From 7f35be65d4b2a0b45c2ca191590ff27672df779e Mon Sep 17 00:00:00 2001 From: Nic Ma Date: Mon, 7 Sep 2020 16:23:50 +0800 Subject: [PATCH 1/2] [DLMED] add load images notebook --- load_medical_images.ipynb | 819 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 819 insertions(+) create mode 100644 load_medical_images.ipynb diff --git a/load_medical_images.ipynb b/load_medical_images.ipynb new file mode 100644 index 0000000000..221342eb1d --- /dev/null +++ b/load_medical_images.ipynb @@ -0,0 +1,819 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Overview\n", + "\n", + "This notebook introduces you MONAI's transformation module for 3D images.\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/Tutorials/blob/master/3d_image_transforms.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup environment" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install -qU \"monai[gdown, nibabel]\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install -qU matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MONAI version: 0.2.0\n", + "Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) [GCC 7.3.0]\n", + "Numpy version: 1.18.1\n", + "Pytorch version: 1.6.0\n", + "\n", + "Optional dependencies:\n", + "Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.\n", + "Nibabel version: 3.1.1\n", + "scikit-image version: 0.15.0\n", + "Pillow version: 7.2.0\n", + "Tensorboard version: 2.1.0\n", + "\n", + "For details about installing the optional dependencies, please visit:\n", + " https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n", + "\n" + ] + } + ], + "source": [ + "# Copyright 2020 MONAI Consortium\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "\n", + "import glob\n", + "import os\n", + "import shutil\n", + "import tempfile\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from monai.apps import download_and_extract\n", + "from monai.config import print_config\n", + "from monai.transforms import (\n", + " AddChanneld,\n", + " LoadNifti,\n", + " LoadNiftid,\n", + " Orientationd,\n", + " Rand3DElasticd,\n", + " RandAffined,\n", + " Spacingd,\n", + ")\n", + "\n", + "print_config()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup data directory\n", + "\n", + "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable. \n", + "This allows you to save results and reuse downloads. \n", + "If not specified a temporary directory will be used." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "root dir is: /workspace/data/medical\n" + ] + } + ], + "source": [ + "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", + "root_dir = tempfile.mkdtemp() if directory is None else directory\n", + "print(f\"root dir is: {root_dir}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download dataset\n", + "\n", + "Downloads and extracts the dataset. \n", + "The dataset comes from http://medicaldecathlon.com/." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "resource = \"https://drive.google.com/uc?id=1jzeNU1EKnK81PyTsrx0ujfNl-t0Jo8uE\"\n", + "md5 = \"410d4a301da4e5b2f6f86ec3ddba524e\"\n", + "\n", + "compressed_file = os.path.join(root_dir, \"Task09_Spleen.tar\")\n", + "data_dir = os.path.join(root_dir, \"Task09_Spleen\")\n", + "if not os.path.exists(data_dir):\n", + " download_and_extract(resource, compressed_file, root_dir, md5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set MSD Spleen dataset path\n", + "\n", + "The following groups images and labels from `Task09_Spleen/imagesTr` and `Task09_Spleen/labelsTr` into pairs." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "train_images = sorted(glob.glob(os.path.join(data_dir, \"imagesTr\", \"*.nii.gz\")))\n", + "train_labels = sorted(glob.glob(os.path.join(data_dir, \"labelsTr\", \"*.nii.gz\")))\n", + "data_dicts = [\n", + " {\"image\": image_name, \"label\": label_name}\n", + " for image_name, label_name in zip(train_images, train_labels)\n", + "]\n", + "train_data_dicts, val_data_dicts = data_dicts[:-9], data_dicts[-9:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The image file names are organised into a list of dictionaries." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'image': '/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz',\n", + " 'label': '/workspace/data/medical/Task09_Spleen/labelsTr/spleen_10.nii.gz'}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data_dicts[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The list of data dictionaries, `train_data_dicts`,\n", + "could be used by PyTorch's data loader.\n", + "\n", + "For example,\n", + "\n", + "```python\n", + "from torch.utils.data import DataLoader\n", + "\n", + "data_loader = DataLoader(train_data_dicts)\n", + "for training_sample in data_loader:\n", + " # run the deep learning training with training_sample\n", + "```\n", + "\n", + "The rest of this tutorial presents a set of \"transforms\"\n", + "converting `train_data_dict` into data arrays that will\n", + "eventually be consumed by the deep learning models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load the NIfTI files\n", + "\n", + "One design choice of MONAI is that it provides not only the high-level workflow components,\n", + "but also relatively lower level APIs in their minimal functioning form.\n", + "\n", + "For example, a `LoadNifti` class is a simple callable wrapper of the underlying `Nibabel` image loader.\n", + "After constructing the loader with a few necessary system parameters,\n", + "calling the loader instance with a `NIfTI` filename will return the image data arrays,\n", + "as well as the metadata -- such as affine information and voxel sizes." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "loader = LoadNifti(dtype=np.float32)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input: /workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz\n", + "image shape: (512, 512, 55)\n", + "image affine:\n", + "[[ 0.97656202 0. 0. -499.02319336]\n", + " [ 0. 0.97656202 0. -499.02319336]\n", + " [ 0. 0. 5. 0. ]\n", + " [ 0. 0. 0. 1. ]]\n", + "image pixdim:\n", + "[1. 0.976562 0.976562 5. 0. 0. 0. 0. ]\n" + ] + } + ], + "source": [ + "image, metadata = loader(train_data_dicts[0][\"image\"])\n", + "print(f\"input: {train_data_dicts[0]['image']}\")\n", + "print(f\"image shape: {image.shape}\")\n", + "print(f\"image affine:\\n{metadata['affine']}\")\n", + "print(f\"image pixdim:\\n{metadata['pixdim']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oftentimes, we want to load a group of inputs as a training sample.\n", + "For example training a supervised image segmentation network requires a pair of image and label as a training sample.\n", + "\n", + "To ensure a group of inputs are beining preprocessed consistently,\n", + "MONAI also provides dictionary-based interfaces for the minimal functioning transforms.\n", + "\n", + "`LoadNiftid` is the corresponding dict-based version of `LoadNifti`:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "loader = LoadNiftid(keys=(\"image\", \"label\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input:, {'image': '/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz', 'label': '/workspace/data/medical/Task09_Spleen/labelsTr/spleen_10.nii.gz'}\n", + "image shape: (512, 512, 55)\n", + "label shape: (512, 512, 55)\n", + "image pixdim:\n", + "[1. 0.976562 0.976562 5. 0. 0. 0. 0. ]\n" + ] + } + ], + "source": [ + "data_dict = loader(train_data_dicts[0])\n", + "print(f\"input:, {train_data_dicts[0]}\")\n", + "print(f\"image shape: {data_dict['image'].shape}\")\n", + "print(f\"label shape: {data_dict['label'].shape}\")\n", + "print(f\"image pixdim:\\n{data_dict['image_meta_dict']['pixdim']}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "image, label = data_dict[\"image\"], data_dict[\"label\"]\n", + "plt.figure(\"visualize\", (8, 4))\n", + "plt.subplot(1, 2, 1)\n", + "plt.title(\"image\")\n", + "plt.imshow(image[:, :, 30], cmap=\"gray\")\n", + "plt.subplot(1, 2, 2)\n", + "plt.title(\"label\")\n", + "plt.imshow(label[:, :, 30])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add the channel dimension\n", + "\n", + "Most of MONAI's image transformations assume that the input data has the shape: \n", + "`[num_channels, spatial_dim_1, spatial_dim_2, ... ,spatial_dim_n]` \n", + "so that they could be interpreted consistently (as \"channel-first\" is commonly used in PyTorch). \n", + "Here the input image has shape `(512, 512, 55)` which isn't in the acceptable shape (missing the channel dimension), \n", + "we therefore create a transform which is called to update the shape:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "image shape: (1, 512, 512, 55)\n" + ] + } + ], + "source": [ + "add_channel = AddChanneld(keys=[\"image\", \"label\"])\n", + "datac_dict = add_channel(data_dict)\n", + "print(f\"image shape: {datac_dict['image'].shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to do some intensity and spatial transforms." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resample to a consistent voxel size\n", + "\n", + "The input volumes might have different voxel sizes. \n", + "The following transform is created to normalise the volumes to have (1.5, 1.5, 5.) millimetre voxel size. \n", + "The transform is set to read the original voxel size information from `data_dict['image.affine']`, \n", + "which is from the corresponding NIfTI file, loaded earlier by `LoadNiftid`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "spacing = Spacingd(keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 5.0), mode=(\"bilinear\", \"nearest\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "image shape: (1, 334, 334, 55)\n", + "label shape: (1, 334, 334, 55)\n", + "image affine after Spacing:\n", + "[[ 1.5 0. 0. -499.02319336]\n", + " [ 0. 1.5 0. -499.02319336]\n", + " [ 0. 0. 5. 0. ]\n", + " [ 0. 0. 0. 1. ]]\n", + "label affine after Spacing:\n", + "[[ 1.5 0. 0. -499.02319336]\n", + " [ 0. 1.5 0. -499.02319336]\n", + " [ 0. 0. 5. 0. ]\n", + " [ 0. 0. 0. 1. ]]\n" + ] + } + ], + "source": [ + "data_dict = spacing(datac_dict)\n", + "print(f\"image shape: {data_dict['image'].shape}\")\n", + "print(f\"label shape: {data_dict['label'].shape}\")\n", + "print(f\"image affine after Spacing:\\n{data_dict['image_meta_dict']['affine']}\")\n", + "print(f\"label affine after Spacing:\\n{data_dict['label_meta_dict']['affine']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To track the spacing changes, the data_dict was updated by `Spacingd`:\n", + "* An `image.original_affine` key is added to the `data_dict`, logs the original affine.\n", + "* An `image.affine` key is updated to have the current affine." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "image, label = data_dict[\"image\"], data_dict[\"label\"]\n", + "plt.figure(\"visualise\", (8, 4))\n", + "plt.subplot(1, 2, 1)\n", + "plt.title(\"image\")\n", + "plt.imshow(image[0, :, :, 30], cmap=\"gray\")\n", + "plt.subplot(1, 2, 2)\n", + "plt.title(\"label\")\n", + "plt.imshow(label[0, :, :, 30])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reorientation to a designated axes codes\n", + "\n", + "Sometimes it is nice to have all the input volumes in a consistent axes orientation. \n", + "The default axis labels are Left (L), Right (R), Posterior (P), Anterior (A), Inferior (I), Superior (S). \n", + "The following transform is created to reorientate the volumes to have 'Posterior, Left, Inferior' (PLI) orientation:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "orientation = Orientationd(keys=[\"image\", \"label\"], axcodes=\"PLI\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "image shape: (1, 334, 334, 55)\n", + "label shape: (1, 334, 334, 55)\n", + "image affine after Spacing:\n", + "[[ 0. -1.5 0. 0.47680664]\n", + " [ -1.5 0. 0. 0.47680664]\n", + " [ 0. 0. -5. 270. ]\n", + " [ 0. 0. 0. 1. ]]\n", + "label affine after Spacing:\n", + "[[ 0. -1.5 0. 0.47680664]\n", + " [ -1.5 0. 0. 0.47680664]\n", + " [ 0. 0. -5. 270. ]\n", + " [ 0. 0. 0. 1. ]]\n" + ] + } + ], + "source": [ + "data_dict = orientation(data_dict)\n", + "print(f\"image shape: {data_dict['image'].shape}\")\n", + "print(f\"label shape: {data_dict['label'].shape}\")\n", + "print(f\"image affine after Spacing:\\n{data_dict['image_meta_dict']['affine']}\")\n", + "print(f\"label affine after Spacing:\\n{data_dict['label_meta_dict']['affine']}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "image, label = data_dict[\"image\"], data_dict[\"label\"]\n", + "plt.figure(\"visualise\", (8, 4))\n", + "plt.subplot(1, 2, 1)\n", + "plt.title(\"image\")\n", + "plt.imshow(image[0, :, :, 30], cmap=\"gray\")\n", + "plt.subplot(1, 2, 2)\n", + "plt.title(\"label\")\n", + "plt.imshow(label[0, :, :, 30])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Random affine transformation\n", + "\n", + "The following affine transformation is defined to output a (300, 300, 50) image patch. \n", + "The patch location is randomly chosen in a range of (-40, 40), (-40, 40), (-2, 2) in x, y, and z axes respectively. \n", + "The translation is relative to the image centre. \n", + "The 3D rotation angle is randomly chosen from (-45, 45) degrees around the z axis, and 5 degrees around x and y axes. \n", + "The random scaling factor is randomly chosen from (1.0 - 0.15, 1.0 + 0.15) along each axis. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "rand_affine = RandAffined(\n", + " keys=[\"image\", \"label\"],\n", + " mode=(\"bilinear\", \"nearest\"),\n", + " prob=1.0,\n", + " spatial_size=(300, 300, 50),\n", + " translate_range=(40, 40, 2),\n", + " rotate_range=(np.pi / 36, np.pi / 36, np.pi / 4),\n", + " scale_range=(0.15, 0.15, 0.15),\n", + " padding_mode=\"border\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can rerun this cell to generate a different randomised version of the original image." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "image shape: torch.Size([1, 300, 300, 50])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "affined_data_dict = rand_affine(data_dict)\n", + "print(f\"image shape: {affined_data_dict['image'].shape}\")\n", + "\n", + "image, label = affined_data_dict[\"image\"][0], affined_data_dict[\"label\"][0]\n", + "plt.figure(\"visualise\", (8, 4))\n", + "plt.subplot(1, 2, 1)\n", + "plt.title(\"image\")\n", + "plt.imshow(image[:, :, 15], cmap=\"gray\")\n", + "plt.subplot(1, 2, 2)\n", + "plt.title(\"label\")\n", + "plt.imshow(label[:, :, 15])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Random elastic deformation\n", + "\n", + "Similarly, the following elastic deformation is defined to output a (300, 300, 10) image patch. \n", + "The image is resampled from a combination of affine transformations and elastic deformations. \n", + "`sigma_range` controls the smoothness of the deformation (larger than 15 could be slow on CPU) \n", + "`magnitude_range` controls the amplitude of the deformation (large than 500, the image becomes unrealistic)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "rand_elastic = Rand3DElasticd(\n", + " keys=[\"image\", \"label\"],\n", + " mode=(\"bilinear\", \"nearest\"),\n", + " prob=1.0,\n", + " sigma_range=(5, 8),\n", + " magnitude_range=(100, 200),\n", + " spatial_size=(300, 300, 10),\n", + " translate_range=(50, 50, 2),\n", + " rotate_range=(np.pi / 36, np.pi / 36, np.pi),\n", + " scale_range=(0.15, 0.15, 0.15),\n", + " padding_mode=\"border\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can rerun this cell to generate a different randomised version of the original image." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "image shape: (1, 300, 300, 10)\n" + ] + }, + { + "data": { + "image/png": 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0Gmi327h+/frY5yEgICDgreKBIGa1BJU8kuTrmw3QxM0Gbv2MxFWpVDA3N4darYZUKoVjx45hfn7e+5Hpk1TZWkmUZJNk8VvLPJPJeCtT2831whqApdYw/cH8U9hJS9IkRVNWptNp5HI5XxdeQwO9GNWsFri9B7SqR6NRrP6qEqTTab+MjBm5+v2+71PrS+a1ktack3BVgbD9ANzwy+vEolQq4d3vfjcqlQo+//nPY2ZmBtlsFleuXEksKyAgIOCt4h1PzNZ6BJIzWo3LVGUtuiRfpLX2eF42m8XExAT27duHfD6PRqOBRx55xC+T6vV6iX7jcWWOqxstVuZ2puU4Go18Okm1LpUorbWskq31m9t62TK4tIh/nBzw2ipF23uhZaoVawPgqHxo/fgZJfVMJuOlby2LSNrYw/rzWSersJC41XJX1wNjBF588UVUKhW0Wi2srq7e9D4GBAQEvBm844nZ+mZvdWwSUSjGkaWel8lkfHKQRqOBVCqFmZkZnD59Gvl8fux6V5KIXb6klnNSUBYt1Gw266OeacFyjXTStZImKEDcnz7ue43UzufzyOfzsQCtJJ81sNP/bP3y1q/NwC363nO5XMziZb/wtd/vxyYn1jfOOuh3SvLsV1tvfp7k3lDfczqdxokTJ1Aul3HmzBnMz8/jzJkzu3k3qoCAgHcY3tHEfDOLM8mfbL9Ta9vKrOO+y+VyfrMJAOh2uzh69ChOnTrls3up1XmzXZ+SJgXWd8wgLpW89XiSmsquLHsc8bJshfWrAvDSMcmSoOyeSqX8JIQSud2SkpMQ3YVKg8o07zWwlYyF5E4LPCl5CSV0u3+yVSis7z5JPRg3SbP9SHUgk8ng4MGDPtXqxsZGiNQOCAi4Y3jHEjN9rkTSoHqzAdd+rgO49U/zs0KhgJmZGUxOTmJjYwOj0QiPP/44HnvsMQDwUchJE4Yk/7G+J2logg8ucxoMBn6PZl2DbHdR0olEUttooap1nmRFckJQKBSQz+dj/aEpLnX5Es8fDAaxgC0lNdbBSsV2IqP1IqGrejAYDHz/EBqMpjK5dRnodzoR0mvqq/YP12Tz+gcOHEA+n0e/38fy8jKWlpZ23PeAgICAN4t3NDED43214yRrK2ne7Fy9Fkl5YmICy8vL2NzcxDPPPINTp055ohlnwZNY1L+aVAebBWtzc9NHcmezWX+MtlsDmpL6yPaDprK0xMOy6MumVUoLmcSulrXNcc3jKVGPUyrGuSDsMic7YdFyOMng5MXeg5tNUrRvrC/exhoQtNw5qdjc3MTc3ByiaCsa/TOf+QzW1tYS2xUQEBBwu3hHErPN7qUDaRIR8H8gnkxE3ydZ1bxOtVrF7Owsstksrl+/jkqlgqeeegpHjx71BDqunjdrg32v6457vV7M4kuSZTX5BtvCV+un5jVu5kelVE9rmSAJJsnyJGJa9QB2kLcer/W0dSWSlmAVi8VYAhLd6ALYkt3VEteJRxLRJk3KrDXP54QTJUr1vBbbPjMzg/e+970YDof49Kc/HSTtgICAt4V3JDETScQ7jngsxiWg4DmUTWu1Gvbt24coitBqtXDw4EE8+eSTmJ6eBoAdg7gtn7DEoIRLq49laIQ1QVJQQlGfriU7BirpsToJ0f8pC+dyOeRyuR3LhWymLUt4jJAmWaoFb5UKqxSwnjcja5IzJw5Jk5oo2loP3ev1fLS2fqfPhwZy6c5Vtj7aFpZJFUCVgkKhgGaziRMnTuCLX/wirl27lvDEBQQEBNwe3nHErPv/EjfzLydhnLyq52UyGTQaDUxPT2MwGKBareLpp5/G/v37kc1md0RCJ5HyOEue32nkMy01+mi1XPpV+V4lWBKibRuJXq1tJXH1FdNC1iVQSlTjrF+1lhlVnUSCKoMr2bEuep6dYOl1ut0uRqMRCoWCn8TYwLd8Pg8AnkiTSJ79Y2V8vZ5GprPPSco66eA69WKxiIMHD+Lxxx/Hpz71qcRnMiAgIOB28I4iZitH32zwS5Iwb2U985h0Oo2pqSlMTEwAAI4fP46jR4+i0WjEBm0gvv7VSr1J0cK0yDjoc+AfDAZYXl7G1atXMRqNfMKSXC7niYHrlZOs46T+YapKlYW1jc4570/O5XKeGJPaZAOkeBzrpOSqPlu+5wQjaamYrbt+p/eMPnemHNV11IyWBuD98VbW1jrZCYDN62192uyLcX3Q7XbRaDTwzDPP4POf/zwWFhbsYxYQEBBwW3hHEbOVSO3/lqiTLGO1/pIIK5vNYt++fZienka5XMaxY8ewf/9+ADuX/NjrJVnzSvr8Xy1GLq967rnn/IDeaDTw+OOP+3XRJOUkn3KSr1QtXkuu/J6knc1mfeQ1y7QWuFqZSt66nlqteF7Lrh+290OvoZMdtfJVcmZdOp0ONjc3veWs2cdYLslb04/qNbQeGqluSdlOMrQdPJb9EEURDh06hPe97334lV/5lZBLOyAg4C3hHUPMdrlRkpXI91a+VOj51oLito20lk+cOIHp6WlvUdlySHxJ5et3SjQkKkqw3W4XnU4H58+fx/Xr1708y72Akyw7S8bj2mtlXvVp61aL6ovW47Q9Vn4ejUbo9Xqe9OzSKJ0EKdmpj1rryLbSerf3W4k6irYSjTi3FS3PCQbJ0U5MAMSWdllQtVCZ3U6ArKqSpMj0ej0UCgU88cQT+NznPofz58/vuFZAQEDArfCOIGYlNL7XQZqf6fHAzkGdnyXJ4KVSCdPT05icnMTU1BROnDiBqakp7/Ol9JzkayWS6mKjrWn5tVotvPHGG2i1WqhUKmg0Gti7dy8GgwGazSZmZ2dRLpdjJKdrb9VCTJKBdc9h7cNsNuuJT/dmtikpkyLC+X44HKLf73ufrxK+Wum0JIG4D571JSmvra3h2rVrWFtbQ6lUwt69ezE5OYlisbjDd6192+v1vOXMRCS8HhOvADekbVU8klQW7SvWLUkFsf3B9vX7fWQyGRw4cAAf/vCH8YlPfAIXL15EQEBAwJvBO4aYrV/Vfp7kb7YEZs8n4RUKBezZswfVahW1Wg2PPvoo6vV6LM2iypbWT8qybPSykh9JeXNzEysrK3juuecwHA5x5MgRTE9Po16vex/q/Pw8jh07hlKpFMtslUQkdqmUPUY3nqDVqgSqZYyb/GjfDQYDdDodT4D0TWs2MO0TXTedBBLzlStXcP36dRQKBd+Xe/bs8aRq28vySc7FYtHn8FY53t4rKhJaRlIcgr2X2j/8syoAg/RyuRxOnDiBxx9/HNevX/cbbgQEBATcDnY9MY9LnjFuoCfUp8xyeJ4OpMVi0ScOKRaLePzxx9FsNmODqbU6LUhKSkKUiWlh9no9bGxs4LXXXsOZM2fQ7/fx5JNP4siRIygUCqhUKnDOodvt4uDBg6hUKjsitFmXcZMQ2z/aB7pHs1q4WlaSVavlDQYDdLtdOOdQLBZjS6s0GYoSve13+tQJ3Wd5Y2MD3W4X7Xbb7yBF4rdQS53SPwCvBuikQBORdLtdXyda4jZugGuXk/rd9j0nPqqI9Pt91Go1nDx5El/4whdw6dKlHfUPCAgIGIddT8yE9endTJIEdhKLDYBKp9N+D+Vms4lyuYwnn3wSzWYzRohJwUJWVrX+R/ptgS35tN1u4/XXX8eXvvQlXL58Gb1eDwcPHkS1WvUBStlsFnv37sXm5qbfPpLts/2gFr9NsWn7Qn2tGhVtSYYEp+dQzgVu7OGsWcF0kkDC73a7MT+v9g+tbJXZnXOoVCrI5/MolUrI5XKoVqtoNBooFAqx+5bUF+p7JzlzAmLbxshrRnKPm2gl+be1L2z2NDv54LVmZ2dRqVR2lB8QEBBwM+xqYlbys69JRGSlVz1WP0+lUiiVSj7vdbPZxOnTpzExMbFjUwQth2Up7PVomTrn0O/30ev18MILL+AP/uAPsLq6iiiKUC6XkcvlsLKyggsXLqBarfroa5IAiQ/YuUex9WFrHXVpkq2b9lUSmY9GI3Q6nR1+eF6DWcFYnvqh1SrXOvB4S7BsRzqdxsTEBPbu3YtUKoVcLoc9e/Z4/7om8tD+T4qa1vozKIx+ZSVT7u3MviXpWp8xv1fp3/q5tVx+zoC4yclJPPvss3j11VdDhHZAQMBt420Rs3PuHIB1AJsAhlEUPe2cmwTwCwDmAZwD8E1RFC2/lfLt8ijFON9xkuy5XVc/uObzeUxPT6PRaKBareLEiROYnJz0iTL0+jaT1bhXALFgKmbDWlpawosvvoi1tbWYrDwcDjEYDNBqtXygEq3t4XCIfD7vSYfkQfK2JGUtX5sQRKX7pEkLXzVhh64JppVNMuOxNjgM2CLvKIq8ZKzBawB2KBFRFKFUKuHAgQMolUpIp9NoNBp+4mAtey1Ly9FJByO2tb5W5bBLzngPdKKh66C1neoi4HGqMug9ePe7343f+Z3fwblz5xKfy92Eu/17DggIuD3c/mbG4/FfRFH0RBRFT2+//wEAn4qi6BEAn9p+/5ah1rDCWi524OS5amE5t7UcZ25uDgcOHMD09DSefvppzM7O7kjjyOPtUiKtl5K9+m97vZ7fJ/nKlStYWVnxdczlcpicnMQjjzyC+fl5TE5OIpPJ+IAqlqvEwXSZtm3j2m37hkubNO+zndBwyRJwQ5bWgDFa8PrZuCVN+v3NFAYl+HK57N0K6lfWV5ZFixxAbK9qJf9+v+8nVbpsjM+EphDl92wny9A+sP3KttESpoRNwmaf1+t1vP/970epVNrxDO1S3NXfc0BAwK1xJ4jZ4usB/Mz2/z8D4BveSiE6ECdZx+MIadzxwJbFNzc3h71792JqagpPPvmkj74eF/iU5GMmVBYmMQwGgx2DNYkjnU6jUqng9OnTeNe73oWpqamY1JvJZPxWi+r/pPVnfZnaD0n9pnVkgFVS8BIl9+Fw6K1+lX9VxrVWLF+V6FgPvqdFSlhZm4SWyWS8G0CDu2y0OPuBcQLlchm1Ws377LXv7X3VZ0UtZB5LCT6fz8cIPyniXCdPOqlTaTyVSuHYsWM4efLkWDVnl+OO/J4DAm4X6WOH8fI/fRbn/96fvN9VuW94uz7mCMBvOOciAD8ZRdHHAcxEUXR5+/srAGbeSsGWcJJk63HH8jMg7h+dmZnBI488grm5Oezbtw+FQsEvidLo7XH+ay2bx5FM8vm836dXiaXZbGJ+fh5LS0tot9vYu3cvDh8+jEKhgHa77QfwYrGIQqGAYrHopeObkaJa9SQh7ackmbnX6/ksX5THdQMKQq1d26/Wn2/9rkpY1v87jhTt+mwGhiURn5bBZCRUARiY1m63vfXPiYZazdzXmpMSm7rTWtdJz5xNVUowPsA5F0vV+Z73vAdnz57d7dtC3rXfc0DA7SBz+BCu/JezePUjH8PqqIMnJ/8Gjv1cF9nLyxiee3gS9rxdYv7KKIouOuf2APhN59wZ/TKKomj7R74DzrmPAvjomO8SiXYcSdrj+MrBM5PJYGZmBsePH8eRI0ewb98+P3Amnbtd91hdSEI6OHPdbD6f94S6sbGB1dVVDAYDpNNp7N27F+VyGb1eD+vr66hWq6jX6wDgrTKSDMvj9TWTlZXkNXuXEoz1pSphDodDrK+vx7Yw5Hfqh9Yds7hkiRb/ONKmWsDobU0sohJ0krTN106n43NMMyWqnWTwWLVi1bJ3zvn+1s842UmSrfv9fqwPbAS/dQvo82IncOxbnjMYDFAqldBsNvHYY4/hM5/5zI4+2EW4I7/nAt4xsn3ALsPzP7gHr33txwAA9VQRr37jTwLfCBz+5Y/i+HcFYr4tRFF0cfv1mnPulwC8F8BV59xcFEWXnXNzABL3wNuejX8cAJJ+7JaErW/SSrZJIOHNzc3hqaeewpEjRzAxMREjpaTgIZVLtSy+cqDPZrMoFouoVqsolUrI5/M+QEsjsJk8pNPpAICXqtkO9d3yOsyuxWU92n4GsFFmpRRvSSNJ7ua+yUkTD05mkuTu4XAYI2h7j3htbiihPnu1mm3UO0m+3W7jueeew6uvvorNzU0cPnwYTz31FCYnJ3359Btrv6nFzuuk02lPzsANyTufz2N1ddUHh/FYbfc4F4E+dxqpbdvIKHB7bq1W85I+rfndhjv1e665yV078wjYvbFVGqsAACAASURBVEiVy8g3uonf/bsP/jj+99/5ENb/1CKweye2dwxv2cfsnCs756r8H8BXA/gygE8A+Nbtw74VwC+/6UrJgAvENw0wdUg8nyRAS/mZZ57BY489hsnJyZgvMSlvsr0mP1NZmZJpqVTya25rtRpKpRIajQbK5XJsiY2u+wXgfZj80yQbtHp1GY++AkChUEChUPAJPjSCWdugbUmyCLW/kiLP2VbK9eOCvfiZTmg4saAFzb+k/Y8BYGFhAa+88opPUcpdtVgv9qO6HOwyJlqqfK9+bU4o6vV6zNq2z5qez+uorK2krM+apvDU7zipqVarmJqa8mrJbsPd/D0HBNwO3MG9+LpjX0787j35HP7y7GfucY3uH96OxTwD4Je2B6EMgH8bRdGvOef+EMAvOue+A8DrAL7pzRRqLWEdMDk4jpM39Rxu3fj000/j1KlTyOfzMStOdxMCdgZ5KXhdDu4M0qrX66jX68jn88hkMjsIn/UkIXU6nR2yK+vC753b2piBft+kdrEeJCMNcrqZ3J/Ut/Y7VRDou1UrWfuDIElySRetW+DG5hGWyFQlGA6H6HQ6fk/jffv2YXJyEqVSCb1ez/ubWR99BpLSoxK0TqkQsL+q1WrM18tJgPV1q+9ey7T9qJMSu84cgO+XWq2GqakprKys7MZ1zXfl9xwQcLsYlXI4XRqfW76c6iEzO4Ph5Sv3sFb3B2+ZmKMoehXAuxM+XwTwgbdabpJ0baVcHTiVoNUPOzExgUcffRSPPvqoD8xSEtbczhyULTlbi41kVa1WvUTNRBYkyFarhVar5dez8hUAqtWqJzCN8lUZFLhB1FayV+ubfcR6qiUq9yKx//iZWoVKKrrzFP94DSt923tBwrU7Nin0GH43MTGBI0eOYGZmxmf9yuVyXqZn/zOBiRKtlm9JMZvNotfrodPpoNFooNvtolgsot/v+4mQ3mOrpOhkxVrP+r39nPWgxVwsFlGr1VAul2ObbewW3K3fc0DA7SK93MJ/XjmBv1JL9JbgA8VN/NV/sB/Hv/3BJ+a7sVzqbcESi1qNSWRsLeZUKuUTVhw6dAilUmmHP5n/W2KyxMXPGZSVyWRQLBYxMTGBiYkJT8rAVgDR0tISFhcX0W63fX04MNPizuVysSVKzA09Go2875lLl5KsXo3Y1j6yfaj1T4ogJgFTrmbd1BongfI6SsTqk6XvGLgR4GWjtfV+qT89irYi25vNJvbu3Ytms4lisejP48RA23YzOd76xznB6Ha76Ha7KJfLaLVafr0026WqgK2zlmknQdbfrn2ux+n93a1ydkDAfUWvj0utm/82/sX7/090vuG996hC9w+7ipitjzOJePUzfc+/YrGIvXv3YmJiAnv27AEQ902rlKzfWUuLBMPjueFFuVz2AV5MJNLpdLCysoJr165heXk5FrBlM0yx3E6n4/cz1nWvus2htVJ5PkGZPCm63E5ibJtIuiRk+ru5jlgtWnsf1JJXklYftD1HfbCc5KgPu16v+1226GOmHG7ldLVMbYR4kuXKfOSLi4t+843hcIhGo+HLYHQ926/XYbtsZrCkPgFuyPcqvTMfOgDMzc3dVIIPCHgY0X5sHz558hM3PeYDxU38ix/9EbQ/8uw9qtX9wa7MlW2tZg6E9hg7QGezWUxNTaFarWJiYgLVanVHkglLNOO2JbRWNAmMsi53jKJvuNVqodPp+MHb+sKtT9a5G3mcaRGShJVUNJhI83DzOBK57bMkqZkkpiqAtpN9rFKxEry1SrV8fq7rolUetvdNrU1gyzKemJjw9ed6ZBKc+rFVQbHPCa9hVQLnHNbX19FqtVCtVrG+vu5JWjfeyGQysZ3FKMnb59D62fWa49aXFwoFAPDXCwgIuAE3HGF51MFUunzT445ny1j8b1oofzKPSLbmfZCw6yzmpAGPVllSwgmCfmVugHDixAk/8KtPU6EWH69FwtAgH0q9JNBWq4W1tTUsLS3h8uXLuHbtms+FrQSplq8SmxKjBkBZH64lRLtrFa11DTqzPmC2Uz+z0jXLVVmbEwD1G5N8rdWoFiVzgJOQaIWyXACx/Zx5nvrdGfXOXad4T6gQqPqQJOlrP7BfcrkcAODChQue8AeDAYrFYkyO7/f7sWhtJVu23z6LvKY+Q1Yt0ec1l8t5az0gIGAL2U/9MZ75je+7rWO/8BU/g/U/98RdrtH9w660mJOsCStpK1KpFMrlMmZmZpDP53H8+HFUq9WYn1a3GUwqUwknk8nE/udAy/W5lLD7/b4PKnPOxaw5lq8DtBKfWn9RFPk1ulauJvHl83lvcallqqSr5GEDy3geyVEVCCUOle8twbPOSZHllN0pretEgcu62F6Vi3WypIFc2gaSMtd1ax/oVo76PGj/8S+bzaLVamFxcdHvd8362b2vkyZ/nGDZSH47CRon5Wtf1Wo1LC0tISAgYBtRBGzeXtrarEtj3/edxdovZRDtskDKO4FdQ8zWx8vPrN8Z2Dn4MQc2l9ocOnRoR5pJDu5aln3P42gpqZXGQRxATK61Plxbd15DSVvXBKuFaUmd59O/zWAlkiCldO0ntsFK4ny1a32txQfECUetT72GRnDT2hwMBj6Lll5Pt14cjUY7/Ngkbf6xDmqldzodn5iD9e/1eigUCrHgsCRwNy9a7u1221+X7eLkZzQaodvtxtptn7mkBCsK7S/7nKZSKbTbbT9B263JRgICdjv+ycFfxn/71X8T+U/+4f2uyh3HriTmm1nHSYNgvV7HiRMncOzYMTSbzcS1xMBOK9Feg2RF61rJXY8nYdllMuMGcmI0GvmIa75PipjWdmoiDa0DA8esXK1lKQkDN9JRDgaDWOYw9YWyXlofWqXAjXW8rDcjzEnMbJPWh5/rBhW6eYeViLlmmYRLpUKP5bU7nY6Xu7XPtF85eWEGsHa7jW636xOWaAS1yvRWSbH3J8nfbCcxdoLGpXq1Wm1XZwELCLgvGDpsRiOk3a29rPszFfyNH/05/B/dv4jMf/qje1C5e4dd5WMmLKlZa5mfOedQqVTwrne9C4899hiazWYsHaQ9X0k1yZetuaA1c5UShiUdW1etnw7cwA2rWcu0g7ptJy0rlW4poWvddYIA3LCaSbLa1s3NzR1WoV2vrX1FMtcIck4yOp1OrD72/rAc3hfK+WwPX3VbSg1IGwwG6Ha7/lzghkuAfmO10oH4XstK0PSrp9Npb4FrelL1X+v9svdRrzOuvZq5Te8lny0AOHLkCAICAm7g5N/6Mv5Du3bbx39DeQPn/lwWSD1Yqxx2JTGr1aEDnh0Is9ksDhw4gMceewzVanWslDyO5PlerVJLsNaSSzrGRoxrPRXqT1XrTqVwJUfm4iaZOOf8hIHWo04mbD1UrtXr0uLe2NhIjBDWiQTbwuxcTKDSbrf9Lk6U/WmBWilc+1Kzr6nvVS14XT9t+11l53w+78uxEyW9N/yOMQec6LAfVRnh8ZwkWBeIdQFof+n5nOjpc6cBZd1uF9Vqdcc9Cwh4mDFqt7EZvbnfxG985IeRKhbuUo3uD3aNlH0raDATX5vNJp566inMzc3tWBZlfa/WQlYpWgdv51xsf2ZL5EmEa4n/VhK8Er29tq7Ltbs60dLt9XoxeVUtPOecl4Htmmi79pbWbqFQ8DJzkrRtN6ZgXXQ9NK1BWtesD+8dyU7laFrBjFLntVgW5Xpek23l9ph2y04lV/aPfU2lUigUCuh2uxgMBl6at5nKaNVbgteJGj/nsYokwtX+7Xa7qFQqmJiYwOLi4o5jAwICHl7sGmK2PjxCrRUd/IrFIo4dO4ZHHnnEW5L6PQN+LPHp9yQxEgoDvXQwv5368Bq6oYRG8NplM0mWvcKuMwa2SK7dbvs1tqoGsL60XFmXbDbrJVumsqSMqku0SIa0ELVtbIdGkKsVnEqlvKRs/e7aN0qMSk6DwQCTk5OxgDBduqSTAbaJ0ek2itv6dhV2PXImk/HBc0xAwjJZD5vmVPtOy016Puz91WQtjOxfWVnBxMQElpeXY89EQEDA7SMFwGV3DZXdEeya1nDAt0EzSdJyNpvF/v378cQTT/i8x3ZAZAAXYf2mJAjKoSQADajSuim5WNDfqdmpNGpbA744SGu9VLYlKTOZCQmXKTytFWcnIzrJ4C5W3AhC80wriVPSZZ8piVppWP2lrINOPnhNO1lSnzvl5HK5jPX19RiR839dPmb7mhb7ONeFle5tMBj7gtnXdELCyQojuEne1tesPnwlfG0nr2f7jf3abrfRaDR8Pu+AgIA3j4OZEs5+bB6Hv+UL97sqdwy7hpiVCG51TKVSwalTp2ISth2UrSWjA7PmRFaiTkptad9ba5A7TRWLxR15pWlxpdPpWPpNDuhJlqUSMzEcDtHtdmOZwSwR2U0hSBgsi+oB/yc5WktNfbrWp8+2qaxMa1p93nYHKJXGdT0v90hWqZsSt5IhX3UDDwDep80JzM2eG/teN9rgXtM8jvXQSQDvrd4/1ss+t0mR9vRZMxqbAW/T09OoVquBmAMC3iLSLoV6tXO/q3FHsWuI2RKAJR61yA4dOoSTJ096i0YH/iRfdFKgjvplOfDSwiMx6YCcZC1rfmXbFh241YpksBStOA7sJAESJus9GAxi65WT+oykpUFiKvOybRrQpH5oko8SrO0r9anylcTG6/MzlYtpheokSKPdVUbWYDMlPd1ExKoNNthMQTl6MBjErFi1eCnlc404d37iRIb3Vl0cbGOSn18/1/dEvV5HLpdDq9VCr9fD+vp6WDIVELCN9OkTaKa/+KbPOzqxgPV9ezG8eOku1OreY9cQMzA+sEoJolar4dSpU5iYmIgtdRkX3WoHRpbDP5ICyxpndSspqk/ZBh7R4qQvWDdrIFnpxhP0m2rwFculfE1Zd5xMq2XYrF2aKYvHbW5u+vqwDPWn2uAsez31lbJcG9lOolOytj5unUSx/9hftKRttDnJm2TP61gfM+8py7dkqZ/r+VZxUBlcrWZ97thf9h7ZyaFzzm+Ewh3Fer0eKpUKVldXdzy7AQEPG174vhq+qvjm4y1+/vB/wns+/F2Y+ngg5jsKSzjAzlSZuVwOR44c8QFf1sfIc+wAnBT9bC1LHazVyk6ygFimZgAjSGgkoHw+72VSrknW62mOapbLSUKv1/PWspKYTghorSrJ2pSZ3A3LEqFNoWnvgxKiEhYtfx5Pi1T7TvtbE4DoJIDlWmJM6mtVDnh+sViMXZfgRiN2IqP3OGlSpX3HyRUtdZ2I6HMCxDcfsWXaaO9SqYRqtYrl5WUAW5OHer2OhYWFIGcHPNSI/uS7Ud2zcb+rsSuwa4jZEiBflYAajQYee+wx1Ov1Heth7eDMMjhY6kBJ65RWlwYvsTwgOb+2BkepDxm4QdYaSKX5mFmWbuigvmFaXZrMQy1S4EY6T20Tr23Jl4TPVJO6uYQNWNJ2qzWoExclSiVR9oXWhySnQWK2rXqfda2yfRbYDvXTq0/e3nebYMYGFWod9Rh1WwyHw1gsgp6vAW90D7As7Sf1j/PcQqGASqXiA76GwyEmJyd9+wICHkqk0vj+f/2z+EBx89bHPgTYNcSsFp5ayvwul8thfn4eBw8e9MtN9HtrVfJPB3zgxlIkm4JSMS4AzVp8Kl+yHF6L8q1GZxcKBU9W6XTaW6u0OnksLWYO+AC8/KxLhfiqAVuUtLV+bDOJQOtt1+CqpK2Wr7UUSVq6vEqlc1UBrN8/afmY+rY5sdF7z7ooCer91UmGThzs/dc+S7r/qmTo5I19xbqxTL1H9jmx1noqtbXZSrlcjsn7DOwLCHgYka7V8Mr3n8Z/9ytP49Vv/Mn7XZ1dgV1DzAoN5iEJTE5O4t3vfjfK5XKMlO0AqxatBhKplMpjraWl17RSOYBYGVq+Wq9KWpS6NXK8UCjEgrIsKbO+6jNmwhO10q30S1m60+nsIC6VaO1kgn5kaz2rVWsD62xfK8EqyXMiwImB3fgjim6krkxyO7DcVqsVs8ApLSvx6j2x9y/pPgOIkao+J5Sf2Xatj7W+tQ8sQev1uVa8VCrFAsB4TKlUQrfbTaxnQMADC+dw5seP4eSB1/FPj/wigMpbLmr4oRWk/lUBowfgd7RriDnJF0hkMhkcOnQIBw4c2DGIE0qOdikSy6eFo+RjLUSVda3Vw/9tgBWP1eupjMtIbA68JFwrXVtLz0acqzxt/bSss65ZtlHSVAqsfG8tWP5vLT89zwaIKRGqz5WEZFUGKzMrsVsLW+8dXQLsY713SeSo0DbZjTEYIc56aOCbRmbb54oWtabfTFJgWG4ul8Pk5CQKhUJsokclJCDgQUe6UUd0aC+uPdvAN33vb+ETkz+JvMvi7ZDy+eEGNn9/AqP+S3euovcRu4aYkyRHDqS1Wg0nT55EPp+PLS2xgUK6A1JSwBYDsTSlZRJJEEoMHPTVGuVyHF6bREQLOJfL+cArEkGr1fIBRQC8pavXjKLIf6Y+dvUtU1ZVoqQ8Tl8lJWDdBxmA/14tOvWLsm0qR7McTmZ4DAO/dOtGOyHS66tCwMkCgFgAGgmakxumDLVbNY6zhK3/3fYtz2W99F5queo/VnWDz4mdVFhfvNaTZWazWTSbTb8fM9uo69YDAh5kXP/zp/CH/9vH5JPs2GNvF3/hi9+OfT/0mbddzm7BriBm9cnqZ7SS9u7di4MHD8ZkU0ua9j3/5ytJjQRpB1YtQ2HL0jrqUhkSFy1WWl25XM7nZtbsYrobk5WReQ0GiSlRqeyshKGBaNaSVRmba7PH5RLXCUChUPAWKmVyXU9t01yyf1VtUHWC95PXS6fTPrK63+9jY2MjRpQkZcr/VuJOmkQB45UUPc4Gv7XbbURRhHK5HKuz1p19S3K2173ZcwTccG0Ui0VUq1VP9FwOFxDwoCNVLuMb/+Zv3e9q7HrsCmK2Foa+LxQKOHr0KCqVSmz9b5JUbYOPrLTJgZGDp/p+tS5JpJ8Unc1jgPiOUozw5RrkiYkJL+faQVslcJV5beS1TiT0HJWR7RIhm5FL813rcbpcSAmQ/+u+yNpG9jUlXt2eknVmXfmZlaPpM46iKJbwhHK8Sr76rKh8r8+A9quqCTbASy37KIq8m6FUKsXiEDTIja+atjVpQjhuMqdydqlUip2je0MHBDyIcE+/C69+pIqfn/gnAIr3uzq7GruCmIG4n1N9b/V6Hfv3748doxG81h9tfYxanmZ0UoK2EwKWk2T9KDHpeZaoSAq0ssYRiVrcag0y+lctTV3OoxMKJUJLtlqmZtZSH7ctl/3b7/dj66uVaDQSnBOidDodcxVoPdj+fr/vZVsSkZXAWT+uf9a1xeP8yJYE9XlK8vna6Gptq1r89p6l02kUCoVYG7UOrKf2vX2G1JVBBB9zwAMN5/DGn6nhxW/7ZwikfGvsGmImlBCz2SwOHTqEqakpP4gmLUNR6GBqJUm7sQWhRJ/kJ1RruN/vewIiWfJPfZYAYmkyeW0roaoFy+spudJy5Ht+r8FGWp5dCsRy7frqTCYT26nKtp3BZ3quTUSi/ULiL5VKAOCJy/Yro9RpFbNszeKWzWZRKpW8T5ptJ2Fr27TuN1M/7P9K2JTq1VLW41SJocWs98z29TiZXQlfJ1ej0chL6QEBDyLSzUl8+rv/MYDy/a7KOwK33JHaOfdTzrlrzrkvy2eTzrnfdM69vP06sf25c879mHPurHPui865p267IiaoyjmHarWK+fl5nyBDB0odXPkdiVEjgCnjKqGMk5OTytVBejgcYmFhAefPn8fKykqMlFX2rdfr/q9Sqezwi+rxth4qIwM31vSSkCjns512v2Ier2Sn1q36rS0BaVvYR/1+H+12O3YdrTfLpX99OBz6IDvbTpXn6Vbg/tKqYHDjDTsBsf1l/cr2+3EpVdk3+jm3mdQELPrHZ0ufw3GwsQB6f7WvdDKmW3zeTdyr33NAAJEqlfDCPzyCidTdU4X+7on/iP4Hn7lr5d9r3M5I8NMAPmg++wEAn4qi6BEAn9p+DwBfA+CR7b+PAvgYbhOWFJ1zmJ6exsGDB2P+QmAngenAr696bJJFmuS71M+thU7rdXV11VuTKhGTmPP5vA9aUnJg2Zws0OLWSYnuaGTJALhB1P1+H51OB+122weWsQ6sr2Yk075IWjucNLFhfXmMEohdqsT/GSBWKpVQKBR2kBvrxYmOnSTlcjkUi8Ud17ZKiT43xO34Z+0xqVTKbyZhP7eTRV5PlYukZ0gnQrYf2b8bGxsxBcJmTruL+Gncg99zQABx6aNP4OyHfxJZl771wW8BG6Muvv9n/wpyv/7/3ZXy7wduScxRFP02gCXz8dcD+Jnt/38GwDfI5/8q2sJnATScc3O3XRkJqMrn8zh06BBqtVpM5hwn/SZZwvxeJWYlaZbH4ywJqk+Wx0xMTODAgQNoNpuxaF0mj9DgIRIogLFEbOtsLWutF7+3ZKDWHutqM1KptUYLXHejUomV77nHNC1YLlnSHapopQNbhNTv99FqtRBF0Q6/eJLESxcFj6WLwMrKt4I+E0nn6nesJ/t1cXERS0s3HnE76dOJjUr4qtYkPZd81TZTCteJwL2UsO/l7zkgIN2cxNf9ld9B2t09NejL/Szmf/gLwAPkCnqrPuaZKIoub/9/BcDM9v/7ALwhx13Y/uwybgLrB2TQ1/z8PLLZLLrd7i3J1A7GKqMyipafWT+v1sMSI8Hy0+k0pqamkMvlMBgMPJmotcS1zEqm+Xw+tvbYyqKsiwY4Jfk6NWCJn5EwlYjU780+pXzLY0nStPR5rJKwSsmESuv8Tic/w+EQnU4H5XLZb6M4LgBL7yfJ3srS1iJl/2hZqmpogpOkczUb22AwwJUrV7CysuLrQH+z7X/uDKb3jP/b4D7bBvZxKpXC6uoqVlZWfD/qM3GfcEd/zwEBhCsU8Hem/gjA3Vun/z3/4HvQbP3+XSv/fuBtB39FURQ55970iOKc+yi25DEty1tPe/bswfT09A7fcBJBa4SxStt2cLbXMfWJvWr59n8GRtGCYqR3v9/HcDhEu90GEF9+pFG/eh1N1kFSAbZ8ye12G/l8HrVazbeFx2jZXHJEP61dK6wgWZOUqE6wvrqbEj/T/td2se2cPGlmLPaPtkstSkq9Smg2eYglR3uP+L0NqLL3S/uc7ebEZGlpCVevXkUqlfKBa+N8yOrTT3qm1G2iz609bm1tDZ1Ox0fEM4bCPt/3A3fi91xA6Y7XK+AdivzdT5yTfgC3M3+rxHzVOTcXRdHlbWnr2vbnFwEckOP2b3+2A1EUfRzAxwGAAwEH5EKhgP3796NUKu2QsJMGL7VwlaA5ECdZ1HqulR8t7OdKVnxVCZeBPZRni8XiDplc66KkNxqNsL6+jvX1dURRhOnpaaRSN/Yn5h7NJHAlUpVrVQpnH5CASQJc4sRgMBK8JTwlSkKXRvV6PeRyuZjVPBptpSBVaV/baPtP66uTMKtqKCmqb1ytV71Her9Jxtyhqtvt4ty5c2i32zhw4ADK5fKOqHx9BngtfSbts3Gz541tWVhYQKfTiRG4VYXuMe7o77nmJh8cTTHgbWH4z4copUJWuzeLtyr8fwLAt27//60Aflk+/8tuC38CwKpIZLcF57aisWdnZ5FOp3esN1aLy0YW2x167NIaypjqE00iZSsVJ1ntev0oijxh9vt9b/VTFubaV55nJVASIneAarVayGQymJ6eRr1e9/IuAJ/mU9uY5KvlJEUTe+h18vm87x/6wZN2SmLfKdHwOgx047KwYrEYI036vnkPldhsXyZFhNt7ZxUMvS/qAkhyjeieynwGrl+/jqWlJT8Bqlar/jx9JVlrQho7UdFnyE4cOOngRh6Li4sx98q4fr+HuGu/54CHF62/8Cz+58P/8a5e4zsvfAWan7126wPfYbilxeyc+zkAXwVgyjl3AcD/CuAfAfhF59x3AHgdwDdtH/5JAB8CcBZAG8C3vdkKpVIpNJtNTE5OxogXQIx4rUWigTocNJX8rCWURLb01+ogn2Q96YDPshj8pbspab5pu1mG+n45aKfTaVQqFRQKBeTzeZ9nm3Wk71ODqtRHSXJLap+1QHlt+p0Zba0kp/5n9oVdP81JR7vd9pK2ZjjTvOSWuFQF0c8trMWs90Pry/+1PMrolKA5UVhZWcHFixexsbGBbDaL2dlZVCoVrK6uxsiXsQHsf1unpLboBIz9xXuzurrqpfN8Po9SqYRGo4FOp5PY9juNe/17Dnh4sXw8jT91F/PmbIy6+H9+8wnMv/Rg+ZeB2yDmKIq+ZcxXH0g4NgLw195KRTioZbNZTE1NeetF/YxKChyElRz1vZan1o5aweqv5XtrhQHYkaXJSs/cKzmXy3mCVOLu9/uxQV1Jn8RMC5dLrSwZ2SU63ClKl2pZS5GWrX6mYH9oOkiVvjc3N71lrWoFz9XPWHapVNqRLMP6/PVcTmpU6k+q67jnRcu/WRsB+HzfvV4PS0tLWFlZQa/Xw549ezA3N4disYi1tTV/75RYqQBoudomXYoGxHcg4z12zmFxcRErKyvI5/OoVquYnJxEtVrFxYuJCvEdx736PQcE7P/0Bn7iL+3Df9+488/2ZjTC84M0Dv/9P8aD6DfZNZm/OIAVi0XMzs6iUCj46NlxwTZKRMAN6VotWsqX1q/JY0kKamkmRW4nWbr9ft8vs2k2m5iYmPBZulgPWs86OeAgz3I0s5dto5W+AeyYjPAztdJUJdDyeB7la8rQ2le0KhnYpaSpRAQgFoi2ubnpLX9K+jxeo7h5DzS6W9uikx5tr5J2EhnbiYk+Wwym6/V6WFlZwcLCAjY3N5HL5XDy5Em//E2fFb2Xdi9pe4/HRfvzPVWTy5cvwzmHRqOBarWKbrfrfc4BAQ8UPvtF/NDvfQipr/wVfLR+6Y4W/fhn/xLmv28FUe/eTGjvNe6rY8vCOYdKpYLJyUkADNqi4gAAIABJREFUyZG7dgkLz7Pl8Hwld0suSbIoz9PjNZWnntfpdHDt2jVcv37dr0u1y4tIvAzS0iVISQO4/dxGX+sOR8wCRsJXf6UlcrV66Q+ndM/Ph8OhD+bq9XqeLNXy06h3tpd9p2uk9X4lLQfiBEh9z2xPEsFpf47zg1vXB8vh8i2u9V5aWsLq6qr3LZ84cSK2DSfbY+unn/EYS9L6v04i0uk0Op0OLl++jEKhgGKxiJWVFaysrPikLAEBDxoe/dtn8WvXT9/RMg//+ndg8t9UMLzwYJIysIssZmBrkK/X6355EBAf7OxaUesvtsfxf7U8eZ7KsEk+ZILnkTh0uc2FCxdw/vx51Go1HDp0yAdpcR0zLTJaXqwHCVQjfNXqUwlaJW8A3pJVaZ7WriVMSrBMpsE/bjPIQDCVap1zPlCNCVKYYISwsraS9nA49BMIJRu2W/tU7wXryXJ0ByrtFzsxsxMZC+6BzeVcV69exWuvvYb19XXU63U88cQT2LdvX2zSwv5n32uCG+vD1vf2+VG3g3MOV69eRbfbRbFYxMLCAvr9vt+8wrpLAgIeBJz5+8fx8tGP4U7ZgL/dBY799Aip//wHd6S83YpdMxpEUeTzTOfz+R1bMlo5NwlqTXFwpcWUdJ61WPWV5anVzCVR9BlfunQJFy5cwPT0tC+Px9HPqoRHOZXrZdWS1GAu9TsDN5J38E9VAF6DyU54LttBK1aPJ5GTFBghnMvlUKvVMD09jVKphNXVVVy6dClmCVvS4ftCoYBMJoOVlRUfsa05y/lKsksiUraNy7gs4VopWZfHJU202N/dbhej0QidTgfnzp3D+vo60uk0ms0mTp8+7UnRbryhFneSjK2KTlKwnRJ8v9/HlStXsLm5iYWFBbRarViiF41BCAh4UHDi7zyPUyt/DZvFCF/85h97W0unjvzGd2Dvf8ig/ICTMrBLiJl+0Vwuh8nJyVg0sg6246RrG/hkLS2+JsnWKvcmWeBqqfb7fSwvL6PVanlim5mZQblc9jKwRjmTZLROJPkkGZ2RutanrXKqlqn1Hg6HWFtb88RsCdBuDcmgLkrKhUIBjUYDzWYTMzMzqNfraLfbqNfruHLlCjY2NmIBdkqK2WwW1WrV91G320Uul/MTCG0P77VONHR7S/ZjFEU+PadVEwgbRGZ96MwhTkv44sWL3sc7NTWF06dPY//+/Wi1WqhUKrh69aovh1a/JhRJumecjCXJ2nRjOOdw/fp1bGxsYGFhARsbGzue33K5vOMZDQh4p2O0vo75v/v7SE810fuLQ5RuIwPYtc0WXh0U8CcKafxed4QfeuNDGPz1CZx4+QWMtpM3PejYFcTMwSibzfrdmFSy1Khj67+1VpK1oHieSp/2fyA+6NsNGnTA73a7aLVaSKW2MkUdPnwYxWLRW2RKjJoSk2XVajWkUim02+2Yb1cDwPL5fEyyJjQymH1kJy42glrbyvM0kQiXeTUaDRSLRZRKJYxGI6ytraHVamE4HHqCpOXJPuY9yOfzqFQq2NzcxMrKil+CpFHK1ueq723kOC14tnHc+maeo7I+FQsSMi3SCxcu4Ny5c749s7OzOHHihCfhVCrlE8RorIG6G9h/9tmw6oFOstLpNFqtFpaXl3Hp0iUfdKaxCOrLD8Qc8CAianfw7L/5H/G9X/dJfO/E6zu+/+CZD+ODM8/hxz/5NShec2ic3cTlP5nC3t/bRPHf/7/YyhT78GBXEDPBAZ7ra9V3Cexcc3wra9p+poO8HVB1QEySa7led2Jiwlt8lUoltm9wr9fz9SdZ8LVYLPoBWVNo6pInWmn6uRKwzXVNa43XZ5AZz7USrEZrc+10rVZDs9lEsVj0CULW19exubnpg5KoDnAPZ01aUi6X/R+JmxMNqgbsS+1j1tPeO5Xm1V+tPliWqZayDTJTRWF1dRWvv/461tfXMTU1hdnZWTzxxBM4cOCA9zWvrKyg2+3G7jvrkGQN6ySIkxS1tvVeXLp0CWfOnMG5c+diUexaZlJO8oCABwWjdhuHf+D38av/8j34F1/7YQzKwOBUG8NuBvO/AJS+8AZ+o/oUjr58Y03y0V+6jxW+z9hVxMxoVSVeHcxtkNc42MAiEhFhSV19igBiVqd+T+l6amrKExuXE3U6HTjnfAAT5VTd2pBERP8xg8j6/b4nH5KOXSbFepCUWX/N6MUJhxJzkpRPYigUCpicnMT09DQqlQp6vR6uXLmCXq/nJxrsQ6bWZDQ4JxnT09PI5/MoFot+IkBizeVyPtDMKg9JFr21VjkZ0clUEpHrfSV4z9imwWCAarWKWq2Gw4cP4/HHH/fWOgOz2PecBGiMgz57StSqCLA8BsuNRlvZxc6ePYszZ854F0gSAWs2t4CABxWbL7+K2R95dcfnQ+BhM4pvil1BzLTmKpWKt9p0oL6VxKcDupW2gRvWWdL+v8CNAVgHYZXCWUfnHIrFos8ORokdgA92ItkqedBvyshm+tBZb41I1jXFOplQqVYzctno66SlSbaPOFnhNpVMaLK0tITl5WV/bU2HSlleLUWuga5UKt5qJzY3N1EoFJDL5bwlau+NStjax1p/VR+0DbZtPJ+v7Ks33ngD58+fR71eR6lUQr1ex+HDh5HP57G2toZyuYzl5WWsra151YJKhNbXThzsREfB+7KysoLXX38dZ86cwerqauwZtZPDpOjugICAhxO7gpgBeAtOB2IgPgDqwKZri5Ug+b2+snxNwqED7LjrqA+bpEmCYkIInTzwe6bljKLIW8MkQ1pSWj7lX64dtrmTSbYaqU4rEsCO9b+sV1JAmxIMpfgoirC6uoq1tbXYUid7XTsB2tzc9PI9c2ar37zf73ur2WbNslncWKa1iG9FWEqaGiUNAMvLyzh//jw6nQ4ajQay2Szm5+dx9OhRbGxseOWCFjUnO7T4kwif9037mPWkX5k5z9944w288MILWFhYiNXVwjnnU5kGBAQE7Bpi5iDZ7XZj2xwm+R+tpE2f77iBnhIjCSNJxrZkrtfXXNYkQfW1FgoFX3+m5qTF1ev1AADFYhHAjWQfSXmXlZhUylViVPIh8WugEycJ6vvUiQjLz+Vyfr9kAJ5ErQxPpYFKAL/n9RcXF/2xJHUeQxmawWw8h0qAZj9TAh434WL9ratCXRCc2F26dAnnz5/3yoBzW9m2jh496l0Ok5OTuHbtmk/DyedIs3/Z58Nem/1OUuYWjtevX8fLL7+M8+fP7yDcm6k/AQEBAbuKmAeDARYXFzE9Pb3DstCBUQOB7IDJz3TQ1M0kOPASluSV9JTk7GcAYoFBGjlM4lNZlDK2Ti6SfKK0UNkfJES1XEk+/N7mZU4iaeu75Q5TtAy57In9YycEaokCN9ZHt9ttdDodtNttTE1NoVgsIp/P+60p+/2+X5eu64RV9dC220mFBScWmmEMgJ84DYdDXLlyBc8//zwWFxf97lyNRgP79+9HrVZDq9VCoVBAq9XCxYsXfa5z9fvbuAPbvwolZQBYXFzE2bNn8eKLL6LVasWeLfusAvCTuUDYAQEBwC4iZkqIXE4C7Ay4UdnQfmehy5Y0AlbPtQSWJIHrtWlJcW9kSr4kBQAx8mQkd9Ja5KRoaZWneZxNLqK5w51zscxdhBKpvQ5wI3sYA5UYAU8LvNVqxeRxToY4EdFgLk4OSPD04y4tLfl13c4578dWWZv3RXej4ntOqBR6r7SvGBDHTSl47Uwmg06ng2q1iqmpKezbt88/W6VSCefOnUO32/VKh3029NnidXm+TlIYfOecQ7vdxvnz5/H8889jdXU1Vn+dMOozxuV2+gwGBAQ8vNg1xExyW19f92tNVd60PmAOxgr9Xi0xPQeIrwe2Mqpey1p3PIayL4+lfKmSJS1Gfm7J3xKyrp1WQrXytk5OdCKhg77WWc/hcSRfug0AoNPpYDgcot1u+2QiajXq1of0p6dSKXQ6HYxGI/R6PWxsbKDRaGB6ehpRFGF5eRndbhfdbhdRdGO9MPvGBlfxniopJxEV28PJ0traGq5cuYKFhQWUSqVYoF2/30ej0cDs7Czy+Tz6/T6q1SquX7+OxcVFb+1SgbCBXklKjMrZ3JPa+pUvX74cS+qiUBUjnU6jWq36zVACAgICdg0xU2ampUZZ0JKOHaiVQKz/j++VMNU6staPTgJsQhPKsrRSKQcrmehEQS0vWpxsA4laLXrWQ6VcGwRl+0GDj8b1kZapCkG328WFCxeQSqVQrVa9DMy6M0qcmcjsRh6U0zmJoFTdbreRy+UwNTWFXC6Hq1ev+k0xmIClXq97/7tOeJSgtU16n9mPo9EI6+vrWF5e9lsp9vt9nxJ0fX0dvV4PMzMzOHr0qN8YhSTMgC8mdqF0T9iJWlIQWiqV8puTjEYjXLt2DS+//DJee+01/6zohErvBf9I7BpsFhAQ8HBjVxAzyY5pFGmVKZKsxiSLBkCM6EiYSoSa3EL9vZp8wyYiiaLIR41rHmjdUELbQ2hCEQ7wDIRSiV3bY8k1ifStkqDrfG09rLVO4r127Ro6nQ5mZmZQqVQwGAx8ZDjryqhyDd6ihczy2Beam5prpFOplL9Op9PBSy+9hH379uHQoUMolUpeVteJkvaFLh1zbmvp2erqKq5evYrl5WXfHvrFae0Ph0NUq1U8+uijOHTokA/QS6VSuHr1Ktrttr8HdntRnQjZ4C99tnRzj8XFRbzyyit46aWXsLGxEZtQ6n3WyRED4dLpNK5du3bT30hAQMDDg11BzEA8qQOXriQFAakPVt9bqExq/ay0VNRC4vV0i0ZLllpeklXLwVcHbxIzpVlamkqobA/LtlHB49QClXR1EsF+tMultA2DwcDn/u50OtizZ88OWX1cn+p9sb5tTlLo4200GkilUlheXkYul8P58+fx3HPP4fr16zh16hQmJye9WmKTdbD8zc1NbGxsYHl5GRsbGxgMBrh+/Tra7TbK5bJfR80lbNy16dixYzh06BCKxSJSqa3leKurq1haWvIydFLf8H+V05VYNUofANbX171fmWXbZ4Ll2uxrpVIJU1NTif0dEBDwcGLXEDOtoUqlgkqlEiMnGxSjBJtEWiQrTYqhf0CyfF0oFFAoFHyeZ7V81VpUUiJx2MGYdSDB67pdSp8kZrWg1JrmMVwOpbK4XkuXCbFeAGITAAtax9ls1vtaG40GcrlcLJKYlqFu6KDR1NYXrmuweVy1WvWft9ttrK+v4+LFi4iiCLOzs6hWq35CRLLrdDpYW1vzO3UtLi5ifX0do9HIW+K0wldWVvy9SKVSqFQqOHbsGE6ePOlJme26evWql+k18l2hioyVtxnQx6VV7XYbr732Gp5//nlcunTJb7Fpn20gvnMY3QSHDx9GJpPZESgWEBDw8GLXEDMAP4jb3aXsQGe/u5WPFYj7jYEb2ZlItrlczmeq4gCcFABkl0XxfEKlULXmtX48LinqWAmP52k0slrHWh8emzQJUZleyVrXLq+urvpNRJgTmxHomkyFFjVJi1s0WvAzVT9SqRT27NmDRx99FJcuXcLi4iIWFxf9Npi6bKjdbqPVamFzcxOlUim2hjyXy2HPnj1ot9vo9Xqxfs7lcti3bx+OHz+OSqUSS3XKYLQoirwEnZRIhPcqaVUAE6mMRiNsbGzg8uXLPg+2JWV9Lq3LJZvNYnZ2Fu9973tjVnZAQEDAriFmDqCMElbLVINogHjSBxskZI9V6DFqMdGiYuYuHmP93IQNgrIEzPbo8ZYoVarlq040VEZVmVqtZQ2SspHfKpPrtbS/6JctFArodDqenIvFYmw/ab7qBhm8prZJ26b1Hw6HWF9f9/0xMzODWq2Gs2fP4sKFC+h2uz5gi35tDUBjnYAtn36r1UKxWEQul/PWLi3j2dlZHDp0CJVKxd9TTjxarZafhAFIDD7Te6P3jm4OnruwsIDz58/jwoULePXVV33Z9lmzQYScFOzZswePPPIITp48iY9//OOxtfUBAQEPN3YNMVM+pvWkpMxXXb5jYf2B9NUqUetgaaO51S+sxyRZ57yOtbT0fP0uyTdMqMzJZUJK5CQOlUDZX7TqaclqoFtSMJn2Advf6/V8UFu73cba2homJyf9zlDdbtefo9YxicROIJjpS5Or0Pple5lr/MiRIyiVSlhYWEC/3/eRzGwfsGUB89koFAqemNU/zF2u5ubmsGfPHpTL5VicAK/P54EuDt2lyhJpUmyDBnqdPXsWi4uLeO2117C6urojIM+C/UNSnpyc9PtfX758ecfxAQEBDy92DTHTP8ltFAHE/MNJwVX831rOSszqL7YSpZKszXJl16AmvZIYtQ0KvaYuK9IJAQd0jdLW42gl6yYRtCKtlUrftUIDm7QfeH3m/c7n857AuC6XVrP6wjVQixMFZh8rlUqo1WoolUpeku73+z7hh/rDndta/713715Uq1WsrKzAOYeFhQWf/IT7RDPeQCcBjAovlUpoNps4cOAAGo2Gl5qZ0IR+aE5wuAxPN+hI6h9Odvg5t73c2NjAyy+/jGvXruHixYtYXFxMnKDxvbpQisUi9uzZg0qlgmaziaeeegq/+7u/i1df3bnbTkBAwMOLXUPMwI3lNnYTCwvrUwaQSOBKeBzcbSRzkgWs1yGshWx9iUnHqFVOIrD+WH1vSZOTFfWHswz1M/OVvtQkf6WVztW/zoQcPL/VavmALc1nTUtXg80o/7Pf6fedmppCpVJBoVDA4uJiLCANwI5JB3e6SqfTPvsb9+dmdrVyuYx6vY5iseh94alUCrVaLWYlcxLDtuhyOZ346KTJPmc6eeI9GI1Gft308vIyrl27lhjslfTs5PN5v7a72Wzi2WefxfLyMn71V38VKysrO84PCAh4eLFriDmKInS7XVy8eBEnT570a2dpnQE7l0apHxWI+5fVt6f7HNtIZZK25p3WXNyKWxHwuKVbSRa6vifUSlcC1aQorLP1WwLwQUmaX1v9ytpnaklzZyv6lnu9nt8OUrejdM75TTmSJja9Xs9PgHq9nt9esVqtolqt+vW9Wmf6bcvlMmq1GprNJrrdLkajEQqFAkqlUiyft7adZKvLl9T3bf22/F7XattJlk5+GKRFP3W/38fa2ppfHtXpdBL90vaZKZfLmJqa8svHnnnmGXQ6HXz+85/HmTNnEl0zAQEBDy92DTFTvl5cXMT169cxNzfnv9OlRuPAwZUSsA0Ao7/UbhjA9cy6ftj6lJOsd7XKVapWJPkcbWBWkuXPdrB+9JUmWXYsn5Z3JpNBsViM+Wz1HF1CphMJTVHa7/f9JIV+Y9aJkxzN0c1gLSVp3rN2u425uTmUSiU8//zzfm9mO8nJ5XLI5/OYmJhALpfz+17TD85lU/QV23Zb+Znt431hfWyGrySlhM8PCZ/yPQO+XnnlFZ/IRMtJsrpLpRL27NkD5xyq1Sre9773odvt4o//+I999HtAQECAItnEEzjnfso5d80592X57O855y465z6//fch+e4HnXNnnXMvOuf+7O1UIoq2El5wZyLuXztObiaJkkhVmtRIZbUKNcqbErFKsLTySDJJ/sft9sX+iJtZPZagk0hZrWj1f9qANzvh4FppkgknHiR09U3rtax1z/6k5altU4k4l8uhVCr5KGhayYpsNotms+kl3Pn5eczPz/u0mCRiWqKsiyZjGQwG6Ha7fkMNSumUzjURys3cDCRhBsfZNcs64dH+1qVRALC0tISXXnoJL7zwApaXl2OTNes+YTnFYhH79u1DoVBApVLB+9//foxGI3z2s5/FysoKOp1OLFr9XuBe/J4DAgLeHm5JzAB+GsAHEz7/kSiKntj++yQAOOdOAfhmAKe3z/lnzrnkNFKCKNpatwpsDeobGxuxZBkkI82OBcTJMsmq5tIelssyKIly60MGUjEftrX+1Kd9MyiJsp5KokquutRIYQd8nYxYErDyuX5PErN1T7LWOTEZDAY+dzPJl0FPJGQG53U6He+/1cmTc84HXzF4bGNjA+12G/l8PhZZzjrwXnFCwuVRqnzontA8T5eYacpTG2ugedhtbICtO0EL3jmHlZUVvPTSS/jc5z6Hq1evxs63/c4yM5kMZmZmMDExgXq9jq/8yq9EOp3G7/7u7+LSpUuoVCp4/fXX74fF/NO4y7/ngICAt4dbEnMURb8N4Ha3vvl6AD8fRVEviqLXAJwF8N7bOXFpacn7eSlX2mhqvtfB3fpZrb/QrrXlAM+gJmb7UhJLCuzi+Tbzlx5vs2DxfPWTsxytt5KNDepK8hMDcTVBj2MZGiw2jnz0vXPO9xUJtFQqoVwue/82I6UHgwHW19cTffyUi3k/u90uLl++jMXFRT/54iRAXQUk306n44PRJiYm0Gw2US6XPbHaZUmchCTlNte+4D23fa+Bhuxf3Vt5bW0NZ8+exec+9zlcuXIlJqPr/dR7nsvlMDMzg2azidT/3965xsZ5nXf+f8iZ4ZAzvJOiKFIiKZGyLpYlW46sOIETNEWSpgkSB0WQfOhlt9jkQ4PdAl1g0/bDFigKdBfbLLrAIoCLFGiDbrJGWiOu64VjO0piOZJtWhIl3u8UObzMcDj3+wzf/cB5jp45fIeiHEkcSs8PIDh8572c9wzf+Z/ncp5TVYWPfexjcLvdeOONNzA3N6f7c2lpCQ+bh/U8C4Lw0fl1YszfVkr9HoBBAH9iWVYIQBeAq2yfpeK2u8KzdukLneLOdjHYnTDd0LxSFQkC/xKmNZNJHGjhBjoXF0tznnK516ZI8Hgnj4XS/dExXFxJtOgeqJ/4dtpmij+d17SUzcUaeGIdDYwovkurHlFymMPhQGdnp44d19fXY3Nzax1kHp+vq6vTBT7IWqYlJe0sbHMqWSwW0+sk03Y+B5nH8+k8duVHaTDBrXH+GXGvBOF0OnXJTSq3ef36dSwuLpYUIzGh+6iursahQ4fQ3NwMp9OJj3/848hms/jZz36GpaUlVFdXw+v16mU3K4j7+jwLgvDR2Y0r247vATgG4ByAFQB/c68nUEp9Uyk1qJQaBLYWAohEIlpgKDOXu3J3k7260xc1WcVc/CmhyePx6CUBaW1ibl2ZMWUunqZYcyGnY023O+3P3d4kuNxFzbOruQDzGDt5Avj6z6YI8YEAd/tzcaI4eyaT0f0Tj8d1LDQYDAIAurq6cOrUKZw4cQI9PT1ob29HTU2NFqbm5mZUV1frLGyaZkSxYmqj2RZeZ5uSyqgt5hSncgJpelKAOyuA8T7h73PLm1z3qVQKc3Nz+PDDDzE3N6dj06Z1zM/hcrnQ3d2NpqYmeL1ePPfcc1qUp6am9LQvr9eLtbW1Sqr2dV+f5xwy97t9gvBY8ZEsZsuydKBNKfV3AF4r/ukDcJjt2l3cZneOlwC8VDyHZVkWUqmUnrfKv8SBO8Jiuq5ZO0q2mxWqgDsJPbSN5k3TesQejwdKKWSzWZ0IZZdUZFqifL6yua8p0Kag8HvkCWzUdh4ztRM1c9Biulq5UNllI/P9LMvSYkgWXTKZhFJKW7y3b9/WxTxoOlVbWxvq6uqQTCa1pR0MBrXQbmxsIBqN6jgwtZ2S1GhdZD4g4a5owozRk0XM74nfe3V1dUlymtkn/JyUCc5F+fr165icnCypSMY/Xz5IcLlcOHjwIFpaWlBfX4/z588jlUppS5lCBDT/mseq95r7/Tw3qBYp/C0IvwYfyWJWSnWyP18EQBmerwL4ulKqRinVB2AAwPu7OSdZVTzTml1P/zaTfsphxm4JPv1nc3MT6XRaV6Wi+bQul0uLs+kWN68B3LH6KHOZizIXC7ukJzuB5BY3uZFpNah0Oq2nQpHbnaxnvq4xCZKdhcktT96/PMM9lUqVxH6JfD4Pj8eD9vZ2OJ1ONDc361KYjY2NaG5u1lOSyC2dSCR0H/FFMOj++ZQws9/4/4A5N51nUFP7+X3RAI/fI//f4DFhspTz+Tx8Ph9u3LiBiYmJkixu0+Km6zqdThw6dEiXA7148SJSqRTefPNN3L59W1dJc7vdaG9vx8rKykPPxt6JB/E8C4Lw0bmrxayU+iGATwNoU0otAfivAD6tlDoHwAIwD+BbAGBZ1ohS6mUAowDyAP7IsqyC3XntIBcyAB1TJFE0rZTi9bZZSaZFSnNYKeGGXMbcUs1mszqBqLq6Gg0NDUgmk8hms6itrS3JiN5p7qqdVcpdyOaAgt8bvc/bCJRmK/OBCVmx5rG0nbtczUEBx26AQNeked8k8iR0SikkEglEIhG98lQ4HNZzeynT2OVyIZlMlgw2zMS0ci5puiezmAr1Ad0jH8TxqXLUfnNJTv7bsqwSSzmTyWBhYQHXr1/H+Ph4yfKXdnPZ6fNtbW1FY2Mj6urqcP78eQSDQbz99ttYXl4uGXg0NDTA7XZjbGys7D0/aB7m8ywIwkfjrsJsWdY3bDZ/f4f9/wrAX32UxuTzeUSjUV0f2S6pykzYsYvxmmLDrUb+pUxf+rlcDqlUSltNSm1la5Olyotp2Lm26fwkWrQvjyUTphjT/uSa5QMHvia1uSaz2Rd0DZ7BzUXZjI2afcHdxnSf6XRaz+MlwfX7/ZiYmEBzc7O24On8TqdTTzcDSi1Z/jfPrjcHXSTGdn1mwvuSJ5SRl6FceVIalNXU1KC2tla7vJeXl3Hr1i2MjY0hkUjs+D9G271eLw4dOoSGhgY8/fTTSCQSeOutt7CysqItZVrY48CBAwiHw7p62l7wMJ9nQRA+GhVT+QvY+tLz+/3o7u4uqUHMv7Q5fJ6rKVD8nPyL1bSw6Hgq00jTg8j1aCaRcbHlwsLbCdjXvaZ9CNM1z6c/kTvYtO7pHsz4qx1ckM1pRvw9vo3fB89ip2smk0nMz88jFArpEqC0oAQlNtEazpZlwePxlCROmXW+zb4wPRB2yVw79aVlWbpymXkc9y7wRK9CoaBF+datW4jH43p/u3gy/R/U1taiu7sbtbW1eOKjEPknAAAgAElEQVSJJxCNRnHp0iUdUyZPAWVit7e3Y3R0dFv2vCAIAqeihBlASSzVdHOWK0lpTmcyv0gpbmpmLNP+JHw0Taq+vh5AaQKZGRflv+2E1WyD+cVOmO2mbdy65Jnb3CVMcGuen9+MsZuDCL7SFcEHIiTM5EXgWevk5uYlOsnKpwUwMpmMztama/H2c6E0PSH83vmgyvQWmHO5Kf7OMcWZypbS/Obl5WXcvHkTIyMjOpPcTvz5dd1uNw4fPoyDBw+ir68P2WwWv/jFL7C0tFRyP5RweODAAWSz2YpK+hIEoTKpOGEuFAqIxWKIx+Pb5jLbCSOJkp11Q/tQRS8+t5jHLrmw0TXdbjcAaFEhTCuNv+bXNOO6pijzeDV3fXPxIdHjCXFcqOxc/XaiYsZYAZSch94zBy00mKEYPdV2pqQ5qiXN7zUcDqNQKKCpqQnZbBb19fWIxWI6u5sPMKh/eT/wgQhwp5Sm+Zmac9OBrVwBO2uU/184HI6ylnI4HN7mlTFj20ptLUpx5MgR9PT04PDhw0in07h8+TIWFxdLQhHUb/X19WhtbcXrr79eErcWBEGwo+KEGQB8Ph8aGhoQi8XQ1tZm66IGtruL6bcp4HyKEVl2du5vEnGa9uNyufTShnxxjHt1X9N+dgMLHm/lsVhz3WbaTpiubX4v/DjTirYTaT5I4IVNzIQn05olYTTbFovF4HK5UF9fj1wuh4aGhhLXPGWum+3l8Wazf83+p9fmnG7z/4O3m8qNkij7/X6MjY1hZGSkRJTt3N/02uv1ore3FwcOHMChQ4eQzWZx+fJlLC0tbStAUlW1Vfa1vb0dhUJBRFkQhF3xUQuMPFCSySQWFxcxMTGhXablrE++8AG5Sk3B5ZYaL8EIlMacga0v8Uwmo7OJaa4tgG3JRGayFm+bnQuUjuFCWy6maq7sZHoJTEvfvA5dy8RucGCXac7jo3buYz7ljMSRrGuaJpXL5ZDNZlFTUwOPx1NyDb7EJW2zrNKlGqltpvAqpUrKd5qlOu3ujSxl8oSQKA8NDSEQCJRkzJs/dM3a2lr09fXhyJEj6O/v14let2/fLplWxfuvpqYGR44cwfr6uqwkJQjCrqhIi9myLMRiMfh8PjidTpw9exa1tbUlCT1mTNd0YdrFfHnZSBJciovyhKjNzU1tNZO1R+c0f5vialqo5jbedm7R87aSANISjbxtZob1TslRHFPI7axC7j6m7Wb83m7OMMWgucWbz+cRiURw4MABxONxXXubz8Gm5DE+uOHn4NYytY/P3Tbvk/Y38wFIIKn+dTgcxszMDG7duoX19fVtgwS7EAUtStHX14eenh74fD5cuXIFfr+/5H+PD14cDocuTTo5OWn7uQiCIJhUlDDTF6DH44HH40E8Hsfi4iKqqqpw5swZ1NbWbvtCNrOmd/qCB+5YrBQr5ZY4rUJEVjWtAcxLeVJCE7+OKbyAfSzajIWbYs1FmSAxpLaRkPHjuXjaxXEJ8xqmUHC3OlmupkjT9Clqj2VZOg+AsrNp32w2i42NDV0NzOPxIBAIaAuYW97UPu5Gp/6iWDvV2jb7nB9v3nNVVZVepMSyLGxsbGB6ehrXrl2Dz+cr+X8yz8fPcfDgQZw/fx7d3d0YHx/H9evXEQwGt32OXJjJWo7FYlheXrY9tyAIgknFCDMlyXi9XihVukJRdXU1WlpacPz4cS2Qdu5VANtEmLbxfXlslGLOfH1fslRzuRzi8TgaGhp0KU9azMFc5IJek7CY4mcnuhy72CrtT8Jn5xUw78/O1W1e07RuOdQPNCeb4t3ccudeAt7XdCwXz0QigcbGRrjdbp2hTWJICXk8M56HFug6vKKZ6Xbn92RC4kiiHIvFMDc3h+HhYSwvL5cM6szPi/9ftbS04MKFC+jv78fQ0BA++OADRCKRkgGSOZii6VRdXV344IMPtq0DLQiCUI6KEObq6mp0dXVBKYVMJqOza+nL3ePxYGFhAT09PfrLmmfzmlm9ZmzQTkS4FWtmQ/NpUiTO/Dx8SUrgTrKWaZHygYFdO+ws5s3NTV2ljNyyBJ2TX89025rnpO3UX3x1JtPypkEA7wfuZubTnXjbeHgAuFPyk96jUqe0fjO/VxJH7mbnnylhijDvM/OzJ6ufW8qRSATT09P48MMPMT8/XxKX5lY2Dw1UVVWhvb0dn/rUp9Db24vBwUF88MEHeslL3jY+GKLrHzhwAPX19ZiYmDD/5QVBEMpSMcJM1iiv40xfklReMZPJ6LV57b6ogTsJP6YVbZeFTZYgWXqm9cjFOxKJlFSJIiEikeRWMl1zJwuZ4GJuJrhR35BYU1v5+XnMl5+T/zbvjcds6TdZeXQ/3GI0k8CoffwezYxpni1NU6gSiYQu3mFam6albd6D3Wdt99nT5+JyubTnIxwOa0t5fn5eu9zLhQ/ofhoaGnDx4kX09/djcHAQg4ODep6z+Tnxvyn7+9ChQ5iamkIkEtnxf0AQBIFTEcK8ubmpF02gL2kqAkGr8fByl3bWsBlrJjHmmC5Lc3oSiQrt53Q6tdWslEIqldIiTvN4qf18XrTdNTncmqbr83vi7QLuzGcmi5QLIV3DnL7Et5t9bQoL/U3JcHQ/5oITZrtNy9/McKd9otEo4vH4tiQpHjO3W6rSdNHTNXh7+P3RilWUE5DJZOD3+7GwsICRkREsLCzocpj8HHafT2NjIz7+8Y9jYGAAg4OD+NWvfrVNlPn+/KeqqgoejwednZ145513pNKXIAj3REUIM3eNUvJXXV0dXC4XMpkM4vG4nuZiFxs1C28Ad7607cSKiwdP+KLsYj6HmNzKlAzFXbipVEoLgTnH2HSpU5voffrbbkqTHXzKGJUr5TFkfn2y4E13uymcpuuVu815LJmLKN2PmY1u3jtZq9QeihPT1DNzkGXmA/D+KveaexUcDof+HJRSSKfT8Pl8mJ2dxeTkJBYXF0tqVHMRpetyUb1w4QLOnj2LGzdu4L333tMLdNhdn5+LBgcHDhxAIpHAxMSErZgLgiCUoyKEWamtOaJkISu1lfwViUSg1Fb5w4aGBr0v/01fluSuNefXEhRf5fuaX8oAdAY0ublpfxIRcidXVVXpohn8fdNCNV2tXNi469eEu3ZNwTTXIObCSdejAYcpqnaWInkHgDueB7tMb27J8vvg92onvKY3gLvW7cSdey5Mj4P5mVEhGEriy2azCIVCWFxcxNLSEiYnJ7G2tqanxJkiyf+uqqpCU1MTzp8/jzNnzmBoaAjvvvsuotGobSjEbD/dv8fjwcDAgK4pLgiCcC9UhDBXV1ejra1Nf7FSIQaPx4OGhga0t7ejo6MDtbW1JV+EdklUQKko88IjtA+3iO2Eko4jy5OsZh635EljiURCW/jmFzfHtPJN0TUFzM4NToMAU+z5AIFb0qaHwK7/qLQmvU/n4W5fuzCCeS/8WtQGeo/3t1nVzO4+7cIVpiCa5TXD4TBmZ2cRCAQQi8UwMzODtbW1Em+K3cCJttfX1+PixYs4c+YMbt68iStXrmhRNttkto1bzG1tbXC73RgeHi4pmCIIgrAbKkKYaQoNLSLR2NiIpqYmtLe3o729Ha2traivr4fL5dKibcZhuUu0nDVHwsGzi/mXrmVZOp7sdDrhdDpLErvMBCiyXvP5PBKJBAqFgl7XuVwymGmd85guL3RiVrLi1yd3remS50tH8ox1c8qUmbRFMVmaXkSDD7syl7wvgTvV0HiRDhr8mLW3TQ+BKe527eNWOvdKUD1zivcvLi5iaGgIkUgEDocDc3NzekqTeT1zEOBwONDe3o4LFy7g6NGjGBwcxPvvv1+SfW1n2fP+oP8FqqM9OTmJhYWFsscIgiCUoyKEmcSjqakJra2tOHDgANra2tDS0qKTv+jLlVfDsot9AnesKTMeXc4i5S5Kmr9sWpHkuuYWOFlDJKS0dCRVFTMtTLsMYLoX0zon7LKSAZRMqaI+JNEyY+Ll3K/c6qTKWPReKpUqaVc5S5PHZnkSGi29yGPhHNO65t6OcqJMfU1zkx0OB9LpNCYnJzE0NIRsNguHw4H5+XlEIpGS9ph9SddwuVzo6OjAZz7zGbS2tuKdd97B0NAQ0um07WdgWst8gONwONDc3IympiZcuXJFkr4EQfhIVIQwO51ODAwMoKOjA21tbWhqakJdXd22ObwEt3btXL78y5PHKXkClJmsZZ47m83qOb1kPZrxXRJrpZQWtUKhgHQ6rS1Pfk7zPoDSDGQ6t51rl85DwsWteODOFCi6N16Zi7vpeV+4XC40NTWhqakJLpdLZ6Dn8/mSedL82vxe6Hq0LCbFjmlww4XXLl5tiradiPNjq6urtShXVVUhHo9jdnYWExMTesrd8vIyotHojvF++vF4PDhx4gQ+8YlPIBaL4dVXX8Xs7Oy2YiDmYMruc6Hym/39/bh9+zamp6fL7i8IgrATFSHMtbW1OHv2LJqbm+F2u22/BKm848zMDJqbm9HT01PisrZLKjItadoOoETY6RxkXZFo0PQoEimarmRmF/NrkiCSuJmDC245mwMELrx2iWFmdjlPdDPvkbfLtO7JSm5vb9erd9H611VVVUin01ps+SCIW628zVz4yYVtdx98EGW6+ume+ECCtlG/8qlQ4XBYJ3ZlMhksLi4iGo3q4jTlYtg0mGlvb8fTTz+NM2fOYHZ2FpcuXUIgENhmHZezluk3xZVpFSnLsvD+++9LbFkQhI9MRQgzFWMwrSUqLLK4uIjp6WmMjo7C7/ejs7MT3/rWt7QQ84xrggsDUGotmWIMlGYaA9CWIwkwz9Tm7maKMedyOZ0ZzMWfMrdpPWNqC/3m7m1eXIW3r9x7dq5ZPg+b2kCC7nA49MIcra2taG5u1is00VxxWgebPAYASgYYdrF56m+7KU/0t124gY4zt5kDHb7gyObmJgKBACYnJxEIBLC2tgafz6eLhpjxdPOzr6urw+HDh/HCCy+gtrZWu66p8ImJKcZ2U/IcDgcaGhrQ1dWFkZER+Hw+23MJgiDshooQZnMKTD6fRygUwvj4OMbGxjA/P49wOKz3X1paws2bN3Hu3DltAZlVo0xx48JhWmlcqLlAc8uOx4C5ZUfisbm5iVwut23uMHBnHi+PPZtWl7mWrylc5gpI5vxlU4xJkCk5ixaRaG5uRnNzM1wuF6LRKACgrq4Om5ubCIfDutALt8bpXObcZO5W5/F3vvayOQjhiWum6PHPjuYD8wFNJpPB2toaJicnsby8jNu3byMcDm/7zOwGKBT/ffbZZ/HUU09hZWUFb775JmZnZ7eVWDXbxLfzfidruaamBh0dHQiHwxgbG9v13HRBEAQ7KkKYAeiEoWAwiNHRUVy7dg2rq6tIpVLbLLBsNosPP/wQR48eRWNjo7Ym7YptAPYVqbibFdhugdI5eFyVZxlzsSdLlJK/uAVMYgWgZFUlWgiDu9y5kHFB4FO/ePtNa59bpjyW7na7UV9fj6amJni9Xr0kIy0qkUwmEY/HS1ZvMq1Xbnlz8SNMUeZ9zO+HW9g87s/34SU16ZhUKoXFxUXMzMxgamoKfr+/pL/552L+7Xa70dPTg4sXL6K1tRU3b97Eu+++i1AoVBJOMP/P6B7oPe4ep7Y7nU492PnlL3+JRCKx7RyCIAj3QkUIs2VZCAQCGBoawvXr1zE/P39Xq2NiYgIvv/wyvvrVr6K5uRlAaSEQO+uY/81F2ZxLbE5HIuuQCyBBX9Y055fc2TSXmsSZLGV+Pm49c6uZW28kclw8+GCB/uaDExJRKmva2Nio63yn02m4XC69znQoFEIikdBTo0xvA90Dt0D5IIU8BtQHZKGb8Vmz77nlT31OU9T4oCWXyyEYDGJhYQHDw8NYWlpCLBbbllhmWudkdTc1NeHJJ5/Es88+i3A4jH/913/F1NQU0ul0SSjDrp12f3Nr2eFwoK6uDl1dXVheXsba2tq2cwmCINwrFSHMyWQSL7/8MmZmZpBKpXZ1jGVZerWg3/zN3yyxcE0rys4K5KJM7nMAJaJsJg2R4FLWLl2TEo7oXGY8lqYOkeuTJ1VRkhV3p9sJJLc8+T3Q/Zpzhmtra3XRE4qP09KaFCqIRCK6vCcAPY+a7t/MzDatcvMeaY1rbl2aoQWCu7bJbc1j9Ja1tUzj6uoqpqamMD4+jvX19ZL28v4wLWWXy4WDBw/iueeew5EjRzA+Po4PPvgAq6urJXXXeXv4Oc2/+T7c1d7c3IxEIoHh4WFbi1sQBOFeqQhhDoVCCAaD93xcoVDAtWvXcPToURw7dsz2C5R+89fcBc3jnTyZjCc80fFcBHgbgNIkKDqGu7HpmplMRlvQZHWZbeNWPz8HwcWTxzq55UiZwvz9QqGAYDCIaDSqQwS8b8ysdnOaFeF0Okvuly/VScfaiZQ5uKDscJqWRsclk0lsbGxgdnYW09PTmJub025rM55s/qbBx4kTJ/Cxj30MSim88847uHHjhi4CY4oytcncZp6b9yUl0TU3N2N4eFhWkBIE4b5REcL86yTLrK+v4/Lly2hra0NjY+M2UTCzdHnslburuShziw8oXaOXW4Bk7QKlLmYeP6XtNNWKrGqqYEYCzd3X3KImt7E5vYiOMwWFx7XJuk8kEvp6vP18AMDF3hzImP3GLWwSTOon7sY2BZALKq/cRcdlMhmEQiEsLy9jamoKc3NzegqUOX+bexOo72pqanDw4EE8/fTTOH36NBYXF3H58mXMz88jnU5rl7yZMGfmG/D3TGjQ43a70draiuXlZfh8PrGWBUG4b9xVmJVShwH8I4AOABaAlyzL+lulVAuA/wugF8A8gK9ZlhVSW99mfwvgCwCSAP7AsqxrD6b5W0xOTmJychLPPPNMSdlM7hLm1hoXA8JMQCreu36fiwp9ifPkLXM/081MljEXYkoGI6hNPCbN3ep2Qkx/8wQ3mkNNx1HMln7zezdFid6zS8oCoAuH8PKfdn3FXf8EdwFTLHlzc6vWeCwWw8rKCmZmZjAzM4NwOFxSpITOa7a9qupOoZQzZ87g7NmzcDqd2koOBoPbBh+8Pfwz4/D3uKVMWditra0IhUKYmpraV1nY++F5FoTHnd1YzHkAf2JZ1jWlVD2AD5VSbwL4AwBvW5b110qp7wD4DoD/AuC3AAwUf54D8L3i7wdGLpfDm2++ic7OThw5cqTE8qUvYxIg08I150CbbmPTuuJf4qbLmfajbbQPF07uqq6q2iovyY8hy53We85ms9rC5NfnqyWZsXTeRhIU3mZzChif52y67SkOzmtnm4Jtxr25KPOsZ4fDoSt3AVvJYsFgEMvLywiHwwgGg5icnEQsFrMtZ8k/Sxrk1NXV4dixYzh79iw6OzuxvLyMq1evYn5+XrvruUVvZwWbn2m51/R5UanYkZERJJPJbeercCr+eRaEx527CrNlWSsAVoqvY0qpMQBdAL4M4NPF3f4BwM+x9SB/GcA/WlvfdleVUk1Kqc7ieR4IlmUhFArhzTffxGc/+1kcPnwYwJ0vYm51mkLG48gcM+vZdBdTJnK5tZ75PGCaRmS6eKk4CXAnbsvbSuckdzbtyy16GmyYBUjofX5NnrjGRd20bOk+qHAKCTg/ju/LMS1l8irQ8ow0D3xjYwPj4+MIBALY2NiA0+nExsYG4vF4SRv5/ZDAV1dX62zop59+GocPH0Y8HsfPfvYzjI6OIhqNlpQotft8efvtBlwET+ijbHaXy4XR0dGyRUkqmf3wPAvC4849xZiVUr0AngbwHoAO9nCuYss1Bmw95IvssKXitgf+IE9MTOiqVh6PZ5vYcBcoT/4iuLuTW3/mOUzR5ZYzYL+qFZ3LnNJF1yQxMmtTk/uZLGR+Xt5uujZZwGQpm1Y1t2h5ARQA2iKmKVLczc7DA7x9XNjKhQ0onkzvra2t4cqVK1hZWUFLSwva2trg8/kQCAT0PG86nq5Nfed0OtHR0YETJ07g3LlzyGazuHnzJm7duqXLc/LPi9q6k6VsF08249hOpxNerxderxezs7NYX1/fdr79RqU/z4LwuLJrYVZKeQH8M4A/tiwragiWpZS6p+wXpdQ3AXzzXo65G4VCAcPDwzhy5AguXrxY4k41v3zNWDSwfcoNjx+b1i7fh85hNyfWjN3azTmmtptVw+g65lxiHiemucZcZKhaFm8zb49lWXogwK1hep97COzi7Hw77w/uZaDYL81NVmor4W11dRXDw8MIBALwer1IJpOYm5tDLBbbVl+aW8g0/en48eM4c+YMHA4HxsbGMDQ0hOXl5ZIkNPo87ATXFHs7i59+aHBDLvOmpiYsLS3B7/dv+5z3Gw/yeXaj7n42VRAeO3YlzEopJ7Ye4n+yLOtfipvXyKWllOoEQN9WPgCH2eHdxW0lWJb1EoCXiue/bymt8XgcN27cwNGjR3Hw4MGSoh3FawHYHo+lfbjblzAFm1vH9LdZq5vvZzcI4FOLuOXM47/0vimwpvVM0DaKC/N2mFY93Zd5H6b72HQF82vQfZtCrpQqWQVqc3MTsVgMfr8f09PTWFhYQDQaRS6X01Ot7OLydJ7m5macPHkSTz75JLxeL8bHxzE6Ooq5uTldQpTaZPaHmSNQ7l7N17yIiNvtRmNjI/x+/yNRRORBP88NqkVS1AXh12A3WdkKwPcBjFmW9V321qsAfh/AXxd//4Rt/7ZS6kfYShKJPOx41NTUFF555RW8+OKL6OjY8sjx+cbcUuVzd/mXtyl8ZtwX2L7Kkvk+nd8Uer6dH8+t5nJxXFN87NpuF2+2u7dy2eR2luRur8+XZrQsC4lEAn6/HwsLCxgfH8fa2lqJmHKhp34ga7u9vR0nT57EU089BYfDgdHRUdy6dQs+n08ndt0t3k2/zX3NPuHHcGuZMrAdDgdu376971eN2o/PsyA8buzGYv4EgN8FcEspdaO47c+w9QC/rJT6QwALAL5WfO91bE2tmMbW9Ip/d19bvAssy8Ls7CxeffVVfOMb34DH49HCyQtM2H2pkxuWW6mmNWdXnctuWg/tz9271AZuae7kSjdd5rx9pvVrNzAguLuZ9jH7YCdBNs9lWs7msoxU7vP27duYm5vDzMwMotGoLtlp9hXdEy02cfLkSZw/fx4OhwOTk5O4fv06lpeXtwmynbhSf9jdP+9Lu9fcWna5XHpu/NjY2L4X5SL77nkWhMcNtZsv4gfeiPvoyuY4nU6cP38ev/3bv43Gxkad3ESiRvduJjHxWC93MwOlSWQ0D5efjwuyKbx0PnMusZk0Zmf50XVNUTQxxaecIO0k/ib8XuhYaislRtGiE4VCAdFoFD6fTwtyIBBALpcrO2Cg+cEtLS144okn8Mwzz8DtdmNoaAjDw8NYXl7WRUbM9pgDDNMVX+5++GszrkyDDCpac/36db0SVwXwoWVZz+51I3aiQbVYz6nP7HUzBKHiecv6se3zXBGVvx4UuVwOIyMjqKmpwRe/+EWd4Wy6lXnSEv+S59OtTPEyq1wVCgW9fjEAbZnzjGk6juYn203VskuwMkW5nMDRvnw/06LkcWW7ZDU7TFGmY6lICE31isfj8Pv9uH37NiYnJ7GysqLd1nYDBjqP1+tFX18fnn/+ebS2tmJmZgaDg4MlpTjpnsrFvOm13edl16fm/tQWmmt94MAB9Pf3Y2hoqJJEWRCEx4BHWpgBIBaL4fLly0ilUnjhhRfQ1dVVktEMlCaCmclAvMSmaVFzi9isn20mEnHrjs9HtnNVl7NsCXNf3nZe6IRfk7CrhlbuOiZ0frKQldpKUkskEggEAlhYWMDs7CyWlpZKlpDk/Ufndzgc8Hg8OHToEJ599ln09fVhaWkJP/nJTzAxMWG73Cf/XMz3eP/bufTtYsnmfVGd7Z6eHpw+fRqjo6OYn58v2x+CIAgPgkdemIE7i11sbm7ixRdfhNfr1YJLU5FIrMzCIgC2CTRwx/1slnqkv804NBdqXqKznCVuVzaTMF3L/Dg6Pz8fUD7Rib9XzsLm5TypsMrm5iaSySTW19fh8/kwMzMDn8+HaDRaYiHTOfgAxe12o7u7G+fOnUN/fz8SiQTeeustjIyMIBQKbVsMw66fdooZl3Pdm4MuHk92u91oa2vDU089hQsXLmBoaAhXrlxBOp0u818lCILwYHgshBnYmmJ0/fp1xGIxfOlLX0J3d7ety9ouU5mqVdGXu50FTfvbiR8XdXN/Oj+PUZuJTaagmNcHUDLP2bSU+T2ZYsX3I5HidaH56laWtVUqNBaLIRgMwufzYX5+Hmtra3pNZxO6/+rqani9XnR1deGpp55CT08PUqkUrly5gqGhIQSDQR2HNu/Zrt1298Pfs7P8+X2SlVxTU4OmpiYMDAzg3LlzOHbsGObn5/HjH/8YiURi2zkEQRAeNI+NMANb4jU1NYUf/OAH+J3f+R0MDAzohCV6v1x8lrur7YTYtG65uJjwBLG7WXhkAXNL3hwAmNa2Xbv4exy+sAUtsMErm+VyOWSzWaRSKYRCIQQCAayurmJpaQnhcFivn22KJf04HA7U1tair68PZ86cQW9vL6LRKK5evYrR0VEEAoGSNZbNdpqDjLtlY/P+431jWskulwv19fXo7+/HyZMncerUKbhcLvz0pz/Fa6+9hnA4XPbcgiAID5LHSpiBLbHx+/344Q9/iM997nM4f/48ampqkMvlSkpU2lmcfGlIO8iaNS1gM/mL4BY4P54nZ/ElH/l16Hjz2qaI8Zg4n5bELWI6XqmtaU6pVAqJRALhcBjhcFgLcigUQigU0kso2t0/nd/lcsHr9aK3txdnzpxBZ2cnQqEQLl26hPHxcYRCIWQymR0T2Th2XoRywmz2Ff+pqalBY2Mjjhw5ogcKSilMTEzg/fffx+Dg4H5cmEIQhEeIx06Yga0v942NDbz66qtIp9P41Kc+VVIyE7hTsxq4Ezem3+TaJgHnYsHLgJoWrJ31bSZHmTFqus6od/oAABWzSURBVB5g787llqGZZMVF2HRT07kKhQJSqRTS6TTi8Tg2NjYQDAYRCASwvr6OaDSKZDKpS3jSNc164HQ9p9OJhoYGDAwM4OTJk+ju7obP58NPf/pTTE1NIRwOa5e3Kcp28WO7/Xb6XM0Kb3TP1K5jx47h1KlT6OnpQSaTwfDwsJ5rvbi4qD0AgiAIe8VjKcxEIpHApUuXEA6H8elPfxrNzc3b3Ms8NmxaqFzMTTExq4KZljH95klntK2cdWznYudCTALMhZiLMLUhm80im80iFoshHo8jEAggEAjA7/cjHA4jkUggk8mU1ODm1zGnW1GmdmNjoxbk1tZWrK6u4pVXXsHc3BwikUjZAh2870wR3q0o89g89xBQDPnYsWMYGBhAd3c3gsEgrl69iqWlJb0O9Pr6ellPiCAIwsPksRZmAAiHw7h06RIikQi++MUvor29Xcd1zYxgnskNbI/t8n3IejPFm0MiRy5yU5zMOKqZbU1C6XQ6t1nHdPzm5tbyjZlMBolEQruoNzY2sLGxgUAggEgkglQqpQuw8GvbuYxJ9HghjoGBAZw4cQJerxdLS0t49913MTs7i3g8vk3w7JK3zOuUE2rTUuf7c69ATU0N2tvbMTAwgNOnT6O2thZ+vx9vvPEGVlZWdLY1lfcUBEGoFB57YSaGh4exsrKCr3zlKzhx4kRJUhh3+9ola5HAcrEkkbQsq2QpQ45dUhNQuvQjd6lbllViDQPQ066oXfl8HtlsFplMBqlUCrFYDLFYDNFoFMFgEKFQSCdtZbNZ5PP5kvsqlwlOr+kea2tr0d7ejieeeAL9/f2or6/H3NwcLl26hIWFBb1alN392d27nVdip+xrfs/UHzQ3mrfL4XBgcXERo6Oj8Pv9SCQSqK6uRiqVQjQaLVkmUhAEoRIQYS6SzWaxsrKC1157DW63G/39/bbJWHYZ1GY2NxczEiceAwZKLTzgjsBSPJmEppz7nGLcJMTZbBbJZBLxeByRSASxWEyLcCQSQTqd1klb5dZRpmvYucn5lKeOjg48+eST6OnpQXV1Nebn5/HGG29gcXERiUSiZMGQcq5oU2jNWLm5r517n3sJvF4vuru78dRTT+HQoUMoFAoYHR3F5OSkzvympLxgMFhS4lMQBKGSEGE2WFpawiuvvIKLFy/i/PnzcLvdJe/T1CXAvsY1FzlasIEEgVvNtGYyUJpoRq5wysgm8SXxz2QyyOfzSKfT2j0dj8eRSCQQjUZ13JgSuugcvL0ECbR5fT4tjNzVDQ0N6OrqwvHjx9Hb24tMJoO5uTkMDQ1hZWUFkUhk21KahBl/32m7WQOb78tfU9tqa2vR3d2NkydP4uTJkwiFQrhx4wbGx8cRDAa1RexyuZBMJrclsgmCIFQaIswGlmXpohmRSATPP/88WlpaSixn0/ql4+g9pe6U3aTa3Jubm3A6ndoqrqmpgVJKu5OpJnQul9PWbS6X06s0kcCk02kkEgkkk0mk02ktNPl8XseIeXZ5OVGk93i5UJo+5XQ69XKHBw4cQHd3N7q7u+HxeBCPxzE4OIiRkRGsra2VlN7k5zUronF3f7n4Me1X7m8eInC5XDh48CDOnDmDJ554Apubm7h8+TImJiawsbGh+4sGP6lUyjbeLQiCUGmIMJchlUrhF7/4BRYXF/HVr34VHR0d2krj2crA9uIipigB0GJHwry5ualjnMlksmTKUiKR0AlJm5ubiEQiCIfDWrTJ4itnofJ2mTFeXh3MzOauq6tDU1MTurq6cPToUbS2tsLj8SCZTMLv92N2dhZzc3MIhUJIJBK21y23zc4dbfYbhws6/SZ3OtWzvnDhAlpaWjAxMYGhoSEsLy/rbHJgS5Tz+TySyeSjsmSjIAiPASLMO5BMJjEyMoKNjQ288MILeP7551FTU4NsNqsFg7K0uSUI3LHuyGVN4pfL5XTcNxgMYmVlBaurq7pwByVwAXdc5FSq0rRM7eKydtnN5jQi7gquq6tDS0uLFuPOzk7U1NTA7/djbm4OS0tLWF1dRTAY1Jnb/NocO+vW3G7Gs8u5vu3i3DU1NWhra8MzzzyD06dPY21tDa+//jpmZ2d1SdDNzU3U1NSgUCggHo9vG0QJgiBUOiLMd8GyLCwvL+Pf/u3fsLa2hk9+8pPo6+tDoVDQLmQ+RQoond9LCVyZTAbBYFDHf2m94nA4rEtSmms786QxaotpAdtZlma2M23nC1I0NDTgyJEj6O3tRXd3N1wuF0KhEG7duoWZmRldXIRc7TtVOyuXPW0KL9+X3585Lc28HxpA9PT04Pz582htbcXY2BgGBwexurqqE7ksy0J9fT02NzcRj8fFShYEYV8iwrxLYrEYfv7zn2N4eBhf+9rX0NfXh9bWVgDQyVlm1a5MJoNQKISVlRXMz88jn8/reb5LS0u2pS3Nv00XtF2hE1OACT7tipY0bGtrQ39/P3p7e9HY2IhAIIChoSEsLCzo6UQ8Vn03zIGAyU6JYJSZTvdkzlGmWHxraytOnz6NEydOIJFI4O2338b09LQWX4qPOxwOPdARBEHYr4gw3wOWZSEQCOAHP/gBuru78aUvfQmNjY0lNbY3NzeRTqcRDoextraG1dVVBAIB5HI59PT0oFAoYGVlRa83bDef14RXBTNXpjLd2LQ/tzSbm5vR3d2Nvr4+tLe3A9jKPn/nnXf0YhRkdZrWv12GdDlM69du7rLdlDOzShq5/1taWtBbrLXt9XoxNjam55tTLJkGQ/l8vmzcWxAEYT8hwvwRiEajGBsbw9raGpqbm9HS0qIFjVZiogIelKjl9XrhcrkQDoeRTCa1gJmFR0i4THe4UlsLQ7jdbrhcLuTzeX0NmpLlcDjg9XrR0NCAxsZGtLW1ob29XWeVLy8v4+rVq5idncX6+jrS6bTOGC/nIrd7fTfxs7PqaTu/Fv9NGeG1tbXo6OjAkSNHcOzYMdTV1WFhYQGXLl2Cz+crseipaprL5UI8HhdRFgThkUCE+SNiWZaupLW4uAiv1wun01lSspO7aJ1Op56LzBel4LFW2o8yuJ1OJ9xuty65SSLES0+63W54vV54vV40NjaipqYGLpcLAPS6yRMTE/D5fPD7/dr9W65M5k6Z0oT5Hp8GtdvjeEY4DSg6Ojpw/PhxdHZ2wu12Y35+HrOzs7qSWC6X04JsWRZqamqQz+cRjUZFlAVBeGQQYf41oVhyNptFXV2d/qmurtZC4nK54PF4EAqFUFNTg66urpIynSTGJFIul0uLDwmx0+lEXV0dvF6vFt9cLqdLhxYKBfh8Pr1MYzgc1sVGyE3NBwy8SEo5V7oppHb7mMlc5c7DX/O5yM3Nzejt7dXTs8LhMEZHRzE/P68zwbkgk5XscDiQSCRkXrIgCI8cIsz3CcuykEgkkM1m0dDQgP7+fni9Xh3vzWQyWFtbQzqdRnNzM2pra0tWgAKg3dUulwvV1dWoq6tDY2NjSZGSRCKBlZUVRKNRRCIRRCIRZDIZPceZKoWVm1pl/s0rjdF2c+qX3fF22+3i0qbbmgYZjY2NOH78OI4ePYqGhgasrKzg8uXLWFxc1PO7KYZMP+Suz+VyZddyFgRB2O+IMN9ncrmcLgXZ39+Pz3/+82hra0Mmk8H6+jpWVlZgWZZ2Ubvdbrjd7hJ3di6X05W9pqenEQ6HdblNEn+ygM1lKfn8amD7Qht285zN1bHKWcHlsLOy7eYi0zSt/v5+nDx5EjU1NVhcXMQ777yD9fV1XQjELJxC1jXF1cVKFgThUUaE+QERj8cxPDyM9fV1nD9/Hr/xG7+Bzs5OdHZ26rnMVOUrEAggkUjoyl+pVEoLME8gKyfE5Upc2sV06bXd/rTtbtbybmLJ/HdVVRVqa2tx8OBBnD17FgcPHsTt27cxNjaG1dVVXdazUCigurpaFwghq1gphWQyuW0qmSAIwqOICPMDJJ/P68pZly9fRnNzM9ra2gBgWz1sqpNN1iIlivE1mMtV27LLpObJWOWONY+xO6e5rdz17AqcVFVV6TjyqVOn0Nvbi3A4jLfffhtLS0vbrF+Xy4Xa2lpEo1GdMS4IgvC4cVdhVkodBvCPADoAWABesizrb5VSfwHgPwAIFHf9M8uyXi8e86cA/hBAAcB/tCzrjQfQ9n1DPp9HMBhEMBjE4uIiamtr4fF44PV6y1q7FPsl93Y5MdxtgtZOAlzOFW1ex7y++R79TXHz2tpaDAwM4NSpU1BKYWRkBBMTEzoznI4tFApaiCORyD30rHAvyLMsCPuD3VjMeQB/YlnWNaVUPYAPlVJvFt/7n5Zl/Q++s1LqFICvAzgN4BCAt5RSxy3LEj8kgEwmg0wmg2g0ioaGBjQ0NMDlcmkrmWKsdusmcze2OR/YzjI2i4+Uy5wud6wZly5XbIS/X11dDY/Hg0OHDuH48eNobW3F4uIixsfH4ff79X3l83ntLZAkroeGPMuCsA+4qzBblrUCYKX4OqaUGgPQtcMhXwbwI8uyMgDmlFLTAC4AuHIf2vvIsLm5qRezoLnI9fX1aGpqArDl6qalHAHoxRjox6yfbed6pt9cZPk2swym+R5QWnWM/+b78znVhw4dwrFjx3D48GGEw2G899578Pl8iMViSKVSKBQKUjJzj5BnWRD2B/cUY1ZK9QJ4GsB7AD4B4NtKqd8DMIitkXgIWw/6VXbYEmwefqXUNwF88yO1+hHCsiy95GMoFNJTpDo7O9HY2Ai3262nQ6VSKSSTSZ0URsJNQs3XjKZzcyGtrq7eca6ynbvc3I8LNAkyVevq7+9HV1cXstksRkZGMDc3h2AwqKc/Scy4crifz3LxfPp5dqPugbVbEB4Hdi3MSikvgH8G8MeWZUWVUt8D8JfYilX9JYC/AfDvd3s+y7JeAvBS8dyPvS/TsixdxSoajcLn86G9vR39/f1oaGhAV1cXmpqacOTIEZ0cFQqFEAgE9FQqKiZiTqfimdbcwjbrbpdrF7e66Yeqk3V2dqKtrQ2nTp1CJpPB6OgoxsbGdJUxEePK434/y0Dp89ygWh7751kQfh12JcxKKSe2HuR/sizrXwDAsqw19v7fAXit+KcPwGF2eHdxm3APbG5uYm1tDX6/XwthXV0dOjo6UFdXhyeffBJPPPEEzp49C7fbjXw+j3Q6jUQigVAopKdipdNpXVObKoSZYsmXr8xkMnq1LGqHw+FANpvVFjK9bmpqQjQaxerqKn71q1+VWPQSN65M5FkWhMpnN1nZCsD3AYxZlvVdtr2zGLMCgBcBDBdfvwrg/yilvouthJEBAO/f11Y/RpC1S0ljoVAIAHDt2jW9LGJnZycOHjyIQ4cO4cCBA2hsbERfXx88Hg8cDoeeHxyPx3Whjnw+j0AggEwmg0Qigdu3b8Pj8WBjYwM+n0/HgWOxmBbsRCKhq5DJWsf7D3mWBWF/sBuL+RMAfhfALaXUjeK2PwPwDaXUOWy5v+YBfAsALMsaUUq9DGAUW1mgfyRZnA8GEtjp6WlMT0/rrGgq0mHW306n03pboVDQ1q1SSk9XUkrpuLUdUuRjXyPPsiDsA1QluBwlxiwIu+ZDy7Ke3etG7ESDarGeU5/Z62YIQsXzlvVj2+e5ym5nQRAEQRD2BhFmQRAEQaggRJgFQRAEoYIQYRYEQRCECkKEWRAEQRAqCBFmQRAEQaggRJgFQRAEoYIQYRYEQRCECkKEWRAEQRAqCBFmQRAEQaggRJgFQRAEoYIQYRYEQRCECkKEWRAEQRAqCBFmQRAEQaggRJgFQRAEoYIQYRYEQRCECkKEWRAEQRAqCBFmQRAEQaggRJgFQRAEoYIQYRYEQRCECkKEWRAEQRAqCBFmQRAEQaggRJgFQRAEoYIQYRYEQRCECkKEWRAEQRAqCBFmQRAEQaggRJgFQRAEoYIQYRYEQRCECkJZlrXXbYBSKgAgAWB9j5vSVgFtAKQdJtKOO/RYltW+x23YEaVUDMDEXrcDlfF5AZXRjkpoAyDtMLF9nitCmAFAKTVoWdazj3sbpB3Sjv1OpfSTtKOy2iDt2D3iyhYEQRCECkKEWRAEQRAqiEoS5pf2ugGojDYA0g4Tacf+olL6Sdpxh0poAyDt2BUVE2MWBEEQBKGyLGZBEARBeOzZc2FWSn1eKTWhlJpWSn3nIV97Xil1Syl1Qyk1WNzWopR6Uyk1Vfzd/ACu+/dKKb9Saphts72u2uJ/FfvnplLqmQfcjr9QSvmKfXJDKfUF9t6fFtsxoZT63H1qw2Gl1CWl1KhSakQp9Z+K2x9qf+zQjofaH/udvXqe5Vne+2e5eN49f54fiWfZsqw9+wFQDWAGwFEALgBDAE49xOvPA2gztv13AN8pvv4OgP/2AK77AoBnAAzf7boAvgDg/wFQAC4CeO8Bt+MvAPxnm31PFT+fGgB9xc+t+j60oRPAM8XX9QAmi9d6qP2xQzsean/s55+9fJ7lWd77Z7l47j1/nh+FZ3mvLeYLAKYty5q1LCsL4EcAvrzHbfoygH8ovv4HAF+53xewLOuXADZ2ed0vA/hHa4urAJqUUp0PsB3l+DKAH1mWlbEsaw7ANLY+v1+3DSuWZV0rvo4BGAPQhYfcHzu0oxwPpD/2OZX2PMuzbM8D+9+thOf5UXiW91qYuwAssr+XsHMH3m8sAD9VSn2olPpmcVuHZVkrxderADoeUlvKXXcv+ujbRbfS3zP33wNvh1KqF8DTAN7DHvaH0Q5gj/pjH7KXfSLPsj179r9bCc/zfn2W91qY95pPWpb1DIDfAvBHSqkX+JvWlp/joaet79V1i3wPwDEA5wCsAPibh3FRpZQXwD8D+GPLsqL8vYfZHzbt2JP+EO4ZeZa3s2f/u5XwPO/nZ3mvhdkH4DD7u7u47aFgWZav+NsP4BVsuS/WyJVS/O1/SM0pd92H2keWZa1ZllWwLGsTwN/hjkvngbVDKeXE1gP0T5Zl/Utx80PvD7t27EV/7GP2rE/kWd7OXv3vVsLzvN+f5b0W5g8ADCil+pRSLgBfB/Dqw7iwUsqjlKqn1wA+C2C4eP3fL+72+wB+8jDas8N1XwXwe8XsxYsAIswldN8x4jsvYqtPqB1fV0rVKKX6AAwAeP8+XE8B+D6AMcuyvsveeqj9Ua4dD7s/9jl78jzLs2zPXvzvVsLz/Eg8yw8728z8wVZW3iS2MuH+/CFe9yi2MvGGAIzQtQG0AngbwBSAtwC0PIBr/xBbrpQctuIZf1juutjKVvzfxf65BeDZB9yOHxSvcxNb/7CdbP8/L7ZjAsBv3ac2fBJbbq2bAG4Uf77wsPtjh3Y81P7Y7z978TzLs1wZz3LxvHv+PD8Kz7JU/hIEQRCECmKvXdmCIAiCIDBEmAVBEAShghBhFgRBEIQKQoRZEARBECoIEWZBEARBqCBEmAVBEAShghBhFgRBEIQKQoRZEARBECqI/w/HDaJ2QpTU1gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "deformed_data_dict = rand_elastic(data_dict)\n", + "print(f\"image shape: {deformed_data_dict['image'].shape}\")\n", + "\n", + "image, label = deformed_data_dict[\"image\"][0], deformed_data_dict[\"label\"][0]\n", + "plt.figure(\"visualise\", (8, 4))\n", + "plt.subplot(1, 2, 1)\n", + "plt.title(\"image\")\n", + "plt.imshow(image[:, :, 5], cmap=\"gray\")\n", + "plt.subplot(1, 2, 2)\n", + "plt.title(\"label\")\n", + "plt.imshow(label[:, :, 5])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleanup data directory\n", + "\n", + "Remove directory if a temporary was used." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "if directory is None:\n", + " shutil.rmtree(root_dir)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 3eed077102c827ec5a970294526a8a0bd3900a86 Mon Sep 17 00:00:00 2001 From: Nic Ma Date: Mon, 7 Sep 2020 22:47:43 +0800 Subject: [PATCH 2/2] [DLMED] update notebook content --- README.md | 2 + automatic_mixed_precision.ipynb | 19 + brats_segmentation_3d.ipynb | 2 +- load_medical_images.ipynb | 672 +++++++------------------------- 4 files changed, 156 insertions(+), 539 deletions(-) diff --git a/README.md b/README.md index 2cb8b12c31..759d04c979 100644 --- a/README.md +++ b/README.md @@ -31,6 +31,8 @@ This notebook compares the performance of `Dataset`, `CacheDataset` and `Persist #### [integrate_3rd_party_transforms](./integrate_3rd_party_transforms.ipynb) This tutorial shows how to integrate 3rd party transforms into MONAI program. Mainly shows transforms from BatchGenerator, TorchIO, Rising and ITK. +#### [load_medical_imagesl](./load_medical_images.ipynb) +This notebook introduces how to easily load different formats of medical images in MONAI and execute many additional operations. #### [mednist_GAN_tutorial](./mednist_GAN_tutorial.ipynb) This notebook illustrates the use of MONAI for training a network to generate images from a random input tensor. A simple GAN is employed to do with a separate Generator and Discriminator networks. diff --git a/automatic_mixed_precision.ipynb b/automatic_mixed_precision.ipynb index 21cfcd8d18..2d37ca94e5 100644 --- a/automatic_mixed_precision.ipynb +++ b/automatic_mixed_precision.ipynb @@ -673,6 +673,25 @@ "plt.legend(loc='best')\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleanup data directory\n", + "\n", + "Remove directory if a temporary was used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if directory is None:\n", + " shutil.rmtree(root_dir)" + ] } ], "metadata": { diff --git a/brats_segmentation_3d.ipynb b/brats_segmentation_3d.ipynb index 292534a1d0..60f6c686e3 100644 --- a/brats_segmentation_3d.ipynb +++ b/brats_segmentation_3d.ipynb @@ -727,7 +727,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.10" } }, "nbformat": 4, diff --git a/load_medical_images.ipynb b/load_medical_images.ipynb index 221342eb1d..2db2667b7a 100644 --- a/load_medical_images.ipynb +++ b/load_medical_images.ipynb @@ -4,11 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Overview\n", + "# Load medical images\n", "\n", - "This notebook introduces you MONAI's transformation module for 3D images.\n", + "This notebook introduces how to easily load different formats of medical images in MONAI and execute many additional operations.\n", "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/Tutorials/blob/master/3d_image_transforms.ipynb)" + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/Tutorials/blob/master/load_medical_images.ipynb)" ] }, { @@ -34,7 +34,7 @@ } ], "source": [ - "%pip install -qU \"monai[gdown, nibabel]\"" + "%pip install -qU \"monai[itk, nibabel, pillow]\"" ] }, { @@ -53,8 +53,8 @@ } ], "source": [ - "%pip install -qU matplotlib\n", - "%matplotlib inline" + "# temporarily need this, FIXME: remove when MONAI v0.3 released\n", + "%pip install -qU git+https://github.com/Project-MONAI/MONAI#egg=MONAI" ] }, { @@ -105,25 +105,26 @@ "# See the License for the specific language governing permissions and\n", "# limitations under the License.\n", "\n", - "import glob\n", + "# Copyright 2020 MONAI Consortium\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "\n", "import os\n", "import shutil\n", - "import tempfile\n", - "\n", - "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "\n", - "from monai.apps import download_and_extract\n", + "import itk\n", + "from PIL import Image\n", + "import tempfile\n", + "from monai.data import ITKReader, NibabelReader, PILReader\n", + "from monai.transforms import LoadImage, LoadImaged, AddChanneld, Resized, ToTensord, Compose\n", "from monai.config import print_config\n", - "from monai.transforms import (\n", - " AddChanneld,\n", - " LoadNifti,\n", - " LoadNiftid,\n", - " Orientationd,\n", - " Rand3DElasticd,\n", - " RandAffined,\n", - " Spacingd,\n", - ")\n", "\n", "print_config()" ] @@ -132,16 +133,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Setup data directory\n", - "\n", - "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable. \n", - "This allows you to save results and reuse downloads. \n", - "If not specified a temporary directory will be used." + "## Load image with default image reader\n", + "MONAI leverages `ITK` as the default image reader, it can support most of the common medical image formats.