From 918680866b9f73f6f3f3370e12320fac69550106 Mon Sep 17 00:00:00 2001 From: Lars Reimann Date: Tue, 14 Mar 2023 15:47:06 +0100 Subject: [PATCH 1/7] build(deps-docs): add mkdocs-jupyter and our examples --- mkdocs.yml | 4 ++ poetry.lock | 131 ++++++++++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 2 + 3 files changed, 136 insertions(+), 1 deletion(-) diff --git a/mkdocs.yml b/mkdocs.yml index 2012704fd..021cdf8ba 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -56,6 +56,10 @@ plugins: - autorefs - glightbox - search + - mkdocs-jupyter + - execute: true + - allow_errors: false + - include_source: True watch: - src diff --git a/poetry.lock b/poetry.lock index 211b7bf2f..6ce4a2f03 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1167,6 +1167,29 @@ files = [ {file = "jupyterlab_widgets-3.0.5.tar.gz", hash = "sha256:eeaecdeaf6c03afc960ddae201ced88d5979b4ca9c3891bcb8f6631af705f5ef"}, ] +[[package]] +name = "jupytext" +version = "1.14.5" +description = "Jupyter notebooks as Markdown documents, Julia, Python or R scripts" +category = "dev" +optional = false +python-versions = "~=3.6" +files = [ + {file = "jupytext-1.14.5-py3-none-any.whl", hash = "sha256:a5dbe60d0ea158bbf82c2bce74aba8d0c220ad7edcda09e017c5eba229b34dc8"}, + {file = "jupytext-1.14.5.tar.gz", hash = "sha256:976e66be8056459a2067e0ec3ff68cc31e00c31895faf9eb893022d319e8f5b4"}, +] + +[package.dependencies] +markdown-it-py = ">=1.0.0,<3.0.0" +mdit-py-plugins = "*" +nbformat = "*" +pyyaml = "*" +toml = "*" + +[package.extras] +rst2md = ["sphinx-gallery (>=0.7.0,<0.8.0)"] +toml = ["toml"] + [[package]] name = "kiwisolver" version = "1.4.4" @@ -1260,6 +1283,31 @@ files = [ [package.extras] testing = ["coverage", "pyyaml"] +[[package]] +name = "markdown-it-py" +version = "2.2.0" +description = "Python port of markdown-it. Markdown parsing, done right!" +category = "dev" +optional = false +python-versions = ">=3.7" +files = [ + {file = "markdown-it-py-2.2.0.tar.gz", hash = "sha256:7c9a5e412688bc771c67432cbfebcdd686c93ce6484913dccf06cb5a0bea35a1"}, + {file = "markdown_it_py-2.2.0-py3-none-any.whl", hash = "sha256:5a35f8d1870171d9acc47b99612dc146129b631baf04970128b568f190d0cc30"}, +] + +[package.dependencies] +mdurl = ">=0.1,<1.0" + +[package.extras] +benchmarking = ["psutil", "pytest", "pytest-benchmark"] +code-style = ["pre-commit (>=3.0,<4.0)"] +compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"] +linkify = ["linkify-it-py (>=1,<3)"] +plugins = ["mdit-py-plugins"] +profiling = ["gprof2dot"] +rtd = ["attrs", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"] +testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"] + [[package]] name = "markupsafe" version = "2.1.2" @@ -1397,6 +1445,38 @@ files = [ [package.dependencies] traitlets = "*" +[[package]] +name = "mdit-py-plugins" +version = "0.3.5" +description = "Collection of plugins for markdown-it-py" +category = "dev" +optional = false +python-versions = ">=3.7" +files = [ + {file = "mdit-py-plugins-0.3.5.tar.gz", hash = "sha256:eee0adc7195e5827e17e02d2a258a2ba159944a0748f59c5099a4a27f78fcf6a"}, + {file = "mdit_py_plugins-0.3.5-py3-none-any.whl", hash = "sha256:ca9a0714ea59a24b2b044a1831f48d817dd0c817e84339f20e7889f392d77c4e"}, +] + +[package.dependencies] +markdown-it-py = ">=1.0.0,<3.0.0" + +[package.extras] +code-style = ["pre-commit"] +rtd = ["attrs", "myst-parser (>=0.16.1,<0.17.0)", "sphinx-book-theme (>=0.1.0,<0.2.0)"] +testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"] + +[[package]] +name = "mdurl" +version = "0.1.2" +description = "Markdown URL utilities" +category = "dev" +optional = false +python-versions = ">=3.7" +files = [ + {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"}, + {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"}, +] + [[package]] name = "mergedeep" version = "1.3.4" @@ -1495,6 +1575,28 @@ files = [ [package.dependencies] beautifulsoup4 = ">=4.11.1" +[[package]] +name = "mkdocs-jupyter" +version = "0.23.0" +description = "Use Jupyter in mkdocs websites" +category = "dev" +optional = false +python-versions = ">=3.7" +files = [ + {file = "mkdocs_jupyter-0.23.0-py3-none-any.whl", hash = "sha256:24ec535d4c0ca2b8f936df6be90c3be3a57cd114b9a07378b362767ccbfdd9a8"}, + {file = "mkdocs_jupyter-0.23.0.tar.gz", hash = "sha256:b1c2e1c9a7c3ce0d43cebd40689b3d097f9e62d7ed63e772e9c3cb197971dc46"}, +] + +[package.dependencies] +jupytext = ">1.13.8,<2" +mkdocs = ">=1.4.2,<2" +mkdocs-material = ">9.0.0" +nbconvert = ">=7.2.9,<8" +pygments = ">2.12.0" + +[package.extras] +test = ["pytest", "pytest-cov"] + [[package]] name = "mkdocs-literate-nav" version = "0.6.0" @@ -2709,6 +2811,21 @@ files = [ {file = "rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055"}, ] +[[package]] +name = "safe-ds-examples" +version = "0.1.0" +description = "Ready-to-use examples for the Safe-DS Python library." +category = "dev" +optional = false +python-versions = ">=3.10,<4.0" +files = [ + {file = "safe_ds_examples-0.1.0-py3-none-any.whl", hash = "sha256:4daf35e358cca0280883ab9b604f82a4596b87a11f784c26389d296dea8d3e18"}, + {file = "safe_ds_examples-0.1.0.tar.gz", hash = "sha256:76b7d3fe02d769a99fbed8a54e7a4e76b36ce78470dc4f8bfbedc5b79ed899ff"}, +] + +[package.dependencies] +safe-ds = ">=0.2.0,<0.3.0" + [[package]] name = "scikit-learn" version = "1.2.2" @@ -2938,6 +3055,18 @@ webencodings = ">=0.4" doc = ["sphinx", "sphinx_rtd_theme"] test = ["flake8", "isort", "pytest"] +[[package]] +name = "toml" +version = "0.10.2" +description = "Python Library for Tom's Obvious, Minimal Language" +category = "dev" +optional = false +python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*" +files = [ + {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"}, + {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"}, +] + [[package]] name = "tomli" version = "2.0.1" @@ -3128,4 +3257,4 @@ files = [ [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "12e549d79adb35e190be5d8f62eef6b15a00e6bde1378ad7b3c03091a0a7b394" +content-hash = "99405e9c42d2838d17ff0e20238574d8f1072a8fcecee8c0af4e6153b0cf7613" diff --git a/pyproject.toml b/pyproject.toml index 5fea393dd..29c0afc4a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,6 +35,8 @@ mkdocs-glightbox = "^0.3.1" mkdocs-literate-nav = "^0.6.0" mkdocs-material = "^9.1.2" mkdocs-section-index = "^0.3.5" +mkdocs-jupyter = "^0.23.0" +safe-ds-examples = "^0.1.0" [build-system] requires = ["poetry-core>=1.0.0"] From 6fb078d841f4c933a24a1afcef23959e82c0acaf Mon Sep 17 00:00:00 2001 From: Lars Reimann Date: Tue, 14 Mar 2023 15:47:26 +0100 Subject: [PATCH 2/7] docs: fix typos --- docs/tutorials/machine_learning.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/tutorials/machine_learning.md b/docs/tutorials/machine_learning.md index 545e3c7a3..05cd0240d 100644 --- a/docs/tutorials/machine_learning.md +++ b/docs/tutorials/machine_learning.md @@ -2,7 +2,7 @@ ## Create SupervisedDataset -Here is a short introduction to train and predict with a machine learning model in safe-ds. +Here is a short introduction to train and predict with a machine learning model in Safe-DS. First we need to create a [SupervisedDataset][safeds.data.SupervisedDataset] from the training data. @@ -24,7 +24,7 @@ to_be_predicted_table = Table({ sup_dataset = SupervisedDataset(table, target_column="target") ``` -[SupervisedDatasets][safeds.data.SupervisedDataset] are used in safe-DS to train supervised machine learning models +[SupervisedDatasets][safeds.data.SupervisedDataset] are used in Safe-DS to train supervised machine learning models (e.g. [RandomForest][safeds.ml.classification.RandomForest] for classification and [LinearRegression][safeds.ml.regression.LinearRegression] as a regression model), because they keep track of the target vector. A [SupervisedDataset][safeds.data.SupervisedDataset] can be created from a [Table][safeds.data.tabular.Table] and @@ -37,7 +37,7 @@ train with (the sum of the rows). The `to_predicted_table` is the table we want does not contain a target vector. In order to train the [LinearRegression][safeds.ml.regression.LinearRegression]-model we need to make the following calls -in safe-DS: +in Safe-DS: ```python linear_reg_model = LinearRegression() @@ -46,7 +46,7 @@ linear_reg_model.fit(sup_dataset) As we can see, a [LinearRegression][safeds.ml.regression.LinearRegression]-object is created. -In safe-DS machine learning models are separated in different classes where the different fit and predictions methods +In Safe-DS machine learning models are separated in different classes where the different fit and predictions methods are implemented for the given machine learning model. ## Predicting new values From 39526a741ff87741aeabcc87de5c8cb01b448c4a Mon Sep 17 00:00:00 2001 From: Lars Reimann Date: Tue, 14 Mar 2023 18:26:48 +0100 Subject: [PATCH 3/7] docs: recreate visualization tutorial using a notebook --- docs/reference/generate_reference_pages.py | 4 + .../data_visualization_notebook.ipynb | 79 +++++++++++++++++++ mkdocs.yml | 9 ++- poetry.lock | 20 ++--- pyproject.toml | 2 +- 5 files changed, 99 insertions(+), 15 deletions(-) create mode 100644 docs/tutorials/data_visualization_notebook.ipynb diff --git a/docs/reference/generate_reference_pages.py b/docs/reference/generate_reference_pages.py index c77277767..5a8adffa2 100644 --- a/docs/reference/generate_reference_pages.py +++ b/docs/reference/generate_reference_pages.py @@ -37,6 +37,10 @@ def list_class_and_function_names_in_module(module_name: str) -> list[str]: # Remove the final "__init__" part parts = parts[:-1] + # Skip private modules + if any(part.startswith("_") for part in parts): + continue + qualified_name = ".".join(parts) for name in list_class_and_function_names_in_module(qualified_name): diff --git a/docs/tutorials/data_visualization_notebook.ipynb b/docs/tutorials/data_visualization_notebook.ipynb new file mode 100644 index 000000000..43ef0c8ba --- /dev/null +++ b/docs/tutorials/data_visualization_notebook.ipynb @@ -0,0 +1,79 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Data Visualization\n", + "\n", + "## Table & Statistics\n", + "\n", + "First, we need some data to visualize. For this, we use the common example of the Titanic disaster, which is included in our [`safe-ds-examples` package](https://pypi.org/project/safe-ds-examples). Naturally, you can also read your own data." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "from safeds_examples.titanic import load_titanic\n", + "\n", + "titanic = load_titanic()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-03-14T16:13:06.844175Z", + "end_time": "2023-03-14T16:13:07.479438Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "data": { + "text/plain": " Name Sex Age \\\n0 Abbing, Mr. Anthony male 42 \n1 Abbott, Master. Eugene Joseph male 13 \n2 Abbott, Mr. Rossmore Edward male 16 \n3 Abbott, Mrs. Stanton (Rosa Hunt) female 35 \n4 Abelseth, Miss. Karen Marie female 16 \n5 Abelseth, Mr. Olaus Jorgensen male 25 \n6 Abelson, Mr. Samuel male 30 \n7 Abelson, Mrs. Samuel (Hannah Wizosky) female 28 \n8 Abrahamsson, Mr. Abraham August Johannes male 20 \n9 Abrahim, Mrs. Joseph (Sophie Halaut Easu) female 18 \n\n Number of Siblings or Spouses Aboard Number of Parents or Children Aboard \\\n0 0 0 \n1 0 2 \n2 1 1 \n3 1 1 \n4 0 0 \n5 0 0 \n6 1 0 \n7 1 0 \n8 0 0 \n9 0 0 \n\n Ticket Number Travel Class Fare Cabin Number Port of Embarkation \\\n0 C.A. 5547 3 7.55 ? Southampton \n1 C.A. 2673 3 20.25 ? Southampton \n2 C.A. 2673 3 20.25 ? Southampton \n3 C.A. 2673 3 20.25 ? Southampton \n4 348125 3 7.65 ? Southampton \n5 348122 3 7.65 F G63 Southampton \n6 P/PP 3381 2 24 ? Cherbourg \n7 P/PP 3381 2 24 ? Cherbourg \n8 SOTON/O2 3101284 3 7.925 ? Southampton \n9 2657 3 7.2292 ? Cherbourg \n\n Survived \n0 0 \n1 0 \n2 0 \n3 1 \n4 1 \n5 1 \n6 0 \n7 1 \n8 1 \n9 1 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
NameSexAgeNumber of Siblings or Spouses AboardNumber of Parents or Children AboardTicket NumberTravel ClassFareCabin NumberPort of EmbarkationSurvived
0Abbing, Mr. Anthonymale4200C.A. 554737.55?Southampton0
1Abbott, Master. Eugene Josephmale1302C.A. 2673320.25?Southampton0
2Abbott, Mr. Rossmore Edwardmale1611C.A. 2673320.25?Southampton0
3Abbott, Mrs. Stanton (Rosa Hunt)female3511C.A. 2673320.25?Southampton1
4Abelseth, Miss. Karen Mariefemale160034812537.65?Southampton1
5Abelseth, Mr. Olaus Jorgensenmale250034812237.65F G63Southampton1
6Abelson, Mr. Samuelmale3010P/PP 3381224?Cherbourg0
