567700 \n", - "8 -0.322646 -0.254167 -0.602655 \n", - "... ... ... ... \n", - "4303 -0.573112 -1.219449 0.756078 \n", - "4304 -0.377726 0.285707 0.072360 \n", - "4305 -0.574519 0.284514 0.491826 \n", - "4306 0.737245 3.037339 -0.636915 \n", - "4307 0.599503 2.253533 -0.473317 \n", - "\n", - " efficientnet_1277 efficientnet_1278 efficientnet_1279 \n", - "4 0.254014 0.080685 -0.808582 \n", - "5 -0.728758 0.519263 1.143726 \n", - "6 0.473745 -1.024547 -0.401801 \n", - "7 0.072269 -1.715632 1.303155 \n", - "8 -0.273222 -0.722049 0.554533 \n", - "... ... ... ... \n", - "4303 -0.434373 -0.617329 2.989479 \n", - "4304 -0.101487 0.592109 -0.326425 \n", - "4305 -0.489022 0.969788 -0.492233 \n", - "4306 0.061156 1.849867 -0.896322 \n", - "4307 0.022974 1.555225 -0.743614 \n", - "\n", - "[4123 rows x 1280 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load features dataframe\n", - "features_dataframe_path = pathlib.Path(\"../1.format_data/data/training_data.csv.gz\")\n", - "features_dataframe = get_features_data(features_dataframe_path)\n", - "\n", - "# split metadata from features\n", - "metadata_dataframe = features_dataframe.iloc[:,:13]\n", - "features_dataframe = features_dataframe.iloc[:,13:]\n", - "\n", - "features_dataframe" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Counts for all phenotypic classes" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Polylobed 1437\n", - "Binuclear 547\n", - "Grape 428\n", - "Prometaphase 327\n", - "Interphase 306\n", - "Artefact 243\n", - "Apoptosis 186\n", - "SmallIrregular 165\n", - "MetaphaseAlignment 160\n", - "Hole 105\n", - "Metaphase 65\n", - "Large 48\n", - "Folded 42\n", - "Elongated 33\n", - "UndefinedCondensed 31\n", - "Name: Mitocheck_Phenotypic_Class, dtype: int64" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "metadata_dataframe[\"Mitocheck_Phenotypic_Class\"].value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Only keep certain phenoytpic classes for analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4123, 1280)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "classes_to_keep = [\n", - " \"Polylobed\",\n", - " \"Binuclear\",\n", - " \"Grape\",\n", - " \"Prometaphase\",\n", - " \"Interphase\",\n", - " \"Artefact\",\n", - " \"Apoptosis\",\n", - " \"SmallIrregular\",\n", - " \"MetaphaseAlignment\",\n", - " \"Hole\",\n", - " \"Metaphase\",\n", - " \"Large\",\n", - " \"Folded\",\n", - " \"Elongated\",\n", - " \"UndefinedCondensed\",\n", - "]\n", - "\n", - "features_dataframe = features_dataframe.loc[\n", - " metadata_dataframe[\"Mitocheck_Phenotypic_Class\"].isin(classes_to_keep)\n", - "]\n", - "metadata_dataframe = metadata_dataframe.loc[\n", - " metadata_dataframe[\"Mitocheck_Phenotypic_Class\"].isin(classes_to_keep)\n", - "]\n", - "features_dataframe.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1D UMAP" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "phenotypic_classes = metadata_dataframe[\"Mitocheck_Phenotypic_Class\"]\n", - "show_1D_umap(features_dataframe, phenotypic_classes, results_dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2D UMAP" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "phenotypic_classes = metadata_dataframe[\"Mitocheck_Phenotypic_Class\"]\n", - "show_2D_umap(features_dataframe, phenotypic_classes, results_dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3D UMAP" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "phenotypic_classes = metadata_dataframe[\"Mitocheck_Phenotypic_Class\"]\n", - "show_3D_umap(features_dataframe, phenotypic_classes, results_dir)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.13 ('2.analyze_training_data')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "ad1083e3dd16f562452e4d714b55bbc8d938c52aa784c0b2ae1ae118cac264ea" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/2.analyze_data/analyze_training_data.py b/2.analyze_data/analyze_training_data.py deleted file mode 100644 index e17b4cec..00000000 --- a/2.analyze_data/analyze_training_data.py +++ /dev/null @@ -1,120 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # Analyze all feature data -# -# ### Import libraries - -# In[1]: - - -import numpy as np -import pathlib -from sklearn.datasets import load_digits -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -import matplotlib.pyplot as plt -from matplotlib.colors import ListedColormap, rgb2hex -from pylab import cm -import seaborn as sns -import pandas as pd -import umap - -from utils.analysisUtils import get_features_data, show_1D_umap, show_2D_umap, show_3D_umap - - -# ### Initalize analysis - -# In[2]: - - -# make random numpy operations consistent -np.random.seed(0) - -# create results dir for saving results -results_dir = pathlib.Path("results/") -results_dir.mkdir(parents=True, exist_ok=True) - - -# ### Load dataframe - -# In[3]: - - -# load features dataframe -features_dataframe_path = pathlib.Path("../1.format_data/data/training_data.csv.gz") -features_dataframe = get_features_data(features_dataframe_path) - -# split metadata from features -metadata_dataframe = features_dataframe.iloc[:,:13] -features_dataframe = features_dataframe.iloc[:,13:] - -features_dataframe - - -# ### Counts for all phenotypic classes - -# In[4]: - - -metadata_dataframe["Mitocheck_Phenotypic_Class"].value_counts() - - -# ### Only keep certain phenoytpic classes for analysis - -# In[5]: - - -classes_to_keep = [ - "Polylobed", - "Binuclear", - "Grape", - "Prometaphase", - "Interphase", - "Artefact", - "Apoptosis", - "SmallIrregular", - "MetaphaseAlignment", - "Hole", - "Metaphase", - "Large", - "Folded", - "Elongated", - "UndefinedCondensed", -] - -features_dataframe = features_dataframe.loc[ - metadata_dataframe["Mitocheck_Phenotypic_Class"].isin(classes_to_keep) -] -metadata_dataframe = metadata_dataframe.loc[ - metadata_dataframe["Mitocheck_Phenotypic_Class"].isin(classes_to_keep) -] -features_dataframe.shape - - -# ### 1D UMAP - -# In[6]: - - -phenotypic_classes = metadata_dataframe["Mitocheck_Phenotypic_Class"] -show_1D_umap(features_dataframe, phenotypic_classes, results_dir) - - -# ### 2D UMAP - -# In[7]: - - -phenotypic_classes = metadata_dataframe["Mitocheck_Phenotypic_Class"] -show_2D_umap(features_dataframe, phenotypic_classes, results_dir) - - -# ### 3D UMAP - -# In[8]: - - -phenotypic_classes = metadata_dataframe["Mitocheck_Phenotypic_Class"] -show_3D_umap(features_dataframe, phenotypic_classes, results_dir) - diff --git a/2.analyze_data/results/1D_umap.png b/2.analyze_data/results/1D_umap.png deleted file mode 100644 index c8021d41..00000000 Binary files a/2.analyze_data/results/1D_umap.png and /dev/null differ diff --git a/2.analyze_data/results/1D_umap.tsv b/2.analyze_data/results/1D_umap.tsv deleted file mode 100644 index 3701f8ff..00000000 --- a/2.analyze_data/results/1D_umap.tsv +++ /dev/null @@ -1,4124 +0,0 @@ -UMAP1 y_distribution Mitocheck_Phenotypic_Class -2.6727352 0.5488135039273248 Polylobed -9.317974 0.7151893663724195 MetaphaseAlignment --5.5003276 0.6027633760716439 Interphase --4.605271 0.5448831829968969 Interphase -6.1979537 0.4236547993389047 Artefact --4.6614175 0.6458941130666561 Artefact -6.171038 0.4375872112626925 Artefact -5.7331967 0.8917730007820798 Artefact -5.630792 0.9636627605010293 Polylobed --5.419625 0.3834415188257777 Interphase --6.1426115 0.7917250380826646 Polylobed --6.1198783 0.5288949197529045 Interphase -8.278043 0.5680445610939323 Artefact --5.4687943 0.925596638292661 Interphase --3.90241 0.07103605819788694 Artefact --4.0342813 0.08712929970154071 Artefact --4.1606755 0.02021839744032572 Artefact --4.13291 0.832619845547938 Artefact --6.2925844 0.7781567509498505 Polylobed --4.190848 0.8700121482468192 Artefact --6.2204747 0.978618342232764 Polylobed --6.3428564 0.7991585642167236 Polylobed --6.269019 0.46147936225293185 Polylobed --6.310519 0.7805291762864555 Polylobed --0.025951887 0.11827442586893322 Interphase --2.6629956 0.6399210213275238 Polylobed --2.3676138 0.1433532874090464 Polylobed --2.667029 0.9446689170495839 Polylobed -1.2909644 0.5218483217500717 Polylobed --2.8516216 0.4146619399905236 Interphase -0.40127257 0.26455561210462697 Binuclear -0.1181941 0.7742336894342167 Binuclear --2.5874496 0.45615033221654855 Binuclear --5.0877395 0.5684339488686485 Interphase -1.1137276 0.018789800436355142 Binuclear -1.4691105 0.6176354970758771 Polylobed -1.6577606 0.6120957227224214 Polylobed -1.652926 0.6169339968747569 Polylobed -11.967536 0.9437480785146242 MetaphaseAlignment -9.145526 0.6818202991034834 Prometaphase -12.373357 0.359507900573786 Metaphase -12.430912 0.43703195379934145 Metaphase -8.945938 0.6976311959272649 Metaphase -8.959034 0.06022547162926983 Metaphase -13.008127 0.6667667154456677 MetaphaseAlignment --4.152686 0.6706378696181594 Binuclear --0.07874505 0.2103825610738409 Polylobed --4.2571216 0.1289262976548533 Binuclear --2.0723577 0.31542835092418386 Polylobed --6.0883126 0.3637107709426226 Polylobed --6.0337844 0.5701967704178796 Polylobed --5.993084 0.43860151346232035 Polylobed --3.8642755 0.9883738380592262 Polylobed -8.187737 0.10204481074802807 Artefact -8.199369 0.2088767560948347 Artefact -8.239755 0.16130951788499626 Large --3.6450117 0.6531083254653984 Large -8.7929945 0.2532916025397821 MetaphaseAlignment -7.0755215 0.4663107728563063 Artefact --5.222765 0.24442559200160274 Polylobed -7.000359 0.15896958364551972 Artefact --3.69352 0.11037514116430513 Artefact --2.113235 0.6563295894652734 Polylobed --2.1792953 0.1381829513486138 Polylobed --2.1925244 0.1965823616800535 Polylobed --3.6009748 0.3687251706609641 Artefact --3.722993 0.8209932298479351 Artefact --2.1748767 0.09710127579306127 Polylobed -1.7159286 0.8379449074988039 Polylobed --5.206784 0.09609840789396307 Interphase -0.33715248 0.9764594650133958 Interphase -1.608113 0.4686512016477016 Polylobed -1.8185472 0.9767610881903371 Polylobed --4.2539363 0.604845519745046 Interphase --5.7786026 0.7392635793983017 Artefact --3.1386635 0.039187792254320675 Artefact --2.95107 0.2828069625764096 Artefact -8.053396 0.1201965612131689 Artefact -8.479537 0.29614019752214493 Artefact -12.404973 0.11872771895424405 Apoptosis -12.468581 0.317983179393976 Apoptosis -8.36281 0.41426299451466997 Metaphase --5.6645193 0.06414749634878436 Metaphase -9.255513 0.6924721193700198 Metaphase -5.918085 0.5666014542065752 MetaphaseAlignment -5.280373 0.2653894909394454 Polylobed -6.3147445 0.5232480534666997 Polylobed -5.16092 0.09394051075844168 Polylobed --3.1711159 0.5759464955561793 Polylobed -10.349653 0.9292961975762141 Interphase -12.370863 0.31856895245132366 Metaphase --5.8915505 0.6674103799636817 Polylobed --5.8854094 0.13179786240439217 Polylobed --5.963112 0.7163272041185655 Polylobed -14.1784725 0.2894060929472011 Metaphase --5.9048085 0.18319136200711683 Polylobed --2.5132551 0.5865129348100832 Binuclear --2.4159567 0.020107546187493552 Binuclear -14.121753 0.8289400292173631 MetaphaseAlignment --2.7053604 0.004695476192547066 Binuclear --2.4368794 0.6778165367962301 Binuclear --5.7917075 0.27000797319216485 Polylobed --5.8185453 0.7351940221225949 Polylobed -9.4396515 0.9621885451174382 Metaphase -9.456206 0.24875314351995803 Metaphase --3.0953712 0.5761573344178369 Large --4.636796 0.592041931271839 Large --3.3120592 0.5722519057908734 Polylobed --3.6600552 0.2230816326406183 Polylobed --6.0674815 0.952749011516985 Polylobed --5.723757 0.44712537861762736 Large --5.7949624 0.8464086724711278 Large --5.775054 0.6994792753175043 Large --5.9366827 0.29743695085513366 Large --5.9721074 0.8137978197024772 Large --6.212404 0.39650574084698464 Polylobed --6.1556273 0.8811031971111616 Polylobed --6.1264963 0.5812728726358587 Polylobed -7.101609 0.8817353618548528 Artefact --6.078765 0.6925315900777659 Polylobed --6.1995506 0.7252542798196405 Polylobed --3.5368052 0.5013243819267023 Artefact --6.131992 0.9560836347232239 Polylobed --3.5536563 0.6439901992296374 Artefact --4.215349 0.4238550485581797 Artefact -1.664766 0.6063932141279244 Polylobed -0.34975076 0.019193198309333526 Interphase -1.9314305 0.30157481667454933 Polylobed --4.3376293 0.660173537492685 Interphase --0.95162207 0.29007760721044407 Polylobed --1.0287273 0.6180154289988415 Polylobed --6.1090875 0.42876870094576613 Polylobed --1.439101 0.13547406422245023 Binuclear --6.105708 0.29828232595603077 Polylobed --6.083616 0.5699649107012649 Polylobed --6.0571923 0.5908727612481732 Polylobed --2.194166 0.5743252488495788 Binuclear --2.197468 0.6532008198571336 Polylobed --2.954754 0.6521032700016889 Polylobed --3.497874 0.43141843543397396 Polylobed --6.1604867 0.896546595851063 Polylobed -9.983681 0.36756187004789653 Large -9.674417 0.4358649252656268 Large --4.5193043 0.8919233550156721 Artefact --4.2853866 0.8061939890460857 Artefact --4.3642554 0.7038885835403663 Artefact -6.2625394 0.10022688731230112 Artefact --4.7014036 0.9194826137446735 Artefact --4.12227 0.7142412995491114 Artefact --4.39106 0.9988470065678665 Artefact --4.3323383 0.14944830465799375 Artefact --4.2202463 0.8681260573682142 Artefact --4.2748895 0.16249293467637482 Artefact --6.1132216 0.6155595642838442 Polylobed --6.02519 0.12381998284944151 Polylobed --6.250376 0.8480082293222344 Polylobed --4.0892515 0.8073189587250107 Artefact --6.217051 0.5691007386145933 Polylobed --6.239643 0.40718329722599966 Polylobed --2.7598915 0.06916699545513805 Polylobed --2.545833 0.6974287731445636 Polylobed --2.7218835 0.45354268267806885 Polylobed -1.4711748 0.7220555994703479 Polylobed -8.352538 0.8663823259286292 Metaphase -11.827969 0.9755215050028858 Metaphase -13.719217 0.855803342392611 MetaphaseAlignment -9.413107 0.011714084185001972 MetaphaseAlignment -9.574092 0.3599780644783639 Polylobed -9.577627 0.729990562424058 Polylobed -13.528788 0.17162967726144052 MetaphaseAlignment --3.7482395 0.5210366062041293 Polylobed -6.4870296 0.05433798833925363 Polylobed --3.7670283 0.19999652489640007 Polylobed --3.8736508 0.01852179446061397 Polylobed -13.122848 0.7936977033574206 MetaphaseAlignment -9.356839 0.22392468806038013 MetaphaseAlignment -9.80283 0.3453516806969027 Large -7.947517 0.9280812934655909 Polylobed -7.866554 0.7044144019235328 Polylobed --5.3548574 0.03183892953130785 Binuclear --5.2036333 0.16469415649791275 Binuclear --5.8412147 0.6214784014997635 Polylobed --5.8241005 0.5772285886041676 Polylobed -13.190508 0.23789282137450862 Prometaphase --5.6769247 0.9342139979247938 Polylobed --5.982045 0.613965955965896 Polylobed --4.590008 0.5356328030249583 Polylobed --6.197586 0.589909976354571 Polylobed --6.156628 0.7301220295167696 Polylobed --5.2457585 0.31194499547960186 Polylobed --5.342562 0.3982210622160919 Polylobed -13.637526 0.20984374897512215 MetaphaseAlignment -13.910844 0.18619300588033616 MetaphaseAlignment --5.026124 0.9443723899839336 Large --4.9280605 0.7395507950492876 Large --5.5479846 0.4904588086175671 Large -9.315415 0.22741462797332324 MetaphaseAlignment -9.223003 0.25435648177039294 MetaphaseAlignment -11.539782 0.05802916032387562 Metaphase -11.562316 0.4344166255581208 Metaphase -7.3446336 0.3117958819941026 Artefact --0.0150256315 0.6963434888154595 Artefact -9.631929 0.3777518392924809 Metaphase -9.196466 0.1796036775596348 Metaphase --5.2358046 0.02467872839133123 Artefact --5.374504 0.06724963146324858 Artefact --4.150786 0.6793927734985673 Artefact --4.9034386 0.4536968445560453 Artefact --5.192016 0.5365792111087222 Metaphase -10.244491 0.8966712930403421 MetaphaseAlignment --3.5625074 0.9903389473967044 Large --6.0742702 0.21689698439847394 Polylobed -10.418693 0.6630782031001008 MetaphaseAlignment --6.058096 0.26332237673715064 Polylobed --6.002243 0.02065099946572868 Polylobed --3.7626414 0.7583786538361414 Polylobed --5.8529625 0.32001715082246784 Polylobed --3.7610774 0.38346389417189797 Polylobed --3.7093441 0.5883171135536057 Polylobed --3.8128488 0.8310484552361904 Polylobed --4.5291357 0.6289818435911487 Large -9.213108 0.8726506554473953 Polylobed -8.466719 0.27354203481563577 Large -8.660996 0.7980468339125637 Metaphase -7.820347 0.1856359443059522 Artefact -7.718887 0.9527916569719446 Artefact -7.7404413 0.6874882763878153 Artefact -9.189223 0.21550767711355845 Metaphase -8.793597 0.9473705904889242 Artefact -8.742666 0.7308558067701578 Metaphase -8.794576 0.25394164259502583 Artefact -8.688946 0.21331197736748198 Metaphase --6.234308 0.5182007139306632 Polylobed --6.274695 0.025662718054531575 Polylobed -6.9276466 0.2074700754411094 Artefact --6.2114983 0.42468546875150626 Polylobed -6.8803487 0.37416998033422555 Artefact -7.4545794 0.4635754243648107 Artefact --6.2354517 0.2776287062947319 Polylobed -7.023982 0.5867843464581688 Artefact -7.153783 0.8638556059232314 Artefact -6.444678 0.11753185596203308 Artefact -7.070881 0.5173791071541142 Artefact --2.1326838 0.1320681063451533 Polylobed --2.12698 0.7168596811925937 Polylobed -7.119784 0.39605970280729375 Artefact -9.654469 0.565421311858509 Artefact -9.589535 0.18327983621407862 Artefact -7.8789344 0.14484775934337724 Artefact --2.1954713 0.48805628064895457 Polylobed -1.9625448 0.3556127378499556 Polylobed -1.7721457 0.940431945252813 Polylobed -8.083367 0.7653252538069653 Artefact -7.999783 0.7486636198505473 Artefact -7.648215 0.9037197397459334 Artefact -7.7238445 0.08342243544201855 Artefact -1.743483 0.5521924699224066 Polylobed -9.21075 0.5844760689557689 Metaphase -9.324499 0.961936378547229 Metaphase -7.6988373 0.29214752679254885 Artefact -1.7528954 0.24082877991544682 Polylobed -7.673036 0.10029394226549782 Artefact -1.919383 0.016429629591474204 Polylobed -7.593922 0.9295293167921905 Artefact -7.6214204 0.66991654659091 Artefact -7.520944 0.7851529120231378 Artefact -7.714098 0.2817301057539491 Artefact -9.799995 0.5864101661863267 Artefact -5.0075827 0.06395526612098112 MetaphaseAlignment --0.9961272 0.4856275959346229 Polylobed --0.9836261 0.9774951397444468 Polylobed --0.94161266 0.8765052453165908 Polylobed -8.511066 0.33815895183684563 Metaphase --6.3087 0.9615701545414985 Polylobed --6.262992 0.2317016264712045 Polylobed --6.375045 0.9493188224156814 Polylobed --6.234981 0.9413777047064986 Polylobed --6.270399 0.7992025873523917 Polylobed -7.3065224 0.6304479368667911 Artefact --6.967111 0.874287966624947 Polylobed --6.9566813 0.2930202845077967 Polylobed --6.978067 0.8489435553129182 Polylobed --6.961561 0.6178766919175238 Polylobed --3.2267559 0.01323685775889949 Large --5.700619 0.34723351793221957 Large -8.439086 0.14814086094816503 Large --6.193547 0.9818293898182532 Polylobed --6.170065 0.47837030703998806 Polylobed --6.117504 0.4973913654986627 Polylobed --6.049613 0.6394725163987236 Polylobed --2.1905081 0.3685846061296175 Polylobed --2.1669748 0.13690027168559893 Polylobed --2.2289634 0.8221177331942455 Polylobed --2.2189977 0.18984791190275796 Polylobed --2.2400846 0.511318982546456 Polylobed -1.6999029 0.22431702897473926 Polylobed -1.673464 0.09784448449403405 Polylobed -1.9343344 0.8621915174216833 Polylobed --0.93588245 0.9729194890231303 Polylobed --0.95844996 0.9608346580630002 Polylobed --0.97441816 0.906555499221179 Polylobed --6.419367 0.7740473326986388 Polylobed --6.443301 0.3331451520286419 Polylobed --6.386738 0.08110138998799676 Polylobed --6.4104557 0.40724117141380733 Polylobed --6.3869863 0.23223414217094274 Polylobed --6.9288883 0.13248763475798297 Polylobed --6.9216003 0.05342718178682526 Polylobed --6.9689145 0.7255943642105788 Polylobed --6.957278 0.011427458625031028 Polylobed -10.349355 0.7705807485027762 Artefact -8.835937 0.14694664540037505 Artefact --4.2443185 0.07952208258675575 Artefact -7.889219 0.08960303423860538 Artefact -9.015814 0.6720478073539145 Artefact -7.732402 0.24536720985284477 Artefact -9.671546 0.42053946668009845 Artefact -7.9985347 0.5573687913239169 Artefact -7.939094 0.8605511738287938 Artefact --6.3764296 0.7270442627113283 Polylobed --6.207442 0.27032790523871464 Polylobed --5.5475764 0.1314827992911276 Interphase -9.231848 0.05537432042119794 MetaphaseAlignment --3.2935054 0.3015986344809425 Polylobed --3.7063506 0.26211814923967824 Polylobed --3.429597 0.45614056680047965 Polylobed --3.8054023 0.6832813355476804 Polylobed -9.810075 0.6956254456388572 MetaphaseAlignment -2.6042628 0.28351884658216664 Binuclear -2.4267209 0.3799269559001205 Binuclear -7.6061463 0.18115096173690304 Polylobed -8.324001 0.7885455123065187 Polylobed -7.537955 0.0568480764332403 Polylobed -1.987085 0.6969972417249873 Polylobed -2.1917422 0.7786953959411034 Polylobed -5.7260895 0.7774075618487531 Polylobed -1.4401226 0.25942256434535493 Polylobed -2.274756 0.37381313793256143 Polylobed -2.4661925 0.587599635196389 Polylobed --0.0142452065 0.272821902424467 Polylobed --0.032700043 0.3708527992178887 Polylobed --0.017721366 0.19705428018563964 Polylobed -0.057529796 0.4598558837560074 Polylobed -0.0022720385 0.044612301254114084 Polylobed -10.07249 0.799795884570618 Polylobed -9.720693 0.07695644698663273 Polylobed -0.59508485 0.518835148831526 Polylobed --0.59475386 0.3068100995451961 Polylobed -0.9510817 0.5775429488313755 Polylobed --1.5990885 0.9594333408334251 Polylobed -1.2914482 0.6455702444560039 Polylobed -0.1290846 0.03536243575549092 Binuclear -0.43877488 0.43040243950806123 Binuclear -2.8472085 0.5100168523182502 Polylobed -2.9508855 0.536177494703452 Polylobed -2.9114935 0.6813925106038379 Polylobed -2.91192 0.2775960977317661 Polylobed --1.9306597 0.1288605654663202 Binuclear --1.8945986 0.39267567654709434 Binuclear --1.9444525 0.9564057227959488 Polylobed --2.1055489 0.18713089175084474 Polylobed --1.9802853 0.903983954928237 Polylobed --1.6187744 0.5438059500773263 Polylobed --1.5947088 0.4569114216457658 Polylobed --1.6293671 0.8820414102298896 Polylobed --1.5449119 0.45860396176858587 Polylobed --1.4581743 0.7241676366115433 Polylobed --1.6751205 0.399025321703102 Polylobed --1.6173016 0.9040443929009577 Polylobed --1.6054939 0.6900250201912274 Polylobed --1.6311435 0.6996220542505167 Polylobed -10.46454 0.3277204015571189 Elongated -1.6090095 0.7567786427368892 Polylobed -1.5116131 0.6360610554471413 Polylobed -1.5118726 0.24002027337970955 Polylobed -1.5884736 0.16053882248525642 Polylobed -1.8605596 0.7963914745173317 Polylobed -1.5661149 0.9591666030352225 Polylobed -2.5071347 0.45813882726004285 Binuclear -2.4530122 0.5909841653236849 Binuclear -1.43648 0.8577226441935546 Binuclear -3.0124137 0.45722345335385706 Binuclear -2.3140168 0.9518744768327362 Polylobed -2.503229 0.5757511620448724 Polylobed --0.099037714 0.820767120701315 Polylobed --0.20846838 0.9088437184127384 Polylobed --2.271131 0.8155238187685688 Polylobed --1.9272963 0.15941446344895593 Polylobed --0.10312254 0.6288984390617004 Polylobed -1.2143236 0.3984342586196771 Polylobed --0.062403195 0.0627129520233457 Polylobed -0.82763374 0.42403225188984195 Polylobed -1.3439931 0.2586840668894077 Binuclear -1.3996035 0.8490383084285108 Binuclear --0.2760603 0.03330462654669619 Polylobed -1.4083074 0.9589827218634736 Polylobed --0.385151 0.3553688484719296 Polylobed -0.8147386 0.3567068904025429 Polylobed -10.011698 0.01632850268370789 Binuclear -0.3966048 0.18523232523618394 Binuclear --4.4672937 0.4012595008036087 Binuclear -2.1291723 0.9292914173027139 Binuclear -1.6207908 0.09961493022127133 Binuclear -0.93535715 0.9453015334790795 Binuclear -14.694672 0.8694885305466322 Artefact -10.05002 0.4541623969075518 Apoptosis -14.762385 0.32670088176826007 Artefact --0.4544839 0.23274412927905685 Binuclear -1.041273 0.6144647064768743 Binuclear --1.9040376 0.03307459147550562 Polylobed --1.8680297 0.015606064446828216 Polylobed --1.8341812 0.42879572249823783 Polylobed --1.8544039 0.0680740739747202 Polylobed --1.5461501 0.2519409882460929 Polylobed --1.6881883 0.22116091534608384 Polylobed --1.6737722 0.2531911937228519 Polylobed --1.6514882 0.13105523121525775 Polylobed --1.5184423 0.01203622289765427 Polylobed --1.6430222 0.11548429713874808 Polylobed --1.5778027 0.6184802595127479 Polylobed --1.613346 0.9742562128180503 Polylobed -11.794662 0.9903450015608939 UndefinedCondensed -12.329601 0.4090540953730616 Interphase -4.447907 0.16295442604660537 MetaphaseAlignment -12.28331 0.6387617573665293 MetaphaseAlignment -2.9858837 0.49030534654873714 Polylobed -0.93335533 0.9894097772844315 Polylobed -2.3942447 0.0653042071517802 Binuclear -0.9477659 0.7832344383138131 Polylobed -2.3779204 0.2883984973314939 Binuclear -1.9205502 0.241418620076574 Binuclear -2.8333108 0.662504571532676 Binuclear -3.0000904 0.24606318499096447 Binuclear --1.6884434 0.6658591175591877 Binuclear --1.9092908 0.5173085172022888 Binuclear -1.8344715 0.4240889884358493 Binuclear -1.5564935 0.554687808661419 Binuclear -1.9618671 0.28705151991962974 Artefact -1.9565095 0.7065747062729789 Artefact -2.0674887 0.414856869333564 Artefact --0.35488424 0.36054556048589226 Binuclear -1.4219342 0.8286569145557378 Binuclear -2.0478563 0.9249669119531921 Binuclear --0.5969379 0.046007310887296926 Binuclear -14.138541 0.2326269928297655 MetaphaseAlignment -14.070833 0.34851936949256324 Binuclear -1.2619698 0.8149664793702474 Polylobed -10.145891 0.9854914276432976 Binuclear -13.8981495 0.9689717046703518 Binuclear --4.5859222 0.9049483455499269 Binuclear -2.861519 0.2965562650640299 Polylobed -2.8606682 0.9920112434144741 Polylobed -1.1168013 0.2494200410564512 Binuclear -1.1414781 0.1059061548822322 Binuclear -2.033594 0.9509526110553941 Polylobed -2.0527759 0.2334202554680963 Polylobed -1.6684144 0.6897682650777505 Polylobed --2.6111856 0.05835635898058866 Binuclear -1.9716467 0.7307090991274762 Polylobed --2.8696992 0.8817202123338397 Binuclear -6.995342 0.2724368954659625 Binuclear -0.8269312 0.37905689607742854 Binuclear -11.321589 0.3742961833209161 Prometaphase -12.554176 0.7487882575401331 Prometaphase -11.051084 0.23780724253903884 Prometaphase -12.108246 0.171853099047643 Prometaphase -13.25979 0.4492916486877381 Prometaphase -11.510959 0.3044684073773195 Prometaphase -13.199712 0.8391891222586524 Prometaphase -11.856732 0.23774182601563876 Prometaphase -12.450613 0.5023894574892614 Prometaphase -7.074538 0.9425835996979304 Artefact -7.132396 0.6339976977446607 Artefact -9.106991 0.8672894054624648 Artefact -8.945024 0.9402096893547673 Artefact -8.992527 0.7507648618863519 Artefact -12.489344 0.6995750602247514 Artefact -2.839991 0.9679655666042271 Artefact -2.8034685 0.9944007896476794 Artefact -2.7913165 0.45182168266975964 Artefact -2.7791665 0.07086977818420837 Artefact -7.161984 0.29279403144051885 Artefact -7.08559 0.15235470568773046 Artefact -14.070464 0.4174863747960118 Prometaphase -12.96127 0.13128932847325603 Prometaphase -13.092492 0.604117804020882 Prometaphase -14.059921 0.3828080591578541 Prometaphase -4.8210516 0.89538588428821 Prometaphase -12.764478 0.9677946717985019 Prometaphase -12.540945 0.5468849016694222 Prometaphase -12.327305 0.2748235698675966 Prometaphase -9.633961 0.5922304187618368 Prometaphase -12.7862835 0.8967611582244098 Prometaphase -12.787934 0.4067333458357483 Prometaphase -10.892351 0.5520782766919708 Prometaphase -10.794381 0.2716527676061459 Prometaphase -12.036967 0.455444149450027 Prometaphase -11.474586 0.40171353537959864 Prometaphase -11.766474 0.24841346508297102 Prometaphase -14.055191 0.5058663838253084 Prometaphase --2.3183239 0.3103808259798114 Large -1.9413948 0.37303486388074747 Large -1.91875 0.5249704422542643 Large -9.2868805 0.7505950229289875 Large -0.12007701 0.3335074657912753 Binuclear -1.5131099 0.9241587666207636 Binuclear -0.5805236 0.8623185468359024 Binuclear --1.1616166 0.04869029597552854 Polylobed --0.28294516 0.25364252425682277 Polylobed --1.054877 0.4461355126592019 Polylobed --1.0317788 0.10462788874247408 Polylobed -0.108046584 0.3484759890334971 Polylobed --0.07574428 0.7400975256176825 Polylobed --0.26907676 0.6805144811428259 Polylobed --0.3338773 0.6223844285660048 Polylobed --2.6661413 0.7105284027223456 Polylobed --2.6150956 0.20492368695970176 Polylobed --2.610284 0.3416981148647321 Polylobed -0.2756647 0.6762424822774629 Polylobed -0.2623048 0.8792347630313271 Polylobed -0.21714903 0.5436780538280952 Polylobed -0.23207967 0.2826996509455366 Polylobed -0.27650064 0.03023525800598259 Polylobed -1.2537247 0.7103368289742134 Large --0.2903597 0.007884103508440377 Polylobed --0.32143876 0.3726790698209955 Polylobed --0.16720715 0.5305372145627818 Polylobed --1.1054567 0.922111461767193 Polylobed --2.4297428 0.08949454503290011 Polylobed -10.8207245 0.4059423219682837 Metaphase -11.162061 0.024313199710157773 Interphase -10.235113 0.3426109843415903 Interphase -1.1993842 0.6222310588397949 Binuclear -1.1821089 0.2790679482285984 Binuclear -1.6455542 0.2097499496556351 Large -1.7814653 0.11570323332709365 Interphase --0.33843833 0.5771402440203418 Interphase -12.343606 0.695270005904686 Interphase -7.49559 0.6719571405958223 Interphase --4.0838313 0.9488610207205074 Interphase -1.1132586 0.0027032138935026984 Interphase --4.067688 0.647196653894036 Binuclear -0.7176855 0.6003922370976396 Interphase --4.0496492 0.5887396099702882 Binuclear -10.380365 0.9627703198402424 Interphase -14.551394 0.016871673370039697 Metaphase -14.16718 0.6964824307014501 Metaphase -14.790142 0.8136786497018634 MetaphaseAlignment -13.845781 0.5098071966215841 MetaphaseAlignment -10.942784 0.33396486959680916 MetaphaseAlignment -13.299221 0.7908401632274049 MetaphaseAlignment -9.348252 0.09724292563242465 SmallIrregular -9.504175 0.44203563772992527 SmallIrregular -8.770857 0.5199523745708382 SmallIrregular -10.4307995 0.6939564109345475 SmallIrregular -8.342732 0.09088573203240946 SmallIrregular -0.3520726 0.22775950153786095 SmallIrregular -8.421112 0.41030156269012563 SmallIrregular -13.49102 0.6232946730201306 Apoptosis -9.725421 0.8869607812174175 Hole -8.283518 0.6188261682413765 SmallIrregular -11.282573 0.13346147093493443 SmallIrregular -9.086276 0.9805801327872824 SmallIrregular -9.116543 0.8717857347554929 SmallIrregular -10.167296 0.502720761145324 SmallIrregular -8.49504 0.922347981796633 SmallIrregular -8.69901 0.5413807937571358 Folded -8.680798 0.9233060678891631 SmallIrregular -8.648732 0.8298973686033432 SmallIrregular -8.83894 0.9682864102942973 UndefinedCondensed -10.770858 0.9197828107781584 SmallIrregular --5.014121 0.03603381742856948 Folded --5.017523 0.17477200416093996 Folded -10.861732 0.38913467710118577 SmallIrregular -8.474019 0.9521426972954208 SmallIrregular -8.417787 0.3000289194759296 SmallIrregular -8.32699 0.16046764388760104 SmallIrregular -8.426648 0.8863046660865599 SmallIrregular -8.28663 0.4463944154832029 SmallIrregular -8.324978 0.9078755943543261 SmallIrregular -11.484546 0.16023046632014326 Hole -11.593667 0.6611175115080995 UndefinedCondensed -9.518745 0.4402637528294918 Hole -10.26581 0.0764867690302854 Hole -2.0894804 0.6964631446525006 Folded -8.237247 0.2473987555391537 SmallIrregular -7.941703 0.039615522579517726 SmallIrregular -10.6458845 0.05994429824957326 SmallIrregular -9.326239 0.06107853706678734 SmallIrregular -10.393795 0.9077329574850395 SmallIrregular -9.63303 0.739883917829101 SmallIrregular -10.668016 0.8980623572137351 SmallIrregular -9.978618 0.6725823112965214 SmallIrregular -8.370239 0.5289399290308832 SmallIrregular -8.273511 0.30444636434737826 SmallIrregular -9.646541 0.9979622513286734 Hole -9.675817 0.36218905893938935 Hole -9.293553 0.47064894921390954 Hole -9.199277 0.37824517492346177 SmallIrregular -8.911074 0.9795269293354586 SmallIrregular -8.486193 0.17465838539500578 SmallIrregular -8.399682 0.32798800090807967 SmallIrregular -8.910688 0.6803486660150015 UndefinedCondensed -10.460359 0.06320761833863064 SmallIrregular -8.327134 0.607249374011541 SmallIrregular -8.375337 0.4776465028764161 SmallIrregular -8.396564 0.2839999767621011 SmallIrregular -8.239327 0.238413280924058 SmallIrregular -8.544875 0.5145127432987567 SmallIrregular -8.457943 0.36792758053704133 SmallIrregular -10.66467 0.45651989126265535 SmallIrregular -9.029832 0.3374773817642399 SmallIrregular -8.790636 0.9704936935959776 SmallIrregular -8.806567 0.13343943174560402 UndefinedCondensed -9.244445 0.0968039531783742 MetaphaseAlignment -9.290789 0.34339172879091606 MetaphaseAlignment -8.995863 0.5910269008704913 MetaphaseAlignment -13.720917 0.6591764718500283 MetaphaseAlignment -15.411592 0.39725674716804205 Prometaphase -13.662381 0.9992779939221711 MetaphaseAlignment -14.636331 0.3518929961930426 MetaphaseAlignment -14.312927 0.7214066679599525 UndefinedCondensed -9.140719 0.6375826945307929 MetaphaseAlignment --4.759999 0.8130538632474607 Artefact -6.940111 0.97622566345382 Artefact -9.735681 0.8897936564455402 MetaphaseAlignment -9.157028 0.7645619743577086 MetaphaseAlignment -9.084597 0.6982484778182906 MetaphaseAlignment -11.406127 0.3354981696758996 UndefinedCondensed -13.04571 0.14768557820670736 UndefinedCondensed -12.970211 0.06263600305980976 MetaphaseAlignment --5.5396657 0.24190170420148482 Interphase --5.4009767 0.4322814811812986 Interphase --5.105779 0.5219962736299825 Interphase --4.992563 0.7730835540548716 Interphase --5.367674 0.958740923056593 Interphase --5.2785563 0.11732048038481102 Interphase --4.986468 0.10700414019379156 Interphase --5.1707377 0.5896947230135507 Interphase --4.998833 0.745398073947293 Interphase -9.214716 0.8481503803469849 Polylobed --0.33047625 0.9358320802167885 Polylobed -1.9422987 0.983426242260642 Polylobed -2.2537663 0.3998016922245259 Polylobed -3.0839612 0.3803351835275731 Polylobed -7.832366 0.14780867669727238 Polylobed -10.074899 0.6849344386835594 Polylobed -11.074613 0.6567619584408371 MetaphaseAlignment -7.107529 0.8620625958512073 Binuclear -7.1287417 0.09725799478764063 Binuclear -10.152017 0.4977769078253418 Metaphase -7.0913353 0.5810819296720631 Binuclear -7.036283 0.2415570400399184 Binuclear -9.044235 0.16902540612916128 Metaphase -0.9801108 0.8595808364196215 Binuclear -2.6916854 0.058534922235558784 Binuclear -13.469704 0.4706209039180729 MetaphaseAlignment -15.399686 0.11583400130088528 Prometaphase -7.7828116 0.45705876133136736 Polylobed -11.106617 0.9799623263423093 Polylobed -13.215565 0.4237063534554728 Prometaphase -14.095699 0.8571249175045673 Metaphase -11.431506 0.11731556418319389 Interphase -1.2192894 0.27125207676186414 Interphase -8.055826 0.4037927406673345 Grape -11.349523 0.39981214000933074 Interphase -11.459124 0.6713834786701531 Interphase -11.304402 0.34471812737550767 Interphase -10.887402 0.7137668684100164 Interphase -8.877314 0.6391868992253925 Interphase -11.03662 0.3991611452547731 Interphase -10.76552 0.43176012765431926 Interphase -10.45567 0.6145276998103207 Interphase -11.196098 0.07004219014464463 Interphase -11.2573185 0.8224067383556903 Interphase -11.978145 0.6534211611136369 Interphase -11.098164 0.7263424644178352 Interphase -9.502262 0.5369230010823904 Interphase -10.3978615 0.11047711099174473 Interphase -11.30621 0.4050356132969499 Interphase --4.0391817 0.40537358284855607 Interphase -10.722264 0.3210429900432169 Interphase -11.541232 0.02995032490474936 Interphase -9.806896 0.7372542425964773 Interphase -11.0550375 0.1097844580625007 SmallIrregular -10.984741 0.6063081330450851 SmallIrregular -10.841647 0.7032174964672158 SmallIrregular -10.926242 0.6347863229336947 SmallIrregular -12.16061 0.959142251977975 SmallIrregular -9.992776 0.10329815508513862 Hole --0.19306618 0.8671671591051991 Folded -10.876501 0.029190234848913255 Hole --0.856485 0.534916854927084 Folded -7.9187803 0.4042436179392588 Folded -7.976497 0.5241838603937582 Folded -7.928984 0.3650998770600098 Folded --0.6536051 0.19056691494006806 Polylobed -0.7517652 0.01912289744868978 Polylobed --3.9493954 0.5181498137911743 Polylobed --5.794185 0.8427768626848423 Polylobed --4.4012165 0.37321595574479416 Polylobed --3.0742214 0.22286381801498023 Polylobed --2.7405472 0.08053200347184408 Polylobed --6.755592 0.08531092311870336 Polylobed --2.0763028 0.22139644629277222 Polylobed --6.7714925 0.10001406092155518 Polylobed --1.9529129 0.26503969836448205 Polylobed --6.7581534 0.06614946211695472 Polylobed -3.0214946 0.06560486720992564 Polylobed -4.063629 0.8562761796227819 Polylobed -2.5573301 0.16212026070883323 Polylobed --6.770141 0.559682405823448 Polylobed -2.639209 0.7734555444490305 Polylobed -2.6743104 0.4564095653390666 Polylobed --4.254515 0.15336887785934572 Prometaphase -13.057699 0.19959614212011434 Prometaphase -0.21242133 0.43298420628118073 Binuclear -0.08271644 0.5282340891785358 Binuclear -0.4466126 0.3494402920485349 Interphase -2.315861 0.7814796002346613 Binuclear -2.4856644 0.7510216488563984 Binuclear -11.787565 0.9272118073731179 Interphase --0.3276556 0.02895254902696054 Binuclear --0.36221483 0.8956912912102034 Binuclear --5.5199723 0.39256878846215104 Interphase --4.145273 0.8783724953799941 Interphase --4.0855713 0.6907847761565296 Interphase --5.0546303 0.9873487570739682 Binuclear --5.3453712 0.7592824517166681 Binuclear -11.550879 0.3645446259967866 Interphase -10.444878 0.5010631728347521 Metaphase -12.505594 0.37638915519435123 Metaphase -12.433835 0.3649118360212381 Metaphase -12.4497 0.26090449938105975 Metaphase -11.587847 0.4959702953734696 MetaphaseAlignment -13.935933 0.6817399450693612 MetaphaseAlignment --1.2849281 0.2773402713052435 Polylobed -4.0147877 0.52437981107722 Polylobed --3.3481908 0.11738029417055718 Polylobed --3.0698438 0.1598452868541913 Polylobed -1.5216709 0.04680635471218875 Polylobed --3.2957258 0.9707314427706328 Polylobed -4.9478273 0.0038603515102610952 Polylobed --0.19451053 0.1785799680576563 Polylobed -10.344162 0.6128667531169923 Metaphase --0.28837797 0.0813695988533053 Polylobed --0.46844253 0.8818965030968323 Polylobed --0.5145246 0.7196201578422882 Polylobed -15.123941 0.9663899714378934 MetaphaseAlignment --6.7120876 0.5076355472407649 MetaphaseAlignment --6.786522 0.3004036831584872 Polylobed -4.9540653 0.5495005727952714 Metaphase --6.775281 0.9308187172979732 Polylobed -11.828828 0.5207614372418605 Metaphase -3.943269 0.26720703186231864 Polylobed --6.7281933 0.8773987891741196 Polylobed --6.763658 0.3719187485124613 Polylobed -2.6190226 0.0013833499989991394 Polylobed -11.504712 0.24768502249231594 Metaphase --0.65322715 0.3182335091770624 Polylobed --0.54817486 0.8587774682319022 Polylobed --0.5604029 0.458503167066445 Polylobed -0.36112195 0.44458728781130075 Polylobed --0.8492049 0.3361022663998874 Binuclear --2.893732 0.8806781230470796 Polylobed --4.3604884 0.9450267769403914 Polylobed --1.0613896 0.9918903291546294 Binuclear --4.265021 0.37674126696098675 Polylobed --0.6813483 0.9661474456271714 Binuclear --2.9353979 0.7918795696309013 Binuclear -10.297425 0.6756891476442668 Metaphase --2.8967924 0.24488947942009942 Polylobed --2.9445798 0.2164572609442098 Polylobed -0.26473665 0.16604782452124567 Polylobed -12.569592 0.9227566102253654 MetaphaseAlignment -8.969981 0.2940766623831661 Metaphase -8.841345 0.45309424544887844 Metaphase --4.368821 0.4939578339872235 Polylobed --5.224334 0.7781715954502542 Polylobed -8.785391 0.8442349615530241 Metaphase --5.187697 0.1390727011486128 Polylobed --6.653475 0.4269043602110737 Polylobed --6.551386 0.8428548878354571 Polylobed -11.622055 0.8180333057558384 Metaphase --6.747227 0.1024137584524164 Polylobed --5.799466 0.156383348867963 Polylobed --5.7942085 0.3041986915994078 Polylobed --6.6321573 0.07535906908334034 Polylobed --5.5362973 0.4246630028405929 Binuclear --3.3133922 0.10761770514958191 Binuclear -9.380825 0.568217593669845 Metaphase -8.060393 0.24655693981115612 Artefact -8.159029 0.5964330653496227 Artefact -8.343031 0.11752564290363765 Artefact --5.6414557 0.9758838684185334 Polylobed --5.5673285 0.9325612038573404 Polylobed --5.2319546 0.3917969385646658 Polylobed --2.5736375 0.24217859412608544 Binuclear --3.289156 0.2503982128535728 Binuclear --5.7155833 0.48339353520239203 Binuclear -9.182591 0.03999280190071697 MetaphaseAlignment --5.843522 0.639705106075127 Binuclear -0.9482358 0.4083029083397448 Polylobed -13.4183655 0.3774065725888873 MetaphaseAlignment --1.310431 0.8093649714891984 Polylobed --1.030646 0.70903546018329 Polylobed --1.2551055 0.954333815392692 Polylobed --3.2603273 0.3519362404956907 Polylobed --3.0797312 0.8975427646494055 Polylobed --3.3076546 0.7699671862500889 Polylobed --3.345111 0.35742465159471304 Polylobed --5.8943334 0.6216654364532578 Polylobed --5.878278 0.2885699576516956 Polylobed -14.068087 0.8743999170748423 Hole -9.344228 0.11242731721231125 Hole -12.880247 0.21243436129404103 UndefinedCondensed -11.382256 0.18303329207992114 Apoptosis -10.421481 0.40302600240428865 Elongated -9.456371 0.7452329600321291 Hole -11.655703 0.5269074490521803 Hole -0.20290174 0.48767632353820756 Hole -1.3634231 0.0005459648969956543 Elongated -10.947374 0.4254017253550547 Hole -12.260119 0.06355377483615843 Metaphase -14.602191 0.20825325212148382 Apoptosis -11.324925 0.9323939389604944 Hole -11.82893 0.2153982043432382 Hole -10.136776 0.8583376386342625 Hole -10.327006 0.8028933715613342 Hole -9.230969 0.15914623694224284 Hole -12.338799 0.6057119572702788 Hole -0.07829083 0.11566187190501331 Hole -12.836848 0.7278881583695115 Hole --4.6728992 0.6374622773722066 Hole -10.49376 0.8119385616910193 Hole -10.14509 0.47938454938918806 Hole -9.765865 0.9148630878333829 Hole -9.877429 0.04934894678843971 Hole -0.04817288 0.29288856502701466 Hole --1.7485098 0.715052597465167 Hole --0.26299316 0.41810921174800086 Hole -13.1222925 0.17295135427115638 Hole -11.921864 0.10721074542854603 Hole -12.669609 0.8173391114616214 Elongated -10.936266 0.47314297846564424 Hole -11.289052 0.8822836719191074 Hole -14.001451 0.733289134316726 UndefinedCondensed -10.852257 0.4097262056307436 Elongated -9.523855 0.37351101415568366 Hole -11.3763075 0.5156383466512517 UndefinedCondensed -11.341602 0.8890599531897286 Hole -11.517209 0.7372785797141679 Hole -9.2848215 0.00515296426902323 SmallIrregular -11.409213 0.6941578513691256 UndefinedCondensed -11.41458 0.9195074069058207 Hole -7.6717625 0.7104557595044916 Artefact -7.6439915 0.1770057815674959 Artefact -7.3888183 0.4835181274274587 Artefact -9.860508 0.1403160179234194 Interphase -9.561636 0.3589952783396321 Interphase -8.59044 0.9371170419405177 Interphase -9.80169 0.9233053075587083 SmallIrregular -8.8209505 0.2828368521760829 Interphase -8.833032 0.33963104416619916 Interphase --4.841573 0.6002128681312939 SmallIrregular -8.575543 0.96319729526038 SmallIrregular -9.547995 0.14780133406539042 Interphase -1.4950207 0.2569166436866691 SmallIrregular -8.787388 0.8735568272907714 SmallIrregular -8.605619 0.4918922317083445 SmallIrregular -8.581326 0.8989610922270317 SmallIrregular -8.315396 0.18551789752317627 SmallIrregular -8.581716 0.5326685874713607 SmallIrregular -8.591605 0.32626963264937237 SmallIrregular -8.96671 0.31654255989247604 SmallIrregular --1.6951305 0.44687696394619913 Polylobed --1.6856192 0.43307744910126844 Polylobed -10.199396 0.3573468796779544 SmallIrregular --1.7888385 0.9149707703156186 Polylobed -0.47108787 0.7317441854328928 Polylobed -0.6360161 0.7275469913315297 Polylobed -0.06706459 0.2899134495919554 Polylobed --3.027121 0.5777094243168404 Polylobed -9.736845 0.779179433301834 Interphase -9.847437 0.7955903685432131 Interphase -9.393717 0.34453046075431226 Interphase -9.872378 0.7708727565686478 Interphase -9.637839 0.735893896807733 Interphase -9.922449 0.14150648562190027 Interphase -9.948929 0.8659454685664772 Interphase -8.541702 0.4413214701804108 Interphase -8.642932 0.48641044888866547 SmallIrregular --2.1293075 0.4483691788979973 Polylobed --2.1450405 0.5678460014775075 Polylobed --2.1656094 0.6211692473670547 Polylobed --2.1586537 0.4981795657629434 Polylobed --2.066395 0.8667885432590956 Polylobed --1.9650301 0.6277347561952844 Polylobed --2.1234288 0.40142794930551995 Polylobed --2.107406 0.41669175690871096 Polylobed --1.9644548 0.8108386151289514 Polylobed --2.0114596 0.3481919427465201 Polylobed --2.0756261 0.21145479578241355 Polylobed -7.9093895 0.059383188005789234 SmallIrregular -1.1762841 0.8760268479205742 Polylobed -1.0741943 0.9185464511903499 Polylobed -1.1803217 0.12012018216347597 Polylobed --3.3117886 0.33447374149611486 Polylobed -1.1617049 0.17537206951524387 Polylobed -1.0787362 0.11589846882587973 Polylobed -1.3319564 0.8998667430000302 Polylobed -8.019861 0.05687725914535546 Polylobed -7.9584994 0.9804856634690068 Polylobed -8.286714 0.09645086069738418 Polylobed -7.908195 0.8634706491935857 Polylobed -8.437476 0.5665061069891627 Polylobed -8.37676 0.36791748781787337 Polylobed -8.361688 0.3423423766251579 SmallIrregular -10.277233 0.7573641432377087 SmallIrregular -9.521051 0.3145732950042872 Polylobed -9.976092 0.6573189166171418 Polylobed -8.396382 0.5173260835160801 SmallIrregular -10.279509 0.4849656451580705 SmallIrregular -9.451441 0.9011621706491616 SmallIrregular -10.126208 0.5546450586202596 SmallIrregular -10.337296 0.8268616030486949 Interphase -10.285744 0.7255735341014894 Interphase -11.498144 0.03855724605899835 Interphase --3.5461042 0.7731100525054192 Interphase -9.0538225 0.21687025009104066 Interphase -9.856668 0.9031496468515715 Interphase -10.040481 0.042924190608832014 Interphase -10.1216135 0.33307203447431877 Interphase --4.48713 0.09973294723475401 Interphase -9.832006 0.47558911708484375 Interphase -8.59701 0.8200224358697518 SmallIrregular -9.83429 0.2981873596630641 SmallIrregular -10.450156 0.1509348973110416 Interphase -8.501003 0.3302670356968992 SmallIrregular -8.55558 0.813880141920636 Interphase -8.575573 0.14038395779934687 Interphase -9.167421 0.2273624490775018 Interphase -8.357146 0.06885196449337394 SmallIrregular -9.570211 0.7057100439896077 SmallIrregular -11.928178 0.3952332435363368 SmallIrregular -8.499433 0.310839977143316 SmallIrregular -8.434722 0.7186263903411519 SmallIrregular -10.067134 0.33597754234025523 SmallIrregular -10.189285 0.727771273214418 SmallIrregular -10.061641 0.8151993953143135 SmallIrregular -8.639828 0.21766284345773845 SmallIrregular -11.13704 0.9738186968459833 SmallIrregular -11.372855 0.16235794791266678 SmallIrregular -10.973674 0.29084090665674256 SmallIrregular -8.418454 0.17979529083354162 Folded -10.562208 0.34550565635633446 Interphase -8.45601 0.4800608878207556 SmallIrregular -8.469894 0.5221758690021191 SmallIrregular -8.136285 0.853606042296272 SmallIrregular -10.423726 0.8894479088158666 SmallIrregular -9.949038 0.22010386078090638 SmallIrregular -12.386624 0.6228940321857525 SmallIrregular -7.8617544 0.11149605729871559 Binuclear -7.8598013 0.4589698601820683 Binuclear -7.901572 0.32233353804661813 Binuclear -7.9903293 0.3165007454536064 Binuclear --5.245288 0.482584241712101 Polylobed --4.2521796 0.7298276355292675 Polylobed --3.4768164 0.0691826587923895 Polylobed --4.4127502 0.8791733376874045 Polylobed --4.1502776 0.7348137746305611 Polylobed --3.4233153 0.17649938888906014 Polylobed --3.3140476 0.9391609090873704 Polylobed -6.9527845 0.5063122240233886 Polylobed -6.979307 0.9998085781169653 Polylobed -0.6054832 0.19725947430073765 Binuclear -0.7275172 0.5349081983832014 Binuclear -0.6342182 0.2902480425599284 Binuclear --0.90315646 0.30417355738924656 Interphase -8.222248 0.5910653808339902 Polylobed -8.397709 0.9217190668708333 Polylobed -8.357773 0.805263855579175 Polylobed -8.286474 0.7239413985013259 Polylobed -7.9207673 0.5591737821017022 Binuclear -8.36571 0.9222985036674477 Binuclear -8.470935 0.49236140669088413 Polylobed -0.7539514 0.8738321783347182 Polylobed --5.309032 0.8339816438766318 Binuclear -8.322833 0.21383534680158656 Binuclear -1.1309971 0.7712254629659102 Polylobed -1.9492378 0.012171156943258765 Binuclear -0.6684107 0.3228295375480875 Polylobed -0.92890126 0.22956744469194978 Polylobed -8.230317 0.5068629584873134 Binuclear -1.6723973 0.7368531616807855 Binuclear --1.1924362 0.09767636744772412 Polylobed --3.1240103 0.5149222019367684 Polylobed -8.024795 0.9384120216943856 Binuclear --3.6936219 0.2286465509829455 Polylobed --3.6498158 0.6771411441114241 Polylobed --4.221709 0.5928802707811576 Polylobed -8.405658 0.01006369565609333 Polylobed -8.375154 0.47582619585722274 Polylobed --0.6473298 0.7087703909410487 Polylobed -8.381012 0.0439754320240906 Polylobed -7.791457 0.8795214830237301 Binuclear --3.5083127 0.5200814166356731 Binuclear --0.68508536 0.03066104832571792 Polylobed --1.9813375 0.2244136119208402 Polylobed --3.7786634 0.953675696427313 Polylobed --2.1119826 0.5823197330520553 Polylobed -1.2135915 0.10747256776880454 Binuclear --2.212798 0.2875445022805615 Polylobed --1.8109802 0.4567036258604842 Binuclear --2.1147244 0.020950069267730465 Polylobed --4.719052 0.4116155136137588 Binuclear --4.480581 0.48945863543469814 Binuclear -0.66279715 0.2436778752812322 Binuclear -0.63258535 0.5886390002919866 Binuclear -0.44976673 0.7532401195921768 Binuclear --0.9078622 0.23583422410563548 Binuclear --5.095191 0.6204999002799877 Binuclear --3.8141677 0.6396222429637416 Binuclear --4.314854 0.948540301128841 Binuclear --5.1257715 0.7782761672964438 Binuclear -0.1299038 0.848345269790006 Binuclear -0.86236924 0.49041990843627514 Binuclear --3.9400842 0.18534858697938283 Polylobed --3.925711 0.995815292959635 Polylobed --4.046902 0.1293557610386996 Polylobed --3.9932084 0.4714573193542654 Polylobed --4.2611656 0.0680930992421066 Polylobed -7.932916 0.9438508573508195 Binuclear -7.847335 0.9649249408484396 Binuclear -7.872158 0.7193890620039736 Binuclear -7.9262013 0.3499928436301689 Polylobed -7.99796 0.25438240111762433 Polylobed -0.84258896 0.2653033245370474 Binuclear -1.702933 0.12729402542007473 Binuclear -0.5488925 0.5258089530767229 Binuclear -0.4670082 0.1418172757014775 Binuclear --4.8629107 0.3167306665534563 Polylobed -8.208327 0.6267064759591049 Polylobed -7.7842274 0.7275436095907498 Polylobed --4.93669 0.024272704622061214 Polylobed -7.5417786 0.4301159843767399 Polylobed -7.676207 0.652124594842139 Polylobed -2.833113 0.8532459761585128 Binuclear -0.72776014 0.4753247822120824 Artefact -3.0460427 0.9692058717176852 Binuclear -0.80390596 0.2656325475414473 Artefact -0.9495729 0.01350870662671888 Artefact -0.8704868 0.4837528647017362 Artefact --4.3127027 0.25611379502001796 Binuclear --4.289543 0.8237176720231694 Binuclear -0.6306074 0.23277267218111208 Artefact -0.2844303 0.3106292182913063 Binuclear -7.114217 0.7912274310191123 Artefact -0.8087705 0.71514325202536 Binuclear -0.5076514 0.5580512366572625 Binuclear -9.468979 0.7049480619227595 Artefact -9.423387 0.4186368635700265 Artefact -7.3968506 0.005310047614667801 Artefact -9.638005 0.011355128512192558 Artefact -0.56810457 0.5112217875736866 Binuclear -0.3776506 0.08329097971899924 Binuclear -0.84005606 0.05107548016925678 Polylobed -0.6329483 0.9655166391374733 Polylobed -0.67613167 0.8590026396580586 Polylobed -3.8681786 0.15202722720956952 Polylobed -3.8372798 0.0006642185902044906 Polylobed -4.605423 0.9416677953897564 Polylobed -3.9951818 0.2783252983201143 Polylobed -4.70048 0.1858976028554975 Polylobed -3.8629289 0.6915081078315353 Polylobed -3.8279476 0.1089037388413262 Polylobed -3.805116 0.2646495980028003 Polylobed -3.8271408 0.9750946802120465 Polylobed -4.311522 0.6394627744740102 Polylobed -4.378474 0.5206777914825536 Polylobed -3.9617243 0.39791861483493773 Polylobed -3.9910452 0.774500954884202 Polylobed -4.0022154 0.14095747652369373 Polylobed -4.3003855 0.9673378020369822 Polylobed --0.9898091 0.8611230080657867 Polylobed --0.91664964 0.6176569825692433 Polylobed --1.1106237 0.042906190402558164 Polylobed --1.068521 0.7008556494477377 Polylobed --1.0884213 0.9132843408854808 Polylobed --1.1162376 0.524577067478626 Polylobed --0.9795341 0.3542248218272628 Polylobed --3.60194 0.12027734497946962 Polylobed --3.3543358 0.7549011041375683 Polylobed --3.866435 0.8850218512118062 Polylobed --0.888139 0.10025174407584858 Polylobed --4.057726 0.7589845547523419 Polylobed --3.2509668 0.017060486259097507 Polylobed --3.4492548 0.9670549180772195 Polylobed --2.8209145 0.6150580206350962 Polylobed --2.9699495 0.5524390589916331 Polylobed --4.919422 0.2959498335889278 Polylobed --5.009096 0.9292916715697834 Polylobed --0.44149524 0.265905627336791 Polylobed --4.8705096 0.8281466132166949 Polylobed --4.777759 0.985108679337132 Polylobed --0.57023036 0.7833966455148058 Polylobed --0.46451142 0.5189899203864318 Polylobed --0.77140766 0.06607426385167192 Polylobed --0.9910651 0.472413789171073 Polylobed --0.7447408 0.43825594697170356 Polylobed --0.77018946 0.20279604118699524 Polylobed --0.75800866 0.42358763671430444 Polylobed --1.0542473 0.3577578840803387 Polylobed --1.9803379 0.16368426115164847 Polylobed --1.0000262 0.44137414333248615 Polylobed --1.9860964 0.26279995632448905 Polylobed --0.7028552 0.5220624206822644 Polylobed --0.9580335 0.03516005971619418 Polylobed --1.8936489 0.9062314197872842 Polylobed --0.59862024 0.8163643055198561 Polylobed --0.598214 0.5525813325105046 Polylobed -3.3247972 0.8518085827572431 Polylobed -4.1486735 0.9623950738108005 Polylobed -4.1482954 0.11052229405266056 Polylobed -3.280106 0.6308318084098038 Polylobed -3.2411807 0.9979940009356586 Polylobed -3.2846859 0.9878891693335261 Polylobed -3.2532518 0.6033229922907446 Polylobed -3.234069 0.12802087045081667 Polylobed --1.1410806 0.5831928309905282 Polylobed --1.0713065 0.0020646355744217137 Polylobed --1.0845288 0.19891133466685962 Polylobed --1.0715196 0.95612315955996 Polylobed -1.5944042 0.3304405726028439 Polylobed -5.3645554 0.6383901057769485 Polylobed -5.5821085 0.2808594946322427 Polylobed -13.8181305 0.9478218871115895 Polylobed --0.5396117 0.7285587299494868 Polylobed --0.42999902 0.3296511575814126 Polylobed --0.442334 0.7917614211803709 Polylobed -9.118693 0.10816552447633765 Polylobed -9.058889 0.3923189400654594 Polylobed -9.040897 0.22121812773159233 Polylobed -9.212812 0.6837264472760463 Polylobed -9.273914 0.10244628177193038 Polylobed -4.433569 0.3970258322826741 Polylobed -4.576901 0.2766497302091896 Polylobed -4.4757786 0.5063429193238042 Polylobed -4.579091 0.3498976805038789 Polylobed -3.2525363 0.706410577667011 Binuclear -3.312203 0.024577024306966067 Binuclear -3.2538605 0.6339869213059075 Binuclear -3.9000998 0.23057128969382001 Polylobed --5.851498 0.2687090287568493 Binuclear --5.7763305 0.8002556035835235 Binuclear -3.7681525 0.9555683939804208 Polylobed -4.186214 0.3165502100270866 Polylobed --3.158562 0.8268052703690757 Polylobed --3.1961503 0.1039908379712331 Polylobed -4.619295 0.6339816530073832 Polylobed -3.917079 0.7510322995349188 Polylobed -3.820103 0.15597792803445654 Polylobed -4.3472195 0.42600238766956056 Polylobed -3.749 0.8927071642489816 Polylobed --2.9567387 0.10357846344837218 Polylobed -3.7862828 0.01809635820147515 Polylobed --3.1726217 0.5905853790479337 Polylobed --3.2696905 0.43553154053093346 Polylobed --1.0779632 0.7986892488839027 Polylobed --1.1871059 0.9234555382041066 Polylobed --1.0569172 0.2991536445438161 Polylobed --1.2741644 0.3884041171691053 Polylobed --0.9434786 0.48627208632931485 Polylobed --0.93171316 0.5881514604352993 Polylobed --5.5805817 0.9838538296816167 Polylobed --5.7713094 0.6973302508300515 Polylobed --3.4246123 0.3895485073455688 Polylobed --0.8547974 0.2637676864504621 Polylobed --5.5045676 0.9446257184221241 Polylobed --3.752491 0.1355484331085064 Polylobed --3.4875398 0.720265852504758 Polylobed --4.940009 0.9253950252924407 Polylobed --5.029217 0.6646655865344386 Polylobed --5.039996 0.42305444016612115 Polylobed --5.209771 0.198990939925484 Polylobed --5.695312 0.36747532225192725 Polylobed --5.6438394 0.7068718094201221 Polylobed --5.6101913 0.6495342241752968 Polylobed --5.5738063 0.9279761665983579 Polylobed --0.77272475 0.8668609136593749 Polylobed --0.84607965 0.8161507522663684 Polylobed --1.0658842 0.9114508753507623 Polylobed --0.7139461 0.2763371527370494 Polylobed --5.6073484 0.3695235401396495 Polylobed --5.6422176 0.3798939037860174 Polylobed --1.0464747 0.5604505887097803 Polylobed --1.8287076 0.668218229542771 Polylobed --5.611396 0.2867166830403943 Polylobed --0.8116301 0.019462467309300346 Polylobed --5.6442 0.3992223836579093 Polylobed --5.692146 0.3085279595544005 Polylobed --1.0011147 0.9421847190180479 Polylobed -16.799686 0.8882650405440435 Polylobed --0.98279864 0.8603106783434196 Polylobed --0.84768325 0.652999760945243 Polylobed --0.89054644 0.3442891646989389 Polylobed --0.7910391 0.5488492673993047 Polylobed --2.9744797 0.8152250407163727 Polylobed --2.9905152 0.09861036872281104 Polylobed --3.1536937 0.8010748802553047 Polylobed --2.8907824 0.04117979132812166 Polylobed --1.1284931 0.8164210312143557 Polylobed --1.0871459 0.8075638041598735 Polylobed --1.0659392 0.051007308828755815 Polylobed --1.0780833 0.6271607114618583 Polylobed --0.9868962 0.5024530743128972 Polylobed --0.012792455 0.16981950317171468 Binuclear -0.07521659 0.14837893767737564 Binuclear --1.0114503 0.7732591260670232 Polylobed -3.3146532 0.5676927488368441 Polylobed --0.6363181 0.9829991349165325 Polylobed --0.114019476 0.9822477772883441 Polylobed -0.01433525 0.9926669934642663 Polylobed -2.8450625 0.11861551836576589 Polylobed -2.2660172 0.9382561369639623 Polylobed -1.9742454 0.2445696090205628 Polylobed --2.9808364 0.458212259758612 Polylobed --2.970934 0.7574065558217259 Polylobed --2.8644388 0.20362093207723198 Polylobed --2.6925697 0.5663116055543299 Polylobed --0.58856946 0.1858167482259957 Polylobed --0.5442644 0.1047361066812933 Polylobed --0.6676874 0.11655861242959853 Polylobed -0.11531066 0.3576390348482188 Binuclear --4.5344534 0.004654836837183485 Binuclear -0.67978483 0.4248539212016895 Binuclear -1.966168 0.664197105081149 Binuclear --0.39038998 0.40168818500398873 Binuclear -0.36562628 0.08579460051983512 Binuclear --1.0044429 0.06268886202364055 Polylobed --0.33416796 0.2781165127648617 Polylobed -0.7349754 0.16931269054360076 Polylobed -1.6754811 0.9650949732179694 Binuclear -2.1877983 0.151230224974085 Polylobed --0.36928815 0.8054624374049966 Binuclear -1.4319882 0.5861079417411448 Binuclear --0.31491357 0.5692869199842944 Binuclear -0.7499517 0.5120807159237846 Binuclear --2.9504309 0.9717630761139511 Binuclear --0.25758946 0.3638447750916378 Binuclear --0.38406655 0.7879157509538762 Binuclear -0.5969208 0.5552941074669596 Binuclear --3.3237388 0.39563366762572616 Binuclear --0.28937006 0.9554659333057809 Binuclear -6.0547338 0.5983159693646871 Grape -6.0859494 0.11891694214601356 Grape --4.5622025 0.4175392009968566 Grape -5.2568088 0.7815817276816076 Grape -6.1988616 0.6937470232392138 Grape -6.021657 0.9163403298365742 Grape -6.020441 0.25937738414152023 Grape -5.9513445 0.7581937163538238 Grape --4.4777756 0.4598752073945209 Grape -5.7566237 0.5736097469668154 Grape -4.9656262 0.955046681006283 Grape -5.7354584 0.9792863199891774 Grape -2.19753 0.8615909639255636 Grape -4.8738394 0.35909708773029625 Grape -4.7881136 0.8877008357273198 Grape -5.7239213 0.638609177641413 Grape -5.213568 0.4299967804707131 Grape -5.214571 0.035742682500483736 Grape -5.026519 0.7701281241915822 Grape -4.9763026 0.502105580724113 Grape -5.233933 0.7861884994650608 Grape -1.3438823 0.7480227993432699 Grape -0.7219608 0.7935673680483519 Grape -9.443157 0.30065115869676684 Prometaphase -5.8470354 0.800798599067631 Grape -5.963529 0.5488463284661673 Grape -5.888558 0.4733262004491432 Grape -6.048824 0.6751259138207569 Grape -5.946815 0.021358682938348306 Grape -10.212333 0.10231681588759423 Grape -10.861255 0.292177365167522 Prometaphase -5.907491 0.9829901099578396 Grape -11.026995 0.13974577886495498 Apoptosis -3.7068737 0.3305963007592103 Grape -3.2793765 0.05105306469192361 Grape -3.647094 0.3312688802927263 Grape -3.355356 0.3203262865108316 Grape -3.3573418 0.9468071709445222 Grape -5.9071474 0.8451540869957204 Grape -6.0038753 0.3827642192427715 Grape -13.080315 0.02476905803924867 Prometaphase -13.5487385 0.8310311139953782 Apoptosis -13.840787 0.6605361771445402 Apoptosis -5.976187 0.15236448365130972 Grape -13.588155 0.9960712710101872 Apoptosis -4.6368523 0.10023343742412305 Grape -4.221854 0.8671145415936629 Grape -15.987793 0.294266164129689 Apoptosis -4.658517 0.43535346632515703 Grape -4.2261324 0.7954565267763146 Grape -4.909205 0.6775083559940103 Grape -4.3412137 0.9378643744440391 Grape -4.9839687 0.6211403254603832 Grape -5.9591684 0.09781016147905275 Grape -15.9645 0.8843603632419503 Apoptosis -4.225968 0.7691555249885911 Grape -4.75555 0.711870450893322 Grape -4.2633476 0.05373354735373537 Grape -6.0137773 0.396222744661744 Grape -6.0574756 0.16743581945953923 Grape -4.273186 0.821903908407869 Grape -4.211769 0.7005286228178628 Grape -6.0122604 0.8830775973191353 Grape -4.239323 0.9665751069241639 Grape -4.2753034 0.7747476141924986 Grape -13.096307 0.9942330832207621 Prometaphase -4.209667 0.6147698861442988 Grape -15.985672 0.037129603891447815 Grape -6.0226297 0.01425151515029588 Grape -4.1495533 0.3421038752037887 Grape -12.822456 0.8234717190675371 Prometaphase -16.012913 0.866134706324731 Grape -4.2761555 0.9608125288226779 Grape -4.25848 0.0651214685488184 Grape -5.952601 0.044571110987664087 Grape -16.009804 0.9132835963670337 Grape -15.959735 0.3050466983628616 Grape -3.8177655 0.5579874006096327 Grape -3.9027894 0.9824448830007099 Grape -3.803213 0.40044853473592334 Grape -3.8682497 0.6658713983098399 Grape -3.8755026 0.4008795636319853 Grape -3.8520486 0.7681946644627797 Grape -3.8708615 0.5277147255728658 Grape -3.9060788 0.23752313798634972 Grape -4.0926304 0.2713061012584662 Grape -5.4137864 0.25805921253840725 Grape -4.27125 0.5323203282585608 Grape -4.356854 0.7031890160156643 Grape -4.5778375 0.9492799000431421 Grape -11.695755 0.6940873750829799 Apoptosis -5.543799 0.7811928439623906 Grape -4.5383525 0.16892611586456563 Grape -4.918751 0.3740626250073257 Grape -5.4176617 0.4137802198582661 Grape -5.9225826 0.686380230000473 Grape -5.9172225 0.2958919763768736 Grape -5.9019804 0.3032919214196661 Grape -5.900378 0.3558891546408681 Grape -5.923134 0.8103020815432322 Grape -6.0225663 0.5775900900433487 Grape -6.3786077 0.07527727983273447 Grape -6.2009296 0.0782460996904688 Grape -5.2159567 0.3712869442824993 Grape -5.1908603 0.7665910506061583 Grape -5.2392697 0.6886834264786805 Grape -5.316527 0.7079823546511866 Grape -5.313385 0.7672100660540969 Grape -5.31761 0.2871527128863294 Grape -5.3652954 0.548256281942088 Grape -5.394088 0.5433526403167696 Grape -5.98623 0.7396325011799493 Grape -5.9867063 0.9568705691056464 Grape -5.254138 0.2779899447954659 Grape -5.4111843 0.7932816727570482 Grape -5.368392 0.6599705485232624 Grape -6.0274897 0.5802378708634601 Grape --1.2033874 0.7748797793474019 Grape -5.405281 0.9440324664280993 Grape -5.9128346 0.03669141786941299 Grape -5.421215 0.14740010251322067 Grape --1.0256219 0.7562872315011158 Grape -5.513608 0.08379135291008455 Grape --0.8267871 0.516123700741093 Grape -5.4083705 0.21986077664160697 Grape -5.438358 0.2742957038427515 Grape -5.440795 0.7018404829444163 Grape -5.466989 0.030192773268711615 Grape -5.4183626 0.8733194279019036 Grape -5.409147 0.44447895535227 Grape -5.4689755 0.5023932939071011 Grape -5.394043 0.5400479636936919 Grape -5.438525 0.6455442945794939 Grape -5.4934998 0.3448565866227481 Grape -5.932757 0.10110749056349133 Grape --2.7176588 0.31837893666617656 Grape --2.5142868 0.1681421180228203 Grape --2.7200797 0.5561331794504739 Grape --2.8105445 0.31802863096038647 Grape --2.8019104 0.9580671779934349 Grape -5.652004 0.9657342779802807 Grape -6.0787387 0.6201258805682415 Grape -5.9397807 0.617497267667286 Grape -4.5518556 0.9853785645671059 Grape --2.436868 0.887283151235186 Grape -5.692212 0.76506994915814 Grape --2.4996343 0.3135906117427012 Grape -4.580105 0.36553902811807637 Grape -5.1601186 0.20126676576086544 Grape -4.565572 0.48714812691737464 Grape -4.851274 0.9903685221395796 Grape -5.3864484 0.9121509530115888 Grape -5.420836 0.11834943401946907 Grape -5.4268656 0.02519028929050615 Grape -5.514053 0.8986376684082662 Grape -5.401099 0.5371701279487074 Grape -5.7844615 0.20018988820845618 Grape -5.016355 0.6736532695846353 Grape -5.5286794 0.64422317792982 Grape -4.487375 0.12208560683646719 Grape -4.774964 0.2596002334976195 Grape -4.7151756 0.06007796431325796 Grape -4.4838295 0.20986047387014195 Grape -4.447888 0.13230567485306288 Grape -4.682057 0.1932362924745561 Grape -4.687396 0.685467145913117 Grape -5.8050694 0.049499744261067735 Grape --3.565559 0.10185461524821648 Grape --3.6118119 0.13417363807828764 Grape -3.7294903 0.316541120958134 Grape --3.5860205 0.2987503108519409 Grape -5.655318 0.2550637853166394 Grape --3.569586 0.7505366534324163 Grape -3.7010536 0.9980227877581006 Grape --3.5654075 0.5339779236287958 Grape --3.584754 0.9442027179139421 Grape --3.628681 0.3966101121260647 Grape -5.575208 0.10668244680271344 Grape --3.5906804 0.4087738294698373 Grape -5.754874 0.2961277734146933 Grape -5.86716 0.49340696228917813 Polylobed -5.9160833 0.6570436769164232 Polylobed -4.689669 0.4610502191524082 Grape -4.5312257 0.9351605120778256 Grape -4.3926163 0.8847648222993799 Grape -4.818026 0.7019775951661972 Grape -4.8325405 0.48968491236454914 Grape -4.7415833 0.13168728181445688 Grape -13.114856 0.3970136667776334 Prometaphase -13.025293 0.7044015394011737 Prometaphase -13.870888 0.2848855205537312 Prometaphase -13.021007 0.10398807754342043 Prometaphase -12.993462 0.9078984575106281 Prometaphase -13.142309 0.7090508098155446 Prometaphase -12.822967 0.6152764266350972 Prometaphase -15.131865 0.7924989056089166 Prometaphase -15.5503435 0.8356460375834757 Prometaphase -15.491017 0.48345899817102334 Prometaphase -15.498199 0.88118825140944 Prometaphase -13.596457 0.9164190107011054 Prometaphase -13.134864 0.2715510954247814 Prometaphase -13.707817 0.6075453597907943 Prometaphase -14.453687 0.5265840288204692 MetaphaseAlignment -14.014506 0.5379457791519024 Artefact -13.95403 0.937663093964562 Artefact -14.029308 0.305188702693029 Artefact -14.207839 0.9834339782345762 MetaphaseAlignment -13.5045395 0.9021312148042321 Prometaphase -14.376318 0.45872288861350263 MetaphaseAlignment -12.939412 0.8174532636181662 Prometaphase -13.086187 0.7690469943201406 MetaphaseAlignment -13.5678215 0.6778949696185212 Prometaphase -13.019624 0.31983388938315427 Prometaphase -13.257431 0.19645099182095394 Prometaphase -6.353147 0.6715276967657582 Prometaphase -14.338263 0.8429732964010842 MetaphaseAlignment -13.48411 0.01625278867736968 Prometaphase -12.988302 0.6428033753085121 Prometaphase -14.825019 0.44287302462221245 MetaphaseAlignment -15.218665 0.8980877551269836 Prometaphase -13.776345 0.32147293085420525 Prometaphase -13.655295 0.47418481226362175 Prometaphase -12.803578 0.5147671040351863 MetaphaseAlignment -13.123301 0.14043952137105298 MetaphaseAlignment -13.841096 0.7128923026989064 Prometaphase -14.233268 0.8304763451209596 MetaphaseAlignment -12.748335 0.05790927689626024 Prometaphase -14.125139 0.29138882053690274 MetaphaseAlignment -15.488096 0.03804468153505791 MetaphaseAlignment -13.229542 0.9565441046882424 Prometaphase -13.566876 0.6671688207038236 Prometaphase -15.454311 0.9642004194547815 Prometaphase -6.35809 0.5314942783313078 Prometaphase -12.820744 0.8020685238956139 Prometaphase -13.647024 0.37441398387974 Prometaphase -12.967696 0.3538190325726409 Prometaphase -5.3089952 0.3782678171051327 Grape -5.3909864 0.6578621337264282 Grape -4.0626416 0.3594531510216522 Grape -5.3384686 0.9003674516042256 Grape -5.4179134 0.9832748650501553 Grape -5.0924435 0.03042651533700591 Grape -3.8920734 0.19362329040249648 Grape -5.3946533 0.112249992587386 Polylobed -3.8376434 0.04236404709144326 Polylobed -3.6841378 0.22774099334728648 Grape -5.22243 0.44679332036908115 Grape -5.3977537 0.8369903652886838 Polylobed -3.7510796 0.2218240305099961 Grape -4.9986854 0.49394525566220127 Grape -3.7532544 0.9296187394467544 Polylobed -3.6359541 0.6672147067866749 Grape -3.552432 0.7980790197559975 Polylobed -5.0015416 0.5509939701854862 Grape -13.823558 0.98046645884063 Grape -4.5983753 0.5886621546814742 Grape -4.7103066 0.04551071396259154 Grape -4.848505 0.19798279993979484 Grape -4.7763405 0.40477362895714797 Grape -4.976149 0.6012771725737763 Grape -4.9272923 0.7719308674089599 Grape -5.2511525 0.41308612590388627 Grape -5.029001 0.7100583050156887 Grape -4.960731 0.789869503109125 Grape -4.715066 0.3172601972499828 Grape -4.812951 0.9792702400358185 Grape -4.5156164 0.6496564952358356 Grape -4.45836 0.8809980607861312 Grape -4.8688245 0.5559376888086067 Grape -4.5544 0.7416031071523237 Grape -4.474982 0.7705440616163653 Grape -4.5258355 0.908248379234579 Grape -4.578113 0.1503497568713703 Grape -4.502328 0.5582834241692485 Grape -4.4270854 0.4283785131058563 Grape -4.4022417 0.9231590211737403 Grape -4.463006 0.1050946942489992 Grape -4.378455 0.9825738886809128 Grape -4.5163097 0.8754513240932963 Grape -4.383171 0.07382628156424853 Grape -4.944598 0.49096638554047445 Grape -4.8137226 0.7175595002125259 Grape -4.882834 0.7381515456910565 Grape -4.9010158 0.9064941252379208 Grape -3.7740934 0.7998654355823109 Grape -4.933601 0.3109303803440895 Grape -4.854418 0.4984347858624524 Grape -4.4660044 0.701785762935854 Grape -14.3142805 0.13843684136435142 Apoptosis -4.386713 0.19399079760019733 Grape --3.6362207 0.4810424481098726 Grape --3.5823677 0.2982457987055127 Grape --3.6206772 0.8625592497840949 Grape --3.6912646 0.586277321520618 Grape --3.6364164 0.3486651977183677 Grape -14.465561 0.8488330256044101 Apoptosis --3.6584074 0.8048784485542388 Grape --3.6392033 0.9983548770992695 Grape --3.7013993 0.8473076883337348 Grape -3.952823 0.4144565348056797 Grape --3.6732254 0.12749888176998847 Grape --3.6793885 0.8406409082004779 Grape -3.9937937 0.05975791796039398 Grape -4.08774 0.3502712073448542 Grape -4.0874257 0.9197379807712738 Grape -4.1566954 0.9607664734451007 Grape -4.040954 0.6405645935212184 Grape -3.9795585 0.6886483169837135 Grape -13.620014 0.04245448872552249 Apoptosis -5.1758103 0.5144803410895479 Grape -5.160064 0.5468681788063412 Grape -5.085512 0.3401007461241111 Grape -5.149137 0.06859683475388434 Grape -5.169267 0.228907599724394 Grape -4.849214 0.35798393686119634 Grape -5.1438622 0.4351419865163296 Grape -4.8525987 0.5909267255335692 Grape -4.7101054 0.7223915186982364 Grape -4.9557667 0.31763187327394615 Grape -4.89927 0.3289537599189831 Grape -4.580171 0.019691642723703717 Grape -4.633592 0.040874860094018306 Grape -4.675017 0.2578216943085364 Grape -4.5590034 0.7402449976749567 Grape -4.557164 0.6283138303739122 Grape -4.591042 0.7697890206778347 Grape -4.4622674 0.7689194362148217 Grape -4.637729 0.8565674676693013 Grape -4.158928 0.7203192659868836 Grape -4.6895065 0.9790109190008228 Grape -4.5874023 0.8988252193018174 Grape -4.293857 0.5867171662232342 Grape -4.302571 0.5881576704911717 Grape -3.756196 0.034267040352229494 Polylobed -4.2857437 0.9985265777083543 Polylobed -3.7048843 0.13157599736614178 Polylobed -3.7277768 0.740347196631592 Polylobed -4.1912837 0.8210151951243089 Polylobed -5.720594 0.3730545293052032 Grape -5.743413 0.19685205466531375 Grape -5.245826 0.09875988679055503 Grape -5.6828523 0.7486060058295778 Grape -5.8875146 0.4526535292056957 Grape -5.7396994 0.7137177590011357 Grape -5.438618 0.9154076488518006 Grape --0.40918738 0.1465837361510567 Grape -5.548887 0.9191710007237996 Grape -5.454758 0.4116264595084367 Grape --0.38204065 0.30526700989728905 Grape -5.6828675 0.9430622606027791 Grape -3.2858343 0.9906516926063994 Grape -5.4891634 0.19889221776744814 Grape -3.3286552 0.6568383469519833 Grape -5.408728 0.10649531377106036 Grape -3.3312287 0.6509140038575058 Grape -5.4358077 0.8273132277758497 Grape -5.3670807 0.6844985465240676 Grape -5.0864944 0.41733314206259575 Grape -5.2229867 0.38306635956376955 Grape -4.3500686 0.39312241522341707 Grape -4.8622127 0.5897118179929232 Grape -5.4443893 0.8815672700724956 Grape -5.418767 0.9290661572687678 Grape -4.2730503 0.05352962020731811 Grape -5.4936666 0.1816223946456883 Grape -5.492807 0.11222431582828851 Grape -5.3808494 0.19333464076691398 Grape -5.591558 0.3466078106091718 Grape -5.5058317 0.5065316826226238 Grape -5.397926 0.6294612270091522 Grape -5.68803 0.7321422191397015 Grape -5.7420287 0.8901115413858071 Grape -5.111787 0.9890884372297908 Grape -4.9698577 0.6628564785571198 Grape -5.7542744 0.845364518667763 Grape -3.8734553 0.7780388469246989 Grape -4.747282 0.30753203921871197 Grape -4.5957594 0.875692270234923 Grape -3.8882086 0.042763137947798846 Grape -4.7849555 0.00036734375145786036 Grape -4.7087374 0.2737326293884642 Grape -4.1006866 0.4620975296274499 Grape -4.955174 0.6383628950006773 Grape -4.104775 0.1017702665445448 Grape -4.829914 0.67301013383484 Grape -3.9922738 0.8018158670596945 Grape -4.8424916 0.1853129195793668 Grape -4.5134525 0.41512525483179386 Grape -4.097115 0.5199849899533098 Grape -4.779746 0.45180701808448065 Grape -4.802551 0.7998299308884707 Grape -4.7891603 0.9605223981193308 Grape -5.725242 0.79895316400818 Grape -5.795175 0.07799281787582457 Grape -14.531895 0.8049355721591271 Apoptosis -14.523376 0.06659633223555117 Apoptosis -3.9959905 0.23597038524157 Grape -3.9751773 0.1530968968357228 Grape -4.7446833 0.19751910684608542 Grape -14.13627 0.5283151270573506 Apoptosis -4.9633584 0.6716898576108536 Grape -14.0742445 0.470321282336041 Apoptosis -3.7261136 0.9596956390292436 Grape -3.840309 0.24029232003016665 Grape -13.382251 0.7631402302547929 Apoptosis -13.241652 0.8701821785071485 Apoptosis -3.44331 0.5620661107547191 Grape -3.4355671 0.4562225019615531 Grape -13.359104 0.5961844467792436 Apoptosis -13.245167 0.42880977016034294 Apoptosis -3.3096323 0.5551938823233694 Grape -6.5333347 0.4169339521288016 Grape -6.533039 0.40046971029413403 Grape -6.673189 0.6953464681395298 Grape -4.4711843 0.09285121306796795 Grape -4.391151 0.16654207272882082 Grape -4.3588123 0.851198471715004 Grape -4.451325 0.771077346815188 Grape -4.5850224 0.2814537272943938 Grape -4.465113 0.3772689326657602 Grape -4.5463843 0.9260265066284805 Grape -4.492279 0.8180772251738613 Grape -4.450132 0.6143462999796309 Grape -4.474313 0.22149017880295774 Grape -4.4109464 0.04425197131526959 Grape -4.4983773 0.43125784684189816 Grape -4.388842 0.6726271392574619 Grape -4.4648886 0.8284804905178516 Grape -4.6191325 0.8526890057422694 Grape -4.572984 0.032775901293363385 Grape -4.49753 0.24415703383687937 Grape -4.4815273 0.33909458847740304 Grape -4.4344254 0.18873221095938586 Grape -4.476879 0.8029753783638378 Grape -4.5112815 0.7674657630849425 Grape -4.4064407 0.5168330403809708 Grape -4.3866935 0.9829264779958555 Grape -4.9984136 0.14405854109084226 Grape -4.9614053 0.8996517033276689 Grape -4.854541 0.11646325416191616 Grape -9.700637 0.1631817055141207 Interphase -9.985517 0.6962192002506188 Interphase --7.3745317 0.10956969204168931 Prometaphase -9.731253 0.5658450954187109 Interphase -10.303422 0.4202335364726536 Interphase -11.539817 0.7284739655410148 Interphase -7.419824 0.9006752388088651 Interphase -0.5963428 0.7698715146690553 Interphase -12.690378 0.8496898764211883 Prometaphase -0.11571981 0.03294544853123238 Interphase -12.043749 0.3101954982442031 Interphase -11.938839 0.5154330866863324 Interphase -11.2952385 0.41595331462977947 Interphase -12.704984 0.23125495297968846 Interphase -11.688139 0.3078740979600233 Interphase -12.66866 0.9454309703916057 MetaphaseAlignment -12.900354 0.29418087987135155 MetaphaseAlignment -13.05836 0.35390411017978574 Prometaphase -0.31152463 0.0037097737500186856 Interphase -0.24662392 0.8450776272778745 Interphase -0.19228208 0.15484070340157663 Interphase -12.864339 0.20414427551372605 MetaphaseAlignment -12.8642025 0.2552645170920256 MetaphaseAlignment --0.5799519 0.8846220568448426 Polylobed --0.458375 0.20645141124631472 Polylobed --0.49808657 0.7975263608679399 Polylobed -1.4329728 0.8080493403229045 Polylobed -12.336446 0.9270205687863958 MetaphaseAlignment -16.896685 0.11556131360031396 Polylobed -16.880646 0.21727897233783133 Polylobed -2.0863764 0.7428982920807213 Binuclear -3.9483683 0.19600075428073904 Binuclear -16.874784 0.286329546873449 Polylobed -16.839327 0.16674158011136264 Polylobed -3.0471 0.1726966861550493 Binuclear -16.879913 0.4815533546282057 Polylobed -16.87185 0.10968306211295853 Polylobed -16.851267 0.3216976184636222 Polylobed -16.872313 0.426593909598879 Polylobed --2.0053356 0.024548116523977592 Polylobed -2.9176466 0.3883331664744526 Polylobed -2.8118465 0.09412243608551696 Polylobed -1.69421 0.49357853114617667 Polylobed -2.9945464 0.8257381885570964 Polylobed -2.8699102 0.8184221621817872 Polylobed -1.7393034 0.08044851553740051 Polylobed -1.6934643 0.6012277576745202 Polylobed -1.7699945 0.8345863819712365 Polylobed -14.051066 0.23797254293463532 MetaphaseAlignment -0.18340445 0.7619265114495991 Polylobed --0.033215698 0.8907643464500933 Polylobed --1.8259095 0.8061241514404441 Polylobed -6.6733747 0.10730103151550452 Artefact --2.0612533 0.009059999522384898 Polylobed -6.700496 0.19172410992184563 Artefact -6.730324 0.27047734094662323 Artefact --0.5816818 0.6161829906159595 Polylobed --0.5790314 0.3842731752423184 Polylobed --0.60822624 0.7034070306614735 Polylobed --0.5072606 0.3530749605216086 Polylobed --0.5539311 0.1544254246546003 Polylobed --0.50042564 0.3126898434860351 Polylobed --0.53061163 0.884324226389135 Polylobed --0.4925915 0.9585323442450648 Polylobed -6.870558 0.20751273406736848 Polylobed -6.828779 0.7884683870244413 Polylobed --0.7386431 0.2733487365398707 Polylobed --0.6645409 0.8871315434314075 Polylobed -0.46324307 0.16554561279625546 Polylobed -0.3469795 0.6659599186940569 Polylobed -0.32146448 0.08421126471318252 Polylobed -0.16924362 0.9738933239738695 Polylobed -0.35009384 0.7006333446405428 Polylobed --0.32271993 0.8418157394050853 Polylobed --0.4106568 0.5666693393630345 Polylobed --0.43308794 0.4768013639288602 Polylobed --0.24872084 0.6218823913943651 Polylobed --0.27818337 0.528741511699448 Polylobed -0.077907726 0.469384355145153 Polylobed -2.2999852 0.7594502514576428 Polylobed -2.253853 0.17820095493926813 Polylobed -4.1769476 0.17117204815515852 Polylobed -1.2881167 0.4318426506497062 Binuclear -1.2642733 0.320747916044946 Binuclear -1.3094954 0.0741245179130241 Binuclear -1.2805709 0.8444704924187344 Binuclear -4.6839604 0.7716028066943498 Polylobed -4.429404 0.5439214998029848 Polylobed -4.7150855 0.979324537264369 Polylobed -4.8085685 0.07260006654760531 Polylobed --2.4104655 0.766669301027875 Polylobed --2.1567762 0.2663703916090968 Polylobed --1.8239195 0.3685989199435754 Polylobed -0.61136204 0.21927938520306756 Polylobed -1.5970422 0.7890378914618652 Polylobed -1.8576787 0.144240102021566 Polylobed -1.8678706 0.840016697735278 Polylobed --5.132107 0.6615776482298339 Interphase -2.321728 0.05902324526911473 Polylobed -2.4109778 0.8109817316001534 Polylobed -1.423948 0.6277557161629794 Polylobed --1.1984875 0.9049823195783415 Polylobed --1.1482488 0.7487223064086382 Polylobed --1.2281781 0.5611209274297748 Polylobed --1.1624126 0.8365471806191977 Polylobed --1.1337825 0.2780502383012221 Polylobed -10.057924 0.5469500618445636 Artefact -10.347092 0.29361681980341015 Artefact -10.357477 0.9682043910905087 Artefact --1.6221018 0.22619630084200637 Artefact -1.7709689 0.015738239670462506 Artefact --0.21511312 0.3258548420037962 Polylobed -0.22102532 0.5025093969698047 Polylobed -0.28453073 0.028362934230075076 Binuclear --4.684536 0.5592483000339074 Binuclear -0.00738223 0.8742827749512302 Binuclear --0.26876992 0.7047321944166701 Binuclear -2.3059726 0.6229683229783043 Binuclear -2.406197 0.9559617507082513 Binuclear --2.1317062 0.9582793338421058 Polylobed --1.9069247 0.8242664697032286 Polylobed -5.09333 0.607741847385851 MetaphaseAlignment --2.0424304 0.4877645594977067 Polylobed --2.0349393 0.01331611324646731 Polylobed --1.9194188 0.6062619062265414 Polylobed --1.8757951 0.9890880753568537 Polylobed -0.17901139 0.818101050024029 Binuclear --1.9161011 0.34060464388435197 Polylobed -0.29408088 0.15204702504442202 Binuclear -0.15366212 0.7840586144671541 Polylobed -0.09124563 0.7439378206317415 Polylobed -0.20216069 0.9670467919793231 Polylobed -4.954898 0.8748423619393388 Polylobed -0.10379368 0.5556626252724074 Polylobed --0.59521574 0.10128424696474214 Polylobed --0.7832663 0.48350065838396006 Polylobed --0.8036857 0.313695054022317 Polylobed --0.72695243 0.5124084820248398 Polylobed --0.87566304 0.3017015746396532 Polylobed --2.3547704 0.8618229919755958 Polylobed --0.91386384 0.8443270139828222 Polylobed --2.3273654 0.3154651571670559 Polylobed --2.3672743 0.599581346373995 Polylobed -1.4194129 0.4301808576390541 Polylobed --2.669323 0.9090927570285632 Polylobed -1.7274363 0.18736090142632278 Polylobed --2.6385994 0.6977284017849593 Polylobed --0.97192895 0.9703753278372166 Polylobed --1.8458267 0.17527551956309018 Polylobed --2.3384166 0.2019664284242032 Polylobed --2.6310377 0.6937233386722279 Polylobed --5.869395 0.7791539245249139 Polylobed -1.844581 0.49054905479393274 Polylobed --2.6052706 0.6096864651471213 Polylobed --0.8801454 0.21268240881182876 Polylobed --0.022408737 0.47661422856732605 Polylobed --2.2937827 0.11207188087491515 Polylobed -6.561867 0.3214219252756647 Polylobed --2.6545005 0.2847797050309543 Polylobed -1.9283762 0.44462536010242004 Polylobed --0.9698464 0.9301263622436969 Polylobed --2.3558223 0.1812676621478424 Polylobed --2.3313682 0.4013882466247205 Polylobed --0.94291097 0.6155972191096447 Polylobed --2.9766047 0.9465570860740017 Polylobed --2.3660784 0.13314819153576818 Polylobed -0.33694822 0.9178766169931135 Polylobed -0.05023207 0.08105378863406154 Polylobed --1.8795382 0.4807413971602644 Polylobed -1.4719217 0.4545898672304828 Polylobed -1.1819156 0.20960272887719555 Polylobed --1.6389401 0.3474596681158475 Polylobed -1.4917725 0.4541652683483981 Polylobed --0.41086283 0.8652114629106292 Polylobed -10.178037 0.9550641453735598 Binuclear --1.4352028 0.5189256786371652 Polylobed --0.40208384 0.8700997902944219 Polylobed -6.565338 0.6081715856767794 Binuclear --1.4554784 0.34908734198767544 Polylobed --1.2018846 0.19419420761346906 Polylobed --0.4003479 0.4131347717827648 Polylobed --0.24696863 0.5228242829238477 Polylobed -10.053961 0.04444338825955618 Binuclear --0.3650464 0.14584116587085072 Polylobed -0.5343853 0.60018442398144 Polylobed --0.08214092 0.22500159467716208 Polylobed -1.2855803 0.8373263760812975 Polylobed --0.7826999 0.3269422497741906 Polylobed --0.38705006 0.10483420725325088 Polylobed --0.97618 0.08353058855531703 Polylobed --0.9264648 0.9371230205317796 Polylobed -0.42216942 0.11802031124187917 Polylobed -0.3457682 0.14090976416206002 Polylobed -5.6350875 0.862666057932753 Polylobed -0.25377214 0.254288130277344 Polylobed --10.178929 0.6659514107544161 Polylobed --10.177283 0.8167256870298913 Polylobed --10.174035 0.6071806401301921 Polylobed --10.171881 0.9574885434478494 Polylobed --10.177391 0.7088828992608414 Polylobed --10.176028 0.11275150990365834 Polylobed --10.1733675 0.5584100265458799 Polylobed --0.015293847 0.7181865260891839 Polylobed -0.086993955 0.8019572403734452 Polylobed --0.07130766 0.02632133528409153 Polylobed --0.22340518 0.7188789152714244 Polylobed -1.1276536 0.8256808210194495 Hole -13.953453 0.7468338109353565 Hole -5.3012857 0.5123491456555981 Polylobed -3.1435235 0.4580209630888029 Polylobed -3.1604583 0.5494185832998159 Polylobed -12.313546 0.704643698826602 Hole -11.664854 0.9229142906324854 SmallIrregular -12.371944 0.617035210641125 UndefinedCondensed -14.4819975 0.8878343062834696 UndefinedCondensed -6.507499 0.7012568474457274 Polylobed -6.467009 0.06833637743884757 Polylobed -6.450914 0.5008281900016218 Polylobed -6.4538083 0.28648634831524233 Polylobed -1.907753 0.2851749281325723 Elongated -1.8800671 0.3559275512159842 Elongated -11.824056 0.3147327720004758 Hole -2.3797588 0.5786099801171002 Polylobed -6.7986064 0.683601500353557 Polylobed -6.783493 0.26874938707673546 Polylobed -6.875347 0.12976262256838134 Polylobed -10.484725 0.0588087209344621 SmallIrregular -2.3978279 0.5757528450305522 Polylobed -2.5268648 0.1861301663330024 Polylobed -11.631546 0.009247994223699374 UndefinedCondensed -2.5104108 0.9277530793237041 Polylobed -12.227074 0.5371404190832406 SmallIrregular -12.372313 0.09244818024697277 Hole -6.4345756 0.8429211121274853 Polylobed --0.7339785 0.9832027382901181 Polylobed -6.4749966 0.4486006620177172 Polylobed -6.457191 0.04248960823815817 Polylobed -0.41522828 0.1175459407560635 Polylobed -6.4632273 0.38165374664543217 Polylobed -6.5204716 0.885522637448877 Polylobed -6.5261593 0.14803867927776204 Polylobed -11.357094 0.8239900940026955 Hole -10.279405 0.014976260219019433 SmallIrregular -1.8611317 0.457388698785811 Elongated -1.8084716 0.6443971409505681 Elongated -2.3713558 0.0603794820255964 Large -10.142161 0.6147627506164867 SmallIrregular -6.894439 0.9444041214695561 Polylobed -6.9986544 0.16025992013659507 Polylobed -6.920438 0.7296113830353528 Polylobed -6.4301724 0.6090938979435434 Polylobed --1.5250976 0.18511638887923054 Polylobed --1.6243454 0.006203414709366029 Polylobed --1.6743718 0.009284450505927078 Polylobed -11.444132 0.5320924091720286 SmallIrregular -3.6752348 0.9427794095611935 Polylobed -3.654321 0.6442986274656124 Polylobed -12.911332 0.7142998462937437 Folded -12.6361065 0.4938654869205371 Hole -7.0933185 0.5818889426345698 Hole -2.4368837 0.12636752533060347 Hole -10.069663 0.8768206204006308 SmallIrregular -3.0625226 0.7607926286668019 Folded -12.403055 0.9981989528446096 Hole -1.4002653 0.29772294755562134 Folded -2.0927145 0.22701777179605787 Folded -2.041552 0.12516165796134293 Folded -11.870586 0.9642097566160448 UndefinedCondensed -2.033465 0.7808851846280236 Folded -11.119727 0.1663246137196387 UndefinedCondensed -1.5054185 0.5526864711082139 Polylobed -1.541243 0.41376820806727677 Polylobed -1.800813 0.1514860078061928 Polylobed -1.8016341 0.16207298502996548 Polylobed -2.8772955 0.9634699947451857 Polylobed -3.1102576 0.3049641829577222 Polylobed -11.7884865 0.9414392916839432 UndefinedCondensed -3.0053968 0.0756106730650844 Polylobed -12.482357 0.4608030422931906 Artefact -12.378896 0.1296190485167601 Artefact -11.710879 0.004787385241172171 Elongated -14.521165 0.5537660737060766 UndefinedCondensed -11.589574 0.11389410481136342 UndefinedCondensed -14.0571995 0.7220245309336797 Hole -1.3268882 0.6981163757068996 Hole -1.6137382 0.17633290746448915 Elongated -11.697943 0.9417421432655166 SmallIrregular -11.789648 0.7210434084324896 SmallIrregular -6.8766594 0.29797026415692995 Polylobed -11.656241 0.7092337639507906 SmallIrregular -6.825235 0.731930277681536 Polylobed -6.9007983 0.3422263312509606 Polylobed -14.436668 0.3755885624614592 UndefinedCondensed -12.453383 0.3591065076533001 Hole -12.249506 0.6166184435520741 UndefinedCondensed -6.454466 0.9004101465401794 Polylobed -1.5596172 0.17319323622858762 Polylobed -7.0393586 0.8751996098363184 UndefinedCondensed -3.3993123 0.027653156505342502 UndefinedCondensed -6.474613 0.6603385959782224 Polylobed -6.426563 0.4144388731896942 Polylobed --0.4165137 0.7912815522324937 Polylobed -0.61342585 0.721198112930903 Polylobed -1.8502868 0.480107807145184 Elongated -2.3982515 0.6438640367225967 Large -10.064434 0.5017731306999452 Polylobed -10.224785 0.8115184706218832 Polylobed --1.6115203 0.476083985968232 Polylobed -9.019294 0.5231559899920758 SmallIrregular --1.5882651 0.25052058641717856 Polylobed --1.6897092 0.6050430168104723 Polylobed --1.6404053 0.30290480866983505 Polylobed -14.224327 0.5772840145429821 MetaphaseAlignment -1.2614901 0.16967811557406987 Binuclear -0.52834797 0.1594690923411588 Binuclear -2.7581635 0.41702974119199354 Binuclear -2.6170068 0.42681951511054084 Binuclear -0.72231823 0.26810926491253906 Binuclear -0.67984444 0.13159685036669 Binuclear -2.4649 0.039210539213897055 Binuclear -1.8990889 0.025231827287816144 Binuclear -1.9781729 0.2715502900064757 Artefact --0.32751238 0.46185344216193713 Binuclear -2.4236147 0.726243281306127 Artefact -2.1318824 0.47487170071400897 Artefact --0.58489543 0.9040508194352818 Binuclear --0.4895575 0.03521980457540708 Binuclear -9.638541 0.18066062154063434 Binuclear -9.854386 0.33851449277014267 Binuclear --2.5944345 0.5774961880651225 Binuclear --1.4883513 0.8527361578939424 Binuclear -2.1397529 0.35020195195657644 Polylobed -2.09644 0.2679886825044201 Polylobed -2.2224243 0.06188916884289575 Polylobed -2.279049 0.8213034776981041 Polylobed -2.2208083 0.37966644320667897 Polylobed -2.2992976 0.5715501954169343 Polylobed -3.662035 0.9835554181712204 Polylobed -3.6239512 0.0015945706042945762 Polylobed -3.5975358 0.14545014069886597 Polylobed -3.6014712 0.7791109940193555 Polylobed --1.8433095 0.8051274852078378 Polylobed --1.6407143 0.7692471190327554 Polylobed --1.7432181 0.5369988910428293 Polylobed --1.7967513 0.9788569810230561 Polylobed -2.407082 0.39618455999532054 Polylobed -1.7615656 0.6019436981131123 Polylobed -2.4729748 0.0633690045488483 Polylobed -1.913135 0.40985744990733874 Polylobed -2.5236545 0.722500087509922 Polylobed -2.007364 0.2387388407706993 Polylobed -2.0888655 0.9438275890731485 Polylobed -2.0386627 0.6867833676432891 Polylobed -1.8922335 0.28757538285757145 Polylobed --0.13294198 0.7689989227058563 Polylobed --0.05135826 0.0831647718081091 Polylobed -2.1713533 0.9747744225104565 Polylobed -0.6076479 0.04928525907355852 Binuclear -1.7556738 0.9334558911487183 Polylobed -0.36625624 0.2528538777482612 Binuclear -2.1283145 0.7578241076908905 Polylobed -0.6849344 7.369943310608917e-05 Binuclear -0.4394817 0.2542400891795997 Binuclear -2.515536 0.7491006066497199 Polylobed -1.7409902 0.5323360708973223 Polylobed -2.0121143 0.11495214963711486 Polylobed -1.0437952 0.3936297458372753 Binuclear -2.382938 0.37554935508454257 Binuclear -2.382843 0.5681622441809498 Binuclear -2.4147806 0.6679770724602178 Polylobed -2.600812 0.84083024241225 Polylobed -3.7143133 0.4972313969773726 Polylobed -2.3450565 0.3920217173051701 Polylobed -2.8855703 0.1439765340953817 Polylobed -1.9816239 0.8048229649897223 Polylobed -2.3271625 0.71337040541456 Polylobed --9.193865 0.40867739745468534 Polylobed -3.6314905 0.5184323099426694 Polylobed -3.3965595 0.6651828444118888 Polylobed -2.2616837 0.1648055898475378 Polylobed -1.117134 0.02719779440792025 Polylobed -1.9787275 0.3175036991866854 Polylobed -10.299426 0.5955850196361631 Artefact -1.8896322 0.4866060912814587 Polylobed -10.4240465 0.6925546268631271 Artefact -1.9879544 0.8196898055745352 Polylobed -1.8349811 0.4884424667302759 Polylobed -10.566929 0.13426702371782484 Artefact -2.49572 0.8506279997989659 Polylobed -2.4396825 0.5749903272741144 Polylobed -2.3906682 0.7399374807828707 Polylobed -1.8303591 0.7046646536082916 Polylobed -1.9757574 0.9682117713312417 Polylobed --0.013148449 0.2953073223358139 Polylobed --0.12792586 0.705306772779262 Polylobed -0.13870446 0.3656763331259183 Polylobed -0.9897576 0.3954107209186417 Polylobed -0.89781624 0.23059463665872149 Polylobed -1.0464703 0.3440101756508539 Polylobed -1.8941748 0.9482967516582631 Binuclear -2.2776177 0.29257084694760094 Binuclear -1.8165139 0.24599060595658673 Binuclear --3.0790215 0.583137978473822 Binuclear -0.39271373 0.2580359567417546 Binuclear -2.067148 0.47338572559429504 Binuclear --0.2061044 0.834176256556237 Binuclear -2.0149817 0.23040031430717556 Binuclear -1.4587897 0.42669141218392004 Binuclear -1.4988945 0.6104897350200319 Binuclear -1.7033646 0.5456289245734854 Binuclear -2.0783563 0.9747232374152373 Binuclear --0.3173916 0.680370254395195 Binuclear -1.720078 0.7399462420484398 Binuclear --0.5328672 0.9669559479667332 Binuclear -0.060562663 0.414438025228521 Binuclear --3.4247105 0.3553798150610512 Binuclear -1.3208716 0.04386242976638255 Binuclear -0.41927123 0.18420430651882713 Binuclear -7.4745674 0.23718963205167587 Binuclear --1.5626245 0.1835044670713385 Binuclear --1.5630045 0.7547838931965645 Binuclear --0.4769825 0.5358829812761117 Binuclear --1.5406624 0.6676338014584673 Binuclear -2.4048243 0.8204621608393736 Binuclear -2.59162 0.23077394128589468 Binuclear -7.2946744 0.32592393982149914 Binuclear -2.5229428 0.7083602670365968 Binuclear -11.907979 0.3927589459044728 Binuclear -2.6246872 0.02927094010104414 Binuclear -12.085156 0.43495521902687795 Binuclear -11.969518 0.9082730864154143 Binuclear -16.774736 0.4090215115928747 Binuclear --1.1285223 0.332248916442747 Binuclear -2.1037621 0.9895250829450272 Binuclear --1.9432235 0.6444155642370132 Binuclear -1.7724489 0.36599802647118085 Binuclear -2.0837224 0.10201953136380737 Polylobed -1.106631 0.7878494449464212 Binuclear -1.1488422 0.7080749334877576 Binuclear -0.43332162 0.9219157992485649 Polylobed -1.014545 0.21727563002800832 Binuclear -1.3792317 0.11492434998823198 Binuclear --1.5597224 0.7240725264090636 Binuclear -2.4247777 0.20339584199869565 Polylobed -1.0893604 0.17610383778949612 Binuclear --1.8195716 0.3198073353960721 Binuclear -1.0422374 0.8168251414784377 Binuclear -2.8094602 0.5395366051096308 Binuclear -3.2834992 0.04585035510228097 Binuclear -2.6148393 0.4638946746593172 Binuclear --1.7064832 0.6839796113664752 Binuclear --1.7537605 0.5383684384690157 Binuclear -0.9281887 0.572450218156556 Binuclear --3.9510434 0.22477732839266462 Binuclear -1.5956632 0.8477393315953481 Binuclear -1.2019011 0.5613987175911241 Binuclear -2.1640172 0.7132460130406766 Binuclear -2.5114853 0.9818642170133025 Binuclear -0.8408097 0.42819865767453213 Binuclear -0.4849013 0.8810666126765085 Binuclear -13.281299 0.007281013941125014 MetaphaseAlignment --3.0963469 0.0334072926151201 Polylobed --3.012171 0.5902799217288169 Polylobed --3.2737868 0.31144939554220064 Polylobed --3.0890765 0.24827656701970024 Polylobed -9.713789 0.27793535720895857 MetaphaseAlignment --3.4212239 0.31840293461058755 Polylobed --3.3719025 0.7289476961434229 Polylobed --3.1787302 0.5691959722309445 Polylobed -0.873276 0.7890359725593334 Polylobed -0.42891395 0.8301965798350519 Polylobed --2.7385128 0.8429348593012821 Polylobed -0.6175554 0.41464414917488757 Polylobed --1.985461 0.4212733963164108 Polylobed --1.9361871 0.926265880140584 Polylobed --1.8579389 0.6617635946044105 Polylobed --2.1288671 0.08046718397913877 Polylobed -13.566809 0.5421869540924683 MetaphaseAlignment -4.063647 0.356007259634907 MetaphaseAlignment -3.996028 0.9874349897163631 MetaphaseAlignment -13.633043 0.013655406462292574 MetaphaseAlignment -14.18884 0.612180873070558 MetaphaseAlignment -12.331034 0.7236230943988581 MetaphaseAlignment -7.6878767 0.2889067713573552 Interphase -12.279067 0.9736415182565321 Interphase -7.187411 0.8595366270217113 Interphase -12.639629 0.9156528409858125 Interphase -10.68325 0.01923205935154848 Interphase -1.1486754 0.569872151314809 Large -1.5119519 0.2946502419545467 Large -12.459472 0.8490286349356629 Interphase -7.220181 0.6328496569149624 Interphase -7.2695355 0.5388770043211097 Binuclear -7.2305174 0.1145881696756057 Binuclear -11.995293 0.5402228047861908 Interphase -4.700133 0.6319041463564267 Polylobed -4.6453605 0.9559123093102045 Polylobed -4.607701 0.5850510104238695 Polylobed -3.5752583 0.9674006017456545 Polylobed -3.5252864 0.9616061156066621 Polylobed -3.5348713 0.6502003358335975 Polylobed -3.566084 0.5059079841180887 Polylobed -3.438071 0.46602174382604933 Polylobed --0.87228453 0.8903785611157826 Polylobed --0.93757373 0.028256682971756764 Polylobed --0.7868531 0.11380819802357156 Polylobed --3.074195 0.10207172911915185 Artefact --3.0906281 0.7569353251210548 Artefact --3.0458522 0.33965102385253476 Artefact --3.0822718 0.637968544740701 Artefact -0.21189246 0.6037829038425656 Polylobed -0.61800605 0.38582796974253164 Polylobed --2.7530208 0.5315677222423986 Polylobed --2.7567863 0.6451385335716601 Polylobed --2.7803824 0.9409503298781383 Polylobed -8.540195 0.5756340728609814 Polylobed -8.563587 0.6143675117683143 Polylobed -8.398903 0.06785581770377547 Polylobed -8.483065 0.952215814760112 Polylobed -10.09177 0.5280819279093862 Binuclear -10.109751 0.8012734198599218 Binuclear -10.151051 0.05029106314738907 Polylobed -8.543061 0.420910135737091 Polylobed -9.660936 0.2569754591527016 Polylobed -9.782582 0.26697589868858995 Polylobed -11.715499 0.7914537299609294 Binuclear -11.699686 0.6238667252688536 Binuclear -10.501505 0.43974531184284804 Artefact --2.8880384 0.010585739586393417 Polylobed -10.357648 0.9649279429455299 Artefact --2.9947252 0.9620232533557238 Polylobed -10.5156555 0.21755221028003846 Artefact --2.85031 0.04134637231655669 Polylobed -11.42968 0.5301993630119022 Apoptosis -11.081481 0.9514108146164075 Apoptosis -9.610788 0.9103958457649349 Artefact -2.037088 0.5846628638893966 Binuclear -6.3566394 0.3035488506537072 Binuclear --3.156158 0.32996088376919597 Polylobed --2.9617221 0.8979135514860276 Polylobed -7.472356 0.49178403563557693 Artefact --3.2271683 0.1311162289671456 Polylobed -2.0642245 0.2484254759736073 Polylobed -7.526918 0.2767949014674991 Artefact -8.034519 0.12354668493190935 Artefact -8.253004 0.46304438152129135 Binuclear -8.300996 0.916050908274991 Binuclear -9.843381 0.6687825429922609 Artefact -9.824925 0.07247392424885613 Artefact -9.7074585 0.005494817809162722 Artefact -9.707195 0.27624766746925433 Artefact -9.81373 0.3626929347939404 Artefact -8.59298 0.7767496690467758 Binuclear -8.673814 0.9670055239303667 Binuclear -9.760473 0.38756717342354363 Artefact -9.725385 0.6866900298168611 Artefact -9.779378 0.9949019073529632 Artefact --2.4729543 0.7456665838750144 Polylobed -6.7084293 0.6361895490858445 Artefact --2.5956762 0.07807485417830429 Polylobed -6.7285776 0.32321520119473024 Artefact --2.6663744 0.9133921584571569 Polylobed --2.8040547 0.2010054816809057 Polylobed -6.7502866 0.8435903592732081 Artefact -6.7272177 0.6963236850805055 Artefact -0.83807826 0.36632437083075464 Binuclear -0.75882465 0.5291742697126566 Binuclear -11.803314 0.5428064687528136 Apoptosis -11.27122 0.7140537850738863 Apoptosis -11.68313 0.5165559410991961 Apoptosis -8.646064 0.13307599018416627 Binuclear -8.672598 0.7734546715219126 Binuclear -7.0473323 0.4062724969061188 Polylobed --3.003515 0.9630938902119273 Polylobed -6.897146 0.28351378229303437 Polylobed -7.6389465 0.2630787802965846 Artefact -7.580985 0.3335073956751826 Artefact -10.070217 0.5723170159586255 Polylobed -7.6512523 0.8948697402009823 Artefact -8.120299 0.1762816401400148 Artefact -8.573278 0.2796787975992202 Artefact -12.196496 0.5816798431478618 Apoptosis -7.4020386 0.4543342421615151 Artefact -7.4961824 0.44732288725572533 Artefact -7.4723167 0.8207342620964065 Artefact -7.533659 0.9238783025113612 Artefact -7.5993886 0.4813069660698819 Artefact -8.329801 0.6873517954472738 Binuclear -8.261107 0.8010587110273939 Binuclear -11.302733 0.5183664103871639 Apoptosis -8.533771 0.29431640895373534 Binuclear -8.556708 0.6380845977222838 Binuclear -9.7706995 0.5851091003060592 Polylobed -9.633275 0.9015628197396104 Polylobed -9.859051 0.05240703676985603 Polylobed -3.1856368 0.9101313871334668 Polylobed -10.137856 0.5344320281653596 Polylobed -11.783686 0.015676116049913924 Binuclear -11.963958 0.3447021787677881 Binuclear -11.718212 0.7243335638753198 Binuclear -11.841572 0.48843308793583295 Binuclear -10.745911 0.9801590374409924 Binuclear -8.844391 0.42261007383104854 Binuclear -8.578905 0.3266352296260078 Binuclear -8.799648 0.8216719344295681 Binuclear -8.845552 0.5479066993580314 UndefinedCondensed --3.1726818 0.6823265790625589 Binuclear --3.109368 0.8057023380386775 Binuclear -7.3190203 0.6714275461256203 Binuclear -7.3448944 0.42240749476866457 Binuclear -11.217415 0.12479644789430067 Artefact -11.245864 0.5802481665713005 Artefact -7.6837206 0.8974333267292596 Binuclear -7.7535033 0.41889242651841496 Binuclear -0.18515265 0.9107252348428405 Interphase -0.5458203 0.503527819782402 Interphase -0.08206499 0.6208415624424766 Interphase -10.995508 0.8329884760556429 Interphase -13.297552 0.5645971096407215 Prometaphase -14.852011 0.0909693392955635 Apoptosis -13.579781 0.9809794008003951 Prometaphase -14.639236 0.24584930191283372 Apoptosis -13.855323 0.7105053286301986 Apoptosis -9.903374 0.5051134386177387 Interphase -14.748236 0.4787726368177081 Interphase -9.964695 0.24394081886689145 Interphase -9.948947 0.7221507657001041 Interphase -11.142213 0.11278826483098725 Interphase --1.2421767 0.9904533093614852 Interphase -11.364064 0.8453735371072039 Interphase -9.175339 0.5345089400057921 Interphase -9.800105 0.42455295605800947 Interphase -6.4842644 0.28646463245986764 Binuclear -6.297207 0.5015914709407675 Binuclear -12.074945 0.879417462024168 Apoptosis -7.2802896 0.2750064007006836 Binuclear -7.231204 0.5005374588031359 Binuclear -13.110663 0.23454990206703086 Prometaphase -13.177057 0.3371491136649515 Prometaphase -4.39831 0.19026050892333246 Polylobed -4.1317744 0.9905391939211878 Polylobed -3.7989504 0.5714974464624556 Polylobed -13.2744465 0.7328151240366955 Prometaphase -14.668777 0.09824991345203671 Apoptosis -14.37031 0.3661175500743158 Apoptosis --0.5252381 0.8926398030473215 Polylobed -0.67113745 0.08443832642394788 Polylobed -1.2248765 0.16548322833961315 Polylobed -11.622419 0.6254176103511686 Interphase -3.371271 0.6227889941042252 Polylobed -2.6516602 0.8382270444539296 Large -3.1645741 0.9354927638714214 Polylobed -12.25228 0.14198647542526377 Interphase -11.136159 0.2593738185269995 Prometaphase -10.952241 0.42746140569325586 Prometaphase -7.3794956 0.0009032855212496305 Large -14.454669 0.06981430295969493 Prometaphase -5.062069 0.22649127794102164 Prometaphase -13.604242 0.48110196401581173 Prometaphase -15.567175 0.2515227381686128 Prometaphase -14.900743 0.876681892223048 Apoptosis -14.917414 0.3242728755420111 Apoptosis -2.6515348 0.9246228121426286 Elongated --4.1984925 0.97478726073471 Interphase -13.641052 0.44986153390757877 Interphase -10.08171 0.22712881689932363 Interphase -14.243447 0.2916661334230033 Apoptosis -13.654064 0.7763336816594462 Prometaphase -5.213883 0.27334970779259105 Apoptosis -14.732125 0.3805828679202846 Apoptosis -14.697562 0.4785758584818498 Apoptosis -14.770357 0.5751111164699841 Apoptosis -14.674148 0.9961004403012381 Apoptosis -10.439027 0.23220975516359232 Interphase -9.62817 0.35342370324816885 Interphase -0.5534479 0.262891177010511 Polylobed -1.2681156 0.3611134366437103 Polylobed -0.83420104 0.10080451689462055 Polylobed -1.4667469 0.3598097672410472 Polylobed -15.023536 0.8878650314643884 Prometaphase -15.038995 0.29858984784644904 Prometaphase -14.241708 0.37193477984906886 Prometaphase -11.602443 0.9444741644379346 Interphase -12.393857 0.7283789963268682 Interphase -11.761605 0.5167387148180483 Interphase -0.8433061 0.7771927284034406 Interphase -0.7591864 0.12317964885450394 Interphase -7.374401 0.4644903532208423 Interphase -7.4126906 0.11823612873586631 Interphase -12.881356 0.23361814501958 Prometaphase -11.588599 0.1418672597402586 Interphase -10.518196 0.3618012391691393 Interphase -14.817737 0.38163932039592585 Prometaphase -11.875395 0.9473085818332408 Interphase -11.90746 0.26412573118752924 Interphase -1.9575465 0.47242884531651685 Elongated -7.355467 0.8113794039634867 Interphase -13.08331 0.8156175864366617 Prometaphase -10.909278 0.7503431870350202 Interphase --2.8667507 0.28783376214007295 Interphase -15.403157 0.49497161941135703 Prometaphase --2.8258617 0.18621159518672326 Interphase -10.943603 0.18839952278280425 Interphase -9.06204 0.4358412131383109 Interphase --2.7903488 0.7385922212429411 Interphase -14.345446 0.5265842599978379 Apoptosis -9.613923 0.8866832208474523 Interphase -11.531108 0.8309087977775759 Interphase --5.6693826 0.03160544282040556 Interphase -12.811387 0.568419042866216 Prometaphase -12.156279 0.6091612643556431 Interphase -14.139719 0.9615751443000815 Prometaphase -13.123135 0.02323662670886173 Prometaphase -15.261664 0.5311037709686289 Prometaphase -14.011674 0.2047813619358524 Prometaphase -14.97746 0.053662906291589096 Prometaphase -0.0018159902 0.5874885247317233 Interphase -15.3061285 0.7726041436328025 Prometaphase -12.799167 0.7748654492251879 Prometaphase -15.369221 0.030288410155906775 Prometaphase -9.461848 0.40694640027158935 Interphase -15.411832 0.0445096271486205 Prometaphase -13.19895 0.24783864699076974 Prometaphase -14.194692 0.19288070275794833 Prometaphase -11.400244 0.21518257924215722 Interphase --1.1114546 0.3391184140345286 Interphase --4.430893 0.2774180418359983 Interphase -10.03689 0.9622799113275983 Interphase --0.07252179 0.3524071200093821 Polylobed -3.1455154 0.8941725152929719 Polylobed -13.681327 0.1810416715209251 Interphase -12.621507 0.7637468671882143 Prometaphase -14.471793 0.061345536024642766 Prometaphase -11.893719 0.4627612294693413 Interphase -11.955898 0.005510440235525937 Interphase -12.7129345 0.810290763069436 Prometaphase -10.086606 0.9504860360377839 Interphase -10.043102 0.035107370827145656 Interphase -9.437544 0.9338463614733145 Interphase -13.330668 0.773853891491353 Prometaphase -11.880792 0.35886157879279534 Interphase -15.180218 0.9088765512228052 Prometaphase -11.326965 0.29625727125090096 Interphase -15.342719 0.409295312234762 Prometaphase -15.304426 0.09671126219202164 Prometaphase -13.764315 0.656938961900144 Prometaphase -13.796129 0.02960082874227188 Prometaphase -15.071791 0.48490527519903726 Prometaphase -13.955015 0.6831918502525338 Prometaphase -15.503216 0.8212376009491109 Prometaphase -10.21475 0.14994138962166215 Interphase -15.484481 0.7540903662487174 Prometaphase -11.389273 0.7190770235550296 Interphase --4.402599 0.5595705341197842 Interphase --4.4654517 0.584644597787864 Interphase -12.979995 0.09127072085677812 Prometaphase -11.958677 0.6004710432403803 Interphase -11.96834 0.3815222070708323 Interphase -12.998655 0.867580851341647 Prometaphase --4.3861055 0.3130988817359902 Interphase --1.3896798 0.5765950773635247 Interphase --1.0644245 0.4265713997011944 Interphase -10.584381 0.8762619382503131 Interphase -14.316653 0.253916666989189 Apoptosis -11.086825 0.07880236030946042 Apoptosis -14.423777 0.7486557209831396 Apoptosis -11.223921 0.41289661690832147 Apoptosis -10.882886 0.5890983591262915 Apoptosis -11.012458 0.026389331713351116 Apoptosis -11.094611 0.952109791397402 Apoptosis -11.000398 0.8776183773849765 Apoptosis -10.591215 0.6238679005772775 Apoptosis -8.553879 0.017029842112618043 Interphase --5.1821055 0.3779764739828766 Interphase -10.606981 0.9787965377421489 Apoptosis --5.132544 0.6813303587402346 Interphase -12.442181 0.16098786131341913 Apoptosis -12.614116 0.4605967486545006 Apoptosis -8.519484 0.966737743843486 Interphase -10.091103 0.5347081744165934 Interphase --3.8716197 0.024614528029824556 Interphase --3.9593704 0.6197720842759777 Interphase --4.473099 0.2712165309311598 Interphase -14.425521 0.09884595187690437 Apoptosis --4.8963366 0.402908444731893 Interphase -14.361209 0.5585230974322448 Apoptosis -14.548788 0.017040718047802716 Apoptosis -10.169043 0.5590468086359475 Interphase -14.484376 0.5112031468396374 Apoptosis -12.1645 0.7939083830987601 Interphase -8.519281 0.42354981681539716 Binuclear -8.4538555 0.5534504296698862 Binuclear -7.403485 0.7420093040571263 Binuclear -7.1796947 0.8971458717819455 Binuclear --5.3957825 0.3800345760236875 Interphase -11.986047 0.6016334957831465 Interphase -10.776126 0.6946109034163818 Apoptosis -9.621929 0.043270676442196176 Interphase -8.597654 0.8007605084861348 Interphase --5.0944157 0.37743018521539384 Interphase -6.0675764 0.6702389414436098 Polylobed --4.203189 0.47166406620494905 Interphase -4.4741387 0.7023391426917073 Polylobed -4.4297633 0.7243309792357789 Polylobed --3.0914283 0.19157101348646732 Interphase --5.004034 0.6621742342864505 Interphase -4.398509 0.8513234378820872 Polylobed -13.786065 0.23556720050497482 Apoptosis -4.4460716 0.7581834698938779 Polylobed -12.231423 0.6338251131552236 Apoptosis --5.161241 0.9551260344725638 Interphase --0.38939565 0.2602465772917608 Interphase --5.0680776 0.15427777765800355 Interphase -12.154317 0.3121210152816999 Apoptosis -8.740906 0.2579587781979187 Interphase -8.020737 0.7568327604735129 Interphase -7.4261436 0.7331093038467983 Binuclear -7.3458424 0.6696849008553338 Binuclear -7.2308807 0.2163791144584878 Binuclear -7.1997337 0.8618068074390897 Binuclear --4.9591312 0.9565715558589277 Interphase --3.854876 0.5316058181086445 Interphase --5.0373282 0.6493727306293858 Interphase --4.43346 0.5006920990205621 Interphase --5.2212334 0.28355771510260386 Interphase --5.121977 0.2861476039966042 Interphase --2.074064 0.31342052273491317 Polylobed --2.189261 0.19085000416415399 Polylobed --2.1799657 0.9019236326293176 Polylobed --2.2060604 0.8583981340466859 Polylobed --2.2218363 0.46163936131883676 Polylobed -13.253701 0.36725141256017046 Apoptosis -13.0140295 0.9390783565470986 Apoptosis --4.023073 0.2223734692328101 Polylobed --4.1005535 0.5535419594668016 Polylobed --4.0881834 0.05449815554216264 Polylobed -5.913186 0.191202197017449 Apoptosis --4.143833 0.6040269333595097 Polylobed -6.6308846 0.6621836528441697 Polylobed -6.662834 0.5809958186732397 Polylobed -6.723793 0.8696014159992898 Polylobed -13.729098 0.993405220822747 Apoptosis -6.8402767 0.7638618421529112 Polylobed -14.59384 0.46509178414375885 Apoptosis -1.7189593 0.8339346694541027 Polylobed -2.5593264 0.12038739416600164 Polylobed -13.854344 0.6832706964459297 Apoptosis -3.6181731 0.518337787453434 Polylobed --4.6161366 0.1780658862557184 Polylobed -14.015387 0.972933617280214 Apoptosis --4.6801004 0.33843741977874753 Polylobed --4.5380936 0.6154234059633834 Polylobed --4.6595755 0.878594237524952 Polylobed --4.642475 0.5066888439148206 Polylobed -13.027678 0.11513785017643186 Artefact -9.090747 0.8187390843472899 Apoptosis -14.829986 0.3532137670588005 Apoptosis -2.8478112 0.9830668688918388 Apoptosis -2.8708842 0.8838254464445581 Apoptosis -2.830445 0.8534493333489572 Apoptosis -2.8136394 0.3562531775378994 Apoptosis -14.767666 0.7571297778090657 Apoptosis -12.538366 0.9863290630015888 Apoptosis -12.465463 0.5263216119513313 Apoptosis -13.1202345 0.4707321549552316 Apoptosis -5.7057176 0.014665820972070809 Polylobed -0.33669984 0.7002037687349363 Polylobed -0.7549919 0.8584775686018541 Polylobed --9.400192 0.24745718608182998 Polylobed --9.399312 0.3150245771146568 Polylobed --9.403695 0.5006770213261564 Polylobed --9.4116 0.07208015736151685 Polylobed --9.397368 0.08964247609873699 Polylobed --9.413044 0.4211107588880808 Polylobed -11.564464 0.26121895270342166 Metaphase -11.477388 0.4060373025327685 Metaphase -13.661449 0.454386298099938 Polylobed -3.542002 0.9762733967391182 Polylobed --3.820741 0.9442598201028373 Polylobed --4.0564723 0.1397301515397661 Polylobed --4.3277154 0.8825859737322465 Polylobed --4.726325 0.08020666233007856 Polylobed --8.365201 0.5076354697177541 Polylobed --8.353474 0.16390990010976536 Polylobed --8.338941 0.36589214535693404 Polylobed --1.8382437 0.7433402256486646 Polylobed --1.752897 0.4245054793568499 Polylobed -2.6801429 0.8069512875713337 Polylobed -2.767459 0.7188157110375418 Polylobed --3.8925085 0.9428394462110162 Polylobed --1.7231561 0.5341560205858773 Polylobed --0.6226531 0.8021479539877953 Polylobed --1.2606115 0.3229071063370519 Polylobed --1.1776272 0.3457458450898646 Polylobed --1.1854719 0.40051046358150544 Polylobed -18.460066 0.41091573429808903 Polylobed -18.473806 0.10325322066597453 Polylobed -18.46152 0.5190989393318636 Polylobed -18.493732 0.09680321926902558 Polylobed -18.464685 0.889762135819764 Polylobed -18.463383 0.06184478322329312 Polylobed -13.390949 0.4496944303093088 Apoptosis -13.285245 0.2846940663904305 Artefact --3.4778812 0.1548694156544791 Binuclear --3.8556237 0.7838366908422123 Binuclear --3.8854594 0.7149323989195256 Binuclear --0.40750667 0.1459478901815131 Interphase -9.702869 0.6327656411063102 Artefact -13.110589 0.7925551673304461 Apoptosis -2.7200186 0.7733322203698869 Polylobed -13.359063 0.09227959766336158 Apoptosis -3.6134148 0.6855127190457763 Polylobed -3.5652912 0.716023920883577 Polylobed -13.571483 0.8621662670420273 Apoptosis -12.719852 0.5080443388649867 Artefact -12.751917 0.46109400030335734 Artefact -18.415619 0.9651163249194699 Polylobed -18.413605 0.7965122557387535 Polylobed -18.402672 0.558730990806489 Polylobed -18.400372 0.3306170713112795 Polylobed -12.271597 0.8452379960035107 Apoptosis -5.958198 0.4554363894644047 Polylobed -5.6708136 0.09268519215816062 Polylobed -5.86755 0.4549042727134398 Polylobed -3.0860803 0.8719683961501463 Polylobed -1.477555 0.44828214689444545 Polylobed -2.630502 0.014349147740350121 Polylobed -2.7530873 0.6114853239164696 Polylobed -2.6863017 0.9958300034113987 Polylobed -15.063767 0.8172485840405118 Apoptosis -18.459108 0.6172379466181479 Polylobed -18.45697 0.914398886797885 Polylobed -18.459293 0.8135816816510619 Polylobed -18.45836 0.49863935274004045 Polylobed -18.461035 0.5912202854073818 Polylobed -12.105857 0.7312928092030199 Apoptosis -1.0823883 0.8464261639788647 Polylobed -13.547745 0.0581764023767114 Apoptosis -1.1469431 0.7748038571611511 Polylobed -0.9262965 0.44750343997820197 Polylobed -13.39552 0.6607985603015407 Apoptosis -13.358701 0.7646330109588501 Apoptosis -13.856033 0.5395019250274483 Apoptosis -10.511607 0.15851485058039905 Artefact --8.372526 0.4095268315182661 Polylobed --8.371178 0.07687299375237977 Polylobed --8.374308 0.6893006911552638 Polylobed --8.374773 0.9537061819630834 Polylobed -6.00013 0.795252812224187 Artefact -5.933635 0.3897864227871187 Artefact -5.8639493 0.5842914960054943 Artefact -5.836703 0.30420067584686916 Artefact -2.3451288 0.541045599554965 Polylobed -2.3152084 0.30072690578464023 Polylobed -2.3617146 0.73832475679861 Polylobed --1.4007821 0.2593415079870255 Polylobed -2.350082 0.9640203904338944 Polylobed --1.2563213 0.6619487321735315 Polylobed --1.4237808 0.06877784768384376 Polylobed --1.2913477 0.01077776350569315 Polylobed --1.225975 0.9970463442051004 Polylobed --9.363577 0.22050557821579353 Polylobed --9.37814 0.3125627484571776 Polylobed --9.374417 0.20841401420210837 Polylobed --9.378793 0.9281017724711823 Polylobed --9.376379 0.6520128596686141 Polylobed --9.373968 0.5043863235561108 Polylobed -2.0009842 0.5576508144925704 Polylobed -1.8328099 0.3178728472991873 Polylobed -1.9346155 0.6141750023638414 Polylobed -1.9560858 0.4196003634549319 Polylobed -1.5995904 0.1992281464241792 Elongated -1.3978671 0.6566050997098815 Elongated -1.3743207 0.37384056925921205 Polylobed -1.272501 0.7850663791171616 Polylobed --1.9401577 0.41539495750952704 Polylobed --1.9396497 0.5082713750461963 Polylobed --1.897402 0.7874664791808963 Polylobed --1.2976366 0.44787809291973113 Polylobed --1.1703254 0.9662211351241645 Polylobed --1.2582542 0.7930286671951117 Polylobed --1.279006 0.8264304053149786 Polylobed --1.2765836 0.9539204667037277 Polylobed --1.2983067 0.3888506288432074 Polylobed -12.011849 0.5800802641821708 Large -2.7822664 0.6935917407956035 Large -8.600944 0.6734584835406308 Large -2.7433624 0.9529367945610195 Large -1.2875377 0.28834863387833454 Polylobed -0.9890072 0.29136152862599196 Polylobed -1.3413732 0.9180044020391467 Polylobed -0.96862537 0.7993056417265596 Polylobed --3.0004532 0.20335662406959776 Polylobed -3.3120644 0.5700468780927677 Polylobed -3.2861497 0.7457637340963282 Polylobed -3.2536821 0.3530363904475091 Polylobed -3.2771628 0.43438225122139007 Polylobed -3.241277 0.575396611660893 Polylobed -10.77043 0.46710855754821057 Prometaphase -12.092266 0.7530423900690328 Prometaphase -10.843776 0.005938753683069731 Prometaphase -12.161235 0.4346325243910505 Prometaphase -12.202439 0.33758137532887156 Prometaphase -0.79388463 0.6091640528298269 Polylobed -0.6452501 0.7759584418813597 Polylobed -0.86379886 0.07614516220153023 Polylobed -0.8792783 0.1580597336011924 Polylobed -0.9481786 0.6650816241420333 Polylobed -3.4054568 0.738402441015417 Polylobed -3.365201 0.5882017770788354 Polylobed -3.332457 0.9828033489679264 Polylobed -3.2511837 0.4994614891065746 Polylobed -1.0643346 0.1362923350976436 Polylobed -0.87085813 0.68629106037108 Polylobed --0.087907076 0.6020755221947981 Polylobed -14.19736 0.5904214750302634 Prometaphase -13.875762 0.5458536352463982 Prometaphase -9.696147 0.9390957095495389 Prometaphase -10.878007 0.129156396826558 Prometaphase -10.880344 0.07319729852497836 Prometaphase -12.83821 0.15411716937731978 Prometaphase -10.860336 0.24499020990495046 Prometaphase -11.978312 0.5929463194364691 Prometaphase -12.760313 0.6124252490967343 Prometaphase -12.55185 0.23260187745708427 Prometaphase -1.2295642 0.4892254993109375 Polylobed -0.12255759 0.48206185276773483 Polylobed -0.66955066 0.45311076628522495 Polylobed -0.75805575 0.8035452949126581 Polylobed -3.2652004 0.47169306987563187 Polylobed -3.132166 0.758365495956166 Polylobed -3.3265986 0.16648113180479807 Polylobed -3.1446106 0.531967344073168 Polylobed -3.353674 0.8241047760631955 Polylobed -3.4187434 0.17771658391625766 Polylobed -3.32924 0.46492216869766234 Polylobed -3.2694988 0.6090793160357167 Polylobed -3.4180596 0.18477457483728732 Polylobed -3.4645295 0.7567656059114031 Polylobed -3.4321299 0.07899624779631742 Polylobed -3.3902626 0.9464155089077187 Polylobed -10.603328 0.46822149421813575 Prometaphase -10.703202 0.3361230863654765 Prometaphase -10.111642 0.11680700716264036 Prometaphase -11.440021 0.4325966430678647 Prometaphase -11.018556 0.6598040293907718 Prometaphase -12.96677 0.09448727914238408 Prometaphase -13.504426 0.3062690951677486 Prometaphase -6.214849 0.8360175827727111 Prometaphase -13.668795 0.6461810255983272 Prometaphase -12.770825 0.2063730118576239 Prometaphase -9.429883 0.16090979579308917 Prometaphase -11.593342 0.9044850262008999 Prometaphase -12.947486 0.28702022134098437 Prometaphase -12.847191 0.1795001688986303 Prometaphase -9.511553 0.9999640185426024 Prometaphase -12.754665 0.3091918277779059 Prometaphase -12.614874 0.12474018780936369 Prometaphase -12.207096 0.4617576297529471 Prometaphase -10.832068 0.4343451101984821 Prometaphase -10.813809 0.926802475830331 Prometaphase -13.455145 0.13420494747630474 Prometaphase -12.9494705 0.09113640612930685 Prometaphase -10.868058 0.0929786709066801 Prometaphase -10.924665 0.9042874658222364 Prometaphase -12.227641 0.4493691779610771 Prometaphase -13.737205 0.15060652815522568 MetaphaseAlignment -12.553914 0.5938300983792906 MetaphaseAlignment -3.7678683 0.5376233556508321 Polylobed -4.1287565 0.19840674774889933 Polylobed -3.9571419 0.603038139426371 Polylobed -4.007933 0.3402312343169456 Polylobed -1.1335361 0.23837076773086996 Binuclear --1.7339995 0.5104006108990281 Binuclear --1.5789672 0.2916492615163876 Binuclear -14.119708 0.22213992176121322 Prometaphase -15.4727955 0.8797224082996742 Prometaphase -13.154847 0.1557625765734173 Prometaphase -15.258485 0.2805899772848325 Prometaphase -13.586805 0.3861383798498257 Prometaphase -13.920229 0.27363831030002284 Metaphase --7.523585 0.2107106425777696 Metaphase --7.5260787 0.771146097054588 Metaphase -12.720012 0.3290161587235815 Prometaphase -15.0592 0.11251295769933845 Prometaphase -15.233128 0.5408951624566958 Prometaphase -13.709446 0.1254884972364103 Prometaphase -15.257977 0.3575196074409279 Prometaphase -13.482862 0.8818267622722838 Prometaphase -9.362053 0.3691611951062197 Prometaphase -14.417317 0.63250897428825 Prometaphase -15.214111 0.5146255360086313 Prometaphase -13.533785 0.1377248457779756 Prometaphase -13.071869 0.6490695977908352 MetaphaseAlignment -15.0827 0.8560349969338062 Prometaphase -15.246159 0.09321247404534405 Prometaphase -10.298638 0.7536800891297916 Artefact -14.819914 0.6902112815189781 Apoptosis -9.887217 0.8666605749971459 Artefact -13.544738 0.10409193213346701 Prometaphase -13.810168 0.3056953793966788 Prometaphase -15.0840845 0.6942914872263741 Prometaphase -15.115445 0.022017978886650247 Prometaphase -15.594616 0.7340756777304887 Prometaphase -14.156025 0.857978579001121 Prometaphase -14.5185375 0.4533040523767575 Prometaphase -15.648889 0.4343299034266521 Prometaphase -15.107624 0.278468962069366 Prometaphase -15.684467 0.005051870005087333 Prometaphase -14.734982 0.18630351666635603 Apoptosis -14.089661 0.9798641184223712 Prometaphase -14.682273 0.8672860771213566 Apoptosis -14.70954 0.2665046387489214 Apoptosis -14.81127 0.9637488571210464 Apoptosis -15.479223 0.36771261595328586 Prometaphase -15.630105 0.2975853747511801 Prometaphase -15.617561 0.15280763544988518 Prometaphase -15.677583 0.901611069510674 Prometaphase -13.659048 0.9869924910668593 Prometaphase -15.627753 0.32741140405268243 Prometaphase -15.6639805 0.7034304233584269 Prometaphase -10.175303 0.33530340980955464 Artefact -10.331766 0.10499073930545522 Artefact -6.6061506 0.3926923068823239 Artefact -13.842596 0.551569960761176 Apoptosis -6.575406 0.12301675917649424 Artefact -6.367671 0.8182727747897611 Artefact -6.497487 0.4973631168932895 Artefact -6.441072 0.23438295250457297 Artefact -6.305611 0.5927595785429434 Artefact -11.886026 0.7932593060544021 Interphase -13.5335865 0.3276243490519758 Prometaphase -0.14154077 0.7014763244005944 Interphase --3.9357603 0.42785917998068734 Interphase -11.295208 0.9640511688456009 Interphase -10.281314 0.7709400866027316 Interphase --5.3104424 0.35635090252202095 Interphase -7.4469085 0.017747959061762075 Interphase -15.447627 0.657519895380428 Prometaphase -13.203911 0.1978721585848523 Prometaphase -15.469913 0.12187917564913275 Prometaphase -15.022985 0.9317764513967938 Prometaphase -15.138904 0.9999489702149168 Prometaphase -9.585735 0.2698888205643851 Artefact -13.644817 0.301768552165938 Prometaphase -9.738539 0.1649889392381687 Artefact -13.609899 0.45768477592962375 Prometaphase -1.006086 0.8658437598357476 Interphase -15.294906 0.7015059521248778 Prometaphase -14.994645 0.8463315580765877 Prometaphase -1.2141985 0.22848165567358047 Interphase -13.752256 0.7304560674084801 Prometaphase -15.182474 0.9185266283960603 Prometaphase -13.645805 0.2814161142143904 Prometaphase -6.219851 0.6906535435911851 Prometaphase -13.78462 0.40065741168985447 Prometaphase -13.670656 0.2902266451589226 Metaphase --0.29466414 0.9696234687543495 Interphase -14.888199 0.3486332299682927 Prometaphase -13.304958 0.10784968126068728 Prometaphase -15.485066 0.3885892096203356 Prometaphase -15.422937 0.44767829095998235 Prometaphase -15.506904 0.7522108254992379 Prometaphase -14.924303 0.9499142669470028 Prometaphase -13.700335 0.8170569837942127 Prometaphase -9.402757 0.930941311255106 MetaphaseAlignment -14.978684 0.4750601299749311 Prometaphase -14.963243 0.7193165682364342 Prometaphase -15.595158 0.30904958501240243 Prometaphase -15.232247 0.06958710851901795 Prometaphase -15.478915 0.36547352532599353 Prometaphase -13.594378 0.6979152283282977 Prometaphase -15.4366455 0.2746221102480437 Prometaphase -10.294031 0.8614058956415209 Metaphase -12.3327265 0.7907728499829019 Metaphase -15.572458 0.19404254918460295 Prometaphase -15.586017 0.33083676249080674 Prometaphase -5.6468115 0.8253026175490917 Apoptosis -13.4512205 0.2439377895112994 Prometaphase -14.705284 0.33160214566713064 Apoptosis -14.680018 0.3430669324230867 Apoptosis -13.463781 0.8010420278257667 Prometaphase -14.689698 0.037030265605016766 Apoptosis -13.301291 0.0737869831214143 Prometaphase -6.0283775 0.5784515481629375 Prometaphase -15.368043 0.39711199099606664 Prometaphase -14.782969 0.6328853196107076 Apoptosis -14.82725 0.26646127617938287 Apoptosis -14.755848 0.9095826774815607 Apoptosis -5.6641364 0.7609418871744561 Prometaphase -13.365039 0.06774622055039603 Prometaphase -14.198706 0.7839054772850341 Metaphase -5.9630866 0.9695823723122513 Artefact -13.3641615 0.942435620587473 Prometaphase -5.7635956 0.7354837980254477 Artefact -13.378722 0.1235937534812156 Artefact -13.968035 0.5760767658404541 Prometaphase -13.151797 0.2842327288404888 Apoptosis --2.9185064 0.9702129436362472 Polylobed -1.0044565 0.7174857253616094 Interphase -11.874771 0.6107655897874192 Interphase -7.105042 0.6950880426047262 Interphase -14.302197 0.4379791291096805 Apoptosis -7.860458 0.37273675143760665 Interphase -7.592079 0.9357995794399642 Interphase -1.4812138 0.33192713479268665 Polylobed -0.9983156 0.04143613173271932 Polylobed --5.1674404 0.4008401541544574 Interphase -2.759292 0.7903403526216722 Polylobed -2.3407872 0.6484683580489194 Polylobed -0.09276174 0.9621707754778747 Interphase --5.2864585 0.10620186761913764 Interphase -1.597798 0.9115463022293834 Artefact -1.4112334 0.3688491939140621 Artefact -1.1736951 0.4740025854219123 Artefact -14.006652 0.42848215448923443 Metaphase -0.7464647 0.11645228109244143 Elongated -12.376908 0.2477964557251887 Metaphase -0.08929908 0.742271569229792 Elongated -0.56003416 0.0230075418456952 Interphase -8.1015415 0.13388777438627453 Apoptosis -8.077209 0.025210168791350074 Interphase -0.710543 0.8573880573235492 Interphase --4.132132 0.6173248277817797 Interphase --0.7964894 0.16992866211855884 Polylobed --2.266342 0.7687722230537536 Polylobed --2.2698624 0.9668005974515538 Polylobed -0.0724818 0.9345603745300182 Polylobed -3.8639984 0.9215475478121653 Polylobed -3.7669275 0.007810215405743226 Polylobed --2.6030695 0.5890134889852785 Polylobed -11.620832 0.25663261663902925 Interphase -14.343468 0.5646472143098588 Metaphase -11.558656 0.31803028916055465 Metaphase -11.615546 0.1536665427726126 Interphase -14.379881 0.4444937233583467 Metaphase -11.5404005 0.4950963271763734 Metaphase --2.3639224 0.9897655068476245 Polylobed --2.155221 0.04238788413854011 Polylobed --1.1671678 0.3011551988972766 Polylobed --1.3529665 0.8518938450883227 Polylobed -3.1085718 0.6596954981260428 Polylobed -3.0024674 0.8266830318458959 Polylobed -2.2369616 0.6602909185597778 Interphase -3.0127301 0.7595363091461329 Polylobed -3.076108 0.4929217499970573 Polylobed -1.0491805 0.2048776928975975 Binuclear -10.632603 0.8100038608371999 Interphase -1.2873522 0.5678206546639691 Binuclear -1.368687 0.13950519378682968 Polylobed -0.56628513 0.8048940268708661 Polylobed -0.439486 0.9585859103625782 Binuclear --0.22890043 0.3461638726284658 Binuclear -1.6876771 0.2935122520864435 Binuclear -1.6762711 0.08177517820756608 Binuclear -1.6761957 0.6644912264494156 Binuclear -7.5611367 0.9589616184964967 Interphase -2.1167843 0.13599268999160763 Binuclear -2.118777 0.4851596313193155 Binuclear -0.8550542 0.09823818465324785 Binuclear -1.4543467 0.034109903709815814 Interphase -0.6679995 0.6645010506676454 Binuclear -10.13022 0.8676871753434979 Interphase --5.671878 0.3375705361260297 Interphase --0.37827292 0.8757651744100993 Polylobed --2.2107055 0.1170530991510319 Polylobed --2.167985 0.2379606819174762 Polylobed --2.029326 0.9016067269067185 Polylobed --0.1260204 0.5085098936906702 Polylobed -1.2314538 0.6081923837970241 Polylobed -1.0904411 0.03801823254250791 Polylobed -1.3603388 0.12838991014788892 Polylobed -1.3540591 0.05579081250085316 Polylobed -1.1579947 0.9050007399496692 Polylobed -1.5767549 0.13479496387094703 Polylobed -0.8240541 0.8496728266087545 Binuclear -1.0875009 0.35103469937245313 Binuclear --0.43445763 0.80059749636578 Polylobed --0.5475767 0.3650435474567474 Polylobed --0.5315521 0.26768403969287646 Polylobed --0.5197303 0.09366070118905723 Polylobed --0.43928596 0.2807065905741337 Polylobed -8.543278 0.47147971671727995 SmallIrregular -8.768412 0.8175087988967955 SmallIrregular -8.611247 0.45251495476869785 SmallIrregular -2.9151876 0.9714355749376175 SmallIrregular -10.138981 0.541633010854823 SmallIrregular -9.296029 0.024945629811443437 SmallIrregular -9.886673 0.41536730019127244 SmallIrregular -11.520132 0.6888991073865137 SmallIrregular -9.251109 0.2342916462498248 SmallIrregular -8.415094 0.6986299508481587 SmallIrregular -8.457442 0.5036973051815162 SmallIrregular -2.7443879 0.025737775583115297 Binuclear -2.5085292 0.7743529494802057 Binuclear --3.1669714 0.5603737038140751 Binuclear --3.1829195 0.08249350308109338 Binuclear --3.1242263 0.475214033462273 Binuclear --2.9832997 0.28729288177474666 Binuclear --3.0499403 0.8796815425879331 Binuclear --3.3459005 0.2849270528072487 Polylobed --3.3537247 0.9416870709741989 Polylobed --3.2200165 0.5461327493471125 Polylobed -8.803148 0.3236137095467213 Binuclear --3.1530697 0.8135450174175773 Polylobed --3.2087204 0.6974003783657658 Polylobed -8.663967 0.4139624956958795 Binuclear -8.697916 0.6296183027065931 Binuclear -8.608532 0.7785842640282813 Binuclear --2.9994767 0.8515577942834893 Polylobed --3.002385 0.8164127137140637 Polylobed -10.164463 0.16607708859341797 Binuclear --2.8707354 0.8283895525035856 Polylobed --2.8948886 0.05862905888377212 Polylobed --2.8486257 0.2001706815505686 Polylobed -0.43489105 0.6229267200659891 Binuclear -2.5780168 0.11469252544690434 Binuclear -0.5833451 0.6033475950562001 Binuclear -2.5231338 0.3079656913635638 Binuclear -2.0195355 0.4294665851546483 Large -2.012611 0.31515150344547804 Large -1.9054637 0.07017356786490447 Large -1.2748775 0.5007587529219609 Polylobed -0.13053428 0.9635994443948874 Polylobed -6.395028 0.6117833481601271 Polylobed -2.3847833 0.3000318716171253 Polylobed -1.5831392 0.9561498341240191 Polylobed -2.2905862 0.38923725195222736 Polylobed --4.3922057 0.6975177338688435 Binuclear -2.3202705 0.6341128049224443 Polylobed --3.0287018 0.9529108480388515 Binuclear --5.031486 0.914602694494837 Binuclear --5.3556156 0.14800405635003122 Polylobed --5.1947083 0.08568587472942246 Polylobed --4.861872 0.8096423277988296 Binuclear --3.3479068 0.904943213296823 Polylobed -0.22276297 0.49297360392289946 Binuclear -9.096457 0.6907612738337751 Polylobed --2.8354018 0.0697882801784182 Polylobed --5.3805075 0.21980439785647188 Binuclear -0.40274552 0.14937619465672114 Binuclear --2.8117373 0.601160583550034 Polylobed -9.138873 0.19753989376610714 Binuclear --2.7904634 0.059846315550850626 Polylobed --5.386556 0.3022072827360107 Binuclear --2.7724054 0.6772124291139527 Polylobed --0.3905708 0.5778637242618545 Binuclear --5.458167 0.3307249654946358 Binuclear --5.5466795 0.8334788758433764 Binuclear --5.5601377 0.2762691080120896 Binuclear --5.479268 0.2605476383656826 Binuclear --5.206007 0.5599474107895882 Polylobed --5.151358 0.47821563984577564 Polylobed --5.3286514 0.44597266060036067 Polylobed --5.421836 0.15578561895406717 Binuclear --3.377611 0.5046461916329911 Polylobed --2.6518707 0.25901891727544424 Polylobed --5.1789784 0.3921798826190154 Binuclear --3.1673882 0.7335704476706063 Polylobed --4.3214235 0.9368628255593556 Binuclear --3.1998813 0.7738629651893243 Polylobed -0.063332066 0.4056338946232637 Large --4.066588 0.5930331393287639 Binuclear -0.3499589 0.746497815875704 MetaphaseAlignment --4.452064 0.4142210500958915 Binuclear -0.3912681 0.3602481588123011 Binuclear -14.690461 0.13524329595586326 MetaphaseAlignment -14.814308 0.7362608014758756 MetaphaseAlignment -14.75341 0.8385085908076249 MetaphaseAlignment -2.7704573 0.31658441102995283 Binuclear -2.1848319 0.7835077656425353 Binuclear --0.1167617 0.6102130583369875 Polylobed --1.4027658 0.784083046403244 Polylobed -0.007987182 0.4113373831496644 Polylobed --0.29373333 0.7475763636496067 Polylobed -2.8419595 0.408509117897626 Binuclear --0.14262158 0.0887579854194529 Polylobed -2.9356358 0.5505789181349441 Binuclear -2.9704797 0.45432872638864563 Binuclear -2.795334 0.25287988720157717 Binuclear --4.2151203 0.8921133349667691 Binuclear --4.187917 0.38285330553600116 Binuclear --3.7556689 0.039126111432281396 Binuclear --3.4047945 0.38213574020259033 Binuclear --3.2839189 0.42450980790036974 Binuclear --4.0820127 0.8616377231941599 Binuclear -10.511173 0.7991832081050435 Apoptosis -11.10314 0.19996658590792427 Apoptosis -14.476388 0.8390292259588709 Apoptosis -12.534425 0.2479272282536652 Apoptosis -14.345451 0.341934148164293 Apoptosis -10.790103 0.07681845397749132 Apoptosis -10.214046 0.055356313134363844 Apoptosis -10.652818 0.6808601208111374 Apoptosis -10.229742 0.923341084521733 Apoptosis -10.672422 0.4930168542378287 Apoptosis -10.900286 0.8440937119585831 Apoptosis -11.137565 0.7937512496926533 UndefinedCondensed -14.209413 0.6646787233813293 Apoptosis -10.897646 0.9782289311177785 Apoptosis -10.745639 0.802187920876386 Apoptosis -11.922367 0.27484439421426143 Apoptosis -10.234656 0.7890142462211079 UndefinedCondensed -12.175127 0.6926616462839036 Apoptosis -9.960591 0.39952044663635167 Apoptosis -11.2081 0.07679747022711503 UndefinedCondensed -2.1140635 0.21435952961809968 Polylobed -2.2260334 0.797237891643355 Polylobed -2.2972739 0.2484852881360492 Polylobed -1.8803413 0.997857983023348 Polylobed -0.64951295 0.6939881399885446 Interphase --4.8666043 0.47722248071760787 Interphase -0.73502713 0.5704366902119271 Binuclear -0.580589 0.6108727235414362 Binuclear -10.657738 0.9689448200326233 Interphase -11.740362 0.16174119463151948 Interphase --4.2420883 0.2723958026607546 Interphase -0.21295892 0.7095910479712436 Interphase -8.989717 0.23985798314356555 Interphase -12.373916 0.8712971622020502 Interphase -0.38193774 0.3465341756581186 Polylobed -0.28469205 0.1808393999158191 Polylobed --2.6912982 0.04762076084981848 Polylobed -11.430855 0.59559504031795 MetaphaseAlignment -13.448156 0.8252821895545517 MetaphaseAlignment -12.653968 0.27379311114879124 MetaphaseAlignment -13.435224 0.9175394862588203 Polylobed --0.35384336 0.7577342701180048 Binuclear -11.920338 0.06130068249878473 Binuclear -11.949901 0.5726329966747499 Binuclear -0.5593276 0.5674409403252657 Large -14.321849 0.20237230493598024 MetaphaseAlignment -14.376605 0.3345443786657215 MetaphaseAlignment -14.612174 0.4582788935837623 MetaphaseAlignment -14.06323 0.04807782123741844 MetaphaseAlignment -9.50464 0.14977640928588787 MetaphaseAlignment -1.2654564 0.1640043891105587 Polylobed --0.17672962 0.3792357544563909 Polylobed -4.0655227 0.7473574586765804 Polylobed -3.8367462 0.8313036590972589 Polylobed -3.8795109 0.16284904601703887 Polylobed -3.8976333 0.7519060360067699 Polylobed --0.42344922 0.6707099583881694 Grape -3.8888474 0.8906873919138784 Polylobed --0.49779448 0.9687912539451718 Grape --0.31270495 0.12446060880994103 Grape -5.813755 0.08067386142081667 Grape -5.6980305 0.7997301289520093 Grape -4.678042 0.16217087308585842 Grape -1.390285 0.1945745242572069 Artefact -1.5311046 0.8820359225258269 Artefact -1.5962887 0.938964897102525 Artefact -5.7283516 0.318471243922974 Polylobed -1.1296288 0.842168100437994 Artefact -1.2398366 0.858856909563651 Artefact -5.6613774 0.6053904805020363 Polylobed -0.91621923 0.566088448063145 Artefact -5.667483 0.5758384092456097 Polylobed -0.8942546 0.30128918764272283 Artefact --3.6755702 0.6188670389389908 Polylobed --3.654926 0.24358027110857006 Polylobed --3.714449 0.17763229007560877 Polylobed -3.317984 0.41295045442400635 Grape --3.6309848 0.7057804229844352 Polylobed --3.6569078 0.6214276873514643 Polylobed --3.6496892 0.9648820793397025 Polylobed -0.8559124 0.6464559113657224 Grape --2.8887296 0.14131404979592044 Grape -0.76311976 0.16121821329251806 Grape -3.3197596 0.09306714007783212 Grape --3.5677848 0.191173764997013 Polylobed --2.8631723 0.597950900679121 Grape --2.709167 0.8382864750148153 Grape -3.3642073 0.8938676520674573 Grape -3.326448 0.40004388668504853 Grape --3.6827056 0.5585830285872317 Polylobed -3.1653643 0.8091995921508134 Grape -3.2472448 0.3120705211951691 Grape -0.68012196 0.5956690044386951 Polylobed -1.126014 0.17408251105366568 Polylobed -3.7913668 0.4765403405180947 Polylobed -0.7315369 0.17307226036346834 Polylobed -0.86649966 0.46242639029914556 Polylobed -0.729912 0.3968196917675796 Polylobed -0.7260939 0.43326482350049333 Polylobed -0.73771966 0.243712226106518 Polylobed -1.2312063 0.13604334237850613 Polylobed --0.9030844 0.2756653279198856 Grape --1.0139408 0.23958003924390914 Grape --0.9516097 0.7342962025844497 Grape --0.79002863 0.7169750729815461 Grape --0.65355384 0.5844829937642724 Grape -4.1531177 0.09133379816877518 Grape -0.24923463 0.566261854280033 Grape -1.362188 0.07923702594059778 Grape -0.67968684 0.018173019276237823 Grape -0.0030543457 0.35259611513765576 Grape -0.6506402 0.2405449190638994 Grape --0.49438307 0.5946476814330426 Grape --0.1759923 0.9286424880696137 Grape -2.74977 0.7198520277711921 Polylobed -2.889884 0.7673608619709871 Polylobed -6.7131248 0.9508931103706227 Polylobed -3.5408912 0.6340910808965676 Polylobed -5.6008067 0.5777309414098256 Polylobed -1.1679332 0.6018679759527085 Grape -6.464727 0.8658052565674659 Grape -1.0564988 0.3880990352069851 Grape --3.8607981 0.795336225388004 Polylobed -6.502949 0.6522569349771614 Grape -4.098923 0.3045715507819634 Polylobed -6.4940257 0.013688410984091681 Grape -4.004807 0.20991999008691997 Polylobed -6.7661147 0.6999887646268611 Polylobed --2.541431 0.5796903097452348 Grape --2.560454 0.27459469982028606 Grape --4.454573 0.43430247140102396 Polylobed -6.4567566 0.6611487655814131 Grape --2.853339 0.12708614048989697 Grape --2.7492373 0.4322795808512164 Grape -6.5432606 0.41662421498197943 Grape -8.8136835 0.5426010201202406 Polylobed -5.2868967 0.521913680218495 Polylobed --4.7730436 0.36772668506784956 Polylobed -6.496758 0.7627336288125462 Grape -3.8835475 0.06722903999121377 Polylobed -3.876965 0.035538472534346055 Polylobed -2.4571881 0.42025933959837536 Grape -4.0848703 0.8966942501708143 Grape -4.150469 0.27756935052966913 Grape -2.86031 0.17230932469765314 Grape -4.190945 0.9282083943293762 Grape -10.332715 0.16822325458257026 Polylobed -10.317544 0.3748405367135256 Polylobed --0.5506227 0.7290155103364921 Grape -3.759678 0.2609240084776018 Polylobed -3.9499187 0.22984952779368129 Polylobed --0.5077147 0.3976672339315541 Grape --0.5012656 0.7873648145968252 Grape --0.788026 0.9109833531625063 Grape --0.52106994 0.03129252790977377 Grape --0.6990242 0.16510812737192027 Grape --0.5841231 0.9586012891469076 Grape --0.46835604 0.5927912915489684 Grape --0.5677468 0.5514102691538197 Grape -3.0613198 0.8135436792839946 Grape -3.0689478 0.039783298028444825 Grape -2.319015 0.013836251172974823 Grape -0.9771514 0.9674940497015918 Grape -1.2196324 0.8285706855726811 Grape -6.891047 0.7473817815582686 Polylobed -6.979371 0.15570077014486672 Polylobed -6.7263355 0.47660392304326005 Polylobed -1.4103769 0.9372935385658475 Polylobed -1.4153309 0.9335995956239547 Polylobed -1.2908144 0.009247106979528796 Polylobed -6.209424 0.7524914090061574 Polylobed --3.577721 0.006237766446710369 Grape --3.6159 0.9843017276705566 Grape --3.5432143 0.4169672019956985 Polylobed --3.623369 0.9735187579800997 Grape --2.8821676 0.3677468090306375 Polylobed -3.273446 0.2931872905734214 Grape --3.6146758 0.3162919638346049 Grape --3.4840257 0.7306790666385901 Polylobed --3.6209848 0.19967283393019697 Grape -3.3120549 0.8640576917926507 Grape -3.2993145 0.5905726936267424 Grape -3.32829 0.9699110259492697 Grape --4.648156 0.9870849034861486 Polylobed -0.7618948 0.18518200130214535 Polylobed -0.49457315 0.9557820535760178 Polylobed -10.791321 0.046538486379183674 Polylobed -2.6247342 0.02786372412310567 Polylobed -2.8266542 0.9438645773285519 Polylobed -3.4351296 0.9623967374499934 Polylobed -3.475472 0.14724767267198968 Polylobed -3.4343195 0.4424158153889981 Polylobed -12.807876 0.4940925279881825 Prometaphase -12.9316845 0.8602812365183045 Prometaphase --4.914608 0.627917243196169 Binuclear --4.55253 0.7745664242016155 Binuclear -0.871595 0.48509684543129095 Polylobed -1.1204187 0.6484137022418283 Polylobed -1.0375719 0.7354093600258587 Polylobed -1.1351956 0.7378812839173013 Polylobed -10.068912 0.0017378267846723805 Binuclear -1.1009578 0.09990807373006061 Binuclear --1.8518429 0.363660441104416 Polylobed --2.1319737 0.0611974471035418 Polylobed --1.76206 0.382989702367717 Polylobed --1.908119 0.38956684847219225 Polylobed --3.228545 0.9202335372777397 Polylobed --2.202113 0.5670718548946909 Polylobed --1.6887108 0.7063512805336583 Polylobed --2.2201123 0.8871986820862284 Polylobed -1.7481097 0.4421293206493173 Polylobed -1.6566632 0.0019621445973821983 Polylobed -1.6697905 0.28533602462541374 Polylobed --2.2396405 0.8141587000374869 Polylobed --2.302059 0.9018411698000748 Polylobed --2.2695255 0.35150958111822106 Polylobed --2.0891736 0.2939912529790484 Polylobed -1.6530684 0.9245052110687334 Polylobed --2.3762133 0.35107373598278 Polylobed --2.5795019 0.6522451880766583 Polylobed -0.15951768 0.2803092350505634 Binuclear -0.35668257 0.0979187987907415 Binuclear --3.711088 0.5012363117860135 Binuclear -9.192234 0.9232318371181676 Interphase --3.703074 0.618640753927305 Binuclear -1.0231704 0.20882662478373437 Binuclear -1.0254914 0.682429573367613 Binuclear --2.517704 0.7683611440976326 Large -10.134183 0.32076056191495883 Binuclear -8.515688 0.5592094014310386 Binuclear --1.2994167 0.6819531314673225 Polylobed --1.3322955 0.16035314538048817 Polylobed --1.3081046 0.9641009397724625 Polylobed --1.2363371 0.4512519614498419 Polylobed --1.0843079 0.9796643904720663 Polylobed --1.3105927 0.1895738658444951 Polylobed --1.3211621 0.4837735463096444 Binuclear --1.3113656 0.1658536536758377 Binuclear --1.3477641 0.010180822383938692 Binuclear --1.2636428 0.23787517257520274 Binuclear -0.14361449 0.4031873370680834 Binuclear -0.4927166 0.4311307377242357 Binuclear --0.016314512 0.8865051551042046 Binuclear -1.7490464 0.6356772638808419 Large -2.2225828 0.4344416552185302 Binuclear -2.518863 0.9879976996098128 Binuclear -3.0888968 0.6142243316266857 Polylobed -7.0741334 0.3226629649849716 Artefact -7.022463 0.6913108812107946 Artefact --3.3055334 0.4956999226830051 Artefact --3.1328325 0.8811676940681722 Artefact -7.2696285 0.8965112620633212 Artefact --3.2485175 0.3673504001211696 Artefact -7.3263755 0.5623390508601441 Artefact -7.300256 0.26577344404630543 Artefact --3.1295624 0.62663675174255 Artefact -2.8479536 0.9923469623196748 Polylobed -2.7125788 0.8325885178221839 Polylobed -0.082402155 0.3664099003028253 Binuclear -2.9557104 0.09538035199349248 Polylobed -0.28999767 0.9666819873735142 Binuclear -3.001029 0.38958477867772023 Polylobed --1.2790188 0.29338089326681516 Binuclear --1.1679943 0.31633539290556756 Binuclear --1.3122069 0.4899233666388467 Binuclear --1.2559804 0.9567513987490006 Binuclear -0.20203476 0.9866223213334914 Binuclear -0.12408173 0.29612672735239554 Binuclear -0.017390098 0.35146870774748507 Binuclear -0.16668238 0.511806560066037 Binuclear -1.5294772 0.008187288629735945 Polylobed -1.3252852 0.17483656324410912 Polylobed -1.2958947 0.9360590205048064 Polylobed --1.6614119 0.029435237061361197 Polylobed --1.7217834 0.1009762797205438 Polylobed --1.68574 0.42064701703371876 Polylobed --1.7115276 0.731450249159012 Polylobed -2.114163 0.2688782303903485 Polylobed --1.3289199 0.30596559055389094 Polylobed -2.146382 0.30547298012438673 Polylobed -2.255019 0.8726019267536065 Polylobed --1.7130164 0.22609760622741792 Polylobed -2.2027924 0.5624018260131903 Polylobed --1.656297 0.9999311470188161 Polylobed --1.8130223 0.7284654948930182 Polylobed -4.2945375 0.336350655296926 Polylobed -4.710465 0.7654589703324095 Polylobed -4.3252435 0.9539157073856089 Polylobed --2.2015657 0.37353780715258433 Polylobed --2.2341492 0.7080112400693306 Polylobed --2.2115235 0.07580426311938915 Polylobed -1.8852518 0.8952968367541417 Polylobed -1.8342499 0.096256724624312 Polylobed -2.0365171 0.37292509626159465 Polylobed --2.257111 0.5425058428622357 Polylobed --2.3182578 0.26357764245021886 Polylobed -1.7146615 0.6492693259871776 Polylobed --2.4014184 0.5544287273924114 Polylobed --0.05255334 0.717185240298106 Polylobed -0.052487396 0.3541393651221608 Polylobed --0.1316694 0.5460138896526734 Polylobed -0.22488576 0.812185400181229 Polylobed -10.440129 0.8106963460014557 Metaphase -10.395469 0.7142399748518815 MetaphaseAlignment -8.989086 0.5419358896149794 MetaphaseAlignment -11.944972 0.6732611007744953 Prometaphase -12.592428 0.08688029011205078 Apoptosis -13.088409 0.4713172007107326 MetaphaseAlignment -14.435717 0.860143500304649 Prometaphase -13.107347 0.6073648217266423 Apoptosis -14.320281 0.94168310582509 Prometaphase -11.330198 0.2621785212940809 Apoptosis -14.73473 0.09630180211077544 Apoptosis -0.44850114 0.6266134356038866 Binuclear -14.224538 0.09092592982038039 Apoptosis -0.6469081 0.7821353634244159 Binuclear -9.053687 0.25674240814905724 Prometaphase -9.258045 0.5387518584436566 Prometaphase --3.9318283 0.8113770403016125 Binuclear --3.5955904 0.7880353687003 Binuclear -11.013643 0.4959750270792086 Prometaphase -11.02402 0.9758464133154727 Prometaphase -7.479558 0.49763315709084066 Prometaphase -11.844647 0.27879141669105545 Prometaphase -10.901928 0.3024312445516868 Prometaphase -7.5117464 0.7436427238772702 Polylobed -7.4409723 0.43710703889059366 Polylobed -7.567253 0.41878848478549024 Polylobed -14.457323 0.02048659526993446 Apoptosis -14.499361 0.30527816042104927 Apoptosis -6.120414 0.8603830798416068 Binuclear -7.234788 0.8035044982271469 Binuclear -0.34506118 0.7465108599826601 Binuclear -0.19306721 0.023128386675719925 Binuclear -9.70789 0.02699029143169318 Artefact -10.70761 0.3114649593908826 Prometaphase -9.386421 0.47445327221813327 Polylobed -7.5419507 0.9097412679862698 Polylobed -8.862806 0.38462981600553936 Polylobed -7.326157 0.28852500286289273 Binuclear -7.289265 0.5711529683047126 Binuclear -10.902541 0.9146942995496945 Prometaphase -14.793705 0.73863871146225 Apoptosis -3.1826534 0.8566662819793105 Binuclear -6.852846 0.18219764492303292 Binuclear -3.1605377 0.1256644453137924 Artefact -3.263777 0.5440711336888929 Artefact -14.861209 0.69737082571725 Apoptosis -14.749036 0.9549880481625318 UndefinedCondensed -11.35449 0.3715344084564789 Prometaphase -12.400323 0.3465568522036966 Prometaphase -14.484859 0.5158542979015847 Prometaphase -10.38959 0.6849379367067089 Prometaphase -11.8707695 0.5660237771153032 Prometaphase -9.351603 0.7230306933325961 Polylobed -7.3172293 0.3803149371124904 Prometaphase -9.427314 0.8621976301852416 Polylobed -9.438816 0.18297559358395976 Polylobed -12.682657 0.315904642828328 Prometaphase -14.820321 0.7736776637703174 Apoptosis -10.997138 0.3356722539793957 Prometaphase -9.119396 0.6681546023700152 Prometaphase -11.00851 0.2877976092729607 Prometaphase -10.8117695 0.18710434243329277 Prometaphase -11.6831875 0.30073256395315373 Prometaphase -6.933818 0.88699329703788 Binuclear -7.0113373 0.460883128691712 Binuclear -7.0642505 0.2254151537382285 Artefact -11.822615 0.321438589165044 Prometaphase -3.2415676 0.002770956625681942 Polylobed -2.894336 0.7558082877570287 Polylobed -2.8734834 0.2256774524328019 Polylobed -3.4519944 0.5979210402705024 Polylobed -3.0825703 0.02510953817714312 Artefact -2.6454215 0.8344984319735175 Polylobed -2.7958033 0.18744475363635515 Polylobed -3.2097096 0.004768377764269971 Polylobed -3.1923125 0.3011878083654256 Polylobed -2.7804356 0.9204871188372717 Polylobed --0.084041074 0.2626142180446358 Polylobed --0.33351263 0.5034788381179114 Polylobed -13.546071 0.4072298443577044 Artefact -13.439323 0.2942492583320867 Artefact -5.9363556 0.9273535786015709 Binuclear -7.089673 0.7889949080537266 Binuclear -11.452225 0.7619133153738572 Prometaphase -12.015169 0.5261071244812398 Prometaphase -13.708824 0.8248616737298372 Apoptosis -10.316683 0.45842865821125367 Prometaphase -1.4277015 0.46518955047155564 Binuclear -0.8497082 0.49377406341346375 Binuclear -10.883144 0.23691022522457095 Prometaphase -10.489811 0.9236518593869899 Prometaphase -9.326237 0.2587506748785492 Prometaphase -9.464852 0.19815949993946502 Polylobed -3.191824 0.24375179333158525 Polylobed -10.420704 0.817352116565939 Prometaphase -9.413386 0.7685456376763616 Polylobed -12.20625 0.4271068084560682 Prometaphase -7.3461947 0.013781904624582508 Polylobed -7.3342314 0.9757350332508857 Polylobed -10.091682 0.13253097749036336 Polylobed -12.2327385 0.8514226765922394 Prometaphase -13.102304 0.7817230866307524 Prometaphase --4.2602043 0.938075781711365 Polylobed -14.532781 0.7733985390852888 Apoptosis --4.578461 0.3313715908270286 Polylobed --5.1650214 0.23191404341416944 Polylobed --4.981185 0.3776908204064149 Binuclear --4.5074544 0.20308717051667458 Binuclear -8.74738 0.7195747477449508 Binuclear -10.937565 0.8265186324941105 MetaphaseAlignment -8.6590805 0.646438892397248 Binuclear -8.932691 0.1860927313091021 MetaphaseAlignment -7.37237 0.03738471102864571 Prometaphase -9.949421 0.5804012668569645 MetaphaseAlignment -14.034855 0.46267310507961357 MetaphaseAlignment -6.098426 0.19970392819903238 Polylobed -12.647578 0.3680839022202911 Prometaphase -6.1536674 0.5414662556089532 Polylobed -6.2732387 0.6329171122765795 Polylobed -0.61045617 0.4766437775428183 Polylobed -6.229863 0.9713928217354079 Polylobed -0.5044222 0.04622640047085602 Polylobed -0.52740854 0.6582079130268984 Polylobed -0.5918019 0.9095579017878257 Polylobed -12.445174 0.236564211113225 Apoptosis -10.097251 0.2137071880691377 Artefact -10.248122 0.7622328074825822 Artefact -10.070768 0.48637379410523596 Artefact -10.065211 0.992177506697882 Artefact -14.687839 0.5891236204917122 Apoptosis -13.905508 0.9234419334879927 Apoptosis -0.34546572 0.012210519663421904 Binuclear -0.54599464 0.15453962047570513 Binuclear -13.954797 0.6844190447515897 Apoptosis -9.977415 0.416466369897446 Metaphase -14.768814 0.8359645694289802 Apoptosis -0.002985856 0.4288980925565181 Binuclear --3.1014068 0.7129966963549856 Binuclear -7.3534327 0.5661600170334582 Binuclear -0.75809604 0.11990356732862284 Binuclear -11.056394 0.7179512067070324 Hole -14.083284 0.7063718013476981 SmallIrregular -14.049843 0.11571815121321471 Hole -8.881051 0.561911457807729 SmallIrregular -12.181396 0.33565001754871837 Hole -10.61004 0.45884978204577553 SmallIrregular -12.068405 0.42275869303077596 SmallIrregular -9.974725 0.1047744042491886 SmallIrregular -14.240604 0.15802298629127975 Hole -8.584566 0.08680861614365021 SmallIrregular -12.425517 0.19244701360336192 Hole -10.976437 0.8524443864977456 SmallIrregular -10.737384 0.024522649262932905 SmallIrregular -10.378216 0.43161725028685605 UndefinedCondensed -9.211275 0.8009363887030907 SmallIrregular -8.980731 0.1002582045273901 SmallIrregular -8.94298 0.22771359015896464 Hole -11.628797 0.7274397075582869 Hole -12.298337 0.6475524325933526 Hole -11.736331 0.5630238367151661 SmallIrregular -14.018194 0.17164081662818076 Hole -10.777851 0.7945310119658656 SmallIrregular -11.441854 0.14141411188129605 SmallIrregular -11.780654 0.13479789472306425 SmallIrregular -9.674935 0.7950350443033544 Folded -11.283437 0.3692233278840753 Hole -14.162828 0.31261711592334684 Hole -11.70948 0.2786720513102364 Hole -14.777371 0.32497210636760143 Apoptosis -14.822589 0.3405875997076052 Apoptosis -14.880553 0.6963676936065396 Apoptosis -14.858528 0.2402622011512725 Apoptosis -14.818089 0.3253264534739049 Apoptosis -8.914299 0.7762115084700661 Hole -8.923119 0.9131505733137121 SmallIrregular -9.565884 0.6128359702284323 SmallIrregular -15.2505865 0.10511822946280702 Apoptosis -11.457194 0.5415846607086229 Hole -15.3134365 0.6277788742244063 Apoptosis -10.987369 0.3140526970069475 SmallIrregular -12.595773 0.8280003729268972 Elongated -11.717777 0.7820788107571781 SmallIrregular -11.237082 0.5753067067202899 Hole -14.462488 0.1450155894271119 Hole -11.435697 0.6973814656840917 SmallIrregular -11.265109 0.8953486638415221 Hole -14.183456 0.2501011097732363 Hole -14.4838085 0.9586858956840082 SmallIrregular -9.144578 0.7713023970302836 SmallIrregular -8.941825 0.30032157477911237 SmallIrregular -11.469523 0.6168211241115817 SmallIrregular -11.05764 0.3078479320806945 SmallIrregular -11.045592 0.09946787609389407 SmallIrregular -11.133306 0.8241486696206854 Prometaphase --3.321058 0.6658070345090323 Polylobed -10.552886 0.6140006512807219 Binuclear -0.12782624 0.14217288071323342 Binuclear -11.814826 0.37737711316979705 Interphase -1.8745507 0.8766360800049111 Elongated -2.1479332 0.1633044607769063 Polylobed -2.6133113 0.04790288699711853 Polylobed -2.8780754 0.1911514768901753 Polylobed -6.5078316 0.250180007174864 Interphase --5.2975836 0.7836425237658834 Polylobed --1.9523029 0.6481414711893314 Binuclear -0.9827797 0.01734274680649217 Binuclear -7.358438 0.01728965274119665 Interphase -9.955495 0.5668964288000554 Interphase -10.794404 0.6332924423782887 Polylobed -10.833889 0.12542188091555395 Polylobed -10.849866 0.6104621520835593 Polylobed -7.1651287 0.24035191041954007 Artefact -9.661601 0.3367213424605472 Large -2.7181885 0.4088315803843413 Large -2.739554 0.11619479623427964 Large --1.4431747 0.963093605444955 Polylobed -0.87365395 0.973764453627561 Polylobed --1.5908695 0.4450016890894074 Polylobed -12.708884 0.2574664780802747 Apoptosis --4.1502695 0.9585944465253112 Polylobed --3.5455875 0.8180573965471908 Polylobed --4.0284486 0.724228605558232 Polylobed --4.5178246 0.4618117872776393 Polylobed --4.5655236 0.41057870144117214 Polylobed --4.360781 0.7793492722768518 Polylobed --5.4729614 0.5196136777646266 Interphase --4.3730245 0.6605821388208081 Interphase -11.581418 0.5661929816084119 MetaphaseAlignment -8.984833 0.7121854071513629 MetaphaseAlignment -8.960002 0.09410500407840094 MetaphaseAlignment --4.045634 0.3776210926357868 Interphase -2.4293885 0.1670891885098651 Polylobed -2.4858234 0.2420439000235356 Polylobed --2.7501035 0.2008242765381808 Binuclear --4.3994026 0.5033222993038652 Binuclear -8.007825 0.018992988509918707 Interphase -10.592302 0.9639676020084441 Elongated -1.308568 0.5189638264095712 Elongated -10.586855 0.05257151420438899 MetaphaseAlignment --5.4889617 0.6820538403617592 Interphase -10.61867 0.26888805009787653 Interphase --4.2403326 0.10676759258842983 Interphase --3.9471788 0.8303036859749948 Binuclear --3.961319 0.1979076029143242 Binuclear -15.2196865 0.10475426885963135 Prometaphase -15.023874 0.05690645260383198 Prometaphase -14.3908205 0.728557743534167 Metaphase -15.32539 0.655271247322768 Prometaphase -15.524673 0.008199140586074072 Prometaphase -14.236869 0.22148054044830368 Metaphase -13.843362 0.7046820311167495 Prometaphase -15.538193 0.15214085470395233 Prometaphase -14.947353 0.5767507831261646 Prometaphase -13.102643 0.2131564667521073 Prometaphase -15.090439 0.4492342848107873 Prometaphase -13.358462 0.8674230618017811 Prometaphase -13.474545 0.302596427300986 Prometaphase -13.623381 0.6859723899252312 Prometaphase -14.223199 0.4382812327828012 Prometaphase -15.052076 0.0961552543014883 Prometaphase -15.323658 0.36063499365180096 Prometaphase -15.282644 0.8412800663481076 Prometaphase -12.6819515 0.9730462422682282 Prometaphase -12.269796 0.8450816706160053 Prometaphase -12.900131 0.16222839077355067 Prometaphase -9.870816 0.3455092984490846 Prometaphase -12.989844 0.8311881586067501 Prometaphase -15.2613945 0.5538959343957655 Prometaphase -15.2025175 0.7761857971144082 Prometaphase -14.391662 0.7777832666167901 Prometaphase -15.179622 0.0710879006301196 Prometaphase -13.5712185 0.3424065656010231 Prometaphase -13.022664 0.4790215054529672 Prometaphase -15.008822 0.945224721580388 Apoptosis -13.138396 0.8982288010160042 Prometaphase -13.104396 0.4230319829312198 Prometaphase -13.200086 0.09566592748691505 Prometaphase -14.555718 0.40454228688038174 Apoptosis -14.08262 0.5359033326412808 MetaphaseAlignment --4.1469727 0.9043971913990313 Interphase -0.14911357 0.29487926714121704 Polylobed -0.3430438 0.30886414566898734 Interphase -0.36140352 0.627183234547841 Polylobed -0.115103066 0.505191555071379 Polylobed -0.1743402 0.19586895905880652 Polylobed --2.7882204 0.14472724884257937 Interphase -11.91166 0.4894972098053466 Interphase -1.0718215 0.7793909564002804 Binuclear -1.8868254 0.7788312515567875 Binuclear --0.10949497 0.9758036873205908 Binuclear --2.2080898 0.3827181981579947 Binuclear -9.486495 0.391989326810261 Prometaphase -10.163938 0.0018194947805292294 MetaphaseAlignment -0.66707915 0.8299173651954892 Interphase -0.65817696 0.6466284440680506 Interphase -13.213105 0.7699221187318909 MetaphaseAlignment -0.8724903 0.47062319994480306 Binuclear -9.815538 0.9201934886408648 Interphase -9.694206 0.029113797832714394 Interphase -0.88327134 0.11528118273788834 Binuclear -0.69160485 0.37601733497150314 Polylobed -14.955225 0.0851059081135721 MetaphaseAlignment -0.3660871 0.08788788196507191 Polylobed -13.0645075 0.5668726793505451 Prometaphase --4.545819 0.2161220195699175 Binuclear -9.74999 0.6265352105571295 Interphase --4.471864 0.9293998221452582 Binuclear -0.57442224 0.8238822097031292 Interphase -6.729565 0.2484303875538083 Prometaphase --2.6266198 0.08966292611132298 Interphase --5.026752 0.9501924682159203 Interphase -11.975221 0.9944959313733248 Interphase -11.557846 0.3744948994651792 MetaphaseAlignment -11.633951 0.460677261142561 Prometaphase -13.026805 0.14533366133006653 MetaphaseAlignment -13.832852 0.7101807927776813 MetaphaseAlignment -13.521587 0.4218336068528409 MetaphaseAlignment -12.896616 0.8030151452544093 MetaphaseAlignment -5.7846375 0.5772967114245704 MetaphaseAlignment -10.364892 0.04278380916663371 MetaphaseAlignment -5.9387074 0.3349545405035974 Prometaphase -10.289635 0.8085961706701537 MetaphaseAlignment -14.019502 0.6881138813839268 MetaphaseAlignment -13.212509 0.6325319473789155 MetaphaseAlignment -13.421048 0.6725046423160782 Prometaphase -13.40321 0.6050368756235982 Prometaphase -9.21563 0.6411243265875745 MetaphaseAlignment -8.170651 0.620421104640871 MetaphaseAlignment -14.134286 0.5334279395046583 MetaphaseAlignment -13.945865 0.7577471254274131 MetaphaseAlignment -8.980915 0.36949256352123194 MetaphaseAlignment -11.564226 0.4752383386405016 MetaphaseAlignment -9.00145 0.40144955691968986 MetaphaseAlignment -7.195388 0.20921049112192747 Prometaphase -12.948433 0.3218889624998843 Prometaphase -12.961907 0.05501507760990065 MetaphaseAlignment -9.686255 0.32552763392261075 Prometaphase -13.181699 0.3905830929331291 Prometaphase -13.276594 0.09338409764882383 Prometaphase -9.784489 0.2204551297120967 MetaphaseAlignment -5.826854 0.24954174744284097 MetaphaseAlignment -5.73022 0.2274702082086888 Prometaphase -13.602087 0.35022170152394927 Prometaphase -12.686043 0.14487610506997384 MetaphaseAlignment -6.31474 0.7315013568444494 MetaphaseAlignment -6.3505344 0.3426548553177532 MetaphaseAlignment -12.56774 0.12236523402169797 MetaphaseAlignment --2.8028135 0.5935997813267829 Interphase -0.17642969 0.1796481751174227 Polylobed -0.27281883 0.05805632067769373 Polylobed --0.1809774 0.16329214202474285 Polylobed --0.539004 0.4199948120491419 Polylobed -0.8852286 0.6935089719274358 Binuclear --4.2925653 0.6415008847712601 Interphase -0.8704735 0.005205979359825141 Binuclear -11.841707 0.793290652514086 Interphase --5.3866863 0.3356877784624941 Interphase -1.0211558 0.6797156909555698 Binuclear -1.5961975 0.032673171706932846 Binuclear --4.3972735 0.3937882433723108 Interphase --4.299732 0.019610501319103024 Interphase -14.169253 0.8163455351451152 MetaphaseAlignment --4.2019277 0.18696718051616434 Interphase -13.066343 0.6745479075190918 MetaphaseAlignment -0.44020653 0.2770661627261115 Interphase --4.4175744 0.13535111433764768 Interphase -15.3495 0.9302953390465766 Prometaphase -14.256313 0.5950246157866221 MetaphaseAlignment -6.681732 0.8906310472988932 Prometaphase -2.329158 0.30659940165063915 Polylobed -6.0751853 0.8298353336783945 Artefact -1.4235255 0.9416823334062824 Polylobed -6.323728 0.3951838267867882 Artefact -1.2196815 0.9041310511984469 Polylobed -6.159349 0.33508574267910995 Artefact -6.240304 0.3245999991272911 Artefact -1.1907356 0.9957326721950958 Binuclear -2.3875093 0.9524104481773523 Binuclear -15.193861 0.228618345971475 Prometaphase -10.449278 0.8372020270371454 Prometaphase -10.300555 0.3424578776409021 Interphase --2.6467335 0.9792433739780269 Interphase -12.07711 0.8498176287600633 Interphase -6.3352084 0.9049193013125382 MetaphaseAlignment -5.0166974 0.5736735114895585 Polylobed -5.1570153 0.2941513523211804 Polylobed -13.07328 0.19752500637671 MetaphaseAlignment -12.900592 0.5278719205461331 MetaphaseAlignment -9.670814 0.6715157252329823 MetaphaseAlignment -12.8542 0.7417886351237006 MetaphaseAlignment -9.322809 0.17038321256296285 Prometaphase -13.54186 0.5358879925710621 Prometaphase -13.869668 0.43610982009521804 MetaphaseAlignment -13.27385 0.20822421097781196 MetaphaseAlignment --1.4984468 0.552962317358824 Polylobed --1.4184647 0.27591372180645746 Polylobed -1.7600772 0.5350545571095502 Polylobed --1.4540145 0.20010934490087728 Polylobed -9.0946865 0.8999425369158052 MetaphaseAlignment --1.4014091 0.33810282491959054 Polylobed --1.4810991 0.610334602657793 Polylobed --4.453915 0.12193298741989589 Polylobed -13.997096 0.6300171925284672 MetaphaseAlignment -0.8277804 0.27204033929140836 Binuclear --4.226399 0.9565315903711672 Binuclear --4.425868 0.39415426604133086 Binuclear -1.3163598 0.607406556711858 Binuclear --4.775046 0.21581336566329634 Binuclear --5.482927 0.8614762028764 Binuclear --5.491547 0.8043329181204663 Binuclear --5.3187394 0.2456353122757715 Binuclear --5.269731 0.9110485080061057 Binuclear --5.5469093 0.040155022034127685 Binuclear --5.31654 0.7525731205167397 Binuclear --2.449352 0.4578687853426443 Binuclear -0.8481752 0.835459394476964 Binuclear --4.0744786 0.465485967436008 Binuclear --5.9427357 0.06558209567040296 Polylobed --4.1911583 0.22654553229584695 Binuclear --5.885033 0.9943748347169384 Polylobed --5.830995 0.3341776027693687 Polylobed --0.6012724 0.16030521702543288 Binuclear --0.62528145 0.8058308258054888 Binuclear --3.888284 0.045069827390500805 Binuclear --4.032479 0.06640875146876257 Binuclear --5.602535 0.2975187794543661 Binuclear --4.7214694 0.04740097463539672 Binuclear -0.25578383 0.9957126838923056 Binuclear --5.50731 0.6911288860821325 Binuclear --3.0380206 0.5195864408573572 Binuclear --5.5506334 0.8051663899562637 Binuclear --4.6124406 0.6624868006119337 Binuclear --5.3595943 0.17326495367589456 Binuclear --4.691992 0.2196337996488893 Binuclear --5.5351 0.8676942857212915 Binuclear --5.4508495 0.3369613267738214 Binuclear --4.539369 0.4476403426248784 Binuclear --5.453465 0.3911357121233465 Binuclear --3.5931723 0.05202981200504242 Binuclear --3.0208344 0.7045737172836195 Binuclear --3.0619133 0.5400740807862157 Binuclear --5.361651 0.6761400818139303 Binuclear --4.6292315 0.27823138966714334 Binuclear --5.3155117 0.4069139083269768 Binuclear --5.3263526 0.19264976227164798 Binuclear --5.2117505 0.41968263143719775 Binuclear --4.871653 0.6534914117970338 Binuclear --4.6088448 0.944689043192468 Binuclear --5.2916107 0.6634571998860077 Binuclear --4.122077 0.5251597299929988 Binuclear --3.0672872 0.4163672224981614 Binuclear --3.1767943 0.10554968426098033 Binuclear --5.4647675 0.4770742969319903 Binuclear -5.0789227 0.15510809316064378 Binuclear --5.3970075 0.5821871830790901 Binuclear --4.1839247 0.7302470776105839 Binuclear --2.962664 0.9464956116881924 Binuclear --2.2741017 0.2968528676879666 Binuclear --2.4928823 0.396905538757533 Binuclear -0.14261316 0.18295193952533229 Binuclear --5.5309896 0.8779405098804326 Binuclear --5.5145073 0.8240772244494228 Binuclear --5.620576 0.7749598946732359 Binuclear --5.3347735 0.057290038549901334 Polylobed --5.113345 0.5567194419749216 Binuclear -3.8492198 0.23501356203546908 Polylobed --4.954163 0.11968519672230693 Binuclear --4.8486967 0.7816131001833894 Polylobed -3.792151 0.21158137510768193 Polylobed --5.2529583 0.5008116982476696 Polylobed --5.227049 0.5260963484005408 Polylobed -2.6006193 0.21328200403897157 Polylobed -1.7781141 0.49226116632238226 Binuclear --4.047312 0.052819897599996946 Binuclear --5.444295 7.2449638492178e-05 Binuclear --5.539017 0.8803219316643702 Binuclear --5.441919 0.4155887020861522 Binuclear --5.2593055 0.9074197989533556 Binuclear --5.1171694 0.49630735972825957 Binuclear --5.4615707 0.4311082945881648 Binuclear --5.0470204 0.29591214065860727 Binuclear --4.641364 0.08168893759303875 Interphase --5.397991 0.30969464926452184 Binuclear --5.1335087 0.6084702651738416 Binuclear --5.250246 0.7627863472229892 Binuclear -9.062414 0.5408255176538437 Interphase --4.8419304 0.7582558436964189 Binuclear --4.507162 0.15775159165898744 Interphase -9.135957 0.5832399316791738 Interphase -8.772195 0.8826032270257104 Interphase --4.559697 0.21796020764080337 Binuclear --4.6369147 0.2276689383929903 Binuclear --4.5960617 0.7484325130680506 Binuclear --5.64203 0.8120567848487955 Binuclear --5.209603 0.24804632969409923 Polylobed --5.6030536 0.3315698041877778 Polylobed --5.6355352 0.6317694004506118 Polylobed -8.26773 0.3984317131891898 Artefact -8.19714 0.6240109701292529 Artefact -8.201624 0.4964300320967251 Artefact --5.575086 0.773651309078725 Polylobed -7.4650183 0.8458886469133242 Polylobed -7.824458 0.8638272093051601 Polylobed --5.4573054 0.6115250777298431 Polylobed -7.6035795 0.6125841045192504 Polylobed -8.978437 0.4496468988194058 Interphase --5.30128 0.02761404496330544 Polylobed --2.8137302 0.29918021946326245 Polylobed --2.8412242 0.0636977185007408 Polylobed --2.7146997 0.4377852488597531 Polylobed --2.7158144 0.8494852489826142 Polylobed --4.9826984 0.5968020177048212 Polylobed --4.932013 0.675532598269557 Polylobed --4.8546352 0.18800797177707496 Polylobed -8.94725 0.26195314970279426 Apoptosis -8.404026 0.16313215297289452 Apoptosis --4.786032 0.8426887622604307 Artefact -8.040436 0.28031301298749256 Artefact --5.3709235 0.5623263097012292 Artefact -8.067019 0.14112861301900848 Artefact -8.005759 0.12398890832731169 Artefact --5.298003 0.34758336321601746 Artefact --4.7976246 0.3303531580600173 Artefact --4.3541536 0.32885323381140785 Artefact --5.364502 0.6009327870197562 Artefact -7.922074 0.31599015604044445 Artefact -8.137592 0.7687358689747713 Artefact -6.20732 0.09067076824664133 Hole --5.591556 0.3247858577376286 Polylobed --5.8878074 0.6691931285020627 Polylobed --5.826344 0.60293211047753 Polylobed --4.052539 0.11951561836705538 Polylobed --4.2973113 0.4394734931348375 Polylobed --5.823081 0.30922146287453667 Polylobed --4.1295304 0.6318704323147726 Polylobed -13.854485 0.12987027957280362 Hole --2.1909702 0.6705659229837077 Polylobed --2.1456194 0.46383253126978974 Polylobed --2.075159 0.274889987919257 Polylobed --2.0058925 0.7474439936640761 Polylobed --2.108618 0.1879098553132461 Polylobed --3.6734574 0.1522379385630518 Polylobed --0.55206823 0.015355936564545813 Polylobed -9.03613 0.861300356312702 Polylobed -9.334572 0.49236554071343774 Polylobed --4.7355633 0.5167156093215455 Polylobed --5.20113 0.11476261670583532 Hole --4.7180424 0.10777613604762737 Polylobed -7.305722 0.33378340489470293 Binuclear -7.292172 0.5162306440186114 Binuclear -11.786669 0.7008722190433495 UndefinedCondensed -7.2942786 0.4992703565129284 Large --2.9193063 0.8315584310390406 Interphase --5.4255576 0.37739233174698905 Binuclear --5.5377054 0.028087991954701463 Binuclear --2.850219 0.42615227502611275 Interphase --2.6966946 0.08500262931736602 Binuclear --4.501505 0.3220544273636916 Binuclear --2.6096272 0.9851546831184075 Binuclear --4.8033223 0.8573542571210433 Binuclear --4.774385 0.23645571315893898 Binuclear -0.5813159 0.3759624313127088 Binuclear -0.5728185 0.43116513094431075 Binuclear -8.709768 0.7638683777001858 Hole -12.777878 0.03345712782056509 Folded -1.0778825 0.038622770027903375 Folded -10.645085 0.11711786752871545 Hole -10.331022 0.6283249825850742 Folded -11.997031 0.4145466172444473 Hole -14.108848 0.8489256287623339 Hole -12.928538 0.28405789022783723 Folded --4.968178 0.4892520298094337 Hole -2.4886572 0.7067857215297443 Hole -2.1859312 0.5452011971650093 Folded -2.265565 0.31679997775695934 Folded -10.383199 0.1525427686133508 Hole -10.861063 0.5406034186244121 Hole -1.6656305 0.14215650226214827 Polylobed -3.8171883 0.9393044596914456 Polylobed -2.011017 0.5398906106106404 Hole -1.8246207 0.1749153048569262 Elongated -2.7157514 0.7206425469169074 Folded -12.618584 0.10354719431977222 Apoptosis -2.715304 0.7716447899740201 Folded -12.753265 0.4572891110790437 Apoptosis -2.5012417 0.13266195551253201 Folded -6.8303137 0.037211036308801515 Hole -14.53418 0.9933779936899051 Apoptosis -13.484756 0.2743682900876313 Apoptosis -13.456126 0.9522486220717061 Apoptosis -11.94492 0.5018363367370507 Apoptosis -15.1732 0.5030153839254815 Apoptosis -12.731173 0.0910697183721334 Apoptosis -12.824013 0.950705818837975 Apoptosis -14.647333 0.7328958645500868 Apoptosis -14.650448 0.5187278381595511 Apoptosis -13.744997 0.12025713907388602 Hole -13.24836 0.17567656948828547 Hole -10.328189 0.43216660514178373 Apoptosis -14.68891 0.36225882073221694 Apoptosis --7.5705385 0.5581380068713327 Apoptosis -14.864514 0.07634397475720878 Apoptosis -9.855524 0.04034601931833981 Folded -11.769449 0.5111030866900542 Folded -12.791755 0.028501380929072284 Polylobed -11.054185 0.0595519540101761 Hole -0.8061346 0.3763414581904063 Folded -2.0822418 0.06177906973480196 Folded -3.7004285 0.3854696266163501 Polylobed -2.341334 0.24175051590943641 Folded -2.329434 0.18777029710908766 Folded -11.78326 0.40467983148626285 Folded -11.792384 0.11990390563125708 Folded -1.3371361 0.3496393671192708 Folded -9.550698 0.4348736293666029 Hole -9.9335785 0.8300029469601379 Folded -1.2836194 0.9328061824822222 Folded -11.145425 0.30833843446623255 Hole -1.8847345 0.2926420497404363 Folded -12.669199 0.5665182742712181 Apoptosis -11.136124 0.13741442640032386 Apoptosis -11.075234 0.34971218472805743 Apoptosis -9.8357 0.05321637916190736 SmallIrregular -13.283168 0.3790681437821276 Apoptosis -11.293934 0.11415134572311048 SmallIrregular -12.651494 0.561812911802296 Apoptosis -7.614324 0.6415937343956617 Binuclear -10.790888 0.9870915366766958 Apoptosis -7.6290503 0.19513866225944 Binuclear -7.6895094 0.2072021488024639 Polylobed -7.84073 0.9474195185897651 Polylobed -7.836595 0.8646408384568184 Polylobed -7.9214716 0.6160540494372189 Polylobed -8.011597 0.841407236750209 Polylobed -7.870788 0.20969214472148845 Polylobed -8.041271 0.874028822863436 Polylobed -7.9541736 0.8673075096746405 Polylobed -7.6319847 0.24811224848006264 Polylobed -9.848025 0.5083733142598797 SmallIrregular --4.0079966 0.9297742638910044 Binuclear --2.905625 0.6791499961674852 Binuclear -11.803462 0.6605191053207405 Hole -11.993844 0.5597646005643659 SmallIrregular -12.056877 0.2688315646932502 Hole -0.787503 0.9784153715112589 Elongated -11.553375 0.44750964562218287 Hole -11.480531 0.31145009655296385 Hole -12.472468 0.7285807096817771 Folded -12.450104 0.6077048132604675 Folded -9.848654 0.3422413082046126 SmallIrregular -9.808794 0.30379159085703733 SmallIrregular -11.473914 0.21450668516253235 Hole -10.586076 0.07848643674025946 Hole -12.416268 0.6257109065192588 Hole -11.381321 0.5056936381437552 SmallIrregular -12.245663 0.23484841075284613 Hole -13.776378 0.05512452558336267 Hole -10.857551 0.0189010682637637 Hole -9.337406 0.7384959296651173 Prometaphase -8.977921 0.18825318915516998 SmallIrregular --5.643834 0.26664361938950665 Polylobed -10.935595 0.01411219378059636 SmallIrregular -10.910711 0.15063905483217888 SmallIrregular -10.841424 0.8744457175624959 SmallIrregular --0.525392 0.24206083474245776 Polylobed --0.26266104 0.9539581984830721 Polylobed -12.810506 0.16434683840013897 Elongated -12.840693 0.25326145865964433 SmallIrregular -6.766338 0.8677099684769924 SmallIrregular -12.546298 0.42660833869182113 Folded -12.460544 0.26782893919360584 Folded -1.0316559 0.6928137627201797 Elongated -2.4137278 0.05122840398223982 Elongated -8.009681 0.8906051765785046 Polylobed -7.982577 0.8415615453566596 Polylobed -8.172984 0.08579703513243586 Polylobed -7.9537225 0.7986187466244946 Polylobed -9.7523985 0.9611704418831957 SmallIrregular -11.501061 0.49206961915489345 SmallIrregular -10.99499 0.5681533501260378 SmallIrregular -10.88184 0.6797967402259275 SmallIrregular -11.19771 0.266726943216011 SmallIrregular --1.1518824 0.6496612589883748 Polylobed --1.0819857 0.4748675001029421 Polylobed --1.3021684 0.40554180461357825 Polylobed --4.786941 0.9028015595875049 MetaphaseAlignment -9.316775 0.7979031099484839 MetaphaseAlignment --7.3918223 0.9346076282036271 MetaphaseAlignment -10.697451 0.8366518248871061 MetaphaseAlignment -9.754166 0.041810601153301374 MetaphaseAlignment -9.657353 0.6065293910758234 MetaphaseAlignment -9.27139 0.6976368815154709 MetaphaseAlignment -10.481313 0.6648574757163 MetaphaseAlignment -9.755424 0.27195643475835074 MetaphaseAlignment -9.162183 0.4256303514649916 MetaphaseAlignment -9.11123 0.03902913808359587 Apoptosis -10.763516 0.09409412769567327 Apoptosis -10.567658 0.5605119006191839 Apoptosis -9.5688 0.5824117644655802 MetaphaseAlignment -10.892598 0.2012292906236388 Apoptosis -9.343732 0.44820272374154624 MetaphaseAlignment -13.926447 0.47207725974866155 MetaphaseAlignment -12.709892 0.011336777535180254 MetaphaseAlignment -12.672068 0.14252078330249296 MetaphaseAlignment -9.5470085 0.6243000572826787 Apoptosis -9.101783 0.9928201077976171 Apoptosis -9.208635 0.6503917479374361 MetaphaseAlignment -10.294509 0.12021666718785995 MetaphaseAlignment -9.341167 0.8214402970991066 MetaphaseAlignment -9.118426 0.43153772118158407 MetaphaseAlignment -14.223945 0.08370020921444421 Hole -10.46433 0.5998245833260225 Hole -14.366695 0.22804609816491894 Apoptosis -14.3447895 0.41678174737952944 Apoptosis -13.434025 0.3182942498743513 Elongated -9.585444 0.6428539475072395 Elongated -14.475661 0.431034672806622 Apoptosis -11.641299 0.10398209321647645 Hole -11.576186 0.2156755661320542 Hole -11.527858 0.5004452736380616 Hole -1.919976 0.7891842270707597 Elongated -0.7753459 0.009743265237635246 Interphase -12.082216 0.6124272839894 Interphase -11.982061 0.11588175867566619 Interphase -9.441887 0.6040494237681695 Elongated -10.342526 0.836807635478249 Elongated -10.588647 0.228482966476935 Interphase -14.373916 0.49551475247726773 Interphase -8.994343 0.7740529258445351 Interphase -8.959222 0.17557747925462064 Interphase -10.705348 0.15256114270196153 Interphase -10.916189 0.9050458686392603 Interphase -9.910276 0.6946466513738965 Hole -9.42926 0.7365733630422217 Elongated -11.1657715 0.8439686276120403 Hole -11.614429 0.5394257808850114 SmallIrregular -10.212889 0.4884054991924638 SmallIrregular -11.609864 0.5562573515833802 SmallIrregular -14.156315 0.7170179689321408 SmallIrregular -11.66433 0.39093730515226144 SmallIrregular -11.537666 0.09516824671698387 SmallIrregular diff --git a/2.analyze_data/results/2D_umap.png b/2.analyze_data/results/2D_umap.png deleted file mode 100644 index 16d0d621..00000000 Binary files a/2.analyze_data/results/2D_umap.png and /dev/null differ diff --git a/2.analyze_data/results/2D_umap.tsv b/2.analyze_data/results/2D_umap.tsv deleted file mode 100644 index 6d08ff29..00000000 --- a/2.analyze_data/results/2D_umap.tsv +++ /dev/null @@ -1,4124 +0,0 @@ -UMAP1 UMAP2 Mitocheck_Phenotypic_Class -5.3745747 5.244098 Polylobed -5.7472153 7.825978 MetaphaseAlignment -2.5254512 9.215811 Interphase -3.1393173 8.55668 Interphase -4.783998 7.814094 Artefact -3.9484844 8.775151 Artefact -5.0187745 7.7024894 Artefact -5.6929164 3.7096727 Artefact -5.9613037 3.6283534 Polylobed -2.7947688 9.298334 Interphase -1.7489855 9.320698 Polylobed -1.2441199 8.769845 Interphase -4.6197767 8.598911 Artefact -2.6632552 9.292688 Interphase -4.2154446 8.277985 Artefact -4.190742 8.3280115 Artefact -4.216244 8.490158 Artefact -4.2090507 8.463135 Artefact -1.3134273 8.815165 Polylobed -4.1873 8.474503 Artefact -1.360845 8.786954 Polylobed -1.3042417 8.817622 Polylobed -1.3403327 8.998921 Polylobed -1.3257658 8.829444 Polylobed -3.6998541 6.7499413 Interphase -1.3438622 7.369575 Polylobed -1.5011358 7.215694 Polylobed -1.3263483 7.3556595 Polylobed -2.6976087 4.2661986 Polylobed -2.3794494 7.0738573 Interphase -3.2052968 6.4362297 Binuclear -3.008185 6.5091996 Binuclear -2.887135 7.3179145 Binuclear -2.6460114 8.689858 Interphase -3.0358865 5.6033425 Binuclear -2.7186294 4.833509 Polylobed -2.816431 4.6742425 Polylobed -2.845017 4.653111 Polylobed -7.046584 5.5864882 MetaphaseAlignment -5.635706 8.192848 Prometaphase -7.5477285 6.3466206 Metaphase -7.5924478 6.197427 Metaphase -5.7189207 8.178514 Metaphase -5.827746 8.471183 Metaphase -7.6239824 5.5289884 MetaphaseAlignment -4.451356 7.9435225 Binuclear -2.9864328 6.466894 Polylobed -4.0514674 8.064722 Binuclear -2.5441191 7.117606 Polylobed -1.5599856 9.051699 Polylobed -1.5798881 8.905468 Polylobed -1.551999 9.065583 Polylobed -3.1792004 8.155487 Polylobed -4.9521155 8.899875 Artefact -4.824244 8.9335985 Artefact -4.9106226 8.743923 Large -1.7898227 7.84467 Large -5.1947274 8.708256 MetaphaseAlignment -4.6399865 8.270188 Artefact -1.9074812 8.442815 Polylobed -4.5000577 8.281356 Artefact -4.7575274 8.212008 Artefact -1.5225303 6.713412 Polylobed -1.4857086 6.7641497 Polylobed -1.5321785 6.823278 Polylobed -4.666369 8.212367 Artefact -4.64871 8.232321 Artefact -1.5264996 6.820948 Polylobed -2.657506 6.123957 Polylobed -2.6982198 9.294284 Interphase -4.3359113 6.7591496 Interphase -2.6625195 6.209566 Polylobed -2.6716943 6.026845 Polylobed -2.2667487 8.047026 Interphase -1.8603169 9.232373 Artefact -3.1486683 7.972922 Artefact -2.9218423 7.5236974 Artefact -4.986943 8.110764 Artefact -5.2749324 7.9540896 Artefact -6.749896 5.1250033 Apoptosis -6.7371387 5.140341 Apoptosis -4.7079296 8.537186 Metaphase -1.9774348 9.164849 Metaphase -5.3725557 7.9919734 Metaphase -5.9953413 3.6248455 MetaphaseAlignment -4.71763 7.8193216 Polylobed -4.7971635 7.866461 Polylobed -4.7870865 3.0128791 Polylobed -4.596084 7.8775187 Polylobed -6.4492636 4.5091558 Interphase -7.8687515 6.1684127 Metaphase -2.1395984 9.498721 Polylobed -2.1143525 9.520704 Polylobed -2.126166 9.488415 Polylobed -8.373879 6.145875 Metaphase -2.0873432 9.395454 Polylobed -2.5366206 7.213905 Binuclear -2.6434982 7.120819 Binuclear -8.485885 5.2943554 MetaphaseAlignment -2.3538227 7.223074 Binuclear -2.5511131 6.9974117 Binuclear -2.2639043 9.575127 Polylobed -2.26026 9.562457 Polylobed -5.7676005 7.7055964 Metaphase -5.777993 7.6595798 Metaphase -1.9090459 7.5070643 Large -1.7423853 8.18137 Large -1.847579 7.775857 Polylobed -1.5353689 7.98486 Polylobed -1.4917989 9.035078 Polylobed -1.6875176 8.785797 Large -1.8111582 8.964556 Large -1.6630613 8.801176 Large -1.6686559 8.981143 Large -1.6146455 9.011199 Large -1.1157439 8.750834 Polylobed -1.1323917 8.703771 Polylobed -1.1562389 8.716876 Polylobed -4.8014674 8.086943 Artefact -1.1980499 8.644874 Polylobed -1.1634202 8.729046 Polylobed -4.7701683 8.12079 Artefact -1.1743747 8.730931 Polylobed -4.6708264 8.14545 Artefact -4.6308923 8.225316 Artefact -2.714594 6.009436 Polylobed -4.398012 6.827593 Interphase -2.6755476 5.9336967 Polylobed -2.493305 7.982347 Interphase -0.9355037 4.672579 Polylobed -0.99511224 4.669166 Polylobed -1.7343863 9.238358 Polylobed -1.8619792 6.9126277 Binuclear -1.662943 9.200086 Polylobed -1.5661076 9.118648 Polylobed -1.6243495 9.123157 Polylobed -2.2464519 6.965285 Binuclear -0.93959665 6.7143764 Polylobed -1.5750216 7.5575185 Polylobed -1.4071562 8.262486 Polylobed -1.2944056 8.64162 Polylobed -6.15913 6.5653996 Large -5.8836875 7.517583 Large -3.787676 8.561645 Artefact -4.4160957 8.583208 Artefact -4.260834 8.528897 Artefact -4.8718543 7.7158504 Artefact -3.9748712 8.789001 Artefact -4.3312874 8.56909 Artefact -4.1088595 8.591505 Artefact -4.2167187 8.503112 Artefact -4.1718907 8.437468 Artefact -4.224615 8.333521 Artefact -1.3496276 8.891758 Polylobed -1.4186943 8.948096 Polylobed -1.3792523 8.924186 Polylobed -4.139178 8.424512 Artefact -1.2671177 8.906617 Polylobed -1.2823727 8.818139 Polylobed -1.4159583 7.5034075 Polylobed -1.5773357 7.309637 Polylobed -1.3145158 7.4599733 Polylobed -2.5215256 4.0995917 Polylobed -5.0855827 9.055153 Metaphase -6.572733 5.7248125 Metaphase -8.105963 5.219667 MetaphaseAlignment -5.832719 7.903572 MetaphaseAlignment -5.805537 6.9288487 Polylobed -5.7410626 7.13731 Polylobed -7.708718 5.4594083 MetaphaseAlignment -4.734627 8.065255 Polylobed -4.8153863 8.015302 Polylobed -4.7316785 8.117567 Polylobed -4.6900635 8.336542 Polylobed -7.2011824 5.261149 MetaphaseAlignment -5.7428007 7.5351596 MetaphaseAlignment -6.0228972 6.9345765 Large -4.6466484 9.0991955 Polylobed -4.768297 9.146405 Polylobed -2.063923 8.957146 Binuclear -2.1193235 8.807236 Binuclear -2.289895 9.558841 Polylobed -2.3094978 9.565916 Polylobed -7.61987 4.316573 Prometaphase -2.4222157 9.548138 Polylobed -1.4687142 8.711973 Polylobed -2.4320912 8.86517 Polylobed -1.3619374 8.920644 Polylobed -1.3451139 8.912921 Polylobed -2.3503115 9.119711 Polylobed -2.1885865 9.164871 Polylobed -7.9407644 5.2098093 MetaphaseAlignment -8.240695 5.2861032 MetaphaseAlignment -1.7100496 8.556338 Large -1.7125391 8.412545 Large -1.6683768 8.573806 Large -5.7384086 7.731587 MetaphaseAlignment -5.664193 8.101798 MetaphaseAlignment -6.185737 5.878901 Metaphase -6.295439 5.8162303 Metaphase -5.638321 5.7687283 Artefact -3.8845043 7.0671706 Artefact -5.845585 7.4539595 Metaphase -5.5661173 8.065933 Metaphase -2.3371117 9.136354 Artefact -2.4839935 9.271971 Artefact -2.5762863 8.685142 Artefact -2.5728498 9.105887 Artefact -2.580775 9.245284 Metaphase -5.9509463 6.6955633 MetaphaseAlignment -1.8311837 7.8743567 Large -1.5814639 9.043517 Polylobed -6.4882703 4.4216733 MetaphaseAlignment -1.5480816 9.061577 Polylobed -1.581986 8.965061 Polylobed -1.5436432 8.02074 Polylobed -1.7224344 8.888665 Polylobed -1.5827678 8.023493 Polylobed -1.4970995 8.002207 Polylobed -1.5523896 8.068497 Polylobed -4.557342 8.744472 Large -5.3344727 7.919019 Polylobed -5.010013 8.570603 Large -5.2679906 8.855581 Metaphase -4.470857 9.1311455 Artefact -4.3374352 9.146426 Artefact -4.3637633 9.146464 Artefact -5.535803 7.8457646 Metaphase -5.429763 8.869651 Artefact -5.443794 8.886522 Metaphase -5.4573708 8.912834 Artefact -5.4233947 8.861427 Metaphase -1.1342893 8.730875 Polylobed -1.2310836 8.820223 Polylobed -4.0653872 9.013164 Artefact -1.2016312 8.677725 Polylobed -4.1640487 8.940343 Artefact -4.253046 9.113713 Artefact -1.178628 8.661201 Polylobed -4.1427155 9.021208 Artefact -4.198062 9.071017 Artefact -4.8874054 3.6423206 Artefact -4.113756 9.0219555 Artefact -1.7020649 6.7889657 Polylobed -1.6072109 6.6925993 Polylobed -4.1543255 9.043764 Artefact -6.56245 7.5984817 Artefact -6.327342 7.5674 Artefact -4.731171 9.00712 Artefact -1.5383708 6.792011 Polylobed -3.6497886 5.4774857 Polylobed -3.1605093 5.6960363 Polylobed -4.8721504 8.813987 Artefact -4.829755 8.933882 Artefact -4.438125 9.272862 Artefact -4.4813232 9.317274 Artefact -2.6639879 6.150799 Polylobed -5.514064 7.8434477 Metaphase -5.5019393 7.877808 Metaphase -4.460236 9.304295 Artefact -2.7257538 5.938541 Polylobed -4.448161 9.276008 Artefact -2.73302 5.9935975 Polylobed -4.413362 9.295254 Artefact -4.432301 9.307878 Artefact -4.402483 9.224319 Artefact -4.9455323 8.018463 Artefact -6.308588 6.9824443 Artefact -4.8055315 3.1056967 MetaphaseAlignment -0.9644867 4.729228 Polylobed -0.9865287 4.75963 Polylobed -1.0251832 4.6681857 Polylobed -4.896177 8.731877 Metaphase -1.361398 9.164039 Polylobed -1.3835939 9.153527 Polylobed -1.3844793 9.202354 Polylobed -1.4246864 9.099544 Polylobed -1.3944335 9.1388445 Polylobed -4.7812862 8.117843 Artefact -1.4618524 10.006827 Polylobed -1.4210095 10.007031 Polylobed -1.4088746 10.060869 Polylobed -1.4224467 10.04828 Polylobed -1.9378769 7.466581 Large -1.649079 8.6730585 Large -4.951434 8.675828 Large -1.158548 8.76547 Polylobed -1.2509083 8.594682 Polylobed -1.2355089 8.74803 Polylobed -1.2327514 8.619476 Polylobed -1.5261587 6.7888494 Polylobed -1.512374 6.7597375 Polylobed -1.5385295 6.7941027 Polylobed -1.5250783 6.7784185 Polylobed -2.670628 6.170346 Polylobed -2.71598 6.103732 Polylobed -2.798733 6.089693 Polylobed -2.6798537 6.0105524 Polylobed -0.9555192 4.6873636 Polylobed -0.9390461 4.632229 Polylobed -0.9727887 4.5621123 Polylobed -1.5604167 9.370455 Polylobed -1.5369242 9.355776 Polylobed -1.51154 9.290015 Polylobed -1.5621717 9.337407 Polylobed -1.5986819 9.342317 Polylobed -1.422574 10.037177 Polylobed -1.4312096 10.013153 Polylobed -1.4246892 10.036852 Polylobed -1.4282702 10.030738 Polylobed -6.5906253 6.3582635 Artefact -4.9771748 8.319833 Artefact -4.922444 8.369664 Artefact -4.5769706 9.086575 Artefact -5.0977874 8.219653 Artefact -4.54255 9.142358 Artefact -6.4785504 7.4057527 Artefact -4.7365565 8.963528 Artefact -4.659488 8.957507 Artefact -1.7030329 9.490515 Polylobed -1.7726771 9.345483 Polylobed -2.1687214 9.092201 Interphase -5.649716 8.053584 MetaphaseAlignment -2.2722435 7.755998 Polylobed -1.8674108 8.118351 Polylobed -2.0726025 7.9511423 Polylobed -2.0393913 8.224585 Polylobed -6.0771303 7.380607 MetaphaseAlignment -3.4675305 4.5717497 Binuclear -3.4804828 4.6572227 Binuclear -4.471617 7.0194483 Polylobed -5.5779157 6.7149577 Polylobed -4.520646 6.943687 Polylobed -3.9479115 5.0647306 Polylobed -3.614944 5.3346047 Polylobed -4.869815 5.206733 Polylobed -2.964505 5.128849 Polylobed -3.3048744 4.558418 Polylobed -3.3175015 4.5421414 Polylobed -1.9687155 4.498462 Polylobed -1.9808599 4.651307 Polylobed -1.8766079 4.9272842 Polylobed -2.0480533 4.50204 Polylobed -1.9636958 4.6520123 Polylobed -6.4596047 6.156412 Polylobed -6.008323 6.566943 Polylobed -3.3549135 5.9690304 Polylobed -2.975822 6.5278225 Polylobed -3.5443964 5.812045 Polylobed -2.595291 6.639078 Polylobed -3.386463 5.3926725 Polylobed -2.8460476 6.029662 Binuclear -3.1756344 6.1236334 Binuclear -4.290809 4.778416 Polylobed -4.263747 4.7692623 Polylobed -4.2259583 4.8165703 Polylobed -4.2208743 4.838309 Polylobed -1.9489977 6.135909 Binuclear -2.0097287 6.108985 Binuclear -1.6715909 6.347813 Polylobed -1.7050955 6.2614264 Polylobed -1.6756696 6.260127 Polylobed -0.26614684 5.927711 Polylobed -0.2558265 5.9204893 Polylobed -0.22782876 5.915134 Polylobed -0.26310587 5.860051 Polylobed -0.2563553 5.818653 Polylobed -0.30711934 5.985943 Polylobed -0.25554478 5.919117 Polylobed -0.22567382 5.898716 Polylobed -0.23638768 5.9010253 Polylobed -6.477617 6.0959306 Elongated -3.900516 4.9768243 Polylobed -3.982599 5.1940284 Polylobed -3.8095882 5.0945706 Polylobed -3.0445895 5.1562014 Polylobed -4.558976 5.489614 Polylobed -3.8390167 5.0693407 Polylobed -3.3692873 4.5741796 Binuclear -3.448548 4.671117 Binuclear -3.476981 5.089439 Binuclear -4.166771 4.777082 Binuclear -3.0535886 4.3913474 Polylobed -3.3513076 4.512809 Polylobed -1.8632331 4.750552 Polylobed -1.8509187 4.7750993 Polylobed -3.293874 7.23276 Polylobed -3.2192178 7.002808 Polylobed -1.8726425 4.7283363 Polylobed -3.173565 6.132173 Polylobed -2.0700064 5.093241 Polylobed -2.437191 5.4294286 Polylobed -3.899987 5.721451 Binuclear -3.6867237 5.710224 Binuclear -3.0315294 6.385401 Polylobed -3.4680116 5.3524017 Polylobed -3.0454385 6.4626255 Polylobed -2.4925334 5.6429253 Polylobed -5.94799 6.5389833 Binuclear -3.105775 6.072213 Binuclear -3.8369033 7.933481 Binuclear -5.445328 6.708706 Binuclear -3.8264697 5.7392817 Binuclear -3.490008 6.4593887 Binuclear -8.904221 5.278068 Artefact -6.2396193 7.0697656 Apoptosis -8.847793 5.297145 Artefact -3.071009 6.4342546 Binuclear -3.2385828 5.792432 Binuclear -1.8017008 6.342374 Polylobed -1.7351656 6.485406 Polylobed -1.7479082 6.2784743 Polylobed -1.7437922 6.2026362 Polylobed -0.24090613 5.9111395 Polylobed -0.2840843 6.0006623 Polylobed -0.23465261 5.913237 Polylobed -0.2575036 5.8935513 Polylobed -0.27386934 5.903135 Polylobed -0.28056923 5.964704 Polylobed -0.2379969 5.871583 Polylobed -0.20406933 5.8826685 Polylobed -7.3897038 6.408512 UndefinedCondensed -6.991736 5.7572813 Interphase -4.364667 2.7388406 MetaphaseAlignment -6.8467913 5.6462398 MetaphaseAlignment -4.194926 4.8655024 Polylobed -3.0687714 5.9713664 Polylobed -3.2361758 4.5099187 Binuclear -3.089873 5.9300904 Polylobed -3.282042 4.564015 Binuclear -2.6731656 4.083282 Binuclear -3.8912175 4.413045 Binuclear -3.9260197 4.2877693 Binuclear -3.0690274 6.8964076 Binuclear -3.056301 6.9263406 Binuclear -4.0795574 5.468417 Binuclear -3.599287 5.438567 Binuclear -4.198044 5.920217 Artefact -4.134238 6.127604 Artefact -4.23855 5.991535 Artefact -2.2383225 6.2445726 Binuclear -3.440813 5.9892855 Binuclear -4.3017983 6.147677 Binuclear -2.187205 6.248456 Binuclear -8.269718 5.227849 MetaphaseAlignment -8.041922 5.5277147 Binuclear -4.41651 6.146612 Polylobed -6.1491013 6.43856 Binuclear -7.941506 5.607551 Binuclear -3.9639 8.085435 Binuclear -4.410007 4.9946685 Polylobed -4.371522 4.841122 Polylobed -2.8195653 5.3831077 Binuclear -2.8002656 5.394721 Binuclear -2.7494612 4.347131 Polylobed -2.8938537 4.156344 Polylobed -2.7141016 4.5462976 Polylobed -2.2974448 6.7718143 Binuclear -3.588849 5.2422047 Polylobed -2.259939 7.2427773 Binuclear -5.2942095 5.669787 Binuclear -2.6264932 5.396754 Binuclear -7.6076403 7.6225104 Prometaphase -7.9753075 6.1271424 Prometaphase -7.502477 7.499296 Prometaphase -8.31874 7.2525797 Prometaphase -8.001344 5.7105603 Prometaphase -7.873742 7.4523144 Prometaphase -8.048142 5.6883783 Prometaphase -7.33761 6.0480237 Prometaphase -7.1309485 5.253108 Prometaphase -5.3230133 5.7218547 Artefact -5.3111753 5.7355666 Artefact -5.3316684 7.81645 Artefact -5.354716 7.51081 Artefact -5.3457665 7.1948133 Artefact -6.732103 5.2842083 Artefact -3.7541218 4.528778 Artefact -3.7668912 4.562955 Artefact -3.7775028 4.6021466 Artefact -3.7844877 4.687505 Artefact -5.529184 5.8934355 Artefact -5.2875633 6.69567 Artefact -8.308635 4.910967 Prometaphase -7.2688866 4.834734 Prometaphase -7.2658577 5.5405293 Prometaphase -8.321182 4.9386096 Prometaphase -3.950756 4.2718906 Prometaphase -7.245741 5.1127806 Prometaphase -7.0027323 5.254316 Prometaphase -7.5123215 5.985398 Prometaphase -6.0974984 7.6774163 Prometaphase -7.6834493 5.596493 Prometaphase -7.5688477 5.227341 Prometaphase -7.173555 7.233872 Prometaphase -7.1292453 7.385371 Prometaphase -8.280103 7.214484 Prometaphase -7.902071 7.4553876 Prometaphase -8.224329 7.4182277 Prometaphase -8.149948 5.213708 Prometaphase -2.4256296 7.0095496 Large -4.441847 5.9974375 Large -4.2745366 5.7746406 Large -5.0320916 6.8746676 Large -3.1585953 6.5655665 Binuclear -2.7124233 4.6974926 Binuclear -3.2205756 6.291357 Binuclear -1.2948638 5.603487 Polylobed -1.9137323 5.5237775 Polylobed -1.3209971 5.6063504 Polylobed -1.4896908 5.601661 Polylobed -2.4007428 5.4627686 Polylobed -2.110823 5.57745 Polylobed -1.4968829 4.706806 Polylobed -1.5927591 4.7480397 Polylobed -1.3533581 7.480493 Polylobed -1.2689142 7.189934 Polylobed -1.3106318 7.2821746 Polylobed -1.5952917 4.5543485 Polylobed -1.6218784 4.5181556 Polylobed -1.6364917 4.508882 Polylobed -1.6016351 4.4783974 Polylobed -1.622746 4.4462147 Polylobed -3.8333762 6.0102477 Large -2.264095 5.183247 Polylobed -1.7424462 3.692882 Polylobed -2.213924 5.1741805 Polylobed -1.2960938 4.8858833 Polylobed -1.215173 6.693371 Polylobed -6.866356 6.250286 Metaphase -4.8347063 5.629777 Interphase -6.180254 6.5550246 Interphase -3.1086383 5.455639 Binuclear -3.6133552 5.740206 Binuclear -4.0924788 5.788809 Large -4.410244 5.5873137 Interphase -3.4340966 6.9253664 Interphase -6.3603773 6.091222 Interphase -5.760163 5.7506127 Interphase -2.312298 7.933097 Interphase -4.4719296 6.329767 Interphase -2.3070781 8.153626 Binuclear -4.3479967 6.4196253 Interphase -2.1572604 8.384277 Binuclear -6.007542 6.0198736 Interphase -8.771648 5.9532843 Metaphase -8.546001 6.0006843 Metaphase -8.991629 5.5952353 MetaphaseAlignment -8.155794 5.2303667 MetaphaseAlignment -7.3038754 7.4175415 MetaphaseAlignment -7.405804 4.638753 MetaphaseAlignment -6.4413786 8.288628 SmallIrregular -6.551669 8.277803 SmallIrregular -6.2728124 8.96342 SmallIrregular -7.1522985 7.7573934 SmallIrregular -5.8400083 9.725235 SmallIrregular -4.2945857 7.14283 SmallIrregular -5.935373 9.778574 SmallIrregular -7.914702 5.1957197 Apoptosis -5.856248 7.3998504 Hole -5.8007703 8.948595 SmallIrregular -6.6231556 6.4233828 SmallIrregular -6.371169 8.469768 SmallIrregular -6.3803086 8.566605 SmallIrregular -6.605766 7.1862974 SmallIrregular -5.846465 9.683458 SmallIrregular -6.0498257 9.36672 Folded -5.9936814 9.44277 SmallIrregular -6.0091724 9.425383 SmallIrregular -6.4445405 8.876057 UndefinedCondensed -7.2617073 7.899946 SmallIrregular -4.151712 9.184232 Folded -3.7005525 9.198817 Folded -7.2627406 7.83162 SmallIrregular -5.8240294 9.699178 SmallIrregular -5.8391285 9.69048 SmallIrregular -5.8494234 9.720146 SmallIrregular -5.9501777 9.797396 SmallIrregular -5.933982 9.850392 SmallIrregular -5.882062 9.771293 SmallIrregular -6.1163974 5.8514223 Hole -7.6153617 7.2113695 UndefinedCondensed -6.0239387 7.546379 Hole -5.6085286 6.6347957 Hole -4.225996 5.8252344 Folded -5.1441665 9.062652 SmallIrregular -5.7962875 9.752965 SmallIrregular -7.2204986 7.5336523 SmallIrregular -6.5428967 8.236731 SmallIrregular -5.633667 6.675996 SmallIrregular -6.5607285 8.055345 SmallIrregular -7.253271 7.4803486 SmallIrregular -6.296867 7.00045 SmallIrregular -5.9390206 9.808647 SmallIrregular -5.901652 9.812653 SmallIrregular -5.730241 7.3438435 Hole -5.961178 7.2518177 Hole -5.047524 7.36069 Hole -6.398418 8.312055 SmallIrregular -6.1481757 8.770729 SmallIrregular -5.2393737 9.276821 SmallIrregular -5.2978797 9.028314 SmallIrregular -6.04917 8.743267 UndefinedCondensed -6.769561 7.028287 SmallIrregular -5.721794 9.594612 SmallIrregular -5.9417953 9.81116 SmallIrregular -5.8777847 9.740197 SmallIrregular -5.8857594 9.801893 SmallIrregular -5.870383 9.457253 SmallIrregular -5.8658314 9.397266 SmallIrregular -7.2391863 7.497413 SmallIrregular -6.124886 8.630963 SmallIrregular -6.106706 8.832973 SmallIrregular -5.8260617 8.635677 UndefinedCondensed -5.662351 7.7982564 MetaphaseAlignment -5.6501403 8.118295 MetaphaseAlignment -5.496685 8.210568 MetaphaseAlignment -8.114949 4.4419036 MetaphaseAlignment -8.953831 4.502922 Prometaphase -8.145148 4.5658746 MetaphaseAlignment -8.852876 5.9118176 MetaphaseAlignment -8.69551 6.3783336 UndefinedCondensed -5.5312023 8.060846 MetaphaseAlignment -4.2426286 8.692002 Artefact -5.1880517 6.4183917 Artefact -6.7558284 7.4827967 MetaphaseAlignment -5.6130157 8.142315 MetaphaseAlignment -5.3964753 8.067704 MetaphaseAlignment -5.7043962 6.67173 UndefinedCondensed -7.918462 5.923708 UndefinedCondensed -7.309295 4.5926723 MetaphaseAlignment -2.639905 9.242107 Interphase -3.0347445 9.425049 Interphase -3.1685023 9.226993 Interphase -3.013696 8.96257 Interphase -3.01932 9.393367 Interphase -3.147722 9.343875 Interphase -3.3359683 9.155512 Interphase -3.3364558 9.360588 Interphase -3.6127756 9.197142 Interphase -5.5086446 7.964391 Polylobed -3.2632732 6.6905804 Polylobed -3.692943 5.635007 Polylobed -4.005207 5.0630603 Polylobed -4.290442 4.4744787 Polylobed -4.663819 8.115722 Polylobed -6.1560516 6.83647 Polylobed -7.130418 7.4300876 MetaphaseAlignment -5.421247 5.684779 Binuclear -5.201567 5.670962 Binuclear -6.8945093 7.4397335 Metaphase -5.0261564 5.9364257 Binuclear -4.835023 5.98 Binuclear -5.652061 8.212607 Metaphase -2.6847618 5.2191606 Binuclear -3.7463057 4.8103695 Binuclear -7.9549985 4.4921694 MetaphaseAlignment -8.945197 4.481616 Prometaphase -5.8909955 5.943661 Polylobed -6.2190037 6.1378784 Polylobed -7.653642 4.5270185 Prometaphase -8.462123 5.8912654 Metaphase -7.487612 6.183301 Interphase -3.299366 5.9942646 Interphase -4.9287405 8.560489 Grape -7.3360066 6.3189597 Interphase -7.090588 6.384024 Interphase -7.2296 6.2889338 Interphase -7.131563 6.4991927 Interphase -6.520878 8.946212 Interphase -7.218727 6.5159345 Interphase -7.3387866 7.427339 Interphase -7.0671363 8.144167 Interphase -7.2569594 6.3753963 Interphase -7.278956 6.399758 Interphase -7.576846 6.654611 Interphase -7.2788286 6.657883 Interphase -6.5630827 8.564286 Interphase -7.211509 8.302425 Interphase -7.3884344 6.294961 Interphase -2.714892 8.01443 Interphase -6.7968216 7.0849795 Interphase -7.2876573 6.3901777 Interphase -6.801912 8.458498 Interphase -7.480317 7.6080475 SmallIrregular -7.4770546 7.7555456 SmallIrregular -7.452103 7.6718903 SmallIrregular -7.4268866 7.781325 SmallIrregular -7.5673404 6.3549404 SmallIrregular -6.296907 7.0450377 Hole -3.6790023 6.856257 Folded -7.423628 7.7673764 Hole -3.3010306 6.982325 Folded -5.094366 7.50832 Folded -5.168746 7.6074185 Folded -5.14661 7.4182773 Folded -1.9856565 6.067193 Polylobed -2.6757789 5.9339848 Polylobed -1.7499495 8.078733 Polylobed -2.23869 9.499356 Polylobed -2.478127 8.182031 Polylobed -2.0503285 7.518428 Polylobed -2.191539 7.035428 Polylobed -1.6161296 9.772836 Polylobed -2.7403793 3.3391955 Polylobed -1.5710865 9.805188 Polylobed -2.766316 3.3235164 Polylobed -1.6007488 9.77342 Polylobed -2.965526 3.1911225 Polylobed -3.9335842 2.0323749 Polylobed -2.820026 3.2436068 Polylobed -1.6107584 9.805941 Polylobed -2.9163673 3.1551073 Polylobed -2.9071388 3.1991577 Polylobed -3.5282602 8.002499 Prometaphase -7.6416903 4.9175453 Prometaphase -4.2891407 7.0121226 Binuclear -4.2797856 7.055437 Binuclear -4.105301 6.7288084 Interphase -3.2071629 4.901087 Binuclear -3.4070358 4.546315 Binuclear -5.2217984 6.199579 Interphase -1.9356691 5.7423434 Binuclear -1.9079428 5.705995 Binuclear -2.6809518 9.3516035 Interphase -2.730172 8.00327 Interphase -2.9349391 7.8951535 Interphase -2.6862314 8.884777 Binuclear -2.4450731 9.049659 Binuclear -5.324435 6.347004 Interphase -6.476982 4.4859653 Metaphase -7.40032 5.8996043 Metaphase -7.594689 6.144546 Metaphase -7.727985 6.176257 Metaphase -6.2343974 5.8698444 MetaphaseAlignment -8.126504 5.029792 MetaphaseAlignment -2.271318 5.4213653 Polylobed -3.0685022 3.2312138 Polylobed -1.5550029 7.4649234 Polylobed -1.697807 7.316876 Polylobed -2.895976 4.265304 Polylobed -1.5305675 7.397787 Polylobed -3.8483734 5.3101916 Polylobed -1.7261147 5.0236564 Polylobed -6.8738523 6.0199857 Metaphase -1.715121 5.1970053 Polylobed -1.6772497 5.194374 Polylobed -1.662111 5.240575 Polylobed -8.851773 4.81035 MetaphaseAlignment -1.7494589 9.779708 MetaphaseAlignment -1.6406687 9.778455 Polylobed -2.34755 5.7141075 Metaphase -1.6188357 9.798143 Polylobed -7.6063733 6.9643736 Metaphase -3.6868892 3.3989737 Polylobed -1.6253864 9.763922 Polylobed -1.6186209 9.784945 Polylobed -2.8208811 3.328334 Polylobed -7.459682 6.195983 Metaphase -1.9899945 5.936789 Polylobed -2.0091326 5.9297256 Polylobed -1.9984674 5.9687676 Polylobed -2.7472134 5.747783 Polylobed -1.5652313 5.2100472 Binuclear -2.1158264 7.2849903 Polylobed -2.6093693 8.090805 Polylobed -2.2272723 6.41901 Binuclear -2.6404257 7.991697 Polylobed -1.7146237 5.239472 Binuclear -1.5832751 7.2017555 Binuclear -6.5859118 7.37849 Metaphase -1.7405624 7.243734 Polylobed -1.8280221 7.3402042 Polylobed -2.6083076 5.8433104 Polylobed -7.023797 5.2075796 MetaphaseAlignment -6.2550435 8.80438 Metaphase -6.3052516 9.011362 Metaphase -1.7451565 8.113861 Polylobed -1.9351426 8.604232 Polylobed -5.466651 8.745554 Metaphase -2.2951355 8.5668125 Polylobed -1.7493782 9.695096 Polylobed -1.7012326 9.677558 Polylobed -6.258563 5.706943 Metaphase -1.6554456 9.764118 Polylobed -1.710938 8.817293 Polylobed -1.5719571 8.793545 Polylobed -1.7021981 9.771717 Polylobed -1.4881531 8.481203 Binuclear -1.5023841 8.018366 Binuclear -5.58223 7.9454327 Metaphase -4.7851834 8.94315 Artefact -4.948874 8.972072 Artefact -5.121119 9.074678 Artefact -2.1937392 9.204746 Polylobed -2.0082731 8.822248 Polylobed -1.9369464 8.549827 Polylobed -2.3558705 6.9921002 Binuclear -2.005289 7.671524 Binuclear -2.0948691 9.044298 Binuclear -5.066362 7.407419 MetaphaseAlignment -1.9235568 8.995045 Binuclear -2.6895492 5.6717544 Polylobed -7.388374 4.9002843 MetaphaseAlignment -2.2736447 5.3982987 Polylobed -2.977515 3.2344396 Polylobed -2.9238954 3.324239 Polylobed -1.5347975 7.4410176 Polylobed -1.701344 7.3030663 Polylobed -1.5785698 7.405097 Polylobed -1.5676123 7.484229 Polylobed -1.8866788 9.139208 Polylobed -1.9021876 9.084459 Polylobed -8.700194 6.7160783 Hole -5.6107187 8.044486 Hole -8.279479 6.457001 UndefinedCondensed -7.9915924 7.650008 Apoptosis -5.3881483 6.7575603 Elongated -5.5645976 7.6314473 Hole -5.530485 6.43045 Hole -4.0480947 6.8642306 Hole -4.9458704 6.0272145 Elongated -6.834675 7.81164 Hole -6.501145 5.599944 Metaphase -8.981553 6.086382 Apoptosis -7.365227 7.125175 Hole -5.053195 6.33893 Hole -6.6783648 8.053423 Hole -6.5569596 8.042011 Hole -5.317745 8.322402 Hole -6.9828544 5.559009 Hole -4.4645905 7.2000003 Hole -7.757717 4.863619 Hole -3.723173 8.429907 Hole -6.9219265 8.068513 Hole -6.595052 7.928763 Hole -6.3612323 7.9384284 Hole -6.408712 7.944627 Hole -4.5789533 6.9910154 Hole -4.5013824 8.096504 Hole -4.3532357 7.4458685 Hole -7.804039 4.87182 Hole -7.6071296 7.1274967 Hole -7.511755 5.832918 Elongated -7.345198 8.159066 Hole -7.6609573 7.507739 Hole -8.563477 6.491204 UndefinedCondensed -5.1122923 6.2118163 Elongated -5.6877017 7.6830783 Hole -7.64502 7.323975 UndefinedCondensed -7.0001454 7.770648 Hole -7.240624 7.5978985 Hole -5.6515193 8.056956 SmallIrregular -7.636522 7.1114697 UndefinedCondensed -7.5060225 7.006394 Hole -5.3164463 6.9503527 Artefact -5.5852423 6.821633 Artefact -4.9443336 6.6986794 Artefact -6.927458 8.526808 Interphase -6.480098 8.076911 Interphase -6.054673 9.39526 Interphase -6.9555693 8.443648 SmallIrregular -6.35958 9.021864 Interphase -6.406352 9.152043 Interphase -4.169659 8.935458 SmallIrregular -6.0877542 9.497968 SmallIrregular -6.457689 8.390594 Interphase -2.9213603 4.954099 SmallIrregular -6.4026613 8.852445 SmallIrregular -6.0978394 9.210783 SmallIrregular -5.9003873 9.3699 SmallIrregular -5.8855867 9.769086 SmallIrregular -5.84488 9.444148 SmallIrregular -5.794326 9.500288 SmallIrregular -5.9972887 9.070578 SmallIrregular -4.1184254 7.5484314 Polylobed -4.2977734 7.8153973 Polylobed -6.992923 8.0508585 SmallIrregular -4.203187 7.714992 Polylobed -3.470257 6.7449436 Polylobed -3.9379804 6.8840256 Polylobed -4.1623983 7.116035 Polylobed -3.3639975 7.4076443 Polylobed -6.9425883 8.659183 Interphase -6.958224 8.740267 Interphase -6.420046 8.221635 Interphase -6.9422336 8.344615 Interphase -6.469951 8.281633 Interphase -6.912484 8.746697 Interphase -7.0337496 8.637889 Interphase -6.176706 9.270512 Interphase -6.0430017 9.138569 SmallIrregular -1.4602095 6.1619835 Polylobed -1.4608034 6.178517 Polylobed -1.4363488 6.1927786 Polylobed -1.4860172 6.202138 Polylobed -1.544701 6.065694 Polylobed -1.2104453 6.372834 Polylobed -1.4224029 6.199114 Polylobed -1.5163332 6.094404 Polylobed -1.2471355 6.373911 Polylobed -1.3139135 6.376704 Polylobed -1.5133028 6.0919423 Polylobed -6.282654 6.717109 SmallIrregular -3.8683395 6.0357275 Polylobed -3.8536844 6.0095487 Polylobed -3.915961 6.0155015 Polylobed -3.3703306 8.324039 Polylobed -3.8845727 6.0318327 Polylobed -3.9346986 6.092871 Polylobed -4.0959225 5.899471 Polylobed -4.7094064 8.798654 Polylobed -4.6776223 8.88112 Polylobed -5.991139 9.509018 Polylobed -6.063195 6.676621 Polylobed -6.0621066 9.719064 Polylobed -6.006137 9.688594 Polylobed -6.0048547 9.556911 SmallIrregular -5.888691 7.1912546 SmallIrregular -6.1838713 6.6794252 Polylobed -6.3060136 6.9108386 Polylobed -5.934184 9.767821 SmallIrregular -7.1589117 8.520278 SmallIrregular -6.4414434 8.389746 SmallIrregular -7.111786 8.509428 SmallIrregular -7.13612 8.493352 Interphase -7.1201315 8.556264 Interphase -7.615589 7.7110543 Interphase -3.981628 8.171242 Interphase -6.2703667 8.65722 Interphase -6.939568 8.650179 Interphase -7.034125 8.574518 Interphase -7.0339174 8.504111 Interphase -3.6666663 8.333282 Interphase -5.861481 7.3068566 Interphase -6.131137 9.352264 SmallIrregular -6.9067717 8.756029 SmallIrregular -7.202376 8.448134 Interphase -6.0081434 9.516885 SmallIrregular -5.883067 9.424349 Interphase -5.980416 9.384761 Interphase -6.2658215 8.530718 Interphase -6.066281 9.17398 SmallIrregular -5.94259 7.90265 SmallIrregular -7.6196876 6.6512012 SmallIrregular -5.903582 9.774616 SmallIrregular -5.9055967 9.765506 SmallIrregular -7.094535 8.530745 SmallIrregular -7.1179194 8.485032 SmallIrregular -6.983486 8.291285 SmallIrregular -5.8990335 9.362935 SmallIrregular -7.4150767 7.7138715 SmallIrregular -7.6350565 7.713303 SmallIrregular -7.280484 7.914586 SmallIrregular -6.053608 9.740412 Folded -6.9795423 8.231553 Interphase -6.0766606 9.7623205 SmallIrregular -6.1005816 9.570008 SmallIrregular -4.995538 8.992536 SmallIrregular -6.2500277 6.736237 SmallIrregular -6.7688665 8.127965 SmallIrregular -7.1364794 5.8733253 SmallIrregular -5.084658 9.727256 Binuclear -5.047284 9.764366 Binuclear -5.0326405 9.752936 Binuclear -4.9633846 9.694872 Binuclear -2.8658066 9.119066 Polylobed -3.3208392 8.373264 Polylobed -3.5580504 8.360062 Polylobed -3.5910523 8.323038 Polylobed -3.413535 8.263455 Polylobed -3.443093 8.370881 Polylobed -3.3340218 8.159602 Polylobed -4.937532 6.482364 Polylobed -4.8476505 6.5971694 Polylobed -4.2116957 6.5472507 Binuclear -4.230781 6.5075936 Binuclear -4.2655234 6.5153737 Binuclear -3.9688303 7.28872 Interphase -5.6841955 9.2172365 Polylobed -5.676204 9.2016945 Polylobed -5.629532 9.240787 Polylobed -5.65926 9.221792 Polylobed -5.70679 7.253618 Binuclear -5.4383135 9.011026 Binuclear -5.810319 9.236803 Polylobed -4.144692 6.334767 Polylobed -2.7010806 9.032083 Binuclear -5.11629 9.209783 Binuclear -4.1524606 6.1466775 Polylobed -4.373457 5.9689775 Binuclear -4.1375046 6.267992 Polylobed -4.1717057 6.26803 Polylobed -5.0848207 9.329122 Binuclear -3.8204584 5.734525 Binuclear -2.7208648 6.405279 Polylobed -5.9715557 7.1019664 Polylobed -4.9631763 9.436122 Binuclear -3.3678117 8.283039 Polylobed -3.3615797 8.284045 Polylobed -4.043465 8.284231 Polylobed -5.3807325 9.249513 Polylobed -5.599582 9.198814 Polylobed -3.9521437 7.3790545 Polylobed -5.404629 9.178845 Polylobed -4.1043077 7.677059 Binuclear -3.6078246 8.059325 Binuclear -2.8612418 6.220876 Polylobed -1.8791988 6.7584133 Polylobed -3.099286 7.763587 Polylobed -1.8484279 6.8550086 Polylobed -3.4919872 5.523573 Binuclear -1.7685082 6.9191203 Polylobed -2.3756428 6.4459043 Binuclear -1.8388075 6.9496713 Polylobed -3.2133102 8.748319 Binuclear -2.6915076 8.285191 Binuclear -3.602361 6.2649746 Binuclear -3.5564692 6.3127236 Binuclear -3.3954911 6.542053 Binuclear -3.199742 7.37553 Binuclear -2.9151752 9.054288 Binuclear -2.7424767 7.7600694 Binuclear -2.7695048 8.041334 Binuclear -2.7645059 8.961437 Binuclear -4.208714 6.990951 Binuclear -3.4776142 5.83436 Binuclear -3.3698509 7.8149962 Polylobed -3.3798738 7.711565 Polylobed -3.3500664 7.9726324 Polylobed -3.2314065 8.005497 Polylobed -3.2747405 8.1336155 Polylobed -5.0683756 9.769765 Binuclear -5.056817 9.737693 Binuclear -4.9733424 9.709112 Binuclear -4.9233375 9.67379 Polylobed -4.8482423 9.584919 Polylobed -3.7300348 6.7428885 Binuclear -4.1261854 5.857041 Binuclear -3.971809 6.4771047 Binuclear -3.9364164 6.4678903 Binuclear -4.61259 9.174041 Polylobed -5.129293 9.117267 Polylobed -4.833452 7.324401 Polylobed -3.2321832 8.71683 Polylobed -4.763457 7.038618 Polylobed -4.562017 7.3128657 Polylobed -4.050876 4.5668526 Binuclear -4.091296 6.1535034 Artefact -4.1657424 4.6002064 Binuclear -4.158258 6.082905 Artefact -4.226581 6.104245 Artefact -4.114672 6.1220293 Artefact -2.8172534 8.109694 Binuclear -2.9158766 8.110887 Binuclear -4.1950464 5.990715 Artefact -3.7121205 6.5471835 Binuclear -5.2242947 5.828767 Artefact -3.8163838 6.276033 Binuclear -3.8543534 6.4807878 Binuclear -6.14295 7.3425975 Artefact -6.1714745 7.6923065 Artefact -4.795471 6.8569393 Artefact -6.27103 7.144314 Artefact -4.1502542 6.5188766 Binuclear -4.19777 7.0127645 Binuclear -2.533722 4.9485507 Polylobed -2.4929583 5.1492987 Polylobed -2.4858525 5.1654735 Polylobed -3.3909404 3.1740618 Polylobed -4.240248 3.7984874 Polylobed -4.0359483 3.195899 Polylobed -3.5031068 3.0556662 Polylobed -4.468086 2.753362 Polylobed -4.221176 3.7911425 Polylobed -3.3340132 3.0979128 Polylobed -4.185052 3.8503609 Polylobed -2.9988759 3.090865 Polylobed -4.415729 2.8062315 Polylobed -3.931042 3.249807 Polylobed -3.944234 3.2872708 Polylobed -3.8561504 3.2844865 Polylobed -3.9510036 3.2091634 Polylobed -4.1996045 2.6243823 Polylobed -1.0867895 4.410538 Polylobed -1.049902 4.4381185 Polylobed -1.1996686 4.8347664 Polylobed -1.0813447 4.49808 Polylobed -1.1879691 4.848753 Polylobed -1.0822456 4.6644497 Polylobed -1.1495057 4.6565566 Polylobed -1.4986736 7.876436 Polylobed -1.8301338 7.7102857 Polylobed -1.5686173 8.098256 Polylobed -1.1069092 4.5283504 Polylobed -1.5076439 8.151743 Polylobed -1.8895154 7.7134027 Polylobed -1.6242732 8.086312 Polylobed -2.230737 7.320689 Polylobed -1.9885283 7.5243063 Polylobed -1.8729032 8.462595 Polylobed -1.6954559 8.440941 Polylobed -1.4358869 3.9010544 Polylobed -1.7361449 8.3255415 Polylobed -1.8022016 8.198001 Polylobed -1.445574 3.8960454 Polylobed -1.4730599 3.8416166 Polylobed -1.1057283 4.08296 Polylobed -0.5429081 3.7862792 Polylobed -1.2223933 4.02362 Polylobed -1.1902841 4.120367 Polylobed -1.2126923 4.0584693 Polylobed -0.5369367 3.798613 Polylobed -0.6112126 5.22177 Polylobed -0.55499065 3.8159137 Polylobed -0.6179564 5.2236767 Polylobed -1.2043061 4.3941293 Polylobed -0.5768477 3.8215287 Polylobed -0.62658787 5.2224884 Polylobed -1.2841337 4.293833 Polylobed -1.3353128 4.423274 Polylobed -3.4814079 3.9478285 Polylobed -3.9531791 2.084177 Polylobed -3.9837818 2.0792742 Polylobed -4.236931 4.205504 Polylobed -4.261132 4.2304926 Polylobed -4.2770395 4.2549624 Polylobed -4.24294 4.2776833 Polylobed -4.2386374 4.2222605 Polylobed -0.45228308 3.7009823 Polylobed -0.52250206 3.7667177 Polylobed -0.51536393 3.7632675 Polylobed -0.53975356 3.7827141 Polylobed -2.9015453 4.8289804 Polylobed -5.0127716 3.1070848 Polylobed -5.0847816 5.102446 Polylobed -8.00789 5.4661374 Polylobed -1.2180117 4.247663 Polylobed -1.3854208 4.2737722 Polylobed -1.4118333 4.1842704 Polylobed -5.36834 7.891092 Polylobed -5.340369 7.8692436 Polylobed -5.3168836 7.79734 Polylobed -5.4261146 7.8182635 Polylobed -5.475125 7.775935 Polylobed -4.3434877 2.6506467 Polylobed -4.3890967 2.193613 Polylobed -4.3889174 2.609144 Polylobed -4.493605 2.3182707 Polylobed -4.0653815 4.103341 Binuclear -3.9889894 4.0095754 Binuclear -4.0304136 3.8904514 Binuclear -4.241907 3.8828297 Polylobed -2.2657776 9.568409 Binuclear -2.2940116 9.523331 Binuclear -3.4394896 3.2246125 Polylobed -3.6674786 3.2219493 Polylobed -3.7409925 8.909746 Polylobed -3.7541676 8.882689 Polylobed -4.1008744 3.197375 Polylobed -3.493863 3.1636746 Polylobed -3.3050416 3.0952146 Polylobed -4.1320963 3.1696076 Polylobed -2.9582274 3.1250167 Polylobed -3.6339166 8.718548 Polylobed -3.42949 3.1676064 Polylobed -3.7017975 8.668841 Polylobed -3.8482513 8.942173 Polylobed -1.3294781 4.99561 Polylobed -1.2382494 5.0380297 Polylobed -1.2221117 4.9752555 Polylobed -1.1932664 5.0900707 Polylobed -1.0751234 4.6487026 Polylobed -1.0949341 4.482249 Polylobed -1.7799407 8.74605 Polylobed -1.4966432 8.630785 Polylobed -1.8430958 7.7489944 Polylobed -1.1370727 4.4620805 Polylobed -1.7699645 8.5620575 Polylobed -1.8225808 7.9205494 Polylobed -1.7441356 7.8294563 Polylobed -1.8656822 8.429706 Polylobed -1.7417374 8.508457 Polylobed -1.7806134 8.46534 Polylobed -1.7579879 8.54191 Polylobed -2.237175 9.254563 Polylobed -2.3012488 9.286538 Polylobed -2.174489 9.193564 Polylobed -2.257126 9.146024 Polylobed -1.004426 4.107469 Polylobed -0.9955629 4.146312 Polylobed -0.5584512 3.8000019 Polylobed -1.1199937 4.091804 Polylobed -2.1122108 9.236944 Polylobed -2.077193 9.196693 Polylobed -0.6375924 3.8895748 Polylobed -0.72848 5.033392 Polylobed -2.1416266 9.249234 Polylobed -0.9950229 4.147093 Polylobed -2.0836039 9.250358 Polylobed -2.087911 9.212605 Polylobed -0.61756146 3.8584566 Polylobed -0.7824227 4.8479505 Polylobed -0.6372656 3.814247 Polylobed -0.7430932 3.9851563 Polylobed -0.74802923 3.9183137 Polylobed -0.88724947 3.972566 Polylobed -2.2569494 7.194484 Polylobed -2.1368327 7.1168404 Polylobed -2.027417 7.3792934 Polylobed -2.151398 7.0611844 Polylobed -0.5169161 3.7653582 Polylobed -0.5130258 3.7538893 Polylobed -0.53433615 3.766561 Polylobed -0.5698957 3.7965364 Polylobed -0.57827944 3.7861137 Polylobed -2.1937444 5.248876 Binuclear -2.1488054 5.199482 Binuclear -0.54017454 3.765888 Polylobed -3.445511 3.9244993 Polylobed -2.2962859 5.144287 Polylobed -2.1304562 5.2231975 Polylobed -2.3492835 5.324536 Polylobed -2.3220978 3.5401368 Polylobed -2.2319167 3.5636647 Polylobed -2.2808554 3.6807587 Polylobed -1.9375165 7.425461 Polylobed -1.9365795 7.4571433 Polylobed -2.017946 7.3037815 Polylobed -2.2421734 6.683618 Polylobed -1.2497749 4.2031813 Polylobed -1.2809855 4.221452 Polylobed -1.2766734 4.216802 Polylobed -3.2764738 6.6837683 Binuclear -2.3457196 8.079334 Binuclear -2.6031976 5.5583415 Binuclear -3.686404 5.475348 Binuclear -2.159011 5.7284117 Binuclear -2.490051 5.747126 Binuclear -2.1975617 6.337466 Polylobed -2.352478 6.034574 Polylobed -2.5662823 5.477976 Polylobed -2.863879 4.3684444 Binuclear -3.1540449 4.5907855 Polylobed -1.4667995 4.370484 Binuclear -1.5507597 4.3130445 Binuclear -1.735437 3.6964002 Binuclear -3.1791077 5.9880376 Binuclear -1.9432639 7.403005 Binuclear -1.7677895 3.670104 Binuclear -1.6891875 3.6933184 Binuclear -3.058975 6.266619 Binuclear -2.112651 7.6537213 Binuclear -1.6914526 3.745641 Binuclear -4.49683 6.5793886 Grape -4.555802 6.769467 Grape -4.360969 8.560695 Grape -5.3142853 3.1185079 Grape -4.5544453 7.091723 Grape -5.639401 3.7357314 Grape -5.627689 3.722093 Grape -5.559412 3.6689792 Grape -4.1628757 8.627733 Grape -5.5147796 3.6524518 Grape -4.667716 3.4036286 Grape -5.537129 3.6403744 Grape -4.0040627 5.307707 Grape -4.634324 3.0970922 Grape -4.8196545 1.9712363 Grape -5.4976873 3.6056118 Grape -4.811374 2.995155 Grape -5.2830524 3.1521244 Grape -4.6048837 2.8853333 Grape -4.8075724 2.7888398 Grape -5.2455997 3.0082357 Grape -2.5966473 4.847534 Grape -2.5590596 5.165198 Grape -5.201686 7.407224 Prometaphase -5.5435553 3.7247136 Grape -5.695631 3.768831 Grape -5.607363 3.6713862 Grape -5.6989994 3.8672276 Grape -5.592465 3.7456834 Grape -4.94191 4.1330047 Grape -6.2679753 4.6696925 Prometaphase -4.8692374 7.711482 Grape -6.4059954 4.8592167 Apoptosis -4.2318063 4.0273123 Grape -4.111582 4.1572495 Grape -4.025469 4.0445046 Grape -3.7995512 4.2083797 Grape -3.9521513 4.338678 Grape -5.9335127 3.9677124 Grape -5.9733143 3.9957833 Grape -7.384091 4.5366077 Prometaphase -7.602047 4.79644 Apoptosis -8.220775 4.6181564 Apoptosis -5.695012 4.0526414 Grape -7.732376 4.7504983 Apoptosis -4.180667 2.316689 Grape -3.851795 2.0873566 Grape -5.0424185 1.7971466 Apoptosis -4.226714 2.2698002 Grape -3.7971509 2.1634684 Grape -4.691277 2.4837022 Grape -3.9028 2.0937774 Grape -4.63259 2.6461406 Grape -5.9120984 3.9108393 Grape -5.0765843 1.7769423 Apoptosis -3.806206 2.1317854 Grape -4.1395307 2.4128594 Grape -3.8477323 2.1342447 Grape -5.952356 3.930602 Grape -5.9587545 3.9249434 Grape -3.8133452 2.3063734 Grape -3.8087018 2.1367707 Grape -5.958394 3.9359803 Grape -3.8079028 2.1442914 Grape -4.1926417 2.5782697 Grape -7.5664506 4.67556 Prometaphase -3.8052495 2.1486797 Grape -5.0535884 1.7820535 Grape -6.0212727 4.0064335 Grape -3.7501698 2.1898339 Grape -7.04973 4.6023016 Prometaphase -5.0062246 1.8215553 Grape -3.841472 2.174375 Grape -3.8260837 2.2128804 Grape -6.134909 4.013303 Grape -5.040737 1.783417 Grape -5.0430894 1.7949914 Grape -3.1337905 2.8738375 Grape -3.2107415 2.915641 Grape -3.1498332 2.8558674 Grape -3.174785 2.8970823 Grape -3.1429448 2.8823473 Grape -3.140258 2.8958824 Grape -3.1219766 2.8805022 Grape -3.228903 2.9124794 Grape -4.8538733 3.8313615 Grape -5.3021894 3.7802887 Grape -5.021129 3.771297 Grape -4.8233666 3.9976323 Grape -4.4589295 3.4254534 Grape -7.1413226 6.191083 Apoptosis -4.984313 5.069771 Grape -4.4139595 3.2766485 Grape -4.709308 3.344874 Grape -4.8655815 3.469317 Grape -5.373302 3.749016 Grape -5.44744 3.8490894 Grape -5.3686485 3.7775745 Grape -5.445014 3.7880301 Grape -5.442019 3.8299847 Grape -5.3936157 3.8942342 Grape -5.487453 4.066205 Grape -5.537512 4.0951552 Grape -4.9471865 3.2698615 Grape -4.897756 3.3740122 Grape -4.908032 3.3616383 Grape -4.997875 3.33921 Grape -5.089955 3.2072892 Grape -4.9304595 3.3816545 Grape -5.045867 3.3891172 Grape -5.314884 3.2975192 Grape -5.7598176 3.8174496 Grape -5.8188734 3.853868 Grape -5.2139077 3.4381132 Grape -5.336877 3.357828 Grape -5.4073024 3.3098693 Grape -5.8307166 3.8342884 Grape -2.6882184 7.044622 Grape -5.3575783 3.3587244 Grape -2.8625321 7.1296268 Grape -5.3456187 3.3436399 Grape -2.627669 6.9401493 Grape -2.7526598 7.0023904 Grape -2.6377764 6.8899302 Grape -5.7328763 3.3601005 Grape -5.1850634 2.7551658 Grape -5.1905956 2.7576392 Grape -5.2886887 2.7918158 Grape -5.7645254 3.3464432 Grape -5.7036304 3.4269016 Grape -5.2198453 2.8133328 Grape -5.183701 2.7306674 Grape -5.2218437 2.8194306 Grape -5.7069416 3.4326298 Grape -4.818318 7.742898 Grape -2.4220748 7.6228123 Grape -2.440414 7.4640684 Grape -2.3801548 7.5755568 Grape -2.4374547 7.587764 Grape -2.4352694 7.661608 Grape -5.741781 3.4925818 Grape -5.7871385 3.6803246 Grape -5.8193393 3.6340556 Grape -4.528639 3.4461205 Grape -2.4290843 7.2687197 Grape -5.9079103 3.6265998 Grape -2.5381167 7.4674754 Grape -4.59446 3.4357948 Grape -5.3863053 2.957455 Grape -4.4991875 3.5213795 Grape -4.6204786 3.41385 Grape -5.449547 2.9167295 Grape -5.463375 2.9715652 Grape -5.4756703 2.9306126 Grape -5.4948425 2.978654 Grape -5.2394285 3.382276 Grape -5.6061325 3.1138504 Grape -4.743201 2.378525 Grape -5.5416207 3.110827 Grape -4.3101444 2.1447942 Grape -4.595489 2.2792425 Grape -4.5486307 2.182591 Grape -4.3515325 2.1160724 Grape -4.2192664 2.014712 Grape -4.427082 2.157824 Grape -4.3963337 2.1532645 Grape -5.145861 3.8659914 Grape -3.0887156 8.464925 Grape -3.108362 8.46819 Grape -3.337689 3.713786 Grape -3.0991075 8.458595 Grape -5.026722 3.8403482 Grape -3.1099396 8.411166 Grape -3.2846966 3.7954285 Grape -3.048619 8.287942 Grape -3.1461685 8.394573 Grape -3.0164661 8.291021 Grape -4.9049573 3.772308 Grape -3.1898026 8.429075 Grape -4.9334335 3.8601224 Grape -5.0120845 3.865198 Polylobed -4.8939524 4.0495815 Polylobed -4.075534 2.6758373 Grape -4.0228357 2.6650023 Grape -3.9621587 2.6600773 Grape -4.111527 2.6332684 Grape -4.103978 2.6775854 Grape -4.051503 2.6180513 Grape -7.4068465 4.4297237 Prometaphase -7.246577 4.7997127 Prometaphase -8.131692 4.3586626 Prometaphase -7.4207225 4.485434 Prometaphase -7.388383 4.5815415 Prometaphase -7.2100406 4.7320395 Prometaphase -6.988402 4.6596446 Prometaphase -8.721134 4.5837145 Prometaphase -8.7235365 4.1815457 Prometaphase -8.979441 4.2500415 Prometaphase -8.92753 4.239575 Prometaphase -8.152905 4.2784886 Prometaphase -7.2722206 4.6063504 Prometaphase -8.085919 4.4415684 Prometaphase -8.741706 5.750706 MetaphaseAlignment -8.154783 5.5168 Artefact -8.180492 5.490272 Artefact -8.264175 5.537407 Artefact -8.404272 4.9945593 MetaphaseAlignment -7.766849 4.8564773 Prometaphase -8.489952 5.597673 MetaphaseAlignment -7.0315747 4.6725526 Prometaphase -8.000883 5.8183913 MetaphaseAlignment -7.8641615 4.587531 Prometaphase -7.419238 4.473271 Prometaphase -7.865577 4.578528 Prometaphase -4.9711947 7.222336 Prometaphase -8.629471 5.7987523 MetaphaseAlignment -8.013818 4.549716 Prometaphase -7.1588054 4.4384527 Prometaphase -9.13695 5.900663 MetaphaseAlignment -8.57225 4.424921 Prometaphase -8.147309 4.3534665 Prometaphase -8.02757 4.374397 Prometaphase -5.6298103 7.4873276 MetaphaseAlignment -7.5088043 4.5629606 MetaphaseAlignment -8.221734 4.5566683 Prometaphase -8.562803 5.0518303 MetaphaseAlignment -7.225388 5.1997795 Prometaphase -8.472773 5.078388 MetaphaseAlignment -9.018363 4.3403325 MetaphaseAlignment -7.8181877 4.3533506 Prometaphase -8.118251 4.2961383 Prometaphase -8.960875 4.304108 Prometaphase -5.2359476 7.4378324 Prometaphase -5.4348936 7.4274483 Prometaphase -7.993311 4.4684157 Prometaphase -7.0744534 4.6135674 Prometaphase -5.1793904 3.272728 Grape -5.1851807 3.2979069 Grape -3.4901006 3.3308978 Grape -5.1711073 3.285933 Grape -5.12531 3.3490512 Grape -5.0430055 3.0667298 Grape -4.601463 3.4685833 Grape -5.1508293 3.7096589 Polylobed -4.112906 4.0328145 Polylobed -4.5128465 3.5176756 Grape -4.719609 3.3698456 Grape -5.2658615 3.5004249 Polylobed -4.544058 3.4892728 Grape -4.7935224 3.379773 Grape -3.8446174 4.0993786 Polylobed -4.401219 3.5432162 Grape -3.6529708 4.1264586 Polylobed -4.673386 3.3667564 Grape -7.915481 4.581059 Grape -4.216913 3.0871572 Grape -4.4497995 3.0488765 Grape -4.3022094 3.1317847 Grape -4.463633 3.0856118 Grape -4.5558567 3.0404015 Grape -4.4722934 2.80956 Grape -5.077651 3.241841 Grape -4.775076 2.6528873 Grape -4.6731153 2.342281 Grape -4.493155 2.579738 Grape -4.5695806 2.0395083 Grape -4.403018 2.5327942 Grape -4.431941 2.498798 Grape -4.4840436 2.2138338 Grape -4.3288226 2.0260775 Grape -4.2667274 2.1208806 Grape -4.31949 2.0652153 Grape -4.340151 2.122347 Grape -4.3081346 2.1511352 Grape -4.2486258 2.167044 Grape -4.3724184 3.2572145 Grape -4.432441 3.2677891 Grape -4.35528 3.2720602 Grape -4.3419614 2.1912568 Grape -4.422359 3.2424881 Grape -4.7717056 2.03235 Grape -4.7273083 2.0750062 Grape -4.6879253 2.0931141 Grape -4.740085 2.1446927 Grape -4.6199346 3.816881 Grape -4.7939053 1.9821118 Grape -4.782825 1.9823868 Grape -4.376133 2.5682235 Grape -8.437605 5.3280034 Apoptosis -4.3698926 2.5774379 Grape -3.0339 8.463674 Grape -2.9755905 8.494538 Grape -2.9766138 8.517359 Grape -2.980108 8.534791 Grape -2.9969819 8.399559 Grape -8.542301 4.916175 Apoptosis -2.9921553 8.5045595 Grape -3.000531 8.423313 Grape -2.9689221 8.470535 Grape -3.9725752 3.2436213 Grape -3.1795998 8.25941 Grape -2.97526 8.444744 Grape -3.9604619 3.1701443 Grape -3.9485564 3.1699367 Grape -4.022591 2.9867928 Grape -3.9585934 2.9830759 Grape -3.8325243 3.082473 Grape -3.9185934 3.1402235 Grape -7.914622 4.763402 Apoptosis -4.9783792 3.3783507 Grape -4.9773097 3.435299 Grape -4.936713 3.4594002 Grape -5.009258 3.5126872 Grape -4.972721 3.5148273 Grape -4.71463 2.6810908 Grape -4.982755 3.5935817 Grape -4.725471 2.5136206 Grape -4.649153 2.5569913 Grape -4.7059865 2.2295043 Grape -4.6717234 2.3290467 Grape -4.2970805 2.9050236 Grape -4.3927207 2.9655452 Grape -4.387556 2.938428 Grape -4.3586545 2.9249067 Grape -4.338267 2.9072845 Grape -4.4057827 2.9582179 Grape -4.321347 2.817477 Grape -4.393668 2.7900097 Grape -4.3047166 2.8167713 Grape -4.39052 2.9659667 Grape -4.344784 2.8539734 Grape -4.313801 2.8172386 Grape -4.305588 2.861667 Grape -3.013222 3.102256 Polylobed -4.034198 1.9093764 Polylobed -3.0059693 3.132531 Polylobed -2.941759 3.1560411 Polylobed -3.9587631 1.9495986 Polylobed -5.7677054 3.551232 Grape -5.745075 3.5724876 Grape -5.2532525 3.69462 Grape -5.697201 3.5099416 Grape -5.710368 3.5790257 Grape -5.739921 3.5423906 Grape -5.7757273 3.4387238 Grape -2.6720176 6.372416 Grape -5.928198 3.5601013 Grape -5.751633 3.3660924 Grape -2.651171 6.3631115 Grape -5.754513 3.5714884 Grape -4.5689774 4.490255 Grape -5.66478 3.341108 Grape -4.596975 4.5068755 Grape -5.7050977 3.421451 Grape -4.5810647 4.374194 Grape -2.865561 7.556766 Grape -5.3401194 3.2947233 Grape -4.8049517 2.82951 Grape -5.3099656 3.0616682 Grape -4.5223336 3.5429852 Grape -4.6388636 3.410072 Grape -5.294034 3.0806751 Grape -5.323773 3.3044648 Grape -4.4071374 3.7252324 Grape -5.378277 3.0552664 Grape -5.4380565 3.066552 Grape -5.3018565 3.2645786 Grape -5.499301 3.1235182 Grape -5.437269 3.092177 Grape -5.3329234 3.1292534 Grape -5.506785 3.181696 Grape -5.539392 3.249938 Grape -4.7886724 2.614747 Grape -4.7170086 2.4628544 Grape -5.519259 3.2313497 Grape -3.0887702 3.773284 Grape -4.5607505 2.1234767 Grape -4.5007205 2.193083 Grape -3.2488654 3.7492545 Grape -4.623223 2.1298656 Grape -4.5843697 2.1313317 Grape -3.515916 2.239919 Grape -4.6460977 2.3046668 Grape -3.5069783 2.2106607 Grape -4.5804443 2.3931994 Grape -3.5170631 2.2382255 Grape -4.5486026 2.5228043 Grape -4.3332086 2.103248 Grape -3.4960113 2.231705 Grape -4.4933805 2.5542777 Grape -4.529646 2.3781855 Grape -4.4855638 2.2230563 Grape -6.325983 4.253891 Grape -6.138497 4.1668983 Grape -8.685077 5.965897 Apoptosis -8.655562 5.920773 Apoptosis -4.062141 3.1638224 Grape -4.095272 3.4135737 Grape -3.7986097 4.1916656 Grape -8.357899 5.5294185 Apoptosis -3.8645003 4.2652955 Grape -8.190456 5.5481186 Apoptosis -4.563274 3.9947722 Grape -4.6094394 3.926569 Grape -7.5746837 4.6789784 Apoptosis -7.409362 4.595326 Apoptosis -4.398092 4.31498 Grape -4.4528112 4.346953 Grape -7.4612684 4.6329722 Apoptosis -7.4150615 4.663931 Apoptosis -4.454386 4.30052 Grape -4.728458 8.461339 Grape -4.78712 8.433149 Grape -4.7736454 8.426999 Grape -4.2844906 3.0452142 Grape -4.293764 3.0402803 Grape -4.228994 3.151313 Grape -4.0289125 1.7566286 Grape -4.2078223 2.7870326 Grape -4.328888 3.079312 Grape -4.036042 1.786739 Grape -4.0118933 2.6753376 Grape -4.0181284 1.7513115 Grape -3.963619 2.6445417 Grape -3.9047744 2.6798759 Grape -4.027804 1.7407479 Grape -4.0362225 1.7726741 Grape -3.9807856 2.7693043 Grape -3.9692388 2.749537 Grape -4.0262146 2.7024856 Grape -4.0299926 2.6764452 Grape -4.0527897 1.769724 Grape -4.0307775 1.7746264 Grape -4.08955 1.8755985 Grape -4.037672 1.7915454 Grape -4.0333104 1.858223 Grape -4.298438 2.4247954 Grape -4.8250327 2.878749 Grape -4.7477126 3.0671365 Grape -4.745991 2.972636 Grape -6.0644665 6.8681464 Interphase -6.2470903 6.9578485 Interphase -8.651149 5.88116 Prometaphase -6.0695443 7.165336 Interphase -5.9617877 6.096204 Interphase -6.2418194 5.8978405 Interphase -5.029166 6.5423007 Interphase -4.4464507 7.0709496 Interphase -7.1750555 5.18578 Prometaphase -4.008955 7.221967 Interphase -6.451029 6.0214653 Interphase -5.2696857 6.234508 Interphase -5.6012526 7.073655 Interphase -6.880288 5.217497 Interphase -5.4222775 6.4687796 Interphase -5.3553376 7.2126646 MetaphaseAlignment -7.4112883 5.3949585 MetaphaseAlignment -7.505732 4.437004 Prometaphase -4.348354 6.553274 Interphase -4.1308117 6.7859097 Interphase -4.4668374 6.499263 Interphase -7.387092 4.891072 MetaphaseAlignment -7.6368303 5.523076 MetaphaseAlignment -1.5079592 4.759656 Polylobed -1.6179197 4.6509542 Polylobed -1.5697572 4.422447 Polylobed -3.3546364 5.388728 Polylobed -6.684104 5.6201315 MetaphaseAlignment -2.451967 3.5371783 Polylobed -2.4263928 3.5679142 Polylobed -3.3116844 4.7466373 Binuclear -4.060706 3.6674085 Binuclear -2.4887507 3.5035856 Polylobed -2.6182082 3.6846552 Polylobed -3.8984978 4.382459 Binuclear -2.4722807 3.5370064 Polylobed -2.477115 3.5501978 Polylobed -2.5138896 3.59846 Polylobed -2.4460266 3.5593472 Polylobed -2.8519015 6.2255697 Polylobed -3.9128344 4.5473046 Polylobed -3.9767783 4.5641456 Polylobed -0.9575129 4.887009 Polylobed -4.0545287 4.505062 Polylobed -3.8566065 4.5674677 Polylobed -0.9744642 4.8924217 Polylobed -0.86950505 4.889696 Polylobed -0.98161834 4.815392 Polylobed -8.335242 5.256297 MetaphaseAlignment -2.0722408 4.510359 Polylobed -1.9016708 4.595995 Polylobed -0.7029076 6.5084305 Polylobed -4.820893 5.939889 Artefact -0.72083944 6.8836775 Polylobed -4.8922157 5.6529694 Artefact -4.9207892 5.6826477 Artefact -1.6078589 3.368923 Polylobed -1.6198835 3.3967628 Polylobed -1.6398445 3.400144 Polylobed -1.6890414 3.4691522 Polylobed -1.6332129 3.390838 Polylobed -1.6537071 3.4379687 Polylobed -1.6511612 3.423217 Polylobed -1.6470503 3.3992722 Polylobed -4.9291596 5.599688 Polylobed -4.869437 5.588301 Polylobed -1.9779574 5.7918067 Polylobed -1.8210176 5.718697 Polylobed -2.1832201 4.5263815 Polylobed -2.2224534 4.345256 Polylobed -2.135827 4.442591 Polylobed -2.0982504 4.460037 Polylobed -2.1716459 4.223987 Polylobed -1.7605234 3.854556 Polylobed -1.774124 4.021656 Polylobed -1.7892495 4.02441 Polylobed -1.8860105 4.26759 Polylobed -1.8531517 4.124043 Polylobed -2.4132214 4.164016 Polylobed -3.195122 4.5290685 Polylobed -3.1699734 4.580944 Polylobed -4.1392007 3.1773417 Polylobed -3.3638532 5.2083254 Binuclear -3.4996982 5.283329 Binuclear -3.3841465 5.217438 Binuclear -3.4396515 5.2325625 Binuclear -4.075247 2.338762 Polylobed -4.1344995 1.9717337 Polylobed -4.1587415 2.3669279 Polylobed -4.1099787 2.3943503 Polylobed -0.8514562 6.629213 Polylobed -0.5928721 6.4659877 Polylobed -0.6082493 6.261476 Polylobed -2.3237314 5.196762 Polylobed -2.7464101 4.9217043 Polylobed -2.9146326 4.5867815 Polylobed -2.987763 4.620791 Polylobed -3.203564 9.141763 Interphase -2.9964132 4.367516 Polylobed -2.829245 4.4177904 Polylobed -2.4977593 4.737507 Polylobed -0.986952 5.57841 Polylobed -0.93669397 5.6292067 Polylobed -0.98721015 5.60824 Polylobed -0.9654643 5.6023064 Polylobed -0.9899138 5.5894666 Polylobed -6.3829446 6.877879 Artefact -6.530995 6.2764883 Artefact -6.3948445 6.0830994 Artefact -0.8461386 6.3106704 Artefact -3.4022136 5.25192 Artefact -2.0621424 4.4811945 Polylobed -2.1074505 4.526633 Polylobed -4.5827246 6.78162 Binuclear -3.2903407 8.764097 Binuclear -2.0359302 3.6858258 Binuclear -1.8062932 3.6873896 Binuclear -3.0124288 4.393805 Binuclear -2.9183562 4.395947 Binuclear -0.35354337 6.5457473 Polylobed -0.41648477 6.4294724 Polylobed -4.746399 4.9757695 MetaphaseAlignment -0.32840717 6.476787 Polylobed -0.37622535 6.5170507 Polylobed -0.39337626 6.4418387 Polylobed -0.33419028 6.4149394 Polylobed -1.9216089 4.4469914 Binuclear -0.3704649 6.4402976 Polylobed -1.9581012 4.4663806 Binuclear -1.9172549 4.4712048 Polylobed -1.9112393 4.445066 Polylobed -1.9736955 4.444289 Polylobed -4.9899173 2.8521209 Polylobed -1.9213811 4.476646 Polylobed -1.4432611 4.017546 Polylobed -1.5631434 3.9034662 Polylobed -1.5814487 3.8063736 Polylobed -1.6401186 3.733226 Polylobed -1.6026444 3.7239087 Polylobed -0.86470914 7.4012027 Polylobed -1.6023878 3.7735248 Polylobed -0.8621329 7.338846 Polylobed -0.87067205 7.420138 Polylobed -2.9506328 5.36752 Polylobed -1.2111032 7.760814 Polylobed -3.329256 5.264128 Polylobed -1.2314624 7.7125726 Polylobed -0.6559492 3.8759305 Polylobed -0.7005907 6.20184 Polylobed -1.750811 6.9487224 Polylobed -1.2849916 7.8239226 Polylobed -1.2953718 8.202838 Polylobed -3.2790017 5.024261 Polylobed -1.3193588 7.789621 Polylobed -0.70137507 3.8959162 Polylobed -2.1113672 4.2410955 Polylobed -1.7819356 7.0275397 Polylobed -6.053551 5.428429 Polylobed -1.2449981 7.8446245 Polylobed -2.310355 4.1482363 Polylobed -0.7105615 3.9078488 Polylobed -1.7689668 6.961663 Polylobed -1.8306352 7.0717254 Polylobed -0.7014866 3.892574 Polylobed -1.3728392 7.973279 Polylobed -1.779021 7.0498924 Polylobed -1.9808537 4.590725 Polylobed -1.9529895 4.5922866 Polylobed -2.3976996 5.9280715 Polylobed -3.097216 5.1702733 Polylobed -3.330915 6.0141816 Polylobed -0.9895072 6.044673 Polylobed -3.192132 5.301411 Polylobed -1.6365849 3.5075111 Polylobed -6.1031437 6.035744 Binuclear -1.1645874 5.6998587 Polylobed -1.6108745 3.5002663 Polylobed -5.6167145 5.686918 Binuclear -1.3670453 5.6776857 Polylobed -1.4617623 5.441077 Polylobed -1.623619 3.563344 Polylobed -1.7514565 3.7722995 Polylobed -6.1928253 6.454573 Binuclear -1.6561053 3.554471 Polylobed -3.0337105 5.866363 Polylobed -1.9197099 3.8774896 Polylobed -2.8591728 5.3459034 Polylobed -1.8597164 5.8617887 Polylobed -1.6539696 3.600828 Polylobed -1.712858 5.7937803 Polylobed -1.7800113 5.8518925 Polylobed -2.2129393 4.357425 Polylobed -2.1113098 4.2920375 Polylobed -4.8126736 5.0377727 Polylobed -2.1165788 4.308441 Polylobed -1.3145524 2.963295 Polylobed -1.3102957 2.9738653 Polylobed -1.3263268 2.9991982 Polylobed -1.3267851 2.9789283 Polylobed -1.313277 2.969996 Polylobed -1.3160232 2.97568 Polylobed -1.3320156 2.996973 Polylobed -1.9489455 4.4388876 Polylobed -1.8884361 4.7032266 Polylobed -1.9312682 4.408268 Polylobed -1.9030566 4.6935735 Polylobed -3.949583 6.4100866 Hole -8.661387 6.754222 Hole -4.623149 5.286523 Polylobed -4.3948436 4.809343 Polylobed -4.375028 4.7707505 Polylobed -7.3544993 6.019578 Hole -7.28205 6.4439816 SmallIrregular -7.2650685 5.898282 UndefinedCondensed -8.850147 6.370615 UndefinedCondensed -5.3405657 4.8183136 Polylobed -5.310534 4.803451 Polylobed -5.30141 4.7636714 Polylobed -5.317814 4.8128314 Polylobed -4.591369 5.94563 Elongated -4.5605726 5.98506 Elongated -7.0391197 6.4627404 Hole -3.2136662 4.6981354 Polylobed -5.127683 5.707271 Polylobed -4.978263 5.7142158 Polylobed -5.074086 5.7442694 Polylobed -6.4106975 6.348066 SmallIrregular -4.308387 5.3670235 Polylobed -4.313475 5.25699 Polylobed -8.100229 7.318536 UndefinedCondensed -4.344586 5.3035765 Polylobed -8.530013 7.120507 SmallIrregular -7.4085064 5.980641 Hole -5.311038 4.765662 Polylobed -2.3857067 5.9312105 Polylobed -5.30335 4.777768 Polylobed -5.3252473 4.7735724 Polylobed -2.9435518 5.859611 Polylobed -5.331514 4.7938027 Polylobed -5.3147507 4.8295016 Polylobed -5.288664 4.8055444 Polylobed -7.7031794 7.3936753 Hole -7.0397906 8.153277 SmallIrregular -4.630545 5.9547625 Elongated -4.587941 5.978824 Elongated -3.217609 4.656864 Large -6.272958 6.589019 SmallIrregular -4.7760205 6.023275 Polylobed -4.8054614 6.1246843 Polylobed -4.7185016 6.10477 Polylobed -5.1207924 4.9422336 Polylobed -2.2361536 6.175113 Polylobed -2.2342896 6.245451 Polylobed -2.1538599 6.26727 Polylobed -8.081245 7.5552607 SmallIrregular -4.0637655 4.237097 Polylobed -3.993628 3.65503 Polylobed -7.4453874 5.744152 Folded -7.4668465 5.7028236 Hole -4.839859 6.3123198 Hole -4.5129 5.840173 Hole -6.6375217 7.9043436 SmallIrregular -4.840767 5.6334743 Folded -7.0643697 5.8028307 Hole -3.9870348 6.136028 Folded -4.234745 5.611277 Folded -4.261317 5.5854936 Folded -7.0570364 6.300441 UndefinedCondensed -4.302467 5.630256 Folded -7.7410264 7.6828914 UndefinedCondensed -2.6575556 4.602594 Polylobed -2.6506007 4.773895 Polylobed -2.778971 4.6443243 Polylobed -2.7785404 4.689135 Polylobed -3.422358 4.153632 Polylobed -3.4732378 4.208905 Polylobed -8.3095255 7.3859625 UndefinedCondensed -3.5301316 4.2564397 Polylobed -7.269248 5.779671 Artefact -7.239815 5.7963076 Artefact -6.0642548 5.7250853 Elongated -8.844251 6.345317 UndefinedCondensed -6.4512324 6.0342517 UndefinedCondensed -8.659367 6.596661 Hole -3.8409991 6.1879654 Hole -4.39915 5.8576784 Elongated -8.198925 7.4545608 SmallIrregular -8.219362 7.431948 SmallIrregular -5.117184 5.731912 Polylobed -8.153454 7.514295 SmallIrregular -5.033661 5.716876 Polylobed -5.0319843 5.7996144 Polylobed -8.809145 6.4571376 UndefinedCondensed -8.566445 7.0519795 Hole -7.5230784 6.3410654 UndefinedCondensed -5.261045 4.7302113 Polylobed -3.460098 5.416473 Polylobed -5.397029 5.5224156 UndefinedCondensed -5.1273284 5.5348654 UndefinedCondensed -5.2681384 4.812591 Polylobed -5.2916903 4.8051257 Polylobed -2.4379587 5.689409 Polylobed -3.2627897 5.8511033 Polylobed -4.6201925 5.9581523 Elongated -3.173219 4.6761575 Large -6.063392 6.435281 Polylobed -5.999982 6.472242 Polylobed -2.3548875 6.248891 Polylobed -6.3434706 8.797472 SmallIrregular -2.190487 6.2525144 Polylobed -2.2297344 6.317567 Polylobed -2.219442 6.2518573 Polylobed -8.394853 5.2368636 MetaphaseAlignment -4.0118985 6.021489 Binuclear -3.7506983 6.3289804 Binuclear -3.884639 4.6761594 Binuclear -3.7089725 4.750013 Binuclear -3.5028949 6.1186113 Binuclear -3.6946876 6.182108 Binuclear -3.7020266 4.9074154 Binuclear -4.3313375 5.576185 Binuclear -2.9243755 4.5151486 Artefact -3.2265065 6.58771 Binuclear -3.0329573 4.219334 Artefact -2.9635313 4.580441 Artefact -3.0112338 6.5544333 Binuclear -3.1272094 6.512045 Binuclear -5.889528 6.6138163 Binuclear -5.8768425 6.386009 Binuclear -1.7732184 7.2160974 Binuclear -2.0369427 6.2814693 Binuclear -2.9797053 4.1516657 Polylobed -2.9204125 4.1125383 Polylobed -2.701623 4.2345295 Polylobed -2.7523918 4.283174 Polylobed -2.9826362 4.0661445 Polylobed -2.7648978 4.2134438 Polylobed -4.16161 3.5467725 Polylobed -4.126434 3.597457 Polylobed -4.061195 3.7396984 Polylobed -4.2046194 3.8027914 Polylobed -2.9391856 6.8950906 Polylobed -2.9879358 6.748459 Polylobed -2.9405255 6.7988358 Polylobed -2.9853015 6.842374 Polylobed -3.060205 4.190968 Polylobed -2.6058836 3.9261453 Polylobed -3.0207055 4.156711 Polylobed -2.6965387 3.9751563 Polylobed -3.1457376 4.2298765 Polylobed -2.6179523 3.9028587 Polylobed -2.2801921 3.6784854 Polylobed -3.6222982 4.9757485 Polylobed -3.6002584 5.068237 Polylobed -2.0492446 3.9942033 Polylobed -1.9766381 4.0689673 Polylobed -3.0299535 4.3336453 Polylobed -2.1704853 4.0089874 Binuclear -2.8381147 4.4336057 Polylobed -2.0870697 4.1533527 Binuclear -2.9416423 4.392269 Polylobed -2.1371903 3.899592 Binuclear -2.0935848 4.0367584 Binuclear -3.3956125 4.65871 Polylobed -2.9240744 4.442074 Polylobed -2.8997247 4.1020236 Polylobed -2.1906247 3.9148457 Binuclear -3.2426255 4.290776 Binuclear -3.269174 4.3946095 Binuclear -3.373233 4.440163 Polylobed -3.4014182 4.4754486 Polylobed -4.0900655 3.5080085 Polylobed -3.3143167 4.624137 Polylobed -3.2878294 3.9011176 Polylobed -2.883263 4.304329 Polylobed -3.0644324 4.368629 Polylobed -3.4268644 3.365726 Polylobed -3.5755575 3.4860454 Polylobed -3.5667083 3.7757642 Polylobed -2.9875295 4.3417954 Polylobed -2.7952795 5.556379 Polylobed -2.7240133 4.5478973 Polylobed -6.466845 5.987308 Artefact -2.7801378 4.514256 Polylobed -6.4653788 5.915211 Artefact -2.7640917 4.442291 Polylobed -2.6430573 4.5309806 Polylobed -6.786877 6.1859107 Artefact -3.0739908 4.2598267 Polylobed -3.0415213 4.266774 Polylobed -3.0647304 4.2898283 Polylobed -3.612418 5.0986876 Polylobed -3.69531 5.1966476 Polylobed -1.9597288 4.075534 Polylobed -1.9194412 3.990437 Polylobed -1.9741144 4.149781 Polylobed -3.0354526 5.5682087 Polylobed -2.499568 6.04957 Polylobed -2.6890342 5.74223 Polylobed -3.526381 5.039964 Binuclear -3.7284706 5.0564475 Binuclear -3.5199826 5.162506 Binuclear -2.316696 7.5348816 Binuclear -2.3024054 5.3962293 Binuclear -4.3959446 5.676923 Binuclear -2.1687174 5.480357 Binuclear -3.360652 5.0026503 Binuclear -3.630184 5.657907 Binuclear -3.3621519 5.2872458 Binuclear -3.1893945 5.2462354 Binuclear -3.9041197 5.4431405 Binuclear -2.3237243 6.1647305 Binuclear -3.5055723 5.1734605 Binuclear -2.986973 6.79769 Binuclear -3.7323704 6.655708 Binuclear -2.5614235 7.463634 Binuclear -2.9009438 5.718331 Binuclear -3.2384272 6.5837765 Binuclear -4.6030717 6.6974125 Binuclear -3.1035333 6.9866962 Binuclear -2.7999656 6.579948 Binuclear -3.0722668 6.7600036 Binuclear -2.767004 6.5541844 Binuclear -3.5014079 4.6083384 Binuclear -3.605402 4.7712874 Binuclear -5.7963147 5.864153 Binuclear -3.9966252 4.700204 Binuclear -6.2802615 5.849209 Binuclear -4.018571 4.673045 Binuclear -6.12506 5.787679 Binuclear -6.027328 5.7769585 Binuclear -2.5949352 4.011511 Binuclear -2.5408316 6.350958 Binuclear -2.8418894 4.3829794 Binuclear -1.6944597 6.619589 Binuclear -2.7590733 4.6852303 Binuclear -3.2064998 4.5416446 Polylobed -3.945744 6.0171304 Binuclear -3.4976306 5.7990413 Binuclear -2.3078253 5.655059 Polylobed -3.9940703 6.067415 Binuclear -3.443697 5.420504 Binuclear -2.8919735 7.029441 Binuclear -3.563638 4.845368 Polylobed -3.9453006 6.072378 Binuclear -2.9044158 7.0463953 Binuclear -3.9756465 6.034343 Binuclear -3.695983 4.399952 Binuclear -3.6499403 4.1508245 Binuclear -3.4521253 4.3305244 Binuclear -3.042291 6.843865 Binuclear -2.925035 6.956199 Binuclear -3.7431087 7.267657 Binuclear -4.3293824 7.5647125 Binuclear -2.9234614 5.1471715 Binuclear -3.2540736 6.0315604 Binuclear -3.5857773 5.0540566 Binuclear -3.6508033 4.78689 Binuclear -3.4993677 5.986053 Binuclear -3.2331967 5.976881 Binuclear -7.36385 5.15783 MetaphaseAlignment -2.3207643 7.4245024 Polylobed -2.241427 7.463161 Polylobed -2.3279707 7.5230517 Polylobed -2.2898488 7.487716 Polylobed -5.856888 7.4729686 MetaphaseAlignment -1.6655126 7.830274 Polylobed -1.6459702 7.7971344 Polylobed -1.593643 7.666172 Polylobed -2.9190438 5.579156 Polylobed -3.0434723 5.858696 Polylobed -1.736832 7.3736014 Polylobed -3.0867102 5.620877 Polylobed -1.9310675 6.395039 Polylobed -1.9390943 6.375307 Polylobed -1.9355583 6.3989177 Polylobed -1.9377133 6.434762 Polylobed -7.784819 5.26591 MetaphaseAlignment -4.1721897 3.153987 MetaphaseAlignment -4.0919104 3.3044178 MetaphaseAlignment -7.882504 5.312875 MetaphaseAlignment -8.471203 5.0608897 MetaphaseAlignment -6.957714 5.787745 MetaphaseAlignment -4.4296727 6.81289 Interphase -7.110125 6.0157948 Interphase -5.169223 6.242528 Interphase -7.419106 5.764676 Interphase -5.5413814 6.3750496 Interphase -2.554676 4.415683 Large -3.0429256 4.9101877 Large -7.548325 5.8646693 Interphase -4.9655557 6.376575 Interphase -5.35383 5.9341583 Binuclear -5.1489487 5.8113613 Binuclear -6.4679623 5.8704114 Interphase -4.495359 2.2969446 Polylobed -4.4375696 2.1164865 Polylobed -4.392986 2.053649 Polylobed -2.7667034 2.5369625 Polylobed -2.72456 2.5361004 Polylobed -2.7158625 2.529643 Polylobed -2.7464068 2.5437024 Polylobed -2.7084546 2.5296302 Polylobed -1.0941626 4.3262544 Polylobed -1.1140126 4.371494 Polylobed -1.0954735 4.338939 Polylobed -2.1566536 7.7054067 Artefact -2.1040034 7.779014 Artefact -2.165216 7.6148295 Artefact -2.169536 7.699687 Artefact -3.6071863 6.825303 Polylobed -3.5263739 6.3516073 Polylobed -3.1340773 7.405899 Polylobed -3.1954424 7.4122295 Polylobed -3.2505274 7.420313 Polylobed -5.978804 9.126582 Polylobed -5.980887 9.13192 Polylobed -5.908568 9.130491 Polylobed -5.9563346 9.192311 Polylobed -6.8888936 8.4431 Binuclear -6.841149 8.374809 Binuclear -6.5360694 6.3071895 Polylobed -6.016365 9.0056 Polylobed -6.267 6.3870187 Polylobed -6.3648405 6.281976 Polylobed -6.687757 6.2729287 Binuclear -6.3329253 6.0332026 Binuclear -6.8888135 6.6331267 Artefact -3.8399854 7.6212783 Polylobed -6.7846203 6.6308923 Artefact -3.932645 7.627336 Polylobed -6.4362807 5.1354117 Artefact -4.036941 7.6013308 Polylobed -6.533512 5.145099 Apoptosis -6.993948 6.488254 Apoptosis -7.034678 8.017878 Artefact -3.7238219 5.3619065 Binuclear -4.593395 5.5003376 Binuclear -3.8068583 7.807999 Polylobed -3.9653833 7.623669 Polylobed -5.3654504 6.4059772 Artefact -3.863232 7.767019 Polylobed -4.1986923 7.2774205 Polylobed -5.55478 6.5482664 Artefact -5.863013 8.531866 Artefact -5.167901 9.363359 Binuclear -5.2751226 9.310688 Binuclear -7.161667 9.355218 Artefact -7.149903 9.279579 Artefact -7.182563 9.371893 Artefact -7.178369 9.359759 Artefact -7.168008 9.358233 Artefact -6.059269 8.925472 Binuclear -6.0184503 9.008263 Binuclear -7.1721535 9.359514 Artefact -7.1364 9.317068 Artefact -7.1715994 9.369675 Artefact -4.117866 7.228837 Polylobed -6.2544804 4.7367086 Artefact -4.0051036 7.4111595 Polylobed -6.2609863 4.7727046 Artefact -3.771759 7.3803997 Polylobed -4.053528 7.453197 Polylobed -6.258447 4.7342024 Artefact -6.2585273 4.7784677 Artefact -4.099649 6.3506646 Binuclear -4.03274 6.2200933 Binuclear -7.5539794 6.5992665 Apoptosis -7.6261134 7.353331 Apoptosis -7.510888 6.7279487 Apoptosis -6.307269 9.358049 Binuclear -6.20227 9.314366 Binuclear -4.0632796 7.593855 Polylobed -3.8888116 7.6819367 Polylobed -4.852278 6.1521206 Polylobed -5.5427723 6.8196855 Artefact -5.267861 6.9718184 Artefact -6.040472 6.5032053 Polylobed -5.51859 6.862229 Artefact -5.1283064 9.127396 Artefact -5.8172884 9.056542 Artefact -7.110493 6.1832547 Apoptosis -4.8935957 7.0036488 Artefact -4.9361243 7.059921 Artefact -4.8830094 7.122653 Artefact -4.978285 7.0882983 Artefact -4.969793 7.180649 Artefact -5.2931437 9.315973 Binuclear -5.052019 9.353167 Binuclear -7.446951 7.089235 Apoptosis -5.9778543 9.072993 Binuclear -6.0011015 9.126998 Binuclear -6.242398 6.489255 Polylobed -5.9991474 6.548383 Polylobed -6.484686 6.165368 Polylobed -4.2607055 4.824073 Polylobed -6.5404177 6.2757916 Polylobed -7.1229935 6.336881 Binuclear -7.2013464 6.336821 Binuclear -7.1278887 6.5176797 Binuclear -7.3450727 6.73695 Binuclear -7.235661 7.942204 Binuclear -6.2904906 8.886183 Binuclear -5.5749207 8.889038 Binuclear -5.670325 8.548168 Binuclear -6.5162177 9.153993 UndefinedCondensed -3.720312 7.799817 Binuclear -3.7002506 7.7022033 Binuclear -5.6681094 5.824396 Binuclear -5.793418 5.799181 Binuclear -6.0449386 6.0109205 Artefact -6.4073014 6.3808036 Artefact -5.0307937 7.2578692 Binuclear -5.0572753 7.2719727 Binuclear -3.8125813 6.913132 Interphase -4.062098 6.6753564 Interphase -3.9075782 6.988754 Interphase -5.162225 6.906052 Interphase -7.8691363 5.4722934 Prometaphase -9.155003 5.902283 Apoptosis -7.9422765 4.297815 Prometaphase -9.099813 5.9339523 Apoptosis -8.21221 5.0281754 Apoptosis -6.140688 7.1084223 Interphase -9.106818 5.877411 Interphase -6.2343097 6.845946 Interphase -6.077264 7.1380196 Interphase -6.8922586 6.1893206 Interphase -3.899196 7.192041 Interphase -6.073989 6.1762633 Interphase -5.4976854 8.357408 Interphase -6.2702174 7.3447266 Interphase -4.847495 6.9395547 Binuclear -4.809945 6.86854 Binuclear -7.395521 5.6807046 Apoptosis -5.7321153 5.779826 Binuclear -5.8349395 5.816569 Binuclear -7.4539437 4.4220366 Prometaphase -7.6854415 4.3180404 Prometaphase -4.2078185 3.1725533 Polylobed -3.926332 3.1592383 Polylobed -3.120671 3.335838 Polylobed -7.846468 5.4099646 Prometaphase -9.011344 5.6974564 Apoptosis -8.698639 6.1529145 Apoptosis -2.6142387 6.306788 Polylobed -2.7247224 6.0096054 Polylobed -3.337542 5.662731 Polylobed -5.378863 6.3969707 Interphase -4.291868 4.249776 Polylobed -4.7703385 5.5195537 Large -4.0912843 4.2586775 Polylobed -5.9714293 6.13309 Interphase -6.4980364 4.56817 Prometaphase -7.4231462 7.3943033 Prometaphase -6.2372246 5.5726 Large -8.835792 6.006784 Prometaphase -7.8321414 4.606119 Prometaphase -7.9102616 4.6072135 Prometaphase -8.916249 4.3086305 Prometaphase -8.80977 5.118848 Apoptosis -9.006451 5.287563 Apoptosis -5.8502326 5.563456 Elongated -4.4650373 7.9094415 Interphase -7.5619497 5.4483933 Interphase -6.3453135 7.0887036 Interphase -8.433803 5.920928 Apoptosis -8.260786 5.607105 Prometaphase -5.130645 3.5723572 Apoptosis -9.067705 5.8197527 Apoptosis -9.003037 5.93975 Apoptosis -9.08893 5.844455 Apoptosis -9.087183 5.920222 Apoptosis -5.953698 6.774292 Interphase -5.6526937 6.1001196 Interphase -3.0279603 6.217967 Polylobed -3.1393676 5.4921904 Polylobed -3.2442095 6.0341506 Polylobed -3.782282 5.220809 Polylobed -8.707864 4.7491775 Prometaphase -8.579184 4.7568135 Prometaphase -8.4668 5.629955 Prometaphase -5.4177713 6.354349 Interphase -6.7575684 6.1010394 Interphase -5.421292 6.3663106 Interphase -4.473652 6.4455476 Interphase -4.47853 6.4762926 Interphase -4.996978 6.329704 Interphase -4.851395 6.4026756 Interphase -7.4352417 5.237026 Prometaphase -5.454652 6.6547723 Interphase -6.0748262 6.84471 Interphase -9.082834 5.797656 Prometaphase -5.1334505 6.2439275 Interphase -5.114983 6.267995 Interphase -4.3270817 5.551617 Elongated -4.8696523 6.4085035 Interphase -7.4775953 5.403494 Prometaphase -5.310542 6.336125 Interphase -2.4889436 7.1855826 Interphase -8.825581 4.3662486 Prometaphase -2.3293798 7.1608377 Interphase -5.2017293 6.456385 Interphase -4.9661727 7.764365 Interphase -2.3613348 7.242229 Interphase -8.532698 5.3476467 Apoptosis -5.70996 7.508014 Interphase -5.243849 6.6600733 Interphase -2.2953844 9.061275 Interphase -7.2919 5.2231684 Prometaphase -6.447515 6.111877 Interphase -8.139611 4.7525983 Prometaphase -7.456794 4.4260187 Prometaphase -8.619569 4.199571 Prometaphase -8.264272 4.63566 Prometaphase -9.084096 5.486589 Prometaphase -4.033398 7.0464253 Interphase -8.929867 4.594204 Prometaphase -7.2026763 4.9136333 Prometaphase -8.827983 4.4270535 Prometaphase -5.1879764 7.2799435 Interphase -8.836038 4.367711 Prometaphase -7.603504 4.4387116 Prometaphase -8.381339 4.9505677 Prometaphase -5.2311635 6.492987 Interphase -4.4314866 7.5176525 Interphase -2.848139 8.11236 Interphase -6.1619754 7.007087 Interphase -2.4263728 6.0342355 Polylobed -4.3062143 4.475692 Polylobed -7.661739 5.836938 Interphase -7.0409174 5.4185815 Prometaphase -8.765999 5.9505963 Prometaphase -5.1873507 6.2674785 Interphase -5.295865 6.2871833 Interphase -5.25884 7.3832893 Prometaphase -6.342232 7.116248 Interphase -6.3956885 6.9833646 Interphase -4.6603904 7.172171 Interphase -7.783345 4.349016 Prometaphase -5.2065864 6.230627 Interphase -8.670879 4.3434253 Prometaphase -5.493112 6.435449 Interphase -8.879148 4.395173 Prometaphase -8.947547 4.531209 Prometaphase -8.096752 4.775263 Prometaphase -8.054857 4.9373426 Prometaphase -8.494247 4.3987455 Prometaphase -8.081077 4.8735414 Prometaphase -8.863547 4.359298 Prometaphase -6.0561643 6.3491793 Interphase -8.862582 4.3306894 Prometaphase -5.11104 6.2720075 Interphase -4.640589 7.744584 Interphase -2.760095 8.144084 Interphase -7.3066063 4.9763813 Prometaphase -5.263194 6.2501183 Interphase -5.2416463 6.34681 Interphase -7.316209 4.8075657 Prometaphase -3.980246 8.102211 Interphase -4.138101 7.5131955 Interphase -4.1887927 7.3335114 Interphase -6.005852 6.9475183 Interphase -8.708595 6.1788697 Apoptosis -7.5081606 7.489722 Apoptosis -8.710405 6.2016797 Apoptosis -7.6074896 7.4282646 Apoptosis -7.420701 7.509421 Apoptosis -7.414743 7.5109925 Apoptosis -7.456542 7.471362 Apoptosis -7.5147386 7.499737 Apoptosis -6.9030924 6.969431 Apoptosis -5.996948 9.336435 Interphase -3.8241394 9.39926 Interphase -6.883994 7.017603 Apoptosis -3.7582448 9.337109 Interphase -7.214735 5.619088 Apoptosis -7.445741 5.696041 Apoptosis -6.1275663 9.510257 Interphase -6.986187 8.199519 Interphase -3.8873818 8.493469 Interphase -3.7473533 8.759382 Interphase -4.230673 8.388722 Interphase -8.625159 5.984647 Apoptosis -3.55064 9.021073 Interphase -8.545368 5.981368 Apoptosis -8.807021 6.00204 Apoptosis -6.2237473 7.051356 Interphase -8.680253 6.043136 Apoptosis -7.31225 6.5270243 Interphase -5.7800694 9.48968 Binuclear -5.9339957 9.584462 Binuclear -4.8332334 6.4422407 Binuclear -5.3149786 5.5239635 Binuclear -3.0362434 9.372625 Interphase -6.686749 5.87067 Interphase -7.0101476 7.3160586 Apoptosis -6.1133485 7.3610406 Interphase -5.857958 9.222831 Interphase -3.6383758 9.372135 Interphase -5.55955 3.6982293 Polylobed -3.7300153 8.022705 Interphase -4.657893 3.544066 Polylobed -4.633772 3.4922848 Polylobed -4.0361905 7.442353 Interphase -3.2135694 9.125082 Interphase -4.5701623 3.5141737 Polylobed -8.22425 5.7279286 Apoptosis -4.5838423 3.5404818 Polylobed -7.5324664 5.942615 Apoptosis -3.6334217 9.371707 Interphase -3.677505 6.9453335 Interphase -5.5285025 9.597509 Interphase -7.347006 6.0902824 Apoptosis -4.614919 7.3358326 Interphase -4.477424 7.1354628 Interphase -4.640195 6.8820825 Binuclear -4.4777594 6.792213 Binuclear -5.7247686 5.7080164 Binuclear -5.541393 5.7061486 Binuclear -3.4993377 9.262503 Interphase -3.7157881 8.702531 Interphase -3.5534985 9.2036085 Interphase -4.15113 8.494672 Interphase -3.5308106 9.35067 Interphase -3.589837 9.382867 Interphase -0.03790229 5.0346913 Polylobed -0.017118584 5.03973 Polylobed -0.002352864 5.04601 Polylobed -0.03376673 5.0208983 Polylobed -0.03169667 5.037342 Polylobed -7.142271 5.1584 Apoptosis -7.056679 5.1931314 Apoptosis -3.1028152 7.7950845 Polylobed -3.1106088 7.88943 Polylobed -3.1712134 7.882221 Polylobed -4.6051006 5.3367105 Apoptosis -3.1922474 7.76202 Polylobed -4.756768 5.4947166 Polylobed -5.0634494 5.549253 Polylobed -5.0764523 5.668966 Polylobed -7.9717326 4.938188 Apoptosis -5.051483 5.6953297 Polylobed -8.913011 6.1135116 Apoptosis -3.3324416 5.920718 Polylobed -3.586016 4.7830315 Polylobed -7.9668894 5.018166 Apoptosis -4.960991 4.547096 Polylobed -3.221171 8.702339 Polylobed -8.172636 5.0320115 Apoptosis -3.4012182 8.735183 Polylobed -3.3690963 8.652931 Polylobed -4.411728 8.502145 Polylobed -4.6167336 8.298602 Polylobed -7.420114 5.646022 Artefact -5.334994 8.026596 Apoptosis -9.202909 5.809384 Apoptosis -5.878555 5.1187778 Apoptosis -5.9049993 5.134609 Apoptosis -5.953359 5.108716 Apoptosis -5.9072723 5.119313 Apoptosis -9.076171 5.653595 Apoptosis -6.692064 5.4429255 Apoptosis -6.6489506 5.481516 Apoptosis -7.781788 5.48132 Apoptosis -4.922604 5.077267 Polylobed -2.706887 6.091479 Polylobed -2.6921527 5.9559917 Polylobed -2.0387104 2.2521408 Polylobed -2.0213084 2.2431786 Polylobed -2.039904 2.262628 Polylobed -2.040236 2.2607663 Polylobed -2.0447433 2.2648532 Polylobed -2.0470376 2.275538 Polylobed -6.1841307 5.3533816 Metaphase -6.1907377 5.325321 Metaphase -7.850092 5.3751225 Polylobed -6.7147107 5.3709855 Polylobed -2.1298685 7.8736353 Polylobed -2.0921621 8.032025 Polylobed -2.1078265 8.352825 Polylobed -2.1113896 8.402795 Polylobed -0.22948946 4.4597883 Polylobed -0.23653163 4.4679823 Polylobed -0.25076842 4.456968 Polylobed -2.8432827 6.9314923 Polylobed -2.8791072 6.8425174 Polylobed -5.715858 5.242908 Polylobed -5.7286167 5.234721 Polylobed -3.126965 7.8349705 Polylobed -3.063813 7.2246695 Polylobed -3.2269409 6.935212 Polylobed -1.217185 5.5961356 Polylobed -1.1810485 5.588431 Polylobed -1.2455938 5.616739 Polylobed -0.32750142 4.873047 Polylobed -0.29998758 4.8695035 Polylobed -0.3305148 4.860817 Polylobed -0.2897304 4.847174 Polylobed -0.36788753 4.9160066 Polylobed -0.32770076 4.85712 Polylobed -7.364672 5.2058496 Apoptosis -5.2242293 5.0663166 Artefact -2.9242246 7.638241 Binuclear -2.8700774 7.8000326 Binuclear -2.819495 7.7893496 Binuclear -3.2599318 6.702708 Interphase -5.3561435 7.2862396 Artefact -7.043896 5.1464324 Apoptosis -3.6245763 4.78178 Polylobed -6.902989 4.992255 Apoptosis -4.7629766 4.511887 Polylobed -4.7503104 4.365599 Polylobed -7.5334754 5.1639166 Apoptosis -7.260877 5.6661325 Artefact -7.414732 5.667092 Artefact -0.23248267 4.6533756 Polylobed -0.250936 4.6653347 Polylobed -0.3115488 4.6657815 Polylobed -0.24957797 4.643037 Polylobed -7.575149 5.5847144 Apoptosis -5.337572 4.2523766 Polylobed -5.386386 4.040176 Polylobed -5.3716617 4.122868 Polylobed -2.8980088 3.56341 Polylobed -3.2875614 5.448091 Polylobed -3.719804 4.615202 Polylobed -3.6575172 4.610631 Polylobed -3.691439 4.6466317 Polylobed -8.942913 5.1030984 Apoptosis -0.24046357 4.8368134 Polylobed -0.2706049 4.8288116 Polylobed -0.24749197 4.839604 Polylobed -0.23840795 4.8329916 Polylobed -0.25621223 4.863924 Polylobed -6.9008265 5.575023 Apoptosis -2.4535463 5.2248216 Polylobed -7.5312095 5.127338 Apoptosis -2.5490878 5.16175 Polylobed -2.5613983 5.2236943 Polylobed -7.046393 4.9245534 Apoptosis -5.998889 5.0605364 Apoptosis -8.050262 4.929531 Apoptosis -5.7478304 6.93635 Artefact -0.18830107 4.495605 Polylobed -0.19394784 4.495467 Polylobed -0.17131576 4.470601 Polylobed -0.18425524 4.486277 Polylobed -5.371648 4.14734 Artefact -5.431493 4.019695 Artefact -5.413947 3.9702523 Artefact -5.479709 4.066364 Artefact -1.193753 4.8816404 Polylobed -1.2202836 4.884674 Polylobed -1.180819 4.8435407 Polylobed -2.2787054 6.5879407 Polylobed -1.1630998 4.867039 Polylobed -2.361557 6.5815725 Polylobed -2.1774826 6.5528564 Polylobed -2.3888524 6.597642 Polylobed -2.3882558 6.5605454 Polylobed -2.0710292 2.2987807 Polylobed -2.071088 2.297081 Polylobed -2.0685568 2.296752 Polylobed -2.0801487 2.3281639 Polylobed -2.0672123 2.2991107 Polylobed -2.1112347 2.3535368 Polylobed -3.2278092 5.0081315 Polylobed -3.0543582 5.3510256 Polylobed -3.020289 5.3162904 Polylobed -3.154438 5.0172224 Polylobed -3.578624 5.565905 Elongated -3.5194805 5.533797 Elongated -2.4532897 4.519136 Polylobed -2.4399776 4.5568295 Polylobed -2.7617018 6.9772596 Polylobed -2.8187826 6.931344 Polylobed -2.7852492 6.9465113 Polylobed -1.1805935 5.5435047 Polylobed -1.1653444 5.574541 Polylobed -1.1999967 5.574941 Polylobed -1.1703917 5.5767426 Polylobed -1.21918 5.533503 Polylobed -1.2272121 5.59253 Polylobed -5.480674 6.185835 Large -5.131816 5.938502 Large -5.9328513 6.3911376 Large -4.9896407 5.6854763 Large -2.8740575 5.2082458 Polylobed -2.7398849 5.295721 Polylobed -3.1761189 5.3335032 Polylobed -2.7757137 5.467851 Polylobed -2.7978199 7.522026 Polylobed -4.7133226 4.5277085 Polylobed -4.668058 4.5414276 Polylobed -4.666917 4.4927306 Polylobed -4.7333703 4.490428 Polylobed -4.7020845 4.484055 Polylobed -7.05037 7.0571165 Prometaphase -7.4185853 6.020473 Prometaphase -7.163414 7.1214333 Prometaphase -6.818905 5.343437 Prometaphase -7.5811415 6.123894 Prometaphase -2.9073558 5.5092936 Polylobed -2.8648317 5.6777363 Polylobed -2.9796114 5.5349116 Polylobed -3.0922296 5.6358776 Polylobed -2.9613805 5.4476457 Polylobed -4.1910872 3.9995215 Polylobed -4.2159348 3.964513 Polylobed -4.198608 4.084743 Polylobed -4.1039457 4.0921807 Polylobed -3.3943927 6.385067 Polylobed -3.4083967 6.4726634 Polylobed -3.2538114 6.7370677 Polylobed -8.389584 4.928895 Prometaphase -8.241988 4.911183 Prometaphase -6.0380454 7.762992 Prometaphase -7.196381 7.280064 Prometaphase -7.2248077 7.2820754 Prometaphase -7.789329 5.894951 Prometaphase -7.0394063 7.1235027 Prometaphase -7.3525124 6.1124816 Prometaphase -7.24457 5.111322 Prometaphase -7.6267085 5.8545947 Prometaphase -2.7447073 5.1692877 Polylobed -2.537551 5.378461 Polylobed -2.5650907 5.3852715 Polylobed -2.7231982 5.3561883 Polylobed -4.6435113 4.601406 Polylobed -4.4332266 4.539272 Polylobed -4.663148 4.53816 Polylobed -4.5266056 4.4917173 Polylobed -4.7947736 4.511504 Polylobed -4.797489 4.5781865 Polylobed -4.5411735 4.418069 Polylobed -4.57366 4.5135016 Polylobed -4.4254 3.9262295 Polylobed -4.444572 3.9743257 Polylobed -4.453821 3.9783695 Polylobed -4.429899 3.9836462 Polylobed -6.7419643 7.2641997 Prometaphase -6.9743266 7.095494 Prometaphase -6.556648 8.285397 Prometaphase -6.6774416 5.674346 Prometaphase -7.350087 7.1954465 Prometaphase -7.1776524 4.533308 Prometaphase -7.7972627 5.0561466 Prometaphase -6.182115 5.255135 Prometaphase -8.025089 5.095232 Prometaphase -7.8278356 5.987016 Prometaphase -5.6445255 5.846938 Prometaphase -6.2372 5.5934305 Prometaphase -7.441884 5.2789016 Prometaphase -7.6022186 5.573115 Prometaphase -5.627372 7.677943 Prometaphase -7.1627216 5.3023868 Prometaphase -7.0794063 5.1622896 Prometaphase -6.873354 5.382227 Prometaphase -7.1377144 7.344899 Prometaphase -7.1774507 7.332037 Prometaphase -8.003624 5.266146 Prometaphase -7.823739 5.7160144 Prometaphase -7.282231 7.2956905 Prometaphase -7.265231 7.3355174 Prometaphase -7.498296 6.1705823 Prometaphase -8.106471 5.1414757 MetaphaseAlignment -7.453105 5.7350216 MetaphaseAlignment -3.4373522 3.3313284 Polylobed -3.561269 3.2155147 Polylobed -3.3976738 3.2864528 Polylobed -3.478347 3.3263962 Polylobed -3.4294338 5.95515 Binuclear -2.682923 6.9204764 Binuclear -2.7825813 6.9552627 Binuclear -8.3124075 4.9522653 Prometaphase -8.954949 4.440103 Prometaphase -7.3936267 4.798655 Prometaphase -8.920009 4.690661 Prometaphase -7.757381 4.678028 Prometaphase -8.181563 5.51959 Metaphase -8.810845 5.4336996 Metaphase -9.02762 5.604394 Metaphase -7.000356 5.26158 Prometaphase -8.797598 4.605029 Prometaphase -8.928314 4.6316557 Prometaphase -8.091472 4.2296906 Prometaphase -8.52573 4.2320538 Prometaphase -7.7217836 4.9291925 Prometaphase -5.4633274 7.580866 Prometaphase -8.557637 4.897295 Prometaphase -8.502763 4.183776 Prometaphase -8.050247 4.346576 Prometaphase -7.5103955 4.517884 MetaphaseAlignment -8.355964 4.173822 Prometaphase -8.6151495 4.4523106 Prometaphase -6.6555285 6.6209254 Artefact -9.181266 5.8067193 Apoptosis -6.2393613 5.525606 Artefact -7.7833886 4.9267993 Prometaphase -7.9230204 4.7853985 Prometaphase -8.747207 4.673997 Prometaphase -8.827513 4.6440473 Prometaphase -8.759451 4.0676126 Prometaphase -8.320105 4.6856704 Prometaphase -8.417083 4.5819583 Prometaphase -8.892631 4.066134 Prometaphase -8.750842 4.624138 Prometaphase -8.930007 4.124434 Prometaphase -9.103837 5.929185 Apoptosis -8.271973 4.6080437 Prometaphase -9.075964 5.9694743 Apoptosis -9.034483 5.9364495 Apoptosis -9.088468 5.61921 Apoptosis -8.658322 4.0740542 Prometaphase -8.848221 4.121016 Prometaphase -8.795666 4.097303 Prometaphase -8.840086 4.049136 Prometaphase -8.011445 4.205626 Prometaphase -8.838743 4.109929 Prometaphase -8.855902 4.1017027 Prometaphase -6.3918753 5.8833966 Artefact -6.362229 5.88204 Artefact -6.2952294 5.4571576 Artefact -8.134129 5.3515306 Apoptosis -6.156792 5.3502164 Artefact -6.0877604 5.1722164 Artefact -6.042297 5.167751 Artefact -5.566432 4.993606 Artefact -5.564275 5.055489 Artefact -5.255812 6.231432 Interphase -7.7327285 4.5086346 Prometaphase -3.8889234 6.8805685 Interphase -3.2920825 7.5046163 Interphase -5.4987755 6.130262 Interphase -3.877789 5.3836584 Interphase -3.0884552 9.311493 Interphase -5.6547823 5.7079935 Interphase -8.951967 4.4464006 Prometaphase -7.395291 4.9480114 Prometaphase -8.938465 4.4527287 Prometaphase -8.681231 4.647249 Prometaphase -8.8134575 4.730193 Prometaphase -6.0429783 7.4662514 Artefact -8.175456 4.3113422 Prometaphase -6.1226726 7.365479 Artefact -8.0484705 4.2243075 Prometaphase -4.428533 6.3178616 Interphase -8.842208 4.594908 Prometaphase -8.7912 4.7845254 Prometaphase -3.2435386 5.5537996 Interphase -8.110712 4.1704516 Prometaphase -8.401303 4.120052 Prometaphase -7.93691 4.8454576 Prometaphase -5.1601834 7.277582 Prometaphase -8.094552 4.920676 Prometaphase -8.110613 4.8533573 Metaphase -3.8251548 6.9891233 Interphase -8.45992 4.2424955 Prometaphase -7.1992774 4.4880285 Prometaphase -8.850562 4.263143 Prometaphase -8.783302 4.309757 Prometaphase -8.830752 4.2543344 Prometaphase -8.292514 4.8551226 Prometaphase -8.0992565 4.515964 Prometaphase -5.540159 7.7360144 MetaphaseAlignment -8.732819 4.697646 Prometaphase -8.64371 4.720362 Prometaphase -8.82771 4.179335 Prometaphase -8.831136 4.5837502 Prometaphase -8.801837 4.210896 Prometaphase -7.893903 4.4585166 Prometaphase -8.740628 4.215246 Prometaphase -6.5998917 6.515169 Metaphase -6.875971 5.8233123 Metaphase -8.686549 4.194913 Prometaphase -8.846198 4.2340355 Prometaphase -5.4675484 4.247207 Apoptosis -7.737181 4.937446 Prometaphase -8.991958 5.869553 Apoptosis -8.97076 5.8345942 Apoptosis -7.9196606 4.376815 Prometaphase -8.928316 5.8003654 Apoptosis -7.4748282 4.8858466 Prometaphase -5.998004 3.7357924 Prometaphase -8.652567 4.1511903 Prometaphase -9.196356 5.7962847 Apoptosis -9.190731 5.815121 Apoptosis -9.036115 5.8170037 Apoptosis -6.560407 4.0511217 Prometaphase -6.697354 4.016014 Prometaphase -8.469486 5.443017 Metaphase -6.300239 4.0160365 Artefact -6.740145 4.0611014 Prometaphase -6.409779 3.9331386 Artefact -6.869546 4.0973377 Artefact -8.12478 4.925348 Prometaphase -7.591144 4.490389 Apoptosis -3.022614 7.4575043 Polylobed -4.745161 6.381092 Interphase -5.225959 6.2413955 Interphase -4.574419 6.355177 Interphase -8.583338 6.1959443 Apoptosis -3.9225833 7.3676515 Interphase -4.930536 6.3315487 Interphase -3.1672883 5.1334147 Polylobed -3.0036798 5.5410795 Polylobed -2.6525295 8.875626 Interphase -4.924905 5.1377015 Polylobed -4.4755683 5.1503 Polylobed -3.571353 7.093144 Interphase -2.908382 9.349553 Interphase -4.3468256 5.4054003 Artefact -4.1016173 5.471833 Artefact -3.8261206 5.888312 Artefact -8.246369 5.3990135 Metaphase -3.8592274 6.126908 Elongated -7.5917788 6.059468 Metaphase -3.7937253 6.8679614 Elongated -4.2900987 6.514993 Interphase -5.9610267 6.441751 Apoptosis -6.0588913 6.471998 Interphase -3.6593845 6.3234816 Interphase -4.2447124 8.082947 Interphase -2.7999601 6.4997916 Polylobed -2.7104561 7.100012 Polylobed -2.7196562 7.093285 Polylobed -2.9412553 6.2437177 Polylobed -4.741177 4.0621715 Polylobed -4.776038 3.9953444 Polylobed -2.7503664 7.476976 Polylobed -6.9461093 7.6531844 Interphase -8.737245 6.0007343 Metaphase -7.0526266 7.704872 Metaphase -7.0105824 7.6903152 Interphase -8.729208 6.0188465 Metaphase -7.072576 7.6809177 Metaphase -2.7004387 7.1999397 Polylobed -2.691634 7.0497217 Polylobed -1.7088238 5.737105 Polylobed -1.8964015 5.737828 Polylobed -4.5213885 4.515361 Polylobed -4.5005536 4.530644 Polylobed -4.8159213 5.8768487 Interphase -4.480137 4.47913 Polylobed -4.515455 4.5145164 Polylobed -3.6577084 6.000952 Binuclear -5.2444634 6.7365804 Interphase -3.718704 5.8947706 Binuclear -2.623428 4.188415 Polylobed -2.4144337 4.253512 Polylobed -3.631322 6.5436444 Binuclear -2.1587725 5.4685216 Binuclear -2.9025967 6.1869073 Binuclear -2.8350646 6.185265 Binuclear -2.850518 6.125897 Binuclear -5.0839314 5.996114 Interphase -3.270603 4.8830576 Binuclear -3.3457572 4.8678446 Binuclear -2.9999573 5.921474 Binuclear -4.7897754 6.2457685 Interphase -2.950049 6.01247 Binuclear -6.2316523 6.69115 Interphase -2.213389 9.115176 Interphase -2.8615355 6.4969625 Polylobed -2.7177007 7.1157403 Polylobed -2.7795742 7.0867558 Polylobed -2.7558587 7.09108 Polylobed -2.8676162 6.351711 Polylobed -2.2827156 4.486273 Polylobed -2.3139806 4.440323 Polylobed -2.286265 4.4681606 Polylobed -2.382562 4.5598187 Polylobed -2.2707593 4.476398 Polylobed -2.5114894 4.4272323 Polylobed -3.517993 5.9412045 Binuclear -3.4117863 6.210458 Binuclear -1.6978987 5.6535296 Polylobed -1.6509119 5.627543 Polylobed -1.6710463 5.6312513 Polylobed -1.6880528 5.6479716 Polylobed -1.6918348 5.624858 Polylobed -5.328325 8.703225 SmallIrregular -6.267232 9.247642 SmallIrregular -6.2069716 9.408806 SmallIrregular -4.2246623 4.5059595 SmallIrregular -6.29154 6.6412287 SmallIrregular -4.9587183 6.745378 SmallIrregular -5.439511 7.1264133 SmallIrregular -5.2527084 6.4583244 SmallIrregular -5.0125027 8.177468 SmallIrregular -6.0552764 9.732044 SmallIrregular -6.034149 9.783629 SmallIrregular -4.2696834 5.607427 Binuclear -4.152609 5.513835 Binuclear -3.857542 7.7437563 Binuclear -3.8726792 7.7494764 Binuclear -3.809055 7.718718 Binuclear -3.875689 7.6172066 Binuclear -3.721335 7.2755 Binuclear -3.4758117 7.7909207 Polylobed -3.42146 7.8083367 Polylobed -3.496217 7.6961064 Polylobed -5.8897285 6.939907 Binuclear -3.43549 7.762139 Polylobed -3.4295661 7.7253437 Polylobed -5.88984 9.120094 Binuclear -5.86364 9.182702 Binuclear -4.6575117 7.4677606 Binuclear -3.573201 7.600729 Polylobed -3.64642 7.6215615 Polylobed -6.1733975 6.474213 Binuclear -3.554314 7.563308 Polylobed -3.5775883 7.577348 Polylobed -3.5510716 7.5516853 Polylobed -4.1272063 6.4243174 Binuclear -3.7463377 4.817151 Binuclear -4.024266 6.387473 Binuclear -3.732569 4.7635436 Binuclear -3.13584 4.851461 Large -3.1665223 4.7159786 Large -3.1604478 4.784593 Large -3.251256 5.6364827 Polylobed -2.5536785 5.8341904 Polylobed -4.687517 5.362079 Polylobed -4.06483 5.271289 Polylobed -3.4675949 5.398222 Polylobed -4.064467 5.378702 Polylobed -3.21288 8.012598 Binuclear -4.039501 5.2683215 Polylobed -3.6216953 7.6298347 Binuclear -4.822283 9.350993 Binuclear -2.4385426 8.89257 Polylobed -2.6536028 8.742734 Polylobed -4.795925 9.152669 Binuclear -2.1845798 7.38965 Polylobed -4.1459713 6.599587 Binuclear -5.0848575 7.817061 Polylobed -1.978574 6.7137413 Polylobed -2.8744452 9.233315 Binuclear -4.3804693 6.574673 Binuclear -1.9613036 6.776009 Polylobed -4.8097267 7.349788 Binuclear -1.934046 6.678898 Polylobed -2.954482 9.259408 Binuclear -1.9385931 6.6938686 Polylobed -3.147114 6.605084 Binuclear -2.9535542 9.4302845 Binuclear -2.9589143 9.39168 Binuclear -2.8975527 9.297578 Binuclear -2.8511078 9.290301 Binuclear -2.8061514 9.122043 Polylobed -2.8352625 9.027704 Polylobed -2.8328307 9.185169 Polylobed -2.7849295 9.173577 Binuclear -2.2590606 7.260418 Polylobed -2.1771579 6.9323215 Polylobed -2.8596246 9.080276 Binuclear -2.1264665 7.380872 Polylobed -4.212209 8.310938 Binuclear -1.9657727 7.8025327 Polylobed -3.647702 6.8132095 Large -3.8763247 7.9838295 Binuclear -3.994321 6.7607374 MetaphaseAlignment -3.653619 7.8989635 Binuclear -4.0033407 6.874581 Binuclear -8.865264 5.358475 MetaphaseAlignment -8.918815 5.3602905 MetaphaseAlignment -8.911784 5.520876 MetaphaseAlignment -3.908149 4.506476 Binuclear -3.3117337 4.6921787 Binuclear -1.8146507 4.9608946 Polylobed -1.7545247 5.343618 Polylobed -1.8416594 4.7982893 Polylobed -1.8030499 5.186356 Polylobed -3.9336903 4.7837486 Binuclear -1.7828921 4.8121376 Polylobed -4.4339237 4.6480837 Binuclear -4.3799243 4.687615 Binuclear -3.9614415 4.6692834 Binuclear -4.0263486 7.9580493 Binuclear -2.8479528 7.922786 Binuclear -3.340102 7.869395 Binuclear -3.0091953 7.457364 Binuclear -3.4825013 7.7263513 Binuclear -3.5739338 7.877495 Binuclear -6.7675986 6.68607 Apoptosis -7.5709968 7.4407687 Apoptosis -8.829972 6.4269066 Apoptosis -8.00674 6.1050005 Apoptosis -8.570293 5.9425015 Apoptosis -7.015123 6.3284774 Apoptosis -6.542201 6.9491496 Apoptosis -6.847109 6.788663 Apoptosis -6.636351 6.7631254 Apoptosis -6.829925 6.867374 Apoptosis -7.313302 7.410989 Apoptosis -7.6652246 7.451861 UndefinedCondensed -8.391367 5.6730266 Apoptosis -6.8809614 4.989404 Apoptosis -6.7556467 6.3741546 Apoptosis -7.2649083 6.2444096 Apoptosis -6.2914352 6.5296693 UndefinedCondensed -7.4057407 5.6706104 Apoptosis -6.4721336 7.112623 Apoptosis -7.6760373 7.4654765 UndefinedCondensed -4.1110706 5.7532864 Polylobed -4.104297 5.550159 Polylobed -4.1143503 5.541556 Polylobed -3.9689085 5.62075 Polylobed -3.613006 6.671849 Interphase -2.9870596 8.995177 Interphase -4.3606544 6.424145 Binuclear -4.2926126 6.5020037 Binuclear -5.126516 6.437913 Interphase -5.214066 6.35628 Interphase -4.2481723 7.992455 Interphase -4.1017327 6.8041015 Interphase -5.300261 8.557748 Interphase -6.8442 5.866238 Interphase -2.3469548 5.3780212 Polylobed -2.2742753 5.634781 Polylobed -2.2220461 7.1182904 Polylobed -6.0490956 5.852007 MetaphaseAlignment -7.6872663 5.2243557 MetaphaseAlignment -6.8449655 5.7731876 MetaphaseAlignment -7.6554155 5.305349 Polylobed -4.1838255 7.171355 Binuclear -5.570658 6.1394925 Binuclear -5.5790377 6.0445766 Binuclear -3.6809216 6.6972075 Large -8.591168 5.9075527 MetaphaseAlignment -8.627055 6.0203576 MetaphaseAlignment -9.050332 5.8069015 MetaphaseAlignment -8.19879 5.2235985 MetaphaseAlignment -5.4607286 7.45751 MetaphaseAlignment -3.6783748 5.725076 Polylobed -3.2130792 6.7366376 Polylobed -3.8424797 3.1300807 Polylobed -4.091183 3.4489086 Polylobed -3.987377 3.2930799 Polylobed -4.009137 3.531804 Polylobed -2.46239 5.58034 Grape -3.970027 3.4945316 Polylobed -2.4199722 5.5390143 Grape -2.5493393 5.5650353 Grape -5.4162626 3.8338728 Grape -5.297896 3.7770407 Grape -4.848833 2.826696 Grape -3.5118966 5.4361444 Artefact -4.0599966 5.5919275 Artefact -4.0405326 5.5852804 Artefact -5.895801 4.9070687 Polylobed -3.571919 5.5070224 Artefact -3.6197662 5.6197176 Artefact -5.7412357 4.604082 Polylobed -3.0690944 5.562325 Artefact -5.706319 4.604895 Polylobed -3.0257576 5.5012045 Artefact -3.1923037 8.490016 Polylobed -3.157373 8.55131 Polylobed -3.2197266 8.496387 Polylobed -3.5300543 3.751744 Grape -3.1857898 8.50519 Polylobed -3.1808264 8.2165575 Polylobed -3.173742 8.534007 Polylobed -3.0952609 6.0700426 Grape -2.4036536 7.276871 Grape -2.884749 6.0467563 Grape -3.5876071 3.6842747 Grape -3.1564438 8.478522 Polylobed -2.3644443 7.2190666 Grape -2.4490938 7.1796966 Grape -3.5354571 3.7198813 Grape -3.5675879 3.689215 Grape -3.135551 8.492236 Polylobed -3.4891253 3.738253 Grape -3.5561817 3.7180877 Grape -3.235535 5.862616 Polylobed -3.2156043 5.726646 Polylobed -4.304517 3.668252 Polylobed -2.6162465 4.930756 Polylobed -2.6759071 4.829497 Polylobed -2.579268 5.161823 Polylobed -2.647471 4.7764883 Polylobed -2.646976 4.9558654 Polylobed -3.3571677 5.607246 Polylobed -2.2655354 6.449809 Grape -2.248597 6.5216985 Grape -2.3088071 6.479 Grape -2.3733356 6.1970706 Grape -2.5757823 5.5384197 Grape -3.5551748 5.182959 Grape -2.7543602 5.129008 Grape -3.452668 5.496513 Grape -2.821814 6.010871 Grape -2.6245108 6.206009 Grape -2.784515 5.8723836 Grape -2.6661108 6.3979344 Grape -2.6872246 6.17377 Grape -5.657183 5.18412 Polylobed -5.6220703 5.226149 Polylobed -5.599555 5.287044 Polylobed -4.7135077 4.144562 Polylobed -5.2799687 3.6669416 Polylobed -3.3217158 5.457881 Grape -4.5275 7.712302 Grape -3.2708147 5.5316043 Grape -2.723553 8.048742 Polylobed -4.524903 7.6792393 Grape -4.1370296 3.2240727 Polylobed -4.552543 7.5923243 Grape -3.9833288 3.207135 Polylobed -4.8292584 7.6858993 Polylobed -2.452117 7.1102023 Grape -2.4557297 7.088779 Grape -2.8569436 8.228371 Polylobed -4.5539517 7.6018925 Grape -2.4630396 7.129474 Grape -2.3360853 6.998381 Grape -4.587567 7.581888 Grape -5.0348063 8.117728 Polylobed -4.6858797 3.603522 Polylobed -2.6743007 8.448953 Polylobed -4.596626 7.6626043 Grape -4.0382805 3.294189 Polylobed -4.0327353 3.3616397 Polylobed -3.5613894 4.8718023 Grape -4.097201 3.1698263 Grape -4.1349597 3.1569748 Grape -4.003167 4.7591405 Grape -4.1926355 3.0355914 Grape -6.124452 5.920792 Polylobed -6.6821723 5.6053057 Polylobed -2.3711498 4.693574 Grape -3.550908 3.459579 Polylobed -3.9256263 3.2921014 Polylobed -2.3415709 4.7607546 Grape -2.4278004 4.787148 Grape -2.356052 5.847264 Grape -2.415943 4.8064046 Grape -2.409504 5.7329555 Grape -2.4445422 5.5410595 Grape -2.512667 5.2740984 Grape -2.4772499 5.3733234 Grape -4.305353 4.626993 Grape -3.6678789 5.4340315 Grape -3.7240212 5.3391223 Grape -2.709668 5.905236 Grape -3.007456 5.491076 Grape -4.839397 5.7637954 Polylobed -5.268445 5.370895 Polylobed -5.2778025 5.3355956 Polylobed -4.057813 5.656876 Polylobed -4.0260825 5.6255565 Polylobed -3.6825876 5.6382766 Polylobed -5.0433245 5.3304596 Polylobed -3.4014523 8.319211 Grape -3.4376783 8.331149 Grape -3.5159225 8.15397 Polylobed -3.4185169 8.3190975 Grape -4.127414 7.323446 Polylobed -3.5739675 3.6902566 Grape -3.4043586 8.320816 Grape -3.8378956 7.7127795 Polylobed -3.3888428 8.327421 Grape -3.5500598 3.714721 Grape -3.544743 3.6748607 Grape -3.5449274 3.7258108 Grape -4.139214 8.697916 Polylobed -3.7847111 6.5320225 Polylobed -4.275944 6.8686123 Polylobed -5.801412 7.16324 Polylobed -5.0549664 5.4403777 Polylobed -5.3581576 5.7922473 Polylobed -4.387309 4.3035 Polylobed -4.452234 4.253055 Polylobed -4.4244804 4.25601 Polylobed -7.295162 4.9216647 Prometaphase -7.2477455 4.6485715 Prometaphase -3.1277494 8.927115 Binuclear -4.748152 8.666217 Binuclear -2.883169 5.744574 Polylobed -2.882443 5.5079937 Polylobed -2.688718 5.629025 Polylobed -2.7647882 5.5296726 Polylobed -6.088607 6.763293 Binuclear -3.3314874 5.9523587 Binuclear -0.55789536 6.3334484 Polylobed -0.52484566 6.4614544 Polylobed -0.5192536 6.27081 Polylobed -0.57271934 6.3505626 Polylobed -3.4113977 8.461696 Polylobed -0.5847819 6.563796 Polylobed -0.86495405 6.1936045 Polylobed -0.8036692 6.816319 Polylobed -1.4384406 5.016723 Polylobed -1.4676763 5.0380044 Polylobed -1.5116844 5.038243 Polylobed -0.7628845 6.908436 Polylobed -0.7877203 6.892555 Polylobed -0.75677246 6.8936 Polylobed -0.9538769 6.6170454 Polylobed -1.4977206 5.025874 Polylobed -0.7613565 6.9146214 Polylobed -0.793159 7.0442977 Polylobed -3.4598298 6.9763885 Binuclear -3.4439237 6.666232 Binuclear -2.3248658 7.9314413 Binuclear -5.479935 8.361981 Interphase -2.39777 7.959585 Binuclear -3.6438863 5.9954767 Binuclear -3.6553922 6.1484423 Binuclear -2.7865784 7.2031765 Large -6.2248254 6.7119875 Binuclear -4.666278 7.570792 Binuclear -1.0497742 5.71736 Polylobed -1.0476158 5.6994023 Polylobed -1.0480849 5.6866627 Polylobed -1.0731988 5.6746984 Polylobed -1.1406825 5.683913 Polylobed -1.045426 5.745994 Polylobed -1.1672616 5.5143824 Binuclear -1.2006129 5.5334334 Binuclear -1.2081852 5.5656257 Binuclear -1.2180866 5.538353 Binuclear -3.687814 6.6433134 Binuclear -3.8900576 6.5736127 Binuclear -3.6780772 6.712853 Binuclear -3.1955826 5.100328 Large -3.6658914 5.0861206 Binuclear -3.80066 4.9106574 Binuclear -4.387889 4.5881376 Polylobed -4.5878525 5.417051 Artefact -4.537898 5.469691 Artefact -3.4257672 8.023148 Artefact -3.1995797 7.8961353 Artefact -5.815864 5.835362 Artefact -3.2117634 7.944078 Artefact -5.8207073 5.913357 Artefact -5.831316 5.8721075 Artefact -2.961989 7.896558 Artefact -4.331936 5.0895944 Polylobed -4.3605843 5.279652 Polylobed -3.7931032 6.5429087 Binuclear -4.4313936 5.1327486 Polylobed -3.8791714 6.5684195 Binuclear -4.44652 5.0117946 Polylobed -1.1357101 5.454404 Binuclear -1.1718459 5.414695 Binuclear -1.1902264 5.449736 Binuclear -1.2095237 5.4525166 Binuclear -3.7034676 6.6541185 Binuclear -3.76115 6.597256 Binuclear -3.6559505 6.665111 Binuclear -3.7971814 6.6438804 Binuclear -3.0798469 5.2102995 Polylobed -2.7402356 5.0015883 Polylobed -2.67272 4.99259 Polylobed -0.6085598 6.170419 Polylobed -0.5861998 6.232036 Polylobed -0.53234893 6.166826 Polylobed -0.5132432 6.1984377 Polylobed -2.078912 4.852656 Polylobed -0.6105859 6.076693 Polylobed -1.9524661 4.8768077 Polylobed -1.9820108 4.8448863 Polylobed -0.60912645 6.2498107 Polylobed -2.0532146 4.825764 Polylobed -0.7445796 6.241867 Polylobed -0.62710816 6.36964 Polylobed -4.2391853 3.0863667 Polylobed -4.5392294 2.2713168 Polylobed -4.3126574 2.716369 Polylobed -0.6529297 6.955358 Polylobed -0.677407 6.8672276 Polylobed -0.6828053 6.909074 Polylobed -1.2137835 4.9712586 Polylobed -1.2127433 4.961318 Polylobed -1.351604 4.996082 Polylobed -0.7112246 6.9129863 Polylobed -0.7160116 7.0905356 Polylobed -1.484856 5.0189514 Polylobed -0.72471094 6.9771075 Polylobed -2.0693011 5.589644 Polylobed -2.1036475 5.589128 Polylobed -2.1512399 5.6922727 Polylobed -2.3610091 5.605579 Polylobed -6.837518 7.270871 Metaphase -6.501651 7.295913 MetaphaseAlignment -6.231071 8.747545 MetaphaseAlignment -6.7315583 5.812275 Prometaphase -7.118055 5.0334115 Apoptosis -7.410007 4.920399 MetaphaseAlignment -8.715426 5.9177775 Prometaphase -7.101439 5.1215773 Apoptosis -8.645055 5.699032 Prometaphase -7.4825096 7.2740984 Apoptosis -9.143493 5.8620515 Apoptosis -4.5135503 6.6914725 Binuclear -8.262086 5.031037 Apoptosis -4.741399 6.3536906 Binuclear -5.6672106 8.556289 Prometaphase -5.9964504 7.8566313 Prometaphase -3.616181 7.579928 Binuclear -3.7359176 7.4465594 Binuclear -7.5263352 7.614105 Prometaphase -7.439588 7.5961823 Prometaphase -4.801607 6.893189 Prometaphase -7.3086987 6.3780303 Prometaphase -7.429476 7.4302416 Prometaphase -6.0230107 6.226333 Polylobed -5.7452517 6.3062687 Polylobed -5.714056 6.4912286 Polylobed -8.794619 6.1800723 Apoptosis -8.825393 6.08331 Apoptosis -5.493429 5.4014983 Binuclear -5.5033436 5.754775 Binuclear -4.0507464 6.7784142 Binuclear -3.9630687 6.7985644 Binuclear -6.1638336 7.353024 Artefact -6.8782616 7.3183727 Prometaphase -6.3877892 7.7386494 Polylobed -5.516426 6.424816 Polylobed -6.1023364 7.7052693 Polylobed -5.2962203 5.8730755 Binuclear -5.443936 5.942834 Binuclear -7.282286 7.272874 Prometaphase -9.1821575 5.8500986 Apoptosis -4.6483855 5.0328283 Binuclear -4.772113 5.2005153 Binuclear -4.4290433 4.8242555 Artefact -4.6397123 4.9836245 Artefact -9.202755 5.756534 Apoptosis -9.132535 5.8931737 UndefinedCondensed -7.5255203 6.9356623 Prometaphase -8.088639 6.333348 Prometaphase -8.871603 6.298147 Prometaphase -6.7518563 6.9386954 Prometaphase -7.4476376 6.557144 Prometaphase -6.524691 7.876601 Polylobed -5.465103 6.1753626 Prometaphase -6.4556046 7.7699475 Polylobed -6.501281 7.8603077 Polylobed -7.6448126 5.8647194 Prometaphase -9.1660595 5.8690305 Apoptosis -7.121656 7.1166854 Prometaphase -6.091226 8.5434065 Prometaphase -7.342779 7.3161964 Prometaphase -7.322208 7.80227 Prometaphase -8.209679 7.4834433 Prometaphase -4.95081 5.5462165 Binuclear -5.239152 5.716805 Binuclear -5.0963798 5.584613 Artefact -7.456085 6.4067307 Prometaphase -3.9422593 4.048821 Polylobed -4.300496 4.9483123 Polylobed -4.1520343 4.855221 Polylobed -4.079048 3.8990788 Polylobed -4.417527 5.0124693 Artefact -4.0837145 4.8688087 Polylobed -4.1825914 4.8589263 Polylobed -3.9476955 4.049085 Polylobed -4.2291465 5.543817 Polylobed -4.1535373 4.8133817 Polylobed -4.1576304 6.8587193 Polylobed -2.3650477 5.920553 Polylobed -7.600058 5.2269692 Artefact -7.5173206 5.3516088 Artefact -5.112525 5.203787 Binuclear -5.2348027 5.5412693 Binuclear -7.5029325 6.8882113 Prometaphase -7.717054 6.457549 Prometaphase -7.889847 5.1582713 Apoptosis -6.519696 7.0309343 Prometaphase -3.3615696 5.51886 Binuclear -3.197311 5.797956 Binuclear -7.3330617 7.298368 Prometaphase -6.4376426 7.152509 Prometaphase -6.2479887 8.417424 Prometaphase -6.4897413 7.862529 Polylobed -4.7333903 5.1720176 Polylobed -6.5583777 6.991006 Prometaphase -6.5758657 7.745525 Polylobed -7.1884837 6.043482 Prometaphase -5.4902124 6.095121 Polylobed -5.27749 6.2305903 Polylobed -5.8649073 6.2206445 Polylobed -7.5639896 6.3622317 Prometaphase -6.974404 4.46631 Prometaphase -2.0898995 8.215562 Polylobed -8.499074 4.727016 Apoptosis -2.0923808 8.233347 Polylobed -2.071741 8.682353 Polylobed -2.9269176 8.890885 Binuclear -3.4408216 8.292553 Binuclear -5.9578466 8.887134 Binuclear -7.0240407 7.1469235 MetaphaseAlignment -5.943389 8.923922 Binuclear -6.092871 8.75206 MetaphaseAlignment -5.373846 6.149797 Prometaphase -6.3431 7.283455 MetaphaseAlignment -8.499547 5.94304 MetaphaseAlignment -4.6191916 6.3106294 Polylobed -7.4684453 5.6184235 Prometaphase -4.5443153 6.643501 Polylobed -4.615641 6.612073 Polylobed -3.4694238 6.205089 Polylobed -4.5433254 6.7271295 Polylobed -2.6179826 5.6725645 Polylobed -2.6427367 5.7909927 Polylobed -2.8784266 5.7622466 Polylobed -6.9326305 5.3216157 Apoptosis -6.3411937 6.6580553 Artefact -6.2568436 6.64826 Artefact -6.2177124 6.7601748 Artefact -6.420023 6.9598627 Artefact -9.051774 5.8544345 Apoptosis -8.286861 5.636902 Apoptosis -4.3016057 6.6500773 Binuclear -4.4660144 6.62193 Binuclear -8.328898 5.6645994 Apoptosis -6.441579 7.453128 Metaphase -9.176433 5.8245206 Apoptosis -3.7201834 6.783208 Binuclear -3.658496 7.659366 Binuclear -4.8925476 6.4574084 Binuclear -3.532519 6.4033318 Binuclear -6.816194 6.6798344 Hole -8.677853 6.6999097 SmallIrregular -8.630661 6.718558 Hole -6.5829997 9.013028 SmallIrregular -8.455007 7.215058 Hole -7.15045 8.115267 SmallIrregular -8.119007 6.565794 SmallIrregular -6.5573344 7.9042363 SmallIrregular -8.751336 6.4720387 Hole -6.1287374 8.91678 SmallIrregular -8.333706 6.426496 Hole -7.12327 7.682047 SmallIrregular -7.312308 8.018111 SmallIrregular -7.1743155 8.45241 UndefinedCondensed -6.1854906 8.561833 SmallIrregular -6.674532 8.984692 SmallIrregular -6.6152916 8.981661 Hole -8.152443 7.5187035 Hole -7.1882434 5.9051785 Hole -7.601226 6.153818 SmallIrregular -8.592767 6.5341024 Hole -7.2366257 7.9265103 SmallIrregular -7.511609 6.98416 SmallIrregular -8.279527 7.4195094 SmallIrregular -5.298584 7.3951526 Folded -7.435139 7.796609 Hole -8.710647 6.620704 Hole -8.090492 7.5022545 Hole -9.108025 5.832538 Apoptosis -9.192647 5.7773023 Apoptosis -9.089122 5.7012906 Apoptosis -9.172716 5.7155933 Apoptosis -9.211009 5.6937947 Apoptosis -6.6856933 8.972855 Hole -6.6473246 8.991024 SmallIrregular -6.367216 8.261314 SmallIrregular -8.898268 4.7031817 Apoptosis -7.787828 7.686209 Hole -8.832493 4.5716467 Apoptosis -7.03224 7.7734804 SmallIrregular -6.208614 5.5673404 Elongated -6.623898 6.1917505 SmallIrregular -7.4506154 7.728006 Hole -8.868126 6.3777585 Hole -7.867154 7.682653 SmallIrregular -7.5779104 7.6966043 Hole -8.726932 6.5863223 Hole -8.863456 6.2606883 SmallIrregular -6.1243677 8.55646 SmallIrregular -6.6022363 9.006358 SmallIrregular -8.015335 7.589402 SmallIrregular -7.372716 7.367118 SmallIrregular -7.519275 7.6869574 SmallIrregular -7.4896894 7.609284 Prometaphase -1.9506927 7.5986176 Polylobed -6.590238 6.406542 Binuclear -3.7180102 7.09374 Binuclear -6.650817 6.083389 Interphase -3.8857415 5.543031 Elongated -3.6207995 5.0795627 Polylobed -3.8982387 4.9651656 Polylobed -3.7822952 4.9508924 Polylobed -5.6361623 6.7158003 Interphase -2.9007568 9.265137 Polylobed -3.1861565 7.1161737 Binuclear -3.3782568 6.427163 Binuclear -5.597915 5.719929 Interphase -5.1115847 6.687649 Interphase -6.87606 6.0089183 Polylobed -6.827221 6.037024 Polylobed -6.8697343 6.0239716 Polylobed -5.691159 6.970368 Artefact -5.9311423 7.1291165 Large -3.7201178 4.6578646 Large -3.6892724 4.6984706 Large -2.633833 6.646631 Polylobed -2.6794045 5.4283557 Polylobed -2.0717726 6.2063727 Polylobed -7.1124945 5.015448 Apoptosis -2.116685 8.15967 Polylobed -2.6825006 7.5657015 Polylobed -2.6237257 7.900525 Polylobed -3.7851236 8.312546 Polylobed -3.6662328 8.313845 Polylobed -3.8148046 8.11669 Polylobed -2.7676694 9.32383 Interphase -3.9716272 8.0981865 Interphase -6.432215 5.941272 MetaphaseAlignment -5.5034027 8.238689 MetaphaseAlignment -5.5627356 8.271128 MetaphaseAlignment -3.4765344 7.907658 Interphase -2.8618393 4.175427 Polylobed -2.9617438 4.156357 Polylobed -3.5318995 7.448586 Binuclear -3.4807205 8.163986 Binuclear -5.8513746 6.5958996 Interphase -6.406472 6.1481924 Elongated -4.325152 6.3728347 Elongated -6.765494 6.1550455 MetaphaseAlignment -2.987289 9.460807 Interphase -6.8182964 6.2023497 Interphase -3.8768375 7.834634 Interphase -3.774214 7.640098 Binuclear -3.7937038 7.907069 Binuclear -8.657892 4.4488153 Prometaphase -8.4734955 4.439358 Prometaphase -8.577449 5.5435557 Metaphase -8.752345 4.366545 Prometaphase -8.909791 4.3397408 Prometaphase -8.353574 5.5796537 Metaphase -8.002527 4.6477633 Prometaphase -8.782167 4.1884747 Prometaphase -8.385264 4.4411106 Prometaphase -7.374614 4.9160824 Prometaphase -8.7698 4.7165065 Prometaphase -7.687834 5.065828 Prometaphase -7.8028917 4.640753 Prometaphase -8.21053 4.6019063 Prometaphase -8.438915 5.145746 Prometaphase -8.822451 4.7872047 Prometaphase -8.814841 4.350849 Prometaphase -8.78421 4.431508 Prometaphase -7.224391 5.3127193 Prometaphase -6.7827897 5.678444 Prometaphase -6.9994793 4.5556417 Prometaphase -5.942476 7.6959014 Prometaphase -7.0592375 4.4694986 Prometaphase -8.668752 4.3634515 Prometaphase -8.597633 4.3454275 Prometaphase -8.5426035 4.8093987 Prometaphase -8.556092 4.4126396 Prometaphase -8.034816 4.383776 Prometaphase -7.328295 5.106914 Prometaphase -8.744195 4.839154 Apoptosis -7.357654 4.8702564 Prometaphase -7.3726115 5.1869345 Prometaphase -7.508948 5.0531363 Prometaphase -8.907522 5.904772 Apoptosis -8.359957 5.0427084 MetaphaseAlignment -4.056469 7.586174 Interphase -2.3605103 5.642559 Polylobed -4.5048532 7.0112934 Interphase -2.5291462 5.7931347 Polylobed -1.9107732 4.4582586 Polylobed -1.9706222 4.4101405 Polylobed -2.307316 7.3231106 Interphase -7.0993423 6.102687 Interphase -3.2426214 5.4048514 Binuclear -3.5993464 5.378123 Binuclear -3.1860366 6.557553 Binuclear -2.1935194 6.690931 Binuclear -5.671407 7.57799 Prometaphase -6.061023 6.532096 MetaphaseAlignment -4.4060717 6.379742 Interphase -4.316609 6.468403 Interphase -7.7262707 5.3397193 MetaphaseAlignment -2.7513263 5.2689486 Binuclear -4.8543115 7.565845 Interphase -4.813924 7.645216 Interphase -2.8572817 5.4914613 Binuclear -3.6280315 6.3188853 Polylobed -8.618514 4.9045496 MetaphaseAlignment -3.3422773 6.3062663 Polylobed -7.1051097 4.4276357 Prometaphase -4.188404 8.586347 Binuclear -4.7455378 7.752249 Interphase -3.8017325 8.193882 Binuclear -3.8405 6.6412582 Interphase -4.901427 7.644265 Prometaphase -2.4057627 7.2286596 Interphase -2.9610052 8.964736 Interphase -5.2482157 6.1658278 Interphase -6.3665714 5.7643266 MetaphaseAlignment -6.662508 4.5762286 Prometaphase -7.3247466 4.7849755 MetaphaseAlignment -8.232694 5.3743467 MetaphaseAlignment -7.5948815 4.6540737 MetaphaseAlignment -6.8411956 4.296558 MetaphaseAlignment -6.0778255 3.73805 MetaphaseAlignment -6.6689067 6.579053 MetaphaseAlignment -6.205231 3.8778524 Prometaphase -6.464605 6.703595 MetaphaseAlignment -8.355419 5.18354 MetaphaseAlignment -7.404876 4.4790616 MetaphaseAlignment -7.7267327 4.3064194 Prometaphase -7.736296 4.3334894 Prometaphase -5.5238404 7.8328085 MetaphaseAlignment -4.79872 8.902399 MetaphaseAlignment -8.32966 4.969326 MetaphaseAlignment -8.089172 4.987309 MetaphaseAlignment -5.508562 8.340321 MetaphaseAlignment -6.4400554 5.2707024 MetaphaseAlignment -5.459673 8.443014 MetaphaseAlignment -5.1671915 5.8215137 Prometaphase -7.3370814 4.715313 Prometaphase -6.9497128 4.4177036 MetaphaseAlignment -5.386321 6.947506 Prometaphase -7.3915887 4.352437 Prometaphase -7.575007 4.3620467 Prometaphase -5.9651914 7.22219 MetaphaseAlignment -6.128696 3.8196852 MetaphaseAlignment -6.412042 4.0483413 Prometaphase -7.799427 4.5011234 Prometaphase -7.274355 5.495501 MetaphaseAlignment -6.1348596 4.422113 MetaphaseAlignment -6.221007 4.5352616 MetaphaseAlignment -7.302876 5.6150894 MetaphaseAlignment -2.4527953 7.1992993 Interphase -2.3074484 5.5060806 Polylobed -2.3565824 5.636479 Polylobed -2.2540221 5.4777737 Polylobed -1.4934224 5.3441706 Polylobed -3.1643732 5.804632 Binuclear -4.0607753 8.107679 Interphase -3.1644998 6.0060534 Binuclear -7.1045957 6.032196 Interphase -2.8147116 9.300202 Interphase -3.2459178 5.7916994 Binuclear -3.249155 5.4381666 Binuclear -4.2213373 8.33763 Interphase -4.152218 7.856327 Interphase -8.509081 5.4845147 MetaphaseAlignment -4.06615 8.044961 Interphase -7.450404 4.9103045 MetaphaseAlignment -4.2330513 6.933102 Interphase -4.4665174 8.376534 Interphase -8.863159 4.449503 Prometaphase -8.572178 5.6461873 MetaphaseAlignment -5.0507517 7.139871 Prometaphase -4.0691667 5.4451575 Polylobed -5.9697022 3.9866118 Artefact -3.978797 5.7494516 Polylobed -6.1108646 4.19855 Artefact -3.88507 6.0189524 Polylobed -6.0188255 4.035175 Artefact -6.139459 4.2712913 Artefact -3.6578991 5.8052597 Binuclear -3.9246626 5.1994824 Binuclear -8.799792 4.456568 Prometaphase -6.909778 7.25015 Prometaphase -5.817213 5.7765656 Interphase -2.4225328 7.2398987 Interphase -5.9232206 6.2023816 Interphase -4.661304 6.87672 MetaphaseAlignment -5.115051 3.470692 Polylobed -5.1155133 3.3772414 Polylobed -7.3328924 4.683276 MetaphaseAlignment -7.2684183 4.7762556 MetaphaseAlignment -5.870231 7.3537087 MetaphaseAlignment -6.9661875 4.4800906 MetaphaseAlignment -6.179219 3.9366388 Prometaphase -7.7539506 5.0831156 Prometaphase -8.199571 5.3666377 MetaphaseAlignment -7.6130786 4.8380475 MetaphaseAlignment -4.213207 7.968453 Polylobed -4.0769343 7.883656 Polylobed -2.6934159 4.6000423 Polylobed -4.011846 7.9357452 Polylobed -5.602684 8.249488 MetaphaseAlignment -1.5582095 5.8352494 Polylobed -1.5049794 5.8716455 Polylobed -2.2602274 8.599528 Polylobed -8.277217 5.2467413 MetaphaseAlignment -3.8694966 6.072227 Binuclear -2.1902194 7.8346376 Binuclear -2.0633676 8.196004 Binuclear -3.9233766 5.988523 Binuclear -2.1016903 8.418316 Binuclear -2.0898974 8.983798 Binuclear -2.1027408 9.002506 Binuclear -3.210204 9.309384 Binuclear -3.1554732 9.351342 Binuclear -2.3955064 9.1306095 Binuclear -3.3795602 9.326344 Binuclear -2.5240843 6.81887 Binuclear -2.8482656 5.6498485 Binuclear -2.5135028 7.790408 Binuclear -1.4749813 8.806978 Polylobed -2.4630432 7.9872518 Binuclear -1.7150179 8.939724 Polylobed -1.6246336 8.872854 Polylobed -2.1870487 6.366854 Binuclear -2.0750859 6.1372747 Binuclear -1.9005437 7.936231 Binuclear -2.0348058 7.820213 Binuclear -2.166219 9.069084 Binuclear -2.0942342 8.525152 Binuclear -3.2405837 6.4758787 Binuclear -2.1758835 8.996648 Binuclear -2.0930212 7.3465343 Binuclear -1.9846741 8.930036 Binuclear -1.9514781 8.205955 Binuclear -1.9001137 8.757437 Binuclear -1.9030675 8.269876 Binuclear -2.1266391 8.958383 Binuclear -2.013973 8.82082 Binuclear -2.2849867 8.152724 Binuclear -2.1235383 8.933554 Binuclear -2.3862872 7.988659 Binuclear -2.2868376 7.548506 Binuclear -2.0872004 7.614218 Binuclear -2.139115 8.827086 Binuclear -2.6979866 8.4133415 Binuclear -2.1510625 8.818371 Binuclear -2.097301 8.754693 Binuclear -2.197787 8.77703 Binuclear -1.9558312 8.55975 Binuclear -2.144706 8.283557 Binuclear -2.0821366 8.809339 Binuclear -1.8418666 8.11017 Binuclear -2.0752692 7.525753 Binuclear -3.9534652 7.6242924 Binuclear -2.5282264 9.215875 Binuclear -3.9701166 5.507546 Binuclear -2.4932709 9.152778 Binuclear -2.0130243 8.244536 Binuclear -2.2399662 7.3441353 Binuclear -2.4828012 6.710417 Binuclear -2.5251205 6.7706485 Binuclear -3.5559974 6.8806634 Binuclear -2.7315714 9.300767 Binuclear -2.3631737 9.171863 Binuclear -2.370298 9.190123 Binuclear -2.3655684 9.10524 Polylobed -1.9180304 8.587973 Binuclear -4.800482 4.20282 Polylobed -1.9755182 8.425628 Binuclear -2.400371 8.72581 Polylobed -4.4256186 3.9760168 Polylobed -2.3202765 8.810177 Polylobed -2.3042643 8.967147 Polylobed -4.127252 4.7553444 Polylobed -3.8772676 5.6099696 Binuclear -2.992392 7.8650346 Binuclear -1.9659442 8.811063 Binuclear -2.0453877 8.986427 Binuclear -2.0628014 8.920524 Binuclear -1.856528 8.718761 Binuclear -1.7762512 8.648369 Binuclear -2.094516 8.911731 Binuclear -1.813097 8.647251 Binuclear -4.0871115 8.597585 Interphase -2.2555869 8.941287 Binuclear -1.7843058 8.580822 Binuclear -2.4814525 8.85079 Binuclear -5.602819 8.826247 Interphase -1.6722693 8.522613 Binuclear -4.2846293 8.457395 Interphase -5.4989266 8.444009 Interphase -5.4881268 8.928099 Interphase -2.2353427 8.231831 Binuclear -3.6925857 8.51421 Binuclear -2.0070944 8.058466 Binuclear -2.155989 9.166465 Binuclear -2.8527856 9.169512 Polylobed -2.161081 9.117101 Polylobed -2.1766024 9.127632 Polylobed -5.034842 9.283735 Artefact -5.0049243 9.267701 Artefact -4.945778 9.301386 Artefact -2.2176108 9.02462 Polylobed -5.7184334 6.448146 Polylobed -5.76505 6.9452586 Polylobed -2.1166284 8.914912 Polylobed -4.306522 7.2491035 Polylobed -5.036076 8.400734 Interphase -2.1594405 8.87818 Polylobed -2.2814271 7.441941 Polylobed -2.0132115 7.048712 Polylobed -2.0207555 7.035669 Polylobed -2.0639088 7.05843 Polylobed -1.7068866 8.374172 Polylobed -1.8014867 8.341947 Polylobed -1.8022273 8.275673 Polylobed -4.5310225 8.51642 Apoptosis -4.9126096 8.880327 Apoptosis -4.681464 9.342857 Artefact -4.7625546 9.341472 Artefact -2.370731 8.895535 Artefact -4.846041 9.066102 Artefact -4.77006 9.343654 Artefact -2.2364912 8.761931 Artefact -4.730879 9.369263 Artefact -3.8178055 8.393398 Artefact -2.2471166 8.814192 Artefact -4.7678413 9.348143 Artefact -4.8261013 9.275039 Artefact -4.342854 7.0350313 Hole -1.7846773 8.804863 Polylobed -1.7047918 9.0156145 Polylobed -1.7910988 9.008742 Polylobed -1.7869021 7.9534802 Polylobed -1.9363872 8.252146 Polylobed -1.7494121 8.963654 Polylobed -1.8138587 7.998045 Polylobed -7.6754165 5.504763 Hole -1.7829207 7.011329 Polylobed -1.7086395 6.867249 Polylobed -1.7599535 6.87698 Polylobed -1.7630441 6.862595 Polylobed -1.7804625 6.8830094 Polylobed -2.0505795 7.7225075 Polylobed -2.4693263 6.417722 Polylobed -5.7815876 7.2682323 Polylobed -5.6553164 7.4637485 Polylobed -2.0528097 8.474021 Polylobed -3.020492 9.154901 Hole -1.8912835 8.408324 Polylobed -5.3809924 6.2137017 Binuclear -5.3465557 6.21797 Binuclear -7.1097684 6.450887 UndefinedCondensed -5.551113 5.9497886 Large -2.136828 7.185369 Interphase -2.5397043 9.123709 Binuclear -2.3952925 9.140916 Binuclear -1.930654 7.3708115 Interphase -1.9693981 7.258751 Binuclear -2.703479 8.140661 Binuclear -2.000055 7.2127986 Binuclear -2.2835348 8.212535 Binuclear -2.3364465 8.225725 Binuclear -3.7637982 6.397608 Binuclear -3.7532575 6.3763895 Binuclear -5.236019 8.152891 Hole -7.7579126 5.709495 Folded -3.6921542 6.0755477 Folded -5.278151 6.7127213 Hole -5.4530654 6.7443624 Folded -6.461307 5.9870706 Hole -8.365333 5.8978486 Hole -6.7779894 4.9882154 Folded -3.8887005 8.998021 Hole -5.0647693 5.262424 Hole -4.219629 5.1335387 Folded -4.357779 5.2244945 Folded -5.733049 6.87927 Hole -5.739787 6.809098 Hole -3.1113331 5.026376 Polylobed -3.164596 3.392439 Polylobed -4.8277454 5.889554 Hole -4.553022 5.942161 Elongated -5.6923385 5.175544 Folded -7.1757407 5.081323 Apoptosis -5.413486 5.102388 Folded -7.28328 4.882789 Apoptosis -4.9425507 5.2764297 Folded -5.929531 6.7708683 Hole -8.835139 6.1462526 Apoptosis -7.799488 4.994733 Apoptosis -7.497869 5.1268826 Apoptosis -6.4563055 5.2587814 Apoptosis -8.871853 4.704074 Apoptosis -7.3988557 5.658423 Apoptosis -7.3820424 5.621351 Apoptosis -8.986355 5.9979496 Apoptosis -8.980731 5.9679475 Apoptosis -8.299049 6.496211 Hole -8.261513 6.3703413 Hole -5.9284096 6.53664 Apoptosis -8.9355345 5.6681004 Apoptosis -9.17973 5.713483 Apoptosis -9.1407795 5.7611527 Apoptosis -6.1073065 6.8810782 Folded -5.5256705 6.1659765 Folded -6.6407776 5.148558 Polylobed -6.7482867 7.7199717 Hole -4.495654 6.3065543 Folded -3.110014 4.7610774 Folded -3.1455207 3.3712468 Polylobed -3.2392492 4.6192102 Folded -3.2417326 4.61884 Folded -5.78512 6.1280117 Folded -5.7354045 6.124693 Folded -4.7924266 6.2412457 Folded -6.152465 8.059557 Hole -6.1170835 6.8925724 Folded -4.735722 6.193293 Folded -6.7695813 7.715059 Hole -4.6021256 5.9709306 Folded -7.5568194 5.572085 Apoptosis -7.44617 7.7333803 Apoptosis -7.4208913 7.736398 Apoptosis -6.413263 7.872668 SmallIrregular -8.133101 5.8847327 Apoptosis -7.8762455 7.461914 SmallIrregular -7.4044666 5.497862 Apoptosis -4.0646777 7.7253914 Binuclear -7.1327257 7.0788727 Apoptosis -4.1085415 7.9244423 Binuclear -4.3013372 8.63706 Polylobed -4.9056206 8.80763 Polylobed -4.639614 8.941039 Polylobed -4.892153 8.894582 Polylobed -4.869942 8.932885 Polylobed -4.7083344 8.914813 Polylobed -4.9464126 9.0556 Polylobed -5.127288 8.936185 Polylobed -4.4335103 7.2135863 Polylobed -6.349265 7.9235687 SmallIrregular -1.7517129 8.00469 Binuclear -2.0390818 7.2387743 Binuclear -8.264908 7.354289 Hole -8.378242 7.2873797 SmallIrregular -8.424417 7.2085094 Hole -4.357261 6.82602 Elongated -7.56543 7.511939 Hole -7.906932 7.6600204 Hole -6.108926 5.6542797 Folded -6.068653 5.677056 Folded -6.33182 7.961729 SmallIrregular -6.102765 7.0856624 SmallIrregular -7.9668307 7.452191 Hole -6.885002 7.394531 Hole -8.607604 6.924929 Hole -7.876573 7.4975967 SmallIrregular -8.492702 7.1304145 Hole -8.140502 5.414482 Hole -7.3233223 7.7967486 Hole -6.407864 8.210192 Prometaphase -6.6336575 8.993702 SmallIrregular -2.2911336 9.087779 Polylobed -7.3472824 8.05699 SmallIrregular -7.567 7.8610024 SmallIrregular -7.4415145 8.022675 SmallIrregular -3.373647 7.0074105 Polylobed -3.089495 6.578645 Polylobed -7.4479637 5.7851634 Elongated -7.7284083 6.040774 SmallIrregular -5.113603 6.026385 SmallIrregular -6.090183 5.6678634 Folded -6.0860467 5.678014 Folded -4.567749 6.30288 Elongated -5.608063 5.542363 Elongated -5.1041303 8.923774 Polylobed -5.094023 8.980282 Polylobed -5.275631 9.171901 Polylobed -5.1522737 8.832571 Polylobed -6.265941 7.896374 SmallIrregular -8.014448 7.564347 SmallIrregular -7.4221573 7.5611696 SmallIrregular -7.4984293 7.9534216 SmallIrregular -7.7656856 7.775919 SmallIrregular -3.378656 7.0764737 Polylobed -3.4206648 6.9124722 Polylobed -3.519807 7.2267413 Polylobed -4.3157907 8.801298 MetaphaseAlignment -6.2074947 8.200284 MetaphaseAlignment -8.339934 5.1715717 MetaphaseAlignment -7.0426664 7.030363 MetaphaseAlignment -6.1594543 7.3807073 MetaphaseAlignment -6.253267 7.6143227 MetaphaseAlignment -5.6621165 7.890736 MetaphaseAlignment -6.950359 7.3507643 MetaphaseAlignment -5.6710405 7.5878806 MetaphaseAlignment -5.6812177 8.135176 MetaphaseAlignment -5.9160905 8.417702 Apoptosis -7.109027 7.302888 Apoptosis -7.0144315 7.442361 Apoptosis -5.2724123 7.376647 MetaphaseAlignment -7.223895 7.4008203 Apoptosis -5.4765954 7.8768783 MetaphaseAlignment -8.267274 5.2622137 MetaphaseAlignment -6.9977546 5.0791173 MetaphaseAlignment -7.0750155 5.1969433 MetaphaseAlignment -5.9415536 6.224873 Apoptosis -5.2736545 8.118973 Apoptosis -6.426313 8.009559 MetaphaseAlignment -5.4921856 6.978946 MetaphaseAlignment -5.505864 7.6527452 MetaphaseAlignment -5.6835613 8.300337 MetaphaseAlignment -8.696521 6.5654445 Hole -6.72876 6.788054 Hole -8.549247 5.8119783 Apoptosis -8.550937 5.8326745 Apoptosis -6.511997 5.5162253 Elongated -5.74097 7.5426054 Elongated -8.750603 5.9496756 Apoptosis -8.10996 7.5178266 Hole -8.122969 7.5219293 Hole -8.030991 7.51322 Hole -4.477188 5.6795635 Elongated -4.0205827 6.7804546 Interphase -7.932023 6.501389 Interphase -6.98485 6.024586 Interphase -5.529541 7.4370713 Elongated -6.4129863 4.5517983 Elongated -7.126073 7.9361687 Interphase -8.629606 6.124641 Interphase -6.513876 8.7347 Interphase -6.454371 8.698109 Interphase -7.3565335 7.9403152 Interphase -6.5060506 6.57969 Interphase -5.447612 7.055908 Hole -5.4519525 7.4720583 Elongated -7.0474763 6.9772787 Hole -6.167009 5.735183 SmallIrregular -6.424451 6.959639 SmallIrregular -7.4758015 6.810749 SmallIrregular -8.409686 5.9538774 SmallIrregular -8.116747 7.5057597 SmallIrregular -7.9556007 7.487187 SmallIrregular diff --git a/2.analyze_data/results/3D_umap.png b/2.analyze_data/results/3D_umap.png deleted file mode 100644 index 7ac43096..00000000 Binary files a/2.analyze_data/results/3D_umap.png and /dev/null differ diff --git a/2.analyze_data/results/3D_umap.tsv b/2.analyze_data/results/3D_umap.tsv deleted file mode 100644 index b512f621..00000000 --- a/2.analyze_data/results/3D_umap.tsv +++ /dev/null @@ -1,4124 +0,0 @@ -UMAP1 UMAP2 UMAP3 Mitocheck_Phenotypic_Class -5.348324 5.728066 2.82352 Polylobed -6.043995 8.450898 4.016086 MetaphaseAlignment -3.159574 9.623679 3.4067802 Interphase -3.7522836 8.957653 3.1624966 Interphase -5.235367 8.0589075 4.4907656 Artefact -4.2096486 9.29514 4.1768928 Artefact -5.3252573 7.992864 4.453043 Artefact -5.4342365 7.13422 5.3293705 Artefact -5.9410996 6.7925973 5.3225446 Polylobed -3.2988484 9.604565 3.1602705 Interphase -3.1667662 9.510121 4.213035 Polylobed -2.4361086 9.4497385 4.2690296 Interphase -4.8283844 9.032083 4.1514587 Artefact -3.2101314 9.614032 3.2066765 Interphase -4.4617605 8.688261 4.4214497 Artefact -4.4641404 8.68151 4.4135704 Artefact -4.5108943 8.887633 4.3415046 Artefact -4.4988217 8.813455 4.4010153 Artefact -3.0745668 9.251463 4.5983825 Polylobed -4.4783397 8.849506 4.3686295 Artefact -3.2379076 9.2212105 4.6182904 Polylobed -3.1962903 9.232353 4.655802 Polylobed -2.851011 9.441216 4.470182 Polylobed -3.0195234 9.245504 4.6276326 Polylobed -3.8848524 7.1671295 2.2869864 Interphase -2.4091296 7.9598804 4.388471 Polylobed -2.8169482 7.5184135 4.430041 Polylobed -2.4574537 7.86717 4.446685 Polylobed -3.5903492 5.8032947 4.592201 Polylobed -3.0143914 7.746252 3.6579492 Interphase -3.8254461 7.181431 3.4840405 Binuclear -3.7003386 7.2785764 3.7193527 Binuclear -3.619725 7.8047853 3.9041736 Binuclear -3.3092535 9.192859 3.4229298 Interphase -3.8009756 6.6837873 3.7777708 Binuclear -3.7273889 5.992346 4.5914936 Polylobed -3.7820482 5.754875 4.4179945 Polylobed -3.7256742 5.719611 4.360693 Polylobed -7.3639317 7.069875 3.2684011 MetaphaseAlignment -5.9951015 8.781748 3.7474475 Prometaphase -7.186592 6.80749 2.4232922 Metaphase -7.287043 6.6956754 2.4014208 Metaphase -5.993569 8.730909 3.5860798 Metaphase -5.9590106 9.012872 3.315607 Metaphase -7.6526594 6.643312 3.4290903 MetaphaseAlignment -4.9228077 8.553041 2.4731681 Binuclear -3.5976055 7.190141 3.5387485 Polylobed -4.45277 8.665237 2.5852113 Binuclear -3.1848817 7.789702 3.5418718 Polylobed -2.833768 9.501115 4.3880258 Polylobed -2.8260834 9.323642 4.403958 Polylobed -2.8400154 9.485868 4.387531 Polylobed -4.149709 8.371105 5.0329013 Polylobed -5.2515373 9.380785 3.6309874 Artefact -5.1882195 9.440468 3.738114 Artefact -5.2651634 9.242302 4.0331073 Large -3.125293 8.441414 4.361377 Large -5.5255327 9.261456 3.600576 MetaphaseAlignment -5.0323544 8.781048 4.590274 Artefact -2.4288611 8.718407 3.8032067 Polylobed -4.9629164 8.703647 4.624763 Artefact -5.114465 8.728399 4.5783954 Artefact -2.302762 7.79599 4.179056 Polylobed -2.2752812 7.875926 4.1401405 Polylobed -2.2597442 7.9273653 4.0801315 Polylobed -5.0417876 8.732829 4.6198945 Artefact -5.093172 8.703356 4.6332436 Artefact -2.272697 7.8902392 4.0974674 Polylobed -4.2023826 7.2473617 4.4697742 Polylobed -3.622191 9.627951 3.6597865 Interphase -4.580017 7.1972604 2.221609 Interphase -4.1436415 7.325913 4.371692 Polylobed -4.222392 7.187188 4.4372525 Polylobed -3.2141027 8.610499 3.8760846 Interphase -3.0252364 9.690509 4.002064 Artefact -3.923492 8.367651 4.457558 Artefact -3.7656581 8.13164 4.277809 Artefact -5.5393505 8.753818 4.032384 Artefact -5.7934194 8.578858 3.880026 Artefact -7.0560827 6.732519 3.7210333 Apoptosis -7.027102 6.721921 3.7014818 Apoptosis -5.0865383 8.930527 4.045346 Metaphase -3.1076186 9.536455 4.0821495 Metaphase -5.70058 8.515761 3.822917 Metaphase -5.8508196 7.229695 5.2359576 MetaphaseAlignment -5.123708 7.986419 4.986692 Polylobed -5.1713014 8.057298 4.7471733 Polylobed -5.631296 6.1934433 5.13918 Polylobed -4.8559127 8.200381 4.855111 Polylobed -6.6921782 7.2125793 4.239132 Interphase -7.76361 6.9210505 2.8424938 Metaphase -3.008941 9.81546 3.7904925 Polylobed -3.0405662 9.83561 3.8157625 Polylobed -3.116202 9.808589 3.813493 Polylobed -8.086964 6.644165 2.5731568 Metaphase -3.0740495 9.77866 3.8592877 Polylobed -3.0363886 7.678303 3.4226763 Binuclear -3.0598552 7.615148 3.3813155 Binuclear -7.9753804 5.8031306 3.2505734 MetaphaseAlignment -2.9017437 7.7743416 3.444748 Binuclear -2.9706614 7.5657973 3.3357427 Binuclear -3.1698127 9.846889 3.666052 Polylobed -3.1531234 9.853591 3.6938512 Polylobed -6.2130117 8.412532 3.8731828 Metaphase -6.2296886 8.3243 3.9909632 Metaphase -3.0666573 8.207206 4.277723 Large -2.7440727 8.761636 4.0425 Large -3.0506544 8.390179 4.3431683 Polylobed -2.909401 8.688282 4.57908 Polylobed -2.9015803 9.493366 4.4563074 Polylobed -2.472452 9.325927 3.8580325 Large -2.6217573 9.522807 3.7461753 Large -2.4735475 9.446952 3.8807132 Large -2.590419 9.609777 3.883356 Large -2.6082351 9.587518 3.9067802 Large -2.3234076 9.421096 4.264198 Polylobed -2.3164139 9.389535 4.343674 Polylobed -2.3157144 9.398057 4.264895 Polylobed -5.2060328 8.641075 4.532722 Artefact -2.3452444 9.303037 4.211842 Polylobed -2.3161051 9.421067 4.283616 Polylobed -5.2329793 8.615313 4.60031 Artefact -2.3299859 9.413254 4.2423396 Polylobed -5.06516 8.647025 4.598395 Artefact -5.0761356 8.781399 4.5001965 Artefact -4.2182064 7.1724176 4.4926057 Polylobed -4.650018 7.2902923 2.2162807 Interphase -4.2500587 7.141713 4.4170384 Polylobed -3.4459774 8.530167 3.8408213 Interphase -1.8601813 6.0773196 4.384926 Polylobed -1.909083 6.0470123 4.382208 Polylobed -2.9701142 9.708973 4.0800943 Polylobed -2.4589155 7.217391 4.197859 Binuclear -2.903148 9.644766 4.1646066 Polylobed -2.8398116 9.599293 4.243806 Polylobed -2.8238406 9.608561 4.1121845 Polylobed -2.8040004 7.4166017 4.060408 Binuclear -2.3558624 7.199607 4.780369 Polylobed -2.674443 8.28028 4.3613014 Polylobed -2.608062 8.871003 4.3138328 Polylobed -2.6152387 9.26946 4.349542 Polylobed -6.242446 7.4323177 3.7285368 Large -6.09992 8.201322 3.9040167 Large -4.187724 9.138923 4.1489987 Artefact -4.629803 9.024216 4.353224 Artefact -4.420105 8.991823 4.3055735 Artefact -5.3209386 7.9141097 4.627112 Artefact -4.210637 9.296473 4.1934605 Artefact -4.591209 9.024914 4.2561145 Artefact -4.42604 9.019446 4.2639103 Artefact -4.49359 8.874535 4.351241 Artefact -4.4969044 8.839159 4.37305 Artefact -4.528741 8.740748 4.324603 Artefact -2.5387084 9.535888 4.187536 Polylobed -2.6066604 9.534411 4.134536 Polylobed -2.8942084 9.3930235 4.4807725 Polylobed -4.458333 8.8202715 4.406882 Artefact -2.6909945 9.505971 4.3646927 Polylobed -2.6927133 9.361007 4.449937 Polylobed -2.4066973 8.098452 4.3370047 Polylobed -2.8321788 7.7147746 4.3921804 Polylobed -2.4206352 7.994832 4.4112773 Polylobed -3.413569 5.6406565 4.849427 Polylobed -5.0863786 9.136582 2.824724 Metaphase -6.557699 6.4492345 2.9511642 Metaphase -7.893382 5.930965 3.4351203 MetaphaseAlignment -6.26245 8.587044 3.8108785 MetaphaseAlignment -6.0958896 7.699391 3.9627526 Polylobed -6.0936813 7.812379 3.9101794 Polylobed -7.646379 6.0814943 3.7682285 MetaphaseAlignment -5.1155653 8.452235 4.7381425 Polylobed -5.1843843 8.443047 4.607396 Polylobed -5.1539717 8.47254 4.6991124 Polylobed -5.0097365 8.737032 4.5059047 Polylobed -7.334627 6.2881193 3.5030067 MetaphaseAlignment -6.13169 8.084767 4.388331 MetaphaseAlignment -6.213199 7.6041193 3.8163662 Large -4.8907976 9.363146 3.7468827 Polylobed -4.9073253 9.402967 3.6769786 Polylobed -2.7488458 9.465655 3.4772503 Binuclear -2.7685626 9.195079 3.6044898 Binuclear -3.255513 9.853975 3.7002745 Polylobed -3.2579546 9.857763 3.705808 Polylobed -8.068748 6.797448 4.1459126 Prometaphase -3.3641002 9.773683 3.6883626 Polylobed -3.0641303 9.183021 4.4770064 Polylobed -3.5126917 9.256833 4.19993 Polylobed -2.8635423 9.4165125 4.4061327 Polylobed -2.6293614 9.490947 4.274486 Polylobed -3.4133704 9.442676 4.0283823 Polylobed -3.1207623 9.454287 3.9147718 Polylobed -7.8455935 5.968487 3.5214896 MetaphaseAlignment -7.9332004 5.86041 3.3854036 MetaphaseAlignment -2.4667032 9.138434 3.7838278 Large -2.504828 9.077262 3.8687909 Large -2.4538183 9.242953 3.8014784 Large -6.12695 8.256477 4.211949 MetaphaseAlignment -6.075757 8.776106 3.7517908 MetaphaseAlignment -6.085223 6.6956286 3.5136595 Metaphase -6.192161 6.5591474 3.3511627 Metaphase -5.8252583 6.4183993 2.5836494 Artefact -4.446766 7.76951 2.597095 Artefact -6.0968285 8.173619 3.431842 Metaphase -5.957099 8.705714 3.8527164 Metaphase -3.3600926 9.479297 4.0090537 Artefact -3.3524601 9.614274 3.7856429 Artefact -3.5867264 9.06906 4.28668 Artefact -3.8040802 9.447303 4.036963 Artefact -3.5141134 9.526387 4.002727 Metaphase -6.2143254 7.4620023 3.5105548 MetaphaseAlignment -2.735839 8.468974 4.0165253 Large -2.8039134 9.543959 4.2974715 Polylobed -6.7427244 6.872772 4.4292164 MetaphaseAlignment -2.8161447 9.484878 4.3811154 Polylobed -2.8314664 9.381591 4.389385 Polylobed -2.8978393 8.72967 4.542005 Polylobed -2.82808 9.351202 4.198413 Polylobed -2.905946 8.707596 4.5295105 Polylobed -2.8937316 8.692509 4.6080203 Polylobed -2.8741386 8.748812 4.502806 Polylobed -4.7686286 9.264601 4.020444 Large -5.7605877 8.551679 4.016855 Polylobed -5.358763 9.147334 4.067867 Large -5.426904 9.392194 3.3909295 Metaphase -4.9778357 9.430708 4.2091384 Artefact -4.877319 9.426531 4.321716 Artefact -4.9161696 9.460778 4.2616506 Artefact -5.9321 8.494302 3.8346343 Metaphase -5.6176977 9.452653 3.3485277 Artefact -5.5805016 9.455937 3.306424 Metaphase -5.6071563 9.494752 3.2986956 Artefact -5.591009 9.428318 3.334862 Metaphase -2.3813076 9.397204 4.3496385 Polylobed -2.508857 9.411615 4.4032555 Polylobed -4.731002 9.211691 4.6876087 Artefact -2.4222927 9.328074 4.3476543 Polylobed -4.8476253 9.027233 4.7877173 Artefact -4.8502975 9.350445 4.4991984 Artefact -2.4473937 9.304664 4.4095364 Polylobed -4.8080306 9.140207 4.6913285 Artefact -4.830186 9.296515 4.585237 Artefact -4.8672366 8.888561 4.8769917 Artefact -4.7479587 9.170283 4.7133474 Artefact -2.4682298 7.7633705 4.081398 Polylobed -2.3919783 7.7686954 4.133405 Polylobed -4.7783275 9.2092905 4.6732707 Artefact -6.6351466 8.265453 3.1504667 Artefact -6.476531 8.253139 3.2376456 Artefact -5.121972 9.393337 3.814559 Artefact -2.2972214 7.863122 4.126282 Polylobed -4.676737 6.743111 4.106482 Polylobed -4.346253 7.008393 4.368102 Polylobed -5.308601 9.2617445 3.9436603 Artefact -5.2879744 9.325605 3.9180334 Artefact -5.0753226 9.595457 4.119097 Artefact -5.0625443 9.605702 4.1362677 Artefact -4.192971 7.247459 4.4663506 Polylobed -5.9580283 8.439987 4.1463895 Metaphase -5.932799 8.4496975 4.0937495 Metaphase -5.0803494 9.583495 4.110337 Artefact -4.1991644 7.152193 4.4016333 Polylobed -5.072848 9.589019 4.1276803 Artefact -4.204619 7.1673126 4.411132 Polylobed -5.045191 9.580826 4.1340284 Artefact -5.072071 9.5865555 4.112351 Artefact -5.017196 9.533688 4.0997725 Artefact -5.2350535 8.780644 4.196793 Artefact -6.256183 7.814998 3.3414664 Artefact -5.266072 6.0350375 5.583446 MetaphaseAlignment -1.8832259 6.158508 4.3686757 Polylobed -1.9030871 6.177015 4.3508334 Polylobed -1.9496083 6.0953035 4.382022 Polylobed -5.2958922 9.22237 4.0007796 Metaphase -2.8134418 9.582706 4.429931 Polylobed -2.7472057 9.560582 4.378852 Polylobed -2.8128142 9.584556 4.422546 Polylobed -2.7630572 9.548415 4.353057 Polylobed -2.8481472 9.53706 4.430134 Polylobed -5.1623697 8.695519 4.4103346 Artefact -3.1809268 10.139187 4.3698373 Polylobed -3.1490984 10.140903 4.3737597 Polylobed -3.14209 10.174021 4.374868 Polylobed -3.183364 10.144129 4.4139457 Polylobed -3.0789473 8.126594 4.122126 Large -2.4718482 9.309809 3.8006344 Large -5.34952 9.154042 4.0794077 Large -2.3437164 9.418968 4.2782683 Polylobed -2.4733706 9.322257 4.360275 Polylobed -2.3933387 9.402283 4.225394 Polylobed -2.433179 9.351531 4.156436 Polylobed -2.2025945 7.851436 4.134717 Polylobed -2.2663097 7.853992 4.131775 Polylobed -2.243506 7.8669605 4.1339397 Polylobed -2.259196 7.8636775 4.113968 Polylobed -4.1108685 7.2608933 4.3978195 Polylobed -4.190865 7.2310605 4.4527435 Polylobed -4.2991333 7.224894 4.4184575 Polylobed -4.2318563 7.1341753 4.4247746 Polylobed -1.8728288 6.1198664 4.391126 Polylobed -1.8295333 6.0780444 4.37901 Polylobed -1.8767488 5.994735 4.4343686 Polylobed -3.4943311 9.50552 4.578108 Polylobed -3.491276 9.493003 4.5733194 Polylobed -3.3921185 9.51691 4.5241647 Polylobed -3.4721198 9.472561 4.553363 Polylobed -3.605348 9.443379 4.526629 Polylobed -3.1571364 10.133964 4.382498 Polylobed -3.140218 10.122157 4.3653398 Polylobed -3.1757867 10.14095 4.389756 Polylobed -3.199805 10.114076 4.414493 Polylobed -6.484722 7.1572156 3.6943657 Artefact -5.512621 8.811826 4.3071003 Artefact -5.4919124 8.860758 4.3426733 Artefact -5.0579524 9.497847 4.030256 Artefact -5.6553097 8.714197 4.283818 Artefact -5.076811 9.5325365 4.043946 Artefact -6.495082 8.045501 3.853159 Artefact -5.024636 9.420825 3.8333204 Artefact -4.9926724 9.420247 3.9029207 Artefact -3.3167033 9.5858135 4.2585588 Polylobed -3.055203 9.679082 4.0719423 Polylobed -2.9719465 9.482535 3.6240551 Interphase -6.0525913 8.668971 3.8563368 MetaphaseAlignment -3.3896227 8.338573 4.4759464 Polylobed -2.879232 8.72846 4.207553 Polylobed -3.1137552 8.590948 4.491713 Polylobed -3.0809064 8.816758 4.3675747 Polylobed -6.2136755 8.025333 3.1763206 MetaphaseAlignment -4.0964866 5.316502 3.5097163 Binuclear -4.1826754 5.357643 3.514409 Binuclear -4.839187 7.554238 3.3748853 Polylobed -5.4491215 7.3661237 3.3379982 Polylobed -4.802925 7.5343227 2.930452 Polylobed -4.631486 6.144775 3.897798 Polylobed -4.694359 6.5879445 4.2177444 Polylobed -5.343143 6.410998 3.932352 Polylobed -3.760109 6.2291713 4.1707854 Polylobed -3.995001 5.381716 3.7633424 Polylobed -4.0595202 5.306575 3.8198948 Polylobed -2.822048 5.6556997 4.3523 Polylobed -2.8104906 5.815137 4.3426156 Polylobed -2.7152467 6.1826596 4.3056655 Polylobed -2.8635998 5.6998634 4.3893647 Polylobed -2.8397002 5.824623 4.3372416 Polylobed -6.068099 6.8289943 3.5063336 Polylobed -5.7056026 7.19403 3.4261856 Polylobed -3.618734 5.9510946 2.761644 Polylobed -3.186651 7.3305264 3.2927225 Polylobed -3.6652892 5.8295674 2.7535112 Polylobed -2.8530402 7.61833 3.5616133 Polylobed -3.6780555 5.5748754 2.7915273 Polylobed -3.4526565 6.819085 3.6299102 Binuclear -3.6478384 6.769833 3.2329144 Binuclear -4.7194734 5.050332 3.1574972 Polylobed -4.6967216 5.0764 3.1934443 Polylobed -4.7089987 5.0864363 3.2015157 Polylobed -4.7244434 5.12611 3.2226915 Polylobed -3.3841584 7.188631 4.9178896 Binuclear -3.4300106 7.1138773 4.91806 Binuclear -3.1616244 7.3409586 4.9048886 Polylobed -3.242862 7.279657 5.0035224 Polylobed -3.2575507 7.2718377 5.00153 Polylobed -1.5368837 6.7718935 5.0215387 Polylobed -1.5354707 6.75393 5.0160537 Polylobed -1.5314243 6.7573886 5.028076 Polylobed -1.5264603 6.7019596 5.0120764 Polylobed -1.5161935 6.6642404 4.991707 Polylobed -1.578727 6.793507 5.016403 Polylobed -1.5263195 6.733903 4.9917455 Polylobed -1.4983999 6.711159 5.0388675 Polylobed -1.5269104 6.7288184 5.0486073 Polylobed -6.3294272 6.881174 3.2636142 Elongated -4.761928 6.4748282 4.4429893 Polylobed -4.7447386 6.540425 4.1756487 Polylobed -4.62528 6.53733 4.2895184 Polylobed -4.303163 6.475079 4.4150095 Polylobed -5.149142 6.5671363 3.5182543 Polylobed -4.63412 6.4590244 4.352362 Polylobed -4.0076036 5.2840376 3.5090592 Binuclear -4.035129 5.3084126 3.5014403 Binuclear -4.0760813 6.026085 3.752812 Binuclear -4.7364593 5.753584 3.8869007 Binuclear -3.8581173 5.4265237 4.116891 Polylobed -4.0609875 5.2496266 3.8076468 Polylobed -2.779885 5.9189553 4.316071 Polylobed -2.772219 6.050812 4.3746753 Polylobed -3.4236572 7.6856923 2.5622785 Polylobed -3.386421 7.5458117 2.710026 Polylobed -2.7752037 5.9068255 4.339935 Polylobed -3.8887932 6.9643245 3.5700576 Polylobed -3.0430832 6.30046 4.3928523 Polylobed -3.3981183 6.448214 4.2938166 Polylobed -4.4294896 6.5409565 3.242674 Binuclear -4.375827 6.5810246 3.3083797 Binuclear -3.4129493 7.244362 3.36374 Polylobed -4.3090477 6.4467463 3.8014996 Polylobed -3.330188 7.178476 3.3638709 Polylobed -3.4981763 6.722871 4.3428817 Polylobed -5.675207 7.2539787 3.3618274 Binuclear -3.5223365 6.722671 3.2979789 Binuclear -4.314994 8.409293 2.6808743 Binuclear -5.5019245 7.414431 3.2507217 Binuclear -4.3418717 6.486996 3.1837282 Binuclear -4.0161476 7.0443215 3.2550337 Binuclear -4.8707347 5.0170746 3.2167704 Artefact -6.333331 7.7464447 3.039511 Apoptosis -4.8960075 4.9973445 3.2132735 Artefact -3.8294523 7.330424 3.6333625 Binuclear -4.0754266 6.7276087 3.5896683 Binuclear -3.2109518 7.3269544 4.8737392 Polylobed -3.0382333 7.3995285 4.8099217 Polylobed -3.248808 7.233525 4.9670944 Polylobed -3.2406497 7.1909266 4.985894 Polylobed -1.5321681 6.757328 5.0139413 Polylobed -1.5554824 6.822433 5.008307 Polylobed -1.5371133 6.7792964 5.0101767 Polylobed -1.5110601 6.7365546 5.004646 Polylobed -1.5328737 6.7752404 4.9956465 Polylobed -1.589123 6.781839 4.995361 Polylobed -1.5119609 6.6978965 5.006006 Polylobed -1.5378975 6.719385 5.013985 Polylobed -7.2767816 6.9295197 2.8263216 UndefinedCondensed -6.9941444 6.540735 3.1586943 Interphase -5.2041974 5.433952 5.7136006 MetaphaseAlignment -6.945593 6.417299 3.0293424 MetaphaseAlignment -4.6247787 6.020208 3.8978114 Polylobed -4.017777 6.9221964 3.6601546 Polylobed -3.9255657 5.2744045 3.6498678 Binuclear -4.061623 7.013002 3.70305 Polylobed -3.9751666 5.3008685 3.538734 Binuclear -3.6483166 5.6171236 4.57977 Binuclear -4.4801145 5.094155 3.8950799 Binuclear -4.5194273 5.164434 3.951343 Binuclear -3.2518194 7.4089603 2.8720822 Binuclear -3.2902017 7.437669 2.8134477 Binuclear -4.5525384 6.6157827 3.8308666 Binuclear -4.197371 6.6171784 4.0601034 Binuclear -4.2708335 6.836992 3.038909 Artefact -4.0805264 6.9596753 3.0209422 Artefact -4.276763 6.8462043 3.0201547 Artefact -3.0597656 7.0313663 3.7610755 Binuclear -4.056882 6.848126 3.5518196 Binuclear -4.217695 6.9903264 3.0144007 Binuclear -2.9273744 6.978682 3.702895 Binuclear -7.952015 5.7145023 3.4622524 MetaphaseAlignment -7.730559 5.844735 3.3889751 Binuclear -4.6380105 6.729901 3.0074222 Polylobed -5.891468 7.0784135 3.3209074 Binuclear -7.5721617 5.899876 3.3418112 Binuclear -4.3665686 8.62576 2.7145638 Binuclear -4.67752 5.208976 3.1374776 Polylobed -4.713017 5.164336 3.1724577 Polylobed -3.7132776 6.5697546 4.2008047 Binuclear -3.8683324 6.636322 4.57323 Binuclear -3.8524692 5.9661965 4.9102373 Polylobed -3.895847 5.779659 4.860762 Polylobed -3.849064 6.163545 4.956069 Polylobed -3.3832922 7.2886295 3.9111862 Binuclear -4.124148 6.0424414 3.4986825 Polylobed -2.855 7.828775 3.3998737 Binuclear -5.3262787 6.378036 3.0034132 Binuclear -3.4177763 6.3346987 3.9870641 Binuclear -7.2555547 7.906104 2.1397698 Prometaphase -7.878369 6.9194617 2.7996395 Prometaphase -7.4472804 8.13138 2.6690223 Prometaphase -7.917892 7.4741836 2.0066164 Prometaphase -8.020011 6.6453357 3.068081 Prometaphase -7.63434 7.910367 2.311418 Prometaphase -8.048777 6.655585 3.0673237 Prometaphase -7.242167 6.8891397 3.1313422 Prometaphase -7.3954344 6.7275233 3.5674613 Prometaphase -5.456089 6.430178 2.6656103 Artefact -5.444313 6.4248543 2.686746 Artefact -5.66471 8.31779 3.9079628 Artefact -5.7220964 8.13572 3.8195133 Artefact -5.7485547 7.9002175 3.7764535 Artefact -6.903867 6.5032477 3.4514015 Artefact -4.2993245 5.702689 3.994854 Artefact -4.2677054 5.7476215 3.9338582 Artefact -4.267718 5.68257 3.8631268 Artefact -4.2233033 5.833148 3.9186885 Artefact -5.6313176 6.633601 2.9766867 Artefact -5.560738 6.874777 2.9367635 Artefact -8.052845 5.868459 3.5240076 Prometaphase -7.511638 7.0325084 3.9740078 Prometaphase -7.3490367 6.6530714 3.443628 Prometaphase -8.111124 5.8470373 3.479408 Prometaphase -4.8696 6.3478246 4.88521 Prometaphase -7.506556 6.711136 3.6552227 Prometaphase -7.160639 6.491902 3.4797163 Prometaphase -7.416634 6.735886 3.0194547 Prometaphase -6.3866105 8.3035965 3.4501467 Prometaphase -7.6843333 6.658774 3.1837525 Prometaphase -7.705671 6.8760023 3.5753465 Prometaphase -7.3616457 8.064464 3.113763 Prometaphase -7.300441 8.240163 2.9994576 Prometaphase -7.9078364 7.4476504 2.1162996 Prometaphase -7.657962 7.8885894 2.2392235 Prometaphase -7.7966228 7.6944838 1.9852508 Prometaphase -7.819375 5.7450194 3.5472672 Prometaphase -3.3443718 7.5076575 4.0502687 Large -4.9355826 6.6957803 3.0111954 Large -4.9662876 6.6093664 3.2628531 Large -5.0920753 7.4364805 2.5761456 Large -3.6152823 7.000173 2.9804313 Binuclear -3.556814 5.6781545 3.7865553 Binuclear -3.618294 6.72192 3.049445 Binuclear -2.4085038 6.1335063 3.2254934 Polylobed -2.8409507 6.328423 3.5956755 Polylobed -2.4160156 6.1437426 3.2517984 Polylobed -2.5449789 6.2647285 3.4692054 Polylobed -3.1486533 6.4133844 3.7042985 Polylobed -3.0433047 6.524455 3.7232697 Polylobed -2.4615366 6.0787077 4.7332263 Polylobed -2.553584 6.222752 4.738993 Polylobed -2.4019172 7.9893146 4.356312 Polylobed -2.3421228 7.6454773 4.4876842 Polylobed -2.3733594 7.746956 4.477692 Polylobed -2.4810746 6.014301 4.6525664 Polylobed -2.5189834 5.9661407 4.6095133 Polylobed -2.555241 5.901073 4.6451287 Polylobed -2.5301883 5.914521 4.6030817 Polylobed -2.5288002 5.8464174 4.5733724 Polylobed -4.1180387 6.31864 2.6106575 Large -3.193573 6.6177483 4.603189 Polylobed -2.5782197 5.3524055 5.253291 Polylobed -3.2098238 6.752706 4.5997925 Polylobed -2.253533 6.3669887 4.8456855 Polylobed -2.2937567 6.9179697 4.740342 Polylobed -6.7771373 6.8613224 3.2335799 Metaphase -5.025075 5.9622188 2.6479769 Interphase -6.2743673 7.309497 3.566988 Interphase -3.716429 6.192104 3.50493 Binuclear -4.011182 6.3589206 3.6023102 Binuclear -4.2811074 6.081445 2.7008138 Large -4.7219 5.9242463 2.702645 Interphase -4.1522393 7.577709 3.654054 Interphase -6.1461525 6.4469905 1.955955 Interphase -5.952352 6.303562 2.3817856 Interphase -3.1493487 8.533638 3.7770152 Interphase -4.6201773 6.68677 2.2008536 Interphase -3.223579 8.733344 4.024385 Binuclear -4.465572 6.6481204 1.9940684 Interphase -3.1012878 8.916739 4.0723085 Binuclear -5.988281 6.7269917 3.149513 Interphase -8.355969 6.2335863 2.435273 Metaphase -8.161568 6.3443885 2.4905784 Metaphase -8.591908 5.946673 2.6917417 MetaphaseAlignment -7.8312635 5.7798157 3.4214382 MetaphaseAlignment -7.272209 8.07346 2.8830664 MetaphaseAlignment -7.6505566 6.3770485 4.0196476 MetaphaseAlignment -6.445269 8.60879 2.6816204 SmallIrregular -6.431871 8.76723 2.7444468 SmallIrregular -6.13266 9.361473 2.6536772 SmallIrregular -6.992696 8.429578 2.7422256 SmallIrregular -5.5576615 9.951856 2.3723755 SmallIrregular -4.67107 7.673255 3.3901842 SmallIrregular -5.618926 10.005955 2.2986252 SmallIrregular -8.00565 6.3199286 3.5376797 Apoptosis -6.227042 8.069088 3.5156693 Hole -5.594399 9.028637 2.3059168 SmallIrregular -6.5711355 6.97214 2.90412 SmallIrregular -6.3289304 8.794325 2.6336572 SmallIrregular -6.3000355 8.913933 2.6197772 SmallIrregular -6.500797 7.912513 2.9999251 SmallIrregular -5.513649 9.924328 2.4171531 SmallIrregular -5.8047643 9.769479 2.519298 Folded -5.7633843 9.850272 2.5197968 SmallIrregular -5.7616234 9.773558 2.4925804 SmallIrregular -6.2854586 9.250878 2.5687459 UndefinedCondensed -7.0874147 8.265591 2.4470367 SmallIrregular -4.3781238 9.3773775 3.0868087 Folded -4.048658 9.426961 3.121165 Folded -7.0339 8.174658 2.437725 SmallIrregular -5.571498 9.906535 2.3845189 SmallIrregular -5.569988 9.917302 2.3546863 SmallIrregular -5.543392 9.941349 2.3633747 SmallIrregular -5.6359024 10.011975 2.284944 SmallIrregular -5.617334 10.031719 2.3061228 SmallIrregular -5.5651865 9.965214 2.3317838 SmallIrregular -6.223951 6.6185436 2.91915 Hole -7.3114266 7.685715 2.166938 UndefinedCondensed -6.0775576 8.3563 3.498286 Hole -5.9875526 7.257777 3.3381085 Hole -4.4936028 6.408729 2.8506916 Folded -5.3457875 9.490042 3.4058309 SmallIrregular -5.5199413 9.857023 2.4503646 SmallIrregular -7.0887747 8.230492 2.6670172 SmallIrregular -6.4731026 8.877127 2.7875268 SmallIrregular -5.9892583 7.398018 3.1020944 SmallIrregular -6.5945783 8.4208765 2.7593467 SmallIrregular -7.076435 8.197163 2.7084408 SmallIrregular -6.2559943 7.706086 2.9994867 SmallIrregular -5.639313 10.075569 2.3125782 SmallIrregular -5.612374 10.009408 2.2959092 SmallIrregular -5.962963 7.959039 3.622152 Hole -6.219427 7.9428325 3.5554404 Hole -5.6158447 7.934854 4.008398 Hole -6.375521 8.73947 2.6852818 SmallIrregular -6.1302786 9.222452 2.7815757 SmallIrregular -5.1222067 9.421597 2.7968423 SmallIrregular -5.341999 9.28588 2.8593996 SmallIrregular -6.090973 9.19971 2.814455 UndefinedCondensed -6.7132277 7.6273613 2.9034414 SmallIrregular -5.4363766 9.784245 2.472731 SmallIrregular -5.6300073 9.9772 2.2875948 SmallIrregular -5.5867853 9.949683 2.294621 SmallIrregular -5.5941935 9.983691 2.3320735 SmallIrregular -5.552914 9.645394 2.283921 SmallIrregular -5.6540694 9.67223 2.2707868 SmallIrregular -7.100212 8.2225485 2.6726692 SmallIrregular -6.148123 9.110957 2.8389137 SmallIrregular -6.0144105 9.302368 2.74174 SmallIrregular -5.871782 9.102351 3.019523 UndefinedCondensed -6.0607696 8.521821 3.8233755 MetaphaseAlignment -6.0076766 8.719114 3.7862358 MetaphaseAlignment -5.8733172 8.880037 3.7124867 MetaphaseAlignment -8.414065 6.5603666 3.841923 MetaphaseAlignment -9.005534 5.8356504 3.5751257 Prometaphase -8.432366 6.5365524 3.6774967 MetaphaseAlignment -8.492399 6.014334 2.3308532 MetaphaseAlignment -8.215043 6.4424067 2.0372963 UndefinedCondensed -5.843365 8.628876 3.6770556 MetaphaseAlignment -4.472588 9.113289 3.623742 Artefact -5.3353963 7.355284 4.011543 Artefact -6.8569746 8.372538 3.3713038 MetaphaseAlignment -6.033257 8.787271 3.7226512 MetaphaseAlignment -5.8304195 8.72489 3.8287418 MetaphaseAlignment -5.851335 7.0529995 2.08101 UndefinedCondensed -8.074567 6.824692 3.0552583 UndefinedCondensed -7.643015 6.858951 4.0865617 MetaphaseAlignment -3.2876964 9.612008 3.1946769 Interphase -3.5274289 9.659647 2.928621 Interphase -3.765595 9.422603 2.9943109 Interphase -3.625587 9.2842 3.1098247 Interphase -3.5164835 9.646844 2.9489481 Interphase -3.607401 9.5872135 2.9239485 Interphase -3.8986104 9.329964 2.8258064 Interphase -3.788865 9.55669 2.7750885 Interphase -4.1725907 9.4363165 2.6069083 Interphase -5.8320365 8.543679 3.8147233 Polylobed -4.166971 7.4958506 3.7416975 Polylobed -4.392412 6.16373 3.5251725 Polylobed -4.5857177 5.811073 3.5615354 Polylobed -4.850537 5.4877877 3.835812 Polylobed -5.085719 8.591385 4.0303807 Polylobed -6.2059293 7.5557084 3.4395561 Polylobed -7.3269258 8.151262 2.8287244 MetaphaseAlignment -5.531858 6.3666844 2.6513705 Binuclear -5.4234686 6.363104 2.7633085 Binuclear -7.0500813 8.238818 3.1675875 Metaphase -5.2914143 6.6044083 2.8145726 Binuclear -5.1919136 6.630458 2.8135397 Binuclear -5.9735765 8.932265 3.5549157 Metaphase -3.4677188 6.341289 3.9386156 Binuclear -4.329934 5.3097014 3.342241 Binuclear -8.328848 6.6279826 3.8499653 MetaphaseAlignment -9.01589 5.930402 3.6400492 Prometaphase -6.113911 6.8919616 2.9739344 Polylobed -6.422006 6.9198427 2.9564114 Polylobed -7.9929442 6.8564687 3.926981 Prometaphase -8.178632 6.3975143 2.7633443 Metaphase -7.264541 6.5053897 3.0646129 Interphase -4.194788 7.112776 3.8979554 Interphase -5.28123 8.96951 3.786548 Grape -7.082112 6.8748064 3.0577772 Interphase -6.9572334 6.844307 2.778971 Interphase -7.036749 6.890325 2.951019 Interphase -6.9989805 7.157899 3.0964453 Interphase -6.2657127 9.259107 2.3272927 Interphase -7.032413 7.1252217 3.0328531 Interphase -7.235182 8.020562 2.5943058 Interphase -6.799722 8.547575 2.3639839 Interphase -7.015704 6.8902683 3.0610547 Interphase -7.0674787 6.955061 3.0121748 Interphase -7.2685795 7.1334176 2.366585 Interphase -7.102874 7.2444873 2.9776456 Interphase -6.458614 8.973971 2.5545073 Interphase -6.8295736 8.502513 2.116424 Interphase -7.111746 6.807751 3.0320034 Interphase -3.4283886 8.589164 3.3450928 Interphase -6.559002 7.553618 2.4844484 Interphase -7.1180005 6.943587 2.909916 Interphase -6.7055793 8.742557 2.4183156 Interphase -7.333382 8.154871 2.5142372 SmallIrregular -7.341215 8.141556 2.4891684 SmallIrregular -7.277187 8.148709 2.5111606 SmallIrregular -7.278168 8.291304 2.4710336 SmallIrregular -7.1397624 6.8647685 2.4399488 SmallIrregular -6.2254133 7.742131 2.9858298 Hole -3.9545531 7.2199173 2.5997162 Folded -7.2633405 8.193624 2.3645442 Hole -3.7153602 7.43338 3.030509 Folded -5.3908443 8.049946 2.669308 Folded -5.422727 8.100589 2.640615 Folded -5.433416 8.005304 2.775808 Folded -2.626662 6.8364134 3.7905788 Polylobed -3.627818 6.967504 4.216695 Polylobed -2.8813295 8.659854 4.270625 Polylobed -3.161649 9.803862 3.7340922 Polylobed -3.2509205 8.714625 3.5806649 Polylobed -2.879522 8.205492 3.7859626 Polylobed -2.9580698 7.7643485 3.796783 Polylobed -3.455779 9.749265 4.3396654 Polylobed -3.4668186 6.495493 5.666541 Polylobed -3.4386344 9.744039 4.3384585 Polylobed -3.568874 6.3660445 5.711972 Polylobed -3.451831 9.715189 4.3644657 Polylobed -3.8263419 6.000679 5.8083544 Polylobed -4.2209954 5.690682 6.1280975 Polylobed -3.6913495 6.1226106 5.832059 Polylobed -3.493432 9.757415 4.3247857 Polylobed -3.7771997 6.0401764 5.822624 Polylobed -3.7399197 6.045774 5.7990003 Polylobed -3.9632475 8.638048 2.950409 Prometaphase -8.070639 6.6601815 3.6231174 Prometaphase -4.537511 7.5029345 2.245328 Binuclear -4.548804 7.5768366 2.3509147 Binuclear -4.3046155 7.2072396 2.1344771 Interphase -3.8936703 5.5722117 3.6531327 Binuclear -4.1380486 5.2021093 3.830243 Binuclear -5.51046 6.5599923 2.1671124 Interphase -2.5538201 6.5580106 3.688238 Binuclear -2.5406644 6.5482693 3.7292721 Binuclear -3.2939916 9.645817 3.1155455 Interphase -3.4093711 8.548778 3.376103 Interphase -3.5412586 8.449667 3.177117 Interphase -3.4512248 9.339304 3.213179 Binuclear -3.1792653 9.489545 3.2883415 Binuclear -5.544977 6.702473 2.174518 Interphase -6.70578 6.9302683 4.3357973 Metaphase -7.366167 6.544642 2.6872556 Metaphase -7.348212 6.663609 2.4792993 Metaphase -7.476983 6.6215715 2.4299612 Metaphase -6.4037395 6.587611 2.9354756 MetaphaseAlignment -7.891779 5.902631 3.638561 MetaphaseAlignment -3.671629 6.742273 5.2616367 Polylobed -3.9189773 6.084619 5.6284137 Polylobed -2.8810782 8.165819 4.6230683 Polylobed -3.012204 7.830131 4.53332 Polylobed -3.936362 6.1389723 5.1228747 Polylobed -2.9292712 8.0582695 4.6578226 Polylobed -4.5957375 6.6835375 4.2791204 Polylobed -2.6244333 6.222597 4.350198 Polylobed -6.9211855 7.2535753 3.5495842 Metaphase -2.634642 6.240285 4.3055944 Polylobed -2.5386434 6.3307133 4.3536735 Polylobed -2.5241017 6.36381 4.40261 Polylobed -8.5743885 5.6905756 3.5673919 MetaphaseAlignment -3.7317014 9.717223 4.208313 MetaphaseAlignment -3.511817 9.763938 4.330786 Polylobed -4.0526986 7.0419235 5.5447693 Metaphase -3.4447167 9.795408 4.345563 Polylobed -7.5182443 7.321469 2.694437 Metaphase -4.514436 5.6930556 5.3100715 Polylobed -3.4756336 9.747316 4.3538632 Polylobed -3.482885 9.763595 4.3421903 Polylobed -3.7073557 5.946067 5.66607 Polylobed -7.2084684 6.7714353 3.0359614 Metaphase -2.6207669 6.7939687 3.7687669 Polylobed -2.6122966 6.7063785 3.8122213 Polylobed -2.6416519 6.7536664 3.7275257 Polylobed -3.3796976 6.6679153 3.8012273 Polylobed -2.711262 6.6448483 4.714049 Binuclear -3.010678 7.970608 3.8723264 Polylobed -3.329866 8.649236 3.6201408 Polylobed -3.3993359 7.373347 4.4873514 Binuclear -3.5705378 8.647609 3.8831167 Polylobed -2.827383 6.638754 4.600561 Binuclear -2.8997993 7.7813325 4.6706157 Binuclear -6.7532663 8.192867 3.558557 Metaphase -2.9716177 7.89095 4.2924633 Polylobed -2.923856 7.9316945 4.260807 Polylobed -3.6380117 7.1601562 4.119553 Polylobed -7.230568 6.3730288 3.5999727 MetaphaseAlignment -6.182173 9.276168 2.7209504 Metaphase -6.1563716 9.4313965 2.640366 Metaphase -2.7428946 8.664406 3.9358618 Polylobed -2.854735 9.020822 3.9167328 Polylobed -5.613014 9.337174 3.3416543 Metaphase -3.0886145 9.112642 3.5909185 Polylobed -3.6094296 9.694123 4.3304014 Polylobed -3.521381 9.706258 4.326107 Polylobed -6.304175 6.4711328 2.9496708 Metaphase -3.494339 9.71084 4.3142986 Polylobed -2.656238 9.409952 3.9126542 Polylobed -2.574373 9.386751 4.0397277 Polylobed -3.4840388 9.755508 4.15851 Polylobed -2.5671113 9.090389 4.1416116 Binuclear -2.5814662 8.660389 4.1993876 Binuclear -5.9448338 8.579261 3.922401 Metaphase -5.070449 9.385291 3.6913164 Artefact -5.2345605 9.456617 3.5758462 Artefact -5.340574 9.512117 3.3068733 Artefact -2.8199384 9.599787 3.472884 Polylobed -2.6617055 9.40001 3.6106737 Polylobed -2.6241932 9.105886 3.686109 Polylobed -3.0186076 7.6093345 3.626144 Binuclear -2.8462594 8.283761 3.834344 Binuclear -2.7650294 9.542179 3.6982408 Binuclear -5.471049 8.032736 4.145097 MetaphaseAlignment -2.8207073 9.4233265 3.7909114 Binuclear -3.7613194 6.86282 4.3822846 Polylobed -7.378474 6.023477 3.8435812 MetaphaseAlignment -3.6348503 6.6574264 5.321975 Polylobed -3.7764459 6.246096 5.6323442 Polylobed -3.7262669 6.414008 5.54909 Polylobed -2.9246788 8.062493 4.608047 Polylobed -3.017307 7.883107 4.5421596 Polylobed -2.9522302 8.042098 4.623014 Polylobed -2.968476 8.121025 4.607673 Polylobed -3.0566118 9.44447 4.011455 Polylobed -2.8652341 9.547692 3.772506 Polylobed -8.2108755 6.748506 1.8649191 Hole -5.9289117 8.502874 3.5559278 Hole -7.8051744 6.5841217 2.0191433 UndefinedCondensed -7.4802504 7.8821564 2.0038626 Apoptosis -5.734891 7.2805605 2.9963052 Elongated -5.879099 8.155196 3.5201101 Hole -5.9041905 6.9116073 2.1750813 Hole -4.2518225 7.2934175 2.3237119 Hole -5.0012307 6.4303937 2.013933 Elongated -6.6131344 7.8524556 2.0184631 Hole -6.6032653 6.1663003 2.6728177 Metaphase -8.5377245 6.2636576 2.122661 Apoptosis -7.397688 7.6411724 2.6632333 Hole -5.2269874 6.6087985 1.8133286 Hole -6.3099585 8.186073 1.9757022 Hole -6.194477 8.161237 1.9488888 Hole -5.359341 8.776932 3.0818217 Hole -7.235931 6.524 2.9660378 Hole -4.798767 7.6789885 2.4917605 Hole -8.130841 6.580331 3.4598346 Hole -4.1411557 8.843813 2.8010495 Hole -6.5562367 8.144247 1.9760602 Hole -6.291385 8.022418 2.0214407 Hole -6.216635 8.048634 2.103189 Hole -6.188022 8.059731 2.0393074 Hole -4.927841 7.3271465 2.2593668 Hole -4.7897434 8.560997 2.8189166 Hole -4.709554 7.9669213 2.594463 Hole -8.13243 6.5936775 3.4819744 Hole -7.160585 7.49944 2.0514169 Hole -7.3723145 6.2628922 2.5558221 Elongated -7.0060654 8.22952 1.9690421 Hole -7.3894677 7.9453087 2.2286863 Hole -8.069905 6.60245 1.9652264 UndefinedCondensed -5.207722 6.4407225 2.3183067 Elongated -5.886899 8.163426 3.2403255 Hole -7.441434 7.714572 2.3192055 UndefinedCondensed -6.7880826 7.82164 1.9513267 Hole -6.907013 7.668486 1.9678874 Hole -5.9493413 8.583615 3.614988 SmallIrregular -7.368162 7.5665016 2.269455 UndefinedCondensed -7.4570203 7.5152965 2.6290307 Hole -5.5726147 7.598736 2.7213812 Artefact -5.644841 7.5545797 2.6874788 Artefact -5.097857 7.3853593 2.9825618 Artefact -6.465838 8.693167 2.0606265 Interphase -6.2104955 8.303519 2.3752592 Interphase -5.6566825 9.539371 2.267586 Interphase -6.5930476 8.722904 2.0979383 SmallIrregular -6.0564494 9.286131 2.3873112 Interphase -6.1115994 9.426686 2.3480926 Interphase -4.5452046 9.142934 2.8076346 SmallIrregular -5.7155886 9.649123 2.3689182 SmallIrregular -6.3619657 8.706467 2.5906813 Interphase -3.693156 6.0414834 4.0242395 SmallIrregular -6.0235634 8.946219 2.0552654 SmallIrregular -5.71285 9.374351 2.188007 SmallIrregular -5.5343676 9.602031 2.3326783 SmallIrregular -5.583153 9.993246 2.3312259 SmallIrregular -5.377426 9.564116 2.31069 SmallIrregular -5.366765 9.645629 2.3137732 SmallIrregular -5.5875983 9.182075 2.221282 SmallIrregular -4.387563 8.177751 3.237692 Polylobed -4.6763434 8.411873 2.8540175 Polylobed -6.698028 8.37021 2.2991014 SmallIrregular -4.525336 8.397898 3.0139167 Polylobed -4.1056323 7.4169636 3.5083709 Polylobed -4.421751 7.4264913 3.4717405 Polylobed -4.469282 7.726343 3.4705212 Polylobed -4.0300784 7.9603615 3.4071546 Polylobed -6.566688 8.817677 1.9369527 Interphase -6.5105996 8.851703 1.9560854 Interphase -6.0796227 8.4555025 2.3354867 Interphase -6.5374603 8.581939 2.1501281 Interphase -6.014327 8.419426 2.2406247 Interphase -6.4943027 8.855247 1.9770759 Interphase -6.5773373 8.7925825 1.9619771 Interphase -5.8382516 9.498614 2.284332 Interphase -5.8543963 9.198953 2.2757692 SmallIrregular -3.7100177 7.2919292 5.7219715 Polylobed -3.6973317 7.31058 5.71038 Polylobed -3.7228336 7.2822156 5.730565 Polylobed -3.7413728 7.358432 5.7181687 Polylobed -3.7637668 7.199008 5.691795 Polylobed -3.199511 7.4631987 5.238423 Polylobed -3.6757689 7.2909055 5.680135 Polylobed -3.758194 7.2235794 5.7033024 Polylobed -3.2826903 7.4749446 5.2410088 Polylobed -3.310403 7.4993563 5.192212 Polylobed -3.7332742 7.211529 5.6995316 Polylobed -5.780586 7.6333756 3.3424335 SmallIrregular -4.217255 6.473281 2.877558 Polylobed -4.207793 6.514922 2.8849447 Polylobed -4.227621 6.5170946 2.858877 Polylobed -4.0811257 8.638942 4.6828113 Polylobed -4.211462 6.48585 2.86234 Polylobed -4.303826 6.5808086 2.8955138 Polylobed -4.385646 6.5134068 2.9860764 Polylobed -5.092876 9.030762 3.9106762 Polylobed -5.063536 9.116221 3.8677027 Polylobed -5.665683 9.614841 2.141725 Polylobed -5.6316 7.365123 3.6346142 Polylobed -5.728095 9.854955 2.144427 Polylobed -5.6464844 9.895451 2.2131658 Polylobed -5.70548 9.693943 2.1639054 SmallIrregular -5.781763 7.896167 2.665413 SmallIrregular -5.870508 7.39391 3.4373264 Polylobed -6.1859403 7.702436 3.2116919 Polylobed -5.60734 9.997211 2.2717326 SmallIrregular -6.6816893 8.719045 1.8902864 SmallIrregular -6.212751 8.644709 2.52745 SmallIrregular -6.649583 8.720064 1.8958445 SmallIrregular -6.697496 8.659358 1.9536983 Interphase -6.6818147 8.701561 1.9285032 Interphase -7.134405 7.7835417 1.9926835 Interphase -4.697019 8.733094 3.0496173 Interphase -5.9269385 8.864081 2.4326441 Interphase -6.5094895 8.768502 1.979494 Interphase -6.571557 8.704562 1.9443161 Interphase -6.60607 8.673652 1.9743912 Interphase -4.1137867 8.891452 2.9365249 Interphase -6.1470966 7.8810797 3.5432615 Interphase -5.8438287 9.500185 2.2048926 SmallIrregular -6.4888144 8.89952 2.002039 SmallIrregular -6.8561425 8.578603 1.9670058 Interphase -5.651686 9.704315 2.3371596 SmallIrregular -5.4850383 9.561497 2.3181243 Interphase -5.5848527 9.546862 2.313523 Interphase -6.0701556 8.847898 2.608089 Interphase -5.72885 9.273481 2.1855135 SmallIrregular -5.7511387 8.350515 2.6348653 SmallIrregular -7.304983 7.096475 2.3376617 SmallIrregular -5.5790486 10.011561 2.3035388 SmallIrregular -5.587839 10.030513 2.3243103 SmallIrregular -6.6308303 8.723252 1.9418525 SmallIrregular -6.617356 8.686322 1.9631599 SmallIrregular -6.7188873 8.578304 2.27123 SmallIrregular -5.567984 9.514273 2.2266011 SmallIrregular -7.0052886 7.858513 2.0735934 SmallIrregular -7.0963397 7.787598 2.00308 SmallIrregular -6.920313 8.011663 2.026222 SmallIrregular -5.723865 9.992268 2.2090938 Folded -6.6478877 8.385835 2.0928931 Interphase -5.7209616 9.907942 2.1807709 SmallIrregular -5.74827 9.719227 2.1461194 SmallIrregular -5.208357 9.356699 3.3401117 SmallIrregular -6.248145 7.3381004 2.7230728 SmallIrregular -6.512667 8.472767 2.4768336 SmallIrregular -7.02351 6.5111384 2.7946556 SmallIrregular -4.9300537 9.454519 2.385327 Binuclear -4.846076 9.583421 2.459021 Binuclear -4.84712 9.559247 2.5186346 Binuclear -4.779144 9.542916 2.580217 Binuclear -3.4789689 9.412075 3.0598617 Polylobed -3.9277306 8.835151 3.505825 Polylobed -4.189517 8.769104 4.062801 Polylobed -4.101849 8.770378 3.4526865 Polylobed -4.0292215 8.665912 3.5277722 Polylobed -4.113319 8.725722 4.369697 Polylobed -4.0630236 8.5346 4.595124 Polylobed -5.1445537 7.3765864 3.9174852 Polylobed -5.138743 7.4122066 3.88017 Polylobed -4.3681383 6.850327 2.035513 Binuclear -4.402094 6.7838764 2.014046 Binuclear -4.4472766 6.772917 1.9682666 Binuclear -4.3524957 7.835491 2.4072206 Interphase -5.375635 9.3204155 2.423248 Polylobed -5.420325 9.36526 2.3934684 Polylobed -5.3841515 9.313308 2.4059792 Polylobed -5.4144697 9.306371 2.4481754 Polylobed -5.4571195 7.7196836 3.1716459 Binuclear -5.2199197 9.117498 2.4968581 Binuclear -5.5696487 9.381985 2.3558257 Polylobed -4.153413 6.53625 2.1651318 Polylobed -3.3660867 9.446958 3.276008 Binuclear -5.0958314 9.407773 3.011459 Binuclear -4.1375403 6.491136 2.1796432 Polylobed -4.9090962 6.6379457 3.0134952 Binuclear -4.100827 6.5363936 2.1491718 Polylobed -4.1264334 6.534742 2.1668446 Polylobed -5.0775366 9.387561 2.8211224 Binuclear -4.4713087 6.5535994 3.2362454 Binuclear -4.0565257 7.408677 4.3350844 Polylobed -6.0173044 7.8701477 3.3991709 Polylobed -4.782194 9.422118 2.7918952 Binuclear -3.964826 8.748966 3.768747 Polylobed -3.9582314 8.794547 3.6624243 Polylobed -4.6858315 8.698969 2.7094424 Polylobed -5.1176214 9.301105 2.5825996 Polylobed -5.343563 9.2664385 2.4416282 Polylobed -3.9417772 7.3438873 4.636037 Polylobed -5.187257 9.201542 2.5225866 Polylobed -4.653145 8.250719 3.139307 Binuclear -4.3067284 8.556591 3.301655 Binuclear -3.8093805 7.1710334 4.688435 Polylobed -2.65564 7.7003922 4.012072 Polylobed -3.8845966 8.109972 3.6728797 Polylobed -2.6471748 7.852298 4.054271 Polylobed -4.0278187 6.1000705 3.1807756 Binuclear -2.5757897 7.979033 3.937832 Polylobed -3.4363532 7.350973 4.160744 Binuclear -2.6233742 8.009373 3.8971884 Polylobed -3.7879198 9.116471 3.222634 Binuclear -3.4223301 8.788747 3.2962687 Binuclear -3.9398026 6.866701 2.5482075 Binuclear -4.0859637 6.9495583 2.8023093 Binuclear -3.6515033 7.080734 2.7897367 Binuclear -3.7617505 8.000958 3.049886 Binuclear -3.5301652 9.405566 3.1242847 Binuclear -3.2574499 8.203482 3.354939 Binuclear -3.3598363 8.478794 3.2065537 Binuclear -3.4357219 9.340568 3.224806 Binuclear -4.660732 7.549376 2.7330513 Binuclear -4.1550455 6.806886 3.6789205 Binuclear -4.0508876 8.286844 3.3389435 Polylobed -4.056615 8.190679 3.3697336 Polylobed -4.172495 8.525143 3.1055527 Polylobed -3.9060338 8.470884 3.2303905 Polylobed -3.8880553 8.58836 2.9864395 Polylobed -4.9075403 9.503121 2.4505188 Binuclear -4.871868 9.512748 2.4542572 Binuclear -4.7989984 9.542718 2.564035 Binuclear -4.7546062 9.511922 2.6332407 Polylobed -4.7190266 9.471261 2.7518103 Polylobed -4.2174854 7.4540973 3.0355592 Binuclear -4.883564 6.663076 3.113812 Binuclear -4.1858454 6.791915 2.252155 Binuclear -4.1761923 6.8059993 2.2695992 Binuclear -4.920323 9.42474 3.406761 Polylobed -5.1905766 9.365339 3.2281857 Polylobed -5.184953 7.7048326 3.4487512 Polylobed -3.8576965 9.099536 4.4892287 Polylobed -5.0943465 7.59356 3.3922746 Polylobed -4.951302 7.744513 3.2727294 Polylobed -4.5014315 5.2285275 3.664872 Binuclear -4.480872 6.726775 2.7215297 Artefact -4.612074 5.2301993 3.653564 Binuclear -4.492271 6.6860323 2.7299922 Artefact -4.547959 6.722944 2.736603 Artefact -4.478861 6.684136 2.749594 Artefact -3.4254034 8.647376 3.1404014 Binuclear -3.509178 8.663799 3.1533127 Binuclear -4.5441403 6.818912 3.5540366 Artefact -3.90948 6.9466357 2.4170582 Binuclear -5.4748373 6.6195683 3.243135 Artefact -4.240928 6.892679 2.734225 Binuclear -4.048794 6.842214 2.3499439 Binuclear -6.396027 8.09817 3.5876827 Artefact -6.307125 8.43054 3.2223384 Artefact -5.048802 7.489687 3.1572402 Artefact -6.51841 7.8703303 3.7973313 Artefact -4.3518357 6.962615 2.1691215 Binuclear -4.4516225 7.4258556 2.201624 Binuclear -3.3799653 6.072721 4.101455 Polylobed -3.280826 6.164678 3.999439 Polylobed -3.2595246 6.1702223 3.9453378 Polylobed -4.381339 5.2016673 5.3623304 Polylobed -4.176784 7.1661296 5.6609836 Polylobed -4.590658 6.143228 5.64108 Polylobed -4.5935717 5.2344594 5.4317937 Polylobed -5.242092 5.594198 5.695529 Polylobed -4.1993337 7.0959992 5.6587186 Polylobed -4.355024 5.1083493 5.400414 Polylobed -4.158957 7.1850543 5.614647 Polylobed -4.0771723 4.966986 5.467356 Polylobed -5.1661587 5.577167 5.674104 Polylobed -4.537101 6.0118294 5.715828 Polylobed -4.878921 5.4400005 5.2573895 Polylobed -4.8370214 5.444486 5.265126 Polylobed -4.96875 5.3802276 5.321838 Polylobed -5.1611433 5.3876586 5.7891426 Polylobed -2.0015845 5.9322248 4.9439864 Polylobed -1.9696113 5.9472003 4.9545226 Polylobed -2.1420114 6.298452 4.8587756 Polylobed -1.9829807 6.032806 4.8961453 Polylobed -2.1309018 6.2428293 4.8620815 Polylobed -2.0014954 6.1556582 4.92599 Polylobed -2.0483785 6.1513734 4.8544383 Polylobed -2.4215534 8.5082 4.209264 Polylobed -2.952286 8.308357 4.279225 Polylobed -2.5822344 8.667296 4.096644 Polylobed -2.028835 6.008764 4.897482 Polylobed -2.4844742 8.7824335 4.043851 Polylobed -2.9132671 8.367887 4.212342 Polylobed -2.747823 8.640559 4.2060933 Polylobed -3.1906416 7.9168444 4.3116093 Polylobed -3.0140936 8.035089 4.2395124 Polylobed -2.5836046 8.896418 3.6824098 Polylobed -2.5063481 9.01063 3.832256 Polylobed -2.3051536 5.4778934 5.119713 Polylobed -2.479271 8.818662 3.8362255 Polylobed -2.6706197 8.783807 3.9447691 Polylobed -2.3090546 5.4897413 5.1087513 Polylobed -2.3551116 5.453755 5.153972 Polylobed -1.9158028 5.57236 5.0226407 Polylobed -1.3731619 5.420098 4.8984723 Polylobed -2.056084 5.5524063 5.0781975 Polylobed -1.9915147 5.6416287 5.026702 Polylobed -2.006409 5.580197 5.035576 Polylobed -1.3671906 5.419839 4.899571 Polylobed -1.7971778 6.509588 5.0407662 Polylobed -1.3809766 5.4334106 4.8986588 Polylobed -1.8294691 6.543802 5.0108104 Polylobed -2.1493888 5.909217 4.8550715 Polylobed -1.4040902 5.427788 4.896487 Polylobed -1.7626776 6.5119348 5.03391 Polylobed -2.2177062 5.8202443 4.8434653 Polylobed -2.2967715 5.870745 4.777065 Polylobed -4.0046444 6.3931637 4.872595 Polylobed -4.7042165 5.260862 6.4467535 Polylobed -4.677069 5.322512 6.406301 Polylobed -4.805703 5.236728 4.090486 Polylobed -4.8075557 5.2242665 4.048001 Polylobed -4.82728 5.2017126 4.038441 Polylobed -4.8276615 5.2331643 3.9992032 Polylobed -4.8555684 5.2347293 4.06509 Polylobed -1.3152112 5.388366 4.89361 Polylobed -1.2847177 5.353918 4.933512 Polylobed -1.345429 5.410895 4.885112 Polylobed -1.3385185 5.4021955 4.8992124 Polylobed -3.8251417 6.050813 4.2215714 Polylobed -5.770891 6.276642 4.9719205 Polylobed -5.6077614 6.409087 3.898231 Polylobed -7.5364776 5.769136 3.1230514 Polylobed -2.1370718 5.71967 4.912471 Polylobed -2.257314 5.7365913 4.8531137 Polylobed -2.2396688 5.6727934 4.878286 Polylobed -5.938068 8.517487 4.2160587 Polylobed -5.9022484 8.479189 4.2077174 Polylobed -5.974848 8.465287 4.180582 Polylobed -5.99086 8.487616 4.158458 Polylobed -6.071197 8.385926 4.1457386 Polylobed -5.030927 5.609636 5.911827 Polylobed -5.1428866 5.4762516 6.2203355 Polylobed -5.135327 5.471831 5.8490906 Polylobed -5.1637764 5.4571 6.126448 Polylobed -4.5558224 5.7380195 4.4515066 Binuclear -4.4601274 5.809149 4.615654 Binuclear -4.447989 5.834065 4.6652164 Binuclear -4.6458597 5.6122465 4.7487264 Polylobed -3.1562443 9.863533 3.7458062 Binuclear -3.2091455 9.836884 3.692819 Binuclear -4.347541 5.1782365 5.3271275 Polylobed -4.425197 6.17438 5.4622993 Polylobed -4.165478 9.208446 4.576496 Polylobed -4.1687856 9.163581 4.6020465 Polylobed -4.617505 6.3907228 5.549579 Polylobed -4.4302597 5.2115946 5.3195753 Polylobed -4.3005166 5.1317177 5.414485 Polylobed -4.6493874 5.8345294 5.5035324 Polylobed -4.004631 5.0035796 5.4471073 Polylobed -4.078848 8.917436 4.580806 Polylobed -4.3428583 5.17096 5.3890977 Polylobed -4.104313 8.885449 4.541659 Polylobed -4.207352 9.173296 4.5284996 Polylobed -2.2808077 6.3405156 4.7937684 Polylobed -2.189626 6.3766103 4.7788296 Polylobed -2.1929185 6.382906 4.7885885 Polylobed -2.1643066 6.4378834 4.801452 Polylobed -2.0004208 6.1803913 4.9169426 Polylobed -1.997448 6.04447 4.892959 Polylobed -2.552777 9.264688 3.8878627 Polylobed -2.4726152 9.197752 4.0447016 Polylobed -2.7629843 8.336625 4.0193667 Polylobed -2.0128915 5.9819074 4.929723 Polylobed -2.6212387 9.194192 3.839549 Polylobed -2.695509 8.505577 3.9183877 Polylobed -2.7109702 8.401995 4.0230947 Polylobed -2.5940754 8.917635 3.699242 Polylobed -2.4959393 9.001929 3.793725 Polylobed -2.4843247 8.976646 3.7458758 Polylobed -2.5296154 9.119493 3.7510417 Polylobed -2.8715477 9.630567 3.553159 Polylobed -2.9644337 9.666988 3.4874194 Polylobed -2.8980494 9.619133 3.4851701 Polylobed -2.9850748 9.582097 3.4982016 Polylobed -1.8703549 5.641197 5.022611 Polylobed -1.8554581 5.6240296 5.0107803 Polylobed -1.3369886 5.4068437 4.903187 Polylobed -1.9320023 5.5975037 4.986815 Polylobed -2.8711786 9.765967 3.6546438 Polylobed -2.8356779 9.725486 3.6133769 Polylobed -1.4036943 5.4526463 4.8878326 Polylobed -1.7855475 6.361696 5.057563 Polylobed -2.8899815 9.737763 3.6117873 Polylobed -1.870494 5.667859 5.0006733 Polylobed -2.8148983 9.704997 3.616595 Polylobed -2.797016 9.666016 3.567421 Polylobed -1.3939983 5.446358 4.8948517 Polylobed -1.829585 6.263187 5.031532 Polylobed -1.436477 5.447619 4.912729 Polylobed -1.716914 5.7102737 4.9591556 Polylobed -1.657554 5.5800133 4.9806857 Polylobed -1.8030031 5.622294 5.011138 Polylobed -3.1293318 7.772002 3.8520958 Polylobed -3.0183363 7.8320622 3.8666077 Polylobed -2.9351552 8.065329 3.8266218 Polylobed -2.9729815 7.714496 3.7984686 Polylobed -1.3330613 5.4063845 4.886408 Polylobed -1.345561 5.406823 4.8921447 Polylobed -1.3581623 5.418079 4.8773055 Polylobed -1.3807148 5.4211597 4.8915324 Polylobed -1.3577693 5.41114 4.8893776 Polylobed -3.1777184 6.412151 4.598175 Binuclear -3.0126245 6.3501334 4.430932 Binuclear -1.3715107 5.4149976 4.8987207 Polylobed -3.9551027 6.441655 4.8954034 Polylobed -2.9573646 6.250226 4.223993 Polylobed -3.0098758 6.3678064 4.3246703 Polylobed -3.158538 6.449945 4.043079 Polylobed -3.174321 5.2411733 5.257598 Polylobed -3.1291893 5.2533364 5.270709 Polylobed -3.1425362 5.314687 5.186899 Polylobed -3.1970146 8.013521 4.408279 Polylobed -3.195731 8.013858 4.444902 Polylobed -3.2238839 8.002129 4.3647847 Polylobed -3.2926264 7.575454 4.3436837 Polylobed -2.1406715 5.702027 4.9383216 Polylobed -2.1788802 5.694768 4.8895273 Polylobed -2.1516032 5.6882715 4.8886533 Polylobed -3.8487334 7.36464 3.2865744 Binuclear -3.055903 8.611479 3.576013 Binuclear -3.5418954 6.76014 4.1456294 Binuclear -4.0172224 5.8250613 3.0846953 Binuclear -3.0602698 7.0055456 4.253115 Binuclear -3.3777144 6.969167 4.181782 Binuclear -3.167472 7.311642 4.294799 Polylobed -3.2509701 7.007202 4.2805285 Polylobed -3.3502848 6.2299557 3.7845762 Polylobed -3.4602067 5.2720904 4.0405536 Binuclear -3.8595166 5.374956 3.7905343 Polylobed -2.3426607 5.9039116 4.7218065 Binuclear -2.377669 5.7008033 4.6810603 Binuclear -2.507645 5.307432 5.213068 Binuclear -3.645288 6.6150503 3.0926073 Binuclear -2.829834 8.050767 3.7582564 Binuclear -2.5266562 5.322523 5.210257 Binuclear -2.4735515 5.355155 5.212389 Binuclear -3.4674954 6.831446 3.1549017 Binuclear -2.8883145 8.239386 3.7784703 Binuclear -2.4819431 5.383679 5.185397 Binuclear -5.162431 7.2866197 4.7557116 Grape -5.1309347 7.3396344 4.6652966 Grape -4.7619123 8.969064 3.7861407 Grape -5.516621 6.363979 5.7714295 Grape -5.0733457 7.5530043 4.483149 Grape -5.426408 7.3170056 5.3234878 Grape -5.5681486 7.150648 5.32659 Grape -5.4516783 7.2021046 5.3562226 Grape -4.554539 8.981056 3.8108587 Grape -5.529974 6.883714 5.3719835 Grape -4.905154 6.262367 5.4926643 Grape -5.6260595 6.939264 5.3139725 Grape -4.898697 6.5818076 4.084599 Grape -5.2450733 5.989716 5.588208 Grape -5.626611 5.256318 6.2892866 Grape -5.7804017 6.5116057 5.2756457 Grape -5.3298926 5.9667563 5.6137257 Grape -5.500864 6.4078774 5.7073507 Grape -5.383918 5.7726083 5.715766 Grape -5.4102106 5.839567 5.728002 Grape -5.5117736 6.3378115 5.7829046 Grape -3.9790173 6.3487396 4.8942018 Grape -4.2172084 6.7840114 4.846136 Grape -5.773903 8.028554 3.9165099 Prometaphase -5.4795003 6.9591002 5.344996 Grape -5.728877 6.989181 5.2773547 Grape -5.667188 6.9697385 5.3001184 Grape -5.7880735 7.0047293 5.1386433 Grape -5.6733937 6.9691114 5.28731 Grape -5.2648115 6.6944804 4.423559 Grape -6.5476913 6.9538264 4.36102 Prometaphase -5.128792 7.7397084 4.880111 Grape -6.6709228 7.095732 4.2283273 Apoptosis -4.4676356 6.870731 5.1455073 Grape -4.5427155 6.2102547 4.6105995 Grape -4.3347917 6.7191334 5.0954843 Grape -4.4470396 6.4181905 4.6643977 Grape -4.573027 6.418009 4.5852847 Grape -5.9423814 6.9414673 5.1802306 Grape -6.0007052 7.057287 5.1820283 Grape -7.733007 6.797286 4.10128 Prometaphase -7.894977 6.266594 3.8114378 Apoptosis -8.416685 6.41823 3.7193365 Apoptosis -5.4303527 7.0878386 4.9880996 Grape -7.9780574 6.371398 3.783527 Apoptosis -4.920149 5.5092163 6.1920366 Grape -4.6334033 5.359045 6.4242024 Grape -5.9423504 5.1599956 6.480917 Apoptosis -4.955379 5.53107 6.2650323 Grape -4.6014867 5.3533206 6.3815236 Grape -5.2096243 5.7916007 6.0413165 Grape -4.6633205 5.3588896 6.4355474 Grape -5.3070836 5.7920985 5.8864064 Grape -5.942621 7.096928 5.2294035 Grape -5.9838796 5.147577 6.4941435 Apoptosis -4.6230264 5.36641 6.4137087 Grape -4.9228106 5.7044926 6.1636915 Grape -4.6372867 5.3789697 6.4245553 Grape -5.896597 7.074219 5.219488 Grape -5.94972 7.0807157 5.226303 Grape -4.606081 5.4849057 6.321879 Grape -4.5898542 5.39169 6.4148154 Grape -5.951296 7.1003146 5.2111654 Grape -4.606045 5.3885593 6.437645 Grape -5.096143 5.341741 5.931322 Grape -7.8204074 6.7803235 3.90275 Prometaphase -4.5829 5.3802457 6.423832 Grape -5.9621143 5.1499553 6.4984026 Grape -6.028339 7.041004 5.1392856 Grape -4.5437436 5.4147925 6.3852525 Grape -7.3152556 7.008916 4.1245356 Prometaphase -5.883581 5.2030373 6.4624934 Grape -4.6249604 5.44306 6.379277 Grape -4.596677 5.438741 6.3863435 Grape -6.244976 6.976441 4.9961724 Grape -5.963872 5.161272 6.5044208 Grape -5.9471064 5.1618466 6.483772 Grape -4.204144 4.863908 5.537639 Grape -4.28276 4.88372 5.53547 Grape -4.198899 4.860123 5.5429153 Grape -4.2479844 4.848786 5.557035 Grape -4.200304 4.8564363 5.5388927 Grape -4.1710224 4.874633 5.5170207 Grape -4.1800528 4.8760467 5.5229316 Grape -4.3007236 4.8862753 5.543674 Grape -5.066304 6.2828255 5.013024 Grape -5.497618 6.558083 5.1354585 Grape -5.112486 6.378453 5.0097933 Grape -4.962804 6.4796414 4.85112 Grape -4.736073 6.130989 5.4000797 Grape -7.0741434 6.839298 3.001802 Apoptosis -5.472879 6.3279276 3.9579723 Grape -4.804689 6.0581517 5.4778376 Grape -4.9697056 6.2410283 5.5346136 Grape -5.1992955 6.385805 5.4967847 Grape -5.299811 7.1203456 5.2860117 Grape -5.321152 7.198209 5.2287774 Grape -5.3273315 7.08088 5.2966075 Grape -5.3291397 7.085727 5.263474 Grape -5.254773 7.1581626 5.2084703 Grape -5.300106 7.2159796 5.1827273 Grape -5.5314584 7.344872 4.937364 Grape -5.48392 7.3659205 4.9894753 Grape -5.438842 6.187551 5.5332994 Grape -5.170819 6.349607 5.59126 Grape -5.169562 6.345272 5.570865 Grape -5.2408996 6.454572 5.6368394 Grape -5.325772 6.4219217 5.7017517 Grape -5.2357078 6.4277773 5.618105 Grape -5.2354584 6.580626 5.61232 Grape -5.06695 6.959494 5.63746 Grape -5.7525573 7.046047 5.3036056 Grape -5.778111 7.0670357 5.264454 Grape -4.680351 7.1999063 5.588427 Grape -4.847566 7.237544 5.679493 Grape -5.4410563 6.788722 5.670665 Grape -5.7687545 7.089321 5.285154 Grape -4.3686333 7.703314 5.0422525 Grape -4.7549925 7.2588434 5.676217 Grape -4.7164187 7.7397165 5.233054 Grape -4.747104 7.246739 5.6863327 Grape -4.324926 7.6646466 5.0523033 Grape -4.57569 7.5888486 5.290739 Grape -4.3268623 7.6000533 5.0504284 Grape -5.654441 6.596681 5.5677094 Grape -5.5533385 6.4859757 6.0361357 Grape -5.5767236 6.5041842 6.028581 Grape -5.556778 6.570004 6.0150313 Grape -5.653283 6.7142954 5.6068735 Grape -5.511202 6.706331 5.647876 Grape -5.5477653 6.5616446 6.013234 Grape -5.5648856 6.464932 6.0272813 Grape -5.5336785 6.5334816 6.03804 Grape -5.246131 7.091803 5.5660286 Grape -4.9554605 7.696202 5.117424 Grape -4.291829 7.6865673 5.399025 Grape -4.330862 7.5730753 5.367503 Grape -4.293196 7.640476 5.391778 Grape -4.276496 7.6904626 5.3938875 Grape -4.2782545 7.7154517 5.389715 Grape -5.604542 6.9687977 5.498877 Grape -5.121675 7.603483 5.384357 Grape -5.557989 7.235907 5.410824 Grape -4.8408146 6.37612 5.5301685 Grape -4.334516 7.415345 5.319945 Grape -5.6583304 7.024281 5.430559 Grape -4.4271216 7.48918 5.3908124 Grape -4.830811 6.197626 5.4581623 Grape -5.2080493 6.663801 5.987155 Grape -4.7976637 6.215567 5.315984 Grape -4.825584 6.336956 5.513231 Grape -5.3480687 6.6764154 5.9811754 Grape -5.358475 6.700272 5.931792 Grape -5.359852 6.7213755 5.9615245 Grape -5.3258667 6.7776637 5.9496913 Grape -5.3380923 6.320745 5.569245 Grape -5.4126897 6.9201603 5.7766943 Grape -5.321149 5.7684836 6.091228 Grape -5.3034706 6.8466544 5.855195 Grape -4.9917054 5.4582105 6.2873554 Grape -5.2811136 5.4822154 6.2271557 Grape -5.361857 5.339024 6.291409 Grape -5.137058 5.3940024 6.3561845 Grape -5.0056868 5.367193 6.424852 Grape -5.147629 5.451792 6.335252 Grape -5.0942864 5.4400654 6.3313947 Grape -5.7356215 6.513822 5.046907 Grape -3.8927197 8.739551 5.2019353 Grape -3.8852031 8.705043 5.2255087 Grape -4.2218256 5.9119725 5.2110934 Grape -3.9315019 8.744933 5.1950345 Grape -5.61134 6.3949337 5.147254 Grape -3.9061394 8.739762 5.1758394 Grape -4.2200446 6.0529504 5.249016 Grape -3.9149625 8.673948 5.184658 Grape -3.9629285 8.709546 5.1889143 Grape -3.8651392 8.602477 5.124292 Grape -5.516685 6.187471 5.2028637 Grape -4.0271297 8.729489 5.1402507 Grape -5.52842 6.4086795 5.039525 Grape -5.550188 6.4581547 5.034758 Polylobed -5.4731503 6.472635 4.8356085 Polylobed -4.7346606 6.002071 6.1284585 Grape -4.630596 6.0243993 6.1725736 Grape -4.5877614 5.996302 6.1029544 Grape -4.7023335 6.0508556 6.1215897 Grape -4.7205515 6.09052 6.1174335 Grape -4.681684 6.048165 6.12072 Grape -7.8193655 6.8419943 4.124294 Prometaphase -7.547916 6.7282534 3.8739595 Prometaphase -8.53548 6.396453 3.9013362 Prometaphase -7.7937403 6.9516916 4.157885 Prometaphase -7.7650247 6.826992 4.0536733 Prometaphase -7.5596356 6.514436 3.9430342 Prometaphase -7.3015084 6.4802895 4.050475 Prometaphase -8.711447 5.779143 3.7264981 Prometaphase -9.0399065 6.0450077 3.9092836 Prometaphase -9.140559 5.9374413 3.7070289 Prometaphase -9.0812435 5.9544315 3.7786438 Prometaphase -8.485582 6.4386578 4.056366 Prometaphase -7.5597935 6.576633 4.08633 Prometaphase -8.27823 6.325505 3.96363 Prometaphase -8.301424 5.9361706 2.488426 MetaphaseAlignment -7.7093935 5.7705474 3.0933046 Artefact -7.6851935 5.7630515 3.0360727 Artefact -7.774431 5.8192625 2.9532976 Artefact -8.195122 5.8262877 3.4779758 MetaphaseAlignment -7.8681974 6.3982487 3.692875 Prometaphase -8.056526 5.810812 2.8312204 MetaphaseAlignment -7.3099785 6.4393215 4.01212 Prometaphase -7.756082 6.4898844 2.9283867 MetaphaseAlignment -8.208446 6.5303774 3.7931511 Prometaphase -7.7594533 6.876991 4.123617 Prometaphase -8.140172 6.576613 3.6720982 Prometaphase -5.3849683 7.650604 4.4135065 Prometaphase -8.345977 6.2868853 2.828032 MetaphaseAlignment -8.328118 6.5423036 3.8025992 Prometaphase -7.4556723 6.6864567 4.2650895 Prometaphase -8.616251 6.005965 2.2472575 MetaphaseAlignment -8.739745 6.134109 3.7541966 Prometaphase -8.492068 6.47834 3.8990066 Prometaphase -8.425968 6.5138984 3.905391 Prometaphase -7.309124 7.28805 4.053595 MetaphaseAlignment -7.8963394 6.850307 3.993448 MetaphaseAlignment -8.47227 6.1700983 3.7589161 Prometaphase -8.39837 5.9972157 3.333696 MetaphaseAlignment -7.4101176 6.532666 3.6212935 Prometaphase -8.361806 6.049035 3.3280416 MetaphaseAlignment -9.076228 5.954845 3.6609106 MetaphaseAlignment -8.304292 6.6309476 4.0192027 Prometaphase -8.508543 6.5050716 3.9920688 Prometaphase -9.119733 5.930342 3.687101 Prometaphase -5.6254063 7.783068 4.4457088 Prometaphase -6.9895124 7.3259206 4.2067156 Prometaphase -8.124518 6.2535324 4.0166545 Prometaphase -7.3212285 6.565199 4.072848 Prometaphase -4.701028 6.974111 5.723469 Grape -4.7354784 6.9348254 5.624739 Grape -4.282186 5.9938765 5.6278625 Grape -4.7242794 6.913625 5.6906085 Grape -4.7572 6.8384337 5.465195 Grape -4.773887 6.6236887 5.8636045 Grape -5.5934534 5.3958077 4.9617486 Grape -4.935041 6.8945675 5.2741313 Polylobed -4.4935255 6.998691 5.096464 Polylobed -5.487982 5.3710694 4.9909987 Grape -4.745988 6.4806314 5.566217 Grape -4.8207736 6.9704742 5.449043 Polylobed -5.5113044 5.40563 5.0115433 Grape -4.7822833 6.5168443 5.544106 Grape -4.169658 6.7787843 4.982295 Polylobed -5.407858 5.313788 4.963708 Grape -4.087233 6.5455537 4.8872395 Polylobed -4.7430468 6.4323244 5.5487103 Grape -7.94157 5.9652286 3.8833382 Grape -4.5692058 6.130232 5.645403 Grape -4.8149204 6.340299 5.7735724 Grape -4.705025 6.317238 5.6311393 Grape -4.862702 6.3405876 5.7578487 Grape -4.930727 6.3466945 5.7521577 Grape -4.9549475 5.987516 6.008343 Grape -5.167284 6.5803237 5.6754074 Grape -5.1904655 6.102519 5.974821 Grape -5.2704496 5.6804824 6.1669846 Grape -5.30736 5.546648 5.870242 Grape -5.326424 5.339804 6.35368 Grape -5.2255282 5.41977 5.89243 Grape -5.214655 5.3971486 6.017116 Grape -5.121703 5.478791 6.299605 Grape -5.1219583 5.3403845 6.3988237 Grape -4.954861 5.40215 6.405094 Grape -5.0856223 5.3931227 6.382147 Grape -4.9778194 5.4496856 6.390957 Grape -5.018341 5.4152226 6.3497376 Grape -4.957585 5.4509416 6.3560743 Grape -4.894224 5.8208203 5.4873943 Grape -4.788046 5.947549 5.557859 Grape -4.7715025 5.9121714 5.544645 Grape -4.9990735 5.509975 6.296083 Grape -4.76135 5.9785247 5.5350986 Grape -5.539921 5.3872085 6.345343 Grape -5.548317 5.3872285 6.345978 Grape -5.489214 5.426043 6.359877 Grape -5.542698 5.465491 6.2883368 Grape -5.2890244 5.9959493 4.696311 Grape -5.5898194 5.3376894 6.3673067 Grape -5.6000576 5.312823 6.3520007 Grape -5.262611 5.3698497 5.8482184 Grape -8.311519 6.2462273 3.1054711 Apoptosis -5.2571883 5.3717256 5.814893 Grape -3.8182075 8.658165 5.266634 Grape -3.829509 8.749477 5.28381 Grape -3.795848 8.755057 5.277985 Grape -3.7884007 8.753429 5.2830267 Grape -3.8202865 8.721801 5.1511936 Grape -8.295079 5.7422223 3.5650258 Apoptosis -3.8253026 8.746411 5.2639832 Grape -3.7910051 8.720539 5.093376 Grape -3.7826266 8.769773 5.149979 Grape -4.932283 5.3613234 5.3522625 Grape -3.916647 8.638335 5.057086 Grape -3.7787886 8.755113 5.092831 Grape -4.842789 5.304612 5.3264976 Grape -4.8990054 5.3610234 5.3840165 Grape -5.0747027 5.3432 5.490537 Grape -4.9857383 5.354191 5.5101476 Grape -4.77712 5.2624774 5.4501934 Grape -4.8101907 5.358976 5.40337 Grape -8.0507145 6.188007 3.780581 Apoptosis -4.327551 7.0868673 5.8054147 Grape -4.299099 7.127273 5.7801495 Grape -4.315353 7.098755 5.8019466 Grape -4.251504 7.159965 5.778967 Grape -4.289532 7.1259685 5.770739 Grape -5.299289 5.7877064 5.886598 Grape -4.2694764 7.1660748 5.7245097 Grape -5.3335342 5.713405 5.9260807 Grape -5.3638673 5.644779 5.9046516 Grape -5.512643 5.4800825 6.1933255 Grape -5.4054995 5.5040197 6.1628504 Grape -4.8884397 6.0075836 5.941785 Grape -4.927877 6.0525093 5.909682 Grape -4.9266357 6.0272064 5.9320254 Grape -5.006285 5.8017964 5.823222 Grape -4.8767033 6.062485 5.968526 Grape -4.988042 5.9495387 5.8753595 Grape -5.0076346 5.8137836 5.86248 Grape -5.037914 5.782873 5.872415 Grape -5.1433263 5.4902644 5.639136 Grape -4.9560556 6.0073633 5.851454 Grape -4.9305205 5.9064255 5.85149 Grape -5.0882754 5.604124 5.6432185 Grape -5.041921 5.646362 5.66627 Grape -4.0461626 4.9979706 5.443432 Polylobed -4.8932343 5.1240993 6.5502152 Polylobed -4.05009 5.061475 5.4157505 Polylobed -3.961283 5.037588 5.454572 Polylobed -4.8153706 5.136261 6.4334927 Polylobed -5.213597 7.450287 5.5114126 Grape -5.2416286 7.422001 5.538453 Grape -4.641542 7.3304315 5.3156195 Grape -5.277844 7.3357286 5.4936795 Grape -5.08501 7.5484133 5.443895 Grape -5.195093 7.439044 5.533469 Grape -5.6748643 6.6522236 5.5472302 Grape -4.1510024 7.2869477 4.8281484 Grape -5.799655 6.725188 5.4760036 Grape -5.6227884 6.6524744 5.6101766 Grape -4.106425 7.2231045 4.8221436 Grape -5.5117507 7.172526 5.467739 Grape -4.8629217 6.364321 4.453231 Grape -5.442818 6.8421025 5.6198487 Grape -5.054336 5.97269 4.264448 Grape -5.4768834 6.8600464 5.5688972 Grape -4.90353 6.2465334 4.4800105 Grape -4.5248427 7.5925426 5.233626 Grape -5.1454296 6.838261 5.6493464 Grape -5.4180665 5.898725 5.694057 Grape -5.6026444 6.167272 5.729449 Grape -4.818835 6.180318 5.2812676 Grape -4.915862 6.172935 5.3917255 Grape -5.4115434 6.4378405 5.7530127 Grape -4.885074 7.0765243 5.681602 Grape -4.742056 6.2057157 5.131875 Grape -5.386292 6.5911846 5.806344 Grape -5.3211246 6.842966 5.73835 Grape -5.578957 6.3841324 5.6126676 Grape -5.3702292 6.829494 5.7212424 Grape -5.399021 6.767662 5.723657 Grape -5.5543747 6.3984337 5.665129 Grape -5.382148 6.9265385 5.7036624 Grape -5.408478 6.9856296 5.650503 Grape -5.310158 5.949771 5.9509025 Grape -5.2813044 5.802135 6.1276574 Grape -5.339183 6.991219 5.623421 Grape -4.0543675 6.0790544 5.365855 Grape -5.3463106 5.360406 6.331311 Grape -5.267798 5.3729925 6.267297 Grape -4.1950345 6.2866726 5.452628 Grape -5.3535247 5.392917 6.2850995 Grape -5.2937684 5.3514123 6.342276 Grape -4.373639 5.415867 6.4234505 Grape -5.3258176 5.56336 6.225802 Grape -4.359615 5.399733 6.432809 Grape -5.144858 5.6494446 6.2110267 Grape -4.3567724 5.416733 6.422382 Grape -5.094091 5.7790294 6.176931 Grape -5.0493307 5.4141035 6.3751764 Grape -4.328381 5.401881 6.428654 Grape -5.065198 5.7896447 6.178065 Grape -5.1160054 5.678113 6.2544327 Grape -5.163331 5.495416 6.320815 Grape -6.4798903 6.511527 4.563122 Grape -6.3261294 6.65989 4.767865 Grape -8.37736 6.3113413 2.4540396 Apoptosis -8.388579 6.2535086 2.4219668 Apoptosis -4.9277267 5.5165424 5.3546243 Grape -4.9939847 5.401438 5.1174526 Grape -4.7640243 6.4474955 5.0839877 Grape -8.25316 6.2999234 3.0333877 Apoptosis -4.8163996 6.577471 5.0808506 Grape -8.051318 6.279918 3.0317404 Apoptosis -4.987765 5.945781 4.7779465 Grape -4.97953 6.0258856 4.7976217 Grape -7.7794266 6.514002 4.032092 Apoptosis -7.742442 6.655238 4.1501274 Apoptosis -4.936958 5.6518993 4.1784143 Grape -4.9664245 5.6028547 4.1713223 Grape -7.782552 6.6029587 4.0935726 Apoptosis -7.6786294 6.595956 4.127271 Apoptosis -4.992628 5.480905 4.0633516 Grape -5.074999 8.80131 4.1384206 Grape -5.1232295 8.78174 4.152039 Grape -5.093929 8.821883 4.072659 Grape -4.895084 5.8802795 5.724831 Grape -4.805869 5.991891 5.7737775 Grape -4.8366446 5.919721 5.6806054 Grape -4.9241176 5.1485 6.7068458 Grape -4.758642 6.003984 6.011525 Grape -4.808617 5.972007 5.701072 Grape -4.94485 5.1706157 6.678277 Grape -4.6870246 5.960502 6.0923233 Grape -4.9434447 5.1636868 6.673951 Grape -4.6098943 5.905459 6.045544 Grape -4.5648937 5.974981 6.0578284 Grape -4.9390464 5.1514916 6.686582 Grape -4.970912 5.1471677 6.662564 Grape -4.542357 6.0375943 5.947337 Grape -4.6089864 6.063587 6.0677996 Grape -4.640644 6.0597177 6.080231 Grape -4.620471 6.047061 6.0344415 Grape -4.9746194 5.1667943 6.650821 Grape -4.93581 5.1260853 6.677681 Grape -4.987771 5.186682 6.604762 Grape -4.962032 5.174623 6.6350927 Grape -4.929304 5.197946 6.611409 Grape -5.083282 5.291489 6.0383472 Grape -5.499792 5.859001 5.6995378 Grape -5.393167 5.9065156 5.6073856 Grape -5.417953 5.8735147 5.643798 Grape -6.129069 7.664071 3.2866764 Interphase -6.241322 7.794779 3.1394994 Interphase -8.269271 6.234782 2.593211 Prometaphase -6.10572 7.9313965 3.034628 Interphase -6.088174 6.7473574 2.9144044 Interphase -6.3511395 6.683481 3.0109391 Interphase -5.192845 7.2418237 2.8728416 Interphase -4.720346 7.5215178 3.1561384 Interphase -7.393913 6.41445 3.2202432 Prometaphase -4.4240775 7.8113027 2.4341555 Interphase -6.620411 6.8024874 3.2326748 Interphase -5.412718 6.531982 1.76625 Interphase -6.2733064 7.6877084 3.773304 Interphase -6.6487727 7.219416 3.8435457 Interphase -5.6098714 6.791683 2.077031 Interphase -6.154458 7.6382604 4.0308924 MetaphaseAlignment -7.5384655 6.4316716 3.4299357 MetaphaseAlignment -7.8994794 6.8639784 4.0814075 Prometaphase -4.600319 7.0798497 2.1494052 Interphase -4.3470416 7.077689 2.1967373 Interphase -4.802247 7.1043887 2.3621109 Interphase -7.676781 6.6513667 3.7906206 MetaphaseAlignment -7.694876 6.541056 3.1790795 MetaphaseAlignment -2.2376356 5.7603526 4.398957 Polylobed -2.353778 5.7151165 4.4163985 Polylobed -2.367445 5.655774 4.5859923 Polylobed -4.0664053 6.4723477 4.182008 Polylobed -6.747343 6.3995643 3.0897052 MetaphaseAlignment -3.4692588 5.271267 5.232739 Polylobed -3.3488603 5.3571053 5.198196 Polylobed -4.129888 5.706023 4.023006 Binuclear -4.780284 5.5837483 5.0789585 Binuclear -3.5271485 5.2303 5.2632723 Polylobed -3.610937 5.3268566 5.1621814 Polylobed -4.4691625 5.6503687 4.0875072 Binuclear -3.4816313 5.27063 5.2436585 Polylobed -3.5118864 5.2504754 5.2500277 Polylobed -3.476777 5.3407106 5.2027645 Polylobed -3.4740772 5.302499 5.2315364 Polylobed -3.6789613 7.078894 3.8477263 Polylobed -3.541205 5.282879 3.2701116 Polylobed -3.5089252 5.284973 3.2453856 Polylobed -2.6648793 5.2804046 3.1621642 Polylobed -3.5925004 5.2802486 3.2930312 Polylobed -3.5150099 5.3144183 3.2865846 Polylobed -2.637672 5.358482 3.147941 Polylobed -2.590655 5.3124757 3.1037912 Polylobed -2.6888802 5.2829614 3.1865935 Polylobed -7.9301414 5.7744374 3.307455 MetaphaseAlignment -3.0064971 5.6983156 4.4720798 Polylobed -2.8515904 5.9008517 4.4732237 Polylobed -2.207623 7.0912323 5.0795593 Polylobed -5.216087 6.759767 3.5312574 Artefact -2.242978 7.240793 5.1285043 Polylobed -4.985135 6.305588 2.9462345 Artefact -5.0533123 6.378965 3.012421 Artefact -2.5617955 5.4965873 5.6672573 Polylobed -2.541114 5.516424 5.668546 Polylobed -2.5718384 5.485114 5.6718326 Polylobed -2.608832 5.49991 5.5943336 Polylobed -2.5665455 5.486559 5.67033 Polylobed -2.5735962 5.5069246 5.634584 Polylobed -2.567749 5.4836144 5.654988 Polylobed -2.5803976 5.4808874 5.6578984 Polylobed -5.599987 6.519996 3.554552 Polylobed -5.398405 6.5546775 3.3974042 Polylobed -2.9515688 6.5161676 3.5616548 Polylobed -2.9239187 6.399642 3.4299586 Polylobed -3.109026 5.9436474 4.616329 Polylobed -3.0568426 5.7656436 4.669277 Polylobed -2.9513118 5.7808957 4.5277534 Polylobed -2.9908278 5.7403936 4.441052 Polylobed -3.0019739 5.670986 4.6613116 Polylobed -2.6946306 5.7425857 5.129991 Polylobed -2.6854968 5.843239 5.0600023 Polylobed -2.7083738 5.826031 5.0096917 Polylobed -2.8126943 5.9398108 4.7845488 Polylobed -2.726044 5.8451834 4.969162 Polylobed -3.3880868 5.930967 4.837434 Polylobed -3.9663048 5.547969 3.8811312 Polylobed -3.971958 5.4790645 3.7555745 Polylobed -4.903581 5.5140734 5.329231 Polylobed -3.8846374 5.9477015 3.2135367 Binuclear -3.9071543 5.935404 3.2331598 Binuclear -3.8396199 5.9342127 3.222291 Binuclear -3.8938859 5.964513 3.2110543 Binuclear -4.6524234 5.800979 6.152864 Polylobed -4.9696236 5.30429 6.4577227 Polylobed -4.6361346 5.866177 6.149377 Polylobed -4.624045 5.8460445 6.1209593 Polylobed -2.0484297 7.3720627 4.761347 Polylobed -1.8865012 7.237576 4.8744135 Polylobed -1.9179485 7.1080747 4.847251 Polylobed -3.1474946 6.2636013 4.12118 Polylobed -3.6881356 6.1128163 4.291571 Polylobed -3.8478146 5.8816295 4.3847404 Polylobed -3.8420658 5.788398 4.3237424 Polylobed -3.7762067 9.382492 2.9949512 Interphase -3.6388953 5.216147 3.9160674 Polylobed -3.5952036 5.2650704 3.9009366 Polylobed -3.2862267 5.515823 3.7891805 Polylobed -2.6267912 5.9446807 3.060791 Polylobed -2.6218915 5.955934 2.9871912 Polylobed -2.601931 5.9665484 2.985885 Polylobed -2.5948884 5.9426303 2.9956865 Polylobed -2.632556 5.9561963 3.00161 Polylobed -6.2897224 7.626293 3.2447383 Artefact -6.3213053 6.868011 3.5435243 Artefact -6.191453 6.745983 3.4490983 Artefact -2.3247108 6.894175 4.9880505 Artefact -4.261004 6.492182 4.2253594 Artefact -2.8926437 5.905043 4.475512 Polylobed -2.993716 5.9386444 4.401525 Polylobed -4.6808553 7.294785 2.2392821 Binuclear -3.917319 9.038761 3.2128408 Binuclear -2.8475177 5.3789034 5.2350397 Binuclear -2.619193 5.3766994 5.2276077 Binuclear -3.669972 5.1926017 3.954858 Binuclear -3.635779 5.191367 3.9276137 Binuclear -1.6699005 7.2496758 5.0234838 Polylobed -1.6911685 7.2034483 5.056015 Polylobed -5.2977858 6.159559 3.900134 MetaphaseAlignment -1.6741518 7.2091002 5.019078 Polylobed -1.6537763 7.23835 5.03492 Polylobed -1.750212 7.118676 5.01609 Polylobed -1.6531868 7.137208 5.047442 Polylobed -2.8671803 5.8239217 4.6331897 Binuclear -1.6974378 7.1695814 5.012462 Polylobed -2.8412013 5.840141 4.594398 Binuclear -2.8286073 5.840188 4.570851 Polylobed -2.7877238 5.8775396 4.6038957 Polylobed -2.8279328 5.8311963 4.601072 Polylobed -5.660398 5.896437 5.5369115 Polylobed -2.8335135 5.876004 4.6417675 Polylobed -2.443153 5.9503145 5.179842 Polylobed -2.4364686 6.0172057 5.2702003 Polylobed -2.4699206 6.0611286 5.387977 Polylobed -2.515528 5.973658 5.4266953 Polylobed -2.4843545 6.010808 5.4082656 Polylobed -2.3280869 7.5628185 5.0715966 Polylobed -2.486753 6.1455407 5.418871 Polylobed -2.2626538 7.49903 5.0452437 Polylobed -2.31981 7.5772963 5.0787683 Polylobed -3.9274302 6.656241 4.4337263 Polylobed -2.265408 8.349282 4.353964 Polylobed -4.2916026 6.5659556 4.294166 Polylobed -2.3142018 8.218964 4.3901978 Polylobed -1.5239803 5.47687 4.9322586 Polylobed -1.999898 7.055265 4.774024 Polylobed -2.7592254 7.8426995 4.4245706 Polylobed -2.2933679 8.398173 4.2915835 Polylobed -2.3552327 8.761842 4.2380567 Polylobed -4.202616 6.2842193 4.4282722 Polylobed -2.3486266 8.305482 4.3469334 Polylobed -1.5676382 5.467103 4.9287596 Polylobed -3.0208545 5.8011208 4.87734 Polylobed -2.8026972 7.905884 4.396874 Polylobed -5.869868 6.9693227 4.2828197 Polylobed -2.3112667 8.388641 4.342974 Polylobed -3.2698123 5.846305 4.7917128 Polylobed -1.55675 5.492901 4.9317484 Polylobed -2.8092897 7.9049115 4.389815 Polylobed -2.8067393 7.9371715 4.330481 Polylobed -1.5697687 5.4832845 4.9300475 Polylobed -2.4367688 8.4925165 4.2706003 Polylobed -2.8322144 7.9381933 4.4049597 Polylobed -2.8714764 5.9168835 4.522174 Polylobed -2.8396864 5.9339533 4.454862 Polylobed -3.2917383 6.927785 4.354456 Polylobed -4.058801 6.5898733 4.3619685 Polylobed -4.337275 7.1160984 3.938523 Polylobed -2.3726606 6.7839246 4.2104526 Polylobed -4.0037804 6.6536026 4.358438 Polylobed -2.6103265 5.6103525 5.573509 Polylobed -6.0490394 6.8002987 3.2550404 Binuclear -2.4760296 6.38646 3.8709328 Polylobed -2.5772061 5.55837 5.5397587 Polylobed -5.828743 6.625436 3.4039395 Binuclear -2.656321 6.46207 3.9529755 Polylobed -2.753406 6.2072415 4.0170565 Polylobed -2.5529583 5.5104613 5.521602 Polylobed -2.6668792 5.631786 5.2793374 Polylobed -6.1629305 7.1910424 3.4853857 Binuclear -2.5624595 5.509378 5.5050325 Polylobed -3.4812717 6.5518885 3.464792 Polylobed -2.7355402 5.512753 5.0498567 Polylobed -3.7558856 6.50859 4.1832356 Polylobed -2.9364443 6.547753 3.4776967 Polylobed -2.542607 5.530452 5.4297504 Polylobed -2.8220284 6.495913 3.4198692 Polylobed -2.8383248 6.552873 3.492716 Polylobed -3.1089907 5.7868924 4.6280866 Polylobed -2.9546976 5.774824 4.666329 Polylobed -5.257776 6.3942823 4.012306 Polylobed -3.0204327 5.687067 4.574824 Polylobed -2.343648 4.937807 5.5838146 Polylobed -2.3155305 4.916954 5.61001 Polylobed -2.33565 4.9381475 5.584427 Polylobed -2.3392885 4.928882 5.5907826 Polylobed -2.3260577 4.929529 5.5978923 Polylobed -2.329378 4.931166 5.594794 Polylobed -2.3545067 4.971842 5.5724435 Polylobed -2.835238 5.9970403 4.673283 Polylobed -2.8383906 6.111048 4.52872 Polylobed -2.9020603 6.092969 4.9014673 Polylobed -2.805154 6.2552342 4.763229 Polylobed -4.154432 6.866985 2.6055605 Hole -8.147915 6.8215823 1.8650411 Hole -5.326403 6.4467134 3.705207 Polylobed -4.8281393 5.7728295 3.7584927 Polylobed -4.8788624 5.792528 3.7747905 Polylobed -7.0799375 6.4863343 2.4430966 Hole -7.232842 6.9689174 2.856252 SmallIrregular -7.075898 6.520934 2.536034 UndefinedCondensed -8.307031 6.4205904 1.985786 UndefinedCondensed -3.6958625 4.7657385 2.4285483 Polylobed -3.6796656 4.753072 2.4194057 Polylobed -3.6863256 4.7758865 2.438911 Polylobed -3.6844022 4.773411 2.4334424 Polylobed -4.601793 5.999057 2.0408926 Elongated -4.614979 6.0620317 2.025559 Elongated -6.9803543 7.0650506 3.0243807 Hole -3.916892 5.3882623 3.5296173 Polylobed -5.5911436 6.8115788 3.4174757 Polylobed -5.4781203 6.75285 3.5115187 Polylobed -5.5390134 6.8811946 3.4047952 Polylobed -6.296686 7.165906 3.0761075 SmallIrregular -4.505119 5.9967337 2.9942038 Polylobed -4.453615 6.0321264 2.9899666 Polylobed -7.6970086 7.6624227 2.0052655 UndefinedCondensed -4.4533315 5.998549 3.025441 Polylobed -8.05729 7.255141 1.8059527 SmallIrregular -7.176901 6.5459456 2.4625514 Hole -3.6851213 4.757925 2.4369833 Polylobed -3.299113 6.795769 3.7067237 Polylobed -3.6847315 4.759964 2.4324183 Polylobed -3.683312 4.7555594 2.4317617 Polylobed -3.5993025 6.7066827 3.6051035 Polylobed -3.6780753 4.749544 2.4245546 Polylobed -3.6925855 4.7959523 2.4576745 Polylobed -3.6817346 4.7754197 2.4340827 Polylobed -7.403354 7.8114233 2.2465954 Hole -6.6944265 8.409407 2.2012541 SmallIrregular -4.6455517 6.037831 2.115902 Elongated -4.6119843 6.055232 2.0380068 Elongated -3.9075122 5.408549 3.5563037 Large -6.0447335 7.287552 3.0854282 SmallIrregular -5.12423 7.0125394 3.3564832 Polylobed -5.1444635 7.094186 3.354115 Polylobed -5.084924 7.086452 3.3712926 Polylobed -3.6833718 6.6777835 4.2115474 Polylobed -3.3914087 7.1501045 4.4397674 Polylobed -3.3776207 7.2187667 4.455356 Polylobed -3.3527918 7.2285914 4.4703918 Polylobed -7.735029 7.8664303 1.9076581 SmallIrregular -4.997916 5.646911 4.245137 Polylobed -4.978749 5.38309 4.685514 Polylobed -7.427316 6.2381225 2.7941785 Folded -7.4912143 6.208266 2.7739775 Hole -5.065316 6.8068695 2.5570514 Hole -4.807731 6.6538186 3.1153624 Hole -6.5978665 8.182844 2.5475986 SmallIrregular -5.187692 6.3617477 3.012063 Folded -7.0575905 6.4239516 2.8808718 Hole -4.2284794 6.7105265 2.72565 Folded -4.415315 5.7829285 2.62417 Folded -4.3894763 5.74809 2.5921648 Folded -6.9395742 6.926876 3.1286626 UndefinedCondensed -4.448112 5.7630997 2.5838337 Folded -7.4842978 8.122615 2.2419622 UndefinedCondensed -3.4936996 5.6950192 4.1123977 Polylobed -3.4600313 5.924517 4.064753 Polylobed -3.6299393 5.739008 4.237155 Polylobed -3.5460055 5.8261046 4.1710963 Polylobed -4.1049337 5.250065 3.9986088 Polylobed -4.164225 5.281874 3.9532955 Polylobed -7.910078 7.624493 1.840966 UndefinedCondensed -4.209679 5.2944326 3.9435213 Polylobed -7.047309 6.2120523 2.593092 Artefact -7.012328 6.22673 2.5946074 Artefact -6.1425433 6.5122232 3.0159287 Elongated -8.311145 6.408083 1.9825511 UndefinedCondensed -6.461571 6.6365757 2.853431 UndefinedCondensed -8.1233425 6.700907 1.9497166 Hole -4.060371 6.669522 2.846775 Hole -4.505788 6.000917 2.2464814 Elongated -7.82617 7.7395105 1.8859513 SmallIrregular -7.8204007 7.713308 1.8903137 SmallIrregular -5.569049 6.818987 3.3484735 Polylobed -7.814644 7.798084 1.8985863 SmallIrregular -5.5325284 6.8274474 3.419796 Polylobed -5.491705 6.837434 3.3634686 Polylobed -8.297446 6.472156 1.9494298 UndefinedCondensed -8.119438 7.124523 1.8055325 Hole -7.300269 6.82164 2.3598888 UndefinedCondensed -3.6901414 4.775341 2.485671 Polylobed -4.3991714 6.553499 4.0641665 Polylobed -5.5404477 6.287462 3.1077516 UndefinedCondensed -5.3205266 6.3391514 3.1121042 UndefinedCondensed -3.6881826 4.7847376 2.4586272 Polylobed -3.6832616 4.7817426 2.4433353 Polylobed -3.1856487 6.6553855 3.6681669 Polylobed -3.775154 6.603476 3.4480839 Polylobed -4.6531224 6.0194225 2.0648766 Elongated -3.942724 5.422243 3.523192 Large -6.233019 7.1883655 2.9680235 Polylobed -6.2190723 7.207098 2.95935 Polylobed -3.2906144 7.187058 4.122452 Polylobed -6.1179767 9.093955 2.520818 SmallIrregular -3.3288035 7.258021 4.4305696 Polylobed -3.388075 7.3307714 4.352093 Polylobed -3.3906393 7.2358294 4.4544296 Polylobed -8.045746 5.753567 3.3356366 MetaphaseAlignment -4.379364 6.439868 2.793439 Binuclear -4.220122 6.8249526 2.7591453 Binuclear -4.48099 5.485689 3.5338385 Binuclear -4.395684 5.465645 3.506357 Binuclear -4.016237 6.5662313 2.7393715 Binuclear -4.0797315 6.6329284 2.5851736 Binuclear -4.334221 5.4827304 3.3015172 Binuclear -4.7808623 6.467212 3.4407697 Binuclear -3.70299 5.359103 4.0591497 Artefact -3.5719082 7.168933 3.1429594 Binuclear -3.8103333 5.278244 3.9952583 Artefact -3.707556 5.3489428 3.923389 Artefact -3.4262965 7.419916 3.3937442 Binuclear -3.5689337 7.302778 3.4351428 Binuclear -5.9599295 7.3787737 2.7112687 Binuclear -5.965084 7.134303 2.7446241 Binuclear -2.4735801 7.818852 3.9672384 Binuclear -2.677684 7.115896 3.8652802 Binuclear -3.934234 5.5562415 4.651114 Polylobed -3.8026547 5.4874067 4.5954413 Polylobed -3.5824225 5.349085 4.1550946 Polylobed -3.5546737 5.367878 4.137595 Polylobed -3.8814478 5.4965224 4.5928135 Polylobed -3.6119611 5.380516 4.1449227 Polylobed -5.156286 5.472909 4.95396 Polylobed -5.093233 5.425064 4.8373375 Polylobed -5.060142 5.359602 4.75767 Polylobed -5.1480517 5.543744 4.7076697 Polylobed -3.1204045 7.3606873 2.8625515 Polylobed -3.0759885 7.316084 2.8715088 Polylobed -3.1084006 7.3584485 2.844822 Polylobed -3.1001246 7.3937693 2.8208191 Polylobed -3.7935975 5.1448812 4.0089517 Polylobed -3.571588 5.470309 4.9328 Polylobed -3.7814617 5.114253 4.070323 Polylobed -3.6432724 5.4702935 4.7610126 Polylobed -3.8830247 5.122303 4.0009933 Polylobed -3.5478961 5.5023794 4.8651547 Polylobed -3.123348 5.325154 5.1530976 Polylobed -4.282789 6.332004 4.474653 Polylobed -4.2287188 6.4558306 4.573995 Polylobed -2.9068155 5.665674 4.9231772 Polylobed -2.824519 5.65621 4.87585 Polylobed -3.7874494 5.2689605 4.0378866 Polylobed -3.0121732 5.49555 4.924675 Binuclear -3.8926787 5.922215 4.757151 Polylobed -2.9376416 5.5472207 4.8674607 Binuclear -3.6641402 5.24895 3.8933406 Polylobed -3.0143483 5.465522 4.970501 Binuclear -2.9571939 5.5440283 4.941678 Binuclear -4.0929484 5.202506 3.6169999 Polylobed -4.0125184 6.008143 4.8497825 Polylobed -3.9601166 5.7483068 4.912113 Polylobed -3.0052147 5.440227 4.995056 Binuclear -4.0132995 5.1368465 3.9806647 Binuclear -4.0036874 5.182781 3.938069 Binuclear -4.010497 5.4405937 3.9064467 Polylobed -4.064412 5.4607053 3.875595 Polylobed -5.1093817 5.4948616 4.982718 Polylobed -3.9883938 5.471309 3.8351533 Polylobed -4.3527107 5.3802404 4.753776 Polylobed -3.7618973 5.6298547 4.374805 Polylobed -3.8049083 5.2617335 3.895957 Polylobed -4.1735563 5.7238727 5.201572 Polylobed -4.302353 5.8911667 5.0672855 Polylobed -4.607043 5.3567214 4.8408093 Polylobed -3.74014 5.300122 4.0226154 Polylobed -3.9009383 6.693254 4.620503 Polylobed -3.6153815 5.8125553 4.3747497 Polylobed -6.33982 6.7643666 3.4944272 Artefact -3.562125 5.5201573 4.0273933 Polylobed -6.2614098 6.6799493 3.3681111 Artefact -3.5771267 5.542782 4.126693 Polylobed -3.453347 5.547958 4.0495334 Polylobed -6.4949784 6.8262997 3.3645933 Artefact -3.7693467 5.1465416 4.024532 Polylobed -3.7406511 5.2185397 4.0045404 Polylobed -3.7734041 5.2030087 3.9530635 Polylobed -4.274867 6.4527326 4.4335666 Polylobed -4.3289304 6.4177914 4.361481 Polylobed -2.790106 5.679949 4.90754 Polylobed -2.789116 5.610328 4.9991198 Polylobed -2.8237035 5.68021 4.8060455 Polylobed -3.9433208 6.798709 3.9435825 Polylobed -3.6326966 7.162082 4.369266 Polylobed -3.8264635 6.9062257 4.4249396 Polylobed -4.019601 5.7603807 3.4137425 Binuclear -4.253399 5.6689725 3.32942 Binuclear -4.044963 5.84529 3.4054556 Binuclear -3.095319 8.013778 3.9427922 Binuclear -3.3446593 6.700504 4.1758947 Binuclear -4.7990646 6.650228 3.5017722 Binuclear -3.1405177 6.6697187 4.2143083 Binuclear -4.0774703 6.0264034 4.02237 Binuclear -4.1056504 6.278134 3.2237034 Binuclear -3.9889436 6.1342196 3.7153585 Binuclear -3.8923583 6.023234 3.9264548 Binuclear -4.280149 6.067604 3.1630764 Binuclear -3.2423646 7.011174 4.0182257 Binuclear -4.1604795 5.988089 3.5507596 Binuclear -3.403904 7.2471476 3.1700857 Binuclear -3.9512122 7.021185 2.346611 Binuclear -3.1394413 7.898938 3.5081086 Binuclear -3.703952 6.24339 4.020503 Binuclear -3.5566392 7.059879 3.085768 Binuclear -5.0257635 7.389606 3.3616064 Binuclear -3.2824297 7.4101543 2.9175806 Binuclear -3.013351 7.229209 2.9257286 Binuclear -3.3463132 7.193099 3.0556843 Binuclear -3.0370042 7.21418 2.940863 Binuclear -4.152203 5.2522364 3.5300877 Binuclear -4.2905393 5.3258247 3.4295988 Binuclear -6.096363 6.583012 2.9246175 Binuclear -4.450911 5.3149514 3.5854406 Binuclear -6.4444156 6.650217 2.9754279 Binuclear -4.5155897 5.2906923 3.6622882 Binuclear -6.240022 6.381826 2.5078776 Binuclear -6.2172885 6.3809795 2.4335601 Binuclear -3.5249858 5.5598454 4.8909364 Binuclear -3.7305372 7.308473 4.4333014 Binuclear -3.5541935 5.331797 4.102236 Binuclear -2.4933443 7.6881866 4.1559386 Binuclear -3.5750287 5.5131493 4.0692873 Binuclear -3.8918343 5.4629025 3.924135 Polylobed -4.16154 6.4806952 2.5890622 Binuclear -4.2106824 6.684907 3.7020996 Binuclear -3.2325222 6.611222 4.007773 Polylobed -4.2049932 6.478269 2.5742958 Binuclear -4.376114 6.5681567 4.015165 Binuclear -3.3423884 7.408093 3.2072687 Binuclear -4.363453 5.363211 3.3545063 Polylobed -4.1465273 6.459138 2.5648363 Binuclear -3.1713915 7.4800897 3.0257194 Binuclear -4.1761723 6.4808455 2.5581946 Binuclear -4.4822273 5.3313255 3.848489 Binuclear -4.432201 5.2212963 4.1208725 Binuclear -4.219859 5.251097 4.0306497 Binuclear -3.2378407 7.4222918 2.8373728 Binuclear -3.134426 7.4412384 2.9211168 Binuclear -4.2599053 7.653229 3.644013 Binuclear -4.827142 8.0638685 2.9174516 Binuclear -3.7728336 6.094876 4.062378 Binuclear -3.8396816 6.666005 3.4716773 Binuclear -4.321952 5.8278337 3.4257956 Binuclear -4.3378396 5.5153227 3.3820174 Binuclear -4.0059137 6.447505 3.1314929 Binuclear -3.7862659 6.5226436 3.3300648 Binuclear -7.4879675 6.1830397 3.623364 MetaphaseAlignment -3.2932262 8.032645 4.0412064 Polylobed -3.250755 8.095117 4.0428953 Polylobed -3.2504659 8.163829 3.9784927 Polylobed -3.2283728 8.09939 3.9611328 Polylobed -6.2384567 8.119167 3.727276 MetaphaseAlignment -2.8098636 8.49685 4.2728915 Polylobed -2.8638206 8.46178 4.3609085 Polylobed -2.8298604 8.310075 4.4270005 Polylobed -3.4837668 6.533377 3.7329617 Polylobed -3.5497913 6.6923566 3.5682893 Polylobed -3.0586631 7.8120584 4.4261074 Polylobed -3.6136322 6.4558034 3.5555906 Polylobed -3.2782364 7.399033 4.551104 Polylobed -3.2728262 7.3630867 4.5382175 Polylobed -3.340109 7.3232584 4.524399 Polylobed -3.2721481 7.492913 4.4908934 Polylobed -7.707349 5.9590964 3.333275 MetaphaseAlignment -5.017692 5.6939936 5.430661 MetaphaseAlignment -4.998982 5.673321 5.241843 MetaphaseAlignment -7.73691 5.9296136 3.3161478 MetaphaseAlignment -8.21383 5.7335463 3.4898884 MetaphaseAlignment -6.970039 6.442078 2.9900768 MetaphaseAlignment -4.816573 7.2593737 2.3857963 Interphase -7.0557613 6.6257687 2.4933293 Interphase -5.4506345 6.7961464 2.3356988 Interphase -7.5777597 6.168924 2.6936662 Interphase -5.6043496 6.963257 2.480091 Interphase -3.2293725 5.6173806 4.3674 Large -3.7454066 5.933046 3.516252 Large -7.3087955 6.377373 2.765956 Interphase -5.215091 6.8262234 2.3486953 Interphase -5.4692364 6.5830903 2.641028 Binuclear -5.3495207 6.4858575 2.6616619 Binuclear -6.5980773 6.7392616 2.9111567 Interphase -5.181168 5.506649 6.189112 Polylobed -5.138699 5.4447002 6.3448615 Polylobed -5.1316442 5.377541 6.404011 Polylobed -3.53831 4.448598 4.9961395 Polylobed -3.515794 4.439365 4.9639406 Polylobed -3.515351 4.439653 4.972289 Polylobed -3.5197604 4.4394712 4.9703183 Polylobed -3.4927258 4.445504 5.004473 Polylobed -1.9449329 5.8027906 4.9793324 Polylobed -1.9796748 5.882568 4.958964 Polylobed -1.9836341 5.806106 4.961338 Polylobed -3.3672466 8.284536 4.500102 Artefact -3.410579 8.3521 4.530745 Artefact -3.4427462 8.23287 4.4749327 Artefact -3.4216213 8.365543 4.501203 Artefact -4.0717936 7.3598413 2.709645 Polylobed -4.0196624 7.0448847 2.8859246 Polylobed -4.0343723 7.8850827 4.320271 Polylobed -4.0422893 7.8704524 4.335192 Polylobed -4.092276 7.879662 4.2761917 Polylobed -5.688787 9.209371 2.1995351 Polylobed -5.7085514 9.232393 2.1498096 Polylobed -5.6204934 9.2354965 2.268358 Polylobed -5.734866 9.245316 2.1660368 Polylobed -6.519062 8.752161 2.3593802 Binuclear -6.632182 8.669997 2.389675 Binuclear -6.2377124 6.9329414 3.3196244 Polylobed -5.796501 9.108842 2.2401168 Polylobed -5.8210917 7.003059 3.4190836 Polylobed -5.8853345 6.849008 3.4879632 Polylobed -6.607383 6.911779 2.616744 Binuclear -6.298576 6.7155347 3.1341822 Binuclear -6.863825 7.5995135 3.9327574 Artefact -4.496286 8.035207 2.7368627 Polylobed -6.774071 7.6505165 3.9769435 Artefact -4.4787245 8.098399 2.5880017 Polylobed -6.6928596 7.3281136 4.1027064 Artefact -4.6419454 8.115651 2.639019 Polylobed -6.745923 7.0046663 4.0087376 Apoptosis -6.920633 7.519601 3.8329709 Apoptosis -6.9122005 8.540995 2.563943 Artefact -4.7894545 6.6739554 4.0301194 Binuclear -5.059353 6.694662 3.745429 Binuclear -4.2621756 8.293403 2.5397623 Polylobed -4.4399424 8.102693 2.6513128 Polylobed -5.4872227 7.1699686 2.7646177 Artefact -4.247417 8.333745 2.4533443 Polylobed -4.7937183 7.589068 2.700774 Polylobed -5.6330743 7.35551 2.6811192 Artefact -5.636719 8.52196 2.5126605 Artefact -4.9340887 9.470523 2.8170948 Binuclear -5.0681777 9.446199 2.7480755 Binuclear -6.4576507 9.510156 1.6026132 Artefact -6.472026 9.444396 1.6595479 Artefact -6.4701014 9.518164 1.5906289 Artefact -6.4515977 9.484102 1.6195337 Artefact -6.4603715 9.504226 1.6118454 Artefact -5.7691693 9.132226 2.3265586 Binuclear -5.8021812 9.14431 2.347626 Binuclear -6.4664183 9.50657 1.6043088 Artefact -6.45678 9.488001 1.6185707 Artefact -6.45943 9.5096245 1.6056926 Artefact -4.5606303 7.679319 2.4908538 Polylobed -6.5270905 7.0238476 4.410918 Artefact -4.5126033 7.818907 2.5347302 Polylobed -6.545819 7.0922046 4.4025764 Artefact -4.3350353 7.896093 2.5688853 Polylobed -4.576607 7.9554067 2.547503 Polylobed -6.501125 7.105174 4.421851 Artefact -6.4820604 7.1177 4.39614 Artefact -4.5905666 6.9166517 2.777041 Binuclear -4.4854016 6.8977904 2.8047543 Binuclear -7.3219256 7.2345967 2.5448022 Apoptosis -7.367155 7.869031 2.4018855 Apoptosis -7.3139987 7.3647776 2.6091826 Apoptosis -5.94859 9.590647 2.2401795 Binuclear -5.871946 9.49542 2.2669628 Binuclear -4.623678 8.056182 2.7727938 Polylobed -4.394527 8.101172 2.715964 Polylobed -5.105038 6.900837 3.2598958 Polylobed -5.673737 7.5655293 2.6882412 Artefact -5.4358783 7.6216 2.67764 Artefact -5.728652 7.161183 3.3538928 Polylobed -5.6438847 7.574513 2.6704314 Artefact -5.1461716 9.273388 2.960786 Artefact -5.718458 9.353309 2.6608305 Artefact -7.045048 6.6926074 2.5170743 Apoptosis -5.2268815 7.61008 2.673207 Artefact -5.2294736 7.656693 2.6566112 Artefact -5.154208 7.676884 2.6715784 Artefact -5.278157 7.7144933 2.6379862 Artefact -5.3109193 7.7357407 2.6603048 Artefact -5.1051803 9.429395 2.7194035 Binuclear -4.8603196 9.407026 2.8435519 Binuclear -7.1348586 7.6726074 2.5046124 Apoptosis -5.692995 9.14429 2.2209065 Binuclear -5.6686482 9.157105 2.201416 Binuclear -5.92566 7.067043 3.4305458 Polylobed -5.665999 7.1088357 3.4251726 Polylobed -6.066409 6.886156 3.453692 Polylobed -4.792983 5.81887 3.7540443 Polylobed -6.149901 6.9035516 3.4228373 Polylobed -6.880432 6.855354 2.5332358 Binuclear -6.931012 6.8330493 2.4649327 Binuclear -6.651074 6.864314 2.2341256 Binuclear -6.838805 7.0023084 2.2128513 Binuclear -6.9758005 8.31359 2.3864236 Binuclear -6.0298505 8.98554 2.215837 Binuclear -5.069705 8.838698 2.4102085 Binuclear -5.3973203 8.619206 2.4387536 Binuclear -6.2113366 9.450488 2.278255 UndefinedCondensed -4.0995817 8.355096 2.3450441 Binuclear -4.1137686 8.282125 2.3303795 Binuclear -5.8629975 6.4479637 2.4388561 Binuclear -5.9989557 6.407398 2.4191449 Binuclear -6.274874 6.563568 2.4207091 Artefact -6.471766 6.8740406 2.5548866 Artefact -5.2585077 7.907459 2.6245189 Binuclear -5.2540817 7.916639 2.5766046 Binuclear -4.029818 7.339923 2.4511013 Interphase -4.3712845 7.073021 2.4868371 Interphase -4.1935763 7.4947553 2.3410006 Interphase -5.440787 7.2855825 2.1124883 Interphase -7.878975 6.6895556 3.2422059 Prometaphase -8.614442 6.0181375 2.2525997 Apoptosis -8.22975 6.2753096 4.130998 Prometaphase -8.584068 6.0773106 2.2047262 Apoptosis -8.211775 6.202801 3.531165 Apoptosis -6.2375374 7.8473077 3.1572237 Interphase -8.543538 6.090207 2.3446743 Interphase -6.3498254 7.59491 3.4462333 Interphase -6.2065887 7.850795 3.4835057 Interphase -6.84152 6.6804357 3.22424 Interphase -4.19318 7.8043413 2.4480693 Interphase -6.35143 6.989565 2.9410381 Interphase -5.509059 8.683914 2.873477 Interphase -6.320227 8.087491 3.111129 Interphase -5.396072 7.591934 4.2063184 Binuclear -5.407418 7.5443254 4.2687473 Binuclear -7.249498 6.9604344 3.8511775 Apoptosis -5.9016137 6.604296 2.8392587 Binuclear -6.035435 6.570873 2.9521053 Binuclear -7.8695574 6.753187 4.1437936 Prometaphase -8.133964 6.651967 4.0811224 Prometaphase -4.685308 6.100663 5.6872973 Polylobed -4.4689264 6.011613 5.593123 Polylobed -3.9450712 5.770142 5.5099344 Polylobed -7.8440332 6.710818 3.3675187 Prometaphase -8.586425 5.925524 2.447845 Apoptosis -8.307818 6.4790597 2.3429146 Apoptosis -4.036505 7.331849 4.8267975 Polylobed -4.1634636 7.102981 4.8724637 Polylobed -4.254959 6.6904564 4.1032386 Polylobed -5.5790744 6.7882 2.075424 Interphase -4.9461536 5.4352655 4.155784 Polylobed -4.985928 5.930605 2.807149 Large -4.740647 5.1523385 3.9668083 Polylobed -6.0434136 6.467638 1.9616544 Interphase -6.739913 6.783256 4.416139 Prometaphase -7.401845 8.008937 2.724046 Prometaphase -6.4623857 6.559188 3.2870045 Large -8.343021 6.179613 2.310335 Prometaphase -8.177496 6.5563145 3.7572105 Prometaphase -8.26999 6.4659243 3.761966 Prometaphase -9.078966 5.88975 3.7723815 Prometaphase -8.698966 6.049563 3.1595688 Apoptosis -8.755717 6.033543 2.9991899 Apoptosis -6.0209723 5.9298897 2.4221866 Elongated -4.963771 8.447067 2.5430224 Interphase -7.5981936 6.1311936 2.4809203 Interphase -6.3194046 7.8469086 2.9057765 Interphase -8.113214 6.4046707 2.5852344 Apoptosis -8.154391 6.4053245 3.0182629 Prometaphase -5.3370476 6.394607 5.240012 Apoptosis -8.578837 6.0059166 2.3075817 Apoptosis -8.548902 6.050993 2.3389955 Apoptosis -8.570514 5.9966464 2.3277352 Apoptosis -8.589896 6.046902 2.3232472 Apoptosis -6.286708 7.596868 3.2100632 Interphase -5.7695155 6.8042603 3.2251093 Interphase -3.908705 7.2169685 3.7060456 Polylobed -3.8949866 6.2473793 3.663953 Polylobed -4.0820827 6.944344 3.683834 Polylobed -4.4472213 6.0980706 3.4823408 Polylobed -8.467468 5.734785 3.5627408 Prometaphase -8.601401 5.946068 3.532399 Prometaphase -8.094072 6.1413927 2.8359516 Prometaphase -5.6621957 6.73203 2.0154736 Interphase -6.402736 6.448695 1.9593153 Interphase -5.778207 6.8018503 2.0945616 Interphase -4.6706376 6.721469 2.0978456 Interphase -4.6651936 6.755241 2.0977738 Interphase -5.198858 6.8744173 2.5857232 Interphase -5.1047835 6.9106 2.4678817 Interphase -7.5337896 6.6612916 3.41607 Prometaphase -5.822893 7.0798435 2.3276434 Interphase -6.069675 7.4768896 2.7702727 Interphase -8.651769 5.947749 2.4136763 Prometaphase -5.351709 6.526142 1.8341998 Interphase -5.36724 6.541335 1.8247548 Interphase -4.5303135 5.828399 2.6090965 Elongated -5.109165 6.755659 2.2625272 Interphase -7.521231 6.24242 3.0680254 Prometaphase -5.4359837 6.7624393 2.3681507 Interphase -2.861583 7.8334136 3.019268 Interphase -8.965865 5.9508452 3.6508503 Prometaphase -2.8147929 7.8674407 3.1152973 Interphase -5.2794676 6.8556776 2.3894982 Interphase -5.126691 8.356258 2.389266 Interphase -2.7996514 7.8635244 3.1049926 Interphase -8.268493 5.885234 3.103775 Apoptosis -5.9164805 8.153176 3.487097 Interphase -5.532501 6.9539485 2.217165 Interphase -2.9586458 9.578081 3.3997984 Interphase -7.466895 6.368983 3.191724 Prometaphase -6.1664133 6.4816885 1.9609103 Interphase -8.249388 6.1367345 3.6955252 Prometaphase -7.7295504 6.6152024 4.1824527 Prometaphase -8.895451 6.1261253 3.820272 Prometaphase -8.350349 6.078422 3.7616687 Prometaphase -8.71158 5.958204 2.616558 Prometaphase -4.3108716 7.590629 2.357477 Interphase -8.897022 5.701396 3.628342 Prometaphase -7.5227623 6.6966667 3.7972884 Prometaphase -8.988799 5.911186 3.6089118 Prometaphase -5.3860517 7.837121 3.681802 Interphase -9.006216 5.970497 3.7173963 Prometaphase -7.963661 6.6540885 4.0529027 Prometaphase -8.171909 5.7458396 3.5628278 Prometaphase -5.427872 6.684731 2.173854 Interphase -4.493464 8.473452 3.4223287 Interphase -3.4835773 8.712553 3.1332278 Interphase -6.033072 7.756726 2.9764812 Interphase -3.2309642 6.6967516 3.589733 Polylobed -4.916065 5.552154 3.9779017 Polylobed -7.4131722 6.0651436 3.2918868 Interphase -7.2087836 6.5261803 3.2528 Prometaphase -8.384275 6.2546945 2.5732982 Prometaphase -5.4118013 6.5116897 1.791798 Interphase -5.461595 6.5081406 1.8196064 Interphase -5.99709 7.8150277 4.038656 Prometaphase -6.2893705 7.8125424 3.0797143 Interphase -6.2576666 7.713408 3.009742 Interphase -4.9364057 7.672574 2.3911946 Interphase -8.154314 6.669338 4.049521 Prometaphase -5.4729958 6.584168 2.0273345 Interphase -8.825093 6.161963 3.7333634 Prometaphase -5.6633863 6.913797 2.3751075 Interphase -9.00259 5.8627725 3.7047355 Prometaphase -8.901748 5.761353 3.6136656 Prometaphase -8.290973 6.4148865 3.4961774 Prometaphase -8.349077 6.3788924 3.3596778 Prometaphase -8.685803 6.116279 3.7717168 Prometaphase -8.248597 6.0836 3.6181848 Prometaphase -9.046975 5.916644 3.6946487 Prometaphase -6.087677 7.1718016 3.5732021 Interphase -9.053868 5.9135013 3.7001426 Prometaphase -5.369493 6.637925 2.2372437 Interphase -4.484416 8.623432 3.4469693 Interphase -3.4262035 8.692458 3.1876738 Interphase -7.5053997 6.4702525 3.7295995 Prometaphase -5.4566965 6.492728 1.7893288 Interphase -5.437194 6.5089564 1.7936796 Interphase -7.668689 6.9044237 3.936166 Prometaphase -4.2691584 8.523524 2.9015892 Interphase -4.457394 7.906287 2.5896912 Interphase -4.5622315 7.8220663 2.462576 Interphase -6.0504475 7.569424 2.4951062 Interphase -8.324378 6.434268 2.3634467 Apoptosis -7.3953714 8.018936 2.552423 Apoptosis -8.340699 6.4264293 2.3286567 Apoptosis -7.4262447 7.942673 2.519525 Apoptosis -7.3021793 8.0894375 2.584375 Apoptosis -7.3708067 8.007256 2.7247927 Apoptosis -7.3616776 8.012454 2.5975559 Apoptosis -7.357493 8.005792 2.6007621 Apoptosis -7.058213 7.9386535 3.6167023 Apoptosis -5.6226254 9.476607 2.409975 Interphase -4.503038 9.569011 2.4647346 Interphase -7.0705 8.007347 3.5061648 Apoptosis -4.311653 9.448991 2.501479 Interphase -7.100739 6.2460194 2.8527882 Apoptosis -7.4580193 6.209743 2.7164152 Apoptosis -5.7267027 9.684467 2.2628958 Interphase -6.6787143 8.534969 2.4715245 Interphase -4.2285233 8.710659 2.281684 Interphase -4.170539 8.879938 2.399443 Interphase -4.7753916 8.847685 2.537074 Interphase -8.26967 6.389996 2.483827 Apoptosis -4.095884 9.27207 2.7846875 Interphase -8.190747 6.479316 2.5535111 Apoptosis -8.390834 6.270794 2.3614984 Apoptosis -6.2248316 7.80413 3.1900048 Interphase -8.326207 6.289879 2.385374 Apoptosis -6.9188833 6.905336 2.1316302 Interphase -5.2987328 9.612377 2.373095 Binuclear -5.5590324 9.795374 2.3312573 Binuclear -5.1139817 7.033106 2.8740869 Binuclear -5.4300427 6.337538 3.0787046 Binuclear -3.5598676 9.694363 2.9605145 Interphase -6.5316763 6.510331 2.4650667 Interphase -7.08195 7.9287415 3.0142133 Apoptosis -6.1131587 8.080222 3.0312579 Interphase -5.457888 9.389515 2.3748088 Interphase -4.1745887 9.5611 2.5173643 Interphase -5.453932 7.2391496 5.3341804 Polylobed -4.1921463 8.452436 2.5829456 Interphase -4.7423487 6.6172857 5.4441423 Polylobed -4.755116 6.590435 5.446656 Polylobed -4.3185544 8.0063305 2.4009697 Interphase -3.7864506 9.37105 2.8435318 Interphase -4.707562 6.580265 5.4610076 Polylobed -8.044771 6.2733684 2.7857685 Apoptosis -4.7107306 6.588436 5.439997 Polylobed -7.4735475 6.861818 2.9891055 Apoptosis -4.194338 9.572986 2.5548651 Interphase -3.8222265 7.390728 2.5914097 Interphase -4.891716 9.657802 2.3727646 Interphase -7.1083436 6.8405766 3.146647 Apoptosis -4.86252 7.9001794 2.3919885 Interphase -4.812466 7.679702 2.4049869 Interphase -4.94956 7.4336314 2.5787086 Binuclear -4.7726755 7.31216 2.6942325 Binuclear -5.8686376 6.3116183 2.5896835 Binuclear -5.763752 6.272419 2.5455186 Binuclear -3.9835708 9.33538 2.499314 Interphase -4.0969634 8.822995 2.3649857 Interphase -4.0333023 9.305741 2.4965558 Interphase -4.675245 8.954568 2.5400085 Interphase -4.0096154 9.512718 2.5429263 Interphase -4.0945396 9.545621 2.5234966 Interphase -1.9957837 5.176407 2.761082 Polylobed -1.9656916 5.164729 2.7424617 Polylobed -1.9764671 5.1612406 2.747368 Polylobed -1.9995196 5.1580467 2.7617702 Polylobed -1.9673554 5.184831 2.7360265 Polylobed -7.1945395 5.9888496 3.308569 Apoptosis -7.1296635 6.0824513 3.2613266 Apoptosis -3.7968981 8.425771 3.2611756 Polylobed -3.8301706 8.490591 3.284583 Polylobed -3.8621643 8.51544 3.2691543 Polylobed -4.793227 6.471893 3.9432106 Apoptosis -3.851686 8.3656 3.2392833 Polylobed -5.056836 6.531406 3.491518 Polylobed -5.2905397 6.570443 3.3292272 Polylobed -5.302167 6.5919003 3.3300905 Polylobed -8.019638 6.2535205 3.663647 Apoptosis -5.4122567 6.555252 3.164715 Polylobed -8.354671 6.2141695 2.1776624 Apoptosis -3.841832 6.3316917 3.078892 Polylobed -4.2288046 5.3594546 3.410932 Polylobed -7.9686136 6.015349 3.690465 Apoptosis -5.696325 5.97931 3.926484 Polylobed -3.9241395 9.098977 3.801181 Polylobed -8.093908 5.9343767 3.4860716 Apoptosis -4.037626 9.147681 3.7279675 Polylobed -4.0850124 9.033269 3.873335 Polylobed -4.5796504 8.940097 3.8641362 Polylobed -4.742884 8.908122 3.8667057 Polylobed -7.411003 6.252076 2.7874312 Artefact -5.613206 8.5819435 3.7869456 Apoptosis -8.665239 5.914174 2.31848 Apoptosis -5.717043 5.6282973 2.8864138 Apoptosis -5.706693 5.6015816 2.9143317 Apoptosis -5.759798 5.591223 2.9123273 Apoptosis -5.6808968 5.6268177 2.9286327 Apoptosis -8.612199 6.004225 2.4686024 Apoptosis -6.893943 6.4518113 3.3640547 Apoptosis -6.804145 6.4655213 3.3238347 Apoptosis -7.7555213 6.4366455 3.1792047 Apoptosis -5.366945 6.37368 3.9498732 Polylobed -3.6929512 7.167558 4.137546 Polylobed -3.7410698 7.0250735 4.184476 Polylobed -2.9203234 4.2169046 5.804324 Polylobed -2.8847237 4.179021 5.8410907 Polylobed -2.917918 4.2153664 5.804661 Polylobed -2.9163043 4.2089567 5.8067594 Polylobed -2.909475 4.2133374 5.8112297 Polylobed -2.9225972 4.224478 5.7971625 Polylobed -6.2372503 6.325118 3.4151814 Metaphase -6.2567835 6.418186 3.4022892 Metaphase -7.433887 5.7555842 3.1444292 Polylobed -6.2809443 6.245084 3.9024477 Polylobed -2.9793568 8.385214 3.6879187 Polylobed -2.891858 8.545456 3.5755625 Polylobed -2.8824816 8.862301 3.4963603 Polylobed -2.902654 8.911748 3.484931 Polylobed -2.5196283 4.8332353 3.2757921 Polylobed -2.543422 4.856441 3.2641058 Polylobed -2.5551162 4.857089 3.2881587 Polylobed -2.9533925 7.4395037 2.9672818 Polylobed -2.977852 7.3816266 2.955965 Polylobed -5.60386 5.6553807 2.7945845 Polylobed -5.579622 5.629743 2.8269358 Polylobed -3.7205243 8.25543 3.2692266 Polylobed -3.566471 7.6960087 3.2106643 Polylobed -3.6838095 7.6141596 3.272617 Polylobed -2.334484 6.034408 3.0940764 Polylobed -2.329491 6.024099 3.073514 Polylobed -2.416651 6.091035 3.1218104 Polylobed -2.3334804 5.0797668 2.9584472 Polylobed -2.295373 5.052704 2.9338095 Polylobed -2.349753 5.1012588 2.9597313 Polylobed -2.3096604 5.0912623 2.9323351 Polylobed -2.3318655 5.1103024 2.9561331 Polylobed -2.341336 5.0775704 2.9567854 Polylobed -7.4339633 5.968571 3.1549535 Apoptosis -5.2532206 5.5872936 3.0284817 Artefact -3.6120324 8.170487 3.28113 Binuclear -3.4660435 8.3176565 3.2490427 Binuclear -3.4734132 8.306978 3.2936447 Binuclear -3.9847467 7.4698915 3.5594463 Interphase -5.604833 7.8749456 3.665448 Artefact -7.1854568 6.029024 3.2704473 Apoptosis -4.2909126 5.284393 3.410986 Polylobed -7.1444364 5.8356886 3.3585892 Apoptosis -5.512077 6.03547 3.9326003 Polylobed -5.5146546 6.008379 4.049367 Polylobed -7.367406 5.820867 3.2901535 Apoptosis -7.2576737 6.3418727 2.7907956 Artefact -7.3329043 6.385402 2.8041782 Artefact -2.3339665 4.9007964 3.0669823 Polylobed -2.3702154 4.9265094 3.0975606 Polylobed -2.3770666 4.922965 3.1327374 Polylobed -2.3721805 4.935096 3.0910695 Polylobed -7.1429067 6.6388903 3.2095752 Apoptosis -5.772609 6.4778624 4.815234 Polylobed -5.7111616 6.3888397 5.0995274 Polylobed -5.7678127 6.446353 4.873142 Polylobed -3.829771 5.9797463 5.4213924 Polylobed -4.01132 6.262558 3.8644164 Polylobed -4.3248096 5.259427 3.488413 Polylobed -4.3031716 5.2534 3.5076373 Polylobed -4.316707 5.2455025 3.5054271 Polylobed -8.643096 5.724958 3.2351847 Apoptosis -2.2930365 5.052376 2.926468 Polylobed -2.298388 5.059951 2.925592 Polylobed -2.3030078 5.0508676 2.936298 Polylobed -2.2907653 5.045435 2.9352653 Polylobed -2.2931173 5.053715 2.9275959 Polylobed -6.892036 6.627761 3.282409 Apoptosis -3.1023376 5.838984 3.2049544 Polylobed -7.457137 5.888899 3.2801354 Apoptosis -3.1835017 5.796703 3.3428266 Polylobed -3.2044463 5.8164806 3.279408 Polylobed -7.199162 5.8836465 3.4692435 Apoptosis -5.6992455 5.623486 2.9825196 Apoptosis -7.989104 5.9373555 3.423216 Apoptosis -6.2146053 7.460569 3.361235 Artefact -2.4400892 4.809594 3.2301242 Polylobed -2.4405265 4.8214607 3.2116942 Polylobed -2.4452107 4.805659 3.219606 Polylobed -2.443208 4.814034 3.2169693 Polylobed -5.6774735 6.465456 5.0092764 Artefact -5.672031 6.4867067 5.044231 Artefact -5.663789 6.4175425 5.0683475 Artefact -5.8032107 6.38028 4.987991 Artefact -3.0292196 5.1605735 3.1510494 Polylobed -3.0230353 5.1618233 3.1560416 Polylobed -3.02378 5.1603823 3.1570225 Polylobed -2.8801277 6.9722037 3.393387 Polylobed -2.9945517 5.145586 3.1391077 Polylobed -2.9119482 6.9997787 3.3417275 Polylobed -2.809132 6.8948803 3.3552144 Polylobed -2.8778248 7.0393558 3.3318286 Polylobed -2.9090672 6.9818864 3.3471153 Polylobed -2.9710128 4.290069 5.766501 Polylobed -2.9528956 4.2739577 5.772505 Polylobed -2.9492805 4.268588 5.777654 Polylobed -2.9483197 4.311129 5.735777 Polylobed -2.954532 4.2773166 5.7675085 Polylobed -2.977994 4.3023205 5.7508416 Polylobed -3.979275 5.7588305 3.6923501 Polylobed -3.9620047 6.0190377 3.7292721 Polylobed -4.0072293 6.058259 3.757913 Polylobed -3.9218094 5.770681 3.604924 Polylobed -3.6939065 5.7023234 2.8346238 Elongated -3.7102947 5.765216 2.8592737 Elongated -3.2768054 5.521907 3.9493003 Polylobed -3.283473 5.56331 3.9314084 Polylobed -2.9186504 7.4477506 2.9612775 Polylobed -2.9288626 7.430379 2.9326832 Polylobed -2.9000068 7.4527764 2.9378002 Polylobed -2.2836897 6.0079045 3.0563536 Polylobed -2.255505 6.087282 3.1485155 Polylobed -2.3156755 6.0028644 3.061463 Polylobed -2.3153448 6.0041623 3.0831914 Polylobed -2.3564208 6.0046067 3.0990214 Polylobed -2.339811 6.0276384 3.0875134 Polylobed -5.746722 6.441873 1.9659781 Large -5.408206 6.297004 2.1664686 Large -6.0950336 7.2282825 2.8310523 Large -5.236561 6.1718197 2.457224 Large -3.9451272 6.2188582 4.2447267 Polylobed -3.6880968 6.340571 4.083305 Polylobed -4.222713 6.492819 4.141082 Polylobed -3.7008455 6.5569005 4.1534925 Polylobed -3.468879 7.7763934 4.346556 Polylobed -5.26084 5.4981966 3.9095993 Polylobed -5.248724 5.544726 3.9034286 Polylobed -5.2328243 5.4825974 3.927204 Polylobed -5.232272 5.495255 3.9009683 Polylobed -5.2330503 5.487312 3.9256618 Polylobed -7.229582 8.014287 3.362539 Prometaphase -7.4300346 7.035831 2.9783146 Prometaphase -7.3400216 8.052692 3.238499 Prometaphase -7.154987 6.891676 3.6669133 Prometaphase -7.509389 7.0392694 2.8686755 Prometaphase -3.6025648 6.394766 3.707149 Polylobed -3.6045048 6.5246944 3.6756067 Polylobed -3.740617 6.3945794 3.688693 Polylobed -3.6357546 6.478051 3.5618873 Polylobed -3.7036242 6.3969975 3.7033882 Polylobed -4.893392 5.017757 4.3350587 Polylobed -4.909533 5.0209394 4.2929606 Polylobed -4.886327 5.04866 4.251965 Polylobed -4.836929 5.0674815 4.238354 Polylobed -3.8444765 6.940546 3.2414715 Polylobed -3.7115884 7.0571275 3.0735164 Polylobed -3.5815778 7.1619534 2.9348998 Polylobed -8.439669 6.1994267 3.3941422 Prometaphase -8.378948 6.3418765 3.5149457 Prometaphase -6.5651736 8.342262 3.6279418 Prometaphase -7.3663697 8.140419 2.9994152 Prometaphase -7.4039345 8.148052 3.0549812 Prometaphase -7.7641225 6.6487026 2.9579256 Prometaphase -7.074664 7.8524227 3.3442516 Prometaphase -7.191871 6.9259195 3.2653768 Prometaphase -7.5005493 6.713414 3.7120712 Prometaphase -7.6505313 7.012816 3.1531656 Prometaphase -3.6151485 6.1732965 3.8956707 Polylobed -3.8528621 6.755757 5.1469107 Polylobed -3.8960924 6.7245607 5.0886264 Polylobed -3.9664347 6.732276 5.0342193 Polylobed -5.2016406 5.6002016 3.931973 Polylobed -4.979628 5.447074 3.8616138 Polylobed -5.2405367 5.543628 3.8880224 Polylobed -5.0936103 5.396518 3.864297 Polylobed -5.2894516 5.645397 3.9706993 Polylobed -5.347992 5.7208157 3.9292152 Polylobed -5.1307154 5.547475 3.9834375 Polylobed -5.0275173 5.6530952 4.0241904 Polylobed -5.116194 5.084568 4.403312 Polylobed -5.129984 5.0800295 4.340762 Polylobed -5.1347013 5.0815234 4.339832 Polylobed -5.123647 5.07706 4.3306756 Polylobed -6.9489846 7.839765 3.18586 Prometaphase -7.1920605 8.067743 3.3488111 Prometaphase -6.516468 8.688176 2.7046616 Prometaphase -6.926443 7.1344056 3.4606342 Prometaphase -7.4114614 8.02149 2.84952 Prometaphase -7.635099 6.928502 4.1448307 Prometaphase -7.833417 6.26939 3.5152106 Prometaphase -6.1485424 7.312375 4.302785 Prometaphase -8.068967 6.2590094 3.3816814 Prometaphase -7.783224 7.027453 3.054598 Prometaphase -5.9123874 7.0358067 3.8095934 Prometaphase -6.5036273 6.9176497 3.621907 Prometaphase -7.639194 6.69101 3.5138216 Prometaphase -7.74231 6.806113 3.390459 Prometaphase -6.1213 8.316609 3.8020618 Prometaphase -7.2011805 6.3958635 3.2116163 Prometaphase -7.354376 6.615785 3.6436744 Prometaphase -7.165141 6.747508 3.5134695 Prometaphase -7.3593526 8.203676 3.0568476 Prometaphase -7.4126835 8.204839 3.0138664 Prometaphase -8.073711 6.585926 3.308233 Prometaphase -7.8690786 6.794219 3.131147 Prometaphase -7.413124 8.161611 3.0109909 Prometaphase -7.431524 8.154634 2.990203 Prometaphase -7.36607 6.9103227 2.9685035 Prometaphase -8.253073 6.4114738 3.3503559 MetaphaseAlignment -7.409397 6.33826 2.9045062 MetaphaseAlignment -4.2777805 5.8218617 5.395239 Polylobed -4.3758965 5.7386494 5.579337 Polylobed -4.2343435 5.7635593 5.531419 Polylobed -4.2764277 5.750512 5.433588 Polylobed -4.057218 6.9984407 3.6049638 Binuclear -3.2212334 7.3223834 3.3922238 Binuclear -3.3192294 7.419497 3.25728 Binuclear -8.224507 5.816383 3.5415487 Prometaphase -8.927393 5.651778 3.6640642 Prometaphase -7.518895 6.3574004 3.8720834 Prometaphase -8.8037 5.6646667 3.5928292 Prometaphase -7.843873 6.1224985 3.8601305 Prometaphase -7.714089 5.8255067 3.0885458 Metaphase -8.367576 5.902903 2.952374 Metaphase -8.561592 5.941478 2.5516388 Metaphase -7.2738333 7.043722 3.804588 Prometaphase -8.713697 5.7873836 3.6513395 Prometaphase -8.772613 5.688773 3.5910378 Prometaphase -8.428096 6.3276377 4.0499954 Prometaphase -8.760311 6.1488285 3.9431908 Prometaphase -7.861983 6.1874285 3.6552916 Prometaphase -6.052626 8.134631 4.0786734 Prometaphase -8.25609 5.749125 3.4596949 Prometaphase -8.810278 6.1625757 3.9588149 Prometaphase -8.425927 6.505074 3.9897294 Prometaphase -7.9188733 6.8815713 3.9706035 MetaphaseAlignment -8.689887 6.1928215 4.0329046 Prometaphase -8.769836 6.026168 3.719447 Prometaphase -6.738655 7.57181 3.9182463 Artefact -8.632234 5.9512644 2.3132699 Apoptosis -6.371358 7.3137603 3.9071026 Artefact -7.7735496 6.0757604 3.9543788 Prometaphase -8.031866 6.069872 3.8556259 Prometaphase -8.539197 5.6655226 3.6558082 Prometaphase -8.709824 5.715912 3.6471527 Prometaphase -9.035173 6.0224986 3.9442022 Prometaphase -8.30487 5.9760966 3.7870677 Prometaphase -8.433016 6.030003 3.816456 Prometaphase -9.129923 5.9354987 3.9232907 Prometaphase -8.721976 5.8682623 3.6385307 Prometaphase -9.127567 5.9286537 3.8796325 Prometaphase -8.556947 6.0533776 2.2347662 Apoptosis -8.320727 6.0706773 3.7670708 Prometaphase -8.548027 6.061951 2.1960537 Apoptosis -8.542092 6.073394 2.2222898 Apoptosis -8.686005 5.991932 2.4617708 Apoptosis -9.020393 6.000567 4.026989 Prometaphase -9.103918 5.930978 3.916882 Prometaphase -9.113714 5.975114 3.9563236 Prometaphase -9.122279 5.960605 3.963853 Prometaphase -8.307008 6.3320985 4.183683 Prometaphase -9.095047 5.919682 3.9312024 Prometaphase -9.123086 5.942394 3.938479 Prometaphase -6.3889136 7.3191814 3.7937381 Artefact -6.3112016 7.0860367 3.698629 Artefact -6.3588943 7.295257 4.1408978 Artefact -8.033962 6.2508874 3.1745572 Apoptosis -6.326506 7.328658 4.1379633 Artefact -6.2489223 7.4750404 4.333579 Artefact -6.282408 7.4838586 4.2932715 Artefact -5.475654 7.525078 4.1294007 Artefact -5.3176847 7.7311482 4.0913415 Artefact -5.4895988 6.553551 2.0118136 Interphase -7.8942857 6.30637 4.080067 Prometaphase -4.076073 7.377271 2.4498985 Interphase -3.973489 7.974133 3.1302223 Interphase -5.640919 6.767132 2.356145 Interphase -4.8616114 6.7023873 4.0766973 Interphase -3.7143784 9.545634 3.087203 Interphase -5.83995 6.296757 2.5052295 Interphase -8.932361 5.695192 3.6738195 Prometaphase -7.5405645 6.404164 3.7829843 Prometaphase -8.972285 5.6875 3.7098658 Prometaphase -8.698848 5.788709 3.691482 Prometaphase -8.734725 5.6932044 3.5695026 Prometaphase -6.188329 8.154827 3.496162 Artefact -8.495914 6.374948 4.0061603 Prometaphase -6.3653617 8.080183 3.6845186 Artefact -8.4092 6.376388 4.1407986 Prometaphase -4.485718 6.6453094 2.2704334 Interphase -8.75922 5.747983 3.683663 Prometaphase -8.602695 5.674794 3.533211 Prometaphase -3.7615128 6.2508435 3.3623762 Interphase -8.4901905 6.257526 4.1439643 Prometaphase -8.801021 6.145324 4.0673103 Prometaphase -7.997501 6.0938897 3.7224472 Prometaphase -5.6276846 7.6573215 4.5483685 Prometaphase -8.17788 6.159197 3.529531 Prometaphase -8.025371 6.153657 3.657212 Metaphase -4.1244664 7.5620737 2.5236633 Interphase -8.773692 6.252707 3.8846884 Prometaphase -7.492753 6.52101 4.123987 Prometaphase -9.04299 6.011134 3.711565 Prometaphase -9.050821 6.0011735 3.777811 Prometaphase -9.053841 6.013664 3.810455 Prometaphase -8.53949 6.1512604 3.569613 Prometaphase -8.3694935 6.213893 3.762703 Prometaphase -6.0116057 8.32434 3.8696082 MetaphaseAlignment -8.594983 5.742609 3.6304572 Prometaphase -8.538819 5.7267785 3.644583 Prometaphase -9.044862 5.8645644 3.880006 Prometaphase -8.869503 5.7498164 3.6428015 Prometaphase -8.961606 5.9034863 3.8535619 Prometaphase -8.160935 6.151564 3.9665709 Prometaphase -8.938676 6.0562315 3.8607287 Prometaphase -6.7268825 7.522657 3.7995155 Metaphase -6.9539065 6.5870357 3.199087 Metaphase -9.00867 6.0136747 3.878795 Prometaphase -9.062743 5.887764 3.824556 Prometaphase -5.902506 5.9712005 4.4646726 Apoptosis -7.906206 6.419938 3.670057 Prometaphase -8.504904 6.121289 2.323169 Apoptosis -8.493221 6.1472607 2.371544 Apoptosis -8.215707 6.5271935 4.015978 Prometaphase -8.524459 6.171143 2.417749 Apoptosis -7.8188663 6.882508 3.8683653 Prometaphase -5.8503075 7.3105574 5.227952 Prometaphase -8.912045 6.0721045 3.9103673 Prometaphase -8.712509 5.9041753 2.293696 Apoptosis -8.684925 5.937237 2.2989466 Apoptosis -8.566813 6.040592 2.4172442 Apoptosis -6.899917 6.624295 4.746608 Prometaphase -7.011347 6.6594105 4.7508993 Prometaphase -8.0533085 5.884299 2.93112 Metaphase -6.4603786 6.869336 4.8985395 Artefact -6.999226 6.6736455 4.736671 Prometaphase -6.5558352 6.7590675 4.9479203 Artefact -7.104582 6.6331925 4.6734257 Artefact -7.9901543 5.8703575 3.540631 Prometaphase -7.972761 6.789722 3.9958434 Apoptosis -3.7197762 7.989939 3.8279963 Polylobed -4.925995 6.7515526 2.16729 Interphase -5.4399366 6.527246 1.8511943 Interphase -4.913835 6.9004683 2.54513 Interphase -8.088722 6.3806467 2.1673853 Apoptosis -4.4401484 7.9256654 2.7650912 Interphase -5.1471844 6.670801 2.1967132 Interphase -3.7827828 6.0096383 3.7538023 Polylobed -3.69277 6.4601116 3.6774688 Polylobed -3.2677522 9.28991 3.2347574 Interphase -5.286727 5.984107 3.1459126 Polylobed -4.750702 5.736436 3.1307442 Polylobed -3.9951336 7.6784906 2.6911938 Interphase -3.402323 9.623459 3.0509462 Interphase -4.475562 6.1699862 3.4331422 Artefact -4.3537545 6.2367086 3.3709972 Artefact -4.1582694 6.397451 3.2336824 Artefact -7.881014 5.8501673 3.1499267 Metaphase -4.0870357 6.556569 2.7762406 Elongated -7.566277 6.885755 2.9188654 Metaphase -3.9886591 7.3446794 2.4065435 Elongated -4.40839 6.7521853 1.9613924 Interphase -6.0248528 7.133318 2.6946137 Apoptosis -6.151988 7.1226993 2.6540904 Interphase -4.303545 6.8533683 2.9397643 Interphase -4.7830973 8.520679 2.6127093 Interphase -3.2956078 7.153111 3.362418 Polylobed -3.080849 7.5738444 3.2391448 Polylobed -3.0968587 7.522047 3.234824 Polylobed -3.4145656 6.916955 3.3898084 Polylobed -5.6459966 5.551812 4.503081 Polylobed -5.5862722 5.5681543 4.4953628 Polylobed -3.2164474 7.906888 3.2999632 Polylobed -6.5386577 7.4344807 1.858898 Interphase -8.295986 6.2977853 2.4080813 Metaphase -6.689896 7.5381446 1.8831911 Metaphase -6.6172357 7.4988794 1.8872211 Interphase -8.266399 6.320801 2.367326 Metaphase -6.6256294 7.4900084 1.8836594 Metaphase -3.1817586 7.6053066 3.3822784 Polylobed -3.173157 7.513656 3.3532612 Polylobed -2.6723642 6.311498 3.2457657 Polylobed -2.8428674 6.3740854 3.2538652 Polylobed -5.0030613 5.367519 3.7783024 Polylobed -5.0012856 5.329953 3.7598772 Polylobed -5.382304 6.8151383 3.3831146 Interphase -4.9750133 5.3675785 3.7921119 Polylobed -5.0160227 5.372522 3.7883773 Polylobed -3.8529031 6.0015574 2.629871 Binuclear -5.413945 6.9642043 2.5303543 Interphase -3.877308 5.9274063 2.6679878 Binuclear -3.5211377 5.793985 4.927623 Polylobed -3.2621567 5.7784104 4.852783 Polylobed -4.2652845 7.2914734 3.2633197 Binuclear -3.134802 6.725151 4.2428656 Binuclear -4.0275297 7.1081095 4.145784 Binuclear -3.939401 7.121538 4.067435 Binuclear -3.934793 7.081849 4.1235113 Binuclear -5.2712994 6.427555 2.268524 Interphase -3.9351015 5.628259 3.526613 Binuclear -4.0225787 5.633448 3.4755294 Binuclear -3.617395 6.7608204 3.4899092 Binuclear -4.963858 6.5031834 2.1523137 Interphase -3.6054604 6.8704495 3.4808948 Binuclear -6.2665887 7.3765683 3.428502 Interphase -2.8327863 9.548377 3.4421585 Interphase -3.2756836 7.0685735 3.2334297 Polylobed -3.1304004 7.5648313 3.2454846 Polylobed -3.1128268 7.55858 3.252177 Polylobed -3.1464555 7.533192 3.216158 Polylobed -3.3647153 6.9841433 3.2705548 Polylobed -3.1037717 5.7236705 4.220186 Polylobed -3.0938506 5.74769 4.2957377 Polylobed -3.0931969 5.767109 4.228317 Polylobed -3.202296 5.772962 4.18871 Polylobed -3.1614301 5.812216 4.256399 Polylobed -3.3902318 5.760522 4.2981315 Polylobed -3.8013234 6.272822 2.9631739 Binuclear -3.873055 6.875604 3.3264592 Binuclear -2.4252598 6.1855774 3.4276514 Polylobed -2.3616803 6.1799498 3.4070451 Polylobed -2.3606222 6.199227 3.3833547 Polylobed -2.364163 6.2004457 3.4240527 Polylobed -2.3777132 6.186405 3.4383705 Polylobed -5.361748 9.076767 3.0714517 SmallIrregular -6.0126934 9.497435 2.3641052 SmallIrregular -5.8875523 9.618658 2.3664696 SmallIrregular -4.732762 5.4241023 3.7409623 SmallIrregular -6.0471635 7.284585 3.160723 SmallIrregular -5.1716404 7.288833 2.9951403 SmallIrregular -5.740096 7.830459 3.374882 SmallIrregular -5.4694324 6.7763743 2.2117057 SmallIrregular -5.0225058 8.624838 2.9472072 SmallIrregular -5.7611604 9.915922 2.1876695 SmallIrregular -5.7145014 9.932634 2.210137 SmallIrregular -4.8072147 6.5088887 3.318177 Binuclear -4.5994897 6.2594666 3.0777001 Binuclear -4.283256 8.361153 2.4200695 Binuclear -4.377931 8.17377 2.8556259 Binuclear -4.22304 8.305275 2.3555346 Binuclear -4.4355807 8.082306 2.821496 Binuclear -3.950389 7.8383894 2.4250014 Binuclear -3.8793335 8.294645 2.4933429 Polylobed -3.841249 8.283171 2.4466817 Polylobed -3.8587403 8.238206 2.4038928 Polylobed -5.801535 7.696737 2.6920714 Binuclear -3.846306 8.225014 2.4186351 Polylobed -3.8075716 8.208785 2.4535804 Polylobed -5.50245 9.265617 2.3851888 Binuclear -5.5094 9.327677 2.4139209 Binuclear -4.8781433 8.054249 2.4163818 Binuclear -3.8106909 8.122693 2.2353334 Polylobed -3.8326068 8.12619 2.2339308 Polylobed -5.9058332 7.133244 3.3484995 Binuclear -3.8117073 8.107173 2.2538006 Polylobed -3.7969832 8.080147 2.2268484 Polylobed -3.802805 8.116292 2.2334294 Polylobed -4.521874 6.887781 2.2730532 Binuclear -4.3879437 5.378819 3.3112702 Binuclear -4.3246775 6.7904077 2.4023738 Binuclear -4.3648233 5.3637714 3.3189092 Binuclear -3.97084 5.7156525 3.6774447 Large -3.8233569 5.665228 3.746275 Large -3.8602498 5.7170196 3.7108026 Large -3.732203 6.5733695 3.6194563 Polylobed -3.2167323 6.5862145 3.629796 Polylobed -5.1830816 6.3608 3.4688551 Polylobed -4.335466 5.9063005 3.1414723 Polylobed -3.963579 6.525131 3.72286 Polylobed -4.3697586 6.007245 3.1268575 Polylobed -3.8897982 8.61832 3.2334392 Binuclear -4.3156576 5.8354454 3.2106667 Polylobed -4.1437783 8.065231 2.9828548 Binuclear -4.6524415 9.434315 2.9419987 Binuclear -3.0194964 9.381128 3.433081 Polylobed -3.2746491 9.1324415 3.3247428 Polylobed -4.683261 9.308261 3.0455515 Binuclear -3.0721936 7.948167 4.001643 Polylobed -4.3752227 6.9564886 2.1842813 Binuclear -5.362914 8.34182 3.9782064 Polylobed -3.1025648 7.536472 4.503481 Polylobed -3.3445513 9.546877 2.8257685 Binuclear -4.6438727 6.9035563 2.1527426 Binuclear -3.1366534 7.575027 4.527331 Polylobed -5.2750187 7.9802675 4.0862093 Binuclear -3.1061149 7.490959 4.5228868 Polylobed -3.46377 9.546943 2.8053813 Binuclear -3.1191971 7.5242486 4.500128 Polylobed -4.0466557 7.5374064 3.736785 Binuclear -3.4317708 9.705922 2.962305 Binuclear -3.4227455 9.650489 2.8820965 Binuclear -3.365739 9.550045 2.837092 Binuclear -3.3717527 9.5273485 2.858824 Binuclear -3.3197742 9.369177 2.9303036 Polylobed -3.3666916 9.2919035 2.9093978 Polylobed -3.3511653 9.413314 2.9024673 Polylobed -3.3260436 9.41846 2.951616 Binuclear -3.4180584 8.084193 4.3069215 Polylobed -3.3023782 7.849243 4.3874197 Polylobed -3.4249449 9.357512 2.9611611 Binuclear -3.3415494 8.046522 4.435998 Polylobed -4.684993 8.769151 2.5299091 Binuclear -3.0294309 8.373545 4.1911397 Polylobed -3.8966458 7.2885504 2.4528656 Large -4.245801 8.549561 2.5040858 Binuclear -4.2037716 7.202015 2.714807 MetaphaseAlignment -4.1158743 8.528308 2.899691 Binuclear -4.277492 7.3748474 2.5199454 Binuclear -8.552971 5.954664 2.9120486 MetaphaseAlignment -8.571908 5.9649024 2.855051 MetaphaseAlignment -8.554556 5.981309 2.7574248 MetaphaseAlignment -4.545642 5.2603703 3.7767262 Binuclear -4.069208 5.7992077 4.2758756 Binuclear -2.6005523 5.909792 4.080832 Polylobed -2.5704143 6.282181 4.0250564 Polylobed -2.5557723 5.72512 4.2768965 Polylobed -2.65423 6.144468 4.0264316 Polylobed -4.498827 5.4200077 3.6255462 Binuclear -2.5196793 5.730035 4.2064424 Polylobed -4.932277 5.6224537 3.7329788 Binuclear -4.8663006 5.6064534 3.7600548 Binuclear -4.511159 5.3745737 3.5980957 Binuclear -4.5188985 8.442173 3.0050921 Binuclear -3.7664018 8.392414 3.352918 Binuclear -3.8965173 8.406804 2.887892 Binuclear -3.682008 8.001911 3.170482 Binuclear -3.9375768 8.222086 2.686223 Binuclear -4.0030265 8.350052 2.747308 Binuclear -6.8016863 7.6283383 3.83573 Apoptosis -7.410449 7.968998 2.4288316 Apoptosis -8.315118 6.4277644 2.0054219 Apoptosis -7.864404 6.703309 2.6522717 Apoptosis -8.1931095 6.454061 2.555378 Apoptosis -7.083199 7.353824 3.9047146 Apoptosis -6.5395675 7.774204 3.5143476 Apoptosis -7.018036 7.7558284 3.6689494 Apoptosis -6.614663 7.621166 3.5872865 Apoptosis -7.056598 7.8415627 3.577479 Apoptosis -7.315292 8.153052 2.7943144 Apoptosis -7.4109197 7.942048 2.3803945 UndefinedCondensed -8.0301 6.068907 2.7771215 Apoptosis -7.08414 7.0394483 4.024965 Apoptosis -6.9986377 7.3858604 3.9124439 Apoptosis -7.2664557 6.789426 2.9392285 Apoptosis -6.08401 7.1635203 3.0678585 UndefinedCondensed -7.3375816 6.9411182 3.8597145 Apoptosis -6.596704 7.9344788 3.7714443 Apoptosis -7.434971 7.9485774 2.3352532 UndefinedCondensed -4.4047174 6.2917547 2.8686247 Polylobed -4.405841 6.126656 2.9857397 Polylobed -4.378255 6.137365 2.977759 Polylobed -4.313027 6.341108 3.0058784 Polylobed -4.216035 7.391775 3.3344576 Interphase -3.6705196 9.177242 3.1519227 Interphase -4.5159736 6.661493 1.9110221 Binuclear -4.384496 6.770806 1.8833662 Binuclear -5.301665 6.947038 2.362415 Interphase -5.4460196 6.716988 2.0574656 Interphase -4.6512294 8.622549 2.4642723 Interphase -4.2653394 7.2598643 2.1231918 Interphase -5.3670607 8.909678 3.059171 Interphase -6.5975256 6.4045486 2.1216094 Interphase -3.2697783 6.5796027 4.416748 Polylobed -3.1965704 6.7135773 4.311141 Polylobed -3.2231607 7.706046 4.1380005 Polylobed -6.1971045 6.58879 2.9227931 MetaphaseAlignment -7.656098 6.2282305 3.1967707 MetaphaseAlignment -6.695998 6.314283 2.2119434 MetaphaseAlignment -7.6354766 6.2207103 3.0900934 Polylobed -4.499887 7.770076 2.4945207 Binuclear -5.769635 6.639097 2.2394567 Binuclear -5.7803054 6.56017 2.207994 Binuclear -4.0543475 7.1387677 2.7493134 Large -8.250065 6.2061486 2.638031 MetaphaseAlignment -8.206768 6.1905875 2.454649 MetaphaseAlignment -8.556386 5.9931984 2.3867722 MetaphaseAlignment -7.867908 5.78499 3.3815033 MetaphaseAlignment -5.852006 8.069555 3.745296 MetaphaseAlignment -4.4257607 6.8810835 3.5717425 Polylobed -4.0472064 7.517134 3.598771 Polylobed -4.7433767 5.5043592 5.4019527 Polylobed -5.0476413 5.5470033 5.114879 Polylobed -4.7622128 5.62769 5.1726213 Polylobed -4.9901123 5.5999064 5.018296 Polylobed -3.5434866 6.5764074 4.6016245 Grape -4.9225564 5.5421734 5.0826855 Polylobed -3.5133939 6.636593 4.611504 Grape -3.5472639 6.6543765 4.530634 Grape -5.1655374 7.1744456 5.1756425 Grape -5.2704678 6.777565 5.2208304 Grape -5.5904922 5.676172 5.4302216 Grape -4.411498 6.554874 3.8991842 Artefact -4.7325473 6.5527263 3.462697 Artefact -4.649363 6.518826 3.4933858 Artefact -6.444349 6.261723 3.526034 Polylobed -4.3835964 6.522269 3.7788994 Artefact -4.5561695 6.67864 3.666324 Artefact -5.4189777 6.917734 4.760687 Polylobed -3.979862 6.625658 3.943399 Artefact -5.2830615 6.967429 4.727775 Polylobed -3.9634154 6.6249456 3.8720505 Artefact -4.007772 8.790379 5.134395 Polylobed -4.0103583 8.801533 5.1268344 Polylobed -4.0501156 8.822711 5.0885696 Polylobed -4.7625155 4.595466 4.6730566 Grape -3.9877565 8.79188 5.12915 Polylobed -4.007044 8.545522 5.031488 Polylobed -3.9865992 8.79116 5.132724 Polylobed -3.769761 7.000827 3.7640238 Grape -3.376504 7.947766 3.8695645 Grape -3.6513517 7.1527505 3.9133422 Grape -4.8320904 4.545751 4.7150745 Grape -3.972316 8.773933 5.061415 Polylobed -3.553074 7.732224 3.8794944 Grape -3.5377188 7.7362447 3.824963 Grape -4.814955 4.535114 4.706814 Grape -4.8227334 4.52504 4.716284 Grape -3.9615598 8.781969 5.1262374 Polylobed -4.7935553 4.5356183 4.700623 Grape -4.8093367 4.5520005 4.701108 Grape -4.1055512 6.7997193 3.8616183 Polylobed -4.1760373 6.8538556 4.0814357 Polylobed -5.129432 5.834716 4.7257557 Polylobed -3.62005 6.0755663 4.5727177 Polylobed -3.6473296 6.0249476 4.575034 Polylobed -3.5512524 6.1458435 4.433927 Polylobed -3.6640563 6.0086403 4.5633626 Polylobed -3.632556 6.1000433 4.5167394 Polylobed -4.2597046 6.659327 4.1653523 Polylobed -3.5660892 7.2314687 4.866074 Grape -3.5533903 7.319689 4.8985443 Grape -3.5665567 7.2326126 4.910576 Grape -3.576623 7.058191 4.825596 Grape -3.6303291 6.769085 4.730077 Grape -4.3399997 6.4879637 4.521924 Grape -3.6945405 6.326947 4.609851 Grape -4.3364806 6.671213 4.202068 Grape -4.042822 6.956045 4.4109025 Grape -3.8754225 7.2298365 4.547227 Grape -3.9519806 6.888787 4.4272346 Grape -3.9398842 7.368636 4.6323752 Grape -3.9428236 7.1903915 4.5443316 Grape -5.570057 5.685546 2.9364855 Polylobed -5.5400367 5.7626395 2.994367 Polylobed -5.56227 5.9400973 3.04014 Polylobed -5.2526593 6.209154 4.5225883 Polylobed -5.600562 6.515006 5.330698 Polylobed -4.106913 6.567788 4.079859 Grape -5.013732 8.011872 4.6994877 Grape -3.9851775 6.648088 4.1860776 Grape -3.3565805 8.546107 3.4944947 Polylobed -4.9866624 7.994262 4.6940093 Grape -5.0623026 5.65734 5.241617 Polylobed -5.0203314 7.9364386 4.671811 Grape -4.9090543 5.568801 5.2680864 Polylobed -5.1851397 8.253356 3.9132035 Polylobed -3.5019083 7.7636895 3.8073707 Grape -3.4778748 7.7826133 3.8171773 Grape -3.4981866 8.786228 3.1373706 Polylobed -5.0381403 7.9504395 4.6750684 Grape -3.4611664 7.8404903 3.8155372 Grape -3.4716518 7.8219438 3.8768446 Grape -5.0396295 7.90204 4.6461296 Grape -5.4307556 8.686955 3.9314468 Polylobed -5.513462 6.205206 4.699621 Polylobed -3.4172218 8.878456 3.3114986 Polylobed -5.0496206 8.000896 4.6802683 Grape -4.936768 5.651721 5.178089 Polylobed -4.9507885 5.588414 5.1288652 Polylobed -4.2785487 5.602301 3.5204978 Grape -5.0560317 5.5739727 5.4395947 Grape -5.068311 5.602331 5.431949 Grape -4.618657 5.551635 3.5830183 Grape -5.1117797 5.5767684 5.4917803 Grape -6.2187824 6.6740985 3.0113707 Polylobed -6.2345266 6.299779 3.8319845 Polylobed -3.560711 6.1026754 4.890931 Grape -4.3825135 5.6382275 5.131222 Polylobed -4.7721195 5.5797 5.194732 Polylobed -3.563639 6.165514 4.8433876 Grape -3.579865 6.1903524 4.8660207 Grape -3.5498776 6.8841166 4.823264 Grape -3.6030095 6.2180243 4.853689 Grape -3.584715 6.8284216 4.826108 Grape -3.5885406 6.654638 4.765228 Grape -3.6078846 6.38704 4.7297435 Grape -3.5949893 6.552549 4.727963 Grape -4.853709 5.7956862 4.0592604 Grape -4.4730577 6.5974407 4.1862736 Grape -4.7120733 6.610111 4.1493425 Grape -3.8930304 6.905931 4.4106593 Grape -4.0713897 6.6495132 4.4093347 Grape -5.112826 6.6753507 3.2574947 Polylobed -5.25695 6.3145037 3.2020395 Polylobed -5.2704644 6.2042623 3.1496775 Polylobed -4.771086 6.5758476 3.2567644 Polylobed -4.7037387 6.605933 3.3316574 Polylobed -4.4711924 6.674832 3.6043403 Polylobed -5.5044336 6.6425023 3.7697928 Polylobed -4.2441015 8.6735735 5.1437693 Grape -4.2280483 8.670419 5.1244655 Grape -4.2278395 8.601776 5.0440583 Polylobed -4.2448015 8.663351 5.118623 Grape -4.818128 7.615405 2.8605468 Polylobed -4.8172474 4.550782 4.716535 Grape -4.239761 8.657045 5.115576 Grape -4.363021 8.450876 4.8176165 Polylobed -4.221409 8.653457 5.106674 Grape -4.807578 4.5339437 4.7147202 Grape -4.8136044 4.522359 4.712561 Grape -4.810409 4.528455 4.711815 Grape -4.4287615 9.072009 3.1668262 Polylobed -4.317715 7.2495685 3.406906 Polylobed -4.624697 7.481614 3.3114927 Polylobed -6.1260185 7.610109 2.685643 Polylobed -5.25544 5.99775 2.8816202 Polylobed -5.496872 6.171991 2.4985857 Polylobed -5.337825 5.4711266 4.1402345 Polylobed -5.358562 5.500596 4.0783434 Polylobed -5.329226 5.461247 4.1845813 Polylobed -7.6200905 6.893309 3.8280973 Prometaphase -7.671651 6.9407415 4.0337424 Prometaphase -4.102007 9.260854 3.2686431 Binuclear -4.8470063 9.009476 3.1532977 Binuclear -3.981462 6.8200493 4.2542644 Polylobed -3.8474104 6.7225018 4.346074 Polylobed -3.8464777 6.797673 4.4554124 Polylobed -3.8477442 6.6907763 4.3545685 Polylobed -5.9505157 7.468924 2.8643048 Binuclear -4.0410028 6.976044 3.7068355 Binuclear -1.8738481 7.098806 5.0015044 Polylobed -1.810074 7.203902 4.9744196 Polylobed -1.7780373 7.095275 4.948556 Polylobed -1.8473281 7.1512737 4.9444475 Polylobed -3.9415305 8.825311 4.522857 Polylobed -1.9122636 7.3027425 4.909389 Polylobed -2.2737827 6.856044 4.981469 Polylobed -2.226985 7.4344454 4.946687 Polylobed -2.857902 5.4345655 3.3489363 Polylobed -2.8636298 5.4508343 3.3505218 Polylobed -2.8714747 5.4567313 3.3670864 Polylobed -2.1737895 7.516804 4.959591 Polylobed -2.2343493 7.4998364 4.909472 Polylobed -2.1733663 7.5207114 4.9325333 Polylobed -2.4051068 7.2907796 4.722072 Polylobed -2.8656306 5.4656925 3.3675935 Polylobed -2.1589992 7.588348 4.9117675 Polylobed -2.2019787 7.686437 4.8780174 Polylobed -3.9810226 7.565047 2.8548968 Binuclear -3.8745508 7.1958776 2.855256 Binuclear -3.0713403 8.510689 3.6571279 Binuclear -5.508072 8.71862 2.8379345 Interphase -3.188232 8.503015 3.655316 Binuclear -3.8914835 6.3638034 2.6371152 Binuclear -3.8830214 6.494271 2.5625064 Binuclear -3.5348823 7.6996956 3.857863 Large -6.169201 7.4151096 2.9423141 Binuclear -4.72023 8.4714365 2.3484921 Binuclear -2.6697633 6.0437946 2.908402 Polylobed -2.6980245 6.0491204 2.9077883 Polylobed -2.7441247 6.0652275 2.9127398 Polylobed -2.7297568 6.0378633 2.9075892 Polylobed -2.9070475 6.043391 2.8796313 Polylobed -2.7459123 6.0698843 2.9026155 Polylobed -2.3801596 5.9706836 3.042984 Binuclear -2.4212902 6.024094 3.0566006 Binuclear -2.3717182 5.9857535 3.054967 Binuclear -2.395121 5.987844 3.0676098 Binuclear -3.8783708 6.990447 2.385911 Binuclear -4.0416255 6.925357 2.3419764 Binuclear -3.9093325 7.0517297 2.367634 Binuclear -3.909359 5.803748 3.5062928 Large -4.3105507 5.7033806 3.2793772 Binuclear -4.428963 5.4631705 3.3315935 Binuclear -4.854496 5.352281 3.6701415 Polylobed -5.399311 6.652127 3.6715229 Artefact -5.239142 6.5759053 3.5394695 Artefact -4.0999093 8.466524 4.3989587 Artefact -3.9541957 8.353969 4.413621 Artefact -6.0587673 6.6471543 3.0525546 Artefact -3.9765146 8.439673 4.4287934 Artefact -6.072123 6.655853 3.018056 Artefact -6.071883 6.629351 3.0682065 Artefact -3.8732166 8.424698 4.5087953 Artefact -4.775902 5.9512367 3.5764732 Polylobed -4.728957 6.0389767 3.3304007 Polylobed -3.9098787 6.934973 2.2465806 Binuclear -4.8985095 6.120358 3.5883691 Polylobed -3.976912 6.9545913 2.2365437 Binuclear -4.9115868 5.987795 3.6188264 Polylobed -2.3711348 5.90188 3.0694761 Binuclear -2.3494487 5.875073 3.0548236 Binuclear -2.3419857 5.884745 3.039904 Binuclear -2.3689964 5.887297 3.0812223 Binuclear -3.9059925 7.0019073 2.384375 Binuclear -3.9154582 7.041059 2.4096184 Binuclear -3.8469272 7.0296183 2.4581704 Binuclear -3.9715774 6.998779 2.347605 Binuclear -4.191369 6.390806 4.2749386 Polylobed -3.7264638 6.2143326 4.3551593 Polylobed -3.7773256 6.212218 4.514121 Polylobed -1.9811497 6.914598 4.9942565 Polylobed -1.9210002 6.997464 5.0429835 Polylobed -1.9413786 6.993945 4.973931 Polylobed -1.8562143 6.9851265 5.0270357 Polylobed -3.3488913 5.472009 3.4384904 Polylobed -2.0088704 6.8441167 4.938968 Polylobed -3.2829633 5.424544 3.345645 Polylobed -3.2723477 5.40407 3.420074 Polylobed -1.9925421 6.975816 4.9936185 Polylobed -3.3102305 5.3891907 3.4086015 Polylobed -2.187103 6.871957 5.0193763 Polylobed -2.0272498 7.0463715 5.030961 Polylobed -5.022382 5.7035446 5.5083594 Polylobed -5.219241 5.460779 6.233398 Polylobed -5.1511173 5.487848 5.783787 Polylobed -2.124825 7.586429 4.9928246 Polylobed -2.105989 7.494246 5.000246 Polylobed -2.127441 7.538302 4.9998465 Polylobed -2.7926352 5.3410707 3.1942816 Polylobed -2.7646613 5.3120294 3.1877985 Polylobed -2.9053762 5.3693867 3.258374 Polylobed -2.1697996 7.492645 4.9959974 Polylobed -2.1494775 7.66669 4.9658303 Polylobed -2.8640425 5.4147105 3.2764902 Polylobed -2.1650703 7.616961 4.976261 Polylobed -2.9238718 6.2991247 3.327061 Polylobed -3.038507 6.2973127 3.3400967 Polylobed -3.008745 6.3808556 3.3828053 Polylobed -3.2469568 6.3946314 3.4710224 Polylobed -7.0904984 8.169109 3.3261347 Metaphase -6.854826 7.93633 3.4181547 MetaphaseAlignment -6.2246103 9.176984 2.7650669 MetaphaseAlignment -6.8023224 6.5438004 3.0770113 Prometaphase -7.3171396 6.9073205 3.7700543 Apoptosis -7.7391996 6.671427 3.758218 MetaphaseAlignment -8.295926 6.078294 2.464645 Prometaphase -7.2716284 6.307435 3.5024784 Apoptosis -8.226626 5.9746246 2.6478863 Prometaphase -7.3644114 7.764564 2.5934823 Apoptosis -8.622411 5.972766 2.3486092 Apoptosis -4.733113 7.106695 2.2249784 Binuclear -8.075347 5.8103375 3.506016 Apoptosis -5.020829 6.8055663 2.3013744 Binuclear -5.5460057 8.875258 2.7742558 Prometaphase -5.8102813 8.195008 2.5766196 Prometaphase -3.884891 8.204206 2.6117897 Binuclear -4.0113845 8.019142 2.4907045 Binuclear -7.342607 8.145662 2.5576208 Prometaphase -7.3731437 8.149536 2.5452774 Prometaphase -5.1940207 7.5003185 2.6268582 Prometaphase -7.217391 7.0052214 2.9946084 Prometaphase -7.3820686 8.13289 2.6524978 Prometaphase -5.936575 6.9229274 3.060585 Polylobed -5.8361173 7.0554056 2.8668945 Polylobed -5.769876 7.2511373 2.7809086 Polylobed -8.396303 6.391072 2.1913798 Apoptosis -8.451073 6.268949 2.3305645 Apoptosis -5.724736 6.431042 3.5611334 Binuclear -5.651541 6.4476585 2.736871 Binuclear -4.176912 7.114331 2.1799018 Binuclear -4.1411366 7.130853 2.1981347 Binuclear -6.166618 8.077642 3.0401664 Artefact -6.981244 7.9622397 2.90608 Prometaphase -6.3267994 8.076969 2.6032963 Polylobed -5.6848793 7.2382884 2.4983447 Polylobed -6.0201297 7.939627 2.5268655 Polylobed -5.537713 6.637232 2.7460072 Binuclear -5.6553273 6.718301 2.7838047 Binuclear -7.4168944 8.042743 2.9290614 Prometaphase -8.711028 5.9517536 2.2183099 Apoptosis -5.090398 6.071677 3.4407632 Binuclear -5.148128 6.201924 3.2988772 Binuclear -4.913552 5.906951 3.7594872 Artefact -5.063968 6.059932 3.5517192 Artefact -8.617427 6.030442 2.3781307 Apoptosis -8.610479 6.0150585 2.296785 UndefinedCondensed -7.319992 7.5848036 2.5779636 Prometaphase -7.7627573 6.770484 2.397246 Prometaphase -8.333264 6.379332 2.0292585 Prometaphase -6.715897 7.6146264 2.9275088 Prometaphase -7.2683725 7.1120887 2.5532987 Prometaphase -6.454624 8.275035 2.6744134 Polylobed -5.6615167 6.9054127 2.6786332 Prometaphase -6.4434457 8.151109 2.6615555 Polylobed -6.466838 8.244548 2.6901853 Polylobed -7.6170545 6.895194 3.1802251 Prometaphase -8.659328 6.025853 2.2333493 Apoptosis -7.1925864 7.827002 3.0020308 Prometaphase -6.145806 9.088567 2.9642882 Prometaphase -7.3207793 8.014487 2.7761183 Prometaphase -7.195383 8.316246 2.5316675 Prometaphase -7.818361 7.762929 1.895456 Prometaphase -5.1071444 6.226773 3.028739 Binuclear -5.3103814 6.404167 2.9749136 Binuclear -5.3255363 6.361855 3.0434895 Artefact -7.241873 7.092788 2.6602275 Prometaphase -4.537549 5.6461444 4.648159 Polylobed -4.5853043 5.9126477 3.6514206 Polylobed -4.5335975 5.827986 3.6974003 Polylobed -4.671938 5.545589 4.729847 Polylobed -4.794524 5.992921 3.5243726 Artefact -4.442233 5.8410006 3.5514085 Polylobed -4.578762 5.886419 3.714248 Polylobed -4.5546484 5.5995827 4.6220055 Polylobed -4.886382 6.633058 3.9583685 Polylobed -4.577478 5.830241 3.6963596 Polylobed -4.462527 7.287629 2.3090308 Polylobed -3.3711321 6.7850766 4.3717866 Polylobed -7.437008 6.2547426 3.2398636 Artefact -7.3558664 6.260433 3.1189003 Artefact -5.5104513 6.3465724 3.6797657 Binuclear -5.4406767 6.1533065 3.0004735 Binuclear -7.45935 7.5551906 2.815912 Prometaphase -7.511224 7.0795937 2.6082923 Prometaphase -7.7999926 6.0441866 3.4777138 Apoptosis -6.6157246 7.742109 3.1795733 Prometaphase -4.0581207 6.3575406 3.4155242 Binuclear -3.7841034 6.59147 3.3376293 Binuclear -7.425475 8.060204 2.8837788 Prometaphase -6.7135167 7.7680464 3.3382485 Prometaphase -6.2538123 8.9721575 2.8779767 Prometaphase -6.490367 8.207372 2.6675258 Polylobed -5.1532335 6.1952558 3.562512 Polylobed -6.5703745 7.723158 3.1711285 Prometaphase -6.472021 8.12694 2.6695368 Polylobed -7.0925465 6.6720123 2.688344 Prometaphase -5.624757 6.872903 2.7324104 Polylobed -5.4071946 6.956803 2.7786202 Polylobed -5.8757668 6.96001 3.1122613 Polylobed -7.256785 6.798415 2.3851361 Prometaphase -7.382513 6.5452175 4.170233 Prometaphase -3.026835 8.698735 3.873741 Polylobed -8.575166 5.998195 3.5060446 Apoptosis -2.9586182 8.749669 3.7248397 Polylobed -2.8593085 9.117785 3.599043 Polylobed -3.600662 9.200042 3.0760365 Binuclear -3.8749707 8.764491 2.8145003 Binuclear -5.7568083 9.187842 2.597154 Binuclear -7.1956177 7.9187207 3.0683746 MetaphaseAlignment -5.6750865 9.118993 2.507446 Binuclear -6.0700126 9.145418 2.781692 MetaphaseAlignment -5.53065 6.8782854 2.7135746 Prometaphase -6.536252 8.003819 3.6082275 MetaphaseAlignment -8.118759 6.407931 2.6275811 MetaphaseAlignment -5.1152 7.187668 4.3280025 Polylobed -7.4602547 6.518823 2.9361534 Prometaphase -5.0886836 7.329638 4.4021287 Polylobed -5.1348634 7.3329916 4.326191 Polylobed -3.897389 6.8910627 3.557152 Polylobed -5.0704126 7.398883 4.400047 Polylobed -3.3302367 6.578916 3.7289853 Polylobed -3.3632188 6.701571 3.5895727 Polylobed -3.4296114 6.6034813 3.532205 Polylobed -7.1043835 6.40852 3.3718722 Apoptosis -6.1448693 7.3991776 3.1563337 Artefact -6.209932 7.398725 3.0342288 Artefact -6.1601377 7.469493 3.0124776 Artefact -6.421193 7.7597466 3.286541 Artefact -8.506894 6.0295186 2.3590722 Apoptosis -8.069229 6.2350936 2.9395924 Apoptosis -4.630898 7.0517583 2.1922073 Binuclear -4.7841287 6.975923 2.1947346 Binuclear -8.093682 6.213621 2.907687 Apoptosis -6.547764 8.150523 3.1468117 Metaphase -8.650416 5.9860454 2.2913241 Apoptosis -3.9268315 7.105355 2.3701744 Binuclear -3.9984665 8.149043 2.4257886 Binuclear -5.1340876 7.17312 2.5781558 Binuclear -4.3340406 7.247576 2.9222407 Binuclear -6.6997533 7.293235 2.9929 Hole -8.15393 6.8145304 1.8771793 SmallIrregular -8.148406 6.7671824 1.9229811 Hole -6.364607 9.335175 2.4500537 SmallIrregular -8.030214 7.349891 1.8032371 Hole -6.8158383 8.261196 2.1452913 SmallIrregular -7.7484565 6.8958187 2.2585018 SmallIrregular -6.3189263 8.014491 2.2084405 SmallIrregular -8.274459 6.603122 2.0349896 Hole -5.958154 9.238094 2.4977047 SmallIrregular -7.9590564 6.726719 2.2091398 Hole -7.0312343 7.9300094 2.1729472 SmallIrregular -7.0215664 8.30985 2.3348215 SmallIrregular -6.8431883 8.605904 2.0716603 UndefinedCondensed -6.127872 8.988332 2.7704532 SmallIrregular -6.5117445 9.328057 2.3734293 SmallIrregular -6.4491234 9.325293 2.465368 Hole -7.692343 7.760308 2.0106504 Hole -7.0855646 6.537526 2.782743 Hole -7.31288 6.6978188 2.865107 SmallIrregular -8.0819645 6.649943 1.9746177 Hole -7.0468273 8.200757 2.3533604 SmallIrregular -7.243181 7.477952 2.3521497 SmallIrregular -7.8852887 7.6715174 1.8585912 SmallIrregular -5.497784 7.968624 3.8373475 Folded -7.114508 7.873069 2.015123 Hole -8.226562 6.6826477 1.8950119 Hole -7.6782002 7.7512207 2.0039892 Hole -8.58288 5.990592 2.3452334 Apoptosis -8.679574 5.917721 2.3605962 Apoptosis -8.636758 5.9044333 2.4671476 Apoptosis -8.673072 5.9304914 2.389855 Apoptosis -8.66505 5.926168 2.3623855 Apoptosis -6.4945297 9.318828 2.4305892 Hole -6.4845796 9.326346 2.4361904 SmallIrregular -6.6288023 8.830366 3.0600145 SmallIrregular -9.008241 5.9484844 3.3124506 Apoptosis -7.352968 7.8600864 1.9808621 Hole -8.9864645 5.9686694 3.502572 Apoptosis -6.8327794 7.90742 2.0937428 SmallIrregular -6.341625 6.1527796 2.5224316 Elongated -6.6639957 7.0512304 3.2869732 SmallIrregular -7.1203814 7.914335 2.0739553 Hole -8.325439 6.382889 1.9732538 Hole -7.377417 7.838291 1.993203 SmallIrregular -7.1533213 7.8872924 2.081151 Hole -8.215563 6.656803 1.9277656 Hole -8.362618 6.3171654 1.9855958 SmallIrregular -6.157221 9.080045 2.9264 SmallIrregular -6.4372926 9.322906 2.4494722 SmallIrregular -7.5574927 7.859201 2.0060456 SmallIrregular -7.325547 8.113534 2.6672568 SmallIrregular -7.265882 8.086817 2.3484921 SmallIrregular -7.1989503 7.917653 2.2878482 Prometaphase -2.8821316 8.218865 4.03594 Polylobed -6.5400043 7.1901946 3.4082174 Binuclear -4.1745872 7.7096868 2.8942766 Binuclear -6.7521653 6.9062576 3.3228655 Interphase -4.190641 5.9407997 2.877733 Elongated -4.1322637 5.8327637 3.4429495 Polylobed -4.441465 5.7139745 3.4728363 Polylobed -4.439688 5.9789515 3.821637 Polylobed -5.7619877 7.4974504 3.3167703 Interphase -3.4539225 9.498236 2.896564 Polylobed -3.3508208 7.613209 2.800519 Binuclear -3.8121886 6.9500966 3.271365 Binuclear -5.8391085 6.2917995 2.4785342 Interphase -5.313131 7.1716447 2.406911 Interphase -6.7167907 6.5957785 3.7957225 Polylobed -6.634462 6.666956 3.8212628 Polylobed -6.685194 6.6430655 3.7734706 Polylobed -5.7574525 7.4942408 3.0365248 Artefact -5.9562573 7.7301645 3.1568851 Large -4.39542 5.243746 3.4066675 Large -4.3433604 5.2926655 3.4467409 Large -3.4060557 7.1690683 3.8531358 Polylobed -3.6511261 6.698717 3.9800472 Polylobed -3.1461856 7.01046 4.2423778 Polylobed -7.3240876 6.567053 3.8218186 Apoptosis -3.0464592 8.7209 4.0286236 Polylobed -3.3277533 8.127148 3.5071838 Polylobed -3.3470142 8.36735 3.533961 Polylobed -4.1818647 8.803134 2.831708 Polylobed -4.058344 8.822795 2.7479239 Polylobed -4.2043004 8.649168 2.679629 Polylobed -3.2795475 9.654837 3.1236484 Interphase -4.364418 8.724168 2.9220798 Interphase -6.447711 6.5940895 2.9966135 MetaphaseAlignment -5.8279877 8.841824 3.561782 MetaphaseAlignment -5.8507633 8.851572 3.538998 MetaphaseAlignment -4.009934 8.379134 2.8377094 Interphase -3.639065 5.1698604 4.159867 Polylobed -3.74132 5.094008 4.1351357 Polylobed -4.070004 7.882345 2.832634 Binuclear -3.931481 8.695541 2.9481924 Binuclear -5.8762164 7.371447 2.6388674 Interphase -6.2711453 6.922646 3.3828306 Elongated -4.741061 7.0021453 2.940448 Elongated -6.673765 6.8801384 3.6547642 MetaphaseAlignment -3.4537864 9.694861 2.9564183 Interphase -6.6059155 6.8191366 3.4107702 Interphase -4.288556 8.352094 2.5139713 Interphase -4.1388803 8.216861 2.4743242 Binuclear -4.176534 8.520617 2.5105894 Binuclear -8.83031 6.051635 3.6797884 Prometaphase -8.694246 6.2201276 3.7359297 Prometaphase -8.342062 6.1691113 2.9034128 Metaphase -8.971909 5.9771285 3.682746 Prometaphase -9.061856 5.843346 3.7338905 Prometaphase -8.188165 6.3357925 2.9483144 Metaphase -8.062504 6.1641226 3.8462558 Prometaphase -9.061957 6.0091643 3.8971424 Prometaphase -8.578713 6.136675 3.7855167 Prometaphase -7.6378164 6.574695 3.7942128 Prometaphase -8.803302 6.06671 3.47823 Prometaphase -7.949582 6.4910607 3.5016677 Prometaphase -8.03629 6.435152 3.862216 Prometaphase -8.371979 6.4940915 3.7040575 Prometaphase -8.204562 5.7903423 3.3672745 Prometaphase -8.731265 5.7366343 3.4924974 Prometaphase -9.016916 5.9352093 3.7099285 Prometaphase -8.883083 6.002335 3.6562836 Prometaphase -7.3330803 6.3807006 3.1940827 Prometaphase -7.112944 6.7413607 3.3619099 Prometaphase -7.3492813 6.5335135 4.131301 Prometaphase -5.7450147 8.152755 2.57639 Prometaphase -7.44635 6.5349517 4.2238526 Prometaphase -8.821399 6.1881776 3.7370565 Prometaphase -8.811206 6.19082 3.7740605 Prometaphase -8.541113 6.1351647 3.4777086 Prometaphase -8.742927 6.1276135 3.7498045 Prometaphase -8.393986 6.5371385 3.9249852 Prometaphase -7.6183276 6.617466 3.626348 Prometaphase -8.742471 6.075173 3.365417 Apoptosis -7.592215 6.415908 3.8265662 Prometaphase -7.426786 6.188963 3.1269343 Prometaphase -7.6951313 6.409711 3.5707414 Prometaphase -8.389585 6.0911174 2.396865 Apoptosis -8.1697035 5.852965 3.4487057 MetaphaseAlignment -4.418043 8.18072 2.4595647 Interphase -3.276878 6.7678795 4.3882413 Polylobed -4.724717 7.4543962 2.2993503 Interphase -3.467967 6.817576 4.2716365 Polylobed -2.8489676 5.824546 4.641007 Polylobed -2.8835754 5.748355 4.632974 Polylobed -2.7835689 7.917027 3.087658 Interphase -6.9357886 6.712224 2.7429802 Interphase -3.8264802 6.220592 3.507667 Binuclear -4.595579 6.65048 4.140379 Binuclear -4.1757784 7.4334106 3.8144276 Binuclear -3.0058465 7.4944515 3.802316 Binuclear -6.116799 8.249909 3.8507047 Prometaphase -6.2150445 7.2247543 2.9273603 MetaphaseAlignment -4.4351907 6.671476 1.9235048 Interphase -4.4400635 6.696851 1.9057857 Interphase -7.7870965 6.6146097 3.431157 MetaphaseAlignment -3.4550345 6.3847427 3.8035755 Binuclear -4.9957056 8.144372 2.3484302 Interphase -4.995229 8.251327 2.3971925 Interphase -3.563571 6.5571046 3.7138672 Binuclear -3.8656886 6.6715546 2.5932527 Polylobed -8.370638 5.733262 3.463335 MetaphaseAlignment -3.6634066 6.8263702 2.972667 Polylobed -7.45064 6.6918697 4.2932725 Prometaphase -4.50889 9.032661 3.2238824 Binuclear -4.894464 8.331978 2.371626 Interphase -4.186359 8.776638 3.1712148 Binuclear -4.394298 7.4714108 3.1833913 Interphase -5.2484164 8.132138 3.9585025 Prometaphase -2.813407 7.8167796 3.0521567 Interphase -3.560421 9.281825 3.0709894 Interphase -5.5134363 6.5078807 1.849436 Interphase -6.43173 6.6475635 3.1706376 MetaphaseAlignment -6.8269196 6.78118 4.2780786 Prometaphase -7.550659 7.1002426 4.006527 MetaphaseAlignment -7.981238 6.108558 3.0959144 MetaphaseAlignment -7.767639 6.2745023 3.8994265 MetaphaseAlignment -7.2399497 6.5337577 4.390532 MetaphaseAlignment -6.0331874 6.818385 5.304132 MetaphaseAlignment -6.746209 7.500696 3.8479347 MetaphaseAlignment -6.2824817 7.040576 5.0491543 Prometaphase -6.5398884 7.54112 3.5849354 MetaphaseAlignment -8.008976 5.8975806 3.3548646 MetaphaseAlignment -7.758537 6.879953 4.1307535 MetaphaseAlignment -8.12761 6.564469 4.0936966 Prometaphase -8.15986 6.5177774 4.0804715 Prometaphase -5.7743654 8.377399 3.5873098 MetaphaseAlignment -5.0139794 9.353451 3.7226794 MetaphaseAlignment -8.075293 5.8427944 3.5167933 MetaphaseAlignment -7.8124 5.8949337 3.6207614 MetaphaseAlignment -5.8892612 8.928599 3.6389651 MetaphaseAlignment -6.589858 6.703807 3.4970708 MetaphaseAlignment -5.850086 9.002183 3.595169 MetaphaseAlignment -5.4118767 6.551887 2.7950785 Prometaphase -7.663562 6.8934646 3.999364 Prometaphase -7.297689 6.549543 4.304689 MetaphaseAlignment -5.5513906 7.554947 3.7412891 Prometaphase -7.704506 7.031123 4.1922107 Prometaphase -7.903513 6.8230343 4.1485205 Prometaphase -6.1318316 7.808185 3.546592 MetaphaseAlignment -6.132539 6.823349 5.1608934 MetaphaseAlignment -6.7031755 6.636036 4.7728987 Prometaphase -7.9217668 6.255168 4.0496216 Prometaphase -7.3966227 6.596068 3.1153271 MetaphaseAlignment -6.220808 7.2082973 4.7425957 MetaphaseAlignment -6.363967 7.21205 4.635011 MetaphaseAlignment -7.4121647 6.6508384 3.192257 MetaphaseAlignment -2.8528347 7.8606057 3.0208817 Interphase -3.2319133 6.614465 4.3362985 Polylobed -3.2461972 6.65629 4.230337 Polylobed -3.0804126 6.3795967 3.771476 Polylobed -2.7145193 6.0075817 3.450831 Polylobed -3.82783 6.695939 3.559205 Binuclear -4.4553757 8.610914 2.7265184 Interphase -3.9650328 6.9803157 3.6923156 Binuclear -6.946242 6.6411834 2.7541947 Interphase -3.3974297 9.576198 3.0983484 Interphase -4.0235453 6.79452 3.7786703 Binuclear -4.39191 6.6933885 4.214979 Binuclear -4.6962056 8.788341 2.528894 Interphase -4.575651 8.380163 2.657683 Interphase -8.08241 5.90517 2.9223537 MetaphaseAlignment -4.4623985 8.5744915 2.6587498 Interphase -7.6116967 6.4876485 3.7712862 MetaphaseAlignment -4.481535 7.348126 2.239205 Interphase -4.840855 8.783857 2.5242927 Interphase -8.946242 5.942364 3.6838915 Prometaphase -8.202123 5.9827037 2.6679745 MetaphaseAlignment -5.1493 7.6315084 3.7717338 Prometaphase -4.559237 6.0685062 3.0386748 Polylobed -5.993165 7.0694203 5.1642823 Artefact -4.3022065 6.2899475 2.9080806 Polylobed -6.2357697 7.172162 4.8870215 Artefact -4.154215 6.4125514 2.6871326 Polylobed -6.0909147 7.1106157 5.07902 Artefact -6.3202744 7.1802106 4.734565 Artefact -4.027747 6.127062 2.9635084 Binuclear -4.425623 5.7463098 3.1200972 Binuclear -8.905407 5.947881 3.6511981 Prometaphase -7.0560026 8.039652 3.305827 Prometaphase -5.913422 6.6139035 3.2417676 Interphase -2.836985 7.818819 3.0504544 Interphase -5.987535 6.5557957 2.10462 Interphase -5.204184 7.4680576 4.4033375 MetaphaseAlignment -5.413232 6.3371153 5.3935065 Polylobed -5.5514226 6.270878 5.4508586 Polylobed -7.5439916 6.411822 3.9726982 MetaphaseAlignment -7.4748006 6.9331007 4.017053 MetaphaseAlignment -6.075894 7.916704 3.5589213 MetaphaseAlignment -7.285548 6.5237813 4.2457566 MetaphaseAlignment -6.286638 7.3751903 4.8223896 Prometaphase -7.6575813 6.299306 3.4859595 Prometaphase -7.883844 5.918935 3.1890118 MetaphaseAlignment -7.755843 6.360261 3.76478 MetaphaseAlignment -4.3458705 8.538281 4.3288293 Polylobed -4.322909 8.533818 4.314966 Polylobed -3.492105 5.6483717 4.063902 Polylobed -4.2858295 8.537368 4.3102665 Polylobed -5.8716736 8.810761 3.4507694 MetaphaseAlignment -2.6729498 6.774546 4.2760916 Polylobed -2.580649 6.7755437 4.340617 Polylobed -3.0333118 9.132522 3.7795572 Polylobed -7.9547424 5.7537894 3.386923 MetaphaseAlignment -4.114799 6.511362 2.8509042 Binuclear -2.868964 8.383439 3.6485794 Binuclear -2.8009667 8.598304 3.6153843 Binuclear -4.2270207 6.4742537 2.8687286 Binuclear -2.7331114 8.891603 3.5505888 Binuclear -2.747689 9.511155 3.5445473 Binuclear -2.823684 9.48208 3.4761753 Binuclear -3.4894967 9.541594 2.8796024 Binuclear -3.5152993 9.5942545 2.9006453 Binuclear -3.0966988 9.542036 3.3167446 Binuclear -3.7716358 9.558552 2.778781 Binuclear -3.4456372 7.5896173 3.9944217 Binuclear -3.757877 6.737339 4.197016 Binuclear -3.4648676 8.29877 3.879322 Binuclear -2.5115285 9.416274 4.0257664 Polylobed -3.3397727 8.533045 3.7995875 Binuclear -2.6364138 9.551685 3.9035916 Polylobed -2.5789747 9.423088 3.9616277 Polylobed -2.828539 7.035173 3.740025 Binuclear -2.7748399 6.8731194 3.7698295 Binuclear -2.6590981 8.537376 3.8223577 Binuclear -2.8108304 8.49535 3.7246983 Binuclear -2.8124008 9.543537 3.4316418 Binuclear -2.9610314 9.010769 3.810333 Binuclear -3.8143463 7.2738166 3.2928183 Binuclear -2.879568 9.484002 3.4430172 Binuclear -2.8370569 8.050175 3.7253869 Binuclear -2.613867 9.407553 3.5072162 Binuclear -2.713254 8.699125 3.6967683 Binuclear -2.5756314 9.407239 3.501208 Binuclear -2.6724892 8.752791 3.731519 Binuclear -2.7486513 9.487824 3.4600399 Binuclear -2.6572587 9.310815 3.6337879 Binuclear -3.0199964 8.764073 3.6098697 Binuclear -2.762064 9.3799095 3.548886 Binuclear -3.2218444 8.578041 3.7002048 Binuclear -3.300913 8.196292 4.0990644 Binuclear -3.313437 8.209677 4.4128904 Binuclear -2.8668108 9.335886 3.6266954 Binuclear -3.3821566 8.893723 3.2700691 Binuclear -2.856981 9.289512 3.613462 Binuclear -2.9594626 9.201099 3.7829807 Binuclear -2.8333719 9.284274 3.5640826 Binuclear -2.7371323 8.90115 3.6609247 Binuclear -2.7807772 8.720687 3.5889938 Binuclear -2.7412775 9.251562 3.5470371 Binuclear -2.8571496 8.744639 3.9836936 Binuclear -3.0796847 8.16675 4.1335115 Binuclear -4.5362782 8.271951 4.1337895 Binuclear -3.0959384 9.59852 3.260676 Binuclear -4.695507 6.8883853 4.3474493 Binuclear -3.1190872 9.545394 3.3092492 Binuclear -2.9488847 8.758832 3.95361 Binuclear -3.3862548 7.969647 4.286604 Binuclear -3.4984906 7.504417 4.0780687 Binuclear -3.5506198 7.598361 4.157257 Binuclear -4.1012053 7.4679127 2.9383261 Binuclear -3.3185983 9.665936 3.0407 Binuclear -3.0446045 9.55049 3.36627 Binuclear -3.0416067 9.572503 3.3426318 Binuclear -3.1474028 9.479719 3.484914 Polylobed -2.6114259 9.111702 3.6446934 Binuclear -5.679978 6.0972233 4.3669496 Polylobed -2.6520307 8.96121 3.617023 Binuclear -3.311537 9.256752 3.8512454 Polylobed -5.268785 5.9985185 4.618052 Polylobed -3.1361744 9.28856 3.79485 Polylobed -3.1104927 9.374639 3.7486172 Polylobed -4.571662 5.4813843 3.5161035 Polylobed -4.7767906 6.851005 3.7959416 Binuclear -3.7219112 8.382139 3.3495002 Binuclear -2.6870615 9.364537 3.5465975 Binuclear -2.694628 9.461534 3.4933748 Binuclear -2.7131338 9.421319 3.4775128 Binuclear -2.5777435 9.324036 3.55661 Binuclear -2.5073907 9.136618 3.7436712 Binuclear -2.7010634 9.410122 3.4964807 Binuclear -2.4767706 9.167546 3.6886573 Binuclear -4.4394093 9.027943 3.3742473 Interphase -2.9372184 9.375766 3.4573538 Binuclear -2.5505838 9.127906 3.7051828 Binuclear -3.1514523 9.314962 3.4393423 Binuclear -5.6890364 9.27931 3.0680256 Interphase -2.4856384 9.091889 3.8359363 Binuclear -4.5958104 8.88909 3.391341 Interphase -5.4838886 8.820471 2.8852584 Interphase -5.589244 9.433911 3.1868129 Interphase -2.8374062 8.672717 3.555665 Binuclear -4.1481643 8.933906 3.3434973 Binuclear -2.6810725 8.555039 3.6543088 Binuclear -2.9475558 9.616232 3.5908964 Binuclear -3.4204493 9.478251 3.1443686 Polylobed -2.8439107 9.629507 3.5304148 Polylobed -2.8191643 9.677133 3.5062513 Polylobed -4.8844004 9.394731 2.9437222 Artefact -4.843469 9.193599 2.901892 Artefact -4.8307877 9.411482 2.9901717 Artefact -2.8323574 9.430807 3.5203893 Polylobed -5.685919 7.0631886 2.9561646 Polylobed -5.8205104 7.652508 2.9754903 Polylobed -2.820185 9.352616 3.5638187 Polylobed -4.6753945 7.6093035 3.560822 Polylobed -5.3508134 8.922504 3.5048685 Interphase -2.865852 9.350826 3.5847924 Polylobed -3.2758408 8.148676 4.2184057 Polylobed -3.030705 7.839172 4.0702076 Polylobed -3.0205033 7.8088336 4.097872 Polylobed -3.044175 7.7816916 4.0688 Polylobed -2.3865721 8.826136 3.8273635 Polylobed -2.3607945 8.677456 3.7861369 Polylobed -2.3818498 8.707699 3.7783513 Polylobed -4.9416933 8.973635 3.6238768 Apoptosis -5.170473 9.305298 3.534316 Apoptosis -4.593503 9.482739 3.094845 Artefact -4.7479367 9.538798 3.107585 Artefact -3.0785263 9.328954 3.244113 Artefact -5.0973077 9.3428135 3.3803391 Artefact -4.7037296 9.498151 3.08745 Artefact -2.9972246 9.20923 3.491632 Artefact -4.680906 9.487179 3.0738745 Artefact -4.637124 8.9058485 2.736645 Artefact -2.9896908 9.282478 3.4110804 Artefact -4.7403073 9.4987135 3.0878208 Artefact -4.8650174 9.505775 3.0644922 Artefact -4.9489036 7.6307144 4.353613 Hole -2.608046 9.387305 3.7990513 Polylobed -2.7102144 9.549838 3.9223096 Polylobed -2.7191412 9.5173025 3.863896 Polylobed -3.0435581 8.646755 4.1476474 Polylobed -3.0923622 8.788829 3.9867733 Polylobed -2.6503274 9.508054 3.9078634 Polylobed -3.0861917 8.6002655 4.191927 Polylobed -7.7636847 6.1521773 2.4724224 Hole -2.507721 7.8734283 3.8640792 Polylobed -2.4521554 7.852751 3.899332 Polylobed -2.4582326 7.830548 3.8773155 Polylobed -2.4497182 7.823821 3.8897789 Polylobed -2.5138514 7.8923216 3.8761058 Polylobed -3.1781323 8.418049 4.411856 Polylobed -3.5792892 7.3593087 4.449195 Polylobed -5.9165998 8.127312 3.62591 Polylobed -5.844394 8.206088 3.5840466 Polylobed -3.0860462 8.98731 4.1219325 Polylobed -3.6330397 9.464651 3.1165774 Hole -2.7349434 8.91429 3.9255705 Polylobed -5.5766835 6.7556596 2.3308604 Binuclear -5.441889 6.7239943 2.2766073 Binuclear -7.042503 6.9894304 2.6266875 UndefinedCondensed -5.7163186 6.7283473 2.8018684 Large -2.9780884 7.900745 3.7046137 Interphase -3.1112077 9.542591 3.319839 Binuclear -3.0229266 9.552399 3.3406339 Binuclear -2.7979183 8.0253525 3.591938 Interphase -2.8077326 7.936875 3.6258152 Binuclear -3.516217 8.686992 3.3264842 Binuclear -2.821595 7.8055367 3.545604 Binuclear -2.8301218 8.638759 3.530518 Binuclear -2.8530257 8.6721 3.5307212 Binuclear -4.075854 6.770067 2.4290664 Binuclear -4.121283 6.781831 2.398994 Binuclear -5.4901204 8.622794 3.6331637 Hole -7.645736 6.49176 2.9610255 Folded -4.062221 6.5982795 3.049097 Folded -5.4474773 6.89276 2.455214 Hole -5.6103516 7.1517963 3.1005685 Folded -6.6075954 6.7984824 3.176279 Hole -8.0187645 6.3247733 2.5599236 Hole -7.1629987 6.416382 3.8989139 Folded -4.2766733 9.261221 2.9236636 Hole -5.227637 5.7248645 2.828871 Hole -4.6455073 6.0648713 3.8989289 Folded -4.813309 5.708975 3.046609 Folded -6.236294 7.457767 3.3504894 Hole -6.5646715 7.304397 3.569728 Hole -4.2381616 6.1075644 4.5902314 Polylobed -4.049437 5.418553 5.3855624 Polylobed -5.0243273 6.1602364 2.2816403 Hole -4.5740643 6.04427 2.1003022 Elongated -5.6592674 5.603634 2.9119587 Folded -7.5059648 6.776659 3.693458 Apoptosis -5.428893 5.6051097 2.9303184 Folded -7.57528 6.8711185 3.8763614 Apoptosis -5.167035 5.7779055 2.8444273 Folded -5.9697213 7.4998593 3.3220716 Hole -8.452522 6.3421235 2.2079685 Apoptosis -7.7845435 6.0701957 3.4006457 Apoptosis -7.3075223 5.8338776 3.2596838 Apoptosis -6.640851 6.522578 3.4858782 Apoptosis -8.910543 5.9679804 3.5329645 Apoptosis -7.4465747 6.3592157 2.8232167 Apoptosis -7.4679193 6.3726 2.8822613 Apoptosis -8.459375 6.1522927 2.2401779 Apoptosis -8.385618 6.1917253 2.2924101 Apoptosis -7.9151545 6.7249923 2.068753 Hole -7.7869496 6.623512 2.2087772 Hole -6.3948717 7.286856 3.4128153 Apoptosis -8.540467 6.043836 2.5430498 Apoptosis -8.744202 5.894247 2.4397004 Apoptosis -8.69781 5.921256 2.4061608 Apoptosis -6.3240194 7.56538 3.0726788 Folded -5.653155 6.609898 2.2885718 Folded -6.764286 5.8724756 3.045602 Polylobed -6.5699472 7.7977467 2.1070266 Hole -4.6036882 6.4690804 1.9756691 Folded -4.0302563 5.6796665 3.9260604 Folded -3.9813805 5.5214643 5.433066 Polylobed -3.935024 5.245295 3.5943372 Folded -3.9062757 5.238665 3.5776873 Folded -6.095666 6.7455263 2.5592172 Folded -6.0322075 6.7659135 2.5744271 Folded -4.887108 6.4526772 2.28493 Folded -6.0459476 8.115034 2.1262822 Hole -6.3396163 7.5524783 3.0543475 Folded -4.80609 6.4438715 2.0780048 Folded -6.5781155 7.737935 2.050859 Hole -4.6091523 6.0342774 2.040937 Folded -7.6168017 6.713656 3.2690692 Apoptosis -7.1324625 8.008084 2.2195406 Apoptosis -7.14972 8.065534 2.289315 Apoptosis -6.1868935 7.9803433 2.0804489 SmallIrregular -7.914721 6.699723 2.951642 Apoptosis -7.571708 7.830192 2.2100422 SmallIrregular -7.488845 6.554 3.2699256 Apoptosis -4.589174 8.128078 3.1820514 Binuclear -7.3331227 7.990572 3.1525776 Apoptosis -4.672997 8.225946 3.712712 Binuclear -4.768547 9.050996 3.9050965 Polylobed -5.1521263 9.02308 3.7320235 Polylobed -4.918208 9.259218 3.771602 Polylobed -5.1061587 9.186764 3.5937698 Polylobed -5.093008 9.217497 3.6073472 Polylobed -4.945679 9.250998 3.7076526 Polylobed -5.1174135 9.338891 3.393212 Polylobed -5.2088313 9.100434 3.0221388 Polylobed -4.7576585 7.567896 3.3894699 Polylobed -6.0838346 7.98545 2.0471318 SmallIrregular -2.760066 8.639089 4.0595484 Binuclear -3.007162 7.960248 3.9196994 Binuclear -7.868408 7.577858 1.8886054 Hole -7.9637046 7.4352155 1.8355407 SmallIrregular -7.996136 7.344329 1.8354558 Hole -4.5191126 7.2658405 2.3580277 Elongated -7.1868978 7.6386213 2.0344734 Hole -7.481013 7.8479366 1.9671348 Hole -6.458808 6.2152457 2.1823003 Folded -6.4771724 6.201169 2.1814873 Folded -6.1108184 8.032058 2.0489867 SmallIrregular -6.097323 7.662049 2.692014 SmallIrregular -7.577962 7.758708 2.0212612 Hole -6.943292 7.956029 2.9481368 Hole -8.106078 7.0534596 1.8375452 Hole -7.54605 7.8063116 2.038277 SmallIrregular -8.03334 7.2602043 1.8277609 Hole -8.221271 6.344029 3.1402104 Hole -7.1473584 8.316239 2.5502248 Hole -6.3381276 8.575486 2.6439972 Prometaphase -6.4236894 9.320322 2.3897579 SmallIrregular -2.879613 9.537886 3.460436 Polylobed -6.9266477 8.169264 1.9948798 SmallIrregular -7.300713 8.250344 2.2530127 SmallIrregular -7.1883755 8.328465 2.2440042 SmallIrregular -3.946911 7.6257305 3.3753595 Polylobed -3.715643 7.2946677 3.377848 Polylobed -7.0171514 6.1587644 2.4358764 Elongated -7.2536044 6.3005557 2.4239783 SmallIrregular -5.5166984 6.906894 3.2186248 SmallIrregular -6.4988036 6.2028265 2.1809382 Folded -6.477572 6.2104416 2.176758 Folded -4.705525 6.5550776 1.9972265 Elongated -5.891654 5.9045067 2.3700216 Elongated -5.2489505 9.188406 3.343138 Polylobed -5.1790123 9.298129 3.2244093 Polylobed -5.2117033 9.396034 3.0057516 Polylobed -5.217713 9.086104 3.0234315 Polylobed -6.0806203 7.988574 2.0588956 SmallIrregular -7.6641145 7.8387914 1.9648603 SmallIrregular -7.245618 8.028962 2.4704707 SmallIrregular -7.2205086 8.314524 2.2166607 SmallIrregular -7.3981323 8.203065 2.1684375 SmallIrregular -3.8076231 7.7372503 3.1081553 Polylobed -3.820087 7.4998503 2.897489 Polylobed -3.8933039 7.8575554 2.7318006 Polylobed -4.5783195 9.240258 3.450512 MetaphaseAlignment -6.439039 8.81922 3.2548592 MetaphaseAlignment -8.069276 5.907904 3.37555 MetaphaseAlignment -7.2078657 7.9684453 3.286461 MetaphaseAlignment -6.3155074 8.170381 3.236764 MetaphaseAlignment -6.4114614 8.324028 3.281745 MetaphaseAlignment -6.080367 8.539432 3.7835884 MetaphaseAlignment -7.0922923 8.226645 3.1986797 MetaphaseAlignment -6.284193 8.058048 3.7469025 MetaphaseAlignment -5.9838543 8.6797085 3.6212695 MetaphaseAlignment -6.1378083 8.968031 3.2425244 Apoptosis -6.9215746 7.868809 2.62808 Apoptosis -6.91205 7.9065204 2.6157691 Apoptosis -5.6227922 7.913946 3.754948 MetaphaseAlignment -6.9954205 7.8637676 2.5747988 Apoptosis -5.9639826 8.565729 3.833225 MetaphaseAlignment -8.004406 6.0414495 3.265268 MetaphaseAlignment -7.316079 6.654698 3.7190583 MetaphaseAlignment -7.279823 6.55261 3.570943 MetaphaseAlignment -6.3985386 7.069759 3.205687 Apoptosis -5.5281043 8.715253 3.5124 Apoptosis -6.375243 8.339583 2.650088 MetaphaseAlignment -5.8247323 7.6706915 3.400552 MetaphaseAlignment -6.076665 8.267826 3.9380388 MetaphaseAlignment -5.857596 8.857153 3.3047435 MetaphaseAlignment -8.184989 6.627116 1.95678 Hole -6.8067036 7.6356125 3.562786 Hole -8.188016 6.167039 2.5391512 Apoptosis -8.195068 6.20034 2.5295548 Apoptosis -6.4327292 5.942656 2.469759 Elongated -5.854267 8.178389 3.340686 Elongated -8.2905035 6.1524863 2.3525076 Apoptosis -7.688562 7.744689 1.9454814 Hole -7.696616 7.770181 1.9559716 Hole -7.6500444 7.820557 2.042878 Hole -4.6483946 5.8837476 2.4568117 Elongated -4.6010594 7.439645 3.220833 Interphase -7.5859284 6.900271 2.315211 Interphase -6.7619934 6.670223 2.787875 Interphase -5.6500406 8.107865 3.4220777 Elongated -6.6756263 7.3550515 4.1414056 Elongated -6.9028015 8.3815975 2.4631763 Interphase -8.129937 6.307232 2.2023125 Interphase -6.3406262 9.053574 2.4851253 Interphase -6.2941155 9.056282 2.4780502 Interphase -7.1003428 8.355445 2.3859367 Interphase -6.4286094 7.1545515 2.5936284 Interphase -5.6868567 7.7755556 3.4442217 Hole -5.647987 8.075115 3.4383433 Elongated -7.1342926 7.7059817 3.1897404 Hole -6.2868533 6.5261555 3.0411758 SmallIrregular -6.5116324 7.779601 3.6632788 SmallIrregular -7.3432755 7.344128 2.660602 SmallIrregular -8.005295 6.378041 2.6632903 SmallIrregular -7.6769767 7.780053 1.9929988 SmallIrregular -7.586943 7.7993703 2.1257002 SmallIrregular diff --git a/2.analyze_data/utils/analysisUtils.py b/2.analyze_data/utils/analysisUtils.py deleted file mode 100644 index 5928e25b..00000000 --- a/2.analyze_data/utils/analysisUtils.py +++ /dev/null @@ -1,199 +0,0 @@ -from distutils.ccompiler import new_compiler -import numpy as np -import pathlib -from sklearn.datasets import load_digits -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -import matplotlib.pyplot as plt -from matplotlib.lines import Line2D -from matplotlib.colors import rgb2hex -import seaborn as sns -import pandas as pd -import umap - -np.random.seed(0) - - -def get_features_data(load_path: pathlib.Path) -> pd.DataFrame: - """get features data from csv at load path - Args: - load_path (pathlib.Path): path to training data csv - Returns: - pd.DataFrame: training dataframe - """ - # read dataset into pandas dataframe - features_data = pd.read_csv(load_path, index_col=0) - - # remove training data with ADCCM class as this class was not used for classification in original paper - features_data = features_data[ - features_data["Mitocheck_Phenotypic_Class"] != "ADCCM" - ] - - # replace shape1 and shape3 labels with their correct respective classes - features_data = features_data.replace("Shape1", "Binuclear") - features_data = features_data.replace("Shape3", "Polylobed") - - return features_data - - -def show_1D_umap(features_dataframe: pd.DataFrame, metadata_series: pd.Series, save_path: str = None): - """show 1D umap with features, colored by metadata categories - - Args: - features_dataframe (pd.DataFrame): features to compress with umap - metadata_series (pd.Series): metadata to color umap - save_path (str, optional): save path for umap embeddings/images. If none nothing will be saved. Defaults to None. - """ - # create umap object for dimension reduction - reducer = umap.UMAP(random_state=0, n_components=1) - - # get feature values as numpy array - feature_data = features_dataframe.values - - # Fit UMAP and extract latent var 1 - embedding = pd.DataFrame(reducer.fit_transform(feature_data), columns=["UMAP1"]) - - # create random y distribution to space out points - y_distribution = np.random.rand(feature_data.shape[0]) - embedding["y_distribution"] = y_distribution.tolist() - - # add phenotypic class to embeddings - embedding[metadata_series.name] = metadata_series.tolist() - - plt.figure(figsize=(15, 12)) - - # Produce scatterplot with umap data, using phenotypic classses to color - sns_plot = sns.scatterplot( - palette="rainbow", - x="UMAP1", - y="y_distribution", - data=embedding, - hue=embedding[metadata_series.name].tolist(), - alpha=0.5, - linewidth=0, - ) - # Adjust legend - sns_plot.legend(loc="center left", bbox_to_anchor=(1, 0.5)) - # Label axes, title - sns_plot.set_xlabel("UMAP 1") - sns_plot.set_ylabel("Random Distribution") - sns_plot.set_title("1 Dimensional UMAP") - - # save umap - if not save_path == None: - embedding.to_csv(f"{save_path}/1D_umap.tsv", sep="\t", index=False) - plt.savefig(f"{save_path}/1D_umap.png", bbox_inches="tight") - - -def show_2D_umap(features_dataframe: pd.DataFrame, metadata_series: pd.Series, save_path=None): - """show 2D umap with features, colored by metadata categories - - Args: - features_dataframe (pd.DataFrame): features to compress with umap - metadata_series (pd.Series): metadata to color umap - save_path (str, optional): save path for umap embeddings/images. If none nothing will be saved. Defaults to None. - """ - # create umap object for dimension reduction - reducer = umap.UMAP(random_state=0, n_components=2) - - # get feature values as numpy array - feature_data = features_dataframe.values - - # Fit UMAP and extract latent vars 1-2 - embedding = pd.DataFrame( - reducer.fit_transform(feature_data), columns=["UMAP1", "UMAP2"] - ) - - # add phenotypic class to embeddings - embedding[metadata_series.name] = metadata_series.tolist() - - plt.figure(figsize=(15, 12)) - - # Produce scatterplot with umap data, using phenotypic classses to color - sns_plot = sns.scatterplot( - palette="rainbow", - x="UMAP1", - y="UMAP2", - data=embedding, - hue=embedding[metadata_series.name].tolist(), - alpha=0.5, - linewidth=0, - ) - # Adjust legend - sns_plot.legend(loc="center left", bbox_to_anchor=(1, 0.5)) - # Label axes, title - sns_plot.set_xlabel("UMAP 1") - sns_plot.set_ylabel("UMAP 2") - sns_plot.set_title("2 Dimensional UMAP") - - # save umap - if not save_path == None: - embedding.to_csv(f"{save_path}/2D_umap.tsv", sep="\t", index=False) - plt.savefig(f"{save_path}/2D_umap.png", bbox_inches="tight") - - -def show_3D_umap(features_dataframe: pd.DataFrame, metadata_series: pd.Series, save_path=None): - """show 3D umap with features, colored by metadata categories - - Args: - features_dataframe (pd.DataFrame): features to compress with umap - metadata_series (pd.Series): metadata to color umap - save_path (str, optional): save path for umap embeddings/images. If none nothing will be saved. Defaults to None. - """ - # create umap object for dimension reduction - reducer = umap.UMAP(random_state=0, n_components=3) - - # get feature values as numpy array - feature_data = features_dataframe.values - - # Fit UMAP and extract latent vars 1-3 - embedding = pd.DataFrame( - reducer.fit_transform(feature_data), columns=["UMAP1", "UMAP2", "UMAP3"] - ) - - # add phenotypic class to embeddings - embedding[metadata_series.name] = metadata_series.tolist() - - fig = plt.figure(figsize=(17, 17)) - ax = fig.gca(projection="3d") - cmap = sns.color_palette( - "rainbow", embedding[metadata_series.name].nunique() - ) - legend_elements = [] - - # add each phenotypic class to 3d graph and legend - for index, metadata_class in enumerate( - embedding[metadata_series.name].unique().tolist() - ): - class_embedding = embedding.loc[ - embedding[metadata_series.name] == metadata_class - ] - x = class_embedding["UMAP1"] - y = class_embedding["UMAP2"] - z = class_embedding["UMAP3"] - ax.scatter(x, y, z, c=rgb2hex(cmap[index]), marker="o", alpha=0.5) - legend_elements.append( - Line2D( - [0], - [0], - marker="o", - color="w", - label=metadata_class, - markerfacecolor=rgb2hex(cmap[index]), - markersize=10, - ) - ) - - plt.legend(handles=legend_elements, loc="center left", bbox_to_anchor=(1, 0.5)) - # Label axes, title - ax.set_xlabel("UMAP 1") - ax.set_ylabel("UMAP 2") - ax.set_zlabel("UMAP 3") - ax.set_title("3 Dimensional UMAP") - - # save umap - if not save_path == None: - embedding.to_csv(f"{save_path}/3D_umap.tsv", sep="\t", index=False) - plt.savefig(f"{save_path}/3D_umap.png", bbox_inches="tight") - - plt.show() diff --git a/2.train_model/README.md b/2.train_model/README.md new file mode 100644 index 00000000..46f2649b --- /dev/null +++ b/2.train_model/README.md @@ -0,0 +1,47 @@ +# 2. Train Model + +In this module, we train a ML model to predict phenotypic class from DeepProfiler-extracted features. + +We train the model in [train_model.ipynb](1.train_model.ipynb). +We use [sklearn.linear_model.LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) for our machine learning model. +We use the following parameters for our Logisic Regression model: + +- `penalty='elasticnet'`: Use elasticnet as the penalty for our model. +Elastic-Net regularization is a combination of L1 and L2 regularization methods. +The mixing of these two methods is determined by the `l1_ratio` parameter which we optimize later. +- `solver='saga'`: We use the saga solver as this is the only solver that supports Elastic-Net regularization. +- `max_iter=100`: Set the maximum number of iterations for solver to converge. 100 iterations allows the solver to maximize performance without completing unnecessary iterations. + +We use [sklearn.model_selection.GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV) to perform an exhaustive search for the parameters below. +This searches for parameters that maximize the weighted F1 score of the model. +We optimize weighted F1 score because this metric measures model precision and recall aand accounts for label imbalance (see [sklearn.metrics.f1_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) for more details). + +- `l1_ratio`: Elastic-Net mixing parameter. +Used to combine L1 and L2 regularization methods. +We search over the following parameters: `[0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]` +- `C`: Inversely proportional to regularization strength. +We search over the following parameters: `[1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03]` + +The best parameters are used to train a final model on all of the training data. +This final model is saved in [log_reg_model.joblib](models/log_reg_model.joblib). +The model derived from shuffled training data is saved in [shuffled_baseline_log_reg_model.joblib](models/shuffled_baseline_log_reg_model.joblib). + +## Step 1: Train Model + +Use the commands below to train the ML model: + +```sh +# Make sure you are located in 2.train_model +cd 2.train_model + +# Activate phenotypic_profiling conda environment +conda activate phenotypic_profiling + +# Train model +bash train_model.sh +``` +## Results + +The weighted F1 score of the best estimators for the grid searches are as follows (can be found in [train_model.ipynb](train_model.ipynb)): +- Final model: 0.79848 +- Shuffled baseline model: 0.19864 \ No newline at end of file diff --git a/2.train_model/models/log_reg_model.joblib b/2.train_model/models/log_reg_model.joblib new file mode 100644 index 00000000..2533d611 Binary files /dev/null and b/2.train_model/models/log_reg_model.joblib differ diff --git a/2.train_model/models/shuffled_baseline_log_reg_model.joblib b/2.train_model/models/shuffled_baseline_log_reg_model.joblib new file mode 100644 index 00000000..55460fc6 Binary files /dev/null and b/2.train_model/models/shuffled_baseline_log_reg_model.joblib differ diff --git a/3.ML_model/scripts/1.train_model.py b/2.train_model/scripts/nbconverted/train_model.py similarity index 84% rename from 3.ML_model/scripts/1.train_model.py rename to 2.train_model/scripts/nbconverted/train_model.py index 47fde5c4..fdd6dae5 100644 --- a/3.ML_model/scripts/1.train_model.py +++ b/2.train_model/scripts/nbconverted/train_model.py @@ -19,9 +19,9 @@ from joblib import dump import sys -# adding utils to system path -sys.path.insert(0, '../utils') -from MlPipelineUtils import get_features_data, get_dataset, get_X_y_data +sys.path.append("../utils") +from split_utils import get_features_data +from train_utils import get_dataset, get_X_y_data # ### Load training data and create stratified folds for cross validation @@ -32,11 +32,12 @@ # set numpy seed to make random operations reproduceable np.random.seed(0) -results_dir = pathlib.Path("../results/") +results_dir = pathlib.Path("models/") +results_dir.mkdir(parents=True, exist_ok=True) # load training data from indexes and features dataframe -data_split_path = pathlib.Path(f"{results_dir}/0.data_split_indexes.tsv") -features_dataframe_path = pathlib.Path("../../1.format_data/data/training_data.csv.gz") +data_split_path = pathlib.Path(f"../1.split_data/indexes/data_split_indexes.tsv") +features_dataframe_path = pathlib.Path("../0.download_data/data/training_data.csv.gz") features_dataframe = get_features_data(features_dataframe_path) data_split_indexes = pd.read_csv(data_split_path, sep="\t", index_col=0) @@ -97,7 +98,7 @@ # save final estimator -dump(grid_search_cv.best_estimator_, f"{results_dir}/1.log_reg_model.joblib") +dump(grid_search_cv.best_estimator_, f"{results_dir}/log_reg_model.joblib") # ## Repeat process with shuffling to create shuffled baseline model @@ -151,5 +152,5 @@ # save final estimator -dump(grid_search_cv.best_estimator_, f"{results_dir}/1.shuffled_baseline_log_reg_model.joblib") +dump(grid_search_cv.best_estimator_, f"{results_dir}/shuffled_baseline_log_reg_model.joblib") diff --git a/2.train_model/train_model.ipynb b/2.train_model/train_model.ipynb new file mode 100644 index 00000000..12278420 --- /dev/null +++ b/2.train_model/train_model.ipynb @@ -0,0 +1,3634 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import pathlib\n", + "\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import (\n", + " StratifiedKFold,\n", + " GridSearchCV,\n", + ")\n", + "from sklearn.utils import shuffle\n", + "from joblib import dump\n", + "\n", + "import sys\n", + "sys.path.append(\"../utils\")\n", + "from split_utils import get_features_data\n", + "from train_utils import get_dataset, get_X_y_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load training data and create stratified folds for cross validation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pathlib.Path(f\"../1.split_data/indexes/data_split_indexes.tsv\")\n", + "features_dataframe_path = pathlib.Path(\"../0.download_data/data/training_data.csv.gz\")\n", + "\n", + "features_dataframe = get_features_data(features_dataframe_path)\n", + "data_split_indexes = pd.read_csv(data_split_path, sep=\"\\t\", index_col=0)\n", + "\n", + "training_data = get_dataset(features_dataframe, data_split_indexes, \"train\")\n", + "training_data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3398, 1280)\n", + "(3398,)\n" + ] + } + ], + "source": [ + "X, y = get_X_y_data(training_data)\n", + "\n", + "print(X.shape)\n", + "print(y.shape)\n", + "\n", + "# create stratified data sets for k-fold cross validation\n", + "straified_k_folds = StratifiedKFold(n_splits=10, shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define model without C/l1_ratio parameters\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# create logistic regression model with following parameters\n", + "log_reg_model = LogisticRegression(\n", + " penalty=\"elasticnet\", solver=\"saga\", max_iter=100, n_jobs=-1, random_state=0\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform grid search for best C and l1_ratio parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameters being tested: {'C': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03]), 'l1_ratio': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# hypertune parameters with GridSearchCV\n", + "parameters = {\"C\": np.logspace(-3, 3, 7), \"l1_ratio\": np.linspace(0, 1, 11)}\n", + "#parameters = {\"C\": [0.1], \"l1_ratio\": [0.0]}\n", + "print(f\"Parameters being tested: {parameters}\")\n", + "grid_search_cv = GridSearchCV(\n", + " log_reg_model, parameters, cv=straified_k_folds, n_jobs=-1, scoring=\"f1_weighted\",\n", + ")\n", + "grid_search_cv = grid_search_cv.fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: {'C': 1.0, 'l1_ratio': 0.6000000000000001}\n", + "Score of best estimator: 0.7984819903105149\n" + ] + } + ], + "source": [ + "print(f\"Best parameters: {grid_search_cv.best_params_}\")\n", + "print(f\"Score of best estimator: {grid_search_cv.best_score_}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Save best model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['models/log_reg_model.joblib']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# save final estimator\n", + "dump(grid_search_cv.best_estimator_, f\"{results_dir}/log_reg_model.joblib\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Repeat process with shuffling to create shuffled baseline model" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3398, 1280)\n", + "(3398,)\n" + ] + } + ], + "source": [ + "X, y = get_X_y_data(training_data)\n", + "\n", + "print(X.shape)\n", + "print(y.shape)\n", + "\n", + "# shuffle columns of X (features) dataframe independently to create shuffled baseline\n", + "for column in X.T:\n", + " np.random.shuffle(column)\n", + "\n", + "# create stratified data sets for k-fold cross validation\n", + "straified_k_folds = StratifiedKFold(n_splits=10, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# create logistic regression model with following parameters\n", + "log_reg_model = LogisticRegression(\n", + " penalty=\"elasticnet\", solver=\"saga\", max_iter=100, n_jobs=-1, random_state=0\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameters being tested: {'C': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03]), 'l1_ratio': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n", + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# hypertune parameters with GridSearchCV\n", + "parameters = {\"C\": np.logspace(-3, 3, 7), \"l1_ratio\": np.linspace(0, 1, 11)}\n", + "#parameters = {\"C\": [1.0], \"l1_ratio\": [0.8]}\n", + "print(f\"Parameters being tested: {parameters}\")\n", + "grid_search_cv = GridSearchCV(\n", + " log_reg_model, parameters, cv=straified_k_folds, n_jobs=-1, scoring=\"f1_weighted\",\n", + ")\n", + "grid_search_cv = grid_search_cv.fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: {'C': 0.01, 'l1_ratio': 0.1}\n", + "Score of best estimator: 0.19864055345533443\n" + ] + } + ], + "source": [ + "print(f\"Best parameters: {grid_search_cv.best_params_}\")\n", + "print(f\"Score of best estimator: {grid_search_cv.best_score_}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['models/shuffled_baseline_log_reg_model.joblib']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# save final estimator\n", + "dump(grid_search_cv.best_estimator_, f\"{results_dir}/shuffled_baseline_log_reg_model.joblib\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.13 ('phenotypic_profiling')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "f9df586d1764dbc68785000a153dad1832127ac564b5e2e4c94e83fc43160b30" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2.train_model/train_model.sh b/2.train_model/train_model.sh new file mode 100644 index 00000000..cd4653d2 --- /dev/null +++ b/2.train_model/train_model.sh @@ -0,0 +1,5 @@ +#!/bin/bash +# Convert notebook to python file and execute +jupyter nbconvert --to python \ + --FilesWriter.build_directory=scripts/nbconverted \ + --execute train_model.ipynb diff --git a/3.ML_model/3.ML_model.sh b/3.ML_model/3.ML_model.sh deleted file mode 100644 index bd8dc473..00000000 --- a/3.ML_model/3.ML_model.sh +++ /dev/null @@ -1,12 +0,0 @@ -#!/bin/bash -# Step 0: Convert all notebooks to scripts -jupyter nbconvert --to=script \ - --FilesWriter.build_directory=scripts \ - notebooks/*.ipynb - -# Step 1: Execute all jupyter notebooks -jupyter nbconvert --to=html \ - --FilesWriter.build_directory=html \ - --ExecutePreprocessor.kernel_name=python3 \ - --ExecutePreprocessor.timeout=10000000 \ - --execute notebooks/*.ipynb \ No newline at end of file diff --git a/3.ML_model/README.md b/3.ML_model/README.md deleted file mode 100644 index a245e67b..00000000 --- a/3.ML_model/README.md +++ /dev/null @@ -1,114 +0,0 @@ -# 3. Machine Learning Model - -In this module, we train and validate a machine learning model for phenotypic classification (mitotic stage) of nuclei based on nuclei features. - -We use [Scikit-learn (sklearn)](https://scikit-learn.org/) for data manipulation, model training, and model evaluation. -Scikit-learn is described in [Pedregosa et al., JMLR 12, pp. 2825-2830, 2011](http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html) as a machine learning library for Python. -Its ease of implementation in a pipeline make it ideal for our use case. - -We consistently use the following parameters with many sklearn functions: - -- `n_jobs=-1`: Use all CPU cores in parallel when completing a task. -- `random_state=0`: Use seed 0 when shuffling data or generating random numbers. -This allows "random" sklearn operations to have consist results. -We also use `np.random.seed(0)` to make "random" numpy operations have consistent results. - -We use [seaborn](https://seaborn.pydata.org/) for data visualization. -Seaborn is described in [Waskom, M.L., 2021](https://doi.org/10.21105/joss.03021) as a library for making statisical graphics in python. - -All parts of the following pipeline are completed for a "final" model (from training data) and a "shuffled baseline" model (from shuffled training data). -This shuffled baseline model provides a suitable baseline comparison for the final model during evaluation. - -### A. Data Preparation - -First, we split the data into training, test, and holdout subsets in [0.split_data.ipynb](notebooks/0.split_data.ipynb). -The `get_representative_images()` function used to create the holdout dataset determines which images to holdout such that all phenotypic classes can be represented in these holdout images. -The test dataset is determined by taking a random number of samples (stratified by phenotypic class) from the dataset after the holdout images are removed. -The training dataset is the subset remaining after holdout/test samples are removed. -Sample indexes associated with training, test, and holdout subsets are stored in [0.data_split_indexes.tsv](results/0.data_split_indexes.tsv). -Sample indexes are used to load subsets from [training_data.csv.gz](../1.format_data/data/training_data.csv.gz). - -We use [sklearn.utils.shuffle](https://scikit-learn.org/stable/modules/generated/sklearn.utils.shuffle.html) to shuffle the training data in a consistent way. -This function shuffles the order of the training data samples while keeping the phenotypic class labels aligned with their respective features. -In other words, this function shuffles entire rows of training data to remove any ordering scheme. -This is necessary because the data as labeled from MitoCheck tends to have phenotypic classes in groups, which can introduce bias into the model. - -We use [sklearn.model_selection.StratifiedKFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html) to create stratified training/test data sets for cross validation. -The training and test data sets that are created have the same distribution of classes. -This ensures that each class is proportionally represented across all data sets. -We use `n_splits=10` to create 10 folds for cross-validation. - -To create the shuffled baseline training dataset, we first load the training data as described above. -We then shuffle each column of the training data independently, which removes any correlation between features and phenotypic class label. - -### B. Model Training - -We train the model in [1.train_model.ipynb](notebooks/1.train_model.ipynb). -We use [sklearn.linear_model.LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) for our machine learning model. -We use the following parameters for our Logisic Regression model: - -- `penalty='elasticnet'`: Use elasticnet as the penalty for our model. -Elastic-Net regularization is a combination of L1 and L2 regularization methods. -The mixing of these two methods is determined by the `l1_ratio` parameter which we optimize later. -- `solver='saga'`: We use the saga solver as this is the only solver that supports Elastic-Net regularization. -- `max_iter=100`: Set the maximum number of iterations for solver to converge. 100 iterations allows the solver to maximize performance without completing unnecessary iterations. - -We use [sklearn.model_selection.GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV) to perform an exhaustive search for the parameters below. This searches for parameters that maximize the weighted F1 score of the model. We optimize weighted F1 score because this metric measures model precision and recall aand accounts for label imbalance (see [sklearn.metrics.f1_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) for more details). - -- `l1_ratio`: Elastic-Net mixing parameter. -Used to combine L1 and L2 regularization methods. -We search over the following parameters: `[0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]` -- `C`: Inversely proportional to regularization strength. -We search over the following parameters: `[1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03]` - -The best parameters are used to train a final model on all of the training data. -This final model is saved in [1.log_reg_model.joblib](results/1.log_reg_model.joblib). -The model derived from shuffled training data is saved in [1.shuffled_baseline_log_reg_model.joblib](results/1.shuffled_baseline_log_reg_model.joblib). - -### C. Model Evaluation - -We train the model in [2.evaluate_model.ipynb](notebooks/2.evaluate_model.ipynb). -We use the final model and shuffled baseline model to predict the labels of the training, testing, and holdout datasets. -These predictions are saved in [results/2.model_predictions.tsv](results/2.model_predictions.tsv) and [2.shuffled_baseline_model_predictions.tsv](results/2.shuffled_baseline_model_predictions.tsv) respectively. - -We evaluate these 6 sets of predictions with a confusion matrix to see the true/false postives and negatives (see [sklearn.metrics.confusion_matrix](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html) for more details). - -We also evaluate these 6 sets of predictions with [sklearn.metrics.f1_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) to determine the final/shuffled baseline model's predictive performance on each subset. -F1 score measures the models precision and recall performance for each phenotypic class. - -### D. Model Interpretation - -We train the model in [3.interpret_model.ipynb](notebooks/3.interpret_model.ipynb). -The final model and shuffled baseline model coefficients are loaded from [1.log_reg_model.joblib](results/1.log_reg_model.joblib) and [1.shuffled_baseline_log_reg_model.joblib](results/1.shuffled_baseline_log_reg_model.joblib) respectively. -These coefficients are interpreted with the following diagrams: - -- We use [seaborn.heatmap](https://seaborn.pydata.org/generated/seaborn.heatmap.html) to display the coefficient values for each phenotypic class/feature. -- We use [seaborn.clustermap](https://seaborn.pydata.org/generated/seaborn.clustermap.html) to display a hierarchically-clustered heatmap of coefficient values for each phenotypic class/feature -- We use [seaborn.kedeplot](https://seaborn.pydata.org/generated/seaborn.kdeplot.html) to display a density plot of coeffiecient values for each phenotypic class. -- We use [seaborn.barplot](https://seaborn.pydata.org/generated/seaborn.barplot.html) to display a bar plot of average coeffiecient values per phenotypic class and feature. - -## Step 1: Setup Machine Learning Environment - -### Step 1a: Create Machine Learning Environment - -```sh -# Run this command to create the conda environment for machine learning -conda env create -f 3.machine_learning_env.yml -``` - -### Step 1b: Activate Machine Learning Environment - -```sh -# Run this command to activate the conda environment for machine learning -conda activate 3.ML_phenotypic_classification -``` - -## Step 2: Execute Machine Learning Pipeline - -```bash -# Run this script to train, evaluate, and interpret DP model -bash 3.ML_model.sh -``` - -**Note:** Running pipeline will produce all intermediate files (located in [results](results/)). -Jupyter notebooks (located in [notebooks](notebooks/)) will not be updated but the executed notebooks (located in [html](html/)) will be updated. \ No newline at end of file diff --git a/3.ML_model/html/0.split_data.html b/3.ML_model/html/0.split_data.html deleted file mode 100644 index e8c53cff..00000000 --- a/3.ML_model/html/0.split_data.html +++ /dev/null @@ -1,14923 +0,0 @@ - - - - - -0.split_data - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/3.ML_model/html/1.train_model.html b/3.ML_model/html/1.train_model.html deleted file mode 100644 index 5a3a4656..00000000 --- a/3.ML_model/html/1.train_model.html +++ /dev/null @@ -1,16901 +0,0 @@ - - - - - -1.train_model - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/3.ML_model/html/2.evaluate_model.html b/3.ML_model/html/2.evaluate_model.html deleted file mode 100644 index 54cdad28..00000000 --- a/3.ML_model/html/2.evaluate_model.html +++ /dev/null @@ -1,16433 +0,0 @@ - - - - - -2.evaluate_model - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/3.ML_model/html/3.interpret_model.html b/3.ML_model/html/3.interpret_model.html deleted file mode 100644 index dbceb303..00000000 --- a/3.ML_model/html/3.interpret_model.html +++ /dev/null @@ -1,15628 +0,0 @@ - - - - - -3.interpret_model - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/3.ML_model/notebooks/1.train_model.ipynb b/3.ML_model/notebooks/1.train_model.ipynb deleted file mode 100644 index be5d09e6..00000000 --- a/3.ML_model/notebooks/1.train_model.ipynb +++ /dev/null @@ -1,3351 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import pathlib\n", - "\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.model_selection import (\n", - " StratifiedKFold,\n", - " GridSearchCV,\n", - ")\n", - "from sklearn.utils import shuffle\n", - "from joblib import dump\n", - "\n", - "import sys\n", - "# adding utils to system path\n", - "sys.path.insert(0, '../utils')\n", - "from MlPipelineUtils import get_features_data, get_dataset, get_X_y_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load training data and create stratified folds for cross validation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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4303SmallIrregular37.0828.268657338.328358LT0106_02287_331LT0106_02_287_33LT0106_02/287/33/LT0106_02_287_33.tifENSG00000186143...-0.0100542.4907910.112932-0.448705-0.573112-1.2194490.756078-0.434373-0.6173292.989479
4304SmallIrregular45.062.742424384.424242LT0106_02287_331LT0106_02_287_33LT0106_02/287/33/LT0106_02_287_33.tifENSG00000186143...0.8288382.3286902.365700-1.219878-0.3777260.2857070.072360-0.1014870.592109-0.326425
4306SmallIrregular52.0105.014085429.056338LT0106_02287_331LT0106_02_287_33LT0106_02/287/33/LT0106_02_287_33.tifENSG00000186143...-0.8909520.3015220.3454630.5944890.7372453.037339-0.6369150.0611561.849867-0.896322
4307SmallIrregular55.093.971429469.214286LT0106_02287_331LT0106_02_287_33LT0106_02/287/33/LT0106_02_287_33.tifENSG00000186143...0.1161830.073442-0.035741-0.0207860.5995032.253533-0.4733170.0229741.555225-0.743614
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3338 rows × 1292 columns

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" - ], - "text/plain": [ - " Mitocheck_Phenotypic_Class Mitocheck_Object_ID Location_Center_X \\\n", - "4 Polylobed 10.0 1212.640449 \n", - "5 MetaphaseAlignment 42.0 69.902174 \n", - "6 Interphase 72.0 517.024390 \n", - "7 Interphase 85.0 1155.936170 \n", - "8 Artefact 100.0 748.324675 \n", - "... ... ... ... \n", - "4302 SmallIrregular 70.0 645.173913 \n", - "4303 SmallIrregular 37.0 828.268657 \n", - "4304 SmallIrregular 45.0 62.742424 \n", - "4306 SmallIrregular 52.0 105.014085 \n", - "4307 SmallIrregular 55.0 93.971429 \n", - "\n", - " Location_Center_Y Metadata_Plate Metadata_Well Metadata_Site \\\n", - "4 21.314607 LT0043_48 166_55 1 \n", - "5 104.782609 LT0043_48 166_55 1 \n", - "6 159.317073 LT0043_48 166_55 1 \n", - "7 191.180851 LT0043_48 166_55 1 \n", - "8 220.935065 LT0043_48 166_55 1 \n", - "... ... ... ... ... \n", - "4302 664.536232 LT0106_02 287_6 1 \n", - "4303 338.328358 LT0106_02 287_33 1 \n", - "4304 384.424242 LT0106_02 287_33 1 \n", - "4306 429.056338 LT0106_02 287_33 1 \n", - "4307 469.214286 LT0106_02 287_33 1 \n", - "\n", - " Metadata_Plate_Map_Name Metadata_DNA \\\n", - "4 LT0043_48_166_55 LT0043_48/166/55/LT0043_48_166_55.tif \n", - "5 LT0043_48_166_55 LT0043_48/166/55/LT0043_48_166_55.tif \n", - "6 LT0043_48_166_55 LT0043_48/166/55/LT0043_48_166_55.tif \n", - "7 LT0043_48_166_55 LT0043_48/166/55/LT0043_48_166_55.tif \n", - "8 LT0043_48_166_55 LT0043_48/166/55/LT0043_48_166_55.tif \n", - "... ... ... \n", - "4302 LT0106_02_287_6 LT0106_02/287/6/LT0106_02_287_6.tif \n", - "4303 LT0106_02_287_33 LT0106_02/287/33/LT0106_02_287_33.tif \n", - "4304 LT0106_02_287_33 LT0106_02/287/33/LT0106_02_287_33.tif \n", - "4306 LT0106_02_287_33 LT0106_02/287/33/LT0106_02_287_33.tif \n", - "4307 LT0106_02_287_33 LT0106_02/287/33/LT0106_02_287_33.tif \n", - "\n", - " Metadata_Gene ... efficientnet_1270 efficientnet_1271 \\\n", - "4 OGG1 ... 1.764085 -0.364659 \n", - "5 OGG1 ... -0.030402 -0.306105 \n", - "6 OGG1 ... -2.070584 -0.419038 \n", - "7 OGG1 ... -1.264048 -0.678396 \n", - "8 OGG1 ... -0.834010 -0.404291 \n", - "... ... ... ... ... \n", - "4302 ENSG00000186143 ... 0.481624 -0.066337 \n", - "4303 ENSG00000186143 ... -0.010054 2.490791 \n", - "4304 ENSG00000186143 ... 0.828838 2.328690 \n", - "4306 ENSG00000186143 ... -0.890952 0.301522 \n", - "4307 ENSG00000186143 ... 0.116183 0.073442 \n", - "\n", - " efficientnet_1272 efficientnet_1273 efficientnet_1274 \\\n", - "4 -0.623983 0.087524 -0.678471 \n", - "5 0.471312 1.111647 -0.395580 \n", - "6 -0.716160 2.525790 -0.300407 \n", - "7 0.076916 3.142620 0.202174 \n", - "8 0.839559 0.230029 -0.322646 \n", - "... ... ... ... \n", - "4302 -0.298825 -1.073172 -0.263557 \n", - "4303 0.112932 -0.448705 -0.573112 \n", - "4304 2.365700 -1.219878 -0.377726 \n", - "4306 0.345463 0.594489 0.737245 \n", - "4307 -0.035741 -0.020786 0.599503 \n", - "\n", - " efficientnet_1275 efficientnet_1276 efficientnet_1277 \\\n", - "4 -1.047430 0.119700 0.254014 \n", - "5 0.265579 0.337486 -0.728758 \n", - "6 0.243762 0.270543 0.473745 \n", - "7 0.331271 0.567700 0.072269 \n", - "8 -0.254167 -0.602655 -0.273222 \n", - "... ... ... ... \n", - "4302 -0.922345 0.761749 0.721974 \n", - "4303 -1.219449 0.756078 -0.434373 \n", - "4304 0.285707 0.072360 -0.101487 \n", - "4306 3.037339 -0.636915 0.061156 \n", - "4307 2.253533 -0.473317 0.022974 \n", - "\n", - " efficientnet_1278 efficientnet_1279 \n", - "4 0.080685 -0.808582 \n", - "5 0.519263 1.143726 \n", - "6 -1.024547 -0.401801 \n", - "7 -1.715632 1.303155 \n", - "8 -0.722049 0.554533 \n", - "... ... ... \n", - "4302 1.400016 -0.244034 \n", - "4303 -0.617329 2.989479 \n", - "4304 0.592109 -0.326425 \n", - "4306 1.849867 -0.896322 \n", - "4307 1.555225 -0.743614 \n", - "\n", - "[3338 rows x 1292 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set numpy seed to make random operations reproduceable\n", - "np.random.seed(0)\n", - "\n", - "results_dir = pathlib.Path(\"../results/\")\n", - "\n", - "# load training data from indexes and features dataframe\n", - "data_split_path = pathlib.Path(f\"{results_dir}/0.data_split_indexes.tsv\")\n", - "features_dataframe_path = pathlib.Path(\"../../1.format_data/data/training_data.csv.gz\")\n", - "\n", - "features_dataframe = get_features_data(features_dataframe_path)\n", - "data_split_indexes = pd.read_csv(data_split_path, sep=\"\\t\", index_col=0)\n", - "\n", - "training_data = get_dataset(features_dataframe, data_split_indexes, \"train\")\n", - "training_data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(3350, 1280)\n", - "(3350,)\n" - ] - } - ], - "source": [ - "X, y = get_X_y_data(training_data)\n", - "\n", - "print(X.shape)\n", - "print(y.shape)\n", - "\n", - "# create stratified data sets for k-fold cross validation\n", - "straified_k_folds = StratifiedKFold(n_splits=10, shuffle=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define model without C/l1_ratio parameters\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# create logistic regression model with following parameters\n", - "log_reg_model = LogisticRegression(\n", - " penalty=\"elasticnet\", solver=\"saga\", max_iter=100, n_jobs=-1, random_state=0\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Perform grid search for best C and l1_ratio parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameters being tested: {'C': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03]), 'l1_ratio': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# hypertune parameters with GridSearchCV\n", - "parameters = {\"C\": np.logspace(-3, 3, 7), \"l1_ratio\": np.linspace(0, 1, 11)}\n", - "#parameters = {\"C\": [0.1], \"l1_ratio\": [0.0]}\n", - "print(f\"Parameters being tested: {parameters}\")\n", - "grid_search_cv = GridSearchCV(\n", - " log_reg_model, parameters, cv=straified_k_folds, n_jobs=-1, scoring=\"f1_weighted\",\n", - ")\n", - "grid_search_cv = grid_search_cv.fit(X, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best parameters: {'C': 1.0, 'l1_ratio': 0.6000000000000001}\n", - "Score of best estimator: 0.7977295975762645\n" - ] - } - ], - "source": [ - "print(f\"Best parameters: {grid_search_cv.best_params_}\")\n", - "print(f\"Score of best estimator: {grid_search_cv.best_score_}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Save best model" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['results/1.log_reg_model.joblib']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# save final estimator\n", - "dump(grid_search_cv.best_estimator_, f\"{results_dir}/1.log_reg_model.joblib\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Repeat process with shuffling to create shuffled baseline model" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(3417, 1280)\n", - "(3417,)\n" - ] - } - ], - "source": [ - "X, y = get_X_y_data(training_data)\n", - "\n", - "print(X.shape)\n", - "print(y.shape)\n", - "\n", - "# shuffle columns of X (features) dataframe independently to create shuffled baseline\n", - "for column in X.T:\n", - " np.random.shuffle(column)\n", - "\n", - "# create stratified data sets for k-fold cross validation\n", - "straified_k_folds = StratifiedKFold(n_splits=10, shuffle=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# create logistic regression model with following parameters\n", - "log_reg_model = LogisticRegression(\n", - " penalty=\"elasticnet\", solver=\"saga\", max_iter=100, n_jobs=-1, random_state=0\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameters being tested: {'C': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03]), 'l1_ratio': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n", - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# hypertune parameters with GridSearchCV\n", - "parameters = {\"C\": np.logspace(-3, 3, 7), \"l1_ratio\": np.linspace(0, 1, 11)}\n", - "#parameters = {\"C\": [1.0], \"l1_ratio\": [0.8]}\n", - "print(f\"Parameters being tested: {parameters}\")\n", - "grid_search_cv = GridSearchCV(\n", - " log_reg_model, parameters, cv=straified_k_folds, n_jobs=-1, scoring=\"f1_weighted\",\n", - ")\n", - "grid_search_cv = grid_search_cv.fit(X, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best parameters: {'C': 0.01, 'l1_ratio': 0.1}\n", - "Score of best estimator: 0.19819771935157435\n" - ] - } - ], - "source": [ - "print(f\"Best parameters: {grid_search_cv.best_params_}\")\n", - "print(f\"Score of best estimator: {grid_search_cv.best_score_}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['results/1.shuffled_baseline_log_reg_model.joblib']" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# save final estimator\n", - "dump(grid_search_cv.best_estimator_, f\"{results_dir}/1.shuffled_baseline_log_reg_model.joblib\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.13 ('2.ML_phenotypic_classification')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "4cc408a06ad49ae0c78cd765de22f61d31a0f8b0861ec15e52107dd82d811e52" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/3.ML_model/notebooks/2.evaluate_model.ipynb b/3.ML_model/notebooks/2.evaluate_model.ipynb deleted file mode 100644 index 33e1de97..00000000 --- a/3.ML_model/notebooks/2.evaluate_model.ipynb +++ /dev/null @@ -1,1724 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import pathlib\n", - "\n", - "from joblib import load\n", - "\n", - "import sys\n", - "# adding utils to system path\n", - "sys.path.insert(0, '../utils')\n", - "from MlPipelineUtils import (\n", - " get_features_data,\n", - " get_dataset,\n", - " get_X_y_data,\n", - " evaluate_model_cm,\n", - " evaluate_model_score\n", - ")\n", - "\n", - "from sklearn.metrics import f1_score" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate best model" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# set numpy seed to make random operations reproduceable\n", - "np.random.seed(0)\n", - "\n", - "results_dir = pathlib.Path(\"../results/\")\n", - "\n", - "log_reg_model_path = pathlib.Path(f\"{results_dir}/1.log_reg_model.joblib\")\n", - "log_reg_model = load(log_reg_model_path)\n", - "\n", - "# load features data from indexes and features dataframe\n", - "data_split_path = pathlib.Path(f\"{results_dir}/0.data_split_indexes.tsv\")\n", - "data_split_indexes = pd.read_csv(data_split_path, sep=\"\\t\", index_col=0)\n", - "features_dataframe_path = pathlib.Path(\"../../1.format_data/data/training_data.csv.gz\")\n", - "features_dataframe = get_features_data(features_dataframe_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate with training data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Mitocheck_Phenotypic_ClassMitocheck_Object_IDLocation_Center_XLocation_Center_YMetadata_PlateMetadata_WellMetadata_SiteMetadata_Plate_Map_NameMetadata_DNAMetadata_Gene...efficientnet_1270efficientnet_1271efficientnet_1272efficientnet_1273efficientnet_1274efficientnet_1275efficientnet_1276efficientnet_1277efficientnet_1278efficientnet_1279
4Polylobed10.01212.64044921.314607LT0043_48166_551LT0043_48_166_55LT0043_48/166/55/LT0043_48_166_55.tifOGG1...1.764085-0.364659-0.6239830.087524-0.678471-1.0474300.1197000.2540140.080685-0.808582
5MetaphaseAlignment42.069.902174104.782609LT0043_48166_551LT0043_48_166_55LT0043_48/166/55/LT0043_48_166_55.tifOGG1...-0.030402-0.3061050.4713121.111647-0.3955800.2655790.337486-0.7287580.5192631.143726
6Interphase72.0517.024390159.317073LT0043_48166_551LT0043_48_166_55LT0043_48/166/55/LT0043_48_166_55.tifOGG1...-2.070584-0.419038-0.7161602.525790-0.3004070.2437620.2705430.473745-1.024547-0.401801
8Artefact100.0748.324675220.935065LT0043_48166_551LT0043_48_166_55LT0043_48/166/55/LT0043_48_166_55.tifOGG1...-0.834010-0.4042910.8395590.230029-0.322646-0.254167-0.602655-0.273222-0.7220490.554533
9Artefact108.0795.484536242.752577LT0043_48166_551LT0043_48_166_55LT0043_48/166/55/LT0043_48_166_55.tifOGG1...-1.4065200.3688180.5680221.618059-0.3206910.5277150.130431-0.293846-0.7559680.025133
..................................................................