\n", + "More details, please check: https://github.com/InsightSoftwareConsortium/ITK/tree/master/Modules/IO" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "metadata": { "tags": [] }, @@ -150,316 +149,86 @@ "name": "stdout", "output_type": "stream", "text": [ - "root dir is: /workspace/data/medical\n" + "image data shape:(128, 128, 128)\n", + "meta data:{'ITK_FileNotes': '', 'aux_file': '', 'bitpix': '64', 'cal_max': '0', 'cal_min': '0', 'datatype': '64', 'descrip': '', 'dim[0]': '3', 'dim[1]': '128', 'dim[2]': '128', 'dim[3]': '128', 'dim[4]': '1', 'dim[5]': '1', 'dim[6]': '1', 'dim[7]': '1', 'dim_info': '0', 'intent_code': '0', 'intent_name': '', 'intent_p1': '0', 'intent_p2': '0', 'intent_p3': '0', 'nifti_type': '1', 'pixdim[0]': '0', 'pixdim[1]': '1', 'pixdim[2]': '1', 'pixdim[3]': '1', 'pixdim[4]': '0', 'pixdim[5]': '0', 'pixdim[6]': '0', 'pixdim[7]': '0', 'qform_code': '1', 'qform_code_name': 'NIFTI_XFORM_SCANNER_ANAT', 'qoffset_x': '-0', 'qoffset_y': '-0', 'qoffset_z': '0', 'quatern_b': '0', 'quatern_c': '0', 'quatern_d': '1', 'scl_inter': '0', 'scl_slope': '1', 'sform_code': '0', 'sform_code_name': 'NIFTI_XFORM_UNKNOWN', 'slice_code': '0', 'slice_duration': '0', 'slice_end': '0', 'slice_start': '0', 'srow_x': '0 0 0 0', 'srow_y': '0 0 0 0', 'srow_z': '0 0 0 0', 'toffset': '0', 'vox_offset': '352', 'xyzt_units': '2', 'origin': array([0., 0., 0.]), 'spacing': array([1., 1., 1.]), 'direction': array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],\n", + " [0., 1., 0., 0.],\n", + " [0., 0., 1., 0.],\n", + " [0., 0., 0., 1.]]), 'affine': array([[1., 0., 0., 0.],\n", + " [0., 1., 0., 0.],\n", + " [0., 0., 1., 0.],\n", + " [0., 0., 0., 1.]]), 'spatial_shape': [128, 128, 128], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.nii.gz'}\n" ] } ], "source": [ - "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", - "root_dir = tempfile.mkdtemp() if directory is None else directory\n", - "print(f\"root dir is: {root_dir}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download dataset\n", - "\n", - "Downloads and extracts the dataset. \n", - "The dataset comes from http://medicaldecathlon.com/." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "resource = \"https://drive.google.com/uc?id=1jzeNU1EKnK81PyTsrx0ujfNl-t0Jo8uE\"\n", - "md5 = \"410d4a301da4e5b2f6f86ec3ddba524e\"\n", - "\n", - "compressed_file = os.path.join(root_dir, \"Task09_Spleen.tar\")\n", - "data_dir = os.path.join(root_dir, \"Task09_Spleen\")\n", - "if not os.path.exists(data_dir):\n", - " download_and_extract(resource, compressed_file, root_dir, md5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set MSD Spleen dataset path\n", - "\n", - "The following groups images and labels from `Task09_Spleen/imagesTr` and `Task09_Spleen/labelsTr` into pairs." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "train_images = sorted(glob.glob(os.path.join(data_dir, \"imagesTr\", \"*.nii.gz\")))\n", - "train_labels = sorted(glob.glob(os.path.join(data_dir, \"labelsTr\", \"*.nii.gz\")))\n", - "data_dicts = [\n", - " {\"image\": image_name, \"label\": label_name}\n", - " for image_name, label_name in zip(train_images, train_labels)\n", - "]\n", - "train_data_dicts, val_data_dicts = data_dicts[:-9], data_dicts[-9:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The image file names are organised into a list of dictionaries." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'image': '/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz',\n", - " 'label': '/workspace/data/medical/Task09_Spleen/labelsTr/spleen_10.nii.gz'}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_data_dicts[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The list of data dictionaries, `train_data_dicts`,\n", - "could be used by PyTorch's data loader.\n", - "\n", - "For example,\n", - "\n", - "```python\n", - "from torch.utils.data import DataLoader\n", - "\n", - "data_loader = DataLoader(train_data_dicts)\n", - "for training_sample in data_loader:\n", - " # run the deep learning training with training_sample\n", - "```\n", + "# generate 3D test images\n", + "tempdir = tempfile.mkdtemp()\n", + "test_image = np.random.rand(128, 128, 128)\n", + "filename = os.path.join(tempdir, \"test_image.nii.gz\")\n", + "itk_np_view = itk.image_view_from_array(test_image)\n", + "itk.imwrite(itk_np_view, filename)\n", + "data, meta = LoadImage()(filename)\n", "\n", - "The rest of this tutorial presents a set of \"transforms\"\n", - "converting `train_data_dict` into data arrays that will\n", - "eventually be consumed by the deep learning models." + "print(f\"image data shape:{data.shape}\")\n", + "print(f\"meta data:{meta}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Load the NIfTI files\n", + "## Load a list of images and stack as 1 training item\n", + "Loading a list of files, stack them together and add a new dimension as first dimension.\n", "\n", - "One design choice of MONAI is that it provides not only the high-level workflow components,\n", - "but also relatively lower level APIs in their minimal functioning form.\n", - "\n", - "For example, a `LoadNifti` class is a simple callable wrapper of the underlying `Nibabel` image loader.\n", - "After constructing the loader with a few necessary system parameters,\n", - "calling the loader instance with a `NIfTI` filename will return the image data arrays,\n", - "as well as the metadata -- such as affine information and voxel sizes." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "loader = LoadNifti(dtype=np.float32)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "input: /workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz\n", - "image shape: (512, 512, 55)\n", - "image affine:\n", - "[[ 0.97656202 0. 0. -499.02319336]\n", - " [ 0. 0.97656202 0. -499.02319336]\n", - " [ 0. 0. 5. 0. ]\n", - " [ 0. 0. 0. 1. ]]\n", - "image pixdim:\n", - "[1. 0.976562 0.976562 5. 0. 0. 0. 0. ]\n" - ] - } - ], - "source": [ - "image, metadata = loader(train_data_dicts[0][\"image\"])\n", - "print(f\"input: {train_data_dicts[0]['image']}\")\n", - "print(f\"image shape: {image.shape}\")\n", - "print(f\"image affine:\\n{metadata['affine']}\")\n", - "print(f\"image pixdim:\\n{metadata['pixdim']}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Oftentimes, we want to load a group of inputs as a training sample.\n", - "For example training a supervised image segmentation network requires a pair of image and label as a training sample.\n", - "\n", - "To ensure a group of inputs are beining preprocessed consistently,\n", - "MONAI also provides dictionary-based interfaces for the minimal functioning transforms.\n", - "\n", - "`LoadNiftid` is the corresponding dict-based version of `LoadNifti`:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "loader = LoadNiftid(keys=(\"image\", \"label\"))" + "And use the meta data of the first image to represent the stacked result." ] }, { "cell_type": "code", "execution_count": 9, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "input:, {'image': '/workspace/data/medical/Task09_Spleen/imagesTr/spleen_10.nii.gz', 'label': '/workspace/data/medical/Task09_Spleen/labelsTr/spleen_10.nii.gz'}\n", - "image shape: (512, 512, 55)\n", - "label shape: (512, 512, 55)\n", - "image pixdim:\n", - "[1. 0.976562 0.976562 5. 0. 0. 0. 0. ]\n" - ] - } - ], - "source": [ - "data_dict = loader(train_data_dicts[0])\n", - "print(f\"input:, {train_data_dicts[0]}\")\n", - "print(f\"image shape: {data_dict['image'].shape}\")\n", - "print(f\"label shape: {data_dict['label'].shape}\")\n", - "print(f\"image pixdim:\\n{data_dict['image_meta_dict']['pixdim']}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "image, label = data_dict[\"image\"], data_dict[\"label\"]\n", - "plt.figure(\"visualize\", (8, 4))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"image\")\n", - "plt.imshow(image[:, :, 30], cmap=\"gray\")\n", - "plt.subplot(1, 2, 2)\n", - "plt.title(\"label\")\n", - "plt.imshow(label[:, :, 30])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add the channel dimension\n", - "\n", - "Most of MONAI's image transformations assume that the input data has the shape: \n", - "`[num_channels, spatial_dim_1, spatial_dim_2, ... ,spatial_dim_n]` \n", - "so that they could be interpreted consistently (as \"channel-first\" is commonly used in PyTorch). \n", - "Here the input image has shape `(512, 512, 55)` which isn't in the acceptable shape (missing the channel dimension), \n", - "we therefore create a transform which is called to update the shape:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "tags": [] - }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "image shape: (1, 512, 512, 55)\n" + "image data shape:(3, 128, 128, 128)\n", + "meta data:{'ITK_FileNotes': '', 'aux_file': '', 'bitpix': '64', 'cal_max': '0', 'cal_min': '0', 'datatype': '64', 'descrip': '', 'dim[0]': '3', 'dim[1]': '128', 'dim[2]': '128', 'dim[3]': '128', 'dim[4]': '1', 'dim[5]': '1', 'dim[6]': '1', 'dim[7]': '1', 'dim_info': '0', 'intent_code': '0', 'intent_name': '', 'intent_p1': '0', 'intent_p2': '0', 'intent_p3': '0', 'nifti_type': '1', 'pixdim[0]': '0', 'pixdim[1]': '1', 'pixdim[2]': '1', 'pixdim[3]': '1', 'pixdim[4]': '0', 'pixdim[5]': '0', 'pixdim[6]': '0', 'pixdim[7]': '0', 'qform_code': '1', 'qform_code_name': 'NIFTI_XFORM_SCANNER_ANAT', 'qoffset_x': '-0', 'qoffset_y': '-0', 'qoffset_z': '0', 'quatern_b': '0', 'quatern_c': '0', 'quatern_d': '1', 'scl_inter': '0', 'scl_slope': '1', 'sform_code': '0', 'sform_code_name': 'NIFTI_XFORM_UNKNOWN', 'slice_code': '0', 'slice_duration': '0', 'slice_end': '0', 'slice_start': '0', 'srow_x': '0 0 0 0', 'srow_y': '0 0 0 0', 'srow_z': '0 0 0 0', 'toffset': '0', 'vox_offset': '352', 'xyzt_units': '2', 'origin': array([0., 0., 0.]), 'spacing': array([1., 1., 1.]), 'direction': array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],\n", + " [0., 1., 0., 0.],\n", + " [0., 0., 1., 0.],\n", + " [0., 0., 0., 1.]]), 'affine': array([[1., 0., 0., 0.],\n", + " [0., 1., 0., 0.],\n", + " [0., 0., 1., 0.],\n", + " [0., 0., 0., 1.]]), 'spatial_shape': [128, 128, 128], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.nii.gz'}\n" ] } ], "source": [ - "add_channel = AddChanneld(keys=[\"image\", \"label\"])\n", - "datac_dict = add_channel(data_dict)\n", - "print(f\"image shape: {datac_dict['image'].shape}\")" + "filenames = [\"test_image.nii.gz\", \"test_image2.nii.gz\", \"test_image3.nii.gz\"]\n", + "for i, name in enumerate(filenames):\n", + " filenames[i] = os.path.join(tempdir, name)\n", + " itk_np_view = itk.image_view_from_array(test_image)\n", + " itk.imwrite(itk_np_view, filenames[i])\n", + "data, meta = LoadImage()(filenames)\n", + "\n", + "print(f\"image data shape:{data.shape}\")\n", + "print(f\"meta data:{meta}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we are ready to do some intensity and spatial transforms." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resample to a consistent voxel size\n", - "\n", - "The input volumes might have different voxel sizes. \n", - "The following transform is created to normalise the volumes to have (1.5, 1.5, 5.) millimetre voxel size. \n", - "The transform is set to read the original voxel size information from `data_dict['image.affine']`, \n", - "which is from the corresponding NIfTI file, loaded earlier by `LoadNiftid`." + "## Load 2D image in PNG format" ] }, { "cell_type": "code", "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "spacing = Spacingd(keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 5.0), mode=(\"bilinear\", \"nearest\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, "metadata": { "tags": [] }, @@ -468,274 +237,110 @@ "name": "stdout", "output_type": "stream", "text": [ - "image shape: (1, 334, 334, 55)\n", - "label shape: (1, 334, 334, 55)\n", - "image affine after Spacing:\n", - "[[ 1.5 0. 0. -499.02319336]\n", - " [ 0. 1.5 0. -499.02319336]\n", - " [ 0. 0. 5. 0. ]\n", - " [ 0. 0. 0. 1. ]]\n", - "label affine after Spacing:\n", - "[[ 1.5 0. 0. -499.02319336]\n", - " [ 0. 1.5 0. -499.02319336]\n", - " [ 0. 0. 5. 0. ]\n", - " [ 0. 0. 0. 1. ]]\n" + "image data shape:(256, 256)\n", + "meta data:{'origin': array([0., 0.]), 'spacing': array([1., 1.]), 'direction': array([[1., 0.],\n", + " [0., 1.]]), 'original_affine': array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]), 'affine': array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]), 'spatial_shape': [256, 256], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.png'}\n" ] } ], "source": [ - "data_dict = spacing(datac_dict)\n", - "print(f\"image shape: {data_dict['image'].shape}\")\n", - "print(f\"label shape: {data_dict['label'].shape}\")\n", - "print(f\"image affine after Spacing:\\n{data_dict['image_meta_dict']['affine']}\")\n", - "print(f\"label affine after Spacing:\\n{data_dict['label_meta_dict']['affine']}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To track the spacing changes, the data_dict was updated by `Spacingd`:\n", - "* An `image.original_affine` key is added to the `data_dict`, logs the original affine.\n", - "* An `image.affine` key is updated to have the current affine." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "image, label = data_dict[\"image\"], data_dict[\"label\"]\n", - "plt.figure(\"visualise\", (8, 4))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"image\")\n", - "plt.imshow(image[0, :, :, 30], cmap=\"gray\")\n", - "plt.subplot(1, 2, 2)\n", - "plt.title(\"label\")\n", - "plt.imshow(label[0, :, :, 30])\n", - "plt.show()" + "test_image = np.random.randint(0, 256, size=[256, 256])\n", + "filename = os.path.join(tempdir, \"test_image.png\")\n", + "Image.fromarray(test_image.astype(\"uint8\")).save(filename)\n", + "data, meta = LoadImage()(filename)\n", + "\n", + "print(f\"image data shape:{data.shape}\")\n", + "print(f\"meta data:{meta}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Reorientation to a designated axes codes\n", + "## Load image with specified image reader\n", + "The `LoadImage` transforms can automatically choose readers based on the supported subffixes and in below order:\n", + "- User specified reader at runtime when call this loader.\n", + "- Registered readers from the first to the last in list, user can register reader when initializing or at runtime.\n", + "- Default ITK reader.\n", "\n", - "Sometimes it is nice to have all the input volumes in a consistent axes orientation. \n", - "The default axis labels are Left (L), Right (R), Posterior (P), Anterior (A), Inferior (I), Superior (S). \n", - "The following transform is created to reorientate the volumes to have 'Posterior, Left, Inferior' (PLI) orientation:" + "And we can set additional parameters for the image readers, for example, set `c_order_axis_indexing=True` for `ITKReader`, this parameter will pass to ITK `read()` function later." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, - "outputs": [], - "source": [ - "orientation = Orientationd(keys=[\"image\", \"label\"], axcodes=\"PLI\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "tags": [] - }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "image shape: (1, 334, 334, 55)\n", - "label shape: (1, 334, 334, 55)\n", - "image affine after Spacing:\n", - "[[ 0. -1.5 0. 0.47680664]\n", - " [ -1.5 0. 0. 0.47680664]\n", - " [ 0. 0. -5. 270. ]\n", - " [ 0. 0. 0. 1. ]]\n", - "label affine after Spacing:\n", - "[[ 0. -1.5 0. 0.47680664]\n", - " [ -1.5 0. 0. 0.47680664]\n", - " [ 0. 0. -5. 270. ]\n", - " [ 0. 0. 0. 1. ]]\n" + "image data shape:(256, 256)\n", + "meta data:{'origin': array([0., 0.]), 'spacing': array([1., 1.]), 'direction': array([[1., 0.],\n", + " [0., 1.]]), 'original_affine': array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]), 'affine': array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]), 'spatial_shape': [256, 256], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.png'}\n" ] } ], "source": [ - "data_dict = orientation(data_dict)\n", - "print(f\"image shape: {data_dict['image'].shape}\")\n", - "print(f\"label shape: {data_dict['label'].shape}\")\n", - "print(f\"image affine after Spacing:\\n{data_dict['image_meta_dict']['affine']}\")\n", - "print(f\"label affine after Spacing:\\n{data_dict['label_meta_dict']['affine']}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "image, label = data_dict[\"image\"], data_dict[\"label\"]\n", - "plt.figure(\"visualise\", (8, 4))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"image\")\n", - "plt.imshow(image[0, :, :, 30], cmap=\"gray\")\n", - "plt.subplot(1, 2, 2)\n", - "plt.title(\"label\")\n", - "plt.imshow(label[0, :, :, 30])\n", - "plt.show()" + "loader = LoadImage()\n", + "loader.register(ITKReader(c_order_axis_indexing=True))\n", + "data, meta = loader(filename)\n", + "\n", + "print(f\"image data shape:{data.shape}\")\n", + "print(f\"meta data:{meta}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Random affine transformation\n", + "## Load image and execute additional operations\n", + "Some image readers can support additional operations after reading the image from file.\n", "\n", - "The following affine transformation is defined to output a (300, 300, 50) image patch. \n", - "The patch location is randomly chosen in a range of (-40, 40), (-40, 40), (-2, 2) in x, y, and z axes respectively. \n", - "The translation is relative to the image centre. \n", - "The 3D rotation angle is randomly chosen from (-45, 45) degrees around the z axis, and 5 degrees around x and y axes. \n", - "The random scaling factor is randomly chosen from (1.0 - 0.15, 1.0 + 0.15) along each axis. " + "For example, we can set a converter for PILReader: `PILReader(converter=lambda image: image.convert(\"LA\"))`." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, - "outputs": [], - "source": [ - "rand_affine = RandAffined(\n", - " keys=[\"image\", \"label\"],\n", - " mode=(\"bilinear\", \"nearest\"),\n", - " prob=1.0,\n", - " spatial_size=(300, 300, 50),\n", - " translate_range=(40, 40, 2),\n", - " rotate_range=(np.pi / 36, np.pi / 36, np.pi / 4),\n", - " scale_range=(0.15, 0.15, 0.15),\n", - " padding_mode=\"border\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can rerun this cell to generate a different randomised version of the original image." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [] - }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "image shape: torch.Size([1, 300, 300, 50])\n" + "image data shape:(256, 256, 2)\n", + "meta data:{'format': None, 'mode': 'LA', 'width': 256, 'height': 256, 'info': {}, 'spatial_shape': [256, 256], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.png'}\n" ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "affined_data_dict = rand_affine(data_dict)\n", - "print(f\"image shape: {affined_data_dict['image'].shape}\")\n", + "loader = LoadImage(PILReader(converter=lambda image: image.convert(\"LA\")))\n", + "data, meta = loader(filename)\n", "\n", - "image, label = affined_data_dict[\"image\"][0], affined_data_dict[\"label\"][0]\n", - "plt.figure(\"visualise\", (8, 4))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"image\")\n", - "plt.imshow(image[:, :, 15], cmap=\"gray\")\n", - "plt.subplot(1, 2, 2)\n", - "plt.title(\"label\")\n", - "plt.imshow(label[:, :, 15])\n", - "plt.show()" + "print(f\"image data shape:{data.shape}\")\n", + "print(f\"meta data:{meta}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Random elastic deformation\n", - "\n", - "Similarly, the following elastic deformation is defined to output a (300, 300, 10) image patch. \n", - "The image is resampled from a combination of affine transformations and elastic deformations. \n", - "`sigma_range` controls the smoothness of the deformation (larger than 15 could be slow on CPU) \n", - "`magnitude_range` controls the amplitude of the deformation (large than 500, the image becomes unrealistic)." + "## Connect `LoadImage` with other transforms\n", + "It's very easy to connect `LoadImage` transform with other transforms to construct a transform chain." ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "rand_elastic = Rand3DElasticd(\n", - " keys=[\"image\", \"label\"],\n", - " mode=(\"bilinear\", \"nearest\"),\n", - " prob=1.0,\n", - " sigma_range=(5, 8),\n", - " magnitude_range=(100, 200),\n", - " spatial_size=(300, 300, 10),\n", - " translate_range=(50, 50, 2),\n", - " rotate_range=(np.pi / 36, np.pi / 36, np.pi),\n", - " scale_range=(0.15, 0.15, 0.15),\n", - " padding_mode=\"border\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can rerun this cell to generate a different randomised version of the original image." - ] - }, - { - "cell_type": "code", - "execution_count": 26, + "execution_count": 23, "metadata": { "tags": [] }, @@ -744,35 +349,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "image shape: (1, 300, 300, 10)\n" + "image data shape:torch.Size([1, 64, 64])\n", + "meta data:{'origin': array([0., 0.]), 'spacing': array([1., 1.]), 'direction': array([[1., 0.],\n", + " [0., 1.]]), 'original_affine': array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]), 'affine': array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]]), 'spatial_shape': [256, 256], 'filename_or_obj': '/tmp/tmpg4lwxckh/test_image.png'}\n" ] - }, - { - "data": { - "image/png": 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0Gmi327h+/frY5yEgICDgreKBIGa1BJU8kuTrmw3QxM0Gbv2MxFWpVDA3N4darYZUKoVjx45hfn7e+5Hpk1TZWkmUZJNk8VvLPJPJeCtT2831whqApdYw/cH8U9hJS9IkRVNWptNp5HI5XxdeQwO9GNWsFri9B7SqR6NRrP6qEqTTab+MjBm5+v2+71PrS+a1ktack3BVgbD9ANzwy+vEolQq4d3vfjcqlQo+//nPY2ZmBtlsFleuXEksKyAgIOCt4h1PzNZ6BJIzWo3LVGUtuiRfpLX2eF42m8XExAT27duHfD6PRqOBRx55xC+T6vV6iX7jcWWOqxstVuZ2puU4Go18Okm1LpUorbWskq31m9t62TK4tIh/nBzw2ipF23uhZaoVawPgqHxo/fgZJfVMJuOlby2LSNrYw/rzWSersJC41XJX1wNjBF588UVUKhW0Wi2srq7e9D4GBAQEvBm844nZ+mZvdWwSUSjGkaWel8lkfHKQRqOBVCqFmZkZnD59Gvl8fux6V5KIXb6klnNSUBYt1Gw266OeacFyjXTStZImKEDcnz7ue43UzufzyOfzsQCtJJ81sNP/bP3y1q/NwC363nO5XMziZb/wtd/vxyYn1jfOOuh3SvLsV1tvfp7k3lDfczqdxokTJ1Aul3HmzBnMz8/jzJkzu3k3qoCAgHcY3tHEfDOLM8mfbL9Ta9vKrOO+y+VyfrMJAOh2uzh69ChOnTrls3up1XmzXZ+SJgXWd8wgLpW89XiSmsquLHsc8bJshfWrAvDSMcmSoOyeSqX8JIQSud2SkpMQ3YVKg8o07zWwlYyF5E4LPCl5CSV0u3+yVSis7z5JPRg3SbP9SHUgk8ng4MGDPtXqxsZGiNQOCAi4Y3jHEjN9rkTSoHqzAdd+rgO49U/zs0KhgJmZGUxOTmJjYwOj0QiPP/44HnvsMQDwUchJE4Yk/7G+J2logg8ucxoMBn6PZl2DbHdR0olEUttooap1nmRFckJQKBSQz+dj/aEpLnX5Es8fDAaxgC0lNdbBSsV2IqP1IqGrejAYDHz/EBqMpjK5dRnodzoR0mvqq/YP12Tz+gcOHEA+n0e/38fy8jKWlpZ23PeAgICAN4t3NDED43214yRrK2ne7Fy9Fkl5YmICy8vL2NzcxDPPPINTp055ohlnwZNY1L+aVAebBWtzc9NHcmezWX+MtlsDmpL6yPaDprK0xMOy6MumVUoLmcSulrXNcc3jKVGPUyrGuSDsMic7YdFyOMng5MXeg5tNUrRvrC/exhoQtNw5qdjc3MTc3ByiaCsa/TOf+QzW1tYS2xUQEBBwu3hHErPN7qUDaRIR8H8gnkxE3ydZ1bxOtVrF7Owsstksrl+/jkqlgqeeegpHjx71BDqunjdrg32v6457vV7M4kuSZTX5BtvCV+un5jVu5kelVE9rmSAJJsnyJGJa9QB2kLcer/W0dSWSlmAVi8VYAhLd6ALYkt3VEteJRxLRJk3KrDXP54QTJUr1vBbbPjMzg/e+970YDof49Kc/HSTtgICAt4V3JDETScQ7jngsxiWg4DmUTWu1Gvbt24coitBqtXDw4EE8+eSTmJ6eBoAdg7gtn7DEoIRLq49laIQ1QVJQQlGfriU7BirpsToJ0f8pC+dyOeRyuR3LhWymLUt4jJAmWaoFb5UKqxSwnjcja5IzJw5Jk5oo2loP3ev1fLS2fqfPhwZy6c5Vtj7aFpZJFUCVgkKhgGaziRMnTuCLX/wirl27lvDEBQQEBNwe3nHErPv/EjfzLydhnLyq52UyGTQaDUxPT2MwGKBareLpp5/G/v37kc1md0RCJ5HyOEue32nkMy01+mi1XPpV+V4lWBKibRuJXq1tJXH1FdNC1iVQSlTjrF+1lhlVnUSCKoMr2bEuep6dYOl1ut0uRqMRCoWCn8TYwLd8Pg8AnkiTSJ79Y2V8vZ5GprPPSco66eA69WKxiIMHD+Lxxx/Hpz71qcRnMiAgIOB28I4iZitH32zwS5Iwb2U985h0Oo2pqSlMTEwAAI4fP46jR4+i0WjEBm0gvv7VSr1J0cK0yDjoc+AfDAZYXl7G1atXMRqNfMKSXC7niYHrlZOs46T+YapKlYW1jc4570/O5XKeGJPaZAOkeBzrpOSqPlu+5wQjaamYrbt+p/eMPnemHNV11IyWBuD98VbW1jrZCYDN62192uyLcX3Q7XbRaDTwzDPP4POf/zwWFhbsYxYQEBBwW3hHEbOVSO3/lqiTLGO1/pIIK5vNYt++fZienka5XMaxY8ewf/9+ADuX/NjrJVnzSvr8Xy1GLq967rnn/IDeaDTw+OOP+3XRJOUkn3KSr1QtXkuu/J6knc1mfeQ1y7QWuFqZSt66nlqteF7Lrh+290OvoZMdtfJVcmZdOp0ONjc3veWs2cdYLslb04/qNbQeGqluSdlOMrQdPJb9EEURDh06hPe97334lV/5lZBLOyAg4C3hHUPMdrlRkpXI91a+VOj51oLito20lk+cOIHp6WlvUdlySHxJ5et3SjQkKkqw3W4XnU4H58+fx/Xr1708y72Akyw7S8bj2mtlXvVp61aL6ovW47Q9Vn4ejUbo9Xqe9OzSKJ0EKdmpj1rryLbSerf3W4k6irYSjTi3FS3PCQbJ0U5MAMSWdllQtVCZ3U6ArKqSpMj0ej0UCgU88cQT+NznPofz58/vuFZAQEDArfCOIGYlNL7XQZqf6fHAzkGdnyXJ4KVSCdPT05icnMTU1BROnDiBqakp7/Ol9JzkayWS6mKjrWn5tVotvPHGG2i1WqhUKmg0Gti7dy8GgwGazSZmZ2dRLpdjJKdrb9VCTJKBdc9h7cNsNuuJT/dmtikpkyLC+X44HKLf73ufrxK+Wum0JIG4D571JSmvra3h2rVrWFtbQ6lUwt69ezE5OYlisbjDd6192+v1vOXMRCS8HhOvADekbVU8klQW7SvWLUkFsf3B9vX7fWQyGRw4cAAf/vCH8YlPfAIXL15EQEBAwJvBO4aYrV/Vfp7kb7YEZs8n4RUKBezZswfVahW1Wg2PPvoo6vV6LM2iypbWT8qybPSykh9JeXNzEysrK3juuecwHA5x5MgRTE9Po16vex/q/Pw8jh07hlKpFMtslUQkdqmUPUY3nqDVqgSqZYyb/GjfDQYDdDodT4D0TWs2MO0TXTedBBLzlStXcP36dRQKBd+Xe/bs8aRq28vySc7FYtHn8FY53t4rKhJaRlIcgr2X2j/8syoAg/RyuRxOnDiBxx9/HNevX/cbbgQEBATcDnY9MY9LnjFuoCfUp8xyeJ4OpMVi0ScOKRaLePzxx9FsNmODqbU6LUhKSkKUiWlh9no9bGxs4LXXXsOZM2fQ7/fx5JNP4siRIygUCqhUKnDOodvt4uDBg6hUKjsitFmXcZMQ2z/aB7pHs1q4WlaSVavlDQYDdLtdOOdQLBZjS6s0GYoSve13+tQJ3Wd5Y2MD3W4X7Xbb7yBF4rdQS53SPwCvBuikQBORdLtdXyda4jZugGuXk/rd9j0nPqqI9Pt91Go1nDx5El/4whdw6dKlHfUPCAgIGIddT8yE9endTJIEdhKLDYBKp9N+D+Vms4lyuYwnn3wSzWYzRohJwUJWVrX+R/ptgS35tN1u4/XXX8eXvvQlXL58Gb1eDwcPHkS1WvUBStlsFnv37sXm5qbfPpLts/2gFr9NsWn7Qn2tGhVtSYYEp+dQzgVu7OGsWcF0kkDC73a7MT+v9g+tbJXZnXOoVCrI5/MolUrI5XKoVqtoNBooFAqx+5bUF+p7JzlzAmLbxshrRnKPm2gl+be1L2z2NDv54LVmZ2dRqVR2lB8QEBBwM+xqYlbys69JRGSlVz1WP0+lUiiVSj7vdbPZxOnTpzExMbFjUwQth2Up7PVomTrn0O/30ev18MILL+AP/uAPsLq6iiiKUC6XkcvlsLKyggsXLqBarfroa5IAiQ/YuUex9WFrHXVpkq2b9lUSmY9GI3Q6nR1+eF6DWcFYnvqh1SrXOvB4S7BsRzqdxsTEBPbu3YtUKoVcLoc9e/Z4/7om8tD+T4qa1vozKIx+ZSVT7u3MviXpWp8xv1fp3/q5tVx+zoC4yclJPPvss3j11VdDhHZAQMBt420Rs3PuHIB1AJsAhlEUPe2cmwTwCwDmAZwD8E1RFC2/lfLt8ijFON9xkuy5XVc/uObzeUxPT6PRaKBareLEiROYnJz0iTL0+jaT1bhXALFgKmbDWlpawosvvoi1tbWYrDwcDjEYDNBqtXygEq3t4XCIfD7vSYfkQfK2JGUtX5sQRKX7pEkLXzVhh64JppVNMuOxNjgM2CLvKIq8ZKzBawB2KBFRFKFUKuHAgQMolUpIp9NoNBp+4mAtey1Ly9FJByO2tb5W5bBLzngPdKKh66C1neoi4HGqMug9ePe7343f+Z3fwblz5xKfy92Eu/17DggIuD3c/mbG4/FfRFH0RBRFT2+//wEAn4qi6BEAn9p+/5ah1rDCWi524OS5amE5t7UcZ25uDgcOHMD09DSefvppzM7O7kjjyOPtUiKtl5K9+m97vZ7fJ/nKlStYWVnxdczlcpicnMQjjzyC+fl5TE5OIpPJ+IAqlqvEwXSZtm3j2m37hkubNO+zndBwyRJwQ5bWgDFa8PrZuCVN+v3NFAYl+HK57N0K6lfWV5ZFixxAbK9qJf9+v+8nVbpsjM+EphDl92wny9A+sP3KttESpoRNwmaf1+t1vP/970epVNrxDO1S3NXfc0BAwK1xJ4jZ4usB/Mz2/z8D4BveSiE6ECdZx+MIadzxwJbFNzc3h71792JqagpPPvmkj74eF/iU5GMmVBYmMQwGgx2DNYkjnU6jUqng9OnTeNe73oWpqamY1JvJZPxWi+r/pPVnfZnaD0n9pnVkgFVS8BIl9+Fw6K1+lX9VxrVWLF+V6FgPvqdFSlhZm4SWyWS8G0CDu2y0OPuBcQLlchm1Ws377LXv7X3VZ0UtZB5LCT6fz8cIPyniXCdPOqlTaTyVSuHYsWM4efLkWDVnl+OO/J4DAm4X6WOH8fI/fRbn/96fvN9VuW94uz7mCMBvOOciAD8ZRdHHAcxEUXR5+/srAGbeSsGWcJJk63HH8jMg7h+dmZnBI488grm5Oezbtw+FQsEvidLo7XH+ay2bx5FM8vm836dXiaXZbGJ+fh5LS0tot9vYu3cvDh8+jEKhgHa77QfwYrGIQqGAYrHopeObkaJa9SQh7ackmbnX6/ksX5THdQMKQq1d26/Wn2/9rkpY1v87jhTt+mwGhiURn5bBZCRUARiY1m63vfXPiYZazdzXmpMSm7rTWtdJz5xNVUowPsA5F0vV+Z73vAdnz57d7dtC3rXfc0DA7SBz+BCu/JezePUjH8PqqIMnJ/8Gjv1cF9nLyxiee3gS9rxdYv7KKIouOuf2APhN59wZ/TKKomj7R74DzrmPAvjomO8SiXYcSdrj+MrBM5PJYGZmBsePH8eRI0ewb98+P3Amnbtd91hdSEI6OHPdbD6f94S6sbGB1dVVDAYDpNNp7N27F+VyGb1eD+vr66hWq6jX6wDgrTKSDMvj9TWTlZXkNXuXEoz1pSphDodDrK+vx7Yw5Hfqh9Yds7hkiRb/ONKmWsDobU0sohJ0krTN106n43NMMyWqnWTwWLVi1bJ3zvn+1s842UmSrfv9fqwPbAS/dQvo82IncOxbnjMYDFAqldBsNvHYY4/hM5/5zI4+2EW4I7/nAt4xsn3ALsPzP7gHr33txwAA9VQRr37jTwLfCBz+5Y/i+HcFYr4tRFF0cfv1mnPulwC8F8BV59xcFEWXnXNzABL3wNuejX8cAJJ+7JaErW/SSrZJIOHNzc3hqaeewpEjRzAxMREjpaTgIZVLtSy+cqDPZrMoFouoVqsolUrI5/M+QEsjsJk8pNPpAICXqtkO9d3yOsyuxWU92n4GsFFmpRRvSSNJ7ua+yUkTD05mkuTu4XAYI2h7j3htbiihPnu1mm3UO0m+3W7jueeew6uvvorNzU0cPnwYTz31FCYnJ3359Btrv6nFzuuk02lPzsANyTufz2N1ddUHh/FYbfc4F4E+dxqpbdvIKHB7bq1W85I+rfndhjv1e665yV078wjYvbFVGqsAACAASURBVEiVy8g3uonf/bsP/jj+99/5ENb/1CKweye2dwxv2cfsnCs756r8H8BXA/gygE8A+Nbtw74VwC+/6UrJgAvENw0wdUg8nyRAS/mZZ57BY489hsnJyZgvMSlvsr0mP1NZmZJpqVTya25rtRpKpRIajQbK5XJsiY2u+wXgfZj80yQbtHp1GY++AkChUEChUPAJPjSCWdugbUmyCLW/kiLP2VbK9eOCvfiZTmg4saAFzb+k/Y8BYGFhAa+88opPUcpdtVgv9qO6HOwyJlqqfK9+bU4o6vV6zNq2z5qez+uorK2krM+apvDU7zipqVarmJqa8mrJbsPd/D0HBNwO3MG9+LpjX0787j35HP7y7GfucY3uH96OxTwD4Je2B6EMgH8bRdGvOef+EMAvOue+A8DrAL7pzRRqLWEdMDk4jpM39Rxu3fj000/j1KlTyOfzMStOdxMCdgZ5KXhdDu4M0qrX66jX68jn88hkMjsIn/UkIXU6nR2yK+vC753b2piBft+kdrEeJCMNcrqZ3J/Ut/Y7VRDou1UrWfuDIElySRetW+DG5hGWyFQlGA6H6HQ6fk/jffv2YXJyEqVSCb1ez/ubWR99BpLSoxK0TqkQsL+q1WrM18tJgPV1q+9ey7T9qJMSu84cgO+XWq2GqakprKys7MZ1zXfl9xwQcLsYlXI4XRqfW76c6iEzO4Ph5Sv3sFb3B2+ZmKMoehXAuxM+XwTwgbdabpJ0baVcHTiVoNUPOzExgUcffRSPPvqoD8xSEtbczhyULTlbi41kVa1WvUTNRBYkyFarhVar5dez8hUAqtWqJzCN8lUZFLhB1FayV+ubfcR6qiUq9yKx//iZWoVKKrrzFP94DSt923tBwrU7Nin0GH43MTGBI0eOYGZmxmf9yuVyXqZn/zOBiRKtlm9JMZvNotfrodPpoNFooNvtolgsot/v+4mQ3mOrpOhkxVrP+r39nPWgxVwsFlGr1VAul2ObbewW3K3fc0DA7SK93MJ/XjmBv1JL9JbgA8VN/NV/sB/Hv/3BJ+a7sVzqbcESi1qNSWRsLeZUKuUTVhw6dAilUmmHP5n/W2KyxMXPGZSVyWRQLBYxMTGBiYkJT8rAVgDR0tISFhcX0W63fX04MNPizuVysSVKzA09Go2875lLl5KsXo3Y1j6yfaj1T4ogJgFTrmbd1BongfI6SsTqk6XvGLgR4GWjtfV+qT89irYi25vNJvbu3Ytms4lisejP48RA23YzOd76xznB6Ha76Ha7KJfLaLVafr0026WqgK2zlmknQdbfrn2ux+n93a1ydkDAfUWvj0utm/82/sX7/090vuG996hC9w+7ipitjzOJePUzfc+/YrGIvXv3YmJiAnv27AEQ902rlKzfWUuLBMPjueFFuVz2AV5MJNLpdLCysoJr165heXk5FrBlM0yx3E6n4/cz1nWvus2htVJ5PkGZPCm63E5ibJtIuiRk+ru5jlgtWnsf1JJXklYftD1HfbCc5KgPu16v+1226GOmHG7ldLVMbYR4kuXKfOSLi4t+843hcIhGo+HLYHQ926/XYbtsZrCkPgFuyPcqvTMfOgDMzc3dVIIPCHgY0X5sHz558hM3PeYDxU38ix/9EbQ/8uw9qtX9wa7MlW2tZg6E9hg7QGezWUxNTaFarWJiYgLVanVHkglLNOO2JbRWNAmMsi53jKJvuNVqodPp+MHb+sKtT9a5G3mcaRGShJVUNJhI83DzOBK57bMkqZkkpiqAtpN9rFKxEry1SrV8fq7rolUetvdNrU1gyzKemJjw9ed6ZBKc+rFVQbHPCa9hVQLnHNbX19FqtVCtVrG+vu5JWjfeyGQysZ3FKMnb59D62fWa49aXFwoFAPDXCwgIuAE3HGF51MFUunzT445ny1j8b1oofzKPSLbmfZCw6yzmpAGPVllSwgmCfmVugHDixAk/8KtPU6EWH69FwtAgH0q9JNBWq4W1tTUsLS3h8uXLuHbtms+FrQSplq8SmxKjBkBZH64lRLtrFa11DTqzPmC2Uz+z0jXLVVmbEwD1G5N8rdWoFiVzgJOQaIWyXACx/Zx5nvrdGfXOXad4T6gQqPqQJOlrP7BfcrkcAODChQue8AeDAYrFYkyO7/f7sWhtJVu23z6LvKY+Q1Yt0ec1l8t5az0gIGAL2U/9MZ75je+7rWO/8BU/g/U/98RdrtH9w660mJOsCStpK1KpFMrlMmZmZpDP53H8+HFUq9WYn1a3GUwqUwknk8nE/udAy/W5lLD7/b4PKnPOxaw5lq8DtBKfWn9RFPk1ulauJvHl83lvcallqqSr5GEDy3geyVEVCCUOle8twbPOSZHllN0pretEgcu62F6Vi3WypIFc2gaSMtd1ax/oVo76PGj/8S+bzaLVamFxcdHvd8362b2vkyZ/nGDZSH47CRon5Wtf1Wo1LC0tISAgYBtRBGzeXtrarEtj3/edxdovZRDtskDKO4FdQ8zWx8vPrN8Z2Dn4MQc2l9ocOnRoR5pJDu5aln3P42gpqZXGQRxATK61Plxbd15DSVvXBKuFaUmd59O/zWAlkiCldO0ntsFK4ny1a32txQfECUetT72GRnDT2hwMBj6Lll5Pt14cjUY7/Ngkbf6xDmqldzodn5iD9e/1eigUCrHgsCRwNy9a7u1221+X7eLkZzQaodvtxtptn7mkBCsK7S/7nKZSKbTbbT9B263JRgICdjv+ycFfxn/71X8T+U/+4f2uyh3HriTmm1nHSYNgvV7HiRMncOzYMTSbzcS1xMBOK9Feg2RF61rJXY8nYdllMuMGcmI0GvmIa75PipjWdmoiDa0DA8esXK1lKQkDN9JRDgaDWOYw9YWyXlofWqXAjXW8rDcjzEnMbJPWh5/rBhW6eYeViLlmmYRLpUKP5bU7nY6Xu7XPtF85eWEGsHa7jW636xOWaAS1yvRWSbH3J8nfbCcxdoLGpXq1Wm1XZwELCLgvGDpsRiOk3a29rPszFfyNH/05/B/dv4jMf/qje1C5e4dd5WMmLKlZa5mfOedQqVTwrne9C4899hiazWYsHaQ9X0k1yZetuaA1c5UShiUdW1etnw7cwA2rWcu0g7ptJy0rlW4poWvddYIA3LCaSbLa1s3NzR1WoV2vrX1FMtcIck4yOp1OrD72/rAc3hfK+WwPX3VbSg1IGwwG6Ha7/lzghkuAfmO10oH4XstK0PSrp9Npb4FrelL1X+v9svdRrzOuvZq5Te8lny0AOHLkCAICAm7g5N/6Mv5Du3bbx39DeQPn/lwWSD1Yqxx2JTGr1aEDnh0Is9ksDhw4gMceewzVanWslDyO5PlerVJLsNaSSzrGRoxrPRXqT1XrTqVwJUfm4iaZOOf8hIHWo04mbD1UrtXr0uLe2NhIjBDWiQTbwuxcTKDSbrf9Lk6U/WmBWilc+1Kzr6nvVS14XT9t+11l53w+78uxEyW9N/yOMQec6LAfVRnh8ZwkWBeIdQFof+n5nOjpc6cBZd1uF9Vqdcc9Cwh4mDFqt7EZvbnfxG985IeRKhbuUo3uD3aNlH0raDATX5vNJp566inMzc3tWBZlfa/WQlYpWgdv51xsf2ZL5EmEa4n/VhK8Er29tq7Ltbs60dLt9XoxeVUtPOecl4Htmmi79pbWbqFQ8DJzkrRtN6ZgXXQ9NK1BWtesD+8dyU7laFrBjFLntVgW5Xpek23l9ph2y04lV/aPfU2lUigUCuh2uxgMBl6at5nKaNVbgteJGj/nsYokwtX+7Xa7qFQqmJiYwOLi4o5jAwICHl7sGmK2PjxCrRUd/IrFIo4dO4ZHHnnEW5L6PQN+LPHp9yQxEgoDvXQwv5368Bq6oYRG8NplM0mWvcKuMwa2SK7dbvs1tqoGsL60XFmXbDbrJVumsqSMqku0SIa0ELVtbIdGkKsVnEqlvKRs/e7aN0qMSk6DwQCTk5OxgDBduqSTAbaJ0ek2itv6dhV2PXImk/HBc0xAwjJZD5vmVPtOy016Puz91WQtjOxfWVnBxMQElpeXY89EQEDA7SMFwGV3DZXdEeya1nDAt0EzSdJyNpvF/v378cQTT/i8x3ZAZAAXYf2mJAjKoSQADajSuim5WNDfqdmpNGpbA744SGu9VLYlKTOZCQmXKTytFWcnIzrJ4C5W3AhC80wriVPSZZ8piVppWP2lrINOPnhNO1lSnzvl5HK5jPX19RiR839dPmb7mhb7ONeFle5tMBj7gtnXdELCyQojuEne1tesPnwlfG0nr2f7jf3abrfRaDR8Pu+AgIA3j4OZEs5+bB6Hv+UL97sqdwy7hpiVCG51TKVSwalTp2ISth2UrSWjA7PmRFaiTkptad9ba5A7TRWLxR15pWlxpdPpWPpNDuhJlqUSMzEcDtHtdmOZwSwR2U0hSBgsi+oB/yc5WktNfbrWp8+2qaxMa1p93nYHKJXGdT0v90hWqZsSt5IhX3UDDwDep80JzM2eG/teN9rgXtM8jvXQSQDvrd4/1ss+t0mR9vRZMxqbAW/T09OoVquBmAMC3iLSLoV6tXO/q3FHsWuI2RKAJR61yA4dOoSTJ096i0YH/iRfdFKgjvplOfDSwiMx6YCcZC1rfmXbFh241YpksBStOA7sJAESJus9GAxi65WT+oykpUFiKvOybRrQpH5oko8SrO0r9anylcTG6/MzlYtpheokSKPdVUbWYDMlPd1ExKoNNthMQTl6MBjErFi1eCnlc404d37iRIb3Vl0cbGOSn18/1/dEvV5HLpdDq9VCr9fD+vp6WDIVELCN9OkTaKa/+KbPOzqxgPV9ezG8eOku1OreY9cQMzA+sEoJolar4dSpU5iYmIgtdRkX3WoHRpbDP5ICyxpndSspqk/ZBh7R4qQvWDdrIFnpxhP0m2rwFculfE1Zd5xMq2XYrF2aKYvHbW5u+vqwDPWn2uAsez31lbJcG9lOolOytj5unUSx/9hftKRttDnJm2TP61gfM+8py7dkqZ/r+VZxUBlcrWZ97thf9h7ZyaFzzm+Ewh3Fer0eKpUKVldXdzy7AQEPG174vhq+qvjm4y1+/vB/wns+/F2Y+ngg5jsKSzjAzlSZuVwOR44c8QFf1sfIc+wAnBT9bC1LHazVyk6ygFimZgAjSGgkoHw+72VSrknW62mOapbLSUKv1/PWspKYTghorSrJ2pSZ3A3LEqFNoWnvgxKiEhYtfx5Pi1T7TvtbE4DoJIDlWmJM6mtVDnh+sViMXZfgRiN2IqP3OGlSpX3HyRUtdZ2I6HMCxDcfsWXaaO9SqYRqtYrl5WUAW5OHer2OhYWFIGcHPNSI/uS7Ud2zcb+rsSuwa4jZEiBflYAajQYee+wx1Ov1Heth7eDMMjhY6kBJ65RWlwYvsTwgOb+2BkepDxm4QdYaSKX5mFmWbuigvmFaXZrMQy1S4EY6T20Tr23Jl4TPVJO6uYQNWNJ2qzWoExclSiVR9oXWhySnQWK2rXqfda2yfRbYDvXTq0/e3nebYMYGFWod9Rh1WwyHw1gsgp6vAW90D7As7Sf1j/PcQqGASqXiA76GwyEmJyd9+wICHkqk0vj+f/2z+EBx89bHPgTYNcSsFp5ayvwul8thfn4eBw8e9MtN9HtrVfJPB3zgxlIkm4JSMS4AzVp8Kl+yHF6L8q1GZxcKBU9W6XTaW6u0OnksLWYO+AC8/KxLhfiqAVuUtLV+bDOJQOtt1+CqpK2Wr7UUSVq6vEqlc1UBrN8/afmY+rY5sdF7z7ooCer91UmGThzs/dc+S7r/qmTo5I19xbqxTL1H9jmx1noqtbXZSrlcjsn7DOwLCHgYka7V8Mr3n8Z/9ytP49Vv/Mn7XZ1dgV1DzAoN5iEJTE5O4t3vfjfK5XKMlO0AqxatBhKplMpjraWl17RSOYBYGVq+Wq9KWpS6NXK8UCjEgrIsKbO+6jNmwhO10q30S1m60+nsIC6VaO1kgn5kaz2rVWsD62xfK8EqyXMiwImB3fgjim6krkxyO7DcVqsVs8ApLSvx6j2x9y/pPgOIkao+J5Sf2Xatj7W+tQ8sQev1uVa8VCrFAsB4TKlUQrfbTaxnQMADC+dw5seP4eSB1/FPj/wigMpbLmr4oRWk/lUBowfgd7RriDnJF0hkMhkcOnQIBw4c2DGIE0qOdikSy6eFo+RjLUSVda3Vw/9tgBWP1eupjMtIbA68JFwrXVtLz0acqzxt/bSss65ZtlHSVAqsfG8tWP5vLT89zwaIKRGqz5WEZFUGKzMrsVsLW+8dXQLsY713SeSo0DbZjTEYIc56aOCbRmbb54oWtabfTFJgWG4ul8Pk5CQKhUJsokclJCDgQUe6UUd0aC+uPdvAN33vb+ETkz+JvMvi7ZDy+eEGNn9/AqP+S3euovcRu4aYkyRHDqS1Wg0nT55EPp+PLS2xgUK6A1JSwBYDsTSlZRJJEEoMHPTVGuVyHF6bREQLOJfL+cArEkGr1fIBRQC8pavXjKLIf6Y+dvUtU1ZVoqQ8Tl8lJWDdBxmA/14tOvWLsm0qR7McTmZ4DAO/dOtGOyHS66tCwMkCgFgAGgmakxumDLVbNY6zhK3/3fYtz2W99F5queo/VnWDz4mdVFhfvNaTZWazWTSbTb8fM9uo69YDAh5kXP/zp/CH/9vH5JPs2GNvF3/hi9+OfT/0mbddzm7BriBm9cnqZ7SS9u7di4MHD8ZkU0ua9j3/5ytJjQRpB1YtQ2HL0jrqUhkSFy1WWl25XM7nZtbsYrobk5WReQ0GiSlRqeyshKGBaNaSVRmba7PH5RLXCUChUPAWKmVyXU9t01yyf1VtUHWC95PXS6fTPrK63+9jY2MjRpQkZcr/VuJOmkQB45UUPc4Gv7XbbURRhHK5HKuz1p19S3K2173ZcwTccG0Ui0VUq1VP9FwOFxDwoCNVLuMb/+Zv3e9q7HrsCmK2Foa+LxQKOHr0KCqVSmz9b5JUbYOPrLTJgZGDp/p+tS5JpJ8Unc1jgPiOUozw5RrkiYkJL+faQVslcJV5beS1TiT0HJWR7RIhm5FL813rcbpcSAmQ/+u+yNpG9jUlXt2eknVmXfmZlaPpM46iKJbwhHK8Sr76rKh8r8+A9quqCTbASy37KIq8m6FUKsXiEDTIja+atjVpQjhuMqdydqlUip2je0MHBDyIcE+/C69+pIqfn/gnAIr3uzq7GruCmIG4n1N9b/V6Hfv3748doxG81h9tfYxanmZ0UoK2EwKWk2T9KDHpeZaoSAq0ssYRiVrcag0y+lctTV3OoxMKJUJLtlqmZtZSH7ctl/3b7/dj66uVaDQSnBOidDodcxVoPdj+fr/vZVsSkZXAWT+uf9a1xeP8yJYE9XlK8vna6Gptq1r89p6l02kUCoVYG7UOrKf2vX2G1JVBBB9zwAMN5/DGn