7Abelson, Mrs. Samuel (Hannah Wizosky)female2810P/PP 3381224?Cherbourg1
8Abrahamsson, Mr. Abraham August Johannesmale2000SOTON/O2 310128437.925?Southampton1
9Abrahim, Mrs. Joseph (Sophie Halaut Easu)female1800265737.2292?Cherbourg1
\n
" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "titanic.slice(end=10)\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-03-14T16:13:07.484433Z", + "end_time": "2023-03-14T16:13:07.492495Z" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/mkdocs.yml b/mkdocs.yml index 021cdf8ba..3fbf20681 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -7,6 +7,7 @@ nav: - Tutorials: - Data Processing: tutorials/data_processing.md - Data Visualization: tutorials/data_visualization.md + - Data Visualization Notebook: tutorials/data_visualization_notebook.ipynb - Machine Learning: tutorials/machine_learning.md - API Reference: reference/ @@ -56,10 +57,10 @@ plugins: - autorefs - glightbox - search - - mkdocs-jupyter - - execute: true - - allow_errors: false - - include_source: True + - mkdocs-jupyter: + execute: true + allow_errors: false + include_source: true watch: - src diff --git a/poetry.lock b/poetry.lock index 6ce4a2f03..d03d15dcb 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1614,14 +1614,14 @@ mkdocs = ">=1.0.3" [[package]] name = "mkdocs-material" -version = "9.1.2" +version = "9.1.3" description = "Documentation that simply works" category = "dev" optional = false python-versions = ">=3.7" files = [ - {file = "mkdocs_material-9.1.2-py3-none-any.whl", hash = "sha256:344a1c66f0e217e5ceaf67739d274e51c61ac9f9d7af55a5b4805a4a2ab54184"}, - {file = "mkdocs_material-9.1.2.tar.gz", hash = "sha256:9ca8c980c30aab3b70e3bfbec691f9ffcbea8319873911c86e8af145147e2a9d"}, + {file = "mkdocs_material-9.1.3-py3-none-any.whl", hash = "sha256:a8d14d03569008afb0f5a5785c253249b5ff038e3a5509f96a393b8596bf5062"}, + {file = "mkdocs_material-9.1.3.tar.gz", hash = "sha256:0be1b5d76c00efc9b2ecbd2d71014be950351e710f5947f276264878afc82ca0"}, ] [package.dependencies] @@ -1764,14 +1764,14 @@ test = ["ipykernel", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>= [[package]] name = "nbconvert" -version = "7.2.9" +version = "7.2.10" description = "Converting Jupyter Notebooks" category = "dev" optional = false python-versions = ">=3.7" files = [ - {file = "nbconvert-7.2.9-py3-none-any.whl", hash = "sha256:495638c5e06005f4a5ce828d8a81d28e34f95c20f4384d5d7a22254b443836e7"}, - {file = "nbconvert-7.2.9.tar.gz", hash = "sha256:a42c3ac137c64f70cbe4d763111bf358641ea53b37a01a5c202ed86374af5234"}, + {file = "nbconvert-7.2.10-py3-none-any.whl", hash = "sha256:e41118f81698d3d59b3c7c2887937446048f741aba6c367c1c1a77810b3e2d08"}, + {file = "nbconvert-7.2.10.tar.gz", hash = "sha256:8eed67bd8314f3ec87c4351c2f674af3a04e5890ab905d6bd927c05aec1cf27d"}, ] [package.dependencies] @@ -2813,14 +2813,14 @@ files = [ [[package]] name = "safe-ds-examples" -version = "0.1.0" +version = "0.2.0" description = "Ready-to-use examples for the Safe-DS Python library." category = "dev" optional = false python-versions = ">=3.10,<4.0" files = [ - {file = "safe_ds_examples-0.1.0-py3-none-any.whl", hash = "sha256:4daf35e358cca0280883ab9b604f82a4596b87a11f784c26389d296dea8d3e18"}, - {file = "safe_ds_examples-0.1.0.tar.gz", hash = "sha256:76b7d3fe02d769a99fbed8a54e7a4e76b36ce78470dc4f8bfbedc5b79ed899ff"}, + {file = "safe_ds_examples-0.2.0-py3-none-any.whl", hash = "sha256:a5b37858a1e17abf7e9b694481cd23bc2e51e725cb152277ff006b3f33eded12"}, + {file = "safe_ds_examples-0.2.0.tar.gz", hash = "sha256:61fd7d2edeb3bd2e978a313629c7cc5154b67bd8e71a5f749120cfe726218df2"}, ] [package.dependencies] @@ -3257,4 +3257,4 @@ files = [ [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "99405e9c42d2838d17ff0e20238574d8f1072a8fcecee8c0af4e6153b0cf7613" +content-hash = "d9215f24f299cd9d159ab03e93109c8f202e3fbd5b783d66327eeaf01adf7935" diff --git a/pyproject.toml b/pyproject.toml index 29c0afc4a..ce9dbee84 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,7 +36,7 @@ mkdocs-literate-nav = "^0.6.0" mkdocs-material = "^9.1.2" mkdocs-section-index = "^0.3.5" mkdocs-jupyter = "^0.23.0" -safe-ds-examples = "^0.1.0" +safe-ds-examples = "^0.2.0" [build-system] requires = ["poetry-core>=1.0.0"] From 041bca6010b7681ff1484248712a719cc8e26406 Mon Sep 17 00:00:00 2001 From: Lars Reimann Date: Tue, 14 Mar 2023 19:04:57 +0100 Subject: [PATCH 4/7] docs: hide notebook for now in navigation --- .../data_visualization_notebook.ipynb | 276 +++++++++++++++++- mkdocs.yml | 1 - 2 files changed, 271 insertions(+), 6 deletions(-) diff --git a/docs/tutorials/data_visualization_notebook.ipynb b/docs/tutorials/data_visualization_notebook.ipynb index 43ef0c8ba..3e3a6140a 100644 --- a/docs/tutorials/data_visualization_notebook.ipynb +++ b/docs/tutorials/data_visualization_notebook.ipynb @@ -7,7 +7,7 @@ "\n", "## Table & Statistics\n", "\n", - "First, we need some data to visualize. For this, we use the common example of the Titanic disaster, which is included in our [`safe-ds-examples` package](https://pypi.org/project/safe-ds-examples). Naturally, you can also read your own data." + "First, we need some data to visualize. For this, we use the common example of the Titanic disaster, which is included in our [`safe-ds-examples` package](https://pypi.org/project/safe-ds-examples). Naturally, you can also use your own data." ], "metadata": { "collapsed": false @@ -25,11 +25,20 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "start_time": "2023-03-14T16:13:06.844175Z", - "end_time": "2023-03-14T16:13:07.479438Z" + "start_time": "2023-03-14T19:00:55.772331Z", + "end_time": "2023-03-14T19:00:56.442240Z" } } }, + { + "cell_type": "markdown", + "source": [ + "Now we have a look at the first 10 rows of the data:" + ], + "metadata": { + "collapsed": false + } + }, { "cell_type": "code", "execution_count": 2, @@ -49,8 +58,265 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "start_time": "2023-03-14T16:13:07.484433Z", - "end_time": "2023-03-14T16:13:07.492495Z" + "start_time": "2023-03-14T19:00:56.442240Z", + "end_time": "2023-03-14T19:00:56.453832Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Next some statistics:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "data": { + "text/plain": " metrics Name \\\n0 max - \n1 min - \n2 mean - \n3 mode ['Connolly, Miss. Kate', 'Kelly, Mr. James'] \n4 median - \n5 sum - \n6 variance - \n7 standard deviation - \n8 idness 0.998472116119175 \n9 stability 0.0015278838808250573 \n10 row count 1309 \n\n Sex Age \\\n0 - - \n1 - - \n2 - - \n3 ['male'] ['?'] \n4 - - \n5 - - \n6 - - \n7 - - \n8 0.0015278838808250573 0.07563025210084033 \n9 0.6440030557677616 0.20091673032849502 \n10 1309 1309 \n\n Number of Siblings or Spouses Aboard Number of Parents or Children Aboard \\\n0 8 9 \n1 0 0 \n2 0.4988540870893812 0.3850267379679144 \n3 [0] [0] \n4 0.0 0.0 \n5 653 504 \n6 1.0850522026992615 0.7491945902631278 \n7 1.041658390596102 0.8655602753495147 \n8 0.0053475935828877 0.006111535523300229 \n9 0.680672268907563 0.7654698242933538 \n10 1309 1309 \n\n Ticket Number Travel Class Fare \\\n0 - 3 - \n1 - 1 - \n2 - 2.294881588999236 - \n3 ['CA. 2343'] [3] ['8.05'] \n4 - 3.0 - \n5 - 3004 - \n6 - 0.7019691946837118 - \n7 - 0.8378360189701275 - \n8 0.7097020626432391 0.002291825821237586 0.21543162719633308 \n9 0.008403361344537815 0.5416348357524828 0.04583651642475172 \n10 1309 1309 1309 \n\n Cabin Number Port of Embarkation Survived \n0 - - 1 \n1 - - 0 \n2 - - 0.3819709702062643 \n3 ['?'] ['Southampton'] [0] \n4 - - 0.0 \n5 - - 500 \n6 - - 0.2362496291260457 \n7 - - 0.4860551708664827 \n8 0.14285714285714285 0.0030557677616501145 0.0015278838808250573 \n9 0.774637127578304 0.6982429335370511 0.6180290297937356 \n10 1309 1309 1309 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
metricsNameSexAgeNumber of Siblings or Spouses AboardNumber of Parents or Children AboardTicket NumberTravel ClassFareCabin NumberPort of EmbarkationSurvived
0max---89-3---1
1min---00-1---0
2mean---0.49885408708938120.3850267379679144-2.294881588999236---0.3819709702062643
3mode['Connolly, Miss. Kate', 'Kelly, Mr. James']['male']['?'][0][0]['CA. 2343'][3]['8.05']['?']['Southampton'][0]
4median---0.00.0-3.0---0.0
5sum---653504-3004---500
6variance---1.08505220269926150.7491945902631278-0.7019691946837118---0.2362496291260457
7standard deviation---1.0416583905961020.8655602753495147-0.8378360189701275---0.4860551708664827
8idness0.9984721161191750.00152788388082505730.075630252100840330.00534759358288770.0061115355233002290.70970206264323910.0022918258212375860.215431627196333080.142857142857142850.00305576776165011450.0015278838808250573
9stability0.00152788388082505730.64400305576776160.200916730328495020.6806722689075630.76546982429335380.0084033613445378150.54163483575248280.045836516424751720.7746371275783040.69824293353705110.6180290297937356
10row count13091309130913091309130913091309130913091309
\n
" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "titanic.summary()\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-03-14T19:00:56.453832Z", + "end_time": "2023-03-14T19:00:56.542297Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "As you can see here, the **idness** of the column `Name` is almost 1. This means, that almost every row has a unique value for this column. Since this isn't helpful for our use-case we can drop it." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "titanic_cleaned = titanic.drop_columns([\"Name\"])\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-03-14T19:00:56.482519Z", + "end_time": "2023-03-14T19:00:56.542297Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Heatmap\n", + "\n", + "Now we have a rough idea of what we are looking at. But we still don't really know a lot about our dataset. So next, we can start to plot our columns against each other in a so-called Heatmap, to understand which values relate to each other.\n", + "\n", + "But since this type of diagram only works for numerical values, we are going to use only those." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from safeds.data.tabular import Table\n", + "from safeds.plotting import correlation_heatmap\n", + "\n", + "titanic_only_numerical = Table.from_columns(titanic_cleaned.list_columns_with_numerical_values())\n", + "correlation_heatmap(titanic_only_numerical)\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-03-14T19:00:56.484790Z", + "end_time": "2023-03-14T19:00:57.006297Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "As you can see, there seems to be some negative correlation between `Travel Class` and `Survived`, meaning that the higher the travel class, the lower the chance of survival. Let's have another look at that." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Lineplot\n", + "\n", + "We'll use a lineplot to better understand this relationship:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from safeds.plotting import lineplot\n", + "\n", + "lineplot(titanic_only_numerical, \"Travel Class\", \"Survived\")\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-03-14T19:00:57.009796Z", + "end_time": "2023-03-14T19:00:57.226271Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "The line itself represents the central tendency and the hued area around it a confidence interval for that estimate.\n", + "\n", + "We can indeed conclude that the higher the travel class, the lower the chance of survival." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Other plots\n", + "\n", + "Some other plots that might be useful are boxplots, histograms and scatterplots:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from safeds.plotting import boxplot\n", + "\n", + "boxplot(titanic_cleaned.get_column(\"Number of Parents or Children Aboard\"))\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-03-14T19:00:57.226271Z", + "end_time": "2023-03-14T19:00:57.302284Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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600NEREREJ+mk5rE7fPiwlJWVne60EBEREdEpOKnAburUqfLYY4+d7rQQERER0Sk4qabYgwcPysqVK2X16tVSWFgodrvd7++33XbbaUkcEREREZ24kwrsPvroI2nVqpWIiGzatMnvbxxIQURERNQwTiqw++c//3m600FEREREp+ik+tgRERERUeNzUjV23bt3P2aT66uvvnrSCSIiIiKik3NSgZ3qX6ccOXJENm7cKJs2bZIJEyacjnQRERERUT2dVGB3++23B33/mmuukf37959SgoiIiIjo5JzWPnZjx47lOrFEREREDeS0BnbvvPOOuFyu07lJIiIiIjpBJ9UUO2TIEL9/A5Dvv/9e3n//fVm6dOlpSRgRERER1c9JBXZer9fv3xaLRZo1aybLli2T3r17n5aEEREREVH9nFRg9+CDD56WH7/nnnvknnvukf/85z8iIlJQUCBXXXWV9O3bV0Rqli677LLL5IknnpBDhw5JRUWF3H333ZKQkHBafp+IiIgolJxSH7v169fLo48+Ko8++qhs2LCh3t9PTU2Vm266SdavXy/vv/++lJeXy6BBg2Tz5s0iIjJ37lx54YUX5E9/+pO8/vrrsm3btlrNwERERERU46Rq7H744QcZOXKkvPbaa+Lz+UREZPfu3dK9e3d54oknJC4u7oS2M3DgQL9//+Y3v5F77rlH3n33XUlNTZVVq1bJY489JuXl5SJSU1OYn58v7777rnTo0OFkkk5EREQUsk6qxm727Nmyb98+2bx5s/z444/y448/yqZNm2Tv3r1yySWXnFRCjh49Kk888YT8/PPPUlpaKuvXr5cjR45Iz5499Wfy8vIkPT1d3nnnnZP6DSIiIqJQdlI1di+99JKsXr1a8vPz9XvNmzeXFStW1HvwxMcffyylpaVy8OBBCQ8Pl2effVaaN28uGzduFIfDoWsElYSEBNm+fXud2zt06JAcOnRI/3vv3r31Sg8RERHRueqkauyqq6vFbrfXet9ut0t1dXW9ttWsWTPZuHGjrF27VmbMmCETJkyQf//73yeTLBERufHGG8Xr9epXWlraSW+LiIiI6FxyUoFdeXm5XHrppbJt2zb93nfffSdz586VHj161GtbDodDcnNzpW3btnLjjTdKUVGR/P73v5fExEQ5fPiw7N692+/zO3bskMTExDq3t3DhQtmzZ49+bd26tV7pISIiIjpXnVRgd9ddd8nevXslMzNTcnJyJCcnR7KysmTv3r1y5513nlKCqqur5dChQ9K2bVux2+2yZs0a/bfPPvtMvvnmGyktLa3z+06nUyIjI/1eRERERL8GJ9XHLi0tTT744ANZvXq1fPrppyIikp+f7zfQ4UQsXLhQ+vbtK+np6bJv3z557LHH5LXXXpOXX35ZvF6vTJkyRebNmyfR0dESGRkps2fPltLSUo6IJSIiIgqiXoHdq6++KrNmzZJ3331XIiMjpVevXtKrVy8REdmzZ48UFBTIvffeK507dz6h7f3www8yfvx4+f7778Xr9UphYaG8/PLLepu33367WCwWGTp0qN8ExURERERUW70Cu+XLl8u0adOCNm96vV658MIL5bbbbjvhwG7VqlXH/LvL5ZIVK1bIihUr6pNMIiIiol+levWx+/DDD6VPnz51/r13796yfv36U04UEREREdVfvQK7HTt2BJ3mRLHZbLJz585TThQRERER1V+9AruUlBTZtGlTnX//6KOPJCkp6ZQTRURERET1V6/Arl+/frJ06VI5ePBgrb8dOHBArr76ahkwYMBpSxwRERERnbh6DZ5YsmSJPPPMM9K0aVOZNWuWNGvWTEREPv30U1mxYoUcPXpUFi9efEYSSkRERETHVq/ALiEhQd5++22ZMWOGLFy4UACIiIhhGFJRUSErVqyQhISEM5JQIiIiIjq2ek9QnJGRIX/729/kp59+ki+//FIASJMmTSQqKupMpI+IiIiITtBJrTwhIhIVFSXFxcWnMy1EREREdApOaq1YIiIiImp8GNgRERERhQgGdkREREQhgoEdERERUYhgYEdEREQUIhjYEREREYUIBnZEREREIYKBHREREVGIYGAX4gDIrl279PJvREREFLoY2IW4yspKGXnLM1JZWdnQSSEiIqIzjIHdr4DDE9HQSSAiIqKzgIEdERERUYhgYEdEREQUIhjYEREREYUIBnZEREREIYKBHREREVGIYGBHREREFCIY2BERERGFCAZ2RERERCGCgd1ZxOW9iIiI6ExiYHcWcXkvIiIiOpMY2J1lXN6LiIiIzhQGdkREREQhgoEdERERUYhgYEdEREQUImwNnYBfAwBSWVnJQRNERER0RjGwOwsqKytl/N2r5fDPe8XicDd0coiIiChEMbA7S5zhXhERqTpypIFTQkRERKGKfeyIiIiIQgQDOyIiIqIQwcCOiIiIKESwj12IUSNwRURiYmIaODVERER0NjGwCzFqBK6IyCMX92zg1BAREdHZxMAuBKkRuERERPTrwj52RERERCGCgR0RERFRiGBgR0RERBQi2McuRJzN9WgDR94ahnHGf/NcxbwiIqKziYFdiDCvR+uKThS73X7Gf0ukZuRtbGzsGfutcx3zioiIzqYGbYq98cYbpbi4WCIiIiQ+Pl4GDx4sn332md9nDh48KDNnzpSYmBgJDw+XoUOHyo4dOxooxY2bM9wrDk/kWfstjr49McwrIiI6Wxo0sHv99ddl5syZ8u6778o//vEPOXLkiPTu3Vt+/vln/Zm5c+fKCy+8IH/605/k9ddfl23btsmQIUMaMNVEREREjVODNsW+9NJLfv9+6KGHJD4+XtavXy9dunSRPXv2yKpVq+Sxxx6T8vJyERF58MEHJT8/X959913p0KFDQySbiIiIqFFqVKNi9+zZIyIi0dHRIiKyfv16OXLkiPTs+b8VFPLy8iQ9PV3eeeedoNs4dOiQ7N271+9FRERE9GvQaAK76upqmTNnjnTs2FFatGghIiLbt28Xh8MhPp/P77MJCQmyffv2oNu58cYbxev16ldaWtqZTjoRERFRo9BoAruZM2fKpk2b5Iknnjil7SxcuFD27NmjX1u3bj1NKSQiIiJq3BrFdCezZs2Sv/71r/LGG29Iamqqfj8xMVEOHz4su3fv9qu127FjhyQmJgbdltPpFKfTeaaTTERERNToNGiNHQCZNWuWPPvss/Lqq69KVlaW39/btm0rdrtd1qxZo9/77LPP5JtvvpHS0tKznVwiIiKiRq1Ba+xmzpwpjz32mDz//PMSERGh+815vV5xu93i9XplypQpMm/ePImOjpbIyEiZPXu2lJaWckQsERERUYAGDezuueceERHp1q2b3/sPPvigTJw4UUREbr/9drFYLDJ06FA5dOiQVFRUyN13332WU0pERETU+DVoYAfguJ9xuVyyYsUKWbFixVlIEREREdG5q9GMiiUiIiKiU8PAjoiIiChEMLAjIiIiChEM7IiIiIhCRKOYoPjXBIBUVlY2dDKIiIgoBDGwO8uOHNgvs/+4TqoP/SKu6ESx2+0NnSQiIiIKEQzsGoDDEynVNmY9ERERnV7sY0dEREQUIhjYEREREYUIBnZEREREIYIdvUiP1OVoXSIionMbAzuSyspKGX/3ajn8816pqjra0MkhIiKik8SmWBIREWe4VxyeyIZOBhEREZ0CBnZEREREIYKBHREREVGIYGBHREREFCIY2BERERGFCAZ2RERERCGCgR0RERFRiGBgR0RERBQiGNgRERERhQgGdkREREQhgoEdERERUYhgYEdEREQUIhjYEREREYUIBnZEREREIYKBHREREVGIsDV0Aoh+bQBIZWWliIjExMSIYRgNnCIiIgoVrLEjOssqKytl/N2rZfzdq3WAR0REdDqwxo6oATjDvQ2dBCIiCkGssSMiIiIKEQzsiIiIiEIEAzsiIiKiEMHAjoiIiChEMLAjIiIiChEM7IiIiIhCBAM7IiIiohDBwI6IiIgoRDCwIyIiIgoRXHmCSLh+65nAPCUiOvtYY0ckXL/1TGCeEhGdfayxI/r/uH7r6cc8JSI6u1hjR0RERBQiGNgRERERhQgGdkQnCYDs2rVLdu3aJQAaOjl0mqnjy2NLdPrx+jpzGNgRnSQODghtlZWVMvKWZ3hsic4AXl9nDgdPEJ0CDg4IbQ5PREMngShk8fo6Mxq0xu6NN96QgQMHSnJyshiGIc8995zf3wHIVVddJUlJSeJ2u6Vnz57yxRdfNExiiYiIiBq5Bg3sfv75ZykqKpIVK1YE/fvNN98sd9xxh9x7772ydu1a8Xg8UlFRIQcPHjzLKSUiIiJq/Bq0KbZv377St2/foH8DIMuXL5clS5bIoEGDRETkkUcekYSEBHnuuedk5MiRZzOpRERERI1eox08sWXLFtm+fbv07NlTv+f1eqV9+/byzjvv1Pm9Q4cOyd69e/1ev3ZqaaeGGIFU18jRhh5R2tC/T0REdCY02sBu+/btIiKSkJDg935CQoL+WzA33nijeL1e/UpLSzuj6TwXHPlln8z+47oGGb1Z18jRhh5R2tC/T0REdCY02sDuZC1cuFD27NmjX1u3bm3oJDUKDk9kg43gdIZ7g/52Xe+fLQ39+0RERKdbow3sEhMTRURkx44dfu/v2LFD/y0Yp9MpkZGRfi8iIiKiX4NGG9hlZWVJYmKirFmzRr+3d+9eWbt2rZSWljZgyoiIiIgapwYdFbt//3758ssv9b+3bNkiGzdulOjoaElPT5c5c+bI9ddfL02aNJGsrCxZunSpJCcny+DBgxsu0URERESNVIMGdu+//750795d/3vevHkiIjJhwgR56KGHZMGCBfLzzz/L9OnTZffu3dKpUyd56aWXxOVyNVSSG4Qa1SoiEhMTI4ZhNHCKzm3m/KTTg+coEVHj0KCBXbdu3Y451YRhGLJs2TJZtmzZWUxV46NGcIqIPHJxT4mNjW3gFJ3bKisrZdpdL0pkaq7Y7faGTk5I4DlKRNQ4cK3YcwRHb55ejjBPQych5PAcJSJqeI128AQRERER1Q8DOyIiIqIQwcCOiIiIKESwj91pwBGBZx/zvG7Mm4bF/CeihsQau9OA646efczzujFvGhbzn4gaEmvsThOOCDz7mOd1Y940LOY/ETUU1tgRERERhQgGdkREREQhgoEdERERUYhgH7sQdabWQz3V7arv12cb6jtnYoThubZurEqvWoovNja2UY26NOfnsZYLDPZ5jiAlOrt4/YUmBnYh6vDPe2X2H9dJ9aFfpKrq6Gnb7qmus6pGDB7+ea+4ohNPaBuVlZUy8pZn5InLh5z2NUjN+XSi6WlI5vyrqqqSpxcOb1Trsqr8tNlscvuI1sf9PNeYJWo4vP5CEwO7EObwREq1zSZVe348vds9xXVWT2bEoMMTcUq/eext1+TTuULln+XIkQZOSXAOT2S9AmSOICVqOLz+Qg/72BERERGFCAZ2RERERCHi3Gl/OocEdkg909s/0Q6v59pAgYbWmDoWn8ygk2DfVwMaDMNo8H1qKHUd1zN93RKFqjNZVjamcvhcwcDuDAjskHqmt3+iHV7PtYECDa0xdSwOHHRyKt+3OMPEZrM1+D41lLqO65m+bolC1ZksKxtTOXyuYGB3hpzpDqknu/1zbaBAQ2tMHYtPNS160IUz7Fcf1NeVl43peBOdS87ktcPrsn7Yx46IiIgoRDCwIyIiIgoRDOyIiIiIQgQ7W9FJ42ilup3qKNaT+S0Rjuasi3lU8Mkck3PtXK9vehvT/p2utDSmfWpszmb5RGcfAzs6aeblozhayd/JLJ12qr8lwtGcdTEfjwP7dktURl69jsm5NjKvvultTPt3utLSmPapsTmb5ROdfQzs6JTUd/moX5OzOZKLo8aOT+VRVVXVKX3/XFHf9Dam/TtdaWlM+9TYMG9CF/vYEREREYUIBnZEREREIYKBHREREVGIYB+7c0hjXes12KjMXbt2iYjIjz/+eMZ+o6731b/r+ltjy8Ngo/fOlZGuJzK6rj6jE8/UiMjT5WyMtDzd+QVAX48NNTr0WNfp6UiT2j5Hv9LJCqVR1AzsziGNda3XwNGxIiJDl/2fuKLiT1taj/yyr9Zv1PXb0+56USJTc6X60C+1/qZGglVVHT2l9JxOwUbvnSsjXU9kDdv6jJ4+UyMiT5ezMRK8PnlwIp/98ccfZe6TG05oe2dKsONxOkesVlZWyshbnpEnLh/C0a90UkJpFDUDu3NMY13rNXB0rCPMc9rTWtcI3GC/Xdff9MjIPaenJvF0CTZC7VwZtXYi6azP6OnGPiLybIwEr0/aT+SzjeFcCkzD6U6TwxNxWrdHvz6N4To5HdjHjoiIiChEMLAjIiIiChEM7IiIiIhCROPrrHUOO5VRq+bvAqjX93bt2nXK61+eaSfyW2dqVNLp3s+TWWdRHScAZ2Sk8PH+frxzqr6jCk82D05kBPOxPtsYR6sdbwTuyZ7X5vVtRUQMw2iU+x/IfC6JnPxIdHO+RUdH6+vmZPPgZEYbm/ch8Dio9082TadyjptHOp+u8+J4o6dPpIw42WN2us/1UBrhejIY2J1GpzJq1Twi5/YRrU/4e2rE27FGJZ5oes+kE/mtMzUq6XTv58mss3j4570y5Y6/iCc2+fSNFD6w/5jnm3kE5/HOqfqOKjyR0bCBTmQEs9qXYCOaG+uox+ONwD3Z89p83lqcYefMmszmc0lETnpN0sDz91RH9tZ3tHGwfTAfB/X+yaYp2Hlzoud4ZWWlnnngdJ0Xxxs9fSJlxMkeM3N5cjrO9VAa4XoyGNidZqcyEvRkR+ScykiesznK9kR+60yOZDyd+3ky6bS7w8/ISOFjba8+IzjrO6rwZPLgeCOYzftS67ONeNTj8fLiZM9rlScWZ1ijmd7oRJiP1amWT2q/T0fZUJ9tBNuHwONwqmkK/H59znE188DpPC+Otz8nkr6TPWZ15fHJCpURrieDfeyIiIiIQgQDOyIiIqIQwabYM+hkOu0DkJ9++umkv3+6BHZmVY7V8f90pTfwt09ksMGZ+O1jLU12vO+fSlrq+u36fK8+nw3cp2N1ZD7R/TOfx6crnXWdk4Gd2U90CbFTWXbsRAam1DUg5Fgd04+VN4H7fyL9hoKVJ8HyT713vKW/1L+P9bcTHdBT198C/36sfTAf++Ntx/x3NZDJnAenc2mzus7RupZ8C7bPxzsOp5K+U83js+VY139gngYOeAn8/vHyP1ieqMEkgRr7QCYGdmfQyXTaP/zzXrnskU0SlZFXq0P52RTYcdv837qW4zJ/51SW7Ar228fb3pn47QP7dvsdh8DlzII5mUEFwbZR16CCE037iX422D4dqyPzie6f+Tw+Vjrrd1yrgp6TwQZYnIhj5UF9vlvXwJTA41F96Be5cOUaeXphTNCO6Spf1Xl3vLx4euHwE0pnsPIkMP/UPgTLk7o6+Af7mz5vHO4TyhNzX6q6zq1j7cPxzt+6BhWZBzKdzoEpgdfOiWzfr7z7//l2vCXYTkf6TiWPz5a6ysJgeRo44EXt1/GWAKzrfDEPUKmrzGmsgzIY2J1hJ9NZ3u4OP6Xvny7mjtvm/x5rOS71nVNdsivwt09ke6f7t6uqqvzeO5tLYh1rUMExv1fPz9a1T8fqyHyi+2c+j+tKZ32Pa7Bz8lQGWJxK5/MT+W6tASFhdadPL3dnOu/q2p7lyJETTmew8qSu/FOfOdYAgWMNjND7cIz0Hescrevcqmsfjnf+1sU8kOl0D0wxXzsnun19jpvy7UwtwXa68vhsCVYW1pWuYNf/iVyndeWJeWnM+hzPhsY+dkREREQhgoEdERERUYhgYEdEREQUItjHroGd6jJkdY08PFfUd6mtxjBSONj7J7Nc2LGW0TqdTnSE6rH273gjNhvSsfahvkvZHW97wb5bn1HsJzPavD5prs9IcvN3A8+P+uTJ8UZ1Hu+3T2WpvfqcvyeyHXP+nej2TmQfgo3EDfbZ4+XtsfI62OhsAH4jN09XHh/v/D2R0dj1yeNjpas+xwzAcfPkZH47Nja2UY2QNVDXWP0QsXfvXvF6vbJnzx6JjIw8I7+xa9cumfbIOjm0f0+tEaSB//1lz48SHp8aMOKzym/kXLDP1PXfPdu/0SOZ6vObp/LZ0/UZ8z6Yl9o63vYC8+ts7kNdv13ffahre+rfVVVVpz2P6zpPzL/p8EQG3Qf1GfNI4bN1Lp3IZ4+1DydyfR3rONSVJ8Hy9kTOTbW9wL/V5/o3bycwzSezvWDnR33yJFgeHy/fTvXaOZnz90TOAfO/T3R7J7IP5s8EHqMTOa7HKiMC067+e2Df7uP+5snmcV3n77Gui2DbPV4eH6ssPJljdiJ5Up/fViPUz/QI2frEMudEjd2KFSvklltuke3bt0tRUZHceeedUlJS0tDJOm1OZeTrsUYenivqu9RWYxgpHOhklwsLtozW6RjZGyx9x0uDedRpnZ85xojNhnSsfTiR43Ks43C879d3FHtdo83rq640n8z2gp0f9cmTYPt9otfDqS61V5/z90S2o/KvPts7kX0IHIlb12ePl7fByog6ZzBQgc4pnGeBv3mi5+/xRmOfcJlzArMw1Gt7J5An9frteoxQP1safR+7J598UubNmydXX321fPDBB1JUVCQVFRXyww8/NHTSiIiIiBqVRh/Y3XbbbTJt2jSZNGmSNG/eXO69914JCwuTBx54oKGTRkRERNSoNOrA7vDhw7J+/Xrp2fN/s15bLBbp2bOnvPPOOw2YMiIiIqLGp1H3sdu1a5ccPXpUEhIS/N5PSEiQTz/9NOh3Dh06JIcOHdL/3rNnj4jUdDw8U/bt2yf7d30vR37Zd9wO4Af27xGLzX7Kn2ns2+M+hOb2uA+NY3uhsA/Mk8bxm419e419H6qqqmTfvn3icDjOWIwh8r8Y5kTGuzbqwO5k3HjjjXLttdfWej8tLa0BUkNEREShLHvF/LP2W/v27ROv99jLyzXqptjY2FixWq2yY8cOv/d37NghiYnBFyBfuHCh7NmzR79++ukn+eqrr2T37t1+75/u19atW0VE5N///vcx/3sin6nPZxtie6GwD8yTxvGb3AfmSWP9zca+Pe5D49ieiMjWrVvPaHyxZ88e2b17t2zdulWSk5PleBp1jZ3D4ZC2bdvKmjVrZPDgwSIiUl1dLWvWrJFZs2YF/Y7T6RSn0+n3ns/nO8Mp/Z+IiIhj/vdEPlOfzzbE9kJhH5gnjeM3uQ/Mk8b6m419e9yHxrE9EZHIyMgzNk+u2fFq6pRGHdiJiMybN08mTJgg7dq1k5KSElm+fLn8/PPPMmnSpIZOGhEREVGj0ugDuxEjRsjOnTvlqquuku3bt0urVq3kpZdeqjWggoiIiOjXrtEHdiIis2bNqrPptbFwOp1y9dVXS2RkZJ3/Xbx4sYjIMT9Tn882xPZCYR+YJ43jN7kPzJPG+puNfXvch8axPfWZwO5fDS3k14olIiIi+rVo1KNiiYiIiOjEMbAjIiIiChEM7IiIiIhCBAM7IiIiohDBwO4Mqa6uPua/gzlb41jOxO/8WsbgnMhxPF2OHj0qP//880l//+DBgyf1m43B6TyfTvSY1fc3T2S7p5qf6jcCf6s+aT1T+3+mt3M6nc3r9lxwNvKjrvPgdJwfjfEca0wY2J2Ar776Sv72t7/pfx85ckRERA4cOFDrs1988YV8/fXX8sUXX8j999+v/22x1J3V27ZtExERwzDq/Mxnn30m999/v/734cOHg35OnfAApLq62u/GsnPnTgFQ63fUd4JtE4AcOXJEqqqqZP/+/bX+pt6rK+1VVVW1/l8VKoEXp/mz9blwv//+e72f5v0Ptj/HyrcjR47U+bvqOB46dEhERO/3zz//fNybbmDa1P/v27cvaDo2b94sn3zyiXz99dcyefJk+fzzz4Om6ciRI7XOC3N6Fy1aJLt37xYAde6XyvNNmzbJJ598os/TAwcOSHV1ddDt79y5M+iN4ViB5PGOp9rva665RqqqqsQwjDpvPgB0vqvtBl6jIjV58NVXX+l9CkwDANm0aZNeW/pYv2n+vvmaNn/evP/qGO7bt8/vOJvPT2Xv3r3yyy+/+J3/6je2bt0q3333nfzyyy8iUpP3Kq3HU1fZY/5tlY///e9/5eWXXxYR/5u+uu7rOn5ffvmlvPjii7J582a59tpr5cCBA0HLl6qqKjlw4MBxz4ODBw8e9zP//ve/5ZVXXvFLo5m5XN6yZYt89913QfMg8HpUgp1Lgd+rq6wMLCODnU+B5aB6r65rOdCXX34p//rXv+Tbb7+Vt99+u1b6zfkReE1+++238vzzz8vDDz8sX3/9td/fgl2/6r3A/VL3BLPDhw/r8+Czzz6TP/zhD35//+6772Tbtm1+54c5Dz755BN57LHHjpkelY7vvvtO3n77bX1uqnuyOU+OHj0aNP9VGR7s/q0c62/nBFCdNm/ejCuvvBIWiwVXX301li5dik8//RRXXHEFXnjhBYwcORLffvut/uwVV1wBh8OBhQsXwmaz4fLLL4fD4cDdd9+NLVu2YN26daiqqsLOnTvxzjvv4K677sKGDRtgGAb+9re/YefOndi4cSPuuOMOzJw5EzNnzsSRI0ewYcMG2Gw23H777dizZw/WrVuHBQsWYNeuXQCA999/H9OmTcOHH36IJUuW6PSMGTMG5eXluPDCC/F///d/aNasGVasWIHvv/8eb7/9Nl5//XVs2LAB7733Hl566SW9zffffx/l5eU4cuQIPvnkE0ydOhWtWrVCcnIynnrqKQBAdXU1Nm7ciLKyMnzxxReorq72y7uffvoJ7733Hi699FJcffXVGDNmDAoKCvDaa69h2rRpOHLkiN/nP/zwQyxevBi7du3CN998g5deegm/+93v8PTTT+Pvf/87Nm/ejE2bNmHx4sW46667dBo2b94MwzBwySWX4PDhwzoP1q1bh+LiYvz000/473//i08++QQfffQRFixYgH/961+46667cPjwYb/tzJo1C6tXr8YjjzyCRx99FB988AEA4IMPPoDD4cCiRYswcuRIvPLKK+jXrx+effZZFBYW4t1338V7772H5557DgcPHsSWLVvw6KOP4tFHH8Xjjz+OiooK/PjjjwCAgwcPAgDWrVuHfv364YsvvsCHH36IpUuX6nwwDAMzZ86E2+2GYRh4+umnUVVV5ZfH7733HgoKCmCz2XD11VfjgQcewJ133ok33ngDH374IZxOJ0QEr776Kr7//nusXbsWf/3rX/228/HHH6NLly545ZVXYBgGrr/+evznP//BihUrMHLkSLz88suw2Wy48sorcdddd+E///kP7rrrLhiGgTfffBMbN27ENddcg8WLF+PTTz/FZZddhs8++wy33XYbZs2ahWuvvRa33XYbvv32W+zZswfr16/Xxw4AqqqqAAD79+/Hu+++C6fTidmzZ6NLly763FZpVcdq9+7dWL16NfLy8rBlyxYcOHAAGzZsgMViwQ033ICrrroKkyZNwpw5c+BwOHDnnXfq3/n000+xcuVKTJ8+HUuWLMHGjRvhdDpx2WWX4aWXXsL27dvx2muvYc2aNfjyyy/x4Ycf4pprrsGiRYvw6aef4qqrrkKfPn1gs9kwceJEvPvuu/j222/x5z//GbfccguGDh2KdevWYfny5TAMAxdeeCFat26Nt956S1+T06ZNw6FDh3QefP7552jbti0WLVqERYsW4fPPP8ejjz4Km82GhQsXIiYmBjfddBP69euH559/Hrm5uXj88cfxzDPP4LPPPsP7779fq9zaunUr/vGPf8DhcOD666/Hyy+/jFWrVuGHH37Ajh078Pnnn+Nvf/sbli1bhoKCArzwwguw2Wxo0qQJ3nvvPb2djz/+GG3atEGvXr3wxhtv4LnnntPnwX//+1+d71dddRWcTidmzZqFkSNHYtOmTfj222+xefNmv+0MGTJEl5cHDhxAdXU1PvzwQ6xatQoHDx7Exo0bMW3aNGzbtg1VVVXYtWsXPvzwQ7zxxhv417/+hb/97W/485//DJfLhaSkJHz77bf48MMPce2112LPnj3YuXMn3nzzTQwcOBCPPvooNmzYgJiYGDz22GO6TPr+++/1NX748GEcPXpUn2NHjx7V+3TnnXfil19+wb59+/Djjz/ib3/7G0aOHInrr78eY8eOxfnnn4/PPvsMn376Kf7+97/jhx9+wMaNG9GvXz/87W9/Q79+/fRx3rp1K95//30888wzuPXWW9GqVSts27YNu3btwpYtW/D444/joosugs1mw/Lly2sdz19++QX79+/Ht99+i/Xr1yM6OhqtW7eG0+lEeXk5XnzxRSxZsgRPPPEEnnjiCcyZMwevvfYaNm7ciOnTp2Pbtm36+ktISIDT6cTo0aMxYMAAbNiwAa+++ipWrVqFUaNGYePGjfp31ff/8Y9/6HJelV0tWrTAwoULsWfPHgDARx99hIkTJ8LpdGLmzJl6X7Zv345//vOfeOihh5CSkoIrr7xS5/vnn3+O22+/HVOmTMGMGTNgs9lgs9nw17/+FVdeeSXat2+Pjz76CFu3bsV3332n7zcvvvginE4nOnbsiLKyMjzzzDMoKSnBv/71L9jtdjRv3hybN2/GuHHj0L17d0yaNAn33XcfDhw4oO8Br776KkaOHIkPPvhAX/dPPPEEXn75ZXz44Yd+9/ZzEQO7OmzYsAEOhwMOhwPjxo2Dy+XCggULUFxcDBGBYRiYMmUK1q9fj8ceewwOhwN2ux3jxo2Dx+PBuHHj4Ha7MX/+fGzYsAEigoqKCnTq1AlRUVFwOBy49NJLER4ejgULFmD9+vXIzs5GTEwMOnfuDBFBYmIiLrvsMrjdbsyYMQPr169HixYtYBgGZs+ejd/85jcYMmQInE4nRo4cCafTidLSUgwfPhwulwt9+/ZF+/btER4eDovFguLiYsyfPx/x8fFISEiAw+FAREQERAQigrlz52Ljxo0IDw/H2LFjMXnyZDidTuTl5cFisSAqKgpFRUUYMGAAOnToAJvNhhEjRuCGG27A7NmzsWjRIjzzzDP4y1/+ghYtWkBEEBYWhvLyclgsFogInE4nrFYrZs2ahRkzZmDRokW49tprISJo2rQp2rVrB5fLhfz8fBiGofNVBTlWqxVXXXWVPkaRkZFIS0tDSUkJevToAavVipycHISFhaFz58545ZVXEBcXh1tuuUXfbF0uF+bPn4/x48ejV69eKCgogMvlQrdu3ZCSkoKWLVsiPj4e6enpGDBgAKxWq96HqVOnwmazYdKkSQgPD8fIkSORmZmJgoICOBwOxMfHw+fzIT4+Hi6XCyICh8OBJUuW4MMPP8T555+P9u3bQ0TQt29frFu3Dm63G5dccgnuvfdeOJ1ODBs2DG63G0uWLMGYMWMQHR2NJk2aoLS0FP3799fH3GazYdy4ccjIyEBZWRny8vLg9Xpht9tx/vnnIz4+HpGRkXC5XEhISIBhGCgvL8fll1+OoUOHwu1263M5LS0NHTp0QGRkJEQEPXr00Oexy+XCVVddBa/XC6vVqm8IIgKbzYapU6fqfY2NjUVJSYn+t4ggJSUFBQUFcDqdWLBgAX73u99h8uTJ6Nq1K3r16oXY2FhYLBbExMToNA0YMABTp07FrFmz8P777+Oiiy5CRUUF4uLiYLFYEB8fj7lz56K4uBhhYWEYP348EhMT0alTJ72N9u3bY/To0ejatSs6deoEwzD80uzxeHDxxRcjJiYGhYWFSElJQV5enj6n1GenT5+OqKgoVFRUwGq1wufzQUTQrVs35OTkID8/HyKCuLg4nT4RgdVqhYggIyMDvXr10uf+1KlTMXjwYEycOBHl5eU6n7p06YKsrCydTqvVinHjxunzzel0YuLEiYiLi8OQIUNgGAZ+97vf4fLLL0f//v0xYsQI/P73v9fnnMqTJk2awGazwW63w2azISoqCrm5uRARuN1uOBwOlJSUwGKxYPTo0fjiiy/w97//XZcNxcXFyMjI0Ne00+mE1+uFxWJBTk4OPB4Pxo8fD4vFgunTp6NTp07Izc1FTEwMWrduDY/HAxFBp06d8Oqrr+KOO+5AaWkpVq9eDcMwMGPGDAwbNgwigqKiImzevBmTJ09GdHQ0oqOjYbVaER4ervPJZrMhLCwMvXr1gohg8ODBuvw0DANutxstW7aEw+HAvHnzsHPnTjzxxBNo1aoVUlNT9XaGDBmCPn36oKKiAnfddRcWLVoEh8OBnj17ori4GM8//zz69Omjt2u321FSUqK/n5ubi6ZNm8IwDDRr1kwfp6ioKH1+TJs2TZflqkxT54q6l4gI7HY7LrvsMgDAjh078I9//APTp0/HsGHDdH6Gh4fDZrPp7agHO5vNpo+L2t5vfvMbGIaBRYsW6fuGy+WCzWaDy+XSxyovL0+fC+rce/jhh3HppZdCRNC1a1c4HA7MmDEDd955py4/RQSLFi3Czp078fjjj+t9GD9+PDweD+bNm4ePPvoIeXl5aNKkCUQEFosFiYmJ6N69O9q2bQu73Y68vDydfsMwYLFY9L+tViuKior0uWOxWFBUVITw8HC0adMGIoLevXsjPDwcw4cPh9vthsViQUREBMLDwzFlyhRcfvnliIqKgsVi0eXCtGnTdFkuIkhLS0OTJk1gGAYyMzNhsVh0BQlQE/ADqFV50ZgxsAti48aNusAbOXIkPB4P5s6di8cee0wXgKqAVzcAEcH48ePh9XoxbNgwWK1WDBw4EGPHjoXVaoXVakVubq4u9IuKiuB0OnHttddi48aNcLlccLvd6Nu3L9xuN0aMGAGHwwHDMBAWFoaysjI4nU5YLBYUFhbC4/GgdevWensOhwNutxvDhw9HVlYWIiMj4fF4cN555yE9PV1fWFarFQ6HQ98c1fterxe33347PB4PJkyYgKioKHg8HiQnJ8MwDLRr1w5hYWFISUnRF4i6+LKzs/V7qnCxWCyIjY3FtGnT4HK5MG7cOIiIDjITExP9CmvDMNC7d29ERkYiLi4ODocDEyZMgM1m0zdSEUGzZs1gtVoxYsQIuN1uXHfddXj66af1b6oL1Gq1wuVyweFw4Pzzz4fb7cakSZP0/kVHR2PgwIEICwuDxWLR6R40aBAyMjL8bgCxsbEQEfTp0wdOpxOdO3dGWFgYRo0ahZycHEyfPl3nsTmYUYW41+uFw+GA0+nE+eefr2/46iY1ePBgZGRk+OXPjBkzcNttt8Fut0NE9M3R/OrSpQuSkpIwb948/ZCgClir1apvOk2bNoWI6BuOOodVvkZEROC6665Ddna2Xxpatmypgx91g4qIiEDbtm31DaWiogIulwujR4+G2+3WN/Rx48b5FcxWqxXjx49HfHw8ysvL9fVjsVhgGAZiY2P1DVzdRM151L9/f9hsNh2Ejho1Sn9uyJAhSEtLw8SJE2G322EYBuLi4hAWFoaJEyeif//+fvnWq1cv+Hw+jBs3Dp988gkiIyNhtVr1NZ+SkqI/26dPH/Tp0wfTp0+HzWbDjBkz4HQ6dfqysrLgcDjQp08fWK1W/cCnbqJ2u11fx5GRkTAMA0lJSfrmaA6IDcOAYRgYPXq0vkbtdjs6deoEu92OXr16ITExEcOGDUNYWBjOO+88REREwOFw6GOrXgsXLkSbNm0wYcIEREREoHXr1vpvbrdbXwc2mw1xcXGwWq3wer2Ijo7W56JhGMjJydE1qT6fD+edd56+1mw2GywWC1JTU3VwqGr+AtOUnp6OsLAw5ObmwjAMhIeHw+VyYfLkyTr/CwsLkZ+fD4fDAZfLhcTERFitVmRlZcFqter8mD17dq3rTJ2P06dP1/mcm5uL6667Ds2aNdPfVfkvImjevDm8Xq9fQKTyvVWrVjqAUGWJyuvx48fDbrejf//+WLBggf6uerhzuVyIj49HVFSUPkcnTZqkH1IdDoc+31WZOXXqVADA+vXr9XVhPp5q/2NjY+F0OhEbG6vzv6ioCCtXrtTHLD09HW63G4sWLcL777+vz8Vhw4bpioF27drB7XYjOTkZFosFmZmZMAwDAwYMQExMjC4fLBYLysvLkZqaipYtW+oybdKkSfjoo4900CYiiI+PR0REBKZMmYJLL70UYWFhOjDPzMxEWFiYLp/CwsKQkZGh96tfv35+5WJBQQEeffRRff1brVakpaXBYrGgtLQULpcLkZGR+j6hrsmpU6ciMjISbdu2xebNm/WDrbrO1PFQD3YejwcTJ05EWFiYrhjo0KEDqqurdSD3008/NWA0cnIY2AX4+uuvER4ejnbt2umLrri4GGlpaYiLi6t1czUHM7GxsXC73X7BjTlwUf+vTnjDMDB58mTYbDY4HA59Y/Z4PHC5XPriN9/Uo6Oj4XK59E1Z/ddqtaK0tBRDhw7Vn4+JicGLL76IVq1aweVy6adsVfultllSUoLS0lLYbDbExsbqgic+Pl5/1u1266DJXAjGxMTAarUiOTnZL50xMTHIyMiA0+lE8+bNYbFY9IWtbt4DBw70yx9VWxYVFQW3242YmBgMGDBA56W6WaiANCwsDCNGjEDz5s1hGIZ+wne73Tp/zRez1WpF27ZtdW2c3W5HSkoKPB4P2rRpA4/HA7vdjvj4eB2Mq+MbHh4Ou93uVxOlChx1/MzBiNVqRUJCAnw+HwzD0MdJ5WW3bt388lKdMyrwzs7O1gGBer9jx476d202GxITE5GVleUXIFssFp3XPp8P4eHhiIuLg9fr9cuLiooKfbysViuWL1+uAyf1WXUuhoWF6fSZj7EK5tXf1WdKS0vh8XiQmZmpn74TExMRERGBsrIyREVF6aZ9j8eDiIgIGIaBUaNG+RXu5geIhIQEuN1uxMXFYezYsQgLC9P5bbVa9U1U5YUK7s15o24q6lqOiYlBSkoKLBaLrsVwOBz6+xaLBb1790ZSUpKuyRQRjBo1Cg6Hwy+tOTk5Oj2GYej0mG8oVqsV0dHRumZn+PDhOrjz+XyIjo7WwbM67ioYCSxDVL6qvOnbt69f7c2MGTN0nnk8Hr0/KpBWwc/EiRN1YGS+FlXNzpAhQ3Q5ExcXp891EUFkZKROX1hYmP6NwPS53W79d6vVqh+UfD4fkpOTYbfbkZ+fj0GDBsFut+trUJ3DzZo108F6aWkpEhISdDpXrVql0xMdHY3BgwcjMjJS1+qptKj/N5+vVqsVo0ePRmRkpA72rFYrmjdvrgNewzB07bhqtfD5fHC73UhJSdHljNpvl8uFFi1aICsrS1+PTZo0gcvlQnp6Ovr06eMXDEVERCA6OhoXXHABysrK9H6pe4D5mKjzpaysDGlpabo1xVzzXlBQABFBZmamXyCmHlbVPlitViQlJfmd8+aHkLCwMB18q7Sb06Jq3MzlncPh0Hltt9tr3f/Uq7CwEBEREbpsNwwDQ4YM0Q+B6pxW15cKrNX7qhLDfF2rcyshIUH/blxcnK7ssNvtugxW54fX69X3N6/XC5fLhZSUFCQmJuLJJ5/EsGHD0KNHD7Ro0QJXXHEFdu7c2dDhyQljYBdg5cqVyMzMxPz589GzZ0990fp8vlqFtPmkNhe+qvA032xVwRYsMFSFj7qhmretLjr1SktL0xec+aRW2/D5fPo7KihUTzfq5uRwONC8eXO/oCJwf9Q2YmJial3A6gndnK5Jkybp4MmcrsBmKZUfqhBST+vqoktOTkZsbKz+TfVft9utC9fAm5yqcXQ6nejbt2+tv5sLFvMN3nzzUdu1WCxo27ZtrYJD/c0cLKjtOZ1OeDweHbCa91HtuzkPVRBrrilQT7bm88vj8aBdu3aYPHmyLhBjYmIgIro2Q+VNbm6urmkybzcrKwterxdt27bV54zVatU3LRWAqBuYYRi62TZYEGE+78znvrnWs67vml9hYWF+tVbqyV3VFKpzVt1QrFYrhgwZ4pdHqqnevN38/Hxd42R+qZoYdQ4FBmZWqxVNmjRB27Ztg+5fYOBus9l0PgWe84HnjKppVzV46mbfu3dv/XBgsViQlJTkF4AEuykGvh/4WXPNn/n98PBw/OEPf/BLq9o/1QxuPm/U/6t9djqdKCkp0TXZKj8DyzB1Yw0MpszXwtixY/0CQJWH8fHxfteUehB1OBxITEyE2+2u9aAbFhamg5GEhAR9fQTLI5/Ph9LSUr/rXQW+KiASEZ0O1fxp3k+73a4DcJvN5pcWczBuLiPUOWC+TwSeI8HOmbS0NP15t9uNrKwshIWF6QoEwzDQsmVLFBQU6M+ZWxrML6fT6VcTrfIjPDw86DkXmIeq9cN8Pqq0qu2YzwN13rRs2RJNmzb1q8FVDw7JycmIiIhAy5Yt0apVKyQmJuqg3XytBbvfWCwWnfdqe1FRUfB6vbXuDaqrR7CKGfU75u2r7RmGgdatW2PhwoUIDw/HoEGD8PPPPzd0iHJCGNgFcfvtt6O0tBQxMTGIjY1FWFgYbDYb+vbtqwsA9YRvPsHVydClSxeUlZXpam510gSenIGFrKoxUjdDddKZm1HUq0mTJpg8eXKtwsEwDP1EaBg1fQbUZ8zNn6pwUk/cKmhRT67mWkvzxRwdHa2fuM2vhIQEXHfddfrpVr2vmrZUXql0qtrIVatW+V2IrVq10n3Spk6dqj+vCh/V1DBp0iQkJib6/U5gc6WquQvcB/Pvde/evVZBol7Jycm1/mYOeM15f8UVV+hg0BzkqCa+nj17wm63+xUu2dnZuvlTRHDjjTfq34qIiND7ftlll+nP5Ofn6/MmIiICSUlJsFqtaNOmDXJzc5GZmYlWrVrp/Y+IiNA1jR6PB23btoVhGLp/T2DTo6oRVs0e6n11k4mMjPRLs0hN7cDgwYPRrVs3nZ8qUBSpaYo29wMy5496DRw4EJmZmbr2rUePHhARXHfddRARfXMwfycxMdHvRmsu7N1uNzIzM+FwONC7d2/92+bzdfny5bqPpNVqRWRkJN5+++1awUFqaioMw0D37t39ugX07t0bixYtqnW+5+Tk6P1W/bzMeZmRkaH/PXr0aL8aht69e+vzU92k1IOcOjdUjbUqhwoLC5GWloawsDB9bqg8djqdiImJgWEYfvtl3g+RmmZ6VeNnsVgwduxY3adOvVRT65NPPonY2FhdlkREROj+l+ZA0FzLab7mevfujby8PL9jYa4lbt26NWJiYvxq0dQ5FxUVhenTp+t+XSoP3G433G63rpkx96lSxyc7O1s3yxuGgeTkZJ0P/fv3R5cuXfzKieLiYixduhTNmjWrVc62adMGs2fP9gtGzX+32Wx+/Zd9Pp8+N7t16+bXr8xcnqjvJCQkwOVy6Zo/1WIgUtP8vGrVKt3FoqioSNfEm9Ohylt1rbtcrlrX7mOPPeZ3j1DXs/l4qO/m5eXVqgFXaVUP3iKCnj17oqysDLfffjssFgsGDhwIp9Opf6egoEDfE9T1rroCbNu2za+LkPpvenq6vn6bNm3qV0upyq3i4mLdny7wvAu816qXzWbT+2S+JtLS0vDoo4/q/oUff/wxXC4X7r777oYOT04IpzuRmqlEPvroI3n00UdlwYIFUlVVJeXl5dKpUyd54oknZP78+TJu3DhZsmSJ/OlPfxIRkaSkJLFYLHqYuRpWHRUVJYcPH5bf/va3ct111wkAyc7OFpvNpqe98Hg8Eh4eLgsXLpSoqCgREbFYLGIYhhw5ckQ6deokWVlZEh0dLTExMXL48GEpKSkRkZppDqKjo+XLL7+UjRs3ioiIz+eTpk2bSkREhJSWlsoXX3whJSUlAkB++uknvZ/bt2+X1NRUiY6Olh9//FHCwsKkSZMmYrVapUmTJmKz2SQlJUWmT58uycnJ8u9//1ucTqecf/75MmXKFPF6vZKfny979uyRWbNmSUxMjIiI2O12+eWXX8TtdsuSJUv0MHjDMGTGjBmydOlSSUlJkS5dukhubq6I1Aw5r66ulosuukiKi4vF5XKJxWKRzZs3y4IFC2TdunXStm1biY6OFhERl8slhmFISkqKOBwOqaqqku3bt0u/fv1EpGaovd1uFxGR4uJiufrqq+Xaa6+VnJwcPdWB1WoVt9stXq9XHA6HiIj885//FIvFoofLO51OERFxOByybds2CQsLkyNHjohhGGIYhoSHh0v37t2lsrJSBgwYIB6PRywWi9x5551y+PBhyc7OltatW0tqaqpYrVa97a+//loMw5D9+/fLyJEjdXrS0tLEZrOJ3W6X6667To4ePSper1cmTJgg1dXVkp+fL7feeqt4PB4RqZnq4OjRozJkyBB55JFHZMKECdK3b1/ZsGGD3HDDDeJ2u/V0AmrKkurqarFarXLzzTfLrFmzxGq1yrp16yQuLk6qqqr0ti0Wi1RUVEhiYqKsXLlS52d4eLjs2bNHIiMjxe12i9vt1vlks9kkPDxcjh49Knv37pWZM2eKiAgAycjIkNjYWNmzZ49ERUVJRESE9O/fXwDIoUOHJCUlRZ+bW7Zske3bt0u7du0EgIwYMUIMw5ClS5eKxWKR8847T2JjY/XnHQ6HbN++XY4cOSLXXnut5OXlSUxMjBw4cEAiIiJk9OjRMnPmTElLS5MxY8ZIZGSkWK1WiY6OFrvdLj/++KN88sknUlxcLBdeeKEcPXpU9u/fL3PmzJHq6mqx2Wz6d+69917p1KmTvPXWW2Kz2XT+VlZWyj//+U9xu93i8/nE5/PJ4cOHpVOnTpKamioZGRmSnp4ulZWVEhcXJ4ZhSHJyshiGIXa7XaxWq7Rq1UrOP/98cTqdsnPnTlm/fr0sXrxYwsPD5ciRI+JyuaSkpET69esn4eHhev9tNptYrVY5ePCgbNmyRf+/1WoVEZE2bdqIiEhERITs2bNHIiIipLKyUmJjY8VqtUpsbKzk5eVJRESEZGRkiM1mk5ycHMnOzhar1Spvvvmm9OvXT1/jDodD/vvf/0pqaqo8//zz4vV6xWKxSEJCgqSmpsp9990nGRkZcvToUcnOzvY7vtHR0dKyZUtp06aNOBwOeeWVV+Q///mPeL1eiYyMlOLiYlmxYoVYrVbp2LGj3HXXXbJv3z7Zs2ePOJ1OSUpK0umurq6W8vJyueWWW/Q527ZtWz09xQUXXCD9+/cXm80mAwcOlISEBH3+//DDD/Lxxx+Ly+USq9UqDodDdu/eLT6fT9LT02X58uUSGxsrAMRiscgFF1wgzz//vEydOlUKCwt12e3z+eT777+XjRs3SmVlpcTExEh8fLx4PB5xuVwiUjPlyM8//ywul0tsNpv07NlTZs2aJSI109DY7XYxDEMsFotERETI7373O0lOTpaff/5ZcnNzZceOHXL06FF9bPfv3y9VVVWSmpoq3333ncTGxuppO7Zt2yZxcXGyaNEifbyGDh0qCQkJUlBQIOvWrdNlUbt27cTsoYcekjfeeEO6deumr2mv1ys9e/YUi8Wiy8qDBw+Kw+GQ/fv36/M/Li5O0tLSZNeuXdK0aVN9L+vVq5fccsstMmrUKElLSxOv1ytNmjTR5+Qnn3wi3333nZ6Oa/fu3RIdHS1Wq1W2b98u3bp1E8MwJCEhQU9BtH//fl1G+3w+ufTSS8Xr9Yrb7RaLxSJRUVEydOhQKS8vlw4dOkjnzp31PhqGoe+92dnZEhUVJV26dBHDMKSqqkq6d+8uTqdT749hGBIWFqa3d++994rNZpMWLVrIF198IeeEBgwqG4WNGzfqJ6aWLVv6NRNmZmbqvmUdOnTAfffdhwkTJvhF/OqpKLDGp1+/fvopKikpCYZh6L5bYWFhaNGiBW6++WZ4vV54vV7d5yUqKgpPP/20X7W90+lERUUF7HY7UlNTsXLlSrRu3Rp2ux1OpxOtW7eG1+tFZmam7psVGRkZ9OlK/v/Ti3oyV/0MVK1HZGSkfmpUfY+Sk5N1rYV6amrevDmaNGniV71u7hdiGDWd3Fu0aKHTkZGRUas63NzB3Jw+EfF7SlPbFBHdD1D1XTHXmqrPdu/eHePHj8fNN98Mu92O2NhYNGvWDA6Hw69fot1uh8fj0b+jOmqrpkL1lK36Kak+QCqNHo8HGRkZGDx4MEpLS/Hb3/4W/fr1Q35+fq1+S126dNHNRuZ+RqtWrUJmZqbOG5WHycnJ+ncCmzSbNm2KMWPG6H1OT09HTk5OrSdZ1UyiPte0aVPdfygwXy0WC+644w4MGDAAF1xwASwWC1JSUvz6KhnG//oLmvvRGIaBrKwsuFwuOJ1OOBwOzJo1C5MmTULXrl0RHx+PlJQU3R9TNbuq/jMjRoxAz5499TmoaioCz1l1vqnrLSoqCm+88QYqKirQrl07jB07Fjk5ORg9ejTmz5+Prl27Yvbs2UhOTkZcXBzy8/P9zqn/+7//0/0dVZNg37599eg4i8Wia+lVDZTKL4fDgfT0dH39q2a9vn37Ii4uTvcVtVgsfvus8lr17VGjpOX/15ao8ywvLw95eXmYP3++rj1TtXjBmriDNauZa81UPqrWBY/HgyZNmqB58+aIi4vT16lKs+rwbs4vS8CgK1Uz1adPH719NchA1VwmJiaiR48eSE5O9uviYLPZdId79dvmfoLh4eHweDx+XTNU64n6bPv27fX1Mm7cOLz22mv6d3Nzc/X13aRJE0RERMDj8ejaTHO/sZSUFHTq1Ek3jZvzzFw2qetXpSczMxNZWVm6CVFdv+o4q/Otb9+++jtq5Lrah/79++syX9XAm1sWvF6vX+uE+RxUxyEmJkbXtjscDgwdOhQFBQW6KTLwO+b9admypV8rTHJyMvr374/k5GTYbDZ9PY8cOVJ/JioqSo+2N/fRE6lpdXnttdcwadIkuN1uDBw4UI8aFvHvj6qalFXahg8fDpvNps9bdQ2pY2Wz2ZCeno7bb78dGRkZyMjIgNfrRXZ2Nq6++mrcfPPNaNWqFYqKioJeI6r/qvk8LCwsRKtWrTBz5ky0adMGLpcLBQUFWLRoEex2O3Jzc7Fr1y50794d9913X0OHLCfkVx3YbdiwQTcZXHzxxcjIyMD48eP1CKjw8HBdda1OXnPw4PP5dBAQ2E8i8EJS/ZfU+3/+859184G5oMvJyanVFKt+w+v1IiMjQwcz5u2pvi1qBJgq4I+XrsCCS/XRMDcjqJFFqlBT2wzWfBkYKLRp06bOvoUqMFCjLINtR72vbujq5jhw4EAYhoEOHTrUajawWCx6pKS5EFZTtBiGofMx8DfV34Plndr3wN9TzQh2ux2DBw+GxWKpdUNV/cjM27XZbHjwwQf1iFNzPpnPAbUt81Qa5t9WNyTV1GHejvn/zX0Z6zom48aN0zdmdW5cdtlluulMfU81X/br169W3qv/T0tLq3Vc1Y1C9QVTeWO+YQTblsj/RuuqfMzJyUFWVha6d++O8PBweL1eXHLJJejatSu6deuG8PBwPeqyZ8+eiIqKgtPp9GtiNJ/PKi02mw233HJL0IEc5hu4OldU1wdzc5kqHwJvIqovpuqyMHjwYL+mINUkZ762zV0MzOdesADP3PVAvQKbVIO9VBoCm0+NIP0YA/ufmvs9qs8GpiswMAm2vcCgtFOnTvr/ExISgg4SGjRokE534OAudVyjoqJqlVWBv+VyuTBx4sTj5pM5r4JdR2r/u3fvrvvABX4/LS3Nr49t4HkT7PciIiIQFxcXtMwSEd03VD0wqO316tXLbxCW+n6w8jawe4TaTuDxVr/hdrtRVlam++yazwG1fXMfSVVxMWnSJL2tfv366X7Rgb8tUlPhcv7559fKn6KiIjRp0gSxsbF+Tah1nX/ml+pnaT4XEhMTMWvWLCQnJyMqKgo5OTlwOBwYNWoUhg4digULFiAzMxNbtmxp6LDlhPxqA7vPPvsMFosFXbt2xbBhw/Doo4+iefPmyM/PR0lJiR4k4HK59NOJ+QagAp+KigrMnTu3zkJPnZyBT3SqI7DL5QraeTVwO4mJibBYLLpQyc3Nxd133+13Iai+UhERETrd5m2YC+3AGhHzKysry2/wQmChYA766goQ1N/UaF3zhdakSRPdJ0d1sjb3GwoscAPzQ928VC2auVBKSEio1XFb/aZ59JyI1OpkH2xf1NxR5kLNnJ/BvqP65qn/ml+qplR9T43ONb9nnkJH7ae6cQULlFThpD5r7qyvbrpq5KH5dwIDzcBt2mw2XHDBBRg0aJBfJ2711KsCF7UN83ZUv7DmzZv7dSRXo0LNN8fA8zTY++p31PdU2s3H2fwglJSUhMGDByMzMxNDhgzRNTfq78H6raq+WWrErPl98/ZVOtT5rfLavC1zja76XRXcmM/p7t2763NXBQGtWrUKemMKPE9O5OVwOPxGRNd1nlv//3QSsbGx+rxV6Q4cSGF+L9hL5V1gzZcKRM3BiblMMR+jYA+k5rw3PxypoKKuflSGUTPlz7G2aRhGrQDYnM66vmtuMQi8hszvq2CnY8eOtT6vrqlgv6PuEccKVNTxMN9jVKtTXl6e/j11PQWWZ+bBcoF5WNdMEGqfzBUM6ncDHy7i4+PhcDh0n8m6zl81WtXn8/mVQYHHyfxvn8+H7t2760EcgcetrnNe5bOqHVX9PNWDqtPpxLRp0+DxeNCvXz/ExsbqCevPBb/KwO7IkSO47rrrYBgGrrzySlx55ZXIzc3V1fzqoKekpOhCTg2xVidNeHg4oqKicO+99+oOzuYTVP2/utGqam3zE7e59sFcaHo8Hn2hmaeRUK9u3bphxYoV+jfVjdx8Qvt8Pr/pF0RqAgZVI2dOR3R0tF9BaW7yCvY6ViEXeONJTEz0u0mq5s/ADq7mJqrAC9dckAcrFFTharFY/Dpim2sWvV6vX81HeHg4UlNT9bB+9b4avKKmaLj33nv18VRNOOpGbq5FNO+TzWZDmzZtUFJSojs4m/M6Pj4e+fn5ejCFOpdU01zgiENzk2VgYW4epFFSUoKOHTvW6gxtfqkbjmraCHaDTk9P1x3iBw0ahP79+6O8vBzR0dF+N/uMjAwdcAeO+vV4PEhNTUVZWRlatWrl94BQV+2s2tf4+Hg9Eaz5WlK13seqcVbbMgwDeXl5aNq0qZ6rSk2vsGzZMuTk5NQaqW0+xwzDf6oSdazVdwJ/05xG87/VyE6R4DfJwBuPquVW55q5Jkblm8fjQc+ePYN2Elc3z2Av1bUksJZS7W9mZqZeRcK8r+ZrV91AzS0Y6tibj1WwGijVbK6aes15pAavBKZNXat17VNBQYGeIFrlv3naDLvdjqKiIj19kfncu+iii/zOJZV35nI5NTVVP3QGnrfqocLn86FLly5+teHm42