4302SmallIrregular70.0645.173913664.536232LT0106_02287_61LT0106_02_287_6LT0106_02/287/6/LT0106_02_287_6.tifENSG00000186143...0.481624-0.066337-0.298825-1.073172-0.263557-0.9223450.7617490.7219741.400016-0.244034
4303SmallIrregular37.0828.268657338.328358LT0106_02287_331LT0106_02_287_33LT0106_02/287/33/LT0106_02_287_33.tifENSG00000186143...-0.0100542.4907910.112932-0.448705-0.573112-1.2194490.756078-0.434373-0.6173292.989479
4304SmallIrregular45.062.742424384.424242LT0106_02287_331LT0106_02_287_33LT0106_02/287/33/LT0106_02_287_33.tifENSG00000186143...0.8288382.3286902.365700-1.219878-0.3777260.2857070.072360-0.1014870.592109-0.326425
4306SmallIrregular52.0105.014085429.056338LT0106_02287_331LT0106_02_287_33LT0106_02/287/33/LT0106_02_287_33.tifENSG00000186143...-0.8909520.3015220.3454630.5944890.7372453.037339-0.6369150.0611561.849867-0.896322
4307SmallIrregular55.093.971429469.214286LT0106_02287_331LT0106_02_287_33LT0106_02/287/33/LT0106_02_287_33.tifENSG00000186143...0.1161830.073442-0.035741-0.0207860.5995032.253533-0.4733170.0229741.555225-0.743614
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" - ], - "text/plain": [ - " Mitocheck_Phenotypic_Class Mitocheck_Object_ID Location_Center_X \\\n", - "4 Polylobed 10.0 1212.640449 \n", - "5 MetaphaseAlignment 42.0 69.902174 \n", - "6 Interphase 72.0 517.024390 \n", - "8 Artefact 100.0 748.324675 \n", - "9 Artefact 108.0 795.484536 \n", - "... ... ... ... \n", - "4302 SmallIrregular 70.0 645.173913 \n", - "4303 SmallIrregular 37.0 828.268657 \n", - "4304 SmallIrregular 45.0 62.742424 \n", - "4306 SmallIrregular 52.0 105.014085 \n", - "4307 SmallIrregular 55.0 93.971429 \n", - "\n", - " Location_Center_Y Metadata_Plate Metadata_Well Metadata_Site \\\n", - "4 21.314607 LT0043_48 166_55 1 \n", - "5 104.782609 LT0043_48 166_55 1 \n", - "6 159.317073 LT0043_48 166_55 1 \n", - "8 220.935065 LT0043_48 166_55 1 \n", - "9 242.752577 LT0043_48 166_55 1 \n", - "... ... ... ... ... \n", - "4302 664.536232 LT0106_02 287_6 1 \n", - "4303 338.328358 LT0106_02 287_33 1 \n", - "4304 384.424242 LT0106_02 287_33 1 \n", - "4306 429.056338 LT0106_02 287_33 1 \n", - "4307 469.214286 LT0106_02 287_33 1 \n", - "\n", - " Metadata_Plate_Map_Name Metadata_DNA \\\n", - "4 LT0043_48_166_55 LT0043_48/166/55/LT0043_48_166_55.tif \n", - "5 LT0043_48_166_55 LT0043_48/166/55/LT0043_48_166_55.tif \n", - "6 LT0043_48_166_55 LT0043_48/166/55/LT0043_48_166_55.tif \n", - "8 LT0043_48_166_55 LT0043_48/166/55/LT0043_48_166_55.tif \n", - "9 LT0043_48_166_55 LT0043_48/166/55/LT0043_48_166_55.tif \n", - "... ... ... \n", - "4302 LT0106_02_287_6 LT0106_02/287/6/LT0106_02_287_6.tif \n", - "4303 LT0106_02_287_33 LT0106_02/287/33/LT0106_02_287_33.tif \n", - "4304 LT0106_02_287_33 LT0106_02/287/33/LT0106_02_287_33.tif \n", - "4306 LT0106_02_287_33 LT0106_02/287/33/LT0106_02_287_33.tif \n", - "4307 LT0106_02_287_33 LT0106_02/287/33/LT0106_02_287_33.tif \n", - "\n", - " Metadata_Gene ... efficientnet_1270 efficientnet_1271 \\\n", - "4 OGG1 ... 1.764085 -0.364659 \n", - "5 OGG1 ... -0.030402 -0.306105 \n", - "6 OGG1 ... -2.070584 -0.419038 \n", - "8 OGG1 ... -0.834010 -0.404291 \n", - "9 OGG1 ... -1.406520 0.368818 \n", - "... ... ... ... ... \n", - "4302 ENSG00000186143 ... 0.481624 -0.066337 \n", - "4303 ENSG00000186143 ... -0.010054 2.490791 \n", - "4304 ENSG00000186143 ... 0.828838 2.328690 \n", - "4306 ENSG00000186143 ... -0.890952 0.301522 \n", - "4307 ENSG00000186143 ... 0.116183 0.073442 \n", - "\n", - " efficientnet_1272 efficientnet_1273 efficientnet_1274 \\\n", - "4 -0.623983 0.087524 -0.678471 \n", - "5 0.471312 1.111647 -0.395580 \n", - "6 -0.716160 2.525790 -0.300407 \n", - "8 0.839559 0.230029 -0.322646 \n", - "9 0.568022 1.618059 -0.320691 \n", - "... ... ... ... \n", - "4302 -0.298825 -1.073172 -0.263557 \n", - "4303 0.112932 -0.448705 -0.573112 \n", - "4304 2.365700 -1.219878 -0.377726 \n", - "4306 0.345463 0.594489 0.737245 \n", - "4307 -0.035741 -0.020786 0.599503 \n", - "\n", - " efficientnet_1275 efficientnet_1276 efficientnet_1277 \\\n", - "4 -1.047430 0.119700 0.254014 \n", - "5 0.265579 0.337486 -0.728758 \n", - "6 0.243762 0.270543 0.473745 \n", - "8 -0.254167 -0.602655 -0.273222 \n", - "9 0.527715 0.130431 -0.293846 \n", - "... ... ... ... \n", - "4302 -0.922345 0.761749 0.721974 \n", - "4303 -1.219449 0.756078 -0.434373 \n", - "4304 0.285707 0.072360 -0.101487 \n", - "4306 3.037339 -0.636915 0.061156 \n", - "4307 2.253533 -0.473317 0.022974 \n", - "\n", - " efficientnet_1278 efficientnet_1279 \n", - "4 0.080685 -0.808582 \n", - "5 0.519263 1.143726 \n", - "6 -1.024547 -0.401801 \n", - "8 -0.722049 0.554533 \n", - "9 -0.755968 0.025133 \n", - "... ... ... \n", - "4302 1.400016 -0.244034 \n", - "4303 -0.617329 2.989479 \n", - "4304 0.592109 -0.326425 \n", - "4306 1.849867 -0.896322 \n", - "4307 1.555225 -0.743614 \n", - "\n", - "[3417 rows x 1292 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "training_data = get_dataset(features_dataframe, data_split_indexes, \"train\")\n", - "training_data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "y_train, y_train_pred = evaluate_model_cm(log_reg_model, training_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "evaluate_model_score(log_reg_model, training_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate with testing data" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Mitocheck_Phenotypic_ClassMitocheck_Object_IDLocation_Center_XLocation_Center_YMetadata_PlateMetadata_WellMetadata_SiteMetadata_Plate_Map_NameMetadata_DNAMetadata_Gene...efficientnet_1270efficientnet_1271efficientnet_1272efficientnet_1273efficientnet_1274efficientnet_1275efficientnet_1276efficientnet_1277efficientnet_1278efficientnet_1279
2542Apoptosis45.0264.076923231.230769LT0109_38381_871LT0109_38_381_87LT0109_38/381/87/LT0109_38_381_87.tifCOPB...0.747397-0.303137-0.441440-0.5582310.5590002.418906-0.536620-0.0600392.196802-1.746036
2636Apoptosis106.0419.000000597.937500LT0089_01175_931LT0089_01_175_93LT0089_01/175/93/LT0089_01_175_93.tifENSG00000159763...0.394264-0.389874-0.1838740.727683-0.044377-1.1513100.2892070.3786870.896932-1.444912
2637Apoptosis106.0404.193548598.161290LT0089_01175_931LT0089_01_175_93LT0089_01/175/93/LT0089_01_175_93.tifENSG00000159763...0.704804-0.400241-0.0276180.955689-0.165457-1.2315690.3604710.5348330.882122-1.371561
2712Apoptosis34.0716.863636232.443182LT0089_01175_601LT0089_01_175_60LT0089_01/175/60/LT0089_01_175_60.tifENSG00000159763...1.1741910.3891170.551532-0.8994690.3545331.228106-1.004360-1.0012670.346599-0.136300
2964Apoptosis145.0726.048780698.609756LT0048_14335_291LT0048_14_335_29LT0048_14/335/29/LT0048_14_335_29.tifPLK1...0.569967-0.616215-0.7029530.1404100.0077901.131292-0.150318-0.6261511.383265-1.793438
..................................................................
687SmallIrregular170.01082.461538553.169231LT0030_17184_391LT0030_17_184_39LT0030_17/184/39/LT0030_17_184_39.tifRGR...-0.6863510.3239890.992241-1.107960-0.143038-0.850287-3.5614550.478179-0.3544171.720881
701UndefinedCondensed47.01182.202703109.581081LT0101_01277_791LT0101_01_277_79LT0101_01/277/79/LT0101_01_277_79.tiffailed_QC...0.2755000.0675560.724037-0.283421-0.288346-0.134525-0.0288590.0650011.887147-1.169897
3174UndefinedCondensed75.0694.145455270.090909LT0041_32132_741LT0041_32_132_74LT0041_32/132/74/LT0041_32_132_74.tifTRPV1...0.3355720.152123-0.596092-0.1837110.1085081.3441670.5626210.0568560.9667240.466382
2059UndefinedCondensed68.0899.866667461.683333LT0027_44292_651LT0027_44_292_65LT0027_44/292/65/LT0027_44_292_65.tifCDK4...0.8253601.854059-0.268961-0.750176-0.248810-0.6677050.8258910.4920011.2121430.265127
598UndefinedCondensed121.01014.135593424.694915LT0030_17184_361LT0030_17_184_36LT0030_17/184/36/LT0030_17_184_36.tifRGR...-0.8249770.6610970.799882-1.043553-0.038244-0.395626-2.1376610.426424-0.7584141.212763
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605 rows × 1292 columns

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" - ], - "text/plain": [ - " Mitocheck_Phenotypic_Class Mitocheck_Object_ID Location_Center_X \\\n", - "2542 Apoptosis 45.0 264.076923 \n", - "2636 Apoptosis 106.0 419.000000 \n", - "2637 Apoptosis 106.0 404.193548 \n", - "2712 Apoptosis 34.0 716.863636 \n", - "2964 Apoptosis 145.0 726.048780 \n", - "... ... ... ... \n", - "687 SmallIrregular 170.0 1082.461538 \n", - "701 UndefinedCondensed 47.0 1182.202703 \n", - "3174 UndefinedCondensed 75.0 694.145455 \n", - "2059 UndefinedCondensed 68.0 899.866667 \n", - "598 UndefinedCondensed 121.0 1014.135593 \n", - "\n", - " Location_Center_Y Metadata_Plate Metadata_Well Metadata_Site \\\n", - "2542 231.230769 LT0109_38 381_87 1 \n", - "2636 597.937500 LT0089_01 175_93 1 \n", - "2637 598.161290 LT0089_01 175_93 1 \n", - "2712 232.443182 LT0089_01 175_60 1 \n", - "2964 698.609756 LT0048_14 335_29 1 \n", - "... ... ... ... ... \n", - "687 553.169231 LT0030_17 184_39 1 \n", - "701 109.581081 LT0101_01 277_79 1 \n", - "3174 270.090909 LT0041_32 132_74 1 \n", - "2059 461.683333 LT0027_44 292_65 1 \n", - "598 424.694915 LT0030_17 184_36 1 \n", - "\n", - " Metadata_Plate_Map_Name Metadata_DNA \\\n", - "2542 LT0109_38_381_87 LT0109_38/381/87/LT0109_38_381_87.tif \n", - "2636 LT0089_01_175_93 LT0089_01/175/93/LT0089_01_175_93.tif \n", - "2637 LT0089_01_175_93 LT0089_01/175/93/LT0089_01_175_93.tif \n", - "2712 LT0089_01_175_60 LT0089_01/175/60/LT0089_01_175_60.tif \n", - "2964 LT0048_14_335_29 LT0048_14/335/29/LT0048_14_335_29.tif \n", - "... ... ... \n", - "687 LT0030_17_184_39 LT0030_17/184/39/LT0030_17_184_39.tif \n", - "701 LT0101_01_277_79 LT0101_01/277/79/LT0101_01_277_79.tif \n", - "3174 LT0041_32_132_74 LT0041_32/132/74/LT0041_32_132_74.tif \n", - "2059 LT0027_44_292_65 LT0027_44/292/65/LT0027_44_292_65.tif \n", - "598 LT0030_17_184_36 LT0030_17/184/36/LT0030_17_184_36.tif \n", - "\n", - " Metadata_Gene ... efficientnet_1270 efficientnet_1271 \\\n", - "2542 COPB ... 0.747397 -0.303137 \n", - "2636 ENSG00000159763 ... 0.394264 -0.389874 \n", - "2637 ENSG00000159763 ... 0.704804 -0.400241 \n", - "2712 ENSG00000159763 ... 1.174191 0.389117 \n", - "2964 PLK1 ... 0.569967 -0.616215 \n", - "... ... ... ... ... \n", - "687 RGR ... -0.686351 0.323989 \n", - "701 failed_QC ... 0.275500 0.067556 \n", - "3174 TRPV1 ... 0.335572 0.152123 \n", - "2059 CDK4 ... 0.825360 1.854059 \n", - "598 RGR ... -0.824977 0.661097 \n", - "\n", - " efficientnet_1272 efficientnet_1273 efficientnet_1274 \\\n", - "2542 -0.441440 -0.558231 0.559000 \n", - "2636 -0.183874 0.727683 -0.044377 \n", - "2637 -0.027618 0.955689 -0.165457 \n", - "2712 0.551532 -0.899469 0.354533 \n", - "2964 -0.702953 0.140410 0.007790 \n", - "... ... ... ... \n", - "687 0.992241 -1.107960 -0.143038 \n", - "701 0.724037 -0.283421 -0.288346 \n", - "3174 -0.596092 -0.183711 0.108508 \n", - "2059 -0.268961 -0.750176 -0.248810 \n", - "598 0.799882 -1.043553 -0.038244 \n", - "\n", - " efficientnet_1275 efficientnet_1276 efficientnet_1277 \\\n", - "2542 2.418906 -0.536620 -0.060039 \n", - "2636 -1.151310 0.289207 0.378687 \n", - "2637 -1.231569 0.360471 0.534833 \n", - "2712 1.228106 -1.004360 -1.001267 \n", - "2964 1.131292 -0.150318 -0.626151 \n", - "... ... ... ... \n", - "687 -0.850287 -3.561455 0.478179 \n", - "701 -0.134525 -0.028859 0.065001 \n", - "3174 1.344167 0.562621 0.056856 \n", - "2059 -0.667705 0.825891 0.492001 \n", - "598 -0.395626 -2.137661 0.426424 \n", - "\n", - " efficientnet_1278 efficientnet_1279 \n", - "2542 2.196802 -1.746036 \n", - "2636 0.896932 -1.444912 \n", - "2637 0.882122 -1.371561 \n", - "2712 0.346599 -0.136300 \n", - "2964 1.383265 -1.793438 \n", - "... ... ... \n", - "687 -0.354417 1.720881 \n", - "701 1.887147 -1.169897 \n", - "3174 0.966724 0.466382 \n", - "2059 1.212143 0.265127 \n", - "598 -0.758414 1.212763 \n", - "\n", - "[605 rows x 1292 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "testing_data = get_dataset(features_dataframe, data_split_indexes, \"test\")\n", - "testing_data" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_test, y_test_pred = evaluate_model_cm(log_reg_model, testing_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "evaluate_model_score(log_reg_model, testing_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate with holdout data" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Mitocheck_Phenotypic_ClassMitocheck_Object_IDLocation_Center_XLocation_Center_YMetadata_PlateMetadata_WellMetadata_SiteMetadata_Plate_Map_NameMetadata_DNAMetadata_Gene...efficientnet_1270efficientnet_1271efficientnet_1272efficientnet_1273efficientnet_1274efficientnet_1275efficientnet_1276efficientnet_1277efficientnet_1278efficientnet_1279
544Large46.0673.963504421.518248LT0042_10144_361LT0042_10_144_36LT0042_10/144/36/LT0042_10_144_36.tifPOLG...-0.869680-0.819131-0.065895-0.5841290.4406761.731456-0.3007260.637247-1.317758-0.620867
545Polylobed47.0239.978723429.074468LT0042_10144_361LT0042_10_144_36LT0042_10/144/36/LT0042_10_144_36.tifPOLG...2.301987-0.809295-0.077291-0.6409481.342124-0.829389-0.514600-0.681411-0.130137-0.984097
546Polylobed47.0219.123288439.972603LT0042_10144_361LT0042_10_144_36LT0042_10/144/36/LT0042_10_144_36.tifPOLG...0.648838-0.948251-0.230179-0.9204181.479645-0.2927600.309152-0.593983-0.226819-1.323268
547Polylobed47.0238.273973456.630137LT0042_10144_361LT0042_10_144_36LT0042_10/144/36/LT0042_10_144_36.tifPOLG...-0.090531-0.5513762.460243-1.0674163.303222-1.2472941.483531-0.815428-1.0675480.053700
548Polylobed47.0213.785714461.857143LT0042_10144_361LT0042_10_144_36LT0042_10/144/36/LT0042_10_144_36.tifPOLG...1.376728-0.958535-0.154087-0.7875872.873853-0.2370600.497598-0.5871011.101417-0.382665
..................................................................
4076Folded83.024.870370692.805556LT0138_03127_351LT0138_03_127_35LT0138_03/127/35/LT0138_03_127_35.tifENSG00000116641...0.004918-0.330484-0.201508-1.1292640.376668-0.0473150.3664330.584675-0.2096160.290683
4077Folded84.0818.500000691.933333LT0138_03127_351LT0138_03_127_35LT0138_03/127/35/LT0138_03_127_35.tifENSG00000116641...0.715451-0.3334790.692772-0.598472-0.312265-0.1500460.276773-0.298540-0.106108-0.438396
4078Polylobed78.0617.656250699.125000LT0138_03127_351LT0138_03_127_35LT0138_03/127/35/LT0138_03_127_35.tifENSG00000116641...0.754502-1.012552-0.005334-1.0344270.010367-0.101921-0.433755-0.0105750.067495-0.720715
4079Folded95.0707.160377802.254717LT0138_03127_351LT0138_03_127_35LT0138_03/127/35/LT0138_03_127_35.tifENSG00000116641...0.134387-0.905355-0.017685-0.983794-0.307310-0.261301-0.1963570.9487980.079491-1.198162
4080Folded95.0706.580000835.860000LT0138_03127_351LT0138_03_127_35LT0138_03/127/35/LT0138_03_127_35.tifENSG00000116641...-0.021910-0.782816-0.024221-0.7822570.4907890.382172-0.1349120.6757140.190705-0.791579
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101 rows × 1292 columns

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" - ], - "text/plain": [ - " Mitocheck_Phenotypic_Class Mitocheck_Object_ID Location_Center_X \\\n", - "544 Large 46.0 673.963504 \n", - "545 Polylobed 47.0 239.978723 \n", - "546 Polylobed 47.0 219.123288 \n", - "547 Polylobed 47.0 238.273973 \n", - "548 Polylobed 47.0 213.785714 \n", - "... ... ... ... \n", - "4076 Folded 83.0 24.870370 \n", - "4077 Folded 84.0 818.500000 \n", - "4078 Polylobed 78.0 617.656250 \n", - "4079 Folded 95.0 707.160377 \n", - "4080 Folded 95.0 706.580000 \n", - "\n", - " Location_Center_Y Metadata_Plate Metadata_Well Metadata_Site \\\n", - "544 421.518248 LT0042_10 144_36 1 \n", - "545 429.074468 LT0042_10 144_36 1 \n", - "546 439.972603 LT0042_10 144_36 1 \n", - "547 456.630137 LT0042_10 144_36 1 \n", - "548 461.857143 LT0042_10 144_36 1 \n", - "... ... ... ... ... \n", - "4076 692.805556 LT0138_03 127_35 1 \n", - "4077 691.933333 LT0138_03 127_35 1 \n", - "4078 699.125000 LT0138_03 127_35 1 \n", - "4079 802.254717 LT0138_03 127_35 1 \n", - "4080 835.860000 LT0138_03 127_35 1 \n", - "\n", - " Metadata_Plate_Map_Name Metadata_DNA \\\n", - "544 LT0042_10_144_36 LT0042_10/144/36/LT0042_10_144_36.tif \n", - "545 LT0042_10_144_36 LT0042_10/144/36/LT0042_10_144_36.tif \n", - "546 LT0042_10_144_36 LT0042_10/144/36/LT0042_10_144_36.tif \n", - "547 LT0042_10_144_36 LT0042_10/144/36/LT0042_10_144_36.tif \n", - "548 LT0042_10_144_36 LT0042_10/144/36/LT0042_10_144_36.tif \n", - "... ... ... \n", - "4076 LT0138_03_127_35 LT0138_03/127/35/LT0138_03_127_35.tif \n", - "4077 LT0138_03_127_35 LT0138_03/127/35/LT0138_03_127_35.tif \n", - "4078 LT0138_03_127_35 LT0138_03/127/35/LT0138_03_127_35.tif \n", - "4079 LT0138_03_127_35 LT0138_03/127/35/LT0138_03_127_35.tif \n", - "4080 LT0138_03_127_35 LT0138_03/127/35/LT0138_03_127_35.tif \n", - "\n", - " Metadata_Gene ... efficientnet_1270 efficientnet_1271 \\\n", - "544 POLG ... -0.869680 -0.819131 \n", - "545 POLG ... 2.301987 -0.809295 \n", - "546 POLG ... 0.648838 -0.948251 \n", - "547 POLG ... -0.090531 -0.551376 \n", - "548 POLG ... 1.376728 -0.958535 \n", - "... ... ... ... ... \n", - "4076 ENSG00000116641 ... 0.004918 -0.330484 \n", - "4077 ENSG00000116641 ... 0.715451 -0.333479 \n", - "4078 ENSG00000116641 ... 0.754502 -1.012552 \n", - "4079 ENSG00000116641 ... 0.134387 -0.905355 \n", - "4080 ENSG00000116641 ... -0.021910 -0.782816 \n", - "\n", - " efficientnet_1272 efficientnet_1273 efficientnet_1274 \\\n", - "544 -0.065895 -0.584129 0.440676 \n", - "545 -0.077291 -0.640948 1.342124 \n", - "546 -0.230179 -0.920418 1.479645 \n", - "547 2.460243 -1.067416 3.303222 \n", - "548 -0.154087 -0.787587 2.873853 \n", - "... ... ... ... \n", - "4076 -0.201508 -1.129264 0.376668 \n", - "4077 0.692772 -0.598472 -0.312265 \n", - "4078 -0.005334 -1.034427 0.010367 \n", - "4079 -0.017685 -0.983794 -0.307310 \n", - "4080 -0.024221 -0.782257 0.490789 \n", - "\n", - " efficientnet_1275 efficientnet_1276 efficientnet_1277 \\\n", - "544 1.731456 -0.300726 0.637247 \n", - "545 -0.829389 -0.514600 -0.681411 \n", - "546 -0.292760 0.309152 -0.593983 \n", - "547 -1.247294 1.483531 -0.815428 \n", - "548 -0.237060 0.497598 -0.587101 \n", - "... ... ... ... \n", - "4076 -0.047315 0.366433 0.584675 \n", - "4077 -0.150046 0.276773 -0.298540 \n", - "4078 -0.101921 -0.433755 -0.010575 \n", - "4079 -0.261301 -0.196357 0.948798 \n", - "4080 0.382172 -0.134912 0.675714 \n", - "\n", - " efficientnet_1278 efficientnet_1279 \n", - "544 -1.317758 -0.620867 \n", - "545 -0.130137 -0.984097 \n", - "546 -0.226819 -1.323268 \n", - "547 -1.067548 0.053700 \n", - "548 1.101417 -0.382665 \n", - "... ... ... \n", - "4076 -0.209616 0.290683 \n", - "4077 -0.106108 -0.438396 \n", - "4078 0.067495 -0.720715 \n", - "4079 0.079491 -1.198162 \n", - "4080 0.190705 -0.791579 \n", - "\n", - "[101 rows x 1292 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "holdout_data = get_dataset(features_dataframe, data_split_indexes, \"holdout\")\n", - "X_holdout, y_holdout = get_X_y_data(holdout_data)\n", - "holdout_data" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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4wHAKALiiffnll2rZsqWuueYaderUye4cAADgAdt6AQAAAMAiX8fXsjuhUK1q/FLk2w4ZMkTHjh2Tj4+PgoKC9I9//EP169fXoUOHNHbsWP32228qV66cJk+erJo1axZ4LIZTAAAAALDIlTacpqSkKCQkRJK0du1azZ49WzExMXr00UfVvXt3RUREaNWqVVqxYoU++OCDAo/Ftl4AAAAAwF9ycTCVpNTUVDkcDp05c0Y//vij++U0nTp10o8//qikpKQCj+Xn1VJAUocqQ+xOuCw5iafsTgAAAMBl+sK5zO6EIsl1lfz1weTkZCUnJ19yeWhoqEJDQy+5/KWXXtJXX30ll8ulefPmKSEhQddee637s6V9fX1VuXJlJSQkuD/XOz8MpwAAAAAAt/fff1+zZs265PKLnzX9Z2+88YakCx/bNmXKFD333HN/6XEZTgEAAAAAbgMGDFDXrl0vuTy/VdM/6tKli1555RVVqVJFJ0+eVG5urnx9fZWbm6vExERVrVq1wPsznAIAAACARZwGvO2Pp+27f5aWlqbk5GT30Llu3TqVLVtW11xzjerXr6+4uDhFREQoLi5O9evXL3BLr8RwCgAAAAD4C86fP6/nnntO58+fl4+Pj8qWLas5c+bI4XBowoQJGjt2rKKiohQaGqrJkycXejw+SgZexxsiAQAAwNtMeUOkjYfr2p1QqDY199nyuCV/TRkAAAAAcMVjWy8AAAAAWCRXDrsTSixWTgEAAAAAtmM4BQAAAADYjm29AAAAAGCRXBfrg55wZgAAAAAAtmM4BQAAAADYjm29AAAAAGARJ+/W6xErpwAAAAAA2zGcAgAAAABsx7ZeAAAAALBILuuDHnFmLHTu3Dk1bNhQb7zxhlcfJzo6WocOHfpbxxg0aJCOHDlSTEXFp5S/n56f1k8LdryuFQemadYXkWoadovdWQUKKR+s8StGKTZloRYditK9fVrbnVQomr3PtF6JZiuY1ivRbAXTeiWarWBar2RmM6zFyqmFVq9ercaNG2vNmjUaNWqU/P39vfI4MTExKl++vG688ca/fIx33323GIuKj4+fj06dOKvRXafr1LGzataugSLnPqnB905U4tEku/PyNWzWE8rJylGvKoNUu3FNvREXqV++O6z4H4/ZneYRzd5nWq9EsxVM65VotoJpvRLNVjCtVzKzGdZi5dRCK1as0JAhQ1S3bl2tW7dOkjRz5kw999xzGjRokDp27Khhw4YpJSVFkpSWlqbIyEh16tRJnTp10ty5c93H6t+/v9544w31799f999/v6ZNm+Z+jD179mjixImKiIjQ119/rdzcXE2ePNl9nMmTJys3N1eS9NFHH6lDhw6KiIhQeHi4Dh48KEkKCwvTvn37JEmzZs1S+/btFRERoS5duig5Odmyc/ZnmelZWjx1jRKPJsnlcmn7F3t08sgZ1Wl0g21NBQkMClDr7ndqwSsfKiMtQz98tVdbYneoXf82dqd5RLP3mdYr0WwF03olmq1gWq9EsxVM65XMbPaWXJdPif+yCyunFtm7d6/OnTunO++8U6dOndKKFSvUvn17SdK3336rlStXqmLFioqMjFRUVJTGjBmjqKgoOZ1OrV69Wmlpaerdu7fq1aunNm0u/J/44MGDeu+995SZmamHH35YTZo0Uffu3bVy5Uo9/vjjuvfeeyVJS5Ys0U8//aTo6GhJF7bsfvTRR+rbt6+mTJmiuLg4Va1aVVlZWe6h9aJz585p/vz52rJliwIDA5WamqrAwEALz1zBylUMUfValRX/c4LdKfmqXreqnLlOHd//e9/B3YfV6J6SuxWZZu8zrVei2Qqm9Uo0W8G0XolmK5jWK5nZDOuxcmqR5cuXKyIiQg6HQw888IC+++47nTx5UpLUtm1bVaxYUZLUo0cPbd26VZK0ZcsW9ezZUw6HQ8HBwerYsaO2bNniPmaXLl3k5+enMmXK6KGHHnLf78+2bNmirl27yt/fX/7+/urWrZv7OHfeeaciIyO1cOFCnTx5UqVLl85z3+DgYN14440aNWqUPv74Y6Wnp8vPr2T8m4avn49GRw3U2o+36tiBk3bn5Kt0cKDSzqXnuSztXLqCQkp7uIf9aPY+03olmq1gWq9EsxVM65VotoJpvZKZzbAew6kFsrKytHr1aq1YsUJhYWF66KGHlJ2drZiYmEtu63K55HA4Lvn1RX/+fX73K8p1F38/a9Ysvfjiizp//rweffRRbdy4Mc/tfH199fHHH6t///769ddf1a1bN+3du7do37gXORwOjZr1mHKycxQ17iO7czw6n5qhoNC8P3TLhJZWesp5m4oKR7P3mdYr0WwF03olmq1gWq9EsxVM65XMbPYWp3xK/JddGE4tsHbtWtWqVUubNm3SunXrtG7dOv373/92b7PdsGGDkpIuvJlPTEyMWrRoIUlq1aqVli9fLpfLpdTUVH3yySdq2bKl+7irVq1STk6O0tPT9Z///Md9vzJlyrhft3rxODExMcrOzlZ2drZWrlypli1bKicnR0ePHlWjRo301FNP6a677tJPP/2Upz01NVVJSUlq3ry5hg8frrp162r//v1ePV9F8fz0fipXKVQTn3hXuTlOu3M8Or4vQb5+vqp+UxX3ZbUa1SzRL/yn2ftM65VotoJpvRLNVjCtV6LZCqb1SmY2w3oMpxaIjo5WeHh4nsuaNGkip9Opb775Ri1bttS4cePUsWNHnTt3TkOGDJEkDRkyRC6XS+Hh4Xr44YfVuXNn3XPPPe5jNGjQQAMHDlSXLl3Utm1b92tMe/furaioKHXp0kVff/21+7WqXbt2VdeuXVWvXj316tVLTqdTY8eOVXh4uDp37qxTp06pd+/eeTpTU1M1dOhQhYeHq1OnTqpYsaIeeOABL5+xgj07uY9uqFNFE/q/o6yMbFtbCpORnqnN0ds04NXeCgwKUINW9dQqopnWLtxY+J1tQrP3mdYr0WwF03olmq1gWq9EsxVM65XMbIb1HC6Xy2V3xNVs5syZSk9P15gxYy7rfv3798/zpkclWYcqQ4rtWJWvq6D3d0xUVkZ2njdvmjlqqdZHf1Msj5GTeKpYjnNRSPlgjZg/WLff30gpZ1I1L3Kx1i/dXKyPUdxo9j7TeiWarWBar0SzFUzrlWi2gmm9kvebv3AuK7ZjedOqXxrbnVCoiFr/s+VxGU5txnBa8hT3cAoAAADvM2U4jT7YxO6EQnWrvcuWxy0Zb7t6FRs2bNhfut/ChQuLuQQAAAAA7MNrTgEAAAAAtmPlFAAAAAAsksv6oEecGQAAAACA7RhOAQAAAAC2Y1svAAAAAFjE6WJ90BPODAAAAADAdgynAAAAAADbsa0XAAAAACzCu/V6xpkBAAAAANiO4RQAAAAAYDu29QIAAACARXJdDrsTSixWTgEAAAAAtmM4BQAAAADYjuEUAAAAAGA7XnMKAAAAABZxsj7oEWcGAAAAAGA7Vk7hdTmJp+xOuCw+d9xqd8Jlc367x+4E4KrkExRkd8Jlc6an251wWfwqV7I74bKZ9veeJPk2qGt3wmXJ/WGf3QkAvIDhFAAAAAAskuti86onnBkAAAAAgO0YTgEAAAAAtmNbLwAAAABYxCmH3QklFiunAAAAAADbMZwCAAAAAGzHtl4AAAAAsAjv1usZZwYAAAAAYDuGUwAAAACA7djWCwAAAAAWyWV90CPODAAAAADAdgynAAAAAADbMZwCAAAAAGzHa04BAAAAwCJOl8PuhBKLlVMAAAAAgO0YTgEAAAAAtmNbLwAAAABYhI+S8YwzUwKdO3dODRs21BtvvFHg7RYsWKAzZ84U6Zhnz57Vww8/rIiICM2bN+8vdUVHR+vQoUN/6b7FKaR8sMavGKXYlIVadChK9/ZpbXdSoabMGaDVm1/Syo2RWrkxUvOWD7U7qUAmnmPTmk3rlWi2Quen22nmpglafWaeRsx50u6cIjHtHJfy99Pz0/ppwY7XteLANM36IlJNw26xO6tApp3jP6p2wzWK3TFBo9/saXdKoUw7z6b1SmY2w1qsnJZAq1evVuPGjbVmzRqNGjVK/v7+ea53Op1yOBz64IMP1KpVK11zzTWFHnPLli0KDQ3Vhx9++Je7YmJiVL58ed14441/+RjFYdisJ5STlaNeVQapduOaeiMuUr98d1jxPx6ztasws9/6RP9ZtcvujCIx8Ryb1mxar0SzFc4knNWSKbFq2q6h/ANL2Z1TJKadYx8/H506cVaju07XqWNn1axdA0XOfVKD752oxKNJdufly7Rz/EdDXwrXvj3H7c4oEtPOs2m9kpnNsBYrpyXQihUrNGTIENWtW1fr1q2TJM2cOVMjR47UkCFDFBERoTlz5igxMVHDhw9XRESEDhw4oKysLE2ePFk9evRQRESERo0apbS0NG3dulVTpkzRzp07FRERoR07dmj16tXq2bOnunTpoi5dumjLli3uxz948KAef/xxhYeHKzw8XDExMVqxYoX27NmjiRMnKiIiQl9//bUt5yYwKECtu9+pBa98qIy0DP3w1V5tid2hdv3b2NJzJTLxHJvWbFqvRLNVvor9Vlvidio5KdXulCIx8Rxnpmdp8dQ1SjyaJJfLpe1f7NHJI2dUp9ENdqfly8RzfFGb9g2VlpKh/207aHdKoUw7z6b1SmY2e4vT5VPiv+zCymkJs3fvXp07d0533nmnTp06pRUrVqh9+/aSpB07dig6OloVKlSQJC1btkwzZsxQ3bp1JUlRUVEKCQnR8uXLJUlvvfWW5s6dqxdeeEHDhw/Xhg0bNGPGDEkXtvl26tRJDodDv/zyix577DFt2rRJOTk5GjJkiJ5//nl16NDBfdvy5ctr5cqVevzxx3XvvfdafVrcqtetKmeuU8f3J7gvO7j7sBrdU7K3ZEnSwKH36fFn2+lY/GktiFqn3Tvj7U7Kl4nn2LRm03olmpG/K+Ecl6sYouq1Kiv+54TCb2wDU89xUJkA9R/aTmOf/Lfad7vD7pxCmXaeTeuVzGyG9RhOS5jly5crIiJCDodDDzzwgCZOnKiTJ09Kku655x73YJqfdevWKTU1VZ999pkkKSsrSzfffHO+tz169KhGjBihkydPys/PT6dPn9apU6f022+/KScnxz2YSlL58uWL8Tv8e0oHByrtXHqey9LOpSsopLRNRUUzf+ZaHTl0SjnZuWrzwK16dVofDXnk/5Rw/KzdaZcw8Ryb1mxar0Qz8mf6Ofb189HoqIFa+/FWHTtw0u6cfJl6jh99tp0+i9mh0yfP2Z1SJKadZ9N6JTObYT2G0xIkKytLq1evVkBAgFatWiVJys7OVkxMjCSpTJkyBd7f5XJp/PjxatmyZaGP9eKLL2rs2LFq166dnE6nbrvtNmVmZsrlcv39b8SLzqdmKCg07w+xMqGllZ5y3qaiovn5h99fb7N2zXe698Fb1eyuOor9eLuNVfkz8Ryb1mxar0Qz8mfyOXY4HBo16zHlZOcoatxHdud4ZOI5rlWvqprcWVtDe862O6XITDvPpvVKZjZ7S64cdieUWLzmtARZu3atatWqpU2bNmndunVat26d/v3vfys6Ojrf25cpU0YpKSnu34eFhWnBggXKyMiQJKWmpurgwfxf55GSkqLrrrtO0oXV2qysLElSrVq15Ofnp08//dR927Nnz+b7eHY4vi9Bvn6+qn5TFfdltRrVNO6F9C6XS44S+nPJxHNsWrNpvRLNyJ/J5/j56f1UrlKoJj7xrnJznHbneGTiOW7U7EZdW628PvhilJasH6vuj7XWXe0aaNZHJfed6k07z6b1SmY2w3oMpyVIdHS0wsPD81zWpEkTOZ1OffPNN5fc/tFHH9W4cePcb4j01FNP6eabb1aPHj0UHh6uvn37ehxOIyMjNWTIEPXp00fHjx9XuXLlJEl+fn6KiorShx9+qPDwcHXu3FkbN26UJPXu3VtRUVHq0qWLbW+IlJGeqc3R2zTg1d4KDApQg1b11CqimdYu3GhLT1GUCQ7QHXfWVil/X/n4OnRv+4Zq2KSGvt1aMt8gwsRzbFqzab0SzVbx8fVRqYBS8vHx+f3XviX3r2oTz7EkPTu5j26oU0UT+r+jrIxsu3MKZOI5/nT5Nxr40D81tMcsDe0xS2s+3q7tm37WS8+8Z3eaR6adZ9N6JTObYT2Hq6Tv44Tx7vcp3s82CykfrBHzB+v2+xsp5Uyq5kUu1vqlm4vt+D533Fpsx5KksuWC9PrbfXV9jYpyOl06evi0PpizXju3/1Jsj+H8dk+xHUvy/jn2BtOaTeuVaM6PT1BQsR1LkvqN66L+47rmuWzhpBgtmrSy2B7DmZ5e+I0ug7fPsV/lSsV2LEmqfF0Fvb9jorIyspWbm+u+fOaopVoffek/BP8VOYmniuU4F1nx/z3fBnWL9Xh/1G9wmKrdcI2mRC4rtmPm/rCv2I51kWk/40zrlbzf/IWz+J5j3jTlxw6F38hmo2/5tPAbeQHDKbyuuIdTbyvu4dQKxT2cAiia4h5OrVDcw6m3FfdwaoXiHk6t4M3h1Bu8MZzCfAynxceu4bTk7hUCAAAAAFw1eLdeAAAAALAI79brGSunAAAAAADbMZwCAAAAAGzHcAoAAAAAsB2vOQUAAAAAizhdrA96wpkBAAAAANiO4RQAAAAAYDu29QIAAACARXLZ1usRZwYAAAAAYDuGUwAAAACA7djWCwAAAAAWccphd0KJxcopAAAAAMB2DKcAAAAAANuxrRcAAAAALMK79XrGmQEAAAAA2I7hFAAAAABgO7b1An/ic/Sk3QmXzadyJbsTLktO4im7E4Bi4UxPtzvhisfPC2s4Tp21OwG4ajhdvFuvJ6ycAgAAAABsx3AKAAAAALAdwykAAAAAwHa85hQAAAAALJLL+qBHnBkAAAAAgO0YTgEAAAAAtmNbLwAAAABYhI+S8YyVUwAAAACA7RhOAQAAAAC2Y1svAAAAAFjEyfqgR5wZAAAAAIDtGE4BAAAAALZjWy8AAAAAWCSXd+v1iJVTAAAAAIDtGE4BAAAAALZjWy8AAAAAWMTJtl6PWDmFcULKB2v8ilGKTVmoRYeidG+f1nYnFaiUv5+en9ZPC3a8rhUHpmnWF5FqGnaL3VkemdZ7kWnPC9N6JZqtYFqvRLMVTOvl7xFrmNYrmdkMa7FyarGwsDD5+/srICBAmZmZatq0qcaPH6/ly5crMzNTjz32WLE/ZnR0tDZs2KAZM2YU+7HtMGzWE8rJylGvKoNUu3FNvREXqV++O6z4H4/ZnZYvHz8fnTpxVqO7TtepY2fVrF0DRc59UoPvnajEo0l2513CtN6LTHtemNYr0WwF03olmq1gWi9/j1jDtF7JzGZYi5VTG8yYMUOrVq3SmjVrdODAAX3xxRfq06ePVwZTb8jJybHtsQODAtS6+51a8MqHykjL0A9f7dWW2B1q17+NbU2FyUzP0uKpa5R4NEkul0vbv9ijk0fOqE6jG+xOy5dpvZJ5zwvTeiWarWBar0SzFUzrlfh7xAqm9UpmNnuL0+VT4r/swsqpjTIzM5WZmanQ0FDNnDlT6enpGjNmjKKjoxUXF6fQ0FDt379fISEhmjlzpipVqpTndpLy/D4rK0vTp0/Xl19+KR8fH11//fWaPXv2JY8bExOjJUuWKDc3V8HBwZowYYJq1aqln3/+Wa+++qrOnz+vzMxM9erVyz0wjx07VmXKlNHhw4d19uxZRUdHW3mq3KrXrSpnrlPH9ye4Lzu4+7Aa3VPytwtdVK5iiKrXqqz4nxMKv3EJYEKvac8L03olmq1gWq9EsxVM680Pf48UP9N6JTObYT2GUxsMHz5cAQEBOnLkiFq3bq3WrVtr165deW7z/fffKzY2VlWrVtXLL7+sRYsW6YUXXijwuHPnztXRo0cVHR0tf39/JSVdunVmx44d+vTTT7V48WL5+/tr48aNGjdunD788ENVr15dCxYskL+/v9LS0tSzZ0/dfffdql27tiRp165dWrRokYKCgorvZFym0sGBSjuXnueytHPpCgopbVPR5fH189HoqIFa+/FWHTtw0u6cQpnSa9rzwrReiWYrmNYr0WwF03r/jL9HvMO0XsnMZliP4dQGM2bMUN26dZWZmalhw4ZpwYIFl9zm9ttvV9WqVSVJt912m77++utCj7t+/XqNHTtW/v7+kqQKFSpccpt169Zp79696tmzpyTJ5XIpOTlZkpSRkaEJEybo559/lsPhUGJiovbu3eseTtu3b2/rYCpJ51MzFBSa94dYmdDSSk85b1NR0TkcDo2a9ZhysnMUNe4ju3MKZVKvac8L03olmq1gWq9EsxVM6/0j/h7xHtN6JTObYT1ec2qjgIAAtW3bNt/BMyAgwP1rX19f5ebmun/tdDrd12VmZrp/7XK5Cn1Ml8ul7t27a9WqVVq1apViY2O1YcMGSdK0adNUqVIlxcTEKDY2Vo0aNcpzfLsHU0k6vi9Bvn6+qn5TFfdltRrVNOKF9M9P76dylUI18Yl3lZvjLPwONjOp17TnhWm9Es1WMK1XotkKpvX+EX+PeI9pvZKZzd6SK0eJ/7ILw6mNnE6nvvnmG9WsWbPI97nhhhv0ww8/yOl0KjU11T1YShfeCfj9999XVlaWJOW7rTcsLEyrVq3Sr7/+KknKzc3Vnj17JEkpKSmqUqWK/Pz8tG/fPu3YseOvf3NekpGeqc3R2zTg1d4KDApQg1b11CqimdYu3Gh3WoGendxHN9Spogn931FWRrbdOYUyrde054VpvRLNVjCtV6LZCqb1XsTfI95lWq9kZjOsx7ZeG1x8zWl2drbq1KmjoUOH6oMPPijSfR944AF9+umn6tixo2rUqKEGDRq4r3vqqaf0z3/+U126dFGpUqVUo0aNSz4+plmzZnr++ec1ePBg5ebmKjs7W+3bt9ett96qwYMHa/To0YqNjdUNN9ygZs2aFev3XVxmDp2nEfMH6+OT85RyJlVvD3m3RP+rW+XrKqjjgLuVlZGtJd+/6b585qilWh/9jY1l+TOt9yLTnhem9Uo0W8G0XolmK5jWy98j1jCtVzKzGdZyuIqyFxT4G+736Wl3wmXxq1zJ7oQrXk7iKbsTAAB/YNrfffw9gvx84Vxmd0KRPPNtf7sTCjXnjoW2PC7begEAAAAAtmM4BQAAAADYjtecAgAAAIBFnC7WBz3hzAAAAAAAbMdwCgAAAACwHdt6AQAAAMAiTjnsTig2Z8+e1ejRo3XkyBH5+/urRo0aeu2111ShQgWFhYXJ399fAQEBkqSRI0fq7rvvLvB4DKcAAAAAgMvmcDj05JNPqkWLFpKkyZMna+rUqZo0aZIkacaMGapbt26Rj8e2XgAAAADAZStXrpx7MJWkxo0b68SJE3/5eKycAgAAAIBFcl0lf1tvcnKykpOTL7k8NDRUoaGh+d7H6XRq6dKlCgsLc182cuRIuVwu3XHHHXrxxRc93vciVk4BAAAAAG7vv/++7rvvvku+3n//fY/3ef311xUUFKR+/fpJkhYvXqzY2FitWLFCLpdLr732WqGPy8opAAAAAMBtwIAB6tq16yWXe1r5nDx5suLj4zVnzhz5+FxY/6xataokyd/fX3379tXgwYMLfVyGUwAAAACAW0Hbd/9s+vTp2rNnj+bOnSt/f39JUnp6unJzcxUSEiKXy6VPPvlE9evXL/RYDKcAAAAAYBGn68p5ZeX+/fs1Z84c1axZUw8//LAk6brrrtPYsWM1bNgw5ebmyul0qnbt2ho/fnyhx2M4BQAAAABctjp16ujnn3/O97qVK1de9vGunLEdAAAAAGAsVk4BAAAAwCJOAz5Kxi6snAIAAAAAbMdwCgAAAACwHdt6gT/JSTxld8IVzycoyO6Ey+ZMT7c7AQC8hr/7AOs4xbZeT1g5BQAAAADYjuEUAAAAAGA7tvUCAAAAgEV4t17PWDkFAAAAANiO4RQAAAAAYDu29QIAAACARZwu1gc94cwAAAAAAGzHcAoAAAAAsB3DKQAAAADAdrzmFAAAAAAswkfJeMbKKQAAAADAdgynAAAAAADbsa0XAAAAACziFNt6PWHlFAAAAABgO4ZTAAAAAIDt2NYLAAAAABbh3Xo9Y+XUy8LCwtS+fXtFRES4v44dO6Z69eopLS3N8p6ffvpJn3zyyV+677Fjx9SiRYtiLrp8IeWDNX7FKMWmLNSiQ1G6t09ru5MKZFqvZF5z56fbaeamCVp9Zp5GzHnS7pwiMe0cSzRbwbReiWYrmNYr0WwF03olM5thLVZOLTBjxgzVrVvX7gxJF4bTDRs26KGHHrI75S8bNusJ5WTlqFeVQarduKbeiIvUL98dVvyPx+xOy5dpvZJ5zWcSzmrJlFg1bddQ/oGl7M4pEtPOsUSzFUzrlWi2gmm9Es1WMK1XMrMZ1mLltATYvXu3evfurfDwcPXu3Vu7d++W9PtK5fTp09WlSxc9+OCD2rFjh/t+ixYt0gMPPKDu3btrxowZ7lXNnJwcPfHEE+rWrZs6duyoyMhIZWVl6ezZs5oxY4a+/vprRUREaOLEiZKk7777Tv3791e3bt3UrVs3bdiwwf0Yixcv1v3336++fftq+fLl1p0UDwKDAtS6+51a8MqHykjL0A9f7dWW2B1q17+N3Wn5Mq1XMrP5q9hvtSVup5KTUu1OKRITzzHN3mdar0SzFUzrlWi2gmm9kpnN3uJ0OUr8l10YTi0wfPhw95bebt265bkuKytLw4cP13PPPafVq1fr+eef1/Dhw5WVlSVJ+u2339S4cWOtXLlSQ4cO1dSpUyVJe/fu1f/93//pww8/1IoVK5SSkuI+pq+vr6ZOnaro6GjFxcUpNzdXK1asUPny5TV8+HC1atVKq1at0ssvv6zk5GSNHz9e//znPxUdHa05c+bolVdeUXJysvbu3at33nlHS5cu1ZIlS/Tbb79Zds48qV63qpy5Th3fn+C+7ODuw6pxy3U2VnlmWq9kZrNpTDzHNHufab0SzVYwrVei2Qqm9UpmNsN6bOu1QEHbeg8dOqRSpUqpVatWkqSWLVuqVKlSOnTokMqUKaOgoCDde++9kqTGjRtr8uTJkqTt27erTZs2qlChgiSpe/fuio2NlSQ5nU79+9//1qZNm+R0OnXu3DkFBgbm+/i7du3SsWPHNGjQIPdlDodD8fHx2rVrl9q2bauKFStKknr37q1PP/20GM7IX1c6OFBp59LzXJZ2Ll1BIaVtKiqYab2Smc2mMfEc0+x9pvVKNFvBtF6JZiuY1iuZ2QzrMZzazOVyyeG4dOn84mX+/v7uy3x8fJSTk1Pg/SRp9erV+vbbb7V48WIFBwdrzpw5Onz4sMfHr1evnhYvXnzJdTt37rzcb8frzqdmKCg07w+xMqGllZ5y3qaigpnWK5nZbBoTzzHN3mdar0SzFUzrlWi2gmm9kpnN3sK79XrGtl6b1apVS1lZWdq6daskaevWrcrJyVHNmjULvF+LFi20ceNGJSUlSZJiYmLc16WkpKh8+fIKDg5WSkqK4uLi3NddvOyiJk2aKD4+3v340oXXwLpcLvdjnDlzRpJKxGtOj+9LkK+fr6rfVMV9Wa1GNUvsC+lN65XMbDaNieeYZu8zrVei2Qqm9Uo0W8G0XsnMZliP4dQCf3zNaUREhL7//nv3df7+/poxY4amT5+u8PBwTZ8+XW+//XaeFdP83HzzzXryySf18MMPq2/fvgoJCVFISIgkqUuXLkpLS1PHjh313HPP6Y477nDfr2XLljp//rw6d+6siRMnqmzZsoqKitLs2bPVuXNndejQQbNmzZLL5dLNN9+sZ555Rn369FHfvn117bXXeucEXYaM9Extjt6mAa/2VmBQgBq0qqdWEc20duFGu9PyZVqvZGazj6+PSgWUko+Pz++/9i25P95MPMc0e59pvRLNVjCtV6LZCqb1SmY2w3oOl8vlsjsCf01qaqqCg4MlSTNnzlR8fLz7DZNKkvt9ehbr8ULKB2vE/MG6/f5GSjmTqnmRi7V+6eZifYziZFqv5P1mn6CgYjuWJPUb10X9x3XNc9nCSTFaNGllsT2GMz298BtdBp4X1jCt2bReiWYrmNYr0WwF03ol7zd/4VxWbMfypo6bhtudUKg198yw5XEZTg326quvaufOncrOztb111+v1157rUSsbv5ZcQ+nMF9xD6dWKO7hFAAAFC+G0+Jj13DKGyIZbPz48XYnAAAAAECxKLkvygIAAAAAXDVYOQUAAAAAizjFR8l4wsopAAAAAMB2DKcAAAAAANuxrRcAAAAALOJ0sa3XE1ZOAQAAAAC2YzgFAAAAANiObb0AAAAAYBG29XrGyikAAAAAwHYMpwAAAAAA27GtFwAAAAAswrZez1g5BQAAAADYjuEUAAAAAGA7tvUCAAAAgEXY1usZK6cAAAAAANsxnAIAAAAAbMe2XgAoAp+gILsTLpszPd3uhCsezwtcKfxq1rA74bLkHI63OwGAFzCcAgAAAIBFXLzm1CO29QIAAAAAbMdwCgAAAACwHdt6AQAAAMAiTrGt1xNWTgEAAAAAtmM4BQAAAADYjm29AAAAAGARJ+/W6xErpwAAAAAA2zGcAgAAAABsx7ZeAAAAALCIi229HrFyCgAAAACwHcMpAAAAAMB2bOsFAAAAAIvwbr2esXIKAAAAALAdwykAAAAAwHZs6wUAAAAAi/BuvZ6xcgrjhJQP1vgVoxSbslCLDkXp3j6t7U4qkGm9knnNnZ9up5mbJmj1mXkaMedJu3OKxMRm054XknnNPC+sYVqzab2SNGpaHy3e+opWfDdR7/53jB7s1dzupEKZdp5N65XMbIa1WDk1SFhYmPz9/RUQECBJatGihcaNG5fvbbdt26bJkycrOjr6kuuOHTum7t27a9u2bZf1+AUd00rDZj2hnKwc9aoySLUb19QbcZH65bvDiv/xmK1dnpjWK5nXfCbhrJZMiVXTdg3lH1jK7pwiMbHZtOeFZF4zzwtrmNZsWq8kffTOOv1r7MfKzsrVdbUqafLSwTr443Ed2HPc7jSPTDvPpvVKZjbDWqycGmbGjBlatWqVVq1a5XEwvZIFBgWodfc7teCVD5WRlqEfvtqrLbE71K5/G7vT8mVar2Rm81ex32pL3E4lJ6XanVJkpjWb+LwwsZnnhfeZ1mxa70VH9p9UdlauJMnlkuSSqt5Q0d6oAph2nk3rlcxshvVYOTXcpk2bNG3aNOXm5qpChQp67bXXVKNGjUtut3jxYi1YsECVKlVS8+Z5t9Zs3LhR77zzjrKyslSqVClFRkaqcePGkqTp06frk08+0bXXXquGDRta8S0VqHrdqnLmOnV8f4L7soO7D6vRPbfYWOWZab2Smc3wPhOfFyY2m8bEc2xas2m9fzT0tW5q172pAkv768CeY/pmw092J3lk2nk2rVcys9lb+CgZzxhODTN8+HD3tt6nn35ar732mhYtWqSbbrpJy5Yt08iRI7Vs2bI899m7d6/eeecdrVy5UhUrVtSECRPc1x05ckRRUVGaP3++goODtX//fg0aNEgbNmzQunXrtG7dOq1cuVKBgYEaOnSold9qvkoHByrtXHqey9LOpSsopLRNRQUzrVcysxneZ+LzwsRm05h4jk1rNq33j2a/Eq13JsSo/u011LBFbWVn5did5JFp59m0XsnMZliPbb2G+eO23sDAQN1888266aabJEndu3fXTz/9pNTUvNvBtm/frrZt26pixQvbaXr37u2+7ssvv9SRI0f0yCOPKCIiQiNHjlROTo5Onz6tbdu26aGHHlKZMmXk6+urHj16WPeNenA+NUNBoXl/iJUJLa30lPM2FRXMtF7JzGZ4n4nPCxObTWPiOTat2bTeP3M6Xfphx2FVrFpOHR9pZXeOR6adZ9N6JTObYT2GU4O5XC45HIVvC3C5XAVef/fdd7sH3lWrVmnz5s2qWLFiofezw/F9CfL181X1m6q4L6vVqGaJfSG9ab2Smc3wPhOfFyY2m8bEc2xas2m9nvj6+qjqDdfYneGRaefZtF7JzGZvcblK/pddGE4N1qRJE/300086ePCgJCkmJka33HKLgoOD89yuRYsW2rhxo86cOSNJWr58ufu6u+66S19++aX279/vvmz37t2SpJYtW+rTTz9Venq6cnNztWLFCm9/S4XKSM/U5uhtGvBqbwUGBahBq3pqFdFMaxdutDstX6b1SmY2+/j6qFRAKfn4+Pz+a9+S/ePNtGYTnxcmNvO88D7Tmk3rlaSy1wSrTafGCgzyl4+PQ7ffXVdtw5vouy0H7E7zyLTzbFqvZGYzrOdwlcTlMeQrLCxMc+bMUd26dd2Xbdq0SdOnT1dOTk6eN0T688e+LF68WO+//74qVqyotm3bav78+e6Pktm8ebNmzJihjIwMZWdn6/bbb9cbb7wh6cIbIn366aeqXLmyWrRoofXr11/2R8nc79OzmM7ABSHlgzVi/mDdfn8jpZxJ1bzIxVq/dHOxPkZxMq1X8n6zT1BQsR1LkvqN66L+47rmuWzhpBgtmrSyWB+nOFnR7ExPL/xGl4Hn8qVMfC7zvDCv2Ypev5qXvpniX1W2Qhm9NPtR3Vi/mnwcDp08cVaxCzbrPx9d3kfYFSTncHyxHesinhfe5+3mL5zLCr9RCdDs05L/iRvfdJhky+MynMLrins4hfmK+z/okb/iHkJwKROfyzwvkJ/iHE6t4I3hFOYzZTi949OX7E4o1Lcd3rDlcUvuXiEAAAAAwFWD4RQAAAAAYDs+5xQAAAAALOJyFf5pG1crVk4BAAAAALZjOAUAAAAA2I5tvQAAAABgESfbej1i5RQAAAAAYDuGUwAAAACA7RhOAQAAAAC24zWnAAAAAGARl8vugpKLlVMAAAAAgO0YTgEAAAAAtmNbLwAAAABYxMVHyXjEyikAAAAAwHYMpwAAAAAA27GtFwAAAAAswrZez1g5BQAAAADYjuEUAAAAAGA7tvUCsJwzPd3uhKuCT1CQ3QmXxcTnhYnNQH5yDsfbnQBcNZxs6/WIlVMAAAAAgO0YTgEAAAAAtmNbLwAAAABYxOWyu6DkYuUUAAAAAGA7hlMAAAAAgO0YTgEAAAAAtuM1pwAAAABgERcfJeMRK6cAAAAAANsxnAIAAAAAbMe2XgAAAACwCNt6PWPlFAAAAABgO4ZTAAAAAIDt2NYLAAAAABZx2R1QgrFyCgAAAACwHcMpAAAAAMB2bOsFAAAAAIvwbr2esXIK44SUD9b4FaMUm7JQiw5F6d4+re1OKpBpvRLNVjCtV5I6P91OMzdN0Ooz8zRizpN25xSJaefZtF6JZiuY1ivRbAXTeiUzm2EtVk4Nlp2drTlz5iguLk5+fn7y8/NTjRo1NHz4cN10001253nNsFlPKCcrR72qDFLtxjX1RlykfvnusOJ/PGZ3Wr5M65VotoJpvZJ0JuGslkyJVdN2DeUfWMrunCIx7Tyb1ivRbAXTeiWarWBar2RmM6zFyqnBIiMj9fPPP2vZsmVas2aNVq5cqQ4dOujgwYN5bud0OuVyXRnvCxYYFKDW3e/Uglc+VEZahn74aq+2xO5Qu/5t7E7Ll2m9Es1WMK33oq9iv9WWuJ1KTkq1O6VITDvPpvVKNFvBtF6JZiuY1iuZ2ew1LgO+bMLKqaEOHz6stWvXauPGjQoNDZUkORwOdejQQZI0c+ZMxcfHKz09XUePHtWiRYs0Z84cbd++XdnZ2SpfvrwmTZqk6tWr69ixY+revbu6deumb775RpmZmRo/fryaNm0qSdq4caPeeecdZWVlqVSpUoqMjFTjxo1t+b6r160qZ65Tx/cnuC87uPuwGt1ziy09hTGtV6LZCqb1msq082xar0SzFUzrlWi2gmm9kpnNsB7DqaF+/PFH1ahRQ2XLlvV4mx07dig6OloVKlSQJA0aNEhjxoyRJC1btkxTp07V9OnTJUm//fab6tWrpzFjxmj79u168cUXtXbtWv3666+KiorS/PnzFRwcrP3792vQoEHasGGD17/H/JQODlTaufQ8l6WdS1dQSGlbegpjWq9EsxVM6zWVaefZtF6JZiuY1ivRbAXTeiUzm1G4s2fPavTo0Tpy5Ij8/f1Vo0YNvfbaa6pQoYIOHTqksWPH6rffflO5cuU0efJk1axZs8DjMZxeIQ4cOKARI0YoIyNDd999t8qWLat77rnHPZhK0qZNm7RkyRKlp6crJycnz/1LlSqlzp07S5KaN2+uwMBA/fLLL/r222915MgRPfLII+7b5uTk6PTp06pYsaI139wfnE/NUFBo3h9iZUJLKz3lvOUtRWFar0SzFUzrNZVp59m0XolmK5jWK9FsBdN6JTObveVKerdeh8OhJ598Ui1atJAkTZ48WVOnTtWkSZM0fvx49e3bVxEREVq1apVeeeUVffDBBwUej9ecGuqWW25RfHy8kpOTJUk33XSTVq1apf79+ys19cLrwcqUKeO+/fHjx/Xmm2/qn//8p+Li4jRp0iRlZWV5PL7L5ZLDceH/OHfffbdWrVrl/tq8ebMtg6kkHd+XIF8/X1W/qYr7slqNapbYF9Kb1ivRbAXTek1l2nk2rVei2Qqm9Uo0W8G0XsnMZhSuXLly7sFUkho3bqwTJ07ozJkz+vHHH9WpUydJUqdOnfTjjz8qKSmpwOMxnBqqZs2auu+++/Tyyy8rJSXFfXl6enq+t09NTVWpUqVUqVIlOZ1Offjhh3muz87O1urVqyVd2A6cmZmpG2+8UXfddZe+/PJL7d+/333b3bt3e+E7KpqM9Extjt6mAa/2VmBQgBq0qqdWEc20duFG25oKYlqvRLMVTOu9yMfXR6UCSsnHx+f3X/uW3L9GTDvPpvVKNFvBtF6JZiuY1iuZ2Xw1S05O1rFjxy75urgwlh+n06mlS5cqLCxMCQkJuvbaa+Xr6ytJ8vX1VeXKlZWQkODx/pLkcF0pb+N6FcrKylJUVJQ+/fRT+fn5KTQ0VJUrV9ZTTz2ldevWKT093f0aU0maOHGi1q9fr2rVqqlZs2ZauXKl1q1b535DpEceeURffvmlMjIy8rwh0ubNmzVjxgxlZGQoOztbt99+u954440id97v07NYv++Q8sEaMX+wbr+/kVLOpGpe5GKtX7q5WB+jOJnWK9FsBSt6fYKCivV4/cZ1Uf9xXfNctnBSjBZNWlksx3d6+Me1v4PnhffR7H2m9Uo0W8G0Xsn7zV84lxXbsbypzrKJdicUavivZTVr1qxLLn/22Wc1bNiwfO/z6quv6uTJk5o1a5Z+/PFHjRkzRmvWrHFf/9BDD+mtt95SgwYNPD4uwyncw+m2bdu8cvziHk4BFE1xD6fe5o3hFABw9TBlOL3p45I/nO5sPzzfVdLQ0FD3J4X80eTJk/Xzzz9rzpw58vf315kzZ/Tggw9q27Zt8vX1VW5urlq0aKHPP/88z3vi/BlviAQAAAAAcPM0hOZn+vTp2rNnj+bOnSt/f39J0jXXXKP69esrLi5OERERiouLU/369QscTCVWTmEBVk4Be7ByCgC4mrByWnwO9Hq5SLfbv3+/OnXqpJo1ayowMFCSdN1112n27Nk6ePCgxo4dq+TkZIWGhmry5MmqVatWgcdj5RQAAAAALHIlfZRMnTp19PPPP+d7Xe3atbVs2eX9g0HJfZtFAAAAAMBVg+EUAAAAAGA7tvUCAAAAgFWuoG29xY2VUwAAAACA7RhOAQAAAAC2Y1svAAAAAFiED/L0jJVTAAAAAIDtGE4BAAAAALZjWy8AAAAAWIVtvR6xcgoAAAAAsB3DKQAAAADAdgynAAAAAADb8ZpTAAAAALCIy+WwO6HEYuUUAAAAAGA7hlMAAAAAgO3Y1gsAVyhnerrdCZfFr3IluxMuW07iKbsTAACm4aNkPGLlFAAAAABgO4ZTAAAAAIDt2NYLAAAAABbh3Xo9Y+UUAAAAAGA7hlMAAAAAgO3Y1gsAAAAAVuHdej1i5RQAAAAAYDuGUwAAAACA7djWCwAAAACW4d16PWHlFAAAAABgO4ZTAAAAAIDt2NYLAAAAAFbh3Xo9YuUUAAAAAGA7hlMAAAAAgO0YTgEAAAAAtuM1pwAAAABgFV5z6hErp1ewsLAw7du3L89l3bp107Zt2y77fiVJSPlgjV8xSrEpC7XoUJTu7dPa7qQCmdYr0WwF03ol85pL+fvp+Wn9tGDH61pxYJpmfRGppmG32J1VINPOsUSzFUzrlWi2gmm9kpnNsBYrpzDOsFlPKCcrR72qDFLtxjX1RlykfvnusOJ/PGZ3Wr5M65VotoJpvZJ5zT5+Pjp14qxGd52uU8fOqlm7Boqc+6QG3ztRiUeT7M7Ll2nnWKLZCqb1SjRbwbReycxmWIuV06vU6dOnNXToUIWHhys8PFwrV67M93aJiYkaPny4evToofDwcM2ZM8fa0D8JDApQ6+53asErHyojLUM/fLVXW2J3qF3/NrZ2eWJar0SzFUzrlcxszkzP0uKpa5R4NEkul0vbv9ijk0fOqE6jG+xOy5eJ55hm7zOtV6LZCqb1SmY2e43LUfK/bMLK6RVu+PDhCggIcP/+8OHDkqSJEyeqTp06mj17thITE9WtWzfdcsstqlu3bp77jxkzRkOGDFGzZs2UlZWlxx57TA0bNtRdd91l5bfhVr1uVTlznTq+P8F92cHdh9XonpK5Vc+0XolmK5jWK5nZ/GflKoaoeq3Kiv85ofAb28DEc0yz95nWK9FsBdN6JTObYT2G0yvcjBkz8gyc3bp1kyRt2bJFY8eOlSRVrlxZbdq00bZt2/LcNj09Xdu3b1dS0u/b39LS0nTw4EHbhtPSwYFKO5ee57K0c+kKCiltS09hTOuVaLaCab2Smc1/5Ovno9FRA7X24606duCk3Tn5MvEc0+x9pvVKNFvBtF7JzGZYj+H0KuZwOAr8vdPplMPh0PLly1WqVCkr0zw6n5qhoNC8P8TKhJZWesp5m4oKZlqvRLMVTOuVzGy+yOFwaNSsx5STnaOocR/ZneORieeYZu8zrVei2Qqm9UpmNnuLi3fr9YjXnF6lWrZsqY8+uvAfaadOndLGjRvVokWLPLcJDg7WHXfcoblz57ovS0hI0KlTpyxt/aPj+xLk6+er6jdVcV9Wq1HNEvtCetN6JZqtYFqvZGbzRc9P76dylUI18Yl3lZvjtDvHIxPPMc3eZ1qvRLMVTOuVzGyG9RhOr1Ivv/yy9u7dq/DwcD3++OMaOXKk6tSpc8ntpk6dqoMHD7rfOOmFF15QcnKyDcUXZKRnanP0Ng14tbcCgwLUoFU9tYpoprULN9rWVBDTeiWarWBar2RmsyQ9O7mPbqhTRRP6v6OsjGy7cwpk4jmm2ftM65VotoJpvZKZzbCew+ViYRnedb9Pz2I9Xkj5YI2YP1i3399IKWdSNS9ysdYv3Vysj1GcTOuVaLaCab2S95v9KlcqtmNJUuXrKuj9HROVlZGt3Nxc9+UzRy3V+uhviuUxchKLdycJzwtrmNZsWq9EsxVM65W83/yFc1mxHcubasyfYndCoeKfGG3L4zKcwuuKezgFcGUq7uHUCsU9nAIA/jqG0+Jj13DKtl4AAAAAgO14t14AAAAAsIrLUfhtrlKsnAIAAAAAbMdwCgAAAACwHcMpAAAAAMB2vOYUAAAAACzi4LNSPGLlFAAAAABgO4ZTAAAAAIDt2NYLAAAAAFZhW69HrJwCAAAAAGzHcAoAAAAAsJ3Hbb1t2rSRw+Eo9AAbNmwozh4AAAAAuHK5Cp+xrlYeh9O33nrLyg4AAAAAwFXM43DavHlzKzsAAAAAAFexIr3mNCsrS9OnT9d9992nO+64Q5K0efNmLVq0yKtxAAAAAHBFcRnwZZMiDaeTJk3Svn37NHXqVPfrUOvUqaOlS5d6NQ4AAAAAcHUo0uecrl27Vp9//rmCgoLk43Nhnr322mt18uRJr8YBAAAAAK4ORRpOS5Uqpdzc3DyXJSUlqVy5ct5oAmzlExRkd8Jl8wkuY3fCZclJPGV3AkogE58Xvw1oaXfCZSv3/ha7E4Crkon/feFMT7c74cpk47bZkq5I23rbt2+vMWPG6OjRo5KkxMREvfbaa+rYsaNX4wAAAAAAV4ciDacvvPCCqlevrs6dOys5OVkPPvigKleurKFDh3q7DwAAAABwFSjStl5/f3+99NJLeumll5SUlKTy5cu73xgJAAAAAIC/q0jDqSQdPnxYn376qRITE1W5cmV16NBBNWvW9GIaAAAAAFxheM2pR0Xa1rt69Wp17dpVP//8s0qXLq19+/apa9euWr16tbf7AAAAAABXgSKtnP7rX//S3Llz1axZM/dlO3bs0OjRoxUeHu61OAAAAADA1aFIw2laWpoaN26c57LbbrtN6by9NAAAAAAUnYv37vGkSNt6Bw4cqGnTpikzM1OSlJGRoenTp2vgwIFejQMAAAAAXB08rpy2adPG/Y68LpdLp0+f1sKFCxUaGqrk5GS5XC5VqlRJTz/9tGWxAAAAAIArk8fh9K233rKyAwAAAACueA7erdcjj8Np8+bNrewAAAAAAFzFivw5pz/99JN27Nihs2fPyuX6fdx/7rnnvBIGAAAAALh6FOkNkT766CP16dNHW7du1bvvvqt9+/bpvffe05EjR7zdBwAAAABXDpcBXzYp0nA6b948zZs3T7Nnz1ZgYKBmz56tt99+W35+RV54BQAAAADAoyINp2fOnFHTpk0v3MHHR06nU23atNH69eu9GgcAAAAAuDoUaemzSpUqOnbsmK677jrVrFlT//3vf1W+fHmVKlXK230AAAAAgKtAkVZOn3zySR08eFCSNGTIEI0aNUoDBgzQkCFDvBoH5CekfLDGrxil2JSFWnQoSvf2aW13UoE6P91OMzdN0Ooz8zRizpN25xSqlL+fnp/WTwt2vK4VB6Zp1heRahp2i91ZhTLteWFar0SzN/S8r7Hef6Wvvpo7XOOfeDDPdc3qX69lkx7Tl3OG6Z3RPVXlmhCbKgtW0s9xfkxrNq1XotkKpv33hWTeOYb1irRy2q1bN/ev27Rpo+3btys7O1ulS5f2WpjJwsLCNGfOHNWtW9fjbaKjo9WkSRPdeOONtjy+yYbNekI5WTnqVWWQajeuqTfiIvXLd4cV/+Mxu9PydSbhrJZMiVXTdg3lH1jydxv4+Pno1ImzGt11uk4dO6tm7Roocu6TGnzvRCUeTbI7zyPTnhem9Uo0e8Pp31L179XbdOetNRXo//tfyWWDAzXl2c6a+N7n+vJ/v+iZbndp0uBOenziUhtr81fSz3F+TGs2rVei2Qqm/feFZN45hvWKtHL6Z/7+/ipVqpQaNGhQ3D1XjZiYGB0+fPiy7+d0OvN8lM/VJjAoQK2736kFr3yojLQM/fDVXm2J3aF2/dvYnebRV7HfakvcTiUnpdqdUiSZ6VlaPHWNEo8myeVyafsXe3TyyBnVaXSD3Wkemfa8MK1Xotlb1n97QBt3HdS5tPN5Lg+7o45+OXFG/92xX1k5uZq78mvVub6SalQpb1Np/kw4x39mWrNpvRLNVjHtvy9MPMfe4nCV/C+7/K23272ah6Si6N+/v2699Vb973//U2Jiojp06KCRI0dqxYoV2rNnjyZOnKh//etfGjNmjFq1aqV3331Xn332mXJzc3Xttdfq9ddfV6VKlTRz5kzFx8crPT1dR48e1aJFi9S1a1d17NhRO3fuVGJiogYMGKB+/fq5H/vTTz/VP/7xD506dUqPP/64+7rJkye7V77Lly+vSZMmqXr16jpz5oxGjBihM2fOSJJatmypcePGSZLHLjtUr1tVzlynju9PcF92cPdhNbqn5G87NVW5iiGqXquy4n9OKPzGNjHteWFar0Sz1WpVv0b7jpxy/z4jK0fHE39TreoVFf/rWRvL8jLxHJvWbFqvRDPyxzlGUfyt4dThcBRXxxUrISFBixcvVlpamtq1a6cePXqoe/fuWrlypR5//HHde++9kqRVq1bpyJEj+vjjj+Xj46MlS5bo//2//6d//vOfkqQdO3YoOjpaFSpUcB/79OnTWrx4sU6fPq0uXbqoadOmuvnmmyVJGRkZ+uijj3Ts2DGFh4era9euKlOmjAYNGqQxY8ZIkpYtW6apU6dq+vTpWr16tapVq6YFCxZIks6dO1ekLquVDg5U2rn0PJelnUtXUAhbzL3B189Ho6MGau3HW3XswEm7czwy7XlhWq9Es9VKB/jrt5S87annM1WmhG3dM/Ecm9ZsWq9EM/LHOUZR8EGlXta+fXv5+PgoJCREtWvX1pEjR1SzZs1Lbrdu3Trt2bNHXbt2lSTl5uYqODjYff0999yTZzCVpB49ekiSKlasqLZt22r79u3u4fShhx6SJF133XUKDQ3Vr7/+qtq1a2vTpk1asmSJ0tPTlZOT4z7Wbbfdpvfee0+TJ09W8+bN1bp16yJ1We18aoaCQvP+ECsTWlrpKec93AN/lcPh0KhZjyknO0dR4z6yO6dApj0vTOuVaLba+cwslSntn+eyMqUDlJaRbVNR/kw8x6Y1m9Yr0Yz8cY5RFAUOp3379vW4Oup0Or0SdKUJCAhw/9rX11e5ubn53s7lcmnw4MHugfPPypQpU+DjuFyuPH9W+T3u8ePH9eabb2r58uW6/vrrtXPnTo0cOVKS1KRJE61cuVJff/21Vq1apblz52rp0qWFdlnt+L4E+fr5qvpNVXT8wK+SpFqNavJCei94fno/lasUqlcema3cnJL9/3fTnhem9Uo0W+2X42fU8a7ft7oF+vvpukpl9cvx0zZWXcrEc2xas2m9Es3IH+f4D1zsPvWkwDdE6tmzp3r06JHvV69evTRp0iSrOq84ZcqUUUpKivv3YWFhWrJkiXs7bVZWlvbu3VvgMWJiYiRJSUlJ2rRpk5o3b17g7VNTU1WqVClVqlRJTqdTH374ofu6o0ePKjg4WB07dlRkZKR++OEHOZ3Ov9TlTRnpmdocvU0DXu2twKAANWhVT60immntwo22NRXGx9dHpQJKycfH5/df+/6l9yKzzLOT++iGOlU0of87yiphKzX5Me15YVqvRLO3+Po45O/nK18fH/m4f+3Q+p0HVLt6Rd17Rx35+/nqyYiW2n/sdIl6valkxjn+M9OaTeuVaLaKaf99YeI5hvUKXDm9uJUTxa93796aPHmy/v3vf2v06NHq0qWLfvvtN/cbF7lcLvXp08e9TTc/VatWVd++fXXq1Ck9/fTTqlevXoGPWa9ePbVv314dO3ZUtWrV1KxZM+3YsUOStH37dr333nvy9fWV0+nUq6++Kh8fn7/U5W0zh87TiPmD9fHJeUo5k6q3h7xbov/Vre+Yzuo/7vf/L7Xrc5cWTorRokkr7YsqQOXrKqjjgLuVlZGtJd+/6b585qilWh/9jY1lBTPteWFar0SzNzwefqee6tLS/fuHWt2iuSu36N1VWzRm9mqN6hem157qoB9+SdBL76yxsdSzkn6O82Nas2m9Es1WMO2/LyTzzjGs53DxlrtGMumzTO/36Wl3wmXxCQqyO+Gy+QQXvO27pMlJPFX4jQAD/DagZeE3KmHKvb/F7gTgqmTif18409MLv1EJ8oVzmd0JRVLrX9PsTijUL8+/aMvjlty1fwAAAADAVYN36zXUunXr7E4AAAAAgGLDcAoAAAAAVuFFlR4VaVtvVlaWpk+frvvuu0933HGHJGnz5s1atGiRV+MAAAAAAFeHIg2nkyZN0r59+zR16lT3Z2nWqVNHS5cu9WocAAAAAODqUKRtvWvXrtXnn3+uoKAg+fhcmGevvfZanTx50qtxAAAAAHAlcbCt16MirZyWKlVKubm5eS5LSkpSuXLlvNEEAAAAALjKFGk4bd++vcaMGaOjR49KkhITE/Xaa6+pY8eOXo0DAAAAAFwdijScvvDCC6pevbo6d+6s5ORkPfjgg6pcubKGDh3q7T4AAAAAuHK4DPiySZFec+rv76+XXnpJL730kpKSklS+fHn3GyMBAAAAAPB3FWk4vbid96K0tDT3r6+//vriLQIAAAAAXHWKNJzef//9cjgccrl+X+O9uHL6008/eacMAAAAAHDVKNJwunfv3jy/P3XqlGbNmqWmTZt6JQoAAAAArkh8lIxHRXpDpD+rVKmSXnrpJU2bNq24ewAAAAAAV6G/NJxK0i+//KLz588XZwsAAAAA4CpVpG29ffv2zfPuvOfPn9eBAwf4KBkAAAAAuAwOtvV6VKThtGfPnnl+X7p0ad18882qWbOmN5oAAAAAAFeZQofT3Nxcbd26Va+//rr8/f2taAIAAAAAXGUKHU59fX311Vdf5dnWC1zJnOnpdidcNhObgStBufe32J1w2Xwb1LU74bLk/rDP7gSgWPB3NdxczFWeFOkNkQYMGKCZM2cqOzvb2z0AAAAAgKtQgSuncXFx6tSpkxYtWqTTp0/rvffeU4UKFfKsom7YsMHbjQAAAACAK1yBw+krr7yiTp066a233rKqBwAAAACuXLxbr0cFDqcu14Uz17x5c0tiAAAAAABXpwKHU6fTqa1bt7qH1Py0bNmy2KMAAAAAAFeXAofTrKwsvfTSSx6HU4fDof/+979eCQMAAACAK42Dbb0eFTicli5dmuETAAAAAOB1RfooGQAAAAAAvKlIb4gEAAAAACgGjFgeFbhyumvXLqs6AAAAAABXsQJXTgEAAAAAyM/kyZP12Wef6fjx41q9erXq1q0rSQoLC5O/v78CAgIkSSNHjtTdd99d6PEYTgEAAAAAl+2+++7To48+qkceeeSS62bMmOEeVouK4RQAAAAALHIlfZRM06ZNi/V4DKcAAAAAALfk5GQlJydfcnloaKhCQ0OLdIyRI0fK5XLpjjvu0Isvvlik+/FRMgAAAAAAt/fff1/33XffJV/vv/9+ke6/ePFixcbGasWKFXK5XHrttdeKdD9WTgEAAADAKgZs6x0wYIC6du16yeVFXTWtWrWqJMnf3199+/bV4MGDi3Q/Vk5hnJDywRq/YpRiUxZq0aEo3duntd1JBTKtV6LZCqb1SjRbwbTeP6p2wzWK3TFBo9/saXdKoUw7z6b1SjRbwbReyczmq1VoaKiuu+66S76KMpymp6crJSVFkuRyufTJJ5+ofv36RXpcVk6vAGFhYZozZ85lvxuWqYbNekI5WTnqVWWQajeuqTfiIvXLd4cV/+Mxu9PyZVqvRLMVTOuVaLaCab1/NPSlcO3bc9zujCIx7Tyb1ivRbAXTeiUzm1GwiRMn6vPPP9fp06c1cOBAlStXTnPmzNGwYcOUm5srp9Op2rVra/z48UU6HiunV7mcnBy7Ey5LYFCAWne/Uwte+VAZaRn64au92hK7Q+36t7E7LV+m9Uo0W8G0XolmK5jW+0dt2jdUWkqG/rftoN0phTLtPJvWK9FsBdN6JTObvcZlwFcRvfzyy9q0aZN+/PFHffXVV1qzZo2uv/56rVy5UqtXr9aaNWs0Y8YMVa5cuUjHYzi9Qk2ePFndu3dX586dNWDAAB0/fuFfs48dO6YWLVpo5syZ6tOnj5YtW6YDBw6oZ8+e6tSpk0aOHKlevXpp/fr1kqTExEQNHz5cPXr0UHh4uObMmWPnt6XqdavKmevU8f0J7ssO7j6sGrdcZ2OVZ6b1SjRbwbReiWYrmNZ7UVCZAPUf2k5z3/rU7pQiMe08m9Yr0WwF03olM5thPbb1XqEGDRqkMWPGSJKWLVumqVOnavr06ZKk3377TbVr19awYcMkSd26ddOAAQMUERGh77//Xr169XIfZ8yYMRoyZIiaNWumrKwsPfbYY2rYsKHuuusu678pSaWDA5V2Lj3PZWnn0hUUUtqWnsKY1ivRbAXTeiWarWBa70WPPttOn8Xs0OmT5+xOKRLTzrNpvRLNVjCtVzKzGdZjOL1Cbdq0SUuWLFF6evolW3cDAgLUoUMHSVJqaqr27dun8PBwSVLDhg1Vr149SRdezLx9+3YlJSW575uWlqaDBw/aNpyeT81QUGjeH2JlQksrPeW8LT2FMa1XotkKpvVKNFvBtF5JqlWvqprcWVtDe862O6XITDvPpvVKNFvBtF7JzGZvcRjwbr12YTi9Ah0/flxvvvmmli9fruuvv147d+7UyJEj3deXLl1aDodD0oV30HI4HO7f/5HT6ZTD4dDy5ctVqlQpy/oLcnxfgnz9fFX9pio6fuBXSVKtRjVL7AvpTeuVaLaCab0SzVYwrVeSGjW7UddWK68PvhglSSod5C8fHx/NqlVZz/YumQOraefZtF6JZiuY1iuZ2Qzr8ZrTK1BqaqpKlSqlSpUqyel06sMPP/R425CQEN10002Ki4uTJP3www/at2+fJCk4OFh33HGH5s6d6759QkKCTp065d1voAAZ6ZnaHL1NA17trcCgADVoVU+tIppp7cKNtjUVxLReiWYrmNYr0WwF03ol6dPl32jgQ//U0B6zNLTHLK35eLu2b/pZLz3znt1pHpl2nk3rlWi2gmm9kpnNsJ7D5XKxsGy4sLAwZWZmytfX133ZAw88oPXr16tatWpq1qyZVq5cqXXr1unYsWPq3r27tm3b5r7tvn37NG7cODmdTjVo0EA//PCDxo0bp6ZNm+rUqVN68803tX//fklSmTJl9MYbb6h27dpF7rvfp3g/8y6kfLBGzB+s2+9vpJQzqZoXuVjrl24u1scoTqb1SjRbwbReiWYrWNHr28B7HzvWb3CYqt1wjaZELiu2Y+b+sK/YjnURzwvvo9n7TOuVvN/8hbP4fvZ4U73Xp9udUKif//GCLY/LcAqlp6e7t/oeOHBA/fv313/+8x+VLVu2WI5f3MMpAOCv8+Zw6g3eGE4BXJkYTouPXcMprzmFdu7cqSlTpujiv1O8/vrrxTaYAgAAAEBRMJxCrVu3VuvWre3OAAAAAHAVYzgFAAAAAKvwokqPeLdeAAAAAIDtGE4BAAAAALZjWy8AAAAAWMTBtl6PWDkFAAAAANiO4RQAAAAAYDu29QIAAACAVdjW6xErpwAAAAAA2zGcAgAAAABsx7ZeAAAAALAK23o9YuUUAAAAAGA7hlMAAAAAgO3Y1gsAAAAAFnGwrdcjVk4BAAAAALZjOAUAAAAA2I7hFAAAAABgO15zCgDAVST3h312J1wWn6AguxMum0/lSnYnXLacw/F2JwBXD15z6hErpwAAAAAA2zGcAgAAAABsx7ZeAAAAALAIHyXjGSunAAAAAADbMZwCAAAAAGzHtl4AAAAAsArbej1i5RQAAAAAYDuGUwAAAACA7djWCwAAAABWYVuvR6ycAgAAAABsx3AKAAAAALAd23oBAAAAwCIOtvV6xMopAAAAAMB2DKcAAAAAANuxrRcAAAAArMK2Xo9YOQUAAAAA2I7htBiFhYWpdevWys3NdV+2YsUK1atXT4sWLSrwvmvXrtXu3bv/dsPMmTM1efLkv32ckiykfLDGrxil2JSFWnQoSvf2aW13UoFM65VotoJpvRLNVjCtVzKvufPT7TRz0wStPjNPI+Y8aXdOkYya1keLt76iFd9N1Lv/HaMHezW3O6lQpj0vJPOaTeuVzGyGtdjWW8wqVaqkzZs3q02bNpKklStXqkGDBoXeb+3atbr11lvVqFEjbycab9isJ5STlaNeVQapduOaeiMuUr98d1jxPx6zOy1fpvVKNFvBtF6JZiuY1iuZ13wm4ayWTIlV03YN5R9Yyu6cIvnonXX619iPlZ2Vq+tqVdLkpYN18MfjOrDnuN1pHpn2vJDMazatVzKzGdZi5bSYde3aVdHR0ZKko0eP6vz586pbt64kKSsrS5MnT1aPHj0UERGhUaNGKS0tTV9++aXWrVunuXPnKiIiQitXrtSpU6fUv39/devWTR07dtSUKVPcjzFz5kw999xzGjRokDp27Khhw4YpJSXFff3Jkyc1aNAgtW/fXk899ZTOnz8vSdqyZYt69+6tLl26KDw8XGvWrHHfZ9asWWrfvr0iIiLUpUsXJScnS5K+++47d0e3bt20YcMGb5/CAgUGBah19zu14JUPlZGWoR++2qstsTvUrn8bW7s8Ma1XotkKpvVKNFvBtF7JzOavYr/VlridSk5KtTulyI7sP6nsrAu7slwuSS6p6g0V7Y0qgInPC9OaTeuVzGz2GpcBXzZh5bSYtWjRQkuWLNG5c+cUExOjLl26aM+ePZKkefPmKSQkRMuXL5ckvfXWW5o7d65eeOEFhYWF6dZbb1W/fv0kSZmZmZozZ47KlCmj7OxsPfHEE9q0aZPuueceSdK3336rlStXqmLFioqMjFRUVJTGjBkjSdqzZ4+WL1+ukJAQPfHEE1q9erV69eqlW265RUuWLJGvr69Onz6tbt26qXXrC9sp5s+fry1btigwMFCpqakKDAxUcnKyxo8fr7lz56py5cpKTExUjx49FBcXp9DQUKtPrSSpet2qcuY6dXx/gvuyg7sPq9E9t9jSUxjTeiWarWBar0SzFUzrlcxsNtXQ17qpXfemCiztrwN7jumbDT/ZneSRic8L05pN65XMbIb1GE6LmcPhUIcOHbRmzRp98sknWrp0qXs4XbdunVJTU/XZZ59JurCSevPNN+d7nNzcXE2ZMkW7du2Sy+XS6dOntXfvXvdw2rZtW1WseOFfTXv06KGJEye679u6dWv38NioUSMdOXJEkpSUlKRx48YpPj5evr6+OnfunA4dOqSGDRvqxhtv1KhRo3T33Xerbdu2Cg4O1q5du3Ts2DENGjQoz/cXHx+vhg0bFvOZK5rSwYFKO5ee57K0c+kKCiltS09hTOuVaLaCab0SzVYwrVcys9lUs1+J1jsTYlT/9hpq2KK2srNy7E7yyMTnhWnNpvVKZjbDegynXtCtWzf17NlTzZs3V/ny5d2Xu1wujR8/Xi1btiz0GO+9956Sk5O1bNkyBQQE6B//+IcyMzPzva3L5ZLD4XD/PiAgwP1rX19f9/0mTJigsLAwzZo1Sw6HQw8++KAyMzPl6+urjz/+WDt37tTWrVvVrVs3zZs3Ty6XS/Xq1dPixYv/6qkodudTMxQUmveHWJnQ0kpPOW9TUcFM65VotoJpvRLNVjCtVzKz2WROp0s/7Dise7vcoY6PtFLs+5vtTsqXic8L05pN65XMbPYWBx8l4xGvOfWC66+/Xi+88IKGDBmS5/KwsDAtWLBAGRkZkqTU1FQdPHhQkhQcHJzndaMpKSmqVKmSAgICdPLkSf33v//Nc6wNGzYoKSlJkhQTE6MWLVoU2pWSkqLq1avL4XDoq6++Unx8vLsjKSlJzZs31/Dhw1W3bl3t379fTZo0UXx8vLZu3eo+xu7du+Vy2ff/qOP7EuTr56vqN1VxX1arUc0S+0J603olmq1gWq9EsxVM65XMbL4S+Pr6qOoN19id4ZGJzwvTmk3rlcxshvUYTr2kd+/el2zZfeqpp3TzzTerR48eCg8PV9++fd3DaefOnRUXF+d+Q6T+/ftr586d6tKlS76rrS1bttS4cePUsWNHnTt37pJBOD8jRozQlClT1Lt3b3322WeqV6+epAvD6dChQxUeHq5OnTqpYsWKeuCBB1S2bFlFRUVp9uzZ6ty5szp06KBZs2bZOpxmpGdqc/Q2DXi1twKDAtSgVT21imimtQs32tZUENN6JZqtYFqvRLMVTOuVzGz28fVRqYBS8vHx+f3XviX3P4fKXhOsNp0aKzDIXz4+Dt1+d121DW+i77YcsDvNIxOfF6Y1m9YrmdkM6zlcdk4a+Etmzpyp9PR09xsglXT3+/Qs1uOFlA/WiPmDdfv9jZRyJlXzIhdr/dKSubVJMq9XotkKpvVKNFvBtF7J+80+QUHFdixJ6jeui/qP65rnsoWTYrRo0spiewyfypWK7VhlK5TRS7Mf1Y31q8nH4dDJE2cVu2Cz/vPRtmJ7DEnKORxfrMfjuex9pvVK3m/+wrms2I7lTbeOmm53QqH2vPWCLY/LcGqgq304BQBcPYp7OLVCcQ6nVinu4RSwA8Np8bFrOOUNkQw0bNgwuxMAAAAAoFgxnAIAAACARXi3Xs9K7jsAAAAAAACuGgynAAAAAADbsa0XAAAAAKzCtl6PWDkFAAAAANiO4RQAAAAAYDuGUwAAAACA7XjNKQAAAABYhdecesTKKQAAAADAdgynAAAAAADbsa0XAAAAACzisDugBGPlFAAAAABgO4ZTAAAAAIDt2NYLAAAAAFbh3Xo9YuUUAAAAAGA7hlMAAAAAgO3Y1gv8iU9QkN0Jl82Znm53AgB4hYk/33wMbAZgHQfbej1i5RQAAAAAYDuGUwAAAACA7djWCwAAAABWYVuvR6ycAgAAAABsx3AKAAAAALAd23oBAAAAwCps6/WIlVMAAAAAgO0YTgEAAAAAtmM4BQAAAADYjtecAgAAAIBFHLzm1CNWTgEAAAAAtmM4BQAAAADYjm29AAAAAGAVtvV6xMopAAAAAMB2DKcAAAAAANuxrRcAAAAALMK79XrGyimME1I+WONXjFJsykItOhSle/u0tjupQJ2fbqeZmyZo9Zl5GjHnSbtzisS0cyyZ12xar0SzFUzrlWj2tlL+fnp+Wj8t2PG6VhyYpllfRKpp2C12ZxXKpHN8kWnNpvVKZjbDWpYOp2FhYWrdurVyc3Pdl61YsUL16tXTokWLCrzv2rVrtXv37r/dMHPmTE2ePPlvH6eoDh48qHr16mnBggV5Lh87dqz7e166dOkl15dE0dHROnTokN0ZGjbrCeVk5ahXlUF6s98MPRc1SDVuuc7uLI/OJJzVkimx+nzhl3anFJlp51gyr9m0XolmK5jWK9HsbT5+Pjp14qxGd52uHnVG6IMpqxU590lVvr6C3WkFMukcX2Ras2m9kpnNsJblK6eVKlXS5s2b3b9fuXKlGjRoUOj9ims4tdry5ct15513asWKFR5v06dPHz322GPWRf1FMTExOnz4sK0NgUEBat39Ti145UNlpGXoh6/2akvsDrXr38bWroJ8FfuttsTtVHJSqt0pRWLiOTat2bReiWYrmNYr0WyFzPQsLZ66RolHk+RyubT9iz06eeSM6jS6we40j0w7x5J5zab1SmY2e43LgC+bWP6a065duyo6Olpt2rTR0aNHdf78edWtW1eSlJWVpenTp+ubb75Rdna26tatqwkTJmjnzp1at26dvv76ay1btkwDBw7UXXfdpRdffFFpaWnKzMxUmzZtNHr0aEkXVkcPHDig9PR0nThxQrVq1dKkSZMUEhIiSTp58qQGDRqko0eP6oYbbtDbb7+t0qVLa8uWLfrXv/6lzMxM5ebm6plnnlHHjh0lSbNmzVJcXJwCAgLkcDj0wQcfKDQ0VN99952mTp2qtLQ0SdLw4cPVtm1bSVJOTo5Wr16txYsXa9CgQfr+++/VsGHDS87JzJkzlZ6erjFjxigrK0uvv/66tm/frgoVKqh+/fo6ffq0ZsyYoejoaMXFxSk0NFT79+9XSEiIZs6cqUqVKrmvCwkJ0c8//6xrr71W//jHPzRlyhTFx8fr1ltv1dSpU+VwOJSamqo333xTP//8szIzM9WiRQtFRkbK19dX/fv316233qr//e9/SkxMVIcOHTRy5EitWLFCe/bs0cSJE/Wvf/1LY8aMUatWrbz9dLlE9bpV5cx16vj+BPdlB3cfVqN7Sv4WJ1OYeI5NazatV6LZCqb1SjTboVzFEFWvVVnxPycUfmObmHiOTWs2rVcysxnWs3w4bdGihZYsWaJz584pJiZGXbp00Z49eyRJ8+bNU0hIiJYvXy5JeuuttzR37ly98MILCgsL06233qp+/fpJkjIzMzVnzhyVKVNG2dnZeuKJJ7Rp0ybdc889kqRvv/1WK1euVMWKFRUZGamoqCiNGTNGkrRnzx4tX75cISEheuKJJ7R69Wr16tVLt9xyi5YsWSJfX1+dPn1a3bp1U+vWF/bCz58/X1u2bFFgYKBSU1MVGBio5ORkjR8/XnPnzlXlypWVmJioHj16uAfIDRs2qEaNGqpRo4a6du2qFStW5Duc/tFHH32kEydOaM2aNcrNzVX//v1VpUoV9/Xff/+9YmNjVbVqVb388statGiRXnjhBfd1q1evVpUqVfT0009rxIgRWrRokUqXLq2uXbtqy5YtatWqld588001a9ZMb7zxhpxOp3v47NWrlyQpISFBixcvVlpamtq1a6cePXqoe/fuWrlypR5//HHde++9xfV0uGylgwOVdi49z2Vp59IVFFLapqIrj4nn2LRm03olmq1gWq9Es9V8/Xw0Omqg1n68VccOnLQ7xyMTz7Fpzab1SmY2w3qWD6cOh0MdOnTQmjVr9Mknn2jp0qXu4XTdunVKTU3VZ599JunCSurNN9+c73Fyc3M1ZcoU7dq1Sy6XS6dPn9bevXvdw2nbtm1VsWJFSVKPHj00ceJE931bt26t0NBQSVKjRo105MgRSVJSUpLGjRun+Ph4+fr66ty5czp06JAaNmyoG2+8UaNGjdLdd9+ttm3bKjg4WLt27dKxY8c0aNCgPN9ffHy8GjZsqOXLl6tr166SpC5duqhLly6KjIxUQECAx/Ozbds2RUREyM/PT35+furYsaO+/fZb9/W33367qlatKkm67bbb9PXXX+e57uIgW79+fVWvXt29WnzzzTcrPj5erVq10rp167R792699957kqSMjAxde+217uO0b99ePj4+CgkJUe3atXXkyBHVrFnTY7OVzqdmKCg07w+xMqGllZ5y3qaiK4+J59i0ZtN6JZqtYFqvRLOVHA6HRs16TDnZOYoa95HdOQUy8Ryb1mxar2Rms7fwbr2e2fJRMt26dVPPnj3VvHlzlS9f3n25y+XS+PHj1bJly0KP8d577yk5OVnLli1TQECA/vGPfygzMzPf27pcLjkcDvfv/zgc+vr6uu83YcIEhYWFadasWXI4HHrwwQeVmZkpX19fffzxx9q5c6e2bt2qbt26ad68eXK5XKpXr54WL158yWOePn1aX331lfbu3auoqChJ0vnz5/X5558rPDzc4/f159Y/+3P7H99c6s/Xebqty+VSVFSUrr/++st+DLsd35cgXz9fVb+pio4f+FWSVKtRTcX/eMzmsiuHiefYtGbTeiWarWBar0SzlZ6f3k/lKoXqlUdmKzfHaXdOgUw8x6Y1m9YrmdkM69nyUTLXX3+9XnjhBQ0ZMiTP5WFhYVqwYIEyMjIkSampqTp48KAkKTg4WCkpKe7bpqSkqFKlSgoICNDJkyf13//+N8+xNmzYoKSkJEkX3sinRYsWhXalpKSoevXqcjgc+uqrrxQfH+/uSEpKUvPmzTV8+HDVrVtX+/fvV5MmTRQfH6+tW7e6j7F79265XC7FxMTowQcf1IYNG7Ru3TqtW7dOkyZNKvCNkaQL255jY2OVk5OjzMxMffrpp4V2X66wsDDNnTvXPXQmJSXp6NGjhd6vTJkyef4M7JCRnqnN0ds04NXeCgwKUINW9dQqopnWLtxoa1dBfHx9VCqglHx8fH7/tW/J/RQnE8+xac2m9Uo0W8G0Xolmqzw7uY9uqFNFE/q/o6yMbLtzCmXiOTat2bReycxmWM+2/0Lu3bv3JVt2n3rqKd18883q0aOHwsPD1bdvX/dw2rlzZ8XFxSkiIkIrV65U//79tXPnTnXp0iXf1daWLVtq3Lhx6tixo86dO3fJIJyfESNGaMqUKerdu7c+++wz1atXT9KF4XTo0KEKDw9Xp06dVLFiRT3wwAMqW7asoqKiNHv2bHXu3FkdOnTQrFmz3MPpn1dI77vvPn3//fc6dszzvxA9/PDDqlSpkjp27Kinn35aDRo0UHBwcJHOaVGNGzdOPj4+ioiIUHh4uJ588kmdPFn4a1d69+6tqKgodenSJc92YqvNHDpP/qX99fHJeRq35Hm9PeTdEv2vbn3HdFbcmXl6eGQntetzl+LOzFPfMZ3tziqQaedYMq/ZtF6JZiuY1ivR7G2Vr6ugjgPuVq0G12nJ928q+uA0RR+cpnu7NbM7rUAmneOLTGs2rVcysxnWcrhcritu1/Mf3/3WRKmpqQoODlZWVpYGDx6s9u3bq2fPnnZn/WX3+5jV7hMUZHfCZXOmpxd+IwCAJfwqV7I74bLlJJ6yOwH4275wLrM7oUhuf2a63QmF2jnnBVse15bXnKJgAwcOVFZWljIzM9WqVSv3myoBAAAAwJXqihxOhw0bZnfC37JsmRn/6gMAAAAAxeWKHE4BAAAAoES64l5UWXxK7luGAgAAAACuGgynAAAAAADbsa0XAAAAACziYFuvR6ycAgAAAABsx3AKAAAAALAd23oBAAAAwCps6/WIlVMAAAAAgO0YTgEAAAAAtmNbLwAAAABYxOFiX68nrJwCAAAAAGzHcAoAAAAAsB3DKQAAAADAdrzmFAAAAACswktOPWLlFAAAAABgO4ZTAAAAAIDt2NYLr/OrXMnuhMviTE2zOwEoFj5BQXYnXBaf4DJ2J1y2nMRTdidc8Ux7HktmPi8+O/Gd3QmX5cFqt9mdAPxlDrb1esTKKQAAAADgsk2ePFlhYWGqV6+e9u3b57780KFD6t27tx588EH17t1bhw8fLtLxGE4BAAAAAJftvvvu0+LFi1W9evU8l48fP159+/bVZ599pr59++qVV14p0vHY1gsAAAAAVjFgW29ycrKSk5MvuTw0NFShoaHu3zdt2vSS25w5c0Y//vij3nvvPUlSp06d9PrrryspKUkVKlQo8HEZTgEAAAAAbu+//75mzZp1yeXPPvushg0bVuB9ExISdO2118rX11eS5Ovrq8qVKyshIYHhFAAAAABQdAMGDFDXrl0vufyPq6bewHAKAAAAABYx4d16/7x993JUrVpVJ0+eVG5urnx9fZWbm6vExERVrVq10PvyhkgAAAAAgGJxzTXXqH79+oqLi5MkxcXFqX79+oVu6ZVYOQUAAAAA/AUTJ07U559/rtOnT2vgwIEqV66c1qxZowkTJmjs2LGKiopSaGioJk+eXKTjMZwCAAAAgFUM2NZbVC+//LJefvnlSy6vXbu2li1bdtnHY1svAAAAAMB2DKcAAAAAANuxrRcAAAAALGLCu/XahZVTAAAAAIDtGE4BAAAAALZjOAUAAAAA2I7XnAIAAACAVXjNqUesnFooLCxM7du3V+fOndWpUyetWbOmwNvXq1dPaWlpBd4mOjpaw4cPL/SxZ86cWeQPvy3I2LFjtWjRor99nL+qlL+fnp/WTwt2vK4VB6Zp1heRahp2i209RdH56XaauWmCVp+ZpxFznrQ7p0hCygdr/IpRik1ZqEWHonRvn9Z2JxXKtGbTeiXznssm/rww8XlhWrNpz2PJjHM8eqJ0d1epaQep/SPSsrgLlx84LPV4SmrR8cLXwBcvXFYSmXCe/8i0XsnMZliLlVOLzZgxQ3Xr1tWPP/6ohx9+WC1btlSFChXszjKGj5+PTp04q9Fdp+vUsbNq1q6BIuc+qcH3TlTi0SS78/J1JuGslkyJVdN2DeUfWMrunCIZNusJ5WTlqFeVQarduKbeiIvUL98dVvyPx+xO88i0ZtN6JfOeyyb+vDDxeWFas2nPY8mMczzoEWniaMnfX/olXhrwvHRLHen6atLbr0rVqkhOp7QkRhrxqrTqPbuLL2XCef4j03olM5thLVZObXLLLbeoTJkyOnbsmAYMGKDw8HB17dpVmzZtuuS2n3zyiZ5++mn377OystS6dWslJCRcctu5c+eqU6dO6tSpkyIjI/OsvJ44cUKDBg1Sx44dNWzYMKWkpLiPN3nyZPXo0UMREREaNWqU+34nT57UgAED1LlzZw0ZMkRnz54t7lNxWTLTs7R46holHk2Sy+XS9i/26OSRM6rT6AZbuwryVey32hK3U8lJqXanFElgUIBad79TC175UBlpGfrhq73aErtD7fq3sTvNI9OaTeu9yLTnsmk/L0x8XpjYbNrz2JRzXOfGC4OpJDkcF/73yHEpNESqXvXCZS6X5ON74fKSxpTzfJFpvZKZzd7icJX8L7swnNpk69atyszM1KhRo9SpUyetXr1ab731lkaNGqWkpLz/ov/AAw9o3759Onr0qKQLw+ptt92mqlWr5rndxo0bFRsbqw8//FCrV69Wbm6uoqKi3Nd/++23evPNN7VmzRoFBwe7r5s3b55CQkK0fPlyrVq1SpUrV9bcuXMlSRMnTlSzZs0UGxuryMhIbd++3Zun5bKVqxii6rUqK/7nSwd1/DXV61aVM9ep4/t/P6cHdx9WjVuus7GqYKY1m9Z7pSjpPy9MfF6Y2Gwak87xq9OkJg9ID/V3qNI10j13/n5d845S4wekN96WnupnX6MnJp1nybxeycxmWI/h1GLDhw9XRESEZs6cqalTp+r48ePq3r27JOmmm25S/fr19b///S/Pffz8/NS7d299+OGHkqQlS5bokUceueTYW7Zs0UMPPaTg4GA5HA716tVLW7ZscV/ftm1bVaxYUZLUo0cPbd26VZK0bt06xcbGKiIiQhEREVq3bp2OHDkiSdq2bZt69uwpSbr++uvVsmXL4j0hf4Ovn49GRw3U2o+36tiBk3bnXDFKBwcq7Vx6nsvSzqUrKKS0TUWFM63ZtN4rgQk/L0x8XpjYbBqTzvH4F6Udn0qLZrp0/z2/r6RK0vY1F75efk6qX8e+Rk9MOs+Seb2Smc2wHq85tdjF15xKUmpq/luKHBf3w/xBr1691LVrV4WFhSk5OTnfIdHlcuV73/z88bYul0vjx48vUYNnYRwOh0bNekw52TmKGveR3TlXlPOpGQoKzfsXRZnQ0kpPOW9TUeFMazat13Sm/Lww8XlhYrNpTDvHvr7SHY2k1V9IH66U+vf4/bqg0tLDEVKrCGnNB9I15W3LvIRp59m0XsnMZq9x8Xa9nrByaqPg4GDVr19fMTExkqSDBw9q7969uu222y65bYUKFdSqVSu9+OKL6tu3b75DaKtWrfTJJ58oNTVVLpdLy5cvV6tWrdzXb9iwwb1lOCYmRi1atJB04V2EFyxYoIyMDEkXhuaDBw9Kku68806tWLFCknT06NE8K7F2en56P5WrFKqJT7yr3Byn3TlXlOP7EuTr56vqN1VxX1arUc0S/WYFpjWb1ms6U35emPi8MLHZNKae45xc6eiJSy93OqWMDOnkKeubCmLaeTatVzKzGdZjOLXZ1KlTFRsbq/DwcI0cOVJTpkzx+O69PXr0UHJysrp27Zrv9W3atFF4eLgefvhhhYeHS5IGDx7svr5ly5YaN26cOnbsqHPnzmnIkCGSpKeeeko333yzevToofDwcPXt29c9nL700kvatm2bOnfurKlTp+quu+4qzm//L3l2ch/dUKeKJvR/R1kZ2XbnFMrH10elAkrJx8fn91/7ltz/62WkZ2pz9DYNeLW3AoMC1KBVPbWKaKa1CzfaneaRac2m9V5k2nNZMuvnhYnPCxObTXsem3COz5yV1vxXSkuXcnOlzdulT/4rtbhd+uob6cd9Fy5PTZMmz77wJkm1a9hdnZcJ5/mPTOuVzGyG9RwuF+vKpoiKitKpU6c0fvx4u1MuS4cqQ4rtWJWvq6D3d0xUVka2cnNz3ZfPHLVU66O/KZbHcKYW/Nmyl6vfuC7qPy7vPygsnBSjRZNWFttjONPTC7/RZQgpH6wR8wfr9vsbKeVMquZFLtb6pZuL9TGKm2nNVvT6BAUV6/G8/Vz2CS5TLMe5yIqfFzmJxbv8Y9rzWPJ+s2nPY8nMn8mfnfjuL9836TfpuVeknw9eWBmtdq3Ur7vUK1z6z3ppxr8vrJQG+Eu33iyNeFqqV/vv9T5Y7dJdZn+Xaf//M61X8n7zF85lxXYsb2rZ9592JxRqy5IRtjwuw6khOnbsKF9fX82fP1+VKlWyO+eyFOdwaoXiHk6tUNz/IYQrQ3H/R723FfdwaoXiHk5xKdOex5KZP5P/znBqB28MpzAfw2nxsWs45Q2RDLFmzRq7EwAAAADAaxhOAQAAAMAq7Fv1qOS+AwAAAAAA4KrBcAoAAAAAsB3DKQAAAADAdrzmFAAAAAAs4nDaXVBysXIKAAAAALAdwykAAAAAwHZs6wUAAAAAq/BRMh6xcgoAAAAAsB3DKQAAAADAdmzrBQAAAACLONjW6xErpwAAAAAA2zGcAgAAAABsx7ZeAAAAALCKi329nrByCgAAAACwHcMpAAAAAMB2bOsFAAAAAIvwbr2eMZzC63IST9mdAFyVnOnpdidcFtN6YQ0Tnxc+QUF2J1y2B6vdZnfCZTHxHJv4XAasxrZeAAAAAIDtWDkFAAAAAKuwrdcjVk4BAAAAALZjOAUAAAAA2I7hFAAAAABgO15zCgAAAAAW4aNkPGPlFAAAAABgO4ZTAAAAAIDt2NYLAAAAAFZxsa/XE1ZOAQAAAAC2YzgFAAAAANiObb0AAAAAYBHerdczVk4BAAAAALZjOAUAAAAA2I5tvQAAAABgFbb1esTKKQAAAP6/9u48rKpyfeP4dwOiMjiGs+VQDlmmqTmbkpkTMphTpFamOaRpaio/UzPLHDupKXosy6EcAWcrJUnLsdGJTMSpFGcFRMb9+8PDPqKA2tG91rL7c11eB9aGtW/WIfZ+1vu87ysiYjgVp4Cvry8tW7akXbt2tG3blrVr1xqS47PPPuPcuXP/83m6du3Kt99+excSmZN3YS9GrxjKqvgFLIydSbMujYyOlCur5QVldgar5QVldgar5QVldoZ2rzVn+ndjWH1uLoNDXzU6zm3RNb73rHaNwZqZxbnU1vsf06ZNo1KlSuzfv5/OnTtTv359ihQpAkBaWhpubvf+Us2fP58GDRpQtGjRe/5cVtZ/Rg/SUtLoWKInFWuU4701Izj86xGO7j9hdLRsWS0vKLMzWC0vKLMzWC0vKLMznDt5gS8mrqJ288dxz5fH6Di3Rdf43rPaNQZrZr4XtFpvzjRyeoNHH30UT09Phg8fzrvvvkuPHj3o2LEjAHPmzKFt27a0bduWESNGkJiYCMD06dMZNGgQPXv25Nlnn2XgwIHs37+fbt260bx5cyZMmOA4/+nTpxkwYADPP/88fn5+hIaGAjBr1izHY/7+/hw6dIht27bRqVMnAgIC8PPzyzKi27VrV9577z26du3Ks88+y9SpU7P8HDt37qRLly4888wzTJ482XH8008/pX379gQEBNCpUycOHDgAQFJSEgMGDKB169a0a9eON954w/E94eHhdOjQgaCgILp168bhw4fv8lW/ffk88tKofT0+G7WYq4lX2fd9NNtW7aZ516cNy5Qbq+UFZXYGq+UFZXYGq+UFZXaW71f9yLY1P3H5fILRUW6LrvG9Z8VrbMXM4nwaOb3B9u3bSU5Oxs3NjZ9//pmFCxfi4eFBVFQUq1atYvHixXh6ejJs2DBmzpzJ0KFDAdi3bx8rVqzAw8ODwMBApkyZwty5c0lLS+OZZ56hU6dOlCtXjmHDhtG3b1/q1KlDSkoKL730Eo8//jh9+vRh2bJljhFcAB8fH7744gtcXV05e/YsQUFBNGrUiIIFCwIQExPDvHnzSE5OpnPnztSsWZNmzZoBcPLkSRYtWkRiYiLNmzfn+eefp1y5cgQEBPDKK68A8MMPPzB69GiWLl3K1q1buXz5MuvWrQPg0qVLAOzevZv169ezaNEi3N3diYqKIiQkhMWLFzv1/5dMpSuVJCM9gz//OOk4FvPbEao3edSQPLditbygzM5gtbygzM5gtbygzJI9XeN7z4rX2IqZxflUnP7HgAEDyJs3L15eXkyfPp3Vq1dTo0YNPDw8ANi2bRutW7fGy8sLgI4dO/L+++87vr9Ro0Z4e3sDULlyZapUqYK7uzvu7u6UL1+eY8eOUaxYMXbu3Mn58+cd35eYmEhMTAwNGza8KdP58+cJCQnh6NGjuLq6cunSJWJjY6lRowYAAQEBuLm54ebmRuvWrdm+fbujOG3ZsiUuLi54e3tTsWJFjh07Rrly5di7dy+zZ8/m0qVL2Gw2jhw5AkCVKlU4fPgw77zzDk899RRNmzYFIDIykujoaDp06ACA3W7n8uXLd+/C36H8XvlIvHQly7HES1fw8M5vUKLcWS0vKLMzWC0vKLMzWC0vKLNkT9f43rPiNbZiZnE+Faf/cf2IJcDq1asdhSlcK8psNluO3583b17Hx66urjd9np6eTkZGBjabjeXLl5Mnz63nM4wZMwZfX19mzJiBzWbjueeeIzk5OduvvTFfds+fkpLCG2+8wcKFC6lWrRpxcXE0adIEgLJly7Ju3Tq2b9/Od999x4cffsjq1aux2+20b98+S5uvkZISruJRIOsfMc8C+bkSn2RQotxZLS8oszNYLS8oszNYLS8os2RP1/jes+I1tmLmeyZDk05zojmnt6lBgwasW7eOhIQE7HY7y5cvp0GDBnd0Di8vL2rVqsWcOXMcx06ePMmZM2cA8PT0JD4+3vFYfHw8pUuXxmaz8f3333P06NEs51u5ciVpaWlcuXKFDRs2ULdu3VyfPyUlhbS0NEqWLAnAF1984Xjs1KlTuLq60rx5c0aMGMH58+e5ePEivr6+rFy5klOnTgGQnp7O3r177+jnvpv+PHgSVzdXSj9cwnGsQvVypp1Ib7W8oMzOYLW8oMzOYLW8oMySPV3je8+K19iKmcX5VJzepqeffho/Pz86d+6Mn58fAH369Lnj80yePJmYmBj8/Pzw8/Nj0KBBjjbZbt26ERIS4lgQafDgwUycOJFOnTrx1VdfUbly5SznqlatGi+//DIBAQE0bdrU0dKbEy8vL8diTMHBwVlGhn///Xc6depEu3bt6NChA7169aJ48eLUqVOHgQMH0qdPH8dWO5s2bbrjn/tuuXolma1hO+j+TifyeeSlWoPKNPCvw8YFUYZlyo3V8oIyO4PV8oIyO4PV8oIyO4uLqwt58ubBxcXlvx+7mvctnK7xvWfFa2zFzOJ8NrvdrnFlC+ratSuvvPLKLQtSM3jWpcNdPZ93YS8Gf9KHJ5+tTvy5BOaOWMS3X269q89xN1ktLyizM1gtLyizM1gtLyhzdlyuu/l7N7wYEkDXkMAsxxa8H87C9yPu2nNkXLly6y+6A7rGN7PaNb4X7nXmbzKW3bVz3UtPt55odIRbilr3liHPq+LUov7JxamIiIiZ3e3CyRnuduF0r+kaS3ZUnN49RhWnWhDJohYsWGB0BBERERERkbtGxamIiIiIiIiT2NS3miPzzvQWERERERGRfwwVpyIiIiIiImI4tfWKiIiIiIg4i9ajzZFGTkVERERERMRwKk5FRERERETEcGrrFRERERERcRKt1pszjZyKiIiIiIiI4VScioiIiIiIiOFUnIqIiIiIiIjhNOdURERERETEWTTnNEcaORURERERERHDqTgVERERERERw6mtV0RERERExElsdvX15kQjpyIiIiIiImI4FaciIiIiIiJiOLX1ioiI/E0uHh5GR7hjGVeuGB3hvqdrfO/pGoulZRgdwLw0cioiIiIiIiKGU3EqIiIiIiIihlNbr4iIiIiIiJNotd6caeRUREREREREDKfiVERERERERAyntl4RERERERFnUVdvjjRyKiIiIiIiIoZTcSoiIiIiIiKGU1uviIiIiIiIs2i13hxp5FREREREREQMp+JUREREREREDKfiVERERERERAynOaciIiIiIiJOYtOU0xxp5FREREREREQMp+JUREREREREDKe2XhEREREREWfRVjI50sipWI53YS9GrxjKqvgFLIydSbMujYyOlCur5QVldgar5QVldoZ2rzVn+ndjWH1uLoNDXzU6zm2x2jUG62W2Wl5QZmewWl6wZmZxLo2cXmf9+vXMnj0bu91OcnIy1apVY8qUKXfl3NOnT+fKlSsMGzaMsLAwNm/ezLRp0zhx4gTt27dnx44dd+V5/tdsVtB/Rg/SUtLoWKInFWuU4701Izj86xGO7j9hdLRsWS0vKLMzWC0vKLMznDt5gS8mrqJ288dxz5fH6Di3xWrXGKyX2Wp5QZmdwWp5wZqZxbk0cvofp0+f5p133mHWrFmsXLmS9evX8+qrxt61TktLy/VzIxmVJZ9HXhq1r8dnoxZzNfEq+76PZtuq3TTv+rQheW7FanlBmZ3BanlBmZ3l+1U/sm3NT1w+n2B0lNtixWtstcxWywvK7AxWywvWzHyv2DLM/88oGjn9j7Nnz+Lm5kahQoUAsNlsVK1aFYDKlSszcOBANm7cyMWLFxk3bhw//PADW7ZsIS0tjY8++oiKFSty5swZ3nzzTRITE0lOTubpp5/mrbfeuqMclStXZujQoURFRVGrVi1OnTqFp6cnR44c4cKFC4SFhREeHs4XX3xBeno6Xl5ejBkzhgoVKpCSksK7777Lzp07KVKkCFWrVuXs2bNMmzbtptHRnEZLf//9d9555x2SkpJITk6mY8eOvPTSSwAMHz78pizOVrpSSTLSM/jzj5OOYzG/HaF6k0ednuV2WC0vKLMzWC0vKLNkz4rX2GqZrZYXlNkZrJYXrJlZnE/F6X9UqVKF6tWr07RpU+rWrcuTTz6Jv78/hQsXBqBAgQKsWLGC9evX07dvXz788EMGDx7Mv//9b2bNmsXkyZMpUKAAoaGheHp6kpqaSo8ePfjuu+9o0qTJHWXJyMhgwYIFwLWC8Oeff2bhwoV4eHiwe/du1q9fz6JFi3B3dycqKoqQkBAWL17MkiVL+Ouvv1i7di3p6el07dqVEiVK3NFzly5dms8++wx3d3cSExPp0KEDjRs3pmLFigBZshghv1c+Ei9dyXIs8dIVPLzzG5LnVqyWF5TZGayWF5RZsmfFa2y1zFbLC8rsDFbLC9bMLM6n4vQ/XFxcmDlzJgcPHmTXrl1s3LiRTz75hNWrVwPQqlUrAKpVqwZA06ZNAXjsscf45ptvAEhPT2fixIn8/PPP2O12zp49S3R09B0Xp4GBgVk+b9mypaMYjIyMJDo6mg4dOgBgt9u5fPkyADt27MDf3x83Nzfc3Nxo06YNP/744x0999WrVxkzZgy///47NpuN06dPEx0d7ShOr89ihKSEq3gUyPpHzLNAfq7EJxmUKHdWywvK7AxWywvKLNmz4jW2Wmar5QVldgar5QVrZr5ntFpvjjTn9AaVKlUiODiYefPm4e3tzc6dOwHImzcvcK2IdXd3d3y9i4uLY/7lvHnzuHz5MsuWLWP16tU0b96c5OTkO85wY/F3/ed2u5327duzcuVKVq5cyapVq9i8ebPjMZvNlu05XV1dycj4bwN5TrmmTp2Kj48P4eHhrFq1iurVq2f5WiMLU4A/D57E1c2V0g//d0S4QvVypp1Ib7W8oMzOYLW8oMySPSteY6tltlpeUGZnsFpesGZmcT4Vp/8RFxfHzz//7Pj81KlTnD9/njJlytz2OeLj4/Hx8SFv3rzExcWxadOmu57T19eXlStXcurUKeDaaO3evXsBqFu3LqtWrSItLY3k5GTWr1/v+L4HH3yQffv2kZGRQUJCgqOgze5nKFGiBG5ubhw8eJDdu3ff9Z/hf3H1SjJbw3bQ/Z1O5PPIS7UGlWngX4eNC6KMjpYtq+UFZXYGq+UFZXYWF1cX8uTNg4uLy38/djXvS7UVr7HVMlstLyizM1gtL1gzszif2nr/Iy0tjenTp/Pnn3+SL18+MjIyGDhwII8+evuTtLt27cobb7xBQEAAJUqUoH79+nc9Z506dRg4cCB9+vQhPT2d1NRUWrZsyWOPPUbnzp2Jjo6mTZs2lCxZkmrVqpGUdK1VokWLFqxfv542bdrw0EMPOdqTb9SnTx/eeustVq1axYMPPkidOnXu+s/wv5reby6DP+nD0ri5xJ9L4KO+/zb1XTer5QVldgar5QVldoYXhrWja8h/p3Y079KQBe+Hs/D9CONC3YLVrjFYL7PV8oIyO4PV8oI1M98T6urNkc1uV9Pz/SQhIQEvLy9SUlLo06cPLVu2dMxPNcqzLsY+v4jIveJi8FSHvyPjypVbf5GIiAV9k7HM6Ai35dkG44yOcEvf/DDSkOfVyOl95uWXXyYlJYXk5GQaNGhw0+JKIiIiIiIiZqTi9D6zbJk17hiJiIiIiIhcT8WpiIiIiIiIk9g0qzJH5l0CUERERERERP4xVJyKiIiIiIiI4dTWKyIiIiIi4ixq682RRk5FRERERETEcCpORURERERExHBq6xUREREREXGWDKMD3F2+vr64u7uTN29eAIYMGULjxo3/1rlUnIqIiIiIiMjfNm3aNCpVqvQ/n0dtvSIiIiIiImI4jZyKiIiIiIg4ic0Cq/VevnyZy5cv33S8QIECFChQ4KbjQ4YMwW63U6tWLd58881sv+Z22Ox2C1wdsbRnXToYHUFE5J5w8fAwOsIdy7hyxegIIiL3xDcZy4yOcFueq/OO0RFuqW23IsyYMeOm46+//jr9+/fPcuzkyZOULFmSlJQU3nvvPRITE5k8efLfel6NnIqIiIiIiIhD9+7dCQwMvOl4diOiJUuWBMDd3Z0XXniBPn36/O3nVXEqIiIiIiLiLBZoXM2pffdGV65cIT09HW9vb+x2O+vWraNq1ap/+3lVnIqIiIiIiMgdO3fuHP379yc9PZ2MjAwqVqzI6NGj//b5VJyKiIiIiIjIHStbtiwRERF37XwqTkVERERERJzFAm29RlFxKnIDt2I+Rke4Y2mnzxgdQeQfSSvfisj9zIorkou1uRgdQERERERERETFqYiIiIiIiBhObb0iIiIiIiLOkmF0APPSyKmIiIiIiIgYTsWpiIiIiIiIGE5tvSIiIiIiIk5i01YyOdLIqYiIiIiIiBhOxamIiIiIiIgYTm29IiIiIiIizqK23hxp5FREREREREQMp+JUREREREREDKe2XhEREREREWdRW2+ONHIqIiIiIiIihlNxKiIiIiIiIoZTW6+IiIiIiIizqK03Rxo5FREREREREcM5pTitXLkyiYmJWY7VrVuXEydO3PG5fH19OXjw4C2/buPGjbRq1YqAgAAOHz6Mv78/V69evePny831P1dqaiofffQRzz33HG3atKFVq1Z88MEHpKam3rXnGz58OAsXLrxr5/s7Tpw4Qd26dQ3N4F3Yi9ErhrIqfgELY2fSrEsjQ/PcSh53NwZOfZHPdr/LikNTmfHNCGr7Pmp0rFxZ7RqD9TJbLS8oszNYLS8oszNYLS8oszNYLW+715oz/bsxrD43l8GhrxodR0zqvm3rXbx4MQMGDKBVq1YArFy58p4+34gRI0hOTmbFihV4eXmRmppKWFgYKSkp5MmT554+9z9N/xk9SEtJo2OJnlSsUY731ozg8K9HOLr/zm92OIOLmwtn/rrAW4EfcubEBeo0r8aIOa/Sp9k4Th8/b3S8bFntGoP1MlstLyizM1gtLyizM1gtLyizM1gt77mTF/hi4ipqN38c93x6byzZM0Vbr6+vLx999BGdOnXC19c3y+jg7t278fPz4/nnn2fcuHHYr+vRPnz4MK+++irt27enXbt2rFixAoD333+fH3/8kcmTJ9O1a1cg6yhnbs+X0zkBvv76a1q2bEnnzp2ZOXOm4/iRI0fYuHEj48aNw8vLC4A8efLQqVMnPD09SU9PZ8KECbRt25a2bdsyYcIE0tPTgWujoaNGjaJbt260aNGCt956y/EzxsXF0b17d9q1a0ffvn25cOGC4zkTEhL4v//7P55//nn8/PwYN26c45xdu3ZlwoQJdOnShWeeeYbJkyc7vm/GjBm0bNkSf39/AgICuHz5MgC//vorXbt2JSgoiKCgIDZv3uz4nkWLFvHss8/ywgsvsHz58jv+//duyueRl0bt6/HZqMVcTbzKvu+j2bZqN827Pm1ortwkX0lh0eS1nD5+Hrvdzs5v9hJ37ByPVH/Q6GjZsuI1tlpmq+UFZXYGq+UFZXYGq+UFZXYGq+UF+H7Vj2xb8xOXzycYHcV4GRb4ZxDTjJxevXqVJUuWcOLECfz8/AgMDCRPnjwMGjSIyZMnU7duXdatW8eCBQsASEtLY8iQIUyaNImKFSuSkJBA+/btqVGjBiEhIRw4cIBXXnmFZs2a3fbz5c2bN8dzFipUiLfffpsvv/ySChUq8O9//9txrv379/PQQw9RsGDBbJ9ryZIlHDhwgLCwMAB69uzJkiVLeOGFFwD4448/+Oyzz7DZbAQGBvLDDz/QsGFDxo0bR506dXj99dc5fvw47dq1o3HjxgCMHz+eOnXq8N5775GRkcGQIUNYsWIFHTt2BODkyZMsWrSIxMREmjdvzvPPP0/hwoX55JNP2LZtG/ny5SMhIYF8+fJx+fJlRo8ezZw5cyhWrBinT5/m+eefZ82aNfz111/MmjWLiIgIHnjgAcaMGXNX/v/+u0pXKklGegZ//nHScSzmtyNUb2LuNtnrFXrAm9IVinH095O3/mIDWPEaWy2z1fKCMjuD1fKCMjuD1fKCMjuD1fKK3C5Di1Obzeb4uHXr1gCUKVOGAgUKcOrUKVJTU8mfP79jjmPr1q0ZNWoUcG20MiYmhjfffNNxjtTUVA4fPkzFihVv+dzZPZ/dbs/xnC4uLjz66KNUqFABgE6dOmUZkczNtm3bCAwMxN3dHYCgoCA2btzoKE6bN29O3rx5AXj00Uc5duwYDRs2ZMeOHYwcORKAsmXLUr9+fcc5IyMj+e2335g3bx5wrdguXry44/GWLVvi4uKCt7c3FStW5NixY5QtW5by5cszdOhQGjduTNOmTfHy8uLnn3/mxIkT9OzZ0/H9NpuNo0eP8vPPP9O0aVMeeOABx8+9fv362/q574X8XvlIvHQly7HES1fw8M5vUKI74+rmwlszX2bj0u2cOBRndJxsWfEaWy2z1fKCMjuD1fKCMjuD1fKCMjuD1fKK3C6nFKdFihTh4sWLeHp6AtdGPRMSEihSpIjjazKLMwBXV1dHi2pO7HY7hQsX/ttzSbN7PpvNluM5N27cmOO5Hn30UY4ePcqlS5eyHT212+1ZCnHIWpjf6c+eec6ZM2dStmzZbB/P7pyurq4sXbqUn376ie3btxMUFMTcuXOx2+1UrlyZRYsW3XSen3766ZZZnCkp4SoeBbL+4fUskJ8r8UkGJbp9NpuNoTNeIi01jZkhS4yOkyMrXmOrZbZaXlBmZ7BaXlBmZ7BaXlBmZ7BaXsnKpq1kcuSUOacNGjRgyZL/vhlfsmQJTzzxBPnz5353p0KFCly9epVdu3YBsGHDBuLj4wEoX748+fLlIyIiwvH1MTExJCT8/T723M5Zs2ZN9u/fz5EjRwBYtmyZ42vKlSuHr68vo0aNcjx/eno6n3/+OYmJiTRo0IDw8HBSU1NJTU0lIiIiyyhoTurVq+eY83r8+HG2bdvmeMzX15c5c+Y4Ctnz589z/PjxXM+XkJDA+fPneeqppxgwYACVKlXijz/+oGbNmhw9epTt27c7vva3337DbrdTt25doqKiOHfuHIDhc07/PHgSVzdXSj9cwnGsQvVypp38f72BH75IIZ8CjOvxb9LTDGzmvwUrXmOrZbZaXlBmZ7BaXlBmZ7BaXlBmZ7BaXpHb5ZTi9P/+7//4888/8fPzw9/fny1btjBx4sRbfp+7uztTp05l7NixPP/88+zdu5dSpUoB4ObmRmhoKOvWrcPPz482bdrwzjvvkJKS8rdz5nbOokWL8u6779K7d286d+6Mq6trlu/94IMPKFeuHO3bt6dt27b4+flx6tQp3N3d6dSpE5UrVyYwMJDAwEAqV67smBuam//7v/9jx44dtGvXjsmTJ9OwYUPHYyEhIbi4uODv74+fnx+vvvoqcXG5t4kmJCTQr18//Pz8aNu2LQ888AAtWrSgYMGCzJw5k48//ph27drRqlUrZsyYgd1up0qVKvTu3ZsuXbrwwgsvZGkdNsLVK8lsDdtB93c6kc8jL9UaVKaBfx02LogyNNetvD6hCw8+UoIxXWeRcvXubS90L1jxGlsts9XygjI7g9XygjI7g9XygjI7g9XyAri4upAnbx5cXFz++7GrKdZmFROx2e0aV5Z761mXDnf1fN6FvRj8SR+efLY68ecSmDtiEd9+ufWund+tmM9dOxdAsTJF+Hz3OFKupmZp2Z4+9Eu+Ddt1V54j7fSZu3KeTPf6Gt8LVststbygzM5gtbygzM5gtbygzM7gjLwuHh537VwvhgTQNSQwy7EF74ez8P2Iu/YcXyV8ftfOdS+1qjrC6Ai3tP7AeEOeV8Wp3HN3uzi91+52ceoMd7s4FREREbmbxakzqDi9e4wqTjWWLiIiIiIiIoYzzT6nIiIiIiIi970MNa7mRCOnIiIiIiIiYjgVpyIiIiIiImI4tfWKiIiIiIg4i9ajzZFGTkVERERERMRwKk5FRERERETEcCpORURERERExHCacyoiIiIiIuIsmnOaI42cioiIiIiIiOFUnIqIiIiIiIjh1NYrIiIiIiLiLGrrzZFGTkVERERERMRwKk5FRERERETEcGrrFRERERERcZYMtfXmRCOnIiIiIiIiYjgVpyIiIiIiImI4tfXKPfdNxjKjI4iIiIiImIM9w+gEpqWRUxERERERETGcilMRERERERExnNp6RUREREREnMWu1XpzopFTERERERERMZyKUxERERERETGc2npFREREREScJUNtvTnRyKmIiIiIiIgYTsWpiIiIiIiIGE7FqYiIiIiIiBhOc05FREREREScRVvJ5EgjpyIiIiIiImI4FadiGQkJCWRkZABw8OBB1q5dS0pKisGpcrdt27bbOiZiFefPnzc6gojIP1p6ejrt27c3OobIPaHiVCyjW7duXL16lTNnztCjRw/CwsIYNWqU0bFyNXHixJuOTZo0yYAkt0cveJKTX3/9lWbNmhEYGAjAnj17ePvttw1OdWvbtm1j4cKFAJw9e5bY2FiDE91/rHiNExIS2Ldvn9ExbotucsqNXF1dKVy4MMnJyUZHuS316tWjfv36Of77R7Lbzf/PIJpzKpZht9vx8PBg7dq1dOzYkf79++Pn52d0rGwdPXqUI0eOkJCQQFRUlON4fHw8SUlJBibL3fUveHnz5jU6zi3Vq1cPm82W4+NmfAPXvn37XDMvX77ciWlu3/jx4/n3v//NkCFDAHj88ccZPny4walyN2fOHKKiojhz5gwvvvgiaWlphISE8OWXXxodLUfnzp1j/PjxnDx5kkWLFhEdHc3PP/9Mly5djI6WLSte46ioKEaNGoWrqyuRkZHs2bOHjz/+mNDQUKOjZWvixImEh4dnOTZp0iTCwsIMSpSzRYsW5fp4cHCwk5LcuSNHjjBixAji4uKIjIxk3759REZG0r9/f6OjZatcuXIEBwfz3HPP4eHh4Thuxmu8YsUK4Nrr28WLF+nUqRN2u50VK1ZQvHhxg9OJ2ag4FctITk4mJSWFLVu20K1bNwBcXMw5+P/TTz8RFhbG2bNnmTt3ruO4l5cXw4YNMzDZrekF797K/P9/8+bNHD58mOeffx6AsLAwHn30USOj5So1NZWHH344y7E8efIYlOb2rFmzhhUrVtChQwcASpQoQUJCgsGpcjdy5EiaNGnCF198AUCFChUYOnSoaYtTK17jadOmsXz5cnr27Alcu9Fy7Ngxg1PdzIo3Offu3QvAhQsX2Llzp2NUbNu2bTRq1MiUryOZxowZQ58+fZgyZQoAVatW5a233jJtcZqYmMgjjzzC4cOHjY5yS6VLlwZg165dji4LuPb37sUXX3T8tygCKk7FQlq3bk29evWoUKECTz75JGfOnDHt6F5gYCCBgYGEhYURFBRkdJw7ohe8e+upp54CYPr06cyfP98xitqsWTNeeuklXn/9dSPj5cjd3Z3ExERH3kOHDpn2v79M+fLlu6mAzm3U2gzi4uLo0qULS5YsAa5dd7PehANrXmMAHx+fLJ+7u7sblCRnVrzJOX78eAD69evHypUrKVu2LADHjx9n6tSpRka7pfj4eJo0aeLI6eLiYuobcJnX2kpOnz7N+fPnKVKkCHBtDYMzZ84YnMogWq03RypOxTJef/11unXrhpeXFy4uLnh4eDB9+nSjY+XKxcWFS5cuUbBgQQAuXrzId999R7t27QxOljO94DlHXFwcycnJ5MuXD4CUlBROnz5tcKqc9e7dmx49enD69GmGDx/Oli1bTD1/Gq6N4u3evRubzUZGRgahoaE88sgjRsfKlZtb1pfly5cvYzfxmxgrXmNPT0/Onj3rKKJ37NiBt7e3waluZuWbnH/++aejMAUoW7as6eciu7q6kpqa6vi9iIuLM/WNIYDDhw8THR2dZXHIgIAA4wLdQvfu3fH396dZs2bAtRb71157zeBUYjY2u5lf9US4dse1bNmyHDp0KNvHb2w1NJN27dqxatWqLMcCAgKIiIgwJtBtstoL3qJFiwgNDb3pBe+FF14wOFnOPvzwQzZv3kzr1q0BWL9+PU2bNmXgwIHGBsvF8ePH2bJlC3a7nUaNGvHQQw8ZHSlXZ86cYdiwYezcuRObzUbt2rWZPHkyRYsWNTpajubOncuxY8fYtm0bffr04YsvvsDPz4/u3bsbHS1bVrzGv/32G6NHj+bEiRNUqVKFI0eOMGvWLB577DGjo+Xo2LFjHDt2jPT0dMexp59+2sBEuXvppZeoU6eOo917xYoV7Nixg88++8zYYLmIiIhg/fr1/P7777Rv356IiAgGDRpE27ZtjY6Wrfnz57NkyRLOnDnD448/zu7du6lTp06WUXYzio6OZteuXdjtdurWrUvlypWNjmSIVqXN2S5+vfV/GjMApOJUTO+1115j9uzZ+Pr63vSYzWZj06ZNBqS6PdkVp35+fqxevdqgRLemFzzniYyMZOfOndjtdurXr0/Tpk2NjpSr1NRUYmNjsdlslC9f/qZRPrNKSkoiIyMDT09Po6PcllWrVhEZGYndbsfX1xd/f3+jI92S1a5xfHw8P/30EwA1a9akQIECBifK2dSpU1m6dCkVK1Z0jOTZbDbmz59vcLKcxcXF8d5777Fjxw7g2uJ1ISEhpl0LINPu3bv59ttvHf/t1a5d2+hIOWrbti1Lly6lS5curFy5koMHDzJ79mzHnFmzSkhI4OjRo1SrVs3oKIZqVbKf0RFuaf3Jjw15Xmu8s5B/tNmzZwPX3shbjY+PD19//TUtWrQA4KuvvjL1iALA0qVLWbZsGV26dOGTTz5xvOCZXZkyZUhPT7fUC56vry81atRwtCOb2e7duxk8eDD58uXDbreTkpLC1KlTefLJJ42OdpOcuiwymbnbAq7d1DJz6//1rl+oJ5OXlxeVKlUyZassXHtz7OnpydNPP83BgwfZsmULzz77rCnnncK1roqNGzfi5eVldJTbVrx4caZNm2Z0jDtWu3ZtateuTUpKCpcuXTI6Tq7c3d3x8PAgIyMDu91OpUqVTLmw1/WstlK2GEPFqVhGbGwspUqVIm/evGzZsoUDBw7QqVMnx3xOMwoJCaFv376OuXmurq7MnDnT4FS50wuec/z6668MHDiQjIwMoqKi2LNnD0uXLuXdd981Olq2xo4dy+TJk6lTpw5wrVgdM2bMTZ0BZtCrV68cHzN7t8UHH3xAv379yJ8/P926dWP//v288847ph09nTlzJnv27HF0Khw8eJAqVapw6tQpxo0b52i1N5Nu3bqxcOFCEhMT6dGjB5UqVWLLli188MEHRkfLlo+Pj6UKU7g2kj579myOHz/OlClTiImJITY2lubNmxsdLUeDBg1i7Nix5MmTB39/fy5cuMBrr71Gjx49jI6Wrfz585OamkqVKlWYNGkSJUuW5OrVq0bHypVVVsoWY5l7prfIdQYOHIiLiwvHjx9n9OjRHD9+3LQrFmaqWLEi69atY9asWcycOZO1a9dSoUIFo2Pl6sYXvAULFljmBS+zNc8KL3iZ+4YWLlwYuJY5s83QjPLmzesoTOHaCEPmYk5mExkZmeM/MxemAD/88APe3t5s3bqV4sWL89VXX/Hpp58aHStHDz74IEuXLiU8PJzw8HCWLl1KtWrVmD9/Pv/617+MjpetzD2zN2/eTMeOHfnkk0/Yt2+f0bFyVKNGDd58802+/vproqKiHP/MbMyYMaSlpREdHQ1cWzhrxowZBqfKXWxsLN7e3mzevJm6desSFRVl6vUhRo8eTWpqKsOHD+fSpUvs2rWLiRMnGh3rlqywUrZT2O3m/2cQjZyKZWQu6x4VFUWXLl3o2bOnaUcTrufq6krhwoVJTk4mLi4OgFKlShmcKmfXv+BNnTqVEydO6AXvHrDavqG1a9dm1apVjnbT1atX06RJE4NT3dqhQ4eyzHurWLGiwYluz65du3j22WcpXry4qbdmiY6OzrKQULVq1di3bx8VK1Y07SrDVtozG2DPnj0ALFiwwHHMZrOZekGkgwcPMmHCBLZu3QpcWyE5IyPD4FS5S0tLA679t/f000+TP39+U/9eVKpUCQAPDw/ee+89g9PcHquslC3GUnEqlpFZ3EVGRjJo0CAA0775ybRt2zaGDx/OuXPncHFxITU1lUKFCrFt2zajo+VIL3jOYbV9Q8PDw5k3bx4jR44Erm19U6hQIRYuXIjNZjPl73RERASTJ092LDQ1e/ZshgwZYur5nEWLFmXkyJF8//339OrVi7S0tCwrtJpN/vz5WbNmjWNF0zVr1uDq6gqYd79TK+2ZDVmLUqu48UZbcnKy6V+vK1asyCuvvMLhw4cZPHiwaTuGbnWz+K233nJSkjs3ZMgQevbsyYkTJ+jatatjpWyR66k4Fcvo3r07bdq0oX79+jz++OMcP37c9AXIpEmT+Oyzzxg0aBDh4eEsW7aMv/76y+hYuTpy5AgjRoxw3AjYt28fkZGR9O9v3mXPBw8ebLkXPKvtG7pixQqjI9yxTz/9lPDwcMeo+pkzZ+jRo4epi9MpU6awatUqnn/+eQoWLMiJEyd4+eWXjY6Vo/HjxzN06FBCQkKw2WxUrFiRiRMnkpSUZNo3yVbbM9tut7N8+XKOHj3KkCFDOHHiBKdPnzblYmSZateuTWhoKCkpKezYsYN58+Zlu+K+mWSO9FauXBkPDw/i4uIYPHiw0bFu4uHhYXSEv6169erMnz/fMitlizG0lYxYVkZGBmlpaaZu3wwKCiIsLIy2bduyZs0aAIKDg1m0aJHByXL20ksv8corrzBlyhRWrlxJRkYGfn5+rF271uhoubLS1hCZrLZvqNVkt5VTdsfk70lPT2f58uV06tSJhIQEAMss3BMfH09sbCzJycmOY9fPqTaT999/n3PnzrFv3z42bNjAhQsX6NmzJ8uXLzc6Wo5SU1OZO3duli2RevXqZZntp+TeSEhIwMPDAxcXFw4ePMgff/xh6pWy76VWxfsYHeGW1scZc5NffyXEMux2O0uWLOGHH37AZrPRoEEDOnbsaHSsXGW+EBcvXpzIyEhKly7NqVOnDE6Vu/j4eJo0acLUqVOB/871NbP33nuP//u//8syByvzmJmVLVuWF154wegYt+XkyZNMmjSJ6OjoLG/ozbzA0IMPPsi0adPo1KkTNpuNJUuWULZsWaNj5cpK19nV1ZWVK1fSqVMnyxSlAOvWrWPChAlcvnyZYsWKcezYMapUqUJ4eLjR0bK1Y8cOIiIiCAwMBHCsYWBmefLkoU+fPvTpY/434Jmio6MZPXo00dHRpKSkOI4fOHDAwFQ5y6m916wdC2C9lbLFGCpOxTImTpzIgQMHCAoKAq7NJzt69Kjp/xBfunSJN954g8GDBxMfH09ISIjRsXLl6upKamqqY75YXFycqReFgGvbmtxo165dBiS5tXr16uU6F8+Mczfh2rZIrVu35sCBA0yePJkvv/ySBx980OhYuXrnnXcYN24c7dq1c9zQMutWPZmsdp0bNGjAhg0baNmypdFRbltoaChhYWH06NGDiIgIvv/+e77++mujY+Uob968Wf5mmH1hIbg2QjZz5ky2b9+OzWajbt269O3b19Q3McaMGcPAgQMZP348c+fOZdGiRXh6ehodK0fXt/cmJyezefPmLIuTmVHmStlr166lY8eO9O/fHz8/P6NjicmoOBXL2Lp1K+Hh4Y7RyFatWhEUFGTK4vTTTz/llVdeoWTJkhQsWJDq1avzzTffGB3rtrzwwgu8/vrrXLhwgenTpxMREeFYgMps1q9fz/r16/nzzz954403HMcTEhJMu81J5tzN5cuXc/HiRTp16oTdbmfFihUUL17c4HQ5u3DhAh06dGD+/PnUrFmTJ554gpdeesnoWNm6/sbECy+8QJcuXYBrC/TExMRQpEgRo6LdkpWuM8DChQu5ePEi+fLlI3/+/NjtdtMukJXJzc2NokWLOhaaatiwoannnFaqVIlVq1Zht9s5ceIEc+bMoVatWkbHylVISAheXl6MHDkSu91OeHg4ISEhTJs2zehoOUpJSaF+/frY7XaKFSvGoEGD6Nq1a677Jhvp9ddfz/L5a6+9xtChQw1Kc3ustlL2PZWhWZU5UXEqlnL93WOzrgQJ17bZeOWVVxg3bpxpW8VyEhAQQJkyZfj2229JSkpiwoQJ1K5d2+hY2SpfvjxNmzZlz549jhVZ4dq8t/r16xsXLBelS5cGrhVQCxcudBwfOXIkL774omNzcrPJbO328PDgr7/+4oEHHjDt4l7Xt4gdPnzYsX1MZuFk5rl6VrrOYM2Fstzd3bHb7Tz00EMsWLCA0qVLc+HCBaNj5Wj48OF88MEHnDlzho4dO+Lr62v6Pb4PHz7sWGcBoFatWo4Vnc0qc5XpggULEh0dTfHixfnzzz8NTnX7PD09Tb+/t9VWyhZjqDgVy2jUqBE9e/YkMDAQm81GeHg4jRo1MjpWtvLmzUvv3r1vGtHL9NFHHxmQ6vbVrl3btAXp9apUqUKVKlXw9fWlUKFCRse5I6dPn+b8+fOOUbzz589z5swZg1PlrHbt2ly8eJEuXboQFBSEu7u7aVs5ry+YAgICTF2M3ii76/zcc88ZHStHmTdbrOSNN94gISGBIUOGMGbMGOLj4xk9erTRsXLk5eXFuHHjjI5xR0qVKpXl79uFCxcoU6aMwaly17p1ay5cuECvXr3o0qULGRkZDBgwwOhYObp+zqndbmfv3r2UL1/ewES3ZrWVssUYWq1XLCMjI4MlS5awbds27HY7DRo0oHPnzqYcQb148SI//PADkyZNyvbFLXNhCzMZMGBArtfSzAV1WloaK1as4MCBA1kWChk/fryBqXK3aNEiZs+e7RjxjYqK4rXXXjPtAknx8fGOrZv++usvEhISHHvimllgYKDluhcyWeE6W2kBJyvbtm0bx44dIy0tzXEsODjYwETZyyyY/vrrL3788UeaNWsGwObNm6lfvz4TJkwwMt5tS01NJTk52dRzZGfMmOH42NXVlQcffJAWLVqYfgHDY8eOcezYsSz7N1+/mOE/RcsHzNkufr0NZ+cY8rwaORXL2LFjB126dHHMH4NrL9hmbN8sVKgQrVu3pmjRotStW9foOLcl802EFY0aNYr09HTH78iaNWtMP/IbHBxMrVq12LVrF3a7neDgYCpXrmx0rGxl5svcgqVUqVIGJ7q/JSUlcerUKdLT03FxceHQoUM8/PDDRsfKltUWcIJr895WrVrF8ePHsxR7Zly/AGDo0KH8/vvvVKlSxdF6alaZi/Q8/PDDWX5nzb6yfiYrFU43zjm1gilTprBs2TIqVqzomGtqs9lMe43FGCpOxTImTpx40wjIpEmTCAsLMyjRrVWvXp0PP/yQEydOMGXKFGJiYoiNjaV58+ZGR7uJGUdzb9eePXtYvXo1fn5+jtHHgQMHGh3rljLbks3OZrNRtmxZLl26RMGCBY2Oc0uHDh1yfJycnExMTAzXNwmZtdCDayPqkydPplChQo5OBpvNZtqRSKst4ATX2npTU1OpXr26JfZX3LNnD2vXrjV9YQrWLJgyWa1wym7KkLe3NzVq1CAoKMiUCw1t2LCBjRs3mnpEWoyn4lRM7+jRoxw5coSEhASioqIcx+Pj40lKSjIw2a2NGTMGHx8foqOjAShRogSDBw82ZXGaqX///rz77ruOOZwXLlxgzJgxpm7rzVxQwdXVlaSkJLy9vTl9+rTBqbLXvn37XNunzTo/0sPDg8DAQJo0aZJlCwMzjjbduLrm9YtMmbnQg2srfa9Zs8YyczmttoATXHtNWb9+vdExbttDDz3E1atXTb2tyY2SkpKYOXOmY1/yhg0b0rt3b/Lnz290tBxZrXB64IEH2Lt3L23atAGu7d/r4+PD+vXriY6OZuTIkQYnvJmPj49lru89p9V6c6TiVEzvp59+IiwsjLNnzzJ37lzHcS8vL9OvWHjw4EEmTJjA1q1bgWur6Zl9j7rjx49nWVyocOHCpl8BsGDBgly6dInGjRvTs2dPChcuzAMPPGB0rGyZ/Xc2Jw899BAPPfSQ0TFuS2RkpNER/jYfHx/LFKZgrYWyMpUtW5aEhATLvEl+6623ePHFF6lVq1aWkV4z3hjK9O6775Kenu7Y13v58uWMHTvW1OsAWK1wio6OZsGCBY7fiU6dOtGnTx9CQ0MJCAgwNlwOatSowZtvvknLli2zrNJr1tFpMYaKUzG9wMBAAgMDCQsLIygoyOg4d+TGhQmSk5Mx+xpk6enppKenO1rIUlNTSUlJMThV7ubMmYOrqyuDBg1i1apVJCQkmPbF+amnnsry+ZUrV4CsG6qbSUxMDIcPH3a067333nskJCQAOPapk/9dZitygwYNmDhxIm3atMny5s2srciZN1sCAgJ46qmniI+P57XXXnMUJWaSuViPt7c37du3p3HjxpYo9saNG0fx4sXx9va2RGsv/HeqRaYnn3ySdu3aGZgoZ5kdWVYrnM6ePZvlPYabmxsnT57E3d3dtO3qe/bsAWDBggWOY2ZunRZjqDgVywgKCiIqKort27cDUK9ePdP/QatduzahoaGkpKSwY8cO5s2bh6+vr9GxctWoUSMGDRrkKDzmz59P48aNDU6Vu8w3bC4uLqYtSm90/PhxBg8ezIEDB7DZbDz66KNMmjSJsmXLGh0ti2nTptG6dWvH59999x3dunXjypUrzJkzhw8//NDAdPePG1uRN2zY4PjY7K3ImTIXyjLrDbjMG0Dly5c3/ZYb1zt16pSl2pAzXblyxXHNzTwF5/qOLLBO4fTUU0/Rq1cv/P39sdlsrFq1ilq1apGYmGja4vT6a/uPZ9K/k2agrWTEMj788EMiIyNp06YNdrudDRs20KxZM1MvfJOamsrcuXOJjIzEbrfj6+vLa6+9Zuq736mpqcyePZvNmzdjt9tp1qwZvXr1Mu2LHVy7UXHjPM7MhSGGDh2Kj4+PQcly9vLLL9OmTRvat28PQFhYGGvWrGHevHkGJ8vqxq1YAgICiIiIAK6tOLxo0SKDkolZNW3alM2bNxsd474xcOBAQkJCKFasmNFRbtucOXNYvXo1bdq0wWazsXbtWtq1a8err75qdLT7RmpqKosXL2bnzp3Y7Xbq1q1L586dTb2VjN1uZ/ny5Rw9epQhQ4Zw4sQJTp8+zZNPPml0NKdrWdj8/y1suDD31l90D6g4Fct47rnnCA8Pd9yJvXLlCoGBgXz11VcGJ8tZTEwMFStWvOUx+d9Mnz6d+Ph4R9t3RESEY+7Q3r17CQ0NNTJetvz9/Vm5cuUtjxnNz88vS3ve9b+/Nz4md8fBgwfZuXMnNpuNunXrmrKl9/oVkW/00ksvOebZm1FCQgIzZ85k+/bt2Gw26tWrR58+fUw737BHjx7s3buXmjVrZmk3NfMidYCj0ylzX/ImTZoYHemWrNadZTXvv/8+586dY9++fWzYsIELFy7Qs2dP0y4EeC+pOM2Z2nrFMooVK5Zlpb+8efOa/k7ykCFDbtr+JrtjZpI5L+tGZp2PBddaTZctW+b4fPjw4bz44ossXLjQsZKh2bi4uHD48GEqVKgAQGxsrClH1FNTU7MsHpNZmCYkJJh+LrIVLVq0iNDQUJo2bYrdbmf27Nn07t2bF154wehoWdzYhny96wsoMwoJCcHLy4uRI0dit9sJDw8nJCSEadOmGR0tW23btqVt27ZGx7hjTz/9tKWKuxu7s6ZOncrPP/9s2u6s2NhYQkJCiIuLIzIykn379hEZGUn//v2NjpajHTt2EBER4di6rnDhwiQnJxucyiAmXxzTSCpOxTKqVq3Kq6++6vijtnLlSp588knHYgZmehE8f/4858+fv2mPxfj4eMcCOGZ1/cI8ycnJbN68mccee8zARLd2+fJlLl68mGX7mzNnzgA3L0plFoMGDSI4OJiqVatis9k4cOBAjjcGjNSmTRtCQkJ4//33HQVqQkICI0eOzDIXVe6O+fPnExERQdGiRYFrf0u6dOliuuLUyisiHz58mDVr1jg+r1WrlqmLPyvtQT1gwIBct8oy82jvhg0bsnRnde/encDAQNMWp++88w59+vRhypQpwLX3SG+99Zapi9O8efNm+f0w++4FYgwVp2IZBw4cAGDJkiWOYz/99BM//fST6RYtWL16NZ9//jmnT5/Osseit7e36efc3LiJ+muvvcbQoUMNSnN7unbtir+/v+N34LvvvuPVV18lMTHRdHNZPvjgA4YPH06TJk14++23yZ8/P3a7nRo1alCkSBGj492kT58+DB8+nMaNG1OuXDkAjhw5wjPPPEO/fv2MDXcf8vHxcRSmAEWKFDHttkhWVapUKc6fP+/47+3ChQuUKVPG4FQ5O3fuHAsWLOD48eOkpaU5jpux0GvWrJnREf42q3VnxcfH06RJE6ZOnQpc68Yx683YTJUqVWLVqlXY7XZOnDjBnDlzqFWrltGxxGQ051TkHrm+Jc/K7HY7bdu2Ze3atUZHyVV0dDS7du3Cbrfz1FNPUaVKFaMjZev6BYZuXGzIzI4ePcr+/fsBePTRRy2z56nVTJ06lXPnzvH8888DEB4eTsmSJXn22WcB824pYyUDBw7kxx9/dBRSmzdvpn79+o6bAmabwvDCCy9QsWJFnnjiiSyt/2YeUY2Li6N48eJGx7gj77//PjExMVm6sx599FHHDU4z3QAH6NixI4sWLaJjx46Eh4cTFxdHnz59CAsLMzpajhISEvjggw8cnRe+vr6MGDECT09Pg5M5X8uCrxgd4ZY2XPrUkOfVyKlYypYtW/jhhx+w2Ww0bNiQhg0bGh0pV+vWrbNccXp9a6ndbmfv3r2W2HahSpUqpi1Ir3f9/UAr3Rt86KGHVJA6QWa76bZt27IcX7ZsmWW2lDG7hx9+OEuR37FjRwPT3Nrly5d59913jY5xR9q3b0/NmjUJDg6mXr16Rse5LVbqzoJrNy1ef/11Lly4wPTp04mIiGDQoEFGx8qVl5cX48aNMzqGOVjo9d/ZVJyKZfz73/9m5cqVjgVuPvjgAwICAujRo4fBybJns9koW7Ysly5domDBgkbHuW3Xzzl1dXWlc+fOtGjRwsBEt/bTTz8xadIkjh8/Tnp6Ona7HZvNdtMbfDNISUlxzEO+/uNMGhn7Z4uIiKBAgQJGx7iv3Th1weweeeQRy41ERkZGsm7dOj788EPi4+MJDg7G39/ftCsig/X24AwICKBMmTJ8++23JCUlMWHCBGrXrm10rGzdasux4OBgJyURK1Bbr1iGn58fX375ZZZFWbp06WLqrSyGDh3Kjz/+SJMmTbIUfWZrG7O6Vq1a0bdvX2rUqIGLi4vjeOnSpQ1MlT1fX98cH9PI2D+b3W7H39+fVatWGR3lvrd161YOHDiQZaVQsxatVt1KJtNPP/3Em2++yeXLlwkMDKRv375Z5lWbSXx8PLGxsVl+L+rUqWNgouylp6fTsWNHVqxYYXSU2zJixAjg2vzunTt3Ur9+feBah0ijRo1Mu1L2vdSywMtGR7ilDZeN2XddI6diKdffdTXzHdhM2bVC7t6926A0t8dKi29kypcvH35+fkbHuC1WXuVU7i2rdltYzeTJk9mzZw+HDh3imWeeYdOmTY43y2Zk1a1k/vzzTxYvXsyaNWuoX78+HTp0YPv27fTo0YOIiAij491k3bp1TJgwgcuXL1OsWDGOHTtGlSpVTLkugKurq2MbFrNv3QQwfvx4APr168fKlSspW7YsAMePH3cs6PRPY9dKxTlScSqW8dhjjzFixAg6dOiAzWZj2bJlpt/iJPNO/OnTpwkPDycsLMz08wz79+9PxYoVqV+/vin33cxOkyZNiIqKMt2cIJE75eHhQWBgoLot7qGoqCjCw8MJCgpi7Nix9OvXj3feecfoWDky88JHOenduzcHDx6kc+fOhIWFUbhwYQCefPJJ1q1bZ3C67IWGhhIWFuYonr///nu+/vpro2PlqFy5cgQHB/Pcc89l+Vth5hbZP//801GYApQtW5bY2FgDE4kZqTgVy3j77bf5+OOPHZPpGzRoQN++fQ1OlbO0tDQiIyNZsWIFv/zyC2lpaXzyySfUqFHD6Gi5suLiG0uWLGH27Nl4enri7u5u6jmnIrnRwlP3nru7O25ubthsNlJTUylevDinTp0yOlaOsts71Nvbmxo1ahAUFJRlKoNZ+Pv706JFi2xvcF6/x6yZuLm5UbRoUdLT0wFo2LAh06dPNzhVzhITE3nkkUc4fPiw0VFuW6FChfj444/p0KEDACtWrHDsTy6SScWpWIaHh4fp99vMNH78eNauXUulSpUIDAzko48+onXr1qYvTMGai29YZd6NyK2Ydd7j/cTT05OkpCRq1qzJ8OHD8fHxMXWXiI+PD3v37nUsBrhu3Tp8fHxYv3490dHRjBw50uCE/5WUlARA06ZNSUlJuenx6/cRNZvMG5sPPfQQCxYsoHTp0ly4cMHoWNlKT0+nZMmSDBgwwOgod2TChAm89957jmk49erVY8KECQanMojJu+iMpOJULCMhIYGZM2eyfft2bDYb9erVo0+fPqace/rll19Ss2ZNevXq5VhG/8Y732Z1+fJl2rVrZ6nFN0qXLk1aWhqxsbHYbDbKlSuHm5v+vIn1nDt3jvHjx3Py5EkWLVpEdHQ0P//8M126dDE62n1j6tSpuLq6MmzYMObNm0d8fLypF2SJjo5mwYIFuLu7A9CpUyf69OlDaGgoAQEBxoa7Qc2aNR2vdTdOYbHZbI7tWszojTfeICEhgSFDhjBmzBji4+MZPXq00bGy5erqyq5du4yOcceKFy9u6v/WxBz07k0sIyQkBC8vL0aOHIndbic8PJyQkBBT/qHbunUrq1evZuLEiVy6dImAgABHq5DZWXHxjT179jBgwADHne+0tDSmT59OtWrVjI4mckdGjhxJkyZN+OKLLwCoUKECQ4cOVXF6F33//ff4+/sDOKaGrFy50nHMbM6ePUuePHkcn7u5uXHy5Enc3d0dBatZREdHGx3hbytWrBje3t54e3vz2WefARATE2NsqFw0bdqUTz75hICAgCxzTs08Og3XVug9duxYlgUXzTxPVpxPxalYxuHDh7PMValVq5Zpi6gCBQoQHBxMcHAw0dHRLF++nKtXrxIcHIyfnx+dO3c2OmKOrLj4xnvvvcf777/vWHFz+/btvPvuuyxevNjgZCJ3Ji4uji5durBkyRLgWquhGecUWtlnn312UyGa3TGzeOqpp+jVqxf+/v7YbDZWrVpFrVq1SExMNF1xer0LFy7w66+/YrPZeOKJJ0w/t3DIkCE3rcyb3TGzmDRpkuN/bTabY60FM49ODx8+nL179/Loo4+aupXeKTLU1psTFadiGaVKleL8+fMUKVIEuPbCV6ZMGYNT3VqVKlUYOXIkw4YN45tvviE8PNyUxennn39O9+7dmThxYraPm3m10KSkpCxbQdSrV88x90nESm5sR798+bLpV/i2ij179vDbb79x4cIFFi1a5DiekJBAamqqgclyN2rUKBYvXsxXX32F3W6nUaNGdO7cmTx58rB06VKj42Vry5YtDB06lKpVqwLw+++/M2nSJBo2bGhwspudP3+e8+fPk5ycTExMjOO/t/j4eK5cuWJwupxZcZT6559/Zs2aNVk6AURupOJULMPDwwN/f3+aNWsGwObNm6lfv76jmDJz8QSQJ08eWrduTevWrY2Okq3M+aXXtwdZRf78+dm+fbtjfu/OnTtN39okkp0WLVowatQoEhMTCQsL44svvqB9+/ZGx7ovxMXFsXfvXpKSkti7d6/juKenp2MfRjPKkycPXbt2pWvXrkZHuW0ffvghixYtomLFisC19tihQ4easjhdvXo1n3/+OadPn6Znz56O497e3rz66qsGJrv/lChRwugIYgE2u27JikXMmDHjpmO7d++mdu3agFa5/F+lpaXxzTffULBgQRo0aMDnn3/ODz/8QPny5enXrx/e3t5GR8zRb7/9xhtvvOFocUtNTWXatGmm3wdXJDurVq0iMjISu92Or6+vadtNrWrr1q00atTI6Bi3lNnNMmHChGwX1DPzDdl27dqxatWqWx4zk9DQUHr37m10jFuqV69etr8PVthCbfTo0Rw6dIjmzZtnaUn/J845fS6f+X/mr64uuvUX3QMaORXLyCw+T58+TXh4OGFhYdjtdhWld8nYsWM5ePAgKSkplClThuTkZJo2bcquXbsYPXo0U6dONTpijqpXr87XX39NbGwsdrudChUqqG1ILGnbtm20a9eOdu3aZTl2fdu6/G9q1arFv/71L44fP86UKVOIiYkhNjaW5s2bGx0ti8xuFk9PT4OT3LkiRYoQFhZGUFAQAOHh4Y4pOWbVu3dvtm3bRkxMDC+++CLnzp3j8uXLlC9f3uhoWVh567SUlBQefPBBDh48aHQUMTGNnIolpKWlERkZyYoVK/jll19IS0vjk08+scS+oVbRunVr1q5dS1JSEo0aNWL79u24u7uTkZFBu3btTLlx+q3mlaq1V6wmMDDwpgVYgoKCCAsLMyjR/WfYsGH4+Pjw7bffsnbtWhITEwkODiYiIsLoaPeFixcvcvz4cYYOHcpff/0FQNWqVZk8eTJly5Y1OF3O5syZQ1RUFGfOnOHrr7/m1KlTDBo0iC+//NLoaHIf0shpzjRyKqY3fvx41q5dS6VKlQgMDOSjjz6idevWKkzvMnd3d2w2Gx4eHjz44IOOlhsXFxfTjkJm7qmXeY/t+v31zL5qocj1jh49ypEjR0hISCAqKspxPD4+Xot73WUHDx5kwoQJbN26Fbg2MpmRkWFwqptdv2hTdszYCrlu3TpGjBiBp6cnycnJTJs2jdq1a5tyP/IbrVmzhhUrVtChQwfg2vzIhIQEg1PdbMCAAbnum27mPcntdjtLlizhhx9+wGaz0bBhQzp06GCZfeDFOVSciul9+eWX1KxZk169ejkWvNEfsrsvJSXFsVLh9R8DJCcnG5wue5s2baJ06dLZPnb9giciZvfTTz8RFhbG2bNnmTt3ruO4l5cXw4YNMzDZ/efGm23JycmmXBHZin/DZs2axeLFi6latSrbt2/n448/pmnTpkbHui358uW76XfDjO81MheFtKKJEydy4MABR7t3REQER44cMfX86XvFrq1kcqTiVExv69atrF69mokTJ3Lp0iUCAgJIT083OtZ95+rVq1lWKrz+YzO+QMO1eciZLZDPP/88y5cvdzz29ttvm3Z/OpEbBQYGEhgYmGWentwbtWvXJjQ0lJSUFHbs2MG8efPw9fU1OtZNcltBOLNd1mxcXFwc28fUq1ePDz74wOBEt69EiRLs3r0bm81GRkYGoaGhPPLII0bHuokV9yLPtHXrVsLDwx1bZrVq1YqgoKB/ZHEqOVNxKqZXoEABgoODCQ4OJjo6muXLl3P16lWCg4Px8/Mz5Z6hVhQZGWl0hDt2/WhHWlpajo+JWEVQUBDHjh3j2LFjWW7CPf300wamur8MGjSIuXPn4unpyeTJk/H19aVXr15Gx7qllJQUvvnmG5YvX87evXvZtWuX0ZFukpqamqXr5sYunIcfftjIeLl6++23GTZsGH/88QdPPPEEtWvXZvLkyUbHuklOe5FnMnuhd/3NbrPe+BZjqTgVS6lSpQojR45k2LBhfPPNN4SHh6s4/QfL7UVOL3piRVOnTmXp0qVUrFgRFxcX4NrvsorT/931czgLFChAQECA4/PFixebcg4nwL59+1i+fDnr1q0jOTmZ9957j1mzZhkdK1s3duDAf7twbDYbmzZtMiLWbfHx8eHTTz8lKSmJjIwM066SbMW9yDM1atSInj17EhgYiM1mIzw83BLbOt0TdvPNczcLFadiSXny5KF169a0bt3a6ChioOTkZMdd+es/znxMxGrWr1/Pxo0bLbGAjNW8++67PPbYY6Zs1czO/PnzWbFiBVevXiUoKIiIiAiCg4Np06aN0dFyZMUOnOtZoWvBytvnDR06lCVLlvDNN99gt9tp3rw5nTp1MjqWmIyKUxGxLCvOkxXJjY+PjwrTe+S9994jIiKCQ4cOERAQQNu2bSlYsKDRsXL0/vvvU79+fcaOHevYgkV/1+6diRMnEhERQfny5S3TtbB161YOHDiQ5WasGYvX9PR0UlJSyJ8/P126dKFLly7Ate3gNAVHbqTiVEQsy+p36UVuVKNGDd58801atmxJ3rx5HcfN/AbZKtq3b0/79u05ceIE4eHhdOnShUqVKtGnTx8qV65sdLybrF27luXLl9OlSxfKly9PYGCg3sjfQxs3bmTTpk2W2R978uTJ7Nmzh0OHDvHMM8+wadMm6tevb3SsbE2ePJkKFSo4tunJtGzZMk6dOmX6ebL3glbrzZmL0QFERETkmj179nDmzBkWLFjA3LlzmTt3Lp988onRse4rZcqU4aWXXqJr167s2LGD3377zehI2apYsSLDhg0jKiqK7t27880333D27FneeusttmzZYnS8+07JkiVNu6d3dqKiovjkk08oWrQoY8eOJSwsjCtXrhgdK1vfffddtquQBwcH89133xmQSMxMI6ciIiIGO3ToEACjR482OMn9y263s2XLFsLCwjh48CCtWrVi6dKljpZZs3J1daV58+Y0b96cc+fOERYWxoQJE2jcuLHR0e4rw4cPp3fv3jRs2BB3d3fHcbMulOXu7o6bmxs2m43U1FSKFy/OqVOnjI6VLRcXF1xdXW867urqqlZ1uYmKUxEREYNlbmVy4xs1u91u+lVOraJJkyb4+PgQFBREv379sNlsJCcnO24MmHmbk4SEBI4ePUq1atXo2bMnr776qtGR7jtz5szhzJkzHDhwINtCymw8PT1JSkqiZs2aDB8+HB8fH9PmTklJISkp6aaW6cTERFJSUgxKZbD7bLXe2NhYhg8fzsWLFylUqBATJkygXLlyf+tcNrsmMIiIiMh9ztfX1/GxzWbLMn/TzDcAoqKiGDVqFK6urkRGRrJnzx4+/vhjQkNDjY52X2nRogVfffWVZUbyzp49S4ECBUhPT2fevHnEx8fTtWtXSpUqZXS0m3z00UfExMTw/vvvOxZ8i4+PZ9SoUTz44IMMGjTI4ITO96xLh1t/kcG+yVh221/brVs32rdvj7+/PytXrmTFihXMnz//bz2vRk5FRETkvmfVBdSmTZvG8uXLHauRP/744xw7dszgVPefcuXKceXKFdPub3qjBx54wPFx3759DUxya/369WP48OE0btzYMZp25MgRfH196d+/v7HhJEeXL1/m8uXLNx0vUKAABQoUcHx+7tw59u/fz7x58wBo27Yt7777LufPn6dIkSJ3/LwqTkVERERMzMfHJ8vn18+JlLvDy8uLoKAgGjdunOX6mnUl2cOHDxMaGsqxY8dIS0tzHF++fLmBqbLn5ubG5MmTOXLkCAcOHMBut1OtWjUeeugho6MZ5k5GJY0yffp0ZsyYcdPx119/PctNhZMnT1K8eHFHW7mrqyvFihXj5MmTKk5FRERE7ieenp6cPXvW0W66Y8cOvL29DU51/6lQoQIVKlQwOsZte+ONN/D39ycwMNC0c00zJSUlAVC8eHGKFy9+03GrbN/zT9O9e3cCAwNvOn79qOm9oOJURERExKSGDBlCz549OXHiBF27duXIkSPMmjXL6Fj3nddff93oCHfEzc3NMgtj1axZM9e5vAcOHHBiGrldN7bv5qRkyZLExcWRnp6Oq6sr6enpnD59mpIlS/6t51VxKiIiImJS1atXZ/78+fz000/AtTf693rk4p8oKSmJmTNn8sMPP2Cz2WjYsCG9e/c27ahe48aN+e6772jSpInRUW4pOjoagFmzZpEnTx46deqE3W5n2bJlltpbVrJXtGhRqlatypo1a/D392fNmjVUrVr1b7X0glbrFRERETGthIQEPDw8cHFx4eDBg/zxxx88++yzmnd6l4WEhJCenk7Hjh2B/87dHD9+vJGxcrRt2zb69u2Li4sL7u7ujm2ntm3bZnS0HHXp0oUvv/zylsfEemJiYhg+fDiXL1+mQIECTJgw4W+3yWvkVERERMSkunXrxsKFC0lMTKRHjx5UqlSJLVu28MEHHxgd7b6yZ88eVq9e7fj8ySefpF27dgYmyt2oUaMYP3481apVw8XFxeg4t+XixYscPXrUsRDSsWPHuHjxorGh5K6oWLEiy5bdnUWeVJyKiIiImJTdbsfDw4O1a9fSsWNH+vfvj5+fn9Gx7ktXrlzBw8MD+O9iPWZVsGBBWrZsaXSMOzJo0CA6duzIY489BsD+/ft59913DU4lZqPiVERERMSkkpOTSUlJYcuWLXTr1g3AMiNlVuLn50enTp1o06YNNpuNtWvX4u/vb3SsHDVv3pwvv/ySVq1akTdvXsdxs86RBWjRogW1a9fml19+wW63U7Nmzb89L1HuXypORUREREyqdevW1KtXjwoVKvDkk09y5syZLMWI3B29evWiSpUqbNu2DbvdzpAhQ0y92NC//vUvAN555x1sNptjzqnZV769dOkSGRkZNG/enMTERC5evEihQoWMjiUmogWRREREREzs8uXLeHl54eLiQmJiIgkJCVn2i5T/TeZCSCtWrDA6yh27dOkSO3fupGzZslSpUsXoOLkKDw9n9uzZpKamsmnTJg4fPszYsWP57LPPjI4mJqK+EBERERETs9ls7N27l127drF//36OHTtmdKT7iqurK4ULFyY5OdnoKLc0ZMgQx9YsFy9epF27dvzrX//ilVdeuWsL0twrn3/+OStWrMDb2xuAChUqcPbsWYNTidmorVdERETEpNatW8eECRO4fPkyxYoV49ixY1SpUoXw8HCjo91XypUrR3BwMM8995xjUSSA4OBgA1PdbP/+/Y4R0pUrV1KxYkU+/fRTTp06xWuvvUaHDh0MTpizPHny4OnpmeWYq6urQWnErFScioiIiJhUaGgoYWFh9OjRg4iICL7//nu+/vpro2PdVy5evMjx48cpUaIEhw8fNjpOrq6fb/zjjz/SvHlzAEqUKIHNZjMq1m0pVKgQsbGxjpwrV66kRIkSBqcSs1FxKiIiImJSbm5uFC1alPT0dAAaNmzI9OnTDU51/1i3bh0jRozA09OTlJQUpk+fTv369Y2Olau4uDgKFizIzp07GTBggOO42duSQ0JCGDx4MLGxsfj6+pIvXz5CQ0ONjiUmo+JURERExKTc3d2x2+089NBDLFiwgNKlS3PhwgWjY903Zs2axeLFi6latSrbt2/n448/NnVx2qtXLwICAsiTJw+1atXi4YcfBuCXX36hVKlSBqfLXfny5Vm2bBlHjhzBbrdTvnx5tfXKTbRar4iIiIhJbdu2jccee4xz584xZswY4uPjGTx4MA0aNDA62n3B39+flStXOj4PCAggIiLCuEC34cyZM5w9e5YqVao4WmTj4uJIT083fYGalJTEqVOnHJ0AgKPAFgGNnIqIiIiYVuYonre3t7bcuAdSU1OJiYkhc6wmJSUly+dmLJx8fHzw8fHJcswKWwstWrSIyZMnU6hQIUdRbbPZ2LRpk8HJxEw0cioiIiJiUsnJyaxatYrjx4+TlpbmOP7WW28ZmOr+4evrm+NjKpzurmeeeYb58+dTunRpo6OIiWnkVERERMSk3njjDVJTU6levTru7u5Gx7nvREZGGh3hH8PHx0eFqdySRk5FRERETKpVq1asX7/e6Bgi/7Np06Zx9epV2rRpk2VLHDO2TotxNHIqIiIiYlJly5YlISEBLy8vo6OI/E8yF5rasGGD45hap+VGGjkVERERMZmJEycC11Zh3bt3L40bN87S1qs5pyJyP9LIqYiIiIjJeHh4ANf2hixfvrzBaUT+vpSUFNzd3UlKSsr28fz58zs5kZiZRk5FREREROSeCAwMJDw83LEv6/Wlh81m48CBAwamE7NRcSoiIiJiUgkJCcycOZPt27djs9moV68effr00RxUsYzY2FiN/sttczE6gIiIiIhkLyQkhIsXLzJy5EhCQkK4dOkSISEhRscSuW2DBw8GoHv37gYnESvQnFMRERERkzp8+DBr1qxxfF6rVi3atm1rYCKRO3P16lW++uor/vzzT6Kiom56/OmnnzYglZiVilMRERERkypVqhTnz5+nSJEiAFy4cIEyZcoYnErk9r355pssWbKEc+fOMXfu3CyP2Ww2FaeSheacioiIiJjUwIED+fHHH2nWrBkAmzdvpn79+hQtWhTQljJiHePHj2fEiBFGxxCTU3EqIiIiYlIzZszI9fHXX3/dSUlE/nexsbHExMTQvHlzEhMTSU1NpVChQkbHEhNRcSoiIiIiIvdUeHg4s2fPJjU1lU2bNnH48GHGjh3LZ599ZnQ0MRHNORURERExsa1bt3LgwAGSk5MdxzRiKlbz+eefs2LFCoKDgwGoUKECZ8+eNTiVmI2KUxERERGTmjx5Mnv27OHQoUM888wzbNq0ifr16xsdS+SO5cmTB09PzyzHXF1dDUojZqV9TkVERERMKioqik8++YSiRYsyduxYwsLCuHLlitGxRO5YoUKFiI2NxWazAbBy5UpKlChhcCoxG42cioiIiJiUu7s7bm5u2Gw2UlNTKV68OKdOnTI6lsgdCwkJYfDgwcTGxuLr60u+fPkIDQ01OpaYjIpTEREREZPy9PQkKSmJmjVrMnz4cHx8fNQKKZZUvnx5li1bxpEjR7Db7ZQvX16/y3ITrdYrIiIiYlJnz56lQIECpKenM2/ePOLj4+nWrRslS5Y0OprIbTl06FCujz/88MNOSiJWoOJURERExKRWrlyJv7//LY+JmJWvry82mw273c7Jkyfx8vICID4+nlKlShEZGWlwQjETtfWKiIiImNRnn312UyGa3TERs8osPseNG0etWrVo1aoVABs2bGD//v1GRhMTUnEqIiIiYjJ79uzht99+48KFCyxatMhxPCEhgdTUVAOTifw9v/32GyNHjnR83rJlSz799FMDE4kZqTgVERERMZm4uDj27t1LUlISe/fudRz39PRk/PjxBiYT+XuSkpLYvXs3tWvXBmD37t0kJSUZnErMRnNORURERExq69atNGrUyOgYIv+z3bt38+abb5I/f34AkpOTmTJlCrVq1TI4mZiJilMRERERk0pKSmL27NkcP36cKVOmEBMTQ2xsLM2bNzc6msgdS0lJITY2FrvdToUKFXB3dzc6kpiMi9EBRERERCR7Y8aMIS0tjejoaABKlCjBjBkzDE4l8vekp6fj7u6Om5sbx44du+U2M/LPozmnIiIiIiZ18OBBJkyYwNatW4Frc04zMjIMTiVy5xYtWsTkyZMpVKgQNpsNAJvNxqZNmwxOJmai4lRERETEpPLkyZPl8+TkZDQjS6zo008/Zc2aNZQuXdroKGJiKk5FRERETKp27dqEhoaSkpLCjh07mDdvHr6+vkbHErljPj4+KkzllrQgkoiIiIhJpaamMnfuXCIjIwHw9fWlV69euLq6GpxM5M5MmzaNq1ev0qZNG/Lmzes4/vDDDxuYSsxGxamIiIiIySxatCjXx4ODg52UROTuyG7EX3NO5UYqTkVERERMpkqVKjz22GM88sgj2T4+fvx4JycSEbn3VJyKiIiImMyKFSuIiIjg6tWrBAQE0LZtWwoWLGh0LJE79tdff2X53GazUaRIkSytvSKZVJyKiIiImNSJEycIDw9n/fr1VKpUiT59+lC5cmWjY4nctnr16mGz2bKsMp2QkECNGjWYOHEipUqVMjCdmI2KUxERERETi4+PZ82aNUybNo0333yTDh06GB1J5H+Snp7O4sWL2bp1K7NmzTI6jpiIilMRERERk7Hb7WzZsoWwsDAOHjxIq1atCAgIoGzZskZHE7lrAgMDCQ8PNzqGmIj2ORURERExmSZNmuDj40NQUBD9+vXDZrORnJzMoUOHAG2/IfeH9PR0oyOIyWjkVERERMRkrt9248b5etp+Q6wkKSnppmMXL15k8eLFnDhxgilTphiQSsxKxamIiIiIiNwTVapUyXKDJXO13gYNGjBixAiKFClicEIxExWnIiIiIiIiYjgXowOIiIiIiIiIqDgVERERERERw6k4FRER+ZuGDx/Ohx9+CMDu3bt57rnnnPK8lStX5ujRo3f1nNf/LM78XhERkUwqTkVE5L7m6+tL9erVqVmzpmMBjsTExLv+PLVr1+arr7665deFhYXRpUuXu/78mbp27cqyZcvu2flFRETuFRWnIiJy3wsNDeXnn38mPDycPXv2MGvWrJu+Ji0tzYBkIiIikknFqYiI/GMUL16cxo0b88cffwDX2mMXLVpEixYtaNGiBQDffvst/v7+1K5dm86dOxMdHe34/v379xMYGEjNmjUZOHAgycnJjsd27NhBkyZNHJ+fPHmS119/nXr16lG3bl3Gjh1LTEwMo0eP5pdffqFmzZrUrl0bgJSUFCZMmEDTpk1p0KABo0aN4urVq45zzZ07l0aNGtGoUSOWL1/+t3/+AQMG0LBhQ2rVqkVwcLDjOmS6cOECL7/8MjVr1uTFF1/kzz//dDwWExPDyy+/zFNPPcVzzz3HunXr/nYOERGR7Kg4FRGRf4yTJ0/y3XffUbVqVcexjRs3snTpUtatW8e+ffsICQlh7Nix7Nixg06dOtG3b19SUlJISUmhX79++Pv7s3PnTlq2bMnXX3+d7fOkp6fz2muvUapUKSIjI/nuu+9o3bo1FStW5J133qFGjRr8/PPP7N69G4BJkyYRGxtLREQEX3/9NadPn+bjjz8G4LvvvuPTTz/l008/5euvv2bbtm1/++dv0qQJX331Fdu2bePRRx9lyJAhWR5fvXo1ffv2ZceOHVSpUsXx+JUrV3jllVdo27YtP/zwA1OnTuWdd965qbgVERH5X6g4FRGR+16/fv2oXbs2L7zwAnXq1KF3796Ox3r16kWhQoXIly8fS5cupVOnTjzxxBO4uroSGBhInjx5+OWXX/j1119JTU2le/fu5MmTh5YtW/L4449n+3y//fYbp0+f5q233sLDw4O8efM6RklvZLfbWbZsGSEhIRQqVAgvLy9ee+011q5dC8D69esJCgqiUqVKeHh48Prrr//t6/D888/j5eWFu7s7/fv3Jzo6mvj4eMfjTZs2pU6dOri7uzNo0CB++eUXTp48yebNmyldujTt27fHzc2NatWq8dxzz93WHFsREZHb5WZ0ABERkXvt448/pkGDBtk+VrJkScfHf/31FxERESxcuNBxLDU1ldOnT2Oz2ShevDg2m83xWKlSpbI958mTJylVqhRubrd+mT1//jxJSUkEBQU5jtntdjIyMgA4ffo0jz32mOOx0qVL3/Kc2UlPT+fDDz9kw4YNnD9/HheXa/enL1y4gLe3NwAlSpRwfL2npycFCxbk9OnT/Pnnn/z2229ZCuz09HTatWv3t7KIiIhkR8WpiIj8o11fbJYsWZLevXvTp0+fm75u586dxMXFYbfbHd/z119/UbZs2Zu+tmTJkpw8eZK0tLSbCtTrnw+gcOHC5MuXj7Vr11K8ePGbzlWsWDFOnjzp+Pyvv/66sx/wP1avXs2mTZuYN28eZcqUIT4+njp16mC32x1fc+rUKcfHiYmJXLp0iWLFilGyZEnq1KnDvHnz/tZzi4iI3A619YqIiPxHhw4dWLx4Mb/++it2u50rV66wefNmEhISqFGjBm5ubsyfP5+0tDS+/vpr9uzZk+15qlevjo+PD1OmTOHKlSskJyfz448/AlC0aFHi4uJISUkBwMXFhQ4dOvD+++9z7tw5AOLi4tiyZQsALVu2JDw8nEOHDpGUlMSMGTNu+XOkpaWRnJzs+JeamkpiYiLu7u4ULlyYpKQkpk6detP3RUVFsXv3blJSUvjoo4944oknKFmyJE2bNuXIkSNERESQmppKamoqv/32GzExMX/rOouIiGRHxamIiMh/PP7447z77ruMHTuWOnXq0KJFC8LCwgBwd3dn+vTphIeHU6dOHdatW8ezzz6b7XlcXV0JDQ3l6NGjNGvWjCZNmrB+/XoA6tWrx8MPP0yjRo2oW7cuAEOHDuWhhx6iY8eOPPnkk7z00kvExsYC8PTTT9O9e3e6d+/Os88+S7169W75c4wZM4bq1as7/o0YMYKAgABKlSpF48aNadOmDTVq1Ljp+9q2bcvHH39M3bp12bdvH5MmTQLAy8uLTz75hHXr1tG4cWMaNWrE5MmTHQW2iIjI3WCzX9/PIyIiIiIiImIAjZyKiIiIiIiI4VScioiIiIiIiOFUnIqIiIiIiIjhVJyKiIiIiIiI4VScioiIiIiIiOFUnIqIiIiIiIjhVJyKiIiIiIiI4VScioiIiIiIiOFUnIqIiIiIiIjh/h/Aqi7mb3nM/wAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_holdout, y_holdout_pred = evaluate_model_cm(log_reg_model, holdout_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "evaluate_model_score(log_reg_model, holdout_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Save trained model predicitions" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "predictions = []\n", - "\n", - "predictions.append(y_train)\n", - "predictions.append(y_train_pred)\n", - "\n", - "predictions.append(y_test)\n", - "predictions.append(y_test_pred)\n", - "\n", - "predictions.append(y_holdout)\n", - "predictions.append(y_holdout_pred)\n", - "\n", - "predictions = pd.DataFrame(predictions)\n", - "predictions.index = [\"y_train\", \"y_train_pred\", \"y_test\", \"y_test_pred\", \"y_holdout\", \"y_holdout_pred\"]\n", - "predictions.to_csv(f\"{results_dir}/2.model_predictions.tsv\", sep=\"\\t\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate shuffled baseline model" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "shuffled_baseline_log_reg_model_path = pathlib.Path(f\"{results_dir}/1.shuffled_baseline_log_reg_model.joblib\")\n", - "shuffled_baseline_log_reg_model = load(shuffled_baseline_log_reg_model_path) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate with training data" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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nql27tmbMmOHeLzg4WIcOHUoymvm3hx56SJ06dVKHDh3UtGlTPfLII8m28ff31xdffKF169bpgQceUKtWrZJcbfdm9913n77++mtt27ZNzZs3V926dfXqq6+qSZMmybYtX768evfurccee0wNGjTQoUOHdN9997nX//777+rSpYtq1qypfv36adSoUSpZsqSioqL0yiuvqG7dunrwwQeVN29e9e7dO932S096v5sPPvhAe/bsUb169fTJJ5+oQ4cOqR7r3XfflZeXl9q0aaMGDRro66+/TnG71H5nf/vwww919uxZNWrUSAMHDtSgQYP0wAMPpPtc4uLi9MEHH6hevXpq2LChLl++nOKHBwAA87O5XC6X0SEAAMhuS5Ys0bfffqtvvvkmyfKKFStq1apVKlWqlEHJAAD/JdtPljE6QrrqlTpudIRkGHEFANzxrl+/rrlz56pr165GRwEAAJlA4QoAuKP9/PPPql+/vgoUKKB27doZHQcAAGQCU4UBAAAAwEO2nCxrdIR0NSh1zOgIyTDiCgAAAAAwNQpXAAAAAICpeRkdAHe+FvYuRke4PTbrfZ5j9/M1OsJtcV6/bnSE/war9WVX+l+FYzpWa2PJmu2M7Fe/utEJbs+2341OcPv428t2q53zjY6QIYkuC753mACtBgAAAAAwNQpXAAAAAICpMVUYAAAAADzEydhhptBqAAAAAABTo3AFAAAAAJgahSsAAAAAwNQ4xxUAAAAAPCRRNqMjWBIjrgAAAAAAU6NwBQAAAACYGlOFAQAAAMBDEl2MHWYGrQYAAAAAMDUKVwAAAACAqTFVGAAAAAA8xMlVhTOFEVcAAAAAgKlRuAIAAAAATI2pwgAAAADgIYmMHWYKreZB165d07333qtx48Zl6+MsWrRIx48f/1fH6NOnj06dOpVFibJW7nz+Gr1wmJZGzNLs41P0YLeGRkdK1/CZAzXvzFQtufKlvvxjgto83czoSEm0f7a5Jv08Rssuz9CQz/okWde6VxN9ufc9LQmbpnFLhip/0bzGhEyH1fqF1fJK5u/HKbFaO9PGnmG1zGbMG9y5tj6Z0Vsr1r2sYaOC3MsrVy2udz7qroUrh2j+8hf06pudlL+Av3t9j96NtXLDCC1d/ZL7VrRYXgOeQXJW+/szY79IjxUzwzwYcfWgZcuWqUaNGlqxYoWGDRsmHx+fbHmcxYsXK1++fCpTpkymj/H5559nYaKsNWjy00qIS9CjRfuoXI3SGrd8hI7tOaGTB84YHS1V895Zog//N1XxcQkqWbGY3l87Wkd2H9fhX//dBwxZ5VLoVc0dH6Laze+VT45/+uW9DSvqqTFd9FKbt3X2yF/q994TGvFVfw1r/ZaBaVNmtX5htbyS+ftxSqzWzrSxZ1gtsxnzXroYoTlfbVLteuXk6/vPv5P+uf30fchu7dq+QImJTg18sbWGjgzSyCHfuLdZ/9MBjX8jxIjYabLa358Z+0V6rJgZ5sGIqwctXLhQ/fv3V4UKFbR27VpJ0qRJk/Tcc8+pT58+atu2rQYNGqSIiAhJUlRUlEaMGKF27dqpXbt2mjZtmvtYPXr00Lhx49SjRw+1aNFCH374ofsx9u3bp7Fjxyo4OFhbtmxRYmKixo8f7z7O+PHjlZiYKEn69ttv1aZNGwUHBysoKEhHjx6VJDVr1kyHDh2SJE2ePFmtW7dWcHCwOnTooPDwcI+12a38cvqqYef79dVr8xQTFaP9mw9q69Jdat6jiWGZMuLkgTOKj0uQJLlcLrlcLgWWK2Jwqn9sXrpLW5f/qvDLkUmW3/9wTW1ctEMn/zirhPhEzXknRNUaVVJgmcIGJU2Z1fqF1fL+zez9+FZWbGfaOPtZLbNZ827a8Ke2/HxI4deuJ1m+c9tRbVz3h6Kj4xQbm6CQhbtUtVoJg1LeHiv9/Zm1X6TFipmzS6LLbvqbGTHi6iEHDx7UtWvXdP/99+vChQtauHChWrduLUn65ZdftGTJEhUsWFAjRozQlClTNHz4cE2ZMkVOp1PLli1TVFSUunbtqooVK6pJkxt/4EePHtWXX36p2NhYPfbYY6pZs6Y6d+6sJUuWqHfv3nrwwQclSXPnztUff/yhRYsWSboxDfjbb79V9+7d9e6772r58uUKDAxUXFycu6D927Vr1zRjxgxt3bpVfn5+ioyMlJ+fnwdbLqniFQLlTHTq7OFQ97Kje0+oWuMqhmXKqEGTn1bLXk3kl9NXh389rh3f7zY6UrpsNptsNluS+5JUukoJhR4/b1SsZKzWL6yW92ZW6sdWbWfaOHtZLbPV8t7q3hp36eTxC0mW1X/gbi1cOUSXL0UqZMFOLV/yq0HpkrPK358V+4UVM8NczFlO34EWLFig4OBg2Ww2tWzZUnv27FFYWJgkqWnTpipYsKAk6ZFHHtG2bdskSVu3blWXLl1ks9nk7++vtm3bauvWre5jdujQQV5eXsqVK5cefvhh93632rp1qzp27CgfHx/5+PioU6dO7uPcf//9GjFihGbNmqWwsDDlyJEjyb7+/v4qU6aMhg0bpu+++07R0dHy8jLu844c/n6KuhadZFnUtWjlzJ0jlT3MY9LAGQrO00vPN35NmxbvUHxsgtGR0rXjxz1q3KmuytxTUj5+3np8RLCcTqd8c2bPNPfMslq/sFrem1mpH1u1nWnj7GW1zFbLe7My5QrriacaadonP7mXbVh7QE8/PlVd2n6oCe+s0BNPNdKDzasamDIpq/z9WbFfWDEzzIXC1QPi4uK0bNkyLVy4UM2aNdPDDz+s+Ph4LV68ONm2LpfLPap1889/u/V+SvtlZN3f9ydPnqwXX3xR169fV8+ePbVhw4Yk2zkcDn333Xfq0aOH/vrrL3Xq1EkHDx7M2BPPBtcjY5QzIOkLXK6AHIqOuJ7KHubidLq0f/OfKlQiv4L6tjA6Trp+W39As8Yt0qtzBmnWwQkKO3VR1yNidPHsZaOjJWG1fmG1vLeySj+2cjvTxtnHapmtlvdvxYrn01sfPKZPP1qlfXtOu5efOnFRly5Gyul06cC+M1o8f6caPVjJwKTJWeHvz4r9woqZs4tTdtPfzMicqe4wa9asUdmyZbVx40atXbtWa9eu1RdffOGeurt+/XpdvnyjEFi8eLHq1asnSWrQoIEWLFggl8ulyMhIff/996pfv777uCEhIUpISFB0dLR++OEH9365cuVynyf793EWL16s+Ph4xcfHa8mSJapfv74SEhJ0+vRpVatWTc8884weeOAB/fHHH0myR0ZG6vLly6pbt64GDx6sChUq6PDhw9naXmk5eyhUDi+Hipcv6l5Wtlppy53U7/ByqJhJz5u51bJpP6l39ZfUtfRAbVqySw4vh06YrL2t1i+sljc1Zu/Hd0I708ZZz2qZrZZXkgoXyaPxHz+uOV9t0poff0974zQ+eDeamf/+rNgvrJgZ5kLh6gGLFi1SUFBQkmU1a9aU0+nUzp07Vb9+fY0cOVJt27bVtWvX1L9/f0lS//795XK5FBQUpMcee0zt27dX48aN3ceoWrWqnnrqKXXo0EFNmzZ1n9PatWtXTZkyRR06dNCWLVvc58Z27NhRHTt2VMWKFfXoo4/K6XTq5ZdfVlBQkNq3b68LFy6oa9euSXJGRkZqwIABCgoKUrt27VSwYEG1bNkym1ssdTHRsdq0aLt6jekqv5y+qtqgohoE19GaWRvS39kgeQsFqGnXBvLL5Su73abaLaur6WMNtHvdfqOjudkddnn7esvusCf52dvXW6WqFJckFSpRQM9NekpLpvyoyKvR6RzRs6zWL6yWV7JGP76V1dqZNvYMq2U2a167wyZvH4fsDpvsdrv75wIFc+u9SU9o6aJdKZ67Wr9hBfnnvnGtjIqVi6lDlzra8vMhT8dPxmp/f2btF2mxYmaYi83lcrmMDvFfNmnSJEVHR2v48OG3tV+PHj2SXIDJzFrYu2Tp8XLn89eQGf10X4tqirgUqekj5mjdN5uy7gFsWft5Tp6CufXady+qbPVSstltOn/yohZPXqmV09dm2WPY/Xz/1f5PjOyoHqM6Jlk2a9xiLf7kR72/aqSKlSmi6MjrWjXrZ309ZoGczn/3suG8nvXTgrK9X2Qxj+TNwr7siX4slzPrjvX/rPR64ZE2lrK8na32tydZL7NH8tavflub9+jdWD2fbpxk2cwZG+VyudTrf010PTouybr2Ld6VJI18vaNq1S0jb28vXbgQrmWLftGSBTtvP++2dEZyb5MVX+Os1o+l7M+82jk/y46VnUKO1TA6QrqCy/5mdIRkKFwNRuFqQllcuHrCvy1cPS07ClekwGp9ORsK12xntTaWrNnOyH63WbgaLosLV4/gby/bWaVwXXS0ptER0tWpnPmuqM3X4Rhs0KBBmdpv1qxZWZwEAAAAAMzJgh8VAwAAAAD+SxhxBQAAAAAPSWTsMFNoNQAAAACAqVG4AgAAAABMjanCAAAAAOAhThdjh5lBqwEAAAAATI3CFQAAAABgakwVBgAAAAAP4arCmUOrAQAAAABMjcIVAAAAAGBqTBUGAAAAAA9JdNmMjmBJjLgCAAAAAEyNwhUAAAAAYGoUrgAAAAAAU+McVwAAAADwECdjh5lCqwEAAAAATI0RV+BWLqfRCW6bPWdOoyPcFuf160ZHALKGBV8vgJQ4fRxGR7gtXt7W+xfWGRdndATA0qz3Vw8AAAAAFpXoYtJrZtBqAAAAAABTo3AFAAAAAJgaU4UBAAAAwEOcshkdwZIYcQUAAAAAmBqFKwAAAADA1JgqDAAAAAAewlWFM4dWAwAAAACYGoUrAAAAAMDUmCoMAAAAAB6SyNhhptBqAAAAAABTo3AFAAAAAJgahSsAAAAAwNQ4xxUAAAAAPMTpshkdwZIYcQUAAAAAmBqFKwAAAADA1JgqDAAAAAAewtfhZA6tZkLXrl3Tvffeq3HjxqW53VdffaVLly5l6JhXrlzRY489puDgYE2fPj1TuRYtWqTjx49nat+slDufv0YvHKalEbM0+/gUPditodGR0uTt46UXp/fT7ONTFHJtpj795V3VaV3D6FhJDJvylObsG6+Fxybo821j1OqJB9zrGgXX0mebR2vh8Y/02abRqt+muoFJU2e1fmG1vJI0fOZAzTszVUuufKkv/5igNk83MzpSuqzUzlZ4rUiJldr4b1bLbMa8wR1qacrUJ7Xyx2F6aXhb9/JSpQpoytQntWTp81qy9Hm9+/5jKlWqgHt9rly+Gv5yOy1YNFgLFg1Wz17GPJf2fVto0uY3tezaVxry+bPu5ZXqltfbK17WgnOf6dvTn2rUnMHKXzSvIRnTY8Z+kR4rZoZ5MOJqQsuWLVONGjW0YsUKDRs2TD4+PknWO51O2Ww2zZw5Uw0aNFCBAgVSOdI/tm7dqoCAAM2bNy/TuRYvXqx8+fKpTJkymT5GVhg0+WklxCXo0aJ9VK5GaY1bPkLH9pzQyQNnDM2VGoeXQxdOX9SQpqN1/tRF1X24pl759kU9U22Iwk5eMDqeJOnbj3/QR8/PUnxcgkqUL6LxIS/q6N7TunI+XMOmPKU3en6qXT/tV50W92jk9Gf0ZK1RunYxwujYSVitX1gtryTNe2eJPvzfVMXHJahkxWJ6f+1oHdl9XId/Nf4DrdRYqZ2t8FqREiu18d+sltmMeS9ditCcWZtVu05Z+fr+8+/kxYuRGjN6scLCrslutym4Qy298moH9fnfDElS/wHN5evrpce7TVHevLn0/gfdFBZ2TT/+8Ltn84de0dx3lqh2i2ryyfHP/1n+eXPp+xlr9cvq35WYkKgBH/XSkGnPaFT7dz2aLyPM2C/SY8XMMA9GXE1o4cKF6t+/vypUqKC1a9dKkiZNmqShQ4eqf//+Cg4O1tSpU3X+/HkNHjxYwcHBOnLkiOLi4jR+/Hg98sgjCg4O1rBhwxQVFaVt27bp3Xff1a+//qrg4GDt2rVLy5YtU5cuXdShQwd16NBBW7dudT/+0aNH1bt3bwUFBSkoKEiLFy/WwoULtW/fPo0dO1bBwcHasmWLIW3jl9NXDTvfr69em6eYqBjt33xQW5fuUvMeTQzJkxEx0bGaNWa+wk5ekMvl0vYVv+qv4+d1d62yRkdzO/VnqOLjEiRJLpcklxRYppAKFsurqGvR2vXTfknSztX7FBsdq8DSBQ1Mm5zV+oXV8v7t5IEzN/UTl1wulwLLFTE4Veqs1s5WeK24ldXaWLJeZrPm3fTzIW3efFjh4deTLI+KilVY2DX3fafTqWLF87nv129QXt/O267Y2ASFhV3TypV71MaAmTybQ3Zp67JfFH45MsnyXav26OdFOxQdcV2x1+O09NPVqlK/gsfzpces/SItVsycXZwuu+lvZsSIq8kcPHhQ165d0/33368LFy5o4cKFat26tSRp165dWrRokfLnzy9Jmj9/viZOnKgKFW68oE6ZMkW5c+fWggULJEnvvfeepk2bphdeeEGDBw/W+vXrNXHiREk3pg63a9dONptNx44d05NPPqmNGzcqISFB/fv31/PPP682bdq4t82XL5+WLFmi3r1768EHH/R0s7gVrxAoZ6JTZw+Hupcd3XtC1RpXMSzT7cpbOI9KVAjUyf2njY6SxIDx3dT8sfryy+mjI3tPaeeafYq7HqfTh/9SvVbVtHP176rXqpri4xJ0/MBZo+MmYbV+YbW8Nxs0+Wm17NVEfjl9dfjX49rx/W6jI6XKyu0smfe14mZWbGOrZbZa3r+FLHtBOXL4yGaz6asvNyZdaUt6p3QZc30YerN7G1bSSZO950nW7BdWzAxzoXA1mQULFig4OFg2m00tW7bU2LFjFRYWJklq3Lixu2hNydq1axUZGakff/xRkhQXF6dKlSqluO3p06c1ZMgQhYWFycvLSxcvXtSFCxd09epVJSQkuItWScqXL1+KxzBCDn8/RV2LTrIs6lq0cubOYVCi2+PwcmjE7MFaNXODTv95zug4SXwy/Bt9OmKeKtcpq3sfqKD42Hg5nS6t+W6bhn/WWz6+3oqPS9Rb/5um2Og4o+MmYbV+YbW8N5s0cIY+GfyFKtevoOpNqio+NsHoSKmycjub+bXiZlZsY6tltlrevwUHTZCfn7datro3yQjszh3H1K1bfY1/Z7ny5culNm2qydfX28CkqStzT0k9PrKjXu/yodFRkrFiv7BiZpgLhauJxMXFadmyZfL19VVISIgkKT4+XosXL5Yk5cqVK839XS6XRo8erfr166f7WC+++KJefvllNW/eXE6nU9WrV1dsbKxcLte/fyLZ6HpkjHIGJH2ByxWQQ9ER11PZwzxsNpuGzxykhLgETR44w+g4KXI6Xdq//agefKSe2j7VRKf+DNXTr3XS8OAPdWTvad1d/S6Nnt1frz42Scf2med8FKv1C6vlvZXT6dL+zX+q+eONFNS3hZZM/sHoSCmyajtb4bXib1ZsY6tltlrem8XExGvZ0l+1aPHzeurJabp6NVqTJ63WwMEtNXP2swoPv661aw+oWTPzjbgVK1tEY0Ne0qdDZ2nf5j+NjpOMFfuFFTNnl8Sk0w6QQeacwPwftWbNGpUtW1YbN27U2rVrtXbtWn3xxRdatGhRitvnypVLERH/XCCnWbNm+uqrrxQTEyNJioyM1NGjR1PcNyIiQiVKlJB0Y5Q3Lu7GCFrZsmXl5eWllStXure9cuVKio9nhLOHQuXwcqh4+aLuZWWrlbbESf1DpvdTviJ5NOaRD5SYkGh0nDQ5vOwKLF1I5e4pqX1bj+jwnlNyuVw69NtJHfzluGo2rmx0xCSs1i+sljc1Di+Hipn4HFertrOVXius2MZWy2y1vLey2Wzy9fNSwYK5JUkRETF6e9xSdek8SU8/NV12u00HD4amcxTPKnxXQb29coTmvr1EP83dZHScFFmxX1gxM8yFwtVEFi1apKCgoCTLatasKafTqZ07dybbvmfPnho5cqT74kzPPPOMKlWqpEceeURBQUHq3r17qoXriBEj1L9/f3Xr1k1nz55V3rx5JUleXl6aMmWK5s2bp6CgILVv314bNmyQJHXt2lVTpkxRhw4dDLs4U0x0rDYt2q5eY7rKL6evqjaoqAbBdbRm1gZD8mTUc5/20V2Vi+vV9uMVF2OuabZ5CuZWkw615ZfLV3a7Tfc9WEVNO9bRnp//1KHfTqjq/eVV9p4bH3KUu7ek7rm/vI6b7E3Gav3CanklKW+hADXt2sDdT2q3rK6mjzXQ7nX7jY6WKiu2s5lfK1JixTa2Wmaz5rXbbfL2dshut8lut7t/rlWrtMqXLyK73aacOX3Ub0BzRUTE6OTJi5KkwGJ5FRCQQ3a7TXXrllXbdjU0e/Zmz+d32OXt6y27w57k5wLF8mn8DyO1bOpqrZj+k8dzZZRZ+0VarJgZGRMbG6vRo0erZcuWCgoK0quvvipJOn78uLp27apWrVqpa9euOnHihHuftNalxuYy+9xQWF4Le5csPV7ufP4aMqOf7mtRTRGXIjV9xByt+8acn4hKNz65nXPiU8XFxCkxwele/lHfz7Q2iz7J9crAVyKlJk8Bf4364hmVqVpCdrtNYacva+nn6/TD7BvZgp5uqg7PNlPeQgG6djFCy7/YoEWfrvlXeRMy+P3Dt8Nq/cIjeW1Z99lknoK59dp3L6ps9VKy2W06f/KiFk9eqZXT12bZY8jlTH+b22SlfuGJ14rsYKU2/pvVMnsir7PJfbe1fc9eDdXryUZJln391c86ceKinurdWIUK5VZsbIL+PBiq6Z+v07FjN77SqUnTSuo/oLn8/f105sxlfT5tnXbtvP2v1PLauu+297nZE690Uo9XOidZNmvsQrlcUs9XO+t6ZEySdR0KPv2vHk+SnHFZ+2GU1fqxlP2ZVzvnZ9mxstO7B9qkv5HBXqqyMv2N/t/YsWNlt9s1YsQI2Ww2Xbx4UQULFlTPnj3VuXNnBQcHKyQkRAsXLtTMmTMlKc11qaFwRbbL6sIVyf2bwtUI2VG4IgVZWLh6RDYUrgAy5nYLV6P928LVCFlduCI5Ctesk9HCNSoqSk2aNNGGDRuSXI/n0qVLatWqlbZv3y6Hw6HExETVq1dPq1atksvlSnVdWhei5eJMAAAAAAC38PBwhYeHJ1seEBCggIAA9/3Tp08rb968mjx5srZv365cuXLpueeek5+fn4oUKSKHwyFJcjgcKly4sEJDQ+VyuVJdR+EKAAAAACZghasKf/3115o8eXKy5QMHDtSgQYPc9xMSEnT69GlVqVJFw4cP1549e9S3b199/PHHWZ6JwhUAAAAA4NarVy917Ngx2fKbR1slqVixYvLy8lK7du0kSdWrV1e+fPnk5+ensLAwJSYmuqcDnz9/XoGBgXK5XKmuS4vFToACAAAAAGSngIAAlShRItnt1sI1f/78qlevnjZvvnF18OPHj+vSpUsqXbq0KleurOXLl0uSli9frsqVKyt//vwqUKBAquvSwsWZkO24OFP24+JMSBEXZwKQQVycKftxcabsZ5WLM7194GGjI6RrRJXvM7zt6dOnNXLkSF29elVeXl56/vnn1aRJEx09elQvv/yywsPDFRAQoPHjx6ts2bKSlOa61DBVGAAAAAA8xOmy2AfL6ShZsqRmzZqVbHm5cuU0f37KHyaktS41d1arAQAAAADuOBSuAAAAAABTY6owAAAAAHhI4h02VdhTaDUAAAAAgKlRuAIAAAAATI2pwgAAAADgIU7ZjI5gSYy4AgAAAABMjcIVAAAAAGBqTBUGAAAAAA/hqsKZQ6sBAAAAAEyNwhUAAAAAYGpMFQZuYXM4jI5w21yJiUZHgAk5/HMZHeG2JEZEGB3httl9fIyOcNuccXFGR4AJHe9orb5891ajEwCZ53RxVeHMYMQVAAAAAGBqFK4AAAAAAFOjcAUAAAAAmBrnuAIAAACAhyQydpgptBoAAAAAwNQoXAEAAAAApsZUYQAAAADwEL4OJ3MYcQUAAAAAmBqFKwAAAADA1JgqDAAAAAAe4mTsMFNoNQAAAACAqVG4AgAAAABMjanCAAAAAOAhiVxVOFMYcQUAAAAAmBqFKwAAAADA1JgqDAAAAAAe4mSqcKZQuMJycufz14vT+6lWy2oKvxihGSPnat03m4yOlaYipQpp0KTeqnz/3YqPTdDPi7br0xe/ljPRaXQ0SdJLU59WjcaV5JvLV1fCwrVg0g/6YdYmVapdVj1HBuvu6qWUmOjU75v/1Kcvz9PlsGtGR07Gav3C7Hm9fbw08MMeqtGksnLn89e542H6aswi7Vrzux7scr8Gf9TTva3NbpNfTl8NbDJGR347aWDq5Mzezu37tlCLHo1V+p6SWv/dVn3Q5zNJ0l2VimvYjL4KLFtEknRk93FNeXGmTh08a2TcFJm9jVNitcxmzPthyzZqUPIu5fD21sWoKH326y59t/93SZKfl5dGNmyih++uKG+7XX9cvKDHFn4rSfJxOPRa4wfVslx5ednt+iX0nF5Zu0ZhUZEezZ/a316luuXVa/QjurtmGSUmOrV34x/6dMhMXf7rqkfzZYQZ+0V6rJgZ5kHh6mHNmjWTj4+PfH19FRsbq9q1a2v06NFasGCBYmNj9eSTT2b5Yy5atEjr16/XxIkTs/zYRhg0+WklxCXo0aJ9VK5GaY1bPkLH9pzQyQNnjI6WqkGTeuvqhXA9VrKf/PPm1Ds/jFL7fi21ZPIPRkeTJH370UpNGPy14uMSVOLuonp36VAd2XtK/nlzauXXGzV27X4lJjjV/91uenHyk3qly8dGR07Gav3C7HntXnZdOHNZL7Udr/OnL6tOy2oa+VU/9WvwqtbN36Z187e5t23R/QF1eynIdEWrZP52vhR6RXPfWaLaLarJJ4dPkuVju3+ssJMXZbfbFNS3pUbMGqh+dUYYmDZlZm/jlFgtsxnzfrprh17+aZXiEhNVNl9+fdPpUR04H6Z9F87rrWYt5LDb1XL2l7oaE6MqBQu593uy+n2qWTRQD8+ZqfC4WL39UEu93qSZ+n2/1KP5U/vb88+bS9/PWKtfVv+uxIREDfiol4ZMe0aj2r/r0XwZYcZ+kR4rZoZ5cI6rASZOnKiQkBCtWLFCR44c0erVq9WtW7dsKVqzQ0JCgmGP7ZfTVw0736+vXpunmKgY7d98UFuX7lLzHk0My5QRRUsX1ob5WxUfG68rYde068c9KlWlhNGx3E4ePKf4uP//vbpcksulwDKFtWvNPv0c8ouiI2IUez1Oyz5fpyp1yxsbNgVW6xdWyBsbHafZ74Qo7NQluVwu7fhxj8JOXlD5GqWTbdu8WwP99M0Wz4dMhxXaeXPILm1d9ovCLycdbYq6Fq2wkxdv3LHZ5Ex0qli5IgYkTJsV2vhWVsts1ryHL19SXGKiJMnlcskll+7Km1dl8ubTQ2XLadTa1bp8/bqcLpf2XTjv3q9kQIA2njqpi9ejFZeYqOWHDuruAgU8nj+1v71dq/bo50U7FB1xXbHX47T009WqUr+Cx/Olx6z9Ii1WzJxdnC676W9mxIirgWJjYxUbG6uAgABNmjRJ0dHRGj58uBYtWqTly5crICBAhw8fVu7cuTVp0iQVKlQoyXaSktyPi4vThAkT9PPPP8tut6tkyZL65JNPkj3u4sWLNXfuXCUmJsrf31+vv/66ypYtqz///FNjxozR9evXFRsbq0cffdRdTL/88svKlSuXTpw4oStXrmjRokWebCq34hUC5Ux06uzhUPeyo3tPqFrjKobkyajFk1eqadcG2rvhgPzz5VKdVjX09evfGR0riQHvdVeLbg3kl9NXR/ac1M7Vvyfb5p4Gd+vkn+cMSJc2q/ULq+WVpLyFAlS8fFGd/CPpVNXCJQvongcq6sOBXxqULHVWbOdbLfxrmnL4+8lmt2nmGwuNjpOMFdvYapnNnPeNpg+pc+WqyuHtrX3nw7T+xHG1Kne3zoWH6/l6DdShUhVdiIrUx9u36oejhyVJ3x3Yp1cbP6jCuXIpPDZWwRUra8OJ4wY/k9Td27CSTh4w3xR9M/eL1FgxM8yFwtUAgwcPlq+vr06dOqWGDRuqYcOG2r17d5Jtfv/9dy1dulSBgYF65ZVXNHv2bL3wwgtpHnfatGk6ffq0Fi1aJB8fH12+fDnZNrt27dLKlSs1Z84c+fj4aMOGDRo5cqTmzZun4sWL66uvvpKPj4+ioqLUpUsXNWrUSOXKlZMk7d69W7Nnz1bOnDmzrjFuUw5/P0Vdi06yLOpatHLmzmFQoozZu/GAHn66mZZc/lIOL4dWzdygzSE7jY6VxCfD5urT4d+ocp1yqtawouJjk46sl6lSXI8PC9Lrjyf/MMRoVusXVsvr8HJo+PRntOabzTpz+K8k65o/1kD7txz6Z3TQRKzWzinpXPQZ+eb0VYsnGun8Kdo4K1gts5nzvrb+J72+Ya3uK1pM9UqUUFxiogL9/VWxYCH9cPSw6s+YqpqBxTQjqKMOX76ko1cu6/jVKwqNiNC2p/sqwenUn5cuavT6tUY/lRSVuaekHh/ZUa93+dDoKMmYuV+kxoqZYS7mHAe+w/09VXjbtm2KjY3VV199lWyb++67T4GBgZKk6tWr69SpU+ked926derVq5d8fG6cq5E/f/5k26xdu1YHDx5Uly5dFBwcrA8++EB//XXjH9GYmBiNHDlSQUFB6tatm86fP6+DBw+6923durWhRaskXY+MUc6ApC9wuQJyKDriukGJ0mez2fT2ipHatHiH2ufppc5F/if/vLn0v7e7Gx0tGafTpf3bj6hgsXxq1/ufqTuBZQrpze+e09QR87R/22EDE6bMav3CSnltNpuGTfuf4uMS9MnQOcnWP9StgVabcJqwZK12TktsdKxWfP6Ths3oqzyFAoyOk4QV29hqmc2e1+lyaVfoWQX659bj91ZXTEKC4hITNXnHNsU7ndpx9oy2nT2tRneVkiS92bS5fLwcqvnZJ7rn04n68chhfRncyeBnkVyxskU0NuQlfTp0lvZt/tPoOMmYvV+kxIqZYS4Urgby9fVV06ZNtWVL8n/6fH193T87HA4l/v95JA6HQ07nP1eijY2Ndf/scrnSfUyXy6XOnTsrJCREISEhWrp0qdavXy9J+vDDD1WoUCEtXrxYS5cuVbVq1ZIc3+iiVZLOHgqVw8uh4uWLupeVrVba1Cf1587vr8J3FVTIlB8VH5egiMuRWvX1etVtXdPoaKlyeNkVWObGxTQKl8ivdxa/qLnvr9BP321LZ09jWK1fWCnvC5OfUr5CARrb4xMlJiQmWVelXnkVKJpXm0J2GZQubVZq5/TY7Db55vRVwWL5jI6ShBXb2GqZrZLXYbfrrjx5dfBi2jMDKhUqpIV/7Ne12BjFJSbq6z27VaNooPL5mWfUrfBdBfX2yhGa+/YS/TTXnFe8tUq/uJkVM2eXRNlMfzMjClcDOZ1O7dy5U6VLl87wPnfddZf2798vp9OpyMhId9Ep3bhi8ddff624uDhJSnGqcLNmzRQSEuIeZU1MTNS+ffskSRERESpatKi8vLx06NAh7dplvn9GY6JjtWnRdvUa01V+OX1VtUFFNQiuozWzNhgdLVXhlyIUeixMQc+2kN1hV648OdWiR2Md+90cV2DNUzC3mnSqI79cvrLbbarVrKqadqqr3zYeVIHAvHonZIiWzVin778ybxtbrV9YJe+gCT1UsmKgRj82UXEx8cnWN+/+gDYt/UXXI2MMSJc+K7Sz3WGXt6+37A57kp/ve+gelateSna7TTlz59Cz7z6hyCtROnXQXOeYW6GNb2W1zGbMWyBHDrW7u6JyenvLbrOp0V2lFFShkraeOaUd587oXES4+tWuJ4fNplqBxVSveEltPHXjPe/3sL/UqVIV5fbxkZfdrh7VquuvyAhdifHsqFtqf3sFiuXT+B9GatnU1Vox/SePZrodZuwX6bFiZpgL57ga4O9zXOPj43X33XdrwIABmjlzZob2bdmypVauXKm2bduqVKlSqlq1qnvdM888ow8++EAdOnSQt7e3SpUqlewrcOrUqaPnn39e/fr1U2JiouLj49W6dWvdc8896tevn1566SUtXbpUd911l+rUqZOlzzurTBowXUNm9NN3YdMVcSlSH/f/3PSf1o159EP1+6CXHh3WXs5Ep/asP6CpQzL2O892LpfaPtVEgz54Qja7TedPX9LUUd9q28o9evylIBUrU1iPDwvS48OC3Lt0vGuQgYFTZrV+Yfa8hUsWUNveDyouJl7fHJrgXj7x+ZlaN3+bvH291LhDHY3tab5znm9m9nbuPqKDerzS2X2/efeGmjV2oU4eOKP+H/ZSweL5FXs9Tod+OaZR7ccrPjb5BwhGM3sbp8Rqmc2W1yXp8WrVNbZZc9lsNp0LD9ebG9dpzbGjkqRnl4fo7Ydaqm/tujobEa6hq1bq2JUbH6a/tWmDRjdupp96Pi0fh12HLl1S3xWe/SocKfW/PZfrxjThJ0Z10hOj/pnC3KHg0x7PmB6z9YuMsGJmmIfNlZH5pcC/0MLexegIt8XmcBgd4bbZc+c2OsJtSbx61egI/wkOq/WLiAijI9w2u49P+huZjPP/Z+UANzv60f1GR7gtd7/0q9ERbht/e9lvtXO+0REypO8vPYyOkK6ptWYZHSEZpgoDAAAAAEyNwhUAAAAAYGqc4woAAAAAHuJ0MXaYGbQaAAAAAMDUKFwBAAAAAKbGVGEAAAAA8BCnbEZHsCRGXAEAAAAApkbhCgAAAAAwNaYKAwAAAICHJLqYKpwZjLgCAAAAAEyNwhUAAAAAYGoUrgAAAAAAU+McVwAAAADwEKeLscPMoNUAAAAAAKZG4QoAAAAAMDWmCgMAAACAhzj5OpxMYcQVAAAAAGBqFK4AAAAAAFNjqjBwC1diotERblvitXCjI8CEEiOjjI5wx3PGxRkdAcgS5Yf+YnSE2+IyOgDwLzjFVOHMYMQVAAAAAGBqFK4AAAAAAFNjqjAAAAAAeAhXFc4cRlwBAAAAAKZG4QoAAAAAMDWmCgMAAACAhzhdjB1mBq0GAAAAADA1ClcAAAAAgKlRuAIAAAAATI1zXAEAAADAQ/g6nMxhxBUAAAAAYGoUrgAAAAAAU2OqMAAAAAB4iFNMFc4MRlwBAAAAAKZG4QoAAAAAMDWmCgMAAACAh3BV4cxhxDWbNWvWTK1bt1ZwcLD7dubMGVWsWFFRUVEez/PHH3/o+++/z9S+Z86cUb169bI40e3Lnc9foxcO09KIWZp9fIoe7NbQ6EhpCh7QWp/seEcrrs/VsC8GGB0nQ4bPHKh5Z6ZqyZUv9eUfE9Tm6WZGR0qX1fqF1fLerHj5oloRNUvDZw40Okq6rNbOVssrkdkTrJC3fb+Wmrx1rJZHfK2h059Nsq7Gg1U14/f3tfTql3p31SgVvqugQSnTVrJSMb374ygtvjBdXx74UA8E1zY6Upqs0C9uZcXMMA9GXD1g4sSJqlChgtExJN0oXNevX6+HH37Y6CiZNmjy00qIS9CjRfuoXI3SGrd8hI7tOaGTB84YHS1Fl85d1pxxC1W7VQ35+vkYHSdD5r2zRB/+b6ri4xJUsmIxvb92tI7sPq7Dvx43OlqqrNYvrJb3ZoMm99afO48aHSNDrNbOVssrkdkTrJD3UugVzX17iWq1qCbfHN7u5QEFcmv0dy/ow76fa9vyX/Xk6100as4gPddotIFpk7M77BqzcIhWTPtJL7d5S9UaV9Ybi4eqX92ROnv4L6PjpcgK/eJWVswM82DE1QT27t2rrl27KigoSF27dtXevXsl/TPCOWHCBHXo0EGtWrXSrl273PvNnj1bLVu2VOfOnTVx4kT3aGhCQoKefvppderUSW3bttWIESMUFxenK1euaOLEidqyZYuCg4M1duxYSdKePXvUo0cPderUSZ06ddL69evdjzFnzhy1aNFC3bt314IFCzzXKKnwy+mrhp3v11evzVNMVIz2bz6orUt3qXmPJkZHS9WmxTu0JWSnwi9FGB0lw04eOKP4uARJksvlksvlUmC5IganSp3V+oXV8t6sadcGirward1r9xkdJV1Wa2er5ZXI7AlWybt5yU5tWbpLEZeTvtc17FBHJw6c0c8Ltys+Nl6z3lyostVKqWTFYgYlTdldlYqpQGA+Lfz4ezmdLv22/oD2bzmk5o83MjpaiqzSL25mxczZxemymf5mRhSuHjB48GD3NOFOnTolWRcXF6fBgwfrueee07Jly/T8889r8ODBiouLkyRdvXpVNWrU0JIlSzRgwAC9//77kqSDBw/qs88+07x587Rw4UJFRPzzRuFwOPT+++9r0aJFWr58uRITE7Vw4ULly5dPgwcPVoMGDRQSEqJXXnlF4eHhGj16tD744AMtWrRIU6dO1Wuvvabw8HAdPHhQn376qb755hvNnTtXV69e9VibpaZ4hUA5E506ezjUvezo3hMqVaWEganuTIMmP61lETP15R8f6XLoVe34frfRkVJltX5htbx/y5k7h3q93kWfDZ1ldJQMsVo7Wy2vRGZPsFreW5WqUkLH9p5y34+JjlXosTCVqlLcwFQpsCX/R91ms6l0VXO2sxX7hRUzw1yYKuwBaU0VPn78uLy9vdWgQQNJUv369eXt7a3jx48rV65cypkzpx588EFJUo0aNTR+/HhJ0o4dO9SkSRPlz59fktS5c2ctXbpUkuR0OvXFF19o48aNcjqdunbtmvz8/FJ8/N27d+vMmTPq06ePe5nNZtPJkye1e/duNW3aVAUL3jgXpWvXrlq5cmUWtEjm5fD3U9S16CTLoq5FK2fuHAYlunNNGjhDnwz+QpXrV1D1JlUVH5tgdKRUWa1fWC3v355841H98MU6XThzyegoGWK1drZaXonMnmC1vLfK4e+rqxeTjsJGXYtWDn9z5T998Jyung9XlyHttOjjlarRtIrubVxZe9YfMDpaiqzYL6yYGeZC4Wowl8slWyqf8kmSj88/50Ta7XYlJCSkuZ8kLVu2TL/88ovmzJkjf39/TZ06VSdOnEj18StWrKg5c+YkW/frr7/e7tPJdtcjY5QzIOkLXK6AHIqOuG5Qojub0+nS/s1/qvnjjRTUt4WWTP7B6Egpslq/sFpeSSpXvZRqPnSv+tUabnSUDLNaO1str0RmT7Ba3ltdj4xVrlsKk5wBOXQ90lz5ExMS9XqXDzRgwpPqOjRIh345ro0Ltik+Nt7oaCmyYr+wYubsYtapuGbHVGGDlS1bVnFxcdq2bZskadu2bUpISFDp0qXT3K9evXrasGGDLl++LElavHixe11ERITy5csnf39/RUREaPny5e51fy/7W82aNXXy5En340s3zrl1uVzux7h06cboihnOcT17KFQOL4eKly/qXla2WmlO6s9mDi+Hipn4HFer9Qur5ZWkak2qqkjpQppzYoq+PfuZugwJUqNO9TRl5ztGR0uV1drZanklMnuC1fLe6uSBMypb7S73fb+cvgosW0QnD5w1MFXKjv9+WkObv6lHAp/VyHbvqGiZwqa9EJ0V+4UVM8NcKFw94OZzXIODg/X777+71/n4+GjixImaMGGCgoKCNGHCBH388cdJRlpTUqlSJf3vf//TY489pu7duyt37tzKnTu3JKlDhw6KiopS27Zt9dxzz6lWrVru/erXr6/r16+rffv2Gjt2rPLkyaMpU6bok08+Ufv27dWmTRtNnjxZLpdLlSpVUt++fdWtWzd1795dRYoYX7jERMdq06Lt6jWmq/xy+qpqg4pqEFxHa2ZtMDpaquwOu7x9vWV32JP8bFZ5CwWoadcG8svlK7vdptotq6vpYw20e91+o6Olymr9wmp5Jen7z9eo192D1fe+l9T3vpe0/LPV2v79rxrRZpzR0VJltXa2Wl6JzJ5glbypvddtDtmp0lVLqmHHOvL29dbjr3TU8d9P6fSf54yOnEyZe0vK29dbvjl89MgLbVWgaF6tmrnR6Fgpskq/uJkVM8NcbC6Xy2V0CGROZGSk/P39JUmTJk3SyZMn3RdvMpMW9i5Zerzc+fw1ZEY/3deimiIuRWr6iDla982mLH2MrNRjdBf1HP1okmUzx3ynWWPmZ92D2LKuEM5TMLde++5Fla1eSja7TedPXtTiySu1cvraLHsMuZxZd6z/Z7V+4ZG8WdgvbtXjtUdUrHxRje85OesOSr+wXF6JzJ7gibw2L+/0N0pDj1c7q8ernZMsm/XmQs16c6FqNrtHAz9+UoXvKqiDO47o/f9NVdjJi//q8bJDn7e7q3XvB+Xl7dC+TQf1yQtf69zRsCw7visha6cdW60fS9mfebUzC/+3ykZtNw42OkK6VjSeaHSEZChcLWzMmDH69ddfFR8fr5IlS+qNN94wxajorbK6cEUKsrFAyRbZUKAgBfQLABn0bwtXpC+rC1ckR+GadcxYuHJxJgsbPdpcX94NAAAAANnBYh/HAwAAAAD+axhxBQAAAAAPcYqvw8kMRlwBAAAAAKZG4QoAAAAAMDWmCgMAAACAhzhdTBXODEZcAQAAAACmRuEKAAAAADA1pgoDAAAAgIcwVThzGHEFAAAAAJgahSsAAAAAwNSYKgwAAAAAHsJU4cxhxBUAAAAAYGoUrgAAAAAAU2OqMAAAAAB4CFOFM4cRVwAAAACAqVG4AgAAAABMjanCAHCncjmNTgDAImze1vqX0BkTa3QEAB5mrVcpAAAAALAwF+e4ZgpThQEAAAAApsaIKwAAAAAgU5o1ayYfHx/5+vpKkoYOHapGjRrp+PHjevnll3X16lXlzZtX48ePV+nSpSUpzXWpoXAFAAAAAA9x6s6bKjxx4kRVqFAhybLRo0ere/fuCg4OVkhIiF577TXNnDkz3XWpYaowAAAAACDLXLp0SQcOHFC7du0kSe3atdOBAwd0+fLlNNelhRFXAAAAAIBbeHi4wsPDky0PCAhQQEBAsuVDhw6Vy+VSrVq19OKLLyo0NFRFihSRw+GQJDkcDhUuXFihoaFyuVyprsufP3+qmShcAQAAAMBDnBa4qvDXX3+tyZMnJ1s+cOBADRo0KMmyOXPmKDAwUHFxcRo3bpzeeOMNPfnkk1meicIVAAAAAODWq1cvdezYMdnylEZbAwMDJUk+Pj7q3r27+vXrpxEjRigsLEyJiYlyOBxKTEzU+fPnFRgYKJfLleq6tHCOKwAAAADALSAgQCVKlEh2u7VwjY6OVkREhCTJ5XLp+++/V+XKlVWgQAFVrlxZy5cvlyQtX75clStXVv78+dNclxZGXAEAAADAQ1wWmCqcUZcuXdKgQYOUmJgop9OpcuXKafTo0ZKk119/XS+//LKmTJmigIAAjR8/3r1fWutSY3O5XK5seyaApBb2LkZHuPPZLDZ5wuU0OgEA4Cb2HDmMjnBbnDGxRke4fbz3ZbvVzvlGR8iQB1YPNzpCuja3SL+Q9DSL/bcLAAAAAPivYaowAAAAAHiIFa4qbEaMuAIAAAAATI3CFQAAAABgakwVBgAAAAAPuZOuKuxJjLjCcnLn89fohcO0NGKWZh+foge7NTQ6UpqCB7TWJzve0YrrczXsiwFGx8mQ9396TSuiZmnpta+19NrX+uLABKMjpctq/cJqeSUye4LV8kpk9gQrvI+0f7a5Jv08Rssuz9CQz/okWde6VxN9ufc9LQmbpnFLhip/0bzGhEyH1d77rNaPJWtmhnkw4mohzZo1k4+Pj3x9fSVJ9erV08iRI1Pcdvv27Ro/frwWLVqUbN2ZM2fUuXNnbd++/bYeP61jetKgyU8rIS5Bjxbto3I1Smvc8hE6tueETh44Y2iu1Fw6d1lzxi1U7VY15OvnY3ScDJs8+EutnLHW6BgZZrV+YbW8Epk9wWp5JTJ7ghXeRy6FXtXc8SGq3fxe+eT4J+O9DSvqqTFd9FKbt3X2yF/q994TGvFVfw1r/ZaBaVNnpfc+q/VjyZqZYR6MuFrMxIkTFRISopCQkFSL1juZX05fNex8v756bZ5iomK0f/NBbV26S817NDE6Wqo2Ld6hLSE7FX4pwugodyyr9Qur5ZXI7AlWyyuR2VOs8D6yeekubV3+q8IvRyZZfv/DNbVx0Q6d/OOsEuITNeedEFVrVEmBZQoblPTOYMV+bMXMMBcKV4vbuHGjOnTooKCgIPXq1UsnT55Mcbs5c+aoRYsW6t69uxYsWJBk3YYNG/TYY4+pU6dO6tq1q3777Tf3ugkTJqhFixZ64okntH79+mx8JhlTvEKgnIlOnT0c6l52dO8JlapSwsBUd6be47ppQdjn+mjjG6rWpIrRcdJktX5htbwSmT3BanklMiN9NptNNpstyX1JKm3S9rbKe58V+7EVM2cXp8tm+psZMVXYYgYPHuyeKvzss8/qjTfe0OzZs1W+fHnNnz9fQ4cO1fz585Psc/DgQX366adasmSJChYsqNdff9297tSpU5oyZYpmzJghf39/HT58WH369NH69eu1du1arV27VkuWLJGfn58GDDD+vJoc/n6KuhadZFnUtWjlzJ3DoER3pukj5urkgTNKiEtQ08ca6M2Ql9T3vuEKPRZmdLQUWa1fWC2vRGZPsFpeicxI344f92jk1wO0YsZanT3ylx4fESyn0ynfnOab8myl9z4r9mMrZoa5MOJqMTdPFfbz81OlSpVUvnx5SVLnzp31xx9/KDIy6TSdHTt2qGnTpipYsKAkqWvXru51P//8s06dOqXHH39cwcHBGjp0qBISEnTx4kVt375dDz/8sHLlyiWHw6FHHnnEc080FdcjY5QzIOkLXK6AHIqOuG5QojvTwR1HdD0yRvFxCVo9c6P2b/lTddvUNDpWqqzWL6yWVyKzJ1gtr0RmpO+39Qc0a9wivTpnkGYdnKCwUxd1PSJGF89eNjpaMlZ677NiP7ZiZpgLhauFuVyuJNNv0touLY0aNXIXwyEhIdq0aZMKFiyY7n5GOHsoVA4vh4qXL+peVrZaaU7qz2Y3+prRKVJntX5htbwSmT3BanklMiNjlk37Sb2rv6SupQdq05Jdcng5dMIC7W3m9z4r9mMrZs4uLpf5b2ZE4WphNWvW1B9//KGjR49KkhYvXqwqVarI398/yXb16tXThg0bdOnSJUlKco7rAw88oJ9//lmHDx92L9u7d68kqX79+lq5cqWio6OVmJiohQsXZvdTSldMdKw2LdquXmO6yi+nr6o2qKgGwXW0ZtYGo6Olyu6wy9vXW3aHPcnPZpUrT07VblndnbNZ94a6t1Fl7Vq1x+hoqbJav7BaXonMnmC1vBKZPcUK7yOpZfT29VapKsUlSYVKFNBzk57Skik/KvJqdDpH9CyrvfdZsR9bMTPMhXNcLSx//vx699133dN78+fPr/feey/ZdpUqVVLfvn3VrVs3FSxYUE2bNnWvK126tN577z2NGjVKMTExio+P13333adq1arpwQcf1G+//aYOHTqocOHCqlevnsLCjD/PY9KA6Royo5++C5uuiEuR+rj/56b+tO7xVzqr5+hH3feb92ismWO+06wx89PYyzhe3g49+UZXlaxUTM5Ep04fPKfXO72vM4dC09/ZQFbrF1bLK5HZE6yWVyKzJ1jhfaT78GD1GNXRfb95twc0a9xiLf7kR738ZT8VK1NE0ZHXtWrWz/r6DeM/CL+VFd/7rNaPJWtmhnnYXGacD4o7Sgt7F6Mj3Pls5vrkPV0up9EJAAA3seew1gVynDGxRke4fbz3ZbvVTvN8mJOWWitHGR0hXb+0GWd0hGQs9t8uAAAAAOC/hsIVAAAAAGBqnOMKAAAAAB7icpn0ctUmx4grAAAAAMDUKFwBAAAAAKbGVGEAAAAA8BAnU4UzhRFXAAAAAICpUbgCAAAAAEyNwhUAAAAAYGqc4woAAAAAHuJyGZ3AmhhxBQAAAACYGoUrAAAAAMDUmCoMAAAAAB7i4utwMoURVwAAAACAqVG4AgAAAABMjanCAAAAAOAhTBXOHEZcAQAAAACmRuEKAAAAADA1pgoDdwCvUiWNjnBbEk6cNDrCf4LN4TA6wm1xJSYaHeG22by8jY5w21wJ8UZHgAlFtK1udITbkuf3i0ZHuG0Jfx4xOgJMwslU4UxhxBUAAAAAYGoUrgAAAAAAU2OqMAAAAAB4iMtldAJrYsQVAAAAAGBqFK4AAAAAAFOjcAUAAAAAmBrnuAIAAACAh7j4OpxMYcQVAAAAAGBqFK4AAAAAAFNjqjAAAAAAeAhThTOHEVcAAAAAgKlRuAIAAAAATI2pwgAAAADgIS6jA1gUI64AAAAAAFOjcAUAAAAAmBpThQEAAADAQ7iqcOZQuMJSgge0VsteTVX63ru0/pvNeq/3J0ZHSpdVMjdpV0PdB7dQ4WL5dOVCuD546Vvt33lcjR6urieeb6mCRfPoYug1ffX+99q6er/RcZPJnc9fL07vp1otqyn8YoRmjJyrdd9sMjpWqqyWN+TKV0nu++Tw0bKpqzTl+a9S3N4szN7O7fu1VMuejVX6npJa/+0Wvf+/zyRJXt4OjZg1UHffV1ZFSxfS0OZvau/GPwxOmzKzt3FKrJbZjHk7t6qhh5tWVbm7Cmr15oMaN+VH97qgZveqR4c6yp83l/YePKu3Pv1RF69EJdnfy2HXrPd7KYeftzr0m+bp+JKkwsXyauDoDqpU/S7FxyVo06p9+uzt5XImOlXj/nLq/2p7FQrMqz/3ntaHIxfo/LmrhuRMjVX+v7iZGfsyrIPC1cLi4+M1depULV++XF5eXvLy8lKpUqU0ePBglS9f3uh42eLSucuaM26hareqIV8/H6PjZIgVMtdseLeeGt5W7wyapT/3nFb+wrklSQWKBGjYh930xrNfadeGg6rzYGWNnNxDTzZ+S9cuRRqcOqlBk59WQlyCHi3aR+VqlNa45SN0bM8JnTxwxuhoKbJa3uB8T7p/9svpq2/PfqafF2wzLlAGmb2dL4Ve0dy3l6hWi2ryzeGdZN2+zX9q0cQf9Mo3gw1KlzFmb+OUWC2zGfNevBKprxdtV93qpeTr88+/kzUql1Dfbg01cMx3Oh16Rc8/9aDGPNdWA17/Lsn+j7evoyvXopXDL4+no7sNHN1BVy9F6vHGb8k/wE/jZjytdt3u1/rlv+mViU/oo1cXavu6g+o5uIVGfNhNLzz2qWFZU2KF/y9uZca+DOvgHFcLGzFihP7880/Nnz9fK1as0JIlS9SmTRsdPXo0yXZOp1Mu151x/bJNi3doS8hOhV+KMDpKhlkh8xPPtdLcSat18LdTcrlcuhQWrkth4SpYNK+iwmO0a8NBSdLOdX8oNjpOgXcVMDhxUn45fdWw8/366rV5iomK0f7NB7V16S4179HE6GgpslreWzXqXE9Xz1/T75sOGh0lTVZo581LdmrL0l2KuJz09SEhPlGLJ/2g/Vv+lDPRaVC69FmhjW9ltcxmzbthxxFt3HlE4RExSZY3rF1Oa7cd0vEzl5SQ6NRXC7epZpWSKl7knwI1sFCAWjWqrJlLtns6dhJFiufTzz/8rvi4BF25GKlffj6kUuUL64EWVXXySJg2/bhP8XEJmv3JGpWpGKgSZQoZmvdWVvj/4mZm7cuGcFngZkKMuFrUiRMntGbNGm3YsEEBAQGSJJvNpjZt2kiSJk2apJMnTyo6OlqnT5/W7NmzNXXqVO3YsUPx8fHKly+f3nrrLRUvXlxnzpxR586d1alTJ+3cuVOxsbEaPXq0ateuLUnasGGDPv30U8XFxcnb21sjRoxQjRo1jHrqyGJ2u01331tC237arxlrX5aPr5e2rN6vGW8v0+HfT+v00TDVe6iKdq77Q/UeqqL4uAQdPxhqdOwkilcIlDPRqbOH/8l1dO8JVWtcxcBUqbNa3lu16NFYa2b/bHSMdFm9na3Aim1stcxWy2v7/1vSJVLZkgV1NuyaJOnF3s009ZtNio1L8HS8JEJmbVbjh6tp745j8g/IodqNK2jWxNW6t05ZHf/zn/aOvR6v0NOXVKp8YZ05fsHAxNZmtb4M86FwtagDBw6oVKlSypMn9Sk2u3bt0qJFi5Q/f35JUp8+fTR8+HBJ0vz58/X+++9rwoQJkqSrV6+qYsWKGj58uHbs2KEXX3xRa9as0V9//aUpU6ZoxowZ8vf31+HDh9WnTx+tX78+258jPCNvwdzy9vFSwzbVNLTrJ0pMSNRrnz2lbgOb6+sPftCaxb9o+EePy8fXS/HxiXpr4CzFXo8zOnYSOfz9FHUtOsmyqGvRypk7h0GJ0ma1vDcrVLKA7m1cRR8+85nRUdJl5Xa2Ciu2sdUyWy3vlt3H9ebz7bR49R6dDr2q3o/cL6fTJT/fG1PhG9cpL4fDro07j6hmlRKGZv1953G17lJXC3eOlsPLodWLf9GWNQdUt2klXbuc9JzcqIgY5cjla1DSO4PV+jLMh8L1DnHkyBENGTJEMTExatSokfLkyaPGjRu7i1ZJ2rhxo+bOnavo6GglJCT9lNPb21vt27eXJNWtW1d+fn46duyYfvnlF506dUqPP/64e9uEhARdvHhRBQsW9MyTQ7aKi4mXJC37erOuXLgx3WjxFxvVbUBz7dl2VE8Pb6vh3T/VkX1ndfe9JTR62lN69anpOvbHOSNjJ3E9MkY5A5K+8eUKyKHoiOsGJUqb1fLerMUTjbV/80H9dcL8ow5WbmersGIbWy2z1fL+su+Ups/foreGtJd/Tl/NW/GLomPidP5ShPx8vTTgicYa8vYio2PKZrNp7Oe9tfK77RrS7VP55fLVC2M7q/fQ1roeFaec/n5Jts/p76frUbEGpb0zWK0vZyeuKpw5nONqUVWqVNHJkycVHh4uSSpfvrxCQkLUo0cPRUbeuGhOrly53NufPXtWb7/9tj744AMtX75cb731luLiUh81c7lcstlu/FE1atRIISEh7tumTZsoWu8gkeHXdeHc1RTPgy5XuZj27Timw7+fkcvl0qG9p3Xwt1Oq+cDdBiRN3dlDoXJ4OVS8fFH3srLVSpv2Yg9Wy3uz5k801upZG42OkSFWbmersGIbWy2z1fJK0qIff1PX575Q2z6fav32w3LY7Tp2+qJKFs2nwEIB+vSNx7RsWl+9PbS9CuTLpWXT+qpooQCPZsydJ4cKF8urpXO2Kj4+URFXo7V68S+q07iiTh0JU5mK/7S3bw5vBZbMr5NHzns0453Gin0Z5kLhalGlS5fWQw89pFdeeUUREf+clB8dHZ3i9pGRkfL29lahQoXkdDo1b968JOvj4+O1bNkySTemGMfGxqpMmTJ64IEH9PPPP+vw4cPubffu3ZsNzyhj7A67vH29ZXfYk/xsZlbIvHrBTrXv1VB5CvjLPyCHOjzVSNvXHtChvadVtU5Zla1cTJJUrkox3VOnjOnOcY2JjtWmRdvVa0xX+eX0VdUGFdUguI7WzNpgdLQUWS3v36rUr6CCxfNpowWuJixZo53Ten3w9vGS9/9Pr7z5ZzOxQhvfymqZzZrXYbfJx9shu90mh90uH2+He1nZkjcu4FekQG4Nf6aFvlv5qyKiYnXs9EV16DdNvYbNVK9hM/X21FW6fDVavYbN1PmLnr3AUPjVaIWevqy2j90vu8OuXLn91LzDfTp+8C9tWXNApe8uqgdaVJW3j5ce7/+QThz6y3Tnt1rh/4ubmbUvwzpsrjvlcrP/QXFxcZoyZYpWrlwpLy8vBQQEqHDhwnrmmWe0du1aRUdHu89plaSxY8dq3bp1KlasmOrUqaMlS5Zo7dq17oszPf744/r5558VExOT5OJMmzZt0sSJExUTE6P4+Hjdd999GjduXIZztrB3ybLn3GN0F/Uc/WiSZTPHfKdZY+Zn2WNkNU9k9ipd6l/t7/Cyq+9rHdS0fU3Fxcbr5xV7NOOdFYqPS1BQjwfU4alGylvQX9cuR2n5rC1aNOPfvckknDj5r/ZPSe58/hoyo5/ua1FNEZciNX3EHFN/N5wn8tocjiw93nNT/iffnL5698ns+a5AV2Jilh8zu9vZ5vXvisker3ZWj1c7J1k2682FmvXmQs089LGKlk56FdMedw9W2MmL/+oxXQnx/2r/W1ntb0+yXmZP5I165P7b2v7pLvX1dJcGSZbNmL9F3674VVPGdFXxInkVHROnFev2adq8zXKm8O9mzSolNHrQw5n6Htc8v/+7vwNJKlspUM+OaKcyFQPldDq1d/sxffJmiK5djlKN+uXU/5X2Klwsn/7ce1ofjJj/r7/HNeHPI/86882s+D9Rdvfl1U7zPveb3T1/rNER0nW4yytGR0iGwhXuwnX79uy5LH1WFq5I2b8tXD0tOwpXJJfVhWt2y47CNbv928LVCFlduOLOcLuFq9GyonD1tKwuXJGcVQrX8t+Zv3A98qj5ClfzzicAAAAAAEAUrpBUokSJbBttBQAAAIB/i6/DAQAAAAAP4etwMocRVwAAAACAqVG4AgAAAABMjanCAAAAAOApTBXOFEZcAQAAAACmRuEKAAAAADA1pgoDAAAAgIe4XEYnsCZGXAEAAAAApkbhCgAAAAAwNaYKAwAAAICnMFU4UxhxBQAAAACYGoUrAAAAAMDUKFwBAAAAAKbGOa4AAAAA4CEul83oCJbEiCsAAAAAwNQoXAEAAAAApsZUYeAOkHjmnNERgP8kV0K80RGALJFn51mjI9yWhFPWygskwdfhZAojrgAAAAAAU6NwBQAAAACYGlOFAQAAAMBDuKpw5jDiCgAAAAAwNQpXAAAAAICpMVUYAAAAADyFqwpnCiOuAAAAAABTo3AFAAAAAJgaU4UBAAAAwGO4qnBmMOIKAAAAADA1ClcAAAAAgKkxVRgAAAAAPIWrCmcKI64AAAAAAFOjcAUAAAAAmBqFKwAAAADA1DjHFQAAAAA8hXNcM4UR1ztYs2bNdOjQoSTLOnXqpO3bt9/2fmaSO5+/Ri8cpqURszT7+BQ92K2h0ZHS5O3jpRen99Ps41MUcm2mPv3lXdVpXcPoWGkqWamY3v1xlBZfmK4vD3yoB4JrGx0pXVbrF1bLK0lFShXS2KXDtfD8dM07PVUDPn5Kdoe530as1s7BA1rrkx3vaMX1uRr2xQCj42SI1dpYsl5mq/SLJu1q6LPVw7V4/9v6Yv1IVa1TRneVL6KPQ17Qd7+N1Xe/jdVbs/rqrvJFjI6aquLli2pF1CwNnznQ6Cjpslo/lqyZGebBiCssZ9Dkp5UQl6BHi/ZRuRqlNW75CB3bc0InD5wxOlqKHF4OXTh9UUOajtb5UxdV9+GaeuXbF/VMtSEKO3nB6HjJ2B12jVk4RCum/aSX27ylao0r643FQ9Wv7kidPfyX0fFSZbV+YbW8kjRoUm9dvRCux0r2k3/enHrnh1Fq36+llkz+wehoqbJaO186d1lzxi1U7VY15OvnY3ScDLFaG0vWy2yFflGzYQU99XI7vTNwlv7cc0r5CwdIkmKiYzWu/1c6f/aK7Hab2vVsqJcn9VD/Nu8bnDhlgyb31p87jxodI0Os1o8la2aGeZj7o3Jkm4sXL2rAgAEKCgpSUFCQlixZkuJ258+f1+DBg/XII48oKChIU6dO9WzQW/jl9FXDzvfrq9fmKSYqRvs3H9TWpbvUvEcTQ3OlJSY6VrPGzFfYyQtyuVzavuJX/XX8vO6uVdboaCm6q1IxFQjMp4Uffy+n06Xf1h/Q/i2H1PzxRkZHS5XV+oXV8v6taOnC2jB/q+Jj43Ul7Jp2/bhHpaqUMDpWqqzYzpsW79CWkJ0KvxRhdJQMsWIbWzGzFfrFE8+30tyJq3Xwt5NyuVy6FHZNl8KuKSoiRufPXrmxkc0mZ6JTgaUKGhs2FU27NlDk1WjtXrvP6CjpsmI/tmLmbOOymf9mQoy43uEGDx4sX19f9/0TJ05IksaOHau7775bn3zyic6fP69OnTqpSpUqqlChQpL9hw8frv79+6tOnTqKi4vTk08+qXvvvVcPPPCAJ5+GW/EKgXImOnX2cKh72dG9J1StcRVD8mRG3sJ5VKJCoE7uP210lJTZkr9Y2Ww2la5q3gLFav3Cann/tnjySjXt2kB7NxyQf75cqtOqhr5+/TujY6XKqu1sJVZsYytmNju73aa77y2pbWv2a8a6kfLx9dKW1fs0461liouNlyTN3zNOOXL6yGa3adYE883SyJk7h3q93kUvtRir1r0fNDpOuqzYj62YGeZC4XqHmzhxYpJitFOnTpKkrVu36uWXX5YkFS5cWE2aNNH27duTbBsdHa0dO3bo8uXL7mVRUVE6evSoYYVrDn8/RV2LTrIs6lq0cubOYUie2+XwcmjE7MFaNXODTv95zug4KTp98Jyung9XlyHttOjjlarRtIrubVxZe9YfMDpaqqzWL6yW9297Nx7Qw08305LLX8rh5dCqmRu0OWSn0bFSZdV2thIrtrEVM5td3oK55e3jpYZtqmvoo5OUmODUa9N6q9ug5vr6/ZWSpC7VR8k3h4+ad679zwisiTz5xqP64Yt1unDmktFRMsSK/diKmWEuTBX+D7PdMrJ2632n0ymbzaYFCxYoJCREISEhWrNmjXr27OnJmElcj4xRzoCkL3C5AnIoOuK6QYkyzmazafjMQUqIS9DkgTOMjpOqxIREvd7lA9VrU1Pfnp6izs+31cYF23TxrHnfzK3WL6yWV7rRf99eMVKbFu9Q+zy91LnI/+SfN5f+93Z3o6OlyortbDVWbGMrZja7uJgbo6rLvv5ZVy5EKPxKlBbP2KA6TSsn2S72epy+n7NVQz/orjwF/I2ImqJy1Uup5kP3auFHK4yOkmFW7MdWzJxdXC7z38yIwvU/qn79+vr2228lSRcuXNCGDRtUr169JNv4+/urVq1amjZtmntZaGioLlww7oJCZw+FyuHlUPHyRd3LylYrbYmT+odM76d8RfJozCMfKDEh0eg4aTr++2kNbf6mHgl8ViPbvaOiZQqb+mIVVusXVssrSbnz+6vwXQUVMuVHxcclKOJypFZ9vV51W9c0OlqqrNjOVmPFNrZiZrOLDL+uC+euZOgbPmx2m3xz+KhgkTzZniujqjWpqiKlC2nOiSn69uxn6jIkSI061dOUne8YHS1VVuzHVswMc6Fw/Y965ZVXdPDgQQUFBal3794aOnSo7r777mTbvf/++zp69Kj7Ik4vvPCCwsPDDUh8Q0x0rDYt2q5eY7rKL6evqjaoqAbBdbRm1gbDMmXEc5/20V2Vi+vV9uMVFxNndJx0lbm3pLx9veWbw0ePvNBWBYrm1aqZG42OlSqr9Qur5ZWk8EsRCj0WpqBnW8jusCtXnpxq0aOxjv1+0uhoqbJiO9sddnn7esvusCf52ays2MZWzGyFfrF6wU6179lQeQr4yz8ghzr0bqztaw+oZsMKKleluOx2m3L6++qZV4IVeS1ap46EGR3Z7fvP16jX3YPV976X1Pe+l7T8s9Xa/v2vGtFmnNHRUmXFfmzFzLg9kydPVsWKFd1fq3n8+HF17dpVrVq1UteuXd3X2klvXWpsLpdZB4Nxp2hh75Klx8udz19DZvTTfS2qKeJSpKaPmKN132zK0sfISoXvKqg5Jz5VXEycEhOc7uUf9f1Ma+dmTW6bl3eWHOdvfd7urta9H5SXt0P7Nh3UJy98rXNHs+6fDFdCfJYd629W6xeeyGtzOLL0eGWrl1K/D3qpbLW75Ex0as/6A5o8+AtdvZA1H2a5ErN+JoLV+kWP0V3Uc/SjSZbNHPOdZo2Zb1Ci9FmtjSXrZfZEv/AqVfJf7e/wsqvvax3VNPg+xcXG6+cVezTj7WWq17yqer7YWgWL5lVcTLwO7T2tL95drhMHQ9M/aBoSTp39V/unpcdrj6hY+aIa33Ny1h7Y5Ux/m9tgtX4sZX/m1U7zvlberNSMd42OkK6TT790W9vv379fEyZM0NGjR/XZZ5+pQoUK6tmzpzp37qzg4GCFhIRo4cKFmjlzpiSluS41FK7IdllduCK5rC5cs1t2FK5ILqsL1+yWHYUrgIz5t4Wrp2Vn4ZptsrhwRXIUrlnndgrXuLg49ejRQ++//7569eqlqVOnqkCBAmrVqpW2b98uh8OhxMRE1atXT6tWrZLL5Up1Xf78+VN9HK4qDAAAAABwCw8PT/H0wICAAAUEBCRZ9vHHH6t9+/YqWfKfD8BCQ0NVpEgROf7/Q3SHw6HChQsrNDRULpcr1XUUrgAAAABgBi5b+tsY7Ouvv9bkycmnyw8cOFCDBg1y39+9e7d+//13DR06NNszUbgCAAAAANx69eqljh07Jlt+62jrzp07dezYMT300EOSpL/++ktPP/20RowYobCwMCUmJrqnA58/f16BgYFyuVyprksLhSsAAAAAwC2lKcEpeeaZZ/TMM8+47zdr1kxTp05VhQoV9M0332j58uUKDg7W8uXLVblyZfdU4MqVK6e6LjUUrgAAAACALPX666/r5Zdf1pQpUxQQEKDx48dnaF1qKFwBAAAAwENsd/B3uqxdu9b9c7ly5TR/fspXek5rXWrM9e3VAAAAAADcgsIVAAAAAGBqTBUGAAAAAE+5g6cKZydGXAEAAAAApkbhCgAAAAAwtVSnCjdp0kQ2my3dA6xfvz4r8wAAAADAncuVfo2F5FItXN977z1P5gAAAAAAIEWpFq5169b1ZA4AAAAAAFKUoXNc4+LiNGHCBD300EOqVauWJGnTpk2aPXt2toYDAAAAgDuKywI3E8pQ4frWW2/p0KFDev/9993nvd5999365ptvsjUcAAAAAAAZ+h7XNWvWaNWqVcqZM6fs9hu1bpEiRRQWFpat4QAAAAAAyFDh6u3trcTExCTLLl++rLx582ZHJgBAFrA5HEZHuC2uW95nAHjO5UYljI5wW/LM+8voCLfNleA0OgLMwqRTcc0uQ1OFW7dureHDh+v06dOSpPPnz+uNN95Q27ZtszUcAAAAAAAZKlxfeOEFFS9eXO3bt1d4eLhatWqlwoULa8CAAdmdDwAAAADwH5ehqcI+Pj4aNWqURo0apcuXLytfvnzuizQBAAAAAJCdMlS4StKJEye0cuVKnT9/XoULF1abNm1UunTpbIwGAAAAAHcYznHNlAxNFV62bJk6duyoP//8Uzly5NChQ4fUsWNHLVu2LLvzAQAAAAD+4zI04vrRRx9p2rRpqlOnjnvZrl279NJLLykoKCjbwgEAAAAAkKHCNSoqSjVq1EiyrHr16oqOjs6OTAAAAABwZ3JxraDMyNBU4aeeekoffvihYmNjJUkxMTGaMGGCnnrqqWwNBwAAAABAqiOuTZo0cV852OVy6eLFi5o1a5YCAgIUHh4ul8ulQoUK6dlnn/VYWAAAAADAf0+qhet7773nyRwAAAAAcMezcVXhTEm1cK1bt64ncwAAAAAAkKIMf4/rH3/8oV27dunKlStyuf75mOC5557LlmAAAAAAAEgZvDjTt99+q27dumnbtm36/PPPdejQIX355Zc6depUducDAAAAgDuHywI3E8pQ4Tp9+nRNnz5dn3zyifz8/PTJJ5/o448/lpdXhgdsAQAAAADIlAwVrpcuXVLt2rVv7GC3y+l0qkmTJlq3bl22hgMAAAAAIENDpkWLFtWZM2dUokQJlS5dWj/99JPy5csnb2/v7M4HAAAAAPiPy9CI6//+9z8dPXpUktS/f38NGzZMvXr1Uv/+/bM1HJCS3Pn8NXrhMC2NmKXZx6fowW4NjY6UpuABrfXJjne04vpcDftigNFxUtS+X0tN3jpWyyO+1tDp/3w3s5e3Q6/Oe04zD32sVXFzVa1xZQNTps1q/cIKedv3baFJm9/Usmtfacjn//SLuyoV16TNb2pB6DQtCJ2md74fobsqFTcwaeqs0M43s1peicyeYLa83l4OjXq6pZZ8+D+t/WygZr75hOpXKy1J8nLY9fbAdlr8wdPaPvNF3VepRIrH8HLY9e07T2rZR308mDxtJSsV07s/jtLiC9P15YEP9UBwbaMjpcls/SIjrJgZ5pGhEddOnTq5f27SpIl27Nih+Ph45ciRI9uCWVmzZs00depUVahQIdVtFi1apJo1a6pMmTKGPL6VDZr8tBLiEvRo0T4qV6O0xi0foWN7TujkgTNGR0vRpXOXNWfcQtVuVUO+fj5Gx0nRpdArmvv2EtVqUU2+OZLOpNi3+U8tmviDXvlmsEHpMsZq/cIKeS+FXtHcd5aodotq8snhk2T52O4fK+zkRdntNgX1bakRswaqX50RBqZNmRXa+WZWyyuR2RPMltdhtynscoT6vfWd/roUrgbVy2jcgHZ6fNRMXbgSqT2Hz2reql/11sB2qR7jiYdr60p4tHL65fFg8tTZHXaNWThEK6b9pJfbvKVqjSvrjcVD1a/uSJ09/JfR8VJktn6REVbMDPPI0IjrrXx8fOTt7a2qVatmdZ7/jMWLF+vEiRO3vZ/T6UzydUT/NX45fdWw8/366rV5iomK0f7NB7V16S4179HE6Gip2rR4h7aE7FT4pQijo6Rq85Kd2rJ0lyIuJ82YEJ+oxZN+0P4tf8qZ6DQoXfqs1i+skndzyC5tXfaLwi9HJlkedS1aYScv3rhjs8mZ6FSxckUMSJg2q7Tz36yWVyKzJ5gxb0xcgqYv3qrQi+FyuaTNvx3XuQvXVKl0ESUkOjXvx93ac+icnM6U/18JLBig1g9U1tfLd3g4eeruqlRMBQLzaeHH38vpdOm39Qe0f8shNX+8kdHRUmTGfpEeK2bOLjaX+W9m9K8uC/xfLqAyokePHrrnnnv022+/6fz582rTpo2GDh2qhQsXat++fRo7dqw++ugjDR8+XA0aNNDnn3+uH3/8UYmJiSpSpIjefPNNFSpUSJMmTdLJkycVHR2t06dPa/bs2erYsaPatm2rX3/9VefPn1evXr30xBNPuB975cqVevXVV3XhwgX17t3bvW78+PHuEfN8+fLprbfeUvHixXXp0iUNGTJEly5dkiTVr19fI0eOlKRUcxmheIVAOROdOns41L3s6N4Tqta4iiF5YA5W6xdWy5uahX9NUw5/P9nsNs18Y6HRcZKxWjtbLa9EZk+wQt78ATl1V9F8Onb2Yoa2H9qzmT6dv1mxcQnZnOw22GwpLLKpdNWUpzobzQr94lZWzAxz+VeFqy2FP3IkFRoaqjlz5igqKkrNmzfXI488os6dO2vJkiXq3bu3HnzwQUlSSEiITp06pe+++052u11z587VO++8ow8++ECStGvXLi1atEj58+d3H/vixYuaM2eOLl68qA4dOqh27dqqVKmSJCkmJkbffvutzpw5o6CgIHXs2FG5cuVSnz59NHz4cEnS/Pnz9f7772vChAlatmyZihUrpq+++kqSdO3atQzl8rQc/n6KuhadZFnUtWjlzM209f8yq/ULq+VNTeeiz8g3p69aPNFI509l7B9WT7JaO1str0RmTzB7XofDrjH92uj7TQd0MvRKuts3qVVeDrtdG345kur5r0Y4ffCcrp4PV5ch7bTo45Wq0bSK7m1cWXvWHzA6WorM3i9SYsXMMBe+iDWbtW7dWna7Xblz51a5cuV06tQplS5dOtl2a9eu1b59+9SxY0dJUmJiovz9/d3rGzdunKRolaRHHnlEklSwYEE1bdpUO3bscBeuDz/8sCSpRIkSCggI0F9//aVy5cpp48aNmjt3rqKjo5WQ8M8nndWrV9eXX36p8ePHq27dumrYsGGGcnna9cgY5QxI+gKXKyCHoiOuG5QIZmC1fmG1vGmJjY7Vis9/0ndnPtX/arykaxfCjY7kZrV2tlpeicyeYOa8Nps05tnWSkhw6r1Za9Pd3s/HS4O6NtILHyz2QLrbk5iQqNe7fKABE55U16FBOvTLcW1csE3xsfFGR0uRmftFaqyYGeaSZuHavXv3VEdVnU7znu9mJr6+vu6fHQ6HEhMTU9zO5XKpX79+7mL0Vrly5UrzcVwuV5LfVUqPe/bsWb399ttasGCBSpYsqV9//VVDhw6VJNWsWVNLlizRli1bFBISomnTpumbb75JN5ennT0UKoeXQ8XLF9XZIzcullC2WmlO6v+Ps1q/sFre9NjsNvnm9FXBYvlMVbharZ2tllcisyeYOe8rT7dU/oBceuGDxUrMwHUQShbNp8CCAfpsVFdJkpeXQ/45ffT9xGf19BvfKPSisa8fx38/raHN33Tfn7Dhda2ZtdHARKkzc79IjRUzZxsXs1YzI82LM3Xp0kWPPPJIirdHH31Ub731lqdy3nFy5cqliIh/LoTTrFkzzZ071z1FNy4uTgcPHkzzGIsX3/jE8vLly9q4caPq1q2b5vaRkZHy9vZWoUKF5HQ6NW/ePPe606dPy9/fX23bttWIESO0f/9+OZ3OTOXKTjHRsdq0aLt6jekqv5y+qtqgohoE19GaWRsMy5Qeu8Mub19v2R32JD+bSVoZvX285O3rnexnM7Fav7BK3tT6xX0P3aNy1UvJbrcpZ+4cevbdJxR5JUqnDp4zOnISVmnnv1ktr0RmTzBr3uFPPqTSxQpoyIQlio1Peq6qt5dDPt6OZD8fO3NRQS98ridenaUnXp2lt75YpcvXovXEq7MUZoILGJa5t6S8fb3lm8NHj7zQVgWK5tWqmeYsXM3aL9JixcwwlzRHXP+eHoqs17VrV40fP15ffPGFXnrpJXXo0EFXr151X0TJ5XKpW7du7qm/KQkMDFT37t114cIFPfvss6pYsWKaj1mxYkW1bt1abdu2VbFixVSnTh3t2rVLkrRjxw59+eWXcjgccjqdGjNmjOx2e6ZyZbdJA6ZryIx++i5suiIuRerj/p+b+tO6x1/prJ6jH3Xfb96jsWaO+U6zxsw3MFVSj4/sqB6vdnbfb/54I816c6FmvblQM/Z9oKKlb1yM6+3vb3zdSY+7B/9zVVmTsFq/sELe7iM6qMcrN/WL7g01a+xCnTxwRv0/7KWCxfMr9nqcDv1yTKPajzfllDortPPNrJZXIrMnmC1v0QK51alZdcXGJej7Sf98x/M7X67Rj1sP6rvxT6pYoRtfczPxpRuvIR1enK7Qi+G6fNM5juGRMXK5XEmWGal590Zq3ftBeXk7tG/TQb388NuKN9MFpG5htn6REVbMDPOwubg0sCVZ6btaW9i7GB3hjmfzMt8oaFpcCeYrcO5Edh9zfm9wapxxcUZHAP6zwp+ob3SE25Jn3i6jI9w23vuy32qneQYF0lL2ow+NjpCuY8+/aHSEZMw1XxEAAAAAgFtwVWGLWrs2/av3AQAAAMCdgMIVAAAAADyFEzUzJUNThePi4jRhwgQ99NBDqlWrliRp06ZNmj17draGAwAAAAAgQ4XrW2+9pUOHDun99993f1fo3XffrW+++SZbwwEAAAAAkKGpwmvWrNGqVauUM2dO2e03at0iRYooLCwsW8MBAAAAwJ3ExlThTMnQiKu3t7cSExOTLLt8+bLy5s2bHZkAAAAAAHDLUOHaunVrDR8+XKdPn5YknT9/Xm+88Ybatm2breEAAAAAAMhQ4frCCy+oePHiat++vcLDw9WqVSsVLlxYAwYMyO58AAAAAHDncFngZkIZOsfVx8dHo0aN0qhRo3T58mXly5fPfZEmAAAAAACyU4YK17+nCP8tKirK/XPJkiWzNhEAAAAAADfJUOHaokUL2Ww2uVz/jBv/PeL6xx9/ZE8yAAAAAACUwcL14MGDSe5fuHBBkydPVu3atbMlFAAAAADckUx6DqnZZejiTLcqVKiQRo0apQ8//DCr8wAAAAAAkESmCldJOnbsmK5fv56VWQAAAAAASCZDU4W7d++e5CrC169f15EjR/g6HAAAAAC4DTamCmdKhgrXLl26JLmfI0cOVapUSaVLl86OTAAAAAAAuKVbuCYmJmrbtm1688035ePj44lMAAAAAAC4pVu4OhwObd68OclUYQDm4kpMNDoCTMgZn2B0BAAWkS9kn9ERbktiQrzREYDMc1FXZUaGLs7Uq1cvTZo0SfHxvEgAAAAAADwrzRHX5cuXq127dpo9e7YuXryoL7/8Uvnz508y+rp+/frszggAAAAA+A9Ls3B97bXX1K5dO7333nueygMAAAAAdy6uKpwpaRauLteNVq1bt65HwgAAAAAAcKs0C1en06lt27a5C9iU1K9fP8tDAQAAAADwtzQL17i4OI0aNSrVwtVms+mnn37KlmAAAAAAcKexMVU4U9IsXHPkyEFhCgAAAAAwVIa+DgcAAAAAAKNk6OJMAAAAAIAsQImVKWmOuO7evdtTOQAAAAAASBFThQEAAAAApkbhCgAAAAAwtTTPcQUAAAAAZB2+DidzGHEFAAAAAJgahSsAAAAAwNSYKgwAAAAAnsJU4UxhxBWWkzufv0YvHKalEbM0+/gUPditodGR0hQ8oLU+2fGOVlyfq2FfDDA6ToYMnzlQ885M1ZIrX+rLPyaozdPNjI6ULqv1C6vllegX2c3bx0svTu+n2cenKOTaTH36y7uq07qG0bHSZaU2/pvVMps9r7ePl16Y/JS+/v1dLTozRZN/Hq3aze+VJD3Y5X4tPjvFfVsS+ql+uPaFytcoZXDq5Mzezrey4v8XVmtjmAsjrneAZs2aaerUqapQoYLRUTxi0OSnlRCXoEeL9lG5GqU1bvkIHdtzQicPnDE6WoounbusOeMWqnarGvL18zE6TobMe2eJPvzfVMXHJahkxWJ6f+1oHdl9XId/PW50tFRZrV9YLa9Ev8huDi+HLpy+qCFNR+v8qYuq+3BNvfLti3qm2hCFnbxgdLxUWamN/2a1zGbPa/ey68KZy3qp7XidP31ZdVpW08iv+qlfg1e1bv42rZu/zb1ti+4PqNtLQTry20kDE6fM7O18Kyv+f2G1Noa5MOL6H5eQkGB0hNvil9NXDTvfr69em6eYqBjt33xQW5fuUvMeTYyOlqpNi3doS8hOhV+KMDpKhp08cEbxcTf6hsvlksvlUmC5IganSp3V+oXV8v6NfpG9YqJjNWvMfIWdvCCXy6XtK37VX8fP6+5aZY2OliqrtbFkvcxWyBsbHafZ74Qo7NQluVwu7fhxj8JOXlD5GqWTbdu8WwP99M0Wz4dMhxXa+VZW+//Cim2cbVwWuJkQI653qPHjx2vHjh2Kj49Xvnz59NZbb6l48eI6c+aMOnfurCeeeEJbtmxR+/btVadOHY0YMULXr19XpUqVdOrUKfXr108PPvigzp8/r7Fjx+rcuXOKjY1V27Zt1bdvX8OeV/EKgXImOnX2cKh72dG9J1StcRXDMt2pBk1+Wi17NZFfTl8d/vW4dny/2+hIqbJav7Ba3pvRLzwnb+E8KlEhUCf3nzY6Sqqs2MZWy2y1vJKUt1CAipcvqpN/nE2yvHDJArrngYr6cOCXBiVLnRXb2WpoY/xbFK53qD59+mj48OGSpPnz5+v999/XhAkTJElXr15VuXLlNGjQIElSp06d1KtXLwUHB+v333/Xo48+6j7O8OHD1b9/f9WpU0dxcXF68sknde+99+qBBx7w/JOSlMPfT1HXopMsi7oWrZy5cxiS5042aeAMfTL4C1WuX0HVm1RVfKx5R+et1i+slvdm9AvPcHg5NGL2YK2auUGn/zxndJxUWbGNrZbZankdXg4Nn/6M1nyzWWcO/5VkXfPHGmj/lkMKO3nRoHSps1o7WxFtjH+LwvUOtXHjRs2dO1fR0dHJpgP7+vqqTZs2kqTIyEgdOnRIQUFBkqR7771XFStWlCRFR0drx44dunz5snvfqKgoHT161LDC9XpkjHIGJH2ByxWQQ9ER1w3Jc6dzOl3av/lPNX+8kYL6ttCSyT8YHSlFVusXVst7K/pF9rLZbBo+c5AS4hI0eeAMo+OkyYptbLXMVsprs9k0bNr/FB+XoE+Gzkm2/qFuDTTvgxUGJEufldrZqmjjf9hMOhXX7Chc70Bnz57V22+/rQULFqhkyZL69ddfNXToUPf6HDlyyGazSbpxnprNZnPfv5nT6ZTNZtOCBQvk7e3tsfxpOXsoVA4vh4qXL6qzR258klu2WmlO6s9mDi+Hipn4XEar9Qur5U0N/SJ7DJneT/mK5NGotm8rMSHR6DhpsmIbWy2zlfK+MPkp5SsUoFe7fJSs71apV14FiubVppBdBqVLm5Xa2apoY/xbXJzpDhQZGSlvb28VKlRITqdT8+bNS3Xb3Llzq3z58lq+fLkkaf/+/Tp06JAkyd/fX7Vq1dK0adPc24eGhurCBeOubhkTHatNi7ar15iu8svpq6oNKqpBcB2tmbXBsEzpsTvs8vb1lt1hT/KzWeUtFKCmXRvIL5ev7HabaresrqaPNdDudfuNjpYqq/ULq+WV6Bee8tynfXRX5eJ6tf14xcXEGR0nXVZsY6tltkreQRN6qGTFQI1+bKLiYuKTrW/e/QFtWvqLrkfGGJAufVZp55tZ7f8LK7YxzMXmcrkYrLa4Zs2aKTY2Vg6Hw72sZcuWWrdunYoVK6Y6depoyZIlWrt2rfviTNu3b3dve+jQIY0cOVJOp1NVq1bV/v37NXLkSNWuXVsXLlzQ22+/rcOHD0uScuXKpXHjxqlcuXIZztfC3iXrnqxufAfYkBn9dF+Laoq4FKnpI+Zo3TebsvQxslKP0V3Uc/SjSZbNHPOdZo2Zn3UPYsu6N6o8BXPrte9eVNnqpWSz23T+5EUtnrxSK6evzbLHkMuZdcf6f1brFx7JS7+wVL8ofFdBzTnxqeJi4pSY8E9bfNT3M62da87MkrXa+G9Wy+yJvI7cuTO9b+GSBTRz33uKi4lPMtI68fmZWjd/m7x9vfTNoY80tucn+m3DH1kRV4kRWX8lXav1C4/8f5HFsruNVzvN+9xvVvHNCUZHSNefr75gdIRkKFyh6Oho9/ThI0eOqEePHvrhhx+UJ0+eLDl+VheuSEEWFigekQ0FClJAvwCQQf+mcDVCdhSusD4K16xjxsKVc1yhX3/9Ve+++67+/gzjzTffzLKiFQAAAAD+LQpXqGHDhmrYsKHRMQAAAAAgRRSuAAAAAOApnKiZKRY7AQoAAAAA8F9D4QoAAAAAMDWmCgMAAACAh9iYKpwpjLgCAAAAAEyNwhUAAAAAYGpMFQYAAAAAT2GqcKYw4goAAAAAMDUKVwAAAACAqTFVGAAAAAA8hanCmcKIKwAAAADA1ChcAQAAAACmxlRhAAAAAPAQG1OFM4URVwAAAACAqVG4AgAAAABMjcIVAAAAAGBqnOMK3AFsdpvREW6LK9HoBACAm9n8fI2OcHsiIoxOAGQe57hmCiOuAAAAAABTY8QVAAAAAJAp/fv315kzZ2S325UzZ069+uqrqly5so4fP66XX35ZV69eVd68eTV+/HiVLl1aktJclxpGXAEAAADAQ2wu899ux/jx47V06VItWbJEvXv31siRIyVJo0ePVvfu3fXjjz+qe/fueu2119z7pLUuNRSuAAAAAIBMyZ07t/vnyMhI2Ww2Xbp0SQcOHFC7du0kSe3atdOBAwd0+fLlNNelhanCAAAAAAC38PBwhYeHJ1seEBCggICAZMtHjRqlzZs3y+Vyafr06QoNDVWRIkXkcDgkSQ6HQ4ULF1ZoaKhcLleq6/Lnz59qJgpXAAAAAPAUC1xV+Ouvv9bkyZOTLR84cKAGDRqUbPm4ceMkSUuWLNG7776r5557LsszUbgCAAAAANx69eqljh07Jlue0mjrzTp06KDXXntNRYsWVVhYmBITE+VwOJSYmKjz588rMDBQLpcr1XVp4RxXAAAAAIBbQECASpQokex2a+EaFRWl0NBQ9/21a9cqT548KlCggCpXrqzly5dL0v+xd99hVdb/H8efBxAUwT0yNVc5yzS3uSI1FwLukdnSHGmamSNzlGma2tDMTNNcOQF3pZHmHum3XGgu3DhQhrI5vz/4cRIFHCn3fdvrcV1ewX2fc3jx6YbD+/4sVq1aRbly5ciTJ0+G5zKiHlcREREREZHMYoGhwncrOjqad955h+joaJycnMiZMyfTpk3DZrMxcuRIBg8ezNSpU8mRIwfjxo1zPC+jc+mx2e32R6jpxIwaObU1OsIjz/b/k9utwp6YaHSE/wabxQbV2JOMTiDyn+WSP5/REe5JwqXLRkcQE1qXtMToCHel/NDPjY5wRwfH9Dc6wm0s9leNiIiIiIiI/NdoqLCIiIiIiEgmsWm8631Rj6uIiIiIiIiYmgpXERERERERMTUNFRYREREREcksGip8X9TjKiIiIiIiIqamwvUB8vLyok6dOiTetNXHsmXLKFOmDPPmzcvwuevXr+evv/761xkmT558V/sgWZlnbg9GLBvIisi5zDsxlRc61jE6UoasljdFg3a1mPHXRFZcm83s4C95+vmyRkfKkNXa2Wp5AQbNeZuFZ6YReHUWsw59TtM3vIyOdEdWa2er5QVlzgw+vZvw9c5PWR29gIHf9zY6ToYeL5Gf5SFfMfDrVwEoW6UEnyzuy+LgCSw8MJ6h371J7gI5jA2ZDqtdF1bLC9bMLOahocIPWP78+dm8eTP169cHIDAwkAoVKtzxeevXr+fpp5+mYsWKDzui5fWZ8gYJcQm0e6wbpSoV55NVQzj+50lCDp4xOlqarJYX4LkXn+GNMZ34pPOXHN55jDyFchkd6Y6s1s5Wywuw8NNAJr05jfi4BIqWeZwJQSM4uvcEf+85YXS0dFmtna2WF5Q5M1w5F8b8T5ZR9aVKuGV1NTpOhnp/2oEj/wtxfO6R0521czfzyW8HSUxMpNfYDrz75St82HGKgSnTZrXrwmp5wZqZxTzU4/qA+fn54e/vD8Dp06eJjo6mdOnSAMTFxTFu3DjatGmDj48PAwcO5Pr162zatImgoCCmT5+Oj48PgYGBXLp0iS5dutCqVSuaN2/O+PHjHV9j8uTJvPPOO3Tr1o3mzZvTp08fIiMjHedDQ0Pp1q0bTZo0oXv37kRHRwOwbds22rdvj6+vL97e3qxevdrxnClTptCkSRN8fHzw9fUlIiICgD///NORo1WrVmzYsOFhN2GGsrq7Uad1TWYPX0jM9RgObAlm24rdNOxS39Bc6bFa3hRdRrRl/if+BO84it1u58q5q1w5d9XoWOmyWjtbLW+KkINniI9LAMBut2O32ylUqqDBqdJntXa2Wl5Q5syyOWAnW5fvIuJK5J0fbKD6vlWJCo/mf5uCHcd2Bx1g88o93IiKITY6nhUzN1C+eikDU6bNateF1fKCNTM/NHYL/DMhFa4PWI0aNTh8+DDh4eEEBATg6+vrODdjxgw8PT1ZunQpy5cvp0CBAkyfPp26devi5eVF9+7dWb58Ob6+vuTIkYNp06bh7+9PYGAg+/fv5/fff3e81h9//MHYsWNZvXo1Hh4eTJ061XFu//79TJw4kbVr15KQkMDKlSsBKF++PAsWLCAwMJBZs2Yxbtw4wsPDCQ8PZ+bMmQQGBrJ8+XLmzZuHu7s7ERERjBgxgokTJ+Lv78+0adMYPny4o6g1QuHShUhKTOLs3+cdx479dZJi5YsYlikjVssL4ORko3SVkuTM58msQ18w/8TX9P7yNVyzZjE6Wrqs1s5Wy3uzPlPeYGXkHGYd+oKw89fYuWav0ZHSZbV2tlpeUGb5h7tHVl5+vwXfjVyW4eOeqfUUpw6fz/AxRrDadWG1vGDNzGIuGir8gNlsNpo2bcrq1atZs2YNP/74I/v37wcgKCiIqKgofv75ZyC5B7Zs2bTnDSYmJjJ+/Hj27t2L3W7n8uXLBAcHU69ePQAaNGhAvnz5AGjTpg2jR492PLdOnTrkyJE8f6RixYqcOnUKgLCwMIYOHUpISAjOzs6Eh4dz4sQJnnnmGUqUKMHAgQOpW7cuDRo0wMPDg71793LmzBm6deuW6vsLCQnhmWeeecAtd3eyeWTleviNVMeuh9/A3TObIXnuxGp5AXIVzEUWVxfqtq7BgBdGkhCfyCj/9+g0tBWzhy8yOl6arNbOVst7s8lvz+Trvt9TrlZpnq1fgfjYBKMjpctq7Wy1vKDM8o8ug735ZcFWLmcwOqd4+cJ0ercZo7pOy8Rkd8dq14XV8oI1M4u5qHB9CFq1akXbtm2pXr06uXPndhy32+2MGDGCWrVq3fE1Zs2aRUREBEuWLMHNzY0PP/yQ2NjYNB9rt9ux2WyOz93c3BwfOzs7O543cuRIvLy8mDJlCjabjZdeeonY2FicnZ1ZvHgxe/bsYfv27bRq1YoZM2Zgt9spU6YM8+fPv9+meOCio2Jwz5H6F1z2HNm4ERltUKKMWS0vQFx0HADLv/6ZsAvXAFj2xWo6DfUzbeFqtXa2Wt5bJSXZObDlMA0718W7RyMCp/xkdKQ0Wa2drZYXlFmSlaxQhMp1y/J2wzHpPqZQ8fx8vOBtpg1bzIEdRzMx3d2x2nVhtbxgzcwPi82kQ3HNTkOFH4KiRYvSv39/evXqleq4l5cXs2fPJiYmBoCoqCiOHTsGgIeHR6p5qpGRkeTPnx83NzdCQ0P59ddfU73Whg0bCAsLAyAgIIAaNWrcMVdkZCSFCxfGZrOxZcsWQkJCHDnCwsKoXr06ffv2pXTp0vz9999UrlyZkJAQtm/f7niNv/76C7vduJ+2s0fO4+ziTOEnH3McK1mxuGkn9VstL0DUtetcOn3F0P/P98pq7Wy1vOlxdnHmcRPPcbVaO1stLyizJKv4fGkKPpGXH/Z8wvx9n9K6V0Oeb16ZyeuGAFCgSB7GLnmHHyetIWjpToPTps1q14XV8oI1M4u5qHB9SNq3b3/bMODu3btTtmxZ2rRpg7e3N506dXIUri1btmTVqlWOxZm6dOnCnj178PX1TbOXtlatWgwdOpTmzZsTHh5+W5GclgEDBjB+/Hjat2/Pzz//TJkyZYDkwrV37954e3vTokUL8uXLR+PGjcmZMydTp07l66+/pmXLljRt2pQpU6YYWtDE3Ihls/8Ouo5qT1Z3NyrULkNtn2qsn7vRsEwZsVreFD//sAGf3k3IlT8HHrmy49e3GTtWm3cuo9Xa2Wp5AXLlz0GD9rXJmt0NJycbVRs/S4MOtdn72wGjo6XLau1stbygzJnFydmJLG5ZcHJ2SvWxWaydu4nXqw/nba8xvO01hjU/bGLX+v0M6zCZvI/l5NNl/Vg5ayNr5mwyOmq6rHZdWC0vWDOzmIvNbqVuFQGSVxW+ceMGgwYNMjrKXWnk1PaBvp5nbg8GzOzJc40qEnklihlD5vPbj5sf6Nd4kDIjr83Z+YG+nrOLM70+78oLHZ4nLiae35du47vBC4iPjX8gr2+/aa/jB0XXRRpsD+4P25z5PBm++F1KPlsMm5ONiyGXCZiylrUzgh7Y18Ce9OBe6//punj4lPnh6zKiLa+MaJfq2JxRi5k7askD+xou+fM9sNfq/F5zHi+Rn896z6bTgOZ0eb8F0ddjUj2mVcn+/+prJFy6/K+enxarXRdWywsPP/O6pAf3M/EwPT3wc6Mj3NH+z/7dz+jDoMLVgv7rhavc7kEXrg/bwyhcJQ0PsHDNFA+hcBWRu/MgC9fM8DAKV7E+Fa4PjhkLVy3OZEF9+vQxOoKIiIiIiEimUeEqIiIiIiKSSbSq8P2x2DgyERERERER+a9R4SoiIiIiIiKmpqHCIiIiIiIimUVDhe+LelxFRERERETE1FS4ioiIiIiIiKmpcBURERERERFT0xxXERERERGRzKI5rvdFPa4iIiIiIiJiaipcRURERERExNQ0VFhERERERCST2IwOYFHqcRURERERERFTU+EqIiIiIiIipqahwiIiIiIiIplFqwrfF/W4ioiIiIiIiKmpcBURERERERFT01BhkUeAzSWL0RHuiT0x0egI/wk2J2utW2jXZSFimKTHCxgd4Z7Ywq4aHeGe6b1PUtg0VPi+qMdVRERERERETE2Fq4iIiIiIiJiahgqLiIiIiIhkFg0Vvi/qcRURERERERFTU+EqIiIiIiIipqahwiIiIiIiIplFQ4Xvi3pcRURERERExNRUuIqIiIiIiIipqXAVERERERERU9McVxERERERkUxi0xzX+6IeVxERERERETE1Fa4iIiIiIiJiahoqLCIiIiIiklk0VPi+qMdVRERERERETE2Fq4iIiIiIiJiahgqLiIiIiIhkEq0qfH/U4yqW45nbgxHLBrIici7zTkzlhY51jI6UISvkbdmjIZM3j2Llte8ZML17qnNu2Vx5+4uuLD49Ff8L3zJh3QcGpcyYFdr5ZlbLC1CwWH5GrxjEsoszWHh6Gr2/fA0nZ3O/jVitna2WF5Q5M5gxb8v21Zk8vzsrd3zIgFG+qc5Vql6CGf5vs3zrB4yf/ioFCuV0nHu2anHGT38V/9+H8MPqfpkbOgPLr85O9W9tzAJ6ffGq0bEy5NO7CV/v/JTV0QsY+H1vo+PcFTNey2IdmfoXh5eXF3Xq1CExMdFxbNmyZZQpU4Z58+Zl+Nz169fz119//esMkydPZty4cf/6de7WsWPHKFOmDLNnz051fPDgwY7v+ccff7ztvBn5+/tz4sQJo2PQZ8obJMQl0O6xbox9+SvemdqNYuWLGB0rXVbIe+X8NRaMW8EvP/x+27l3vn4dzzwedKs8iDaP92Da+/MNSHhnVmjnm1ktL0Cfya9z7VIEHYr2pGfVQVSsV46WPRsbHStDVmtnq+UFZc4MZsx75VIkC777nV+W7011PEcud4ZP6MAPU4No02AcRw6eY+inbR3nY6Lj+Xn5Hr774pfMjpwhn9yvOv61L/wWcdFxbFq63ehYGbpyLoz5nyzj51m/GR3lrpnxWhbryPRb5fnz52fz5s2OzwMDA6lQocIdn/egCtfMtnTpUmrWrMmyZcvSfUzHjh159dVXMy/UfQoICODkyZOGZsjq7kad1jWZPXwhMddjOLAlmG0rdtOwS31Dc6XHKnm3LN/NtpV/EBEWlep4kaceo2bz5/iy90zCL0eSlGTn6N6TxoTMgFXaOYXV8qZ4rHgBNi7ZRnxsPFdDw9n985+m/oPDau1stbygzJnBrHm3BB1i24ZgIq7dSHX8ea9yhBy/yKb1B4mPS2DutN8oWfoxihbPB8DhA2f5dfVfXDh71YjYd6Vu6xpcuxjOvs3BRkfJ0OaAnWxdvouIK5FGR7krZr2WDWG3wD8TyvQ5rn5+fvj7+1O/fn1Onz5NdHQ0pUuXBiAuLo7PP/+cXbt2ER8fT+nSpRk5ciR79uwhKCiIrVu3smTJEl577TWef/553n33Xa5fv05sbCz169fn/fffB5J7VY8ePcqNGzc4d+4cJUuWZMyYMXh6egIQGhpKt27dOH36NE888QRffvkl2bJlY9u2bXzxxRfExsaSmJhIjx49aN68OQBTpkxh1apVuLm5YbPZmDNnDjly5ODPP/9kwoQJXL9+HYC+ffvSoEEDABISEli5ciXz58+nW7du7Nu3j2eeeea2Npk8eTI3btxg0KBBxMXF8fHHH7Nz507y5MlDuXLluHz5Ml999RX+/v6sWrWKHDly8Pfff+Pp6cnkyZPJnz+/45ynpyeHDx+mYMGCfPjhh4wfP56QkBCefvppJkyYgM1mIyoqirFjx3L48GFiY2OpUaMGQ4YMwdnZmS5duvD000/zv//9j4sXL9K0aVPee+89li1bxv79+xk9ejRffPEFgwYNonbt2g/7crlN4dKFSEpM4uzf5x3Hjv11kor1ymd6lrthtby3KlutFBdPXabLh615sePzhF24xrxP/NkcuNvoaKlYrZ2tljdFwJS1NGhfm782HsQjd3aqvVSJH0YuNjpWuqzWzlbLC8qcGayWt1ipAhw/Eur4PDYmnvNnwihWKj+nT142MNnda9SlHuvnbTI6xiPHateymE+m97jWqFGDw4cPEx4eTkBAAL6+vo5zM2bMwNPTk6VLl7J8+XIKFCjA9OnTqVu3Ll5eXnTv3p3ly5fj6+tLjhw5mDZtGv7+/gQGBrJ//35+//2fYY5//PEHY8eOZfXq1Xh4eDB16lTHuf379zNx4kTWrl3rKC4Bypcvz4IFCwgMDGTWrFmMGzeO8PBwwsPDmTlzJoGBgSxfvpx58+bh7u5OREQEI0aMYOLEifj7+zNt2jSGDx9OREQEABs2bKBYsWIUK1YMPz+/DHtdUyxatIhz586xevVqZs+ezf79+1Od37dvH4MGDWL16tU8+eSTqYZY79u3jyFDhvDTTz+RNWtWBgwYwMSJE1m9ejVHjhxh27ZtAIwdO5Zq1ao52jksLCxVtvPnzzN//nwCAwNZsmQJJ0+epHXr1jz99NMMGzaM5cuXG1K0AmTzyMr18NR3d6+H38DdM5shee7Eanlvla9wHko8XZTr4TfoVLIPX/efw3vfvUXRMo8bHS0Vq7Wz1fKm+Ov3gxQvX4TAsFn8GPINR/YcZ8vyXUbHSpfV2tlqeUGZM4Pl8rq7cj0qJtWx61GxZHN3MyjRvclfNC/P1CvPurkbjY7yyLHatSzmk+k9rjabjaZNm7J69WrWrFnDjz/+6CjOgoKCiIqK4ueffwaSe2DLli2b5uskJiYyfvx49u7di91u5/LlywQHB1OvXj0AGjRoQL58ycNS2rRpw+jRox3PrVOnDjly5ACgYsWKnDp1CoCwsDCGDh1KSEgIzs7OhIeHc+LECZ555hlKlCjBwIEDqVu3Lg0aNMDDw4O9e/dy5swZunXrlur7CwkJ4ZlnnmHp0qX4+fkB4Ovri6+vL0OGDMHNLf1f3jt27MDHxwcXFxdcXFxo3rw5f/zxh+P8c889R6FChQB49tln2bp1a6pzjz32GADlypWjcOHCjl7msmXLEhISQu3atQkKCuKvv/5i1qxZAMTExFCwYEHH6zRp0gQnJyc8PT0pVaoUp06donjx4ulmzkzRUTG450j9Cy57jmzciIw2KFHGrJb3VrEx8cTHJbDg0+UkJSaxb3Mwf248RJWGT3P68Dmj4zlYrZ2tlheSf7eNXT2U1d+tp1/d4WT1yMqA73rw5thOzBiywOh4abJaO1stLyhzZrBc3htxuGdP/XeOe3Y3om/EGpTo3jR6uR4HtgRz4eQlo6M8cqx2LT9MWlX4/hiyHU6rVq1o27Yt1atXJ3fu3I7jdrudESNGUKtWrTu+xqxZs4iIiGDJkiW4ubnx4YcfEhub9i9Fu92OzWZzfH5z4ejs7Ox43siRI/Hy8mLKlCnYbDZeeuklYmNjcXZ2ZvHixezZs4ft27fTqlUrZsyYgd1up0yZMsyff/tiNZcvX2bLli0EBwc7enujo6P55Zdf8Pb2Tvf7ujXrrW7NfvNCV7eeS++xdrudqVOnUrRo0Xv+GkY7e+Q8zi7OFH7yMc4evQBAyYrFCTl4xuBkabNa3lud2HfK6Ah3xWrtbLW8AJ55PCjwRD6WT/2Z+LgE4sOi+OWHDbw6qr1pC1ertbPV8oIyZwar5Q05dpFG3pUcn7tlzUKhIrkJOWaNQrDhy/VY9Nlyo2M8kqx2LYv5GLKPQdGiRenfvz+9evVKddzLy4vZs2cTE5M8xCQqKopjx44B4OHhQWTkP5PPIyMjyZ8/P25uboSGhvLrr7+meq0NGzYQFhYGJC8qVKNGjTvmioyMpHDhwthsNrZs2UJISIgjR1hYGNWrV6dv376ULl2av//+m8qVKxMSEsL27f+sOvfXX39ht9sJCAjgpZdeYsOGDQQFBREUFMSYMWPuOFy4Ro0arFixgoSEBGJjY1m7du0dc98rLy8vpk+f7ihIw8LCOH369B2flz179lT/D4wQcyOWzf476DqqPVnd3ahQuwy1faqx3qRDeqyS18nZiSxuWXBydsLJ2eb4eN/mw1w6fYUOA71xcnaifK2nqFivLH+s22d05FSs0s4prJYXIOJKJOePh+L9ViOcnJ3IntOdRl3qcXxfiNHR0mW1drZaXlDmzGDWvE7OTmRxdUl+33D65+Otvx2ieKkC1HmxHFlcXXi5ewNO/B3qmN9qs9nI4uqCs4uT42MXF2dDv5cU5WuVJl/h3Pxu8tWEU6R+7/7nY7My67Us1mHY1d2+ffvbhgF3796dsmXL0qZNG7y9venUqZOjcG3ZsiWrVq3Cx8eHwMBAunTpwp49e/D19U2zl7ZWrVoMHTqU5s2bEx4efluRnJYBAwYwfvx42rdvz88//0yZMmWA5MK1d+/eeHt706JFC/Lly0fjxo3JmTMnU6dO5euvv6Zly5Y0bdqUKVOmOArXW3tWX3zxRfbt28eZM+nfWerQoQP58+enefPmvPXWW1SoUAEPD4+7atO7NXToUJycnPDx8cHb25s333yT0NDQOz6vffv2TJ06FV9f31RDlDPb5N4zcM3myuLQGQxd0I8ve31n6rt1VsjbabAPq659T4eB3jTsVIdV176n02AfEhMSGdn2c6o1eZaA0On0+/oNPnvzW04fOX/nF81kVmjnm1ktL8CodpOo+lIllpyfzuzgL0hMSGLagDlGx8qQ1drZanlBmTODGfN2erMeq3Z8SIfX69KwxbOs2vEhnd6sR/jVG3w8cBGv9n6RZRsHU+aZwowdvNTxvGeeK8aqHR/yyZQuFCyUi1U7PmTMN10M/E7+0ahLPTYH7iL6ljm6ZtV5WGvWRC+g42A/Gnapx5roBXQe1troWBky47Us1mGz2+2P3Cjrm1fptaKoqCg8PDyIi4ujZ8+eNGnShLZt2975iSbVyMm62a3CyS2r0RHuSVKsNf4osDqbszl6Me6W3UTTEkT+a5yetdbKrvb9h42OcM/0O+7hW5e0xOgId+W5Hp8bHeGO9kzrb3SE2xgyx1Uy9tprrxEXF0dsbCy1a9d2LPAkIiIiIiLyX/RIFq59+vQxOsK/smSJNe4WiYiIiIiIZIZHsnAVERERERExpUduombmMO/SYyIiIiIiIiKocBURERERERGT01BhERERERGRTGLTUOH7oh5XERERERERMTUVriIiIiIiImJqGiosIiIiIiKSWTRU+L6ox1VERERERERMTYWriIiIiIiImJqGCouIiIiIiGQSm11jhe+HelxFRERERETE1FS4ioiIiIiIiKmpcBURERERERFT0xxXERERERGRzKIprvdFPa4iIiIiIiJiaipcRURERERExNQ0VFgePpu17o84ZctqdIR7dv2lZ4yOcE+yBewwOsJ/gnPhQkZHuCcJp84YHeGeuTxZ0ugI9yzh6HGjI4gJrV37o9ER7knTph2NjnDvDvxtdIJ7Zk+INzrCI8mmocL3xVoVhYiIiIiIiPznqHAVERERERERU9NQYRERERERkcyiocL3RT2uIiIiIiIiYmoqXEVERERERMTUNFRYREREREQkk2hV4fujHlcRERERERExNRWuIiIiIiIiYmoaKiwiIiIiIpJZNFT4vqjHVURERERERExNhauIiIiIiIiYmoYKi4iIiIiIZBKtKnx/1OMqIiIiIiIipqbCVURERERERExNhauIiIiIiIiYmua4ioiIiIiIZBbNcb0vKlwzkZeXF66urri6upKUlETPnj1p3rx5uo8vU6YMe/bsIXv27Ok+xt/fnw0bNvDVV19l+LUnT57MjRs3GDRo0H3nBxg8eDBPP/00L7/88r96nX9j0Jy3qez1NFmzu3H1wjUWT1jJ2plBhuW5VRZXF97+/BUqv1ABz9zZOXf8IrNGLmX3ur8AqNSgPG9P6kL+Ink5vPs4E976jounr2RqxlZNK9OsQQVKFsvH+s3BjJnyk+OcV+0yvN6+NgXyehJ6OZLpCzaxaedRAF5vV5tXWtcgLj7R8fhXB/zAudDwTM2fFs/cHrw7oydVGlck4nIkM4cu4LcfNxsdK11WyVvfuzKd3nmJAo/n4uqlSCa+9yOH/xfCoC+78FTFohQskof3O0xh3/ZjRkdNk9nb2X/vR6k+d82ahdULtvHNxysoUDg3P/w2mOjrsY7zS77byI9Tf83smBkyexunxWqZfXo3oXHXBhR/5gk2/LiFz17/2tA8VZqk/jwmFjr6wLB+cPQkDB4Dp88mnytfBj7oC08WT/58xx6Y+gMc/BtyeMKvizInc8v21WnUshLFnyzIhp/2MXFEoONcpeoleHtwc/I/lpPD+88yYUQAF8//8772Rt9GNPF7DoCfAvcw88t1mRP6Fi17NqbxK/Uo/nRRNizayoQ3vwXAJYszQ+a+zVPPleSx4vl5r+HH/PX7IUMy3onVfvbEXFS4ZrKvvvqK0qVLc/DgQTp06ECtWrXIkyeP0bEsZeGngUx6cxrxcQkULfM4E4JGcHTvCf7ec8LoaAA4uThx6WwYA5uM5eLpK1R/qSIfzOlFjxrDiL4ew/D5ffj87e/ZvuZ/dP2wFUN/6EU/r48zNePlsCh+WLad6pWK4+b6z6+BfHk8+LBvM4aMC2T73hPUeq4kH7/nTZse33Et4gYAv245zMdfrcnUvHejz5Q3SIhLoN1j3ShVqTifrBrC8T9PEnLwjNHR0mSFvJXrlOa1QS34tM8cDv/vFHkK5HCcO7D7OIHfb2To1FeNC3gXzN7OrSoPd3zsli0LP279kE1r96V6TJuqI0lKTMrsaHfN7G2cFqtlvnIujPmfLKPqS5Vwy+pqdBz++OdeJzeioa4fvPRC8ucF8sKXo+DxxyApCRYEwIBRsHxW8vls2aBVM2geC9/Oz7zMVy5FsuC736la+0lc3f5538uRy53hEzrw+UfL2f77Ebr28mLop23p13UGAM1aV6XWC2Xp2f4b7HY7Y6e9woWzV1m9dHfmhU/5Hs5fZcHYQKo0qohbtiypzu3fchj/r35i2I99Mz3XvbDaz56Yi+a4GqR8+fJkz56dM2fO0LVrV7y9vfHz8+P333+/7bFr1qzhrbfecnweFxdHnTp1OH/+/G2PnT59Oi1atKBFixYMGTKE69evO86dO3eObt260bx5c/r06UNkZKTj9caNG0ebNm3w8fFh4MCBjueFhobStWtXWrZsSa9evbh69eqDbop7FnLwDPFxCQDY7XbsdjuFShU0ONU/Ym/EMW9MIKGnLmO329nx059cCLnMU5WL83zLqoQcOsumgF3Ex8Yzd0wAJZ95gqKlC2Vqxt93/M2mnUeJiIxOdbxAXg+ibsSyfW/yTYBte44THRNP4cdyZmq+e5XV3Y06rWsye/hCYq7HcGBLMNtW7KZhl/pGR0uTVfK+3L8JC776heC9Idjtdq6EhnMlNJyE+EQCv/+dA7tPmLqgsko7p6jb5BmuhUWxf7c5bsLdDau1MVgz8+aAnWxdvouIK5FGR7nNLxshTy6oWjH58xyeULgQ2Gxgt4OTM5w6+8/jK5YDn5egyOOZm3NL0CG2bQgm4tqNVMef9ypHyPGLbFp/kPi4BOZO+42SpR+jaPF8ADTyfpZlc7dy+WIEVy5FsmzuNhp5V8rc8P9vS+Autq7YTWRY6usgIT6RgMk/cWDrYf1Otgib3fz/zEiFq0G2b99ObGwsAwcOpEWLFqxcuZLPPvuMgQMHEhYWluqxjRs35siRI5w+fRpILmSfffZZChVKXexs3LiRFStWsHDhQlauXEliYiJTp051nP/jjz8YO3Ysq1evxsPDw3FuxowZeHp6snTpUpYvX06BAgWYPn06AKNHj6ZatWqsWLGCIUOGsHPnzofZLHetz5Q3WBk5h1mHviDs/DV2rtlrdKR05SqQgyJPFiTk0FmKlSvM8f2nHedib8Rx/sRFipUrbGDCfwQfC+XkmSs8X7UUTk426lZ/kviERI6FXHY85vmqpVgzuzdzv3gV35eeNTDtPwqXLkRSYhJn//7nZs6xv05SrHwRA1Olzwp5nZxsPPVMUXLm9WDmhqHM3TaCnqNa4eqW5c5PNgkrtPPNXvStwq+Be247/sOGwcz9fSj9x7YlR253A5Klz2ptDNbMbGaBPyUXojZb6uPVm0OlxvDJl9DduNlFd1SsVAGOHwl1fB4bE8/5M2EUK5U/+XzJAhw/csFx/viRCxQrVSDTcz4K9LMn/5aGCmeyvn374ubmhoeHBxMmTKBv3760bt0agCeffJJy5crxv//9Dy8vL8dzXFxcaN++PQsXLmTgwIEsWLCAfv363fba27Zto1mzZnh4eADQrl07xowZ4zjfoEED8uVLvoPYpk0bRo8eDUBQUBBRUVH8/PPPQHIPbNmyZQHYsWMHw4YNA6Bo0aLUqlXrAbfI/Zn89ky+7vs95WqV5tn6FYiPTTA6UpqcXZwZPLMH6xZs4fSR82TL7kb45dR3Sq+HR5PNI6tBCVNLSrLz08YDjOjXHFdXFxISEvlwwkpiYuMBCNp6mOXr/uRq+A3KP1WI0QNbEnU9lvWbgw3Nnc0jK9fDU99Fvx5+A3fPbAYlypgV8ubK50kWVxfqNH2W99pOITEhkeHfvUHHPo34YYL5hoqnxQrtnCJ/oVw8U70kX3yw1HEs4up1+rb6imOHzpMjlzu9R/jy/oSODHtjpoFJU7NSG6ewYmazOhcKu/6E0Wksn7FzdfIw4sCfkocNm1U2d1fCr15Pdex6VCzZ3N0AyOruyo2o2JvOxeCe3S1TMz4q9LMn/5YK10yWMscVICoqKs3H2G69bUlyEern54eXlxcRERFpFpB2uz3N56bl5sfa7XZGjBhhmqL0biUl2Tmw5TANO9fFu0cjAm9aYMgMbDYb78/oTnxcAl+/OxeA6OuxuOdI/QvaPUdWoqNijIh4m6oVn6BXl/r0GbGII8dDKVPyMT4d4st7o5dx9OQlTp75ZxGp/YfPsXT1HhrUKm144RodFXNbu2bPkY0btwyFNgsr5I2LSb5ZsfKHTVy9FAFAwIwNlipcrdDOKV70fY6Df5wk9Mw/0zFibsTx9/7kMZbXrkQx9aNAFmz9EPfsbty4acEmI1mpjVNYMbNZLf8ZnnsGiqQz28U9G3Twgdo+sHoO5M2dufnuRvSNuNsKUffsbkTfSP4Zi7nlvJl+/qxGP3s3sZt0LK7JaaiwgTw8PChXrhwBAQEAHDt2jODgYJ599vbhl3ny5KF27dq8++67dOrUKc0CtXbt2qxZs4aoqCjsdjtLly6ldu3ajvMbNmxwDEMOCAigRo0aQPJqx7NnzyYmJrl4ioqK4tix5BVCa9asybJlywA4ffo027Zte4At8GA4uzjzuInmuKZ4d+ob5C6Qk487TyYxIXkV3pBDZyn59BOOx7i5u1KoRAFCDp1N72Uy1VPFC/DnwTMcPhaK3Q7Bxy5w8O/zVKtYLM3H2+1wd7dKHq6zR87j7OJM4Sf/ua1fsmJx0y72YIW8URHRXDp3FbuF31yt0M4pXvR9jvUBf2T4GMf/iru8QZkZrNTGKayY2ayW/wy+L2X8mKQkiImB0EuZk+lehRy7SMnS/1wLblmzUKhIbkKOJQcOOZ76fMnSjxFy7GKm53wU6GdP/i0VrgabMGECK1aswNvbm/fee4/x48enu8pwmzZtiIiIwM/PL83z9evXx9vbmw4dOuDt7Q1Az549Hedr1arF0KFDad68OeHh4fTq1QuA7t27U7ZsWdq0aYO3tzedOnVyFK4ffPABO3bsoGXLlkyYMIHnn3/+QX779yxX/hw0aF+brNndcHKyUbXxszToUJu9vx0wNNet+n7ZlaJlCjG87eeOniuArSv/oHj5wtTxqUoWtyy8PNiXE/tPc/rI7QttPUzOTjZcszjj5OSE0/9/7Oxk49DRC1QsV5gniyfP7XmqRAGeLVeEoyHJb+B1qpXC8//vPJd78jHaNKvMpl3Gb4MScyOWzf476DqqPVnd3ahQuwy1faqxfu5Go6OlySp51y3ZScuudcmZ1wOPHNnwfb0+O349CEAWV2ey/P/KnFmyuDg+NhOrtHO5ysXIVzAnm376K9XxMhWLUrhEPmw2G5653OnxYUv+3H6MGyYZoQHWaeObWTGzk7MTWdyy4OTslOpjI+3dDxcvQ5MXUh/fsgsOHoHERIi6DuO+Tl6wqdT/3/9MSoLYWEhISL4ZExsLcfG3v/6D5uTsRBZXl+Q2dPrn462/HaJ4qQLUebEcWVxdeLl7A078Hcrpk8lrO6xf9SetXq5F3vye5MnvSZsutVm38n8PP3B630M610EWVxey/P8aBDd/bCZW/NkTc7HZrXw7/T9m6tSpXLp0iREjRhgd5Z40cm7/wF4rZz5Phi9+l5LPFsPmZONiyGUCpqxl7YwHt4+rU7Z/N9+0QNG8zD00ibiYOBIT/lnd78u+s/lt8TYqNyhP70ldKFA0H8G7jzHxrRmEnrqcwSve2fWXnrmnx7/erjavt6+d6tj3i7by/eKttGpamXbNnyNPruxci7iB/9r/sXBl8rL/I/s3p9qzxcni4sylsEgCfvofS+9jYaxsATvu+Tl34pnbgwEze/Jco4pEXolixpD5pt4bLjPyujzx7xa8cHZxoscIPxr4VCEuNp5Nq/7HzE9XEh+bwOzNH1KwSOqbbF3rfMTFm4a63quEUw/+rvvDbmeXJ0v+69fo81Er3LJlYcLA1Bta1m/+LK++24RceT24ERXDnq1/8/34NVy9nPY0k7uVcPT4v3r+raz2swfWy9xlRFteGdEu1bE5oxYzd9SSB/Y1fj735z09fsQEiI6B8cNSH//pN/jq++QeVjdXeLosDHgLypRKPr9zL3Ttl3rUQLVKduZ8eW95mzbteE+Pf/mtBnTpkbrKnjvtN+Z9u4HKNUrSe1AzChTKRfD+M0wcHkjo+WuOx73xTiOa/v8+rmsD7n8fV/uBv+/reSm6fNiaLh+2TnVs7sfLmPvxMuYc+ZLH/v+ms+PxT/UlNOTf/X1hT3iwdxUe9s/euqQH9zPxMNXqNNHoCHe0bcGAu3rc1atXef/99zl16hSurq4UK1aMjz76iDx58nDixAkGDx7MtWvXyJUrF+PGjaN48eIAGZ5LjwpXi2jevDnOzs7MnDmT/Pnz3/kJJvIgC9fM8G8LVyPca+FqtIdRuMrt/m3hmtkeRuH6sD2IwjWzPejCVR4N91q4Gu1eC1cz+LeFqxEedOH6sKlwfXDutnC9du0ahw8fdkxBHDduHOHh4YwZM4ZXXnmF1q1b4+Pjw/Lly1m2bBlz5swByPBcejRU2CJWr17NihUrLFe0ioiIiIiItURERHDmzJnb/kVERKR6XK5cuRxFK0ClSpU4d+4cV65c4eDBg7Ro0QKAFi1acPDgQcLCwjI8lxHzTUoSERERERF5VFlgvOsPP/zAlClTbjv+9ttv06dPnzSfk5SUxI8//oiXlxfnz5+nYMGCODs7A+Ds7EyBAgU4f/48drs93XPprfUDKlxFRERERETkJl27dk1zQdgcOXKk+5yPP/4Yd3d3Xn75ZQ4ePPjAM6lwFREREREREYccOXJkWKTeaty4cYSEhDBt2jScnJwoVKgQoaGhJCYm4uzsTGJiIhcvXqRQoULY7fZ0z2VEc1xFRERERETkvnz++efs37+fr7/+GldXVwDy5s1LuXLlWLVqFQCrVq2iXLly5MmTJ8NzGVGPq4iIiIiISCaxJd35MVbx999/M23aNIoXL06HDh0AKFKkCF9//TUjR45k8ODBTJ06lRw5cjBu3DjH8zI6lx4VriIiIiIiInLPnnrqKQ4fPpzmuVKlSrFkSdpbFGV0Lj0aKiwiIiIiIiKmph5XERERERGRzGKB7XDMSD2uIiIiIiIiYmoqXEVERERERMTUNFRYREREREQkk9g0VPi+qMdVRERERERETE2Fq4iIiIiIiJiahgqLiIiIiIhkFrvGCt8P9biKiIiIiIiIqalwFREREREREVPTUGEREREREZFMolWF748KV3n47ElGJ7gnSTduGB3hnmX/eZ/REe6Jta4I60q6cNHoCI+8hGMnjY4g8kBU+7Cn0RHuSd79O4yOcM/siYlGRxCxNA0VFhEREREREVNTj6uIiIiIiEhm0VDh+6IeVxERERERETE1Fa4iIiIiIiJiaipcRURERERExNQ0x1VERERERCSTaDuc+6MeVxERERERETE1Fa4iIiIiIiJiahoqLCIiIiIiklnsGit8P9TjKiIiIiIiIqamwlVERERERERMTUOFRUREREREMolWFb4/6nEVERERERERU1PhKiIiIiIiIqamocIiIiIiIiKZRUOF74t6XEVERERERMTU1OMKeHl54erqiqurK0lJSfTs2ZPmzZtneo7Zs2fj7e1N3rx5/9XrdOnShddff50XXnjhASUzF8/cHrw7oydVGlck4nIkM4cu4LcfNxsdK11mz5vF1YW3P3+Fyi9UwDN3ds4dv8iskUvZve4vACo1KM/bk7qQv0heDu8+zoS3vuPi6SsGp76d2dv5VlbI27JHIxp1qUfxp4uyYfE2Jnb79rbHdP6gFa982JrBzcawN+iAASkzZoV2vtmgOW9T2etpsmZ34+qFayyesJK1M4OMjpUhq7UxWC+z2fJmcXFmcGcvapR7ghzZs3L64jW+DtjC1v0nKVEoDx+93oQi+XMCcCjkIp8t/I0T58MA6NK4Ci1ql6dQ3hxci4pmyW9/MveXPwz7XlIsvzo71eeu2VxZOe0XpvabnebjzcBs18XdsGJmMQ8Vrv/vq6++onTp0hw8eJAOHTpQq1Yt8uTJA0BCQgIuLg+/qebMmUPt2rX/deH6qOsz5Q0S4hJo91g3SlUqzierhnD8z5OEHDxjdLQ0mT2vk4sTl86GMbDJWC6evkL1lyrywZxe9KgxjOjrMQyf34fP3/6e7Wv+R9cPWzH0h1708/rY6Ni3MXs738oKea+cv8qCTwOp2qgirtlcbztfqGQB6vpV58q5qwakuztWaOebLfw0kElvTiM+LoGiZR5nQtAIju49wd97ThgdLV1Wa2OwXmaz5XV2shEaFkm3z5ZwISyCOs+U4NO3mtN+5FwuXbvO+9NWcf5KBE42G+1eeJax3ZvRYdQ8AGw2GyO+/5m/z1yiSP5cfN2/FaFXI/ll1xFDvpcUPrlfdXyc1d2NRWe/ZdPS7cYFugtmuy7uhhUzPwxaVfj+aKjwLcqXL0/27NkZPHgwH3/8MW+88Qbt2rUDYPr06bRo0YIWLVowZMgQrl+/DsDkyZPp378/3bp1o1GjRvTr14+DBw/yyiuv0LBhQ8aNG+d4/YsXL9K3b1/atGmDt7c306ZNA+Cbb75xnPPx8eHo0aNs27aN9u3b4+vri7e3N6tXr3a8TpcuXfjkk0/o0qULjRo1YtKkSam+j507d9KxY0defPFFJkyY4Dj+/fff07p1a3x9fWnfvj2HDh0CIDo6mr59+9KsWTNatmzJO++843hOQEAAbdu2pVWrVrzyyiscP378Abf63cvq7kad1jWZPXwhMddjOLAlmG0rdtOwS33DMmXECnljb8Qxb0wgoacuY7fb2fHTn1wIucxTlYvzfMuqhBw6y6aAXcTHxjN3TAAln3mCoqULGR07FSu0882sknfL8t1sW/kHEWFRaZ7v/fmrzPzgR+LjEzI52d2xSjvfLOTgGeLjktvTbrdjt9spVKqgwanSZ8U2tlpmM+aNiUtg+srtnL8Sgd0Om/46wbnL4ZQrVoCo6FjOX4kAwGaDxCQ7RfPncjx3zs+7CT51kcQkOyGhV9n4v2M8++TjBn0naavbugbXLoazb3Ow0VHSZcbr4k6smFnMRT2ut9i+fTuxsbG4uLiwd+9e5s2bh7u7Oxs3bmTFihUsXLiQ7NmzM2jQIKZOncrAgQMBOHDgAMuWLcPd3R0/Pz8mTpzIjBkzSEhI4MUXX6R9+/YUL16cQYMG0atXL6pVq0ZcXByvvvoqzzzzDD179mTJkiWOnl+A/Pnzs2DBApydnbl8+TKtWrWiTp065MyZPPzm2LFjzJo1i9jYWDp06EDlypUdw4PPnz/P/PnzuX79Og0bNqRNmzYUL14cX19fXn/9dQC2bt3KiBEjWLx4MZs3byYiIoI1a9YAEB4eDsDu3btZu3Yt8+fPx9XVlY0bNzJ06FAWLlyYqf9fUhQuXYikxCTO/n3ecezYXyepWK+8IXnuxGp5AXIVyEGRJwsScugszd/04vj+045zsTfiOH/iIsXKFeb0kfMZvErmslo7Wy1vWuq2qk5CXAK7fv7T6Cjpsmo795nyBo271ieruxt/7znBzjV7jY6ULiu2sdUyWyFvHk93niiYm+Pn/plGsuHLnmRzc8XJZmPaiq3pPrfSU4Xx/31fZsS8a4261GP9vE1Gx8iQFa6LW1kxs5iLCtf/17dvX9zc3PDw8GDy5MmsXLmSSpUq4e7uDsC2bdto1qwZHh4eALRr144xY8Y4nl+nTh08PT0BKFOmDGXLlnXMmy1RogSnTp2iQIEC7Ny5k7CwMMfzrl+/zrFjx3j++edvyxQWFsbQoUMJCQnB2dmZ8PBwTpw4QaVKlQDw9fXFxcUFFxcXmjVrxvbt2x2Fa5MmTXBycsLT05NSpUpx6tQpihcvzv79+/n2228JDw/HZrNx8uRJAMqWLcvx48cZNWoU1atXp0GDBgAEBQURHBxM27ZtgeQegIiIiAfX8Pcom0dWroffSHXsevgN3D2zGZQoY1bL6+zizOCZPVi3YAunj5wnW3Y3wi9HpnrM9fBosnlkNShh2qzWzlbLe6us2d147aP2DGnxqdFRMmTVdp789ky+7vs95WqV5tn6FYiPNWePNlizja2W2ex5XZydGP1mU1ZtPcjJC/9MG2jwzjdkdXXBu3Z5zl+JTPO5b7WshZPNxoot5pkfn79oXp6pV55J3W+f028mZr8u0mLFzGIuKlz/3809nQArV650FK2QXLDZbLZ0n+/m5ub42NnZ+bbPExMTSUpKwmazsXTpUrJkyXLHTCNHjsTLy4spU6Zgs9l46aWXiI2NTfOxt+ZL6+vHxcXxzjvvMG/ePCpUqEBoaCj16tUDoGjRoqxZs4bt27fz+++/8/nnn7Ny5UrsdjutW7dONXTYSNFRMbjnSP0LLnuObNyIjDYoUcaslNdms/H+jO7ExyXw9btzAYi+HntbfvccWYmOijEiYrqs1M5gvby3euXDNvy6YDOhJy8ZHSVDVm7npCQ7B7YcpmHnunj3aETglJ+MjpQmK7ax1TKbOa/NBh+93oT4xETG//jbbedj4hJYuvEv1k/qQZvhP3D1psztXniW5rXK8eb4xcQnJGZm7Aw1erkeB7YEc0G/3x44K2Z+aJI0yfV+aI7rXapduzZr1qwhKioKu93O0qVLqV279j29hoeHB1WqVGH69OmOY+fPn+fSpeRfjtmzZycy8p+7kpGRkRQuXBibzcaWLVsICQlJ9XrLly8nISGBGzdu8NNPP1GjRo0Mv35cXBwJCQkUKpQ8P3HBggWOcxcuXMDZ2ZmGDRsyZMgQwsLCuHbtGl5eXixfvpwLFy4AkJiYyP79++/p+36Qzh45j7OLM4WffMxxrGTF4qad1G+lvO9OfYPcBXLycefJJP7/HxEhh85S8uknHI9xc3elUIkChBw6a1TMNFmpncF6eW9V6YUK+PR6iR9Pfs2PJ78mf5G8fDCvL+0GtDA6WipWb2dIHgXxuInnuFqxja2W2cx5h3dtTN4c7rz/zUoSEpPSfIyTzUZW1ywUyOXhONby+Qq82rQaPScu4+LVtOfQG6Xhy/VYN/d3o2PckZmvi/RYMbOYiwrXu1S/fn28vb3p0KED3t7eAPTs2fOeX2fChAkcO3YMb29vvL296d+/v2Po7SuvvMLQoUMdizMNGDCA8ePH0759e37++WfKlCmT6rUqVKjAa6+9hq+vLw0aNLjj9jceHh6OhaE6d+6cqkf58OHDtG/fnpYtW9K2bVu6d+9OwYIFqVatGv369aNnz560bNmSFi1a8Ouvv97z9/2gxNyIZbP/DrqOak9Wdzcq1C5DbZ9qrJ+70bBMGbFK3r5fdqVomUIMb/s5cTHxjuNbV/5B8fKFqeNTlSxuWXh5sC8n9p821fxWsE47p7BKXidnJ7K4ZcHJ2SnVx4OajuGtKoPoVWMovWoMJez8Vb58eyYrpq0zOnIqVmnnFLny56BB+9pkze6Gk5ONqo2fpUGH2uz9zTzDKG9ltTYG62U2a94hL79IiUJ56DdlObHx//SY1ij3BGWK5sfJZiN7VlfebVefyBsxju1wmtYoS2+/5+k1yZ+zl8ONip+m8rVKk69wbn43+WrCYN7rIiNWzCzmYrPb7eqrtiAr7dXayKntA309z9weDJjZk+caVSTyShQzhsw39R5gmZHX6aabEPeqQNG8zD00ibiYOBIT/rlj/mXf2fy2eBuVG5Sn96QuFCiaj+Ddx5j41gxCT13+V3mTbty484Puka6L2zm53r6Fzb14eVgrugxrnerY3NHLmDfaP9WxHw5/wRc9v/vX+7gmxcX9q+en5aG3s+3B3f/Nmc+T4YvfpeSzxbA52bgYcpmAKWtZO+MB7+NqT7tn7H5Z7WcPrJc5M/KGvXH3o8gey+PJ6nFvEhufQOJNPa1j5v1KfEIiPX1qUyC3B7HxCRw4GcrkZZs5ejb5fWPF2NcpmMuDuJuGB6/ZEczYefd2Uzzv7B339Pi78c7UN3Fzd2P8q18/8NcGsCc+2CHRVruO4eFnXpe05IG91sNUv9l4oyPc0cY17xsd4TYqXC3qv1y4yu3+TeFqhIdRuMrt/m3hmtkeRuH60D3AwjXTPODCVR4N91K4msHDKFwftgdduMrtVLg+OGYsXLU4k0XNnTvX6AgiIiIiIiKZQoWriIiIiIhIJrFpvOt9seAYJxEREREREfkvUeEqIiIiIiIipqahwiIiIiIiIplFa+PeF/W4ioiIiIiIiKmpcBURERERERFT01BhERERERGRTKJVhe+PelxFRERERETE1FS4ioiIiIiIiKmpcBURERERERFT0xxXERERERGRzKI5rvdFPa4iIiIiIiJiaipcRURERERExNQ0VFhERERERCST2OwaK3w/1OMqIiIiIiIipqbCVURERERERExNQ4VFHgH2uHijI4gJJcXFGR3h0WdPMjqByAORd1+k0RFE/jv01nFf1OMqIiIiIiIipqbCVURERERERExNQ4VFREREREQyiVYVvj/qcRURERERERFTU+EqIiIiIiIipqahwiIiIiIiIplFI4Xvi3pcRURERERExNRUuIqIiIiIiIipaaiwiIiIiIhIZtGqwvdFPa4iIiIiIiJiaipcRURERERExNRUuIqIiIiIiIipaY6riIiIiIhIJrFpiut9UY+riIiIiIiImJoKVxERERERETE1DRUWERERERHJLNoO576ocBXL8cztwbszelKlcUUiLkcyc+gCfvtxs9Gx0pXF1YU+U7vx3IvP4JnHg3NHL/D9BwvY9dP/jI7m0LJnYxq/Uo/iTxdlw6KtTHjzW8e5Si9UoM9Xr5G/aF6Cdx5lwpvfcvHUZQPTps1q14XV8lrhOk6LldpZbZx5rJbZp3cTGndtQPFnnmDDj1v47PWvjY5EyzbVaNz8WYqXKsCGdfuZ8PEKAFxcnBjyUSueKvc4jxXKxXu9fuCvPSGO53V5sz4dX61DfFyi49hbL0/jwrlrmf0tpLL86uxUn7tmc2XltF+Y2m92mo83A6tdx2DNzGIeKlxvsnbtWr799lvsdjuxsbFUqFCBiRMnPpDXnjx5Mjdu3GDQoEH4+/uzYcMGvvrqK86cOUPr1q3ZsWPHA/k6/zabFfSZ8gYJcQm0e6wbpSoV55NVQzj+50lCDp4xOlqanF2cuXT6MgMajODiqctUb1aZYYvepXvFAYSGXDI6HgBXzl9lwdhAqjSqiFu2LI7jOfJ6MmJxfyb1+I7tq/bw6si2fDC/D+/UHWFg2rRZ7bqwWl4rXMdpsVI7q40zj9UyXzkXxvxPllH1pUq4ZXU1Og4AVy5HsmDWJqrULIWbW+o/J/f/eRr/RTsY9kmbNJ+7cf0Bxo0MzISUd88n96uOj7O6u7Ho7LdsWrrduEB3wWrXMVgzs5iH5rj+v4sXLzJq1Ci++eYbli9fztq1a3nzzTcNzZSQkJDh50YyKktWdzfqtK7J7OELibkew4EtwWxbsZuGXeobkuduxNyIZe6oJYSGXMJut7Nj9R4unLjIU1VKGh3NYUvgLrau2E1kWGSq43V8q3Hy4Bk2LdtBfGw8cz9eRsmKxSha5nGDkqbNateF1fKCNa7jW1mtndXGmcOKmTcH7GTr8l1EXIm884MzyZYNwWz9/TCR4dGpjickJBGwaAcH/jxNUpI1h0PWbV2DaxfD2bc52Ogo6bLidWzFzA+LLcn8/8xIPa7/7/Lly7i4uJArVy4AbDYb5cqVA6BMmTL069eP9evXc+3aNUaPHs3WrVvZtGkTCQkJfPnll5QqVYpLly7x7rvvcv36dWJjY6lfvz7vv//+PeUoU6YMAwcOZOPGjVSpUoULFy6QPXt2Tp48ydWrV/H39ycgIIAFCxaQmJiIh4cHI0eOpGTJksTFxfHxxx+zc+dO8uTJQ7ly5bh8+TJfffXVbb2q6fWyHj58mFGjRhEdHU1sbCzt2rXj1VdfBWDw4MG3ZclshUsXIikxibN/n3ccO/bXSSrWK5/pWe5XrgI5KVK6ECEHThsd5Y6KlS/C8b9OOT6PuRHL+eOhFCtfmNOHzxmYLDWrXRdWy5sWK1zHVm9ntfHDYcXMj5qadUqz9OeBhF2JZMXSXazy/8PoSKk06lKP9fM2GR0jQ1a8jq2YWcxFhev/K1u2LBUrVqRBgwbUqFGD5557Dh8fH3Lnzg1Ajhw5WLZsGWvXrqVXr158/vnnDBgwgO+++45vvvmGCRMmkCNHDqZNm0b27NmJj4/njTfe4Pfff6devXr3lCUpKYm5c+cCycXi3r17mTdvHu7u7uzevZu1a9cyf/58XF1d2bhxI0OHDmXhwoUsWrSIc+fOsXr1ahITE+nSpQuPPfbYPX3twoULM3v2bFxdXbl+/Tpt27albt26lCpVCiBVFiNk88jK9fAbqY5dD7+Bu2c2Q/LcK2cXZ4bM68svczaaqvBLTzYPN65dTn2H/3r4DbJ5mKu9rXZdWC3vraxyHVu5ndXGD48VMz9KNq4/wOrAP7gWdp2yFQrz4di2REXGsGHdAaOjAZC/aF6eqVeeSd2/vfODDWTF69iKmcVcVLj+PycnJ6ZOncqRI0fYtWsX69evZ+bMmaxcuRKApk2bAlChQgUAGjRoAMDTTz/NunXrAEhMTGT8+PHs3bsXu93O5cuXCQ4OvufC1c/PL9XnTZo0cRSKQUFBBAcH07ZtWwDsdjsREREA7NixAx8fH1xcXHBxcaF58+b88ce93cWMiYlh5MiRHD58GJvNxsWLFwkODnYUrjdnMUJ0VAzuOVL/gsueIxs3IqPTeYZ52Gw2Bs3pQ0JcAlPenml0nLsSHRVL9lveUNxzZCM6ylztbbXrwmp5b2al69iq7aw2frismPlRcurkP4v7Hdx3hsBFO6nrVd40hWujl+txYEswF06ad145WPM6tmLmh0arCt8XzXG9RenSpencuTOzZs3C09OTnTt3AuDm5gYkF7iurv8sjODk5OSY7zlr1iwiIiJYsmQJK1eupGHDhsTGxt5zhlsLw5s/t9vttG7dmuXLl7N8+XJWrFjBhg0bHOdsNluar+ns7ExS0j8D1tPLNWnSJPLnz09AQAArVqygYsWKqR5rZNEKcPbIeZxdnCn85D89ySUrFrfEpP4BM3qSu2BORrWZSGJC4p2fYAIhB89QsuITjs+zurtRqGRBQg6eNTDV7ax2XVgt782sdB1btZ3Vxg+XFTM/yuzYSedPF0M0fLke6+b+bnSMO7LidWzFzGIuKlz/X2hoKHv37nV8fuHCBcLCwihSpMhdv0ZkZCT58+fHzc2N0NBQfv311wee08vLi+XLl3PhwgUguZd3//79ANSoUYMVK1aQkJBAbGwsa9eudTzviSee4MCBAyQlJREVFeUodtP6Hh577DFcXFw4cuQIu3fvfuDfw78RcyOWzf476DqqPVnd3ahQuwy1faqxfu5Go6Nl6J1vuvFEucJ82HIccTFxRse5jZOzE1ncsuDk7JTq4y3Ld1G8QlHq+FUji1sWOg/z48S+U6Ybumi168JqeVOY/Tq+lRXbWW388Fkxc3q/o43NZCOLqzNOTjacnJySP3ZOrkCzZHEmi6tz8scu/3wMUKtuaTw8swJQpvzj+LatzrbfD2f+N5CG8rVKk69wbn43+WrCYM3r2IqZxVw0VPj/JSQkMHnyZM6ePUvWrFlJSkqiX79+lC9/9xPGu3TpwjvvvIOvry+PPfYYtWrVeuA5q1WrRr9+/ejZsyeJiYnEx8fTpEkTnn76aTp06EBwcDDNmzenUKFCVKhQgejo5OEXjRs3Zu3atTRv3pxixYo5hjzfqmfPnrz//vusWLGCJ554gmrVqj3w7+Hfmtx7BgNm9mRx6Awir0TxZa/vTH23rsAT+WjxVmPiYuJYfP47x/EvenxL0AJz7F3WeagfXT5s7fi8Yee6zP14GXM/XsZH7b/g7S9fZdDs3gTvPMqYlycbmDR9VrsurJbXCtdxWqzUzmrjzGO1zJ2HteaVEe0cnzfsUo85oxYzd9QS4zK9Vo8ub/6zGmzDphWZO2Mjc2dsZObi3jxWKBcAY796GYAufl8Sej6cBo2eZsCwlmTJ4sLlixEsnreVdWv+MuJbuE2jLvXYHLiL6KgYo6PcFatdx2DNzA+FRgrfF5vdrkHWj5KoqCg8PDyIi4ujZ8+eNGnSxDEf1iiNnIz9+v8FNpcsd36QidgT4o2OICIiN7FVf8boCPfmj4NGJ7hn9kRzD/1/FKxLMu5mzr1oVHu00RHuaN3WYUZHuI16XB8xr732GnFxccTGxlK7du3bFnoSERERERGxGhWuj5glS6xxp0lERERERORuqXAVERERERHJJDbN1LwvWlVYRERERERETE2Fq4iIiIiIiJiahgqLiIiIiIhkFg0Vvi/qcRURERERERFTU+EqIiIiIiIipqahwiIiIiIiIpklyegA1qQeVxERERERETE1Fa4iIiIiIiJiahoqLCIiIiIikklsWlX4vqjHVURERERERExNhauIiIiIiIiYmoYKi4iIiIiIZBYNFb4v6nEVERERERERU1PhKiIiIiIiIqamocIiIiIiIiKZRUOF74sKV5FHgD0h3ugIIv9NNgsOXLInGZ1ATMj2599GR7gnSYmJRkcQkUxmwXdcERERERER+S9R4SoiIiIiIiL3bNy4cXh5eVGmTBmOHDniOH7ixAnat2/PSy+9RPv27Tl58uRdncuIClcREREREZHMkmSBf3fpxRdfZP78+RQuXDjV8REjRtCpUyd+/vlnOnXqxPDhw+/qXEZUuIqIiIiIiMg9q1q1KoUKFUp17MqVKxw8eJAWLVoA0KJFCw4ePEhYWFiG5+5EizOJiIiIiIiIQ0REBBEREbcdz5EjBzly5MjwuefPn6dgwYI4OzsD4OzsTIECBTh//jx2uz3dc3ny5MnwdVW4ioiIiIiIZBKbBbbD+eGHH5gyZcptx99++2369OljQCIVriIiIiIiInKTrl274ufnd9vxO/W2AhQqVIjQ0FASExNxdnYmMTGRixcvUqhQIex2e7rn7kSFq4iIiIiIiDjczZDg9OTNm5dy5cqxatUqfHx8WLVqFeXKlXMMBc7oXEZsdrsF+qrF0ho5tTU6gojIw2Gz4BqH9ntYLlL+M5zcshod4Z4kxcYYHUFMaF3SEqMj3JUmz35odIQ7+unPj+/qcaNHj+aXX37h8uXL5M6dm1y5crF69WqOHTvG4MGDiYiIIEeOHIwbN46SJUsCZHguIypc5aFT4SoijywVrvKIUOEqjwIVrg/O3RaumcmC77giIiIiIiLyX6I5riIiIiIiIplFA17vi3pcRURERERExNRUuIqIiIiIiIipaaiwiIiIiIhIZtFQ4fuiHlcRERERERExtUwpXMuUKcP169dTHatRowZnzpy559fy8vLiyJEjd3zc+vXradq0Kb6+vhw/fhwfHx9iYh7s0uk3f1/x8fF8+eWXvPTSSzRv3pymTZvy6aefEh8f/8C+3uDBg5k3b94De737cebMGWrUqGFoBs/cHoxYNpAVkXOZd2IqL3SsY2ieO/Hp3YSvd37K6ugFDPy+t9Fx7iiLqwvvzujJvBNTWR4+h2/+GE+1JpWMjnVHVrsurJYXlDkzFX7yMVZfn8ugOW8bHeWOrNjGVstshbwtezRk8uZRrLz2PQOmd3ccf6FDbQIvfef4t/zKDH6OnsuTlYsbFzYdVmjnm1ktL1gzs5jHIztUeOHChfTt25emTZsCsHz58of69YYMGUJsbCzLli3Dw8OD+Ph4/P39iYuLI0uWLA/1a//X9JnyBglxCbR7rBulKhXnk1VDOP7nSUIO3vuNkMxw5VwY8z9ZRtWXKuGW1dXoOHfk7OLMpdOXGdBgBBdPXaZ6s8oMW/Qu3SsOIDTkktHx0mW168JqeUGZM1OfKa9zeNcxo2PcFSu2sdUyWyHvlfPXWDBuBVUbPoNrtn/e635buJXfFm51fN7o5bp0GuLD0b0nDUiZMSu0882slhesmVnMwxRDhb28vPjyyy9p3749Xl5eqXoVd+/ejbe3N23atGH06NHYbxoTfvz4cd58801at25Ny5YtWbZsGQBjxozhjz/+YMKECXTp0gVI3Tua0ddL7zUBfvnlF5o0aUKHDh2YOnWq4/jJkydZv349o0ePxsPDA4AsWbLQvn17smfPTmJiIuPGjaNFixa0aNGCcePGkZiYCCT3og4fPpxXXnmFxo0b8/777zu+x9DQULp27UrLli3p1asXV69edXzNqKgoPvjgA9q0aYO3tzejR492vGaXLl0YN24cHTt25MUXX2TChAmO502ZMoUmTZrg4+ODr68vERERAPz555906dKFVq1a0apVKzZs2OB4zvz582nUqBGdOnVi6dKl9/z/90HK6u5GndY1mT18ITHXYziwJZhtK3bTsEt9Q3NlZHPATrYu30XElUijo9yVmBuxzB21hNCQS9jtdnas3sOFExd5qkpJo6Oly2rXhdXygjJnpgbtaxN17QZ7g/YbHeWOrNjGVstslbxblu9m28o/iAiLyvBxjV6uw/r5WzIp1d2zSjunsFpesGbmhybJAv9MyDQ9rjExMSxatIgzZ87g7e2Nn58fWbJkoX///kyYMIEaNWqwZs0a5s6dC0BCQgLvvfcen332GaVKlSIqKorWrVtTqVIlhg4dyqFDh3j99dd54YUX7vrrubm5pfuauXLl4sMPP+THH3+kZMmSfPfdd47XOnjwIMWKFSNnzpxpfq1FixZx6NAh/P39AejWrRuLFi2iU6dOAPz999/Mnj0bm82Gn58fW7du5fnnn2f06NFUq1aNt99+m9OnT9OyZUvq1q0LwNixY6lWrRqffPIJSUlJvPfeeyxbtox27doBcP78eebPn8/169dp2LAhbdq0IXfu3MycOZNt27aRNWtWoqKiyJo1KxEREYwYMYLp06dToEABLl68SJs2bVi1ahXnzp3jm2++ITAwkHz58jFy5MgH8v/7fhUuXYikxCTO/n3ecezYXyepWK+8gakebbkK5KRI6UKEHDhtdJR0We26sFpeUObM4u6Zja4j2/J+o9E0eT3t9y8zsWIbWy2z1fJmpMATeXm6TlkmvjXD6Ci3sVo7Wy0vWDOzmIuhhavNZnN83KxZMwCKFClCjhw5uHDhAvHx8WTLls0xp7JZs2YMHz4cSO7lPHbsGO+++67jNeLj4zl+/DilSpW649dO6+vZ7fZ0X9PJyYny5ctTsmRyr1P79u1T9WRmZNu2bfj5+eHqmjx0plWrVqxfv95RuDZs2BA3NzcAypcvz6lTp3j++efZsWMHw4YNA6Bo0aLUqlXL8ZpBQUH89ddfzJo1C0guxAsWLOg436RJE5ycnPD09KRUqVKcOnWKokWLUqJECQYOHEjdunVp0KABHh4e7N27lzNnztCtWzfH8202GyEhIezdu5cGDRqQL18+x/e9du3au/q+H4ZsHlm5Hn4j1bHr4Tdw98xmUKJHm7OLM0Pm9eWXORs5ffic0XHSZbXrwmp5QZkzy6sfteOn73/j0pkrRke5K1ZsY6tltlrejDTsVIf9Ww6bctqJ1drZannBmpnFXDKlcM2TJw/Xrl0je/bsQHJvaVRUFHny5HE8JqVwA3B2dnYMe02P3W4nd+7c9z13Na2vZ7PZ0n3N9evXp/ta5cuXJyQkhPDw8DR7Xe12e6oiHVIX7ff6vae85tSpUylatGia59N6TWdnZxYvXsyePXvYvn07rVq1YsaMGdjtdsqUKcP8+fNve509e/bcMUtmio6KwT1H6l9w2XNk40ZktEGJHl02m41Bc/qQEJfAlLdnGh0nQ1a7LqyWF5Q5M5R6thiVX3yGnlUGGR3lrlmtjcF6ma2WNyMNO9dh4fgVRsdIk9Xa2Wp5wZqZHxabtsO5L5kyx7V27dosWrTI8fmiRYt49tlnyZYt4zssJUuWJCYmhl27dgHw008/ERmZPE+wRIkSZM2alcDAQMfjjx07RlRUxnMrMpLRa1auXJmDBw9y8uRJAJYsWeJ4TPHixfHy8mL48OGOr5+YmMgPP/zA9evXqV27NgEBAcTHxxMfH09gYGCq3tP01KxZ0zHH9vTp02zbts1xzsvLi+nTpzuK3LCwME6fzngoZ1RUFGFhYVSvXp2+fftSunRp/v77bypXrkxISAjbt293PPavv/7CbrdTo0YNNm7cyJUryXf/jZ7jevbIeZxdnCn85GOOYyUrFtek/odgwIye5C6Yk1FtJpKYcOebKUay2nVhtbygzJmhYv0KFCyen/knp7Lo7Le0HeBN3VY1mLrrU6OjpctqbQzWy2y1vOkpX+sp8hbKzaaAXUZHSZPV2tlqecGamcVcMqVw/eCDDzh79ize3t74+PiwadMmxo8ff8fnubq6MmnSJD766CPatGnD/v37efzxxwFwcXFh2rRprFmzBm9vb5o3b86oUaOIi4u775wZvWbevHn5+OOP6dGjBx06dMDZ2TnVcz/99FOKFy9O69atadGiBd7e3ly4cAFXV1fat29PmTJl8PPzw8/PjzJlyjjmombkgw8+YMeOHbRs2ZIJEybw/PPPO84NHToUJycnfHx88Pb25s033yQ0NDTD14uKiqJ37954e3vTokUL8uXLR+PGjcmZMydTp07l66+/pmXLljRt2pQpU6Zgt9spW7YsPXr0oGPHjnTq1CnVcGQjxNyIZbP/DrqOak9Wdzcq1C5DbZ9qrJ+70dBcGXFydiKLWxacnJ1SfWxm73zTjSfKFebDluOIi7n/n6nMYrXrwmp5QZkzw5rv1tP1qb70eO59ejz3Pqu+XceONXsY0vQTo6Oly2ptDNbLbJW8qd/rbLe91zXqXJfNgbuIjnqwWxM+KFZp5xRWywvWzCzmYrPb1VctD1cjp7YP9PU8c3swYGZPnmtUkcgrUcwYMp/fftz8QL/Gg9RlRFteGZH6RsWcUYuZO2pJOs8wVoEn8jH/5DfExcSRmPDPsnJf9PiWoAXmbWerXRdWywvKnCbbw7sJ1WV4Gx5/8jHGvTLlwb6w/cEuF6nr4uHLjLxObln/1fNf/sCPLsNapTo2d7Q/8z4JIItbFhaenMzHHb/ifxsO/quvkyIp9sEXwLouHr6HnXldkjn/trpV03JDjI5wR2sPjTU6wm1UuMpD96ALVxER03iIhetD84ALV3k0/NvCNbM9jMJVrE+F64NjxsLVgu+4IiIiIiIi8l9imn1cRUREREREHnlJGvB6P9TjKiIiIiIiIqamwlVERERERERMTUOFRUREREREMovWxr0v6nEVERERERERU1PhKiIiIiIiIqamwlVERERERERMTXNcRUREREREMovmuN4X9biKiIiIiIiIqalwFREREREREVPTUGEREREREZHMoqHC90U9riIiIiIiImJqKlxFRERERETE1DRUWEREREREJLMkaajw/VCPq4iIiIiIiJiaClcRERERERExNQ0VloduXdISoyOIiIiIiJiDPcnoBJakHlcRERERERExNRWuIiIiIiIiYmoaKiwiIiIiIpJZ7FpV+H6ox1VERERERERMTYWriIiIiIiImJqGCouIiIiIiGSWJA0Vvh/qcRURERERERFTU+EqIiIiIiIipqbCVURERERERExNc1xFREREREQyi7bDuS/qcRURERERERFTU+EqlhEVFUVSUhIAR44cYfXq1cTFxRmcKmPbtm27q2MiVhEWFmZ0BBGR/7TExERat25tdAyRTKfCVSzjlVdeISYmhkuXLvHGG2/g7+/P8OHDjY6VofHjx9927LPPPjMgyd3Rm6Gk588//+SFF17Az88PgH379vHhhx8anOrOtm3bxrx58wC4fPkyJ06cMDjRo8eKbRwVFcWBAweMjnFXdANUbuXs7Ezu3LmJjY01OspdqVmzJrVq1Ur333+S3W7+fyakOa5iGXa7HXd3d1avXk27du3o06cP3t7eRsdKU0hICCdPniQqKoqNGzc6jkdGRhIdHW1gsozd/Gbo5uZmdJw7qlmzJjabLd3zZvzjrnXr1hlmXrp0aSamuXtjx47lu+++47333gPgmWeeYfDgwQanytj06dPZuHEjly5d4uWXXyYhIYGhQ4fy448/Gh0tXVeuXGHs2LGcP3+e+fPnExwczN69e+nYsaPR0dJkxTbeuHEjw4cPx9nZmaCgIPbt28fXX3/NtGnTjI6WpvHjxxMQEJDq2GeffYa/v79BidI3f/78DM937tw5k5Lcu5MnTzJkyBBCQ0MJCgriwIEDBAUF0adPH6Ojpal48eJ07tyZl156CXd3d8dxM7bxsmXLgOT3t2vXrtG+fXvsdjvLli2jYMGCBqcTK1HhKpYRGxtLXFwcmzZt4pVXXgHAycmcgwb27NmDv78/ly9fZsaMGY7jHh4eDBo0yMBkd6Y3w4cr5f//hg0bOH78OG3atAHA39+f8uXLGxktQ/Hx8Tz55JOpjmXJksWgNHdn1apVLFu2jLZt2wLw2GOPERUVZXCqjA0bNox69eqxYMECAEqWLMnAgQNNW7hasY2/+uorli5dSrdu3YDkmzCnTp0yONXtrHgDdP/+/QBcvXqVnTt3OnrTtm3bRp06dUz5PpJi5MiR9OzZk4kTJwJQrlw53n//fdMWrtevX+epp57i+PHjRke5o8KFCwOwa9cux+gMSP599/LLLzt+FkXuRIWrWEazZs2oWbMmJUuW5LnnnuPSpUum7RX08/PDz88Pf39/WrVqZXSce6I3w4erevXqAEyePJk5c+Y4el9feOEFXn31Vd5++20j46XL1dWV69evO/IePXrUtD9/KbJmzXpbcZ1Rb7cZhIaG0rFjRxYtWgQkt7tZb9CBNdsYIH/+/Kk+d3V1NShJ+qx4A3Ts2LEA9O7dm+XLl1O0aFEATp8+zaRJk4yMdkeRkZHUq1fPkdPJycnUN+dS2tpKLl68SFhYGHny5AGS10y4dOmSwakMYtKhuGanwlUs4+233+aVV17Bw8MDJycn3N3dmTx5stGxMuTk5ER4eDg5c+YE4Nq1a/z++++0bNnS4GTp05th5ggNDSU2NpasWbMCEBcXx8WLFw1Olb4ePXrwxhtvcPHiRQYPHsymTZtMPV8bknv/du/ejc1mIykpiWnTpvHUU08ZHStDLi6p35YjIiKwm/gPHCu2cfbs2bl8+bKjwN6xYweenp4Gp7qdlW+Anj171lG0AhQtWtT0c5+dnZ2Jj493XBehoaGmvmkEcPz4cYKDg1MtVOnr62tcoDvo2rUrPj4+vPDCC0DysP233nrL4FRiJTa7md8RRUi+U1u0aFGOHj2a5vlbhy+aScuWLVmxYkWqY76+vgQGBhoT6C5Z7c1w/vz5TJs27bY3w06dOhmcLH2ff/45GzZsoFmzZgCsXbuWBg0a0K9fP2ODZeD06dNs2rQJu91OnTp1KFasmNGRMnTp0iUGDRrEzp07sdlsVK1alQkTJpA3b16jo6VrxowZnDp1im3bttGzZ08WLFiAt7c3Xbt2NTpamqzYxn/99RcjRozgzJkzlC1blpMnT/LNN9/w9NNPGx0tXadOneLUqVMkJiY6jtWvX9/ARBl79dVXqVatmmMI+bJly9ixYwezZ882NlgGAgMDWbt2LYcPH6Z169YEBgbSv39/WrRoYXS0NM2ZM4dFixZx6dIlnnnmGXbv3k21atVS9c6bUXBwMLt27cJut1OjRg3KlCljdCRDNC1sziHoN1t71nydQypcxfTeeustvv32W7y8vG47Z7PZ+PXXXw1IdXfSKly9vb1ZuXKlQYnuTG+GmScoKIidO3dit9upVasWDRo0MDpShuLj4zlx4gQ2m40SJUrc1jtoVtHR0SQlJZE9e3ajo9yVFStWEBQUhN1ux8vLCx8fH6Mj3ZHV2jgyMpI9e/YAULlyZXLkyGFwovRNmjSJxYsXU6pUKUcPoM1mY86cOQYnS19oaCiffPIJO3bsAJIX0hs6dKhp1x5IsXv3bn777TfHz17VqlWNjpSuFi1asHjxYjp27Mjy5cs5cuQI3377rWOOrllFRUUREhJChQoVjI5iqKaFehsd4Y7Wnv/a6Ai3scZfHfKf9u233wLJf+RbTf78+fnll19o3LgxAD///LOpeyIAFi9ezJIlS+jYsSMzZ850vBmaXZEiRUhMTLTUm6GXlxeVKlVyDHE2s927dzNgwACyZs2K3W4nLi6OSZMm8dxzzxkd7Tbpjc5IYeZRGpB8w8vM0wludvOiQSk8PDwoXbq0KYffQvIfztmzZ6d+/focOXKETZs20ahRI1POc4Xk0Rjr16/Hw8PD6Ch3rWDBgnz11VdGx7hnVatWpWrVqsTFxREeHm50nAy5urri7u5OUlISdrud0qVLm3KRsZtZbUVvMR8VrmIZJ06c4PHHH8fNzY1NmzZx6NAh2rdv75g/akZDhw6lV69ejrmAzs7OTJ061eBUGdObYeb4888/6devH0lJSWzcuJF9+/axePFiPv74Y6Ojpemjjz5iwoQJVKtWDUguZEeOHHnbiAIz6N69e7rnzD5K49NPP6V3795ky5aNV155hYMHDzJq1CjT9rpOnTqVffv2OUY4HDlyhLJly3LhwgVGjx7tGL5vJq+88grz5s3j+vXrvPHGG5QuXZpNmzbx6aefGh0tTfnz57dU0QrJPfDffvstp0+fZuLEiRw7dowTJ07QsGFDo6Olq3///nz00UdkyZIFHx8frl69yltvvcUbb7xhdLQ0ZcuWjfj4eMqWLctnn31GoUKFiImJMTpWhqyyoreYl7lnnYvcpF+/fjg5OXH69GlGjBjB6dOnTbuyYopSpUqxZs0avvnmG6ZOncrq1aspWbKk0bEydOub4dy5cy3zZpgy3M8Kb4Yp+6Lmzp0bSM6cMnTRjNzc3BxFKyT3TKQsLGU2QUFB6f4zc9EKsHXrVjw9Pdm8eTMFCxbk559/5vvvvzc6VrqeeOIJFi9eTEBAAAEBASxevJgKFSowZ84cvvjiC6PjpSllT/ANGzbQrl07Zs6cyYEDB4yOla5KlSrx7rvv8ssvv7Bx40bHPzMbOXIkCQkJBAcHA8mLeE2ZMsXgVBk7ceIEnp6ebNiwgRo1arBx40ZTr0cxYsQI4uPjGTx4MOHh4ezatYvx48cbHeuOrLCid6aw283/z4TU4yqWkbI0/caNG+nYsSPdunUzbS/EzZydncmdOzexsbGEhoYC8PjjjxucKn03vxlOmjSJM2fO6M3wIbDavqhVq1ZlxYoVjiGsK1eupF69eganurOjR4+mmmdXqlQpgxPdnV27dtGoUSMKFixo6u1lgoODUy1qVKFCBQ4cOECpUqVMuxqylfYEB9i3bx8Ac+fOdRyz2WymXpzpyJEjjBs3js2bNwPJKzknJSUZnCpjCQkJQPLPXv369cmWLZupr4vSpUsD4O7uzieffGJwmrtjlRW9xbxUuIplpBR+QUFB9O/fH8C0fxil2LZtG4MHD+bKlSs4OTkRHx9Prly52LZtm9HR0qU3w8xhtX1RAwICmDVrFsOGDQOSt+/JlSsX8+bNw2azmfKaDgwMZMKECY5Fr7799lvee+89U88fzZs3L8OGDWPLli10796dhISEVCvJmk22bNlYtWqVY+XVVatW4ezsDJh3P1cr7QkOqQtWq7j1JlxsbKzp369LlSrF66+/zvHjxxkwYIBpRxrd6Uby+++/n0lJ7t17771Ht27dOHPmDF26dHGs6C1yt1S4imV07dqV5s2bU6tWLZ555hlOnz5t+uLks88+Y/bs2fTv35+AgACWLFnCuXPnjI6VoZMnTzJkyBDHTYIDBw4QFBREnz7mXbp9wIABlnsztNq+qMuWLTM6wj37/vvvCQgIcPTGX7p0iTfeeMPUhevEiRNZsWIFbdq0IWfOnJw5c4bXXnvN6FjpGjt2LAMHDmTo0KHYbDZKlSrF+PHjiY6ONu0f0FbbE9xut7N06VJCQkJ47733OHPmDBcvXjTlwmgpqlatyrRp04iLi2PHjh3MmjUrzZ0BzCSlh7hMmTK4u7sTGhrKgAEDjI51G3d3d6Mj3LeKFSsyZ84cy6zoLeaj7XDEspKSkkhISDD1kNBWrVrh7+9PixYtWLVqFQCdO3dm/vz5BidL36uvvsrrr7/OxIkTWb58OUlJSXh7e7N69Wqjo2XISttbpLDavqhWk9Z2VGkdk/uTmJjI0qVLad++PVFRUQCWWUQoMjKSEydOEBsb6zh28xxuMxkzZgxXrlzhwIED/PTTT1y9epVu3bqxdOlSo6OlKz4+nhkzZqTa1ql79+6W2UJLHo6oqCjc3d1xcnLiyJEj/P3336Ze0fthalqwp9ER7mhtqPk6APQbRCzDbrezaNEitm7dis1mo3bt2rRr187oWBlKeZMuWLAgQUFBFC5cmAsXLhicKmORkZHUq1ePSZMmAf/MLTazTz75hA8++CDVnK+UY2ZWtGhROnXqZHSMu3L+/Hk+++wzgoODU/2xb+bFjp544gm++uor2rdvj81mY9GiRRQtWtToWBmyUjs7OzuzfPly2rdvb5mCFWDNmjWMGzeOiIgIChQowKlTpyhbtiwBAQFGR0vTjh07CAwMxM/PD8CxZoKZZcmShZ49e9Kzp/n/OE8RHBzMiBEjCA4OJi4uznH80KFDBqZKX3pDhs060gGst6K3mI8KV7GM8ePHc+jQIVq1agUkz18LCQkx/S/p8PBw3nnnHQYMGEBkZCRDhw41OlaGnJ2diY+Pd8xPCw0NNfUCFZC8Ncutdu3aZUCSO6tZs2aGc//MOFcUkrd2atasGYcOHWLChAn8+OOPPPHEE0bHytCoUaMYPXo0LVu2dNzsMut2Qyms1s61a9fmp59+okmTJkZHuWvTpk3D39+fN954g8DAQLZs2cIvv/xidKx0ubm5pfqdYfZFjiC5Z23q1Kls374dm81GjRo16NWrl6lvcIwcOZJ+/foxduxYZsyYwfz588mePbvRsdJ185Dh2NhYNmzYkGqhNDNKWdF79erVtGvXjj59+uDt7W10LLEQFa5iGZs3byYgIMDRi9m0aVNatWplysL1+++/5/XXX6dQoULkzJmTihUrsm7dOqNj3ZVOnTrx9ttvc/XqVSZPnkxgYKBjMSyzWbt2LWvXruXs2bO88847juNRUVGm3aolZa7o0qVLuXbtGu3bt8dut7Ns2TIKFixocLr0Xb16lbZt2zJnzhwqV67Ms88+y6uvvmp0rDTdfNOiU6dOdOzYEUheLOjYsWPkyZPHqGh3ZKV2Bpg3bx7Xrl0ja9asZMuWDbvdbtrFulK4uLiQN29ex6JXzz//vKnnuJYuXZoVK1Zgt9s5c+YM06dPp0qVKkbHytDQoUPx8PBg2LBh2O12AgICGDp0KF999ZXR0dIVFxdHrVq1sNvtFChQgP79+9OlS5cM94U20ttvv53q87feeouBAwcalObuWG1F74cqSTM174cKV7GUm+86m3XFSkjeKuT1119n9OjRph1+lh5fX1+KFCnCb7/9RnR0NOPGjaNq1apGx0pTiRIlaNCgAfv27XOsHAvJ8+xq1aplXLAMFC5cGEgurubNm+c4PmzYMF5++WXHxuxmkzJc3N3dnXPnzpEvXz7TLjR287Cz48ePO7bASSmqzDw30ErtDNZctMvV1RW73U6xYsWYO3cuhQsX5urVq0bHStfgwYP59NNPuXTpEu3atcPLy8v0e5gfP37csa4DQJUqVRwrT5tVymrYOXPmJDg4mIIFC3L27FmDU9297Nmzm37/cqut6C3mo8JVLKNOnTp069YNPz8/bDYbAQEB1KlTx+hYaXJzc6NHjx639QSm+PLLLw1IdfeqVq1q2mL1ZmXLlqVs2bJ4eXmRK1cuo+Pck4sXLxIWFubo/QsLC+PSpUsGp0pf1apVuXbtGh07dqRVq1a4urqadnjozcWUr6+vqQvVW6XVzi+99JLRsdKVciPGSt555x2ioqJ47733GDlyJJGRkYwYMcLoWOny8PBg9OjRRse4J48//niq329Xr16lSJEiBqfKWLNmzbh69Srdu3enY8eOJCUl0bdvX6NjpevmOa52u539+/dTokQJAxPdmdVW9Bbz0arCYhlJSUksWrSIbdu2YbfbqV27Nh06dDBlz+u1a9fYunUrn332WZpvfCmLbJhJ3759M2xLMxfbCQkJLFu2jEOHDqVatGTs2LEGpsrY/Pnz+fbbbx09xRs3buStt94y7WJNkZGRju2nzp07R1RUlGPPXzPz8/Oz3KiHFFZoZystJmVl27Zt49SpUyQkJDiOde7c2cBEaUspps6dO8cff/zBCy+8AMCGDRuoVasW48aNMzLeXYuPjyc2NtbUc3KnTJni+NjZ2ZknnniCxo0bm34xxVOnTnHq1KlU+1PfvLDif0WTfOYcgn6zny5PNzrCbdTjKpaxY8cOOnbs6JivBslv5mYcEporVy6aNWtG3rx5qVGjhtFx7krKHxhWNHz4cBITEx3XyKpVq0zfY9y5c2eqVKnCrl27sNvtdO7cmTJlyhgdK00p+VK2kXn88ccNTvRoi46O5sKFCyQmJuLk5MTRo0d58sknjY6VJqstJgXJ8+xWrFjB6dOnUxWCZlwvAWDgwIEcPnyYsmXLOoazmlXKgkFPPvlkqmvW7DsApLBSUXXrHFcrmDhxIkuWLKFUqVKOua02m820bSzmo8JVLGP8+PG39Zx89tln+Pv7G5TozipWrMjnn3/OmTNnmDhxIseOHePEiRM0bNjQ6Gi3MWMv8N3at28fK1euxNvb29Fr2a9fP6Nj3VHKUGezs9lsFC1alPDwcHLmzGl0nDs6evSo4+PY2FiOHTvGzYOLzFoEQnJP/IQJE8iVK5djBITNZjNtD6bVFpOC5KHC8fHxVKxY0RL7R+7bt4/Vq1ebvmgFaxZTKaxWVKU1DcnT05NKlSrRqlUrUy569NNPP7F+/XpT92SLualwFdMLCQnh5MmTREVFsXHjRsfxyMhIoqOjDUx2ZyNHjiR//vwEBwcD8NhjjzFgwABTFq4p+vTpw8cff+yYM3r16lVGjhxp6qHCKYs7ODs7Ex0djaenJxcvXjQ4Vdpat26d4ZBss87HdHd3x8/Pj3r16qXahsGMvVS3rgJ684JXZi4CIXlF8lWrVllm7qjVFpOC5PeUtWvXGh3jrhUrVoyYmBhTb81yq+joaKZOnerYd/3555+nR48eZMuWzeho6bJaUZUvXz72799P8+bNgeT9ifPnz8/atWsJDg5m2LBhBie8Xf78+S3Tvg+dVhW+LypcxfT27NmDv78/ly9fZsaMGY7jHh4epl9Z8ciRI4wbN47NmzcDyav+mX0PvtOnT6da6Ch37tymX6kwZ86chIeHU7duXbp160bu3LnJly+f0bHSZPZrNj3FihWjWLFiRse4K0FBQUZHuG/58+e3TNEK1lq0K0XRokWJioqyzB/Q77//Pi+//DJVqlRJ1UNsxptGKT7++GMSExMd+5YvXbqUjz76yNTrDlitqAoODmbu3LmOa6J9+/b07NmTadOm4evra2y4dFSqVIl3332XJk2apFpN2Ky92mI+KlzF9Pz8/PDz88Pf359WrVoZHeee3LpIQmxsLGZfDy0xMZHExETHsLT4+Hji4uIMTpWx6dOn4+zsTP/+/VmxYgVRUVGmfeOuXr16qs9v3LgBpN5M3kyOHTvG8ePHHUMAP/nkE6KiogAc+/DJv5cyvLl27dqMHz+e5s2bp/rDzqzDm1NuxPj6+lK9enUiIyN56623HAWLmaQsHOTp6Unr1q2pW7euJQrB0aNHU7BgQTw9PS0xXBj+mb6R4rnnnqNly5YGJkpfykguqxVVly9fTvU3houLC+fPn8fV1dW0Q+D37dsHwNy5cx3HzDwcW8xHhatYRqtWrdi4cSPbt28HoGbNmqb/ZVe1alWmTZtGXFwcO3bsYNasWXh5eRkdK0N16tShf//+jqJkzpw51K1b1+BUGUv5Y87Jycm0BeutTp8+zYABAzh06BA2m43y5cvz2WefUbRoUaOjpfLVV1/RrFkzx+e///47r7zyCjdu3GD69Ol8/vnnBqZ7dNw6vPmnn35yfGz24c0pUhbtMuvNuZSbQyVKlDD9tiE3u3DhgqWGNqe4ceOGo83NPK3n5pFcYJ2iqnr16nTv3h0fHx9sNhsrVqygSpUqXL9+3bSF681t+59n0t+TZqftcMQyPv/8c4KCgmjevDl2u52ffvqJF154wdSL8MTHxzNjxgyCgoKw2+14eXnx1ltvmfqueXx8PN9++y0bNmzAbrfzwgsv0L17d9O+EULyTYxb542mLFIxcOBA8ufPb1Cy9L322ms0b96c1q1bA+Dv78+qVauYNWuWwclSu3U7GV9fXwIDA4HklZHnz59vUDIxqwYNGrBhwwajYzwy+vXrx9ChQylQoIDRUe7a9OnTWblyJc2bN8dms7F69WpatmzJm2++aXS0R0Z8fDwLFy5k586d2O12atSoQYcOHUy9HY7dbmfp0qWEhITw3nvvcebMGS5evMhzzz1ndLRM1yS3+X8Wfro6484PymQqXMUyXnrpJQICAhx3cG/cuIGfnx8///yzwcnSd+zYMUqVKnXHY/LvTJ48mcjISMdQ8sDAQMdcpf379zNt2jQj46XJx8eH5cuX3/GY0by9vVMN+bv5+r31nDwYR44cYefOndhsNmrUqGHKYcI3r9x8q1dffdUxr9+MoqKimDp1Ktu3b8dms1GzZk169uxp2vmNb7zxBvv376dy5cqphrCaecE8wDFCKmXf9Xr16hkd6Y6sNqrLasaMGcOVK1c4cOAAP/30E1evXqVbt26mXZTwYVLhen80VFgso0CBAqlWJHRzczP9Hej33nvvti180jpmJinzwG5l1vlfkDx8dcmSJY7PBw8ezMsvv8y8efMcKy6ajZOTE8ePH6dkyZIAnDhxwpQ98fHx8akWskkpWqOiokw/99mK5s+fz7Rp02jQoAF2u51vv/2WHj160KlTJ6OjpXLr0Oab3VxcmdHQoUPx8PBg2LBh2O12AgICGDp0KF999ZXR0dLUokULWrRoYXSMe1a/fn1LFX63juqaNGkSe/fuNe2orhMnTjB06FBCQ0MJCgriwIEDBAUF0adPH6OjpWvHjh0EBgY6tt/LnTs3sbGxBqcyiMkX6jQrFa5iGeXKlePNN990/MJbvnw5zz33nGNhBTO9QYaFhREWFnbbHpKRkZGOxXjM6uZFgmJjY9mwYQNPP/20gYnuLCIigmvXrqXawufSpUvA7QtkmUX//v3p3Lkz5cqVw2azcejQoXRvGhipefPmDB06lDFjxjiK16ioKIYNG5Zq7qs8GHPmzCEwMJC8efMCyb9LOnbsaLrC1corNx8/fpxVq1Y5Pq9SpYqpC0Mr7bHdt2/fDLf7MnMv8U8//ZRqVFfXrl3x8/MzbeE6atQoevbsycSJE4Hkv5Hef/99Uxeubm5uqa4Ps++yIOajwlUs49ChQwAsWrTIcWzPnj3s2bPHdAsorFy5kh9++IGLFy+m2kPS09PT9HN8bt1A/q233mLgwIEGpbk7Xbp0wcfHx3EN/P7777z55ptcv37ddHNnPv30UwYPHky9evX48MMPyZYtG3a7nUqVKpEnTx6j492mZ8+eDB48mLp161K8eHEATp48yYsvvkjv3r2NDfcIyp8/v6NoBciTJ49pt3ayqscff5ywsDDHz9vVq1cpUqSIwanSd+XKFebOncvp06dJSEhwHDdjEfjCCy8YHeG+WW1UV2RkJPXq1WPSpElA8iges96oTVG6dGlWrFiB3W7nzJkzTJ8+nSpVqhgdSyxEc1xFHpKbh/lZmd1up0WLFqxevdroKBkKDg5m165d2O12qlevTtmyZY2OlKabFzu6deEjMwsJCeHgwYMAlC9f3jJ7ulrNpEmTuHLlCm3atAEgICCAQoUK0ahRI8C82+JYSb9+/fjjjz8cRdaGDRuoVauW44aB2aZFdOrUiVKlSvHss8+mmk5g5p7Y0NBQChYsaHSMezJmzBiOHTuWalRX+fLlHTc/zXRzHKBdu3bMnz+fdu3aERAQQGhoKD179sTf39/oaOmKiori008/dYzY8PLyYsiQIWTPnt3gZJmvSc7XjY5wRz+Ff290hNuox1UsZdOmTWzduhWbzcbzzz/P888/b3SkDK1Zs8ZyhevNw1Xtdjv79++3xNYRZcuWNW2xerOb7xVa6b5hsWLFVKxmgpQhrNu2bUt1fMmSJZbZFsfsnnzyyVQ3ANq1a2dgmjuLiIjg448/NjrGPWndujWVK1emc+fO1KxZ0+g4d8VKo7og+YbG22+/zdWrV5k8eTKBgYH079/f6FgZ8vDwYPTo0UbHMAcLvf+biQpXsYzvvvuO5cuXOxbb+fTTT/H19eWNN94wOFnabDYbRYsWJTw8nJw5cxod567dPMfV2dmZDh060LhxYwMT3dmePXv47LPPOH36NImJidjtdmw2221//JtBXFycY97zzR+nUI/af1tgYCA5cuQwOsYj7dbpEGb31FNPWa4HMygoiDVr1vD5558TGRlJ586d8fHxMe3KzWC9PUZ9fX0pUqQIv/32G9HR0YwbN46qVasaHStNd9o2rXPnzpmURKxOQ4XFMry9vfnxxx9TLRDTsWNHU2/HMXDgQP744w/q1auXqiA021A0q2vatCm9evWiUqVKODk5OY4XLlzYwFRp8/LySvecetT+2+x2Oz4+PqxYscLoKI+8zZs3c+jQoVQrmpq1oLXqdjgp9uzZw7vvvktERAR+fn706tUr1TxuM4mMjOTEiROprotq1aoZmChtiYmJtGvXjmXLlhkd5a4MGTIESJ5PvnPnTmrVqgUkjyypU6eOaVf0fpia5HjN6Ah39FOEufaVB/W4isXcfLfWzHduU6Q1vHL37t0Gpbk7VloIJEXWrFnx9vY2OsZdsfJqrPJwWXWUhtVMmDCBffv2cfToUV588UV+/fVXxx/SZmTV7XDOnj3LwoULWbVqFbVq1aJt27Zs376dN954g8DAQKPj3WbNmjWMGzeOiIgIChQowKlTpyhbtqwp1yFwdnZ2bCVj9u2nAMaOHQtA7969Wb58OUWLFgXg9OnTjsWl/mvsWlH5vqhwFct4+umnGTJkCG3btsVms7FkyRLTb9OScgf/4sWLBAQE4O/vb/p5jX369KFUqVLUqlXLlPuKpqVevXps3LjRdHOQRO6Vu7s7fn5+GqXxEG3cuJGAgABatWrFRx99RO/evRk1apTRsdJl5kWY0tOjRw+OHDlChw4d8Pf3J3fu3AA899xzrFmzxuB0aZs2bRr+/v6OwnrLli388ssvRsdKV/HixencuTMvvfRSqt8VZh52e/bsWUfRClC0aFFOnDhhYCKxGhWuYhkffvghX3/9tWNif+3atenVq5fBqdKXkJBAUFAQy5Yt43//+x8JCQnMnDmTSpUqGR0tQ1ZcCGTRokV8++23ZM+eHVdXV1PPcRXJiBbBevhcXV1xcXHBZrMRHx9PwYIFuXDhgtGx0pXW3qienp5UqlSJVq1apZoeYRY+Pj40btw4zZufN++hayYuLi7kzZuXxMREAJ5//nkmT55scKr0Xb9+naeeeorjx48bHeWu5cqVi6+//pq2bdsCsGzZMsf+6yJ3Q4WrWIa7u7vp9xNNMXbsWFavXk3p0qXx8/Pjyy+/pFmzZqYvWsGaC4FYZZ6PyJ2YdZ7loyR79uxER0dTuXJlBg8eTP78+U09uiR//vzs37/fsTDhmjVryJ8/P2vXriU4OJhhw4YZnPAf0dHRADRo0IC4uLjbzt+8T6rZpNz0LFasGHPnzqVw4cJcvXrV6FhpSkxMpFChQvTt29foKPdk3LhxfPLJJ46pPTVr1mTcuHEGpzKIyUffmZUKV7GMqKgopk6dyvbt27HZbNSsWZOePXuacq7rjz/+SOXKlenevbtjK4Bb75ibVUREBC1btrTUQiCFCxcmISGBEydOYLPZKF68OC4u+vUm1nPlyhXGjh3L+fPnmT9/PsHBwezdu5eOHTsaHe2RMWnSJJydnRk0aBCzZs0iMjLS1IvDBAcHM3fuXFxdXQFo3749PXv2ZNq0afj6+hob7haVK1d2vNfdOi3GZrM5tpwxo3feeYeoqCjee+89Ro4cSWRkJCNGjDA6VpqcnZ3ZtWuX0THuWcGCBU39sybmp7/sxDKGDh2Kh4cHw4YNw263ExAQwNChQ035S3Dz4KhBmAAAIfhJREFU5s2sXLmS8ePHEx4ejq+vr2P4kdlZcSGQffv20bdvX8cd84SEBCZPnkyFChWMjiZyT4YNG0a9evVYsGABACVLlmTgwIEqXB+gLVu24OPjA+CYbrJ8+XLHMbO5fPkyWbJkcXzu4uLC+fPncXV1dRSzZhEcHGx0hPtWoEABPD098fT0ZPbs2QAcO3bM2FAZaNCgATNnzsTX1zfVHFcz92pD8krCp06dSrX4o5nn5Yq5qHAVyzh+/HiquTFVqlQxbYGVI0cOOnfuTOfOnQkODmbp0qXExMTQuXNnvL296dChg9ER02XFhUA++eQTxowZ41gZdPv27Xz88ccsXLjQ4GQi9yY0NJSOHTuyaNEiIHn4ohnnMFrZ7NmzbytS0zpmFtWrV6d79+74+Phgs9lYsWIFVapU4fr166YrXG929epV/vzzT2w2G88++6zp5zK+9957t60gnNYxs/jss88c/7XZbI61Hczcqz148GD2799P+fLlTT08P1Mkaajw/VDhKpbx+OOPExYWRp48eYDkN8UiRYoYnOrOypYty7Bhwxg0aBDr1q0jICDAlIXrDz/8QNeuXRk/fnya5828qml0dHSq7Sxq1qzpmGslYiW3DnGPiIgw/UrkVrFv3z7++usvrl69yvz58x3Ho6KiiI+PNzBZxoYPH87ChQv5+eefsdvt1KlThw4dOpAlSxYWL15sdLw0bdq0iYEDB1KuXDkADh8+zGeffcbzzz9vcLLbhYWFERYWRmxsLMeOHXP8vEVGRnLjxg2D06XPir3be/fuZdWqValGEIjcCxWuYhnu7u74+PjwwgsvALBhwwZq1arlKLTMXFgBZMmShWbNmtGsWTOjo6QpZT7rzUOOrCJbtmxs377dMZ94586dph8uJZKWxo0bM3z4cK5fv46/vz8LFiygdevWRsd6JISGhrJ//36io6PZv3+/43j27Nkd+0yaUZYsWejSpQtdunQxOspd+/zzz5k/fz6lSpUCkofcDhw40JSF68qVK/nhhx+4ePEi3bp1cxz39PTkzTffNDDZo+exxx4zOoJYnM2uW7liEVOmTLnt2O7du6latSqg1Tj/rYSEBNatW0fOnDmpXbs2P/zwA1u3bqVEiRL07t0bT09PoyOm66+//uKdd95xDJuLj4/nq6++Mv0+vyJpWbFiBUFBQdjtdry8vEw7hNWqNm/eTJ06dYyOcUcpo2DGjRuX5uJ+Zr5Z27JlS1asWHHHY2Yybdo0evToYXSMO6pZs2aa14MVtoEbMWIER48epWHDhqmGuf8X57i+lNX83/PPMfPv/KBMph5XsYyUwvTixYsEBATg7++P3W5XwfqAfPTRRxw5coS4uDiKFClCbGwsDRo0YNeuXYwYMYJJkyYZHTFdFStW5JdffuHEiRPY7XZKliypoUhiSdu2baNly5a0bNky1bGbh8LLv1OlShW++OILTp8+zcSJEzl27BgnTpygYcOGRkdLJWUUTPbs2Q1Ocu/y5MmDv78/rVq1AiAgIMAxzcesevTowbZt2zh27Bgvv/wyV65cISIighIlShgdLRUrb/8WFxfHE088wZEjR4yOIhalHlexhISEBIKCgli2bBn/+9//SEhIYObMmZbYF9UqmjVrxurVq4mOjqZOnTps374dV1dXkpKSaNmypSk3jb/TPFYNFxar8fPzu20xmFatWuHv729QokfPoEGDyJ8/P7/99hurV6/m+vXrdO7cmcDAQKOjPRKuXbvG6dOnGThwIOfOnQOgXLlyTJgwgaJFixqcLn3Tp09n48aNXLp0iV9++YULFy7Qv39/fvzxR6OjySNIPa73Rz2uYnpjx45l9erVlC5dGj8/P7788kuaNWumovUBc3V1xWaz4e7uzhNPPOEYxuPk5GTa3suUPQNT7r/dvH+g2VdXFLlZSEgIJ0+eJCoqio0bNzqOR0ZGaqGxB+zIkSOMGzeOzZs3A8k9mklJSQanut3NC0ilxYzDK9esWcOQIUPInj07sbGxfPXVV1StWtWU+63fatWqVSxbtoy2bdsCyfMxo6KiDE51u759+2a4L7yZ91y32+0sWrSIrVu3YrPZeP7552nbtq1l9rkX46lwFdP78ccfqVy5Mt27d3csvqNfcg9eXFycY0XFmz8GiI2NNThd2n799VcKFy6c5rmbF18RMbs9e/bg7+/P5cuXmTFjhuO4h4cHgwYNMjDZo+fWG3GxsbGmXLnZir/DvvnmGxYuXEi5cuXYvn07X3/9NQ0aNDA61l3JmjXrbdeGGf/WSFmg0orGjx/PoUOHHEPIAwMDOXnypKnnaz8sdm2Hc19UuIrpbd68mZUrVzJ+/HjCw8Px9fUlMTHR6FiPnJiYmFQrKt78sRnfvCF53nPKsMo2bdqwdOlSx7kPP/zQtPvvidzKz88PPz+/VPMC5eGoWrUq06ZNIy4ujh07djBr1iy8vLyMjnWbjFY6ThmCazZOTk6OLXBq1qzJp59+anCiu/fYY4+xe/dubDYbSUlJTJs2jaeeesroWLex4l7rKTZv3kxAQIBj26+mTZvSqlWr/2ThKvdHhauYXo4cOejcuTOdO3cmODiYpUuXEhMTQ+fOnfH29jblnqhWFBQUZHSEe3ZzL0lCQkK650SsolWrVpw6dYpTp06lukFXv359A1M9Wvr378+MGTPInj07EyZMwMvLi+7duxsd647i4uJYt24dS5cuZf/+/ezatcvoSLeJj49PNVrn1tE7Tz75pJHxMvThhx8yaNAg/v77b5599lmqVq3KhAkTjI51m/T2Wk9h9iLw5hvhZr0pLualwlUspWzZsgwbNoxBgwaxbt06AgICVLj+h2X0Bqg3RLGiSZMmsXjxYkqVKoWTkxOQfC2rcP33bp4zmiNHDnx9fR2fL1y40JRzRgEOHDjA0qVLWbNmDbGxsXzyySd88803RsdK060jd+Cf0Ts2m41ff/3ViFh3JX/+/Hz//fdER0eTlJRk2tWcrbjXeoo6derQrVs3/Pz8sNlsBAQEWGJrqofCbr559VagwlUsKUuWLDRr1oxmzZoZHUUMFBsb67ibf/PHKedErGbt2rWsX7/eEovZWM3HH3/M008/bcrhn2mZM2cOy5YtIyYmhlatWhEYGEjnzp1p3ry50dHSZcWROzezwmgHK28BOHDgQBYtWsS6deuw2+00bNiQ9u3bGx1LLESFq4hYlhXn5YpkJH/+/CpaH5JPPvmEwMBAjh49iq+vLy1atCBnzpxGx0rXmDFjqFWrFh999JFjGxn9Xnt4xo8fT2BgICVKlLDMaIfNmzdz6NChVDdqzVjYJiYmEhcXR7Zs2ejYsSMdO3YEkre007QeuRcqXEXEsqx+d1/kVpUqVeLdd9+lSZMmuLm5OY6b+Y9nq2jdujWtW7fmzJkzBAQE0LFjR0qXLk3Pnj0pU6aM0fFus3r1apYuXUrHjh0pUaIEfn5++iP/IVq/fj2//vqrZfb/njBhAvv27ePo0aO8+OKL/Prrr9SqVcvoWGmaMGECJUuWdGw1lGLJkiVcuHDB9PNyHwatKnx/nIwOICIiIsn27dvHpUuXmDt3LjNmzGDGjBnMnDnT6FiPlCJFivDqq6/SpUsXduzYwV9//WV0pDSVKlWKQYMGsXHjRrp27cq6deu4fPky77//Pps2bTI63iOnUKFCpt2zPC0bN25k5syZ5M2bl48++gh/f39u3LhhdKw0/f7772mult65c2d+//13AxKJVanHVURExGBHjx7l/9q786Co7/uP48/lWA2gohY5jPWswXpEFOuJMQSvaERN1aZWbZLG2yQezSCTMR6dWBLN4RGxY9TYOLEeIFXx+FXr1RAtNkaMOkYETwQvFHBll3V/f6RuJRgVr+93yesx48x+P5/d7752xxl97+cCeOeddwxOUnG5XC527dpFUlISR48epWfPnqxcudI9DdesvL29iYmJISYmhosXL5KUlERCQgJRUVFGR6tQ4uLiGDlyJB07dsRqtbrbzbppl9VqxcfHB4vFgsPhIDg4mHPnzhkd67a8vLzw9vYu0+7t7a3p71IuKlxFREQMdvM4lh/+J87lcpl+N1ZP0blzZ4KCgujfvz9jxozBYrFQXFzs/tHAzEe1FBYWcuLECZo2bcprr73GH/7wB6MjVTh/+ctfOH/+PIcPH75tkWU2/v7+2Gw2IiIiiIuLIygoyLS57XY7NputzDTsoqIi7Ha7QakMVsF2Fc7KyiIuLo78/HwCAwNJSEigXr16D/19LC4tmBAREZEKLjo62v3YYrGUWi9q5h8HduzYwZQpU/D29mbbtm1kZGQwf/58EhMTjY5WoXTr1o3Nmzd7zAjghQsXqFq1Kk6nkyVLllBQUMCQIUMICwszOloZH3/8MZmZmbz77rvuzecKCgqYMmUKP//5zxk/frzBCR+/rl4D7v4kg/3fjVX3/NyhQ4fy4osvEhsbS0pKCmvWrGHZsmUPPZNGXEVERKTC89TN3ObMmcPq1avdu6Y3b96ckydPGpyq4qlXrx7Xrl0z7fmtP/Szn/3M/Xj06NEGJrm7MWPGEBcXR1RUlHsULjs7m+joaMaNG2dsOPlRV69e5erVq2Xaq1atStWqVd3XFy9e5NChQyxZsgSA3r17M2PGDC5dukSNGjUeaiYVriIiIiImFhQUVOr61jWY8nAEBATQv39/oqKiSn2/Zt3x9vjx4yQmJnLy5ElKSkrc7atXrzYw1e35+Pgwa9YssrOzOXz4MC6Xi6ZNm1K3bl2joxmmPKOZRpk7dy7z5s0r0z527NhSPzjk5OQQHBzsnqru7e1NrVq1yMnJUeEqIiIi8lPh7+/PhQsX3FNY9+zZQ5UqVQxOVfE0aNCABg0aGB3jnr3xxhvExsbSr18/065tvclmswEQHBxMcHBwmXZPOYLop2bYsGH069evTPuto62PmwpXEREREZOaNGkSr732GqdPn2bIkCFkZ2ezYMECo2NVOGPHjjU6Qrn4+Ph4zCZdERERd1w7fPjw4ceYRu7VD6cE/5jQ0FByc3NxOp14e3vjdDrJy8sjNDT0oWdS4SoiIiJiUi1atGDZsmX85z//Ab4vAowc8aiobDYbn3zyCV9++SUWi4WOHTsycuRI044GRkVFsXPnTjp37mx0lLs6cuQIAAsWLMDX15dBgwbhcrlYtWqVR52dK7dXs2ZNmjRpwvr164mNjWX9+vU0adLkoU8TBu0qLCIiImJahYWF+Pn54eXlxdGjR/nuu+/o2rWr1rk+ZPHx8TidTgYOHAj8b63ozJkzjYz1o9LS0hg9ejReXl5YrVb30VlpaWlGR/tRL730El988cVd28TzZGZmEhcXx9WrV6latSoJCQmPZOq9RlxFRERETGro0KF8/vnnFBUV8eqrr9K4cWN27drFn//8Z6OjVSgZGRmsW7fOfd2qVSv69OljYKI7mzJlCjNnzqRp06Z4eXkZHeee5Ofnc+LECfemTCdPniQ/P9/YUPJQNGzYkFWrHv2GUypcRUREREzK5XLh5+fHhg0bGDhwIOPGjeOFF14wOlaFdO3aNfz8/ID/bRxkVtWqVaNHjx5GxyiX8ePHM3DgQJo1awbAoUOHmDFjhsGpxJOocBURERExqeLiYux2O7t27WLo0KEAHjPC5kleeOEFBg0aRK9evbBYLGzYsIHY2FijY/2omJgYvvjiC3r27EmlSpXc7WZdkwvQrVs3IiMj2b9/Py6Xi4iIiEeyDlIqLhWuIiIiIib1/PPP065dOxo0aECrVq04f/58qUJFHo7hw4cTHh5OWloaLpeLSZMmmXrjo48++giAadOmYbFY3Gtczb5D75UrV7hx4wYxMTEUFRWRn59PYGCg0bHEQ2hzJhERERETu3r1KgEBAXh5eVFUVERhYWGp8zDlwdzclGnNmjVGRym3K1eusHfvXurUqUN4eLjRce4oOTmZhQsX4nA42Lp1K8ePH2f69OksXbrU6GjiITTXRERERMTELBYLBw8e5N///jeHDh3i5MmTRkeqULy9valevTrFxcVGR7mrSZMmuY+Xyc/Pp0+fPnz00Ue88sorj2VznAfx2WefsWbNGqpUqQJAgwYNuHDhgsGpxJNoqrCIiIiISaWmppKQkMDVq1epVasWJ0+eJDw8nOTkZKOjVSj16tVj8ODBdO/e3b1BE8DgwYMNTFXWoUOH3COrKSkpNGzYkMWLF3Pu3DlGjBjBgAEDDE7443x9ffH39y/V5u3tbVAa8UQqXEVERERMKjExkaSkJF599VXWrl3Lv/71L7Zs2WJ0rAolPz+fU6dOERISwvHjx42Oc0e3rm/et28fMTExAISEhGCxWIyKdU8CAwPJyspy50xJSSEkJMTgVOJJVLiKiIiImJSPjw81a9bE6XQC0LFjR+bOnWtwqoojNTWVyZMn4+/vj91uZ+7cubRv397oWHeUm5tLtWrV2Lt3L6+//rq73exTnePj45k4cSJZWVlER0dTuXJlEhMTjY4lHkSFq4iIiIhJWa1WXC4XdevW5a9//Su1a9fm8uXLRseqMBYsWMCKFSto0qQJX331FfPnzzd14Tp8+HD69u2Lr68vrVu3plGjRgDs37+fsLAwg9PdWf369Vm1ahXZ2dm4XC7q16+vqcJSLtpVWERERMSk0tLSaNasGRcvXmTq1KkUFBQwceJEOnToYHS0CiE2NpaUlBT3dd++fVm7dq1xge7B+fPnuXDhAuHh4e5pt7m5uTidTtMXrzabjXPnzrlnEADu4lvkbjTiKiIiImJSN0f/qlSpomNDHgGHw0FmZiY3x3HsdnupazMWVUFBQQQFBZVq84TjkZYvX86sWbMIDAx0F9wWi4WtW7canEw8hUZcRUREREyquLiYv//975w6dYqSkhJ3+1tvvWVgqoojOjr6R/tUVD1czz33HMuWLaN27dpGRxEPpRFXEREREZN64403cDgctGjRAqvVanScCmfbtm1GR/jJCAoKUtEqD0QjriIiIiIm1bNnTzZu3Gh0DJEHNmfOHK5fv06vXr1KHetjxunYYk4acRURERExqTp16lBYWEhAQIDRUUQeyM1NrzZt2uRu03RsKQ+NuIqIiIiYzHvvvQd8v1vswYMHiYqKKjVVWGtcReSnRiOuIiIiIibj5+cHfH/2Zf369Q1OI3L/7HY7VqsVm8122/4nnnjiMScST6URVxEREREReST69etHcnKy+9zZW0sPi8XC4cOHDUwnnkSFq4iIiIhJFRYW8sknn/DVV19hsVho164do0aN0ppX8RhZWVmaNSAPhZfRAURERETk9uLj48nPz+ftt98mPj6eK1euEB8fb3QskXs2ceJEAIYNG2ZwEvF0WuMqIiIiYlLHjx9n/fr17uvWrVvTu3dvAxOJlM/169fZvHkzZ86cYceOHWX6n3nmGQNSiSdS4SoiIiJiUmFhYVy6dIkaNWoAcPnyZZ588kmDU4ncuwkTJvC3v/2NixcvsmjRolJ9FotFhavcM61xFRERETGpN998k3379vHss88CsH37dtq3b0/NmjUBHYsjnmPmzJlMnjzZ6BjiwVS4ioiIiJjUvHnz7tg/duzYx5RE5MFlZWWRmZlJTEwMRUVFOBwOAgMDjY4lHkKFq4iIiIiIPFLJycksXLgQh8PB1q1bOX78ONOnT2fp0qVGRxMPoTWuIiIiIia2e/duDh8+THFxsbtNI63iaT777DPWrFnD4MGDAWjQoAEXLlwwOJV4EhWuIiIiIiY1a9YsMjIyOHbsGM899xxbt26lffv2RscSKTdfX1/8/f1LtXl7exuURjyRznEVERERMakdO3bw6aefUrNmTaZPn05SUhLXrl0zOpZIuQUGBpKVlYXFYgEgJSWFkJAQg1OJJ9GIq4iIiIhJWa1WfHx8sFgsOBwOgoODOXfunNGxRMotPj6eiRMnkpWVRXR0NJUrVyYxMdHoWOJBVLiKiIiImJS/vz82m42IiAji4uIICgrS9ErxSPXr12fVqlVkZ2fjcrmoX7++/i5LuWhXYRERERGTunDhAlWrVsXpdLJkyRIKCgoYOnQooaGhRkcTuSfHjh27Y3+jRo0eUxLxdCpcRUREREwqJSWF2NjYu7aJmFV0dDQWiwWXy0VOTg4BAQEAFBQUEBYWxrZt2wxOKJ5CU4VFRERETGrp0qVlitTbtYmY1c3C9E9/+hOtW7emZ8+eAGzatIlDhw4ZGU08jApXEREREZPJyMjgwIEDXL58meXLl7vbCwsLcTgcBiYTuT8HDhzg7bffdl/36NGDxYsXG5hIPI0KVxERERGTyc3N5eDBg9hsNg4ePOhu9/f3Z+bMmQYmE7k/NpuN9PR0IiMjAUhPT8dmsxmcSjyJ1riKiIiImNTu3bvp1KmT0TFEHlh6ejoTJkzgiSeeAKC4uJjZs2fTunVrg5OJp1DhKiIiImJSNpuNhQsXcurUKWbPnk1mZiZZWVnExMQYHU2k3Ox2O1lZWbhcLho0aIDVajU6kngQL6MDiIiIiMjtTZ06lZKSEo4cOQJASEgI8+bNMziVyP1xOp1YrVZ8fHw4efLkXY/KEbmV1riKiIiImNTRo0dJSEhg9+7dwPdrXG/cuGFwKpHyW758ObNmzSIwMBCLxQKAxWJh69atBicTT6HCVURERMSkfH19S10XFxejVV7iiRYvXsz69eupXbu20VHEQ6lwFRERETGpyMhIEhMTsdvt7NmzhyVLlhAdHW10LJFyCwoKUtEqD0SbM4mIiIiYlMPhYNGiRWzbtg2A6Ohohg8fjre3t8HJRMpnzpw5XL9+nV69elGpUiV3e6NGjQxMJZ5EhauIiIiIySxfvvyO/YMHD35MSUQejtvNFNAaVykPFa4iIiIiJhMeHk6zZs34xS9+cdv+mTNnPuZEIiLGUuEqIiIiYjJr1qxh7dq1XL9+nb59+9K7d2+qVatmdCyRcjt79mypa4vFQo0aNUpNFxa5FypcRUREREzq9OnTJCcns3HjRho3bsyoUaN46qmnjI4lcs/atWuHxWIptRt2YWEhLVu25L333iMsLMzAdOJJVLiKiIiImFhBQQHr169nzpw5TJgwgQEDBhgdSeSBOJ1OVqxYwe7du1mwYIHRccRDqHAVERERMRmXy8WuXbtISkri6NGj9OzZk759+1KnTh2jo4k8NP369SM5OdnoGOIhdI6riIiIiMl07tyZoKAg+vfvz5gxY7BYLBQXF3Ps2DFAR4hIxeB0Oo2OIB5EI64iIiIiJnPr0SE/XB+oI0TEk9hstjJt+fn5rFixgtOnTzN79mwDUoknUuEqIiIiIiKPRHh4eKkfX27uKtyhQwcmT55MjRo1DE4onkKFq4iIiIiIiJial9EBRERERERERO5EhauIiIiIiIiYmgpXERGR+xQXF8eHH34IQHp6Ot27d38s7/vUU09x4sSJh3rPWz/L43ytiIjIvVDhKiIiFVp0dDQtWrQgIiLCvRlIUVHRQ3+fyMhINm/efNfnJSUl8dJLLz30979pyJAhrFq16pHdX0RExAgqXEVEpMJLTEzk66+/Jjk5mYyMDBYsWFDmOSUlJQYkExERkXuhwlVERH4ygoODiYqK4rvvvgO+n3K7fPlyunXrRrdu3QD45z//SWxsLJGRkfzmN7/hyJEj7tcfOnSIfv36ERERwZtvvklxcbG7b8+ePXTu3Nl9nZOTw9ixY2nXrh1t27Zl+vTpZGZm8s4777B//34iIiKIjIwEwG63k5CQQJcuXejQoQNTpkzh+vXr7nstWrSITp060alTJ1avXn3fn//111+nY8eOtG7dmsGDB7u/h5suX77Myy+/TEREBL/73e84c+aMuy8zM5OXX36ZX/3qV3Tv3p3U1NT7ziEiIlJeKlxFROQnIycnh507d9KkSRN32z/+8Q9WrlxJamoq3377LfHx8UyfPp09e/YwaNAgRo8ejd1ux263M2bMGGJjY9m7dy89evRgy5Ytt30fp9PJiBEjCAsLY9u2bezcuZPnn3+ehg0bMm3aNFq2bMnXX39Neno6AO+//z5ZWVmsXbuWLVu2kJeXx/z58wHYuXMnixcvZvHixWzZsoW0tLT7/vydO3dm8+bNpKWl8ctf/pJJkyaV6l+3bh2jR49mz549hIeHu/uvXbvGK6+8Qu/evfnyyy/54IMPmDZtWpnCV0RE5FFR4SoiIhXemDFjiIyM5Le//S1t2rRh5MiR7r7hw4cTGBhI5cqVWblyJYMGDeLpp5/G29ubfv364evry/79+/nmm29wOBwMGzYMX19fevToQfPmzW/7fgcOHCAvL4+33noLPz8/KlWq5B5d/SGXy8WqVauIj48nMDCQgIAARowYwYYNGwDYuHEj/fv3p3Hjxvj5+TF27Nj7/h5+/etfExAQgNVqZdy4cRw5coSCggJ3f5cuXWjTpg1Wq5Xx48ezf/9+cnJy2L59O7Vr1+bFF1/Ex8eHpk2b0r1793ta0ysiIvIw+BgdQERE5FGbP38+HTp0uG1faGio+/HZs2dZu3Ytn3/+ubvN4XCQl5eHxWIhODgYi8Xi7gsLC7vtPXNycggLC8PH5+7/zF66dAmbzUb//v3dbS6Xixs3bgCQl5dHs2bN3H21a9e+6z1vx+l08uGHH7Jp0yYuXbqEl9f3v11fvnyZKlWqABASEuJ+vr+/P9WqVSMvL48zZ85w4MCBUsW30+mkT58+95VFRESkvFS4iojIT9qthWhoaCgjR45k1KhRZZ63d+9ecnNzcblc7tecPXuWOnXqlHluaGgoOTk5lJSUlCleb30/gOrVq1O5cmU2bNhAcHBwmXvVqlWLnJwc9/XZs2fL9wH/a926dWzdupUlS5bw5JNPUlBQQJs2bXC5XO7nnDt3zv24qKiIK1euUKtWLUJDQ2nTpg1Lliy5r/cWERF5UJoqLCIi8l8DBgxgxYoVfPPNN7hcLq5du8b27dspLCykZcuW+Pj4sGzZMkpKStiyZQsZGRm3vU+LFi0ICgpi9uzZXLt2jeLiYvbt2wdAzZo1yc3NxW63A+Dl5cWAAQN49913uXjxIgC5ubns2rULgB49epCcnMyxY8ew2WzMmzfvrp+jpKSE4uJi9x+Hw0FRURFWq5Xq1atjs9n44IMPyrxux44dpKenY7fb+fjjj3n66acJDQ2lS5cuZGdns3btWhwOBw6HgwMHDpCZmXlf37OIiEh5qXAVERH5r+bNmzNjxgymT59OmzZt6NatG0lJSQBYrVbmzp1LcnIybdq0ITU1la5du972Pt7e3iQmJnLixAmeffZZOnfuzMaNGwFo164djRo1olOnTrRt2xaAP/7xj9StW5eBAwfSqlUrfv/735OVlQXAM888w7Bhwxg2bBhdu3alXbt2d/0cU6dOpUWLFu4/kydPpm/fvoSFhREVFUWvXr1o2bJlmdf17t2b+fPn07ZtW7799lvef/99AAICAvj0009JTU0lKiqKTp06MWvWLHfxLSIi8qhZXLfOERIRERERERExGY24ioiIiIiIiKmpcBURERERERFTU+EqIiIiIiIipqbCVURERERERExNhauIiIiIiIiYmgpXERERERERMTUVriIiIiIiImJqKlxFRERERETE1FS4ioiIiIiIiKn9P/blm0qFiCRgAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_train, y_train_pred = evaluate_model_cm(shuffled_baseline_log_reg_model, training_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "evaluate_model_score(shuffled_baseline_log_reg_model, training_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate with testing data" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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9pm3btik1NVX+/v4KCAjgLXYAwFA0rgAAr6tQoYJmzJih999/X02aNFHLli01Z84cz918c1KtWjW98cYbGjt2rO666y6tX79eM2fO9Fyz+sQTT+i9995TgwYNNGfOHM924eHhOnDgQKbRzIvuvfdede3aVZ07d1arVq3UvXv3K9YJDg7WBx98oPXr16tZs2Zq27ZtprvtXurOO+/URx99pO3bt6t169Zq1KiRXn75ZbVs2fKKdW+66SY99thjevDBB9W0aVMdOHBAd955p+fxH3/8UT169FC9evXUv39/vfjii6pcubISExP10ksvqVGjRrrnnntUokQJPfbYY7nWLze5/WzefPNN7dmzR40bN9a7776rzp07Z7uvSZMmydfXV+3bt1fTpk310UcfZbledj+zi9566y2dPHlSd999twYOHKhBgwapWbNmuX4vqampevPNN9W4cWM1b95cMTExWf7zAABgfw632+22OgQAAIVtxYoV+vTTT7Vo0aJMy2vWrKmvvvpKVapUsSgZAODvZEfkjVZHyFXjKkesjnAFRlwBANe88+fPa+HCherZs6fVUQAAQD7QuAIArmnffPONmjRpouuuu04dO3a0Og4AAMgHpgoDAAAAgJdsjaxmdYRcNa3ym9URrsCIKwAAAADA1mhcAQAAAAC25mt1AFz72jh7WB3hmudTvLjVEa5KRmys1RH+Fpz/fXsYU7hSU62OcNVMq7FkZp1R+Jz1brU6wlVx7/3V6ghXzZ2RYXWEa97XrsVWR8iTDDdjh/lB1QAAAAAAtkbjCgAAAACwNaYKAwAAAICXuBg7zBeqBgAAAACwNRpXAAAAAICt0bgCAAAAAGyNa1wBAAAAwEsy5LA6gpEYcQUAAAAA2BqNKwAAAADA1pgqDAAAAABekuFm7DA/qBoAAAAAwNZoXAEAAAAAtsZUYQAAAADwEhd3Fc4XRlwBAAAAALZG4woAAAAAsDWmCgMAAACAl2QwdpgvVM2LYmNjVbt2bY0fP75Qj7Ns2TIdOXLkL+2jX79+OnbsWAElKljFSgZrzNKhWhU/T/OPzNA9vZpbHSlHpuWVpEmrh2pV1Htafny6lh+frtk7x1kdKVem1dm0vH7+vhoys58+PvCOlp+ZrXe3j1eD++6wOlauTKozNfYe0zKbkLfTA400bd4TWr3tZT3/SudMj7Voc5v+tWSglm8apVmLn1aTVrdYEzIHnQa01fTt4xWRME8vzOlvdZw8MeG8uJyJmWEfjLh60erVq1W3bl19/vnnGjp0qPz9/QvlOMuXL1fJkiV144035nsf//rXvwowUcEaNP1xpaem64Hy/VS9blWNjxip3/YcVeS+E1ZHy5JpeS+aMWyhvpj3jdUx8sy0OpuW1+nrozMnzmlom7GKPnZOjdrV1YsLBumpBiN0OvKs1fGyZVKdqbH3mJbZhLznzsRr4ZxNatDkJvkH/O/Py+vKFNOwsV31ynOLtGvrITVqfrNenPiAHun4tmL/SLQwcWbnTsVo4YTlqn/fHQoIKpy/zwqaCefF5UzMDPtgxNWLli5dqgEDBqhGjRpat26dJGnatGl65pln1K9fP3Xo0EGDBg1SfHy8JCkxMVEjR45Ux44d1bFjR82aNcuzrz59+mj8+PHq06eP2rRpo7feestzjJ9++knjxo1TeHi4tm7dqoyMDE2cONGzn4kTJyojI0OS9Omnn6p9+/YKDw9XWFiYDh8+LEkKDQ3VgQMHJEnTp09Xu3btFB4ers6dOysuLs5rNbtcYJEANe92l+aO/kTJicn6ect+bVu1S637tLQsU05My2sq0+psWl5JSklK0fxxy3Q68qzcbrd2rNmt34+e0c318v8PssJmWp2psXeYltmUvFvW/6JtG/Yr7s+kTMtLlwtRYnyydm09JEnaufmgks+nqeL1Ja2Ima0tK77V1lW7FH8u3uooeWLKeXEpEzMXlgy30/YfdsSIq5fs379fsbGxuuuuu3TmzBktXbpU7dq1kyR99913WrFihUqXLq2RI0dqxowZGj58uGbMmCGXy6XVq1crMTFRPXv2VM2aNdWy5YVf8MOHD+vDDz9USkqKHnzwQdWrV0/dunXTihUr9Nhjj+mee+6RJC1cuFC//PKLli1bJunCNOBPP/1UvXv31qRJkxQREaEKFSooNTXV09BeFBsbqzlz5mjbtm0KDAxUQkKCAgMDvVi5zCrVqCBXhksnD0Z5lh3ee1R1WtxqWaacmJb3Uv8Y3VWPjumqE4dO66Oxy7V3y69WR8qWaXU2LW9WSpQN0fU3l7f1f8lNrzM1LhymZTYt7+UO7julY0fO6q4WNbVz8wE1blFTaanp+u3gaaujGc3E88LEzLAXe7bT16AlS5YoPDxcDodD9913n/bs2aPTpy88abdq1UqlS5eWJHXv3l3bt2+XJG3btk09evSQw+FQcHCwOnTooG3btnn22blzZ/n6+qpo0aK6//77Pdtdbtu2berSpYv8/f3l7++vrl27evZz1113aeTIkZo3b55Onz6toKCgTNsGBwfrxhtv1NChQ/XZZ58pKSlJvr7W/b8jKDhQibGZ/5ubGJukIsWCstnCWqblvWjOK0v0aL0RevjWoVozd5NeWTRIFaqWsTpWtkyrs2l5L+fj66MRc5/W1/O/0fEDUblvYBGT60yNC49pmU3LezmXy621n+/RiPHdFLHtZY0Y301TJ6xWSnKa1dGMZuJ5YWJm2AuNqxekpqZq9erVWrp0qUJDQ3X//fcrLS1Ny5cvv2Jdt9sth8NxxecXXf51Vtvl5bGLX0+fPl3PPfeczp8/r0ceeUQbN27MtJ6Pj48+++wz9enTR7///ru6du2q/fv35+0bLwTnE5JVJCTzE1zRkCAlxZ+3KFHOTMt70a/fHdH5hBSlpaZr7SdbtW/HITVsU9vqWNkyrc6m5b2Uw+HQsA/6Ky01Xe8++5HVcXJkap2pceEyLbNpeS9Xr1E1/XNwGw19Yq463DVWQ/t9qGdfDle1GuWtjmY0E88LEzMXFpectv+wI3umusasXbtW1apV06ZNm7Ru3TqtW7dOH3zwgWfq7oYNGxQTEyPpwo2VGjduLElq2rSplixZIrfbrYSEBP373/9WkyZNPPtduXKl0tPTlZSUpC+++MKzXdGiRT3XyV7cz/Lly5WWlqa0tDStWLFCTZo0UXp6uo4fP646deroiSeeULNmzfTLL79kyp6QkKCYmBg1atRIgwcPVo0aNXTw4MFCrVdOTh6Iko+vjyrd9L8XvGp1qtp2Kp1pebPjVvb/GLED0+psWt5LPfd+P5UsV1xjH3xbGekZuW9gIVPrTI0Ll2mZTct7ueo1y+vH3ZE6+Mspud1uHdh3Sr/+dEJ3Nq5mdTSjmXhemJgZ9kLj6gXLli1TWFhYpmX16tWTy+XSt99+qyZNmmjUqFHq0KGDYmNjNWDAAEnSgAED5Ha7FRYWpgcffFCdOnVSixYtPPu47bbb9Oijj6pz585q1aqV55rWnj17asaMGercubO2bt3quTa2S5cu6tKli2rWrKkHHnhALpdLI0aMUFhYmDp16qQzZ86oZ8+emXImJCTo6aefVlhYmDp27KjSpUvrvvvuK+SKZS85KUWbl+1Q31d7KrBIgG5rWlNNwxtq7byNuW9sAdPyShf++1k/9Db5BfjK6ePUPT0aq3aTGvpu3U9WR8uWaXU2Le9Fg6c9pso1K2l018lKNWCan4l1psaFz7TMpuR1+jjl53/hdcPp/N/nv/58UrfXu8Ezwlq9ZnndXreK7a5xdfo45RfgdyH/JZ/blSnnxaVMzAx7cbjdbrfVIf7Opk2bpqSkJA0fPvyqtuvTp0+mGzDZWRtnjwLdX7GSwXp+Tn/d2aaO4s8laPbIBVq/aHOBHqMgeSOvT/HiBbav4tcFa+xnz+j6myvI5XLp+IEofTxhpXZv2Fdgx8iIjS2wfV3EeXElZwG+5VbZG0pr3oF3lJqcqox0l2f5OwPnaP0nWwvkGK7U1ALZz6UKu86m1Vgq+Dqb9rsnmZfZK88X9f7aDXIefqKV+jyZ+W+See+v1/xZG9TpgUbq0vsulSgVrNg/ErV68bdaOv+vndPuvQV7w8A+L3dXn9HdMy2b99oSzRu7pMCO4c4o2BkUpp3HUuFn/tq1uMD2VZhW/lbX6gi5Cq/2g9URrkDjajEaVxSEgmxcvaEwGldcqSCbKm8ojMa1sJlWY8nMOqPw/dXG1dsKunH1hoJuXHElUxrXZYfrWR0hV12r77Y6whV4OxyLDRo0KF/bzZs3r4CTAAAAAIA92XfyPgAAAAAAYsQVAAAAALwmg7HDfKFqAAAAAABbo3EFAAAAANgaU4UBAAAAwEtcbsYO84OqAQAAAABsjcYVAAAAAGBrTBUGAAAAAC/hrsL5Q9UAAAAAALZG4woAAAAAsDWmCgMAAACAl2S4HVZHMBIjrgAAAAAAW6NxBQAAAADYGo0rAAAAAMDWuMYVAAAAALzExdhhvlA1AAAAAICtMeIKXAMc/n5WR4ANuTMyrI5wzXOlplodASgQqdcVsTrCVeFVD/j7oXEFAAAAAC/JcDPpNT+oGgAAAADA1mhcAQAAAAC2xlRhAAAAAPASlxxWRzASI64AAAAAAFujcQUAAAAA2BpThQEAAADAS7ircP5QNQAAAACArdG4AgAAAABsjanCAAAAAOAlGYwd5gtVAwAAAADYGo0rAAAAAMDWaFwBAAAAALbGNa4AAAAA4CUut8PqCEZixBUAAAAAYGs0rgAAAAAAW2OqMAAAAAB4CW+Hkz9UzYZiY2NVu3ZtjR8/Psf15s6dq3PnzuVpn3/88YcefPBBhYeHa/bs2fnKtWzZMh05ciRf2xakYiWDNWbpUK2Kn6f5R2bonl7NrY6UI9PyLvttSqaPiFPvqv+EB6yOlSvT6mxaXknqNKCtpm8fr4iEeXphTn+r4+SJaXU2La9EZm+we14/Px8Nfa69Fs17Sp+veFazZvxDjRpW8zweEOCrZwe10YrFg7R6+bN6+83eFqbNGs9v3mFiZtgHI642tHr1atWtW1eff/65hg4dKn9//0yPu1wuORwOffzxx2ratKmuu+66XPe5bds2hYSE6JNPPsl3ruXLl6tkyZK68cYb872PgjBo+uNKT03XA+X7qXrdqhofMVK/7TmqyH0nLM2VHdPydq02xPN5QBF/Lfppor5Z9b2FifLGtDqblleSzp2K0cIJy1X/vjsUEOSf+wY2YFqdTcsrkdkb7J7Xx8ep6DNxevaFhYqOjlPjRtU15sVOeuzJD3T6dJyef7adfHyc6vvP2YqPT9ZN1ctaHfkKPL95h4mZYR+MuNrQ0qVLNWDAANWoUUPr1q2TJE2bNk0vvPCCBgwYoPDwcM2cOVPR0dEaPHiwwsPDdejQIaWmpmrixInq3r27wsPDNXToUCUmJmr79u2aNGmSvv/+e4WHh2vXrl1avXq1evTooc6dO6tz587atm2b5/iHDx/WY489prCwMIWFhWn58uVaunSpfvrpJ40bN07h4eHaunWrJbUJLBKg5t3u0tzRnyg5MVk/b9mvbat2qXWflpbkyY1peS93d8c79efZBP20/ZDVUXJkWp1Ny3vRlhXfauuqXYo/F291lDwxrc6m5ZXI7A0m5E1OTtNH87bo9Ok4ud3S9h2HFfV7rGreXF6Vry+lpnfdpDff/kKxseflcrl14OBpqyNfgee3wmdi5sLicjtt/2FHjLjazP79+xUbG6u77rpLZ86c0dKlS9WuXTtJ0q5du7Rs2TKVKlVKkrR48WJNnTpVNWrUkCTNmDFDxYoV05IlSyRJb7zxhmbNmqUhQ4Zo8ODB2rBhg6ZOnSrpwtThjh07yuFw6LffftM//vEPbdq0Senp6RowYICeffZZtW/f3rNuyZIltWLFCj322GO65557vF0Wj0o1KsiV4dLJg1GeZYf3HlWdFrdaliknpuW93L0979J/PttudYxcmVZn0/KayrQ6m5ZXIrM3mJZXkkqWKKLK15fSkcizqnVLBZ2OjtM/Hmmu++69TediEvXRvM3atPmA1TGNZuJ5YWJm2AuNq80sWbJE4eHhcjgcuu+++zRu3DidPn3hP5MtWrTwNK1ZWbdunRISEvTll19KklJTU3XLLbdkue7x48f1/PPP6/Tp0/L19dXZs2d15swZ/fnnn0pPT/c0rZJUsmTJAvwO/5qg4EAlxiZlWpYYm6QixYIsSpQz0/Jeqkylkqrd5Ga9PWSe1VFyZVqdTctrKtPqbFpeiczeYFpeHx+nXhwRpi+//knHj8eoRbMaqnZjGW3a/Ku693pXt9WqpNfHddfRyHM6djxv9+nAlUw7LyQzM8NeaFxtJDU1VatXr1ZAQIBWrlwpSUpLS9Py5cslSUWLFs1xe7fbrTFjxqhJkya5Huu5557TiBEj1Lp1a7lcLt1xxx1KSUmR2+3+699IITqfkKwiIZmf4IqGBCkp/rxFiXJmWt5L3fvAXdq345BOH7P/Hxam1dm0vKYyrc6m5ZXI7A0m5XU4pFHDOyotPUPvTP9akpSSmq60tAzNW7BVLpdbe348rt17jqlh/ao0rn+BSefFRSZmLiwZclgdwUj2nMD8N7V27VpVq1ZNmzZt0rp167Ru3Tp98MEHWrZsWZbrFy1aVPHx/7sWIzQ0VHPnzlVycrIkKSEhQYcPH85y2/j4eF1//fWSLozypqamSpKqVasmX19frVmzxrPuH3/8keXxrHDyQJR8fH1U6abynmXV6lS17UX9puW91L09GmutAdOEJfPqbFpeU5lWZ9PySmT2BpPyDn3ufpUsUURjXluhjAyXJOm3385YnOraZNJ5cZGJmWEvNK42smzZMoWFhWVaVq9ePblcLn377bdXrP/II49o1KhRnpszPfHEE7rlllvUvXt3hYWFqXfv3tk2riNHjtSAAQPUq1cvnTx5UiVKlJAk+fr6asaMGfrkk08UFhamTp06aePGjZKknj17asaMGercubNlN2dKTkrR5mU71PfVngosEqDbmtZU0/CGWjtvoyV5cmNa3otqNaim0hVKGHE3Ycm8OpuW9yKnj1N+AX5y+jgzfW5XptXZtLwSmb3BlLxDBt+nKjdcp1Gjlyo1Nd2zfM+PxxUdHaeHejWR0+nQ7bdWUt06lbXzO+vfXu9SPL8VPhMzw14cbrvPDYXx2jh7FOj+ipUM1vNz+uvONnUUfy5Bs0cu0PpFmwv0GAXJG3l9y5Qu0P0NeqO3AoL8NXng3ALd70XpZ84W+D45L67k8PEp0P31ebm7+ozunmnZvNeWaN7YJQWyf3dGRoHs51KcF4WPzIXPG3nTWzfI97blyobok/n9lZqa7hlplaS33vlSa9ftU9UqpfXCkHaqVq2MTp+O05y5m7R5y8G/lNdv/e6/tP3lCvv5TSr45zjTzmOp8DN/7VpcYPsqTJP2tc99JYsNu3VN7it5GY0rCl1BN664UkE3roWtMBpXXKmgG9fCVhiNK4C8+SuNqxUKunH1Bp7jCh+Na8GxY+Nq3zkQAAAAAACIuwoDAAAAgNdwV+H8YcQVAAAAAGBrNK4AAAAAAFujcQUAAAAA2BrXuAIAAACAl7jcjB3mB1UDAAAAANgajSsAAAAAwNaYKgwAAAAAXpLBVOF8oWoAAAAAAFujcQUAAAAA2BpThQEAAADAS1xyWB3BSIy4AgAAAABsjcYVAAAAAGBrTBUGAAAAAC/hrsL5Q9UAAAAAALZG4woAAAAAsDWmCqPQOXx8rI5w7XPyPyhcyRkcbHWEq5IRG2t1hKvmDAqyOsJVc50/b3UE2NCJUH+rI1yValvMyitJSk21OsFVc2dkWB3hmuRyc1fh/OCvXQAAAACArdG4AgAAAABsjcYVAAAAAGBrXOMKAAAAAF6SwdhhvlA1AAAAAICt0bgCAAAAAGyNqcIAAAAA4CW8HU7+MOIKAAAAALA1GlcAAAAAgK0xVRgAAAAAvMTF2GG+UDUAAAAAgK3RuAIAAAAAbI3GFQAAAAC8JMPtsP1HXk2cOFGhoaGqWbOmDhw44Fl+5MgR9ezZU23btlXPnj119OjRPD2WExpXAAAAAMBVu/fee7VgwQJVqlQp0/IxY8aod+/e+vLLL9W7d2+NHj06T4/lhMYVAAAAAOARFxenEydOXPERFxeXab0GDRqoQoUKmZadO3dO+/btU8eOHSVJHTt21L59+xQTE5PjY7nhrsIAAAAA4CWuq5iKa5WPPvpI06dPv2L5wIEDNWjQoBy3jYqKUrly5eTj4yNJ8vHxUdmyZRUVFSW3253tY6VKlcpxvzSuMEqnAW113yMtVPX2G7Th062a/Ph7VkfKlYmZy15fSgP/70HdUv9GpaWma3PEbr0/eolcGS6ro2WrWMlgPTe7v+rfV0dxZ+M1Z9RCrV+02epY2TItryRNWj1UtzSopoz0DEnSuag/9c9GL1mcKmem1XnYnCdVt9VtCiwSoD9Ox2rxlM/1xUcbrY6VI9NqLJmX2YS883t3V92KFZTuuvA6cTo+QW3/9ZEkqUed2/Vkk4YqXbSIvjtxSiP//ZWiExKtjJsl037/TPz7woRzGRf07dtXXbp0uWJ5SEiIBWkuoHH1stDQUPn7+ysgIEApKSlq0KCBxowZoyVLliglJUX/+Mc/CvyYy5Yt04YNGzR16tQC37e3nTsVo4UTlqv+fXcoIMjf6jh5YmLmgf/3oP48G6+H6o5UcEgRjf90oDr+o4VWzdlgdbRsDZr+uNJT0/VA+X6qXreqxkeM1G97jipy3wmro2XJtLwXzRi2UF/M+8bqGHlmWp0/nRyhKf3nKC01XZVrVNCkL0bq0J5IHfrhqNXRsmVajSXzMpuS99Wv1mvx3p8yLWtUuZKeb9lMDy9aosiYP/RS61aa0ul+PbRwsUUps2fa75+Jf1+Yci7jQoOa3ya1QoUKOn36tDIyMuTj46OMjAxFR0erQoUKcrvd2T6WG65xtcDUqVO1cuVKff755zp06JC+/vpr9erVq1Ca1sKQnp5u2bG3rPhWW1ftUvy5eMsyXC0TM5e74Tp9s+p7paWk648zcfpu/S+qUjP3JxSrBBYJUPNud2nu6E+UnJisn7fs17ZVu9S6T0uro2XJtLymMrHOkb+cVFrqhedYt9stt1uqWK2sxamyZ2KNTctsWt7L3XNTNa3Zf0CHzp5Tmsuld7fuUKMbrtcNJYpbHe0Kpv3+mfb3hennckFyuZ22//grrrvuOtWqVUsRERGSpIiICNWqVUulSpXK8bHcMOJqoZSUFKWkpCgkJETTpk1TUlKShg8frmXLlikiIkIhISE6ePCgihUrpmnTpqlMmTKZ1pOU6evU1FRNmTJF33zzjZxOpypXrqx33333iuMuX75cCxcuVEZGhoKDg/XKK6+oWrVq+vXXX/Xqq6/q/PnzSklJ0QMPPOBppkeMGKGiRYvq6NGj+uOPP7Rs2TJvlgpetnL2erXoXF97tx1QcPEiahB6q+ZNirA6VrYq1aggV4ZLJw9GeZYd3ntUdVrcamGq7JmW91L/GN1Vj47pqhOHTuujscu1d8uvVkfKlql1Hjilr9o83FyBRQJ08Iej2vnlHqsjZcvEGpuW2aS8L7RqphdaNdeRmD/01qYt2nnshBwOhxyO/13Pd/Gzm8tcp2N/xloTNAcm/f6ZxqRzGXk3btw4ffXVVzp79qweffRRlShRQp9//rleeeUVjRgxQjNmzFBISIgmTpzo2Sanx3JC42qBwYMHKyAgQMeOHVPz5s3VvHlz7d69O9M6P/74o1atWqUKFSropZde0vz58zVkyJAc9ztr1iwdP35cy5Ytk7+/f5Z359q1a5fWrFmjBQsWyN/fXxs3btSoUaP0ySefqFKlSpo7d678/f2VmJioHj166O6771b16tUlSbt379b8+fNVpEiRgisGbOnHbQfV7qFmWnrgTfn4+ujrT7dr6xr7vngHBQcqMTYp07LE2CQVKRZkUaKcmZb3ojmvLNGxX08pPTVDLbs20iuLBunpFq8q6ugZq6NlydQ6Tx/ykWY8/7FqNb5Zde6+RWkp1s1yyY2JNTYtsyl531i/WYfOnVNahksdatXU+93CFf7hfG08fERvh3fQot17dfSPPzSw2V1yud0K8vWzOnKWTPr9M40p5zKuzksvvaSXXrryfhfVq1fX4sVZXxKQ02M5YaqwBS5OFd6+fbtSUlI0d+7cK9a58847PXO977jjDh07dizX/a5fv159+/aVv/+F6xyyGnJft26d9u/frx49eig8PFxvvvmmfv/9d0lScnKyRo0apbCwMPXq1UvR0dHav3+/Z9t27drRtP4NOBwOjVs0UFv//YO6VH9OD9w6TMHFi+ixlzpbHS1b5xOSVSQk8wtf0ZAgJcWftyhRzkzLe9Gv3x3R+YQUpaWma+0nW7VvxyE1bFPb6ljZMrXOkuRyufXztgMqU6mUOvYLtTpOtkyssWmZTcm7J+p3JaamKTUjQ8t/2qfvT55Sy+o3alvkcU3dvE3Tu3TUxv7/1InYOCWmpOr3+ASrI2fLlN8/05hyLsO+aFwtFBAQoFatWmnr1q1ZPnbRxQuXL37ucv3vzq4pKSmez91ud67HdLvd6tatm1auXKmVK1dq1apV2rBhgyTprbfeUpkyZbR8+XKtWrVKderUybR/mta/h2Ili6hspVJa9cFGpaWmK/6PRH396TY1vPc2q6Nl6+SBKPn4+qjSTeU9y6rVqWrbmz2Yljc7brkzTQG0m2uhzk5fpyrY+Bo7E2tsWmbT8l7kdrs904IXfL9HbWbN1V3T3teXvx6Uj9OpA2fOWpovL+z++2caU8/lwpAhh+0/7IjG1UIul0vffvutqlatmudtbrjhBv38889yuVxKSEjwNJ3ShTsWf/TRR0pNTZWkLKcKh4aGauXKlZ5R1oyMDP3004U7AMbHx6t8+fLy9fXVgQMHtGvXrvx/c4XE6eOUX4CfnD7OTJ/bmWmZ42ISFRV5Vh363i2nj1NFQ4LU+oHGOrLvpNXRspWclKLNy3ao76s9FVgkQLc1ramm4Q21dp4938bAtLzShf+K1w+9TX4BvnL6OHVPj8aq3aSGvlv3U+4bW8S0OhcvU0wtuzdWYNEAOZ0O1W9dW/f0aKI9G36xOlq2TKuxZF5mE/IWCwhQ8xuryN/HRz4OhzrdeosaVr5e3xyJlL+Pj24ufZ0kqUJIMY1r31of7dqtuEv+MW4HJv7+mfb3hQnnMuyNa1wtcPEa17S0NN188816+umn9fHHH+dp2/vuu09r1qxRhw4dVKVKFd122/9GwZ544gm9+eab6ty5s/z8/FSlSpUr3gKnYcOGevbZZ9W/f39lZGQoLS1N7dq10+23367+/ftr2LBhWrVqlW644QY1bNiwQL/vgvDQqK7qM7q75+vWD92tea8t0byxSyxMlTMTM497/F968rVu6vH0fXK5XNq75YDeH23fvJI07enZen5Of312erbizyXonQH/svV/cU3L6+vno74vdtb1N1eQy+XS8QNRevXhd3Xi0Gmro+XIqDq7pY7/vFeD3/mHHE6noo+f1cxhC7Tt8++tTpYjo2r8X6ZltnteP6dTQ1o0VbVSpeRyu/TbuT80YNkqHYn5Q8UCAvRWp/a6oUQJJaamaumPP+vtb66caWY5A3//TPz7wu7nMuzN4c7L/FLgL7jP70GrI1zzfP7732xTpJ+OtjrC34JPcfu93UROMmLtd4fR3DiDzLupiOs815PhSkcnNLU6wlWpNnZ37ivZjPu/M+JM4v7vpWqm+Nplv/cHzspT3/WxOkKuZtafZ3WEK9h3PgEAAAAAAKJxBQAAAADYHNe4AgAAAICXuNyMHeYHVQMAAAAA2BqNKwAAAADA1pgqDAAAAABe4pLD6ghGYsQVAAAAAGBrNK4AAAAAAFtjqjAAAAAAeEmGm6nC+cGIKwAAAADA1mhcAQAAAAC2RuMKAAAAALA1rnEFAAAAAC9xuRk7zA+qBgAAAACwNRpXAAAAAICtMVUYAAAAALzExdvh5AsjrgAAAAAAW6NxBQAAAADYGlOFUejcGRlWR7jmpZ+OtjoCbCgjNtbqCNc81/nzVkcACsSNL++wOsJVcQQFWR3hqrn4ewj/5RJThfODEVcAAAAAgK3RuAIAAAAAbI2pwgAAAADgJdxVOH8YcQUAAAAA2BqNKwAAAADA1pgqDAAAAABe4nIzdpgfVA0AAAAAYGs0rgAAAAAAW6NxBQAAAADYGte4AgAAAICX8HY4+cOIKwAAAADA1mhcAQAAAAC2xlRhAAAAAPASl5gqnB+MuAIAAAAAbI3GFQAAAABga0wVBgAAAAAv4a7C+cOIayELDQ1Vu3btFB4e7vk4ceKEatasqcTERK/n+eWXX/Tvf/87X9ueOHFCjRs3LuBEV69YyWCNWTpUq+Lnaf6RGbqnV3OrI+XItLwSmb3BtLwSmb3BtLwSmb3BtLydBrTV9O3jFZEwTy/M6W91nKtSsXo5rYqepWH/esLqKLky7byQzMwM+2DE1QumTp2qGjVqWB1D0oXGdcOGDbr//vutjpJvg6Y/rvTUdD1Qvp+q162q8REj9dueo4rcd8LqaFkyLa9EZm8wLa9EZm8wLa9EZm8wLe+5UzFaOGG56t93hwKC/K2Oc1WefvNhHfj+iNUx8sS080IyMzPsgxFXG9i7d6969uypsLAw9ezZU3v37pX0vxHOKVOmqHPnzmrbtq127drl2W7+/Pm677771K1bN02dOtUzGpqenq7HH39cXbt2VYcOHTRy5Eilpqbqjz/+0NSpU7V161aFh4dr3LhxkqQ9e/aoT58+6tq1q7p27aoNGzZ4jrFgwQK1adNGvXv31pIlS7xXlGwEFglQ8253ae7oT5ScmKyft+zXtlW71LpPS6ujZcm0vBKZvcG0vBKZvcG0vBKZvcG0vJK0ZcW32rpql+LPxVsd5aq07NZIiX8m6YeN+6yOkisTzwsTMxcWl9th+w87onH1gsGDB3umCXft2jXTY6mpqRo8eLCeeeYZrV69Ws8++6wGDx6s1NRUSdKff/6punXrasWKFXr66ac1efJkSdL+/fv1/vvv65NPPtHSpUsVH/+/FwcfHx9NnjxZy5YtU0REhDIyMrR06VKVLFlSgwcPVtOmTbVy5Uq99NJLiouL05gxY/Tmm29q2bJlmjlzpkaPHq24uDjt379f7733nhYtWqSFCxfqzz//9FrNslOpRgW5Mlw6eTDKs+zw3qOqcuv1FqbKnml5JTJ7g2l5JTJ7g2l5JTJ7g2l5TVWkWKD6vNhFs1781OooeWLieWFiZtgLU4W9IKepwkeOHJGfn5+aNm0qSWrSpIn8/Px05MgRFS1aVEWKFNE999wjSapbt64mTpwoSdq5c6datmypUqVKSZK6deumVatWSZJcLpc++OADbdq0SS6XS7GxsQoMDMzy+Lt379aJEyfUr18/zzKHw6HIyEjt3r1brVq1UunSpSVJPXv21Jo1awqgIvkXFByoxNikTMsSY5NUpFiQRYlyZlpeiczeYFpeiczeYFpeiczeYFpeUz3yUld9+fE3OnsyxuooeWLieWFiZtgLjavF3G63HI4rh+MvLvP3/9+1IU6nU+np6TluJ0mrV6/Wd999pwULFig4OFgzZ87U0aNHsz1+zZo1tWDBgise+/7776/22yl05xOSVSQk8xNc0ZAgJcWftyhRzkzLK5HZG0zLK5HZG0zLK5HZG0zLa6JqtSurXqtb9XTzMVZHyTMTzwsTMxcWu07FtTumClusWrVqSk1N1fbt2yVJ27dvV3p6uqpWrZrjdo0bN9bGjRsVE3PhP4PLly/3PBYfH6+SJUsqODhY8fHxioiI8Dx2cdlF9erVU2RkpOf40oVrbt1ut+cY586dkyRbXON68kCUfHx9VOmm8p5l1epUte1F/abllcjsDabllcjsDabllcjsDablNVGdu29RuRtK6+N9b2rhwbfVbVA7NetUX9M3vWJ1tGyZeF6YmBn2QuPqBZde4xoeHq4ff/zR85i/v7+mTp2qKVOmKCwsTFOmTNE777yTaaQ1K7fccov++c9/6sEHH1Tv3r1VrFgxFStWTJLUuXNnJSYmqkOHDnrmmWdUv359z3ZNmjTR+fPn1alTJ40bN07FixfXjBkz9O6776pTp05q3769pk+fLrfbrVtuuUVPPfWUevXqpd69e6tcuXKFU6CrkJyUos3Ldqjvqz0VWCRAtzWtqabhDbV23karo2XJtLwSmb3BtLwSmb3BtLwSmb3BtLyS5PRxyi/AT04fZ6bP7WrNhxv16B3D9HSz0Xq62Wh9/sEG7fxyj17s8qbV0bJl4nlhYmbYi8PtdrutDoH8SUhIUHBwsCRp2rRpioyM9Ny8yU7aOHsU6P6KlQzW83P66842dRR/LkGzRy7Q+kWbC/QYBcm0vBKZvcG0vBKZvcG0vBKZvcEbeR0+PgW2rz4vd1ef0d0zLZv32hLNG1twM7ecQYV3XeTDI8NVsVo5Teo3q0D3m5GQUKD7M+08lgo/89euxQW2r8LUYdNgqyPk6vMWU62OcAUaV4O9+uqr+v7775WWlqbKlSvrtddes8Wo6OUKunEFAAAFqyAbV28ozMa1sBR044or0bgWHDs2rtycyWBjxphzEwEAAAAAyC/7XnAAAAAAAIAYcQUAAAAAr3GJt8PJD0ZcAQAAAAC2RuMKAAAAALA1pgoDAAAAgJe43EwVzg9GXAEAAAAAtkbjCgAAAACwNaYKAwAAAICXMFU4fxhxBQAAAADYGo0rAAAAAMDWmCoMAAAAAF7CVOH8YcQVAAAAAGBrNK4AAAAAAFtjqjAAAAAAeAlThfOHEVcAAAAAgK3RuAIAAAAAbI2pwsA1wOnvb3WEq+JKTbU6AgDgEg7DXkcyEhKsjgDAy2hcAQAAAMBL3Fzjmi9MFQYAAAAA2BqNKwAAAADA1pgqDAAAAABe4hJThfODEVcAAAAAgK3RuAIAAAAAbI2pwgAAAADgJS7uKpwvjLgCAAAAAGyNxhUAAAAAYGtMFQYAAAAAL3EzVThfGHEFAAAAANgajSsAAAAAwNaYKgwAAAAAXsJdhfOHEVcAAAAAgK3RuAIAAAAAbI2pwgAAAADgJdxVOH8YcYVxipUM1pilQ7Uqfp7mH5mhe3o1tzpSjkzL6+fvqyEz++njA+9o+ZnZenf7eDW47w6rY+XKtDqbllciszeYllciszeYlleShs15UgsPT9WyqPc154dJate3pdWRcmVanU3LK5mZGfbBiKtBQkND5e/vr4CAAElS48aNNWrUqCzX3bFjhyZOnKhly5Zd8diJEyfUrVs37dix46qOn9M+vWnQ9MeVnpquB8r3U/W6VTU+YqR+23NUkftOWJorO6bldfr66MyJcxraZqyij51To3Z19eKCQXqqwQidjjxrdbxsmVZn0/JKZPYG0/JKZPYG0/JK0qeTIzSl/xylpaarco0KmvTFSB3aE6lDPxy1Olq2TKuzaXklMzPDPhhxNczUqVO1cuVKrVy5Mtum9VoWWCRAzbvdpbmjP1FyYrJ+3rJf21btUus+9vxPrml5JSklKUXzxy3T6cizcrvd2rFmt34/ekY317vR6mjZMq3OpuWVyOwNpuWVyOwNpuW9KPKXk0pLTZckud1uud1SxWplLU6VPdPqbFpeyczMsBdGXA23adMmvfXWW8rIyFCpUqX02muvqUqVKlest2DBAs2dO1dlypRRo0aNMj22ceNGvffee0pNTZWfn59GjhypunXrSpKmTJmif//73ypXrpxq167tjW8pR5VqVJArw6WTB6M8yw7vPao6LW61MFX2TMublRJlQ3T9zeVt/d9Q0+psWl6JzN5gWl6JzN5gWt5LDZzSV20ebq7AIgE6+MNR7fxyj9WRsmVanU3LK5mZubDwdjj5Q+NqmMGDB3umCj/55JN67bXXNH/+fN10001avHixXnjhBS1evDjTNvv379d7772nFStWqHTp0nrllVc8jx07dkwzZszQnDlzFBwcrIMHD6pfv37asGGD1q1bp3Xr1mnFihUKDAzU008/7c1vNUtBwYFKjE3KtCwxNklFigVZlChnpuW9nI+vj0bMfVpfz/9Gxw9E5b6BRUyrs2l5JTJ7g2l5JTJ7g2l5LzV9yEea8fzHqtX4ZtW5+xalpaRbHSlbptXZtLySmZlhL0wVNsylU4UDAwN1yy236KabbpIkdevWTb/88osSEhIybbNz5061atVKpUuXliT17NnT89g333yjY8eO6aGHHlJ4eLheeOEFpaen6+zZs9qxY4fuv/9+FS1aVD4+Purevbv3vtFsnE9IVpGQzE9wRUOClBR/3qJEOTMt76UcDoeGfdBfaanpevfZj6yOkyPT6mxaXonM3mBaXonM3mBa3su5XG79vO2AylQqpY79Qq2Oky3T6mxaXsnMzLAXGleDud1uORy5TzVwu905Pn733Xd7muGVK1dq8+bNKl26dK7bWeHkgSj5+Pqo0k3lPcuq1alq22mspuW91HPv91PJcsU19sG3lZGeYXWcHJlWZ9PySmT2BtPySmT2BtPyZsfp61QFG1/jalqdTcsrmZm5sLjd9v+wIxpXg9WrV0+//PKLDh8+LElavny5br31VgUHB2dar3Hjxtq4caPOnTsnSVqyZInnsWbNmumbb77RwYMHPcv27t0rSWrSpInWrFmjpKQkZWRkaOnSpYX9LeUqOSlFm5ftUN9XeyqwSIBua1pTTcMbau28jVZHy5JpeS8aPO0xVa5ZSaO7TlZqcprVcXJlWp1NyyuR2RtMyyuR2RtMyytJxcsUU8vujRVYNEBOp0P1W9fWPT2aaM+GX6yOli3T6mxaXsnMzLAXh9uOw2rIUmhoqGbOnKkaNWp4lm3atElTpkxRenp6ppszXf7WNQsWLNBHH32k0qVLq1WrVpozZ47n7XA2b96sqVOnKjk5WWlpabrzzjs1fvx4SRduzrRmzRqVLVtWjRs31vr166/67XDaOHsUUAUuKFYyWM/P6a8729RR/LkEzR65QOsXbS7QYxQkb+R1+vsX2L7K3lBa8w68o9TkVGWkuzzL3xk4R+s/2Vogx3ClphbIfi7FeVH4yFz4TMsrkdkbvPI6ElRw1xkWL11ML80fpGq1K8vhdCr6+FmtnPG11szdUGDHcJ0v+OmlnBeFr7Azf+1anPtKNtBwjf3fGeTb9hOsjnAFGlcUuoJuXHGlgmxcvaEwGlcAQP4VZOPqDYXRuMJ8pjSu9de8aHWEXH3XfrzVEa7AVGEAAAAAgK3RuAIAAAAAbI33cQUAAAAAL3G7c39XEFyJEVcAAAAAgK3RuAIAAAAAbI2pwgAAAADgJS6mCucLI64AAAAAAFujcQUAAAAA2BqNKwAAAADA1rjGFQAAAAC8xO22OoGZGHEFAAAAANgajSsAAAAAwNaYKgwAAAAAXuLm7XDyhRFXAAAAAICt0bgCAAAAAGyNqcIAAAAA4CVMFc4fRlwBAAAAALZG4woAAAAAsDWmCgPXAOcN11sd4aq4Dv1mdYS/Bae/v9URroorNdXqCFfNGRRkdYSr5jp/3uoIsKGE+++wOsJVCdkTbXWEq5bOax/+y8VU4XxhxBUAAAAAYGs0rgAAAAAAW2OqMAAAAAB4idttdQIzMeIKAAAAALA1GlcAAAAAgK3RuAIAAAAA8mX9+vXq3LmzwsPDFRYWpq+++kqSdOTIEfXs2VNt27ZVz549dfTo0b90HK5xBQAAAAAvcV9Db4fjdrs1bNgwLViwQDVq1ND+/fvVq1cvtW7dWmPGjFHv3r0VHh6ulStXavTo0fr444/zfSxGXAEAAAAA+eJ0OhUfHy9Jio+PV9myZfXHH39o37596tixoySpY8eO2rdvn2JiYvJ9HEZcAQAAAAAecXFxiouLu2J5SEiIQkJCPF87HA69/fbbGjBggIoUKaLExES9//77ioqKUrly5eTj4yNJ8vHxUdmyZRUVFaVSpUrlKxONKwAAAAB4iQlThT/66CNNnz79iuUDBw7UoEGDPF+np6fr/fff14wZM1S/fn199913GjJkiCZNmlTgmWhcAQAAAAAeffv2VZcuXa5YfuloqyT98ssvio6OVv369SVJ9evXV1BQkAICAnT69GllZGTIx8dHGRkZio6OVoUKFfKdicYVAAAAAOBx+ZTg7JQvX16///67fvvtN1WrVk2HDx/W2bNnVaVKFdWqVUsREREKDw9XRESEatWqle9pwhKNKwAAAAB4jdvqAAWoTJkyeuWVV/TMM8/I4bgwBfr1119XiRIl9Morr2jEiBGaMWOGQkJCNHHixL90LBpXAAAAAEC+dOrUSZ06dbpiefXq1bV48eICOw5vhwMAAAAAsDVGXAEAAADAS0y4q7Ad0bjCOMVKBuu52f1V/746ijsbrzmjFmr9os1Wx8qWCXnDHm6i1l0a6Maa5bUh4ge9NeLCtI4bqpfV85N6qsINFy6kP/TzSc0cu0rHDkdbGTdLJtT5Uqbl9fP31cCpj6pe6O0qVrKoTh0+rQ9Hf6ZdX+2xOlqOTKvzsDlPqm6r2xRYJEB/nI7V4imf64uPNlodK0em1VgyL7MJeUcPaq/6tW9QUICfzv2ZqIUrd2n1uh9V9fpSenlge1UsV0KS9Otvp/X2h+t09ESMpXl53bOGiZlhHzSuBktLS9PMmTMVEREhX19f+fr6qkqVKho8eLBuuukmq+MVmkHTH1d6aroeKN9P1etW1fiIkfptz1FF7jthdbQsmZD3XHScPnnvP6rfvIb8A/0yLR8/eL6iT/4hp9Ohjg810YgpvTWg09vWhc2GCXW+lGl5nb4+OnPinIa2GavoY+fUqF1dvbhgkJ5qMEKnI89aHS9bptX508kRmtJ/jtJS01W5RgVN+mKkDu2J1KEfjlodLVum1VgyL7MJeect36nX3/tKaekZuqFiKU1/9QEdOHpaJ3+P1YtvrtbvZ+LkdDrUtW1dvfpsR/V94WNL8/K6Zw0TM8M+uMbVYCNHjtSvv/6qxYsX6/PPP9eKFSvUvn17HT58ONN6LpdLbve1cf+ywCIBat7tLs0d/YmSE5P185b92rZql1r3aWl1tCyZknfrVz9r29p9ivszKdPyxPhkRZ/848IXDodcLrcqVLnOgoQ5M6XOF5mWV5JSklI0f9wynY48K7fbrR1rduv3o2d0c70brY6WLRPrHPnLSaWlpkuS3G633G6pYrWyFqfKnok1Ni2zKXmPnDintPSM/37lltvtVqVyJZSQlKLfz8RJkhySXC63ri9fwqqYHrzueZ+JmQuN24APG2LE1VBHjx7V2rVrtXHjRs97LDkcDrVv316SNG3aNEVGRiopKUnHjx/X/PnzNXPmTO3cuVNpaWkqWbKkJkyYoEqVKunEiRPq1q2bunbtqm+//VYpKSkaM2aMGjRoIEnauHGj3nvvPaWmpsrPz08jR45U3bp1Lfm+K9WoIFeGSycPRnmWHd57VHVa3GpJntyYljc7i3e9oqAi/nI4HZr3ztdWx7mCaXU2LW9WSpQN0fU3l7f1f8lNrfPAKX3V5uHmCiwSoIM/HNXOL+07HdvEGpuW2aS8z//zXt3f6jYFBvjp199Oa9vuI57Hvpj7tIIC/eV0ODT70y0WpswbXvcKnomZYS80robat2+fqlSpouLFi2e7zq5du7Rs2TLPG/3269dPw4cPlyQtXrxYkydP1pQpUyRJf/75p2rWrKnhw4dr586deu6557R27Vr9/vvvmjFjhubMmaPg4GAdPHhQ/fr104YNGwr9e8xKUHCgEmMv++9obJKKFAuyJE9uTMubnR4NXlFAkJ9ad6mv6FN/Wh3nCqbV2bS8l/Px9dGIuU/r6/nf6PiBqNw3sIipdZ4+5CPNeP5j1Wp8s+rcfYvSUtKtjpQtE2tsWmaT8r45+z+a8sE63V6jourder1S0zI8j7X7x7sKDPBV+5a36fezcRamzBte9wqeiZlhLzSu14hDhw7p+eefV3Jysu6++24VL15cLVq08DStkrRp0yYtXLhQSUlJSk/P/IeQn5+f5/2XGjVqpMDAQP3222/67rvvdOzYMT300EOeddPT03X27FmVLl3aO9/cJc4nJKtISOYnuKIhQUqKP+/1LHlhWt6cpJxP078X7dAn21/WE+3fVGxMotWRPEyrs2l5L+VwODTsg/5KS03Xu89+ZHWcHJlcZ5fLrZ+3HdC9DzZVx36hWvme/UZ8JDNrbFpm0/K6XG7t3X9Sbe+upS733aEla3Z7HktOSdeKr/fo8zkD1PvZD/VnnD2/h4t43StYJmYuLNxVOH+4xtVQt956qyIjIxUXd+G/ljfddJNWrlypPn36KCEhQZJUtGhRz/onT57U66+/rjfffFMRERGaMGGCUlNTs92/2+2Ww3Hhl+ruu+/WypUrPR+bN2+2pGmVpJMHouTj66NKN5X3LKtWp6ptpyualjc3DqdDAUH+Kl0u+5F+K5hWZ9PyXuq59/upZLniGvvg28pIz8h9AwuZXOeLnL5OVbDxNa4m1ti0zKblvcjHx6lKWVzL6nQ4FBjgqzKlink/VD7wuldwTMwMe6FxNVTVqlV177336qWXXlJ8fLxneVJSUpbrJyQkyM/PT2XKlJHL5dInn3yS6fG0tDStXr1a0oUpxikpKbrxxhvVrFkzffPNNzp48KBn3b179xbCd5Q3yUkp2rxsh/q+2lOBRQJ0W9OaahreUGvn2fPtIkzJ6/Rxys/fV04fZ6bP6zW9WdVrVZTT6VCRogF6YmRHJcSet93bAphS54tMy3vR4GmPqXLNShrddbJSk9OsjpMr0+pcvEwxtezeWIFFA+R0OlS/dW3d06OJ9mz4xepo2TKtxpJ5mU3IWyIkSPc2ramgQD85nQ41uqOKWje7Rd/9eEwN61TRzVXLXngdCfLXoL6tFJ+QosiT5yzNzOue95mYGfbicF8rt5v9G0pNTdWMGTO0Zs0a+fr6KiQkRGXLltUTTzyhdevWKSkpyXNNqySNGzdO69evV8WKFdWwYUOtWLFC69at89yc6aGHHtI333yj5OTkTDdn2rx5s6ZOnark5GSlpaXpzjvv1Pjx4/Ocs42zR4F+38VKBuv5Of11Z5s6ij+XoNkjF9j6PcC8kdf3pmp/afuHBrXWw4PaZFo2f9rXijx4Wo88e59Klyuu1JQ0HfjxhD6YvEZHf/39Lx0v/dBvf2n7rHBeXMnp719g+yp7Q2nNO/COUpNTlZHu8ix/Z+Acrf9ka4Ecw5XDLJD8Kuw6O4MK7tqs4qWL6aX5g1StdmU5nE5FHz+rlTO+1pq5GwrsGJLkOl+w0/JM+92TzMvsjbxJ3e7K97YlQoI07vkw3VSljJwOh34/G6fF/96t1f/5UffcVUP9HmymMtcFKyU1XfsP/a73Fnyjw8f+2ttohez5a42kt1/3pIJ/7TPtPJYKP/PXrsUFtq/CdPPicVZHyNXBHi9ZHeEKNK7wNK47duwolP0XdOOKK/3VxtXbCqNxxZUKsnH1hsJoXAtbQTau3lLQjSuuDX+lcbXCX21crcBrX+EzpXG96TP7N66HHrBf48pUYQAAAACArdG4Qtdff32hjbYCAAAAwF/F2+EAAAAAgJfwdjj5w4grAAAAAMDWaFwBAAAAALbGVGEAAAAA8BamCucLI64AAAAAAFujcQUAAAAA2BpThQEAAADAS9xuqxOYiRFXAAAAAICt0bgCAAAAAGyNqcIAAAAA4C1MFc4XRlwBAAAAALZG4woAAAAAsDUaVwAAAACArXGNKwAAAAB4idvtsDqCkRhxBQAAAADYGo0rAAAAAMDWmCoMXMbh42N1hKvmOnbC6giwIXdGhtURrnmu8+etjgAUiJAdx62OcFUyon63OgKQf7wdTr4w4goAAAAAsDUaVwAAAACArTFVGAAAAAC8hLsK5w8jrgAAAAAAW6NxBQAAAADYGlOFAQAAAMBbuKtwvjDiCgAAAACwNRpXAAAAAICtMVUYAAAAALyGuwrnByOuAAAAAABbo3EFAAAAANgaU4UBAAAAwFu4q3C+MOIKAAAAALA1GlcAAAAAgK3RuAIAAAAAbI1rXAEAAADAW7jGNV8Ycb2GhYaG6sCBA5mWde3aVTt27Ljq7eykWMlgjVk6VKvi52n+kRm6p1dzqyPlqNOAtpq+fbwiEubphTn9rY6TKz9/Xw2Z2U8fH3hHy8/M1rvbx6vBfXdYHStXpp0XpuWVzDuXJfPqbFpeiczeYELesL7N9U7Ec1p1cLKee7N3psfqNrtZs9aN1PJfJ+n/PnlaZSuVtChl9nh+8w4TM8M+GHGFcQZNf1zpqel6oHw/Va9bVeMjRuq3PUcVue+E1dGydO5UjBZOWK76992hgCB/q+PkyunrozMnzmlom7GKPnZOjdrV1YsLBumpBiN0OvKs1fGyZdp5YVpeybxzWTKvzqbllcjsDSbkPXc6Tp9M/Vr1W94i/0A/z/KQkkX10vuP6e3hn2jH2p/1yPP3a+S7fTWk89vWhc0Cz2/eYWJm2Acjrn9TZ8+e1dNPP62wsDCFhYVpxYoVWa4XHR2twYMHq3v37goLC9PMmTO9G/QygUUC1LzbXZo7+hMlJybr5y37tW3VLrXu09LSXDnZsuJbbV21S/Hn4q2OkicpSSmaP26ZTkeeldvt1o41u/X70TO6ud6NVkfLlmnnhWl5LzLtXDatzqbllcjsDabk3frFXm376kfF/ZGYaXmz9nUUeeB3bf58j9JS0jV/yhe68daKur56WYuSZo3nt8JnYuZC43bY/8OGGHG9xg0ePFgBAQGer48ePSpJGjdunG6++Wa9++67io6OVteuXXXrrbeqRo0ambYfPny4BgwYoIYNGyo1NVX/+Mc/VLt2bTVr1syb34ZHpRoV5Mpw6eTBKM+yw3uPqk6LWy3J83dQomyIrr+5vK3/G2raeWFaXlOZVmfT8kpk9gbT8l7uhhrldeSXU56vU86nKirynKrUKK8Th6MtTGY2E88LEzPDXmhcr3FTp07N1Ix27dpVkrRt2zaNGDFCklS2bFm1bNlSO3bsyLRuUlKSdu7cqZiYGM+yxMREHT582LLGNSg4UImxSZmWJcYmqUixIEvyXOt8fH00Yu7T+nr+Nzp+ICr3DSxi2nlhWl5TmVZn0/JKZPYG0/JeLqhIgGJjEjItS4w/r6CigRYlujaYeF6YmBn2QuP6N+ZwOHL82uVyyeFwaMmSJfLz85MdnE9IVpGQzE9wRUOClBR/3qJE1y6Hw6FhH/RXWmq63n32I6vj5Mi088K0vKYyrc6m5ZXI7A2m5b3c+aQUFQnO3KQWCQ7U+cRkixJdG0w8L0zMXFjc3FU4X7jG9W+qSZMm+vTTTyVJZ86c0caNG9W4ceNM6wQHB6t+/fqaNWuWZ1lUVJTOnDnj1ayXOnkgSj6+Pqp0U3nPsmp1qtp6Gqupnnu/n0qWK66xD76tjPQMq+PkyLTzwrS8pjKtzqbllcjsDablvdyxA7/rxlsrer4OCPJXhSqlFXngdwtTmc/E88LEzLAXGte/qZdeekn79+9XWFiYHnvsMb3wwgu6+eabr1hv8uTJOnz4sOcmTkOGDFFcXJwFiS9ITkrR5mU71PfVngosEqDbmtZU0/CGWjtvo2WZcuP0ccovwE9OH2emz+1s8LTHVLlmJY3uOlmpyWlWx8mVaeeFaXkvMu1cNq3OpuWVyOwNpuS98Jzg+9/nB4fn861f7FXVGhXUrH0d+QX46qFn2+roL6dsd30rz2+Fz8TMsBeH281gNQpXG2ePAt1fsZLBen5Of93Zpo7izyVo9sgFWr9oc4Ht3+HjU2D7kqQ+L3dXn9HdMy2b99oSzRu7pMCOUZCZy95QWvMOvKPU5FRlpLs8y98ZOEfrP9laIMdwpaYWyH4uVdjnRUHzRl7TzmV3RsGP7HNeFD4yFz5v5PW9vtJf2v6hIe308JB2mZbNn/KFFkz5QnWb19CA17qp7PUl9evuY3rz+YWKPhGTzZ7yJiOqYEdsvfFaXdDPcaadx1LhZ/7atbjA9lWYqsyZZHWEXEU+PszqCFegcUWhK+jGtbAV9B/73mBa5sJoXHEl086LwmhcAeTNX21cva2gG1dv4Dmu8NG4Fhw7Nq72nQMBAAAAAIC4qzAAAAAAeI/bkfs6uAIjrgAAAAAAW6NxBQAAAADYGo0rAAAAAMDWuMYVAAAAALzEwXu65AsjrgAAAAAAW6NxBQAAAADYGlOFAQAAAMBbmCqcL4y4AgAAAABsjcYVAAAAAGBr2U4VbtmypRwOR6472LBhQ0HmAQAAAIBrlzv3HgtXyrZxfeONN7yZAwAAAACALGXbuDZq1MibOQAAAAAAyFKernFNTU3VlClTdO+996p+/fqSpM2bN2v+/PmFGg4AAAAAriluAz5sKE+N64QJE3TgwAFNnjzZc93rzTffrEWLFhVqOAAAAAAA8vQ+rmvXrtVXX32lIkWKyOm80OuWK1dOp0+fLtRwAAAAAADkqXH18/NTRkZGpmUxMTEqUaJEYWTCNcbh42N1hKvivuxcN4HD39/qCLAhE89l05j2/CZxXiBrZ1vfYHWEq1Ji7kmrIwD5Z9OpuHaXp6nC7dq10/Dhw3X8+HFJUnR0tF577TV16NChUMMBAAAAAJCnxnXIkCGqVKmSOnXqpLi4OLVt21Zly5bV008/Xdj5AAAAAAB/c3maKuzv768XX3xRL774omJiYlSyZEnPTZoAAAAAAChMeWpcJeno0aNas2aNoqOjVbZsWbVv315Vq1YtxGgAAAAAcI3hGtd8ydNU4dWrV6tLly769ddfFRQUpAMHDqhLly5avXp1YecDAAAAAPzN5WnE9e2339asWbPUsGFDz7Jdu3Zp2LBhCgsLK7RwAAAAAADkqXFNTExU3bp1My274447lJSUVBiZAAAAAODa5OZeQfmRp6nCjz76qN566y2lpKRIkpKTkzVlyhQ9+uijhRoOAAAAAIBsR1xbtmzpuXOw2+3W2bNnNW/ePIWEhCguLk5ut1tlypTRk08+6bWwAAAAAIC/n2wb1zfeeMObOQAAAADgmufgrsL5km3j2qhRI2/mAAAAAAAgS3l+H9dffvlFu3bt0h9//CG3+3//JnjmmWcKJRgAAAAAAFIeb8706aefqlevXtq+fbv+9a9/6cCBA/rwww917Nixws4HAAAAANcOtwEfNpSnxnX27NmaPXu23n33XQUGBurdd9/VO++8I1/fPA/YAgAAAACQL3lqXM+dO6cGDRpc2MDplMvlUsuWLbV+/fpCDQcAAAAAQJ6GTMuXL68TJ07o+uuvV9WqVfWf//xHJUuWlJ+fX2HnAwAAAAD8zeVpxPWf//ynDh8+LEkaMGCAhg4dqr59+2rAgAGFGg64XKcBbTV9+3hFJMzTC3P6Wx0nT4qVDNaYpUO1Kn6e5h+ZoXt6Nbc6Uq6GzXlSCw9P1bKo9zXnh0lq17el1ZFyZVqdTcsrkdkbeI7zDtMym5D3tX7tteatJ7T+3ae1ZMKjCr/7ds9j4XffrmWvP6aNMwZq6pCuKl2iqIVJs2dCnS9lWl7JzMywjzyNuHbt2tXzecuWLbVz506lpaUpKCio0IKZLDQ0VDNnzlSNGjWyXWfZsmWqV6+ebrzxRkuOb6pzp2K0cMJy1b/vDgUE+VsdJ08GTX9c6anpeqB8P1WvW1XjI0bqtz1HFbnvhNXRsvXp5AhN6T9HaanpqlyjgiZ9MVKH9kTq0A9HrY6WLdPqbFpeiczewHOcd5iW2YS8cz/fqbEffqW09AxVKV9S7w9/QL8ei1aRAH8N6NZc/Sct1rHTf+j53vdo/JMd9OTEz6yOfAUT6nwp0/JKZmaGfeRpxPVy/v7+8vPz02233VbQef42li9frqNHj171di6XK9PbEf3dbFnxrbau2qX4c/FWR8mTwCIBat7tLs0d/YmSE5P185b92rZql1r3sfcIZuQvJ5WWmi5JcrvdcrulitXKWpwqe6bV2bS8Epm9hee4wmdaZlPy/nbqnNLSMzxfu91uXV+2hO6uW03/2XVAv506p/QMl+as3q47a16vSmWKW5j2SqbU+SLT8kpmZi4sDrf9P+zoL90W+O/cQOVFnz59dPvtt+uHH35QdHS02rdvrxdeeEFLly7VTz/9pHHjxuntt9/W8OHD1bRpU/3rX//Sl19+qYyMDJUrV05jx45VmTJlNG3aNEVGRiopKUnHjx/X/Pnz1aVLF3Xo0EHff/+9oqOj1bdvXz388MOeY69Zs0Yvv/yyzpw5o8cee8zz2MSJEz0j5iVLltSECRNUqVIlnTt3Ts8//7zOnTsnSWrSpIlGjRolSdnmQu4q1aggV4ZLJw9GeZYd3ntUdVrcamGqvBk4pa/aPNxcgUUCdPCHo9r55R6rI2XLtDqbllciM7JmYo1Ny2xS3uEPh6pjs9sUGOCn/ZGntWXvEd1erbwcl6zj+O9X1SuV1skzsdYEzYJJdZbMyyuZmRn28pcaV4fDkftKf3NRUVFasGCBEhMT1bp1a3Xv3l3dunXTihUr9Nhjj+mee+6RJK1cuVLHjh3TZ599JqfTqYULF+r//u//9Oabb0qSdu3apWXLlqlUqVKefZ89e1YLFizQ2bNn1blzZzVo0EC33HKLJCk5OVmffvqpTpw4obCwMHXp0kVFixZVv379NHz4cEnS4sWLNXnyZE2ZMkWrV69WxYoVNXfuXElSbGxsnnIhZ0HBgUqMTcq0LDE2SUWK2X+a/fQhH2nG8x+rVuObVefuW5SWkm51pGyZVmfT8kpkRtZMrLFpmU3KO3H+Or2xYL1q31RB9WtWVmp6hrbsPaIJT3XU0g17dfz0n/pnp7vkcrkVGGCvt1Q0qc6SeXklMzPDXuz1rHENateunZxOp4oVK6bq1avr2LFjqlq16hXrrVu3Tj/99JO6dOkiScrIyFBwcLDn8RYtWmRqWiWpe/fukqTSpUurVatW2rlzp6dxvf/++yVJ119/vUJCQvT777+revXq2rRpkxYuXKikpCSlp/+vEbnjjjv04YcfauLEiWrUqJGaN2+ep1zI2fmEZBUJyfyEXDQkSEnx5y1KdHVcLrd+3nZA9z7YVB37hWrle19bHSlLptXZtLwSmZE1E2tsWmbT8rrcbu05eErt76ql7vfcoU/X7taslVs18ekwBQcFaNFX3ykpOVXRMQlWR83EtDqbllcyMzPsJcfGtXfv3tmOqrpcrkIJdK0JCAjwfO7j46OMjIws13O73erfv7+nGb1c0aI534HP7XZn+lllddyTJ0/q9ddf15IlS1S5cmV9//33euGFFyRJ9erV04oVK7R161atXLlSs2bN0qJFi3LNhZydPBAlH18fVbqpvE4e+l2SVK1OVeNuQuD0daqCja9xNa3OpuWVyIysmVhj0zKblvciHx+nrv/vdayL1+3R4nUXLje5oVwJPRZ2lw6fPGtlvCuYVmfT8kpmZi40bmat5keON2fq0aOHunfvnuXHAw88oAkTJngr5zWnaNGiio//3803QkNDtXDhQs8U3dTUVO3fvz/HfSxfvlySFBMTo02bNqlRo0Y5rp+QkCA/Pz+VKVNGLpdLn3zyieex48ePKzg4WB06dNDIkSP1888/y+Vy5StXYXL6OOUX4CenjzPT53aVnJSizct2qO+rPRVYJEC3Na2ppuENtXbeRqujZat4mWJq2b2xAosGyOl0qH7r2rqnRxPt2fCL1dGyZVqdTcsrkdlbeI4rfKZlNiFvyWJBatOopoIC/OR0OHTXbVXUtvEt+vaX4/L39VH1StdJksqVKqZRfdvok6+/V3xSisWpMzOhzpcyLa9kZmbYS44jrhenh6Lg9ezZUxMnTtQHH3ygYcOGqXPnzvrzzz89N1Fyu93q1auXZ+pvVipUqKDevXvrzJkzevLJJ1WzZs0cj1mzZk21a9dOHTp0UMWKFdWwYUPt2rVLkrRz5059+OGH8vHxkcvl0quvviqn05mvXIXpoVFd1Wf0/0Z/Wz90t+a9tkTzxi6xJE9eTHt6tp6f01+fnZ6t+HMJemfAv+z930W31PGf92rwO/+Qw+lU9PGzmjlsgbZ9/r3VyXJkWp1NyyuR2Rt4jvMO0zLbPa9bUvd77tDIR+6Vw+HQ7+fi9daiDdr0w2EFBwVo7BP36/qyJZSUnKrVm3/WzOVbrY6cJbvX+XKm5ZXMzAz7cLi5NbCRTHqv1vv8HrQ6wlVxZzOd286chr2nsus817Pg2uDw8bE6wlUz8TkOhe/PfzSxOsJVKTF3m9URYENfuxZbHSFPqr39ltURcvXbs89ZHeEK9p1/BAAAAACAuKuwsdatW2d1BAAAAADwChpXAAAAAPAWLtTMlzxNFU5NTdWUKVN07733qn79+pKkzZs3a/78+YUaDgAAAACAPDWuEyZM0IEDBzR58mTPe4XefPPNWrRoUaGGAwAAAAAgT1OF165dq6+++kpFihSR03mh1y1XrpxOnz5dqOEAAAAA4FriYKpwvuRpxNXPz08Zl90+PyYmRiVKlCiMTAAAAAAAeOSpcW3Xrp2GDx+u48ePS5Kio6P12muvqUOHDoUaDgAAAACAPDWuQ4YMUaVKldSpUyfFxcWpbdu2Klu2rJ5++unCzgcAAAAA1w63AR82lKdrXP39/fXiiy/qxRdfVExMjEqWLOm5SRMAAAAAAIUpT43rxSnCFyUmJno+r1y5csEmAgAAAADgEnlqXNu0aSOHwyG3+3/jxhdHXH/55ZfCSQYAAAAAgPLYuO7fvz/T12fOnNH06dPVoEGDQgkFAAAAANckm15Dand5ujnT5cqUKaMXX3xRb731VkHnAQAAAAAgk3w1rpL022+/6fz58wWZBQAAAACAK+RpqnDv3r0z3UX4/PnzOnToEG+HAwAAAABXwcFU4XzJU+Pao0ePTF8HBQXplltuUdWqVQsjEwAAAAAAHrk2rhkZGdq+fbvGjh0rf39/b2QCAAAAABggJSVFEyZM0LZt2xQQEKC6detq7NixOnLkiEaMGKE///xTJUqU0MSJE//SwGeujauPj4+2bNmSaaowcDXcGRlWR7jmubjeHADwF1y3an/uK9kIf1nAaO5rq6964403FBAQoC+//FIOh0Nnz56VJI0ZM0a9e/dWeHi4Vq5cqdGjR+vjjz/O93HydHOmvn37atq0aUpLS8v3gQAAAAAA147ExEStWLFCzzzzjGegs3Tp0jp37pz27dunjh07SpI6duyoffv2KSYmJt/HynHENSIiQh07dtT8+fN19uxZffjhhypVqlSm0dcNGzbk++AAAAAAAHuJi4tTXFzcFctDQkIUEhLi+fr48eMqUaKEpk+frh07dqho0aJ65plnFBgYqHLlysnHx0fShVm8ZcuWVVRUlEqVKpWvTDk2rqNHj1bHjh31xhtv5GvnAAAAAIBLGHBX4Y8++kjTp0+/YvnAgQM1aNAgz9fp6ek6fvy4br31Vg0fPlx79uzRU089pXfeeafAM+XYuLrdF6raqFGjAj8wAAAAAMB++vbtqy5dulyx/NLRVkmqWLGifH19PVOC77jjDpUsWVKBgYE6ffq0MjIy5OPjo4yMDEVHR6tChQr5zpRj4+pyubR9+3ZPA5uVJk2a5PvgAAAAAAB7uXxKcHZKlSqlxo0ba8uWLWrevLmOHDmic+fOqWrVqqpVq5YiIiIUHh6uiIgI1apVK9/ThCXJ4c6hK61Vq5YqVqyYbePqcDj0n//8J98Hx99DG2eP3FcCAAM5/nvtjkm40zuy4lOqpNURrkpGzB9WR4ANfe1abHWEPLn5/6ZYHSFXB0cMyfO6x48f16hRo/Tnn3/K19dXzz77rFq2bKnDhw9rxIgRiouLU0hIiCZOnKhq1arlO1OOI65BQUE0pgAAAACALFWuXFnz5s27Ynn16tW1eHHB/TMhT2+HAwAAAACAVfJ0cyYAAAAAQAGgxcqXHEdcd+/e7a0cAAAAAABkianCAAAAAABbo3EFAAAAANhajte4AgAAAAAKjoNrXPOFEVcAAAAAgK3RuAIAAAAAbI2pwgAAAADgLUwVzhdGXGGcYiWDNWbpUK2Kn6f5R2bonl7NrY6UI9PySmT2BtPySmT2hk4D2mr69vGKSJinF+b0tzpOnphWY8m8zKblvahll4aatfVVrYicpg++Ha/b7rrJ6kg5Mq3OpuWVzMwM+2DE9RoQGhqqmTNnqkaNGlZH8YpB0x9Xemq6HijfT9XrVtX4iJH6bc9RRe47YXW0LJmWVyKzN5iWVyKzN5w7FaOFE5ar/n13KCDI3+o4eWJajSXzMpuWV5Lqtaylx0Z31ev/nKVfvz+qUuWKWx0pV6bV2bS8kpmZYR+MuP7NpaenWx3hqgQWCVDzbndp7uhPlJyYrJ+37Ne2VbvUuk9Lq6NlybS8Epm9wbS8Epm9ZcuKb7V11S7Fn4u3OkqemFhj0zKblveiPsM7aeHkCO3/7ojcbrfO/f6nzv3+p9WxsmVanU3LK5mZudC4DfiwIRrXa9TEiRPVrVs3derUSX379tXJkyclSSdOnFDjxo01bdo09erVS4sXL9ahQ4fUo0cPdezYUS+88IIeeOABrV+/XpIUHR2twYMHq3v37goLC9PMmTOt/LZUqUYFuTJcOnkwyrPs8N6jqnLr9Ramyp5peSUye4NpeSUyI2sm1ti0zKbllSSn06Gb61ZR8euK6YOd4zRv70QN+L9e8g/0szpatkyrs2l5JTMzw15oXK9R/fr109KlS7Vq1Sp17NhRkydP9jz2559/qnr16lq0aJF69eqlYcOG6eGHH1ZERIT69u2rH3/80bPu8OHD1adPHy1ZskRLly7Vpk2btGXLFiu+JUlSUHCgEmOTMi1LjE1SkWJBFiXKmWl5JTJ7g2l5JTIjaybW2LTMpuWVpBJlQ+Tn76vmne7UC2Fv6OlWY1W9dmX1eq6D1dGyZVqdTcsrmZkZ9sI1rteoTZs2aeHChUpKSrpiOnBAQIDat28vSUpISNCBAwcUFhYmSapdu7Zq1qwpSUpKStLOnTsVExPj2TYxMVGHDx9Ws2bNvPSdZHY+IVlFQjI/wRUNCVJS/HlL8uTGtLwSmb3BtLwSmZE1E2tsWmbT8kpS6vk0SdKqf61XzOlYSdKy99aq13P366MJKyxMlj3T6mxaXsnMzIXFYdOpuHbHiOs16OTJk3r99df15ptvKiIiQhMmTFBqaqrn8aCgIDkcDkmS2+2Ww+HwfH0pl8slh8OhJUuWaOXKlVq5cqXWrl2rRx55xGvfy+VOHoiSj6+PKt1U3rOsWp2qtr2o37S8Epm9wbS8EpmRNRNrbFpm0/JKUkJsks6cjJHbbc5f56bV2bS8kpmZYS80rteghIQE+fn5qUyZMnK5XPrkk0+yXbdYsWK66aabFBERIUn6+eefdeDAAUlScHCw6tevr1mzZnnWj4qK0pkzZwr3G8hBclKKNi/bob6v9lRgkQDd1rSmmoY31Np5Gy3LlBPT8kpk9gbT8kpk9hanj1N+AX5y+jgzfW5XJtbYtMym5b3oq0VbFd4vVMVLF1Nw8SLq8uS92vnVXqtjZcu0OpuWVzIzM+zF4Tbp32HIUmhoqFJSUuTj4+NZdt9992n9+vWqWLGiGjZsqBUrVmjdunU6ceKEunXrph07dnjWPXDggEaNGiWXy6XbbrtNP//8s0aNGqUGDRrozJkzev3113Xw4EFJUtGiRTV+/HhVr149z/naOHsU3DerC+8B9vyc/rqzTR3Fn0vQ7JELtH7R5gI9RkEyLa9EZm8wLa9E5qw4LnneLQh9Xu6uPqO7Z1o277Ulmjd2SYEdw52RUWD7kjgvvMEbeX1KlSzY/fn66KkJPXVPt0ZKTU7TNyt3afarS5WWUjDvZpAR80eB7OdSnBeFr7Azf+1aXGD7Kkw1x06xOkKufn15iNURrkDjCiUlJXmmDx86dEh9+vTRF198oeLFC+Y91wq6cQUAuyjoxtUbCrpxxbWhoBvXwlYYjSvMR+NacOzYuHJzJuj777/XpEmTPNeijB07tsCaVgAAAAD4q2hcoebNm6t58+ZWxwAAAACALNG4AgAAAIC3cKFmvtj3VoUAAAAAAIjGFQAAAABgc0wVBgAAAAAvcTBVOF8YcQUAAAAA2BqNKwAAAADA1pgqDAAAAADewlThfGHEFQAAAABgazSuAAAAAABbY6owAAAAAHgLU4XzhRFXAAAAAICt0bgCAAAAAGyNqcIAAAAA4CUOpgrnCyOuAAAAAABbo3EFAAAAANgajSsAAAAAwNa4xhUAgHxyZ2RYHQEoEI7AQKsjAH8fXOOaL4y4AgAAAABsjcYVAAAAAGBrTBUGAAAAAC/h7XDyhxFXAAAAAICt0bgCAAAAAGyNqcIAAAAA4C1MFc4XRlwBAAAAALZG4woAAAAAsDWmCgMAAACAtzBVOF8YcQUAAAAA2BqNKwAAAADA1pgqDAAAAABe4mCqcL4w4goAAAAAsDUaVwAAAACArTFVGAAAAAC8hanC+cKIKwAAAADA1mhcC1BoaKiaN2+ujIwMz7KlS5eqZs2amj9/fo7brl27Vnv37v3LGaZNm6aJEyf+5f3YWbGSwRqzdKhWxc/T/CMzdE+v5lZHypFpeSUye4NpeSUye4NpeSUye4NpeSWp8k3l9PqnA7Vk30TN2fyymrarY3WkXJlWZ9PySmZmhn0wVbiAlSlTRps3b1bLli0lSStWrNBtt92W63Zr167V7bffrjp17P/EbrVB0x9Xemq6HijfT9XrVtX4iJH6bc9RRe47YXW0LJmWVyKzN5iWVyKzN5iWVyKzN5iW1+nj1OgP+unf87boxV7vqvZdN+mVuU9oYNtJOnnkjNXxsmVanU3LK5mZGfbBiGsB69Kli5YtWyZJOn78uM6fP68aNWpIklJTUzVx4kR1795d4eHhGjp0qBITE/XNN99o3bp1mjVrlsLDw7VixQqdOXNGffr0UdeuXdWhQwdNmjTJc4xp06bpmWeeUb9+/dShQwcNGjRI8fHxnsdPnz6tfv36qV27dnriiSd0/vx5SdK2bdvUs2dPde7cWWFhYfr8888920yfPl3t2rVTeHi4OnfurLi4OEnSnj17PDm6du2qDRs2FHYJcxRYJEDNu92luaM/UXJisn7esl/bVu1S6z4tLc2VHdPySmT2BtPySmT2BtPySmT2BtPyShdGW68rV1zL/7VeLpdbe7Ye1L5vjyi0W0Oro2XLtDqbllcyM3OhcRvwYUOMuBawxo0ba+HChYqNjdXy5cvVuXNn/fTTT5Kk2bNnq1ixYlqyZIkk6Y033tCsWbM0ZMgQhYaG6vbbb9fDDz8sSUpJSdHMmTNVtGhRpaWl6fHHH9emTZvUokULSdJ3332nFStWqHTp0ho5cqRmzJih4cOHS5J++uknLVmyRMWKFdPjjz+u1atX64EHHtCtt96qhQsXysfHR2fPnlXXrl3VvPmFKRpz5szRtm3bFBgYqISEBAUGBiouLk5jxozRrFmzVLZsWUVHR6t79+6KiIhQSEiIt0srSapUo4JcGS6dPBjlWXZ471HVaXGrJXlyY1peiczeYFpeiczeYFpeiczeYFpeSXI4slooVb2lgtez5JVpdTYtr2RmZtgLjWsBczgcat++vT7//HP9+9//1qJFizyN67p165SQkKAvv/xS0oUR2FtuuSXL/WRkZGjSpEnavXu33G63zp49q/3793sa11atWql06dKSpO7du2vcuHGebZs3b+5pLOvUqaNjx45JkmJiYjRq1ChFRkbKx8dHsbGxOnLkiGrXrq0bb7xRQ4cO1d13361WrVopODhYu3fv1okTJ9SvX79M319kZKRq165dwJXLm6DgQCXGJmValhibpCLFgizJkxvT8kpk9gbT8kpk9gbT8kpk9gbT8krS8UOn9efZeHXvf6+W/2u97mhaQ7Xvukl7tx60Olq2TKuzaXklMzPDXmhcC0HXrl3Vo0cPNWrUSCVLlvQsd7vdGjNmjJo0aZLrPj788EPFxcVp8eLFCggI0Msvv6yUlJQs13W73XJc8u/NgIAAz+c+Pj6e7V555RWFhoZq+vTpcjgcatu2rVJSUuTj46PPPvtM33//vbZv366uXbtq9uzZcrvdqlmzphYsWJDfUhS48wnJKhKS+QmuaEiQkuLPW5QoZ6bllcjsDabllcjsDabllcjsDabllaSMdJde++dsDRjbXT0GtNbBPcf0TcRupaWmWx0tW6bV2bS8kpmZC4vDplNx7Y5rXAtB5cqVNWTIEA0YMCDT8tDQUM2dO1fJycmSpISEBB0+fFiSFBwcnOk61fj4eJUpU0YBAQE6ffq0/vOf/2Ta14YNGxQTEyNJWr58uRo3bpxrrvj4eFWqVEkOh0NbtmxRZGSkJ0dMTIwaNWqkwYMHq0aNGjp48KDq1aunyMhIbd++3bOPvXv3yu227rft5IEo+fj6qNJN5T3LqtWpatuL+k3LK5HZG0zLK5HZG0zLK5HZG0zLe9HRX05pWPep6ll7pF56+D2Vv6G0ft0daXWsbJlWZ9PySmZmhr3QuBaSnj17XjEN+IknntAtt9yi7t27KywsTL179/Y0rp06dVJERITn5kx9+vTR999/r86dO2c5StukSRONGjVKHTp0UGxs7BVNclaef/55TZo0ST179tSXX36pmjVrSrrQuD799NMKCwtTx44dVbp0ad13330qXry4ZsyYoXfffVedOnVS+/btNX36dEsb1+SkFG1etkN9X+2pwCIBuq1pTTUNb6i18zZaliknpuWVyOwNpuWVyOwNpuWVyOwNpuW9qGqtivIL8FVAoJ+6PRmqUmVDtHbxTqtjZcu0OpuWVzIzM+zF4bayC0G+TJs2TUlJSZ6bMdldG2ePAt1fsZLBen5Of93Zpo7izyVo9sgFWr9oc4EeoyCZllciszeYllciszeYllciszd4I69vxYK9cdLjL4Wr7YNN5Ovno592HtZ7Ly9R1NGzBbb/9FNRua90lTgvCl9hZ/7atbjA9lWYbh86xeoIufrpjSFWR7gCjauB/u6NKwAAKFgF3bgWtsJoXGE+GteCY8fGlZszGWjQoEFWRwAAAAAAr6FxBQAAAAAv4a7C+cPNmQAAAAAAtkbjCgAAAACwNaYKAwAAAIC3MFU4XxhxBQAAAADYGo0rAAAAAMDWaFwBAAAAALbGNa4AAAAA4C1c45ovjLgCAAAAAGyNxhUAAAAAYGtMFQYAAAAAL3FYHcBQjLgCAAAAAGyNxhUAAAAAYGtMFQYAAAAAb+GuwvnCiCsAAAAAwNZoXAEAAAAAtsZUYeAa4AwKsjrCVXGdP291hL8Fp7+/1RGuiis11eoIwN+Wu1SI1RGuivOPP62OcNV47cNFDqYK5wsjrgAAAAAAW6NxBQAAAADYGlOFAQAAAMBbmCqcL4y4AgAAAABsjcYVAAAAAGBrTBUGAAAAAG9hqnC+MOIKAAAAALA1GlcAAAAAgK3RuAIAAAAAbI1rXAEAAADASxxc45ovjLgCAAAAAGyNxhUAAAAAYGtMFQYAAAAAb2GqcL4w4goAAAAAsDUaVwAAAACArTFVGAAAAAC8hLsK5w8jrjBOsZLBGrN0qFbFz9P8IzN0T6/mVkfKkWl5JWnYnCe18PBULYt6X3N+mKR2fVtaHSlXptXZtLx+/r4aMrOfPj7wjpafma13t49Xg/vusDpWrkyrs2l5JTJ7gwl5wx5srKmL+mvVrlf0/NiumR4LCPTT0y+G6dONI7V0y0t648N/WpQyZ6a99plwXlzOxMywD6+OuIaGhio1NVUbN26Uj4+PJGnp0qUaNWqUXn75ZT388MPZbrt27VqVLVtWderU+UsZpk2bpqSkJA0fPvwv7SevDh8+rPvvv18jR47UP/7xD8/yESNG6Pbbb9fDDz+sRYsWKSUlJdPjdrRs2TLVq1dPN954o6U5Bk1/XOmp6XqgfD9Vr1tV4yNG6rc9RxW574SlubJjWl5J+nRyhKb0n6O01HRVrlFBk74YqUN7InXoh6NWR8uWaXU2La/T10dnTpzT0DZjFX3snBq1q6sXFwzSUw1G6HTkWavjZcu0OpuWVyKzN5iQN+ZMvBbN2qD6zW5SQIBfpseeGR0up49TT3R+R/Gx51WtZgWLUubMtNc+E86Ly5mYGfbh9RHXMmXKaPPmzZ6vV6xYodtuuy3X7dauXau9e/cWZrRCsWTJEt11111aunRptuv06tXL9k2rJC1fvlxHjx61NENgkQA173aX5o7+RMmJyfp5y35tW7VLrfvY87+ipuW9KPKXk0pLTZckud1uud1SxWplLU6VPdPqbFpeSUpJStH8cct0OvKs3G63dqzZrd+PntHN9az9R1ZOTKuzaXklMnuDKXm3/Geftq3/RfF/ns+0/PqqpdW41S2a+tpKxf6RJJfLrUO/nLIoZc5Meu0z5by4lImZC43bgA8b8vo1rl26dNGyZcvUsmVLHT9+XOfPn1eNGjUkSampqZoyZYq+/fZbpaWlqUaNGnrllVf0/fffa926ddq6dasWL16sRx99VM2aNdNzzz2nxMREpaSkqGXLlho2bJikC6Oqhw4dUlJSkk6dOqVq1appwoQJKlasmCTp9OnT6tevn44fP64bbrhB77zzjoKCgrRt2za9/fbbSklJUUZGhp566il16NBBkjR9+nRFREQoICBADodDH3/8sUJCQrRnzx5NnjxZiYmJkqTBgwerVatWkqT09HStXr1aCxYsUL9+/fTjjz+qdu3aV9Tk0lHg1NRUjR07Vjt37lSpUqVUq1YtnT17VlOnTtWyZcsUERGhkJAQHTx4UMWKFdO0adNUpkwZz2PFihXTr7/+qnLlyunll1/WpEmTFBkZqdtvv12TJ0+Ww+FQQkKCXn/9df36669KSUlR48aNNXLkSPn4+KhPnz66/fbb9cMPPyg6Olrt27fXCy+8oKVLl+qnn37SuHHj9Pbbb2v48OFq2rRpYZ8uV6hUo4JcGS6dPBjlWXZ471HVaXGr17PkhWl5LzVwSl+1ebi5AosE6OAPR7Xzyz1WR8qWaXU2LW9WSpQN0fU3l7f1f8lNq7NpeSUye4NpeS9Xs/b1io76Uw8PCNW9Hesq5my85r+3TlvW7rM6WpZMee0z8bwwMTPsxeuNa+PGjbVw4ULFxsZq+fLl6ty5s3766SdJ0uzZs1WsWDEtWbJEkvTGG29o1qxZGjJkiEJDQz1TayUpJSVFM2fOVNGiRZWWlqbHH39cmzZtUosWLSRJ3333nVasWKHSpUtr5MiRmjFjhmd68E8//aQlS5aoWLFievzxx7V69Wo98MADuvXWW7Vw4UL5+Pjo7Nmz6tq1q5o3vzD3fs6cOdq2bZsCAwOVkJCgwMBAxcXFacyYMZo1a5bKli2r6Ohode/e3dNcbtiwQVWqVFGVKlXUpUsXLV26NMvG9VKffvqpTp06pc8//1wZGRnq06ePypcv73n8xx9/1KpVq1ShQgW99NJLmj9/voYMGeJ5bPXq1SpfvryefPJJPf/885o/f76CgoLUpUsXbdu2TU2bNtXrr7+uhg0bavz48XK5XJ7G9IEHHpAkRUVFacGCBUpMTFTr1q3VvXt3devWTStWrNBjjz2me+65p6BOh6sWFByoxNikTMsSY5NUpFiQRYlyZlreS00f8pFmPP+xajW+WXXuvkVpKelWR8qWaXU2Le/lfHx9NGLu0/p6/jc6fiAq9w0sYlqdTcsrkdkbTMt7udLliuvGm8try9p9eujeSap1R2W99m4fHTt8RsePnLE63hVMee0z8bwwMTPsxetThR0Oh9q3b6/PP/9c//73vz0jmpK0bt06rVq1SuHh4QoPD9e6det07NixLPeTkZGhSZMmqVOnTuratasOHjyo/fv3ex5v1aqVSpcuLUnq3r27tm/f7nmsefPmCgkJkcPhUJ06dTzHiImJ0eDBg9WxY0c9/vjjio2N1ZEjRxQcHKwbb7xRQ4cO1WeffaakpCT5+vpq9+7dOnHihPr166fw8HD169dPDodDkZGRki5ME+7SpYskqXPnzlqzZo1SUlJyrM+OHTsUHh4uX19fBQQEZKqPJN15552qUOHCtSF33HFHpvrceeednia3Vq1aql+/vooVKyZfX1/dcsstnlzr1q3TnDlzFB4eri5duujnn3/WkSNHPPtp166dnE6nihUrpurVq2f7M7DC+YRkFQnJ/ARXNCRISfHns9nCWqblvZzL5dbP2w6oTKVS6tgv1Oo42TKtzqblvZTD4dCwD/orLTVd7z77kdVxcmRanU3LK5HZG0zLe7nU5DSlpaVr4awNSk/P0I/fHdWeb4/ozqY3WR0tWya89pl4XpiYubA43Pb/sCNL3g6na9eu6tGjhxo1aqSSJUt6lrvdbo0ZM0ZNmjTJdR8ffvih4uLitHjxYgUEBOjll1/Otil0u91yOByerwMCAjyf+/j4eLZ75ZVXFBoaqunTp8vhcKht27ZKSUmRj4+PPvvsM33//ffavn27unbtqtmzZ8vtdqtmzZpasGDBFcc8e/astmzZov3792vGjBmSpPPnz+urr75SWFhYtt/X5Vkvd3n2jIyMbB/Lbl23260ZM2aocuXKV30Mq508ECUfXx9Vuqm8Th76XZJUrU5V205XNC1vdpy+TlWw6XU+knl1Ni3vpZ57v59Kliuul8InKSPdPs8NWTGtzqbllcjsDablvdyRg79bHSHf7PzaZ+J5YWJm2Islb4dTuXJlDRkyRAMGDMi0PDQ0VHPnzlVycrIkKSEhQYcPH5YkBQcHKz4+3rNufHy8ypQpo4CAAJ0+fVr/+c9/Mu1rw4YNiomJkXThpkKNGzfONVd8fLwqVaokh8OhLVu2eEYoExISFBMTo0aNGmnw4MGqUaOGDh48qHr16ikyMjLTaO7evXvldru1fPlytW3bVhs2bNC6deu0bt06TZgwIcebNEkXplKvWrVK6enpSklJ0Zo1a3LNfbVCQ0M1a9YsT0MaExOj48eP57pd0aJFM/0MrJCclKLNy3ao76s9FVgkQLc1ramm4Q21dt5GS3Nlx7S8klS8TDG17N5YgUUD5HQ6VL91bd3To4n2bPjF6mjZMq3OpuW9aPC0x1S5ZiWN7jpZqclpVsfJlWl1Ni2vRGZvMCWv08cpP39fOZ0OOZ3//dzHqR+/O6ozUbF68PEWcvo4dWvdG1SnwY36bstBqyNnYtprnynnxaVMzAx7sWTEVZJ69ux5xbInnnhC06dPV/fu3eVwOORwODRw4EBVr15dnTp10siRI/XFF1/o0UcfVZ8+ffTMM8+oc+fO+v/27ju+xrv/4/jrJARJ7MZWqzVbpaitmqpakcQeRVuldqlRUjeKVs3eRQmlWqMoktg6pFJas9XWShWxiU0SkXl+f/jl3CKDKLmuS9/PxyOP5lzXGe9cvZxzPtd3FSpUKEUrbe3atfHz8+PUqVOUKlWK4cOH3zPT4MGD+eCDD/j8888pV64c5cqVA24Xrv379+fWrVvY7XYqVqxI48aNyZYtG7NmzWLy5Ml89NFHxMXFUbx4cfz9/QkMDEyx5M7LL7/M6NGjOX067StLHTp0IDQ0lObNm1O4cGEqVapEdPTD7ULh5+fH5MmT8fb2xmazkTVrVvz8/NJsgU3Svn17Jk6cyBdffMGwYcMMmZwJYEbfeQye35tvwucRcTmST/t8buqrdVbLix1avPUyAz59HZuTExdOXcJ/2BK2r//N6GTpstpxtlreAk8+QfMeLxN7K5ZlJ2Y5tn/abz4/LvvFwGTps9pxtlpeUObMYIW8nXo25LXe/+tW+7JXFRbPDmbx7GA+eGcJA8f40K57A8LPXmPK+ys5fdxky2hZ8LPPCufF3ayYWczDZrfbTdqL+cFl9lqtD1tkZCTu7u7ExsbSu3dvmjRpQtu2bY2O9cBecbJudqtwymGtiQ0SH/LFGEmdk4uL0REyJDE21ugIIv9azs+UMzpChtiPmmf+jfulz75H7/vEFUZHuC/P9/rE6Aj39Jv/IKMjpGBYi6uk7Y033iA2NpaYmBjq1KnjmOBJRERERETk3+ixLFz79+9vdIR/ZMUKa1wtEhERERERyQyPZeEqIiIiIiJiSo/dQM3MYciswiIiIiIiIiL3S4WriIiIiIiImJq6CouIiIiIiGQSm7oKPxC1uIqIiIiIiIipqXAVERERERERU1NXYRERERERkcyirsIPRC2uIiIiIiIiYmoqXEVERERERMTUVLiKiIiIiIhkEpvdbvqfBzFz5kzKlSvH4cOHAQgLC6N9+/a8+uqrtG/fnuPHj/+j46bCVURERERERB7YgQMH+P333ylSpIhj2+jRo+nUqRPffvstnTp1YtSoUf/oNVS4ioiIiIiIyAOJjY1l7NixjB49GpvNBsDly5c5ePAgLVq0AKBFixYcPHiQK1euPPDraFZhERERERERcbhx4wY3btxIsT1XrlzkypUr2bZPP/2Uli1bUrx4cce2c+fOUbBgQZydnQFwdnamQIECnDt3jnz58j1QJhWuIiIiIiIimcUCy+F89dVXzJw5M8X2fv360b9/f8ftvXv3sm/fPoYMGfLIM6lwFREREREREYdu3brh6+ubYvvdra27d+/m2LFjvPzyywCcP3+e7t27M2LECMLDw0lISMDZ2ZmEhAQuXLhA4cKFHziTClcRERERERFxSK1LcGp69uxJz549Hbc9PT3x9/enbNmyLF26lHXr1uHt7c26deuoUKHCA3cTBhWuIik4585tdIQMu9G4gtERMsRtxQ6jI/wrOOXNY3SEDEkMv2B0hAxzfqac0REyLGH/X0ZHEBPa8N03RkfIkKZenY2OkHG/7jc6gZiEzQJdhR+GMWPGMHz4cGbNmkWuXLmYOHHiP3o+Fa4iIiIiIiLyjwUHBzt+L1OmDCtWrHhoz63lcERERERERMTU1OIqIiIiIiKSWf4lXYUfNrW4ioiIiIiIiKmpcBURERERERFTU1dhERERERGRTPJvmVX4YVOLq4iIiIiIiJiaClcRERERERExNXUVFhERERERySzqKvxA1OIqIiIiIiIipqbCVURERERERExNXYVFREREREQyiWYVfjBqcRURERERERFTU+EqIiIiIiIipqbCVURERERERExNY1xFREREREQyi8a4PhAVrpnI09MTFxcXXFxcSExMpHfv3jRv3jzN+5crV47ffvsNNze3NO8TEBDAli1bmD59erqvPWPGDG7evMl77733wPkBhg8fzjPPPMNrr732j57nn8iZ15135/WmWuPK3LgUwXy/r/lx6TbD8tyPSWuHUr56aRLiEwC4fO4ab70w0uBU/zO6X1OqPfMkObJl5fK1KJas3cPa4H3J7vNmm9q81bYOA8avYM++kwYlTZvVzgur5QUoUCwf/T7uQPlqpYiLjWfbur3MGbWSxIREo6OlyezH2atDTV7xfp6STxckZOOfTP1PgGNftuxZeWtwExo0foYsWZw5dvg8Q9+YZ2Da1Jn9GKfGapnNmHdJAARugsPHoPnLMGHE7e2/H4Dp8+HgYXBygheqgN87UCD/7f2xsfDRDPhhK8THQ9VnYMxgKOiRuflbtq3BKy2qUPKpAmz5bj9TP1jt2NfEuyrtu9Ujb353DvxxkqljV3PlUmTmBrwPZjwv7sWKmcU8VLhmsunTp1O2bFkOHjxIhw4dqF27Nvny5TM6lqX0n9md+Nh42hXqQZkqJflw3QiO/XGcEwdPGx0tXbOGfc2mRVuNjpGqhUG7+Mj/O+LiEyhRJB8zR7fjcFg4f4VdAKBowdy8VPNpLl4x3wd3EqudF1bLC9Dv4w5cuxRB5yojcM/lyofL+9Hi9Qasmb/F6GhpMvtxvnIxgqVzt1Ct7lNky5Y12b53Rnnj5OxET59PibgeTelyhQ1KmT6zH+PUWC2zGfN6PAG9usC2XRAT+7/tNyKgnRfUewGcnWH8f+H9j+Hzybf3L1x5u7gN+gJyusF/JsP4T2HG+MzNf/lSBF9/8RPVaz2FS/b/fR1+9vkSvNHnZYb1/oozJy/Te3BTRnzYmqFvf5W5Ae+DGc+Le7FiZjEPjXE1SMWKFXFzc+P06dN069YNLy8vfH19+emnn1Lcd8OGDbz99tuO27GxsdSrV49z586luO/cuXNp0aIFLVq0YMSIEURFRTn2nT17lh49etC8eXP69+9PRESE4/kmTpxImzZt8Pb2ZujQoY7HhYeH061bN1q2bEmfPn24evXqwz4UGZLdNRv1Wtfiy1HLuBV1iwM/h7J9zR4adXnR0FxWF3b6MnH/3xpst9ux2+0ULZjHsf/dN15m1pKtxP//fczGaueF1fImKfhkfrau+Y24mHiuXrzBrz8eooRJiymwxnH+efNBtv94iIhr0cm2Fyv5BDUblmf62NVcv3qTxEQ7Rw6dNShl2qxwjO9mtcxmzdu4ATSqD3lyJ9/eoBY0eQnc3SBHdujUCn67owPP6XNQtwY8kQ+yZYNmL8OR45kaHYCffwxle8hf3Lh+M9n2WvXL8tPmg5w4dpH4+ESWzA+h8vMlKVw0b+aHTIdZz4v0WDHzo2Kzm//HjFS4GmTHjh3ExMQwdOhQWrRowdq1a5k8eTJDhw7lypUrye7buHFjDh8+zKlTp4Dbhexzzz1H4cLJvzCGhISwZs0ali1bxtq1a0lISGDWrFmO/b/++isTJkxg/fr1uLu7O/bNmzePnDlzsnLlSlavXk2BAgWYO3cuAOPHj6dGjRqsWbOGESNGsGvXrkd5WO6paNnCJCYkcubv/xXtR/88TomKxQxMdX9eH9WK5Uc+Yeqm4VSuW87oOCkM6f4ywQsHsOy/b3L5WhTb94YB8FKtssTHJ7D99zCDE6bNaueF1fImWT3vRxr4VCNbjqzkL5Sb6p4V+fXHg0bHSpNVjzNAuWeLceHcNV7r48nykBHMXtWPuo0qGh0rBSseY6tltlreu+35A54q9b/bbZrD3v1w4RJE34J130P9msblu5vNZsNmS34boGSZAgYlSp0VzwsrZhZzUeGayQYMGIC3tzczZsxgypQpnDlzhtatWwPw1FNPUaFCBX7//fdkj8mSJQvt27dn2bJlAHz99dd07tw5xXNv376dZs2a4e7ujs1mo127dmzfvt2xv2HDhjzxxBMAtGnThh07dgAQHBzMmjVr8Pb2xtvbm+DgYE6evD2GcefOnbRt2xaA4sWLU7t27Yd7QDIoh3t2ou66Ohp1/SauOXMYlOj+zB+zkjeqDue1ikPZ+OVPjFnan8IlM3lAzz1Mmb+ZRt1m0GvUMkJ2/U1sfAI5smWlV4d6/PerH42Oly6rnRdWy5tk3/a/KVGuMKsOT2Xx3o/4+4+T/LLxD6NjpcmqxxngiYK5KfV0IW5GxtD55UnM+mgdQ8a3pngpc71vWPEYWy2z1fLe6a+jMPsrGNrrf9tKFofCBeDF1jZqNINjJ6BPN+My3m3Xz3/ToFElSj1VAJdsWej81oskJtrJlj3rvR+ciax4Xlgxs5iLxrhmsqQxrgCRkamPF7Tdeanv/7Vr1w5fX188PT25ceNGqgWk3W5P9bGpufO+drud0aNHG16U3o/oyFu45kr+BueWKwc3I6LTeIQ5/PXr/1orf1j2Cw1bv0CNV55lzefBBqZKKdFu58+/zvBq/Qq0euU5CnnkYtPWg5y7eMPoaOmy2nlhtbxw+31p/NJ+bFy0jcFeU8nulo1B017jzZE+fDE+yOh4qbLicU4SeyuOuLh4vp67hcSERPb9epw/dofxfJ2nOBV20eh4DlY8xlbLbLW8SU6chp7DYER/qP7c/7Z/MO32mNjta+24Zod5S+HtYbDc37isd/p9dxiL5m7hPxPb4eaenYClO4i+GcOlC+b6HLTieWHFzI+M3aR9cU1OLa4Gcnd3p0KFCgQGBgJw9OhRQkNDee6551LcN1++fNSpU4d3332XTp06pVqg1qlThw0bNhAZGYndbmflypXUqVPHsX/Lli2ObsiBgYHUrHm7b46npydffvklt27dAm4X1EePHgWgVq1arFq1CoBTp04la8E1wpnD53DO4kzRpwo5tpWuXNJyg/rt3P9FBiM4OztRtGAeqj3zJG2bPM/aOb1YO6cXBZ7IyfiBLXitZQ2jIyZjtfPCankBcuZ1pUDRfKz5IoS42Hgirkbx/fLt1Hi5ktHR0mTF45wk7O/zRke4L1Y8xlbLbLW8AGfOw5uDoXdX8H41+b7Qo+DbFPLkAhcXeK0V/HnIxtVrhkRN1doVu3mz9UzavzqFbcEHcXZ24vjRC0bHSsaS54UFM4u5qHA12JQpU1izZg1eXl4MGTKESZMmpTnLcJs2bbhx4wa+vr6p7n/xxRfx8vKiQ4cOeHl5AdC7d2/H/tq1a+Pn50fz5s25fv06ffr0AaBnz56UL1+eNm3a4OXlRadOnRyF6/vvv8/OnTtp2bIlU6ZMoW7dug/zz8+wWzdj2Bawk24ftCe7azYq1SlHHe8a/LAoxNBc6XHLlYNqnpXImi0LTs5OvNS2Js/WLsuvwfuNjgZA3lw5aFSnHDmyZcXJZqPmcyV4pU55fj1wkgHjVvDakC/pNmwh3YYt5NKVKCZ+/gOrvv3d6NjJWO28sFpegBtXojh34hLNu9XHydkJt1w5aNSuJmEHzxgdLU1WOM5Ozk5kdcmCk5MNJ6f//93ZiX2/Hufiuet06N4AJ2cnKlZ5ksrVS/Hrz38bHTkZKxzju1kts1nzxsdDTAwkJkJCwu3f4+Mh/CK8MQg6+UAH75SPe7YcrP4WIiIhLh6WBkGBJ+zkzZO5+Z2cbWR1ccbJ2Qknp6Tfb/+3RJnbXfI9CubiHT8vgpbtJDLiVuYGvAeznhfpsWJmMReb3a62aquYNWsWFy9eZPTo0UZHyZBXnNo+1OfLmdedwfN78/wrlYm4HMm8EUse6hpgzrlz3/tOGZA7vzvjvnmHYk8XJjExkVOHz7Hwo9Xs3fLwJrW50bjCAz82T84cfPiuF0+V8MDJZuP8pRus2LiXNXet4wqwasZbTJj73T9ex9VtxY5/9PjUPOrz4mHLjLxZCj7cyURKVyrG22NbU6piMRITE/nz58N8NmI51y8/nGWS4sMffovGI3+/eOafTbT2Wm9PXuvtmWzb4tnBLJ4dTIkyBRg4xodSZQsRfvYaX834nl+CD/2j1wNI2P/XP36OO1nt3x5YL3Nm5P32bMbGq89cAJ99mbznUN/X7dhsMHOBDdccyb9e/rrp9n+vXoePpsMve24Xrk+Xgvf6QuUMfow19Uo510dGvNbjRbr0bJhs26K5WwhcuoMpc9+gSLG83LwZy3drf+er2cEkJv7zr8uJvz7cC9ZWO4/h0Wf+PnHFQ3uuR6l2p6lGR7in7V8PNjpCCipcLaJ58+Y4Ozszf/58PDzMNTnHvTzswvVRe9iFa2b4J4WrER5F4SopPezC9VF7FIXro/ZPC1cjPOzCVR4PGS1cjfZPC1cjPOzCVVJS4frwmLFw1eRMFrF+/XqjI4iIiIiIiBhChauIiIiIiEhmUX/XB6LJmURERERERMTUVLiKiIiIiIiIqalwFREREREREVPTGFcREREREZFMYks0OoE1qcVVRERERERETE2Fq4iIiIiIiJiaugqLiIiIiIhkFi2H80DU4ioiIiIiIiKmpsJVRERERERETE1dhUVERERERDKJTV2FH4haXEVERERERMTUVLiKiIiIiIiIqamrsIiIiIiISGaxq6/wg1CLq4iIiIiIiJiaClcRERERERExNXUVFhERERERySSaVfjBqHAVuUvC9etGR8iwnKt/MzpChiQaHeBfIuHSZaMjPPYS9v9ldASRh6L6mN5GR8iQAn/9aXQEEclk6iosIiIiIiIipqYWVxERERERkcyirsIPRC2uIiIiIiIiYmoqXEVERERERMTUVLiKiIiIiIiIqWmMq4iIiIiISCbRcjgPRi2uIiIiIiIiYmoqXEVERERERMTU1FVYREREREQks9jVV/hBqMVVRERERERETE2Fq4iIiIiIiJiaugqLiIiIiIhkEs0q/GDU4ioiIiIiIiKmpsJVRERERERETE1dhUVERERERDKLugo/ELW4ioiIiIiIiKmpcAU8PT1p0qQJLVu2pEWLFqxfv96QHF9++SWXL1/+x8/TpUsXfvzxx4eQyJxy5nVn9KqhrIlYxOKwWbzUsZ7RkdJltbxZXbIwyL8HCw9/SuDFeXy240OqN37O6Fj3ZLXjbLW8AC37vMrMHR+yLnIRQ+b3NjrOfbHacbZaXlDmzGClvMUL5GH7jP6Mf6MJAM+WKsSsd1rx49TebJ78NhN7NOeJXG4Gp0xbkTIFWXNhLsM+72l0lHuy0nmRxIqZxTzUVfj/TZ8+nbJly3Lw4EE6dOhA7dq1yZcvHwDx8fFkyfLoD9XChQupU6cO+fPnf+SvZWX9Z3YnPjaedoV6UKZKST5cN4JjfxznxMHTRkdLldXyOmVx5uLpywx9ZRwXTl7mhSZVeH9Jf3pVH074iUtGx0uT1Y6z1fICXD57ha8/CqRa4+fIlsPF6Dj3xWrH2Wp5QZkzg5XyDu/gycHj4Y7bOV2zE7B1H9sPriMhIZH3OrzEmG6N6Tcj0MCUaes79TUO/xZmdIz7YqXzIokVMz8KmlX4wajF9S4VK1bEzc2N4cOHM27cOLp37067du0AmDt3Li1atKBFixaMGDGCqKgoAGbMmMGgQYPo0aMHr7zyCgMHDuTgwYN07dqVRo0aMXHiRMfzX7hwgQEDBtCmTRu8vLzw9/cHYPbs2Y593t7eHDlyhO3bt9O+fXt8fHzw8vJK1hLcpUsXPvzwQ7p06cIrr7zCtGnTkv0du3btomPHjrz88stMmTLFsf2LL76gdevW+Pj40L59ew4dOgRAdHQ0AwYMoFmzZrRs2ZJ33nnH8ZjAwEDatm1Lq1at6Nq1K8eOHXvIR/3+ZXfNRr3Wtfhy1DJuRd3iwM+hbF+zh0ZdXjQsU3qslhcg5mYMi8cHEH7iEna7nZ0b93L++EWerlrK6GhpstpxtlreJD8H7eaXNXuIuBxhdJT7YrXjbLW8oMyZwUp5G1cvS0R0DLv+OunY9suB4/zw299E3YrlVlw8y7f8wXNlihiYMm0vtn6BqGs3+T3koNFR7slK50USK2YWc1GL61127NhBTEwMWbJkYe/evSxevBhXV1dCQkJYs2YNy5Ytw83Njffee49Zs2YxdOhQAA4cOMCqVatwdXXF19eXqVOnMm/ePOLj43n55Zdp3749JUuW5L333qNPnz7UqFGD2NhYXn/9dZ599ll69+7NihUrHC2/AB4eHnz99dc4Oztz6dIlWrVqRb169cidOzcAR48eZcGCBcTExNChQweqVq3KSy+9BMC5c+dYsmQJUVFRNGrUiDZt2lCyZEl8fHx48803Afjll18YPXo033zzDdu2bePGjRts2LABgOvXrwOwZ88eNm7cyJIlS3BxcSEkJAQ/Pz+WLVuWqf9fkhQtW5jEhETO/H3Ose3on8ep3KCiIXnuxWp5U5OnQC6KPV3I1FdDrXacrZbXqqx2nK2WF5Q5M1glr1t2F3p71aHXf1fiU/eZNO/3/NNFOXb2nw+Lethcc2any/u+DPeaTJOu9Y2Oc09WOS/uZMXMYi4qXP/fgAEDyJYtG+7u7syYMYO1a9dSpUoVXF1dAdi+fTvNmjXD3d0dgHbt2vHRRx85Hl+vXj1y5swJQLly5ShfvjwuLi64uLhQqlQpTp48SYECBdi1axdXrlxxPC4qKoqjR49St27dFJmuXLmCn58fJ06cwNnZmevXrxMWFkaVKlUA8PHxIUuWLGTJkoVmzZqxY8cOR+HapEkTnJycyJkzJ2XKlOHkyZOULFmS/fv3M2fOHK5fv47NZuP48eMAlC9fnmPHjvHBBx/wwgsv0LBhQwCCg4MJDQ2lbdu2ANjtdm7cuPHwDnwG5XDPTtT1m8m2RV2/iWvOHAYlSp/V8t7NOYszw7/sy/eLt3Lq8Ll7P8AgVjvOVstrVVY7zlbLC8qcGaySt3fLOgT9vJ/wq5Fp3ufpok/Qo3kt3p29JhOT3Z+uI1vx7cKtXDpz5d53NgGrnBd3smJmMRcVrv/vzpZOgLVr1zqKVrhdsNlstjQfny1bNsfvzs7OKW4nJCSQmJiIzWZj5cqVZM2a9Z6ZxowZg6enJzNnzsRms/Hqq68SExOT6n3vzpfa68fGxvLOO++wePFiKlWqRHh4OA0aNACgePHibNiwgR07dvDTTz/xySefsHbtWux2O61bt07WddhI0ZG3cM2V/A3OLVcObkZEG5QofVbLeyebzcawL3oTFxvPZwO/MjpOuqx2nK2W16qsdpytlheUOTNYIW/ZYh7ULP8kHT9cnOZ9invkZkZ/X6Z8s4W9R85kYrp7K/1scao2rEjfeqONjnLfrHBe3M2KmR+ZRA1yfRAa43qf6tSpw4YNG4iMjMRut7Ny5Urq1KmToedwd3enWrVqzJ0717Ht3LlzXLx4EQA3NzciIv43diwiIoKiRYtis9n4+eefOXHiRLLnW716NfHx8dy8eZNNmzZRs2bNdF8/NjaW+Ph4ChcuDMDXX3/t2Hf+/HmcnZ1p1KgRI0aM4MqVK1y7dg1PT09Wr17N+fPnAUhISGD//v0Z+rsfpjOHz+GcxZmiTxVybCtduaRpu7FaLe+d3p3Tg7wFczOuw39JiE8wOk66rHacrZbXqqx2nK2WF5Q5M1ghb/WyxSiSPxcbPnqL7yb2pEujanhWfZolfp0AKJwvJ7MHtubz9TtZv/OQwWlTqly/PAWffIKFB6fy9d//pXX/JtRtWY2ZP40xOlqarHBe3M2KmcVcVLjepxdffBEvLy86dOiAl5cXAL17Z3w5iClTpnD06FG8vLzw8vJi0KBBjq63Xbt2xc/PzzE50+DBg5k0aRLt27fn22+/pVy5csmeq1KlSrzxxhv4+PjQsGFDRzfhtLi7uzsmhurcuXOyFuW//vqL9u3b07JlS9q2bUvPnj0pWLAgNWrUYODAgfTu3duxXNDmzZsz/Hc/LLduxrAtYCfdPmhPdtdsVKpTjjreNfhhUYhhmdJjtbxJBsx4k+LlijKq1RRib8UZHeeerHacrZY3iZOzE1mzZcXJ2SnZ72ZlteNstbygzJnBCnkDtu6j5X++oOOHi+n44WJWbv2TbfvD6Ds9EI88bvgPasM3W/5g1dY/jY6aqo0LQnjjuWH0rTuKvnVHsf6LLez69g/e951qdLQ0WeG8uJsVM4u52Ox2u9qqLahLly68+eab9yxWzeAVp7YP9fly5nVn8PzePP9KZSIuRzJvxBJ+XLrtob7Gw5QZeZ1cHt7SJAWefIJFhz8l9lYsCfGJju2f9pvPj8t+eSivkRgb+1Ce5046L1KyOTs/1Ofr8p82dBnVJtm2RWNXsmjcyofy/PaEh9+yr/Pi0VPmRy8z8l7umbFeZOl5u0UtinvkYeSCTfRsXoteXrW5eSv5+369gZ/9o9co8PWjK4JfG+FNkdIFmdRj7r3vnAEJkWmP/30QVjuP4dFn/j5xxUN7rkfpxWaTjI5wTyEbhhkdIQUVrhb1by5cJaWHWbhmhkdRuEpKD7twfdQeReEqIvfnYRaumeFRFq6PysMuXCUlFa4PjxkLV03OZFGLFi0yOoKIiIiIiEimUOEqIiIiIiKSSWzq7/pAzDurhoiIiIiIiAgqXEVERERERMTk1FVYREREREQks2hu3AeiFlcRERERERExNRWuIiIiIiIiYmrqKiwiIiIiIpJJNKvwg1GLq4iIiIiIiJiaClcRERERERExNRWuIiIiIiIiYmoa4yoiIiIiIpJZNMb1gajFVURERERERExNhauIiIiIiIiYmroKi4iIiIiIZBKbXX2FH4RaXEVERERERMTUVLiKiIiIiIiIqamrsMhdbM7ORkfIMJu7m9ERMuZKrNEJ/hXsCQlGRxARiyjwy2WjI2SIPVafI2JhiUYHsCa1uIqIiIiIiIipqXAVERERERERU1NXYRERERERkUyiWYUfjFpcRURERERExNRUuIqIiIiIiIipqauwiIiIiIhIZlFP4QeiFlcRERERERExNRWuIiIiIiIiYmrqKiwiIiIiIpJZNKvwA1GLq4iIiIiIiJiaClcRERERERExNRWuIiIiIiIiYmoa4yoiIiIiIpJJbBri+kDU4ioiIiIiIiKmpsJVRERERERETE1dhUVERERERDKLlsN5IGpxFcvJmded0auGsiZiEYvDZvFSx3pGR0pXyz6vMnPHh6yLXMSQ+b2NjnPfXvStwdxfPiDoxAy+2P0hlWo9ZXSkdFntvLBaXlDmzGC1vKDMmcEKeb061GT60t6s2TOGweNaJduXLXtW+r7vxfKQEaz6eSSTF7xlUMq0ZXXJwiD/Hiw8/CmBF+fx2Y4Pqd74OaNjpcsK58XdrJhZzEMtrnfYuHEjc+bMwW63ExMTQ6VKlZg6depDee4ZM2Zw8+ZN3nvvPQICAtiyZQvTp0/n9OnTtG7dmp07dz6U1/mn2ayg/8zuxMfG065QD8pUKcmH60Zw7I/jnDh42uhoqbp89gpffxRItcbPkS2Hi9Fx7kvVFyvw5qhWTHhrLn/9dpx8BXMbHemerHZeWC0vKHNmsFpeUObMYIW8Vy5GsHTuFqrVfYps2bIm2/fOKG+cnJ3o6fMpEdejKV2usEEp0+aUxZmLpy8z9JVxXDh5mReaVOH9Jf3pVX044ScuGR0vVVY4L+5mxcxiHmpx/X8XLlzggw8+YPbs2axevZqNGzfy1lvGXhGMj49P97aRjMqS3TUb9VrX4stRy7gVdYsDP4eyfc0eGnV50ZA89+PnoN38smYPEZcjjI5y37q815Kvp6wj9Ncw7HY7l89f4/L5a0bHSpPVzgur5QVlzgxWywvKnBmskvfnzQfZ/uMhIq5FJ9terOQT1GxYnuljV3P96k0SE+0cOXTWoJRpi7kZw+LxAYSfuITdbmfnxr2cP36Rp6uWMjpaqqxyXtzJipkfFVui+X/MSC2u/+/SpUtkyZKFPHnyAGCz2ahQoQIA5cqVY+DAgfzwww9cu3aN8ePH88svv7B161bi4+P59NNPKVOmDBcvXuTdd98lKiqKmJgYXnzxRYYNG5ahHOXKlWPo0KGEhIRQrVo1zp8/j5ubG8ePH+fq1asEBAQQGBjI119/TUJCAu7u7owZM4bSpUsTGxvLuHHj2LVrF/ny5aNChQpcunSJ6dOnp2hVTauV9a+//uKDDz4gOjqamJgY2rVrx+uvvw7A8OHDU2TJbEXLFiYxIZEzf59zbDv653EqN6iY6VkeV05ONp6uUoIdm/7gi13jyZo9K9s3/M68MSuJvRVndLxUWe28sFpeUObMYLW8oMyZwWp571bu2WJcOHeN1/p48nKLKly5FMHi2cH8/MNBo6OlK0+BXBR7upBpWwKteF5YMbOYiwrX/1e+fHkqV65Mw4YNqVmzJs8//zze3t7kzZsXgFy5crFq1So2btxInz59+OSTTxg8eDCff/45s2fPZsqUKeTKlQt/f3/c3NyIi4uje/fu/PTTTzRo0CBDWRITE1m0aBFwu1jcu3cvixcvxtXVlT179rBx40aWLFmCi4sLISEh+Pn5sWzZMpYvX87Zs2dZv349CQkJdOnShUKFCmXotYsWLcqXX36Ji4sLUVFRtG3blvr161OmTBmAZFmMkMM9O1HXbybbFnX9Jq45cxiS53GUp0AusrpkoV7L5xniNZn4uARGL+pDx3eb89VHQUbHS5XVzgur5QVlzgxWywvKnBmslvduTxTMTamnC/HzDwfp/PIkKjxXnLGfdeHk0YucCrtodLxUOWdxZviXffl+8VZOHT537wcYwIrnhRUzi7mocP1/Tk5OzJo1i8OHD7N7925++OEH5s+fz9q1awFo2rQpAJUqVQKgYcOGADzzzDN8//33ACQkJDBp0iT27t2L3W7n0qVLhIaGZrhw9fX1TXa7SZMmjkIxODiY0NBQ2rZtC4DdbufGjRsA7Ny5E29vb7JkyUKWLFlo3rw5v/76a4Ze+9atW4wZM4a//voLm83GhQsXCA0NdRSud2YxQnTkLVxzJX+Dc8uVg5sR0Wk8QjIqNvp2q+qaz3/kSvh1AAJm/0DHd5uZtnC12nlhtbygzJnBanlBmTOD1fLeLfZWHHFx8Xw9dwuJCYns+/U4f+wO4/k6T5mycLXZbAz7ojdxsfF8NvAro+OkyYrnhRUzPzKaVfiBaIzrXcqWLUvnzp1ZsGABOXPmZNeuXQBky5YNuF3gurj8b4IdJycnx3jPBQsWcOPGDVasWMHatWtp1KgRMTExGc5wd2F452273U7r1q1ZvXo1q1evZs2aNWzZssWxz2azpfqczs7OJCb+r8N6WrmmTZuGh4cHgYGBrFmzhsqVKye7r5FFK8CZw+dwzuJM0af+15JcunJJ03blsaLI6ze5eOYKdgu9qVrtvLBaXlDmzGC1vKDMmcFqee8W9vd5oyNkyLtzepC3YG7GdfgvCfEJRsdJkxXPCytmFnNR4fr/wsPD2bt3r+P2+fPnuXLlCsWKFbvv54iIiMDDw4Ns2bIRHh7O5s2bH3pOT09PVq9ezfnztz8IEhIS2L9/PwA1a9ZkzZo1xMfHExMTw8aNGx2Pe/LJJzlw4ACJiYlERkY6it3U/oZChQqRJUsWDh8+zJ49ex763/BP3LoZw7aAnXT7oD3ZXbNRqU456njX4IdFIUZHS5OTsxNZs2XFydkp2e9m9t3SX/Du4UnuJ3LintsV37dfZtd3fxodK01WOy+slheUOTNYLS8oc2awSl4nZyeyumTBycmGk9P//+7sxL5fj3Px3HU6dG+Ak7MTFas8SeXqpfj157+NjpzCgBlvUrxcUUa1mmLaOR2SWOW8uJMVM4u5qKvw/4uPj2fGjBmcOXOG7Nmzk5iYyMCBA6lY8f4HjHfp0oV33nkHHx8fChUqRO3atR96zho1ajBw4EB69+5NQkICcXFxNGnShGeeeYYOHToQGhpK8+bNKVy4MJUqVSI6+nb3i8aNG7Nx40aaN29OiRIlHF2e79a7d2+GDRvGmjVrePLJJ6lRo8ZD/xv+qRl95zF4fm++CZ9HxOVIPu3zuamv1nX2a0WXUW0ctxt1rs+isStZNG6lganS9/WU9eTK5878neOIvRXH1tV7WPrJBqNjpctq54XV8oIyZwar5QVlzgxWyNupZ0Ne6+3puP2yVxUWzw5m8exgPnhnCQPH+NCuewPCz15jyvsrOX3cXEvMFHjyCZr3eJnYW7EsOzHLsf3TfvP5cdkvBiZLmxXOi7tZMfMjYZ1ObaZis1upP6DcU2RkJO7u7sTGxtK7d2+aNGniGA9rlFecjH39jLI5OxsdIcOccucyOkKGJFy5anQEERG5g/Mz5YyOkCH2w2FGR8iwxNhYoyM89r5PXGF0hPvySp3xRke4p+9/GWl0hBTU4vqYeeONN4iNjSUmJoY6deqkmOhJRERERETEalS4PmZWrLDGlSYREREREZH7pcJVREREREQkk9g0UvOBmHtqUxEREREREfnXU4uriIiIiIiIZNjVq1cZNmwYJ0+exMXFhRIlSjB27Fjy5ctHWFgYw4cP59q1a+TJk4eJEydSsmTJB34ttbiKiIiIiIhkFrvd/D/3yWaz8dZbb/Htt9+ydu1aihcvzpQpUwAYPXo0nTp14ttvv6VTp06MGjXqHx02Fa4iIiIiIiKSYXny5KFmzZqO21WqVOHs2bNcvnyZgwcP0qJFCwBatGjBwYMHuXLlygO/lroKi4iIiIiIiMONGze4ceNGiu25cuUiV65cqT4mMTGRpUuX4unpyblz5yhYsCDOzs4AODs7U6BAAc6dO0e+fPkeKJMKVxERERERkcySaHSAe/vqq6+YOXNmiu39+vWjf//+qT5m3LhxuLq68tprr3Hw4MGHnkmFq4iIiIiIiDh069YNX1/fFNvTam2dOHEiJ06cwN/fHycnJwoXLkx4eDgJCQk4OzuTkJDAhQsXKFy48ANnUuEqIiIiIiIiDul1Cb7bJ598wv79+5k7dy4uLi4A5M+fnwoVKrBu3Tq8vb1Zt24dFSpUeOBuwqDCVUREREREJNPYMjBrr9n9/fff+Pv7U7JkSTp06ABAsWLF+OyzzxgzZgzDhw9n1qxZ5MqVi4kTJ/6j11LhKiIiIiIiIhn29NNP89dff6W6r0yZMqxYseKhvZaWwxERERERERFTU4uriIiIiIhIZnmMugpnJrW4ioiIiIiIiKmpcBURERERERFTU1dhERERERGRzKKuwg9EhavIXewJCUZHyLCEK1eNjiAiIlZ2/IzRCTIkMTbW6AgiksnUVVhERERERERMTYWriIiIiIiImJq6CouIiIiIiGSWRKMDWJNaXEVERERERMTUVLiKiIiIiIiIqamrsIiIiIiISCaxaTmcB6IWVxERERERETE1Fa4iIiIiIiJiauoqLCIiIiIiklnUVfiBqMVVRERERERETE2Fq4iIiIiIiJiaugqLiIiIiIhkFnUVfiBqcRURERERERFTU+EqIiIiIiIipqauwiIiIiIiIplFXYUfiFpcRURERERExNQypXAtV64cUVFRybbVrFmT06dPZ/i5PD09OXz48D3v98MPP9C0aVN8fHw4duwY3t7e3Lp1K8Ovl547/664uDg+/fRTXn31VZo3b07Tpk35+OOPiYuLe2ivN3z4cBYvXvzQnu9BnD59mpo1axqaIWded0avGsqaiEUsDpvFSx3rGZrnXqyWF5Q5M1gtLyhzZrBaXlDmzGC1vHcqUqYgay7MZdjnPY2Ock9WO85WywvWzCzm8dh2FV62bBkDBgygadOmAKxevfqRvt6IESOIiYlh1apVuLu7ExcXR0BAALGxsWTNmvWRvva/Tf+Z3YmPjaddoR6UqVKSD9eN4NgfxzlxMOMXQjKD1fKCMmcGq+UFZc4MVssLypwZrJb3Tn2nvsbh38KMjnFfrHacrZYXrJlZzMMUXYU9PT359NNPad++PZ6enslaFffs2YOXlxdt2rRh/Pjx2O/oE37s2DHeeustWrduTcuWLVm1ahUAH330Eb/++itTpkyhS5cuQPLW0fReL63nBPjuu+9o0qQJHTp0YNasWY7tx48f54cffmD8+PG4u7sDkDVrVtq3b4+bmxsJCQlMnDiRFi1a0KJFCyZOnEhCQgJwuxV11KhRdO3alcaNGzNs2DDH3xgeHk63bt1o2bIlffr04erVq47XjIyM5P3336dNmzZ4eXkxfvx4x3N26dKFiRMn0rFjR15++WWmTJnieNzMmTNp0qQJ3t7e+Pj4cOPGDQD++OMPunTpQqtWrWjVqhVbtmxxPGbJkiW88sordOrUiZUrV2b4/+/DlN01G/Va1+LLUcu4FXWLAz+Hsn3NHhp1edHQXGmxWl5Q5sxgtbygzJnBanlBmTOD1fLe6cXWLxB17Sa/hxw0Oso9We04Wy0vWDPzI5NogR8TMk2L661bt1i+fDmnT5/Gy8sLX19fsmbNyqBBg5gyZQo1a9Zkw4YNLFq0CID4+HiGDBnC5MmTKVOmDJGRkbRu3ZoqVarg5+fHoUOHePPNN3nppZfu+/WyZcuW5nPmyZOH//znPyxdupTSpUvz+eefO57r4MGDlChRgty5c6f6WsuXL+fQoUMEBAQA0KNHD5YvX06nTp0A+Pvvv/nyyy+x2Wz4+vryyy+/ULduXcaPH0+NGjXo168fp06domXLltSvXx+ACRMmUKNGDT788EMSExMZMmQIq1atol27dgCcO3eOJUuWEBUVRaNGjWjTpg158+Zl/vz5bN++nezZsxMZGUn27Nm5ceMGo0ePZu7cuRQoUIALFy7Qpk0b1q1bx9mzZ5k9ezZBQUE88cQTjBkz5qH8/35QRcsWJjEhkTN/n3NsO/rncSo3qGhgqrRZLS8oc2awWl5Q5sxgtbygzJnBanmTuObMTpf3fRnuNZkmXesbHeeerHacrZYXrJlZzMXQwtVmszl+b9asGQDFihUjV65cnD9/nri4OHLkyOEYU9msWTNGjRoF3G7lPHr0KO+++67jOeLi4jh27BhlypS552un9np2uz3N53RycqJixYqULl0agPbt2ydryUzP9u3b8fX1xcXFBYBWrVrxww8/OArXRo0akS1bNgAqVqzIyZMnqVu3Ljt37mTkyJEAFC9enNq1azueMzg4mD///JMFCxYAtwvxggULOvY3adIEJycncubMSZkyZTh58iTFixenVKlSDB06lPr169OwYUPc3d3Zu3cvp0+fpkePHo7H22w2Tpw4wd69e2nYsCFPPPGE4+/euHHjff3dj0IO9+xEXb+ZbFvU9Zu45sxhUKL0WS0vKHNmsFpeUObMYLW8oMyZwWp5k3Qd2YpvF27l0pkrRke5L1Y7zlbLC9bMLOaSKYVrvnz5uHbtGm5ubsDt1tLIyEjy5cvnuE9S4Qbg7Ozs6PaaFrvdTt68eR947Gpqr2ez2dJ8zh9++CHN56pYsSInTpzg+vXrqba62u32ZEU6JC/aM/q3Jz3nrFmzKF68eKr7U3tOZ2dnvvnmG3777Td27NhBq1atmDdvHna7nXLlyrFkyZIUz/Pbb7/dM0tmio68hWuu5G9wbrlycDMi2qBE6bNaXlDmzGC1vKDMmcFqeUGZM4PV8gKUfrY4VRtWpG+90UZHuW9WO85WywvWzPyo2LQczgPJlDGuderUYfny5Y7by5cv57nnniNHjvSvsJQuXZpbt26xe/duADZt2kRERAQApUqVInv27AQFBTnuf/ToUSIjIx84Z3rPWbVqVQ4ePMjx48cBWLFiheM+JUuWxNPTk1GjRjlePyEhga+++oqoqCjq1KlDYGAgcXFxxMXFERQUlKz1NC21atVyjLE9deoU27dvd+zz9PRk7ty5jiL3ypUrnDp1Kt3ni4yM5MqVK7zwwgsMGDCAsmXL8vfff1O1alVOnDjBjh07HPf9888/sdvt1KxZk5CQEC5fvgxg+BjXM4fP4ZzFmaJPFXJsK125pGkH9VstLyhzZrBaXlDmzGC1vKDMmcFqeQEq1y9PwSefYOHBqXz9939p3b8JdVtWY+ZPY4yOliarHWer5QVrZhZzyZTC9f333+fMmTN4eXnh7e3N1q1bmTRp0j0f5+LiwrRp0xg7dixt2rRh//79FClSBIAsWbLg7+/Phg0b8PLyonnz5nzwwQfExsY+cM70njN//vyMGzeOXr160aFDB5ydnZM99uOPP6ZkyZK0bt2aFi1a4OXlxfnz53FxcaF9+/aUK1cOX19ffH19KVeunGMsanref/99du7cScuWLZkyZQp169Z17PPz88PJyQlvb2+8vLx46623CA8PT/f5IiMj6du3L15eXrRo0YInnniCxo0bkzt3bmbNmsVnn31Gy5Ytadq0KTNnzsRut1O+fHl69epFx44d6dSpU7LuyEa4dTOGbQE76fZBe7K7ZqNSnXLU8a7BD4tCDM2VFqvlBWXODFbLC8qcGayWF5Q5M1gtL8DGBSG88dww+tYdRd+6o1j/xRZ2ffsH7/tONTpamqx2nK2WF6yZWczFZrerrVoerVec2j7U58uZ153B83vz/CuVibgcybwRS/hx6baH+hoPk9XygjJnBqvlBWXODFbLC8qcGTIjr/P/r4rwKLw2wpsipQsyqcfch/acCf+gh11adF48eo868/eJK+59JxNoWmGE0RHuaeOhCUZHSEGFqzxyD7twFRERkYfrURauj8KjKFzF+lS4PjxmLFxNsY6riIiIiIiISFpMs46riIiIiIjIYy9RHV4fhFpcRURERERExNRUuIqIiIiIiIipqauwiIiIiIhIZtHcuA9ELa4iIiIiIiJiaipcRURERERExNRUuIqIiIiIiIipaYyriIiIiIhIZtEY1weiFlcRERERERExNRWuIiIiIiIiYmrqKiwiIiIiIpJZ1FX4gajFVURERERERExNhauIiIiIiIiYmroKi4iIiIiIZJZEdRV+EGpxFREREREREVNT4SoiIiIiIiKmpq7C8sh9n7jC6AgiIiIiIuZgTzQ6gSWpxVVERERERERMTYWriIiIiIiImJq6CouIiIiIiGQWu2YVfhBqcRURERERERFTU+EqIiIiIiIipqauwiIiIiIiIpklUV2FH4RaXEVERERERMTUVLiKiIiIiIiIqalwFREREREREVPTGFcREREREZHMouVwHohaXEVERERERMTUVLiKZURGRpKYmAjA4cOHWb9+PbGxsQanSt/27dvva5uIVVy5csXoCCIi/2oJCQm0bt3a6BgimU6Fq1hG165duXXrFhcvXqR79+4EBAQwatQoo2Ola9KkSSm2TZ482YAk90cfhpKWP/74g5deeglfX18A9u3bx3/+8x+DU93b9u3bWbx4MQCXLl0iLCzM4ESPHyse48jISA4cOGB0jPuiC6ByN2dnZ/LmzUtMTIzRUe5LrVq1qF27dpo//0p2u/l/TEhjXMUy7HY7rq6urF+/nnbt2tG/f3+8vLyMjpWqEydOcPz4cSIjIwkJCXFsj4iIIDo62sBk6bvzwzBbtmxGx7mnWrVqYbPZ0txvxi93rVu3TjfzypUrMzHN/ZswYQKff/45Q4YMAeDZZ59l+PDhBqdK39y5cwkJCeHixYu89tprxMfH4+fnx9KlS42OlqbLly8zYcIEzp07x5IlSwgNDWXv3r107NjR6GipsuIxDgkJYdSoUTg7OxMcHMy+ffv47LPP8Pf3NzpaqiZNmkRgYGCybZMnTyYgIMCgRGlbsmRJuvs7d+6cSUky7vjx44wYMYLw8HCCg4M5cOAAwcHB9O/f3+hoqSpZsiSdO3fm1VdfxdXV1bHdjMd41apVwO3Pt2vXrtG+fXvsdjurVq2iYMGCBqcTK1HhKpYRExNDbGwsW7dupWvXrgA4OZmz08Bvv/1GQEAAly5dYt68eY7t7u7uvPfeewYmuzd9GD5aSf//t2zZwrFjx2jTpg0AAQEBVKxY0cho6YqLi+Opp55Kti1r1qwGpbk/69atY9WqVbRt2xaAQoUKERkZaXCq9I0cOZIGDRrw9ddfA1C6dGmGDh1q2sLVisd4+vTprFy5kh49egC3L8KcPHnS4FQpWfEC6P79+wG4evUqu3btcrSmbd++nXr16pnycyTJmDFj6N27N1OnTgWgQoUKDBs2zLSFa1RUFE8//TTHjh0zOso9FS1aFIDdu3c7emfA7fe71157zfFvUeReVLiKZTRr1oxatWpRunRpnn/+eS5evGjaVkFfX198fX0JCAigVatWRsfJEH0YPlovvPACADNmzGDhwoWO1teXXnqJ119/nX79+hkZL00uLi5ERUU58h45csS0//6SZM+ePUVxnV5rtxmEh4fTsWNHli9fDtw+7ma9QAfWPMYAHh4eyW67uLgYlCRtVrwAOmHCBAD69u3L6tWrKV68OACnTp1i2rRpRka7p4iICBo0aODI6eTkZOqLc0nH2kouXLjAlStXyJcvH3B7zoSLFy8anMogJu2Ka3YqXMUy+vXrR9euXXF3d8fJyQlXV1dmzJhhdKx0OTk5cf36dXLnzg3AtWvX+Omnn2jZsqXBydKmD8PMER4eTkxMDNmzZwcgNjaWCxcuGJwqbb169aJ79+5cuHCB4cOHs3XrVlOP14bbrX979uzBZrORmJiIv78/Tz/9tNGx0pUlS/KP5Rs3bmA38RccKx5jNzc3Ll265Ciwd+7cSc6cOQ1OlZKVL4CeOXPGUbQCFC9e3PRjn52dnYmLi3OcF+Hh4aa+aARw7NgxQkNDk01U6ePjY1yge+jWrRve3t689NJLwO1u+2+//bbBqcRKbHYzfyKKcPtKbfHixTly5Eiq++/uvmgmLVu2ZM2aNcm2+fj4EBQUZEyg+2S1D8MlS5bg7++f4sOwU6dOBidL2yeffMKWLVto1qwZABs3bqRhw4YMHDjQ2GDpOHXqFFu3bsVut1OvXj1KlChhdKR0Xbx4kffee49du3Zhs9moXr06U6ZMIX/+/EZHS9O8efM4efIk27dvp3fv3nz99dd4eXnRrVs3o6OlyorH+M8//2T06NGcPn2a8uXLc/z4cWbPns0zzzxjdLQ0nTx5kpMnT5KQkODY9uKLLxqYKH2vv/46NWrUcHQhX7VqFTt37uTLL780Nlg6goKC2LhxI3/99RetW7cmKCiIQYMG0aJFC6OjpWrhwoUsX76cixcv8uyzz7Jnzx5q1KiRrHXejEJDQ9m9ezd2u52aNWtSrlw5oyMZomlRc3ZBv9PGM+ZrHFLhKqb39ttvM2fOHDw9PVPss9lsbN682YBU9ye1wtXLy4u1a9calOje9GGYeYKDg9m1axd2u53atWvTsGFDoyOlKy4ujrCwMGw2G6VKlUrROmhW0dHRJCYm4ubmZnSU+7JmzRqCg4Ox2+14enri7e1tdKR7stoxjoiI4LfffgOgatWq5MqVy+BEaZs2bRrffPMNZcqUcbQA2mw2Fi5caHCytIWHh/Phhx+yc+dO4PZEen5+fqadeyDJnj17+PHHHx3/9qpXr250pDS1aNGCb775ho4dO7J69WoOHz7MnDlzHGN0zSoyMpITJ05QqVIlo6MYqmnhvkZHuKeN5z4zOkIK1vjWIf9qc+bMAW5/ybcaDw8PvvvuOxo3bgzAt99+a+qWCIBvvvmGFStW0LFjR+bPn+/4MDS7YsWKkZCQYKkPQ09PT6pUqeLo4mxme/bsYfDgwWTPnh273U5sbCzTpk3j+eefNzpaCmn1zkhi5l4acPuCl5mHE9zpzkmDkri7u1O2bFlTdr+F21+c3dzcePHFFzl8+DBbt27llVdeMeU4V7jdG+OHH37A3d3d6Cj3rWDBgkyfPt3oGBlWvXp1qlevTmxsLNevXzc6TrpcXFxwdXUlMTERu91O2bJlTTnJ2J2sNqO3mI8KV7GMsLAwihQpQrZs2di6dSuHDh2iffv2jvGjZuTn50efPn0cYwGdnZ2ZNWuWwanSpw/DzPHHH38wcOBAEhMTCQkJYd++fXzzzTeMGzfO6GipGjt2LFOmTKFGjRrA7UJ2zJgxKXoUmEHPnj3T3Gf2Xhoff/wxffv2JUeOHHTt2pWDBw/ywQcfmLbVddasWezbt8/Rw+Hw4cOUL1+e8+fPM378eEf3fTPp2rUrixcvJioqiu7du1O2bFm2bt3Kxx9/bHS0VHl4eFiqaIXbLfBz5szh1KlTTJ06laNHjxIWFkajRo2MjpamQYMGMXbsWLJmzYq3tzdXr17l7bffpnv37kZHS1WOHDmIi4ujfPnyTJ48mcKFC3Pr1i2jY6XLKjN6i3mZe9S5yB0GDhyIk5MTp06dYvTo0Zw6dcq0MysmKVOmDBs2bGD27NnMmjWL9evXU7p0aaNjpevuD8NFixZZ5sMwqbufFT4Mk9ZFzZs3L3A7c1LXRTPKli2bo2iF2y0TSRNLmU1wcHCaP2YuWgF++eUXcubMybZt2yhYsCDffvstX3zxhdGx0vTkk0/yzTffEBgYSGBgIN988w2VKlVi4cKF/Pe//zU6XqqS1gTfsmUL7dq1Y/78+Rw4cMDoWGmqUqUK7777Lt999x0hISGOHzMbM2YM8fHxhIaGArcn8Zo5c6bBqdIXFhZGzpw52bJlCzVr1iQkJMTU81GMHj2auLg4hg8fzvXr19m9ezeTJk0yOtY9WWFG70xht5v/x4TU4iqWkTQ1fUhICB07dqRHjx6mbYW4k7OzM3nz5iUmJobw8HAAihQpYnCqtN35YTht2jROnz6tD8NHwGrrolavXp01a9Y4urCuXbuWBg0aGJzq3o4cOZJsnF2ZMmUMTnR/du/ezSuvvELBggVNvbxMaGhoskmNKlWqxIEDByhTpoxpZ0O20prgAPv27QNg0aJFjm02m83UkzMdPnyYiRMnsm3bNuD2TM6JiYkGp0pffHw8cPvf3osvvkiOHDlMfV6ULVsWAFdXVz788EOD09wfq8zoLealwlUsI6nwCw4OZtCgQQCm/WKUZPv27QwfPpzLly/j5OREXFwcefLkYfv27UZHS5M+DDOH1dZFDQwMZMGCBYwcORK4vXxPnjx5WLx4MTabzZTndFBQEFOmTHFMejVnzhyGDBli6vGj+fPnZ+TIkfz888/07NmT+Pj4ZDPJmk2OHDlYt26dY+bVdevW4ezsDJh3PVcrrQkOyQtWq7j7IlxMTIzpP6/LlCnDm2++ybFjxxg8eLBpexrd60LysGHDMilJxg0ZMoQePXpw+vRpunTp4pjRW+R+qXAVy+jWrRvNmzendu3aPPvss5w6dcr0xcnkyZP58ssvGTRoEIGBgaxYsYKzZ88aHStdx48fZ8SIEY6LBAcOHCA4OJj+/c07dfvgwYMt92FotXVRV61aZXSEDPviiy8IDAx0tMZfvHiR7t27m7pwnTp1KmvWrKFNmzbkzp2b06dP88YbbxgdK00TJkxg6NCh+Pn5YbPZKFOmDJMmTSI6Otq0X6Cttia43W5n5cqVnDhxgiFDhnD69GkuXLhgyonRklSvXh1/f39iY2PZuXMnCxYsSHVlADNJaiEuV64crq6uhIeHM3jwYKNjpeDq6mp0hAdWuXJlFi5caJkZvcV8tByOWFZiYiLx8fGm7hLaqlUrAgICaNGiBevWrQOgc+fOLFmyxOBkaXv99dd58803mTp1KqtXryYxMREvLy/Wr19vdLR0WWl5iyRWWxfValJbjiq1bfJgEhISWLlyJe3btycyMhLAMpMIRUREEBYWRkxMjGPbnWO4zeSjjz7i8uXLHDhwgE2bNnH16lV69OjBypUrjY6Wpri4OObNm5dsWaeePXtaZgkteTQiIyNxdXXFycmJw4cP8/fff5t6Ru9HqWnB3kZHuKeN4eZrANA7iFiG3W5n+fLl/PLLL9hsNurUqUO7du2MjpWupA/pggULEhwcTNGiRTl//rzBqdIXERFBgwYNmDZtGvC/scVm9uGHH/L+++8nG/OVtM3MihcvTqdOnYyOcV/OnTvH5MmTCQ0NTfZl38yTHT355JNMnz6d9u3bY7PZWL58OcWLFzc6VrqsdJydnZ1ZvXo17du3t0zBCrBhwwYmTpzIjRs3KFCgACdPnqR8+fIEBgYaHS1VO3fuJCgoCF9fXwDHnAlmljVrVnr37k3v3ub/cp4kNDSU0aNHExoaSmxsrGP7oUOHDEyVtrS6DJu1pwNYb0ZvMR8VrmIZkyZN4tChQ7Rq1Qq4PX7txIkTpn+Tvn79Ou+88w6DBw8mIiICPz8/o2Oly9nZmbi4OMf4tPDwcFNPUAG3l2a52+7duw1Icm+1atVKd+yfGceKwu2lnZo1a8ahQ4eYMmUKS5cu5cknnzQ6Vro++OADxo8fT8uWLR0Xu8y63FASqx3nOnXqsGnTJpo0aWJ0lPvm7+9PQEAA3bt3JygoiJ9//pnvvvvO6FhpypYtW7L3DLNPcgS3W9ZmzZrFjh07sNls1KxZkz59+pj6AseYMWMYOHAgEyZMYN68eSxZsgQ3NzejY6Xpzi7DMTExbNmyJdlEaWaUNKP3+vXradeuHf3798fLy8voWGIhKlzFMrZt20ZgYKCjFbNp06a0atXKlIXrF198wZtvvknhwoXJnTs3lStX5vvvvzc61n3p1KkT/fr14+rVq8yYMYOgoCDHZFhms3HjRjZu3MiZM2d45513HNsjIyNNu1RL0ljRlStXcu3aNdq3b4/dbmfVqlUULFjQ4HRpu3r1Km3btmXhwoVUrVqV5557jtdff93oWKm686JFp06d6NixI3B7sqCjR4+SL18+o6Ldk5WOM8DixYu5du0a2bNnJ0eOHNjtdtNO1pUkS5Ys5M+f3zHpVd26dU09xrVs2bKsWbMGu93O6dOnmTt3LtWqVTM6Vrr8/Pxwd3dn5MiR2O12AgMD8fPzY/r06UZHS1NsbCy1a9fGbrdToEABBg0aRJcuXdJdF9pI/fr1S3b77bffZujQoQaluT9Wm9H7kUrUSM0HocJVLOXOq85mnbESbi8V8uabbzJ+/HjTdj9Li4+PD8WKFePHH38kOjqaiRMnUr16daNjpapUqVI0bNiQffv2OWaOhdvj7GrXrm1csHQULVoUuF1cLV682LF95MiRvPbaa46F2c0mqbu4q6srZ8+e5YknnjDtRGN3djs7duyYYwmcpKLKzGMDrXScwZqTdrm4uGC32ylRogSLFi2iaNGiXL161ehYaRo+fDgff/wxFy9epF27dnh6epp+DfNjx4455nUAqFatmmPmabNKmg07d+7chIaGUrBgQc6cOWNwqvvn5uZm+vXLrTajt5iPClexjHr16tGjRw98fX2x2WwEBgZSr149o2OlKlu2bPTq1StFS2CSTz/91IBU96969eqmLVbvVL58ecqXL4+npyd58uQxOk6GXLhwgStXrjha/65cucLFixcNTpW26tWrc+3aNTp27EirVq1wcXExbffQO4spHx8fUxeqd0vtOL/66qtGx0pT0oUYK3nnnXeIjIxkyJAhjBkzhoiICEaPHm10rDS5u7szfvx4o2NkSJEiRZK9v129epVixYoZnCp9zZo14+rVq/Ts2ZOOHTuSmJjIgAEDjI6VpjvHuNrtdvbv30+pUqUMTHRvVpvRW8xHswqLZSQmJrJ8+XK2b9+O3W6nTp06dOjQwZQtr9euXeOXX35h8uTJqX7wJU2yYSYDBgxI91iaudiOj49n1apVHDp0KNmkJRMmTDAwVfqWLFnCnDlzHC3FISEhvP3226adrCkiIsKx/NTZs2eJjIx0rPlrZr6+vpbr9ZDECsfZSpNJWdn27ds5efIk8fHxjm2dO3c2MFHqkoqps2fP8uuvv/LSSy8BsGXLFmrXrs3EiRONjHff4uLiiImJMfWY3JkzZzp+d3Z25sknn6Rx48amn0zx5MmTnDx5Mtn61HdOrPhv0eQJc3ZBv9OmS3ONjpCCWlzFMnbu3EnHjh0d49Xg9oe5GbuE5smTh2bNmpE/f35q1qxpdJz7kvQFw4pGjRpFQkKC4xxZt26d6VuMO3fuTLVq1di9ezd2u53OnTtTrlw5o2OlKilf0jIyRYoUMTjR4y06Oprz58+TkJCAk5MTR44c4amnnjI6VqqsNpkU3B5nt2bNGk6dOpWsEDTjfAkAQ4cO5a+//qJ8+fKO7qxmlTRh0FNPPZXsnDX7CgBJrFRU3T3G1QqmTp3KihUrKFOmjGNsq81mM+0xFvNR4SqWMWnSpBQtJ5MnTyYgIMCgRPdWuXJlPvnkE06fPs3UqVM5evQoYWFhNGrUyOhoKZixFfh+7du3j7Vr1+Ll5eVotRw4cKDRse4pqauz2dlsNooXL87169fJnTu30XHu6ciRI47fY2JiOHr0KHd2LjJrEQi3W+KnTJlCnjx5HD0gbDabaVswrTaZFNzuKhwXF0flypUtsX7kvn37WL9+vemLVrBmMZXEakVVasOQcubMSZUqVWjVqpUpJz3atGkTP/zwg6lbssXcVLiK6Z04cYLjx48TGRlJSEiIY3tERATR0dEGJru3MWPG4OHhQWhoKACFChVi8ODBpixck/Tv359x48Y5xoxevXqVMWPGmLqrcNLkDs7OzkRHR5MzZ04uXLhgcKrUtW7dOt0u2WYdj+nq6oqvry8NGjRItgyDGVup7p4F9M4Jr8xcBMLtGcnXrVtnmbGjVptMCm5/pmzcuNHoGPetRIkS3Lp1y9RLs9wtOjqaWbNmOdZdr1u3Lr169SJHjhxGR0uT1YqqJ554gv3799O8eXPg9vrEHh4ebNy4kdDQUEaOHGlwwpQ8PDwsc3wfOc0q/EBUuIrp/fbbbwQEBHDp0iXmzZvn2O7u7m76mRUPHz7MxIkT2bZtG3B71j+zr8F36tSpZBMd5c2b1/QzFebOnZvr169Tv359evToQd68eXniiSeMjpUqs5+zaSlRogQlSpQwOsZ9CQ4ONjrCA/Pw8LBM0QrWmrQrSfHixYmMjLTMF+hhw4bx2muvUa1atWQtxGa8aJRk3LhxJCQkONYtX7lyJWPHjjX1vANWK6pCQ0NZtGiR45xo3749vXv3xt/fHx8fH2PDpaFKlSq8++67NGnSJNlswmZt1RbzUeEqpufr64uvry8BAQG0atXK6DgZcvckCTExMZh9PrSEhAQSEhIc3dLi4uKIjY01OFX65s6di7OzM4MGDWLNmjVERkaa9oP7hRdeSHb75s2bQPLF5M3k6NGjHDt2zNEF8MMPPyQyMhLAsQ6f/HNJ3Zvr1KnDpEmTaN68ebIvdmbt3px0IcbHx4cXXniBiIgI3n77bUfBYiZJEwflzJmT1q1bU79+fUsUguPHj6dgwYLkzJnTEt2F4X/DN5I8//zztGzZ0sBEaUvqyWW1ourSpUvJvmNkyZKFc+fO4eLiYtou8Pv27QNg0aJFjm1m7o4t5qPCVSyjVatWhISEsGPHDgBq1apl+je76tWr4+/vT2xsLDt37mTBggV4enoaHStd9erVY9CgQY6iZOHChdSvX9/gVOlL+jLn5ORk2oL1bqdOnWLw4MEcOnQIm81GxYoVmTx5MsWLFzc6WjLTp0+nWbNmjts//fQTXbt25ebNm8ydO5dPPvnEwHSPj7u7N2/atMnxu9m7NydJmrTLrBfnki4OlSpVyvTLhtzp/PnzluranOTmzZuOY27mYT139uQC6xRVL7zwAj179sTb2xubzcaaNWuoVq0aUVFRpi1c7zy2/3omfZ80Oy2HI5bxySefEBwcTPPmzbHb7WzatImXXnrJ1JPwxMXFMW/ePIKDg7Hb7Xh6evL222+b+qp5XFwcc+bMYcuWLdjtdl566SV69uxp2g9CuH0R4+5xo0mTVAwdOhQPDw+DkqXtjTfeoHnz5rRu3RqAgIAA1q1bx4IFCwxOltzdy8n4+PgQFBQE3J4ZecmSJQYlE7Nq2LAhW7ZsMTrGY2PgwIH4+flRoEABo6Pct7lz57J27VqaN2+OzWZj/fr1tGzZkrfeesvoaI+NuLg4li1bxq5du7Db7dSsWZMOHTqYejkcu93OypUrOXHiBEOGDOH06dNcuHCB559/3uhoma5JXvP/W9h0dd6975TJVLiKZbz66qsEBgY6ruDevHkTX19fvv32W4OTpe3o0aOUKVPmntvkn5kxYwYRERGOruRBQUGOsUr79+/H39/fyHip8vb2ZvXq1ffcZjQvL69kXf7uPH/v3icPx+HDh9m1axc2m42aNWuaspvwnTM33+311193jOs3o8jISGbNmsWOHTuw2WzUqlWL3r17m3Z8Y/fu3dm/fz9Vq1ZN1oXVzBPmAY4eUknrrjdo0MDoSPdktV5dVvPRRx9x+fJlDhw4wKZNm7h69So9evQw7aSEj5IK1wejrsJiGQUKFEg2I2G2bNlMfwV6yJAhKZbwSW2bmSSNA7ubWcd/we3uqytWrHDcHj58OK+99hqLFy92zLhoNk5OThw7dozSpUsDEBYWZsqW+Li4uGQT2SQVrZGRkaYf+2xFS5Yswd/fn4YNG2K325kzZw69evWiU6dORkdL5u6uzXe6s7gyIz8/P9zd3Rk5ciR2u53AwED8/PyYPn260dFS1aJFC1q0aGF0jAx78cUXLVX43d2ra9q0aezdu9e0vbrCwsLw8/MjPDyc4OBgDhw4QHBwMP379zc6Wpp27txJUFCQY/m9vHnzEhMTY3Aqg5h8ok6zUuEqllGhQgXeeustxxve6tWref755x0TK5jpA/LKlStcuXIlxRqSERERjsl4zOrOSYJiYmLYsmULzzzzjIGJ7u3GjRtcu3Yt2RI+Fy9eBFJOkGUWgwYNonPnzlSoUAGbzcahQ4fSvGhgpObNm+Pn58dHH33kKF4jIyMZOXJksrGv8nAsXLiQoKAg8ufPD9x+L+nYsaPpClcrz9x87Ngx1q1b57hdrVo1UxeGVlpje8CAAeku92XmVuJNmzYl69XVrVs3fH19TVu4fvDBB/Tu3ZupU6cCt78jDRs2zNSFa7Zs2ZKdH2ZfZUHMR4WrWMahQ4cAWL58uWPbb7/9xm+//Wa6CRTWrl3LV199xYULF5KtIZkzZ07Tj/G5ewH5t99+m6FDhxqU5v506dIFb29vxznw008/8dZbbxEVFWW6sTMff/wxw4cPp0GDBvznP/8hR44c2O12qlSpQr58+YyOl0Lv3r0ZPnw49evXp2TJkgAcP36cl19+mb59+xob7jHk4eHhKFoB8uXLZ9qlnayqSJEiXLlyxfHv7erVqxQrVszgVGm7fPkyixYt4tSpU8THxzu2m7EIfOmll4yO8MCs1qsrIiKCBg0aMG3aNOB2Lx6zXqhNUrZsWdasWYPdbuf06dPMnTuXatWqGR1LLERjXEUekTu7+VmZ3W6nRYsWrF+/3ugo6QoNDWX37t3Y7XZeeOEFypcvb3SkVN052dHdEx+Z2YkTJzh48CAAFStWtMyarlYzbdo0Ll++TJs2bQAIDAykcOHCvPLKK4B5l8WxkoEDB/Lrr786iqwtW7ZQu3ZtxwUDsw2L6NSpE2XKlOG5555LNpzAzC2x4eHhFCxY0OgYGfLRRx9x9OjRZL26Klas6Lj4aaaL4wDt2rVjyZIltGvXjsDAQMLDw+nduzcBAQFGR0tTZGQkH3/8saPHhqenJyNGjMDNzc3gZJmvSe43jY5wT5uuf2F0hBTU4iqWsnXrVn755RdsNht169albt26RkdK14YNGyxXuN7ZXdVut7N//35LLB1Rvnx50xard7rzWqGVrhuWKFFCxWomSOrCun379mTbV6xYYZllcczuqaeeSnYBoF27dgamubcbN24wbtw4o2NkSOvWralatSqdO3emVq1aRse5L1bq1QW3L2j069ePq1evMmPGDIKCghg0aJDRsdLl7u7O+PHjjY5hDhb6/DcTFa5iGZ9//jmrV692TLbz8ccf4+PjQ/fu3Q1OljqbzUbx4sW5fv06uXPnNjrOfbtzjKuzszMdOnSgcePGBia6t99++43Jkydz6tQpEhISsNvt2Gy2FF/+zSA2NtYx7vnO35OoRe3fLSgoiFy5chkd47F293AIs3v66act14IZHBzMhg0b+OSTT4iIiKBz5854e3ubduZmsN4aoz4+PhQrVowff/yR6OhoJk6cSPXq1Y2Olap7LZvWuXPnTEoiVqeuwmIZXl5eLF26NNkEMR07djT1chxDhw7l119/pUGDBskKQrN1RbO6pk2b0qdPH6pUqYKTk5Nje9GiRQ1MlTpPT88096lF7d/Nbrfj7e3NmjVrjI7y2Nu2bRuHDh1KNqOpWQtaqy6Hk+S3337j3Xff5caNG/j6+tKnT59k47jNJCIigrCwsGTnRY0aNQxMlLqEhATatWvHqlWrjI5yX0aMGAHcHk++a9cuateuDdzuWVKvXj3Tzuj9KDXJ9YbREe5p0w1zrSsPanEVi7nzaq2Zr9wmSa175Z49ewxKc3+sNBFIkuzZs+Pl5WV0jPti5dlY5dGyai8Nq5kyZQr79u3jyJEjvPzyy2zevNnxRdqMrLoczpkzZ1i2bBnr1q2jdu3atG3blh07dtC9e3eCgoKMjpfChg0bmDhxIjdu3KBAgQKcPHmS8uXLm3IeAmdnZ8dSMmZffgpgwoQJAPTt25fVq1dTvHhxAE6dOuWYXOrfxq4ZlR+IClexjGeeeYYRI0bQtm1bbDYbK1asMP0yLUlX8C9cuEBgYCABAQGmH9fYv39/ypQpQ+3atU25rmhqGjRoQEhIiOnGIIlklKurK76+vuql8QiFhIQQGBhIq1atGDt2LH379uWDDz4wOlaazDwJU1p69erF4cOH6dChAwEBAeTNmxeA559/ng0bNhicLnX+/v4EBAQ4Cuuff/6Z7777zuhYaSpZsiSdO3fm1VdfTfZeYeZut2fOnHEUrQDFixcnLCzMwERiNSpcxTL+85//8NlnnzkG9tepU4c+ffoYnCpt8fHxBAcHs2rVKn7//Xfi4+OZP38+VapUMTpauqw4Ecjy5cuZM2cObm5uuLi4mHqMq0h6NAnWo+fi4kKWLFmw2WzExcVRsGBBzp8/b3SsNKW2NmrOnDmpUqUKrVq1SjY8wiy8vb1p3Lhxqhc/71xD10yyZMlC/vz5SUhIAKBu3brMmDHD4FRpi4qK4umnn+bYsWNGR7lvefLk4bPPPqNt27YArFq1yrH+usj9UOEqluHq6mr69USTTJgwgfXr11O2bFl8fX359NNPadasmemLVrDmRCBWGecjci9mHWf5OHFzcyM6OpqqVasyfPhwPDw8TN27xMPDg/379zsmJtywYQMeHh5s3LiR0NBQRo4caXDC/4mOjgagYcOGxMbGpth/5zqpZpN00bNEiRIsWrSIokWLcvXqVaNjpSohIYHChQszYMAAo6NkyMSJE/nwww8dQ3tq1arFxIkTDU5lEJP3vjMrFa5iGZGRkcyaNYsdO3Zgs9moVasWvXv3NuVY16VLl1K1alV69uzpWArg7ivmZnXjxg1atmxpqYlAihYtSnx8PGFhYdhsNkqWLEmWLHp7E+u5fPkyEyZM4Ny5cyxZsoTQ0FD27t1Lx44djY722Jg2bRrOzs689957LFiwgIiICFNPDhMaGsqiRYtwcXEBoH379vTu3Rt/f398fHyMDXeXqlWrOj7r7h4WY7PZHEvOmNE777xDZGQkQ4YMYcyYMURERDB69GijY6XK2dmZ3bt3Gx0jwwoWLGjqf2tifvpmJ5bh5+eHu7s7I0eOxG63ExgYiJ+fnynfBLdt28batWuZNGkS169fx8fHx9H9yOysOBHIvn37GDBggOOKeXx8PDNmzKBSpUpGRxPJkJEjR9KgQQO+/vprAEqXLs3QoUNVuD5EP//8M97e3gCO4SarV692bDObS5cukTVrVsftLFmycO7cOVxcXBzFrFmEhoYaHeGBFShQgJw5c5IzZ06+/PJLAI4ePWpsqHQ0bNiQ+fPn4+Pjk2yMq5lbteH2TMInT55MNvmjmcflirmocBXLOHbsWLKxMdWqVTNtgZUrVy46d+5M586dCQ0NZeXKldy6dYvOnTvj5eVFhw4djI6YJitOBPLhhx/y0UcfOWYG3bFjB+PGjWPZsmUGJxPJmPDwcDp27Mjy5cuB290XzTiG0cq+/PLLFEVqatvM4oUXXqBnz554e3tjs9lYs2YN1apVIyoqynSF652uXr3KH3/8gc1m47nnnjP9WMYhQ4akmEE4tW1mMXnyZMd/bTabY24HM7dqDx8+nP3791OxYkVTd8/PFInqKvwgVLiKZRQpUoQrV66QL18+4PaHYrFixQxOdW/ly5dn5MiRvPfee3z//fcEBgaasnD96quv6NatG5MmTUp1v5lnNY2Ojk62nEWtWrUcY61ErOTuLu43btww/UzkVrFv3z7+/PNPrl69ypIlSxzbIyMjiYuLMzBZ+kaNGsWyZcv49ttvsdvt1KtXjw4dOpA1a1a++eYbo+OlauvWrQwdOpQKFSoA8NdffzF58mTq1q1rcLKUrly5wpUrV4iJieHo0aOOf28RERHcvHnT4HRps2Lr9t69e1m3bl2yHgQiGaHCVSzD1dUVb29vXnrpJQC2bNlC7dq1HYWWmQsrgKxZs9KsWTOaNWtmdJRUJY1nvbPLkVXkyJGDHTt2OMYT79q1y/TdpURS07hxY0aNGkVUVBQBAQF8/fXXtG7d2uhYj4Xw8HD2799PdHQ0+/fvd2x3c3NzrDNpRlmzZqVLly506dLF6Cj37ZNPPmHJkiWUKVMGuN3ldujQoaYsXNeuXctXX33FhQsX6NGjh2N7zpw5eeuttwxM9vgpVKiQ0RHE4mx2XcoVi5g5c2aKbXv27KF69eqAZuP8p+Lj4/n+++/JnTs3derU4auvvuKXX36hVKlS9O3bl5w5cxodMU1//vkn77zzjqPbXFxcHNOnTzf9Or8iqVmzZg3BwcHY7XY8PT1N24XVqrZt20a9evWMjnFPSb1gJk6cmOrkfma+WNuyZUvWrFlzz21m4u/vT69evYyOcU+1atVK9XywwjJwo0eP5siRIzRq1ChZN/d/4xjXV7Ob/2/+9taSe98pk6nFVSwjqTC9cOECgYGBBAQEYLfbVbA+JGPHjuXw4cPExsZSrFgxYmJiaNiwIbt372b06NFMmzbN6Ihpqly5Mt999x1hYWHY7XZKly6trkhiSdu3b6dly5a0bNky2bY7u8LLP1OtWjX++9//curUKaZOncrRo0cJCwujUaNGRkdLJqkXjJubm8FJMi5fvnwEBATQqlUrAAIDAx3DfMyqV69ebN++naNHj/Laa69x+fJlbty4QalSpYyOloyVl3+LjY3lySef5PDhw0ZHEYtSi6tYQnx8PMHBwaxatYrff/+d+Ph45s+fb4l1Ua2iWbNmrF+/nujoaOrVq8eOHTtwcXEhMTGRli1bmnLR+HuNY1V3YbEaX1/fFJPBtGrVioCAAIMSPX7ee+89PDw8+PHHH1m/fj1RUVF07tyZoKAgo6M9Fq5du8apU6cYOnQoZ8+eBaBChQpMmTKF4sWLG5wubXPnziUkJISLFy/y3Xffcf78eQYNGsTSpUuNjiaPIbW4Phi1uIrpTZgwgfXr11O2bFl8fX359NNPadasmYrWh8zFxQWbzYarqytPPvmkoxuPk5OTaVsvk9YMTLr+duf6gWafXVHkTidOnOD48eNERkYSEhLi2B4REaGJxh6yw4cPM3HiRLZt2wbcbtFMTEw0OFVKd04glRozdq/csGEDI0aMwM3NjZiYGKZPn0716tVNud763datW8eqVato27YtcHs8ZmRkpMGpUhowYEC668Kbec11u93O8uXL+eWXX7DZbNStW5e2bdtaZp17MZ4KVzG9pUuXUrVqVXr27OmYfEdvcg9fbGysY0bFO38HiImJMThd6jZv3kzRokVT3Xfn5CsiZvfbb78REBDApUuXmDdvnmO7u7s77733noHJHj93X4iLiYkx5czNVnwPmz17NsuWLaNChQrs2LGDzz77jIYNGxod675kz549xblhxu8aSRNUWtGkSZM4dOiQowt5UFAQx48fN/V47UfFruVwHogKVzG9bdu2sXbtWiZNmsT169fx8fEhISHB6FiPnVu3biWbUfHO38344Q23xz0ndats06YNK1eudOz7z3/+Y9r190Tu5uvri6+vb7JxgfJoVK9eHX9/f2JjY9m5cycLFizA09PT6FgppDfTcVIXXLNxcnJyLIFTq1YtPv74Y4MT3b9ChQqxZ88ebDYbiYmJ+Pv78/TTTxsdKwUrrrWeZNu2bQQGBjqW/WratCmtWrX6Vxau8mBUuIrp5cqVi86dO9O5c2dCQ0NZuXIlt27donPnznh5eZlyTVQrCg4ONjpCht3ZShIfH5/mPhGraNWqFSdPnuTkyZPJLtC9+OKLBqZ6vAwaNIh58+bh5ubGlClT8PT0pGfPnkbHuqfY2Fi+//57Vq5cyf79+9m9e7fRkVKIi4tL1lvn7t47Tz31lJHx0vWf//yH9957j7///pvnnnuO6tWrM2XKFKNjpZDWWutJzF4E3nkh3KwXxcW8VLiKpZQvX56RI0fy3nvv8f333xMYGKjC9V8svQ9AfSCKFU2bNo1vvvmGMmXK4OTkBNw+l1W4/nN3jhnNlSsXPj4+jtvLli0z5ZhRgAMHDrBy5Uo2bNhATEwMH374IbNnzzY6Vqru7rkD/+u9Y7PZ2Lx5sxGx7ouHhwdffPEF0dHRJCYmmnY2ZyuutZ6kXr169OjRA19fX2w2G4GBgZZYmuqRsJtvXL0VqHAVS8qaNSvNmjWjWbNmRkcRA8XExDiu5t/5e9I+EavZuHEjP/zwgyUms7GacePG8cwzz5iy+2dqFi5cyKpVq7h16xatWrUiKCiIzp0707x5c6OjpcmKPXfuZIXeDlZeAnDo0KEsX76c77//HrvdTqNGjWjfvr3RscRCVLiKiGVZcVyuSHo8PDxUtD4iH374IUFBQRw5cgQfHx9atGhB7ty5jY6Vpo8++ojatWszduxYxzIyel97dCZNmkRQUBClSpWyTG+Hbdu2cejQoWQXas1Y2CYkJBAbG0uOHDno2LEjHTt2BG4vaadhPZIRKlxFxLKsfnVf5G5VqlTh3XffpUmTJmTLls2x3cxfnq2idevWtG7dmtOnTxMYGEjHjh0pW7YsvXv3ply5ckbHS2H9+vWsXLmSjh07UqpUKXx9ffUl/xH64Ycf2Lx5s2XW/54yZQr79u3jyJEjvPzyy2zevJnatWsbHStVU6ZMoXTp0o6lhpKsWLGC8+fPm35c7qOgWYUfjJPRAUREROS2ffv2cfHiRRYtWsS8efOYN28e8+fPNzrWY6VYsWK8/vrrdOnShZ07d/Lnn38aHSlVZcqU4b333iMkJIRu3brx/fffc+nSJYYNG8bWrVuNjvfYKVy4sGnXLE9NSEgI8+fPJ3/+/IwdO5aAgABu3rxpdKxU/fTTT6nOlt65c2d++uknAxKJVanFVURExGBHjhwBYPTo0QYneXzZ7Xa2bt1KQEAAhw8fpmnTpnzzzTeObrhm5ezsTKNGjWjUqBGXL18mICCAiRMnUr9+faOjPVaGDx9Or169qFu3Li4uLo7tZp20y8XFhSxZsmCz2YiLi6NgwYKcP3/e6FipcnJywtnZOcV2Z2dndX+XDFHhKiIiYrCk5Vju/hJnt9tNPxurVTRo0AAPDw9atWpF3759sdlsxMTEOC4amHmplsjISE6cOEGlSpXo0aMHb731ltGRHjtz587l4sWLHDp0KNUiy2zc3NyIjo6matWqDB8+HA8PD9Pmjo2NJTo6OkU37KioKGJjYw1KZbDHbFbhsLAwhg8fzrVr18iTJw8TJ06kZMmSD/11bHYNmBAREZHHnKenp+N3m82WbLyomS8OhISEMGrUKJydnQkODmbfvn189tln+Pv7Gx3tsdK4cWO+/fZby7QAXrp0iVy5cpGQkMCCBQuIiIigS5cuFClSxOhoKXz66accPXqUjz76yDH5XEREBKNGjeLJJ59k0KBBBifMfK84tb33nQz2feKK+75v165dad26Nd7e3qxevZpVq1axcOHCh55JLa4iIiLy2LPqZG7Tp09n5cqVjlnTn332WU6ePGlwqsdPyZIluXnzpmnXb73bE0884fi9T58+Bia5t759+zJ8+HDq16/vaIU7fvw4np6e9O/f39hwkqYbN25w48aNFNtz5cpFrly5HLcvX77MwYMHWbBgAQAtWrRg3LhxXLlyhXz58j3UTCpcRUREREzMw8Mj2e07x2DKw+Hu7k6rVq2oX79+suNr1hlvjx07hr+/PydPniQ+Pt6xfeXKlQamSl2WLFmYMmUKx48f59ChQ9jtdipVqkSJEiWMjmaYjLRmGmXGjBnMnDkzxfZ+/folu+Bw7tw5ChYs6Oiq7uzsTIECBTh37pwKVxEREZF/Czc3Ny5duuTowrpz505y5sxpcKrHT+nSpSldurTRMe7bO++8g7e3N76+vqYd25okOjoagIIFC1KwYMEU262yBNG/Tbdu3fD19U2x/c7W1symwlVERETEpIYMGUKPHj04ffo0Xbp04fjx48yePdvoWI+dfv36GR0hQ7JkyWKZSbqqVq2a7tjhQ4cOZWIauV93dwlOS+HChQkPDychIQFnZ2cSEhK4cOEChQsXfuiZVLiKiIiImFTlypVZuHAhv/32G3C7CDCyxeNxFR0dzaxZs/jll1+w2WzUrVuXXr16mbY1sH79+vz00080aNDA6Cj3FBoaCsDs2bPJmjUr7du3x263s2LFCkutnSupy58/PxUqVGDdunV4e3uzbt06KlSo8NC7CYNmFRYRERExrcjISFxdXXFycuLw4cP8/fffvPLKKxrn+pD5+fmRkJBAu3btgP+NFZ0wYYKRsdK0fft2+vTpg5OTEy4uLo6ls7Zv3250tDR17NiRpUuX3nObWM/Ro0cZPnw4N27cIFeuXEycOPGRdL1Xi6uIiIiISXXt2pXFixcTFRVF9+7dKVu2LFu3buXjjz82OtpjZd++faxdu9Zx+/nnn6dly5YGJkrfqFGjmDBhApUqVcLJycnoOPfl2rVrnDhxwjEp08mTJ7l27ZqxoeShKFOmDCtWPPoJp1S4ioiIiJiU3W7H1dWV9evX065dO/r374+Xl5fRsR5LN2/exNXVFfjfxEFmlTt3bpo0aWJ0jAwZNGgQ7dq145lnngHg4MGDjBs3zuBUYiUqXEVERERMKiYmhtjYWLZu3UrXrl0BLNPCZiVeXl60b9+e5s2bY7PZWL9+Pd7e3kbHSlOjRo1YunQpTZs2JVu2bI7tZh2TC9C4cWOqV6/O77//jt1up2rVqo9kHKQ8vlS4ioiIiJhUs2bNqFWrFqVLl+b555/n4sWLyQoVeTh69uxJ+fLl2b59O3a7nSFDhph64qP//ve/AHzwwQfYbDbHGFezz9B7/fp1EhMTadSoEVFRUVy7do08efIYHUssQpMziYiIiJjYjRs3cHd3x8nJiaioKCIjI5Othyn/TNKkTKtWrTI6SoZdv36dXbt2Ubx4ccqXL290nHQFBgYyZ84c4uLi2Lx5M8eOHWPs2LF8+eWXRkcTi1BfExERERETs9ls7N+/n927d3Pw4EFOnjxpdKTHirOzM3nz5iUmJsboKPc0ZMgQx/Iy165do2XLlvz3v//lzTffzJTJcf6Jr776ilWrVpEzZ04ASpcuzaVLlwxOJVairsIiIiIiJrVhwwYmTpzIjRs3KFCgACdPnqR8+fIEBgYaHe2xUrJkSTp37syrr77qmKAJoHPnzgamSungwYOOltXVq1dTpkwZvvjiC86fP8/bb79N27ZtDU6YtqxZs+Lm5pZsm7Ozs0FpxIpUuIqIiIiYlL+/PwEBAXTv3p2goCB+/vlnvvvuO6NjPVauXbvGqVOnKFSoEMeOHTM6TrruHN/866+/0qhRIwAKFSqEzWYzKtZ9yZMnD2FhYY6cq1evplChQganEitR4SoiIiJiUlmyZCF//vwkJCQAULduXWbMmGFwqsfHhg0bGDFiBG5ubsTGxjJjxgxq165tdKx0hYeHkzt3bnbt2sWAAQMc283e1dnPz4/BgwcTFhaGp6cn2bNnx9/f3+hYYiEqXEVERERMysXFBbvdTokSJVi0aBFFixbl6tWrRsd6bMyePZtly5ZRoUIFduzYwWeffWbqwrVnz574+PiQNWtWqlWrxlNPPQXA77//TpEiRQxOl75SpUqxYsUKjh8/jt1up1SpUuoqLBmiWYVFRERETGr79u0888wzXL58mTFjxhAREcHgwYOpU6eO0dEeC97e3qxevdpx28fHh6CgIOMC3YeLFy9y6dIlypcv7+h2Gx4eTkJCgumL1+joaM6fP+/oQQA4im+Re1GLq4iIiIhJJbX+5cyZU8uGPAJxcXEcPXqUpHac2NjYZLfNWFR5eHjg4eGRbJsVlkdasmQJU6ZMIU+ePI6C22azsXnzZoOTiVWoxVVERETEpGJiYlizZg2nTp0iPj7esX3YsGEGpnp8eHp6prlPRdXD9fLLL7Nw4UKKFi1qdBSxKLW4ioiIiJjUO++8Q1xcHJUrV8bFxcXoOI+d4OBgoyP8a3h4eKholX9ELa4iIiIiJtW0aVM2btxodAyRf2z69OncunWL5s2bJ1vWx4zdscWc1OIqIiIiYlLFixcnMjISd3d3o6OI/CNJk15t2rTJsU3dsSUj1OIqIiIiYjKTJk0Cbs8Wu3//furXr5+sq7DGuIrIv41aXEVERERMxtXVFbi99mWpUqUMTiPy4GJjY3FxcSE6OjrV/Tly5MjkRGJVanEVEREREZFHwtfXl8DAQMe6s3eWHjabjUOHDhmYTqxEhauIiIiISUVGRjJr1ix27NiBzWajVq1a9O7dW2NexTLCwsLUa0AeCiejA4iIiIhI6vz8/Lh27RojR47Ez8+P69ev4+fnZ3Qskfs2ePBgALp162ZwErE6jXEVERERMaljx46xbt06x+1q1arRokULAxOJZMytW7f49ttvOXPmDCEhISn2v/jiiwakEitS4SoiIiJiUkWKFOHKlSvky5cPgKtXr1KsWDGDU4ncv3fffZfly5dz+fJl5s2bl2yfzWZT4Sr3TWNcRURERExq4MCB/Prrr7z00ksAbNmyhdq1a5M/f35Ay+KIdUyYMIERI0YYHUMsTIWriIiIiEnNnDkz3f39+vXLpCQi/1xYWBhHjx6lUaNGREVFERcXR548eYyOJRahwlVERERERB6pwMBA5syZQ1xcHJs3b+bYsWOMHTuWL7/80uhoYhEa4yoiIiJiYtu2bePQoUPExMQ4tqmlVazmq6++YtWqVXTu3BmA0qVLc+nSJYNTiZWocBURERExqSlTprBv3z6OHDnCyy+/zObNm6ldu7bRsUQyLGvWrLi5uSXb5uzsbFAasSKt4yoiIiJiUiEhIcyfP5/8+fMzduxYAgICuHnzptGxRDIsT548hIWFYbPZAFi9ejWFChUyOJVYiVpcRUREREzKxcWFLFmyYLPZiIuLo2DBgpw/f97oWCIZ5ufnx+DBgwkLC8PT05Ps2bPj7+9vdCyxEBWuIiIiIibl5uZGdHQ0VatWZfjw4Xh4eKh7pVhSqVKlWLFiBcePH8dut1OqVCmdy5IhmlVYRERExKQuXbpErly5SEhIYMGCBURERNC1a1cKFy5sdDSR+3LkyJF09z/11FOZlESsToWriIiIiEmtXr0ab2/ve24TMStPT09sNht2u51z587h7u4OQEREBEWKFCE4ONjghGIV6iosIiIiYlJffvlliiI1tW0iZpVUmI4fP55q1arRtGlTADZt2sTBgweNjCYWo8JVRERExGT27dvHn3/+ydWrV1myZIlje2RkJHFxcQYmE3kwf/75JyNHjnTcbtKkCV988YWBicRqVLiKiIiImEx4eDj79+8nOjqa/fv3O7a7ubkxYcIEA5OJPJjo6Gj27NlD9erVAdizZw/R0dEGpxIr0RhXEREREZPatm0b9erVMzqGyD+2Z88e3n33XXLkyAFATEwMU6dOpVq1agYnE6tQ4SoiIiJiUtHR0cyZM4dTp04xdepUjh49SlhYGI0aNTI6mkiGxcbGEhYWht1up3Tp0ri4uBgdSSzEyegAIiIiIpK6MWPGEB8fT2hoKACFChVi5syZBqcSeTAJCQm4uLiQJUsWTp48ec+lckTupDGuIiIiIiZ1+PBhJk6cyLZt24DbY1wTExMNTiWScUuWLGHKlCnkyZMHm80GgM1mY/PmzQYnE6tQ4SoiIiJiUlmzZk12OyYmBo3yEiv64osvWLduHUWLFjU6iliUClcRERERk6pevTr+/v7Exsayc+dOFixYgKenp9GxRDLMw8NDRav8I5qcSURERMSk4uLimDdvHsHBwQB4enrSs2dPnJ2dDU4mkjHTp0/n1q1bNG/enGzZsjm2P/XUUwamEitR4SoiIiJiMkuWLEl3f+fOnTMpicjDkVpPAY1xlYxQ4SoiIiJiMuXLl+eZZ57h6aefTnX/hAkTMjmRiIixVLiKiIiImMyqVasICgri1q1b+Pj40KJFC3Lnzm10LJEMO3v2bLLbNpuNfPnyJesuLHI/VLiKiIiImNTp06cJDAxk48aNlC1blt69e1OuXDmjY4nct1q1amGz2ZLNhh0ZGUmVKlWYNGkSRYoUMTCdWIkKVxERERETi4iIYN26dUyfPp13332Xtm3bGh1J5B9JSEhg2bJlbNu2jdmzZxsdRyxChauIiIiIydjtdrZu3UpAQACHDx+madOm+Pj4ULx4caOjiTw0vr6+BAYGGh1DLELruIqIiIiYTIMGDfDw8KBVq1b07dsXm81GTEwMR44cAbSEiDweEhISjI4gFqIWVxERERGTuXPpkLvHB2oJEbGS6OjoFNuuXbvGsmXLOH36NFOnTjUglViRClcREREREXkkypcvn+ziS9KswnXq1GHEiBHky5fP4IRiFSpcRURERERExNScjA4gIiIiIiIikh4VriIiIiIiImJqKlxFREQe0PDhw/nkk08A2LNnD6+++mqmvG65cuU4ceLEQ33OO/+WzHysiIjI/VDhKiIijzVPT08qV65M1apVHZOBREVFPfTXqV69Ot9+++097xcQEEDHjh0f+usn6dKlCytWrHhkzy8iImIEFa4iIvLY8/f3Z+/evQQGBrJv3z5mz56d4j7x8fEGJBMREZH7ocJVRET+NQoWLEj9+vX5+++/gdtdbpcsWULjxo1p3LgxAD/++CPe3t5Ur16dDh06EBoa6nj8wYMH8fX1pWrVqgwcOJCYmBjHvp07d9KgQQPH7XPnztGvXz9q1apFzZo1GTt2LEePHmX06NH8/vvvVK1alerVqwMQGxvLxIkTadiwIXXq1GHUqFHcunXL8Vzz5s2jXr161KtXj5UrVz7w3z9gwADq1q1LtWrV6Ny5s+M4JLl69SpvvPEGVatW5bXXXuPMmTOOfUePHuWNN97ghRde4NVXX2XDhg0PnENERCSjVLiKiMi/xrlz5/jpp5+oUKGCY9sPP/zAN998w4YNGzhw4AB+fn6MHTuWnTt30r59e/r06UNsbCyxsbH07dsXb29vdu3aRZMmTfjuu+9SfZ2EhATefvttihQpQnBwMD/99BPNmjWjTJkyfPDBB1SpUoW9e/eyZ88eACZPnkxYWBhBQUF89913XLhwgc8++wyAn376iS+++IIvvviC7777ju3btz/w39+gQQO+/fZbtm/fTsWKFRkyZEiy/WvXrqVPnz7s3LmT8uXLO/bfvHmTN998kxYtWvDLL78wbdo0PvjggxSFr4iIyKOiwlVERB57ffv2pXr16nTq1IkaNWrQq1cvx76ePXuSJ08esmfPzjfffEP79u157rnncHZ2xtfXl6xZs/L777/zxx9/EBcXR7du3ciaNStNmjTh2WefTfX1/vzzTy5cuMCwYcNwdXUlW7ZsjtbVu9ntdlasWIGfnx958uTB3d2dt99+m/Xr1wOwceNGWrVqRdmyZXF1daVfv34PfBzatGmDu7s7Li4u9O/fn9DQUCIiIhz7GzZsSI0aNXBxcWHQoEH8/vvvnDt3ji1btlC0aFFat25NlixZqFSpEq+++up9jekVERF5GLIYHUBERORR++yzz6hTp06q+woXLuz4/ezZswQFBbF48WLHtri4OC5cuIDNZqNgwYLYbDbHviJFiqT6nOfOnaNIkSJkyXLvj9krV64QHR1Nq1atHNvsdjuJiYkAXLhwgWeeecaxr2jRovd8ztQkJCTwySefsGnTJq5cuYKT0+1r11evXiVnzpwAFCpUyHF/Nzc3cufOzYULFzhz5gx//vlnsuI7ISGBli1bPlAWERGRjFLhKiIi/2p3FqKFCxemV69e9O7dO8X9du3aRXh4OHa73fGYs2fPUrx48RT3LVy4MOfOnSM+Pj5F8Xrn6wHkzZuX7Nmzs379egoWLJjiuQoUKMC5c+cct8+ePZuxP/D/rV27ls2bN7NgwQKKFStGREQENWrUwG63O+5z/vx5x+9RUVFcv36dAgUKULhwYWrUqMGCBQse6LVFRET+KXUVFhER+X9t27Zl2bJl/PHHH9jtdm7evMmWLVuIjIykSpUqZMmShYULFxIfH893333Hvn37Un2eypUr4+HhwdSpU7l58yYxMTH8+uuvAOTPn5/w8HBiY2MBcHJyom3btnz00UdcvnwZgPDwcLZu3QpAkyZNCAwM5MiRI0RHRzNz5sx7/h3x8fHExMQ4fuLi4oiKisLFxYW8efMSHR3NtGnTUjwuJCSEPXv2EBsby6effspzzz1H4cKFadiwIcePHycoKIi4uDji4uL4888/OXr06AMdZxERkYxS4SoiIvL/nn32WcaNG8fYsWOpUaMGjRs3JiAgAAAXFxdmzJhBYGAgNWrUYMOGDbzyyiupPo+zszP+/v6cOHGCl156iQYNGrBx40YAatWqxVNPPUW9evWoWbMmAEOHDqVEiRK0a9eO559/ntdff52wsDAAXnzxRbp160a3bt145ZVXqFWr1j3/jjFjxlC5cmXHz4gRI/Dx8aFIkSLUr1+f5s2bU6VKlRSPa9GiBZ999hk1a9bkwIEDTJ48GQB3d3fmz5/Phg0bqF+/PvXq1WPKlCmO4ltERORRs9nv7CMkIiIiIiIiYjJqcRURERERERFTU+EqIiIiIiIipqbCVURERERERExNhauIiIiIiIiYmgpXERERERERMTUVriIiIiIiImJqKlxFRERERETE1FS4ioiIiIiIiKmpcBURERERERFT+z8n/baIkhlRXgAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_test, y_test_pred = evaluate_model_cm(shuffled_baseline_log_reg_model, testing_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "evaluate_model_score(shuffled_baseline_log_reg_model, testing_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate with holdout data" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_holdout, y_holdout_pred = evaluate_model_cm(shuffled_baseline_log_reg_model, holdout_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "evaluate_model_score(shuffled_baseline_log_reg_model, holdout_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Save trained model predicitions" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "predictions = []\n", - "\n", - "predictions.append(y_train)\n", - "predictions.append(y_train_pred)\n", - "\n", - "predictions.append(y_test)\n", - "predictions.append(y_test_pred)\n", - "\n", - "predictions.append(y_holdout)\n", - "predictions.append(y_holdout_pred)\n", - "\n", - "predictions = pd.DataFrame(predictions)\n", - "predictions.index = [\"y_train\", \"y_train_pred\", \"y_test\", \"y_test_pred\", \"y_holdout\", \"y_holdout_pred\"]\n", - "predictions.to_csv(f\"{results_dir}/2.shuffled_baseline_model_predictions.tsv\", sep=\"\\t\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.13 ('2.ML_phenotypic_classification')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "4cc408a06ad49ae0c78cd765de22f61d31a0f8b0861ec15e52107dd82d811e52" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/3.ML_model/notebooks/3.interpret_model.ipynb b/3.ML_model/notebooks/3.interpret_model.ipynb deleted file mode 100644 index 4273b323..00000000 --- a/3.ML_model/notebooks/3.interpret_model.ipynb +++ /dev/null @@ -1,762 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import pathlib\n", - "\n", - "from joblib import load\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpret best model" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# set numpy seed to make random operations reproduceable\n", - "np.random.seed(0)\n", - "\n", - "# results dir for loading/saving\n", - "results_dir = pathlib.Path(\"../results/\")\n", - "\n", - "log_reg_model_path = pathlib.Path(f\"{results_dir}/1.log_reg_model.joblib\")\n", - "log_reg_model = load(log_reg_model_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compile Coefficients Matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1280, 15)\n" - ] - }, - { - "data": { - "text/html": [ - "
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ApoptosisArtefactBinuclearElongatedFoldedGrapeHoleInterphaseLargeMetaphaseMetaphaseAlignmentPolylobedPrometaphaseSmallIrregularUndefinedCondensed
00.0000000.0000100.0167200.0000000.0445511.830712e-078.667781e-030.0309630.0186600.0184560.0359470.0413430.0427260.1095600.000000
10.0000000.0000000.0542290.0055970.0460185.784630e-023.182227e-020.0231690.0072220.0000000.0000000.1283530.0000000.0351890.000000
20.0122210.0489490.0498480.0039020.0000000.000000e+003.472109e-070.0059790.0038620.0000000.0187710.1365870.0219030.0316740.011061
30.0000000.0294290.0897900.0367830.0107683.443468e-023.275226e-020.1307960.0081590.0170260.0176890.0272120.0097730.0073060.000000
40.0679880.0824890.0916060.0002440.0050411.366581e-043.782586e-020.0073590.0000000.0626710.0146530.0571200.0011710.0538910.015763
\n", - "
" - ], - "text/plain": [ - " Apoptosis Artefact Binuclear Elongated Folded Grape \\\n", - "0 0.000000 0.000010 0.016720 0.000000 0.044551 1.830712e-07 \n", - "1 0.000000 0.000000 0.054229 0.005597 0.046018 5.784630e-02 \n", - "2 0.012221 0.048949 0.049848 0.003902 0.000000 0.000000e+00 \n", - "3 0.000000 0.029429 0.089790 0.036783 0.010768 3.443468e-02 \n", - "4 0.067988 0.082489 0.091606 0.000244 0.005041 1.366581e-04 \n", - "\n", - " Hole Interphase Large Metaphase MetaphaseAlignment \\\n", - "0 8.667781e-03 0.030963 0.018660 0.018456 0.035947 \n", - "1 3.182227e-02 0.023169 0.007222 0.000000 0.000000 \n", - "2 3.472109e-07 0.005979 0.003862 0.000000 0.018771 \n", - "3 3.275226e-02 0.130796 0.008159 0.017026 0.017689 \n", - "4 3.782586e-02 0.007359 0.000000 0.062671 0.014653 \n", - "\n", - " Polylobed Prometaphase SmallIrregular UndefinedCondensed \n", - "0 0.041343 0.042726 0.109560 0.000000 \n", - "1 0.128353 0.000000 0.035189 0.000000 \n", - "2 0.136587 0.021903 0.031674 0.011061 \n", - "3 0.027212 0.009773 0.007306 0.000000 \n", - "4 0.057120 0.001171 0.053891 0.015763 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "coefs = np.abs(log_reg_model.coef_)\n", - "coefs = pd.DataFrame(coefs).T\n", - "coefs.columns = log_reg_model.classes_\n", - "\n", - "print(coefs.shape)\n", - "coefs.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Diagrams for interpreting coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# display heatmap of average coefs\n", - "plt.figure(figsize=(20, 10))\n", - "ax = sns.heatmap(data=coefs.T)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/seaborn/matrix.py:654: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n", - " warnings.warn(msg)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# display clustered heatmap of coefficients\n", - "ax = sns.clustermap(data=coefs.T, figsize=(20, 10), row_cluster=True, col_cluster=True)\n", - "ax = ax.fig.suptitle(\"Clustered Heatmap of Coefficients Matrix\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# display density plot for coefficient values of each class\n", - "sns.set(rc={\"figure.figsize\": (20, 8)})\n", - "plt.xlim(-0.02, 0.1)\n", - "plt.xlabel(\"Coefficient Value\")\n", - "plt.ylabel(\"Density\")\n", - "plt.title(\"Density of Coefficient Values Per Phenotpyic Class\")\n", - "ax = sns.kdeplot(data=coefs)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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zM7V8+XIdOXLEphhnemt+7HBrnwEDBqhJkybasmWL9bXVXXfdZXQsl/P2228rIiJCtWvXtq7lZDKZeC1VgAkTJignJ0ebN29Wv379FBsb6zazgCidnOSf6w+gcJdGgeHqGjZsqHfeeUdHjx7V22+/rf379yshIUEdOnQwOppLWrRokd566y2VLVvW+iLcZDLxQukyJpNJ1apV019//aUyZcoYHcel8ULJfjt27NCKFSsonK7g0ovIS2vvSHmjw9xlvQZn4jUVHO25555Tdna2GjZsKG9vb6PjuDR2uLXfpWlRKNyqVau0Zs0aRorbYceOHYqJiVFQUJB11Nzzzz9vdCy7UDo5WUZGhubOnWvdErlVq1YaOnSoSpQoYXQ0l8HUMPtNmjRJ/v7+io+PlyRVqlRJL774IqVTIT755BPFxsZaF3lEwXx8fBQSEqI2bdrYjCjgHV9bvFCyX/Xq1XXhwgWVLFnS6Cgua8aMGfk2hpg5c6YiIyMNSuS6ClrLsFSpUrr33nsVGhrKzkeS9u3bZ/04MzNT+/fv1+UrajDC19ahQ4e0cuVKo2O4BXa4vbJevXpdcXQh66nZ8vf353WUnS4tsG42m5WRkaFSpUrp5MmTBqeyD6WTk02ZMkU5OTkaN26cpLxfPK+99ppbzMV0tmeffVZTpkyxLph29uxZTZo0iel1l9m7d6+mT5+ujRs3SspbTDU3N9fgVK7L39+fwskO1atXV/Xq1Y2O4fJ4oWS/l156SY888oiaNGliM4qAIjPvj92DBw8qLS1N69evtx5PTU1VRkaGgclc12233aadO3eqW7dukqS4uDj5+/tr5cqVio+P1/jx4w1OaLwhQ4bY3L98xyxG+OZXrVo1paWl8TvdDuxwe2Wsw3dt7r33Xr3wwgvq3Lmzza51jBrPr0yZMvrrr790//33a/DgwSpXrpxuu+02o2PZhdLJyS4Ni7ukcePG6tGjh4GJXNeRI0dsVugvV64cu9P8w6V/+C/JzMwUewPkd+kd3/vuu08zZsxQt27dbP5h4x3fPPv379eBAwesU1def/11paWlSZIGDhxoZDSXcqkY4IWS/aZOnaqKFSuqVKlSTLH7h23btikyMlKnT5/W/Pnzrcd9fX3546UQ8fHx+vzzz60FZp8+fTRs2DDNmzdPPXv2NDaci1i7dq3REdzCpUXpS5UqpV69eun++++nGL8Kdri9smbNmtncP3/+vCTbtejwtx07dkiSPv/8c+sxlioo2Icffiiz2axRo0Zp+fLlSktLc5t/8yidDHD+/HnrLx7exSxcTk6OcnJyrH+gZGdnKysry+BUriUwMFDz5s1TVlaWNm/erAULFqh9+/ZGx3I5/3zHd9WqVdaPecf3b++99566du1qvf/DDz9o4MCBOn/+vD788EO98847BqZzHZcXAxIvlOxx4sQJpq4UIiQkRCEhIYqMjFRoaKjRcdzC6dOnbd508fT01PHjx+Xt7c16PLgml16P16xZUzVr1jQ4jXu4VIb37NlTzZo1U2pqqp5++mnrLA7kOXLkiF588UXt2bNHJpNJd999t2bOnKlq1aoZHc2lXP4aCld26W9iDw8PtymbLqF0crKgoCD16dNH3bp1k8lk0ooVKxQcHGx0LJfUunVrjRo1yjrCYuHChbr//vsNTuVaRo0apfnz56tkyZKaOXOm2rdvr6efftroWC6Hd3ztc/jwYXXq1Ml6v0SJEtYt7dna/m+8QLp2d911l06ePKkKFSoYHcVlhYaGav369fr5558lSS1atKDALESzZs00ZMgQBQcHy2Qyafny5WrSpInS09MpnXBNWJT+xlza4ZZR9vlNmDBBvXv3Vq9evSRJkZGRmjBhghYsWGBwMtdisVi0ZMkSHTp0SKNHj9bRo0d18uRJNW7c2OhoLqdFixb51gu7tJ7hmDFj5O/vb1CyqzNZ+C3hdJdeVFosFt13331q06aN0ZFcUnZ2tj744AOtW7dOFotF7dq105AhQ3hBeZn9+/erdu3aVz2Gv+3du1e//PKLTCaTmjdvztS6ywQFBdlM/738Z+mfjyEPJYF9Bg0apJ07d6pRo0Y2UxFZo+9v77zzjtauXatu3brJYrFo1apVateundvsTONM2dnZ+vrrr/XLL7/IYrGoefPm6tu3b74p54C90tLSNHfuXP38888ymUxq0aKFhg0bxhpPdnrggQe0bt06o2O4lODgYC1btuyqx251b7zxhs6cOaNdu3Zp1apVOnv2rAYPHsyC6wWYPXu2UlNTraOio6Ojrb+jdu7cqXnz5hkZ74oY6WSAtm3b8oeJHby8vPTMM8/wLtQVjB49Ot9uRwUdQ55FixZp3rx5euCBB2SxWPTBBx9o6NCh6t+/v9HRXEJ2drbNQqqXCqe0tDSmthbgnyXBrFmztH37dkqCAnTv3l3du3c3OoZLW7VqlaKioqzTfR577DGFhITw81QALy8vPfroo3r00UeNjoKbxLhx4+Tr66vx48fLYrEoKipK48aN03vvvWd0NJdx+Y6I/3Tx4kUnJnEPHh4eOnDggGrVqiVJSkhIYE3DAmzevFnR0dHW3cvLlSunzMxMg1O5ph9++EERERHW++Hh4XrkkUf0xRdfWDfWcFWUTk4ycuTIK26fybu9+V1a3PGfWNRRSk5OVnJycr5tkFNTU60LFiK/hQsXKjo6WuXLl5eUdx379etH6fT/unXrpnHjxumNN96wFk9paWkaP368zVpPyENJYL9LLyZRuAoVKqhEiRLW+8WKFWM6YiESEhI0btw4JSUlae3atdq1a5fWrl2rZ5991uhocFMHDhxQbGys9X6TJk0oyv/hn+tjXu7yEazIM2rUKA0YMED16tWTyWTSnj17Cv3b5lZWrFgxm7+R2YW7cOfOnVNKSorNzu6nTp2SlH9zKVdD6eQk7dq1MzqC27l8l4fMzEytW7dO99xzj4GJXEdMTIw+++wznTx50mYb5FKlSumpp54yMJlr8/f3txZOkuTn5+c2W406w7BhwxQeHq77779fNWrUkCQdPHhQDz74oEaMGGFsOBdESWC/M2fO6PPPP9eRI0ds3hHnDZe/1atXT0899ZS1oFu2bJkaN25s3S2REdJ/mzx5soYNG6a3335bUt61e+mllyidcN0qV66s5ORk+fn5Scr7Y65q1aoGp3ItrI9pnzfffFPh4eFq06aNXn31VZUoUUIWi0X33nuv9ecLf6tTp46WL18ui8Wio0eP6sMPP1STJk2MjuWSHn30UQUHB1tfD/zwww966qmnlJ6e7vJrYLGmk5MlJSWpYsWKRsdwS2lpaRozZozef/99o6O4hMunh8E+s2bN0pkzZ/Twww9LkqKiohQQEKCHHnpIkljf6f8dOnRIu3fvliTdfffdql69usGJXNMbb7yh/fv325QEd999t/UffkqCv/Xv31+1a9fWv/71L5vpBYyA+tuVpoqZTCYtXLjQiWlcW69evbR06VL17NlT0dHRkmTzMXCtnn/+ef3666/WN4nXrVunli1bWt+oYpQ97BUSEmJd5uLyj1GwtLQ0vfnmm9ZSs3379nr55ZdVsmRJg5O5pvj4eG3ZskUWi0XNmjVT3bp1jY5kF0Y6OVmvXr3UqFEjDRgwQC1atDA6jlspWbKkDh8+bHQMlxIXF0fpdA0uDZ3ftGmTzfGIiAiZTCZ99913RsRyOdWrV6dossOePXskSYsXL7Ye27Ztm7Zt2yaTyUTpdJlz585pypQpRsdwaeyKaD+z2azs7GzrlIykpCR5eHgYnAru7I477rB546l3794GpoE7u3w8B2M7rs7X11dTp041OobbqFu3rtsUTZejdHKytWvXKi4uTu+8845SU1M1YMAABQcHsztGAS6f92yxWLRz507VrFnTwESuxWQyqVq1avrrr79UpkwZo+O4hejoaJUuXdroGLhJUBLY784772Skrx02bNign376SSaTSa1atVKrVq2MjuSS+vfvr2eeeUZnz57V7NmzFR0drVGjRhkdC26MTWvgKFlZWdb1Vi//+BJG1edZtGjRFR8fMGCAk5K4j23btmnmzJk6cuSIcnJyZLFYZDKZ8r2Z7oqYXmegbdu26YUXXtC5c+cUEhKi4cOH26w3c6v7z3/+Y/3YbDbr9ttvV8eOHV1+oTRnGjNmjH799Ve1adPGZg0shoHnZ7FYFBwcrOXLlxsdBTeR1NRUJSQk2Oy00rRpUwMTuaZBgwZp586datSokc2Cs6zp9LePPvpIy5Yts+5AExcXp549e2rQoEEGJ3NNW7du1ffffy+LxaL27dsrMDDQ6Ehwcxs3btSePXtsfp9TRuFatW/fvtDHGFX/t5dffllS3vppv/zyi1q2bCkpbzZC69at2TmyAF26dNHw4cN177332ozurVKlioGp7MNIJwMcO3ZMX3/9tWJjY9WyZUuFhYXp559/1qBBg1iP4DL8Q391BU2D2rp1q0FpXBsjw+BocXFxmj59us6dO6cKFSro8OHDqlu3Lus3FKB79+7sBHUVy5cv19dff20d+fzoo4+qX79+lE7/kJOTo969e2vp0qUUTXCYt956Szt27NC+ffv04IMP6rvvvrP+EQxcCxZct8+0adMkSSNGjNCyZctUrVo1SdKRI0c0a9YsI6O5rOLFiysoKMjoGNeF0snJhg4dqr1796pv376KjIxUuXLlJEmNGzdWXFycwelcC7sdXd2lYu7kyZOKiopSZGQk88evwMfHRyEhIYwMg0PMmzdPkZGR1jcMfvzxR61evdroWC6JBcPtc/lUe6bdF8xsNqtcuXLKzMxkm3Y4zPr16xUVFaXQ0FC99tprGjFihCZPnmx0LOCmd+zYMWvhJEnVqlVTQkKCgYlcV5s2bbR+/Xq3XDOU0snJgoOD1bFjR5vdey65tMgx8jz77LOqXbu2WrZsWeD1utVdvHhRa9eu1dKlS/W///1PFy9e1Mcff6x7773X6GguiwWy4Uienp4qX768cnJyJEmtWrXS7NmzDU7lmkaOHGld9PmSUqVK6d5771VoaCiLQEu655579PLLLyssLEwmk0kRERG65557jI7lkmrUqKEBAwaoU6dONm8gsAYIrpe3t7c8PT1lMpmUnZ2tihUr6sSJE0bHAm56ZcuW1Zw5cxQWFiZJWrp0qcqWLWtsKBe1ePFiffDBBypZsqS8vb3dak0nSicnycjIkCQ98MADysrKyvd4iRIlnB3J5bHbUeGmTZumFStWqE6dOgoJCdG7776rrl27UjhdBVM24UiX/sGvXr26Pv/8c1WpUkVnz541OpZL8vf3186dO23WK/L399fKlSsVHx+v8ePHG5zQeK+++qrmzJlj3cXnvvvu0/Dhww1O5ZrS09N155136sCBA0ZHwU2iZMmSysjIUKNGjRQeHi5/f3/e8AScYPr06Xr99det08ZatGih6dOnG5zKNS1dutToCNeNhcSdpG7dutZ3ef95yU0mk3Xrbfxt1KhRCg8PZ7ejAjRs2FCNGjXSsGHD1KJFC0myrkGAwp05c0bTpk3T8ePHtWjRIsXHx2v79u3q16+f0dHghjZt2qR77rlHZ86c0aRJk5SamqoXX3xR9913n9HRXM6AAQO0YMECeXt7S5IyMzM1bNgwzZs3Tz179mR6OeyWk5OjOXPmaOTIkUZHwU3k9OnTKl26tHJycrRgwQKlpqZq4MCBCggIMDoaAFhdvHhRCQkJMplMqlGjhjw93WMMkXukvAnEx8cbHcHtnDt3Tj169GC3owJs3LhRMTExmjFjhv766y/17NnTOsUHhRs/frzatGmjL7/8UpJUq1YtjRkzhtIJ16VChQoqVaqUSpUqpU8//VSStH//fmNDuajTp0/b7Dzq6emp48ePy9vb21pE3erS0tI0d+5c/fzzzzKZTGrRooWGDRvG2k7/YDabtWXLFqNj4Cbz448/Kjg4WJKsIwyXLVtmPQag6GzatEmHDx+2WcOX6dL57dixQyNHjrSOtL948aJmz56t+vXrGx3tqiidDHD27Fn99ttvMplM+te//sW81UKw21HhSpcurQEDBmjAgAGKj4/XkiVLdOHCBQ0YMEBBQUHq27ev0RFdUlJSkvr166fFixdLypsexVoyuF6jR4/Ot1NdQccgNWvWTEOGDFFwcLBMJpOWL1+uJk2aKD09ndLp/40bN06+vr4aP368LBaLoqKiNG7cOLaNLsADDzygjz/+WD179rRZ04mlCnC9Pv3003wFU0HHADhWeHi4du7cqbvvvpsprVfx+uuv64033rDurPnzzz9rypQp+vrrrw1OdnWUTk62YcMGjRkzRvXq1ZMk/fHHH5o5c6ZatWplcDLXw25H9qlbt67Gjx+vsWPH6r///a+ioqIonQrxzyGo586dY7c/XLPk5GQlJycrMzNT+/fvt/4Mpaam6vz58wanc00TJkzQ119/rW+//VYWi0WtW7dW37595eXlpW+++cboeC7hwIEDNhuKNGnShDdeCjFz5kzrrclksi6mylIFuFY7duzQ77//rrNnz2rRokXW42lpacrOzjYwGXBr2L59u2JjY21GQ6NgGRkZ1sJJylv/6tK60a6O0snJ3nnnHS1atEi1a9eWlDcVY8yYMZROl/nss8/02GOPacaMGQU+zvb2BfPy8lLXrl3VtWtXo6O4rI4dO2rChAlKT09XZGSkvvzyS/Xq1cvoWHAzMTEx+uyzz3Ty5EkNHjzYerxUqVJ66qmnDEzmury8vPToo4/q0UcfNTqKy6pcubKSk5Pl5+cnKW9UdNWqVQ1O5ZpYsgCOkpSUpJ07dyojI0M7d+60Hi9ZsqSmTZtmYDLg1lCpUiWjI7iNEiVK6Oeff7au5/vLL7+4zQhfSicnu3jxorVwkqTatWvbzF+FrOs3XT5kHnCEp556SsuXL9e5c+e0fv16PfroowydxzV77LHH9Nhjj2nevHkaOnSo0XFc2qU3EaZPn27dTONyvInwNx8fHwUHB6tdu3aSpHXr1qlly5bWN2C4VoDjdejQQR06dNDGjRvVunVro+MAt5waNWro8ccfV4cOHWym27OmU37jxo3Tc889Z71O2dnZbjMFn9LJyfz8/BQZGanQ0FBJUlRUlPVdTeR5+OGHtXLlSjVu3Fj33XefPvvsM/3000+qWbOmRowYYXQ8uLFNmzapR48e6tGjh82xy4eqAvYaOnSoNm3apP379+uRRx7RmTNndO7cOdWsWdPoaC7j0psIJUuWNDiJ67vjjjt0xx13WO/37t1bW7duVbVq1QxM5VpatGhRYHl5aXrdpk2bDEiFm0GTJk3073//W0eOHNHbb7+t/fv3KyEhQR06dDA6GnBTy8rK0u233669e/caHcXlNWzYUKtXr1ZCQoIsFotq1arlNtMSTRYWNHGalJQUHTlyRGPGjFFiYqIkqV69enrrrbd4UXmZCRMmaO/evcrKylLVqlWVmZmpBx54wLpbzaxZswxOCHcVEhKSb5Hn0NBQRUZGGpQI7uzDDz/U+vXrderUKa1evVonTpzQqFGj9NVXXxkdDW7s5MmTioqKUmRkpCwWi1avXm10JJdx7NixKz5epUoVJyXBzWbs2LHy9/fX999/rxUrVig9PV0DBgxQdHS00dEA3OKutm6TO0yxY6STk8TFxenll19WyZIllZmZqffee0+BgYFshVyArVu3asWKFcrIyFDr1q31888/y9vbW3369LEZoQLY69ChQzp48KDS0tK0fv166/HU1FS3WYAPric2NlZLly5VWFiYpLx1CdLS0gxO5VouX5i3IAyfz3Px4kWtXbtWS5cu1f/+9z9dvHhRH3/8se69916jo7kUSiUUlb1792r69OnauHGjpLzRmbm5uQanAm5+FotFixcv1k8//SSTyaRWrVopLCyswFGtt6pGjRpZN82QZL027rSJBqWTk7z//vv6+uuvVa9ePf3888+aM2eOHnjgAaNjuSRvb2+ZTCb5+Pjo9ttvt85b9fDwcJshhHAt27ZtU2RkpE6fPq358+dbj/v6+mrs2LEGJoM7K168eL7fSbxIsnX5wrwo2LRp07RixQrVqVNHISEhevfdd9W1a1cKpwKMHDnyiv8fe/fdd52YBjeTf/4uz8zMZHdbwAlmzJihPXv2WJeeiY6O1sGDB1nH8DLfffddoW+6uMvrLEonJ/Hw8FC9evUk5a1J8OabbxqcyHVlZWVZtyG//GMp70UAcK1CQkIUEhJis54acKMqVaqkrVu3ymQyKTc3V/PmzdOdd95pdCyXcqXdny5NM7/VffXVV2rUqJGGDBli3ZGG8rJglxZZBxwtMDBQ8+bNU1ZWljZv3qwFCxaoffv2RscCbnobN25UVFSUPD3zaokuXbooNDSU0ukyzzzzjHV5kIcfflhLliyxPvbqq6/mWzrEFVE6OUl2drZNefLPMuXyxUNvdRcuXLDZhvzyj3khjhsRGhqqw4cP6/Dhw8rJybEeb9u2rYGp4K5effVVjR07Vn/++af+9a9/KTAwUG+99ZbRsVxaVlaW/vvf/2rJkiXauXOnda2+W9nGjRsVExOjGTNm6K+//lLPnj1tfj/hbyEhIUZHwE1q1KhRmj9/vkqWLKm33npL7du315AhQ4yOBdwSLv/7jr/18rt81OU/d713lxGZLCTuJFd6t8RkMum7775zYhrg1jRr1ix98803ql27tjw8PCTl/f9v4cKFBieDO8vIyFBubi47tF3Brl27tGTJEsXFxSkzM1Ovv/66HnzwQRUvXtzoaC4lPj5eS5YsUWxsrGrXrq2goCD17dvX6FguY8aMGVd8nHfGca1Ydw4w1vTp0/XHH38oJCREJpNJUVFRqlOnDstfXObyjZD+uSlSQZskuSJGOjnJ2rVrjY4A3PJWrlypNWvWsIA/HIaRc1e2cOFCLV26VBcuXFBoaKiio6M1YMAAdevWzehoLqlu3boaP368xo4dq//+97+KioqidLqMj4+P0RFwk5kyZYruuecepkYDBhkzZowWL16s//73v7JYLOrQoYP69OljdCyXkpmZaZ0hdfnHlx5zB4x0AnDL6N+/v7788kujY+AmMWPGDEVHR6tmzZqMnCtE3bp11bJlS7322muqVq2aJOnBBx9kdC8Al7B06VJFR0frwoUL6tmzp7p3764yZcoYHQu46eXk5CgrK0slSpSwOZ6RkSFvb2+ZzWaDkrmem2HGFKUTgFvGjBkzdOLECXXu3FnFihWzHmdkCq5Hx44dtWzZsnwvmPC3/fv3a8mSJYqJiVHNmjUVEhKi//znP4z+xQ3buHGj9uzZY/Mu7zPPPGNgIrizo0ePKioqSitXrlSdOnU0bNgw3XXXXUbHAm5a06dPV61atRQWFmZzfOHChTpx4gTTpW8ylE4AbhmPPvpovmOMTMH1euyxx/Txxx9bd1xB4XJycvT9999r6dKl+vHHH9W5c2cFBQXp/vvvNzoa3NBbb72lHTt2aN++fdaRcy1btmQhf9yQ1NRUxcbG6r333tMLL7yQ749hAI7TrVs3LV++PN+IppycHAUHBys2NtagZCgKlE4Abnr79u274uPsHonrsWfPHr399ttq1aqVvL29rcdZePbKzpw5o8jISC1btowXlbguQUFBioqKUmhoqJYvX66kpCRNnjxZc+fONToa3IzFYtGGDRsUGRmpvXv3qkuXLurZs6d1OjCAohEUFKSYmJhrfgzuibdnAdz0Lm17/M9tWC0Wi9vMhYbr+fDDD3Xq1Cnt2bOHtQfskJaWpkOHDql+/foaPHiwnnrqKaMjwU15e3vL09NTJpNJ2dnZqlixok6cOGF0LLihNm3ayN/fX6GhoRoxYoRMJpMyMzOtb1bxphRQNLKyspSRkZFviYL09HRlZWUZlApFhdIJwE2P9WNQFHbt2qVvv/02X5mJ/NavX68JEybIbDZr7dq12rFjh+bMmaN58+YZHQ1uqGTJksrIyFCjRo0UHh4uf39/il9cFy8vL6WkpOiTTz7RggULdPkEEN6UAopO165dNXbsWL3xxhvWXaVTU1M1YcIEde7c2eB0cDSm1wEAcB2GDBmid955RyVLljQ6isvr1auX5s2bp8GDBys6OlpS3gvOuLg4Y4PBLZ0+fVqlS5dWTk6OFixYoNTUVD366KOqXLmy0dEAAHa4ePGiwsPD9d1336lGjRqSpIMHD6p9+/aaPn0662XeZPivCQDAdfD19VVoaKjuv/9+mzWd2HGlYP7+/jb3L79mwLW47bbbrB8PHz7cwCQAgOvh6empt956SwcPHtSePXtksVhUv359Va9e3ehoKAKUTgAAXIdatWqpVq1aRsdwCyVLltTp06etUxE3b96sUqVKGZwK7urAgQOaN2+eDh8+rIsXL1qPL1myxMBUAAB7ZWRkSJIqVqyoihUr5jv+z7We4N6YXgcAAIrU77//rokTJ+ro0aOqW7euDh48qPfff1/33HOP0dHghoKCghQcHKz69evbrOXUrFkzA1MBAOxVt27dK66JuWfPHiemQVGjdAIA4DpkZGRo7ty5+umnn2QymdSqVSsNHTqUd+cKkZqaqm3btkmSGjVqpNKlSxucCO4qJCREUVFRRscAANyg999/X15eXurTp48sFosiIiLk5eWlgQMHGh0NDkTpBADAdRg3bpxycnLUu3dvSX9P7Zk2bZqRsVxSWlqafHx85OHhob179+rPP//UQw89xLpOuC6zZs1SYGCg2rRpY3QUAMAN6Nevn7766qurHoN7Y00nAACuw44dOxQTE2O937hxY/Xo0cPARK5r4MCB+uKLL5Senq5BgwapTp062rBhg958802jo8ENtWzZUsOHD5eHh4e8vb1lsVhkMpm0adMmo6MBAK5BSkqKDh06ZF1A/PDhw0pJSTE2FByO0gkAgOt0/vx5+fj4SPp78UvkZ7FY5OPjoxUrVqh379569tlnFRQUZHQsuKkJEyZo2rRpql+/vjw8PIyOAwC4TqNGjVLv3r2tazzu3r1bU6ZMMTgVHI3SCQCA6xAUFKQ+ffqoW7duMplMWrFihYKDg42O5ZIyMzOVlZWlDRs2WNdpoCzA9SpTpow6d+5sdAwAwA3q2LGjAgMD9b///U8Wi0WNGjWSn5+f0bHgYJROAABchyFDhqhu3bratGmTLBaLRo8ezRozhejatatatGihWrVqqXHjxjp16pSKFStmdCy4qQ4dOuirr75Sly5dbH6OWMQfANzPX3/9pdzcXHXo0EHp6elKSUlR2bJljY4FB2IhcQAArtGlBcSXLl1qdBS3ce7cOfn6+srDw0Pp6elKS0tTxYoVjY4FN1S3bl3rxyaTybqmE1tsA4B7iYqK0gcffKDs7Gx99913OnDggF577TV9+umnRkeDAzHSCQCAa2Q2m1WuXDllZmYyYsdOJpNJO3fuVGZmpvUYpROuR3x8vKS8d8d/+eUXVatWzaaIAgC4h88++0xLly7VgAEDJEm1atXS6dOnDU4FR6N0AgDgOtSoUUMDBgxQp06drIuJS7K+cMLf4uLiNH36dJ07d04VKlTQ4cOHVbduXUVFRRkdDW5k9OjReuqpp1S3bl2lpKQoODhYvr6+Onv2rEaNGqWwsDCjIwIAroGXl5dKlixpc8xsNhuUBkWF0gkAgGuUkpKiI0eOqFKlSjpw4IDRcVzevHnzFBkZqUGDBik6Olo//vijVq9ebXQsuJndu3dbRzQtW7ZMtWvX1ieffKITJ07o6aefpnQCADdTtmxZJSQkyGQyScr73V6pUiWDU8HRKJ0AALgGcXFxevnll1WyZEllZWVp9uzZatmypdGxXJqnp6fKly+vnJwcSVKrVq00e/Zsg1PB3Vw+lfXXX39Vhw4dJEmVKlWy/sECAHAf48aN04svvqiEhAS1b99exYsX17x58/6vvXuPrfl+/Dj++hwcOo02pWjNpC5DIuZo0TIt1Y26lpDZ3KKLjhCMYTrLxGS+bgmdS23u0o1IFenmtro2o1ZByUiVMrTqbsrpRZ3vH8tO9Keb7fs7px9On4+/nPf78/mcVz4S6uX9eX/MjgUXo3QCAOBfWLlypTZv3qzWrVvr2LFjWr58OaXTC1itVjkcDjVp0kSbNm1So0aNdO/ePbNj4RVUUFAgHx8fHT9+XBMnTnSOP7tXGADg1RAUFKStW7fq8uXLcjgcCgoK4vE6D2QxOwAAAK8Si8Wi1q1bS5JCQ0P18OFDkxO9/CZNmqTCwkJ98sknSktL0/Lly/XFF1+YHQuvmLi4OMXExOjdd99VcHCwmjdvLkk6deqUAgMDTU4HAPhflJSUyGL5o5bIzc1VTk6OyYngaobD4XCYHQIAgFdF79699fXXX+vPvz4nTpxY7vOf/xAG4Hq3bt3S7du31apVK+cjdQUFBSorK6N4AoBXTFJSkhYtWiRfX1/nn+mGYSgtLc3kZHAlSicAAP6FyMjIv5zjB6WKFRcXa+fOnbp69aqePHniHJ8+fbqJqQAAgJl69OihjRs3qlGjRmZHgRuxpxMAAP/C/v37zY7wypk0aZJKS0vVtm1bWa1Ws+MAAICXgL+/P4VTFcBKJwAA4FbR0dHatWuX2TEAAMBLJCEhQUVFRerTp0+5N5SyVYFnYaUTAABwq8aNG6uwsFDe3t5mRwEAAC+J7du3S5J2797tHGOrAs/DSicAAOAWCxYskPTHRs9nz55V165dyz1ex55OAAAAno2VTgAAwC1ee+01SVJQUJCCgoJMTgMAAF4GJSUlslqtstvtFc57eXlVciK4EyudAAAAAABApRg4cKBSUlLUqlUrGYahZysJwzB07tw5E9PB1SidAACAWxUWFmrFihU6duyYDMNQaGioxo0bxx5PAABUQbm5uayArkIsZgcAAACeLT4+Xvfv39esWbMUHx+vBw8eKD4+3uxYAADABFOnTpUkjRo1yuQkqAzs6QQAANzq0qVLSk1NdX4ODg5W3759TUwEAADMUlRUpD179uj69es6dOjQc/MREREmpIK7UDoBAAC3CgwM1N27d+Xn5ydJunfvnl5//XWTUwEAADNMmTJFW7Zs0Z07d7R69epyc4ZhUDp5GPZ0AgAAbjV58mSdOHFC3bt3lyQdPHhQYWFhqlu3riRp+vTpZsYDAAAmmDdvnmbOnGl2DLgZpRMAAHCrZcuW/e38hAkTKikJAAB4meTm5urixYuKiorSo0ePVFpaKl9fX7NjwYUonQAAAAAAQKVKSUnRqlWrVFpaqrS0NF26dElz5szR+vXrzY4GF2JPJwAA4Hbp6ek6d+6ciouLnWOscAIAoOrasGGDkpOTNWzYMElS06ZNdfv2bZNTwdUonQAAgFstWrRIZ86cUU5Ojnr06KG0tDSFhYWZHQsAAJioRo0aql27drmxatWqmZQG7mIxOwAAAPBshw4d0po1a1S3bl3NmTNH27Zt0+PHj82OBQAATOTr66vc3FwZhiFJ2rFjhxo2bGhyKrgaK50AAIBbWa1WVa9eXYZhqLS0VA0aNNCNGzfMjgUAAEwUHx+vqVOnKjc3V5GRkapVq5YSExPNjgUXo3QCAABuVbt2bdntdtlsNn366afy9/dn+TwAAFVcUFCQtm7dqsuXL8vhcCgoKIifDzwQb68DAABudfv2bdWpU0dlZWVat26dHj58qJEjRyogIMDsaAAAoJLl5OT87Xzz5s0rKQkqA6UTAABwqx07dmjAgAEvHAMAAJ4vMjJShmHI4XAoPz9f3t7ekqSHDx8qMDBQ+/fvNzkhXInH6wAAgFutX7/+uYKpojEAAOD5/iyV5s6dq+DgYEVHR0uSdu/erV9//dXMaHADSicAAOAWZ86cUVZWlu7du6ekpCTneGFhoUpLS01MBgAAzJaVlaVZs2Y5P/fq1Utr1641MRHcgdIJAAC4RUFBgc6ePSu73a6zZ886x2vXrq158+aZmAwAAJjNbrcrMzNTISEhkqTMzEzZ7XaTU8HV2NMJAAC4VXp6ut5++22zYwAAgJdIZmampkyZIi8vL0lScXGxFi9erODgYJOTwZUonQAAgFvZ7XatWrVKV69e1eLFi3Xx4kXl5uYqKirK7GgAAMBEJSUlys3NlcPhUNOmTWW1Ws2OBBezmB0AAAB4ttmzZ+vJkyc6f/68JKlhw4ZatmyZyakAAIDZysrKZLVaVb16df3222/KyckxOxJcjD2dAACAW2VnZ2v+/PlKT0+X9MeeTk+fPjU5FQAAMFNSUpIWLVokX19fGYYhSTIMQ2lpaSYngytROgEAALeqUaNGuc/FxcXi6X4AAKq2tWvXKjU1VY0aNTI7CtyI0gkAALhVSEiIEhMTVVJSooyMDK1bt06RkZFmxwIAACby9/encKoC2EgcAAC4VWlpqVavXq39+/dLkiIjIxUXF6dq1aqZnAwAAJglISFBRUVF6tOnj2rWrOkcb968uYmp4GqUTgAAwC2SkpL+dn7YsGGVlAQAALxsKlr1zJ5OnofSCQAAuEWrVq3Upk0btWjRosL5efPmVXIiAAAAVCZKJwAA4BbJycnavn27ioqKFBMTo759+8rHx8fsWAAAwER5eXnlPhuGIT8/v3KP2MFzUDoBAAC3unbtmlJSUrRr1y69+eabGjdunFq2bGl2LAAAYILQ0FAZhlHuTbaFhYVq166dFixYoMDAQBPTwdUonQAAgNs9fPhQqampSkhI0JQpUzRkyBCzIwEAgJdEWVmZNm/erPT0dK1cudLsOHAhSicAAOAWDodDR44c0bZt25Sdna3o6GjFxMSocePGZkcDAAAvoYEDByolJcXsGHCh6mYHAAAAnik8PFz+/v4aNGiQxo8fL8MwVFxcrJycHEm8EhkAAJRXVlZmdgS4GCudAACAWzz7KuT/u3cDr0QGAKBqstvtz43dv39fmzdv1rVr17R48WITUsFdKJ0AAAAAAEClaNWqVbn/jPrz7XWdO3fWzJkz5efnZ3JCuBKlEwAAAAAAAFzOYnYAAAAAAAAAeB5KJwAAAAAAALgcpRMAAKgSRowYoa1bt5od4/9l586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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# display average coefficient value vs phenotypic class bar chart\n", - "pheno_class_ordered = coefs.reindex(\n", - " coefs.mean().sort_values(ascending=False).index, axis=1\n", - ")\n", - "sns.set(rc={\"figure.figsize\": (20, 8)})\n", - "plt.xlabel(\"Phenotypic Class\")\n", - "plt.ylabel(\"Average Coefficient Value\")\n", - "plt.title(\"Coefficient vs Phenotpyic Class\")\n", - "plt.xticks(rotation=90)\n", - "ax = sns.barplot(data=pheno_class_ordered)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# display average coefficient value vs feature bar chart\n", - "feature_ordered = coefs.T.reindex(\n", - " coefs.T.mean().sort_values(ascending=False).index, axis=1\n", - ")\n", - "sns.set(rc={\"figure.figsize\": (500, 8)})\n", - "plt.xlabel(\"Deep Learning Feature Number\")\n", - "plt.ylabel(\"Average Coefficient Value\")\n", - "plt.title(\"Coefficient vs Feature\")\n", - "plt.xticks(rotation=90)\n", - "ax = sns.barplot(data=feature_ordered)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpret shuffled baseline model" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "shuffled_baseline_log_reg_model_path = pathlib.Path(f\"{results_dir}/1.shuffled_baseline_log_reg_model.joblib\")\n", - "shuffled_baseline_log_reg_model = load(shuffled_baseline_log_reg_model_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compile Coefficients Matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1280, 15)\n" - ] - }, - { - "data": { - "text/html": [ - "
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ApoptosisArtefactBinuclearElongatedFoldedGrapeHoleInterphaseLargeMetaphaseMetaphaseAlignmentPolylobedPrometaphaseSmallIrregularUndefinedCondensed
00.0259590.0000000.0736970.00.00.0000000.0638430.0000000.00.00.0096170.0000000.0229610.0000000.0
10.0000000.0000000.0211660.00.00.0210710.0000000.0201760.00.00.0000000.0000000.0000000.0051650.0
20.0054410.0000000.0335130.00.00.0282900.0000000.0560900.00.00.0000000.0437080.0006320.0000000.0
30.0000000.0000000.0000000.00.00.0172580.0000000.0000000.00.00.0000000.0000000.0258860.0000000.0
40.0000000.0091210.0000000.00.00.0000000.0000000.0084930.00.00.0000000.0000000.0000000.0000000.0
\n", - "
