From 385bd2da20fa065449d7e70ef639ad4c64356b75 Mon Sep 17 00:00:00 2001 From: EhsanGharibNezhad Date: Wed, 8 Nov 2023 12:28:10 -0800 Subject: [PATCH 1/3] add the whole Sphinx docs files to main --- docs/Makefile | 20 + docs/cite.rst | 9 + docs/code.rst | 40 + docs/conf.py | 103 + docs/figures/All_infographs.pdf | Bin 0 -> 12159179 bytes docs/figures/TelescopeML_modules.png | Bin 0 -> 173975 bytes docs/figures/convolution_graph.png | Bin 0 -> 32030 bytes .../convolution_graph.001.png | Bin 0 -> 32030 bytes docs/index.rst | 47 + docs/installation.rst | 181 + docs/knowledgebase.rst | 9 + docs/make.bat | 35 + docs/publications.rst | 2 + docs/tutorials.rst | 37 + .../1__BrownDwarf_Data_Exploration.ipynb | 816 +++ ...Models_Predict_BrownDwarf_Parameters.ipynb | 5766 +++++++++++++++++ docs/tutorials/3__Train_CNN_Model.ipynb | 2625 ++++++++ ...oncepts_ML_knowledge_Train_CNN_Model.ipynb | 1984 ++++++ 18 files changed, 11674 insertions(+) create mode 100644 docs/Makefile create mode 100644 docs/cite.rst create mode 100644 docs/code.rst create mode 100644 docs/conf.py create mode 100644 docs/figures/All_infographs.pdf create mode 100644 docs/figures/TelescopeML_modules.png create mode 100644 docs/figures/convolution_graph.png create mode 100644 docs/figures/convolution_graph/convolution_graph.001.png create mode 100644 docs/index.rst create mode 100644 docs/installation.rst create mode 100644 docs/knowledgebase.rst create mode 100644 docs/make.bat create mode 100644 docs/publications.rst create mode 100644 docs/tutorials.rst create mode 100644 docs/tutorials/1__BrownDwarf_Data_Exploration.ipynb create mode 100644 docs/tutorials/2__Deploy_ML_Models_Predict_BrownDwarf_Parameters.ipynb create mode 100644 docs/tutorials/3__Train_CNN_Model.ipynb create mode 100644 docs/tutorials/Concepts_ML_knowledge_Train_CNN_Model.ipynb diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 00000000..0a538e9b --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = ../../TelescopeML_docs/TelescopeML_docs + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/cite.rst b/docs/cite.rst new file mode 100644 index 00000000..01d4d4a9 --- /dev/null +++ b/docs/cite.rst @@ -0,0 +1,9 @@ +Cite: +====== + +(Under construction) Please cite the following papers if you are using the ``TelescopeML`` package or any of its concepts, modules, or +functions in your work: + +1. Will be updated after publishing the package! + +2. Will be updated after publishing the first case study! \ No newline at end of file diff --git a/docs/code.rst b/docs/code.rst new file mode 100644 index 00000000..9adc07de --- /dev/null +++ b/docs/code.rst @@ -0,0 +1,40 @@ +The Modules +============ + + +TelescopeML\.DeepBuilder Module +-------------------------------- + +.. automodule:: TelescopeML.DeepBuilder + :members: + :undoc-members: + :show-inheritance: + + +TelescopeML\.DeepTrainer Module +-------------------------------- + +.. automodule:: TelescopeML.DeepTrainer + :members: + :undoc-members: + :show-inheritance: + +TelescopeML\.Predictor Module +------------------------------------ + +.. automodule:: TelescopeML.Predictor + :members: + :undoc-members: + :show-inheritance: + + +TelescopeML\.StatVisAnalyzer Module +------------------------------------ + +.. automodule:: TelescopeML.StatVisAnalyzer + :members: + :undoc-members: + :show-inheritance: + + + diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 00000000..152f0f12 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,103 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys +sys.path.insert(0, os.path.abspath('/Users/egharibn/RESEARCH/ml/projects/TelescopeML_project/TelescopeML/')) +autodoc_mock_imports = ["sklearn", "tensorflow", "bokeh", "matplotlib", "ipython3"] + + +# -- Project information ----------------------------------------------------- + +project = 'TelescopeML' +copyright = '2023, Ehsan (Sam) Gharib-Nezhad' +author = 'Ehsan (Sam) Gharib-Nezhad' + +# The full version, including alpha/beta/rc tags +release = '0.0.2' + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ 'sphinx_copybutton', + 'sphinx.ext.autodoc', + 'sphinx.ext.coverage', + 'sphinx.ext.napoleon', + 'sphinx_gallery.load_style', + 'nbsphinx', + 'sphinx.ext.mathjax', + ] + + +nbsphinx_allow_errors = False + +nbsphinx_execute = 'always' + + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The master toctree document. +master_doc = 'index' + + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This patterns also effect to html_static_path and html_extra_path +exclude_patterns = ['**.ipynb_checkpoints'] + +#exclude_patterns = [] + + +# -- Options for HTML output ------------------------------------------------- + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_rtd_theme' + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + + +# Add any paths that contain custom themes here, relative to this directory. +html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] + +# ------------------ + +# Output file base name for HTML help builder. +htmlhelp_basename = 'TelescopeMLdocs' + +highlight_language = 'none' + +html_theme_options = { + 'display_version': True, + 'prev_next_buttons_location': 'both', + 'sticky_navigation': False, + 'navigation_depth': 3, + 'includehidden': True, + 'titles_only': False, +} + + +#nbsphinx_prolog = """ +#{% set docname = env.doc2path(env.docname, base=None) %} +#.. note:: `Download full notebook here `_ +#""" +###.. only:: html diff --git a/docs/figures/All_infographs.pdf b/docs/figures/All_infographs.pdf new file mode 100644 index 0000000000000000000000000000000000000000..cb6f261bd89557dc7792faca1815ed84285fa704 GIT binary patch literal 12159179 zcmV(_K-9k_P((&8F)lL-CB)_ObY*fNFGg%( zbY(HAV%78Q@czT`+kyWaZ|^_-{Pw#){PFny z=Rbnl@BZ`qzxg-szx&(Y|Hr?1KfeFlfBXLL{^s|C@xQ*y>2UvcJlw81#PNFhcK^5? 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TelescopeML documentation master file, created by + sphinx-quickstart on Tue Dec 27 15:39:09 2022. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +TelescopeML +============== + + +``TelescopeML`` is a Python package comprising a series of modules, each equipped with specialized machine learning and +statistical capabilities for conducting Convolutional Neural Networks (CNN) or Machine Learning (ML) training on +datasets captured from the atmospheres of extrasolar planets and brown dwarfs. The tasks executed by the ``TelescopeML`` +modules are outlined below: + + - *StatVisAnalyzer*: Explore and process the synthetic datasets (or the training examples) and perform statistical analysis. + - *DeepBuilder*: Specify training and target features, normalize/scale datasets, and construct a CNN model. + - *DeepTrainer*: Create an ML model, train the model with the training examples, and utilize hyperparameters. + - *Predictor*: Train the module using specified hyperparameters. + +or simply... + + - Load the pre-trained CNN models based on the latest synthetic datasets + - Predict the stellar/(exo-)planetary parameters + - Report the statistical analysis + +.. image:: figures/TelescopeML_modules.png + :width: 1100 + + + + + +====================== + +.. toctree:: + :maxdepth: 1 + :hidden: + + Installation + Tutorials + The Code + KnowledgeBase + Github + Publications + What to Cite + + diff --git a/docs/installation.rst b/docs/installation.rst new file mode 100644 index 00000000..cdd9cde2 --- /dev/null +++ b/docs/installation.rst @@ -0,0 +1,181 @@ +Installation +============= + +.. toctree:: + :maxdepth: 1 + :caption: Contents: + + + +.. note:: + `TelescopeML` requires python >= 3.8. + + +Step 1: Create your directory structure +---------------------------------------- +Let’s start by creating the folder structure. Here is the folder structure of +an example project, named TelescopeML_project with two sub-directories: *reference_data* and +*notebooks*. + + +| TelescopeML_project +| ├── reference_data +| │ ├── training_datasets +| │ ├── observational_datasets +| │ ├── figures +| │ └── trained_ML_models +| └── notebooks +| + +.. note:: + You need to be inside the `TelescopeML_project` directory if you want to follow method 2 + (i.e., `Install with Git`) and clone the github repository. + + +Step 2: Download Input Data and Trained ML Models +-------------------------------------------------- +First things first, after creating the home directory, you need to download the following datasets, 1--4, +as well as the trained CNN models to fully utilize the code and apply it to your datasets. + + + 1. for **training_datasets/** directory: `Link to the pre-trained ML models to deploy `_ + 2. for **observational_datasets/** directory: `Link the synthetic Brown-dwarf training datasets `_ + 3. for **trained_ML_models/** directory: `Link to the observational telescope spectra for few brown dwarfs `_ + 4. for **notebooks/** directory: `Download the Jupyter notebook tutorials to deploy the ML models `_ + + + +Step 3: Install the Package +---------------------------- + +Method 1: Install via Pip (Straightforward) ++++++++++++++++++++++++++++++++++++++++++++ +The easiest way to install the most stable version is with *pip*, the Python package manager, +but do not forget that you still need to create a virtual environment using the `Anaconda distribution `_ +and then install ``TelescopeML`` there by the following steps: + +1. Create a conda virtual environment using `python>=3.8`: + +.. code-block:: bash + + conda create --name TelescopeML python=3.9 + +2. Activate the new environment: + +.. code-block:: bash + + conda activate TelescopeML + +3. Now install `TelescopeML` via pip: + +.. code-block:: bash + + pip install TelescopeML + + + +Method 2: Install with Git (Recommended) ++++++++++++++++++++++++++++++++++++++++++++ +If you want to access the latest features or modify the code and contribute, we suggest that you clone the source code +from GitHub by following steps: + +1. Clone the repo and Create `Conda` environment named *TelescopeML*: + +.. code-block:: bash + + git clone https://github.com/ehsangharibnezhad/TelescopeML.git + cd TelescopeML + conda env create -f environment.yml + + + +2. Activate the new environment: + +.. code-block:: bash + + conda activate TelescopeML + +3. Install the library via the `setup.py` file: + +.. code-block:: bash + + python3 setup.py develop + + +Now, you should have the package installed alongside the trained models and telescope datasets. + +Step 4: Set input file environment variables +--------------------------------------------- + + +For Mac OS +++++++++++++++++++++++++++++++++++++++++++++ + +follow the following steps to set the link to the input data: + +1. Check your default shell in your terminal: + +.. code-block:: bash + + echo $SHELL + +This command will display the path to your default shell, typically something +like `/bin/bash` or `/bin/zsh`, or `/bin/sh`. + +2. Set the environment variables : + + * If your shell is `/bin/zsh`: + + .. code-block:: bash + + echo 'export $TelescopeML_reference_data="/PATH_TO_YOUR_TelescopeML_project/" ' >>~/.zshrc + source ~/.zshrc + echo $TelescopeML_reference_data + + + * if your shell is `/bin/bash`: + + .. code-block:: bash + + echo 'export $TelescopeML_reference_data="/PATH_TO_YOUR_TelescopeML_project/"' >>~/.bash_profile + source ~/.bash_profile + echo $TelescopeML_reference_data + + * if your sell is `/bin/sh`: + + .. code-block:: bash + + echo 'export $TelescopeML_reference_data="/PATH_TO_YOUR_TelescopeML_project/"' >>~/.profile + source ~/.profile + echo $TelescopeML_reference_data + + +.. note:: + - Replace `PATH_TO_YOUR_TelescopeML_project` with the actual path to your TelescopeML directory. + - *echo* command is used to check that your variable has been defined properly. + + +For Linux +++++++++++ +In Linux, the choice between `~/.bashrc` and `~/.bash_profile` depends on your specific use case and how +you want environment variables to be set, but `~/.bashrc` is a common and practical choice for +modern Linux system. + +.. code-block:: bash + + echo 'export $TelescopeML_reference_data="/PATH_TO_YOUR_TelescopeML_project/" ' >>~/.bashrc + source ~/.bashrc + echo $TelescopeML_reference_data + + +Replace `PATH_TO_YOUR_TelescopeML_project` with the actual path to your TelescopeML directory. + +For Windows +++++++++++++ + +For Windows users, we recommend installing Windows Subsystem for Linux (WSL) before proceeding. +WSL is a compatibility layer for Windows that allows you to run a Linux distribution alongside +your Windows installation. + + + diff --git a/docs/knowledgebase.rst b/docs/knowledgebase.rst new file mode 100644 index 00000000..a9603152 --- /dev/null +++ b/docs/knowledgebase.rst @@ -0,0 +1,9 @@ +KnowledgeBase +=============== + +ML Concepts: Data processing and Model Training in CNN +------------------------------------------------------- +.. toctree:: + :maxdepth: 2 + + Neural Network Arch diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 00000000..954237b9 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/publications.rst b/docs/publications.rst new file mode 100644 index 00000000..4ced0874 --- /dev/null +++ b/docs/publications.rst @@ -0,0 +1,2 @@ +Publications: +============== \ No newline at end of file diff --git a/docs/tutorials.rst b/docs/tutorials.rst new file mode 100644 index 00000000..f7900072 --- /dev/null +++ b/docs/tutorials.rst @@ -0,0 +1,37 @@ +Tutorials +========== + +Exploring the Datasets +----------------------- +.. toctree:: + :maxdepth: 3 + + Brown Dwarf Synthetic Dataset + + + + + +Predict Atmospheric Parameters +------------------------------- +.. toctree:: + :maxdepth: 3 + + Deploy CNN Model to Predict Brown Dwarf Atmospheric Parameters + + + + + + +Train a Regression ML Model +---------------------------- +.. toctree:: + :maxdepth: 3 + + Train a Regression ConvNN Model using the BOHB Tuned Hyperparameters + + + + + diff --git a/docs/tutorials/1__BrownDwarf_Data_Exploration.ipynb b/docs/tutorials/1__BrownDwarf_Data_Exploration.ipynb new file mode 100644 index 00000000..ce202731 --- /dev/null +++ b/docs/tutorials/1__BrownDwarf_Data_Exploration.ipynb @@ -0,0 +1,816 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "260a5d61-ff91-48fc-af4e-1f5a22ea4483", + "metadata": {}, + "source": [ + "# 1. Exploring the Brown Dwarf Synthetic Dataset" + ] + }, + { + "cell_type": "markdown", + "id": "723c394d-987d-4f2d-8bd6-378a23b1218e", + "metadata": { + "tags": [] + }, + "source": [ + "\n", + "\n", + "In the following steps, you will: \n", + "\n", + "- Load the brown dwarf dataset used to train the ML models\n", + "- Check the variables and parameters in the dataset\n", + "- Visualize them for few cases" + ] + }, + { + "cell_type": "markdown", + "id": "32491e1e-619b-4553-a77c-2b370d90fd05", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "aeb60d40-df78-4734-9a88-ab41b2e18ad9", + "metadata": {}, + "source": [ + "Before going through this tutorial, make sure to check you have installed `TelescopeML` successfully as discussed in this [installation link](https://ehsangharibnezhad.github.io/TelescopeML/installation.html). \n", + "\n", + "In a nutshell: \n", + "\n", + " [1] You are at **TelescopeML_project/notebooks** directory because you have created your directory structure \n", + "\n", + " [2] The trained ML models and datasets are now in your **TelescopeML_project/reference_data** directory.\n", + "\n", + " [3] Path is defined to your **reference_data**. Confirm it by os.getenv(\"TelescopeML_reference_data\") \n", + " \n", + " [4] And last, you should be able to execute `import TelescopeML` with no issue!\n", + "\n", + "Happy *TelescopeML*ing! ;) " + ] + }, + { + "cell_type": "markdown", + "id": "8a80b0be-236f-4635-bef8-96ffe1e6462e", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "6a0e9b08-df67-4377-9ad7-61865ed8f859", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's first import libraries we need in this tutorial!\n", + "from TelescopeML.StatVisAnalyzer import *" + ] + }, + { + "cell_type": "markdown", + "id": "98309fb6-0ed0-4f20-a65e-45b54910a8c8", + "metadata": { + "tags": [] + }, + "source": [ + "## 1.1 Load the Synthetic spectra\n", + "\n", + "We computed a low-resolution spectrum ($R$=200) utilizing atmopshric brown dwarfs grid model, [*Sonora-Bobcat*](https://arxiv.org/pdf/2107.07434.pdf) for spectral range $\\sim$0.9-2.4 $\\mu m$. An open-source atmospheric radiative transfer Python package, [*PICASO*](https://natashabatalha.github.io/picaso/) was employed for generating these datasets. This dataset encompass 30,888 synthetic spectra (or instances or rows). \n", + "\n", + "Each spectrum has 104 features (or wavelengths in the astrophysics context, i.e., 0.897, 0.906, ..., 2.512~$\\mu$m) and 4 parameters:\n", + "- gravity ( log$g$ ) \n", + "- temperature ( T$_{eff}$ )\n", + "- carbon-to-oxygen ratio ( C/O )\n", + "- metallicity ( [M/E] )" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "cc47b0e4-8644-49fc-89e0-fd5d39b0df5f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/egharibn/RESEARCH/ml/projects/TelescopeML_project/reference_data/'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read the `TelescopeML_reference_data` path\n", + "\n", + "import os # to check the path \n", + "\n", + "__reference_data_path__ = os.getenv(\"TelescopeML_reference_data\")\n", + "__reference_data_path__" + ] + }, + { + "cell_type": "markdown", + "id": "b438caa7-be1e-48d4-a0de-2edfee40bcbc", + "metadata": {}, + "source": [ + "### Load the dataset and check few instances " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "68bfc730-ea01-488a-ae40-ad33704a34b4", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "

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        " + ], + "text/plain": [ + " wl\n", + "0 2.511960\n", + "1 2.486966\n", + "2 2.462220" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wl_synthetic = pd.read_csv(os.path.join(__reference_data_path__, \n", + " 'training_datasets', \n", + " 'wl.csv'))\n", + "wl_synthetic.head(3)" + ] + }, + { + "cell_type": "markdown", + "id": "42a6b2d7-98f1-4a2c-8377-f2299f1d70de", + "metadata": { + "tags": [] + }, + "source": [ + "## 1.4 Visualize the Brown Dwarf spectra for different parameters\n" + ] + }, + { + "cell_type": "markdown", + "id": "312be00f-4549-4646-a163-09c71b4ff71c", + "metadata": {}, + "source": [ + "### 1.4.1 Effective Temperature \n", + "\n", + "Plot the brown dwarf datasets for $T_{eff}$= 400-1800K while other paremeters are constnat (log$g$=5, C/O=1, [M/E]=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "73d7fc34-4306-4f6e-a8f1-74d8df4ea484", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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5TdONrP7whz/kb/7mb/jUpz5Vsryvr2/WMtmBQIBUKjVl+UzkZXI7zuX1zRG9zs7OKf/r6OgAxDgvLy9HKTVtPlBXV1fJ37l+Px25+t3vfsfKlSvzBPjBBx+cIn89E1pbW6cVoSjGbDwlACtXruTuu+8mm82WGLE5UYzZSnDnYJom11xzDb/5zW9oa2ub1SDIT3/6UxYvXlxyLNM0ufrqq9m4ceOM+2lra+Opp57ihhtumDE/CM78tT1XuOOOO/joRz/Kt771LT75yU/OuN501+9M4lSv3+n2rTPdN124cHF6cImQCxcOQqEQV199NVu3bmXVqlUlIUKTsWHDBn7yk5/wyU9+kqqqqmnXySlLzdbT8NrXvpb3ve993HXXXRw6dIiGhgZe8pKXnFL7Tgfn6jjF+K//+i/Wrl2b//t//ud/yGazeTW/M9UmpdQUxa97772X9vZ2Fi5cmF92vHs3d+5cenp66O7uzntL0uk0v/3tb2fVhnN5fcPhMJdeeik/+9nP+PznP5/3oti2zQ9/+EMaGxtZvHgxSinWrVvHz3/+cz7/+c/n2zQ2Nsavf/3rkn3W1NQAIvhQjN/85jf88Y9/5C1veUt+2ZkM3xocHOTXv/41a9asmeJpnIybbrqJ7373u/z0pz/Ni0EA/OAHP6C+vp5LL710Vm0qxoc+9CHuu+8+3vKWt/CLX/xiyn3LZDJs3LiRl7/85YAY8rfccsu0+/nNb37DO97xDu65554SsmNZFm9/+9vRWvOhD33ouO15PobGATQ0NPCBD3yAvXv3cvvtt8+43kzX70zhVK/f6fats9E3XbhwcepwiZCL5zV27tw5rXrQggULTil/5ytf+QpXXnklV111FW9/+9uZO3cuo6OjHDhwgF/96lf54qNf/OIXufLKK7n00kv5P//n/7Bw4UK6u7v55S9/ybe//W2i0SgrV67M7/P222/H6/WyZMmSGQsHlpWVcdNNN3HXXXcxNDTE+9//fgyjVOF+tu07XZyr4+Tws5/9DI/Hw7XXXptXjVu9enWJIXQm2nTjjTdy1113ccEFF7Bq1SqeeuopPve5z00ZmT/evbv11lv56Ec/ym233cYHPvABkskk//qv/zprBaozdS6zxac//WmuvfZarr76at7//vfj8/n4xje+wc6dO7n77rvz3pV//ud/5mUvexnXXXcd7373u7Esi8997nNEIpF82A6Iqtkll1zCF7/4Raqrq1m+fDmPPvoo3/3udwG45JJL8utGo9FTCl193eteR3NzM+vWraOqqor9+/fzhS98ge7u7in5Sg8++CDXXHMNH/3oR/noRz8KwA033MC1117L29/+dkZGRli4cCF33303Gzdu5Ic//OEUT4tSig0bNuQl0qfD+vXr+eY3v8k73vEO1q5dy9vf/naWL19OJpNh69atfOc732HFihW8/OUvZ9u2bRw8eHDasK4rrriCL3/5y7znPe/hyiuv5J3vfCfNzc35gqqPP/44X/7yl0+oHHaq1xaEtI6PjzM6OgoIqf3f//1fQPLUiouizubanCxOJA1+vOt3pnCq1+9k+taZ6JsuXLg4y3hOpRpcuDhFnEg96Lvf/a7W+uRV47TWurW1Vb/pTW/SDQ0N2uv16urqan355ZfrT3ziEyXr7d69W7/mNa/RlZWV2ufz6ebmZn3HHXeUFFv80Ic+pOvr67VhGBrQ999/f0n7J6s5/e53v8ufw759+6Y999m270Q4UUHV2Rwnp4zW29s7q31v2LBBL1++fMr2Tz31lH75y1+uI5GIjkaj+rWvfe20ilmn0yattR4cHNRvfvObdU1NjQ6FQvrKK6/UDz/8sN6wYcMUxaqZ7p3WWt933316zZo1OhgM6vnz5+uvfe1rM6rGTdeO2Z7LTDjZgqoPP/ywftGLXqTD4bAOBoP6sssu07/61a+mbH/PPffolStX5vvzZz7zGf2ud71Ll5eXT2n79ddfryORiK6urtZ33HGH/t73vqcBfejQoRO2/0T49Kc/rdesWaPj8bg2TVNXV1frm266ST/xxBNT1s09y5OVz0ZHR/W73vUuXVdXp30+n161apW+++67p2w/Ojp6Uopw27Zt07fffrtubm7WPp9Ph8NhfeGFF+qPfvSjeRXJ//t//++M6mU5bNq0Sb/61a/WtbW12uPx6JqaGn3zzTfrxx57bFbtOB20tLTMSiFtttdmtqpxx0OxatxM1+98KKiq9ez71un2zbPRdhcuXJRCaX0SwvsuXLhwcQZx55138vGPf5ze3t4ZQwxdTMUdd9zBAw88wIEDB/J1s84GMpkMa9asoaGhgd/97nczrpdIJLjmmmuIxWJs3LjxrLTlbOG+++7jxhtvZPv27XlP4Oli2bJl3HDDDcdVRHs+YLbX5kz3xzN5/bRTL+g//uM/ePOb38yWLVvOmMDO2cbzue0uXDxf4IbGuXDhwsXzEEeOHMHr9bJ8+XJ27tx5Rvb55je/mWuvvZY5c+bQ1dXFt771Lfbs2cNXvvKV/DpPPvkk//u//8sVV1xBNBpl3759fPWrX6Wvr49HH330jLTjXOL+++/ntttuO2MkCKbmTz1fcTLX5kz2xzN5/X7xi19w0003nbH9nUs8n9vuwsXzBa5HyIULF88ZXI/QqeHw4cN5lbpgMMjy5cvPyH5vueUWHnvsMXp7e/F6vVx00UV8+MMf5vrrr8+v88ADD/CP//iPHDhwgEQiQX19PS996Uv5yEc+Mq2SnIs/f5yt/ngmMDQ0xIEDB/J/L1u2rCQH6nzG87ntLlw8X+ASIRcuXLhw4cKFCxcuXPzFwTjxKi5cuHDhwoULFy5cuPhzwUMPPcTLX/5y6uvrUUrx85//vOT/Y2NjvPOd76SxsZFgMMjSpUv55je/WbJOKpXiH/7hH6iqqiIcDvOKV7xiSo3FwcFB3vCGNxCPx4nH47zhDW9gaGjoLJ/d7OESIRcuXLhw4cKFCxcu/oIwPj7O6tWr+drXvjbt/9/73vfmZd337NnDe9/7Xv7hH/6BX/ziF/l13vOe93DPPffw3//93zzyyCOMjY1x4403lpSTeN3rXse2bdvYuHEjGzduZNu2bbzhDW846+c3W7ihcS5cuHDhwoULFy5c/IVCKcU999zDq171qvyyFStWcOutt/KRj3wkv2zt2rW89KUv5V/+5V8YHh6murqa//zP/8wXB+7o6KCpqYn77ruP6667jj179rBs2TI2b96cLxa8efNm1q9fz969e1myZMk5Pc/p4KrGnSXYtk1HRwfRaDRfsNCFCxcuXLhw4cLF+QOtNaOjo9TX108pYv5cI5lMkk6nZ72+1nqKzen3+/H7/Sd97CuvvJJf/vKXvOlNb6K+vp4HHniAffv25VVEn3rqKTKZDC95yUvy29TX17NixQoee+wxrrvuOjZt2kQ8Hs+TIIDLLruMeDzOY4895hKhP2fkWLELFy5cuHDhwoWL8xvHjh2jsbHxuW5GHslkknnzGujqGpj1NpFIhLGxsZJlH/vYx7jzzjtP+vj/+q//ylve8hYaGxvxeDwYhsG//du/ceWVVwLQ1dWFz+ejvLy8ZLva2lq6urry69TU1EzZd01NTX6d5xouETpLiEajgDxYsVjsOW6NCxcuXLhw4cKFi8kYGRmhqakpb7edL0in03R1DXC49b+JxU4smz4yMsHcebdNsTtPxRsEQoQ2b97ML3/5S1paWnjooYd4xzvewZw5c3jxi18843aTvVLTRUVN57l6ruASobOE3A2OxWIuEXLhwoULFy5cuDiPcb4Y5pMRC/mIhWZBZrJZWf8M2J2JRIIPf/jD3HPPPbzsZS8DYNWqVWzbto3Pf/7zvPjFL6auro50Os3g4GCJV6inp4fLL78cgLq6Orq7u6fsv7e3l9ra2tNq45nC+RUM6cKFCxcuXLhw4cKFC4HWs5/OEDKZDJlMZkrOlGma2LYNiHCC1+vl97//ff7/nZ2d7Ny5M0+E1q9fz/DwME888UR+nccff5zh4eH8Os81XI+QCxcuXLhw4cKFCxfnI2wNDvk44XongbGxMQ4cOJD/u7W1lW3btlFRUUFzczMbNmzgAx/4AMFgkJaWFh588EH+4z/+gy9+8YsAxONx3vzmN/OP//iPVFZWUlFRwfvf/35WrlyZD51bunQp119/PW95y1v49re/DcBb3/pWbrzxxvNCKAFcIuTChQsXLly4cOHCxfkJ254lEZrFOkV48sknufrqq/N/v+997wPg9ttv56677uK///u/+dCHPsTrX/96BgYGaGlp4ZOf/CRve9vb8tt86UtfwuPxcMstt5BIJLjmmmu46667ME0zv85//dd/8a53vSuvLveKV7xixtpFzwXcOkJnCSMjI8TjcYaHh90cIRcuXLhw4cKFi/MQ56u9lmvXwNHZiyVUNN923p3H+Q7XI+TChQsXLly4cOHCxfmIs+QRciFwiZALFy5cuHDhwoULF+cj9CyJkHaJ0KnAJUIuXPwZQ9spsMbQdgJ0BnS2MMcAwwvKC8qHMnzgqUAp97XgwoULFy5cnA9Q2kbNguTMZh0XU+FaPC5cnCfQ2gJrHLCdkR0b0GDGhaTMZh92CrL9aGvUIUDpyWvkVgSlwFKAKvxHtaI8lShfHcqMnJHzcnFuIOmeFtgp0GmwU2idBjuN9CUA7dzoXGqoBdpy+pvzG8AIgRmRPmBGQPnP2xobLly4cPFnDTc07qzCJUIuXDxH0NoGaxRtDUN2GG2NTbueMgIQXjmjp0bbGSE/mT4hQADYYGdl0hRepNou2MAgZMj0guEBpcE00ZledKYXZYZR/iaUp3yao7p4LqB1FqwJsJNonRKS48y1Tp250AhrVPqm86dSHrQZRXmrHK+hW4LOhQsXLs4JspZMs1nPxUnDJUIunrfQWoM9BtYE2p4AewIA5a1zjLXzbwRbyM8wOtMP2QHxAuX/aTkkJWdkKtApWSd5EAKLp5yTTrVjp446f9lgJaW6tGWBNkArFKqwP8yi3453wLYAC40GpVG+INpjoq1x9MSzGJGLZu2RcnFmIH07AfY42nL6tj0xjYdvKpTygOEH5ZOwRwyZlDNhOERGoSl4BAsF+TQYzvGtUbQ1IQQsO4jODqKUifZWobzVYETOy+fMhQsXLv5s4HqEzipcIuTieQettXhAUm2S+zL5/9kRlBEEfwN4Ks+L0Wuts+jUMcj0iVEpS0GDshU6k4Jsqoi0OGsoGzxZtFIoMwa+Oc7+NDp9DJ1qBzsjxCeTBNtAaSWRUEoBBlg43iGnKJu2UNpGG17wBsDjB48XheNtSKUgpcHvAY8Psv3547o489Bai1fHGhWvoDWOtsdn9O4ow4dWQVSO5GiH1Noa7CzKSqOzSbCGJEyOqRUSNEpIkban/T8Y4I2ifGXgrZZ+oCcg0yNkLN2NTnfLc+atAW81yvCesWviwoULFy4caD07b79bDeeU4BIhF88baG3LqHS6TUbJcUawtYGysuisY/T5vGg0OnEAZXZCaDlKmSfa/Vlqs0PakofROuMsNYX8pMbASkOe/hjgiYJh5m1TlRlGZy0wRrE5jGFGwIigU0fR6XbIjEEmi7I9Qn6sNKRTQog4/ki9sjOQykBqNL9M+0IQiqGsMSFndgLb7MF0idAZgxCfJGRH0NYIWCMzeHoMEbHAFLJj22BlwUqiskMIw51m/9PtByjkCTlrFXsjUaBMmdAScpcZRmeGC6uYQQjUogIxsEfR2QG0nUCnjkDqKMpbifLWghl1vUQuXLhwcabgeoTOKlwi5OK8Rb7WrzWKzvZBpr/gTdE2pNOQSecN/pzppRMp8Njg96GtcTHSAvPOffvtFDrZis4OOgsUKqvQqaFCe5UXvGVioyZHUaNdFJuyGgs8FqgwGAl0Yh94ytHpLsiOQzqLytpCgFJJwBDDWZngCRSFRuF4DorC5EwPeH1gKDGKx7tR6QlIT6AjtWKs6zHIDKKtCZR54oJuLqaHqPcNo7PDMrczU1dSfpQ2wLLQVgoyoydWAVKmhMGZPpThB9MPZkBIixmQyfChlHIIWE4UwckXy5EfZebJi9YarASkhyE9hM4MCeG2EjB+GMYBXxkqUAc+jyPOMS45apk+8RL5asVL5CoQupgBWtuOgqUj6KEzIu6R7/NOmKaszFSKX0S288TbFE9pXg2zMLnk3MXzFrbj8Z/