6nhxW/7ZwikfGvsGmImlBCz2SwOHTqEqakpP4gmLUNR6GBqJUm7sQWhRJ/kJ1RruN/vewIiWfJPfZYAYmkyeW0roaoFy+spudJy5Ht+r8FGWp5dCsRy7frqTCYT26nKtp3BZ3quTUSi/ULiL5VKAOCJy/Yro9RpFbNszeKWzWZRKpW8T5ptJ2Fr27TuN1M/7P9K2JTq1VLW41SJocWs98z29TiZXQlfJ1ej0chL6QEBDyLSzUl8+rv/MYDy/a7KOwK33JHaOfdTzrlrzrkvy2eTzrnfdM69vP06sf25c879mHPurHPui865p267IiaoyjmHarWK+fl5nyBDB0odXPkdiVEjgCnjKqGMk5OTytVBejgcYmFhAefPn8fKykqMlFX2rdfr/q9Sqezwi+rxth4qIwM31vSSkCjns512v2Ier2Sn1q36rS0BaVvYR/1+H+12O3YdrTfLpX99OBz6IDvbTpXn6Vbg/tKqYHDjDTsBsf1l/cr2+3EpVdk3+jm3mdQELPrHZ0ufw3GwsQB6f7WvdDKmW3zeTdyr33NAAJEqlfDCPzyCidTdU4X+7on/iP4Hn7lr5d9r3M5I8NMAPmg++wEAn4qi6BEAn9p+DwBfA+CR7b+PAvgYbhOWFJ1zmJ6exsGDB2P+QmAngenAr696bJJFmuS71M+thU7rdXV11VuTKhGTmPP5vA9aUnJg2Zws0OLWSYnuaGTJALhB1P1+H51OB+122weWsQ6sr2Yk075IWjucNLFhfXmMEohdqsT/GSBWKpVQKBR2kBvrxYmOnSTlcjkUi8Ud17ZKiT43xO34Z+0xqVTKbyZhP7eTRV5PlYukZ0gnQrYf2b8bGxsxBcJmTruL+Gncg99zQABx6aNP4OyHfxJZl771wW8BG6Muvv9n/wpyv/7/3ZXy7wduScxRFP02gCXz8dcD+Jnt/38GwDfI5/8q2sJnATScc3O3XRkJqMrn8zh06BBqtVpM5hwn/SZZwvxeJWYlaZbH4ywJqk+Wx0xMTODAgQNoNpuxaF0mj9DgIRIogLFEbOtsLWutF7+3ZKDWHutqM1KptUYLXHejUomV77nHNC1YLlnSHapopQNbhNTv99FqtRBF0Q6/eJLESxcFj6WLwMrKt4I+E0nn6nesJ/t1cXERS0s3HnE76dOJjUr4qtYkPZd81TZTCteJwL2UsO/l7zkgIN2cxNf9ld9B2t09NejL/Szmf/gLwAPkCnqrPuaZKIoub/9/BcDM9v/7ALwhx13Y/uwybgLrB2TQ1/z8PLLZLLrd7i3J1A7GKqMyipafWT+v1sMSI8Hy0+k0pqamkMvlMBgMPJmotcS1zEqm+Xw+tvbYyqKsiwY4Jfk6NWCJn5EwlYjU780+pXzLY0nStPR5rJKwSsmESuv8Tic/w+EQnU4H5XLZb6M4LgBL7yfJ3srS1iJl/2hZqmpogpOkczUb22AwwJUrV7CysuLrQH+z7X/uDKb3jP/b4D7bBvZxKpXC6uoqVlZWfD/qM3GfcEd/zwEBhCsU8Hem/gjA3Vun/z3/4HvQbP3+XSv/fuBtB39FURQ55970iOKc+yi25DEty1tPe/bswfT09A7fcBJBa4SxStt2cLbXMfWJvWr59n8GRtGCYqR3v9/HcDhEu90GEF9+pFG/eh1N1kFSAbZ8ye12G/l8HrVazbeFx2jZXHJEP61dK6wgWZOUqE6wvrqbEj/T/td2se2cPGlmLPaPtkstSkq9Smg2eYglR3uP+L0NqLL3S/uc7ebEZGlpCVevXkUqlfKBa+N8yOrTT3qm1G2iz609bm1tDZ1Ox0fEM4bCPt/3A3fi91xA6Y7XK+AdivzdT5yTfgC3M3+rxHzVOTcXRdHlbWnr2vbnFwEckOP2b3+2A1EUfRzAxwGAAwEH5EKhgP3796NUKu2QsJMGL7VwlaA5ECdZ1HqulR8t7OdKVnxVCZeBPZRni8XiDplc66KkNxqNsL6+jvX1dURRhOnpaaRSN/Yn5h7NJHAlUpVrVQpnH5CASQJc4sRgMBK8JTwlSkKXRvV6PeRyuZjVPBptpSBVaV/baPtP66uTMKtqKCmqb1ytV71Her9Jxtyhqtvt4ty5c2i32zhw4ADK5fKOqHx9BngtfSbts3Gz541tWVhYQKfTiRG4VYXuMe7o77nmJh8cTTHgbWH4z4copUJWuzeLtyr8fwLAt27//60Aflk+/8tuC38CwKpIZLcF57aisWdnZ5FOp3esN1aLy0YW2x167NIaypjqE00iZSsVJ1ntev0oijxh9vt9b/VTFubaV55nJVASIneAarVayGQymJ6eRr1e9/IuAJ/mU9uY5KvlJEUTe+h18vm87x/6wZN2SmLfKdHwOgx047KwYrEYI036vnkPldhsXyZFhNt7ZxUMvS/qAkhyjeieynwGrl+/jqWlJT8Bqlar/jx9JVlrQho7UdFnyE4cOOngRh6Li4sx98q4fr+HuGu/54CHF62/8Cz+58P/8a5e4zsvfAWan7126wPfYbilxeyc+zkAXwVgyjl3AcD/CuAfAfhF59x3AHgdwDdtH/5JAB8CcBZAG8C3vdkKpVIpNJtNTE5OxogXQIx4rUWigTocNJX8rCWURLb01+ogn2Q96YDPshj8pbspab5pu1mG+n45aKfTaVQqFRQKBeTzeZ9nm3Wk71ODqtRHSXJLap+1QHlt+p0Zba0kp/5n9oVdP81JR7vd9pK2ZjjTvOSWuFQF0c8trMWs90Pry/+1PMrolKA5UVhZWcHFixexsbGBbDaL2dlZVCoVrK6uxsiXsQHsf1unpLboBIz9xXuzurrqpfN8Po9SqYRGo4FOp5PY9juNe/17Dnh4sXw8jT91F/PmbIy6+H9+8wnMv/Rg+ZeB2yDmKIq+ZcxXH0g4NgLw195KRTioZbNZTE1NeetF/YxKChyElRz1vZan1o5aweqv5XtrhQHYkaXJSs/cKzmXy3mCVOLu9/uxQV1Jn8RMC5dLrSwZ2SU63ClKl2pZS5GWrX6mYH9oOkiVvjc3N71lrWoFz9XPWHapVNqRLMP6/PVcTmpU6k+q67jnRcu/WRsB+HzfvV4PS0tLWFlZQa/Xw549ezA3N4disYi1tTV/75RYqQBoudomXYoGxHcg4z12zmFxcRErKyvI5/OoVquYnJxEtVrFxYuJCvEdx736PQcE7P/0Bn7iL+3Df9+488/2ZjTC84M0Dv/9P8aD6DfZNZm/OIAVi0XMzs6iUCj46NlxwTZKRMAN6VotWsqX1q/JY0kKamkmRW4nWbr9ft8vs2k2m5iYmPBZulgPWs86OeAgz3I0s5dto5W+AeyYjPAztdJUJdDyeB7la8rQ2le0KhnYpaSpRAQgFoi2ubnpLX9K+jxeo7h5DzS6W9uikx5tr5J2EhnbiYk+Wwym6/V6WFlZwcLCAjY3N5HL5XDy5Em//E2fFb2Xdi9pe4/HRfvzPVWTy5cvwzmHRqOBarWKbrfrfc4BAQ8UPvtF/NDvfQipr/wVfLR+6Y4W/fhn/xLmv28FUe/eTGjvNe6rY8vCOYdKpYLJyUkADNqi4gAAIABJREFUyZG7dgkLz7Pl8Hwld0suSbIoz9PjNZWnntfpdHDt2jVcv37dr0u1y4tIvAzS0iVISQO4/dxGX+sOR8wCRsJXf6UlcrV66Q+ndM/Ph8OhD+bq9XqeLNXy06h3tpd9p2uk9X4lLQfiBEh9z2xPEsFpf47zg1vXB8vh8i2u9V5aWsLq6qr3LZ84cSK2DSfbY+unn/EYS9L6v04i0uk0Op0OLl++jEKhgGKxiJWVFaysrPikLAEBDxoe/dtn8WvXT9/RMg//+ndg8t9UMLzwYJIysIssZmBrkK/X6355EBAf7OxaUesvtsfxf7U8eZ7KsEk+ZILnkTh0uc2FCxdw/vx51Go1HDp0yAdpcR0zLTJaXqwHCVQjfNXqUwlaJW8A3pJVaZ7WriVMSrBMpsE/bjPIQDCVap1zPlCNCVKYYISwsraS9nA49BMIJRu2W/tU7wXryXJ0ByrtFzsxsxMZC+6BzeVcV69exWuvvYb19XXU63U88cQT2LdvX2zSwv5n32uCG+vD1vf2+VG3g3MOV69eRbfbRbFYxMLCAvr9vt+8wrpLAgIeBJz5+8fx8tGP4U7ZgL/dBY799Aip//wHd6S83YpdMxpEUeTzTOfz+R1bMlo5NwlqTXFwpcWUdJ61WPWV5anVzCVR9BlfunQJFy5cwPT0tC+Px9HPqoRHOZXrZdWS1GAu9TsDN5J38E9VAF6DyU54LttBK1aPJ5GTFBghnMvlUKvVMD09jVKphNXVVVy6dClmCVvS4ftCoYBMJoOVlRUfsa05y/lKsksiUraNy7gs4VopWZfHJU202N/dbhej0QidTgfnzp3D+vo60uk0ms0mTp8+7UnRbryhFneSjK2KTlKwnRJ8v9/HlStXsLm5iYWFBbRarViiF41BCAh4UHDi7zyPUyt/DZvFCF/85h97W0unjvzGd2Dvf8ig/ICTMrBLiJl+0Vwuh8nJyVg0sg6246RrG/hkLS2+JsnWKvcmWeBqqfb7fSwvL6PVanlim5mZQblc9jKwRjmTZLROJPkkGZ2RutanrXKqlqn1Hg6HWFtb88RsCdBuDcmgLkrKhUIBjUYDzWYTMzMzqNfraLfbqNfruHLlCjY2NmIBdkqK2WwW1WrV91G320Uul/MTCG0P77VONHR7S/ZjFEU+PadVEwgbRGZ96MwhTkv44sWL3sc7NTWF06dPY//+/Wi1WqhUKrh69aovh1a/JhRJumecjCXJ2nRjOOdw/fp1bGxsYGFhARsbGzue33K5vOMZDQh4p2O0vo75v/v7SE810fuLQ5RuIwPYtc0WXh0U8CcKafxed4QfeuNDGPz1CZx4+QWMtpM3PejYFcTMwSibzfrdmFSy1Khj67+1VpK1oHieSp/2fyA+6NsNGnTA73a7aLVaSKW2MkUdPnwYxWLRW2RKjJoSk2XVajWkUim02+2Yb1cDwPL5fEyyJjQymH1kJy42glrbyvM0kQiXeTUaDRSLRZRKJYxGI6ytraHVamE4HHqCpOXJPuY9yOfzqFQq2NzcxMrKil+CpFHK1ueq723kOC14tnHc+maeo7I+FQsSMi3SCxcu4Ny5c749s7OzOHHihCfhVCrlE8RorIG6G9h/9tmw6oFOstLpNFqtFpaXl3Hp0iUfdKaxCOrLD8Qc8CAianfw7L/5H/G9X/dJfO/E6zu+/+CZD+ODM8/hxz/5NShec2ic3cTlP5nC3t/bRPHf/7/YyhT78GBXEDPBAZ7ra9V3Cexcc3wra9p+poO8HVB1QEySa7led2Jiwlt8lUoltm9wr9fz9SdZ8LVYLPoBWVNo6pInWmn6uRKwzXVNa43XZ5AZz7USrEZrc+10rVZDs9lEsVj0CULW19exubnpg5KoDnAPZ01aUi6X/R+JmxMNqgbsS+1j1tPeO5Xm1V+tPliWqZayDTJTRWF1dRWvv/461tfXMTU1hdnZWTzxxBM4cOCA9zWvrKyg2+3G7jvrkGQN6ySIkxS1tvVeXLp0CWfOnMG5c+diUexaZlJO8oCABwWjdhuHf+D38av/8j34F1/7YQzKwOBUG8NuBvO/AJS+8AZ+o/oUjr58Y03y0V+6jxW+z9hVxMxoVSVeHcxtkNc42MAiEhFhSV19igBiVqd+T+l6amrKExuXE3U6HTjnfAAT5VTd2pBERP8xg8j6/b4nH5KOXSbFepCUWX/N6MUJhxJzkpRPYigUCpicnMT09DQqlQp6vR6uXLmCXq/nJxrsQ6bWZDQ4JxnT09PI5/MoFot+IkBizeVyPtDMKg9JFr21VjkZ0clUEpHrfSV4z9imwWCAarWKWq2Gw4cP4/HHH/fWOgOz2PecBGiMgz57StSqCLA8BsuNRlvZxc6ePYszZ854F0gSAWs2t4CABxWbL7+K2R95dcfnQ+BhM4pvil1BzLTmKpWKt9p0oL6VxKcDupW2gRvWWdL+v8CNAVgHYZXCWUfnHIrFos8ORokdgA92ItkqedBvyshm+tBZb41I1jXFOplQqVYzctno66SlSbaPOFnhNpVMaLK0tITl5WV/bU2HSlleLUWuga5UKt5qJzY3N1EoFJDL5bwlau+NStjax1p/VR+0DbZtPJ+v7Ks33ngD58+fR71eR6lUQr1ex+HDh5HP57G2toZyuYzl5WWsra151YJKhNbXThzsREfB+7KysoLXX38dZ86cwerqauwZtZPDpOjugICAhxO7gpgBeAtOB2IgPgDqwKZri5Ug+b2+snxNwqED7LjrqA+bpEmCYkIInTzwe6bljKLIW8MkQ1pSWj7lX64dtrmTSbYaqU4rEsCO9b+sV1JAmxIMpfgoirC6uoq1tbXYUid7XTsB2tzc9PI9c2ar37zf73ur2WbNslncWKa1iG9FWEqaGiUNAMvLyzh//jw6nQ4ajQay2Szm5+dx9OhRbGxseOWCFjUnO7T4kwif9037mPWkX5k5z9944w288MILWFhYiNXVwjnnU5kGBAQE7Bpi5iDZ7XZj2xwm+R+tpE2f77iBnhIjCSNJxrZkrtfXXNYkQfW1FgoFX3+m5qTF1ev1AADFYhHAjWQfSXmXlZhUylViVPIh8WugEycJ6vvUiQjLz+Vyfr9kAJ5ErQxPpYFKAL/n9RcXF/2xJHUeQxmawWw8h0qAZj9TAh434WL9ratCXRCc2F26dAnnz5/3yoBzW9m2jh496l0Ok5OTuHbtmk/DyedIs3/Z58Nem/1OUuYWjtevX8fLL7+M8+fP7yDcm6k/AQEBAbuKmAeDARYXFzE9Pb3DstCBUQOB7IDJz3TQ1M0kOPASluSV9JTk7GcAYoFBGjlM4lNZlDK2Ti6SfKK0UNkfJES1XEk+/N7mZU4iaeu75Q5TtAy57In9YycEaokCN9ZHt9ttdDodtNttTE1NoVgsIp/P+60p+/2+X5eu64RV9dC220mFBScWmmEMgJ84DYdDXLlyBc8//zwWFxf97lyNRgP79+9HrVZDq9VCoVBAq9XCxYsXfa5z9fvbuAPbvwolZQBYXFzE2bNn8eKLL6LVasWeLfusAvCTuUDYAQEBwC4iZkqIXE4C7Ay4UdnQfmehy5Y0AlbPtQSWJIHrtWlJcW9kSr4kBQAx8mQkd9Ja5KRoaZWneZxNLqK5w51zscxdhBKpvQ5wI3sYA5UYAU8LvNVqxeRxToY4EdFgLk4OSPD04y4tLfl13c4578dWWZv3RXej4ntOqBR6r7SvGBDHTSl47Uwmg06ng2q1iqmpKezbt88/W6VSCefOnUO32/VKh3029NnidXm+TlIYfOecQ7vdxvnz5/H8889jdXU1Vn+dMOozxuV2+gwGBAQ8vNg1xExyW19f92tNVd60PmAOxgr9Xi0xPQeIrwe2Mqpey1p3PIayL4+lfKmSJS1Gfm7J3xKyrp1WQrXytk5OdCKhg77WWc/hcSRfug0AoNPpYDgcot1u+2QiajXq1of0p6dSKXQ6HYxGI/R6PWxsbKDRaGB6ehpRFGF5eRndbhfdbhdRdGO9MPvGBlfxniopJxEV28PJ0traGq5cuYKFhQWUSqVYoF2/30ej0cDs7Czy+Tz6/T6q1SquX7+OxcVFb+1SgbCBXklKjMrZ3JPa+pUvX74cS+qiUBUjnU6jWq36zVACAgICdg0xU2ampUZZ0JKOHaiVQKz/j++VMNU6staPTgJsQhPKsrRSKQcrmehEQS0vWpxsA4laLXrWQ6VcGwRl+0GDj8b1kZapCkG328WFCxeQSqVQrVa9DMy6M0qcmcjsRh6U0zmJoFTdbreRy+UwNTWFXC6Hq1ev+k0xmIClXq97/7tOeJSgtU16n9mPo9EI6+vrWF5e9lsp9vt9nxJ0fX0dvV4PMzMzOHr0qN8YhSTMgC8mdqF0T9iJWlIQWiqV8puTjEYjXLt2DS+//DJee+01/6zohErvBf9I7BpsFhAQ8HBjVxAzyY5pFGmVKZKsxiSLBkCM6EiYSoSa3EL9vZp8wyYiiaLIR41rHmjdUELbQ2hCEQ7wDIRSiV3bY8k1ifStkqDrfG09rLVO4r127Ro6nQ5mZmZQqVQwGAx8ZDjryqhyDd6ihczy2Beam5prpFOplL9Op9PBSy+9hH379uHQoUMolUpeVteJkvaFLh1zbmvp2erqKq5evYrl5WXfHvrFae0Ph0NUq1U8+uijOHTokA/QS6VSuHr1Ktrttr8HdntRnQjZ4C99tnRzj8XFRbzyyit46aWXsLGxEZtQ6n3WyRED4dLpNK5du3bT30hAQMDDg11BzEA8qQOXriQFAakPVt9bqExq/ay0VNRC4vV0i0ZLllpeklXLwVcHbxIzpVlamkqobA/LtlHB49QClXR1EsF+tMultA2DwcDn/u50OtizZ88OWX1cn+p9sb5tTlLo4200GkilUlheXkYul8P58+fx3HPP4fr16zh16hQmJye9WmKTdbD8zc1NbGxsYHl5GRsbGxgMBrh+/Tra7TbK5bJfR80lbNy16dixYzh06BCKxSJSqa3leKurq1haWvIydFLf8H+V05VYNUofANbX171fmWXbZ4Ll2uxrpVIJU1NTif0dEBDwcGLXEDOtoUqlgkqlEiMnGxSjBJtEWiQrTYqhf0CyfF0oFFAoFHyeZ7V81VpUUiJx2MGYdSDB67pdSp8kZrWg1JrmMVwOpbK4XkuXCbFeAGITAAtax9ls1vtaG40GcrlcLJKYlqFu6KDR1NYXrmuweVy1WvWft9ttrK+v4+LFi4iiCLOzs6hWq35CRLLrdDpYW1vzO3UtLi5ifX0do9HIW+K0wldWVvy9SKVSqFQqOHbsGE6ePOlJme26evWql+k18l2hioyVtxnQx6VV7XYbr732Gp5//nlcunTJb7Fpn20gvnMY3QSHDx9GJpPZESgWEBDw8GLXEDMAP4jb3aXsQGe/u5WPFYj7jYEb2ZlItrlczmeq4gCcFABkl0XxfEKlULXmtX48LinqWAmP52k0slrHWh8emzQJUZleyVrXLq+urvpNRJgTmxHomkyFFjVJi1s0WvAzVT9SqRT27NmDRx99FJcuXcLi4iIWFxf9Npi6bKjdbqPVamFzcxOlUim2hjyXy2HPnj1ot9vo9Xqxfs7lcti3bx+OHz+OSqUSS3XKYLQoirwEnZRIhPcqaVUAE6mMRiNsbGzg8uXLPg+2JWV9Lq3LJZvNYnZ2Fu9973tjVnZAQEDAriFmDqCMElbLVINogHjSBxskZI9V6DFqMdGiYuYuHmP93IQNgrIEzPbo8ZYoVarlq040VEZVmVqtZQ2SspHfKpPrtbS/6JctFArodDqenIvFYmw/ab7qBhm8prZJ26b1Hw6HWF9f9/0xMzODWq2Gs2fP4sKFC+h2uz5gi35tDUBjnYAtn36r1UKxWEQul/PWLi3j2dlZHDp0CJVKxd9TTjxarZafhAFIDD7Te6P3jm4OnruwsIDz58/jwoULePXVV33Z9lmzQYScFOzZswePPPIITp48iY9//OOxtfUBAQEPN3YNMVM+pvWkpMxXXb5jYf2B9NUqUetgaaO51S+sxyRZ57yOtbT0fP0uyTdMqMzJZUJK5CQOlUDZX7TqaclqoFtSMJn2Advf6/V8UFu73cba2homJyf9zlDdbtefo9YxicROIJjpS5Or0Pple5lr/MiRIyiVSlhYWEC/3/eRzGwfsGUB89koFAqemNU/zF2u5ubmsGfPHpTL5VicAK/P54EuDt2lyhJpUmyDBnqdPXsWi4uLeO2117C6urojIM+C/UNSnpyc9PtfX758ecfxAQEBDy92DTHTP8ltFAHE/MNJwVX831rOSszqL7YSpZKszXJl16AmvZIYtQ0KvaYuK9IJAQd0jdLW42gl6yYRtCKtlUrftUIDm7QfeH3m/c7n857AuC6XVrP6wjVQixMFZh8rlUqo1WoolUpeku73+z7hh/rDndta/713715Uq1WsrKzAOYeFhQWf/IT7RDPeQCcBjAovlUpoNps4cOAAGo2Gl5qZ0IR+aE5wuAxPN+hI6h9Odvg5t73c2NjAyy+/jGvXruHixYtYXFxMnKDxvbpQisUi9uzZg0qlgmaziaeeegq/+7u/i1df3bnbTkBAwMOLXUPMwI3lNnYTCwvrUwaQSOBKeBzcbSRzkgWs1yGshWx9iUnHqFVOIrD+WH1vSZOTFfWHswz1M/OVvtQkf6WVztW/zoQcPL/VavmALc1nTUtXg80o/7Pf6fedmppCpVJBoVDA4uJiLCANwI5JB3e6SqfTPvsb9+dmdrVyuYx6vY5iseh94alUCrVaLWYlcxLDtuhyOZ346KTJPmc6eeI9GI1Gft308vIyrl27lhjslfTs5PN5v7a72Wzi2WefxfLyMn71V38VKysrO84PCAh4eLFriDmKInS7XVy8eBEnT570a2dpnQE7l0apHxWI+5fVt6f7HNtIZZK25p3WXNyKWxHwuKVbSRa6vifUSlcC1aQorLP1WwLwQUmaX1v9ytpnaklzZyv6lnu9nt8OUrejdM75TTmSJja9Xs9PgHq9nt9esVqtolqt+vW9Wmf6bcvlMmq1GprNJrrdLkajEQqFAkqlUiyft7adZKvLl9T3bf22/F7XattJlk5+GKRFP3W/38fa2ppfHtXpdBL90vaZKZfLmJqa8svHnnnmGXQ6HXz+85/HmTNnEl0zAQEBDy92DTFTvl5cXMT169cxNzfnv9OlRuPAwZUSsA0Ao7/UbhjA9cy6ftj6lJOsd7XKVapWJPkcbWBWkuXPdrB+9JUmWXYsn5Z3JpNBsViM+Wz1HF1CphMJTVHa7/f9JIV+Y9aJkxzN0c1gLSVp3rN2u425uTmUSiU8//zzfm9mO8nJ5XLI5/OYmJhALpfz+17TD85lU/QV23Zb+Znt431hfWyGrySlhM8PCZ/yPQO+XnnlFZ/IRMtJsrpLpRL27NkD5xyq1Sre9773odvt4o//+I999HtAQECAItnEEzjnfso5d80592X57O855y465z6//fch+e4HnXNnnXMvOuf+7O1UIoq2El5wZyLuXztObiaJkkhVmtRIZbUKNcqbErFKsLTySDJJ/sft9sX+iJtZPZagk0hZrWj1f9qANzvh4FppkgknHiR09U3rtax1z/6k5altU4k4l8uhVCr5KGhayYpsNotms+kl3Pn5eczPz/u0mCRiWqKsiyZjGQwG6Ha7fkMNSumUzjURys3cDCRhBsfZNcs64dH+1qVRALC0tISXXnoJL7zwApaXl2OTNes+YTnFYhH79u1DoVBApVLB+9//foxGI3z2s5/FysoKOp1OLFr9XuBe/J4DAgLeHm5JzAB+GsAHEz7/kSiKntj++yQAOOdOAfhmAKe3z/lnzrnkNFKCKNpatwpsDeobGxuxZBkkI82OBcTJMsmq5tIelssyKIly60MGUjEftrX+1Kd9MyiJsp5KokquutRIYQd8nYxYErDyuX5PErN1T7LWOTEZDAY+dzPJl0FPJGQG53U6He+/1cmTc84HXzF4bGNjA+12G/l8PhZZzjrwXnFCwuVRqnzontA8T5eYacpTG2ugedhtbICtO0EL3jmHlZUVvPTSS/jc5z6Hq1evxs63/c4yM5kMZmZmMDExgXq9jq/8yq9EOp3G7/7u7+LSpUuoVCp4/fXX74fF/NO4y7/ngICAt4dbEnMURb8N4Ha3vvl6AD8fRVEviqLXAJwF8N7bOXFpacn7eSlX2mhqvtfB3fpZrb/QrrXlAM+gJmb7UhJLCuzi+Tbzlx5vs2DxfPWTsxytt5KNDepK8hMDcTVBj2MZGiw2jnz0vXPO9xUJtFQqoVwue/82I6UHgwHW19cTffyUi3k/u90uLl++jMXFRT/54iRAXQUk306n44PRJiYm0Gw2US6XPbHaZUmchCTlNte+4D23fa+Bhuxf3Vt5bW0NZ8+exec+9zlcuXIlJqPr/dR7nsvlMDMzg2azidT/3965xsZ5nXf+f8iZ4ZAzvJOiKFIiKZGyLpYlW46sOIETNEWSpgkSB0WQfOhlt9jkQ4PdAl1g0/bDFigKdBfbLLrAIoCLFGiDbrJGWiOu64VjO0piOZJtWhIl3u8UObzMcDj3+wzf/cB5jp45fIeiHEkcSs8PIDh8572c9wzf+Z/ncp5TVYWPfexjcLvdeOONNzA3N6f7c2lpCQ+bh/U8C4Lw0fl1YszfVkr9HoBBAH9iWVYIQBeAq2yfpeK2u8KzdukLneLOdjHYnTDd0LxSFQkC/xKmNZNJHGjhBjoXF0tznnK516ZI8Hgnj4XS/dExXFxJtOgeqJ/4dtpmij+d17SUzcUaeGIdDYwovkurHlFymMPhQGdnp44d19fXY3Nzax1kHp+vq6vTBT7IWqYlJe0sbHMqWSwW0+sk03Y+B5nH8+k8duVHaTDBrXH+GXGvBOF0OnXJTSq3ef36dSwuLpYUIzGh+6iursahQ4fQ3NwMp9OJj3/848hms/jZz36GpaUlVFdXw+v16mU3K4j7+jwLgvDR2Y0r247vATgG4ByAFQB/c68nUEp9Uyk1qJQaBLYWAohEIlpgKDOXu3J3k7260xc1WcVc/CmhyePx6CUBaW1ibl2ZMWUunqZYcyGnY023O+3P3d4kuNxFzbOruQDzGDt5Avj6z6YI8YEAd/tzcaI4eyaT0f0Tj8d1LDQYDAIAurq6cOrUKZw4cQI9PT1ob29HTU2NFqbm5mZUV1frLGyaZkSxYmqj2RZeZ5uSyqgt5hSncgJpelKAOyuA8T7h73PLm1z3qVQKc3Nz+PDDDzE3N6dj06Z1zM/hcrnQ3d2NpqYmeL1ePPfcc1qUp6am9LQvr9eLtbW1Sqr2dV+f5xwy97t9gvBY8ZEsZsuydKBNKfV3AF4r/ukDcJjt2l3cZneOlwC8VDyHZVkWUqmUnrfKv8SBO8Jiuq5ZO0q2mxWqgDsJPbSN5k3TesQejwdKKWSzWZ0IZZdUZFqifL6yua8p0Kag8HvkCWzUdh4ztRM1c9Biulq5UNllI/P9LMvSYkgWXTKZhFJKW7y3b9/WxTxoOlVbWxvq6uqQTCa1pR0MBrXQbmxsIBqN6jgwtZ2S1GhdZD4g4a5owozRk0XM74nfe3V1dUlymtkn/JyUCc5F+fr165icnCypSMY/Xz5IcLlcOHjwIFpaWlBfX4/z588jlUppS5lCBDT/mseq95r7/Tw3qBYp/C0IvwYfyWJWSnWyP18EQBmerwL4ulKqRinVB2AAwPu7OSdZVTzTml1P/zaTfsphxm4JPv1nc3MT6XRaV6Wi+bQul0uLs+kWN68B3LH6KHOZizIXC7ukJzuB5BY3uZFpNah0Oq2nQpHbnaxnvq4xCZKdhcktT96/PMM9lUqVxH6JfD4Pj8eD9vZ2OJ1ONDc361KYjY2NaG5u1lOSyC2dSCR0H/FFMOj++ZQws9/4/4A5N51nUFP7+X3RAI/fI//f4DFhspTz+Tx8Ph9u3LiBiYmJkixu0+Km6zqdThw6dEiXA7148SJSqRTefPNN3L59W1dJc7vdaG9vx8rKykPPxt6JB/E8C4Lw0bmrxayU+iGATwNoU0otAfivAD6tlDoHwAIwD+BbAGBZ1ohS6mUAowDyAP7IsqyC3XntIBcyAB1TJFE0rZTi9bZZSaZFSnNYKeGGXMbcUs1mszqBqLq6Gg0NDUgmk8hms6itrS3JiN5p7qqdVcpdyOaAgt8bvc/bCJRmK/OBCVmx5rG0nbtczUEBx26AQNeked8k8iR0SikkEglEIhG98lQ4HNZzeynT2OVyIZlMlgw2zMS0ci5puiezmAr1Ad0jH8TxqXLUfnNJTv7bsqwSSzmTyWBhYQHXr1/H+Ph4yfKXdnPZ6fNtbW1FY2Mj6urqcP78eQSDQbz99ttYXl4uGXg0NDTA7XZjbGys7D0/aB7m8ywIwkfjrsJsWdY3bDZ/f4f9/wrAX32UxuTzeUSjUV0f2S6pykzYsYvxmmLDrUb+pUxf+rlcDqlUSltNSm1la5Olyotp2Lm26fwkWrQvjyUTphjT/uSa5QMHvia1uSaz2Rd0DZ7BzUXZjI2afcHdxnSf6XRaz+MlwfX7/ZiYmEBzc7O24On8TqdTTzcDSi1Z/jfPrjcHXSTGdn1mwvuSJ5SRl6FceVIalNXU1KC2tla7vJeXl3Hr1i2MjY0hkUjs+D9G271eLw4dOoSGhgY8/fTTSCQSeOutt7CysqItZVrY48CBAwiHw7p62l7wMJ9nQRA+GhVT+QvY+tLz+/3o7u4uqUHMv7Q5fJ6rKVD8nPyL1bSw6Hgq00jTg8j1aCaRcbHlwsLbCdjXvaZ9CNM1z6c/kTvYtO7pHsz4qx1ckM1pRvw9vo3fB89ip2smk0nMz88jFArpEqC0oAQlNtEazpZlwePxlCROmXW+zb4wPRB2yVw79aVlWbpymXkc9y7wRK9CoaBF+datW4jH43p/u3gy/R/U1taiu7sbtbW1eOKjEPknAAAgAElEQVSJJxCNRnHp0iUdUyZPAWVit7e3Y3R0dFv2vCAIAqeihBlASSzVdHOWK0lpTmcyv0gpbmpmLNP+JHw0Taq+vh5AaQKZGRflv+2E1WyD+cVOmO2mbdy65Jnb3CVMcGuen9+MsZuDCL7SFcEHIiTM5EXgWevk5uYlOsnKpwUwMpmMztama/H2c6E0PSH83vmgyvQWmHO5Kf7OMcWZypbS/Obl5WXcvHkTIyMjOpPcTvz5dd1uNw4fPoyDBw+ir68P2WwWv/jFL7C0tFRyP5RweODAAWSz2YpK+hIEoTKpOGEuFAqIxWKIx+Pb5jLbCSOJkp11Q/tQRS8+t5jHLrmw0TXdbjcAaFEhTCuNv+bXNOO6pijzeDV3fXPxIdHjCXFcqOxc/XaiYsZYAZSch94zBy00mKEYPdV2pqQ5qiXN7zUcDqNQKKCpqQnZbBb19fWIxWI6u5sPMKh/eT/wgQhwp5Sm+Zmac9OBrVwBO2uU/184HI6ylnI4HN7mlTFj20ptLUpx5MgR9PT04PDhw0in07h8+TIWFxdLQhHUb/X19WhtbcXrr79eErcWBEGwo+KEGQB8Ph8aGhoQi8XQ1tZm66IGtruL6bcp4HyKEVl2du5vEnGa9uNyufTShnxxjHt1X9N+dgMLHm/lsVhz3WbaTpiubX4v/DjTirYTaT5I4IVNzIQn05olYTTbFovF4HK5UF9fj1wuh4aGhhLXPGWum+3l8Wazf83+p9fmnG7z/4O3m8qNkij7/X6MjY1hZGSkRJTt3N/02uv1ore3FwcOHMChQ4eQzWZx+fJlLC0tbStAUlW1Vfa1vb0dhUJBRFkQhF3xUQuMPFCSySQWFxcxMTGhXablrE++8AG5Sk3B5ZYaL8EIlMacga0v8Uwmo7OJaa4tgG3JRGayFm+bnQuUjuFCWy6maq7sZHoJTEvfvA5dy8RucGCXac7jo3buYz7ljMSRrGuaJpXL5ZDNZlFTUwOPx1NyDb7EJW2zrNKlGqltpvAqpUrKd5qlOu3ujSxl8oSQKA8NDSEQCJRkzJs/dM3a2lr09fXhyJEj6O/v14let2/fLplWxfuvpqYGR44cwfr6uqwkJQjCrqhIi9myLMRiMfh8PjidTpw9exa1tbUlCT1mTNd0YdrFfHnZSBJciovyhKjNzU1tNZO1R+c0f5vialqo5jbedm7R87aSANISjbxtZob1TslRHFPI7axC7j6m7Wb83m7OMMWgucWbz+cRiURw4MABxONxXXubz8Gm5DE+uOHn4NYytY/P3Tbvk/Y38wFIIKn+dTgcxszMDG7duoX19fVtgwS7EAUtStHX14eenh74fD5cuXIFfr+/5H+PD14cDocuTTo5OWn7uQiCIJhUlDDTF6DH44HH40E8Hsfi4iKqqqpw5swZ1NbWbvtCNrOmd/qCB+5YrBQr5ZY4rUJEVjWtAcxLeVJCE7+OKbyAfSzajIWbYs1FmSAxpLaRkPHjuXjaxXEJ8xqmUHC3OlmupkjT9Clqj2VZOg+AsrNp32w2i42NDV0NzOPxIBAIaAuYW97UPu5Gp/6iWDvV2jb7nB9v3nNVVZVepMSyLGxsbGB6ehrXrl2Dz+cr+X8yz8fPcfDgQZw/fx7d3d0YHx/H9evXEQwGt32OXJjJWo7FYlheXrY9tyAIgknFCDMlyXi9XihVukJRdXU1WlpacPz4cS2Qdu5VANtEmLbxfXlslGLOfH1fslRzuRzi8TgaGhp0KU9azMFc5IJek7CY4mcnuhy72CrtT8Jn5xUw78/O1W1e07RuOdQPNCeb4t3ccudeAt7XdCwXz0QigcbGRrjdbp2hTWJICXk8M56HFug6vKKZ6Xbn92RC4kiiHIvFMDc3h+HhYSwvL5cM6szPi/9ftbS04MKFC+jv78fQ0BA++OADRCKRkgGSOZii6VRdXV344IMPtq0DLQiCUI6KEObq6mp0dXVBKYVMJqOza+nL3ePxYGFhAT09PfrLmmfzmlm9ZmzQTkS4FWtmQ/NpUiTO/Dx8SUrgTrKWaZHygYFdO+ws5s3NTV2ljNyyBJ2TX89025rnpO3UX3x1JtPypkEA7wfuZubTnXjbeHgAuFPyk96jUqe0fjO/VxJH7mbnnylhijDvM/OzJ6ufW8qRSATT09P48MMPMT8/XxKX5lY2Dw1UVVWhvb0dn/rUp9Db24vBwUF88MEHeslL3jY+GKLrHzhwAPX19ZiYmDD/5QVBEMpSMcJM1iiv40xfklReMZPJ6LV57b6ogTsJP6YVbZeFTZYgWXqm9cjFOxKJlFSJIiEikeRWMl1zJwuZ4GJuJrhR35BYU1v5+XnMl5+T/zbvjcds6TdZeXQ/3GI0k8CoffwezYxpni1NU6gSiYQu3mFam6albd6D3Wdt99nT5+JyubTnIxwOa0t5fn5eu9zLhQ/ofhoaGnDx4kX09/djcHAQg4ODep6z+Tnxvyn7+9ChQ5iamkIkEtnxf0AQBIFTEcK8ubmpF02gL2kqAkGr8fByl3bWsBlrJjHmmC