" - ], - "text/plain": [ - " Apoptosis Artefact Binuclear Elongated Folded Grape Hole \\\n", - "0 0.025959 0.000000 0.073697 0.0 0.0 0.000000 0.063843 \n", - "1 0.000000 0.000000 0.021166 0.0 0.0 0.021071 0.000000 \n", - "2 0.005441 0.000000 0.033513 0.0 0.0 0.028290 0.000000 \n", - "3 0.000000 0.000000 0.000000 0.0 0.0 0.017258 0.000000 \n", - "4 0.000000 0.009121 0.000000 0.0 0.0 0.000000 0.000000 \n", - "\n", - " Interphase Large Metaphase MetaphaseAlignment Polylobed Prometaphase \\\n", - "0 0.000000 0.0 0.0 0.009617 0.000000 0.022961 \n", - "1 0.020176 0.0 0.0 0.000000 0.000000 0.000000 \n", - "2 0.056090 0.0 0.0 0.000000 0.043708 0.000632 \n", - "3 0.000000 0.0 0.0 0.000000 0.000000 0.025886 \n", - "4 0.008493 0.0 0.0 0.000000 0.000000 0.000000 \n", - "\n", - " SmallIrregular UndefinedCondensed \n", - "0 0.000000 0.0 \n", - "1 0.005165 0.0 \n", - "2 0.000000 0.0 \n", - "3 0.000000 0.0 \n", - "4 0.000000 0.0 " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "coefs = np.abs(shuffled_baseline_log_reg_model.coef_)\n", - "coefs = pd.DataFrame(coefs).T\n", - "coefs.columns = shuffled_baseline_log_reg_model.classes_\n", - "\n", - "print(coefs.shape)\n", - "coefs.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Diagrams for interpreting coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# display heatmap of average coefs\n", - "plt.figure(figsize=(20, 10))\n", - "ax = sns.heatmap(data=coefs.T)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/roshankern/anaconda3/envs/2.ML_phenotypic_classification/lib/python3.8/site-packages/seaborn/matrix.py:654: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n", - " warnings.warn(msg)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# display clustered heatmap of coefficients\n", - "ax = sns.clustermap(data=coefs.T, figsize=(20, 10), row_cluster=True, col_cluster=True)\n", - "ax = ax.fig.suptitle(\"Clustered Heatmap of Coefficients Matrix\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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YPPPMM0ybNo3PPvuMpKQkfD4f69at44UXXgDglltu4b333uPAgQMkJyczYcIEOnXq5F/StmrVKn788Ue8Xi9paWn8+uuvxMTEnPPa/9WgQQNKlSrF+PHjcTqdpKWl8ccff2Rqd8MNN7B3714WLlyI2+3G7XazadMm/6bU55LVs/4vh8NBx44dee655yhfvjzXXHMNAMnJyVitVkqUKIHH42HKlCkkJSVlOUa1atVIS0vj+++/x+12M336dFwul//8Pffcw8SJE/2JvWPHjvk3lP/ll1/4559/8Hq9hIaGYrPZsp072cnN3835uphrn2s+Z/f7mzZtGikpKezYsYPPP/+cm266yX+ua9eufPHFF3z33XfZfpduuOEG4uLimDt3Li6Xi6SkJDZu3Jhl2yeffJIvvviCd955x1/BtG3bNh577LFMbVNSUjAMgxIlSgDw2WefZdi0ffny5f7nUrRoUQzDwGKxsGnTJjZu3Ijb7SY4OBi73X7ec0BERCS/KfkkIiKSjdNvzmrdujUzZsygb9++vPrqq/7zTz75JFWqVOGuu+6iYcOG9OnTJ8d7/Hz55Ze0bduWhg0b8r///Y/XX389y3YDBw5k+vTpREZG+t+IB+l/NG/fvj3bag1Ir1564403ePnll2natCmrVq1ixowZWe4PlZWOHTsyYcIEPvvsM1q1akXz5s2ZNGkS7dq1A+COO+6gS5cu9OzZk3bt2mG32/2JqXLlyjFt2jRmzpxJs2bNaN26NbNnz86wLDGnrFYrM2bMYN++fbRp04brr7+e5cuXZ2oXGhrK7NmzWbZsGa1ataJly5aMGzcuQ2LnbIYMGcIzzzxDZGRklm+7O+3WW28lOjo6w7Nv2bIl119/PR06dKBt27YEBgZmu3QtLCyMESNGMHz4cK6//nqCg4MpW7as/3zv3r1p27Yt/fr1IyIigrvuusu/D1BcXByPPPIIjRo14qabbiIqKuqsCcis5Obv5nxdzLXPNZ+z+65ERUVx44030qdPH/r160fLli395xo1aoTFYuHqq6+mYsWKWV43NDSUOXPmsGrVKlq0aEGHDh0yvKHvTA0bNuS9997jl19+oX379kRFRfHCCy/49wU7U82aNenXrx933303zZs3Z/v27TRs2NB/fvPmzdx5551ERETw4IMP8vzzz1OpUiWSk5MZPnw4UVFRtGnThmLFitGvX79zPj8REZGCZJhZ1YOLiIhIobZw4UIWLFjARx99VNChiBRKBw8epF27dvz1119nXa7Zu3dvOnfufF5LF0VEROT8qPJJRETkEpOSksKHH35I9+7dCzoUkUvapk2b+Pvvv+nUqVNBhyIiInJZU/JJRETkEvLjjz/SrFkzwsPDueWWWwo6HJFL1tNPP03fvn157rnn9KY4ERGRPKZldyIiIiIiIiIikmdU+SQiIiIiIiIiInlGyScREREREREREckz2b/64xJy/HgyPp9WD8rlKzw8lPj4pIIOQyRPaZ7LlUDzXK4EmudyJdA8l8udxWJQvHhIro13WSSffD5TySe57GmOy5VA81yuBJrnciXQPJcrgea5SM5p2Z2IiIiIiIiIiOQZJZ9ERERERERERCTPKPkkIiIiIiIiIiJ55rLY80lERERERETkUuf1ejh+PBaPx1XQocgVwmazU7x4KazWvE0PKfkkIiIiIiIiUggcPx5LUJCDkJCyGIZR0OHIZc40TZKTT3L8eCwlS5bL02tp2Z2IiIiIiIhIIeDxuAgJKaLEk+QLwzAICSmSL5V2Sj6JiIiIiIiIFBJKPEl+yq/5puSTiIiIiIiIiIjkGSWfRERERERERCRLJ0+epG3b5kyaND5Pr7Ns2WL27993UWMMG/YI0dEHcykiyU1KPomIiIiIiIhIlr75ZjlXX30N3377NW63O8+us2zZYg4c2H9RY4wbN5kKFSrmUkSSm/S2OxEREREREZFCxrb5HwI2bcuTsd0N6uK5pk6O2i5duoiHHnqU99+fy5o1P9CmTXtmz57J3r17SElxEhMTQ5UqVXj22RGEhobidDqZOPENtm79C4AOHW6iZ88+AAwZMpBateqwY8c/xMYepW3bGxk0aDBLly7in3+2MnHiOGbNms7gwY/SsGEk06e/xa+//gxAkybNefDBh7FarXz55ed8/PGHBATYMU0fo0a9RpUqVenWrTOvvz6B6tVrMmfO23z77dfY7YEYBkyePJOwsLA8eZ5ybko+iYiIiIiIiEgmO3Zs5+TJkzRq1Jhjx+JZunQRbdq0B2DTpg28++6HlCgRzpgxI5k79x2GDBnK3Lnv4PP5mDdvAU5nMoMG9aNGjVo0a9YCgL17dzNx4jRcLhcPPNCX+vUbcPPNXVi+fAn33NOLFi1aAfDFF5+yY8d25syZD6QvqVu06Atuu60b06ZNYt68BZQpUxaXy4XP58sQ98mTJ/noow9YsmQFgYFBOJ3J2O2B+fjk5L+UfBIREREREREpZDzX1MlxdVJeWbr0Szp2vBnDMGjdug0TJrxBbOxRAJo3b0WJEuEA3HJLVyZOfAOAdet+49FHh2EYBiEhobRv/3+sW/ebP/nUqdMt2Gw2bDYb7dr9H+vX/+5POJ1p3bpfuemmWwgICADgpps6s3r1Km67rRsNGzZmzJhRtGp1Pc2atcy01C4kJITKlaswatQLNGnSnObNW+FwhOTZc5Jz055PIiIiIiIiIpKB2+3mm2++YunSRXTr1pl7770Tj8fD8uVLMrU1TQDj9CcMI+N5478H/P3MM/plHvO//U5/HjPmDQYNeoiUlFQeeeQB1q79KUM7q9XKzJnv0q3b3cTGHqV//57s3LnjXLcseUjJJxERERERERHJYPXq76lcuSpffLGMTz9dzKefLmbChCksW7YYgJ9/XsPx48cBWL58MQ0bRgIQGdmEJUu+xDRNnM5kVq5cQWRklH/cr75ahsfjISUlhVWrVvr7hYSEkJyc5G/XuHETli1bjMfj8Se9IiOj8Hg8HDoUzVVX1adXrz5ERTVlx45/MsTudCaTkJBAREQj+vcfRPXqNdi9e1eePi85Oy27ExEREREREZEMli1bzP/9X6cMx+rXb4DP52Pjxg1ERjbm1VdHcehQNJUrV2HIkMcA6NNnABMmvE7v3t2B9A3HmzZt7h+jTp26DB36EHFxsbRp096/5K5Ll9uZOnUiH330Pg899ChdutzGwYMH6Nu3BwBRUc3o3Pk2vF4vr7zyEklJiRiGhTJlyvDAA0MyxJmUlMTzzz+Fy5WGz+ejdu26tG7dJs+elZybYabXuV3S4uOT8Pku+dsQyVapUmHExiYWdBgieUrzXK4EmudyJdA8lytBXs3zmJh9lC1bJdfHzW2zZ88kJSWFIUOGnle/IUMGZthUXAqHrOadxWIQHh6aa9fQsjuRQso4mUbADwfAq8SqiIiIiIiIXLq07E6kkLJtO0bgqv34yoVA2SIFHY6IiIiIiAgA/fsPuqB+U6a8ncuRyKVClU8ihZXTDYBtc1wBByIiIiIiIiJy4ZR8EimkDKcHANu2eMw0TwFHIyIiIiIiInJhlHwSKaQMpxvTAMPtw7v5aEGHIyIiIiIiInJBlHwSKaQMpwdf+VB8Rex4fz9c0OGIiIiIiIiIXBAln0QKKSPFjekIwHNNKXx/x0Gyu6BDEhERERERETlvSj6JFFKG0+NPPuEzsf2tjcdFRERERCR/nTx5krZtmzNp0viztvv44w85fvxYjsY8cSKBBx7oR58+Pfjww3kXFNeyZYvZv3/fBfWV/Kfkk0ghZTjdmA4bvjIOjLKhBOitdyIiIiIiks+++WY5V199Dd9++zVud+bVGD6fD9M0+fjjj3KcfFq37jfCwsKYO/dDevTofUFxLVu2mAMH9l9QX8l/toIOQESy4PZiuH2YjgAwDKyNy2Eu3oGRkIpZLKigoxMRERERkTxm/ftrrH8ty5OxvVffhPeqDjlqu3TpIh566FHef38ua9b8QJs27Zk9eybR0QdJSXESHX2Qdu3+j7i4WIYPfxq7PZARI0ZTsWIl3n57Gn/++Qdut4caNWrwxBPPsm3b30ydOgmnM5k+fXrw2GNPcuTIET755CM8nvTk1uDBQ4mMjAJg7949TJo0jmPH4jFNk3vu6YXP5+Off7YyceI4Zs2azuDBj9K4cZM8eVaSO5R8EimEDKcHANOR/hW1RpbDs3gHti1xuFtWLMjQRERERETkCrFjx3ZOnjxJo0aNOXYsnqVLF9GmTXsA/vxzPXPmzKdYsWIALF68kNGjx1K9ek0A5s59h5CQEGbNSl9WN23aZN5//10GDRrMgAEP8PPPPzJ69OtA+jK8G2/sgGEY7N+/l0cffYgvvliGx+PhmWeeYODAh2jbtr2/bdGixVi+fAn33NOLFi1a5fNTkQuh5JNIIWQ40zP+ZnAAAJaSDrwVw7BtilXySURERETkCuC9qkOOq5PyytKlX9Kx480YhkHr1m2YMOENYmOPAtCsWQt/4ikrP/20muTkZL7//jsA3G4XNWvWyrJtdPRBXnrpeWJjY7HZbBw7Fk98fBwnTpzA6/X6E08ARYtmf00pvJR8EimE/lv5BOBpUIrAZbsxYp2YpRwFFZqIiIiIiFwB3G4333zzFXZ7IF99tRQAj8fD8uVLAAgOPvvfJKYJTzzxDI0aNT7ntV566XmGDHmM66+/AZ/PR/v2LXG5XIB50fchhYM2HBcphIyUU5VPjgD/MW/ZEAAsCWkFEpOIiIiIiFw5Vq/+nsqVq/LFF8v49NPFfPrpYiZMmMKyZYuzbB8SEkJSUpL/c8uW17NgwXzS0lIBcDqT2bt3T5Z9k5KSKFeuPABLlnx5KvEElStXxWq18t133/rbnjiR4L9ecnJSprGkcFLySaQQOl35xBmVTwSl/2ykeQsgIhERERERuZIsW7aY//u/ThmO1a/fAJ/Px8aNGzK179btbsaMGUWfPj3Ys2c3PXv2oUaNWgwY0Jv77rubBx8cwL59WSefHnnkcZ57bhgPPtifw4cPUbRoUQBsNhuvvTaeL7/8jN69u3Pfffewdu1PAHTpcjtz575D3749+P33X3P57iW3GaZpXvJ1bPHxSfh8l/xtiPgFfL+fwO8PkPRCM7BaKFUqjLidcYRMWEdq5xp4GpUt6BBFcl2pUmHExiYWdBgieUrzXK4EmudyJcireR4Ts4+yZavk+rgiZ5PVvLNYDMLDQ3PtGqp8EimEDKcHM8gK1n+/omagNf1cqiqfRERERERE5NKh5JNIIWQ43f433fnZrZiAkeYpkJhERERERERELoSSTyKFkOH0ZHjTHQAWAwKtoD2fRERERERE5BKi5JNIIWSkuDO86e40M8imZXciIiIiIiJySVHySaQQyrLyifR9n7TsTkRERERERC4lSj6JFEKGM+vKJwKtoMonERERERERuYRkLq0QkYLl9mK4fdkvu0tyFUBQIiIiIiJypenWrTN2ux27PRCXK41rr43giSeeYcmShaSlpdG9+725fs1lyxbz888/Mnr067k+thQcJZ9EChnDmb6szgzOetmdJV6VTyIiIiIikj9Gjx5L9eo18Xq9DB58Pz/88B233tqtoMPKMY/Hg82m1EdB029ApJAxnG4ALbsTEREREbmCJe35msTdy/Jk7LDqNxFarcN59XG5XLhcaYSFFWH27JmkpKQwZMhQli1bzDfffEVYWBF2795FWFgoo0e/Tnh4yQztgAyf3W43M2dO5ddff8ZisVK+fAVefXVcpusuX76Ezz//BK/XS2hoKMOGPUPlylXZtWsn48e/RmpqCi6Xiy5dbuOuu3oA8MorL+FwODhw4AAJCceZM+eDi35mcnGUfBIpZIyUU5VPWW04HmTThuMiIiIiIpJvhg9/Grs9kOjog0RFNSEqqimbN2/M0Gbr1r95772PKFOmLGPHjubTTxcwaNDgs477/vvvcuhQNHPmzCcgIICEhIRMbTZu3MB3333D1KmzsNvtrF37E6++Oorp0+dQrlw5Jk6cht1ux+l0MnDgfURFNaNq1WoAbNmymSlT3iY4ODjXnoVcOCWfRAqZ05VPZLXnU6AVw2uCxwc2vS9ARERERORyFVqtw3lXJ+WF08vu0tLSGD78KT7++MNMbRo0uJYyZcoCcPXV9fn991/POe7PP69hyJChBASk/91TrFixTG1++mk1O3fuYODAPgCYpkli4kkAUlNTmTLlNXbu3I5hWIiLi2Xnzu3+5NMNN7RT4qkQUfJJpJDx7/mUReUTgenHjFQPZqg9P8MSEREREZErWGBgIM2bt+Lnn3+kbt2rMpyz2//928RiseL1pm8VYrVaMU2f/5zL9e/Lk0zTPOc1TRNuvrkLAwY8kOnczJlTKVEinDlz5mOz2XjsscEZxnc4lHgqTFQ6IVLYnN7zKasNx4Os6T+kad8nERERERHJPz6fjz///INKlSrnuE+FChX5559t+Hw+nM5kfv75R/+5Fi1a8fHHH+F2p//9k9WyuxYtWvHVV0s5evQIAF6vl23btgKQlJRI6dJlsNls7N69k40b/7zwm5M8p8onkULGcHowA61gzZwbNgPTk09Gqodz/zuBiIiIiIjIxTm955PH46ZatRr06XM/n3zyUY763nBDO7777lt69bqLChUqUadOPf+5nj37MHPmFPr27YHNFkDFihUZPfr1DP2vu64hAwc+xDPPPI7X68PjcdOmTXvq1q3Hfff15+WXX2TFiuVUqFCB666LyNX7ltxlmDmpdSvk4uOT8Pku+dsQASDw03+wRifhfLSR/1ipUmHExiZi2XsCx9wtpPS+Gm/1YgUXpEgeOD3PRS5nmudyJdA8lytBXs3zmJh9lC1bJdfHFTmbrOadxWIQHh6aa9fQsjuRQsZI8WS93xNAoJbdiYiIiIiIyKVFySeRQsZwujGzeNMdgHnGhuMiIiIiIiIilwIln0QKGcOZfeXT6Q3HDVU+iYiIiIiIyCVCySeRQuZslU9adiciIiIiIiKXGiWfRAoTtxfD7cMMzmbPJ6sF02bRsjsRERERERG5ZCj5JFKIGM70pFK2lU+kL73TsjsRERERERG5VCj5JFKIGCnnTj4RaANVPomIiIiIiMglIpu1PSJSEAynGyDbDcdBlU8iIiIiIpI/unXrjN1ux24P9B979dVx3HlnF1asWI3D4cjXeHbs+If9+/fTrt2N59338OFDDBjQi6VLV+ZBZHIuSj6JFCKnk0+cbdldoJJPIiIiIiKSP0aPHkv16jULOgwAduzYzs8//3hByScpWEo+iRQm/j2fzvLVDLRhnHTmU0AiIiIiIlIQjhz4iiMHlufJ2GUqdaJMpY65MtbWrX8xceI4UlNTCAoKZujQYdSrd7W/0qhLl9v55ZefSE1N5ZlnXuTaa68D4LPPFvDJJ/8jNDSMZs1a8PnnH7N06Uo8Hg9PPTWUEydOkJaWxlVXXc2TTz6H05nMO+/MwOlMpk+fHlx3XQRDhz7JX39tYcaMt0hOTgZgwIAHaN685alrfMzHH39IeHhJIiIa5cr9yoVR8kmkEPEvu8vubXekL7tDlU8iIiIiIpIPhg9/2r/szmq1Mnv2+/5zbreb559/imeffZHGjZuwbt1vPP/8UyxYsBCAEydOUL9+AwYNGsyKFcuZMWMy06fPYefOHbz//lzeffdDihcvzqRJ4/1jWq1WRowYTdGixTBNk9GjR7B06Zfcems3Bgx4gJ9//pHRo18HIDExkXHjxvDGG5MpWbIkcXFx3H9/b+bNW8CRIzHMmzeHd9+dT4kS4Ywb91r+PTTJRMknkULEcHowA61gzf5dAGagDSNNG46LiIiIiFzOylTqmGvVSRfjbMvu9u/fR0BAAI0bNwEgMjKKgIAA9u/fh8PhIDjYQYsWrQC4+uprmDJlIgAbNvxBs2YtKF68OAA33dSZFSuWAeDz+fjoow/45Zef8fm8JCYmEhQUlOX1t2zZyOHDhxg27BH/McMwiI4+wObNm2jevCUlSoQD0LXrbaxa9c3FPxC5IEo+iRQiRor77G+6Awi0Yrh84DPBYuRPYCIiIiIiIv9hmiaGkflvktOH7PZ//7axWCx4vR5/P8j6b5lvvvmKTZv+ZNq0WTgcIcybN4cDB/Znc32oUaMWU6fOynRu06aN53k3kpeyL68QkXxnOD1n3++JU8vuQEvvRERERESkQFWpUhWXy8X69esAWL9+HR6Ph0qVqpy1X0REI3755ScSEhIA+OqrJf5zSUmJFC1aDIcjhKSkJL755iv/uZCQ9GOn1a/fgIMH9/uvD+l7UJmmScOGkaxd+xPHjx8DYMmSLy/6fuXCqfJJpBAxnG7MUPtZ25iB6V9bI9Vz1r2hRERERERELtaZez4BPPPMcP/PAQEBvPLK6xk2HB89eiwBAWdfzVGrVm169OjNAw/0pUSJcCIjowgJCQWgY8db+PHH1fTseRelSpXi2msjSEtLA6BRoyg++ugD7rvvHiIiGjJ06JO89tqbTJ06iUmTxuPxuClfvgJjx06gZs1a9OrVlwcf7E+JEuE0a9YyD56O5JRhpte7XdLi45Pw+S752xDBMWEd3qpFSLutdobjpUqFERubCID17ziCP/4H5wPX4SsbUhBhiuSJM+e5yOVK81yuBJrnciXIq3keE7OPsmXPXjV0uXA6k3E40v+emT17JtHRB3nxxZcLOKorU1bzzmIxCA8PzbVrqGxCpBAxnG7M4HPt+XTqa6tNx0VERERE5BI1ffoUNm/e6K9Weuqp5ws6JMlDSj6JFBZuL4bbd+49nwLT93wyUrXnk4iIiIiIXJqeeOLpgg5B8pE2HBcpJAznqTc/nONtd6c3HDe04biIiIiIiIhcApR8EikkjJTTyadzFCRq2Z2IiIiIiIhcQpR8EikkDKcbyEHlk5bdiYiIiIiIyCVEySeRQuJ08ongc1Q+BVgwLQaGKp9ERERERETkEqDkk0hhkcM9nzAMCLSC9nwSEREREZE81K1bZ3r0uIM+fXrQp08PJk8en23b9evX0b9/ryzPHT58iJtvbnfe1z/bmHJp0dvuRAoJ/7K7c1U+AWaQTcvuREREREQkz40ePZbq1WsWdBhyiVPySaSQMNK8mDYL2M5dkGgGWrXsTkRERETkMrb70FfsPLQ8T8auWb4T1ct3vKC+v/zyMzNnTsHn81GsWHGefPI5KlaslKndZ599zMcff0h4eEkiIhplOLd27RrmzZtDWpqLgIAAHn74cerXvwaAt9+exsqVKyhVqjT16l19QTFK4aPkk0ghYbh9YM/hSthAqyqfREREREQkzw0f/jR2eyAAvXr1YcKE13nrrbepVq06S5YsZOTI4cya9V6GPjt37mDevDm8++58SpQIZ9y41/znoqMPMnfubN588y1CQkLZvXsXw4Y9wuefL2XNmtX89NNq3n33QwIDA3n22WH5eq+Sd5R8EiksXF7MAGuOmppBNoyEtDwOSERERERECkr18h0vuDopN5257G7NmtXUqFGbatWqA3DTTV0YP34sTmdyhj4bNvxB8+YtKVEiHICuXW9j1apvAPj117VERx9k8OCB/vZer5djx+LZsGEdbdveiMPhAOCWW7ry3nuz8/weJe8p+SRSSBhuH2YOK5/MQCsWLbsTEREREZF8ZWIYOWhlmmc916RJM154YdR59ZNLW7687e748ePcf//9dOjQgc6dOzNkyBCOHTsGwJ49e+jevTsdOnSge/fu7N27Nz9CEil8XF7IaeWTlt2JiIiIiEg+u/rqBuzcuZ19+/YCsHz5EmrVqoPDEZKhXcOGkaxd+xPHj6f/3b9kyZf+c1FRTfn117Xs3r3Lf2zr1r8AaNQoiu+++5aUlBS8Xi/Lli3K4zuS/JIvlU+GYTBgwACaNGkCwNixYxk3bhxjxoxhxIgR9OjRg65du/Lll1/y4osvMm/evPwIS6RQOZ/KJ4JskOYB0yRH//QgIiIiIiJykYoXL87w4aMYOfJ5vF4vxYoV58UXX87UrmbNWvTq1ZcHH+xPiRLhNGvW0n+uUqXKvPjiy7z22sukpaXh8bi55pprqVfvalq0aMWWLZvo27cHJUuWIiKiEbGxsfl5i5JHDLMA6tq+/vprPvroI8aPH0+HDh349ddfsVqteL1emjRpwooVKyhRokSOx4uPT8LnU3meXNqCZ/yJGWYn9d6rMp0rVSqM2NhE/+eANQcJ/HYfSc81BXvOqqVECrv/znORy5HmuVwJNM/lSpBX8zwmZh9ly1bJ9XFFziareWexGISHh+baNfJl2d2ZfD4fH330EW3btuXw4cOUKVMGqzX9j2er1Urp0qU5fPhwfoclUuAMtxczh4kkMzC9nZGqfZ9ERERERESkcMv3DcdffvllHA4HPXv25O+//86VMXMzGydSUFI8JgFFAilSKizL86XOOO4pnYgbKBEShKWU5r9cPkplM/9FLiea53Il0DyXK0FezPOjRy3YbPleIyJXOIvFkuf/3c7X5NPYsWPZt28fM2bMwGKxUK5cOY4cOYLX6/Uvuzt69CjlypU7r3G17E4uByGpHlK8Pk5kUb7737Jea5qbYOD4oZP4rJr7cnnQMg25Emiey5VA81yuBHk1z30+Hx6PL9fHFTkbn8+XaT5fssvuJkyYwJYtW5g6dSp2ux2A8PBw6tWrx5IlSwBYsmQJ9erVO6/9nkQuG24f5HDDcTMoPW9spGnZnYiIiIiIiBRu+VL5tGPHDmbMmEHVqlW5++67AahYsSJTp07lpZde4plnnmHatGkUKVKEsWPH5kdIIoWLx4fhMzEDcrh5+Kk9n0jz5l1MIiIiIiIiIrkgX5JPtWrV4p9//snyXI0aNfjkk0/yIwyRwst9qrQ2p5VPgacqn7ThuIiIiIiIiBRy2slMpBAwXOkVTDmtfDKDTr3tTpVPIiIiIiIiUsjl+9vuRCQL7lNJpBxWPmFX8klERERERPKex+Phvfdm8+23X2O12rDZbFSsWJH+/R+gWrXqBR2eXCKUfBIpBAxX+rK7HO/5ZDEw7VbQsjsREREREclDY8aMJDU1lbfffo+wsDBM02TVqpXs27cnQ/LJ5/NhGAaGYRRgtFJYKfkkUhj4K59ymHwifemdKp9ERERERC5Pa2O/4qejy/Nk7BalO9GsVMdztjtwYD+rV6/i88+XERYWBoBhGLRt2x6A2bNnEh19kJQUJ9HRB5kyZRbz5s3hzz/X43a7KVasGM8++yJly5bj8OFDDBjQi06dOrNx43rS0tJ44olnuPbaiPT7XbuGefPmkJbmIiAggIcffpz69a/Jk/uX/Kfkk0gh4K98yumyO9I3HdeG4yIiIiIikle2b/+HihUrU6RIkWzb/PnneubMmU+xYsUA6NmzD0OGDAVg8eKFTJ8+mZEjXwXgxIkT1KhRkyFDhrJhwx+89NLzLFiwkNjYo8ydO5s333yLkJBQdu/exbBhj/D550vz+hYlnyj5JFIYnNpwnJwuuwMIsoIqn0RERERELkvNSnXMUXVSftqzZzcjRw4nNTWVpk2bExYWRrNmLfyJJ4BffvmJzz//hJQUJ15vxr9XAgIC6NDhJgAiIhoRGBjI/v372LTpT6KjDzJ48EB/W6/Xy7Fj8ZQoEZ4v9yZ5S8knkULAcF9I5ZMVw6nKJxERERERyRu1a9fh4MH9JCYmEhYWRrVq1Zk790M++2wB27ZtJSwsjOBgh799TMxh3nrrTWbNmkf58hXYvHkjI0cOz3Z80zQxDAPTNGnSpBkvvDAqP25LCkDO/9IVkbxzAZVPWnYnIiIiIiJ5qVKlyrRs2ZqxY0eTlJTkP56SkpJl++TkZGy2AMLDw/H5fCxc+FmG8263m2+++QqAjRs34HK5qFy5ClFRTfn117Xs3r3L33br1r/y4I6koKjySaQQME5tOH4+lU9adiciIiIiInnt+edfYu7cdxgwoDc2m42wsDBKlixFz559WLPmhwxta9SoSZs27enZsztlypQhIqIRGzdu8J8vWrQoBw8e4P777yMtLZWXXnqFgIAAKlWqzIsvvsxrr71MWloaHo+ba665lnr1rs7v25U8YpimaRZ0EBcrPj4Jn++Svw25gtlX7cf+wwGSXmwOlsyvJi1VKozY2MSMfVbsJeC3wyQPb5ZfYYrkqazmucjlRvNcrgSa53IlyKt5HhOzj7Jlq+T6uIXB6bfdLV26sqBDkf/Iat5ZLAbh4aG5dg0tuxMpDNxeTJsly8RTdsxAK4bHBx5fHgYmIiIiIiIicnGUfBIpBAyXDwLO8+sYdGp/KC29ExERERGRQq5cufKqerqCKfkkUhi4vZj2nG82DukbjgPadFxEREREREQKNSWfRAqBC6l8Mk9VPhmqfBIREREREZFCTMknkcLgAiqfCDydfFLlk4iIiIiIiBReSj6JFAKGy4dpP8/Kp1PL7khV5ZOIiIiIiIgUXko+iRQGLi8EnO+eT1p2JyIiIiIieadbt87s3r0zw7H+/Xuxfv268+4nVzYln0QKAcPtPe/KJ04v03Mr+SQiIiIiIiKFl62gAxARwOU7/8qnUxuUG25fXkQkIiIiIiIF6Nujm1lxdFOejP1/pRvQvvQ1FzXGsWPxvPHGqxw6dBDTNLnnnl506nRLpnZxcXFMnPg6R47EkJaWRvv2Hejdu99FXVsuPUo+iRQCxoVsOH46WaXkk4iIiIiI5JHhw5/Gbg/0fz5wYB8AEyeOo3r1Grz66jji4uLo3/9e6tSpS/XqNTP0Hz36Rfr0GcB11zXE7Xbz6KMPUq/eVTRu3DRf70MKlpJPIoWBywcB57nszmpgWg0Ml5bdiYiIiIhcbtqXvuaiq5Nyw+jRYzMklPr37wXAunW/MWTIUABKlixJs2YtWb9+XYa2KSkpbNjwBwkJCf5jTmcye/fuVfLpCqPkk0hB8/owfOb5Vz5BesJKlU8iIiIiIlIADMM462fT9GEYBu+8Mw+bTemHK5k2HBcpaK5TyaPzrXwCzAArhjYcFxERERGRfBYZGcWiRV8AEB8fx9q1PxEREZmhjcMRwrXXRvDBB3P9x44ciSE+Pi4/Q5VCQKlHkQJ2OnmkyicREREREblUDB06jDfeGMN9992NaZo88MAQqlevkandiy++zOTJb9K7d3cgPSH17LMvEh5eMr9DlgKk5JNIQTtd+WS/gMonu1V7PomIiIiISJ749NPFmY7Nnv2+/+dXXx1/zn7h4SUZOXJM7gcnlxQtuxMpYP7KpwBVPomIiIiIiMjlR8knkYJ2unLpApbdpe/5pOSTiIiIiIiIFF5KPokUMOPUsjvzAjYcT6980rI7ERERERERKbyUfBIpaO6LqHyyW/zJKxEREREREZHCSMknkQJ2cZVPVlU+iYiIiIiISKGm5JNIQbuYyqcAi/Z8EhERERERkUJNySeRAnbxlU9KPomIiIiIiEjhpeSTSEG72D2ffCZ4lIASEREREZHc1a1bZ3bv3nnWNsuWLWb//n0Fdn25NCj5JFLADJcP02qAxTj/zqerpVT9JCIiIiIiBWDZssUcOLD/vPv5fD5M08yDiKQwshV0ACJXPLf3gqqeAMyA9H6G24sZrK+ziIiIiMjlYuWRPXxzZHeejH1jmeq0K1Mtx+2HDBlIvXpXs2XLJuLi4mjbtj0PPvgwS5cu4p9/tjJx4jhmzZrO4MGP0rhxE+bPf4/vv1+J1+ulZMnSPP3084SHl2T27JlERx8kJcVJdPRBpkyZRb9+99K+fQc2b95IXFwsd911D3fc0d1/7e+++5axY18hPj6Oe+7p6T83ZcpE/vxzPW63m2LFivHssy9Stmw5jh8/xksvDef48XgAIiOjeOSRJwCyjUvynv5aFSlghsuHeYHJJ1U+iYiIiIhIfjhyJIapU2fhdDrp3r0rt9zSlZtv7sLy5Uu4555etGjRCoCvv17GwYMHmTlzLhaLhS+++JQpUyYyYsRoAP78cz1z5synWLFi/rGPHYtn6tRZHDsWT9++93LttQ2pWbMWAKmpqcyc+S6HDx+id+/udOrUGYfDQc+efRgyZCgAixcvZPr0yYwc+SorViynbNmyTJo0DYCTJ0/mKC7JW0o+iRQww+X9N4l0nk4nrQyXFxWsioiIiIhcPtqVqXZe1Ul5rU2bdlgsFkJDQ6lSpRrR0QepVKlypnZr1qxm27at9OvXEwCv10NoaKj/fLNmLTIkngBuuaUrACVKhNO8eUs2bPjDn3xq3/7/AChXrjxhYUWIjT1KlSpV+eWXn/j8809ISXHi9Xr9Y1199TUsWPAhU6dO4rrrGtKkSbMcxSV5S8knkYLm9qrySURERERECjW7PdD/s8ViyZDwOZNpmtx3Xz9/Qum/goMdZ72OaZoYZ2yHa7fb/3NdDzExh3nrrTeZNWse5ctXYPPmjYwcORyA+vUb8O678/n991/5+utlfPDBXKZPn33OuCRvacNxkQJmuHwXXvl0qp+h5JOIiIiIiBSAkJAQkpOT/J9btryeL7741L/czeVysWPH9rOOsXz5EgCOHz/OL7/8TERE5FnbJycnY7MFEB4ejs/nY+HCz/znDh2KJiQklPbtO/Dww4/xzz/b8Pl8FxSX5B5VPokUNLcXM9R+7nZZObXhOO6s/9VBREREREQkL3XpcjtTp07ko4/e56GHHqVjx5s5cSKBhx8eCKS/1e622+6kVq3a2Y5RpkxZHnpoAPHxcfTq1YcaNWqe9Zo1atSkTZv29OzZnTJlyhAR0YiNGzcAsGHDH/zvfx9gtdowTR9PPvksFovlguKS3GOYl8G7DePjk/D5LvnbkCuU4631eMs4SLurbobjpmli4sNiWClVKozY2MRMfY1YJyFTN5B6e208DUrlV8gieSK7eS5yOdE8lyuB5rlcCfJqnsfE7KNs2Sq5Pm5h1q1bZ15/fQLVq5894SR5J6t5Z7EYhIfn3p5YWnYnUtDcXshiz6e1f7/G178/hNvjzL6vXZVPIiIiIiIiUrgp+SRSwAyXz79305mOJmwm/uQ2ftw8Ep/Pk2Vf7fkkIiIiIiKXsk8/XayqpyuAkk8iBS2Lyievz0VyymGKhlTjUPwvfL9+HFmukPXv+aTkk4iIiIiIiBRO2nBcpCB5TQyvifmf5FOi8xAmPupXvZdjiTvYuGMBNqM09SrfmbG/zcAEDJeW3YmIiIiIiEjhpOSTSEE6vVfTf5bdnXTuB6BISCXKFrmatPg/iN48Bd/xXVx97TP/NjQMsFtU+SQiIiIiIiKFlpJPIgXodMXSfyufEk5up6gb9m14A+fJnQAEYnDk4FeUrXY74UX+fR2oGWDF0IbjIiIiIiIiUkhpzyeRguQ6VbH0n8qnxOgfKO4Bq8VO1XoP8H/dFlG6UgdsPpOt+xZkHCNAlU8iIiIiIpL7unXrzO7dOws6DLkMqPJJpACdrlj6b+WTJzUeI8DBta2mAxBSJIzQIjWJBZKd0RnamgFW7fkkIiIiIiKFisfjwWZTykHSaSaIFKRsKp9MtxNbSPkMx4JDKqZ3SYnJOIb2fBIRERERueysPHKIb2IO5cnYN5YtT7sy5c/dMAtTpkzkzz/X43a7KVasGM8++yJly5bj8OFDDBjQi9tvv4t1636jQ4dOXHttQ8aMGUlqagq1atXh4MED3Hdff1q0aEVcXBwTJ77OkSMxpKWl0b59B3r37pfLdyqFhZJPIgUoq8qnlNR4rKZJkKNshrZBIRUA8KYl4PN5sFjSv75mgAVDyScREREREckHPXv2YciQoQAsXryQ6dMnM3LkqwCcOHGCqlWr0b//IAD69etJ9+496NDhJrZt+5uBA/v4xxk9+kX69BnAddc1xO128+ijD1Kv3lU0btw0v29J8oGSTyIFKYvKp2PHN2MAjrCqGZoGOcoBBjafSYornpCgMqf6WiHJlS/hioiIiIhI/mhX5sKrk/LSL7/8xOeff0JKihOvN+P2H3Z7IG3b3ghAcnISe/bs4sYbOwJQt+5V1KhRE4CUlBQ2bPiDhIQEf1+nM5m9e/cq+XSZUvJJpABlVfl04sTfABQrWjdDW4slAFtgcWyeYySnHvEnn8wACxaXKp9ERERERCRvxcQc5q233mTWrHmUL1+BzZs3MnLkcP/54OAgDMMAwDTBMAz/5zOZpg/DMHjnnXnaF+oKobfdiRSk0xuFn1H5lJS4B4Bixa7O1DwopDwBPkhOPfLvQbsV3NpwXERERERE8lZycjI2WwDh4eH4fD4WLvws27ahoaFUrVqNb775GoB//tnG7t27AHA4Qrj22gg++GCuv/2RIzHEx8flafxScJRiFClAp/dqOrPyKdV5CAMy7fkE4AitysljW0hK+Tf5pD2fREREREQkrwwdOhir9d+/V9q0aU/Pnt0pU6YMERGN2LhxQ7Z9hw8fyauvjuJ///uAOnXqUaNGLUJDQwF48cWXmTz5TXr37g6kJ6SeffZFwsNL5u0NSYFQ8kmkIJ2ufDoj+eRJPUaALQjDsGZqHhJWFQvgdB7492CAVW+7ExERERGRXPfpp4uzPD506DD/z6c3Fy9XrjxLl67M0K5cuQq8/fZ7GIbBnj27efjhQVSrVgOA8PCSjBw5Jo8il8JGySeRAmS4fZhWAyzp66B9phfTk4ItOHPVE0DwqTfeORPPTD5ZMDw+8Jn+cURERERERAra5s0bmTp1EmAC8PTTz1OkSJGCDUoKhJJPIgXJ5c1Q9ZScEoPNZxKYxZI7gKCQiundzlx2Zz+1X5TbB4GZq6VEREREREQKQlRUU6Ki9PY60YbjIgXKcPkwz9hs/PjJ7VgBR2iVLNsHOcoB4E078e/BgPSEk6FNx0VERERERKQQUvJJpAAZ7oyVTycS/gagaJE6Wba3WAKw2MOw+Ny4PEkA/yavtO+TiIiIiIiIFEJKPokUpP9UPiUl7gYgrGjNbLvYg0oT4IPk1FNL7/yVT0o+iYiIiIiISOGj5JNIAfpv5VOq8xAAQY7y2fYJCqlAgAlJzhjgjD2fXFp2JyIiIiIiIoWPkk8iBcnlxQz4N/nkTo0HSwC2gNBsu4QWqYEFSErel37gVOWUKp9ERERERESkMFLySaQAGW4fnKpccnucGJ40rIHFztonrEgtAJJPLdHzJ6+04biIiIiIiOSibt0607VrB7zef//WWLp0ES1bRvLZZwvO2nf16u/5++8tFx3D7NkzmTJl4kWPIwVLySeRguTyYp5adnfSeQCbCYHBZc7aJTi0EgCpydHpB7ThuIiIiIiI5JHw8JL89tta/+fly5dQp069c/b78cfv2br1r7wLTC4ptoIOQORKZrj/3XD8RPJebCY4QquctU+Qoxwm4E6JA/Anrwzt+SQiIiIictn4LuY43x4+nidjty9XnLZli+eobadOnVm2bAnNmrXk0KFo0tJSqV69BgBut5u3357Gn3/+gdvtoUaNGjzxxLNs3ryRNWtWs27dbyxe/CXdu/cgKqopL730PMnJybhcLpo3b8FDDz0KpFc37d27h5QUJzExMVSpUoVnnx1BaGj6diSxsUcZNuwRDh2KpkKFirz88liCgoJYt+43Zs2ajsuVhtfrpXfvfrRv3wGAOXPe5ttvv8ZuD8QwYPLkmYSFhfHXX1uYMeMtkpOTARgw4AGaN2+Z249Y/kPJJ5GC5Pp3w/ETJ/7BAIoUrX3WLhZLAIYtGJ/rRPoBVT6JiIiIiEgeadgwki+++ISTJ0+yfPkSOna8mW3btgIwf/57hISEMGvWPACmTZvM+++/y6BBg2nZ8nrq1q3HHXd0ByAtLY2xYyfgcDjweDw8/vgQfvnlZ5o2bQ7Apk0bePfdDylRIpwxY0Yyd+47DBkyFIB//tnKrFnzCA0N5fHHh7BixXK6dLmN2rXrMm3aO1itVo4di6d//15ERTUD4KOPPmDJkhUEBgbhdCZjtweSmJjIuHFjeOONyZQsWZK4uDjuv7838+YtICwsLJ+f7JVFySeRguI1Mbymv/Ip6eRuDMARUvGcXa2BxTCch/H5PFhO7flkaM8nEREREZHLRtuyOa9OykuGAW3b3sjKlStYuXIF06fP9ieffvppNcnJyXz//XcAuN0uatasleU4Pp+PadMmsXnzJsAkPj6eHTu2+5NPzZu3okSJcABuuaUrEye+4e8bFdXUnxy66qr6REcfBCAh4TivvjqKgwf3Y7XaOHnyBPv376NevauoXLkKo0a9QJMmzWnevBUORwhbtmzk8OFDDBv2yBn3ZxAdfYC6da/K3QcnGSj5JFJQTieLTlU+pSRH4wCCHOXP2dUeXAZ38mGSU2MJCyp7ajxVPomIiIiISO7r1OkWBg3qw3XXNaRo0WL+46YJTzzxDI0aNT7nGAsWzCcx8SRvvz2XwMBAxo59BZcrLcu2pglg+D/b7YH+ny0Wi38D9PHjX6NFi+sZM+YNDMPg7rtvx+VKw2q1MnPmu2zevJH169fRv39Pxo9/C9OEGjVqMXXqrAt6DnLhtOG4SAE5vUeTGWDBNH240+IBA3tw6XP2DQ6tjAVIStoNFgPTZsFwKfkkIiIiIiK5r0KFitx//0Pcd9+ADMdbtryeBQvmk5aWCoDTmczevXsACAkJISkpyd82MTGR8PCSBAYGEht7lDVrfsgw1s8/r+H48fQ9rpYvX0zDhpHnjCsxMZFy5cphGAa///4L0dEH/HEkJCQQEdGI/v0HUb16DXbv3kX9+g04eHA/69ev84+xdetfmOnZLslDqnwSKSinK5XsVpxpcVh9Xiz2olgs5/5ahhWpwTHgxIntlCvdIn3fJy27ExERERGRPNK16+2ZjvXs2YfZs2cyYEBvLBYLYNCv3/1UrVqNDh1u4pVXRrJq1Uq6d+/BnXfezQsvPE3fvj0oXbpMpmqpyMjGvPrqKA4diqZy5SoMGfLYOWN68MEhjB8/lg8+eI8aNWpSo0b6kr+kpCSef/4pXK40fD4ftWvXpXXrNgQGBvLaa28ydeokJk0aj8fjpnz5CowdOwHDMM5xNbkYhnkZpPji45Pw+S7525ArjOVwEo6ZG0m5qy4Hy+7l758fp1iRWjS6/p1MbUuVCiM2NtH/OfHEDjauHoCjwg00bDgSx5u/461ejLRbs15fLXIp+O88F7kcaZ7LlUDzXK4EeTXPY2L2Ubbs2d9+fTmaPXsmKSkp/g3GJX9lNe8sFoPw8NBcu4aW3YkUFH/lk4WTyfuxmeAIzdn/0ISEVcUEUp2H0g8EWFX5JCIiIiIiIoWSlt2JFJB/93yycjJpH1YgNKxajvpaLAGYlgDcqfHpY9i155OIiIiIiFya+vcfVNAhSB5T5ZNIQTmj8intVAVTUMi533R3mmEPwXSfKvUNsOptdyIiIiIiIlIoKfkkUkD8lU92K67UOACCHDlPPlkDS2DxuPD5fJgBFgwtuxMREREREZFCSMknkYJyulIpwII3LQGAIEe5HHcPcpTDAqSkHDr1tjtVPomIiIiIiEjho+STSAE5s/LJdCeDxYYtoEiO+5/enDzh+BZMu9U/noiIiIiIiEhhouSTSEE5nXyyWcCTiiUgDMMwctw9rGhtABJP7lTlk4iIiIiI5Lpu3TrTtWsHvN5//6F76dJFtGwZyWefLThr39Wrv+fvv7dcdAyzZ89kypSJFz1OTu3du4eWLSP5+OMPMxx/5ZWX/Pe8cOGnLFgwP99iulDLli1m//59BR0GoOSTSIEx3D5Mq4HLTMZmgi2w+Hn1L1asHgDO5AOYAVbt+SQiIiIiIrkuPLwkv/221v95+fIl1KlT75z9fvzxe7Zu/SvvAssjS5Z8SaNGjVm6dFG2bW69tRvdu9+bj1FdmGXLFnPgwP6CDgMAW35cZOzYsXz99ddER0ezePFiatdOr9ho27YtdrudwMBAAIYNG0arVq3yIySRgufyQoCV1LRjBJgQEFz6vLo7gkrjBVyp8f9WPpkmnEf1lIiIiIiIFE6ror2sjM6b1Q3tKlhoU8Gao7adOnVm2bIlNGvWkkOHoklLS6V69RoAuN1u3n57Gn/++Qdut4caNWrwxBPPsnnzRtasWc26db+xePGXdO/eg6ioprz00vMkJyfjcrlo3rwFDz30KJBe3bR37x5SUpzExMRQpUoVnn12BKGhoQDExh5l2LBHOHQomgoVKvLyy2MJCgpi3brfmDVrOi5XGl6vl969+9G+fQcA5sx5m2+//Rq7PRDDgMmTZxIWFsZff21hxoy3SE5OBmDAgAdo3rwlAB6Ph2++Wc7Uqe8wbNgjbNv2N3XrXpXpmcyePZOUlBSGDBmK2+3mzTdfZ8OGPyhevDi1atXm2LF4Ro9+nWXLFvPNN18RFlaE3bt3ERYWyujRrxMeXtJ/LjQ0jF27dlCqVGmGDn2SadMmceDAAerVu4oXX3wZwzBITk7irbcmsGvXDlwuFxERkTz88GNYrVaGDBlIvXpXs2XLJuLi4mjbtj0PPvgwS5cu4p9/tjJx4jhmzZrO4MGP0rhxk4ubOBchX5JP7dq1o3fv3tx7b+bM4OTJk/3JKJErieH2YdotJCfvxwCCQ3L+pjsAw7CAxYbbdSJ9zycT8JgQoOSTiIiIiIjkjoYNI/nii084efIky5cvoWPHm9m2bSsA8+e/R0hICLNmzQNg2rTJvP/+uwwaNJiWLa+nbt163HFHdwDS0tIYO3YCDocDj8fD448P4ZdffqZp0+YAbNq0gXff/ZASJcIZM2Ykc+e+w5AhQwH455+tzJo1j9DQUB5/fAgrViynS5fbqF27LtOmvYPVauXYsXj69+9FVFQzAD766AOWLFlBYGAQTmcydnsgiYmJjBs3hjfemEzJkiWJi4vj/vt7M2/eAsLCwvj55zVUrFiZihUr0anTLSxZsijL5NOZvvzyM44cieGDDz7G6/Xy8MODKF3638KCrVv/5r33PqJMmbKMHTuaTz9dwKBBg/3n5s37H6VLl+Gpp4YycuRwpkx5m6CgIPr378m6db/RuHET3nprAtdd15BnnnkBn8/HyJHDWbp0EV263AbAkSMxTJ06C6fTSffuXbnllq7cfHMXli9fwj339KJFi4Iv8smX5FNkZGR+XEbk0uJOr3xKStoLgCOk0nkPYdgC8XqSIMRyxphaTSsiIiIicqlrU8Ga4+qkvGQY0LbtjaxcuYKVK1cwffpsf/Lpp59Wk5yczPfffweA2+2iZs1aWY7j8/mYNm0SmzdvAkzi4+PZsWO7P/nUvHkrSpQIB+CWW7oyceIb/r5RUU0JCwsD4Kqr6hMdfRCAhITjvPrqKA4e3I/VauPkyRPs37+PevWuonLlKowa9QJNmjSnefNWOBwhbNmykcOHDzFs2CNn3J9BdPQB6ta9iqVLv6RTp1sA6NjxZvr2vZeHH37Mv1orK+vX/0HHjjdhs9mw2Wy0b9+BTZs2+M83aHAtZcqUBeDqq+vz+++/ZjhXunQZAGrVqkPZsuX81V41a9YiOvoAjRs3Yc2a1Wzd+hf/+1/6PlOpqan+fgBt2rTDYrEQGhpKlSrViI4+SKVKlbONuSDkS/LpbIYNG4ZpmjRq1IjHH3+cIkVy/rav08LDQ/MgMpG8lWZYMB0B4D0KQIVK9SlVKizb9lmdCwgMI80ZS2gJB24gvEgwluLBeRWySJ4723dA5HKheS5XAs1zuRLkxTw/etSCzVa4/jHZarXQuXMX+vfvTUREI8LDS2AYBhZL+oqLp556lsjIqEz9Trc5fT+ffPIhSUmJzJkzj8DAQF599WU8Hhc2mwWLxcAw/m175meLxSAoKMh/zmaz4nan93vzzddo1ao1r78+HsMwuPPOW/F63QQGBjB79nts2rSRP/74jQEDejJhwhQsFoOaNWsxY8bsTPHGx8fz+++/snPnDubOfQeAtLRU1qz5ng4dOmW4H4vl358NI/0ZnY7Pas0Ye2Bg4Bmx2/D5fNmcs2a4T6vVhmn6Tn02ef31N6lQoWKWzzk4+Mx+VsB3KjYDq9U455yyWCx5/t/tAk0+zZ8/n3LlyuFyuXjllVcYNWoU48aNO+9x4uOT8PnMPIhQJO8EJaVhGHDyRAwAHk8pYmMTs2xbqlRYlucs1iIY3hhOpKTgAI7FnMT0ePIybJE8k908F7mcaJ7LlUDzXK4EeTXPfT4fHk/heou11+ujTJny3H//Q1x1VX08Hh+maeLzmbRocT0ffvgB9erV9y9vO3r0KFWrVsPhcHDyZKL/fk6cOEmJEuFYrQEcPhzD6tU/cOutd+Dx+PD5TH766UdiY+MpXrw4S5YsIiIi0n/O5zP945z5+eTJREqXLovXa/L772s5ePAAXm/6caczhQYNImjQIIJNmzaxY8dOmjZtzoED+/ntt99o2DB9hdbWrX9Rt+5VLFmyiBtuaMeLL77sv/dvvvmKRYu+pF27Dv57/m9M113XiOXLl9K6dTu8Xi/ffLOCkiVL+tuZZsbYT3/O6tyZ93nm9Vq0uJ65c99l2LBnsFqtJCQk4HQmU758BUzTxOvN2O/05//+DrLj8/kyzWeLxcjVQp8CTT6VK1cOALvdTo8ePXjwwQcLMhyRfGW4fZhBNtxpCZiA3V7svMewB4XjApIsx3AAhsuH0rAiIiIiIpLbuna9PdOxnj37MHv2TAYM6I3FYgEM+vW7n6pVq9Ghw0288spIVq1aSffuPbjzzrt54YWn6du3B6VLl6FRo8YZxoqMbMyrr47i0KFoKleuwpAhj50zpgcfHML48WP54IP3qFGjJjVqpC/5S0pK4vnnn8LlSsPn81G7dl1at25DYGAgr732JlOnTmLSpPF4PG7Kl6/A2LETWL58CYMHD80wfqtWNzBu3KscPnwo2xhuvfUOdu7cTq9ed1G6dFnq1KlLamrquR/oeXj00SeYNm0yffrcg2EYBATYeeSRJyhfvsJZ+3XpcjtTp07ko4/e56GHCnbDccM0zXz7W7Vt27bMmDGD2rVr43Q68Xq9hIWFYZomEydOZOfOnUydOvW8x1Xlk1yKgqduwAwP4tsyI/EmH6H1LauybZvdv6xs3TKB+D0LqVH5eWp9EYKz7zX4qpz/0lWRwkD/Ui5XAs1zuRJonsuVIK/meUzMPsqWrZLr4xZ2Z7497lLkdCbjcITgcrl45pnHadOmPZ0731rQYeVYVvPukqx8Gj16NCtWrCAuLo6+fftSrFgxZsyYwcMPP4zX68Xn81GjRg1GjBiRH+GIFAqG24vPbsXrcYI14ILGcASXJx5I9B4A6mK4vbkao4iIiIiIiJzdo48+hNvtxuVKIzIyyr9pufwrX5JPw4cPZ/jw4ZmOL1y4MD8uL1I4uX0QYMH0pGKxBl3QEKGh6W8wcHqOAHXTxxQREREREbmE9O8/qKBDuCizZr1X0CEUeoVrG32RK4jh9mIGWMHnxhoQckFjBAWXBsDlTUgf06XKJxERERERESlclHwSKQimCS4fZoCBxefDGnBhr7UMOLVJudtzIv2AKp9ERERERESkkFHySaQgeEwMIMWWjAUIsBe9oGFsp/p5vOmbHWrPJxERERERESlslHwSKQinkkRJ1iMA2ANLXNAwFosN0zi1aTmo8klEREREREQKHSWfRAqAcSpJlMxRAAKDS134WLZATG8KpgGGS8knERERERERKVyUfBIpCKc2Bk8hHoDgoDIXPJRhCwavG+xWf0WViIiIiIjIxerWrTM9etzBfffdQ69ed/Htt1+ftX3LlpE4nc6ztlm2bDHDhz91zmvPnj2TKVMmnk+4WXrllZf47LMFFz2OXBxbQQcgciU6XfmUah4DINhR/oLHstpCMVLj8QaY/nFFRERERERyw+jRY6levSbbt2/jgQf6ExnZhGLFihV0WHKJUfJJpCCcqlBK8yUAEBpS6YKHstmLYDUhNTiVQFU+iYiIiIhcFvbugb27jTwZu2p1k6rVzq9P7dp1cTgcHD4czYgRz5KQcByr1crAgYNp2rR5hrYrV37D118v5fXXJwLgcrm4887OvP32e5nG/eCDuXz99TIA6tW7mqFDn8ThcABw5EgMw4Y9QkxMDFWqVOHZZ0cQGhqK2+3m7ben8eeff+B2e6hRowZPPPEsDoeD2NijjB49goSEBMqXL4/Xq7+RCgMtuxMpAKf3ZnL7TmICgUHhFzyWPbA4FsAZmKw9n0REREREJE+sX78Ol8vFqFEvcOONHXjvvf/xwgsv8/LLL3D8+PEMbVu3bsOuXTs5dCgagO+++4arrrqGMmXKZmi3du1PfP31MmbMmMO8eQvwer3MnfuO//ymTRt47rkRfPDBx4SEhPrPzZ//HiEhIcyaNY+5cz8kPLwU77//LgATJ77BtddG8N57HzFkyGNs2LA+Lx+L5JAqn0QKwqnlcR5vEqZhwTCsFzyUPTAcA0gOPk4JLbsTEREREbksVK0GVauZBR0Gw4c/jd0eSEhICC+++DIvvPA0N93UBYBq1apTs2Yd/vprMy1bXu/vY7PZ6Nr1dhYu/IyHHnqEzz//hPvvfzDT2OvW/Ua7dv9HSEgoAF263M6kSeP855s3b0WJEun/UH/LLV2ZOPENAH76aTXJycl8//13ALjdLmrWrAXA+vV/MHTokwBUqFCRyMjGuf1I5AIo+SRSAIxTy+O8PieG5eK+hkHBpQFwBhzDSFVJqYiIiIiI5J7Tez4BJCcnZdnGMDIvD+zS5Xb69buXli2vJykpkcjIqCx6mpn6ZjUWgGkCGP6fn3jiGRo1UmLpUqFldyIF4VSFkulNBVvQRQ0VHFwOgJSABP+4IiIiIiIiuS0kJJSaNWuzfPkSAPbt28uuXdu56qr6mdoWK1aMyMgoXnrpeW677c4sk0qRkU1YuXIFTmcypmmyZMnCDEmqn39e41/St3z5Yho2jASgZcvrWbBgPmlpqQA4ncns3bsHgEaNIlm6dBEAhw5Fs27d77n4BORCqfJJpAAYrlMVSj43lsBiFzXW6TflpdpO/juuiIiIiIhIHhgxYjRvvDGGjz/+EKvVyvDhoyhevHiWbW+5pSurVn1Lp063ZHm+WbMW7Nq1g0GD+gJQt+5V3Hdff//5yMjGvPrqKA4diqZy5SoMGfIYAD179mH27JkMGNAbi8UCGPTrdz9Vq1bj0UeHMXr0CFatWknlylVo3LhJ7j4AuSCGaZoFv4j0IsXHJ+HzXfK3IVeQgB8OYF+1j2WNniewWE2atpp91valSoURG5uY5TlX2jF+W3Ebdksdbth2P84nVHoql6azzXORy4XmuVwJNM/lSpBX8zwmZh9ly1bJ9XELyty57xAfH88TTzxd0KHIWWQ17ywWg/Dw0Fy7hiqfRAqA4faSGpCKBbDZi17UWAEBRQBwk4yhZXciIiIiIlII9Ox5F1arlTfffKugQ5FCQMknkYLg9pEUcgwDsAeWuKihDIsN07DgNZzg1rI7EREREREpeB988HFBhyCFiDYcFykAhstLYsgxAIKCSl38gFY7ppmK4TXBqyWoIiIiIiIiUngo+SRSENw+nEEJAAQGlbno4QxrECbuU2Or+klEREREREQKDyWfRAqA4faRYj8JgCOk3EWPZwkIAdPjH1tERERERESksFDySaQguLykBqQnn0IcFS96OJs9DItp4jHcqnwSERERERGRQkXJJ5ECYLh9uKxJQO7s+RRgL4YFSAlIUuWTiIiIiIjkim7dOtOjxx3cd9899Op1F99++3WBxPHxxx9y/Pixix5nyJCB/PTTj7kQkZwvve1OpCC4vXgsyfgMA4vVftHD2QPDMYCkoGMUdSn5JCIiIiIiuWP06LFUr16T7du38cAD/YmMbEKxYsUA8Hg82Gx5n1b4+OOPiIyMonjxi3tTuBScHM+SlStX0rp163yZWCKXO8Ptw2OkYljO/n16f+8uQmw2BpWqf9Z2gUElAUgOPEYxLbsTEREREbnkJf0NyX8ZeTJ2yNUmoVedX5/atevicDh45ZURlC9fgQMHDpCQcJw5cz7ggw/m8vXXywCoV+9qhg59EofDwezZM9m/fy/JyckcOLCfOnXq0bPnfUyZMpGYmMO0bt2WwYMfBSAuLo6JE1/nyJEY0tLSaN++A7179+O992YTFxfL8OFPY7cHMmLEaOLj45g1azouVxper5fevfvRvn0HIL26qVatOuzY8Q+xsUdp2/ZGBg0a7L+PP/9czwcfzCUuLo62bdvz4IMPA/DRRx+wcuUKvF4Pdnsgw4Y9Q61adUhNTWX06BHs3bsbq9VG5cpVePnl1wBYvnwJn3/+CV6vl9DQUIYNe4bKlate5G/n8pTjTNKkSZN4/vnnuemmm+jatSvXXnttXsYlcnlzeTFJw7AGZtskJsXJx/v3YAIliji4oUj2y/OCgssC4LQngJbdiYiIiIhILlu/fh0ulwubzcaWLZuZMuVtgoODWbv2J77+ehkzZszB4Qhh9OgRzJ37Dg899AgA//yzjXfeeZ/g4GD69evJjBlTGDduMl6vlzvv7EKXLrdRqVJlRo9+kT59BnDddQ1xu908+uiD1Kt3Fffd15/Fixf6K7AAwsNLMm3aO1itVo4di6d//15ERTWjSJEiAOzdu5uJE6fhcrl44IG+1K/fgBYtWgFw5EgMU6fOwul00r17V265pSuVKlWmY8ebueeengD8/vuvvPHGq7z99lx+/XUtiYmJfPDBJwCcPJm+d+/GjRv47rtvmDp1Fna7nbVrf+LVV0cxffqcfP29XCpynHxatGgR27Zt48svv+Thhx8mODiYrl270qVLFypWvPgNk0WuJIbbh4kHi61otm2+ijmEgUGDYsUZ/+dG7Fc1oHnJ0lm2dQSnvzEv1X4SQ5VPIiIiIiKXvNCrIPQqs6DD8FcchYSE8MorY1mx4iuuuuoagoODAVi37jfatfs/QkJCAejS5XYmTRrn7x8V1ZTQ0PRzNWvWpEaN2tjt6VuPVK5chejog5QsWYoNG/4gISHB38/pTGbv3r00btw0U0wJCcd59dVRHDy4H6vVxsmTJ9i/fx/1618DQKdOt2Cz2bDZbLRr93+sX/+7P/nUpk07LBYLoaGhVKlSjejog1SqVJl//tnK+++/y8mTJ7BYLBw4sP9UzLXYv38v48ePJSKiEc2btwTgp59Ws3PnDgYO7AOAaZokJp7Mrcd+2TmvNXR169albt26PPXUU6xdu5bXXnuNt956i4YNG9K9e3duueUWLBbtYS5yLqbbi2F6sQWEZnne7fPxTUw0UeElGVa3PiO2/sm4bVt45ZqG1CtaLFN7R0h6AjgtIBG055OIiIiIiOSSMyuOAFas+AqHI/iMFiaGkXF54Jmf7fZ/V3tYLFYCA+1nfLbg9XoxTR+GYfDOO/NytNXP+PGv0aLF9YwZ8waGYXD33bfjcqVl2dY0TSC7eNKv73a7eeGFp5kyZRZ16tQlLi6WW2/tBECFChWZP/8T1q37nV9++Ym3357Ke+/9D9OEm2/uwoABD5wzXrmAt93t37+fqVOn8tJLL5GWlsYjjzzCnXfeyfz583nkkUfyIkaRy4vXh8twYgVs9qwrn36OO8oJt5tO5SoSZLUyvnkzwu2BjPprIwedyZnaBwaVwARcNqfediciIiIiIvkmMrIJK1euwOlMxjRNlixZSGRk1HmN4XCEcO21EXzwwVz/sSNHYoiPjwMgJCSEpKQk/7nExETKlSuHYRj8/vsvREcfyDDeV18tw+PxkJKSwqpVK2nYMPKs1z+9d1Tp0mUA+PzzT/znjh49gsVi5frrb+CRR54gIeE4iYknadGiFV99tZSjR48A4PV62bZt63nd95Ukx5VP8+fP58svv2Tfvn106tSJ119/neuuu85/vkOHDjRv3jwvYhS5vLh8JAUexwDsgcWzbLL88EHKBAUTceptDsUDAxl1TQRP/rmOEZs3MLFhE8ICAvztDcOKaVjwWFNAy+5ERERERCSfNGvWgl27djBoUF8A6tZN36fpfL344stMnvwmvXt3B9ITUs8++yLh4SXp1u1uxowZRVBQECNGjObBB4cwfvxYPvjgPWrUqEmNGrUyjFWnTl2GDn2IuLhY2rRp719yl52QkFD69x/E/ff3pkyZsjRt+m9uY9euncyYMQUAn89Lz559KFmyFCVLlmLgwId45pnH8Xp9eDxu2rRpT9269c773q8Ehpleg3ZOgwYN4tZbb6Vdu3b+9Zn/tWbNGlq2bJmrAeZEfHwSPl/Br4UVyQnjZBpxc+ewtdr/KFXjLupcNTjD+QPOZB5ct5Y+1WrSrVJVAEqVCiM2NpEtCcd5ZtMfPFr7Km4sWz5Dv9XLbgRXAO2LTMfVtkp+3Y5Irjk9z0UuZ5rnciXQPJcrQV7N85iYfZQtq/9f/mIMGTKQe+7pdc6Ek/wrq3lnsRiEh2e9TcyFyPGyu6ioKDp16pQp8fTuu+/6fy6IxJPIJcftIznwOACBwZk3EF9++CA2w+DGMuUznbuqaDFCbDa2nTyReVxbIKbFrbfdiYiIiIiISKGS4+TT1KlTszw+ffr0XAtG5EpguLw47enJI4cjY4Ip1etl5ZHDtChZmqJZVBhaDIO6YUXZdjIh8zmbA9PwYLi07E5ERERERK5MU6a8raqnQuicez6tXbsWSN8865dffuHMVXoHDx4kJCQk76ITuRy5felvpQNCHBUynFoTe4Rkj4dO5Spm271ukaJ8uC+eZI+HkDPeBGENCMPLEVU+iYiIiIiISKFyzuTT888/D4DL5eK5557zHzcMg1KlSjF8+PC8i07kMmS4faQFpL+pIeg/y+6WHz5IJUcIVxctlm3/ukWKYgLbE08QUTzcfzzAXgQP4HFlfhueiIiIiIiISEE5Z/Lpu+++A+Cpp57i9ddfz/OARC57bi9umxMTsFiD/Yd3JZ3kn8STDKxRG8Mwsu1eJ6woBrD15H+ST4HFSQWSfDEEZ9tbREREREREJH/leM8nJZ5Ecofh8uG1pmJabBmSTL/ExWIB2pYud9b+DpuNKiGhmTYdDwwsCUCyeSTXYxYRERERERG5UGetfOrUqRPLly8HoHXr1tlWY3z//fe5HpjIZcvtxWdxYbVk3FB8b3IS5YMdhAYEnHOIumFF+TH2CD7TxHLqexkUXAYAJ/G5H7OIiIiIiIjIBTpr8unll1/2//zGG2/keTAiVwLD7cM03Bi20AzH9yYnUSM0LEdj1C1SlK9iojnoTKZySPo4wcFlAUg1juduwCIiIiIicsX67rtvef/9OZgmuFxp1K5dl5deeiVXxp49eyYpKSkMGTKUZcsW8/PPPzJ69OscPnyIAQN6sXTpyly5zsXGJhfvrMmnyMhI/89RUVF5HozIlcB0eTHwYQ34N/mU4vVwODWFdmXOvuTutLpFigLp+z6dTj45QtLfkJdmScjdgEVERERE5IoUFxfHm2++xuzZH1CmTFlM02Tnzu0FGpPH48F2xlu///u5IBWmWAqbHD+Vd999l6ZNm1KvXj3+/PNPhg4ditVqZdy4cURERORljCKXFY8rCQtgsxf1H9uXnP6Gumo5rHyqEOwgzBbAtpMn6FCuAgAORzlMIM2SlNshi4iIiIhIPrP94cL2uztPxvY0DsDTyH7OdseOxWG12ih66m3chmFQq1YdAFq2jOT++x/kxx9/4MSJEzz99POsW/cbv/76Mx6Ph5dfHkvVqtWIj4/jpZeeJzk5GZfLRfPmLXjooUfPK96WLSN56KFH+PnnNVx7bQRHjx7B4XBw4MABEhKOM2fOByxfvoTPP/8Er9dLaGgow4Y9Q+XKVXG73bz55uts2PAHxYsXp1at2hw7Fs/o0a9nqm7Krtpp166djB//GqmpKbhcLrp0uY277uoBwCuvvJQpFsksx8mnuXPn0q1bNwDGjx9Pnz59CAkJYcyYMXzyySd5FqDI5cbpjsMC2AOL+4/tTU4EoGpIaDa9MjIMg7pFirIt8d9Nx60WOz7AY0kG04SzvDFPRERERETkXGrWrM1VV13NHXfcTEREIxo0uI4OHW7yJ6NCQ8N45515fPfdtzz77BOMHPkqDzwwhPnz32PevDm8+OLLhIaGMXbsBBwOBx6Ph8cfH8Ivv/xM06bNzysWn8/HlClvA+kJny1bNjNlytsEBwezceMGvvvuG6ZOnYXdbmft2p949dVRTJ8+hy+//IwjR2L44IOP8Xq9PPzwIEqXLn1e1y5XrhwTJ07DbrfjdDoZOPA+oqKaUbVqNYAMsUjWcpx8SkxMJCwsjKSkJP755x/mzp2L1Wpl7NixeRmfyGUnyXsYgMCgkv5je5KSCLZaKR0YlONx6hYpyu/H4khyu//dpNyw4bGmgseEACWfREREREQuVZ5G9hxVJ+Uli8XCq6+OZ/funWzYsJ4ff/yeDz98n3nz/gdAu3b/B0CdOnUBg+bNW576XI8fflgFpCeNpk2bxObNmwCT+Ph4duzYft7Jp06dbsnw+YYb2vmTPT/9tJqdO3cwcGAfAEzTJDHxJADr1/9Bx443YbPZsNlstG/fgU2bNpzXtVNTU5ky5TV27tyOYViIi4tl587t/uTTmbFI1nKcfCpXrhzr169n586dREZGYrVaSUpKwmq15mV8Ipcdpy8WDAgM/jfbvic5iWohodm+UTIrdcPSl+39k3iCRiVOJbKMAHwWF7i9EGDJ1bhFREREROTKVL16TapXr8kdd9xFz553smHDHwDY7enJMYvFgt3+71u7LRYLXq8XgAUL5pOYeJK3355LYGAgY8e+gsuVdt4xBAc7Mnx2OP5N9pgm3HxzFwYMeCBTP9M0gaz/zrJarZimz//Z5XJl2W7mzKmUKBHOnDnzsdlsPPbY4Axtz4xFspbjv06feuopHnnkEWbMmMFDDz0EwKpVq7jmmmvyLDiRy1GqLw4AR3D65uKmabI3OZFqITnb7+m02kWKYCF90/HTDEtQ+pv0XL7sO4qIiIiIiORAbOxRtmzZ5P989OgREhKOU65c+RyPkZiYSHh4SQIDA4mNPcqaNT/kepwtWrTiq6+WcvToEQC8Xi/btm0FoGHDSFasWIbH4yEtLY3vvvvG369ChYr88882fD4fTmcyP//8Y5bjJyUlUrp0GWw2G7t372Tjxj9z/R4udzmufGrdujVr1qzJcKxjx4507Ngx14MSuZylmQkAOBzp/8GOTUvF6fXmeL+n04KtNqqEhLLtjOSTzerA40lIr3wSERERERG5CF6vl9mzZxITc5jAwCBM08eAAQ9Su3bdHI9x551388ILT9O3bw9Kly5Do0aNcz3O665ryMCBD/HMM4/j9frweNy0adOeunXrceutd7Bz53Z69bqL0qXLUqdOXVJTU4H05XLfffctvXrdRYUKlahTp16W4993X39efvlFVqxYToUKFbjuOr107XwZZnoNWo4kJiayZ88ekk+9meu0Zs2a5Xpg5yM+PgmfL8e3IVKgfl94P2nW7TTpsJgAexF+jY/l5b828sZ1kdQrUizLPqVKhREbm5jp+LQd21h19DD/a34DVsPg15WDcDu30fK6hVCpeOaBRAqx7Oa5yOVE81yuBJrnciXIq3keE7OPsmWr5Pq4VzqnMxmHIwSXy8UzzzxOmzbt6dz51oIOq9DIat5ZLAbh4edXIHE2Oa58+vzzzxk1ahQOh4OgoH83RTYMg5UrV+ZaQCKXOzdJmIAtIP2LvDc5CYAqjvP/YtctUpRlhw9ywJlM1ZBQ7AFFcQOpzhiCUPJJRERERETk0Ucfwu1243KlERkZlWnzcsl7OU4+TZgwgUmTJtG6deu8jEfksuc1UjCxYBjpW67tSUqkbFAwDluOv45+dYukbzq+9URCevIpMJxkINkZTRBZl4yKiIiIiIhcSWbNeq+gQ7ji5XjDca/XS8uWLfMyFpErgs9I48y8797kpPPe7+m0ckHBFAkIYFti+r5PgUGlAEhJjbnoOEVERERERERyQ46TT/fffz/Tp0/H59NbtEQuhmm4MYz0V5Kmer0cSnFS7QKTT4ZhUDesKP+c2nQ8OLhM+riuuNwJVkREREREROQi5Xidz9y5c4mLi+Odd96hWLFiGc59//33uRyWyOXMg9UoBsABZzI+uODKJ4BqIaGsOxaPx+cjKDT9DXppnmO5EKeIiIiIiIjIxctx8umNN97IyzhErgim14eBidXiAGBPcvobMqqFhl3wmOWDHfgwOZKaQomwigC4PAkXHauIiIiIiIhIbsjxsruoqKhs/09EcsbtPIEVsFnSk017k5MItFgoGxR8wWOWD05PZB1KSSEopAQ+wO3V641FREREROTitGwZidPpzHDs5pvbcfjwofMeq1u3zuzevfOc7Vav/p577+1G37492L9/L3369CAtLfW8r3c2Z96Xx+PhnXdmcPfdt9Oz513ce2833nprAh6PJ9eu98orL/HZZwtybbwLcfjwIW6+uV2BXT/HlU8ul4upU6eyZMkSEhIS+OOPP1izZg179+6lZ8+eeRmjyGXDmXgQALs1/S11e5LSNxu3GMYFj/lv8smJEV4SEwOPmXzxwYqIiIiIiOSzL7/8nP79H6Bt2/YAzJ37YZ5eb8yYkaSlpTJnzvs4HCF4PB6WLl2Ey+XCdgFvJJes5fhJjhkzhiNHjjBu3Djuv/9+AGrVqsWrr76q5JNIDjmTDwMQGFAC0zTZm5xE85KlL2rMIgEBhFhtHEpJz9ybWPGZKRcdq4iIiIiIFBzbn0cJ2HAkT8Z2R5TBc93F/R0C6dVMHTvezO+//0p8fBz33NOTO+7oDsDGjRsYP/41AgMDufrqazBN099v//69TJr0JidOJOB2u7nrrnu4+eYuTJ48nk2bNrB//z6++OIT3nprJi1bRrJixWocDsdZr5fdmAA//PAdM2dOpUiRojRt2twfx4ED+1m9ehWff74MhyMEAJvNRteutwPg9XqZPv0tfv31ZwCaNGnOgw8+jNVq5ZVXXsJut3PgwH6OHj3C1Vdfw/DhIzEMg9jYo4wePYKEhATKly+P1+v1XzM5OYm33prArl07cLlcRERE8vDDj2G1WhkyZCD16l3Nli2biIuLo23b9jz44MMAzJnzNt9++zV2eyCGAZMnzyQsLIy//trCjBlvkZycXoAwYMADNG/eEoDPPvuYjz/+kPDwkkRENLro3/fFyHHy6dtvv2XFihU4HA4slvTVemXKlOHIkbz5MohcjtJSYgAItIcT70oj0eO+qM3GIf2Nd+WDHf7kk8UMwGe4LjpWERERERGRc0lNTWXmzHc5fPgQvXt3p1OnzthsNkaMeI4XX3yZhg0jWbnyGz79NH3Zmcfj4aWXhjNixGiqVKmK05lM//69qF+/AY888gTbt//DPff0okWLVjm+nt1uz3bMIkWKMHbsK8yYMZvKlasyf/57/rG2b/+HihUrU6RIkSyvtWjRF+zYsZ05c+YDMGzYIyxa9AW33dYNgN27dzFx4jQsFgt9+97LunW/0rhxUyZOfINrr42gX7+BREcfpE+fHjRp0gyAt96awHXXNeSZZ17A5/MxcuRwli5dRJcutwFw5EgMU6fOwul00r17V265pStFixbjo48+YMmSFQQGBuF0JmO3B5KYmMi4cWN4443JlCxZkri4OO6/vzfz5i3gyJEY5s2bw7vvzqdEiXDGjXstd37hFyjHyaeAgIAM2TqAY8eOZXrznYhkLzU1DoCgwFLsTU4C0t9Wd7HKBwez7eQJACxmIF5Tez6JiIiIiFzKPNeVzpXqpLxgnLFtSPv2/wdAuXLlCQsrQmzsUdxuN0FBQTRsGAlAu3Y38sYbrwDp1Ub79u1hxIjn/GO43W727t1DlSpVz3ntrK7n8/myHdNqtVC7dh0qV04fu0uX25k+/a1TrUzOZt26X7npplsICAgA4KabOrN69Sp/8qlVqxsIDAwEoE6dOkRHH6RxY1i//g+GDn0SgAoVKhIZ2dg/5po1q9m69S/+97/0hFZqaiqlS5fxn2/Tph0Wi4XQ0FCqVKlGdPRBypevQOXKVRg16gWaNGlO8+atcDhC2LJlI4cPH2LYsEf8/Q3DIDr6AJs3b6J585aUKBEOQNeut7Fq1TfnfL55JcfJp44dO/L000/z7LPPAnD06FHGjBnDzTffnGfBiVxuXGnHAHAElfMnny628gnS9336MfYIbp8PK0H4SLjoMUVERERE5MpWrFhxTp48gcORvs+sx+MhKSmJYsWK+9vY7Xb/zxaLBa/Xk2GJ3X+ZpknRosUueC+nrK4HRrZj/vjj99mOVbt2XQ4e3M/JkyezrH4yzYyJNsj4OTDwzFismQp2smYyZsw4KlSomOVZuz3wjDEteL1erFYrM2e+y+bNG1m/fh39+/dk/Pi3ME2oUaMWU6fOyjTOpk0bcxBL/snx2+4ee+wxKlasSJcuXTh58iQdOnSgVKlSDB48OC/jE7msuD0nAQgOLc+epCRKBQYSeiqLfjHKBzvwATGpKdhwYAF8XvdFjysiIiIiIleuxo2b8OWXn/s/L1r0BVdffQ1BQUFn7VelSlXS0tL488/1AKxa9S1JSen/+F65chWCgoL46qul/vb79u0l+dQ/zl+Is41Zv34Dduz4hwMH9gOwePFCf5tKlSrTosX1vPHGGJzO9D2TvF4vH3/8EU6nk8aNm7Bs2WI8Hg8ej4fly5cQGRl1zngaNYpk6dJFABw6FM26db/7z7VocT0ffPCeP1GVkJDAoUPRZx3P6UwmISGBiIhG9O8/iOrVa7B79y7q12/AwYP7Wb9+nb/t1q1/YZomDRtGsnbtTxw/nl4AsWTJl+eMOy/luPJp//79VK9enUGDBuH1emnfvj116tTJy9hELjseTyImYA8qyp6je6kaEpYr4575xrsgipAGpKQeISQk62y6iIiIiIjIuTzyyBNMmjSO++67G8OwULp0GV54YdQ5+6XvwfSKf8Pxhg0bU6ZMWSB9Q++xYycwefJ4PvrofbxeHyVKlGDUqAvfk+hsYxYvXoKnnnqep59+jCJFivrfonfa8OEjmTPnbfr160VAgA3TNGnatAV2u50uXW7j4MED9O3bA4CoqGZ07nzbOeN59NFhjB49glWrVlK5chUaN25yxrknmDZtMn363INhGAQE2HnkkScoX75CtuMlJSXx/PNP4XKl4fP5qF27Lq1btyEwMJDXXnuTqVMnMWnSeDweN+XLV2Ds2AnUrFmLXr368uCD/SlRIpxmzVpe4NPNHYZ5tno40kvinnvuORYuXEjZsmUpXbo0R44c4ejRo3Tt2pUxY8ZkKkPLb/HxSfh8Z1+rKVIYrPnqLrzuo7Rovozb/1rLHZWqcF+1mufsV6pUGLGx2e/jlOh2c8/aH+hXvRb11s8m3viBOk3foFSpc2flRQqLc81zkcuB5rlcCTTP5UqQV/M8JmYfZctWyfVxRc4mq3lnsRiEh1/8FjGnnbPyacGCBfz2228sWLCABg0a+I9v2rSJJ554gv/973/cc889uRaQyOXM50sF00oMbnyYVDr1Os+LFRYQQJgtgEMpTiKM9PXXKSl6E6WIiIiIiIgUvHPu+fTll18yfPjwDIkngAYNGvDcc8/x5ZcFu25Q5FJimmkYvgBiPGkAlAkKzrWxywcHcyjFSbCtJACpSj6JiIiIiIhIIXDO5NOuXbto3LhxlucaN27Mrl27cj0okcuW6cZq2jlyKvlUNleTTw4OpzgJtpYCIC0tLtfGFhEREREREblQ50w+eb1eQkOzXucXGhqKz+fL9aBELlumF6s3kJi0FOwWC8XPeE3oxSof7CA2LQ2bvQwArtTjuTa2iIiIiIjkj3NsyyySq/Jrvp1zzyePx8Mvv/ySbUCnXw8oImfn9XmwADbTQUxKCmWCgrHk4mb9p994dywgDBPwpJ3MtbFFRERERCTv2Wx2kpNPEhJSpMBf7CWXP9M0SU4+ic2We0UR2Tln8ik8PJznnnsu2/MlSpTI1YBELlepKUcwgABfCDGpKee95M40zbP+D1C54PTxDgX48GHgcestMyIiIiIil5LixUtx/HgsSUkJBR2KXCFsNjvFi5fK++ucq8F3332X50GIXAmcKTEABJphxKSmUL9o8Wzbek2TKdui2XbSSarXS6rPJMXtpX254gypWzHLPqcrnw4ZbkqYFnweZ+7fhIiIiIiI5Bmr1UbJkuUKOgyRXHfOPZ9EJHeknEo+YRQhxes9a+XTmqMnWBlznDJBAUSUCOPmqqVpWCKMFYePsysxJcs+obYAigQEEI0Lw7RhelPz4jZEREREREREzouSTyL5JDU1FoAUS3rFU9ngrJNPXp/J//YcoWpIEC82qMojdSvyWEQ1nri6EmE2K/N2x2R7jfLBDqJ9aVh8dkyfK/dvQkREREREROQ8Kfkkkk9caXEAJNqKAVAmm8qnH44mEJ3ioke10hk2JA+xWelWpRQbjiWx8XhSln3LBzk45EnD4gsE05O7NyAiIiIiIiJyAZR8EsknrrTjAMT5k09Bmdp4fCb/23OU6qFBNClZJNP5myuEUzIwgHm7YrJ8A2X54GDivW6s3iAspqnXtIqIiIiIiEiBU/JJJJ+4XScwgcP2MIoF2Am2Zt7v/7uY48Skuri3Wpks32xnt1q4t1oZdiSm8HPsyUznT2867jWDMYC0tGO5fRsiIiIiIiIi50XJJ5F84nUnpSefAowsl9y5fT4+3neU2mHBRIaHZTvODWWLUTkkkPd3x+DxZaxsOp18SiMUAGfKody7AREREREREZELoOSTSD7xupMxsXLY4snyTXffHj7O0VQ3PbKpejrNahj0rl6WQykuvj2csbLpdPIpyRICQIrzcC7egYiIiIiIiMj5U/JJJJ/4vCngsxJreDK96c7t8/HJvljqFnEQUSL0nGM1Dg+jbhEHnx+Iy7Cvk8Nmo1iAnWOW9P2iUlOP5u5NiIiIiIiIiJwnJZ9E8ovXBWYAPoNMlU9/JTiJS3Nze+VSZ616Os0wDFqXKUZMiovDKa4M58oHOzhqT08+paXG5V78IiIiIiIiIhdAySeR/OJ148MOZH7T3YZjidgMg+tyUPV0WqNT+0KtP5aY4Xj54GD2BhUD/n3DnoiIiIiIiEhBUfJJJJ8Ypg836UmnskGODOc2HEuiXlEHQdacfyXLBtspH2znj/j/Jp8cHAwohkn6G/ZERERERERECpKSTyL5wO1OxgKkEYwNg/DAQP+5Y2lu9ian0rBE9m+4y06j8DA2JyST5vX5j5UPduC1BOADPK6TuRC9iIiIiIiIyIXLl+TT2LFjadu2LXXq1GH79u3+43v27KF79+506NCB7t27s3fv3vwIRyTfOZ3RACRZHJSx2rGesa/TxuNJAOe15O60hiXCcPlM/j6R7D92+o13PsOC15OcXVcRERERERGRfJEvyad27doxf/58KlSokOH4iBEj6NGjB19//TU9evTgxRdfzI9wRPJdSkoMACesoZS1BWY4t+FYEkUDrFQLDcqq61nVLxZCgMXIsPTOn3zCis+TchFRi4iIiIj8P3v3HSZXdd9//H1umz7b+6qvupBAEggwvWPANsE27iWOHZe4xHES9xY77vnZcdxiJyY2uGFTTDfddATqQr1u7236Lef3x6xWWkmAJHZXgL6v57nPvXPunXvPzM5qmQ/nfK8QQrx0kxI+LV++nLq6ujFtvb29PPfcc1x55ZUAXHnllTz33HP09fVNRpeEmFTZbCcAXVacWmd/yBRozZr+FCeXxzGO4C53BwuZBotKY6zqS422hU2TMt/Aw0b7+ZfeeSGEEEIIIYQQ4iWwjteF29vbqampwTRNAEzTpLq6mvb2dsrLy4/qXBUVRz9dSYjJtHdPsfZSr5XktLISqqqK9Z229qcZKHicM61qtO35PN/+c6ZW8oM1u/GiNnWxkYLm2BSUQyQYftHzCvFyIp9XcSKQz7k4EcjnXJwI5HMuxJE7buHTeOrtTREE+nh3Q4jnNdDfDsCQUUJSm3R3F6fJPbCnG4BZljXadjhVVYnn3T835ABw77YOLm+oAKDaN8mqECX+wAueV4iXkxf6nAvxaiGfc3EikM+5OBHI51y82hmGGteBPsftbnd1dXV0dnbi+z4Avu/T1dV1yPQ8IV4NCoUBAIZUObXR6Gj7mv5hpsXClIfsYz53Q8ShOmyPmXpXoxyGjQgG4LmZYz63EEIIIYQQQgjxUh238KmiooL58+dz++23A3D77bczf/78o55yJ8QrgVcYIgDyKkJNrBg+5f2AjQMZTjmGu9wdSCnF0vIE6/pTuEEAQI1hkzaK18lk21/S+YUQQgghhBBCiJdiUsKnr33ta5xzzjl0dHTw3ve+lyuuuAKAL3/5y1x//fVceumlXH/99XzlK1+ZjO4IMel8N4WvFElPEYsU73a3YSCNp/VLDp8AllUkyPoBmwaLo5yqzRAZFQMgk217yecXQgghhBBCCCGO1aTUfPr85z/P5z//+UPaZ82axY033jgZXRDiuAq8LD4mdVkDzOJd7Vb3DeMYigUlsZd8/pNKY1hKsapvmMVlcWrsEKlCEoBctusln18IIYQQQgghhDhWx23anRAnksDP4SqLusL+X7nVfSkWlsQImS/91zBqmSwoifJsb7HuU3UozLAaCZ9y3S/5/EIIIYQQQgghxLGS8EmIyeAXyCmbOtcEoCfn0pzJc/I4TLnbZ0l5nD3pHMOuR9ix0UEJAIV837hdQwghhBBCCCGEOFoSPgkxCZT2yakQdV4xfNo4mAZgSdn4hU9zEsUC49uHs2jHIJKrAMDN94/bNYQQQgghhBBCiKMl4ZMQEywIPJTW5FSY2qBYZm37UAbHUEyLhcftOk3JCADbhrJgm1TmYwSA6w6N2zWEEEIIIYQQQoijJeGTEBMsn+9FAVkVoS6wAdg2nGVmPIJpqHG7TswyaYiG2DqcQTsGtVkTXyn8QmrcriGEEEIIIYQQQhwtCZ+EmGCZTBsAWRWjyrDxtWZnKsfskZFK42lOIsK2oSyBbVCbU7gYeF563K8jhBBCCCGEEEIcKQmfhJhg2WxncUMnsEIWrZk8OT+gKTH+4dPsZIT+gkcvmpqcwlU2gZcb9+sIIYQQQgghhBBHSsInISZYLtcFgO2WoB2zWJMJmD0R4dNI0fEthQK1OUVB2ajAHffrCCGEEEIIIYQQR0rCJyEmWCHXA0A0X4IOmWwbzhAxDeqjoXG/1ox4GEsptmXz1OYUORVCBf64X0cIIYQQQgghhDhSEj4JMcFy+X4ASjLlEDLZPpxlViKCocav2Pg+jmkwPR5mWypLJFC4KoyBJgi8cb+WEEIIIYQQQghxJCR8EmKCZXN9aKAmU4rrGOxK5Sak3tM+sxMRtg9nCWwTTQwF5HLdE3Y9IYQQQgghhBDihUj4JMQEKxSG8IHanMNuC9xAT0i9p31mJyNk/IDmhIlBAoBMpn3CrieEEEIIIYQQQrwQCZ+EmGC+l8FTBjU5xTYjAKApOXHh05zkSNHxEouQLgEgm+mYsOsJIYQQQgghhBAvRMInISaY9rN4yqQ6p9iqPeKWSW3YmbDrNURDhE2DzXGDmFcKwICMfBJCCCGEEEIIcZxI+CTEBDP8Aq6ysbVim+fSlIigJqDY+D6mUjQlImyJKMoK5QAMZTon7HpCCCGEEEIIIcQLkfBJiAlmaJ9AhSgo2F1wJ7TY+D6zExF2hBSV2UqgWPRcCCGEEEIIIYQ4HiR8EmICaa0xdYA2IuyIG/hMbL2nfWYno7gKfK8MDRQKgxN+TSGEEEIIIYQQ4nAkfBJiArluGgMwiLE1YQJM6J3u9pkzEnC1mjY+ELipCb+mEEIIIYQQQghxOBI+CTGBOoeaAbBVgs0JkxLbpDJkT/h1q0I2JQFsCSt8ZaL8zIRfUwghhBBCCCGEOBwJn4SYQF3DewGI6CRbEyazJ7jY+D5KKeZisjmq8A0LI8hP+DWFEEIIIYQQQojDkfBJiAnUn2oHIBqUsidq0JSMTtq15yiLvRGDwHCwAhet9aRdWwghhBBCCCGE2EfCJyEmUCbbCRQLfwdKTUq9p32aLAutFL4RxtI+Q547adcWQgghhBBCCCH2kfBJiAlUyPcC0KcrAJg1ieHTTNsBwFNRLDSd2eykXVsIIYQQQgghhNhHwichJpDvDgCwW1VS6mnKJ6HY+D4VIYu4q8mrGAroTHVP2rWFEEIIIYQQQoh9JHwSYgIpN4UP7LKizPAmvtD4GCGLWWmfIRIA9I4UPxdCCCGEEEIIISaThE9CTBCtNWaQIVAGexzFjGCSf91sg1kpn54gCcBQumVyry+EEEIIIYQQQiDhkxATJuV5OEGewLDIG4oZanJ/3bRjMjMd0K9KAchnOyb1+kIIIYQQQgghBEj4JMSE6cpncXDxVbHw93TDmtwOjIx8GlKVAPgjxc+FEEIIIYQQQojJJOGTEBOkK5fD1j6eEcbQmqnO5BUbh+LIp+mZgNRI+KS8AbTWk9oHIYQQQgghhBBCwichJkhXPoepA/IqQkM2wAlN8sgnxyQUQKlZTgCEgxQpz5vcPgghhBBCCCGEOOFJ+CTEBOlO9WICwzrOzFSAnuTwSdvFX+9pRPEVRIIsnbnspPZBCCGEEEIIIYSQ8EmICTKc3gNAHwlmpn1wzMntwMj1ZmDiKpOwztOVz01uH4QQQgghhBBCnPAkfBJiguSz7QCkVCkz0wE6NMnhk6nQCmZ6igIWIV2gS0Y+CSGEEEIIIYSYZBI+CTFBgnwXAEOqnJlpf/LDJ6XAMZnpQkGFcLRHZ05GPgkhhBBCCCGEmFwSPgkxAXK+j+P1A5A3qqnJaZjs8IniHe+qCgEFI4JFQFcuPel9EEIIIYQQQghxYpPwSYgJ0J3PEdcpABJmPQbFIGjShUyMfABWHAX0Z/onvw9CCCGEEEIIIU5oEj4JMQG6clmiOk0ANKjyYuPxGPkUtiDnYTqlAGSyPZPeByGEEEIIIYQQJzYJn4SYAF25HBGdxVOKGRRDp0mv+QTosInK+UTDlQDYXh8pz530fgghhBBCCCGEOHFJ+CTEBOjK5wgHeVwsZroKbSiwJv/XTYctVM6jNFYDQFL30yVFx4UQQgghhBBCTCIJn4SYAF25HCHtUlAO0/IaHLN497nJFjIh51Nb0ghAMhikM5ed/H4IIYQQQgghhDhhSfgkxATozmex8XCNCMlCcFym3MHIyKe8R0m8EQ3E9RBdeRn5JIQQQgghhBBi8kj4JMQE6MpmsbRGWwlU3j++4ZOviZjlBEA8SNMlI5+EEEIIIYQQQkwiCZ+EGGdeEJDN96EAyymBvH9c7nQHxYLjAJEgga8gojO0ZSV8EkIIIYQQQggxeSR8EmKc9eTzJINeACKRyuLIJ+f4hE+ELQDMgkFgGER0jtZM5vj0RQghhBBCCCHECUnCJyHGWVc+S0nQB0BprAZVOI7T7kauq3I+mCFCOk9vIX9c+iKEEEIIIYQQ4sQk4ZMQ46wrlyOpBwGoTjYe52l3xZFPKu9hWlEc7ZEPfNKed1z6I4QQQgghhBDixCPhkxDjrDOXJTESPiVijce14Pi+aXcq52HaCSwCDO1L0XEhhBBCCCGEEJNGwichxllHLktcpwGIRaagCj4cp5pP+wqOk/NxQqUARLXc8U4IIYQQQgghxOSR8EmIcdaezRINMvhASMUB0CHruPRFHzDyyQlXAhAPhtiRSh+X/gghhBBCCCGEOPFI+CTEOGvPZQnrHIFhYBQCgOM37c420KpYcDwUrgKgNOhjp4RPQgghhBBCCCEmiYRPQowjNwgYdAuEdR4Mp1hsHI5bwXGUKtZ9yntEI3UAJINB2nKZ49MfIYQQQgghhBAnHAmfhBhH+2opObqAssKokfBJH6eaT1CceqdyHtHYFABKGaIvnz9u/RFCCCGEEEIIcWKR8EmIcdSRywFgax/TjhWLjXMcp91RLDqucj7RSB0aKCFNxnePW3+EEEIIIYQQQpxYJHwSYhx15rIY2sdCYznJ4z/tjgNGPoXK8YEEGQIChgqF49YnIYQQQgghhBAnDgmfhBhHezNpYn4KANsp2z/t7jiGT4RMyPmYZojAMIjq4tTA9YNDx69PQgghhBBCCCFOGBI+CTGO9qTSlOo+AJxw5Wj4xPGu+ZT3ig9Mm5AuTg3cPJg6bn0SQgghhBBCCHHikPBJiHHUkctRogcAiESqYbTmk3Xc+rRv2h2AsiKYfrHY+O50+rj1SQghhBBCCCHEiUPCJyHG0YCbp0wXp7NFIrWovIc2FFjquPVJh01UIQBfY9lxVOChgPZs5rj1SQghhBBCCCHEiUPCJyHGSdb3cLVPOcXpbNFYY7HgeMgE9dLDp7zvkvbyR//E8Mioq7yHZZeg0MQNTb8rBceFEEIIIYQQQky84zcXSIhXmfZssZB3qUqjgWikHpXvRB9DvaecX2Bnuott6Q62pzrYtb6bXcNdmMrgdXXLeHPDGSTtyBGda1+xc5XzsUNl+EC9kWOLa5NyfeL2cSyGLoQQQgghhBDiVU/CJyHGybah4oinOBl8BSE7gSq0HdWd7h7o3sAfWp6kOdtLgAag1I6yoKyR00pm0Z0f4ua2ldzVuZY31p/GG+pPJWI6L3hOPTLySeU8QuEqcsAUc5AtbpQ96RwLS2PH9oKFEEIIIYQQQogjIOGTEONk6/AwACGdRSsTpYz90+5ehNaaG1uf5Jd7H2Z2rJa3NJ7J7HgtTbFaKpw41dVJuruL539jwwr+b+9f+VXzI/y541ne2vgaLq85Gdt4nuvsC5/yHpFILYNAvTkEqpbtw2kJn4QQQgghhBBCTCgJn4QYJ3szGdBg+Tn8kdFIKu+joy/8axZozc93388t7c9wXuUCPtl0xfMHScC0aBVfnHcNm4ZbuW7Pw/xk173c1PY0fzf9fM6qmHfI8To8cq6cT7SsDoByBgHYPJTi9VQfy8sVQgghhBBCCCGOiBQcF2KcdOezmMoCP4+yivWYVN5/wZpPbuDznW23cUv7M7y+bjn/PPuqFwyeDjQ/0cA3F76Vry24lpgV4t+33MJjvVsOOe7AaXex2FQAQn4xfNqdSh/VaxRCCCGEEEIIIY6WjHwSYpwMuQUSpo0KXEw7XmwsPP+0u6xf4GtbbmbVwC7eO/Vc3tRwOuoo74qnlGJZ6QwWJRr59Mbf8p1tt1EdKmF2vHb0mAMLjseitQSA5Q0B0JXPorU+6usKIYQQQgghhBBHSkY+CTEOXD+gEHjUOgoFWE4SKNZZOlzB8QE3w6c3/pY1A7v5x1mv5c2NZ7ykAChk2nxx3jWU2FG+vOmPdOeHDti5f+STZUYIlAJ3GANFPvDozrvHfF0hhBBCCCGEEOLFSPgkxDjYnkqD0kxzCgDYoTIINKoQjIY/+3TlB/nU+l+zO9PNF+b9DZfULB6XPpQ5Mb46/03kApcvb/ojGT9f3GGq4tS/vAeANiwCN0W5EwJ8dqdy43J9IYQQQgghhBDicCR8EmIcbBgojjSaZhVrKYXClcUpdzCm5pOvA7659c/0uxn+fcFbOL189rj2Y1q0is/OeT27M918a+uf8XVQ7EPYROWK/VFWmMDLUh+JgJLwSQghhBBCCCHExJLwSYhxsH24WLi7ThVDqHC4GrUvfDpg2t0tbSvZNNzKR2ZewsJk44T0ZVnZTD4082Ke7t/Bz3c/UOxD2ELliiOfDCsKQYEp0RhK+ewczk5IP4QQQgghhBBCCJCC40KMi5ZsMXyKBP3kgUi0HvLF8GlfwfGWbC+/an6E08uaOL9ywYT258rapbRl+7m5fSWN4XLeFLJgZOST5STx053UhUNoNDvkjndCCCGEEEIIISaQjHwSYhx053NYysAv9AIQizWg8vtHPvk64D+234ljWHx01mWTcne5900/nxVlTfxk1708VdY3OvLJdkpRQK1V7F9nLkvW8ye8P0IIIYQQQgghTkwSPgnxEmU8n4zvkrRCuPk+AiASqtofPjnm6HS7D824mHInPin9MpXBv855HdNjVXyt+lkG/eL0OidcAUCpLgZlWvnsSUvdJyGEEEIIIYQQE0PCJyFeomJw41MTDuMVhvAVhOzkaMHxZjU0adPtDhYxHf5l9uvIKI+f1W0ttoVrAAi73ShA47NTio4LIYQQQgghhJggEj4J8RLtGs4CPtNiMXw3hTYslDJQeR8fzff6HpjU6XYHmxat5BpvLnfWtrNpqIVIrA6AQq6DqlAYy5A73gkhhBBCCCGEmDgSPgnxEm0eSoGC6bEYgZcB0wFA5X1unNbKpmz7pE63O5x3Wkuozjn8146/EI40AJDPdtIQiWIbmp0pueOdEEIIIYQQQoiJIeGTEC/RzpG7xdVGIuDnMawIAM35Pn7etIczSid/ut3BwqEI/7BlJjuzXTyW7gcgn++jPhLF1S67U1l8rY9rH4UQQgghhBBCvDpJ+CTESxBoTXsuA0B1yIHAx7Tj+DrgO+pxQoHBP8y69LhMtxsjbHFeZyXLIlP5deuz+IBbGKAuEsHTAfkgoD1bOL59FEIIIYQQQgjxqiThkxAvQWeuQCHwAChXBRRgOSXc0bGa58xePr5jNuWhxPHtJKDDJgrFh5NnUdA+vjLwC0PUR6IjR3gjtauEEEIIIYQQQojxJeGTEC/B7lQOjU/CstGFPgBMp5TfND/GKfkqLhqqP849LNJhC4Apfpw3NqwgrxSum6JuJHwylM8uKTouhBBCCCGEEGICWMe7AwAXXHABjuMQCoUA+NSnPsXZZ599nHslxIvblcqhlE99JEI60wrAXs9j0Mvwd33LwDm2XzGtNTuHNE93BzzTpcnrfsocTWVYURWGyrCiMqyYGldURl58Sp8OmwConM+1s87gno02jpej0nEwgISt2CVFx4UQQgghhBBCTICXRfgE8J//+Z/MmTPneHdDiKOyO5XDUAG14SiZ9GYAns2lOKN6OQu2lUAoOOJzuYFmfa9mZXfAyq6A3nxxaOK8MsWMpEXroMuGvoC+HOw7qwGcU29w7SyT2ugLhFChkV/1nEfYtImGK7BTrdzZ8SxVoTAQyMgnIYQQQgghhBAT4mUTPgnxSrRrOINPceRTZqgdgE5CfHHKOai/tqGjR/Yr9liHz0+f80m5EDbh5ArF26oNllUZlDiKqqoE3d3DAPh+QH8BunPwdFfAnXsDHmkPuLjR4E0zTcrDh4ZQ+6bdqbwPQEmsnkKqlZv2PEBj7Dw6cx59BY+BgkfpMY7WEkIIIYQQQgghDudl8y3zU5/6FFprli1bxic/+UmSyeQRP7eiIj6BPRPi8NKuR0c+CybMr60g39OJBpY2nsGp02eS81sxkmESVc9fcDxdCPiPlWnu2eUzv8LivSdFWFZnY/f7eI9kCO7Oo3OabC5NPK/ROQ15TUmpwczFYc5cHObdl9v8aluOP2/L80BbwDVzwrx9YYTS8NiSbllLETMM7KoEpeWNdHWupNwfoJ9dpP0GNJp+UzH7BforxESrks+fOAHI51ycCORzLk4E8jkX4si9LMKnG264gbq6OgqFAl//+tf56le/yne/+90jfn5vb4og0BPYQyEO9dxAGiiOJEq6BpsH9hIHrqm7gO7uYaKpAgU0gyMjlg62qT/gB+s9urPw5lkGb5oKoU0pCr8q4G0vnjeYYqJjilCtQ0574Ch0CIyuAPPJDP7DGaImvH+WyWvnWPwmYfD7TTlu2Zrjo4sszqjdH0BFQxbZvgwD3cMooxqAc+Nl/E+mGcOPY1DG6tY+ZpjmhL5vQjyfA0f4CfFqJZ9zcSKQz7k4EcjnXLzaGYYa14E+L4vwqa6uDgDHcXjb297Ghz70oePcIyFe3O50DpQHgGkU8AtD+IZJXaQMAo3KuOiYfcjzvEDzhx0+f9oZUBWBry8xWbzSxbreRWU0QanCvSiEt9xGlxXDo2RVgqGD/7h5GmO3j7XJw9zsMe32PJ+OKd50aYgfoPjOWo/35EyummaglCrO58sVQ61YfDoAc02LiGGRNVopN6dL3SchhBBCCCGEEOPuuIdPmUwG3/dJJBJorbnzzjuZP3/+8e6WEC9qdyqHaQSUOiFubH2Ck7WHaceKO3MeSnNI+NST03x7jce2Qc359QYfCGnK/ieDGtb4iyy80xz8JhOMF7+DHZYiaLIoNFlwFRitPs4tOZpuyvGtGSbfOsXml1t8urKa984z0SELlSuGZdFoAwHg5bq4vOYqbmp/nLDVz65UZJzfJSGEEEIIIYQQJ7rjHj719vby0Y9+FN/3CYKAWbNm8aUvfel4d0uIF7UrlcU2AspDAQ/1bGRF4GOFSgFQaRcAHd0fPvXnNV9c6TJYgE8tMjlvrYv9cAFdYZD7hyhB42Gmu/k+Rv8gfns7zq42tGURVJUTVJWjY1FQ+0OqoMEk96Eo1rMukTvzfPGWHP99doib9wZ05zSfCdtE8iPhU6gcT0Eh18NbG1dwU9tK+oMtuLlyCn6AYxqH9kUIIYQQQgghhDgGxz18mjJlCrfccsvx7oYQRyXQmt3pHK5y6fP3UorCBOxwJXBA+DQy8mm4oPnKMx59efjKTIMlf8xitga4K2wKV4XBGQmRsjmc1c9htnZg9A6gBoZQWuMCoYP7EAkXg6jKcryZjfhN08FQeKc6eAttnLtyfPCveWpnmfwUm09XNPLVvbtwANuK4yuFXxggbocoMacyGOzApIe9mTxNCRkBJYQQQgghhBBifBz38EmIV6KObIGc7xGYQ3QWOnhfaS30QiTaAIwNnzKe5qvPerRlNF+KwCn/kwFbkXt3BH9hMZxSqQzO02uxV29EFVz8yjL8mgqC+U0EFaWUNDXQq2yU52H09GN09WL09GF29WFv2IKzagN+VTmFM5fizZsFUYPCNRG8Ux1e/9sM1cMFvrkgxMcbZ/L5tKYhpsAKEbgpAGZFZrAu3YJvbWHH0BIJn4QQQgghhBBCjBsJn4Q4BrvTOTQegdFKzIyw2DHpAmKJaQAYI+FTLmzz9VUeu4Y1nzU0p/05jz/bJH9tBJ00UIPDOE+uwV63CfwAb94sCmcuJaiuGHM9oyoB3cPokIMfi+JPa9i/MwiwntuO8/izRG69j+CRleTPWIq3cDbBVJPsR2Kcfl2Wbz+b54tLbL7yjMs3T7dRdhwKPfh+nsZonOdSDXjGTh7v28qlDWdM1lsphBBCCCGEEOJVTsInIY7B7lQOjG4whrm67lzyAw8DkEw2AcWRTwWl+MY2xeZ+zaeU5uy/5PFOtshfGwHfI3T3I9hrNwPgnjSHwumnoMtL91/EzaIG2lCDrRQ29WC37kTlhgiStejSRnRZI0FpI8Qr8RbNwVs4G2vLTpzHniVyx4MEjz5D/pzT8BbOJveBKHN/2MfX1yg+darB1591ucIpgXQPhWwXdZEIBb+MkB1nbWo1vl6BqaTukxBCCCGEEEKIl07CJyGOwc7hLFi7UdrhjQ3LWdf2WzSQTMwGwE+7fGN+E2v6NB8n4KL7CrhLbQpvDqP6B4jcdA9GTz/u0kUUzjgFnYwDoHp3Y629BXPHo6h0z+j1CoAZKUWHE1i7nkD57ug+bYXQFTPxFl6GN+9ivLlvwty+h9BjzxK57X7cXc3kLj0H7zUF5vx5kM+vreaLS2xanApmsoN8tpP6yDQUitmRRWzIPMkD3Ru5uPqkyXxLhRBCCCGEEEK8Skn4JMQx2JJqwVeDVFpNhEwbN9uDrxSOXQyRfhSU8ERJkr8n4Ir7C7jLbQpvDGNt3Un4jgfRpkn2LVfhz2iEwMPY/gjW2psxm1ehTRt/5lnoqlno0gaC0gYqZs2hZ3jk4jpADXejBlpGllbM5lU4D/w/9KM/w59/Kd7i15N519U4jz2L8+gzRDu6cecvRzmdnLykmo9u8XhsXg0zgWymnbqq+QDMjDayIZXkV3sf5fzKBVjGYe7AJ4QQQgghhBBCHAUJn4Q4SkMFj95gM8qwmR8vTrPzCoNghQG4r8XnnlCSt3SkuGajhXuaTeH1NqEHHsdZuQ6/vobs1ZegwwbW0zdgrr8VY7iLIFGNe+b78RZdAdHSMddU4QQMj6RPykAna9DJGpi6rHh9rVEdz2GtuxVzw+1Ya2/GbzwZ7+Rr8N9yJeE/30/o8QfAqMc/SXNhfYi2DVPQCVjTspOLplyBAZhGgOnPoafwDPd2r+fympMn6V0VQgghhBBCCPFqJeGTEEfpga5daKMHw29kajRRbPSyGOFydg0F/HyTz8mDed690cJdYeNe5BP53d1YLR0Ulp1E/sIzMNrW4fzxWxhDHfhTlpI/96MEM88A4xh/JZVC1y3ErVuIe/aHsZ67C3PdrYRu/wL+9NPJvvlDhO98BrOrmdAjj5O95kIuSE9jcw90DuzliXZNVTjCgJunzKrHNSv4TfNjXFi1COdY+ySEEEIIIYQQQgBSUViIo3Rn59OgbdBVNEZjBIGLCnyMUDnfWesRBz6zVqNqs3jnZon+358wO3vIvv4i8ucvx37kx4T+9I9gmOTf/EMK1/wHQdPZxx48HSxairf8reTfcwOFcz+K0bIG5+YPUZjjo70qrF07iV5/M4k5NXgGVHmd/HC9R9IM057NMjcRJarn01MY5s6O1ePTJyGEEEIIIYQQJywJn4Q4CjvTnTTnm4mpaShMGiNRMqm9KKC5UE1nBj73bJ4yP4U/t5fIb24DNJl3/w1+WYHQb96PteZPeEuuJv/2XxDUT2BRb8PEP+Ua8u/8JUHtfOxnf4wfuYP8wnkYPf1U3fQUlhem1OynPqPZOxCmJZuhKRGhN1vCouRUft/6BDm/MHF9FEIIIYQQQgjxqifhkxBH4fctT6C0RY09DYD6SJTBwa0A7Cg08Ld7PBZqILwVZ8tToDXZa1+LsfUmQn/4KHgF8n/zPdzzPw52ZFL6rEvqKFz9XQrn/ROY7Ri7/p3CyTGMwWHO23ERoYLm84aP8iNkfY+6iIUGzio7lQE3w+0y+kkIIYQQQgghxEsg4ZMQR6gl28sjvZtR/jRCpkG5EyJqWezt2QlARXY6b2zxcC8cRjnbQQdk33Qx1mPfwl55A/6Cy8i/438JRoqETyql8BdfiRr6OCQWYW78OYVpvYS9MCt2nk/Z6b1cnQsB8MQmHwDXK2Fp6QxubH2SrIx+EkIIIYQQQghxjCR8EuII/b7lCUxlYfgzyAYujdEoQwXN5s5mNPDWjdNxr/YIPXEvEJA9+1TsB7+M0byawsX/invxv0AodvxegKFQoTKCmn/APfP9mK13k048ge3bJG68j4uXFv85eMLPUWbYbB3K8o4pZzHkZbmt/dnj128hhBBCCCGEEK9oEj4JcQQ6cgM80L2RpsgcDOXQm8/REIny/fUeEb8LH6hYUU7oiTtRrof2yrHW/Tuqv5nC676Bv/Dy4/0SANAhC5X38U57O/krv0rE7aQQfhBVKDDjpr/QlHUp9VMMF8JsHsgwP9HAqaWz+GPbU6S9/PHuvhBCCCGEEEKIVyAJn4Q4Aje2PoWhDMLBLBqjDhnfYygfYXWPpsrtx/FihLfdi8rnKTQ1EsT/F+XlyL/x+wQzVhzv7o/S4WL4BBA0ncPu89+ONgbwrXvAL/D/NrSwMNyH5YcZ8Dza+vO8fcpZDHs5/twho5+EEEIIIYQQQhw9CZ+EeBE9+WH+0rWOi6tPYm9aURMu/to80xFmeZ9PmAynNp+FymTJnT0dY8/3gBD5N/8QXTvvJV1be+AOwPAun/RmGHoW+h9S9NylGHhUkd4EhS4IvCM8X8RCZdzRx/aUFTzZCJlYAV/fRth3+dCqzfxtvLj/548NMzdWy4qyJm5qfYq0l3tJr0cIIYQQQgghxInHOt4dEOLl7qa2pwl0wPkVy7hvbwdR2wQg5Eb4xK48QekplKfLyJ1eg/XY18FuBO896PIpR30ttx/SzynSuzXt+WF2W33sjfbRE0phaIWlTSxlYBsGZpeB1WFS4kZoSlUz3S4lXG5gV4BdoQlPBTM69vw64WA0D40+TkQbSNuwelYlZ2amY265n+r8BVy1+WGumzaHVeE8j9yT5x1nn8VH113HLe3P8PYpZ72k91MIIYQQQgghxIlFwichXsCAm+HOztWcX7WQgbwNQHPKA23w0ecMknOeIbptGq11zVSv/xNB3UKMwnvBOPJBhUEBBrZ4PLarja1uL3ujvTRP6SdvFIcz2Rg0hEoICHDx8XSAq33cIMANAgq6OI0urC2aslU0tVQz+7kapubLSDaZJBZrnDpQCnTSQQ0VINBgKEJ2Cb5hkHcHKFz+RYb5BjWbHyXUfTbf9rfy+RlxfqQr+Y9N5ZxRPpub21byurrlJKzw+L/ZQgghhBBCCCFelSR8EuIF3NK2kkLgcW3DGdzblsVSil2pPDE/zHk1zTjbNtMV30XV4DPoqiYKr/8GkV/tQEdf+FdLa8i3wI5Nw/wltZ3Hy3eSKS/gYDIjWsrFJTNoipfTFC9jSqSEupoSuruHD3uunnyGDUNdbBjsZsNgF7dE10IDONpkyUAjF9w+n9mxchJLNCWxEE6gURkXHXdQSmHYMYJCCgwTdfE/c0v/Z3lDx9Ms7FvBx421/Gd9I9/bq/jgkjN4wt/GzW1P866p50zE2y2EEEIIIYQQ4lVIwichnse+IttnV8xjSrSCrUM7MHUIjwGu6A2w2x8lUxqQzK+kEC9Bv+HbEIqj0i5BeeSw59QBDK4LeGxLOw/Gt7GppB0zolgRn8KVM5pYVFKFqY6uFFtlKMp5VdM5r2o6AAOFHBuGulg70MmD5m5Wlu1hVq6SC56dzynD9VSEIdLmYc5xALBC5ajcHnwvR9wJ89umN1ATvZ8zdqzlvJ4l1Jc+yYcTZ/LY46WctWwut7Q/w9V1p5KwD/8ahRBCCCGEEEKIA0n4JMTzuK39WbJ+gWsbz8DXmq1DOTw/TlUwzHtb29ARB8O9FdcK6Ln041RGSwFQaRcdsw85X7rT58aV27gvsoX+hgzlRoS315/EZfWzKHfGL8gpdcKcVTmVsyqn8t7pJ3Nv505ubdvCz8OPUOlHObd1DmfeU0XFFkXZOZpwpAZ3cA+5bCexxDRmxBP8bMolzC5ZTdWzW5mzHb7UFOcrNUv44IblPDZlC39qe5r3TDt33PoshBBCCCGEEOLVS8InIQ4j6xe4pX0lK8qamBmr4a/tGTwdsLDf4+PtbdiBj28+jK/yPF0HJ1UvLj6x4KPcYEz4FOTh0Sc7uc59hs7SIRbZNXxo1lJOr2g46lFORytq2by+YS5X1s/m6b42bt27iT+Za7ijcT2Xdi7iouvnYS2YgsvTpIZ2FMOnWJxn+npR536A33T9B29r3sPZ2+FNM2P8NjqLMwpz+HP7s1xdfyoldvTFOyGEEEIIIYQQ4oQ2sd98hXiFuqNjNcNejrc0nkne1/zyuTRozSd7NzArXcCPrgY1wOoZpaQcCDtlQHHUE4CO2WgNzZsyfOX+x/iW+QA6FPCl2efyrRUX8JrKKS8peNJa4wUeOT+D1vpFjzeVwRkVjXxz8UX899O1LKOEW+vW8rVFd9LWUQ1Af9s2AGbEEwRoWrNZnp73Jm6dWYM2uvnAroeYlu8i3b2CXFDgj61PHXP/hRBCCCGEEEKcOGTkkxAHyfsuN7U9zSkl05mXqOf/Nrj06Rxv6OpkZn8bXZHdlOld5N/wfYbXfBJthFEjQdK+8CmjbX53z2ZuiazHTwS8pfwkrp03H8cwX/T6gfZpzexix/B6dgxvpCWzA58COS+PGxRwg+JaEwAQMiLURqZSG5lKXWQadZFp1EanURVqwDIO+hU3FbN1nK/0lfL4uYv56Y5n+W19Nx8ags7du6jqU0w9NQ7A7nSKk0pj/LDiIi4sf5zE00N8e+dtvGvetczun8tt6lleX7ecylBiHN99IYQQQgghhBCvNhI+CXGQv3Sto99N8+nG17N1wOfPLQFLM518ZM8OhsJpSvXTFC7/OkHNXLSXxYiU739yyuXxyiQ/7HuStvggp5j1fGTJMuqi8ee9Xt7PsmN4A9uHN7BjeAO7Us+R8zMAJO1ypsfnUhotJXANLOXgGCFsw8EyHCxlMVDooT27h61Da3mq597R85rKojE6i1PKz2ZZxfnURBoB0EkHNVRgWVkdP156OTc1r8Nf/0fa49vYNLiBi/4wD2eBwc7UMK+pbOQ3u7t4atG1nJP7Nc5al//e9gfePu91eP42btj9KB+fe/nE/CCEEEIIIYQQQrwqSPgkxAHcwOfG1qdYkGhkXryRf37AozGT41O71uFbAVH+wj1zX8c5M8/ELwyjdIAdLoZPfl5z/ZY2fr94I2U6wmdnnM2Z9Q0opQ57rbbMLh7quJUne+4h52dQKBqis1hReTGzEouYFV9IqW/ipVoIBV30d2/DHW5Be1mUYYGyUKaFUhYYcZSxhMAKkY0k6QlbtJo5NqU2ckvzL7il+RdMiTaxrOI8ziybQl1nEgDbMLl22ik8sNGijBzX16/jqeqdlGdL2LorxTsrIjiGYv1ghtMv/wgq+/+IbbX48bY7+PsFJ/GXnnVc3bicqbGqSfsZCSGEEEIIIYR4ZZHwSYgDPNC9ge7CEB+bdRl/2uDT7Guua36QqkIeHbqPuyrn07PoDQBkM20AhCM19PcU+O6zT7GmooUVPWX848Xnkogdegc7L3BZ3fcID3XcwrbhtVjKZlnFeZxedQnTQ9PRnevItj9JYevvyQy3kPbzo89VVgQ7MQXDiaMDH/wMQcFDBx5oD+17+IVBcNNUApUoliUaIHEGHU7AxnQ7d6R+wS0NMLWkjqUtV3BWzZUk7TIMO0bUzfK1hefxg+1P02G10usl6PztKcw9Oca6/jQog/zVn0Dd8D0aWhJ8bVsbn1hs83+rH+ILZ71pMn48QgghhBBCCCFegSR8EmKErwP+0Poks2O1lOtp/Knd43M71tCQaadgr2Gwtpbv17+WLydKABge2g5AKlfJx9fdTX80w/uGm3jHRpfMG8YGT735Th7pvI1Hu+5gyO2jMlTHNVM/yOklZ2J0riez9k90dz4LgYcZLscpm0OkZil2YgpWYgq1M+YzkA497yiqfbTW+JkuCgPbKfTvKK4HtlOZauNc4FwzRNqpYT1p7tzzC+5o+RVn1VxBvZNA5QY5ubSG/zrlcj63/q9sT3fz3bn3Mbt5HuurUwxkPEqjFrm3fRL1P//Byb0JPrPF4d8W7GDTc3uYv2DahPxchBBCCCGEEEK8skn4JMSIv/Zsoi3Xz2fnXM2Pn/Y4o7+dc4dWUbDa6In18cCpX8Rr76QpXiywPTy8C4Cbc8NoQ/PNpotY+ngfxIZGz5lyB7m95Toe6rgVjeakstM5r+wipgz0k9n8CP09PwcdYMXqSc65hmjjOYQq5o8WMN/HiSVQmeEXfQ1KKaxYDVashmjDa0bbAzdNvm8LmZZHMXc9yOleP6e32PSUlPF46lZcP2AmsLdvFdMqlvN3M5by6XWP0xoZoK3+KQK/lkfvSnPphSXYpRbZ93yU6E9/xCXdUTp35fhp+YP8oO6dUPbiBdWFEEIIIYQQQpxYJHwSAgi05vctTzAtUknnrhkMuGl+2PwA2sqSCa3m7hVfYpsbUBeOELdtUhmXtXvXUQuUM4evrLiM0kgIle5Ex2y8wOPhzlu4rfk6sn6ac2qu5JKySzF2PkBq/bfp9/PYJdMpWfBOYo1nY5fOetFRTS+FYceI1CwlUrOUqrJ3oG+8m77T2jCHnuR1A5oARc7U3LHqn1BTz+G82regCHNl7Sw2DjezZbiFm0oKzPndOTScbxObGyHzvg8Q/ekveOfecrrCm3nyto2c/raTwJq41yGEEEIIIYQQ4pXHePFDhHj1e6JvK3uyPVxUcjp/6vb59rYHsIMsvvVX/m36u2mYModtw8PMTiTp6MzwqSfuR+tufAX/ctZrKY2EiidKF1hduZmvrH0vv9/9Q6bF5/KFmV/lkq4C6b98nOHttxKbej71l/0vDZdfR9lJ78Upa5rQ4OlgOhkhlp1GVfm7aHzdH6i96EcY9csJ+/DaXsWS5x7jrpUfpt76Pe2ZVr6z+CIanEY6w118Y97drH1okN77FEEoSfbdb0cbmk9uq+Sp2K2Yt2Ym7XUIIYQQQgghhHhlkPBJnPD0yKinunAZTz83i/e1Psu0fBeB9RRPn3wNqxLzmBK16c7nSBZM/mnjX+i2hqlRPr5hEHGK0/DaMrv4zrQf8x+1PwY0H6v9CG/rjeA9+AUyzQ+RmP0GGq/8DZUrPo1TOvP4vd6kA4AaKqCUQbhyIeVL/4H2KGSqZjHFruVvehQfbt1GbevXuGXHD7iwegqGN4NCxOM7C+/hwbbddP5OUTBryF77OtBhPrNdcVfvbVgrC8fttQkhhBBCCCGEePmR8Emc8J4Z2Mm2dAdzs6dSlW7m6p71BOZ23GXL+FPZWUyLhejMZ9DkuXtwA8pQfGveRZhBBmVFcYM8f9z9E76y9m/ZEd/Lu9NX8uHBWuJP/Zh81xpKFr6Lxqt+T8XSj2LFao73ywXHRIdN1ND+O+klYlPxFQw5isbX/prqs7+Ojs7ion5Y9OytWJveT7V+grc2nM7sZDm/nPk4v4k/Q+tvNWk9g9RlZ2ME5Vw1uJ6he9ZgtPjH8QUKIYQQQgghhHg5kZpP4oQWaM11ex+m0iqhta2en+76M4HRhzvLIHv2R9j0+BYurC3j1i3bQA1R45byb8vOpSJu0hG4aLuMr6/7e9qyuziv9FIue3iIodK7ce0IZUs+SKLpdRh29Hi/zEMEiRDG8P4RSkoZ+JaDm+1CGSbRhteQcZr477V/4aPmGk7pfJaThm5hdf4B3rPsX3k0Podb2UpzvJ/33XkWU09dQuviFuavUyTDv8X6dZTcRxdAXPJtIYQQQgghhDjRyTdDcUJ7uOc5dqa7KOk5ja/uvA+HLF7VHgpXfont6QJZ36e1ay+r/N2EdIT/OPMiqkuiZDNtALR4XaTdQT6ZfCtnb3qGodInSZaeS8MVv6Zk/ltelsETFKfeqaGx0+OMUCkUhtFaAzAjnqDZqaNl/idpfO2v2Rufw4qBYdSDn2N68w/5h+nT2Rvt4xsn3cWqdT2Egit4qkYRcefg2T8m/H87wNPH4dUJIYQQQgghhHg5kfBJnLDcwOdXex+hwq/i7c3dTMkP48fWU7jmyxCKs7pvkMBs5ll2EQpinF45i1jYpjPbwg0bPwdAmV3NJwbrCK//HZZdzszdH6Rq9scww2XH98W9iGL4lB/T5sTqUDogn2kHoDYcIWyY7EqlcJJTSC/+Bt8s/ydyJVOY076bqU98hk8Ya0iENf9v3n3cObyVTORqNiRzhN2FBNlv49y4F7QEUEIIIYQQQghxIpPwSZyw7upcQ0d+gLObZ3BJ3y58exP5v/kYuqSedM7l5r2rwBji6uAk8kSYk0zyUMfN/Nu692GmOinJw5KOHvyhFipO/RQNM79ONDcFHbOP90t7UTrhoNIu+PuDoViyCYCBvnUAGEoxLRZnV3oYgJNKY7TZ0xlY8kNC53yZTDjO1N2P87HmH/M3ejs3TnmGOxI7+X3DbFrDPo57Eqrl37Ae6pj8FyiEEEIIIYQQ4mVDwidxQsr4eX7T/BiL+mr5xz2bCYxO8pdfTtCwmL5Ujk89cT9pY4jFzOWkhTWYZNnY931+s+v7nK6mc16/QdyDyIxLabjiehKzrsTIBADo6Mu/lFqQDKE0qNT+qXclZQvRwEDf2tG2GfE4u9IpAq2ZHg+TsEzW9aepqz+PZVf8mZ6T30LKCDiv9T6+0vc7+iKPsS2p+ddFmkHbxPIXYK38IuaG3uPwKoUQQgghhBBCvBy8/L8lCzEBbm5bSZDN8K1NNgZZcqfW4i+8nPb+NJ9b8yC9ZgbDn8YVC2axqnclVdYv6Eil+UiwlJI9a/AMRXvU5MwVnx49p0q7AOjo+Ix88j1IZyCTgs4Ol84ORSYN6ZEln1MjR2pQoGD/GjBNiEYhGodYDGIxTTRW3E5EQoQANVRAl4QASESn4CpIDW4b7cP8ZAl3t7eyO51iZjzBorIYa/tTaK0xDIPl8z7I4Mw38ciqLzC1eSP/2H07G6KruTl5Gh9fnOanqxOEglmouz5Lruzb6IaScXlvhBBCCCGEEEK8ckj4JE44A4U0f9z7FP+5Lk7SC3BnpvHO/0d2dg/yhY0Pkjc8Loydwn1DWVLug6zu/U8aCxHeO1wJ6dXEm17PpvZb0bHqMedVaRcdtsA6tgGF+Tx0d0FXp6K7C4YG1QF7CygDYlGIxaG+AcJhjVL7SyodvPY9yGSKQVV3J3jegf2qwGk6nfK1ARWDUFEJ8ZJ6Cgbk062jRy0uKQdg3UAfM+MJTqtI8kT3ENuHs8xOFouplzgVXHn6j1nf9FeeXv0tTu5r4bOZNh5MTOeLC+bzjQ0lGEED4d99luz7vwPJl2cRdiGEEEIIIYQQE0PCJ3HCuWHbY/z9Do8FqSiF0r3kr/4CG9v6+cr2hzC1wdemXchvB7uoC/+WW/Y8ztmD5bxmcBAzEqXyvO/ixypR7bcQTswYc16Vdo+q3lOhUAybujsVXV0wOFAMm0xTU1kFjVMC4oniSKUpU2NkMmmUepGTPg+twXU16RTF0VP9AdnHeugMV7JxvaI4ZCpBqCSE9jLs3NpLTX0FVfEw9ZEo6wb6eUPjNE6rTGAqeLx7aDR82uekynOYfcFy7trxI9TWO7hweBdpYy83TTuLN++pBS9H+JefIfeBb0IkcmwvRAghhBBCCCHEK46ET+KE0p7qJ7NtFW9sTeLZ7eTf/glWtg/xjT2PkPBDfHXO+YTKh2hp/yq1bgtvGywnlumnv+YsFr/mXzGdBDu33wBAacWSMec2jiB8cl1obYY9u4uBE1phmJrKSli0OKCqGsrLwTDHPi8eN8hmj/11KwWOA045lJUDjYrYrTtwZ2VJnTedvl5Nb49iW1s10MyGNTt49tkqEklNY3kZ6wc68PyAhG2xuDTO492DvGtmDeqgNCxsRrl6zj+zu/5Kbtv4NU5qb2F+8DBbK5cwp2ceOr+V8C/+mdz7vwXh2LG/ICGEEEIIIYQQrxgSPokTyu//+he+uDmCVkPkrr2WBzvzfL/9CWoKSf7tpPPodNbw83X/xrLhApcN2Gg74Gdlb+I9y96B6SSAYkFuDdRUv2bMuVXaJag6dEqZDqCrC3bvUrQ2g+8r4nHN/AVQUxtQXlGszzSplCre8W44j+NAbR3U1mn6ghmwt5l587agQqfR1qqIdJaTLW/ll3ekWFJVwvxECauzrexO55gRP/wIpunx+Xzo1P/jL62/5Q9b/5cLzbWUFwwqh+ZAbgOhX/4T+fd+B8KJSX7hQgghhBBCCCEmm4RP4oSxceVuPvrcHkwdYviik7l9OMTPeh9jZr6SLy89i0fSv+G+7b/iqr4Yc9Ieds1y/trwVp7rHGB6LD56nuzwTlzDIBGbOub8Ku2ip+8f+TQ0WAyc9u6GbFZh25ppM2Da9ICKSo55Ct140ckQxlBhTFtl2WI6mv9KIbeRRYth9lzNgnQZDz8Lw+V9tLWVUPBKUFWt/HblEG+dFqGhsTiq6mCWYfHaKe+kLLKAH275GmcmVvGu7TZVw4sgtQ7nlx+j8J7vQ0SKkAshhBBCCCHEq5mET+LE0OdT8eTvKXWTdM42uMWZyg19K1mUreOTy07iV51fYKBrJR/ui+AUcqysfhNvPO9DPLdhDdOiMUIjQ5O01gTZPoxw6dgpZ16AynoEUZu2VtiySdHTrVBKU1sHS5YG1DcchxFOLyBIOJjtqTFtVSWL2KsgNbBlf1vMYVo0Rr/Tx+vOnE5vj8mzW2JsyQ/yzFN1rFqpqa2HqdM0dfVgHfSvyhmVy3h28IPcq25nw5I1fG6DQdPAYnR6Ndb/vofCO3+CkaydjJcshBBCCCGEEOI4OLbbcgnxSuJr9v7uf5mdStKa7OX/pp/KDcNrWZGext8treH7uz9EafMq3t1pEjZL+EHZJ0jMeyug2D48TFMiOXqqXLYdpX3CiWljLqG7s2xLVnP7QCOP/dUgnYbFJwdc+XrNWedqpkx9eQVPADrpoIYK+2+PB5QlmnBNEy/fh+fuD6YWl5bz3NAAPsW6VBfPSNJv5pl/ToZZs6GvF558zOC2mxVPPa5ob4Mg2H+t9047F8eeix9/A19dHLCqohnLPQU7W471f28n89zN6AP6IYQQQgghhBDi1UPCJ/GqN/zru1gwWGDA6eWnS87i9twWLkjP5tyFvfxsyz/w2tZ+zusPiDWew8oF32CPM4MVlQnaclmGPZfZ8f3hU3fXkwCUlJ0EFAuIb9kEdzwa5dG6uShLcdrpAa+9SjN3PoRfxjd100kH5QWQ9UbbTMMmHJ8CQHpw+2j7ktIy8kHAluFBAM6oKk6V2+gOcfJSzZWv05x7QcCUadDeDo8+XAyiVj2j6O2BqBnmgzMuYmd2iHOmXsN/LjiHZyo6sdzlOIWpJO7/AT23vo9cz4ZJfAeEEEIIIYQQQkwGmXYnXt3ufpa69m0UzDxfOnk2q2nnqswcwjPu47Gtd/J3fTbhQFGx/J+Iz7qSx57ZzrySKGWOzV9b2wE4pax89HT9PavQQEnJGaxbo9ixHTxXUWNnOatlOyWfmI+yj3MxpyOkEyEAjOECQXR/raqyilPI9u9maGATJZUnA7CopAwFrBvoZ1FJGRUhm3nJKI93D3Lt9GqUAdU1UF2jWboMOjo0e3crdu2EHdsM4gnN1GlzOTU5m+tbHuZLC97OD7xT+MTmGzil/1RcFVC/ZyetQ//A0KzzKVvyAex43XF4V4QQQgghhBBCjDcZ+SRetYwNu4mueZBAaf5lUYjVoYBrClPpqf4xzvY7eWuXIhFtoP6Sn5JouorOnMuuVI7TK4sjnVb29jAlGqM2sv8OdunB7bgKnnh4Dls2F+8Sd+ElAZe4O6mP5FH2K+dXKkgWq4Srg4qOV1eeigf0964abYvbNrPiCdYN9I22nVmVZFcqR3s2P+b5hgn1DXD6azSvu1qzfEVAJALPbTCYs/FKYn6C7229hQ8tjPOtpr9nQ0mUcH4FaWbQ2Afm1kdovfNd9K35GX5heOLeACGEEEIIIYQQk+KV801ZiKPR3U/ojt9i6DD/PmeYtfEqriHG7tDXuXDXLs4cUiRmXkndJT/DKZ0JwBPd+6eUZTyPDYP9nFpeCRSn121cr3Ez3RRI0Nhoc9kVmjNeoymvAKMrg18dfd7uvBzpxL7waWx4VFWykIIB6aHtY9oXl5azeWiQnO8D+6fePd419LzXsG2YMRPOu1Bzxes0y08KcUXqbxj2M3x//W1cYoX54ow3sbGslorMafQbC2gc8AilEgxt+i2tt72VgQ2/InDT4/nShRBCCCGEEEJMIgmfxKtPLkf4up9gBaX8emovD1bWc5nTw0DuP3hHa446P0TVmV+i8rRPYVjh0ac92TPEjHiY2ojDmoE+PK1ZVlrJ1s1w522KTRt7MfGIljSy4kxNIjHyxIKP6s8RvFLDp+GxI5/CTimEkni5PgJ/fzC1pLQMT2s2Dw0AUBNxaEpERkO7FxONwbwF8I5La3hf3aW0hXez1n2UU9JRPjXttTxeMYva9Em0myuYOdhPJu+QduoZ2PC/tNz2Vgaeu4HAzYzLaxdCCCGEEEIIMXkkfBKvLr5L6GffxfbKeKSyh19Oi3G6vZa6nj/xNz0QK51Nw2X/S2zq+WOe1p932TyYGZ1y91RPNxFlsfuRUtauNigthcVLVgJQVrlwzHON7gwKCKpjk/EKx49lEMRsjIOm3QHEEjNRQGpo52jbgpJSTKVYO9A/2vaaqhK2DmfZm84d8WWVgqtnLuaymiWsjj3BvCXbuCLq8OUp5/LnypOYkp5Om3EJy/o9Qt1beLB0On5iOgPrfk7L7W9lcNPvCLzsS3rpQgghhBBCCCEmj4RP4tUj8Aj/z7exM2Vsiw/zpflZTtOPcGHbOpamFMl511J/4X8dtpD1Uz1DaOD0yhKamzWPd/RSk64kHjE49/yAcy/QFHIr0UBl1YoxzzW6iqNxguqX8a3tnodOOIfUfAIor1oOQF/PytG2iGkxJ5EcU/fporoyHENxW0vvUV/7QzMuZnaslv/pu4NLThvgHxfb/Gjqafy6YQVTM6V08UaahkK8dcdubvLXsbL+NRilM+lf+1NabntbMYQqpI7hVQshhBBCCCGEmEwSPolXBx0Q+vV3sXpL6AwV+NSSvVyRfoK3tA1SpRLUnPttyk/+EMq0D/v0J3qGqHYcdj0V5vanUmSNAudPq+CCizXVtcVjUoNbcRVUlC4Y81yjK4M2FbrsFRg+JZ1Daj4B1Fa9hgDo61k1pn1xaTnbhofJeB4AJY7FeTWlPNjRz5DrHdW1HcPic3OvxlIGX9tyM8trPT691OI3dYv42exzqc0ZFNy3kqeGz2wGp+sxvmFvZP3MizBLptK/9qc0//nN9K3+EV6645jfAyGEEEIIIYQQE0vCJ/HKpzXO7/8Duy1Kv+3xhZNX857+9VzSr4nULGPqa/+PSN1pz/v0zgGftX1pKvtLSA0pvJldGMBFTRUotf84N9NJYEdwrPiY5xvdGYKqKJiKV5qgMorRmwU3GNNeGp+OaxpkhneNaV9SWkaAZuPgwGjb66ZUUgg0d7f2cbRqwiX8y5zXsSfTzQ923M2ySsXnllrcWtLE9xZeQonvUz94MS3OGbylBT6yzebB7H18PbSVp6e8FqPyVIa2/omW299G1+NfId+7+ZjeByGEEEIIIYQQE0fCJ/HKpjXqph/g7LJIWQE/XfAQ7+vZw4ycSdnSj1J/7ncxw+WHfWohD2tWKf734SECNGfXJbn8Ks0OepibLKHEdvYfm+tF+QWcaP0h5zG6Mq+4YuP7+FMSKF9jtI+dvqaUgRWpQucH0dofbZ+XLMExDJ7p7xltmxoLc3JZnDtbe3GDsSHWkVhWOoN3Tj2Hh3qe4+b2lSypMPjiMouHo4388ymvx4vFmTkwlbR+G0sGs3x5Y5QZg7XcZ9zBN+xnuKv0jaRLribT+jTt936Q9vs/RqblUXRwdCOxhBBCCCGEEEJMDAmfxCuX1uRv+y9iWwNyJtw1816uGhrAc8qYcukvKJlzDUodOhrJ92HLpuId7LZu0ewq6aExEuK1SyMMB3m2p4Y5tbxyzHMG+tYBEC+dO/ZkWQ9jqPCKDZ+CKcVb9pl7hw7ZFyuZg0IzNLB/NJFjmKyoqOLhrg7y/v5Q6vVTKukreDzWdWR3vjvYtQ1ncGb5HH6++wFuaVvJwnKDLy+32GaV8K6Fr6O7YTYlOR87+7fESPC5rbt5w665xHJTWZ38PT+O/4Wbom9kt/E+0n1ddD36eZr//Bb61/4cd7jlmPokhBBCCCGEEGJ8SPgkXpl0QP/N36HiuRyuYbK24X7mu2lWljUx57U34JTOPPQpGvbuhrvvUKxbY1BRCTPOTtOhc7xuSgVKKZ7pK47oOa2iasxze7v3FRs/dUy70T1SbLzqlRk+6bhDUB7GbB4+ZF9V1RkAdHQ8NKb9stoGUp7H4z1do22nlMdpjIb4c0svWuuj7oehFJ+e83rOLJ/Dz3bfzx9anmBuqcE3V9iEwhbvqDuHJxedjaEzlPRfhpd8Ldd0beVzW1qY0XMO0cJUNpdfxx+mXM8fypex3vgYvdk5DGz6Ha13vIPdd36M/q13y13yhBBCCCGEEOI4kPBJvOJoz6Xzt59lytaAgBC7ah5CO1l+UXM6Z5/7XSJO7JDndHfB/X9RPPWEgW3DOecFnH2e5sHBXmKWwXm1ZQA83ddDVSjEtOjYcwwPbBopNr5oTPv+O929MsMnAH9KErN5qJjOHaCu9lw8BX3tj41pP6m0jLpwhHs6WkfbDKW4qrGC7cNZnhvMHFM/bMPkM3Nez7mV8/nl3oe5fu8jNMbg26fbnFql+Lwzh5+d8Tq0bRPtSKD0xyl3Svjqjke4stXFGbqWuqEVtETv4bYpP+DOpjzPVP8rLeoD5Ab7GFz1TXb98Rq23PkdmjesJpeVaXlCCCGEEEIIMRms490BIY5GLpei74ZPM6d7ClpH2VP7V9ZUxbkuuZTPnPROasOlY44fGoR1axXtrYpIVHPqioBp00EZ0JNzebx7kNc3VhI2DdwgYE1/HxfU1B0yXa+QbsM3baKhsSOijK4M2jHQJaEJfuUTx5+awF7bherNoSv337HPseN4kTLMdCuFXC9OuAIoBk2X1jVw3a7tNGfSTBkJ6i6oLeP6nZ38uaWHhaWHBoBHwjJM/nn2VTjK4oaWxyhon/dOPZd/Pdnipl0Bv9lWzvoVb+Ab258k2bWNaPZS3GkZLun5A2cOtvCjKSvYoz7A7KCD7tKHuDfydarnNLIifjXT+hox2h8iMnQ//oY7aF5fQtp+DbrsHGJ1SymvcigtA0v+VRRCCCGEEEKIcSVfs8Qrxp7uFiJ//BxzBk9CY7Ox/nEemH0uN5LiA9Mv5JTS6aPH5rKwcYNi1w4wTVi0OGDOXDAP+MTf1dYLGl7bUAxV1g/0kwv8Q+o95XO94Oewko2HhFJG18id7oxX3p3u9vGnJAEwm4fwDgifABJVS3H33E9Xy700Nr1ltP2imnqu372De9pb+btZcwAImQaX1pdz095umtM5psTCx9QfUxl8oum12IbFja1PUghc/n76RVwz02RWUvG9dfCO6efwlbmzOfnRR3D2BvixDxNLPsBndj3I3ugGflK/nLb021jqZcmaj3Fbzw+xlM2ieSs4tfQfqes28VqeJDn0MEb3nfhdUfZwBmvV2QTJFZRVRCmr0JSUQLIEHOfF+y2EEEIIIYQQ4vAkfBIve1pr7t3wJK95+PvUDJ9BYATcMW0v2076J24evI+LqhbxhrrlABQKsGWTYtsWCAKY2QQLFmnCB+UgBT/gnrY+TqtMUhMpJgsr+3oIGQaLS8vGHNveei8AldWnH9I3oyuDP7fskPZXEl0ZQYdNzL1DeKfUjNlXXX0GO/feT0fzHWPCp1LH4fSKKh7obOddM2bhGCYAr5tSyd1tffxkaxtfP3nGYQu+HwlDKf5h5iU4hskt7c/gBj4fmXkpJ1cafO8Mm2+t8fjnoTrOu+gaPr5uNfGO9TippXhTz6MxfxPf2H4X2xNT+UH9KezMXMRpqSspr9jOluG/sqb/UcJmjKWzz+a08i8wLReQ2vM4ZtujVLj3o4dMMsML6Ni5jG0sJcUCQhG7GEQlIVmiSUooJYQQQgghhBBHTMIn8bI27Ba44eH/5oMbHiGefQ2uWeAbs+ZQe8rbubvrBubE6/jorMvwPcW2rcXgyXUVU6ZqFp6kSSQPf95HugYZcn2ubCyOesr6Hg91dbCsvIKQaY45tm3PrXgKmma8bUy7ShUwMi7uK7jeEwCGGqn7dJii4yWLWGdBKLWX9PAuYokZo/suq2vg0Z4unujp5tzqWgBKHYv3zKrlv7a0cl9HPxfXlR9zt5RSfGD6hYQMm9+3PsGgm+Fjsy6nOhLhmyssbt4V8Med8PS05Xx4dhOXPP4IdnMXWl1FoQlm9f6RH265lU3lc/l+zSIecWtZ2PsxzormKFStZlXfIzzefTdJu5ylNeeyaO4/M71g4XeuJdS5mljfr4Dr0CqEy0kMDS2jo+sUtgez0dgAOI4mFodYDGJxiMf16HY0BoZU1RNCCCGEEEIICZ/Ey9e6znbaHv4En2i2sQpnk7ILfHjuW7nopFLu6LuesGnz2aar2bPNYtNGRT6vqGvQLDopoPQFBiNprbmtpYdpsRAnjdQm+kt7G8Oey980Th9zbC7bjZ9uw0g0EBmpebTP6J3uqo+tvtHLiT8lgbWtHzIuRO3R9li4Fj9chnb76W75C7H5fz+6b3FpOTXhCHe3t46GTwAX1ZXxQEc/v9zewakVSUqdY/9nRinFu6eeQ8IKc93eh/ngml/wiabXclrZLN48y+SsWoP/3uTx3d5Sbj37Kj7bvZ3GjasIbRsmsN+IOzPPvPY/8rO+LbSWzeL6srncaDcQ6ZnLafnzmFXbS3voSR7vvouHOm/GUjazk4tZuOg8FsQ+QtnwALmu1eQ6V+EM/oxKANNGxZrwnLlkjfkMBfMY6J9Ka6uBDg5Im1RxxF0kUlzCEYhE9JjH4TA4IQmphBBCCCGEEK9uEj6Jlx038PnDE9dx8abrOb1/Doa3hA4nzIfmvZs3LgyzMnMTXflBPl76NlbeW0I2o6iu0SxaHFBR+eLn3zSYYWcqx4fnNqCUwg0Cbm7Zw6KSUuYlS8Ycu3PbdSigbvrVh5zn1XCnu338qfvqPg3jz90/WkkpxbT6S+jd9ns6m+9m2rz3o1QxKTGU4tLaen61ewetmTQNI4XHDaX4yNwGPr5yO/+zvZ1/WjDlJfVNKcU1DSs4uXQ63912O1/adCOX1Szh/dMvoD4W4kvLLB7tCPjfzT7vCzdxxVVNvGf9NpI71xDakiNw3oY706eu707+deedfDxayYMVy/hF2TAPphwaul/DCvNNNFT3MxTdyHNDK/njnh8DUOpUsaBkOU0Nb2S6XU9pqp9C3xbyvZuh/x7i3i3EgQYrilM5ByM+C9+ZTsGYTlrPIJtPkM1COg09PVDIHz5lsh1NKMT+JVxcO47GtsF2KK7t4lS/fW0HDdITQgghhBBCiJclCZ/Ey8rTLc/Ss/KrvK01TTx9JkbQyKrYFL40+wLeOt9kXe4Onh3YxQXDl9O/eyrlFcU72NXUvvi5AQKtuW5HB0nb5LyaUgAe7uqgp5Dno3PmjzlWa01f24MUDIMZU153yLmMrgw6YqHj9iH7XmmC+jjaUJjNQ2PCJ4B5U9/IX3b9gWi+j8HeNZRWLh3dd3FtPTfs2ck9HW387czZo+1TYmHeOK2K3+3u4oLaUk4pT7zkPs6K1fCDxe/m+uZH+VPrU6wZ2M0nm67gpJKpnF1nsrTS4PptPrc3B9xTNZuL5zXxrjXbKW9bQ2hzGm1ejjclhq2e5vLme7isPcSuqqX8OTKVW0t78QdMyroaOVmdxt9UhnDKd7E19Sxr+h7l8e67AAibUabH5jFj9iJmxN7INJ3EHm4n37eZfO8Wcs13oL0cADEgGS7HTk7Hrp6GnZyKEa3HN+spqBryeYdcHvI5yOcV+ZHtVAr6eiGfB61feEiUYewPp5wDAqr9YZXGNMGyi3fxO+wyss8w4BhLdAkhhBBCCCHEC5LwSbws9Ob6ufvRz3J2+yZe01OKk78EpRP8su40bm5YxIcXBvyh5Sa2eXs4te9CljonM/+cgLr6o/vCfH9HP5uHMnx8XiNh0yDQmj8272ZGLM7SsrHT6gb71oKbJlx1EqYZOuRcRlemOOrp1fCN3TEJamOHrfsUC1dTXX8R/u576dhz+5jwqcwJsaKikvs623jn9FnYB8wfe+PUKh7pHOAnW9r44WmzCZkvfW6ZY1j87bTzWFHWxPe2386/bvwNV9edyrunnUvMtvj7BRavnaq5ZbfP3W0BdzfO5tzFTbxn2y7qdmzG2tWOUjPwS5cSlLUzo+cBPuE+wUfDSXZXnMK9kZncFs/x4GCA3W8yP7iA5Yk301jrYoTaaUlvYWfqOe5p+y2B9gFI2uXURaZR1zSXusjF1KkklYUAK92HN7SHwuBuUrvuRnvZA16JwoxWEYvVURqvx4rVYZZXYUWrMKNVWJEqlBXF9zQFF1wX3MLI4lJsK4Drqv1tI+tMpriv4ELgH8V7rvTzB1QHBlUmWPb+Y80XON6UUEsIIYQQQggBKK21Pt6deKl6e1MEwSv+ZZyQfM/l3qf+ndq2B5k9AJWDMzDdZRSI8OnZF9NTVcsVMZffpW6k1+7kCu8K3jRvEVXVR/+Fdsj1+NBTW5kSDfHvp8zEUIrHe7r49+fW8c/zFo2pWwSw6sl/JNW9iqbTv0V91UF3utOa2DefwjupivyVs17am3AEqqoSdHcfGgyNJ+fundjPdJL+9AqwxoYWA6mdPPHX95LQFmdcegemtf/2gWv7+/jc+lW8deoM3j597Huxvj/F59bsYkVlkn9dOBXLGL8UIusX+J/dD3JH52oqnARvqFvO5TVLiI30rSeruXW3z72tAXkfTqtQXN0zxCmrNmMGO1BmGm3Y+FUxgvBeVN8jKC9FEC2nvfwUnrYauSOSYGfMBcDUiqm6jHmRSuZUJymN9TNQ2EVbZhft2T20Z/eQ89Oj/YuaCWojUygP1VLuVFGtElT4FomCR6SQhUwPXqoNL9WOn+s95PUpO1YMoyJVmOHykaUUM1xW3A6VYoTLMEMlKOPw/x8hCMD3wHuxxQXPUy9+rFtca33kP0d1UKhlHhxkHbAvWeKQz+cPCLkOWB8UdEmoJV6pJuPfcyGON/mcixOBfM7Fq51hKCoq4uN2PgmfxHGhA491635OsP2P1Gd8qgZNEtllGP4MWuxqPjHvIiqiUeb2p7mv8g9krCE+0fgGLpzWdMzX/K/NLdzX0c8Pls9mWjyM1pp/WrOSYdflp6eegan2By6Bn+exuy4nb9tceMldo3WO9lF9WWL/uYrca2finVZ3zH06UpPxx83c2EPkxi1k/m4xQeOh0+QeevJDWN3P0XTyZ6idctmYfd/bvIGHuzr53imnMvugWwze3tLDf29r59yaUv5xfiPGOCcG6wb38NuWx1kzuIeoGeLymiW8vm45VaFiP4YKmjv2+ty5JyDlQdKGsxzNVdtbmLV3G8psRRkFNIqgNIwOd6JTq1FeB9owKJQ10RydzyprCn91EmyPDVIwi6Oewtqi0SphWrSEaSVJqqIBtuon43bQnttDV7aF3nwn/YUuPO2O6XfEjJO0y0napZSYSSp0lLLAJOkrop5HqJDHyqcwcoPo/BBBfgAC9+CXDyiMUBIzVIYZLsMIl46GU2a4DCNUgukkMUJJDCf5gmHVkQp8cL3nD7bGtqsX2b8/2DqWUOuFRl7tH7GlRwMs+6Dphpa9v80wJdASE0++rIgTgXzOxYlAPufi1W68wyeZdicmlQ581m/8b/wtN1FecIllYMpgHaZ7KgQR7qw4me9PO4U5OZvp/X3cW/d7AsPlmwvewsJk4zFfd9Ngmr+093P1lEqmxYsjY9YP9rN1eIiPNM0bEzwBtDXfjdI+pbXnHxI8Adiru9AK/DkvcFu9V5hgSjFwMvcOHTZ8mjfnA2zu+QR7d/72kPDp72fNZd1AP/9vy0a+v/Q0HGN/JewrGyvJ+gG/3tlJxDT40Jx61Dh+w19cMo3FJdPYnurgj21PcXPbSm5tf4bzKhdwTf0KpseqeGuTxTUzNKt6NI92BNzXFXBn3RQqp0/hXBVw8dYOprfsxexpQ1lJ4Fy0ZREkAkLZdpp6nqJJ3cmbTI1fOoue0Ey2qTrWqRI2RE2ezrVx//DO0T6FsKg251MTWU5DMsYpoQgJ2yes0gR6kJzXx4DbxZDbz1Chn5b8Xp4r9JPxD/oPmNDIApiYlBollBOhVDskApN4AFFPE/EDQl4BJ9uCM7QDy81h+IXnfc+UFcUIJTCcEsxQcSkGU8WA6sDAat8+ZUVGf26GCSFzf99e2JH/j4Hy8hgdHaniCCt//0irw4dc6rD78vligfexodaRTT8cHaV1QI0s+8B6WfsCrAOmHe5vO/Q4y5IwSwghhBBCCJDwSUwS382x4bmfEmy/nTLXI+9DQ2+SkuwijGAK/WaSb8w6i3WJOl7jWsyZ1s51hT8QNm2+Nv8dTI9VHfu1A81PtrRRGbJ5y/Tq0fYb9+6m1Ha4sPbQkUstu27EU9A06x2HntALsFZ14s8pR5eGD93/CqWTIYLSEGbzMIcbX1NTdjLPRSrID+0mndpLLD51dF/ctvnYnPl8acMart+9c0zxcYA3Tasm6wX8cW83EdPgPbNqxzWAAmiK1/LpOa/nvVPP5eb2ldzduY77ujcwP9HAqWWzOLV0Jiuqazi9xiLraZ7uCnikPeCWXoM/Ta0nPL2eBSas6B7i9D3N1Ka7MPK9KDMAagAIwmAMDlHntlGn13O2GkaHArzkTNLWFNqpZidlbDGTNEcMOkJpNg50k7UOfkcVSWMqZc4cykJhSiNhpibDRE0DS+UxVAaDPErngRyBzuIFWdwgTcZPkfZS9AU5Cn6WfJAl7+dG15oAACuAaACRACL+2O1IkC4u2Q4iaTW6LxQ8//vrK4Vr2XiWg2c5+FaYwA6j7SjaiYIdAyeOCiUw7ORIqJXENsPYysE2HGwjNLIe2VYOlmFjjAS8pqlwRoqnv7gjC7W0hiDQY6YNuu6hUwld94BRWgcdl88f0OYdXS0ty3qBQGtM25EFWsZLL50mhBBCCCHEpJPwSUyo/PBetqz7EXbr05QEmpRWRIZKWDBcg+EuJsDit7XL+L/aRTSaNp+bZbA3soZf7H2ASifB1xdcS0249CX14ZbmHnanc3x60VQiVnFEzqbBAVYP9PGeGU1jRukA5LNduKlm/FgFpfEZh5zP2tSLkXbJn3qEt9h7BfGnJDF3DoCvwRwbDimlmD7nPTSv+R4bnvwnlp/3K0wrMrp/WXkll9U2cHPLHs6oqGJ+SemY579zZg1ZP+Dm5h5Sns/7muqIWmPf+/FQEy7lgzMu5u2NZ3Fn5xoe693Cr/b+lV/t/SvldpzlZTM5tWwmy6tncG59iKGCZn2fZkNfwMZ+zU9Kk/ykdCFhtZB5aOb15ljc0cWMdDflwz0YVgyl9k8t1HmNmS0Q0oOU67UsUBmuVGmCkMKPllGwq8moGnp0Oe26hFaVpDPkMGQXGLKzDNpZWpwehqwcBcN/nldVHAJlUEbMtInZDjHTJm45RGyLpGkRMkwcZWKbARYBlnIxlI+Bi4GHUi5oF3BR+OTwyBPQj0ugPQLt4nk58DKoQgblZjHdLIabx/Lz2J6L7bk4novj5gjn+on6mnCgMTl8kKjR5AwYMiBrQNYcWRuQO6DNNUw8yyFwQrimg7ZCWKNB1aGB1aHtL7bPKe6zQtiOQ3TkGEvZB4WgRxZoHRxm7dt2D27z1P6g64D2bPbQels8z3t4MMPULzp9sNimD9N2aPgltbOEEEIIIcRkkJpPYtxpHdDX8iAtG68jOdCMRtNiWkwdKGPBkEJ5yzGCJJtiDfz7tDMZjCR5T5PJ7Jpe/nPnPWwabuWUkun8y+yrKHViL6kvj3QO8N3nmjmjqljwWilFfyHPP656GqUU/7XsdGLW2Ax264b/pGvXnyib9y4Wzn7fIeeM/O96VKpA5h+WwjgW0H4hkzWn3NzcS+R3m8lfOh33jIZD9gfa5+6H30xiuIeq+guYu/SLY768ZzyPjz77JIZS/HDZ6YRN86Dna67f2clNe7spD9l8dF4Dp5QfOsVvvPUX0jwzsJNn+nfw7MAu0n4eUxnMiFYxfd8Sq2ZGtAoVRNk0ABv6ArYMalpSmsLIiCAF1Pma2cMF5vYNMD07QG1hgLJgkIiRwiSFOqi2k0YDeVB5tMqByqFVHm1bBJZDYEbxzDieKiGvSximjH6/hAGjlEErTMbyyJouWbNAxiyQtYrbWbtAxi5QMD0Khk9eeRSUR0H5eOoFhjA9DxNFyLAIKRN7X5BlGCNrk5Ax0m6YOKYxum0rRVjnCAXDhL0UtjeM46ax/DS2m8L0MlheGtPNYHrZkXUe8wWmBQK4pkneNCkYJnlTFcMqE7JKkzEC0sonpTyyhh4Ns/IGuEee46AwcIwQjhkmZIRxjPDoY8cIEzL3t4WMCI4ZGnkcHt0OGeGR40eOMUI45sjaCGMdQX0trYvTB92DRmONBlpj2tShbQeO5jrKovAHTzU87DTDfXWzDmrbV2/LNA9YRh5LqPX8pEaIOBHI51ycCORzLl7tpOD4YUj49PKQT7Wyc8uv8XY/SMLNkzI1W5wI83qrWN7vo7yFmEE5Q2aE/248g7vLp3N+vcnb52ju6HqCG1ufJGqGeP/0C7iwatFLnpa1um+Yf1u3hznJCF9ZMoOQaeAGAZ9b9yw7UsN8++TlzIqPLY6ttc9j91xFzk9z1sW3EHbG1nQyOtJEf7qG/CXTcc88NJyZKJP2x01rwjdswtw7ROYfTkEnDy3qs63lz2xe/z3KPJix4MM0zLp2zP51A318dt0qLqyp42Nz5h9STwtgy2CGH2xuoSWT5+K6Mt47q464Pf6joA7HC3w2p9pY2b+DbakOdme66Xf336UuaUWYHq2iNlxKhROnzI5j6Dg5N8ZgLkZXOkJLRtGdZTSU2ifia+qyBaZl0jTmhqnLp6jwspSSJqkHiQQpQkEe0w8wXuSfLI0LykUbARgabSoCw0IbFtp08FWIwAjjE8FXMXwVxyOBRwmudshrg6xWZCmu86amYHijYVXB8EYWf8zaNXxc5RfXho83uh3s3x5Ze4ZPQfkEL/ZiDsPQPtGgQDTIE/fzJAOXhF8gHhSI+wWiOk80yBMNckR0jnCQJayzhHQO4wVGKAUoPBXGVQ6eCuEpG085uMrGMyxcZVJQFq5pUFCKgqHIG5qcEZA3NVnlkTdcfMPFI4enCrjk8FQeT+WP+nWaWNiEcQjhqDC2ChM6cL0vtFLF0Co0EmyFrMjoOmSFCNlhQla4uB4Jt0JmGEs5h/xbWZxq+CLTDA8YdXVo20HHeRD4R/vvsT4kkDo4pLIO02aaev+2Uawvti/M2vfYMEbazJE244Bt8+U/NVG+rIgTgXzOxYlAPufi1U7Cp8OQ8On48dJdtO+8mYE995BI9QGwx4FuXc3SvhIWDOUx3fkYupQhM8qN1Uv4U80cqhMWfzffwrRb+cGOu2jJ9nFB1ULeP/1CSu3oS+7XlsEMX1i7i7qIw9dPnkncNtFa81/bNnNPRyv/Mm8R51QfOm1uzdP/SqrzSazaUzn91O8esj9023astd2kP7kcovZL7ueRmsw/bqovS/RHq/HmV5B/49xD9vt+nj8//k7iqR4inmbRGd+jtHLpmGN+vXsHv9+7i5NKyviX+Ysocw4NsQp+wG93d3Hz3m4sQ3FWdQmX1JUzvyQ67vWgXsyAm2FPppvdmW52p4vrrvwQA26a4KCgw0ARt8JEzRBhI4RthDB0CK0d/CCE71u4nknBtcgHFvnAJMACLNAmYKGwsH1IeJq455PwA0rdPPX5QWrcYSq9FGVumqSfI+oXCAcBdqAxNZhaobRJcazSkX/LDtAEhiYwFL6hCAwD3zDxDQvPMPFNC9+08Awb33TwTKe4tsIEhl0MvpS5PwBTJigDMPGVIlAKzwAf8JTGU+COrsEzAjw0Hh4+Ab728fDx8fEoBluBqcn5HgW8YtvI4h+w7eFj6ix2kMHRGZwgQ1hncXSOUJAnpHOEgwIRXSASFJfwmG33BcMrAB9FTjlkDYecYZNXxaWgLPKGSUHtWxQFYyTEGgmyXAMKClwV4CmNr4r9DgyNj4cyfBTeyOLCyJRIhQe4qKMdtaYVYKO0XVzjYOjiWuFg4KCwMUa2TTWyNvavTeVgGjamcrCUVZymaIYIWU5xhJfl4JhhLMPG1AZKG6iguBjBAY+1gfINVGBCYI5sGyjPxPBNdGDg++D7I4Xk/QOWkcdHc8fD56X02FDq+cKrkWOUURzEOubxSNvotqFRBx+jDjr+kOcUR3/te54a2VdZEaO/Pz16DjWy78DtfYsQr1TypVycCORzLl7tJHw6DAmfJpeb7qJt5x8Z3vMg8VQ3AB02bA0lqR6u47yeYcrzUZQ3F0PHGTBj3FC7lD9XNzE9aXLNLJOZJcP8ofUJ7uxcQ02ohI/OvJRlZTPHpX/N6RyfXrWTmGXyraUzKQsVQ6I721r48fbNvGnKdN49o+mQ5+3c+kvatlyHGy3nnPP/gGkcFC7lPGLfW4m3qJL862cf8vyJNNl/3JwH9+I83Ez2XQvxZ5Yesj+V7eD+VZ8k2d9KyIyy9NzrCEdrxhxzf2cbP9q2mbhl85kFJzE/eeh5AHalstzZ2sdfOwfI+gFToiHOri5hXkmMOcnIhNSFOlK+Dhhw0/QWUvQVUvQWUvQWhkl5OVJenrSfI+3lSfv50XU+cPH10U97O5jSBoY2UNosLpj7Q6dAEfV9KlyfCtel3PUoL7iU+h4lrkfcC4gFxbvghX1N2NeEAo0TKGwNlmbkXAYKE7QFFIMkRq6xb1FHOoftCPgUC5d7SuErA08ZY7YDZRSDMFUMxoJ9a8MkMAwCw0CPrFHFbT2SMmhj37r4LT4wFNpQaIORNop1zAyFUh4GeQydR6liYXdD51FBDqVHliCLCrIQZCHIQ5AbXRf35TCCwhFP8dsnUCa+svENm0BZ+IaNryx8ZRdHaWGOvB+KYkylCRR4gIfGV7q4phhuBSPhlq+KAZ5PgK+KoZ5vFKM9X/n4RjG60yogUMXqVgHF7Cqg+Fjva3++/Sj0yOdCY6KxRtbmoe16X5uBxmDf58vAwsTEwMY0iuPCTGVhGRa2srGUhWUUQzBL2SM1u+xicXpsbIr1u0xt4xDC1g42DpYOYQUOtg5h42AEFuZIABYEiiAAP4DAL44K80fWQQB6ZB3oQx/v2z7qH/Q4UErvD6gULxhWjdl+ntDr6M6jj/k849UX8comX8rFiUA+5+LVTsKnw5DwaWIFbprhjpW0N/8Ft3sdiWwKgA5bszkcg0Idy3vjLBscwPIaUUE9CoNus4z/aziZv1RMZ16ZyRtnGmDv5faO1TzZtw2AN9SfyjunnEXYPKLbW72oR7sG+cmWVkxD8a2lM6mLFEfcrB/o5/PrV7G0rILPL1yCedB/2XZ3PMbmlZ+lYNmccd5viEaqDzm3/VQbobt2kfnAEoL68fslPBKT/sfN9Yn+eA2YiswHTwbr0BE2ucIADz3zCaK9u3DCFcw9+TOUVi4fM2ppZ2qYf39uHT35HO+bOYcr6xufd1RT1vN5tHuQv7T1sWUoCxS/7k2NhZiZiFAVsqkMO1SFbMpDFjHLJGqaREwDc5Jqbx0pXwe4gUch8CkE3v5FF9fuQe35wMPTPp72cYP9azfwcD0fz/Xx3JFtz8fz9+/3ggBXe3g6KE6HozgtzqM4Va44Pe6gMExrIoFP1PeI+R7hwCfs+8V14BMKfCKBT8jziQYBMQ8SniLuQ9Qr3hkv7FMMtLTGCYpLcWQWGLoYa4EBuhg8qNEAYn8bh7SNPAe1/3i9v02/wHOKIZka17DsSIwJaJQGVazzpRWg9CHbqOCg7QCt/JEzFbc1frEdH72vTfto5RXXRjBy3gBNQGAEI8eNHKsCAhUcdq0Jis8/4PGRvmX7Ayg1ElSpMSFV8Rh9wLH7/y5rtf/RmPbR/Rzadpjro8a2H+5YPfKCioGaGtMGav+i1NjHI21q32dwZFsphVLG6LaBgVImShkYyiiuMUfbUcWwGGWiVHHUY3FtgrJx7BCeq0BZKG2BMtEYxeBOG6NBXjDyOdfaIMBCK3sk1LPR2qJ4awF7JOwrbgcj+319QFswcmxg42uzeA6tRsM1rTlkm/EYgfaSHFnwZhy0HDiibXQ5oG3/6Df9/PsPajvsuQ/aL2HZoeRLuTgRyOdcvNpJ+HQYEj6Nr8DLM9y9iu6W+8l3riWS6sagOIWmxTFotZKEc1Wc0m+xYDiF5dei/KkobLLK4aGy2dxSPZvdkXKWVhtcNtWj2d3IHR2rac72krQiXFqzhCtqTn7Jd7LbJ+X6/GxbGw93DjA7EeGTC6bQEA2htebezjZ+sWMr5U6I751y2iEFxjOpvTz78HvwtM+CM/6Dmoplh15Aa6I/Wo0OmWTfv2Rc+nw0jscfN3NrH5HfbCJ/4TTcsxsPe4zrZXjk6Y9h9G3D0pAoW8TUue8ZE0KlXJfvbdnIyr4eGiNRzq2u5bzqWuoizz+9MuX6bBvOsGUww5ahDHvSefryLs83nsgxFLahMJXCUsW1qRSmobAU+9uN/futkW3bKG7b6sC1UWw/aL9tGCPXKq73bdsHbI9dq0mfQniIQKPzGi/n4eV9vLyH63kUXJ+C61LwPAreyNovLvnAI++7xaAMj7z2yFOcDje6HilunqdY6DxnFNtyhkvOcCkYLnYQYOsAZ2R9yOORNkcHhHxNzFdEPQgHirBvEApUcfHBCcAJ1EjQpbADXRzFtW+tNVagMYOgGIIRYASMTLErBjVK6WL4si+qUIeLLChmECNJx4H1yxUKNfql3BgNKQ4Xno2uR4O2g4K1wx4/Nmw7cH00UyyPliagOC5Nj4RexSCs+CbsC6hGtpUeOU4fsD0SKI3U/hoN29S+t2DkmAOCOT3y/GLOUfypBCOhXADFQE0X2wLAVwHByBRMX40saHwjINA+vrFvmqO/fxTYyOPi9UeCwJHPw+h4LrU/UjswXgNG2xSALk67G3m5I/sPXY/kj8WfnD5oTfEtGrMGDK1GtyeDZl9yY6EMG0wLZTgos7gYhoMyQxhmCGWEwLDBcFCGM7qt1chjVdxmdNsuPj5gW1M8Rit7/zYOGmts6DWyLm6r4va+EWgHbh9w/OGWA0e1jS5j2sb/32Sl9BGHWGNqlR3mGPPgcOyg4573uS+w/3j8GZIv5eJEIJ9z8Won4dNhSPh07AI3Q75/O31dTzPYswZ/YA+x3DAGxa8cbQ60WSF8v5TqdDnLBgzqs6CCalRQhcLAw2BlYjq31MxmTaKe+WUmK2o94pFW1g9v54HujeQCl7nxOq6sXco5lfNxjuAOUEdCa83qvhT/taWVvoLLtdOqedO0aixD0ZXL8sOtm1g90MfCZCmfnLeQmnBkzPM9N81TD1yLVximbuHfM2fm2w57HdWTIfZfq8m9YTbeyYeOippox+uPW/h3mzC3D+C+poHC6fUQOfTn5gcFHt/wNfraHqbMNzECn0iyicYZbySWnEUkMQ1lODzY2c59ne2sH+wHYG6ihDMrq2iIRKkOR6gJRw4JBsdeR9NXcOnJu/TlPTK+T8r1Sbsead+nEAS4+xYdFEcCBRovKG57WhMEI19sdfFrpq81fjAyHSwAT2t8DV4A3r5hFuyLII7NIcHUYUIs21Q4hoGtFMZICGaOhGaG2r+9f9m/zxp5bBywzzLUmMf7zzP2XMZhzvtCxxlH8Q0m0Jp84JLzXXKBS84vjKxdckHhgPbiOu+74EB/Oj16TP6AY7Ijz8kHLvnAO6qfgYEiZNiETbu4HtkOG2OXkLKxDQtHmTiquLaVNbrtKAsHCwejWEVJFyspFcMvVQy9Ao0ZgBUUty0N2gsIggDXC/ACjecHeL6PF/h4foC/r00Xj/N1gB+MbAcBgQ7w/eLop0AHqCAA7aODAKX90cdqZM6YEQQoRta6OKZG6QBTFx+bemSinAZLB5haY+iRSXNaj45eM3VxrNn+0Wwjx2lVHIem9481U2O2DxyFNvmj0Q6mRycO7g+cRkMotX+s1ujoLbV/GuK+/YFSxWBMjQ3LAorTGAMVjLaNBmX7tpUeE5p5xkiIZgTFmmdKj27vG5EWEKCN/SPfAvaPktP4qJE2pTw0xZ+3UvrgcVyjYdi+xdAjI4kOCMFGxxKObJt6/2MTNabdHHnewZOhR/8L7DAj2MY8Pmh/oCwC0wLDRCsLbdpo0wazGHJh7QvF7P3h2P9v796DpKju/o+/T/fsTZbbopBdiaJ5fBDFQgQ1gqtEQSBcBBTReIlQXhArVGJiQWlJ8mDUYKq8lmhSxltp/YxUFBQUDYqKxqzmV4A+iv4UUQisrOyisOxlZrrP74/u6bnssrvA3qKflzVOX06fPt1zduj+zjmnYwU4bl4UIHOcAkysANctwHELcd2CdPAstW1GcA2TDpp5qVZgzQSsmgSxvJbWmyZpvBa29b2W1ndQcKwdgljpl221lVlJyWHs2VOX1Tosd8y05sZX6+rfa0QOhIJP8l2n4FMzFHxqmbUWP/4tjXu2snv3h+zZ/QkN336JW7uTnvG66Fqw1rHscg0NFGGTPTiqtoTBe116J2Lg98H4h2MIusftzOvBuj6D+L+9BvK/xQMY2Mfwo8N3Qmwrn+zbyid7d+BjyXdinN1vCJNLT+G/i0vb7Zjqkx5v7PyGVdtr+HJfA0ceVsANQwZyXK/D8K3lxcp/89jnn2EMXHnMfzGxdGCzN84bK25kT9W75A88m9OHL97/Dj0fd8u3wfhHXdDFq6v+cTO1cQpWbib2cQ22wCVxemkQhMoZbN23Hp9tX8WXX/2dul0f0DsZ3HSHuRArLKGwxw8pLOpP0unBtkafz+oTVMUtvgnGhPEx5Ll5FDgxCLuyYIIxY5JAwhoSlujV6Ls0mhgJk0eCvODpZsTa/crVAPmOQ77jhsEih5gJX44TBmicYPQkE3T/CrrrpG+8rQ3bU9jgaWy+NfjW4ltD0rckrCXpQ8K3WGuCoJhNB8qSqfku/poz0ExgKh3wSrUey21Zljkfy5zOee9dXEhjfTy9zHGIGcJ5J3wPgknWJIMWLyTx/GQwkLlNkvQTYTfEJAmbJOGnAlbpQFZjzntmECzhJ0lYr93PnYMh5gSjHQXnzsUN65FjnGjaDV+OcXDCeuQYE3QszHlvab0hOO+myXZBh8igPgZ1NGvaGlIhjPSycCBwa4IbYz9M56fX2XBZ0DLFCab9cD9e0C/RhFFf4wUt1GK+R8xPEvMtMesR8zxc6wXLrY/r+cTwgrTWx7Uejg2mY9YPWrlhw218XIJAWiqg5tpwVKqM+XTgzeYE2Gw6EJPZYimj1VIUYLPZf9+p1nBRd70uCrY1CbKlpk3msubXWZpJ0+J26fms/Zpm0rZQDttiGVNdQ23We7RPJ9VGLR0wxKT3HE3nvOcu90zw8o0Jp0308h0TjEfngBeOS+c5Bs9x8I3Bd9xgOhyXznMcrBs8yMFzXazr4rsufiwIrBnHwRoX67iY8MENwb91QeTGMQ7WBGPwWYJ1ljzCWko0vlo4Zh/WCf82nYxXOO+7wXrfBOmtGy53sH64re9grQtemI8fPBjA+sH2vm/CeRfrBdv6fvCwC0P4kAFcwIRjEwbLUq01D6rFprHZg/mb/QSscgNabQxupaftQW6Xvb9mHz6gYNr3hoJP8l33nQw+bdmyhYULF/LNN9/Qp08flixZwqBBg9q8/fc9+OR7jXj1NTTsq+TbPZ+zd8826morSezbSV5DDT0S+yhMDYBswVhLA9CAg/WK6dHYm8MbetCvMZ8iLx9jewfBJoIAgw9U5RexofhI/tVrIO/36klRrwb69NpNXmw3tf4u/t++HcT9JA6G/y4uZVjvozm5z9EMKT6SArf9ngpnreX/fFHFC//exb6kzzHFhUw6sh9nD+hDgRtc5KzasY0HP/uE4X1L+MVxQ+if09op05efPUX1vi2cfNJCnHZqjdURuvofN+erfeS/sY3YpmpszMH2KcDvlY/tmY8tzo/GhLIG6tnDF/67bPXe5lt/c9A9yoe8jF/NO7J7STAejRO+Un0OXHBiYMIuI2HXkWC6AJwCLAX4phBLIZ4pIuEXkCSfhC0g7ufTaAtoNDEajUO9NTQYhwZraDTQaH3i1qcRG023xzdSEKxxMroLOtndCo2T1arJMSZoqZDV0skJux06OemdKL0Jg2cOFtdvxPEbw/cGXK8ex9+L49VhvFrwGzHhCz+OE83HgxtJm9G5yZpgIG3ySKTeM17x8NVAAfW2gEZTSNwU0kghjaaIuCkkTiHWHHqNaS7Y5ZgwqGDCsIFJ3SqF3a3CsZhM1BUtmLfGx9pwXKbwvenNdsaNdca0DTuP2mg6db7CaetnzIfd2aKpzHxpcr6z1kV1MN3dzGZ+Nlnz4VJL9rwAqe8sN3y+ZOZ/JhjryZpojbFO2KLMId93cK1DnjW4vkPMQsx3yDMOTjJoQeb6Qau5mO8T8w2x8HvS9YJWR1FrOmuCdwzGBsH9KHgWrouCaD7p6bDFWnPTrg1HlQpbx7lRfuGoU6nprABdEP5I7bvrHgeRXVfTQalUa7aw62j4d2dyglsGCzZoMRZ1tcz6O27hZbLnbRvSZP39pspnMsubWt50PvM9s7tr2D4uO52J9pIViEstz/xWyeweG5XQNE2TPXZbOnBHzhGm9pdann432ctMcMYz10fvJnMbk5NvGCjPCO5GgXKTns7KL/p0DdaEQXNjghpggnEEg1aN4XVDtI/gR7HUMt+44X5SY7AFY7rZVDtAkwq6mSAYZ1Njx7lhODo1Zlw477jhOHHBdLAs9eOVg5NKF/4QZ4xJjy/nBHk5jpMed84Jf8RwgsCf6zjh2GhusMwJbiCD/ZgwYGbAmGC5E/77Fw6oFgTPgrSOY8Jx18IfPEz4DyeG9Lh5hEMMhK/U+ihNcGYy05OT3uSkb5puP/vo6DKEafY3hEJXX5+LdLTvZPDpiiuu4IILLuD8889nxYoV/O1vf+OJJ55o8/b/6cEn68XxEvV48Xrq6r+hrr6ahsZvaWzcQzy+h2T8WxobdpNo/BaTrMXx6sjz68n34xR5SYoynqyVl4TCBBjf4hlwEr0oivcn5hVS4BVS6OXj2AKMLQR7WBRgSql3DDvzi/jksCP4oPiHbD+shESvXtT0eIt9TiV7vd3EbSJKX+TkM7CohBN7DeTk3oMY2uuH9IgVdNi58nzLwvWbGVCYz0+P7MeQ3oc1+QdhV2MDX+7bxyl9S7p+vJ120l3+cXN27iO2fifOt3HM3ozXfv7+PONRn1fLvvw91OXvpS5/Dw15dXjGwzcJPCeBbzys4+Gb4BUMjpzqfhLeVBiwTuriGggHTCYckJmsl42mTTPLyLh8bY8gmA2vjg3hr81Ru4egpUhuR5ho7yZjRBmTuTzMFzLSpJYZgluB1MV19v+zbxKiWxAybxEyL8cd6+PiBS1DCG4q2yo6myZ3wOlMJrs7FkHwOzj3mbcQLfNwwifCuXgmRtLEwvlgOhk+Kc4zsaAFnImRNPkkyCeRSmtjeI4bvBO0YLDW4Icl8cPz70efmQXrYfBwwvegTqXnTTgf5BKmDeudk1pvU8uCd8cmSbX1S41HFXRfS9dLE97sm+jzMtHwVKnza6I6kD7XfkY9Sh8T6RuorOW56dM3YakL76h+ZkjfGFrIKUOQt4uHi2+CAbN96+IZl1QN80zwNLzgbMSCtOE2HjHC9kvhsli4nZN9M5A+CfthM8qYuyp1BvygBMbimKAkxgSfG9ZLf36kWtxkhgFN9NcWLE8NvJ76m8tYH70H4QKMjb6XUvkTlsXYzOU2rPl++hO1hOVO31anP08nfVOd+1nv5/PP/KwPms3ukhcEz0x63s+ZD9cH3VRNk/m8jO2DoBxhgA7ywv3k++EYb9aEY72RDtpZGwT7UsGxqAzh8ztzymMwYbdSE74yuiJaE43X5aTSQNZYXm6bv8VE0rIDljSZtmY/y7Hh9156eZSXyUiT2tZkXwNk5hulMy1vA+nrieyyZ2+bmSZd1oxtmtl3c/lGZcwpV+ax526fe75sZt4Z29gw38xro3S+ZOSTfXUVlSHrus0Jg55OmI8TPuAiFcByogAnqfeM5dEyByxu+CNbuDz8zo6a2ZGRPtVS0qTyypwOApYQPBXYGBPuKwhQpgKoQQvMdHlS26daYUbvYaATJ/3gDYyL46TL4DguGBMEVp0gmAphy+1wneO6YRA1CNRG2+AEgdhwewj3aXIf6hHkjQlaWqYCrsFDPfQd3NG+c8Gn6upqxo8fT0VFBa7r4nkep59+Oq+88golJSVtymP37n3/ccGnj/7XULkDCpIfc7S3OLyxaVncWBocaHAMDcal0XWoc/KoNQXUOYfR4PTk8i+3cni8LtrGxM/C9XsB4VetBXDZVVDIZ0XFVOX3paawH4mintjiHvTonU/vnobSYsMPigy98yFhPe7+bBV5JsaAwj6UFvbmB4V9KCvsS+9Y0+CPtL9+/Yqprq7t6mK0zNqM647M6Yz5cNr4FhI+JulD0sckfEh6kLTRtEnYcPAlHxIeJmnDtB54GfmRs6/wJs06JmwLT9A9wzXptvCuwbqQdBMk3QbibgMJp56EW0+CehKmgSQNeCaBHw6x7ftxkjaOb+NYL4Fn4/g2gbVJrJ8A64etTXywNj0dXtzk/pW0Nt/iqT6AtLn7CLpJRB2JCG7JgidxBZ2Ugqdn+eRhTQE++Xjk41FA0hSQwCWJE7b+CX7BzzxWP9VCyPpgkgQBQi9qRYRJBW48jE3imiSO8XBsEjcIM4WtMKJb6qhlhpMxb8jpEnUQ5y/z8r+5h3sd/Hk26WvejPKlf4/v3pq/7UkHpDLjP022iW42msujZS2dl4zQHOmzaXJSpPeSNVh41rLmy93aspaW5xb8gLffT1ZZ2dr9p9vfdrnvWcv2U5gorW1++9a0d90+0L/tzhZ0jU7/nGGDO8no28pYJ93iJvVtFXZndcLtTNgVNggyZ6SxYRdaG7xSATFyg2Ck02R/1wSt5MJvpGY/+9TA98bmrs898y19ErkB6mB/1gb3tLaZdcF0Zhg7XGfT6XKnmt9bZuY2J032923m8WX+EJKZzmStT6fLzDe1jYEm59Wks0unT12XkP4xJvUZBlmlf7CK1jfz/3S69NGkx9dLrU8fbbo7cPZZyEyTro+ZabKPdL/T9gDTp48S6TpNWzW3NN/KtMnNsaV/5Zpunw4GHsi+c/dJRtVteXub9c2Ss77ZPLL32XTLzD/2nPxs7vKm4cz9l7mFfFtKm7PuYK5hm25jML360vu6mw8it+Z1eT+jyspKBgwYgOsGjbdd16V///5UVla2OfjUt2+Pjixihyg/OzU1AljR6fsfGL7a6s4jLu2ookgbtWfUWURERERERKSzdOTQKyIiIiIiIiIi8j3X5cGn0tJSdu7ciecF3c48z6OqqorS0vZ7MpqIiIiIiIiIiHSNLg8+9evXjyFDhrBy5UoAVq5cyZAhQ9rc5U5ERERERERERLqvLh9wHGDz5s0sXLiQPXv20KtXL5YsWcKxxx7b1cUSEREREREREZFD1C2CTyIiIiIiIiIi8t3U5d3uRERERERERETku0vBJxERERERERER6TAKPomIiIiIiIiISIdR8ElERERERERERDqMgk8iIiIiIiIiItJhun3wqb6+nl/+8peMGzeOCRMmsHbt2v2mfeaZZxg3bhxjx45l8eLF+L4PwJo1a5gxYwaTJ09m0qRJPPLII51VfJH92rJlC7NmzWL8+PHMmjWLL774okkaz/P4n//5H8aOHcu4ceNYtmxZm9aJdBeHWs8feOABJk2axNSpU5kxYwbr1q3rxNKLtM2h1vOUzz//nGHDhrFkyZJOKLXIgWmPev7iiy8yZcoUJk+ezJQpU9i1a1cnlV6kbQ61nldXV3PNNdcwZcoUJkyYwO9+9zuSyWQnHoFI69pSz9966y1mzJjB0KFDm1yXHPR9qO3m7r//fnvTTTdZa63dsmWLHTVqlK2trW2SbuvWrba8vNxWV1dbz/PsnDlz7HPPPWettXbDhg32q6++stZau2fPHjt27Fj73nvvddoxiDTn8ssvt8uXL7fWWrt8+XJ7+eWXN0nz3HPP2Tlz5ljP82x1dbUtLy+327Zta3WdSHdxqPX8zTfftHV1ddZaazdt2mRHjBhh6+vrO+8ARNrgUOu5tdYmk0l72WWX2RtuuMH+4Q9/6LSyi7TVodbz999/306cONFWVVVZa4Nr8oaGhs47AJE2ONR6/vvf/z76Do/H4/bCCy+0q1at6rwDEGmDttTzL774wn744Yf2rrvuanJdcrD3od2+5dNLL73ExRdfDMCgQYMYOnQob775ZpN0L7/8MmPHjqWkpATHcZg5cyYvvvgiAMOGDWPAgAEA9OzZkx/96Eds37698w5CJEd1dTUfffQRkydPBmDy5Ml89NFH1NTUZKV78cUXmTlzJo7jUFJSwtixY1m9enWr60S6g/ao5+Xl5RQVFQEwePBgrLV88803nXocIi1pj3oO8Oc//5kxY8YwaNCgziy+SJu0Rz1/7LHHmDNnDkcccQQQXJMXFBR07oGItKA96rkxhn379uH7PvF4nEQiEd2HinQHba3nRx99NCeccAKxWKxJHgd7H9rtg087duzgyCOPjOZLS0v56quvmqSrrKykrKwsmi8rK6OysrJJus2bN7NhwwZ+/OMfd0yBRdqgsrKSAQMG4LouAK7r0r9//yZ1NrdeZ9b/ltaJdAftUc8zLV++nKOOOoof/OAHHVtwkQPQHvX8448/5q233uLKK6/stHKLHIj2qOebN29m27ZtXHrppUyfPp2lS5dire28gxBpRXvU83nz5rFlyxbOPPPM6DVixIjOOwiRVrS1nreWx8HchzYNY3Wy6dOns2PHjmbX/eMf/2jXfVVVVTFv3jwWLVqkCLSIyH+Qd999l3vvvVdj9sl3TiKR4JZbbuGOO+6ILgRFvos8z+OTTz7h0UcfJR6Pc9VVV1FWVsa0adO6umgi7Wb16tUMHjyYxx9/nH379nH11VezevVqJkyY0NVFE+lyXd7y6bnnnqOioqLZl+u6lJWVZXWRq6ysbPZX79LS0qwg1o4dOygtLY3mq6urmT17NldddRU//elPO/agRFpRWlrKzp078TwPCC7IqqqqsupsKl1mvc6s/y2tE+kO2qOeA6xfv54bb7yRBx54gGOPPbZzCi/SRodaz7/++mu2bt3KNddcwznnnMPjjz/OM888wy233NKpxyHSkvb4Pi8rK2PChAnk5+dTXFzMueeey/vvv995ByHSivao508++SRTp07FcRx69uzJOeecQ0VFRecdhEgr2lrPW8vjYO5Duzz41JoJEybw17/+FYAvvviCDz74gPLy8ibpxo8fz5o1a6ipqcH3fZYtW8bEiRMB2L17N7Nnz+bSSy9l5syZnVp+keb069ePIUOGsHLlSgBWrlzJkCFDKCkpAXePlgAACcBJREFUyUo3YcIEli1bhu/71NTUsGbNGsaPH9/qOpHuoD3q+fvvv8+vfvUr7rvvPk488cROPwaR1hxqPS8rK6OiooLXXnuN1157jZ///OdcdNFF3HrrrV1xOCLNao/v88mTJ/PWW29hrSWRSPDPf/6T448/vtOPRWR/2qOeDxw4MBqfOB6P884773Dcccd17oGItKCt9bwlB3sfamw372xdV1fHwoUL2bRpE47jcOONNzJ27FgA7r33Xvr3788ll1wCwNNPP83DDz8MwOjRo1m0aBGu67JkyRKeeuopjjnmmCjfK664ggsuuKDzD0gktHnzZhYuXMiePXvo1asXS5Ys4dhjj+Xqq69m/vz5nHTSSXiex+LFi3n77bcBuPrqq5k1axZAi+tEuotDrecXXHAB27dvz+oqfeeddzJ48OAuOR6R5hxqPc90//33U1dXx4IFCzr7MERadKj13Pd9lixZwptvvonjOJx55pksWLAAx+n2v4XL98ih1vOtW7fy29/+ll27duF5Hqeffjo333xzs4M2i3SVttTzf/3rX9xwww3U1tZiraVnz57cdtttlJeXH/R9aLcPPomIiIiIiIiIyH8u/dQgIiIiIiIiIiIdRsEnERERERERERHpMAo+iYiIiIiIiIhIh1HwSUREREREREREOoyCTyIiIiIiIiIi0mEUfBIREZHvtc8//5xp06YxfPhwnnjiCRoaGpg7dy4jRoxg/vz5PP/888yZM6fVfB566CFuvvnmTijxoVm4cCF33313VxdDREREvkdiXV0AERERkbZ44YUXePTRR9myZQs9evTg+OOPZ+7cuYwcOfKQ8n344Yc57bTTWL58OQDLly9n165dVFRUEIsFl0pTp05tNZ+5c+ceUjlS/v3vf3Puuefy4YcfRvvPtHLlSu666y5effVVjDHR8mQySXl5Obfffjs/+clP2qUsIiIiIu1BLZ9ERESk23v00Ue5/fbbmTt3Lm+//TZr167lZz/7Ga+++uoh571jxw6OO+64rPlBgwY1G/jpDsaNG8eePXt49913s5avW7cOYwzl5eVdVDIRERGR5in4JCIiIt3a3r17ue+++1i0aBHnnXcehx12GHl5eZxzzjksWLAAgHg8zm233caZZ57JmWeeyW233UY8Ho/yWLt2Leeffz4jR47k4osv5uOPPwbgiiuuoKKigsWLFzN8+HBuuOEGli5dyksvvcTw4cNZtmwZzz77LJdcckmU16effsrs2bM57bTTGDVqFA899BAA999/P7/5zW+idBs2bODiiy9m5MiRTJ06lYqKimjd5Zdfzj333MPFF1/M8OHDmTNnDjU1NQBcdtllAJx66qkMHz6c9evXZ52PgoICJk6cGLXUSlm+fDlTpkwhFosxf/58Ro8ezYgRI7j00kv59NNPmz23uccGMHjwYL788svovC5ZsoQxY8YwatQoFi1aRENDQyufmIiIiEg2BZ9ERESkW1u/fj2NjY2MGzduv2kefPBBNm7cyIoVK3j++ef54IMPWLp0KQAffvghN910E4sXL6aiooJZs2Yxb9484vE4TzzxBCNHjmTRokWsX7+eu+66i2uvvZaJEyeyfv16Zs6cmbWf2tpaZs+eTXl5OevWreOVV17hjDPOaFKenTt3cu2113Ldddfx7rvvsmDBAubPnx8FmCDoPnfHHXfwzjvvkEgkeOSRRwB48sknAXjvvfdYv349w4cPb5L/tGnTePnll6NA0N69e1m7di3Tpk0D4KyzzuLll1/mnXfe4YQTTsgKih2IP/7xj2zZsoXly5fzyiuvUFVVxQMPPHBQeYmIiMj3l4JPIiIi0q1988039O3bt8VucC+88ALXX389/fr1o6SkhOuvv57nn38egGeeeYZZs2YxbNgwXNdl+vTp5OXlsWHDhgMuy+uvv87hhx/OnDlzKCgooLi4mGHDhjVJt2LFCs466yzOPvtsHMdh9OjRDB06lDfeeCNKM2PGDI455hgKCwuZMGECmzZtanM5RowYweGHH87f//53AF566SUGDRrEkCFDALjwwgspLi4mPz+fX/ziF3z88cfs3bv3gI7VWsuyZcu46aab6NOnD8XFxVx77bWsWrXqgPIRERER6Z6DGYiIiIiE+vTpw+7du0kmk/sNQFVVVVFWVhbNl5WVUVVVBQRjOC1fvjxqUQSQSCSi9QeisrKSo446qtV0O3bsYPXq1axduzZalkwmOf3006P5I444IpouKiqirq7ugMpy/vnnR13tVqxYwfTp0wHwPI+7776b1atXU1NTg+MEvzXu3r2bnj17tjn/mpoa6uvrmTFjRrTMWovv+wdUThEREREFn0RERKRbGz58OAUFBaxZs4YJEyY0m6Z///5ZA4dXVlbSv39/AEpLS5k7dy7XXXfdIZeltLS0TS1/SktLOf/88/n9739/wPvIfIJdS6ZNm8bSpUtZv349Gzdu5J577gGCVmCvvvoqjz76KAMHDmTv3r2ceuqpWGub5FFUVJQ1htPXX38dTfft25fCwkJWrVrFgAEDDvg4RERERFLU7U5ERES6tZ49ezJ//nwWL17MmjVrqK+vJ5FI8MYbb3DnnXcCMGnSJB588EFqamqoqanhgQceYMqUKQDMnDmTp59+mo0bN2Ktpa6ujtdff53a2toDLsuYMWPYtWsXjz32GPF4nNraWjZu3Ngk3dSpU1m7di3r1q3D8zwaGxupqKjgq6++anUfJSUlOI7Dtm3bWkx35JFHcsopp/DrX/+aUaNGRS2p9u3bR35+Pn379qW+vp677rprv3kcf/zxfPrpp2zatInGxkbuv//+aJ3jOMycOZPbb7+d6upqIBjLat26da0eg4iIiEgmBZ9ERESk25s9ezYLFy5k6dKlnHHGGYwZM4annnqKsWPHAjBv3jyGDh3K1KlTmTp1KieeeCLz5s0D4KSTTuLWW29l8eLFnHrqqZx33nk8++yzB1WO4uJiHnnkEdauXcvo0aMZP3581lPsUkpLS1m6dCl/+tOfOOOMMzj77LP5y1/+0qYua0VFRcydO5dLLrmEkSNHtjg21fTp09m+fXs00DgELaLKysooLy9n0qRJnHzyyfvd/phjjuH666/nyiuv5LzzzmPEiBFZ62+88UaOPvpoLrroIk455RSuvPJKtmzZ0uoxiIiIiGQytrk22CIiIiIiIiIiIu1ALZ9ERERERERERKTDKPgkIiIiIiIiIiIdRsEnERERERERERHpMAo+iYiIiIiIiIhIh1HwSUREREREREREOoyCTyIiIiIiIiIi0mEUfBIRERERERERkQ6j4JOIiIiIiIiIiHSY/w9sF9cmSIgb+QAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# display density plot for coefficient values of each class\n", - "sns.set(rc={\"figure.figsize\": (20, 8)})\n", - "plt.xlim(-0.02, 0.1)\n", - "plt.xlabel(\"Coefficient Value\")\n", - "plt.ylabel(\"Density\")\n", - "plt.title(\"Density of Coefficient Values Per Phenotpyic Class\")\n", - "ax = sns.kdeplot(data=coefs)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# display average coefficient value vs phenotypic class bar chart\n", - "pheno_class_ordered = coefs.reindex(\n", - " coefs.mean().sort_values(ascending=False).index, axis=1\n", - ")\n", - "sns.set(rc={\"figure.figsize\": (20, 8)})\n", - "plt.xlabel(\"Phenotypic Class\")\n", - "plt.ylabel(\"Average Coefficient Value\")\n", - "plt.title(\"Coefficient vs Phenotpyic Class\")\n", - "plt.xticks(rotation=90)\n", - "ax = sns.barplot(data=pheno_class_ordered)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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PrtZPTVVuK6Hk1vZsfnqNkyvdAcjU0o0t42EVcX21+MpCLJe+GJSfq5TzVIvl+j5IG53Qqp5rDK1ynj3fdaVQy3Mp9dtQqlOfi4oYsVKskWQpnNV7fVONQr0vFdXLhOyR0ivEN1PrZbIQvb1n2pqx9lft7cKB6t6rhF0/8cQT83vf+15u2LAhDz/88LzuuusyM3PTpk2j7nhSelwprqCWQ1TqAzgflfH3yvM/tQZUHERt3NlU+GHUN6PGVbsnNhe93h2l7NmqVavyIx/5SB555JF58skn5+WXX16iw5Tuear1PVN7A574Zkp3PDO1vhllH6j9oKZz1e4yzaYiF6MWByHOvpTcqtW0zUVFzkTJV6DW1nchOF+BsuktczG9vlFG6Ual+N0k1bUgxHzV+p6p1XgSsqv0flSmvu9cgVqvOpqWdXK9+TZKb/Zl+o5Fpl6eq+VddcofXW71UYReoORLrV5woMXbtr3GQeaj1x54s1GpJ1fo6apmz+ZirH1QPZdU6zDKHyXGVYvltvL1qRriap1L6UaFe1eqdzcU3nRRG3egUpdTcqD21pNSnyfKnimdHzL16lfmo0efnJBbtVoutTjbUtALvaKWP1Oqq1erU1fTY4TsqvWfoVCr/SagY2LVsaulkJsk6DUnNxcqcf0B1fv6Y3Ruq1yq6ttnPfZpV3pvUq0/6Gx6fhdSzUZS4yr14aV0mNJbT+T3ItZXtX5WoWdBplZvLrVYkFL+u/V5mjjzjM2ZkHuBsL+tbe/Yb6Z2z7MVamd1qgc+MW7Pd7TV7nNNjl91RlWLD1M6TOlOORW3auWDVMUVlN62fTamjbWp5SYXorc1aEmFnaT2A51PrrRnavuBzkFU5yZPPvnknX++8MILc82aNfn9738/P/GJT+R73/veqcZUq2OioPYZIbuuMZlBqb5xEoX7XL7rujA91hup5XiWQq/Ysai9MUCMS81VzW9U6tWu1i93KfRDpFhu77rOpudam0kqz5NqsdzWvkK1f0fkuXqXW8JvbOnnK9SOVn8zpfmq+XdKcX0KpbtnmVr96lXltjpOrhTLVYuTq9Vot46Vj9G3lCxQa6D+LnOFf6feW78K4pup1zZWofSGxXz0GmNSuoeYqfVG6FKw6RW1+sQZgvLJ1e6ZELiP567xVPoALoU6RIpe3/dRqyFeCnVBY2Wh9T7r9S1epbyvmj2j5tvKB+k5b2Q7+ewQ8ZUeYzbU2ZfaZ2o9IWZD9Xlabr25KL9RLc81H1W6pnK+am89E+M6zsai9FaM0vkhs918q+LkSm8iqr1BQ4zbwj4o2F+12JVrjri1VboPMhcV5x013ahWzzUXY2OuavFGNRmbjx7tjtoZQqn+vSUVsSDinNpSvpR61VXNlfJBCN2oFs9Wg9CNLX3cCn9Uqef3s9FbvEKtH9N8jD1D0TJW6YOove9Djasku0p9V9RyG5Q9U4sFKb25odRzNFPLH1V7Z5GyZ0oxJrW6XLV79cR8W8dce73PNRe9xq6WwjupPdeeEeurlttQewuQ+GYt8zA93ylX6umqts8olPq/z0XPb9Co3f0m1kHtngklC0r1jZROUPNH1fuDUPnkijwXtc+U6qPU7iEq5WIou0OtgftHc6jF7+hvVpmfbV2v0GtcUKmPp1pNm2ti2sZWKr6ZUr1CZlsbsdx0mFI/NbW4IDVfpToTtRoAtXeZCdTqoxait1x9pq7PVGnXW/pMtE2fVsaouIJa/I7QC9RcqW9G6cbFyPsul/rGueg550mMqyZfarHnhejN7lCovW07sJzfApxNVf87Vd+5EqX7FZla50kqv6PWe1Xpm7XQYQr3Jlv5Yj339vHZdwalt2KWUv+Z3moLWtidVn1txqytWm0QASULavnZVmeI6p7fZL1+VT2qks5V6pGaqZf/ng+iN/fY2gIlPU7KgdI7C2pxQfXcBvWGRYVNV7I7FGp3eOai1976anFntTsLS6EvRu9Unx9OOOGEnX9++9vfnjfeeGNmZv7zP/9znnLKKaPHr5yvWm6DQsnPpeIVSj0WM9vJGOXfVYzbKn9GrUEFlNy2vIfY65vq1LjqPQuqUOr/3qJWv9JGEL4zKV/EPlPrOaLaR5umZx+E2hNKNeVKb0JkcjZCSecq1ZNntj1Tj409UzkINV3TKtbW4nzW2zt4A9VxBUqXv/Wtb81vfetb+dWvfjWPPvrovOKKKzIz86qrrsp3vOMdU42p1qedPp+1es+EYszaKtVMZuq96zoXVK/nsajFrmZTbc8U3hhs9c16XtvW9qxXP5fOR1XaSbWeI2p3LAhU874Kb5SpyZeS3VGrK1DzcSnZVapZUMtBKL1d2NJG0jGxHvttUHa99T3tMWurVsdE+PpqOkytLlfpbQy1nEnLWsxe+9oq5b7Jceei12+m1geAmi9xRmtdn10tYxV6nFoDtfuCavZMqRegWs8gKh6kVEesVmuzEL3lv9XeAqRQuufZYq6VeQil/u+kH0boG2r/HnDAAblq1ard/jvooINy1apVecwxx5T+WwMV9XfVsSult55axsnH1sQo6dtM7psp+c4USuf0Z6O3uD7l46rlfZVqR9VqACgdptbTRamPJy1j1X6YUkxMre8Z6YtF0RyXJMSFmbmU8fDfa17zmsLZ705vTXqpAyzV/OjEE0/M733ve7lhw4Y8/PDD87rrrsvMzE2bNuVJJ53U1VwpGVu1alV+5CMfySOPPDJPPvnkvPzyy0suX1Dr8La3vS2vuOKK3LZtW37xi1/Myy67LB988MG84IILcu3atVOPSyjkt7zlLXnFFVfkjh07dv5sx44d+Zd/+Zd52mmnTT1XalxCxtT2g5KuyeT2A+Gsnn/++bl+/fr8xje+kWeeeWa++93vzh//+MeZmaPWgFpbpX32bEzrK1DfjNpnaj6I0sPVJ5988s4/X3jhhblmzZr8/ve/n5/4xCfyve9979RzpXwQ4gBLyRelxxdizPmB0rmEviHOfJmc3FI6l7JnSo2KqLWldCM1LmHP1NaAGpcKnBK2h9Jhat+MsmfUfBdijF0nkoGUzlV6ZIss7iFiV3TD+bkYI7fEmVrNV5j8ZpmZp556ambO+HjHHnvs1ONSutE+COOTU7qG2g/U+Uwt9kzEgqhvRsUr1OLkSrpRzddXKiakdBgVs6EKWyhZUKrboOwvJWPUNyPOZ+S5hCiqfMMb3jDV754NSsaI8z+1zyibTuXkqPmqFZwT86UKopXOUmQOkSjaVWtIQsyXsumUzqXGpc5RSnFBam3V/EZCFijdqBZvVKoXpOIVahetqXVYiOpczFhfTO1MTcRtqFhQJpNDVKrVz2R0LiVfmVqNxikdpnZBj1hfSi9QOpc4R1G6Rs1vpM4mVC6GkAW1miul+CglX5SuUcsnE+tL5UzU7gZROozw8cg8F7EOSjosU8vXV7oDkMnIgtodz0zGt6HOO0r3nsn7s0Tulzr7qtV9t3qUpCofNfDMM8/k2rVr86yzzsonnnhilL4h9i+Z9yVy9Wq5DSJmo1Q3Ss6XqjFp2Tx0rL6hfBDKH6VkgajbIHN9hF5Q8hUyGX+JsunU2qr1iaHGVaoBWIgxsVyle5Nq9oGKNxIyplanruaTU3adqv2m8urEeVKtdyNlfwl7RsktZScpuSV8EDUdRp19lfoWUD6IWr0gIWOUn0/JV6ZW7xWlO3hqPRbUvhmhG9XuFpC5GOI8SciC2n0uIi5I1jwT+ShKvqh7uWp6QamnK+XbULJAzFctnt3yPmbF2ZfSN0r3qdX8RuJcotZPjZLbheitlyklt7ZnM1A6V+nOkdodC7WaNiKurxZfIXwxtb4YlJ+rlPMkY7mE/fV9ELa2gMh/U+cSKuep1Hc0UytfoFQDoGYfKN+ZiBErxRozuXtXamd1pTfVqPoKtb5UVO03MS61zyhZIL6ZWi8T6ny2EGP8JcofJeyv2tuFVO9Vwq5P/r1Vq1bN+7vnitobocR81XKISn0AM7n4O3H+p9aAioOojUv4YdQ3o8ZV6y9I+Apq55IhBvDUU0/l17/+9Tz77LPz0EMPzfe97327+evPFaV7nmp9z9TegCe+mVqPFKVvRtkHaj+o6Vy1u0xELkYtDkKcfSm5Vatpo3ImSr4Ctba+C8H5CpRNp2SMilcQ5zNKNyrF7zK5XgjEfNX6nqnVeBKyq/R+VCa3f6kY00JMmz9T61VH5fpa9onp1bdRerMv03csMvXyXJSNoPxRpfooyu4QeoGSL7VeJlR9hVIchJovpWuoenIq5qrU01XNnhH2gbwrR8gYpcMof5QYVy2WS60tVZtM6Vzq3EeMS9kHtbsbSm+6qI1L6HJKDsi3nuZjTJ26Up8nyp4pnR/I+Sq9o6X0Dh5Zy0XYX7U4m5peoGJiSnV9am9TEeOq1akrvTuSyciuWv+ZhRiztmq130p9YqjYlVpukvhmSjm5TC7GRNUsELG2lnepK3Qu5Y8qvX1Gra1S/D1Tq68tZR+U3oVUs5HUuEr9oykdpvTWE9k3mVhftfpZpZ4FmVq9udRiQUr5b0qHZTKyQORMSN1I2F9yvsQ3U7vnuRBjYmJKNXhUbpIaV+mOttp9LuKMqhYfpnSY0p1ypXcLM7m4gtLbtplMrE0tN6m0BplavUyo/UCdzwh7prYfKF1O5SYn5ejEE0/Mp556auf/7u0eMaXHqbogap8RskvWmBDrS8mCUn1jptZ9Lko3UnlqpbrcTK1+OWrnSUrGlN4uVBqXmqua36jUq12tX65aP0Tq7EvFnpVqR5VqbTKZ86RaLJeYL+nfETEmNbkl/EYy76tUO0p9M6X5qvl3SnH9TOa8o3T3LFOrX72a3FJxcqVYLll7thDT5gvUarQJWaD0LSUL1H5Qe5eZ8O/UeutTsVHim6n1hKRiNkpvWGRqxZiU7iFS81Xr6UrZM+p+MnGGoHxytXsmSnFnar5Kbz1n6t0TW4hpz1GUH0bpMGK+ajXESnVBmYwskPuM8BvV8lxq9ow4R1HzpXwQpbyRWr++lnUQY+MrZD55Iab1QaizL7XP1HpCEPl69+bi/EalPBepa4j5qr31rHRXTi3Olsn4C0pvxVDnh2djWl+Bmi8VJ1d6E1HtDRpiXPKMqmR/1WJXrjni1lbpPkgmc95R042LUc81ph8TEXMldfl8jFkDJRlTszvUGYKKNyrVv2cyeTkqFkScU8kzqlKvOmqulA9C6Ea1eDZVA7AQY+wkoRvJfDLhj1JxBaU8F7XPlPoxkfF3SsYIH0TtfR9qXCXZpXxcQoep5TYoe6YWC1J6c0Op52imlj9K6RqlGpNMrRiTWl2u2r16Yr5kzJW6zzUfY86oSrErtXdS1WrPiPUl9xlhf9XeAiS+GVlro3SnnLzzOx/T6nK1fabWl5qKgyi9QaN295tYB7V7JpQsKNU3UjpBzR/122czEHkuap9RMQCl3txqsSulHAS1Bmr9oyl/VOndAsruUN+MyM+S9xuJuCBVB6HUx1Otpo2UsfmornnutSYmk/lmSvUKmW1tRK+96tR6cxFxffcymUGpzkStBkDtXeaFmNZOqtVHKeXqMzkZo2rKCbtO+SCUTSdkjIorqMXvCL1AzZX6ZpRubLnPeq5vXIje4vpK7zeqyZda7FnJ7mRq1RGr9XRVysVQ79VRvjOVM1HqnawWYyJ8Jiq/sxg9ysf4NUrfjNJhmVr3JilfTKm3j8++Myi9FaPWf0aptoC0O4RupNZWrTZoIaa165QsqOVnKT+XOvMQ5z6qHlVJ5yr1SM3U6q1H6fFMprZASY+TcqD0zoJaXFCtHyJhzyibrmR3MrXigkp3yinbS9a5EvtX7c6CWl8MYv8q1e9kZr7+9a/f+eff/M3f3O13Y2LPxHzVchuUTaf0AlHzTMUrlHosZnIyRsUriHEpWaDWgNi/lNxSNl3pTXVqXLWeBQsxxqYr9X8na/UJG0H4zqR8EftMreeIWh9tpRgTNS61J5RqypXehMjkbISSzlWqJ89sG8sdG3umchBquoaSBco+UDFtYr5UXKFFPdfRRx+92++WS592av9SsdyF6K0GQKlmMlPvXVcqbkPkN9RiV5Q9o2wEce6jvpnS2pIxJiU/l/JHCTup1nNE7Y6F+yZpvVGmJl9Kdofsg0D4YWo+LiW7SjULajkIpbcLydqzVnW5PffboOw6sX+ptVWrYyJ8fTUdplaXq/Q2hlrOpGW9fq99bZVy3+S4St9MrQ8ANV/ijEbeQyRkjNDj1Bqo3RdUs2dKvQDVegZR8SBKxgi7o1Zro5T/VnsLMFPrjoXSXDOZPIRS/3fSDyP0DbV/P/WpT+Vv//Zv5z333LPbvzUWSjdSsSult54oWSBqYtTu1VPfTMl3zmRyiErn9EytuD7l46rlfZVqRxfjLYTM6et9KR2m1tNFqY8nJWOUH6YUE1Pre0b6YjF6dksY4sLMAQcckMccc8weCnnVqlV58MEHj5ku5vgQRTPUAZZqfjT5vWfPc1pZoOZKydiwH5566qn8+te/nmeffXYeeuih+b73vW+3wPJzhVqHSSOamXnqqadm5oyBPvbYY6cel1DIb3jDG6b63WKNS8iY2n5Q0jWZ3H4gnNUTTzxx55+feeaZXLt2bZ511ln5xBNPjFoDam2V9lkm4ytQ34zaZ2o+iNLD1ZPf+8QTT8ynnnpq5/8eswaUD0IcYCn5ovQ4dX6gdC6hb4gz3+S41XJL6VzKnik1Kmrhg1TqRmpcwp6prQE1LhU4JWwPpcPUvhllz6j5UnadSAZSOlfpkS3Kb8xkYlfUeYeSW+JMreYrnHLKKXnXXXdlZubNN9+cp59++s7fvfGNb5x6XEo32gdhfHJK11D7gTqfqcWeqQfBBiq/GRWvUIuTK+lGNV9fqZiQ0mFUzIYqbKFjxJn9121Q9peSMeqbEeczyqZncg+5X3nllfnMM8/s/NkzzzyTV1xxRf7Wb/3W1HNtIWNV539qn1E2vUWz9cr5qhWcE/OlCqKVzlKUfGUyRbtqDUmI+VI2ndK51LjUOUopLkjXeGZq+I2ELLQ4/yvEG5XqBal4hdpFa2odyFxMtS+mdqYm4jZULCiTySEq1epnMjqXkq9MrUbjlA5Tu6BHrC+lFyidSz9Qm8n4NZXjUv5di3xfZS6GkAW1miul+CglX5SuUcsnE+tL5UzU7ga1eMS8yscj81zEOijpsEwtX1/pDkAmIwuUHifP1IRvQ513lO49k/02iNwveU9Mpe47k3uUhJCF4447bo+frVu3Ls8444w5f/eTQuxfMu9L5OrVchtEzEapbjSTmy9VY0I2D63WN5QP0sIfrZQFom6DvHdF6AUlXyGT8ZfUel2p9YlpcVbvvQaAiuUq3ZtUsw/0/bNKGVOrU1fzySm7TtV+Kz3Uq9a7kbK/hD2j65iq7ST56CsZI1bQYdTZV6lvAeWDqNULEjJG+fmUfGVq9V5RuoOn1mNB7ZsRulHtboHa3SCyn1qmxn0uIi5I1jzT+ahK+aLu5arpBaWerpRvQz6sVD1ftXg2eR+TOPtS+kbpPrWa30icS9T6qVFyq9TLlJJb27MZKJ2rdOdI7Y6FWk0bmafO1IivuC9Gm7xRZt85TzKWS9hf3wdhawuI/Dd1LqFynkp9RzO18gVKNQBq9oHynYkYsVKsMZO7d6V2Vld6U42qr1DrS0XVfhPjtthnlbJAfDO1XiZK75lmcv4oeR8kU+PtQqr3KpVD3LRpU373u9/NI444Iq+//vrMnPHze8xBKMUV1HKISn0AM7n4O3H+p9aAioOojUv4YdQ3o++6qvQXVLo72uJ8NrB58+b87Gc/26UsKL3VRp3/qbiN0jdT65Gi9M0o+0DtBzWdq3aXicjFqMVByPpZos9E9Vwn55tZX1dP5EyUfAU6xlQ9rtJdiBb9mCptOiVjVLyCOJ+1qCvI7Dt+l8n1QiDmq9b3TK3Gk5DdFveYKr8ZtX+pGBORP1PrVUfl+sg+MSq+DbXP1HI8Sncs1PJclI2g/FGl+ijK7hB6gZIvtV4mVH2FUhwkU7cHHvXuSGXMVbGna6aGPSPsA3lXjpAxSoe1yHlWjasWyyXjIERtMqVzqXMf+e5IZq19ULu7ofSmi9q4hC6n5ICy6VSdeov4aNW4lD1TOj8825x6q19R88kJuSVruQj7qxZnU9MLVExMqa6P8vWV6urV6tSV3h3JZGRXrf9Mi7VVqv3O7L9PDBW7UstNEt9MKSeXycWYqJoFItZG3qUmdG6L+qje3z5T69NOjavU15a0DyrvQqrZyBbv8FTuB0KXUzqsRa4+s77OtfJ7ZTLrq1Y/q9SzIFOrN5daLEgp/03psExGFuiaq2rdSNhfcr5kva/KPU8qJqZUg0e+uUmMq3RHm6xjGqjcD8QZVTU+TNXEZPZ/p1zp3cJhLKrn6EDvb9tmMrE2tdyk0hpkavUyofYDdT4j7JnafqB0OZWbfOMb35ibNm3K22+/fQ+ZmpS/54JaHRNVF0THQSpll6yVI9a3Rb1C7/WNmVr3uSjdSOWpibMUtbbDHFX65aidJykZU3q7UGlcuheiit+o1KtdrV+uWj9E6uxLxZ6VakeVam0ymfOkWiyXmC/p31ExJiW5JfxGMu+rVDtKfTOl+ar5d0px/UzmvKN09yxTq1+9mtxScXKlWC5Ze0bU8KjVaBOy0OLeRqUsUPtB7V1m0r/L1OitT8VGiW+m1hOSitkovWGRqRVjUrqHmKn1RqiSTc/k7icTZ4gWuT61eya9x50ztfp4KvUBJOsQiXMU5YdROoyYr1oNsVJdUCYjC+Q+U3qLVynvS9oz4hxFzZfyQZTyRmr9+sg6iOr4CplPJnwQ6uzbYp9l9t8TgsjXuzcXX8ulkOcidQ0xX7W3npXuyqnF2TIZf0HprRjq/JDJ+ArUfKk4OWXXlWqpld4TJ8+oSvZXLXblmiNubZXug2Qy5x013UjJAtWPiYi5UrqcWgMlGVOzO9QZgo43KtS/ZzJ5OSoWRJxTyTOqUq86aq50T5fMOt2oFs+magAoO0noRjKfTPij9H2QzP7zXNQ+U+rHRMbfKRkjfBC1932ocZVkt0VNTJUOU8ttUPZMMRY00PubG9TZl1pbJX9U7Z1Fyp4pxZjU6nLV7tUT8yXzZ0SMmDqjKsWu1N5JVas9I9aX3Gf029SVZ0lqXOKbkbU2SnfKKR+E0OVq+4zKRyn1f8/UeoNG7e43sQ5q90zoXrGZ/dc3UjpBzR/122czEHmuFu9+VcYAlHpzq8WulHIQZM/gAYX+0ZQ/SoyrGr/LrP1mRH6WvN9I3tfPrK2DoOs2VN73IXwQam3JmmeVmphM5psp1StksjaiWodNzjezTo9R+0ypn5p7mcygVGeiVgPQImfS+90gtfoopVx9JidjVE05YdcpH4Sy6YSMUXEFtfgdoReouVLfjNKN5D5Tqm9Uiusrvd+oJl9qsWclu5OpVUdM2Qi/Bci9V0f5zlTORLV3skKMiYxXZNbmdyj/jvJrFL9ZtQ7L1Lo3SfliSr19fPadQemtGGptKR2mVFtA9lggdCO1tmq1QYRdp/u5qORnyfsrxJmH7slcWY+qpHOVeqRmavXWI3ukELUFSnqcrINQemdBLS6o1g+RsGeUTVeyO5lacUGlO+UtevtW11IT+1ftzoJaXwxi/yrV72RmrlmzJi+55JLcvn17rl+/Pv/mb/4mMzOvvvrqfNvb3tbVfNVyG6RNH6D0QlXNc4t4Y6UsUHlf8s4vEa8gxiXfTyLWgNi/9Dkqs9amK72pTo2r1rOAyicr9X+nzv+ZjI0gfGeybxKxz9R6jqj10VaKMVHjUntCqaZc6U2ITM5GKOlcpXryTDaWS/UsIPupK+gasr6CsA9K7+BRcYUW8ZUPf/jDu/1utpxMM6ZCn3Zq/1KxXKUaAKWayUy9d12puA15NyhTI3ZF2TPKRhDnPtK3UVlbMsak5OdS/ihhJ9V6jqjdsXDfJK03ypTlq3e7Q/YBpN+aUPBxKdlVqllQy0EovV1I1p5RdblK/TYou07sX2pt1eqYCF9fTYep1uVWj6v0dhL1zch6feKOhdIbv0o9UjO1vplaHwBqvsQZjezNRcgYocdpG6lyX1DNnin1AlTrGUTXMWVyfmOV3VGrtVHKf6u9BZipdcdCaa6ZTB5Cqf876YcR+oa0Z//0T/+Ub3nLW/LLX/5yZuao88gApRup2JXSW0+ULBA1MaQOI3Qu9c2UfOdMJoeodE7P1Irrt+iLoZD3VaodJWsAiHpfOu+bqdHTRamPJyVjtB+W2X9MjJYvlZx6ZubeYeYlM3f++TWvec1uv3vmmWemGnP//fePL3/5y7Hffvvt8bvXvva1U405cPzxx8f++++/27wHtm3bNvW4Dz/8cJx44okREfH2t7893vzmN8d73vOeuPjii+O4446baszzzjsvNm7cGL//+78fJ510Upx++umxYsWKqec48OpXvzrWrVsX733ve+OII46Ir33ta7F69eq45pprYt9995163BUrVsQdd9wRP/rRj2L79u1xww03xKGHHhp33nln7Nixo6u5kjIWEbHPPvvEcccdF8cdd1zcf//98ZWvfCUuvvji+Nu//dupxqPWYe+994677747fuEXfiFuueWWeP7znx8REXvttVfsvff0qm/FihVxwAEHRETET//0T8ev/uqvRkTEL/7iL0495r777ht//dd/HW9605t27oPMjCuvvDJe+MIXdjcuIWNq+0FJ10Rw++HJJ5/c+efHHnssDjnkkIiIeOUrXxk//vGPpxrzqaee2vnnFStWxJo1a2L9+vVxzjnn7PbvPVeotVXaZxGMr0B9M2qfqfkghB8WMfO9h3EH9tprrzjppJPis5/97FRjPvXUU3HHHXdEZsaKFStin3322W3ssVT7II899li87nWvi4iIyy67bOd6HHPMMfHJT35yqjEp+aL0OHV+oHQuoW+IM98k1XJL6VzKnhG6JoLZv9TaUrqRGpewZ2prQI1LyG0EZ3siGB2m9M1Ie0bMl7Lrk+Ndd9118Rd/8Rexzz77xC//8i/HCSecMNWYlM6l7M6LXvSiuOCCC+IP/uAP4qqrroqvfOUr8fGPfzyOPvroePOb37yHD/GTQO5dInZFnXcouSXO1Gq+wnve85447bTT4md+5mfigQceiEsvvTQiIh588MGdMjENlG60D8L45JSuofYDdT5Tiz0TsSDqm5F5GKU4uZJuHFDx9YkcRATjg1A6jIrZED5uBCcLSnUblP2lZIz6ZsT5jNSL5513Xtx4442xdu3a2H///SNiJmbz7W9/e+ox161bF2vWrImLLrpop5xt3rw5DjzwwFi3bt2oudIyVnX+p/YZZdOpnBw1XyreSMgCNV9i7w7jqpylKPmKiLjrrrt2fptjjz02Lrroovid3/md+MxnPtPVmBFcXJCYL2XTKZ1LjUudo5TigtTaqvmNhCxQulEt3qhULzhQHa+gbDqV2xioXgdqvoQvpnamJuI2VCwogskhKtXqRzA6l6w9o/YZcY4aqNZhVByTkjFifSm9QOlc4hxF6RpFv3Gg8mxC+XeELKjVXCnFRyn5onSNWj6ZWF8qZ6J2N4jSYYSPR+a5iHVQ0mERWr6+0h2ACEYW1O54RjC+DXXeUbr3TPZCoHK/EfVnX6W67whmP1Cy8IpXvCKuvfbaWLly5c6ffehDH4pLL700vvCFL0w9LrF/ybwvdQdPKbdBxGzU6kap+VI1JpSvT+gbygeh/FFKFoi6DfLeFRkrV/AVIhh/Sa3XlVqfGGpcpRoAKpardG9SzT5Q8caBShlTq1NX9MkJu06d/ylfgThPqvVupOwvYc9IfzSCqZUj5Jaqy1XSYdTZV6lvAeWDqNULEjJG+fmkH6bUe0XpDp5ajwW1b0boRrW7BWp3gwhZULvPRcQFyZpnIh9FyRd1L1dNLyj1dKV8G1IWquerFs8ma1KJsy+lb5TuU6v5jcS5RK2fGiW3Sr1MKbm1PZuB0rlKd47U7lio1bQRcX21+Ir7YnB+rlLOk4zlEvbX90HY2gIi/02dSwaqc55KfUcjtPIFSjUAivaBildUx4iVYo0R3L0rtbO60ptqVH3FgEpfKqr2mxi3xT6rlAXim6n1MlF6zzSC80cJ+6v2diHVe5Ww6+95z3vi9NNPj7322isuvfTSuOyyy+KBBx6I++67L9auXTv1XNXeCCXmq5ZDVOoDGMHF34nzP7UGVBxEbVzCD6O+GdlvI0Knv6DS3dEW57OBl73sZXHuuefGueeeO3p8hXuean3P1N6AJ76ZWo8UpW9G2QdqPwyo6Fy1u0xELkYtDkLmaIm3AIm5kvfECD9MyVeg1tZ3Idjeq4RNp2SMildQ57OIet2oFL+LYHshVM9Xre+ZWo0n1Utc5f2oCG7/UjqMyJ+p9aqjcn2UblTybZTe7IvwHYsIvTwXZSMof1SpPop8O6laL5DyNTnv3nuZUPUVSnEQar6Urmnx7khlzFWpp6uaPSPsA3lXjpAxSodR/igxrlosl1xbojaZ0rnUuY8Yl+wvqHR3Q+lNF7VxCV1OyQFl06k6daU+T5Q9Uzo/kPNVekdL6R08spaLsL9qcTY1vUDFxJTq+tTepiLGVatTV3p3JILrlzug0H+GWlu12m+lPjFU7EotN0l8M6WcXAQXY6JqFohYGzVXSudS/qjS22dqfdqpcZX62pJxfZV3IdVsJDWuWv/oCOaeicpbT/Tds4ja9VWrn1XqWRCh1ZtLLRaklP8m7+VSdYjVORNSNxL2l5wvdU6N0LnnScXElGrwqNwkNa7SHW21+1zEGVUtPjxQrcOU7pQrvVsYwcUVlN62jWBibWq5SbU1UOplQu0H6nxG2DO1/UDpcio3+cQTT8Q555yzc19s3rw59ttvv3j00Uen1o1qdUxUXRBZfxZRK7vkXKn39eh6hd7rGyO07nNRupHMqavU5UZo9ctRO09SMqb0dqHSuOS9yQgdv1GpV7tiv9zJf6P3fojkOylE7FmpdlSp1iaCO09G6MRyByrnS/p3RIxJTW4Jv5HM+yrVjpJ3qdXmq+LfUfOl9Bhx3lG6exah1a9eTW4pv0YplkvGVogaHrUabUIWyPcFCFmg9oPau8yEf6fWW5+KjVJ9qQcUekJSMRulNyyGsVViTGr3EJXeCFWy6RFc3Td5hqj2yZXvmfQed6bmq/TWc4TePTHiHEX5YZQOo/omDSjUECvVBUUwskDuM6W3eJXyvqQ9I85R1HwpH0Qpb6TWr486UxPxFTKfTPgg1NmX2mdqPSGIfL17c7FxwQiNPBepa4j5qr31rHRXTi3OFsH4C0pvxZB1coSvQM2X8p2V3kRUe4OGGJc8oyrZX7XYlWuOuLVVug8SwZx31HQjJQtUPyYi5krpcnINVGRMze5QZwjy/myETv07kZejYkHEOZU8oyr1qqPmSvkghG5Ui2dTNQCUnRyo1I10f99qf5SKKyjluah9ptSPiYy/UzJG+CBq7/tQ4yrJ7gBZE1Olw9RyG5Q9U4sFKb25odRzNELPH43QeWeRsmdKMSa1utwBlXv1A5XzJfNnRIyYOqMqxa7U3klVqz0j1pfcZ4T9VXsLkPhmZK2N0p1yygchdLnaPlPrS03FFZTeoFG7+03uCZV7JtQaKNU3UjpBzR/122czEHkuap9RMQCl3txqsSulHAQptwMK/aMpf1Tp3QLK7lDfjMjPkvcbibggVQeh1MdTraaNWlvKD1OqiYlgvplivUIE0+tZpVedWm8uIq7vXiYzqNWZROjUAKi9y0zYSbX6KKVcfQQnY1QND2HXyTpiwqYTMkbFFdTid4ReIO9SD1R+M0o3UvtMrb5RKa6v9H6jmnwNqMSelexOhFYdsVpPV6VcDPVeHeU7U7kNpd7JajEmwmei8juUf0f5NUrfbGBSh23evDm++tWvjtJhEVr3Jsl3mVV6+/jsO4PaWzFK/WeUagtIfUvoRmpt1WqDCLtOykKETn6WvBdDnHmIcx9Vj6qkc5V6pEZo9dYj43dEbYGSHifrINTeWYjQiQuq9UOkzn2ETVeyOxFacUGlO+WU7SV1GLF/1e4sDKj0xSD2r1L9TkTEBRdcEB/96EfjqKOOin333Tf+/M//PD74wQ/GEUccEf/tv/23ruarltugbHoLvVBV80zFK5R6LEawta5UjrZ6XFIWiDUg9m+L/rOVNl3tTXViXLWeBVQ+Wan/O9mrjrIREbW+M+nnE/tMreeIWh9tpRgTNS75btBA7zXlSm9CRLD3qSM0dK5SPXkE982o2HME209dQddQskDZB6V38CifkbyP+eijj8YLXvCC+JM/+ZOdP7/vvvvip37qp6YaU61PO7V/qViuUg2AUs1khN67rlTchshvKMauCHtG2Qji3EfmaFXWlowxKfm5lD9K2Em1niNqdyzcN0nrjTJl+erd7pBxK6r2TMnHpWRXqWZBLQcxoPB2IVl7RugbtX4blF0n9i8Za1SqYyJ8fTUdplaXq/Q2hlrOhBpXqa+tUu6bHFfpm6n1AaDmS5zRyN5chIwRepxcgwid+4Jq9kyxF2C1LEQw/jMVD2rhN1a+5RGhU2ujlv+O0HkLMELrjoXSXCOYPIRS/3fSByFr6wl7dtBBB8UXv/jF+OQnPxnveMc7RvWHGaB0I3m/IkLnrafquUYwNTFq9+rJ2mQl35nIISqd0yO04vrkWVIp76tUO0raB6Lel9Jhaj1dlPp4UjJG+WFKMTG1vmfkfMdVPCxxiAszr3/96+MHP/jBnM7JscceO/VcI/Sa9BIHWKr50Xve8544/fTTY6+99opLL700Lrvssrj//vtj8+bNsXbt2q7mSsnYXE7Jy172sjj33HPj3HPPnXpc8puddtpp8TM/8zPxwAMPxKWXXhoREQ8++ODO4qxpIBTyunXrYs2aNXHRRRft/G6bN2+OAw88MNatWzf1XKlxCRlT2w8tdc0DDzwQ991339S6ZhiX2A+Es/qKV7wirr322li5cuXOn33oQx+KSy+9NL7whS9MPVdqbWfvs8yMzZs3x6te9aru9lkE4ytQ34zaZxFaPojSw9VPPPFEnHPOOTt9hs2bN8d+++0Xjz76aFnxxUCFD0IdYAn5ovQ4dX6gdC6hb4gzXwQnt5TOpfxGpUZF1NpSupEal7Bn5BqcffbZO/937+OSRQfVtofSYWr7YSF7tmbNmu7mS9l1IhlI6Vy1R7YIvzGCiV1R5x1KbokztZqvcPTRR8c3v/nNuOuuu+KVr3xlvOAFL4iIiJe+9KW7+b3PFcrXV/NBiHEpn5zQNdR+oM5narFnIhZExlyJeIVanJzIIUYwsqvm6ysVE1I6jLIPA9U+LnneUarboAr5CRmjvhlxPiPzRkRR5f/3//1/8T//5/+MLVu2xL333hsRET/7sz8bL3nJS0aNG8HIGHH+p/YZZdOpnBw1X7WCc2K+ZCMslbMUJV8RTNGuWkMSYr6UTad0LqnLiXOUUlyQrAui/EYif0bEACjdqBZvVKoXpOIVEVoXral1oOZL+WJKZ2oibkPGgogcolKtfgSjcyn5itBqNE7q8gidC3rE+pJ6gYoxDVSdo8j8DuU3Er4zdTYhHwAeqJIFtZorpfgoJV8RXF29Uj6ZWF8qZ6J2N4jMJ1f7eGSei1gHJR0WoeXrK90BiGBkodUdz4g635lsbFh93lG690zWbRC5X+rsq1T3HcHsB0oWPvrRj855Ln//+98/uqlyRO3+JfO+RK5eLbdBxGyUaurnm29FfpaqMaF8fULfUD4I5Y9SskDVbVD3rgi9oOQrRDD+klqvK6V+TOS4SjUAVCxX6d6kmn2g4o2EjKnVqav55BGMXadqv5Ue6lXr3UjZX+oMQcgtZScpuSV8EDUdRp19lfoWkPVRSvWChIxRfj5ZAzCg0ntF5Q6eWo+FCK1vRuhGtbsFLe8GVcTKI+plQe0+FxEXJG06kY+i5Iu6l6umF5R6uraIMVXKAjFftXg29c2osy+lb5TuUyv6jRG15xK1fmpUXF+plymZ37E943Su0p0jtTsWAyo1bURcXy2+4r4YnJ+rlPMkdQ1hf30fhPWdifw3dS6hcp5KfUcjtPIFSjUAavaB8p2JGLFSrHGS6rxky7N6zzFtlTxqhF5fKqr2m3zEvHqfUbIQUf/N1HqZKL1nGsH5o4T9VXu7kLrDQ9j1VatWxYYNG3b+78MPPzxuvfXWePnLXx4vfelLp55rhNYbocR81XKISn0AIzg/jDj/U2tAxUHUxiX8MOqbtdgPAz33F1S6OxrB2LNPf/rTo8eYC7V7nq36nlXk6tXegL/kkkti7dq1pd9MrUdKi3eOB8Z+MzLmSsfDBnrWuWp3mYhcjFochDj7UnKrVtNG5UyUfAVqbX0XgvMVKD8sgr+fXBmvGKg8n1G6USl+F8H1QiDmq9b3TK3Gk5Bd8r6+Us/vAaK2oDp/ptarjsr1UbpRybdRerMvwncsIvTyXC3ijZX+qFJ91EC13SHsLyVfar1MqPoKtTiIUg888u0kIuaq1NNVzZ5R/SvId7+rZYzSYZQ/Sp0nlWK51NpSNcSUzqXOfdS9K8I+qN3dUHrTRW1cQpdTckDGrog6dbU+T4Q9G77ZhRdeGC9/+csjM2Pz5s3xqle9qrvzw+R8FepX1HxyQm7pWq5q+6sWZ4tg9QLxbl8Ec/9soPd3jtXepiLGVatTV3p3JIKRXbX+M9TaUvuX0jdKfWKo2JVabpL4Zmo5OTKuP1BZs0DE2qi5km/8Ev6o0ttnLfu0U/H3Xt+bVOsPqvQupJqNpMYl3+IdqNLllA5TeuuJ+l4RzPqq1c8q9SyI4ORB6W0MNTtJrAMZcyVkgciZkG/7EPa3lS6v+maK9zyJmJhSDR6Vm6TGVbqjTekbaj8QZ1S1+DD5BrzSnXKVdwsjuLiC0tu2EUysbfbZpKomRineSJ7PlHqZUPuBOp8R9kwtVx/B6HIqN/ntb397zp8/73nPi09+8pNTjalYxxRRXxdE7TNCdslauYHK9aVkQam+MULrPlcE9xbAQGWeWqkudxKFfjlq50lKxpTeLlQal5qrmt+o1qu9Vb/cnuOCZL8c4uxLvuuqUjuqVGsTwZwn1WK5xHxJ/46IManJLeE3knlfpdpR6pspzVfNv1OK60cw5x2lu2cRev3qleSWipMrxXLJODlRw6NWo03IAqVvKVmg9oPau8yEf6fWW5+Mv1d/M7WekFTMRukNiwitGFOEzj1Ear5qPV1b+HeV95OJMwT5NobSPROluDM1X6W3niP07okR5yjKD6N0GDFfsoaYeGdRqS4ogpEFcp8pvcWrlPel38uJqD1HUfOlfBClvJFivz7izEfEV8h8MnUfkzj7krmYFj0hqu49E/l69+Zq887TQK95LlLXUHddld56VrorpxZnm6TSX1B6K4Y6PwxjV/sK1HypOLnam4hKb9AQ45JnVCX7q/bWRIRrjqi1VbsPQtUnK+lG8q4rcf6neokRupxagwgdGVOzO/QZojreqFT/HsH3iq2MBRHnVFK+lHrVUXMl32oj7K9SPJuqAaDsJKEbyXwU4Y9S+QKlPFcEVzuq0o+JjL9TMkb4IGrv+1DjRujILtl3pVqHqeU2KHs2+/3VqnEpPdbyzY1e+79Ta6vkj6q9s0jmIVRiTGq9fdXu1VNvxVByS8SIW5xRe49dqb2TqlZ7Rr07Qt4NGqjsL9jqLUC6T9vnP//5qcYka22U7pRHcD5ItS5X22cDKn2pqTiI0hs0EVp3v4l1ULtnQtYWqNQ3UjpBzR/122czUHkuYp9RMQCl3txqsSulHAS1Bmr9oweq/VFiXLX4HfXNqPwsdb+RiAtSdRBKfTzVatqoGBPlh6nVxFB+o0q9wjDGbCpshFKvOrXeXERc371MZqDrTCrHVawBUHqXmbCTavVRSrn6CLaOeKAyt0HZdcIHoWw6IWNknZxS/I7QC9RciRx1BHsXgthnavWNSnF9pfcbI7TkSy32rGR3IrTqiNV6uirlYqh+EOQbcBFMD8uB3nsnq8WYCJ+Jyu+Qd6kJv0bpm82lw/bbb7+StwCV7k1GML6YUm8fn313ofJWjFr/GaXaAlK+CN1Ire1i1AaNiQcRdr1Fj9SBnvOzlJ9L2Uni3EfVFSjpXKUeqRFavfXIHilEbYFSPTl5flB6Z0EtLqjWD5GwZ5RNV7I7EVpxQaU75aTtpWqpByr3r9qdBbW+GMT+Vavfef7znx9/+Id/GL/3e78Xd999d+zYsSN+7ud+Ll784hdPPSY1XypvpGbTqXgFUfNM3iNW6bEYwckYFa8gxqVkgVqDgep6ckJuW9j03t9Un2/csTZCrWcBlU9W6v9OvpdD2AjCdybvJhP7TK3nSIRWH+0BhRgTNS61J6izOrEn1N6EaHG/caBXnatWT059MyL2TOUg1HQNJQuUfVB6B4+KK1C6/NOf/vScP3/hC18Yn/nMZ6YaU61PO7V/qViuUg2AUs1khN67rlTchshvqMWuKHtG2YiBynMf9c2U1paMMSn5uZQ/SthJtZ4jEVp3LFr1Teo576v2RpmSfCnZHbKugPDD1HzcCEZ21WoWlHIQSm8XknkjQt8o9tsg7Dqxf8k7bUp1TJSvr6TD1Opyld7GUMuZUOMq9bVVyn2T4yp9M7U+ANR8iTMa2ZuLkDFCj1NroHZfUM2eRej0AqRkIYLxn8m+mISMEXZHrdZGKf+t9hbgJAp3LNTmSuQhlPq/U/fkIhh9Q9qziJm7nh/4wAfihhtuiA0bNowej9KNVOxK6a0nShaoN12U7tWTeTkV3zmCySEqndMj2sb1x+Y2KB9XLe87W8YyM+6///4ua0dJXUPd8yR0mFpPF6U+npSMUX6YUkxMre8Z6oulec489thj+eCDDy72NPZg3bp1ed111835u4svvnjqcf/+7/8+jzjiiDz++OPziCOOyO985zuZmfnAAw/khz/84anHneT666/Pz3/+8yVjZc58o1tvvTVvueWW3LJlS9m4A08//XTefPPN+cADD4wei55rFffccw86PrEODz/8cN500035yCOPlIyXmfntb387V65cuXMvnHnmmfmmN70pf+3Xfi2vvPLKUWM/9NBDecstt+Qtt9ySDz30UNGMuXG3bt2aGzduzNtuuy0ff/zxkjFV9sOAkq4h9sOTTz6ZF198cR522GH5G7/xG3nAAQfkwQcfnGeddVbefffdU425devW3LZt25y/u/3228dMdzcq1zaT22fVEL4C/c3ofda7D0L5YXfeeWeeccYZuXLlyjz++OPz+OOPz5UrV+bb3/72vOOOO0bPe5Lt27dPrRMyOR/kd3/3d+fUiffee2+edtppJf/Gd7/73TL5IvQ4dX6YTZXObWUjMsef+Si5pdeg2p5RuobYvy3lK3O8bqTHbeGX974G1eO2sDuZNb4Nff6fTa/fbDbVZ4jZjJ0vZddXrVqVxxxzTK5atSpXrVqV9913X2ZmPvLII3nyySdPNSalcym7c9JJJ039d39SKv3GhWJXf/VXfzVq7Gr70MofrWCp+ArVVOtG+yBsjKlK17TeD1U5OZXYMxELIr8ZEa+YTe9x8tn0rBvVfH0iB5HZ1gep0mHV9qGFjzsJZX97rduYpNLXnwtqDcZ+MzIHQdr0J598Mj/2sY/lGWeckb/+679eOjZFlYwR538SwqaTtVHEfKl4IyULZHyU3ru9n6UoH/fss8/ODRs27PHzT3ziE3nggQd2M2YmZ3eo+c6Fgl9DQsffx9A6DkJRIWPPPPPMzj9v3749b7755ty8eXOXuZgW5/+BnuONKvWCreIVVTViVFyBWgc6DkL6YtV1fbPpNW5DjUnVv6vV6s8Focsr/Ttin1WPSekwKo45l4ytXr26TMaIb0bXqVfpXOocRfl3rfzGa665Jh9//PFRuoY6m1C5mJZn6l5rrkiqZbfV2Zfy73rPJ5PrW50zUbsbROmw+ajw8Yg8V+t1GIt63KbX/OwAXR81xAUffvjhsjEV7ngSvg1dt6F075mAyP1SZ18yDkLIQUtfv1eo/UvZyFZ3mTL7zW0MkPHsnutG56JqvlSNiZqPm9muz0S1P1olCy16FhC1mJV6oaWvUBHPbtFHrAo6n6zSJ4ZCqQaAjuWq3pu85pprcvv27V3aByreSOqFSTn42te+Vjp2izr13n3ygSq7Ttuz6m9GxoNUejcOVNvfFmeIqjwX7dso3okZ6FWHUWffpdC3oPqMqlAv2DLPNQZSvlrcS6VkoWrclnfwVHos9P7N5oPQjT3fLWgBlUMkZaHX+1wta6kr1qBFPqpKvlrmzjL71QtKPV23bt2aDz/88Jw98MfMlZIFcm1V4tm0fNFn3yp9o3afOlPHbyTOJbTcztZhVXH9hx56aOe411577Wi5VeplOtCqp1yFPSNkjPZtqnWu0p2jVv5SVc5EraatZW2QSnylAtW+GISfq5LzJL8ZZX99H2TXONU6gch/U7Z3ds7z0UcfzVtuuWW0XKj1HVXLFyjVAFBjKtWpt4gRD3t3bO8oyp617pfba412i5h273nUH/zgB6Pn9pNQtQ5U7TcxLrXPWtnIqm9G2YetW7fuHJe4l1sJ5S/R9xDJfMFgJ++7774u3y6kcgWqsYXM/t8InU1lHYRKDnE2g2689957u+wDSPlhZP07JQtUbFRlXPJ8Rn2z6nHV+gsOKNwdnc21116bn/nMZ7r1G5XveVJvxfT+nulc32w+f28aiG+m3CNl27Zto3tNkvuM9hur9kMrndv7GUKtj4fKXZtM5uxLya3qG6FUzIbUYwp32lqOOxa1fmpzUV2vUCVjVLyCOJ+RNl2lLiizXW+f66+/Pj/3uc+VjKXS90yxj3Yrn6lah/Xe85uKMZE9g1R61VHn6Va6saqmjX7PJLP/3r7zsVzvWEzS83tXczHWRlBr2+Kb9X5vYzaVeqFavrZt2zZvHqPHXiZ0j1S1OIhCDzwqhknFXJV6us5Hr/aMsg/U2YyQMVKH7dixY+efH3vssbzpppvy/vvvHzXmbAg/l8r7DvRaT07FXCmdS+lGYtyW94Iy++0To/Smi9q4hC6nY6PVNr31u5C99nmapNqeDd9sw4YN+cMf/rBsXLXzzoBirK3nd/CIvBFpf1XibLOh9ELVOlAxsZZvn42t65sN9TbVhg0b8uabby47sxJ7WKVOfUD13ZGBMfpG7Y5Yq7XtvfZbqU9MizfKJuk1N0l8M7WcHBVjonwFItam/qZrlT+q9PZZKyi7o/DepEp/UCr+3jKH2KuNbOWP9tw/urUO6/Wtp0kq9WKL9e29flaxZ8EkSr251N4pn/x3esx/z0WFDiNlgay5qlzXFva3cr7EN1O750nFxNRq8IY5E/uselzlO9q93+dqEW98+umn88/+7M+6jQ+3PkP0fqc8k68XHPvNqLjCUnjbtqqvjVpukoA4nyn1MqH3Q/X5jLRnqvuh6o5269xkBSp1TPRduep9RsouEbNp2V+fqp/ttb6xVS7mmmuuKRtroEo3tspT916Xq9YvZy56PU9SMqb0dqHauMSYLf3GT3/602XjKfZqp3rg9R4XnI+xPgh9Z7A6JqZUO6pWa0OcJ9ViuWoxMSLGpCa381F57qv085VqR6lvpjRftZ7fajqMPKurvOkyG6V+9VU+uVLPgkmUYrmzqbCRRA2Pao12q/pGgip/iVoDlXeZqfqouei1tz5p06u/GakTiPg7HbNRe+9KJcY0SZXP1ErXVH63avlSs+mt+jxXnCFa+aK93zNRijtnavXxVOoDOEDYSOIcRflhlA4j5kvbhy1btuTGjRvztttuy8cff7y8H9NAr3VBpD0jfVHFt3gHes37UvaMqhck7xxV+yBKeaMBlX59rXsn90qLHmXVNTxEvFGpJwSRr3dvLs5vVM1zVUPNV+mtZxLVONuGDRvypptumvedpucCec9E5a2YuajIG7XsFTt2vq3i5L3fB5lE7Q2aqnEp+6Bmf+dC4a2JTNccTVK1tir3Qaj65ElUdeNYWaBtenWuntDlrfwaVRkj6LX2jDo/KNW/ZzJ5Oeq8Q55TiVijUq86+hzVygep1I29x7OpGgDKTpI5KUK+5vJHV69eXeqPVsUVlPNc1N3v5dyPaZIqGaN8EKX3fVrJQs+yS+lxQoep1RAPUP7S5PurN954Y0kuhkDtzY25oGKuVdx33325YcOGsrd4W7w7sn379rz55pvL5FaxxiST85kGxpyj1Hr7qt2rp+ZLy21ljJg6oyrFrtTeSVWrPaPWl9pnLXpj9v4W4NatW/fwDYZvWL0nKmKNynfKq/JclC5X2mdqfampOEiLN2geffTRvOWWW8rl4vrrr88vfOELo8chdUL1nlC7Z5K5aw02bNiw824fRc/1jdU6Qc0f9dtnM1B1Vy3r9cfGANR6c2fqxK7IHMTWrVt3u6NdAbEGav2jKX90KbxbQDH2m7Xow0uc/yvjgq3qIBT6eCrVtE1StbYt7zFVU1lD3OKb9VyvkMnbCMVedb335qLi+rP9u6r3rhR7mdB1JhXj0nt3Um6J2mT67mjPd4PU6qOqUeuzSMVBWtRNVt+7It/9roKMKyjF7wZa6IWxc539zSbzURWxIHoN6NqzKqptj1pcX+n9xkl6ly/Spqv0OCLtTos6Yppee7oq3Qdpcf8us853ps7/ir2TB3qPMQ208Bur6u+q50rHyRW+2Vw6rCouqHRvcjYbNmzIz372s6P/jRa6vNJv9Nl3TyrXt1X8rtf+M62psD3EN2v9dvBYqNqgyTsLk3cce6xDzKyXBbX87Gyq/FyqFpM499F1Bco6t9ceqZnavfWqIGoL1OrJSR939tjzrcu0KNTfzUWv/igVuyJ7ulI2XcXuKMcFq1DrrT9JZWyU2L9qdxbU+mIQ+1e5fqeSFvPtPbdB2fS5eiwMjNELZM0zHW/svcfiQLWMUXF90m+slgVqrnRPiEy+NnmsTVd6U50ct3U+asw3o+IVav3f56Li/E/YiJZxECI2Wrl/yZ4jij0hq+rq1WJMLd4jGhi7J+izOrEn1N6EqLYRajpXsZ6csuuVsefWOYiedQ0hC63qiKsg5kvHFVTuY85F733aJ9m2bdvo/UvFRtVqADK1aibnotd3Xam4zUL5jQMOOGDqceei19gVdS6hbESLOObA2G+mtrZzUakTFPxcyh8lc4h0vJGKZ/d+x6JF36RHHnkkb7755rIYpkpv7oGh5+a1116bn/vc50aPN0llD4+WOZNe7c4wt2pd0+KtiYFefdzZVPv6KjULk/Seg1B6uzCT02EtcxtjUeuhNaAYE+u9jqlFTWrvOkytLpcaV+ntJOqb0bKg0NdWMfdNjqvwzQbU+gBQ8608o7WwD5UyRuhxag1U7wvOpld7Npvqnq6VkLJA5gta1bqOlTHC7qjV2tAo9WknbK/SHQuluWayeYhJWfja1742eq4Ec9nIoaccdZd6jL5ZKu9ojYWyvUpvPVFzbXGfq/d79QO0H9Z7H20yh6hwTm/J008/nTfeeCNyz5PycXvP+w4ytmHDhrzxxhuR98Sr9i8hty3qfWkd1nNPl7notY9nZr2Mte4NqhATU+t7VrkfVmRmhvmJefjhh+NFL3rR6HG2bt0a9913X0REvPzlL48Xv/jFo8ck+dGPfhR33XVXvPKVr4wXvOAFiz2deTniiCPihBNOiDe/+c1x4IEHYv/O448/HnfccUf8wi/8QrzwhS/sdkxyXDPDjh074tZbb42Xv/zl8dKXvnTUWNu2bYt777039t5773jFK14R/+bf/JuiWdbygx/8INasWRNXX311rFixIl74whfGE088Eaeffnr83u/9Xjz/+c8f/W889thj8S//8i/x7//9vy/VOd4PM1Svw/bt2+Puu++OHTt2xM/93M+V2rRqWai2v9dcc0285jWviYiIRx55JC666KK4/vrr46CDDoo//uM/Hq0XVKnylyL0fCZCl5N+2JYtW+Lee++NiIif/dmfjZe85CVlY6vq8u3bt8fjjz8e/+7f/bvysaug1raaFr7Nd77znfhP/+k/lY1Hrm2lbqSgdC6payap3L/VuqaFPVOQsUqo8//WrVvj4x//ePzwhz+M173udfHWt7515+/OP//8+NSnPlX2b0XUya2Sz6RiH1rLghqPP/54PPjgg/GKV7xisaeyB9V25wc/+EHsv//+FVObF9LHrYxdTVLth5FUri+twxy72kWVb6MScx2gbbqCz+h9xufkVOIKLaD2hJKdVNALZA6iGkqHVduHFj6uCrY7e1Kpw+i4IKXDrr/++rj22mvjnHPOKR97LFu3bo0//dM/jXvvvbdJvKL6/E/ZyKpxVWqjFoLKc1GxoMr53nDDDbFhw4byvauU86yOgaxYsWJOPbtp06b4pV/6pS7GnKTa7tDzVaRqP8yutbn44ovju9/97uham1ZxfcKe9Z6b/Na3vhUf/OAHY7/99ot169bF+973vvipn/qpeOihh+KSSy6JY445ZvScSXz+r4c+q/fuN5o9oXwxit5jYi1yG1QOkaB6rkr1FZMQ+6znGEhEuzgmtR8q17eF3FbYyRbnqCrfefJc8uijj8aFF14Y119/fbzqVa+KNWvWTC0LmzZt2uNn73rXu+LP//zPIzO7PUtW+3c+U+/Jcs7RUjpM1aYroLa2SvnkASI+TK+D4ysMvccF/+7v/i4+9KEPxcte9rJYv369XFywCsK3aXXeUfJBqufaIvdL6MbeYzb29dvs3xZ7tyJXT/mNdHxULZ5NQfphVTUmSr6+2jlqoFrftLpHXE3vsfJJKn0F2l8i62d7z/VFcPaMGpfQuaq6MaLfeEWrHIRKD7zK/dAyv0P4o0rn/4EqXd7i/F/lK7SKBynYSQrFmjZK51b7uGr3tCP4+1w999qIYL+Z2l05hbOJSl1QRDt71qtPvhDL7Q6e2R1Fn7wSJT02QH0zalwih9jCplfko1r5osS9XErnKvV6rliDFj3wJ6mWBTX/WalGrPe8r9p96giduL5SnI3SYcO411xzTUQErhspKs9Rar3E1XrrV8UbXXu2J1WyQOpG0j6QcX1yn6nEV5Z7vGLAPvnuVNhflT6L1Jgk1flvyvb+8R//cbzvfe+Ll7zkJXHdddfF+eefHy9+8Ytjy5Yt8bGPfSz+83/+z6PmrVQ7OhcV+0xNdknU9DgVr6iIEU/u3e9+97tx3nnnle7datwvd2Gqz3093y0Y3iM69dRT41WvelXx7HidS90Tqxi39T6rigUp1DG1vJfbc71gq1hQhf2lfdyBat+GusOjsM9mQ8ZBlGp4KsesloOhlymtG1vE35944olR+61F/buS3Kqh9P5b7/VRrfzRnmsL3M9lbghd3vt+oPje974XF1xwQey1116xfv36WL9+ffy///f/Yt99943Pfe5zU5+zqbeDZ1OlG4mzybC2z3ve82LdunW7re3nP//50euiGiPu+Yw6G7XY80DPfTzos2TvOnc2Sv5dxPKs8WyBmtxGMLJQeY5yX9u5GZs3Us7LKdwdJWJBlK8f4bc8B9R6BhGorIGiDlOpvyNzfYRubJXvI2LEin6jIgq1NqqyQL2ZWzmuWm6jkmq7Q9ZyketL90itjoO4J3O7evKKmKtqT1eFM/VcKPWviKiRsWodNuSpVd8jVqP3mGsEo3Mp3dii7iqi//6CBKpvukRo9Pah/VG1taVQm+9YWvUcUelrWznu5Nu2jzzySFx00UUlb9uahVGwv2p9baqgYjZEHoJ6m5qqmZwrt/GSl7wkHnrooVG5ja1bt8bHP/7x+OEPfxive93r4q1vfevO351//vnxqU99avTcVeIVSvnZSVTzBT2jXnPUc5821TfKIlj7S/g2vefkqmNMre4nV8TalkJfvcr9oFinrtarTuGuegsb2fsb0lTMVdXHpajaDy10OeXnUzqMmK9KDfEkPcdsIjhd0+qsQ8b1Kf95DPPFng866KD44z/+49G2p4WN6H1PzEWlLFTWXCnbdDonp1DvS45biVIN3gBV21id8yTXdrnHhyvPqOrvfqtB7F+1OleqJxVJxTosdNd12t4r5P3ZVlTLWAtd03MvE/qcqhBzpVHxQRTXtncU78rRVOoE1TekSCrXtzo+Sp8hquNBSyFPXUELPaZ2NomoiV1RMmbZZWs8VexDq7gNiUKM2OxJdV1fdex5NgpvnynU2kTo1kcp5bmqxyRzMSpyO0n1+hKxIJX8GfnNCL3QSsaWK0o9+wZUYrmTEGff5ZyjVYuT+23qNnViPb9TNhsFnaDQC6HFuOrviY+Btum0HqvaZy3ziAp9JiK4mL5CjImC1jWU/+z48C6q6r6Va+vVqNQJKjVtSueo5R4Lap1LHqvDWsy3ap/RveoIPd5yP/R4D3EuiHp9xXhjRL95VNcx7UI1rlDh67euy63y7+yT68WeB9R0OXn3arn15orQizvT57PqmI1ivZGaTqj0P6rldq53PP7tv/238eCDD45+x0Pdb4zo8y0xVVr4ziox7eWe9x1QsGc0KrKgBiELXtvdUXvbVsU+kCi830hTLWOK8ZXqNfBb7UyOp2UecbnWQSj1qmsx155jFiTuhTCD0jebhIq/q73jUeXnqsUVfObTe3+VitmoxYIq57t169b40z/907j33nuxd9om6bV2JYLfv1XnqCEX87KXvSzWr1/vN9WXMVR9I+GP0nKrGFshqYzbeG1nUOvpSqMYI66uj1L7ZiS9156p3NdXumMxF732aR8gfX3F2u/lqsOIfdYqH6XS/71yP8z1Xv2LX/zi2LJly6j36iffPnv00UfjwgsvjO9+97tx0EEHxZo1a0bHiJVq2lT6AA5n36uvvjpWrFhRfrePQsk+qL1XrxaDp2WBynP1/Ia0cq86sqat11yMqh4f6N13pvxR0s9V0+MEym+1k3HBnns8taC6xoT2QVxLXY/SGUKtvmKAzm1U6DH1N9XHxPXJt1cU3z5rEbchZWEMQ67gec97Xqxbty7Wr18fGzZsiBe96EUy768q0asctEDlbELHQUi7TsVBqvvPkjpXpQ+Ayn4goPJRc1Gpcx1/Z1E6n/mb7aJK5x5xxBFxwgknxKmnnor6nr3bCMIHmczVP/LII3HRRRfF9ddfH6961atKcvUEam8B0lB5yar9QJ3/Ve950lDnyepxe8/10SjErug3mVX61bc6m1Tqct8z2RPnz5Z3vHGSalmotmcqupHCd3PbUZ2fVXpbPoI599FxK9J/JuykyrmEHpeAnqt7Mo+HvINHs1y/Wav7Z0QMj6y5os5RFeMq31mgqb5T3rvtbbV/lXyFCGa+VWMS+1e5foeAnG/vcX1KJyj2f1NCOR5G5WjJt8+qqZqrYo/yVhA9gwhfgfKfK84PijUxylSeJRVtBHmWJMZWOfuq1GIqx5jIcSkq9Y2CjLWqn+393Beh049JHSL2rNgnhoCaL6nHiTNE1Xxb+IxqMkbosKo1oPOoarFc04ZeezdELI27ENWxq0p7RtgI5Thm72sb0eZsVn0HfkDhDGE7qd8nZkwOca47MS95yUvioYceKr+vX0m13LbouakQW5mEmm/vb5+38sOUcnIRffc4Jqn2QZZKLFeldkUht6Hy1iI5rsJd6gEy3qh655fycSvsTqu6vmqd675Ju0PIGHX+H3j88cfjoYceip//+Z9/zn+3VR1TlR7funVrfPzjH48f/vCH6D5T6EVsdkctf1apF5T0uJr/oTbfSRR6WBKo1Xi2WtsKu0PPVU2PRzB+Y8/1FRFaPftV86iVeQiqTxsRb6R7ys2maq8p1PrOR5UOa2V7VXQuDfUG/FiU7MOAyv5dCrVc1VTFRud628Y9eGpQrQdRQumc3oqee5lMUrl/1WIgk/Sux0iQ/ZtmXm699dY85ZRT8tRTT81Nmzbl2WefnYccckgeddRRuXHjxqnGvOuuu/KMM87Iww47LFevXp2rV6/Oww47LM8444y88847a/8PQGzfvj1vvvnmfPjhh6ce4/DDD8+LL7546nWcj1WrVuVHPvKRPPLII/Pkk0/Oyy+/PLdt2zZ63G9+85v5K7/yK/mGN7whb7jhhjz66KPzjW98Yx5++OF51VVXdTMmOe6WLVvyggsuyHe+8535pS99abffnXfeed2NO8jYrbfeOvUYrbjnnnvyXe96Vx5wwAF54IEH5uGHH56HHHJIXnLJJfnkk09OPe7VV1+9888/+tGP8gMf+EC+7nWvy/POOy8feOCBqcd929velldccUVu27Ytv/jFL+Zll12WDz74YF5wwQW5du3aqcb8oz/6o3zooYcyM/Mf//Ef89WvfnWuXr06jzzyyPyHf/iHqedK7YeFOP7446f+u9Q3W4x1mBZKFij7e/LJJ+/884UXXphr1qzJ73//+/mJT3wi3/ve9049LiULA1u3bs2NGzfmbbfdlo8//viosQh/KZP7ZpTdoXT5wJYtW3Ljxo25cePG3LJly+jxBiplQVWXV64ttXeptZ1NhZ+fye2H22+/fY//jjrqqNy0aVPefvvtU41JrS2lGxdijA+yGOfUMfOdi2uuuWbU36d0DbW2lIwRemzS9l5++eW7/W6M7aXO/+eff36uX78+v/GNb+SZZ56Z7373u/PHP/5xZmaedNJJo8evZi4ZW7ly5SgZo+yZkn3I5GSB9vUHHn300bzlllvykUceKRuzalzKJ58cn/CdK8ddrHP6tPaX2mdz0Wssl9JhSjGbTE6HEb4N9c0o34bwGymfkcrvqO0z2p4NVNhe6uzbag0mGXOWpPZESzs5MO06qMUr5qLCVxio9O8oHdYyZjM2tpLJ+QrEuGp2h1rbVjqswp611mGV+qYSpdgVJV8tbW+FblwMn6mCyhwiPW71mEo5T7W4Qksq9i8xrlr9HbUfqFobwk5SdkctN3niiSfm9773vdywYUMefvjhed1112Vm5qZNm0b5IEScyef/GZTqZ5eC31gBnUOk8meTUPY3s8+4oFK8YhKFWjkKwk5SOZNJyP1bVS/YYh0y+66DoMal/DulGgDKd6bOJQcccECuWrVqt/8OOuigXLVqVR5zzDFTj6u+z6r0zXxU5KNUYkyEr0/ZdKK2cb5xK3QjdY4i9i+lE9Tu8MyG0mGTTKtv1GK5avEVop6LklvHBTkd1vqM2ms+WalOrNVducwaf3Qx6q6mRS1HrRZjImi5Hyqg7E6L+GgVdH+Uar3SKhZEn//HQugbtXu5SvqmpX2gz5OVtdTkPus1lkv5YWo9R5TspFr9u1I9CJWDUOuBR+wHam0nUejtsxA91m0Y/v6oQs6EQq12dC4qYpitfZAxukYtF6Pkj6rd0VY6myyFuqCKc2qLHGLvsXK1WmrKpiuN2yInV9nLRK3ORKnXZMvzWa/xu0kqz75K8bBMTucq9Xqm1oDogT8fvfY9U+r3lal3n2shKnI8Le5zjUUt5zkXY/cvtR8oHdZSN1KM/WZqvcTVYlezaVGjPS1qvRuUZGGwDytXrsTtQ0WMiVpbpfiKUg1xJhevUOr/1jqPOrbGQqnPovL9KIUzxAknnLDzz29/+9vzxhtvzMzMf/7nf85TTjllsaa16PR8p3whentfT0mPq0HtXaW39TI5Xa4kY3T8rtqWUe8RUXfgZ9NzL5MWY1ZC+KOUTqD69bU8Q/Tax5OCspOuF+Sg4iBKNTzUXCk5oHSj2jvlA5V+rpLcPhtj1lbpHTxqbZdCfRTFWB+EOvNRunEx6O0egNp+oHImb33rW/Nb3/pWfvWrX82jjz46r7jiiszMvOqqq/Id73jH1OMSZ/WWdqfi/EutbesYsdp9wd50zbPRW33UfPSaQ1TSuWr+XUudWxELan0ntcd+Luq9iDLH6zDKRqrlZ5VQqwtq5dtU9Qwi/FG1tzyV7mhT41L3EJXWYC6o3lwVZ0ml+jtFWtW09fy2/EL0ViP2bCyXXAwlCy2/GRGvqNDlarkNwreh7A5VyzUfxNh07coYHaZUt9EyZtPrOwCTVNodtZ6QSv0FlfYYidJ7xGq+qHsszA+hy3uve54PhfNZxdqq9TJRsmdqa0uhdPeb2rtqPUeUYs/U27aULCi9jTkXPb8PotbXRqnn91xUyMLk/l27dm3Z/qViYlRu4/zzz8/169fnN77xjTzzzDPz3e9+d/74xz/OzJz63KcWr2gZA6iQhdYx7Z7vbUxC3SNWqzkaG9N2zmQGtdqg2SjEySepnG91rwmS6h4Lqvuhch3UatqoeAV9F6Jyn1E2Uil+R0HtB6V4RaZWH0/KD6PWgJgv+b0IO6n2/ttcjD0/kGcdpTwEpcOo2DNlI4g9QdW/K0HGrQi73mLvKvTxaKXDyJyJWmyBoNc1UOslTteuVOoEtXe//c2WRp1rha5RiwsSd12pfi4L0VvvlRYxEIUe+NQ5VSnGtBA91sTQtSsKeSO1mOtsqHznJFS/jWnHVdMJaj1SlHoyU2cItbtMhB5rocOW89mkVeyqhY2YlpY1bQNjdCNR40m9MUj5NpTOpWSB2Gdqcqs237no9W2M+ajK/VJvjyrEFeaj136ISnkutRwiTfVcifVtWU++HHsGKfV/o/w7tZ4QSt9MLZZLnX1bx/DUfOcxUPtX6W3quaiwZ1RckPLFiPmq6QSlXgjkuHMx9oyqHleoqusjzurqd216zk22Xtte6xvVfBvCf16MexC95dRJCHumdvalz9QKPVLVUOrto6bH56LSRirEnal9RvWqo88lLb4ZmUPs0faq3V+ZHL9KFug86iQV50m1OiYCv9k3N1Xxitb1jT3qxrlizytXrhytFyjZJXT5UvAbFeqCKs9n6nHnCh2m1Lsik/NBlN67ovwPSm6pdzwoWsVtlmNdUCs7WZUzofQjsQ7O+7a3Z2Q+qse7Z5QsUL6+0v1Ram1V+21U+vqUnWxhH8heYj3faVuM+tkxUDJG1PZRa0utgVItV6Z+PKhSL1Tno5TqIJYCFftBLWYxH8vl7WQSpW82FxW6USmHmMn4uUpxBQpyrkp9qdT2w1xUxIKUauWIHh4U1P0oav9S56jFyMWo1WhPO65aDzz6Ld5K+0DJLaUX1GSB0DfU2lKxUWptlXq6tohbkf1MqfcFquJhxDdbDJ+xx7uj1Ljkff0WtdQVqNVSEz3VleIKmX6vLrPtG2Xk2zaZfdaDzEVlHXGlr0C9V0+9fTZQ7S8RsVy1PoDU2VcpfqfU34gcV82uz0WlzlV4r07JnlGxXGpcpVwMHcOcix79MCW7M4nCfS4qJqb0zVrbSOIuIt07aoxeULonNhcKdxEVepm0rEOs+Ga0Dqv+Zqp1uQp2UuntUertlfno9Q0L6kzd+gwxxvZS76+q9X8noOSAsjuUfaB0bksZI/zn5XgHntC5Sv0rMrXuEVNrQOWjKJ1L9XlSi9vMRa/x94Xo7S6TWpyN0rmrVq3Kj3zkI3nkkUfmySefnJdffnmJ7SXmq3Zvks7Vz0VvPvlcVPhhLePZFfpWzW8cqIwFqelcyrchxlXN9VX7H0r1IK3jgmOh9i91NpmLqtho9blErW+S0vsr1BqoxRuV+nO3rvseqxfU3qBpeTe3176FaudJpTdz54K+szAGSt8QulHpXEKOS9B6rr3W+xKQ+pbKq8/HGLuzGN9sOb2lPUDe/a6COkcpvbWgth9mU+HXLAXb2/M6LMQYWSDm22INiLyvQs1za8b6d9S5RK2Wi+7/Vim7avWCFC1lrNf3xOhvpuDjUmugth8ou074z9T5oWVNTIVOaH1vcowvqnTmy2T2L3mWJMam5kv1IloKdwt69RUolPK+mVo9+6n6WercR+hctX5MC9Fbn+dJKs+oan1iFkIlF9Pz2zaz6fmtNqWcJ6XDqDVonUftEcpfUrujrVY3uRj0VpM6m55rrlqgcL9xLnrtH9XybFbhg6ieISrlVu19EOqOhZLdoe7EtIpnV9kdouemWk5dqQd+67PZ2DO1Uk4uU6/HMbEnKB9ELZbbmh57C6j1DFKKuyrdpZ6Lnt9kompXKHtG2R2qrk9J51I+uXrv1RY5iGltGlXHRNkHpfpZCrX+M9R8W5/Rqv3Gypi2QpyN8pfU3qBV85mIdVB6xyNTT8bmYmwMgJqrWq0Nsc/U6ismqby7Qe8H1TxqBVSfNiLeSPaUI/aa2nuILXOIvdYLqubUW9HjXNXeM1XycxfjHmKP70IStaPUmVrtDQuqHoSQ3Rb7gfadK+qN1OqulHqZtDz79lqLmcmsg1qcnDz7Rs0UlyaEwX/LW96SV1xxRe7YsWPnz3bs2JF/+Zd/maeddlrFtOekN4eKKkYaEpdPPfVUfv3rX8+zzz47Dz300Hzf+963m8F6rhANhakmxdS4VJKVGpeSMULRE4XAmVwif9Jhz8w89dRTM3NGlx177LGjx6w8BFD7Ya5A0fDfa17zmqnHpb4ZtQ7EQYCSBcr+Tq7fiSeemE899dTO/z3G9lKyQAS2qMu11Dej7A6ly6mHqwlZUNPlxCGrhe2tXFsqcErtByIZSK0tpRspH4TSufPN9bbbbhs1XyJpR+kaam0pGSP0GGV7yfP/wDPPPJNr167Ns846K5944onufOdMRsYoe6ZkHzI5WVAruifGpfSC0mUv8vEywl+g9hmVXCPWl9JhLR+yq4DSYYRvQ30zSocRNp3yGanYu9o+o2SBsL3U2ZdaA+rsS+0Jyk4S66AWr1C65ETpsJYxm54fSyDGVbM71NpSOoywZ2SzV6Vma0qxK0q+WtrenhuHUMWP1EUJYlxqrko5z8WKK0xbC0HJLbV/iXHV6u+o/UDV2hB2ksxBKOUmJ+e0atWqeX/3XCHiTD7/z6BUP6vmN1L2jDr3KT2CpBYXVIpXZGrVylGXsgg7SenbTOYSKFUvSK2DUh0ENS7l3ynVAFC+M3Uu+dSnPpW//du/nffcc8/On832oadBbZ8R+obyFdRiTIQsUDad0mHUuNQ+I8al5qp2h6flAxdj9Y1aLFctvkLIGCW3jgvyvQUya+1ZJuPnqjVrIvYENddMxh9djAdqp9XjZNN9Im5D7l8CIkZM7gdC31B2h4qPEnKr1h+F8sOUHnHLZPRN63u5Y+MglL4hvhlpH4jzJPXNqH2mFMul/DC1niNkHnE+pvVH1erflepBqByEWg88Yj9Qa5up1dtHrW5jIcbk+yh/dCF6i+Uq5UwytWraKN1I3YkhdC6la6i1nYwFXX755bv9bky9oJI/qnZHW+lsolYXlMmcU5VyiJmMXlCrpVbzFYhxlXJymXp1Jkq9JqlxleJ3mczZVykelsnpXKVez9QaED3wM7X6nin1+8rkZIG6Z0KdU6n7XEo1AEq9iKj9QOkwatyF6O3xbrVe4otRR9zb43CZjA5T692gJAvkm2pEjIlaW6X4iuMVMyj1fyPzqESNhVKfRbX7UZnMe0RU7vv1r3/9zj//5m/+5m6/ox6077EGQOlOOXU+I3Sjkh5/NsbILXFngdq7Sm/rZfJv5mTWroNSf1AqHka9R0TpXKVeJtRcF2KMviH8UUonUP36WvaV77WP57MxrYxRdtL1ghxUHESphoeaKyUHlG5Ue6ec8HOV5DaTW1uld/Ba1Fco1EdRMRvCB6HOfJRupFC6B6C2H1r04Tn66KN3+11FHKTyrE7WvxPnX2ptiXiF2n1BJV1Dzpc69ynlEJV0rpp/p/bWE/HNqL2rJLeZWn2p1fraUjJG1VIvxLTxUbW6IMq3oXoGEf6o2lueSne0qXG9BjNQ+4w4SyrV32Vy8bvBnt16661TjzEXVL5P6W15pRoxcr5KuRhKFqhvRsUrCF2ultsg7Dpld6harkxGxlrWxFToMOq7ETWp1LlvMd6VH+MrEHZHrSekUn9BsjaqddxmjNwqvUes5otSfmPL+orbb789b7vtti7zXMT5TK1/BbUniLVV62WiZM/U1pbaZ0p3v6m9S/UcoeyOUuyZetuWkgVCbqkzX6bW+yBqfW2o70bpckIW6P1bHRMj7zINPPPMM7l27do866yz8oknnpj63KcWr6DsDnXeIfSN2r0Nam2XQs3R2Jg2Zc8o+0DJmFJtELUfqG9GzZfqNUGsA9VjQW0/EOugVtOm9rYNsc8oG0nJLZFHpcalawAy+49XZGr18aT8MGoNiPmS34uwk2oxG+L8QPafVcpDUN+Mil1RNoLYE2QfT8L+Ktn0TMauk3e/lfp4UONS/d+oszqRR6R8cmoNiPmq9RKnzhCETlB793spfLOVK1eO+mZqda6UrlGLCxJ3Xal+Lkq9V8gztd/RavPmdZWvoFYTQ52pqbwRgVrMlbqjTckuMe5ixJ0z+3tDivpmSj2ZqTOE0n3BTEaPkTWphG+jdjahYleUjSBklzpHkbpxoKrGk7IPlG9D6VxKFoh9pia3avOl+gso9dGm9AIVV1iI3t5PptZWKc+llkOk9i7x1mQms75ULCjTPYMyOb1A+M6Uf6fWE0KpZ59aLJc6+xJndTXfeSF6vPes9DZ1JmPPqLig0jtlSjohU6sXAjkucUZViytQdX2EHiN75RC+jVJuklxbpT6L1P6l7roS/rPa+43UuFRukrBnamdfalylHqmZjN2hdA2Z/56P3vR4JvPNKBuplNtocXc0s65XHXUuoeLvxHmHsr2ZjB5Tu79C7F8yxkScJ9XqmBait7fPlN7sy+TiFYQuJ3XjQvRWl0vJLqHL1fxGqi6IOvsS5zO1uDOhw8jaFUIWKF2j9N4V5X9Qcku945HJ+PqU36hUF5TJrC1lJ6mcCaUfiXVw3pe1Z63ef+v57hklC5Svr3R/lFpbtX4bhK9P2Ukl+5DJ2cmFmDauQMmXUk49kznztHjfp3INlGq5MvVqXQm9QOUQleogMtv0ZL788stLxqX2g1LMgvLJKR1G2TOlN9XInAmhG6n9QH0zws9Viitk6vXLVepLRe0HtbdiqG9GyC7Rw2P2XKv8GipvRO1f6hxF5WLUarSJcdV64FH1jYR9oORWrWeQUk0MtbZUbIVaW6WermTcitALVF0fFScnvlnLd3h6vjuqdCeVrKVWqolR6tNOntWJuI3ae3XEGZW8e0boG7V6ECpfQPgK1Hv11NtnVP93Ipar1geQOvsqxe+U+huR4yrlCzI5nav0Xp2SPaNiudS4SrkYSo+r+WGU3aHyZ0r3udTijWp5akLnUvlZSi8o3RPL1LqLuBi9TKaFrEMkvhmlw6hvplaXq2Qnld4epd5eydR6w4Ial5CFFra38v1Vtf7vhK9P6QTK7lD2gdK5lIxR/rPSHXiltzGU+ldkat0jptaAykdROlct/t6yp3qv8Xelu0xqcTZa5z711FP59a9/Pc8+++w89NBD833ve99uNqmH+ZLxioWYVj9SuXpqn1E+COGHtaxDrNC31P5Vypmo6VzKtyHGVcv1UTFXpdgVGRcc9MKtt95aMl4mt3+ps4lSbFStb5LS+yvUGqjFG5X6c5PvmRB6Qe0NGuqOo1LfQmptqVpM8h7PfIyxv0QNAHXeofQNoRuVziXkuES8gqxjUqr3VboDMHvsqrw6ZXeob0bNlzqbKL3fQNkd6hxFjUt8M7X9QPg1arY3U2sdKFkg5kt+MyLuqvR+Iwnh31HnErVaLqr/GyG7avWClB/Wsia1IkdLnCepb0bd1ydkgVoDalzK16fsOuE/U+eHljUxFTqB0OWUL0q+R6SiG8k3yoixqflSPoja3QJKL1A2QqkvFaVvlHr2U/Wz1LmP0Llq/ZgoWaDGneuMunLlylFnVLU+MUshF9Pz2zZEjTZVT076eNW06ClH3cGrvJ9MycJCTHuepPwltTvaanWTlIwp1aRSfampGDFRp56pdb9RqX8U2ZuL8EHUzhCE3Kq9D6LUJyaTiQVRd2Kob0bZHaLnplpOnbwLMR/Tyhh5NiPO1Eo5uUy9HsfEnqBzECqxXMLuUH7NszGtvlHrGaQUd1W6S52p9SYTVbtC2TPS7gxU1vX53SC93qvUO3iETaPqmCj70LJ+dmxu8tmYdn3V+s9Q8yVkjPQbCbuuFGej/CW1+J2az0SsA/nGIJGLUZMxIhdDzVWt1obYZ2r1FZnM3Q1KxpTyqJlMDQDVp42IN5I95Yi9RuaTCVreT+61XlAtp74Q054lybkSMSa1fupKbwy27P1eIWMtcn1VtaPUmVrtDQuqHoSQXfLdMyXfWa3uSqmXCbV/lWoxM5l1UIuTk30xY+zkljKEwX/DG94w1e9+EpQcKqoYafKbDWzevDk/+9nPjlpfoqEw1aSYGpcsZiDGpWSMUPREIXAml8g/5ZRT8q677srMzJtvvjlPP/30nb974xvfONWY1CGA2g8HHHBAHnPMMXsEi1atWpUHH3zw1ONS34xaB+IgQMkCZX/f+MY37gxmzl7LSf32XKFkgQhsUZdrqW9G2R1KlysFOdV0ObG21N6l1pYKnFL7gUgGUmtL6UbKB6F0LjVfImlH6RpqbSkZI/QYfeabpOL8f9xxx+3xs3Xr1uUZZ5wx5+9+UqggOiFjLYrCM/u2D5mcLKgV3RPjUnpB6bIXZXcyGftL7TMquUasb4v4XbUsEFA6jPBtqG9G6TDCplM+IxV7V9tnLWJMVbaXjCsMVK4BdZak9gRlJ4l1UItXKF1yonQYGbNReiyBGFfN7lBrS+kwwp5ROiyTfcSsGqXYFSVfpO1VahxCFT9SFyWIcam5KuU8ybgCUYdIya3SA7Vq9XfUfqBqbQg7Sdkdxdzkpk2b8rvf/W4eccQRef3112fmjH9XEResjDP5/D+DUv2smt9I2TPq3Kf0CJJaXFApXpGpVStHXcoiczHV+jaT+Wb0RWsyd9R7HQQ1LuXfKdUAUL4zdS7JzPynf/qnfMtb3pJf/vKXMzNHP3ibqbfPCH1D+QpqMSZCFiibTukwui6XuutKnH2r56p8h6f6gYtqfaMWy1WLrxAyRsmt44KcDqPsWSbj56o1ayL2BDXXTLYvRmatbiT0OKXDMpm4Dbl/CYgYMbkfCH1D2R0qPkrIrVp/FNIPU3nELZPRN2r3clvcp676ZqR9IM6T1Dej9plSLJfyw9R6jlDjEv6oWv27Wj0IkYNoUf9eOS61H4i1zdTq7aNWt0H126T8UaVYrlLOJFOrpo3SjS2aYWfW6NwWvRsr15aqF1TyR6m1VcvFKPWlJh9sIs6pSjnETEYvqNVSq/kKxLhKOblMvToTpV6T1LhK8btM5uyrFA/L5HVuZv+9nqk1IHrgZ2r1PaNyJmo1YtQ9E+qcqvQomFrOk67lqtwPlA6jxlV6vFutlzjliyk9DpfJ6DC13g1KskC+qUbEmKi1VYqvOF4xQ4v8d5VfTvZTI2oslPosqt2PymTOEFTue82aNXnJJZfk9u3bc/369fk3f/M3mZl59dVX59ve9rapx1WrAVC6U670vp6SHs/k5JbIR1F7l1pb6u53C1+fiuH13h+0RTxsoOI9ohb3YnrvZULNtUW8IrPGHyXPO2S/vsz6vvIqfTwzGRlrYSeXa70gBRUHUarhadFTPrNODijdSO0zpR5HSnKbyd7BG+j9HTxqbdXqo6iYDf22bfWZj9CNFEr3AFT3A5kz+fCHP7zb72bb/OcCcVYn69+J8y+1tkS8Qu2+oJKuydSqjxrmq5JDVNK5av6d2ltPxDdT7eei0ouI0GFqfW0pGaNqqcn77yp1QWRfG6JnEOGPqr3lqXRHmxrXazADtc/o/Gxm3/V3mVz8jrJnVL5P6W15pRoxcr5KuZgW76RWfjMqXkHocrXcBmHXybzvbCpquTIZGSNrYggdRn03oiaVOvdRuoaqaSPsTotaaoUzNfHNyNoows8l71eovEes5ou26DlSXV+hlOcizmfUGVXtfEasrVovEyV7pra21D5TuvtN7V2q50iLu5iZfceeqbdt6btnCm9jZmq9D6LW10ap53cmIwvU/qViYlRu47jjjtvjZ+vWrcszzjhjzt/9JKjFKyi7Q513CH2jdm+DWlvFmqPqmDZlzyj7QPc4yuy/NojaD9Q3o+ZL9Zog1oHqsaC2H4h1UKtpU3vbhthnLeqYKuWWeoOGGLfFfug9XjF7vpX7gYjltqgLqlwDYr7UXDMZO6kWsyHOD2T/WaU8BPXNqNhVizqxqj1B9vEk7K+STZ89pyq7Tt79Vurj0aK/T2X/N+qsTuQRKZ+cWgNivmq9xKkzBBVfUXr3299Mr86VvF8xoBAXnPy7VXddiTEztXqvkGdqv6Ol9ea1ak1M9ZmayhsRqMVcqTvalOwS45I6V+kNKbWeX5QvRpwhlO4Lzv671W8yETWpPptwsSvKRhCyS52jKN1I1Hi28J0rfZtMRudSskDsMzW5VZsv3Q8xs/8+2pReoOIKSu8nU2urlOdSyyFSe5faD8T6UrGgTPcMyuT0AuE7U/4dNS7lKyj17FOL5bbIR1Wd1dV8Z8pfovav0tvUmYw9o+KCSu+UKemETK1eCOS4xBlVLa5A1fUReozaZ7PHrvJtlHKT5Noq9Vmk9i9115Xwn9Xeb6TGpXKThD1TO/tS4yr1SM1k7A6la6gztZIez2S+GWUjlXIb1D6jetVR5xLqmxHnHcr2ZjJ6TO3+CiELZIyJfidVoY5J6e0zpTf7Mrl4BaHLSd2oVJdLyS6hy9X8RqouiDr7EucztbgzocPI2hVCFihdo/TeFeV/kL02iHc8Mhlfn/IbleqCMpm1pewklTMh7xYMVK2D876sPaPef1O6e0b3Y6LqrhTuj5LvEQ8o9NsgfH3KTirZh2Fcwk7Scf1K+VLKqWcyZx5qbak1UKrlytSrdSX0ApWPUqqDyNTqydzivmDvMQu1t5Ppuo3M/t9UI3MmhG6kezJn1n4zws9Viitk6vXLVepLRe0HSjfS/Wervxkhu0QPD2quVN6I2r/k/UYiF6NWo033U1PogUfVN1L2gZBbtZ5BSjUx1NrSPYOr11appytZx0TX8FTW9VFxcuKbke/wKN0dVbqTStZSK9XEKPVpb3FWz6yL26i9V0ec+8jeq4S+UasHofIFhK9AvVdPvX1G9X8nYrlqfQCps69S/I46o6q9V6+UL8jkdK7Se3VK9oyK5bboCdl7LobS42p+GGV3qPyZ0n0utXijWp6a0LlUfraFXuj9nlim1l1Eqg6RgKxDJL4ZpcOob6ZWl6tkJ9XeHiXeXsnUesOiRc/vKlloYXsr31+ldDm1DoSvT9e5Evf6BirtA6VzKRkj33tWuQOv9DaGUv+KTK17xNQaUPkoSueqxd/Jnuoq8Xelu0xqcbYWdmdg8+bN+dnPfra7HkdkvII4U1O5emqfUT4IeaeNqEMk9C21f5VyJmo6t8W9yapx1XJ9VMxVLXZFxQUJvUDtX+psohQbVeubpPT+Cn0/KlMj3qjUn5uyZ5mMXqBkjJRd4o6jat/CyrWlajEp20Plk4kaAOq8Q+kbQjcqnUtajVt9jymzvo5Jqd5X6Q7A7LGr8uqU3aG+mdpb2krvN1B2hzpHUeMS30xtP5C1jZkatjdTax1a5Eyq5kt+MyLuqvR+IwkdV6g8l6jVclH93wjZVasXpPwwsiaVyNES50nqm1H39QlZIN8XIMalfH3KrhP+M3V+IGtiSJ1QqcspX5R8j0hFN5JvlBFj03V91T6I2t0CSi9QNkKpLxWlb5R69lP1s9S5j9C5av2YlPo8ZzJnVLU+MYq5GPqMWnmGoN8dqawnJ328aigd1uIOXuX9ZEoWiBhxi16mCne0VesmM2tlTKkmlepLTcWIqXOU0v1Gpf5RZG8uwgdRO0MQckvrcZU3LJR6TdL39cm4YKXdIXpuquXUqfkSvjN5NiPO1Eo5uUy9HsfEnmjRU26g51guYXfIt20JfdPiHJXZ/1uLSm+qtejt0/ubTHQvhMxae0bZHaquj9K5SvlZtd6r1Dt4hE2j6pgo+6BUP5vJ+Apq/Weo+RIyRvqNhF1XirPR8TCV+J2az0SsA/nGIHGeVJMxIhdDzVWt1obYZ2r1FZnM3Q1KxpTyqJlcDQDRp42KN1I95Yi9RuaTCcj7ySr1gmo5deIsSeowIsak1k9d6Y1Bsve70ruQRO0odaZWe8OCqgchZJfaD5lavrNa3ZVSLxNq/yrVYmYy66AWJ6f2b2bm3mHmJTN3/vk1r3nNbr975plnphpz3333jb/+67+ON73pTbFixYqd/86VV14ZL3zhC6efbEQcf/zxsf/+++8274Ft27ZNPe6KFSvigAMOiIiIn/7pn45f/dVfjYiIX/zFX5x6zIF99tknjjvuuDjuuONi8+bN8dWvfjUuvvji+Nu//dupxpvr//vLXvayOPfcc+Pcc8+dep4rVqyIO+64I370ox/F9u3b44YbbohDDz007rzzztixY0c3Y5LjPvXUU7v9G2vWrIn169fHOeecE08++WR34w5Mytj9998fX/nKV0bJ2Ite9KK44IIL4g/+4A/iqquuiq985Svx8Y9/PI4++uh485vfvIeu+EnYe++94+67745f+IVfiFtuuSWe//znR0TEXnvtFXvvPb2afuqpp+KOO+6IzIwVK1bEPvvss/N3e+2119Tjvuc974nTTjstfuZnfiYeeOCBuPTSSyMi4sEHH9ypH54rr371q2PdunXx3ve+N4444oj42te+FqtXr45rrrkm9t1336nnSu2H/fffP7785S/Hfvvtt8fvXvva1049LvXNqHWY1LnXXXdd/MVf/EXss88+8cu//MtxwgknTDUmJQuU/X3iiSfinHPO2bkWmzdvjv322y8effTRUd+MkoWHH344TjzxxIiIePvb3x5vfvOb4z3veU9cfPHFcdxxx001JuEvRXDfjLI7lC7ftm3bzm82sNdee8VJJ50Un/3sZ6cel5AFNV1OrC21d8m1Jfx8aj+cd955sXHjxvj93//9OOmkk+L000/fqR+mhVpbSjdSPgilc6n5nnfeeXHjjTfG2rVrY//994+IiGOOOSa+/e1vTz0mpWuotaVkjNBjlO2lzv+veMUr4tprr42VK1fu/NmHPvShuPTSS+MLX/jC1OMSvnMEI2OUPVOyDxGcLFDrO7mfHnvssTjkkEMiIuKVr3xl/PjHP+5qXEovUL4zMS5ldyIY+0vts8ceeyxe97rXRUTEZZddtnOdjznmmPjkJz859bjE+lI6jJQFAkqHEb4NGXMdqNRhhE2nfMaB6ti72j6jZIGwvdTZl1oD6ixJ7QnKThLroBavoHwFwr+jdBgVVyBiKxGcr0CMq2h3iLWldBhhz0jfhtI3BEqxK0q+yHEp3ThQ6TPdddddO+Xz2GOPjYsuuih+53d+Jz7zmc+Mmi+RQ6TGpeaqlPMk4wpEHSIlt9T+JcZVq7+j9gNVa0PYScruqOUm3/Oe98Tpp58ee+21V1x66aVx2WWXxQMPPBD33XdfrF27dupxByrjTD7/745C/aya30jZM+rcR+XPCDupFhdUildEaNXKEbomgr3HU30nJoLLU1P3giLq7Y5SHQQ1LhkjVqkBoHxn6lwSEXHQQQfFF7/4xfjkJz8Z73jHO0bJ1WxU9hmhbyhfQS3GNFApC5RNp3QYadMj6vcZOW71mGp3eCgdRugbtViuWnyFkDFKbh0X3EX1OYqyZxGMn0vdLVCqEyP9JcIfpXQjocfJvg1E3IbcvwREjJjcD4S+oewOFR+l5HZAoT8KmfMk4o2UnST0jdq93Bb3qau+GWkfiPMk9c2ofaYUyyVzfQMKPUeocQl/VK3+Xa0ehMhBqPXAo/YDld9R6u2jVrdB9duk/FGlWO6ASs5EqaaN0o3UnRhC55K+M7G2ZL3g5Nx79kfV7mgrnU3U6oIimHOqUg4xgtELarXUAyq+AjGuUk5uEpU6E6Vek9S4SvG7CObsqxQPi+B0rlKvZ2oNiB74EVp9z5T6fUVwskDdM6HOqdR9LqUaAKVeRNR+oHQYNS5lJ4lvptZLnPLFqG9G1WhTtQUDCr0blGSBvLdBxJiotVWKryyFeEVFTblS/zcy10fUWCj1WVS7HxXBnCGo3PcFF1wQH/3oR+Ooo46KfffdN/78z/88PvjBD8YRRxwR/+2//bepx1WrAVC6U670vp6SHo/g5JbIR1F7V+ltvWG+A9VnCDqG13t/0BbxsIGK94gonavUy4SaK6UbCX+U0gnUvVwyrq/SxzOCkTHKTrpekIOKgyjV8FBzpeSA0o3UPlPqcaQktxHsGXWg93fwqLVVq4+iYjZUL3HizEf3ea5G6R6A2n4YqK7x3H///ePRRx+NF7zgBfEnf/InO39+3333xU/91E9NPU/irE7m5Kg75cTaEvEKtfuCSrqGnC917lPKISrpXDX/Tu2tp4HKb6bWz2VApRcRocPU+tpSMkbVUlNx/Yh6uVW6OzrMl+gZRPijim95Tv4bPd/Rpsb1GsxA7TPiLKlUfzeMMVAZv6PsGZXvU3pbXqlGLMK5mAi29pv4ZuR9zGpdrpbbIOw6ZXeoWq4IRsaoeAWlw6jvRvVOiqg/91G6hjr7EnZHrSekUn9BsjaK8HMpuVV6j1jNF23Rc6TSB1HLcxHnM7X+FdSeINZWrZeJkj1TW1tqnynd/SZrz4ieI5TdUYo9U2/b0m8y0W9jVr3PrfQ+iFpfmwGFnt8RjCxQ+5eKiVG5jVe84hVx7bXXxsqVK3f+7EMf+lBceuml8YUvfGGqMdXiFZTdoc47hL5Ru7dBra1azRER06bsGWUfKBlTqg2i9gP1zaj5Ur0miHWgeiyo7QdiHdRq2tTetiH2WYs6pkq5pfKoxLjUflCLVyj18aT8MGoNiPmS7yEQdlItZkOcH8j+s2p5iIj6b0bFrlrUiVXtCbKPJ2F/lWx6BGPXyZycUh8Palyq/xt1VifyiJRPTq0BMV+1XuLUGYKKryi9++1vplfnSukatbggcdeV6uei1HuFPFP7HS2tN6/VamIGqs/UVN6IQDHmSvVCIGSXGJfUuUpvSKn1/KJ8MeIMoXZfkNRjRJ2rzyZsnpqwEYTsUucoSjcSNZ7k+1yUTiB0LiULxD5Tk1u1+VL9BZT6aFN6gYorKL2fTNYQq+S51HKI1N6l9gOxvmQsyD2DOL1A+c6Ef6fWE0KpZ59aLJe8t1F9VlfznSl/idq/Sm9TRzD2jIoLKr1TpqQTIrR6IZDjUm89DSjEFai6PkKPkfXvhG+jlJsk11apzyK1f6k7AIT/rPZ+IzUulZukevYrnX2pcZV6pEYwdoe8bxRRf6ZW0uMRzDejbKRSboPaZ1SvOupcQsbfq887lO2NYPSY2v0VQhbIGBNxnlSrY1J6+0zpzb4ILl5B6HJSNyrV5Q5Uyy6hy9X8RqouiDr7EucztbgzocPI2hVCFihdo/TeFeV/UHJLvuNB+Ppkr1iVuqAIZm0pO0nlTCj9SKyD876sPSPOJmp3z8j+PhHMHSliXKWaGLV+G4SvT9lJJfswjEXYSSoXQ8iXUk49gjnzUGtLrYFSLVeEXq0roReofJRSHUSEVk/mFvcFe49ZqL2dTNkzpTfVyJwJoRup/UDey632c5XiChF6/XIHFPpSUftB7a2YgepvRsgu0cODmiuVN6L2L3WOonIxajXaxLhqPfCo+kbCPlByq9gzKEKjJoZc24HqWpuI+reDlXq6kmtL6AWqro+Kk5N3QqrlVu3uqNKdVLJmUqkmRqlPO3lWp+I2ETrv1VFx8oh63RjB6Bu1ehAqX0D4CtR79dTbZ1T/dyKWq9YHkDr7DijE75T6G5HjKuULIjidq/RenZI9o2K5LXpC9p6LofS4mh82UG13qPyZ0n2ulvHGXn0F8uxL6FwqP0v2xlS5JxahdReRqkMkIPNGVHw0ol6HUd9MrS5XyU6qvT1KvL0SofWGRYue31WyQNaCEO+vqvV/J3x90geJqLc7lH0gzyaEjFE6V+kOvNLbGEr9KyK07hFTa0DloyidqxZ/J9/BU4m/K91lUqvra2F3Bl72spfFueeeG+eee+7U41J9z6h4BXGmpnL11D6jfBCqvjGC6TVJ6Ftq/yrlTNRyGy3uTVaNq5bro2KuarErKi6o1E+NOpsoxUbV+iYpvb9CrYFavJEal5AF0p5Rb6opvUFD3XFU6ltIrS1Vi0nZHiqfTNQAUOcdssazWjcqnUtajVsVryBtulK9r9IdgAgmr07ZHeqbqb2lrfR+A2V3qHMUNS7xzdT2A+HXqNneCK11aJEzqZov+c2IuKvS+40khH9HnUvUarmo/m9kvb5KvSDlh5GxZ7pvcNV5kvpm1H196tw3UJ3bIMZt0eO40q4T/jN1fqBqYiidMFCpyylflHyPSEU3km+UEWOT842o90HU7hZQeoGyEa36UvWa943Q6tlP1c+S577Jf6NC56r1Y1Lq8xzBnFHV+sSo5WLU3rah3x2prCenfaZKKB1G7jPifjIlC2SMmIjfKd3RVqubpGRMqSaV6rtCnUuoc5TS/Ual/lHkXWrCB1E7QxByq/Y+iFKfmAhGh1F3Ygaqvxlld4iem2o5dWq+hO9Mns2IM7VSTi5Cr8cxsSda9JQb6DmWS9gd8m1bQt+o9QxSiruq3aVWepOJql2h7Blld6i6PvV3g3rO1SvlICIYm0bVMVH2Qa1+lvIVBhR6flHzJWSM9BsJu64UZyPfb4zQid+p+UzkdyN6g1C5GGK+1NoSuRhqrmq1NsQ+U6uviGDublAyppRHjeBqAIg+bVS8kXxronqv0b25qyHjKyr1gmo5deIsSeowIsak1k99QOGNQepcovYuJFE7Sp2p1d6woOpBCNkl42xKvrNa3ZVSLxNq/yrVYkZw7ywMKPRpJ+Ns/Z1AOoIw+OvWrYs1a9bERRddtHOzbN68OQ488MBYt27d6PmqOFRzHVb222+/0cGBT3/601P/3YVYqKHwmjVryscc06R4rnHvv//+2Lx586hxF0qyfv7zn+9uXCoANVCp6IlC4IiZRP7ZZ5+9839XJfKPPvro+OY3vxl33XVXvPKVr4wXvOAFERHx0pe+dDdd+VygDgHUfnj9618fP/jBD+bUt8cee+zU41LFF5S+IQ4ClCxQ9ne+QObznve8UY41tX+pJhTE5drZ3ywz4/777x/9zaiiIUqXKwU51XQ5sbaUHqfWlgqcUvshoj4ZSK0tpRspH+SSSy6JtWvXlttJar5E0o7SNZQ9o2SM0GPUmY86/3/0ox+dU57e//73jyq8oILoc8nY5s2b41WvetXUMkb5okr2IYKTBbWie2JcSi8oXfai7E4EZ3/pArLK5BqxvpQOI2WBgLIRhG9DfTMqrkDY9BYN0QYqYu9q+4ySBcL2Umdfag0oW0btiQjGThLroBavULrkROkwwj5EsI8lEL4CMW5Lu1ORk6PWNoLRYdSlXUqP04+jV6IWu6Iu+BDjqjUOoYofqYsSxLjUXJVynpTdieAaCg9Uyq3SA7Vq9XfUfqBqbSg7SdgdKl5B5X1XrVoVGzZs2Pm/Dz/88Lj11lvj5S9/ebz0pS+delwizuTz/wxK9bMRWn4jZc+oHKLSI0hqcUGleEUEVytHxSsi6nUNUUdM3YmJYL4ZVS9I2R2lOghqXPL+mUoNAHGXK4I7lww8//nPjw984ANxww037OZLT4vaPiP0DeUrqMWYCFmgfFFKh1HjUvuMGJeaq9odHkqHEfqGsmcRTCxXLb5CxEfJu2et7ixU1BYQcUHqHEXZswjGz1Vr1kTsCfKhMcIfpXQjocepvNEklXEbOsZUDREjJvcDoW8Gu3PhhRfGy1/+8oio8cnJ+GhErdwq9kch/DClR9wiGH1DnaOoOAjZ2LH6m5H2gThPUt+sRVy/91gu5Yep9RyhxiX8USp+R9WIRWjVg0TU5yCoeAU5LuU3Vq9thFZvH7W6DarfJuWPKsVylXImkyjUtFE+E3lfv1rnUrqGsjtUvaCSP0rJLRVfoe5YkH2phzhbVX4ngqvLpc6prXOIPd7nUqqlVvMViHEXIyfXY+yZyhdE6PSabFEj1iJ+N/YeInH2VYqHDRA6t2Wv5wo/hFgDogd+RNu+Z2P9Z6V+XwOUbxNRf/6nzqlKj4JRcX2lXkTUfqB0GDWu0uPdar3EqdiV0uNwEYwOU+vdoCQL5J02IsZE9nRVia8shXhFRU05JQtErTrpk1PvNxBndaJHCqXDqLhzBHOGoHLfz3/+8+MP//AP4/d+7/fi7rvvjh07dsTP/dzPxYtf/OKpx4zQqwFocae8KmZDvq9H9NsgcpNkfIWS24GqfBS1d6n7XNTdb8q3oWRMqT8oFQ+j3iOi8lxKvUyouVK6kfBHKZ1A9eujzhBKfTwjGBmj7CR1RlWqF6Qg72Mq1fBQda6EHFC6Ue2dcsrPVZJbam3V3sEj1latPoo67xA+CHXmo3QjhdI9ALX9QNV4znemfuELXxif+cxnyscdC2XPiPMvtbbE/QK1+4JKuoacL3XuU8ohqulcJf+Omi8VCyK+mVo/F7VeRIQOo95eUaufHaiupSZiruQ7x1T9HeHbUHcxCX90Kb0hPbbGU6VnkNI9dXJcap8RZ0n6nePqu+pU/G6g2p5R+T6lt+XJGjGiNsi5GE4WqJg2Fa8gdLlaboPwbSi7Q8WHIxgZo+IVZByT+G5ETWqL3iCVuoaqaaN6LBA9IdTO1MQ3a1EbVennUnKr9B4x5deQ8TvqzVzCB1HLc1F1zxE6/SuoPUGsrVovEyV7pra2A9X7jJqvkj2jeo5Qdkcp9ky9bdsiZjNAvI1Z9T630vsgan1t1Hp+E7JA7V8qJkblNj760Y/OGQN7//vfHyeccMJUY7Z8w6IqDkLYHeq8Q+gbtXsb1NpSvRuocYmYNlmHGFFvHygZU6oNovbDQPU3o+ZL5X0HKteBuntG1QZR+4FYB7Watoh2b9tUzJfYZ1RdLiW31FsTxLjUflCKV0Ro9fGk/DDqHEXMl3wPgbCTajEb4vxA9p+N0MlDUN+Mil2Rb3lU7wnqzBfB2F8lmx7B2XVq7yr18aDGpeogqLM6kUekfHJyDQaq5rsYvcTHxBUoXUPphIg2735X+LlqPVKpWBDRi7iFr1+pa9Ti5MRdV6qfi1LvFfJMrfiOVvW9SWp9CV9BrSaGOlPTeaNK1GKu1B1tSnaJccm8UYs3pHp/w0KtJ3MEc4ZQuS8Ywegxsrax1dmk5zt4EUzsirIRhOxS5yhKNxI1npTvTPWZGKjWuWQeonqfqcktFW+k5kv1F1Dqo035jVRcQen9ZOqNowitPJdSDpHau2Ruo3p9yXpy9wzi+qQSvjMVu1LrCUHpckLG1GK51NmXiOur+c6Uv0TtX8Ifpc6+EYw9I+vEVN4pa6kTqu66qvRCIMclzqhqcQWqro/QjWTcmfBtlHKT5Noq9VkkY64R9XddKf9Z6f1GalwqN0nYM7WzLzVuqx6pVfUVZB988l79QNWZWkmPE9+MspFKuQ2qtpHqVUedS6hvRpx36HepImr1mNr9FUIWqJh+BPe2jVIdk9LbZxE6b/ZFcPEKQpeTurFFXW5EjV6gZJfQ5ZQej2D8RrIuaKDy7Eudz5TizpQOo2pXCFmgctTke1fVtaNk3oiQW+odj0kqfX3qPKlUFzRJ5dpS8RUqZxLB6EfCX3DedwbKnhFnk5Z3zyr6C7ToSz1Q/VZb5bhKNTFq/TYIX5+0k63tQ492kogrUPI1oJJTn6sm5v777x915qHWluwPqlLLFaFX60roBSofpVQHEaHVk5naD0oxC7W3kweq7ZnSm2pkzoTQjWT/d6ouqNrPVYsrqPXLVepLRdYRR+i8FUN9M0J2iR4e1FypHDW1f6lzFJWLUavRJsZV64FH1TcS9oGSW6o3iJosEPqG0rlUbIV6O5iI30Uw+T6yjonQC1RNDBUnJ2oLKLlVuztK30mtrDmi3yOK0KiJUerTTp7V6btBA9R7db32Eqd0YwSjb9R6EVP5AsJXoN6rp94+I/u/V8dyKR+XmGsEd/ZVit9RfW2pe2JqMTwq70vpXKX36pTsGRXLpcZVysVQelzNDyPveUbU58+U7nNRtZhKvgJ59iV0LpWfpfQCkS8g9bjSXUSyL3U1ZB0i8c0oHUZ9M/K9SaIuV8lOqr09GlH/9kpEuzcsKuIr1H1qQhYo20u9v6rW/32g0tdvoRMGKuwOZR8onduizqRS5yrdgadqqYmaI6X+FRFa94ipPUbloyidS9XKqcVtlOLvSneZWsbZKmoAKJ07nz86FmK+ZLyCOFNTuXqylzjhgxB+GOWTU/qW7p2kkDNpWUvd8ztaxLhquT4q5kq9TUW+eUXEBQcU+qlRZxOl2Kha36QInfdXqDVQizcq9ecm3zMh9AIVu6Jkl7rjqNS3kFpbqhaTqnWl8slU3UZE/XmH1DfVulHpXEKOS8QryDompXpfpbfPI5i8OmV3qG+m9pa20vsNA9V2J4LrIU2MS3wztf1A+DVqtjdCax0oWSDmS34zIu6q9H4jCeHfUecStVouqv8bIbtq9YKUH0bJGPmeWPV5kvpmlI9LyAK1BuT7yRH1vj5l1yn/mTg/UDUxlE4gdDlZU0++r6egG8k6G2Jsar5kPyaluwWUXhgg+uAPkH2pes37Rmj17KfqZ6lzH6Fzyf6CKjUm5LjEGVWtT4xaLkbtbRuiRpuqJ2/xDm0lhA5rfR9k7P1kShaIGDHlL1E1YmR9FDFfpXqQCK4mlVhbquaKihEPVJ+jlO43KvWPIntzUX3wlc4QhNyqvQ9C1rmq9Jqk7sRQ34yyO0TPTTKnTvWvIOZL+M7k2Yw4U6vl5CK0ehwTe4LyQdRiuQOVdofMTRL6hvJt1N5abPGmWu93qek3mSrXgaqpJ31cwu5QdX2UzlXKz6r1XqXO/4RNo+qYKPugVD8bwfgKaj2/KBtByBjpNxJ2XSnORtVXUHaH6vNM9lMn7BkRx6TfXomoPU/SMlZ9NiFyMdRc1WptqH2mVF8RwdzdoPpSKeVRI7gagIj6Pm1UvDGC6ylH9Hmi8skElA5TqxdUyqkTZ0lahw1UxZgon1ypt/4krfpijjmnt3wXsuJNdaJ2lDpTk29YKL3fQJypyXu5pO9cnY9Sq7tS6mXS4t5k77WYEcw6qL1BS8VXIiIizXNi27Zt+dhjj+WDDz44apyHHnoob7nllrzlllvya1/7Wsnc1q1bl9ddd92cv7v44ounHvfb3/52rly5Mo844oj8zne+k2eeeWa+6U1vyl/7tV/LK6+8cqox77nnnj1+ds0110w9x9Y8/fTTefPNN+cDDzzQ9ZiV427dujW3bdu228+Gb3b77beXjfvoo4/mLbfcko888sioceeSsQpOOukkZNyHH344b7rpptywYUPedttt+fjjj5eM+8wzz+z88/bt2/Pmm2/OzZs35913310yfjX33XdfbtiwITds2JA//OEPy8d/5JFH8uabbx6tw2czKbfEuNXfrEovrFq1Ko855phctWpVrlq1Ku+7777MnFnnk08+edTYjz32WN566625YcOGvOmmm/Lhhx8eNd4AYX9bsn379lGy8Pd///d5xBFH5PHHH7/TrmdmPvDAA/nhD3+4apqZmSX+Uuaub7Zhw4a88cYbR++zuezZwBi7k7lLlz/yyCNleuHOO+/MM844I1euXJnHH398Hn/88bly5cp8+9vfnnfcccfU45KyMOzf6n320EMP5caNG/O2227La6+9drQOo9Z2kmo9Prm2Dz300OjxFvLz/+qv/mrU2JRvM8l3v/vd/PznP18y1uTabtmypWTMzBmdM8jt448/ntu3by/3QyrPUZN2skLGaJ588sn82Mc+lmeccUb++q//+ujxduzYsfPPjz32WN500015//33jx43s96ezUVVvGKSCj22devWPXy5wRaPtb1KkL5zJr9/r7nmmnz88cdH27OtW7fu9JeGM2qVrz9w/fXX5+c+97nSMVswdn2ffPLJvPjii/Owww7L3/iN38gDDjggDz744DzrrLO6G3e2XpiUhTF6gfLvWviNVLyCYsOGDfnZz352tD373d/93TnHuPfee/O0004bNXZm/RmC0mGkD0KxZcuW3fzcChsxmwrfhjink3GFzDY2fey5ZHbsvTI2OvnNMuvOO8Q+o2SBsumZ9fH3wabPPvtm1vr6VPx9oPocpeiPVq9BZs3+pXwFyr8jzzuUfaiOrczH2NwGNW6LGObTTz+df/Znf1ZeA5A54y9V+2FVvj5pzwYG+3DfffeN1mH02UQBOnaVWRvTJ8et1o2U33j22Wfnhg0b9vj5Jz7xiTzggAOmHpfKIRLj0rlvKuc58PTTT+eNN95YXntWZXeIOsSF5PbAAw+casxJKN+mclwqZzLXuAMV5zO6nkuJ66+/Pj/96U+Xj1utE1TyvkOcaa7YQjXE+Z+QhSrU6mcnufbaa/Mzn/lMeQ6xalzans1m7LmPznO1ii0QEDU8w7gV8YrqOHmLnOfA2DWgdU2lnSTvxBDfjKoXnL0OVfFRpToIctxM3r9TqQHI5O5d9c5c+ma+mNNzgZJbsj55oCqH2DLGVFGfTObrZ49ZVf9O3eGp1o2UPfvBD34w6u/PBS0H1TXP27ZtQ2JXLfJRmXWyMEmlPaPulM9FdXylKsdDxEcpuZ3NcowLtuotUGnPCD+Xzs9W3z8j9wSRkyP6YsymqmaB0ONUrX4mG7dpUZtcARkjJvYDqW9m+40V/hIRHyXkluq7QuV3ZlOV56LO/y3qmAaqavBa3B2tpDpe0fKbVdSjtjpPVsTvKL9mKcRyx55RW/UcqWT2uBUyRsYVVOvfqVrX3mlxv7HyDi2VVydo1dunskdKK8bWbVA6jPJtSJ1b3deGquujxm1R00bdF6zymUj/jvJtBqieI1XxdyoWpOiPDvTe13Y21NlkkN177723rC81Vat//fXX5xe+8IWSsVrE36vmS+U2WtQRV9epD7b32muvLbFnVH0FVR9F1G0MVPvkqrFnIl8wSeVZnbofVS0LreJ3VfcQqbMv0buhVZ0NdS9moOouBBHDq7Y7FK16C1D+aFVPyLnotUasxfk/s863oe7wtKgBqI7rV8eCWu1fordv71B2kvxmdJy88vw/SZV9oL4ZlVMndBhVK6cWu2qV921R4zn2zlF1/9nZVPk2VBwkk4lXEPuMrimnZWGA8Msr778TfbSU+ixW6zCy9ypxhmhZ11eBWg2A8p1ymsq+o5WxFeL9Rkpuqf6CLRlrI8m4cwtff2DsflDqD9qyp00l1XkupV4m1FzJM2p1TQz1djIN9Q6eQh/PzLb36isgarSV6wWroWPElXVXmWwutbLeqMW7chS9v/tF3i2gc/W9v71C9Xpu4edW15hQurG67oo+79D3z4j3QcwMlX14qvuuzDUu0VPO1OrGVnfKKyHuF7To/a5Ua0O8DzJQkfel66Oq7SRx/32A1rnEmaf6Pgh99q3qRTxQLV8t7GR1H09KbmldXmV/SR1G5VGpfrkDVbqGqqUmYq5kv1yyL0Z1/L1Fb+5KyB4exLvyVF/MVj2DxkL2BqX8O+K9WOrOM3WWJPMw1WdJKn7X6m5QFUpvy8+GfgO+1/go/d5EJhvXr/Rzq/v7Ur4+6TOp9NukfBu1vC95nlR6W4/wF4geKdSdGEoOqHqjFnaHuqdO11eMzRdQ34zyyQk/V61OrlXOk+yxUEV1DY/a3SBKlxPnM7Uz6lxU1K+0yqlfc8015e8GV9d4DlT1F1DqPzubqrVttc+ou9/UWarn2jPS7qjE31sz9u4ZkU8mewAo1nINMaYNGzbkzTffjMhaRd0V1W+T0uWKsmD4Hh6VvTEJ+0DX9VH5gsw6v4a6t0GvbYuao97f4qnOmVD2ofWd37H2l6hZoPYD9c2o+VLxd2IdqLtnZI+jSariINQ6UHVMLd7not4+q8rxkHkuui53YKwep96aoMad3A/VdUHVNSat8kYKfTzp+yCV56hMdr6Vc21hJ6veqyd7jmYy54cWeryqBo/w7xTv+5J3+2Yzxv5SfW0zGftL9QFs/fZqZU/Iyvxsiz4e1T55dT0IFSenzupEHpHynak1IN+rm8xH3XTTTWWxXMJfInq6qvawzKx9y5Oyk6rfjKqfzaypY6J0DZWLmQ+V/iBqVNfKVZ6pld/Rou7gVa1vi7dtM7l+LhUxAOpM3eLeBklFvSC1ti17IVTKLtl7pbInVas3pKg3LIgYU1Weq9XbhVWo3BdciDExJrI+u8XZhLqDR72JWNU3mOrjQchuq3MU3buhAtp3HtZgbFyBopUsVOwzsm/SbCpsOtnjKJPz76pzqYp9tKv9Rsr2Kr6fXP3G0Wyo2rPKcYn3EJTeb2yV26jM0VbHgjI5v3FyvlX5M1qPV+vclue+qrqC6jrEuajujVmpy0kZa/EeQq/1RnNB5RF79p1b95+p0gvk29SVZ1/KnpG13wOVfeVb1SFW98Wo1AkteiFUygLZY6HyjDrXu8EDxP2oXt+gHSBzPFX175mMb0OeH2gq11apzyJ135fuRUTei6l+02US0icfC3lGbdUzqLKmfqCqHyJR49mi1qYyj0rYHUrXUO8GUXqcqk0mvhllI9VyG5NU3x0lIN4doePvKj3KSZ+p2l+i7q+QskCczVr0UyPvWGSOj9u0ePuMoiqPqtSPeYCKXRG1IEp1uXRf28remGT8jsyfVdt06uxLnc8IP2xy3MpzegsbWfluMBkHoeqNyP6Vk1TXAFTdv2vdu2EsLXq1V/mNSnVBmczaUvGVVjmTKj+XqoWg+gCq9sCvtGdq964m6b0GgDqnUvVcLWpiquY619lksOs99tsgczH0ewhVd8+ofDIlty1r8MbmJmmfkXo/efaZh7CZVXlfwh+lzuqZ9XEQKh5E7TNCL5A5iBa1mFX1Cmo9mTO5XB/xBlyLmpiqOAgxV8qetXpTjar9rvpmrXrFVMTwWr2/WhUHoeu+K30FtX651JtqLftSjd0Pam/FUN+sdb+cMVD7LJPp/75169ad+7fnHHVreq7RpiDiYVT9LIVi38JWPeWq+yFW+87EfZDqtaVqJsm3gzP5+F1F/0ayvxGhF6iamFb55Iq8HC231eNS36zVndTKmiPC7ijVxMyG6glZBZWDIOI2au/VEWdUUjcqvwFfvX+r8wWKZ4hqWq1B5T1t2sel+gBWQZ3PWsbvqmqTld6rV+xRXq1zyXw90Rszc0YWhhjAtdde2609y+R6bRC1NpNU9XRpcS4hqY4FVcotlTeivhl9n6tSj1O1mFRtsuJb7Zm1Ole1Vp/sFVsZW2lx9q06l6i9uUnkEDOZb0blJqlvRr03QdXlKtnJ+Rjb41hVl5NvWFTHV+j3GzJnfJvKftcKbxHNpqIGYKBa57Y6n1XUuVK9Qci7clTuiPAXKJ2rdAde7W2MTOY9k7mo0GGt7hH3fDeoBdU5dZX4O/22nGL8nepxVOGLUb1B6PootRrtFvOtuEfc+l2XairsDuWDEHfaqBzEAHUntdpvVMqZtKqlrr4XM5vqd7QGKt/iqajPzuRqTOg4OfUuM/3ecxVUPzUqXkGiEBtt/TZ19XmyqhcRSYu3MTPH2wfKt6F9poHKN3MHiDsA5BsDg52syKVmtpPdsbTIn1XmYoh4Nl2LWR0To86+RA0AnTeiajwHCN1Y+Q5eJtM3iRi3Rf+Z6rXN5Ot96bfPx/bFmEQpt1FNq/7vY6H6Cyq939CqXoGwD5l19Y2kXiDfB62k1V3MqncW5qJi3BbrQN7RJt+bqP5uVbJAxF2V328kIOMKVXH9uaiIC6rVzxKyS/VCoGoWWvn6A1V9v6v3GXGepOL6VK0+IQtzrcFwnhizBtTa0r4+FV8ha20U8juZ9TqhVV+bSl+UuP9O6UbirmsmW5tMxG2q64LIXF/Ld/t69RUoG9GqLxVBpY2g39yga8SqeeSRR/Lmm28e7SdQvlgm39f22muvzc985jMy36xKxshauRZ9YhTq1Kl3tMhY0EDFXVfirhydn211/6yS6p5ydD+mKihZIGLEdN/z2ajkv6tr2maPO7bGotV7RARVa0vl5KhcPXWOUurDo5Yzoc7/5H19qifkgMK9mOoaYtpOtnonZazvrNRrkooFKfYnp2t9K/2lHTt27PzzY489ljfddFPef//9o8Yk6yvIsxlxpqZyclRcYeD666/PT3/60yVjETI2H2NtT4s3mSmq7jK1sDuVcfJW9VxVNeUt3lqk4q49v6k2e9wq3djqTaaKdaB6IcxHdc8CtVxMi3eDpq2PIvtXtOq9WlGf3OodPAVov7EKpdrvlr31K2Ji5PuYmYyvQPR+I+x6q/hw5f3G6vfEW8Xvqvo8k/WCc1HlM1X6jeT73MR5kn5PvPpsQuZiqHOUyj3i2RB36yvfF8xkbCT51mJ1Xyq1PrHKNQBqVNUytXwfpJrq+8kq9YKTVOYgJqmKg7SMO1dA9o8i3l4h4uRUnprqi0nGcinfeeDpp5/OP/uzP0PulBNUnqlb9RypusdUDXEfhOwT28p3roy50nVXmWz+m+h3XTVm9T2TVrWY1bG2ynVoGSevhIivRMkoS5Rbb701TznllDz11FNz06ZNefbZZ+chhxySRx11VN56661TjXn77bfv8d9RRx2VmzZt6rrJ+CQVyWa1ddiyZUt++MMfzne+8535pS99abffnXfeeVONefjhh+fFF188tSzNx9VXX73zzz/60Y/yD/7gD/J1r3tdnn/++V1+sz/6oz/aqdD+8R//MV/96lfn6tWr88gjj8x/+Id/mHrcLVu25AUXXFD6zTIZRX/PPffku971rjzggAPywAMPzMMPPzwPOeSQvOSSS/LJJ5+cety/+7u/y1/5lV/J4447Lm+44YY8+uij841vfGMefvjhedVVVxX+PxjPsAYHHnhg6RrMJV9vetObRssXJbeT41533XVl41L6Zj7GHAq/+c1v7iG3q1evHi23anaH/GbVgWlK31L7d7ad/MAHPpCve93r8rzzzhtlJym9MKDwaCS1zygbMVC5tpQeH84lb37zm3eeS/7jf/yPedRRR+XGjRtHzXmSqqJSyrcZdGPl/2dK31JrQO2z1r5CNddff/3o5O1cPkiF70zZMyJeMXu+VXqMmutSYWxCZaH1nVZfUrpm2GdveMMbSvcZsQYkk/7oI488UuaPDihcyqJkYYC+7FXtN1baBwr6vDObsck14gxByS3lg1BQ5zPCX6DkloorzDfumLi+Wkyf9kGq9xklCwOVtpfau9TZl5IxIq6QyfmjhIxRa9A6/t7jJSf1805FbCWTizER46rF7+aKEVfHgghfv9KetT6XZDLFhGqMiV1ROURq3NlU6UbKb6QeO83kLjoR4xJjKulytToIUm4nqdq/xLhUzoSskyNz9Qoo6YRMvbwv4ZdT+4HO1Vef1SkfhKifpePv1eNS9ozOIdJNqyvspNp+IO4WtPDvKmWBrLsi8Dfbk6p6QboGQKEOghqXkNtMrRoAag3UoPMFdPOBCn1Dyi0RY6LsJLEOVP07ZScpvUDZM8LPbXGnrbKmjfL1B6p1GCELavaM+mbUuEQ8iK4xqY4LErVnFC1q2irt2UI89thj5ee0qrsF1ffPiD3RMs/Vuz9aDXlOJ3ssqOSjiBjxYuR9t2/fnv/6r/861d+lZIy8n1wNed5RynPNpireOB+EnSTuLPR8jmrtN47RNS3OUZXnyVZ3aOl91mMsl9KNSjnETK172q3iINX17z3eRW6FWo8FtbrJzPo8BH1vQ6VuozVjfBsK6l6M4rjVUPnZ1vmCsXdiiJo2+g5edfy9Rb1g7/4oJbdqPbSGnq6VskvFRufau8cccwxieyvOqdR8Kd9GKUZM2V61+9SELFBxNkoWKLujVGvTQue2iLmO8e/o83T12ZeuTa6Gmi+1fwmdS9md1ow9RyndWVDrA0DdM1HthzibsT0hiHNfa70wZv8q5WGWEmO+mdL5P5N7N4hG4ZFLtTomJVlQq/Gkzn2Ub0P45JRuVNtnLeOuY89nLfOole83ULGgStlVi7lO0uIM0esD062pqgGo9JmoeCOlb4j9S51Rld5vzGzXp60CsleOUn9BpXch6T6ereJhY3GPFHZMAkLfKN0XzGwXc+25j6card+g6THPRdHCd66sYyL2L+XXqOXq1d79yqz3cyn7oFZLTef7FN4zofO+rfoAVN/bqOwlXl2nrgZdx1S5vnTflerv3urNjZ5p4YMonH8JnbsYvkJFb32Vnl+ta46q307q/c0NlV6Tre8AjD37toqJ9fzWU+u3bcd+M7W7QfPRY72gWnyFqqUmoPYZFXNtFX+newaNQa2WizpHkf1yq32bxXiTeYweVt1nlWdJ6pstRvxuTO2Zkj2bROFt+RZ+bmVtEFlDq3LXlYpjttYL1b25K3S5Ui1XJuPbqOV9F6JSxnqvXaFiYkRNKv120mx6fldepb8gVV+hlOcia55V/dxKCP9OrQdP63v1PfbFXIgqXV59PiOg66Oo+pXKtW2xfytrPKn+AgSUriHrZwmoWBtxlloqtWdjz76UL6ZU/67U26dFn4kW93LHyhd1jlKru6LPOwp1fYaLO9P3fVu9OzI2hknkCxajZ3CPNZNUPVer+GhFTJusaSOgcp6t9AJZGzR2P7SOYVbEgoheE+Q6VPdYoGIAdBykch0ou0PVIVK6hrxDW73P1HonU719iHEpG6lW46nUx1Ot/l2pn3omYyepfaZ0fiC/mdIdC6U6iEyteCNZ00bYX6V3qTIZ29Pi7bPKMwT5NgZZD9IqTl5d7zswJo/Yut/m2DUg5jvXGXX16tXd3pWje7oq9LBU6/+u9M3U3kmdj7GxUbJ3g5KfS6AkYy3ktuU7WtPWuqrlqVvf2xijE9RqtDOZvBEBVS/Y6j2Tqnyn0l11tRrPxaiDGLMOrXvFUmffMSjdF8xk5tvirmulb0PJLZWrb5WHqLIRSj6T0tsFLfpXXHfddV2vQSYnC8Q+W4z3EHvLG2Xqv7kxUP0GXM+1owMKsdyFGBPDI/vKq/RT8/uNu1M9VyJH2zJOXuE3Uvmz+RgrC1TuiPCd1d6wUOsVOx9j4uRqd2Joe1a5z9R8Z4rFeDdo2rVp2Vevwp5RcYXFqHWddk9QuQ2qnpzqhaB+V448o45dW7U3aAnduBgxzMo4SFXcmbBni7G2PfZZJO8yESjJgpo/SuUmCfurdp+LrPGsrrVZjLdix9aeEbTuNTmGxehLXZ0zyayzkQq5jdY1z2Oh9wP9zSrOO+TZjLo3qdIDb5JKWWjxbgHZW4+Ig5A1qRUo5VEXosd+zJnMmUfJX8rUeic1k/HLF0MnVPqNVXVB5F3X6vMZtc9o3ajQ9zyTvUes8t4VEbOhvpnaOSqT8fVbvc/Ve10Qsbat4iuVORMV/5nqA0j5d8QaLIYOG3PvilpbalzyPYTW9wB6uxukdu/ZuRhOvlrl1Xt+A56MuRK0fD95rOzSMRuVPk+zqYqDKMWI56JSL1Tnowg7qRYHIcedpGo/tHwDrtf4ipo9m4+xcWfCt1G7z9X6nFp1n6vyzET3f6/WNar9cok8RPX5jHznuCVj89RqNakESvtMKUdNQvbxpOOYVfqGioep1YNk6vUtrI7fUb31KJ+J2GdqNZOUjaBjz5UyRteuUHqBfl+gKh5G1Bao1oNQ36x6XMLHI99/V6qJod9vrGQx/LAxcRulniOZWue+SRTegG/1xmBlf0GVMwQJsQbEGYLycdXe8aDOZ4RupGq5KPugVtPWKuZa3dO1Ml9P+Uyt+l1V3nWl3tGqrrVR62VCQPmNlNySfakpqn0byj6ovZ+s9Fb7XJA9yse+kUL3f6/UCy1iK5VnX1rGFHqZ0G9YZtZ+M6X+FZl658nMejtJ6bDWa9tjH8C5qLBnVHyFvk9dWQ+i9BZRJlcDQJ6pq1Gru6LPfWrvlM+mWuf2fgd+LsbWUhNvF1ByS8eIq30Q4kyt1heDOvdRb26o+6PLMf5O+GJqNl2tRlvpHrEaVP37fIzp8zwXFX6Y0nsbmexddQqVHniULqdkjLgPonYHYKA65krlPFu+eVWBUnylFT3HRumeVNV6gXy7sNrHU6uDUBuXeDO3VQ1xZr1euO6668r7C6jYnfkYE8ulfBAqnk3VYrbqFVtNdQ0AAbXPCN2o1suk9V2mMTkTtdozqpe46v2oSug3mcn+7+Qbvwp3FojYFQFhHzK17gYp9V2ZROFtjBa9QSvXgconK61vCztJ1Ccr1Dy3hPLvKuN31ePOR+/1s5WyS8VyqTyXeryCvMOT2WdfjEzmvj4hC2S/PmJt1d6mJnJSrd4yoei5t75a3fdC9BZrI31nIm5D1QVRuT5qfdV8BcpG0L1XKmMAdD/TSn+h1VuAVSzGG1LTrq3am8xq8x2oPKNS9ysouVXqhTAXVXanVe03+Vbb2DttSnlEOhbU6h4iReW78lVQuTPqfiPl31E1ba3v1vcuY5VrO1DdJ5aKEdN92qrj+i3vn43tH0XQ0vZW+CBKd1InIe7ltngrtmINqFgQ5Tsr9ZpUqomhaLEfKm36XL5+r7Grlvah4kyt1jOY7nFULWPEmaf1/fexUHeZCLujFidX6/lFnSEIn4nK79Ax12ofhIiVU3ZHrfaMgtK5RBxErX9FKx+PzEH0iup9X4XabypeQdfEEL3qqB4LZO0ZEVuolFtKDlr2lKuMV7R4p7wy3kj126j0ycl9Rpwnqfm2rk8ec3+FqrWh7hFT+6HVW4DVufrKbzZQfXeD7g+q0Cd2IXqsAVCD0AtUTQxFqzeke64XVIu5EpAxEKUYExknV7nbp9Y3Sal+lporlYdR61GuZB8GqL7UKj2DqbMv4d+1PEuS7yGM7V9BnVOJdWh9pu71HmJmZoz620uct771rfmtb30rv/rVr+bRRx+dV1xxRWZmXnXVVfmOd7xjqjEPOOCAXLVq1W7/HXTQQblq1ao85phjCmdfz5YtW3Ljxo25cePG0QkKtXU4//zzc/369fmNb3wjzzzzzHz3u9+dP/7xjzMz86STTppqzFWrVuVHPvKRPPLII/Pkk0/Oyy+/fN6D0XPh5JNP3vnnCy+8MNesWZPf//738xOf+ES+973vnXpc6pudcMIJO//89re/PW+88cbMzPznf/7nPOWUU6Yel/hmz8bxxx8/1d9729velldccUVu27Ytv/jFL+Zll12WDz74YF5wwQW5du3aqedz4okn5ve+973csGFDHn744XnddddlZuamTZuwNZgWag0o+VIbl9I3BJTcqtmdxfhm0+owSt9S+4Gyk9R8F2Lab0ZB7TPKRgxU+riUHBDnkmdjjHxR34zQjZS+pdaA2mdKvgIF5YOo6QVivouhw5YTSrErap+pydikP7p27doyf3Sg0rehUIpXPBvT+kyLcX4Yg9p8CV+Mklu1/UD5uYQuV4srEOOqxfTVfBBKFgiovasW16fOvpQ/SsgYtQZq8feFmNa/W0rnnTFxQUrGiHHV4ndKsSCKFnNVOFPTVK4BlUOka0Gq5WAx/EYqh1gxrsI+U9Lli+WH9ZanVoKy6ZRupHP1CijphEy9vC8hY9R+UMvVK8WuHH+fgc4hKqC2Hwh908K/q/TJ1eyO6jebC8InHzPmYuhctTPqcqnRJliMe0E9olYftRDLpdaGspPEOqjVAFB6gbJnhCz4Ttuz05tvs1j2bNp1UMr7Zmrd/VaqMaFQq2l7NsaeTarPJUqxZ7V6I6W7o4sVG+2tx4ISajGmpdQHYNo1UDvvKK3tJAp2Uu0cpVTH1OIcVSljdPyOimcv53pBpRxippaMqdV4quU8CdR6LKidJwcUcvX2QfTGVbszqBQDUKv3pSD2r+odPJV7JoSMqcUrKHtGyC6lF5XyRpncfJV8fbXejWq6nJAFtZ7f1HyVam2sc/XO02q1yWp9bQidq3TmI1G6s6AW16eg9Y3CvXrfT15atfoUvdUAKJ3/M7X8RjXU6piUZEGtpo2yO0o1AGq6US0mRvQBUNIJmVqxIDX5mkThDLGU6K2HllK/60xm/6rdHV2I3mpMKBajV86YNVDLUxOoxe8o1PpSkSjUqWcy9kytf8VixFx7szuTKPjOajkIJRuh5jsT+1ftTRcKNV0+ULl/ld6PytSKuVJQ+2wp5X17O1MryReJUh2TUj1qppYfRqHmg1AQOlfNV1Dr+WUboec3EvpGrcZE7RylFBdUiitkasWuKPvg+AoHtc+otV1KZ99pUau1oXwFpXOfmu9M7zOFvBH1zby2i0dvNc8t/NzK2iClXkRq9+qXkm/TW299CsJXWGrnkt78ZwqlfIFan6eFWC46jPIVlPqOLtYdnt7eGKQg1lct/r6U/LCeUTinuj5Kb/8q5UzcQ2sGpfvUSnuXRKn3KoVSjadanwm1+gq1uquFWI7+6HKF8kGW0n3fMfuB0AtquY0BhTupmVrroJSTy9R7A26AiFcoxEAWCyUfZMxcVWtHK6H0rVIPrUy+9rtS36j1TqapXFtKDtRqPGnfZjnXvyv1U89kZFctFrQQvb3PnanV/03N7ijFG5V80Wejx32m9CYThVIu5tnoraZtKdHbGqjdlVtKunxalOoVyHEJ1N5JpVDr3aCEkoypye2z0dsdPKX1pXSCWo3nQvTm31HfTG1tle5NtojfKdQAUOuwGD2OetMLSveuMn3XNVOvX45r1ZeWDpsWtf4VFEq9JtXunqn2y5mL3t7ipVA7Qyih1lee2L9Kb5QronRfn0LtDKHU40gtp76UesX2dm9SLR9F7DM135lC6Wyi9oY0daZWqnVVe1dO7a660l0594mZgdhnavE7CsKeeW1nUMv1KcmCqj+q0AdA7T6XUhxksXRjb7FnpfioknwtFr3Jl1osV2k/ULSIBVXaX7UY8UL01vObQi0OQqAUd56EqiuoHpfw8dTsg9I7qZnMN1PSCZMo1O88G73dtVHSjS1858r5qtUQE2+UUd9M7Rw1iUKPI4LFilf09l7dgMJ9kEyt/jNKa6DW/51aW7VedUq1NpNU6hu1e89K9YJquUnn1fV6eCjdP1OL2ajFxJR6urYYtxJC5yqffVvRY/93pbtBavaMgrBnauc+NX1DnNXV+r8vRI9yoBTHVPWXqlH6ZotBb/tMKUdNonZvktA3PvvqodQrJ5PTC8Q+U6uZpGyEUux5sfyP3mrKB6r9RqWc52KcUXvsSUXYdbVYEKUXlO7bqPlhSnUbFGpxQQr3wNNF4c6C0rmEROltW6I2OZOzD0qxoElUYs8ElM9E+2IK9eSOPXNQfqPqnSMFKPugFL+jaKETWtnJMecSpf7vSrGVTNudzDbfrHKfKflhmXrnyQEFf1R1bRXwm0x69kHpLcBJFPoLUHKrdu5Tu09NoObnKt1ZUIsR+24Qtx+oOLn90d1ROJ8RvpiaTVfzR5XuEauxGLFcqr5iufQHVfMbBxTsA7W2lIwRvo3aHYCB6jOfag1AS3q716aEWu2Z2p0jYh3U6iDUxiXsmVqP46XUX6A31PJRAwr3uSZRiL8PqPiNhB5T0zVKOUS1d7SUYkFq5wc1n9xv0Oih9s7xQvTWl1pJblvk1Htfg0y9vJxSfYVpg0KfdqX7IC1QeL9RqQd+5uLkaHvr6ULXLLSM2fR2RlVbW6X6KNuHGZT8uxa5gt5jbaTvTOwJtdoVan1VcxsK41IxAKWziVr9nVIfALXzjtp8BxR0AiW3Sr0QJqm2D/bLteKjqrqGQsFfonQYdb9RTZer1ZQTqN3nolH4Zotx/6y3vrYtbK/CnSOl+gq1NVC6r6+Ge3Pr7QclX1+tB75azbNajyPizKOmx5V6fivpmky9/aD0TopqT7m5GOODEPuXWgPHG2dQ8nNdA2AGfN+XgzqfqdXEKPVYWAx6u9+o1lNO7a0YpX4bavuMmq9SDECt/o7aD0pvAbY4PyjULAwo5OQMC6EXfD+ZRalHudLaqtVBUGurFicfULBnajlEAmqulHyp9ShXsg+TLOf9q3TuUzpLkijVUqudqclYUIz620ucyQ969NFH7/a7aY3dpz71qfzt3/7tvOeee3b+bNWqVVON9VwYUyBw11135RlnnJGHHXZYrl69OlevXp2HHXZYnnHGGXnnnXdONeZircO0nHjiiTv//Mwzz+TatWvzrLPOyieeeGJqWRjk66mnnsqvf/3refbZZ+ehhx6a73vf+/Lqq6+eeq6T8znxxBPzqaee2vm/x8gB9c1e//rX7/zzb/7mb+72uzHzJb5ZZubtt98+73+vec1rphpz0ihlZp566qmZmbljx4489thjp57r5P/P2d+qN4edWgNKvtTGpfTNli1b8oILLsh3vvOdefnll+/2u/POO2+qMSm5VbM71DcjdBilb6n9QNlJar7zfa/bbrtt6m/2bEw7X2qfUTaC8HFpPZ5Zdy7JZHRCJvfNCN1I6VtqDah9Rq3DYjDtXqN8EDW9QMyXmutSojdZoHQNtc/UZIzyRwnf5tnoTedOnlG/9KUv7fa7ac+omYxPTtkHisWY75hxCV+Mklul+F0m7+tn1ulytbgCMa5aTF/NB6FkYSGmHZfau2pxfersS/mjhIxRa0DtXyX/Tu28Q8UF6X1WOa5q/C6z/1jQszHtuORcF+NM3RvEGlA5RGrcudZg5cqVo+WA8hspXU6NO6zvypUrm+yzHs8mSrHcTE4W5qM3u0ONS9l0SjdSPpMSSjphclyVvC8hY9R+UMvVUz4IcVZXi79T8QqleCM1rtp+IPQN6d8R577FsDvL6ZsRcUzKz6d0LlU/S8WCiPmq1WgTdpJaAzUWoz6qt9iVUjw7k7OTxDqo1QBQeoHO91XKgu+0zUD5CoQskPaM0LlKed9MrbvfSjUmFGo1bZnMPqPOJUqxZ7V+G63vjo6RL7I2WanHwmLQW71vppaMUetArIHaeUdpbTO17KTaOUqpjok8R5E5k8xaP4zaD64X1MohZmrJmFqNp1qtDQHl36nFnimI+lm1+J2SD6I2rtqdwcWIASyXel+lmja1O3iUT67kj6rFK1rYsyrZbdF/tmXeqEIvVM63Re/VKp2r1rtRTZcTskDJbYt+G1SPlN5rbdR0rpKueTZ68xvVaqlpO5lZp3Od951B6c6CWly/xdm3cv9S50niLYCldD+5t1iQGko1AErn/9l/d7neHc30eyaz57SczxBKOROlGgA13agcEyNyJmqx3N5jQWryldm+B15v90yejR5rC+jeK733u85k9q/a3VGlGhMKtbogtTy1UixoIXq0O5ReoOKYCzHt+irVqWcy+kbtrRjKH1XylzK1+ker5SCU+vuo+c7E/lV704VCTZcTdwuU3o/K1Iq5Zmr1XlXL+yqdqZ1PnkGpjkmpHjVTyw+jIH2QxTirTwuhc9V8BbWeX0o+yLPRW92VUr5ArcZEqRdxplZcUCmukKkVu1J7c3Mx4iu91Qu22GeVa6t29iVQq7WhY0wK5z4135mShRb3Nqp8G+qbeW1Z5tPjY3qfq70Br5aXI+wvXT+bqaFzFeP6CrVcmYyvoJb3zWRkQc3uKN3NVevzZB3G+Qot+46OlQXyDg8hC0pxhUxmfdXi72p+2GIwZn0JvUD5Cq6P0tu/SjkTpfuzz0Zvd9oyGVlYSrVnPcaClOqulGo81d7nVquvUKu7Wgx/tMd7AMsdygdRu+9L7QdCL6jlNpTupGa2XYexb/Eo5eQy9d6AI2R3KeXqe5QxYh0IHzdTr3ZUKR9F20mVHA9x11Wtd/JC9BbPpuRArcaT8m1c/67VTz2TkV21WBDhL5HfjLK/xDooxZ0zteKNi1XTNu0eVttnSm8yPRvTjq2Ui8nkzpMtztSVNbTz0WNcYSF66/OsFldYiN7yRkr1CuS4C9GbHl+Mt9p6PEOo+bkESjJGyi2VLyDsr1qeeiF66+eiVuOpVEvdIrehsLZK9ybJGk+lezHUOlBxVyW9oHTvipqvWlxQrV/OYtSq99ajTE2HEaj1r6BQ6jWpdvdMrV8O5Sso1fa1PkOMzW1Q8Qr3lWf272K8UZ45vey2znFl9ncHbzHiYZn95c8Wosc69YXoLRejFstVqmNSiwUp2TM133kheryjTayv2hvS1JlaqdaV0mFq4y6Fu3K93m+kbDp1/if22WLF74ixe7NnS2ltx4yrlutTkgU1f1SpD4DafS6lOAipG1vXAPR4H3MhlOJsmf35ClRuYyHUYrnTQu0HpbwRGQsi7K9ajJjYv+Q3U8r7UuMSa6AUd87kfGdqXMLHI/0lJRmjfDHim1E64dmYdmwihpnpuzaZWrqR9J3pOwAKNcRKtStq56hMrR5HC9FjvIKu21DIRyn5z67FXJz+7z32Y1LrVadUa5PJ2p3quarlYoh9ppabXIy8em/jKr01mal1/0wtZqOWM6HspFJ8lFpbQucq9p9ZiJ56QmZqvQGn1Is4s03et/KeGGHP1M59Sv2YMpmzulr/dzU5UI1jKvhLC9HbPstcHB9kWpT2mVKOmkTt3iShb9TOvotBb7Kr1Csnk9MLxD5Tq5mkbIRS7Jn0GZXuLND3GxVyntS41N2CFveuquz6YvUn7+2e2GLdt5kGtT5PSnUbz4ZSXJC8IzUtVHxlMXoR93aGoCDeM81k/FGlcwkJ/batUj/1avugFAvK1OqR+mz0dpeJGpe4F6PmO1M6TCkHQfmNaneOlHQNZR+U4neZzDcj81GEzqXOJUr938nYipKMKdkd8psR/mhLP6zn+1yUjBExALXzmdL+fTZ6i68o3ad2TcwMSjpXre5K7dyndp96IXqru6Lq35XuLKjFiNXuBhFQ+0FNFtT8UaXzGd1DS8Gmq/mjSveIM7XOfZSvT8UxiXGV6oIytXqJZzKxILVzSYtcanVv/UyNOwDUmU+tBoBC6V4bhVLeSK0nldIdPLU6CLVx6fuN1T2OlfSCmt1RWlu1eLZSr+dMRhbU/EZCj6npGqUcoto7WkqxILXzg5pPTvlM1LhK9b7UmNTaKr0RqnbXhpBbtZz6s7Fc8nJK9RWZ7XVub/uMhLzPlakRv1sK36zX9xsp3ah2f4War5IsUPf1lc6o9NqqxJgIX5/6ZotBb+cztbrvTJ1YW4sex5l1e0KtdoVa35a5jYq3R5XexqBrqTP7P5ssRv3dGJT6AKidd9Tmq6QTaLlV6IWQydmdxfDLp/1u1J02pTyimq6hUPKXWtQbKeRnqXVQqyknUHtzU+keIjVfSpcvRl/baSFt73K/k5rJ9HlWWwOl+/rPRm93cylfQak/udp+oHx9pTqmTMZOqtU8q/U4UrrrSkHpXDou2DKu0Fu+Ty32TPhMaj3lKB+E2L/UGjjeOENrP3eMD+IagGent3MUhe/7cuNS5zO1mhilHguZjCwQvoLa25hq8QrVeGPluGrvQtK9VzL7j+Wq1t9V7weltwCpb5bJ3N1Qyqmr6TAzA6EXyH1GsBhxkN5shFrMVS3PpRTXV4uTK9kztRwiATVXSr7U3pBWsg+ZWn2p1c6+ZA5R4SyZycWzlWqp1c7UZCxo7zDzkpk7//ya17xmt98988wzU4153nnnxcaNG+P3f//346STTorTTz89VqxYMWqeA5s2bZr3d1u3bp163A9+8IPxX/7Lf4n/8T/+R+y1114RMfP//8orr4wPfehD8b//9/9+zmOS60Dw1FNP7fzzihUrYs2aNbF+/fo455xz4sknnxw19j777BPHHXdcHHfccXH//ffHV77ylbj44ovjb//2b6ee6x133BGZGStWrIh99tln5++G7zcN1Dd79atfHevWrYv3vve9ccQRR8TXvva1WL16dVxzzTWx7777Tj0u9c2OP/742H///XfTDwPbtm2basy999477r777viFX/iFuOWWW+L5z39+RMx8r733nl5Nr1ixIu6444740Y9+FNu3b48bbrghDj300Ljzzjtjx44dU49LQK0BJV9q4w5U65s1a9bEz//8z8drX/va+F//63/F//2//zf++3//77H33nvHv/7rv041JiW3anZnoPqbETqM0rekfSDsJDVf4ptFMH4jtc8oG0H4uJQcEOeSCE6+qG82UK0biTGpNaDtGbG2BIQOo3wQNb1AzJeaqxpUzEYpdkXtMzUZo/xRwreJ0NK5s8+o/+f//J/RZ9QIxmeiz//VUPOldCPhi1FyqxS/i+D8XEKXq8UViHHVYvpqPgglC4RupPauWlx/oPrsS/mjlIxF1K8BtX+V/Du18w4VFxygYkyV46rF75RiQRGMPSPtA3WmVoJYAyqHSI1LyQFl0yldTo07rO/xxx9ftr5UXEFJl5N5I0IWqG+mNC55fhio1I103lcBJZ0wiUrel5Axaj+o5eopH4Q4q6vF36l4hVK8kRw3Qmc/EPqG1DXk2bf6m1HypfbNCJ+cOvNROpc+o1bvB9doM3aSvMulBHWGoHSuUq0N6ddE1NtJYh3UagAovUDX8FTKgu+0zaDk25D2jMz9KuR9I7TufivWmFSjVtMWwewz6lyiFHtW67ehdHeU9GuUeixQKN3LjdCSMWodiDVQO+8orW2Elp1UO0cp1TGRfj4hY5QfRu0H1wtq5RAjtGRMrcZzQKXWhkCtx4Ja/yiifnZAKX6n4oOojat2Z5Aa1/W+WjVtanfw1O6ZEDKmFq8YqLZnhOxSepHMGxE6l85z0b1XK3SuWu9GNV0+UCkLaj2/qfkq1dqo6VwlXROh1Z9Mqd4ogpMxQuc67zuD0p0Ftbg+dfal9i91niTeAlC7n6wUC1JDrQYgQuP8H+G7owN+z8RniAGlnIlaDUCEjm5Ui4kR+1ctlqsUC1KTrwjGZ6LvTVajdNc1grtPrdLvepLq+PuAwt1RpRoTCrW6ILU8tVIsSM3uqPWlItZXqU49gtE3am/FUP6okr8UodU/Wi0HodTfR813Jvav2psuFGq6nLhboPR+VIRezFWp96pa3lfpTO188gxKdUxK9agRWn4YBemDUGcTAkLnqvkKAyo9v5R8kAituiulfIFajYlSL+IIrbigUlwhQi92FaHz5ia1tkr1gtQ+o9ZW7exLoFZrQ5+jlM59Kr4zJQut7m1UniWrv5nXlkWp5lnNz1WqOVK7V6/m2yj11idquSIYX0Et7xvByIKa3VG6m6vW58k6jPUVBnrvO0rW1Cu9MUhBrK9a/F3ND6Og4piEXiBzGwPLtT5Kbf8q5UyU7s9G6L0LSciCWu0Z2Y9JpfcqhVKNp1qfCbX6CrW6K8ofVbuLuNyhfBC1+77UfiD0glpuQ+lOaoTWOijl5CL03oAjvplart59Yri5qtWOKuWjlHpoRXC6kbjrqtY7WSmeTcmBWo0n5du4/l2rn3oEI7tqsSCl97kjtPq/KcWdI7TijaQfRth1tX02oPAmUwT39tlA77mYCL2aNqKGVi2uoNTnWS2uoJg3UqhXIMdV0uOU/aXkVq13gxJKMkb6jVS+QOkOnpJeoHSCWo2nUi01qceV1lbp3iQZW1GqAaDWgYq7KukFpXtX1HzV4oJq/XKo/dvq7cKeawuU+l2r9a+gUOo1qXb3bEClX47aW7xKb58p1fVR46r1lR+o3L/k+42ED0L1iVG6g0fGG5XyZ2p3XZVyMWqxXLU6JqVYkJI9U/OdSbsz0PvZRO0NaepMrVTrqta/Uu2u+oDCXTn3iZmB2GekfBG2R8meqa0tNa5ark9JFtT8UbU+ABE697mU4iCkbiT0AqVvqbOJUpwtQstXULpb0OLOUSXUflDKGw1U250I7nw2oBAjVur5HaGV96XGpdZgoPe4cwTnO1PjEj4eGctVkjHKFyO+mdo9YqX6nQituzYDCrqR9J2J+arVECvVrqidoyK0ehypxSuU3qtTug8yiUL/GaU1UOz/PlDdU44aV+k9BKUex2r3npXqBdVyk9R8lXLqSm9NRmjdP1OL2ajlTNR6uiq9V0foXMX+Myo9ISO03oBT6kUc0S7vW3VPjLBnauc+pX5Mk1Se1dX6vyvJwSQKcUw1f0lpn0WwNTzVKO0zpRw1idq9SULfqJ19KZR6HCv1yong9AKxz9RqJikboRZ7jmBqPJXuLJB1BREaOU9qXLWeVIRdJ3PqpP1VeQuAQK3Pk1LdRoTe3SC1PrxKPfCUzhAUxHumEYw/qnQuIaHftlXoOTJQbR+UYkERencsCJ1L+UzUuEr15GqxZ6UcBOU3qt05UtI1lH1Qi98R34zMRxE6lz6bKfR/J2MrSjKmZHfIb0bsMzU/TK0Gj4oBROicz5T2b4RWLzGl+9SuiZlBSeeq1V2pnfvU7lMr3YGn6t/V7iwoxYjV7gYRkH0AlGRBzR9VOp8RvpiaTVfzR5XuEUdonfsoX5+KYyr1B6VqQZR6iUcwsSC1c8kA4StU+zZqdwCoM59aDQCF0r02CqW8kVpPKqU7eGp1EGrjEvaMPP8r6QU1u6O0tmrx7AGFXs8RjCyo+Y2EHlPTNUo5RLV3tJRiQWrnBzWf3G/QzKD09pnaO8dqfalV5FYxp65UX6G0vmr9QSndqJSTi9Dq0650H4RE6f1GpR74k1THK5R6ulCyQNXqK51R6bVViTERvj7Z74tA6XymVvcdoRNrI31nYk+o1a7QMTyV3IbSuFQMQOlsotT7LUKrD4DaeUdtvko6ga5TV+iFEMHZB0rnEv4zdadNKY+opmsolPwlssZTKT9LrYNaTTmB2pubSvcQqflSulypty9pe5f7ndQIRhbU1mBA4b5+hFb/aLXe3ARq+6FVz5Ge65gitHSj2rtBSn081HKTSj2/ybiC0psbarHngUqfSa2nHOWDEPuXvG80sJzjjUp+rmsAZlA6R1H4vi83LnU+U6uJUeuxQMiC0v1GtZ5yam/FDCj021B7F5Kar1IsV63+bqB6P6i9BUjdmyTubijl1NV0mNmdSr1A93muRq2Pp1KPcue5ZlCK66vFyZXsmVoOkYDs2zBQ3XOEGJdCyT5EaPWlVjv7DhBxEIWzZAQXz1aqpVY7U6P7Ic28/O7v/m4+8sgje/z83nvvzdNOO23U2E8++WR+7GMfyzPOOCN//dd/fdRYAwcccEAec8wxuWrVqj3+O/jgg6ce9w1veMNUv/tJINaB4Oyzz84NGzbs8fNPfOITeeCBB0415kknnTRyVnOzatWq3eTgvvvuy8zMRx55JE8++eTR41d/syeffDIvvvjiPOyww/I3fuM38oADDsiDDz44zzrrrLz77runHpf4ZpmZxxxzzM41nc1RRx011Zh///d/n0cccUQef/zxecQRR+R3vvOdzMx84IEH8sMf/vDUc/32t7+dK1eu3DnmmWeemW9605vy137t1/LKK6+celwCag0o+VIbl9I3J5544s4/P/PMM7l27do866yz8oknnpj636TlVsXuUN+M0GGUvqX2A2UnqfkS3yyT8xsz6/cZZSMIH5eSA+pcQskX9c0I3UjpW2oNBqr3GbUOFIQOo3wQNb1AzJeMrShB2V6l2BW1z9RkjPJHqfidks4lzqiZjM9E2QcKar6UbiR8MUpuleJ3mZyfS+hytbgCGddXielT86X2GfXNCN1I7V21uD519qX8UULG6PN/9f5V8u/UzjtUXJCSMWJctfidUiwok7FnpA9C1sSoQKwBlUOkxqXkgPIbKV1OjUusL5nvzNTR5ZlMHQQhC9Q3UxqXkgNKN9I+kxIqOkEt70vIGLUfBlRy9ZQPolQvqJaPUoo3UuOq7QdS3xD+HeGTU9+Mklu1b0b45NSZj9K5SmfUTNdoZzJ2kvbvVKDOEJTOVaq1odaAspPEOqjVAFB6gbJnhCz4TtsMSr4Nac+IdVDK+2Zq3f1WqjGhUappI/YZdS5RjD2r9NtQujtK1iYr9VigULqXm6knY5n160D5jZk6550BlbVVspNq5yilOiZyPxAyRvlh1H5wvaBeDlFJxtRqPNVqbQjUeixMjq/QP0opV28fRG9ctTuD1Liu99WqaVO7g6d2z4SQMbV4BWXPCNml9CKZNyJ0LjVfpd6rAyq9G9V0OSELdH62Whao+SrV2qjpXCVdk6nVn0yp3iiTkzFC5zrvO4PSnQW1uD6lG6n9S50nler1HQvSQ6kGQOn8n+m7owNK95MHVM5namcIpZyJUg2Amm4cUImJEftXLZarFAsaUJGvTM3eXNUo3XXNZOwvpReUcjFqd0eVakwo1OqCBlTy1EqxIDW7o9aXilhfpTr1TEbfqL0VQ/mjSv5Splb/aLUchFJ/HzXfmTxPqrzpQqGmy4n9q/R+VKZWzDVTK7ehlvdVOlM7nzyDUh2TUj1qppYfRkH6IGQutRpC56r5Cmo9v5R8kEytuiu1fEGmTo2JUi/iTK24oFpcQSl2pfbmJrW2SvWC1D6j1lbt7EuiUmtD+QpK5z4135mSBaV7G9Q389qyKNU8q/m5SjVHavfq1Xwbpd76lA4jfAW1vG8mIwtqdkfpbq5anyfrMM5XUOo7St7hUXpjkIJcX5X4u5ofRkHFMQm9oOTfZerlJjN19q9SzkTp/mym3ruQhCyo1Z5R34ySMaW6K7Uaz0ydPhNq9RVqdVeUP6p2F3G5Q/cXVLnvS+0HQi+o5TaU7qRmaq2DUk4uU+8NOOKbqeXq3SeGm6ta7ahSPkqthxalG8n7+iq9k5Xi2ZQcqNV4Ur6N69+1+qlnMrKrFgtSep87U6v/m1LcOVMr3kjWtBF2XW2fqb3JRHwztVyMUk1bJnPuU4srKPV5VosrKOWNlOoVyHGV9PhAtf2l5Fatd4MSajJGjUnlC5Tu4A0o6AW69kylxlOplpr6Zmprq3RvktxnSjUA1DpQcVclvaB07ypT766r++Vw+1fp7UI1HUag1r+CQqnXpNrdM7V+OUpv8WZqvX2mlIOgxlXrK0/sXzJmQ/ggavWzZI6WiDcq5c/U7roq5WLUYrlKdUxqsSAle6bmO1M6TOlsovaGNHWmVqp1pXSY2ri+K+c+MQPEPiPjd4TtUbJnamtLjauW61OSBTV/VKkPgNp9LqU4CKkbCb1A6VvqbKIUZ6Pm67sFuyBiuQTUflDKG5F2h7C/ajFipZ7fmVp5X2pcYg2U4s6ZnO9MjUv4eGQsV0nGKF+M+GZkjprwmZTqdzK17tqo6cZMxncm5qtWQ6xWu0KOS6DU40gtXqH0Xp3SfZBMrf4zSmswoNL/nVpbtV51SrU2mYy+Ubv3rFQvqJabpOarlFNXemsyU+v+mVrMRi1nQtlJpfgotbaknVTqP6PUE1LtDbhMnV7ESnnfTLYuRuXcN6DQjymTOaur9X9XkoNMrTimmr+ktM8y2Tdzq1HaZ0o5ahK1e5OEvlE7+1Io9ThW6pWTyekFYp+p1UwOVNsIpdgz6Qso3Vmg/Ea1nCcxrlpPKsKuk725iT2h9hYAgVqfJ7W6DbW7QUp9eKn4iuI9MRWU7iwonUtIlN62pXQCZR+UYkGZencsCJ1L+UzUuEr15GqxZ6UcxEC136h250hJ11D2QSl+l8l8MzIfRehc6lyi1P+djK0oyZiS3SG/GbHPlPywTL0aPCV/VG1tKZR6iSndp3ZNzAxqOjdTp+6Kmq/aO+VKdZPUGqjdDcrUeduWsttqd4MIqP2gJgtq/qjS+YzwxdRsupo/qnSPOFPr3EfZHSqOqdQflKoFUeolnqllH6i1pWSM8G3U7gBQZz61GgAKpXttFEp5I7WeVEp38NTqINTGJewZGbdS0gtqdkdpbdXi2Wq9nglZUPMbCT2mpmuUcohq72ipxYIydc4Paj455TNR4yrV+1JxNmptld4IVbtrQ+YKVHLqmVr1FQNK66vSH5TSjUo5uUytPu1K90FIlN5vVOqBn8nFK5R6ulCyQMVBlM6oamurVB9Fvw9Sjdr5LFOn7jtTL9ZG+M7EnlCrXRmoXl+13IbSuJQOUzqbKPV+y9TqA6B23lGbr5JOoORWqRdCJmcfKJ1L+M90LEghj6imayiU/CVKh6nlZ6l1UKspJ1B7c1PpHmImM19Klyv19h0gbK/SnSOl+gq1NVC7r6/UP5ryFZR0mNp+UOs5kqnTA1+t5nlApceR0l1XCkrnEv4SGVdQenNDLfZM+ExqPeUoH4TYv9QaON44g5Kf6xqAGZTOURS+78uNS/fQUqmJUeqxkMnIguL9RpWecmpvxSj121B7F5Kar1IsV63+jtoPSm8BknXfSjULSjm5TJ8hSAi9QO4zArU+nko9yp3nmkEprq8WJ1eyZ2o5RJLquVLypfaGtJJ9yPT+zdQ69ymdJTM5X0GpllrtTE3qsBj1t5cpjz32WD744IMlY11//fX5+c9/vmQsKuj/lre8Ja+88sp85plndv7smWeeySuuuCJ/67d+a+pxJ6lcB4KtW7fmtm3b5vzd7bffPtWY99xzz5gpPWe2b99e+phO9Td77LHH8tZbb81bbrklt2zZMno84ptlZq5bty6vu+66OX938cUXTz3uww8/nDfddNOcyr6Kp59+Om+++eZ84IEHsH9jDOQaVMuX2riUvjnuuOP2+Nm6devyjDPOmPN300DJbe92h/pmhA6j9O0Atc9mU2Unq+dL2Z0Wl1sq9xlhI0gft5Xcjj2XUPKVyXwzQjeSPnkL/65qn7U+m4ylhQ6r9kFU9MLkOPR8K2MrCrS+WNpr7GoS+oyqJmNj/VHKt1HSudQZlfSZWtmHKqrnS8oX7YtROqz3+F1mGz93oEKXq8UVyHF7j+nPRtUHGfvNKN1I7V2luH7rsy/lj46RsVZrULV/Ff27gd7PO9QaUDJGjasUv5uPXmNBpK9P+CAtamJ6h1gDKodIjdtaDsb6jZQup8Yl1rdVzFVFl2fWzpWQBeqbqY07F2PlgKzbaBkLUqB3naCW982slzG6jmlANVc/1gdRrhfsPR81H73GG4lx1fZDC31TaXcIn5z6ZpTcqn0zwidvERvNrNO5SmfUTNdoZzJ2spV/p8rYMwSlc5Vqbag1oH0bIlauUgPQSi9U2TNSFpb7nTYl34aUW2Id1PK+S+Hud481Jq1QqGkj9lnr3KRC7Fmh30amzt3RSSpjrso9FqpQu5erJmOTVK1DizVQO+/0vrZqdnIYQ+EcNR891jGR+6GljI2VL2qurhecH4Uc4iQ9yphajadirQ2NWo+F3vtHKeXq7YNojqt2Z5AY1/W+ejVtk/R+B0/tngkhY2rxilb2rEp2W8ZGK/ZuS507dr7KvVd77904m951eUs/t/ee37Opnm/vtTZKOldN1yj1J1OqN1oIqh5krM513lfrzoJaXL/V2bdq/1LnSbV6/eUeC1JDqQZA7fw/H8vp7mim9v3k3s9namcIpZyJUg2Aum5Ui4kNEOczlVhu77GgSRTkS7k3VxVqd11b5mh77XdN7F+1u6NqNSYt6bUuaDa956mVYkFqdmc2vfelItZXqU59ISrspNJbMYQ/quYvKfeP7j0HsRT6+/TqO2fy8QrVN12qUNHl1P5Vej9KKeaaqZXbUMv7LoUz9XLLJ89Hr3VMxJhKfZPUqfRBWt+3GQuhc5V8haXS86tXH0Sp7kotXzBJ7zUmAwq9iBdCKS6oEFfI7Dt2pfbm5myq1latXjCT32eVelzp7NuC3mttKF9B6dyn6DsTsqB0b4P8Zst9bUmUap4HlPxcYlzl3qsDvepcpbi+Ui1XZps4iELel5AFNbujfjc3s98+T9Zhu6j2FdT6js5FhV+j/MYgTaXfqBB/V/LDKKh4I6EXlP27SXrNTU6isH8n6Tlnkql1f1b1XcgB8izVa+1Z67tBPfZepVCr8ZxEoc/EXPRaX7HQv9Nj3RX1zdTvIi5HWvggvd/3bX0+q/QXes9tKN5JnQuldeg9J6fyBhzxzdRy9e4Tw+4HpdpRpXzUbFR6aFXrxhZ2p/feyUrx7AHijKpU4zkfY30b179r9VMfoOykSixI6X3uuei5/5tS3Hkheo03TlIZryDsuto+U3uTifhmarkYpZq2TObcpxZXUOrznKkVV1DKG6nXK/T8zdTeSW0ttyq9G3pGTcaoMalYruIdvAFFvVAdJ++9xlOtlnouqHpBtbXt9d7kXFTMVakGYD6qZLc67qqkF9TuXanddXW/nPkZu3+V3y4c6FWHUSyF/hUUPfaaVLt7ptYvR+kt3kytt8+UchDkuJP03lee2r+U3SF8ELX62fno9b6+Uv5M7a6rWi5GKZY7m57rmOaj11iQmj2bi159Z0qHqZ1NlN6QHqDO1Cq1rnPRa/9KetzlfFduPpZbn5j5qNhnhE4gbI+iPVNZW3LcTJ1cn5oszPfv9OiPKvUBULvPNZve4yCZzH4g9AK5BsTZRCnOlqnlKyjfLaDr+iog9oNS3oi0O4T9VYsRq/X8Vsr7UuMSa6AWd6Z8Z9onr/bxqFiukozNprpesMU5tdd7xGr1O0p3bdR04ySVvnOL+fZeQzwfvdautBi3EqUeR2rxCqX36tTugyj1n1Feg977v1Nrq9arTq3WpmWOR+Xes0K9oEpucnKcyvkq5dQV35pUuX+mGLNRypnMR489XalxW69tpZ1U6D+j1hNS6Q24SXrvRayU952E9MV6P/fNpvd+TORZXaX/u5IcZGrHMQd69ZfU9llrX2wMavtsEsUcNUmv9ybno9Jv7P3sS6HU43g+euyVM3ucSr1A7DO1msnZVNsIhdgz6ecr3Vmg/Ea1nCcxrlpPqtlU2HVSNxJ7Qv0tgCqU+jyp1W2o3Q1S6sM7H732wFsKZ4ixqN1ZIMZUfcdD8W3bgbE6gbIParEgtTsWlM6lfCZiXKV68tn0HntWykHMptJvVLpzpKRraPugEL/LZPcZsQaEzqXOJWr93yeptA9KMqZsdyq/GeWPqvhhmXo1eEr+qNraUqj1EptE4T71cq+JydTSuZOo1F0N9H7um03v96mV7sCr3Q2apPe3bVu+W5DZ792gFtA2vVdZUPNHlc5nA6QvpmDTM/X8UZV7xGrnvrkYa3eoOKZSf9BWtSC99xJXtA8DVWvb2m8kesX2egeAPP8r1QBQKN9rq0Ipb6TWk4oal1oHpToItXHng7BnFWMq6YVMLbujtraT9B7PVuv1TMiCot84FxV6TEnXqOUQVd7Rmo9eY0GT9H5+WCo++XJ6gyZT6+2z+ej1nWOlvtTz0avcTtJ7Tj1Tq75iNgrrO9B7f1BKDtRycsp92qvG9TfT63etdn9FqacLJQuUrlE6o6qtbaZufdTkv0HYnbGons8yNeq+1WJtAy1iTJV7Qq12pWp91XIbauPOBRUX7PVsMknvvd8GFPoAqJ131OarqBOq5VatF0LLb5Y5XjcS/nOrWFDPeUQ1XUOh6C+1sr295mcHqHVQqimnUHlzU+0eIjFfSpcr9/attL2Kd44U6isytdZA7b7+Uugf3Wtvbgql/TAbpZ4jCj3wlWqeZ6PW42ig57uurei15/cklXKg9ubGJL3Hnlv6TL32lGvtg4zdv8QaON64MAp+7nKtAVgK56gKfN9X+x5x7zUxSj0WMhlZUL7fqNZTrve3YtT6bSi9C5nZ1p4pxXJ7rb+j/UaFtwDno+KbKdUsqOXkfIbgaBljonRjNb338ZwPhR7lyzXPpRbXJ8ZVu9vnHGIbquZKyZfqG9Kz6dU+eP/OT4/nPrWzJO0rqNRSU2PORa9xkMzMFZmZYZYE69evj2OPPTZ+9Vd/dY/f/cmf/En84R/+4VTj/su//EusWbMmbr311thvv/0iImLz5s1x4IEHxtq1a+M//If/MGrexhhjNDnnnHPi7LPPjpUrV+7280svvTQ+//nPx/e+971Fmpkx5tmg/EYl7OMao4t1mFHEcmtoKN9GSXZ9RtVDSb6MMaYV1o3G7ML+nTG6qNkz5wu8BhFeAxpifdV0jeG+mdq4xhijjM/qemtgO2kGlM48li9Do7Qf1FCzk8Y6N8JrYIwxxpjFwecSFvt4hsTyZQyP7aShUZIxaq5Ka2BYlGRBaa7GTGLZNYbHZ3Xn6kmsx43ZHetc61xjTBvUdI3tg1HEcquHmm6kzpNq60Dg/auH4yscandHrcM4vLbGmNnYZ9LDvbn05mv7a4xpgZKuUdPjFNQ3I9bXdeqGhtoPljGvgTHKeP/qoeSTG2OMmRvrcqOIUozJmAHrWzPgsy+HWr2gMca0wPc2OLy2xhizJ9ZhevibzaC0Do4rmAHLgjG6UPFGQi8o2UgSrwOH7ZlzEIr4mxljWmF9Y4yZxL7zDF4HPYhvpparV7PpjoNweG31sN3RimcbFn8zY4zRR+1sYvzNFCHOfWpyoDZfJby2evibeQ2MLo7lclgvGGNm49izIbF8zWD7y+G3C40xZk/UavuUoNbAa6sH4YO4fpZFaR2U5hqhN19jDIt1AotjbcbswvvBazBA2B7bsxn8xoBRxLpRD+sEvTXwfL0GZhfOG81g+6uHmowReA24vWudMINlTA/CZ3L9jh5qOkxtvkYPJRnz2ZdDSQ6MMdpY3xhFnFP3GdXsQk0WlOKjamtL4fs2hsR5XzNgnWBolGSM8peU1iDCutwY0wY13WiM0cTvXZkBf7MZbH/18DfzGphdWBZsz8wuLAt6qN2xsM71PiNxDkIP6xo91PaZda4eSjKmNFcSpX2mNNcITsbU1oFAbf/atzGKWNeYAddN6tkdYwyPfX1jeGx/TYSWzxjhmlSjieXL0NimG2NmY73A4bXV822UakfV1tZwWBaM0UWp3lctLmg47OPO4D2hByG7lByo7TP7o/5mRhclWVCaqxpeWz3si7J4TxhFlN6wIMc1xmhinaCH2jcj/Ge1WJDhsL9kaCwLerkYpdozw+L9axRx7NkoYhtpjC5q/pLtpDHGmLmwfdCDOkP4bGIGLAvG8Nj+eg0Mj1LcRmmuEd6/RhPLLYfa+UFN5yrhtTVGF+9fQ6LmKxiWFZmZiz0Jo8GWLVvi3nvvjYiIn/3Zn42XvOQlizwjY4wxi8m2bdtixYoV8aIXvWiP323atCl+6Zd+aRFmZYwxzw37uMYYY4xZSixn38ZnVGOMMcaYpYX9O2NMa5bzmXrAa+A1oPH6GmOMMdr4rO41MPrYJzdmF94P9dhOGmOMMcYY89zwucQYY4yZH9tJQ6MkY9RcldbAsCjJgtJcjZnEsmuMIXGunsd63BgzYJ1rjGmBdY0xxuyJqm6sPk+qroMxEY6vGOswEq+tMcYsHewz6WD7a4xpgXWNHorfzHXqhoLeD5Yxr4Exynj/6qDo3xljjNkd63KjiOXWKGK5NbPx2dcYY0xLfG+Dw2trjDG7sA7Tw99sBsV1cFzBDFgWjDGzqdQLijaSwOvAY3tmjDHGGGPMT4Z95xm8DnoQ38y5egavA4fXVhfbHQ6vrR7+ZsYYY4wx8+NznzHGGLM0sE03xpj2OPZsSCxfRg37o8aYpYBr++qh1sBrayIsB8YYY4z5yXGszZhdeD94DYwxZi6sG40xxrTAeaPdsf3VQVXGKvEa7ILau8tdJ1jGzCSu39FDTYepzdfoYRkzEZYDY0w7rG+M0cJnVDOgKgsK8VHVta3G62BInPc1xpj5We7ndOtyY4wxxpifjOXuNyrib2aMMWYpYHtmBiwLeviOhR7eZ/VYbvXwN9ND9ZtZ5+qgJGNKc22B0j5TmSstYyrrQOD9a0w7lrOuMbuznGXBdscYMxv7+sbw2P4asye2D4bE8mUobNONMbOxXuDw2u5CzbdRqh1VW1vDYVkwRg/7CkYRy61RRUl2leY6yXL2R/3NjDpKsqA0VzW8tsbsjveEUUThDYsW4xpjNLFO0GM5fzPVWJDhsL9kaCwL9ajdQ7Tt0cX71xhjWGwjjdHH/pIxxhhjWkKdIXw2MQOWBWOMMUsJpbiN0lyNMWZA9fxgncvhtTVGF+9fQ6DqKxiGvRZ7AqYNJ5xwwugxXvKSl8TBBx8cBx988E6DVDGuMcYYTfbdd985HcqIiPe///2NZ2OMqWK5+Xf2cY1ZWnj/GkUst6aSlr5Nb7LrM+rSojf5MsaYHrBuNMsN+3fGLE16tmfOF3gNIrwGNK3W199MD7UYpmXMGLNc8Vl9aa2B7eTyRP3MozRX0z/q+6FHlpKdNN4PEV4DY4wxxvD4XNIer68hsXwZU4vtpKFRkjFqrkprYFiUZEFprsZMYtk1ZnFYLvvMuXoe63Fjnp3lsiesc40xLVhKuma52AeztLDc9omqbqw+T6quQyu8f/vG8ZW29Hh31DqMw2trjHku2P72jXtzzU2P87X9Nca0YKnomh71OMVifLOx6+s6dUNB7wfLmNfAGGW8f3VYKj65McYsZ6zLjSKKMSZjrG/NbHz2bUuP9YLGGNMS39vg8NoaY8wurMP08DebQXEdHFcwA5YFY5YWvdUnK9pIAq8Dj+3Z3HgN9PA3M8a0wvrGmOWLfecZvA56EN9sKeTqe5Rbx0E4vLa62O7MTW/xbNMGfzNjjFmaWJfr4W/WJ63PfWpyoDZfJby2evibeQ1M3ziWuzhYLxizvHHs2ZBYvubH68ChVr9ijDHVLIXavt6g1sBru7RQukdsX3QGpXVQmmuE3nyNMSzWCXU41mbMLrwfvAYLQayD13YGvzFgese6cWngb6a3Bp6v12C54bzR7tj+6qAqY5V4DXZB7d3lrhMsY0sL9+ZafqjpMLX5Gj3UZUxprj2jLgfGGB2sb8xSYbnk1H1GNQOqsqAQH1Vd22p838aQOO9rfhKsEwxNrzLW8pze4xpYlxtjFpsedaMxZunRWzzMtMHfbH68Dnr4m3kNzC6WmyzYnpkBy4IeS+GOxXKTMe+zepyD0MO6Rg/VfWadq4OSjCnNtQVK+0xlrrSMqawDwVLav8vlmxldlrOuMbuznOsml5LdMcbUYF/fGB7bX/NsqOlF16Sa3rF8GQrbdGPMbKwXOLy2u1DzbZRqR9XW1nBYFozRY6nU+1rXLC/s4z473hN90lp2ez0/kCxnf9TfzKijJAtKc1XDa7s08Derw3vCKKLwhkWLcY0xmlgn6LEUvpnfZDJV2F8yNJaFuek5F6NUe2ZYvH/NUsFya3rFNtIYfZaCv6Q2X2OMMW2wfegT6gzhs4kZsCwYs7jY/noNTC1KcRuluc6H2nyNibDcjkX1/LAUdG6veG2N0cX71xCo+gqGYe/FnoCpY9OmTfP+buvWreXjZuaocY0xxmhD2R1jDI/3r31cY5SxDjOKWG4NDeXbKMmu0lzNDP5mxhizJ9aNxuzC+8EYXdT2r/MFXoMIrwENsb5qusa0r23sdVxjjFHGulFvDWwnzYDSmcfyZWiU9oMa3r96+Jt5DYwxxhizOPhcwmIfz5BYvozhsZ00NEoy1vq+YI9rYFiUZEFprsZMYtk1hsdnda8BifW4MbtjfeM1MMa0QU3XqM3XmAjLrSJq38w9ITm8Bno4vsKhdnfU+5fDa2uMmY31gh7uzeX5GmPMXCjpGqW5kiidqV2nbmj8pjqH18AYXbx/9bCfa4wx+liXG0WUYkzGDFi+zIDPvhy2D8YYsye+t8HhtTXGmD2xDtPD32wGpXVwXMEMWBaM0UWpdlTJRpJ4HThszyxfivibGWNaYX1jjJnEvvMMXgc9WsYres3Vq9l0tfkq4bXVw3ZHK55tWPzNjDFGH/ujevib6dGyB96YMUnU5quE11YPfzOvgdHFssvhtTXGzMaxZ0Ni+ZrB9pfDdSbGGLMnarV9StjumAGlfJTlawaldVCaa4TefI0xLNYJLI61GbML7wevwYDS+UwNn1ONItaNelgn6K2B5+s1MLuwvzSD7a8eajJG4DVoX1ew3HSCZUyPln6j63f6RU2Hqc3X6KEkY9aNHEpyYIzRxvrGKOIYsdZcDYuaLCjFR9XWlsI615BYvsyAv5mhUZIx57lmUJuvMUYT6xpjTAv83pUZ8DebwfZXD38zr4HZhWXB9szswrKgh1rs2TrX+4zE8qWHdY0eamtrnauHkowpzZVEaZ8pzTXCeQgStf2rNl9jIqxrzC7UYlcESnM1xrTBvr4xPLa/JkJPDmwfjCKWL0OjpsuNMTzWCxxeWz3fRin+rra2hsOyYIwuSvW+9mvMgGVhBq+DHkq6UU2+7I/6mxldlGRBaa5qeG31ULM7anhPGEWU3rAgxzXGaGKdoIfaN1OKCxo97C8ZGsuCXi5GqfbMsHj/GkWsa4willtjdFHzl6xvjDHGzIXtgx5q8Uajh2XBGB7vM6+B4VGK2yjNNcL712hiueVQW1s1nauE19YYXbx/DYmar2BY9l7sCZg6jj/++Nh///0jM/f43bZt27o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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# display