oYBiIiItC9e3ddCxkWFoaYmBh4PB6MHTtWd61R511g2a3yKXD/1eeaNm2qu9WoORxVdxzL/x9AYRhG0Cbdjh076u5A5lYkNY2UOiYWi0VPZmzOM8PwH5ijvhMeHu7XlSQpKQlpaWlBKx3U+a4mRRf53yAh87yRgde+WrFFlRmqSdk8pYx5+itVxqrBjebzPCoqCjExMejSpQvKy8uRkZGB++67D8OHD8emTZsaOmypl19lYAcAX331FS677DJEREQgOzsbpaWlSElJwaJFi3Rfu9TUVHTv3l0/yZWWlqJp06aIjY1FdHQ0HA4HXn75Zbz++uu69szlcmHHjh1o3ry533xPNpsNaWlpmDZtmi4gunTpgilTpiAiIkKvRqEKl8DpPswFi+r/oibnbN26tV8tWqdOnXRfsmbNmqFFixa6v9aKFSv8tjls2DD88ssvtYJC83/NU7Wo//bu3RtxcXF+/dWKioqQnp6uZwlPTU3VBY1ITd+4rl27+hXc5r445hG1PXv2rFW7JVIzW7zql6dqg9RKHSqP2rZtW+vmoPYhsHD2eDx6ri7zDbhJkyZ69YfFixdjxIgRGD16NDp37qzTK1JTOzFo0CC0atUKbrdb/00t3WM+bqrv49VXX42BAwfqAFLdvNSILlUbpOZPCzaFQdu2bfHVV1/h1ltv1fsWExODiooKWCw1oxrT0tKC1iCrwjkwjyIjI/UIQMMwdHNp+/bt8dxzz6Fr1656f+Lj4/V21NQh5nNQPbUXFRXVaq5Vn1NTMHTr1g2jRo1CZGQkCgsL9e+rm4YKrMzTG6jCOPC/gdeMx+NB9+7d4XA40LVrV9x999248847kZiYqPumBssfr9eLiooKGEZNv1JVY26x1EyubW7eUteKmibHnK8qzT6fD06n0y+PzNeAmpA1ISEBLVq00POk3XnnnXpSYLXvb7/9Nt5++22MGDHCrxlLjao3n/MqX6ZPn44ff/wRs2bN0n0hVcBkni5DnUdJSUkYN24cOnTo4FeTVVZW5ndNqht0u3btatWKjRw50m8C4JSUFHz//fc6AAycHkiN5lbbVcGYmvYnMN+sVit69+6NIUOGoEWLFroP87Jly9C6dWu/Bwl1DapVXVT+mM8X8yjaPn366P1p3ry5/my3bt30KGv10O7z+XDrrbfq+SdV+uLj42Gz2fCHP/wBGRkZeuS3yiM1Afwf//hH9OjRA3l5eRg+fDhKSkr88kL16VUjetWI8fT0dB2ohYWFYdiwYRgwYACmTZuG6OhohIeHo1+/fnA4HOjQoQM6duyou0Go6UfMffDcbrcuz5OSktCxY0eUl5frfuKxsbG4+eab0bt3b/3Aompg58yZ43ds+vXrhyeffBKGYeg5Ce12OxISEtCnTx/ceeed6N+/P8rKynTLicvlQk5ODsaNGweLpWZlDPXQrvqEqmAvMjJSB3zLli2DYRh46623cOutt2L58uUQqWkOnzJlit52UlISoqOjMXLkSH1vXrFihT4OPp8Pc+fOxc6dO/Hqq6/qZtcDBw40bLByEn61gR0A/Pe//8XcuXPRvXt3/OUvf0FFRQXKysrQuXNn3S8jOjpaN42pCR5jY2P1yTh9+nS93mCHDh1gGDVLslx88cX6hFKFxbBhw/SEryI1S0o98MADuqkrKioKY8eOrVUNrWpAcnJyMGLECL8LqFevXvrkV1XxeXl5frWPXbp0QY8ePZCVlYWUlJRatQ0VFRX699SNSKUxMjLSr7OrCmY2bdoEq9WKli1b6tqf7OxsdO7cGYsXL8Yll1yipx9QN7PCwkIYhuHXfBZYUxcdHY2oqCgMHjxY/7/5hqNuhE2bNkXz5s31JMOq9koVZo888kitm32PHj10IaEGX6gbhNfr9VtVRC3ZlJOTg+3bt2P8+PFo1apVrTQ1b95c10iZbw4OR82awVOmTNE3MlXjpJ4e1VxtKo3h4eF+zVgpKSnIycnR/VjMN/cuXbr4TRmh+nmWlZVh1KhRfjdd9UpMTPQbCKHyRXUoNw8CUMfEvByUKmhtNhu6dOmCefPmwWq1oqCgAKmpqSgqKqo1DYz6ryrgVZCvbvZDhw7Vn1dL16nVKebOnYtRo0bpiVsDp+iIjY31mzxYBYEqGFOfy87Oxt/+9jdYrVb9nbi4ODRv3jxo4Kua8tPS0mC316z8MHfuXN38rLpWqD6E5hpCkZomfnWNBfbNy8rK0n0YrVarTmdUVBQ2bdrkVxOn8r5nz55+HeDvuece/UC0bt26WrVDw4cP14GyyrPS0lLs3bsXV111FZo2bapXR1APReY5ytR5mJWVhU6dOukAMCoqSk+rYs4v80ATdU4ZhoFrrrlGB8Dq/M3IyIDH49HNzoFN6UlJSX7lU2lpKTIzM3VzXuDDkmEY+vdUVxRVG6sCMHXuqz55jzzyiF9trDpnAx+i16xZox/8VDpHjBiBqqoqPQ+oOf0pKSnYsGGDDu7i4+PRt29fPc2Q3W7XS/Sp4+n1evHwww/j3XffRYcOHeBwOHDffffVOidVkKj6Uns8HgwbNgzt2rXTeTt06FD07t0b0dHRfq0WmZmZmDFjBiZOnIiZM2fqpsvOnTvr715yySWw2+1o3bo12rRpA6/Xi/DwcL99d7lcKC8vh2HUzKmprjtzPqtyTNWSqrJMTYOkXsOGDcPatWv19alawwIffszbKCsrw5gxY5CUlKRXQEpPT4fL5UJRUREGDhyIoqIiFBcX69q4iy++WA/CUcdbpOYB5f3338f+/fuRlpamVxXq27cvgHNr+bBgftWBHQBs2bIFjz32GBYuXKj7trjdbr1eZGC/C3XBqIKhXbt2+Ne//oXx48frQj4pKclvpKWq2VD/Ntd8mJvq1NJhHo+nVnOix+NBeno67r33Xrz44ouIiIjQAYqqXg7WvKaaCM3z1qnRh6pAvPrqq/XvqImG1UUQGxuLp59+OmjTSnR0NHr37o2hQ4fqvgpRUVG49NJLMW7cuGM2C6n5n9SFrD6r+qKoC1HdVNTgA3VhmmsYVd8zFchGRkbi448/9uu35/V6de2GKjjUiCuVZ2qUsToW5iWBMjMz9RqH5hpNFRSZC3m1asZ///tfjBw5Uo8sDMzDCRMm6MLMXJAZhoFhw4bhiy++wPz581FUVIT4+Hi/2pDAEbcqT1q0aAGHw4Hu3btjzpw5Ov9UJ+arrrpKr46izhtzk64KDgYPHqy3r47JzJkzdX+gZcuW4fzzz9eztefk5CAqKuqYzWaB+x+sWd3pdKJdu3bo06cPevbsqUeTJiQkBB3dp0aFm5t8VE2Q+XNqLjc1Uay6dswrvgSmU23T6XTqUe6ZmZl47rnnanUaN9f2qAdAc4CimvhVv0fzSEK1PFRiYiK6dOmiywe3241Vq1bpG2fggKgBAwbA4/H4dee45pprcOjQIaxbtw6zZ8/WwWZOTg42bNiAsWPHIiUlxa97Q2CwpK47c9BiHiChmj/VA4q5FirwpVYWMTdDW601KwSY54BzuVy4/vrrMXnyZDRp0kT/XufOnVFWVqbPMXUjN/+Gy+Xyq+FSeREWFobi4mK/ps1g3RTMx9+cF6o/qLn5snPnznjppZeClm0pKSlITU2tNYpYBZw2m03fTyIjI/2a/tWawOagRjVPqu96PB690k94eDgGDhyI/v3766BVPch5vV6/YEs9nJSWlmLatGn4/PPP0bp1a9xyyy16Gb3FixdjyZIlfi0ogX0s63olJSXpcydYP8fIyEi//n9qn9966y288847OP/88/XqOKo5OVg5kZ+fjyuvvFKPhjWMmpkXLrjgAj3g0fydvLw8v/SY58K89NJLAQDffvstOnTogIceegjh4eHo0aPHOR/UAb+SwG7r1q264+PWrVvx/vvv679t2rQJPp8PQ4YM0c1v6sRQHefNTQfqRA92U1IXd0ZGhm4+CTzR1ZOE6iAaWINltVrRs2fPoP1bVFratWvndxGrSZTVqLDAtJkn+ly4cCH27dun98ncrFhYWIjf/OY3tQrmYBeZ+RUREVFrNN+xXuZ+ND6fT6dBBRrR0dFBR60F9rMzT0Zrbm5RBWXgzUr9u7CwUDcdqt9UeRkREeE3Ya/5+6qDebB+kWoErmqaSEhI0NsJFhS73W5dOKk0q/5PkZGRWLduHcaMGaOng0hJSQk6mCBwUI/I/5p8b7nlFkyYMEH/XU3f0rFjR781XM3fDdxWly5ddFBdUFDgV+uogj+VR2pAUGAazcctcPJf9T1zPqk8vPTSS3HnnXfq5mHVTBuYzsD+ccGaV9VLLSGVlpamB0UFO0bmSWlVU7i6tsznZmC/2cC0qfUs1fZVwBQWFoaWLVvCZrPpMkENpFKDZQKvJ9XHzXwTD7zp/vWvfwVQM42T6mIQmEfqt8zfCwxmzS/z8m1RUVGYMGEC2rdv77ffdY3WrOsmbe5zqd6Pi4vDsGHD/MoDc36q8zdYGgNXDlC/HRMT41fTF3icysvLawUlY8eOrbMcC9aPWET0g3Zgepo0aVLrnFeBbV37YJ7sN/CYmGs/ExMT4fV6ER8f73dvstvtfl0qzL/h8XjQuXNnXTtqPk5PPfWU37WoWi/UyHLztW5Ol9Pp1Mdf7YMK1Mz9k4NNvaOuKY/Hg8GDB8MwjKCrmqh/m/dT/a179+61uquUlJQgPT3dr6+kKj9UP+effvpJj3adOXMmwsLCUFRU1BAhymkX8oHdpk2bkJycjFGjRuHee+9FWloa5s2bB6Bmcd9+/fph0qRJiIuLQ//+/WsVAOnp6UE7e6vO3eYLOyIiAjfffDOGDx+ua/sC+3WpGgPVLyqwRlAVoBaLBWlpaUhOTtYXpxp4ENhHym6361oKr9eL+fPn+y0Zpgpnh8OBESNGYMGCBbX2M9hTe7B+S6oPWLBCLyMjo1ZeBY52VX9Xy8mYl9pRTal9+/atNVjA6/WioKDA76ksLCwMvXr1qpVuVaAFG22san9U4K5qVtS+Bs4DZ7HUzEFWXl7uV5AE6ytlHg3WunVr5ObmIjU1VQeEwfLMPO2Hqv3o2bOnPp/MBa+IfyCujkVgWlT/Io/Hg7y8PL1YvDlvAtPRs2dPXHfddX6FaosWLXDgwAE96tvcjUAF5+qmFfgAYP691NRUv8BJ1d6o3wkW7EVERGD16tW6z5q6marpfAI/bw4gzAMNgo3MVGnt2rUrYmJi9PmsFj8PvJmYm+3UzamuEZjBrmXz9WSxWDBgwADY7fZazZoqACkoKPBrdm7atKlfFwx1EzMHB/b/v7qI0+nE2rVrceutt8Jqtfo11x9rSg11vAP3zTzdUGCfwsDzLtgDhvlaNW9XXfPm68J8fqkyIvBcDQxQ66pNClZDpL6nHpLrGmnavHlzv4eowHxzu91BR7yb923VqlVBz9O6ajbNx3bo0KF+wWlg7aC5L5zT6dRTZwXbVl3nqjoegQ8OgeeveqALzMvAfTF3ScjJydEPfeZ5Q9UxyMrKQqtWrfy206tXLzRr1kw/7AQGzw6Hw69FJDs7Gx06dKh1rpaWluqHVvN9U3ULatOmDaKjo3UFSXh4OIYNG4aYmBisWrUKCQkJ2LFjRwNHLacupAO7jRs36j5B5iDI5/Ph008/xZ49e5Cfn4/09HSMGzeuVodoVYuiJhANrLkzX2itW7dGVlYW/vjHP2Lp0qWYOHFirYvYPOGtKshUE5F5JJa6SLp27Yq3335bF1LBpvewWP43GjYhIQElJSXo169fnUFNsOH5qiOqeb9Vk566uBMTE9GuXTvExMSgvLwcWVlZtYJW80sFdOa+f8HmAlT5l5eXh+bNm+NPf/qT7oNknjKgX79++mYV7DcD+3ep3w4cramCOLXdli1bYsSIEfrCN3/f3NfRHNg5HA6/Rb4D06L63qnmIPV+9+7d9RO/0+nUwb05ncFuVOb3Vq1ahcWLF+vBGYHH2W63o7S0VNeIBD65W61WPZHxlClTdOGvajbU+phqe+PGjUNycnKtvnlqWxaLBREREfjNb37jN++Y+Rw3/37gb6na5sDPmGulzLU8q1evxoABA/xu1Kp2wW63+y3XpQZfBDs/1Ss6OlpPb3SsYM18nFV/xsBaqsLCQl2rKVJ7RLg53wyjZlDKxRdf7FcmqFpk8+9u2LABhw8f1teRYRi6eV6lxWazoXnz5ujdu7e+njMyMvQ0OREREejZs2etACBY7Yj5uhSpCeoCp0dyu92IjY3VD5nmhxPVF0uNVjXfwINNtWFuEjZ3HTCnS+Wjamo/77zzEB0djYKCgqBLNqpmYvX7qjlVNYmr600Fp4ZhYMCAAX6TIYvUlI19+/b1S3dKSgpuv/12pKSk1Aq6PB4PfD5frQd/Ef/BIurfdrvdr/9ZWFgYkpKSapUDgYG1qjkvKSnBVVddBZGaygaVdvPMCuayQaQmKJ88eTJatWqFNm3aBH1QN1+35mst8FoIrDmbOnUqkpKScPnll/vNvWo+32JiYvymYlK/lZubi4ceesgvaFbdHVRTa1xcHNxuN66//npERETo4xUVFYUZM2YAAN544w1kZGTo66xp06YwDANTp05FZmYmcnJyEBkZiS5duuCFF17A8OHDsXnzZhw4cACVlZUNHLWcHiEb2KmgLiYmBmPGjIFh1ExmO3jwYDidTnTv3h0bN25Edna2vkk6nU79FKACoKysLPz000/o0KEDHnvssaBPvebh/1FRUbqPzezZs9GuXTtdKCQmJuoCMrCGw3zBqJM/JycHmZmZSExMDLo+q5orrFmzZjCMmiH1Y8aMwdixY/3WFzV/vk2bNsjKyvJrtlQdctUNUv1/RESELpzj4uKQkpKC6OhotGzZEg6HQ+eRqkk0FwQjRozQI9liY2P9+hImJSX5FQ5qjcbmzZvrfMnJyalVva+aNlVfRPO+GYbhN5O8es/8JKkKRvOcTampqejYsaO+sauRuVOmTIHL5ULXrl11ENSrVy+/mhOHw4Hzzz+/VqErUhPEn3feeX61gdHR0cjIyPDrzxZ44zEX5IHzhqnm44SEBLRq1Qrp6emYPHly0GYw1UG7ZcuWuoAzPzi88sor+Oijj3ThmJycrJvWevTo4dd/zDAMrFq1qs7mLHN+m0camvMkOzsbs2bN0p+zWq1B+ykFCzKys7N1fydzM7j5moiMjKz19K6ui7qCOxUoderUCb169ao1/Yn5xhX4cNinTx+MGDFCXwciNbX7qilV9YNSU0gEpsHn82HDhg345ZdfdE2vSutjjz2mByw5HA6cd955+npW/UDz8/Mxc+ZM3HrrrWjatKke3f+f//xH14aUlpZi7NixiIyMREFBgV7rNbAridvt1oNDVDrMTV/mIMn8crvdePfdd3V3FrUqhtPphNfr9auNi4iI0FNwmIOkcePG6SDVvD6yiOD888/X5aX5AWjChAk4evQoFi9eDJvNhlatWvkdO3UO1lWbZxj/m4MxNTVVrwrh8/nQp08fjB49GhEREfrYidTUZpprrtxut34oevLJJxEdHe3X+qAGh6hzQw26U+ds79699XV22WWX1XqwNJdd5lHL5prc1NRUOBwOPPTQQxgzZgzS09ORlJSku4IEPgAHXh/Z2dn47W9/q8t1c/mgJiA3DAPl5eV+14HNZtP74XQ6df9GwzB0v85BgwbB6XTijjvuQNu2bf3Kf3Mwbe5HqLY3fvx4v7x+9tln9Xq3IjUjk8PCwjBv3jwUFhbC4/GgRYsW2L59O44ePYqbb74ZhvG/1X0iIyPRq1cvhIWF4eGHH8Zzzz2H5cuXw+l04vnnn8fhw4cbOlw57UIysPvwww/1BIPJyclwOBzIy8tDcXExjh49imbNmsHtduPmm2/GmDFjIOI/R1BcXJyuNXK5XLjlllvQsmVL/PnPfw7atGNuksjMzMRtt92GsrIyTJo0CRdffLFfLVFUVJQeKh7YtGHerhoF5vF4MHLkSBQWFuLGG2+sdeNr27atntZC1bT16dMHVqsVzz//vF9hYbfb/aq61fsejwdNmzZFs2bN9JQJAwYMgNVq9etsrGpK1Oz9Ki9SUlL85g0SETz00EPYvn273xJsqmlF/YbFYkG3bt3wr3/9Sw9siIuL0094qnnJnE9xcXGYOXMmNm3ahNGjR/v9psPh8JsuwxzwqHyIiYnx68idmJiIkpISJCYmoqioCA6HAy1atMB3332HzMxM3HfffUhMTESrVq3QtGlTdOnSRacnLCxMj7Ls1q1breOnmraaNGmibzbqqT03N1cHFurGbj630tPTccEFF/jV0qhjUFFRgWeffRY+n89vKhrzjSQwgAlc/LusrAyJiYlo3ry5HoXn8/mQlJSEsLAwdOrUSY8Adblcejogq9WKKVOmBJ1fUe13amqq301FzTo/bNiwWiM+1U0vJycnaL8jdX6Zp7RR+RQWFlarhkvlnfn6dDgcQSejTk9P10G+eohRtQkqqFLNr+bfSE9PR15eHkpLS/1GnorUDIhRtWDR0dF6rjLz9abOxdatW6Nv375++Th48GAcOHBAD9Ix1y6rtKpphQoLC+FwOHR/TqvVimeffRYffvihnoxanR/dunXDlVde6RdcqzQVFhbinXfe0b8xfPhwnb+pqan6evH5fHoJJ/OUTT/88IN+6FIDFcyjbNXNd/DgwXpZL/M8naoJTkT0PGqqK4o6P0RqRn2q5cFUMGJ+GFWBR3h4uN/cc4EBTqtWrXDrrbfq9D3wwAPIzc2F1WrFddddh5EjR8JqtSIrK0sHdm+88Qaqqqr8amPVclqHDx/G5MmT/WqYk5KS0L59+1qBrPkheOXKlX6VCuZ9VQ9G5uvkvPPOQ9u2bf1Giqr+rz6fD6mpqWjTpg3WrVuHv//973ognjquag5OdU65XC6MHz8eZWVlyM7O1v1B1f6pJR3V1Dtq0F1SUpKevscwDB1Yq4cO9ZtRUVG4+OKLERERoUe8h4WFIScnx68MzsnJ0RMpq0C2qKgIRUVFSExMRJ8+ffTnVRPy6NGjdYVNRkaG7lfYv39//cA9evRov64if/jDH3SMUFlZiaKiIvzmN79pwEjlzAnJwO69997D0qVLsXHjRsTHx2PChAn49NNPERERgbvvvhuffvop7HY7YmJicNddd+nlw1RNWceOHf0KAjXqSF2Qah1RdcGogl81Az344IMYPXo0ysrKUFxcjOjoaMTGxiI+Pl4HN9ddd51fFX+wTtjJyckIDw/H008/jaSkJD3kXV00v/3tb3UhMHXqVF3zM3HiRPTs2RNff/21X4dcNX2LuRbDbrcjJycHvXv3RlZWlt9NKj09Hf369dMXVWpqqq5lMa8XqZ4UA2/I6gapCq1u3brpgtLcOb2oqEjPW6UuQpXXEyZM0KsE5ObmwmKxYPDgwejRowfS09NrBcQi/2u2CezkrvJBzXcVeKNVx2fMmDEAgPLycjz00ENo1qwZSkpKkJubC4/Hg4SEBL8+jIZhoF27die0dJMKqhISEuB2u/2WclLN+Wq+p/POOw99+vTxW9ZOjRy84447cPXVV6Nz5876xqYm/iwoKPAL6FVNpBrgYQ6Y0tLS8M9//hO5ubk6j9SIxU6dOmH8+PHIy8vT0wyUlJRg4sSJejvmuRXN+W3+nfz8fL2WqPqb1+tFfn4+li1bhvz8fF3r6XQ6kZOTA5/Ph9jYWF37VVRUVCvIVQGZOW9Vh3jVLGyxWDBjxgz84Q9/0DccNUgmsBY8PDwcBQUFOtAODw8P2nfJ4XAgOjoaeXl5uOiii/xqKc3XsepbqWol1LxhajvDhw/H7373O9x///36/WHDhmH79u2Ii4vD9ddfr9OSmZmJJ554wm8AiYhg6tSpuO+++1BRUQGfz4frrrsO+fn5GDx4sK4FVtvwer1wuVzo0KGDLtcMo2YeTPUAp6YPUjd+NbP/smXLdBChztnExET9/+np6X55pWpozN0vysvLER8fj4KCAt0E6/V69QOEuo7UsTMvTWUYNQNvVLCrpulRQbR55Ku5htycBnUuitR07VBBS3h4uG5SLigoQNeuXfHWW2/pJcYsFgseeeQRjBw50m8ydxW0bdmyRc8xp35/6NChyM/Px+233+7XLzawr616yFZpXrRokT6/zQ8tdrtdTyFlHqQUGLQ2a9YMb731FgCgqqpKl3UxMTGYNm2aX79mVUaq+4rH40FBQYHf+avy3marWTVD5aW6hgzDQHJyst9qJaWlpboMjomJwd///nfdHKrSmZOTo+9DTmfNur2GYaBz5876YUqdv+YHH7VmuQro1fsqyIyKikJ+fj7mzJkDn8+HadOm6S4ADz74oB7xeujQIXTp0gV33HFHg8UpZ1JIBnZmxcXF6N69OzZv3oxu3bqhoqICN910E7Kzs+HxeDBo0CB8+OGHeOWVV9ChQwekpaXpDvnmC9Vc+Hs8Hj3RruqfExcXh6ysLN1kFBERgcTERPzud79Ds2bNYLVa0a5dO31h3HHHHZg9e7buyK8CxMCCW3WUbdWqlW4uUnPL5ebm6gDrggsuQG5urj7p09PT8eqrrwad3PbCCy/0e+pWhcu0adP0ihwejwfR0dF6DimXy4WWLVviueee003ZgdtVT+pqu2oak/T0dD2vn7oIzXmqnsw8Ho+eIV2tdKC2a57532q1YuDAgfrCt9lsKC4u9ivkVHAYHx/vVzCEh4f7BYPmp1RViKsbQGxsLObOnaubkcLDw9GnTx80a9YMo0aN8iuolixZgq+++qpWzaU6hupm6XA4cMMNN8Dn8/n1bezcuTO6deuGZs2aITU1VY8MjouLw8iRI/1Gfaqb5nvvvYdt27ahZcuWer9mzpyJQYMGoU2bNn5pWL58OcaPH+83mlqkZhJZn8+nJxFu164dysvL8fe//x3FxcVo3749evTogfbt29daizM6Oho+n8+v/5VKpzkfVMEaWLNYWlqKzZs3IzU1FS1atIDdXrNKgGpKNAen6qHLfIMONjhBpGauuAMHDuhVYXw+H2bPnu3Xlyiwps/c1Gfu9xV4EzafN61bt9YPNE6n02/+sGDnwD//+c9afVnT0tJQWFjoV7Nw9913o0+fPrpPrsPhQGpqqu5ekpOTo0fX/uc//8Gnn36qp3IwX1fmUbF5eXmYPHkynnzySbz//vvIzc3F//3f//kdO5Uf6iasbrrmvm/qeNrtNaumBM65GewV2MleBWOqnGnSpIkObtQ1b96eChhV2aC2aV5Avq5mR/V5q9WqazfNXSHUQ485rT169MCKFStqzflpsVj8VrUwp8+8DdUfTE1eXFe+eDweFBUVBe2/FqzP7SOPPILS0tI6uxaocjwmJga///3v8cwzz/jV7plr/wLP/YsvvhhNmjTRgWdgPqvPtmnTxm+y6MCuBoZhoFOnTn7/VhUD6p6raiPNK8mYB56tWLHCb7/sdruu8VPdhlTaVHckVTMsUlMhUl1djR9++AFHjx7Vg7cWLVqEr7/+GgcOHMDixYuRmpqKr7/+uqFDlDMipAK7/fv3Y+/evdizZ49+74MPPtA3STU/lOpnpk6CcePGoVu3bhgxYgRmzZqlmyJUYSIi+sbu8/nQtGlTPbt1nz59dJOYugGpkzQvL0/3J1M1C126dMHYsWN1E426kRQWFtZarsW8tqUauKGaVsLCwlBaWurX/0U1B6qLOdhACXUzCbwptmjRQi8YbZ5UWY1GMvfBmzVrlt9oTvWbql+D3W5HixYt8Mknn+iLPDs7W+/reeedpxdZFhE9ctTtdqNfv35YunSpXz8MEanVbzBw7Vjz1BWqQMzMzNSzipvfF/nfepOBwaC5kE1OTkZ+fj7eeustLFmyBNdddx2mTJmChIQE/VvZ2dkICwuDz+fDtm3b/OZ/ExH9JKoKIpvNpgNZc3N8cXEx3nzzTb/pAQzDQFlZmV+thfkG0K9fP3Tq1Mmvv+Hw4cN1YGIe5Tl8+HDd3K1qvlTw1apVK6xdu1ZPBty7d2+89957ugOyz+fDmDFjEBYW5pfv6sl61KhROlh3u90YOHAgRET3uVLpVcffnD/5+fl+a84GBgJqlYfAG54KhMx94S677DJ9Tr333nuYMWMGnM6aZbn69OlTq1+rSkvggJpgL/P8hoFBg+rrNH36dF0TbV7azjwVkPlGq95TLzUCXzWbqe2roNtqtaJfv35o27YtDKNmwueHHnrIbz5GcyCjyiFVM+dwONCtWzc9b57qs6gmelXXkjlfVd8wi8Wij1NgIGJe6N48Cjew3DHnu/laNQ94UeWWehAwT7Ni3lbgwBpzeakCnKioqFqTdEdFRaFVq1a6/DcfB/NgEXM/ssCHEvODq3nbKq/MD6KBL/UZtc3AMtpms2H+/Pk6f9RSl+b9CnZuqrJFBZI5OTmwWCy6tl99L3C+P5XfeXl5+jgGXovmmu3ly5f7rR09fPhwJCQk+A22UNd/4LVsGIae