5Lc3oSiQrt53Q6tdWslEIqldIiTvN4qf18XrTdNTncmqbr83vi7QLuzGcmi5QLIV3DnL7Et5t9bQoL/U3JcHQ/5oITZrtNy9/McKd9otEo4vH4tiQpHjO3W6rSdNHTNXh7+P3RilWUE5DJZOD3+7GwsICRkREsLCzocpj8HHafT2NjIz7+8Y9jYGAAg4OD+NWvfrVNlPn+/KeqqgoejwednZ145513pNKXIAj3REUIM3eNUvJXXV0dXC4XMpkM4vG4nuZiFxs1C28Ad7607cSKiwdP+KLsYj6HmNzKlAzFXbipVEoLgTnH2HSpU5voffrbbkqTHXzKGJUr5TFkfn2y4E13uymcpuuVu815LJmLKN2PmY1u3jtZq9QeihPT1DNzkGXmA/D+KveaexUcDof+HJRSSKfT8Pl8mJ2dxeTkJBYXF0tqVHMRpetyUb1w4QLOnj2LGzdu4L333tMLdNhdn5+LBgcHDhxAIpHAxMSErZgLgiCUoyKEWamtOaJkISu1lfwViUSg1Fb5w4aGBr0v/01fluSuNefXEhRf5fuaX8oAdAY0ublpfxIRcidXVVXpohn8fdNCNV2tXNi469eEu3ZNwTTXIObCSdejAYcpqnaWInkHgDueB7tMb27J8vvg92onvKY3gLvW7cSdey5Mj4P5mVEhGEriy2azCIVCWFxcxNLSEiYnJ7G2tqanxJkiyf+uqqpCU1MTzp8/jzNnzmBoaAjvvvsuotGobSjEbD/dv8fjwcDAgK4pLgiCcC9UhDBXV1ejra1Nf7FSIQaPx4OGhga0t7ejo6MDtbW1JV+EdklUQKko88IjtA+3iO2Eko4jy5OsZh635EljiURCW/jmFzfHtPJN0TUFzM4NToMAU+z5AIFb0qaHwK7/qLQmvU/n4W5fuzCCeS/8WtQGeo/3t1nVzO4+7cIVpiCa5TXD4TBmZ2cRCAQQi8UwMzODtbW1Em+K3cCJttfX1+PixYs4c+YMbt68iStXrmhRNttkto1bzG1tbXC73RgeHi4pmCIIgrAbKkKYaQoNLSLR2NiIpqYmtLe3o729Ha2traivr4fL5dKibcZhuUu0nDVHwsGzi/mXrmVZOp7sdDrhdDpLErvMBCiyXvP5PBKJBAqFgl7XuVwymGmd85guL3RiVrLi1yd3remS50tH8ox1c8qUmbRFMVmaXkSDD7syl7wvgTvV0HiRDhr8mLW3TQ+BKe527eNWOvdKUD1zivcvLi5iaGgIkUgEDocDc3NzekqTeT1zEOBwONDe3o4LFy7g6NGjGBwcxPvvv1+SfW1n2fP+oP8FqqM9OTmJhYWFsscIgiCUoyKEmcSjqakJra2tOHDgANra2tDS0qKTv+jLlVfDsot9AnesKTMeXc4i5S5Kmr9sWpHkuuYWOFlDJKS0dCRVFTMtTLsMYLoX0zon7LKSAZRMqaI+JNEyY+Ll3K/c6qTKWPReKpUqaVc5S5PHZnkSGi29yGPhHNO65t6OcqJMfU1zkx0OB9LpNCYnJzE0NIRsNguHw4H5+XlEIpGS9ph9SddwuVzo6OjAZz7zGbS2tuKdd97B0NAQ0um07WdgWst8gONwONDc3IympiZcuXJFkr4EQfhIVIQwO51ODAwMoKOjA21tbWhqakJdXd22ObwEt3btXL78y5PHKXkClJmsZZ47m83qOb1kPZrxXRJrpZQWtUKhgHQ6rS1Pfk7zPoDSDGQ6t51rl85DwsWteODOFCi6N16Zi7vpeV+4XC40NTWhqakJLpdLZ6Dn8/mSedL82vxe6Hq0LCbFjmlww4XXLl5tiradiPNjq6urtShXVVUhHo9jdnYWExMTesrd8vIyotHojvF++vF4PDhx4gQ+8YlPIBaL4dVXX8Xs7Oy2YiDmYMruc6Hym/39/bh9+zamp6fL7i8IgrATFSHMtbW1OHv2LJqbm+F2u22/BKm848zMDJqbm9HT01PisrZLKjItadoOoETY6RxkXZFo0PQoEimarmRmF/NrkiCSuJmDC245mwMELrx2iWFmdjlPdDPvkbfLtO7JSm5vb9erd9H611VVVUin01ps+SCIW628zVz4yYVtdx98EGW6+ume+ECCtlG/8qlQ4XBYJ3ZlMhksLi4iGo3q4jTlYtg0mGlvb8fTTz+NM2fOYHZ2FpcuXUIgENhmHZezluk3xZVpFSnLsvD+++9LbFkQhI9MRQgzFWMwrSUqLLK4uIjp6WmMjo7C7/ejs7MT3/rWt7QQ84xrggsDUGotmWIMlGYaA9CWIwkwz9Tm7maKMedyOZ0ZzMWfMrdpPWNqC/3m7m1eXIW3r9x7dq5ZPg+b2kCC7nA49MIcra2taG5u1is00VxxWgebPAYASgYYdrF56m+7KU/0t124gY4zt5kDHb7gyObmJgKBACYnJxEIBLC2tgafz6eLhpjxdPOzr6urw+HDh/HCCy+gtrZWu66p8ImJKcZ2U/IcDgcaGhrQ1dWFkZER+Hw+23MJgiDshooQZnMKTD6fRygUwvj4OMbGxjA/P49wOKz3X1paws2bN3Hu3DltAZlVo0xx48JhWmlcqLlAc8uOx4C5ZUfisbm5iVwut23uMHBnHi+PPZtWl7mWrylc5gpI5vxlU4xJkCk5ixaRaG5uRnNzM1wuF6LRKACgrq4Om5ubCIfDutALt8bpXObcZO5W5/F3vvayOQjhiWum6PHPjuYD8wFNJpPB2toaJicnsby8jNu3byMcDm/7zOwGKBT/ffbZZ/HUU09hZWUFb775JmZnZ7eVWDXbxLfzfidruaamBh0dHQiHwxgbG9v13HRBEAQ7KkKYAeiEoWAwiNHRUVy7dg2rq6tIpVLbLLBsNosPP/wQR48eRWNjo7Ym7YptAPYVqbibFdhugdI5eFyVZxlzsSdLlJK/uAVMYgWgZFUlWgiDu9y5kHFB4FO/ePtNa59bpjyW7na7UV9fj6amJni9Xr0kIy0qkUwmEY/HS1ZvMq1Xbnlz8SNMUeZ9zO+HW9g87s/34SU16ZhUKoXFxUXMzMxgamoKfr+/pL/552L+7Xa70dPTg4sXL6K1tRU3b97Eu+++i1AoVBJOMP/P6B7oPe4ep7Y7nU492PnlL3+JRCKx7RyCIAj3QkUIs2VZCAQCGBoawvXr1zE/P39Xq2NiYgIvv/wyvvrVr6K5uRlAaSEQO+uY/81F2ZxLbE5HIuuQCyBBX9Y055fc2TSXmsSZLGV+Pm49c6uZW28kclw8+GCB/uaDExJRKmva2Nio63yn02m4XC69znQoFEIikdBTo0xvA90Dt0D5IIU8BtQHZKGb8Vmz77nlT31OU9T4oCWXyyEYDGJhYQHDw8NYWlpCLBbbllhmWudkdTc1NeHJJ5/Es88+i3A4jH/913/F1NQU0ul0SSjDrp12f3Nr2eFwoK6uDl1dXVheXsba2tq2cwmCINwrFSHMyWQSL7/8MmZmZpBKpXZ1jGVZerWg3/zN3yyxcE0rys4K5KJM7nMAJaJsJg2R4FLWLl2TEo7oXGY8lqYOkeuTJ1VRkhV3p9sJJLc8+T3Q/Zpzhmtra3XRE4qP09KaFCqIRCK6vCcAPY+a7t/MzDatcvMeaY1rbl2aoQWCu7bJbc1j9Ja1tUzj6uoqpqamMD4+jvX19ZL28v4wLWWXy4WDBw/iueeew5EjRzA+Po4PPvgAq6urJXXXeXv4Oc2/+T7c1d7c3IxEIoHh4WFbi1sQBOFeqQhhDoVCCAaD93xcoVDAtWvXcPToURw7dsz2C5R+89fcBc3jnTyZjCc80fFcBHgbgNIkKDqGu7HpmplMRlvQZHWZbeNWPz8HwcWTxzq55UiZwvz9QqGAYDCIaDSqQwS8b8ysdnOaFeF0Okvuly/VScfaiZQ5uKDscJqWRsclk0lsbGxgdnYW09PTmJub025rM55s/qbBx4kTJ/Cxj30MSim88847uHHjhi4CY4oytcncZp6b9yUl0TU3N2N4eFhWkBIE4b5REcL86yTLrK+v4/Lly2hra0NjY+M2UTCzdHnslburuShziw8oXaOXW4Bk7QKlLmYeP6XtNNWKrGqqYEYCzd3X3KImt7E5vYiOMwWFx7XJuk8kEvp6vP18AMDF3hzImP3GLWwSTOon7sY2BZALKq/cRcdlMhmEQiEsLy9jamoKc3NzegqUOX+bexOo72pqanDw4EE8/fTTOH36NBYXF3H58mXMz88jnU5rl7yZMGfmG/D3TGjQ43a70draiuXlZfh8PrGWBUG4b9xVmJVShwH8I4AOABaAlyzL+lulVAuA/wugF8A8gK9ZlhVSW99mfwvgCwCSAP7AsqxrD6b5W0xOTmJychLPPPNMSdlM7hLm1hoXA8JMQCreu36fiwp9ifPkLXM/081MljEXYkoGI6hNPCbN3ep2Qkx/8wQ3mkNNx1HMln7zezdFid6zS8oCoAuH8PKfdn3FXf8EdwFTLHlzc6vWeCwWw8rKCmZmZjAzM4NwOFxSpITOa7a9qupOoZQzZ87g7NmzcDqd2koOBoPbBh+8Pfwz4/D3uKVMWditra0IhUKYmpraV1nY++F5FoTHnd1YzHkAf2JZ1jWlVD2AD5VSbwL4AwBvW5b110qp7wD4DoD/AuC3AAwUf54D8L3i7wdGLpfDm2++ic7OThw5cqTE8qUvYxIg08I150CbbmPTuuJf4qbLmfajbbQPF07uqq6q2iovyY8hy53We85ms9rC5NfnqyWZsXTeRhIU3mZzChif52y67SkOzmtnm4Jtxr25KPOsZ4fDoSt3AVvJYsFgEMvLywiHwwgGg5icnEQsFrMtZ8k/Sxrk1NXV4dixYzh79iw6OzuxvLyMq1evYn5+XrvruUVvZwWbn2m51/R5UanYkZERJJPJbeercCr+eRaEx527CrNlWSsAVoqvY0qpMQBdAL4M4NPF3f4BwM+x9SB/GcA/WlvfdleVUk1Kqc7ieR4IlmUhFArhzTffxGc/+1kcPnwYwJ0vYm51mkLG48gcM+vZdBdTJnK5tZ75PGCaRmS6eKk4CXAnbsvbSuckdzbtyy16GmyYBUjofX5NnrjGRd20bOk+qHAKCTg/ju/LMS1l8irQ8ow0D3xjYwPj4+MIBALY2NiA0+nExsYG4vF4SRv5/ZDAV1dX62zop59+GocPH0Y8HsfPfvYzjI6OIhqNlpQotft8efvtBlwET+ijbHaXy4XR0dGyRUkqmf3wPAvC4849xZiVUr0AngbwHoAO9nCuYss1Bmw95IvssKXitgf+IE9MTOiqVh6PZ5vYcBcoT/4iuLuTW3/mOUzR5ZYzYL+qFZ3LnNJF1yQxMmtTk/uZLGR+Xt5uujZZwGQpm1Y1t2h5ARQA2iKmKVLczc7DA7x9XNjKhQ0onkzvra2t4cqVK1hZWUFLSwva2trg8/kQCAT0PG86nq5Nfed0OtHR0YETJ07g3LlzyGazuHnzJm7duqXLc/LPi9q6k6VsF08249hOpxNerxderxezs7NYX1/fdr79RqU/z4LwuLJrYVZKeQH8M4A/tiwragiWpZS6p+wXpdQ3AXzzXo65G4VCAcPDwzhy5AguXrxY4k41v3zNWDSwfcoNjx+b1i7fh85hNyfWjN3azTmmtptVw+g65lxiHiemucZcZKhaFm8zb49lWXogwK1hep97COzi7Hw77w/uZaDYL81NVmor4W11dRXDw8MIBALwer1IJpOYm5tDLBbbVl+aW8g0/en48eM4c+YMHA4HxsbGMDQ0hOXl5ZIkNPo87ATXFHs7i59+aHBDLvOmpiYsLS3B7/dv+5z3Gw/yeXaj7n42VRAeO3YlzEopJ7Ye4n+yLOtfipvXyKWllOoEQN9WPgCH2eHdxW0lWJb1EoCXiue/bymt8XgcN27cwNGjR3Hw4MGSoh3FawHYHo+lfbjblzAFm1vH9LdZq5vvZzcI4FOLuOXM47/0vimwpvVM0DaKC/N2mFY93Zd5H6b72HQF82vQfZtCrpQqWQVqc3MTsVgMfr8f09PTWFhYQDQaRS6X01Ot7OLydJ7m5macPHkSTz75JLxeL8bHxzE6Ooq5uTldQpTaZPaHmSNQ7l7N17yIiNvtRmNjI/x+/yNRRORBP88NqkVS1AXh12A3WdkKwPcBjFmW9V321qsAfh/AXxd//4Rt/7ZS6kfYShKJPOx41NTUFF555RW8+OKL6OjY8sjx+cbcUuVzd/mXtyl8ZtwX2L7Kkvk+nd8Uer6dH8+t5nJxXFN87NpuF2+2u7dy2eR2luRur8+XZrQsC4lEAn6/HwsLCxgfH8fa2lqJmHKhp34ga7u9vR0nT57EU089BYfDgdHRUdy6dQs+n08ndt0t3k2/zX3NPuHHcGuZMrAdDgdu376971eN2o/PsyA8buzGYv4EgN8FcEspdaO47c+w9QC/rJT6QwALAL5WfO91bE2tmMbW9Ip/d19bvAssy8Ls7CxeffVVfOMb34DH49HCyQtM2H2pkxuWW6mmNWdXnctuWg/tz9271AZuae7kSjdd5rx9pvVrNzAguLuZ9jH7YCdBNs9lWs7msoxU7vP27duYm5vDzMwMotGoLtlp9hXdEy02cfLkSZw/fx4OhwOTk5O4fv06lpeXtwmynbhSf9jdP+9Lu9fcWna5XHpu/NjY2L4X5SL77nkWhMcNtZsv4gfeiPvoyuY4nU6cP38ev/3bv43Gxkad3ESiRvduJjHxWC93MwOlSWQ0D5efjwuyKbx0PnMusZk0Zmf50XVNUTQxxaecIO0k/ib8XuhYaislRtGiE4VCAdFoFD6fTwtyIBBALpcrO2Cg+cEtLS144okn8Mwzz8DtdmNoaAjDw8NYXl7WRUbM9pgDDNMVX+5++GszrkyDDCpac/36db0SVwXwoWVZz+51I3aiQbVYz6nP7HUzBKHiecv6se3zXBGVvx4UuVwOIyMjqKmpwRe/+EWd4Wy6lXnSEv+S59OtTPEyq1wVCgW9fjEAbZnzjGk6juYn203VskuwMkW5nMDRvnw/06LkcWW7ZDU7TFGmY6lICE31isfj8Pv9uH37NiYnJ7GysqLd1nYDBjqP1+tFX18fnn/+ebS2tmJmZgaDg4MlpTjpnsrFvOm13edl16fm/tQWmmt94MAB9Pf3Y2hoqJJEWRCEx4BHWpgBIBaL4fLly0ilUnjhhRfQ1dVVktEMlCaCmclAvMSmaVFzi9isn20mEnHrjs9HtnNVl7NsCXNf3nZe6IRfk7CrhlbuOiZ0frKQldpKUkskEggEAlhYWMDs7CyWlpZKlpDk/Ufndzgc8Hg8OHToEJ599ln09fVhaWkJP/nJTzAxMWG73Cf/XMz3eP/bufTtYsnmfVGd7Z6eHpw+fRqjo6OYn58v2x+CIAgPgkdemIE7i11sbm7ixRdfhNfr1YJLU5FIrMzCIgC2CTRwx/1slnqkv804NBdqXqKznCVuVzaTMF3L/Dg6Pz8fUD7Rib9XzsLm5TypsMrm5iaSySTW19fh8/kwMzMDn8+HaDRaYiHTOfgAxe12o7u7G+fOnUN/fz8SiQTeeustjIyMIBQKbVsMw66fdooZl3Pdm4MuHk92u91oa2vDU089hQsXLmBoaAhXrlxBOp0u818lCILwYHgshBnYmmJ0/fp1xGIxfOlLX0J3d7ety9ouU5mqVdGXu50FTfvbiR8XdXN/Oj+PUZuJTaagmNcHUDLP2bSU+T2ZYsX3I5HidaH56laWtVUqNBaLIRgMwufzYX5+Hmtra3pNZxO6/+rqani9XnR1deGpp55CT08PUqkUrly5gqGhIQSDQR2HNu/Zrt1298Pfs7P8+X2SlVxTU4OmpiYMDAzg3LlzOHbsGObn5/HjH/8YiURi2zkEQRAeNI+NMANb4jU1NYUf/OAH+J3f+R0MDAzohCV6v1x8lrur7YTYtG65uJjwBLG7WXhkAXNL3hwAmNa2Xbv4exy+sAUtsMErm+VyOWSzWaRSKYRCIQQCAayurmJpaQnhcFivn22KJf04HA7U1tair68PZ86cQW9vL6LRKK5evYrR0VEEAoGSNZbNdpqDjLtlY/P+431jWskulwv19fXo7+/HyZMncerUKbhcLvz0pz/Fa6+9hnA4XPbcgiAID5LHSpiBLbHx+/344Q9/iM997nM4f/48ampqkMvlSkpU2lmcfGlIO8iaNS1gM/mL4BY4P54nZ/ElH/l16Hjz2qaI8Zg4n5bELWI6XqmtaU6pVAqJRALhcBjhcFgLcigUQigU0kso2t0/nd/lcsHr9aK3txdnzpxBZ2cnQqEQLl26hPHxcYRCIWQymR0T2Th2XoRywmz2Ff+pqalBY2Mjjhw5ogcKSilMTEzg/fffx+Dg4H5cmEIQhEeIx06Yga0v942NDbz66qtIp9P41Kc+VVIyE7hTsxq4Ezem3+TaJgHnYsHLgJoWrJ31bSZHmTFqus6od/oAABWzSURBVB5g787llqGZZMVF2HRT07kKhQJSqRTS6TTi8Tg2NjYQDAYRCASwvr6OaDSKZDKpS3jSNc164HQ9p9OJhoYGDAwM4OTJk+ju7obP58NPf/pTTE1NIRwOa5e3Kcp28WO7/Xb6XM0Kb3TP1K5jx47h1KlT6OnpQSaTwfDwsJ5rvbi4qD0AgiAIe8VjKcxEIpHApUuXEA6H8elPfxrNzc3b3Ms8NmxaqFzMTTExq4KZljH95klntK2cdWznYudCTALMhZiLMLUhm80im80iFoshHo8jEAggEAjA7/cjHA4jkUggk8mU1ODm1zGnW1GmdmNjoxbk1tZWrK6u4pVXXsHc3BwikUjZAh2870wR3q0o89g89xBQDPnYsWMYGBhAd3c3gsEgrl69iqWlJb0O9Pr6ellPiCAIwsPksRZmAAiHw7h06RIikQi++MUvor29Xcd1zYxgnskNbI/t8n3IejPFm0MiRy5yU5zMOKqZbU1C6XQ6t1nHdPzm5tbyjZlMBolEQruoNzY2sLGxgUAggEgkglQqpQuw8GvbuYxJ9HghjoGBAZw4cQJerxdLS0t49913MTs7i3g8vk3w7JK3zOuUE2rTUuf7c69ATU0N2tvbMTAwgNOnT6O2thZ+vx9vvPEGVlZWdLY1lfcUBEGoFB57YSaGh4exsrKCr3zlKzhx4kRJUhh3+9ola5HAcrEkkbQsq2QpQ45dUhNQuvQjd6lbllViDQPQ066oXfl8HtlsFplMBqlUCrFYDLFYDNFoFMFgEKFQSCdtZbNZ5PP5kvsqlwlOr+kea2tr0d7ejieeeAL9/f2or6/H3NwcLl26hIWFBb1alN392d27nVdip+xrfs/UHzQ3mrfL4XBgcXERo6Oj8Pv9SCQSqK6uRiqVQjQaLVkmUhAEoRIQYS6SzWaxsrKC1157DW63G/39/bbJWHYZ1GY2NxczEiceAwZKLTzgjsBSPJmEppz7nGLcJMTZbBbJZBLxeByRSASxWEyLcCQSQTqd1klb5dZRpmvYucn5lKeOjg48+eST6OnpQXV1Nebn5/HGG29gcXERiUSiZMGQcq5oU2jNWLm5r517n3sJvF4vuru78dRTT+HQoUMoFAoYHR3F5OSkzvympLxgMFhS4lMQBKGSEGE2WFpawiuvvIKLFy/i/PnzcLvdJe/T1CXAvsY1FzlasIEEgVvNtGYyUJpoRq5wysgm8SXxz2QyyOfzSKfT2j0dj8eRSCQQjUZ13JgSuugcvL0ECbR5fT4tjNzVDQ0N6OrqwvHjx9Hb24tMJoO5uTkMDQ1hZWUFkUhk21KahBl/32m7WQOb78tfU9tqa2vR3d2NkydP4uTJkwiFQrhx4wbGx8cRDAa1RexyuZBMJrclsgmCIFQaIswGlmXpohmRSATPP/88WlpaSixn0/ql4+g9pe6U3aTa3Jubm3A6ndoqrqmpgVJKu5OpJnQul9PWbS6X06s0kcCk02kkEgkkk0mk02ktNPl8XseIeXZ5OVGk93i5UJo+5XQ69XKHBw4cQHd3N7q7u+HxeBCPxzE4OIiRkRGsra2VlN7k5zUronF3f7n4Me1X7m8eInC5XDh48CDOnDmDJ554Apubm7h8+TImJiawsbGh+4sGP6lUyjbeLQiCUGmIMJchlUrhF7/4BRYXF/HVr34VHR0d2krj2crA9uIipigB0GJHwry5ualjnMlksmTKUiKR0AlJm5ubiEQiCIfDWrTJ4itnofJ2mTFeXh3MzOauq6tDU1MTurq6cPToUbS2tsLj8SCZTMLv92N2dhZzc3MIhUJIJBK21y23zc4dbfYbhws6/SZ3OtWzvnDhAlpaWjAxMYGhoSEsLy/rbHJgS5Tz+TySyeSjsmSjIAiPASLMO5BMJjEyMoKNjQ288MILeP7551FTU4NsNqsFg7K0uSUI3LHuyGVN4pfL5XTcNxgMYmVlBaurq7pwByVwAXdc5FSq0rRM7eKydtnN5jQi7gquq6tDS0uLFuPOzk7U1NTA7/djbm4OS0tLWF1dRTAY1Jnb/NocO+vW3G7Gs8u5vu3i3DU1NWhra8MzzzyD06dPY21tDa+//jpmZ2d1SdDNzU3U1NSgUCggHo9vG0QJgiBUOiLMd8GyLCwvL+Pf/u3fsLa2hk9+8pPo6+tDoVDQLmQ+RQoond9LCVyZTAbBYFDHf2m94nA4rEtSmms786QxaotpAdtZlma2M23nC1I0NDTgyJEj6O3tRXd3N1wuF0KhEG7duoWZmRldXIRc7TtVOyuXPW0KL9+X3585Lc28HxpA9PT04Pz582htbcXY2BgGBwexurqqE7ksy0J9fT02NzcRj8fFShYEYV8iwrxLYrEYfv7zn2N4eBhf+9rX0NfXh9bWVgDQyVlm1a5MJoNQKISVlRXMz88jn8/reb5LS0u2pS3Nv00XtF2hE1OACT7tipY0bGtrQ39/P3p7e9HY2IhAIIChoSEsLCzo6UQ8Vn03zIGAyU6JYJSZTvdkzlGmWHxraytOnz6NEydOIJFI4O2338b09LQWX4qPOxwOPdARBEHYr4gw3wOWZSEQCOAHP/gBuru78aUvfQmNjY0lNbY3NzeRTqcRDoextraG1dVVBAIB5HI59PT0oFAoYGVlRa83bDef14RXBTNXpjLd2LQ/tzSbm5vR3d2Nvr4+tLe3A9jKPn/nnXf0YhRkdZrWv12GdDlM69du7rLdlDOzShq5/1taWtBbrLXt9XoxNjam55tTLJkGQ/l8vmzcWxAEYT8hwvwRiEajGBsbw9raGpqbm9HS0qIFjVZiogIelKjl9XrhcrkQDoeRTCa1gJmFR0i4THe4UlsLQ7jdbrhcLuTzeX0NmpLlcDjg9XrR0NCAxsZGtLW1ob29XWeVLy8v4+rVq5idncX6+jrS6bTOGC/nIrd7fTfxs7PqaTu/Fv9NGeG1tbXo6OjAkSNHcOzYMdTV1WFhYQGXLl2Cz+crseipaprL5UI8HhdRFgThkUCE+SNiWZaupLW4uAiv1wun01lSspO7aJ1Op56LzBel4LFW2o8yuJ1OJ9xuty65SSLES0+63W54vV54vV40NjaipqYGLpcLAPS6yRMTE/D5fPD7/dr9W65M5k6Z0oT5Hp8GtdvjeEY4DSg6Ojpw/PhxdHZ2wu12Y35+HrOzs7qSWC6X04JsWRZqamqQz+cRjUZFlAVBeGQQYf41oVhyNptFXV2d/qmurtZC4nK54PF4EAqFUFNTg66urpIynSTGJFIul0uLDwmx0+lEXV0dvF6vFt9cLqdLhxYKBfh8Pr1MYzgc1sVGyE3NBwy8SEo5V7oppHb7mMlc5c7DX/O5yM3Nzejt7dXTs8LhMEZHRzE/P68zwbkgk5XscDiQSCRkXrIgCI8cIsz3CcuykEgkkM1m0dDQgP7+fni9Xh3vzWQyWFtbQzqdRnNzM2pra0tWgAKg3dUulwvV1dWoq6tDY2NjSZGSRCKBlZUVRKNRRCIRRCIRZDIZPceZKoWVm1pl/s0rjdF2c+qX3fF22+3i0qbbmgYZjY2NOH78OI4ePYqGhgasrKzg8uXLWFxc1PO7KYZMP+Suz+VyZddyFgRB2O+IMN9ncrmcLgXZ39+Pz3/+82hra0Mmk8H6+jpWVlZgWZZ2Ubvdbrjd7hJ3di6X05W9pqenEQ6HdblNEn+ygM1lKfn8amD7Qht285zN1bHKWcHlsLOy7eYi0zSt/v5+nDx5EjU1NVhcXMQ777yD9fV1XQjELJxC1jXF1cVKFgThUUaE+QERj8cxPDyM9fV1nD9/Hr/xG7+Bzs5OdHZ26rnMVOUrEAggkUjoyl+pVEoLME8gKyfE5Upc2sV06bXd/rTtbtbybmLJ/HdVVRVqa2tx8OBBnD17FgcPHsTt27cxNjaG1dVVXdazUCigurpaFwghq1gphWQyuW0qmSAIwqOICPMDJJ/P68pZly9fRnNzM9ra2gBgWz1sqpNN1iIlivE1mMtV27LLpObJWOWONY+xO6e5rdz17AqcVFVV6TjyqVOn0Nvbi3A4jLfffhtLS0vbrF+Xy4Xa2lpEo1GdMS4IgvC4cVdhVkodBvCPADoAWABesizrb5VSfwHgPwAIFHf9M8uyXi8e86cA/hBAAcB/tCzrjQfQ9n1DPp9HMBhEMBjE4uIiamtr4fF44PV6y1q7FPsl93Y5MdxtgtZOAlzOFW1ex7y++R79TXHz2tpaDAwM4NSpU1BKYWRkBBMTEzoznI4tFApaiCORyD30rHAvyLMsCPuD3VjMeQB/YlnWNaVUPYAPlVJvFt/7n5Zl/Q++s1LqFICvAzgN4BCAt5RSxy3LEj8kgEwmg0wmg2g0ioaGBjQ0NMDlcmkrmWKsdusmcze2OR/YzjI2i4+Uy5wud6wZly5XbIS/X11dDY/Hg0OHDuH48eNobW3F4uIixsfH4ff79X3l83ntLZAkroeGPMuCsA+4qzBblrUCYKX4OqaUGgPQtcMhXwbwI8uyMgDmlFLTAC4AuHIf2vvIsLm5qRezoLnI9fX1aGpqArDl6qalHAHoxRjox6yfbed6pt9cZPk2swym+R5QWnWM/+b78znVhw4dwrFjx3D48GGEw2G899578Pl8iMViSKVSKBQKUjJzj5BnWRD2B/cUY1ZK9QJ4GsB7AD4B4NtKqd8DMIitkXgIWw/6VXbYEmwefqXUNwF88yO1+hHCsiy95GMoFNJTpDo7O9HY2Ai3262nQ6VSKSSTSZ0URsJNQs3XjKZzcyGtrq7eca6ynbvc3I8LNAkyVevq7+9HV1cXstksRkZGMDc3h2AwqKc/Scy4crifz3LxfPp5dqPugbVbEB4Hdi3MSikvgH8G8MeWZUWVUt8D8JfYilX9JYC/AfDvd3s+y7JeAvBS8dyPvS/TsixdxSoajcLn86G9vR39/f1oaGhAV1cXmpqacOTIEZ0cFQqFEAgE9FQqKiZiTqfimdbcwjbrbpdrF7e66Yeqk3V2dqKtrQ2nTp1CJpPB6OgoxsbGdJUxEePK434/y0Dp89ygWh7751kQfh12JcxKKSe2HuR/sizrXwDAsqw19v7fAXit+KcPwGF2eHdxm3APbG5uYm1tDX6/XwthXV0dOjo6UFdXhyeffBJPPPEEzp49C7fbjXw+j3Q6jUQigVAopKdipdNpXVObKoSZYsmXr8xkMnq1LGqHw+FANpvVFjK9bmpqQjQaxerqKn71q1+VWPQSN65M5FkWhMpnN1nZCsD3AYxZlvVdtr2zGLMCgBcBDBdfvwrg/yilvouthJEBAO/f11Y/RpC1S0ljoVAIAHDt2jW9LGJnZycOHjyIQ4cO4cCBA2hsbERfXx88Hg8cDoeeHxyPx3Whjnw+j0AggEwmg0Qigdu3b8Pj8WBjYwM+n0/HgWOxmBbsRCKhq5DJWsf7D3mWBWF/sBuL+RMAfhfALaXUjeK2PwPwDaXUOWy5v+YBfAsALMsaUUq9DGAUW1mgfyRZnA8GEtjp6WlMT0/rrGgq0mHW306n03pboVDQ1q1SSk9XUkrpuLUdUuRjXyPPsiDsA1QluBwlxiwIu+ZDy7Ke3etG7ESDarGeU5/Z62YIQsXzlvVj2+e5ym5nQRAEQRD2BhFmQRAEQaggRJgFQRAEoYIQYRYEQRCECkKEWRAEQRAqCBFmQRAEQaggRJgFQRAEoYIQYRYEQRCECkKEWRAEQRAqCBFmQRAEQaggRJgFQRAEoYIQYRYEQRCECkKEWRAEQRAqCBFmQRAEQaggRJgFQRAEoYIQYRYEQRCECkKEWRAEQRAqCBFmQRAEQaggRJgFQRAEoYIQYRYEQRCECkKEWRAEQRAqCBFmQRAEQaggRJgFQRAEoYIQYRYEQRCECkKEWRAEQRAqCBFmQRAEQaggRJgFQRAEoYIQYRYEQRCECkJZlrXXbYBSKgAgAWB9j5vSVgFtAKQdJtKOO/RYltW+x23YEaVUDMDEXrcDlfF5AZXRjkpoAyDtMLF9nitCmAFAKTVoWdazj3sbpB3Sjv1OpfSTtKOy2iDt2D3iyhYEQRCECkKEWRAEQRAqiEoS5pf2ugGojDYA0g4Tacf+olL6Sdpxh0poAyDt2BUVE2MWBEEQBKGyLGZBEARBeOzZc2FWSn1eKTWhlJpWSn3nIV97Xil1Syl1Qyk1WNzWopR6Uyk1Vfzd/ACu+/dKKb9Saphts72u2uJ/FfvnplLqmQfcjr9QSvmKfXJDKfUF9t6fFtsxoZT63H1qw2Gl1CWl1KhSakQp9Z+K2x9qf+zQjofaH/udvXqe5Vne+2e5eN49f54fiWfZsqw9+wFQDWAGwFEALgBDAE49xOvPA2gztv13AN8pvv4OgP/2AK77AoBnAAzf7boAvgDg/wFQAC4CeO8Bt+MvAPxnm31PFT+fGgB9xc+t+j60oRPAM8XX9QAmi9d6qP2xQzsean/s55+9fJ7lWd77Z7l47j1/nh+FZ3mvLeYLAKYty5q1LCsL4EcAvrzHbfoygH8ovv4HAF+53xewLOuXADZ2ed0vA/hHa4urAJqUUp0PsB3l+DKAH1mWlbEsaw7ANLY+v1+3DSuWZV0rvo4BGAPQhYfcHzu0oxwPpD/2OZX2PMuzbM8D+9+thOf5UXiW91qYuwAssr+XsHMH3m8sAD9VSn2olPpmcVuHZVkrxderADoeUlvKXXcv+ujbRbfS3zP33wNvh1KqF8DTAN7DHvaH0Q5gj/pjH7KXfSLPsj179r9bCc/zfn2W91qY95pPWpb1DIDfAvBHSqkX+JvWlp/joaet79V1i3wPwDEA5wCsAPibh3FRpZQXwD8D+GPLsqL8vYfZHzbt2JP+EO4ZeZa3s2f/u5XwPO/nZ3mvhdkH4DD7u7u47aFgWZav+NsP4BVsuS/WyJVS/O1/SM0pd92H2keWZa1ZllWwLGsTwN/hjkvngbVDKeXE1gP0T5Zl/Utx80PvD7t27EV/7GP2rE/kWd7OXv3vVsLzvN+f5b0W5g8ADCil+pRSLgBfB/Dqw7iwUsqjlKqn1wA+C2C4eP3fL+72+wB+8jDas8N1XwXwe8XsxYsAIswldN8x4jsvYqtPqB1fV0rVKKX6AAwAeP8+XE8B+D6AMcuyvsveeqj9Ua4dD7s/9jl78jzLs2zPXvzvVsLz/Eg8yw8728z8wVZW3iS2MuH+/CFe9yi2MvGGAIzQtQG0AngbwBSAtwC0PIBr/xBbrpQctuIZf1juutjKVvzfxf65BeDZB9yOHxSvcxNb/7CdbP8/L7ZjAsBv3ac2fBJbbq2bAG4Uf77wsPtjh3Y81P7Y7z978TzLs1wZz3LxvHv+PD8Kz7JU/hIEQRCECmKvXdmCIAiCIDBEmAVBEAShghBhFgRBEIQKQoRZEARBECoIEWZBEARBqCBEmAVBEAShghBhFgRBEIQKQoRZEARBECqI/w/HDaJ2QpTU1gAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "deformed_data_dict = rand_elastic(data_dict)\n", - "print(f\"image shape: {deformed_data_dict['image'].shape}\")\n", - "\n", - "image, label = deformed_data_dict[\"image\"][0], deformed_data_dict[\"label\"][0]\n", - "plt.figure(\"visualise\", (8, 4))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"image\")\n", - "plt.imshow(image[:, :, 5], cmap=\"gray\")\n", - "plt.subplot(1, 2, 2)\n", - "plt.title(\"label\")\n", - "plt.imshow(label[:, :, 5])\n", - "plt.show()" + "transform = Compose([\n", + " LoadImaged(keys=\"image\"),\n", + " AddChanneld(keys=\"image\"),\n", + " Resized(keys=\"image\", spatial_size=[64, 64]),\n", + " ToTensord(\"image\"),\n", + "])\n", + "test_data = {\"image\": filename}\n", + "result = transform(test_data)\n", + "print(f\"image data shape:{result['image'].shape}\")\n", + "print(f\"meta data:{result['image_meta_dict']}\")" ] }, { @@ -786,12 +383,11 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ - "if directory is None:\n", - " shutil.rmtree(root_dir)" + "shutil.rmtree(tempdir)" ] } ], @@ -811,7 +407,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.10" } }, "nbformat": 4,