average coefficient value vs feature bar chart\n", - "feature_ordered = coefs.T.reindex(\n", - " coefs.T.mean().sort_values(ascending=False).index, axis=1\n", - ")\n", - "sns.set(rc={\"figure.figsize\": (500, 8)})\n", - "plt.xlabel(\"Deep Learning Feature Number\")\n", - "plt.ylabel(\"Average Coefficient Value\")\n", - "plt.title(\"Coefficient vs Feature\")\n", - "plt.xticks(rotation=90)\n", - "ax = sns.barplot(data=feature_ordered)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.13 ('2.ML_phenotypic_classification')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "4cc408a06ad49ae0c78cd765de22f61d31a0f8b0861ec15e52107dd82d811e52" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/3.ML_model/results/0.data_split_indexes.tsv b/3.ML_model/results/0.data_split_indexes.tsv deleted file mode 100644 index f0e917ae..00000000 --- a/3.ML_model/results/0.data_split_indexes.tsv +++ /dev/null @@ -1,4124 +0,0 @@ - label index -0 holdout 107 -1 holdout 108 -2 holdout 109 -3 holdout 110 -4 holdout 111 -5 holdout 112 -6 holdout 113 -7 holdout 114 -8 holdout 115 -9 holdout 116 -10 holdout 117 -11 holdout 118 -12 holdout 119 -13 holdout 120 -14 holdout 121 -15 holdout 122 -16 holdout 123 -17 holdout 124 -18 holdout 125 -19 holdout 126 -20 holdout 127 -21 holdout 128 -22 holdout 129 -23 holdout 130 -24 holdout 131 -25 holdout 132 -26 holdout 133 -27 holdout 134 -28 holdout 135 -29 holdout 136 -30 holdout 137 -31 holdout 138 -32 holdout 139 -33 holdout 140 -34 holdout 141 -35 holdout 142 -36 holdout 143 -37 holdout 144 -38 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3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 -y_train Interphase Binuclear Grape Prometaphase Apoptosis Hole Polylobed Elongated Hole Polylobed Grape Artefact SmallIrregular Apoptosis Polylobed Binuclear Polylobed Interphase Polylobed MetaphaseAlignment Interphase Hole SmallIrregular Polylobed Binuclear SmallIrregular Polylobed Prometaphase Polylobed Folded Polylobed Polylobed Polylobed Polylobed Prometaphase Prometaphase Polylobed Prometaphase Polylobed Prometaphase Grape Large Metaphase Polylobed SmallIrregular Prometaphase Prometaphase MetaphaseAlignment Grape Binuclear MetaphaseAlignment Polylobed Polylobed Polylobed Hole Interphase Interphase Binuclear Polylobed Binuclear Polylobed Prometaphase Polylobed SmallIrregular Polylobed Polylobed Artefact Polylobed SmallIrregular Polylobed Polylobed Hole Binuclear Polylobed Grape Interphase Grape Interphase Apoptosis Polylobed Prometaphase Grape Polylobed Interphase Grape Polylobed Polylobed Apoptosis Polylobed Polylobed Grape Polylobed Grape Polylobed Prometaphase Artefact Polylobed Binuclear Polylobed SmallIrregular Artefact Metaphase Polylobed Large Polylobed Polylobed Polylobed Polylobed SmallIrregular Prometaphase Apoptosis Prometaphase Polylobed Polylobed MetaphaseAlignment Grape Interphase Binuclear Grape Polylobed UndefinedCondensed Polylobed Elongated Polylobed Polylobed Polylobed Hole Polylobed Grape Interphase Polylobed Polylobed Interphase Artefact Grape Polylobed Prometaphase Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Binuclear Apoptosis Hole Polylobed Binuclear Polylobed SmallIrregular Interphase Polylobed Apoptosis Prometaphase Binuclear Polylobed Grape Artefact SmallIrregular Interphase Binuclear Polylobed Binuclear Polylobed Artefact Polylobed Polylobed Artefact Binuclear Binuclear Prometaphase Polylobed Folded Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Polylobed MetaphaseAlignment Grape UndefinedCondensed Binuclear Binuclear Interphase SmallIrregular Polylobed Metaphase Polylobed Polylobed Polylobed Metaphase Grape Apoptosis SmallIrregular Binuclear Prometaphase Polylobed Grape UndefinedCondensed Artefact Polylobed Polylobed Artefact Polylobed Binuclear Polylobed Polylobed Grape SmallIrregular Prometaphase Binuclear Binuclear Binuclear Binuclear Grape Polylobed Polylobed Binuclear Interphase Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed SmallIrregular Polylobed Polylobed Grape Grape Polylobed Polylobed Polylobed Interphase Interphase SmallIrregular SmallIrregular Binuclear Binuclear Grape SmallIrregular Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Artefact Large Artefact Grape Polylobed Polylobed Grape SmallIrregular Polylobed Grape Polylobed Polylobed Prometaphase Polylobed Polylobed Binuclear Polylobed Polylobed Folded Polylobed Interphase Artefact Apoptosis Polylobed Binuclear Polylobed Prometaphase Binuclear Binuclear Polylobed SmallIrregular Grape Prometaphase Interphase SmallIrregular Apoptosis Polylobed Polylobed MetaphaseAlignment Interphase Grape Binuclear Prometaphase Prometaphase Folded Artefact Grape Grape Polylobed Artefact Binuclear Hole Prometaphase Grape Polylobed Interphase Interphase Polylobed Artefact SmallIrregular Polylobed Polylobed Binuclear Binuclear Binuclear Binuclear Large Artefact SmallIrregular Polylobed Binuclear Folded Apoptosis Binuclear Polylobed MetaphaseAlignment Polylobed Polylobed Apoptosis Artefact Polylobed MetaphaseAlignment Polylobed Interphase Binuclear Prometaphase Polylobed Grape Artefact Grape Prometaphase Artefact Grape Polylobed Interphase Polylobed Binuclear Interphase Polylobed Binuclear MetaphaseAlignment Binuclear Polylobed Binuclear Elongated Polylobed Polylobed Polylobed Binuclear Interphase Polylobed Grape Binuclear Artefact MetaphaseAlignment Polylobed Binuclear Binuclear Metaphase Binuclear Polylobed Hole Polylobed Polylobed Interphase Polylobed Apoptosis Binuclear Prometaphase Polylobed Large Hole Polylobed Polylobed Binuclear Polylobed Prometaphase Polylobed UndefinedCondensed Large Grape Polylobed Polylobed MetaphaseAlignment Metaphase SmallIrregular Polylobed Interphase Interphase Polylobed Artefact Polylobed Interphase Polylobed UndefinedCondensed Interphase MetaphaseAlignment Binuclear Binuclear Binuclear Binuclear Metaphase MetaphaseAlignment Grape Binuclear Polylobed Apoptosis Artefact Polylobed SmallIrregular Polylobed Apoptosis Interphase Polylobed MetaphaseAlignment Polylobed Metaphase Interphase Prometaphase Hole Polylobed Polylobed Binuclear Prometaphase Grape Polylobed Polylobed SmallIrregular Polylobed Hole Interphase Prometaphase Polylobed Artefact Binuclear Prometaphase Polylobed Binuclear Binuclear Artefact Polylobed Prometaphase Polylobed Polylobed Binuclear Polylobed Interphase Polylobed Polylobed Polylobed SmallIrregular Polylobed Grape Binuclear Apoptosis MetaphaseAlignment Hole Apoptosis Polylobed Prometaphase Polylobed Artefact MetaphaseAlignment Grape Binuclear Interphase Polylobed Polylobed Interphase Artefact Binuclear Grape Binuclear Polylobed Grape Interphase Polylobed Binuclear Polylobed Polylobed UndefinedCondensed SmallIrregular Polylobed Polylobed Polylobed Binuclear Polylobed Apoptosis Binuclear Prometaphase Apoptosis Artefact Grape Prometaphase Polylobed Polylobed Polylobed Polylobed Apoptosis Prometaphase Prometaphase Polylobed Binuclear SmallIrregular Interphase Polylobed Binuclear Polylobed Polylobed Polylobed Grape SmallIrregular UndefinedCondensed Polylobed Hole Grape Apoptosis Polylobed Binuclear Polylobed Grape Hole Prometaphase Apoptosis Apoptosis Polylobed Polylobed Folded Apoptosis Metaphase Polylobed Apoptosis Hole Interphase Polylobed Interphase Grape Polylobed Hole Prometaphase Grape Hole Polylobed Artefact Grape Grape Artefact Binuclear MetaphaseAlignment Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Grape Polylobed Binuclear Polylobed Apoptosis Metaphase Polylobed Polylobed Artefact Prometaphase Hole Binuclear Binuclear Polylobed Large Polylobed Folded Polylobed MetaphaseAlignment Prometaphase Grape Polylobed Interphase Grape Prometaphase Interphase Polylobed SmallIrregular Polylobed Prometaphase Interphase Folded Grape Interphase Apoptosis Interphase Polylobed Interphase Polylobed Polylobed Polylobed Interphase MetaphaseAlignment Polylobed Binuclear Apoptosis Prometaphase Grape Artefact MetaphaseAlignment Interphase Polylobed Polylobed UndefinedCondensed Apoptosis Polylobed Prometaphase Polylobed Polylobed Polylobed Binuclear Grape Apoptosis Grape Interphase Grape Interphase Polylobed Polylobed Binuclear Binuclear Prometaphase Grape Polylobed Prometaphase Polylobed SmallIrregular Grape Polylobed Metaphase Binuclear Grape SmallIrregular Polylobed Hole Artefact Grape Grape Apoptosis Polylobed Polylobed MetaphaseAlignment Grape Metaphase Polylobed Prometaphase SmallIrregular Polylobed Grape Polylobed Interphase Polylobed Prometaphase Polylobed Prometaphase Polylobed Interphase Polylobed Polylobed Hole Polylobed Prometaphase Artefact Binuclear Grape Apoptosis Polylobed Binuclear Artefact Binuclear Binuclear Artefact Grape Polylobed Polylobed Polylobed Interphase Binuclear Polylobed Polylobed MetaphaseAlignment Prometaphase Polylobed Prometaphase Binuclear Polylobed Grape Polylobed Interphase Interphase Polylobed Polylobed Polylobed Polylobed Artefact Apoptosis Polylobed Polylobed Artefact Polylobed Binuclear MetaphaseAlignment MetaphaseAlignment Binuclear Artefact Hole Folded Artefact Polylobed Apoptosis Grape Grape Grape SmallIrregular Elongated Grape Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Grape Prometaphase Polylobed Polylobed Metaphase Polylobed Prometaphase Polylobed Grape Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Apoptosis Artefact Binuclear Polylobed Grape Interphase Binuclear Polylobed Interphase Polylobed Artefact Prometaphase Polylobed Interphase Apoptosis Polylobed MetaphaseAlignment Polylobed Artefact Binuclear Polylobed Polylobed Artefact Artefact Interphase Polylobed Binuclear Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Prometaphase MetaphaseAlignment Binuclear MetaphaseAlignment Binuclear MetaphaseAlignment Grape Binuclear SmallIrregular MetaphaseAlignment Polylobed SmallIrregular Polylobed Apoptosis Binuclear Interphase Polylobed MetaphaseAlignment Polylobed Grape Binuclear Grape Polylobed Artefact Binuclear Grape Prometaphase Polylobed Polylobed Polylobed Polylobed SmallIrregular Polylobed Grape Polylobed SmallIrregular Interphase Binuclear Polylobed Polylobed Polylobed Interphase Elongated MetaphaseAlignment Artefact MetaphaseAlignment SmallIrregular Metaphase Binuclear Binuclear Polylobed Apoptosis Binuclear Polylobed Interphase Polylobed Apoptosis Artefact Prometaphase Polylobed Grape Grape Grape Polylobed Interphase Grape Binuclear Artefact Polylobed Binuclear MetaphaseAlignment Artefact Polylobed Polylobed Prometaphase Apoptosis Polylobed Polylobed Polylobed Polylobed Polylobed Hole Polylobed Prometaphase Polylobed Polylobed Artefact Polylobed UndefinedCondensed Prometaphase Polylobed SmallIrregular Apoptosis Grape Binuclear Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Interphase Polylobed Polylobed Polylobed Polylobed Polylobed SmallIrregular SmallIrregular Polylobed Polylobed Polylobed Grape SmallIrregular Polylobed Polylobed Binuclear Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Polylobed Apoptosis Grape Hole Prometaphase Hole Artefact Interphase Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed SmallIrregular Folded Artefact Polylobed Interphase Grape Polylobed Binuclear Polylobed Interphase Polylobed Large Hole Interphase Polylobed Polylobed Polylobed Interphase Binuclear Binuclear Grape Apoptosis Prometaphase Grape Polylobed Binuclear Polylobed Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Prometaphase Binuclear Binuclear Hole Interphase Prometaphase SmallIrregular SmallIrregular Binuclear Binuclear Polylobed Prometaphase Polylobed Prometaphase Polylobed Prometaphase Apoptosis Metaphase Binuclear Polylobed Polylobed Grape Binuclear Polylobed Interphase Large Polylobed Prometaphase Polylobed Grape Polylobed Polylobed Artefact Polylobed Metaphase Binuclear Grape Polylobed Polylobed Binuclear Apoptosis Binuclear Interphase MetaphaseAlignment Polylobed Apoptosis Polylobed Polylobed Interphase Binuclear Polylobed Metaphase Binuclear Polylobed Polylobed Binuclear Grape Polylobed Elongated Grape Polylobed Grape Polylobed Hole Polylobed Artefact Binuclear Apoptosis Interphase Polylobed Polylobed Binuclear Grape Binuclear Grape SmallIrregular Binuclear Binuclear Polylobed Polylobed Interphase Binuclear Polylobed Interphase Grape Grape Hole Polylobed Polylobed Polylobed Grape Polylobed Polylobed Apoptosis Interphase Polylobed Apoptosis Polylobed Artefact Polylobed Grape Polylobed Interphase Prometaphase Polylobed Polylobed Artefact Interphase SmallIrregular SmallIrregular Polylobed Binuclear Binuclear Artefact Apoptosis Grape Interphase Hole Large Grape Polylobed Polylobed Interphase Artefact Polylobed Polylobed Binuclear Interphase Polylobed Polylobed Polylobed SmallIrregular Polylobed Polylobed Binuclear Hole Interphase Artefact Interphase Binuclear Polylobed Binuclear Artefact Grape Polylobed Binuclear Polylobed Hole Polylobed Prometaphase Interphase Large Polylobed Artefact Prometaphase Interphase Polylobed MetaphaseAlignment Binuclear Grape Elongated Prometaphase Artefact Polylobed Artefact Binuclear Polylobed Binuclear Binuclear Artefact Polylobed Prometaphase Artefact Binuclear Prometaphase Polylobed Polylobed Prometaphase Interphase Artefact Polylobed Prometaphase Binuclear Binuclear Prometaphase Artefact MetaphaseAlignment Apoptosis Binuclear Interphase Grape Prometaphase Metaphase Interphase Elongated Metaphase Polylobed Artefact Binuclear Artefact Grape Polylobed Prometaphase SmallIrregular Artefact Polylobed MetaphaseAlignment Prometaphase Polylobed Polylobed Polylobed SmallIrregular Grape Grape MetaphaseAlignment Polylobed Prometaphase Prometaphase Polylobed Prometaphase Artefact Polylobed Polylobed Interphase Apoptosis Polylobed Artefact Apoptosis Artefact Binuclear Polylobed Interphase SmallIrregular Polylobed SmallIrregular MetaphaseAlignment Binuclear Interphase Grape Prometaphase Polylobed Artefact Polylobed Binuclear Interphase MetaphaseAlignment Hole Large Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Artefact Apoptosis Polylobed Grape Polylobed Grape Prometaphase Binuclear Prometaphase Polylobed Polylobed Polylobed Hole Binuclear Polylobed Polylobed Binuclear Binuclear Polylobed Binuclear Polylobed Polylobed Interphase Polylobed Grape Polylobed Polylobed Interphase SmallIrregular MetaphaseAlignment Polylobed Interphase Polylobed Artefact Apoptosis Prometaphase Grape Prometaphase Binuclear Interphase Prometaphase Binuclear Artefact Prometaphase Grape Prometaphase Interphase Polylobed Polylobed Polylobed Artefact Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Prometaphase SmallIrregular Binuclear Binuclear SmallIrregular Large Polylobed Polylobed Polylobed Grape Binuclear MetaphaseAlignment Polylobed Apoptosis Polylobed MetaphaseAlignment Artefact Grape Polylobed Apoptosis Prometaphase Interphase Interphase Binuclear Polylobed Polylobed Polylobed Polylobed MetaphaseAlignment Folded Polylobed Polylobed Large Polylobed Polylobed Interphase Polylobed Artefact Grape Polylobed Binuclear Polylobed MetaphaseAlignment Polylobed Binuclear Artefact MetaphaseAlignment Polylobed Interphase Apoptosis Artefact Polylobed Polylobed Binuclear Polylobed Interphase Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Prometaphase Interphase Polylobed Polylobed Prometaphase Polylobed Binuclear Prometaphase MetaphaseAlignment Apoptosis Polylobed Prometaphase Elongated Interphase Polylobed Polylobed Grape Prometaphase Polylobed Grape Polylobed Metaphase Polylobed Polylobed Grape Grape Hole Polylobed Polylobed Grape Interphase Binuclear Grape Grape Polylobed Polylobed Prometaphase Prometaphase UndefinedCondensed Interphase Polylobed Polylobed Interphase Artefact Binuclear Polylobed Binuclear Apoptosis Hole Interphase Artefact Binuclear Polylobed Artefact Grape Polylobed SmallIrregular MetaphaseAlignment Polylobed Polylobed Grape Polylobed Polylobed Binuclear Interphase Binuclear Binuclear Polylobed Polylobed Polylobed Binuclear Binuclear Hole Polylobed Polylobed SmallIrregular Artefact Polylobed Polylobed Hole Polylobed Polylobed Polylobed Polylobed Binuclear Artefact Artefact Polylobed Polylobed Binuclear Artefact Hole Polylobed Hole Hole Apoptosis Large Artefact Interphase SmallIrregular Polylobed Polylobed Folded Polylobed Binuclear Binuclear Polylobed Polylobed SmallIrregular Polylobed Grape Interphase Interphase Apoptosis Interphase Polylobed Polylobed Prometaphase Binuclear Polylobed Interphase Binuclear Polylobed Prometaphase Polylobed Polylobed Apoptosis Hole Interphase Artefact Grape Binuclear Apoptosis Grape Binuclear Artefact Grape Polylobed Elongated Artefact Binuclear Grape Polylobed Metaphase Polylobed Polylobed Binuclear Prometaphase Polylobed Binuclear Grape Polylobed Polylobed Prometaphase Grape Polylobed Polylobed Hole Polylobed Binuclear Interphase Grape Polylobed Polylobed SmallIrregular Binuclear Polylobed Apoptosis Polylobed Polylobed Elongated Polylobed Polylobed Binuclear Binuclear Polylobed Prometaphase Polylobed Apoptosis Binuclear Binuclear Polylobed Interphase MetaphaseAlignment Polylobed Polylobed SmallIrregular Binuclear Hole Binuclear Polylobed Polylobed UndefinedCondensed Grape Grape Polylobed Polylobed Interphase Polylobed Grape Grape Prometaphase Interphase MetaphaseAlignment Binuclear Binuclear MetaphaseAlignment Binuclear Metaphase Polylobed Polylobed Apoptosis Interphase Polylobed Polylobed Polylobed Interphase Grape Prometaphase Binuclear Interphase Polylobed Prometaphase Binuclear Polylobed Binuclear Grape Prometaphase Prometaphase Polylobed Hole Artefact Polylobed Prometaphase Grape Hole Grape Grape Artefact Grape MetaphaseAlignment Polylobed Polylobed Binuclear Polylobed Polylobed Artefact Binuclear Binuclear Polylobed MetaphaseAlignment Binuclear Binuclear Polylobed Apoptosis Grape Binuclear Polylobed Binuclear Polylobed Polylobed Interphase Elongated Polylobed MetaphaseAlignment Interphase Metaphase Polylobed Binuclear MetaphaseAlignment Polylobed Polylobed Prometaphase Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Binuclear Apoptosis Binuclear Grape Polylobed Artefact Polylobed Polylobed Binuclear MetaphaseAlignment Prometaphase Apoptosis UndefinedCondensed Prometaphase Metaphase Binuclear Apoptosis SmallIrregular Grape Folded Grape Binuclear Prometaphase MetaphaseAlignment Binuclear Prometaphase Hole Binuclear Folded Prometaphase Folded Prometaphase Prometaphase Polylobed Interphase Polylobed MetaphaseAlignment Interphase Prometaphase Binuclear Prometaphase Artefact Interphase Polylobed Binuclear Polylobed Polylobed Binuclear Grape Binuclear Grape Prometaphase Polylobed Interphase Apoptosis Polylobed MetaphaseAlignment Prometaphase Polylobed Polylobed Grape Prometaphase Prometaphase Apoptosis Binuclear SmallIrregular Polylobed Grape Polylobed Metaphase Folded Large Grape Prometaphase Large Apoptosis Artefact Metaphase Interphase Prometaphase Binuclear Prometaphase Prometaphase Artefact Polylobed Interphase Polylobed Binuclear Binuclear Polylobed Binuclear Grape Prometaphase Grape Polylobed Binuclear SmallIrregular Grape Polylobed Prometaphase Polylobed Polylobed Interphase Binuclear Artefact Interphase Grape Binuclear Polylobed Binuclear Artefact Artefact Binuclear Binuclear Polylobed Grape Prometaphase Polylobed Hole Polylobed Polylobed Interphase MetaphaseAlignment MetaphaseAlignment Interphase Binuclear Polylobed Prometaphase Artefact Grape Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Hole Apoptosis Polylobed Binuclear Hole Apoptosis Binuclear Prometaphase Interphase Interphase Interphase Polylobed Polylobed Polylobed SmallIrregular Metaphase Grape Binuclear Grape Polylobed Prometaphase Binuclear MetaphaseAlignment SmallIrregular Polylobed Polylobed Interphase Grape Artefact Artefact Binuclear Binuclear Prometaphase Polylobed Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Grape Prometaphase Polylobed Hole Polylobed Polylobed Binuclear Polylobed Grape SmallIrregular Apoptosis Polylobed Artefact Polylobed Prometaphase Grape Elongated Binuclear Prometaphase UndefinedCondensed Polylobed Grape Binuclear Interphase Prometaphase Grape Metaphase Binuclear MetaphaseAlignment Polylobed Hole SmallIrregular Polylobed Prometaphase Binuclear Apoptosis SmallIrregular Polylobed Polylobed Interphase Prometaphase Interphase Grape Grape Polylobed Polylobed Grape Polylobed Metaphase Polylobed Binuclear Interphase Artefact Apoptosis Grape Grape Polylobed Artefact Artefact MetaphaseAlignment Apoptosis Interphase Artefact Polylobed Polylobed Hole Metaphase Polylobed Polylobed Binuclear Polylobed Prometaphase Hole Polylobed Artefact Artefact Polylobed Polylobed Interphase Polylobed Binuclear SmallIrregular Polylobed Polylobed Binuclear Large Prometaphase Apoptosis Apoptosis Polylobed Polylobed Folded Interphase Apoptosis UndefinedCondensed Interphase Hole Polylobed Polylobed Binuclear Grape SmallIrregular Hole MetaphaseAlignment Grape Polylobed Prometaphase Polylobed Polylobed Metaphase Prometaphase Hole Polylobed Binuclear MetaphaseAlignment Polylobed MetaphaseAlignment Prometaphase Polylobed Prometaphase Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Interphase Binuclear Binuclear Interphase SmallIrregular Binuclear Polylobed Polylobed Polylobed Binuclear Artefact Hole Polylobed Binuclear Polylobed SmallIrregular Binuclear Binuclear Grape Metaphase Polylobed Interphase Binuclear Interphase Polylobed Apoptosis MetaphaseAlignment Prometaphase Polylobed Interphase Grape Polylobed Binuclear SmallIrregular Prometaphase Polylobed Binuclear Interphase Grape Polylobed Hole Polylobed Polylobed Apoptosis Polylobed Interphase Polylobed Apoptosis Apoptosis Polylobed Polylobed SmallIrregular Binuclear Polylobed Polylobed Prometaphase Interphase Polylobed Polylobed Grape Metaphase Prometaphase Prometaphase Artefact MetaphaseAlignment SmallIrregular Artefact Polylobed Apoptosis Grape Binuclear Grape MetaphaseAlignment Polylobed Polylobed Hole Artefact Prometaphase Grape Hole Polylobed Binuclear MetaphaseAlignment SmallIrregular Grape Polylobed Binuclear Binuclear Polylobed Interphase Polylobed SmallIrregular Artefact Binuclear Artefact Artefact Polylobed Interphase Binuclear Polylobed Grape Polylobed Artefact Polylobed Apoptosis Polylobed Polylobed Binuclear Binuclear Polylobed MetaphaseAlignment SmallIrregular Prometaphase Polylobed Grape Polylobed Grape Binuclear Prometaphase Elongated Polylobed SmallIrregular Polylobed Polylobed SmallIrregular Polylobed Polylobed Elongated Polylobed Prometaphase Binuclear Polylobed Polylobed Artefact Polylobed Artefact Grape Polylobed Binuclear Polylobed Interphase Grape Polylobed Polylobed Grape Large Polylobed Binuclear Binuclear Apoptosis Grape Binuclear Binuclear Polylobed Elongated Artefact Polylobed Apoptosis Polylobed Apoptosis Polylobed Folded Artefact Polylobed Grape Binuclear MetaphaseAlignment Polylobed Interphase Folded Prometaphase Polylobed Prometaphase Artefact Artefact Artefact Binuclear Polylobed Polylobed Polylobed Artefact Prometaphase Binuclear Apoptosis MetaphaseAlignment Polylobed Polylobed Polylobed Grape Artefact Polylobed Polylobed Polylobed Artefact Polylobed Polylobed MetaphaseAlignment Artefact Polylobed Interphase Binuclear Polylobed Prometaphase Polylobed Apoptosis Artefact Interphase Polylobed Polylobed Polylobed Interphase Polylobed Binuclear Interphase Folded Prometaphase Hole Polylobed Interphase Artefact Binuclear Polylobed Artefact Folded Interphase Apoptosis Hole Binuclear Binuclear Interphase Prometaphase Grape Apoptosis Polylobed Grape Artefact Prometaphase Binuclear Binuclear Polylobed Large Polylobed SmallIrregular Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Hole Polylobed Binuclear SmallIrregular Grape Polylobed Binuclear Binuclear Polylobed Artefact Binuclear Prometaphase MetaphaseAlignment Binuclear Grape Prometaphase Grape Polylobed Metaphase Prometaphase Polylobed Polylobed Polylobed Binuclear Interphase SmallIrregular Prometaphase Grape Polylobed Binuclear Grape Polylobed MetaphaseAlignment Metaphase Binuclear Interphase Polylobed Grape Polylobed Polylobed Polylobed Grape Metaphase Large Grape Binuclear Polylobed Binuclear Polylobed Binuclear UndefinedCondensed Interphase Artefact Binuclear Polylobed Polylobed Prometaphase Polylobed Polylobed Grape Prometaphase Apoptosis Binuclear Polylobed Binuclear Grape SmallIrregular SmallIrregular Folded Apoptosis Prometaphase Binuclear Binuclear Interphase MetaphaseAlignment Polylobed Polylobed Polylobed Artefact Interphase SmallIrregular Prometaphase SmallIrregular Polylobed SmallIrregular Large Interphase Hole Binuclear Artefact Artefact Interphase Binuclear Polylobed Folded Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Apoptosis MetaphaseAlignment Artefact SmallIrregular Binuclear Apoptosis Artefact Interphase Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Grape Binuclear Binuclear Grape Polylobed Interphase Interphase Prometaphase Polylobed Polylobed Grape Polylobed SmallIrregular SmallIrregular Binuclear Artefact Prometaphase Binuclear Polylobed Polylobed Binuclear Polylobed Binuclear SmallIrregular UndefinedCondensed Binuclear Polylobed Polylobed Hole Polylobed SmallIrregular Polylobed Polylobed Artefact Artefact Interphase Prometaphase Polylobed Polylobed Hole Interphase Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Large Binuclear Binuclear Polylobed Polylobed Polylobed Large SmallIrregular Artefact Polylobed Artefact Folded Prometaphase Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Prometaphase Grape Interphase Binuclear Metaphase Binuclear Polylobed Binuclear Prometaphase Binuclear Polylobed Polylobed Polylobed SmallIrregular Polylobed Interphase Apoptosis Polylobed Prometaphase Interphase Polylobed Polylobed Binuclear Artefact Hole Interphase SmallIrregular Prometaphase Large Binuclear Polylobed Grape Polylobed Grape Interphase Polylobed Apoptosis Polylobed Polylobed Binuclear MetaphaseAlignment MetaphaseAlignment Large Apoptosis MetaphaseAlignment Grape Polylobed Polylobed Apoptosis Polylobed Hole Grape Polylobed Polylobed Polylobed Polylobed Binuclear Binuclear SmallIrregular Polylobed Grape Prometaphase Artefact Prometaphase Interphase Binuclear Polylobed Polylobed Hole Prometaphase Prometaphase Binuclear Prometaphase Artefact Prometaphase Grape Grape Polylobed MetaphaseAlignment Artefact Polylobed Binuclear Binuclear Polylobed Polylobed Artefact Prometaphase Polylobed Polylobed MetaphaseAlignment Grape Polylobed MetaphaseAlignment Prometaphase Grape UndefinedCondensed SmallIrregular Grape Binuclear Prometaphase Hole Artefact Prometaphase Apoptosis Polylobed Polylobed Polylobed Interphase SmallIrregular Polylobed Polylobed Polylobed Artefact Binuclear Polylobed Polylobed Interphase Grape Interphase Binuclear Apoptosis Prometaphase Elongated Polylobed Apoptosis Polylobed Polylobed UndefinedCondensed Apoptosis Polylobed Binuclear Prometaphase Prometaphase Polylobed SmallIrregular Polylobed Binuclear Interphase Large Binuclear SmallIrregular Polylobed MetaphaseAlignment Prometaphase Prometaphase Grape MetaphaseAlignment Polylobed Binuclear Polylobed Polylobed Artefact Polylobed Prometaphase Polylobed MetaphaseAlignment Metaphase UndefinedCondensed Binuclear MetaphaseAlignment Polylobed Artefact Grape Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Apoptosis SmallIrregular Polylobed Polylobed Prometaphase Binuclear Interphase Artefact Apoptosis Polylobed Binuclear Artefact Polylobed Polylobed Polylobed Binuclear Grape Large Artefact Polylobed Binuclear SmallIrregular Binuclear Polylobed Binuclear Polylobed Artefact Prometaphase Polylobed Large Polylobed MetaphaseAlignment Polylobed Hole Folded Polylobed Polylobed Binuclear Metaphase Grape Polylobed Interphase Prometaphase Binuclear Polylobed Polylobed Binuclear Polylobed SmallIrregular Metaphase Polylobed Polylobed Prometaphase Polylobed Grape Apoptosis Interphase Binuclear Prometaphase Polylobed Artefact Binuclear Polylobed MetaphaseAlignment Binuclear Polylobed Binuclear Polylobed Polylobed UndefinedCondensed Binuclear Polylobed UndefinedCondensed SmallIrregular Polylobed MetaphaseAlignment Polylobed Interphase Binuclear Metaphase Binuclear Prometaphase Artefact Artefact UndefinedCondensed Polylobed Prometaphase Binuclear Polylobed Interphase Polylobed Binuclear Polylobed SmallIrregular Binuclear Interphase Metaphase Artefact Binuclear Folded Grape Interphase Artefact Metaphase Binuclear Grape Grape Binuclear Binuclear Polylobed Polylobed Hole Polylobed Elongated Apoptosis Binuclear Prometaphase Prometaphase Polylobed Polylobed Binuclear Polylobed Artefact MetaphaseAlignment Polylobed Prometaphase Grape Artefact Polylobed Interphase MetaphaseAlignment Interphase Hole Prometaphase Hole Prometaphase Interphase Interphase MetaphaseAlignment Hole Binuclear SmallIrregular Interphase Interphase Polylobed Prometaphase Grape Prometaphase Polylobed Interphase Interphase Polylobed UndefinedCondensed Grape SmallIrregular Prometaphase Binuclear Binuclear Polylobed Prometaphase Binuclear Polylobed Artefact Polylobed Hole Prometaphase Grape Polylobed Interphase Polylobed Binuclear Polylobed Grape Polylobed MetaphaseAlignment Apoptosis Hole Binuclear Prometaphase Polylobed SmallIrregular Polylobed Polylobed Grape Polylobed Metaphase Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed SmallIrregular Grape Metaphase Binuclear Polylobed Polylobed Polylobed Grape Polylobed Polylobed SmallIrregular Polylobed Polylobed Prometaphase Artefact Binuclear Artefact Grape Polylobed Binuclear Polylobed Polylobed Interphase Prometaphase Apoptosis UndefinedCondensed Folded SmallIrregular Polylobed Polylobed Prometaphase Binuclear Large Polylobed Polylobed Binuclear Apoptosis SmallIrregular Grape Polylobed Polylobed Apoptosis Polylobed Large SmallIrregular Prometaphase Polylobed Polylobed MetaphaseAlignment Polylobed Prometaphase Polylobed Prometaphase Binuclear Polylobed Polylobed Polylobed Large Binuclear MetaphaseAlignment Apoptosis Polylobed Binuclear Interphase Interphase SmallIrregular Binuclear Binuclear Polylobed Binuclear Binuclear Polylobed Polylobed Grape Artefact SmallIrregular Polylobed Polylobed MetaphaseAlignment Binuclear Apoptosis Interphase Polylobed Grape Prometaphase Polylobed Folded Polylobed Polylobed Prometaphase Hole Grape MetaphaseAlignment Elongated Binuclear Polylobed Polylobed Polylobed Prometaphase Polylobed Interphase Artefact Prometaphase Polylobed Binuclear Polylobed Polylobed Elongated Polylobed Interphase Prometaphase Polylobed Binuclear Polylobed SmallIrregular Grape Polylobed Binuclear Binuclear Apoptosis Grape Interphase Grape Prometaphase Polylobed Polylobed Binuclear Polylobed Grape Binuclear Interphase Binuclear Interphase Polylobed Polylobed Artefact Hole Interphase MetaphaseAlignment Polylobed Polylobed Polylobed Interphase Interphase Polylobed Apoptosis Prometaphase Artefact Binuclear Polylobed Interphase Polylobed Grape Binuclear Binuclear Polylobed Binuclear Polylobed MetaphaseAlignment Polylobed Polylobed Binuclear SmallIrregular Polylobed Polylobed Polylobed Prometaphase Elongated Binuclear Polylobed Polylobed Hole Binuclear Polylobed Apoptosis Polylobed Interphase Grape Polylobed Polylobed Apoptosis Binuclear Folded Prometaphase Polylobed MetaphaseAlignment Grape Binuclear Metaphase Binuclear Binuclear Binuclear Polylobed Apoptosis Folded SmallIrregular Interphase Binuclear Apoptosis Polylobed UndefinedCondensed Apoptosis SmallIrregular Grape Polylobed Polylobed SmallIrregular Hole Polylobed Polylobed Polylobed Grape Binuclear Binuclear Binuclear Grape MetaphaseAlignment Apoptosis Elongated Binuclear Binuclear Polylobed MetaphaseAlignment Metaphase Grape Polylobed Grape SmallIrregular Binuclear Polylobed Polylobed Polylobed Prometaphase Elongated Binuclear Grape Polylobed Interphase Interphase Polylobed Polylobed Artefact Grape Binuclear Interphase Binuclear Polylobed Polylobed Interphase Prometaphase Prometaphase Polylobed Polylobed Artefact Large Grape Interphase Binuclear Binuclear Polylobed Polylobed Polylobed Grape Folded Polylobed Binuclear Polylobed Binuclear MetaphaseAlignment Grape Polylobed Artefact Polylobed Polylobed Artefact MetaphaseAlignment Elongated Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Polylobed Grape MetaphaseAlignment Artefact Grape Interphase SmallIrregular Polylobed Interphase SmallIrregular Artefact SmallIrregular Prometaphase Polylobed Polylobed Hole Binuclear Prometaphase Prometaphase Prometaphase Metaphase Polylobed Grape Interphase Polylobed Polylobed Metaphase Polylobed Apoptosis Artefact SmallIrregular Prometaphase Artefact Polylobed Grape Apoptosis SmallIrregular Polylobed Polylobed Grape Polylobed Metaphase Metaphase Apoptosis Interphase Artefact Interphase Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Grape Binuclear MetaphaseAlignment Grape Hole Polylobed Polylobed Polylobed Grape Polylobed Artefact Polylobed Polylobed Binuclear Grape Prometaphase Binuclear Binuclear Polylobed Polylobed Prometaphase Polylobed Polylobed Artefact Polylobed Apoptosis Binuclear Artefact Polylobed Prometaphase Artefact Polylobed Polylobed Hole Prometaphase Artefact Grape Polylobed Artefact SmallIrregular Polylobed Apoptosis Apoptosis Polylobed Grape Interphase Apoptosis Interphase Artefact SmallIrregular Binuclear Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Binuclear Grape Prometaphase Prometaphase Polylobed Polylobed Interphase Grape Polylobed Polylobed UndefinedCondensed Prometaphase Binuclear Binuclear Prometaphase Binuclear Polylobed Polylobed Interphase Polylobed Binuclear Artefact Grape Polylobed Polylobed Polylobed Binuclear Binuclear Interphase Prometaphase Interphase Artefact SmallIrregular Binuclear Grape Interphase Artefact Apoptosis Prometaphase Polylobed Binuclear Prometaphase Polylobed SmallIrregular Grape Prometaphase Apoptosis Grape Apoptosis Polylobed Polylobed Polylobed Binuclear Metaphase Prometaphase Binuclear Apoptosis Interphase Binuclear Polylobed Polylobed Grape Large Polylobed Apoptosis Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Polylobed Binuclear Interphase Elongated Binuclear Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Binuclear Binuclear Interphase Grape Interphase Polylobed Polylobed Polylobed Polylobed Prometaphase Binuclear Prometaphase Polylobed Polylobed Prometaphase Prometaphase Artefact Apoptosis MetaphaseAlignment Binuclear Grape Polylobed Prometaphase Interphase Polylobed Grape Apoptosis MetaphaseAlignment Apoptosis Binuclear Interphase MetaphaseAlignment Prometaphase Polylobed Binuclear Prometaphase Polylobed Polylobed Polylobed Interphase Binuclear Polylobed Metaphase Polylobed Interphase Elongated Apoptosis Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Binuclear Prometaphase Polylobed Polylobed SmallIrregular Prometaphase Binuclear Apoptosis SmallIrregular Prometaphase Polylobed Interphase Prometaphase Interphase Polylobed Polylobed Polylobed Metaphase Interphase Grape SmallIrregular MetaphaseAlignment Prometaphase Interphase Prometaphase Apoptosis Apoptosis Polylobed MetaphaseAlignment Polylobed Polylobed Artefact Interphase Polylobed Apoptosis Polylobed Interphase Polylobed Prometaphase Prometaphase Polylobed Polylobed Interphase Interphase Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Binuclear Apoptosis Binuclear Polylobed Binuclear Artefact SmallIrregular Polylobed Hole Binuclear Grape MetaphaseAlignment -y_train_pred Interphase Binuclear Grape Prometaphase Apoptosis Interphase Polylobed Elongated Hole Polylobed Grape Artefact SmallIrregular Apoptosis Polylobed Binuclear Polylobed Interphase Polylobed MetaphaseAlignment Interphase Hole SmallIrregular Polylobed Binuclear SmallIrregular Polylobed Prometaphase Polylobed Folded Polylobed Polylobed Polylobed Polylobed Prometaphase Prometaphase Polylobed Prometaphase Polylobed Prometaphase Grape Large Metaphase Polylobed SmallIrregular Prometaphase Prometaphase MetaphaseAlignment Grape Binuclear MetaphaseAlignment Polylobed Polylobed Polylobed Hole Interphase Interphase Binuclear Polylobed Binuclear Polylobed Prometaphase Polylobed SmallIrregular Binuclear Polylobed Artefact Polylobed SmallIrregular Polylobed Polylobed Hole Binuclear Polylobed Grape Interphase Grape Interphase Apoptosis Polylobed Prometaphase Grape Polylobed Interphase Grape Polylobed Polylobed Apoptosis Polylobed Polylobed Grape Polylobed Grape Polylobed Prometaphase Artefact Polylobed Binuclear Polylobed SmallIrregular Artefact Metaphase Polylobed Large Polylobed Polylobed Polylobed Polylobed SmallIrregular Prometaphase Apoptosis Prometaphase Polylobed Polylobed MetaphaseAlignment Grape Interphase Binuclear Grape Polylobed UndefinedCondensed Polylobed Elongated Polylobed Polylobed MetaphaseAlignment Hole Polylobed Grape Interphase Polylobed Polylobed Interphase Artefact Grape Polylobed Prometaphase Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Binuclear Apoptosis Hole Polylobed Binuclear Polylobed SmallIrregular Interphase Polylobed Apoptosis Prometaphase Binuclear Polylobed Grape Artefact SmallIrregular Interphase Binuclear Polylobed Binuclear Polylobed Artefact Polylobed Polylobed Artefact Binuclear Binuclear Prometaphase Polylobed Folded Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Polylobed MetaphaseAlignment Grape UndefinedCondensed Binuclear Binuclear Interphase SmallIrregular Polylobed Metaphase Polylobed Polylobed Polylobed Metaphase Grape Apoptosis SmallIrregular Binuclear Prometaphase Polylobed Grape UndefinedCondensed Polylobed Polylobed Polylobed Artefact Polylobed Binuclear Polylobed Polylobed Grape SmallIrregular Prometaphase Binuclear Binuclear Binuclear Binuclear Grape Polylobed Polylobed Binuclear Interphase Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed SmallIrregular Polylobed Polylobed Grape Grape Polylobed Polylobed Polylobed Interphase Interphase SmallIrregular SmallIrregular Binuclear Binuclear Grape SmallIrregular Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Artefact Large Artefact Grape Polylobed Polylobed Grape SmallIrregular Polylobed Grape Polylobed Polylobed Prometaphase Polylobed Polylobed Binuclear Polylobed Polylobed Folded Polylobed Interphase Artefact Apoptosis Polylobed Binuclear Polylobed Prometaphase Binuclear Binuclear Polylobed SmallIrregular Grape Prometaphase Interphase SmallIrregular Apoptosis Polylobed Polylobed MetaphaseAlignment Interphase Grape Binuclear Prometaphase Prometaphase Folded Artefact Grape Grape Polylobed Artefact Binuclear Hole Prometaphase Grape Polylobed Interphase Interphase Polylobed Artefact SmallIrregular Polylobed Polylobed Binuclear Binuclear Binuclear Binuclear Large Artefact SmallIrregular Polylobed Binuclear Folded Apoptosis Binuclear Polylobed MetaphaseAlignment Polylobed Polylobed Apoptosis Artefact Polylobed MetaphaseAlignment Polylobed Interphase Binuclear Prometaphase Polylobed Grape Artefact Grape Prometaphase Artefact Grape Polylobed Interphase Polylobed Binuclear Interphase Polylobed Binuclear MetaphaseAlignment Binuclear Polylobed Binuclear Elongated Polylobed Polylobed Polylobed Binuclear Interphase Polylobed Grape Binuclear Artefact MetaphaseAlignment Polylobed Binuclear Binuclear Metaphase Binuclear Polylobed Hole Polylobed Polylobed Interphase Polylobed Apoptosis Binuclear Prometaphase Polylobed Large Hole Polylobed Polylobed Binuclear Polylobed Prometaphase Polylobed UndefinedCondensed Large Grape Polylobed Polylobed MetaphaseAlignment Metaphase SmallIrregular Polylobed Interphase Interphase Polylobed Artefact Polylobed Interphase Polylobed UndefinedCondensed Interphase MetaphaseAlignment Binuclear Binuclear Binuclear Binuclear Metaphase MetaphaseAlignment Grape Binuclear Polylobed Apoptosis Artefact Polylobed SmallIrregular Polylobed Apoptosis Interphase Polylobed MetaphaseAlignment Polylobed Metaphase Interphase Prometaphase Hole Polylobed Polylobed Binuclear Prometaphase Grape Polylobed Polylobed SmallIrregular Polylobed Hole Interphase Prometaphase Polylobed Artefact Binuclear Prometaphase Polylobed Binuclear Binuclear Artefact Polylobed Prometaphase Polylobed Polylobed Binuclear Polylobed Interphase Polylobed Polylobed Polylobed SmallIrregular Polylobed Grape Binuclear Apoptosis MetaphaseAlignment Hole Apoptosis Polylobed Prometaphase Polylobed Artefact MetaphaseAlignment Grape Binuclear Interphase Polylobed Polylobed Interphase Artefact Binuclear Grape Binuclear Polylobed Grape Interphase Polylobed Binuclear Polylobed Polylobed UndefinedCondensed SmallIrregular Polylobed Polylobed Polylobed Binuclear Polylobed Apoptosis Binuclear Prometaphase Apoptosis Artefact Grape Prometaphase Polylobed Polylobed Polylobed Polylobed Apoptosis Prometaphase Prometaphase Polylobed Binuclear SmallIrregular Interphase Polylobed Binuclear Polylobed Polylobed Polylobed Grape SmallIrregular UndefinedCondensed Polylobed Hole Grape Apoptosis Polylobed Binuclear Polylobed Grape Hole Prometaphase Apoptosis Apoptosis Polylobed Polylobed Folded Apoptosis Metaphase Polylobed Apoptosis Hole Interphase Polylobed Interphase Grape Polylobed Hole Prometaphase Grape Hole Polylobed Artefact Grape Grape Artefact Binuclear MetaphaseAlignment Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Grape Polylobed Binuclear Polylobed Apoptosis Metaphase Polylobed Polylobed Artefact Prometaphase Hole Binuclear Binuclear Polylobed Large Binuclear Folded Polylobed MetaphaseAlignment Prometaphase Grape Polylobed Interphase Grape Prometaphase Interphase Polylobed SmallIrregular Polylobed Prometaphase Interphase Folded Grape Interphase Apoptosis Interphase Polylobed Interphase Polylobed Polylobed Polylobed Interphase MetaphaseAlignment Polylobed Binuclear Apoptosis Prometaphase Grape Artefact MetaphaseAlignment Interphase Polylobed Polylobed UndefinedCondensed Apoptosis Polylobed Prometaphase Polylobed Polylobed Polylobed Binuclear Grape Apoptosis Grape Interphase Grape Interphase Polylobed Polylobed Binuclear Polylobed Prometaphase Grape Polylobed Prometaphase Polylobed SmallIrregular Grape Polylobed Metaphase Binuclear Grape SmallIrregular Polylobed Hole Artefact Grape Grape Apoptosis Polylobed Polylobed MetaphaseAlignment Grape Metaphase Polylobed Prometaphase SmallIrregular Polylobed Grape Polylobed Interphase Polylobed Prometaphase Polylobed Prometaphase Binuclear Interphase Polylobed Polylobed Hole Polylobed Prometaphase Artefact Binuclear Grape Apoptosis Polylobed Binuclear Artefact Binuclear Binuclear Artefact Grape Polylobed Polylobed Polylobed Interphase Binuclear Polylobed Polylobed MetaphaseAlignment Prometaphase Polylobed Prometaphase Binuclear Polylobed Grape Polylobed Interphase Interphase Polylobed Polylobed Polylobed Polylobed Artefact Apoptosis Polylobed Polylobed Artefact Polylobed Binuclear MetaphaseAlignment MetaphaseAlignment Binuclear Artefact Hole Folded Artefact Polylobed Apoptosis Grape Grape Grape SmallIrregular Elongated Grape Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Grape Prometaphase Polylobed Polylobed Metaphase Polylobed Prometaphase Polylobed Grape Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Apoptosis Artefact Binuclear Polylobed Grape Interphase Polylobed Polylobed Interphase Polylobed Prometaphase Prometaphase Polylobed Interphase Apoptosis Polylobed MetaphaseAlignment Polylobed Artefact Binuclear Polylobed Polylobed Artefact Artefact Interphase Polylobed Binuclear Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Prometaphase MetaphaseAlignment Binuclear MetaphaseAlignment Binuclear MetaphaseAlignment Grape Binuclear SmallIrregular Prometaphase Polylobed SmallIrregular Polylobed Apoptosis Binuclear Interphase Polylobed MetaphaseAlignment Polylobed Grape Binuclear Grape Polylobed Artefact Binuclear Grape Prometaphase Polylobed Polylobed Polylobed Polylobed SmallIrregular Polylobed Grape Polylobed SmallIrregular Interphase Binuclear Polylobed Polylobed Polylobed Interphase Elongated MetaphaseAlignment Artefact MetaphaseAlignment SmallIrregular Metaphase Binuclear Binuclear Polylobed Apoptosis Binuclear Polylobed Interphase Polylobed Apoptosis Artefact Prometaphase Polylobed Grape Grape Grape Polylobed Interphase Grape Binuclear Artefact Polylobed Binuclear MetaphaseAlignment Artefact Polylobed Polylobed Prometaphase Apoptosis Polylobed Polylobed Polylobed Polylobed Polylobed Hole Polylobed Prometaphase Polylobed Polylobed Artefact Polylobed UndefinedCondensed Prometaphase Polylobed SmallIrregular Apoptosis Grape Binuclear Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Interphase Polylobed Polylobed Polylobed Polylobed Polylobed SmallIrregular SmallIrregular Polylobed Polylobed Polylobed Grape SmallIrregular Polylobed Polylobed Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Polylobed Apoptosis Grape Hole Prometaphase Hole Artefact Interphase Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed SmallIrregular Folded Artefact Polylobed Interphase Grape Polylobed Binuclear Polylobed Interphase Polylobed Large Hole Interphase Polylobed Polylobed Polylobed Interphase Binuclear Binuclear Grape Apoptosis Prometaphase Grape Polylobed Binuclear Polylobed Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Prometaphase Binuclear Binuclear Hole Interphase Prometaphase SmallIrregular SmallIrregular Binuclear Binuclear Polylobed Prometaphase Polylobed Prometaphase Polylobed Prometaphase Apoptosis Metaphase Binuclear Polylobed Polylobed Grape Binuclear Polylobed Interphase Large Polylobed Prometaphase Polylobed Grape Polylobed Polylobed Artefact Polylobed Metaphase Binuclear Grape Polylobed Polylobed Binuclear Apoptosis Binuclear Interphase MetaphaseAlignment Polylobed Apoptosis Polylobed Polylobed Interphase Binuclear Polylobed Metaphase Binuclear Polylobed Polylobed Binuclear Grape Polylobed Elongated Grape Polylobed Grape Polylobed Hole Polylobed Artefact Binuclear Apoptosis Interphase Polylobed Polylobed Binuclear Grape Binuclear Grape SmallIrregular Binuclear Binuclear Polylobed Polylobed Interphase Binuclear Polylobed Interphase Grape Grape Hole Polylobed Polylobed Polylobed Grape Polylobed Polylobed Apoptosis Interphase Polylobed Apoptosis Polylobed Artefact Polylobed Grape Polylobed Interphase Prometaphase Polylobed Polylobed Artefact Interphase SmallIrregular SmallIrregular Polylobed Binuclear Binuclear Artefact Apoptosis Grape Interphase Hole Large Grape Polylobed Polylobed Interphase Artefact Polylobed Polylobed Binuclear Interphase Polylobed Polylobed Polylobed SmallIrregular Polylobed Polylobed Binuclear Hole Interphase Artefact Interphase Binuclear Polylobed Binuclear Artefact Grape Polylobed Binuclear Polylobed Hole Polylobed Prometaphase Interphase Large Polylobed Artefact Prometaphase Interphase Polylobed MetaphaseAlignment Binuclear Grape Elongated Prometaphase Artefact Polylobed Artefact Binuclear Polylobed Binuclear Binuclear Artefact Polylobed Prometaphase Artefact Binuclear Prometaphase Polylobed Polylobed Prometaphase Interphase Artefact Polylobed Prometaphase Binuclear Binuclear Prometaphase Artefact MetaphaseAlignment Apoptosis Binuclear Interphase Grape Prometaphase Metaphase Interphase Elongated Metaphase Polylobed Artefact Binuclear Artefact Grape Polylobed Prometaphase SmallIrregular Artefact Polylobed MetaphaseAlignment Prometaphase Polylobed Polylobed Polylobed SmallIrregular Grape Grape MetaphaseAlignment Polylobed Prometaphase Prometaphase Polylobed Prometaphase Artefact Polylobed Polylobed Interphase Apoptosis Polylobed Artefact Apoptosis Artefact Binuclear Polylobed SmallIrregular SmallIrregular Polylobed SmallIrregular MetaphaseAlignment Binuclear Interphase Grape Prometaphase Polylobed Artefact Polylobed Binuclear Interphase MetaphaseAlignment Hole Large Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Artefact Apoptosis Polylobed Grape Polylobed Grape Prometaphase Binuclear Prometaphase Polylobed Polylobed Polylobed Hole Binuclear Polylobed Polylobed Binuclear Binuclear Polylobed Binuclear Polylobed Polylobed Interphase Polylobed Grape Polylobed Polylobed Interphase SmallIrregular MetaphaseAlignment Polylobed Interphase Polylobed Artefact Apoptosis Prometaphase Grape Prometaphase Binuclear Interphase Prometaphase Binuclear Polylobed Prometaphase Grape Prometaphase Interphase Polylobed Polylobed Polylobed Artefact Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Prometaphase SmallIrregular Binuclear Binuclear SmallIrregular Large Polylobed Polylobed Polylobed Grape Binuclear MetaphaseAlignment Polylobed Apoptosis Polylobed MetaphaseAlignment Artefact Grape Polylobed Apoptosis Prometaphase Interphase Interphase Binuclear Polylobed Binuclear Polylobed Polylobed MetaphaseAlignment Folded Polylobed Polylobed Large Polylobed Polylobed Interphase Polylobed Artefact Grape Polylobed Binuclear Polylobed MetaphaseAlignment Polylobed Binuclear Artefact MetaphaseAlignment Polylobed Apoptosis Apoptosis Artefact Polylobed Polylobed Binuclear Polylobed Interphase Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Prometaphase Interphase Polylobed Polylobed Prometaphase Polylobed Binuclear Prometaphase MetaphaseAlignment Apoptosis Polylobed Prometaphase Elongated Interphase Polylobed Polylobed Grape Interphase Polylobed Grape Polylobed Metaphase Polylobed Polylobed Grape Grape Hole Polylobed Polylobed Grape Interphase Binuclear Grape Grape Polylobed Polylobed Prometaphase Prometaphase UndefinedCondensed Interphase Polylobed Polylobed Interphase Artefact Binuclear Polylobed Polylobed Apoptosis Hole Interphase Artefact Binuclear Polylobed Artefact Grape Polylobed SmallIrregular MetaphaseAlignment Polylobed Polylobed Grape Polylobed Polylobed Binuclear Interphase Binuclear Binuclear Polylobed Polylobed Polylobed Binuclear Binuclear Hole Polylobed Polylobed SmallIrregular Artefact Polylobed Polylobed Hole Polylobed Polylobed Polylobed Polylobed Binuclear Artefact Artefact Polylobed Polylobed Binuclear Artefact Hole Polylobed Hole Hole Apoptosis Large Artefact Interphase SmallIrregular Polylobed Polylobed Folded Polylobed Binuclear Binuclear Polylobed Polylobed SmallIrregular Polylobed Grape Interphase Interphase Apoptosis Interphase Polylobed Polylobed Prometaphase Binuclear Polylobed Interphase Binuclear Polylobed Prometaphase Polylobed Polylobed Apoptosis Hole Interphase Artefact Grape Binuclear Apoptosis Grape Binuclear Artefact Grape Polylobed Elongated Artefact Binuclear Grape Polylobed Prometaphase Polylobed Polylobed Binuclear Prometaphase Polylobed Binuclear Grape Polylobed Polylobed Prometaphase Grape Polylobed Polylobed Hole Polylobed Binuclear Interphase Grape Polylobed Polylobed SmallIrregular Binuclear Polylobed Apoptosis Polylobed Polylobed Elongated Polylobed Polylobed Binuclear Binuclear Polylobed Prometaphase Polylobed Apoptosis Binuclear Binuclear Polylobed Interphase MetaphaseAlignment Polylobed Polylobed SmallIrregular Binuclear Hole Binuclear Polylobed Polylobed UndefinedCondensed Grape Grape Polylobed Polylobed Interphase Polylobed Grape Grape Prometaphase Interphase MetaphaseAlignment Binuclear Binuclear MetaphaseAlignment Binuclear Metaphase Polylobed Polylobed Apoptosis Interphase Polylobed Polylobed Polylobed Interphase Grape Prometaphase Binuclear Interphase Polylobed Prometaphase Binuclear Polylobed Binuclear Grape Prometaphase Prometaphase Polylobed Hole Artefact Polylobed Prometaphase Grape Hole Grape Grape Artefact Grape MetaphaseAlignment Polylobed Polylobed Binuclear Polylobed Polylobed Artefact Binuclear Binuclear Polylobed MetaphaseAlignment Binuclear Binuclear Binuclear Apoptosis Grape Binuclear Polylobed Binuclear Polylobed Polylobed Interphase Elongated Polylobed MetaphaseAlignment Interphase Metaphase Polylobed Binuclear MetaphaseAlignment Polylobed Polylobed Prometaphase Polylobed Polylobed Prometaphase Polylobed Polylobed Grape Binuclear Apoptosis Binuclear Grape Polylobed Artefact Polylobed Polylobed Binuclear MetaphaseAlignment Prometaphase Apoptosis UndefinedCondensed Prometaphase Metaphase Binuclear Apoptosis SmallIrregular Grape Folded Grape Binuclear Prometaphase MetaphaseAlignment Binuclear Polylobed Hole Binuclear Folded Prometaphase Folded Prometaphase Prometaphase Polylobed Interphase Polylobed MetaphaseAlignment Interphase Prometaphase Binuclear Prometaphase Artefact Interphase Polylobed Binuclear Polylobed Polylobed Binuclear Grape Binuclear Grape Prometaphase Polylobed Interphase Apoptosis Polylobed MetaphaseAlignment Prometaphase Polylobed Polylobed Grape Prometaphase Prometaphase Apoptosis Binuclear SmallIrregular Polylobed Grape Polylobed Metaphase Folded Large Grape Prometaphase Large Apoptosis Artefact Metaphase Interphase Prometaphase Binuclear Prometaphase Prometaphase Artefact Polylobed Interphase Polylobed Binuclear Binuclear Polylobed Binuclear Grape Prometaphase Grape Polylobed Polylobed SmallIrregular Grape Polylobed Prometaphase Polylobed Polylobed Interphase Binuclear Artefact Interphase Grape Binuclear Polylobed Binuclear Artefact Artefact Binuclear Binuclear Polylobed Grape Prometaphase Polylobed Hole Polylobed Polylobed Interphase MetaphaseAlignment MetaphaseAlignment Interphase Binuclear Polylobed Prometaphase Artefact Grape Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Hole Apoptosis Polylobed Binuclear Hole Apoptosis Binuclear Prometaphase Interphase Interphase Interphase Polylobed Polylobed Polylobed SmallIrregular Metaphase Grape Binuclear Grape Polylobed Prometaphase Binuclear MetaphaseAlignment SmallIrregular Polylobed Polylobed Interphase Grape Artefact Artefact Binuclear Binuclear Prometaphase Polylobed Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Grape Prometaphase Polylobed Hole Polylobed Polylobed Binuclear Polylobed Grape SmallIrregular Apoptosis Polylobed Artefact Polylobed Prometaphase Grape Elongated Binuclear Prometaphase UndefinedCondensed Polylobed Grape Binuclear Interphase Prometaphase Grape Metaphase Binuclear MetaphaseAlignment Polylobed Hole SmallIrregular Polylobed Prometaphase Binuclear Apoptosis SmallIrregular Polylobed Binuclear Interphase Prometaphase Interphase Grape Grape Polylobed Polylobed Grape Polylobed Metaphase Polylobed Binuclear Interphase Artefact Apoptosis Grape Grape Polylobed Artefact Artefact MetaphaseAlignment Apoptosis Interphase Artefact Polylobed Polylobed Hole Metaphase Polylobed Polylobed Binuclear Polylobed Prometaphase Hole Polylobed Artefact Artefact Polylobed Polylobed Interphase Polylobed Binuclear Hole Polylobed Polylobed Binuclear Large Prometaphase Apoptosis Apoptosis Polylobed Polylobed Folded Interphase Apoptosis SmallIrregular Interphase Hole Polylobed Polylobed Binuclear Grape SmallIrregular Hole MetaphaseAlignment Grape Polylobed Prometaphase Polylobed Polylobed Metaphase Prometaphase Hole Polylobed Binuclear MetaphaseAlignment Polylobed MetaphaseAlignment Prometaphase Polylobed Prometaphase Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Interphase Binuclear Polylobed Interphase SmallIrregular Binuclear Polylobed Polylobed Polylobed Binuclear Artefact Hole Polylobed Binuclear Polylobed SmallIrregular Binuclear Binuclear Grape Metaphase Polylobed Interphase Binuclear Interphase Polylobed Apoptosis MetaphaseAlignment Prometaphase Polylobed Interphase Grape Polylobed Binuclear SmallIrregular Prometaphase Polylobed Binuclear Interphase Grape Polylobed Hole Polylobed Polylobed Apoptosis Polylobed Interphase Polylobed Apoptosis Apoptosis Polylobed Polylobed SmallIrregular Binuclear Polylobed Polylobed Prometaphase Interphase Polylobed Polylobed Grape Metaphase Prometaphase Prometaphase Artefact MetaphaseAlignment SmallIrregular Artefact Polylobed Apoptosis Grape Binuclear Grape Prometaphase Polylobed Polylobed Hole Artefact Prometaphase Grape Hole Polylobed Binuclear MetaphaseAlignment SmallIrregular Grape Polylobed Binuclear Binuclear Polylobed Interphase Polylobed SmallIrregular Artefact Polylobed Artefact Artefact Polylobed Interphase Binuclear Polylobed Grape Polylobed Artefact Polylobed Apoptosis Polylobed Polylobed Binuclear Binuclear Polylobed MetaphaseAlignment SmallIrregular Prometaphase Polylobed Grape Polylobed Grape Binuclear Prometaphase Elongated Polylobed SmallIrregular Polylobed Polylobed SmallIrregular Polylobed Polylobed Elongated Polylobed Prometaphase Binuclear Polylobed Polylobed Artefact Polylobed Artefact Grape Polylobed Binuclear Polylobed SmallIrregular Grape Polylobed Polylobed Grape Large Polylobed Binuclear Binuclear Apoptosis Grape Binuclear Binuclear Polylobed Elongated Artefact Polylobed Apoptosis Polylobed Apoptosis Polylobed Folded Artefact Polylobed Grape Binuclear MetaphaseAlignment Polylobed Interphase Interphase Prometaphase Polylobed Prometaphase Artefact Artefact Artefact Binuclear Polylobed Polylobed Polylobed Artefact Prometaphase Binuclear Apoptosis MetaphaseAlignment Polylobed Polylobed Polylobed Grape Artefact Polylobed Polylobed Polylobed Artefact Polylobed Polylobed MetaphaseAlignment Artefact Polylobed Interphase Binuclear Polylobed Prometaphase Polylobed Apoptosis Artefact Interphase Polylobed Polylobed Polylobed Interphase Polylobed Binuclear Interphase Folded Prometaphase Hole Polylobed Interphase Artefact Binuclear Binuclear Artefact Folded Interphase Apoptosis Hole Binuclear Binuclear Interphase Prometaphase Grape Apoptosis Polylobed Grape Artefact Prometaphase Binuclear Binuclear Polylobed Large Polylobed SmallIrregular Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Hole Polylobed Binuclear SmallIrregular Grape Polylobed Binuclear Binuclear Polylobed Artefact Binuclear Prometaphase MetaphaseAlignment Binuclear Grape Prometaphase Grape Polylobed Metaphase Prometaphase Polylobed Polylobed Polylobed Binuclear Interphase SmallIrregular Prometaphase Grape Polylobed Binuclear Grape Polylobed MetaphaseAlignment Metaphase Binuclear Interphase Polylobed Grape Polylobed Polylobed Polylobed Grape Metaphase Large Grape Binuclear Polylobed Binuclear Polylobed Binuclear UndefinedCondensed Interphase Artefact Binuclear Polylobed Polylobed Prometaphase Polylobed Polylobed Grape Prometaphase Apoptosis Binuclear Polylobed Binuclear Grape SmallIrregular SmallIrregular Folded Apoptosis Prometaphase Binuclear Binuclear Interphase MetaphaseAlignment Polylobed Polylobed Polylobed Artefact Interphase SmallIrregular Prometaphase SmallIrregular Polylobed SmallIrregular Large Interphase Hole Binuclear Artefact Artefact Interphase Binuclear Polylobed Folded Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Apoptosis MetaphaseAlignment Artefact SmallIrregular Binuclear Apoptosis Artefact Interphase Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Grape Binuclear Binuclear Grape Polylobed Interphase Interphase Prometaphase Polylobed Polylobed Grape Polylobed SmallIrregular SmallIrregular Binuclear Artefact Prometaphase Binuclear Polylobed Polylobed Binuclear Polylobed Binuclear SmallIrregular UndefinedCondensed Binuclear Polylobed Polylobed Hole Polylobed SmallIrregular Polylobed Polylobed Artefact Artefact Interphase Prometaphase Polylobed Polylobed Hole Interphase Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Large Binuclear Binuclear Polylobed Polylobed Polylobed Large SmallIrregular Artefact Polylobed Artefact Folded Prometaphase Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Prometaphase Grape Interphase Binuclear Metaphase Binuclear Polylobed Binuclear Prometaphase Binuclear Polylobed Polylobed Polylobed SmallIrregular Polylobed Interphase Apoptosis Polylobed Prometaphase Interphase Polylobed Polylobed Binuclear Artefact Hole Interphase SmallIrregular Prometaphase Large Binuclear Polylobed Grape Polylobed Grape Interphase Polylobed Apoptosis Polylobed Polylobed Binuclear MetaphaseAlignment MetaphaseAlignment Large Apoptosis MetaphaseAlignment Grape Polylobed Polylobed Apoptosis Polylobed Hole Grape Polylobed Polylobed Polylobed Polylobed Binuclear Binuclear SmallIrregular Polylobed Grape Prometaphase Artefact Prometaphase Interphase Binuclear Polylobed Polylobed Hole Prometaphase Prometaphase Binuclear Prometaphase Artefact Prometaphase Grape Grape Polylobed MetaphaseAlignment Artefact Polylobed Binuclear Binuclear Polylobed Binuclear Artefact Prometaphase Polylobed Polylobed MetaphaseAlignment Grape Polylobed MetaphaseAlignment Prometaphase Grape UndefinedCondensed SmallIrregular Grape Binuclear Prometaphase Hole Artefact Prometaphase Apoptosis Polylobed Polylobed Polylobed Interphase SmallIrregular Polylobed Polylobed Polylobed Artefact Binuclear Polylobed Polylobed Interphase Grape Interphase Binuclear Apoptosis Prometaphase Elongated Polylobed Apoptosis Polylobed Polylobed UndefinedCondensed Apoptosis Polylobed Binuclear Prometaphase Prometaphase Polylobed SmallIrregular Polylobed Binuclear Interphase Large Binuclear SmallIrregular Polylobed MetaphaseAlignment Prometaphase Prometaphase Grape MetaphaseAlignment Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed MetaphaseAlignment Metaphase UndefinedCondensed Binuclear MetaphaseAlignment Polylobed Artefact Grape Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Apoptosis SmallIrregular Polylobed Polylobed Prometaphase Binuclear Interphase Artefact Apoptosis Polylobed Binuclear Artefact Polylobed Polylobed Polylobed Binuclear Grape Large Artefact Polylobed Binuclear SmallIrregular Binuclear Polylobed Binuclear Polylobed Artefact Prometaphase Polylobed Large Polylobed MetaphaseAlignment Polylobed Hole Folded Polylobed Polylobed Polylobed Metaphase Grape Polylobed Interphase Prometaphase Binuclear Polylobed Polylobed Binuclear Polylobed SmallIrregular Metaphase Polylobed Polylobed Prometaphase Polylobed Grape Apoptosis Interphase Binuclear Prometaphase Polylobed Artefact Binuclear Binuclear MetaphaseAlignment Binuclear Polylobed Binuclear Polylobed Polylobed UndefinedCondensed Binuclear Polylobed UndefinedCondensed SmallIrregular Polylobed MetaphaseAlignment Polylobed Interphase Binuclear Metaphase Binuclear Prometaphase Artefact Artefact UndefinedCondensed Polylobed Prometaphase Binuclear Polylobed Interphase Polylobed Binuclear Polylobed SmallIrregular Binuclear Interphase Metaphase Artefact Binuclear Folded Grape Interphase Artefact Metaphase Binuclear Grape Grape Binuclear Binuclear Polylobed Polylobed Hole Polylobed Elongated Prometaphase Binuclear Prometaphase Prometaphase Polylobed Polylobed Binuclear Polylobed Artefact MetaphaseAlignment Polylobed Prometaphase Grape Artefact Polylobed SmallIrregular MetaphaseAlignment Interphase Hole Prometaphase Hole Prometaphase Interphase Interphase MetaphaseAlignment Hole Binuclear SmallIrregular Interphase Interphase Polylobed Prometaphase Grape Prometaphase Polylobed Interphase Interphase Polylobed SmallIrregular Grape SmallIrregular Prometaphase Polylobed Binuclear Polylobed Prometaphase Binuclear Polylobed Artefact Polylobed Hole Prometaphase Grape Polylobed Interphase Polylobed Binuclear Polylobed Grape Polylobed MetaphaseAlignment Apoptosis Hole Binuclear Prometaphase Polylobed SmallIrregular Polylobed Polylobed Grape Polylobed Metaphase Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed SmallIrregular Grape Metaphase Binuclear Polylobed Polylobed Polylobed Grape Polylobed Polylobed SmallIrregular Polylobed Polylobed Prometaphase Artefact Binuclear Artefact Grape Polylobed Binuclear Polylobed Polylobed Interphase Prometaphase Apoptosis UndefinedCondensed Folded SmallIrregular Polylobed Polylobed Prometaphase Binuclear Large Polylobed Polylobed Binuclear Apoptosis SmallIrregular Grape Polylobed Polylobed Apoptosis Polylobed Large SmallIrregular Prometaphase Polylobed Polylobed MetaphaseAlignment Polylobed Prometaphase Polylobed Prometaphase Binuclear Polylobed Polylobed Polylobed Large Binuclear MetaphaseAlignment Apoptosis Polylobed Binuclear Interphase Interphase SmallIrregular Binuclear Binuclear Polylobed Binuclear Binuclear Polylobed Polylobed Grape Artefact SmallIrregular Polylobed Polylobed MetaphaseAlignment Binuclear Apoptosis Interphase Polylobed Grape Prometaphase Polylobed Folded Polylobed Polylobed Prometaphase Hole Grape MetaphaseAlignment Elongated Binuclear Polylobed Binuclear Polylobed Prometaphase Polylobed Interphase Artefact Prometaphase Polylobed Binuclear Polylobed Polylobed Elongated Polylobed Interphase Prometaphase Polylobed Binuclear Polylobed SmallIrregular Grape Polylobed Binuclear Binuclear Apoptosis Grape Interphase Grape Prometaphase Polylobed Polylobed Binuclear Polylobed Grape Binuclear Interphase Binuclear Interphase Polylobed Polylobed Artefact Hole Interphase MetaphaseAlignment Polylobed Polylobed Polylobed Interphase Interphase Polylobed Apoptosis Prometaphase Artefact Binuclear Polylobed Interphase Polylobed Grape Binuclear Binuclear Polylobed Binuclear Polylobed MetaphaseAlignment Polylobed Polylobed Binuclear SmallIrregular Polylobed Polylobed Polylobed Prometaphase Elongated Binuclear Polylobed Polylobed Hole Binuclear Polylobed Apoptosis Polylobed Interphase Grape Polylobed Polylobed Apoptosis Binuclear Folded Prometaphase Polylobed MetaphaseAlignment Grape Polylobed Metaphase Binuclear Binuclear Binuclear Polylobed Apoptosis Folded SmallIrregular Interphase Binuclear Apoptosis Polylobed UndefinedCondensed Apoptosis SmallIrregular Grape Polylobed Polylobed SmallIrregular Hole Polylobed Polylobed Polylobed Grape Binuclear Binuclear Binuclear Grape MetaphaseAlignment Apoptosis Elongated Binuclear Binuclear Polylobed MetaphaseAlignment Metaphase Grape Polylobed Grape SmallIrregular Binuclear Polylobed Polylobed Polylobed Prometaphase Elongated Binuclear Grape Polylobed Interphase Interphase Polylobed Polylobed Artefact Grape Binuclear Interphase Binuclear Polylobed Polylobed Interphase Prometaphase Prometaphase Polylobed Polylobed Artefact Large Grape Interphase Binuclear Binuclear Polylobed Polylobed Polylobed Grape Folded Polylobed Binuclear Polylobed Binuclear MetaphaseAlignment Grape Polylobed Artefact Polylobed Polylobed Artefact MetaphaseAlignment Elongated Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Polylobed Grape MetaphaseAlignment Artefact Grape Interphase SmallIrregular Artefact MetaphaseAlignment SmallIrregular Artefact SmallIrregular Prometaphase Polylobed Polylobed Hole Binuclear Prometaphase Prometaphase Prometaphase Metaphase Polylobed Grape Interphase Polylobed Polylobed Metaphase Polylobed Apoptosis Artefact SmallIrregular Prometaphase Artefact Polylobed Grape Apoptosis SmallIrregular Polylobed Polylobed Grape Polylobed Metaphase Metaphase Apoptosis Interphase Artefact Interphase Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear MetaphaseAlignment Grape Hole Polylobed Polylobed Polylobed Grape Polylobed Artefact Polylobed Polylobed Binuclear Grape Prometaphase Binuclear Binuclear Polylobed Polylobed Prometaphase Polylobed Polylobed Artefact Polylobed Apoptosis Binuclear Artefact Polylobed Prometaphase Artefact Polylobed Polylobed Hole Prometaphase Artefact Grape Polylobed Artefact SmallIrregular Polylobed Apoptosis Apoptosis Polylobed Grape Interphase Apoptosis Interphase Artefact SmallIrregular Binuclear Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Binuclear Grape Prometaphase Prometaphase Polylobed Polylobed Interphase Grape Polylobed Polylobed UndefinedCondensed Prometaphase Binuclear Binuclear Prometaphase Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Metaphase Grape Polylobed Polylobed Polylobed Binuclear Binuclear Interphase Prometaphase Interphase Artefact SmallIrregular Binuclear Grape Interphase Artefact Apoptosis Prometaphase Polylobed Binuclear Prometaphase Polylobed SmallIrregular Grape Prometaphase Apoptosis Grape Apoptosis Polylobed Polylobed Polylobed Binuclear Metaphase Prometaphase Binuclear Apoptosis Interphase Binuclear Polylobed Polylobed Grape Large Polylobed Apoptosis Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Polylobed Binuclear Interphase Elongated Binuclear Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Binuclear Binuclear Interphase Grape Interphase Polylobed Polylobed Polylobed Polylobed Prometaphase Binuclear Prometaphase Polylobed Polylobed Prometaphase Prometaphase Artefact Apoptosis MetaphaseAlignment Binuclear Grape Polylobed Prometaphase Interphase Polylobed Grape Apoptosis MetaphaseAlignment Apoptosis Binuclear Interphase MetaphaseAlignment Prometaphase Polylobed Binuclear Prometaphase Polylobed Polylobed Polylobed Interphase Binuclear Polylobed Metaphase Polylobed Interphase Elongated Apoptosis Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Binuclear Prometaphase Polylobed Polylobed SmallIrregular Prometaphase Binuclear Apoptosis SmallIrregular Prometaphase Polylobed Interphase Prometaphase Interphase Polylobed Polylobed Polylobed Metaphase Interphase Grape SmallIrregular MetaphaseAlignment Prometaphase Interphase Prometaphase Apoptosis Apoptosis Polylobed MetaphaseAlignment Polylobed Polylobed Artefact Interphase Polylobed Apoptosis Polylobed Interphase Polylobed Prometaphase Prometaphase Polylobed Polylobed Interphase Interphase Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Binuclear Apoptosis Binuclear Polylobed Binuclear Artefact SmallIrregular Polylobed Hole Binuclear Grape MetaphaseAlignment -y_test Prometaphase Polylobed Polylobed Grape Polylobed Grape Prometaphase Apoptosis Artefact Grape Polylobed Hole Polylobed Polylobed Binuclear Interphase Polylobed Polylobed Apoptosis Interphase Prometaphase Polylobed Polylobed Polylobed Grape Grape Interphase Polylobed Polylobed Binuclear Polylobed Interphase Polylobed Binuclear Grape Polylobed Polylobed Grape Hole Prometaphase Artefact Prometaphase UndefinedCondensed Artefact Grape Polylobed Polylobed Apoptosis Polylobed Binuclear Artefact Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Binuclear Polylobed Polylobed UndefinedCondensed MetaphaseAlignment Artefact Grape Polylobed Polylobed Hole Interphase Hole Binuclear Polylobed Apoptosis Polylobed Interphase Interphase Prometaphase Interphase Binuclear Apoptosis Polylobed Prometaphase Interphase MetaphaseAlignment Polylobed Polylobed Polylobed Binuclear Polylobed SmallIrregular Polylobed Artefact Binuclear Polylobed Prometaphase Polylobed Polylobed SmallIrregular Interphase Polylobed Grape Interphase Grape Binuclear Polylobed Binuclear Prometaphase Apoptosis MetaphaseAlignment Polylobed Polylobed Interphase Hole Artefact Polylobed Prometaphase Interphase Polylobed Polylobed Polylobed Interphase Polylobed Binuclear Polylobed Binuclear Binuclear Interphase Polylobed Grape Binuclear Interphase Polylobed Polylobed Artefact Polylobed Large Binuclear Polylobed Polylobed Apoptosis SmallIrregular Binuclear Polylobed Binuclear Polylobed Binuclear Polylobed Prometaphase Polylobed Interphase Polylobed Interphase Polylobed Apoptosis MetaphaseAlignment Polylobed Interphase Polylobed Binuclear Polylobed SmallIrregular UndefinedCondensed Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed SmallIrregular Binuclear Prometaphase Grape Large Binuclear Apoptosis Binuclear Grape Prometaphase Prometaphase Grape UndefinedCondensed Polylobed Grape Prometaphase SmallIrregular Polylobed Polylobed Prometaphase Grape Binuclear Binuclear Polylobed MetaphaseAlignment Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Metaphase Polylobed Prometaphase Binuclear Polylobed Apoptosis Polylobed Grape Prometaphase Interphase SmallIrregular Polylobed Polylobed Grape Interphase Grape Binuclear Binuclear Apoptosis Folded Artefact Artefact Polylobed Artefact Binuclear Polylobed Polylobed Polylobed Polylobed Artefact Binuclear Artefact Grape Interphase Polylobed Polylobed Polylobed Apoptosis Polylobed Apoptosis Polylobed Polylobed Polylobed Hole SmallIrregular Polylobed Metaphase Grape Artefact SmallIrregular Polylobed SmallIrregular Prometaphase Polylobed Polylobed Polylobed Grape Binuclear Folded Artefact MetaphaseAlignment Prometaphase Grape Artefact Metaphase Hole Binuclear Apoptosis Polylobed Polylobed Polylobed Polylobed Apoptosis Binuclear Polylobed Binuclear Polylobed Polylobed Apoptosis Metaphase Polylobed MetaphaseAlignment Interphase Prometaphase Binuclear Binuclear Polylobed MetaphaseAlignment SmallIrregular SmallIrregular Grape SmallIrregular Interphase Polylobed Polylobed Grape Grape Binuclear Polylobed Polylobed Grape Binuclear Polylobed Binuclear Binuclear Polylobed Prometaphase Prometaphase Polylobed Binuclear Prometaphase SmallIrregular Polylobed Polylobed Grape Elongated Folded Artefact SmallIrregular Polylobed Polylobed Interphase Polylobed Binuclear Apoptosis Interphase Prometaphase Elongated MetaphaseAlignment Interphase Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Grape Prometaphase Grape Prometaphase Polylobed Polylobed SmallIrregular Binuclear Polylobed Interphase Polylobed Polylobed Prometaphase Polylobed Interphase Interphase Binuclear Artefact Binuclear MetaphaseAlignment Artefact Polylobed Polylobed Artefact Grape Polylobed SmallIrregular SmallIrregular Polylobed Polylobed Hole Polylobed MetaphaseAlignment Binuclear MetaphaseAlignment Artefact Polylobed Artefact Polylobed Polylobed Polylobed Polylobed SmallIrregular Hole Artefact Polylobed Polylobed Grape Polylobed Polylobed Binuclear Hole Binuclear SmallIrregular Polylobed SmallIrregular Hole Prometaphase Grape Hole Prometaphase Grape Interphase Interphase Binuclear Apoptosis Polylobed Hole MetaphaseAlignment Polylobed Interphase Binuclear Polylobed Binuclear Grape Large Hole Artefact Metaphase Polylobed Binuclear Polylobed Polylobed Polylobed Apoptosis MetaphaseAlignment Polylobed Binuclear Interphase Interphase Polylobed Polylobed Grape Binuclear Binuclear Polylobed Polylobed Apoptosis Binuclear Elongated Polylobed Folded Apoptosis Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Interphase Interphase Polylobed Polylobed Polylobed Polylobed Prometaphase Interphase Polylobed Polylobed Polylobed Prometaphase Apoptosis Polylobed MetaphaseAlignment Prometaphase Large Polylobed Interphase Binuclear Apoptosis Polylobed Binuclear Metaphase MetaphaseAlignment Binuclear Polylobed Grape Polylobed Prometaphase SmallIrregular Metaphase MetaphaseAlignment Prometaphase Grape Artefact Binuclear Polylobed Interphase Apoptosis Polylobed Artefact Binuclear Large Artefact Grape Artefact Artefact Large Polylobed Artefact Hole Elongated Prometaphase Prometaphase Prometaphase Polylobed Binuclear Interphase Polylobed Artefact Binuclear Polylobed Binuclear Grape Apoptosis Prometaphase Polylobed Prometaphase Polylobed Grape Polylobed Polylobed MetaphaseAlignment Interphase Grape Binuclear SmallIrregular Grape Polylobed Polylobed Interphase Binuclear Grape Binuclear Artefact Polylobed Grape Binuclear Polylobed Binuclear Binuclear Polylobed Binuclear MetaphaseAlignment Artefact Polylobed Interphase SmallIrregular Polylobed Artefact Polylobed Polylobed Metaphase Polylobed Prometaphase Artefact Prometaphase Binuclear Artefact Grape Polylobed Polylobed Folded Interphase Prometaphase Prometaphase Interphase MetaphaseAlignment Polylobed Elongated Grape Binuclear Polylobed Polylobed Binuclear Binuclear Prometaphase Grape Binuclear Prometaphase Polylobed Polylobed Polylobed Polylobed Binuclear Metaphase Apoptosis Polylobed Grape Prometaphase -y_test_pred Prometaphase Polylobed Polylobed Grape Polylobed Polylobed Prometaphase Apoptosis Artefact Grape Polylobed Hole Polylobed Polylobed Binuclear Interphase Polylobed Polylobed Apoptosis SmallIrregular Prometaphase Polylobed Polylobed Polylobed Apoptosis Grape Interphase Polylobed Polylobed Binuclear Polylobed Interphase Polylobed Hole Grape Polylobed Polylobed Grape Hole Prometaphase Binuclear Prometaphase SmallIrregular Artefact Grape Polylobed Polylobed Apoptosis Polylobed Binuclear Binuclear Polylobed Polylobed Prometaphase Polylobed Binuclear Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Apoptosis MetaphaseAlignment Artefact Grape Polylobed Polylobed Hole Interphase Hole Binuclear Polylobed Apoptosis Polylobed Interphase Interphase Hole Interphase Polylobed Apoptosis Binuclear Prometaphase Interphase MetaphaseAlignment Polylobed Polylobed Polylobed Binuclear Polylobed SmallIrregular Polylobed Artefact Interphase Polylobed Prometaphase Hole Polylobed SmallIrregular Interphase Polylobed Grape Interphase Grape Binuclear Polylobed Prometaphase Prometaphase Apoptosis Metaphase Grape Polylobed Interphase Hole Artefact Polylobed Prometaphase Interphase Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Binuclear Binuclear Interphase Polylobed Grape Binuclear Interphase Apoptosis Binuclear Artefact Polylobed Polylobed Binuclear Polylobed Polylobed Apoptosis Interphase Binuclear Polylobed Binuclear Polylobed Interphase Polylobed Prometaphase Binuclear Binuclear Polylobed Interphase Polylobed Prometaphase Prometaphase Polylobed Interphase Polylobed Binuclear Polylobed Interphase UndefinedCondensed Polylobed Polylobed Apoptosis Polylobed Polylobed UndefinedCondensed Binuclear Artefact Grape Large Binuclear Apoptosis MetaphaseAlignment Grape Prometaphase Prometaphase Grape UndefinedCondensed Polylobed Grape Prometaphase Interphase Binuclear Polylobed Prometaphase Grape Polylobed Polylobed Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Apoptosis Binuclear Polylobed Apoptosis Polylobed Grape Prometaphase Interphase SmallIrregular Polylobed Polylobed Grape SmallIrregular Grape Binuclear Binuclear Apoptosis Folded Artefact Artefact Polylobed Artefact Binuclear Polylobed Polylobed Metaphase Binuclear Artefact Polylobed Metaphase Grape Interphase Binuclear Polylobed Polylobed Apoptosis Polylobed Apoptosis Polylobed Polylobed Polylobed Hole SmallIrregular Polylobed Metaphase Polylobed Artefact SmallIrregular Polylobed Hole Prometaphase Polylobed Polylobed Polylobed Grape Binuclear Folded Artefact MetaphaseAlignment Prometaphase Grape Artefact Interphase Hole Binuclear Apoptosis Polylobed Binuclear Polylobed Polylobed Apoptosis Binuclear Polylobed Binuclear Grape Polylobed Apoptosis Metaphase Polylobed MetaphaseAlignment Interphase Prometaphase Binuclear Binuclear Polylobed Prometaphase SmallIrregular Hole Grape SmallIrregular Interphase Binuclear Polylobed Grape Grape Binuclear Polylobed Polylobed Grape Binuclear Polylobed Binuclear Binuclear Binuclear Prometaphase Prometaphase Polylobed Binuclear Prometaphase SmallIrregular Polylobed Polylobed Grape Elongated Polylobed Artefact SmallIrregular Polylobed Polylobed Interphase Binuclear Binuclear Apoptosis Interphase Apoptosis Elongated MetaphaseAlignment Interphase Polylobed MetaphaseAlignment MetaphaseAlignment Polylobed Interphase Polylobed Grape Prometaphase Grape Prometaphase Polylobed Polylobed SmallIrregular Binuclear Polylobed Binuclear Polylobed Polylobed Prometaphase Polylobed Interphase Prometaphase Binuclear Artefact Binuclear Polylobed Artefact Polylobed Polylobed Polylobed Grape Polylobed SmallIrregular SmallIrregular Polylobed Polylobed SmallIrregular Polylobed Artefact Binuclear MetaphaseAlignment Artefact Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed SmallIrregular Hole Artefact Polylobed Polylobed Grape Polylobed Polylobed Polylobed Hole Binuclear SmallIrregular Polylobed SmallIrregular Elongated Prometaphase Grape SmallIrregular UndefinedCondensed Grape Interphase Interphase Binuclear Apoptosis Interphase Apoptosis Metaphase Polylobed Interphase Binuclear Polylobed Artefact Grape Large Hole Prometaphase Prometaphase Polylobed Binuclear Polylobed Polylobed Binuclear Apoptosis MetaphaseAlignment Polylobed Binuclear MetaphaseAlignment MetaphaseAlignment Polylobed Grape Grape Binuclear Binuclear Polylobed Polylobed Apoptosis Binuclear Interphase Polylobed Metaphase Apoptosis Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Interphase Polylobed Polylobed Polylobed Polylobed Prometaphase Interphase SmallIrregular Polylobed Binuclear Prometaphase Apoptosis Polylobed MetaphaseAlignment Prometaphase Large Polylobed Interphase Binuclear Artefact Polylobed Binuclear Metaphase MetaphaseAlignment Polylobed Polylobed Grape Polylobed Prometaphase SmallIrregular Interphase MetaphaseAlignment Prometaphase Grape Artefact Binuclear Polylobed Interphase Apoptosis Polylobed Artefact Binuclear Large Artefact Grape Artefact Artefact Interphase Polylobed Artefact Apoptosis Elongated Prometaphase Prometaphase Prometaphase Grape Binuclear Prometaphase Polylobed Polylobed Binuclear Polylobed Interphase Grape Apoptosis Prometaphase Polylobed Apoptosis Polylobed Grape Polylobed Polylobed MetaphaseAlignment Interphase Grape Interphase Binuclear Grape Polylobed Apoptosis Interphase Binuclear Grape Folded Artefact Polylobed Grape Binuclear Polylobed Binuclear Binuclear Polylobed Binuclear MetaphaseAlignment Artefact Binuclear Hole SmallIrregular Polylobed Artefact Polylobed Polylobed MetaphaseAlignment Polylobed Prometaphase Polylobed Prometaphase Binuclear Artefact Grape Polylobed Polylobed Binuclear Interphase Prometaphase Prometaphase Interphase MetaphaseAlignment Binuclear Elongated Grape Binuclear Polylobed Polylobed Binuclear Binuclear Prometaphase Grape Binuclear Prometaphase Polylobed Polylobed Apoptosis Polylobed Binuclear Metaphase Apoptosis Polylobed Grape Prometaphase -y_holdout Grape Polylobed Grape Prometaphase Grape Polylobed Grape Grape MetaphaseAlignment Artefact Large Polylobed Apoptosis Apoptosis Grape Grape Hole Polylobed Grape Grape Polylobed Grape Binuclear Grape Grape Grape Apoptosis Polylobed Grape Grape Polylobed Grape Grape Polylobed Grape Grape Polylobed Grape MetaphaseAlignment MetaphaseAlignment Grape Polylobed Polylobed Grape Polylobed Large Grape Elongated Grape Grape Grape Grape MetaphaseAlignment Polylobed Polylobed Grape UndefinedCondensed Large Folded Grape Grape Polylobed Grape MetaphaseAlignment Elongated Grape Polylobed MetaphaseAlignment Grape Grape Grape Grape Grape Prometaphase Grape Polylobed Polylobed Grape Grape Polylobed Grape Grape Polylobed Large Polylobed Grape Grape Hole Large Grape MetaphaseAlignment Grape Grape Grape Grape MetaphaseAlignment Grape Grape Grape Polylobed Metaphase Prometaphase Grape Grape Polylobed Grape Polylobed Folded Polylobed Polylobed Prometaphase Polylobed Grape Interphase Grape Grape Artefact Artefact MetaphaseAlignment Grape Hole Grape MetaphaseAlignment Large Grape Grape Apoptosis Polylobed Hole MetaphaseAlignment Grape Apoptosis Grape Grape MetaphaseAlignment Grape Large Grape Grape Polylobed SmallIrregular Folded Metaphase Grape Grape SmallIrregular Grape Grape Grape Polylobed Polylobed Grape SmallIrregular Grape Grape Polylobed Polylobed Grape Apoptosis Polylobed Grape Grape Grape Polylobed Grape Apoptosis Grape Binuclear Grape MetaphaseAlignment MetaphaseAlignment Grape Grape Grape Folded MetaphaseAlignment Interphase Apoptosis Prometaphase Grape Grape Grape Polylobed Polylobed Grape MetaphaseAlignment Folded MetaphaseAlignment Polylobed Artefact Large Grape Folded Grape Grape Polylobed Grape -y_holdout_pred Grape Polylobed Grape Prometaphase Interphase Polylobed Grape Grape MetaphaseAlignment Artefact Large Polylobed Apoptosis Prometaphase Grape Polylobed Hole Polylobed Grape Polylobed Polylobed Grape Polylobed Polylobed Polylobed Grape Apoptosis Polylobed Grape Grape Polylobed Grape Grape Polylobed Grape Grape Polylobed Grape Prometaphase MetaphaseAlignment Grape Polylobed Polylobed Grape Polylobed Polylobed Grape Elongated Grape Grape Grape Grape Prometaphase Large Polylobed Grape SmallIrregular Large Interphase Grape Grape Polylobed Prometaphase MetaphaseAlignment Elongated Grape Polylobed MetaphaseAlignment Grape Grape Grape Grape Grape Prometaphase Grape Polylobed Polylobed Grape Grape Polylobed Grape Grape Polylobed Polylobed Polylobed Grape Grape Hole Interphase Grape Interphase Grape Grape Polylobed Grape MetaphaseAlignment Grape Grape Grape Polylobed Metaphase Interphase Grape Grape Polylobed Grape Polylobed SmallIrregular Polylobed Polylobed Prometaphase Polylobed Grape Interphase Grape Grape Polylobed Artefact Prometaphase Grape Hole Grape MetaphaseAlignment Large Grape Grape Apoptosis Polylobed Elongated Prometaphase Grape Apoptosis Grape Grape MetaphaseAlignment Grape Polylobed Grape Grape Polylobed Binuclear Interphase MetaphaseAlignment Grape Grape Hole Grape Grape Grape Polylobed Polylobed Grape Interphase Polylobed Grape Polylobed Polylobed Grape Apoptosis Polylobed Polylobed Grape Grape Polylobed Polylobed Apoptosis Grape Polylobed Grape MetaphaseAlignment MetaphaseAlignment Grape Grape Grape Folded MetaphaseAlignment Interphase Apoptosis Prometaphase Grape Grape Grape Polylobed Polylobed Grape Artefact Folded MetaphaseAlignment Polylobed Artefact Large Grape Folded Grape Grape Polylobed Grape diff --git a/3.ML_model/results/2.shuffled_baseline_model_predictions.tsv b/3.ML_model/results/2.shuffled_baseline_model_predictions.tsv deleted file mode 100644 index 9fb5693d..00000000 --- a/3.ML_model/results/2.shuffled_baseline_model_predictions.tsv +++ /dev/null @@ -1,7 +0,0 @@ - 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 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3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 -y_train Interphase Binuclear Grape Prometaphase Apoptosis Hole Polylobed Elongated Hole Polylobed Grape Artefact SmallIrregular Apoptosis Polylobed Binuclear Polylobed Interphase Polylobed MetaphaseAlignment Interphase Hole SmallIrregular Polylobed Binuclear SmallIrregular Polylobed Prometaphase Polylobed Folded Polylobed Polylobed Polylobed Polylobed Prometaphase Prometaphase Polylobed Prometaphase Polylobed Prometaphase Grape Large Metaphase Polylobed SmallIrregular Prometaphase Prometaphase MetaphaseAlignment Grape Binuclear MetaphaseAlignment Polylobed Polylobed Polylobed Hole Interphase Interphase Binuclear Polylobed Binuclear Polylobed Prometaphase Polylobed SmallIrregular Polylobed Polylobed Artefact Polylobed SmallIrregular Polylobed Polylobed Hole Binuclear Polylobed Grape Interphase Grape Interphase Apoptosis Polylobed Prometaphase Grape Polylobed Interphase Grape Polylobed Polylobed Apoptosis Polylobed Polylobed Grape Polylobed Grape Polylobed Prometaphase Artefact Polylobed Binuclear Polylobed SmallIrregular Artefact Metaphase Polylobed Large Polylobed Polylobed Polylobed Polylobed SmallIrregular Prometaphase Apoptosis Prometaphase Polylobed Polylobed MetaphaseAlignment Grape Interphase Binuclear Grape Polylobed UndefinedCondensed Polylobed Elongated Polylobed Polylobed Polylobed Hole Polylobed Grape Interphase Polylobed Polylobed Interphase Artefact Grape Polylobed Prometaphase Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Binuclear Apoptosis Hole Polylobed Binuclear Polylobed SmallIrregular Interphase Polylobed Apoptosis Prometaphase Binuclear Polylobed Grape Artefact SmallIrregular Interphase Binuclear Polylobed Binuclear Polylobed Artefact Polylobed Polylobed Artefact Binuclear Binuclear Prometaphase Polylobed Folded Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Polylobed MetaphaseAlignment Grape UndefinedCondensed Binuclear Binuclear Interphase SmallIrregular Polylobed Metaphase Polylobed Polylobed Polylobed Metaphase Grape Apoptosis SmallIrregular Binuclear Prometaphase Polylobed Grape UndefinedCondensed Artefact Polylobed Polylobed Artefact Polylobed Binuclear Polylobed Polylobed Grape SmallIrregular Prometaphase Binuclear Binuclear Binuclear Binuclear Grape Polylobed Polylobed Binuclear Interphase Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed SmallIrregular Polylobed Polylobed Grape Grape Polylobed Polylobed Polylobed Interphase Interphase SmallIrregular SmallIrregular Binuclear Binuclear Grape SmallIrregular Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Artefact Large Artefact Grape Polylobed Polylobed Grape SmallIrregular Polylobed Grape Polylobed Polylobed Prometaphase Polylobed Polylobed Binuclear Polylobed Polylobed Folded Polylobed Interphase Artefact Apoptosis Polylobed Binuclear Polylobed Prometaphase Binuclear Binuclear Polylobed SmallIrregular Grape Prometaphase Interphase SmallIrregular Apoptosis Polylobed Polylobed MetaphaseAlignment Interphase Grape Binuclear Prometaphase Prometaphase Folded Artefact Grape Grape Polylobed Artefact Binuclear Hole Prometaphase Grape Polylobed Interphase Interphase Polylobed Artefact SmallIrregular Polylobed Polylobed Binuclear Binuclear Binuclear Binuclear Large Artefact SmallIrregular Polylobed Binuclear Folded Apoptosis Binuclear Polylobed MetaphaseAlignment Polylobed Polylobed Apoptosis Artefact Polylobed MetaphaseAlignment Polylobed Interphase Binuclear Prometaphase Polylobed Grape Artefact Grape Prometaphase Artefact Grape Polylobed Interphase Polylobed Binuclear Interphase Polylobed Binuclear MetaphaseAlignment Binuclear Polylobed Binuclear Elongated Polylobed Polylobed Polylobed Binuclear Interphase Polylobed Grape Binuclear Artefact MetaphaseAlignment Polylobed Binuclear Binuclear Metaphase Binuclear Polylobed Hole Polylobed Polylobed Interphase Polylobed Apoptosis Binuclear Prometaphase Polylobed Large Hole Polylobed Polylobed Binuclear Polylobed Prometaphase Polylobed UndefinedCondensed Large Grape Polylobed Polylobed MetaphaseAlignment Metaphase SmallIrregular Polylobed Interphase Interphase Polylobed Artefact Polylobed Interphase Polylobed UndefinedCondensed Interphase MetaphaseAlignment Binuclear Binuclear Binuclear Binuclear Metaphase MetaphaseAlignment Grape Binuclear Polylobed Apoptosis Artefact Polylobed SmallIrregular Polylobed Apoptosis Interphase Polylobed MetaphaseAlignment Polylobed Metaphase Interphase Prometaphase Hole Polylobed Polylobed Binuclear Prometaphase Grape Polylobed Polylobed SmallIrregular Polylobed Hole Interphase Prometaphase Polylobed Artefact Binuclear Prometaphase Polylobed Binuclear Binuclear Artefact Polylobed Prometaphase Polylobed Polylobed Binuclear Polylobed Interphase Polylobed Polylobed Polylobed SmallIrregular Polylobed Grape Binuclear Apoptosis MetaphaseAlignment Hole Apoptosis Polylobed Prometaphase Polylobed Artefact MetaphaseAlignment Grape Binuclear Interphase Polylobed Polylobed Interphase Artefact Binuclear Grape Binuclear Polylobed Grape Interphase Polylobed Binuclear Polylobed Polylobed UndefinedCondensed SmallIrregular Polylobed Polylobed Polylobed Binuclear Polylobed Apoptosis Binuclear Prometaphase Apoptosis Artefact Grape Prometaphase Polylobed Polylobed Polylobed Polylobed Apoptosis Prometaphase Prometaphase Polylobed Binuclear SmallIrregular Interphase Polylobed Binuclear Polylobed Polylobed Polylobed Grape SmallIrregular UndefinedCondensed Polylobed Hole Grape Apoptosis Polylobed Binuclear Polylobed Grape Hole Prometaphase Apoptosis Apoptosis Polylobed Polylobed Folded Apoptosis Metaphase Polylobed Apoptosis Hole Interphase Polylobed Interphase Grape Polylobed Hole Prometaphase Grape Hole Polylobed Artefact Grape Grape Artefact Binuclear MetaphaseAlignment Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Grape Polylobed Binuclear Polylobed Apoptosis Metaphase Polylobed Polylobed Artefact Prometaphase Hole Binuclear Binuclear Polylobed Large Polylobed Folded Polylobed MetaphaseAlignment Prometaphase Grape Polylobed Interphase Grape Prometaphase Interphase Polylobed SmallIrregular Polylobed Prometaphase Interphase Folded Grape Interphase Apoptosis Interphase Polylobed Interphase Polylobed Polylobed Polylobed Interphase MetaphaseAlignment Polylobed Binuclear Apoptosis Prometaphase Grape Artefact MetaphaseAlignment Interphase Polylobed Polylobed UndefinedCondensed Apoptosis Polylobed Prometaphase Polylobed Polylobed Polylobed Binuclear Grape Apoptosis Grape Interphase Grape Interphase Polylobed Polylobed Binuclear Binuclear Prometaphase Grape Polylobed Prometaphase Polylobed SmallIrregular Grape Polylobed Metaphase Binuclear Grape SmallIrregular Polylobed Hole Artefact Grape Grape Apoptosis Polylobed Polylobed MetaphaseAlignment Grape Metaphase Polylobed Prometaphase SmallIrregular Polylobed Grape Polylobed Interphase Polylobed Prometaphase Polylobed Prometaphase Polylobed Interphase Polylobed Polylobed Hole Polylobed Prometaphase Artefact Binuclear Grape Apoptosis Polylobed Binuclear Artefact Binuclear Binuclear Artefact Grape Polylobed Polylobed Polylobed Interphase Binuclear Polylobed Polylobed MetaphaseAlignment Prometaphase Polylobed Prometaphase Binuclear Polylobed Grape Polylobed Interphase Interphase Polylobed Polylobed Polylobed Polylobed Artefact Apoptosis Polylobed Polylobed Artefact Polylobed Binuclear MetaphaseAlignment MetaphaseAlignment Binuclear Artefact Hole Folded Artefact Polylobed Apoptosis Grape Grape Grape SmallIrregular Elongated Grape Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Grape Prometaphase Polylobed Polylobed Metaphase Polylobed Prometaphase Polylobed Grape Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Apoptosis Artefact Binuclear Polylobed Grape Interphase Binuclear Polylobed Interphase Polylobed Artefact Prometaphase Polylobed Interphase Apoptosis Polylobed MetaphaseAlignment Polylobed Artefact Binuclear Polylobed Polylobed Artefact Artefact Interphase Polylobed Binuclear Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Prometaphase MetaphaseAlignment Binuclear MetaphaseAlignment Binuclear MetaphaseAlignment Grape Binuclear SmallIrregular MetaphaseAlignment Polylobed SmallIrregular Polylobed Apoptosis Binuclear Interphase Polylobed MetaphaseAlignment Polylobed Grape Binuclear Grape Polylobed Artefact Binuclear Grape Prometaphase Polylobed Polylobed Polylobed Polylobed SmallIrregular Polylobed Grape Polylobed SmallIrregular Interphase Binuclear Polylobed Polylobed Polylobed Interphase Elongated MetaphaseAlignment Artefact MetaphaseAlignment SmallIrregular Metaphase Binuclear Binuclear Polylobed Apoptosis Binuclear Polylobed Interphase Polylobed Apoptosis Artefact Prometaphase Polylobed Grape Grape Grape Polylobed Interphase Grape Binuclear Artefact Polylobed Binuclear MetaphaseAlignment Artefact Polylobed Polylobed Prometaphase Apoptosis Polylobed Polylobed Polylobed Polylobed Polylobed Hole Polylobed Prometaphase Polylobed Polylobed Artefact Polylobed UndefinedCondensed Prometaphase Polylobed SmallIrregular Apoptosis Grape Binuclear Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Interphase Polylobed Polylobed Polylobed Polylobed Polylobed SmallIrregular SmallIrregular Polylobed Polylobed Polylobed Grape SmallIrregular Polylobed Polylobed Binuclear Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Polylobed Apoptosis Grape Hole Prometaphase Hole Artefact Interphase Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed SmallIrregular Folded Artefact Polylobed Interphase Grape Polylobed Binuclear Polylobed Interphase Polylobed Large Hole Interphase Polylobed Polylobed Polylobed Interphase Binuclear Binuclear Grape Apoptosis Prometaphase Grape Polylobed Binuclear Polylobed Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Prometaphase Binuclear Binuclear Hole Interphase Prometaphase SmallIrregular SmallIrregular Binuclear Binuclear Polylobed Prometaphase Polylobed Prometaphase Polylobed Prometaphase Apoptosis Metaphase Binuclear Polylobed Polylobed Grape Binuclear Polylobed Interphase Large Polylobed Prometaphase Polylobed Grape Polylobed Polylobed Artefact Polylobed Metaphase Binuclear Grape Polylobed Polylobed Binuclear Apoptosis Binuclear Interphase MetaphaseAlignment Polylobed Apoptosis Polylobed Polylobed Interphase Binuclear Polylobed Metaphase Binuclear Polylobed Polylobed Binuclear Grape Polylobed Elongated Grape Polylobed Grape Polylobed Hole Polylobed Artefact Binuclear Apoptosis Interphase Polylobed Polylobed Binuclear Grape Binuclear Grape SmallIrregular Binuclear Binuclear Polylobed Polylobed Interphase Binuclear Polylobed Interphase Grape Grape Hole Polylobed Polylobed Polylobed Grape Polylobed Polylobed Apoptosis Interphase Polylobed Apoptosis Polylobed Artefact Polylobed Grape Polylobed Interphase Prometaphase Polylobed Polylobed Artefact Interphase SmallIrregular SmallIrregular Polylobed Binuclear Binuclear Artefact Apoptosis Grape Interphase Hole Large Grape Polylobed Polylobed Interphase Artefact Polylobed Polylobed Binuclear Interphase Polylobed Polylobed Polylobed SmallIrregular Polylobed Polylobed Binuclear Hole Interphase Artefact Interphase Binuclear Polylobed Binuclear Artefact Grape Polylobed Binuclear Polylobed Hole Polylobed Prometaphase Interphase Large Polylobed Artefact Prometaphase Interphase Polylobed MetaphaseAlignment Binuclear Grape Elongated Prometaphase Artefact Polylobed Artefact Binuclear Polylobed Binuclear Binuclear Artefact Polylobed Prometaphase Artefact Binuclear Prometaphase Polylobed Polylobed Prometaphase Interphase Artefact Polylobed Prometaphase Binuclear Binuclear Prometaphase Artefact MetaphaseAlignment Apoptosis Binuclear Interphase Grape Prometaphase Metaphase Interphase Elongated Metaphase Polylobed Artefact Binuclear Artefact Grape Polylobed Prometaphase SmallIrregular Artefact Polylobed MetaphaseAlignment Prometaphase Polylobed Polylobed Polylobed SmallIrregular Grape Grape MetaphaseAlignment Polylobed Prometaphase Prometaphase Polylobed Prometaphase Artefact Polylobed Polylobed Interphase Apoptosis Polylobed Artefact Apoptosis Artefact Binuclear Polylobed Interphase SmallIrregular Polylobed SmallIrregular MetaphaseAlignment Binuclear Interphase Grape Prometaphase Polylobed Artefact Polylobed Binuclear Interphase MetaphaseAlignment Hole Large Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Artefact Apoptosis Polylobed Grape Polylobed Grape Prometaphase Binuclear Prometaphase Polylobed Polylobed Polylobed Hole Binuclear Polylobed Polylobed Binuclear Binuclear Polylobed Binuclear Polylobed Polylobed Interphase Polylobed Grape Polylobed Polylobed Interphase SmallIrregular MetaphaseAlignment Polylobed Interphase Polylobed Artefact Apoptosis Prometaphase Grape Prometaphase Binuclear Interphase Prometaphase Binuclear Artefact Prometaphase Grape Prometaphase Interphase Polylobed Polylobed Polylobed Artefact Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Prometaphase SmallIrregular Binuclear Binuclear SmallIrregular Large Polylobed Polylobed Polylobed Grape Binuclear MetaphaseAlignment Polylobed Apoptosis Polylobed MetaphaseAlignment Artefact Grape Polylobed Apoptosis Prometaphase Interphase Interphase Binuclear Polylobed Polylobed Polylobed Polylobed MetaphaseAlignment Folded Polylobed Polylobed Large Polylobed Polylobed Interphase Polylobed Artefact Grape Polylobed Binuclear Polylobed MetaphaseAlignment Polylobed Binuclear Artefact MetaphaseAlignment Polylobed Interphase Apoptosis Artefact Polylobed Polylobed Binuclear Polylobed Interphase Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Prometaphase Interphase Polylobed Polylobed Prometaphase Polylobed Binuclear Prometaphase MetaphaseAlignment Apoptosis Polylobed Prometaphase Elongated Interphase Polylobed Polylobed Grape Prometaphase Polylobed Grape Polylobed Metaphase Polylobed Polylobed Grape Grape Hole Polylobed Polylobed Grape Interphase Binuclear Grape Grape Polylobed Polylobed Prometaphase Prometaphase UndefinedCondensed Interphase Polylobed Polylobed Interphase Artefact Binuclear Polylobed Binuclear Apoptosis Hole Interphase Artefact Binuclear Polylobed Artefact Grape Polylobed SmallIrregular MetaphaseAlignment Polylobed Polylobed Grape Polylobed Polylobed Binuclear Interphase Binuclear Binuclear Polylobed Polylobed Polylobed Binuclear Binuclear Hole Polylobed Polylobed SmallIrregular Artefact Polylobed Polylobed Hole Polylobed Polylobed Polylobed Polylobed Binuclear Artefact Artefact Polylobed Polylobed Binuclear Artefact Hole Polylobed Hole Hole Apoptosis Large Artefact Interphase SmallIrregular Polylobed Polylobed Folded Polylobed Binuclear Binuclear Polylobed Polylobed SmallIrregular Polylobed Grape Interphase Interphase Apoptosis Interphase Polylobed Polylobed Prometaphase Binuclear Polylobed Interphase Binuclear Polylobed Prometaphase Polylobed Polylobed Apoptosis Hole Interphase Artefact Grape Binuclear Apoptosis Grape Binuclear Artefact Grape Polylobed Elongated Artefact Binuclear Grape Polylobed Metaphase Polylobed Polylobed Binuclear Prometaphase Polylobed Binuclear Grape Polylobed Polylobed Prometaphase Grape Polylobed Polylobed Hole Polylobed Binuclear Interphase Grape Polylobed Polylobed SmallIrregular Binuclear Polylobed Apoptosis Polylobed Polylobed Elongated Polylobed Polylobed Binuclear Binuclear Polylobed Prometaphase Polylobed Apoptosis Binuclear Binuclear Polylobed Interphase MetaphaseAlignment Polylobed Polylobed SmallIrregular Binuclear Hole Binuclear Polylobed Polylobed UndefinedCondensed Grape Grape Polylobed Polylobed Interphase Polylobed Grape Grape Prometaphase Interphase MetaphaseAlignment Binuclear Binuclear MetaphaseAlignment Binuclear Metaphase Polylobed Polylobed Apoptosis Interphase Polylobed Polylobed Polylobed Interphase Grape Prometaphase Binuclear Interphase Polylobed Prometaphase Binuclear Polylobed Binuclear Grape Prometaphase Prometaphase Polylobed Hole Artefact Polylobed Prometaphase Grape Hole Grape Grape Artefact Grape MetaphaseAlignment Polylobed Polylobed Binuclear Polylobed Polylobed Artefact Binuclear Binuclear Polylobed MetaphaseAlignment Binuclear Binuclear Polylobed Apoptosis Grape Binuclear Polylobed Binuclear Polylobed Polylobed Interphase Elongated Polylobed MetaphaseAlignment Interphase Metaphase Polylobed Binuclear MetaphaseAlignment Polylobed Polylobed Prometaphase Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Binuclear Apoptosis Binuclear Grape Polylobed Artefact Polylobed Polylobed Binuclear MetaphaseAlignment Prometaphase Apoptosis UndefinedCondensed Prometaphase Metaphase Binuclear Apoptosis SmallIrregular Grape Folded Grape Binuclear Prometaphase MetaphaseAlignment Binuclear Prometaphase Hole Binuclear Folded Prometaphase Folded Prometaphase Prometaphase Polylobed Interphase Polylobed MetaphaseAlignment Interphase Prometaphase Binuclear Prometaphase Artefact Interphase Polylobed Binuclear Polylobed Polylobed Binuclear Grape Binuclear Grape Prometaphase Polylobed Interphase Apoptosis Polylobed MetaphaseAlignment Prometaphase Polylobed Polylobed Grape Prometaphase Prometaphase Apoptosis Binuclear SmallIrregular Polylobed Grape Polylobed Metaphase Folded Large Grape Prometaphase Large Apoptosis Artefact Metaphase Interphase Prometaphase Binuclear Prometaphase Prometaphase Artefact Polylobed Interphase Polylobed Binuclear Binuclear Polylobed Binuclear Grape Prometaphase Grape Polylobed Binuclear SmallIrregular Grape Polylobed Prometaphase Polylobed Polylobed Interphase Binuclear Artefact Interphase Grape Binuclear Polylobed Binuclear Artefact Artefact Binuclear Binuclear Polylobed Grape Prometaphase Polylobed Hole Polylobed Polylobed Interphase MetaphaseAlignment MetaphaseAlignment Interphase Binuclear Polylobed Prometaphase Artefact Grape Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Hole Apoptosis Polylobed Binuclear Hole Apoptosis Binuclear Prometaphase Interphase Interphase Interphase Polylobed Polylobed Polylobed SmallIrregular Metaphase Grape Binuclear Grape Polylobed Prometaphase Binuclear MetaphaseAlignment SmallIrregular Polylobed Polylobed Interphase Grape Artefact Artefact Binuclear Binuclear Prometaphase Polylobed Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Grape Prometaphase Polylobed Hole Polylobed Polylobed Binuclear Polylobed Grape SmallIrregular Apoptosis Polylobed Artefact Polylobed Prometaphase Grape Elongated Binuclear Prometaphase UndefinedCondensed Polylobed Grape Binuclear Interphase Prometaphase Grape Metaphase Binuclear MetaphaseAlignment Polylobed Hole SmallIrregular Polylobed Prometaphase Binuclear Apoptosis SmallIrregular Polylobed Polylobed Interphase Prometaphase Interphase Grape Grape Polylobed Polylobed Grape Polylobed Metaphase Polylobed Binuclear Interphase Artefact Apoptosis Grape Grape Polylobed Artefact Artefact MetaphaseAlignment Apoptosis Interphase Artefact Polylobed Polylobed Hole Metaphase Polylobed Polylobed Binuclear Polylobed Prometaphase Hole Polylobed Artefact Artefact Polylobed Polylobed Interphase Polylobed Binuclear SmallIrregular Polylobed Polylobed Binuclear Large Prometaphase Apoptosis Apoptosis Polylobed Polylobed Folded Interphase Apoptosis UndefinedCondensed Interphase Hole Polylobed Polylobed Binuclear Grape SmallIrregular Hole MetaphaseAlignment Grape Polylobed Prometaphase Polylobed Polylobed Metaphase Prometaphase Hole Polylobed Binuclear MetaphaseAlignment Polylobed MetaphaseAlignment Prometaphase Polylobed Prometaphase Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Interphase Binuclear Binuclear Interphase SmallIrregular Binuclear Polylobed Polylobed Polylobed Binuclear Artefact Hole Polylobed Binuclear Polylobed SmallIrregular Binuclear Binuclear Grape Metaphase Polylobed Interphase Binuclear Interphase Polylobed Apoptosis MetaphaseAlignment Prometaphase Polylobed Interphase Grape Polylobed Binuclear SmallIrregular Prometaphase Polylobed Binuclear Interphase Grape Polylobed Hole Polylobed Polylobed Apoptosis Polylobed Interphase Polylobed Apoptosis Apoptosis Polylobed Polylobed SmallIrregular Binuclear Polylobed Polylobed Prometaphase Interphase Polylobed Polylobed Grape Metaphase Prometaphase Prometaphase Artefact MetaphaseAlignment SmallIrregular Artefact Polylobed Apoptosis Grape Binuclear Grape MetaphaseAlignment Polylobed Polylobed Hole Artefact Prometaphase Grape Hole Polylobed Binuclear MetaphaseAlignment SmallIrregular Grape Polylobed Binuclear Binuclear Polylobed Interphase Polylobed SmallIrregular Artefact Binuclear Artefact Artefact Polylobed Interphase Binuclear Polylobed Grape Polylobed Artefact Polylobed Apoptosis Polylobed Polylobed Binuclear Binuclear Polylobed MetaphaseAlignment SmallIrregular Prometaphase Polylobed Grape Polylobed Grape Binuclear Prometaphase Elongated Polylobed SmallIrregular Polylobed Polylobed SmallIrregular Polylobed Polylobed Elongated Polylobed Prometaphase Binuclear Polylobed Polylobed Artefact Polylobed Artefact Grape Polylobed Binuclear Polylobed Interphase Grape Polylobed Polylobed Grape Large Polylobed Binuclear Binuclear Apoptosis Grape Binuclear Binuclear Polylobed Elongated Artefact Polylobed Apoptosis Polylobed Apoptosis Polylobed Folded Artefact Polylobed Grape Binuclear MetaphaseAlignment Polylobed Interphase Folded Prometaphase Polylobed Prometaphase Artefact Artefact Artefact Binuclear Polylobed Polylobed Polylobed Artefact Prometaphase Binuclear Apoptosis MetaphaseAlignment Polylobed Polylobed Polylobed Grape Artefact Polylobed Polylobed Polylobed Artefact Polylobed Polylobed MetaphaseAlignment Artefact Polylobed Interphase Binuclear Polylobed Prometaphase Polylobed Apoptosis Artefact Interphase Polylobed Polylobed Polylobed Interphase Polylobed Binuclear Interphase Folded Prometaphase Hole Polylobed Interphase Artefact Binuclear Polylobed Artefact Folded Interphase Apoptosis Hole Binuclear Binuclear Interphase Prometaphase Grape Apoptosis Polylobed Grape Artefact Prometaphase Binuclear Binuclear Polylobed Large Polylobed SmallIrregular Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Hole Polylobed Binuclear SmallIrregular Grape Polylobed Binuclear Binuclear Polylobed Artefact Binuclear Prometaphase MetaphaseAlignment Binuclear Grape Prometaphase Grape Polylobed Metaphase Prometaphase Polylobed Polylobed Polylobed Binuclear Interphase SmallIrregular Prometaphase Grape Polylobed Binuclear Grape Polylobed MetaphaseAlignment Metaphase Binuclear Interphase Polylobed Grape Polylobed Polylobed Polylobed Grape Metaphase Large Grape Binuclear Polylobed Binuclear Polylobed Binuclear UndefinedCondensed Interphase Artefact Binuclear Polylobed Polylobed Prometaphase Polylobed Polylobed Grape Prometaphase Apoptosis Binuclear Polylobed Binuclear Grape SmallIrregular SmallIrregular Folded Apoptosis Prometaphase Binuclear Binuclear Interphase MetaphaseAlignment Polylobed Polylobed Polylobed Artefact Interphase SmallIrregular Prometaphase SmallIrregular Polylobed SmallIrregular Large Interphase Hole Binuclear Artefact Artefact Interphase Binuclear Polylobed Folded Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Apoptosis MetaphaseAlignment Artefact SmallIrregular Binuclear Apoptosis Artefact Interphase Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Grape Binuclear Binuclear Grape Polylobed Interphase Interphase Prometaphase Polylobed Polylobed Grape Polylobed SmallIrregular SmallIrregular Binuclear Artefact Prometaphase Binuclear Polylobed Polylobed Binuclear Polylobed Binuclear SmallIrregular UndefinedCondensed Binuclear Polylobed Polylobed Hole Polylobed SmallIrregular Polylobed Polylobed Artefact Artefact Interphase Prometaphase Polylobed Polylobed Hole Interphase Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Large Binuclear Binuclear Polylobed Polylobed Polylobed Large SmallIrregular Artefact Polylobed Artefact Folded Prometaphase Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Prometaphase Grape Interphase Binuclear Metaphase Binuclear Polylobed Binuclear Prometaphase Binuclear Polylobed Polylobed Polylobed SmallIrregular Polylobed Interphase Apoptosis Polylobed Prometaphase Interphase Polylobed Polylobed Binuclear Artefact Hole Interphase SmallIrregular Prometaphase Large Binuclear Polylobed Grape Polylobed Grape Interphase Polylobed Apoptosis Polylobed Polylobed Binuclear MetaphaseAlignment MetaphaseAlignment Large Apoptosis MetaphaseAlignment Grape Polylobed Polylobed Apoptosis Polylobed Hole Grape Polylobed Polylobed Polylobed Polylobed Binuclear Binuclear SmallIrregular Polylobed Grape Prometaphase Artefact Prometaphase Interphase Binuclear Polylobed Polylobed Hole Prometaphase Prometaphase Binuclear Prometaphase Artefact Prometaphase Grape Grape Polylobed MetaphaseAlignment Artefact Polylobed Binuclear Binuclear Polylobed Polylobed Artefact Prometaphase Polylobed Polylobed MetaphaseAlignment Grape Polylobed MetaphaseAlignment Prometaphase Grape UndefinedCondensed SmallIrregular Grape Binuclear Prometaphase Hole Artefact Prometaphase Apoptosis Polylobed Polylobed Polylobed Interphase SmallIrregular Polylobed Polylobed Polylobed Artefact Binuclear Polylobed Polylobed Interphase Grape Interphase Binuclear Apoptosis Prometaphase Elongated Polylobed Apoptosis Polylobed Polylobed UndefinedCondensed Apoptosis Polylobed Binuclear Prometaphase Prometaphase Polylobed SmallIrregular Polylobed Binuclear Interphase Large Binuclear SmallIrregular Polylobed MetaphaseAlignment Prometaphase Prometaphase Grape MetaphaseAlignment Polylobed Binuclear Polylobed Polylobed Artefact Polylobed Prometaphase Polylobed MetaphaseAlignment Metaphase UndefinedCondensed Binuclear MetaphaseAlignment Polylobed Artefact Grape Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Apoptosis SmallIrregular Polylobed Polylobed Prometaphase Binuclear Interphase Artefact Apoptosis Polylobed Binuclear Artefact Polylobed Polylobed Polylobed Binuclear Grape Large Artefact Polylobed Binuclear SmallIrregular Binuclear Polylobed Binuclear Polylobed Artefact Prometaphase Polylobed Large Polylobed MetaphaseAlignment Polylobed Hole Folded Polylobed Polylobed Binuclear Metaphase Grape Polylobed Interphase Prometaphase Binuclear Polylobed Polylobed Binuclear Polylobed SmallIrregular Metaphase Polylobed Polylobed Prometaphase Polylobed Grape Apoptosis Interphase Binuclear Prometaphase Polylobed Artefact Binuclear Polylobed MetaphaseAlignment Binuclear Polylobed Binuclear Polylobed Polylobed UndefinedCondensed Binuclear Polylobed UndefinedCondensed SmallIrregular Polylobed MetaphaseAlignment Polylobed Interphase Binuclear Metaphase Binuclear Prometaphase Artefact Artefact UndefinedCondensed Polylobed Prometaphase Binuclear Polylobed Interphase Polylobed Binuclear Polylobed SmallIrregular Binuclear Interphase Metaphase Artefact Binuclear Folded Grape Interphase Artefact Metaphase Binuclear Grape Grape Binuclear Binuclear Polylobed Polylobed Hole Polylobed Elongated Apoptosis Binuclear Prometaphase Prometaphase Polylobed Polylobed Binuclear Polylobed Artefact MetaphaseAlignment Polylobed Prometaphase Grape Artefact Polylobed Interphase MetaphaseAlignment Interphase Hole Prometaphase Hole Prometaphase Interphase Interphase MetaphaseAlignment Hole Binuclear SmallIrregular Interphase Interphase Polylobed Prometaphase Grape Prometaphase Polylobed Interphase Interphase Polylobed UndefinedCondensed Grape SmallIrregular Prometaphase Binuclear Binuclear Polylobed Prometaphase Binuclear Polylobed Artefact Polylobed Hole Prometaphase Grape Polylobed Interphase Polylobed Binuclear Polylobed Grape Polylobed MetaphaseAlignment Apoptosis Hole Binuclear Prometaphase Polylobed SmallIrregular Polylobed Polylobed Grape Polylobed Metaphase Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed SmallIrregular Grape Metaphase Binuclear Polylobed Polylobed Polylobed Grape Polylobed Polylobed SmallIrregular Polylobed Polylobed Prometaphase Artefact Binuclear Artefact Grape Polylobed Binuclear Polylobed Polylobed Interphase Prometaphase Apoptosis UndefinedCondensed Folded SmallIrregular Polylobed Polylobed Prometaphase Binuclear Large Polylobed Polylobed Binuclear Apoptosis SmallIrregular Grape Polylobed Polylobed Apoptosis Polylobed Large SmallIrregular Prometaphase Polylobed Polylobed MetaphaseAlignment Polylobed Prometaphase Polylobed Prometaphase Binuclear Polylobed Polylobed Polylobed Large Binuclear MetaphaseAlignment Apoptosis Polylobed Binuclear Interphase Interphase SmallIrregular Binuclear Binuclear Polylobed Binuclear Binuclear Polylobed Polylobed Grape Artefact SmallIrregular Polylobed Polylobed MetaphaseAlignment Binuclear Apoptosis Interphase Polylobed Grape Prometaphase Polylobed Folded Polylobed Polylobed Prometaphase Hole Grape MetaphaseAlignment Elongated Binuclear Polylobed Polylobed Polylobed Prometaphase Polylobed Interphase Artefact Prometaphase Polylobed Binuclear Polylobed Polylobed Elongated Polylobed Interphase Prometaphase Polylobed Binuclear Polylobed SmallIrregular Grape Polylobed Binuclear Binuclear Apoptosis Grape Interphase Grape Prometaphase Polylobed Polylobed Binuclear Polylobed Grape Binuclear Interphase Binuclear Interphase Polylobed Polylobed Artefact Hole Interphase MetaphaseAlignment Polylobed Polylobed Polylobed Interphase Interphase Polylobed Apoptosis Prometaphase Artefact Binuclear Polylobed Interphase Polylobed Grape Binuclear Binuclear Polylobed Binuclear Polylobed MetaphaseAlignment Polylobed Polylobed Binuclear SmallIrregular Polylobed Polylobed Polylobed Prometaphase Elongated Binuclear Polylobed Polylobed Hole Binuclear Polylobed Apoptosis Polylobed Interphase Grape Polylobed Polylobed Apoptosis Binuclear Folded Prometaphase Polylobed MetaphaseAlignment Grape Binuclear Metaphase Binuclear Binuclear Binuclear Polylobed Apoptosis Folded SmallIrregular Interphase Binuclear Apoptosis Polylobed UndefinedCondensed Apoptosis SmallIrregular Grape Polylobed Polylobed SmallIrregular Hole Polylobed Polylobed Polylobed Grape Binuclear Binuclear Binuclear Grape MetaphaseAlignment Apoptosis Elongated Binuclear Binuclear Polylobed MetaphaseAlignment Metaphase Grape Polylobed Grape SmallIrregular Binuclear Polylobed Polylobed Polylobed Prometaphase Elongated Binuclear Grape Polylobed Interphase Interphase Polylobed Polylobed Artefact Grape Binuclear Interphase Binuclear Polylobed Polylobed Interphase Prometaphase Prometaphase Polylobed Polylobed Artefact Large Grape Interphase Binuclear Binuclear Polylobed Polylobed Polylobed Grape Folded Polylobed Binuclear Polylobed Binuclear MetaphaseAlignment Grape Polylobed Artefact Polylobed Polylobed Artefact MetaphaseAlignment Elongated Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Polylobed Grape MetaphaseAlignment Artefact Grape Interphase SmallIrregular Polylobed Interphase SmallIrregular Artefact SmallIrregular Prometaphase Polylobed Polylobed Hole Binuclear Prometaphase Prometaphase Prometaphase Metaphase Polylobed Grape Interphase Polylobed Polylobed Metaphase Polylobed Apoptosis Artefact SmallIrregular Prometaphase Artefact Polylobed Grape Apoptosis SmallIrregular Polylobed Polylobed Grape Polylobed Metaphase Metaphase Apoptosis Interphase Artefact Interphase Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Grape Binuclear MetaphaseAlignment Grape Hole Polylobed Polylobed Polylobed Grape Polylobed Artefact Polylobed Polylobed Binuclear Grape Prometaphase Binuclear Binuclear Polylobed Polylobed Prometaphase Polylobed Polylobed Artefact Polylobed Apoptosis Binuclear Artefact Polylobed Prometaphase Artefact Polylobed Polylobed Hole Prometaphase Artefact Grape Polylobed Artefact SmallIrregular Polylobed Apoptosis Apoptosis Polylobed Grape Interphase Apoptosis Interphase Artefact SmallIrregular Binuclear Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Binuclear Grape Prometaphase Prometaphase Polylobed Polylobed Interphase Grape Polylobed Polylobed UndefinedCondensed Prometaphase Binuclear Binuclear Prometaphase Binuclear Polylobed Polylobed Interphase Polylobed Binuclear Artefact Grape Polylobed Polylobed Polylobed Binuclear Binuclear Interphase Prometaphase Interphase Artefact SmallIrregular Binuclear Grape Interphase Artefact Apoptosis Prometaphase Polylobed Binuclear Prometaphase Polylobed SmallIrregular Grape Prometaphase Apoptosis Grape Apoptosis Polylobed Polylobed Polylobed Binuclear Metaphase Prometaphase Binuclear Apoptosis Interphase Binuclear Polylobed Polylobed Grape Large Polylobed Apoptosis Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Polylobed Binuclear Interphase Elongated Binuclear Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Binuclear Binuclear Interphase Grape Interphase Polylobed Polylobed Polylobed Polylobed Prometaphase Binuclear Prometaphase Polylobed Polylobed Prometaphase Prometaphase Artefact Apoptosis MetaphaseAlignment Binuclear Grape Polylobed Prometaphase Interphase Polylobed Grape Apoptosis MetaphaseAlignment Apoptosis Binuclear Interphase MetaphaseAlignment Prometaphase Polylobed Binuclear Prometaphase Polylobed Polylobed Polylobed Interphase Binuclear Polylobed Metaphase Polylobed Interphase Elongated Apoptosis Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Binuclear Prometaphase Polylobed Polylobed SmallIrregular Prometaphase Binuclear Apoptosis SmallIrregular Prometaphase Polylobed Interphase Prometaphase Interphase Polylobed Polylobed Polylobed Metaphase Interphase Grape SmallIrregular MetaphaseAlignment Prometaphase Interphase Prometaphase Apoptosis Apoptosis Polylobed MetaphaseAlignment Polylobed Polylobed Artefact Interphase Polylobed Apoptosis Polylobed Interphase Polylobed Prometaphase Prometaphase Polylobed Polylobed Interphase Interphase Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Binuclear Apoptosis Binuclear Polylobed Binuclear Artefact SmallIrregular Polylobed Hole Binuclear Grape MetaphaseAlignment -y_train_pred Grape Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Binuclear Polylobed Grape Polylobed Binuclear Polylobed Polylobed Binuclear Binuclear Binuclear Polylobed Grape Grape Binuclear Polylobed Polylobed Binuclear Polylobed Prometaphase Grape Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Grape Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Interphase Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Grape Artefact Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Binuclear Polylobed Binuclear Binuclear Polylobed Polylobed Grape Polylobed Polylobed Grape Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Grape Binuclear Polylobed Grape Grape Polylobed Polylobed Binuclear Binuclear Grape Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Grape Binuclear Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Binuclear Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Polylobed Binuclear Grape Polylobed Binuclear Polylobed Binuclear Binuclear Polylobed Polylobed MetaphaseAlignment Grape Polylobed Polylobed Polylobed Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Binuclear Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Binuclear Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed SmallIrregular Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Prometaphase Binuclear Interphase Polylobed Binuclear Polylobed Binuclear Polylobed Polylobed Binuclear Binuclear Artefact Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Polylobed Grape Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Binuclear Polylobed Polylobed Grape Polylobed Binuclear Polylobed Polylobed Grape Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Grape Polylobed Binuclear Binuclear Polylobed Polylobed Binuclear Grape Grape Interphase Polylobed Polylobed Binuclear Binuclear Binuclear Polylobed Binuclear Polylobed Interphase Polylobed Artefact Polylobed Polylobed Binuclear Binuclear Binuclear Polylobed Polylobed Binuclear Polylobed Binuclear Polylobed Polylobed Prometaphase Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Binuclear Prometaphase Polylobed Polylobed Grape Binuclear Binuclear Binuclear Polylobed Polylobed Grape Binuclear Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Grape Polylobed Binuclear Polylobed Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Grape Polylobed Prometaphase Binuclear Prometaphase Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Polylobed Polylobed SmallIrregular Binuclear Polylobed Polylobed Binuclear Binuclear SmallIrregular Binuclear Polylobed Binuclear Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Grape Polylobed Grape Grape Artefact Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Prometaphase Polylobed Binuclear Polylobed Interphase Polylobed Prometaphase Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Binuclear Polylobed Polylobed Polylobed Grape Polylobed Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Grape Binuclear Polylobed Polylobed Polylobed Prometaphase Binuclear Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Grape Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Binuclear Binuclear Binuclear Polylobed Polylobed Grape Polylobed Binuclear Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Binuclear Binuclear Interphase Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Polylobed Binuclear Binuclear Polylobed Grape Grape Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Grape Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Artefact Binuclear Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Binuclear Polylobed Binuclear Grape Polylobed Polylobed Grape Grape Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Artefact Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Polylobed Grape Polylobed Binuclear Prometaphase Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Grape Polylobed Prometaphase Binuclear Polylobed Polylobed Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Grape Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Artefact Binuclear Grape Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Binuclear Binuclear Polylobed Binuclear Polylobed Binuclear Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Polylobed Artefact Artefact Polylobed Polylobed Polylobed Grape Binuclear Binuclear Polylobed Binuclear Binuclear Binuclear Binuclear Prometaphase Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Prometaphase Polylobed Binuclear Polylobed Polylobed Binuclear Grape Polylobed Polylobed 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Grape Binuclear MetaphaseAlignment Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Binuclear Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Grape Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Interphase Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed SmallIrregular Grape Polylobed Polylobed Polylobed Polylobed Interphase SmallIrregular Polylobed Binuclear Binuclear Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed 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Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed -y_test Prometaphase Polylobed Polylobed Grape Polylobed Grape Prometaphase Apoptosis Artefact Grape Polylobed Hole Polylobed Polylobed Binuclear Interphase Polylobed Polylobed Apoptosis Interphase Prometaphase Polylobed Polylobed Polylobed Grape Grape Interphase Polylobed Polylobed Binuclear Polylobed Interphase Polylobed Binuclear Grape Polylobed Polylobed Grape Hole Prometaphase Artefact Prometaphase UndefinedCondensed Artefact Grape Polylobed Polylobed Apoptosis Polylobed Binuclear Artefact Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Binuclear Polylobed Polylobed UndefinedCondensed MetaphaseAlignment Artefact Grape Polylobed Polylobed Hole Interphase Hole Binuclear Polylobed Apoptosis Polylobed Interphase Interphase Prometaphase Interphase Binuclear Apoptosis Polylobed Prometaphase Interphase MetaphaseAlignment 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Polylobed Apoptosis Metaphase Polylobed MetaphaseAlignment Interphase Prometaphase Binuclear Binuclear Polylobed MetaphaseAlignment SmallIrregular SmallIrregular Grape SmallIrregular Interphase Polylobed Polylobed Grape Grape Binuclear Polylobed Polylobed Grape Binuclear Polylobed Binuclear Binuclear Polylobed Prometaphase Prometaphase Polylobed Binuclear Prometaphase SmallIrregular Polylobed Polylobed Grape Elongated Folded Artefact SmallIrregular Polylobed Polylobed Interphase Polylobed Binuclear Apoptosis Interphase Prometaphase Elongated MetaphaseAlignment Interphase Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Grape Prometaphase Grape Prometaphase Polylobed Polylobed SmallIrregular Binuclear Polylobed Interphase Polylobed Polylobed Prometaphase Polylobed Interphase Interphase Binuclear Artefact Binuclear MetaphaseAlignment Artefact Polylobed Polylobed Artefact Grape Polylobed SmallIrregular SmallIrregular Polylobed Polylobed Hole Polylobed MetaphaseAlignment 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Polylobed Elongated Grape Binuclear Polylobed Polylobed Binuclear Binuclear Prometaphase Grape Binuclear Prometaphase Polylobed Polylobed Polylobed Polylobed Binuclear Metaphase Apoptosis Polylobed Grape Prometaphase -y_test_pred Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Binuclear Binuclear Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Binuclear Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Polylobed Polylobed Artefact Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Grape 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Polylobed Binuclear Polylobed Binuclear Binuclear Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Grape Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Prometaphase Polylobed Binuclear Prometaphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Prometaphase Polylobed Polylobed Polylobed -y_holdout Grape Polylobed Grape Prometaphase Grape Polylobed Grape Grape MetaphaseAlignment Artefact Large Polylobed Apoptosis Apoptosis Grape Grape Hole Polylobed Grape Grape Polylobed Grape Binuclear Grape Grape Grape Apoptosis Polylobed Grape Grape Polylobed Grape Grape Polylobed Grape Grape Polylobed Grape MetaphaseAlignment MetaphaseAlignment Grape Polylobed Polylobed Grape Polylobed Large Grape Elongated Grape Grape Grape Grape MetaphaseAlignment Polylobed Polylobed Grape UndefinedCondensed Large Folded Grape Grape Polylobed Grape MetaphaseAlignment Elongated Grape Polylobed MetaphaseAlignment Grape Grape Grape Grape Grape Prometaphase Grape Polylobed Polylobed Grape Grape Polylobed Grape Grape Polylobed Large Polylobed Grape Grape Hole Large Grape MetaphaseAlignment Grape Grape Grape Grape MetaphaseAlignment Grape Grape Grape Polylobed Metaphase Prometaphase Grape Grape Polylobed Grape Polylobed Folded Polylobed Polylobed Prometaphase Polylobed Grape Interphase Grape Grape Artefact Artefact MetaphaseAlignment Grape Hole Grape MetaphaseAlignment Large Grape Grape Apoptosis Polylobed Hole MetaphaseAlignment Grape Apoptosis Grape Grape MetaphaseAlignment Grape Large Grape Grape Polylobed SmallIrregular Folded Metaphase Grape Grape SmallIrregular Grape Grape Grape Polylobed Polylobed Grape SmallIrregular Grape Grape Polylobed Polylobed Grape Apoptosis Polylobed Grape Grape Grape Polylobed Grape Apoptosis Grape Binuclear Grape MetaphaseAlignment MetaphaseAlignment Grape Grape Grape Folded MetaphaseAlignment Interphase Apoptosis Prometaphase Grape Grape Grape Polylobed Polylobed Grape MetaphaseAlignment Folded MetaphaseAlignment Polylobed Artefact Large Grape Folded Grape Grape Polylobed Grape -y_holdout_pred Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Artefact Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Interphase Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed diff --git a/3.evaluate_model/README.md b/3.evaluate_model/README.md new file mode 100644 index 00000000..8267a382 --- /dev/null +++ b/3.evaluate_model/README.md @@ -0,0 +1,30 @@ +# 3. Evaluate Model + +In this module, we evaluate the final and shuffled baseline ML models. + +After training the final and baseline models in [2.train_model](../2.train_model/), we use these models to predict the labels of the training, testing, and holdout datasets. +These predictions are saved in [model_predictions.tsv](evaluations/model_predictions.tsv) and [shuffled_baseline_model_predictions.tsv](evaluations/shuffled_baseline_model_predictions.tsv) respectively. + +We evaluate these 6 sets of predictions with a confusion matrix to see the true/false postives and negatives (see [sklearn.metrics.confusion_matrix](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html) for more details). + +We also evaluate these 6 sets of predictions with [sklearn.metrics.f1_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) to determine the final/shuffled baseline model's predictive performance on each subset. +F1 score measures the models precision and recall performance for each phenotypic class. + +## Step 1: Evaluate Model + +Use the commands below to evaluate the ML models: + +```sh +# Make sure you are located in 3.evaluate_model +cd 3.evaluate_model + +# Activate phenotypic_profiling conda environment +conda activate phenotypic_profiling + +# Evaluate model +bash evaluate_model.sh +``` + +## Results + +Each model's evaluations can be found in [evaluate_model.ipynb](evaluate_model.ipynb). \ No newline at end of file diff --git a/3.evaluate_model/evaluate_model.ipynb b/3.evaluate_model/evaluate_model.ipynb new file mode 100644 index 00000000..94c7e47d --- /dev/null +++ b/3.evaluate_model/evaluate_model.ipynb @@ -0,0 +1,1730 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import pathlib\n", + "\n", + "from sklearn.metrics import f1_score\n", + "from joblib import load\n", + "\n", + "import sys\n", + "sys.path.append(\"../utils\")\n", + "from split_utils import get_features_data\n", + "from train_utils import get_dataset, get_X_y_data\n", + "from evaluate_utils import evaluate_model_cm, evaluate_model_score" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load necessary data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# specify results directory\n", + "results_dir = pathlib.Path(\"evaluations/\")\n", + "results_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "# load features data from indexes and features dataframe\n", + "data_split_path = pathlib.Path(\"../1.split_data/indexes/data_split_indexes.tsv\")\n", + "data_split_indexes = pd.read_csv(data_split_path, sep=\"\\t\", index_col=0)\n", + "features_dataframe_path = pathlib.Path(\"../0.download_data/data/training_data.csv.gz\")\n", + "features_dataframe = get_features_data(features_dataframe_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate best model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "model_dir = pathlib.Path(\"../2.train_model/models/\")\n", + "log_reg_model_path = pathlib.Path(f\"{model_dir}/log_reg_model.joblib\")\n", + "log_reg_model = load(log_reg_model_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate with training data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"... ... ... ... ... \n", + "4646 ... -0.397249 -0.565566 -0.588207 \n", + "4647 ... -0.295010 0.310557 0.524240 \n", + "4648 ... -0.691697 0.809051 -0.522286 \n", + "4650 ... 1.127832 0.492408 -0.531921 \n", + "4652 ... -0.041231 0.998568 0.006131 \n", + "\n", + " efficientnet_1273 efficientnet_1274 efficientnet_1275 \\\n", + "0 -1.377590 0.454974 0.188488 \n", + "1 -1.431541 -0.063308 -0.412793 \n", + "2 -1.427502 -0.901764 -0.355080 \n", + "3 -1.365543 -0.276932 0.023856 \n", + "4 -1.584454 0.891666 1.223252 \n", + "... ... ... ... \n", + "4646 -0.944316 1.137498 -0.536326 \n", + "4647 -1.558440 -0.013856 -0.466041 \n", + "4648 -0.956816 0.112946 -0.087137 \n", + "4650 -0.766331 0.286463 0.493081 \n", + "4652 -0.857846 1.163148 0.904470 \n", + "\n", + " efficientnet_1276 efficientnet_1277 efficientnet_1278 \\\n", + "0 0.141427 -1.553405 2.346107 \n", + "1 0.452684 -1.906647 1.962141 \n", + "2 0.418053 -2.298449 1.098266 \n", + "3 0.376514 -1.700348 1.833686 \n", + "4 -0.359166 -0.826366 2.115734 \n", + "... ... ... ... \n", + "4646 -1.618058 0.579486 -1.083401 \n", + "4647 -3.544024 0.174894 -0.085268 \n", + "4648 -1.078033 0.191389 -0.921300 \n", + "4650 0.520599 -0.713538 0.553553 \n", + "4652 -0.321917 0.480036 0.449932 \n", + "\n", + " efficientnet_1279 \n", + "0 -1.774278 \n", + "1 -0.223039 \n", + "2 -0.069326 \n", + "3 -0.625385 \n", + "4 -1.241848 \n", + "... ... \n", + "4646 1.938486 \n", + "4647 1.764378 \n", + "4648 1.250694 \n", + "4650 0.480614 \n", + "4652 1.926145 \n", + "\n", + "[3398 rows x 1293 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "training_data = get_dataset(features_dataframe, data_split_indexes, \"train\")\n", + "training_data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_train, y_train_pred = evaluate_model_cm(log_reg_model, training_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1266Anaphase32909.93103499.413793LT0098_1321771LT0098_13_21LT0098_13/LT0098_13_21_77.tif...0.4016011.268759-0.4654240.1692120.1170112.817199-0.689718-0.5221470.739060-1.661322
1003Anaphase1031101.338983292.186441LT0100_0393841LT0100_03_93LT0100_03/LT0100_03_93_84.tif...0.423295-0.0766680.132334-0.510567-0.3498150.6629000.766846-0.0508151.4946430.513932
..................................................................