0sR8zMwPQ127dtU1pU2bNoXdbtdN8+a5XdUSkap/u3rwuOCCCyBS86D9l7/8BQsWLNAzDOTm5iI9PR2dO3dGSkrKObX2a32FTGC3efNm9O7dG61bt0ZycjIeffRRAMDatWv9ngzS0tIwcuRI7N6922828d/97neoqKjQVb2BN5fc3FyUlZWhe/fu6NKlC0aMGKGbbQJvCOb+DCL/m97B4/EgJSUFmZmZMIz/DVpo06aNXi1AFTbmVRbM/1UndmD/JjUNhAoIzEvNBN6IVIFpGIaeET0qKspvhnH1m0lJSbraXN1YA2erV00DqolajZgbPHiw7vOi0hkbG4vNmzejXbt2GDRokL6ptm3bFjabDeedd56umVDD0m+44QbYbDa/Jg31Ki4u1uur5ufnw2az6X1WNYsZGRl+fToCq/FV4dKmTRt8//33ehmeyZMno3379njggQdQXV2N119/3e9YT5o0CXv27MGVV16JtLQ0/P73v9eFl8rzwMJfPama81kFT6pGLDw8XPf3Us17gfutth8YnKrgWu1fv379/JpLRQQPPvigrj3OysqCxWLR5+SECRMQGxur0xYTE4OJEydi6dKlKCwsRGFhoV+g2bNnTzz55JOIiorSk6Kam/rVSx2jYKOaU1NTUVBQgL59+2Lw4MG46qqr9I02cNRvWFgYWrRogZYtW6JFixb4/e9/r2sZVL6aB8c0a9YM//d//4fKykpkZmb61fqp2jA1Sat5Hj3zcVPXSk5ODvr06aOX5FKTqQ4aNAgPPvigPrbmqVaWLFmilxtUxyAmJgYTJkxAXl4eMjMzMXDgQBQXF8PhcKB379560MXYsWMh4j9lUExMDBITEzFkyBA96MDcp9BisejrU/XzS05ODtqkrF6qBtjn8+lBDCo/VeDRpUsXv/nKAs/DYO8FngPmpf7UYIXExETdv1VEMGvWLB24qYevwGAmPDxc9+UKTI9q+Rg0aJDuz2quDVJBgbnvsEqn6o9tPr+zsrJw8803Y8GCBbqcs1hqVkcxN78aRs0IdPPoW5GaGi11/qgBAQ6HQwfr6lxS27HZbLoywGarWdps0aJFuO666/S8cIFTxKjzXZV9al1ylX/qgbWuuQZVNyTVncJckWDON1W2DRs2DD169MB9990Hu92uW4XMc9gF/kZiYiKmTp3qd74EG4muBtAEPoAPGDBA19CVl5dj7dq1uPbaa/2+a7PZUFJSohcHyMvLQ0lJCd58803s3LkTL7zwAm6//Xbcd999ermwUBUSgd3mzZsRExODuXPn4o9//CPmzZsHu92OJ554Am63G5dddpmedFJF/2omeRHB66+/DqBmTbjAfjyG8b+lgq6//nrdTKv6CYnUPJ2bL4SRI0di0KBBtQqOVq1aoVWrVnr2cBVgbdy4UdcSFhcXo3Xr1npxePNNUp3s5idec585c3Pd4MGD4fP5/JbuUb8ZbNoUVZCaA8iIiAi8+uqr2Llzp98oKVXjFx8fr1fpSEhI8FuySqQmiOvYsSPuueceNG/eXHe4HTZsGDp06IDzzz+/1s0gMTERbrcbixYtwg8//ICCggJcf/316Natm27eUINbEhIS0KZNG8TGxuoaMPNTbV5enu7/6HA4dNNRkyZNag3Bb968OTp37oxffvlFrz2otpOdnY21a9fi0KFDWLhwIcLCwjB9+nRUV1fj6NGjOHLkCNq0aYMePXpg2bJlWLp0qe5bVdfcYXl5eaioqPAb9adqdtWNaPDgwYiPj/cb8WkO3s3HW+2PahZRNxhVI2PuXH7nnXdi8eLFCA8Px4UXXqiPmZqHsH///mjRooXuz1RaWopLL70U7dq1Q+fOndGpUyd9c/F4PDpwv/baa/1qJMw1zgkJCX6FuPlBITMzE2+++SYAYNeuXcjLy9PNKtOnT9dP5h6PBwMHDtQPES+//DLuuOOOWg86iYmJcDqdOqB5++23AQBDhgxBdnY2iouLa9UkmI+NWvLKvCqJWmJJ3WzUviQkJGDSpEn4/vvv9fq/agLiwsJC5Ofn44MPPtDvidT0l+vatauuscnNzYXP50NZWRk6dOig1/KcMWMG8vLy4HA4dO2x6jO1evVqPPTQQ7pfpap9Xb58Of7zn//U6tOo+qeq9xISEvxGKqobcrD5GVVTbF03YZVn5hux+szDDz+MW2+91W+KEXOgNXDgQL8JcK+88kqIiF6rNdg0ROaaUjUQTZ37qhYyIyMDLpcLl112GXbu3KkfOmJiYpCamoqvvvqq1ryFTZs21UGyy+WCw1Gzhu9NN92EH374QV9HqampqKys1LVD5haS5cuX6/PG7Xbj4YcfxqhRo2rNBKCmPXrsscf08o8qiPb5fOjRo4fflFELFy7EX//6V7z66qv44osvUFRUVGfZYrFYMH78eCxcuNAv39VL5al6X93Hnn/+eb91u9V+9O3bV4+yj4iIwO9+9zssWLAAQE0/ZK/Xq/uOm8+RXr16BZ1KJTMzU3dBUoGyOhfNtc/mbjPqWOXm5uLSSy/FunXr/M4NtRZsixYt4PF48Mc//hFbtmzBjz/+GBIrSdTXOR/YVVZWonfv3rjkkkv83ldr6C1atAgA8PjjjyM/Px8JCQmIiYnB+PHj0bp1a1gsFjz66KO49dZb8cADD2DBggUYOnQoMjMzdcGlntRU4ZWYmIgrrrhCX5xLlixB69ata9WcqAtErctoGAaefvpprFy5EiNGjIDVWjOn2B133IGVK1fqET0XXXSRrv4uLS2FyP8mRE5KSvKbq0jkf6NV1Y2yffv2fjd7w6iZfmTy5Mm68DOPQFQFjs/n85sAdsiQIThy5AgAoH///vop1+l0IikpSVebL1++HEOGDPFb9sputyMyMhJPP/00AOCVV17RtVFqagaRmqf4mJgYvwl5p02bpoOmIUOG6LTOmjVLN3GsXr0aS5Ys0c17ap/V07E5P6ZMmaLn3SsrK8PixYvRo0cP9OnTRxcijzzyCAoKCjBp0iRERETodX9V/gwcOBAAcPToUf0gAEDnz6RJk+B2u9G3b1/ddKkKHHOBZ+5vdv311yMrK0sf48LCQsyZMwfXXHMNevXqhcjISFxyySW6T4t5vyIjI3HhhRfqJbdiYmLQqVMnPP7440hKSkJiYqKeTsE8KKJ3797Iz8/HggUL0LJlS71OcHJyMqxWqw6SRWrWWUxISEBcXBx++9vfom3btnp5NPNDkrq5paWlIS4uDhMnTtRP7YH91dToO/PNU6RmkAwA/Pzzz3j//fexY8cOjB07Fh07dsTs2bMxYsQI2Gw2PPLII+jUqRPi4uLw/vvvIzs7229EodrXjIwMPVBm/vz5AIChQ4eipKQE27dvR6tWrfz6P5mDu5kzZyI1NbXW39VNMiwsDNdee61uVk9NTUWvXr3gdruxcuVKdOnSBQUFBRg8eLCedLdly5a6T6K6bp999lncdNNNWLNmjZ5aZ9iwYbrmT10jU6ZMwcKFC9GzZ09UVFSgZcuWePbZZ7FlyxaEh4cjKysL27Ztw1dffYXq6mpUV1fjH//4h76ebDYb0tPTa02fM3PmTF2umdcoDjbyX+VPly5dat2sVc3lwoUL0aFDB7Ro0UIvmRUeHo5rrrnGr2wMDDQyMzMRFhaGrKws3HPPPZg3b55+YFADvtTDjHqp4M1ms+ngt1mzZrrvqkhN95LVq1dj0KBBfvvo8Xjwj3/8A8OGDau1H8uXL9fzpvl8PlxwwQXIzMzE1q1b9ZQgYWFhusuFYRjIz8+H01mzNGK7du1w8803633MycnB6NGja7UCqdcFF1wAi8Wil79S55iqCVNBuWEYmDdvHnbu3AkA+Oijj9CtWzeEh4cjLi6u1vYLCwsRGxurV2WYM2eO/pt6QFfntAqGmzdvjtLSUr0qUnR0NKZNm4a4uDikp6frB6IlS5agqqpKT1z+m9/8ptb0U507d8a0adNq9TFMSEhAcnIyBg4ciOzsbL+gODMzU3ebUaudqJYLkZr+v2qmC6fTiYULFyI+Pl7P9BAXF6fPm6eeekqX0XfccQceeOCB0x16NGrnfGC3fft2lJSU4I033gBQc+MFgAEDBqBFixYAakYHHT16FJ9++im8Xi9uvfVW7Nu3D2VlZXrUaXFxMbp27Yq4uDjccMMNGDt2LDp16qSfuFRw0b17d2zfvh3Dhg1Dz5499Uzhbdu21Z2+1Y1GBQ2vvfaaPrH79++Po0eP4quvvtJ9OVSnXvX0fv755+tRk0uXLsXcuXPRuXNnJCQk6Lm5EhMTUVhYCKfTicWLF+s+Bx6PR0/CqC5in88Hu92O4cOH6/5s5kJbzYulmofVFBeqVmrJkiV+yyo1adIEU6ZM0UPsy8rK8NNPP/ntu3qpNfm2bNmCli1bYsqUKcjLy0O/fv0QFRWF1157DUOGDMG///1vHYiuXbsWR48exT333IPf//73cLvd6NChg55DLzw8HNdff71uqkxMTNQLOZv7hKhgeubMmTp4ys3NBQB8+eWX6N+/P+x2O5KTk7F582YUFRXB5XJhwIABiIyMRLdu3TBo0CDdsfvVV18Neg7u2bMHZWVlcLlcyMrKwocffoiVK1fi5ZdfRuvWrf2WqlODPHw+H8aPH48ePXroRbDtdjsmT56MMWPG6HmynnzySbz11lsQqRlRpwboiAj+9Kc/oaSkxG/Op5YtW+rg469//SvS0tIwceJE/OEPf8C2bdsAAFdccQVatmyJZcuWYdGiRcjPz9d5r24UqoBUEz+npaX5zV0VExODNm3awGaz4dJLL9UBt81mw9ixY9G5c2e9sLfqd6SOl3qg6dGjhw6+RowYgdmzZ+Pqq6/Grl27AAD//e9/0bx5cwwbNgwRERHIysrCokWLMHHiRD2paGZmpp43MiEhAUuXLoXH4/Fruh8zZgx27NgBkZqRwAcOHMDIkSORkpKia43HjRunl2NSAyIGDRqE6Ohofe77fD5ER0cjIiICzZo1Q3JyMjwejx5126xZM3zzzTd48803dX9Eu92OsWPH4uDBg1i+fDk6dOigJxZu2rQphg0bBo/Hg5ycHHTr1k03javr3+fzITw8HCtWrNDlWGFhIS655BJcffXVui+VWipQqaqqQo8ePXQtiDo/VFCpmiTVNWtelL68vDxoc2p6ejr69OmjryWRmtaJF198ERMnTtQ1kiUlJToPhwwZ4tdfVqVFlVVqOwMGDMCECROQkJCAlStXokOHDoiJiUFycjJiY2PRs2dPtG7d2m/EZps2bTB16lRMnz4ddnvNqgJqihb1G/feey9ycnLQoUMHv36WKSkpfn271EPhn//8Z4waNUrPFvDaa6/B7Xbj/vvvR9OmTf36WqqBFiI1tf5q2b/Fixfr9XPN07HYbDa/5QrVPUKtUCRSU2ual5eHK664Quez0+nEww8/DMMwcPnll2P+/PmYNGkS7r77bpSUlOC1117zWx3FvP0HH3wQ0dHRGD16tG5qVcG1eSS6Kl/Uw6/a1m233YZvvvkG999/P/7xj38gLCwMw4cPR1VVFVauXIkNGzbggw8+QK9evXS5lJubiw4dOsBisaB37946TSpAs9vtGD9+PMaMGYOEhARkZWWhWbNmekCTz+fTDyLTp09HRUWFfkC+7LLL4HQ6MXjwYN1SFxkZiVatWsFiqRl53bJlS/3wvW3bNhQXF6NPnz5+fe9D3Tkf2AHA559/rv9fTTa4ZMkSjBs3DgD0U+w333yDwYMHY/jw4Th8+DDWrFkDkZrBFKoZxeVy4ZtvvsHHH3+MiIgIbNq0CT///DMuvPBCiNRMort3716kpqYiJSVF115UVFSgefPmMAwD999/v65Gz8zMRHV1Nd5++234fD4UFhaiqqoK1dXVup9FeXk5pk+fjpUrV+LQoUPYu3cv0tPTER8fj2+//Rb/+Mc/MGTIED29wB//+Ed8/vnnaNu2LZKSknQTU3h4ONq2bYvi4mIMGTJEN0OOHTsWLVu2RFlZmZ7zRz0JqkJSjey65ppr8NRTTyEyMhLR0dH49NNP8dvf/hZhYWFo3rw5IiIi9PQjatRdQkKC7uT/17/+1W/kWEREBP75z3/i8ssvR2pqKiZMmIARI0Zg3rx5yM7OxuHDh3HgwAEAQE5ODqxWK9auXYulS5fqIE/169i+fTvuv/9+tGjRQjf1vfTSS9i4cSNiYmJw3nnnoUmTJrjlllsgIhg/fjwef/xxXTiq/i4XXnghduzYgbVr1yIyMhLNmjXD/Pnzdb4kJibirrvuwp///GccOXIEWVlZyM7ORmxsrG4yVFQ1/7fffourrroK4eHh+OSTT/T7n332Ga655hp06dJF16RNmjQJbdq0QV5enl4X2GazYfny5frm9+6776Jjx4546qmnUF1djR9//BFTp07VgdGYMWNQVVXlN9rY4XDoqXq8Xi+mT5+O1NRUvP7669i3bx9++eUXne5LLrkEWVlZuP/++/VqImpOvsWLF2Py5Mm60H/55ZexbNkyPZpXNWlff/31aNKkCT777DOMGDFC3zSKi4uxd+9eHVilpKTA4/GgU6dOuuZZ1fCqiYaLi4v1vGCqf+y2bduwYMECdOzYER999BFuueUWhIeHY8aMGRg3bhw++OADpKam4qWXXtJTeNxzzz14/fXXdfBSWlqKO++8EwCwevVqPXVNQkIC1q9fDwCYO3eu7l8WFxeHbt26oXXr1rjyyiuRlJSEPn366BonVZOrbjQejwepqanIyMhAcnKyrhn4+OOPcdFFFyE3Nxdbt27V58qQIUPQrl07jBo1ChdddBHmz5+PlStXYurUqWjXrh3atm0Lj8eD6dOn+63k8dxzz+ljN2LECNx555347W9/i4yMDN2B/P7770dlZSWOHj2Ko0eP4tVXX9XdEMwPc6qGXNXamLt8qDWMVfcJdYO32Wy47rrrUFJSgtatW+uBFJdffrn+vSlTpuhaK1WuuFwupKam6vJQrcut5jo0DEOfa+PGjcPUqVPx5Zdf4vLLL0dKSgrWrFmDTz/9FL/88gsqKyuRkpKia6a8Xq+eQik+Ph7t27fXDyxqf+12O3r27Int27fjkksu0SPP1X7Nnj1bD1qx2Wzo3r07Jk+erB9SvF4v4uLisGrVKmRmZuomXfODu+ruYh6Q8d5776GsrEznaevWreFyuTBnzhy/EeOqdtC8zc6dO2P8+PEoKSnR65UvXboUTzzxhO7vmJubi1tuuQXx8fH4/vvv8c477/g1S6uXGmzzyCOPYNGiRXC73bobhXnKKYvFoleDueiii/T9rnPnznj66afx1Vdf4YorrtAVCJ9++qlfE+dPP/2EzMxMvPjii0hMTITdXrOihmqZUrWk6gFywIAB+PjjjzFy5EiMGjUKLVq0wOWXX45x48ahd+/emDJlCqxWK8rLy7FlyxYMHjxYX3uxsbF48skncejQIRQVFWHOnDn48MMPdQ1306ZNsW3bNp2+TZs24b///e/JBxjnoJAI7BRVW7d161ZMmTIFFRUV+m833HADbr31Vjz11FMwDANvvfUW9u3bh4SEBB2U2Ww2vPfeewCAZ555Bvn5+di9ezeqq6v17Oo2m03XVlx44YVISUnBkiVLdN+SuLg4rFu3DkOHDtXD+KdPn47FixfrguDJJ5/EBx98gE8++QRFRUW4+eabdTqrq6tRVVWFpk2bIiUlBRs3bsQ333yjm31feOEFfcJ+8sknugOpmt9IPcEkJSXhgw8+wF133QWRmo7+L7/8cq3520aPHg2Hw4HMzEwkJSXh5Zdf1kPBs7OzMXDgQKxfvx65ublwOBy6f93s2bMRFhaGFStW6OY2h8OBPn36ICIiArNmzdJP7Sptarmjjz76CGvXrtWF+bJly3DJJZfAZrMhMjISc+bMgcvlwqOPPop//etfuOeeeyAiePrpp/H000/rARperxeLFi3Ce++9h9deew1Tp05FRkYGvvjiC11jp7argrvRo0frav6ioiI9c73D4cDKlStx4YUX6tFSVVVVuOSSSxAXF4d///vfGDZsGGJjY/HWW2/pvnWvvPKKfmp977330LZtW7/jCQALFy7UeX7PPfdg8eLF6NWrF5YsWYJZs2bBZrPpNSHDwsLwwgsvYOHChUhLS8M333yDo0ePorq6GhdeeCGioqLQv39/3Hzzzbj55pvhcrkwcuRI5OTkIC0tDTExMZg6dSri4uIwYMAAFBcXIzs7G7feeiuOHDmCgwcP6nS1a9cOFRUViI6OxnnnnYfS0lLk5uYiKSlJd3RXNX/KunXrdPNpWVkZmjZtiksvvRSRkZEYP3687ryumkdUs65qHlM1GeaaBYvFgsWLF2PAgAFISUlB9+7d8cMPPwAAdu7ciV27duka97Zt26K8vFxf22r/li5dijZt2iAhIQGtW7f2WwD+k08+wcGDB7Fv3z4MGzYMbdu2xbp16/Q+ffnll7jwwgt1c47NZkPfvn0RExODp556SjdVqdrsp556CllZWVixYgVGjhyJ9u3b6yb8kpISdO7cWc95tmHDBgDAF198gUWLFiEqKgqzZs1CQUEBnn/+ebzzzjtYvHgxoqKi8MADD6B9+/aIjY3Ftddei0WLFun8Oe+883DTTTdh3rx5iI6OxmeffYZ9+/Zh+/btGDBgAGJiYhAdHY1FixZh7dq1qKysxJw5c/Syc3369NEPNypQUyMFVbNhbGws8vLy8M4772D79u1+68G63W7MmDED559/vu5/abPZMGfOHOzatQv79+/H0aNHkZaWpgeWlJSUICwsTHdTyM/PxwsvvIA5c+boGsuYmBj885//xJ133gkRwbx58/Ddd99h9uzZ6Nevn37oO3r0KL755hucf/75fquaWCwWdOrUSU8JExcXh0suuUQHu6pPruoL9u6772Lo0KEQqVn9QT10X3755X4DayyWmjWiBwwYABHBnXfeicLCQt08qmqn+vTpg2uvvdavj/Wzzz4LAH6BjM/nwwcffICjR4/qUa+qtUb9nrousrOzYRg1y8apwTjp6en45ptv8Pjjj+uAdMOGDbpGfPr06X4BYlxcnN+I8TfeeANjx47Vy4mZaypFamrqBw8ejLlz56JLly547rnn9BrZLpcLzZs3R3Z2Np5++mm/Mk7dcw8cOIDCwkJdU5eXlwebzebXV+/NN9/ExRdfrFtSVCvMyJEjkZCQgKSkJFitVvTu3RsxMTF44IEHkJ6ejnbt2mHOnDkwDEM/7EydOhXfffcd0tLS0Lp1a8ybN89vcJi6l/4a+9cBIRbYATVPymlpaWjfvj369u2LqqoqLF26FIZhYOPGjTh06BB69+6NESNG4IsvvkDLli11DdOYMWOwc+dOVFVVYf78+ejSpYuuvt2/fz/GjRunL4j4+HikpKTg5Zdfxpw5c3Ttlyrsbr75Zr+BCCq4ULPFjxo1CgDQr18/tG7dGocPH8Z3332Hhx9+GAMGDIDNZkNGRgbS09NRUlICn8+HJUuW6BveoUOHsGnTJvTo0QNOp1M/yfbq1QsvvfQSNm/ejI8++ghpaWl6ZGTgqNL8/Hz8+9//RteuXXXn3hYtWiA5ORkvvfQSrFYrvF4vLrjgAr+h5qpv1FtvvYWXXnpJ9w0cOXIkrrjiCjz++OOoqqrC888/jyVLlmDs2LHo1q0bfD4fFi9ejKqqKgDA66+/joKCAhQXF2PgwIG6w7fT6URCQoJeo1cFPmFhYbjgggsQHR2N8ePH46abbkJZWZkOTNW8ebfddhuioqJ0/punYFAzzC9evBh//OMf4Xa74fF48Oc//xn9+/fH3//+d/zyyy948cUXMXDgQFgsFj1h8YEDBzBs2DD4/l973x1W1ZW9fe69lAuX3ot0kA4KKFKk9w4WVEBBEBEBK1ZAHRWsMWMMsbdYsMRo5qfO2DUZTayAJbYAaowFjFGxUt7vD7695l7ElslMMuS8z+Ojwrnn7rPPLmuvtd53aWigT58+SE9Ph4WFBYyNjSl07+vrCycnJ2zevBkLFy7EgQMH0KVLF/To0QORkZG4ePEi8vLyYGtrSx5QFi5mOWvOzs6wtrbG2bNnaWFavnw5BAIBzp49i4yMDPKmDRo0CGpqapTIffz4cYwePRqGhoaYPXs2SQdERETQHGlqakJzczPGjh2L2NhYxMbGQiBoE4DduHEj1q1bhw0bNsDExAQODg7IyckB0LZR3blzB6tWrZKZB7a2tjA1NUVAQACdmplHOCYmBnl5eVTVgomBS5OT4uLiyEP7xRdfQCAQYM+ePbh58yb279+PJUuWoKamBvfu3UN4eDh0dXXpebKyssBxbYnaL168IM+7mpoa1NXVYWVlhZiYGAQGBmLYsGGYPXs2zp07h/v376OmpgY7d+6kfB8/Pz+oqqoiLCwMI0aMQGFhITw8PCAQCEhD7fz587hw4QI+++wzfPHFF1i1ahX09PTw17/+Ffb29pg5cyb69++PrKwsZGRk4NWrV7h37x6mT58OExMTOjQEBwfD0NAQFhYW6N69OzZu3Ej1qtk8VVZWxokTJ0gWSUNDA7169UJmZiZmzJgBX19fbNmyBXPnzkV5eTlGjx4NHx8fKvNkYGCAVatW0cbKDinm5uZwc3PDgAED6D2yg2NaWhoeP36M+vp6zJo1i0K9bO5z3L9q8DJChZqaGnx8fCgCwQ5JK1asgLy8PKytrXHu3Dlcu3YNs2fPRvfu3bFt2zby0H700UcwMTFBQEAARCIRGU/btm2jg/Y//vEPMlyUlZUpNYblozE9Mx8fH5m1VlVVFUZGRujevTs++ugjnDx5kg6thoaGWLJkCZqbm/H06VPU1dUhJSWFyv4x4pyNjY1MfjL7Xha6bO8lMzY2RlZWFgYOHEj1er/99lscP34csbGxUFZWpnGrqqqKrKwsqvKQlJQkI8q9cOFCqhYyZ84cPH78GKtXrybSEZtD0gclaY8k+7mtrS3k5OTg7e0tk7rRvXt3qnDUrVs35ObmYujQoRg+fDjc3NywYsUKfP/999i1axf69euHwMBAmJiYwNzcnKJjNTU1WLp0KTHM16xZg5iYGNoj3d3dqWyhqqoqlixZQmFmJnXDCIM+Pj5wd3fH+fPnAQB37twhIg3TBZwxYwZVmmDec+kcwZSUFJnoxJ8Rncqwq6yspCRcFRUVpKWlYf78+VBUVKSwCwCMHj0aIpEIlpaWMnVIGRvN3t4eEokEY8eOxa1bt3D+/HmEhobShBGLxVBTU8OqVavQvXt3qsfJjBADAwMUFBS8NuHCwsJoEdDW1sb333+PW7du4fr166isrIS5uTmVsTEyMkJubi5yc3Nhbm5OLEtLS0v8+OOPANo8dikpKXR6dHd3B8e1sXwZE8/CwoImVfvcOnby3LRpEzZt2gRNTU1ERkZi6tSppN/HvGMsN4RtOoWFhXj69Cl5H6qqqnDr1i2oqqqiW7duWLduHdTU1LB8+XJcuHABmpqaGD16NAlCstPUL7/8gmfPnqGyspJCGmwTY98vXU6K49pyQQwMDHDv3j3s2bMH+vr6JNdhaWmJr776iqp2DB48mBKRWZ7LP/7xDwBtp80VK1Zg69at0NDQwPDhw3Hz5k00NzdjxYoVkJOTQ5cuXei7gDZdROlySGKxmBJ+R44cif3798PY2Bje3t7kHTE1NYWBgQFqampQXV1N4R224e7cuROVlZXIzc1FSEgIevXqhejoaHz88ce4evUqGcIs5WDw4MEQCNqKWvv4+OCbb75BWloaPD09aaFWVFSkEFdWVhZ0dHQQHh6OcePGISEhAfHx8QgNDUV4eDj09PRI68zNzQ0lJSW4ceMGTE1NoaOjA2NjY9K/unnzJr766isMGDAAqqqqxMJlciHMw6OgoIDi4mJ8++23mDdvHhITE7Fjxw4qEj537lyaC6xqwKlTp1BdXQ03NzeqtsJyEjU1NWFmZkZl7wIDAzF37lwoKChg1KhRUFBQoFxSxgBlc8/ExARZWVmwsLAgKYSEhARoa2tj8+bNOH78ODZu3EjJ8ocPH4aZmRmNdX19ffTr1w8nT55EdXU1zMzM4OHhAX19fURFReH06dPIyMhAv3798OTJE5w9e5ZCtStXrsS8efOQlZWF8vJytLS04Nq1aygtLUVsbCwiIyNx4sQJfPzxx2SYMHLC1q1bceLECQgEbSKv0sLeAoGAQvsuLi7Q1dVFbGwsjhw5gsOHD+Pw4cO4efMmvvvuO5iYmNCzME8RqzrCfsbmloKCArFRBQIBedzYmGde0Pb1b+Pi4pCYmAixWIx+/frhwIEDKC0txdq1a0laoqWlBdevX8ehQ4fIkGTfKa01yf4oKytj+fLlqKiokCF1MINGWVmZiBesvBhrp4eHB1RVVUmbU09PDxoaGujduzeSk5MhFAqRnJyMqqoqWouuXLmClJQUyMnJwdHREZs2bcKVK1cQFBSEWbNmwdTUlDxz0nmLLKTcXuyYGcM6OjrkeWN5k127doW/vz+lbVRWVlKKC/ucioqKTHk0S0tLdOvWDUZGRhT2ZYQ11h4ms6WrqwsFBQX06dOHCBlbtmyBl5cXjh07hm7dulENWtavgYGBkEgkCA8Pp72GVWPR1NRERkYGJk6cCENDQ5JK+stf/gIzMzO4u7tDTU2NyHC+vr70DNI5uAkJCTh48CAZ15qamqiqqsLXX3+N3bt30/srLS2V2dvr6uowfvx4SCQS3L59G4GBgdDS0oKPjw8EAgG+/PJL3L17F2VlZVBTU8OdO3f+c4bG/wA6jWFXWVlJMhn19fU0GdTU1OjU19zcjIsXL1KeENNMsrKyQnV1NUkHKCgoUJ1IFlJJSUmBgoICFdJmuQ/Dhw/H9u3baZFycHAgHTFpVXaWV6GgoEByG0KhELm5ubh58yasrKyQm5sLkUiE7OxsJCUl0QKal5eH/Px82kCtra0xffp08qax0w8zOMPDw6GkpIRJkybh+PHjMouFl5eXjKbVwIED0dLSglOnThHLT3rRYjVrpVlpMTExePjwIQBg69atRFxJT0+nxHCOa0s+f/HiBbKzs5Genk5M12PHjmHGjBmoqqrCw4cPcfToUWhoaMgsbGzz8vDwoLAaW1ALCwvh6OiIcePGwdjYGIWFhfDy8qKar8ePH0deXh5yc3PJ+GL5QpqamjA1NcWqVasAtHlimYfms88+w9///nd8+eWXEIvFyMnJQX19PRwdHTFz5kzcu3cP9vb2cHJygqqqKsRiMYYNGwaxWEwhID09PYwbNw4VFRUQi8WU+M7aK/2cbEENCwuDuro6fH19SbeO5Z/5+vpixYoVaGpqwr179+jvH374AfX19Xjw4AElEUdHRyM0NFRGh4uRQ1iR9/YbskQiocR3RjoQiUQICAigkB27liUsi0QiKpSuo6MDNTU1Yh2bmZkhJSUFEydOBMf9S6SY5dapq6uTp5QJhguFbWW/WltbMXnyZHTv3h12dnaYPn06xo8fLzMm3NzcqKoGyzdVUlKi72ebLPOg29nZkUxDUlKSjByPvr4+Fi1ahK+//ppyEq9fvw4DAwOMHj0avXv3RmRkJPT19XH06FEcOHAAenp6yMjIQGNjIx0qGAGDaTAqKSkhNzcXTk5OUFJSQkBAALy9vSEUCjF27FgKU7K+tbKygrGxMUaPHo3m5mY0NzfDxsYGkyZNgpKSEhWkl+4HFmqLi4uDvLw8ySh99913qK+vx+rVq3H8+HFoamqif//+kJeXJ4kVaVYqO/AxLyrThmP91JFHSloXkRGumNHXp08fFBcX0/2Sk5MxZcoUTJo0CWfOnCGNShUVFcpXs7Ozk2lTWFgYRCIR7OzscPz4cSI06enpQVtbGxkZGZTrVVFRIbP+MjkOeXl5+Pn5kSHbvlIJq9TRp08fjBo1iuamjo4OHBwcoKCggOnTp8PHx4fKZw0bNgz6+voU4WHeIhZ61tTUfE1sWyAQwMHBgdonLW7u7OyM4uJiAG0sV3YgY5WNRCKRzFrs6emJzz//nCIQrDShlpYW0tPTqSoFE6cWCoU0V6RJc0wHkBHkRowYAaFQCBcXF0yZMoWqwnz00Uc4f/485SFfvHiRPKPSTgJTU1M0NjZiw4YNEAqF6NevHwYNGoRhw4bBzs4O5eXlqK2tpRC6dDnGAQMGYNmyZdi7dy955JgSxf79+/Ho0SOKWjx48ABubm7IzMwEx3EYP348Jk2aRGxwAPj555/h7u7e6XXq3oVOYdhVVVVBUVGRpE1aWlqIrXTx4kW67tmzZ0hISIC/vz+mTJlCmmoJCQn46KOPYGhoSELEYrGYTrTm5uak7wWAiALsRM7K1bR3gTOPCQuHsbIms2fPJkFgFRUVWFpa0snb3d0dioqKcHNzg4KCAnR0dODn54fY2FgkJiZSYjIrs3L16lVUVlYiMTFRxmDLzMxEY2Pja6WvunTpgp49e5L2l6OjIyVsMwFl6UWcbYrSxqFQKER6ejrS09OptND+/ftRUFBAk5edsD///HM4ODggLi4Ou3btgq+vL2lFGRoaUlkm6WLo7StnMNKAUCiEsbExDA0N4ezsDG1tbQQEBOD27dv0bE5OTtDV1ZUpQebq6koSMmzz79mzJ548eYIXL17A19cXBw4ckFmMFRQU0KtXLyxfvhx9+/ZFjx49cO7cOTIYFBQUaEMSCoVUQcHe3l4m9Ovk5ITo6GgYGBiQBAjzQDIihEDQpokYEhKCYcOG4erVqwgKCoKCggI0NTXh6emJiIgIDB06FCNHjsTQoUORl5eHoUOH4vTp0yT3c+HCBfTv3x+bNm2i/qiurkZSUhIUFRURGhoKZ2dn2NjYkEeWebrMzMxgbm4Oe3t7kvdhHgb2PlVVVaGnp0eebUNDQ/j6+iIiIoJKNYlEIjImGWOZPS/zhCgqKmLSpEk4deoUbeROTk4oKiqCmpoa1q9fj27duqGmpoakU1gdXgsLCwr5xcfHUxhYWVkZrq6ucHR0hFgsxvDhw6GhoQF/f38oKSnBzc2N8rOkx7dYLIapqSnEYjHCwsJoHllZWUEikcDb2xvW1tawtbWleqY6OjooKyvDnj17aANleaby8vKYOnUqtm7dSnPl0qVLAIDy8nKIRCKYm5ujX79+cHBwILKLUCiEu7s7Ll++DAAIDw+n8DgzvDjuX6kELNTMmMdsrDs6OsLFxYXqGTMPDiudx/S+2IYsEokwYsQIYkiya5l3kx2oLC0t0b1799dYl97e3h3qzDEygvTP/f39oaioiJiYGDosdvQ+5OTkKOeZGY/W1tYwNjaGjY0NFBUVYWtrC2tra4SFhcHa2prm+6xZs3Ds2DEZlik7RDAPOvsuBwcHmYojzEO5ZcsWpKSkyLB2Wf6wh4cHtYlVp2ACwNIVKqQPV4yl21EFCU1NTSxbtoxyURMTE0k3UDp1xsLCAmpqasRuZWsz001khr+FhQV9n6qqKhm1I0eOpIM6Y0AHBwcDaCMcJiQkwM3NDampqUhJSYG5uTkiIiLw5MkTWk8TEhKgoqKCiRMnYuzYsTKVkoqLi/Hpp59SbqCOjg6Jrg8fPhyhoaEknmxlZQUbGxsy/pleZlBQEOLj4zFhwgTKz8vIyMCDBw8AtEV5Hj16hNDQUGRkZMDf3x8BAQH4+eefaY9vbW1FY2Pjf97o+IOjUxh2J0+epJMPS+Zk0ibl5eV0XUtLCzw9PWFkZAQvLy9MmDAB48ePh5mZGQwMDGBjYwMFBQX07dsXOTk5MqKcEomEBtHAgQNhZ2cHiUQCCwsLaGhoYMSIEeA42VJXeXl5aGxsxMKFCyESiRAcHAw3Nze4uLggJCQEEokEmZmZtBjEx8dj165dlDyroqKCXr16UZ6IpaUlKioqSKxRVVUVUVFRGDJkCGxsbNC3b1+a4GlpaYiNjaXcNjbhWBtZaIBt2tILnqamJrp27UrGWftTKFuMHR0dqd6poqIiJdyqqqqitLSU6lkyLxA7se7atQsxMTGYMmUKaVENGjQIkyZNwuzZs3Hw4EHMmDGDWMZDhgzBqFGj4OPjQ0W8WdUObW1tnD17Fv7+/mSASlfB0NPTQ/fu3VFSUoI5c+bA3d2diqW3tLRQwfV9+/ahf//+kEgkGDBgAPbu3Yvx48fDwMAAH3/8MdTV1TFmzBiYmJhg7dq1sLS0RGBgIHnuWL+bmJhgypQp6NevH224ampqtDErKSlh27ZtWL16NYlySucw+fv7A2jLW/H29oaLiwuCg4Nhbm5ORB1bW1v6+/z58zJyP4wcERISgt69e9O4ZzVzx44di82bN6OlpYXq/UokElhaWhJbUzoHjuM4GSYfO02zTd7CwgJhYWEkM6GnpwcLCwsZ4WUTExPy3CUkJGDcuHHUrsbGRhw6dIiMwMrKSnzxxRfQ0dHB+fPnYWlpCaFQiI8//hgGBgYQidrqMnfp0gXW1tZISkrCmTNnEBgYCDU1NQQFBUEoFFJ6gnR5PGZYM+OM5VEx7zx7NhY+l/68pqYm5ZaxccV00LS1tSlCIBS2ldybM2cOHBwc4ODggMGDB6O1tRXV1dWUt1pWVoaoqChoaWlR7q5IJMKWLVswb948iEQiGV1Itimz+STtUZNW+mcGh4qKCpKSkii83LdvX9rw7ezsaN6zOtSamppkaLEUDZYSwfqDGfrS41XayJA2kEQiEfLz82ldVFRURE5ODoqLi8lDz/Td2L2k62RL659Jf6e09AwzZNg6xuQuWOlBlofKiEnsAMXuy2SemPwQCwMmJSVRIj8jZDAiiZaWFh1+2xu5zABsXy2FPR/LE2PrFHMcKCkpQVdXF9ra2pgzZw4kEgkcHR1l3iuTo2Jedzbu1NTUEB0dTftFly5dsHz5coSEhFD7DA0NsXr1aixatAiXL1+mHD2xWIwLFy4AAC5dugQ1NTX4+vpi/vz5qKmpwfXr13H9+nWaEyYmJkROMzU1JbKUQCCAp6cnrSdMV5C1fdiwYQgMDIRIJIK3tzf8/f2pzjGbQ8HBwYiOjqa8VHd3d8o/liZvAW2pVJqamjh//jwxznnIolMYdu3BcreYtAkLb/zyyy8IDw9HcHAwZs6cicmTJ0NJSQmrV6/Gnj17MHnyZKirq6NXr1745ZdfsHr1amhpaSEoKIg8R+Xl5Rg5ciQSExMRGRmJ6OhoKiX16aefYt++fZg2bRrU1NTg6OiIpqYm/PjjjygoKICDgwOOHTuGf/7zn/j0008RFBSE/v37U9FjY2NjXL9+HevXr6cTb35+PmxtbTFp0iSEhYVh0aJFsLe3pxO4uro6du3aRRT4devWQV9fnxLId+7ciT179qB3796IiYmhsBHbIIRCIQwMDJCcnAwvLy/Y2tqSzhjbbO/evYuUlJTXCl1ramri0KFDWLVqFbKysqCiogJ/f394eHigV69eVHyZLWJMvHfw4MFkgBcUFEBJSQnDhw8H0EZQaGpqQmtrK7799luIxWLY2NigubkZRUVFpMoeFBQEX19faGlpwcrKigwJT09PlJSU4MyZM0hNTYWdnR1yc3Nhb2+PZ8+eobi4GCKRCGVlZQDajIsBAwZg5MiRiImJwe7du2kc3bp1CykpKcjIyKCcNENDQ1RUVGDAgAGoq6vDhQsXkJWVRYaLp6cnmpubUVlZCXt7e5iamhK139jYmE7wrBzR9u3bER8fT/kuYrEYdXV1aG1txdWrV+Hl5YXo6Gh4eXkhMTERAOiEyv5+m9xPc3MzXr16hSFDhsDIyAiampr4+uuv0dzcjJUrV5IBo6ysjEOHDlFYhnl3tLS0YG5uDiUlJcTFxUFOTo48WMxbqaOjg7i4OCxbtgwNDQ3IyMgAx3Hw9fWFlZUV9u3bh549e5L47vjx40mrrqmpCdeuXUN4eDhiYmLQ3NyM58+fw9LSEhEREViyZAksLCygrKyMkpISjBo1ijbK8PBwVFVV0bMzmQsmpcGMLZFIRN425n1nGw8zZOTk5Kj6i76+PqysrMiLoqKigoqKCpw8eRKGhoZU1YLjOJSXl2PHjh24d+8eBgwYQB7PxMRETJgwAVOmTIGjoyPu3r2LR48eQSwWIyIiAo2NjWhubiZtQeadYdVV9u7di6amJhw+fBgxMTHQ0dFBt27dXhP6Zc/GNnDGXM/KyoKnpyd69OgBa2trqhO8ceNGPHz4kAx1U1NTzJw5k9aULl26ICQkhLTMWC6cvLw8EhMTsXbt2tdCs0ZGRqirqyPvHSMkDRkyBE1NTcR4dXV1xU8//YTKykoYGhpixYoVcHd3p+oqxsbGdKhg2o8CgQARERHYtWsXifiydWjNmjVobm7GgQMHqAIPW6uZcSd9aPX09MTjx4/h5+cnYyxv2rQJampqiIuLg6WlJRnzSUlJWLx4Mdzc3ODp6Ynw8HA6wLF8ZolEQoYWM3SlDV/m1WJeKfb+JBIJiouL8de//pXe3bfffouamho4OjpCXV2dDrEWFhbENvb390fv3r0xaNAgSlHp3bs3oqOjYWJigpEjR6K1tRUZGRnkSTYxMcGRI0col7ClpQV79+6Fp6cnzMzMcOvWLfzyyy+Ii4tDUFAQHj58SNcBkEmrUFdXh7KyMgYMGIDvv/+e8qLZwdXZ2RmGhoZkpDPCm0QigYeHB0xNTfHll19CJBJh9+7dOHnyJAYPHkyHjJMnT6K5uRlTpkyBg4MDwsLCSGaqtbUVTU1N2L9/P3nBeXSMTmnYMXzxxRfgOA7ffPMNgDaD78SJE4iIiMCAAQNga2uLFStW0PV3796lk+c333yDhw8fYtWqVdDX16fTCMe1SYeYmZmhZ8+eEAgEGDRoEFJTU2FiYoIxY8YAAOVRMd2zAwcOQCRqqx35448/QltbmxaHDRs2YOzYsbSQamhooKCgACNHjoRYLIaBgQEKCwvh7++P4OBgWFlZUV4dk1+R9vYEBQXRpEpPT4eRkRHlMwUFBcmEPxhbUUVFhXIXWD4UW4BCQkJgaGiIHTt20AbBBDnZIshKPaWlpSEtLQ3Jycl02rW1taWwKjuhBQYG4tGjR9i3bx9JAmzYsAGPHj1CS0sLGShMMX379u24c+cO7O3tSYKAMSlnzJiByZMn06I7duxYAMCcOXPg6uqK//u//4OzszMePnyI+vp6Sr5neRlMukUgEOCrr76i8dDa2kr0/+3bt0MgECA4OJg8GkePHsWWLVvwxRdfYOzYsXSS/+abb/Dzzz/jxx9/RJ8+fZCUlAQAxELs0aMHpk2bhqSkJLS2tuLIkSMQiUS02fr4+NBhZNKkSRAKhVizZg2EQiEuXLhAZAppKv+tW7ewefNmfPHFF6isrMTkyZMRHh5O12RlZUEgEJA+IVPkFwgEZAApKyvDzs4OwcHBFE5mC3dWVhYddpi3t1u3bjLloSZMmICmpiYsWrQI+vr6yM3NhaurKxoaGhAVFYXY2FiSaygsLMTt27cxZswYhIeHo6CgABzXpmJ/9epVnD59Gi4uLqSJlZycjJaWFvTv35+8v6qqqti3bx+eP3+OGTNmICYmBocPH8bZs2fx2Wefobi4mOQumGeEberSIXlpA4UlipeXl2Po0KH0XTNmzAAATJgwAYaGhtDQ0CBvcUxMDJ4+fYrS0lK4uLhg/fr1EIvFyM/Px+XLl6GsrIywsDCcOXOG8tHWr1+PR48eYc+ePaSxxWQ4tLW1UVlZiVGjRiExMRGHDh2CtbU1duzYQesKMx6YdIWZmRlcXV2pQoezszMcHR1RXV2NzMxMpKSkoG/fvmhtbUVDQwPNISsrKzx//hwXL16UeY/MwGVzqmfPnigsLMSsWbOQmZkpY9AUFRVhxIgRdHDhuLayYX369EFraysePHiAkJAQcFxbXtTNmzfJaJGOdnh7e2Px4sVkcIaEhJARt2jRIixZsoTSWjiOI8HwlpYWIkSMGTMGYWFhGDp0KBwcHOjdq6io4Pnz52hpacGGDRtkDOSoqCj84x//gJaWFoWpBQIBcnJysHfvXly6dInWOCcnJ5SVlaGkpAShoaGwt7eHjY2NTIqGj48PjI2NyRsoXX+WEYwmT54MY2NjlJSUEKN9zJgxqKurw9ChQ0mLVCKRIC0tDVFRUVBWVoaxsTGcnJwwceJE+k4tLS1aQ4cMGYLhw4dDX18faWlpdOBl+c5MQxVok/lh8jrl5eVkzO/du5fknBoaGshZEBgYCIFAQMSP+/fvY86cORAIBFTB5sKFC1i6dCl27txJJeoSEhIwbdo0HD16FNbW1igvL6cUEAsLC8TExFBFm82bN+Pp06e4d+8eVFVVUVJSQjJThw8fRnZ2Nuzs7HD37t1/30DoxOjUht358+fRtWtXJCUlydCfv/vuO/j7+8PNzQ1btmyhk8mrV68QHBwMe3t7okw/fvwYK1asoPwLVmpFIBDgu+++I3kHVnXAwMAAN27cQN++fWFtbY2UlBQcP34cSkpK6NmzJ7y9vYnlxQgYQqEQw4cPJxq6nJwcJc8uWrQIHNeW48Jy+JisBzM2hUIhrKyssG7dOuTl5UFNTU2G6cZc/yzRl+VbMMON/Vvahd4+AZgtIpcvX6awnvQGIxaLKQeGJdgqKytTjqOhoSGUlJQolyc3NxcAMG7cOLi7u8voQzGB2tbWVuzatQsSiQR9+/bF48ePkZiYSMnMSkpKcHR0xMCBAyk/iyX2FhYWIi8vD3FxcSgoKIC/vz+ePn2KpqYmGBkZwdTUFK9evSIpHFbEPSYmBhcuXMCVK1cwatQokoZgoVFdXV3a7FRVVeHu7g59fX3Exsaid+/esLW1pbHT0tKCvn37oqSkBJWVlfSuGenG3t6eknwXLFgAgeBfyvxBQUEoKCiASCSCtbU1/va3v8lcL432TM3AwEB4e3sjMDAQAMgAZpuLtDL/xIkTX/PAsA1IV1eXwkXM++Dq6krhOW9vb5mNkBVfFwqFcHR0RGRkJPz8/PDgwQP0798fM2fOBABUVFSQXIFIJIKhoSGF3dlGOHPmTDx79gyXL19GbGws9uzZQ/miLFWB4zgsWbIERUVFEAgEOHPmDK5cuYIFCxbg2LFjiImJofrHpqam0NDQIMOKhWElEgkcHByotrO6ujpsbW3x/PlzrFixgnJafX19ceHCBaSmppIXhFWHKCwsxPfffw8/Pz+Eh4fj4cOH2LJlC/Uhm4tDhgyhzzHvLMdxqKurQ0tLC/bs2UMhRibWfe7cOYwfPx4eHh44efIkZs6cCRsbG/JA+vn5wcDAAMbGxoiNjcWIESMQFxdHeoJ9+vSBvLw8Zs+ejejoaLx8+RJAm6CskZER6UpWV1fDzs4OZmZmJKyrrKwMHR0dmUR5tj54enpi8+bNZNxJJBKkpqZSuoeKigpycnJw7do1WFpa0tonHRJnXh6WOxsQEID6+nro6OjA0dERO3fuhJ+fHwQCARwdHaGvrw8PDw9oa2tDW1sbAwYMoHU9KiqKxhJro7OzM3r06EEH2czMTDQ1NWHo0KFUHoytw0lJSWTssnYJBALs3r0bX375JVRVVSkveu3atZg2bRrk5OSQkZGBqKgo5ObmkvQKu2d+fj58fX0hFotpvRQI2pjn7D3r6uqid+/exNrX1dWFSCTCmTNnUFBQQGLAzDBmqR9sPba0tKR2s3VeW1sbvXr1gqamJuWQxsfH4+nTp6+tG8bGxjIpLK6urkhJSUFVVRXGjRsHJycn2uOmT59OToNhw4YhNDSU8vX8/f0hkUgwfPhwCAQCVFRUQENDA+rq6pRX6+PjIyOzI52neOPGDYwaNQoCgYBSRcaPHw9vb2/88MMP6NevH4WbpTUoeXSMTmvYXbt2jQwwBQUFyiWora2l2ntisRijRo3Cw4cP8ejRIxQXF8PQ0BCFhYUylOn09HRIJBKcP3+eBBv79u2L+fPn06ZYUFBADMqAgACYmJhQzUNlZWUUFhZi3759UFVVRVlZGeTl5REaGkp1/jiurW6gdLhz0aJFGDZs2Gu5HEKhEAMHDoSCggKOHz+O2bNno6ysDAJBmwQCq2/IwhAs54h57eTl5SkEJ71ge3p6QiwW0+bX/nv9/f2Rn59P3iWJREKLKNuk09LSoKenR+XSmG5XS0sLysvLKf/G2dkZsbGxUFVVhbq6OnJychAaGkrhg/379+P58+eYNGkSVdw4cuQIGT4eHh6UgM1CP+ykL0328Pf3h4aGBiorK6muK2OKJScny0jhHD16FEZGRnBycqIFkcl45ObmkrBxdnY2bXa5ubkYM2YMFaofN24c1NTUcOvWLRQVFcHQ0BDbt2+nZOD2BJHAwEB8+umnGDNmDG30zFumqKiI2NhYWFhYoKCgANbW1qRjyFBXVwdjY2NMmjQJjY2NpHfHcRy6deuG6dOnU3gsPT2dNmKRSIRly5bh8OHDNIbZ5xITE0lDUDpXjo1JVuWBbSZMVZ9dp6mpSbWCKysrUVxcDFNTU1y5coW8BUwwVk9PD1lZWdDS0qKDDfMu1tXVAQBVEmHjTF5eHk5OTnBxcUFhYSHk5eWxadMmHDp06LXcOo7jaPywDZCFWBnZoE+fPrh79y4iIyMhELQxe/ft24ejR49SIru6ujptYqwEHBtfZWVl9H0ZGRmor69HdXU1GQhs3LC5m5iYSP0qFAqxf/9+PHv2TGZcCQQCDB48GJmZmdDW1sauXbtoPWPEl8GDBxPZgOM4CgMzzx/rhwMHDuDixYtQV1fH3LlzsX79etJR4zgO+fn5SE9Pl/EqsdzG9gY/+7Ny5UoUFhZCRUUFampqJH+TmZlJOXEuLi4y5JDBgwfLiIWHh4e/FlrOy8ujcnYnT56kcOyqVauwZ88eSCQSKqWoqKiIxYsXY9iwYXQP9o5YP0p7lNn4Yf9m7FBmaLIcQjaudXV1ScqKyT6xZ3B1dYWCggKCg4Ohrq5OaxEjvnFcGzlDLBYjOTkZ8vLyZNyw7zc3N5fRlGP9nZmZifr6enzyyScQiUTkVWb3YMaziYkJsXsXLVpEB045OTn6OQtzSiQSGQkQtm4UFhaib9++UFFRgY6ODnJzc+mdMgO+vLycDnDSKQtMMUFaukZeXh7Tp09HamoqXZOTk0PPwIhU7P6MvHPp0iWcPXsW2dnZ+P777wEAf//736Gqqort27cjOzsbWlpaMmRIHm9GpzTsGhsbkZGRgfT0dNoUsrOzabNNTEzEwYMHZXIYPD09SUBUmjK9Y8cOmJmZ4bvvviPJCCYYyYpUGxgYoLa2FhUVFXTSZpUl5OTk4OfnB6BNboXRzoVCIUpKSlBSUkLGiYaGBpSUlGQ0o5gnjE0ctggpKipi3759MqWrcnJyMHDgQKpjyMR7hUIhzMzM6HklEgmFgdkmwgxgtgHp6+tDWVmZciXYoiWRSGBtbU31+aRPikZGRqivr8fOnTtx8eJFBAQEID8/n3KFmPBmSUkJioqK0LNnT2hqakJLSwuffPIJDh8+jMWLF5P3x9nZGTo6Ojhy5AhcXV3Ru3dv5OTkID8/n6o8sLwjHR0dEqxkbEEWNmFMQ6DNwIyKiiJFfiaFw3DgwAGoq6vD3NwcI0aMwPnz5zFhwgSYmJggMzMT7u7umD17Np2ytbW1KXxXVFSE8vJyGBsbIzg4GAYGBhRKVVVVRXx8PPr160ebDtsANDQ04O3tjXPnzmHNmjUyLEehUEjFsaW1GBmWLVuGgIAAYoMNHToUxsbG9N6Yhzk4OBgZGRmvFetmmz9LKmeSC6zeLBsXbNF2dnYmlpy8vDwuXbqE+/fvw8nJicgsxsbGKCgowMcff4yRI0dSyJLNATaPiouL4e3tDT8/P4waNQo///wz1NTUMGvWLDg7O2Px4sWoqqrC/Pnz6TBgYmICV1dXYsYylXtW49bT05MYtNLjm80bxmJkYV6hUIjKykr88MMPmDp1KpSVlaGnpwdDQ0PY29tDX18fXbp0oXmrrq6OIUOGQEdHhzx2AoFA5rDk4+NDDF1VVVVcunQJQUFBlKTv6OgIOTk5TJw4kca6k5MTFBUVyTvC5lNcXBxOnTqFoUOHIj09nZiHampqlFzO2qqmpkYeQXNzc/JKFhYWor6+HocOHYKVlRWx8CMjI8nIZblsc+bMoTktbXRJkw6EQiE0NDSIZbpgwQIEBATA2dmZ6s7u2LEDRUVF8PT0hK2tLbKzs+l+LGXB2dkZ/fr1IwUDgUAAW1tbbNu2DQ4ODhS1YJ6eKVOm0PoxatQo6nM9PT2Zwxwj2bDDiImJCYV72Zxg/05NTYWXlxcUFBRQUFBABurFixexefNm8k7379+fRHP9/PygqalJB2X2/hkrXCKRwMTEBEpKStDX1ycPM8s9Zest0+PU0tIiwoWnpye9sxUrVsgYoUxTjpFj2Htia1hVVRXJaeXl5ZEzw9fXF7q6uqitraX9gq0bixcvJg9hVFQUysrKIJFIoK6uTqFXFiqWl5fHJ598Ak9PT9jb25PMEyNQsPxhCwsLqKurIzo6GoGBgZQfzOogx8XFoWvXrlQrW01NjXQHe/XqhZUrV9L6FhERARsbGwgEAt5T9wHolIbds2fP8Omnn6KiogKtra0yBZSXL1+OLVu2AGg7tbDQUllZGWpra6l8E6NMNzQ04Mcff6Rk83PnzuHUqVPkbi4qKqJyOhcuXKCSWuXl5fjpp58QGxsLDw8P7Ny5ExEREbC3t4eHhwfMzc2hr6+PgQMHyjDAOK6tHiXLpWG6eBMmTCBXNTu5jx8/HvX19QCAx48fY8GCBfD398eGDRswcOBAxMXFoaioCCdPnqTFjBkLLHeKLT7STC6JRCJT1J0Zl4zdaWVlhUuXLpFho62tTZ4oHx8fEvPNyMhASkoKamtrERgYSPVmWV/4+vrCwMAACQkJsLKyIomW+Ph4+Pr6oqKigogEP/zwA7E/Wa5IY2MjMVkDAwPxz3/+E8uWLYOSkhKFRIF/MaUZ+vfvD7FYTAs1Q3NzM+bPn48RI0bg4cOH9LlBgwZBRUUFdnZ2GDp0KEaMGAFLS0vExcWhT58+KCkpgUAgQEhICHr06AEtLS1ERUXh+++/x7FjxxAaGorq6mosWbIE69atw6RJkyhpPyIiAgJBW4mg+/fvY+/evTA1NcWyZcuwdu1abN++HXv37iVR6vZYunQpLC0tcfbsWTx79gyxsbHgONk8SX19fbi4uCA7Oxtdu3aFj48PhVTk5eURGRmJ+Ph4rFixAhUVFTIeUKZtx07YTGtKJBLB09MTALB3715YWlpi9+7dePjwIWkcMvkV6URn6Xm0dOlSmJmZIS8vD1evXsXMmTMp1MrGnra2Nkk9zJgxA2fOnHlN0DY0NBQLFy5ESEgIlfVieYPMSGYG2ujRo5Geng4tLS0y8oqKihAfHw9zc3OcO3cON27cwIkTJ3D06FH89NNPePDgAaqrqxETEwMPDw/Ku2VVKtTV1cmLyHL4WNiXbUYs1MuMDW9vb1y6dAlJSUnw9fXF+vXrMWPGDGRnZ+PcuXPYsmULBAIBxo4di5s3b9J6BoDKBGpqamLHjh00Vuvr61FbW4uCggJ06dIFJ0+epPsw4+7Bgweora3F/Pnz8cknnyA2Npb6MjY2Fvfu3cO6devo3TNjn+PaPI9ubm4wMjJCYGAgVq9ejatXr+L58+eorKxEdnY2srKyyOMCtAl629jY4PHjx1QJIjo6mjxVsbGxmDRpkowB4+XlBQ8PD+jo6KBLly6YOnUqGVPS80xHRwfz5s3D9OnT0adPH/j6+mL27Nk0Ttl7YId35q1j4tnsEKmtrY19+/bh5MmTcHR0xMmTJ3Hjxg1ERkZCLBZj5cqVVA8XaMuVVVBQgJ+fHxISEmBpaYlNmzZh5cqV8PDwoAORo6MjjIyM0KNHD2zduhW7du2ithw5cgQ//PAD5s+fD19fX5ibmxNLmFWCGTZsGNzc3GBvb4/PP/+cDGkfHx9ary9evEhEhzt37sDDwwPHjh2TaW96ejqSk5M7XDcWLVqEq1evYtasWRAIBAgMDISRkREUFRVRVlZG44cZuI6OjpCXl4eHhwfWrVuHL7/8kqJCUVFRePDgAS5duoTi4mIidb148QLTpk0jD56JiQkUFBQwa9YsDBkyBHJycpgyZQr27t2LwsJCGBgYYNu2bQCAL7/8Ejt37vzT69J9KDqlYQfgNS0bVkBZ2hh6+fIlTp06BT8/P5iZmdHm+eLFC5w9exZPnjyRuYd0snpHWjmtra2or6+XYePevn0bgwcPhpKSEkJDQ9HQ0IDHjx9j8ODBVF6GLaIWFhYIDAyEvb09/Pz8yG09YsQI1NbW4smTJ9i0aRPlgrHnuXPnDu7du4cXL15QbcqDBw/C1tYWz549Q2trK+VoaGtrkyipt7c3hX+Dg4MpHCgQCGixZyffqVOnYty4ceQtWLRoEYKCgnDu3Dk0NDTgzJkzdLIbMmQIGhoaqJzY/fv3UVtbi5MnT2LgwIHUF/X19TT5N2zYADU1Nfztb38jRqc0njx58hr7kzFlQ0NDifTw6tUrrF69GnJycrC1taV3xjSQAODEiRMU5muPW7du4cSJE/T/mTNnUrhg8eLF6NGjBzw9PeHh4UFixwKBADt37kRrayttrCy5uP2YbG1txe3bt2WKWjO5gvHjx+P8+fM4deoUmpqa3qtwNcv9s7a2phAca0tNTQ2VaWMEltDQUNy9exc3btygOaGgoICVK1dSojsL9ZSWlmLnzp3Izc0Fx7WFjZl3UVdXFy4uLgCAH3/8EXfu3MHjx49l5gJ7H+3BfsfabmlpiT59+hCbUSAQYPny5cjOzoarqyscHBzg7u5OTGtDQ0OkpKQgLCwMn3zyCYyNjTF48GBcuXIFqampGDRoEO7evUs1hpm3e+TIkTRX1q5dS0ZE//79ceDAAVy/fv21Q4A0fvrpJ5rLfn5+SE5OhpKSEoKCgrB06VJoaGhgz5495NGPiopCQ0MDEUZmzZqFbdu2Yc2aNVThQ3qss/Hxyy+/AJBds9hYaG5uxr1798hzJ80wZvVUGxsbZbS9pO/Drn306BHu3buHW7du4datW1i7dq3MNcxATU5OJlkfe3t72Nvbw8rKSmZ+Pn36FE+fPiXCT3uEhYXR+jVv3jwoKytDXV2dmKscx2HFihVYuXIlkXmGDx+Or7/+Gl5eXrCwsKADEBvb9+7dQ0FBAQICAlBTU4PHjx/TWGPeuZiYGFRVVWHq1KngOI7YmmycVVVVUW7t8OHDUV9fj59//pn6+NatWyQjJD2Wp06dCnNzc+Tm5iImJgY7duyg/DVG/ggJCcG9e/dw9OhR9OjRA9OnTwfQVpaPHXxfvXqFxsZGFBUVYcCAATLvij3vqFGjcPz4cdTX12P37t04dOgQamtrkZ+fT2UpGR49ekRrZGtr62sMeWlUV1fLrBvSfVtTU4OcnBwEBARQP7P+vHr1KjZu3Ej1W1+9eoVFixZh+PDhMt/x+PFjGsds/G/ZsgUPHjzAwYMHoaWlhR49eiA8PJzmYHNzM27evInU1FTk5ORQ6gyPD0enNewYHj16RH+kGXm3bt3CmDFjkJiYiOrqajLuampqMHLkSHh4eJDn4UPB6l0yNu7t27cxefJkHDx4kCjnLAGXEQ1qamqwfft2KuZuaWmJ8PBwarOvry+KioqQn59P9StZzlC3bt0QHByM/Px8JCUl4enTp/jpp58QExMDoM1z1qVLF9pwWH7P3Llzcfv2bUyYMAEHDx5ES0sLrKyskJqait69e5Nrfe/evRg7diw8PDwo8Z6x1gAQ0+rmzZvkiSosLERBQQHMzMyQkJCA58+fo7S0FCUlJZg4cSIOHjwI4F+bfEtLC6ytrTF+/HhMnTqVirwDQGlpKRWxZ9eeO3cO0dHRKCwspGtbWlpQWlqK+fPnUxJ/fn4+AGDWrFkYOHDga1461v6O0NDQgNGjR2Pv3r30swsXLhDZIjExEVZWVujbty+1c8GCBSgtLYWrqysVMO/o+7Zv3w6O4zBt2jQZQ93d3R2RkZHIy8tDUlISbfZvQ01NDbZs2YJp06ZRW9g7mTNnDoUIPT09sWXLFowaNQoJCQlobGwkMeMePXrg9u3bGDVqFBl2bPzW1tbCx8cH0dHR+Pbbb6GlpQUVFRVoa2vj1atXKC8vR0ZGhsw7el+0b3tdXR3OnDmD5uZmYjVHRkYiKCgIaWlpiI6OxrZt2yCRSCAQtLGYv/rqKwgEAly+fBnjxo1D79690dLSgubmZpISYka0m5sbwsPDkZ+fD4lEQv3OnvVdkJ7Lt2/fxqRJk2gsW1pakkYfy1MtLCzEd999h0GDBiExMRFPnz5FS0sLoqOjERMT0+FYnz9/Pm3K7VnEo0aNQnx8PJ4/f94hwzghIYEIBdIMyDddKz2+Nm/eDI5rY64uWrSIEvSVlZXxz3/+E7GxscTqZHO/tLQUkZGRmDdvHmkotkdAQADWrVsHAMjMzISqqir09fUxb948zJw5k77z9u3bFGK3tLTE7du3iSE6depUGtsMbHy0f966ujqUlpaC4ziMGzcOt2/fptw7MzMzlJeXg+Pa8u+mTJlCh58xY8bI9I30/JU2+KdOnYpevXrRGExLS8OCBQvQ1NSEuro6pKamws/Pj/qVvWvpedz+fmFhYWhtbUVzczP69+8vc+Bzd3dHREQERo4cSe16nzVS+v4dXXv16tXX1g3pvnVxcaEauqWlpTLjx8LCAvr6+iTb87b2sDktDR8fH3IaTJo0SWYOjhs3Tqb/eHw4OrVhd/HiRYSFhaF79+4wMjLChg0bUFFRQd4cOTk58nD98MMPVEZJIpG8lnv1IXj58iXCwsJkihE/evSIGGkAcOTIEcrNmDVrFoC2yVhRUYGYmBj4+/tTofOkpCRalFj+3qZNm2T0uFiIlT1Pc3Mz/P39YWhoSCGhly9fwsnJiRhvSkpKKC0txaxZs1BTU4OGhgZ4eXlh/fr12LRpExQUFNC/f3/k5+dDLBZj4MCB8PDwIFFQPT09HD9+HECbgfb8+XMEBQVRqJGFjM+ePUvs08rKytf6gkkw9OrVCxs3bsTUqVMRGRkJADKfY2BlmyZOnEjXtra2oqioCBzHkdxHt27doKSkRMnmcnJyr3nqGIuS6Ym1BzuJs3BHdXU1unfvjurqakydOpUqS0yZMoXaOWbMGMTHx79VAf3FixcIDQ3FoEGDsHbtWsjLy1OCMtNaY+/yfbFixQoZ5mNraytGjx6N+Ph4rF27lpKqGamlubkZDQ0NsLGxgUgkohyY3bt30/htbGzE8+fPkZycjNmzZ6O5uRnHjh2jOpBML6v9O/pQSLedvZNhw4YhJiYGffr0wezZs3Hjxg3s3bsXr169ouoCMTExWLRoEezs7PDw4UMUFBQgKysLL1++pLSKlStXQk5OjvqXzSMXFxesXbv2tbn6LkiP30ePHuHFixc0dz7//HPcv38fXl5eyMvLk2H/njt3Di0tLXj+/Dn69++P2bNnv3GsS+uISbOI5eTkcObMmTf+juUyMrztPuza9tewGsnSuXZMsDwkJASmpqaIjIwktrVAIMCuXbtem0fMOJ04cSI+//xz5Ofnw8jICDU1NZg9ezaUlJSwcOFCWsvYOHRxcYFIJJJpZ/uxDaDDedb+WZgmp0AgoBqz0gQKJpDOCG1v6kd2bwD0zo4dO0b32b59O13HxuCLFy9k5s677ie9hrH2tF8TmIH0rjWyo/u/6dq39e2TJ09eGxss541p5r1vewDQ+E9OTiZiYFRUFLy9vWkOsv7ryNvP4/3QaQ07Vj9zzJgx2LhxI8aMGQN5eXmcO3cOQUFB0NLSQnV1NV3/8uVLqgv7WzBv3lWM+NWrV4iNjYWpqSnlkQFtbnNTU1Pk5eVh2LBhmDdvHhQVFSl3ixWtvnDhAiXsMlFNaWOgqakJRUVFCAgIoMWguroaysrKUFBQQHx8PBElBAIBJk+ejPHjx8PGxgZ1dXWkV8d+HxcXBzU1NWRmZsLT0xPLli0jkdjNmzfj2rVrVCC6rq7uNfawNPu0I5SUlMDa2hp1dXWYNm0asrOzO/xcVVUVJBIJxo8fDwB0bVlZGRQVFSmnadq0acjKysKgQYPAcW0srNOnT8t8pzRzevLkyRSiB/61KLY/NU6ZMgU9e/bE/fv3MW3aNKowIRKJ8Je//AUTJkyAhoaGzNh6E0pLS6GqqoqffvqJilr37NnztbH5vmDMx3nz5mH9+vWYMGEC1NXVScS3/bhvbW1FcXExbGxsqJIE6wsmH3Pnzh1itV69ehUtLS0oLi6m/CtG9nnbu/2Qtk+YMIHuraioiMGDB9N3szYzMBYzk/sYMGAA1NXVySvLvOOtra2v9e/w4cNprP8WhcNLSkrofsXFxTSWO1priouLYWJigqtXr751rLO2Ax2/uzf9rj3e59r212hoaGD69Omoqqqi/1dWVmLatGkk2MtKKe7cufON8wgAVq9eTSQP6QT4OXPm4MqVK/SdmpqayM7OhqmpKXr16iXTzo7G9pvmWftnUVBQgIGBAfLy8mBoaAixWAwFBQX06NGDitC/Tz8yA0f6nTFJm549eyIzMxNpaWkyY1B67rzP/RQUFHD69OkOx2x1dXWHn3nT2vq+176rb9v3JytLx9aD920Pg3SfsDnMxOz79u0r0388fh06pWH34MEDqp8pDX9/f3Tr1u2100RLSwtRyzs6qX0I3qcY8ebNm5GdnQ0NDQ36vq1bt8owCFkyK6sPKBAIaAG6f/8+CVKmpqbS7xgLleHOnTt0gm5oaEBYWBiys7Ph7u6OEydOQFtbG7q6usSWVVRURFFRETIyMohswVh/FhYWSEtLg5qaGrXjwIED8PLygr6+Puzs7NC1a1ecOnVKhj3MWKpvYjSxvtDU1KS+YM/e/nN37tyBgYEBufybm5uJ5cnESVni9qxZs4gcIJFIXlMqZwxSaaYhSzDvCBcvXiRjhz0/a6dEIkGXLl1gY2ODgIAAmWoIHUF6jEgXtZZ+l78WjPko3RZpJiq7t/QYTE5OBse1FbFfsmQJkTnMzMyQlpZGY5KNUWlpElVV1d+MrbZ7926oqqpCVVWV2JpisZhCnYCsYXfhwgXk5ORQXV/GYpYGe3bWvxzHoU+fPtDU1KTN598pHC49fsvKyujf0vOA9Xn7OQ68eay3b39H4+Jtv/uQ+7zpmlevXr32GTavGNHh2LFj75xHV65cQVFRER06pcOE0u8nOTkZWlpaSElJ6bCdHY3ttz0vq+6ioaGBU6dO0VqhoqIis6Z+SD+yPpB+Z5cvX0ZRURFCQkJoDHb0rt/3fu3HbEftete4+dBr39W37fuz/TO9z3d01Cetra2UQqGqqorU1FTeqPsN0CkNu7t378rUz5RmB3l6enYY4vrqq686PFX9WrytGHFVVRWio6OJjg68ziBkzNvq6mpiETKwHAeWcMw2KMZCBV5ngrI+OXr0KBobG/HkyRN89NFH8Pf3R1RUFHls0tPTERQUhNzcXNjZ2aG6uhoff/wx5aQcPnxY5r4NDQ24evUqzp07h/r6+g7Zw2/zgHbUF2/63J07d5CYmCjDMmZh1oEDB8LCwgKZmZm4ceMGFZg3MTHpcKGRZk4DeI09KI0bN24gMTER9vb2MgcC6XY+ePAAd+/e/aC8TEboWLlyJU6fPv3ae/61aN8W6XfCwPq9qqoK5eXlmDx58mt9kZqaivj4eBqTbIxu2bKFJBx+S12pZ8+eYd68eVi6dCmOHz8OLy+vN76T2tpaREREwNLSkkKcHREf2LOz/t2yZctr4+3fKRwuPX6l/91Rn3fEEn7XHOnoPu/zu19zbftrOvoMm1esze87j6TFcaWNc/Yd27dvJ0mRt7XzfedZc3MzZsyYgbCwMFy6dImIVh2tqR/Sj6wPOnpn0mOwo3f9vvdrP2Y7atf7rK0feu3b+rZ9f/6a73hbn7S2tuLx48dvJS/xeH90SsMOeHv9TGlIM/n+m5DOZ2Bon1PANpuOkkhZCONd7CdpZm/7PmGMrLS0NLx8+ZIYWXl5ebhx4wYlxr6JhdfU1NShl+Nd7OH26Kgv3vQ5aWYiYxmzazdu3EjMRKAtb/Jt4bW3MafZM0oz5DoqOP1rDQJpvCns+1uio3tL5+K9qS/Gjh0rw7r84YcfALR5uX6LZ28P6Xu+evXqje+EMa3fhzncvn87Gm//DqTv1z53tD06yht6Vz++bVx8yJh5n2vbX9PRZxobG2Xa/LZ5xJjhLS0tqKmpeet3sr75reaB9NrI/v+mNfVDv/N9xv6H5Ii1v9/7rAkfMv9+q3Xqbc/0W/cJj1+PTmvYMbwPO+h/mVb9a57vTYwsAEhOTiY5gPdh4b0vc/O3hDQzEZBd/Biz9kPwPuzBNzFcOxvepy/el2Twn27Hn+Wd/K/gXe+MMfZ58ODxn0WnN+yA92cH/a/i1zzfmxhZAoEAc+fOfW8W3m8ROvw1eBOz1svLCxs3bvzg+70Pe/DPgj9KX/xR2sHj/fG2d/Z7rRU8ePzZIOT+BADAcRzHycnJcSYmJtyCBQu4efPmcadPn+ZcXV1/59b9+/g1z9f+MwsXLuTmzZvHnTp1ipswYQLXu3dvrr6+njt79izn5ubGCQQCDgCXnJws87tu3br9tx5TBmpqapyCggL9XyAQcIsXL+YaGho4Hx+fD76fQCB44zN27979t2z6Hx5/lL74o7SDx/vjbe/s91orePD40+H3syn/+/gQJtH/In7N872JkfXvsvD+m+iIWftr8Ud9xt8Df5S++KO0g8f7g39nPHj8fvhTeOwYwsPDOY7juOPHj3MeHh6/c2t+e/ya5+voM46OjtzZs2c5FxeX165/2+9+Lzg4OHC3b9/mvv7669/Ek/NHfMbfC3+UvvijtIPH+4N/Zzx4/D4QAP8/JvcnwdOnTzmJRPJ7N+M/hl/zfO0/A4ATCAQdXvu23/2eePXqlUxo9t/BH/UZfw/8Ufrij9IOHu8P/p3x4PH74E9n2PHgwYMHDx48eHRW/KlCsTx48ODBgwcPHp0ZvGHHgwcPHjx48ODRScAbdjx48ODBgwcPHp0EvGHHgwcPHjx48ODRScAbdjx48ODBgwcPHp0EvGHHgwcPHjx48ODRScAbdjx48ODBgwcPHp0EvGHHgwcPHlJIT0+nmqfSf65fv/57N40HDx483gm537sBPHjw4PFHQ0REBLdmzRqZn+nq6n7QPVpaWjiBQMAJhfz5mQcPHv898CsODx48eLSDoqIiZ2BgIPPnr3/9K+fs7MxJJBLOxMSEy83N5RobG+kza9eu5TQ0NLivvvqKc3Bw4BQVFbmbN29yL1++5MaPH88ZGxtzEomE8/T05I4cOfL7PRwPHjw6NXjDjgcPHjzeA0KhkFu8eDF38eJFbt26ddyhQ4e4CRMmyFzz7Nkzbu7cudzKlSu5ixcvcnp6elxeXh534sQJrqKigquurub69evHRUREcNeuXfudnoQHDx6dGXytWB48ePCQQnp6OrdhwwZOLBbTzyIjI7lt27bJXLd9+3YuJyeHa2ho4DiuzWOXkZHBVVZWcq6urhzHcdzNmzc5S0tL7ubNm5yRkRF9NiQkhOvZsydXWlr6X3giHjx4/JnA59jx4MGDRzsEBgZyn332Gf1fIpFwBw4c4MrKyrjLly9zjx8/5pqbm7kXL15wz54945SVlTmO4zgFBQXOxcWFPnf+/HmupaWF69q1q8z9X758yWlra/93HoYHDx5/KvCGHQ8ePHi0g0Qi4aytren/dXV1XExMDDdixAhu9uzZnJaWFvfNN99wmZmZ3KtXr8iwU1JS4gQCAX2usbGRE4lE3JkzZziRSCTzHSoqKv+dh+HBg8efCrxhx4MHDx7vwJkzZ7jW1lZu4cKFxHLdunXrOz/XvXt3rqWlhbt//z7Xu3fv/3QzefDgwYMnT/DgwYPHu2Btbc01NTVxn3zyCVdTU8N9/vnn3NKlS9/5ua5du3IpKSnc4MGDuR07dnC1tbXcyZMnubKyMm737t3/hZbz4MHjzwbesOPBgwePd8DV1ZX76KOPuLlz53JOTk7cxo0bubKysvf67Jo1a7jBgwdz48aN42xtbbmEhATu1KlTnKmp6X+41Tx48PgzgmfF8uDBgwcPHjx4dBLwHjsePHjw4MGDB49OAt6w48GDBw8ePHjw6CTgDTsePHjw4MGDB49OAt6w48GDBw8ePHjw6CTgDTsePHjw4MGDB49OAt6w48GDBw8ePHjw6CTgDTsePHjw4MGDB49OAt6w48GDBw8ePHjw6CTgDTsePHjw4MGDB49OAt6w48GDBw8ePHjw6CTgDTsePHjw4MGDB49OAt6w48GDBw8ePHjw6CT4f3KbQ+LSgeePAAAAAElFTkSuQmCC" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from safeds.plotting import histogram\n", + "\n", + "histogram(titanic_cleaned.get_column(\"Fare\"))\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-03-14T19:00:57.312507Z", + "end_time": "2023-03-14T19:00:58.343092Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from safeds.plotting import scatterplot\n", + "\n", + "scatterplot(titanic_cleaned, \"Travel Class\", \"Number of Parents or Children Aboard\")\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-03-14T19:00:58.343092Z", + "end_time": "2023-03-14T19:00:58.457212Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-03-14T19:00:58.472493Z", + "end_time": "2023-03-14T19:00:58.473207Z" } } } diff --git a/mkdocs.yml b/mkdocs.yml index 3fbf20681..0cbaa0d88 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -7,7 +7,6 @@ nav: - Tutorials: - Data Processing: tutorials/data_processing.md - Data Visualization: tutorials/data_visualization.md - - Data Visualization Notebook: tutorials/data_visualization_notebook.ipynb - Machine Learning: tutorials/machine_learning.md - API Reference: reference/ From ee0b287334c25242833a371558a27e06b376cab1 Mon Sep 17 00:00:00 2001 From: Lars Reimann Date: Tue, 14 Mar 2023 19:12:51 +0100 Subject: [PATCH 5/7] chore: ignore generated reference pages --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 1fdfac9a5..9d4da087a 100644 --- a/.gitignore +++ b/.gitignore @@ -34,6 +34,8 @@ coverage.xml # mkdocs /site/ +/docs/reference/safeds/ +/docs/reference/SUMMARY.md # MegaLinter report/ From df5732b3b25d517a25e2fe1947cfff121405ca53 Mon Sep 17 00:00:00 2001 From: Lars Reimann Date: Tue, 14 Mar 2023 19:14:11 +0100 Subject: [PATCH 6/7] fix: import --- docs/tutorials/data_visualization_notebook.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/data_visualization_notebook.ipynb b/docs/tutorials/data_visualization_notebook.ipynb index 3e3a6140a..8b85a9728 100644 --- a/docs/tutorials/data_visualization_notebook.ipynb +++ b/docs/tutorials/data_visualization_notebook.ipynb @@ -18,7 +18,7 @@ "execution_count": 1, "outputs": [], "source": [ - "from safeds_examples.titanic import load_titanic\n", + "from safeds_examples.tabular import load_titanic\n", "\n", "titanic = load_titanic()" ], From 496f7f5fc40b23a78ba1f6bad8da0be88d29c217 Mon Sep 17 00:00:00 2001 From: Lars Reimann Date: Tue, 14 Mar 2023 19:16:11 +0100 Subject: [PATCH 7/7] chore: remove empty cell --- docs/tutorials/data_visualization_notebook.ipynb | 13 ------------- 1 file changed, 13 deletions(-) diff --git a/docs/tutorials/data_visualization_notebook.ipynb b/docs/tutorials/data_visualization_notebook.ipynb index 8b85a9728..a76aad886 100644 --- a/docs/tutorials/data_visualization_notebook.ipynb +++ b/docs/tutorials/data_visualization_notebook.ipynb @@ -306,19 +306,6 @@ "end_time": "2023-03-14T19:00:58.457212Z" } } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-03-14T19:00:58.472493Z", - "end_time": "2023-03-14T19:00:58.473207Z" - } - } } ], "metadata": {