640SmallIrregular93979.030769553.276923LT0094_01319601LT0094_01_319LT0094_01/LT0094_01_319_60.tif...0.1418350.496487-0.754865-0.6622460.8235770.594171-0.5858570.2739090.0481650.719115
1912UndefinedCondensed48621.619565230.152174LT0027_44292921LT0027_44_292LT0027_44/LT0027_44_292_92.tif...1.1373671.9367491.337562-0.866048-0.3275550.1372860.744572-0.5522441.728855-0.621845
1244UndefinedCondensed75690.226415280.264151LT0041_32132651LT0041_32_132LT0041_32/LT0041_32_132_65.tif...1.0743091.1111210.266230-0.5872650.9998430.7663800.460926-0.1076521.5633490.345567
1885UndefinedCondensed126698.066667765.213333LT0027_44292651LT0027_44_292LT0027_44/LT0027_44_292_65.tif...0.7375191.4694391.067551-0.9885901.1853341.174448-0.712216-0.7405001.937956-2.176477
1873UndefinedCondensed18854.27272793.327273LT0027_44292651LT0027_44_292LT0027_44/LT0027_44_292_65.tif...0.4047780.9089701.159503-0.8742101.2538752.004828-1.048763-0.2657261.903799-1.792228
\n", + "

598 rows × 1293 columns

\n", + "
" + ], + "text/plain": [ + " Mitocheck_Phenotypic_Class Mitocheck_Object_ID Location_Center_X \\\n", + "3735 Anaphase 296 553.963636 \n", + "1938 Anaphase 191 108.709091 \n", + "861 Anaphase 95 1067.354839 \n", + "1266 Anaphase 32 909.931034 \n", + "1003 Anaphase 103 1101.338983 \n", + "... ... ... ... \n", + "640 SmallIrregular 93 979.030769 \n", + "1912 UndefinedCondensed 48 621.619565 \n", + "1244 UndefinedCondensed 75 690.226415 \n", + "1885 UndefinedCondensed 126 698.066667 \n", + "1873 UndefinedCondensed 18 854.272727 \n", + "\n", + " Location_Center_Y Metadata_Plate Metadata_Well Metadata_Frame \\\n", + "3735 955.818182 LT0043_48 166 36 \n", + "1938 810.927273 LT0027_44 292 95 \n", + "861 604.629032 LT0048_14 335 1 \n", + "1266 99.413793 LT0098_13 21 77 \n", + "1003 292.186441 LT0100_03 93 84 \n", + "... ... ... ... ... \n", + "640 553.276923 LT0094_01 319 60 \n", + "1912 230.152174 LT0027_44 292 92 \n", + "1244 280.264151 LT0041_32 132 65 \n", + "1885 765.213333 LT0027_44 292 65 \n", + "1873 93.327273 LT0027_44 292 65 \n", + "\n", + " Metadata_Site Metadata_Plate_Map_Name Metadata_DNA \\\n", + "3735 1 LT0043_48_166 LT0043_48/LT0043_48_166_36.tif \n", + "1938 1 LT0027_44_292 LT0027_44/LT0027_44_292_95.tif \n", + "861 1 LT0048_14_335 LT0048_14/LT0048_14_335_1.tif \n", + "1266 1 LT0098_13_21 LT0098_13/LT0098_13_21_77.tif \n", + "1003 1 LT0100_03_93 LT0100_03/LT0100_03_93_84.tif \n", + "... ... ... ... \n", + "640 1 LT0094_01_319 LT0094_01/LT0094_01_319_60.tif \n", + "1912 1 LT0027_44_292 LT0027_44/LT0027_44_292_92.tif \n", + "1244 1 LT0041_32_132 LT0041_32/LT0041_32_132_65.tif \n", + "1885 1 LT0027_44_292 LT0027_44/LT0027_44_292_65.tif \n", + "1873 1 LT0027_44_292 LT0027_44/LT0027_44_292_65.tif \n", + "\n", + " ... efficientnet_1270 efficientnet_1271 efficientnet_1272 \\\n", + "3735 ... 1.462225 -0.616369 -0.531454 \n", + "1938 ... 0.929248 3.647632 -0.114033 \n", + "861 ... 1.111943 0.418418 -0.964839 \n", + "1266 ... 0.401601 1.268759 -0.465424 \n", + "1003 ... 0.423295 -0.076668 0.132334 \n", + "... ... ... ... ... \n", + "640 ... 0.141835 0.496487 -0.754865 \n", + "1912 ... 1.137367 1.936749 1.337562 \n", + "1244 ... 1.074309 1.111121 0.266230 \n", + "1885 ... 0.737519 1.469439 1.067551 \n", + "1873 ... 0.404778 0.908970 1.159503 \n", + "\n", + " efficientnet_1273 efficientnet_1274 efficientnet_1275 \\\n", + "3735 -1.440759 -0.084943 1.122296 \n", + "1938 -0.869640 1.028330 2.456478 \n", + "861 -1.614682 5.774443 -0.975210 \n", + "1266 0.169212 0.117011 2.817199 \n", + "1003 -0.510567 -0.349815 0.662900 \n", + "... ... ... ... \n", + "640 -0.662246 0.823577 0.594171 \n", + "1912 -0.866048 -0.327555 0.137286 \n", + "1244 -0.587265 0.999843 0.766380 \n", + "1885 -0.988590 1.185334 1.174448 \n", + "1873 -0.874210 1.253875 2.004828 \n", + "\n", + " efficientnet_1276 efficientnet_1277 efficientnet_1278 \\\n", + "3735 0.536537 -0.670603 2.340016 \n", + "1938 -1.666183 0.174904 0.183448 \n", + "861 -1.797358 -1.633382 0.516337 \n", + "1266 -0.689718 -0.522147 0.739060 \n", + "1003 0.766846 -0.050815 1.494643 \n", + "... ... ... ... \n", + "640 -0.585857 0.273909 0.048165 \n", + "1912 0.744572 -0.552244 1.728855 \n", + "1244 0.460926 -0.107652 1.563349 \n", + "1885 -0.712216 -0.740500 1.937956 \n", + "1873 -1.048763 -0.265726 1.903799 \n", + "\n", + " efficientnet_1279 \n", + "3735 -0.063247 \n", + "1938 1.116518 \n", + "861 -1.135858 \n", + "1266 -1.661322 \n", + "1003 0.513932 \n", + "... ... \n", + "640 0.719115 \n", + "1912 -0.621845 \n", + "1244 0.345567 \n", + "1885 -2.176477 \n", + "1873 -1.792228 \n", + "\n", + "[598 rows x 1293 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "testing_data = get_dataset(features_dataframe, data_split_indexes, \"test\")\n", + "testing_data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_test, y_test_pred = evaluate_model_cm(log_reg_model, testing_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "evaluate_model_score(log_reg_model, testing_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate with holdout data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Mitocheck_Phenotypic_ClassMitocheck_Object_IDLocation_Center_XLocation_Center_YMetadata_PlateMetadata_WellMetadata_FrameMetadata_SiteMetadata_Plate_Map_NameMetadata_DNA...efficientnet_1270efficientnet_1271efficientnet_1272efficientnet_1273efficientnet_1274efficientnet_1275efficientnet_1276efficientnet_1277efficientnet_1278efficientnet_1279
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2229SmallIrregular152467.472973598.716216LT0039_45136621LT0039_45_136LT0039_45/LT0039_45_136_62.tif...0.439038-0.2188600.1841200.124753-0.716705-0.394543-1.4423620.0720970.3527961.657756
2230SmallIrregular159499.466667622.613333LT0039_45136621LT0039_45_136LT0039_45/LT0039_45_136_62.tif...-1.269705-0.4496380.5168880.145913-0.175916-0.139407-1.648394-1.174195-0.7247521.740923
2231SmallIrregular1121176.418919428.513514LT0039_45136531LT0039_45_136LT0039_45/LT0039_45_136_53.tif...0.113412-0.644244-0.3902510.530301-0.452048-0.9070920.2559160.5152821.2452450.852617
\n", + "

478 rows × 1293 columns

\n", + "
" + ], + "text/plain": [ + " Mitocheck_Phenotypic_Class Mitocheck_Object_ID Location_Center_X \\\n", + "3291 Prometaphase 44 689.846154 \n", + "3292 Prometaphase 84 593.157895 \n", + "3293 Prometaphase 85 541.015873 \n", + "3294 Prometaphase 86 482.838235 \n", + "3295 Prometaphase 88 608.475410 \n", + "... ... ... ... \n", + "944 Folded 95 706.580000 \n", + "2228 SmallIrregular 87 1260.900000 \n", + "2229 SmallIrregular 152 467.472973 \n", + "2230 SmallIrregular 159 499.466667 \n", + "2231 SmallIrregular 112 1176.418919 \n", + "\n", + " Location_Center_Y Metadata_Plate Metadata_Well Metadata_Frame \\\n", + "3291 395.230769 LT0064_14 3 22 \n", + "3292 686.333333 LT0064_14 3 22 \n", + "3293 691.968254 LT0064_14 3 22 \n", + "3294 697.647059 LT0064_14 3 22 \n", + "3295 720.688525 LT0064_14 3 22 \n", + "... ... ... ... ... \n", + "944 835.860000 LT0138_03 127 35 \n", + "2228 333.675000 LT0039_45 136 62 \n", + "2229 598.716216 LT0039_45 136 62 \n", + "2230 622.613333 LT0039_45 136 62 \n", + "2231 428.513514 LT0039_45 136 53 \n", + "\n", + " Metadata_Site Metadata_Plate_Map_Name Metadata_DNA \\\n", + "3291 1 LT0064_14_3 LT0064_14/LT0064_14_3_22.tif \n", + "3292 1 LT0064_14_3 LT0064_14/LT0064_14_3_22.tif \n", + "3293 1 LT0064_14_3 LT0064_14/LT0064_14_3_22.tif \n", + "3294 1 LT0064_14_3 LT0064_14/LT0064_14_3_22.tif \n", + "3295 1 LT0064_14_3 LT0064_14/LT0064_14_3_22.tif \n", + "... ... ... ... \n", + "944 1 LT0138_03_127 LT0138_03/LT0138_03_127_35.tif \n", + "2228 1 LT0039_45_136 LT0039_45/LT0039_45_136_62.tif \n", + "2229 1 LT0039_45_136 LT0039_45/LT0039_45_136_62.tif \n", + "2230 1 LT0039_45_136 LT0039_45/LT0039_45_136_62.tif \n", + "2231 1 LT0039_45_136 LT0039_45/LT0039_45_136_53.tif \n", + "\n", + " ... efficientnet_1270 efficientnet_1271 efficientnet_1272 \\\n", + "3291 ... 0.736642 -0.445215 -0.265348 \n", + "3292 ... 0.995399 0.249642 -0.018367 \n", + "3293 ... 1.308651 0.418540 -0.601356 \n", + "3294 ... 1.689349 0.191744 0.316645 \n", + "3295 ... 0.693373 0.102049 0.672704 \n", + "... ... ... ... ... \n", + "944 ... 0.477016 -0.853186 -0.190448 \n", + "2228 ... 0.673718 0.277680 0.095618 \n", + "2229 ... 0.439038 -0.218860 0.184120 \n", + "2230 ... -1.269705 -0.449638 0.516888 \n", + "2231 ... 0.113412 -0.644244 -0.390251 \n", + "\n", + " efficientnet_1273 efficientnet_1274 efficientnet_1275 \\\n", + "3291 1.071087 0.218429 1.898012 \n", + "3292 -1.448828 -0.108095 2.250121 \n", + "3293 -1.432083 -0.305420 4.157056 \n", + "3294 -1.242803 1.226191 3.854381 \n", + "3295 -1.385639 -0.001937 1.760955 \n", + "... ... ... ... \n", + "944 -1.310051 0.905157 1.072310 \n", + "2228 0.041264 -0.449543 -1.188860 \n", + "2229 0.124753 -0.716705 -0.394543 \n", + "2230 0.145913 -0.175916 -0.139407 \n", + "2231 0.530301 -0.452048 -0.907092 \n", + "\n", + " efficientnet_1276 efficientnet_1277 efficientnet_1278 \\\n", + "3291 0.610592 -0.385365 1.464963 \n", + "3292 -0.114802 -0.816467 2.097812 \n", + "3293 0.277327 -0.861576 0.808444 \n", + "3294 -0.198574 0.205935 1.721441 \n", + "3295 0.095366 -0.617366 2.316967 \n", + "... ... ... ... \n", + "944 -0.121663 0.484607 0.499503 \n", + "2228 0.380463 0.079736 0.245942 \n", + "2229 -1.442362 0.072097 0.352796 \n", + "2230 -1.648394 -1.174195 -0.724752 \n", + "2231 0.255916 0.515282 1.245245 \n", + "\n", + " efficientnet_1279 \n", + "3291 -0.312188 \n", + "3292 -0.739505 \n", + "3293 -0.388083 \n", + "3294 -1.363270 \n", + "3295 -0.068435 \n", + "... ... \n", + "944 -0.828424 \n", + "2228 0.793815 \n", + "2229 1.657756 \n", + "2230 1.740923 \n", + "2231 0.852617 \n", + "\n", + "[478 rows x 1293 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "holdout_data = get_dataset(features_dataframe, data_split_indexes, \"holdout\")\n", + "X_holdout, y_holdout = get_X_y_data(holdout_data)\n", + "holdout_data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_holdout, y_holdout_pred = evaluate_model_cm(log_reg_model, holdout_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "evaluate_model_score(log_reg_model, holdout_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Save trained model predicitions" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = []\n", + "\n", + "predictions.append(y_train)\n", + "predictions.append(y_train_pred)\n", + "\n", + "predictions.append(y_test)\n", + "predictions.append(y_test_pred)\n", + "\n", + "predictions.append(y_holdout)\n", + "predictions.append(y_holdout_pred)\n", + "\n", + "predictions = pd.DataFrame(predictions)\n", + "predictions.index = [\"y_train\", \"y_train_pred\", \"y_test\", \"y_test_pred\", \"y_holdout\", \"y_holdout_pred\"]\n", + "predictions.to_csv(f\"{results_dir}/model_predictions.tsv\", sep=\"\\t\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate shuffled baseline model" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "shuffled_baseline_log_reg_model_path = pathlib.Path(f\"{model_dir}/shuffled_baseline_log_reg_model.joblib\")\n", + "shuffled_baseline_log_reg_model = load(shuffled_baseline_log_reg_model_path) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate with training data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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j8gcPHrBr1y6cnJzeep9z5syhcuXKCVpoTMpiCojr169n8ODBODo6vrJ9oUKF8Pf3J3fu3IkRnsVs3LiR48ePJ5tC4+AlvchXKjcz+s3n+tlbVG3hS/+FX2Ows2PLwh2WDi/e2EqeYF251vu0Ci7uziyfvIGrp2/ilt6Fxt1rMWbTAPo3HMWR7acAcHF35u8Nx7h47CqPHz0lUw5PPulZhzGbv6N7xe+5fv62hTN5d9Z0PF/FVvL8N4/M6fh8RCvu3XhAGre3/xySlNnK8Zw/dCmrpm2ItXzIsj6EhYRxdv8FC0QV/2zleIJt5QrW8T6Ur1BmRs7owJnjN/hlwBIMGGjazpefp7bl/zrO4tTRf4qHdnYGBo5qTiGfbCyetZNTR67imNqBPAUy45jawYJZvLv6nWvi4uHCsnGruXryOm6erjTpWZ9xu4fS74OhHN5y3NIhxhtbuT5tJU+Rd5HkCo3lypVj1apVJoXGNWvWkCFDBrJkyWLB6JKfq1evcuTIEXx9fdmxYwebN2+mTp06r9zG2dmZYsWKJU6A8kZK1/ahRM2i/NRiDFsW7QTgyNYTZMzmyecjWrPNfxeRkZEWjtJ8tpInWF+uE76ZS+C9YJNlf288xszDw2n2TV1joXHeT8tN2hzbeYbT+y/w298/UeWTsswdaro+ubC24/kytpLni3pM7sixv04R/OAxfo3LWjqceGNLx/PWxTvcunjHZJm3XwHSeroyf+hSIiOT/+BPtnQ8bSnXGNbwPtSmazUeBz9nwBdzCXkeBsChvRf4z6qv+axnLb5pN93Y9sPmZSlZIS/ftJvO6WPXjcv3/XU20eOOL+O7TedRQJDJsv1rDzP73Hia92toNYVGW7k+bSXPJCP5/5q2OUlujMZ69eqxfv16QkNDjctWrVpFnTp1MBgMJm1v375Nr169KFOmDEWKFKFly5YcP/7Pm3TVqlW5ceMG8+fPx8vLCy8vL5YuXQrA8uXLad68OaVLl6ZUqVK0bt2ao0ePmux//Pjx+Pj4cPToUZo0aYK3tze1a9dmy5Ytcca+Zs0aatWqhY+PD23atOHqVdPbAEaOHEn9+vXx8fHBz8+Pnj17cvfuXZM2Bw4coGXLlpQoUQIfHx/q16/PsmXLTNps3bqVpk2bUqRIEcqWLcugQYN4+vRprHj++OMPDAYDQ4YMwdPT09i78VX27t2Ll5cXx44dMy4LDg6mV69e+Pj4ULZsWUaMGMG0adPw8vKKtd2OHTv45ptv8PHxoUqVKvz2228m++/bty/16tXjr7/+on79+hQpUoQWLVpw7do1Hj16xFdffUXx4sWpXr06q1evjhXf63J/kzj69u3LsmXLOHfunPG86Nu372tfG0up0LA0T4OfsW3xbpPl62ZtIX0Wd/KXyWOhyOKXreQJ1pfri0VGgOdPQrh6+iaeWd3faNuI8OT7YczajufL2Eqe/1atpS9FKhZg3BczLB1KvLPF4/lvH3SoQmRkJOtmxf2ZLrmxpeNpS7mC9bwPFSyWjaN/XzYWGQGePQ3l+MHLFCqWDff0zsblDVqU5fjBKyZFxuTuxSIjwPMnz7ly8jqe76e3QEQJw1auT1vJU+RdJblCY9WqVYmIiGD79u0A3Lhxg0OHDlG/fn2TdoGBgbRo0YLTp0/z3XffMX78eFKnTk3btm25f/8+ABMmTMDT05NatWrh7++Pv78/lStXBuD69es0aNCAsWPHMnLkSDJlykTLli25dOmSyfOEhYXx9ddf07BhQyZMmED27Nnp1q0bZ8+afqN26tQpZs6cSa9evRg2bBiXL1+md+/eJm3u379Pp06dmDp1Kv379+fGjRu0bt2a8PBwAB4/fkynTp1wdnZm1KhRTJo0iY8//pigoH9+Ma1du5YuXbqQL18+JkyYQO/evdmwYQP9+/eP9Vr++eeflCxZkixZslC7dm22b99OYGDgWx+Tfv36sXXrVnr37s3w4cO5dOkS8+fPj7Pt4MGDyZEjBxMnTqRSpUqMHDnSeCxjBAQEMHLkSLp06cLIkSO5fv06vXv3pmfPnuTNm5fx48dTqFAhevfuzY0bN94p91fF0bVrVypVqsT7779vPC+6du361q9LYslRKBtXT90gMsK0EHPxaPTYdzkKZ7NEWPHOVvIE28jVyTU1eYpm58qp2GMv2tkZsHdISda8mfhqQnse3g1k/bzke4uJLRxPsJ08Y6T1dKXLqLbM6LeQezceWDqceGdrx/PfnFxT49eoDIc3H+f25QBLhxMvbOl42lKu1vQ+lNI+BWFh4bGWh4VFAJAjb0YA0md0JVOWdFw+d4d23auzcFMf/vx7EFOXdKN6/WKJGXKCc3J1Im/xnFw5kfzGnHwZW7k+bSVPkXeVpG6dBnB0dKR69eqsWrXK+G/u3LnJnz+/SbvZs2cTFBTE4sWL8fDwAKBcuXLUqFGDGTNm0KdPHwoWLIiDgwPp06ePdTtwt27djP+PjIykQoUKHDt2jGXLltGzZ0/jurCwMLp06UKTJk0A8PX1pUaNGkydOpVff/3V2C44OJjly5fj7u5ufDxgwABu375NpkyZABg2bJixfUREBD4+PlSsWJE9e/bg6+vLpUuXCA4OpmfPnsbegv++hTwqKooRI0ZQp04dhg4dalyePn16OnXqRNeuXcmbNy8Ax48f5+LFi7RtGz1YdP369ZkzZw5r167lk08+eePjcf78eTZs2MDw4cNp0KCB8TWoVatWnO1r1qxJ9+7dAShbtixbt25l3bp1VKxY0dgmMDCQBQsWGMeBvHv3Lj/88AMdO3bkiy++AMDb25sNGzawceNG2rZt+1a5vy6ObNmy4e7uzs2bN5PFbeKuHs7cung31vLgB4+N662BreQJtpFrt19b45jGgYUjV8Zat/z2VBwc7QG4fu4WfeoMT9Z/QNnC8QTbyTNG94mfcv3MLVZOWW/pUBKErR3Pf6vSrAKOTqlY+x/r6M0ItnU8bSlXa3ofunoxgALe72MwGIiKir4P0i6FHV6FswLg+r+xJ9NncAWgev1i3LsbxKSf/+TJ4+fUblSCXj80IqV9CtYuPWCZJOJZ9wmf4pgmFQt+WmrpUOKNrVyftpKnyLtKcj0aIbootmXLFp48ecKqVati9WYE2LlzJ2XKlMHNzY3w8HDCw8Oxs7OjZMmSJrf9vsyFCxf44osvKF++PAUKFKBQoUJcunSJy5cvx2pbo0YN4/9TpEhB1apVOXz4sEmb/PnzG4uMgLGIdvv2P5MbbNu2jWbNmlGiRAkKFixoLL7FPGe2bNlwdnZm8ODBrF69mgcPTP/wvnTpEjdu3KB27drGnMPDwylVqhQGg8HktvGVK1dib2/PBx98AECRIkXIkSPHG90+/W8xr2W1atVMXoOYnqEv8vX1Nf7fzs6OXLlymbwGABkyZDCZbCZHjhwAlC9f3rjM1dUVd3d347Zvk/ubxpGcxHwgi3tdIgaSwGwlT7DuXNsMaEjVT8oxtd8ik1mnY/SsMZSvqv3A8M+m8jT4OcP//D+y589sgUjjjzUfz3+zlTx9G5WmbL0SjOo01dKhJChbOZ4v+qB9FQLvBbFz+X5LhxKvbOl42kKu1vY+9MfCPWTNkZ6u/erikcGF9Bld+bJ/fTK+5wZgHCvVYBc9VJZ9qpR8120uf204wcHdFxja+7+cO3mTlp9XtlQK8artkE+o3qoiU3rOtrpZp23h+gTbyVPkXSS5Ho0QXXBKkyYNkyZN4uzZs0yaNClWm4cPH3L48GEKFSoUa122bK/uqvz48WM6dOiAu7s7ffv2JXPmzKRKlYoBAwYQEhJi0tbe3h43NzeTZR4eHgQEmN5q4+rqGms7wLi/o0eP0rVrV6pVq0bHjh3x8PDAYDDw8ccfG9u4ubnxn//8h3HjxtGnTx8iIiIoWbIkAwYMwMvLi4cPHwIYe/296NatW0B0D83Vq1dTunRp7OzsjLdeV6tWjZkzZ3Lr1i3ee++9V75GMQICArC3t8fFxSXWaxCXF9vZ29vHGj/yZa/Vi9s6ODgYX5s3zf1t4kgugu4/xtXDJdZyF/fob8pivjlL7mwlT7DuXFv2/YgWfT7kP9//zsppm+Jsc/5IdPHx9P6L7Fl9mJmHf6bdoCZ833xcYoYab6z5eP6breTpmCYV3cd/yooJa7l/86FxhteUDtEfmdK4ORERFsHzpyGv2k2SZyvH80U5vbPhVTI3S8etJiw09m2cyZUtHU9byNUa34fWrziEW7o0NO9Yifoflwbg5JGr/D5nF5908OP+3ei/V4IfRX9ev37pHndvmQ75dGD3eZp9WhG3dGkIfPgkcROIR60GNqHVgCbM7L+AFRPXWjqceGUL1yfYTp5JhUGF22QnSRYaU6RIQe3atZk5cyY+Pj68//77sdq4ubnh5+dHjx49Yq1zcHB45f4PHz7M7du3mTp1qskt2cHBwcbbnGOEhYURGBhoUmy8f/8+np6eb5XTxo0bcXZ2ZsyYMdjZRXck/ff4gzGKFCnC9OnTef78OXv37mX48OF88cUXbNy4kbRp0wIwcOBAihQpEmvbDBkyANEToty9e5e7d+9SqlSpWO1WrVpFx44d3yhuT09PwsLCCA4ONinexYyDmVjeNHdrdOn4Vao0q4BdCjuTcUByekcX1C8fv/qyTZMVW8kTrDfXln0/ovW3DZj703L8f/3zjbZ59vg5187eIkuejAkcXcKx1uP5IlvJ0y29K+6Z0tL0m/o0/Sb2HRXLH/yHnSv2M7jRSAtEF39s5Xi+6IP2VQBYO3OzhSOJX7Z0PG0hV2t9H1o8awfL5+8mczYPnj0N4e6tQL4cUJ9nT0M4978xnW9ef8jzZ6Fxbh8zLeirepIlda0GNqHt4E+YPdifhcOWvX6DZMYWrk+wnTxF3lWSvHUaoEmTJlSpUoV27drFub58+fJcuHCB3Llz4+3tbfLz79mQ7e3tY/VSfP78uXFdjIMHD8ZZ+APYsGGD8f8RERFs3ryZokWLvlU+z58/x97e3mTm7JUrY49dFsPR0ZFKlSrRvHlzrl+/TkhICLly5SJTpkxcu3YtVs7e3t5kzBj9h/off/yBk5MTs2bNYs6cOSY/hQoVeqvbp729vQHYtOmfnkkREREvnXk7obxp7m8qrvMiqdq5bC9OLqnxa1zWZHmNNpW5d+MBp/eet1Bk8ctW8gTrzLVFn/q0/rYBC0b8wfyfV7zxdq7uzuQslJWbcYxzk1xY4/GMi63k+eD2I76p+n2sn/3rDhPyLJRvqn7PrO/8LR2m2WzleP6bvUNKqrXw5fS+81w+YT2z2YJtHU9byNWa34fCwiK4cuEud28F4pnJjYq1CrNm6QFCQ6J7GEdGRLJ762nez5WejJnTmmxbokJebl69T9Cj5HmXUssBjWk7+BPm/fg784b8bulwEoQtXJ9gO3mKvKsk2aMRoECBAnHeMh2jXbt2rFy5klatWtGmTRsyZ87MgwcPOHLkCBkzZjQWKHPlysWePXvYuXMnrq6uZM2alWLFiuHk5MT333/P559/zp07d5gwYUKcxSp7e3smT55MSEgIWbNmZeHChdy5c4fPP//8rfKpUKECs2fP5ocffqBGjRocOnSIFStM/xjfunUrv//+O9WrVydz5szcu3ePefPmUbx4cVKlSgVA37596dWrF0+fPqVy5cqkTp2amzdvsm3bNr7++muyZMnChg0bqFmzpslEMjEaN27MkCFDOHfunMnkKS+TJ08eatSowY8//sizZ8/InDkzixYtIjw83KRomtAMBsNrc8+ZM+cb7y937twsWbKEVatWkT17dtKlS0fWrFkTMIN3t3/tYQ6sP8KXkzri5Jqam+dvU6W5L6Vr+zCs1VgiIyNfv5NkwFbyBOvLtVG3WrQZ0Ij9G46yb90R8pfKZbL+9P6LOLmmZtiKXmxZvIebF+4Q8iyMLHky0qBLDewd7N+qOJnUWNvxfBlbyTMsJIyj207GWl6rbSUiIyLjXJcc2crx/LfyH5XC1cOFGf0XWjqUeGdLx9MWcrXG96HsuTPgW70g507eJCw0nJz5MvFxez9uXn3AnImmPYznTNxEyQp5+XFia+ZN2cLTJyF80LAEufJl5Kc+/7VQBuZp0rMe7YY0Y9+aQ+z78yAFypj+HXZq7zkLRRa/bOH6BNvJU+RdJdlC4+ukS5cOf39/xowZw8iRI3n06BEeHh4ULVrUZPKWnj17MnjwYLp3786TJ08YNmwYjRo1YuzYsYwYMYKuXbuSI0cOBg8ezPTp02M9j729PaNGjeL777/n7NmzZM2alXHjxsWaBft1KlWqRK9evZg3bx5Lly6lePHiTJ061WT25mzZsmFnZ8eYMWO4d+8e6dKlw9fX12QW7Nq1a+Pq6sqUKVOMPSKzZMmCn58f6dOnZ8uWLQQHBxtniH5R3bp1GTZsGCtXrjTZ76v89NNPDBkyhBEjRuDg4EDDhg3JkycPixYteqvXwFyvy/1tNGnShKNHj/LDDz/w6NEjGjZsyM8//5wQYceLwY1H0mFoc9p+/wku7s5cO32Doc1Hs9V/l6VDi1e2kidYV65laxcDoFSNIpSqEXtogw9c2xP2PIyLx65Ru10lPLO44+Boz8M7QRzdcZofW03k6pmbiRx1/LKm4/kqtpKnrbC14/lB+yo8e/zcavOzpeNpS7lai/CwCIqWysVHzcvi6ORAwO1AVv++H/+ZfxHyPMyk7a3rD+nVfgYdetTgy+8+JGXKFFw8c5vBPRaw76+zFsrAPGXrlQSgdG0fStf2ibW+hl3TxA4pwdjK9WkreSYJyXe0BJtliErOg1wksPHjxzNz5kwOHTpk6VCSnObNm5MyZUrmzp1r6VAsypo+FIhtSeHsbOkQEkXEYw3GbVUMSXbEl/gVZRs9IQwpUlg6hEQTFRFh6RAkPtnIe1EKb6/XN7ICEUdPWToEkbe2IXKxpUNINCU6jrZ0CAnuwG9fWzqEeJVsezRK4lm3bh23bt3Cy8uLp0+fsmrVKg4ePMjEiRMtHZqIiIiIiIiIiCQRKjTKazk5ObFixQouX75MWFgYuXLl4pdffqF69eqWDk1ERERERERERJIIFRpfoXv37nTv3t3SYVicn58ffn5+lg5DRERERERERESSMBUaRUREREREREQkyTFoWpFkxzZGMhYREREREREREZEEpUKjiIiIiIiIiIiImE2FRhERERERERERETGbxmgUEREREREREZGkR0M0Jjvq0SgiIiIiIiIiIiJmU6FRREREREREREREzKZCo4iIiIiIiIiIiJhNhUYRERERERERERExmyaDERERERERERGRJMegyWCSHfVoFBEREREREREREbOp0CgiIiIiIiIiIiJmU6FRREREREREREREzKYxGkVEREREREREJOnRGI3Jjno0ioiIiIiIiIiIiNlUaBQRERERERERERGzqdAoIiIiIiIiIiIiZlOhUURERERERERERMymyWBERERERERERCTJMWgymGRHPRpFRERERERERETEbOrRKCLyL3apHC0dQqK4+mURS4eQKLL8tMvSIUg8SuHmaukQEkVEYJClQ0gUdi4ulg4h0UQGB1s6hMRhsI0+DFEREZYOIVGsXrvI0iEkirrlP7R0CIki/PIVS4cgIjbCNj4NiIiIiIiIiIiISIJSj0YREREREREREUl6NEZjsqMejSIiIiIiIiIiImI2FRpFRERERERERETEbCo0ioiIiIiIiIiIiNlUaBQRERERERERERGzaTIYERERERERERFJcgyaDCbZUY9GERERERERERERMZsKjSIiIiIiIiIiImI2FRpFRERERERERETEbBqjUUREREREREREkh6N0ZjsqEejiIiIiIiIiIiImE2FRhERERERERERETGbCo0iIiIiIiIiIiJiNhUaRURERERERERExGyaDEZERERERERERJIcgyaDSXbUo1FERERERERERCSZWbNmDV27dqVSpUoUK1aM+vXrs2DBAiIjI03abdu2jQYNGuDt7U2NGjWYP39+nPubMWMGVatWxdvbm8aNG7N37963jkmFRhERERERERERkWTmP//5Dw4ODvTp04cpU6ZQvXp1hg4dyi+//GJsc+jQIbp27UrBggX57bffaNiwIT/++COLFy822deMGTMYPXo0LVu2ZNq0aWTPnp2OHTty5syZt4pJt06LiIiIiIiIiIgkM1OmTMHd3d34uGzZsjx9+pT58+fz9ddf4+DgwMSJEylYsCA//fSTsc2tW7cYO3YsjRs3xs7OjtDQUCZPnkybNm349NNPAShdujT169dnypQpjB49+o1jUo9GERERERERERFJeqKirP/HDP8uMsYoUKAAISEhPHr0iNDQUPbs2UPdunVN2tSvX5+AgABOnjwJwMGDBwkODqZevXrGNilSpKBOnTps27aNqLeIUz0aRURERERERERELKBatWqvXL9p06a32t+BAwdImzYtHh4eXLp0ibCwMHLlymXSJk+ePABcuHCBwoULc+HCBYBY7XLnzs2TJ0+4c+cOmTJleqPnV49GERERERERERGRZO7YsWMsXbqUtm3bkiJFCgIDAwFwdXU1aRfzOGZ9UFAQDg4OODo6mrRzc3MD4NGjR28cg3o02ojx48czYcIE4+N06dLh5eVF9+7dKVmy5BvtY+nSpfTr14/du3fH2T33Zby8vOjTp4/xPv+EfK63MWTIELZu3crmzZsTZP/xyTGNI+1/bEalpuVwcXfm2umbLBq+jK3+uywdWrxK7exIy++akKdoDnL75CStpytzvv8vc79f/PqNk6jUzo606NeA3EWykbtodtJ6ujL3x6XMG7rslduN3NAfb9/8/DFlAxO/npNI0b5emVzvU79ofoply0wmNxeCn4dw4sYdJm3Zw8mbd03atipbjGZlipI1nSuBz0LYdPI8YzbsJOh5yEv3n9vTnSVftMQhZUqaTlrAiZt3EjqleGEr16i15Zmr8Pu0G9CAHAWz4ObhQujzMK6fv83K6VvYvDh6hj07OwMNOleneJWCZC+QBZe0Tty9/oDdqw/z3zFreBL0zMJZvJvcRbPT/sdm5CycDTdPV0KehXL9zE3+mLyOTfN3WDq8d/ImxxOgUJk81GhRntze2cheIDMOqexpW7Qvd67dt2D05us1ows121R66fovfQdweu/5RIwoYXiVzE3bwU0pWC4vBoOBM39fYNagxZzcfdbSocWr5HyN9hsGy9caXrp+4aQoihWCA0dh2Vo4dQ7OXYKwMAMbF0WR5b3Y2xSoFPf+en4eRceW8RV5/CpUMgefdK1Gfp/sOKSy597tR2xaeoCFEzYa26RIaceHbX2p0aQUmbOnJyw0nKvn7jB92EpOHbxiwejfTbEqhanWyo+C5bzwfN+DJ4+ecvbvC8z74XfOHbxo6fDilbV9JhLLetseiy8TEBDAl19+ibe3Nx07djRZZzDE/T767+VxtYm5Zfpl28dFhUYb4ujoyOzZswG4ffs2kydPpl27dixZsgQvLy8LRyevMnhJL/KVys2MfvO5fvYWVVv40n/h1xjs7NiyMGl/2Hwbrh4u1O1YnYtHrrBrxT7qfFbd0iGZzdXdmTodKnPx2DV2rzxA7Q5VXrvNh52rkzl3xkSI7u01K10ENydH5u4+xIW793FP40S7CsVZ2KkZn89ext6L1wDo80FFWpf34T87DrD7wlVyZ/Cge7VyFM6aiRZTFxEeGRlr33YGAz82qsnDp8/J6Oqc2KmZxVauUWvL09ktNQE3HrJ1yT7u3XqEo1MqqjYtQ5+pn5ExW3oW/vonDqkdaPl/9dm2ZB9r5/5F0P3H5Cmanebf1KXsB0XpXvVHQp+HWTqVt5YmbRoCrt1ny6Kd3LvxEMc0qajWwpe+c7qTMXsGFvy01NIhvrU3OZ4AxSoVwKdSAc4fvcbT4GcU9ctv4cjjx/yhS1k1bUOs5UOW9SEsJIyz+y9YIKr4la9ELn7d/B1n9l9gRPvJGAzQ9Jv6jFj3Lb1rDOXU3nOWDjHeJOdrtEsb+OTD2GN5de0HDg7g/b9Lbs9B2H0ACuQBZyfYd/jV+61VKYp2n5guy5w0Py5R+UMfev3anL9WH+HXbxbx7GkI72XzwCOjm7GNnZ2B76a0o1DJnPw+bQsnD1zB0cmBvIWz4ujkYMHo3139zjVx8XBh2bjVXD15HTdPV5r0rM+43UPp98FQDm85bukQ4421fSaS5C84OJiOHTvi6OjI5MmTsbe3B/7pkRjTczFGUFAQ8E/PRldXV0JCQggJCSFVqlSx2sXs502o0GhD7OzsKFasmPFxkSJFqFq1Kv7+/gwcONBygckrla7tQ4maRfmpxRi2LNoJwJGtJ8iYzZPPR7Rmm/8uIuMo2iRHd64E0NC9HRBddLSGQuOdq/do/F5nAFw9nF9baMyYLT3th3zML59NZZD/V4kQ4dv5YeVmHjwx7cG149xl1n7dns8rlWbvxWtkcElDq3I+LNx7hFHroz9o7b5wlQdPnjLy4zo0KF6Q3/+O/UGzbfniZHR1Zsb2/Xxb7/UF2aTCVq5Ra8zz6M6zHN1p2gtq3/qjZMyentpt/Vj465+EPgulXbF+BD98YrLd3ev3GTCrC771i5v0lksujm47ydFtJ02W7f3zIJlyZKBux2pJuojxMm9yPAEW/LKK+SNWAtC4W02rKTTeuniHWxdNe4F7+xUgracr84cuJTLSvMHmk4K2g5vy+NFTvq03nJBnoQAc3HSc2WfG8PnwFnxd+XsLRxh/kvM1mi1L9M+/7TsMDwMNdG4TRYoU0cu6tIEv2kX/f+ai1xcaPdyhWKF4DjYBeGR05cuhTVizcA8TB/5znI7uMS32f9jWl5KV8tOr6QROH75qXL5/y6lEizW+je82nUcBQSbL9q89zOxz42ner6HVFBqt8TNRUmZI/r++ElxISAhdunTh3r17+Pv7ky5dOuO6bNmyYW9vz8WLF6lYsaJx+fnz0Xc55M6d2+TfCxcuULBgQWO7CxcukCZNGjJmfPNvdjRGow3LnDkz6dKl4/r160RGRjJlyhSqVq1K4cKFqVmzJrNmzXrl9o0aNaJXr16xlo8aNYry5csTFvbyHh7+/v7Url2bwoULU7lyZUaPHk14eHisdlevXqVNmzYULVqUqlWr8vvvv8dqc+jQIdq0aUOxYsUoUaIE33zzDffvm97+dOfOHTp37kzRokXx8/Nj+vTpr8wtKanQsDRPg5+xbfFuk+XrZm0hfRZ38pfJY6HIJCH0mNiBg5uPs+uPA5YOJU4vFhkBnoaGcT7gPpn+1wux6PvvkTKFHdvPXjJpt/V09C0zNQrmjbWP7B5p6VatHD+s3MzjkNAEiDzh2Mo1ait5AgTdf0xEePQfCJGRUSZFxhhnDlwGwDNLwgzvYSmB94OICI+wdBjx6t/HE3irWROTuw86VCEyMpJ1s7ZYOpR4Uah8Po5uP2UsMgI8e/ycYztOU6i8F+6Z0louuESSXK/RJX+CwRBF4zr/LLOz0r9Ea31ShtRpUrF4yquvu4/a+XF830WTImNy92KREeD5k+dcOXkdz/fTWyCihGFLn4kk6QsPD6dHjx6cPn2a6dOnkyWL6Tc9Dg4OlC1bljVr1pgsX7VqFZ6ensaiYvHixXFxcWH16tXGNhEREaxZs4ZKlSq91a3TVvr2Lm/i8ePHBAYGkiFDBkaMGMHYsWP58MMPmTJlCtWqVWPYsGFMnDjxpds3bdqU9evXG7vSQvSJuHz5cj766CNjV90XzZ07l4EDB1K2bFkmT55Ms2bNmDFjRpy9Knv27EmFChWYMGECZcqUoX///mzfvt24/tChQ7Ru3RoXFxdGjx7NDz/8wLFjx+jSpYvJfrp27crx48cZPHgwgwYNYv369WzcuPHFp0uSchTKxtVTN4iMMP1W7OLR6HFbchTOZomwJAF80K4SXiVzJakxGd+EcyoHCr6XgfN3owv89imjuyqEvvCHUHhkJJGRUXhliv1Bc0iDGmw7c4ktp5Pf+D22co1ac54GgwG7FHa4eThTr0NlSlQtyOKxa1+5TbGK0b3grpy+mRghJhhj7uldqN+5JiVrFsX/lz8sHZZZ3uV4WiMn19T4NSrD4c3HuX05wNLhxIuUDikJC4n9RXbMspyF30/skBKcNVyjwY9h/TYoWxyyxjH+4pv6cyMUqwFFqkPjjrB09eu3sQTv0rkIeviErLk9mbDqa1adHc7CfYPp9mNjnJyjb0dM/54bmd535/KZW7TtVZsF+wax6uxwpqztRfVGbzZ+fnLh5OpE3uI5uXLimqVDiTfW/JlIkp8hQ4awZcsWOnfuzPPnzzl8+LDx5/HjxwB88cUXHD9+nAEDBrB3714mT57M4sWL6dGjB3b/+9bHwcGBLl26MGvWLGbOnMmePXvo06cP165do3Pnzm8Vk26dtjExvQZv377N8OHDiYiIoHz58vTp04f27dvz1VdfAeDr68uTJ0+YPn067dq1I02aNLH2Vb9+fYYPH86qVato0aIFADt27ODOnTs0adIkzuePiIhg4sSJfPDBBwwaNAgAPz8/DAYDo0ePpkuXLrz//j8fEj/66CM6depkbHf16lUmTZpk7PL766+/UrhwYSZMmGCssOfNm5f69euzbds2KlWqxPbt2zl+/DizZs2iXLlyAJQqVYrKlSubdClOqlw9nLl18W6s5cEPHhvXS/LnkTkdHYc1Z3p/fx7cemTpcN7Kd/WrktrBnqnb9gFw4X8FR5/smdl36bqxnU+2zNjZGUjrZDqTWYsyRcmXMT31Fs1OvKDjka1co9acZ7eRLanbPnoSjdCQMCb3XcTq2dtf2t7jvbS0H9iIswcvs3fd0cQKM0F8OfFT6nWqAUTnPvGrWfw5LXl8Efcyb3s8rVWVZhVwdErF2v9YR29GgKunbpC/TB4MBoOxZ6pdCjvyl4ruPeSSjN+HXsYartE/N8HzEAON6757b+J61aOoVBYyZYAHj+D3P6H/cAPXbkXR483nm0wUHhndSJXagf4T2uA/ZTNTf/iDfEXep9VXNcmRLxO9Pp5I+v+N1Vi9cUnu3Qpk0qBlPA1+zgfNyvDNyGaktE/BWv/kNyxHXLpP+BTHNKmS9O3+b8uaPxNJ8rNjR/RQVb/88kusdXPmzKFMmTL4+PgwadIkRo0axfLly8mUKRMDBgygadOmJu07dOhAVFQUc+fO5d69e+TLl49p06a99ZweKjTakKdPn1Ko0D8Dm7i5uTFw4ECcnJwICwujTp06Ju3r1q2Lv78/p06dinNmamdnZ2rXrs2SJUuMhcYlS5bg4+NjvL//RRcvXuThw4dxPteoUaM4cOCASaGxRo0aJu1q1KjByJEjiYiIIDQ0lIMHD9KnTx8iIv7pOZUzZ048PT05duwYlSpV4ujRo7i4uBiLjDG5lylThrNnk8cMha+6zcuG7gCzal+Oa8/FY9dYMzN5/UHYvVo56hcrwI8rNxtnnT5z+x77L12ng29JLgc8ZNeFq+T2dGfQh9UIj4jk38OEZU7rwtc1fRm2eiv3nzy1UBbms5Vr1FrzXDRqNWvn/kXa9C6U+aAoXUe0wDFNKpZMWB+rrXNaJ37w/xKDwcBPn05N9rfhLhi2jDUzNpM2gytl65Wg27gOODql4vdRqywd2jt7m+NpzT5oX4XAe0HsXL7f0qHEmxUT1/HNb53oNrYdC35ejp2dgVYDGpMxe3RP+SgrGIfyRdZwjS75E9K6RVHD79338ct3po9rVoIufaOYPh9aNwb3tGaFGK8MdgZSOdozc9x64+3Tx/ZeIDwsnM4DG1CsQl7j7f8ODvYM7DCDuzcfAnBwx1nGrfCgRfcaVlFobDvkE6q3qsiE7jOsbtZpa/1MlCTp9XylzZs3v1G7SpUqUalSpVe2MRgMfPbZZ3z22WdmxaRCow1xdHRk3rx5GAwG0qVLx3vvvYednR0rVqwAwNPT06R9+vTRH9oePXr00n1+/PHHNGvWjNOnT5MhQwY2b97M4MGDX9o+ZqajmH3HiHnuF2dC8vDwMHns7u5OWFgYDx8+JCIigoiICIYNG8awYcNiPdetW7cAuHv3Lu7uscfQSp8+fbIoNAbdf4yrh0us5S7u0d+UxXxzJsmXb8NSlKzpTc9qP5LGzclkXUr7lKRxc+L5k5AkNyZT1ypl6VKlLGM27GDB3iMm675etIqfGtVidPN6AISGhzN71yHK5c6Gq+M/s5gNqFeVc3fuseHEeVz+t9zRPvpXk1Mqe5xTOST5MRtt5Rq15jwDbjwg4MYDAPZvjB6ovv13Ddm4cBeB9//Jy9nNiWFLe+LxXlr6fvQrt6/cs0i88Sng2n0CrkX3Qt635jAAn/7UnA1zthF4L9iCkb27Nz2e1iyndza8SuZm6bjVhIXGHgM7uVo3extunq606NeA+p2jv4w+sfssi0f/SbPeH3LvxkMLRxj/kvs1euYCHD9joHWTKBzieSLl+jVh624Dx09HUbFs/O7bHMEPn0BOTw5uP2Oy/O9tpwHIUygLuzecAODaxbvGImOMA3+doVnXarh5OCfr96xWA5vQakATZvZfwIqJ1jV8hTV/JhKJDyo02hA7Ozu8vb1jLU+bNi0A9+7dM5lJ6N69eybr4+Lj40PevHlZsmQJWbJkwd7entq1a7+0fcy+XpysJSAgeuygF6dMv3//vklMDx48wN7ennTp0hESEoLBYKBTp05Urx57duKY26IzZMjAgwcPYq2PyS+pu3T8KlWaVcAuhZ3JOCA5vaPH/rh83HoGkLZVOQpmJaV9SsZtHxxrXZ1Pq1Dn0yoM/ngMu1cmnQliulYpS7dq5ZiwaTfTtsXuLfPgyTM6z12Oe5rUpHdOw81HQYSEh9O8dBHWnzhnbJc3owdZ0rmxd0DXWPuY/WlTgp49p+zQyQmai7ls5Rq1lTwBzhy8RL0OlcmUw9P4R56zmxPDlvUkU/b09G3wK5dO3rBwlAnjzP4L1O9ck/dyZUwWRYw3EdfxtHYftK8CwNqZb9bLITn578iVLBu3hix5M/E0+Dl3r96jx8RPefb4udX1mIpLcrtGl0RP9k6TuvG/75heY0ltUplLp29RoHiOONZED/MUFRnFrav3ef407i9SY+ZbiErGsxa3GtiEtoM/YfZgfxYOW2bpcOKdLX0mEnkXKjQK3t7e2Nvbs2bNGpNbq1evXo2Tk5PJ1OZxadq0KZMnT8bDw4M6derEOZ5jjJw5c+Lu7s6aNWuoWbOmyXMZDAZKlChh0n7Dhg0mz79hwwYKFSpEihQpcHJyolixYly8eDHOAuq/8wsODmb37t3G26cDAwPZu3dvshijceeyvdTtWB2/xmXZ9t9dxuU12lTm3o0HnN573oLRSXzYMPcvjm4/FWv5L+v7s/OPv1k+YR2XT16PY0vL6Fy5DN2qlWPylj1M2rLnlW0fPHlmnKm6VdlipHawZ/6ef3o/fuO/mlT2pr+KfPPmoGPFUgxesdE4wUxSZivXqK3kCVDUNz8REZHGCTSMRcYc6fm20WguHLOeAe1fVLRyISIiIrl18Y6lQ4k3Lx5Pa2fvkJJqLXw5ve88l08knd8d8SksNNyYm+f7HlRqWpY1MzYT+jz2RDHWJjldo6Gh8McGKFIginy54n//f6wH+5RRFMoX//s2x861x6jTohwlK+fnwsl/JgwrVSV6ErHTh68QGRHJ7o3H8f2gCBmypOPuv3rjlqyYn5uX7xH0MHkOKdNyQGPaDv6EeT/+zrwhv1s6nARhS5+JRN6FCo2Cu7s7rVu3ZubMmTg4OFC8eHF2796Nv78/3bt3x8nJ6ZXbf/TRR/z66688fPiQH3/88ZVtU6RIwRdffMEPP/yAu7s7VapU4eTJk4wbN45GjRqZjM8IsGLFChwdHSlYsCCrV6/m77//Ztq0acb1ffr0oW3btnz11VfUrVsXV1dXbt++za5du2jUqBFlypShYsWKFCpUiN69e9OrVy9cXFyYOnUqLi6xu7snRfvXHubA+iN8OakjTq6puXn+NlWa+1K6tg/DWo0lMhl/2xmXUh8UwzGNI04u0ROGZC+QFb/G0ffD7Ft90DimTXJSsmYRHNOkwsk5Jqcs+DYsBcD+tUe4c/Ued67G3cP2/s2HHP3rdKLF+jrtKhTny+rl+evsJbaduUSRrJlM1h+9fhuAJiULA3DtQSAujqnwy5eDxsULM2bjTk7duhur/b9lSesKwIkbdzlxM+n/IWUr16g15vnl6NY8DX7GmYOXeXQ3CFcPZ/w+KkHlRqVZPG4tgfcf4+Boz9DfvyJ3kfeZ+q0/KVKmIH/Jf/5iDrwXzK1kWMD6akpHngY94/T+8zy6E4hrehcqNilHlU/K4//LH8mip9SL3uR4Arh5OONdIboykaNAFgBKVi9M4P1gAu895tiupD+syquU/6gUrh4uzOi/0NKhxLschbLi27A0Zw9cJCwknFxFsvFJ7w+5cf42swYvtnR48coartGNOyAwyEDjz+MeYO3BI9h/OPr/Z//XGXX73ujxFtOlhdLFopfNWAgXLkPZEpDJE+4/hCWrYed+A93aR5EubUJm8fYO7jjLno0naNG9BgaDgdOHr5LXOystv6zJ3k0nOfH3ZQDmjlpHyUr5+XFWR+aPXc/Tx8+p9UkZchZ4j2Hd51k2iXfUpGc92g1pxr41h9j350EKlMlrsv7U3nMv2TJ5scbPRCLxSYVGAaB37964urqyePFipk2bxnvvvUffvn1p167da7dNmzYtpUqV4tatW/j4+Ly2fatWrUiZMiWzZs3C398fDw8PPv30U7p37x6r7a+//sqoUaOYOHEiHh4e/PDDDyYDmBYvXpwFCxYwfvx4+vXrR1hYGJkyZaJs2bJkz54diB7QdNKkSQwaNIiBAwfi6upKmzZtuH37Nlu3bn3j18iSBjceSYehzWn7/Se4uDtz7fQNhjYfzVb/Xa/fOJn5clJHMuXIYHxc6ePyVPq4PACtcnblzpXk9wd993HtyJT9nzFQKzYuQ8XGZQBo4/X1S4uMSVHl/NEFFr98OfHLlzPW+oIDRgNgwECb8j68l9aVqKgoTt26y5cL/mDzaeu8rc1WrlFry/PU/gvUbFGB6s3K4+yWmmdPQrh0/DojOk1n8+LoQfjTebriVSL6XO/yc/NY+9iwYBe/dvtPosYdH07tOUfNtpWp0aYSzmmdePb4ORePXOHnNuPZNH+HpcN7J29yPAGy58/MgFldTLbt/msrAI7uOEOfD0cmatzx7YP2VXj2+HmyvS5fJSw0nGKVC9Hgi1o4OjsScPU+f/62Cf8Rf/D8aYilw4tX1nCNLvkTnFJHUbda3OvPX4KvBhlMlg0ZHf24VLEo5oyNXpYrG2zZCdv2QFAwpEoF+fPAyIEv37elDes+l5Y9alK7eVlaflmT+3eDWDZzOwvG/TMp1a2r9+n9ySTa96nDl0ObkMI+BRdP3uD7z//Dvs2x73RJDsrWi55AtHRtH0rXjv13YQ27prGWJVfW9pkoKTOobpvsGKKS+3SJYnGPHz/Gz8+P7t2706FDB0uHk6is6ZelRLNL5WjpEBLFtW+KWzqERJHlJ33YsyYpXjFmsDWJCAyydAiJIoWbq6VDSDSRwUm/91m8MCSxwfISSFRE0pqcLaGsu3HI0iEkirrlP7R0CIki/PIVS4cg8WhDpHX14n6V8p/8aukQEtwu/28sHUK8Uo9GeWePHz/mwoULLFiwAIPBQKNGjSwdkoiIiIiIiIiIWIgKjfLOTpw4QZs2bXjvvfcYPnz4K2enFhERERERERER66ZCo7yzMmXKcObMGUuHISIiIiIiIiLWSIP9JTu2MZCKiIiIiIiIiIiIJCgVGkVERERERERERMRsKjSKiIiIiIiIiIiI2VRoFBEREREREREREbNpMhgREREREREREUlyDJoMJtlRj0YRERERERERERExmwqNIiIiIiIiIiIiYjYVGkVERERERERERMRsGqNRRERERERERESSnigN0pjcqEejiIiIiIiIiIiImE2FRhERERERERERETGbCo0iIiIiIiIiIiJiNhUaRURERERERERExGyaDEZERERERERERJIcg+aCSXbUo1FERERERERERETMpkKjiIiIiIiIiIiImE2FRhERERERERERETGbxmgUEREREREREZGkR2M0Jjvq0SgiIiIiIiIiIiJmU49GEZF/iQwNtXQIiSLriP2WDiFR6AtQ6xL5+ImlQ0gcUZGWjiBR2MzxBKIiIiwdQiKxjTztUqe2dAiJonyvzpYOIVG4XN5t6RBERKyKejSKiIiIiIiIiIiI2VRoFBEREREREREREbPp1mkREREREREREUlyDBoLKdlRj0YRERERERERERExmwqNIiIiIiIiIiIiYjYVGkVERERERERERMRsGqNRRERERERERESSnigN0pjcqEejiIiIiIiIiIiImE2FRhERERERERERETGbCo0iIiIiIiIiIiJiNhUaRURERERERERExGyaDEZERERERERERJIcg+aCSXbUo1FERERERERERETMpkKjiIiIiIiIiIiImE2FRhERERERERERETGbxmgUEREREREREZGkR2M0Jjvq0SgiIiIiIiIiIiJmU6FRREREREREREREzKZCo4iIiIiIiIiIiJhNhUYRERERERERERExmyaDERERERERERGRJMegyWCSHfVoFBEREREREREREbOpR2MCGT9+PBMmTDA+TpcuHV5eXnTv3p2SJUtaMLI3M2vWLHLmzEmlSpUS7Dlat26Nk5MTU6dOTbDnsBapnR1p+V0T8hTNQW6fnKT1dGXO9/9l7veLLR1avHJM40j7H5tRqWk5XNyduXb6JouGL2Or/y5LhxavchfNTvsfm5GzcDbcPF0JeRbK9TM3+WPyOjbN32Hp8N5JamdHWvZvSO4i2cldLAdpPV2Z+8MS5v6wxKRdofJe1GxTkTzFcpC9UFYcUtnTOu+X3Llyz0KRxw9bOXetLc83PW8B8hTLwWfDWlCgTB4iwiM4vPUE0/5vAbcv3bVA5PHD2o4n2PZ7kTUez5extlxTOzvSou9H5C6SjdxFskeft0OXMe+nZSbt1j2Z89J9XDtzk8+K903oUN9IiYLv80GFAnjnzUxGdxeCn4Zw+tJtZizbw5nLpu+ZXjky0K2ZH4XyvEdERCQHTl5j3ILt3AwINLZxTJWS/p/VJF+ODKRP64ydwcCte4Fs2H2GhWsP8DwkPLFTfCf6PJ88r8+XsZU8Rd6FCo0JyNHRkdmzZwNw+/ZtJk+eTLt27ViyZAleXl4Wju7V5syZQ+XKlRO00ChvztXDhbodq3PxyBV2rdhHnc+qWzqkBDF4SS/ylcrNjH7zuX72FlVb+NJ/4dcY7OzYsjB5FuDikiZtGgKu3WfLop3cu/EQxzSpqNbCl75zupMxewYW/LTU0iG+NVcPZ+p8WpWLR6+y64+/qfNp1Tjb+VQthE/Vwlw4cpknQU8pVrlQIkeaMGzl3LW2PN/0vH3fKzO/bBzAxSNX+LHFOBwc7Wk7qAmjNg+kS6l+BN4LTuTI44e1HU+w7fciazyeL2Ntubq6O1OnfWUuHrvG7lUHqd2+cpztelT+Ptay/KVy0+WXVuxceSBBY3wbjaoVxc3Zkf+uO8ilGw9I65KaFnVKMGNwc3qMWMqBk9cAyP5eOib1b8rZKwH0H/8nqexT0LFxeaZ89wlt+s/lUfAzAFKmSIHBYGDhmgPcvBtEVFQUxfJnpUPDshQvkJXuP8f+cigp0uf55Hl9voyt5CnyLlRoTEB2dnYUK1bM+LhIkSJUrVoVf39/Bg4caNI2KiqKsLAwHBwcEjlKSQ7uXAmgoXs7IPpDijV+MCld24cSNYvyU4sxbFm0E4AjW0+QMZsnn49ozTb/XURGRlo4yvhxdNtJjm47abJs758HyZQjA3U7VkuWhcY7V+7RKENH4H/n6Ev+uJ8/dBnzfozOr8nXda3ij3tbOXetMc83PW/bDGpCWEg43zUYydP//eF77uAl/nNyFE161mXGt4sSLeb4Yo3HE2z3vchaj2dcrDHXO1fv0ThLFyC6WP6yQuPp/RdiLav7aRUiIyNZN3tbAkb4dkbO3sTDoGcmy/Ycvczvv3ag3YeljYXGjk3KExoWwTe/Lufps1AATl+6w+JfO9CyTkkm+v8FwOOnIQyY8KfJ/vafuIpDyhS0rl+KzJ5uJj0gkyp9nk+e12dcbCXPJCNSgzQmNxqjMRFlzpyZdOnScf36dfr27Uu9evXYtm0bH374Id7e3mzatAmAjRs30qBBA7y9valQoQLff/89T548Me5n7969eHl5sX37drp3746Pjw+VKlVixYoVwD+9EUuVKkX//v0JDQ01ieP27dv06tWLMmXKUKRIEVq2bMnx48eN66tWrcqNGzeYP38+Xl5eeHl5sXRp9Ifx5cuX07x5c0qXLk2pUqVo3bo1R48eNdn/+PHj8fHx4ejRozRp0gRvb29q167Nli1b4nxd1qxZQ61atfDx8aFNmzZcvXrVZP3IkSOpX78+Pj4++Pn50bNnT+7eNb3t4sCBA7Rs2ZISJUrg4+ND/fr1WbbM9HaTrVu30rRpU4oUKULZsmUZNGgQT58+fe1xk8RRoWFpngY/Y9vi3SbL183aQvos7uQvk8dCkSWewPtBRIRHWDqMBBUVZX0fFGzl3LWVPF9kl8KOMnV82LFsn7HICHD36j2ObD1JhY9KWTC6d2erxzOGtb0X2dLxtKVcXye1syN+DUtz7K/T3LyYdIZxeLHICPAsJIxLN+6TwcMFgBR2BnyL5WLL/nPGIiPA7fvBHDh5jUolX38cHwZHf46PUEEnybCV69NW8hR5Vyo0JqLHjx8TGBhIhgwZALh79y5Dhw6lffv2/PbbbxQoUIBNmzbRrVs3cuTIwYQJE+jatSsrVqzgiy++iLW/77//ngIFCjBhwgSKFStG3759+eWXX9ixYwfff/89PXr0YMWKFcycOdO4TWBgIC1atOD06dN89913jB8/ntSpU9O2bVvu378PwIQJE/D09KRWrVr4+/vj7+9P5cqVAbh+/ToNGjRg7NixjBw5kkyZMtGyZUsuXbpkEltYWBhff/01DRs2ZMKECWTPnp1u3bpx9uxZk3anTp1i5syZ9OrVi2HDhnH58mV69+5t0ub+/ft06tSJqVOn0r9/f27cuEHr1q0JDw83vq6dOnXC2dmZUaNGMWnSJD7++GOCgoKM+1i7di1dunQhX758TJgwgd69e7Nhwwb69+//jkdT4luOQtm4euoGkRGmHxYvHr0Svb5wNkuElaAMBgN2KexwS+9C/c41KVmzKP6//GHpsOQt2cq5ayt5vihz7ow4OqXi0rGrsdZdOnaVzLkzYp/K3gKRmcdWj6e1sqXjaUu5vk7lJmVJ7ezImiTUm/Fl0qR2wCtHBi5dj/57I0vGtDimsufCtdjjop6/do+sGdPiYJ8i1roUdgacUjtQtkgOWtQpwbpdp7lzP3kOX2GNbOX6tJU8Rd6Vbp1OYDHFsNu3bzN8+HAiIiKoVasWf/75J4GBgUyfPp0iRYoY23/99dd4e3szZswY4zI3Nze++eYb9u7dS5kyZYzLa9euTdeuXYHo27I3bNjAn3/+yfr16423YO/bt4+1a9fSuXNnAGbPnk1QUBCLFy/Gw8MDgHLlylGjRg1mzJhBnz59KFiwIA4ODqRPn97k1m+Abt26Gf8fGRlJhQoVOHbsGMuWLaNnz57GdWFhYXTp0oUmTZoA4OvrS40aNZg6dSq//vqrsV1wcDDLly/H3d3d+HjAgAHcvn2bTJkyATBs2DBj+4iICHx8fKhYsSJ79uzB19eXS5cuERwcTM+ePY1jX5YrV864TVRUFCNGjKBOnToMHTrUuDx9+vR06tSJrl27kjdv3lccRUkMrh7O3Irj2/jgB4+N663NlxM/pV6nGgCEhoQx8atZ/Dlto4WjkrdlK+eureT5Ilf36LyCHz6JtS744WPs7OxwSZeGB7cfJXJk5rHV42mtbOl42lKur1OrbUWCHz5hx/K/LR3Ka/VuV43Uqez5z4q9ALg5OwIQ+Ph5rLZBj59hZ2fAJY0j9x/9895bvawXP3ara3y8cttxfp6xIYEjl7dhK9enreQp8q7UozEBPX36lEKFClGoUCGqVavG3r17GThwIH5+fkD0TNT/LjI+efKEU6dOUbt2bZP9fPDBB6RMmZK//zb9EFG+fHnj/11cXHB3d6dkyZIm4zzmyJGDW7duGR/v3LmTMmXK4ObmRnh4OOHh4djZ2VGyZEmOHTv22pwuXLjAF198Qfny5SlQoACFChXi0qVLXL58OVbbGjVqGP+fIkUKqlatyuHDh03a5M+f31hkBMidOzcQXZiNsW3bNpo1a0aJEiUoWLAgFStWBDA+Z7Zs2XB2dmbw4MGsXr2aBw8emDzHpUuXuHHjBrVr1zbmHB4eTqlSpTAYDCa3jYtlvepWNiu7yw2ABcOW8UXpfvSvN4x1/9lCt3EdaNKznqXDkndgK+eureQZl1fnnjyTt+XjaY1s6XjaUq4vk71AFgqUzsMW/12EhYRZOpxX+rxJeT6oUIAx87fFmnX6VQfsxeO89+hl2n03n65DFzP5vzuoUiovw3rUx2BIiKjlXdnK9WkreYq8C/VoTECOjo7MmzcPg8FAunTpeO+997Cz+6e2G9OjMEZwcDBRUVGkT5/eZHnKlClJmzYtgYGmgxy7uLiYPHZwcMDV1dVkmb29vckYjQ8fPuTw4cMUKhR70PNs2V7dxfvx48d06NABd3d3+vbtS+bMmUmVKhUDBgwgJCQk1vO6ubmZLPPw8CAgIMBkWVzxAsb9HT16lK5du1KtWjU6duyIh4cHBoOBjz/+2NjGzc2N//znP4wbN44+ffoQERFByZIlGTBgAF5eXjx8+BAgztvPAZNCrFhO0P3HuHq4xFruEtOb6H/fEFqTgGv3CbgWfQvRvjWHAfj0p+ZsmLMt2c5ia4ts5dy1lTxfFBTTO8E9du8El3TOREZG8vhR8hvv11aPp7WypeNpS7m+Sq22lQCS/G3TnzYsS4cGZZn83x38vuGwcXlMT0Y3l9SxtnF1Tk1kZBSPn5r+fRH8NITTl+4AcPDUNW7cecSP3etRsUQetv19PuGSkDdmK9enreSZZKhwm+yo0JiA7Ozs8Pb2ful6wwtfv7m4uGAwGIxjJcYIDw/n0aNHsQp378LNzQ0/Pz969OgRa93rZrw+fPgwt2/fZurUqeTPn9+4PDg42Hibc4ywsDACAwNNYr5//z6enp5vFe/GjRtxdnZmzJgxxiLtjRs3YrUrUqQI06dP5/nz5+zdu5fhw4fzxRdfsHHjRtKmTQvAwIEDTXqQxogZM1Ms69Lxq1RpVgG7FHYm453k9I4ugF8+Hnt8NGtzZv8F6neuyXu5MqrQmIzYyrlrK3m+6OaFOzx/GhLneEs5Cr/PzQt3knxvorjY6vG0VrZ0PG0p15dJaZ+Cas3Kc/bgJS4eTbr5ftqwLB0bl+e3JbuY/cc+k3U37jzieUgYubOmj7Vd7vfTc/3OI0LDXj1B3omL0XdAZcuULv6CFrPYyvVpK3mKvCvdOp2EpEmThgIFCrBmzRqT5evXryc8PJySJUua/Rzly5fnwoUL5M6dG29vb5OfmPENIbpn4Yu9FJ8/f25cF+PgwYNxFv4ANmz4Z8yUiIgINm/eTNGiRd8q3ufPn2Nvb29SlF25cuVL2zs6OlKpUiWaN2/O9evXCQkJIVeuXGTKlIlr167Fytnb25uMGTO+VUySMHYu24uTS2r8Gpc1WV6jTWXu3XjA6b3W/0110cqFiIiI5NbFO5YORd6CrZy7tpLniyIjItnz50F8G5Qi9f/GFAPwfN+DopULsmP5fgtG9+5s9XhaK1s6nraU68uUrVuctJ6urEvCvRnbNyhDx8blmbl8DzOW7Ym1PiIyih2HLlK5VB6cHP/52yKjhwslCmRl69/nXvscJQq8D8D1O4/iLW4xj61cn7aSp8i7Uo/GJKZbt2588cUX9OzZkwYNGnDt2jVGjRpFuXLlTCaCeVft2rVj5cqVtGrVijZt2pA5c2YePHjAkSNHyJgxI+3atQMgV65c7Nmzh507d+Lq6krWrFkpVqwYTk5OfP/993z++efcuXOHCRMmxFmos7e3Z/LkyYSEhJA1a1YWLlzInTt3+Pzzz98q3goVKjB79mx++OEHatSowaFDh1ixYoVJm61bt/L7779TvXp1MmfOzL1795g3bx7FixcnVapUAPTt25devXrx9OlTKleuTOrUqbl58ybbtm3j66+/JmfOnO/2giaiUh8UwzGNI04u0X/oZi+Q1fjLbd/qg4Q8C33V5kne/rWHObD+CF9O6oiTa2punr9Nlea+lK7tw7BWY4mMjHz9TpKJr6Z05GnQM07vP8+jO4G4pnehYpNyVPmkPP6//JFsezOWqlUUxzSpSO0cfRtUtgJZ8GtUGoi+NTzkWShu6V0oUrEAADkLv/+/7YoReC+IRwFBHPvrtGWCN4OtnLvWmuebnLdzhyyh9K5i/LC8N/6//IGDoz1tBjUh6F4wS0b/acnw35m1Hk+wzfciaz6eL7LWXEvWLIKjU6p/Puflz4xvg1IA7F93xORz3gdtK/L8aQib/7vbIrG+TovaJejUpAK7j1xi56FLFMr9nsn6Exeihy36bckuZg5pychvGjJ35T4cHFLSsXF5HgU/Z8HqA8b2Dap6U8wrK3uPXebO/cekTpWSYl5ZaVqzGEfP3mD7geRT1NHn+eR5fb7IVvIUeVeGqOQ6gnkSN378eGbOnMmhQ4fiXN+3b1+OHz/OqlWrYq3bsGEDEydO5Pz587i6ulKrVi169epFmjRpANi7dy9t2rTh999/N7k1u2rVqlSuXJmBAwe+Mo6AgADGjBnDtm3bePToER4eHhQtWpR27dpRvHhxAM6dO8fgwYM5deoUT548YdiwYTRq1Ijt27czYsQIrl69So4cOfjmm2+YPn06Tk5OTJ061eQ5Z82axffff8/Zs2fJmjUrvXv3plq1asY4WrdubbIdwLFjx2jSpAlz5swxFlZ/++035s2bR2BgIMWLF2fgwIHUqlWLPn368Omnn3Lx4kXGjBnD0aNHuXfvHunSpcPX15eePXua3Kq9c+dOpkyZYpz8JUuWLPj5+dG1a9dY412+qRp2Td9pu3cx9+JEMuWI+zbvVjm7cudKQJzrkhPHNI50GNqcik3L4eLuzLXTN1j08zK2+u9KvCAMCd/Ru1a7ytRsW5lsBbLgnNaJZ4+fc/HIFdbM3Mym+TsS/PkBDClSxPs+55wdS6YccQ+P0Drvl9y5co8iFQswcuN3cbY5su0kvWv8GK8xRYUnzi2tSeLcTQSWztOQ0v71jd7Sm5y3AHl9cvLpT80pWDYPEeGRHN56gmn/Nz/OWSfNZSvnbUIcT7Dd9yJLH8/EZOlc7VLHHlfQXLNP/kqm7HGft20K9OTO1ej3Is8s7sw+NYrNi3Yx8vNp8R7HvwU2LPZO203q35Ti/+ttGJeyrUYZ/++VIwPdmvlROE9mIiIj+fvkNcYv2MaNu/+MTe+d9z3a1i9NvhwZSeviSEREFNfuPGTLvnMsWHOAkNDwd4ozhsuCxCvY6vO89bB0nhsiFyfK8yQFlWuPsHQICW7rmj6WDiFeqdAo8e51RVZrkpiFRkkkiVBoTAoSotCYFCVWwUYSR0IVppIaWzlvbeV4gu0cU1uREIXGpOhdC43JTWIWGkXiiwqN1sXaCo228Re1iIiIiIiIiIiIJCgVGkVERERERERERMRsKjRKvOvevbtN3DYtIiIiIiIiIiL/0KzTIiIiIiIiIiKS9GhakWRHPRpFRERERERERETEbCo0ioiIiIiIiIiIiNlUaBQRERERERERERGzaYxGERERERERERFJcgwaojHZUY9GERERERERERERMZsKjSIiIiIiIiIiImI2FRpFRERERERERETEbCo0ioiIiIiIiIiIiNk0GYyIiIiIiIiIiCQ9mgwm2VGPRhERERERERERETGbCo0iIiIiIiIiIiJiNhUaRURERERERERExGwqNIqIiIiIiIiIiIjZNBmMiIiIiIiIiIgkOYYozQaT3KhHo4iIiIiIiIiIiJhNhUYRERERERERERExmwqNIiIiIiIiIiIiYjaN0SgiIiIiIiIiIklPpKUDkLelQqOIiC2K0m9sEbEsg73tfAyNCg+zdAgib83t9wOWDiFRRKW0t3QIiULvQyKSWHTrtIiIiIiIiIiIiJhNhUYRERERERERERExmwqNIiIiIiIiIiIiYjbbGRxHRERERERERESSDUNUlKVDkLekHo0iIiIiIiIiIiJiNhUaRURERERERERExGwqNIqIiIiIiIiIiIjZNEajiIiIiIiIiIgkPRqiMdlRj0YRERERERERERExmwqNIiIiIiIiIiIiYjYVGkVERERERERERMRsKjSKiIiIiIiIiIiI2TQZjIiIiIiIiIiIJD1Rmg0muVGPRhERERERERERETGbCo0iIiIiIiIiIiJiNhUaRURERERERERExGwao1FERERERERERJIcg4ZoTHbUo1FERERERERERETMpkKjiIiIiIiIiIiImE2FRhERERERERERETGbCo0iIiIiIiIiIiJiNk0GIyIiIiIiIiIiSU+UZoNJblRoTAR//PEHc+bM4dKlS0RFRZExY0aKFy9Oz5498fDwSJQYxo8fz8yZMzl06BAA169fp1q1aowdO5YPPvgAgNatW+Pk5MTUqVMTJab4sHfvXtq0acPvv/+Ot7e3pcNJMKmdHWn5XRPyFM1Bbp+cpPV0Zc73/2Xu94stHVq8spU8cxfNTvsfm5GzcDbcPF0JeRbK9TM3+WPyOjbN32Hp8OJV7mI5aD2gMV6lcpMmbRoCrt5j86Kd/D5qFSHPQi0dXrxxTONI+x+bUalpOVzcnbl2+iaLhi9jq/8uS4cWr2wlT6+SuWk7uCkFy+XFYDBw5u8LzBq0mJO7z1o6tHhlje+5qZ0dadH3I3IXyUbuItlJ6+nK3KHLmPfTslhta7erTN3PqpA5dyYiwiK4fPI6i0f/yb51RywQufls5frUeZt8z9vUzo606NeQ3EWzk7vo//L8cQnzflxqbGNnZ6BBtw8oUd2b7AXfx8U9DXev3mP3ygP4j1zJk8CnFszgzaR2dqRl/4bkLpKd3MVyROf5wxLm/rAkVts8xXLw2bAWFCiTh4jwCA5vPcG0/1vA7Ut3LRB5/LCV9yJbyVPkXejW6QQ2bdo0+vTpQ8mSJRk9ejSjR4+mcePGHD9+nLt3k+8vEElcrh4u1O1YHftU9uxasc/S4SQYW8kzTdo0BFy7z8wBC+lf72dGtJvInSsB9J3TnRbfNrJ0ePEmW4EsjNk+hIw5PJn8zRwGfjSCrf/dTasBjek370tLhxevBi/pRY02lZg7ZDHf1vmJM3+fp//Cr6nS3NfSocUrW8gzX4lc/Lr5O1KltmdE+8mMaD8JB0cHRqz7lgJl8lo6vHhlje+5ru7O1GlfGXsHe3avOvjSdm0GNOKriR048/dFfmw5jpGdphEWGsYPS7+hwoclEy/geGQL1yfovE3O562rhzN1Pq2CfaqU7F55IM42DqkdaDWgMXeu3mNK77l81+AX1szcQp1PqzJ6yyAcHO0TOeq3F51n1ehz9I+/X9rufa/M/LJxAPYOKfixxTh+/XwaWfO+x6jNA3FL75KIEccvW3kvspU8Rd6FejQmsLlz59KwYUP69u1rXFapUiU+++wzIiMjLRjZu3n+/DmOjo5vvDy5iIqKIiwsDAcHB0uHEqc7VwJo6N4OiP6AXeez6pYNKIHYSp5Ht53k6LaTJsv2/nmQTDkyULdjNRb8tPQlWyYvVZtVIFVqB4Z8PJpbF+8AcHjrCdzfS0vdjtVxTpuGx4+eWDhK85Wu7UOJmkX5qcUYtizaCcCRrSfImM2Tz0e0Zpv/rmT5fv8iW8mz7eCmPH70lG/rDTf2uj246Tizz4zh8+Et+Lry9xaOMP5Y43vunav3aJylCxD9x37t9pXjbFezTUWO7zzD+K9mG5cd3HyCRRfHUaOlLztfURxIimzl+gSdt8n5vL1z5R6NM30O/C/PDlVitQl9Fkrb/F8R/OCxcdnR7ae4e+0+3y3sgW/D0mxeuDPRYn4Xd67co1GGjsD/ztFPq8bZrs2gJoSFhPNdg5E8DX4GwLmDl/jPyVE06VmXGd8uSrSY44utvBfZSp4i70o9GhNYcHAwGTJkiHOdnd0/L3/VqlUZMmQIM2fOpGLFivj4+NCnTx9CQkI4deoUzZo1o1ixYjRu3JgzZ86Y7GfmzJk0btyYEiVKUK5cOTp16sSlS5fMjn3p0qV4eXlx6NAh2rdvT7FixRg+fDh79+7Fy8uLrVu38uWXX1K8eHF69OgBQFBQEIMHD8bX15fChQvTqFEjduwwvRU0KiqKCRMmUKFCBXx8fOjWrRvbtm3Dy8uLvXv3AtG3dnt5ebF27VqTbYcMGULVqnH/sn6b16Nv377Uq1ePbdu28eGHH+Lt7c2mTZvMfclEzBJ4P4iI8AhLhxFvwsOic3nxNqfHj54SERFJeGi4JcKKdxUaluZp8DO2Ld5tsnzdrC2kz+JO/jJ5LBRZ/LKVPAuVz8fR7adMbu1/9vg5x3acplB5L9wzpbVccBJvIsIieBL0zGRZWEgYoSFhhD4Ps1BU785Wrk9bZ23nbVwiI6NMiowxzvx9AQDPrIkz7FRCs0thR5k6PuxYts9YZAS4e/UeR7aepMJHpSwY3buzlfciW8kzqTBEWv+PtVGhMYEVKlSIRYsWsXjxYgICAl7ZdtOmTezZs4cffviBb775htWrV/PDDz/Qp08fmjZtytixYwkNDaVHjx4m35Dcvn2bVq1aMWnSJH788UciIyNp1qwZjx49ipccevXqRbly5ZgyZQofffSRcfnAgQPJli0bEydOpH379oSGhtK+fXu2bt3KV199xeTJk8mdOzedOnUyKY7OnTuXCRMm0LBhQ8aPH0/27NkZNGhQvMQKb/563L17l6FDh9K+fXt+++03ChQoEG8xiLwJg8GAXQo73NK7UL9zTUrWLIr/L39YOqx4s2HudoIfPubLCZ+SKWcGUjs7UqZOcep2rMbKyet4/jTE0iHGixyFsnH11A0iI0w/JVw8eiV6feFslggr3tlKnikdUhIWEvsP9phlOQu/n9ghSQJYPmk9JWt4U6tNRZzTOuGeyY3Pf25BGlcnlk9eb+nw3pqtXJ+2ztrO27dRrHIhAK6cvG7hSOJH5twZcXRKxaVjV2Otu3TsKplzZ8Q+VdK/TfxFtvJeZCt5irwr3TqdwAYNGkS3bt0YMGAAAFmzZqVKlSq0a9eOrFmzmrQ1GAxMmDDBePvuvn37WLx4Mb/99hsVK1YEIDIyks6dO3P27Fny588PwLfffmvcR0REBBUqVKBcuXKsW7eOTz75xOwcmjdvzmeffWZ8HNPrsFq1avTq1cu4fMmSJZw+fZoVK1aQJ0/0tzh+fn5cvnyZSZMmMXbsWCIiIpg2bRqNGjUybuvr68v9+/dZtiz2gNfv4k1fj8DAQKZPn06RIkXi5XlF3taXEz+lXqcaAISGhDHxq1n8OW2jhaOKP3euBPCV30AGLf6GOWfHGZcvG7+GyT1nv2LL5MXVw5lbF2OPuRvTI8PVwzmxQ0oQtpLn1VM3yF8mDwaDgaj/zXJol8KO/KWif6+5WEmetm7ZxHWEPAul2+g29Jwc/Rkn6P5jBjUdzck95ywc3duzlevT1lnbefumPDKno8MPn3Dm7wvsXX3I0uHEC1f36Gsy+GHsIWSCHz7Gzs4Ol3RpeHD7USJHZh5beS+ylTxF3pUKjQksX758rFq1it27d7Njxw7279/P3LlzWbp0KfPnzzfpRVeyZEmTMQJz5MiBnZ0dZcuWNVkGcOvWLWOh8fDhw4wdO5aTJ0+a9Nq7fPlyvORQqVKlN1q+c+dO8uXLR44cOQgP/+eWyHLlyrFq1SogurdhQEBArNufq1WrFm+Fxjd9PdKlS6cio1jUgmHLWDNjM2kzuFK2Xgm6jeuAo1Mqfh+1ytKhxYuM2T0Zsqw3D+8GMuTjUQQGBJG/dB5afNuI1M6OjPo8+cxw/zoxBam41yViIAnMFvJcMXEd3/zWiW5j27Hg5+XY2RloNaAxGbOnByAq0koStXE1W/vR5ZeW/DF1I/vXHyWlQ0qqt6jAYP+vGNJiHAc2HrN0iG/NFq5PW2eN5+3ruKRLw4/Le2MwGPip9fhXnufJ0auv2+SZq628F9lKniLvQoXGRODg4EClSpWMhbm//vqLTp06MXHiRCZMmGBs5+rqarKdvb09jo6OJsVHe/voLvQhIdG3HN68eZMOHTpQuHBhvv/+ezJkyIC9vT2dOnUytjGXh0fcY6G4u7ubPH748CEnT56kUKFCsdqmSJECwHj7+Ivbvvj4Xb3N6/GyvEQSS8C1+wRcuw/AvjWHAfj0p+ZsmLONwHvBFowsfnw6tDlOrqnpUrKv8TbpYztOE3g/mF7Tu7Bh7naO/XXKwlGaL+j+Y1w9Ys8O6RLTWyGOsaaSI1vJc93sbbh5utKiXwPqd47ucXxi91kWj/6TZr0/5N6NhxaOUMzlnNaJL0a1Ye2sbfz2r8kW/l5/lBFr+vHl2Ha0LfSNBSN8e7ZyfdoyazxvX8c5rRPD/uyHR2Z3/u+Dody+9OphqJKToJieb+6xe765pHMmMjKSx4+exlqX1NnKe5Gt5CnyrlRotAA/Pz/y58/PhQsXzN7XX3/9xdOnT5kwYYKxUBkeHk5gYKDZ+34dg8Fg8tjNzQ0vLy+GDh360m08PT0BePDggcnyFx+nSpUKgLAw03GyXpfX27weL8YvYmln9l+gfueavJcro1UUGnMXzc6VUzdijcV49n8Duuco/L5VFBovHb9KlWYVsEthZzJWT07v6PF5Lh+PPf5ScmQreQL8d+RKlo1bQ5a8mXga/Jy7V+/RY+KnPHv8nHMHL1o6PDFT1rzv4eiUijMHY0+cd+7QJYpWLIBjmlQ8f5J8xpG1pevTVlnjefsqzmmd+Hn1t2TK4cn/1f6JS8evWTqkeHXzwh2ePw2Jcyy/HIXf5+aFO3GOF5zU2cp7ka3kmWSoi2iyo8lgEti9e/diLXv+/Dm3bt0iffr0Zu//+fPnGAwGUqb8p2a8Zs0ak1uXE0v58uW5du0aGTJkwNvbO9YPQKZMmfD09Iw1w/PGjabj0nl4eGBvb29SjA0NDeXvv/9+ZQxJ6fUQeVtFKxciIiKSWxfvWDqUeHH/1kNyFMyKY5pUJssLlM0HwL3r9y0RVrzbuWwvTi6p8Wtc1mR5jTaVuXfjAaf3nrdQZPHLVvKMERYazuUT17l79R6e73tQqWlZ1szYbDUzu9qy+7ceAVCgVO5Y6/KXykPwg8fJrlhja9enLbLG8/ZljEXGnBnoV+9nLhy5YumQ4l1kRCR7/jyIb4NSpHZ2NC73fN+DopULsmP5fgtG9+5s5b3IVvIUeVfq0ZjA6tevT5UqVfD19SVDhgzcvXuXuXPn8vDhQ9q2bWv2/mPGb+zXrx/NmjXj/PnzzJw5M9Zt2ImhQYMGLFq0iDZt2tChQwdy5MhBcHAwJ0+eJCwsjG+++YYUKVLw+eef89NPP5E+fXrKlCnDnj17jBPM2NnZGf+tUaMG8+fPJ3v27KRLl465c+e+thdiUno94lupD4rhmMYRJ5foDyPZC2Q1/nLbt/ogIc9CLRlevLGFPL+a0pGnQc84vf88j+4E4prehYpNylHlk/L4//KHVfRmBFg6bg2Dl3zDz2v7s3TsaoLuBZO/TF6a/d9HXD5xjf1rD1s6xHixf+1hDqw/wpeTOuLkmpqb529TpbkvpWv7MKzVWCIjI1+/k2TAVvLMUSgrvg1Lc/bARcJCwslVJBuf9P6QG+dvM2vwYkuHF++s8T23ZM0iODql+ien/JnxbVAKgP3rjhBw/T47lu+ndocqhIWGs2/dEewdUlKjpS+Fy+dj1ve/WzL8d2Ir12cMnbfJ97wtWbMojmn+nWcWfBuWBqLP46ioKH5a2ZfcxbIzpdc8UqRMQf7SeYzbB94LinMSjqSmVK3oPFM7pwYgW4Es+DWKznPfmsOEPAtl7pAllN5VjB+W98b/lz9wcLSnzaAmBN0LZsnoPy0Z/juzlfciW8lT5F0ZopLrKLPJxPz589myZQtnz57lwYMHpEuXDi8vLz777DOTSV6qVq1K5cqVGThwoHHZ+PHjmTlzJocO/TO72vXr16lWrRpjx47lgw8+AGD58uVMmDCBu3fvUqBAAfr3789XX31lsr8X9xXXflq3bo2TkxNTp0ZP0LB06VL69evH7t27TcZQ3Lt3L23atOH333839lSM8fjxY8aPH8/69esJCAggbdq0FCxYkBYtWlC5cmUgeuDciRMnsmDBAp4+fUq5cuWoW7cu33zzDStWrDBOcvPgwQO+++479uzZQ5o0afjss8+4fPkyW7duZfPmzS+N5U1ej759+3L8+HHjJDXvqoZdU7O2fxtzL04kU44Mca5rlbMrd65Yx7g1Fs/TkPAdvWu1q0zNtpXJViALzmmdePb4ORePXGHNzM1smr8jwZ8fwGCXOEMHFK1UkE/6fERO72ykcXMi4Np99vx5kEXDlyfK+DVREREJ/hwAjmkc6TC0ORWblsPF3Zlrp2+w6OdlbPXflSjPn1gsnachpX2CP0eWvJn4enJHchTKiqOzIwFX77N18W78R/wRaxiAhBIVnni9Ji35nmuXOnWC7Hf2yV/JlN0zznVtCvTkztV72Key56PO1anWvAIZs3sSERbB9fO3+WPqBrb47473mCKfPYv3fb7I0tdnYrL0Z4WEOHeT4nlLAvwOnX1mzMvz9OoBwJwzY1+6/fq52/m1Y/xOJpcQk3zNOTuWTDnizrN13i+5cyX6rre8Pjn59KfmFCybh4jwSA5vPcG0/5ufIMXUxPrdYivvRZbOc0Ok9X35+TI1Kvxo6RAS3IadAywdQrxSoVGShNGjRzNr1iz27t2Lo6Pj6zdIIhKz0CiJJBEKjUlBYhUaLS2xCo2SOBKj0JgUJGah0ZISqtCYFCVGoVESj82cuzbyOzQhCo1Jka38brEVNlVoLG8DhcZd1lVo1K3TkuguXLjAH3/8gY+PD/b29uzbt48ZM2bQvHnzZFVkFBERERERERGRf6jQKInO0dGRw4cPs2jRIh4/fkzGjBn59NNP6d69u6VDExERERERERGRd6RCoyS6LFmyMHv2bEuHISIiIiIiIiIi8cg2BiMTERERERERERGRBKUejSIiIiIiIiIikuQYNH9xsqMejSIiIiIiIiIiImI2FRpFRERERERERETEbCo0ioiIiIiIiIiIiNk0RqOIiIiIiIiIiCQ9GqMx2VGPRhERERERERERETGbCo0iIiIiIiIiIiJiNhUaRURERERERERExGwqNIqIiIiIiIiIiIjZNBmMiIiIiIiIiIgkPZGWDkDelno0ioiIiIiIiIiIiNlUaBQRERERERERERGzqdAoIiIiIiIiIiIiZtMYjSIiIiIiIiIikuQYoqIsHYK8JfVoFBEREREREREREbOp0CgiIiIiIiIiIiJmU6FRREREREREREREzKZCo4iIiIiIiIiIiJhNk8GIiIiIiIiIiEjSo8lgkh0VGkVE/i0q0tIRJIqoCEtHIPL2osLDLB2CxKPI5yGWDkHknUSFhVs6hMRhM5+J9KFIRCQ+6dZpERERERERERERMZsKjSIiIiIiIiIiImI2FRpFRERERERERCTpiYqy/h8zXLlyhYEDB/LRRx9RsGBB6tWrF2e7bdu20aBBA7y9valRowbz58+Ps92MGTOoWrUq3t7eNG7cmL179751TCo0ioiIiIiIiIiIJDPnzp1j27ZtZM+endy5c8fZ5tChQ3Tt2pWCBQvy22+/0bBhQ3788UcWL15s0m7GjBmMHj2ali1bMm3aNLJnz07Hjh05c+bMW8VkiIrSFD4i76qGXVNLhyAiIpI8GWzo+24bmVTDVhhS2ls6hMRhI+etJoOR5GhD5OLXN7IStUoMsnQICW7dge/fedvIyEjs7KI/U/Xt25fjx4+zatUqkzafffYZgYGBJoXF7777ji1btrB9+3bs7OwIDQ2lfPnyfPzxx/Tp0weAiIgI6tevj5eXF6NHj37jmGzoE56IiIiIiIiIiIh1iCkyvkxoaCh79uyhbt26Jsvr169PQEAAJ0+eBODgwYMEBweb3HqdIkUK6tSpw7Zt23ibPooqNIqIiIiIiIiIiFiZq1evEhYWRq5cuUyW58mTB4ALFy6Y/Ptiu9y5c/PkyRPu3Lnzxs+Z0pyARUREREREREREEoQNjOJQrVq1V67ftGnTO+87MDAQAFdXV5PlMY9j1gcFBeHg4ICjo6NJOzc3NwAePXpEpkyZ3ug51aNRRERERERERETEShkMhtcuj6tNzC3TL9s+LurRKCIiIiIiIiIiYgHm9Fh8nZgeiTE9F2MEBQUB//RsdHV1JSQkhJCQEFKlShWrXcx+3oR6NIqIiIiIiIiIiFiZbNmyYW9vz8WLF02Wnz9/Hogeg/Hf/8aM1RjjwoULpEmThowZM77xc6rQKCIiIiIiIiIiSY4hKsrqfxKSg4MDZcuWZc2aNSbLV61ahaenJwULFgSgePHiuLi4sHr1amObiIgI1qxZQ6VKlXTrtIiIiIiIiIiIiDV79uwZ27ZtA+DGjRs8fvyYtWvXAlC6dGnc3d354osvaNWqFQMGDKB+/focPHiQxYsXM2TIEOzsovsfOjg40KVLF0aPHo27uzsFCxZk8eLFXLt2jVGjRr1VTCo0ioiIiIiIiIiIJDP379+nR48eJstiHs+ZM4cyZcrg4+PDpEmTGDVqFMuXLydTpkwMGDCApk2bmmzXoUMHoqKimDt3Lvfu3SNfvnxMmzYNLy+vt4rJEBWVwP00RaxYDbumr28kIiIisRlsaASfqEhLRyDxyJDS3tIhJA4bOW+jIiIsHYLIW9sQudjSISSaD4oNtHQICW7t4SGWDiFe2dAnPBEREREREREREUkounVaRERERERERESSHt2Em+yoR6OIiIiIiIiIiIiYTYVGERERERERERERMZsKjSIiIiIiIiIiImI2jdEoIiIiIiIiIiJJj8ZoTHYs3qOxb9++1KtXL851Q4YMoWrVqmY/x7Fjx/Dy8mLv3r1vve3KlSupWbMmhQoV4qOPPgLAy8uLGTNmmB3Xm3rw4AFeXl4sXbrUZHlYWBjz5s2jadOm+Pj44O3tTd26dZkyZQpBQUGJFl98HafEZs55kdgc0zjSZXQ7Fl2fyp9P5zPl4C9U/qS8pcOKd7aSZ2pnRz4b3oqf1w5g8Z0ZbIhcTOtBTS0dVryzlTzBds5dW8nTVs5dWzieRSoVZEOEf5w/BcrktXR48coWjmcMa8s1tbMjnw1rzrA/+/LfG1NYH7qA1t81jtWuUHkvvp7SkYl7hrIqeDbrQxeQMXt6C0Qc/wpV8OLHP/6PJXenszJoDv85OZqW3zaydFjxSr9brIut5CnyLtSj8RUeP37Mt99+S7169Rg2bBjOzs4A+Pv7kzlzZovGFhoayueff87ff/9N8+bN6datG6lSpeL06dMsXLiQK1euMGzYMIvGKPFn8JJe5CuVmxn95nP97C2qtvCl/8KvMdjZsWXhDkuHF29sJU9XDxfqdqzOxSNX2LViH3U+q27pkBKEreQJtnPu2kqetnLu2srxBJjx7UKObD1hsuzS8asWiiZh2NLxtLZcXT2cqfNpVS4evcquP/6mzqdxf4HvU7UQPlULc+HIZZ4EPaVY5UKJHGnCqNKsAn1mfcH2xbv5pf0knj1+znu5M+LxXjpLhxav9LsleV6fL2MreYq8CxUaX+HatWuEhoby4YcfUqJECePyYsWKWS6o/xk3bhx79uxh2rRpVKxY0bi8bNmytGjRIln00pM3U7q2DyVqFuWnFmPYsmgnAEe2niBjNk8+H9Gabf67iIyMtHCU5rOVPAHuXAmgoXs7IPpDp7V+0LSVPG3l3LWVPME2zl1bOp4AN87f4tTec5YOI8HY0vG0xlzvXLlHowwdgf+957yk0Dh/6DLm/Rh9h1OTr+taRaHRI3M6vprckdW/bWR895nG5Ue2nbRgVAlDv1uS5/UZF1vJU+RdWfzW6Te1dOlSvLy8OHHiBJ999hnFihWjZs2aLF++PFbbSZMmUaFCBXx8fOjWrRsPHjyI1SYqKooZM2ZQq1YtChcuTLVq1Zg1a5Zx/fjx42nQoAEA7dq1w8vLi/HjxwOxb51u3bo1nTp1Ys2aNdSqVQsfHx/atGnD1aum35SHhoYyatQoqlSpQuHChalduzYrV66MFdt///tfqlatStGiRWnbtm2s/YSEhDB//nyqV69uUmSM4eDggJ+fn/Hxo0eP6N+/P2XLlqVIkSI0adKEHTtMv2V50xzu3LlD586dKVq0KH5+fkyfPj3W8wPcvn2bXr16UaZMGYoUKULLli05fvy4SZuqVasyZMgQ5s2bR5UqVShRogRdu3Y1OV5hYWEMHz7c+Jr5+vrSuXNngoODjW2CgoIYPHgwvr6+FC5cmEaNGsXKD97svEiKKjQszdPgZ2xbvNtk+bpZW0ifxZ38ZfJYKLL4ZSt5ivWxlXPXVvK0FTqe1sWWjqct5fqiKCscp6x2h6qkdnbE/5c/LB2KxANbuT5tJU+Rd5VsCo0xevfuja+vLxMnTiR//vz07duX8+fPG9fPmzePsWPH8uGHHzJu3DiyZs3Kd999F2s/Q4cOZdy4cTRo0IBp06bRsGFDRo4cycKFCwFo2rSp8dbjgQMH4u/vT9OmLx9D49SpU8ycOZNevXoxbNgwLl++TO/evU3a9OjRA39/f9q3b8/UqVPx8/Ojd+/ebNu2zdhmy5YtfPfdd5QpU4YJEyZQtmxZevbsabKfY8eO8fTpUypVqvTa1ysiIoKOHTuyceNGvv76a8aPH0/69On5/PPP2bNnz1vn0LVrV44fP87gwYMZNGgQ69evZ+PGjSZtAgMDadGiBadPn+a7775j/PjxpE6dmrZt23L//n2Ttps3b2bLli0MHDiQ/v37s2/fPn744Qfj+qlTp7Jo0SI+++wzZs6cyXfffUeGDBkIDQ0Foou37du3Z+vWrXz11VdMnjyZ3Llz06lTJ86cOWPcz5ueF0lRjkLZuHrqBpERpt+KXTx6JXp94WyWCCve2UqeYn1s5dy1lTxtha0dz+7jO7A2ZAHLH/6HYWu+pVAFL0uHFK9s6XjaUq62wNuvAEH3g3nfKzOT//6ZNc/m898bU/ly4qc4uaS2dHjylmzl+rSVPJOMqCjr/7Eyye7W6ZYtW9KyZUsAihYtytatW1m/fj158uQhIiKCqVOn8tFHH/F///d/APj5+REQEMCqVauM+7h69Srz5s3j+++/55NPPgGgfPnyPH36lIkTJ/LJJ5+QKVMm8uaNHiQ8T548r71dOjg4mOXLl+Pu7m58PGDAAG7fvk2mTJnYs2cPmzdvZsaMGfj6+gJQoUIF7ty5w/jx441Fw8mTJ1OyZEljkdPPz49nz54xdepU43PdvXsXgEyZMr329dq6dStHjx5l2rRpxufw8/OjXr16TJw4kbJly75xDtu3b+f48ePMmjWLcuXKAVCqVCkqV65MunT/jKEye/ZsgoKCWLx4MR4eHgCUK1eOGjVqMGPGDPr06WNsGxUVxeTJk3FwcADgypUrzJgxg8jISOzs7Dh27Bi+vr7GYw5Qq1Yt4/9XrlzJ6dOnWbFiBXny5DHmd/nyZSZNmsTYsWPf+LxIqlw9nLl18W6s5cEPHhvXWwNbyVOsj62cu7aSp62wleP5JPApS8eu5si2EwTdf0yWPJlo+k19ft08iAH1h/P3+iOWDjFe2MrxBNvK1Rakz5yOVE6pGLDoK/yHr2Dynjl4lcxF60FNyVHofXpWHmzpEOUt2Mr1aSt5iryrZNejMaZIB+Ds7Mx7773H7du3gejbde/evUuNGjVMtvl3YQpg165dANSsWZPw8HDjT7ly5QgICODWrVtvHVf+/PmNBTqA3LlzG2MC2LlzJ2nTpqVs2bKxnvPUqVNEREQQERHBiRMnXht/zG0TBoPhtXH9/fffpEmTxqT3o52dHbVr1+bQoUNERES8cQ5Hjx7FxcXFWGQEcHNzo0yZMibPuXPnTsqUKYObm5sxTzs7O0qWLMmxY8dM2pYqVcpYZIToom5YWJix52PBggXZtm0b48eP5+jRo7HGuti5cyf58uUjR44csV7XmOd60/MiKXvVrTLW9AWIreQp1sdWzl1bydNW2MLxvHD4MpN7zmbXir85vuM062ZtpYfvd9y/9ZDPfm75+h0kI7ZwPGPYUq7WzmBnR6rUDiz6eTmLRqzg6PaTLB61ipkDFlG4Qn58qha2dIjylmzl+rSVPEXehcV7NKZIkcKk2PVvERERpExpGqKLi4vJY3t7e+NttAEBAQAmxTLA2KsuxsOHD4mKijLpzfdvt27dIkuWLG+eBODq6horLogeTzHmOR89ekShQnEP2hwQEECKFCkIDw+PFX/69OlNHmfMmNEY5+sEBQXF2j5mn2FhYTx9+tT4mr4uh7t378aKLWZfZ8+eNT5++PAhhw8fjjPXbNlMu5G/7jm7dOmCnZ0dy5YtY8KECbi7u9OyZUu++OILDAYDDx8+5OTJk3E+V4oUKYA3Py+SqqD7j3H1cIm13MU9+puymG/OkjtbyVOsj62cu7aSp62w5eP5JPApe/88SP3ONXFwtCf0eZilQzKbLR1PW8rVFgQ9CAbe4+8NR02W7197GEa1JY9PTg5tPh7ntpL02Mr1aSt5irwrixca3d3duXfvXpzrAgIC4ixsvYynpydArEk+XhwX0M3NDYPBwIIFC4yFrX/LmTPnGz/nm3Jzc8Pd3Z1p06bFud7d3Z0UKVKQMmXKWPG/+Pp4e3uTJk0atm/f/spxI2OeN67X9969e9jb2+Pk5PTGOWTIkCHOCVRe3L+bmxt+fn706NEjVtt/9158Ew4ODnTv3p3u3btz5coVlixZwvjx48maNSsNGjTAzc0NLy8vhg4d+tJ9vOl5kVRdOn6VKs0qYJfCzmQckJze0UXby8evvmzTZMVW8hTrYyvnrq3kaSts/XjG3BViLb1ObOl42lKutuDSsasULJsv1vKYG7eiIq3kIrURtnJ92kqeSYYm8E52LH7rdKlSpQgKCmL//v0my4ODg9m3bx+lSpV6431lypQJT09PNmzYYLJ83bp1Jo9jbv199OgR3t7esX6cneN/TIXy5cvz4MED7O3t43xOBwcHUqRIQcGCBV8bf6pUqWjRogUbN25k586dsZ4rLCzMOOtyiRIlePLkCdu3bzeuj4yMZO3atfj4+Bh7/b0Jb29vgoOD2b37n9m1AgMD2bt3b6xcL1y4QO7cuWPl6eX17oOvZ8+enZ49e5I2bVouXrxofK5r166RIUOGOF9XePPzIqnauWwvTi6p8Wts2gO3RpvK3LvxgNN7z79ky+TFVvIU62Mr566t5GkrbPl4OqdNQ5m6xTl/6BJhIcm/NyPY1vG0pVxtwY6l+wAoVauYyfLStX0AOLXvXGKHJGawlevTVvIUeVcW79Ho6+tLyZIl6datG1988QV58+bl7t27TJ8+nZQpU9K6des33leKFCn4/PPPGTp0KB4eHlSoUIEdO3bEKmLmzJmTli1b0qdPHz799FOKFi1KWFgYly9fZu/evUyaNCm+06RChQpUqVKFzz77jM8++wwvLy+ePXvG+fPnuXLlirFHXufOnenatSv9+vWjTp06HD9+PM4JS7788kuOHTtG586dadGiBRUqVCBVqlScO3eO+fPnU6xYMXx9falcuTJFihShT58+9OzZk4wZM7Jo0SIuXbrEwIED3yqHihUrUqhQIXr37k2vXr1wcXFh6tSpsW5nb9euHStXrqRVq1a0adOGzJkz8+DBA44cOULGjBlp167dGz9n165dKVSoEAULFiR16tRs2bKFR48eGW97b9CgAYsWLaJNmzZ06NCBHDlyEBwczMmTJwkLC+Obb7554/Miqdq/9jAH1h/hy0kdcXJNzc3zt6nS3JfStX0Y1mpsrHErkytbyTNGqQ+K4ZjGEScXRwCyF8hq/LCyb/VBQp6FWjK8eGMLedrKuWsrecaw9nPXVo5nv3nduXv1HmcPXCTwXjBZ8maiydf1SJfRjV86TLZ0ePHGVo4nWG+upWoVxTFNKlI7R8+0nK1AFvwalQZg35rDhDwLxS29C0UqFgAgZ+H3/7ddMQLvBfEoIIhjf522TPBmOLDxKLtX/k2rAY2wszNwau858pXIRavvmrBn1QFO7Dxj6RDjlX63JM/r80W2kqfIuzJEvWoU00Ty+PFjxo0bx4YNG7h79y7Ozs6ULVuWr7/+mhw5cgCwdOlS+vXrx+7du01up65Xrx6FCxfm559/BqIHZZ04cSILFizg6dOnlCtXjk8++YROnToxZ84c48QlUVFRzJ8/H39/fy5duoSTkxM5c+akdu3axkLYsWPHaNKkicl2AF5eXsYiJUDr1q1xcnIymRk6rm1DQ0OZNm0aK1eu5MaNG7i4uJA3b14aNWpEgwYNjNsuWrSIKVOm8ODBA4oWLUrPnj1p1qwZw4YNo1GjRsZ2YWFhLFq0iOXLl3Px4kXCw8PJnj071apVo127dsaZoB89esSIESPYtGkTT58+xcvLix49euDn52fc15vmcPv2bQYNGsTu3btxdXWlTZs23L59m61bt7J582bjtgEBAYwZM4Zt27bx6NEjPDw8KFq0KO3ataN48eIAVK1alcqVK5sUPNeuXUuPHj3YtGkTWbNmZfr06axZs4YrV64QERFBzpw56dChA/Xq1TM5f8aPH8/69esJCAggbdq0FCxYkBYtWlC5cuW3Oi/eVg27V9+6Hl8c0zjSYWhzKjYth4u7M9dO32DRz8vY6r8rUZ4/sdhKngBzL04kU44Mca5rlbMrd64EJHJECcNW8rSVc9dW8gTbOHctfjwNCX9jzSd9PqLyx+XIlDMDqZ0dCXrwmBM7z7Dw5+Wc/ftCgj+/UVTC/9Fp8eOZiCydqyFl7KGXzDXn7Fgy5fCMc13rvF9y58o9ilQswMiN38XZ5si2k/Su8WP8BpUI5y2Ag6M9rb5rQtVmFXB/Ly33bz5k88KdzPvhd8JCwxP8+aNeMl9AQtDvFuth6Tw3RC5OlOdJCj4o1N/SISS4tSdePhRccpQkCo0iyVViFRpFRESsTiIUGpOMRCrYSOJIiEJjkmQj521iFhpF4osKjdbF2gqNFr91WkRERERERERE5EUG9Y1Ldmzoq2QRERERERERERFJKCo0ioiIiIiIiIiIiNlUaBQRERERERERERGzaYxGERERERERERFJejRGY7KjHo0iIiIiIiIiIiJiNhUaRURERERERERExGwqNIqIiIiIiIiIiIjZVGgUERERERERERERs2kyGBERERERERERSXoiNRlMcqMejSIiIiIiIiIiImI2FRpFRERERERERETEbCo0ioiIiIiIiIiIiNk0RqOIiIiIiIiIiCQ9URqjMblRj0YRERERERERERExmwqNIiIiIiIiIiIiYjYVGkVERERERERERMRsKjSKiIiIiIiIiIiI2TQZjIiIiIiIiIiIJD2aDCbZUY9GERERERERERERMZsKjSIiIiIiIiIiImI2FRpFRERERERERETEbBqjUUREREREREREkh6N0ZjsqNAoYoYNkYstHYKIiIiIiIiISJKgW6dFRERERERERETEbCo0ioiIiIiIiIiIiNlUaBQRERERERERERGzaYxGERERERERERFJeiI1GUxyox6NIiIiIiIiIiIiYjYVGkVERERERERERMRsKjSKiIiIiIiIiIiI2TRGo4iIiIiIiIiIJD1RkZaOQN6SejSKiIiIiIiIiIiI2VRoFBEREREREREREbOp0CgiIiIiIiIiIiJmU6FRREREREREREREzKbJYEREREREREREJOmJirJ0BPKW1KNRREREREREREREzKZCo4hIItm/fz9PnjyJc92TJ0/Yv39/IkckIiIiImK+0NBQ1q1bx9WrVy0diohYmAqNIkncgwcPGDlyJG3btqVWrVqcO3cOgNmzZ3P48GHLBpeArl+/zq5du3j06JGlQ4k3bdq04cKFC3Guu3TpEm3atEnkiBJOeHg4x48f5/79+5YORUREREQSmIODA7169eLWrVuWDkVELExjNIokYSdOnKBdu3akSZOGEiVKsG/fPkJDQwG4c+cOs2bNYsyYMZYNMh78/PPPRERE0L9/fwA2bNjA119/TXh4OG5ubsyYMYPChQtbOErzRb1ifJFnz57h6OiYiNEkLDs7O5o1a8a0adMoX768pcMReSvnzp1j0qRJHDt2jNu3b+Pv70+hQoUYPXo0xYsXp1KlSpYOMV48ePCAmTNnGvOcMGECefPmZfbs2RQtWpRixYpZOsR4ExgYyLlz57h16xYVK1bEzc2NkJAQ7O3tsbOzru/dbSXXCxcuGM/dxo0b4+npyZUrV/Dw8MDZ2dnS4ZmtX79+dO3alffffz/Wuhs3bjBhwgSGDRtmgcjEHNZ+3ubKlctqC41ve+dRqVKlEigSGxSpMRqTGxUaRZKwYcOGUaxYMSZNmoTBYODPP/80ritatChr1qyxYHTxZ8OGDXz55ZfGx6NGjaJSpUr06NGDESNGMGbMGKZPn27BCN/d4cOHOXTokPHxypUrOXDggEmbkJAQNm3aRK5cuRI7vARjZ2dH1qxZCQoKsnQoCSJ//vwYDIY3bn/q1KkEjCbh9OvX763aW8MfvTt37qRTp04ULFiQunXrMnXqVOO6lClTsnDhQqsoNNrKF1mRkZGMGTOGuXPn8uzZMwwGA7///jtubm5069aNokWL0q1bN0uHGS9sJddnz54xYMAA42egqKgo/Pz88PT05NdffyVr1qz06dPHwlGab9myZTRv3jzOQuPDhw9Zvnx5sn3PrVq16lv9Dt20aVMCRpM4bOW87dmzJz/99BN58uSxik4C/9a6dWsMBoOx48C/z+GoqKhY53Ry/ewnEh9UaBRJwo4dO8b48eOxt7cnIiLCZJ27u7vV3JYaEBBA5syZAbh69SqXLl3il19+IV++fLRu3Zr/+7//s3CE727Hjh1MmDABiP5AMnfu3FhtUqZMSe7cuRk0aFBih5egOnfuzOTJkylevDgZMmSwdDjxqnfv3sYPlOHh4cyfP58UKVJQtWpV0qdPz71799i0aRORkZG0atXKwtG+u+PHj5s8DggI4NGjRzg7O+Ph4cH9+/d5/PgxadOmxdPT00JRxq9ff/2VOnXqMGLECMLDw00KjQUKFGDx4sUWjC7+2MoXWWPHjmXevHn07t2bMmXKULduXeO6qlWrsnjxYqsovoHt5Dp8+HD27NnDlClTKFmyJMWLFzeuq1SpErNmzbKKgs2rXLlyhbRp01o6jHdWuXJlk6LMxo0bCQoKomzZssbfoXv27MHNzY3q1atbMNL4Yyvn7ciRI3n06BFNmzYlXbp0eHh4mKw3GAz88ccfForOPL///rvx//fv32fgwIGULFmSDz74wPiZaO3atfz9998MGTLEgpGKWJ4KjSJJWOrUqXn8+HGc627evJmsP2T+m4uLi7FounPnTtzc3Izfgjo4OBASEmLJ8MzSrVs34x92+fPnx9/fn6JFi1o4qsSxdu1a7t+/T/Xq1fn/9u48rqb8/wP467QwKUWLEtOgqGSpFBVCosYyhsFYxsiexIx1MmMfhhBGEdn3ryVSWSJj37exjBlLI0u0aJeUlt8fHu7PnWKGbp17z309H495POp8zvC67ql77/t8Pu+PtbV1qW82Q0NDRUpXNkOGDJF9vWDBAtja2mLZsmXQ1NSUHX+z7C0tLU2MiAoRFRUl+/rEiROYMWMGgoKC0LJlS9nxU6dOYdq0aZgwYYIYERXu7t27GD9+PACUmJ2gr6+P9PR0MWIpnLrcyNqzZw/GjRuHfv36lXicFhYWePTokUjJFE9dHmtMTAwmTZoEd3f3Eo+zVq1aSEhIEClZ2W3duhXbtm0D8Pr3z4QJE1C5cmW5c/Lz85GQkAAvLy8xIirEtGnTZF+vWbMGZmZmiIqKgr6+vux4ZmYmhg8fDlNTUzEiKpyUr9u32dnZSW4m4xtvP64xY8agU6dOJSZDdOjQAYGBgdixY4ckVj8QfSwWGomUWKtWrRAaGgpXV1fZmy9BEPDy5Uts3LhRMi9gTk5OWLp0KVJTU7FmzRq5u9d///03atasKWI6xfnrr7/EjlChcnJyULduXbnvpWjPnj2YN2+eXJERADQ1NdGvXz8EBASo9KzcNxYsWIAxY8bIFRmB17+nRo8ejQULFkjid5KBgQGSk5NLHYuPj5fMzE11uZGVkZEBS0vLUseKiopQUFBQwYnKj7o81hcvXrzz5zA3N7eC0yhWjRo1ZMWMu3fvom7dujA0NJQ7R1tbG/Xq1UPPnj3FiKhwGzduxPTp0+WKjMDr38XDhw/HzJkzMWzYMJHSKY6Ur9u3zZs3T+wIFeLkyZOyFUv/1KpVK0nMHicqCxYaiZTYxIkT0bdvX3h5eaFFixYQBAFLlizBvXv3IAgCvv/+e7EjKsSPP/6IiRMnYuHChbCzs8PYsWNlY5GRkXBychIxneLs378fT548wdChQ0uMrVmzBubm5vj8889FSFY+SlsmLkUvX75850yEhIQElZ6R+7b3LdUzMDDAw4cPKzZQOfH09ERwcDCaNm2Kzz77DMDrGzwpKSlYs2aNSs8iepu63MiqU6cOTp8+DVdX1xJj58+fR/369UVIVT7U5bFaW1vj0KFDaNWqVYmxY8eOqfRsKk9PT7mbre/aDEZKMjMzkZ2dXepYdna2ZHo9S/m6VUe6uro4e/ZsiZuvwOvVWbq6uiKkkrD3bKhJyomFRiIlZmpqioiICKxfvx5nzpyBhYUFMjIy0LVrVwwaNEgyM05MTU2xcePGUsfWrFmDSpUqVXCi8rFy5Up89dVXpY598sknWLVqlaQKjerC09MTCxcuxCeffAJPT09UrVoV2dnZOHz4MBYtWiSZ/lJWVlYICwuDk5OT3M6Yz58/R1hYGKysrERMpzjjx4/HjRs38MUXX6BBgwYAXt8MefToEerWrSuZWQrqciPLx8cHU6dOhZaWFry9vQEAiYmJ+P3337Fp0yaV3UyjNOryWP38/ODn54fc3Fx4e3tDEARcv34d0dHRCA8Px6pVq8SOqBBSeb7+jYuLCxYuXIiaNWuiefPmsuPnz59HUFAQXFxcREynOOpy3QJAVlYWYmJicP/+fdkmY2+bMmWKCKkUq1+/frLVWO3bt5f1aIyNjcXevXsxevRosSMSiUooLmZ5mIioItjb28tmEP3TuXPnMHLkSLkdqqWgqKgI586de+ebzUGDBomQSrGeP3+OH3/8EYcPHwbwenOfN0sUPT09MXfuXLnCnKq6cuUKhg4dCg0NDbRo0UL2pvr8+fMoLCzE6tWr0axZM7FjKsSrV68QGRmJM2fOID09HQYGBnBzc0O3bt0kc+MDeP1h8M2NrDeP09XVVVI3sgBg3bp1CA4ORm5urmy3UB0dHYwZM0YSv4Pepi6P9eDBg5g/fz6ePHkiO2ZmZoaAgABZkVUKTp06hZiYGCQmJpaYHS8IAjZs2CBSMsVJTk7GyJEjcevWLVStWhXVq1dHeno6srOzYWtri9DQUMn0aVSH6zY+Ph59+vRBfn4+cnNzYWhoiMzMTBQUFMDAwAB6enqS2EUcADZv3oywsDAkJyfLdqM2MTHB8OHDMWDAALHjScrnn34ndoRyd+DRr2JHUCgWGolUzKVLl3D//n00a9YM9erVEzvOR/P19UVAQADq1KkDX1/f956rypuGvK1FixaYOnUqunTpUmIsKioKs2bNwsWLF0VIVj5SUlIwYMAAxMfHy96AAfIbbPz5559ixVO4uLg4XL9+HSkpKahRowYaN278zn5pqurZs2dYv3697HGamJigSZMmGDhwoGR6F5I05eTk4OrVq7KCqqOjoyRuAJRGnR7r/fv3ZY9Tar9vV69eLZvpV69ePWhra5c4Z8WKFSIkKx8nTpwo8dri7u4udqxyIeXr1tfXF8XFxfj1119hb2+P8PBw2NjYYP/+/Vi8eDGWLl0qqWXiRUVFSExMlF23ZmZm0NDQEDuW5LDQqHq4dJpIiY0fPx6VKlWSLZ/Ztm0bZs6cCeD1bswrV64sdXacKsjJyZHtuifVTUL+ydnZGWFhYfDw8ECVKlVkx1+8eIHVq1fLLRmSgnnz5qFatWo4fvw42rRpgx07dsDY2BiRkZGIiIhAWFiY2BEVytLSUnIfGP7J2NhYMrtL/5v79+/LFY4bNWqk0jd31J2urm6pvdGkSJ0ea926dWWbjuXn50tqxvHWrVvRr18/TJ06Ve4GnVS5u7tLtrD4T1K+bq9fv445c+bIHtOrV6+gqamJrl27IiMjA7Nnz8b//vc/kVMqjoaGBszNzWFubi52FGnj3DiVw0IjkRK7fPkyJk2aJPs+LCwMvXr1QkBAAGbMmIGQkBCVLTS+vVGIumwaMnbsWPTp0wcdOnSAl5cXatSogeTkZMTExODVq1dYtGiR2BEV6uLFi5gyZYrcTDdzc3PZ3e5Zs2Zh9erVIiZUnFevXmHXrl24ceMGEhMTMW3aNNSpUwf79++HtbW1pAqQmZmZuHv3Lp4+fQp3d3cYGBggLy8P2trakriLn5OTg2nTpuHAgQMoKiqSLYXX0NCAt7c3fv75Z5Vt8t61a9f/fK4gCIiMjCzHNBXjxIkTyMrKks0kf/r0KX788UfExcXBzc0N06ZNk7vxo8oiIiLeOSYIAqpWrQobGxuV/0AcERGB7Oxs2dLEO3fuwN/fH48fP0azZs2wZMkSGBkZiZyy7DIyMtChQwe1KDICr39W37yGjhw5Eubm5rh48SIsLCwksXRaXa7b/Px86OnpQUNDAwYGBkhOTpaNWVlZ4a+//hIxnWLdvXsXy5cvl12327dvh52dHRYvXgxHR0fJbKpG9DFYaCRSYmlpaahRowYAyD7Yf/vtt9DV1UX37t3x3XfSn0YuJZaWlti1axeWLl2KQ4cOISMjA9WqVYObmxv8/f1lO9xKRXZ2NgwNDaGhoQE9PT2kpqbKxuzt7SUzo/HRo0fw8fFBWloabGxs8Pvvv8tm6V68eBEnT56URFP/oqIiLFmyBJs2bUJubi4EQcCuXbtgYGAAf39/NG3aVBIbpcyePRtHjx7FrFmz0LFjR+jr68sa28+dOxezZ89W2efTzs5ObYoWbyxdulRuk61Zs2YhLi4OnTt3RmRkJJYuXYqAgAAREypOQECA7Pl9uzPS28cEQYCnpyfmz58PHR0dUXKW1Zo1a9CnTx/Z9z///DO0tbXx448/YtOmTVi0aBHmzJkjYkLFaNeuHS5fvqyyN5T/q7S0NPj5+eHatWswMTFBSkoK+vTpA3Nzc4SHh0NHRwfTp08XO2aZqct1W6dOHSQkJMDZ2RkNGzbE1q1b4ebmBi0tLWzfvl32uUbVnT59GiNGjEDDhg3RuXNnrFy5UjampaWFbdu2sdBIao2FRiIlVq1aNSQkJMDJyQknT56EiYkJ6tevDwAoLCxEUVGRyAkVQ51mnHz22WcICgoSO0aFqF27tuxOtpWVFfbu3Yt27doBAGJjYyWz2cTs2bNhaGiInTt3Ql9fX673kLOzs2Rmqv7666/YvHkzJk6ciBYtWqBz586yMQ8PD+zcuVMShcaYmBhMmDABPXv2lB3T19dHr169kJ+fj0WLFqlsoXHevHliR6hwDx48gI2NDYDXGzedPHkSCxcuhLe3N+rXr4+QkBDJFBp37NiB8ePHo1u3bmjfvj0MDQ2RlpaGw4cPIzIyEjNmzMDTp08xb948BAUFqezOrwkJCbJZ4mlpabh8+TJWrFgBd3d3GBoaIjAwUOSEitGjRw/MnDkTeXl5cHNzg76+folz7OzsREimWHPmzEF6ejqioqJQp04duddQV1dXSfToBtTnuu3cubNs1uJ3332HIUOGoHnz5rJe3ar6+vlPQUFB6NSpE+bPn4+CggK5QqOtrS127twpYjoi8bHQSKTE3N3dsXDhQvz111/Ys2cPunXrJhu7e/cuateuLWI6xVGnGSfqpG3btjh9+jQ6deqEkSNHYtSoUXB1dYWWlhaePXsmmV5/Fy5cQFBQEAwNDWV9R994MztDCvbs2YNx48ahX79+JR6nhYUFHj16JFIyxapcufI7f7d++umn0NKS3lun4uJi5OTkQFdXV3IzHt8sewcg22yrdevWAF4/n8+ePRMtm6ItXrwYvXv3xrBhw2THTE1NYWtrCx0dHaxatQobNmxAeno6Nm/erLKFRg0NDbx69QoAcP78eWhpacHFxQXA69+56enpYsZTmCFDhgAAVq1ahVWrVsn9bL6ZnSqFDdWOHz+On3/+GVZWViVeW2rWrImkpCSRkimWuly3b+9ub29vj+joaJw4cQJ5eXlwcXFBgwYNREynOHfv3sX48eMBoMTrpr6+vmSeT6KPJb13y0QS8sMPP6CwsBCnTp1CmzZtMHr0aNnY4cOHZR+WVJ06zTh58OABdu/ejfj4eOTl5ZUYl9IOkm/egAFAmzZtsHXrVhw5cgQvX76Em5ubZJaUaGpqyi1TfNuzZ88kMxs3IyPjnb0mi4qKUFBQUMGJykePHj2wbds2tG7dusQH+61bt6JHjx4iplOsCxcuICQkBFevXkVBQQG0tLTg6OiI0aNHw8nJSex4ClGvXj1ERkaiadOm2L59OxwcHGQ9NlNSUiQzsxoArl69iqFDh5Y61rBhQyxbtgwA0KRJE6SlpVVkNIWysbHB1q1bYWZmhk2bNsHFxUW28cSTJ09gbGwsckLF2Lhxo9gRKkRhYeE7XyezsrJK3W1bFanLdftPNWvWxNdffy12DIX7Z//Jt8XHx8v1JycF4GYwKoeFRiIlVrVq1XcuMdi2bVsFpyk/6jLj5Pr16xgwYADMzc0RHx8Pa2trZGdnIyEhAWZmZrCwsBA7Yrlq0qQJmjRpInYMhXN2dsa6devg7u4uu47fLBHasWOHZPpr1alTB6dPny718Zw/f17W1kHVGRgY4NatW+jYsSPatWsHIyMjpKam4ujRo8jPz0ezZs2wbt06AK+fZx8fH3EDf6TTp09j+PDhqFOnDkaMGAFjY2OkpKQgJiYGPj4+CAsLg5ubm9gxy8zPzw/fffcdIiIioKmpKXcz58SJE2jYsKGI6RTL0NAQMTExaNmyZYmxgwcPwtDQEMDrDY8MDAwqOp7CjB07Fr6+vvjiiy+gq6sr+3kEXrflaNy4sYjpFKd58+ZiR6gQTZo0QXh4eKk3H/ft2wdHR0cRUimelK/bP/7444POl8KSf09PTwQHB6Np06ayHuuCICAlJQVr1qyBl5eXyAmJxMVCIxGJTl1mnCxYsADe3t745ZdfYGdnhzlz5sDOzg5XrlzBhAkT5Ja7SYnUd5KcMGEC+vbti06dOqF9+/YQBAFbtmzB3bt38eDBA8n06fHx8cHUqVOhpaUFb29vAEBiYiJ+//13bNq0STJ9l97uqVnajKK3e6yqcqFxyZIlcHd3x/Lly+Vmbvr7+8PPzw9LliyRRKGxffv2OHDgAG7dugVra2vUqVNHNubg4ABra2vxwinY8OHDMWPGDDx+/Bjt2rWT9Wg8cuQIzp07h5kzZwIAzp07p9JFjWbNmuHo0aOIj4+HhYWFXO/Cnj17Su6mXVxcnOw19KuvvoKJiQkePHgAIyMj6OnpiR2vzL7//nt8++236N+/P7y8vCAIAmJjY7Fy5UocP34cW7duFTuiQkj5uv3qq6/+U9sNKS35Hz9+PG7cuIEvvvhCthz8xx9/xKNHj1C3bl1J9KwmKguh+F3rvYhIKVy8eBHbt29/51LbqKgoEVIp1pEjR/Ddd9+hsLBQNuOkVatWAF7vopmRkSGJJcXNmzdHUFAQWrVqBVtbW2zdulV2pz48PBybNm1CRESEuCEVqLSdJHft2gU7OzsEBARIZidJ4PXO0yEhITh9+jQyMjJgYGAAV1dXjBkzRqU/PPzTunXrEBwcjNzcXNlycR0dHYwZM0auLxMpv6ZNmyI4OBju7u4lxk6cOIHRo0fj2rVrIiSjsjhy5AhWrFiBP//8U7Yc3tbWFiNHjoSHhwcAIDMzE1paWrIbeqSccnNzMWXKFOzfv182S/7Na+iYMWNQu3ZtTJo0SeyYCnH16lUEBQXh6tWrKCwshCAIsLe3x6RJk+Dg4CB2PPoXFy5c+KDzpTJb99WrV4iMjMSZM2eQnp4OAwMDuLm5oVu3brJl8aQYn9ca/e8nqbgDCcFiR1AozmgkUmInT57EiBEj4Orqips3b8Ld3R0vX77ElStXYGZmBmdnZ7EjKoS6zDgRBAHa2toQBAFGRkZ48uSJrNBoZmaG+Ph4cQMqmLrsJAm8XuIvlR0j32fQoEHo3bs3rl69KntT7ejoKIlZNeqmSpUq79xkISkpSTK9Rd948ODBO2/YdezYUYREilVQUIC//voL9vb22LlzJ4qKipCWlgZDQ0NZS4c3VHnZ9BtFRUU4d+4c7t+/j/z8fLkxVZ5p/LbAwECcO3cOK1euhJOTk9wS4jZt2mD9+vWSKTQ6ODhg8+bNePnyJTIzM6Gvrw8dHR2xYymcVK9bqRQOP5S2tja++uorfPXVV2JHkb6iIrET0AdioZFIiQUHB2PgwIGYMGEC7Ozs8N1338HOzg4JCQkYOnSobLc6Kfj000/x6aefljgupQbSlpaWePToEVxcXGBvb4+1a9eiQYMG0NLSQlhYWKmPX5Wpy06S3377LaZPn17qRin379/H9OnTJdXUX1dXVzbjWMry8vLw6NGjUgtTUugv5eHhgaCgIJiZmcltLHbq1CksXrwY7du3FzGd4jx//hz+/v44d+6cbFYYIL9LqBSW8WloaKBPnz6y3poaGhqS3VwiJSUFAwYMQHx8/DufU1Ut2LwtJiYGkyZNgru7e4nX0Fq1aiEhIUGkZOWncuXK0NbWxieffCJ2FIVTl+tWXTx//hz5+fmy3rcAEBkZibi4OLi4uEimPzfRx2KhkUiJxcXFYezYsdDQ0IAgCMjNzQXw+g3m6NGjsXTpUnTr1k3klIpx//59hIWF4fLly8jMzISBgQGcnJxkmxVIQe/evfHkyRMAwLhx4zB48GDZ86ejo4OlS5eKGU/h1GUnyQsXLiAnJ6fUsefPn+PSpUsVnEhxPnQp/5dfflkuOSpSfn4+Zs6cib1795b4cP+GFApTkyZNwp07dzBs2DDo6enJNr3JyclB48aNJTNTasGCBUhJScHWrVvRr18/hISEwMDAAJGRkTh37pxcz01VpqGhgdq1ayMrK0vsKOVu3rx5qFatGo4fP442bdpgx44dMDY2RmRkJCIiIhAWFiZ2RIV48eLFO3euffN+UCpOnTqFZcuW4ebNm7Il/3Z2dhg1apTcjRBVpi7XrYeHx7/2azxy5EgFpSk/EydORI0aNWS9b0NCQmSvL2FhYQgKCkKnTp1ETkkkHhYaiZRY5cqVUVRUBEEQYGJigocPH8LJyQnA62VviYmJIidUjJs3b2LAgAGoVKkSPDw8YGxsjGfPnuG3337DgQMHsHnzZpWdQfT8+XPo6upCEAS5IoylpSX279+P33//HS9fvoS9vT2MjIzEC1oO1GUnyfe5evWq3N1uVRMQECD3/ZsPD2+3d377A4UUCo3Lli3DqVOnMG/ePEyYMAHTpk1DlSpVEBkZiYcPH2Lq1KliR1QIAwMDbN++HUePHsWlS5eQnZ0NAwMDNGvWDG3bti2x3FZVnTx5EmPHjkXTpk0BADVq1ECTJk3g7OyMwMBArFu3DosXLxY5pWL4+voiNDQUjo6OqFGjhthxys3FixcxZcoUuSKcubk5fH19UVxcjFmzZmH16tUiJlQMa2trHDp0qNQZ5MeOHZNrR6LKwsPD8dNPP8HJyQnjx4+X3fQ4fPgwhg8fjp9//hk9e/YUO2aZqct127Zt2xKFxoyMDFy+fBmCIEhmtvyNGzdkfcaLi4uxdetWjBgxAmPHjsXcuXOxZs0aFhpJrbHQSKTEbGxscP/+fbRs2RKurq5YsWIFqlevDi0tLSxZskS2y5mqW7BgAWxsbLBmzRq5GXAvXrzAkCFDsGDBAqxfv168gGXg7OyM7du3o0mTJiWW2Orq6qJly5YiJyw/Ut5JcuXKlVi5ciWA14W2gQMHlnhjnZ+fj8LCQvTr10+MiApx9uxZ2dcPHz7E2LFj0bVrV3h5eck+DB48eBDR0dGSKdYcPHgQ/v7++PzzzzFhwgQ0adIEjRo1wpdffomAgAD89ttvpRbPVZGGhgbat28vmQ9+pUlLS0PNmjWhqakJHR0dZGRkyMbc3d0xerR0GswfPHgQqamp8PT0hLW1dYmbV4IgSKI3bnZ2tqz3pJ6eHlJTU2Vj9vb2kpkZ5ufnBz8/P+Tm5sLb2xuCIOD69euIjo5GeHg4Vq1aJXZEhVi2bBl69OiBX375Re64j48PJk+ejOXLl0ui0Kgu1+20adNKPZ6fn4+RI0dKZpVSZmYmqlevDuD1hIn09HTZderh4YGdO3eKGY9IdCw0EimxgQMH4vHjxwBeL7X19fXFyJEjAbzePCQkJETMeApz7do1LFq0qMQy2ypVqmDo0KGYMGGCSMnKrnLlyrIeb+9bYitFDg4O2LhxI4KCghAYGIji4mKsWLEC9vb2WL9+vcrOUgVeP7bBgwejuLgYy5YtQ+fOnWFmZiZ3jra2NiwtLdGuXTuRUpbdmzfRwOvfQV9//TVGjBghO2ZqaoqGDRuiSpUqWLRoETZs2CBGTIVKTExE3bp1oampicqVK8stRe3atSvGjRsnWyqlahwcHP51SdsbgiDg8uXL5Zyo/JmZmSE9PR0AUKdOHfz222+ynbavXLmCypUrixlPoXJyclC3bl2576Wodu3aSE5OBgBYWVlh7969st+zsbGxqFatmojpFKdt27ZYtGgR5s+fj6ioKADAzJkzYWZmhoULF0qmB1xaWho6d+5c6ljnzp1x4MCBCk5UPtTlun2XSpUqyW64f/PNN2LHKTMTExPcu3cPTk5OOH78OGrVqiXrtZ6bmwstLZZZFOqtlTSkGvgTQKTE3p41Y2pqit27d+PBgwd4+fIl6tWrh0qVKomYTnG0tbXf2W9I1V+sra2tMX/+fNkH2507d+LEiROlnisIAkaNGlWR8cqdVHeSbN68uWyXRUEQ0KtXL5iamoqcqnxdvXoVQ4cOLXWsUaNGWLFiRQUnKh8mJiay4mLt2rVx/vx5uLm5AYDK7ww/ePBguUJjYWEhQkND0bt3b8kutW3ZsiXOnDmDDh06YODAgQgICMD169ehra2N69evY9CgQWJHVJhNmzaJHaFCtG3bFqdPn0anTp0wcuRIjBo1Cq6urtDS0sKzZ89U+ubkP3l7e8Pb2xv3799Heno6DAwMSt14TJU1bdoUf/zxR6krPG7duoXGjRuLkErx1Om6fZf09HTJ3ADx8vLCggULcObMGZw4cULu/dGtW7fw2WefiZiOSHxCcTHLw0QkrjFjxuCPP/5AWFiY3BvouLg4+Pr6ws7ODkuWLBEvYBncunULs2bNQlxcHJ4/f47KlSu/c0aRIAi4cuVKBSck+m88PDzg6uqKOXPmlBibPHkyzp8/j99++02EZIr1448/olq1apg0aRLWr1+P+fPno3379tDW1kZsbCy6dOlSYomfqiosLISdnR3Cw8NVeobx++Tm5iI3N1fWK/Xw4cM4ePAg8vLy4Obmhj59+kimH6W6un79Oo4cOYKXL1/Czc1NMq0NpOztFgYPHjzAuHHj0L17d3h6esLQ0BBpaWk4fPgwIiIisGjRIlmPVSmR6nV76NChEsdevXqFuLg4bNmyBc2bN0dwcLAIyRSroKAAK1aswM2bN9GwYUP4+vrKJoCMGjUKzZo1w+DBg0VOKR2fm/mJHaHcHUhcLnYEhWKhkUjJxcXF4fDhw0hMTJQtwX1DEARJfOB9+vQp+vfvj8TERFhZWcHExATPnj3D3bt3UbNmTWzZsqXEslRVZGNjgx07dqBJkyZiRyk3s2fP/qDzp0yZUk5JKtaDBw+we/duxMfHl/g5BSCJ2X47duzAtGnT4OzsDE9PT1mPxtjYWFy8eBGzZs1C7969xY5ZZikpKUhPT5f1wF2/fr1cYWrUqFHv3E1d1ahDoVHdFBUV4dy5c7h//z7y8/NLjEtpBqc6ePr0KWJjY/H06dNSn09VfQ21sbGRu+n65uPou479+eefFRuQPpqNjU2px7W1tdGhQwdMmTJFpTfJI3Gw0Kh6WGgkUmIRERH48ccfoa2tDTMzs1KXSr/p26PqcnJyEB4ejsuXLyMrK0u2+2mPHj2gq6srdjyFuHDhAuzs7CTzeErj4eHxn88VBAFHjhwpxzQV4/r16xgwYADMzc0RHx8Pa2trZGdnIyEhAWZmZrCwsMDGjRvFjqkQR48exYoVK/DHH3+goKAAWlpasjv5H/LcK6uCggL89ddfqFmzpuR2gS+NuhUaU1NTS70RYG5uLkIaxUtJScGAAQMQHx8PQRBKLd5IqWCTlJSEpKSkUp9TZ2dnERIp1v79+zFp0iQUFxfD0NAQ2tracuOq/Bq6e/fu/9wvFgC6d+9ejmkqltSv24SEhBLHKleuDCMjow96zlWJ1F9blMHnpiPFjlDuDiSp/mZtb2OhkUiJeXl5wdraGnPnzpV0cUrdxMXF4caNG0hMTMRXX30FExMTPHjwAEZGRtDT0xM7Hn2gN0XGX375Ra5oc+XKFUyYMAEzZ85E69atxY6pUEVFRUhLS5PtoCkVRUVFaNKkCcLCwmR9GaVMHQqN6enpmD17Ng4dOoSCggK5seLiYknNlho/fjwSEhLw66+/ok2bNtixYweMjY0RGRmJiIgIhIWFwcLCQuyYZfbo0SNMnDgR165dA/D/M9/ekMpz2qFDB9jZ2eHnn39G1apVxY5DZaQu1626UKfXFmXAQqPqUd0dFojUQHJyMmbMmCH5IqOtrS22b99e6pLimzdvolevXpJ4sc7NzcWUKVNkOygWFxejdevWMDExQVBQEGrXro1JkyaJnJI+1O3btzF8+HBZwe3NXW1HR0eMGjUKQUFBkis0amhowNjYWOwYCqehoYHatWvL7TStDqQ6ywR4vbT0woULGDJkCKysrErMCpOSixcvYsqUKTAxMZEdMzc3h6+vL4qLizFr1iysXr1axISKMWXKFDx9+hSzZs2S9HOalpaGr7/+Wm2KjI8fP8aVK1eQmZkpW9VSq1YtsWMpjLpctxcvXnznmCAIqFq1KurWravyG1qq02sL0cdgoZFIiTk5OeHOnTtwdXUVO0q5et/E6oKCAmhqalZgmvITGBiIc+fOYcWKFXBycoKjo6NsrE2bNli/fr2kCo379+/HkydPSt2peM2aNTA3N8fnn38uQjLFEgQB2traEAQBRkZGePLkiey5NTMzU+mditWx56avry9CQ0Ph6OgouZ2YHRwcSi0q9u/fv8RxQRBw+fLliopWbs6fP48pU6bgyy+/FDtKucvOzpbNMtbT00NqaqpszN7eHmFhYSKmU5zr168jMDAQHTt2FDtKuXJ3d8fvv/8u+feAhYWFmD59Onbv3o2ioiLZcQ0NDXz11VeYOXOmJGbOq8t1O2DAgBK9Nv/5+vLJJ5/g66+/xqRJk1T2uVWn1xaij8FCI5GSeXsnvrFjx2LSpEmoXLkyWrZsWepd7WrVqlVcOAVKSUlBcnKy7Pu///67REExLy8P4eHhkulxEhMTg0mTJsHd3R2FhYVyY7Vq1Sq1r40qCwsLQ48ePUod++STT7Bq1SpJFBotLS3x6NEjuLi4wN7eHmvXrkWDBg2gpaWFsLAwfPrpp2JH/Ggfsou0IAiSKDQePHgQqamp8PT0hLW1dYlejYIgIDRUNZe3DB48WNKzF0ujr6+P6tWrix2jQtSuXVv2umplZYW9e/eiXbt2AIDY2FiVfb/wT6ampipbnPgQM2bMwLhx47B48WK4uLhAX1+/xDlSaHkQHByMiIgIfP/99+jcuTNMTEyQkpKCffv2ITg4GCYmJhgzZozYMctMXa7blStXYsaMGXBxcUH79u3ldhG/cOECJkyYgDt37mDt2rWoUqWKyj636vTaQvQxWGgkUjIuLi4l7gTOmDHjnR8OVXVJ8fbt2xESEgJBECAIAiZPnlzinOLiYmhqamL69OkiJFS8Fy9eyC1pe1tubm4Fpyl/8fHxqF+/fqljlpaWuH//fgUnKh+9e/fGkydPAADjxo3D4MGD0a1bNwCAjo4Oli5dKma8MvmQQqNU5OTkoG7dunLfS8Xo0aPFjlDhhgwZgk2bNqFly5bQ0pL22962bdvi9OnT6NSpE0aOHIlRo0bB1dUVWlpaePbsGSZMmCB2RIX4/vvvsWrVKjg5OUmmeFqa58+fIycnBytXriwxG1VKPeD27t2LMWPGYPjw4bJjtWrVkn2/bds2lS1GvU1drtvw8HB06dIF48ePlzvu6emJoKAgHDhwACEhIQD+/7lXRer02qIUiritiKrhTwWRkvnll1/UYsZJ9+7d0bx5cxQXF2PgwIGYNm0arKys5M7R1tZGnTp1JHPH0NraGocOHUKrVq1KjB07dgyNGjUSIVX5qVy5stzSvbelpKRI5o3Z28tmLC0tsX//fvz+++94+fIl7O3t1WL3YinZtGmT2BGojP655D8uLg4dOnSAs7NzqbPCpDATF4DcB/s2bdpg27ZtiI2NxcuXL2FraytisrLz9fWV+z4xMREeHh6wtbUtsdpDlWcdv23SpElITEzE1KlTUadOHcn2gHv27Nk7Z2ba2dnh2bNnFZxIcdTxuj158iT69OlT6piLiws2b94MAGjRogXWrFlTkdEU6u+//1ab1xaijyGNT3lEEvKupaZSU6tWLVmT740bN6Jhw4aS33HZz88Pfn5+yM3Nhbe3NwRBwPXr1xEdHY3w8HCsWrVK7IgK5ezsjLCwMHh4eKBKlSqy4y9evMDq1avRvHlzEdOVH11dXbRs2VLsGOUiPT0dW7ZsweXLl2UN+52cnNCvXz+VvyFw/PhxmJiYoGHDhgBezxiaM2eO3Dm6uroYO3asGPHoA/xzJu6bm3eXLl0qca5UlvyXpnHjxmjcuDGA1607vv/+e5XtJ/bPmcVv754tpVnHb7t58yaCgoLg6ekpdpRy9emnn+Lo0aOlvm4ePXpUpduPqON1q6uri/Pnz8PNza3E2Pnz52UbXL569Uql3/cfPXpUbV9biP4LFhqJVEB2djZu376NlJQUmJiYwNraWlK7EL4pON2/fx/Xr1+XPc7GjRujXr16IqdTnLZt22LRokWYP38+oqKiAAAzZ86EmZkZFi5cKLmG72PHjkWfPn3QoUMHeHl5oUaNGkhOTkZMTAxevXqFRYsWiR1RISIiIt459maHRRsbG5XvNfrw4UP0798f6enpcHBwQJ06dZCSkoLQ0FD873//w5YtW+Q+RKmS48ePw8/PD9u3b5cdKyoqwubNm2FiYiKbSZSamgpra2t06tRJrKj0H6jjkn+pU8eZxhYWFiX6OUvRwIEDMX36dKSlpeHzzz+HsbExUlNTceDAAezfvx8zZ84UO+JHU8frtk+fPli2bBnS0tLQrl07WY/GI0eOYPfu3fD39wcAXLlyBTY2NiKn/Xh8nSF6P6H4fdu9EpGoioqKsGTJEmzatEmuh5+Ojg6++eYbfP/995LYkfnFixeYOnUqDhw4gKKiImhpaaGgoAAaGhrw9vbGzz//LLsDKhX3799Heno6DAwMYGlpKXaccvPgwQMsXboU58+fR0ZGBqpVqwZXV1f4+/vjs88+EzueQtjY2Mjuar/9kvr2MUEQ4Onpifnz50NHR0eUnGXl5+eH+Ph4rF69Wq5o+vTpUwwdOhR16tTBsmXLREz48Xx9fVG1alUsWLBAdqywsBB2dnYIDw+XLetbsGAB7t27h5UrV4oVleijvJnRKIWefuri1KlTCAoKwqJFi+T6xkrRpk2bsHz5cqSnp0MQBBQXF8PQ0BCjRo1C//79xY5HH2jjxo1YtWoVUlJSZM+nsbExRowYgQEDBgB43dJCR0dH5W/CUsXwNh7+7yepuIPPwv79JBXCGY1ESmz+/PnYvHkzhg0bho4dO8ruCsbExGD16tV49eoVAgICxI5ZZj///DOOHj2KWbNmoWPHjtDX10dWVhZiYmIwd+5czJ49G3PnzhU7ZpmFhISgV69eMDU1Rd26deU+OCQnJ2PHjh2yO71S8dlnnyEoKEjsGOVqx44dGD9+PLp161Zih8XIyEjMmDEDT58+xbx58xAUFKSyS2nOnz+POXPmlPhQULNmTfj7+6vs4wKAGzduYNq0af96nrOzM/bu3VsBiUiR0tLSsGHDBly7dk02Y75p06YYOHAgDA0NxY5HH+HevXtYsWJFied0+PDhaNCggdjxFCIwMBDJycno3LkzatSoUWpPv8jISJHSKdaAAQPQv39//P3338jMzES1atVQt25dye3SrA7XLQB8++23+Oabb5CYmCh7nGZmZnLPpxRusqelpWHt2rW4ceMGEhMTERISgvr162PDhg1o2rQp7O3txY5IJBoWGomU2J49e0rsxGdqagpbW1vo6Ohg7dq1kig0xsTEYMKECejZs6fsmL6+Pnr16oX8/HwsWrRIEoXGZcuWwd3dHaampiXGkpOTsWzZMskVGtXB4sWL0bt3bwwbNkx27O2f01WrVmHDhg1IT0/H5s2bVbYg92a2cWm0tLRQVFRUwYkUJzMzs8SmPZqamvj111/lloPr6ekhIyOjgtNRWVy7dg1Dhw5FYWEhXFxc4OjoiNTUVGzatAmbN2/G2rVr0bRpU7Fj0gc4duwY/P39YWpqCg8PDxgZGSE1NRW//fYbevTogZCQELRt21bsmGVmZ2cnuU3i3kdDQ6PEpoBSoi7X7RsaGhowNzeX7IzFP/74Az4+PtDV1UWzZs1w4cIF5OfnAwCSkpKwfv16LFmyRNyQRCJioZFIib1ZulcaOzs7yfTuqVy5MmrXrl3q2KeffiqZ3Ynf16kiJSWl1B3rVNm33377r+ds3LixApKUr6tXr2Lo0KGljjVs2FC2nLhJkyZIS0uryGgK5ejoiOXLl8PJyQnVqlWTHc/MzERoaCiaNWsmXrgyqlq1KlJSUkoc9/Lykvs+JSVFUv1x1cHMmTNhZWWFsLAwuecuOzsbw4YNw6xZsxAeHi5iwrJxcHCQtWl4H6m8XwBer/Zo3bo1li1bJjdDavLkyfDz88P8+fMlUbCZN2+e2BHKzbp16/7zuYIgwMfHp/zCVBB1uW4B4O7du1i+fLlspt/27dthZ2eHxYsXw9HREW3atBE7YpnNnTsX9vb2WL58OQRBwL59+2RjTZs2xYEDB0RMRyQ+aXx6J5IoLy8v7Nu3r9Sd+Pbt24cOHTqIkErxevTogW3btqF169ZyH5iKi4uxdetWld6JOzo6GtHR0QBev1kODAwsUajIz8/HzZs34ejoKEbEcqOjo1PiA3BGRgZu374NfX192NraipRMsQwNDRETE1Pqz+nBgwdlSzNzcnJgYGBQ0fEUJiAgAP3790e7du3g4uICExMTPHv2DGfPnkWlSpUQGBgodsSP1rhxYxw4cACff/75e887cOAAmjRpUkGpSBHu3buHX3/9tcTv3apVq2LYsGEqv4v44MGD/1OhUUoeP36MgICAEstqNTQ00K9fP64MUAEf8nohlUKjuly3p0+fxogRI9CwYUN07txZrqexlpYWtm3bJolC440bNxAcHAxtbe0SN3IMDQ2RmpoqUjIi5cBCI5ESc3Z2xuLFizFgwAB4enrKllnExsbi4cOHGDt2LA4dOiQ7v2PHjiKm/XgGBga4desWOnbsiHbt2ske59GjR5Gfn49mzZrJ7n6r2hvOV69eIScnB8Drwmlubm6JN5mVKlVCt27d3jkrTlW9a8OM1NRUjBw5Et26davgROVj+PDhmDFjBh4/flxih8Vz587Jdsw8d+4cGjduLHLaj1e/fn1ERkZi/fr1uHTpEuLi4mBgYICvv/4aPj4+MDMzEzviR+vfvz9GjBiBZcuWYeTIkSV+RouLixEaGorDhw9jxYoVIqWkj/HZZ58hKyur1LHs7Gx8+umnFZxIsUaPHi12hApnbW2Nx48flzr2+PFj1K9fv4ITlZ9Tp04hJiYGiYmJyMvLkxsTBAEbNmwQKVnZ/PXXX2JHqHDqct0GBQWhU6dOmD9/PgoKCuTeC9ra2mLnzp0iplMcHR0dPH/+vNSxJ0+eyK38IAUo4v7FqoaFRiIl9qb/YlJSEi5evPjOceD1G05V3U1y0aJFsq9LW0r79mYiqlZo7N69O7p37w7gdbPzGTNmSKIBdlkYGRlh2LBhWLhw4b/OIFMFffr0gYmJCVasWIHAwEAUFBRAS0sLtra2WL58OTw8PAAA/v7+KtcGoFOnTli8eDGsra0BAGZmZrCzs8PIkSNVenbmP7Vp0wbDhw9HcHAwtm/fDhcXF9SsWRPA69+/Z8+eRXJyMoYNGyaJmRjqZOLEiZg1axZq1qyJ5s2by46fP38eISEhmDp1qojp6GNMmzYN48aNg46ODjw9PVG1alVkZ2fj8OHDWLdunWQ2IFu9ejUWLlyImjVrol69eqhSpYrYkcpFYWEhNDU1xY5R7tTlur179y7Gjx8PACVmW+vr6yM9PV2MWArXqlUrhIaGwtXVVdb6SBAEvHz5Ehs3buR7BVJ7QvH7moYRkagSEhI+6PxatWqVUxIqq7y8PPTs2ROTJk1C69atxY4jupiYGEyePBlXrlwRO4pCFRUVIS0tDYaGhpLYLdPGxgY7duyQLRcuLCxEo0aNsGvXrnf2j1Vlx44dw7p163D16lVZU/dKlSrB0dERgwYN4gcHFdS1a1ckJycjKysLVatWRfXq1ZGeno7s7Gzo6+ujRo0asnOltIuvlDk4OKCgoAAFBQUAXi/HfPtrbW1t2bmCIODy5cui5CwrDw8PtG3bFlOnTpX08viWLVuie/fu6NGjB+rVqyd2nHKjLtdtq1atMH78eHTv3l3Waz48PBx2dnbYsWMHVqxYgd9++03smGWWlJSEvn374vnz52jRogViY2PRunVr3Lt3D4IgYMeOHSU2maOP52047N9PUnEH01aJHUGhVGtqBZGaYeFQOipXrozk5GS1uGv/xh9//FHi2KtXrxAXF4dly5ZJstedhoYGjI2NxY5RrqR8f7Jt27Zo27YtCgsLkZGRgeLiYlSvXl2tfm6lRt127lUH6tKXMiMjAx06dJD8Y/3qq6+wd+9erFmzBg4ODujVqxe8vb2ho6MjdjSFUpfr1tPTE8HBwWjatCk+++wzAK8LpykpKVizZk2JTdZUlampKSIiIrB+/XqcOXMGFhYWyMjIQNeuXTFo0CAunSa1xxmNRCoiNze3RH8eAJJ5IUtPT8eWLVtw+fJlZGZmwsDAAE5OTujXrx+qV68udjyFeDMrYdasWWJHqRA2NjYl3lS/eclp2rQpFi5cqPL90d6Qah+t0mY0vj07gYiIysf48eNRt25dyWwS8j5FRUU4efIkwsPDcfToUVSqVAmdOnVCz5490bRpU7Hj0QfIzs6Gj48Pbt++jQYNGuDWrVuwsbHBo0ePULduXWzYsAG6urpixyQV411dWn3sS3MwfbXYERSKMxqJlNibDQj+97//ISUlpdRzVLUv49sePnyI/v37Iz09HQ4ODqhTpw5SUlJkj33Lli2wsLAQO2aZOTo6YtGiRfD19YW7uzuMjIxKFOJUdUOf0pTWb7Ny5cowMzODqampCInKh7r00XqbOszKINWXl5cHV1dXLFiwAO3btxc7DtEH6dGjB2bOnIm8vDy4ubnJ+sC9TSo3fDQ0NNCmTRu0adMG6enp2Lt3L3bt2oVdu3bB0tISPXv2RI8ePUr9NyDlUrVqVfzvf/9DZGQkzpw5g2rVqsHAwAD9+/dHt27dUKlSJbEjElEF4IxGIiW2bt06LF++HEOHDsXixYsxcuRIaGpqYt++fXj16hV8fX3Rs2dPsWOWmZ+fH+Lj47F69WqYm5vLjj99+hRDhw5FnTp1sGzZMhETKoaNjc17x1V5Qx91JuU+WjY2NtDR0ZF7XC9evChxDFDtnlIkXa1atcIvv/wCd3d3saOQAj148AC7d+9GfHx8qas9pLA7/D/fM7z9O7e4uFiy7xnu3LmD8PBwREZG4sWLF3BycsLVq1ehoaGBwMBAlb5pIPXrNi8vD9999x0GDx4st/mWVHh4eHzQ+7wjR46UYxr1whmNqoczGomU2K5duzB69Gj0798fixcvhqenJ+zs7ODn54eRI0fi4cOHYkdUiPPnz2POnDlyRUYAqFmzJvz9/TFlyhSRkinWv73heNMUXGqSkpKQlJRU6ptqZ2dnERIplpT7aKnDkj2Sti+//BK7du1ioVFCrl+/jgEDBsDc3Bzx8fGwtrZGdnY2EhISYGZmJokVEEDpqwKk6vnz54iOjkZ4eDhu3rwJKysrjBw5Et26dYOBgQGeP3+On3/+GXPmzFHZQqM6XLeVK1fGxYsX4ePjI3aUctG2bVu593qxsbHIysqCi4sLjI2N8ezZM5w7dw4GBgbw9PQUMSmR+FhoJFJiCQkJsLW1haamJrS0tJCVlQXg9RKTfv364aeffsK4ceNETll2RUVF0NIq/deRlpYWioqKKjhR+Shtc5/U1FTs27cPUVFRuHnzpqRmJzx69AgTJ07EtWvXAPx/f0ZBECQ1G6Ndu3a4fPkyXF1dxY6icCw0kqrT19fH1atX8cUXX6B169YlWlYIgiDZD8VStWDBAnh7e+OXX36BnZ0d5syZAzs7O1y5cgUTJkzAsGHS2J1UijPCSjNp0iQcOnQIgiDg888/x08//QR7e3u5c/T09NCvXz/s3btXnJAKoC7XbcuWLXHmzBm4uLiIHUXhpk2bJvt6zZo1MDMzQ1RUlNyS/szMTAwfPlxSLYKIPgYLjURKrFq1anjx4gUAwNzcHLdu3ZIVM9LT0/Hy5Usx4ymMo6Mjli9fDicnJ7nNbTIzMxEaGopmzZqJF64c5OTk4PDhw4iKisK5c+dQWFiIxo0bS2bm5htTpkzB06dPMWvWLFhZWUFbW1vsSOVCnfpoEamaRYsWAQBSUlJw586dEuMsNKqe27dvY/jw4dDQ0AAA2Wx5R0dHjBo1CkFBQWjdurWYEekD3Lt3DwEBAejSpQv09PTeeZ6VlZVKz/JUl+v2q6++wvTp0/HixYtSb+4A0nhPtHHjRkyfPr3Eez4DAwMMHz4cM2fOlEzxWClIZNKJOmGhkUiJOTo64saNG2jTpg26dOmCkJAQPHv2DFpaWtixY4dkZlAFBASgf//+aNeuHVxcXGBiYoJnz57h7NmzqFSpEgIDA8WOWGYFBQU4fvw4oqKicOzYMeTl5cHU1BRFRUX49ddf4eXlJXZEhbt+/ToCAwMltcFNaYYMGQIAWLVqFVatWqU2fbSIVMFff/0ldgRSMEEQoK2tDUEQYGRkhCdPnsDR0REAYGZmhvj4eHEDKoiNjc07W3IIgoCqVavCxsYGAwcOhIeHRwWnU5zdu3f/p/N0dXVVepanuly3I0aMAABs3rwZmzdvlux7oszMTGRnZ5c6lp2dLVuFRqSuWGgkUmL+/v5ISkoCAPj6+iIrKwvR0dGymVNTp04VOaFi1K9fH5GRkVi3bh0uX76MuLg4GBgY4Ouvv4aPjw/MzMzEjvjRLl68iKioKMTExCAzMxPVq1dHjx490LVrV1haWqJ58+YwNDQUO2a5MDU1ld25lzJVnmFBRKRqLC0t8ejRI7i4uMDe3h5r165FgwYNoKWlhbCwMHz66adiR1SIcePGYevWrdDW1kbbtm1hZGSEZ8+e4ejRoygsLMQXX3yBixcvYtSoUVi4cCE6d+4sduT/LCMj44POf3u1i6pSl+tWXd4Tubi4YOHChahZs6ZcAfz8+fMICgqS5NJxog/BXaeJiMrRmxkJrq6u8PHxQcuWLaGpqQng9R1PZ2dnbNq0SRKbovzTwYMHsW7dOqxcuVISHxKISDW9evUKu3btwo0bN5CYmIhp06ahTp062L9/P6ytrWFpaSl2RPoAERERePLkCfz8/BAXF4fBgwcjOTkZAKCjo4OlS5eiVatWIqcsuwULFiA+Ph7BwcFyN+2Kiorg7++Pzz77DD/88APGjh2L+/fvIyIiQrywH+h9szVLI4UZcOpw3ebl5WH+/Pno1q0bmjRpInaccpWcnIyRI0fi1q1bqFq1KqpXr4709HRkZ2fD1tYWoaGh7NOoQN4Gg8WOUO4OZq4VO4JCcUYjESmN7Oxs3L59GykpKahRowYaNGiAqlWrih2rTGxtbfHnn3/iwoULEAQBaWlp8PT0fG8fIqmIiIhAYmIiPDw8YGtrW+K5FAQBoaGhIqVTvIsXL+LSpUvIzMyEgYEBnJ2d4eTkJHYsIrX26NEj+Pj4IC0tDTY2Nvj999+Rk5MD4PXP7MmTJzF37lyRU9KH+PLLL2VfW1paYv/+/fj999/x8uVL2Nvbw8jISLxwCrRnzx7MmzevxMoADQ0N9OnTBz/88AN++OEHdOnSBWPHjhUp5cf55ZdfPqjQKAXqcN1WrlwZu3fvlmQ7oH+qUaMGwsPDceLECVy/fh0pKSkwMTFBkyZN4O7uLnY8ItGx0EikxIqKirBz507ExMQgMTFR1jj6DUEQEBsbK1I6xSkqKsKSJUuwadMm5Obmyo7r6Ojgm2++wffffy+bBahq9uzZg7///huRkZHYt28fAgICULlyZbRt2xbt2rWT9BvtnJwcWFhYyH0vRS9evIC/vz/OnDkDLS0tVKtWDRkZGSgsLISbmxtCQkKgo6MjdkwitTR79mwYGhpi586d0NfXR6NGjWRjzs7Oss1iSHVERESgTZs2qF69OoDXvftatmwJ4PWS3IiICLmijqp6+fIlnj59WurYkydPZO8Jq1SponKbrfXo0UPsCBVOXa5bBwcHXLt2TaX7aX4Id3d3FhYrAhfhqhwWGomU2IIFC7Bu3To4OjrCyclJ5d5I/lfz58/H5s2bMWzYMHTs2BGGhoZIS0tDTEwMVq9ejVevXiEgIEDsmB+tXr16+P777/H999/j999/R1RUFA4ePIiYmBgIgiDrZyO15dObNm0SO0KFWLhwIa5du4agoCB4e3tDU1MThYWFiImJwbRp0xAUFCS5HcWJVMWFCxcQFBQEQ0NDFBYWyo2ZmJggJSVFpGT0sSZPnozt27fLCjZve/z4MSZPniyJgo2HhweCgoJQpUoVtGvXDnp6enj+/DmOHDmCoKAgeHp6Ani9m/Fnn30mctqyy83Nxa1bt2SrAuzs7PDJJ5+IHUth1OW6HTNmDCZOnAgtLS24u7vD0NCwxE11KbXTSUpKQlJSUonJIID03tcTfQgWGomUWFRUFPz9/eHv7y92lHK1Z88ejBkzBsOHD5cdMzU1ha2tLXR0dLB27VqVLjS+zd7eHvb29vjpp59w6tQpREdHIzY2FrGxsTA3N8eRI0fEjkgf6NChQxg/frxcI35NTU106tQJ6enpCA0NZaGRSCSampp4VzvyZ8+eoUqVKhWciMrqfe3ls7KyoKurW4Fpys+MGTMQEBCAiRMnQhAEaGlpoaCgAMXFxejQoQOmTZsGADA3N8e4ceNETls2oaGhWLVqFXJzc2XPb5UqVTB8+HD4+vqKnE4x1OW67dOnDwAgMDAQ8+fPL/UcKfTcfPToESZOnIhr164BKPn8SmV3baKPxUIjkRLLz89Hs2bNxI5R7goLC2FnZ1fqmJ2dXYlZKFKgoaEhW27x8uVLHDlyBFFRUWLHKrN169aha9euMDY2xrp16957riAI8PHxqZhg5SgrK+udu0VaWFggKyurghMR0RvOzs5Yt24d3N3dZb3uBEFAcXExduzYAVdXV5ET0n9x/PhxnDx5Uvb92rVrYWxsLHdOXl4ezp07B1tb24qOVy709PQQEhKCuLg43LhxA8nJyahRowYaN24st4FRx44dRUxZdhs2bMCvv/6K3r17o0uXLjA2NsazZ8+wb98+LF26FFWqVMG3334rdsyPoo7Xrbr035wyZQqePn2KWbNmwcrKSrKrzog+FguNREqsa9eu+O233yT/QcjLywv79u2T9ap52759+9ChQwcRUlWcTz75BJ07d5abEaeqAgMD0axZMxgbGyMwMPC950ql0GhpaYmIiAi0bt26xFhERASsrKxESEVEADBhwgT07dsXnTp1Qvv27SEIArZs2YK7d+/iwYMH2Llzp9gR6T+Ij4/Hb7/9BuD1a8elS5dQqVIluXO0tbVRv359lZ/d90+WlpaS3hl969atGDJkCCZOnCg7Vq9ePTRv3hx6enrYsmWLyhYa1fG6VZf+m9evX0dgYKDKF/pVRXFRkdgR6AMJxe+bx01EooqMjMSSJUtgb28PNzc36OvrlzhHCi9wERERWLx4MSwsLODp6QkjIyOkpqYiNjYWDx8+xNixY+WWt0nhMZN0xMbGYvTo0WjatCm8vb1lszEOHjyI69evY+nSpbJeWkRU8R49eoSQkBCcPn0aGRkZMDAwgKurK8aMGSO3YRWpBg8PDyxfvhw2NjZiR1G4P/74A5aWlvjkk0/wxx9//Ov571oNokoaN26MlStXws3NrcTY6dOn4evrixs3boiQTLGkfN2qI29vb0yYMIHv7yqIl95AsSOUu5jnG8SOoFAsNBIpsX97MyKV/h8f8qZLKo9ZHcXFxeHOnTswNDSEk5OTyu4kXpojR45g2bJl+PPPP1FcXAxBEGBrawt/f394eHiIHY+IiFSAjY0NduzYgSZNmsDGxuadS1DfvM5I4f1Q+/bt8eWXX2L06NElxoKDgxEREcH+1UruQ/poCoKA0NDQckxTMQ4ePIh169Zh5cqVktrcRlmx0Kh6uHSaSIn92xurgoKCCkpSvv7tcb569Yq9T1TIli1bcPjwYRQUFMDb2xvffPMNpk2bhp07d8o+HFlZWWHDhg0wNDQUO65CtG/fHu3bt8eLFy+QnZ2NqlWrcpMJIiXw7bffYvr06aUuPb1//z6mT5+OjRs3ipCMyiItLQ1r167FjRs3kJiYiJCQENSvXx8bNmxA06ZNYW9vL3bEj7Jx40bZtaou12WvXr2wdOlS5Ofn4/PPP4exsTFSU1Nx4MABrF27ttQCpKqS6nWbk5MjdoQKFxERgcTERHh4eMDW1hZVq1aVG5dKQZXoY7HQSKTEatWqVeJYamoq9u3bh6ioKNy8eVMSd7PV5XGqgw0bNmDu3Llo0aIF9PX1MX/+fNy6dQuHDx/GpEmTYGlpiTt37mDFihVYvny55HZjrlKlCguMRErkwoUL7/wQ/Pz5c1y6dKmCE1FZ/fHHHxg4cCD09PTQrFkzXLhwAfn5+QCApKQkrF+/HkuWLBE35Edq3rx5qV9L2YgRI5CZmYl169Zh9erVsuOampoYMGAARowYIWI6xZHydbtp0yaxI1S4nJwcudYb6lhsJXofFhqJVEBOTg4OHz6MqKgonDt3DoWFhWjcuLHkijTq8jilbOfOnRg+fLisqfnBgwcxduxY/PTTT/jmm28AAO7u7tDS0sKWLVtU9rmdPXv2B52vqo+TSMquXr0qmVnV6mTu3LlwcHDA8uXLIQgC9u3bJxtr2rQpDhw4IGI6+q/u3r2L7du34/Hjx6hRowYWLFiAKlWqIDMzEwYGBmjSpAmqV68udkyF4XUrLepYXBUVu/2pHBYaiZRUQUEBjh8/jqioKBw7dgx5eXkwNTVFUVERfv31V3h5eYkdUSHU5XGqi0ePHsntHt6qVSsUFxeXaFjfqFEjPH36tKLjKcybXST/C0EQWGgkqkArV67EypUrAbz++Rs4cGCJXnf5+fkoLCxEv379xIhIZXDjxg0EBwdDW1sbhYWFcmOGhoZITU0VKVnZde3a9T+fKwgCIiMjyzFN+bl06RJ8fHxQWFiI6tWrIzMzEzt37sS0adPQt29fseOVCylft4cOHfqg86W2qWNxcTFycnKgq6v7zr6qROqGhUYiJXPx4kVERUUhJiYGmZmZqF69Onr06IGuXbvC0tISzZs3l8QMDHV5nOomLy8POjo6su/ffF2pUiW580p7o61KPqTQSEQVy8HBAYMHD0ZxcTGWLVuGzp07w8zMTO4cbW1tWFpaol27diKlpI+lo6OD58+flzr25MkTld6Ywc7OTi0KFSEhIbCyskJoaChq1qyJ58+fY/LkyViyZIlkC41Svm7HjBnzn8+VyiZGwOvWHCEhIbh69SoKCgqgpaUFR0dHjB49Gk5OTmLHIxIVC41ESmbAgAEQBAGurq7w8fFBy5YtZbvzZmdni5xOcdTlcdJrUvvgtGLFCvTo0QM1atSQHbt48SLs7OzkejQ+evQIy5cvx9y5c8WISaSWmjdvLutvJwgCevXqBVNTU5FTkaK0atUKoaGhcHV1hb6+PoDXz/PLly+xceNGtGnTRuSEH2/evHliR6gQt2/fxsyZM1GzZk0AgJ6eHn744Qd4enri6dOnsuNSIuXrVh13BT99+jSGDx+OOnXqYMSIETA2NkZKSgpiYmLg4+ODsLAwuLm5iR2TSDQsNBIpGVtbW/z555+4cOECBEFAWloaPD09oaenJ3Y0hVKXx6mOSlum2L9/f7ljxSrea+XXX3+Fm5ubrNBYWFiIb7/9Frt27ZJbJp6WloaIiAgWGolE4u/vL3YEUrCJEyeib9++8PLyQosWLSAIApYsWYJ79+5BEAR8//33Ykcsd3FxcYiMjMTYsWPFjvJR0tPTS8wyflNcTE9Pl2ShUcrXbWmbOkrdkiVL4O7uLuu5+Ya/vz/8/PywZMkSFhoVqUi1PzeoIxYaiZTMnj178PfffyMyMhL79u1DQEAAKleujLZt26Jdu3aSmRmmLo9T3ajLh/rSCqWqXjwlkqKioiLs3LkTMTExSExMRF5enty4IAiIjY0VKR19DFNTU0RERGD9+vU4c+YMLCwskJGRga5du2LQoEEqvQT1fZKSkhAdHY3o6Gj8+eefqFSpksoWGtWRul63UnXnzh2MHj26xOcVQRDQt29fjB49WqRkRMpBKOYnIyKl9vvvvyMqKgoHDx5EamoqBEGAp6cnvv32Wzg7O4sdT2HU5XGSNNjY2GDHjh1o0qQJgNczGu3s7BAeHi43o/HatWvo06ePZPoREamawMBArFu3Do6OjrCysoK2tnaJc6ZOnSpCMqJ/l52djYMHDyIqKgqXLl1CcXExbG1t0bNnT3Tu3BkGBgZiR/woNjY20NHRKVGkefHiRYnjgiDg8uXLFR2RPoCDg8N/niAglefT1dUV48aNQ69evUqM7dy5E4sWLcLZs2dFSCZNXjoDxI5Q7mJypbWTOWc0Eik5e3t72Nvb46effsKpU6cQHR2N2NhYxMbGwtzcXDJ9UdTlcRIRUcWJioqCv7+/2sy2lipfX9//fK4gCAgNDS3HNOUrPz8fv/32G6Kjo3HixAnk5+fjs88+w6BBg7B27VpMnjxZ5W/A8udRWgYPHqx2K5E8PDwQFBQEMzMztG7dWnb81KlTWLx4Mdq3by9iOiLxsdBIpCI0NDTg7u4Od3d3vHz5EkeOHEFUVJTYsRROXR4nSZO6vdEmUnb5+flo1qyZ2DGojI4dOwZdXV00bNhQ7CjlavLkyTh8+DBycnJgZGSEPn36oGvXrmjcuDGys7OxZs0asSMqhDoWGl+9eoV169bh4MGDePr0aaltHFR1pp86LhOeNGkS7ty5g2HDhkFPTw9GRkZITU1FTk4OGjdujEmTJokdkUhUXDpNRET0gUpb9lXakq/i4mK8fPmSS6eJRPLzzz9DQ0MDP/30k9hRqAyGDRuGs2fPokaNGujcuTO6dOkCa2trsWMpnI2NDQRBQKtWrTBr1iy5TVGys7Ph7OyMTZs2qfyMRnU0ZcoUREREwMPDA3Xr1i21jYM6FmBVWVFREY4ePYrLly8jKysLBgYGaNasGdq2bQsNDQ2x40mK1yf9xY5Q7mJebhE7gkJxRiMREdEH4ocBItXQtGlTLFmyBKmpqXBzc4O+vn6Jczp27ChCMvoQq1atQnp6Og4cOIDo6GisWbMGlpaW6Nq1K7p06QJzc3OxIyrEjz/+iH379uHkyZNo3749nJyc0LVrV3h5eXHGvIo7dOgQJk+ejP79pV8wycrKQkxMDO7fv4/8/PwS41OmTBEhleJpaGigffv2XCZNVArOaCQiIiIiSbKxsXnvuCAInHGsghISEmQ7MN+7dw8ODg7w8fGRTNH40aNH2Lt3L/bv34+///4b2traaN68Oc6cOYP169ejRYsWYkekD9SuXTvMnDkT7u7uYkcpV/Hx8ejTpw/y8/ORm5sLQ0NDZGZmoqCgAAYGBtDT01PZvuvZ2dmYO3cuOnfujJYtW5Z6zunTp7Fv3z788MMPKrtZkzLijEbVw0IjEREREUlSQkLCv55Tq1atCkhC5eHFixdYvnw51q5dCw8PD4SEhIgdSeFu3ryJqKgoHDhwAMnJyahSpQq8vLzw5ZdfsuCoQjZu3IgzZ84gJCQEWlrSXVTo6+uL4uJi/Prrr7C3t0d4eDhsbGywf/9+LF68GEuXLkWjRo3EjvlRVq5ciT179iA6Ovqdz2FBQQG++OILeHt7Y8yYMRWcULpYaFQ90v0tR0RERERqjUVE6SkoKMCJEycQHR2No0ePQldXF/369UPPnj3FjlYuGjVqhEaNGiEgIADnzp1DZGQkYmNjERERwdm4KuTbb79FcnIyOnbsCCcnp1LbOEhhSfH169cxZ84cVKpUCcDrTXA0NTXRtWtXZGRkYPbs2fjf//4ncsqPExUVhX79+r23UKylpYV+/fph+/btLDQqUHER58apGhYaiYiIiEgyHBwc/nM/O1Xe6VXdnD9/HtHR0YiJiUFhYSE8PT0RHBwMNzc3tdh4QRAEuLq6wtXVFTNnzsSxY8fEjkQfIDIyEmvXroUgCDh79myJzWAEQZBEoTE/Px96enrQ0NCAgYEBkpOTZWNWVlb466+/RExXNg8fPvzXdhwA0KBBAzx8+LACEhEpLxYaiYiIiEgyBg8ezI0zJKZNmzZIT0+Hu7s7Zs2aBQ8PD9mMKamLi4vDjRs3kJiYiK+++gomJiZ4+vQp3NzcxI5GH2DRokXw8vLCzz//DD09PbHjlJs6deogISEBzs7OaNiwIbZu3Qo3NzdoaWlh+/btqFGjhtgRP5qWlhby8vL+9by8vDxoampWQCIi5cVCIxERERFJxujRo8WOQAqWlJQELS0tnD59GmfOnHnvuVKZpZqbm4spU6bgwIEDAIDi4mK0bt0aJiYmCAoKwqeffoqJEyeKnJL+q8zMTPTu3VvSRUYA6Ny5s2zW4nfffYchQ4agefPmEAQBxcXFmDt3rsgJP56VlRVOnz6N1q1bv/e806dPw9LSsoJSESknFhqJiIiIiEhp+fv7ix2hwgUGBuLcuXNYsWIFnJyc4OjoKBtr06YN1q9fz0KjCmndujWuXbsGV1dXsaOUq0GDBsm+tre3R3R0NE6cOIG8vDy4uLigQYMGIqYrmy+++ALz589H69at37nr9JkzZ7Bt2zZMmjSpgtMRKRcWGomIiIiISGmpY6ExJiYGkyZNgru7OwoLC+XGatWq9Z92VCfl0atXL8yaNQu5ublwcXEpdTMYOzs7EZKVr5o1a+Lrr78WO4ZC9OnTB7GxsRg2bBg8PT3h7u6OmjVrQhAEPHnyBCdOnMCRI0fQvHlz9OnTR+y40lJcJHYC+kAsNBIRERERESmRFy9ewMTEpNSx3NzcCk5DZTVs2DAAwMqVK7Fy5Uq5PrLFxcUQBEFSu4jfvn0biYmJpfY07NixowiJyk5LSwthYWFYunQptm7dikOHDsmex+LiYujq6mLw4MEYPXo0ezSS2mOhkYiIiIiISIlYW1vj0KFDaNWqVYmxY8eOoVGjRiKkoo+1ceNGsSNUiDt37uD777/H/fv3UVxcXGJc1QuqlSpVwoQJEzBmzBjcuHEDSUlJAABTU1M0btxYbTapIvo3LDQSEREREREpET8/P/j5+SE3Nxfe3t4QBAHXr19HdHQ0wsPDsWrVKrEj0gdo3ry52BEqxI8//ghNTU2EhoaiTp060NbWFjtSuahUqRKaNWsmdgwipSUUl3argYiIiIiIiERz8OBBzJ8/H0+ePJEdMzMzQ0BAALy9vUVMRh/r4sWLuHTpEjIzM2FgYABnZ2c4OTmJHUthHBwc8Ouvv8Ld3V3sKAoXERHxQed/+eWX5ZJDHXXQlEafz/c5XLhd7AgKxRmNRERERERESsbb2xve3t64f/8+0tPTYWBgAEtLS7Fj0Ud48eIF/P39cebMGWhpaaFatWrIyMhAYWEh3NzcEBISAh0dHbFjlpmNjQ3S0tLEjlEuAgIC5L5/uz/jP48BLDSSemOhkYiIiIiISEnVrVsXdevWBQDk5+ezD5wKWrhwIa5du4agoCB4e3tDU1MThYWFiImJwbRp0xAUFIQpU6aIHbPMpk2bhh9++AEmJiZo0aIFtLSkU244e/as7OuHDx9i7Nix6Nq1K7y8vGBkZITU1FQcPHgQ0dHRWLx4sYhJicTHpdNERERERERKJCIiAtnZ2RgwYACA15ts+Pv74/Hjx2jWrBmWLFkCIyMjkVPSf9WqVSv4+fmhX79+Jca2bNmC0NBQnDp1SoRkipWfn4+ff/4Zu3btgoaGBipXriw3LggCLl++LFI6xRk0aBBcXFwwYsSIEmMrVqzA2bNnsWHDBhGSSROXTqse6dxiICIiIiIikoA1a9agT58+su9//vlnaGtr48cff8SmTZuwaNEizJkzR8SE9CGysrLw6aefljpmYWGBrKysCk5UPqZOnYr9+/ejQ4cOqFu3rmQ3g7l69SqGDh1a6lijRo2wYsWKCk5E6uz+/fuYPXs2Ll++DB0dHXTu3BkTJkzAJ598IlomFhqJiIiIiIiUSEJCgqwfY1paGi5fvowVK1bA3d0dhoaGCAwMFDkhfQhLS0tERESgdevWJcYiIiJgZWUlQirFO3ToEAICAtC/f3+xo5QrQ0ND7N+/Hy1btiwxtm/fPhgaGoqQSsKKi8ROoLSysrIwcOBAmJubY+nSpUhLS8PcuXORkZGBhQsXipaLhUYiIiIiIiIloqGhgVevXgEAzp8/Dy0tLbi4uAAATExMkJ6eLmY8+kCjRo3C6NGjkZCQAG9vbxgbG+PZs2c4ePAgrl+/jqVLl4odUSH09fXfOXNTSnx9fTFt2jQ8fPgQnp6esh6NsbGxuHjxImbNmiV2RFIT//vf/5CVlYWIiAhZgVtTUxMTJkzAyJEjRdtAjIVGIiIiIiIiJWJjY4OtW7fCzMwMmzZtgouLi2wTmCdPnsDY2FjkhPQhPD09ERISgmXLliEwMBDFxcUQBAG2trYICQmBh4eH2BEVwsfHB1u3boWbm5ukNoL5p969e8PExAQrVqzAggULUFBQAC0tLTRs2BDLly+XzPNJyu/EiRNwdXWVm0Xr5eWFH3/8EcePH2ehkYiIiIiIiICxY8fC19cXX3zxBXR1dbFu3TrZWGxsLBo3bixiOvoQ+fn5OHr0KGxtbbF79268ePEC2dnZqFq1KqpUqSJ2PIV6/Pgx/vrrL3Ts2BFOTk7Q19cvcY4UdtcGgHbt2qFdu3YoKipCWloaDA0NoaGhIXYsUlHt27d/7/iRI0dKPR4XF4evvvpK7lilSpVgYWGBuLg4heX7UCw0EhERERERKZFmzZrh6NGjiI+Ph4WFhVzBpmfPnrCwsBAxHX2ISpUqYcKECVi9ejUsLCxQpUoVyRUY3zh69Kis2Hbp0qUS44IgSKbQ+IYgCCgsLERRURELjeXkcNFOsSOUu38rNL5LVlZWqQV9fX19ZGZmljXWR2OhkYiIiIiISMno6emhUaNGJY63adNGhDRUFvXq1cPTp0/FjlHufvvtN7EjVJiTJ08iODgYt27dQlFREXbu3Ak7OztMnToVzs7O+OKLL8SOSCrkXTMWP9ab9gxiYaGRiIiIiIhIyRQVFeHcuXO4f/8+8vPz5cYEQYCPj484weiDjRs3Dr/88gusrKxKLR6TaomOjsbEiRPh5eWFHj16YMaMGbKxTz/9FLt372ahkSqEvr4+srKyShzPzs4WrT8jwEIjERERERGRUklJScGAAQMQHx8PQRBQXFwMAHIzVFhoVB0LFy5ERkYGevXqherVq8PIyEhuXBAEREZGipSubNLS0pCcnAwbGxu543/99ReWL1+OuLg4GBsbY+DAgZLZJGX58uUYOHAgAgICUFhYKFdorF+/PjZs2CBeOFIrlpaWJXox5ufn4+HDhyV6N1YkFhqJiIiIiIiUyLx581CtWjUcP34cbdq0wY4dO2BsbIzIyEhEREQgLCxM7Ij0AaQ8i3HRokX4448/sGfPHtmxhIQE9O/fHy9fvoS1tTXu3r0Lf39/bNiwAc7OziKmVYxHjx69s4WBjo4OsrOzKzgRqSt3d3eEhoYiPT0d1atXBwAcPnwY+fn5orbZYKGRiIiIiIhIiVy8eBFTpkyBiYmJ7Ji5uTl8fX1RXFyMWbNmYfXq1SImpP/i7t272L59O9LT01GjRg14eXmhZcuWYsdSqCtXrqBnz55yx9avX48XL15g1apVaNWqFV6+fIlBgwZh1apVkig0mpiY4O+//4arq2uJsdu3b8Pc3FyEVKSO+vTpg82bN8PPzw9+fn5ITU3FvHnz0LVrV1GXTnNbJCIiIiIiIiWSnZ0NQ0NDaGhoQE9PD6mpqbIxe3t7XL58WcR09F9cunQJ3bt3x5YtW3D9+nWEh4dj6NCh2LZtm9jRFCopKQn169eXO3b06FHY2tqiVatWAIBPPvkEAwYMwO3bt8WIqHBdunRBcHAwzp49KzsmCALu3LmD1atXsz8jVRh9fX1s2LABVapUwejRozFv3jx06dIFs2fPFjUXZzQSEREREREpkdq1ayM5ORkAYGVlhb1796Jdu3YAgNjYWFSrVk3EdPRfhISEwMrKCqGhoahZsyaeP3+OyZMnY8mSJejbt6/Y8RRGEAS53qHPnj3D48ePMXDgQLnzatSogfT09IqOVy78/f1x9+5dDBo0SPazOGzYMKSlpaFt27YYPny4uAFJrdStWxdr1qwRO4YcFhqJiIiIiIiUSNu2bXH69Gl06tQJI0eOxKhRo+Dq6gotLS08e/YMEyZMEDsi/Yvbt29j5syZqFmzJgBAT08PP/zwAzw9PfH06VPZcVVXt25dnDlzRjZ78ejRoxAEocQS8ZSUFBgaGooRUeEqVaqE0NBQnDt3DmfOnEF6ejoMDAzg5uYGNzc3seMRiY6FRiIiIiIiIiUyfvx42ddt2rTB1q1bceTIEbx8+RJubm6iNvmn/yY9PR1mZmZyx94UF9PT0yVTaBwwYAB++OEHZGVlwdjYGNu2bYOFhUWJgtupU6fQoEEDkVKWDxcXF7i4uIgdg0jpsNBIRERERESkxJo0aYImTZqIHYOohC+++AKJiYnYvHkzsrOzYWdnh+nTp0NL6/9LDampqTh69ChGjx4tYlLFS0pKQlJSEvLy8kqMSWHTG6KPJRQXFxeLHYKIiIiIiIjksZChumxsbKCjoyPXvxAAXrx4UeK4IAjc4EeFPHr0CBMnTsS1a9cAAP8sqQiCgD///FOMaERKgTMaiYiIiIiIlAgLGarP399f7AhUTqZMmYKnT59i1qxZsLKygra2ttiRiJQKZzQSEREREREpkYEDByI+Ph7+/v7vLGQ0atRIhGRE5ODggMDAQHTs2FHsKERKiTMaiYiIiIiIlMj169dZyCBSUqamptDQ0BA7BpHS4k8HERERERGREmEhg0h5ff/991i1ahUyMjLEjkKklLh0moiIiIiISIkcPHgQ69atw8qVK1GtWjWx4xDRW3x9ffHnn38iOzsbtra2qFq1qty4IAgIDQ0VKR2R+Lh0moiIiIiISGS+vr5y3ycmJsLDw4OFDCIlk5OTAwsLC7nviej/sdBIREREREQksn8WK1jIIFJOmzZtEjsCkVLj0mkiIiIiIiIiIiIqM85oJCIiIiIiIiJ6h3Xr1qFr164wNjbGunXr3nuuIAjw8fGpmGBESogzGomIiIiIiJTMvXv3sGLFCly7dg0pKSkwMTFB06ZNMXz4cDRo0EDseERqxcbGBjt27ECTJk1gY2Pz3nMFQcCff/5ZQcmIlA8LjURERERERErk2LFj8Pf3h6mpKTw8PGBkZITU1FT89ttvSEpKQkhICNq2bSt2TCIiohJYaCQiIiIiIlIinTp1wmeffYZly5ZBQ0NDdryoqAh+fn54+PAh9u/fL2JCIvXSvXt3zJ8/H/Xr10dISAh69eoFU1NTsWMRKSWNfz+FiIiIiIiIKsrjx4/Rt29fuSIjAGhoaKBfv354/PixSMmI1NPdu3dlu78vW7YMSUlJIiciUl7cDIaIiIiIiEiJWFtbv7OY+PjxY9SvX7+CExGpt1q1amHnzp3Iy8tDcXExbt26hby8vHee7+zsXIHpiJQLl04TEREREREpkRs3bmDcuHHw8/ODp6cnqlatiuzsbBw+fBihoaEICgpCkyZNxI5JpDaioqLw008/4dWrVwCA0soogiCguLiYm8GQ2mOhkYiIiIiISIk4ODigoKAABQUFAAAtLS25r7W1tWXnCoKAy5cvi5KTSJ08f/4cjx49Qvfu3TF37tz3zixu1KhRBSYjUi5cOk1ERERERKREBg8eDEEQxI5BRG/R09ODra0t/P394ebmxs1giN6BMxqJiIiIiIiIiD7Q06dP8fTpU9jY2KBKlSpixyFSCtx1moiIiIiIiIjoP9q+fTtat24NDw8P9O/fH/fv3wcAjBo1Chs2bBA5HZG4uHSaiIiIiIhIyTx48AC7d+9GfHx8qbvbrlixQoRURLR+/XosXLgQAwcOhIuLC4YNGyYba968OQ4cOICBAweKmJBIXCw0EhERERERKZHr169jwIABMDc3R3x8PKytrZGdnY2EhASYmZnBwsJC7IhEamvz5s3w8/ODn58fCgsL5cbq1q0rm91IpK64dJqIiIiIiEiJLFiwAN7e3oiOjkZxcTHmzJmDI0eOYOvWrdDQ0JCbQUVEFSspKQkODg6ljmlrayM3N7eCExEpFxYaiYiIiIiIlMjt27fRpUsXaGi8/rj2Zum0o6MjRo0ahaCgIDHjEak1c3Nz3Lhxo9Sxa9euoU6dOhUbiEjJsNBIRERERESkRARBgLa2NgRBgJGREZ48eSIbMzMzQ3x8vHjhiNRc7969ERoaip07d+L58+cAgIKCAhw7dgxr1qzB119/LXJCInGxRyMREREREZESsbS0xKNHj+Di4gJ7e3usXbsWDRo0gJaWFsLCwvDpp5+KHZFIbQ0ZMgRPnz7FtGnTMH36dABA3759AQD9+vVD//79xYxHJDqhuLi4WOwQRERERERE9FpERASePHkCPz8/xMXFYfDgwUhOTgYA6OjoYOnSpWjVqpXIKYnU26NHj3D69GlkZGTAwMAArq6uXDZNBBYaiYiIiIiIlFpOTg5+//13vHz5Evb29jAyMhI7EhERUalYaCQiIiIiIlIiERERaNOmDapXr15iLCMjA8eOHcOXX35Z8cGI1NTFixc/6HxnZ+dySkKk/FhoJCIiIiIiUiK2trbYvn07mjRpUmLs5s2b6NWrF/78808RkhGpJxsbGwiCgDflE0EQZGPFxcVy3wPgzyepNW4GQ0REREREpETeNxckKysLurq6FZiGiHbt2iX7OjU1FdOmTYOTkxO8vb1hZGSE1NRUHDx4EJcuXcKsWbNETEokPs5oJCIiIiIiEtnx48dx8uRJAMDmzZvh7e0NY2NjuXPy8vJw7tw5mJmZYdOmTWLEJFJ7Y8aMQa1atfDDDz+UGAsMDMTDhw+xbNkyEZIRKQfOaCQiIiIiIhJZfHw8fvvtNwCvl2VeunQJlSpVkjtHW1sb9evXx7hx48SISEQATp48iZCQkFLHWrVqBX9//wpORKRcWGgkIiIiIiIS2cCBAzFw4EAAgIeHB5YvXw4bGxuRUxHRP+nq6uLs2bNo2bJlibHTp0+ztQGpPRYaiYiIiIiIlMibmY1EpHz69euHpUuXIjU1Fe3bt5f1aIyNjcXevXsxevRosSMSiYo9GomIiIiIiJRMWloa1q5dixs3biAxMREhISGoX78+NmzYgKZNm8Le3l7siERqa/PmzQgLC0NycrJsN2oTExMMHz4cAwYMEDsekahYaCQiIiIiIlIif/zxBwYOHAg9PT00a9YM+/fvx65du2BnZ4f58+fjyZMnWLJkidgxidRaUVEREhMTkZKSAhMTE5iZmUFDQ0PsWESi49JpIiIiIiIiJTJ37lw4ODhg+fLlEAQB+/btk401bdoUBw4cEDEdEQGAhoYGzM3NYW5uLnYUIqXCQiMREREREZESuXHjBoKDg6GtrY3CwkK5MUNDQ6SmpoqUjIgAIC4uDocPH0ZiYiLy8vLkxgRBwC+//CJSMiLxsdBIRERERESkRHR0dPD8+fNSx548eYJq1apVbCAikomIiMCPP/4IbW1tmJmZoVKlSmJHIlIqLDQSEREREREpkVatWiE0NBSurq7Q19cH8HqW1MuXL7Fx40a0adNG5IRE6is0NBSenp6YO3cudHV1xY5DpHS4GQwREREREZESSUpKQt++ffH8+XO0aNECsbGxaN26Ne7duwdBELBjxw4YGRmJHZNILb3pn+rq6ip2FCKlxC2RiIiIiIiIlIipqSkiIiLwzTffICUlBRYWFsjIyEDXrl0RHh7OIiORiJycnHDnzh2xYxApLc5oJCIiIiIiIiL6D27duoVJkybhm2++QcuWLVG1atUS57CPKqkzFhqJiIiIiIhE5uvr+5/PFQQBoaGh5ZiGiN7FxsZG9rUgCKWe8+eff1ZUHCKlw81giIiIiIiIRHbs2DHo6uqiYcOGYkchovf45Zdf3llgJCLOaCQiIiIiIhLdsGHDcPbsWdSoUQOdO3dGly5dYG1tLXYsIiKiD8JCIxERERERkRJIT0/HgQMHEB0djd9//x2Wlpbo2rUrunTpAnNzc7HjEaktDw+Pd85i1NLSgpGREZydnTFgwAAYGxtXcDoi5cJCIxERERERkZJJSEhAdHQ0oqOjce/ePTg4OMDHxwcdO3YUOxqR2pk1a9Y7C42FhYVISUnBhQsXUKlSJWzbtg0WFhYVnJBIebDQSEREREREpKRevHiB5cuXY+3atfDw8EBISIjYkYioFNnZ2ejfvz+srKywaNEiseMQiYabwRARERERESmRgoICnDhxAtHR0Th69Ch0dXXRr18/9OzZU+xoRPQOVatWxZAhQzBv3jyxoxCJioVGIiIiIiIiJXD+/HlER0cjJiYGhYWF8PT0RHBwMNzc3KChoSF2PCL6F6ampsjOzhY7BpGoWGgkIiIiIiISWZs2bZCeng53d3fMmjULHh4eqFSpktixiOgD3L17FzVq1BA7BpGoWGgkIiIiIiISWVJSErS0tHD69GmcOXPmvecKgoDLly9XUDIi+i8uXbqEZcuW4csvvxQ7CpGoWGgkIiIiIiISmb+/v9gRiOgdunbt+s6xoqIiPHv2DFlZWWjWrBnGjBlTgcmIlA93nSYiIiIiIiIieoeAgAAIglDqmKamJgwNDeHs7IxWrVq98zwidcFCIxEREREREREREZUZty4jIiIiIiIiIiKiMmOhkYiIiIiIiIiIiMqMhUYiIiIiIiIiIiIqMxYaiYiIiIiIiIiIqMxYaCQiIiISye7du2FtbS37r2HDhnB3d8fkyZORlJRUIRk8PDwQEBAg+/78+fOwtrbG+fPnP+jPuXLlCoKDg5GVlaXoiAgICICHh8e/njdgwAB06dJFIX/nm+fmxo0bCvnz3v4zHz9+rLA/k4iIiEiZaIkdgIiIiEjdzZ07F/Xq1cPLly9x6dIlrFy5EhcuXEBUVBSqVKlSoVns7Oywfft2WFlZfdD/d/XqVYSEhKB79+7Q19cvp3REREREpMxYaCQiIiISWf369dG4cWMAgIuLCwoLC7F8+XLExsbiiy++KPX/yc3NhY6OjsKz6Onpwd7eXuF/LhERERFJH5dOExERESmZN4W+J0+eAHi9dNjBwQG3b9/G4MGD4eDgAB8fHwBAfn4+li9fDm9vbzRq1AguLi6YPHky0tLS5P7MV69eYf78+WjZsiWaNm2Kvn374vr16yX+7nctnb527Rp8fX3RokULNG7cGJ6enpgzZw4AIDg4GPPnzwcAtG/fXrYU/O0/Y//+/fj6669hb28PBwcHDBkyBLdu3Srx9+/evRteXl5o1KgRPv/8c0RERHzUv+G73LhxA2PHjoWHhweaNGkCDw8PjBs3DgkJCaWen5WVhcmTJ6N58+awt7eHr68vHj16VOK8M2fOYODAgXB0dETTpk3Rp08fnD17VqHZiYiIiJQdZzQSERERKZkHDx4AAAwNDWXHXr16hZEjR6JPnz4YNmwYCgsLUVRUBD8/P1y+fBlDhgyBo6MjEhISEBwcjOvXryM8PByffPIJAGDq1KmIiIjA4MGD0bJlS9y9exf+/v7Iycn51zwnT57EyJEjUa9ePQQEBKBmzZpISEjA6dOnAQC9evVCZmYmNm3ahJCQEJiYmACAbPn1ihUrsGTJEvTo0QMjR47Eq1evsGbNGvTv3x87d+6Unbd7925MnjwZ7du3R0BAALKzsxESEoL8/HxoaCjm/nhCQgLq1q2Lzp07w8DAACkpKdi2bRt69uyJffv2yf2bA8BPP/0ENzc3LFy4EImJiViyZAkGDBiAyMhI2RLxvXv34ocffkD79u0RGBgILS0tbN++HUOGDMGaNWvg6uqqkOxEREREyo6FRiIiIiKRFRUVoaCgAHl5ebh48SJCQ0Ohq6srtwHKq1evMGrUKHz11VeyY/v27cPJkycRHByMjh07yo7b2NigZ8+e2L17N/r164e4uDjs2bMHPj4+mDRpEgCgZcuWMDIywoQJE/4136xZs1CzZk3s3LkTlStXlh1/k8XMzAw1a9YEANja2qJ27dqyc54+fYrg4GB88803mDJliuy4m5sbvLy8EBISgiVLlqCoqAiLFy+GnZ0dli1bBkEQAADNmjWDl5cXatSo8UH/pu/i7e0Nb29v2feFhYVo27YtWrZsiejoaHz77bdy5zdq1Ai//PKL7HsrKyv07dsXW7ZswciRI5Gbm4tffvkFbdu2xbJly2TntWnTBt27d8eiRYuwc+dOhWQnIiIiUnZcOk1EREQkst69e8POzg6Ojo4YMWIEjI2NsWrVKhgbG8ud5+XlJff90aNHoa+vj3bt2qGgoED2n62tLUxMTHDhwgUAkC1h7tq1q9z///nnn0NL6/33ne/fv4+HDx+iZ8+eckXG/+rUqVMoKChAt27d5DJWrlwZzs7Osoz3799HcnIyunTpIisyAkCtWrXg4ODwwX/vu+Tk5GDBggXo0KEDGjZsiIYNG8LBwQEvXrxAXFxcifP/+W/m6OiIWrVqyf5Nr169ioyMDHTv3l3u8RUVFaF169a4ceMGXrx4obD8RERERMqMMxqJiIiIRBYYGAhLS0toaWnByMio1Nl7Ojo60NPTkzuWmpqKrKwsNGrUqNQ/Nz09HQCQkZEBALIlzW9oaWmhWrVq7832ptejqanpf3koJTx79gwA0LNnz1LH3yyJfpP1n8XVN8fe1UPxQ40fPx7nzp2Dn58fGjduDF1dXQiCgOHDhyMvL6/Uv7u0Y2/+Td88vjFjxrzz78zMzKzw3cOJiIiIxMBCIxEREZHILC0tZbtOv8vbs/zeqF69OqpVq4bVq1eX+v/o6uoCgKyYmJKSIlcwLCgokBXM3uVNz8KkpKT3nvcu1atXBwAsXboU5ubm/3rem8Ld20o79jGys7Nx7Ngx+Pv7Y/jw4bLj+fn5yMzMLPX/eVceCwsLAP+fe+rUqWjatGmpf4aRkVFZoxMRERGpBBYaiYiIiFRU27ZtsW/fPhQVFb2zyAUALVq0AABERUXJzX48cOAACgoK3vt31K1bFxYWFggPD8egQYNQqVKlUs97c/yfswJbtWoFLS0tPHz4sMTS73/+PSYmJoiOjsagQYNkhdWEhARcvXpVIT0aBUFAcXFxicewc+dOFBYWlvr/REVFyeW+cuUKEhISZDM0HR0doa+vj3v37uGbb74pc0YiIiIiVcZCIxEREZGK6ty5M6KiojB8+HAMGDAATZo0gba2NhITE3H+/Hm0b98eHTp0gKWlJb744gts2LABWlpacHNzw927d7FmzZoSy7FLM23aNIwcORK9e/eGj48PatasiadPn+LkyZMICgoCADRo0AAAsGHDBnTv3h1aWlqoW7cuateujTFjxmDJkiV49OgR3N3doa+vj2fPnuHGjRvQ0dHBmDFjoKGhge+++w5TpkzBqFGj0Lt3b2RlZSEkJKTU5cvv8vz5cxw8eLDEcUNDQzRv3hzOzs5Ys2YNqlevjlq1auHChQvYtWuXbAfpf7p58yZ++ukneHt7IzExEYsXL4apqSn69esH4PWs0SlTpiAgIACZmZnw8vKCkZER0tLS8NdffyEtLQ0zZ878z/mJiIiIVBkLjUREREQqSlNTE6Ghodi4cSP27t2LsLAwaGpqwszMDM7OzrLiHwDMmTMHxsbG2LNnDzZt2gRbW1sEBwdj3Lhx//r3tG7dGps3b8ayZcswe/Zs5OXlwczMTG5X7BYtWmDEiBHYs2cPdu7ciaKiImzcuFF23NLSEhs3bsS+ffuQn58PExMTNGrUCH379pX9Gb169QIArF69Gv7+/qhVqxZGjBiBixcvyjaN+TdPnz7Fd999V+J48+bNsWnTJgQFBWHOnDlYsGABCgoK4OjoiHXr1mHEiBGl/nlz5szB3r17MW7cOOTn56NFixb46aef5HpbduvWDebm5li9ejWmT5+OnJwcGBoawtbWFt27d/9PuYmIiIikQCguLi4WOwQRERERERERERGpNg2xAxAREREREREREZHqY6GRiIiIiIiIiIiIyoyFRiIiIiIiIiIiIiozFhqJiIiIiIiIiIiozFhoJCIiIiIiIiIiojJjoZGIiIiIiIiIiIjKjIVGIiIiIiIiIiIiKjMWGomIiIiIiIiIiKjMWGgkIiIiIiIiIiKiMmOhkYiIiIiIiIiIiMqMhUYiIiIiIiIiIiIqs/8DAYgjEKNpbe8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_train, y_train_pred = evaluate_model_cm(shuffled_baseline_log_reg_model, training_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "evaluate_model_score(shuffled_baseline_log_reg_model, training_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate with testing data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_test, y_test_pred = evaluate_model_cm(shuffled_baseline_log_reg_model, testing_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "evaluate_model_score(shuffled_baseline_log_reg_model, testing_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate with holdout data" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_holdout, y_holdout_pred = evaluate_model_cm(shuffled_baseline_log_reg_model, holdout_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "evaluate_model_score(shuffled_baseline_log_reg_model, holdout_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Save trained model predicitions" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = []\n", + "\n", + "predictions.append(y_train)\n", + "predictions.append(y_train_pred)\n", + "\n", + "predictions.append(y_test)\n", + "predictions.append(y_test_pred)\n", + "\n", + "predictions.append(y_holdout)\n", + "predictions.append(y_holdout_pred)\n", + "\n", + "predictions = pd.DataFrame(predictions)\n", + "predictions.index = [\"y_train\", \"y_train_pred\", \"y_test\", \"y_test_pred\", \"y_holdout\", \"y_holdout_pred\"]\n", + "predictions.to_csv(f\"{results_dir}/shuffled_baseline_model_predictions.tsv\", sep=\"\\t\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.13 ('phenotypic_profiling')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "f9df586d1764dbc68785000a153dad1832127ac564b5e2e4c94e83fc43160b30" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/3.evaluate_model/evaluate_model.sh b/3.evaluate_model/evaluate_model.sh new file mode 100644 index 00000000..4e178900 --- /dev/null +++ b/3.evaluate_model/evaluate_model.sh @@ -0,0 +1,5 @@ +#!/bin/bash +# Convert notebook to python file and execute +jupyter nbconvert --to python \ + --FilesWriter.build_directory=scripts/nbconverted \ + --execute evaluate_model.ipynb diff --git a/3.evaluate_model/evaluations/model_predictions.tsv b/3.evaluate_model/evaluations/model_predictions.tsv new file mode 100644 index 00000000..3768ff2a --- /dev/null +++ b/3.evaluate_model/evaluations/model_predictions.tsv @@ -0,0 +1,7 @@ + 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 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3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 3396 3397 +y_train Polylobed MetaphaseAlignment Polylobed Artefact Apoptosis Prometaphase Prometaphase Polylobed Apoptosis Polylobed Polylobed SmallIrregular Interphase Polylobed Artefact Elongated Grape Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Prometaphase Grape Grape Polylobed SmallIrregular Binuclear Polylobed Binuclear Artefact Artefact Binuclear MetaphaseAlignment MetaphaseAlignment Polylobed Metaphase Artefact Polylobed MetaphaseAlignment Grape Polylobed Binuclear Polylobed Artefact Polylobed Apoptosis Polylobed Interphase Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Interphase Polylobed Interphase Prometaphase Polylobed Prometaphase Grape Polylobed MetaphaseAlignment Hole SmallIrregular Binuclear Polylobed Interphase Binuclear Polylobed SmallIrregular Anaphase Prometaphase Prometaphase Binuclear Prometaphase Polylobed Artefact MetaphaseAlignment Grape Polylobed Polylobed Large Artefact Artefact Interphase Anaphase Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Hole Hole Grape Binuclear Grape Polylobed Polylobed UndefinedCondensed Binuclear Prometaphase Artefact Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Hole Polylobed Binuclear Apoptosis Grape Artefact Polylobed Interphase Polylobed Grape Interphase Polylobed Artefact Polylobed Prometaphase Grape Apoptosis Binuclear Grape Artefact Binuclear Prometaphase Polylobed Binuclear Binuclear Polylobed Polylobed Hole Polylobed Polylobed Grape Interphase Binuclear Binuclear Polylobed Apoptosis Prometaphase Hole Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Metaphase SmallIrregular Polylobed Apoptosis Binuclear Anaphase Binuclear Polylobed Polylobed Polylobed Prometaphase Artefact Polylobed Polylobed Polylobed Binuclear Interphase Interphase Binuclear Prometaphase Interphase Binuclear Grape Polylobed Binuclear Polylobed Polylobed Folded Grape Artefact MetaphaseAlignment SmallIrregular Binuclear Grape Polylobed MetaphaseAlignment Binuclear Polylobed MetaphaseAlignment Binuclear Grape Binuclear Hole Polylobed Artefact Polylobed Prometaphase Polylobed Grape Polylobed Binuclear Grape Polylobed Prometaphase Binuclear Polylobed Binuclear Binuclear Interphase Polylobed Apoptosis MetaphaseAlignment Prometaphase Polylobed Polylobed Grape Artefact Binuclear Interphase Polylobed Binuclear Artefact Polylobed Binuclear Grape Apoptosis Binuclear Binuclear Grape Interphase Binuclear Interphase Artefact Polylobed MetaphaseAlignment Apoptosis Artefact Prometaphase Interphase Polylobed Polylobed Prometaphase Polylobed MetaphaseAlignment Apoptosis Polylobed Polylobed Binuclear Polylobed Polylobed Grape Polylobed Polylobed Prometaphase Artefact Polylobed Polylobed Polylobed Large Binuclear Binuclear Polylobed Polylobed Binuclear Anaphase Interphase Binuclear Binuclear Polylobed Binuclear Apoptosis Anaphase Elongated Polylobed SmallIrregular Binuclear Artefact UndefinedCondensed Polylobed Grape Polylobed MetaphaseAlignment Metaphase Interphase Polylobed Prometaphase Grape Polylobed Polylobed Polylobed Binuclear Apoptosis Polylobed Binuclear Polylobed Polylobed Artefact Prometaphase Artefact Polylobed Grape Prometaphase Interphase Binuclear Polylobed Interphase Polylobed Polylobed Binuclear Binuclear Binuclear Interphase Binuclear Artefact Interphase Large UndefinedCondensed Binuclear Polylobed Large Binuclear Polylobed Polylobed SmallIrregular SmallIrregular Prometaphase Anaphase Polylobed SmallIrregular Apoptosis Polylobed Binuclear Binuclear Apoptosis Interphase Polylobed Grape Polylobed Polylobed MetaphaseAlignment Binuclear Polylobed Prometaphase Prometaphase Grape Grape Polylobed Grape Polylobed Polylobed Binuclear Polylobed Anaphase Interphase Prometaphase Artefact Binuclear Grape Grape Binuclear Artefact Polylobed Grape Interphase Metaphase Polylobed Polylobed Interphase Polylobed Binuclear Grape Prometaphase Prometaphase Prometaphase Binuclear Polylobed Artefact Interphase Grape Polylobed SmallIrregular Polylobed Polylobed Apoptosis Prometaphase Interphase Binuclear Grape Polylobed Apoptosis Polylobed Binuclear Polylobed Interphase Prometaphase Prometaphase Large Binuclear Polylobed Artefact Grape Binuclear Grape Artefact Polylobed Polylobed Polylobed Large Binuclear Polylobed Polylobed Prometaphase Artefact Polylobed Binuclear Grape Interphase Polylobed Polylobed Grape Interphase Prometaphase Binuclear Grape Polylobed Anaphase Polylobed Polylobed Artefact Polylobed Polylobed Artefact Binuclear Metaphase Polylobed Polylobed Polylobed Elongated Polylobed Polylobed Grape Polylobed Binuclear Apoptosis Binuclear Grape Polylobed Grape Interphase Artefact Hole Grape Apoptosis Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Interphase Polylobed Polylobed Prometaphase Interphase Prometaphase Prometaphase Binuclear Folded Polylobed Polylobed Polylobed Artefact Polylobed Polylobed SmallIrregular Polylobed Polylobed Grape Binuclear Hole Polylobed Interphase Binuclear Binuclear Grape Polylobed Polylobed Binuclear SmallIrregular Grape Prometaphase Binuclear Prometaphase Artefact Binuclear Binuclear Interphase Grape Polylobed UndefinedCondensed MetaphaseAlignment Apoptosis Polylobed SmallIrregular Interphase Polylobed Polylobed Grape Prometaphase Polylobed Prometaphase Binuclear Polylobed SmallIrregular Interphase Binuclear Interphase Interphase Artefact Binuclear Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Apoptosis Grape Binuclear Apoptosis Polylobed Polylobed Polylobed Binuclear Polylobed Apoptosis Binuclear Polylobed Prometaphase Polylobed Artefact Binuclear Interphase Hole Apoptosis Polylobed Polylobed Prometaphase Prometaphase MetaphaseAlignment Artefact MetaphaseAlignment Apoptosis Polylobed Binuclear Polylobed Polylobed Interphase SmallIrregular UndefinedCondensed Polylobed Artefact Polylobed Polylobed Polylobed Binuclear Binuclear UndefinedCondensed Binuclear Grape Polylobed Hole Polylobed Prometaphase Interphase MetaphaseAlignment Apoptosis Binuclear Polylobed Artefact Polylobed Binuclear Grape Polylobed Grape Polylobed Polylobed Metaphase Polylobed Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Binuclear Binuclear Apoptosis Polylobed Binuclear Artefact Polylobed Polylobed Polylobed Polylobed Apoptosis Apoptosis Polylobed Binuclear Interphase Interphase Prometaphase Artefact Polylobed SmallIrregular Polylobed Polylobed Hole Grape SmallIrregular SmallIrregular Binuclear Prometaphase MetaphaseAlignment Binuclear Binuclear Binuclear Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Artefact Grape Interphase MetaphaseAlignment Binuclear Interphase MetaphaseAlignment Binuclear Binuclear Metaphase Polylobed Polylobed Grape Polylobed Prometaphase Polylobed Artefact Binuclear Interphase Polylobed Prometaphase Interphase Interphase Artefact Polylobed Polylobed Prometaphase Grape Polylobed Polylobed SmallIrregular Polylobed Grape Grape Polylobed Interphase Polylobed Prometaphase Binuclear Interphase Grape Interphase Artefact Polylobed Binuclear Polylobed MetaphaseAlignment Polylobed Grape Binuclear Polylobed Polylobed Binuclear Hole Metaphase Hole Grape Binuclear Grape Artefact Polylobed Polylobed Interphase Interphase Prometaphase Polylobed SmallIrregular Polylobed Polylobed MetaphaseAlignment Apoptosis Polylobed Polylobed Grape Binuclear Polylobed Hole Polylobed Polylobed Polylobed SmallIrregular Binuclear MetaphaseAlignment Interphase Polylobed Prometaphase Polylobed Artefact Polylobed Grape Polylobed Polylobed Polylobed MetaphaseAlignment Artefact Apoptosis Polylobed Binuclear Prometaphase Apoptosis Prometaphase Binuclear Apoptosis Grape Artefact Polylobed Grape Polylobed Polylobed SmallIrregular Prometaphase Interphase MetaphaseAlignment Anaphase Polylobed Polylobed Large Large Polylobed Prometaphase Binuclear Binuclear Polylobed Interphase Polylobed Polylobed MetaphaseAlignment Binuclear Polylobed SmallIrregular Binuclear MetaphaseAlignment Interphase Grape Apoptosis Binuclear Prometaphase Binuclear Artefact Binuclear Polylobed Interphase Binuclear Polylobed Polylobed Artefact Prometaphase Polylobed Polylobed Polylobed Binuclear Hole Polylobed Polylobed Hole Interphase Binuclear Polylobed Artefact Polylobed Polylobed Interphase MetaphaseAlignment Polylobed Interphase Polylobed Polylobed Apoptosis Polylobed Interphase Artefact Binuclear Artefact Binuclear Polylobed Polylobed Polylobed Prometaphase Grape Polylobed Anaphase Apoptosis Interphase Apoptosis Polylobed Binuclear Polylobed Binuclear Anaphase MetaphaseAlignment Interphase Polylobed Interphase Grape Binuclear Binuclear Artefact Artefact Interphase Prometaphase Elongated Polylobed Prometaphase MetaphaseAlignment Interphase Polylobed Prometaphase Polylobed Polylobed Folded Grape Interphase Apoptosis Polylobed Interphase Polylobed Interphase Polylobed Binuclear Apoptosis Grape Artefact Prometaphase Prometaphase Binuclear Polylobed Binuclear Interphase SmallIrregular Binuclear Prometaphase Polylobed Polylobed Interphase Polylobed Binuclear Large MetaphaseAlignment Binuclear Polylobed Prometaphase Prometaphase Prometaphase Polylobed Interphase Interphase Polylobed MetaphaseAlignment Prometaphase Grape Artefact Polylobed Large Binuclear Artefact Polylobed Polylobed Polylobed Interphase Artefact Binuclear Binuclear Apoptosis Polylobed Grape Prometaphase Polylobed Polylobed Apoptosis Polylobed Polylobed Polylobed SmallIrregular Grape Polylobed UndefinedCondensed Polylobed Grape SmallIrregular Grape Polylobed Hole Anaphase Grape Prometaphase Prometaphase Anaphase Large Polylobed Grape Apoptosis Grape Polylobed Large Grape Artefact Elongated Binuclear Interphase Apoptosis Binuclear Interphase Polylobed Folded Binuclear Prometaphase Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Binuclear Grape Interphase MetaphaseAlignment Interphase Prometaphase Polylobed Apoptosis Prometaphase Hole Binuclear Grape Interphase Polylobed Polylobed Polylobed Binuclear Grape Apoptosis Grape UndefinedCondensed Apoptosis UndefinedCondensed Prometaphase Artefact Prometaphase Polylobed Prometaphase Binuclear Polylobed Polylobed Artefact Apoptosis Grape Polylobed Binuclear Binuclear Polylobed Binuclear Artefact Polylobed Binuclear Polylobed Prometaphase Polylobed Polylobed Polylobed Binuclear MetaphaseAlignment Binuclear SmallIrregular Metaphase Prometaphase Grape Artefact Polylobed Binuclear Prometaphase Interphase Prometaphase Prometaphase Apoptosis Polylobed MetaphaseAlignment Artefact Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Interphase Large Grape Grape Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Prometaphase Hole Interphase Binuclear Binuclear Binuclear Polylobed Binuclear Prometaphase Binuclear Polylobed Polylobed Polylobed Polylobed Apoptosis Artefact Polylobed Prometaphase Interphase Interphase Grape Binuclear Polylobed Polylobed Hole Artefact Apoptosis Hole Prometaphase Polylobed Interphase Artefact Artefact Polylobed Polylobed Interphase Polylobed Polylobed Elongated Polylobed Prometaphase Polylobed Grape Interphase Polylobed Polylobed Polylobed Prometaphase Polylobed Large Grape Polylobed MetaphaseAlignment Polylobed MetaphaseAlignment Polylobed UndefinedCondensed Polylobed Interphase Apoptosis Binuclear Grape Polylobed MetaphaseAlignment Prometaphase Grape Polylobed Polylobed Prometaphase Polylobed Interphase Binuclear Prometaphase Polylobed Interphase Prometaphase SmallIrregular Prometaphase Binuclear Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Binuclear Polylobed Anaphase MetaphaseAlignment SmallIrregular Binuclear Grape Prometaphase Binuclear Grape Binuclear Polylobed Grape Binuclear Polylobed Polylobed Apoptosis Hole Polylobed Hole Polylobed Large Polylobed Interphase MetaphaseAlignment Large Binuclear Binuclear Prometaphase Grape Polylobed MetaphaseAlignment Polylobed Polylobed Binuclear Grape Polylobed Artefact Polylobed Binuclear Polylobed Apoptosis Polylobed Grape Artefact Polylobed MetaphaseAlignment Polylobed Polylobed Metaphase Interphase Anaphase Polylobed Hole Artefact Interphase Interphase Interphase Hole Polylobed Binuclear Polylobed Anaphase Artefact Artefact Polylobed MetaphaseAlignment MetaphaseAlignment Polylobed Binuclear Large Grape Interphase Polylobed Polylobed Polylobed Grape MetaphaseAlignment Apoptosis Interphase MetaphaseAlignment Polylobed Interphase Polylobed Apoptosis Polylobed Prometaphase Polylobed Interphase SmallIrregular Binuclear Polylobed Interphase Polylobed Polylobed Grape Apoptosis Polylobed Apoptosis Polylobed Artefact Interphase Polylobed Polylobed Polylobed Large Polylobed MetaphaseAlignment Polylobed Prometaphase UndefinedCondensed Polylobed Apoptosis Polylobed Large Apoptosis MetaphaseAlignment Apoptosis Polylobed Prometaphase Prometaphase Anaphase MetaphaseAlignment Artefact Grape Polylobed Prometaphase Binuclear Interphase Grape Polylobed Prometaphase Polylobed Prometaphase Polylobed Grape Artefact Prometaphase Polylobed Polylobed Interphase Prometaphase Polylobed Polylobed Prometaphase Polylobed Grape Polylobed Binuclear Binuclear Polylobed Artefact Interphase Polylobed Binuclear Interphase Polylobed Prometaphase Interphase SmallIrregular Polylobed Apoptosis Interphase Binuclear Binuclear Grape Metaphase Interphase Binuclear Polylobed Grape Polylobed Elongated Polylobed Artefact Grape Interphase Binuclear Binuclear Binuclear Hole Binuclear UndefinedCondensed Polylobed Polylobed Polylobed Polylobed Grape Apoptosis Interphase Artefact Grape Polylobed Artefact Binuclear Apoptosis Interphase Apoptosis Binuclear Polylobed Binuclear Polylobed Polylobed SmallIrregular Artefact Polylobed Grape Artefact Polylobed Polylobed Binuclear Artefact Polylobed Prometaphase Polylobed Interphase Polylobed Apoptosis Grape Polylobed Grape Hole Artefact Polylobed Polylobed Polylobed Prometaphase Artefact Polylobed Grape Binuclear Hole Polylobed Binuclear Binuclear Prometaphase Polylobed Polylobed Polylobed Polylobed Binuclear MetaphaseAlignment Prometaphase Binuclear Polylobed Polylobed Elongated Grape Artefact Hole Grape Polylobed Large MetaphaseAlignment Apoptosis Interphase Polylobed Interphase Interphase Grape Binuclear Polylobed Interphase Interphase Grape Binuclear Interphase Binuclear Polylobed Polylobed Interphase Artefact Prometaphase Apoptosis Apoptosis Large Polylobed Binuclear SmallIrregular Binuclear Large Binuclear Interphase Apoptosis Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Binuclear Artefact Grape Apoptosis Interphase Binuclear Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed MetaphaseAlignment Metaphase Polylobed Binuclear Prometaphase Polylobed Binuclear Grape Apoptosis Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Artefact Polylobed Binuclear Polylobed UndefinedCondensed Metaphase Prometaphase SmallIrregular Apoptosis Binuclear Polylobed Binuclear Metaphase Binuclear Prometaphase Polylobed Grape Apoptosis SmallIrregular Interphase Prometaphase Anaphase Polylobed Binuclear Elongated Polylobed Grape Artefact Grape Prometaphase Binuclear Apoptosis Polylobed Binuclear Binuclear Grape Polylobed Polylobed Binuclear Binuclear Artefact Prometaphase Large Metaphase Interphase SmallIrregular Grape Grape Polylobed Binuclear Polylobed Interphase Anaphase Binuclear Prometaphase Interphase Apoptosis Interphase Polylobed Binuclear Apoptosis Artefact Artefact Interphase SmallIrregular Polylobed Polylobed Binuclear Polylobed Grape Artefact Artefact Polylobed Grape Anaphase Grape Polylobed Polylobed Polylobed Interphase Apoptosis Polylobed Polylobed Anaphase Polylobed Binuclear Grape Apoptosis Grape Polylobed Large Binuclear Binuclear Grape Polylobed Interphase Polylobed Elongated Polylobed Hole Polylobed Apoptosis Apoptosis Binuclear Polylobed Grape Binuclear Interphase Grape Polylobed Binuclear Polylobed Artefact SmallIrregular Interphase Interphase Grape Binuclear Interphase Binuclear Elongated Polylobed Binuclear Binuclear Binuclear Artefact Polylobed Polylobed Polylobed Binuclear Apoptosis Apoptosis Polylobed Grape Polylobed Polylobed MetaphaseAlignment Artefact Binuclear Polylobed Binuclear Polylobed Large UndefinedCondensed Polylobed SmallIrregular Binuclear Hole SmallIrregular Polylobed Polylobed Polylobed Polylobed SmallIrregular Polylobed Polylobed SmallIrregular Binuclear MetaphaseAlignment Grape Polylobed SmallIrregular Artefact Grape Grape Binuclear Grape Metaphase Binuclear Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Interphase Prometaphase Artefact Polylobed Polylobed Binuclear Hole MetaphaseAlignment Interphase Polylobed Polylobed Grape Grape Polylobed Interphase Prometaphase Apoptosis Prometaphase Interphase Polylobed MetaphaseAlignment Hole Elongated Polylobed SmallIrregular Polylobed Polylobed Polylobed Binuclear Apoptosis Artefact Polylobed Prometaphase Polylobed Prometaphase MetaphaseAlignment Artefact Binuclear Polylobed Grape Interphase Grape Artefact Interphase Artefact Grape SmallIrregular MetaphaseAlignment Interphase Polylobed Polylobed Prometaphase Polylobed Grape Prometaphase Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Interphase Binuclear Large Grape Binuclear Polylobed Polylobed Prometaphase Polylobed MetaphaseAlignment Binuclear Binuclear Interphase Prometaphase Polylobed Binuclear Prometaphase Apoptosis Polylobed Hole Polylobed Polylobed Interphase Binuclear Polylobed Polylobed Artefact Artefact MetaphaseAlignment Apoptosis Metaphase Grape Binuclear Grape Prometaphase Interphase Polylobed Prometaphase Grape Apoptosis SmallIrregular Hole Binuclear Artefact Polylobed Binuclear Binuclear Polylobed Polylobed Folded Interphase Polylobed Hole Polylobed Polylobed Polylobed Grape Polylobed Metaphase Binuclear Binuclear Binuclear Binuclear Polylobed Polylobed Prometaphase Folded Polylobed Polylobed Grape Artefact Polylobed Grape Grape Binuclear Grape Polylobed Prometaphase Polylobed Binuclear Anaphase Grape SmallIrregular Apoptosis Polylobed Polylobed Interphase Polylobed Hole Folded Polylobed Artefact Polylobed Interphase Interphase SmallIrregular Binuclear Polylobed Grape Folded Polylobed Polylobed MetaphaseAlignment Binuclear Polylobed Binuclear Grape Prometaphase Prometaphase Grape Polylobed Binuclear Artefact Polylobed Polylobed Interphase Polylobed Interphase Artefact Artefact Binuclear Grape Artefact Binuclear Polylobed Binuclear Artefact UndefinedCondensed Binuclear Binuclear Artefact SmallIrregular MetaphaseAlignment Interphase Binuclear Polylobed Binuclear Interphase Interphase Binuclear Grape Hole Polylobed Grape Metaphase Grape Binuclear Polylobed Hole Prometaphase Binuclear Interphase Grape Prometaphase Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed SmallIrregular Polylobed Binuclear Grape Apoptosis Polylobed Prometaphase Binuclear Grape Interphase Binuclear Grape Polylobed Grape Folded Binuclear Binuclear Binuclear Prometaphase Interphase Binuclear Interphase Polylobed Polylobed Binuclear Grape Elongated UndefinedCondensed Polylobed Polylobed Polylobed Prometaphase Binuclear Prometaphase Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Prometaphase Polylobed Prometaphase Grape Apoptosis Prometaphase Binuclear Polylobed Grape Polylobed Grape Polylobed Polylobed Polylobed Polylobed Grape Artefact Polylobed Interphase Prometaphase MetaphaseAlignment Polylobed Grape Polylobed MetaphaseAlignment Polylobed Grape Binuclear Polylobed Polylobed Polylobed Prometaphase Polylobed Prometaphase Polylobed Polylobed Binuclear Polylobed Interphase Binuclear Grape UndefinedCondensed Grape Artefact Grape Grape Apoptosis Binuclear Prometaphase Polylobed SmallIrregular Interphase Interphase Grape Interphase Polylobed Polylobed Binuclear Interphase Polylobed Polylobed Metaphase Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Apoptosis Hole Prometaphase Polylobed Polylobed Binuclear Polylobed Prometaphase Artefact Prometaphase Polylobed MetaphaseAlignment Interphase Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Interphase Polylobed Apoptosis Prometaphase Polylobed Polylobed Interphase MetaphaseAlignment Polylobed Grape MetaphaseAlignment Grape Grape Grape MetaphaseAlignment Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Grape UndefinedCondensed MetaphaseAlignment Binuclear MetaphaseAlignment Artefact Grape Binuclear Artefact Polylobed Polylobed MetaphaseAlignment Grape Prometaphase Apoptosis Large MetaphaseAlignment Prometaphase Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Grape Polylobed Metaphase Interphase Interphase Grape Large Polylobed Apoptosis Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Anaphase Metaphase Polylobed Grape Apoptosis Artefact Polylobed Polylobed Polylobed Grape Binuclear Polylobed UndefinedCondensed Grape Binuclear MetaphaseAlignment Polylobed Artefact Polylobed Interphase Apoptosis Interphase Artefact Grape Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Interphase MetaphaseAlignment Folded Polylobed Large Binuclear MetaphaseAlignment Binuclear Grape Prometaphase MetaphaseAlignment Polylobed Binuclear Polylobed Elongated Folded Binuclear Binuclear Polylobed Prometaphase Interphase Polylobed SmallIrregular Prometaphase Polylobed Artefact Polylobed Prometaphase Artefact Polylobed Binuclear Binuclear Grape Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Interphase Apoptosis Polylobed Polylobed Polylobed Prometaphase Grape Grape Interphase Prometaphase Polylobed Prometaphase Interphase Grape Interphase Polylobed Prometaphase Hole Prometaphase Interphase Large Binuclear Interphase Polylobed Large Polylobed Polylobed Grape Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Artefact Prometaphase Metaphase Polylobed Polylobed Artefact Interphase Prometaphase Grape Apoptosis Polylobed Metaphase Polylobed Polylobed Grape Polylobed Prometaphase Grape Polylobed Interphase Binuclear Prometaphase Polylobed Polylobed Artefact Grape Interphase Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Binuclear Grape Hole UndefinedCondensed Binuclear MetaphaseAlignment SmallIrregular Prometaphase SmallIrregular Apoptosis Grape Binuclear Polylobed Prometaphase Polylobed Hole Prometaphase Polylobed Binuclear Polylobed Hole Prometaphase Artefact Polylobed Apoptosis Binuclear MetaphaseAlignment Grape Polylobed Polylobed Binuclear Polylobed Interphase Polylobed Grape Interphase Polylobed Polylobed Grape MetaphaseAlignment Polylobed Binuclear Apoptosis Grape Binuclear MetaphaseAlignment Polylobed Grape Artefact Anaphase Grape Polylobed Polylobed Grape Polylobed Polylobed Polylobed Grape Polylobed Apoptosis SmallIrregular Polylobed Polylobed Grape Binuclear Binuclear Binuclear Anaphase Polylobed MetaphaseAlignment Polylobed Grape Polylobed Elongated MetaphaseAlignment Polylobed Binuclear Grape Polylobed Apoptosis Polylobed Grape Artefact Polylobed Grape Binuclear Artefact Binuclear Prometaphase Grape Polylobed Polylobed Polylobed MetaphaseAlignment Grape Binuclear Polylobed Polylobed Metaphase Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Grape SmallIrregular Polylobed Polylobed Polylobed Prometaphase Interphase Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Grape Binuclear Prometaphase Polylobed Polylobed Prometaphase Polylobed Artefact Polylobed Polylobed Interphase Anaphase Binuclear Prometaphase Binuclear Polylobed Polylobed Binuclear Polylobed Interphase Polylobed Metaphase Polylobed Binuclear Artefact Polylobed Polylobed Artefact Polylobed Artefact Polylobed Polylobed Apoptosis Binuclear Binuclear SmallIrregular Grape Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Large Polylobed Polylobed Artefact Grape Polylobed Polylobed Polylobed Polylobed Grape Prometaphase Artefact Polylobed Apoptosis Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Binuclear Prometaphase Polylobed Binuclear Grape Grape Prometaphase Apoptosis Prometaphase Polylobed MetaphaseAlignment Polylobed Anaphase Polylobed Interphase Polylobed Polylobed Apoptosis MetaphaseAlignment Polylobed MetaphaseAlignment Polylobed Interphase Binuclear Prometaphase Polylobed Interphase Polylobed Interphase Polylobed Grape Interphase Grape MetaphaseAlignment Apoptosis Polylobed Polylobed MetaphaseAlignment Interphase Interphase Polylobed Binuclear Binuclear Grape Hole Prometaphase Polylobed Interphase Grape SmallIrregular Artefact Polylobed Artefact Polylobed Polylobed Polylobed Binuclear MetaphaseAlignment Polylobed Metaphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Metaphase Hole Polylobed Apoptosis Polylobed Interphase Metaphase Grape Hole Grape Polylobed Polylobed Interphase Polylobed Artefact Interphase Grape Grape Anaphase Polylobed Binuclear Polylobed Prometaphase Binuclear Prometaphase Polylobed Prometaphase Polylobed Binuclear Apoptosis Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Prometaphase SmallIrregular Apoptosis Polylobed Grape Elongated Metaphase Polylobed Prometaphase Metaphase Grape Polylobed Interphase Binuclear MetaphaseAlignment Metaphase Polylobed Polylobed Binuclear Prometaphase SmallIrregular Apoptosis Interphase MetaphaseAlignment Interphase Prometaphase Polylobed MetaphaseAlignment SmallIrregular Binuclear Polylobed MetaphaseAlignment Interphase Polylobed Interphase Binuclear Interphase Binuclear Polylobed SmallIrregular Artefact Polylobed Interphase Polylobed Apoptosis Apoptosis Binuclear Grape Polylobed Prometaphase Grape Polylobed Binuclear Binuclear Prometaphase Polylobed Polylobed Artefact Artefact Interphase Artefact Interphase Artefact Polylobed Anaphase Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Binuclear MetaphaseAlignment Polylobed Binuclear Polylobed Artefact Binuclear Polylobed Prometaphase Grape Polylobed Polylobed Artefact Interphase Artefact Polylobed Hole MetaphaseAlignment Binuclear Polylobed Polylobed Binuclear Hole Large Polylobed Prometaphase Polylobed Interphase Interphase Interphase Binuclear Interphase Large Artefact Interphase Binuclear Grape Grape Prometaphase Polylobed Interphase Grape MetaphaseAlignment Polylobed MetaphaseAlignment Binuclear Hole Polylobed Grape Polylobed Large Artefact Polylobed Grape Binuclear Polylobed Polylobed Binuclear Artefact Binuclear Grape Grape Polylobed Polylobed Interphase Grape Binuclear SmallIrregular Polylobed Grape Binuclear Grape Prometaphase Binuclear Binuclear Polylobed Prometaphase Binuclear Interphase Polylobed Grape Polylobed Binuclear Grape Polylobed Large Interphase Interphase Grape Grape Grape Apoptosis Binuclear Interphase Polylobed Binuclear Polylobed Polylobed Apoptosis Interphase Binuclear Polylobed Polylobed Apoptosis Interphase Interphase Polylobed Binuclear Binuclear Grape Binuclear Polylobed Apoptosis Interphase Polylobed Interphase Interphase Polylobed Polylobed SmallIrregular Binuclear SmallIrregular Polylobed Grape Binuclear Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Grape MetaphaseAlignment Binuclear SmallIrregular Polylobed Binuclear Interphase Binuclear Polylobed Polylobed Polylobed MetaphaseAlignment Hole Polylobed Binuclear Artefact Grape Interphase Artefact MetaphaseAlignment Polylobed Polylobed Binuclear Binuclear Polylobed Apoptosis Polylobed Polylobed Metaphase Metaphase Binuclear Apoptosis Polylobed Interphase Prometaphase Binuclear Anaphase Binuclear Prometaphase Grape Hole SmallIrregular Grape Artefact Grape Interphase Polylobed Metaphase MetaphaseAlignment Polylobed Prometaphase Polylobed Polylobed Binuclear Artefact Grape Prometaphase Prometaphase MetaphaseAlignment Prometaphase Polylobed Polylobed Artefact Polylobed Prometaphase Interphase Polylobed Interphase Polylobed Polylobed Elongated Apoptosis Polylobed Polylobed Apoptosis Polylobed Polylobed Binuclear Binuclear Interphase Folded Apoptosis Polylobed Hole Prometaphase Polylobed Polylobed Polylobed Interphase Binuclear Polylobed Binuclear Prometaphase Apoptosis Binuclear Artefact Interphase Prometaphase Interphase SmallIrregular Prometaphase Binuclear Anaphase Elongated Polylobed Polylobed Polylobed Artefact Polylobed Polylobed MetaphaseAlignment SmallIrregular Grape Prometaphase SmallIrregular Polylobed Polylobed Binuclear Polylobed UndefinedCondensed Polylobed Polylobed Artefact Interphase Polylobed Anaphase Grape Polylobed Polylobed Polylobed Grape Artefact Prometaphase Grape Binuclear Binuclear Artefact Polylobed Prometaphase Polylobed Binuclear Polylobed Interphase Grape Polylobed Anaphase Binuclear MetaphaseAlignment Elongated Binuclear Artefact Interphase Artefact Interphase Polylobed Polylobed Grape MetaphaseAlignment Prometaphase Polylobed Polylobed MetaphaseAlignment MetaphaseAlignment Polylobed Apoptosis Polylobed Binuclear Apoptosis Polylobed Polylobed MetaphaseAlignment Grape Polylobed Binuclear Interphase UndefinedCondensed Apoptosis MetaphaseAlignment Polylobed Prometaphase Polylobed Polylobed Grape Prometaphase Interphase Binuclear Large Prometaphase Binuclear Prometaphase MetaphaseAlignment Binuclear Binuclear SmallIrregular Polylobed Polylobed Polylobed Prometaphase Polylobed Artefact Binuclear Polylobed Polylobed Interphase SmallIrregular Polylobed Grape SmallIrregular MetaphaseAlignment Anaphase Binuclear Binuclear Elongated Polylobed Polylobed MetaphaseAlignment Binuclear Interphase Interphase Apoptosis MetaphaseAlignment Binuclear Artefact Binuclear Artefact Polylobed Polylobed Polylobed Grape Prometaphase Elongated Binuclear Polylobed Binuclear Polylobed Binuclear Polylobed Binuclear Binuclear Polylobed MetaphaseAlignment Artefact Interphase Polylobed Interphase Grape Binuclear Polylobed Binuclear Polylobed Grape Interphase Polylobed Grape Artefact Interphase Polylobed Polylobed Binuclear Grape Large Interphase Polylobed Binuclear Polylobed Binuclear Prometaphase Artefact Apoptosis Interphase Binuclear Binuclear Polylobed Prometaphase Grape Grape Binuclear Polylobed Polylobed Apoptosis Artefact Polylobed Grape Binuclear Prometaphase Prometaphase Polylobed UndefinedCondensed Artefact Polylobed Large Polylobed MetaphaseAlignment Anaphase Polylobed Polylobed Polylobed Binuclear Hole Polylobed Binuclear Interphase Prometaphase Prometaphase Polylobed Apoptosis Binuclear Binuclear Polylobed Polylobed Apoptosis Metaphase Interphase Interphase Polylobed Apoptosis Polylobed Polylobed Polylobed Prometaphase Artefact Binuclear SmallIrregular Prometaphase Interphase Apoptosis Polylobed MetaphaseAlignment Artefact Binuclear Apoptosis Prometaphase Polylobed MetaphaseAlignment Artefact Binuclear Polylobed Grape Polylobed Polylobed Interphase Prometaphase Binuclear Polylobed Artefact Binuclear Polylobed Grape Binuclear Interphase Polylobed Interphase Interphase MetaphaseAlignment MetaphaseAlignment Binuclear Interphase Interphase Polylobed Interphase Grape Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Binuclear Binuclear Artefact Apoptosis Polylobed Grape Binuclear Interphase Polylobed Artefact Prometaphase Grape Polylobed Artefact Polylobed Prometaphase MetaphaseAlignment Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Binuclear MetaphaseAlignment Prometaphase Polylobed Polylobed UndefinedCondensed Apoptosis SmallIrregular Artefact Polylobed Polylobed Polylobed Binuclear Polylobed Grape Binuclear Grape Polylobed Interphase Polylobed Interphase Polylobed Grape Polylobed Grape Interphase Binuclear Grape Interphase Prometaphase Polylobed Interphase Polylobed Polylobed Grape SmallIrregular Interphase Polylobed SmallIrregular Grape Polylobed Polylobed Artefact Polylobed Binuclear Polylobed Large Polylobed Polylobed Apoptosis Interphase Polylobed Polylobed Polylobed Hole Grape Interphase SmallIrregular Polylobed Large Prometaphase Polylobed Interphase Apoptosis Grape Binuclear UndefinedCondensed Artefact Polylobed Interphase SmallIrregular Binuclear Artefact Polylobed Metaphase Artefact Polylobed Polylobed Polylobed Apoptosis Binuclear MetaphaseAlignment Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Grape Polylobed Binuclear Binuclear Apoptosis MetaphaseAlignment Grape MetaphaseAlignment Apoptosis Interphase Binuclear Artefact Polylobed Polylobed Grape Apoptosis Grape Polylobed Polylobed Polylobed Hole Binuclear Polylobed Hole Polylobed Prometaphase Binuclear Large Elongated Polylobed Binuclear Binuclear MetaphaseAlignment Binuclear Hole Prometaphase Interphase Binuclear Binuclear Elongated Interphase Prometaphase Binuclear Polylobed Polylobed Binuclear Interphase Binuclear Prometaphase Binuclear Polylobed Polylobed Prometaphase Binuclear Polylobed Polylobed Interphase Apoptosis Apoptosis Polylobed Interphase SmallIrregular Polylobed Binuclear Artefact Grape Interphase Interphase Binuclear Polylobed Polylobed Interphase Binuclear Interphase Binuclear Binuclear Grape Interphase Polylobed Polylobed Polylobed Prometaphase Apoptosis Hole Polylobed Elongated Binuclear Grape Hole Binuclear Polylobed Binuclear Binuclear Elongated MetaphaseAlignment MetaphaseAlignment Grape Polylobed Anaphase Polylobed Binuclear Polylobed Binuclear Artefact Polylobed Artefact Polylobed Grape Polylobed Polylobed Binuclear Hole Polylobed Polylobed Hole Polylobed Grape Interphase Polylobed Grape Binuclear Binuclear Polylobed Apoptosis Polylobed Artefact Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Binuclear Polylobed Polylobed Prometaphase Polylobed Apoptosis Prometaphase Polylobed Interphase Interphase Binuclear Folded Polylobed Polylobed Prometaphase Binuclear Polylobed Polylobed Prometaphase Binuclear Binuclear Anaphase Grape Interphase Grape Artefact Prometaphase Polylobed Polylobed Large Polylobed Grape Binuclear Interphase Hole Polylobed Polylobed Polylobed Artefact Grape Apoptosis Polylobed UndefinedCondensed Binuclear Binuclear Prometaphase Polylobed Grape Prometaphase Polylobed Binuclear Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Prometaphase Artefact Interphase Grape Binuclear Prometaphase Artefact Hole Interphase Interphase Binuclear Polylobed Prometaphase Polylobed Artefact Polylobed SmallIrregular Prometaphase Polylobed Grape Interphase Apoptosis Binuclear Polylobed Prometaphase Polylobed Polylobed Artefact Hole Polylobed Polylobed Apoptosis Binuclear Prometaphase Binuclear Polylobed Polylobed Apoptosis Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Grape Binuclear Binuclear Polylobed Polylobed Polylobed Binuclear Metaphase Interphase +y_train_pred Polylobed MetaphaseAlignment Polylobed Artefact Prometaphase Prometaphase Prometaphase Polylobed Apoptosis Polylobed Polylobed SmallIrregular Interphase Polylobed Artefact Elongated Grape Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Prometaphase Grape Grape Polylobed SmallIrregular Binuclear Polylobed Binuclear Artefact Artefact Binuclear MetaphaseAlignment MetaphaseAlignment Polylobed Metaphase Artefact Polylobed MetaphaseAlignment Grape Polylobed Binuclear Polylobed Artefact Polylobed Apoptosis Polylobed Interphase Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Interphase Polylobed Interphase Prometaphase Polylobed Prometaphase Polylobed Polylobed MetaphaseAlignment Hole SmallIrregular Polylobed Polylobed Interphase Binuclear Polylobed SmallIrregular Anaphase Prometaphase Prometaphase Binuclear Prometaphase Polylobed Artefact MetaphaseAlignment Grape Polylobed Polylobed Large Artefact Artefact Interphase Anaphase Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Hole Hole Grape Binuclear Grape Polylobed Polylobed UndefinedCondensed Binuclear Prometaphase Artefact Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Hole Polylobed Binuclear Apoptosis Grape Artefact Polylobed Interphase Polylobed Grape Interphase Polylobed Artefact Polylobed Prometaphase Grape Prometaphase Binuclear Grape Artefact Binuclear Prometaphase Polylobed Binuclear Binuclear Polylobed Polylobed Hole Polylobed Polylobed Grape Interphase Binuclear Binuclear Polylobed Apoptosis Prometaphase Hole Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Metaphase SmallIrregular Polylobed Apoptosis Binuclear Anaphase Binuclear Polylobed Polylobed Polylobed Prometaphase Artefact Polylobed Polylobed Polylobed Binuclear Interphase Interphase Binuclear Prometaphase Interphase Binuclear Grape Polylobed Binuclear Polylobed Polylobed Folded Grape Artefact MetaphaseAlignment SmallIrregular Binuclear Grape Polylobed MetaphaseAlignment Binuclear Polylobed MetaphaseAlignment Binuclear Grape Binuclear Hole Polylobed Artefact Polylobed Prometaphase Polylobed Grape Polylobed Binuclear Grape Polylobed Prometaphase Binuclear Polylobed Binuclear Binuclear Interphase Polylobed Apoptosis MetaphaseAlignment Prometaphase Polylobed Polylobed Grape Artefact Binuclear Interphase Polylobed Binuclear Artefact Polylobed Interphase Grape Apoptosis Binuclear Binuclear Grape Interphase Binuclear Interphase Artefact Polylobed MetaphaseAlignment Apoptosis Artefact Prometaphase Interphase Polylobed Polylobed Prometaphase Polylobed MetaphaseAlignment Apoptosis Polylobed Polylobed Binuclear Polylobed Polylobed Grape Polylobed Polylobed Prometaphase Artefact Polylobed Polylobed Polylobed Large Binuclear Binuclear Polylobed Polylobed Binuclear Anaphase Interphase Binuclear Binuclear Polylobed Binuclear Apoptosis Anaphase Elongated Polylobed SmallIrregular Binuclear Artefact UndefinedCondensed Polylobed Grape Polylobed MetaphaseAlignment Metaphase Interphase Polylobed Prometaphase Grape Polylobed Polylobed Polylobed Binuclear Apoptosis Polylobed Binuclear Polylobed Polylobed Artefact Prometaphase Artefact Polylobed Grape Prometaphase Interphase Binuclear Polylobed Interphase Polylobed Polylobed Binuclear Binuclear Binuclear Interphase Binuclear Artefact Interphase Large UndefinedCondensed Binuclear Polylobed Large Binuclear Polylobed Polylobed SmallIrregular Interphase Prometaphase Anaphase Polylobed SmallIrregular Apoptosis Polylobed Binuclear Binuclear Apoptosis Interphase Polylobed Grape Polylobed Polylobed MetaphaseAlignment Binuclear Polylobed Prometaphase Prometaphase Grape Grape Polylobed Grape Polylobed Polylobed Binuclear Polylobed Anaphase Interphase Prometaphase Artefact Binuclear Grape Grape Binuclear Artefact Polylobed Grape Interphase Metaphase Polylobed Polylobed Interphase Polylobed Binuclear Grape Prometaphase Prometaphase Prometaphase Binuclear Polylobed Artefact Interphase Grape Polylobed SmallIrregular Polylobed Polylobed Apoptosis Prometaphase Interphase Binuclear Grape Polylobed Apoptosis Polylobed Binuclear Polylobed Interphase Prometaphase Prometaphase Large Binuclear Polylobed Artefact Grape Binuclear Grape Artefact Polylobed Polylobed Polylobed Large Binuclear Polylobed Polylobed Prometaphase Artefact Polylobed Binuclear Grape Interphase Polylobed Polylobed Grape Interphase Prometaphase Binuclear Grape Polylobed Anaphase Polylobed Polylobed Artefact Polylobed Polylobed Artefact Binuclear Metaphase Polylobed Polylobed Polylobed Elongated Polylobed Polylobed Grape Polylobed Binuclear Apoptosis Binuclear Grape Polylobed Grape Interphase Artefact Hole Grape Apoptosis Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Interphase Polylobed Polylobed Prometaphase Binuclear Prometaphase Prometaphase Binuclear Folded Polylobed Polylobed Polylobed Artefact Polylobed Polylobed SmallIrregular Polylobed Polylobed Grape Binuclear Hole Polylobed Interphase Binuclear Binuclear Grape Polylobed Polylobed Binuclear SmallIrregular Grape Prometaphase Binuclear Prometaphase Artefact Binuclear Binuclear Interphase Grape Polylobed UndefinedCondensed MetaphaseAlignment Apoptosis Polylobed SmallIrregular Interphase Polylobed Polylobed Grape Prometaphase Polylobed Prometaphase Binuclear Polylobed SmallIrregular Interphase Binuclear Interphase Interphase Artefact Binuclear Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Apoptosis Grape Binuclear Apoptosis Polylobed Polylobed Polylobed Binuclear Polylobed Apoptosis Binuclear Polylobed Prometaphase Polylobed Artefact Binuclear Interphase Hole Apoptosis Polylobed Polylobed Prometaphase Prometaphase MetaphaseAlignment Artefact MetaphaseAlignment Apoptosis Polylobed Binuclear Polylobed Polylobed Interphase SmallIrregular UndefinedCondensed Polylobed Artefact Polylobed Polylobed Polylobed Binuclear Binuclear UndefinedCondensed Binuclear Grape Polylobed Hole Polylobed Prometaphase Interphase MetaphaseAlignment Apoptosis Binuclear Polylobed Artefact Polylobed Binuclear Grape Polylobed Grape Polylobed Polylobed Metaphase Polylobed Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Binuclear Binuclear Apoptosis Polylobed Binuclear Artefact Polylobed Polylobed Polylobed Polylobed Apoptosis Apoptosis Polylobed Binuclear Interphase Interphase Prometaphase Artefact Polylobed SmallIrregular Polylobed Polylobed Hole Grape SmallIrregular SmallIrregular Binuclear Prometaphase MetaphaseAlignment Binuclear Binuclear Binuclear Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Artefact Grape Interphase MetaphaseAlignment Binuclear Interphase MetaphaseAlignment Binuclear Binuclear Metaphase Polylobed Polylobed Grape Polylobed Prometaphase Polylobed Artefact Binuclear Interphase Polylobed Prometaphase Interphase Interphase Artefact Polylobed Polylobed Prometaphase Grape Polylobed Polylobed SmallIrregular Polylobed Grape Grape Polylobed Interphase Polylobed Prometaphase Binuclear Interphase Grape Interphase Artefact Polylobed Binuclear Polylobed MetaphaseAlignment Polylobed Grape Binuclear Polylobed Polylobed Binuclear Hole Metaphase Hole Grape Binuclear Grape Artefact Polylobed Polylobed Interphase Interphase Prometaphase Polylobed SmallIrregular Polylobed Polylobed MetaphaseAlignment Apoptosis Polylobed Polylobed Grape Binuclear Polylobed Hole Polylobed Polylobed Polylobed SmallIrregular Binuclear MetaphaseAlignment Interphase Polylobed Prometaphase Polylobed Artefact Polylobed Grape Polylobed Polylobed Polylobed MetaphaseAlignment Artefact Apoptosis Polylobed Binuclear Prometaphase Apoptosis Prometaphase Binuclear Apoptosis Grape Artefact Polylobed Grape Polylobed Polylobed SmallIrregular Prometaphase Interphase MetaphaseAlignment Anaphase Polylobed Polylobed Large Large Polylobed Prometaphase Binuclear Binuclear Polylobed Interphase Polylobed Polylobed MetaphaseAlignment Binuclear Polylobed SmallIrregular Binuclear MetaphaseAlignment Interphase Grape Apoptosis Binuclear Prometaphase Binuclear Artefact Binuclear Polylobed Interphase Binuclear Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Polylobed Binuclear Hole Polylobed Polylobed Hole Interphase Binuclear Polylobed Artefact Polylobed Polylobed Interphase MetaphaseAlignment Polylobed Interphase Polylobed Polylobed Apoptosis Polylobed Interphase Artefact Binuclear Artefact Binuclear Polylobed Polylobed Polylobed Prometaphase Grape Polylobed Anaphase Apoptosis Interphase Apoptosis Binuclear Binuclear Polylobed Binuclear Anaphase MetaphaseAlignment Interphase Polylobed Interphase Grape Binuclear Binuclear Artefact Artefact Interphase Prometaphase Elongated Polylobed Prometaphase MetaphaseAlignment Interphase Polylobed Prometaphase Polylobed Polylobed Folded Grape Interphase Apoptosis Polylobed Interphase Polylobed Interphase Polylobed Binuclear Apoptosis Grape Artefact Prometaphase Prometaphase Binuclear Polylobed Binuclear Interphase SmallIrregular Binuclear Prometaphase Polylobed Polylobed Interphase Polylobed Binuclear Large MetaphaseAlignment Binuclear Polylobed Prometaphase Prometaphase Prometaphase Binuclear Interphase Binuclear Polylobed MetaphaseAlignment Prometaphase Grape Artefact Polylobed Large Binuclear Artefact Polylobed Polylobed Polylobed Interphase Artefact Binuclear Binuclear Apoptosis Polylobed Grape Prometaphase Polylobed Polylobed Apoptosis Polylobed Polylobed Polylobed SmallIrregular Grape Polylobed UndefinedCondensed Polylobed Grape SmallIrregular Grape Polylobed Hole Anaphase Grape Prometaphase Prometaphase Anaphase Large Polylobed Grape Apoptosis Grape Polylobed Large Grape Artefact Elongated Binuclear Interphase Apoptosis Binuclear Interphase Polylobed Folded Binuclear Prometaphase Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Binuclear Grape Interphase MetaphaseAlignment Interphase Prometaphase Polylobed Apoptosis Prometaphase Hole Binuclear Grape Interphase Polylobed Polylobed Polylobed Binuclear Grape Apoptosis Grape UndefinedCondensed Apoptosis UndefinedCondensed Prometaphase Artefact Prometaphase Polylobed Prometaphase Binuclear Polylobed Polylobed Prometaphase Apoptosis Grape Polylobed Binuclear Binuclear Polylobed Binuclear Artefact Polylobed Binuclear Polylobed Prometaphase Polylobed Polylobed Polylobed Binuclear MetaphaseAlignment Binuclear SmallIrregular Metaphase Prometaphase Grape Artefact Polylobed Polylobed Prometaphase Interphase Prometaphase Prometaphase Apoptosis Polylobed MetaphaseAlignment Artefact Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Interphase Large Grape Grape Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Prometaphase Hole Interphase Binuclear Binuclear Binuclear Polylobed Binuclear Prometaphase Binuclear Polylobed Polylobed Polylobed Polylobed Apoptosis Artefact Polylobed Prometaphase Interphase Interphase Grape Binuclear Polylobed Polylobed Hole Artefact Apoptosis Hole Prometaphase Polylobed Interphase Artefact Artefact Polylobed Polylobed Interphase Polylobed Polylobed Elongated Polylobed Prometaphase Polylobed Grape Interphase Polylobed Polylobed Polylobed Prometaphase Polylobed Large Grape Polylobed MetaphaseAlignment Polylobed MetaphaseAlignment Polylobed UndefinedCondensed Polylobed Interphase Prometaphase Binuclear Grape Polylobed MetaphaseAlignment Prometaphase Grape Polylobed Polylobed Prometaphase Polylobed Interphase Binuclear Prometaphase Polylobed Interphase Prometaphase SmallIrregular Prometaphase Binuclear Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Binuclear Polylobed Anaphase MetaphaseAlignment SmallIrregular Binuclear Grape Prometaphase Binuclear Grape Binuclear Polylobed Grape Binuclear Polylobed Polylobed Apoptosis Hole Polylobed Hole Polylobed Large Polylobed Interphase MetaphaseAlignment Large Binuclear Binuclear Prometaphase Grape Polylobed MetaphaseAlignment Polylobed Polylobed Binuclear Grape Polylobed Artefact Polylobed Binuclear Polylobed Apoptosis Polylobed Grape Artefact Polylobed MetaphaseAlignment Polylobed Polylobed Metaphase Interphase Anaphase Polylobed Hole Artefact Interphase Polylobed Interphase Hole Polylobed Binuclear Polylobed Anaphase Artefact Artefact Polylobed Prometaphase MetaphaseAlignment Polylobed Binuclear Large Grape Interphase Polylobed Polylobed Polylobed Grape MetaphaseAlignment Apoptosis Interphase MetaphaseAlignment Polylobed Interphase Polylobed Apoptosis Polylobed Prometaphase Polylobed Interphase SmallIrregular Binuclear Polylobed Interphase Polylobed Polylobed Grape Apoptosis Polylobed Apoptosis Polylobed Artefact Interphase Polylobed Polylobed Polylobed Large Polylobed MetaphaseAlignment Polylobed Prometaphase UndefinedCondensed Polylobed Apoptosis Polylobed Large Apoptosis MetaphaseAlignment Apoptosis Polylobed Prometaphase Prometaphase Anaphase MetaphaseAlignment Artefact Grape Polylobed Prometaphase Binuclear Interphase Grape Polylobed Prometaphase Polylobed Prometaphase Polylobed Grape Artefact Prometaphase Polylobed Polylobed Interphase Prometaphase Polylobed Polylobed Prometaphase Polylobed Grape Polylobed Binuclear Binuclear Polylobed Artefact Interphase Polylobed Binuclear Interphase Polylobed Prometaphase Interphase SmallIrregular Polylobed Apoptosis Interphase Binuclear Binuclear Grape Metaphase Interphase Binuclear Polylobed Grape Polylobed Elongated Polylobed Artefact Grape Interphase Binuclear Binuclear Binuclear Hole Binuclear UndefinedCondensed Polylobed Polylobed Polylobed Polylobed Grape Apoptosis Interphase Artefact Grape Polylobed Artefact Binuclear Apoptosis Interphase Apoptosis Binuclear Polylobed Binuclear Polylobed Polylobed SmallIrregular Artefact Polylobed Grape Artefact Polylobed Polylobed Binuclear Artefact Polylobed Prometaphase Polylobed Interphase Polylobed Apoptosis Grape Polylobed Grape Hole Artefact Polylobed Polylobed Polylobed Prometaphase Artefact Polylobed Grape Binuclear Hole Polylobed Binuclear Binuclear Prometaphase Polylobed Polylobed Polylobed Polylobed Binuclear MetaphaseAlignment Prometaphase Binuclear Polylobed Polylobed Elongated Grape Artefact Interphase Grape Polylobed Large MetaphaseAlignment Apoptosis Interphase Polylobed Interphase Interphase Grape Binuclear Polylobed Interphase Interphase Grape Binuclear Interphase Binuclear Polylobed Polylobed Interphase Artefact Prometaphase Apoptosis Apoptosis Large Polylobed Binuclear SmallIrregular Binuclear Large Binuclear Interphase Apoptosis Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Binuclear Artefact Grape Apoptosis Interphase Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed MetaphaseAlignment Metaphase Polylobed Binuclear Prometaphase Polylobed Binuclear Grape Apoptosis Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Artefact Polylobed Binuclear Polylobed UndefinedCondensed Prometaphase Prometaphase SmallIrregular Apoptosis Binuclear Polylobed Binuclear Metaphase Binuclear Prometaphase Polylobed Grape Apoptosis SmallIrregular Interphase Prometaphase Anaphase Polylobed Binuclear Elongated Polylobed Grape Artefact Grape Prometaphase Binuclear Apoptosis Polylobed Binuclear Binuclear Grape Polylobed Polylobed Binuclear Interphase Artefact Prometaphase Large Metaphase Interphase SmallIrregular Grape Grape Polylobed Binuclear Polylobed Interphase Anaphase Binuclear Prometaphase Interphase Apoptosis Interphase Polylobed Binuclear Apoptosis Artefact Artefact Interphase SmallIrregular Polylobed Polylobed Binuclear Polylobed Grape Artefact Artefact Polylobed Grape Anaphase Grape Polylobed Polylobed Polylobed Interphase Apoptosis Polylobed Polylobed Anaphase Polylobed Binuclear Grape Apoptosis Grape Polylobed Large Binuclear Binuclear Grape Polylobed Interphase Polylobed Elongated Polylobed Hole Polylobed Apoptosis Apoptosis Binuclear Polylobed Grape Binuclear Interphase Grape Polylobed Binuclear Polylobed Artefact SmallIrregular Interphase Interphase Grape Binuclear Interphase Binuclear Elongated Polylobed Binuclear Binuclear Binuclear Artefact Polylobed Polylobed Polylobed Binuclear Apoptosis Apoptosis Polylobed Grape Polylobed Polylobed MetaphaseAlignment Artefact Binuclear Polylobed Binuclear Polylobed Large UndefinedCondensed Polylobed SmallIrregular Binuclear Hole SmallIrregular Polylobed Polylobed Polylobed Polylobed SmallIrregular Polylobed Polylobed SmallIrregular Binuclear MetaphaseAlignment Grape Polylobed SmallIrregular Artefact Grape Grape Binuclear Grape Metaphase Binuclear Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Interphase Prometaphase Artefact Polylobed Polylobed Binuclear Hole MetaphaseAlignment Interphase Polylobed Polylobed Grape Grape Polylobed Interphase Prometaphase Apoptosis Prometaphase Interphase Polylobed MetaphaseAlignment Hole Elongated Polylobed SmallIrregular Polylobed Polylobed Polylobed Binuclear Apoptosis Artefact Polylobed Prometaphase Polylobed Prometaphase MetaphaseAlignment Artefact Binuclear Polylobed Grape Interphase Grape Artefact Interphase Artefact Grape SmallIrregular MetaphaseAlignment Interphase Polylobed Polylobed Prometaphase Polylobed Grape Prometaphase Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Interphase Binuclear Large Grape Binuclear Polylobed Polylobed Prometaphase Polylobed MetaphaseAlignment Binuclear Binuclear Interphase Prometaphase Polylobed Binuclear Prometaphase Apoptosis Polylobed Hole Polylobed Polylobed Interphase Binuclear Polylobed Polylobed Artefact Artefact MetaphaseAlignment Apoptosis Metaphase Grape Polylobed Grape Prometaphase Interphase Polylobed Prometaphase Grape Apoptosis SmallIrregular Hole Binuclear Artefact Polylobed Binuclear Binuclear Polylobed Polylobed Folded Binuclear Polylobed Hole Polylobed Polylobed Polylobed Grape Polylobed Metaphase Binuclear Binuclear Binuclear Binuclear Polylobed Polylobed Prometaphase Folded Polylobed Polylobed Grape Artefact Polylobed Grape Grape Binuclear Grape Polylobed Prometaphase Polylobed Binuclear Anaphase Grape SmallIrregular Apoptosis Polylobed Polylobed Interphase Polylobed Hole Folded Polylobed Artefact Polylobed Interphase Interphase SmallIrregular Binuclear Polylobed Grape Folded Polylobed Polylobed MetaphaseAlignment Binuclear Polylobed Binuclear Grape Prometaphase Prometaphase Grape Polylobed Binuclear Artefact Polylobed Polylobed Interphase Polylobed Interphase Artefact Artefact Binuclear Grape Artefact Binuclear Polylobed Binuclear Artefact UndefinedCondensed Binuclear Binuclear Artefact SmallIrregular MetaphaseAlignment Interphase Binuclear Polylobed Binuclear Interphase Interphase Binuclear Grape Hole Polylobed Grape Metaphase Grape Binuclear Polylobed Hole Prometaphase Binuclear Interphase Grape Prometaphase Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed SmallIrregular Polylobed Binuclear Grape Apoptosis Polylobed Prometaphase Binuclear Grape Interphase Binuclear Grape Polylobed Grape Folded Binuclear Binuclear Binuclear Prometaphase Interphase Binuclear Interphase Polylobed Binuclear Binuclear Grape Elongated UndefinedCondensed Polylobed Polylobed Polylobed UndefinedCondensed Binuclear Prometaphase Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Prometaphase Polylobed Prometaphase Grape Apoptosis Prometaphase Binuclear Polylobed Grape Polylobed Grape Polylobed Polylobed Polylobed Polylobed Grape Artefact Polylobed Interphase Prometaphase MetaphaseAlignment Polylobed Grape Polylobed MetaphaseAlignment Polylobed Grape Binuclear Polylobed Polylobed Polylobed Prometaphase Polylobed Prometaphase Polylobed Polylobed Binuclear Polylobed Interphase Binuclear Grape UndefinedCondensed Grape Artefact Grape Grape Apoptosis Binuclear Prometaphase Polylobed SmallIrregular Interphase Interphase Grape Interphase Polylobed Polylobed Binuclear Interphase Polylobed Polylobed Metaphase Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Apoptosis Hole Prometaphase Polylobed Polylobed Binuclear Polylobed Prometaphase Artefact Prometaphase Polylobed MetaphaseAlignment Interphase Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Interphase Polylobed Apoptosis Prometaphase Polylobed Polylobed Interphase MetaphaseAlignment Polylobed Grape MetaphaseAlignment Grape Grape Grape MetaphaseAlignment Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Grape UndefinedCondensed MetaphaseAlignment Binuclear MetaphaseAlignment Artefact Grape Binuclear Artefact Polylobed Polylobed MetaphaseAlignment Grape Prometaphase Apoptosis Large MetaphaseAlignment Prometaphase Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Grape Polylobed Metaphase Interphase Interphase Grape Large Polylobed Apoptosis Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Anaphase Metaphase Polylobed Grape Apoptosis Artefact Polylobed Polylobed Polylobed Grape Binuclear Polylobed UndefinedCondensed Grape Binuclear MetaphaseAlignment Polylobed Artefact Polylobed Interphase Apoptosis Interphase Artefact Grape Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Interphase MetaphaseAlignment Folded Polylobed Large Binuclear MetaphaseAlignment Binuclear Grape Prometaphase Prometaphase Polylobed Binuclear Polylobed Elongated Folded Binuclear Binuclear Polylobed Prometaphase Binuclear Polylobed SmallIrregular Prometaphase Polylobed Artefact Polylobed Prometaphase Artefact Polylobed Binuclear Binuclear Grape Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Interphase Apoptosis Polylobed Polylobed Polylobed Prometaphase Grape Grape Interphase Prometaphase Polylobed Prometaphase Interphase Grape SmallIrregular Polylobed Prometaphase Hole Prometaphase Interphase Large Binuclear Interphase Polylobed Large Polylobed Polylobed Grape Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Artefact Prometaphase Metaphase Polylobed Polylobed Artefact Interphase Prometaphase Grape Apoptosis Polylobed Metaphase Polylobed Polylobed Grape Polylobed Prometaphase Grape Polylobed Interphase Binuclear Prometaphase Polylobed Polylobed Artefact Grape Interphase Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Binuclear Grape Hole UndefinedCondensed Binuclear MetaphaseAlignment SmallIrregular Prometaphase SmallIrregular Apoptosis Grape Binuclear Polylobed Prometaphase Polylobed Hole Prometaphase Polylobed Binuclear Polylobed Hole Prometaphase Artefact Polylobed Apoptosis Binuclear MetaphaseAlignment Grape Polylobed Polylobed Binuclear Polylobed Interphase Polylobed Grape Interphase Polylobed Polylobed Grape MetaphaseAlignment Polylobed Binuclear Apoptosis Grape Binuclear MetaphaseAlignment Polylobed Grape Artefact Anaphase Grape Polylobed Polylobed Grape Polylobed Binuclear Polylobed Grape Polylobed Apoptosis SmallIrregular Polylobed Polylobed Grape Binuclear Binuclear Binuclear Anaphase Polylobed MetaphaseAlignment Polylobed Grape Polylobed Elongated MetaphaseAlignment Polylobed Binuclear Grape Polylobed Apoptosis Polylobed Grape Artefact Polylobed Grape Binuclear Artefact Binuclear Prometaphase Grape Polylobed Polylobed Polylobed MetaphaseAlignment Grape Binuclear Polylobed Polylobed Metaphase Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Grape SmallIrregular Binuclear Polylobed Polylobed Prometaphase Interphase Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Grape Binuclear Prometaphase Polylobed Polylobed Prometaphase Polylobed Artefact Polylobed Polylobed Interphase Anaphase Binuclear Prometaphase Binuclear Polylobed Polylobed Binuclear Polylobed Interphase Binuclear Metaphase Polylobed Binuclear Artefact Polylobed Polylobed Artefact Polylobed Artefact Polylobed Polylobed Apoptosis Binuclear Binuclear SmallIrregular Grape Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Large Polylobed Polylobed Artefact Grape Polylobed Polylobed Polylobed Polylobed Grape Prometaphase Artefact Polylobed Apoptosis Binuclear Polylobed Grape Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Binuclear Prometaphase Polylobed Binuclear Grape Grape Prometaphase Apoptosis Prometaphase Polylobed MetaphaseAlignment Polylobed Anaphase Polylobed Interphase Polylobed Polylobed Apoptosis MetaphaseAlignment Polylobed MetaphaseAlignment Polylobed Interphase Binuclear Prometaphase Polylobed Interphase Polylobed Interphase Polylobed Grape Interphase Grape MetaphaseAlignment Apoptosis Polylobed Polylobed MetaphaseAlignment Interphase Interphase Polylobed Binuclear Binuclear Grape Hole Prometaphase Polylobed Interphase Grape SmallIrregular Artefact Polylobed Artefact Polylobed Polylobed Polylobed Binuclear MetaphaseAlignment Polylobed Metaphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Metaphase Hole Polylobed Apoptosis Polylobed Interphase Metaphase Grape Hole Grape Polylobed Polylobed Interphase Polylobed Artefact Interphase Grape Grape Anaphase Polylobed Binuclear Polylobed Prometaphase Binuclear Prometaphase Polylobed Prometaphase Polylobed Binuclear Apoptosis Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Prometaphase SmallIrregular Apoptosis Polylobed Grape Elongated Metaphase Polylobed Prometaphase Metaphase Grape Polylobed Interphase Binuclear MetaphaseAlignment Metaphase Polylobed Polylobed Binuclear Prometaphase SmallIrregular Apoptosis Interphase MetaphaseAlignment Interphase Prometaphase Polylobed MetaphaseAlignment SmallIrregular Binuclear Polylobed MetaphaseAlignment Interphase Polylobed Interphase Binuclear Interphase Binuclear Polylobed SmallIrregular Artefact Polylobed Interphase Polylobed Prometaphase Apoptosis Binuclear Grape Polylobed Prometaphase Grape Polylobed Binuclear Binuclear Prometaphase Polylobed Polylobed Artefact Artefact Artefact Artefact Interphase Artefact Polylobed Anaphase Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Binuclear MetaphaseAlignment Polylobed Binuclear Polylobed Artefact Binuclear Polylobed Prometaphase Grape Polylobed Polylobed Artefact Interphase Artefact Polylobed Hole MetaphaseAlignment Binuclear Polylobed Binuclear Binuclear Hole Large Polylobed Prometaphase Polylobed Interphase Interphase Interphase Binuclear Interphase Large Artefact Interphase Binuclear Grape Grape Prometaphase Polylobed Interphase Grape MetaphaseAlignment Polylobed MetaphaseAlignment Binuclear Hole Polylobed Grape Polylobed Large Artefact Polylobed Grape Binuclear Polylobed Polylobed Binuclear Artefact Binuclear Grape Grape Polylobed Polylobed Interphase Grape Binuclear SmallIrregular Polylobed Grape Binuclear Grape Prometaphase Binuclear Binuclear Polylobed Prometaphase Binuclear Interphase Polylobed Grape Polylobed Binuclear Grape Polylobed Large Interphase Interphase Grape Grape Grape Apoptosis Binuclear Interphase Polylobed Binuclear Polylobed Polylobed Apoptosis Prometaphase Binuclear Polylobed Polylobed Apoptosis Interphase Interphase Polylobed Binuclear Binuclear Grape Binuclear Polylobed Apoptosis Interphase Polylobed Interphase Interphase Polylobed Polylobed SmallIrregular Binuclear SmallIrregular Polylobed Grape Binuclear Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Grape MetaphaseAlignment Binuclear SmallIrregular Polylobed Binuclear Interphase Binuclear Polylobed Polylobed Polylobed MetaphaseAlignment Hole Polylobed Binuclear Artefact Grape Interphase Artefact MetaphaseAlignment Polylobed Polylobed Binuclear Binuclear Polylobed Prometaphase Polylobed Polylobed Metaphase Metaphase Binuclear Apoptosis Polylobed Interphase Prometaphase Binuclear Anaphase Binuclear Prometaphase Grape Hole SmallIrregular Grape Artefact Grape Interphase Polylobed Metaphase MetaphaseAlignment Polylobed Prometaphase Polylobed Polylobed Binuclear Artefact Grape Prometaphase Prometaphase MetaphaseAlignment Prometaphase Polylobed Polylobed Artefact Polylobed Prometaphase Interphase Polylobed Interphase Polylobed Polylobed Elongated Apoptosis Polylobed Polylobed Apoptosis Polylobed Polylobed Binuclear Binuclear Interphase Folded Apoptosis Polylobed Hole Prometaphase Polylobed Polylobed Polylobed Interphase Binuclear Polylobed Binuclear Prometaphase Apoptosis Binuclear Artefact Interphase Prometaphase Interphase SmallIrregular Prometaphase Binuclear Anaphase Elongated Polylobed Polylobed Polylobed Artefact Polylobed Polylobed MetaphaseAlignment SmallIrregular Grape Prometaphase SmallIrregular Polylobed Polylobed Binuclear Polylobed UndefinedCondensed Polylobed Polylobed Artefact Interphase Polylobed Anaphase Grape Polylobed Polylobed Polylobed Grape Artefact Prometaphase Grape Binuclear Binuclear Artefact Polylobed Prometaphase Polylobed Binuclear Polylobed Interphase Grape Polylobed Anaphase Binuclear MetaphaseAlignment Elongated Binuclear Artefact Interphase Artefact Interphase Polylobed Polylobed Grape MetaphaseAlignment Prometaphase Polylobed Polylobed MetaphaseAlignment MetaphaseAlignment Polylobed Apoptosis Polylobed Binuclear Apoptosis Polylobed Polylobed MetaphaseAlignment Grape Polylobed Binuclear Interphase UndefinedCondensed Apoptosis MetaphaseAlignment Polylobed Prometaphase Polylobed Polylobed Grape Prometaphase Interphase Binuclear Large Prometaphase Binuclear Prometaphase MetaphaseAlignment Binuclear Binuclear SmallIrregular Polylobed Polylobed Polylobed Prometaphase Polylobed Artefact Binuclear Polylobed Polylobed Interphase SmallIrregular Polylobed Grape SmallIrregular MetaphaseAlignment Anaphase Binuclear Binuclear Elongated Polylobed Polylobed MetaphaseAlignment Binuclear Interphase Interphase Apoptosis MetaphaseAlignment Binuclear Artefact Binuclear Artefact Polylobed Polylobed Polylobed Grape Prometaphase Elongated Binuclear Polylobed Binuclear Polylobed Binuclear Polylobed Binuclear Binuclear Polylobed MetaphaseAlignment Artefact Interphase Polylobed Interphase Grape Binuclear Polylobed Binuclear Polylobed Grape Artefact Polylobed Grape Interphase Interphase Polylobed Polylobed Binuclear Grape Large Interphase Polylobed Binuclear Polylobed Binuclear Prometaphase Artefact Apoptosis Interphase Binuclear Binuclear Polylobed Prometaphase Polylobed Grape Polylobed Polylobed Polylobed Apoptosis Artefact Polylobed Grape Binuclear Prometaphase Prometaphase Polylobed UndefinedCondensed Artefact Polylobed Large Polylobed MetaphaseAlignment Anaphase Polylobed Polylobed Polylobed Binuclear Hole Polylobed Binuclear Interphase Prometaphase Prometaphase Polylobed Apoptosis Binuclear Binuclear Polylobed Polylobed Apoptosis Metaphase Interphase Interphase Polylobed Apoptosis Polylobed Polylobed Polylobed Prometaphase Artefact Binuclear SmallIrregular Prometaphase Interphase Apoptosis Polylobed MetaphaseAlignment Artefact Binuclear Apoptosis Prometaphase Polylobed MetaphaseAlignment Artefact Binuclear Polylobed Grape Polylobed Polylobed Interphase Prometaphase Binuclear Polylobed Artefact MetaphaseAlignment Polylobed Grape Binuclear Interphase Polylobed Interphase Interphase MetaphaseAlignment MetaphaseAlignment Binuclear Interphase Interphase Polylobed Hole Grape Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Binuclear Binuclear Artefact Apoptosis Polylobed Grape Binuclear Interphase Polylobed Artefact Prometaphase Grape Polylobed Artefact Polylobed Prometaphase MetaphaseAlignment Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Binuclear MetaphaseAlignment Prometaphase Polylobed Polylobed UndefinedCondensed Apoptosis SmallIrregular Artefact Polylobed Polylobed Polylobed Binuclear Polylobed Grape Binuclear Grape Polylobed Interphase Polylobed Interphase Polylobed Grape Polylobed Grape Interphase Binuclear Grape Interphase Prometaphase Polylobed Interphase Polylobed Polylobed Grape SmallIrregular Interphase Binuclear SmallIrregular Grape Polylobed Polylobed Artefact Polylobed Binuclear Polylobed Large Polylobed Polylobed Apoptosis Interphase Polylobed Polylobed Polylobed Hole Grape Interphase SmallIrregular Polylobed Large Prometaphase Polylobed Interphase Apoptosis Grape Binuclear UndefinedCondensed Artefact Polylobed Interphase SmallIrregular Binuclear Artefact Polylobed Metaphase Artefact Polylobed Polylobed Polylobed Apoptosis Binuclear MetaphaseAlignment Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Grape Polylobed Binuclear Binuclear Apoptosis MetaphaseAlignment Grape MetaphaseAlignment Apoptosis Interphase Binuclear Artefact Polylobed Polylobed Grape Apoptosis Grape Polylobed Polylobed Polylobed Hole Binuclear Polylobed Hole Polylobed Prometaphase Binuclear Large Elongated Polylobed Binuclear Binuclear MetaphaseAlignment Binuclear Hole Prometaphase Interphase Binuclear Binuclear Elongated Interphase Prometaphase Binuclear Polylobed Polylobed Binuclear Interphase Binuclear Prometaphase Binuclear Polylobed Polylobed Prometaphase Binuclear Polylobed Polylobed Interphase Apoptosis Apoptosis Polylobed Interphase SmallIrregular Polylobed Binuclear Artefact Grape Polylobed Binuclear Binuclear Polylobed Polylobed Interphase Polylobed Interphase Binuclear Binuclear Grape Interphase Polylobed Polylobed Polylobed Prometaphase Apoptosis Hole Polylobed Elongated Binuclear Grape Hole Binuclear Polylobed Binuclear Binuclear Elongated Prometaphase MetaphaseAlignment Grape Polylobed Anaphase Polylobed Binuclear Polylobed Binuclear Artefact Polylobed Artefact Polylobed Grape Polylobed Polylobed Binuclear Hole Polylobed Polylobed Hole Polylobed Grape Interphase Polylobed Grape Binuclear Binuclear Polylobed Apoptosis Polylobed Artefact Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Binuclear Polylobed Polylobed Prometaphase Polylobed Apoptosis Prometaphase Polylobed Interphase Interphase Binuclear Folded Polylobed Polylobed Prometaphase Binuclear Polylobed Polylobed Prometaphase Binuclear Binuclear Anaphase Grape Interphase Grape Artefact Prometaphase Polylobed Polylobed Large Polylobed Grape Binuclear Interphase Hole Polylobed Polylobed Polylobed Artefact Grape Apoptosis Polylobed UndefinedCondensed Binuclear Binuclear Prometaphase Polylobed Grape Prometaphase Polylobed Binuclear Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Prometaphase Artefact Interphase Grape Binuclear Prometaphase Artefact Hole Interphase Interphase Binuclear Binuclear Prometaphase Polylobed Artefact Interphase SmallIrregular Prometaphase Polylobed Grape Interphase Apoptosis Polylobed Polylobed Prometaphase Polylobed Polylobed Artefact Hole Polylobed Binuclear Apoptosis Binuclear Prometaphase Binuclear Polylobed Polylobed Apoptosis Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Grape Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed Metaphase Interphase +y_test Binuclear Artefact Polylobed Polylobed Hole Polylobed Interphase Polylobed SmallIrregular Polylobed Polylobed Prometaphase Polylobed UndefinedCondensed Polylobed Polylobed Grape Metaphase Polylobed Prometaphase Interphase Binuclear Hole Grape Hole Polylobed Apoptosis Artefact Polylobed Prometaphase Polylobed Binuclear Hole Prometaphase Anaphase SmallIrregular MetaphaseAlignment Polylobed Polylobed Interphase Polylobed Apoptosis Binuclear Apoptosis Polylobed Apoptosis Polylobed Polylobed Polylobed Grape Interphase Elongated Grape Interphase Grape Polylobed Prometaphase Prometaphase Grape Polylobed Grape Polylobed Grape Polylobed Polylobed Artefact Binuclear Polylobed Artefact Grape Artefact Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Polylobed Artefact Apoptosis Binuclear Artefact Polylobed Polylobed Interphase Prometaphase Polylobed Polylobed Polylobed Polylobed Apoptosis Binuclear Artefact Apoptosis Grape Binuclear Interphase Binuclear Apoptosis MetaphaseAlignment Prometaphase Large Polylobed Polylobed Polylobed Grape Polylobed MetaphaseAlignment MetaphaseAlignment Polylobed Grape Polylobed Interphase Artefact Binuclear Polylobed Polylobed Polylobed Polylobed Prometaphase Prometaphase Polylobed Polylobed Polylobed Interphase Artefact Polylobed Hole Interphase MetaphaseAlignment Binuclear Prometaphase Interphase Interphase Interphase Polylobed Grape Polylobed Binuclear Interphase Polylobed Grape Binuclear Interphase Grape Interphase Artefact Interphase Polylobed Polylobed Binuclear Polylobed Polylobed Anaphase Grape Binuclear MetaphaseAlignment Binuclear Polylobed Binuclear Interphase Prometaphase MetaphaseAlignment Interphase Polylobed Prometaphase Grape Polylobed Apoptosis Grape Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed SmallIrregular Polylobed Interphase Polylobed Hole MetaphaseAlignment UndefinedCondensed Polylobed Binuclear Prometaphase Grape Interphase Binuclear Apoptosis Binuclear Polylobed Interphase UndefinedCondensed Grape Grape UndefinedCondensed Polylobed Interphase Elongated MetaphaseAlignment Polylobed Polylobed Polylobed Polylobed Grape Binuclear Polylobed Artefact Polylobed Polylobed Artefact Polylobed Binuclear Polylobed Polylobed Polylobed Prometaphase MetaphaseAlignment Metaphase Polylobed Polylobed Polylobed Binuclear MetaphaseAlignment Apoptosis Polylobed Grape Prometaphase Polylobed SmallIrregular Polylobed Polylobed Binuclear Prometaphase Grape Binuclear Binuclear Apoptosis Binuclear Artefact Apoptosis Polylobed Artefact Interphase Binuclear Polylobed SmallIrregular SmallIrregular Polylobed Polylobed Artefact Binuclear Artefact Prometaphase SmallIrregular Polylobed Grape Metaphase Interphase Grape Artefact Polylobed Polylobed Polylobed Folded Binuclear Binuclear Artefact MetaphaseAlignment Prometaphase Binuclear Artefact Polylobed Polylobed Grape Binuclear Anaphase Polylobed Prometaphase Polylobed Polylobed Anaphase Binuclear Polylobed Binuclear Polylobed Prometaphase Apoptosis Interphase Interphase Polylobed Prometaphase Interphase Grape Prometaphase Binuclear Binuclear Polylobed MetaphaseAlignment SmallIrregular Prometaphase Elongated SmallIrregular Interphase MetaphaseAlignment Polylobed Polylobed Grape Grape Binuclear Polylobed SmallIrregular Folded Binuclear Polylobed Artefact Binuclear Polylobed Prometaphase Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Grape Binuclear Binuclear Artefact Prometaphase Polylobed MetaphaseAlignment Interphase Prometaphase Binuclear Anaphase Polylobed Hole Prometaphase Binuclear Large Interphase Polylobed Polylobed Binuclear Polylobed SmallIrregular Polylobed Grape Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Interphase Polylobed SmallIrregular Polylobed Polylobed Interphase Hole Binuclear Artefact Binuclear Large Artefact Polylobed Polylobed Artefact Grape Prometaphase Prometaphase Polylobed Polylobed Polylobed Grape MetaphaseAlignment Metaphase Binuclear Metaphase Artefact Polylobed Apoptosis Polylobed Polylobed Polylobed MetaphaseAlignment Prometaphase Grape Apoptosis Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Grape Binuclear Polylobed Polylobed Polylobed Grape Polylobed Grape Grape SmallIrregular Grape Interphase Interphase Artefact Apoptosis Polylobed Grape Metaphase Polylobed Hole Binuclear Polylobed Binuclear Grape Interphase Grape Artefact Interphase Polylobed Binuclear Polylobed Prometaphase Polylobed Apoptosis Large Polylobed Binuclear Interphase Hole Polylobed Polylobed Elongated Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Apoptosis Binuclear Binuclear Polylobed Binuclear Apoptosis Polylobed Polylobed Binuclear Polylobed Polylobed Prometaphase Binuclear Interphase Polylobed Interphase Polylobed Interphase MetaphaseAlignment MetaphaseAlignment Prometaphase Interphase Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Metaphase Polylobed Interphase Polylobed Hole Binuclear Anaphase Polylobed Binuclear Interphase Interphase Binuclear Prometaphase Polylobed Grape Polylobed Prometaphase SmallIrregular Interphase Large Prometaphase Grape Apoptosis Binuclear MetaphaseAlignment Interphase Apoptosis Polylobed Artefact Binuclear Interphase Artefact Artefact Artefact Polylobed Grape Binuclear Binuclear Binuclear Polylobed Binuclear Apoptosis Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Large Interphase Grape Binuclear SmallIrregular Grape MetaphaseAlignment Polylobed Hole Binuclear Grape Binuclear Artefact MetaphaseAlignment Grape Binuclear Polylobed Binuclear Binuclear Polylobed Binuclear Large Artefact MetaphaseAlignment Interphase Prometaphase Polylobed Artefact Polylobed Polylobed Interphase Polylobed Prometaphase Artefact Prometaphase Binuclear Apoptosis Grape Polylobed Polylobed Binuclear Interphase Prometaphase Prometaphase Interphase Large Polylobed Binuclear Grape Binuclear Polylobed Polylobed Binuclear Binuclear Prometaphase Grape Binuclear Prometaphase Polylobed MetaphaseAlignment Polylobed Polylobed Binuclear Interphase Apoptosis Polylobed Grape Prometaphase +y_test_pred Binuclear Artefact Polylobed Polylobed Hole Polylobed Interphase Polylobed SmallIrregular Polylobed Polylobed Prometaphase Polylobed UndefinedCondensed Polylobed Polylobed Grape Metaphase Polylobed Prometaphase Binuclear Binuclear Hole Grape Hole Polylobed Prometaphase Artefact Polylobed Prometaphase Polylobed Binuclear Hole Prometaphase Artefact SmallIrregular MetaphaseAlignment Polylobed Polylobed Interphase Polylobed Apoptosis Polylobed Apoptosis Polylobed Apoptosis Polylobed Polylobed Polylobed Grape Interphase Elongated Grape Interphase Grape Polylobed Apoptosis Prometaphase Grape Polylobed Grape Polylobed Grape Polylobed Polylobed Artefact Binuclear Polylobed Grape Grape Interphase Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Apoptosis Polylobed Polylobed Artefact Apoptosis Polylobed Artefact Polylobed Polylobed Interphase Prometaphase Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Artefact Apoptosis Grape Binuclear Prometaphase Binuclear Apoptosis MetaphaseAlignment Prometaphase Large Polylobed Polylobed Polylobed Grape Polylobed MetaphaseAlignment Prometaphase Polylobed Grape Polylobed Interphase Artefact Binuclear Binuclear Polylobed Polylobed Polylobed Prometaphase Apoptosis Polylobed Interphase Binuclear Interphase Polylobed Polylobed Interphase Metaphase Prometaphase Binuclear Prometaphase Hole Interphase Binuclear Polylobed Grape Polylobed Binuclear Interphase Polylobed Grape Apoptosis Interphase Grape Interphase Artefact Interphase Polylobed Polylobed Binuclear Polylobed Polylobed Anaphase Grape Binuclear MetaphaseAlignment Binuclear Polylobed Binuclear Grape Prometaphase MetaphaseAlignment Polylobed Polylobed Prometaphase Grape Polylobed Interphase Grape Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed SmallIrregular Polylobed Interphase Polylobed Hole MetaphaseAlignment Apoptosis Polylobed Polylobed Prometaphase Grape Prometaphase Binuclear Apoptosis Polylobed Polylobed Interphase SmallIrregular Grape Grape UndefinedCondensed Polylobed Prometaphase Artefact MetaphaseAlignment Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Artefact Polylobed Polylobed Artefact Binuclear Binuclear Polylobed Polylobed Polylobed Binuclear MetaphaseAlignment Metaphase Polylobed MetaphaseAlignment Polylobed Binuclear MetaphaseAlignment Apoptosis Polylobed Grape Prometaphase Polylobed Interphase Polylobed Polylobed Binuclear Prometaphase Grape Artefact Binuclear Apoptosis Interphase Polylobed Artefact Polylobed Artefact Grape Binuclear Polylobed SmallIrregular SmallIrregular Polylobed Polylobed Artefact Polylobed Artefact Prometaphase Hole Polylobed Grape Metaphase Interphase Grape Artefact Polylobed Polylobed Polylobed Folded Polylobed Binuclear Artefact Artefact Prometaphase Binuclear Polylobed Polylobed Binuclear Grape Binuclear Prometaphase Polylobed Prometaphase Binuclear Polylobed Apoptosis Binuclear Polylobed Binuclear Polylobed Interphase Apoptosis Interphase Interphase Polylobed Prometaphase Interphase Grape Prometaphase Binuclear Binuclear Polylobed Polylobed Interphase Prometaphase Elongated Prometaphase Interphase Metaphase Polylobed Polylobed Grape Grape Polylobed Binuclear Folded Elongated Binuclear Polylobed Artefact Binuclear Binuclear Prometaphase Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed Grape Binuclear Polylobed Artefact Prometaphase Polylobed MetaphaseAlignment Binuclear Prometaphase Binuclear Anaphase Polylobed Hole Prometaphase Binuclear Large Hole Polylobed Polylobed Binuclear Polylobed SmallIrregular Polylobed Grape Polylobed Grape Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Polylobed SmallIrregular Polylobed Polylobed Interphase Hole Binuclear Artefact Binuclear Binuclear Artefact Binuclear Polylobed Artefact Grape Prometaphase Prometaphase Polylobed Polylobed Polylobed Grape MetaphaseAlignment MetaphaseAlignment Binuclear Prometaphase Artefact Polylobed Apoptosis Polylobed Polylobed Polylobed Prometaphase Interphase Grape Prometaphase Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Grape Binuclear Polylobed Polylobed Binuclear Grape Binuclear Grape Grape UndefinedCondensed Grape Apoptosis Interphase Artefact Artefact Polylobed Grape Metaphase Binuclear Interphase Binuclear Polylobed Binuclear Polylobed MetaphaseAlignment Grape Artefact Interphase Grape Binuclear Artefact Prometaphase Polylobed Apoptosis Polylobed Polylobed Polylobed Interphase SmallIrregular Polylobed Polylobed Elongated Interphase Polylobed Binuclear Polylobed Polylobed Polylobed Apoptosis Binuclear Polylobed Polylobed Polylobed Apoptosis Polylobed Binuclear Binuclear Polylobed Polylobed Prometaphase Binuclear Interphase Polylobed Polylobed Apoptosis Interphase Prometaphase MetaphaseAlignment Prometaphase Interphase Polylobed Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed Interphase Polylobed Interphase MetaphaseAlignment Hole Binuclear Anaphase Polylobed Prometaphase Large Interphase Binuclear Prometaphase Polylobed Grape Polylobed Prometaphase Polylobed Interphase Large Prometaphase Grape Apoptosis Binuclear MetaphaseAlignment Interphase Apoptosis Grape Artefact Binuclear Interphase Artefact Artefact Artefact Polylobed Grape Polylobed Binuclear Binuclear Polylobed Binuclear Apoptosis Polylobed Polylobed Polylobed Polylobed Grape Polylobed Binuclear Large Interphase Grape Interphase SmallIrregular Grape MetaphaseAlignment Polylobed Hole Binuclear Grape Polylobed Artefact MetaphaseAlignment Grape Binuclear Polylobed Polylobed Binuclear Polylobed Binuclear Interphase Artefact MetaphaseAlignment Interphase Prometaphase Polylobed Artefact Polylobed Polylobed Interphase Polylobed Prometaphase Artefact Prometaphase Binuclear Apoptosis Grape Polylobed Polylobed Binuclear Interphase Apoptosis Prometaphase Interphase Interphase Binuclear Binuclear Grape Polylobed Polylobed Polylobed Binuclear Binuclear Prometaphase Grape Binuclear Prometaphase Polylobed MetaphaseAlignment Polylobed Polylobed Binuclear Interphase Apoptosis Polylobed Grape Prometaphase +y_holdout Prometaphase Polylobed Polylobed Polylobed Grape Apoptosis Apoptosis Prometaphase Polylobed Artefact SmallIrregular Polylobed Polylobed Polylobed Polylobed Grape Apoptosis Polylobed Interphase MetaphaseAlignment Grape Polylobed Grape Polylobed Grape Prometaphase Elongated Interphase Grape Interphase Polylobed Prometaphase Interphase Grape MetaphaseAlignment Apoptosis Artefact Grape Polylobed MetaphaseAlignment Prometaphase Polylobed Apoptosis Interphase Polylobed Prometaphase Grape Grape Hole MetaphaseAlignment Interphase Polylobed Apoptosis Polylobed Grape Apoptosis SmallIrregular Grape Polylobed Artefact Polylobed Grape Grape Polylobed Polylobed Polylobed Grape Polylobed Apoptosis Polylobed Metaphase Apoptosis Folded Grape MetaphaseAlignment Polylobed Grape Polylobed Polylobed Apoptosis Hole Apoptosis Polylobed Prometaphase Artefact Grape Polylobed Artefact Prometaphase Polylobed Prometaphase Interphase Polylobed Polylobed Anaphase MetaphaseAlignment Grape Interphase Polylobed Artefact Grape Grape Polylobed Grape Grape Folded MetaphaseAlignment Apoptosis Artefact Binuclear Polylobed Polylobed Polylobed Grape Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Artefact Polylobed Apoptosis Folded Grape Polylobed Prometaphase Apoptosis Apoptosis Artefact Polylobed Prometaphase Large Prometaphase Grape Grape Polylobed Artefact Apoptosis Artefact Artefact Prometaphase Polylobed Binuclear Polylobed Polylobed Interphase Polylobed Interphase Grape Polylobed Apoptosis Folded Apoptosis Prometaphase Polylobed Polylobed Grape Polylobed Polylobed Grape Grape Polylobed Polylobed Polylobed Grape Large Grape Hole Binuclear Artefact Apoptosis Interphase Polylobed Interphase Polylobed Prometaphase Polylobed Polylobed Artefact Polylobed Grape Grape Grape Large Apoptosis Polylobed Grape Apoptosis Polylobed Grape Prometaphase Grape Prometaphase Polylobed Grape Anaphase Artefact Polylobed Hole Apoptosis Anaphase Polylobed Polylobed Polylobed Polylobed Binuclear Hole Polylobed Apoptosis Polylobed Interphase Polylobed Grape Polylobed Polylobed Interphase Anaphase Polylobed Anaphase Grape Grape Prometaphase Interphase Polylobed Prometaphase Apoptosis Polylobed Prometaphase Polylobed Artefact Apoptosis Polylobed Anaphase Polylobed Polylobed Polylobed Grape Folded Grape Polylobed Polylobed Grape Polylobed Polylobed Grape Polylobed Grape Apoptosis Apoptosis Apoptosis Prometaphase Polylobed Artefact Polylobed Grape Grape Grape Artefact Artefact Polylobed Polylobed Polylobed Folded Apoptosis Polylobed Artefact Polylobed Folded Grape UndefinedCondensed Polylobed Grape Polylobed Artefact Grape Grape Polylobed Apoptosis Artefact Artefact Grape Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Artefact Interphase Polylobed Polylobed Polylobed Prometaphase Grape Grape Folded Grape Polylobed Polylobed SmallIrregular Apoptosis Polylobed Artefact Polylobed Prometaphase Polylobed Polylobed Interphase Polylobed Grape Interphase MetaphaseAlignment Interphase Polylobed Grape Artefact Grape Interphase Grape Grape Prometaphase Apoptosis Polylobed Polylobed Grape Polylobed Polylobed Grape Apoptosis Artefact Grape Grape Polylobed Grape Polylobed Artefact Polylobed Prometaphase Folded Artefact Anaphase Prometaphase Hole Polylobed Polylobed Anaphase Polylobed Grape Grape Grape Polylobed Polylobed Grape Polylobed Grape Polylobed Polylobed Artefact Apoptosis Prometaphase Polylobed Large Artefact MetaphaseAlignment Artefact Apoptosis Binuclear Polylobed Polylobed Folded Polylobed Grape Binuclear Apoptosis Artefact Polylobed Interphase Polylobed Grape Polylobed Grape Artefact Artefact Grape Binuclear MetaphaseAlignment Polylobed Grape SmallIrregular Large Polylobed Polylobed Polylobed Polylobed Polylobed Folded Grape Apoptosis Polylobed Anaphase Polylobed Prometaphase Grape Apoptosis Grape Polylobed Polylobed Grape Apoptosis Polylobed Grape Folded Anaphase Binuclear Polylobed Grape Polylobed Grape Polylobed Grape Artefact Polylobed Grape Polylobed Artefact Large Polylobed Prometaphase Prometaphase Polylobed Polylobed Folded Grape Grape Grape Folded Polylobed Polylobed Apoptosis Binuclear Polylobed Grape Grape Polylobed Interphase Polylobed Polylobed Polylobed Prometaphase Apoptosis Grape Artefact Polylobed Polylobed Apoptosis Polylobed Apoptosis Polylobed Interphase Interphase Polylobed Binuclear Apoptosis Binuclear Grape Grape Polylobed Grape Prometaphase MetaphaseAlignment Polylobed Polylobed Grape Polylobed Polylobed Apoptosis Polylobed +y_holdout_pred Prometaphase Polylobed Interphase Polylobed Polylobed Apoptosis Prometaphase Prometaphase Polylobed Artefact Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Interphase Apoptosis Polylobed Polylobed Polylobed Polylobed Polylobed Prometaphase Elongated Binuclear Grape Interphase Grape Prometaphase Interphase Polylobed Apoptosis Apoptosis Interphase Polylobed Polylobed MetaphaseAlignment Prometaphase Polylobed Apoptosis Interphase Polylobed Prometaphase Binuclear Grape MetaphaseAlignment Interphase Interphase Binuclear Polylobed Polylobed Polylobed Apoptosis Interphase Polylobed MetaphaseAlignment Binuclear Polylobed Grape Polylobed Polylobed Polylobed Polylobed Grape Polylobed Apoptosis Polylobed Anaphase Apoptosis Interphase Polylobed Prometaphase Polylobed Grape Polylobed Interphase Apoptosis Elongated Polylobed Artefact Prometaphase Interphase Polylobed Polylobed Artefact MetaphaseAlignment Polylobed Apoptosis Binuclear Polylobed Polylobed Elongated MetaphaseAlignment Polylobed Interphase Binuclear Artefact Polylobed Polylobed Polylobed Polylobed Grape Elongated MetaphaseAlignment Apoptosis Elongated Interphase Binuclear Polylobed Polylobed Grape Polylobed Artefact Polylobed Polylobed Polylobed Elongated Polylobed Polylobed Apoptosis Interphase Polylobed Polylobed Interphase Apoptosis Apoptosis Polylobed Polylobed Prometaphase Binuclear Prometaphase Binuclear Grape Polylobed Artefact Polylobed Artefact Binuclear Prometaphase Prometaphase Binuclear Polylobed Polylobed Interphase Polylobed Interphase Polylobed Polylobed Apoptosis Elongated Apoptosis Prometaphase Folded Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Binuclear Interphase Binuclear Hole Binuclear Binuclear Apoptosis Binuclear Binuclear MetaphaseAlignment Polylobed MetaphaseAlignment Polylobed Polylobed Apoptosis Binuclear Grape Grape Grape Binuclear Apoptosis Polylobed Grape Apoptosis Polylobed Polylobed Prometaphase Artefact Prometaphase Polylobed Grape Interphase Large Polylobed Anaphase MetaphaseAlignment Anaphase Artefact Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Interphase Anaphase Polylobed Anaphase Polylobed Polylobed MetaphaseAlignment Interphase Polylobed Prometaphase Apoptosis Polylobed MetaphaseAlignment Binuclear Polylobed Apoptosis Polylobed Artefact Polylobed Binuclear Polylobed Polylobed Elongated Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Polylobed Apoptosis Prometaphase Apoptosis Prometaphase Polylobed Large Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Prometaphase Polylobed Polylobed Grape Interphase Polylobed Artefact Prometaphase Polylobed Binuclear Artefact Polylobed Grape Polylobed Apoptosis Apoptosis Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Artefact Interphase Polylobed Polylobed Polylobed Prometaphase Grape Interphase Binuclear Grape Polylobed Polylobed Interphase Apoptosis Polylobed Apoptosis Polylobed SmallIrregular Polylobed Polylobed Artefact Polylobed Grape Binuclear Interphase Binuclear Polylobed Grape Artefact Polylobed Prometaphase Grape Grape Hole Apoptosis Polylobed Polylobed Polylobed Polylobed Polylobed Hole Apoptosis Artefact Polylobed Polylobed Polylobed Binuclear Polylobed Artefact Polylobed Prometaphase Polylobed Polylobed Interphase Prometaphase Hole Binuclear Polylobed Prometaphase Polylobed Grape Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Hole Polylobed Elongated Apoptosis Apoptosis Hole Polylobed Interphase MetaphaseAlignment MetaphaseAlignment Binuclear Apoptosis Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Binuclear Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed Apoptosis MetaphaseAlignment Grape Interphase Apoptosis Polylobed Binuclear SmallIrregular Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Apoptosis Polylobed Anaphase Polylobed Prometaphase Grape Apoptosis Polylobed Binuclear Polylobed Binuclear Apoptosis Grape Polylobed Elongated Anaphase Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Prometaphase MetaphaseAlignment Polylobed Polylobed Polylobed Polylobed Binuclear Apoptosis Polylobed Polylobed Polylobed Apoptosis Binuclear Polylobed Grape Binuclear MetaphaseAlignment Large Artefact Polylobed Interphase Apoptosis Apoptosis Binuclear Polylobed Polylobed Binuclear MetaphaseAlignment Polylobed Apoptosis Polylobed Interphase Binuclear Artefact Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Prometaphase MetaphaseAlignment Polylobed Polylobed Grape Polylobed Polylobed Grape Grape diff --git a/3.evaluate_model/evaluations/shuffled_baseline_model_predictions.tsv b/3.evaluate_model/evaluations/shuffled_baseline_model_predictions.tsv new 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990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 3396 3397 +y_train Polylobed MetaphaseAlignment Polylobed Artefact Apoptosis Prometaphase Prometaphase Polylobed Apoptosis Polylobed Polylobed SmallIrregular Interphase Polylobed Artefact Elongated Grape Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Prometaphase Grape Grape Polylobed SmallIrregular Binuclear Polylobed Binuclear Artefact Artefact Binuclear MetaphaseAlignment MetaphaseAlignment Polylobed Metaphase Artefact Polylobed MetaphaseAlignment Grape Polylobed Binuclear Polylobed Artefact Polylobed Apoptosis Polylobed Interphase Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Interphase Polylobed Interphase Prometaphase Polylobed Prometaphase Grape Polylobed MetaphaseAlignment Hole SmallIrregular Binuclear Polylobed Interphase Binuclear Polylobed SmallIrregular Anaphase Prometaphase Prometaphase Binuclear Prometaphase Polylobed Artefact MetaphaseAlignment Grape Polylobed Polylobed Large Artefact Artefact Interphase Anaphase Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Hole Hole Grape Binuclear Grape Polylobed Polylobed UndefinedCondensed Binuclear Prometaphase Artefact Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Hole Polylobed Binuclear Apoptosis Grape Artefact Polylobed Interphase Polylobed Grape Interphase Polylobed Artefact Polylobed Prometaphase Grape Apoptosis Binuclear Grape Artefact Binuclear Prometaphase Polylobed Binuclear Binuclear Polylobed Polylobed Hole Polylobed Polylobed Grape Interphase Binuclear Binuclear Polylobed Apoptosis Prometaphase Hole Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Metaphase SmallIrregular Polylobed Apoptosis Binuclear Anaphase Binuclear Polylobed Polylobed Polylobed Prometaphase Artefact Polylobed Polylobed Polylobed Binuclear Interphase Interphase Binuclear Prometaphase Interphase Binuclear Grape Polylobed Binuclear Polylobed Polylobed Folded Grape Artefact MetaphaseAlignment SmallIrregular Binuclear Grape Polylobed MetaphaseAlignment Binuclear Polylobed MetaphaseAlignment Binuclear Grape Binuclear Hole Polylobed Artefact Polylobed Prometaphase Polylobed Grape Polylobed Binuclear Grape Polylobed Prometaphase Binuclear Polylobed Binuclear Binuclear Interphase Polylobed Apoptosis MetaphaseAlignment Prometaphase Polylobed Polylobed Grape Artefact Binuclear Interphase Polylobed Binuclear Artefact Polylobed Binuclear Grape Apoptosis Binuclear Binuclear Grape Interphase Binuclear Interphase Artefact Polylobed MetaphaseAlignment Apoptosis Artefact Prometaphase Interphase Polylobed Polylobed Prometaphase Polylobed MetaphaseAlignment Apoptosis Polylobed Polylobed Binuclear Polylobed Polylobed Grape Polylobed Polylobed Prometaphase Artefact Polylobed Polylobed Polylobed Large Binuclear Binuclear Polylobed Polylobed Binuclear Anaphase Interphase Binuclear Binuclear Polylobed Binuclear Apoptosis Anaphase Elongated Polylobed SmallIrregular Binuclear Artefact UndefinedCondensed Polylobed Grape Polylobed MetaphaseAlignment Metaphase Interphase Polylobed Prometaphase Grape Polylobed Polylobed Polylobed Binuclear Apoptosis Polylobed Binuclear Polylobed Polylobed Artefact Prometaphase Artefact Polylobed Grape Prometaphase Interphase Binuclear Polylobed Interphase Polylobed Polylobed Binuclear Binuclear Binuclear Interphase Binuclear Artefact Interphase Large UndefinedCondensed Binuclear Polylobed Large Binuclear Polylobed Polylobed SmallIrregular SmallIrregular Prometaphase Anaphase Polylobed SmallIrregular Apoptosis Polylobed Binuclear Binuclear Apoptosis Interphase Polylobed Grape Polylobed Polylobed MetaphaseAlignment Binuclear Polylobed Prometaphase Prometaphase Grape Grape Polylobed Grape Polylobed Polylobed Binuclear Polylobed Anaphase Interphase Prometaphase Artefact Binuclear Grape Grape Binuclear Artefact Polylobed Grape Interphase Metaphase Polylobed Polylobed Interphase Polylobed Binuclear Grape Prometaphase Prometaphase Prometaphase Binuclear Polylobed Artefact Interphase Grape Polylobed SmallIrregular Polylobed Polylobed Apoptosis Prometaphase Interphase Binuclear Grape Polylobed Apoptosis Polylobed Binuclear Polylobed Interphase Prometaphase Prometaphase Large Binuclear Polylobed Artefact Grape Binuclear Grape Artefact Polylobed Polylobed Polylobed Large Binuclear Polylobed Polylobed Prometaphase Artefact Polylobed Binuclear Grape Interphase Polylobed Polylobed Grape Interphase Prometaphase Binuclear Grape Polylobed Anaphase Polylobed Polylobed Artefact Polylobed Polylobed Artefact Binuclear Metaphase Polylobed Polylobed Polylobed Elongated Polylobed Polylobed Grape Polylobed Binuclear Apoptosis Binuclear Grape Polylobed Grape Interphase Artefact Hole Grape Apoptosis Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Interphase Polylobed Polylobed Prometaphase Interphase Prometaphase Prometaphase Binuclear Folded Polylobed Polylobed Polylobed Artefact Polylobed Polylobed SmallIrregular Polylobed Polylobed Grape Binuclear Hole Polylobed Interphase Binuclear Binuclear Grape Polylobed Polylobed Binuclear SmallIrregular Grape Prometaphase Binuclear Prometaphase Artefact Binuclear Binuclear Interphase Grape Polylobed UndefinedCondensed MetaphaseAlignment Apoptosis Polylobed SmallIrregular Interphase Polylobed Polylobed Grape Prometaphase Polylobed Prometaphase Binuclear Polylobed SmallIrregular Interphase Binuclear Interphase Interphase Artefact Binuclear Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Apoptosis Grape Binuclear Apoptosis Polylobed Polylobed Polylobed Binuclear Polylobed Apoptosis Binuclear Polylobed Prometaphase Polylobed Artefact Binuclear Interphase Hole Apoptosis Polylobed Polylobed Prometaphase Prometaphase MetaphaseAlignment Artefact MetaphaseAlignment Apoptosis Polylobed Binuclear Polylobed Polylobed Interphase SmallIrregular UndefinedCondensed Polylobed Artefact Polylobed Polylobed Polylobed Binuclear Binuclear UndefinedCondensed Binuclear Grape Polylobed Hole Polylobed Prometaphase Interphase MetaphaseAlignment Apoptosis Binuclear Polylobed Artefact Polylobed Binuclear Grape Polylobed Grape Polylobed Polylobed Metaphase Polylobed Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Binuclear Binuclear Apoptosis Polylobed Binuclear Artefact Polylobed Polylobed Polylobed Polylobed Apoptosis Apoptosis Polylobed Binuclear Interphase Interphase Prometaphase Artefact Polylobed SmallIrregular Polylobed Polylobed Hole Grape SmallIrregular SmallIrregular Binuclear Prometaphase MetaphaseAlignment Binuclear Binuclear Binuclear Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Artefact Grape Interphase MetaphaseAlignment Binuclear Interphase MetaphaseAlignment Binuclear Binuclear Metaphase Polylobed Polylobed Grape Polylobed Prometaphase Polylobed Artefact Binuclear Interphase Polylobed Prometaphase Interphase Interphase Artefact Polylobed Polylobed Prometaphase Grape Polylobed Polylobed SmallIrregular Polylobed Grape Grape Polylobed Interphase Polylobed Prometaphase Binuclear Interphase Grape Interphase Artefact Polylobed Binuclear Polylobed MetaphaseAlignment Polylobed Grape Binuclear Polylobed Polylobed Binuclear Hole Metaphase Hole Grape Binuclear Grape Artefact Polylobed Polylobed Interphase Interphase Prometaphase Polylobed SmallIrregular Polylobed Polylobed MetaphaseAlignment Apoptosis Polylobed Polylobed Grape Binuclear Polylobed Hole Polylobed Polylobed Polylobed SmallIrregular Binuclear MetaphaseAlignment Interphase Polylobed Prometaphase Polylobed Artefact Polylobed Grape Polylobed Polylobed Polylobed MetaphaseAlignment Artefact Apoptosis Polylobed Binuclear Prometaphase Apoptosis Prometaphase Binuclear Apoptosis Grape Artefact Polylobed Grape Polylobed Polylobed SmallIrregular Prometaphase Interphase MetaphaseAlignment Anaphase Polylobed Polylobed Large Large Polylobed Prometaphase Binuclear Binuclear Polylobed Interphase Polylobed Polylobed MetaphaseAlignment Binuclear Polylobed SmallIrregular Binuclear MetaphaseAlignment Interphase Grape Apoptosis Binuclear Prometaphase Binuclear Artefact Binuclear Polylobed Interphase Binuclear Polylobed Polylobed Artefact Prometaphase Polylobed Polylobed Polylobed Binuclear Hole Polylobed Polylobed Hole Interphase Binuclear Polylobed Artefact Polylobed Polylobed Interphase MetaphaseAlignment Polylobed Interphase Polylobed Polylobed Apoptosis Polylobed Interphase Artefact Binuclear Artefact Binuclear Polylobed Polylobed Polylobed Prometaphase Grape Polylobed Anaphase Apoptosis Interphase Apoptosis Polylobed Binuclear Polylobed Binuclear Anaphase MetaphaseAlignment Interphase Polylobed Interphase Grape Binuclear Binuclear Artefact Artefact Interphase Prometaphase Elongated Polylobed Prometaphase MetaphaseAlignment Interphase Polylobed Prometaphase Polylobed Polylobed Folded Grape Interphase Apoptosis Polylobed Interphase Polylobed Interphase Polylobed Binuclear Apoptosis Grape Artefact Prometaphase Prometaphase Binuclear Polylobed Binuclear Interphase SmallIrregular Binuclear Prometaphase Polylobed Polylobed Interphase Polylobed Binuclear Large MetaphaseAlignment Binuclear Polylobed Prometaphase Prometaphase Prometaphase Polylobed Interphase Interphase Polylobed MetaphaseAlignment Prometaphase Grape Artefact Polylobed Large Binuclear Artefact Polylobed Polylobed Polylobed Interphase Artefact Binuclear Binuclear Apoptosis Polylobed Grape Prometaphase Polylobed Polylobed Apoptosis Polylobed Polylobed Polylobed SmallIrregular Grape Polylobed UndefinedCondensed Polylobed Grape SmallIrregular Grape Polylobed Hole Anaphase Grape Prometaphase Prometaphase Anaphase Large Polylobed Grape Apoptosis Grape Polylobed Large Grape Artefact Elongated Binuclear Interphase Apoptosis Binuclear Interphase Polylobed Folded Binuclear Prometaphase Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Binuclear Grape Interphase MetaphaseAlignment Interphase Prometaphase Polylobed Apoptosis Prometaphase Hole Binuclear Grape Interphase Polylobed Polylobed Polylobed Binuclear Grape Apoptosis Grape UndefinedCondensed Apoptosis UndefinedCondensed Prometaphase Artefact Prometaphase Polylobed Prometaphase Binuclear Polylobed Polylobed Artefact Apoptosis Grape Polylobed Binuclear Binuclear Polylobed Binuclear Artefact Polylobed Binuclear Polylobed Prometaphase Polylobed Polylobed Polylobed Binuclear MetaphaseAlignment Binuclear SmallIrregular Metaphase Prometaphase Grape Artefact Polylobed Binuclear Prometaphase Interphase Prometaphase Prometaphase Apoptosis Polylobed MetaphaseAlignment Artefact Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Interphase Large Grape Grape Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Prometaphase Hole Interphase Binuclear Binuclear Binuclear Polylobed Binuclear Prometaphase Binuclear Polylobed Polylobed Polylobed Polylobed Apoptosis Artefact Polylobed Prometaphase Interphase Interphase Grape Binuclear Polylobed Polylobed Hole Artefact Apoptosis Hole Prometaphase Polylobed Interphase Artefact Artefact Polylobed Polylobed Interphase Polylobed Polylobed Elongated Polylobed Prometaphase Polylobed Grape Interphase Polylobed Polylobed Polylobed Prometaphase Polylobed Large Grape Polylobed MetaphaseAlignment Polylobed MetaphaseAlignment Polylobed UndefinedCondensed Polylobed Interphase Apoptosis Binuclear Grape Polylobed MetaphaseAlignment Prometaphase Grape Polylobed Polylobed Prometaphase Polylobed Interphase Binuclear Prometaphase Polylobed Interphase Prometaphase SmallIrregular Prometaphase Binuclear Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Binuclear Polylobed Anaphase MetaphaseAlignment SmallIrregular Binuclear Grape Prometaphase Binuclear Grape Binuclear Polylobed Grape Binuclear Polylobed Polylobed Apoptosis Hole Polylobed Hole Polylobed Large Polylobed Interphase MetaphaseAlignment Large Binuclear Binuclear Prometaphase Grape Polylobed MetaphaseAlignment Polylobed Polylobed Binuclear Grape Polylobed Artefact Polylobed Binuclear Polylobed Apoptosis Polylobed Grape Artefact Polylobed MetaphaseAlignment Polylobed Polylobed Metaphase Interphase Anaphase Polylobed Hole Artefact Interphase Interphase Interphase Hole Polylobed Binuclear Polylobed Anaphase Artefact Artefact Polylobed MetaphaseAlignment MetaphaseAlignment Polylobed Binuclear Large Grape Interphase Polylobed Polylobed Polylobed Grape MetaphaseAlignment Apoptosis Interphase MetaphaseAlignment Polylobed Interphase Polylobed Apoptosis Polylobed Prometaphase Polylobed Interphase SmallIrregular Binuclear Polylobed Interphase Polylobed Polylobed Grape Apoptosis Polylobed Apoptosis Polylobed Artefact Interphase Polylobed Polylobed Polylobed Large Polylobed MetaphaseAlignment Polylobed Prometaphase UndefinedCondensed Polylobed Apoptosis Polylobed Large Apoptosis MetaphaseAlignment Apoptosis Polylobed Prometaphase Prometaphase Anaphase MetaphaseAlignment Artefact Grape Polylobed Prometaphase Binuclear Interphase Grape Polylobed Prometaphase Polylobed Prometaphase Polylobed Grape Artefact Prometaphase Polylobed Polylobed Interphase Prometaphase Polylobed Polylobed Prometaphase Polylobed Grape Polylobed Binuclear Binuclear Polylobed Artefact Interphase Polylobed Binuclear Interphase Polylobed Prometaphase Interphase SmallIrregular Polylobed Apoptosis Interphase Binuclear Binuclear Grape Metaphase Interphase Binuclear Polylobed Grape Polylobed Elongated Polylobed Artefact Grape Interphase Binuclear Binuclear Binuclear Hole Binuclear UndefinedCondensed Polylobed Polylobed Polylobed Polylobed Grape Apoptosis Interphase Artefact Grape Polylobed Artefact Binuclear Apoptosis Interphase Apoptosis Binuclear Polylobed Binuclear Polylobed Polylobed SmallIrregular Artefact Polylobed Grape Artefact Polylobed Polylobed Binuclear Artefact Polylobed Prometaphase Polylobed Interphase Polylobed Apoptosis Grape Polylobed Grape Hole Artefact Polylobed Polylobed Polylobed Prometaphase Artefact Polylobed Grape Binuclear Hole Polylobed Binuclear Binuclear Prometaphase Polylobed Polylobed Polylobed Polylobed Binuclear MetaphaseAlignment Prometaphase Binuclear Polylobed Polylobed Elongated Grape Artefact Hole Grape Polylobed Large MetaphaseAlignment Apoptosis Interphase Polylobed Interphase Interphase Grape Binuclear Polylobed Interphase Interphase Grape Binuclear Interphase Binuclear Polylobed Polylobed Interphase Artefact Prometaphase Apoptosis Apoptosis Large Polylobed Binuclear SmallIrregular Binuclear Large Binuclear Interphase Apoptosis Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Binuclear Artefact Grape Apoptosis Interphase Binuclear Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed MetaphaseAlignment Metaphase Polylobed Binuclear Prometaphase Polylobed Binuclear Grape Apoptosis Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Artefact Polylobed Binuclear Polylobed UndefinedCondensed Metaphase Prometaphase SmallIrregular Apoptosis Binuclear Polylobed Binuclear Metaphase Binuclear Prometaphase Polylobed Grape Apoptosis SmallIrregular Interphase Prometaphase Anaphase Polylobed Binuclear Elongated Polylobed Grape Artefact Grape Prometaphase Binuclear Apoptosis Polylobed Binuclear Binuclear Grape Polylobed Polylobed Binuclear Binuclear Artefact Prometaphase Large Metaphase Interphase SmallIrregular Grape Grape Polylobed Binuclear Polylobed Interphase Anaphase Binuclear Prometaphase Interphase Apoptosis Interphase Polylobed Binuclear Apoptosis Artefact Artefact Interphase SmallIrregular Polylobed Polylobed Binuclear Polylobed Grape Artefact Artefact Polylobed Grape Anaphase Grape Polylobed Polylobed Polylobed Interphase Apoptosis Polylobed Polylobed Anaphase Polylobed Binuclear Grape Apoptosis Grape Polylobed Large Binuclear Binuclear Grape Polylobed Interphase Polylobed Elongated Polylobed Hole Polylobed Apoptosis Apoptosis Binuclear Polylobed Grape Binuclear Interphase Grape Polylobed Binuclear Polylobed Artefact SmallIrregular Interphase Interphase Grape Binuclear Interphase Binuclear Elongated Polylobed Binuclear Binuclear Binuclear Artefact Polylobed Polylobed Polylobed Binuclear Apoptosis Apoptosis Polylobed Grape Polylobed Polylobed MetaphaseAlignment Artefact Binuclear Polylobed Binuclear Polylobed Large UndefinedCondensed Polylobed SmallIrregular Binuclear Hole SmallIrregular Polylobed Polylobed Polylobed Polylobed SmallIrregular Polylobed Polylobed SmallIrregular Binuclear MetaphaseAlignment Grape Polylobed SmallIrregular Artefact Grape Grape Binuclear Grape Metaphase Binuclear Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Interphase Prometaphase Artefact Polylobed Polylobed Binuclear Hole MetaphaseAlignment Interphase Polylobed Polylobed Grape Grape Polylobed Interphase Prometaphase Apoptosis Prometaphase Interphase Polylobed MetaphaseAlignment Hole Elongated Polylobed SmallIrregular Polylobed Polylobed Polylobed Binuclear Apoptosis Artefact Polylobed Prometaphase Polylobed Prometaphase MetaphaseAlignment Artefact Binuclear Polylobed Grape Interphase Grape Artefact Interphase Artefact Grape SmallIrregular MetaphaseAlignment Interphase Polylobed Polylobed Prometaphase Polylobed Grape Prometaphase Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Interphase Binuclear Large Grape Binuclear Polylobed Polylobed Prometaphase Polylobed MetaphaseAlignment Binuclear Binuclear Interphase Prometaphase Polylobed Binuclear Prometaphase Apoptosis Polylobed Hole Polylobed Polylobed Interphase Binuclear Polylobed Polylobed Artefact Artefact MetaphaseAlignment Apoptosis Metaphase Grape Binuclear Grape Prometaphase Interphase Polylobed Prometaphase Grape Apoptosis SmallIrregular Hole Binuclear Artefact Polylobed Binuclear Binuclear Polylobed Polylobed Folded Interphase Polylobed Hole Polylobed Polylobed Polylobed Grape Polylobed Metaphase Binuclear Binuclear Binuclear Binuclear Polylobed Polylobed Prometaphase Folded Polylobed Polylobed Grape Artefact Polylobed Grape Grape Binuclear Grape Polylobed Prometaphase Polylobed Binuclear Anaphase Grape SmallIrregular Apoptosis Polylobed Polylobed Interphase Polylobed Hole Folded Polylobed Artefact Polylobed Interphase Interphase SmallIrregular Binuclear Polylobed Grape Folded Polylobed Polylobed MetaphaseAlignment Binuclear Polylobed Binuclear Grape Prometaphase Prometaphase Grape Polylobed Binuclear Artefact Polylobed Polylobed Interphase Polylobed Interphase Artefact Artefact Binuclear Grape Artefact Binuclear Polylobed Binuclear Artefact UndefinedCondensed Binuclear Binuclear Artefact SmallIrregular MetaphaseAlignment Interphase Binuclear Polylobed Binuclear Interphase Interphase Binuclear Grape Hole Polylobed Grape Metaphase Grape Binuclear Polylobed Hole Prometaphase Binuclear Interphase Grape Prometaphase Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed SmallIrregular Polylobed Binuclear Grape Apoptosis Polylobed Prometaphase Binuclear Grape Interphase Binuclear Grape Polylobed Grape Folded Binuclear Binuclear Binuclear Prometaphase Interphase Binuclear Interphase Polylobed Polylobed Binuclear Grape Elongated UndefinedCondensed Polylobed Polylobed Polylobed Prometaphase Binuclear Prometaphase Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Prometaphase Polylobed Prometaphase Grape Apoptosis Prometaphase Binuclear Polylobed Grape Polylobed Grape Polylobed Polylobed Polylobed Polylobed Grape Artefact Polylobed Interphase Prometaphase MetaphaseAlignment Polylobed Grape Polylobed MetaphaseAlignment Polylobed Grape Binuclear Polylobed Polylobed Polylobed Prometaphase Polylobed Prometaphase Polylobed Polylobed Binuclear Polylobed Interphase Binuclear Grape UndefinedCondensed Grape Artefact Grape Grape Apoptosis Binuclear Prometaphase Polylobed SmallIrregular Interphase Interphase Grape Interphase Polylobed Polylobed Binuclear Interphase Polylobed Polylobed Metaphase Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Apoptosis Apoptosis Hole Prometaphase Polylobed Polylobed Binuclear Polylobed Prometaphase Artefact Prometaphase Polylobed MetaphaseAlignment Interphase Polylobed Polylobed MetaphaseAlignment Polylobed Polylobed Polylobed Interphase Polylobed Apoptosis Prometaphase Polylobed Polylobed Interphase MetaphaseAlignment Polylobed Grape MetaphaseAlignment Grape Grape Grape MetaphaseAlignment Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Grape UndefinedCondensed MetaphaseAlignment Binuclear MetaphaseAlignment Artefact Grape Binuclear Artefact Polylobed Polylobed MetaphaseAlignment Grape Prometaphase Apoptosis Large MetaphaseAlignment Prometaphase Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Grape Polylobed Metaphase Interphase Interphase Grape Large Polylobed Apoptosis Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Anaphase Metaphase Polylobed Grape Apoptosis Artefact Polylobed Polylobed Polylobed Grape Binuclear Polylobed UndefinedCondensed Grape Binuclear MetaphaseAlignment Polylobed Artefact Polylobed Interphase Apoptosis Interphase Artefact Grape Polylobed MetaphaseAlignment Binuclear Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Interphase MetaphaseAlignment Folded Polylobed Large Binuclear MetaphaseAlignment Binuclear Grape Prometaphase MetaphaseAlignment Polylobed Binuclear Polylobed Elongated Folded Binuclear Binuclear Polylobed Prometaphase Interphase Polylobed SmallIrregular Prometaphase Polylobed Artefact Polylobed Prometaphase Artefact Polylobed Binuclear Binuclear Grape Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Interphase Apoptosis Polylobed Polylobed Polylobed Prometaphase Grape Grape Interphase Prometaphase Polylobed Prometaphase Interphase Grape Interphase Polylobed Prometaphase Hole Prometaphase Interphase Large Binuclear Interphase Polylobed Large Polylobed Polylobed Grape Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Artefact Prometaphase Metaphase Polylobed Polylobed Artefact Interphase Prometaphase Grape Apoptosis Polylobed Metaphase Polylobed Polylobed Grape Polylobed Prometaphase Grape Polylobed Interphase Binuclear Prometaphase Polylobed Polylobed Artefact Grape Interphase Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Binuclear Grape Hole UndefinedCondensed Binuclear MetaphaseAlignment SmallIrregular Prometaphase SmallIrregular Apoptosis Grape Binuclear Polylobed Prometaphase Polylobed Hole Prometaphase Polylobed Binuclear Polylobed Hole Prometaphase Artefact Polylobed Apoptosis Binuclear MetaphaseAlignment Grape Polylobed Polylobed Binuclear Polylobed Interphase Polylobed Grape Interphase Polylobed Polylobed Grape MetaphaseAlignment Polylobed Binuclear Apoptosis Grape Binuclear MetaphaseAlignment Polylobed Grape Artefact Anaphase Grape Polylobed Polylobed Grape Polylobed Polylobed Polylobed Grape Polylobed Apoptosis SmallIrregular Polylobed Polylobed Grape Binuclear Binuclear Binuclear Anaphase Polylobed MetaphaseAlignment Polylobed Grape Polylobed Elongated MetaphaseAlignment Polylobed Binuclear Grape Polylobed Apoptosis Polylobed Grape Artefact Polylobed Grape Binuclear Artefact Binuclear Prometaphase Grape Polylobed Polylobed Polylobed MetaphaseAlignment Grape Binuclear Polylobed Polylobed Metaphase Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Grape SmallIrregular Polylobed Polylobed Polylobed Prometaphase Interphase Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Grape Binuclear Prometaphase Polylobed Polylobed Prometaphase Polylobed Artefact Polylobed Polylobed Interphase Anaphase Binuclear Prometaphase Binuclear Polylobed Polylobed Binuclear Polylobed Interphase Polylobed Metaphase Polylobed Binuclear Artefact Polylobed Polylobed Artefact Polylobed Artefact Polylobed Polylobed Apoptosis Binuclear Binuclear SmallIrregular Grape Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Large Polylobed Polylobed Artefact Grape Polylobed Polylobed Polylobed Polylobed Grape Prometaphase Artefact Polylobed Apoptosis Polylobed Polylobed Grape Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Binuclear Prometaphase Polylobed Binuclear Grape Grape Prometaphase Apoptosis Prometaphase Polylobed MetaphaseAlignment Polylobed Anaphase Polylobed Interphase Polylobed Polylobed Apoptosis MetaphaseAlignment Polylobed MetaphaseAlignment Polylobed Interphase Binuclear Prometaphase Polylobed Interphase Polylobed Interphase Polylobed Grape Interphase Grape MetaphaseAlignment Apoptosis Polylobed Polylobed MetaphaseAlignment Interphase Interphase Polylobed Binuclear Binuclear Grape Hole Prometaphase Polylobed Interphase Grape SmallIrregular Artefact Polylobed Artefact Polylobed Polylobed Polylobed Binuclear MetaphaseAlignment Polylobed Metaphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Interphase Metaphase Hole Polylobed Apoptosis Polylobed Interphase Metaphase Grape Hole Grape Polylobed Polylobed Interphase Polylobed Artefact Interphase Grape Grape Anaphase Polylobed Binuclear Polylobed Prometaphase Binuclear Prometaphase Polylobed Prometaphase Polylobed Binuclear Apoptosis Polylobed Polylobed Polylobed Polylobed Apoptosis Polylobed Prometaphase SmallIrregular Apoptosis Polylobed Grape Elongated Metaphase Polylobed Prometaphase Metaphase Grape Polylobed Interphase Binuclear MetaphaseAlignment Metaphase Polylobed Polylobed Binuclear Prometaphase SmallIrregular Apoptosis Interphase MetaphaseAlignment Interphase Prometaphase Polylobed MetaphaseAlignment SmallIrregular Binuclear Polylobed MetaphaseAlignment Interphase Polylobed Interphase Binuclear Interphase Binuclear Polylobed SmallIrregular Artefact Polylobed Interphase Polylobed Apoptosis Apoptosis Binuclear Grape Polylobed Prometaphase Grape Polylobed Binuclear Binuclear Prometaphase Polylobed Polylobed Artefact Artefact Interphase Artefact Interphase Artefact Polylobed Anaphase Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Binuclear MetaphaseAlignment Polylobed Binuclear Polylobed Artefact Binuclear Polylobed Prometaphase Grape Polylobed Polylobed Artefact Interphase Artefact Polylobed Hole MetaphaseAlignment Binuclear Polylobed Polylobed Binuclear Hole Large Polylobed Prometaphase Polylobed Interphase Interphase Interphase Binuclear Interphase Large Artefact Interphase Binuclear Grape Grape Prometaphase Polylobed Interphase Grape MetaphaseAlignment Polylobed MetaphaseAlignment Binuclear Hole Polylobed Grape Polylobed Large Artefact Polylobed Grape Binuclear Polylobed Polylobed Binuclear Artefact Binuclear Grape Grape Polylobed Polylobed Interphase Grape Binuclear SmallIrregular Polylobed Grape Binuclear Grape Prometaphase Binuclear Binuclear Polylobed Prometaphase Binuclear Interphase Polylobed Grape Polylobed Binuclear Grape Polylobed Large Interphase Interphase Grape Grape Grape Apoptosis Binuclear Interphase Polylobed Binuclear Polylobed Polylobed Apoptosis Interphase Binuclear Polylobed Polylobed Apoptosis Interphase Interphase Polylobed Binuclear Binuclear Grape Binuclear Polylobed Apoptosis Interphase Polylobed Interphase Interphase Polylobed Polylobed SmallIrregular Binuclear SmallIrregular Polylobed Grape Binuclear Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Grape MetaphaseAlignment Binuclear SmallIrregular Polylobed Binuclear Interphase Binuclear Polylobed Polylobed Polylobed MetaphaseAlignment Hole Polylobed Binuclear Artefact Grape Interphase Artefact MetaphaseAlignment Polylobed Polylobed Binuclear Binuclear Polylobed Apoptosis Polylobed Polylobed Metaphase Metaphase Binuclear Apoptosis Polylobed Interphase Prometaphase Binuclear Anaphase Binuclear Prometaphase Grape Hole SmallIrregular Grape Artefact Grape Interphase Polylobed Metaphase MetaphaseAlignment Polylobed Prometaphase Polylobed Polylobed Binuclear Artefact Grape Prometaphase Prometaphase MetaphaseAlignment Prometaphase Polylobed Polylobed Artefact Polylobed Prometaphase Interphase Polylobed Interphase Polylobed Polylobed Elongated Apoptosis Polylobed Polylobed Apoptosis Polylobed Polylobed Binuclear Binuclear Interphase Folded Apoptosis Polylobed Hole Prometaphase Polylobed Polylobed Polylobed Interphase Binuclear Polylobed Binuclear Prometaphase Apoptosis Binuclear Artefact Interphase Prometaphase Interphase SmallIrregular Prometaphase Binuclear Anaphase Elongated Polylobed Polylobed Polylobed Artefact Polylobed Polylobed MetaphaseAlignment SmallIrregular Grape Prometaphase SmallIrregular Polylobed Polylobed Binuclear Polylobed UndefinedCondensed Polylobed Polylobed Artefact Interphase Polylobed Anaphase Grape Polylobed Polylobed Polylobed Grape Artefact Prometaphase Grape Binuclear Binuclear Artefact Polylobed Prometaphase Polylobed Binuclear Polylobed Interphase Grape Polylobed Anaphase Binuclear MetaphaseAlignment Elongated Binuclear Artefact Interphase Artefact Interphase Polylobed Polylobed Grape MetaphaseAlignment Prometaphase Polylobed Polylobed MetaphaseAlignment MetaphaseAlignment Polylobed Apoptosis Polylobed Binuclear Apoptosis Polylobed Polylobed MetaphaseAlignment Grape Polylobed Binuclear Interphase UndefinedCondensed Apoptosis MetaphaseAlignment Polylobed Prometaphase Polylobed Polylobed Grape Prometaphase Interphase Binuclear Large Prometaphase Binuclear Prometaphase MetaphaseAlignment Binuclear Binuclear SmallIrregular Polylobed Polylobed Polylobed Prometaphase Polylobed Artefact Binuclear Polylobed Polylobed Interphase SmallIrregular Polylobed Grape SmallIrregular MetaphaseAlignment Anaphase Binuclear Binuclear Elongated Polylobed Polylobed MetaphaseAlignment Binuclear Interphase Interphase Apoptosis MetaphaseAlignment Binuclear Artefact Binuclear Artefact Polylobed Polylobed Polylobed Grape Prometaphase Elongated Binuclear Polylobed Binuclear Polylobed Binuclear Polylobed Binuclear Binuclear Polylobed MetaphaseAlignment Artefact Interphase Polylobed Interphase Grape Binuclear Polylobed Binuclear Polylobed Grape Interphase Polylobed Grape Artefact Interphase Polylobed Polylobed Binuclear Grape Large Interphase Polylobed Binuclear Polylobed Binuclear Prometaphase Artefact Apoptosis Interphase Binuclear Binuclear Polylobed Prometaphase Grape Grape Binuclear Polylobed Polylobed Apoptosis Artefact Polylobed Grape Binuclear Prometaphase Prometaphase Polylobed UndefinedCondensed Artefact Polylobed Large Polylobed MetaphaseAlignment Anaphase Polylobed Polylobed Polylobed Binuclear Hole Polylobed Binuclear Interphase Prometaphase Prometaphase Polylobed Apoptosis Binuclear Binuclear Polylobed Polylobed Apoptosis Metaphase Interphase Interphase Polylobed Apoptosis Polylobed Polylobed Polylobed Prometaphase Artefact Binuclear SmallIrregular Prometaphase Interphase Apoptosis Polylobed MetaphaseAlignment Artefact Binuclear Apoptosis Prometaphase Polylobed MetaphaseAlignment Artefact Binuclear Polylobed Grape Polylobed Polylobed Interphase Prometaphase Binuclear Polylobed Artefact Binuclear Polylobed Grape Binuclear Interphase Polylobed Interphase Interphase MetaphaseAlignment MetaphaseAlignment Binuclear Interphase Interphase Polylobed Interphase Grape Polylobed Binuclear Polylobed Polylobed Polylobed Binuclear Binuclear Binuclear Artefact Apoptosis Polylobed Grape Binuclear Interphase Polylobed Artefact Prometaphase Grape Polylobed Artefact Polylobed Prometaphase MetaphaseAlignment Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Binuclear MetaphaseAlignment Prometaphase Polylobed Polylobed UndefinedCondensed Apoptosis SmallIrregular Artefact Polylobed Polylobed Polylobed Binuclear Polylobed Grape Binuclear Grape Polylobed Interphase Polylobed Interphase Polylobed Grape Polylobed Grape Interphase Binuclear Grape Interphase Prometaphase Polylobed Interphase Polylobed Polylobed Grape SmallIrregular Interphase Polylobed SmallIrregular Grape Polylobed Polylobed Artefact Polylobed Binuclear Polylobed Large Polylobed Polylobed Apoptosis Interphase Polylobed Polylobed Polylobed Hole Grape Interphase SmallIrregular Polylobed Large Prometaphase Polylobed Interphase Apoptosis Grape Binuclear UndefinedCondensed Artefact Polylobed Interphase SmallIrregular Binuclear Artefact Polylobed Metaphase Artefact Polylobed Polylobed Polylobed Apoptosis Binuclear MetaphaseAlignment Binuclear Binuclear Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Grape Polylobed Binuclear Binuclear Apoptosis MetaphaseAlignment Grape MetaphaseAlignment Apoptosis Interphase Binuclear Artefact Polylobed Polylobed Grape Apoptosis Grape Polylobed Polylobed Polylobed Hole Binuclear Polylobed Hole Polylobed Prometaphase Binuclear Large Elongated Polylobed Binuclear Binuclear MetaphaseAlignment Binuclear Hole Prometaphase Interphase Binuclear Binuclear Elongated Interphase Prometaphase Binuclear Polylobed Polylobed Binuclear Interphase Binuclear Prometaphase Binuclear Polylobed Polylobed Prometaphase Binuclear Polylobed Polylobed Interphase Apoptosis Apoptosis Polylobed Interphase SmallIrregular Polylobed Binuclear Artefact Grape Interphase Interphase Binuclear Polylobed Polylobed Interphase Binuclear Interphase Binuclear Binuclear Grape Interphase Polylobed Polylobed Polylobed Prometaphase Apoptosis Hole Polylobed Elongated Binuclear Grape Hole Binuclear Polylobed Binuclear Binuclear Elongated MetaphaseAlignment MetaphaseAlignment Grape Polylobed Anaphase Polylobed Binuclear Polylobed Binuclear Artefact Polylobed Artefact Polylobed Grape Polylobed Polylobed Binuclear Hole Polylobed Polylobed Hole Polylobed Grape Interphase Polylobed Grape Binuclear Binuclear Polylobed Apoptosis Polylobed Artefact Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Binuclear Polylobed Polylobed Prometaphase Polylobed Apoptosis Prometaphase Polylobed Interphase Interphase Binuclear Folded Polylobed Polylobed Prometaphase Binuclear Polylobed Polylobed Prometaphase Binuclear Binuclear Anaphase Grape Interphase Grape Artefact Prometaphase Polylobed Polylobed Large Polylobed Grape Binuclear Interphase Hole Polylobed Polylobed Polylobed Artefact Grape Apoptosis Polylobed UndefinedCondensed Binuclear Binuclear Prometaphase Polylobed Grape Prometaphase Polylobed Binuclear Polylobed Binuclear Binuclear Polylobed Polylobed Polylobed Prometaphase Artefact Interphase Grape Binuclear Prometaphase Artefact Hole Interphase Interphase Binuclear Polylobed Prometaphase Polylobed Artefact Polylobed SmallIrregular Prometaphase Polylobed Grape Interphase Apoptosis Binuclear Polylobed Prometaphase Polylobed Polylobed Artefact Hole Polylobed Polylobed Apoptosis Binuclear Prometaphase Binuclear Polylobed Polylobed Apoptosis Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Grape Binuclear Binuclear Polylobed Polylobed Polylobed Binuclear Metaphase Interphase +y_train_pred Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear MetaphaseAlignment Polylobed Binuclear Binuclear Prometaphase Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Artefact Binuclear Polylobed Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Prometaphase Interphase Binuclear Polylobed Binuclear Polylobed Polylobed Apoptosis Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Grape Binuclear Polylobed Polylobed Polylobed Binuclear Binuclear Polylobed UndefinedCondensed Interphase Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Interphase MetaphaseAlignment Polylobed Polylobed Binuclear Binuclear Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Interphase Grape Binuclear 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Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Artefact Interphase Polylobed Binuclear Binuclear SmallIrregular Polylobed Polylobed Polylobed Polylobed Prometaphase Grape Polylobed Polylobed Polylobed Prometaphase Binuclear Polylobed Polylobed Metaphase Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Grape Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Binuclear Polylobed Binuclear Polylobed Polylobed Binuclear Large Grape Polylobed Polylobed Interphase Polylobed Interphase Polylobed Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed Binuclear Artefact Polylobed Interphase Polylobed Polylobed Polylobed Polylobed Polylobed Prometaphase Interphase Polylobed Polylobed Polylobed Grape Binuclear Polylobed Polylobed Metaphase Polylobed Polylobed Polylobed Binuclear Large Interphase Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed MetaphaseAlignment Polylobed Interphase Polylobed Binuclear Binuclear Artefact MetaphaseAlignment Polylobed Binuclear Prometaphase Binuclear Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Binuclear Prometaphase Interphase Polylobed SmallIrregular Polylobed Polylobed Polylobed Polylobed Polylobed Hole Polylobed Hole Polylobed Hole Polylobed Polylobed Polylobed Metaphase Binuclear Polylobed Binuclear Polylobed Polylobed Interphase Grape Binuclear Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Grape Polylobed Prometaphase Prometaphase Polylobed Polylobed Binuclear Polylobed Polylobed Prometaphase Apoptosis Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Artefact Polylobed Polylobed Polylobed Prometaphase Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Large SmallIrregular Polylobed Polylobed Polylobed 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Binuclear Elongated Polylobed Binuclear Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Polylobed Binuclear Polylobed Polylobed Polylobed Polylobed diff --git a/3.ML_model/scripts/2.evaluate_model.py b/3.evaluate_model/scripts/nbconverted/evaluate_model.py similarity index 76% rename from 3.ML_model/scripts/2.evaluate_model.py rename to 3.evaluate_model/scripts/nbconverted/evaluate_model.py index af49308d..f8d2d9be 100644 --- a/3.ML_model/scripts/2.evaluate_model.py +++ b/3.evaluate_model/scripts/nbconverted/evaluate_model.py @@ -10,58 +10,58 @@ import numpy as np import pathlib +from sklearn.metrics import f1_score from joblib import load import sys -# adding utils to system path -sys.path.insert(0, '../utils') -from MlPipelineUtils import ( - get_features_data, - get_dataset, - get_X_y_data, - evaluate_model_cm, - evaluate_model_score -) - -from sklearn.metrics import f1_score +sys.path.append("../utils") +from split_utils import get_features_data +from train_utils import get_dataset, get_X_y_data +from evaluate_utils import evaluate_model_cm, evaluate_model_score -# ### Evaluate best model +# ### Load necessary data # In[2]: -# set numpy seed to make random operations reproduceable -np.random.seed(0) - -results_dir = pathlib.Path("../results/") - -log_reg_model_path = pathlib.Path(f"{results_dir}/1.log_reg_model.joblib") -log_reg_model = load(log_reg_model_path) +# specify results directory +results_dir = pathlib.Path("evaluations/") +results_dir.mkdir(parents=True, exist_ok=True) # load features data from indexes and features dataframe -data_split_path = pathlib.Path(f"{results_dir}/0.data_split_indexes.tsv") +data_split_path = pathlib.Path("../1.split_data/indexes/data_split_indexes.tsv") data_split_indexes = pd.read_csv(data_split_path, sep="\t", index_col=0) -features_dataframe_path = pathlib.Path("../../1.format_data/data/training_data.csv.gz") +features_dataframe_path = pathlib.Path("../0.download_data/data/training_data.csv.gz") features_dataframe = get_features_data(features_dataframe_path) -# ### Evaluate with training data +# ### Evaluate best model # In[3]: +model_dir = pathlib.Path("../2.train_model/models/") +log_reg_model_path = pathlib.Path(f"{model_dir}/log_reg_model.joblib") +log_reg_model = load(log_reg_model_path) + + +# ### Evaluate with training data + +# In[4]: + + training_data = get_dataset(features_dataframe, data_split_indexes, "train") training_data -# In[4]: +# In[5]: y_train, y_train_pred = evaluate_model_cm(log_reg_model, training_data) -# In[5]: +# In[6]: evaluate_model_score(log_reg_model, training_data) @@ -69,20 +69,20 @@ # ### Evaluate with testing data -# In[6]: +# In[7]: testing_data = get_dataset(features_dataframe, data_split_indexes, "test") testing_data -# In[7]: +# In[8]: y_test, y_test_pred = evaluate_model_cm(log_reg_model, testing_data) -# In[8]: +# In[9]: evaluate_model_score(log_reg_model, testing_data) @@ -90,7 +90,7 @@ # ### Evaluate with holdout data -# In[9]: +# In[10]: holdout_data = get_dataset(features_dataframe, data_split_indexes, "holdout") @@ -98,13 +98,13 @@ holdout_data -# In[10]: +# In[11]: y_holdout, y_holdout_pred = evaluate_model_cm(log_reg_model, holdout_data) -# In[11]: +# In[12]: evaluate_model_score(log_reg_model, holdout_data) @@ -112,7 +112,7 @@ # ### Save trained model predicitions -# In[12]: +# In[13]: predictions = [] @@ -128,27 +128,27 @@ predictions = pd.DataFrame(predictions) predictions.index = ["y_train", "y_train_pred", "y_test", "y_test_pred", "y_holdout", "y_holdout_pred"] -predictions.to_csv(f"{results_dir}/2.model_predictions.tsv", sep="\t") +predictions.to_csv(f"{results_dir}/model_predictions.tsv", sep="\t") # ### Evaluate shuffled baseline model -# In[13]: +# In[14]: -shuffled_baseline_log_reg_model_path = pathlib.Path(f"{results_dir}/1.shuffled_baseline_log_reg_model.joblib") +shuffled_baseline_log_reg_model_path = pathlib.Path(f"{model_dir}/shuffled_baseline_log_reg_model.joblib") shuffled_baseline_log_reg_model = load(shuffled_baseline_log_reg_model_path) # ### Evaluate with training data -# In[14]: +# In[15]: y_train, y_train_pred = evaluate_model_cm(shuffled_baseline_log_reg_model, training_data) -# In[15]: +# In[16]: evaluate_model_score(shuffled_baseline_log_reg_model, training_data) @@ -156,13 +156,13 @@ # ### Evaluate with testing data -# In[16]: +# In[17]: y_test, y_test_pred = evaluate_model_cm(shuffled_baseline_log_reg_model, testing_data) -# In[17]: +# In[18]: evaluate_model_score(shuffled_baseline_log_reg_model, testing_data) @@ -170,13 +170,13 @@ # ### Evaluate with holdout data -# In[18]: +# In[19]: y_holdout, y_holdout_pred = evaluate_model_cm(shuffled_baseline_log_reg_model, holdout_data) -# In[19]: +# In[20]: evaluate_model_score(shuffled_baseline_log_reg_model, holdout_data) @@ -184,7 +184,7 @@ # ### Save trained model predicitions -# In[20]: +# In[21]: predictions = [] @@ -200,5 +200,5 @@ predictions = pd.DataFrame(predictions) predictions.index = ["y_train", "y_train_pred", "y_test", "y_test_pred", "y_holdout", "y_holdout_pred"] -predictions.to_csv(f"{results_dir}/2.shuffled_baseline_model_predictions.tsv", sep="\t") +predictions.to_csv(f"{results_dir}/shuffled_baseline_model_predictions.tsv", sep="\t") diff --git a/4.interpret_model/README.md b/4.interpret_model/README.md new file mode 100644 index 00000000..9301532a --- /dev/null +++ b/4.interpret_model/README.md @@ -0,0 +1,30 @@ +# 4. Interpret Model + +In this module, we interpret the final and shuffled baseline ML models. + +After training the final and baseline models in [2.train_model](../2.train_model/), we load the coefficents of these models from [models/](../2.train_model/models). +These coefficients are interpreted with the following diagrams: + +- We use [seaborn.heatmap](https://seaborn.pydata.org/generated/seaborn.heatmap.html) to display the coefficient values for each phenotypic class/feature. +- We use [seaborn.clustermap](https://seaborn.pydata.org/generated/seaborn.clustermap.html) to display a hierarchically-clustered heatmap of coefficient values for each phenotypic class/feature +- We use [seaborn.kedeplot](https://seaborn.pydata.org/generated/seaborn.kdeplot.html) to display a density plot of coeffiecient values for each phenotypic class. +- We use [seaborn.barplot](https://seaborn.pydata.org/generated/seaborn.barplot.html) to display a bar plot of average coeffiecient values per phenotypic class and feature. + +## Step 1: Interpret Model + +Use the commands below to interpret the ML models: + +```sh +# Make sure you are located in 4.interpret_model +cd 4.interpret_model + +# Activate phenotypic_profiling conda environment +conda activate phenotypic_profiling + +# Interpret model +bash interpret_model.sh +``` + +## Results + +Each model's interpretations can be found in [interpret_model.ipynb](interpret_model.ipynb). \ No newline at end of file diff --git a/4.interpret_model/interpret_model.ipynb b/4.interpret_model/interpret_model.ipynb new file mode 100644 index 00000000..4053cd08 --- /dev/null +++ b/4.interpret_model/interpret_model.ipynb @@ -0,0 +1,768 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import pathlib\n", + "\n", + "from joblib import load\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpret best model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "model_dir = pathlib.Path(\"../2.train_model/models/\")\n", + "\n", + "log_reg_model_path = pathlib.Path(f\"{model_dir}/log_reg_model.joblib\")\n", + "log_reg_model = load(log_reg_model_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile Coefficients Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1280, 16)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Anaphase Apoptosis Artefact Binuclear Elongated Folded Grape \\\n", + "0 0.007907 0.000000 0.002906 6.380548e-09 0.000000 0.000000 0.005263 \n", + "1 0.000000 0.012269 0.020812 2.386640e-02 0.013439 0.016868 0.009444 \n", + "2 0.000000 0.035544 0.031964 5.343236e-06 0.000721 0.000000 0.000000 \n", + "3 0.000000 0.000920 0.057642 5.459898e-02 0.026499 0.001331 0.018668 \n", + "4 0.026603 0.000000 0.027513 5.134753e-02 0.003103 0.002732 0.011911 \n", + "\n", + " Hole Interphase Large Metaphase MetaphaseAlignment \\\n", + "0 7.795729e-03 0.055444 0.028652 0.007294 0.058497 \n", + "1 5.394298e-07 0.000000 0.020314 0.000000 0.013486 \n", + "2 1.184528e-02 0.026249 0.010501 0.000000 0.010033 \n", + "3 2.611519e-02 0.145121 0.000000 0.001165 0.024942 \n", + "4 1.870821e-02 0.054121 0.000000 0.019289 0.016082 \n", + "\n", + " Polylobed Prometaphase SmallIrregular UndefinedCondensed \n", + "0 0.055951 1.164984e-02 0.049322 0.008320 \n", + "1 0.030805 1.976936e-02 0.031717 0.000000 \n", + "2 0.021676 4.566904e-02 0.062578 0.000000 \n", + "3 0.077565 1.402689e-02 0.000948 0.003738 \n", + "4 0.001906 1.538151e-07 0.018743 0.019257 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coefs = np.abs(log_reg_model.coef_)\n", + "coefs = pd.DataFrame(coefs).T\n", + "coefs.columns = log_reg_model.classes_\n", + "\n", + "print(coefs.shape)\n", + "coefs.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Diagrams for interpreting coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# display heatmap of average coefs\n", + "plt.figure(figsize=(20, 10))\n", + "plt.title(\"Heatmap of Coefficients Matrix\")\n", + "ax = sns.heatmap(data=coefs.T)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/seaborn/matrix.py:654: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n", + " warnings.warn(msg)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# display clustered heatmap of coefficients\n", + "ax = sns.clustermap(data=coefs.T, figsize=(20, 10), row_cluster=True, col_cluster=True)\n", + "ax = ax.fig.suptitle(\"Clustered Heatmap of Coefficients Matrix\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# display density plot for coefficient values of each class\n", + "sns.set(rc={\"figure.figsize\": (20, 8)})\n", + "plt.xlim(-0.02, 0.1)\n", + "plt.xlabel(\"Coefficient Value\")\n", + "plt.ylabel(\"Density\")\n", + "plt.title(\"Density of Coefficient Values Per Phenotpyic Class\")\n", + "ax = sns.kdeplot(data=coefs)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# display average coefficient value vs phenotypic class bar chart\n", + "pheno_class_ordered = coefs.reindex(\n", + " coefs.mean().sort_values(ascending=False).index, axis=1\n", + ")\n", + "sns.set(rc={\"figure.figsize\": (20, 8)})\n", + "plt.xlabel(\"Phenotypic Class\")\n", + "plt.ylabel(\"Average Coefficient Value\")\n", + "plt.title(\"Coefficient vs Phenotpyic Class\")\n", + "plt.xticks(rotation=90)\n", + "ax = sns.barplot(data=pheno_class_ordered)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# display average coefficient value vs feature bar chart\n", + "feature_ordered = coefs.T.reindex(\n", + " coefs.T.mean().sort_values(ascending=False).index, axis=1\n", + ")\n", + "sns.set(rc={\"figure.figsize\": (500, 8)})\n", + "plt.xlabel(\"Deep Learning Feature Number\")\n", + "plt.ylabel(\"Average Coefficient Value\")\n", + "plt.title(\"Coefficient vs Feature\")\n", + "plt.xticks(rotation=90)\n", + "ax = sns.barplot(data=feature_ordered)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpret shuffled baseline model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "shuffled_baseline_log_reg_model_path = pathlib.Path(f\"{model_dir}/shuffled_baseline_log_reg_model.joblib\")\n", + "shuffled_baseline_log_reg_model = load(shuffled_baseline_log_reg_model_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile Coefficients Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1280, 16)\n" + ] + }, + { + "data": { + "text/html": [ + "
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AnaphaseApoptosisArtefactBinuclearElongatedFoldedGrapeHoleInterphaseLargeMetaphaseMetaphaseAlignmentPolylobedPrometaphaseSmallIrregularUndefinedCondensed
00.0000000.0000000.0000000.0457570.00.00.0000000.00.0000000.000000.00.00.0150240.0000000.0000000.0
10.0000000.0090150.0000000.0543330.00.00.0619990.00.0162490.025570.00.00.0072660.0000000.0182770.0
20.0000000.0000000.0141050.0000000.00.00.0000000.00.0000000.000000.00.00.0000000.0000000.0206690.0
30.0000000.0000000.0225400.0000000.00.00.0000000.00.0000000.000000.00.00.0000000.0000000.0000000.0
40.0228860.0000000.0000000.0000000.00.00.0074390.00.0000000.000000.00.00.0000000.0133880.0000000.0
\n", + "
" + ], + "text/plain": [ + " Anaphase Apoptosis Artefact Binuclear Elongated Folded Grape \\\n", + "0 0.000000 0.000000 0.000000 0.045757 0.0 0.0 0.000000 \n", + "1 0.000000 0.009015 0.000000 0.054333 0.0 0.0 0.061999 \n", + "2 0.000000 0.000000 0.014105 0.000000 0.0 0.0 0.000000 \n", + "3 0.000000 0.000000 0.022540 0.000000 0.0 0.0 0.000000 \n", + "4 0.022886 0.000000 0.000000 0.000000 0.0 0.0 0.007439 \n", + "\n", + " Hole Interphase Large Metaphase MetaphaseAlignment Polylobed \\\n", + "0 0.0 0.000000 0.00000 0.0 0.0 0.015024 \n", + "1 0.0 0.016249 0.02557 0.0 0.0 0.007266 \n", + "2 0.0 0.000000 0.00000 0.0 0.0 0.000000 \n", + "3 0.0 0.000000 0.00000 0.0 0.0 0.000000 \n", + "4 0.0 0.000000 0.00000 0.0 0.0 0.000000 \n", + "\n", + " Prometaphase SmallIrregular UndefinedCondensed \n", + "0 0.000000 0.000000 0.0 \n", + "1 0.000000 0.018277 0.0 \n", + "2 0.000000 0.020669 0.0 \n", + "3 0.000000 0.000000 0.0 \n", + "4 0.013388 0.000000 0.0 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coefs = np.abs(shuffled_baseline_log_reg_model.coef_)\n", + "coefs = pd.DataFrame(coefs).T\n", + "coefs.columns = shuffled_baseline_log_reg_model.classes_\n", + "\n", + "print(coefs.shape)\n", + "coefs.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Diagrams for interpreting coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# display heatmap of average coefs\n", + "plt.figure(figsize=(20, 10))\n", + "plt.title(\"Heatmap of Coefficients Matrix\")\n", + "ax = sns.heatmap(data=coefs.T)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/roshankern/anaconda3/envs/phenotypic_profiling/lib/python3.8/site-packages/seaborn/matrix.py:654: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n", + " warnings.warn(msg)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# display clustered heatmap of coefficients\n", + "ax = sns.clustermap(data=coefs.T, figsize=(20, 10), row_cluster=True, col_cluster=True)\n", + "ax = ax.fig.suptitle(\"Clustered Heatmap of Coefficients Matrix\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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5HA6ZzW7/KhwoMiVijx0AKA7Oih1j9sFO5v46wQHXyWg3ynPrn/JZu0+GVKvslQPkNamj7A3LyFYzWJJk+itGHh7+F/fZifk1234zg6Q0O0uxAQAAAAAA5NfChXN13313qUePTurV62ZNmTJZ586dc2kTFfWgHn10rLZs+VB3332nunZtr0OHMgoH3n9/g/7zn57q1q2DxowZqb1796hjxwht3rzJpY/Nmzdp0KD+6tq1vXr1ulmLF8+X1Wq9as8TyAsxJYBSKz2Ppdic++sE1JXPqt0yHYuXJKW1qyT7jTXlF+4rxSTJWjNYHj+ekumvWElSeEizjH12zv+sqhV6ZOnXGexQsQMAAAAAAIqJw+GQ5ZJtK64mL6NRBoOhyPuNiTmvgQMHKzy8jGJjY7R27VuKinpQb7653qUi58CB/Tp9+pSGDh2pgIAAlS1bTjt37tArr0zTbbf1UmRkNx08+LumTn0iyz3Wrn1TCxfOVb9+AxQVNVZHjhzRkiULZLfbNXLk6CJ/TkBhEOwAKLUszmAn94qdsNSqMh2Ll8PLpNQ768pWN0xm88WCR1v1YDkkmc4ky5CQdmGfnXU57rPjdSFIomIHAAAAAAAUB4fDocd+2aP98Ql5Ny4G9QMDNKNZoyIPdyZPnuL8t81mU6NGTdS79y366acf1Lp1W+e5hIR4LVu2WmXLlnMeW7VquVq2bKXHHntSktSmTTulpVm0cuVSZ5vk5CQtX75EAwbcp+HDM/Z7b9WqrUwmk+bPf1UDBgxUUFBwkT4noDBYig1AqZWWS7Bjt1sVG39IkhQeV1GSZK0TKlvdsKwd+XnIXsFfkmQ6HKuw4Iv77KSkns3S3FmxY6NiBwAAAAAAIL++/vorjRjxgG68sbM6d26j3r1vkSQdO3bUpV2tWrVdQh2bzaaDB39Xhw7Xu7Tr1Kmzy+Pdu39TSkqyunTpJqvV6vxp2bKVLBaL/vrrz2J6ZkDBULEDoNTKLdiJTzoim90is9lPwaf9JcXIXjEgx75sNYNlOpko01+x8mxSVsGBtRUb/7vOxvySZTm2zKXfLCzFBgAAAAAAioHBYNCMZo2uqaXY9u/fq0mTxqtTp866995BCg4OlcFg0PDh98tiSXNpGxIS4vI4NjZGNptNwcEhl7ULdXkcFxcrSXrggXuzHcOZM6ev8FkARYNgB0CpZLNbZXNk/D99r2z22Lm4v04dmX7NCGBsFfxy7q9msPTVPzL9FSM5HCoT0iwj2DmfXbBzoWKHpdgAAAAAAEAxMRgM8jaZ3D2MIvPFF5/L399fzz47XUZjxkJUp06dzLbt5aFScHCITCaTYmNjXI7HxJx3eRwQEChJeuGFl1WuXDldrkKFioUeP1CUCHYAlEqZ1TpS9hU7mfvrhPjWkTHOIkmyl/fPsT9b1UA5TAYZ49NkiE5ReEhTHTq6TtGxv2Vpm1mxk0bFDgAAAAAAQL5YLKkym80uoc22bVvyda3JZFKdOnW1c+cO9et3t/P4l1/ucGnXuHFTeXt76+zZ0+rcuUvRDBwoBgQ7AEqlzGoZo8Esk9Ejy/nMip0wazVJkj3UW/LOZcr0MMlWNVDmw3Ey/xWroMY1JUlJySflcNhlMFzc0iyzQoiKHQAAAAAAAFc2m12fffZxluO1a9fV+vVrNHv2S7r++i7as+c3ffTR5nz3O2jQEE2a9IhmzHheXbp018GDB7R1a8b1mWGRv7+/hgwZoQUL5urMmTNq0SJCRqNRJ078oy+//EIvvPCSvL29i+aJAleAYAdAqZRmy6iWyW4ZNpvNorjEvyRJ4XEVJSXKViHnah3ndTVDZD4cJ9NfsfKJqCODwSS7I10plnPy9S7rbOdcis1GsAMAAAAAAHCptDSLnnpqUpbjkydP0ciRo7Vhw3pt3rxJjRs31Usvvaq7774zX/127NhZEyZM0urVK/XRR1vUoEFDPfLIY5ow4WH5+1/83ufuu+9VmTJltG7dW9qwYZ3MZrMqVaqs9u07yWzm63T8O/BOBFAqZYYq2S3DFpvwhxwOm7w8guV/2ktSouz5CnaCpU8k0+FYGR0m+XqXU1LKCSUln8w22LGwFBsAAAAAAIDTkCHDNWTI8Fzb3HPPIJfHO3f+4PJ43rwlOV7bq1cf9erVx/l406b3JUnXXVfHpV337jeqe/cb8zNkwC0IdgCUSrkFO879dYLqyfxbkiTlK9ixV/CXw9ssQ6pVxpMJ8vOpoKSUE0pOOSmpqbOdJ0uxAQAAAAAAXFXx8XFasWKpWraMkK+vn/bv36vVq1eqU6fOqlChoruHBxQIwQ6AUulisJN1KbbM/XVCfOvIGJMqSflaik1Gg2w1gmTeHy3TX7HyDasgSUpKOenSzLkUGxU7AAAAAAAAV4XZbNaJE//o448/UkJCvIKDQ3Tjjbdo5MjR7h4aUGAEOwBKpcw9dnKr2AmzVpMk2YO9JF+PfPVrrRnsDHb8KmcGOydc2mTu65M5BgAAAAAAABQvX18/vfTSq+4eBlAkjO4eAAC4Q05LsaVbk5SQdEySFB6bEczkZxm2TLaaIZIk07F4+XmWl5RLxQ5LsQEAAAAAAAAoIIIdAKWSM9gxugY7CYlHJTnk7RUu39MmSflchu0CR6i37EFeMtgcCogNkCQlp5xyaZO5/JvFmiKHw1HYpwAAAAAAAACgFCLYAVAq5VSxk5yaEcL4+VSQ6WSipIJV7MhgkK1msCQp8ISXJCnFclY2m8XZJHMpNodssjnSCjV+AAAAAAAAAKUTwQ6AUsni3GPH1+V4UsppSZKvZ1kZzmWEPwUKdiRnsOP7l03mC8FRcupp53kPk/fFcVhZjg0AAAAAAABA/hHsACiV0nOs2MkIYPyswTJIsgd4yuHvWaC+bTWCJUnmU8ny9cq6z47RYJKHMSPcSbsQMAEAAAAAAAD/Z+++o6Oq1jeOf8/UTJJJpyR0gkiXXjQioiAqXEVR8doLVuyIiteGBWzYCyog9h/2ckWFa0VRRLxXkaICIlIS0tv0Ob8/JhmICSUFQuD5rOWSOWefs98ZyIGVJ+/eIrtDwY6IHJB2tBSbpyLYiS9PBCCcUbtuHQAz3kGoRVzkPuFUAMrKN1UZUzmvP6yOHREREREREREREdl9CnZE5IC0bSm2v3XsVCzFFl8YCWZCtVyGrVLlcmzu0sj/yzxbqpyvXALOF1THjoiIiIiIiIgIwKxZM8nK6s+JJx5LOByudn7SpCvJyurP5MlX1+q+8+a9wuLFixqoyppNnHhRresSqSsFOyJyQNpRx07lUmzx2ZHjtd1fp1I02NkaCYjKPOrYERERERERERHZFZvNRlFRIT/++EOV44WFhXz//Xe4XLE7uHLH5s17lcWLv26oEkUanYIdETkgVQYqlZ0zAIFAKYFgKQDuLU6gHsFOu0RMi0FCoRuA8u322InMGwl2Kvf6ERERERERERERsNvtDB58KAsWfFTl+KefLiAtrRkHH9ylkSoT2Xco2BGRA5I/WH0ptspuHYfVjSPkJBxnx3Q76jaBw0q4TQLx3mQAynYQ7PgV7IiIiIiIiIiIVHH00aP4/PNPCQQC0WMLFnzEUUeNrDY2JyebqVNv4fjjj2L48MO4/PIJrFq1Mnp+3LgxbNmymbfeep2srP5kZfXnww/fB2D+/A+49NILOPbY4YwadSQTJ17EihXLq9x/1qyZjBhxOCtX/sKECWczfPihnHHGOL7++qsaa//004WcfvpJjBhxOFdeeQkbN/5V5fxTTz3G2WefxogRh3Piicdy221TyM3NrTLmp5/+y+WXT+CYY45gxIihnH32acyf/0GVMd98s4gJE85h+PDDGD36aB54YBoej77PdKBQsCMiB6RtHTvVg51YUoGKbh3DqPMcoZZxxPsiwU4gWIo/UBI9p2BHRERERERERKRmWVmHEw6H+fbbyPJpW7ZsZvnynxgxYlSVccXFxVx22YX89ttqrr76eu6++15iYlxcddUlFBTkA3DPPfeTmprKsGFH8fTTc3j66TkMGZIVve+oUcdz553Tue22u2jRogUTJ17En3+urzJPMBjk1lunMGrUaO6++z5atWrDlCmTWLv29yrjfvvtV1599UUuueQKpky5jQ0b/mTq1FuqjCkoyOess87jvvse5qqrrmPLls1MnHgRwWAQgLKyUiZPvpq4uDhuv/1upk17gH/8YywlJdu+r/TZZwu58cZryczsxD333M+ll17JF198xvTpUxvg05emwNbYBYiINIaa9tgp91Tsr+NPAuq+DFslMzkGR9hBTNiN11JCmWczDru7yrwKdkRERERERERkTzBNE1/YbJS5nRYDox4/LOt0xnD44UewYMHHHH74MBYs+Ih27Tpw0EGdq4x7/fVXKS0t4dln55KcnAJAv34DGT9+LK+++iKXXXYVnTt3wW53kJKSQo8ePatcf955E6K/DofDDBgwiJUrVzB//gdcfPHl0XOBQIBzzjmf0aNPAGDgwCGMHz+WF16Yw+233x0dV1pawuzZL5OcnFzxupR7772LnJxsmjdvAcCUKbdFx4dCIXr06MXYscexbNlSBg4czIYNf1JaWsrFF08kM7MTAP37D4xeY5omTzzxCMOHj+DGG7eFRikpKUyefA3nnHMhHTtm1uFTl6ZEwY6IHJAqAxXndnvslHu3ABBfmgBAqJ7BTjg5JnI/fwremBLKPZtJToj8A8RhiQQ7vlB5veYQEREREREREfk70zS58YdNrCryNcr8XROdTOuXUa9wZ+TIY5kyZRLl5eUsWPARI0eOqjZmyZJv6dOnP253QrTjxWKx0KtXb1auXLHLOf74Yx0zZz7B8uU/RTt8ADZsWF9t7NChR0Z/bbVaOeywodWWY+vUqXM01AFo374DADk5OdFgZ/Hir5k7dxbr1q2hrKysypwDBw4mI6M1cXFxPPDANMaNG0/fvv2r3HPDhvVs2bKZK6+8LvqeAXr37odhGKxevVLBzgFAwY6IHHCCYT9hM/IXX40dOwVxAIQz6hnspETuHV+eRG7MekrLN0XPOWyRQCmgjh0RERERERER2QPqHqnsG/r3H0hsbBzPP/8ca9eu4eijj6k2pqiokF9++ZlhwwZXO9eqVeud3r+8vIxrr51IUlISV1xxDS1apON0Opg+/S78fn+VsTabjYSEhCrHkpOTycurujeO2+2u8tputwPg90cCtpUrf+HGG6/l8MOP4MwzzyEpKQXDMLj44nPx+SJzJiQk8NBDTzBr1jPcddethEIhevXqzTXXTCYzsxOFhYUATJkyqcb3lZ29ZafvW/YPCnZE5ICz/fJnNe2xE+9NwnTZMBOd9ZrHTIpc7y5PghQo92zeNq9FS7GJiIiIiIiIyJ5hGAbT+mU02aXYINIVM3z40bz22kv06NGLjIxW1ca43QkMGnQoEyZcUu2c3e7Y6f2XL/+ZnJxs7r33oSpLvJWVlQLNq4wNBoMUFxdXCXcKCgpITU2r1Xv68svPiY+PZ+rU6VgsFiCyz8/fdevWgwcffBSfz8uyZUt54olHuOmm65g3710SEhIBuOaayXTv3qPatWlpzWpVkzRNCnZE5IBTGabYLA4sxrbHYGWwE+dLiizDVs9/gGC3EnY7cHsj7bJl2wc72mNHRERERERERPYgwzCIsTbtvp3Ro08gJyebkSOPrfF8//4D+eST+bRr1wGXy1XjGACbzV6tC8fn8wLbumoAfv75f2zevIkOHTpWu8eXX34W3WMnFArx9ddf0q1b9WBlZ3w+LzabrUro9ckn83c43umMYciQLDZu/ItHHnkQn89Hu3btad68BZs2beTkk0+t1fyy/1CwIyIHHH/FvjaVXTMAobAfry8PgHhfEuF67q9TyUyOIb5wx8GO9tgREREREREREanZQQcdzLRpD+7w/PjxZ7BgwUdMnHgRp5wynhYtWlJYWMCKFb+QlpbGaaedAUD79u354YelfP/9t7jdCaSnZ9C9e09crlhmzLiXM888l61bc5g9+xmaNWtebR673c7cubPx+/2kp2fw9ttvkJOTvdPaajJgwCDmzXuVhx66j6FDj2T58p/4+OMPq4z55ptFfPDBuwwdOowWLVqSn5/HG2/Mo2fPQ3A6I6vDTJx4DXfccTNer4chQ7JwuVxs2bKZxYsXcdFFl9O2bbta1SVNj4IdETngVHbJbL8Mm8ebA4A1bMcZjMXXQMFOOMVFfHYk2Cn3bME0wxiGRR07IiIiIiIiIiL1lJiYxMyZc3j22ad46qnHKC4uIjk5hW7dejB06LDouIsuupwHH5zOzTffQHl5GVOm3MZxx43hzjun88QTD3PjjdfRpk1bJk26iZdfnlttHpvNxu23382MGfeydu3vpKdncPfd99Gp00G1qnfIkCwuvfQK3nxzHh9++D49ex7Cffc9zOmnnxQd07p1aywWg2eeeZKCgnwSE5MYOHAwF198eXTM8OFH43bHM3fu7GjHT8uW6QwadCgpKam1/BSlKTJM02ychRb3AwUFZQSD4cYuQ0RqaW3BUl5afh3NYztySb85AOTk/cBXP1xLgieNsT9eTdkV/TFTd9bCayE5OW6XzwH7F39i/2wtLw25A9MIc9zQN3DFNOO3/G959ZcbSI/vzIQ+zzb4exSRvWN3nwUisv/Sc0BE9BwQEWjcZ0Eg4CcvbzOpqem73FdGam/WrJm89tpLLFjwVWOXIo1sV19rKSlxWK2WvVLL3plFRGQfEl2KbbuOncr9deK9SZhOK2ZyTIPMZSbHYMFKXKhyObZNVeZWx46IiIiIiIiIiIjUhoIdETng1LQUW7knEuzE+ZIIpceDpWE2FwxXBETx3qr77CjYERERERERERERkbpQsCMiB5wagx3vFgDifUmEG2h/HYBwSkWwU5YIQJlnS8XcsQD4KrqHRERERERERERk33PBBRdrGTbZ5yjYEZEDjj9cGezERo9FO3b8iQ0a7BBrx3RYcVd27JRXX4pNW52JiIiIiIiIiIjI7lKwIyIHHF9wx3vsRJdiayiGQTg5hnhfJNgpr1iKzRmd2yQY9jXcfCIiIiIiIiIiIrJfU7AjIgecbR07kXDFNMOUe3OASLBjViyf1lDM5Bjc3hRg2x47dsu2ObTPjoiIiIiIiIiIiOwuBTsicsAJhKouxeb15WOaQQzTgsuZBtaGfTSGU7Z17Hh8uYTCfgzDgt2ybTk2ERERERERERERkd2hYEdEDji+UNWl2Mq9WwCI9bsxEuMafL5wcgwxgTispgMwo/v5VM5fWY+IiIiIiIiIiIjIrijYEZEDjj9UdSm2yqAlzpdEONHZ4POZyS4MDNz+qsuxVe6zo44dERERERERERER2V0KdkTkgFMZpDgrlmIr90aCnXhfEmZSwwc74eTIfjrx5UkAlHk2AduCpco9f0RERERERERERER2RcGOiBxwKoMde3Qptu07dmIafD4zyYlpgNsT2WenvKJjp3L+gDp2RERERERERESYNWsmWVn9OfHEYwmHw9XOT5p0JVlZ/Zk8+epa3XfevFdYvHhRA1VZs4kTL6p1XXvL66+/RlZWf+6++/Yaz9999+2cddap0dfLli0lK6s/q1at2EsVNo4vv/yct956vbHLqBMFOyJywIkuxWapuhTbnurYwWrBTHQS74sEO5VLsTm0FJuIiIiIiIiISBU2m42iokJ+/PGHKscLCwv5/vvvcLlia33PefNeZfHirxuqxCZnwYKPAPjii8/w+by7HH/wwV14+uk5tGvXYU+X1qi++upz3n5bwY6ISJNQbY8d7xYg0rFj7oE9dgDCyS7ivRXBTnnVYMcXKt8jc4qIiIiIiIiINDV2u53Bgw+NhhGVPv10AWlpzTj44C6NVFnTtHHjX6xYsZyBA4dQXl7GokVf7fKauLh4evToicvl2gsVSl0o2BGRA872e+yYphnt2InzJe6RpdgAzOQY3H/r2Knc40cdOyIiIiIiIiIi2xx99Cg+//xTAoFA9NiCBR9x1FEjq43Nyclm6tRbOP74oxg+/DAuv3wCq1atjJ4fN24MW7Zs5q23Xicrqz9ZWf358MP3AZg//wMuvfQCjj12OKNGHcnEiRexYsXyKvefNWsmI0YczsqVvzBhwtkMH34oZ5wxjq+/rjkg+fTThZx++kmMGHE4V155CRs3/lXl/FNPPcbZZ5/GiBGHc+KJx3LbbVPIzc2tMuann/7L5ZdP4JhjjmDEiKGcffZpzJ//QZUx33yziAkTzmH48MMYPfpoHnhgGh5P9e8xffLJfAzDYPLkKaSmprJw4UfVxvxdTUuxlZaWMnXqLYwYMZTRo4/myScf4cUXnycrq3+165Ys+Zbbb7+ZESOGcvLJo3n55blV7l+59Nt33y3m7LNPY/jww7jssgvZtGkjxcVF3HrrTYwceQSnnnoC//nPJ9Xq29V735067r77dubP/4B169ZG/1zsaKm6fZGtsQsQEdmbTNPEX9Eh47C6CARLCVa8jrWk4Xda98i84ZSYaMdOIFiCP1CipdhERERERERERGqQlXU4994b5ttvv+bww4exZctmli//iWuvvaFK8FJcXMxll12Iy+Xi6quvJz4+njfemMdVV13Ca6+9TXJyCvfccz/XX38VPXv2Zvz4MwFo1ao1AFu2bGbUqONp1ao1gUCAhQs/YuLEi3j++Vdp27ZddJ5gMMitt05h/PgzyMjI4O2332TKlEnMmfMyHTt2io777bdfKSh4kUsuuYJwOMSjj85g6tRbmDlzTnRMQUE+Z511HmlpzSgsLOC1115m4sSLeOmledhsNsrKSpk8+Wp69erN7bffjd3u4I8/1lJSUhK9x2efLeS226Zw3HFjuOCCi8nLy+Xppx+npKSYO+6YVuWzXLjwYw45pA8tW6YzfPhI3nnnDYqLi0lISKjV78k999zBsmXfc9llV9KyZUveffctfv11dY1jH3hgGscccxz33HM/X3zxGU899RiZmQcxePCh0TF5eXk89dRjnHPOhdhsVh5++AGmTr0Fl8vFIYf0YcyYE3jvvXeYOvUWunfvScuW6bV+7zur49xzL6SwsID16//g1lvvAiA5OblWn0ljUrAjIgeUYNiPSWTzPYfVRXll90wgFqvbvcfmDSfH4Aw7cYbi8VlLKfdsxl4R7AQU7IiIiIiIiIhIAzNN8IcaZ26HFQyj7tc7nTEcfvgRLFjwMYcfPowFCz6iXbsOHHRQ5yrjXn/9VUpLS3j22bkkJ6cA0K/fQMaPH8urr77IZZddRefOXbDbHaSkpNCjR88q15933oTor8PhMAMGDGLlyhXMn/8BF198efRcIBDgnHPOZ/ToEwAYOHAI48eP5YUX5nD77XdHx5WWljB79svRgKC0tJR7772LnJxsmjdvAcCUKbdFx4dCIXr06MXYscexbNlSBg4czIYNf1JaWsrFF08kMzMSGvXvPzB6jWmaPPHEIwwfPoIbb7wlejwlJYXJk6/hnHMupGPHTABWrVrJ+vV/cMoppwMwcuQoXn/9VT77bCEnnHDSbv9+rFu3li+//Ix//esORo06PvoZnH56zfcYNmw4F1xwMQD9+g3gm2++4vPP/1Ml2CkpKebJJ5+jffvIPj65uVt56KH7OeOMczj33AsB6NKlO19++Rlffvk5p556eq3e+67qaNWqNUlJyWzZsrnan4umQMGOiBxQtu+OsVtjyKtYhi3el4SZtGeWYQMwkyMhTrw3GV9cKWWeLTgs2mNHRERERERERBqeacKj3xisK6hHulIPHZJNrjzUrFe4M3LksUyZMony8nIWLPiIkSNHVRuzZMm39OnTH7c7gWAwCIDFYqFXr96sXLmi2vi/++OPdcyc+QTLl/9EQUF+9PiGDeurjR069Mjor61WK4cdNrTacmydOnWu0vVRGVrk5OREg53Fi79m7txZrFu3hrKysipzDhw4mIyM1sTFxfHAA9MYN248ffv2r3LPDRvWs2XLZq688rroewbo3bsfhmGwevXKaLixYMF8bDYbw4cfDUDXrt1p3botCxZ8VKtgp3JJtqysI6p8BoceejhvvPFatfEDBgyO/tpisdCuXXtycnKqjElLaxb9fADatIl0SG0fYrndbpKSksnJya71e9/dOpoqBTsickCpXIbNbonBYlgp924BIM6XRDjJucfmDSdHQiN3eRJ5cRso82zGadceOyIiIiIiIiIiNenffyCxsXE8//xzrF27hqOPPqbamKKiQn755WeGDRtc7Vzlcms7Ul5exrXXTiQpKYkrrriGFi3ScTodTJ9+F36/v8pYm81Wbemy5ORk8vKq7o3j/ttqMHa7HQC/3wfAypW/cOON13L44Udw5pnnkJSUgmEYXHzxufh8kTkTEhJ46KEnmDXrGe6661ZCoRC9evXmmmsmk5nZicLCQgCmTJlU4/vKzo58ryscDvOf/yygT59+GIYlupTb0KFH8OqrL5GdvYUWLVru9DOqlJubi81mIz4+vtpnUJOaPofy8qo/2Pz3e9lsth1eW/n57e57r00dTZWCHRE5oPjDkRClcn+b8oqOnThfEmbingt2cNkwXTbcvshfeGWeTThiDo7UpGBHRERERERERBqQYcCVh5r4Q2ajzF/fpdgg0hEyfPjRvPbaS/To0YuMjFbVxrjdCQwadCgTJlxS7Zzd7tjp/Zcv/5mcnGzuvfehKku8lZWVAs2rjA0Gg9X2pSkoKCA1Na1W7+nLLz8nPj6eqVOnY7FYgMg+P3/XrVsPHnzwUXw+L8uWLeWJJx7hppuuY968d0lISATgmmsm0717j2rXpqU1A2DZsqXk5m4lN3crxx57ZLVxCxd+zBlnnLNbdaelpREMBiktLa0SyBQUFOzW9Q1ld9/7gUDBjogcUCpDFIc10i1T2bET70sivAeXYoNI1068tzLY2UxKWu8qNYmIiIiIiIiINBTDAGcT/+7v6NEnkJOTzciRx9Z4vn//gXzyyXzateuAy+Xa4X1sNnu1Lhyfzwts66oB+Pnn/7F58yY6dOhY7R5ffvlZdI+dUCjE119/Sbdu1cOFnfH5vNhsNoztUq9PPpm/w/FOZwxDhmSxceNfPPLIg/h8Ptq1a0/z5i3YtGkjJ5986g6v/eST+bhcLqZNezAaIlV6/PGHWbBg94OdLl26AfDVV59z7LGjgcrP4KsdX7QH7O573101/bloKpr4l7aISO1U7mezrWMnsq7mHu/YIRLsuP9KqZh3My0qagiEFeyIiIiIiIiIiPzdQQcdzLRpD+7w/PjxZ7BgwUdMnHgRp5wynhYtWlJYWMCKFb+QlpbGaaedAUD79u354YelfP/9t7jdCaSnZ9C9e09crlhmzLiXM888l61bc5g9+xmaNWtebR673c7cubPx+/2kp2fw9ttvkJOTvdPaajJgwCDmzXuVhx66j6FDj2T58p/4+OMPq4z55ptFfPDBuwwdOowWLVqSn5/HG2/Mo2fPQ3A6I9+7mjjxGu6442a8Xg9DhmThcrnYsmUzixcv4qKLLqdly3S+/PIzjjhieJU9ayqNHn0CM2bcy9q1a6rsSbMjHTp0ZOjQI3nkkQfw+by0aJHOu+++SSgUrBJS7WmGYezyvbdt226379e+fXs+/PA9Fiz4iDZt2pKYmER6esYefAcNR8GOiBxQAqG/LcW2XcfOng52zGQXsWsiLbseX160hsqwSUREREREREREdl9iYhIzZ87h2Wef4qmnHqO4uIjk5BS6devB0KHDouMuuuhyHnxwOjfffAPl5WVMmXIbxx03hjvvnM4TTzzMjTdeR5s2bZk06SZefnlutXlsNhu33353RRjyO+npGdx993106nRQreodMiSLSy+9gjffnMeHH75Pz56HcN99D3P66SdFx7Ru3RqLxeCZZ56koCCfxMQkBg4czMUXXx4dM3z40bjd8cydOzva8dOyZTqDBh1KSkoq33zzFaWlpYwadXyNdRx99Egee2wGCxZ8VOW+O3PTTbfy0EP38cQTj+BwOBg1ajTt23fknXferNVnUF+7eu+1MXr0CaxY8QsPP3w/RUVFHHvsaG6++fY9UHXDM0zTbJyFFvcDBQVlBIPhxi5DRGrhf9kf8e6v08hMHsj4rnfxzn9GAnDqspsJ3TBitxeAtdksJCfH1eo5YFu2BesHy3ll8J0ADB70OLN+mki8I5VrB71VtzckIo2qLs8CEdm/6DkgInoOiAg07rMgEPCTl7eZ1NT0Xe4rI7U3a9ZMXnvtJRYs2LvLjjUFl156ATabjccem9nYpewVu/paS0mJw2q11HBlw1PHjogcUPzbdeyUeyPLsNlCdhxxyXj2cOuomRyDPezEFnYQtPgxQ74qNYmIiIiIiIiIiOyLPv/8P2RnbyEz8yC8Xg8LFnzEzz//j3vueaCxSzsgKdgRkQPK9nvsVC7DFudLwkyK2eNzh5Mjc7h8bkpceYSDkVr8IQ+mae7VNUlFRERERERERER2l8sVy8cff8iGDRsIBgO0bdueW2+9s8qSd7L3KNgRkQNKtGPH4qLcE+nYieyvs+eDHTPBiWkxiPVHgp1QoLTyDMGwD7t1z9cgIiIiIiIiIiK774ILLuaCCy5u7DIa3aBBQxg0aEhjlyEV6rzgm9/vb8g6RET2ikBlsGOL3a5jJ5FwonPPT24xMJNjiPUnRGoJFAORLp3KTiIRERERERERERGRnalzsDN06FAefPBBNm3a1JD1iIjsUdGl2Cwuyj3ZQOVSbHsh2CGyHJsr4AbA68vHUdGlo312REREREREREREZHfUOdg58sgjefHFFxkxYgSXX345ixcvbsi6RET2iOhSbFYX5d5IsBPvSyK8F5Zig0iwE+uvDHbycFhcVeoSERERERERERER2Zk6BzvTpk3jiy++4Oqrr2bVqlWcf/75HHvssbz00kuUlpbu+gYiIo2gMkBxWmMp91QuxZaEuTeWYgPM5Bhc0WAnF4dVwY6IiIiIiIiIiIjsvjoHOwCJiYlMmDCBhQsX8vjjj5Oens7dd9/N0KFDmTp1KmvWrGmoOkVEGoS/Yik2u8WJx7cVgDh/EmaCY6/MH05xRffY8fjytgU7YQU7IiIiIiIiIiIismv1CnYqGYbBUUcdxaRJkxgwYADl5eW88sorjB49miuuuIK8vLyGmEZEpN6inTHhAKYZwjAtxDjTwNogj8Ndqtqxk4fDGhupK1i+V+YXERERERERERGRpq3e38kMBoN88MEHnH766Zx88sn89ddfTJo0iU8//ZQpU6awdOlSJk+e3BC1iojUW7QzJuQDINbvxkiK3Wvzb7/HTjBUjsNwVK1LREREREREREREZCfqHOxkZ2fzyCOPMGzYMCZNmoTVauXhhx9m4cKFXHjhhWRkZHDWWWdxxx13sHTp0oasWUSkzqIdO8FIsOPyuwnvpf11AHBYsbrisQcjczoqHsPaY0dEREREREREDnSzZs0kK6t/9L/Ro4/mqqsu5X//+3G37/Hhh++TldWfwsLCWs2dldWfV155sVbX1HWu2pgx417GjRuzx+4vTZOtrhcOHz4cq9XK8ccfz9lnn03Xrl1rHNemTRtSU1PrXKCISEOq3GMnXLH0mSsQj5kWs1drMFNcuAJuAjYfNrOyLgU7IiIiIiIiIiJOp5NHHnkagK1bs5k7dzZXXXUps2a9RGZmp0auTmTfUOeOncsvv5zPP/+cadOm7TDUAejatSuffvrpDs+vX7+eW2+9lRNOOIFu3boxevToGsd98cUXnHjiifTs2ZMRI0bw8ssv71adgUCABx98kKysLA455BDOOussVq1atVvXisj+xTTD+ENeAILBUqCiYydpL3bsUHU5NgU7IiIiIiIiIiLbWCwWevToSY8ePTnyyKOZPn0GoVCId999s7FLE9ln1DnYycjIwDCMGs8VFhbyzjvv7NZ9fvvtN7744gvatWtHZmZmjWN+/PFHLrvsMrp168azzz7L2LFjueuuu3j99dd3ef9p06bx8ssvc+WVV/Lkk09is9k499xz2bp1627VJyL7j0DYB0SSlGBgW7Bj7s2l2AAzOQZXRbBjCQeBbZ1EIiIiIiIiIiKyTcuWLUlMTGLz5k2Ew2FeeGE2p5zyD448cgjjx49l3rxXdnr9+eefydSpt1Q7PnPmE4wZM5JgMLjDa9999y3OOGMcRx45hJNOOp5nnnmyxvF//bWBK6+8hKOOOoxx48bwwQfvVhuzfPlPXHnlJRx9dBbHHHMEt99+MwUF+VXG5OZu5YYbruGoow7jxBOP5ZVXXtjpe5MDV52DnZtuuokNGzbUeO6vv/7ipptu2q37DB8+nC+++IJHH32U7t271zjmiSeeoFu3btxzzz0MHjyYyy67jHHjxvHII48QDod3eO/s7Gxee+01rrvuOk499VQOO+wwHnvsMUzTZO7cubtVn4jsP7aFJwZ+fyEAsQE34aS9uxRbpGMnIVJJOFBRmzp2RERERERERKThmCYEA43zn2k23PsoKyulpKSYtLRmPPHEIzz33NOMHHks9977EIcfPoxHH53B888/t8Pr//GPE/n8808pKSmJHguFQnz00b8ZNep4bLaadyt5443XuP/+e+jbdwDTp8/gxBNP5pVXXuD++++pNvb226cwYMAg7rnnAfr27c/06Xfy7bffRM8vX/4TV1xxMXFx8dxxxzQmT76ZVatWcMMN11a5z403XseqVSuYNOkmrrvuBj7//FO++uqL2n5kcgCo8x475k6+On0+H1ardbfuY7HsPFvy+/18++23TJo0qcrxMWPGMG/ePFasWEGPHj1qvHbRokWEQiGOP/746LH4+PhomPT3e4rI/q0yPHFYY/B68wCI8cfv9Y6dcMq2jh2Cviq1iYiIiIiIiIjUl2nC9x8ZFG6tecWlPS2pmcmAUSY7WPBplyq7YrZuzeHxxx8iFArRv/9A7rzzVsaPP4MJEy4FYODAwZSVlfLyy3M59dR/EhsbW+1eI0aM4vHHH2bhwo8ZO3YcAEuWLGbr1hyOP/4fNc4fCoV4/vnnOPLIo7nuuhsAGDRoCIZh8MwzT3L22efTqlXr6PhRo47nrLPOi47buPEvnn/+OQYPPhSAp59+nC5dunLPPfdHV8Hq0CGTc84Zz+LFixgyJItvv/2GVatW8MgjT9Gv3wAADjmkLyeffDyJiUl1+yBlv1Wrjp1Nmzbx/fff8/333wOwYsWK6OvK/7766iuef/550tPTG6TAP//8k0AgQMeOHasc79QpslHWmjVrdnjtmjVrSEtLIykpqcrxzMxM1q1bt9NuHxHZ/2wLdmLx+SLBjsuSBI7dC6Ibipnsiu6xEw56qtQmIiIiIiIiInIg83g8DBs2mGHDBnPKKf9g2bIfuOaaybhcsQSDQYYPH1ll/NFHH4PH4+G331bXeL+4uHiGDx/Bv//9XvTYv//9Hj179qJ9+w41XrN+/R8UFhZy1FEjqs1lmiY///y/KseHDh1W7fWqVSsIhUJ4vV5+/vl/HHnk0YRCIYLBIMFgkLZt25GamsbKlSsAWLFiOfHx8dFQByAhIYG+ffvv/AOTA1KtOnbeeustHn/8cQzDwDAM7rjjjmpjKjt5br755gYpsKioCIj8Id5e5evK8zUpLi7G7XZXO56YmEggEKC8vJz4+Pg612a11nklOxFpBEG8ADgtMXgDWwCIiW2GzVb7r+XKr/86PQeSnLjCiQCEA2Vgg0DYU6c6RKRx1etZICL7BT0HRETPARGBxn0WhMPV22IMAwaMMgkFG3BNtFqw2qhzt47T6eSJJ54FDJKSkmjevAUWi4WPP/4QgNTU1CrjU1PTACgu3vH3if/xj7Fccsn5/P77b6SlNePrr7/iuutu3OH4ymXbUlKqzlX5uri4uMrx5OSUaq+DwSBFRYWEQiFCoRCPPjqDRx+dUW2unJxsAPLycklKSq52PiUllTVrft9hrbL3Wa1Gjd/Hq+uf+bqoVbBz7LHHctBBB2GaJldffTXXXnst7dq1qzLG4XBw0EEH0bp16x3cpW6MHXwqOzq+s/M7W0auNhISXA1yHxHZOzb5Il/7sTYXJiEA3CktcCXH1fmedX0OBGKbARAMloINQvhIrkcdItK49G8CEdFzQET0HBARaJxngddrJTfXUuM3m+32vV5OvVgsBhaLpcatN5KTkwAoKiogPb1l9HhhYX70vM1mwWKJfD/YZtv2efTu3ZuOHTOZP/89WrZMx263M3LkMdU+r8rPMCVl21zbj8nJKaiYK7HKXMXFhVVqilxnIzU1Bb/fj2EYnHPO+RxxxJHV3ldiYqTuZs2aUVhYUK2mgoL8ivejHyBobOFw5M9nYmIsMTF7d8/uv6tVsJOZmUlmZiYA06ZNY9iwYSQnV08RG1JiYuSn2v/emVOZiv69k2d7CQkJ1dLTymvtdnuNay7WRnGxh1BIy7mJNBX5RZG/fG3hyF+EzkAswdgYCgrKan0vq9VCQoKrzs8BR0zkJzzCph+LCR5/aZ3qEJHGVd9ngYg0fXoOiIieAyICjfss8Pt9hMNhQiGTYLBpP4fC4cgP5db0Pjp37obNZmPBgk/o1Ong6PEFCz7G5XKRmXkwwWB4u3tU/TxGjz6RF16YRVJSCsOHj8DhiKk2T+VnmJHRhqSkZBYu/ITDD98WxnzyyUcYhkH37odUmeuzzz4lM7NzdNznn3/GwQd3xTQN7HYnPXr0ZN26tVx44aU1vu9gMMzBB3ejtLSU7777LrocW3FxMT/88D2JiUlN/vd2fxAKmYTDYYqKyvF4QtXOJya6sFj2TgBXq2Bne2PHjm3IOnaobdu22O121q5dy9ChQ6PHf/890n5WGTTVJDMzk7y8PAoLC6vss7NmzRo6dOhQ7w85FArrC0qkCfH6I8GJzYx87cf63YRSnPX6Oq7rc8AZ78YedBKw+bCZ4At59DwRacL0bwIR0XNARPQcEBFonGdBKNQ4y63tbUlJSYwbN57XXnsJh8NBz56HsHTpEt599y0uuOBiXK6dd0uNGnUcTz/9OIWFa7nxxn/tdKzVauW88y7koYfuJykpmcMOG8rq1auYPXsmxx03hoyMVlXGf/TRv3E6nXTu3IX//OcT/ve/H7n//oej5y+77CquuupSbr31Jo46aiRut5utW3P4/vvvOO64MfTt25/Bgw+lc+cuTJ36Ly655ArcbjcvvDCH+PjqW41I49pRiNpAC4XtlloFO48//jinnHIKLVq04PHHH9/pWMMwuPzyy+tVHESWdhs8eDDz58/n3HPPjR7/4IMPaNasGd26ddvhtVlZWVgsFubPn8/pp58OQFlZGZ9++imnnHJKvWsTkabFFyoHoLIL2eV3E050NkotpttJbGkCRbat2EzwhzyNUoeIiIiIiIiISFNx2WVX4na7ef/9d3jxxTm0aNGSiROv5rTTztjltQkJifTu3YecnGx69Oi1y/Enn3waVquNefNe4d133yIlJZXTTz+L88+/qNrY22+/m6effpw5c54jOTmZyZNvZsiQrOj5nj0P4cknn2PWrJlMm3YHgUCAZs1a0L//AFq3bgNEvp8+ffqDPPDANO6/fxput5tx48azdWs233yzqBafkhwIDLMWG8506dKFefPm0atXL7p06bLzGxsGK1eu3OU9PR4PX3zxBQAvv/wyGzZs4MYbIxtXDRw4kJSUFH788UfOPPNMxo4dy5gxY1i2bBmPPvooU6dOrRLQjBgxgoyMDObOnRs9NnXqVN59911uvPFGMjIymD17NsuXL48GQ/VRUFCmn8YRaUK++vNFPlv/HD2dnQkX/kpmTh/6Dr+TcEbtf/LBZrOQnBxX5+eAfckmPv/jZrYkreUvOxTZDG7J+myX+4aJyL6lvs8CEWn69BwQET0HRAQa91kQCPjJy9tMamo6drtjr87dlJSVlXLiicdx/vkXcfrpZzZ2OdIE7eprLSUlDqt1H1yKbdWqVTX+uj7y8vK46qqrqhyrfP3CCy8waNAg+vTpw5NPPsmMGTN45513aNmyJf/617+qdd2EQiHC4aoPzhtvvJHY2FgefvhhSkpKOOSQQ5g7d269Qx0RaXr84UhXjDUcIgy4/PGN1rETdjuIDUQCJZsJYBIIe3FYteGqiIiIiIiIiEhDKS8vY926dbz99usYhsHxx49p7JJE6q3Oe+w0lNatW7N69epdjjviiCM44ogjdjrm008/rXbM4XAwadIkJk2aVOcaRWT/4A9GlmIzAn4AXKEEiLXv7JI9xnQ7cfm3D3Yiy7Ep2BERERERERERaTirVq3kyisvoXnzFtx88+0kJCQ2dkki9VbnYMfn8xEIBIiPj48e+/DDD1mxYgWHHnoohx56aIMUKCLSUCo7dsygD4AYewo00tJnZoKDWH8CAE6sQEj77IiIiIiIiIiINLC+ffuzaNHSxi5DpEHVecG366+/nrvuuiv6+oUXXuDaa6/lueee44ILLojumyMisq+oDE7Miv/HxKQ1Wi1mnANXxVJsdiLhkoIdERERERERERER2ZU6Bzs///wzhx9+ePT1iy++yD/+8Q+WLl3KiBEjmDVrVoMUKCLSUCqDk1BF544zvvGCHawGMdYUAGxmZC02f6i88eoRERERERERERGRJqHOwU5+fj4tWrQAYMOGDWzYsIEzzzyT+Ph4xo0bx2+//dZgRYqINAR/yIPFBJMgAE5380atx+VMBcAaDoOpjh0RERERERERERHZtToHOzExMZSUlADwww8/EBsbS8+ePQFwOp2Ul+snz0Vk3+IPebBFmmOwhRzYkpIatZ6Y2GYAGJhYULAjIiIiIiIiIiIiu2ar64WdO3fm5ZdfJiMjg1deeYVBgwZhVGxCvnnzZtLSGnGJIxGRGmwf7Lj8bsJJzkatx+KOxx6MIWDzYjPBH1awIyIiIiIiIiIiIjtX546dyy67jO+//54TTzyRVatWceGFF0bPff7553Tv3r1BChQRaSj+UPl2wU48ZmLjBjtmgpNYvxsAu5ZiExERERERERERkd1Q546dIUOG8OGHH/LLL7/QtWtX2rRpEz03ePBgunbt2iAFiog0FH/Ig7vi166AG9PdyMGO20HsZjdFsVsjHTshLWEpIiIiIiIiIgeuWbNmMmfOs9HXSUlJZGYexPnnX8Qhh/RpxMp2z7x5r9CmTVuGDMnaY3NMnHgRsbGx3Hffw3tsDtn31TnYAWjVqhWtWrWqdnz8+PH1ua2ISIMLmyECYS/2yo4dIwmsRqPWZCY4cPkTACqCHXXsiIiIiIiIiMiBzel08sgjTwOwdWs2c+fO5qqrLmXWrJfIzOzUyNXt3Lx5r3LooVl7NNgRgXoGOwB5eXls3LgRn89X7dyAAQPqe3sRkQYRCHkBokuxxdiS63W/Al+QRWvy6OO24zTqFhCF3U5iA1qKTURERERERESkksVioUePnhWvetK1aw9OOWUM7777Jtdee0OVsaZpEggEcDgce79QkUZU52AnJyeHyZMn891331U7Z5omhmGwcuXKehUnItJQKkOTaLDjTK3zvYr8IW78YRObygN0SnByR++WxNuttb5PpGMnEuzYULAjIiIiIiIiIvJ3LVu2JDExic2bN3H33bezatUKLrvsSp5++gnWr1/HrbfexfDhR/Pll58zZ84zrF//B/Hxbo44YjiXXnoFsbGxACxbtpQrr7yEBx54lPfff5slS77F7U7g4osv55hjjuP111/j1VdfpLy8nGHDhnPttTdUCYxycrJ5+unH+e67b/B4vHTt2o0rrriWLl0iW5KMGzeGLVs289Zbr/PWW68DMGXKbRx33Bjmz/+A9957mz/+WIdpmnTqdBCXXXYl3br1iN5/1qyZvPbaSzz66NPMmHEva9b8Tnp6BpdddhWHHXZ4tc/l008X8uyzT5Kbu5WuXbtzww3/olWr1tHzTz31GIsXL2Lz5k3ExcVzyCF9uOKKa0lLS4uO+emn/zJz5hP8/vuvhMMm6enpnH76WRx77OjomG++WcScOc+yZs3vxMa6GDbsKC6//GpcLlcD/Q5LXdQ52LnzzjtZuXIlkyZN4uCDD1YqKiL7tMrQxG4agElMbNrOL9gBTzDM1P9uYVN5AIDfi33c+uNmpvZJr32447ThCicBaI8dEREREREREZEalJWVUlJSTFpaM4LBILm5uTzyyIOcc84FNG/egubNW7Bo0RfcfPP1HHnk0Vx00eVs2rSRmTOf4M8/1/PII09Wud+DD97L8ceP4cQTT+a9997h7rtvZ82a31m3bg3XX38TmzZt5LHHHiIjoxVnn30+AMXFxVx22YW4XC6uvvp64uPjeeONeVx11SW89trbJCencM8993P99VfRs2dvxo8/EyAatGzZsplRo46nVavWBAIBFi78iIkTL+L551+lbdt20dqCwSC33jqF8ePPICMjg7fffpMpUyYxZ87LdOy4bRm63377lYKCF7nkkisIh0M8+ugMpk69hZkz50THFBTkc9ZZ55GW1ozCwgJee+1lJk68iJdemofNZqOsrJTJk6+mV6/e3H773djtDv74Yy0lJSXRe3z22UJuu20Kxx03hgsuuJi8vFyefvpxSkqKueOOaQ3/my27rc7BzpIlS5g8eTInn3xyQ9YjIrJH+CpCk8qOHWd881rfIxA2mf5zNr+X+EiwW7hhYBvuXbKBNSV+bvtxC3f0qX3njsueEq1LHTsiIiIiIiIi0lBMEwg00uR2qOPK9UAk4ADYujWHxx9/iFAoxLBhR7Fw4ceUlBTz4IOPVul2ue22m+jSpRtTp24LGxISErjjjn+xbNlS+vbtHz0+fPjRnHvuhQB07dqDL7/8jIULP+b//u8d7HY7AD/++AOffbYwGuy8/vqrlJaW8Oyzc0lOjnwvp1+/gYwfP5ZXX32Ryy67is6du2C3O0hJSdluKbmI886bEP11OBxmwIBBrFy5gvnzP+Diiy+PngsEApxzzvmMHn0CAAMHDmH8+LG88MIcbr/97ui40tISZs9+meTk5IrXpdx7713k5GTTvHkLINItVCkUCtGjRy/Gjj2OZcuWMnDgYDZs+JPS0lIuvnhidO+i/v0HRq8xTZMnnniE4cNHcOONt0SPp6SkMHnyNZxzzoV07Ji5099H2XPqHOwYhkF6enpD1iIissf4Qx4ME6xEkh1nUu2CnbBp8uiKrfw334PTYnB73wwGt0kiyQxz0/cb+b3EV6dwxxkTWRLObkJZUB07IiIiIiIiIlJ/pglFLxsEN9YjXakHWyuTxDPMOoU7Ho+HYcMGR1+73Qlcc81kBg0awsKFH5OUlFQl1CkvL+e3337lssuuqnKfI488mrvuuo2ffvpvlWBn+/AiPj6epKRkevfuGw11ANq0acePP/4Qfb1kybf06dMftzshGjpZLBZ69erNypUrdvme/vhjHTNnPsHy5T9RUJAfPb5hw/pqY4cOPTL6a6vVymGHDeXrr7+qMqZTp87RUAegffsOQGT7lMpgZ/Hir5k7dxbr1q2hrKysypwDBw4mI6M1cXFxPPDANMaNG0/fvv2r3HPDhvVs2bKZK6+8LvqeAXr37odhGKxevVLBTiOqc7AzatQoPvvsMw499NCGrEdEZI/whz1YK7p1jLAFR2JaRcSza6ZpMvu3PL7MLsVqwI29WnBwUgwA7d1O7uyTzr9+3MzvJT5u/+8Wbq/Fnjsxsc0AsACBYNnOB4uIiIiIiIiI7OecTidPPPEsYJCUlETz5i2wWCzR80lJKVXGl5aWYJomqalV91O22WwkJiZRXFxU5bjb7a7y2m63Ex8fX+1av98ffV1UVMgvv/xcJXCqtP2+NjUpLy/j2msnkpSUxBVXXEOLFuk4nQ6mT7+ryhyV8yYkJFQ5lpycTF5e7i7fA4Df7wNg5cpfuPHGazn88CM488xzSEpKwTAMLr74XHy+yJwJCQk89NATzJr1DHfddSuhUIhevXpzzTWTyczsRGFhIQBTpkyq8X1lZ2/Z6fuWPavOwc6xxx7LLbfcgmmaHHnkkSQlJVUb07179/rUJiLSYAIhD5U/d+EKxGMmxOz2tW//WcT7G4oBuLJrM/qmxlY5397t5K6KcOe34tqFO9aEeGx+J0Gbj1BIwY6IiIiIiIiI1J9hQOIZJgR298daG1g9lmKzWCx06dJth+f/ft/4eDeGYZCfn1fleDAYpKiokISExLoVsh23O4FBgw5lwoRLqp2z23e+9/zy5T+Tk5PNvfc+xEEHdY4eLysrBaquKBMMBikuLq4S7hQUFJCaWru9or/88nPi4+OZOnV6NBTbsmVztXHduvXgwQcfxefzsmzZUp544hFuuuk65s17N/q5XXPNZLp371Ht2rS0ZrWqSRpWnYOdc845B4CXXnqJl19+uco50zQxDIOVK1fWrzoRkQbiC5VH99dxBdwQu3uPv/9sKmHu75EW2fMPSmFYurvGcZWdO7dUhDvTfsrmrr7pGLv4V4zpduLMjiNo8xEOaCk2EREREREREWkYhgHsPHPYL8TGxnLQQZ359NOFjB9/ZvT4F198Gu1Cqa/+/QfyySfzadeuAy6Xa4fjbDZ7tS4cn88LUGWpt59//h+bN2+iQ4eO1e7x5ZefRffYCYVCfP31l1WWntsdPp8Xm81W5ftSn3wyf4fjnc4YhgzJYuPGv3jkkQfx+Xy0a9ee5s1bsGnTRk4++dRazS97Xp2DnWnTpu16kIjIPsIf8mwLdsKJu/VjIwW+IE+u2grA2LaJnNA2aafjO1SEO5O+38jyQi/rywK0j9/5v6DCCQ5cG+Ipi8nHDHl3672IiIiIiIiIiMg2559/ETfdNInbbpvCsceOZtOmjcyc+Tj9+g2ssr9OXY0ffwYLFnzExIkXccop42nRoiWFhQWsWPELaWlpnHbaGQC0b9+eH35Yyvfff4vbnUB6egbdu/fE5Yplxox7OfPMc9m6NYfZs5+hWbPq+z/b7Xbmzp2N3+8nPT2Dt99+g5ycbKZNe7BW9Q4YMIh5817loYfuY+jQI1m+/Cc+/vjDKmO++WYRH3zwLkOHDqNFi5bk5+fxxhvz6NnzEJxOJwATJ17DHXfcjNfrYciQLFwuF1u2bGbx4kVcdNHltG3bro6fqNRXnYOdsWPHNmQdIiJ71PbBTowlabeu+XZrOUETMt0Ozu6UsusLiIQ7/VJj+S63nK+zS2kfv/PrTLeTWH+kvdYIBTDNMIZh2ek1IiIiIiIiIiKyTVbWEdx11308//yz3HTTdcTHuxk58jguvfSKBrl/YmISM2fO4dlnn+Kppx6juLiI5OQUunXrwdChw6LjLrroch58cDo333wD5eVlTJlyG8cdN4Y775zOE088zI03XkebNm2ZNOkmXn55brV5bDYbt99+NzNm3Mvatb+Tnp7B3XffR6dOB9Wq3iFDsrj00it48815fPjh+/TseQj33fcwp59+UnRM69atsVgMnnnmSQoK8klMTGLgwMFcfPHl0THDhx+N2x3P3Lmzox0/LVumM2jQoaSkpFabV/YewzTNei+0uHbtWgoKCujatSuxsbG7vmA/UVBQRjAYbuwyRGQ3LFz3NOvWvkpKCHp6j6fzPybv8ppblm3ipwIv53ZKYWy7JHK8XkoCQeJsNtxOO62aJVBa5Kn2HPhiSykzfskhw2XnySGtd7ocm1Hs45c372Blq6/Js8K5wz7CYd1xS6+I7FtsNgvJyXH6N4HIAUzPARHRc0BEoHGfBYGAn7y8zaSmpu9yvxfZd82aNZPXXnuJBQu+auxSZAd29bWWkhKH1bp3fmC7zh07AO+88w4zZsxg69bIUkVvvPEG3bt356qrruKwww7j1FO19p6I7Bu279hxOnbdfVPsD7G8MLI02qHN4/ijtIxrlv1E8G9ZuMNiIdZqxWWzEmu1cmSLZoxo2RKHxWCTJ8C6Uj8d3c4dzmPGOYj3JwGRB7I/5FGwIyIiIiIiIiIiIjtU5/ho/vz53HjjjXTr1o1bbrmF7Rt/unfvzvz5O96MSURkb6uyFFtM2i7Hf5dbRtiEjm4HLVx23tu4maBpEmOxEGPZ9uj0h8MUBgJs9nhZU1rGnLXr8YaC9EuNhDNfZ5ftfCKrgcuSDIDNBH+ovG5vUERERERERERERA4Ide7YeeaZZzjppJO45557CIVCTJ06NXquY8eOvPjiiw1SoIhIQ/CHyrFX/Domvtkux3+TEwlkDm0eR3EgwBc5uQBM7dWNrokJGBYDh9vJptxiSnwBykMhZq35g19LSvlkSzZZLZJZvLWcRTmlnJmZvNPl2FyOyJqkdjMSQImIiIiIiIiIyIHlggsu5oILLm7sMqSJqHPHzpo1azj++ONrPJeUlERhYWFdby0i0uD8wfJtHTvunQc7pYEQP+VHApYhzeL4ZHM2/nCYjvFxdElwA2C1GCQ47LRwxdA+Po5uiQmMaZUOwEebsumT6sJpMdjiCbKmxL/T+WJiIvXYTPAF1bEjIiIiIiIiIiIiO1bnYMflclFSUlLjuezsbBITE+tclIhIQwsES6nsmXEktdjp2O9zywma0DbOTnqsnQ83bQFgTKuWO+28OaxZKol2G3l+Pz8VFtI/LRaARdmlO53PGR9ZGs4CePz5u/eGRERERERERERE5IBU52CnT58+vPzyy1X21qn01ltvMXDgwHoVJiLSkIL+yNJqtqATIzFup2O3X4ZtSV4+W31+3DYbhzfb+d48douFo1tGQqP5m7LJahGZ5+ucshqflZUs7jhMM/I49ni37t4bEhERERERERERkQNSnYOdyy+/nP/+97+MGzeOF198EcMw+OSTT7jkkktYunQpl1xySUPWKSJSL+FgpGvGGYwDp3WH48qDYX6sXIateTwfbIx06xyT3gKndcfXVRqV3gID+LGgkIxYCzFWgxxvkF+LfTu8xkxwghnZ8szjU7AjIiIiIiIiIiIiO1bnYKdnz548++yzlJeXM336dEzTZObMmaxbt45nnnmGzp07N2SdIiL1Yga9ADhNN+xkObUf8soJhE0yXHYMAvxUWIQFODaj5W7N09IVQ7+UJAD+k53DwIrl2L6u6AKqsTa3AyPsAMDnL9iteUREREREREREROTAZKvPxYMHD2b+/Pn8+eef5ObmkpycTIcOHRqqNhGRhhP2A+Bi5/t/VS7DNqR5HP+u2FtnUFoKzWOcuz3VsRktWZpfyH+25HBpp658mV3GouxSzu2UgqWGUCmc4MQachCygd9fuNvziIiIiIiIiIiIyIGnTsFOfn4+r732GkuXLiUnJweA5s2bM2jQIE499VSSk5MbtEgRkfoIm0EsZhiAWEvqDsf5QmF+yC0HoE+Kkzt+iSyLNqZVeq3m65eSTHOnkxyfD0+oHJfVIM8XYnWRj65JMdXGm24H9pCLEBDwFddqLhERERERERERETmw1HoptsWLFzNy5EgeffRRvvvuOwoLCykoKOC7777joYceYtSoUXz//fd7olYRkTrxhzzRFDvW0WyH45blefCFTZrH2FhTVogvHKZdXCw9EhNqNZ/VMDgmvQUAn2zJZlCzOAAWZZfWfEGMDXsosmRb0K9gR0REREREREQOTJ98Mp8JE87mmGOOYOTIIzjjjHFMn34nBQX5e62GWbNmMmLE4dHXmzdvIiurP599tjB6bOLEi5g8+eq9VlNDWLZsKVlZ/Vm1akVjlyINoFbBTn5+PldffTVut5uHH36YpUuXsmjRIr7++muWLl3KjBkzcLlcXHnllRQUaJ8IEdk3+EMebGbk13ExzXc4rnIZtsHNYvlwUzYAozNaYuxgTx6ztBwjJ6/GcyPSm2MzDFaXlJKZELn+65wywqZZ43iH4QYgHCzf9RsSEREREREREdnPvPji89x556306tWHO+6Yxh133MPxx/+DVatWkpu7tbHLE9mn1GoptjfeeINwOMyrr75Ky5ZVNxJ3uVwcd9xx9O7dmxNOOIE33niDCRMmNGixIiJ14QuVR4MdZ1xajWMCYZOluZFgJyUmxGavlziblWEtau7wMfIK8c19C2eZh/C4Ywkd1L7K+WSHg0PTUvlyay5/lBcRZ7NR4A+xstBL92RXtfs5rZG9f8yQt47vUkRERERERESk6Xrzzf/j2GNHc8UV10SPDRlyGP/859mEw+FGrKxufD4vTmf1Jfl3dLypME2TQCCAw+Fo7FIOaLXq2Fm0aBEnn3xytVBnexkZGZx00kl89dVX9S5ORKQhbN+x40xoUeOY/+V7KA+ZpDit/FiYC8CIli2IsVqrjTXKynG88j6UeTCAmH9/hlFaVm3csRmRub7MyaVfqhOARTnVxwHE2FIAME0/5g66ekRERERERERE9lelpSWkptb8A7kWy7ZvY48bN4YZM+7l1VdfYuzY4xgx4nDuvPNWfD4fv/22mksvPZ+jj87iwgvPZs2a36vc59VXX+LCCyNLvY0ePYLJk6/mzz/X17v2Dz98n6ys/ixf/hNXX30ZRx+dxeOPPxJd/uybbxbxr39NZuTII7jllhsBKCkp4YEHpnPCCcdw5JFDOP/8M1my5Nsq9zVNkzlznuUf/ziGESMOZ8qU61m8eBFZWf1ZtmwpUPNScQAzZtzLuHFjdlr37nwed999O2eddSqLFy/inHNO58gjh7Bo0Zf1/ciknmrVsbN27VrOOuusXY7r378///73v+tclIhIQ/L4CqmMZ5xJNQc7X+dE9r/pmWzns9wiDOD4jBpCbH8A17wPsRQWY6QmEbJZsWTnEfP+p3jGj4btlm3rnphA21gXf5Z7cNn9QGS5tws7p2L92/JusTGpEAIDk2CoHLstrt7vW0RERERERESkqTj44K68++5bZGS04tBDs3YY8gAsWvQlmZmdmDx5Cps2beSxxx7C4XDwyy8/c9ppZ5CSksJTTz3GLbfcwEsvvR4NhrZuzebkk0+lRYuWlJeX8c47b3Lppefz6qtvkZCQWO/3cMcdt/CPf4zl7LPPx+Fw4vf7ALj//nsYOfJY7rlnHBaLhUAgwDXXXE5+fh4TJlxGs2bN+eSTD7n++quYPftlMjM7AfDGG//H7NnP8M9/nk2/fgNYunQJ998/rd51VtrdzyM3N5dHHnmQc865gObNW9C8ec3fX5O9p1bBTnFxMSkpKbscl5KSQnGxNgAXkX1DedkWAEzTwJpY/S/pYNhkydbI3jZeM9JR0z8lmZauv7XFhsO43lmAdctWTFcMjgnj8BSV43xuHrY//sL+3X8JDO4THW4YBsdltOTp39exvDifOFsShf4QvxR46ZVSdTk2Z2wyoRKwAl5fvoIdEREREREREakf04RAI81tp8oPv+6O6667gSlTrufee+8CID29FYcddjinnfZP0tMzqo2/554HsNvtAPz44w+8//47PPDAowwefCgA4bDJDTdcw5o1v3PQQZ0BuPLK66LXh0IhBgwYxOjRI/nss/9wwgkn1eWdVjF27Mn8859nR19XdtVkZR3BpZdeET3+73+/x2+/reb551+lQ4eOAAwaNIQ///yT559/jjvvnE4oFOKll57nuOPGRK8dOHAwBQX5zJ//Qb1rhd3/PEpKinnwwUfp1q1Hg8wr9VerYMfv90e/WHZ6U5uNQKCxnhoiIlV5i7MBMLFiOKs/9pYXeCgNhkmwGfy3MB+A0a3Sqw4yTZwff4ltzXpMmxXf+ONxNU/BtDvxHX0YMfO/wPnFEkLtWhFObx697MgWzXh+7Xo2lHvol5jCj3khfswvrxbs2GMTCJaC1YwEO+64Ng38KYiIiIiIiIjIAcM0iXmqEOv6YKNMH2pvw3tJUq3CnY4dO/Hii/NYuvQ7liz5jv/+9wfeeOM1PvzwfZ544hkOOujg6NjevftW+T51mzbtsFgs9Os3YLtjbQHIycmOBjvLl//Mc889xa+/rqa4uCg6dsOGP+v6VqsYPPiwGo8PGVL1+JIl35KZ2Yk2bdoSDG77PerffyALF34MwNatOeTl5ZKVNbTKtVlZRzRYsLO7n0dSUpJCnX1MrYIdiCzHZq1hz4m/jxER2Vf4PHkAGGbNm7otyY1062TEB1lRGqKVy0Xv5KqdPY5vluH470pMw8B7wghovW2ZtsAhXbGu24B91Vpc7y6k7Lxx4IzMFWuzcUSLZny8OZuSUCngYnWRr1oN9ng3wa3gNMHrz2uIty0iIiIiIiIiB7LaNczsE+x2O0OGZDFkSBYA3323mMmTr2bOnOe45577o+Pi4+OrXGez2XA6nVXCnspfVy6HtmXLFq69diJdunTl+utvIi2tGXa7neuvvzo6pr6Sk2te7So5ObnK66KiQn79dTXDhg2uNrbye++5uZE9oJOSql7793vVVW0+j6SkXa/iJXtXrYOdm266aZdjTNPEqGWrnYjInuLzFQBgwVnj+VVFXkxMtvgjS0iObtUSy3bPMNtPq3B+uSRyrxFZBDt3qPrwNAy8o4Zh3ZSDpaCImAWL8I4eHj19XEZLPt6czZqyYkwc/FbsIxQ2sVq2zeFwJxKseOn1KtgRERERERERkXowjEjHTBNaiq0mgwYNITPzINavX1fve3333Td4POXcfff9uN1uAILBYJVOlfra0ffE/348ISGRzMyDuOmmW3Z4r7S0yB5DhYUFVY4XFFR97XBEfrg4EKjanbWrrVJq83noW/37nloFO9OmNdzGTCIie0sgEPkLyWpxVTvnC4X5o9QPBMj3+3BZrQxvsW0pNeu6DcTM/yIydnAfAv120HbqcuL9x1G4Xn4P+8+rCXZsQ7DbQQB0jI+jS4KbVcUlOGxe/ME4/ij1k5mwLWhyJCRS+devtzSn/m9aRERERERERA5shgE1L16yT8rPzyMlJbXKMZ/PS05OdnQfmvrw+XwYhoHNtu1b4p9+upBQKFTve9dW//4DWbz4a9LSmpGW1qzGMc2aNSc1NZWvvvqCww8fFj3+1VefVxmXnJyC3W6vEn75/X5++um/WCyWHdawL30eUnu1CnbGjh27p+oQEdljgqFSAGyWuGrn1pb4CZkQYw/gMaFfShKxtkjLqyU7F9dbH2OEwwS6HYR/2KCdzhNqk4H/0L44v/6BmI++pCyjBWZSAgDHZrRgVXEJGB5MYllV5K0a7Djioh07nrLshnjbIiIiIiIiIiJNxtlnj+ewww5n4MAhpKWlkZu7lTfe+D+Kigo55ZTT633/yv137rnnDk444ST++GMtr776EvHx7nrfu7ZGjTqed999i4kTL+b008+kTZu2lJaW8ttvqwkEAlxyyUSsVitnnnkejz76ICkpqfTt258ffvieZcuWAkRDG4vFwtChw3jzzXm0bt2GxMQk3njjtV3WsC99HlJ7O47sRET2E6GwBwC7PaHauV+LvQA4rZF+mW6JkTFGUQmuef/G8AcItsvAe/yRu9V36s/qT6hVSwyfH9d7CyEcBuDQtFRshoHfDAIhfi2uulapzeIgXLH4rcebW7c3KiIiIiIiIiLSRJ1//kXk5uby+OMPcfXVl/HYYw8RGxvHI488xdChw+p9/8zMTtx0062sXr2SyZOvYcGCj7nrrnur7dezNzgcDh599CkOOyyLF16YzbXXTuTBB6ezatUKevXqHR03btxpnHfeBP797/eYMmUS69ev49JLrwAgLm5b3VdfPZk+ffrx8MP3c//99zBkSBZZWUN3WsO+9HlI7RmmaZqNXURTVVBQRjAYbuwyRGQX3vx4FBgeUt1HM2xI1bVL7/85m69ySrHZtxIwwzzctxeZNjuxL72NNbeAULMUys88EWKq7s9js1lITo6r8TlgFBYTN/t1DJ8f32H98A8dCMCkZT+xuqQUI5RIeoybmYe2rXLdE58eQ8ugl3hLBscc/WrDfxAi0uB29iwQkQODngMioueAiEDjPgsCAT95eZtJTU3Hbm9Ca69JnTzzzJP83/+9zIcf/genM6axyzmg7OprLSUlDqt17/TS1GopNhGRpsjEjwE4XSnVzkU6Z4IEzDAuq4X28XHEvLsQa24BYXccnlOPrxbq7HK+pAS8o47A9e4CHN8sI9S+NaG2GXRNdLO6pBTT8LPFE6TIHyLRYY1eZzFcgBdfeOeb24mIiIiIiIiIyP7vjz/W8fHHH9Kz5yHY7TaWLfuB1157iRNPHKdQ5wCnYEdE9mvhUADDiGz65opvXuVcoS9IjjcIRgCAg91urIEgtl8jm815xo7ETKhb+2mwWycC6zZg/2kVMe8tpOyCU+makMA7bMZuDRIKw+oiLwObbdv3x2aJBQoImGWYZgjDsO54AhERERERERER2a/FxMSwYsVy3n33LcrKSmnWrDmnn34W559/UWOXJo1MwY6I7Nd8xZH9akwgxl012Fldsc9NrD1IWTiyv451/UaMUIhwkptwRot6ze0dkYV1w2YsBUXEzP+CrqOPBCBgBjAIs7rIVyXYcdjcmD4wDBOfv4gYZ/UOIxEREREREREROTC0bJnOI4881dhlyD5o7yz4JiLSSPyF2QAEAafTXeXcrxXBTgg/AF0T3dh+Xx8Zn9kODKN+kzvseE4YgWmxYF+9lpSSMtJdFW2yRoDVxd4qw532eIIVv/b68+s3t4iIiIiIiIiIiOyXFOyIyH7NW7IVgKABDquryrlfi3yYhPCGg1iAzvHx24KdTu0aZP5wejNCbdMBsK3dQNeESLhkGn5+K/YRCpvRsQ5HHMGKLMnrU7AjIiIiIiIiIiIi1SnYEZH9mq+05mAnbJr8XuwFI9Kt0yE+jvi8AiylZZh2G6G2GQ1WQ7BjWwBsa/6kW2ICAFYjgDdksr7MHx3niImPBju+8twGm19ERERERERERET2Hwp2RGS/5vXmAZXBTmz0+F9lAcpDJhZLAPjbMmwd2oCt4bYgC2VGgh3rn5vo7oqES2EjgInJ6iJfdJzTsS3Y8RbnNNj8IiIiIiIiIiIisv9QsCMi+zWPb7tgx7KtY6dyfx27NbKrTbeEhGiwE2qgZdgqhVOTCSfEY4RCtM0twG2zYWICQVYXbdtnx2mLU8eOiIiIiIiIiIiI7JSCHRHZr3mC23fsbB/seDEJ4w1HlkLrbrNh3RzpkglWdNg0GMOILsdmX7uBLhX77GD4WV28XceOdbs9djx5DVuDiIiIiIiIiIiI7BcU7IjIfs1DIQAmVqyWbcur/VrkAyOyDFtzp5Pmf20BINSyGWZ8XIPXUbkcm23tn3RNjAQ7puFnU3mA4kAIiARPwYrx3kB+g9cgIiIiIiIiIiIiTZ+CHRHZr3mNksgvrM7oMV8ozPoyP1DD/joNvAxbpWC7VpgWC5aCIvoQacuxWCL77Pxasc9OlY6dUOEeqUNEREREREREZF919923c9ZZp9Z4bsaMexk3bky951i1agVZWf1Ztmxpra/95JOPGD9+LEccMYhzz/0nAFlZ/XnllRfrXdfuKiwsJCurPx9++H6V48FgkDff/D8mTDiHESOGMnz4oZx55qm88MJsSkpK9lp9DfX7tLfV589FY2i43cFFRPY1YROftQwAq23bMmy/F/sIm2CzBQgAPeLjsa3bAOy5YAeng1Drltj+3ETn7DxshkHQDGMQYnWRl/5psVX22PGaRXumDhERERERERERqbWyslKmT5/K0Ucfw5QptxEXFw/A00/PoWXL9Eatze/3c/31V/PTTz9y4onjOP/8i3A6nfz++6+89dYb/PXXBqZMua1Ra5SGpWBHRPZboeISQtbIHjpWe3z0+K/FPkxMQkTO9Sspw/AHCMfFEm7ZbM/Vk9kW25+bcK7bQKeD2rKquASMAKsqOnYcVlc02AlYPIRCPqzbdRqJiIiIiIiIiEjj2LRpI36/n2OOOY5evXpHj/fo0bPxiqowa9ZMli37nvvvf4TBgw+NHu/btz9jx57SZLpQZPcp2BGR/Za/MAeAMGCzxUaP/1rsA4KEMYm1Wmm1MRuAYKe2YBh7rJ5gx7Y4P/sW6/pN9OjdhVXFJZiGn9+KvYRME6c1jnBFvRbA688nztW4P/EhIiIiIiIiIrKv+fDD97nnnjuYNeslnnnmSf73v2WkpTXjnHMu4NhjR1cZ+/zzz/HWW6/j8ZQzYMBgxow5odr9TNPk1Vdf4r333iY7ezNpac0ZN+5UTjvtDCASnMyZ8ywAV111KQDnnTeBCy64mKys/lx22VX8859nATBx4kXExsYyatRonn32SXJzt9K1a3duuOFftGrVOjqn3+9nzpxn+eST+eTn55GR0YpzzrmQkSNHVantvffe5oUXZlNQkE+PHr2YMOGyKud9Ph9vvfU6hx8+rEqoU8lutzNo0JDo6+LiIp588lEWLfqC8nIPmZmZTJhwGQMHDo6O2d33kJu7lfvvv4elS5fgdidw6qmn1/j7lZOTzdNPP853332Dx+Ola9duXHHFtXTp0jU6Zty4MRx6aBbt2rXnlVdepLS0hL59+zN58r9ITk4GIsvNzZz5BJ9+uoD8/DwSEhI4+OBu3HrrncTHR36ou6SkhJkzn+Crrz6juLiYDh0yueSSiVXeH+zen4t9mYIdEdlv+YojwU7QAMf2wU6RF4xIt04Xdzz2n/8LQKhT+z1aT7hZCmF3HJaSMoaU+3gDMIwAnpDJhrIAyfY4MCAIOACvN0/BjoiIiIiIiIjUjWlCINw4c9ste/SHZyvdeectjBlzIuPH/5N3332Le+65gy5dutGhQ0cA3nzz/3juuac5/fSz6N9/IEuWfMt9991T7T6PPPIA77//DmeffT7duvVg+fKfeOqpx3A6nZx44jjGjDmR9PQM7rnnDq699gY6d+5C8+bNd1jXb7/9SkHBi1xyyRWEwyEefXQGU6fewsyZc6Jjbr31Rn766X+cd94E2rdvz+LFX3PnnbfgdrsZMuQwAL7++ivuu+9ujjtuDEcdNZJVq1Zw++1Tqsy1atVKPJ7y6DU7EwqFuO66K9m48S8uvvhymjdvzttvv8n111/FQw89Qd++/Wv1Hm688Tq2bs1m0qSbiI+P58UXn2fr1hysVmt0THFxMZdddiEul4urr76e+Ph43nhjHldddQmvvfY2yckp0bGLFn3JX39t4Nprb6CoqJBHH32Qhx++jzvumAbAiy/O4Z133uTSS6+gQ4eOFBUVsmTJtwQCke/zBQIBrrnmcvLz85gw4TKaNWvOJ598yPXXX8Xs2S+TmdmpVn8u9mUKdkRkv+UtzQUiQYnTGgl28n1Bcn0hsAQAGGJYsBQWY1otBNu33tGtGoZhEOzYBsf/VtE5Ow9irJhGEAizusjLES0iNQYNcJjgK94KyXu2JBERERERERHZD5kmrtk/Yd1Q3CjTh9ok4Dm/1x4Pd0466VROOukUALp378k333zNF198SocOHQmFQrz44vMcc8xxXH75VQAMGjSEvLxcFi78OHqPjRv/4s035zFp0k2ccMJJAAwYMAiPp5w5c57lH/84iebNW9CxYyYA7dt32OXya6WlJcye/XK006S0tJR7772LnJxsmjdvwbJlS1m06EtmzHg82kkyYMBgtm7dyuzZM6Mhzdy5szjkkD7R/XEGDRqC1+vlxRe3hSu5uZEfbG7evMUuP6/FixexcuUv3H//wwwZklVxz0M5++zTmD37mSrBzq7ew7fffsOqVSt45JGn6NdvAACHHNKXk08+nsTEpOh9Xn/9VUpLS3j22bnREKdfv4GMHz+WV199kcsuu6pKjdOnz8DhcADw118beOWVFwiHw1gsFlau/IWBAwdFf88Bhg07KvrrTz6Zz2+/reb551+NhnuDBg3hzz//5Pnnn+POO6fv9p+LfZ2lsQsQEdlTfOV5QEVQYnUBlcuwgaUi2OmfVwRAqG0rcNj3eE2hjm0BiF3/F61cMZGDhp/VRd5ojZX77PhKtu7xekREREREREREmqrtl9eKjY2jefMWbN0aCTq2bs0hN3crQ4ceWeWaI488qsrr77//DoBhw4YTDAaj//XrN5C8vDxycrJrXVenTp2jgQhEwiCAnJxIbUuWfEtCQiJ9+/b/25wD+PXX1YRCIUKhEKtXr2To0GFV7r19kAGRZeQAjN0I0f73v/8SGxsXDXUALBYLRx55NMuX/0QoFNrt97BixXLi4+OjoQ5AQkJClXCo8r326dMftzsh+j4tFgu9evVm5coVVcb27t03GupE5uxIMBikoCAfgM6du7B48TfMmjWTlSt/IRwOV5srM7MTbdq0rfK59u8/kFWrInPt7p+LfZ06dkRkv+X154Htb8FOkQ+TEGFCWA2DVhu3ABDs1G6v1BRs3xrTMLDmFTLEZucNvJhGgNVFPiyGFbvFRdDwROovy90rNYmIiIiIiIjIfsYwIh0zTWwpNqvVWu2b9ZXC4TA2W9VvZ8fHu6tOa7fh90eW5crNjXxfZftwIvI6pcrroqJCTNPk+OOPrnHe7OxsWras3VL5bvff64r8MLHf74vOWVxcxLBhg6tdC5CXl4vVaiUUClWrNyWl6utmzVpU1Llll3WVlBRXux4gNTWVYDCIx+OJ7lWzq/eQl5dLUlL1pWZSUlJZs+b36OuiokJ++eXnGt/r9vv1ANG5t81pq5gz8nt69tnnYxgGH330b+bMeZakpGROOukUzjtvAoZhUFRUyK+/rq5xrsrl4Xb3z8W+TsGOiOy3fIHC7YKdyDJnvxZv21+nu9OJ/a+9G+wQ4yTUuiW2DZs5rKiUNyyA4eev8gAlgRBOW2w02PF58/ZOTSIiIiIiIiKy/zEMcFh3PW4fkpSUTF5ezd8PycvLrfbN+J1JS0sDoKCgoMrxyu6PSgkJiRiGwZNPPhcNL7bXtm3Df8/I7U4gKSmZBx54pMbzyckpWCwWrFZrtXrz86u+7tKlK7GxcXz77deMGXPiTudNSEiodj1AXl4eNpsNl8u12+8hNTWNwsKCasfz86v+/rndCQwadCgTJlxSbazd7qh2bGccDgcXXHAxF1xwMX/9tYF///s9Zs9+hoyMVowadTwJCYlkZh7ETTfdssN77O6fi32dlmITkf2WN1wIbOvYCZkmvxX7MI3IMmwjy70YpkkoLRkzKWGv1RXq2AaATjkVf9EZAUwitTmtsdGl2Lz+pvUXioiIiIiIiIhIffTu3ZfS0hL++99lVY6Xlpby448/cMghfXf7Xs2aNSc1NY0vv/ysyvHPPvtPldeVS4kVFRXRpUu3av/FxsbV8d3s2IABAyksLMBms9c4p91ux2q10rlzF7788vMq137+edX6nU4nY8eO46uvvuD777+tNlcwGGTJksjxXr16U15exrfffhM9Hw6H+eyz/9CjR69oV8vu6Nq1O6Wlpfzww/fRY8XFxSxbtrTKuP79B/LHH2tp165DtfeZmdlpt+f7u9at23DxxZeTkJDI+vV/ROfatGkjaWnNavxcYff/XOzr1LEjIvstD5H9cyqDnb/KAnhDJobVjwn0yS2MnN9b3ToVgh3b4vxiCfEbNpPSKo38UBgIsKrIi9MaS0llsBMq3Kt1iYiIiIiIiIg0poEDB3PIIX2YMuV6zjtvAh07ZpKbu5VXXnkBm83GuHHjd/teVquVM888l0ceeYCUlFQGDBjEd98t5n//+7HKuLZt23HSSadw1123cvrpZ9GtWw+CwSAbNvzJjz8uZdq0Bxv6bTJgwGAOO+xwrrvuCs4442wyMw/C4/Gwbt1aNm7cwI03RjpOzjnnfG688TruuecOjjpqJKtWrWDBgo+q3e+CCy5m1aoV3HDDtYwdO44BAwbjcDhYt24Nb731Ot2792TgwMEMGZJF167dueuuW7noostp1qw57777Jhs2rOfaayfX6j0MHnwonTt3YerUf3HJJVfgdrt54YU51ZbHGz/+DBYs+IiJEy/ilFPG06JFSwoLC1ix4hfS0tI47bQzdnvOm266joMP7spBBx2My+Xi66+/pLi4KLqvz6hRx/Puu28xceLFnH76mbRp05bS0lJ++201gUCASy6ZuNt/LvZ1CnZEZP8UCuO1FgMQJLIU26/FXkzCmEYQi2nSsmIZttBeDnbCLdIIx8ViKStnVCDMKxagYp+dTGvcto4ds3Cv1iUiIiIiIiIi0pgsFgv33/8wzz33NK+99hK5uVuJj4+nb98B3HXXfdFltHbXuHGnUVpawltvvc7bb79O//4Duf76KUyefHWVcVdffT1t27bj3Xff4vnnnyMmxkXbtu0YPrzmfXcawl133cdLLz3PW2+9QXb2ZuLi4unYMZPjjhsTHZOVdQSTJt3ECy/MZuHCT+jWrTu33343l1xyfpV7ORwOHnzwMd555w0++uhD3nvvbUKhEK1ateaII4Zz2mn/BCJh14MPPsoTTzzCzJmP4/F4yMzsxH33PRwNR3aXYRhMn/4gDzwwjfvvn4bb7WbcuPFs3ZrNN98sio5LTExi5sw5PPvsUzz11GMUFxeRnJxCt249GDp0WK3m7NnzED79dCGvvfYSoVCINm3acdttdzFgwKDo5/Doo08xe/YzvPDCbPLycklMTKJz54MZO/aU6H1298/FvswwTdNs7CKaqoKCMoLBRtqATER2rtDLO4tHE7IG+NUJJ3S7nc9ye/Dx5lxMawFDvX6mLluJGeOk9KpzwVK7lSltNgvJyXF1fg7EfPAp9p9Xs7xrJhOT4yHsxG1J5YS0Z1mbu4jOPrCYVk4c+R+MOmw2KCJ7R32fBSLS9Ok5ICJ6DogINO6zIBDwk5e3mdTU9FrvWSIiu29XX2spKXFYrXtn9xvtsSMi+6VQUSEha2QvnaABDlscq4t9mIYfgJGlnsi5jm1rHeo0hGDHtgB0zM6NHDAClAZDhI2YaMdO2AgRCJbu9dpERERERERERERk36VgR0T2S76iHADCGJgGGIaLDaV+IBL29MzJB/b+/jqVgh1aYxoGsflFZPgDYISBEN5QTKTecGSlTK8nr1HqExERERERERERkX1Tk9lj56yzzmLJkiU1npsxYwbHH398ra778MMPyczMbNAaRWTf4SvdChDtfsnzOQhhgiVAC6+fxMJiTMMg2LFN4xToiiGc0RzrxmyO9/h51mEHw09ZyAmAEbZjWoL4irIhoX3j1CgiIiIiIiIiIiL7nCYT7Nx2222UllZdkmju3Ll88sknDBkyZKfX9u3blxtuuKHKsdatWzd4jSKy7/CVRzpdgkS2Ecv22ol065gcURx5loRatwRXTCNVGFmOzboxm8GFJTybGIdpBCgKVK7PGXk8+0q2Nlp9IiIiIiIiIiIisu9pMsFOp06dqh277rrrOOyww0hJSdnptQkJCfTu3XsPVSYi+yKvNw8cEKjo2NlYbgOjDIAjiyL/DzXSMmyVgpltcX71Pe1y8rC1aU7Q8JPvd5AIhC1WALyluY1ao4iIiIiIiIiIiOxbmuweO8uWLeOvv/5izJgxjV2KiOyDvP6KPXQMAIMNZRZMw09MKMRBeYWRc40c7IRbNiMcG4MtEKR7STkYIfL9kbw9ZIk8nn0eBTsiIiIiIiIismumaTZ2CSL7tX3pa6zJBjsffPABLpeLo446apdjlyxZQu/evenZsydnnnkm33///V6oUEQakzdcCESCHac1lj/KAmAE6FtUii0cJpzkJpya3LhFGgahDm0BOLrUA0AIOwCBiqdzZUAlIiIiIiIiIlITqzWy6off72vkSkT2b5VfY1Zr4y+E1vgV1EEwGOSjjz7iqKOOIjY2dqdjBwwYwAknnED79u3Jyclh1qxZnHfeebz44ov06dOnXnVYrU02FxPZ73nNYiAS7NitsZR4AmALc1hBCQDhg9pjs1vrfP/Kr//6PgfMg9rBL78yqLAYWjUjaEQey36rSUwYfKFCbDY9a0T2VQ31LBCRpkvPARHRc0BEoLGfBRbi4tyUlhYC4HA4MQyjEeoQ2T+Zponf76O0tJC4ODcOR82xyt78smuSwc7XX39NXl4eo0eP3uXYK6+8ssrrYcOGMXr0aJ588kmeffbZetWRkOCq1/UismeY/hBe67Zgx2qNByMApslhhZFgx9W3C/HJcfWeq77PAbPvwfjeXUDzolJS/QE8FWGT3xICwGsWkdwAdYrInqV/E4iIngMioueAiEDjPQuSkmLZvHkzhYWFlJc3Sgki+zXDgNTUZNLT0/eJ4LRJBjsffPABSUlJZGVl1fra2NhYjjjiCD7++ON611Fc7CEUCtf7PiLSsIx8Dx57KRAJdsJhJ6YR5KAyD0k+P6bdRnFKKhSU1XkOq9VCQoKrQZ4DzvTmWDblMLCgmE+bR4Idj+GP/J9CCupRp4jsWQ35LBCRpknPARHRc0BEYN94FrhciTid8QSDIWDf2QtEpOkzsNmsWCxWCgt3nJwmJrqwWPZO116TC3a8Xi//+c9/GDNmDHa7vU73aKhNjkKhMMGg/tEmsq8x8svx2iNhSNAAM+wEAgyuWIYt2KENQQxogK/fhngOWDq2xbkph0MLS1nYIrLvTzmRNTu9ljL8fj8WS5N7XIscUPRvAhHRc0BE9BwQEdgXngWGvocgsgeEwxAO7/xru4Fih93S5BaA/fTTTykrK2PMmDF1ur68vJwvvviCnj17NnBlIrKvCBYXEK5YyiwEeEIxYATpWxQJdkId2zZiddUFO7YBoF9RCabpjBzDxDANMEx83oLGLE9ERERERERERET2IU0u2Hn//ffJyMigX79+1c5NmTKFbt26RV8vXbqUSy+9lLfeeotvv/2W9957jzPOOIOtW7dy+eWX782yRWQv8hVvBcAw7ZgGlASdGIToXOYBINSqRWOWV004vTlhVwyxwRBdSwKYpgEGOAORvXV8hdmNXKGIiIiIiIiIiIjsK5pUX15RURFfffUV55xzTo0bFIXDYUKhUPR1s2bN8Pv9zJgxg8LCQlwuF3369OGOO+6gV69ee7N0EdmLvOW5ABhGZAm2gOmgjcdHXCiMabcRTktu3AL/zmIh1KE1lhW/M7CghGWJMdjw4AjH4qUUX1EOZDR2kSIiIiIiIiIiIrIvaFLBTmJiIsuXL9/h+enTpzN9+vTo63bt2jFr1qy9UZqI7EN8njxwARVryoZw0rM0srFZqGUz2EubmNVGsGNb7Ct+Z1BhCd/jwIYHmxELgLd0ayNXJyIiIiIiIiIiIvuKfe+7myIi9eT15wMQjgY7drpUBDvh9OaNVtfOhDpE9tnpXObBHowswWa1RIIdnyev0eoSERERERERERGRfYuCHRHZ73hDBQCErJFHXMhwRIOd0D4a7JjxsYQT3QC0KU0BIGyPAcDny2+0ukRERERERERERGTfomBHRPY73nARAIGKJ5wRspFZ5gX23WAHttXWpjQJgDJrJNjxBhTsiIiIiIiIiIiISISCHRHZv/iCeK0lkV9awgC08dhxmCZhVwxmkrsxq9upUEYk2GlfGqmx2GYHwBMubKySREREREREREREZB+jYEdE9itGiR+PoxQAjxkEoFOJFYBwejMwjEarbVcq9/9pXxLZW6fQEnlEeylutJpERERERERERERk36JgR0T2K5YSPx57pGPHE44sv9ap1AT27WXYAEItm2EaBql+O25/DHlmpG6vVcGOiIiIiIiIiIiIRCjYEZH9ilnswWsvB8CHD4CDSiOdO/t6sIPDTjg1CYDWJSkUVHQcBaw+gt6yRixMRERERERERERE9hUKdkRkv+IvzgPDBNMghIkzaKOdJwBsW+psXxbOaAFA29JUgqYXSziyz46vIKcxyxIREREREREREZF9hIIdEdmv+Mq2AuAw4sGAjNJULEDIHYcZH9u4xe2GUHozANqUpGAxfFjDCQD4ihTsiIiIiIiIiIiIiIIdEdnPeD15AFgt8QC0Lol06YQz9v1uHYBQeqRjp3VpClbKCZAIgK9UwY6IiIiIiIiIiIgo2BGR/YzPlw9A0BIHQJvSFADCFYHJvi7cPIWwxSAu6KSFF8osSQD4yvMatzARERERERERERHZJyjYEZH9ijdYAIDH6gKgTWkSsG2Js32e1YonLRJKdSiNId8SWYrN681tzKpERERERERERERkH6FgR0T2H6aJN1wEQClO4gJO0nwuTCDUsokEO0CgZTIA7UvjyLW4AfAGChqzJBEREREREREREdlHKNgRkf2HN4jHWgxAkWmjTUlkGbZAciLEOBuzsloJVnQXtStJoNyIdO94Q4WNWJGIiIiIiIiIiIjsKxTsiMh+w1Lix+soBaAotG1/HTKaxv46URnpALQuS8JnxgDgobARCxIREREREREREZF9hYIdEdlvGCV+PPZIsOM3oE1JKgDhjOaNWVatWZu1wGP14wjbaO6JPKa9luJGrkpERERERERERET2BQp2RGS/YRRvC3aChKMdO6H0phXs2KxONsYXAtC+PHLMYy/F9PkbrygRERERERERERHZJyjYEZH9Rri4FL/dA0Ci3yA+EEPIMAm3SG3kympvc0IJAJmlYUwMTCOMv2BrI1clIiIiIiIiIiIijU3BjojsN3yluQCYWGlfZgOgMMkKNltjllUnOcleADqXBfDgBsBbmN2YJYmIiIiIiIiIiMg+QMGOiOw3/OV5AHhJoGOZHQBPs9jGLKnO8pKCALQvC+EPJwHgK1GwIyIiIiIiIiIicqBTsCMi+w2vLx+AMhLoUOICINAyoTFLqjNfnJ1iuwcrkOhpBoC3XEuxiYiIiIiIiIiIHOgU7IjIfsMbKIj8HzdtS+MA8LdsevvrADjtcWxwR4KqZmXJAHi8uY1ZkoiIiIiIiIiIiOwDFOyIyP4hbOINFwIQ700mJmzDZwlAWhMNdqyxbIiPLC3Xsjwe2BZciYiIiIiIiIiIyIFLwY6I7BeM8gAeWwkAzcvdAPwVX4DT4W7MsurMad3WsdOm1AGAJ5zfmCWJiIiIiIiIiIjIPkDBjojsF4wSP15HKQAZZTEA/OXOx2mNbcyy6sxhjWVDfCTIaeGz4Ag6KDYKG7coERERERERERERaXQKdkRkv2CU+PHYIx07bcvsAGyIz8NpjWvMsurMaYvFY/dTEBsCIK08hXJrcSNXJSIiIiIiIiIiIo1NwY6I7BeMEj9ljjIsYQttyw0A/koowmZxNHJldVMZSOUk+QBIK0shZC3B9PoasywRERERERERERFpZAp2RGS/YJT48NhLSPYkYTOhzOajPNbAMIw9Om+h38OqkhxM02zQ+zqsLgBykzwApJalgmHiK9jaoPOIiIiIiIiIiIhI02Jr7AJERBpCqKSYsNNPs7IUADa483Da99z+Ov5wkDm/LmHO6iV4QgGOSOvIxMws4mwN0yFU2bGzKaEISCWtLBVM8BVuISa9dYPMISIiIiIiIiIiIk2Pgh0R2S/4yvLACSnlaQBsiM8nZg/sr2OaJl/lrWPOH0vI9pVGj3+Ru5ZVJTnccPBwurib13sepy1S+8b4AkIGxAVjiA24KC/JIbHedxcREREREREREZGmSsGOiOwXvN5cAFLLKoIddz4Oa7MGnePXkq08s+5bVpRkA9DcFc957QbQzB7P/b9+RravlEk/vc9Z7fpxSqtDsNRjGTinNdJtVEYphYkJpBYWk1aWQk7ZVtIb5N2IiIiIiIiIiIhIU6RgR0T2C55AHraQjRRvpNNlQ3we6bZ2DXLvXF8Zc9cv5T9bfwPAabFxWptDmNBzCJ4SP8FgmMd7n8RjaxbxZe5a5q5fyv8KN3HdQUeQ6qxb15CjItjxBcvxtUyDwmLSylP53Z/bIO9JREREREREREREmiZLYxcgIlJvIZMiCkkrT8HAoCDGpNThI8YaX6/bekNBXvlzGROWvR4NdY5qdhDP9j2FM9v3I8Zmj46Nszm4ofORXN3pcJwWG/8t2sTl/32bJfl/1mnuyqXYfKEyHK1bApBWlkJhuKBe70lERERERERERESaNnXsiEiTZ5T5yYktI60sBYDspDAATltsne4XNk2+2LqGOeu/J9dfBkBXd3Mu7jCEzu4dL+9mGAYjWxxMV3cL7v31U9aW5XP7yk84Ib0757cfiN1i3e0aKpdiC5tB7K0iy8ullaVQyq91ek8iIiIiIiIiIiKyf1CwIyJNnlHipyCmhC45kWAnLzkIgMNa+2XQSoM+bl/xybZ9dJzxnN9+IIendsDYzT1z2sQmMaPXP5jzx/e8u/kX3t38Cz8Xb+HGzkfSOjZpt+7hsLqiv/Yku3BZwBF2kOj31/o9iYiIiIiIiIiIyP5DS7GJSJNnlPgpd5SQVp4KQEFKJPyIsdU+2Hnpz2WsKMnGZbFzTrv+zOwzjqFpHXc71KnksNi4uOMQbus6kgSbk7VleVzxv3f4JPtXTNPc5fUWw4rdEgl3fHjYmhADQKtyC4FQuNbvS0RERERERERERPYPCnZEpMkzSnxY8RDvjyMM5CeWAuCsZcfOhvJC/r1lBQD/6no0p7XujdNav8bGQSlteaL3SfRKTMcXDvLw718yZ/33u3Vt5VJyvlAZnhaR5dgyymLZVFBWr5pERERERERERESk6VKwIyJNnqXET0tPZP+aLbF2yiyR4MNZy46d5/74jpBpMii5LX2SWjVYfanOOO7ufizntO0PwFsbf+bP8oJdXlcZTPlDHuxt2wLQrDyFP7I3N1htIiIiIiIiIiIi0rQo2BGRJq+4uJiW5W4AtqYm4wuVA7Xr2FlW8BffF2zAahhc0GFQg9doNSyc1qY3Q1LaEcbk+fVLd3mNs2KfHV+wjMQOkWAnpTyZPwo2Nnh9IiIiIiIiIiIi0jQo2BGRJm+9P4+0shQAAukt8QUrl2KL3a3rQ2aYZ//4DoAxLbvT2pW4ZwoFzmnXHwsG3+av55fiLTsd66gIpnyhMqwpSXitAaymFbNg0x6rT0RERERERERERPZtCnZEpMn7K5xPWnkk2HG0brmtY2c3l2L7aMtq1pcX4LY5Ob1tnz1WJ0Db2GRGtugMwOw/lmCa5g7HbttjpxwMg+w4LwAtSor2aI0iIiIiIiIiIiKy71KwIyJNXplZSEwwhpARJqVNK3yhij12dmMptrKgnxf//AGAM9v2xW1z7tFaAc5o0xenxcrKkhwW56/f4bjKjiN/RVCVHxcJgdqV+fGHwnu8ThEREREREREREdn3KNgRkaYtGCYmFFl6bUusD7fLjj/kAXYv2Hltw48UB720cSVybIuue7TUSqnOOE7M6AnA8+u/J2TWHNJU1u8LRoIdnzsGgHZlBhvLA3uhUhEREREREREREdnXKNgRkSbNKPHT3BcJOTa7t3W3wLalzHZkk6eIdzf/AsCFHQZjs+y9R+K4Vr1IsDn5y1PEJ9m/1jjGEV2KLdKBZG8WWW6uucfO+vzSvVOoiIiIiIiIiIiI7FMU7IhIk+Yr8tHKYwBQmOyKdrdYDQc2i2On187+YwlBM0y/pNYMSG5Tq3mNNX4CC4swNgQgvON9cnYkzuZgfJvIfj4v/fkD3lD1Dpxox05FsJPQrBVl9nIsGBT8ubHWc4qIiIiIiIiIiEjTZ2vsAkRE6mNjQTkHlUcCnGCLVLwVy7Ltqlvnp6JNfJO/HgsGF3YYtNvzWdb5cSwox7omQABwAPZYg1Cmg1BnO6GDHJjJ1t261/Etu/Lepl/Y4ivh7U3LOb0i6KlUuceOLxgJdlzJLcmNyyOuMJaYLdm7XbOIiIiIiIiIiIjsP9SxIyJNWm5uEa6QjZARwpXRKtqxE7OT/XVCZphn1n0HwLEtu9AuNnmX81jWB4h5rhDX00VY1wQwrWDpGoMZY2CUm9h+9uF8s5TY6fm47s/H8U4J1lU+MHfczWO3WDm7XX8A3tj4E0UBT5XzLpsbAE+wBABnUkty4/IBaFmQv8uaRUREREREREREZP+jjh0RadLKC/IAKIwppk1yawqDmwFw2nYc7Pwn5zfWluURZ3VwZtt+O72/ZUMA+4JybKv9AJgWCA6IITwinuSOCRTklhJe58f6W+Q/y4YgltwQltwQ9sVeAgNi8J8UDxajxvsPTevIWxt/4veyPF7d8F8u6Tgkei7GngCAtzLYcSaR6yoAoHNpMb5QGKdV+byIiIiIiIiIiMiBRMGOiDRptvJcAPJjC0iLbU52/u8AOKw1L8VWHvQzd/1SAE5v04dEe0yN44wtQRwflWFbuV2g0y+GwPBYzBQrNltFoGI1CLe3E25vJzAiDjxhrGsDWFf7sS3xYv/eCxbwn1hzuGMxDM5rP5Cbf5nPh1tWckJGd9JjIoHO3zt2DMNCcUwQgJa+ID/lldCheWKtPzMRERERERERERFpuvSj3iLSpLUoLwRga2wJNpsLXyiyH02MLb7G8a9v/B8FAQ8ZMQmMSe9W4xhjaxDXU4XYVvoxDQj0c+KZlIJ/nBszZRf757gshLo78Z/kxneaG9MA+3deHO+W7nBZtj5Jreib1IqgGeaFitAJtgU7lR07AA5bPMWOyD5CeX9s2nktIiIiIiIiIiIist9RsCMiTZYvFKZdeWRfmrz4cMWxSLDjrKFjJ9tbwlsblwNwfvuB2C01hDTeMDEvFGN4TUJtbHiuS8Z/agJm6i4CnRqE+sTgP6Ui3PnWi+O9HYc757UbCMAXuWv5rTTShRRTEez4Qx5C4UinTpw1hbyKfXaCm7bUuiYRERERERERERFp2hTsiEiTlZ1bTErAxMSkLNEBgC9YDoDTWn2PnTnrvydghuiVmM6QlHbVbxg2cf5fCZacEOEEC75zEjGb1W/FymC/GPwnV4Q733hxvF9WY7iTGZ/Kkc0yI3X+sQTTNKt0HVV27cQ4UsmLjQQ7ibl59apNREREREREREREmh4FOyLSZJWs2wxAsbMEV0IasF3Hjq1qsLOiOJsvc9diABPaD8Ywqu93Y/9PObYVfkwb+M5OwHQ3zCMyOCAG/0mRkMb+tQfHv2sOd85u2x+bYeG/RZtYVrgRi2HFaY1c5wkWAxDjSiMvtgCANsXFDVKfiIiIiIiIiIiINB0KdkSkyQpuygYgL7aA1PiWAPiClUuxbQt2wqbJM+u+BWBki4PJjE+tdi/rLz4cCyPdPv6xbsJt7A1b60AXvspw5ysP9vnVw50WMW5GV+z7M2f9EsKmictWGexUdOy4m0eDnXSfD09JeYPWKSIiIiIiIiIiIvs2BTsi0mTF5keWIsuLLeD/2bvvaLmu8vzj332m19urere6rOIqN2zjgg2YTiihhBYChBBMCcmPEJIQQhJagARMhwA2prjj3i1bsmT13m7v987c6TNn//44V5JlS7Jc1Ozns9Zdlmb22Wfv46WztPz4fXcsfPiKnfv6trN1tI+IE+BdE5c8ax7TUyb0Ky84KZ0bobw0fEzWWz4zQuH1XlATvD9H4PZnhztvHb+IqC/Azswg9/VtJxJIAgeCnVBVE0V/keGgd7bQ4J6uY7JWEREREREREREROTkp2BGRU1bLWCuywegQ4dC+YGffGTtRAKy1/Lp9DQBvmbCQ2mD04ElyLuGfpDBFS2VqgOJrnn02z0upfHaEwuvGwp37cvgfyR30fVUgzJvHLwTgp3tXERxrxbbvjJ1Ioglgf9XO6N7OY7peERERERERERERObko2BGRU1I5l6elkAf2BTtee7VCeRQ40IptU7qXttwIIcfP1c1zDp7EtYT+L40zUMGtdsi/Iwm+Z5+985Kv/ZwIhSu99QVvz2CGKwd9/7qWedQFo/QWRhkqexU9+dJYK7axyqThWJ93fW//MV+viIiIiIiIiIiInDwU7IjIKWlor3e+TjqYoeAvEnlmxc5YK7Y/9WwB4Lz6KUT9wYPmCPwpg39LERuAwruTED9+r8TyeREqk/2YIgRvHj3ou7DPzzvHWsbtznkVPftasQUDSRzroz86CEDT0PBxW7OIiIiIiIiIiIiceAp2ROSUlGv3gp2hiNeSLByqBaBQHjtjxxcjVynxQP9OAF7dOOug631P5Qne64UmhTcmcMcFjsu693MMhdcnsA741xXxbSke9PUljTOYEKki5/qAA63YjHEI2yoGx1qxNeXzkCsc37WLiIiIiIiIiIjICaNgR0ROTd29gHfWTNDE8flCAOQrY8GOP8aD/TvJu2Vaw0nmJpv2X2pSFUI3eEFJ6bwIldPDx3nxHtvip3xuBIDgH9JQsvu/8xmHq1rmUDbevrKl1P7vIr4aCv4ifd5XFDt6jt+iRURERERERERE5IRSsCMip6TqQa9iZSA6RDjgna9TccuUXa96JeyL8aeerQC8umkWxhw4OydwdxZThMoEP8UrYsd55QcrXhrFTTo4Ay6B+7IHfXdhw3TAC50GCgP7Pw8HvOqktmgZgJE9XcdnsSIiIiIiIiIiInLCKdgRkVNPqUzzqHcuzWB0iEjYC3aKlQPBSG+xyMZ0Dw6GSxpn7P/c9JXxP573xl8ZA5/hhAo5FK/ywqXAfVlMf2X/Vwl/iOmJ8QD05Pr2fx4JNwDQFfVayVU6VbEjIiIiIiIiIiLySqFgR0ROOW7vAD4g7XfJBnKEo17Qsa8Nm98JcVffDgCW1oynNhjdf23wjizGhfJpQdypweO+9kOpLAhRmRHAlMdastkDLdmW1XmhVKaUIl8pARCKefsdiXjt2aoHB4/zikVEREREREREROREUbAjIqecTJtXodIVLYCBcMQLOgrlsfN1fDHu6d0OeG3Y9nHaSvjXFbAGipef2BZsBzGGwuviWB/4t5bwrS/u/2pO1WQAHAo82L8LgHCyEYBMqB+AhmwO8oXju2YRERERERERERE5IRTsiMgpp9jZC0BvNA1AJFQPQGGsYseaIEOlHNWBMGfUTPQuspbgbd735dND2Bb/cV71kdkGP6ULvcqi4B9HoeBV7UQDSQD8FLijezMA4ZgX7AR8Q3SHAgA4XX3PnFJERERERERERERehhTsiMgpJ9jrVaoMRocACIe8M3b2VexkXe/cnIsbZuB3vNecb1sJ344S1gelV59E1TpPU7ooilvr4KRcAnd7e4n4vWDHwWVzupO27DCRsf36TYotMS8MKnTonB0REREREREREZFXAgU7InJqcV3qR0YASIe9MGN/sDNWsTNS9qpdLm2aOXaNJbCvWufsCLbGdzxXfPQChuLr4t4vH8xhussEfREM3nr9FPhTzxbCYxVKZX+WbbEQALn27hOzZhERERERERERETmuTolg58Ybb2TWrFnP+vna1772nNf+7ne/4/LLL2f+/PlcddVV3HbbbcdhxSJyrDgDQwRdl4zPoRjygp19rdjy5VEAKgSYnWhkYrQGAN/aAr7OMjZkKL4qemIWfpQqp4Uozw1iXAj9fhQDRPxe2OOjyF192zC+GI71Wsl1xbzqpEjvwIlasoiIiIiIiIiIiBxHJ9chE8/hBz/4AYlEYv/vm5qajjj+9ttv57Of/Swf/OAHOffcc7nrrrv45Cc/SSKRYPny5cd6uSJyDLid3lkyO6NBjFMBIByqBSBXSgFQIsQVTbO8C8qW4B1etU7pwgjETv48u3h1HN/WQXy7SvifLBAJJMmWR6j1GzpKeR4faiNsqsnSTyZcBqA2kyGdL0A4dIJXLyIiIiIiIiIiIsfSKRXszJ07l9ra2qMe/41vfIPLL7+cT33qUwCcddZZ7Nq1i29+85sKdkROUfmOXhLAnrFKlZCvCscJANCeHWtHZiKcVzcFAP/jeZxBFzfhUFp+clfr7GNrfJQuiRG8LUPgzgzhy7yKndOr6ugYrHBHzxYu9teSLfdTa7N0BwM0F0v4evqpTBp3glcvIiIiIiIiIiIix9LJ/7+uv0BtbW3sG1151AAA50pJREFU3LmTq6666qDPr7rqKtauXcvg4OAJWpmIvCjdXsVOR7QEQDhYt/+r3aMdAEyMtxL1B6HgErxrrFrnkigEzXFe7AtXOjeCjRucIZdozgt25sSTADw53I4JVgMwMTfE1rgXWJnO3hOyVhERERERERERETl+Tqlg56qrrmL27NlcfPHF/M///A+VSuWwY3fu3AnA1KlTD/p82rRpWGv3fy8ipxBrSQx4oWxvJAdAONoAQLZcZKDgnTMzt2oyAIEHcpiMxa33UV4WPv7rfTECZn+FUbQnAkDIlFmQbMEC3T6vSqkxN8TWuPd9oaPnhCxVREREREREREREjp9TohVbQ0MDH/vYx1i4cCHGGO655x6+/vWv09PTwz/8wz8c8pqRkREAksnkQZ9XVVUd9P2L4fOdUrmYyCnPDI4QLpcpGkPZnwYgGqnH73d4uG83js0DMLNqIv6cJfCgF/5UrozjD/le0rXs+/N/LN8DdnkUe1+W6GgU6qHgjnJF61LWprrYVioxG/CTYmvMC4Cc7j78fr2XRI6n4/EuEJGTm94DIqL3gIiA3gUiAuY4Ngs6JYKd8847j/POO2//75cvX04oFOInP/kJH/7wh2lsbDzsteYZT9Nae8jPX4hkMvKi5xCRo1fZ204J2BUNU1tJgR9qq1upqYlx17pt+CkA0FTXROyOIuWCxZkSInlB7UvyZ/5Qjul7oAaKF5eIbvNasblOltfNmMd3djxCb8nPbCAfSNM9Flgn06OEwn5MJHTs1iQih6S/E4iI3gMioveAiIDeBSJyfJwSwc6hXHHFFfzwhz9k06ZNhwx2nl6ZU19fv//zVCoFPLuS54VIpXJUKu6LnkdEjtLWvUSArfEINZURXACb4Kn2dtYNdrF0LNip9Pop3ZfCAPnLo+SGsy/5Unw+h2QycuzfA8sChLfEABjpHiA3ucCrGqbzRNsuADKhFNFQlJ5ggKZiidTmPbiTxx279YjIQY7bu0BETlp6D4iI3gMiAnoXiAhUVUVwnONTtXfKBjvPZd/ZOjt37mTatGn7P9+xYwfGmGedvfNCVCou5bJe1CLHi9vRSwTYFoswnjRZIBio47bOzWBdfBQBiK70YSpQmRagNNkPx/DP6TF/D0QgNKEWgHzfMOWyy6WNM7mv/REARkPDTKv42BqP0DRYwnb0UB7fcuzWIyKHpL8TiIjeAyKi94CIgN4FIq9kY83CjotTtunjrbfeis/nY86cOYf8fsKECUydOpVbb731oM9vvvlmFixYQG1t7fFYpoi8VKwl1NsPwPZYBOvzqu8CgVru7tuGjxL7mq0lHw8AUDrv5VH+HJztva9y5TTOnhJTY3U0xScBUPEVWVjI7z9nh66+E7VMEREREREREREROQ5OiYqd97///Zx11lnMnDkTgLvvvpvf/OY3vPvd76ahoQGAz3/+8/z+979n48aN+6/7+Mc/zic/+UkmTpzIOeecw913383DDz/MD37wgxOyDxF54cxolnC+QAXoDIbJ21EANufzDJfyNPoNlCBElEDWj1vnozIreGIX/RIJV1fDXsgFRgncl6Xw51Vc2jyXob4wEfJMzfRzV9wLsdyu3hO7WBERERERERERETmmTolgZ8qUKdxwww10d3fjui6TJ0/m85//PO9617v2j3Fdl0qlctB1V1xxBfl8nu9973tcd911TJo0if/6r/9i+fLlx3sLIvIiOT1etc7eSIiGch7rdwHDPYNekHFmTT19vRAtxAEonRsGxxxuulNK2J8AvGDHv7FIsbvMhQ3T+IUTI+Lm6SjtZWf1bAAiwylGC0UIvTxCLRERERERERERETnYKRHsfOELX3jOMV/5ylf4yle+8qzPr7nmGq655ppjsSwROY583V6Lse2xCNOyI5CEYKCaJ4Y7AFiYrOWuXojm4tiQobwkfCKX+5KK7A92sri4BO/LEntbkrC/AYoDrAm3YWLL6A0GaCyW8PX0U5nYeoJXLSIiIiIiIiIiIsfCKXvGjoi8stixYGdrPMq03DAABV8MF8vsRCNxx6vYi5bilJeGIfzyeb3tq9ixxqXgz+FbU8AMVBifmAJAZ6SPlnCQrWPt2JxunbMjIiIiIiIiIiLycvXy+S+fIvKyZrq9VmzbYhEaSykA+ipe0eFlTbPIDQ8DEC0mKJ0TOSFrPFYCvhB+x2utlp5ZwFgIPJBlQu1UAMKMEnSG2RKLAgeqm0REREREREREROTlR8GOiJz8cnlC6VEAtkcjRG0agEEbIOz4WV43hcKeAQDC8Spsve+ELfVYifiTAKTPLAPgfyJPzDQBELcZuvKd+yt26FKwIyIiIiIiIiIi8nJ1SpyxIyKvbL4er1qnMxQEf5BSwAt2sibC+fVTiZb85PuHYQKEWmuOy5r6M/Bwe5EdPdAQhfFVMKEK4qFjc7+wP0G62E+2KUtlkh/fnjLJjdXgh7ibYW95mEzMC3/8g8NQKEIoeGwWIyIiIiIiIiIiIieMgh0ROek5PQfasDWXfaRDXiu2rIlwadNM/CvzZB0v7Ik0Hrtgpy8DT3XBU12GthGA0r4V7h9THbaMr4LxVZYJVV7gUxV+8feOjJ2zky+PUrooiu/HKaqeSMLZECGPz5ZJRXL0BQM0FEv4evupTGh98TcWERERERERERGRk4qCHRE56fn2na8TjzAjC33RYQAioXrmxBoJPDJEdobXqi0SrHpJ7907CmvGwpyOlNn/uQFmNztMqnLpSVvaR7zgZzhvGM7D+p4DY5Mhy8IWuOo0S+gFvnXDY8FOrpymclqQSouPUFcMvxui7BSI2SyjzhBbYmEaiiWcrj4FOyIiIiIiIiIiIi9DCnZE5OTX7Z0Zsy0W4ZKBMrtb0gSAeXWz8G8p4Qy6ZENjFTtjZ9G8GBUX7t8FT7QbutIHAhrHWKbXwaIWy+njHSY2RxgaylAuWwDyZegYsbSNQPuIoX0EekYhVTA8uBs29sI7F1mm1D7/NUWeFuxgDKULo4T/L00s18BIrJ3x5QIbg2W2xEMsH/LCsNJzzCkiIiIiIiIiIiKnHgU7InJyK5bwD40AXrDzvu0Z2sblADizcT6B33i/zsTHKnZeZLCTLcKPnzRs7fcCHcdYZtZ7Yc68ZoiPHVvjP8TbM+yHaXXeD3hhT6EM2wcsN6w3DGQN33wELpkOl820+J1nz3E44cC+YMdrQ1eZH8K9NUN8LNhZloGNQdiS8OIcZywMExERERERERERkZcXBTsiclJzegcw1jIQ8DMSDLKpphMHi4thWnYcvh0prAM5/yi4EA288GCnZxR+8IShL2MI+iyvnW05vRViwRe+/pAf5jbB1FrLjRu8KqA7t8OmXnjn6ZbmxNHNc+CMHa8yCZ+hfFaY2O56AKan81ADWxIFAJyBISiWIBh44YsXERERERERERGRk87z+P/FRUSOP1/P2Pk6sQg1TpCVjV0AGH81wUeKABTmGoquV7kT8b+wM3Y298F/PeSFOjURyyfOtSyf/OJCnaeLBOAdiyzvWewSDVjaU4avPWi4fye49iiuH6tEypdG939WOiNCtNgAgFNOMzveyGDI0Bd0MBx4diIiIiIiIiIiIvLyoWBHRE5qTs+B83VajKEjPgBAMtyCf3UegNRZZQAMDmF/7HnNby08sAv+93FDvmyYUmP5m+WWcS/+qJ5DWtQKn7nAclqDpewafrfR4XsrDMO5I18X9seBA63YAIg7RBtaAMiERrkoMRmALfGxNnJqxyYiIiIiIiIiIvKyo2BHRE5qvu6xip14hLA7TJQMAHWZFkwZKuP8ZJq9z8L+BMYc/Wut4sL16ww3bnBwrWHZeMtHz7IkQi/9Pp6uKgwfOsPypnkuAceytd/wbw8YVnUc/pr9FTv7WrGNCc2dAEAmNMT5xWYAtiS8EiCfgh0REREREREREZGXHQU7InLyqlRw+gcBr2Jn0B0gbr0QJ95RA0D53AjZsbDj+ZyvkynCd1cYHtlrMFheO9vlzxZa/L6XeA+HYQwsnwyfPt8yscqSKxl+ttrhT9sOPT48dsZO7hnBTnhSKwCZ0AB1a1zGhRrZqoodERERERERERGRly0FOyJy0nL6hzAVl1GfQ2fIYbt/hLj1zphJDDfgxg3lhSHyY+3J9lW1PJfuNPznQ4btA4aQ3/IXyyyvmuaFLcdbYxw+ca7l0ulelc2tWxwe2/vscZHDBDuRcD3G+rBOheLOIS5pmH6gFdvAMBRLx3T9IiIiIiIiIiIicnwp2BGRk5bTM9aGLRYh4MviGqgrZwGI5RsoL4uA35DdF+wcRcXO1n74+sOGgayhLmr563Mtc5uO3R6Ohs+B15x2INz5zTrDhp6Dx0QCXrBTrGSpuOX9nxvjI4pXvZSln2tSExkI+ugPgrEWX2//8dmEiIiIiIiIiIiIHBcKdkTkpOXr7AVgayyK8aXAWqJjFTvxfAPlZWEAciUv2In6q444X88oXLfSkC8bptVaPrnc0pI4hht4nq6cZTljvMW1hh+vMuweOvBd2B/f/+t8efSg66L+egAy4X6Sj5UJUH2gaqdL7dhEREREREREREReThTsiMhJy9flla1sSgTJ2zRhCuB41SrhcS3YOu9AnGx5xPsscPiUJl/yQp1C2TC11vKRsyzx4DHewPNkDLx1gWVOo6XkGv73cUPPWIbjGD8hXwyA/DPasUVDXsnRaLgX3/YSTU7L/mDH6JwdERERERERERGRlxUFOyJyciqVcXoHAdiQqACwIOWdFxMp1OAuOxDi5Ete0HG4ih3Xwi/WGHpHDVVhy3uXWPwn6dvP58CfL7ZMrLZkS4bvrTCM5L3vwvvP2UkddE001gxAJtYFwOJUI1viXuhVbu84TisXERERERERERGR4+Ek/U+bIvJK5/T0Y1yXwYCf7kgGgCVDXhVKvNhIZW5o/9h9FTuHO2Pnru2wrsfgcyzvW2JJhA457KiZ0QF862+nsvpWnG0P4du7Gqd7M87AHky6DwoZsO4Lnj/khw8uszTELEM5w/+sMORKENkf7DyjYifZCkAm5AU7524KszFRDUBsOAPZ/Atei4iIiIiIiIiIiJxc/Cd6ASIih+LrHGvDFg+Dk8cAUzMOmxsgEm2CgNk/9sAZO88Odjb0wG1bvLFvnmeZVPMCF2RdfHtWEnjqJvw7HsZYlxJwpIzI+sPYYAQbq6U070pK818DgfBR3S4egg+fafnGw9CZNly3EhIxL9h5Ziu2SNW+YGcQt87h9F4/KV8teyJ9TMpBpb0D38xpL2TXIiIiIiIiIiIicpJRsCMiJyVfZy8AGxNeYeG8UjX4+gGINLceNHZfa7LwM4KdvlH42WqDxXDuJMtZE5//OkxmgMD62wisvRkn1b3/c7flNPyxKsqZNLaYxYz9UMxixqp1TDmPKechO4Tv3m8RXPFzikvfSmnhayEYfc5710Xhg2dYvvUobB8wjLOHqdiJeK3YRsPDlOZA6EGHaDnJhqSfSbkyvdu30qJgR0RERERERERE5GVBwY6InJR8XV7Fzsakd67ORV2NjIYfBSBa/4xgZ1/FztNaseXLcN1KQ75smFJjuWauPfqbWxffnicJrP2jV53jemf82FCM0pzLKC24Cqd5OtGaGJmhDOXy09quWQuVIqaY80KeYhZf10aCj/8SJ9VN+IHvEXr8lxQXv4ni6ddAOHGYRXjGV8H7l1r+ZwUM5ZKEg5ArPSPYCTcBUPYVKdQPEwxV0ZIJsT4e48qeEejoPtTUIiIiIiIiIiIicgpSsCMiJx2TzeEMe+HF5mQJMFywbQKPzu0DDgQZANZasmMVO5FA1dhn8H9rDN2jhmTI8p4lFv9RnChmMoNedc66W3BGOvd/XmmZS3Hh1ZRnXri/ldphpzMG/CGsPwTRaizgNk6nNO9K/JvvIrTiFzhDbYQe+SHBlb+mePo1lBa/CRutPuy6ZtbDOxZZrt/khUBbB9JcMOnA9z5fiJBNUDBpctkuIosbmTUSYkN9HTDChME8fflRasLx534IIiIiIiIiIiIiclJTsCMiJx1nrA3bnoifUb9hmr+B+kyc0Yj3+b7WYwDFSg7Xlr3Px1qx3b0Dnuo2+IzlvUstVUdxrE1gzR8I3fdtTMWrELLBGKU5l1Ja8FrchqkvflM+P+W5l1OefSn+rfcRXPFzfP27CK34OcFVN1Ba+FqKS9+Kjdcd8vLF42BDf4JdKdg9lGbHAEx72tCYU0/BpsmlOymdfRbLfhHm9glx0j5IVGD9tvWcN/+sF78PEREREREREREROaEU7IjIScfXOdaGbaxL2ZW94yn6M5T8OeDgip1cecS7xgTxOyE29cItmw0Ab5xnmVLzHDerlAjd802Ca2/yfts8m+LC11KedSEEIi/dpvZxfJRPu5jyrIvwb3+Y4GM/w9e7leCq3xBY8ztKC66mcN4HDnnvOY1xdqXAmDQ/X2O49nxLJOB9Fw02MljYRSbXjW3yszgZx+KwIRHmrOE8Qzt3goIdERERERERERGRU95RNCcSETm+fGMVOxuTLli4+MkmRsNeG7ZQoBq//0DokSt7LduigSQDWcNPVxsshrMnWs6Z9Oy5n85kh4hc/ymCa2/CYiic90Gyf/YdyvOuODahzkE3dyjPOI/sO/+H7Bv+jUrLXEylRHD1jUR//QnM6MCzLomMVSQFfWmGcoYb1psD38W8KqZsyXt2wbNjhCoBNia8dKyhL0VHbuTY7klERERERERERESOOQU7InJysRZflxdObEoYIraKmmyI0UgHANFI00HDsyUvrAj7k1y30pArGSZVW9441x7xNk7vNqI//zD+jrXYYIzcNf9C8Yw/887IOZ6MoTLlTLJv/zbZN34VN1KFr2cr0V9+BKdvx0FDw34vpKmKpHCMZVWHYZX3WIhUjQMgyyAUK1ROC9JYCLFhLNiZl7Lc27f9+O1LREREREREREREjgkFOyJyUjFDI5h8gYKBHTHD9HQjAJloGwDRcPNB43OllPfPYhVdaUMiZHnvEovfd/h7+LfcR/T/PoaT7sGtHkf2z75DZerZz2udtlIi07WOfO86igObKQ7toDSyh/JoJ+VsL5X8EG4xjVvOY93KUWzcUJl8hreWmgk46V6iv/oYvl0r9g+JBLyQpmLTXDrd++z6dYahHESTLQCMhoZxhvLgM0wLRdkUj1IBWgvwVNs2rD1y4CUiIiIiIiIiIiInN52xIyInlX1t2LbFDWVjuGxHK9ZYRiNdAEQjzwh2yl6wM5jzQo+3LbBUH66LmnUJPvIjQo/9DIDypGXkrvoHCCeOam22UiLfvYpc233k2x/CLY4e3aaMj3DLMqJTLycy7myML3T4e4wFTZE//j3+tjVEfvc5Cq/6BKVFryMyVrGTL6d59QzL5j7YM2z4+Wp4x1zvuWRCw5iBHDTFWDilivvaetgVDTM9m6e+L8WW0T5OSzQe3bpFRERERERERETkpKNgR0ROKgfO1zFg41zYncDWFsmEh4Bnt2LLjFXsuLaK01stcw/++oBilvCt/0xgx8Peb5e8hcL5HwTnyK9BWymR73mS3N57ybU9hC0dCHP8kWrwx7CVMtYtgev907plcEtPn4R852PkOx/DBOJEJ11EdMplBOvnYg7V+i2cIPfGfyd8538Q2HA74bv/C2e4nfA57wSg7BZxbYF3nh7i3x+AHYOGVd3exvPBDO7ACFDPsvHVsNewPhFjejbPvJTlvr7tCnZEREREREREREROYQp2ROSk4uvsAWBjwhCoVJMo+6jEhsmEhoFnt2LbPuAFO36T5A2HOVfHDHcQ+f3f4RvYjfUFyF/6t5TnXnbYNRwpzHHCtUQmnE9iyqtonnUWwyN5ymX32XNYC7aCdUtUMj1kd99FdtefqGR7yWy/icz2m/DHxxGdchnRKZfij7c840EEyF/2Gdya8YQe+gHBVddTPdxBsNqh6HPJl9M0xEK8Ya7lV2sNt2+vYnkghEuB/HAnIaZRFfQTIMCGRJTX9wwwN235Rf9OPjDlLHxGnThFRERERERERERORQp2ROTkUa5gevsB2BR3aMjW4dY6OMU0o2PBTuxprdj6MrBnOEUgAHMakyQO0eHMt2cVkZv/EZNP4cbqyL3un3Bb5hzy9tZasjtvY2TN93ALqf2fO+EaIhMuIDrxQoIN8zGOD7/fwTiHP8jHGAPGj3H8OFWTqVr4FyQXvI9Czxqyu+4g13Y/5dEOUut+SGrdDwk2LiQ29XKiky/F7KsiMobime/ErWolfPu/EtjxCB+NOVw3w2tBlwjVc+YE2NhrWdvtkLPNhMwecuku9j2KunCYDckYAKelLdlCjtXDHSytmXCU/1JERERERERERETkZKJgR0ROGk5vP07FZdgPnaEY5w3GKC8NYx5NUQhkAYiGvZZj1sL16wwWL4CZVffsc3J8Ox8j8vvPY6xLpXk2udf9EzZef8h7V7J9DK34GvmuFd5aDhHmvFjGOISbFxNuXoy79BPk2h8iu/N2Cj2rKfY+RbH3KUa3/p7asz5LoHrK/uvKp72KbKKRyB/+jtbMCB/bCP1TNsPUqRgDb11g2T0EmYoX7GTy3VSPXTslFuPR/AjDfj/V5TIz05Z7+7Yr2BERERERERERETlFqRePiJw0nA6vDdumhMFQzfzBEJVZDpmAd75OwB8nEIgD8Hg7bO03+Bwv2IkGqw6eq2crkZu/iLEupZkXkX3r1w8Z6lhryey8ne5b3uOFOk6AqkUfpuX111Oz7K8JNS16SUKdZ+01ECU25dU0XPyfNL/u1yQX/gUmmKA0uIWe2z9IauMvsW5l/3h33Dyyf/YdBqNBqouGKTd9Hd/OxwCIBeHPFlkK1qtm2h3MQtG7dkF1Aox3zg7AvJTl0YE95CslRERERERERERE5NSjYEdEThq5tnYANiYcjI1zZiIJbunA+TpjbdjSBfjDRgNALDgCQMSf3D+PSfUQ+d3nMKU85YlLyF/5d+B/dp+2Srafgfs/x9BjX8GWMgTqZtN0xQ9IzHnbgXZox4E/1khy7jtpvvJHhFvPArdEas3/0nfnxyil9u4fZ6vH8fuz5rI9YfGVi0R+/3l8u7wKo9MaoKXGq2baWFMm01MA4Mz6JFhYl4wCsGzER94t8+jgnuO2PxEREREREREREXnpKNgRkZOG6ewGYGMigs8N0LQogTOSZzQ8DEA07AU7N24wZEuG8UkLxqvYiQTGgp3CKJHffRYnM0Clfgq51/4j+A4Oaay1ZHb9yavS6XwMnADJhR+g8dJvEaiadHw2ewi+aD11F/wrNWd9BhOIURzYSM9tf0F602/2V+8Eog38YBZ0TpiGsS6Rm/4fTvdmABZPaATA8fXxf9sCWAsNkSA+E2Bjwgt25g1bsF47NhERERERERERETn1KNgRkZOCzeaoSXtVJltiNVSVArizQ5iRwtMqdppY3wOrOw2Osbx5QZl8eRQYq9iplIj88R/w9e/CjdWRu+bfIBQ/6D6V3AADD3yBoUf/BVsaJVA7k6bL/5fk3Hcc1yqdwzHGEJt6BU2v+RGhlmVQKTKy+jv03f3XlNPtxIN1VBx45PT5lCcuwZTyXnXScCeJqFexE6ab9dkQK9q8OWsCIbbEopSNIeqWacnDk0MdDBdzJ3CnIiIiIiIiIiIi8kIo2BGRk0LPrh0AtIcNaX8145ww+AxmpMDoWLATDDRxwzqvBduFU6AhNgpYACK+BOE7/wP/3iexgTC5a76CTTYedI/s7rvpueW95DseBsdPcuFf0Pjq7xConnLc9nm0/NFG6i/8KjVn/C3GH6XYt46eW99Pc187WEiXh8m99ktUGqbjZIeI3ngtMbyqnLDpAyr8YZMhXYBJsRhFn8POmHfOzqV9EVwsDw7sPIE7FBERERERERERkRdCwY6InBT6d3mtwTbFgxgcZjUmAHCGC4yGhrzvBlsYzhvqopbLZ1lyJa8NW8gXI/L4LwlsuB1rHHJXfRG3acb+ua21jDx1HYOP/BNuMUWgZl+VzjtPiiqdwzHGEJt+FU2v+SGhpsXYSoHq7Q9yRR8Usj0QipF7w1dwE004Q+3U3vYf+F0DpsKUcju5kuF3Gwzzqr1nuTYRAeDCLu+8oXv7dpywvYmIiIiIiIiIiMgLo2BHRE4411oCXf0AbEp4rdOWTKgGwIzk97diW93rnbHz1gWWoA+y5REAzhgMEHrkRwAULv5rKlPP2j+3tZaRJ79DesPPAEjMfSeNl32HQPXUY76vl4o/1kz9q/6D6mWfxPqCtBQMS3ZupTzahY3Xk3vjV7HhBP6uTVzUHsRYeE3/kxgsT3YaGkwSLKwbO2dncq6IY2FzupeO3MgJ3p2IiIiIiIiIiIg8Hwp2ROSE25TqZvpICYCN8WqwMGesysSmRskFvXN08raFMyZYZtZ71+VKKaal4DVbvXCisOztlBa+dv+81roMP/GfjG65HoDqJR+nauFfnNRVOodjjCE+43WEL/pn0j5LvFSh908fpTi0HbduErnX/TPWF2DcSJEzOyGZ28v5E10A7tsWxjF+NiS8Vmx+k+LsHu8h3tW77YTtSURERERERERERJ4/BTsicsKt3r2Z6hKUDeyMRYn6gviMgVyZDIMAVGyEcDDB62bbAxf27+TPt4HPQmnWRRTP+8D+r6xbZujRr5DZfhMYh5ozP0N81huO99ZecsnaudzcBIMBi5sfpO+uT1DofYrK+AXkr/wCFjht0BA1a7iqLkNNxDKUcwgTpj8UYCgSxhh4854aAO7u3UbFuid2UyIiIiIiIiIiInLUFOyIyAlVdl1Se3YBsCccoeg4NIW9s2CcwRxd8QwAedvMG+ZCLOhdZzIDzLnn10Qqhp6aKvKXfxaM90qzlRKDD3+J7O4/gXGoPfvviE274vhv7hgI+aOUAxFubQSn7jRsKUPfPX9Lru1ByjMvYM+8pQC0FHYQ23A7b5o3FoSVvDZsG8da3S0YsSRKAfqLGdYMd56QvYiIiIiIiIiIiMjzp2BHRE6o1cMdTBne14bNa782Ne61DHP6szzY5APAF2ji9Naxi4pZIr/7HJFsmr6Q5dGzzgV/CABbLjDw4N+Ta3sAnAB1532J6OSLj++mjrFEsJaiA6Vl7yc8fjm4JQYe+n9ktt9Mau4FrK/3wpzg+u+yILeShS2WUMV7tqtjYQD8wSEuaRsPwJ29W0/MRkREREREREREROR5U7AjIifUff3bmZ32goh1VV6lzsIaL4TY2WPpjmcBmFTfhDGAdYnc8mV8PVvJB4NcNwt8Me+8GLeUpf/+z5LvfAzjC1F/wb8QGb/8+G/qGIsFawEYraSpW/5FYtNeA9Zl6PGvEercwMomaIsHMbZC5KZ/4K2NW6m2SQDWJrxnTGCQK9smAPDIwG7SpfwJ2YuIiIiIiIiIiIg8Pwp2ROSEyVdKPN63m5mjXrCzKeFVkyypS+JauD5XQ5guABqSzQAEH/8//DsfwfqD3LVkLgNhiPiTuMU0/fd+mkLPaow/Sv1F/064ZdkxW7tbhHyXS34r5FbC6N2G1I2GoR8aBr5pGPqRIX2LIfs4FHeBm3np7p0I1gEwWhzEOH6qz/hbEnPf6a1r++1MysJ944u4/umYYpaGWz7DuyYMg/WxIxqh5PNhKkWmjzpMH0lSti739e946RYoIiIiIiIiIiIix4z/RC9ARF65VgzuZVy6TMiFgvHTHg4RMH6SgQCP7YU2X4RFdAMQjTTjdG4g+PB1ABRe9Qn2cjcMQ8wG6Lv7U5SGtmKCCRou/CrB+tkvyRptGYpbodxjqIyAOwKVEbA5gNzYqGdn5JUcVHoBzP7PTNTibwBfI/gbLP5G8NWBeZ5v4vjTgh0AYwxVC/8CX7iW4VXfojkPftcybC6hqr6Cr38XFzz+WaomfJSRgGFrLMHc1DCMS3HF3ol8a/567uzZytUtc5/v4xEREREREREREZHjTMGOiJww9/fvYE7aBWBXPIE1htpgiHwJbt7sBSJVbgeuD+JOFZGb/gljXUqzLqI070pyq28kVIHkk7+kNNqDE66h/qKvEayZ9qLXVklDfo0hvwZs1hxyjBMBpwqcpMVXDU6VxVcFThwqKS/YKfcZKn1QGfTmKe2B0h7YH/gYi68ewoss4flgAs+9tvi+VmzFgYM/n/UGnFAVA4/8E/VFQ1/y9/gv/wrx3/8tvsE9/D/nN/zNpGt4Kh71gp2aIS7ZNIvvzt3A9swAOzMDTI3VvdBHJiIiIiIiIiIiIseBgh0ROSHSpTwrh9q5dux8nbVVUQCmxuL8aZthtGhoyqewiSGw0PjYjTipbtyqFvKXfgqMIV8a4eIBcAo9OJF6Gl71HwSqJr2odZU6Ib/KUNgMuF744iQswRngq7Y4VeCrhnQ8zRazh5XdWxgt5Sm4RQpuiWKuRCFT8n4dKVEYX6LQWiLiBJnqH8fU0ngmj45nct8EEl1JyDtU+iBzpyH7kCV8OkQWW5zY4de4r2In/YxgByA6+WK2dd5M9e4nIdjNwOpv4XvN3xO9/lMs7l/JW8INrEsuhE4g3UsyeTrndrdwf2snd/Zs5UNTz35Rz09ERERERERERESOLQU7InJCPDywm7J1WTDsABVWx0MAzIgnuX+jN+b13avYmLTMHPITbn8Y6/jIvebvIRTHWsui7kGaCwb8ERou+uoLDnVsBQpbIL/SUO46UJ3jH2+JLPFCnZIpsT61i1XDW1jVtZXd2a7ndY9sJc9AKcUTbIIwMAFqpiaYFhrHlNx4JuyYwKTuCdQ9Uk3ucQjPg8gyi6/22XPtC3YypcFD3ivYfDqbh55kzrAhP7iKvt1Jmi78KJF7vsGH2u/gH6a2ePsbHCZ3tuHKVRO5v7WTe/u2877JZxBwfM9rbyIiIiIiIiIiInL8KNgRkRPivr4dxMqW1kIFgE3xMFjo662hYg2z/TnGF3fTkYczOr12bcVz34/bMgeAkXU/YmrGxcVSfc7nCFRPfd5rcDOQXwO51QabGQt0fJbQbAgvdumq6uXekS2s2rqFdamdFNzS/msNhtnVE5kfn0a1P07QCRBygoQcP0EnQNgJjn0WIOgESJUzbBttZ1umne2j7ezN9TBUSrOytJmVbIbJwGSYmh/PxXvO5ay1p5NfEyA4EyJnWALjDqw7HvDSnkNV7AAkohMZDUBXsJbW0jC5vffSNzVK0+xLCW26k0/t/TVdoTfRUsjTEx9h6XAT9bkw/ZE8Kwb3srx+yvN+liIiIiIiIiIiInJ8KNgRkeOuv5BhXaqLxWmLAUYiEYaDAQIE2NoXxDGWNxe6yAYHuKAN/K6lPHEJxWVvAyC7605G1/8UgEdrHN4y7rzndf9yP+RWGAqbgMpYu7W4JbzIElhY4f7cav6v/W46dvcddF1dIMni6pksqZ7FsrpZTG5sYmgoQ7nsHsVdG5iTmLz/d/lKkZ3ZTraPhT3bMh3szfawM9zOzlm/5pcz/sjyzqVc3HYurVsb8Y+zRM7wqocSYxU72dIIFbeMzzn4VZ6Me5VL3bE0i7Lvp7f0AzI7b6Fv5htx94xjYraDYdqABnZv6qVxyWwua5vAL2Zu487erQp2RERERERERERETmIKdkTkuHugfwcWeFVPBBhla1UcAL/rnbOzfBKMWzHCkF1FbcFQDAYpXvF5MA6F3rUMrvgqAOsSlo7aWowxh7nTwayF/BOQecDsD3T8LZbIUotvpsv9Q6v55bY76cj3AxAwfuYnp7JkLMyZFGnefy+/33lRzyDsCzInMfmgsGe4NMqdvU9wS8+jdBcGuWP8g9wx/kHmDE/nks5zWfz7eYRqHMJnVGHwYamQKQ2RDDUcNHcsOg6DoeQvEBgdT815f8vQiq+S3vpb2qa+htpNA1QXdwMNNA11c/eCpVyxdiK/mLmNVUPt9Bcy1IeOcMiPiIiIiIiIiIiInDAKdkTkuLuvbwcASwe9s1yeioQB8JcTxAKWy2danFtX0uDrAGDX0otojddRTrcz8OAXwC1RbpzLE8H1tAbrj+qelRSM3mIo7fWCmcBUS/Rci9Pi8kD/Gn6x7k7a816FTtIf5U2tF3JV8zlEfeGD5rHW0pHLsCUzxI6dKdb09TJQyGEB643A2n2/Bjv2e4DqYIgZ8WpmJWuYGa9meqKaiO/Aa7g6EOfN4y7ija0X8OTwVm7qeYQnhjaxsXo7G6u3U11MclHXWVx4z1lET68h4+tntDjwrGDH5wSJBZsZLXaRyu2lato7cUsZRp78byYM3MIfWs7gHXs3AzA928l/dxjOHF/L/IFa1tUNcnffNt46ftFRPVcRERERERERERE5vhTsiMhx1Z4dZntmAF8Fmkt5MLAmFgIgUq7iitmW2FAnxrkBgPX1FjNpCZXCCP33fRa3kCJQexrdsy6E3etJPCPUeCZrobARMncabMFAwBJ/lSWwwOWhwbX84qk/sTfXC0DCH+WNrRfw2uZz9wc6+UqZ7aMjbEoNsjE1yJbUEKly8QXtva+Qo6+Q45GBLgAcYGIsycxENbMSNcxMVDMxmsRnHJbWnMbSmtPoLQxxa89j3N67gmFS/G7Sn/jjhLs4J+8DHwxuGaRlCTyzaCkRn8zoYBdp001VvkzitDfjFkdJr/8JiyuPc1fDbC7pKhKwQeYOruXXUxZw5YaJrKsb5M7urbxl3MKjroQSERERERERERGR40fBjogcV/f1e9U6l3bV4ZhuXGPYFouAdZgciXP2+AqRX/wzxuQYDPl4sqnM8mAdAw/+A+V0O75oE/UX/DMbem4EIBk8fLDj5mD0T4bi5rH2aa2W2GsqPGrX8Yu1f2JPrgeAuC8yFugsJ+YPM1ws8IeOLTwx2MOO0REq+0puxgQdh1mJGk5vamJaKElTMIoxYACDwQCYp/16THc+y9b0EFvTw2xND9FfzLM7k2J3JsWfuvcCEHZ8LKpp4PLmSZxe00hjqIb3TLyCd4y/lEcG13NT98OsT++iL5Sn1sKKPU8ybtfZJC83+BIH7pVITqJr8FFGIn1MHMzjtsZJzn8PtjjK6Nbf0hzYTDYwgWgpyDt6fsbnE//IGb6pRMrr6CTFxnQPc5PNL+rftYiIiIiIiIiIiLz0FOyIyHFjreW+Xi/YeU2Hd65OdyJBwedg3BBvne8j8viP8fVvwNog900s4hqwm/9AsfcpTCBG/YX/ii9SR7rgnYOTCB26FVtxt9d6zR01YLy2a6nFg/zzjl+xLr0TgJgvzBtazuf1LecR80fYnUnxg52bua+3nZJ1989VGwwxO1nLnGQtpyVrmRqrIhL0U1MTY2goQ7nsHnINz9QYjrKg+sB6Bwp5tqaH2DIW9mwbHSJXqfDYQDePDXTTFIpyWcskLm2aQE0wzAX1i7igfhGPD23i+s1fhvIIjzY/zJo0/PmvXs+8c8YTmuNV7yRiEwEYifThDOZwW+MYY6ha8lGe6uth2tBDjEY6iJZqSJb9/EXXP/OrCV/m/M5x3DFxL3f2bFWwIyIiIiIiIiIichJSsCMix8220X46CylCZR9zyl4ty+pIFIAqJ8rs/FqCj/0UgHz5YlLhWxmXdSgOPADGoW75FwlUTwUgVfTOw0k844wdW4LM/Yb8Km9+X60l9hqXhwKr+fa6G8lW8kScEG9oPZ9rWs4n6gvz5FAvf+hYw+rhvv3zzExUc2XLZOZX1dMYihyTtmR1oTBnh1o4u74FgIq17MmkuLunjbt62+gpZPnp7k38Ys9mzqlr4YqWycyvquOMmtmkmi/kkfafE8FlW9Vu/j75DS7ccibv2HYFrZfGSMQmAV6wYwZz++9pjAMLPsXqFYOcG+6HFFhbz6LMzewY+D86zbnAXh7o28mHpp5NxBd4yfctIiIiIiIiIiIiL5yCHRE5bu7r2w7Aud3N+GIjkIJ18SAAF1b5CN/6ZYx1ccNnMuBroC4P47NeG7TqpX9NuGXZ/rn2VewkQ437Pyv3QPomQ2XAC2HCp1vc8zL8R9uN3D+wBoDZ8UlcO+PPqA1Uc29vG3/o2ElbbhTwzrw5u76F14+bxmmJmuN+xozPGKbGq5gar+Ldk2fzUH8Ht3btYUt6iAf7O3mwv5NxkRhXtEym3l8LwBlVkxkXXMB9A6u5t+UxVpSe4k03XcaV8+YCkA2lqAwMARP332d+fYL317yLePlHTOx1MTYGNsw1/T/gG+Nm0pyJ0x0b5cH+Xby6aeZxfQYiIiIiIiIiIiJyZAp2ROS4qFiXB3q9FmiXdI3DKa0AYFMiAq7lz7f/ECfdi1szHpu6iuHYrUz18hbis99KfMZrD5rv6RU71oXcCsg+ZMA1mJglcYVlU/12/n3j/9FfHMHB4R0TLuXyhnO5tWsvt3U9QapcBCDi8/Pq5olc3TqF5nDsOD2RIwv5fFzcNJGLmyayc3SE27p2c19fOx25DD/YuYF69jAVyBaH+ey8d3BV6hy+s+137KSTn076Pfd2reBKN4HrpBlN7yXKwv1z14b8hP1x/rvh7byqfRXxQoxMuJVYYSd/0f1luoMfozs2yl2dWxTsiIiIiIiIiIiInGQU7IjIcbFupJvBSo5EMcDSmjims0zW8dMWCXH5wFpq9jyCdfzkLvsCwR/ugYZVOBhK1eOpWvShg+YqlLMUK1kAEk496d8Zitu96prgTEvo0hI/6b+NGzc+gMXSGq7n09Pfzu5Ry1+uuo9MpQxAUyjK1eOm8OqmiUT9J2/LsanxKj46YyHvnTKX+/vaubVrN70Zr4VdW6aTG9q28dpxU/nW4r/mtu7H+PGu22mLd9GRgRYXegv9TH7GnNPicZ5KpXmkZhyv7h6mQBInHCKRH+FDXb9mZf0y1md7aM+NMD5Sddz3LCIiIiIiIiIiIofmnOgFiMgrw33dXhu2CztbKTelANgST1BfHOHjbX8AoHjOe7G+cXQ134DPFMk7Fv+8t3vnwjxNeqxaJ+SLkbs+5oU6fkv8CpfBV3fxyR3f5Ldd92OxXNF4Jp+d9gF+tLON/96+lkylzLRYFZ89bSn/u+xVvH7ctJM61Hm6qN/PFS2T+ebpF/DJWecD4CfDj3dv5COr7uXR/m5e03w2P1z2GS6OL2XYFwbgtxMfY+dDfVh7YK4z6qrBwmPJJACxQivtiQJlx8/U/Db+etdmAO7q2npc9ygiIiIiIiIiIiJHpmBHRI65klvh4YFdALwqO5Hhri4AnqyKcO3uG4lW8lRa5lBc9lZSG39OJrYNF9iWhGTNac+aLzV2vk4s20C502BCluRbXG5veJCPrfs6O7OdVPljfH7Gu6n3z+fatY+wMTVI2PHxganz+M/Tz2d5Qys+c2q+Ao0xLK2fBoBDhfoA9OSzfGXzSq5d+xBd+SKfnv82ljYsB8AN9PA39uvcdudqbMmbY1FtDPDzRFUCayBUTGB81XRVedVMV/c/yUUDe7i7aysV656IbYqIiIiIiIiIiMghqBWbiBxzTwzuJUOJhlyY+umt1D/+IAARu5Ol6e1UfEFyl3+OfM9qRrp/A8CuuCXrd4hHxz9rvuEBr2InmmvASVgibyrxzdT13NW+CoBl1adxecMl/Gz3Ttpz3kE9y2qb+Mi0+TSGo8dsn9Za3EqOUmGYUmGIUmGI8tivXbdAMNJIKNJEKNpMMNKEzx9+wffyOyHC/gT5cpovz53LA4MVftu+nU2pIT615kEubBjHVbWL2dD5O5pLJfKRAt9M/ILV92zm42dew/iqEAETJB0os6emmsmDwzQ1v4f2nm/QH7PUZ+AzOx7jA9EanhxqZ1ntxJfwSYmIiIiIiIiIiMgLpWBHRI65B/buAOCirnE80TjKm90SWb+fa/puBWB0+QdxQ0EG7/0nwBLIzqS/fgvxaCs+X+iguUpt0LeyH8ZBwtTjvD3D/+v8CWtTO3Bw+PMJV9CdreZfN60FoDoQ4sPT5nNufQvGmBe9l1JxhJGexylk9rDXphlN9VHMD1EuekGOWykc9Vz+YDWhaDOhaBPBscBn/0+kGV/gyCFUIlhHvpymWB7hzyYt4dXNE/nZ7s3c3dvGfX0dPNWX5vVAzOZ4x+jZ/DL2GA8mV7FlzW4+M+kdTIrG2Z7N8mB1gsmDw0QGAlQv+Rh9K79JpASxYokvbXuAnyRPY9kZCnZEREREREREREROBgp2ROSYypaLPJZtAwPTE9MptbcDYH29RG2JpxIzmLLoKgbu/iRuIUWoMp58eSqwhURs0kFzFbZA+iZDZqLXii04Pcbf7v4W7fk+Ir4QVzdeyU3taYZLbQBc3jyJ90yZQ/xFnKFjrSWX3s1wz6MMdz9KenADcOTWZI4vRCBUgz9YQyBUTSBUg3ECFHO9FHI9FLLduOUs5eIw5eIwmeHNh5wnVjOb2ubl1LQsJxyf+KxgKhaopY/dZEqDANSHInxy1ulc3TqFH+zcwPoRSxk/fqfM/EKCr879S/5t2y/pDQ1wbee3meMsx1LHPYkI7wJ8ezqIX/NeypluOjf8hin9MD07zJmbb2Tk9FdRFXjhFUYiIiIiIiIiIiLy0lCwIyLH1KPduymaChPScVYkxvOu3U8AECtvJxMI8Zu57+dDq79HaWATJpigdefbWN18PwDJ+OT98+RWQ+ZPBjBka71WbLcNPUU71dQFqpgUOosb2wYAmBCJ81czFjK3qu4FrdmtFEkNPMVw96MM9zxKIdt10PfR5DSS9QuoqmmhbGM4/ioCoZqxn2ocX+SI1UHWWiqlUQrZbgq5borZnrFfe/8sZnsol1JkhjaRGdpE26bvE46Np6blXGqalxOvnYMxPhJBb3/p4sBB809PVPOvC87h0YFuNq+po8rt4X/i61mUa+Hriz/Od574A49E1rCOBzD+BnZGl5CPRwmPZvHt7qDq9A9TyfbRUb6XiYNwVd927r/nZyy+7AMv6HmKiIiIiIiIiIjIS0fBjogcU/fv2QbArNwU+gslpuR6vC+cHr498TUsCXSQ2fZ7AOoWXktoPQxHveAmEZuEtZB90JB71AtKwossvVV7IA8jLkyINuOWZ7N2OIffGN46YSZvmjCdgON7XussFYYY7n6MoZ5HGel9AreS2/+dcQIk6xdT03w21U1nEYo24/c71NTEGBrKUC4fuYLnmYwx+IMJ/MEEseoZhxxTzPUx1P0IQ90Pk+pfTT7TTtf2X9O1/df4g9XUNJ9DlcnhWBh9RrCz7x7n1LdgErPoHOmhyh3kps5drBrq5ROLr+D0dbP4gfkdBV8f5eB9PFm3kHNGs/h37KEyYzK1Z3+WvtwA/cW1NIzCWRt+RXnJxbj1U5/XXkVEREREREREROSldUoEO7fddhs33XQTGzZsYGRkhAkTJvD2t7+dt73tbTiOc9jr3vWud/H4448/6/Nbb72VadOmHcsliwgwXMjypO0GA73h05g3sBfvT2yKR6on8lRiPFfv+h8AEvPeTcw3D1h/INiJTmb0dkNhrRfqRJZXuHnSvQzu6iYINEcmszc7g4JboSEU4bOzlzIrUXPU67PWMtK7gs5tvyQ9sA6w+78LhGqpbj6b6qazqWpYjM9/5PNuXmrBSANNU15H05TXUS5lGOl9nKHuhxnufpRycZi+vbcSAy4CSqX76PNPpablfPyB2EHzVFdPoXPkAS7pT9M1MUxnLsPn1j3MNeOn89XRj/Opkeso+4a4MbGec2jGv2MvBWsxvhD153+Z9pGPECl2EC9WKNzwd5j3XQfB4/ssRERERERERERE5IBTItj50Y9+RGtrK9deey11dXWsWLGCf/7nf6atrY3PfOYzR7x28eLFzxozfvz4Y7lcERnz0PYduMbSnK0lX6nh6sFfAVD2D/DtiVfwweGfYyoFQs1LSc77c5yVPeT9GQr+UQDM3ZMobDdgLOFLy/wgeSO3732UsygBsDMzlQqGhdX1XDtrCVXB0FGty1rLUPfDdG75KZmRrfs/j1XN3B/mxKpnYszhg+PjyR+IUTfuIurGXYTrlkkPPMVQ10P0dtyNv5jCP9rHztX/hm/dt2mcfDXNU99IMNIAQKJqMgBB3wDfbV3K93K7uae3nd+2b+eJaIIlwdfzeOkOVtV0kXdcwqlR3J5enOYmnFCS1su/xrbfv595fVmSmS5yd3yN8lV/D0doNSciIiIiIiIiIiLHzikR7Hzve9+jtrZ2/+/POussstksv/jFL/jkJz9JMBg87LXJZJJFixYdh1WKyDPd37MdQmDsTCbnNjEukwLi3DXhNF4z+ieayn34oo3UnvMFjOPD9GcZjnjVOuFKM5XtUfBbnKuy/EvlZzzZu5UQZQzg4lAhxpvGT+ddk0/DdxQhjLUug10P0LnlZ2RTOwBwfGGapryepqlvIBRpPIZP46XhOH6qGpZQ1bAEd8Jy/rDmb5hsq5hqq8iPttG1/Vd077iBuvGX0DL9rSRikwAYifSR6C/yN4sXc059C9/e9hR7s2naGMVx51N2qllZPcjywRh33X8DS655F3XBJP54C6OzPs3u8j8xfcAlsvUecmsXUl74uhP8JERERERERERERF6ZTo7/Hf05PD3U2Wf27NkUCgWGh4eP/4JE5Dl1D42wITQAFpKF8fxl99cwxLHGsiE2yOL8BirGR+3yL+ILVwPg9OcYivYDEB2djAlbeFOaL+S/y5MjWwmaADV4QUWZOJ+fvYz3TJnznKGOtRX62+9m3b3vY/sTXySb2oHjj9I6450suvRXTJz74VMi1HmmRKiOtB+2RSoseNVPmHnmv5CoW4C1Zfrbbmfdve+la/3/ECobir48xe4OAM6qa+G/l1zE8vpWLBbj5HBsEx3JKQBM7y/wsZVfZ2NqDwALl1zAzxqvpCfh3Td09zdwerYeck0iIiIiIiIiIiJybJ0SFTuHsmrVKqqrq6mrqzviuMcff5xFixZRqVRYuHAhn/jEJ1i2bNlxWqXIK9cdGzcAEC2P4819v6Qu5wJQrI9zWfoGAFKzPsCk+jkHLurL09VYGbtuEpU3j/CF3u+xN9dLxIlQLs2mYjsBGB8bxzn1rUdcg3XL9LffTefWn5PPtAHg88donvYmmqe+EX8w+ZLueR/XhdwoVMqABTv2461p7CSffZ+N/dMYiCYhHDv6LmeJoPf+y5dHKbslaprPoab5HEYHN9K5/VcMdT3ISO/jNADFMnRl/sQ4dyHG8VMVCPHZ2Ut5oK+Df9+8Gmsq3NDg8NbdMD8Vplzu5tr13+GjU97AFS1nckbrVXzL9PLZHY+TKLiEbryW3Pt+DqH4S/78RERERERERERE5PBOyWBn3bp13HjjjXz0ox/F5/MddtyyZct43etex+TJk+nt7eW6667jve99Lz/72c84/fTTX/Q6fL5TouBJ5LgrVsrcXtoOATh70Merh64H9xwAhtyn8OHyRGQhV53+Vvx+78+Rmy7TV5hFKuKdwxNZ0Mjner5DR76fkBOlWJyDIcppMYdKBhqizfuvfSbXLdG35w7at/ycfMYLgvzBJK0z3kLLtDfgD7z4MGLfn39rDaODDiODkBrwftJD4FZe4Lx+iFVBvApi1d4/49UQS4LzjNddzJfE7wQpu0Xy7hCRkBd0VTfOo7rxy+TSbXRu+w1du/5I0EJH9A4G7l5H68y30DjpSnz+MK9qmcDdnQVWjWyhNwTbo36mZ8u8bc9M/nf6Zr6x+3p2ZDr40IKr+dHgIn46IcUHdm0mmB0m9MfPUXnbt3Xejryi7XsX6O8EIq9ceg+IiN4DIgJ6F4jI8f1PZKdcsNPX18fHP/5x5s+fzwc+8IEjjv34xz9+0O8vvPBCrrrqKr7zne/w/e9//0WvJZmMvOg5RF6ObnliLSOBIolSgI+2/whjAWccVCAX2EWPr46b6t/BBxu9ihm3aGn7ySgFXzXZ8G4Afpx9go5yhYCJUi7OxW+i/NW804nm+7h7GzRWtVJTE3vWvbv33MeGR79GbrQLgGC4hqnz38Xk2W/CH3z2+OejkLMM9roM9VYY6nUZ7M2SHrL7q3GezueHYMiA8V7qxoBxnvZrAxhwjDemUraMDlsq5QMB0dMZA/FqQ1WtQ7LWoabRoXWyj2S4nsFsJyaUfdbzqKk5jdaJ/8BDj/nZu/kGqooB8tlOdq75Om2bfsTUee9g6vw/4/Ip41j1ZDeYLCvqIkzPpplRDPPq7ou4s+k+bu57hL2FHq6pW8r/BDNMzmW4sqON4N51uI9/n+jln3xRz1Xk5UB/JxARvQdERO8BEQG9C0Tk+Dilgp10Os0HPvABwuEw3/3udwkEAs/r+mg0ygUXXMAdd9zxkqwnlcpRqbgvyVwiLye/2bkGQvCJXZtpKHXhRmfijBgqToF0KMX36z5Ca6KOoaEMtgxD10Ox3aFihikGvTN2tpUK+E0StzSPqkCSv5u7jIU1DVy/wavACdpqhoYy++9ZzA+wc803GOi4D4BAuJZxM/+M5ilX4/NHSGeATIbnK5uCjh3QuRMyI4ceEwxDss77qaqFRJ1XYWPMIRKfZzkwxnUhm4bMMIyOwOiw95MZgXIJ0kOW9FAFdnjlQMaAO7EWnE52tnVQ7Uw/5B3C0amkAhAfncjM1gtpL9xOPtPJllXfYdfG62mc/l6MWw++KL4Zs6BtJfMHRvnizCou6XsND9fexdrUDrp8A8Qq9fz7+OVMztzD3OEezH0/Z7B6OmbWq57XcxV5ufD5HJLJiP5OIPIKpveAiOg9ICKgd4GIQFVVBMc5PlV7p0ywUygU+MhHPkJ/fz+//vWvqampeUHz2EP9r/UvUKXiUi7rRS3ydDu7e9kYGuDswQ4uG3wUgGLTcsIjGTLhHn5TfTUdgRYurUlSyrukfmco7TQYx2Uo9kcARk2AiqnClOfRHK7hS3PPYlw0TrnsMpLvAyDmr6NcdrHW0t92O3vWf4dKKQ3GoWXaWxk368/x+cNYeN5/TksF6NkDnTsNw70H11BGEpZkLVTVG1onhnBCBfzBZ89feYGt2MIx76du3IHPrIVCzgt8MiMwOmIY6oHMiMHkaiEGax4fIL3S0jAeGiZYquu9CiGAWHgCACPRflpHzqb2Ne9goONe2jZ+n2Kuh/51X+UDvvHcGb6MFVVLeHMkRCJXYNZInntrLfMzr6Ij+Di9kX78gVEqtonPT76G72/5GY25DMGbvkS2eiKmbuoL27TIy4D+TiAieg+IiN4DIgJ6F4i8kr2E0cNzOiWCnXK5zCc+8Qk2b97Mz3/+c8aNG/fcFx1CNpvl/vvvZ/78+S/xCkVkn99v2kCVyfPZnY8DkF94NXZ9B1BNqrmKh6JLwRrOqasi/Qcv1MFvGWldyY+b7mVZGUacOKY8nxnxBv7f3DOpCYb3z58uehU9iVA9+UwXu576D1J9KwGIVs1g6qJriVXPeN7rdivQ3wldOw19beC6+wIdS20LtE61NEyAQND71O831NT4GRoqUC6/4Md1VIyBcNT7qWv11gSQTVuGNtUyVICSf4DMkCEzArs3GAKhsZBnvCXRMAmATGgEd88Axsygfvwl1LacR/fOG+nc+nOayu28M3MdO4qPUJpwOb6tnXzIjfNxU2ZdbJS68kKmpnayM7kLTAcD4QJfmvBPfHXnp4mWKwR/81cU3/cbTOjFn18kIiIiIiIiIiIih3dKBDtf+tKXuPfee/n0pz9NPp9nzZo1+7+bPn068Xicz3/+8/z+979n48aNAKxcuZLrrruOSy+9lNbWVnp7e/nRj35EX18f3/jGN07QTkRe3jLZAvf6dvPFHY9TW85QqZ1Ej9vLxNxEAO6aeTmksgQIEr0zSHG7AZ+l5+o9fLHnRuaU0wAMM5GlNeP57OylRHwHXlPWWtLFPrBQ6VrJuu3X41byGCfI+NPeQ8u0t2Cco3+tWeudZdO5w9C9G0qFA9U58WpLy1RLy1QvUDkZRRPQ0lzHlj1QP2OA+SGXvnZDf4e3l84d3t6MU43TWIXLCOmRvYTLZ4DfwfGFaJ3xdhomXsHDq/6XUN/tTCttYls2y3zO4rTuIb560RV8ddMqesgy5JvArOE4W6rXYZ1+NlXfzTfH/SN/u/cLhLNZyr/+IO47f445TiWnIiIiIiIiIiIir0SnRLDz0EMPAfDv//7vz/rupz/9KWeeeSau61J5Wu+jhoYGisUi//mf/8nw8DCRSITTTz+df/zHf2TBggXHbe0iryTXr9zMJYPbuHBwL9b4GDxtCc6GFRgmU6mK8kDFOxersRyltNUBn6X9qp18afg6sv4i1UWv9KWleibvnXMG/mcEBLlyinCxyNwM9A39DIBE3UKmLvo04fj4o15npQLtW6FtiyGbOhDmBMOWlinQMs2SqPEqZU528WAtANnKoLf2KRbXheFeS1+bobcdcmmDKU6C4FoeGpdkxuMlWhaH2FcIFQhVM3/x3/A3D8/l4vzN+GPbcXHxD4xQs/1m/mvhG/j2jo08MtDFjngN49IL6Yg/Rd7fw5+aVtBSfDfv7vop8b5Ohm77LP7XfPUEPhEREREREREREZGXt1Mi2Lnnnnuec8xXvvIVvvKVr+z//aRJk7juuuuO5bJE5GkqZZcV+VV8b/cTAGQXXE7/7t/TnDvd+376VDpyGQCm9yfBsey9cjtfGrmOvFsEW0WNWwTg9VPOe1ao41aK7N30fc5OgYPB548xce6HaZj0Gow5ugoR14XO7bBjraGQ9VIbx2dpnACt07yWa6dasUk8WAfAaHFg/2eOA7XNUNtsmbkURoctTzw1iaHiWnKhTrZsj7B1p6VpEkyYaaluhLqwHycyhev972JXspPTO3dRlY5TWnM/HX0384HZf8GCqrn8YNcmeiJVBMszKPp3UPB38cNJGaaUzuD8/sep3vw4A3XfJXTWR07UIxEREREREREREXlZOyWCHRE5+f16RRuf3HMH8UqJfP0MOlKPgXWJl6cAMDyumUK/10Zt2WA17Vfu5EtpL9QxbjXh8gwS9i4AkvHJB82dHlzPztX/Tn50Dw6GkUiUC8/7McFIw1GtzbrQtQt2PGXIjXqBTihqmTrfq9DxB1+653C8JcaCnfTTgp2nMwYSNTB+8gSGtkJVeQNhX47hSoTuXdC9yxCvtoyfaTmzupZb+oZZ6bZiF02CB1fSmJ1Ee+5udj75z0yuns2/Tn8/X+sYprtQj7EVLO2UnRSfn+7jF8UmJqV6qH701wzVjCM867XH81GIiIiIiIiIiIi8Ipxi/2+6iJyMMkVw2n7BklQPBSdAX2OUSn6AcHg6gbwP6zjcvdc7qMZYP4nl/fzj6A/2hzoRdx6f2+JV64SCNYSCVQC4bpm9G/6HjQ9+jPzoHvBHWRO3DLTOP6pQx1ro3g2P3GRY/7BDbtQQDFtmLXNZfo1lwqxTO9QBiAe8VmyZ4jCurRx23L6wrBDazQXpDZz1Gpdx0y2O3zI6bNj8uEN8fQ1YL+9fUVMNQE2miQkz3o/ji5AZ3kRq5af5TGAN51TVYmwMYycRKtdinQrvP62FVCiM34XYn/6TfNsjx3TvIiIiIiIiIiIir0QKdkTkRbvngY28u9OrtumfsojRoafAF6Sh6Z0AFGNNPFzw2rBFfaN8Ofd9cm4B41ZR7ZzO11JTmVQaAiARmwRAIdvDpoc+Qdf2/wMs9RMuI3faa+kJQiJ05FDHWuhtg8duNqx9wCEzYvAHLTMWe4HOpNng8x2jh3GcxYLVGBwsFTKl4cOOq0meBkAq0k9xsI9k0mXuOZYL3mQ57QyXWLWlIR8mUIkA8NPuPKV4AlOpMCFwHgsv+Tl14y8FLEN7b+byjm/wDt8oxjoUTSPh4hRyfj8fnD2NkuMQLYL/lr+n0Lf+ODwFERERERERERGRVw4FOyLyorQPljhz678Qsi5b482ksisBqFn6CUI9eQAyoxPYnhjBmmHSgbvJVvJgkzT5l/K1RRcwt8cyHOkDIBmbxFD3I6y/7wOMDm3A548xfdkXmbb4c6TdFACJYP0h12ItDHTCitsMa+51SA8ZfAHLtIUu573BMmUe+APH4aEcR47xUxVqBGAw137YcaFgFYnYRAD6Y3txerygLRCEiafBOVdbzrjMMsOpAaDTN8ou640vrt5DIFjH9CV/x+xz/4tIYjLl4jDTe6/nQyN301QZouTU4i/Ppi0S5+9mTsECNZkKpZv/huLQ9mP4BERERERERERERF5ZFOyIyAtmLfTf/hOm5dpJ+YJUklnAEp18KdHJl+Pb0QFAW7CBXKCfsv8hKhTAJpkYOIuvLbqQcaEYTk+GkWgvWHBG2tm64vOUSyliVTOZd+H/Utd6IQDpQj8AyUNU7KQGYOWfDKvuckj1Gxy/Zco8y3nXWKYt9AKMl6u6qBfAHCnYAairngdAX6INpzN90HfGQE0TXD27CqwPayxP1Xht3oK79/LQ76F9K8RqTmfehd9nwpwP4fjCNJe7+dDwrVyWeZCATeKrzOORmhZ+NL4FgKbBIqO3f5JS6shrExERERERERERkaOjYEdEXrBtT23g3K5fALC1Jgo2hT85kepln6TyYB9OuUiFEDcs6afsfxBMEWyCaaGz+erCC6gPRXD6sphChZFINw0FyPc+CUDT1Dcy57xvE46N23+/dNELdp7eiq1cgi0rDY/dahjqMRjHMnG2F+jMWGwJho/jAzlB6iLjAejP7T3iuNqquQD0Jfbi6xw95JjF9TEc6z20lXMNFZ+faGWU4NAgGx9zeOhGw56NARonv50FF/+U6pbzcbCcWdjIXw/+hLmFbpzyfH487jQerkniAK3daYb+9NeUs70v3aZFREREREREREReoRTsiMgLks/lmPjgv+Bg2ZSoIhYYxvjD1J33T5S2R+Exr0IjVVvPvYHfjoU6cWZHlvNvCy+kOhgCwGlL0V29njgjhKzB8UeZsexLTJ7/MRzfwWU2qcJYu7axVmx97fDIHw17NhqwhubJXqBz2jJLKHIcH8YJVhd5fhU7/fF26Bw+5Jio32FaPAnAk6NpKlO8YG1+3S5CUUshZ9j2pMMDvzW0bW5iyoIvMTjjLxl04oTNKG/M3MK70rdSV57Al6adQVs4RMCF5q5++u7+FJX8oe8rIiIiIiIiIiIiR0fBjoi8IEM3f4+GYgdDgSBOdASAmjM/g+2fRPpmQ8R6IcNPGjdiTQFsjBbfufzLgguIjx1047oldrdfx4Ypv8YBSo7D/At+QG3r+c+6X6lSIFf2ztgJuQ2sfcCw+h6HfMYQjllOf5XLgvMt4djx2f/JpHasYmcge+SKnURsIgFfnLKvxEh2FxQrhxx3fmM1WIeSddnV7FVH1af2cN41lrnnuMSqLOWiYec6wwO/NbSm38DPat7AvdEFYP1MKe/hwyO/4Kx8ir+beQ5ZxyFWhIb2Nnrv+VvcUuYl3b+IiIiIiIiIiMgriYIdEXne0htXMHPvHwDoS1ZwHYjPehMBcxGp3xtMpUSIbgAeqhoGG8VXXso/zz+fiM8PQCHbxcYHP06Hucub02cpN84iHG899D3H2rD5TZiVtyTo3m3AWCbNsZzzWkvD+GO86ZNY/b6KnXwnri0fdpwxDrXVcwDoi+/F6Tl0wHJGQxysV1F1a9wrfXI6evAVcoybDue81rLwApdkncWtGHq2BJk1vJAHogu4yf9n1FSW4KPCefnHeW1+FddNmo0L1GShun07nfd+GlsuvIRPQERERERERERE5JVDwY6IPD/ZEZJ3fxWAvfEQlWCFYvVMos0fJvVbA2WD27oJg6UtUqQrHMBXXkq1mURr1AsLBrseYt19HyAzvAl/OUw8NZeRICTiUw57277BsWCnWE+l6JCotZx1pWXWUstYAdArVjLUgN8J4doyw/nuI47d147NO2cnfcgxrdEA9cE4AHdmMlQa6zCAb4dXEWQMNE2CM6+0LLnEpabJsnBkHj7Xx5M1LtuG/orJ479Ixd9EtZtitm8DDzfVANCYhqq9G9l5799i3cOHUCIiIiIiIiIiInJoCnZE5OhZS+Hm/yBeHGAkECEbLzDqRGiZ92XSNwSwRUNqyhDrzZ0APF5TIlA+A2PrOL22FuuW2bP+O2x7/AtUSqPEwzM4Y8tfko16r6JkfPKzbulWYMdTsGqFd75OsNLArKUuZ15pSdYdt52f1IxxqBtrx9afazvi2KcHO07n6GHHnVNfA9aQrVToHt8MgH/Hwa3ejIG6Vlh2meX8i0MsrswE4JYJW1m7/iJiVT9mb+gdVPBTSAyxs9oA0DICte3rWH33x6hUii9s0yIiIiIiIiIiIq9QCnZE5KjZlb+jvu0BKjgMJnNUjKFtyvvJ/rEJmzeMjB/iM5O/waIBHwDpcXOwthZjHS6u87H50U/TveM3ADRPewsLzbVEijUMx7zQJhGbfND9hnrg0ZsNO55yKPq8ip1xLfVMmgOO3l4H2XfOzuBzBDs1VbMxOIyGh8n3dBx23JkNsf3t2B6o9qp3/Lv2QuXQ5/LUNsOHz5yPwbA71kZvaJCenTH8Ax/kTueHdLOYnXWW7jgYYNwQTOzcxCO3v49UfuQF7FhEREREREREROSVSf9pVESOitO9mdiD3wGgNwn5IPyxajGznroamzGkmof5zPRvEBvNMjEXxDXQ2TAFg2F8uRf71CdI9a/G8UWYsexLTJr3lwTas1RMmbTTCxyo2KlUYMtKwxN3OGRGDMGwJTnRC39qYg0nZP8nu7qxc3YGskcOdgL+KMnoZG9scSsUDh3UzK4OEzbe+Tq/rVRwI2FMvoivo+ewc7dEkiyv8+Zu9z1BY7FIrGIYn5nMvc5/8YjzD6xtqmYwAj4L4wdh5kA7K+94H9uGdzzPHYuIiIiIiIiIiLwyKdgRkeeWT+P7wz/is2UGwyGGoy6rIxPIVF5LdCjCSP0I1572DUbdFMuGkgC445p5bDTDwsJK3pX5FqV8L+HYBOae/11qW8+HsovTOcpQtAdLhYA/TiTUQHoIVtxi2LPRa9s1brrl3NdZKmEv2EkG60/YYziZ1UcmADDwHBU7AHW184Gxdmzdh27HFnAMp9dWg4X+Uonhia0A+DcdOYB547gFADzY3MakzjbOnphjUb2lqWTY41zC7eaXrGq5inQQAi5MGITZ6QHa7vlr/tTxwNFuV0RERERERERE5BVLwY6IHJm1BG//KuHRLrK+EH1VBXoDCX5cfzYXtc1jpDrFtXO/zqg7AjbIW/PTARga18i5qd9wde5GfJSpbj6XuRd8l2hyMgBOdwZTdump885tqauez55NhsduMYwOGwJhy+mvcpl7jiUQglRhrF1bSBU7h1L7fIKdp52z4zvCOTtnNcT3t2N7uMUL1ALrt0C+cNhrZiYaWFI9HtexfH3+WhoeTLHkPJf3nO0SNZYRX5wH3Gt5IPk1sn4/oYpXuTMrlybx6L/w7S0/o+iWjnrfIiIiIiIiIiIirzQKdkTkiAKrf0tox4NUcOipLlAyAb7bcD4tmXHU+kJ8euHXyVgv1PnLCX/G5F4vKNgycj1Lio9jMdROfw8zz/gn/IH4/nl9bSkAehraAcj3z2frSgfrGurHW8652tIw/sA6hvPdAFSFGo/Tzk8tdWPBTrrYT7GSPeLY2uq5AAzEO7Gdg4cdt7guirFhAH7tGCp1NZhiicDazUec/yNTzyZgfDzR2Md9yXYCd2doaYH3nmlxsHSGYEPkDB5KfJ+CEyJagtYhmFDIc9GaH/PllV+mOz/wfLYvIiIiIiIiIiLyiqFgR0QOy+naRPD+7wHQm3TJB+HG5HI6gjWcOTKJaxd9nawdBhvgE1PeyTXUYfIFyr4SBfMUORPmluRHmDH3PRhz8OvG15bCYukO7gKgMLAQx2+Zc5bL6RdZQpEDYzPFYTIlL4Coj046Lns/1UQCCaKBagAGcu1HHBuLtBLyVeE6FUYGtxx2XHXQx/R4Aiy05/P0LTwNgOCq9eC6h72uNVLFWycsAuDb89aTfySFs6fEjHp4wzwLwJYo9E+eymPJf6eCn2QBGlNQVa7wnh3389sHPsZjfauexxMQERERERERERF5ZVCwIyKHlk8Tvvkfcdwyw2Efw1EYKVzEnbXjiJR9/HbizWTtMMYG+NS0d3N582xKD98BQG+8nW5fEz+Mf5TmlvOePbe1VNozPNyapGRSYIPUJGdx9lWW8TPBmIOH92W98Kcm3ErQF3n2fAIcqNoZyO494jhjDHVVXju2frsN8uXDjj2rIQE2CMBdjTXYcAhnOIVvx54j3uPN4xYwPlLFYLjAdadtJHR9GkqW5ZPhvMleuPNgAZJXLGDHzC9gMdRloSpjCFq4cqCT3COf4ZervkyunD/aRyAiIiIiIiIiIvKyp2BHRJ7NWsK3fwVfqpu8z0dPVQVTmsuPGxdhcckH9pJnGIOfa6f/ORfXT2X3E18hvjsNQNukKD9OfJghp5ELmqqfNf3QjhL3VC2gvcarLIkFZ3PmFQFiyUMvpy+7G4CG6JRjsduXjf3BznNU7ADU1s0HoDexF1/X4c/ZWVp/oB3b/YMjFBfNBiD4xLojzh9wfHx06rkA/GHybraU+gnekQHg9XMss+otxYrh5xsNsUsvJHPORwFoTVtCuRABC1OyZWbuvYs773wLG9vvec49iYiIiIiIiIiIvBIo2BGRZwk8eQOBHQ/jYuiqqVA0zWwr/SXrq7uxpo2KSWPw87np7+WceDUbHvwYgQ3b8Vk/hYTDr8e9mZIJEiDMaVXh/fO6Fdi6yvDEIyFygTAmsAaACeMX4BzhbdSb2QlAQ2zyMdz1qe9AsHPkih2AurFzdvoSezEd6cOOmxIPMiHqnY20bXSU3nmzsMbg39OB03vkc3AWVrdyccMMrIGvLXwKHs7g7Cric+DPF1saY5bhvOG6lYbiGW+iuOQtAEweLuPLV+G3UJ+HcfkU6VVf4q77P0o203VUz0JEREREREREROTlSsGOiBzE6dpI6IF95+pYRgNRwj3/wo8mr8WaNjA5DH6+MPO9LGSU9fd/kOzIVsaPzADAXXYm69NeUDArUYXf8fqqZUZgxW2G3RsMYJiU6sIXWQNAXc2CI66pd6wVW6Mqdo7o+VTs1CRn4eAjFxwl1737sOOMMVw5rgZsAICHiwXKs7x/D4GVR67aAXj/5DOI+0Nsrxrhxsk7vZZsRUs0CB9YZokGLHuGDb9aa8if/2FKs16FQ4UZQxki2Xr8FmryhkAFEsMbWH3XO1i/+r+plLNH8URERERERERERERefhTsiMgBuRSRm/8R41ZIhWEganAGvsgNrb30xDaAyeMQ4B9n/QVTBlez+ZFPUy6maPAvJJGrwvocNkxspei6YA0XNddgLXRsg0dvMaQHDYGQZVlhB6elVpIxfYCzv3rkUKy1+8/YaYxNPU4P4tRUFz1QsWOtPeJYny9EdWgaAIPpjUcce0FzHP9YO7a7e/opLfWCuMCGrZhs7ojXVgcjvG/SMgB+OHsz/ZkMwdu81m8NcXjPEotjLKs6DHftcMhf8XlKsy7CUGZSapBkupWgtdTnDXk3hN+6ZPZez6O3vo09G27HWvc5noqIiIiIiIiIiMjLi4IdEfFYS/iOf8NJ9VD0QVcVDOX+ijWROm6eeDOYPAY//zLzz6nZ8QvaNn4PcKmfcDmnOa8HoDxzKveNZsbmC7EwGWPdg4YNjzq4ZUNts+Xsy8qM72ynN7EbgOrEdAL+2GGXNVocIF8exeDbX5Eih1YTbsXgUKzkGC0euU0aQG3dPAD6nB2Y0eJhx8UDPs6oqwFgezrNcFMdleZ6TLlCYM2m57zPq5tmMSfRRN5X4Rvz1xJ4JI+z3bvfzHp44zwvhLpli8NTvX7yV36B0twrMNaldbSL2uwcHCzTcgX6qWHIBAjYFN3bv8JjN/0Fu9Y9jFs5cpAlIiIiIiIiIiLycqFgR0QACKy6fuxcHeiogV3mtWzxz+c7c34MJg/Wz79NfiPOU//GYOe9GONn8oJPMnXu3xDc5J2BU1o0m0f6BwFo8MfY8qcA3bsNxlhmnO6y5BJLbCiNsdBd3wZAXc38I65rXxu2usg4/E7wmO3/5cDvBKkOtwAwkGt7zvF19V7lTV+iDd+WIwdB10yqB+vHAjd19lDcV7Xz5HqoVI54rWMMfzXtXHzG8FBLNw83dRG6IQ0Fr9rm3Elw3mQvmPnFGkN72kf+sk9TXPR6DJamkY00cTZYw8LsEOFiNQ9EWsgbB8fupHfn37Hipg+zdeUKSkUFPCIiIiIiIiIi8vKmYEdEcDrWE3rwfwDoTcK20BLWORfzw5k/A5MD6+cjydkUn/wiufQuAqFaZi//Ok1TXkdgy05MoYhbnaS9sY7BYgEsjB+sJZ8xROKWZZdbpswH44DTlvLuU70XgPrq5zhfJ+OFRg0xna9zNA6cs/PcwU7tWAu8oVg3dnPHEcfOSoaoDyQB+EN7F7lZU3FjEZx0Bv+WXc95r8mxWt7Q6oV4X1+4jnyqSPDmzP7vXz/HMqvBUqwYfvCEIVVwKLzqExSWvd1ba9ejjE9cDG6Y6cU+rhrI8av4PB6MNlPEwTFbGOr4DE/c/Fesf3AVqQEFPCIiIiIiIiIi8vKkYEfkFc4MdxL5w9/tP1dnR3w8d4Zfz+8n3QAmh3F9vK5QonnHj6mUM8Rr5zHvwu+TqPXaeAWe8lpxlRbO5qGuIW9SG2RqLknzFMvZV1mqGw7cz9eWouDLMezrBKC+5sjBTl92NwCNUZ2vczSeT7ATDTcSDTRijctw30bIlw871hjD6yc0gXXIVsrc0z9A6XQvGAquXHtUa3v7hMU0heL0hnP8ZNYWAo/n8W3xWrL5HPjzxZbGmGU474U7+bKheN4HKZzzPgAS2+5icuOrcJxmEnaYz3VtJVuZwdfrFvBIpIkSBp9vA5nBT7Huvr/msZvX0rYVyofvMiciIiIiIiIiInLKUbAj8kqWSxG58TM4uRHyfthZFeeXybfwSOMdYHKEKvC2VBuLU6sAS+Pk1zP73P8iGK4DwOkfxN/ejTWGrrpZ3LqzH4CgG+bipUHmL7f4n949zbX42tL0JfcClnh0POFQ7RGXuK8VW0N08ku//5ehuujRBzvwtHN2Ynvwbxs84tiLW5L4rXce0q/3dlBYNAfrOPg6enA6e5/zXmGfn7+cei4Av5m2g+3JEYI3pCHntWSLBuADZ1iiAcveEcN3VxiyZUPx7HeTv+AvAYisu5XJtUsIJBbgmDzvG3iID+8dx12xuXy9bj6PRRopY/D7n8JUPs6etX/LAzduYP3DhqEesCrkERERERERERGRU5yCHZFXqnKRyB//Ht9QGyUHdtc6/KjujaypWQEmT0uhwgeHt3BasRPjBJh6+meYsvCvcZzA/ikCT20GYLhuMo8+HqE/MArAuQ11TJhuMObgW5r+LCZfprtqD/Dc1TrWuvRldgPQqFZsR+X5VOwA1FV5VTe9yT34Nx35nJ14wMe59Q1gDX2FAo8V8pTnTAeOvmpnWe0Ezq2bjGss/7F4LaQq3nk7rpe4NMTgI2d64c6eYcN3HzNkilBa+hbyl/wNFkNo3S1MDLQSnXwVGMtsbuG/NxvG5c/itvhUvlE3nyciDVRwCARWEot8lOGua3nyri08/AfDrvVQyB3VckVERERERERERE46CnZEXomsJXzHV/G3P0XFQFst/LL2MlZWbQdTYOlojveNrKO+ksYJ1TNn+bdomHjFwXOUK/jWbgFgo53D1ugAGMD6eMu0ukPe1teWBqC3zgsd6p7jfJ2RQg8lN4fPBKiNjHtxe36F2BfsDOW6qLil5xzfWLcEgK6qnZR3dkCpcsTxV06oBhsF4Pq9HRSWeufm+DftwIxmjnDlAR+acjYRX4CNyUFumrIH//oigdsPXDuhGj56tiUWtLSNGL7zmGG0CKWFryV/xeewxiG44XZaB7NUL/4rwMEfuZtrO+7k7zdcjY8Z3JyYxDfr5vJkuBEXQyCwgnj8Q1C+ll1rV/HADbDmPkNfO7hH3rKIiIiIiIiIiMhJRcGOyCtQ8OHrCGy+Cwt01MAtNYu5u2YIhzyvHenn6uwGgpQZjk5h0UU/IF5z2kHXWwtD9+7Cl8+T9cUZrJrA5pYuAOqCMcbHgoe4q3e+TtkpMRDaCzx3xU5vxmvDVh+dhGP8L3LXrwyJYD0BJ4KlwlC+8znHJ+NTSMan4joV2pLr8O0cPuL42VUhxoWqwMK29CjrIiHK45sxrkvgyQ1Htcb6UIx3T/QCpf+dv4nBUJ7g/Tn8jx0ooxmXhL8625IIWTpShv9+1JAuQHnOq8lf9Q9Yx0dgyz3Ub15N/fn/ghOswQZ2MSn6D/znumresestZJw6/pCcyDdr5/JUuAU7FvDEYp8iFnsPQ103sfqeIvf9xrDuIUNvG1QU8oiIiIiIiIiIyElOwY7IK0xg3S2EVvwcgK4quLtmOr+u85F0s3xoeBdLCrsBeCwyh0XLv04gVH3Q9fksPHmXIbxuEwC9Tacx6dIcXSWvDdtrWpsPe29fW4r+eDsuFcKhOmKR1iOuVefrPH/GGOoi4wEYyLUf1TUTWy4BYGf92udsx2aM4crxNWAjAPy2rYPSUi+gC6zeAOXyUd3zqpY5TI/VM0qRb13stfQL/n4U3+bC/jEtCS/cSYYsXWnDtx41jOShPPNCcq/7MtYXILDjYWrv+T7/n73zDrCjKvv/58zMbdtbNsmmF9IrEEggdJEOhv6qKK9YUfAHNuRFReW1iyCI0l5BQZp0BZQeeoAkBEgCyab37e3WmTm/P2Zu25LdJJuQxecDk9PPlLv33Jnznec5Q4/4GZERR4JycEpv59jEn/nT659nXvPhNJkFPFQyjN9XTOWtyChcI4RprqOg4DeUlJyNadzCtrV1LH3e8ESelxQ7NoDTt1MRBEEQBEEQBEEQBEEQhH2KCDuC8B+Eue5NQk//FoD6IniucgT/N6iUCakmvtG0kiGpRpIEeaD4CIonXMjgSGle++3r4bXHFPGNrVQnNqGByjMmce+WzQAYOsSpwyu633lHCqMhxvaSdQBUlk1HdV6EpxN1vsWOrK+za2TX2dnQp/rDhxwLwLbStSRr14Kjd1r/6KFFBCgCDW81NlM7vBq3pAgjGsdavrpP+zSVwTfGHY6B4jlrPa8f1oTSELq7DWNLVlEZXASXHKYpC2t2tCtufE3RHANn7DxiZ/0aN1KGWVdL8QPfpnroJymfdyUqUIgOrUAP/gpfWePy20VXMCE+hUYzwuPFg/hFxWSeL56KHaxEqVbC4bspKTmPouKrwV3O1rWKpS94Is+yhYpt68Hu3audIAiCIAiCIAiCIAiCIOwTRNgRhP8QjLo1hB/9AUq7tIThuarB3F5dySc7NvLpltWE3CRucjh/LD+JDQUHcM7wmZm2dgree0XxzosGqaRigrscAGfMCKJFEd5orAfgwLJKIlb3w4q5qRWA7f76Or25YQOoy1jsiLCzK1QWpIWdjX2qXxgZSmXpNFCa9UVLMTe07LR+ScBkfnUp6BAAD27eRurAaQAE33rX89XXByYUD+LUoZMB+N8hb7B2cgyV1IT+3IJqzvpEG1ToiTsVEU1dh2e50xgFZ8QsohfcijN0KirRQcGjV1G+aT2DT7qN0JCDQSWxy2+gvPSn/ODtU/npkssZ6YwjYVi8EIlwTcloHio9hGjheMDBNJ6jqOhrVFR9hYLCp3Fsh23rFMteNHjhAcU7Lyq21EIi2qfTEwRBEARBEARBEARBEIS9ggg7gvAfgGqvJ3z/tzDsOB1BeH5IJfdUVXJh84fMi+0AYOjWk7m57HiazWLOHzGbYsubtG/eAa89rthSqwDNmCkOo6Oe66zUrMn8Y/M2HK1Bm5w3enCPx2BubMXFoa5gPQBVZTsXdlxtUxf1LE6qC8fu6SX4jyJtsVMf7ZvFDsCIjDu2dzBX1Pda/4SaYpRbBMDCHfVsmTwObVmY2+sxN27t834/P2oOBxRV0Won+M6UV9g+PIHR6hK6owXibvacCjxxp6pA0xD1xJ36DtDFg4iedx3JA88CILToboqf/DWDDr2SsjmXocwwbmgJiZoLGaHf4X9fuZgrV36DGkagDcU7IZdfFpRzW/kxNJTNAWXhpFYQsH5K5aBzGTziDiJFW3Btxfb1ivdeMXjx7wavPqZY+aaibpNY8wiCIAiCIAiCIAiCIAj7FhF2BOHjTjJK6G+XY8abSJjw0uASnikr4qKWlQyzo1iBEqZt/D5vm4ezoyDF4FARpw2dguvC6qWKRf9SxNoV4ULNnBM0k0vWYXREcQsiJMaP4tFN3iR+ZaCEiaXh7o9Ba6yVDTQVbsdWCSyrkNLinYs1jbEtODpJwAhTFupZMBK6MrhwHADb2lfhuH1THYYNPhqFQUPxZqJrVvZqdTOlLMzwggJwgzha83B9A6lpEwAIvLWsz8caMQP8ZMoJDAuXUpfq4NtzX6e5LIW51SF0d2ueW7jyiLfmzqBCTVPME3e2tQFmgMQxlxA75YfoQBhrw2IK7/oypQVjGXzybQSrpgFR7IpfYA/9PpMbCvnVwsv4n3VfY6iqAaXZGGjj9wGD35R/gtUVx6CC5djJeuKt/0fQPJ8hI77O0NGPUFLRDGjamxUbViiWPGfw/H2KN/+lWLMMmuvAdXs4WUEQBEEQBEEQBEEQBEHoB0TYEYSPM65N4O4rCbRtwDbg9cEFvF9s8an29YS1S3HlDA50fk9w+1T+NmEVAJ8beTDJdpNFTynWLFOgFUPHauadpikfDIF3VgBgT5/Im82ttNop0IrThw3pcc0cY0s7Rn2M7WWetU5l2TSUMnd66HXRdQAMKhiNUjJU7QqDCkZTECgj5cbZ1La8T23CoXKqKw4CYH14McaW9p3WV0qxYFQpShcC8K+t22me5blVsz5ch2pu7fPxlgYiXDP1RCqDBWxINnPFJ94kGraxPkwRfKQ9T2Qqi8Al8zRDijQtccXvXlYs3uKV2ZOOJfqZm3EqRmK01xO5//8RWfU6g467jtJZXwEjgGO9SmLYp7Er/sSkjUP41Yvf4jurv8hgqkE5tFn1/NVs4yfFB/Ny5Yno0qmAItb6LtGmazHcMxl5wJWMm/EcNePjhIs02lU0bVesXmqw6EmDF+5TLH1BsfEDaGsCLUKPIAiCIAiCIAiCIAiC0I/IbKkgfFzRGuNvvyTcuBQXWFoVZFuhw8xkIxrFsAmfY+qoX1H4SiF/PeBDOiybcYWVjGsex+v/ULTWK6ygZvoRLtPnawJBUK3tmLWee6/kzMncv34zAIYu4BM1JT0eirXMc/e2rcaz7unNDRvAjo41AFQXyvo6u4pSBmNKDwRgXfOSPrcbUZPjjm15Xa/1jx1azOiCItAWCdflsWQCe/RwlNaEn365z2vtAAwOF3PN1JMoskKsSNbxw5OXkDJcAoviBJ6J5vVVEvYsd8ZXahKO4i+LDf7+rsJ2wK0cRfQzfyI18RiU6xB+/kYiT/wvxePPYPCJNxOsngk6hV34N5Ij/wu34u/M2HIAv174Xb6z8otMcMaCAtts4mmznquDVdxUeRabBp+OWTQarW1adrxC/fqriTctYNiYXzLrqLeZdIhN9UiNFdTYKcWODYoVbxi89rjBc/d6Fj0fvq3Yvh7iHX2+LIIgCIIgCIIgCIIgCILQBRF2BOFjiE5p9J03ULj9aTSwqtKkvjhFpU6gA2VMPuy3DJ/w34QejLIl0sEjY9cBcETjoax83cSxFeVDNIedphmao6sElq1EaY09soY1oQCr2ttAw8HlVZQEe7DAcVysd+vQaHaEagGoKu9d2KmLrgVgUIEIO7vD6LLZAKxtXtznNjXVR2ASoLWgnra1vbtTM5XioglVKNez2nlk01bajpmLNk2s1esJLH5/l455VEE5V0/+JCHD5E17Cz8/5V1cNMFnogQfas9zy1YUgq8dqjl+vJf38nrF9a966+4QLCB+yg+JH3MJ2jAJfPg8BXd/lZBjMOi466g88mdYJaPQbiupwhtIjb0AVf08M3ZM5oevfINfvv0djojPwcQE1cF2cx23unX8IHwgDw76b1oGn4QZHoRjd1C/4QnWLr2MhvXnUTHoTxx6wioOOclh/CyXiiEa09I4tmfRs+59xTsvGix80ODFBzyrnrXvQeM2WadHEARBEARBEARBEARB6DvWR30AgiD0L06ri77rV5THngJgY6lic6mDpSBeOIvDjvgRgVA5geejmFsdbj1kBbZyGR0bTtmW4RiGZtwszeipkOdZTWsCy1YCkJo5mUc2+f6vdJhTR5T3eDxmbTNGNMWOqu0k3BZMI0R5ycRez2NHhy/siMXObjGmzLPY2dT2PkknRtCM9NomYBUypGoem+sXsi7wJpPrPokeVLDTNjMrIsypqGBRcxvtts2/XYczjplL+JlXCD33Ks6oGtyqij4f95SSwVw58RP8eMW/eVato/T0EJc8PpHAojiq2SHxmRIIe+8kmAacMkkzpkJz9xLFxhbFb16Cz8zSTB+iSB14Fs7giUT+cTVm4wYK7voqybkXoA4+l3DNIXSseYrWZf+HG99CIvRjrEn3EYhezLANs/jKov/iM9bpvDD5NZ6oeIlW2tBqA8u04l2nCqPgZI4tszjUrSXU+CbJeB1bV9/D1tX3EIxUU1Z9KGOnH0JJ5UHEOgporYeWekVLPbQ1QyKm2LEBdmzwv2RKU1gKxWVQVKYpLIOiMigoAvFEKAiCIAiCIAiCIAiCIOQiwo4gfIxIrbdJPXYVg5Ovo4HNpVBbqUkZirbKi/jk4Z9GKQNVZxN4poNnh+3g+SGbQcPh9YdSVq2ZOs+bYO6MuXYTRksbOhykYcwIXnx7KQDlgWJmVvQsGljvbAdg1XjPeqNm8JGYZmin52G7SRrjmwCoFoud3aI8PIzS0GBaEtvZ2Poe48rn9KndiGHHs7l+IWurljFt+Q7so0b32ua/D6jkrUX1uKqNe9dv4oRDD8RaswFrzUbCjz5D9PNngbXzNZVymVMxgssOOJLfrnqRh/iAsnML+NxDI7E+TKH+1EziwlJ0Wba/KdXw7SM1dy6GdU2K299SHD1Wc9okDcOmEb3gVsL/vAZrw9uEXr6VwPtPET/2UorGn0rB6ONoX3E/bSvuwe5Yic2lhGYfRiD6FYo+HM2p736CkziGxWPe5YmRL7KK9WhVh2PU8bRj8YweREHpuZwcdJmUeJ9A8zKSsR3sWP84O9Y/jlIWxZUzKBt8KKOnHkqkeBSOrWht1Fmxpw7iUUVHM3Q0A2QVVcPUFJZAUTkUlWYFn0hRJ+FVEARBEARBEARBEARB+I9BhB1B+BigNcTeSpB8/VJGJD9AAxvLYE0F1JuVJIb8hNPnTPUmgl1N4MF2Xhri8MuZbwMwu20qR8wqZ8RE3eNkceCd5QCkpk7kqfp6HK1BW5w0tBKjp0ZxG2tlA45Ksd5aBC6Mqjmx1/NpiG3E1Q5hq4jiYNWuXxABpRSjy2bzzvanWNv8dp+FnSFVhxJQBURDrTSue4uSPgg7wwuDnDhkME9sb6cpmeShTVs495RjKbj9PswdDYRefIPEcYft0vEfV30Arak4t657g/+LLaHkMxHO+Hs15laH8B+aSfx3KW5N9iesPAKXzNM8vhJeWKN4YY1iXRN8/kBNeUE5sbN/g7XiaUIv/hGjaSMFD36H1ISjSBz1dUqmf57C8afR+t6ddKx+nET9qyTU64QOOhwreSasnM2ctbOYs3YWa4o38Oq4t3ilZAltqgOtttLBVh6wQyhjBKHSmRwRSDFDb6S8fQVObBut9YtprV8M7/+RYGQwZYMPpaz6UEZMnM3oqZ5FVCKqaW2E9mZob1a0N0NHC7iOoq0J2pogV/AxLU1BCRQUeyJPpFhTUASRYggXgiFWPoIgCIIgCIIgCIIgCB9bRNgRhAGOTsH2p1oJbvgqo5JbcIH1FVBbBksDJ1FUcxn/NTuYEWyi/4ix0Axzy8RnSZhJRjqD+d7hh1BS0vM+VHMr1ofrAIjNnMjjtV5cuYV8YljPDa3l9ShHs370GlJuB5FwNdUVs3s9p7qO7Po6SswSdpsxZQfxzvanWLcL6+yYZohhg45k3Y6nWK/eYEbzqeiycK/tPjO+kud21BGnhbvWbmBGWSlTTj6Ggr8/SXDRO9hjR+CMGbFLx79g2HSaUzEe2LyM3ze+RvHnjuHYB8swtjuE/9hM4jPFOJOy1l+mAZ+aohlbrvnbO4p1TYrfLITPzNZMqVbYUz6JPe4wQq/+mcCShwl8+CLWmjdIzr2A5MHnUj7nMoomnEnLO7cS3/Qyie0vkeAlrAPGEAqfCVuOZ+zWkYxdOpL/4gyWV6zitTGLeaNwGQmVQKuNxI2NPK0LeNatRkU+xfhCmG9uZ2RiDap1OcnYdnase4wd6x5DGQGKK6ZTXDWT4vKplA+ezKDhhYC3bpB2IdauPbGnJV/wcWxFWyO0NabPPvs9UUoTLvQEn4JiT/SJFHmCT7gAghERfgRBEARBEARBEARBEAYyIuwIwgDGaYX3/rGJMU0XMyzeiqugthLerijidfUjRg0+hPNnaQwFjg1r/+2wpqmAf458lsZgE2VGAf978HGUhHbiJktrwk++iHJd7NHDWQi0pFKgDWaWlTMo3PMwEnhnBwCrhy/zrHWGnoBSvbvk2h5dA4gbtj1lTKm3zs7W9lXEUm1EAsV9ajd8xPGesFP5LjNXbEfPG9Vrm5KAyWdHD+O2NQlcI84vl3/IDQfPxDpwKsHF7xP+x3NELzoXXdD7Wj+5XDhqDi2pOP/e8SG/2PwiRZ89gTmPFmCuThG6o5XkGUXY8/L7nDEUako0dyyGTS2KWxYpZgzRnD5ZU1VYROKYS0hNO5nQs9dhbX6X0Mu3Yr3/FIljvwmjD6bqyGtItayj/cOHia79F3brWuzW36IiN1Nw5MlY9gLs1TVM3zGJ6Y2TuNA4m6VVy3l91BIWR5ZjqyiuuQ5Yxwc6xIdOOcqcQWH54RwRaGeivZ6y9hXo+I6sNQ8ABgUlYyiqmEpRxTSKy6cQKR5GQYmiGkgLPq4LsTZNRyvE2iHWpoi2Q6zNS7uO8vLboXEb5Io+AChNKAwhX+gJF0CoQBPKxL3QlDsEQRAEQRAEQRAEQRCE/RKZthGEAUpineYfLy3m9ObvU5FM4ij4YBA8NGg2a1M/Y0JVAZ8/UGMa0LQd3l8I0ViAN8uXsqpoLZYy+MHU46gMFex0P9Z7H2Kt24S2TOKfnM/f164HQLkFLBhV1mM71RzHXN9CR6CVre67AIzsgxs2gDVNbwEwrHhyn+oL3VMcqqIqMpL62AbWtyxlUtURfWpXXTGbsColHmihrvZlKueO7NOCLiePKOWJTZVsSW2jLpHgDx/W8p1j5mKu34LZ0EToiReIn3XiLi0Oo5TikvHzabXjvN64gZ+seYZfnnsyk/4dJvBWnNAj7RjbbZInFUEo229VIXzzMM8120trYdk2xfvb4cgxcPwBmoJB44id9/uMezazaSMFD347454tUDqa8jmXUTrzi3SseYr2Dx/Gad9Cx9r7gQcIjz2MyOELYMdBmCsDHLJjFofsmEWHGeOtwe+waMRSlofWkFIJtNqGZhttGp5wSnhKV6Eip1EVsjlE1TFOb6csvgGVqCPaWku0tZYd6x4DwAqWUVQxlWJf7Ckqm4hhhigsJWctLJ05b60hEdMZkSfa5os8bRCPQiIKWisSMUjEoDV7pbtcezOgCYYhFPasfILh9KYJRvLzrYCs+SMIgiAIgiAIgiAIgrCvUFpr3Xs1oTuamjqwbfejPgzhPwytoe7NJA9seIhLNt9Moa2xDXhziMVfBn2ZRPw8RpfBxXM1yobVSxWbPvRmXLcG1nP/iH+jleaScfM5acikne5LdUQpvOVeVDxB4ui5vDNlHN9d+h5omF48kv89cFiPrtICCzcQem4970x7k6Ulj1JZNp2jD7mx1/NrTdRx3aKzAcW3Dn2YwmD5rl6ifYJlGZSXF+7348ATq3/HW1sf4ZCaMzlx3Df73O6dZdexetvDjKqfxqFzfopzQEWf2r1R18H/LtuINhtBwSUTxnGiYVJwx4Mo1yV+4lGkZk/Z5fNIODZXLX+K91u3URYI863xRzF3aQXBf0UBcEsNkp8qwpkS6tJ2axs8ulyxss77Wy0MaE6cqDlspOe+jUR7xj2b0i7aCpM85NMkZy+AsGflpLVLfMsbtH/4EImtb2b6tkpGEBl5LMGio3A2jiX5gcJt8vaTMJKsLK3l3eqVLKv8gC3WjvwD0xZKl6N0MUqXUOQqptLIFFXP4OQmgtH1oO28JkqZhItHUVAyloLiMURKxlJQMpZgpLpPbgu1hmQc4h2eyOOJPSoj+sT9zbV3TaVRhiYQhECIbJjegrpLvuXHPw6C0EAZCwRB2HvIOCAIgowDgiCAjAWCIEBFRSGmuW/834uwswfIQC3sa3QKXnp6G2sS1/Hl9a8TciBlwKOjyvh32a9wopOoKdZcfKimYQ3ULlPYSW/WtKhjO9dPeYI2K8UJgyfyzfG9W2+EH32GwPJVONWVRC88i6ve+4B3mptRboTrD57CmOKuk+jegWoK/vA2qj7KI0f8iVZnMwdO+S5jhp/S6z7f3voY/1z9W4YVT+GiWX/cpeuzLxkoN2wr6l/kgRU/ZFDBaL520J19btfctppnX7sIgBM2XUrRhWeC0RfhQPODJVtZ1lyPNtsJGgbXHTiDse99SPi519CWRfQLZ+NW7rpg124nuOK9J1jT0QDAKUMm8+XULEoeiWM0eZ+BPTVI8vQidFlXl38rdngCz7Z27zwGF2nOmKKZ4vk5w6irzbhnA9CBMKlpJ5M88Gx0WU2mn1TrBs9N25qn0HYsk28VDyc84ihCBUfibJuIvUlhbwEcb3/1oUaWlX/Au1Urea9sFTEj3uniKaAIpUtQuhjLLWSo3cwk6hnrbqcysZGA3dLttTGtQiIlYygoGUOk2BN7CkrGYgX75n4v7zA02ClPAErGvDARg2RceXk5+ck42KndV2aU0hmRJ08QCkIglBWLrKAnAmXCAJiB/WOtoIEyFgiCsPeQcUAQBBkHBEEAGQsEQRBhZ8AgA7WwL6nbkOCuZc8yK34bJ21sJOBCwoQ7p81ihXk1rR0VDCrUfHq0ZvM7imirN9lagsPE9Q38cM5z1Ba3MqFoEL+efioBY+dr3Zi16ym4/wm0UkQ/fybbykq4aJG3Fsi88pFcOWN4j22NzW0U3LqUHaWbeXLqHzGNEKcc/TABq7DX87zn/StY1fgax4z6IkeMvGAXrtC+ZaDcsEVTLfzm9TMAzeWHPkRRsLLPbd9+5+es2/4Ule01HDvqt7gH1fTeCFjTluDyRZtwjCYwkowuLOA3s6ZR9sCTWOs24QypIvq5M8Hsfb2lzsQdmzvWL+KxrcsBGBou4fLRRzJrURGBhTGUCzqoSH6yAPuwCJj5ooPjwmsb4MkPFR2+6DlpkOaMyZqhJYDWWCufI7jobsx6b60nrQzs8fNJHnwebs3UTF9uqoPYpleIbXiR+NZF4KYyZWbhECIjjiRccxRGcjL2RoPURkVqM2ArbOVQW7yelaW11JatZ1XJetrMjq4nrAMoXQIUotwCSh3NYKeNGreJ0TRT5dRRkNiBovu/wUC4ikjRSMKFwwgVDiNcWEOosIZwYQ2mtXM3jH3FsSGVgFTSDxO5adW1zI+7zp6b6piW7iL4dBZ/AkGN6ecHMnleaAW8tYT2xGpooIwFgiDsPWQcEARBxgFBEEDGAkEQRNgZMMhALewL3ITmqZfeZ2PyNo5vWsrUHWC5ELXgiSMvZHHr59neZlIa1BwLJLZ5M5TBsGZiOMHYV5r534MW88ywTZQFwvx+5gKqQr0ILMkUhbfei9HaTvKQmSSOO4wfvfMBi5sbMHSQ/5s7i8pwz0t0BZ+oJbhoC68c/G9WBxcycujxzJl+Va/nmnLi/Pr107DdJF858P8YXDhuVy7VPmUg3bDdsviLbOtYxZkTf8C06k/0uV080cS/X/w0KaIcuulMhn/2GxDsmxjzj40t3PLhDrTZAMrl5JohXDxkMIW33ee59ps7m+Qxc3f3lFjavJlrVy2kPtmBgeKsYdP5bGgmxQ9HMdd77sucYRbJM4twhwe6tI+m4JlVihfXgeMqFJqZQ+GwUZoDKkGhMde/TfDt+7DWZV2vOUOnkjz4XOzx8yFHHHVTUeKbXyO2cSHxLW+gnaw1jhmpIjz8cEKDZxOsnIFuqiC1EVIbFfZm0EmFRlMXbmR18TpWl6xndcl61hdtxlHd/G1pBURQuhClCzB0mEF2kmqnncFOPTVuE9VOPYVO806voRUqJ1w4jHBBVuwJFQ4jVDCEQKgcpfbujYhj5wg+SbB94SeZADuZFYSSCXBSYCc9SyI7Ca7bj/7blMayPIEnV+wxA3j5OWFaSMrkWRAKG5RXRojGYqBcTCvvT0MQhP8ABtI9gSAIewcZBwRBABkLBEEQYWfAIAO1sLdZ/0EDz676EweknmNWvUONv9J5W9Dg7U/+mofWHUxHSlFgaA5ugiJHoQzNqMkw3opT/LdW7h9byx+mvYepFD+bejLTS4f2ut/Q0y8TfOtd3NJiOr54Hm+1tfPj91YAcMygEVw+ZUTPjR2Xgt8uQsei3Hf4r0m5UY446FqqKw/qdb8fNLzCfcuvpCw0hEvm3NunNUM+KgbSDdvTa/7Ia5vvZfbgUzhtwnd3qe2qtfezbNUfCKUKOLnotxhH9W19HK01N3/QwBNb6tBmEwBXTp3IEQ0tRB76FxqIffp0nFHDdvV0MrTbCW5e8zrP1q0CYHRBOd8afxQTlxcSfLIDFdNoBfYhYVJHRtBVXcXI+g54fIXinW3Zv7VBhZrDRmrmjICiIBj1awi+9QDWymdQjmeV45bWkDzwbFLTToRgvuWLa8dJbF1EdMOLxDe/hrajeeVWyUhC1TMJVc8kWDUT3ToIexs4dQq7Hpw60AlF0kiyrmgztcUb2Fi4lc0F29hcuJ24mejhiijQYZQOA2FCjkm1E6PSiVPhRKlwW/2tmbAb66EPvydlEYxUEQwPIhjxt3BuWE0gXI5SH42C4Tq+yNNJ8MnPUz3kZzdPJOt/lKFzxKDuN8PMTese63XXbj8eGgXhP5KBdE8gCMLeQcYBQRBAxgJBEETYGTDIQC3sLZLRFP968W9EUg8wJN7OjG1Q6Ht52lJZwXvzbuGB1YNwtaLM0RzYqghrGDxKc8CBmqI2m/BNTSwpqeNbh72GqzRfHTOP03PcSPWEsXk7BX95CAVEzzuVlpFD+eLri+lwbEIU8tfDpxOxeh6gzA8aiNyznDXDlvPSqL8RCVdz0hH39ent/8dX/Yol2/7JITVncuK4b/b1cn0kDKQbtlWNr3PP+9+jLDyUS+fcu0ttXdfm2RcupNXeyMQdc5n+qZ+ii4J9amu7mp+8s40lzTvA6KDQNPn9wbMY8fxrBJeuwC0uJPq5M9ElRbtzWhlebVjHDbUv05KKYymDz4w4kLNLp1HwRBRrSVYEsScFsQ+P4BwQ6DIzvrkVXlmveHsTJHwXYaahmTUUDhupGVsBRrSBwJKHCb7zGCruqaw6VERq6gnYE47GqZkKnf7OtZMgvu1t4lvfJLnjHVLNa7ocv1lUkxF6QoOmYRTWoNsNnHqw68CpV168Hlxb0xBqZlPhNjYXbMuEmwu2kzRTXfrOPxgT8ISfsKuocJKUOwkq3BjlTgcVThvlbislbjt90g2UQTBcRTBcRSBUTiBUgRUqy8S9sJxAuBzTKtrvhFqtPashO+WFTgpsP8yL255I1GNdW+HakEp5gtO+wMgVgszexKBsXWMnbXKFpv3soxKE/Z6BdE8gCMLeQcYBQRBAxgJBEETYGTDIQC3sDd5d9CKbtt1EhbudES0wvgFMDbaCTQeezAtV3+Oldd6s29AEzOiAsgrNxDmaisGgmhzCf2qmLtHOl455kRYrybGDxvOtA47qfWLVcSj4898x6xpJTZtA/LTj+OXyD3i5rgG0yVfHTeSUEWU77SL0wAoC79fz73n3slW9x6Sxn2Pq+It6PW+tXX73xlm0pxr5zLTfMK58Tl8v2UfCQLphSzpRfvXaqbja4ZI591Ie7t1qK5cd9Yt5afFlKK04wf4Rhacc0+e27SmHb7+5mc3J7aBSTCop5meTJ1B254MYjS24kTDxTx2PM7rnNZv6QnMyxg21L/Na43oAJhVX860DjmLE1giBhTHMlUmU/2vnVpukDo9gHxiGYP53Im7D4s3w6gbFppZs2ZAizbxRmjnDoEDFCLz/L4JvP4DRvDlTxy2sxB5/BPaEo3CGT/dm0TvhJlpJ1C0jsf0dEnXvkGpaDTr/70dZEQJl4wiUj8sJx6KMCG6zJ/A4zeC2KtxWcFrAbnWp1y3UhRuoCzeyI+KFdSEv3hJs69uF1ApDWxS7UOo4lLgpb3MSlLgJStwYJW6UYjeKQd9vH5QKYIXKCYY9sccKlmIFS7ACJVihdLyUQLAkU2aYfRMQP2pyx4Jk0s0Rg3wRqMdNdZvvOt3X7481ifqKYXa1IDJ6EpBMTzwyuuTRVVBKp/fNPa4g7DMG0j2BIAh7BxkHBEEAGQsEQRBhZ8AgA7XQn+zY9CFLl1xHqbscy4EpO6DaX0u9NRKk9dRfc8eaWaxt9yb3JkRhstJMmK2pGee9Ya222oT/r4VUe4pvHP0yHxY1M66wkt9MP42Q2fOaOGmCr7xNaOEi3EiY6JfP5+WODn6x/EPQMDRYzR/njcPcmTgUsyn8zetEzRb+fvBvAJcT5v+NooLe3W1tblvB7Uu/StAs4DtzH8M0uq6Lsj8x0G7Y/vzON9jY+i6nHvAdDhxy6i63f+PVK9nU/grVraM54qibYFAv6zTlsCWa4ltvrqdd14HSTCkp5qrhNQx+/DnM7fVopUgefSjJQ2ftkamA1prn6lbzxzWvEnVShAyTL4w+hFOGTMFscAm8GsN6M45Kej97OqJIHRLGnhtBV3R1KbahGV5dr1i8BZL+pLqhNGPKYdIgzaRKh1FNbxD84Hms2ldRyY5MWzdSij1+vifyjDjQm83uBjfZTqLuPZJ175DY/g7JplXgdmd5o7CKh2WFntIxWMXDMIuGYlgRr68EuG3gtoDTBm6LL/y0QiyWYIfbxA7VSGOohaZgC03BVppDfhhspTXY3udrbWhNoZuixElR7DoUZbYUhW7KD5MU6QQhbfe537wzNsOYgRKCIU8AMgOFmFYhVqDIiweKsKx03M+3vHwzUIixj8aQfTUWaBecHkQfxwa3c57TvXDUk3i0L4UjpXT3wk9PlkSB7tdB6rIektXFaE4Q9gkD7Z5AEIT+R8YBQRBAxgJBEETYGTDIQC30B63t23jjtRspir6CgaYkBtO3Q8QGDWwfNZn1U67jb7Vh2pXC1DA7qjl0AoyZ5i3kDWCsSRK+sxXiLr+Yu5SnqjdQYoW4fuanGBwu7vU4VEMThbffj3JcYqcdR92E0Vz85lLabRvcQn484wAOrCzYaR+BVzYRenotyya8xpKqf1JVNoOjDrmhT9fh+XW389LGvzC56ijOmfyTPrX5KBloN2wvrr+DFzf8mWHFU/jCzJt22S1WNLadfy/8DI5KcXj7Fxhy5ud3qf2yxhg/XLoOx2gGpakKBfmfieOZ9spiAu9+AEBq4hjipxwLoT2z1NiRaOe6VQtZ2rIFgOGRUk4cPIlPVB9AiRPEeitO4NUYRkP2c3NqLJxJQZxJQdwRFhjZ6xNLwdubPZFnS1v+dSsKaiYOgskVCWbEF1O8biGB1S9nXLWB567NHn84qQOOwhl5IATCPR67dm3s1o2kmmtJNtWSal5NqqkWN97YYxsjUolVNAyruMYPh2EV1WAVD8MI5n/3tQs6Bm7U23Q0HVckYzZN8TYa7FYa3RZadQdtdNCqOmizOmgNtNMe6KAt4MV7df3mY2k3I/gUujZFbooC1yaibQpcx4u7DgXaJuLaFGh7l6yBekKpEIZViGUVYaUFn2ARVqAQM5gvEJlWAaYVwTDDfhjJpA0ztNPvy0AbC3oi7ZpuZ1ZDjuO5n8uWq7w2drcCUza+t9Y0ysUwdVboyQlzhSAv1D2W54pH4ppO6Asfl3FAEITdR8YBQRBAxgJBEETYGTDIQC3sCdGO7byx5E4CDU8TIAUuDG6xmNpgYwApU7F5xiU813YWbyiFbUDE1Zw2CA6cqQnnaCzmewlC97SCrbnzkNX8echyDBTXTD2RWWV9WJxeayJ3P4q1cSv22BFEzzmZn7z/AW81NoG2mFUynJ/MHrrTyU3VFKfgprchZfPwEX+izdnCQVO/y+hhp/Tpetyy+CK2dazmjAnfZ+bgE/vU5qNkoN2wtScb+f2b52G7yd12dbdy2a28v+0uIoliTpx+G8boIbvU/t+bW7lx5Ta02QTKIaAU3zhgLCdsayD09Esox8WtKCV21om4VRW7fHy5uFrzj63LuXP9W8R8CxhLGcyvHMNJQyYxrWgw1gcpAq/EMGpTGTdtALpQYU/0RB5nQhAi2R/k+g5YWQcr6xSr6rPr8aQZVqKZXGkzI7mEEdsWUrTuJYxoU7Zvw8StPgCnZhrOsGk4NdPRRZW9no8TbyKVI/SkWjfitG/BTbbutJ0KFGIWDMKMDMIsqPLiBYMwI9m4ESzZ6Xdba8AGnfCsgrS/xeNJWuNROlJxOpw47XacqBOjw43T4caI6jgdxOggTpQYbUaUdiNGzIwTM+KkehCGlNaEtJMReQpc2087hLRD2N9Cbjpu58QdQrp/v48ahSKEoSKY/mYY3mYaYUyrgFC4EFcHUWYI04pgBiIYgQhmIIwZDGMEI5jBEEYgiGGGMMygtxkhlBHY79Yf2hto7VsddRKJ0sKP260Y1MnqqMvaR9l8vddEI51vFZQWffwwswV1XjpTHszmGaaIRB9XBto9gSAI/Y+MA4IggIwFgiCIsDNgkIFa2B3amj5g2bI7MZpfx8D7+2l3Qhy2PUV11Eu3FQ3i9dIbWWoMZWUEUFAT1HxxrqaiJL8/67UYwUfbiRkpfnbUO7xU5K358cXRh3DmsBl9OqbAkuWEn3oRHbDo+OJ5PBWLceOHtaDBcCq5/pDRjC4O9dyB1oTveh+rtomVk9/jjfJ7scwIJx/1IAGrd5ddLfHtXP/muSgMvjX3YQoCZX067o+SgXjD9q/aG3ljywMML5nGf8+4cZcnkx0nwdNPf5oOo54JbYczbcE1qF1cLOOOVQ08tKEJbbSAkQDg9GFD+VIkQuEjT2O0tqMDFvFTjsGePH6X+u6OqJ3khfpanty2ktqOhkz+iEgZJw2ZxHGDxlOcCGJ9mMRckcT8MImKZ38WtQHuqADucAtnuIU7zEJXmmAobBfWNXkiz8o68tbkSRMxbQ5V73FQ24uMqXuJSGxHlzpu6VCcmqk4NdNxhk3DrRztzQD3ATfRit2+BbttM3b75rzQjTf13gGAGcQMl2OEKzBDZRjhcj9dlhNWYITKMEIlqG7WDtodbNchGk/QnojRnozTkYgTTyWJ2QlithdG7QRtTow2N06HGyeq40RJEtcJEiRJqhQpUtgqhYMNSmeEobB2CGfEIJuwmxZ+nC7xkHYIapeAHwa1S5B99L3WCgigCGIQymxKBTGUHxohlBHEMHxhyMgRiKx0GERZQQwr4G2BgCckBYJe3AximAGUEfDaG0GUGUApa8ALS1qD6/rCT87aRnaqOxFIdSsO5YpE6XLo3+uilCf+mMFOolCeCKS7EY3yxSKxItr/GIj3BIIg9C8yDgiCADIWCIIgws6AQQZqoa9o7dK07TVWLb8b2pdn8repCJObXWY0Jgg4nuu11SUn8Wr4e7xfZNDgu1k7dJjmnJkay8jrlMDTUYLPRllf1MZVR77JBqsNSxl8Zcw8Th4yqU+Tdaqtg8Jb70UlksSPO4yN0yfy9beWknRdlFPMZ8cM59wx5Tvtw3pnB+GHP6Aj3MYjB9+A7UaZMfEbHDDqnD5dnze3PMyTtdcxomQ6/z3zxj61+agZiDdsbYl6fv/mf+HoJBdM/x1jyg7c5T62rn+RVz/4IQDjIsczc/73UapvIgR46+A8vaWNmz+sJ0kbGN66NDPKSrli9Aiqn3gBa50nTiYPmUnimLn9stK61ppV7fU8uX0lL9TVknC9dV+Chpmx4plSPBjlgrE+hbkiibUyibHD6dpXSOHWeCKPM8wXeypM2lzFB3XwYb1iUwtsbwcn14pAayrt7RwQW8Z0+z3Gxd6jsn1NRuDNVAsW4tRMwa0ah1M5CrdiJG7FSOiDS8Vc3FQUJ1qHE6vzwmgdTrTez/NCN9G8y9dSWRGMYHFmU+l4qDgvv/OmAoWoPgpWu4PWmpS2iTtJYm6CmJOk3Y7TkorSkorRlorRlorTlorTkYoTdbw6cTdJwk2S0ClSbgqbFLa2cXQKixQBnSSg7YzYE3QdArgEXZcgOUKQdgjkxD2hKBu3/LSlNRYu+9VSMDoABFEqgCKAoYJeaAQxVAAzbVlkBjDMQMbiSFkBjDwxyROYDCMtIAVQpi8iGX47IysuKbNTXSO434hMua7pckWitOhjp7ek6pTOiaeydftVJFK6B1EoXyBKi0iBTuVmAALBPuvHQh8YiPcEgiD0LzIOCIIAMhYIgiDCzoBBBmqhNxw7Tv3Gf7H+g/vQCW+9DwfFqkAFE1riHLGjg4i/rnjcLOC14qt5vWQua8IaVyksQ3PaZM2Rozu9netogg+3E3gzzotDt/Dzg5cQUzaVwQL+Z9InmFRc3bcDdF0iDz6FtXo9ztBq2i74FN9a+j617e2gAxxdNZLLp1bvfKKtI0nhjW+jYjbPzn+ETe5blJdM4phDb+rzhP/d732H2qZFHDf6Kxw+4tN9O/aPmIF6w/bk6ut4c+vDjCqdxednXL9bfWx49g7etO8ApRkROZyDD//xLi9Uv7o1wS/f3c72RLtnvaM01aEQ/zNlApMWv0/otSUA2CNriJ9xPLpo5+s77QpRO8nzdat5cvtK1nRk164ZVVDOcYMOYFrpEMYVVhIwTFSDg7kmibHZ9rYtNsru2qdWoEsMdIWJW2GgK02S5SbbCiw2WiZbEt76PJtboSOZ/T6FnQ7GxpczPvYu42PvMS72PmEd6/a4E+FyEqWjSJWPhMpRGFUjsapHQfGg3X59XztJnFgDTrwRN96MG2/CiTfhxptxEn4Yb8KNN+EmWmCP1r5RqEBhjthThBEsQgUKMKxCLwx0FxZi5OT1l8XQrmC7Dm12glY7SmsqTmsqRpsTp8NOEHUTOJZDY0c7HakEcTdJ3BeMkm6KpJsilRaNtI2jbVxtAylMN0UAGwtP+LG0JpCJ+0IQ2heE/Dx0l/J02kJj5ohHptZYWmPSKa8f1jDaW2gsUBYYAVAB8IUffEEpXzDyBCLT9DbDCGIaQSzLC00zKyh5Fk+BTgJTML/PTFnQd5HXPzfjaZGoW+EnL60yYlAq2UlASgtE/ehyThkayxd+zM7iUK5A1EU0yo/vJ1rcR8pAvScQBKH/kHFAEASQsUAQBBF2BgwyUAs9kYw3sKX2IbaufQTD8SwS4irA0lAlo9uTnL61kRLPCxWOMlkV+RQvl3yZZSVhOvwZkkmDNGdN0wzq7Mks4RK6tw1WxLlt8gruGb8agOklQ7hi4rGUB/s4AZ5MEXnk31i1G9CGQfTCs7grFuOe9RtBK8YVDOVXB40k2MtgFHpwJYF361g7djULh9yBUibHzb2V0uJxfTsMJ8qvXzsdR6f42kF3MqhgdN+O/yNmoN6wtSZ2cMObn8bRKT4/4/eMKp25653YLtsev5vXIn/GNRyGBGdx6BG/xDLDu9RNW8rhuvfreLOhBW02g3IIGopPDhnMf8WT1Pz7ZVQyhVsQJjV7KqlZU9AlRbt+vD2gteaD9jqe3LaShfW1JNysdU5AmUwormJy8WB/q6YsGAFHo3Y4mFtsjE02xuYUxlYHldz5T6k2QBcZuMUGzaUmm4oDbAubNAZMGg2DJq1otBXRlMOwxFrGxpdTk1jH0OR6hibXU2HX9dh3QoVpCVbTHqqiI1hFNDKIeHgQiUgVqcIq7MJq3IJSQgGTgAFWzmam42Z+fufNNEBpB53qwE22db8lWnGT7X66FZ1sx0224ibb0Ha83z43ZYa6F4GsApQVQQUiGFbEi1thz8LIz8/ErWwcc8+sRPZ0LNBak3RTtNsJ2u04bXaMdidBh52gw0nQYWeti2J+mHC7F45S2sbWWQHJ1ja6G5dySmtMXxAyM8JQjgDUU366TUZE0vmCUqZcdxGScut6Ybaf/RUXE9ew0CqANgJoIwhG0BOdDE8sSlskGUYIwwz5IlMI0wphmSEsK0zADBG0IgSsEAEz7LvRC2VEJMMM+f14eT0JShkrop4shJI5AlGyeyHJTnprF/UnZkB3L/z0Jg59jNYfGqj3BIIg9B8yDgiCADIWCIIgws6AQQZqIRetNdGWD1m36gFat76Aob3X+puMAl4PVzI0ZvP5zdsYFPW+ci6wIXIUbxV+i/erStngv4VbGtIsmKqZObTrJIe5IkHw4XZaYzF+fPBbLK6qB2BBzTS+MPoQzD6+Xaza2ok88CTm9nq0ZRI//ROsGFrNt5YsQwMlRjl/OOQAykI7fzPe/LCRyN/eJ27FePSwm4jbTUwacwFTD/hin6/bW1se4Yna31EeHsY3Dr57v3HD0xsD+Ybtn6t+y9vbHmNM2UFcMP3a3evE0TT882FeDtyEbaaosiYy74jfEgzsorswrfn7umbuXtOAazSDkQQ8p0Unh8J8felKCppaANBKYY8fRerAqThjRvTrLGC7neD5uloWN21iRdt2Wu1Elzo14RJP5CmpZnLxYEYWlHnfOa2hQ2M0OKhGB6PRQTW6XtjgoFpdVB9/aZMGNIZMGsImjcUmDYUWbSGDpBkj5G6m0N5IaWojlcn1DE6upzq5GYuu7uI642DSYlXQbFXRbpYQNYrpMEu8zSjKxL38Yj9eiK2CmetsKt1F7DENMJXnLc9U2bRpgJGTtlSKkNtGSLcRctsIum0E3DYst4OAG8VyOzDdDiw3iul0YDh+6EZRtpdWbtfPpF9QBsoMdxKEPFEoXxAK5wlC6bgVilBaXkZb1MUl6AlPVsgLdzJJv69wtEs8RwxKWxNl8jLu6DyRKOGmSDgpP8/2y7Nlycxmk9J2RlBKuTbu7qxLlCMy9SoWZayQcvJ3Kkzlx9N9WLn7zNlXYF+tq9QLDiauEUCrAK4R8K2XgmD67up8AShPSDI90SgjJAUiBM0QphXOW5Mp3V67QVw3hOsEcZwQjh3EsVWOGKR6EI2yae32s/VQd67l8lzIaSzLsy4yA2Ti6XWH0uFHIRIN5HsCQRD6BxkHBEEAGQsEQRBhZ8AgA7UAEGtbT/2mZ9m68Wl0bGsmf4NVxquRCiqSLl/fsJERbU5mTYWtoWm8X3klG4cPY1E7JB2FQnPkGDhpgibc2atVu0vosXasdxKsKGvih4e8yY5wjLBh8c3xR3DUoL5ZxwAYOxqI3P9PjLYO3IIwsbNP5p2CED96dyUp7WLqEL87aBpjikM77yhhU/CHxRitCV6e929q1UKKCkbyiXm3YZq9tE1fu1QbN771GWJ2CyeOvZRDhp3V5/P4qBnIN2zN8W3c+NancbXDf8/8AyNKpu1eR66m9cl/8aL6HUkrTpkxisPm/5ZIeNAud7W0Mcpv391BixNDqygY3iS+5bqcFU1wzo4mqrZlrVbcshJSs6eQmjEJXRDZvePvAa01m+OtrGjdzoq27axo28H6aFOXehEzwLBwCYPDxQwJFVPth4PDRVSHigmbvjBqa1S7621t/pYbb3NRUQ0xjYq6qN51GsATgTosl0SgnlSgEYxGlNGIaTRgqQaCNBCigbBu6rKOT19xMEkaIRIqTMKIkDTCPcRD2ARwlEVKBXGUha0C2MrCVkFsZeH4aUeZaAw0yttUTrxL2kArL452sVQSS8exSPhhnAAJAjpGUMcIpPN0ggBxAtqLWzpdL4GlE1gkCOhU3rlm5oF119VQcoU51U2dbvPSbZTpb5YXYnprDSnLF328tMJAqfSm/LRCofyQbNqPp41dlL9nTxhX/v4UKAP8vsATR/NPLJ1WndKdypXhz5abaMPMSWdDbZhopXCUwgYcBTZgK7DRpAAbl6QCW2tSSpNUkAJSyitPAkmlc0KXhNIkdHpzieMSd10vrlxiuMRxSOKQwsbWDrqvSmpnfMEnVwBKWxdl1kfKpHWOOzzfjV4mni3vku7kTi/tas/cDyyXXGWhMy7wLJSyPJd0vqs6wwz4Lu780AigCAIBIIDW3ua6AXADuNrCdYO4TsDfLBwniGNbuLaJbVs4tum3N9HaAtJbOm0CVk6Z6Yc9KzdKacxOok/3QpDOE4RMCwwLTDObzs03jJ4Fo4F8TyAIQv8g44AgCCBjgSAIIuwMGGSg/s8lEd1Kw6bnqNv8HPHW2kx+CpOVoUreCZVyQCzKf23ZxqhWB9P/ljUGh7N+7PdpGTeVZ7d7a24AjCrTnDNdM7y00460xlqcIPiPdlRU8/iodVw/411SyqUmXMIPJh/PqILyPh+3uWYDkYf/jUqmcCrLiJ17Ck/HY9zwwRpcNOgA35k0kSOHlPTaV/DJWoJvbGFLzUaeHn0zAEfN+T1V5X137fVU7fUs2vIQgwrG8JUDb8NQ+37tjN1loN+wPb7qVyzZ9k/GlR/CZ6b9evc70pqOp1/iheQviQfbMQkwasSpTBh9HoWRobvUVcx2+dfmVh7e0EJTMoE2OsDIrjczPeXwlZYOJq3bhJX0JuW1aWBPGkdq9lSc4UP22mvabXaCD9p2sKJ1ByvatvNBWx0xN7XTNuWBCNWhIoaEi6kOFTMoVEixFaIkEKbEClMSCFFihQmZOX/3WkMKVNT1hJ6YJ/qouIaERiVcP9SZMDeeF2Z+3R0wmsFo9MN2UO0oP8To8MP2nDCK2t3JcUH4CNAoMBRamWAYnkCoDHQmrnD9uKsUbjrEizsoXKWw8eOAjcJBYaN8gcoLU4CtVEaUSipIKkXC8ATXhKFJGJqkH6YMSCkDRylSSmErA9vw9uEob78acuVML1Qapb20t7kYeOKToTWmSruzI09U6lZk6qGsbyvh7Y+YaEzwBZ9sPF8g0jotBHUViHKFo851u5ZZoAwMZaEMC8P0BFnDtDAMC9O0CIaDuFqhDBPTNDEsC9MyMU0Lw/LqmqaJaXlp07Qw03UsE8MysSyFYQ5813SC8J/IQH82EAShf5CxQBAEEXYGCDJQ/2eRjDfQuPl56jY9S7R5RSbfQVEbqOLdUDFJTE5p3MERDS2Ux8Dy/zw6AmVsn3YZtaOP5JXNBhtbvKf1goDm1EmauSM9t0W5qEaH4ENtWKtSJAyH6w59jycGrQNgbsUovnXAURRawT4ff2DJckL/WojSGntkDR1nnsBftm7nwY2bvQpumAvHjOas0RW99mVsbCVy+zs4RopH599Cu72VMcNP58Ap3+rz8ezoWMPNi7+IxuGz065lbPlBfW67PzDQb9iaYlu48a3PonH41MT/YUb1J3e/M62Jv/AmbzReT33xJgCUMhg+5Dgmjv6vPq+3lCbhuDyzpY2H1jdTl0iBioIZzawVEnYcPtnQytk7GhnZ2p5p55aV4AwbglNTjTO0Gndwlbd4zF7A0S6bYi1si7eyLd7G9kQ72+NtbE+0sS3eRtTZueiTS8gwKbbCvuDjCT/FVogCM0iBGSCSs3WfDmIZnW4a0gJRQkPC9cKkL/ok/fyU7pSfG9qQiKFSMUjGUXYcUnEUCSAOKuFvXlypJCgbsHPCVA9pF8/UREPGksj1TFy6bHj1lV+/c5jpQ/sWKgqdfq1eedY/XuhZsmg8KxbPMkgBhteLn0fGYojM9LqbqW/i+uVupsyzPlKmie2Cow1PMND+0WlN+j+0RuPi3Xa5aO16x54XOt65pPM82SFncwDXlx4cDOWitBdXysHQDga2f1b59DhH3MNdYE9WS7nWSZ0tlbqrk87vrk4mX+9aWua78/G+AQqt/L8Yhf/36QlHDuAqhZNO+6HtC1uOIiMuaUWmvVbZb5nXNzhGtr2rtC+SaU84U+CivTXF8PpJf+Baac+SKucD1enQ0LholNJ4Nmy+cJURtP5z6GKllLbyS1syKQtF1gpQKROl/HzlWVelQ8MIeFZXZhDDsPy8oCdGmV4d0/QssQwzgGn5cSvgi05BTCvgC1F+OyO9D2vAuM4VhH3FQH82EAShf5CxQBAEEXYGCDJQf/xJJVto2vISOzY9Q3vDOyh/BkwDawNVvBcqpjZYxKFtTZy9fTtj2lJEcuZzE4ESGg78Eq8OOoVXNpi0JryH4IChmTMcTp6oKerstczRWK/GCP6rAzeleXnYVu6YvYo1RjMK+NzIgzln+EyMvj5Qa03whTcIvb7EO6dpE2g+4Qh+/WEtixp891JuIScOHsrFkwf1/qAet4nc/g5mXZRFB7/EiuC/CIeq+ORhdxII9G1he601d733LdY2v82kyiM4d8o1fTuX/YiPww3bv9f8gdc33w8oTjvgO8wecsoe9We9spGmN57l3eEL2VqWtWQbUjWPEUOPo6J0MoWRYX2eDEq5mhe2tfH3dc1sjaVAxdEqDiqZmSyc2B7l9G0NHFffRNjN/zlzDYPkoAoYNhhdMxi3phq3omyfvAbdZifYEW9jW6KN7fF2tifaaExGaU3FabUTtKbitNkJbN0/fzuWMggZFkHD9DeLkGESMMycfC/snM7PS7f165oWljIwlUFAKUzXwEopLFthJZUXTykCCYWRBCOFZy2U7CoYqaTOL0un+66B7RbaAoIKHVJeGFQQ6hT2kN+5DgHQAQWW8r1Bqb0+Frja2xwXHA2uH9puNu50LnddHDuF6yRxHAfXdXBcB9ePa8fB1em0jXZdtOuXuTbaddDayYaODdoB7aC1i3fn6IlUWrueaKU9Qcq7rdR5ZV46Hffba1+Q878DnsSgfVsZz1IlPy+njvbELUNrDGxM1/WsWbTjWbRoB5UT98r8Oq4nknlt0324mdDr29+Hby2TjivSdXQ2nVuHtIWN64tQvlPBngSvzumd1fsPwAEShkHcNIgbhhf3t4TppROGImkYJA1F0lAk/Hgqd1PpOF6ZqUgqhWN41zItGJk6PzS0J6mYkBe3tCcuWYCZydd+3BejdNaSyvD/FnJDlfnb8oXY/WQ9pz1B6yCe2OS54lP4bvxUNq6MIEoFfbEpmBGeDD9umEEvbvqik+WFphnwxaSgLzilRaYgZiCAZQYxAum8AEoNXNsz4ePDx+HZQBCEPUfGAkEQRNgZIMhA/fHDTnXQ1rCM1volNNctIda6OiPmAGy0Kng3VMr7oSIqbJvzd2zhiIZWymLZN0o1io7BM2mcfi7/cg/lra0Wtr/AcGlIM3+0Zt4oKOpsbBN1CSyKY70aI96e5MmRG3hgwhq2hjoAKLZCfHfCMRxUPnwXTsgm/I/nCKzwJtkT8w9m65zp/ODdlWyMRkGD4Zby6TE1nD26DLOXCW9VFyVy73JUQ5R3xizknaFPAzBv1v9SUz2/z4e1sn4h96/4AaYKcvHBf6E8vGsuu/YHPg43bFq7PFl7HW9tfRSAk8b9P+bULNijPq3F2wj9aw2N1gbeG/YS6yrfz85WAsFAKeWlk6goneILPTWEg+VYVmGPgo/jat5vjvNWQ5Q366NsjiYBB1QSrZIYRoqInWRqWweT2qNMbo8yuS1Kmd11sZq4ZVJXUkRHYYRYYQGJogLs4kLckiJUSRFWYQEB08RSioBhYBkKSxkEDIWlFJZheKFSe/y2staamJOixY7Tlkr4YVb4iTkpYk6KqB/mblEnSdRJ9Zsw1F+kRaC80PBCS6lMnmWYmMrATK8XoxWG9oxzDK08Ix3Xz3cVRjrtgOF65Ybj13dUtszflKPyjX4884BMPGtBkt5/fn539dO1s0YIylvCxlAYpmch5M0IK5ShwABlenHTUJimiWX418UPTcO/PqYfGiamqbx8y69nmF7cMjBNw3fl5LexDAzL9GefvX1628CSAtLiVWZz89NO5/Ie6nWp20O5zq3n+pYoLrhaYbueaOZ0CvPyNNgO2Lrneik35zNIC1H4FlUZyyoHExulHUx/wt8khalTmCSxdMpPJ7B0MmedqCSWThDQSX+9qZRfN+mXpQiQJEDKS5Py6yQxsTF1EkN7++nJmqonASrve+WHRue8PsaNnD72FXHDIGoYxE2TqGkQMw1ihknMNIiaJjFfWErHY2a6zKsXNw2ifn1vM0n6FoK7hC8WZYQlrTFQmBoMFIZWvlM4A1ODZ6ejvFArf1UiheXXC2RCT4gKAJarPTsf3wWfqbXnuk97LvxM7Yuh/t+jwsbQdtr5IIpUflztX783uXhu9IL+FvBC5YVKBUB5AhMqkCM0+XEjmBWajGDGmskwg741ky8wpeNpgcn0Q19sMqyg51rPVOJK7z+Uj8OzgSAIe46MBYIgiLAzQJCBeuDj2HHaG9+jpX4xLXVLiDZ/AJ3eotxmlvBuuJwPgkUMTSU4rKWRoxpaGNLhEMyZN04WVpCctYAVQ07l2a0VrGrIPtGNLNUcNVYzcyhYnb7bqs4m8EoM66049WaMh8es5dHR62gPeK+xF1shTh4ymdOHTqE8WNDnc1PRGJG/P4W5eRvaMIiffDQrR9Vw1bIVdDg2aIMSo4LvTx/BtPLeF583P2gg/NAHuMk4r0x+jLVlngXQhNHnM33C1/p8XCknwR/f/hzNiW3MH3EBx47+Yp/b7k98XG7YtNb8e+0feGPzAwAcP+Zi5g0/b886jdsElmwn8Npm2pOb+WDwm+wo3UBj4VZcZXfbxDCChIPlhPzNsiKYRshfqDvkv8EbwlAWbTZs7HBY3+GyOebiaNNzWqW8Bdu9d+Q1VUkY0+4wtsNlfLvL2A6bUC8fVcJQ7AgG2BEK0hSwaLNM2i2TNtOkzTJpsyw/NIkFLGIBC8eysMy04GNgKIWhwFQqEzfILnhv4AsEKp2Xlhc8VCaVn4ffj6Hw+kX5Z+p4Lr6URmvPlZfn2Mj13delXZV5ce2nHe1gaxdHOzjaxdaOv/lxN5t2tcbRLi4ujta42t0PlnoXACxXEXRMQq5JyDEIOiZB1yTkGn5oEtQGQdcipA2C2vQ2TEK5cUxCyiSASVBZBDEIYxFUJkHlWXWFDIuQMrEME+2LWJ6YBZgK7fnRwv+j9/JyytPCkzaz5en22ugmL1Mvp0+DATFjqn2rqpQDqXSYE0/2kO/FVdf8Hutm447eHXHB9sWhZKcwgUmSgE5iZvLz65g5dS2SnuiEJ0Dlpk2d9EUlr41JIu+lGXRWHDL6GO8tr0ud/v1483AB28iuneRZDBl5VkRpq6KEUiRNbz2mlG99lMixPIqnQzO9FpTvIi9ty6YyNm0Z13sa/PWisi74Otd3u6mvyTp71Jiei0nf1aSL6bml1N6qTq4fN5QvuOP9lhkaLIys6OQ7jQtiEEAT0P4GBLSDpbW/7pO/5hMupusJm6b2hCVT+6KnTmFo2xdBbZROYZDyRajUfrsOnGfJFERrX2DqIjR5eVmhKceyyfBFJzPou70LZtznKV90MgwLw1/fSVkWpr/mk+Gv2eS52jMzcU9s8tZxUqb38oCwd/i4PBsIgrBnyFggCIIIOwMEGagHHq6ToL1pOa31S2muW0xH0wrQ+RPNjUYBa4MlrA1GaFYhJsY6OLqpkSltHRQnIJLKThC4pknH6ENZMfoCXk9NZmWDImF7pYbSzBgCR43VjC7rNA+lNUZtisBLMayVSVaVtHD/uNU8O2wzjuF9JWvCJSyomcZx1RMI5y6u3guqpY3A4vcILl2BiifQ4SAtn/okj5omd65dj4sGbTG1eAhXTB9GWbAX9xVaE3hpI8Hn1pOwOnhu5n3UhdaglMmsSf+PsSNO7/OxASzc8BdeWH87xcFBfP3gvxI0exeV9kc+TjdsWmueX38bL2+8C4C5w87jsOHnUxTsfb2lneK4WMvrCbyyCXNbB46yaSzcRl3ZZuqqttIY2UzUaMbWsX44i95RWlEWK6UkUURhspDCZIG/FVKQLKDQDu9237G0yyDTIO5PysVz8tKTdtnF05U/EZjdUn657ddxjJy63dS3lfLXy1CZdTNy8+yctTT0XpgI19nVNzI5XbecfNU1X6MxlPJcGxkqM39vGoYfV54eoLKClqnwrX8UluEJaKZvRZWtn803/bx0PN2XyhyNxv8/vRqOn9YZ55sab6I+7YZMu9pfFsdzsxWwTJJxG9dx/eVxtO91TOO6nhBmuy62L4zZ2sXBT6fzVDqdE1d+XHn56d+HjxrTVQRdg7BjZcSkUEZcSm9GNu7m5nltQq6ZaRf2w6Cfn017odlpWj4jFvnCks4VjvxQ5wlMufmdLJvS7XNFKkPl7yO3304iVfd1/fxuRClt+n98nsKbVnazmy9c6dy8nPxu83zSVkNpwSfpdC8AJbsIQ90ISd3Wy4/bbv7n0me09iyRSGZEodywq6DUfZ2dte0sPAV0gqCb9CxiOgs/br4glLe5XQWjTJvdO/s+4wKu4b204Krs+keZzeiUTtfrId/11z7aVyZSNjm/Rz3EnXQcw//N8u3alJGTb2TyvdDEUUZmzbO0QJVxfpiTp/2XItIh+J+9/5KFAQTQWLi+6ORkBCdLO56FEzamTotPnpWdobMWdwbdv7Cyv+Kt45TezJy4n1bZtFLZtLeWk+mvr5Re1ymdNv26Jsrw4pnQjxtGbmhl4+lyM1tumF6+J1h5+abpCVOGH0/nd9lX+jg+ghcAPk7PBoIg7D4yFgiCIMLOAEEG6v2bVKKZaGst0ZZaoq21tDWvJtG+vouQ02JEWBsoZU0wwjYrQlUqxTFNDcxpbmVozCaS7Prw3lE1nuXDz+Jp61jWtIW9B0afkpDm4OFwxGhNniGM1qjtDtbKJNaSOGyzWVS9g/vGrWbxoPpMtWklQzizZjqHVIzcpXV0zI1bCby5DGvVOpT/tU5VlHLPITO5u72dhOuZFykd5PyRozl/TGXv/Sccwo9+iLW8nubIDp6Z9Tc6VD0Bq4hDZ/6YwZUH9+34cvjN62cQTTWzYOJVTK8+fpfb7y98HG/Y0qIbgKFMJlbM58ChpzG27CCU2oMfJa0x17YQeG0T5ppmlJP/s5MyErQWtdJS2UFrYQvRUBvxQIJUIEHSSGAbCVIqiatTaJW1RPEsVXwrFe1bomjXn3zXuJkF6nVmnQ9y1snofEaGa1CQimTEnrAdJOgECe0kNAbIst4OdBKB8oWflPLeHE+H2TUsDJL+2+dJw1unIrfMq5/NS7+lnvTXxPDWwFDEVXq9DE+g2h8IGgYhwyBkGoRN04sbBiE/HjZz46aX9su9uBcWBCyqywtJdiQxtSLs99eba8vdQWudFYZ8y6qkY5O0HZK2Q8JOkXIckraf5/rljkPS9cKEa5NyHRKun6cdb3MdktomqV2S2iaBQwKvLIFNQnV1bbivMF2F5Rqe2yk/DLgGpmsQyMs3vNBVfr4XT5d5bRSBnHxTe66tjJytL3ld6qAwXT+PnHquZ8WwK33n5qk+zMLniUDpIUn5/+SW0X2YEX67q9Mlnp0g18obW2zDF5V9KxRPpPatWFCZctsgL+0or77tW5K4/njk+v3qdBrfIhOvjZdWGbEiXSfdNm296ZLb1nO9BykUCXRmnRvHuy/UDuCFiqx7sozLPO2gsLMu87SNqVOEdIyQGyeoEwScBEGdJOgmCOgUQdcTmQJuOu65xAu6XhhwUwS0jeXaXpnrYGkbYy/aQ+rMdVKZ0FXKE4n8a66V9kJ0flr5crdK18tts/N03t/VfoZN+oUKIxN6L1V4YX5+1zpeewPti1Te37LyLKDwvl86baOm/FcKlPLXEQNfdsLCs2Yy/dDCxdSagHYx8aycTL88uzaUmw1z1gpLp/+T0doTeNKrYqn0qlkqG+bmZQQhcsWhbKgMK0886iIqGSamaREKh0ilNCijq4iVK1yl40ZWsOoijnUjWikj55jyyi3/GAfGfakgfJz5OM4TCIKwa4iw0w1r167lmmuu4e233yYSiXDKKafw7W9/m3C49zesH374YW6++WY2b97MqFGj+PrXv85JJ520x8ckA/X+gevaxNs3ZAScjpZa2ltW4yabuq3froKsCRazySoghUVVMsnstjamtHcwJOYQSXqL4+ZiBwtoGjSDFaVH8qI+mHXukLzyEaWaKdUwdbBmeGnO8gYJjVmbxFzpbdGOBO+XN/JuZSMLa7awvqgd8NwrHVE1lgU105hQPKjvJ5+yCSxfReCtdzF3NGTPccRQHq2p5s8G2Olj0SZFRhHfnzaGGRW9u3RTTXHC97yPuSPK5vJaXpxyLykdozAyjMMO/DklhaP6fpw5vLzxLpJOjGNGffEjeZuuv/i43rC9X/ccr29+gM1tyzN5ZaEhjCidTmloMKWhwZSEqikOVuK9HesvoI6L46ZIOFESdgcJp4OEEyXpREnYUT/dQcLuIBlvJ5Fs9+rpGEkjgf4IXKrkrg+RefleZ0MzJy+9YLblx9Nh0LUIuBama2K5FpZrYjlWNu5m44Y2MbQiYAcJOiECToiAEyTgBrCcQKau6VqY2sRw0w7ccixdchepgPwyf+H2/R0H5Ys8pu+GyCDhx1OmiW0a2KZJyg/tnDzHNPx8g5TlrW2RNA0Spu/iSEFCQVJDXGkSWhNHE9MQ05o43oTvXnPl5U/UeW+BQ8g0KFAmYVMRNkzChvLEIX8Np6BSflwRRBH013MKKkVQeWsWBZUi4G9B3zIpqPz1nkyDgGlgmSaWaRIwTe8GUnlvrXuWIgZYphfu0alpUtoXhBybhGtnxSHXIe7aeeJRws9Ph0nXJu5k44mcstw6CccrS+mPTkjar9DZNVcya1GRLyh5ZWQEIYUvDLm5opG3Vovpi1qmVn5oEHBzBCWyglRGsMoRm7oLdzWvb+28Y83mdU73Xift9nJ3SAtC3pYWjsA1FK4FTkDhWgrHUmhL4QQURtAgpTS2Ca6pcEyvjuunbT/PMT3RyzE9Mcz2hTDHUJ5jTWWj3BiGG8dwYxhODOUkMJ1YdrO9zXJiWHYcy4kRsGMEnBgBN0bQiRFyYwTdOCE3RngfWcbuDM/SxsRVJo7v6s1Rafdvvtu5tCCictzOKf8XzheXvDydEZ5A56Q9Wx1wfVdtaWdz2c3EzRMnM7+m6bzceA95eb+0/fRz0p3AlLHo7ZSXyhGUckUou5PglH6Jw82Iosq34s0RSMmKcMq/tsrTnnzRyF+fKU9QyoZGznpRmbgfmmj/viobmply7d9r5bbJ7bP7eNpxYDoUOpMWqwKeY0Rl+utMpS2wAjmWVr71leGFhh96YlR2M3LKPbeA2XzP9V/OllvH78vI7TMvbnouB5UnfnnrXokwJQx8Pq7zBIIg9B0RdjrR2trKqaeeSk1NDRdffDGNjY38/Oc/54gjjuA3v/nNTts+9dRTfPOb3+TLX/4yhx9+OM888wx33XUXt912G/Pn932x9+6QgXrfYafaSUa3k4huy2ztHVuItm/CiW5C9TAB1GBE2G6GaDVDuK7BkITN2I4YY2JxqhI2IRtCdtdnMtsMUFc6nvdLjua1wDzWqVF5E4EBQzNxEEyt1kyuhrK0ZU5KY2y3MdamsD5IUr+llfdKG3i3spF3KxqoLWnNvrkIFJgBThw8idNrplIdKurz9VAtbQSWvE9wyXJUPAGADljsOGAMt5SX8GyuVZIOUBUo4bxRQzhmaDGhPg4ukT8uxtzegVNocf+BPyPptFJVNoO5s35KKFjW52P9uPJxv2Hb3lHL4q2P827d08Tt9n2yTwODkIoQ0hFCOkzYCROyQ4TsICE7hOVYmP4b94ZjYDgK0zUwdHYztdkpbfiCSjrubf6CNZ6rJOW7TFKA4b3xjaFwTIeUmSJppEiaSZJmkpgZI2q202F20KHaiJrtxFQzKDdP9PEWrM5J+3m7Ov9juCZBJ0wwFSHkRAjaEYJ2GMsJEnCDWE4Qyw0ScLy44ZqYvohkumbmemQmN5WGgAEBAx3wJ/wt5R2cYaD866GU60+QaZT2LKHQXlz5K84rV6NcF217aRwNjsbIbGA6YLoQGABfke4Osa83SLmGEvsrDmTX/VBZy6rseiCd4pl63lvqWQstM2PVlVBZ666EYZLw2ydUWrhTJJVBQnltHEN5f1/+36NSWXvXtJcxSBuGeKuEKOX6ZdqLk5641SjlicrptGfJlw3z8rTrp3LqaH8yGJ2Je+THu4Sqh/wew17q9NPEsNLaFzey409uPC/P9SZtc/My/ZDVkHPzcss659GpjWcZ0v2WtRbsrqz7/N11J6k0mP6KZFkhzPu9SQtACvzfkqw4Zrg54pibFZOsToJZXy2wrJ3UsbSR2ZeljUyYFt68fRpZMQ7Dd0Vpem4rDS9uWH6+5Qm8pmlgmAbaMtCGwrVsbBXDVQkclcDVcTRxtI6j3ThaJ9E6hdYplJtE6yRKJ1FuClwvz3CTGG7KD73NdFOYbhLL30ydJOAmMfnPEGc9l3BpN3CAH2YFo6xbTxT540yOcEQPIhOdy/x0bvnu1u923346PbJmLLwgY+nlqKzFr+sLUhm3ep3KHbKuYzMhhi9o+q75jLT9nPIFzrRFGb4LvmxfjuFda1dl14zKXM/MvY5/Iionz6+pMnGVaa209h/zdPb9GaUzVlXepjNhWmAylMoRqXIFKLoKU+kXhrQvbuWKXTorjGXKcveT00/aFiyd/3EjLQl661DluApMi1AEsoIU+aKVF09bL3lxQ1lgeH1l4oaZFbJyBCcjV8hKC1QqK1RlRKu0gJWua+a2Nb2/JeXf16TjkJcvfLz5uM8TCILQOyLsdOKWW27hpptu4rnnnqOiwlvz4fHHH+fb3/42TzzxBOPGjeux7UknncSECRO4/vrrM3kXXXQRbW1t3H///Xt0XDJQ7zlaaxy7AzvZQirRTCreSCK2jVjHVjo6tpCIbseO7UA5HTvtJ45BixEmiUk4pShKaSqTNkPiSSqSDhEbgt0IOGlSRoCtReNZHJ7Hksh8NofGem87+5hKM7QERpV5Ys74Sk2o3cXYavubA1tTrE808155VsjZVtD1Dcmh4RKmlgxmaskQjqgcQ4EV3KVrFlj0DqHnXsu8me+WFpM8aBoPVJRw+5Zt2YpuiAlFFXx6TDUHVhbssnVM8N9rMBriJE4ex8rGB7GdGJPGXoBp7Nrxflz5T7lhSzkJapveoCG2idbEDloS22lJ7KA92eg/pHgTZkqZmMokZBURMgsIWYWEzAKCZgEhs5CQ5Yd+WTa/kJAZIWQWYhmhAWnFpbVLe6qJ1sQOWhN1tCS20RTbQlN8K03xLTTHt+LoFPgP9Z3FHktDWAUIqxABDAzXQTsJtB5Yfvt3iv8mfdqyKS06Wb4QZWWsnrz8dDxT1zWxcvKzbdLlFpY2URnxLjt5urtv7O9LfIeB/uRgVgrILJyuIO3KJ3dZk9w3oU2dO6G0f+KCLwIZGdEn15VfViQyMm4A0/lZV1wqY0XhkHXflXG9lY77E5FZd1PZSUhXedcOcibtdM7EnX9nbKSnBHWOsRxZSz/wrEIsf2IuoDWWC5bW3rodOmdzvTfTA1r7goq3mVpjuf4b8Tl5Bm4m39DZ8ty46WY/e9N/Q/7jStrlW8oXf1LpeF6oOqW9erZK56kuZflplek7bx95eWon+/etpPfT3zGlPQHLSotUblY0Srsz9ASnXKuudH2jB+Ept76BRVZ4CmgIKNe30tAYyhNjDTRmjnCbtqDxVtTR+BICGhuFm3GVp7TnNk/pFIZOYOgkhvbWUjLwBCZDu5jaRun0GjkOhnazE+N5cX9yXLv+hLrrl7t+P07GDZ9YhuxfdJbIexOyoBsxq1Nebrq7frvrO7OpbHv/lZiceNoiSmXap+PpOq7Kb+OStapKW665OfcHbsbqKseqrZv9QfaeQSu8NQJV9l7DK9c55+7LXkp3vZ7aEyI9zUznD3N+OYAyNGm3x+AJZOjsixzee1Q6Y73V3We0s2veuXx/u8XzPr+Mc0RvUwbpkTBdlnGgqIzM1fDyvc1VJmmZz8VCq3SZJ2hpTLQvbqXLctfH0hnhy+tDYeK9vWV4z27K9F54IADKz9OGL5p59dP1DN99oeG7/zMwQJmYWNk6mJiGtyKiMvw7b+V//hlhS2X+bvKELj/DyHnDR6md1+lOKEuHRm5aqUxd1bluOs/v0PDzDL+CkemvU7nKnku2by9t+BkBS1FeXkBLSwzH1nnn7YXZ9tmc9PGpnHoMyGdjQRD2rbDT9xXZP0IWLlzIvHnzMqIOwAknnMCVV17Jiy++2KOws3HjRtasWcPll1+el3/qqafy/e9/n8bGxrw+hd1HaxfHjuHaURw7imPHcOwodrKVWKKJWLyReKyJRLyZVLwFN9mCtlsw3XaMnbzRZ7gQ1N4Eio1BMGUQTkHYhoijidjeFnZcLDeK1ctzl60MmoIVbAmMYm14KutCU9kSGkOjVZ0RcgKGZlQpDC/RDC/1XKsNKQbL/04Gnu4g8GoMFc3eVrpovnTUC6wubc0/fhRjCyszQs6UksFUBHt3g7YzzB0NKK2xRw0jdfB07PGjwDCwN2wibBgYOsLssirOHzuI0UW7L8IkPzk2E59Y+uk9OmZh4BIwQ0yqOvKjPoz9GqUMioOVFAcrGVY8uUu51i5tyXoaY1tojm+hMe6FTfGtNMU2E7VbgJS/kVnL2AD/LU4IqgAFRgFhM0wACxMDS2XdDWnXAX+tocyWXmNIu7kHk/Nk7Ef8CW4ySf/JH8+9k1ecOyOi8prnPpak34fNeR8ZpbzQwX9bOWMeYHvn7JenZ9MVGpTrx33LDL83hWaXH280OVZcyrfY6p0+C0KazAQMSvkP6d7Tolbeg3T6QR3DewtVE0AZFuggnruUEEoHUYRAB1AEQFugzWzoptMmyrVQronhmhiO6a+ibuBqA+0otGugHAPtKpTrTbIqXyzIvO2rvYnVtEub9LRDZo0GfwLUTNfLWc/B0umFxrOhqTUBNz8/oF0COe8PGUDY1YTd/4w3+fcXbKX9t9u1L3rpnLVotJdOfz9zp86UF2asDchN06k8p1SlhbB8K5W8LVNGXp7ZeabVx8Sz/gvt9Ex7e1dt37zLlhZ4bJUjJilvfbGMS7f0ZC1ZKwitsmsLeUKm9tcZ8o7c8T83TTpPZ9YT0v5nm54kdozcCePcyWcnb0KaTH7XSeo+5aX7N70yV3lrGaWUSwp/TRjtZKY5rRzhxMLFcF1cv56RdqGWk84VW8y0KNP5gitQWuUo2p59rI33K6Py/2BzYtkTzy1J7yUbZvM0Blp77uO0Tjsg836Z0q7PvC3djoz1a+5/ZFzGZWSA3CtLxuLQdzGn0t9R7bdT5LX3fqzTe+zs3sy3EvHFazMvz/tNNX3RK+tmLXu9M9c95zch/ZkojR/qzJUwtPebonKsTdJx1U3cu+q79tuuOoVdvtr75qu+E7o7gI/8oPYL+kuU69yu8zhGl7Gvaz55ZaqrsNSNwJVOq9y2XcZQf91Pld1/d8fSnSCX3o+r8o8xUyc9tuccjwueF4KcfLdTf7l95B5X7rntKek79nSYfikpk0Z1uY4ZMTGnn9zRoOd6XZ89IPdvReX1mZ/uXJbfX+90X7f3HhT5f0E91NrpUJH+9dK97FF1OsvdO+bc8rTldHq09+L+6O0rZdnPwvA/Zz+eae9vmReo/VClXWfkxrP9ptuovLTKum70XzRN1/fqGb7g5tf1RU2MbFyl+1Q5aVROWTrPwFDp/vLLjM51DD9uGJ4YiifOeRaeBobhvXRoKMNf79qPo7w2WmMY2f4M08LAQJn+enHK8KwOMTxvG/6zplJpt9tZUVYpI+e6pUPh48iAEHZqa2s566yz8vKCwSAjR46ktra2x3Zr1qwBYOzYsXn548aNQ2vNmjVrRNjpI1prNn/4FzqaP8Cxo7ipKHYqRjLRgXZjsAu+u9NrWeSSxKDDsOgwLJrNEM1mkEkdUWbG2jJ1imMuw5t7f2NOK4UTiuBGSlnpjmBtYAJrQ9PZFBpLk1WdeVWjKKipKIAREZgZgZoSl+GlUF2Us0ZON5jLk6io9m6sqk3cIRaJoYpYSBPGYlJxNVN8IWdi0aBdtsjpjfiJR5E4Zi66MF8gOnvkcM4eObxf9yUIwp6jlEFJqJqSUDUwq0t5wu7IWPc0+aJPWgRqSdSR0kkSpGjTLWC39GGH9MsD2n6LTr/x6Yn+6UcG1UO+F8+dhOpaT0EXi4zu8mAPrWH8nzCVE/dPqecpn/TO9/iFH/+BRpnZh5v0zX/6iuQ/WXv/6HRS55Xr9AQjaXdoOZORmTNKu/BLv9VveJZXvvWWmWOh5bmaSrtbzLfoMrWB8q2vlC/QefH0ujNGJp1flnXBlW5vpGejyRUoskeevuZZOUPnXBo/V+WlfKHEc1/oKrdL3EWjlZupl62fm9dTe899nKvSfeTGdae0724up2zAjQU5glDms9b+A6+b62LT3/y/jzwXnF1cdBqZtnntXc9VZ35/xi71Z2rvredcAtrbAM9FZfrE/uNIj7YD4nHzY4WDxlHaX7spHe+0GRoH1y9PjxlZ8SmlfGFJuf4EbVoETluLZAXiXMuRTFrlTPplJnWzaTdju5HdVE45/jiuc3NzRLjMNGLn327/tysvnV43iHRZrpu1nLRys4KU/xuXlvTSL0Pki1Xk5KWPR+efme50FXKPT5NzDPjnkv8Si8rJg87XIN1P3tR2p76y1wutOvWbniTOjmFK5+R1Ej0zcd25Zd5eOsXz87rZY9/a6s55ve0r07CbvD0t66Fc5Qu0+Rs7KfM3tQt1u6vTZf876ye/rLOYnP4jSgvR+eV+mcq/Ej28l5Fzpbq/Zj1faZ3zb+d2fei7a/VsPd1TeR/6U5nbwF7p9RYse9u5e331eh67Vt75tLL3yb3XhZ7/Bnqq3/nRo9c2aidl3RyL6qGezukqr7yP59ktvZxL52PrNn8nfXcu73x32d1n1ePn2ek4dDd5Pe5DZX5les7vVCfjppb0LzA5QiHZ/Iw1afZ1F+8FyXR5NnTTv8BK5ezLS6fjmXsNlbWEzbx8SbpfI8djhgL/xZxsvez6jzm/ut4LPngvdJK+M0jX9+8czj33R5SUVLEvGBB32q2trZSUlHTJLykpoaWlpcd26bLObUtLS/PKd5fS0kjOG8sfc7RL4YxP9aFi7hfV94FM2n2M98Pt5tTJ3rp6D+1Ke4q1iUHITWC5cTw7Vc9DumnbXr+Gpz5j+G9AG5a/pc2MPabaMNHxxGtDgamy8d2e67i60Hsd0cz2UAg8yBd2t0dhAJJ+4eE/ahwQ9hKFDKUamNltaWZ9EO3i4mTXAslZHwR0l7u1nT0q5T2Iq25zoZuUl9O5Xde63cV67Kfbqj31sLP+yH4xc+r2fFRdzz+/Ro6lgj8Rk3mYBf/6Z9+YdrW/Zos/GZRet4XcMOdzyzyw6tyH7G7OqS/5u/hgt7+xswe37jNz4pnPPH+CSeXle/ck3v89TAp1c1+ge0jlTjVkPRqnP990vOe8TH3ddS87Y998rjlXUeV+I3ZluiDvCmW+L+Sm+4k9uSa70zZ3IsDxt/QTbf5flveE3+MV1Hk1d7rHnU3m9MWycKd1dvPD6MsY39fd9G3SLj+VnpTZvcPfs7/AvozL6UlxOuf11EZ3d0VzYrrHkp0frCAIgvAR8lFNEsjkxN5Drq3QN1Rh2T7b14AQdnpCa90nc7LOddIP4XtqimYYH2dv5p0xKCzZ19YgRXvcQ6HZD4chCDvhP2scED46ZDATBEEQBEEQBEEQBEEQPAbEjGRJSQmtra1d8tva2rq15EnTk2VOuq+dtRUEQRAEQRAEQRAEQRAEQRAEQdjfGBDCzrhx47qspZNMJtmwYQPjxo3rsV16bZ30WjtpamtrUUp1WXtHEARBEARBEARBEARBEARBEARhf2ZACDtHHnkkr7/+Ok1NTZm8p59+mmQyyVFHHdVjuxEjRjB27FieeOKJvPx//OMfzJgxg4qKir12zIIgCIIgCIIgCIIgCIIgCIIgCP3NgBB2zj//fIqLi7n44ot56aWXeOSRR/jpT3/Kaaedlmexc+WVVzJlypS8tpdeeilPPvkkv/vd73jjjTf42c9+xiuvvMKll166r09DEARBEARBEARBEARBEARBEARhj7A+6gPoCyUlJdx5551cc801XHLJJYTDYU499VS+/e1v59VzXRfHcfLyTjrpJOLxOH/605+4/fbbGTVqFL/73e+YP3/+vjwFQRAEQRAEQRAEQRAEQRAEQRCEPUZprfVHfRCCIAiCIAiCIAiCIAiCIAiCIAhC7wwIV2yCIAiCIAiCIAiCIAiCIAiCIAiCCDuCIAiCIAiCIAiCIAiCIAiCIAgDBhF2BEEQBEEQBEEQBEEQBEEQBEEQBggi7AiCIAiCIAiCIAiCIAiCIAiCIAwQRNgRBEEQBEEQBEEQBEEQBEEQBEEYIIiwIwiCIAiCIAiCIAiCIAiCIAiCMEAQYUcQBEEQBEEQBEEQBEEQBEEQBGGAIMJON7z44ot86lOfYvr06Rx//PHcfffdfWqXSqX47W9/y/z585k5cyYXXHABK1euzKvz6quvcvnll3Pssccyc+ZMTjrpJP74xz+STCb3xqkIgtALa9eu5aKLLmLWrFnMmzePa665hng83qe2Dz/8MCeeeCLTp0/n1FNP5cknn+xSpy/jgiAIHy17cxxYu3YtP/3pTzn55JOZNWsWxxxzDFdeeSV1dXV741QEQdgD9vY9QS7XXHMNEydO5Cc/+Ul/HLogCP3EvhgHVq1axVe/+lUOOuggZs+ezZlnnsnixYv78zQEQdgD9vY4sGnTJi6//HLmz5/P7NmzWbBgAY899lh/n4YgCHvI7o4FTzzxBJdccglHHHEEEydO5Pbbb++2Xn/MF4qw04klS5Zw8cUXM2XKFG699VYWLFjANddcwwMPPNBr25///OfcfffdXHrppdx0001YlsWFF16YN3lz77330tbWxiWXXMItt9zCOeecwy233MK3v/3tvXlagiB0Q2trK5///Ofp6Ojg97//Pd/73vd4/PHHueqqq3pt+9RTT3HFFVdw/PHHc+uttzJ37lwuu+wyXn755bx6fRkXBEH46Njb48Arr7zCokWLOPfcc7n55pv5f//v//Hmm29y3nnn0dHRsTdPTRCEXWBf3BOk+eCDD3jwwQcpKirq79MQBGEP2BfjwMqVKzn//PMpLCzk2muv5cYbb+TEE0/s86SxIAh7l709DiQSCS666CLef/99rrzySv7whz8wefJkvvOd7/Dvf/97b56aIAi7wJ6OBRs3buSYY47Zab1+mS/UQh4XXXSRPvvss/PyrrrqKn344Ydrx3F6bLdt2zY9efJkfdddd2Xy2tra9CGHHKJ//etfZ/IaGhq6tL3zzjv1hAkT9KZNm/rhDARB6Cs333yznjlzZt738rHHHtMTJkzQq1ev3mnbE088UV966aV5eV/4whf0Oeeck0n3dVwQBOGjY2+PAw0NDdp13bw6K1as0BMmTNAPPfRQP5yBIAj9wd4eC3L5zGc+o6+//np9zDHH6B//+Md7fvCCIPQL+2IcOO+88/Tll1/efwctCEK/srfHgTfffFNPmDBBv/baa3n1Tj75ZP3Nb35zz09AEIR+YU/Gglz9YMKECfq2227rUqe/5gvFYieHZDLJ66+/zimnnJKXf9ppp1FXV8fy5ct7bPvyyy/jOE5e26KiIo499lhefPHFTF5FRUWXtlOmTAFgx44de3oKgiDsAgsXLmTevHl538sTTjiBYDCY973tzMaNG1mzZg2nnnpqXv6pp57KsmXLaGxsBPo+LgiC8NGxt8eBiooKlFJ5dSZOnIhpmvK7Lwj7EXt7LEjz2GOPsWnTJr70pS/17wkIgrDH7O1xoLa2liVLlvDZz35275yAIAh7zN4eB2zbBqC4uDivXnFxMVrr/joNQRD2kN0dCwAMo3e5pb/mC0XYyWHDhg2kUinGjh2blz9+/HjAuxHridraWqqqqigrK8vLHzduHGvXrsV13R7bvvXWW5imyahRo3b/4AVB2GVqa2sZN25cXl4wGGTkyJE7/b6vWbMGoMtYMW7cOLTWmfI9GRcEQdg37O1xoDuWLFmC4zhd9isIwkfHvhgL2tvb+dWvfsV3v/tdIpFIPx69IAj9wd4eB5YuXQpAW1sbZ5xxBlOmTOHYY4/lr3/9az+ehSAIe8LeHgcOOuggxo8fz7XXXsvGjRtpa2vjvvvu47333uP888/v57MRBGF32d2xYFf674/5QmuPj+RjREtLCwAlJSV5+el0urw7WltbuyjuAKWlpaRSKaLRaLd+tDdv3sxtt93GggULurXmEQRh79Ha2trl+w7ed35n3/eexorS0tK88t0dFwRB2Hfs7XGgM6lUip/97GeMGTOGo48+ejePWhCE/mZfjAU33ngjo0aN4uSTT+6PQxYEoZ/Z2+NAfX09AN/5znf4whe+wMyZM3nuuee45pprKC0t5fTTT++X8xAEYffZ2+NAIBDgL3/5C1/72tf4xCc+kcn7xS9+wbx58/rlHARB2HN2dyzYlf77Y77wYy/stLW19cnVyYgRIzLxzi5TesvfWfnOTCk7Ojq45JJLqKys5Hvf+16vxygIwr5Ba93r9x26fufT3/fc/F0dFwRB2D/oz3Egl5/+9KesWrWKu+66C8v62N+GCcKAp7/GgtWrV3P33Xdz//339/9BCoKwV+mvcSD99u1ZZ53FV77yFQDmzp3Lhg0b+NOf/iTCjiDsx/TXOBCPx7n00ktxHIcbb7yRoqIinnvuOb7//e9TUlLCkUce2f8HLwhCv9HXsaAv9Md84cd+RuHpp5/m+9//fq/1HnnkkR7fsm1tbQW6Ku+5lJSUZOp1bhsIBCgoKMjLT6VSXHrppWzfvp177713p30LgrB36Ol729bWtlMXSbljRVVVVSa/81ixq+OCIAj7nr09DuRy44038ve//50bbriB6dOn7+mhC4LQj+ztseDnP/85J554IsOGDcuUua5LKpWitbWVoqKiPvnjFgRh77G3x4F0vblz5+a1nzt3LgsXLiSVShEIBPbsJARB2CP29jjw97//nXfeeYeFCxdmvPbMmzePLVu28Otf/1qEHUHYT9jdsWBP+9/V+cKP/dPDmWeeyQcffNDrNnnyZEaOHEkgEOjiF3/16tUAO/3gxo0bR0NDA83NzXn5tbW1jBkzJu9BzXVdvvvd77JkyRJuueWWPGshQRD2HePGjeviGzOZTLJhw4adft/TfnM7jxW1tbUopTLluzIuCILw0bC3x4E0d999NzfccAM/+tGPOO644/rp6AVB6C/29liwdu1aHnvsMebMmZPZtm7dyv3338+cOXNYu3ZtP5+RIAi7yr54NugJwzD67Q1gQRB2n709DqxevZrBgwd3WYph8uTJbNy4sT9OQRCEfmB3x4Jd6b8/5gtlVjGHYDDI3LlzefLJJ/Py//GPfzBo0CCmTJnSY9v58+djGEZe246ODp577jmOOuqovLo/+clPePrpp7nxxhuZOnVq/56EIAh95sgjj+T111+nqakpk/f000+TTCa7fG9zGTFiBGPHjuWJJ57Iy//HP/7BjBkzMjdpuzIuCILw0bC3xwGAf/7zn1xzzTVceumlnHfeef1/EoIg7DF7eyy49tpr+ctf/pK3VVVV8YlPfIK//OUv1NTU7J0TEwShz+ztcWD27NmUlpby2muv5dV77bXXGDdunLhoFYT9gL09DtTU1LB9+3YaGhry6r333nsMGzasH89EEIQ9YXfHgr7SX/OFcufQia9//et89rOf5aqrruK0005j8eLFPPDAA/zkJz/JU8uOP/54ampquPPOOwEYPHgw559/Pr/5zW+wLIuamhr+7//+D4DPf/7zmXY333wz99xzDxdeeCEFBQUsXbo0UzZy5Mguqr0gCHuP888/n7vuuouLL76Yiy++mIaGBn7xi19w2mmn5SnwV155JY888gjLly/P5F166aVcdtlljBw5ksMOO4xnn32WV155hdtuuy1Tp6/jgiAIHx17exxYtGgR3/ve9zj44IM5/PDD8373KyoqGDly5D45T0EQds7eHgtmzZrVZZ+hUIjBgwdz6KGH7tVzEwShb+ztcSAYDHLxxRfzm9/8huLiYmbOnMnzzz/PCy+8wB/+8Id9eq6CIHTP3h4HTj/9dG655Ra+9KUv8aUvfYmSkhKefvppnn/+ea6++up9eaqCIOyEPRkLVq9enfH+BfDhhx/y1FNPEYlEMqJNf80XirDTidmzZ3PTTTdx7bXX8sgjjzBkyBCuuuoqzjnnnLx6juNkFj9Mc8UVV1BQUMB1111HW1sbM2fO5M4772TQoEGZOi+//DIAd9xxB3fccUde+5///OeceeaZe+fEBEHoQklJCXfeeSfXXHMNl1xyCeFwmFNPPZVvf/vbefVc18VxnLy8k046iXg8zp/+9Cduv/12Ro0axe9+9zvmz5+fV68v44IgCB8de3sceOONN0ilUixatKiLtc6CBQv4xS9+sfdOThCEPrMv7gkEQdi/2RfjwIUXXohSir/85S/cdNNNjBgxgl/+8pd84hOf2OvnJwhC7+ztcWDIkCH89a9/5brrruOaa64hGo0yatQorrnmGs4+++x9co6CIPTOnowFTz75JDfeeGMm/cgjj/DII48wbNgwnnvuuUx+f8wXKq213s1zFARBEARBEARBEARBEARBEARBEPYhssaOIAiCIAiCIAiCIAiCIAiCIAjCAEGEHUEQBEEQBEEQBEEQBEEQBEEQhAGCCDuCIAiCIAiCIAiCIAiCIAiCIAgDBBF2BEEQBEEQBEEQBEEQBEEQBEEQBggi7AiCIAiCIAiCIAiCIAiCIAiCIAwQRNgRBEEQBEEQBEEQBEEQBEEQBEEYIIiwIwiCIAiCIAiCIAiCIAiCIAiCMEAQYUcQBEEQBEEQhF1i5cqVfP/73+fYY49l+vTpzJ49mwULFnDrrbfS3Ny8V/f9xBNPcMoppzBjxgwmTpzIihUrAPjrX//K8ccfz7Rp05g4cSKtra1cccUVHHvssbu8jwsuuIALLrigvw89j9WrV3PDDTewadOmXut+/etfZ8aMGbS2tvZY51vf+hZTp06lvr6+z8cwceJEbrjhhj7XFwRBEARBEARh/8D6qA9AEARBEARBEISBw/3338+Pf/xjxowZw0UXXcT48eOxbZv33nuPe++9l6VLl/KHP/xhr+y7sbGR7373u8yfP58f/ehHBINBRo8ezYoVK7jmmms455xz+NSnPoVlWRQWFnLxxRfzuc99bpf386Mf/WgvHH0+q1ev5sYbb+SQQw5h+PDhO6179tln88wzz/D444/zmc98pkt5W1sbzzzzDEcffTRVVVV765AFQRAEQRAEQdhPEGFHEARBEARBEIQ+sWTJEq6++moOO+wwbrrpJoLBYKbs8MMP57//+7956aWX9tr+165dSyqV4vTTT+eQQw7J5K9atQqAc889lxkzZmTyR44cuVv7GT9+/J4daD9z5JFHUl1dzYMPPtitsPOPf/yDeDzO2Wef/REcnSAIgiAIgiAI+xpxxSYIgiAIgiAIQp+4+eabUUrx05/+NE/USRMMBjnuuOMyadd1ufXWWznxxBOZNm0a8+bN47vf/S7btm3r0vbVV1/l85//PAceeCAzZ87k/PPP57XXXsuUX3HFFXz6058G4LLLLmPixIkZl2nf+c53ADjnnHOYOHEiV1xxRaZNZ1dsruvy17/+lTPOOIMZM2Zw8MEHc+655/Lss89m6nTnii2ZTHLTTTdlzmXu3Ll8//vfp7GxMa/esccey1e+8hUWLlzIggULmDFjBieeeCJ///vfM3UeeughvvnNbwLwuc99jokTJzJx4kQeeuihbq+7aZosWLCA999/nw8++KBL+UMPPcSgQYM48sgjaWxs5Oqrr+bkk09m9uzZzJs3j8997nO89dZb3fadyw033MDEiRO77X/ixIld3MY98cQTnHfeecyaNYvZs2dz0UUXsXz58l73IwiCIAiCIAjCniEWO4IgCIIgCIIg9IrjOLz++utMnTqVoUOH9qnN1VdfzX333cdnP/tZjj76aDZv3sz111/PokWLeOihh6ioqADg0Ucf5Xvf+x7HHXccv/zlL7Esi/vuu4+LLrqI22+/nXnz5nHxxRczffp0fvKTn3D55Zdz6KGHUlRUBHgWK3/84x/5+c9/ztixYzP9dscVV1zBY489xtlnn82ll15KIBBg+fLlbN68ucc2ruty8cUX8/bbb3PRRRdx4IEHsnnzZm644QaWLVvGgw8+SDgcztRfuXIlv/zlL/nSl75EVVUVDzzwAP/zP//DqFGjmDNnDkcffTSXX3451157LT/84Q+ZOnUqsHMLo7POOotbbrmFBx98kCuvvDKTv3r1apYtW8aXv/xlTNPMrHH0jW98g6qqKqLRKE8//TQXXHABd9xxB4ceemjvH1wf+NOf/sR1113HmWeeyde+9jVSqRS33347n/nMZ3jggQf2O6snQRAEQRAEQfg4IcKOIAiCIAiCIAi90tTURCwW63U9mDS1tbXcd999fPrTn+YHP/hBJn/KlCmcc8453HnnnVx22WXEYjF+9rOfcfTRR+etzXPUUUexYMECrr32Wh544AFGjhyZEQtGjRrFrFmzMnXTgsgBBxzA9OnTezymt956i0cffZSvfvWrXHbZZZn8I488cqfn8uSTT/LSSy9xww038MlPfjKTP2nSJM4++2weeuihjDUReNfqnnvuoaamBoA5c+bw+uuv8/jjjzNnzhwqKioYNWoU4Ll9yz2XnkiLQo899hjf+c53CAQCADz44IOAJ/wAjB07lquvvjrTznEc5s+fz+bNm/nrX//aL8LO1q1bueGGG/jsZz/LVVddlck/7LDDOOGEE7jxxhu57rrr9ng/giAIgiAIgiB0j7hiEwRBEARBEASh33njjTcAWLBgQV7+jBkzGDduXMbN2pIlS2hubmbBggXYtp3ZXNfliCOO4N133yUajfbLMS1cuBCg23Vqdsbzzz9PSUkJxxxzTN4xTp48mUGDBrFo0aK8+pMnT86IOgChUIjRo0ezZcuWPTr+s846i6amJp577jkAbNvmscce4+CDD2b06NGZevfccw8LFixg+vTpTJkyhalTp/Laa69RW1u7R/tP8/LLL2PbNmeccUbe9QiFQsyZM6fL9RAEQRAEQRAEoX8Rix1BEARBEARBEHqlvLycSCTSZZ2Vnki7BKuuru5SVl1dnRE56uvrAbj00kt77KulpYWCgoJdPOKuNDY2YpomgwYN2qV2DQ0NtLa2Mm3atG7Lm5qa8tJlZWVd6gSDQRKJxC7ttzMnnngi11xzDQ899BAnnHACL774IvX19Xz729/O1Pnzn//ML37xC84//3y++c1vUl5ejmEYXH/99axZs2aP9p8m/ZmdffbZ3ZYbhrw/KAiCIAiCIAh7ExF2BEEQBEEQBEHoFdM0mTt3Li+99BLbtm1jyJAhO62fFjd27NjRpe6OHTs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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# display density plot for coefficient values of each class\n", + "sns.set(rc={\"figure.figsize\": (20, 8)})\n", + "plt.xlim(-0.02, 0.1)\n", + "plt.xlabel(\"Coefficient Value\")\n", + "plt.ylabel(\"Density\")\n", + "plt.title(\"Density of Coefficient Values Per Phenotpyic Class\")\n", + "ax = sns.kdeplot(data=coefs)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# display average coefficient value vs phenotypic class bar chart\n", + "pheno_class_ordered = coefs.reindex(\n", + " coefs.mean().sort_values(ascending=False).index, axis=1\n", + ")\n", + "sns.set(rc={\"figure.figsize\": (20, 8)})\n", + "plt.xlabel(\"Phenotypic Class\")\n", + "plt.ylabel(\"Average Coefficient Value\")\n", + "plt.title(\"Coefficient vs Phenotpyic Class\")\n", + "plt.xticks(rotation=90)\n", + "ax = sns.barplot(data=pheno_class_ordered)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# display average coefficient value vs feature bar chart\n", + "feature_ordered = coefs.T.reindex(\n", + " coefs.T.mean().sort_values(ascending=False).index, axis=1\n", + ")\n", + "sns.set(rc={\"figure.figsize\": (500, 8)})\n", + "plt.xlabel(\"Deep Learning Feature Number\")\n", + "plt.ylabel(\"Average Coefficient Value\")\n", + "plt.title(\"Coefficient vs Feature\")\n", + "plt.xticks(rotation=90)\n", + "ax = sns.barplot(data=feature_ordered)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.13 ('phenotypic_profiling')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "f9df586d1764dbc68785000a153dad1832127ac564b5e2e4c94e83fc43160b30" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/4.interpret_model/interpret_model.sh b/4.interpret_model/interpret_model.sh new file mode 100644 index 00000000..71f4d2f1 --- /dev/null +++ b/4.interpret_model/interpret_model.sh @@ -0,0 +1,5 @@ +#!/bin/bash +# Convert notebook to python file and execute +jupyter nbconvert --to python \ + --FilesWriter.build_directory=scripts/nbconverted \ + --execute interpret_model.ipynb diff --git a/3.ML_model/scripts/3.interpret_model.py b/4.interpret_model/scripts/nbconverted/interpret_model.py similarity index 91% rename from 3.ML_model/scripts/3.interpret_model.py rename to 4.interpret_model/scripts/nbconverted/interpret_model.py index 52c9c7f4..5b1de928 100644 --- a/3.ML_model/scripts/3.interpret_model.py +++ b/4.interpret_model/scripts/nbconverted/interpret_model.py @@ -21,13 +21,9 @@ # In[2]: -# set numpy seed to make random operations reproduceable -np.random.seed(0) +model_dir = pathlib.Path("../2.train_model/models/") -# results dir for loading/saving -results_dir = pathlib.Path("../results/") - -log_reg_model_path = pathlib.Path(f"{results_dir}/1.log_reg_model.joblib") +log_reg_model_path = pathlib.Path(f"{model_dir}/log_reg_model.joblib") log_reg_model = load(log_reg_model_path) @@ -51,6 +47,7 @@ # display heatmap of average coefs plt.figure(figsize=(20, 10)) +plt.title("Heatmap of Coefficients Matrix") ax = sns.heatmap(data=coefs.T) @@ -109,7 +106,7 @@ # In[9]: -shuffled_baseline_log_reg_model_path = pathlib.Path(f"{results_dir}/1.shuffled_baseline_log_reg_model.joblib") +shuffled_baseline_log_reg_model_path = pathlib.Path(f"{model_dir}/shuffled_baseline_log_reg_model.joblib") shuffled_baseline_log_reg_model = load(shuffled_baseline_log_reg_model_path) @@ -133,6 +130,7 @@ # display heatmap of average coefs plt.figure(figsize=(20, 10)) +plt.title("Heatmap of Coefficients Matrix") ax = sns.heatmap(data=coefs.T) diff --git a/README.md b/README.md index 8ba45264..06f39c28 100644 --- a/README.md +++ b/README.md @@ -1,18 +1,59 @@ # Phenotypic Profiling Model +## Repository Structure: + +This repository is structured as follows: + +| Order | Module | Description | +| :---- | :----- | :---------- | +| [0.download_data](0.download_data/) | Download training data | Download labeled single-cell dataset from [mitocheck_data](https://github.com/WayScience/mitocheck_data) | +| [1.split_data](1.split_data/) | Create data subsets | Create training, testing, and holdout data subsets | +| [2.train_model](2.train_model/) | Train model | Train ML model on training data subset | +| [3.evaluate_model](3.evaluate_model/) | Evaluate model | Evaluate ML model on all data subsets | +| [4.interpret_model](4.interpret_model/) | Interpret model | Interpret ML model | + ## Data -Instructions for data download/processing can be found at: https://github.com/WayScience/mitocheck_data. +Instructions for data download/preprocessing can be found at: https://github.com/WayScience/mitocheck_data. + +This repository downloads training data from a specific version of [MitoCheck_data](https://github.com/WayScience/mitocheck_data). +For more information see [0.download_data/](0.download_data/). + +## Machine Learning Model + +We use [Scikit-learn (sklearn)](https://scikit-learn.org/) for data manipulation, model training, and model evaluation. +Scikit-learn is described in [Pedregosa et al., JMLR 12, pp. 2825-2830, 2011](http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html) as a machine learning library for Python. +Its ease of implementation in a pipeline make it ideal for our use case. + +We consistently use the following parameters with many sklearn functions: + +- `n_jobs=-1`: Use all CPU cores in parallel when completing a task. +- `random_state=0`: Use seed 0 when shuffling data or generating random numbers. +This allows "random" sklearn operations to have consist results. +We also use `np.random.seed(0)` to make "random" numpy operations have consistent results. + +We use [seaborn](https://seaborn.pydata.org/) for data visualization. +Seaborn is described in [Waskom, M.L., 2021](https://doi.org/10.21105/joss.03021) as a library for making statisical graphics in python. + +All parts of the following pipeline are completed for a "final" model (from training data) and a "shuffled baseline" model (from shuffled training data). +This shuffled baseline model provides a suitable baseline comparison for the final model during evaluation. + +## Setup + +Perform the following steps to set up the `phenotypic_profiling` environment necessary for processing data in this repository. -This repository compiles training data from a specific version of [MitoCheck_data](https://github.com/WayScience/mitocheck_data). -For more information see [0.download_data/README.md](0.download_data/README.md). +### Step 1: Create Phenotypic Profiling Environment -Formatted data (feature data + metadata) is saved in [training_data.csv.gz](1.format_data/data/training_data.csv.gz). +```sh +# Run this command to create the conda environment for phenotypic profiling -## Analysis +conda env create -f phenotypic_profiling_env.yml +``` -We anaylze the feature data with UMAP in [2.analyze_data](2.analyze_data). +### Step 2: Activate Phenotypic Profiling Environment -## ML Model +```sh +# Run this command to activate the conda environment for phenotypic profiling -We train, evaluate, and interpret a model to predict mitotic stage from nuclear staining data using [DeepProfiler](https://github.com/cytomining/DeepProfiler) features in [2.ML_model](2.ML_model). \ No newline at end of file +conda activate phenotypic_profiling +``` diff --git a/3.ML_model/3.machine_learning_env.yml b/phenotypic_profiling_env.yml similarity index 86% rename from 3.ML_model/3.machine_learning_env.yml rename to phenotypic_profiling_env.yml index a68a726d..21a17628 100644 --- a/3.ML_model/3.machine_learning_env.yml +++ b/phenotypic_profiling_env.yml @@ -1,4 +1,4 @@ -name: 3.ML_phenotypic_classification +name: phenotypic_profiling channels: - conda-forge dependencies: diff --git a/utils/evaluate_utils.py b/utils/evaluate_utils.py new file mode 100644 index 00000000..bb50c334 --- /dev/null +++ b/utils/evaluate_utils.py @@ -0,0 +1,73 @@ +import pandas as pd +import numpy as np + +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import confusion_matrix, f1_score + +import matplotlib.pyplot as plt +import seaborn as sns + +from train_utils import get_X_y_data + +# set numpy seed to make random operations reproduceable +np.random.seed(0) + +def evaluate_model_cm( + log_reg_model: LogisticRegression, dataset: pd.DataFrame +): + """display confusion matrix for logistic regression model on dataset + Args: + log_reg_model (LogisticRegression): logisitc regression model to evaluate + dataset (pd.DataFrame): dataset to evaluate model on + Returns: + np.ndarray, np.ndarray: true, predicted labels + """ + + # get features and labels dataframes + X, y = get_X_y_data(dataset) + + # get predictions from model + y_pred = log_reg_model.predict(X) + + # create confusion matrix + conf_mat = confusion_matrix(y, y_pred, labels=log_reg_model.classes_) + conf_mat = pd.DataFrame(conf_mat) + conf_mat.columns = log_reg_model.classes_ + conf_mat.index = log_reg_model.classes_ + + # display confusion matrix + plt.figure(figsize=(15, 15)) + ax = sns.heatmap(data=conf_mat, annot=True, fmt=".0f", cmap="viridis", square=True) + ax = plt.xlabel("Predicted Label") + ax = plt.ylabel("True Label") + ax = plt.title("Phenotypic Class Predicitions") + + return y, y_pred + + +def evaluate_model_score(log_reg_model: LogisticRegression, dataset: pd.DataFrame): + """display bar graph for model with scoring metric on each class + Args: + log_reg_model (LogisticRegression): logisitc regression model to evaluate + dataset (pd.DataFrame): dataset to evaluate model on + """ + + # get features and labels dataframes + X, y = get_X_y_data(dataset) + + # get predictions from model + y_pred = log_reg_model.predict(X) + + # display precision vs phenotypic class bar chart + scores = f1_score( + y, y_pred, average=None, labels=log_reg_model.classes_, zero_division=0 + ) + scores = pd.DataFrame(scores).T + scores.columns = log_reg_model.classes_ + + sns.set(rc={"figure.figsize": (20, 8)}) + plt.xlabel("Phenotypic Class") + plt.ylabel("F1 Score") + plt.title("F1 Score vs Phenotpyic Class") + plt.xticks(rotation=90) + ax = sns.barplot(data=scores) diff --git a/3.ML_model/utils/MlPipelineUtils.py b/utils/split_utils.py similarity index 52% rename from 3.ML_model/utils/MlPipelineUtils.py rename to utils/split_utils.py index 553e6035..c91b9389 100644 --- a/3.ML_model/utils/MlPipelineUtils.py +++ b/utils/split_utils.py @@ -1,25 +1,14 @@ import pandas as pd import numpy as np import pathlib -from typing import Tuple, Any, List, Union - -from sklearn.utils import shuffle -from sklearn.metrics import confusion_matrix, f1_score -from sklearn.linear_model import LogisticRegression - -import matplotlib.pyplot as plt -import seaborn as sns - # set numpy seed to make random operations reproduceable np.random.seed(0) def get_features_data(load_path: pathlib.Path) -> pd.DataFrame: """get features data from csv at load path - Args: load_path (pathlib.Path): path to training data csv - Returns: pd.DataFrame: training dataframe """ @@ -31,14 +20,10 @@ def get_features_data(load_path: pathlib.Path) -> pd.DataFrame: features_data["Mitocheck_Phenotypic_Class"] != "ADCCM" ] - # replace shape1 and shape3 labels with their correct respective classes - features_data = features_data.replace("Shape1", "Binuclear") - features_data = features_data.replace("Shape3", "Polylobed") - return features_data -def get_image_indexes(training_data: pd.DataFrame, images: List) -> List: +def get_image_indexes(training_data: pd.DataFrame, images: list) -> list: image_indexes_list = [] for image in images: @@ -50,13 +35,11 @@ def get_image_indexes(training_data: pd.DataFrame, images: List) -> List: return image_indexes_list -def get_random_images_indexes(training_data: pd.DataFrame, num_images: int) -> List: +def get_random_images_indexes(training_data: pd.DataFrame, num_images: int) -> list: """get ramdom images from training dataset - Args: training_data (pd.DataFrame): pandas dataframe of training data num_images (int): number of images to holdout - Returns: List: list of unique images for holding out """ @@ -66,15 +49,13 @@ def get_random_images_indexes(training_data: pd.DataFrame, num_images: int) -> L return images -def get_intelligent_images(training_data: pd.DataFrame, num_images: int) -> List: +def get_intelligent_images(training_data: pd.DataFrame, num_images: int) -> list: """get images from training dataset and try to balance labels present in these images add an image if it has at least class not represented by the other images if the image doesn't have a contribution via new class, try a new image - Args: training_data (pd.DataFrame): pandas dataframe of training data num_images (int): number of images to holdout - Returns: List: list of unique images with intelligently balanced phenotypic classes """ @@ -116,15 +97,13 @@ def get_intelligent_images(training_data: pd.DataFrame, num_images: int) -> List def get_representative_images( training_data: pd.DataFrame, num_images: int, attempts: int = 100 -) -> List: +) -> list: """get images from training dataset and such that every phenotypic class is represented returns None if no combintation of images are found that represent every phenotypic class within number of trials - Args: training_data (pd.DataFrame): pandas dataframe of training data num_images (int): number of images to holdout attempts (int): number of times to try getting representative images - Returns: List: list of images with every phenotypic class represented or None if this list cannot be curated """ @@ -150,117 +129,4 @@ def get_representative_images( trial += 1 print("No combination of images found that represents all classes!") - return None - - -def get_dataset( - features_dataframe: pd.DataFrame, data_split_indexes: pd.DataFrame, label: str -) -> pd.DataFrame: - """get testing data from features dataframe and the data split indexes - - Args: - features_dataframe (pd.DataFrame): dataframe with all features data - data_split_indexes (pd.DataFrame): dataframe with split indexes - label (str): label to get data for (train, test, holdout) - - Returns: - pd.DataFrame: _description_ - """ - indexes = data_split_indexes.loc[data_split_indexes["label"] == label] - indexes = indexes["index"] - data = features_dataframe.loc[indexes] - - return data - - -def get_X_y_data(training_data: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFrame]: - """generate X (features) and y (labels) dataframes from training data - - Args: - training_data (pd.DataFrame): training dataframe - - Returns: - Tuple[pd.DataFrame, pd.DataFrame]: X, y dataframes - """ - - # all features from DeepProfiler have "efficientnet" in their column name - morphology_features = [ - col for col in training_data.columns.tolist() if "efficientnet" in col - ] - - # extract features - X = training_data.loc[:, morphology_features].values - - # extract phenotypic class label - y = training_data.loc[:, ["Mitocheck_Phenotypic_Class"]].values - # make Y data - y = np.ravel(y) - - # shuffle data because as it comes from MitoCheck same labels tend to be in grou - X, y = shuffle(X, y, random_state=0) - - return X, y - - -def evaluate_model_cm( - log_reg_model: LogisticRegression, dataset: pd.DataFrame -) -> Tuple[np.ndarray, np.ndarray]: - """display confusion matrix for logistic regression model on dataset - - Args: - log_reg_model (LogisticRegression): logisitc regression model to evaluate - dataset (pd.DataFrame): dataset to evaluate model on - - Returns: - Tuple[np.ndarray, np.ndarray]: true, predicted labels - """ - - # get features and labels dataframes - X, y = get_X_y_data(dataset) - - # get predictions from model - y_pred = log_reg_model.predict(X) - - # create confusion matrix - conf_mat = confusion_matrix(y, y_pred, labels=log_reg_model.classes_) - conf_mat = pd.DataFrame(conf_mat) - conf_mat.columns = log_reg_model.classes_ - conf_mat.index = log_reg_model.classes_ - - # display confusion matrix - plt.figure(figsize=(15, 15)) - ax = sns.heatmap(data=conf_mat, annot=True, fmt=".0f", cmap="viridis", square=True) - ax = plt.xlabel("Predicted Label") - ax = plt.ylabel("True Label") - ax = plt.title("Phenotypic Class Predicitions") - - return y, y_pred - - -def evaluate_model_score(log_reg_model: LogisticRegression, dataset: pd.DataFrame): - """display bar graph for model with scoring metric on each class - - Args: - log_reg_model (LogisticRegression): logisitc regression model to evaluate - dataset (pd.DataFrame): dataset to evaluate model on - """ - - # get features and labels dataframes - X, y = get_X_y_data(dataset) - - # get predictions from model - y_pred = log_reg_model.predict(X) - - # display precision vs phenotypic class bar chart - scores = f1_score( - y, y_pred, average=None, labels=log_reg_model.classes_, zero_division=0 - ) - scores = pd.DataFrame(scores).T - scores.columns = log_reg_model.classes_ - - sns.set(rc={"figure.figsize": (20, 8)}) - plt.xlabel("Phenotypic Class") - plt.ylabel("F1 Score") - plt.title("F1 Score vs Phenotpyic Class") - plt.xticks(rotation=90) - ax = sns.barplot(data=scores) + return None \ No newline at end of file diff --git a/utils/train_utils.py b/utils/train_utils.py new file mode 100644 index 00000000..37615f95 --- /dev/null +++ b/utils/train_utils.py @@ -0,0 +1,50 @@ +import pandas as pd +import numpy as np +from sklearn.utils import shuffle + +# set numpy seed to make random operations reproduceable +np.random.seed(0) + +def get_dataset( + features_dataframe: pd.DataFrame, data_split_indexes: pd.DataFrame, label: str +) -> pd.DataFrame: + """get testing data from features dataframe and the data split indexes + Args: + features_dataframe (pd.DataFrame): dataframe with all features data + data_split_indexes (pd.DataFrame): dataframe with split indexes + label (str): label to get data for (train, test, holdout) + Returns: + pd.DataFrame: _description_ + """ + indexes = data_split_indexes.loc[data_split_indexes["label"] == label] + indexes = indexes["index"] + data = features_dataframe.loc[indexes] + + return data + + +def get_X_y_data(training_data: pd.DataFrame): + """generate X (features) and y (labels) dataframes from training data + Args: + training_data (pd.DataFrame): training dataframe + Returns: + pd.DataFrame, pd.DataFrame: X, y dataframes + """ + + # all features from DeepProfiler have "efficientnet" in their column name + morphology_features = [ + col for col in training_data.columns.tolist() if "efficientnet" in col + ] + + # extract features + X = training_data.loc[:, morphology_features].values + + # extract phenotypic class label + y = training_data.loc[:, ["Mitocheck_Phenotypic_Class"]].values + # make Y data + y = np.ravel(y) + + # shuffle data because as it comes from MitoCheck same labels tend to be in grou + X, y = shuffle(X, y, random_state=0) + + return X, y