Nei5OGu5XysV5AxldPorODDJ9uA5isGU1OjHqEAkP+CrEGLQsGS23RyGbFWPSZ4p3yFOB8sRPvW06g053oowQeMpOaOBpK4Ge2JHPAVKWB50YIO/98cZBBSAxiho5Ov35Gh4Jc8ta6GTaiYLySrhTdhxSGVRiHDIahdchOh7IpCE1BozM/gQNE0JlEKyEZD9qrBuMDDrsAzPliCe0zH5/f+GQnJoRtDUkQhh2ctIaSrw9NuhsGtJjKD1a/F8HJnhCKE8QzBB4QuAJCtExfCjD4xAcS0Iks0nIjMPEkMzT45CdQBse2dYblO29ISFN2pbt7AzayspvgEAZBKtQoTmSTWZnIdUHEx3oVD+kh2TCRAVrIDgX9AQ62yfPcfKwDEB4KlG+WjeX6C8IWlsOsUnniY62M4W/86Qne+KdncrxZ1iulMfxeBZy5abOnUEBTCenzoTcwJLyFJGq5z7c2sVfEFyP0FmFS4RcPOfQdkbC3dLdTPsZUyZKBR1CMAh2RgxFTxwyNmqoSwzA3P68YYhVotID6EwajCQ6eQDCq09phFprCbPT2SGndQrliaM8Fc6otzF1/eQhMQiUH5JJyAwKBQrWS9ja8DHITBQ28schUAm2453JpMFKQXIQdAaFhfZ4QY2IAZvKopIJOX/LkBHRREIS3J02Ypjgj4IvLAaw6ZVlylk/NQaJIZlsC8b6YbwfKlpAZSA1AMkx8BiiJOdvdo3ZGZAT7tDZYcgOFan3FUH5hdhm0uj0GEoLUc1fUeUBbxQ8YdBOeGQ2IyGMyQzY/WB3g87mFQF1ThlwNkidBDHOnZcvCqEqCFZBqBJVWSf9MtGJnmiH7Dg60QmJTvEShRrBVE4uUaJIgTBUyCVyvUTPe2iddcQ8UkLynd+iZjiNt3NGKBFiUV4wisU9lOPlKXiwp+RPTvlWOAMCOuMQroxDuDKFNs+SfJ1oXF0ps+BtcrxPygiCGQcj6L4nXZxZuAVVzyrcL5KL5wRiOI6jMz3oTK8TqpOFbAZSWfm4mH6U4UdnxyXxG+ezaAbB9qEGOwo7VAYEKiA9isqMQ/8EOloNlg2ZCbThg+QRVHDB9G3J9knyt7cG5aspXSHThc4OgTJQyi8GXnYInR1CZXohtKyUDGV6JPfDTkNiXMKblAeMOKr/qBiSAKYPYs1CfoY7oG/HtNdK6Qm0iqISo2jKwcii0kkJi0vKHOVHPt+G1AaykXyS0R5HFc6BYUpInOmFYBzK50DdavAHoGcf9B2A/iMQiMmybEK2t8bAGgZP2Und5z9n5MlPJhe2OckANPwo7QUri06NoaxJHh/lEc8gXlH6y0ygJvoh23bqjTL9DvGdNOksZBKQnXDmCRk8MEx51gxHQt30yrOY6BfilB6VaahVzjlYAdFGiNajwi2QGYHxY+hEV8FLZAYh1IgKNosyYrZflOesw46XqEIk2c2Yq0b4PEAhp23UqU82Mo2HsxR5omD4AG/BE6MRz7XGye+0UDlPpD0uxB7ygzWqRO3QEfhQHvGW57w0huPpcbyeyiztU9J+hxwV12jLCYUUhYvqfF0ti0IIae531glp1TLIpS0gmU/Vy0vNGz60GUd5ypwacK6IiIvTg7Jt1CyI0GzWcTEVLhFycc6gdUaKfFrjTl6B4xHRWcimIZ1GaUfUwAnXgTHHaDRRgRq08kN/KyrbJ+vFGiFcLwRqrBe8XgjYMN4No90or0arEJhjaNWNNiMire18nHR2GJ06Im0CtDWKgY3y1Tl/T4g8tc5CBjHgAo0oUuh0uxgGyQMQWCR5GHZaksftNCTHUdojAghp2wmBQ8KSYs0wPgLtu4SwgFjH0VrwxxwlNz+MdsFgGyoxgjbKUKkRyf1JpVGpNKQzoD2ycSIJWbsoXn4a2JZMmZR4ewbbZblhQrwWGi6Enj2QHEElkhDyiFiDlZaRfZcISdhjtk8UAIsMQqVMMMJiGGUSkB7MCxLks9i8cTACQnySY6jRtulH8TwB8EVl8vjF4DM9BcNvuqkozyff1mxKPH/eCIRqTmqkWltpIUQTvTJPDkFiQKaeZ8RLFGuA6CJUbJEQook2ySUa3Q9jJio4B0KLUSRl0MOayOcSAeIpMuMStmrGnjNRExel0FYCrEF01qknNY2XR9QL/ZKTo1VetZA8sUmCPeK8x2c20I7nfTmVjAdtBlHeOPicyRt1CPeJSfeJno5C0eJMgVzlcpusMXRO+MQWTyiAMsPSx82oPPtGwA2tc3FyyJc2mMV6Lk4aLhFycVagrQn5gNoJ54M4IQnjJStJor9OJ0RIABPMKCgn0dvjFePPtiGdgOFOIQIghmKgBgY7of3Z0v36glC7BDVyGJ1JoYykeISMFHbyEHBIiJDy5QmQeHtCaHsUO9kq8gLeGgmp05YcP6PRqcMwfhjtjUOgHOiXmkDKhwrMRadaJQQjkwDbBF8ljA6h0iMymll5AUyMwdFtBU9NIAqV8yFSByO9kByFsT4hKslRUGnw+lETw2hVDtk0Kp2FZMJRfRsDMyz78ocgXg/BMvD6nSkAvoD8VgqsjBDP0T4Y7pRrmEnCYAeM9MDiy4VIDhyCxJjIaPv96MwAOmD9RRmr4vVJyih4fjS8qB8rA2VEJWwzNYbKtJfuwAiAv1y8fqlx1HCvoxRYBE9Qws/8cRlF11qIfXoMxkdBjTkeUud5MJx5zmwrCiHS2SQkRyA5LFOmqK3eADpUCaEKCFdKH9G20x9SMrfScvxIFQTLUZE5EHEk2zMJGGuH0XaHEPXJ1LMDYk1QsRgVnQcTnejxIxI2N9EGE21oX5l4iUJhyA6KYIQ14bwnJtDpTkChvFUof6MUEXZxzpCrW6Wz/ZAZkLpsJVBghlH4HKn+NNgJUS8sUR88Hoq9j155BxdPyvFKSovIC3oU/85FDmjLKRaddbwzToiolQQrIUQu2eXsy0B7IyhvTI5T7GUqzhEyTHleTX9eYGQyZJkzKEGw+Oo419GWd0R2qKiPj4skfvF+HEKEEXR+B+VvNf1xXfyFI5uVaTbruThpKK1dCnk2MDIyQjweZ3h4mFgs9lw355xA2xkJMcv0FghG6RoSS20rdFpCdBROmIQZglQKlTxBHoNSEKqDiREx2nMIlkG0Bka7ITEs61XNg2Q3mjQ6FAafCaFqJ0SiaJdmGTo5Dql+lD+I9oqRqcyYhLhlE5DMoAw/yhdHJ/vIjVVqXwh8ErahvJVCiqwEJNIoW0HCyfEwfeCvhv5WMXJBjM361ZIP0r4Teg5NP6KjLfArUejyGkICE6OQtmAiKSQoVgOeEHqgFz02OPPl8wUhFIVgFFVeC7XzIByHxAjsf6zgIZp7EYx3oCeOoGNhKC8HfzlGeJkU0vwzRqHobZ8IHUw3Gu6Jg/YK6UgNUBQYA96YowRow/ggKtFb6vUxfdIPfVEJ3ZwYhrEeR+DiLMAbEKJzsq96jw+idRCrk7m/IIutMxNCiEbbxFOUQ6wRKpbIuaUHxUuU7CF/fQwfKtQAgRoZubcdCfFJghLKW+0SorMMre1J5Ke4JpsSGXQ8EtqZSaDSwzOQHuWoFYZQnlBBsdCQ0DhteJxwOBuVJzBOyFlubhf9bXic/fnkXWeKKMiszsnOSLhmeljk39PD4p0/aRhOG/zSB3NqjJ6cGmNwViFv2k4X1CLtCbnGx8vjUEYJMSohSZiOByo7yRuVLXjdlKMemSuobPjdnLxZ4Hy113LtGnr0c8QiwROvP5ag7IoPnHfncb7DJUJnCefrg3U2IMU929GpNkoMQjOSlwPGSqEz4yir2Csk65AYQ+WMQGWIZySbltwEKyXLQtUQrhUvSNdOCb8wDJizCqoWymj5cLfkPYy2w8AR2V/AJ8TGY0EgLh8yf6XkURimGHETnfl2azQqEEF7nFF2OwOJCZRtQKAeFZsnH+pEJ3r0oHy8AzG0x5b1tSUy2Gkt22lTjELbC4PHpE3BMmhYDVktnqHh7sIliddCtBoCEfEUjQ9A61OSiBzwCBlSQMaGsTEggDYD6IlUwcNkmKhImaM0I6FwOpMqhOBNggrHoXYuqm4eDB6Ftl1OO/1gTKBDXnS8HEIRVLARI7TsVLvKeQuRjB5xyM9AqaKVMlBGRLyVmJAaF3GAYs+Qr0L6VSaDGu+BiQJZBiRPJ1wDeGBiFMa6pyc+Hi/4ItI/cxO6EG5kOZNtOfvXzmGc36ZP+nkgLnlegRjK9IoiXGJQxDAmBmCiX0ic4XEMTq/MTZ/04bHeqf3FF4LYHIjVQ6wO5fHLtUv0Q/+z4kXMIVIPlYtRgXK0lYSJdvR4W+k1M3wofyUEquTa6ZQUSc4OFS69twrla0SZJzYCXBSQ8/CQHXSK8zpeFGeuc/kvJVASvmUb6PQ4KjXElJA25ZEiu74ytCfshMRZqGxSxF8y4zK3UnlBjzOSwK1MCRE1/YW8N1+o8NszfbiZPNdJyAxDZrTIq+Q8P87fea+SlXKI0yzMIuURQuRx5Oq9UfBXoUz/jJvk85WcSIlCxETC8TSfeXNMcqnEyyXEyPmtCoTpL90Ldb7aa3ki9PD/mz0RuuqD5915nO9widBZwvn6YJ0O8i9xZebDo7SVQCcPoC3HqDOCKO1FJ0dRmVGmvtidPAmtZLQ8nQtNMyW8JqtFxcwXkWR+b0DCe0Y6ZMolA0aqoWkdjPRB/1EYaBcDEWDOEiirho6tYsyFfGiPB/xeCZubBipQjfZVwcheNDYqGEebtoQzZTRkbVTCGTGNzIGy+WgDGNwu64cr0EoS3kllUamE5AUFKiGVhpFu8VI1rYNoPRzYBH0OWTMMqFkAjSvknEf7YWQAPdoPY4MoZYlhbU1AOCTEcGwYCKLHx9AEJLE4VolqWY6uakKZXgiEUIZRuHeZlHiSJkZhfBjdewzd31aiRqPmrUJV1sD+R4VUmgl0LIyOxyEWA38lRuQiGXV8HkMMxQkn5G1EpK6LyI9SXvBWghGFTBKVHhTZ6OLRZcMHwTmgvajRDsmlKYY/LuRdK+eetpeSC6UkTC1aK1O4GuXxOe1zSKy2ZUT8LBgqWusZ96ttS8jSSJfkqY33laoWKSBcJcQo3ogKV6KTQ0KIxopETALlUDYPog3Sb5O9kOiSa1mi4KXAF0eFm0X1MT2JEJkRGVhxCgpLnsVzb7zJOzELOiUkT6fEoLVT4v32xM9ZwnyezGcHHQJ0fEEDwCH5MQndTI9DapAS8mP4Ub5yCW3EhHQSlRyA5ECp6uVsoIySfLZCeJop78DcMtsJ0cymhJjMhkgpJfLwoWooX4Dyn/o3VzzCzj20kvLbSoKVdHKnksf3MnmiqBy595XNOh9I6iqliohRsuh34XildZKKJL0x0Nohck5/nK1EucoTI19hf8X7zx2PqXmIfw44X+21PBF68DPEIif2jo+MJSnb8H/Ou/M43+ESobOE8/XBOlno7BA60+2MXiXzHyV5GQec0UYbMFCWD50cRBW/fA0/yhuT0UPLEmIx3lNQTjM8QoAyFvQdFE/Q8RCIQs1SGYHb94jk0eTgDUiuC0BlE1Q3Qvs2UBaEwmjThNqVKJ1GpwaEWHjCEJ6HGuuB4SPoSCXoCXHueCNS1NLWMD4xRb4VXxQdq4VUJxpQoRr0RDcqm4FE0vGGmUI+DBPmXQGjQ3D4KTGIlRLy07QSUkn0vi3ojgPT3ASNUV4mIXo6KUao9qHHR9E6IB6d1degR4ehdTe62/E8KUO8OOEohOOoylpU5Rwoq0KZDpHNpqHnGLrrILptn2zWvBQ1dxlsuxdSPeiyCJTFxSsUKEMF52P4G0/Qc84/SG2fAalTZY1MMRKU8oCnAswyyQlLdEBmqHQnykT5KtCBGkgnUEOtksuTQ6DcIT+GkN/h9lLVPl8Iypokj8sfE8nyoS4Y6pR8sJyYRfFr2ReEsnpR+Curh2Bs1saIzqZhfFC2mWEQYFb7sbLixRrplCkxXLpCtBbqV6KidejUCAzsE89szog1vBBvhvhclD8mRl96CFJ9EmqaLbqGnigqOh/tDUG6vVCQuAhKmRISagTFS4EjJJGbDC+oM0+WtJ2R3EfLERHIv/+OD2WGpf6YWSZecBRSSDSVn0sx0bT8r7i2TU4tbUoqf1EfsZMi265LibYyRYRCvNvFuTZOiFpGQoJLyI8nIvmXyifv66QjkDGdTLsyRfzF63gvPSH52xOYKuaR85hr7ZwThXMqynMrvmd5opl1yglkE6Wep9x88j0I10D5wpMWCJktdFE+ElYSsgl0ekC8TsX3RXlQ/grwV4lwgxmQZSfZJqlHZwMnt22hplOq0Mccwi7S57P0fuXPR6FULr/JX8h1Us7fz1MRiPPVXssToQc+PXsi9MIPnXfncb7DJUIzoLW1lTe96U10d3djmiabN28mHA7Pevvz9cGaLUT97LDkvJwQXpgYlVwYkJd9sB7wo1KjkOiVELTJeRKxZvGW9O0vjJQHYlC9UEYDE07Ct+GFskYxIL1BOPgEdDoCCYEI1C2GymaIVomXZfefxJiMVELIBxODkh8UCMoHOVIH0SZ0IA4j7aj+vYVCkiDkRjuGmdaQTKGyloxsly2AoUMwclTquBgmlDdB2vEGaCA5gcpYkBgHy5CQjpb1cHCLCBSAhMAtvlKiM/ZtQbfvzx9f+UMQq4RohcTnH9kFWqMiAZQhoVB6fARtO6FMZQ3ozmOlifFKlRrTxTA9qPIaVP1c1NylqIj0T310D/a2P8rm9QtRRhr6noWoDx2Lo+NlEAqDJ4oRWobynP/9WvLWHAnn7AilRoohoUBmTCYri0p0ihR0cS6ENyYhXP5KtPKiRtqkD+REDwwvlM0Vw3ykS8ILrUJ/wh+RvlvWBOmUyJMPdcL40KmdlD8M8Trp+/6Q/O1z5pmk5M6N9so8dwylhETVLoCqFpT39HJvdHpchDZG2h2y5zzb0RoJV43WiuE6fBSGW0u9B8EKiDRApA7li8j+cuFzY0cKniJvDBVdIInu9ph4na3x2ZMP5ZE8FzMmyo1G+KQNNa0tkUa3RhziM72XRQaGcqFGjuy/nXRynyZ5TpRxZsLFpm2HBzzlol6YTkKqV/JlTmTsmmEI1oh3MzEgXr3iPgzy7gxWSNFlf1wIZzYj9zY9DumiuZUuIl25aZYn4Q1I/TN/RCID/LkpCt6pNXqk/EHCkXk/XOqR9EWFEMWaUMbZF3nRVlqIZapvqgc5B+Vx8owCKGcu+UdhUbg7h2SiIC2e82g6dZdKVPGyzvITi2JIflKw4L01I/JsnOc4X+21PBH60ydnT4Re9P+dd+dxvsMlQjNgw4YNfOITn+Cqq65iYGCAWCyGxzP7pMPz5cHS1hho+6SMVp3uFknpnPSvrw7lKUOrgHzM7KJRpWQPOtEn3hIzDOEWmBhGjbRO/ZB6w0JCApViqPU+WxAOCJbJSPpwn4ySx2qgolE8O8oQw3GwQ8Lg0k6IWuNymH+xhIGBiARk0jKIuuN3YhT6gyI0YFsQiYBR/BFV5L/O/piEVQwelH2VN0B2CLIalZhwRpgjEKmBqgXSpvbNkOhDe0IQKxfvQTYDiZTkPKUQD1bLFbD3IRmZ9/phwaVQNQ+9+1EhObnWzFmAWnIxKlZVdC9ScGQn9p5Nch9DfsgkRe9BmeiMEjIJEI5hzFuGmr9MBBES4zAxih4fgZFBdH8Xur9TjPHiq1DTiJq3DNW8CHqPYT/9W7BtVNCPYgwCGh2NoaMxiDp5J8qHCq+cVYicttPodAc4ghJikJ6d8AoZsXfC3ayRgkR77lyNIHgrpT/jR2WGIdWPTvZDscCHGUKFGtDBOZL/MNYpU6rIE+INQXwepNPSl3P9EoSYlLdIcdp0GnoPQU9rwWOZQ7hMSE28Toi76XHUq5wJJc/KYAcMOap+J1srothTCkKKyhugeq7sP5MsmpxQpHitPH/RqhMaZjo1Dl27oP/AJEK0UkQWACZ6HCO1kI8n1ykq74TIHKkDprMwdhg9frRARr1xVKheQmq9jjfFnhBSpFOOoVY6iXdlcn6LgTIj4pXxVh5XiEHbSXS6S2qCTTIApXBmFOWJghERcmxn8uFTeS+B4YdANdoMoGwnYb5EfEOVhiQZ/oLRqG00RYpp2IWBjemeHeUBFZT8tVSPeNumW8fwOeIDPkfIwI8MZA2gxjoKnnqQfJxQlbyvTb8Qq4l+GO+V8OXnwnIwTAiVS0hpbgrES0iOTo/D0EEYPlLwYpk+iM+VHNFiT1TeM4UQPX8ZeM/M+0nCkUcKpCg7MQvhBgNyCnfeOPhi4Ckl8Nq2IDvuTGOizpgZl307og5CsPyOcIUj9GAE5Bk4xXOT0L20E66XcvKbUqBLo0UmQ8QmCsToVAYkzjbOF3ttMvJE6I+fIBaeBREaT1J2zf89787jfIdLhKbBrl27ePe7380f/vCHU97H+fBg6eww9sRuAIzgYjFCj7e+1ujUUcdozYVz1KFSI+jMiLzUZ3qRB+pF9a24LoonKOQiVClhAeMDMHRUQmtyBlMgJipbA10SGjQbBGNwwQtQZXPQI33ojoPQeRA9OiDnunQ9NCyE7fdCclyMdzJiZC64CpL9EraTTcoHsmqZfOz7D0no2XinkIzy+TCwD6WBjIaUY+gapshdV86Dnq1SsDVYIeStbz8qkxLZY3ww73I4+JSMzPtDsOZlkEqhn/4delyMalU3D7XkUlS8Cj0ygO5pl6m3HcZHUJV1qKZ56ANPFt0sG7IedCoF4RjmpddCbROMj2G3HQaPF6OuARWJTrnHjA6iezvQh/cWwuhAtrnipSiPib3lPinwqcZRUT86GoGyGDpcI54qsigzhAqtmFFOWwyBXvEsFhmTyvCDpxLlrQIjdFpGh4S7jaAtx8gsUb5yjmeGpXaUp1KMyUQ3OtXnjJQXf7xNVLAGQg1SH2X4CIwem5QLocQ4jDaJtHjP7oInzuNzyM9ciafv2gc9ByFVtL3XD9XzhGTE68QTOtiNHuwSQ9MXFI+gPwiBsPSZSHkh18vKSsjdaJ/sNzUuU3pCJsMjYhuxaumP0WqUP4SeGHbI2CEYG5j9Bfb6obweKprEoxSIzJxXlB6Hrt3i4c0/3440fOV8lC8sEtyj7TDeJdLbxZ8f0wdl82UEH+0QomPkK1Y69whf1KkTI0YijlFfYixqG+xxpwio482ZHApZ1C+UGXRybEbR6U50tnCNlBEQ4mRG0UZYBF9S/ZAakPfiiYxbI4AKVEOwBu0tQ+m0kw/jldpj2sqHV5Um6k8TCjdZbc2Z6+zYVPLjK4NALXhi4lG20vLOs5IyzybFU58temZMnwhdeKPy7pzol/yw6UKWTU+pqIcvVJib/qIcICmOimGWhMA5N6pwvrnf6QSkRmVKjzu/x+T3dOaKYYg4SKgCyltQ8XrZnZWB4cPivT2ZfCbDKyGugTJnKgfPVE/UqUDbliPH71x/hzRrKymkZtq+5AjuKI+QHyvJqbNQ5/qXSIcX/e0UqVW5ArW5orW5vw2PEHzT74TGyeBwQQQiKYVxrTGpqzStB1WhPFEZkPCUnfY34EzgfLDXpkOeCP3+n2dPhK796Hl3Huc7npdE6KGHHuJzn/scTz31FJ2dndxzzz286lWvKlnnG9/4Bp/73Ofo7Oxk+fLlfPnLX+aqq66a1f5//vOfc9ddd2HbNm1tbbz61a/mwx/+8Em18bl+sLSdRI/vKHz8lYERWiEf/+nW1zY6ebCo0GGl5N+k+iatqeTFmIMZAE85auBQYTQxUA7li8RIyYkcjE0ayQ7GhQD1HiuMpHu8ULcEqlrE2Os/VlBUi1SKQVZWDxUNkE6it9+P7j5c1LRCOJhafDGqrgW2/kqWxWMyWhwsgzkrJEwpMy5GVM+z0L1L2md6oaIWkkV5CdqEsREJcfMFYWKo8L/qBZDuk5FHb1DqByVGIaNE0GF0tECCVr8UOg5h790shkkggrroxaiqRvRwP/ZTD6K7jkx/Q31+1PwLoG23owZnopNpCIZRa69B9/ZgHz6I7usu2UxFYqi6Bow5jRhzF0gdl+L7Pj6KPrwHfWg3enQQTA/mC28CU2E/9jPURBeqLATlMXSsDMLlEJsLZhKtMyhPOSq4ZGq4ip2S/pR1yJ4Zlpyy7GDJyKGEL4VlxNDIzY/vZdJ2WvLWskMFkY7iczaC4IlJSJTpPHuJbnSic6qxaAYLYW++clR6VLyCox3kjQ1lSs5BzmsxcAS690j4JkjYTt0KiDdA32EJ2xwpEk7w+MT7Uj1fDMXuI0J8BrtEtv0EUOE4avHF0Lj4uCOpuVf5iYwKPT4kpGioU4xTb6B0si3xPg22Fzy2+evlgVAcQmWFKVKJCsUL+09POB6ig6XFgmP14k2NN6IMUwzV8W4ZeBjrLoSnml55f5QvEGN/og2dHhLierzk71xukOET5S5vXGo4eXM5MglHTGAg3y/zm5ohQJXI/itPHOWbg9Z+VHpAiE96YGZj1QyIepkp+RJkx4VwF3uUlAflrwK0GL9WYhZegpOArwwVqEWbEcnFHDkm77njwfAI+fHF5N01dGzqoJRhCtGIVItIRrhK7lPKIeDpRNHvCXlHKeWIIBTV6VGGLDN98lzkJtNb+O2b3jDWtiWEaGJApoQzn9xHK+ZC01qUV3LitLYLHl1tFXEIXZhr5PuVGp7es2H6hWz5YhI94I8JIZ+lrPdsUFC4G3Gkv53Bx+n6vOETT5EnLDldXmdAwC4iu7ZDsHLTrOs9nQSUp0CKcp4nMyieW08ElBbPrUOMph2QMHxglkmxbk/8OZH7fq7ttZmQJ0K/vXP2ROi6O8+78zjf8bwkQr/5zW949NFHueiii/irv/qrKUToxz/+MW94wxv4xje+wRVXXMG3v/1t/u3f/o3du3fT3NwMwNq1a0mlUlP2/bvf/Y5HH32Ut73tbWzbto2amhquv/56PvzhD3PttdfOuo3P5YOltYWe2ClhQUZYDDlrROrghFZOUTDS2kIn9olKk7YhCyqV+3gqGc30Vzr1USIFxbhsEnp3Sb4MyMehepWMJHbtlPCJYgTjEG+UQfjO/VI/BSS3oXk11C2SD2VqQurcKIV2jM28VK/W0LEf/cxD6EwSDANV04KaswDq5sFhJ4wMUAvWoIIBOLJVQuIiwcKIoi8EFfNg8LCMqIN8jK2M/C8SEQNCKyEzSsHCF4qxO9YDPXsLctg1CyDRJb+zaZgYBzyQNSE5ISRo5fXonY+ge2UbVb8QteqF8g3esQl7/3Zpm2GgquagqhtQNY0QimBv/h26X/av5i4RD8LwEPgC6Ggd9uFDRRdZYdTOQVsWuq+ofkvuf3MaMRYsxpi3qIQUacvCfuTX6PZD4PVhXvMa9M4HoGs3KuqDeBQdiUI0Kve5fBk6e0wInRM6iVPPQmuHqGhbCLivEXz1hdHv7KCTszM0rdGhvBUo//xplbZ0dgidOFBS20cZAfmAOonhyvBKiFyyV0I3k30UPD8K/BWoQA34K1GekMT1j3XCUGspAQ5WQfk8J5SzGwZaJ3kzo0KAghVwdLt4W3LCCEoJoa9bJAZk5yF0+370UClRzan9UV6HCkbQqYR4HlMTkJpAT4zkyYSKlKOWXAL1C8/JCKq2bRjtEUXGgTYJ0Zvpc1E9F+ZehIoUvM7ayki+VP9B2U8OHl8hZypWhzI8YqyOdkD/Xsn1ADGWKxZD2TxnHS0CJ+lhyIxIjRgr4RCo433GDFGkc2Sf8ZXL+vl+WJRDowypYeStkzyX0VZ0apIaoDLBVy5J8L5yJ9wq592xHbWzpBiIHr/UmUr2oJO9M5MeZebDmHKiLJPPSOXWKxm9NxAS5kN746iJPiE/iUleP9MvAgb5KShzyxbSM3SsVADDMEXQI1Ir5MfwifT6SK9Mo70nFrY5VXi84tUs8mwq/0wDeFo8RRMDkp/Xt08unMcLjWuhcsFJigvYkBoRT1lyUKb0yMz93htySFFMBgBD1flQ7TMByX+aEPlvbYsYhSeCMn0nvx+dq9+UC7XMhVsW53FZFIrVOpLiRb9FJMJRtpsNsTJDKG9EcvS8EbRHwtOVNSzvcmtk0jdAJNyVpwy8FTKodQ5w3hOh+z4yeyL00n+Z9XnMxqmwZ88ePvjBD/Lggw9i2zbLly/nf/7nf/K2dCqV4v3vfz933303iUSCa665hm984xs0NhYElQYHB3nXu97FL3/5SwBe8YpX8NWvfpWysrLZX4yziOclESqGUmrKzbv00ku56KKL+OY3v5lftnTpUl71qlfx6U9/+oT73LRpEx//+MfZuHEjAJ/73OcA+MAHPjDjNqlUqoRYjYyM0NTUdM4fLK01OrnfETkwIOW8/AJ+wJKXTGhpgcxoC5141in4lhFhAFsDBio0ByJzUZ6wvAAT/SIRnByWj0O2yO1dvkhG9rt2Fj6ohiFJ07F6ce33t0m4UC6UyBuAljUw5wIY6UMf2yskJ5MWo69lOTRdIOE5iVEZQW/fj+5qBUDFq1AXvrgkpwZAH9qOvfNhWWfuClR6SD7c8RqonSsfy2JhAV9IZK0jNfDsb8UwCMYlP6lrr1Rrrl4stX+KpIx11y5o2yr7qJkPiW4pTJoxxHAYGXE8QS8TEtR9GEwPxsoXQNNSdOse7KcflJAoQDUuwLjwBahoWen5WFnsrQ+j920rLDQ96EgtdtsxQGG0zMeYuwCjeX6e4Oh0Gt3Tid3Vjj56GLu3s2ivCqNpLuaqtaiGZod0ZrEf+Bm6px38QYzmuegjT6P8NioegXAIXVYFQZEvpnwROnV4xr6ozCgqsEDWTfXK6KGvvHD9tF3I9bDGnJAKIaVKeVCBeRJC5xiYOnWsKGwzhPLOEQLkeJB0NgHJXingmR6kxJT0RlHBOSJ3bfjE2B7vgrEu6df5RhuiYhifJ32k/6AYicWy16FyqF0q+S9HtkPH7gI5CpfDnMVSzLe3Dd22F93XXnpdqptQ1U1QVgtlNSiPU+fHtlHeUgNHZzPQ+gz6wFYh/iAy6YvWQU0zynucmiWZFPQcRQ92o+JV0LA4H2J3KtC2Jc/GxJB4OSecqdj7VbMA5l2ECpWVbpsckdDT/oOluVSmR94P5c0yyGB4pEhr356CJ8P0i/x2LkzJF2VaZTE7Le8wOy3GY3oYnR6cSj6UKV6ZYJ2oeSkhRWgL7alApUfQY4chnSMTyiFSleCvkDBfOyt9JzkooWXZZGFejGCFhFFGG4TYpYdkv3ni4xASh0hNuea6WOkt69SQyhTmud/JQfGuFRuV4RoIz5EcpGxSCMNkYYPifm0Yzr1oEQ/DUKfkpI30FgaLJsMw5R3nc6bcb8MstNu2J/22xIuTTUnbs+nSaTr4Qw4xcnJHI5XTX6/xATiyWYgRSK5ay2WowKl/g8UTNSwEKT0i89RIaT5VHkruebhWrr+/7DkP+zpb0HbWkRgvkhm3U+jshKjn2dNdH5yBhDJR3/SVg6HBGhIPf1EonRFoQfnqz8m5nPdE6Nf/d/ZE6MZPzPo8TuRUOHjwIJdccglvfvObee1rX0s8HmfPnj1cfPHF1NTUAPD2t7+dX/3qV9x1111UVlbyj//4jwwMDPDUU09hOuq0N9xwA21tbXznO98B4K1vfStz587lV7/61SlclTOPPzsilE6nCYVC/OQnP+Gmm27Kr/fud7+bbdu28eCDD55wn9lslosvvpg//elPxONxXvnKV/J3f/d33HjjjTNuc+edd/Lxj398yvJzToQyvdiJAzLSk8qgHI+KVgqCATA84hkKLAQzgk7slZH5zASksyL57I2hyldKnsPIMRmpTQ5MHzLgj0sl+e5nYdhR6/F4ReK6cr6MJnftl49qfpswNCyF+mViqD37hAgdTAfDQHkD6OI8C6Uk9G3RWgmxSSXEeO8+htY2xpqroGM/9vb7ZRcXXFoowtqyBlpWizdn4JB8rOqWy4e+a58Yp8ceFyPYMGWEPxgXw3j/JjF0m1aJ+pYy4NiTElpnGKIe13dYvAHjSZGrXX4NuuMw+uhuMEyM9a+E8jr01oewnxUSpeKVGBdtQM1pkXtlWehjrVgHnkW3HUFVVmOuWw/pcezHfw+2jQ5VYXd2gjLwvOilmAuXFPrAxAT4fKhJ4h56dBj70H7sg/tKSJGqqMZctRZj4RKwLOw//gQ92AsmKCONMhKoeAhiIXSsDuKVoDMSghOuhEw3xfUsUF4n/6JKjNChXRIiBBJCEayV3AXfVCNBW+NOXSq538pbgfLVo5OtBZLkq0P5WwAloSPJXhltz04K5/FEnNyMOpQ3Kh7M4aMwcqRU8jrXj6MNEGuRZPCuXTBWFBbqj0jITcU8+d22SzyNOcOtohHmXiQJ6Ed2wbE9QkRy17i8DtWwCOoXirE/2IMe7EUP9sBgL3pYni9VXo2qboDqBlRNAyrokNpMCn1oOxzahs4UjEUVrYCKOajyOqiog0wa3XsUeo6gB7tLRrJVOI5auBaalpxRBS09PihFf3tbCwvrFkHLhSUhc+CQ39EeIZdDx8Qgz8EwoHKBeNl8QXn39O+dmt+RS2oPlImSmOnkLJg+J0zHU0S2HS9SagjSg0KMrCIipkzpI4FaWX+sVfqUHCg/GIQZEvI81uXkNx1PTVOJJ6iEFCkIV0O0UcLQFA4xGXfmTg5M1pGCztWQOtlwJn9ciLzhh8E2GDxSKt8+palKakGVt4hnY6ANug+KsMtkRCpEmdPJPSMYlVBEKyPkNp0Ub2aO6Hodj5jPmXt94PUdP7zTtkXlM+d1GumZPqfNH5JnrqIJKhryEQPg9LGevdC+Xc7dMES4o3bZme332ZT0idSIEKVE39T3iumXb0yoRq7v5LwpZRbmqD8r0qSttOQ9ZUYhMyp5bJlxSnP+kO+Fv0LCk71h0EmwhlD+ueeskPJ5T4R++eHZE6FXfOqUzmM6p8Jtt92G1+vlP//zP6fdZnh4mOrqav7zP/+TW2+9FYCOjg6ampq47777uO6669izZw/Lli1j8+bNXHrppQBs3ryZ9evXs3fvXpYsWTLtvs8l/uyIUEdHBw0NDTz66KNcfvnl+fU+9alP8YMf/IBnn312Vvv9zW9+wz/90z+hteYlL3kJX/ziF4+7/vngEdJ2Bj2+TRSTUilUJiuj8Z6gGAFKQyAgIWAolBEUadf0CGRAaQMVnov2lKGGDzsx1UXkxxuUF3qgwomTjkouz+FNMrpnmEIqypvh6A4hQMUf4YpGIUAVzeLd2f2oGGsgks5zFqCaLoB4NbTvRx/dhR52jFGlUPFqCSFqXgbRCvTBnegDz4jRXgRV14yx4ZVwaBv2ns1CQC5YC8e2ywqRCli4HlVeL6Psh7cWRvW9AbjgKji6qfARbVgHex4qDY/wh6BhOTQskxHIwaNFDfDByDDEqtGhOvR+ETkwLn4pVDZgP3ovulNygYwVl6FWXIoyDPTwENa2LdiH9qHTU5NMjYYWjFUXYu/ajn20VUjQ1TegDR9WVxd2Tw92dzd6XIiQZ/EivMuWYTQ2TCUcw0NYO5/G2rszH2OvQhE8170SFYth//7H6OE+VLIPFfGgykUsgXAFum4FpLsAjSpbIYpe0/XHZC96aJczIu+E8hSFtGEEULkcDm9MvDZOmJROt6NT7RR7dcRLNB9UCMZanVCj4lFH5eRIVEOgRsLetBYv5uT+rAwRPYjMgXCdjMoPHBEClAvpzItizJecCCstRuLRZwo5FJEKmH8JZDLo1h350EcAFYxCy3JUw2Lw+tHHDkg+VrFAxYkQiWMsXYdauFI8Y+kk+tA2aNsnYXMngIqUC0Hqas33KRWMCCFqXooynbC0XDheckK8HRVzRLThJKBH++Dw04VCwSDqc9XzoXouKhApXV9rScYfdEhR0jmfyYRorFOIR3LIkeE/ATlQhtzPQLlIPQcrwB9HKcMR8BiFZBc60V1KivLbm6hQI0Ra5B0weFDaMJmQ+WPyPvSG5Xhex7tjSv0inZkQYYjR9tKQy2K1ypOF6RPyYXqdfCjntyco7+XRHvG8Fef4+MPyri4RNAhLWJfW0HdURD1Gi4i/YYiyYFm9hKeFymB8BIZ70EM9MNIPyXF0OnHSKobK65M2BSMiCBKMoAJhkYMPRkQcxCwM4mgr4xQk7pEBtaGOqYWJYzVSQqF2Yb6f6dQYHH2iMEAXiEk463SeKm07Ne3qxCsWrjol0qQz4zDeI965id7pay/NBE9Qim1H6iE4vcdryvG0Lc/GeI+QMtNf6IeeYCEE0siFbmppUzbh1GRKFH5nE5NIsyqaFansGaa8y40iQYXiZaavUE/K9E/y3toi+JAaFLXO9CBTcqDMoBCjUIOEs54DnPdE6Ocfmj0RetWnOXbsWMl5+P1+/P7j595OtqVt2yYej/NP//RPPPLII2zdupV58+bxoQ99KL/On/70J6655hoGBgYoLy/P72v16tW86lWv4uMf/zjf+973eN/73sfQ0FDJ8crKyvjSl77EG9/4xtldjLOIc5+Vdo4wXa2BkxltueGGG7jhhhtmvf5sOtrZhk61Su5EelxUzgw/qmqthF8M75E6HYkkeDPg9cpLKTMMGQOFF+JLYaQDNbKrsFN/XAohhuvysqI6PSECCO3bYcgJ+wmVSa2c/jbY8vOCdHYoLqPDtYtQgQh6qAf95G/y4W2YHtTCi1DzV5eG+cxbiZq3Ej3SJ4Z6vNox2jS6/RD64V+jRwrGhYpVQHU9+siz6K6j2I9tRF1+A6qvHd17DH1kH2rRpTKKPzYA2+4VieziWHePTySEDzwOiy4To7hivtT/0VryIKLV0L5LjMZDW2QUfNV1Mvo72iMfgmEnNDBQXiBBq14I4XLs396dFyUw1l+P0bwIrTXWjqfJPv5wIR8kFMFYsASjeR72oX1Ye3ditx/BbneMTGVgXvliUlu2Ynd2Te0M6TTZnbvI7tyFisXwLL0A75rVGBExElS8DM8VL8Jcdzn27mewdm5FT4yR3fhzvDe/HuPqv8L69V3orI3CAG3IQF42iRrtQlfOh7GD6OE9Qj48BaNZ2xaM7ENPOAa/JyoeRk9I8iUSXRK+ZidFwCCR804ptCeMCregQo0oT4UILljjEmYXXATpUfTg5gKhyoU5BarBX5WPndeZCXT/XvEAFSeLB8qlHlS0XrxY433QtUdGznPeCdMD1Uug9gIxIkZ64NmHhATlDAV/COatg3A1eueDJeFvqqYFNXclurIeOo9gP/0gur211MgIx8T7U14N5TUyV4ao+fW2Q087eqgXxoaxt/wR1d+FcfGLUL4A6oLL4ILLRGhhsAs90AWDneihXgl9q2pC1TRL6FxIPoY6m0Ed2Yk+8DQ6MYbe8SDq2cfRyij1thZBldVATYvsq7z2hJK3KloFK1+CHumF1ifFuzDcLdOBTehYjSMaMQ+VKwrrJN7rhjWSe9f5jAyu9O6XMLrKBVC3HBVtkPPQthh8yUEhRZkJIahWSgZjckpqmQmZRtvz/UQHKwphS9FFqOgi8f4kutHJLvHIhRoh3CzGYu8e8R7mZasNUcGM1Mn7EEOIXCYhHvOMExqXSYi3wBeS90fjlRI6NNoGI22lOVDesNTKyc9DjnGZywHKCQyIx4BcDlLGCcXLyPEY7IKRpwr8yvRA+Vy5fr6wEKPkqITujrTLPDFaGvKmlAjS1CyASBX0d4rHsnUPerT/+ITH9EjBXn8QfAFpazYlUv3ZtHhHnXebzqSlzEFRFICevK+qRlRti/S/UAzK6mRqXiVhpMNdIqYzcExyTXP97Ngz6JXXoeK1KH8EvfBqebaPbRGinTzB4MF4P3TuktDj2BwhRbH6GXOUJkN5w069uXkOSRlwSFGP0z8nka/iM88mhHQPHgTTj47MkfdUqLpUETEz4YiMnATZUiba45dn5WTI2elCmWhv0Cmy65Ajb8gpPN2EQjsS4/1S5NzJ+9MT7Y4yZNm5a+v5DFvPbsDBlv7U1NRUsvhjH/sYd95550kdsqenh7GxMT7zmc/wiU98gv/3//4fGzdu5Oabb+b+++9nw4YNdHV14fP5SkgQQG1tLV1dYpd0dXXlw+iKUVNTk1/nucafHRGqqqrCNM0pF7inp4fa2trnqFVnHzo7KHlBmTFI2yjlR1WulQ+dPYSOLZW6F6MHIJOVOF5Dg22gCICvBtX5dIHAxOdKknKgTEaREoPQdxA91CahC8WoXSqGwY4/yAcWJIRi4WUQrxPyNNiFve1+dE9htFi1LEctuURGA3PnMdCD7u9EheMSghWtBMuC/i7s3g5028G8cAD+IMbyS1AtSwohRC1LsB64B31sPzwVQF14LeqhH4tSVn8v6rJbJYynY4+oYoGEuy28DMIV8PQv5ByO7IDVN8AzG4X0hMtg6QtRphfdtFKM4oOPyyjqjt/DimvEeOtrA8YgVI59cIec5+KLoaoJ67d3y8h7KIL5gleiKmrQI0NkH/gtdmcbAEZ9M+ZFl6LmNObzOYzGFsw1l2A9vRlr325QCmPdlaQe3oweGwOfD7O5GUIR8PmxMTEMjRofIbt/P3pkhMzjT5DZ8qQQonVrMaskr0r5A5gXXoKxfA2Zn9+NHuwj89tf4H3FrajyavRYj3yvs7Z4GK2shH8kk5Ionh5ED2wHX8yRgHUUipzwBxVugdjCgsR2oEombcmoYEbyzUSGOAXZMfTwLkh0osqWiTy3nUCrIIwfRo8eBLRTbHMh+MsL+W52Fj18REKqJoq8hIankPfjj8FYL7Rtmxqa5fEL+aleItt07RPSWxyaEy6H+gugej760Hb0U3+UD5RhCplvWQGGid6/HfvRjQXJdYSsq7kXSH8tygMTz8gYaDBaFkPLYlmeTqH3P4O9/RH0oV3YIwMYV70839dVIAxzFohQCE5YkWJawqI8XlhwIcxdiTqySwjRJMU65Q/KSD0aPdIvI/9DPeh9WyQ8taJOjPLJ8AdR81aJ6AOgYtWw+gZ0cgx6D0NfKwx1OfkmPXDwCXS00vEUzUOF4kKKorUQvRY92j2FEOlItcjwhypkHmtBxedOaYokc6eF/Cb6xRBNDEguzUSvTP3Pyqh5tNEJh1yEEXeueTYh+UnDhwvew1CNGLfhGnk/Dh6DnkdFQON4jp3EsBSdNT0Sela5QO6tlZQR+lz9s5waWnJIFPSsjJxDLofGShf+PpFAQaQaqhaKAd/bCnsenD7MrRhldUJ+4vXQ3y4Kkv3tU1ZT3gCU1UBZNSpeI7XKfEJ8lOfEAgHatoQApZOiSJoYkz6YHBOBncSY1D3LJNHdh/OKoCpSDrUtqJoWqKwXb1FFo0ysRydGhRB17JUadNvuRS9/MapKch+pmCukZqiNnCBNaYiaM2UmRAxlpENCoweP5QVxdDAu/TPkkOlg/IQeI5XzOoeqgOXTX5N8Dpgl/XW0w+kDKemDw4fB8KIjdUKcc56fYuTC7wLlTj+ZlLNmpWX/xR7NnBcxN3mdeYkKni6ZOTexkK9mW04Ip/NbO39byYKnSVvyvZgcMphrd7BSnudgFSoy31l/SOTp/ZVTt/lLha3zJOeE68G0HqGTPqRDvF75ylfy3ve+F4A1a9bw2GOP8a1vfYsNGzbMuO1kx8NM+Y/nSyjonx0R8vl8rF27lt///vclOUK///3veeUrX/kctuzsQWsLnTwkL89MBoUPylfKyF/fHgCULwrVy6H6CtR4K4y3iWKQGQTLj+pzQgb9cai7EBUoRyeG0W1bS5XVQLzk4Sr52MYbJbTimd/IR8YfgnkXixcIoPsI9sGn0f0dhc0bl6AWrUNFZRRBZ9JS0+bgDvRAkboUSL6RbZWOhpgejAsuQi1dh/KVPuCqrhnj8peKAtqBHSh/EHXRtejHfo4+tgdVOQe1+Ap0wzJo2ykenjkFWWK96np4+pcyuvj4T+S8TS+suDZvuCjDhDmLxZjbdq8Yd7v+BPMvBscDpNMOEZizAOavwf7D/0AqgSqvxnjhTahgGGvvTrKP/kk8Xh4vnss2YCxbVchvSKXI7j+A8vkwamsxN7wEc+16sodaST34MDqdxk5lyaY86B1HmAwjHiWw9jI8UT/ZXbux29vJ7tpNdtduzLlz8V1yMWZTo9M/fHivfxWZe/4L3ddN9oHfYlTWQcd+Mb7SWTEO7CjYFmpoP7rxciHe2VH05PwcI4AqX47yV0qo21CrfGTDdSjTK+QlR4qcLqWtFCQ60SMHIT2A7tmEii2EUD1qaLuEwoGM2scvcEKdbPR4t5Cfsc7S0c5QtRAgx5vAwGE49HCp/LnpcRTMmiE+R0bge1vF25cj9YYpYhj1F0CsVuSvH/qJGGCAqp2LWvECSKewn9mEPrK30F+DEYy5F6DmXgBlVZLf1dmGfegAerDfmQby+URGQzPG0lUide7zo5ZfjCqvxnr0PnRfJ9bGH2G+4OWoyrop93s2QgjK9MD81RKyN9gtz1cgLPWLirbXyXHJM+o5Cr1HJYS0WKp+EvSRXaiGRTKwEZHnWgUi0LQCmlZI0dW+I3JtBzsk1Gm0Hw5tQUcqoWYeVM8XUlRCiHaIGthIt0w5eLzokIS84fFPnbxhqKgqhAOlRx2xlz7J8ckkYGC/TN4wOtooRthQayH0LlgFVUtlJHvoGHTsFK9VsT0SjIvHJRca582FyPmFcPcfEpLTd1CmQFQ8NWh0cljIUmq0NOz2hDfRyUHKhUB5A04O2zzpd+27Yc/DpbLS/rDk9ASd8LDc3B8Wz0/7PvTWB0res6qiDiobhPSUOTlBlgVDveihPhjog3RKQi5zUyopRaANAxWSUDcVdMLgQs7vUFT2axhTKyVpjRrpQ3cfkf430Cn5o2OD6IPbRFikfiGq8QIhRUpJCGrDMnTdItj1R/EU7fwdeslVqDmSf6A8fpFuPxEq5zsDEwNCiIY7nAKyw6Wqekqhg2UQLC8p8HqyinEqnx9kSphuZI54kib6YKxDiJGVkvdbYauTEmSQAqwJ2Y/pk5pIZ1D2e8bjatshRBMFD21mQr4ZySFpz1iHTCDfh2CFPHfBShG1ceEg50GcxXpALBY77RC/qqoqPB4Py5YtK1m+dOlSHnnkEQDq6upIp9MMDg6WeIV6enryqSl1dXV0d3czGb29veeNc+J5mSM0NjbGgQMHALjwwgv54he/yNVXX01FRQXNzc15+exvfetbrF+/nu985zt897vfZdeuXbS0tJyTNp7LmFM7dQydPCIJwVkvKjwPlRiTytogL5icgRgoh4pF6EAckn0w3IEadzpp1XKp3TF4BHr3lSaLG6Yk1ZY1QbxBRojTCdhzvwgiANQuhMVXCHHoOFAqgqCUEKDF61DhMgARBdi3FXvn4zJSCI4cdiNMjKFHhwoPfyAkktLV9ajmxfKRLYJOTKB7u53jtKAP7sR+4g+yyw2vhLE+9LNPSFPmr0YtXZ+PQ9e2BUd2oY/ulhH9eAVsv69gnKx4Mfgi6N2boHIOau5KlE/idfVon5ChbJp8HaNoNXZHm7Tl6tejtz2KProPAiHM616HCkextj9JdrMIdxhzmvC88DpUTBLL7fFxMk9tJfPMM1As8R4IYFRWYre3o7MW2Yk0lhl2wmbAiIYxy+IY0RDp1jYJgwSUz0tgzTJ88xqw9uwmu39/fpdmUxO+K9ZjNghZsDuOkbn3p2BbGPPmoroOoNKDqKgHVR6F8joZFbaTEvpXtwqV6JLkbE/QqSORy5MwJIyjc0tBzlcZTmhSI0Tqpv0g6+wEemh3kWqXiXiYDFT8Agg1SCjSSJuEPhWrN/kiQn5izShvSMI4e/eXKgUapozQlzdDbI4IbmgtoVyHtsioMoiB2bwa5iyRsMnuVvTBbXlSrwIR1Mqr0ME4+qkHSnJ/VNUc1JKLUE0LxWjq68Hetwt7/16RyJ4MZVBcXFIFghiLl2MuXYkqq0CPDGI/9Ev0yIDkvF3yYtS8pSeuGWTbMNwPkfgURbrZQtu2hF6NTpO0jkb3tqE7DxZOpXEJasnF+ed8yhbphIiK9LRKvkfxJ6iiAVouzHuSAXRiSEIYx/ud+jGDswsT8QWd0LtqCfUKVUgOmm0JGRptF/GDyTlHwQopsmyGoGuHEJjiNoYrpe+UN6P80eM2QWst5Kn/kLxXi3NbimF6pE5NICYkx/QVau0U//YEwDMp78K2xCPSvrvwLgYhOw3LRMXQ9Ilne9QhFQ65YLBLlAkdqFil5LQ1LJJ2OIIeeqBHBD5GBk6OtM0Ew0BFyiBeIWHNsQpUrByi5SWDWzqdhN5jEknQc6Tk2VHBqNTWarogT761bcGzD0t+KsD8ddC85vSKNmdTMNol38PEoFO7aBrPnGE4A4RzZApVnDCc9ITH1lrenWMd0k9D1RCqOaMS3eBct+SIEJW8p8eZ29nSZVpTkN3OebR0IdRPmc6AhK8gIZ/vuzlBE1PIUKLP8dz2Tw3Zq1qGqjw3ifTnfY7Q/7yfWOjEXp2RiRRlt3z+jIklXH755SxYsKBELOGmm24iGAzyox/9KC+W8MMf/pBbbrkFgM7OThobG6eIJTz++ONccsklADz++ONcdtllrljC6eCBBx7g6quvnrL89ttv56677gKkoOpnP/tZOjs7WbFiBV/60pd4wQtecM7aeE6JkJ1BDzwCmSTKLIeUhZpwPCu1q8XoHNgvsce5j77XMVqTg2KEzVknH/6jT8hIJohhH6+XkI5YfYE4WBk4tgOOPSOjjoYpBKhusdQ+eeaBgsS1xyujz/NXy4fLge4+hr3lT/JhBVS0XBLC5y1DBYLOcSwYG5L9R+KTPv42dut+7APPonu70OMFj4TnimswV6zBfuoBUWbzBzFueD0c3IpufUaOF6tEXfQSGBtC73kMPV4Y7TPWvgRMBQc2QdNKqJyLfuSnBQED0yOy3PPXSO2XkR7Ydp+EsSgDrULo4X6R//aEsbc/CoaBec1roGoO1tOPYz35qOxqzSWYl1yJUgp7bIz0psfJ7tolI6+AKi9Heb3YfX15489OJMlOWNjBONrWqDn16FgMnc5iT6TQyTSe2nKCtTEy+w5i9Q0698IksG4V/mULye7aQXbnrvw+zeZmIUT19Vh7d5B98HdgZTC8aZQ1jFEeRpXFIBqDqvmgEvLhqlmFKp9+pFWPd0PnkxKaYXjkQ1gcIqHMQvV2f0xG932xAjGZaEeP7JPQCyOAqlgtRfq6nxYSlIPph1ij9POAyHPr8T5R8xs8UjCafWGoWQJVCwp1qbJp8VJ0Plso3mt6oXkVNK4AFBzbiz60rdBHlELNXw2LLoYDO7CfeUyMBKVQzYsxllyIqpqDTkxg79uN9ewu9GBhUEEFwxL6WF6BKq+UKV4OE2OSC7Z3J3qicJ3MFRdhXv5CyKSxN22UWk8gQgpzl0q4Xaw0RlsP9oo8++E9Ti2rIMbaF0pY3lkIR9DDvTLwkcv9UwrVsBhVO1dG7ovCX0u2SyeFFPW2Sqhq7nMUqxFCVNk0TbFeSyT8xwccpbWUEwKUKoSPZRJTDXbDkBH8WD1UzEUF4xJKN+aQIm3JQJC/TArmdu8p5HVFqoQ8lzVJ7onWQpiHu5w8HefYmVz+Tlo8KPVLobK5UEB26JiEaHn8QnyCDvnxTl9EFJCcmNS4Ez43Jp7K5Fgh72dyjldlkwi5hCvRR3ZC9xEhPzOE1algRKTVGxdDKC6iHod2zSzq4Q+iKmrE2+MLgD8gynC+oJAYn188nxMS/kZiHO3MmRhDT4zOTAgBVVFbCCENFoVNaw39Hei2vdBxsOR8VFktqmkJNDqDFoe2SG0vEDK4aP1pk5KSdqQnCgVdJxxylJ50Hzy+QvmI2Bz55haHOuakw3N/+yP5YsMn3SYrI0TG8YgejyhprcULOd4vOW65AYbjqQueafhCziBFpcyD5eI5SvQLOZroh/pLUKGqc9Kc854I/fc/zp4I3faFWZ/HiZwK99xzD7feeitf//rXufrqq9m4cSPvec97eOCBB7jyyisBkc/+9a9/zV133UVFRQXvf//76e/vnyKf3dHRwbe//W1A5LNbWlpc+ew/d5zLB0uPHkKPHpDkb2KokWNiZNZfjIrMKayXTcLQIQn/sHIeGA/MuViMkJ69YjyYHlF/q1qYr84t26fFYDyyTT72IKpZy14kYgltz6J3PixhPoYh4W+TRBD06BD2dsdDAmKgrbkSNX/57FRy0insvTuxdjyNHitOfFWoSFSWKQPvS29G1TVg/+5u9FAfqn6eeIa6j6C3/VFGpYu39gWhvFbi0g0D49JXQFUDJMeEBCXG8jkQesTxGBgGavElqEVrJWxn70MQLMc+/KyQt2UvQG/+nax68TWohSuxnngUa9vjAJjrrsCz9jIAskeOkrr3PnRC2mXMqcNomUeqYwDSGSkIm07B+BiZjl7wBbCymnTWgzanelVy1yS0cgGh+XWkd+4h29blnKuX4KVr8C2bT3bb9hJC5H/JtXhXriD76P1YO55CjbRjlPlRsQBGLAJhv+TJNCyHkcPSf+Zeg/IWiSVoW0IyB5x7HCiTPuYNi0LhSLskjk9X8V4Zkp9WtUzysaykFEYN1Erf7NgsBohSQnxiTflkYm1bot7X+2ypNzNSLbk/ZU35cDoGO2TUuLdIxMAwxWhqXiPJ0ge3wcFthfo9Xh+0rEDNWyWkZPPv0H0i9KDmzJV7HImhR0ewtj+JtXdHweAzPBjzFmIuXoZqbMmHocmIbwJ7YAC8XszaWrRto4+2Yu3dgX3kEKDzfUVrjd6xGXvPkyXGpKqoRc1bCraFbt0joUv5f6o8KShu59mAHuwWQlSUCwhOnkdlA6qqXubTECOdGBVlx859RQSkQu5HVUuJktgJ22FlxLgb63W8Sb2ltcNASEh5i5CiQEz6T+9+Ccdzyg4QqYLGi1ARSfbV6SR0H4CuZ6eXdZ4O/pDUSqu/YObCoOkJCRUcHxCSkxovzHPv2uPBGxDPT/0y0Bp9cCv6yO4pxq0KxyFSLsps0XKIVqLj1ai+TuxDu+S9XBxSF4qiKmpQ5TWQmwfDp+dh0RrGR2FkAD0yIKI3zm+SRWRCKVRdi5CixgUlHk1tZUUJ8dhekYvP9e9wHHXlX6H8IfSxnTKYBRJ6ufTqMyqfPeW8kqOSYzTaKR6k4us4W3j8olJZveiEtY+0tuV4/a1CsIvvtWFKn8gV0PUGZIAnMSQkY7q2ebziUTc8U5XhjCJlOKXIy38zzW+ddWpCOYMDVqr07+kIl1IQLCuQo1AlBOKnVffsZHDeE6EfvXf2ROh1X5r1eczGqfC9732PT3/607S1tbFkyRI+/vGPl6SZJJNJPvCBD/CjH/2opKBqsWDDwMDAlIKqX/va19yCqn/uOKdEyEqhB3cAflS/sHsa1svI5uHHhPRUzJcPvscnH/zRNkkcjs+HjiL1t/JmaFqL8hWNxI32ibhA94GC8RWMiWpWzXywsuIFapM8IxWvdgqdFpIddSqB3vk49v7teaNbLVqFsepySdKefE4597vWkkfRfhS7/aiICuTkngNBjGWrMRqaUVW14PWSvX8j9v7dKJ8f782vBzsrIgW2hbHuRRiLV6NTE+itf5D8B9ODWnAhauGFYvw++Vt050GUx4e65GVyXmODqHAZ6sqbJdym5yh6/1PoASdEqnkpatULxRv00P+gh3tRjRdgH5RwLLVwJeYlLya76QGsZ54CwHPZBszV69Bak9n8OOnH5INtVFVhrrmQ5J5WMgem5v0A6HSGTBqyZgAMA3/LHLxNtRh+L8rrBdMgfeAYiWdzCnOK0JrFhOfXktqynWy3GMkqGCB05Tp8C5vIbH6c7N5nwTQJ3nYrRk01mR98E91/BCPmxQh7URE/qrwCQmH5aEWjQkpC1RLfnhl3EmNHZEQeoGw+VK+YYoDk8zaSQ06xQmfKEXRPAGpWQUTyAHRqFNo3yTEMLzRcigpVO31rDPoOyJQzGg1DcjFqlqDCDoG1MpJQfWxHac5bTtmwbrGMVHYeQu96pJADFIqJB6h5mfSRZ7eKl8/Kgscrnpb5y2F4EGvbFhG0cEI6VXUd5gUrMRYsRvkD2KOjZPc+i93fjz0wgD0wWBL+aDY347vqCsw6yQGydm0j+8gf5ZK88HrMJZJ0rTNpEQ45vBfdVaRsloNhoBrmi4e1tgm992nsXY/Ls2d6MFZfgVq8pkDIrKzII48NozOZkhpGpwLtFD+mv70ggV8EVVkvIVj1C/NhpvltU+OSv9e+u/C+UUoGWyKVQo4ilVJU0zf13TFte7QW79Fot5Dl0c7S8LpQmRhoOdnpQAwa1kgocC5ssutZ6D9a2M4wRWUtEHFydgLO3AkFGmiT/pbrk0pBVYuQIm2J0Mpon3iWZlDuy8P0SD5PICqepsCkyeuXotQHnkZ3HCgQg3i1EPeyGgjHC179bBaG+9CdR7AP7YKxovyXSBxj3jLpO9MQZj0xjh7oQycmZHAmnZa8oFRK5umU1AuKl6FiZfk5oRMTKJ0YRx/dLzLzOVEcAI8X1bgQY95SqGsujQ5ITUD7Pik6nByXml2Xv0pURrsPwt4H5J6V1Ykk+OQCrpYzNzzyPSuS4D5VaG2Lp2WkU6bxvsIzanqdUEdnnpNCH+sp9SpFa0T4orw5H0Kcz1/qPyS5u8Xk3uMvhLGdCIbp5DZVFsiHP3pWvMWToa20EP5xx/Mz3ldaZDmHhtWoOSvPenvgeUCE/uPdsydCf/OV8+48zne4ROgs4Vw/WHZqBHX0QacGyGKINMCBByScIodcbkTNYlS4SsJCDj4kCaGGCfOvRJU1iVE02CEF+QbaRJ40h1BcwsXqFku4x9iQyGGP9MsI3pJLRA7bMXx1Ji2G456n8i9tVdeMceELRDLYgchiH8V65ins9qPHfZmrsgopALpo2dSiodks2V/9D3ZPJypegfem16Jbd2M//SCYHszrX4+KV+TDLIiWl9RL0VYWvemXeZIDoAJhGWUMld5H3boDe4fk+ajqZlTDIuxtfxSjpaIF3boXVVGLce2t2Af2kn1gIwCeK6/BXL4GPTFB8r6NWEeEsJiLFpGxvKR3OfHtSuFffQFGZQXW8JhMg6MkWrvQXh8ahRWMkBxOTvmAmeEAFZcswu7tI7nvqOzO6yW2YQ3+ijCJh7dgDQxJt4jHCL3wEvSBZ7EOHULFYoT++nVk/3gv9oHtGH4bI6hQYRNVWS/1hGwLGlbBaOv0SZyGR0Q3oo3Sz7r3yMe+vKmEZJdcz1zNn57thRC6SB1Em6B7myh/ecNC8n1hGG6XEfyRDvIJ7L4gVC+GqkWicoXjyWzfLQQoZ5R6fFIUt24xRKuFbI30i0ezr8257xHUsvWSL4GSkKGdm/PeFlXbhHHpS8D0kH3sfuwDz5JrSF79r15Gxey2djJbt5E9cGAqaQFULCYKgI6R7Vm8GN8V6zEqKsg+/jDWtifE03n9qzCa55Vet+SEyMYf3S+Dsi0XSB6dfxLBGB7AfuIPIs8NqLIq8PnRY8OiWje5TRU1qPr5qIZ5UFFbyNlJJvIJ83qwF7TGWLQKVV0//X1NJ2GgQyTGJxMjw5Bnp3Ex1M4rUR/TmZSo9rXvnt5QAjEic4U7vf7Cb19IFCej1dN6YXQ2LaPog0fEUM3dE28A6ldJOLAyRDmw9clSohKtkn5TuyDfx2aCeJkOS52yoRNIxYbiQvCCUQmT8kdEyMIfAY9vmuLDWcnbGulDt+8vrWFV3SS1oqoaZOBoqE8UOQd7YKAHPdxf+tx6vBLWOX+ZFPPNiUyMjWD39aCLp2n6yqzg8TrEqByjuhaVm/zTX0M9Mog+vFfCO4uImoqWo5asEaJW7CUaHUA/8r/oTFpq0q27Xs5joA12/v644XhTUNEoHrbKk/NEzgRtZeR6m94ZQ/S0tuVb3Ldf3m2514THJ0IY3oB4f4olwL1+RyZ9vgg2gFMnKOmEaObCNhNC9gIxIT2BE6venUvo9LgQx/G+Qj7gwheiYnNOvPEZwHlPhO76h9kToTu+et6dx/kOlwidJZxTj5CdhaMPSoXrYBXE5kLrYxKDHIjKR32gtVT1Jlwp5CdX+2bhCyWpuGM3HNleGo5hGFA1V+Ldy+YUDKLOQ+JZyaZFnW3t9agqp95HNoPet11CeJwEV1VWhbHmKlT93KK229j792A98xR6oEjyuBgeL0Z9E0Z9E6qhGVVZfXyVnIlxMj/7L/T4KEbTPMzrX4V+4B5011FUrBzjhTdPP9LpyDnqdFLC4cYGUV4/6oqbS7xbJdt0HcZ+amNpmFLLCuw94vkyr3k1+MOkf/pDyGYw112OZ+169MQEE//9P+jBQfB4MNdcSGLbAexRMTJ8i+dhx8oZe2ofdnJSSI+tsU0PiRRIoVKF4fWAoVCGQtsaOymelUBjFRWXLCSxbR/pNsmB8VTEib/kEozkOImHtmCPi5FnVpfjtcYgMYE5dy7exiqsLQ9i2KOogMaIByS0qXmZ1Ofx+qHpQhg95kiwOrVQfBFRMjK94q05+JCMYuYQqS4km09DiiT5e59MxcZasALqL5Nwp6NPlI6exuqgahGUNRaR8KR4Ftp2FZKbg1EJtapbVFhvbFCksI/sEoPYMFELLxRD0vSI52XHpkK4mdeHsfpK1KJV6M42sn+8L28cGs3zMS+6DKN2jpDyPXvIbN2O3Vvo20ZDPUZVNVqZ2BkbK5HGHhwBbLxBhe7I1b5ReFauwHf5eqzHH8Levxs8XryvuBWj+tTUdrTWIiSy9eGpoWIeLyoSF3GHSUWKCYRQ8UoJX0pME9KIQwxXXAo1jSd4PkdlBL99v9QJy8H0oJqWohaskRCuojaTGpdQtPF+8aCM9hdU/U4Ef1iKgkarJfcoVlNasDObcqSVbfGam170aC/se0wUIUGM0NqFojAZcTyMqQkY6HJyPTLiGbeyTh5IVsIBG5fkyZ0eGxDPet9h2V+kUkhVtEq8WzPkdcgAwQiM9Mv1GumX3+NDk26AQtUvlIGoeDW6v0tUDDsPT39dfAFUZZ3k4jQtzJMKu68He/d27EP7pxf2QKHKykWwxudH+XySF+T3OzlCPvESDQ+ih4fQI0Po0RGYQWtcxctRVbUYNXVCjGrmoMyCkS6DVl3YrbvRh/cWhHW8fowFy8WzGZH+ovvasTf/AmwbtWANxvIrnWvfL+8BpRzpaF9REr9XfidGhPgWE9b8gMkSiFadM7lfnR4vKA2mJz1vhimeyoq5ebGXs9oWrSflvznz/DTpb9sClNgNxaFzxfMc6c95dieRTZ3zqJ+hvK4T4bwnQt9/5+yJ0Bu/dt6dx/kOlwidJZxTItS7GwaelXCiyuVw8GEx6KI1sGADyuN34rJ7ZQS9OIHcMGHh1TA+LPV1ciFD/rAk3VY0Qnl9PrkcHK/JrkfRh3cAoCrqUOtuyMf96552rE0bJdQGUNEy1IrL5INbFPNrD/ZjPfg77G7H++LxYi5ZIRLSwZDz0lQSZjFNrLCeSGAdO4Y9PIw9NIQeHgGvh8D116HHRsj8/G6wsnhecC1Gy3wJkUuMiXrbC1+FqhBjUqdT+bA9Y8EK1NoXiujDoe2S8B2vknPuPCK5GJMV6wa70Y//Gp1OoLx+dFkTunUPqqYR4+qbyNxzN7q/R9Thbnw1ZLMk/ud/sbu7UdEoxrKVTDzyNDprYVaW471oFaNP7CXT4whJmCZmPIIZDWFlbcaP9JGxFEopwgvrqbtpPb6qIsMxazHw2G76/rANOyVhhLGLFhJtrmD04W1Yo3KPAwsaib94HVbrURKbnkanM5jxMN7sCNg23kVzof0AaqILI2KiyiMYsbgoMaX7ZWSydimqae30/XK4HVofFRLi8cto5HhvqT3kjzhJ42WSs+FI0ko43Ih4ghL9kg9Ud5EQ+iObZR85SdyqhSXx9DqTgrYdQoJysfChuCTf1yxAGYYooXW1oo/sLB1Jr5uPWn4lKhxDd7RKPluOFHi8Itu+5CLw+bG2PoG15VFAo8oq8LzopRjVkuOT3bGT9KbN6HHnefJ4MOfPx8ooUvuPojMzj077FjbhMTLYx6RdRnU1gdtuwfrdL7Hbj6CCYbyvem1eZfBUoCfG0G0HxZCNxCESlyT4vFLbOLrjMLrjELrzyNScgkgcVVaNKq9Cj4+iD+8phLxW12MsvxTmtJw4FGqkH92+D9r3oycKxEbNWSAGffnMhE9bGfHU5MQJ8kZaSkLcRvumr6FjemRgp2YeVDSVGJI6k5RE+469hXXnroXG5QXiPNIv4hlt+04YhqR8QZi/GjVvZWnB6JnOKZMSL89wX5706JG+Gb0ZyheAWCWU1aJaVki/HejB3rGpIKoBktdTXiMiBxW1kusTihTudzaLfWifEKDc+xhkUKC8EqOqBlVVi6qqQVVWnbQCobYsGB0WUjTQj93bLSI3o8NT1lXBMMayVZhLV6HCk961mbTUOdq3VVRFc9s0LsBYciHUNEL7Puynfy/NX7kBNe/kwqv0xLDkD3btKw2hDUQKhDpaDdHKku/i2UA+D6jvoHh6ylvEq26eOWnpvPBH3xEhg9M9T2cb4TLJx4tWyjxSOavn5UzhvCdC//73sydCb/76eXce5ztcInSWcM49Qt3bpajp4c0SRlLeDPOumHa0SGeSkk8x1gNzVkDPERFAACFA89ZC7aLpycfoAPqp3+YFA9SCNSJFbZgih71zM/YukakmFMVYtR41d2lpjRLLkmTypzZJTRqvD/PCS4UAzRAmUWh7huzBg2T37MU6fITpZHTN+fMJvOoV2DueJrvpAckXuuUOQGM/+HMZ2fd4Ma54KaQS2NseKUnSVQtWiDxxzkiYGMN++FcSs24YqHnLMJauFdnXXLvGR9D7t0BZPfam34G2Ma95DdaB/Vg7n0b5g3hf8wYIhEj+7OdYR49CIABNC0k+I7lVnpYGsr4IEzvFgDGCAcKXrcD2+Bjf38FEaxc6I4aXGQ5Q+eILGW0fZGjbERmA83owPCaGzyS6uJ6ay5fQ/8B2hp+UUDsz5KfmurXo4WHGNu0Q4wRF5OKlRNYtYfTuX6ITSXyNlRj9XUIivUmMzCBGeQgV9aPCflRZLSx7gZAcw4BlN5YSEa0l4bzzGSEs4UqY/wKUPyxJ4YNHYejo1JosOUSqoOkSVNgJYcwmROGwZw+0bZV1qhZA8yWTjNhpCFCkQghQ9TwhV5kU+tB2kUsvKiiqaueKCmB1Izo5gf3UA+gjTm0tj1eU4C64COUPohMTZP94H3Z7LqRxGeZV14DHi7X/AKlHHhVPH6CiUcwlS8gMJ0nt3F/or6aJWVmGEY2gPR5Rph0YRPc7IabBAMGVC7H374FkEu8lF+O79GIyv/wxur8XFa/A86IbMGqm1hMqhk6nsY8ewqiZc8rESVsW9Lajx0ekz8crp9Tv0uMj2Lu3oA/uypMDVVYFlXWoeCUqXikFkmdItNdaQ1+bJPn3HC3cl4p61MI1UNNy6mpao30w0ive7+Hu0jA3j9chRfPF6D20pWD41S6ABZdKv52pfdEKCV8zPc7klblhiLJZLs/M45VitvNXlxaQnhgRJbSBTpmPTUPcQN470QoxvmNVQn5ileAvqM3poT4hQMcOFNo3b5kUnZ6kKpg//sgQ1u5nRKkw5/1RBsa8RZjLVqHq6s9IaNhM0MkEurdbiFFPJ7q7E517FxsmxvzFmCsuxKgtDZHSWgtR37dViLoD1bgQ46ob0fufRO8VURrjkhtRdXNPvm05UZXOfeLFm470BmOOVy/nbaw+r0LOZoK2LRjqFPLTd6SU8M2EXBiq1xFfKPldNBlOKYASae0iuW0rK0p7o453d6aw14WXoZrcHKF4PM7Qd98xeyL0lm+cd+dxvsMlQmcJ5/rB0lrD/j/J6FEwDhdc74QmjUuOjzcg+RNef4m7WbfthP2Oss78ddC4ctoPn86kJBH34DYhL76gCCLUtsj/x4axH70vn+Cq5i2TJPJJBpPd1UH2kT+g+2WU3Wiah+cF16IiM9fjsIdHsFpbybYeFgKRLYyOGlVVGNXVGGVxCARIP/QwWBa+F1yFd+1FZH5+N7q3C2PeIrwveYWozj3ya3TX0ZJjqGg5qnlRnsSp+csxLr0WBrqxHvqVeJIMo7TgYNNCjJWXo8oKYXPW479HH9yJqmtGLbiQzMZ7APBefxOqeR6pe39D9tlnwePBiteQaZPr4Fu1lNH93VhDo4AivGYRactk6KmCUQPgiYeJLGtG+wP0PLIHK3kcz0JFmJbXrMcb8ND5s8dIdYqHKbyonqoXrmRiyy4mdovcseH3Eb6gEWvPXlCKQEsldHdiDLZhVvgwYj6MoImKBsQYnnshZEckpj1eL2FpyWHxEiUGCkVLqxdB07rpCXk2JSpGiWGZJ4ck9Mm2QCH7bFgjCcVtT0ueEUDdMmi4sGAAphNOcv2uUgI09yKomisEKJuB1mekD+dy1XxBaFmGalmOCsXEuDq8F/upB6Q4JAgBWn4pKhCU/x9tJfvQ7yUUzvTguerFmEuWYx1rI/Xww9idTv8PBjGXryA9lCC9a1++4rdZVYEVipJNa7I9g9iJUkUwT9iP32ejJ5xwxbAXMzmM4fMSvO0WjLK49GlHMdFomoe5dv1UQ3FsFGvnVuw9z0gCu8eLZ8N1mAvPbs0GPTGG3vMk9oEd03sxvH5UeZXkMbUsmfJ+ANAjfVKvqX1fkefakFCzaKWEqUYrIVYBwZNL8NZaS7hbzyHoPTS9SEG4HBZfgSpzilu27RMClFOLxPFYLViDqpg5h0Hblni6DjyNztVgMkzJh7KyUsg0OU1uViACcSE7KlYJsSoIl00/MGVZEja2f3uBuAOqZQnGystKBmsAdCqJ7urA7jiG3dkmtddytavCUYxlqzEvWIEKzSyUodNp7L5+dCKBzqQhlUan0+hUTjghLUWgq6owqioxKitRs6xsry0Lu/UA9s6nSzxTRvUcjJUXYsxfXBI2B6CH+7H3bUMf3Am2LeHXS9eit/8JfXSPiINccTOqrGZWbZi2Xdm09JucwMVor6j6TYZhQrwOyudAWb2E00337rMt+S6PDwoZGB8Q73jTClSo7OTbN+KQfNNTqD3l8TphgH75DVJnqu+wFJ0tllM3PRL9EaspkBxPoCTv7mwRPJ0ad0iRIxwy2ifXduVLUFUtZ+WYk3HeE6HvvI1YcBZEKJGi7K3fOu/O43yHS4TOEs45EercCe3b5EW89AZUsAzdsRf2P1Y6kmV6JSSkbokIKey+X5bPX4dquXDqfjMpqaOyb0u+jo6qbhIS5Ixs2u2t2Jt+46gF+TEuuQajpdTg0okE1uMPYT27U/bhD2Je/kKMRaVFIXU2K+Fu3T1YPT3Y3T3okdJcABWL4Vm0CDsYwR6dwJ5IoieS2IkkanQIIzmE8ngI3voalN9L5mf/BdrG85JXYM5bJB/bJ/6AbpWcC2PlZajFF6JME/vwXuxNG0FrVF0zurdD4v1jFRgveIV4kHZvKYSdGIaMuC67BCZGsX59F2iNcdUryf5xIzqVwFxxEZ4rriZ1/wNknt4KhoGubyG1vw1ME//Fqxl+XHKBPGVRQutX0/fQbjKDMpocXlhPeEkjoQVzmOgepnPjNlL98hEONVXS8LKL8MVD2BkLO50lMzxB271PkR6QUb6q9YtouGENQ0/sp++PW9EZC+U1qbrmQsJNFQz/7nHSnZKrEagMYoyNoLwmPjOJMdyJp8yHEVIYfhsqKkVG2+OFNS+D/b9n2gKLhgHNl6JmU829uJ+kJ6B9qyQFg3zUw5UwLDLVNF6EqpNK1zo5CkefEUn3YrnlYgJkW3B0j/RfxwOkohUieT5nQUFFa2wEe8sf8/kUqqwK49JrUZXicdGDA2Q3PYB9rNX5fyWea29ElVeSefwJ0o8+Jsf3evGuXk0mrUlu2VkQP2icg+UNMLGvQ8LyCr0Zb2UcT2Wc1NGufD5YqLEcNTiAzmYxk8N44kGMsjihv/lrSCfJbnlMvEU5cYbGuZgXXQamKYIjhwr5VcrrE4MVMJevwVy/4ayO8gMi4NB1VGSRh/thuN8pkFzUV0yPGOwLVkDVnCmERifHHe/d7ryE+RQoJYIFvkItG3wB8ZTEa6CsekaypLUW47HnoMioa1u8hw3LHCGYQZHaH+gqtLd5mXh1inKYTngttBbZ/gNPFvZV3P54jRRqrqiHiroS8ZYp+7JtKXLafUym3vaSsEXVtAhj5fr84IzOZNAdx6YlPjkYjXMxl69GNc+fQrZ0Novd24fd3Y3V1YXd3Y3d18/JQsViDimqwqiuwmxswIjOPPgFYPd2Y+/cinVgb8HLGAxjrl4n0QOTwvPsAzukiLYyMK+9FSqqJWS59xjKH0Jd9eopgjfg3J9MyulLsw/H0plkKTEa6poqdW56hBiVzZEw77EBIT0TQzMXBa6eCy1rUNHq6f+fO75tCaFv3yUez5OFNwBVzeIRLW846++Ek4GU4DDPWZvOeyL0rb+bPRF627fPu/M43+ESobOEc1tHqBv2/V6+b3PXSxzx/kfFpQ8S7mZbM9ejaFwOC9cXDMfOgxL+MdhdEqqhwmWo5ZeLupNSUu9k52bsnRKCoCrrMK54WYkQgbYs7D3PYD25KR96YS5ZgXnpVZIHlFsvnSaz/RkyTz6VHw0vHFhh1Ndj1NRiWZDpHiR7tGP6D4nWeLxZTI/GiEQI/c1fY+14Gmvb46hQBO8tt6P8Afn49bZLRfNAqeFhH9mH/dh9eaNN1c/DuPyG0ornQ/3Y2x/JEyIVr4RQRPKI5rSgQ9VY255AVVTjvfl1WK1HSP5CNPTVogtIPHMQAM/KZYxuPYS2LHxzqrGrqxh+UrxAnniY+tdciW9OJX2b9tG7aR+ZYbk23niQ+hsuRPt99O9ow/R78Yb9eCN+fJEAkaYKeh/aTe+jMkrsKwsx97Yr8JeH6fzZo0wcFGLhq4xR/dKLYWyUoXsfBa0JVfphfBxzvB+vP4snpDH9WVTQlrynqgqRx25eBaEQdO91cn1ihSlcjfIV3d/BDiHhs0w41qPdcGxLwbOkFLRchqpaICOIB58QAzZfgLO6UG8mp3jV3Sq5bE4hVBWMoi64VKrRO15RPTqEvedJ9CGn5ophYqy4FLV0Hco00akk1lObsXZuFUPZMDFXXIi5bj0YJqnf/4Hsrt1yv1Ysh6o6Jh55Oi964W1pQJdVMLbjcN7742+eg1lfjZWxyIylSXUNku6TZPJIZVBy3RR4yiJ4UyOobBqvnsCMBPAsX0bg+uuk7cNDWFsfL5HrLoZR34y5ai2qsQXryU35+lWqug7vi2+cNlROaw2WNUWNccb7lJiAVBLi5SfOB7KyMDqE7jyCPrgzX0wZQMUqpKDy/OVTw+60hoTUnWG0TzwzI/3ybprF50v5gkKIympRZdVSx8g79Rh5D6O24dAz2Huc0F2PV4hzy4q81LeEfx4ueJZzSeD530pycRrmF+1Xw0CnlBkIhIX4lNeWKOVNe91y16zrCLq7bWrOhj8oSpxL10kOEEKY7H27sLY8NkXlTcXKRHhmTqPMJ3njdTpNZsdOsrv3lBRyLtlHOIyKhOVe+X0on6/kt56YwO7rx+7rK+TJTd5HWRyzsRGzqQmzqXFGYqQTExK+t3t7/lxUICjKocvX5PuL1lqiEo7ug0gc8/rXgwL96M/QI/3iUZwzX8KgU7kpIYMvtk2+CPDCi2YUxzkeRNRiSMLphjok9Ox4+TWmVwZuwhWSIzPYIWFqOZQ3QMuaEoEiyA0W7RHhjVxYmWFAeaP0u7wkeK5ga6YwUBSKi4R7VYuIhpyGGIG2sjAxKkIeiRERQZkYkXO2raLJlrnl/K01hGMQr0bFqyCey7c6/nNwNnHeE6Fv/B2x4InzwkYSacre4RKhk4VLhM4SzikR6m+FI5uEADWug+33ySgVwPyLoXl1nrgw0i0j6L2tEp5RswCWXQ3pBPrwzim5E4AUqVtwITQvK9QdSSWxH7svH5+tFq3GuOgFhRH2XBjRpgfRw07Sf0U1nquuwahrKLTdssg8vZXMlifzxURVJILZ2ICqqUFrg+zQGJnWNqzegeJmYZbH8c5vwohFMUIB0DC28QHIWviCNoahMVta8L/y5WR/+p/o4UEhYRteckKjzT66H3vbwxgtS1Ar108flqI1+ug+7CfvzyvjARgbXkVm468gm8F73StRtQ1M/OA/0OMTqLnzSTzbBrZG1dYxflRGVwMLmxgbTOfD18ouWUL5hpV0/WEHA1tb0VkxRrzRANVXLMHfUMWR3+5k5PD0I4GeoI8lr1uPP+TlyI8fIzUgBkTNC5ZSf8MaxnYeoec3T5IdEWIVXlRPIGSQ3NOK4TUJ+LKovk48TOAr82AG0hhxP6qiCmPhKhEtMEy47LYTjGBb4pXMJZ/7w1A9T6Z47XHvg9Z2QdyjdhmqrFFCibbdW1AMm8ZY0KODIoPd68iG+4KoxeugZXmhf/Z3Ye95qlDYF1A1DZIbFpPcJPvZXViPPyRy0YDRsgDP+g2oeDk6kSD5y19jtbWJutu6daSO9JI5IopvRjyKZ/FCRncdxRqUtnoq41BWzvD/z95/R8l1Xmfe6O/EyqFzzgHdSI2cQUTmKFHJVrLGtmzPN8vjeGfmu/Nde649wROcxmEsyxpZgcqUxEwCIHIGupEbjdQ5x+quXOec9/7xNqrRBEACpEjDc7XXqlUdqk6dVOfsZ+9nP8/lgXdN4D35PrRUHJFKQzyGSQrdb2C6bFSXifvpp9AbG+b203QE+8wJ7I6LAGh1C1CXrkTNn08FcnpuYL0tu5SK6UJ76GEUrx8xMYoYH5PPE+OITBq1pAy1pkFSke4wsO50XsO52i6l7oWDWlSKtmwNSlXtvQFdIWBsEOfaeXkMbtLodEMKlixY8Z6mr8Kx5fcuk5RgLJOEdFJ2rhPTUuJ7euz2fa1qciasrAGKqucryL2jC6QUVKC0bEfxziXpYrhPimiMDfBeoeSXoq7YgpL/7rNc87Yrk5bdnsEueX291eMHpHhMYTlKUQVKUQWE5xcXnN4ued2dnJV59wVQK2pQSstRS8rvSkMW8Tjp1jYyZ87O87ZSPB7UoiLU4iK04iL5s98vZ0ITKZxkCpF9JHGSaRSXiV6Yh5afA+m09M2aBUb20DDO8PDt+2oWGOnNTWgVFXeQC7dxrl7CbjuBmJ6S7zHdqEtWoC1ZLgtc6RT269+C2LTsNm54XJpiH/zhbfe1dwuluAalYSVKzr0ft3eGFCiagMlB2dFWFAl6bvpgufy3b2N0AnrO3rnI4/JJCvDojTlw6vJKNdfS5nf11BKODY51z+IOwrYkYExGIRmDZExeexNRWZSIT99FUfD9h+LPmQVHBZIaGi78yAQTHngg9Fdfvncg9K++8sBtx4MePwdCH1J85NS4+KSsynccksanhgsWbkfJLb/z6+2M5OMGC2F8EOf4y9lkRHH7pOxrbinkFN6W6IrIOM6BlyTVRdNR1+yURnc3/5+IS2PTmzQitxdt1QbU5iXzAIU9Okrq9Tez0sJKKISxejXC6ydzrZt0RydO9Jabl6JgVJRgNtSgFuaTHp8mMzCGk0wjMhZO2oKJMdT4DKoqMN0WiqrifupJ1ICHzMvfB8hKWN+2TxIJcLvvWyJVpBI4p/cjutpRKhsR7hzs00dlN+gTnyf12uvSrNTrIzXtIDIWSkE+sd4pbpqdTl0fIzM+g+Z1UfqZLQhVo/OFQ2Sm5c3GV5VPwcYmjIIg3a+dY6JdJmGqoVOyoR7N1MnEUmRiKeJDEeLDMnkq37aQyocXMvB6G2NHpWiCuyhEzWc3484PMLb3HBMHziNsB4HA79cgncIMe9HHelAnhnAXmuhBBTXXh+JzS3qQC8mZL1+M0nD7voTZyuWF3ZJ+BJImcuvciOmRg8ae2S6Sxy/n27zhO1OZUjFoe0Umue4ALNqOEpxL9kUmheg4ieg8K5MIVZVmuQ0rUXR5ExHjQzhnDiGGb1GKK61GbV4NhbP+KckE1v5dOF1yfynhPPQNW1ErqgFwJqdI/PgnUhDBMNAWLSF2/KKU7NU1XCsWkxiNk7jcJTfb58G1qJ6p9n4y45LuaIT9uMvycJfn4y7Lw8gNMHOxm7G3WhG2g+Y28Rf5sAZHUMaGcRcE0d0OuikTU+8XP4/qfwdASc1SV99FcETMTGPtfgVnZPCur3lnqEWlEhAFQ9jXO3C6rs9XkVPUORpeTh7asjWodQtum+W46zqlU4juy4grZyWN7uZiKxulSl/++/cSEbYl5aanRhBTI9LPKHaLJ41uQHGtNHeNTma7QGi6lF6uWjQHsMeHcc4dnhvO13SU6ibZJbo5GM5NqlV6HsBTqhZIyfW7yPYzNSaH/wc7EaOD8zt8iopSUIJSXIVSXAW5hXcszDjjo9jHDuD0dcm3mW60letQF7W8K8XImZoic+o0mYuXsvOXSjiMuWolWk01SiAA6QyZ/mGs3gEyfUNYA8MSqL9XqCpafg56UT5aQS56YT56cT6KoWMPDGD39GL39t0GjNSSEsz1a9Gqq28HC46Dc+0ydttxxNRskc1woS5ehrZ0JcxMYu/+vqQor3sEtXaRFPm51iqpti6vvKe5vODygNsrr0XT43Kea/D63K7PK5PdwILbgdmHGXek/d4awUIoXwwF1R94dicrBNJ7GabHIBG7OxX1naHpkibqCYA3KAsGpkcWyTQNFO2Wn1X5DDAziYiMQmQMIqNyzvMdoS7cKI3OP4J40IHQ5F/8yj0DoZx//dUHbjse9Pg5EPqQ4p/iiyWGrkL7PvnLiqdRQu9dzRKxKcSBHyIySZRQvuz8lNbf9eLq9HdK2lgmDd4A2kPPZOkYAM5A75yviqqhLV2Jtmz1vORMOA6Z062kDx2WlS23G/OhzTiKQeLAnNEngGIaGHVVmLWVOKpOsmuI5LU+rKmZu2yQwCSJ7tbRNAvdcFBDIbxf+iJO+zmsI3ImSt+wDW3JCvmWZAL7yD7sq5dQyyrRdzyF4rk3x/p5Hx2PIlQN6zv/gEin0Hc+hRAayZ++jBACS/FiJyzUvBziIwkcy8bdVEOkcxxrJoGR46f8Sw8zcbaHwbfOgpCgperTG3DlBeh89SxDR69JGo+mUrK+noqHF+HYYHgMdI+Boig4lk3nK2fp2yspW6HaQpq/uInkwATd3z9CZiaJoikU71xK8bZFWNNxhl86RvRyH6qq4NEzgMBMjGPODOEKg14UQA26UE0Hwvmoy7fCtaOSkrH207c5sYvpUWlimIrJeaLm7ZBTKk16RzphvGf+sO6tESyA2jUoOXMGnfNBkB+WP4XilpVt4TjQ2464fCxbpVSKqlEWb0LxhbPHxjl7WM6FgaTBVDVJ9b9bjH2dvm6svW9kz1991QbUpSuzSb3V00PqldcQiQSK34/SuJD4YalkZ9RVopSVM33wnJz1URT8axaRjFtETs9SIYNeip/bgKssj2jXCNHOEWKdI8QHJlF1jaJNC0hc6SU9PIkQAreaQRMWRmwCsygH02OjKgKtuhr3x597X8mZsG3sE4ewL55BcXtQcvOlN1duvnwYhuz4dF6dL6V8SyjBMGrDQrSGJjBM7POtOBfPZGeRFH8QbWELhMIoHq+kwXq8Uq77Luss6WbdOJdPzxMzUQpKUcrrJI0mnH9X5bl72nYhsgak9F9BJO4gVlBQgdKyLTtPIiLjOOeOzKmxKSpK/WLURWtvk9Kf91mxGfm+m+ecqklgt3A1KIrs+vTfQAx0yUr7reEPoZRUo5RUyc7Pu8hVi5mIpG92XATELH1zGdpyKfJxx/fYNnZ/P9a581gdc11RtbgYc/Uq1MpKMtd7yPQOYPUOYg2P3XE5AIrLRHG7UN0ulNmHE09gD48j0nf+jmv5ORi1lZg1FRhVZSAc7P4BrOs3sC7NATK1qAhz3Vq0uts7jcJxcDqvYrcez3rQKTl5GB//HOLyaZxzR6Qlw2O/eJtoxLuFmJmUgKjv8hw9OlSAUr0EcoqkCfdH5G8j0vFZIZhZ6m5BrZRyDxa+95vfa9nxGURvu5yhTNzhfqpqsijq9oPHBy4fiscnC1deCXyk+NIHA4jSJyw+28GdA0dKy3aUgjsXcn/W8cADoT/75XsHQr/9Dw/cdjzo8XMg9CHFR94RSkzDyRclH7hmJUr1ivn/d2zpT2FlIKdYeqlkUpIyEJ1ECReibPjYXXm6wrIQ547gXD4NgFJQhrr5qex8jRDiHb4qcphczc2ftxxnbJzkrt04AzLB0mprUBuaSBw9k73ZKh43rqY6jLoqMkmL5OVuEld6EZlbhoJVFbOiCFdVMZrfg2IaiFSGydePgpXBbWRQdRVDzKD5vZibN2GuWY11+ij2KTnYrm97HFQN+/Dbc5KtyCROf+w51DyZIIt0WvprdF1DKSxBW7z8rlLEdttxrBOH5PY//SkS//hNyZf3hkiPx1F8HlLCgzU5gxoKEo1kEGkLV3EuJZ/aTO9Lp5m5Jmk5eWvqKX96JcMnO+l6/RxWQiYVBcurqH6ihejIDFdeOcd03xSApLSFPLhDHoqWVeDPcXPlO8exkmkMv5vmL2zEX5ZDzw+PMXVeJpqu/AAVz64m2FhC19++SrJvDJffQE/FYXgAHxOYIRWzNITqUVCDbggEJJdeTckB4dImaNgovVsSEdlp7GqTN25vSKr/vEMJSTi27BTFp2bfNyOfY5NzFdDccqhbI4d67wCCskPolw5nZ9kUXxhl8eY5NUPLkknRpZPZLoZS3YS6dOM7ZtksCQ7OzZ7foVz0HU9kjUuFZZE+eEiKXQBqUSGU1RA/3AqA2dxAYjpD6oakxpnF+biWNjJ24GKWfhheuwB3TSn9b5whNXYXIA+EF1fgCZpETnQgUin0yCTuoI7Lp6J7TUy3LX0hKypwPbwTNSd812WJRAKr44qcwcibP/dw61zMXd8fnZGA6MYVRCyKWlUrBU4Kim9PTFNJnEtnsc+1zvs+zQtVQwkE5/zC7tK9EpOjOJdbEd2Xb59RMd1SDCBcgBIIg8cvEzSvX87e3GsnSgiYHJIzOwOSiqQ0r8t2gYQQiIsnZEI9G0p1sxRXCYTv6TMAxMQwTusBxEif/IPhmp3buGW7NF0CntIaCX7eY/kik8HpuobTcQGnvxduimbULkBfswkldPv7RTKF1dWJfe0GVlfXPPqbVl2NuWYVSlExqdYLJI604sTnV+nVUACjvAS9ogSjvBg1FJAgaJ41goOTTKGaBugaTmQGe3QCa3gMe2QMa2Qce+wdEuGqilFejFFTgVFTgRryYZ1uJXP23BwgKiiQgKih/vbzTghE13Wp5piMo61Yh7ZyPc7eFxHDvSg5BaiPfOa+B+9FYkaqF3ZfnN/J1vTZ2ZZCqUQXKvjA4Eg4tgTYdysSODYI566mu/f8ObYl/dN6Ls33TzNMKGtEKaqWnXm3/2cCcv65xAMPhP7Hv7h3IPS7X3vgtuNBj58DoQ8pPlofIVsmitMjECqCZU9JoGNbiKun5dD4zMSc4aHpgbJ62Z4e60Nx+1Ae+tQ8f4t5yx8fwjn6Zna4WWlYirpiazbhEMkE1tuvZ6lwWuMitE3b51UxhWWRPn6CzImTcj0MA2PjRlK9o6QvXJldLwP32mVo5WXEL9wgfuFGVkULQAv48DRX426sQA0FSPaNkxqYwLEshOUgbFuKKExPo4kMLtNBScdwBXQUnxffr/wL8Hhk9+dC67xtVMJ5aCvXYZ84LCl/uoG+aQdiegrnQpuUIJ57NWpNvRxELyqdo85k0mRe+CoimUDf9jiZK11YHR0It4fURBpQcHILSQ5NoZgmsaSCsBy8NcUUPLOOG/+4n9TYDKpLp/L5tbhL82j/+iFiQ1MA+MtyqPv4KmwbOl45y1Tn+M3V4U5+PGWrq6l7pJmObx4m2j+JoihUPraEyocXMXWuh76XT5GJyGQntKic4s1N9H9zD04qgzfHhdLXgyvSi6fAxCwJoplplNxclKBbVrdXPQqXZ1UHFeX2WYy8Cli47b5MB0U6LkHUQPvc8gy3nP+4FQRNjUgANCaBh2K45RxQzZI548vBbuwTu+eMffOKpaT7LVQrIQSitxPr2MHsTIW2sAVt3RYUQyYd9vAwqdfewJmQ57++ZDGW4iZ5/Kz8vaGWmcuDCMtC0TQCDy0nPpkgckKe12Z+kKLn1jPVMcTw3ovZY+Yty8VfU4i/uhBfTSGTbZ30v9aKsAVmro/iTQsYe+MkYngUT9iFaUVxFQQx8gNo6RmZJOo65ob1GCtXzE9I4wnSp0+TaTsDmQzoOu4nH0evr7/nY/F+Q1iWTND7eiTNJhGHRCIrW34zFMNEbV6CtmTlu8ytRBGd7YjJkdmK8V18dm4NlwfFF5C03rxiaZwcyntXgHTzNpj9LqcSOEfemFMRLKtFbdkovZHeRwghZPen7SBiZnYb/CHU0hqU0hooLH9PgQohBGJ4AKfjEs71jnn7Uy2rkvTj4tJ573FiMayOK9jXr2P39c+X//d40OrrMJYvQw3nkGy7SOLwaZyYBLFqKIjZWI1RUYJeXoIW9OMkUiQ7B0j3DGNH49jxJE48hZNI4cSTt1yvpRqiUZyLUZKHWZyHUZyHFvQhkikyXf1kbvSQvtGLE5mvCqrl5+Ddug6jopjM6TYyZ87IcxhQ8/MwN268Y4fIvnEFa9fLoKgYz38OxePBfu2bkE6iLliOunLrPR6td+z3VEKah4/2SjrXnWThNV36q+WXoeSXSxGMd6GsSRPTSRjpRoz0IMb75SzjgjVQ0XxH6uPdl+VIz6qxm8BGnRPsyD5UKWIwcH0e7U3JL0OpXDhPQfP9hqShTkIyLn+2Mnd8CMeWJuu5xZLmeZ/mvB9GPPBA6L996d6B0O//7wduOx70+DkQ+pDiIwVCXa3QeVryn1c/j+L2S8rbqTflhXs25AVHnc//VTXUTc/f5rEgUgnJWe+7jui7JpNStxd17cOoZbXZ1zljI1hvvSQdwjUdfdMOtKbF85ZldXeT2rM3azKp1daiNS0kuvsITkRWxj1rWlCrK5nef4ZUz5zErB7y411aj7u+glQ0RexSD7EbQ1iROw++CsfBLVLoHh1TJNFNBd2Oogc96EuX4n54h+xe7X0D++olSSNZvgZt+RoUTZegbverWbPM7L4L5aI1L8Hp685y8AGUvAK0piWoDc04ly9gHdsvaUMbHib5wx8hAEsPYs8kEG4viUgGFJWM20c6ksRTVUjhsxu4/rW3SU/FceX6qf/VHcRGo7R/4xB2MoPhc1H95DJymku58N2TjF6S8x2qoVG9pZHah5vRDI1kJEFqOsnk9VGuvHwOIQS59QUs+6UN9LxxnsHZGaGcBSU0fX4jmqExuOssIwfbEbZANTSKNzUwdfACQgi8iXFckz24QyruUr8UTCgpQynMh3hEygg7MelJMXsu4Q1J6kS4FMqa33eVVMQj0HlKysOCNK1c/rQEQdfacC4dzn6mUrMUpXFVdrBWZNI4Zw4irp6Tr/H4UZdtkvMctw6VD/RinziUpX8pbg/6lkdRq+uy51LmxEnSR4/J+R+vF/ORnaSu9JI8LWXgjcVNTJ/rRmQsXJXF+NYvZfiNVtKjcg4ld/NiQmsX0P3dI0RnhS0KNzdR+tgyNLe8sTmWTeTGKKquoWnQ9a2DpCaiKJqCP9eDNTaJNj6Kp8CPKeLoIT/+xzYjOq9JXy1kxdz12COofj/pk6dkNf1mB9Xlylb/zYc2Y6xa+U9S6RWWBYm43O9nTyImbwJ5FbW+Scoi572HZLBlSUnuqTE5VxObngVbs4Pcd5Mk1nSUcL4ERqU1UFx512RTjA1iH3oFZr2i1FXbpMT3O9bDOd8qxSJcbhSvF9xeFO9NGqAPJTfvtiTvpjktXv9sF+EehCXiMSnc0XExKzwDoARCqAsWoTUuRAnM71ALyyLT2kb62PG58wBQc3PR6urQ62tRi4tBCJJnLpE4dDqrdKiGAng3r8a1ZAEiY5PqHiTZOUDqxgDpwXHuWHW5x1A9bsySPNx1ZbgbKzGKcnEmI6Q7e8l09pG50YNIy/XVSwrxbluPXpxHpu2MBPWz57FaUoJr8ya0ijnqlBAC662XZOe+oBjjuV9ADHbh7P+pfM+WZ+fduyQlKwHxGUQ8KoFLSdV7C7hEp+TMWWR07vmd4EjTUXJLJCjKL5ddIzsjZ3FGeiQAugMtE5Dy/s0boOg91sWxpcfV1VPz5t7eKxS3DyqbUSqa70sGPvu5mfTsjM+ElMWfnoDIBCI6dXsx7F7WJ5QnjZdzi6RdQTj/nru6P6t44IHQn/zSvQOhf/P1B247HvT4ORD6kOIjBUKpGLTvh9ImlMJaRP8VxNm9CCsjK+WLNkJ+mRxoFA6M9iH6O2ByGGXRRpRieXMQjiOrltfOSY7+LaeGUtmIuno7imuOc25fuYR1YJf02QmE0R95ep5SlROZJrV/P/ZVya1XfF7Mh7aQGhwneXSWYhQK4t2+nuiFbuIX5ByFout4F9XgbWkgHc8wc66LaEcvIjM3NKpoKu7yfDyVhTKh1FTseEoO/qdTeHUbVXVwiQSaS8H0CFSXieeLn0fLz5f88htXUPMKUXLeYTroONjHD2KfO4WSV4i2Yi1qdX02cXImxnDOt2JfaQdn9gao6bPSpRn0LY+QvngVu6sbEQiTGokhgER8dli0oIBY/xSa10Xxp7fQ+Z0jWNEk7qIQ9V/eyUhrN50vtSGEIFRfxMJf2sz4tREufOckmXgaVVOp2FhH3aOLcIfuPAMw2j5I2z8cxkpm8BX4WfUbW4l2j3L1+yew0xZmyEvT5zaQ01hMYniKnh8dJ3p9GCPgJqcun+iFLpShAYLWCC6PjafUgxZU0YoKURoXS0EOTUfd/llIx6WikevOsxsiPoM4vx90A6WkDgqr7lkqVUyPwng3lDRJgN/TjnNmjzwHyhpRmtfN8wYRowPYR9/IKm0pjctkJf+WhNQZHZYA6Cag1XQpib1sdXamwpmJknzl1TkKZ0M95qZNxHYfIX1Fdj7NlS1ETl5FZDK468pRq8oY23UGYTvoQS+ln9qMZUPXC4ewYik0t0HVpzaQ01JFeibJxKV++bg8iJ2UyV/himpqn15G/yunmTwrAZYWncZlCtweFZM0rrCJFgoS+pVP4XR1kdq3XyaIiiLPr+x8RSHmunVoNdWk9+6T4AjZ0XLt2P4zSTbs/gGcSAS9rvaeTTNhrhNnnzmFMzhH0VErauRxKCm/f9GSm4ltIia7LhMjiPEhxMSwnGm8NTw+1OomlJqF2S6PEALR0YbTdlDSkAJh1E1PzZshy9Kwju6TxZ93CcXtQdu0A63u/k1sheMgem5gX76A09M5J56gG2i1jagLFt1xHwkhsK9dJ7Vvf9Z/TS0oQF/YjF5Xi5qTA4A9NU3q0lWSp87jTM8CoIAf7+ZVmEsWkLjQycyJS6T7R25Lbo38MK6aUvTcIKrXjepxoXldqB43qs+N6jZxYknSwxNkhsZJD42TGZogM3q73LkW8OFuqMDTWIG7tgwUSBxrI3n8DCIjz2OjqkwCovwc0qdOkTndmj3HtepqzM2b0ApnacyxKJnvfR2RSaGv34q2dCXO6X04HW2yU1hSPQt8ZiTQfYcQgZJTgLr8IZTiyns/VjfB0Xi/7FCP9d82/K/ohuyS3Lr9qibFGAoroaBCdpyunMoWKpW8MpSFG1ByiuZ/nm3JuZ5rrdnZHsVwQ2WTLIbKlZLnjBBzDwWU/AoorHjPApWwLIhGJLiZmZp7npnKdtjvGIZLzs3pxvyHpstrvm5IH72pcWm+ficwqKooOYUozatQKxtu//+HEA86EJr4L18k6L4HIJRMk/tv//GB244HPX4OhD6k+MhnhGb5/mKoC+fEKwAoeaUoKx5G8by7cZ1wHOkGf+XsvAuTEs6XPhjl9Sh5cxdjIYQczj0tufNqRQ36jieyfH8hBJmTp2Ql3bKk2tuyFiivIr7rMPakTCBcS5sQoVwie1tlpRQF3/JG/OsWMXOpn8njl7PzFQBGjp/Akmr8C8rxVBaiGBrWTJJbT+GRl44xc6ELzUrhcitokXFcBQEMTXaJtIoK3J98/t4qsankuw93JxM4V9ux28/PSdX6g6jbnyT5wvcQGYu0ZSKESlr1YCVt1LwcZgblPs57dBUDb1/ETmTwluVQ+6XtdL52luETsgtSsr6BqieXcfknbfQfl8l3qCKHll/agL8oSDqW5vIbl+g/24fu0nEH3biCbtwBN2XLyjHdOqf+134SE3EMr8mKX9mEO+Di0tcPEh+Sx6B4XT21zyxHMzUu//mrJIYiBBuLUaenSV28hi/Wj89v4Sl2ScGEgBu1phlFTUlvjqZ1qI2r7r4PJ4cQJ16dL7V6U764pBaC+VK5yXC/Jx1EDHfLc1sIlLrlqIs2zv3PtqWn1cUT8g9eP9raR1BKquZek4hLgDtr6ouiojUtQVu5bp5E9K2CCJgmrh3bEaEcoi++KTuYqopr/Uoihy/hpNKYFcWkhEH8huxk+hdWUvzxDYwe6mBw93m5OmU51HxhC3ba4eoPTzB9Y3TeeWv43VjxNMJx8OQHaPrCRlL94/T86Dj2+CS+sBs9OoWnMIhpR9EDHlSfB//TO9FL8km/vQ/riqTiqcXFcp6itgaRSJG60IFeVYbo6yG9bz8g54vczzyF4nYjkilpljkwgD04iIjF0Rvq0Zcsvk2Z7mbYAwOkDx/NdqQwTYyWFowVy+76nruFMzKEffaUNIGd7TYoBcVoy1bPK0C83xBCyAr2+DBitB/RcxXSt9CDcgpQahbK/80KIiiVjVJK/RZPI2dyHPvw3my3WPH60Zatlp+RiEM8JqXW43FEdBqRkF1rtXYB+qbt83zT7rqukUkJfq5cmuf/oxaVojYtRq1dgGLeOSGyR0dJ792P3SuBpeLzYW7ehL5QmlbfBD/p9utYgyNzy/b78GxciXv5QpJdg0TePE56aE69T88N4a4pwVVTirumFC14O4VaCIEdS2JFk9jRBKqp4yrKQXXNFTyEZZMZnSTVM0zySg/JGwMy4b55HFQVs7II/8om3PXlJI6cJnnqfLbLZzbW4N2yFtXnJnPsOJnztxgWNzdhbliPGg5jXzqHdXAX6AbmJ78IPh/OW99FTN7FdNTtRfEGJHi+KfZRVou6fPN9iSzcui+YmZDdn7E+CZBuLtcXhsJKlMIqyCu9rSAkMinE1dPSSPimiWxZA0rTOqly131Bzi3dNId2eVDqVkD14g/kwyOiEcTVs7JwMBO5M0C5NVwelFCupJwG8+TPwdz7FjIR8agsVIwPye/nxJA0ZgfUDY+jVje97226n3jggdB/+sK9A6H/+xsP3HY86PFzIPQhxT+Jalwqjtj7HUQ6gVK1EGXJ1ntKIpy2Azjtckgcl0f6eNQtvuPArrBt7AO7sK/MepYsW4O2ZtMctz6RIPn6G9idXfL/5WUY6zeQONtB6mw7MHvj3bqWmXNdJK/Km7a7tozA1hVEznQxdfIKwp69wfk9hFY1EFxajVmUQ6x7VCptdY8S6x7Fis2v9Kq6gluXEtVuJ4GuC8z0DHrQjemyUE0Dc+MGzHVr73v/3i2EEIiRIZzu66jV9aSPnybT0YGdsLEUD8LlJhGxQFGJ2ybCdgivaWLkXB9WNIm/uoCaL26h49vHmLg8gKIo1H1sJYGGYlq/coD4WAwUqHtkIQ2PL0E4gmv7rtD+xiUy8TurMimaytp/sZ7C+gJOf+UAU90TKKrCwk+upHxNNdd/0srgEUmVM/xu6j++En9JiI6/eA3HcijevIDIG0dxDXUSDKVwF3kwS/NQXWmUUA7q6i2I9iOS1/7wF+/ILxf9V3Da9khTymAeFFRKs974nSuKiumWFLiSOkl3M29RGpwcxjnyY9l9LF+Asnzn3Dk3NohzfFdWelmpbkZdtW3OaNFx5BD/ycPZWS+1vgl91cZ5Q+VZAH/wkHxNQQGup58kfbWH2G6pcKiFg7g2r2HilWM4yRSuymKStk68cxjF0Ch6ei3+xdV0vXCI6cuym1SwoZHyZ1Yx0zPBhb/flxW98JfnkruwjLzFZQQq85juHKP9G4dITcZQdY3aZ5YTu9zLdHsf2nQEl8/AraTRvQa+Ii9idpbDvaYF37b1OEODCCHQKioQqTTJ42dIHD+DSGdQdJ3Axx9F1QTJV1+DTAYlGETR9ezs022hquj19RjLWlDLpbS4PTQkAVBXV/Y1it+f7T6gquiLFmKuWomae39JpIhMYZ87JdXPbspOB8NoLatRaxrel5LjHT/HthEDnXL2aODGfDqdqsqOQOOyW+aF7mCq27JK0mnvMt8gbBu79Rh22wnZXXJ70TbvQKttvP218RjOjSs41y7PU+lT3B7UxkUSAOXc3eBTxOOkDh/BOidBN7qOsXIF5prViFSG5PnLt4EfkJ0W16IGXEuayIxNMfXmcZLXpaCD6nYR3NyCt6UBPTQHbO1EmtjVfmJXB8hMRbGjSaxoAiuaAOcdqYSiYOYGcJXk4i7JxVWSg6s4ByM3IIt2GYtk9yDJK70kr/SSGZ/KvtUszif82DqM/CDxAyey9w4AV0szvq3rwMqQPnx4TvVOVTE3bcRYtRLr5e/jDPahllWhP/m87AJdOQumG7x+KfPsC0ihjeysaxzn/DHEtXOzHRQVtWEpypJ185gQ9xtCOBIYaSaK795yARGfkSqYfR3ZbVM0c65b5PajNKyQ3n7vc7bnpqm409E2p4h4axguSbn0h2Uu4A/L34N5d1Uj/KAhZ6ciiPEhKR7iufPc8s86Hngg9Mefv3cg9O+/+cBtx4Me7wsIvfTSS/f9QQ8//DCen9GN7J9D/FN0hMTxVxAj3bJCs/mT93SBdPqu4xyQx1NduVW6u9/lfSKVxNr1iqyIKqqcB1q4NPt/e3iY5EuvyKRI1zG3bcW2FOJ7jyES8gLuXrkYtbyMyVePYscSKLpO+NG1ZByVsV1t2HGZqHqqCsnZsJDAwgpiPeNMnuli8nwPVvQd/gYKKKoCYvYiKsAVMNEScUQ0isejYKRjuPK9GAETzY6jKAqeT31yHr/cmZkhc/YcelUlWkXF/ez6+ftzcpL4176OE4uTttxgukhaLhzLIe1oZGwVT1Uhac1k5soQnpIw9b/+MB3fPMrE5QE0U2fhL29BMXVO/e1+MokMnlwvLV9YT259IT0nuzn34hkSkzIJDpaGWfjkIjRTIzWdJDmTYuzqCEMXpYHf6i+upWJlJee/fZyBU7KSXbm5noXPr2Sme4wr3z+e7Q7lNpWSv6CQgVdbUXQVjx1Hv95B0BfDU+TFLA2jBXUUnweleRXK2HVEIoq6dCtK9dwMhaQYnUBcOSkPUVE1yspHUHRzTr544BoMd0MiemfPCk2XYKi2Baw04tCPEOmklDZe+xSKqknjyXNHJO0FJIhfvWMencIZ6sc6tAcxPiuvm1eIvmnHbUPlIpki+eab2NdmZa4XLcTYuJHYmwdJX5Z/M5vqcK1qYfSFXTiJJGZFEZY3yMz5LlTToPLLjyE0nRtf30tqPIpqaFR+cj15K2sZO99L+z8exslYBGsKaP7CRty5fuyMzejFAfpPdmGnLRY8uZTe3ecZPy8T0mB5GLt/DDE9gzfsRo9P48nz4aotxVcWkhVzQCvMI/DcI2jhIImT50gcbUXMDq4rblf2Z99jWzCrikm++BNE9JbubyiEVlqCWlKCYhhkzl/I0gJBzpYoweAcAFIUCXjWrUMJBrCv3yB98tS892gVFeCa7ajefKgqitslwdVdgJJIxLEvtOFcOCPNUW+uoy8gZb7zC1HyClDzCiEY+kDzTiKVQHRfkRLXVgZ17cPzxDSc/h6s3a9mVfCkqe7WO6qy3Smc0WEpxz7bMVbrmtA3bQdFwem6PmdKm527UVArqiX4qap7d4GHdFraEJw8lZ0D0hsbMR/ajOLxkDjaSuJo27yui1FVhrmwHldTHarPizU1Q2TPKWJnrgICRdPwr1lIcOsKNK8bIQTpsWmi7T1E2/uIdw3dDnhuCc3rQvN7cOIpCY7uEKrbxNdYRqC5An9TOZpXFjysiWli568xc+hcVnTB3VBB+NF1aLpCbN9x0u2zHTtdx7NhBZ51y3EmJkgfOozdLa9vns9/FtVlkP7+N8Cx0Lc+hrZg0Xseq+x+jYzjtB1EDHTO7jSXVApsaPnIZ1dEZBRx6UhW4U3xhVDqV0LFgvftHyRsG9FzBdHRipiYA8dKSRVKVRNKMAcCYanO+CHMEt6LWuVHHQ88EPr/fu7egdD/51sP3HY86PG+gJB6n1QFRVG4evUqtbW17/3i/0PiIwdCnedxzu+X4gcPfUpW4G/+z7ZgbAgx0gvpFEpNM0puEWJmCvuNFyCTek9VHWdiDOvNn0pHb93AePhp1Mqa7P8zFy6S2r0HbFsao27YSPz4Oax+aZSnFeTi2bqO6NlO4uflzcwoyiOwZQWjb58nOSCr+a7iXIqeXoOnspCx41cZ3n+J9OScMILuMwnUl0ilreoCPKU5qLq8IaSnYlz8rz/FSVkECrxYExHU0RG8RUFMawY95MNTU4AYHkTx+fB+4XMoXi9WxxWSu3ZnB3H1xkbMLZtR38dxS761C+v8BazpJJYRwDa9pGYyOI5CPKOhe90E1i9i4K1zqIZGw796lM6Xz2VB0OJf20YqYdH2tcM4GZtwTR6rfn0Lusvg7I/auLZXVj89OV4WP7OEqrU1Egjeei44glPfOkHXEUmxW/GLq6jdXM+NXe10vCz9iXIbClnxy5vQXTq9b1+i560LOJZN7qIyPLogcrEPJifxT/UTcM3gzXfhKvGh5YdR/boUzli6GnHpiLw51y2XfPVEFGbGEZFZqmDdcpSF69+Vky4cR6rCpeIQGUNcb0NMz1JzVE0CqHRCenls/Jj8/Z2KcDXNqCu2ZCu3wnGwTx7GPiOpcorpRluzEbV56W1dUutGJ6m39yIiEUl7274NSkqJ/uB1SeNUVXw7N+L4Akz8aC8iY2GWF6EUFzFxpB1FVSn/pZ2k4xm6v38EJ21j5vqo+6VteMtyGTx6javfO44QgrzF5TR9YSMz/RH6T3Qy2Nozr6unmTqLP7MKEiluvNSGY9noiQQun4EWncEVMHFZcYyQl8DGpXhriom9+jZOPIGiayimmZU91vJz8G5Zi9lYQ/S1fdmqumf9CjzrWrCvXUPx+VFLilF9t1de7ZERMmfPYbVfnjdwry9slgAoHCJzvQdreAz30ibUgA+7f4D0iZPYN26829dE0mWXLsHcsF4KDdzpvMikcS5fwL50NmuceVvoBkoojBLKnX3OQQnnooRz3tVY9l7C7riItf8t2dEJ5aJv3JY11b2fELYlO0ptJwCB4vJIKwBnDqCoBSWo9QukEa3v3amFwraxzl8gffQYIj4L0AoLcW3dglpWSurcZeL7jmfNqPXSIlwtTVnwI4Qg1TVI7MxV4mevztKSwbuknvDO1ei5QdJj00webSfa3kt6fH4H1ywM428qx1UURg940f1uNL8H3edG0eeSc2smQWpoguTgJKmhSVKDE6SGJ7PdfgAUBW91Ef7mCvzNFZgFIZx4kul9rURPXJLXBhT8KxcQ2r4KZ3qa2K5D2fuKGvDh3bYe15IFpF59DavjCmpZKZ5PfwrnzEmsEwdRXB6MT//SPVET5+3nwW6ctgNSmAMkMMgtkr55OYXy2f/BgDjMCmiM9Em62S2efPNeM9YnVd+Kat43VVQkE4hr53CunoVZ2qYsODWjLlguRQvez3KFgOkpnNFhxOgQIhaTFEPLkpTAzKxiXCaTPe+VUC5qcSlKcSlqURmE70045MOKBx0Ijf/BZ+8ZCOX9h28/cNvxoMf7BkJDQ0MUFt6bqVcgEODs2bM/B0IfUoiZCZz93wPHRl28CaV2mfx7MoFzei+i7/ptqjZKYRmkU4ipMZT8UtQdn7hrtcu+3oG1702wMtJj59Fns6IIwrZJ7z8gFX2QvkCUVRPbfUQqbRk6nofWYusuIm+dmK30KQQ2LEH4A4y8eRqRsVHdJgWPrCDQUsvY0SuMHGzHiklgonkMwksqyWmpJlBfjKprOJZNfChCfHgaRVXkQ9eIXx9iZP9FVF3Bozs4E1O4TQdTs3CHXWhFeZhGBjE5iVZZiRLwY12UhodKOISYmh2A1nXM1atQgkHEzAzOzAwikUSvrEBftPCOPH0nGiX+9/+AE0uQTmk46CQtE6GoJC0DR1HJ2bqUwf0dOBmbsqdXMNo+Mg8EzYxEufCdkwghKFxUyvJf3oiVtjn294cZ6ZA3/+YnFtH8mOwC3fWcEIIz32/NAqeWTyyncWcTIxf6OfO/j2ClLFwhD4s+tYrilnJmesY58xdv4Vg2dc8uZ+LQJZLXB/BODhB2z+AJqVIwoaQILdcDVhpl3aMoHYdvk0UGJMVo6VYpzXqfkfUHunoKMSnnbhRfCGXT8yguL87lVpxWOeuCN4C2ZodUArv5/mQCa89rWTEEbcFitLUP3UatcianSO3bh31DVn6VQAD3M09hTcWI/nQXImOhBv34P/4YiesDRHbLDpcURihn9A1JJy355Caig9MM75PnUXBBCTWffQjNa9K76yKdr54B5DxW0fp6zn7zGLHhOR8hd8hD6epqprrGmLgmO1fl62upXFvNub/egx1N4DUEaiaFN2Ci22ncPg1FU3DXlZPz5HoSew6TviYr4lpOCM/m1bgWN2KNTRE92Y6rvhxGRojvPw6Aa2ED/md2oOg6QgiciSkyfUNYvYM4sTiuRQ2YzfUomib9Z9rbcWaiGIsWoublYo1OEHvrIJnOm4qBKu6lTXjWL0fLy8EZH8ceGJC0M0fMdmsFOA52X192n2MYmGtWS/lv4+4zDiKdQoyPIcZHEGMjOOOjiImx24bdbw0llINSWIJaWIxSWCK7SfdQ0RdCYJ86gt16TG5a3QL0rY+9p7z1e4UzMiS7Q1OzFM5wnlTLq2+6pw6TEAL7ylVShw5lr1NKOIS5cSP6gkYy3f3Edx3K+rGpoSC+HevlcVQU0kPjxM9eI3buGvb0XDfQVV1K+NG1uMoLcTIW4/vPM773HMKanVHRVLy1JRKsNJVj5t39fiaEwIqlpAKi+/bjKSyb5MA4M5d6ibb3khqaD3DNvCDhdQvI3bAQKxIlsusE8YsSVCuGTnBTC/6NS7GudRN7e051VC8uwLt+Gendb4Fl4XrycfSGBjI/fgExPoJa14Sx88n33Me3ra/jIDov4Zw9DHfyxjJcKLmFKDmFkF+CUlR+zzQ6MTWGuH4Bp+uyFPkAlJqFqCseui8qXlaafaBLgqWbUtWZtOymz4IQbr1Oe3yoDS0o9Uvvi+YmqWvTs6BnFviMDr/DXuL+Q3F5UIpKJDgqKpXf1w/4fbufeOCB0P/zi/cOhP7ohQduOx70eF9A6Etf+hJ/+Zd/SSDw7kP4N+M3fuM3+KM/+iPy89+fB8M/x/hIgdD1MzgXD0na0LpnZkUTeqR61s3Kj8uDUiQpX6L32pwKkcuD9thnpe/Grcu0LJzu6zhXLuH0yBuRWlaFvvPJ7IVTxOMkX35F+lMAxprVpCNpUmdkQmg2VONavYypva2kuqTks1mcT+ixtYwfucLMhS4A/E3lFH9iE9HrI/T+9CSZaXlTcOX6Kdq2iLzVdSi6xkz3OKNnepjuGiXaN4mTuYOfAxDOc2NF4rjcKmomhTY2gqcohIs4ms+NZ3kT4lp7VnkIwFi7BnP9OpzxCVJv78Xp77/7Dne5MJYsxli2DDU0d2xT+w+QOXUaK5rEUv2kZ9JYLj+O7iIRdzCLwiQdneTgFMGmUlKKzvj5viwImuyZpP1FSfMqW1vDkl9Yw/TQNIf/9gDx8Ri6S2fNl9ZTtkxS+mzL4dKuDjpP9qCbKqbPhctn4va7qF5VQX5NHud/co6ON+XxaHpsIYufXUpseJrTXzlIbEQmEcXLKlj0yZWMnO7kxk9b0Uydxk+uovuru9B6u8j3RvGGBJ4SD3pFKVp9DYx0oxSWozYtRdw4B24vuP0oHr90HQ8XvS9p1ltDCAHjA5LuWb0YxRuUnaC9LwJIP6tlm+crwo2PYr35klQ30nT0rY+i1c8fuBXpNOkTJ8mcPCUTdVXFWLEcY+0aksfOEj8kAY9RVYb/6Z1M7TpJ7JycpwqsXYxaUcLAd/aDEBQ8upJUymFoVhShePtiSh9fBorC9RdP03/gMgCVDy8mf2UNx/98D+lYCs3UKV5WTtnaGvIailBUBcd2uPb6Ba69eREE+IuDhHLdTHUMYqSTmB4DPRHFFfQQWliG3d2HyFjo4QB5v/AIYnwchMC1qAFrKsr03tPEzl5D0q4Ucp7aiOHRiL6yBxyBXlaE6vOS6R3MUldvDTXgx7OmBdfyhahuOW/lxJPED56QlDwh5ExQQW42+QYwF9TiWb8Co7z4rsfW6ukhfeAgzrCk5ig+H+bGDbLIcI8Vb2HbEJ1GTE0iIpO3PE/MExqY2yAdNb8AtboOtWHhHb2LhG1h7XsT55o8btrytWirN96uzhaPkzp0GKd/ADU/H7W0BK2kGLWw8F0TOGFZiP4e8AdQcvPvTbRFCOyeHtIHD+MMy2KI4vFgrl+HvnQJztQ0sd2HSV/tkv9zmXg3rca9eglOMk2stYPYuetkhucEEFS3K6vM6aouQVEUoh19DP/0WLYD5GsoJWddE96GMrRbRA/sVIaZa0OkJ6KkI3EyU7HZ5zjp6TjCmhU3yPXhKQ7jKc7BUxLGXZyDuzCY7eADpCejRC/1EG3vJX5jMNstMgtCct5uQTmp7iGm3jxGqneWXRD0k/P4OtyNlSRPnSNx6DQinZ59nx81Ooni9+P90hcRkUkyP34BhIPx2MdQq+YXY4VtQ2wGMS3tH5Ti0jseE2Hb0sdqYkR6Wk2MIKZG7yjXruQWohRVSuW5grJ554NIpxBdlxE3LkqRgJvh8mTBEC6P7G6/Q+7/TiGGenDOHpYKbPcQSm4hyoIVKJWN71kUELaFmJxAjI8iJkbl8/ioFAR5Z2g6al6BNFoOhcGQCnGKYYKug2GCYUhBB1VBjI3iDPVLX6yRodvlxxUVff0WtCUrbv+sDyEeeCD0//6FewdC//E7D9x2POjxc7GEDyk+cmrcSPesApcXcf4ozgVZ+VUCOajrHpHVqpvDv/GoVIgZ6pGJZJFMrIUQiME+CX5uXJ1X6ddaVktRhJsS0pNTJH74IzkPZBiYW7YSb23PUhY8m1aRTitED59DzHaGgttWQiDAyKsnsabjKJpK4eOr8C6sou/HJ4i0S/DhLgxS8kgLOUurSE7GGD7ZycipThJjc1V0AN1j4isNoygyiczMJEmMzeAKujGTCZy0hctOolkpPD4Vw6Phdjmgqfg3LcM6eQIlGMT9xGOopaXYw2NouWEwdKzLHWTOnkXRdJRgADUQAE0jc+EiYmoquw6KzzfrHeLJKm6logI7AynFizBcxFIqiqriWlDJ5MUBdJ+LvO1Luf7j06i6xpLf2E5kYJqL3z8FQO3DzSx4poWRjmGO/O1BrJSFr8DPxl/fTKgsDED/hUGOf6eV6eE7Cw+ousrOf72FkqYi2l+/xMWXpHRy1dpqVn1+LUIIrr1+gRu72hFCYHhNmp5bxkTrDaauDhOoykcZGcM+106OOUMonMFV5MGsKEJdthKlZ3Y25ckvSsWgdzs3HQdx9ay8MZbXve/BYzEzhf3mC5LeWbdYqnrdkii8W+cSZqvVHVdI7z+QnY/RqqpwbdsKPh/Rn+7KJpOeNS241i5n7Lu7SfcNg6KQ89Qm1Lwcer/2FsKyCa9rwqwuputbUlyh6lPryV/bgJOxufztI4y2yQ5N3cdXkbekgqN/upvkVJxQZS5r/tU2DK+cmRq6OEjHW+2kY2lW/OIqFMvhzD8eITWdBNtGTyYxDQWvC9RMBo9XQw94qPj8VqZePoQ1EUHRdXI/tgVXVTHT+1qJtXbM0ookBfVmEhx8aDneumJmfvhGNnmUO0JDLy3EKC9B0TSSbRdwYrNVatPAvWwhaihA/NCpLGgyG2vw7dyIlhsm0ztI4mhrVlocJDVP0TSZRNqO7DDYtlzeuuW4li/EvnqV9MHDWbEFJRzCXLNGKp19gHkMkUxIAZORIcTIIGJkaL5yIQpqeRXqgkVSmU6f9RB78yWcoT6ZiD20E61pyfzlOg7W+fOkDh7OUmnnhaqiFhailRSjL2hEKyt739sAs4DxyFGc/tnZK8PAXLUSY9VKUFQSh08TP3JaJuSKgnvlYrwPrQFFZfrQWWaOns/KUCuahruxEl9LPZ7GShRDJuiZqRjDrxxn5nwXAHrQS9HTawksqc5+v5yMRaS9n8kzXUTa+3DSd+/E3c3gWa6DgrswRKi5nNDiCnyVc2DQTmWYOXuDkTdOY8fkOeZfWEnRU2swcgMkLnYy9eYxrCl5H3BVl5Dz5Eb0gIf4gRPS20s4uPwKimNhrF2Da9NGrGMHsM+eRPEF0Ba1IKYjs48pRCw6VxQE1PJq9Id23ubLdKcQti19dCZHJKgZ7suajs8tUEUpKEMpLEdMT8xnZygqSnktSu0iKes9PoRzYvec6EtxlbSsuJNo0dgQzrnD0uYCJBCpWwz+0JxktWHOSlZLEILhkgp5dwFXzvAgYqAX5ybomZqct2/mDqIqZ/QKiiTwKShCyXl3w+L32o9ifAQxNIAzNIAYHkDEoxiPPpf1dPuw40EHQmP/7t6BUP5//jkQut/4wEBo+/btbNmyhT/4gz+Y9/fJyUmef/553n777Q+0gv9c45/qi+VcOYtzSu5zpXaRFEB4D+dm4TjYZ07itJ9DROcSa8UfRG1olo9bVIvskRGSP3wRkUighELo6zcS23UEJxpDcbvwbFlH5PClrAqQp6ka38YWxveeI3pZDoGbBSFKP7OF+OAUPS8ex0nbKLpK8Y4lFG9fTHo6QfebFxg+cSOb0GmmTt7icnIXlhKoysdTEJh3Uc/EUpz8Ty+TiSbJrcwhPTCOiEzjDbnQo5O4C4J4CzwwM4OWn0vwE4+ihkOIdIboK2+TvtKJ6vXg2bAC98rFd6TqCCGwb3SSaW2bkw6+JexYigxe0pEklidERuikLQV3VRETN8ZBQMWn1nP1x21YiTQ1Ty1D9Xs5920JXOseXciCp1sYbh/i8N8cwM7YFDYVse5XNuLyu4iOxTjxvVZ62uR+dAfdLHq0CU/AjZ2xScXSDF4eZvDSELrb4PHf305eVS6dh69z+tsnEY6gsLmYDV/ehOExmO6d5NwLx5nulWa39Y82M3r4ClYijccE2q8QVmcIh5K4iny4SkKoNbVoeX5E/433nC0TmTTO4VclbQPkTbS4EqWqUcqym/fmPSMyaZxd35NUzrxi1J1zYiDCcbBPHMI+Kzs57+xcghTySO3dl00olWAQ17YtaHV1WD0DRF99G3siApqG/8ltqHl5jL7wFvZ0FNXtIv8XHiaTcuj/9tuIjI1/YSW525Zx5W/exMnYFG1bRPlTK7ESaS7+wwGmrg6h6hoLfnE9ocZijv3ZbmIjM/iKAqz/7YcxvCZ9bb1cfuMSU7P7Xq6YQvPjC6nbXM/5bx9n7PIQ1ugUoaIAppPGMDXMTALDa2LmBSn7xS1M7zlJ8trsMPVN4AG46ysI7ViFWVbA9P42Invk/vEtayS4YRGp1otoeWH08hL0kvm0MZGxSF28QuJYG/bYLesHaPm5+B7ZjFFdRvRkO5nBMXwrm3BVFGGNTpA41kbqfMfdzU1nQy/Mx/foZvSyIjJtZ0ifOAnJWVWsQABzzWr0xYt+JhSZ7BzDQB/O1Us4g31zu9x0odYtwBnoRUQmUQyX9EQrr5q3DHtomNTuPdmujFpQgLl2Dc7UFPbgIM7AoJRcvyXM9esw1q2977kOu7eP9JEj2W47moaxdAnG2jWoPh/pGz3EXt+ftSIw6irxPbwZLRRg5thFpg+0ZQUHzLIC/Cub8S6uRfXMfd+EZTNx5BJju87gpDOgKuRuWEj+w8vR3CaOZTN9eYDJs11MXezFSc1V7V25frwVeRghL2bYhxnyzv7sRQ94cFIWiaFJksMREgOTJIamSAxNYifmZs0AjJCH0MJywosrs7RnO5FmbHcbE0cugSNQdI28hxaTt60FRYHpQ2eZPnBmVgRCwb9mIaEdq0jsPUKy7RKaR0cXcelH90tfQPH5yPzwG3K+9U6h6pICPT0t57Z0A33NJtRFy+77uIl4FDHcixjqQQz3SK+id4QSypOqrNVNKO75c0vCtqWVxYXjkvap6VKoYcEK+d2eGsM5d0QCKpBAq34p6qI170thTQiB6OvGbjs+7zuRXVfTJSmlNx+5BbKT+SHS1m7S73B73jN3+VnFAw+E/u1nCLruAQil0uT/l+8+cNvxoMcHBkKqqpKXl8fGjRv59re/jW926HZ4eJjS0lJs+10qR/8Hxz+JfHZkXIof2Bbqss2oC+/u73JrWCcOYbfNdpAME7W2EbVx4R0N++zePhI/+Smk06gFBSjNi4ntPgq2jZafi7lyKZNvnsRJpdECPnKe2EBiLMro7jZExkbRVPK2tZCzoZm+V1oZPyGFE/x1RVR9Yh2qx6T7zQsMHb2GM8tRz1lQQtHqGvKXVqC5DKyUxUz/JJHeSeKjM6iaimrqUjltZJrRE9dQDZ2AB6ypGEY6jqFYeIMmRq4ft55GJFN4NqzAqK0k+pNd2cHim6H6vHg2rEAvK0ZxmSguA9XtRjHnwJETiyFmoohEHBFP4ESmmdlzHDuRIZXRsW2VhOpBdZmQn0esb4KcZVXEU4Lx830EKvIoeqiJs988BgKqty2g+ePLGW4f4sjfHsTO2JQsKWX9lzehGRoj18fY9Rf7ycTTKKpCzdoqIpNJei7M0SJ0Q0M3NZxYEl1X8QTcPPHvdhIsDDB4YYBjf38YK2URKguz6V9twZvjlZSs1yQlS9VVmp5cTPcrbdhjEbzRCUL2JOFAAnehD3exBzU3jLF1J87pt8FwoX3sV+/oYSESMZz9P5XVUk1HCYTnBo8BVFVSQlRNJuGqLrtGpdWySuqVQ+NCCJzDryF6roDbK6mcN/8Xj2HtfiV7E9eWrkJbuzmbwNykMFnnZ72DdB1z7RqMVSsR6Qyx3YdJnZM0KDXgJ/DJx0mPR5l4cR/CsjDyw+R/9jHivWMMfP8AOAJ/UwWFz67jyl+/SXoqTrCplPpf3k4mmuL83+0l2jeB5jJY9MsP4a/I4/hf7mG6bwpPjpe1v72TkY5hLr/RzsxsN0936dQ+VE9qOkn38S55zlflsvqLazn11/uID01hWhlcbg2fC1RNIZDjwo4l0XxuKr70MKnLnUwfkLRKV1UJoZ2rcVeXkInEiJy+hq++FHtsgomfSEqfu66c/F94GNVl4qQzpPtGSHUNkuoawo7G8a1YQGDtItA1Mtd7JCAan5r1m1mENT7F+I8PyG7ZbLiqSghsXIqnqQoRjUu5ZlUFXR5fRddAVcn0DBDffzyrZOdaWI9350ZUt4vM2XNkTp3OigAoPh/G6lUYzU13FVV4PyEiU9hXLkq/nncUf/THP4aaO0fjFskk6UOHs4a0mCbmxg0Yy1rmJcpCCMT0NPbgIPa1G1gdUvpYq6rC/cTjKN737oTa/f1Snrx3bvbKWLoUY81q1IAfZyZGbNdBUpfkdVP1+/A9+hBGQzXxtitE9p7GnpHXMqMgh9DDa/A0Vc27jqfHp5k6eZXI6atZnzZPVSHFz23AXZqLsB2G911kaO+FecDFzPWR01JNztIqvBV578vwNhOJE+0cYepCL9OX+7CTc+BKc+sEF5RRsHEBgbpiUsNTDL90jNg1WbzQQz6KnlpDYEk1diTK1BvHsvNDqsdNcNMSMkdPINIZXDkmSiqOVl+H59lnpFfVqSOyOBIMowRDKIEQSjAEXul9I6YmsPbvkh1BQC0sQdvyyLxz4X63l5lJxFAvYlYMQa1dCLlF7015m57EObkHMTxb4AjnQzgf0XU5+xqlZiHqkvUo/vvPL4QQiO7rWK3HEaOz9w9VQ62qRckvkjS3vALw+T+YKqNtQ8aanVOypFiCbaPm5tyXAfOHHQ88EPp/3QcQ+q/3DoQOHDjAf/tv/43Tp08zODjIj3/8Y5577rk7vvbXfu3X+MpXvsKf/dmf8Vu/9VvZv6dSKX7v936P73znOyQSCXbs2MHf/M3fUF4+p8o7OTnJb/7mb2YVp5955hn+5//8n4TD4fdcx48ifiZAqK2tjV/7tV8jFovx8ssvU11d/XMg9FFT42wb563vICZHZUt928fu6QLmDPaReen7gEBfv1Wqat1laNm6dp3kK6/KC1lZGU5uMckTMjkw6qsR+QVM7z8LCFxVJfg2LmPkjdbsQKy3ppjij29AoHDjG/tJDE6BAqWPLqNox2LG2nq49qOTZGZFEnIai6l+ooVgTQHx8Rj9JzoZau1hZihyV+qFEIJQ0MCOp/AFTJREEiU6gzvowkzOYIa9+BrLEN1d896n5YbxP/cw9sg48YMns0O47wyjshT32mWYDdW3VQtj+46ROHSKTCRB2vCTjKRw/EG8TZWMXRhA1VUKH1/BtR+3ouoa5U8s4/JPziKEoHJTPYs+vYrhS0Mc/l8HcTI2pS1lrPuVjWiGxsClIfb81UHstEV+TR45tXlc3HeDTOrOc1KO7eBCYLo0/AV+nvi3O/GGPEx0T3Dor/eTmk7iCrhZ8QsrKV9RiRCCU/9rP6MXBwmWh8ktCTC09wLqwCD5RowcfwJ3WMPTUIQWcKOuWIcycBli06hrH0Gtmy9PK6YnsPf9BKIROYe25VmU/BJEZALR0yEli99JI7k1FAWlrA61fgliahTnzCFpgLrjE1Log1lp7F2vyHkQ3UDf9tg8nxbr2nWSr78BsxQwvWmBlBb2+0mdbSe258icrPvyRXi2rWPm8PksoPA0VpL3ie1MtV5n+CU5OB9aXkfRx9Zz7St7iHaN4i4MsuA3nyAdTXL+b98mOR7F8LtZ8uvb8RaHOPlXe5m4Porpd7Hut3fSeayLy69LDy7Da9KwrZH6bY24/DIp6D3dw+lvnyQTT6OZOkW1uUSuDuNMzhAo8OHCwtBV8lZUI8YmSQ6Mo5oG5V/Yjm6qCEfgqi7BSaQY23ueyaOXsgWI0s9swXBrjH13NyKTwSjIQXGZZAZGs13Xed+JgI/gluX4VzXNea1YNtMH2mRF3rZRTUOaX3b0ZDtRRl6YwMYl+JY1ImwHJ5HCSSRx4inZpVBVXBUFJA6dknQmQNE1PBtW4lm3HFRFqqKdODlf4jscQispQSspQS0tQc3P/8ByxkIISQfquIiwMuibdqB45yrrdv8AyZ++lO306M1NmFseQjFdJI61YQ0Mo5eXYDZWo+Xnzu9QX2ontWs3WBaK34/7mafRSm6fmxKWhXXlKpmz5+bkx1UVffFizHVrUAMBhOOQPH1BWhHMns+eNS14tqwh1TXE5OtHscZld0gP+QluX4VvWcMcldmyiV7sZurElSywANB8bgofX0VoVQOKopAYnKTre0eI90p6lhHyktNSRc6y6nk0tnfuQyueJjkeJTkeJTEeRdVVvIUhvMVBXDl3Ntl0LJuZa0NELvQydamXTGSum5a3uo7yp1eieV3MXOhm5JUTZKbkueBbUE7xc+sxcwMkb/Qz+eoRMiPyWmL6DfR0DNXUMbQkigLuT3wcvarqts+/UwghcNrPYx87IKnhqoa2bDXairXv26/n/YYQQgo1tB6YbwBcUY+6dMP7UnkTjoNz4yp223HExKzBrKajLWxBa1l526zwPS0zncbuH8Du7cXu7UPMzEjAY1nv2hVWi4rQKsrRysvQyspR3P90wOhBB0Kjv/fpewZCBf/9e/e8Ha+//jqHDx9mxYoVPP/883cFQj/5yU/4wz/8Q0ZHR/n93//9eUDoN37jN3j55Zf5+te/Tl5eHr/7u7/LxMQEp0+fRpu9Pj/++OP09fXxla98BYAvf/nLVFdX8/LLL9/bjviQ42cChIaGhgiFQvyLf/EveOutt/jBD35Ac3Pzz4HQR/jFctoO4rSfAtON9sTn5yrmto0YHcIZ6EMM9MrOzfI1qJU1iFSSzA+/iYhOozUuQt/22F2Xn7lwkdRbu0AItNpaLMNP6sysHO/aZcQnkyTOy3a9f2UzaQwmj12Wr/e6KHxyDYGWGkb2XWRo93kcy8EIuKn57GZcxTlc/cEJxs5Kqpm/LIfa51YSri9i8HQ3PYevZdW0boYr5CFUnoO/OIgA7LRFbHia8Ssjkj5kpxGWjUdkUIWDx3DQFBuvV0V1GQQaCrE65ee5WprxP/pQttsjbJvU2XaSbZdw4glEOoNIpeb5Z6ihIJ5VS1CDPkQqjUhliB8+hT0dJRkVZNKClOHDyA+TUkxS41HyNzTSf7qXTCxF2dZmbhzuxEpmKFtbw9LPrb0NBK3/1U2oukp3ay/7v3IEx3IIV4RJ2TDWMwVASUM+Sx9twhN0Y1s2ji0YvDJC68sXcTIOHlNF1xRyK3N47Pe2Y3pNYmNRDv/tQSL9chnlKytZ/pmVKI7gwH98jUw8TfW2Rkb2nMO+0k2ukSDPH8MbFLjrSzFy3CjhHLQlixFnD6PkFqHu+ASkkpBKIKIRnJN75M3bH0Lb+jHpTXFLCCGk9HVazsFIdTFbGglev4gYG+Cdoa7ajtrYIpOV861Yxw5IaeNwHsajz6CE52aVMufOyyQUUIsKcW3bhlZWijU4QmzXITI9cvlaYR7+J7aiFeQx/sO9JC53ARDc2ELw4dWMv32Osd0SGOVsXEjhU2vo/eExxo5fQ/MYNP3mEwhFpe3P3yQTTeLJD7Dk17fjyvFx+u8PMHpxEMNjsPY3dxCdinP4rw8AsOjpJTTsWIBxB2Wt+GSck18/xkjHMI7loMzE8HgM3LrANBV8Jqgug6bffJzRV08Qvz6IoqmUfHIzgYWVTBy8yPjBCzjJWQAY9MrKv6JQ9PRafFX5jH3rDezYXOKpBf24qotxV0sPnekDZ7KzGHo4QHDbSoy8IBM/PUhmVFLlPAuqyHl6E3rIjz0dY+bYBaIn27OUrHcLoyiP3Gc3oxkqsTcPkumVx0P1evCsX4575RLQVKxL7WRa23DGxm5fiK6jFhWiFRWhlhSjFRWjhD+4nPHNsLq7Sf7kJbAs1NxcXDu3o5aXk77SSeytg7cVS9RQELOxGrOhBqNSql7Zo6PSW21qSgLAbVvRW5bKucbItJQnv3BhjlJ305B27VrUUFB2Ua73EHv7CPaIBCd6SaGkb+aGmXrjGNGTUgxF83kIPrQc/5qFWRnr9Ng0k8cuEzl9NevRhqLgqy8lvKaRwMJKlFkVzuF9FxncdQ5hOWgeg4pnV5O7qu62/RkdmGS0tZv4yHQW/Nw0Cb5TaKaOt0iCIm9hCF9ZDuH6wnkCDEII4r3jjB2/ytjxqyCkVULZU6vIW12HyNiM7zvH+L5zCNtBMTQKdi4nd9MiUBSiJy8x9cYxhGXh8TgowsGV70WJRVBzc/F84XP3BZpFbAbr4B6cbnlPU8J56OsekmIKH1CW/bbPSiVxLl8Alwt1weLbhTmScalal0qiLlqNknd3EZI7Lj+dRsxEEKND2GdOISISNCqGibpoGdrSlfclLS4sC3tgALu3D7unF2do6D1psIAUSzANQJES2+8ItaBAAqOKcrSysp+ZgfK9xAMPhH7nPoDQn947ELo1FEW5IxDq7+9n7dq1vPnmmzz55JP81m/9VhYIRSIRCgoK+OY3v8mnP/1pAAYGBqioqOC1117j0Ucfpb29nYULF3Ls2DHWrpVG9seOHWP9+vVcvnyZBQsW3Nd6fhjxgYGQpmkMDg5mpbT/+I//mD/+4z/m3/ybf8Mf//Ef/xwIfRSqccO92Ht+CIC6+WnUinpgtmK+57V51I+bodY0AgKn8ypKIITxic/fdV4jfbqV9D4pV6w1N5NJK6QvSBUtz/YNRC/2kuoZQlFVQg+vYap9MFt1DK1soPDJ1cQHJul98TjJEbkuwQUlVH16I7GhCO3fOEwmmkTRVCofWUzlzkVMXBvl8o/bmJ5N1lEgv7GI0jU1FDSX4ArefjOyMzYH/ugVEhNxQvkerMkozMTwhV0Y6SSG18DnVVHsNK6qYnzV+Rjlxbia6ohfvEFkzynMknz8axdhVhTOuyEJIXBmoiRPXSDZdvGOKlsAmek4GSNIciKOHQzjqi1l8sqI9NooK2D8Yj/+8lyUsJ+hs/2Eq/NY/zsPM3Z9lIN/uQ87Y1O2rJx1v7IRVVe5dvgGh75+AoQgXJHDUE8E23ZweQ0W72jkytkBhm6M37YemhCYqgJC4DNVVFWCoUd+exvugAs7Y9P+xiUuv34R4QhMv4sVn1mJoau0fvUQKBD0qiTbrhBW4uT64gRDaYyqUtzFXlBAf+LjiMMv3fUmqOQWoW597jYe/L2EmBqXnhed7ZBJyXm3tQ+jKArWycO3SBs3oW95OMslF0KQOXac9JGjAOiLF+F6eCfOdJT420dJXZLnraLreLeswb2mhfTgOBMv7iMzOomiaeQ+9xDeJfUMv3RMgnmg4OEV5O1oYWT/JfpePg0K1P/KDvx1RZz587eI9k3gL89lya9vQ/e4aPvaIYbP9aMaGmv+1TZcYS+7/uMbZOJp6rc1svzTK+edW33nBohNxmnYVIemqwgh6HiznfM/OUtqZJpQgQ89kcCb48GrC1QEZq6Phl/dydiuVmbOSZECzevKJryuklwKHl2Jf0EZwy8dZ/KoLFzkbVlCzrpGYqcuYxTk4KouQQvPp8AIyyZ6+jLT+1qxo/NlgzWfh/CTG/EuriV2pZ9k7xjBlfWYOX6cVJrY6Q5mjp7PAilF01C9blSPC9XrIjM8iZNIAgqBdYsI7liFdb2b2NvHcCKzogkeN551y/GsXopiGohkCntoCGdgQFLPBofuLFbgcs0Bo5IStNKS95VQWdeuk3z5FXActOpq3M88hTMdI/rWATLXZQFFDfhxL19Ipn+ITGffvO+BYui4ljbj27kRHJvkm29hX5V0Nr2xUSaTt3gtKX4/xtIl6EuXZD2drMERYnuOkOmSVC3FZeLdth73ikVkhicY/8HbWVAaWL+E0I5VkoYLpIanGNt7lukzN6S6HxIQh1c3ElrdiJkz51UUH5ik+3uHiffJBDm0sIzKT6zHDM19b+1UhpG2boaOXmO66w6gFDBDXty5Pjz5fuy0TXw4QnIsmqU43xqqoZPbVEJ+SwW5i8owvHP3nmj3KD0/OCoZA0jadOXza/EUhUmNRhh68QjxG1KF1FWcS8nzG/BUFjL1xjGmD59FNxRMJYWiKpg+gWJbmFu3YK68PwUyIQTOjSvYh97OGuoCKMGwpI/lF0qhgLzC93WOiWQC+3wrzvm2rDCRWlkrpdrvc3kinUIM9CJmpuce0WmYmX6HSIj0VFOXrkBbvPyeQJ1Ip+UMXP8Adl+/lMV/R16nBAJolRVolZWo+XmSVaIbUpDDMOQsk6LIApgjEPGYBFJ9fbKLdIsA0c0wtzyEuWrlbX//MOKBB0K/fR9A6M++R29v77ztcLlcuN6DingnIOQ4Djt37uTZZ5/lX//rf011dfU8IPT222+zY8cOJiYmyMmZK3a2tLTw3HPP8R/+w3/ga1/7Gr/zO7/D1DuOcTgc5s/+7M/40pe+9N474kOOD9zrfSeO+vf//t/T3NzMF7/4xQ+66J/HPYZIxOQMRnUTakW9HB5vO4596ijMGvgppeWopRWI6Yh0be+8MvtuBX3747eBIOE42N3dZM6ew74ub9j6smWkxmJkrnaBquDZsp7IsQ6syWlUt4vQo+sY2XeR9GgE1TQo/cwWPNWF9P70FBOn5TKMoIfyZ1cRXlpF/952Ol8+gxACX2kOTZ9dD4bO6a8cZPSSvNEZHoPq7U2Ur6vFk/OOwVJHkJxOopkahttAMzSaP76C1q8eYnosgc/UsHUdK5YGBQxHkMyAR1NJdQ/hX7sYs6GaydeOMHNUqqBlRieJnbuKWZyPf90iPAsq0fxSaUcLBvBtX4938ypS56+Quij3oZwhkn5BiWOXsRMpLMOFoqlM98yqdS2poufgVRRNJX9lDRdfPIOiKCz+zGqm+iazwgjFi0uzIOj60U4O/W85u5VXl0//9Qls26F8YRF5tXkceeUSVsZGNzTcPhPHFjhCkElksCwb1RGYhkrKAZ+pMdEzyev/dQ+P/u42vGEPi59eQllLGSf/8TiR/imOffUIK35xFWWrq+k/2cVUXwSXaWCnwbYVHEeBtAU5BVJKtrcbpXYx4trs7ISmS9NBlxvyS1CXP/S+h12VcB7Kqm0oyzbB1BjkFcsq+mAfdqvcJ/q6LahLV86pIToO6b37yJw5K8+dtWvQly8ntvuwlHueTVRdixvxbl2H4vUw9eZxZo5eAASa30v+Lz6Cnp9D79d3EbvSL7soz6wjd0MzIwfbJQgCyp9aSaipjCvfO060bwLD72bxr25F97o4+/UjEgTpKqt+7SFClbns/e+7ycTT5Fbn0fL88ux2RsdiHHvhFH3nZOGgfc9VNnxhNUUNBSx4tJmek91MpDIkIwlcpoqdskjpJqFcF+mJGFf/fjcNv/4IesDD5OFL2PEUZl6Q/EdWEGypQVEU7GSawmfWooe8jL5xmvH957Gm45R8YhOKrknwOD5DvGuYRNcwVjRBzvpmAmsX4VveSPTEJaYPnMFJJPEtbyT82HoUTWPoxSNMnZBzMKN72gguqSF38yICG5bgX7cIJ5pA9biy6mQ3w44lmHrjGLEzV5g5doH4pS5yntpIzr/8LKkLV0gcOoU9GSG+9yiJY2141i7D3dKMXl0F1ZLiJIRATExgDw3jDA1jDw/hjIxCKoXd04Pd08PN6RYlNwettDT7UHLf3bwxc/kyqdfekN3s+npcD+8kfugUiaNtWbl1z7rleDetmusipzOkO3tJX+kkc60bJxYnefo81sAwgU8+jvvpp8icbiV94CDWlSvZz9IqKzCWLUOrq83S2OypaeL7jpG6MPs6VcWzagmeTatRPC5mDp8jsvskwrbRAj7yPr4Vd73k4ycHJxh7+6xUgJu9N/sWlJOzvhl/YxmKNkfndSyb4b0XGdw92wXymlQ8t5rcFbXZpDXaO8HgkWuMtHZhp+QeVTSVvEVlhOqL8OT5cecHcOf60Mzb0wnHsklOxIgPThEfniY+EiFyY5TkeJSx872Mne9F0VTC9UXkL60gb0k5/qoCmn7rSUYPXWbgzTNErw/T/qevULR1ESU7l1D55ceInL7GyKsnSA1N0PU3r5KzdgF5W5YQPX0ZK5HECLohncJx+dHiU6SPHrvvOTNFUdDqFqCWVWKfPIzT04mITku1uekpnBsdc6/1B6VnVXkVannlu6rOiUQC+/xpnAtt0nAUUEK5iJlpnJ4bZH74DfQdT6CWVrznOgrHwWk/h33qyJ1lrW+un8sDgSBabaMUgbiDD97NcGZmsPsHJPDp78cZHb3tNYrPJzs3lZVoFeUI04XV1Ufyeg9O2xVJjctYiIwlJeMz1qzBqqRya3k5GFVlGNXVeDZsBGb9xXr7sft6EROTqHnvrkb6/08hLIHQ3rtnISz5moqK+efOH/zBH/CHf/iH9/25f/Inf4Ku6/zmb/7mHf8/NDSEaZrzQBBAUVERQ0ND2dfcyXO0sLAw+5p/6vjAQKizs/M2f6Dnn3+eBQsWcPr06Q+6+J/HPYRa3YSSWwQeHyKTwXrjJzgDs1XLhoWS937LhU9dsAj74G6c4QG01RtRi+XMhRACZ3gY6/oNrEvtWUlbAGP9OpLdY2Sud4Om4dm6nsm9Z3ESSfRwAN+WVQy+chI7kcII+yn74g7ig1Pc+JOfSmNUBQo2LqD0seUoqsLlbxzOygsXr62j7hOr6D/eRfuLbTgZG1VTqXyogfrHFmH6JEhLziTpa+1lsnuCSP8U0wMR7MxcZUp36fgKA4Rr85m6MUbGEmhuEzsRl0pGTgbFEij5YcTEBFOvH2XmyPnswHdg3WKcVJr4ueukh8bkYDmSc2+WF2KWFeJdXIueE8C9YhHuFfPnYiL7WhGajpVOg9cNbjd2JI27KMhEl6y2Fq2u5casyWnN9gWgaxz477vJJDIUNBSy/ssSBPWdH5CdIKBwQRHdl0dwbEFRXR4z8QwdP5GzFVWLi3n0S2skNS5jY1k2fR0jvPo3R0nbDgYKDmBpKm5TIzIYkWDod7bhz/eRU5nLzn/3KOd/epYruy5z9gdtbPvdHYxfHWF6LIKSARsNW6jYFjipFHiDMDWKc+0y+md/FXXJujmp1p9xKLoB+ZKuJVJJrLdfBwRa4yK0ljkxEGFZpF5/M5tkmtu2Inwhpv76W9mZCqOmAt+ODejFBSSu9jL50ivZroVvaQPhJ9Zjpyy6/uYV0iNTKIZG6ae3EFxSzeiRDnp/IlXXincuoXDLQkZOdzJ45CqKotD0+Y2YQQ/nvnmMwbZeVE1lxa9uJr+pmLbvnmayewLDa7LuV+XxdWyH9j1XaPvpeayUhaKpGG5dHp8/2U3jQ3WsfH4ZdVvqifRPYcVSeP1uMokkmsvBv7SW2MUuUqMzXP3bt2j49YfxVBaC4xBcWoOia6Qmogy+eYbx0zcI1BVR+8Wt6AEvgz86RKTtOpmpmJQe7hrODs3fjGh7L/6mCoqeXiNNLNcsxJ6JY+SFSPSOMvDd/aTHpqVkc2keyf4xps/eYPrsDbw1xeQ+tBh/cwXYDpnJKJlIDGtaes4oCoSf2IivpYGJlw5iTU4z9sKbeBfWEH5sHeFfXyAV6w6dwp6YIr7vGPF9x9DCQfSKEozKUulnlZeLkZcHi6Rpr7BtnLExCYwGB7EHBhGTk4iJSayJSawLcjZLCYcxFi1Eb26e5wMGkDl/QVKAAb25GW3Zcqa++j2cWQNSo7YS/6ObUXPDJC52kh4ax9NUhau8ENeCWlwLaiWwvNbNzEu7sQZHiPzD9wl84gnMVSvRiotIHz6CWlCA0dIyL9lzYnESR1tJnDg3B9oXNeLdtg4tHMSejjH2j6+RvC47RJ6manKfewjN5yHRN8bYnjNEL80pWQYWVZG/owV32fz7sxVLMnb8KqNHrpCelBSl8KJyKp5fl+0CTVwepPPlNqJ9c3N8nvwAJRvqKVpTixm4t46Fqmt4C4N4C+f2sxCCWP8kY+d6GTvXS2xwismOQSY7Brn2w5PkL6uk/vnVFG1dRHhpFb0/Pk7kUj9Du88zeaaLqk+tJ7yqAX9zBSOvniRy+iqTxy4zc6mHUEMVyfNXSCXBhSAzFkXL80JCiqa4H3lYJuZTEZyJCZzJSfmYmEDRdMwtm9GK51PPFLcHffNOue7JBGJs1tR3bFj+HJmUACk6nQVHSigXtbwStbwKpbQCxXQhEnHss6ewL56RhqeAkluAtnIdak0DYnwUa/eriMgEmZd/gLZyHdqKdXdVrnN6OrGO7p8z6A2EUPIL5XMgKMGYPyB/fg91TmdmhszxE1idXfPu+9l9EAyilc0WEyoqICeMPTQqaZund2P13V9Ca49PYo9PkmyV9zEtfxYYVVXi2bAeWcB9cMQU/qlDOHdWMr/T64A7doTuN06fPs1f/MVf0Nra+r6EUW59z91mC39WNOYPGu+bGjd9hy/LneJBajN+lPFP1Wq1Dr2NfbFNDo9v3onWuPCOrxNCQDyG4vMjLIv0ocNYlzvmc3ddrmzCENt3gvS1bhRdw711A5N7pDyrWVaAe/lChn56DGE7eCoLKPn0FvpeOsXUxdkbdkmYyk+ux19VwMSlAa796CSJsRlUXaPuYyvJXVrBhRdOMHJBVsXzFxSx6DOr8RUEEEIw3D5E56HrDJzrx7HecTVQlGzl82aULillumMQO57C5WQwFAd/wEA3VFyKDbpCIKjjxGTyp7pd5H58K97makBWrGOtV4iduUJmZJJ5ygyKgndJHcFNLZglMsEQtkNmZJLRf3yVzOQM8aiNcHlIOhpoOnlbFtG9px3NZRBaXk3v4Rt4cr2s/PWtHPjLfSSm4uRU5bLlt7ZjeAzGOsd547+/jZWyCFfkMNA9hXAEBTW5DPZFSCUyGC6dhz7VwmDvJK1vX8Vx5u8D09BQbUAIgl4DYQv8OR5ciuyieXO8PPq72wgVB7Pnw4G/3MdI+xA5Vbm0PLuUo//lNezuQXLNNPmuOGF/Ak+ugWfrBrT4GCKVwHjyE7fJDN/xfEsmJFXiA8iuZt5+HefqpduonCKdJvnTl6WcuarifuJxrKRN9JW3QQj0ony8OzZi1lZgTU4T2XOK2FlJkdNDfnKe2YynsZJ45zB939yDHUuiB71U/NJO3GX5jB69Qs8PJRWvaNsiyp5cQWJkmtb/8QZ2KkPVo0uoenwp5184Qd/RGyiqwvJf3kRxSzm9p7o59tUjAGz6v7ZQsqSUsa4JjnzjJBM9MskMFAWJJzMko2nCeT6iw9NyyDvoZuXHW7jww9Mkx2O4VYGOwB/Qcef4WP5bD3P9q2+THJnGDHtp/JeP4soLkJlJMLj7HGPHrmbNLQHcRSEavryT9PAk/d/aKyWTZ0PRVNzl+XhrinEyFlNHL0v/L00lZ+Mi8ne0oJo64/vOM7arDeE46CEfpZ/ajK++lOTAOBMHLjB9tjMrvKC6TZxU5rbvp9zvPko/uQlPVSHT+1qZPnQWhEBRVbzLGgk+tAw9J0D60lUSx89KBbp3hOJxY1SUYFSUzEmAv+P8EvEE9uAAdv8g9sCAnGe4hdajVVSgL2xGb2wgc/5ClgKsL12CunAxMy+8hEilUYN+fI9sxlxQS7p/lKnXj5LqmUv+zLIC/GsW4VtSl+2A2ZMRpn/wmpztUVX8j225rXgCYI2Okzx+luT5juy6GVVl+HZuRC8pRAhB4uINJl46hJNIoug6OU+sx7eqGZGxGXrxMJG2WTllRSGwpJr87S24S+ZX1GO944wevsxkW2f2Oqr73ZQ/s4rcFbJ76GRsOl89Q99eSaNUdY38lgpK1jcQqi+8a+IihCAxESc+OkNsZAZFU/AVBvEVBnAF3e+a8MRHpxmfBUU3aXeGz0X986soWFENwNT5Hnp/cpJMRF6zCzYtoOyJFWgug9i1AYZ+fIT02DSKqeP3KTixOJ5cN0psBi3kRU9NoSgKSiiEiETuui6oKuamjRirVt5zkibSKQmMBnoRfd3SIPTWrFVRUQuKcCbG5gBQXiH6yvUo1fNnsEQmjX14L3aHBAhqSTn69ifmmf86E2PYR/fj9HXJZbk8aKs3SKGj+5X7TiRIHz8hu+i3fC/UwkIJfMrKUEtLpWJhNEa6s4/MjR4y13tw4vM7UFp+DmZdFVpRPoohaXGKoYMunxVdywoxZQaGyXT3Y3X1Y43cTrXU8nPxbl6Na1HDfW3P+40HnRo3/BufumdqXNHffv9nMiP053/+5/zO7/wO6i3nlG3bqKpKRUUFXV1d/8dQ4943EFJnOZ93i5to7+czQh/dF8vp6ybzqpwVMp54HrWi+p7el9p/gMyp2e6dYaBVV6HX16M3NoCA6R++RuZ6jwRBWzYwuacVJ5XGVVGE1lDD6JutIISsQD6xms5v7CfeP4mqqxQ/vJSirYtIzyS5/uPTWUEEV9hL8xc3YzuCtq8dJjWdRNVUFjzbQvW2BSiKwnD7EG3fPZ2VGQYpK1y8uJRwWYhQWRh/QUCaqSYzTHSNy2F0RaFmZRkjZ/rIDI4TKg3iSicxfC4CuS6cmTj+mgIYHsEoyiH/0ztR3G6mTnbgKgzjqy/NDhs7yTTpwTHSfSMkr/aR7OzProurugQcQXpgbNbTAjKRBCl3AKFqJDMq/ppCIlMpEqMzFK6qpetED0IIVn55M2d/ep5I/xSBkhDbfncHLr+L6eEZXv3Pu0hFU/gLAowMRRFCUNKYT393hEQsRXFNHhueX8Jb3z7FSO/UvGOpalI5zHEcAj43TtpG0xQCHoNM0iKQ58WtyoF8w2uy/nOrqF0jgUx8Ms5bf/Q6mXiahU8toW/3BaJnbpCjpchzp8jxxvGFbMymetwrmnGuXnxPkQ0Ap/Mamd2vgqqg1TRIafbSivu6advXOrD2vAIoGM9+eq6LmUyR+PFPpNqWYeB+9hkyQxPEdkmTU9fSJvxPbscajzB9oI3YuWuziblCYP1iQjtXo5oGU6euMvTiYYTt4C7Lp/yLOzBCPsaOX6X7+3LeqGjrQsqeWomTsWn7H28QG5oi3FDMkt/YxqUfttJz8BoosPxLGylZUcnM8DS7/9ObWCmLpscXseTZpfSe7WfPXx0EITA8Bnl1+XSeH8Kx5y7DvqCJLgTWrHRxTr6X1GQMJZrAG/ZgppKYPpOS9fVUPbaEa3+3KwuGcpbXMHqkI+v5EmwsIW9tPX0vSTVDI+Sh/ld2ogqb8QMXcBWG8dQU46nIR72FwpYajTD88nFiHbOy5D43Rm6AZK+kyQSW1lDysQ2oHgl2tFmzv8xUjMkjl5g83pEVa1A0FT3oRQ/5MEI+kn1jpMfldzpnXROFT6zGmphm6s1j2W4HioJvWQPBh1Zg5Idwkims/iEyPQNYvYNk+odvm1NAVWdNYYvRK0oxyotRfe+g06bTWFeuYl1qn5OoBtB1qXIFMgmurWfmO68g0mn08mKCn3kakbaY2n2CWNssJdYwcNeUkLzen1XMU90ufCsX4F+9ECNPepTNvLSb9GUJVNwrF+N7ZLOUEe/sJXHsDJkbc10cvaQQ75a1GHWVKIpCZmyKyVcOZ/eLWZxP3qe2YxTkkJmM0veNPSQHxkFRCC2rJW9bC66icHZ5jmUzda6bkUOXiXXPJZzeijwKNy4gZ1l19rjHR6Zp/8dD2S5Q6aZGqp9owfDNryZbyQxDZ/uIDkaIjUwTG5khPha9vUh1c5tmO/XewgC+wiCBkhD5TcUY3tsTu5neCa585yjRfjn7lLeknIZPrsEV8mIn0/S9fJqxY7KI4crzU/XpDQTqJHjv/ptXSQ6ME6jJx+kfRFEUPC4LhIOrOIASuUWl0jBQc3NRc3NQc3JQc3PJdHRk57i06mrcjz/6viTbRSqJGOjD6evC6eue51+kFBRLAFRZ8675k321HevALmkO7fKgb30EpagU++QR7PZzgJCKdouXS0W7+xRwEOk0mdY20idPZVU1tfIyjDWrpVCBaeIkkmR6Bsh09pLp6rvNT0wxDYyaCsy6Koy6StB1Eh092FNR2XWz7Flq3C3Plo2wHcziXFy1ZVKcRQgyPf1kuuXjpihI4GOP/hwIza7X0K/dOxAq/rufDRAaHx9ncHBw3mseffRRPv/5z/OlL32JBQsWZMUSvvWtb/GpT30KgMHBQcrLy28TSzh+/Dhr1qwB4Pjx46xbt+6fv1jC/v37sz8LIXjiiSf46le/Stk7nLS3bNnywdbwn2l85PLZqSSZ7/8jIh5FW7QMfdOO217jzMxAKo2aP98cNfGtF0AIXA/vlI7uuo49PUOy9SKptos4sUS2EzTx1mlEJoOrqgSKipg4JOkmOeuaCK5r5vo/vE16Mobud1P/y9vxlOXSv/8y3a+fw05bKKpK2ZYFVD6ymIFTPVz64WmEI/AXB1n2pY0Ey8IkphKc/WErvadkgmB4TarWVFOzqZZwec5t23VrnPjfR+k+3kW4IgxTMeL9E3i9Oh4DPF4Nb3EIxiZAUaj69cfxVBWRHonQ9809pEdlpVB1mwQWVhJYUo2vsQxVn1MbSg+MMn3oHPEL1+dVulWXiVGUy2THEI6qk8woCFUntLKO/uOdGD4XTsDHdH+EkuUV+GsKOPP9Vkyfi4f//WN4c7zEIwle+8+7iY5F8eX7mJxMkknZlDYVMtAbIRZJUFiZQ+OGKt7+fhtW2sYXcvOxf7mJquYiNF1FVVWununn2/9lN8IR5IS9pOMZQnleTCAeSRLM8+L3GUQG5PbWrK1i/WdXYXpNek52c/wfjqCoCrmFPiYOXCQkEuS60uT6k/hDFq7qEjw7HsI5fxzFMDG+8Bt37fTY1y5LOts7+vqK14/a0Izi9UmevGVBJgOOjVJUKv0sZg1RRXSGzA/+EZFOoa1Yh756o/x7PEHixRdxhkfA5cL9sWdJX+0jfkhS2Dxrl6E1NTBz4ExWDQ7AXVdOaOdqXOWF2PEkQz85xvRZOcMWWFxN6acfQjV1CYJ+cBQEFG5upvzZVSiKwuVvH2H4xA3MoIeVv/8Eg2f7uPi9U6BAy+fXUbamhkwiw57/uouZwQiFC4rY/JtbsTM2P/5/XiM+GaeoqYhkymbgqkxOK5eWUN5cxOmXL5KKZxCOIBh2kY4k0TQFrw7WTJJgwMDQFTyzLMTKhxdTtrmRq//rrawQCchEt+zJFQQbJK0wPRnl6t/vITkcQXMb1P3SVgKz/7sZwnaI909gxVNZc8vo5V6GXzkx991wGRQ/u57gijpS4zN0zsrg57RUUfjQQnyVskvqpDKkx2fQAx40//yOgJPKMPLGKSaPyK6DmRek5JOb8dYUkeoZJrLvNMmrN0GKgq+lHt+qZlwVhbfIeFtYg6NYfUNkegew+oZuq1CDlMW/SaczKkpQc+ZU5ZzINFZ7O5mLl7LD2ub6dVBWycx3X0ZkLIzKUgIff4zo6Q4i+9vk7APga2kg9Mha9KBPdpBPXyZ6sj1LtQTwLq4j92NbUAydxKFTxPfPzraVFSFSGeyxucTcbKrDs3YZevnsLFw6w/T+NmYOn0PYNoqmEXxoGcGHlqPoGrEbQ/R/6+2sl1TZ57bjq52jdFmxFKNHOxg90pGVpVZ0lZylVRRsaponhS2EYOj4da7/6BR22sLwuWj8hXXkL5k/ZxAbnaF7/xX6jt7AuoNsv6qpePJ9spPvCAmQxqN3tDpQNZW8BUUUL6ugcEkZrsBcIu9YNr17LtHz1gUcy0b3mNR9bCVFa+Ts0nTHAN3fP0J6an53KNE9Qu8/vAmqQrDQiz0xhbvAjzo9ierzENi5DjUcRMnJQfHdLukthMA6d57Uvv1S8tznw/XEY+iVlbdvwH2EmI7gDPah+PwoZZX33mmKTJHZ/QpibNarSzeyHSW1pgF97UMoofD9rYttY507T/rY8axXl1pQgLl5I2pZmSwydPaR6erDGrp9Nkgvyseoq8Ksq0QvL8aeiZNo7yJ+qYtU1yB39bW4ayiYZfm4a8tw15ZhVhaBZZHpHsCoKkP1/mwV+u4WDzwQ+vKnCL7LXFf29ek0xV+5dyAUjUa5dk2C/+XLl/Onf/qnbNu2jdzcXCrvcN6/UywBpHz2K6+8wte//nVyc3P5vd/7PcbHx2+Tzx4YGODv/u7vACmfXVVV9X+OfPbNCAQCnD17ltra2p/F4v7Zx0f9xcrseQ3nWjtKKAfj+c9lh9Stnh6syx3Yvb2IqVkX8nVrMTesByFIvPAdnOERtMYGjDXryHT2kr7eQ6Zzrlqq+n24H1rLxBsncRJJXDWlUFjIxEEJggoeXYmruojrX9uLncjgLgxS/ys7yKQsrnznGDOzggGh+iIanl+NuyDApR+cpveIrJKWrKhk6efWohoanYeuc/ZHZ7CSGVAU6rc2sOjpJZjvqB4KIUhEksQn45g+E3fAjeHWSUaSvPEHr2ClLAorw0R7xxHjEfx5Pjyqje42yKnJJdU7gr+pnPDaJga+ux8nlUEPekEIrJlbZIW9LoJLawiuqMdTWZC9iVmTM8Qv3kDzezDLC9HzQkwd72Dox0dwBCQsDSPoIWZrZKYT5LZU0dvah+7SWfvbO9n7P/aQSWRY8dnV1G2uJ53I8MZ/28NEzySesIdE2iE2nSK3PMTUTIqZ8Ti5JUECJX7aT0iAWN9SyvZfXMGV833MTMWJR1PEo0kSsTR2zGa8ZxpdV/GaBlbapqgqByeaIh5JEir007C8jMt7ryAcgTfHy+ZfXkdJUxHH/uEIvSe7caJJ9PEJfMkouWaSXG8Sf66CpySIuX4t2lQfIjqN/vDT8/x7boZ9+TzW/l2AQGtYiLpwKc6VdpzrHYj0nVX3sqGoqCXlqNV1OJ3XcAZ7UQqKMZ79DIqm4USjJH/4Is74OIrHg+v5j5FsuyxFEQDPlrVk0jC9rzW7SO/CGgKbl+Eql4ObMxd7GHrxMFY0AapC/o5l5O9YBsDAG2cY2i2XVbBpARXPrUFRFAYOX+Xq94+jKApL/6+dGCEvh/7L69hpm6aPLaN2RzPCERz66/0MXRzEHfKw8/9+FE/Iw4nvtXJpVwdCUbB0nXQig25oLH10AZdb+5gei7PykUZSUwk6DnfiOIJI5xg5RQGKygLYiTRaIoUn6KZiRTkTZ+WMXd3HVlK4vIobX9+HsG2KH24hvLgie64mJ6KYQQ9O2uL61/cRvT6MoqtUfWIdZtjHTOcI0RvDxHrGsp0kb1kOVZ/agLc8D2HZTB5tJzk4Sf7OZZi5ASbP99D93cPYyTl6HYC/tpCiLQsJLSy/zWzUmkmSHI1IU9q6IuLXBxn4wSGsSAwUhdzNiyl4eDmqqZPqHWZ6XyuJK3PdkpsdGHd9Oe66cvSC8Lxk3pmMkOkbkslc7+A8oHEzVJ8Xs6Ea15IF6JWlWVEAZ2gYkUnjOCoz331FmulWleHeup7xU5IjTAABAABJREFUH+zFmpQg0ywvIueJ9bgqim5btnAckld7mTl+keTVPkBgFueR/9lH0cMBUh03iP50F2KWkqgYOq7li/CsXoqWE8puR6K9i6nXjmBF5FySp7GS8BMbMPLkayaPXWb4p8cQjoO7NI+yL+zIqsAlRyKMHGhn/NR1nNn5SSPkoWB9I/lrGzGC82d7rESaK987np3XDDcU0/S5DbjC3uz6jF8ZpmtvByMXB7J5rq8wQH5TMb7CAL6CAL6iIO4cL6o2v8trZ2wS41FiI5IyFxueZrJzjOjQLdR6BfIaCilqqaC4pRz37GfHBqbo+M7R7P0jt6mUhs+sxZ3ju2N3qPJT65nae5bYtQH8VfmIwUEUwBNUIZXCu3k13i1rs8fKmZrGHpvEGp3AHpeKkd6H1iCScVKvvIYzIc8fY+0azA3r75t2dq8hhMDu6UFxuW6bTxK2jX3iIPY5ydhQ8ovQ12+5JyGFectJprBu3CB95GiWGqiEQpibNqDV1ZFqvUj84MmsyfHN0PJyMKrL5aOqDMXjwhqdIt7eSeJSF+mB+WDJLM7HLC/I0uIUXUPRdRRdzf6OgFTfMKnrA2TGp+a9X9E0zIpC3LVleBfVYBR+NIIJDzoQGvzVewdCJX9/70Bo3759bNu27ba/f/GLX+TrX//6bX+/ExBKJpP8/u//Pi+88MI8Q9VbBRsmJiZuM1T9q7/6q/9zDFVvxs+B0Pz4KL9Y9vUOrN2ztKHnfgG1qARh29IN/dSdBSv05mbU/DzSBw8hFBVLC2YdyW+GUVWGe+Vi1MICRr72CvZMDLO8CLWilPH9ksNc9Mw6tLwQN76+Dydj468uoPZL2xg4fJXuN88jbAfdY1L73AqK19YxcXWE9hdbme6bAgUWPN1C7cPN2Gmb1hdO0n28C4DcmjxW/OJqcipkB0gIwdDlEa4d7WSqP0JkeEaCpVtC1VVyysJULSmm441LaCqYwsEejxAqDuLzqqgZa5Y6MTLPRNJbU0zZ57ah+dwkukaYOd/F9PnOeUPkZl6QnI0LCa9pnEcjurl+N/70x6RHprAUnXQGzLI8JronceX4SKk6sZEo9Y8vZmosRvfRTnKqctnxbx7BsR12/+V+BtuHMX0uFJ+LiYFpvGE3KUdheiJGuNBPXnWY80e6UDWVnb+wguK6HL7557uZidxeCReOoCgnTDySIqfAjx3LYKVtyurzyUzFiU0l8ed6WflE8/+Pvf8Oj+O+zv7hz8xs71jsAoveQQIgAIK996reLckq7jWJ4yeJk9iOS+I4ibvjuFuWZPUukZIoUey9F5Ag0Xvv2/vM+8dAC0KkbdlW/Oh5fz7XtRcIcDGYmZ3vzLnPOfd907SnGf+wHwSBtZ9ejqc8g7e+8Qbe9mHE0UlsSgSnNoLTGMZiB1OGHm1RPob5VSTPn0QsLEW7+eaZ1+SFMySO7AVAqqhFWrl+OmlNJpC72lE621AUGUGjUyVWtVqQZVWdafwd1UiNFt3t9yE4nMh+P+Fnn0OZ9CKYzRjuuI3Q0fNEz6sdBvPmVURGAiklQHNtGbZVdWgz1GspGYowtO14ilehy3CQfddKjHluktE4nU8fZrJeTcA96+aQfV0dgiAwWt/DpV8fQFEUiq6fS+76So59fxeTnWOkl2ew6K/XIQgC5547Q8vuJiStxJq/34CzwMlY9wTb/+1NghMhYoqAzmrAledg9poS9j1znmho2oclLdNK7ZoSuk730Hmqh8hYkNL5OWgSCeRgBItVjy3XQf6CfDpfOwfArA8uw7No5r3X3zNO52vnGL/cjyXXyZyPrUZr0dP51GEmpkDUO0OaajUlw3EEUVCVujbVpK53OZGkf8dZhvap3jWWQjdZW+YydrKNiXMdKFMjfnqXlbSaAmLeEJFhL9ER3wzQZC31UHjvCiSdhqHtx/GenuJsWY2kr61NrbFo7zD+oxeItPYih2aCZ8lqxlCag6EoB31xNhq7Zcb/y6GIOk7X06+O1PUPz5C4Fu1W9NWzMFTPQkpPI9bejf/Z11ASSbTF+RhWLWbksTeQQxEkqxnHpkWYalXj0UQgzNjeeiJ9Y6Qtr8Q6p2BGpT/aM8ToE2+SDIaRzEZcH9yMPi+TxMg44YMn0WRloK+rRLzCRDI+PMHkm8dS4E9jt+C4bhnGikIVsCWSDG47xuRxlZBvqyki686VCFqJQOsgQwcu4b00PbprykkjY3UlabWFM7raoN6vxhv6aH3hFJHxAIIkUnhdLXnrKhBEkWQ8Sd+JDrr2NeMfmObUuCuzKFwzC1eF508iOgcGfQye62HwfA++npkjV1nz8qm8cz56qwE5KdO37zKdr9cjJ5JIBq0qpLCw6NrdoUXFBM80gyDgyLcTHxjB4LYi+sYRNBK68iISoxMkxyavHq1E9bGy3LYZbXYG0X37SVyY4urkZGPYuvUqcY0/NeSJSaJ79pLs7ARUkRfdvLqr3zfQixIJIxaW/t7zriQSyMMjqtz84BDJwUGUielzLJhM6JYuQZpTRbylk9DuIyQnVWAqWi1oi/PQFuSgK8pDtJpVX73uIcJNXUQau98BXgT0BR5MlYUYKwoRzSYS/hBKQlY9DN/+mpSnR+NkBUO2E226KgASbe8n0tFHpK2fpG/aQDntumVYl1b/saf2D4r3OxDq/+i7B0LZD/1xo3H/X46/AKH/pfhzLiy5t4vE3jcQZ1WhWbRC9bt59bWUS7mmqgpNWQlSTi6J5maiu3bPGOtKaq3EfREEjYSmIAddcT660gKk9DSSviDDv36V+NgkWncaUkkRYwfUBDPzhkUIdisdTxxEScjYK3Ioun8lbS+dZeComti4avIovWMh8XCCxpfPpgQRtEYtcz+0DHdVNv4hH0d+fghfvxdBFJhzUw2zNlcgCAKxUIzWox007W3Fe2UVEUAQMNoNqlz0FaMaZStL8LUN4x/yIYUi6CQBvZDE6DBgFJMICLirswleVhOOtKUV6rG8M1mQZUJtA3hPt+Jv6E6RyzU2E+lranAsKicZjOKrb8d3tp1I/xhyUiGckBAkgTBakgmZ9AXFdB/vQmvSUf3gUg7+SB0rXfeFjTiL0tn/iyN0nuxGo9dgyXHQ1zyKzqhBm2ZiuHsSq9NE0fwcju24jCAI3PG5Vfj8QV5+5DDJpEJmjoPZdfkYzXrMVgNtl/o5d6QNk0mHLqF2gyoX5tN7cYhEPElBRQZxbwTvcABBgNpNs4j7InSe7EJv0XPLv16Hr2+SPd98g2jbAE5dkjR9HKcuhNWcwJwuoivKwbh1E8lDb6bc1982RlVCQeQ21X9HqlmAtGTVH64645tUO0GdrcijwynhD0VRiDz7PMneXgSbDcMdtxE+Xp/qBFluWk+wbZjgGfXvp12/HOuSOYA6HuY91cLksUa1CyQIpK+uxrVhLqJWQ2wySNvDewn1jqsdkzuXkr6gBIDJ1iEu/HQPciKJZ0kp5XcvpvWNBlpeu4DWqGXFF6/DmGai/WArp59QR/OWfmI5ufPykWWF1765k5H2MSZGghjSLcxeWYQx3czhFy6oHLASF3NWFnH4xQuEfGrCn1eRwejlYcaaBskocpKRYUKOJtDG4+gtemofXEqkd4zefZcRRJGqj60mvSqH4MAknTvqU3y8t0NnN1H9iTWYc9Lo3XaK4QOX0TlMWIoysBRnYinKwOBxkAhG6X35BONnOwEwZNjIv3Mp+nQrHY8fINCuChdkrq4k+7q6VJId84YYOdTIyNEmkuGZRQp1vYI+zUI8EEaOJdGYdRTevQJ7ZS7+hm6Gth8jPqEmQhq7Gdf6Whzzy6YlvgfHiLT2EmnrI9o5kOLlvB0apx1DcTb6omwMxdlIlnfwg+IJ4r0DRC82E7vcmurMgMrNSQyNgiyjKy1Ev2JhCgTpctxkPHg9olGPHI2rhrUHLqhCEFNhzHfj3rIAc8n0uGFi0s/I428SHxpD0Ghw3rYGc3XJVaclNjCKb98ZQpdULyhBkrAur8G2ug5xSp474Q/T+/gewp1DIAi4N88nfU01cixBx2MH8F7uS51jR2UuGasrsRRnXrXuktE4Qyc76NvfSGhqlNLosjL7geXYCtSxxtBYkLO/Ooh3CqBIOg25S4ooWF2OJfO9f5aFRgMMne9l8HwPE+3qqKjOomfO3QvxzFWryqFhH01PHsXXoRZI3HUFlN21CK1JP7M7JEB6uZtw2wDG3HTE4SFQZMyZJhTvzOeHoJGQnGlILvUVbWxL8VNMa5diXDaPRFMT0Z271LFdptTTMjMRPZmqoEDmH+khlEgQP3Wa2PETKW7a26GdW4tu7Zo/jEPZ30+84TLy4KBqPnwNbzfBZkNbPQftvDoSw2ME3zpMok8V/BDNJkxrFqOvnQLCwTDhpm4izd1E2vpmmCQLkoShJAdjxRT40esINPXhPddG4HI3Svzd8cK1Dgum0izMpdmYS7ORLAYSYz6iHX1E2vqwr1+A1v27R+Hfq3jfA6EP/wFA6OG/AKE/NN5TIFRfX09RUdF7sbn/5+PPzhGKhEGrU3kTjz+hzv/qdBg2b0LwZBHv7CXe1QfxOIbZhUR3Td3cjWYiY1GQRByfuBeNS73xxAbG8B+9QKi+FSWZRGO3oJ9XyfCbZwHIuH4hmEx0Pn0YFEirLSD/zqU0PXWM0fPdCIJA6Z2LyFhQROvrF+jY05QS0MhfWUrp1jnorQb6zvdy4tdHSUQTGOxGlnx0Ge5yVSmp42Q3x584TTSo3oQ1eg3uUjcGhxFZVojFEkQDMQwWPQaLjlgwRsveFow2A8vuncf5Z04THfVhTzch+oNY3BacWVZiI17ss7OxuEwYCzOx15UgJ5J4L/agTTNjynFeVUGVo3G8Z1oZ3VuvjvIwpYoVucJRXRQQ0uwEhwJINhPe8QjGDBuhOIRGg5RdX03nmV4meyYoXFbMgvsXceKZs1ze1YQgiWRWZdF2pg9REsiYlUFb/QBavYZ5W2ez9/lzAGy+fwG9faMc36Mm+rVLirnrU2vQG6alq2PRON/7xxcYHfSSl+9molvlLay+pZozO5pIJmVmLcrDbNTSdLhT/TyLnBCKEhwLkl+Xy9rPrODlv32eiVNt2MU4LqNMmj6M3RTD5NKiz05Dv3IFwlD71d2bqZDmL0Wav/Q9lciMX7pMdMcboNFgfOA+IqcvET6mXpOWG9YRaOwj1NAOCDhvXY25phT/pW4mTzYTbOlPFQB0brvaBcpXx+SC3aO0PbKXuDeMxqyn5MNrsBSp40+B3nHO/2gXiUiM9OpcKj+8Cl/vBEe/+xaKrFD74FJyFhYy3DTEgf/eh5KUqbqxmsrrVQDWuLeFY0+cIjARRjbosLrMOAqdtJxWCfDVq0vQ2/VMDPmZv66crguDnNpxmWRSxjfgRQrGsFh1lM3LIe4NoUFGrxGRdBJLPr+Bgf2XGTrZoZpUVmYzVt+TWmvu+YV4FpfQ+sJJQoNeJL2WigdXkF6VQzIaR9RpfuvnM3mxh+4Xj6U4JpJRSzIcRzJoKPjActJqCkhE4kQnQ5gybantJKNxxk61Ee4bR+e0YMiwY3Db0LusiFoNkWEvHY8fIDRFiM9YOZucG+YjAJOnWxjdfT61xrRpFlwb5mKfWzKjUKHEE0S7Bom09xFp7yfWN8I7+QmadDv6fA/6AvWlSZ/mBynxOLHmDqIXmoi1dU9fF7OK0S9bwMijO5DD0yBI0GrUfXvrbGp01pDjwlTsYeJYY8ofxTwrl4wt8zFkqzxMORpj7NndqS6Pfd0CbGvmIQgC0e4hfPtnjv8ZK4pwbFqE1uVQ91NR8J1tY2j7cZKhKKJBR849q7HMziMeiND6q92EesYQtRLpC0vIWFmBIeNqD5voZIi+g00MHGklMWW4qzHo8CwtpWDzHDRGNckaaejn3KNHiYdiaE06SjdXkbu0+JrCBlf9jUAU34AX34AXBAGLy4I53YzJaUbUvLuk3tszTv1vjqW6UNkLC6i8Yz46sx5FlunZdYnOHfUosozeYWb2/ctwlKrrtO3Xe5hs6MVWlkmyawBFlnGUuoh3D6DLcGCZlY1kNSO5nEjpDpUvdOX4ZjxOYMd+ovXqvVVXVojlpg0QiRB5cydyX9/VO4wKMMTMDNXAtyAf0e3+nfe8RHc30V17Ul0aKT8P/fp1JFrbiB1URV6kwgIM11+PYPjdssdKKET0wCESDQ0z98loVE2FMzMRPR4kTyaCyURywktwz1Fil1VeiKDRYFxah3HpPBKTAUIN7YSbe4j1DnPlepLMRgxleRhn5WMozUPQawm1D+I7147/QifJ8BVASatB1ErqWJwkTn+VJASNKuYT7R9DSc4Ea3pPGubSbEyl2ZiKPUj6996O4bfF+x0I9T347oFQzqN/AUJ/aPzRQOi2226b8f327dtZt24d5ilX7LfjxRdf/OP37v/h+L+1sMKvbCfZ2orodKJdv57g3uMkemYqf0huJ9brVpNsayN0rpVkIIR+3hykwgKiXYNEuwamEgs1dLmZGOdXMPDCURRZxr1xHqLLTsfjB0GB9EWl5N40n0sPH2KiaQBRIzH7geWg0XDxmZOEx9WxhYw52cy+pQ6Lx4aiKDS9eZkLr9SDouAuy2Dxx5ZhtBsJecMce/wU3WfVRNHitmB2Wxjt9+MbnTm+987wdo/jcJkoW16M4gsx3jaMJh5HKyexp5uw56chD4+DAhV/dyOm7DQSwShtj6rcCQBRr8FSlIG1JBPnvGJ0julrWk4k8Z5sngGITEUebHOLMZVmc+m725FjCeJ6A9FgnLQ5efSe70dn1pO3sZL658+iNenY8vXraTvaycln1SQ+Z14ezSdUXlbRwnwuHu0EYP51FRx+9SKyrLD0+kra2gdovzyAIMDWuxex9qa5M4nosox3IsDooJ9ffONVFAVmzcqlr2kUjVZi9S3VHH/lErIsU7mskJKaLA4/eYZoKA6yjFaR0ek1rPzoEroOt9Kx/Ty2ZASnPo7TGMduSWB26TDYJLRlxRiu34zcdFGVMZckECWQJES3B7Hgve0OK+EwoYcfRQmH0a1cQSIsEzqodl/Mm1bib+wj0tKDIEmk37kOWaOj/5kDJIPTI1Wmkiwci2ZhnVOAONVpGDnYSN9rp5ETMkaPnZKPrEOfrsrVhkf8nPvhTmL+MPbSTKo/tRYQOPSfOwgO+cmqy2PuR5YTHAmw+7/eIhaMkrewgMUfUQFgaDLMS//yGhF/FJ8/imTQIeskYnEZURRZe18dnU3DXDjckdrHWQvymLemjIYD7Vw+3MFk2whmrUhBdRYWncpp8RQ4CPR7MaaZWPJ/NtL23HHGGqYTNVdtPoVbajBnOwCVC3Lp4YNMNKlqWiW3zidn9ezp6yaRxN89jrd9mEQ4RvaKcgxpZhLhGH2vTnMxjFkOih9cg8FtI9A7zsVf7ic6GcSan07Oqlm45xYgamcWEa4VciJJ32tnGD6gjjOactIoum8Vhgy7Kt99opmxPefVzh1q0cFSnoOlMh/LrBykd5Co5XCUaPeg2i3qGCA2eLUkr2gyoM/3YJyVj6mqGNGoJplyIET0UgtKPIFUmDcDBLkfuI5Q+yDDb5xOCUZonVYytszHWqOOZyX8IUZ3nWPyRHNq3NZWW4x7y3x0TiuKLDO58zj+w6rxsKmyCDkSI9L+9uclYKopwbaqDl3mNCciNhFg8KUjKeU+vcdJzgfXoM9wEB0P0PrLXUSGfWjMOko+uh5LgXvG8SqKgr9zlL6DTYyc604lnkaXlZxVs8hcXIJmqoCiKAqtOy7SsuMiKGDPd1L30RWY0mc+z99+72TvJJM9E/j6vXj7J/H2eYlcY0RXPTwBk9OkAiOXBavHRnZNNtbf0l1KxpO07rhI21uXQAG9zUD1vYvImKMKMfm6Rmn8zWHCo34EQSBvQxUFW6qJjvi49N3toIBrTjahxm70GXa0gUmURALX3RsxVc28JymKQtIbID48gaDVoCvwEDt3mcCbByCZRLTbsN25FY3HjRKJkBwaRh4aQh4eJjk0lOLdzjhcoxEpLw+pIF8FRnYVmMrBILH9B0lcVq95wWRCt2Y10qxyEq1dCAY9QjRE5PUdkEggOp0Ybrsl9fvv3O9E/QWiBw9BdArYVlSgKSlG9HgQbNYrRpFlEoMjxBqaCV9pLF1bgXnNEhRJwrvrJIGTl7kS/Og8Loyz8zGU56Ob4lVGB8bxnm3Dd659xti4xmbCVluMbW4xhpz031v8kmMJQp1DBFv6CbX1E+kfnzGlIogiGTcswrn82vYf73W834FQ7wPvHgjl/uYvQOgPjT8aCL1b7e+HH374j9n8//Pxf2NhJVpaiWzbrsrIrllLcM8JlLCaBGqyMtAWZBO92IIcCKrqL0V5ajVdlIgktClyLaA+vKqKsS6rRjAa6fyf7STDUWx1JZiri2l/dB9KUsG1tIycG+Zz8Rf78LYNqxXnD62k72wPvUdVJS6Dw0TVBxaQWa0+yJLxJKefOEnXMTX5K1ldxtw75yFqRDpP93D0NyfVLpAgYHJbmBgJkXib+GvQ4ClxYXObsbktmBxGIv4owckwQ22j9FwcJNA7TnZ5BuWL8+g73Y0yGcRsN6CNRjA4jGSWuwm2D5FWW0D2lrm0PrSH6KgfUa9B1IgkgtNdHkEj4lpUimd99VWAKNw5hM5lRzv18+GDl+l5+SSSWc/kZAxJqyGm1xP1RSm9bg6XdjUTD8Wou3s+mVXZvPQvryEnZPLm5dF0sgdFgbJlhVw40kUykaRqZTHnj7YTiySoWVFMWIly9nAbRpOOD/7NembPzWegb4w9O04zMjTB2IiX8VE/SVmmoNhDdUUZ+7afx2zVU1KYTfuFAfQmLatvrubw8+pIVnq2nbX31nH21UsMto6iROOYjBr0Fj1F1R6anz+FIRDAqYvj1Mew2WQs6TqM+gjavCxMH/0w4p+J8BjZ+RaJCxcRnU6U0krCUypcpo0rCLYMEm7qQtBocN27iXgkSf9TandGYzNhn1+GY0EZOtf0WoxNBul8+jD+FnU8xF6ZQ9EHV6akoGO+MOd+uJPwqB9LThq1f70RjVHHpedO07m/Gb3dyMovbkWQRHb/5078gz6chems+T/rkXQqGNj388N0nuwmHIqT1GoIB2PIeg1mu5EbPr2Mw6830HK2D1ESKa3NpuVsH2/fkmctyKOnfpDRxiGMskx6rp3Cygyi40EKFhcS6holNBrAWeJm/idX0fr8CeSETP6GKqz508qQckJWTVwTSVqfP8nAUbUanLW0DJ3NwGTbMP6uMeT49IiOxqRn1j1LcNWoo0n+tkFCfeO4l5Srinrnu2l8/AjJ2DvGeiwGspeXkbW8DL3dhKIohIZ8+LtG8XeP4e8aIxlPULi1BvfcAryXeul8+jCJYBRRJ+FZX03GygokvRY5lmDi2GXG919MASIARAFTQSaWyjwsFfnoXLarEi8VGA0R7R4k2j1IrHckJXEP6niPcVY+pppSjOX5CFoNscExhn/96gwQNHbgImN7VQAjmQ241s8lbfGsq8ZoAWKjPkZ2nkkpEIoGHbn3r8Ncmg1A4ORlJl49lAJLV3omvS2EAOpI7sTRRkbeOI0ciyNIIq4NdaSvmoOgkQj1T9D6q13EvWF0DhNln9w4owsU84cZOtHB4PE2QkPTibq9NJPcNbNJr8qZ0QmJBaOcf/QoI5fUgln+ilIqbp+HpL2aVzTcOETDqxcYa7saaAKYnGZsWTYEUSAwGiQ0Gphhen1l2HMc5M7PJ29+3jVB0WTnGOcfO0pwSO1o5ywuovL2eWhNOpLROK0vnmbwmHotW/PSmf3AcoZ21TN+uh1LkQuGxpBjcdJme4i196Bx2km7YTnx4YnUKzE8PsNPy1xbhvPmVSRHx/E9vwPZ6wdJwrJ1NYa5VyflSiRKcngIeWiYZG8vyZ7e1Bjd26GakeaQaG9PgRZtbQ26FSuQYzECr+5NSaibN65AV5BF5OVXUIJBBKMRwy03IWVnp7aXHBoiumuP6omFqvqmX78OKUd9j5JMkhgYUeWou/tIdPenOpYwZSy9YTmS20nwdCOTb6kiSKAKcxgrCjGW5yPZzCiKQnRgHF99J/6LnaliAKjXt626UC0CFntAEAi0DxEd9aPIisoLkqe4Qcnp71HAmJOGrSwLjVktaCSCEUJtAwRb+wm1DhAb85H3kU1YZuVe89p5r+P9DoR67v3AuwZCeU8+8747jvd7vGejcX+JmfHnH42LEnrkUdUQNT2TyJQXhMbjxnrHViSHTa0MDY/if2r7tNSsohDXWogHY2gcVgxleegLPBiKspFsZpLhGJ0/3k5sxIsx303aujpVGCEh45xXRN5ti7j4qwN4W4fQGHVUfmw17bubGLrQBwIUrplF+Q01aPQaFFmh90w3l15vSPGB5t41j9I15SiKwtlXLlD/qtriN9iNhGNJQn4VlKRl26heX05+bRbByQje0SDekQAhXwSTzYDFYURv1LL750fxdquS2VmlLmR/mPh4AHu6CU08htGsxVWZTaR5yh9latxH5zRT+tH1GDLthAcmCbQNMnGhO9UlEjQiroWleDbMBESp868oXPrWK0SGfQhOK/7hIDqXjYmhIHqbgYxFxTS+cQlbtp1NX97Kvp8fput0D1aPjZHBAMmETMmifNouDRGcDFNYncXgwCTjg36Kq7PImp3Om8+dQhQFPvHF6ymdk0Pz5R5+9t2XCIWiV+0PwK0fWMWFQ10M9kxQOb8AggrdjcNYHEY2f3ABB585R2AyjEYrsfKuWi7tbMI/HkKTTGIwaDCZtYQ7h9GOeUnTJXBqI1iNSWwOMNpBl+NCv34duvnz3stL+ZqR7Osn/PQzAIh1CwgdOQ+Aae0SIuNhAicuIUgS7gevIzoRov9Z1avHVlNE9gdWXZW4jp/toPuFYyTDcUStRO5NC3AtLU8l1KERHw2/3E9oyIvRZWXu5zahsxkZuTzAyR/vA2Dhp1fjrsrm7NOnad3XjDHNxPp/3ITRoXIG+i4O8NYP9hGPJgjFFRRBIBBNoDPpuO3v1rD/pXp6mofR6CRu+uRSMgucSILIvhfO03C0U6289/uR4kkMiSRmg4ayxXkQiKDRa1j9t2s59eN9JCJx8peXUHX3whmAIDQWpH1nA73HOkifncncB5ehMWrp3XOJ9m1nrzrHWosBe7Gb6EQIf4/KlchZOYuim+uQpsQSFEWh560GOqZEGtJmZ1F6x0JGz3bTf7iZ6BRpXdRIWHLTCA36SFw5PnpF5KyeTfFNdSSCUTqfPIS/VU3sNBYDnvVzcC9VBRMUWSbcM0rgcjeByz1EB2eS67VOK5bZuVhm52Eq9lwlZAKgJJLE+keJdPQRqm8jPjytKCca9BgrCgk3ds0Yhxs72MDobvU401dXk76udtovyRdmaF8D4YEJsjbXYinMSG0v0j/G4EtHCXcPI0giWXeuxF6ncoMi7X1M7jyOLicD24paNGnTRpkA0aFJBp4/RLhb5WEZCzPJun05+gwHAIGOIVof2kMyHMfosVP68Q3oHGbkRJLxy/0MHm9jvKE/BbYknQbX3HxyVs3Gmne1Atdk1xhnHzpEeDyEqJWYc/dCchfPHG9XFIWhS4Nceu0iY1McHlEr4SpxYc92YMu2Y8+2Y8uyozVqr/rdiDdCcCxAcCRAYDTIWPsow01DM0aj7DkOcuflkTs/H5tn+nmZjCdpfrWejj2NoKhFtdoHl5Jepp7vkXNdND9zgkQoiqTTUHRdDUM7TqMkFTLm5hG42IHWYcagRFLJ/jtDEEU06Xbio5OgKOhyM3Hfs1EVoXhlF7HWTgB0pYVoi3LReNxIHhei/uqxNSWZVAUKurpIdHcjDwzOFOhwu9Fv3IDoySR67hLBtw6pXLUrjMGNi+diWFxD9OVtyCMjIEnoN29CU1RE7PAR4ufOqRvTatEtX4a2bi6yL0D0YrNqUto7MAP4AAgGPdr8bAzz56ArKSDaPcTEq4eIDUwZ2GY4SbthOYaibBX89I/hu9CJv74z5fkFKq/KMjsPe10x5lm5iFoNcV+YsVOtjB5vJTrq512HAKbcdGzlWdjKszEXuqf5hhMBNBbDNdfy/0a834FQ9z13v2sglP/U0++743i/xx8NhL74xS9yyy23pAyS/hIz48+9sKK7dhM/X4+MSCwkgCCir5mNYcVC1Qi0tZdoRz9KUsZ5/RIi+4+q3SKTidB4DEGrJetvP5BSXkpGYkwcbWT84EWSwQgau5nMO1bS9sg+5GgCR1Uu+R9YRsOvD6ogyKCj6uOrad7RwGjTEKJGZN7HVpAxJwdFVug63kHjG5dT5qhak46lH19OZoWHWDjOwYeO0nOuD0UBc4aF0cGA+m+HkdUPLEBr0XFqRyMtp3r5XZdseDyE2aghMuQju8yNWS9ALIEUjaE3SFgMIhqDlsxZbvxNqnDD20p3WsvVngX+tkEGdtankjRRK5GxqoLMtdNz9YGuEQZ31eO91IegEQnEROSkTEzQkEgqlN9Yw8U3G4mHYiz5+HK0VgNvfGs38WiChEaDrEDeHA8TExGGusZxZtuwZFpoPNmNLd3M6rtqeOqnqgLbbR9ZwbJNVZw50czD//Mq8USSkvJsVqytJT3DjjvDzsVz7Tzx0Fvo9Vo++Te38Oh3dpJMKlx/z2IaD3cz1D2B02Plnr9fx74nz9JRr56HrII0JrsnkBNJjBIIiSTCZAB9MIhTl8ShiWE3xLDaFUxuPTqrFl1VBcY7b/+d16YSChHZtRtBp0dbPQcxO+sP4g0pySThx59AHh1DKp9FqLkfJRzBuGw+SZ2JyTePAQKuuzcQ9ccYfOkIAI6F5XhuW5aqfsvxBIH2YUaPt6RU00x56RTdu2JGRX2soY/Gxw6TCMfQ2U3M/euNGN1WgiN+jnx7J/FQjPyVpcz5wELG2kfZ8+1doCis+tu1ZM5W5W9joRivfP0NgmNBEgpEkxBPysQUyCpNxx+MMtwzicGkY8uHF/LK40fwe8MUzfKw+oYaXBk29j1XT/3BNkbbxrFqBCwakaxyF1k5NmL+CKVry8mZk8Wpn+0HBaruWkDBqjLC40Ha3lQBkHxFsmlyWZj/yVVYs+yMnO+mZ1cDpkwb9uIM7CUZGDPUzoqcSNLx6nl6904pw+U6qXhwBfo0E81PHmP4TCcAOatmU3zLvJRkspxIMnahl979jSlSO6jJuCXPiTXfha0gHX/3GD171G3bCl1UfGgleoeJibMd9L95PpVM6RwmPBtrSF9QMtPLa9xP4HIPgcvdhNoHZyTUglbCXJKFuTwXU7EHvSftmtdabHCM0PlWgvWtM5Sq3gZB44cvM/KWKr2eecMinCtVvlc8EGFo70VGDjdNd9AFyFxVSfaWuYg6Tepa63/2IP56tevt3rKA9DXVv/W6TwQjjO2rZ+LwJZSkjKjXkrF1AY4ls1O/M3mxh47HD6TUOUs+ug6NSa8qdL5RT8w33TWzFbrwLC7BXVeQuk9dGfFQjK4DLbTuuIiclDG5LMz72ApsV/i0KYrCYMMAl167yHiHCowlrUTxqlJmbaxIAf4/JqKBKP3ne+k908NQ40xQlFWdzYL7F2OwTd+PJ9pHOP+bY4RGAyBA+fU1lGyuVLlWE0EanzjKZMsgkk5Ddl0uE6fbMeWmIfn9JIMR0uYWkGjrRrKa0Gakqa9MJ9qMNJU7JklE2voYffot5EgUyWrG/cHNaLNdhA+fJrTv2FXHIDntaDwZaLLcSB43mqyMGSqAoBqXJnv7SPb2IaY50FRVIvuDBF7dk7Ko0ORkYrlxA7HmdkJ7VPNmfWUp5i2rie7cSbJN7TCi10+Pwc2ahW7NKgSjkfDxc4QPnEBJTHfeBKNB9c4qyEFbkIOUoY6rJQMhJnceT5kCiwY99nXzsSyqIjbqxXumDf+Fd4AfrYSlPBdrTRGW2blIBh2KLONrHmD0WAveSz0ptUjJoMFcmIGY4gVd8RIFBElCSSYJdAwTHpicca5EvQZrSSa28mys5VkYMuzvKb/0d8X7HQh1feDdA6GCZ/4ChP7Q+JNG41577TUkSeLGG2/k5ptvZsOGDeivUSX5fzG+//3v86tf/QpFUdiwYQM//OEP/6BF+WeVz56qliuyTDyhR06CcclcyM5h/Pm9KRPAt0M0GnDfuYZ4Rze+c50k/CFsq+ow1pYT6R0l3DOC90xbSghAm2Yl6wOr6Hz2KJFhH9ZSD0UPruLSrw8x2TKIxqCj8iMradrRwHjrCJJOw4JPriJ9ViajbSOcffo0k1MKRDqznrJ15ZSuKUdn1uEb9rP7RwfxThFsRZMOv1e92ZcsyKNgXg6ndzbT3zo9imF1mrC7LdhdZkx2A2F/FP9EiLE+H/6xIJHRACadiEZRsFl1WO0GhGAYk92ASasgKgo5K8qIdgxiKcog75aFv7fy5G8fon/H2ZRilsasJ2NlBf7WwRRIQlA5KMONQyBJBKIKRocJ98JiGt9owJZtZ+OXt/Dav7/FaOc4fp9qhphR7EQw6Giv78dg0VO7vpS9L5xHFAWu+/hiXnz0ELFIgmWbqrjtIyvY/9ZZnn5kF4oCc+eX8pG/vhGdbnr/FUXh2197kvaWfuoWllFeUshrTx5HEODuT6/l4HMXmBwJ4M6xc9fn19BxfoADz5xDlmWsZi1yNIkSjaOXFIJtwziEBE6DTJo+gd2QwOoQMNo1GHRRNFkZmD/zKYTfsu6VSJTws8+plc23rz9XOprqarSVlaDVoPj8yJOTKF4vciCA6HQiZXkQHKpPTOzkKWIHDoLBgJJXSrS+CcntRLd0IWPP7QEgbetSYgmB4ddUzlDa8koyb1xMdMSHt7EPX1M/gbahVPIqiAKeDdV41lenkmxFUejeeZGuHfUoioKtyE3lh1eit5uIh2Ic+e5OgkN+HIXpLP6cKgf+1jffxNc/ScHSIhY9uCR1jAd+dZT2Y50IkogvlFANJhMysgKiUSIaTWBNM3LDx5fw7K/24x2fnrcHcHlsLN9cxeHnG+g8049ZK2IRweYwUr2uFG+7ej5X/vUagv0TNL1yHkEQ8MzLY+hcbwoApZdnkLu0hObt5wmPh5B0GuZ+aCmZNb9/5GT8Uj+NTxwhHogg6bUY0i0E+ycQJJHS2xeSvbwMRVFQkspVZHh/zzihwUnMOWmYMu1X+cuMXuih6YmjJMIxtGY9s+9bjrMyW+1snG5nYOf5lCSy3mUlY/lsHLUF6OwzleDkaJxg6wCBph4Cjb0p3t7bIZn0GAszMRd7MBZ5MGQ5Ea7YF0VRiHYOEDrfipJIkHb9csaPNjLypmo7kHH9QtJXVZMIRhjc28DI4UbkmHoNmQtc6J2WlLqe3mWl4APLsBZnprY9/NpJxg+qEsxpS2aTefOSGWNpyWic8YMXGT9wMaVCZ5mdh+fWZamRW0VRGDnUSM8rJ0EBR1UuRfevQtRq6NlzifZXVMCmtRjIXFiMZ0kJZs/VvBKAqD9C574mug+0EJ9S9suszqHm/iUzBBEmeiY48+TJ9xwAXXOfAlH66/voPd2dAkV6q4GFDywmq3p6JCwRTdDwzEn6TnQC4JqVSe2Dy9DbDOpEwffewN89RtbiEoL17cjxJBkLCgmca0VjMVLyhTsQfw8BPz7mZfTxN4iPTiJoNKTfvgbTnBISA8PEWjpJDI6QGBhB9geu+fuarIwp350ctHnZCLrpv6coCtGzDQR3HUaJxRE0EqbVSzAsriXpDyFoJBId3fi37Vb5mvnZWO7YSuLkyZQNhpCWhn7DOjT5+cR7Bwm8vjeldKfJ9aCvKkebn50CPm+HHI4SPNuEd89p5Kj6XDfPm41j0yIQJUZ2nmbiaGOqKyVoJSyz8rDVFGKenZcSLoh5Q4ydaGH0eCuxiem1Zi5041pcRlptwbsWOYh5Q/hbB/A1DeBv7ifun9mxy94yl6yNNe9qW39qvN+BUOddd2PTvgsgFI9R+OxfgNAfGn/SaJyiKBw6dIjt27ezbds2+vr62LhxIzfddBM33HADLpfrvdzXP1uMjIywZMkSGhoa0Gq1rFq1iu985zssXbr0XW/jz7mw4hcbiO7ajSzpiHkTiDYrYlVlykxSl+XCVFWMviibidcPE+sbQetOwzy3jMm3ToBGQ1RjnkF+BFVZy7W2FmtNIZ1PH2HiXCc6h4nZf3sdLc+fYuRcN5JBS9XH19D02kXGW4bRGLQs/MxqjG4r9S+eo+uoWhHVmnTM3lxJyepStFME3aHWEfb8z0GiAXWsIQ6EQwm0eg3L76mjr2Occ7tVkrYkicxeWkB6vgNZUQhMhAlMhomEYljTTKRlWNBoJPb85jSJUAy9BLFhPxn5dkwaASmZxOYwYDBp0CbiaC0GFn/tltTIT2jIS/dbDYgaEZPHjjnLgTnbgc46/dBXFAVvQy99r50mMnxFxUwScM4vJnNNFQ2PHCbYP0FSpyMSTlK0fjZNB9tT3aBoNMmhXx8j5IuS1Gkwp5nImuPhwsEOJI3E+g8t4NWHjpGIJ1l5SzVHD11mcjRA2ZwcPvpPW3lz23G2P38YgJXrarnnIxsQryGz2ts9wjf/+VFkReGz/3AbDce6ObW/GZ1Bw32fXc+OX5/APxFGZ9Bw0yeWYbEaePa/9iAnZexmHcl4kuiwD3EygI0EDm2CdJOM3SRjtQmYjDIGQxRtTgbGG29AM+tqQ1UlFiP8wkvI/f0qgbiokERzy7RcrCiqD97fdgsyGJCyPOrcfSKBZtFiAvvVa9q4bjnjO0+jJJNYl8xBttkZfvUEAOlra3FtqmPgzfMpU9S3Q2s3YZuVjXtpOeb86ftTIhyj6YmjjF5Qq7TZK8opuXU+okZCTsqc+ul+RhsHMThMLP+HTejtRi7vaODiK/XoLHq2fO169BYVDLaf6OLAL46o4y4GLUF/DEGvIRCMEU8kiaPg9Fi5+dPLePx/djMxEiAzx8F9n9vA2SOtHHvrEqEpnlpwJIwQVNAmwCSAUVQomp9HxbICOg61YbAb2fjlLTS+eIb+k9PeQOnlGZRurU6NEMUCUc7++hBjzSqQL7tuDqVb56SSpagvwkTHKJPtIyRiCYrXV2ByWYh6QzQ+doTJKR6V1qyn8sOrcJRlEg/FOP3Lg0y2j5I1L5+C1eU4Cqe5SdeKWDBK1BfBmmUnPBbg8sMH8feMqaT3jXMo2FKNKInI8QSjx1oY2HWBRGAqQRLAUpRBWm0hjur8q0CRoihEhyYJNvaoXIPO4atGhESDDnN5Dva5U6M97xiZHN1bz8gbpwDI2LqAtOWVDOy6wPDBy9Nms3npZG+Zi22WasjqvdRL1/PHiHtDIEDGitlkb61LJYTjBy8y9NpJUBQslfnk3LMGBJg42sjYvvqUmIchOx335nmYZ+WmPpfYRIDOZ46keGzpi0opuGMJgiTSs7shNeKY//a5uwZ3CSA8EaJj92W6D08brVo8Nko2VZK9sHBG0tx3vpfjDx0lGUsgaSVKVpdRvnE2Rvt7C4CuFd6+SY49dBRf/yQApWvLqbl1bopzB9B7rJ2GZ0+RjCXR2wzMfXAZ6bMyGW8c4MJPdyNqJPKWFDJ2rAVDph29EiM+7se9cR6uDXNT25HjCWKjPmLDk0RHvIg6DWlLKiCZZPS53URa1HuBfc08bOsWzAQWwRCJoVESAyMkBkdIDgynvHhSIQposjNVYJTjIXzi/HQXKNeD9cb1iE4H/sP1eN86gaDT4r5vC4Icx//cDpRYDMnlxHbvjShDQyihEJo5VSjxBKG9R4mcVgG2YDRg3rAcfc1091BRFOIDo4Rbeog09xDtHuJtIQRdtpu0G5ajy83Af6GToe3HU89+S2U+9roSLLNyU6BRjieYbOhh7GQbvqZpU13JpCN9fjGuxWUYs9LUUd6WIcIjPtU/SFaQk7LKDUpOfZ+QAQVLrhNnRTZasz61v+GBSfzN/WrRqn2I0o9vwFo602D2fyve70Co48573jUQKnruqffdcbzf4z3lCF2+fJnt27fzyiuvcPLkSZYsWcJNN93EPffcQ05Oznv1Z/7XY2RkhAULFnD+/HkMBgOrVq3iqaeeoqTkag+I3xZ/7oUVa+vE9+R2ABSXh3C/OgNvXTwHx9YlaktalklM+Bl+aPsM81TZaiM8FkbQShiy0zHmujGVZGGpUN3hR4400f3CcQRJoOzTmxk81Un/oWZEjUTVx1bTvq+F4YZ+NHoNi/5mHZJBy77v7UmpCBUuK6b61loM1ulRh7ZjnRx+5DhyQkZv1ePzx0kkZKzpJlbcN58Dz9cz3KV2keauL0Nr0XF2Xyu+sd+jGjfox+4wQTCGXpGRUPB4zOgNGrSxGHqrAZtRTbRm3bsUz+ISvO0jXPzlvpSs7JXhqsmj9LYF6NOmeUFKUmb0RCujR5uwFGeSuboSXZoFX+coZ7//BooCwbiAoBHJWl5G674WbNl21n5hIy99+TUCY0H8vhhaq4GsikxaL6hKXls+voR9L51nbMBH2dwcYlKChtNduDx2/uYbt3LpQge/+pH6Gd94x3Kuu1VVJgsGQnR1DtDV2U9Hex+9PYPUzJ2FRjGw6/VTuDPs/PM3H+TR77xJ26UBHOlmPvwPW9j12Gk6GtQEa9Hm2WgQOLe7Ba1GRJOUmewYw6TEVSAkxknTJbBbBKwWsGgjGF06tE4Turm1GK7bMuO8KYkEkRdfJtnTA3o9xrvuRMpwo0SixC9fJlFfjzyqVjLRaBBtNoQ0B4LZjDw6hjw0NMPwUMzJIRaVSPQMoMnPIdDrQw5HMM4uxLhwDj0P7wRZwb1pHunraul58TgjR9TxD1t5FrbZOdhmZWPIvHrcItA7zuXfHCY05EXUSJTeuZCsJaWp/39bHEHSSSz9/EZseWn4h/zs/MYO5HiSRR9aQsESlVcRGA3yyr++QTwUw1XmpuvyCAgQiCSRZYVQPI6kk7jr71az/cljjAx4cXlsfPqrN2GfusaikTgn9zVxcEc9/e3jjLV5MaPBppew6iTceXbWfWYFXQdb8Q94yanLY9GHl3DhsWMkY0mKNlSkANCVISdkGl86S+d+9by4K7PQmfVMdIwQeocao86sZ/4nV5JW7EaRZXr3NuLrGqX4xjqMbivhiRAnf7yXwDu8vRwFTgpWl+Opy0fSSiiygrd7nJHLA4xc6meycwwUlZBfecd8FEWm/eUz9B9S98nospK/uZqM+YWIkqhKcZ9oZeJcJ4HOK2TaBbAUZ5JWU4BjTt61eXuJJJH+MULtg4Q6hgh1Ds2QuxcNOqxVBdjmFmMuyWL8cEOqo+jePB/nqjm0P7ovZVJqynWSvXkutoqcq66hRDhG3/ZTjB5Xyft6p4WCu5dhLVETOV99B/3PHEBJJDFkp5MIhFPJp85tx71pHtbqwhmJ7NiJVnq3nSQZSSBqJXKun4d7hZrsdu9qoGO7CoIKttZQuOXqyrmiKPj7Junc30z/ic5Ul9Ce76RkUyWZtbkzjkNRFFr2NHP++bOgKGRWelj44JJ3DYCSCRnvoI+J3klQFMxOM5Z0M6Y041Udwd+5nViS+pfO0bp3av1mO1jy0aXYcxyp9wQGvZz51SH1+hOgdMscSrdUUf/jXXjbhvEsKiZ8uYtkOE7G4mICp5sRdVocS2YRG/YSHZ5UPavekf4Y8tzkfWgDkkl/ldKf8/a1KV+na+63L5CyqYh39qpCC+8IQSNhWrsUw8Ia5FCEsRf2EWntmf5/rQb3vZvRWA34ntqOHAgiWs3Y7r4RKSOdWGMbwTcPIgfU9aqvmY15w3JEk5FkKKL6bLX0EGnpIRmcqeKndadhXVqNef4s4hMBBl85llIk1LlseG5dlhL2UBSFYNcIY6famDjXOcMXzFKSiWtxKWk1BYhaDXIiyfCZLnr3XibYP5O/97tCEASshS7SK3NwVuVgznakrkc5nkAQxRnd2//NeN8DodvvxfougJA/HqPohSffd8fxfo//NbGEkZERtm3bxrZt21i5ciV///d//55t+8CBA3z729/m9OnTDAwM8NJLL3HLLbfMeM9PfvITvv3tbzMwMEBVVRU/+MEPWLly5bv+Gz/60Y/40pe+hEaj4VOf+hTf/OY3/6B9/HMuLEVR8D3+suoTZDQRmowjiCJpNyzHVFNGoKmXwKVuAk29KAmZrBsX4H39MEoyiWgy4ptMIABFn7s55X+ROo7mflof2oOSkMm9eQGRcJLO19UxnNkPLKfvXB8DZ7oRtRKLPrsGrdWQAkFWj42FDywmvdiV2s+xrgmaD7TRfEBNGAwOE2MjQRAEPCXplK8sZvfjZ4hF4hjMOnKrMmk530d4qkJuTTNSUOHB4jBiTVMFEvzjISZHg/S1jjLcM4GvP4A7w0JyMoTsj5Bb6sTmMKCVk+h1EvYMM4kxH+bsNAqvq+Hyo4eR4wlshS7SZmURHPQSGpgkPOJHURQkg5ai62rJXln+O03ump48yuDxNkSLEb83hqPYxXCvL9UNGu3xcv7Vi4T8UWS9DlErEogkQYA199TR0TTM5RNd2NLNLLxhFq88egRJI/I337gVjU7kP778GyKROJtvXMSt96xGURReem4Xr7y095q8qX/454/wxC92MTkR4Ppbl7J+60J+9C8vMTLgJbfYxSe+dANHtzdw8GW1a+IpdCIHEwQmQthseiaahkmO+3FbROzESdPFsell7GYZi13A4NSj18WRsjIxf/qTqXOjJJNEtm0n2d4BWi3GO25Hys6asW+Koqg+GlodgsV8VWKpJJOqO/rAAIrXi2JJI/DmQZAkEnY3sYExdLmZpN22lq6fvkYyGME+r5TM25bR/dwxxk+3gwD5ty3GvWzWtdeNLNOz5zJdO1TXep3dRNVHVmErnO4UdR9q5eLTanI872Mr8MzNQ1EUDvxgL8NNQ2RWelj512tUbo2s8Oa3dzPUMkJaroPR0RCRQAy9w8j4aBBRKxEIR3HnOQgpEYZ6J3G4LHzmqzcRDIYY7B+nqrYI05SSUiwa56ufeJTuM0NYNHqseg1mUcCol5izoZzl9y9gz7feQk7ILHhgMUXLri1XHhoPYrAZU+NrvUfbufj0yRn8IQSweuw4ilx4e8bx9UwgakRq7ltC9oKCGdsLDHo58T/7iEyG0NuNVN4+j+EL6n3g7W3qLHrSil2Mt44QD11bLMFZlsG8j61AZ9YzfLqT1hdPEZ/q/hjdNgo2zyFjfmHquopNBpmo72LifBfBK0ERapfGMScPR3X+b+UWKLJMpHcM34WOq+R/JZOe5FQhxL1xHs411XQ8doDJiz2IWonCu5fjqC2YTtSSMmP1PYSGfeSsmpXi4fia++l69iixiSCCJFB493Kc89TPJdQxSO+ju1OeKxq7GffGOuzzSmckfDFviO7nj6YAmLnQTeHdyzG41edI91sX6Xj1HACF19VSsLl6+hinwM/A2W4Gz/YQHJ5OxtPLMyjZVEX6rKuNVuWkzLlnz9C2X+3AF68spe7u+b8VwMRCMca6J5jonUx9nez3TlX8Z4YgCpgcJizpJszpZmyZVvLn5pCW6/id4+YDF/s5+ehxov4Iolai5tZaStdOC5okYwkanjudUidNL8+gaFUZlx8+gKiRKFhZysjBy+icZswWiWj/+FV/QzTo0Gc40LntBC53kwxF0aZZyfvIRvQZDgJnmpjYdhAlmUSymtHnZ6LLcqHNdqHLdiGZfztITE76VGDU2Uu8ZwApPQ3L5lVI6Q4irb2MPb+HZDCMoNHg2LKE8OVOIm29CJKE6+6N6LKc+J7aRnJ0AkGnQ5OTmeooSU475uvWoivMJRmKMPn6EYLnW7lS/lrQalXj0/I8DKV5aNKsKIkkYwcuMrrnHEo8iSCJpK+tJX1NtWoqPRFg7HQ746fbZ0w96BwmnAtKSJ9fnOJTJsIxBo600Lu/iZhXXUuSXoujLFP1EZriBYmSajCufi+iJGUmWwYJvoMjpHeYcVZm46zMJq38Lz5CML1f7be9eyBU/OJfgNAfGn8yEAqHwyiKgsmkjil0dXXx0ksvUVlZyaZNm96TnXxn7Nixg8OHDzNv3jxuv/32q4DQM888w/33389PfvITli9fzs9//nN+9atfcenSJfLz8wGYP38+0ejVHYCdO3diNBq5++67eeqppzAajWzdupV//dd/ZdWqVb91n6LR6Izt+Xw+8vLy/iwXZLShGf9LO0EQiCR0yAmZtK3L0BZk0fPIrqtG3rROK57NtfgPnCMu6gj2jGOtKiD3gfWp94T6xunfcTblWO6ozsc2r5hLDx0AoOS2BUz2++g+3IYoicz/xEr06Rb2f18FQfYcB6v+di0Gq4FoIErTgTbajnaqXCDUQpzBaWJsOIggCJQvLcCe62D/0+dQFIXMQidJCbqb1FGedI+N5TdVkTs7g6AvTCgYJRSIkogncbqtuLPsiKLIj//uFSb6fBj1WhLeELpInHSPBZfLhIiCxSihNWgxiUnkK8il6VU5VDy4YsbNN9A/QcszJ/BNJV3WAhfldy3Cknu1+lLMH+H4119Wxy1EDfG4gr3cQ/+lQWzZdlZ+bi0v/NN2IsEYwVAcyaAlFJcRtBLzNpaTlODoq5eQNCK3fHYFT/9iL9FwnOvuWcSKrdX81788Rl/PKGWzcvnbL38ASRJ58dm3ePlFlSPjzkijsDCHgqJsujr6OXniIiVledx4w3p+8cNtaCSRL/7Hg+g0Wn70Ly8T9Ecoq87hgb/dSE/TMC/9+BDhYIzswnQme70oskJiyEtiNECGVcIuJUjTJrEbktisYLGJGEwCeimENssFWq06D6/Vgqyg+Hyq6elttyLm5hC90ISg16ErLfyDq3xKLM7ET59A9gfQlBXju6h6BWV+9nb6nj5IpHcUQ3Y6eR/fTNfTR5hs6EUQBQrvmU5C3xmR8QCNjx/B26ZeX66aPMo/sHiGYMZY0xAnfrwXRVYov6GG0i1VAHQebefko8eRtBKbvnIdFrcqMHL+tQbOvlSPRq8hrdRN5/kBTHYDI6MhZEUhpsgkEkkEs0AwEsWWZuIzX70JfyDE9/7taeLxBFqthpp5JSxeUUllbRHP/HQf+54/j+JPYhQljAqYRIXcykxu+fp1jDYOcuHl82j0GjZ+aQuWDOvU+lIYaR7m8huXGL48iDXLztKPL8eerSYxk51j9Bxpw+Aw4ihy4ShIT3FEEtEE5x89wlC9uvavHKOb6Bjl1E/3Ew/FMGdYWfjZtSmvmag/Qs/hNroPtRKZnL7naI1a0mdl4q7MxlWRha93gnMPq9LbJpeZBZ9ajcVjJxmN03ewmd49l4hPmSibMmwUbKnGXVcwU/J5IsDE+S4mL3QT6BqZ4aVqyLBhr8rDWurBUpRxzYRKURTCnUP4zrXju9CZGk9zrZ+La/1cOp44yMT5LkSNSMlH12ErVyvliXCMwWNt9O5vJDrFkTB57FR9dDWmDPU+n4zE6X7+qModEiDv1kVkLFc9m6JDk4zsOoupIAPH4lkzuImKojBxrpPuF4+TDMUQNCLZm+eSuaYydezdOy+mFPveBkGKouDv9zJwpusq8CNqRDLmZFO0roK04muPqsfDcY796jCDDQMgCNTcNpfyDbOuCVJC3jDnXrlIy6E2FPnq1EFj0OLMdSBqRILjIYLjwWuCIwBbpo2ihfkULswj7Ypuz5UR8UU4+ZvjDF5UBV08c7JZ9KElqTFUgL4THVx8+hTJWAJrth2bXcdk8yAZ8wuJt6vck6y1lchePxqrMQV89BkOJIthejx0xEvvw28RG/MhGfXkPLAec7GHaNcgo0/tvKrDAiBZzeimQJEuNwN9gQdR/9sTViWZxLv7FL6D5wEFrTsN190b0GY4URJJRp/dTfhyB4Io4rx9LcayXPzPvk68Rz1+RBHTsvkYl89H0GoINbQzsf1Qat+07jQM5XkYy/PRF3gQpOmRwlDHIAMvHiE2PAmofFbPrcvQu+1ExwP0vHwC76Xe1FoSdRJpNQWkLyjBUupJnafIRJC+/Y0MHG0lGVE7RTq7iZxVs8haVorW9O544pGJIOOX+hhv6GOieWiGfL+okSi+eR45q65dxHqv4/0OhNpu/eC7BkIlLz3xvjuO93v8yUBo06ZN3HbbbXzqU59icnKSWbNmodPpGB0d5Xvf+x6f/vSn36t9vWYIgnAVEFq8eDHz5s3jpz/9aepnFRUV3HLLLfzHf/zH793mc889x759+/jxj38MwLe//W0UReELX/jCb/2dr33ta3z961+/6ud/jgsy1tlL8PW9JJIi4eEAWpcDy8al9D+1HzkWR+uwYK0pxFyWw+ALh4lPBrAvKMO5ooqOH74CikLWB9eRCMcJD04S7p9ICQAIokD64lIy1lRx9vs7iQci5K6pQDHqaX71AghQ95HlWLLT2Pe93TNAkKTTcGlXU0oxDVTSbc6cLEYHfIz0qhWnhbfMwR+IcvqNJgCK63Lo6xpjYjiARidx3YcX48iysOflszSe67n2SQAkjYhe0CKHZfz9AWxGLcpkEKvDQE6eHZ1ewqQBURBwFzoITUkEe5aUUnbXomtWPxVFYeBwCx3bz5GIxBAEgZzVsynYWoPGoFWlZU+0077tLPFABNGgwxdIIOk0hBWBRCTBko8vZ7B9nIY3LxP0R1EMOmQgnJBx56VRvb6E1x5SPXFu+cxyjuxpoL1xkMLyTD7ztZt48qG3OLS3HqvVyJf+80M40iy8/MJuXnxuFwAffOAGNl+3PLXPk5N+vvC33yESifGZv7mb04dbuXC2nQxPGv/0b/cx3DvJz//9VeKxJC6PnQ///WaUpMIvv/QayYRM8SwPg22jTDYPo4vFcBoEnFoZh0HGYQSbFUwmMIphDDYJXYb16k6ZKGK45SY0RUUEdx1W/aoA0WrBMH8OhrmViJZpjocSi5MYHiU57kVy2JAy01PytMF9xwgfOoVosxBJ6kmM+7CtnEvYn2DyRBOSSU/+J7fS/fIp/C2DiBqRogdW46jKm96+LBMPRIn5I/g6R2jfdpZkJI6k11J6+wIyFxXPSPx6j7Vz8ZlTyPEk2QsKqH1QHUOM+CO8+bXXiQWj1Nw2l1mbKgAY7Rjjtf/chZKUKV9bxtm3WhAEcBan0900gtGmZ2w8SDAQQTYrWGxGPvPVm9DoRf7zy4/h94cxGnWEw9PdE4vVSG5WBq2nBxlr82ERdaSZdVh0IjaHgfLlRWz5h/Uc+MFeRlqGcRals/bvNjB4eYDGHZdSUsep9aGVqLt7PoXLin9nJf7t677p5XO0724EIHthAZ7aPM49ehQ5nsRR4GTBp9egs1yd9MhJmeELfQSH/ThL3dgL0q9aW/7+SU79/ADhsSAavYa5H16WMsxMROL0H2yid+/lFCDSO0xkLCgic2HxVUIAcV+YyYYeJi92428dRLki8RYkAVOeC2upB2tJJpbCjJSyW+pYkzLBtgGURBLzrBy6njnK+Ol2BI1IyYNrsFfmTiV/TQwebU3JgWstBgRRIOYLozHqmP3ActIrc1Lnr/eVkwwfnDp/W2rxbKj5rec9Muyld9upVOHJlOuk8O7lGLOmVdyuBEFF188lf9Mc5ITMuUeOMHjFfVHUiLgrs8iaV0DGnOyUaeq1IjQR4tD/7MfbN4mklVj0kaXk1uVd9b54JM7FNxppeKuRxBRXypxuJj0/jbRcB848B868NCwu81XjdmFvhMBYUH2NBhntGKP3wsCMQpQ9yz4FivJxZM18XiqKQuu+FupfPIccT2J2W1j+qZUzR+WGfBz97lvEQzHKt1TS99YFBEGgaGMFw3suorUbmfNPt1712b8zEoEwvY/uvkr6XI7FifUOE+sfJdY/Srx/lPiYlxkIHEAQ0OVkYCjOxlCUjS4/MzVOl5jwMfrsHmK9qiWDZWElaVuXIidlRnaeQTLpSV9Tw8TL+wmebwEEnDevxFxbRnDXIeRACNPapWhcaSSDYSa2HyLUoHbDtO40nLeuRp+XOWN3YqM+fBc68Nd3EumfEr4wG8i8YRG2KUn3kcNN9L1+JsWBs5Zmkr6gBEd1AdIV146/e4ze/Y2MnOlKybObPHby1lWSMa8QQSMy0TZCYNCnTigoTH1VVNCsgDJ1vhwF6aQVuxHEqe5ePIG3ZZixKWAUGQ8w55NrU+vpfzve70Co9eb73jUQKn3l8ffdcbzf408GQi6Xi/3791NVVcWvfvUrfvSjH3H27FleeOEFvvKVr3B5ykX5fyveCYRisRgmk4nnnnuOW2+9NfW+z33uc5w7d479+/f/3m0eO3aMT3/60xw9ehStVstNN93EJz7xCW6++ebf+jv/NztCALHBcYZ++jyKrGCYX8XY8RaQFcyl2eTctw5panQj2D5I9y92qH4JbjuxES/6HBcTXZNXbdNZV0jW5rnoXVYafrWfsYu9mLPTyFg5m4tPqsT0qrsWkDk3jz3feovgaAB7joPVn19H97k+Tr9wnsiUEow53Ywj30k8nmSgZZRIMIakEVnzoYU0nemj6YRqKFeyIJfGsz3Eo0kcbgsrbq/m9OFmWi6oCYIoCjgzrJgseoxmPZJGYnzYx+igl0RcRk7KyF4ZvUaLEogjjwWwmjWUVGeh1wqYrToIx7Bm27CatTgrs8nbUIUgCKnxsmslK1FviLaXTjNyViWk6+wmCjbNYehUR0oq2OSxo5iMjLaMYPTYGe31YvXYWP1/1vPCP28nOBEmGE2mukGSTmLxzVXsf6keWVZYc0ct6OHVJ46jM2j4u/+6k5bmHh756esIAvzNP91JRXUh217ay/PP7ATgnvuvY+v1V498vg2U3BlpfOmrn+LbX32SiXE/1XXFfObvb6Ovc5RHvruTydEAOoOGez+7jtEuL/ueP4/eqMVuMdJ9qgvRGyLdJJKuB6dBxm4WsFkETFIMkzaKsciD9ZYNaHM9qjphPI4SiyO60hGtVsJHzxLcrYo7CEZDyuAXUURXrvJqklMA6J2hAiIXsdYuSCaRKmfjP9uGaDJgXDFfFUcQBPI+vJHh421qBV+vofTDa7GWZTHZOkTH9rOERwMkgtGrxgdtRW5m37cMo2vayyUZT3L5+dN0H24DwF2VxbyPrVT5LorCkZ8fov9cL468NNb/0yZESSQeTbDtX9/AP+QntzaHvo5xAuNhShblc+Fol5oEGCWC/gi+cAjJJHHvX61jdl0e3/7qEwz0j5Ob7+bvv3ovw4MTHD/UwMnDl/H5QiiywkRnGH1Uh1nSYdFpMWlF9HKS7FkZLHtgIblVWbz1jR3Ew3EMdmOKmydqJYqWF1O4pIiL2y4w9LZh5sIC5n1wYUq05MpQFAV56toE6D7cSsMzp2ZU/10VHuZ9bCUavUYV1QhEMaWZrtrW74tYIMqZXx1kvFXlUc2+eS5F66cJ34lwjP6DzfTumwZEANb89JQ09Ntk69TnF4mpKoGN/fhbB2coW4HqB2Yt9Uxxi/LRXPH7iqLQ/dxRRo+3IogCRQ+sxlKcSesLJxk52z0j+ctdU0HGgkKS4TgNvz6Ar2MEQRAovL52xv1k4K16Bt5UPa8yVlaQe/NM0n0iGGXgrfOMHGlCSSoIkoBnQw2edXNSwgfvlDMvumEu+RvnoCgK9Y8do+9EJ6Ik4q7KIqsun4zqnN8Jft6OkeZhjj10hIg3jMFuZPmnV+J8h9hFMiHTfKCV89sbUvdyV3E6C+6Yi6f8ah5aMiEzOehDTipY003ozbpry5eH4/Sc76PzVDd9FwdmdI3y5uaw4kOLZ3R9ACZ7Jznys4MERwNo9BoWfmjJDNDW+sZFml+9gDnTijPTzHhDH+65+ST7R4iNB8m5vg7PuuoZ25TjCaKjfiKjfjQGLZZSD0oiSf8zB/Bf6ARI8Q6vGieMxogPjqXAUbRzgMTkTF6QIEnoctzoctwEzzQhR2OIBj3OW1ZhqiomMjBO3+N7iI2qRUH7vFI8dyxn8rUjBE6qn3fa1qVYl6kcMEVRCF1oY+LVw6ovkiBgW1WHfc28lFdadMSL/0InvvoOogNXjAOKAo6F5WRsWYBk0hMZ8dH17JGUEqqlJJOCO5bMsBKQ40lGznXRf6gZX+d0YcVR5iFvXSVpFaoVwkT7CE3bzqtr+V2GzqInszoHz9w8nOWZKQPft02YDenmlJjR/3a834FQ8433v2sgVL79sffdcbzf408GQiaTicbGRvLz87nrrruoqqriq1/9Kj09PcyaNYtQKPT7N/InxDuBUH9/Pzk5ORw+fJhly5al3vfNb36TRx99lKampne13S996Uu8+OKLiKLI+vXr39fy2QAjT7xJuLETye3E16fejO3zSsm6fTmCRkKOJ/A1DZAMR0mOTjK2TyWBIggIGekE+yYxZjmwlmZhzHJgKcpI3RAHjrbS/PQxRI1E4a3zufjsGRRZoXhDBaVb57Dve7uZ6BrH7Law7gsbaTvWxclnVHUvSa9BNOqYGAnOOH9mh5E1H1nE4Zcu0tcygiSJlCzM5fzhDhRFoajKQ0FNBq8/cxJZVpAkgfmryll3cx2ua0jDyrJMw6kuHv3eTkITEexGM94eL4aEjElQyCp2ku40otGIGCUFQRBY+aXrsGap2+o72cml506TjCbQGLVoDFp0Vj2Fq2fN4EiMNw7Q+twJwlcYx0l6LQVbqslYUMzer25DjicR08z4R4LU3F5HMBDj/GsNTA4H0KaZUSSRcCyJM9vO8IiXWDhBzcpiltxQwQ+/9BLJhMydn1hF4exM/v2LjxKLJbj+tmXceMdydrx6kKcefx2Au+7ZzA03r5lxHhRFYXRkHJ1Ox1f++X+YmPBx7wPXUzG7lO98/Sni8QTX37qUG+9cQcAb5rEfvkXbVIK8/ua5dJwdZLh7krwSNx3HOol2j+MyS7hNAg5tEodVxGoAsyaKxSFhcBoxrVqIZcvqqz6TSH0jgW1q18q0binGRbVEL7USOX2RRN/gVe8XzSakdAfJCd9V8rSa3CwCAwHkUATzojmMHG1BScq4t8wHk4muZ48iSALln96MpSiD4dOdND15dEbVWRAENGY9OpuRjPmF5K6tmNGpCI0FOfurg3h7JkCAsuuqKd1SlbpuW/Y0ce7ZM4gakXX/uIm0PLVaf+aleupfa8CUZsI928Olg+1Y003Y8hw0n+rF4bHS3ztBNBonqoljdZj45/++h5997yUaG7qxO8z807/dR1r6FUaSSZmGc+385LsvMdrpw6G1Q0BBp4iYRQF3pgWrRYc908qt37ie4cuDnHhY9SDRGLSUrC6jbF15iuiuKApNb17m4rZ6FFnB4ray5OPL0Bp1THSPM9E1rn7tniAZSzD/gwspXKqOFY42DnL2oUPEw3FyFhZS/cHFiBqRiD/CgR/sxds3iavMTcmqMnLm5qYSmisjHonTe7qbrmOd+If9zLt3ATm1ucgJmUvPnUoBz7RiF7NuqsVZOp1ky/EkY5f6GDrRzvilabNQUSPhrMwmY14hzqocpGtU+6PjAfytgwTaVJn72BUje4IkYC3Lwjm3EHtVHv07zqoCGwIU3bcSa3k29T/eTWDKnDqt3EPO2gqcFdkzFcQSSdpePE3/4SkRirn5zLp3aWokb/jgZXpeVnlmzvnFFNyldhdHjjUz8OY5ElP8R3tVLrk3zJ+RiAYHvTQ+dji1D0U31pG/oQpFUWh86Swde5oQBIH5n1yZ6qj9vhjrGKNhez1Dl9Q1aMt2sPKvVmFyXiEIoyh0ne7h9Iv1+KdG7ayZVubfVkvBPFVkIR5JMNozwWj3BCOd6texXu8M7plGJ2FxmrCmm7A4zVicJuyZVgpqsjBMAZ1YKEb3uT46TnbTf0n1hTKlmVj9iWVklrlnfp6BKMd+eZjhJrWrUnVjNRXXqWs0Ho6z76vb1K7Q9XPoe0PlshZvrWJoZz2SUUvWxlqiIz4ioz6io35ik8EZTR3X0jLyb1sMgsDw66cYP6DyJx0Ly/HcsvQqY+Z3RmLCT6Sjn2hHP5H2/hkeVQD6fA/pd6xDk2bFe6aVgRcPo8STaGwmEoEwyAq22mKy7lqJd9eJlFiDff1CLPNmMb79EOHGTgB0nnSct61Bl+Ui7gvhPdGM70In0cGZ4Mdcko21uhDrnAI0ZgOKLDN84DL9b6gdNlGvIfeG+TMMpaMTQfoPtzBwtDXF2xM1Eq65+eSuno01XwXMvr5JmredZ7ihf+o9IumzMlUfIWGqqCgKCIL6QlDFMMaaBlPy7QAavQb3nGw8tXm4K7PeFZB/L+P9DoQar3/gXQOh2a/95n13HO/3+JOBUE1NDR/72Me49dZbmTNnDm+88QZLly7l9OnTXH/99QwOXp3wvJfx24DQkSNHZshd//u//zuPPfYYjY2N/6v783b8ORdWpLWX4UdfA0EgYU8nOuzFVldC9gdWEWgfSpmeJSNq6zv3xvmE6tuI9I1iyM9kvH0MQRSo+udb0TstM7YdGvFx5ts7SEbj5KytoP1QB4lInKx5+dQ8sIQjPzvE4MV+dGY9676wkZ6L/Zx46gzhQAxRryUOUzdByCxxkT/HQ96cLLQmLS9+dz8TQ370Ri0Fc7M4d1Bt89etKUUwixx4TX0IzF1awqY759PU2M3hvfVEwzFESUQURXR6DflFHkrKcygpz+bZnx2gub6XxEQSQjL4I5gFhfRsG7l5dkQBnG4z0YkghWvKqbxjPl0HWmh49tRvPb8Fq8qouG1eimyejCfoeauBvgNNpM3OouTmeejTzHQdbKHhmVMYHEZGR0IIosimr2zl1W++hW84QDguozHpCMUVBI2IaJQIBqIUzM7ktr9ewU/+dRtjQ34q5xfwob/bxA/+/VmaL/cwqzKfz33xTpobO/mPf1O9re74wCZuunWt+hmFwly82Mz5c5c4d7aBkZFxCgtzuW7rBn79y5cwm418+wd/z8VzHTzyUxVEferztzB3YRmJRJLXnjjGwR2qFOuy9ZVc3NuBLCvEx8LEusdwGEQyLBJ2KY5dr2A3g80mYbKK6LVxDGUFpH32/hnnLNbahe/ZV0FWMC6ei2nD8pmV8IFhopfbEI16pEw3msx0RPN0V0EORdRRuaFRZF+AWFjGf/IyGqedqMZIpHcMa1UB6Zvn0/iD15BjSXKun0fm2ip691xKyQq7avMp2FyNzmpAY9b/VvL3SEM/5x49SjwUQ2vSMfdDy3BXTgs8jHWMse+7u5ATMnV3z6d0jSoX7hv28/JXXkdOyNTcXM2xFy+gKLDkrrnsfvIMgiBgcpsY6feSlJKEk3FWX1/NRMjHkf0X0Ou1/P1X7yHdbcPrDeDJcs04T9/52pPUn+yAgER8JIFNqyfNZsCRZkQTT5CebadkaSErP7qE9kNtJMJxipaXoDNf+6E52jbC8YeOEhr/3eqLCAILH1iUAkPh8SD+/kncVdmpEcH939+bkjl+O/RWA4XLiilZWYLJaWa4aYjOox30ne0hGU/O2P78Dy6keEWJ2ok52MLll86lpJ3dVVnMuqEWW17ajO3H/BGGT3cwdLIjBQ5ALUakV+eSMa+QtFmea8pIK4pCdMSXElwIX6lwJaAmxAIU3rMce2Ue9T/Zjb97DK3FwJxPrMFW8LvtIAaOtND6winkRBJzloOqj6o2AgBjp9vpevowiqxgK88i5g0RGVK7oEaPndybF6Z4SG/va/+BJtq3n0OOJ9Ca9ZR9YDHuWpXj2rbzEk3b1E5Tzf1LyF1c9Dv3DWCie5yG7RcYuKAmrYIkUrSsmJrb5qI1TieeyYTMoV8fo+OE2v02WA3MvWkOZStLECWB5iOdnHn9MhP93msq32sNGjRaibD/ag7u2yFKArmVHsoW51M0Lxf9FD9tvGeCfT87gm/IhyAK1N1SQ/XWincAT5nzL5xNqcrl1OWx6ENL0Og1tL3ZQNP2eswZVly5NkbPd5NenYs44SU8eHXXGUAyatGnWwn1jYMCabUFFN67AlEjMXH0MoOvHANFQe9xYirxYPCkofc40XvSfueonaIoJMZ9RDv6iXYPoc1QVdsURWF4+3Emjqm5iLksh+x7VhNqH6T/yX0osoy1upDsD6zCf+g83j3qs0nQaFASCQRJwra6DtuquSCK+M60MbjtWEoRURBFTGXZ2KoLsVblI5mmeY/hoUm6njlCsEvt7tjKs8i/cyl6p0W1h2gbpv9gE6P1vamCg95hImtZGVnLSlN2EsERP82v1jNwphsU9Rmfu6SI0uuqMb6L7rCckBlvHWbwfA9D9X1EvdP8K1Ej4prloXDtLFyz/yKfbbfbuXzduwdCFa//BQj9ofEn9x2/8pWvcO+99/L5z3+e9evXp8DHzp07qaur+5N38A8Nl8uFJElXAbDh4WEyMzN/y2/9vx1yJIpoNCBlpBNsGUbQSmRsXcDo8Ra6nz+WqnhpzDoSwRgDb52n/NObCDX3MTnFI0irK5oBgpLxBH37Gul+q4FkNI69JIOx7kkSkThpxS6q71vM2adPM3ixH0krseKzq+i7NMiJp84Q8kYIhuIY9VokjUTFyiLqtlZgy7AQnAzT2zTCW4+cJByIYks34y5xpkDQ0usrGRwZ58K+TgDW31qHzirwvX9/msmJa5vYtTX3s/dNtQPlcqUhyzIJKYlWKxIIJ9DrRSK+KIJeC7E4yamHat+JTrRmPS2vqVW/wtXlFK2fTTwcJxGOMXJpgLadl+g60IK3e5y6j67AmGZC0mpUovLWmTP/b6sXiVYjwmiYzAoPHad6iIfjhIMxdOkWkqCKNjhNDA/7MFr03P65lTz+37sZG/LjzLBy1ydWc+xAA82Xe9BqNdz/ic1EIjF+8dPnURSFVWsXpEDQjh37eOzRF0hc0fkA6OzsBTFJbl4mvT1DbHt5L/fefz3dHYPseeMMD//kNf7pG/eTlZPOzQ8ux5lh45VHj3DiQCNLVldybm8rvskwegSSMiiiiCyIKKKCopVIiiKJpIw2ESU54SXw5gEkmwXBNPWg3LEfZAV99SxMG5ZDIokypRoEqvGgJuvq0Zq3QzQZ0BXmQmEuCW+A8R88A4CuvBDvgUsIWg0ZNy6i7eF9yLEktvIsMlZX0Pr8yZQUc+6aCopvrvudSn/JeJKmbefp3Kt2iu35Tuo+uiIlAAAQC8Y49qvDyAmZ3Hl5lKwuS/3fyWfPIidkPLMzuXy4A0WBWcsLuXi0E4C8ykwaz/eSTMgE4hEkrUQkGeHI/gsIAnzsr2/E4bTw5X/6EaMjEzjT7VTXllNTW05VdSkLls2mpamX8dEQJrMRUZRICgLhQIw0l5nARIj2Y52ULi+iZGUpvy9cJW42fHEzpx47Tv/5PkSNiD3HQVq+k7QC9dVxqI22/S2c/M0JFAWKlhVjdJoxTnUM3u4E+fonMdiNLPnoMoabh2k/2ErEG6bpzUs07byM3qIneoVJojXTRsGSQvzDfrqOdnD68RNEvGEqrquiYFU5mTW5tO64SM+RdkYaBhhpGCBrfj7lN9RgngIUOquB3DUV5K6pINA/wfDpTkbOdBEZDzB8qoPhUx1oTHpcNXmklXuwl2agn/IbEgQBQ4adrA01ZG2oITLsZeJ8pwqKphSsCu5cir0qnws/3aOCILOe2s9uwJztuOpcBof9RCZCOMszEASBrGVlmDwOLj18gODAJGd/8CY1n16HJddJ+vxiNEYd7b/Zj69Z7cBqzDqyt9ThWlw2Q0AkOhmi6cmjTDSp73NWZFN+z5LUcfQcaUuBoIpb634vCJrsneTSqxfoO6dKJSMIFCwppPK6OSmhj7cjEU2w96eH6Ls4gCCJ1GytZM6W2WgNWvqbRzj81BmGO6YBqNlhxF2QRnqeA3ehE1eeA1uGBUEQSMSSBMZDU68g/jH130Nto4z1eum+MED3hQFE6SQFNVmULsqncG4ON/7LJo4+for2Y52cefE8g03DrPzoEow2NaEXNSJ1H5iPI9fBmadO0Xe2hz3DfpZ9aiUFq8tp391IcNhP7sIChPoexi70MvvuRUye7UBrMaB32zC4rOhdNvRuGxqzXh3vOt9Fx5OqSEYyEqP4wTWkLa1A47DQ/+Q+ooPjM7stgoDOZUPvScPgcWLIc2HMz0iNoQuCgDbdjjbdjmWByiWMTQToe3wPkd5REIQpcY5aBFHEVl2IcP86+h7fg/9CJ31JmZwPrkXUa5nYcRQlkUCX48Z5y2p0nnQS/jADLx4mcEkdKzfkuEhbNhtr5UzwA6px7/DBywy8VY+SkJEMWnJvWkD6olLVgmFgkqYnj+LvHkv9jr00k5yV5aTPmfbbikyGUmv07Rp6Vl0eZTfUYMl894m3qBFxzfbgmu2h6q4FTHaOMXS+l6HzPQRHAgw39JNZ+/tNn/+/EooioCi/fyLp3bznL3F1vCfy2YODgwwMDFBbW5sydzxx4gQ2m43Zs2f/yTv5u+K3iSXMnz+fn/zkJ6mfVVZWcvPNN78rsYT3Iv7cFYZEIETXT3cQG/WSvroa2WCg7zW1Ku6sK8S9YjbmfBdN//MGwa5R0moLyN4yl4ZvvQIKzP7b6xANesJjAcJDXvoONKXGv2yFLtLmFnLphbOIGpGVX7qO3nN91L9wFgSBZZ9aQSQU5+BDxwj5IgQCcUxuC6UL81l+bx0DbeNcONDGQNsYId90YuTOT0Nn09FyTuX/rL1rLg31nbQ3DiJpRDbeXsfevacZe3t+2mFm4/ULyS/2ICdlkkmZYCBMR+sAbU299HQNoyjgNDmI+pP4e0PI4zHMCthNEvlVHmxGVcozza4nFpiuWJZuqaLs+uqrxh+HL/Zx/tGjxMNxdGY9tQ8swV2VzTtj+GIfp352AEEUSBj0RHwR5t+/iOPPnsc77CcYSSLoJKKKgKSTkHUikVCMjR+cT2/fKCf3NWEwafmrr9+C2W7ga3//EMFAhFvvXsXmmxbzy58+x8H9Z3BnpPGN//ocRqOeI4dP8YPv/xoAj8dN3bwqamoraG3p5IXnd+DxuPnIR+7me9/6DRqNxDe//be43Gn88JvP0tLYS5rTyme/cDu5+W4UReGh/9pB47ke8kvcSFGRxsNdiMN+7AaB7HQDdiGBwyxgt0qYDGAkgkkfx5DrvCbY0JbkY7vremIDY4w8+jqCVoNlYQWWhRVIlqurhnIsTnxoHMlsRLKbU4pHYy/uJXi2GX2Bh4AvSWxokvS1NUSjCsMHLqMx65n1N1tpffkMYxd61XGYW+aRu6bid64Z/4CXcw8fxt+vVooLVpUx+9a6GaNdiqJw5GcH6T/fh8VtZf0/b0I3Vb3uvdDPrh/uR5BE8hYWcPlQBya7gerNs9nz5Bn0Jh0Zpek0nelBZ9EyHvCTlmGmZ1RNcO/+0HpWbZjLd/7zES7Wt1y1f5IkUlCQTdvFUcJjSdJ0dpSAgiYOHo+NtHQTyUAEh9OEPdvGzV/dikY3sxOSiCboqe/HXZSOxTXTayfsDaM361OdziuP+ewzp2nb1wKCwIL7FlK0XCVWRwNR9n9/D94+FQSt+T/rsE4lQHJSpr++j7YDrQxfnjJgNenIX1hAweJCnEXpKe7MxW0XaNzRAEDJ6jLqPjA/RZxOVZtPqwmeIAhkLyqkeENFapT1nfvr7xpj+EwnI2e7iPlmqnuZMmzYSzNxlGbOAEZXRmTYixxPondZufDzvXjbhtGY9NR+dv0MlchENMHg2W56j7Yz3qbyIQpWlVF55/zpsSJviIZf7sffM4bGqKPmM+tTo0T+9iF6t53CUpRB1sYaNO9Q2Bo520XzsydIhKKIWg3FN9eRvWJ6ZGnwXA9nHjoEChRvrGD2zXOveT58/V4GLw0yeLGf4eZhVaZTEMhfWEDl9VWpz+zKiIVi7Prv/Qy3jiJpJdZ+ZgW51dn4RgIcffYcrSdVQQatQcP8G6qoXFWcAid/aEz0+2g92U3L8S4m+qdlmjVaieoNZSy6rYb2Y50cf/I0yXgSo8PI6o8vwzNrZvFkrH2Uwz87SNQXQWfWs+yTK/B1jdL0ynlMLguZJU6GT3XgrMim+lPrfu9++Zr7aXtkH3I0gbnARelH16Mx64lPBgm1DRAZHCc6ME50YEIdZXtnCAL6zDRMhRkYizyYCjPRTnlcBZp66X96P8lQFMmkJ/vu1Vhm5SLHEwzuuoBk1pOxsoJgcx+9v9mNkkhimZ1Hzn1ribb3kQyEMc8tR5BEfPUdDL50hGQoiiCJuDbUkb66egagVhSFQPsQYyfbmKjvSokh2CtyyL9jCTqH+arOo6TTkLGgiOyV5Viyp7ux4YkQHbsu031k2pDXXZnFrBtndm3j4Tih8eAVnUJlZtdQURBEAZvHftV9J7XPgz6GzveQt7wUvfWPu77+0Hi/d4QatnzoXXeEqt545H13HO/3+KOB0Be/+EVuueUWFi1a9F7v0++NQCBAa6vqQ1NXV8f3vvc91q5di9PpJD8/PyWf/bOf/YylS5fyi1/8gl/+8pc0NDRQUFDwe7b+3sSfe2F5z7TS/8wBRIMOU20Zw4fVCrdn/Ryyt9alHqSh/nEav/8aiqxgykkj1DeBqcDF+EDwKkNRnd1E8Y1zcVRkc/AbrxMLRCm/oQZTtoPDPz0IikLtnfOw5aWx83t7CU6ECfhjGN0WiupycJe7Oberhcnh6U6OIAik59jJKkmnt3OMvrZRJI3IpvsXcPzgZbpbRzCadGy8ax6vvHiAYCCCM93K1luWsGTVHLS/gzx58Vw7//OtF0AWsGBhvNuPGFAwJhXsOpHMwjRyi9JIhGJklbnwdajdsNm3zqV4/W9PmkOjAc786iC+3klgKmG+ZS6SToOclGl5/QJtOy+BAvYiF/0to2gMWgrXzeL8totMDAXQOk2EQgnQa7BlWBjsn8TmNFG7sZgdz5xCEOCj/7iV2XPzefgnr3H80CVy89380zfu5/zZRn743ccRBIEvffUTlM8u5FJDC9/4t/8mkUhy3fVr+dCH70ztbyQS5bOf/jJ+f5BPffo+Th1v5GJ9C4VF2Xz5658iGonz3a8/xeDAOHq9lo/9zY1U15UwMern23//LLFIglWbq9n32Fki7WOkGSWy0vQqR8gsYrdJGIljFqOY853YFpWjtZqQQ2HkYBglFEbKSMeyZTXJYJihn788Q3pWkCRMc4qxLKxEjsaIdg4Q6Rgg3j+SGsdAENDYLWjSbEQ6+gEF08r5jOyuRzTocN+6nI7HDgJQ+tF1DJzpZuhkO6JGYvb9y3DPLUCRFSY7RxEkEZPLgtakSyXiXfubaXz5HHJCRmfRU3Pf4mtyLJp3N3H+uSle0Bc2kpavJsXJhMwrX92Bb8hHwcJ8Gk/0ICcV1n1kEbuePEs4EGXJTVXse7meZFImrksSiyewevQMDI+wbHU1D3xyS0oCXafT8sWvfoKAP0j9uWbqzzUzOKhen96hGA6LE8UrkhyXsekNZOamYdaIGExahEgMi91A7Y1zqLtZJYOHfREa97TQuLeFaDCKpJWYd1stFevLEcV3U1lUOPfMGVr3NafAUHZtLvt/sBdv78RVIOidERj2E5oIkV7suiZnCKBlbzPnnj0DikJOXR6LP7w0JdIA4OuZoOnV84w0DKR+llGVTdGGCpyl7t/qFTTZOsz4pX68rUMEesevEsmw5KThqs3HVZOHyTPtOZSMJbj4i31MtgzOADCKouDtGqf3aBv9p7pSqmm8/ecVyFlcRM0HF6fAXCIc48LP9uLrHEEyaKn+5DrsxTP5LldGzB+m9YVTKTEWa146s+9fhilzGviNNQ9x8if71M7k0mKq712U2vdYKMZw4xCDDQMMXhogPDGTm5s7P5+qG+ZguwaQBFUW+63v72OidxKtSceGv1mFI9vO6Vcvcf7NJuSkjCBA5eoSFt1ag8k+M0FVFIXJ4QDDXROMdE8iywpWpxGr06S+0k0YLfprfmZjvZO0Hu+m9UQXk0PqsyKzJJ3Nn1lOIpJg388Oq7YLgsDcm+ZQe0PVjO2EJkIc+dlBJrrG0VsNbPqXrRz6pvq8mn1jNb1v1qMkZeZ+bvPv/AzejkDXCG0P7SYRjGHMclD68Q3orgGeE/4w0cFxIgPjRPvHCfeMpEQPrgytw4Le4yDQ1AeKgiHXRc5969ClWYh5Q7Q/so9gt7rWM9dUknPDfEKt/fQ8ugslnsRclkPug+sRtRqSoQiDLx3FV98BgCE7nay7VmLImgbr0TE/Y6faGDvVRuyKEVi9y0rWphqc81TVyKg3RPOTxxhvVEclnRXZzLp3KTrbtDdScMRP+85L9F1hyHtNHl9CpnVfMw3bL0yvj98RWpOOvPn55C3Ixz3VUf2/Ge93IHRh84ffNRCqfvPh991xvN/jjwZCH/7wh3nttdeQJIkbb7yRm2++mQ0bNqDXvzsN+T8l9u3bx9q1a6/6+YMPPsgjjzwCqIaq3/rWtxgYGGDOnDl8//vf/50+QO91/DkXlpxI0v6dF4hPBDDXFDNyRq2k5twwD8/aOYDaGh+72EciHCM5NsnwgWmulL4km7GmQUStBmO6BYPLgq3ARc7qWUh6LfVPHKf3aDsWj4059y9h//f2kIgmKF5ZSumGWbz+zV14R/xMjoSwZNvJqchkeNCPd8qt3mDRU7mskJxyF9Z0E9FIgtd/fZyxAR8Gk44bP7GUN186RX/XGCaLnvV3zOXFZ/YRjcYpKPbw11+4HVEjsm/3CQ7uO40oiaQ5baSnO3C506iuLaOgUO3SfPtrT9Le0k9mmouRVh/eDj82jQaHXsLhNFGxJJ/oWABnoZN0j5WMqmyyFxb+3nOcjCdpevkcnfvVsStzppXK2+bRtvNSqjKcv6KUUCRJ17EO8hcV0nFhEO+Qn0AwjqIRiYkiOqOOmCATiyaoWVPM0f2XUBS4+YFlrLyumssXOvnhfzyHIMAXvn4f6W4rX/yHH+DzBbnh5tXcdc8WensH+PKXvkMoGGbx4rl8/u8+lurEvh3bt+3isd+8iNvt5Ktf+zxf+9JPCARCrN+4hAc/ejPBQIRf/OAVmi51Iwhw1wPrWbt5Hkd2NvDirw+hM2hgXGb84iAOrUCmXYdTr2C3iDhMIkYlitWuAgzHhoU4Ni+++pyFIgz/4hXiY5PoPC6sy6vxH7+Uko+9VkhmI3I0jpKY+TA1VRXj7fYSG/PhXFXN4IkOEoEIGStnYyzOouGh/QiCQPVn1pNW7iEejnPu4cOMXJpOojUGLSaXGQQBX4/KD3FXZlFz3xL016hsj7WPsve7u1GSMnX3LKD0ipG4i282cuq5sxhsBgwZNvoahymal4POYeLMzibSs+1kV7o5/kYj5jQDQxOTGExaJqLjJBWFL//nhxgdHeN733oUgE/91V0sWzFzlLivd5h/+af/ZmwogMOcTnAggVUwY9Hp0SHhdJmxGLUo0Thmkxa9Scu6z6yk+1wfbUc7UpwcrVGbIiZnlLpY/qHF2D2//56kKArnnj2T4mKY0s2ExoIY7EZWf34dtnexjd8XvWe6Of7ro8gJGVeZm6UfX4HhHZ/FRMcoHbsvM3h+2uPEUeCkaEMFntq8FPi4ViTCMbztI0y2DF0TGJkybKTX5OGak0vnG/VMNA4gGbTUfHo9tkIXgUEf5x45nCqCAJhcZnKXlJC7pIix5iHqHzuOoihk1eVR++CyVKU7EYlz8Zf78LYOIem1zPn4GhxlM8ez35bfb3v5DIlQFEEQyNtQRcGW6hk8p8muMU78t3rfzazJoe6jKxAlUVWOe/EcLXuaUa4QKZC0Eu7yDDxVWWTNyU75S10r/CMBdn5/H/5hPwabgU3/Zy2+0SB7Hjqe4vnkVWWy7O55uPIcKsela4LB9nGGusYZ6Z5kuHuC+O9JgDVaVTjB4bZQOj+XWYvyMNunk25FUeg408eeh44RDamG2hs+sZTs2Rkcf/I0rYfV0ePChfms+PCSGd3PZCzJm//2OsGRALV31KERFBpfOocx3Uz27AwGj7XiKPNQ+1cbSMYSRCdDRMaDRCfUV2Q8iNasp2BLNRqjjvDgJC2/eIu4N4zOaabskxsxuH7/9Z7whwl1DhHuHCLUOaRKVl+huOhYMpvMGxcjaiSC3aO0PbKXuDeMZNCkOLxZG6rJ3lpHsG2A3kd2IcfimEqySFsym6Ftx0j4wyAKuNbW4lpXq4ohxRKMn+tk7FQbgbbp+6tk0JBWW0j6wlLMhdPFg9H6HpqfPkY8eO3Oo79/kradl+g/3ZVac85SN6Vb5lxlyDvaNsKZJ0/h7ZsEVJAjSiK8LZZwRagjk4kZJstGh4m8hfnkLyzAkZf2fwUUvd+BUP3Gj7xrIFTz1q/fd8fxfo8/aTROURQOHTrE9u3b2bZtG319fWzcuJGbbrqJG264AZfrd5NL//85/pwLa/zwJYa2HUNjNRE3mgn3T5CxqoK8mxfibR+h/2ATYxd7ScbUG23JLfOYOHSJ2GQIU66T0b4AcjxB7V9vxFE680H99sgXwPxPreLkE6cIT4TImJ3Jog8vY8e3djPeO8FonxdLtoPsWRmMjoeZHPJjshrILHXi84bpab5aVtPuMnPjJ5fy0iOHGOqbxGo3suy62bzy/EESSZmKOQXc87EN7Nt9gj1vHScUily1jbfD5U5j/oJKPJkZPPeb/YgI6GIm+s+OYBa1pOk12EwSZYvy0SYSCMD1/3FzSvY3ForRsqcZ/5CP0HiQ8GSYRCRB2fpZVGytTN2cRy4NUP/E8RnkTo1ew5x7F5FZncv2f3yJeDhOyfpZ1L/ZxORQAI3TTCgUB70Gq9vM0IAXR4aFibCfoD/KkvUV3P6xlcTjSf7tHx9mZGiStZvncdcD6/jhdx/jzKnL5OV7+Nq/f5ZAIMiXv/htRkbGKS8v4itf+xw63dU3yGg0xl9/9itMTvr4+CfuITMjg+/85yMAfPZv7mHxshoSiSRPPvQWR/arHKm1m+dxx31r+Pk3XqP98gDhgQjxHj82USHDqiXdLGDTQZoZrCYBs02DUadgri7C86nbZvx9JZFk+NHXiHYOINkseD55C5Jtynizd5jAsYuEGjqQrCb0hVnoC7MwFGWr7ueKguwPkZjwkRj3I4cjxJICw6+eRDIb0BTmMHG+C2OWg+KPrOfMd3cQD0TI21BF8Y11hMeDnPrZfvz9XkSthNakm/F5gSotPfuWuRSsKrvmg7f3bA+nHz9JLBgld34+Sz62bLqrOhnmxS+9SiKaoGxtGefeakGURDZ9djkv/uAAiqxw418t56WfHSYWSWDM0DMy7CWr2EFLZzd5BRl84vM38pV//h+CwTAbNi3h+ptW0dc3SFFxPlbr9Ajbd/7zYc6eaiQ8LiDF9aTp7SR9MmZJS3ahE70goNWKSIkERuNMpaX0QidzNlfw/2Pvr8OjOvfuf/w1bhmJu3tCEggECO6UCtTdXal7e2rn1N176qVKFS/uFhLi7u42ScZn9u+PgYE0tKWn5+mvn+f7rOtKhyZ77u173+sta0VmhlG9p45D3xbgsNgRSyVMWJZG6qJEz2R6qHuYgbZB+lsGsZntpC9JRuGlGEOGFDolc+6c58ksDLQN0lPfS/j4MBS/ItBwFMauIUp+rqCvpZ+pF03EL9pdLtZV2cm+d3djN9uRKqQkLkomYUESUsXo7O9I1xD12ypoOVDnkVtW+aiJmB5HWHbsCcnsL2EfsdJb0kJPYRP9lR2jVAXBLbqQduNc9DEBmHpHOPDyZiyDZsQyCUHjwwifGuvpCTqKjoJmCj7eh8vpIiA1hAnXzvBkwZw2B6Uf7KS/sh2xTErqNbPwSXYHbczdQ1R9c5CBancZoVeYDwkXTPGU0R1Fy/46Sla6Pa184vzJumWuZ/zKzRXuEmXcPVhBqcEEpgQRkBA4Krv2a+hvHWDTKzswD5jx8vNi0V1z6GroZ/N7+3A5BQxBWqZfOIHIDLdIRk/LANu/OEx9cfuYsSRSCf7hBvwjDMjkEoy9Job6TQz1mkaVRB+FSCQiPDmApKmRJEwKR33k/Bm7h9n49l5PL1LmaclMOVIqt2/FIVwOF4Hx/sy7ZeYoie36vbXkrshBqVex6JEl7P7XOndWaGk6rRuLcDmcyDSKUVLsv4Q2wpf0m+cjVcmx9g1T/e/NWLuHkGmVxFw1F02E3x+aqLusdszN3Zibe1AGe+OV5Jb77jtcR+NKtyeXMlBP7NVzMVa00fyj25Yi5JTxBC9Mx1TfSfNHm3DZjimsyQMMhJw/E1W4O7s10tRD/Re7sR5VMhWBLj4Y36xYDOMiRgk6OK12an/Mo32/u6LGK8yHpMume7y5Bhp7qf25lM4jdhXgDhbFnZKKd8xYBb+iHwpo2OcmqHKNgvSzM37Xp0xwCXRXd9F4sIHWgpZRpEgbqCM8K5KIrIhfzTb/T+DvToQKF5w8EcrY8n9E6I/iv9IjdBTl5eWsWbOGVatWkZuby5QpU1i6dCkXXXQRoaF/jTHW3wV/5Y1l7Rqge+NhUMjpPFiHWC5h3MPn0FfeRuUX+z0RUJmXEvuwBblWRcolU+jYXorEV0/rvhq8wnzIvGeJ5wFmN9mo+KmA5n1uWduI6bHYxRJqtlehDdQx++757Pz3PtrKOuhpHkAZoMMnTI9dJKareQBj3whSL9mommWZQopCJUOhkhEQ7s2EBbF88+4OBvtMGHw1ZC1K4KeVOxEEmDQ1kdmnZPDK858xPOwu8wgJ8WfJGbPw9tHR32ekt2eA1uZOigorsVrdLwqxWER4YBTdnYPI7Qp6K4eQmUGvkKKTiwmJ9ycy0Y+RriHSz5lA4sIkbCM2dr2+nf7GPk6EhIVJpJ89/lgZyoiVkq9y6ChoQRdmYMLVM9AEaGnObeTAB/tQ+2qQ+mppPNzCQJ8ZiUaBVRCQKGRYBScul0D0hGCK8+vxC9Jxz4vnI5VK+HbFNrZuyMPg7cVjL1xNYX4Fb7/xNVKphCeevoXgEH8effglamsbCQry559P34tO94tmZ4eDsrJKgoICyMst4eOPvsXX18BrbzzO6h92sPqn7SiVcp54+laCQ9y9QRvX5PDT126yu+z8GUyaksLL939Le2Uvoi4rPmIRflopfl4SdFIX3nopOq0UlRJUzhGUYf4E334+Ul+9p/Ss77vtjBRVI5bLCLhuGfIg3zHHVRCEk5pUuOwOap//DofRhPesNFq3l4MAibcvoWlrOd0FTWiCDGTeswRj2wB57+7COmRBoVMy8YZZGCJ9cdqdmHuHMfUMYxkw45sQiOYEUXKHzUHht/nU7XZPFLwjfZh1+1xPXxDA7g8PULu/Ht8oX4bNdvpajaQvSqClvp/m8k4SssJR6JUc/LkcnyAtje1diEQiNMFSOjv7OOuiWezZm0NjQxux8eHcvPxCHrz/WYxHpHYDAnyJi4siNi4S44CJjev3MdTjwKDxwd4PQj+EhfsiF0lQq+WoZSJEgFomQiqTEJYewrjFyQQm+NNV30fVvgaCE/wJjPFh/4pc2o6UmnmHGZDIJAy0DY4pZwmI82fRXXORyt3+SWXrSumq7GDixVkeEtTXMsCG57diN9mQyCREZUWQNCcOvyO9QEfR29hH8YZyGvKaOdowIFPLWXTHbPxj3MGygZYBclcc9NyDSr2K1DPSiMqOHqP0Zx2y0LizisZd1Z5JlEgsIjA9jIiZcfgmBJ7UdeUw2+grb6OnqJm+Mnd50NGsjWXQzIFXtmDqGcYrSMeU5fN/k2h1l7aR98EeXHYnvgmBTLxhlofIOe0Oyj/eTW9pK2KphOTLp2PqHqLx52JcdgdimZSoJemEzkkata9Om4OSb3JpPegug/JLDmLCVdORHbkWWwtb2PfuHhAExp+fSfy8xN/d5+PRXt7J9nf2YDPZMIQaWHTnHJpLO9j6wQEEAeKnRjL/2qlIpGJMQxb2fl9MwbZqBJeARCImLDmAwEgfAiK9CYgw4BOs+1VVRofdyXC/maE+E+21vVQebKS97lhjvkgsIiIlkKQpkSRPjUQiFbPvmwKKtrgJeEiiP4tunI6xw8i2t92kWR+kY+Edczy9by6Hiw3/WIupb4Tx52ciRaD8h3xUPmrCM0JpPa4KQqKQofTWoPDRoPTWINeraN1ZgX3Eii7Kj7Qb5yFVybEPmal5fwumVncGWaKUogw0oAw0oAo2oApy/1umU53UNScIAm0b8unY6lbp1KeEEn3JTCRK9znt3FFKy5o8wK3uGjgnFXNTF00fbsJlteMzMxX/RZmIZVJ3NnF7KW0/5yM4BWR6NQHTE/GZGIPcoBmzbmNDDxUr9mLuGXKrvM1LIerUdMRSCVajheIvDnpksBFBUEY4cYtTxyg3CoJAw746in4oxHaEVEYdUR/8pffT78Fpd9JR1k5TTiPtRa2jlCXHLUsneUnqHxrvP8XfnQjlL7gGrfQkiJDDxoQtH/7t9uPvjv8qEToe3d3drF69mtWrVzNz5kzuueee/4nV/G3xV99YgiBQ/uo6zC19BM5NReKnp/prd8mG//gIwual4BXqTe4zazH3DBF92njC5qeQ8+QqrAMjhC5IRR3sg91kwzpkoWl3tUdMICw7hohZ8Wx5djOC08WsO+ZSm9NM5a4a+loHkerUqH3UyAwqupoG6OsaQuIlQ6aQEhjhTdqMGNKmR6M/TomrqriFT1/ehNVsJzDUQMasGH5cuQNBgGmz05g6J4mXn/sUk8lCeEQQ55y/kAkTk0/4srHZ7JQUVbN+zW6qKhuIjAiju8WEsdOM3KTA1e9EL5ehl4vx8deQOjsWY0MPhggfZt8+10OC5F4KkhanoPZRo/ZW01Pb44m2xs1JYPwFmZ71C4KAqXsYlY/GUwqz+40ddJS2EzsnnqKtNXQ39KHw02JzCrjkEqQqGcYhM/5hetp7+7CY7Vx86zwyZ8STs7eMj95aB8DNd59F4rgI7r/rZfp6Bzn7vAWcec58Vq/azOcrfkSjUfPMs/cTFHwsQtfV1c3mzTvYsmUH/f0D+Pr68Prrz3H3nf+kr2+Aq64+j0WLZ/HcPz+korye8IggHvvnzciPuJ7v2V7E5+9vRCoR8+hzV1Ge18SXL21jpMZIgEKCr5eMQIMMncyFQSdFq5GgxI5aZEEd4ecug5BKkXprEasUWJs6EInF+F+2BGVcGNbOfsQKuadx+I+gd1cxXesOITN4IQ4Lor+gAX1KKLrxsZR/tgeRRMyEO09hpHeEgk/dUVZtiJ5JN805KSnXo+hv7ufgh/sY6nDX+ScuSmbc0vRRTb1dtT2sf2bzkb8ncXh9BQqNnMnnZvDzhweRyiSce/9cPv3nJpwOF9ETgynOqyck2oeqxgYkYjEpWaEc3F+IVqvhyWdu5d13V1BYUI5CIcdqtY3aJqfDRV+XGb2XN3q1uzzOW6FFq1IjR4yPnxaVQoLYJeAdrOX0u2bj5etF/eEWCjdW0F59zAQxNDmAmZdOpK++j5yV+aMisWKpGEOIHkOInuaiNuwmG1FZEcy+ftoJ77nhnhHWPbsZ84AZqVKGw3IsYu0dZiBxdhxafy9KNlXQXnZMwTM0LQS72UZXjbuPbuEdswmMc1/HgiDQnNtEyaoiRnrcpFAXoiftrAyCx4WM2Q6nzUH74Saa9tYwUH9sUq328yJ8WiwB40LwCtaf1ATVaXcgOAWkShm2ESsHXtnCcIcRla+G7LsWojyuhOvX0FvdRe47O3HaHHjH+DHpxtke0uJyOCn/bC89hU2jvuOdGEz8eZM9Mtue49tpJP+DPQy1D4IIEk5LJ3bxscx0f3M/21/YgtPmcItNXDjxpDMVgx1G8r4vpCnfrSLnF+PLwuWzqcltZuenhxAESJ4Zw5yrJiO4XORvrmbvj8VYj1wv8ZPCmXPRBLwDf73c7mQw0DVM5cEmKnIa6TxOiU7np2HZ8pkEx/hSk9PEto8OYrc4UGkVLLxxGl4GJZtf3Ymp34RKr2LB8ln4Rrp7ZGp3VXP4y1xUBjWL/rGE3f9ch9VoIfW8iehD9UjVcpTeGiRK2ZjjNdzSR9HbW8eQIYfZRuNXexisbEM4zvj1eEhUMtShvmii/PGK9EcT6YdU8wvlNoudhi93M1DqPu6Bc1MJPdWtanm8mXf75iLafi4AIPysyQTMSMIxZMJldSA/Up5nGzTR8NUeho5kE70zIok4d+oY8Q1BEDDW99C2u5LuArcpsMKgIenSaZ4yzd7qLgo+3ovVaHELk2RFErsoBa8T+PUNtAxw+KtD9Na6nyv6UAOZF0/CL/b3e69+D3aLnbaCFpoONdJZ3sGcu+bjF/fnxz0Z/N2JUN686/A6CSI07LAxcdv7f7v9+E+xevXqP/ydhQsXolL9/vP6ePxhImQ2m+nr6xuT4SktLSU19a9h7/8v4K++sQZKm6n9aDtiuQS/eRnUrS0AIGRGAnHnZiESiRjuGKTzUD0tW0qQKuVEL51A9cqDCC4YcYrH1NprArWMu3AyvvEB7H13N20FLQSnheCdEMShbw4z2DWMUypFqVdiiPSmpaaXge5hUEqQq2XMvWA8yKC6uJXGqk4UKhk6Hw1avYqK/CacToGY5GCSskL59ottCALMnJfBxGlxvPz8p1gsNhKTorjr/itRqX4/0tTe1s0Dd7+CIAjERsRRX96BqcWBZEiEXq5Ar5SgVUlJnh2HMDiC4HShDdYz1D6I3EvB7DvmYQgzjBqzdncNh7/MBUEgekYsEy/JOuGEw9RvYt1Dq0FwG80eXl1CV0MfqmA9ZoeAWCrB4nIikoiImRhMUV49gWHe3P38ubQ2dfP8Y19itzs4ZekUzrxwFj99v5Ufvt2Cr5+B516+i4H+Qe668ylsNjs33nQp8+a7zYJbW9v5+OMvyMsrGNMUfuut1+F0iPjwg28wGHS8+faTjIxYePT+1zEaR5g2YzzX3XQuEok76v/m899TWlhPfFIYtz90Pncue5vuwl4C5BJ81BJC/JRoZQIGnRSNAlQOMxo/t3Goy2zxRPuPwufM2XhNTMJY3EDr59tAJEKbFoXvzFRUEaPVnxzDZobLmzHVdSBWyJDq1O4fLyVt3+zCabLiu2ACzRuL3YpZ18yjbMV+HCYrUadmIPPVUfDJPsBdyjHh6uknbconCALV26oo/tEtnqDUq5h85VQCk0d7WDisDtY8tZHBDiPRUyKpK+nENGhh6rnp5GyqYqjPxPSz0+nqGKRwVy1RqUE0NHcwbLQQleZPSWkt/kE6GtsaEIlE3PfQ1TQ0NvLpJ98jl8t49vkHMBh01NU2UVvbSGVlHYfzSmht7CUiIgLboBSJTY6v1htnnxM/fz0yQYRKI0cjd/tqJc2Ipq2iC+OR/jyxRETEuGBayjpx2J2IJSIyFieROiuWttJ2lDol3qF6dIFaTzS/vbyTza+5m/LTlqQw8ZyMUcfBOmxl/bNbGOwwYgg1sOS++Rg7jVTsqKHhUNNovyDc0f7oyZGMOyUZkVhMR00P9fvq6KrpRqqQsuD22QQlHLsenHYntbtqKF9f6ok4+ycEkH7OBHwifTgRjK0DNO2ppi2nYVR2S6FT4pcYhO8Rqd7fIzQOi52Dr29jsKkPhV5F9p0LUPt5nXBZQRBw2p1Ijys96q/vIfftHe6MRbg3k2+bd4wMOV1Ufr6PrsNu2f6YMycSmBU95nnSlttI8Zc5OG0OFFol46+chm/isZJl84CZrc9uwjxgIjAliBm3zP7VTMzxsAxZKVxbQsWOGnc/kUhEwsxYsi6YQPmuWnZ/4bYgGDcvjpmXTqS+sJ3tXxym70hgICDCm7mXZBKZemJvF6fTRV+7ka6WARBA66NG76vGy6BC8jtGpP2dQ1QebKJgWzXGnhEkEjFzL53IhAXxDHYOs/GtPfQ0DyASwcxLJxGTGcqW13bS3zKAVCFlzo3TCUsLwWl3suHRtZgHTGRekoXY4aT8+8OovNXMfuyMEyqVHY/hlj4K39qKw2RFF+VP2k3zPM8Rl8OJtXcIS/sA5o4BzJ0DWDoGsPYMIbjGTqOUATo0kf5oIv1R+mlpXnUIc/sAYqmYiPOz8Z3oVmIcau6j7ONdyNQK0m6ah0yjoHVDPh1b3CXLEedNxX9qgmfcgdJmGr/Zh2PEilguIfzMyR4ZbM+5sDvoPtxI667KUX5bAZlRxJ2XhUztLnut21RG5doiEMArSEfmtTNOSIAsQxbK1pVQt6sGwSUgVUhJXZpO3Jz4k7r2/igsQxYUvyKu8T+BvzsRyp17/UkToUnb/33S+7Fr1y5eeOEF8vLyaG9vH6XAbLfbeeSRR1i/fj11dXXo9XoWLFjAs88+S0jIMeVcq9XKPffcw1dffYXZbGb+/Pm8/fbbhIUdkz/v7+9n+fLlHmKzdOlS3njjDQwGw29u3y97n38PIpGI6upqYmJi/tj3/ggR+u6777jzzjvx8fFBEATef/99pkxxN0lnZmZy+PDhP7Ty/834K28sQRCoeHUdppY+9OmRtB52y5yGzU0meFYS7YcaaMtrZKhtEEEQ0GtlOM1WdyTK5cIlk2G2uNAEaNGG6JGq5OhCDYRPj0Mik9Bd1cWOl7ciEosYd84EDnydj2nQjMnsQOXrRUhqENWFbfR3DoFCgkwlRaQRM2L59Z4ecBul+kZ4sepbt/rXnIXjSZsUzasvfobVaiclNZY77r0MpdJNgnp7+tm3Lw+1WkVcfBRhYUFIJKNfsG+++iU5B4pJiI+hubqfpoJetE41GpEMvVyCXiEhLCWQ6HGB9B+JIv8aCTqKhv11HPosx6NuddS87yhM/SZ2vbadoQ4jvrF+jFhd1B5sxIEIQSlHUEgRyyUMm6wERXnT2N6F3erk8jsXEpMcxDOPrKC3x0hqRjS33Hs2/f1GHrjrZaxWO7csv4jJ2Wk88/RbFOSXkZISx2NP3IlIJGJoaJi7736Eri53/1VaWgqLF8+ns7OLFSu+ISQkmFdffZo7lj9BT08/S5cu4NLLz6a0uIbnn/4IQRDInJTMTbddiEIhp7d7kCfv+xir1c4l1yxk+zdFlPxch79IjI9GSrCvAp0C9FoZGmxoZC60ET6EXDALfWYsjoFhHH1GHH1GJDoN6uQoLG19NLy9FsE+uvRKFRmAd3YyjiETQyWNmJu6xxCp4yH31yMODqDvcD265FCsLjG9pa1ow31JuW4Ou/+1HrvJRsT0WFLOn+TpfRloGUAXqPvVfgnrsJWcTw7QUeIuCQnJCGXSZVNOWOJx4Ms8KrZVoTKoCM0Mp3BTFfoAL2Kyo9jzfRE6Pw1n3DaDDx5ZjyAIzDw3jQ3fHUJrUDHCEEajCe8ABa0d7Sw+dTrTZ43noQeew+Fwcs21F7D4lNmj1icIArfe/CjVlU0o5VpkLjU6tQ/2XgG1WEFggA9YXGh1Sry85AjHEQClRk7q3DjS5ieg8VYx2DXMni/zaChw76eXj5qp56QjU8oY7BxioMPIQOcQg53DOGwOUmdEU7nNXZaUfVkWibPdHkUOq4OfX9pGT10vam81qUtS6GkZIH5KJMEJ/thMdmr311O5sxbTgInY7GjGLUpCEEHOj8VUHvFaihofgtjuoLOyC4lcyoLlswhOGt2faDPZqNhYTvW2So9kb+SUKMYtS0ftc+LMosPqoD2vkfb8Jvpquj3fOwqvIB3+KcH4p4bgExswamLstDs59NZ2+mq6kanlZN+1YMyk0GF10FneQVtRK+3FbTgsdrJvmEHwuGMTA2NLPzlvbsc2bB1Dho6q2nmFGJB5/SJjYHdS/sNhmo6UZfrEBzDhymkojiNvDpuDHS9tpb+xD22wnnn3LhhVtnnCY2JzUrGtisL1ZZ4sYGhaCJPOHY93qJ7D68rY/63bl2j8KUlkn5/Blk9zKdjqlnRXa5XMPD+DtNkxnonJYO8InY19dDYN0NXcT1fzAD1tgzhPkDERiUR4GZRuYuSjwTtQS+LEcCKSxqqFWUZsbHj/ANW57ndY0pRITrl2CmKJmF0rcik/4jeXtWwcGYsT2fHuXtrLOhCJRUy9dBKJs+Ko3lZJwcrDqH01LHpkCbv+uQ7roJnUCyYROTN+zPb9EkPNfRS9tQWH2YYu2t+dGfqNoIrL4cTSNYipuZfhhi5GGrqxdI1VjwOQ6VTEXDkHr0h3pqOvrI2yT3bjPFLarYvyI/3m+YjlUlrX5tG5o8xt8nvhdLwzImlZm0f3HrcirDrUm+hLZ6EMOHaNWvpHaNtTRcf+Gk8vlFgmJWBiFKEzEzxS8LYRK4Wf7veIyYROjiL1gqwxfXkOm4PqrZVUbCz3ZH3DMsPJOC/T0197FIMdRg58kUtf0wDgfn4dU9E+IqMtCIgkYoIS/ImfEUPouBAkv0NO/wr83YlQzh8gQpP/ABHasGEDe/fuJTMzk3POOWcUERocHOTcc8/luuuuIyMjg/7+fu644w4cDge5uccM6G+66SbWrFnDJ598gq+vL3fffTd9fX3k5eV55mdLliyhpaWFf//73wBcf/31REVFsWbNmt/cPrFYTEdHBwEBv+45eDy0Wi2FhYX/s0Ro/PjxbN68GX9/f3Jzc7niiit4+OGHufjii5kwYQL5+fl/aOX/m/FX3lgDJc3UfrwdsUKKJDSA/qoOAjKjCJieQN67u0aVrQB4+agQBt2lJ4JLYMQuRiQVM+uRU8e8+AVBYOuzm+hv7CM0M4K6wjZMgxYG+8yo/L0ISw2iuriDwd4RzFY7Xr5qBKWAyWZDJpMQHh9ASJQP/iF6/AIMWM12BvtG0OiUVFTUs2ur+wW84NRJJKSF8vpLn2Oz2UnLSOD2uy9FLpfR0tLOqp82s2d3Ds7jlJEUCjmxsRFMn5HFjJlZqFRKGhvaePSBN9wy3V5B1OZ3orIoUTll6BVS9AoxvsE6xi9KpLOo5XdJ0FE0HWrk0KcHcDlc6EL0TLthJtpALcYOI7te246534TKW834i7LY/PpO2iq6UAUbGDLZkWsVWF1OEIsIT/OnvLiZ0Gg/bnvqTN549jsqy5rwDzTw4D8vQ61R8t5bK9m7O5/4hEgeeeIG9u/L49VXPkIqlfDiy48QEhKIy+Xin/98kcOHCwkMDOAf/7iP0NBgwJ21ve662xkeHuG++25HJlPwwnPvIRKJ+Mdjt5M6LoG8Q6W888Y32Gx24hMiufPey/HSqtm6PpdvP9+OWq0gNiyCnZ8W4u0U4auWEugtQ6cSY1CJ0UodaPUyVFo5frPGEXLBWEVGx7CZhjfXuNUM40MIWDKJvr3lGAtqRylcHYUy1A+vxFAEARxGE44hEw6jGZfNjs/cDOpXus2B/eam0bC5FLFUQuY9S6jdVknL/jq0wXqm338KYqkYu8VOzsf7aStsRalXkbQ4mZgZcaMIUXdVFwc+3Odphs84Zzyxs39FPOGIZxDA9Kunsu3jQzjsTuZdPZlNnx/GarJx2o3TKDpQT2VuM8mTI+kfGaKmtI3kSWEcOlyGQiFl0NqDy+XisX/exJtvfkJrSwcTJ6Vx3/03nnC9n37yHat/2kJvpwlvnS86lQ8j7Q7CQwMQjYhRqeXIEaNUyQgJ1yOXSxk3L56EaVHIFFLsVgfNFV34hujQ+3tRn9/Kni/yPBmjX4NCIydlWiRV291eQgtum0VIahDb3tpNS1EbcrWcjDPT2fl5nica7hOqI3VuPInTolAcmZybjRby1pZRvLXaI78rEosQXALxUyIQzDbayzqQyCTMXz6LkOSx2YaR3hFKVxfReLABcAtdxM9LJGlx8m+SAKfdyUBdDz2VHfRUdDDY3AfHve0kcil+SYH4p4bglxhE2bd5dJW2IVXKmLJ8HvojUunmATNtxa20F7XSWdE5hlxJFVJm3zkPn6hjfXBDbQMcfH3bCcnQiTDcaaTg42PqdHGLU4k7ddyoaLsgCOz/915a85uRaxTMf2DRGENUz747XPQ19dFZ1U359mpGet3n2zvcm6zzxhOSEoQgCOSuKiHnJ3e/yqSlqWSdOY5NH+VQtKMWkUhE1mnJZC9N9ZzPwZ5htnx1mOK99Sdcr1wpxT/MgFQqYbBvhKE+0wnJEYDWW0XKlChSp0YSnhgwquw47+dKdnyVj8vlwidIx9LlM/APN3DopxIOrXJv77h5cUy7MJMDnx+idp97ezLPziB1QSLrHlmN1Whh0mWTETmclH2bh9KgZvKtc9yG2RY7jiOfdrMNh9mO0qAmbGoMYqmYoaZeit7eisNsQx8TQNqNc5EoTi7DDOAYsTLS3MNIfRfDjd2YWnpRh/oSddF0T/9O+/4aqlfmILhc6OMCGWkbwGGy4psaSso1sxGJRTT/mEP33koQgcJX6xFECJiVTOhpmR5lweG2fho3FNFb3OKpDFD6eBEyI56gqXHINMcCO/11PeR/tBfLgAmxTELqeRMJyx4tcCC4BBoO1FO6uhjzgLtH1zvSh/SzxxOQOFb5sGp3LTlf53sEmU4WSp2S2Oxo4qdHYwg5sbT7X4G/OxE6OOeGkyZCU3a89x/tx4k8OX+JQ4cOMXnyZBobG4mIiGBwcBB/f39WrFjBBRdcAEBbWxvh4eGsX7+exYsXU15eTkpKCgcOHPAkTg4cOEB2djYVFRUkJv56b+NVV13F66+/jlZ7cmW4N910E0899dQfFmr7Q0QoNTWV0tJSz//39vZy9tlnM3/+fH766af/ywgdh7/yxhosa6H5xxxUUf605DS6DSXPn0Lpd4dx2pzowgxEzkpAH+HD3uc34nK68A/RYu4aROylZGjQjneMH9l3LRwzds2OKvK/zkOqlGFICKQup4m+jiFUgToConzo7h6hv3OInq4hdCFeiOQihm0WOtp6UQdIkSiOE0uQSkgdH8PEqYkc2ldO0eFaRCI495K5+Idoee2lz7HbHYzPTOLWOy5GJIJ33lrBnj3Hog/JyXFIJGJqaxsxm4+p/yiVcmbOmsIpS2az8stNFOZXopbq6G+xIvSLUdll6OTurJCXl4z0xUmMmxePb4wfGt+T61vpqe1m/7/3Yhk0I1PLSTltHOUbSrENW9EG6Zh1+1wqdtZw6NsCetuM2KVSFH4aZGo5QyMWDIFedPT14bC7uPaBJZSV1bNpbQ4KhYz7n7qUkDA/aqqbePLRdwB4/F+3EBDozZ23P8ng4BDnnX8a551/GgBffvkdK1f+iFwu47nnniA6erQ/1tG/x8XF8MILT/Leu1+wbes+fH0NvPDSw3h5aaiqaODl5919WCEh/tzz4FX4+Op57h9f0FjXgZdMQ8v+XjQWAV+lmACdDINKjEElwqCX4eUlQSFyoE0IJe6B80atX3A4afpgI6b6DuS+OqJuPd3jdm43mhjYX46xqB6ZtxdeKZFoUyJ+s3+o4as99ObWoUsKoa99GEvvMNGnj0cbG8j+l7cAkH3XArxj/BnuGmLPO7vd/RXHQWVQk7QkhejsGCo2llG2vhQEAW2gjqnXTcMQ5n2iVWMZsrLq8Q2YB80kz09geMROxZ56guL88Ivz4+DaMvzCDMy/chIfP/4zIpGIs26bzmevb0EsFhGR4UdJYS2hkT5U19cQGhpAbFIQmzbuxmDQ8cJLD9PU1ExFRRXBwYEEBwcREhKESqWioryGfzz6Mm3N/URGRGAbkCB1KvDWGJC7pBi0XohdoJRLCU3w5+JHFyISiTD2jpC/uZrCHTVYhq1IJGImLEoge9k4ZHIpeWtLqdxbj1KrwBCoRR+oxRCkQx/oxe7P8+iq78MnVEdIpDf1OY1IFVKCkwNpLmhFIpMw5bIsdq7Iw2a24xdhYKBjCIfNTRCkcgkJUyNR61UUbq7EfkQWODQpgKnnZWAasPDzW3sQXAJJM6JxDltoK2lHIpMw+cJM4mfGntDrqK+hl6LvC+iu7gLcmdyUU1OJnhE7qjzt12AbsdJb2Ul3WRvdpe1Yh8ZmrMUyCZNvmePxSDk+AHIUal8NIemhhKSHUrGpnK7yDhRaJfPuWziKmJwsGWrNqafk61ycNsdvmjYX/1RIxc9liKViZt8xb1T/hN3qoLuuh86qbjqru+mu7RlVoqgyqJh4Vjox2dGIxSLMQ1b2ryzwZFimnJPOxNNT+PmDgxTvdJOgU2/MJnV6tPvYWezs/qmY/evLcNiciEQiAsINBEZ4ExBuOPLjjd5PM3pCLQiYjFaMfSMM9rqJUWttDxWHmrEcrxZ2lBRlRxGe4JZ5bqvpYdXruxnqMyGVSVhwRRbpc2Ip3lrN7s9zEQSInRTOguunUry+nMK1JYilYs555gxa85oo/C4fjb8XCx8+hd1PrcPyC+XIEyF0SjTpl05xZ9yPJ0OxAaTd8MfI0K9BEAQa1hfRtMld+haYFUPChVMYanL3KLnsDoKmxpFwoXvS2PTtfnoOurOEUi8lURdNR590rDWh42At1d8ewnUk626IDyJ0ViK+40JHGV0LgkDD9koqfipAcAlo/L2YcO1MdKGGUct0lLZT/GOhRw5b7ashbVk64VmRYzN4Q1b2fnqQ5iOm6EFJgUw6J+NYZkkk4uhXRCIRiMBmslOf00jt/gYsx92DfjG+xM+IITorErnqzx/nP4K/OxHaP/sGvKS/3x4w7LCSvfM9mpubR+2HQqH4XWubkyFCW7ZsYdGiRQwMDKDT6di2bRvz58+nr68Pb+9j78+MjAzOPPNMnnjiCT766CPuuusuBgYGRo1lMBh45ZVXuOqqq353v34PRqPxT523P0SE5s6dy2uvvUZ6errndzabjSuuuIJvv/0Wh+OPRQP+N+OvvrFcDifln+ymp7gFVbA3fR3Dbm+O5CAmXjcTyZGJQsEn+2jLbSQgNRjvYB0NB+qxDtvIuHwqoZOjPeM5rA4KVh6mfq9bNS5mdjzFW6vpqO1BGaDDO1QPajnttX10tvSjDfLC5nJglzpoa+5B7iNGpXO/9NVqBTK5lMGB0VFomUzKVbechljq4vWXP8fhcJI5KfkICRLx8kvvcyinCJFIxOTJGSw9cxHx8VGA+4Hd2tpB/uFStmzeTXt7t2fM66+/lA/f+xFjnwWN2ICpzYnSIkMrlaFTSPGSiYhMC+GiV89GqT32cLBbHXTX9tBR2UVHZSd9LYOkn5pC+qkpnmXMA2b2/3sPvXXHmtC9I32Yedsc5Bo53z+4lqaiNhwSMYNGG9pQPVaXEwHwj9FTX9dJVEIg5988myfu+Qiny8X1dywjc3ICgiDw5D/eoba6mZmzM7nupvP493tfsmXzHkJDA3n+xYeQyWTk5Bzm6adfAuCOO25izpwZY66HwUEj1123HJvNzhNPPEhiYjz33fM0HR3dTJ8+kdvvvAaAluZOXnz2Y/p6B/H21vHQY9djtzp55uEV9HeO4GwRUA6Dv0KCn06Kn1aGQSNGp5OhUYDCakIT6UfE9UtQRwYgOhKh7PhxH/0HKhArZETdcjqKwBOTjJOBpcdI2XOrEFwCfvPSaNhUisxLSdbDSznw6haG2gYJy44h/ZIpdJS2c+DDfdhNNpR6FVOvncZg2yAVG8o8kU2JTOKZJEZmR5N54aQxJSFHIQgC29/eQ1N+C/pgPdlXTOaHf21GEGDJ7TP56Y29OGwOzrpjFnvWldJY3smEuXH0Dw9TcqiB9KkxHMovwW534B+morGplQmTEtl/wC2T+/Ajt+If4MMddzyAzTY6c6vX6wkNDaKqohXjgBm1QocMDQatD9ZuFz4GPQqHFLlcilIsQaaQMufCCbTX9VKV2+zJ1Ci9FFiOCJ8oNXKmnZnG+AXxSH/F6HS4z8TKx37GPGQlNisMzHY6Ko54k4hETL9yMgd+LMHYM0JIgj9L752Lw+6kcl8DJduq6W8bXRbkF2Eg+/zxhKcG0ddupKO+D6kItr7vVicbNzcO26CJ1iJ32Z53mIHJF2QSnBz4y01DEATai9so+r6AoU73euReCuLnJRI3Ox7570h4Hz+OsaWf7tI2ukrbGGjoRSwWM/H6mR4S0l7Sxt53diM4XRgifAibEEZIeii6kGMCDHaznR0vb2WguR+vAC3z7ls4qqzyt8iQw+qg9JtDtOY0AOCbEEDGFdNO2MdUs7Oa/K/cAaHJV04lcmq0Zz/yvi+kdHPlmCyrQqMgIN6P4ORAEmbEIlW4DaBLtlWT82MxVpP7ept+0QTSFybw8/sHKdldh0gk4vSbp5GcHYXL5aJgZy3bvslneMBNJCKTAznl8iyCo8cqQZ4snA4ntUVtlB1sHEOKIpMCOWf5THQ+GszDVta+vY/6I9fGuJkxLLoqi4aCNja/tx+X00VocgCnLp/F1td30lndTeqiJCYsS2Pdw6uxDVuZfOVUZFIxxV/mIJaIkKpkSJUyZCo5UqUMqVqGWCqh9UA9giAQszCZpGXjATA29lD89jYcFhvqAB3aKD/UATpUATrU/jqU/l5IfsPk+5dwOZxUfX2AzkPuDFbk4jQil6Qf8/cpbqbsw10IgkDEwnFEnz4eweWi7ecC7EYzoadmIjtieOpyOKn9MY+2Pe4SVp+UUGKWTkATbBizXsuAidKVuXQWuQlLcGYE4y6ajOw4wjHQ0k/h9wV0lbsFGGRqOclLUombHX/C0uLWknb2fHwQ86AZsVRM5lkZxEyNpL/dnbU6+vw5OsUUXIJbKVQsIjDGF6lcSmtJG9V76mgpavMsL5FJiJwYTsr8BI/E/v80/u5EaN+sG0+aCE3b9e6Y3z/22GM8/vjjv/nd3yNCFouFGTNmkJSUxOeffw7Al19+yVVXXYXVOlqSftGiRURHR/Pee+/x9NNP88knn1BVVTVqmYSEBK666ioefPDB39yuF1988TfF1oxGI4sWLeLAgQO/Oc5v4eTvYGDFihVIpaO/IpfL+eqrr7j11lv/4434P/x5WPpG6C1pxWl10F3fh0gmJTA9lPFXTR/l7B49P4m23Ea6yzoISAvDOmxDppaj8NPSdsQ7wGlzUrqm2K2eJRKRvCSF7jYjw30mXIhRG9Tow72pymuht8OI2k+N2WzDoXDS0zGIRCvQ3ddDqMFAUlok4RFBhIQGEBjgT2lBPXkHK7FZ7Vx/+zI6u7v44PXvcTicZE0ex03LL0AikfDWG59yKKcImUzK/Q/cRHpG8qj9FYlEhIUFExYWzOlnzKe0pIqvvlxFdXUDBQXFJCVHU5BXxWCvEZdDhkIkxeEScAqAWIx5yEJLUStx0921pA25Tez+6OCY1P7hHwpRahUkzHQ3tqoMKubcNZ/8lXnU7aohIDmIaTfMQKaU0VnTzVD3sNszw0uJQqdEIhUj2Jxo9EoaG92R7FMumMzPqw7idLlISY8ic7K7EXbv7nxqq5tRKuWce+Fiqqvq2bJ5DwDXXX8xMpmM9vYOXnvNnTFasmThCUkQgF6vY+HCeaxbt5EffljDE088yG3Lr+TRR15k7948MiemMXPWZMLCA3n0yRt58ZlPaG3p5KN//8ADj17LwtOz+PGL3QyahpEJSlyACxHIJLgkYlyAw+JAJgIEaPr3BkRSCcoQH6Q6DUMlDSASEXLhbBSB3rjsDkRSyX/U/NqxpRjBJaBLDqWryK24FDYnieb9tQy1DSJTy0lclkHlpnKKfiwEQcAn2pdpN8xEZVDhHx9AdHYMdXtrqfi5DMugGalCSuZFkzwTyl9D9Z46mvJbEEvFzLxmKjtWuCPR8VMiqC1sx2FzEBLnB1IRjeWdSGUSUrIjee/pdYhE4BuqwZ7jwNdPR3NLIw67g7y8AgBOP2M+6RnJPPLIP7HZ7ISGhqDVetHW1oHRaGRwcJDBwUFGhu1odd70dw3hrZdjt9sRpCKkCjFiqRiJROIOdLhgx9fHypPDkwOZdEoSsRNCaCzpYPuX+fS0DLDtizwOb65k+tnpyFUyjD0jGHtH3J89IzidLrLOTmfP57nUHmphyllpWIet9LcMMOXCTIq212LsGUHnr2HaRROoK2wjOj2Y9AUJpM2Pp72qm5LtNZgGzYybG09sVjhWk53tXxwmb1OlOxM0JZI5V01m+0c5lGyvYcKSJLIuDKJwdQn9LQNsfGkb4eNDyTpvArrj1MlEIhEh6aEEpQRTv7+Oyo3ljPQMU7q6iMqNZcTMiiNhfhIqw2+LIohEIvThPujDfYg7ZRy2EatHKAOOZH/f24PgdBGeFcmUq7NPeO3KVDJm3DKbbS9sdmci39rJ7DvneTJU2hADU5bP4+Br2xhs7ifnjW1Mvm0e5t4R8j/ey0jXEIgg/tQ04hanjhGscTlcFHx3mNod7n6dpCWpo67Zql21lPxcDoDaW01ggj+B8f4EJQSgD9aN2uaWsk52f5FHX6s7U+oXbmDGJRMJTvBjw3sHKN1bj0gs4vSbp5M8NZL6knY2rjhER6NbOtonSMvCiyeSlBXxp5vYJVIJCZnhJGSG47jWSV1xGyX7G6g41ERjRSfv3r+Gc26bRWx6COfeO4eDa8rY/W0hJbvrMPaOcPZdszn9rtlseH03reVd/PTsVjIXJ9JZ3U3lzhrST0slcUESxT8VUr6hjMWPnUrIpMjf3CbvaD+Kv8yhbnM5Cq2S6HlJ6CL9SLtpHsXvbMPUZcT0i94fkUiEwluDOkCHOtiANsIHbbgvSj+vMcfIYbZR+uEuBqrdiprx508mONvde+e0OUAkwi8tnPgLplD19QGaNpcg16kInZVI6KmZo8ayDpgo+3gXxoYeRCIRkUvSiVg0bsw6Tb0j1G0uo2V/HS6nC7FETPI5mUTMHC2uUL+3lsNf5eJyuBBLxcTNSSDplJQT9ko6bE7yvi+gfKt7YqsP1jPrumy6m/pZcc8aHL8oHT0RpHIJ8VMiSZ0bx7xbZmI2Wqg70Ej1njoG2wepO9CAf7TvX0aE/u5wCaN8eX9zOeCEGaE/A7vdzoUXXojL5eLtt9/+3eV/aY1xoufFydpnPProo/j6+p4wczQ8PMzixYsxGk/ck3ey+ENE6HgViF9i+vTpf2pD/g9/Ds1bSt1GlBIJIpEU34RAJlw9w9MMbOoboW5vHbZhK94xfvTX9VC60h1h1Ef5sv35zWPGVOpVTLk6G6lGwaFVGxjoHEIb7kNAvB9VeS0M9owgVklwulzYJA6GBkdwSm0MDA8i0whYHRYK8yspzHc3d/r46Ljy2rM488JZuFwuVv+4nR++dZc1TclO54ZbzkMikfDB+1+za1cOEomYO+++dgwJ+iVEIhHj0hK59vqLuP/eZ9i37zA333QF5WV1tA324qPyR+KUIHKIQeL+MRutNBW4iVBzYSs739+P4HSh9lYTlBhAUGIAA21GyjZXsH/FIdR6FWHp7kixWCpm4sVZpJw6DqVe6bmZ6w40YhmxIlHKGB6xoQ1VMzRsQSyXoPKW4xp0q+Tp/dQc2OWucz/9HPd9097WzWcfrXL/btkcvL11vPHaRwDMnjOVlNR4nE4nzz33GiMjJpKSErjmmkt/87gsW7aEDRs2U1hYQnV1LfEJsZx73mms/GYtH37wNUlJsfgH+OLra+Cu+y7nwXtepbysjt078zj17Gls+OEADpw4cOESxDgcAojFuBDhsAs4nHZEwQbkQd44BkdwWWxu0QPc2Tn/RZloUyIwtfZR9e4mxDIpgbOS8Zsa7/HNOAqH2UZ/QQPG8haQiJEo5UhVcsRyKX157vIdZYQ/puIipCo53qlh7HvRfc0mnTmehv0NFP1QALg9LTIvmjQqACCRS4ifm0DM9FjailvxifL93ZJIY9cQOV+7y30nLEunubyTztpe5CoZqfPi+frprQDMPC+Dnz8/BEDWokRyd7snCOOyoqkobwTAJ1BDc5eAS3DisNuIjArloouXsmnTNkpLK1Ao5PzjH/cSGOguyRoZGaG5uY2HH34Kl2DC4bDjdNlRa2WYBs1otV4MD5lRyuWInSKcDhcGrQoESJ4WyaTFSQREHsvCRaeHEDkuiJJd9ez+tpCBrmHWvbvvV/f94LpyJp+VzoHvCsn5qZjT75qNd5COgz8W01HTg0ItY85Vk/n2hR2YjBaUXgrSZsUwfn48IYkBhCS698PlclG0o5bdKwsxHSmDEYlEVBxsRKVVMOvyiez6LI/8DRVMPiuNc54+nYI1JVRsr6a5oJXWknaS5yWQcXrqqH4gsVRM7Mw4oqfF0JLXRMXGcgZbB6jaXEHN9ioip0QTPikCvzj/UdfBr0F+XA/FQMsAe97ahdPuJCg1mMlXTP3NF7bKoGLmrbPZ9sIW+up7OfjhPrKvn+Hp79GGGJhy+zEytO+FjZj7TLicLpQGNeOvzPaU4h0P86CZA+/vpafGfT+lnJ5GymnHlFm763o4+JXbcybz7IxRmevjYewZYd/Xh6nNdQcRlBo5U85JJ2VOHAgC697ZT/n+BkRiEWfcPJ3EKRFs+jyXfWvdZfBKtZxZ56QzZXHSb6q/2W0OutsH6GodQBBA5+0WR9D7aJD9RumiVHaMFPV1GFn56k46Gvr4/JktzDwzjTnnZTB1aSpB0T78+Ooumso6+eaZbZx77xyW3T+Pda/spLuxn/0/FqPx1TDSO0LF9mpSFiRQubmCoU4jzXlNRGT9NhEKnxaL1Wiham0R5T/ko9CpCJkUiS7Kj6yHz2CwtgtTlxFztxFTpxFz1xAOiw1L3zCWvmH6KtqO7ZNagTbcB22EL9oIXxTeGiq/2MdI+wAShYyUK2fik+J+n/TVdJH3791IVTKy71pIcHYcNqOZhvWF1P6Qi1ynxH/8sW0fqOmk7OPd2IctSFVyki6fjm/KaBXf4U4jtZvKaMtp8GRkfOL8ST4709P7Bm4lw6LvC6je5n5Hh2SEMv68TDS/opTY1zLArvf3M3CkbC5pXgLjl6ax75t8T5mll48amUJyXFmc+/NoaZx1xMZQr4ny3XWU767DL8JA6pw4EmbFkrookZ76Xqr31BMz5bfP1/+XIAgiBOFkfKrcy+h0uv9aZstut3P++edTX1/Ptm3bRo0bFBSEzWajv79/VGlcV1cX06ZN8yzT2dk5Ztzu7m4CA8dm/X+JFStWcNlll+Ht7T0qWzU8PMyiRYvo6+tj165df2IP/yAR+jVYLBaKioro6urC5Rqdnl+6dOl/YxX/h9+ApW+Yztx6nHYnFhuIlZBwhtv/pLu6i8rNFbQXt3lUuaKnRkFdjycVbXO4P1XealR6lduZOljvMUjb9tZuTINmJEoZEoWU6sJ2LCYbVqcTtVbJiN2C1W5jYMiIS2UDqR2Dn5ZLrzgdgLbWbooKK+nu6ufl5z9l+swJgDsDAnDa0lmcf9EpAHz+2Q9s3rQbkUjErbddyaRJ6ZwsoqPDSc9IpqiwnJq6OvR6L+TKAcRSEEQCToeA3eXC7nRnhFpL2mkqaGHHu3sRnC6iJ0cy89psxGKRO/slEhE5MZzGvGa2v7uXJffOGxWhOj7q7HS4aDjUxEi/GalaDg4rw4MWRBopCrWclja3Qt28ZePZ8NN+XIJAakY0MfEhWCxWXn/pcywWG0nJ0Zy2dBYlxZWUlFQhlUo4/0hf0MaNW2loaEKr9eK++5aPyc7+EgEB/sycOY2dO/fw449rue++2znr7MUU5JdSVVXPyy99wMOP3oqXlwb/AB/OOX8hX32+nq9WrGf8hCQCw7xpzOvB6QSnCxwuFw4XOF0i7FY7SrkEFDJ02an4TY3H3juEuaUHS0sPUq0Kn1njcJht1H26A6fJhhMbLWvyaN9chF92AgEzkjB3DNCXW8tAcdOoPoxfQpccQleheyIXMjORqnXFHr8Wv3Eh/PwPtwdT2pkZJC4+sd8UuAlR+MSI372Wuut62fPRARxWB4EJAYSmh/Dt4xsBdylRwbYanE4XUeOCGR6y0F7f5yZI06J4/dEfAZgwM5a3XylEJIKhEXfEShC5M46nnTaPwUEjn3zyJQCXXnqBhwQBaDQakpLiGTcumfz8YiwmC0q1FNOIGZlUg0wpRYIIqVyKTCYFO4SkBnD2zTM8je2WERuFu2qpyGsmIMxA9mkppM+JJWlqBDnryinf14BCLUfnp0bnq0Hnp0HrrWbbF3n0dRhpa+wjcVoUlfsa2PzufhKy3f8WiUUsuCGbHd8UYjJaEIlFWIatHFpfTu6GCqLSgslcmIBCLWPr53kejxifYB3zL5+EZcTG2rf2kr+lihnnpDP9ogns/SqfnB+LGewaIvvc8STOjuPQynxaS9op3VRB/aEmpl2W5QlGHIVYIiZichThWZG0l7RR8XMZvbU91O+tpX5vLVKFlMCUYELSQwgaF4JS++umqAAjPcPsfmMHdpMN31g/sm+Y8buSy+B+Xs64ZRY7X91OW2Er+d/kkXnRJM91qA0xMHn5PHJe38ZIt1uoJjAtlLRLp4wiYUfRW9fDvvf2uPsRVTImX51NSNqxya7ZaGH7O3txOVxEZoaTtuRYsMhpd9LTPEBnXS+dtT3U5bbgsDvdqp/z4ph8ZhpKLwUup4t17+yj/EAjYrGYM26dTkJWOJu/yPOQoKxFScw9NwP1cWaygiDQUtdNe1Mfna39dLX209Xm9o/7tUJ7tUaO7ggp8g3QkTQhgvhxoWMIkk+QjmueXMLGzw6Ru6WKXT8W0VzVxdm3zSIqLZgLHprPdy/soL22h6//tYXz7p/HWQ8tYM2L2zF2j2CRiZACZVsqSV2YSPz8REpXF1G+oZTwSb+fyYpdnIJtyELDziqKVhxA7qXALykIuU6F/4TRE3NBELAPWzB3DWHqMjLc0sdQU69H9KC/sp3+yvZR35HrVKTdMNej3na8Ea/dZOPwv3cx5Y4FRCwah81opm1PFRUr9iHTKNHHBdC6s4K6VfkILheaEG9Sr541yoPK2DpA7aZS2g83eYRB/JKCiF2cim/8aLJtG7Gx/4O9nlK41DPSSD419YTHyOlwUbqpgoLVxe7MqVbJjKumoA3Usuq5rR5p86wz05i09MRjHH/c2qt7KNtRQ01OEz1NA+z8LJd93xQQPzWSlNmxTLs86zfP0//X4ELkrsg4ieX+mzhKgqqrq9m+fTu+vqMzdBMnTkQmk7F582bOP/98ANrb2ykpKeH5558HIDs7m8HBQXJycpg8eTIABw8eZHBw0EOWfgvnnnsuAwMDXHzxxaxbt465c+cyPDzMKaecQk9PDzt37jwpQvVb+NOGqj///DOXX345PT09Y/4mEolwOn8/Tfq/EX9lzWnjxmIa1hficIIVCb4JAUxZPp/mvCYOfrjPQ3g0/l6MdA+jDzOgEJyMdA5hiPajo3EAp83BvPsW4hszWm2jp6GPNf/cSHtlN8ogHWa7C7FCRm/3EBp/NTanAzNWGus7kHq7GBzpJzBCx5XXnMn8RVM941itNn74dgs/r9vjiVCJxWKuuHopcxe4m0K//mo1P3z/MwA33HgJ8xccyzIKgkBeXiE7d+7Bx8ebadOmkJAQO+aBW1xUwVNPvo5cLiM1eRy7txQhdahQWNRIh6V4SWTo5GL0KinBcX7IjvSGREwIY9oVU2gsbKVyXz0tZZ0Igrt8RK9X0FHRiVKr5NQHF6ALGKtgUrS+jLzvC2it7EbQKEApxzhsQeOvwT/aQH19ByGRvlxyxzyeuOcjXILAfU9cQnRcMO+++Q379xZiMGh58tnb0Ou9eOShF6iubuCUJbO5+poLGB4e5qab7mZoaJgbbriSJUvGCluc8NpobOb22x9AJBLx+uvPER4eSkd7Nw8+8BwjIyYio0J55NHl6PVanE4njz/8No0NbWRPz0Cwyln/ySEU/TICpFJ8NFIC/VXo5CJ0cgG9Xo5aKyN4TgpRF40t0RMEgdqPtzNY2oLcR0PQvDS6dpdj6Rw8wZaCKtiAT2Y0YoUMp9mG02LHabEhOAWUEf5Ufn0QiVxKzAVTKfhkPyKRiOkPnELt7lpqdlRhiPBhwYOL/lTZjt3qIP+nIsq2VIEgoNQpOfXBhWx+dx8dtb1EjAtiyvnj+fThDQiCwCWPL+Knd/bS3TrI7LPT6R0c4sDWchIzwghOMLD2+31ExwVTVl2G1WLF4hhCoZTx/ofP8frr75Kbm09CQhzPPvvYCT0T1q3bxPvvf4rVIqBSahnsseLr44dGakDkEKOTG5CKJagFORKphFteWobd5iR3UwVFe+uxHyepLRaLGDctmulLxxEY8es9W61V3Xz5z80ILoGFV2ZRvaeO7oZ+z99nXT6Rlto+infWotTIufSJxfS3D3F4c5Wnl+N4yFUyZpydzoSF8fR3DtNS043D7GDb5+5sxsIrs3BZHBz4zq0gKVNIyVo2jvRFibSXd3Dw68MMdbr7DmKnRTP5gkwUv9EL1F3dReOBBtpL2kY3yItE+ET5EJQSTEBiID7RvqOyRRajhe0vbGG4ewhdiIG5d88/6Z6jo2g53Mz+9/eCIDBuWTrJS0Z76w21DVC5qhD/1JAx5UlHUbe7hvxv8nA53D5n02+cgTbw2DvE5XSx6eXtdFR2oQ/SsejuubSUddJZ30tXXS/djQMehb6jCE0OYMbFE/ELNwDQ125k08c5NJV1IhaLWbZ8BnETw9j69WH2HFFlO+2aqWQtHK3o1FzXzdrP91NbNnqCfxRqjZyAUG/EEjHG/hEG+0aw2048B1CoZCRPiCAtK5qkCREofiFRXby3njXv78NmcaDRKzn3tllEjwumu3mAb5/bxvCAGUOglgsemIdUKuGrh9dhGbGhkYsRCQJTLppIbHYU6x9Zg91kI/v6GYRlhp9wW46HIAgUfLSX9vxmJHIpU26fhyHy5Eu0XA4nI20DDDW7idFQUy+mjkG8wrxJuWomSh93tqX9cBOFn7r7nPwSAxls7sdushE8MYLxV04DQaDskz30FDYhVcrRxwfQW+wOBgVMiibhgime3t/hDiOVqws8PUAAAeNCiDtlHIaosds+1Glk79u7Geo0IpFLmXzl1F89Nl21Pez77JAnCxSWHsL0K6fQXt3N1g8OYjPbPWa3oUkBmIasnqCrIBzrEeLIv0ViEVofNSKRO4BSsbee0u01DHQMedbpH+lN1plpRE8I/eXm/I/g794jtG3aLSfdIzRv31snvR/Dw8PU1LiFOCZMmMDLL7/M3Llz8fHxISQkhHPOOYfDhw+zdu3aUWTDx8cHudz9bLzppptYu3Ytn3zyCT4+Ptxzzz309vaOkc9ua2vjvffeA9zy2ZGRkb8rn308nn/+ef71r3+xatUqHn30Udrb29m5c+cYT9P/BH+aCMXFxbF48WL+8Y9//GlW9r8Jf7WPUMeheg5/uBeRTMrkW+diMdk58MFeBJdA2MQIxi1NQyKXsu6hVSBA5nkTaNlbg09qKBWbK/AK0HLKE6eNejG3l3ey8/199LUMMNhnBqUcNAr6uodQ+apAImLYYaa9rQenykZHTycRCd6cvmw2/cZe2lo7yZ6WyfQZWej1bvJQU93Eh+/9wODAEDfedgHpGe7+mO+/28A3X7tviquuPo8lp84F3KU1+/bl8N13q2hoGO3K7uPjTXb2ZE47bREhIUGeY/Hg/c9RV9dETFQM1WVtjPQ5kZk0aGwqvCRy9Go5BrUUvUGFzl9D6Lhg4mbHs/X9/Z4GYgCJVIzT4SJucjiW7mH6m/vdTua3z8b3uIlk1e5a9n2aw8igBbPFQf+ABYlBhdlqxztExwhWrBY7l9w2n6LiKvbvKiU1I5rb7j+XrZsO8OlHqxCLxTz46LUkJkeTl1fMc8+8g1wu4423nsTbW8+HH65gzZqfCQsL5bXXnhnjn/RbePrpl8jJOUxKSiL/+tejiEQiGhtb+eeTrzM4OERISACP/uN2fP28qatt4YlH3nZ7DGWks+PHEkStIvylcrxVEgIMcgwqCd4GGTqNFIXIgXdKGOMePGvMeju2FtO6Ph+xVEzibUtQh/kiCAKDZS107ihluK4LqUaO94RofCfFoQ7z+VUSU/DaJgbrugidk0RXTS8DjX1Ez08ibFocG59cj+B0MfvOeaOkXZsKWijdVEnCrFhip0b97nFqKW5j/+e5HqnhmKlRZJ0/gcp9Dez9Oh+ZUsrFT5/Gpk8OUXO4hYSscGInhfP9G7tQquVc9dQpvHz/dzgdLm78x+l8/O5aenuMpE2M5OChfMRiAZPNSGpqPPMWTOGll95CKpXyyitPEx5+4od5T08v1167nCGjCb3Wj74eMxHh4cgFLyRiGQqbCrlMjq9Wi3XIjspLgXn4WOOqf6iejFmx1Jd2UHscSYmfEMrUU1OQK6SMGC2MDFo8nwAatZwDq0uRyiScc9cstrx3AMuwlYxFiWgCvNj8ySFEIhHn3jeX6LRgz7j9nUMUbK2meGctlhEbabNjmXV+Biqtgn1ry9i+Mh+nw8W4adEEhejZ91MJIpGIM26ZjsFfw54v8uisc2eQDEFaZlyUSWhSAId/KqZsSyUIAiqDimmXZRGe8dsvQEEQ6G/so724jbbiNgaa+kb9XSKT4BvnT0BiIH4xfuSvPMxgSz8aPy/m3rPghH1Gdoud7vpeumt76artwWF1MPPqqXj5HSuzPOpjA5B5SRaxM+N+czuPwml3UrDyMHVHfIRCJ4STdcUUZL8gCIdW5lO6qQKpQsop985n8/v76WsdXSOv1MgJiPElKNaX4IQAQpPd8tQOm4MDq8s4uKYUp9OFVCbhjFtnEJcZyrZv8tn9k1vJ7NSrpjB5cZJnvIHeYTZ8nUPebnevklQmJjopmMBQbwJDDQSEehMY6o1Gpxx1DwuCgHnExmDfCMYBNzFqqeuh5FA9xn6TZzmZXEJCehhpk6NJmxzjIUU9rYOsfHUHXc0DiEQi5pw3nllnpTHQNczKZ7cx2D2Ml7ea8x+YR9XeevLXl6NSSRHbHWh8NZz9r9Mp31BK+boS9GHeLHx48UkFSpx2J7nv7KS3qhO5l4LsuxaiOUEA7GRxtD/nKJr31VL8VQ4IbuGCjMuz6a/tJuet7QgugYTT04k7JRWn3UHxu9sZrHGXFokkYmKXZRIyK9GzH93l7eR/sMdtJCyC4PHhxC5KRRd+4mDH8YIyah8N026aifcJlrWZbOR9X0jlrloQBBQaBVkXTCAqK4KD3xdR8HMFAEFxfiy+ZTrGPhOrX9+Dsfe3pfnBnR3OXJTIuBnRyFUyBEGgrbKb0u3V1Oa24HK6mH/dVJKm/3YP538Lf3citHXarWhOggiNOKzM3/fmSe/Hjh07mDt37pjfX3HFFTz++ONER5/4+G/fvp05c+YA7oqwe++9ly+//HKUoWp4+DFi3dfXN8ZQ9c033/xdQ9Vf4sEHH+T5558nKiqKnTt3/ma7zh/BnyZCOp2O/Px8YmNj/ysb9L8Ff/WNVbWmiJqNpejDvYlclMqBD/YhOF2EZYbjnxpCc2EbraXtGOt7MAR4kbQ4mcyLJrHz1W10VXQSmhVJ9LRYtAFeqA0qitaXUbC6BASBvo4hJFolg4NWhsx2RHIxSr2CEawMD5vpHx6gc7AT/3ANcxZMRKOTsmH9Ds+2SSRiJmSO48KLziAiItTtyO50ekq71qzeworPfgDgssvP5oylCwB3n8Rjjz1LTc2RHhGlgvnz52A0Gjl06DAWi3vCJxKJmDVrOuedt4ywsBD278vjlZc/xOkAuURDW80QBqkvOrxQOCTolHJ0aikqkYgJZ6SQsjiZda/swmF3ovPXkDQjhoTsKIZ6Rlj9ovvFNPnMcbQVtLidzJUy5t0yg5DkIOoPNbHz3/vcoS+5lP5eM709I1gcLtQBanzCdLS09+AXpOOKexfx1H0f4xIE7n/yUpyCnaef+DcOh5OLLjuVJafNRBAE7rv3aRobWlm2bCGXXHYWra3tLF9+P06nk8ceu58JE06+XBCgq6ub5cvvx2Kxcv31V3DqqYsAaG/v4qknXqOnpx8/P28efex2goMD+OKztWxcvxfBLsHSJcbW4MRXpMBXKcZPJyfAT4XOS4pGIUJhNaGL9CP9ifNHGUQaq9upfm8zCBB5fjZ+U8YaGdoGTUg1Co8Xxq9hoKaTwjc2I5ZKiL9sBoc/2INYKmbuU8vI+zKX1vxmgsaFMPNWtyGp0+4k97tjzbwAky/KJGX+if0KbCYbB77Mo+5AAwAaXw3TLssidFww/e1GVv7jZxx2J3OvmowhVM/nj29EJBJx5dNL+PrlHfS2G5l7/gSMphF2ri0iOjGIaacn894rq9B4KdEFSKmtaUIsdWCyjHDBhafz06rVDA0NcdFF53LBBWNJ5PG4666Hqampw2oWIzglaDXeeOt8kApqRHYJGokalUqB2CxChMjdED05gqyFiUQmB3omTO31vexZVULZwUZ+77GfPDkCkV2gobgd3xA9y5ZPZ6BtCIVWwTfPbMPlcjH7ggmYzFaqC1pJmxHDhNlxKI9kUBw2B06HC4VaTm+7kZ/e3kNzdfeodcxYNg6XxUnB1mokEjHn3DOHyHFBVO6tZ/9Kd9kdQFRGCDMumYjFaGbvxzkYj6jFxUyNYsqFmSds6D4RTP0mOkra6KrqoquyE6txrHy2Qqdk3j0L8Doy6RUEgca8ZtrLO+mq7aG/dXCM8a9vlA9L7luA9DhlreJVRVRsKAWRiClXZ/9uf4p12Mq+93bTU90NIhHjlqWTdIISz/pDTex8by8Ac26cTtWhZqoPNqHSKkjIjiIwxpeAGF90/pox360rbGPLZ7kMHMmuRaeHsOCKSXgHatm+Mp+dPxQBsOSKyUw5UmpnMdnYtjqf3euLPZmdiTPjOeWCLLz9/nNiIAgCTTVdFOfUU5xTR2/nsWyAb6CWS5cvIDzWXcpltzpY/8lB8re7CWLmvHhOv3YqIwMWVj67jd62QVReCk67MZuNb+zBaXeiUYgQCTDzmqmEpYWw/uHVOKwONP5ebkIiErkl2kUiRGJ3D4suSEfGeceuJ7vZzsHXtmBsGUDlq2HaXQtHmdv+p6jfVkH5D+6y8PBpsYy7MMsjktG0p4aSr939hpnXziBofDgOs43id7djM5pJunQa+thjJW5Ne2so/ToXQRDwifNn3IVZY7wAjz/m1duqKPwuHwQB31g/pt04c0y5qCAINOQ2k/P1YcxHMqpx02OYdO54HA4nm97eS3u1uwJo/ClJTD03g4qDjfz8/kGcDvc1crQfSHSkP+h4GW2XU/C0UMiVMsbNiiFzYQI+we55knnISuW+esbNjR91T/1P4u9OhLZknzwRWrD/5InQ/ws4++yzR/3/+vXrycjIGJMJ+uGHH/7jdfxpInT11Vczffp0rrnmmj8zzP86/JU3lt1sZ8c/VmE320k6O5P8HwpwOVz4xgfQ2TyI9bgIsX3YishixS/Gj/n3L+Tnx9djGbJgE0s8afajhocAAYkB1BV3MDJoxeR00dHUT0CCLy6FgMlmpa29G4t0GKvDTHxqMAuWZPHF5z8BcNrp86gor6W21t0wrtaoeOTR24iLiwLcD9yV36zl++82AHDhRWdw9jlLAHA4HDz11AsUFpag0ag5/fRTOP30xWi17rICm81GYWEpGzduJTfX/VIRiUTMnTuTG264knvu+hetrZ2YjQIjfRCoDUJhUyI1S9DK5XjJxEQkB3Lmg/NZ98pOrCY7kenBLFk+C4lUjMPmoLWqh97GXvZ/W4RILOLU5TOp2FxBR2UXYqmY5PmJlG+txOVwEZsdTen+Rrqa+hFrFPT0DKMP0WKTOrA7nZxz7UyqahrYv6uUceOjuenus3jonldpb+8ha/I4br3TLRl+lMSp1UreeOsptFoN//rXSxw6dJhJkybwyCO/LiP5W1i/fhP//venKJUKXn/9OQIC3B4kPd19PPXka7S3d6PXa3nqn/dg8Nbx4D2v0lzfxXC7gKRHga+gxF8hwd9bQWCgGi+lBJXIgcppxRDth0StQBcfjDYhGFWwN7UfbsMxYsV3chxRF/x+HfBvoeidrfRXtBM8LZ6hQSudRa2ET4slZGoM257fDCIRix45BX2oAWPnEDve20ffkeh/QJwfXTXul/aEs9JJPzVl1ASxo6qL3R8cYKRvBEQiUhYkMOHMdGQKKS6XwI9Pb6Gjpofw1EBOu2s2Xz6xmfa6XsbNiiE0OYAf39qDykvBdU+fxov3rcRmcXDtA0tYt2ov1ZUtzJyfzsbNO3A6nZhtg0hlEtIyYsjLyycyMpyXXvrn7/Z6ffPNj3z11XcITikSiRJjr42goAC8ZL64nKByqJHJZMyYl0pAiDdpM2LQ/oZqWm+7kX1rS6k41IRMIUWjV6LRKdHolSjVcg7+XIHL6eK0q6dyaHUpwwNmxs2KYea5GXz6yAZMRguJkyOIzgzlhzd3e8aVKaSkz4hh8uIkAiO8EQSBnJ8r2PxVHg6bE4VaxpIrJiMIsOpd92T+jOuyaSntpDLHvS2n3zyd+Ilh2Mx2Dq0qoWhzJS6ngEwpZe6Vk4nODCP/pyJKN1d6otQZS1NJnB3/hxzqBUFgqMNIZ0Un3ZWddFV1IZaImXX7HI+flCAIHPgij8ojim1HofHVEBDrh2+kD8Xry7COWEmYFTeqr0EQBPK/zqN2ZzUiiZhpN84Y1eNzPIY63Wpzw11DyFQyplwzjeBxY32E+lsHWff0JhxWB2lLUpAb1OxakYtILOLMB+YTkuB/gtFhqM/Ets/zqMxxZ9S9DCrmXTqRxCnunpkd3xWw40hZ4uLLs8g+NQWn08XBreVs+i6X4SOEMSY5mDMuneohKP8tCIJAe1MvxQfrObSzkoHeESRSMaddPIWZS9I89+vhbVWsef8AgiCQNj2aM2+egdVk47vnd9BR7xYxCY/2pqOqB61OjstkwxCiZ9kTSyhbW0LZupLf3ZaApEBm3jbHk72xGi3sf3kzpp5h5BoFXsE6lAb1kR8VSm8NSoMKlUGN/BfZsBPtZ82GEqrXu7cjen4SSWeOH/Odsm/zaNhZhUQuIfvOhejCvT2Bi+NNZytXF1K32a0YGJoVRdolU361n81pc3L461wa9rmDiicSlAEY6h7mwBe5tJa4Sx91gTqyL5tEcFIgjUVtbH3/AOYhK3KVjHnXTCE6M5TdK4s4eKSnLHZCKKffNM3Tp3giWE02SvbUk7+pir6OY5nMqHHBTFycSHRG8AnLhP8n8XcnQpum3nbSRGjRgTf+dvvxZ3CyPkMff/zxf7yOP02ETCYT5513Hv7+/qSlpSGTjU7jL1++/M8M//8s/sobq25LORU/FaAJ0OIVF0jdrhpUvhr6+y04bU68/L2InhRBT/MAVTurkVhs+Ed6Y4jwYaCpD4vZgVMmRaaW47A6EJwuJDIJSQsSKd1dz8iAicEBC93dI4iVEpT+SqzY6e7uR1DbaGxuITzem8WnTWPd+k24XAIXXbyUs852CyA0N7fx3jtfUFVVj1qt5MGHbiEmNoL33vmCXbvcfirHm4UKgsA773zEpk3bUCjkPPPMY8TERP3q/tfU1LFy5Y/k5LjLUW666WoQpHz4wTe01PeiVfqhlRmQmpWIjCK8xDL0KhkRSQHIFDJcThchCf6cfvccXE4X+Vuryd1QgcloITI1CG+D0hN1PefRheT/WETDoWNlelFZEaCQUbqjhvb6PkReCqwOJxKNFJvMgc6g5rpHTuWfD3ziyQa1tXfw3lsr8fJS8+Jr96DWqHA6ndx951O0tXV5jkdBQTGPP/4sEomE1157lrCwsROkk4EgCDz88FOUlVWSkTGOxx9/wPNSHRgw8s+nXqepsY1x4xJ49LHbKcyv5Pl/fUR9YR96iw9+aPBXiPHVKwgJ1aKSCqicdnQ+CnR+akQneIqoQ71JvG0JIqmEjv01SFVyfNPCfjUDZGzoobugEZfd6Y4g4pYPbt9fjUgsJvXGeRx8czsIMPPhUzn0eQ69tT1ETYsh6/Ip1B5oYP+KQzisDhQaBTOunkJYeggFq0soXOOefKQtSSHz7HQEl0DhmlIK1x0xVQ3QMuvaqfgf6ZETBIHD68o58F0hMqWUi/51KlW5LWxdkYtcJeOqZ07j039upK9jiAUXZTJitbD5h8OERvly1nXTefbRz5GIxcw9I53VP25DpZLRP9SDl1ZFX38HIpGI559/gvj438+k19c3cuedDzEybEGv86e/20RoaCjB/iEM9lrwknmBVUxcagg3PnqG53sul4uKgmZKcxvwCdAxaVYCep/fNw8+OjFWeSlYdv001rzh7uvT+qgZ6jPhH25gyQ3ZfPT4BmwWB8mTI+nrMNLZdKyPKPKIB1BjubukJyYtmGU3TEN/RI1q2zf57PqxCLFEzIX3zCF/YxWNJe6m7djxocy7bCLegVr6241s/+igJwKdPDOGmZdMZKBtgD0f5zB4xDTXy9+LiWelE/UfSjsLgoDgEkaVLxWtL+PwD4VuC4F58QQmBBAQ64f6OJLZWtrO5ld3giAw46opHjn+o2Me+uQAjQcbkMgkzLh19qjSTXD3M+17dw+2EStqXw0zbpmNPmRsRN9msrHmX5sY6hwiODmQ9KVp/PTsNlxOF9MuGM+E48QSHDYH3S2DdDX209nQR9neBmwWOyKRiImLE5l+dppnorrz+0K2f1sAwKJLJzHt9FQcDicrXt1Maa47gOUfrOf0S6aSMnGsoebxsNsd9PUY6etxT269fbR4+2pRKE++z8o8YmXlezspznH77CRnRnDhTXPRHMlalO5v4Ps3d+NyukieHMm5y2fisDn54eWdNFd0IZdLkDpdIAjuXiFg/m2zCEsPYaBlAKfV4fGzOXrOBZeA3Wwn7/McHFYH8fMTGX/eMbnqke4hDry6FevvGLJK5FJ0YQa0IQa0oQZ0oe5/S5Xu0q/yH/Jp2O5WZ0s4PZ3YxSknPJ4up4vcd3bSU9GB0qBm+r2LRmWinHYnRZ/tpz2/GYD4U8cRt2SsdDa4vXsaD9ZTsroYc78JRCIyzp1A/LyEMSWMZZsrOfxjEU67E7FUTPppqaSdkozT4WLvV4cp2+UmUX7hBk65dQZKrYK1b++j9oiZ6tQzUpl5fsZJ33+CINBY0kHepkrqCto8ZM8Q4MX4+Qmkz45BeZLZ3j+LvzsR2jh1+UkTocUHXv/b7cffHX+aCH3wwQfceOONqFQqfH19x2iH19XV/emN/H8Rf+WNZRkwUb+9ErWfF3kr8xnqHsYhlSJRytCH6NGFe1Nf0IZ50EJvZQcGbyWBYTqkcimCS8A4bMMpEjPnpunETo7EPGihtbKLnZ/m4rA7kSokdHWP0FzdjcJPhUgrwmyzYpdZaGlvxxCkIDUjmoamWsxmK7PnTOWmmy9FJBJ5rgez2cJzz7xNWVkNSqWc8PAQqqsbEItFXHf9xaOEEVav3sBHH32OSCTigQfuZMqUiSd1HH76aR2ffPIlwcFBvPLK01xz1b3U13Ti7eWH1KVGZFSitMjRyZR4eynw8ffCy1uFX4SB0++azeHN1RzeXIX1OHM/gBlnp9Oc30JP8wCGQC/OuHcuFVurKNtcSVh6CH6Jgez9Kh+rycawxcHggBlNoIYhsxmJl4RTL5pMZ28PO7cUkJIWxa33n8ODd79Ce3sP51+0mNOXzQFg29Z9vPvO53h5aXjz7SdRKOTceedDNDW1cNppi7nuusv/1HXS2trOnXc+iM1m59Zbr2PBgjmev3V19nDXnU9hs9m5+ZbLmD1nKjdc9TjF+1vwMnvj49IQqJDirZYSFqlH6XTgJQOfKB8SLpqCNsTAUFUbxoo2Rpp6kKgVJC1fgsJXS9PmEurXFgCgMKgJmZlIcHYcMo1buaq3uJmW7RUYG7pPvOFAYFY0Dpmcpt01BKSGEDQ1hv3v7UEik3DKk6fTXNTG/hXukpLAhABmXjuVxqJ2CjdVMmFJMk6Tjdxv3ZnD+Bkx9LcN0lPnVvKLnRbN1IsnenoxjD0j7F6RS0Ohu6dmzhVZhKcF8+H9a7FbHSy6ejJIRPz0zl7UWgU3Pn8GL9y9ErPJxmV3LOTw4XIO7ilj8vRkmtoaqattQaOT0dvXg96gorung4UL53LLLdee1HkTBIHrr7+Djo4unHYZTrsIncYbPz8/BLMcl0NAgxaJRMLdz5+LWqskZ1sFB7dXMNAz7BlHJILEjHCy5iSROjHyVw1VnQ4n7z+yno6GPhImhhEZ68/eH9y9I0qNnIseXcj3b+6mo6GPyKRALn90EWKx20fp0KZKynMacR3JKMsUUhZdMpFJR5ruD26roLq4hVMvnsKObwoo3luPQi3j8ocWUpPbSu6GcpxOFxKphCmnpzB1aQpiiZhDq0rIW1OKIIB3iI5FN03HJ0RH9Z46ClaXeEp4fKN8mHTO+BOasf4R1OytY8/HB4HfLqsEKFhTQsGqYiQyCac9tBCf4/otXA4X+9/fQ1thK1KFlNl3zsPnSPN6U04Dhz47iMvhwifKl+k3z0KpG6tqZx22su3tPXRWdaH2VrPo7rmsfmE7Q70mojNDmXX5JMr3NbqJT2M/vW2Dnoz+UQTH+rHoqiwCo9xqZXabg+0rCzzqcAsunsiMpeNwOJx8/toWSg41IJWJOf2SbKYuSEZ6XPDCZrVzOKeKzrY+ensG6e0epKdrcIxh9lGo1QoM3l54++kweHvhH2hg3PgYQiP8f9VfZP/mMlav2IfD7sLgq+GS2+YTneTuRavIbeLbV3fidLiInxDK+XfOAQHev3s1wwNm/HzVmActGLyV2Ics+Mf6ceoDC353gt6S38z+99y+bVlXTCEq+xipdVgdDLX2Y+43YRkwYTn6OWDG3G/CajR7VNrG7L+fBrlGwUCjO0udcm4mUXOOXU+CIFC/tw65Wu4RLLCbbOx7cRMjXUMYonyZcvt8JDIJ1iELef/exUB9L2KJmLRLJo8yQj8eneUdFH5fwGCLO0Ch9tEw8ZIsglKDRy3ncro48EUuVbvc5ulBSYFkXzoJfZCO5tIOtn14kOE+EyIRpC9MZOq5GQz1m/jhpZ30trmz3KdcN5Xk7EhK9jXQWtuDy+nC6XDhcrncpXDOY58AsRkhZMyM9YgVDXQOkb+1mqIdtZ7379Sl45h1fsZvnrP/Fv7uRGjDlJMnQksO/u8hQkVFRYwbN+6kM4SlpaUkJib+bpXFL/GniVBQUBDLly/ngQce+MvTmX9n/P/jxirfUMrhb/LoaRpAF+2HPkRPR8vQsbS6WER/bTcKuRgfvQKtnwarycaQ1UV30wA+CYHIFFJ8QvV0HZG8jUgLZthkp2BHDcMmK0NWC4ZIDQNmI2aXiRGrkZAIA37BXtTVNZKUFMu991/PM8+8QmdnN9dddwVTp04CwGKx8vxz71JS7I6KqVQK7rr7OjLGH/O/OHgwj2effQVBELj66ktZunTJSe+/xWLh2muXMzw8wn333c66NTs4sK8YuViNc0SByqFFL9KiQopWrcBLJiF+cgRn3j+PjR+7G+ABfEP0TD49BYfNweZPDrlVlW6bzv6v8xnqNeHlo2bZffOQK6V0N/Wz7pWdCAJ4BXrR3jxIR0s/Mq0ci9iO3l/N8qfP5qn7P8Zud3Dnw+fTPzDA2298jZeXmpfeuA+VSkFHezf33/c0ZrPV0ye1fftuXnvtXby8NLzzzsuessA/g6NkUaNR88Ybz+Pjc2zStuqnTXzx+U94eWl45bV/8PJzn7J9XRHyQS0GhxdBMil6jYyQYA1qwYm3txytj4rweSkkXDDFM47TYgcRSBQyBmo6KXpzC4IgIFXJcZjdLzmJXIpfRjiDtd1Y+tyTdbFUgv+ECI+qkiC4/yORS/GdEMHup3/GZXeSdfMc8r45zFCnkaQlqSSfksJ3D67BYrSQuiiJ1FOS2fnpIery3OdTJBax7N65DHcNsf/zXE+Ph0wtZ9qlk4ie7O7fcDpcFG6s4NCqEhw2J2KJmImnpzBpWSo/vryL2oJWQhP8ueDBebx59yoGuoZZdOkkLE4b67/KwT9Yz3WPnMqjt7+P0+Xi+jvO4NWXP0UQBGyuYQTBCWIrNpv1D/d6ffDBCtau/RkRSiRiOSajEz8/X0L9wunvG8FX7Y112InBV4Ox3+QhImovBeOzY+lo7qOuosMz3tHfK1QyrBYHNqsdm8WOzepAJpcyfUEqXz2/DafDxbIbplGf30ZjaQdn3jGLspxGDm2udJPA585A94ssk7FvhLwtVfR3DzPnnAx8gnSYR6x89/4uCg+4A2PBET7c+OgZrHxpB40Vnej9NFz71KnYLQ42f3rIkx3S+WlYcPkk4jLDaCnrZMu/9zMyYEYqkzD9ogmkzo3DYXNSuqmCkp/L3Q3jQEhqMAmzYglLC/nDfQYtxW1sfXM3gtNF6uJkss4bj93qoL/NSF/rAL0tg/S1un9cThen3zWbgp+KaS1uQxug5YxHFo3yPHLanOx5ayddlZ3INQrm3D2f1vxmSte4yWXohHAmXzXVY8J6PAY7jGx5fRdDXUNIlTIW3zWHnFWlNBS2ofPXcMa9c/n6X1vHNKgrvRQERnkTEOFNaLw/8ZPCPESgqaKTVe/to7fdnbmZf2EmM89Mw+l08cXrWyg6WI9EKubqe08hMeNYw7PD4WTv9iLW/7j/V0mPXC7F18/9vhvoH8Zstp1wOQA/fz0Zk+IYnxVPbELomPlDa0MPn7+2he72QcRiEYvOm8S8ZeMRi8XUFrXx1YvbcNicRKcGcdG988jbWMnubwvx0ikRTFZEiFBJRYgQOOW++QQl/H5JX+maYsrWlSCWiplz1/wxKqq/BpfDhalnCGPLAMbWfoZaBxhqHfiFaiGkXzKFsKmjs4YF3xymZoe7n3H6zbMISXeXUA53Gtn/4ibsZjuhWVHELk4l990dmHpGkKlkZF43E9+EsYR/oGWAoh8K6Dyi7CdTy0k+JYW4OQlIfnEvOKwOdr6/j+aCVnc/24WZJM2Lx2F1svebfEqP9GXp/DXMu2YqoUkBNJZ2sOqNPViGrXgZVJx112wCo7zZ8HEOhzZXntTxAlBp5ExckEDWoiT0Rzzd7FYHZXsbyN9axdl3zUb3O15v/y383YnQusm3nzQROi3ntb/dfvynkEgkdHR04O9/4rLfX0Kn01FQUEBMTMzvL3wc/jQR8vHx4dChQ/8nlvAL/NU3ltPuZP0ja2gtaUOkUuAdF0BP1zCCC8JTg5hwajIOq4PvHtuAtXeEkAgdWoMKu0ugvWkQhyDCK3h0SUbGokTip0fzyUPrqS9pR1CJEXmJMIssiLVOWlrbCYrSMmlyMnv3H0Qmk/LSK4+wYsXX7N170DPO7NkzuO66y/Dy8sJms/Hu25/T0tLBLbddQWTksbr5kpJynnzyOWw2O4sXz+fGG6/6w2Uun3++ku++W0V8fCyJCSn89MNmRowOBJMKf3UAapcamU2CRi4nNjWIG14/ix1f5VO4vQapTMKSG7JJOlI7LwgCq9/YQ2VOEzo/DefePYeNb+1hoGMItU7JzEsnsv3jHGxmO4nTo6gsaKOjoR+by4nFYUfqIyV7QQpyvYj1Px0gMiaI+5+8hIfvfY3W1i7OOX8hy86eh91u59GHX6KuromkpFgee+IOxGIxt912Py0trVx22QWcc85/x4/L5XJx//2PU11dS0bGOB5++G6PDKbD4eCB+5+lqbGNWbMmI0bJj1/uQuhRoDN74S+V4aOREuCjxFsrw+CtRCkV8E8LY+K9p45Zl23ITN4LG7ANmgjMiibhgql05TfSurOC4ZZjCl4yjYKQGQmEzEhArjtxb0vNz6VUrS1CF2YgaFoc+V/motAqWfLk6VTtqePQN4fR+GqYcmkW2z46yEi/GbFEjH+kgc66PpReCs57fDE9dT3s/fggftG+o9S+2qu72fHJIfpa3aVWIYn+zL4iC58QPRUHGln95h4kEjFXPH0qTZWdrP73fjR6JTe/sJQX7lnJsNHChTfPobW9iw2rDhCXEEri+BC+/Xoj3j5a2rtaUKnkDBi7UCoVrFjxnue4nwyKikr5xz+exmJxotV4Yxp24OvtR1REOF0tI8glcmQ2hed+iU4MInthCmmToz1eLd3tg+TurOTQzspRil0nQnJmBPHxbiUxpVrOjc+fgUarpCq/hW9f3QnAJQ8soKdnkMqCZibMiGdcVhQSydhgWGtDDyte3UxPhxGJRIRCKcM0YiNjagxnXz2Tjx7bQG+7kZAYX654dDFypZSqnGa2fZHHUJ97O5OnRrL42ik4bU62vH+ApmL3JC96QigzL52I1leD2WihcE0JlbtqEY5EnqUKKRETwojOiiAkJeh3zVV76nv5+cVtOKwOYqZGkbwoiW0fHKC3eeBXPXIMQVqW3TuXDc9vZaR3hLCMUObfOnPUs8tusbPr1e30NfQilknc5Z9AwsIk0s8e2ycC0FbewfZ39mI32dD4aliwfBZ1+W0c+K4QiVTM2Y8sZM8PxdQcbkHnqyF9TiyBUT74Rxg88sTHw2q2s/XrwxzaVIkgCHgZVJx29RSSJ0fidLr46q1tFOyrRSIVc+Xdi0me4Pbbcrlc5OwtZ933e+nuct8fPr5a0ibE4uuvx9dfd+RTj5dWNWq9ZpOVgf5h+nuN9PcOMdA/TFNdB2XFjdjtx6TdtVoV6RPjGD8pnuT0KE8Gymqx88OHuz1qdfFpoVx2+0LUXgoayjr48vmt2CwOwhMCOPuWGXz8wDrsNgd+PmosQ1Z8/NRYB0yEpoWw8PbZv3nuwU1M9r23h7aCFpR6FQseXHxC9cCThW3EylDLAMOdRvSRPqNkuAVBoPDbfI+ZKbhJy8KHT/GYPfdUdHDorR0IgoBYKsblcKH20zDpxjl4BY2eV5j6TZSuKaZhfz0cWT52djzJS1JPKChiHbay9c1ddNX0IJZKmH3DNCInhNFa3snWDw4w1Ou+99Lmx5N93nhkSin5W6rY8lkugksgKNqXs++ahUIj5/vXd1OR24RIJCJzXjwanRKJVIxI7BZvkUjFiEUixBIRFpOdw9ur6T8ikCEWi0iZGsXUJcmExZ/chPe/jb87EVo7+Y6TJkKn57z6t9uP/xRisZjrr78etVp9Usu//fbblJWV/fVE6M4778Tf35+HHnrozwzzvw5/9Y3VcKCe/e/vpbOmB4mfFpdMhlQpI35KBAuuz0YkFtFY0s7KRzYw0jpAcKwvqbNiqMtvpam0A224D2c+tAB9gJau+l68fDX4hun57sUdVOU209czzIDJjEvrRKIT6OzrxjdUSUJSBN297fT09nP2Oacgljj58svvkEqlzJ49nW3bdiEIAt7eBh544E4SE08sJVtf38jDDz+FyWQmKyuTBx644w9JRB/FwMAg1113O3a7nTOXLWXtmh20NfajlnrjqwlAapYjMYlRuaTEpoeQNieW4p21iEQili2fSUJWOA67k72rS8jbVk32qSkUbq7C2DNC0pRIFlw+kTUv7qCnecCzzpAEf5LnxbP6zT10Ng+gCVDT0z+I2k/FVfcu5sN3V2MasXL9HcuwOy289dpXqNVKXn7jPtQaFZ98/C3r123Hy0vDCy8+hK+fNwcP5vHMMy+jVqt4//3X0Gj+e5GxpqYW7rnnEWw2O5mZGTzwwB2eSXl1dQOPPPQCgiAwPXsKG1fnYmkXozNp8ZXI8VNK8fdREBCoQaOWorBb0YV5M/3Z85AojvUHCi4Xxe9up7+yHXWQnsy7TvH8XRAEBmu76ClsRhOsJyArGons11PZTruTHY+txmq0kH75VErWlzHUYWT8+ZlEz4jj+wfXYBow4xvrR0t1D4LgnpwuunEa3iF6fvjXZrob+/GLMHD2wwsRi0WeCbHT4eLQT8UcXleGILhLv6ZfNIHE6dEen4sP7luLyWhh2llpZC9L5fU7fmSwZ4TFl2fhwMmqz/bhE6DljmfP4dE7/s3IsIXrb1/K99//THNTB34BXrS0tRIY5E1zSwNZWZk8/PDdf+icOZ1OrrjiJgYHjQgOJSBBrdCj03mhlftiGrGSPSUdXz89WbMTCQr3+Y2xXFQXt1BR0IxYLEKukCFXSpErZIhEsHrFfpwOF5cun8+h9ZW01vQQmx7CaVdP4b2H1mI12ZmxbByxE0J4+4nVHoJg8NUwbVEqU+Ylo9Eq3WIDW8pY9dmREic/Ly67fQEup4t3n1qD0ylw6kWTGT8llg8eXY9pyIreT8OSKyaTOCkcu9XB/p9KObS+HJfLhX+4gbPumIU+wIvCjZXs/7YQ1xEJ6IlLUxm/OAmpXIKxa4iqXbXUH2rySKGDe5IZOSGMkNQgAmL80PiOJgvGriHWP7MFy5CF4JQgsi/N4oentzAy4I7qK70U+Ibr8Q014BOqRx/oxZZ/H2BkwEzyzBgyFsSz7pktuBxOMs/OIP3UlFHH3TpsZcfL2zC2DSASi5hw4URiZ41VUwSo3FnDgS/cE86AOD/m3jyTvjYjq57fhuASmHNlFhark60rcpFIxFzy+GKCon/9nNcUtrLm/f0M9riPx4S5cSy6ZBIqLwUul4uv397O4T01SKRiLr9zIakTo9wT9dwaVq/cTVuru4xUp1Oz5KxsZsxLdxv5/oewWmyUFjVQmFtN8eFaTKZjgj4hYX5cu/wMQsKOZWMO7azkx4/2YLM6CI7w4fqHT0OrV9NS3c3nz2zBYrIREutLcKg35fsa8A30wtJrQioVIxdciESw9PEl+IQZfnfb7BY7257fjLFtEJ9oX+bcOX9MJuXPQhAEin4ooGqzW4I68+JJ1O+to7+xD59oX+bevcAjfNC4q5rSlbkAGKJ8mXjDLBS/UHqr2VFF0fcFOI8Q7LCJEaQtS/eoH/4SI30mNr2yg8H2QWRqOQtum4VPuDcHviukaIs7O6X1VTPvmqmEpQRiM9vZ8lkuJbvdGd3k7CiWXDcFm9XJVy9so7mqC6lMwlm3zCD1JKwKXC4XVYdbOLC+nIayY5nqsHh/pi5JJnlyBJLfURP9b+LvToRWZ508EVp66H8PEZozZ84fDoZ/+eWXBAcH//6Cx+FPE6Hly5fz2WefkZGRQXp6+hixhJdffvnPDP//LP5qH6HN/9pI46FGLHYBo8mOIcqPxGlRTDkvg9I99RTvqKW/c4i+ul6UTgc+QTpSFyZQva+ervp+AtNDuPbNczwTxOF+Eyuf205PywAdjX04pSL6R4boGRlA6utCqnHiH6xn6vQUNmzYjq+vgYsvXcpLL70BwM03X8OiRfOoqKjmtdfepb29A29vA6+//tyYEq/29g4eeOBJBgcHSUlJ5PHHH/hD0fJf4p13PmLjxq3Ex8fTWN9FU103foZgVCItwqAU6YgUH7WG4CgfFCr39brwyiwmLEigoayDtR8coKfNHfmUyiScdeN01r+3H8ElMP/ySaROj2btyzvorO1F56dh0S0z+Oa5bYwMmunpMCJSSrDJHPiG6Mg+LYkfvtpJYLA3/3j+Kh594A1amjs569wFnHXufHJyCnnxebfJ2P0P3sTEiWkIgsD99z9OVVUNZ599BpdffuF/fCx+DUVFpfzrXy9itdrGZIY++vAbft6wE4lIhqkfBpqc6M16fEQK/BRiAvxUBIVqUYpB6bShDzMQnB1H8PQEvMK83T5FR0x+JXIpE+5eguZXJF1PBi376yj64iBKg5qUiyez+/UdSBVSTn/2TE82yDRkxaWUIxKJSJkVw4yLJyJTSnHYHJiHrHz7+EbMQ1bip0ay8IZsRCIRg13DbH5vH5217kle4vQopl+YiUp77IXz84cHKdpeg0+wjiufPpX87dWs++ggWm8VN7+4jBfuWYmx38Q5187EIbbzxQeb8PXTccUti/nnY+8hk0qwCSNYrVb03gq6u7u48carOOWUBX/4OLzyyjvs3LkHudQLQRAjOOSoVWpSEpNoqO7yeFMdD0EQaGnsQqvXYPA+udLKDV/nsPWnfPQ+aq66+xQ+eWIjDpsTjU7JiNFCeEIAF94zh1cf/oH+7mEi4gPo6zR61MVkcgmZM+KxmGyeUriUiZFccOMcBMFFS1M3fW3D/PDRHkQiuOb+JXh5qfjutV0MdLvLJBMyw1hy5WS8A7S0VHbx02u7MRktKDVylt46g6i0YHqaB9i9Ipe2Kndvmc5fw8xLJhI1PtSz7z31vdTlNNGQ24R5YHSzu8qgIiDGD/8YX3wivNn/eS5DXUP4RHgz95aZrH15J32tg3iH6Fh6z1y8fMZGJVvKOln9wjYEARbdOA3B7mDfpzluJcO75hCSHDRqefOgmarNFQSnhYwRTgBwuf5/7J13VFTn/vU/04feOyiKFAUFFRBs2HvvxiQmJjE9Mf2m9+SmmtwkJmoSNYkt9t5FVMACiBQBAem9l+nlvH8cg/Fqem57f9lrzZphOHPOc8qceb5l7y1w/vsL5B0VqwQ94wIZfHssBq2R718+iLZNT+jgQPqMDmbja0ewWKyMvi2ageNv5DBZrVaaazs4vSubzCSR/+HsYc/UpfEE9fXtWmbzFydIP1mITCbh1kfH0je2B5XlDXy3+iClxeIk1dZWxbhpgxg5rv9vEj/4NTCbLRTlV5J5/jLnU/PRdOpRKOTMu30kQ0ddI9/XlDex6s19dLTp8PBxYunzk3Fxd6CmtJlv3jiMrtOAdzcX2qvFSoOrkwqDxoS7hy26Fi09BnUn4Z5fp2DZWd/B0b8fxqQ1Eji4J9G3xf4ho+YfQxAEsndmUXDoEnDNa0rTpOHImwcxaY03CDaUnbyMvk1HrwkRN1Q18w5eImenqPrn3suDfrOifralr6WqlSMfJaFt0WLrYsvYZQnIVAr2vJ/YZWoaPiKIwfP7o7RRUF3cyN4VKbTWdSCRSBg2L5JBU/rQ1qjh27eO0FTTjtpWyYInR+If7EFzQwcGnRGDzoRBb0L/49daI2azhaA+voRFBSCVSqkpbebsgUtkJ5dgMYuVXEc3O8beMpC+f/kI4eTkxK6Yx351IDT9/PL/uv34b8cfDoRuZsbUtXKJhOPHj/+R1f/P4t/5xaovqCPxg2PU5NdhViuR2tvSKz6Q6Fn92PpeIvqr8tkWk5WO+g5kOgP2tgq8errRXNOOwWDBpFTgG+6Nf5gnPj3cSN2dQ3ujBoVKTkNjB3U1rWgkWrRWLTpZOz6BTiy6fRLffrcVo9HErbfNYMPGTej1BiZOHMu9997RNT69Xs/jj79AdXUN8fExPP30o10/KvX1Dbz44lvU1dUTGNiNN9984Q9XP2pqannggSexWq3IpfbU17Tj4uiJVadA2qHGWeaAg1KNo4MNLp72xE2LIG5aHw6sO9flVWHnpMbJ3Y7q4ib8gz2IiOnGye/FH5sJd8fRO647RefL8Qn1YM+nydRcaUKmlNHeqae1tRO5i5wh48M5l55Da0snt94zHpWtlE+Wr8fGVs2H/3gKrVbHU0+9hVajY+rU0dy2eDYgtgi+8MIbKBQKVq36CBcX5z90PH4KOTl5vPHGe+j1Bvr27cMLLzyJSqVCq9Xx+LLXqaqso73RgrlFhZPeFTeuSmi72eAT4ITCaMBOLcHF2xGuzhFsvZ1wDfOlKikfQRAIXTQY79jfVqb+MQRB4PRbB+ioaSNsRhS1xU1UXaggKCGYfrP7s+3ZPXQ2a+jsNCG3UzFicQzhI3th1Jk4viGD7BPFxE+PoEeEN7vfO47VIjB4fhS2jmqSvk3DpDejslUw4o5YesV2u27bFXl1bHzzKAALXxiLq48jnz6+A53GyKQ7B2GRWtj21Wmc3ex4ZvkC3npuHTXVzcy9dSRXyko4lZRBr2B/cvJysbFR0dZRD8CqVR91yZf/FiQnn+W99/6BYJWhUtqBRY5aZU9goD/15SKZ+bUP78HDy5n62hbOnMwh9WQuLc3i5MYvwJ0+/QLp3TeQXmEBKG/CSQGRSP/B01torG1n2MQIPNydOfytmJG2sRd5QQe+P0da0mVcPOx54p25yORSMlOLOX0gm6rSpq51yWQSJi4cRMLkflSW1fPpu9toa9UwbkoMlk4JZ4/nY2Or5JE3Z+HkYsupndmk7M3FYrYiV8oYPrMfg6eEo23Xs+vjU9RcaUIikZAwP4qYyaJSWuGZMlI2Z3ZVbrr382HoLQNx9r6WDbdaBeoLGyjLqKC+qJHmytau9rkfw87NjolPj+bol2eoyqvHztmG2S+OxeFnuApnt2WRticXpY2C+a9NIHtfLoWnr6CyUzH5+bE4/kojTqPORNKqFKqyRZGO/jP60W9yH7StevYuP0FjeSuufo5MeWIkG984Qmt9J8ED/Zn+6DCaqttpqGqjobKVhqo2Gqtaaaxux2y65usSOz6M0Qv6o1Rfq8xuWZXEucQCpFIJtz46hn6DelJZ3sDyNzah6dSjUikYNWEgY6fEYGt3o5DDn432Ng1rP9/PpaxS8RjEBHPrPROwu+pR1ljbxso399LS0Imzmx33vjAVDx8n6spbWPXcXixmK916uNNY0Yp3gDMdNe3Y2CqQGExIpBIG3x6DvbsdNo422DqpUdopfzLAqcur5eQ/ToAgEDVvAMGjflow47cgZ082eVelvPsvjKZXwrWqYNXFSlI+FyXpB983DL+onzaMFASBS/tyubRX5Jr1mdKXPpPDfzZgq71cz7FPT2HSGnHycWLcYyMw6s3sfj+RzmYtdi42jLprEN0ifMSWyH15nN6ShdVqxcHVlikPDCEgzJOa0mbW//0ona06HN3suPVvY7AIFla/vf8X225/gIuHPXGj+zBoZBj2TjZ0tuk4f7iAtCMFaNr1zF2W8KuqS38G/tsDoW0DH//VgdDs9A//6/bjvx2/v7Z9FYmJiX/GOP7CH4CuVYdBY0Rup0LTacbJR0W3KF+2vHMcvcaIQi1HZiOnqrCB5po2XGwUyK1WBEFA26rDqlIgSCU017TTXNNO1tVgwMXLARs3G5paNZgFM+2dWtqFZjwD7Rk+YiDZuZcwGk2EhwdzMesier2B8PAw7rrr1uvGp1areeyx+/nb314lNfU8iYmnGDVqOEVFV3jjjQ9obW3Fy8uTl19+5k9pAfPx8SY+PoaUlHNodVpsbJVYLEYMOisOShvkSikgwWKxMmBcKINnRbD5g0QKL4gyoANHhzBm4QCMBjMrntpFZWEDvWO7MXB8KOmHCjj01VkkEuibEMSRteepudKE2k6Jvbcd7Xl6jFYzCokCqVIkDDs52xE7pDevvfg5AOMnDMbWzoYP3l+FVqOjV6/uLFw0vWv827fvAWDUqOH/siAIICKiNy+99AyvvfYO2dmXeO21d3nxxaewtbXhzrvm8e7fv6CppR47qycWLFgEAStgNFswmS1ITRYUAe70mNGfzvJGmrIr0da2oa0Vq2necb1+UxBkMVloyK3G0K7HqDFg0hrRt2joqGkTBRP6+IhyxkBQQjCXTxaha9NhMliQ2Spx8XWkz4ggKvLq2L/qDG1Xqwupu3Lo0c+HIQsHcOq7dFI2Z3Zt07uXO2PvG4yj+7XrThAEck5e4cRGUWUucmQvAsI82f7ZKXQaI96BrkSOCOL9J78HIGFKJJfzKqipbkatVhAZE8TGTaKDttpOvMV6errQ1lFPQIDf7wqCAAYMECvuOp0eg0GBRGJBYlBSVlZFWFAYZVfq2LxObBMqulzV9TmVSoHRaKKqopGqikaO7EtDIZcR2MsHhVKOyWjGbLZgMpoxmcwolQpGThnI9i+TOX0wh4den0mPcG/K8uqY+cAQyorqSEu6jEQCCx8YhfqqMEBMQijRw0Mou1zHqQPZNDd0MP32wQSGepOTeYXVH+/GYDABcHjveZY+Mo3aihbKCutY+/5BHn5jJqMXDKDf0J7sX3OWktxajm++wMWTxcy4fwgLXxjDkXVpZCcVc2LTBepKmxl/9yBC4gMJjPIjbU8uFw8VUJZVQ0XuPrr38yV0SA8CI32RKWR4h3riHSoS5s0GM41lzTReaaLhShP1VxqRyqSMXZbAmW1ZVOXVo1DJmfxYAg5udug1RtoaOmlr0Fx9Fl9LpBLG3RlDZV4dtUWNHP4ihalPjKS5spWm0maOfJzElGfH/qLpq1Fn4vDyRBqvNIlS20vi6BEjWh7sW55EZ7MWGwcVEx4cyvHv0mmt78TR3Y4J9wxiy/Ik8n4k5/9jyJUyfALdGLtoIN1Cr4kFaDsNbPtSFK+QSGDRw6PpN6gnNVVNfPzmZjSderr39ObBp2bh6PTvIawDODrZ8fAzczi6L41dm09y4XwhpcW13PXQFHqF+ePu7cSDr0xn5Rt7aahpY8Uru1j6/GR8urkRHh9I1qkrcLWlrKGmHXuVDJ3WhLunHbpmjVit+xGkcilqBzU2jmrcA12JnhvVpR7p1dubyDn9ubglg4tbL2Dnbo97Lw8UNorfXR26tC+nKwiKmjfguiAIwC/Sn5AxYVw+ms/5b87i7O+MnfuNlVxBEMjZlUX+QbGq1HdGJGET+tyw3I9RdqGSpJUpWM0WPHu5M/qh4bQ3adnzfiK6DgMuPg5Me3oU9i62tDdp2PdFKhVX5e9DY7sxfkksansVxdnVbP7wBEadCa9uLiz622jaWjSsfmsfWo0RpUqOrb0KlVqBylYpPqsVqGwUqG2UWMxWss4U09LQyYFN5zi8NY2+sT2IH9OHEXMiGTo9grxz5YTFdPvZ/fm/BIGfFCW8Ybm/8NvxhwOhv/CfR0B0N9TezrS0G1G5KFHbq0jcnImu3UBzfQe2HrZINVJUDioEJOj1ZuyVSjqatFgF0OnNOHk5EjU6GIVKTmVBAypbBaikXDpTRluLBrm9DFOHEavUQo9gHwKDvFm18jgymZTBQwbw2YpVSKVSHnjg7ptKFwYHB7FgwSzWr9/CqlVrMRqNrF27Ab3eQPfuAbz44lN/6qR/5swppKSco7m5CU93HzRtegSrGpWDKBlu0JkI7OPN6NsGsu+rMxReqEKulLHo6dH0iBD7S23sVYy/NZrdq1JJ3JLJvW9PQRAg43ABB788S0V+fVfP9OjbY9i24hSd7TqUdgqcXO24kCG2t4yeGM2lnGLKy2pQq5WMmziE3JzL5ORcRqGQ8+iyJV3HrKSkjIyMi0gkEmbMuFGA4M+G2Ir4LK+++ndyc/P55pvNLF26mNjYSOztbUTCq8WKVWLFJAhYrGAyCZj1ZhRSCQggtVHR545hmHVGGi6W05BeilQlp9ccUS3QpDORs+EschslPUaF3uB8LggCNenlFOzKFL0uboJuQ4OoSK9AsAp4BHti525P9oE8rFYBkwBSiYTIcaGc2HiBtANiNcrR3Q43H0dKsms4sOoMi9+cQENpM/mnS5BIIGZGXwZO6XOdd0xdaTNH1p6n+qoJq2c3FxIW9Kckp4asU1eQSCRMuTuOrDNXaGnoxN5RTdzo3nyxfCcAg0f0Jf28mCDo1t2bK1dKAbAiZuYHDoz63efKxsaGfv3CSU/PxEatwmIGW7UKi1nAzkmcvOVkiv4rEgn06deD+OER9BvYC6PeSH5uOZeySsnLLqWluYPC/Mqf3NbZM7lExQeRmVrMjq9P8cDL0zDozFgFKx88tQWAEdOi0Oi1bP0ukej4MAKDfJBIJASGehMYeq0l7NSxTDZ+fRSrIBAW3g0PLxdOHb/Id18e4sEnZ7P+4+PUVbWy6bNEFj8xDg9/Z25/YRw5KSUc/OY8TTXtrHvjMHMeGc6Euwfh3cOVY9+kk3emjPryVibcMwi/YA8Gz4ui97CenN6QQXl2DSUXqii5UIXKTknwoG6EDumBV0/R4kGukuMd4nmDktiZrRcpSClFIpUw4aGhmEwWvli2k/bGm6ukgXj9jrk3ni0vH6SuuIkL+y8x5pEE9l71/Tn26SnGPT7yJ9XrjDoTRz46QeOVJpS2SsYuG4FHTzfKc2o4+OlpTHozLj4OTH5sBCU5NRScK0cqlTLtoaFkJBaRd74cmVyKV3cXPP2dcfd1wsPfGQ8/J5w97W9QYyvIquD7L07Q1qxFKpWw4MGRRMYHUVfdzEdvbKajQ0dAd08e+RIJaGAAAQAASURBVNvcrkrMP0PTqaWpqY3WlnZaWztobemg7epza2sHMpkUZxdHXFyvPlwccXV1wsXVEWcXh5+Vt5VIJIydEkNwb3+++nQvDXWtfPD6RqbMHsLEGXE4u9nz4CvTWf32PqpKm1jxym7ufnYSsePCyDp1hcorDXh7OdFa34lDgDMtFa1YZVKC4nugadGia9Ojb9dj0BhEtbcWLdoWLU1lzZgMZobfHd81luBRIbRWtFB2poTkFSd/GCAKGwUKGwVKW6X42laJjZMNDl4OOPo44eDtiI3z9cIReQdyu5QCRS+fm1eY+s6IpLG4geaSJlJXJzPqqbHXGaUKgkDWtkwuHxX5RZFzBxDyM/LuZoOZi3tzyT6YB4JAQJQfCUuH0FDW3GUm7t7NmWlPjsTGUU3BuXIOfXVWTKKq5Iy5PZqI4T2RSCRcPFXMri9SsFqs9Aj3Zv7jI6mtbObLd/aj15roFuzJ3c9MwvYXAv/piweTmVpM6pFcyosayEwpJjOlGC9/F+LH9GHgsOCbCq/8X4WABKvwy8G3wJ/Tvvl/DX84EHr77bfx8vJiyZIl173/9ddf09DQwDPPPPNHN/EXfgGl6RVo2nRoOwzY+DhTW9mGRCWnub4DOw9b7Jxs6D+iF9pOA3WFjVjNViRKOa017SidbNF0GKgsasQr1J2BY0KJnxHBwW/Ok3miCCTg6GNLU2kbFpkJe0cV/QeE8u032wCYM3cyR46K7Y9jx47Ez++nSWqzZ08jIyOLvLwCvvhCdAGOjIzg6acf+VPFAEAMvFxcnGlp7sBsMWMwmLBT24FcwKQ3Y9JZ0LTr2bM6lYzjhUgkEuY8PJweET4IgsCpA9kk7spk7OwB9Ozrw5XsGvasSuWOl8djtVjJPFbYFQTFTYugorhB9E4QrChs5Hh1cyIjqwYbGyXDRkfywTtrARg5ZhD2DrZs33YAgFGjB+Plfa1C8EM1aMiQQfj4XM8v+FchLCyYp59+lFdffYcDB44wYsRQQkKC8PX1Jk9VjsQEgkk0HjQLAlarFZPBjL2zCrPBRFtFM/7xPZHbKPGJ64VP3PWCGHnb0rvM/ypSivEM96XHmN649vKgtaSRvG0ZXR4bKicbXHq4obBVobBTorRToXJU49nXnwMv7wUgaMS1apDl6rWstFFwZl9elyRw34QgRi0agGAV+Ppv+2iubSd5ew4jFsfg2cMNzx6uePW8puCk7zRwamsWmccKEQQBhUrOkFn9GDg+BEGAvV+dASB6TAi+Pd347tNjAIyYGkljQxu5F8XgasS4AXz4rnht9w7vya7dBSiVCmprRZWzPxIIAcTGDiA9PROr1QQoEDABcsorqujTN5C21k5ihvRh0NA+uLhea8lSKuVEx4cRHR+GIAjUVjdTWlSDRCpBoZChUMhRKOWYTRZWfbSLgkvlxN7dh4KLFVReaeTs8XyGTojgq3cOoO004BfoRv+hPXn7he8wmcwc3Z9Gj14+jBg3gIFxocjlMgRBYMfGkxzeK2bh44eHs+ju8QBUVzZQfLmajWuPcMvD41j91j5y0kpZ8/4hZi0ZirObPX2H9CQ4yp/tK05xOb2S7z88wdSlg+k/JgSPAGd2fnyKpuo2Nrx2hKjRwQyfF4mLjyNTnxhBc3UbBcklFKSUomnRkXO8iJzjRTh7OxAQ4Y1noCvu3V1x9XXsCoRzE4tI3ytm2EfcEYOjpz3fvXIYbYfIfbJ1UOPkYYeThz1OHvbYOKg49X0mxReqCB4YwIg7Yjm0IpmMfZfw7+PF2EcT2Pf3o9QXNZC89izD74m/oZJg0ps4+vEJGoobUdoqGf/ESNy6u5J7ooikq+pcvqEeTHxkOO1NGo5/mw7A8PlRCBI4tkmsWk5eMogBo0J+9tox6E3sW3+GlCPiPnr4OLHggZF0D/aivraF5W9upq1Ng1+AO48+N++mQVBleS27d57gbGoWv7erXi6XEda7B5H9w4gaEIaXt9tNlwsM8uH5txazae1RzpzKZc/WZPJzyrh32XTsnWy578WpfPXOAUov17Hyzb0seXICvkFuVBc3YedmS2t9J82NGmQyCc01HYxYMgifH6mSWcxW9O16dG06WqraSF53jitnSvEL9yYoXuSmSCQSBt4Sg9lgpjanWhQjEARMWiMmrRFt008HyHKVHHsvRxy8HJDKpZSlikmKvjMiCRkT9pOfk8qlxN09hCNvHqSlrJmL2y7Qf77opffPctv9Fwyk14ifPu8VF6s4syG9SzQkZHgQcYuiqcqvZ//HJzEbLfgEuzP5sQQkUkkXJxLAu4cbUx4cjKu3I4IgkLglk6RtYkU+YnAPZtw/hJL8Gta8fwijwUzP3j4seXoCaptf5pAplHJiEkKJSQilsqSB1COXuJBcRF1lCzvXJrN/41mmLIpj8LjwX1zX/wVYrz5+zXL/v+Odd9750+OKPxwIrVy5kg0bNtzwfnh4OAsWLPgrEPo3wMZRjY2zDTJbFdoOA4JKTmN1G44+9rh6O5Iwrx856aWkJxXS1qrBQaXCLJfh7OGA3mjGggSL1UpZfj1l+fVIpRKsVgGJRMKQGeEc3HGetjYNOosOH2cH0jMuotMZCA3tibePK5cvF6FSKZk/f+bPjlMqlfLYY/fz6KN/Q6fTM3LkMB588OYVpD8DPXsGUlNTj06rQ0CK2kaG2WrEYpBgo1RjMprJOC7Kso6/LZqwmG6YjGa2rj7ZJde657sz3Pv8FCoLGygvqCd5dy5j74gBIPNYId0jvPEL8+Dbt45gtVrhaiJMbxH9M6Lje1NRXsPlglLkchkTJg8lP6+InJzLyOUyZswY3zXe2to6Tp8WJ9yzZk39lxyTn0L//v1ISBhKUtJpVqz4kvfffx0fHw+UKjlmsxVBZsViEjBZBaxWMJmtSFUKTHozbeXNP7neuqxKKs+UgAQ8evvQkFdDfW419bnV2Lrbob2aaZcp5QSN602PUWHIbsJdqUgrw9CuR+1kg1dvH1Je2ocggBnRtNcqk9JS046to5oJdw+i1wB/2po05KSUMGROPw59eZbz+/MIiQmg7+jr21GKM6s4sOoM2qtk/95x3RlxywAcrpLjk7ZdpKmmHXtnG0YvGMDF1Cs01rZha68ifmw4G74+DEBUdDDNzS1UVzegViuxCOI14B/gTX5BDra2NvTu/fOT1V9CTMwAPv/8azo17ajkjnRYOrBTOVNf38S9D84lOLT7L65DIpHg4+eGj9/NJ6BjJsdwYNcZDu45w9i5g9i9LpWD359H064nP7MCuULKggdHsuHrw5hMZlzdHGhv1VBSVENJ0T62fZfIsNGR1FY3k35WrIpOnTOESTOvBQJ3PzKNt55dR2V5A8knM5m7dDhbViZxKb2MotwqJs6PZcj4cNR2SuY/PpI9q1LITCpm1xfJaNr1DJkazl3vTiFxQwY5J69w4ehlCtMrGbs4muDoAFx9nYifG8Wg2ZFU5dVRkFxCcVoFrbUdXYRwEMVQ3Lo54+LjSEFKKQDR08LpOTCA9a+KQZBXoCvz/zbq5i73gsCJTRc49m0ad749mT4JQVxKKuboqjPMf20Co+4fyuGPTlByrgwHD3sGzLzmHWXSi5Wg+qJGFLZKxj0+EtduonLXDwFZ6OBARt4Zi8ViZfcnpzGbLPSM9CVyVBCrnt2H1WKld2x3+o+8ufrcDyi9XMumFYk01oqJgqETIpi0MBalSkFTQxsfvbmZ1pZOfHxdWfb8fOwdrpeMLi2pYvf2RNLO53a95+hoh5OzA84ujjg724uvnR1xcrZHsAq0tLTT3NxGS3O7+Ghpp7WlHbPZQk52ETnZRaz/Zi/e3u5EDgglsn8ooWE9rlOjU9soueP+SfTuG8iGrw5TmF/Jh69v4tHn5+HkbM89z01m7QeHKMyu4st39jNkZATVxU3UVLZgYy/+Hnbr6UZTeQsX9ufh8+i1QEgml2Lnaoudqy3uPdzQtGjJ3JVN6ndpeAS5d3G7ZEoZg+8dCoieUCa9CZPOiFFjxKS7+lprRNOkpaOunY7adjobOjEbzLSWN9P6o/tjxPR+N7SwmY0W0rdlorJTETlV5PjYudkRe2ccyZ+dpCjxMh7Bnvj19yd9/XlKTheDRMLAW6LpOezmKqyaFi3nNmZQliEmoOxc7Rh0ywC6RflzJb2CQyvEqk63CG8mPDwMvcbIlneO01jVJvLJJvdh6Jy+yOQytO16tn92iqKrBtODp4QzdtFA8i6U883yw5hNVkL6+XPHE+NQqq4XyxIEAZ3WgFajR9OpR6vRY9CbCAr1w8FRvL/69/Bg7tIEpiyKI/1UIalHcqmrasX1V3Lr/i9AECQIv6Yi9CuW+V/Hd999xzPPPMOIESM4ceLEn7LOPzwDra2tvalUnYeHBzU1NX909X/hV8AnzAsHfxds6jU0NWioK27CwdseJ3c7zCoL6z87jsFgpKGuGb3ZhEomQ9umx6u7Ky1XmjCYLdg4qOkR4UNbYyfNtaI6zIwHhnA+uYDODh2C3ILECsjMVFZVY2ur5oGHbuP1198DYOrUideZc/4UPD09ePfd16itrSc6+ub+GX8WevToTlraBdpbNShUdiAV0GoN2KBAZadA22nAyVXOoAm9iZvUh5bGDtZ+cJiqkkakUgkuHvY01XVwYs9Fxt8Ww57VqRzblIGdo6pLZc7OSc0Xz4qVCp9ebmivGHHxsKfkith2FD04jL27RR7dkOEDcHFx5LNP1wGQMCION/drx2z9+i0IgkD//v3o2TPwX3ZcfgpLliwiIyOT0tJy9u49hK+fN0qlAlOnGavMgoCABdGJ3AoIMilmvYH2qlasFut1LWYAhg492RvEakDPUWGEzeyPpr6DksR8Ks+UiEGQBALigwiZ3BeV00/7dRQliYFpz6FBFKeWoGvVYRUELBIJMpmE1mYtEqmEBc+Pwc3XkYzEQg59ex6D1kRAsAe9BweSl1J6tUVuInKFDLPRzImNF8g4ImZXXX0cGXtHDN3Dr1XimmraObVTbGeZcHsMKlsFR3dkADB8Ul/aWjs5lyxOWifOiGfvHvFcxw+J5MIFkQugUoktUVFRff9w0O/q6kJISC8uXy7C3sEWvc6Ei6s9Lc0aTp/M+FWB0C9hwvRBpCRl01DXitaopVuwJ+WF9V37PXlhHHm5pRQWVKJSKXjipYXIFTJOH8/i5NFM2lo17NuRCoBcJuXWpROIGyZmdnU6A9VV9fQM8ufuR6by0Vvfk3oyl6BQf5a9PYdtX56k9HIdu9alkHG6kDn3DMcv0J3p9w3BzsmG5N05HN2QjrZdz9hFA5m0NJ7wIT049PU5Wus62PHRSYKjAxhzezQOrrZIpRICwr0JCPdm+G3RlF2spr6kifrSZhrKWjDpzdQVN3UpB4YN7cHAqX3Y9n4STdVt2LvYMvuJhJsHQUD0pDCKLlRRWVDP/pWpzH4ygZrCBlqq2zm6+gyTH0tgyO0xnF5zlqx9udi72xEyLAiTwczRj5O6gqDxj43A2deJI1+kUHhW5PtETwsndmZfLCYLe1ek0Hw1GJ90XzwH1p2nqbYdRzc7pi29sdL0A4wGE8d2XOD4rguirLybHfPuG0FIX5GE39LUzvI3NtPc1IGXjwuPPj+/a4IKUFRYzq7tx7l4QQxoJRIJ0bHhTJs5ku6Bvr/52hIEgeqqBrIyC7h4oYCC/BJqaxup3d/Iof3JqNVKRoyOZc78cSiV1ybVg4b2oVsPLz56czPVVU188NomHnt+Hi5ujix5agLr/3GMnLRSziUXYG+vorNFR0D/AMqya+jUiAmJ0swqLiUV4xbgjJOn/Q3ntN/kcGry6qi7XE/SqhQm/W0sMvn19zSZUoZMKUPt+PPCEVazFU1TJ+217XTUdtBR145roOsNculWq8CpL1O7AhZ7N1t6DRF5lb59/Qgd25uCI3mkfXuWirQyKjMqQCIh5vZYAuNv5F9arQL5xy+TsSMLs8GMRCqhz5hQoqb3RaGSk59cwvGvziJYBYKi/Rl772A07Xo2vXWM1roO0Vvq/sFd98DKwga+/yiJ9iYNcqWMqffEEzksiKyzV1j/yTEsZivh0d257dGxIIHtG5K4nFfeFfRoNfqb+m/JZVIGxoeRMLY/PYPF68jGTsXQCREMGR9O2eU6ut/ELPb/Kv6qCF3DwIEDmTRpEleuXGHXrl1ERET8YR/TPxwIBQQEkJycTI8e18scJicn4+v722+Uf+G3o72hk7KL1Wjb9Oj0ZixWK+4BTpiUVi6lX6GutgmtSYvJYMFGb4eNRIHZImDUmzBbrOi1Jhx8HZi4OBYPfydqS5uRSCWcOpRNfmYFHe1aLAoTaqWMppYmvHycuXPJPLKzc6mursHBwZ6ZMyf/6vEGBPgREOD3ywv+QfTsGYjk6kTZxlaJ2WpEpzVjZ2ePwl6GQWMiYnAPxt8eTXVZIyvf2IemQ4+dg5rbHh2DnaMNy/+2lZy0UgaNDmPItAiSd+ewZ/UZlGoFEYN7sP2zU7Q3aXD1dkDpKH6dPPwcqciqxsnZDrWtjMyMfCQSCZOmDKPwcglZF/OQSiXMnHmtGnTyZDKnTqUilUpZuHDOT+3SvxROTo4sXryQTz9dzcaNW7n9tkUo1XLarFosggWL1IpVAIsVrAKYLQISkwWryUJHVStO3a75mAiCQM6m8xg7DTj4OBE8RcyE23k6EDE/huBJfanNrMA1yAMHX+efHVdrZSuNhQ1IpBJ6DOnJ4Y+SEACrRIpEIqByskFi6KRnpC8KlYzv3j5KcVZ11+crChuIHR9KWU4tTdVtpOzIIWxQN/Z8lkzTVZn0geNDSZgfhfxH1ShBENj71RnMJgu9In0Jjw8k53wpdZUtqG0VDBkfwdb1iQgCRET1wNnVjrRzYvDTK7QbBw4dRaGQ09rWIm7jD7bF/YCYmP5cvlzEDzGVySKqQp5NzWLR4ildE0hBECjIKyE97RI2ahV+AV4EdPPGy9vtZz26VGolMxcMZ+0XBziw8wwPPD6Lr94+iNUqEBzhR+gAf97421oAZt2SgJuHyPmaPGsw46cN4mJaIScOXaCxoY077p9IaLgYnDU1tfL317+krraJeQvHM2X6CKbPH87OTSfZvOYoT75yCw++Op0zRy+xf+M5Koob+Pi57Qyb1Jdxc6IZe8tA7BzVHP4ujZS9uWja9UxbGk/3cG+WvD2JlJ25nNt7icK0Ckqzawgf2oOoUcF4dheTDUobBcFx3QmO6951fNrqOmkobaa+tLnLj+jouvOU5daiUMmZ/UQC9i4/beYnkUiYtDSONc/tp7KgnovHihh3/xC2vXaY8uwazm67SPzcKDoaNFzcm0Pqt+dR2Sm5dPQydYUNKGwUjH9sBK7dXDjwj1OUXqxGKpMw8s5Ywob2RK8xsmN5EhX59chkUqY+OIQr2TVknihCIpEw68GhKNRyLmdX0lTXTktjB831HbQ0dNLS2HGdgtfAYcHMuGMINnZiANDa0snyN7+nsaENDy9nlj0/v0tivaa6gW++3kVuTnHXfsYPiWTqjJH4+XvecBx+LSQSCX7+nvj5ezJxyjC0Wj252UVczCwg60IBra0dHNx3muzMy9z70DwCe1z7nfDxc+OJlxby8VvfU1/bwvuvbmTZ8/Px8HLm1mVj+PujG2lt0tC9tyeVeQ20tWmRyaQ013XQracrTeWtJK65JpagslPi5Gl/9eGAT6gHw++OZ/erB2kqbSZjRxYxc6N+135K5VIcvBxx8HKEyJsvIwgC5zamdwVBAGc3ZeDT2xu7q9XoiOn9aLzSQFNxI5UZFUikEmLvjKdbzI0Jj8aSJlK+TaP5agXKvacbg2+P7fJOyj52mZNXWyvDhvZg5J2xdDRr2fTWMdoaRPGNhc+PwcnDXhzbwXwOr0/DYrbi5u3IvMdH4NXNhfRTl9m0QrzvRQ0OYsEDI5FKJaz5bB/nU/Nvuq8KhRw7ezV29mqsFis11c2cPX2Js6cv0b2nNyPG9mdgfBhKpbyLZ/gXrsEqiI9fs9z/71i7di0XL15k8uTJJCUl8emnn1JcXIy7uzvh4eGsWbPmN6/zDwdCd999N8uWLcNkMjFq1CgAjh07xtNPP80TT/w208C/8Ptw+UyZ2CYkCHS06bFxVtPaqaEop4LK8nqsNgYkcpBKpFh0FkxWC4JMQnN1OyhlIIHK8kY2fnGMUdMHEBETyNHtGZw9Lt7U7D0VlOV3orA3Y2evJiIihP4Dwnn4YbHtce7cGX86x+fPQFBQIAAmsxG1Wk5bkx4rahS2csxWC70HBzLnkeF0tGn5+t2DaDr0+AW6sfiJ8bh6iGX5oRMiOLk/m13rUnni3TkYtEbSjl5m+2enqbjc0EWgn3hnLF+9L/J+dGaxxWpgXCgH9yUDEB0bjo+vB39/WySaDxs+CE8v0euhvr6hizM1b95MQkL+WHbjj2D06AQSE0+Rm5vP8cSTKFVyTGYjJqsJs2DBgoBFEDCZrFiMZuRXidhnPz5G0IRwAkeEIlPIqD5fSt3FSqQyKZG3x9/gfaFyUNN92M+38/yA4pNiNcgvyh9Nq47W6jaMOhNGqQy5QkZzkwYEsHe35bOndmPUmZArZYya15/OVh0pe3M5vTuXsXdEs+sfpzm7N5fz+y5hsVixdVQz6d54ekbemLTJOn2Fkpwa5EqZKJdttrJvw1kAho7vi05n4OwpsVVo0ox4TiWlYzZb6Bnk3yWS0Cc8mPNpYrtj//79btjG70F0dH/Wr99CS2szMokdDY2NeLj40Nmp40JaHt0CfUg5dYHk05k0NrTc8HmFQo6vnyc+vh4oVQpkUikSiQSpVIJUKsXO3paJU4YSeOQCpcW1nE25xNTb4rmUUcb8+0aw5vN9GI1mgsP8GT4m6rp1y+UyBsaFMTDueg5EQ30zb7/+Zdd4tm4+QmjvHoyfGkvJ5SouZhSz6qNd/O2N24gfG06f6EB2r0vh4pkrJO3NovhSNfc8O5nBU8KxdVCxe2UKF08W09mqY9aDw7BzUjN8XiRhcd049NU5aoobyTxWSOaxQnyC3Ikc2YuwuG5d0tEgTsqdvR1w9nboCo7O7b1E1gnRYHnaQ0Nx8XYgaXsWTTVt6DVGdJ1G9BoDOo2xi0x+x0vjGXXrQA59dZZTWy4S2M+HkUtiObIylYx9ebj6OhE1PYKOxk6unCklccVp8TzYKBj32AjcAl1J/PocpRerkStkTFo2nIBwbzpbtGx5N5GGilaUNgpmLhuOg7sd698TOZnDZ/bFP9iDFa/soryo4SevF0cXW6YvHkJk3LUKwuW8Cr7+dC+tLZ24uTvy2PPzuzhlVZX1vP3aKtrbNchkUoYMH8DU6SN+ksvzR2BrqyZmUAQxgyJE/ktGPmtW76Cqqp5XX1jBjNmjmTI9oStw9/R24YmXFrL8zc2iiMJrG1n23Dy8/dyIG9OHg5vP09regVQqofpKE72j/CnLqUVqoyR8RBAtNe201XeiaRGVVutLmqkvudq6tgdG3TWIwXfEkvjZKXIP5eHb2wu/iN9mzvhrkbX/EvmJhSCRMPyeeC4dLaDxShOn155l3GOikeQPfKGjbx3CqDUSd1c8/gNuVFO7uC+XCzuzQRBQ2CqJnh1JyPAgJBIJBq2RlM2ZXLrqJdVvTAhDFw2gtb6TzW8do71Jg7OXAwueG42jmx1GvYndq1LJSRE5TX0GdWfavYNR2ypJPXqJ7V+dQhAgZkQoc5cORyKR8M3KA5xPzUcukzJv8Wh8/d2xtVdjayc+/lmqv7S4hhOHL5CWkkfZlVrWrTzAtg0nGDqyH8PHROHq/pf0848hIPlVQgj/F8QSfuAIHTlyhN69e3e939jYSHZ29u9a5x8OhJ5++mmam5t54IEHMBrFErRareaZZ57h2Wef/aOr/wu/AgOn9EGukrPtgySMehMaq4lqQyNVlQ1YbQwEhfkxZ9Fojh0+z4W9pZgsFgxmK3a+TjTWtCPIQKaWUVHcyLoPD+Pq6UBzvdhHHzMqmL17T2NFwCQYcLdzIiY2ktWr19He3k5AgB8TJ/52c8h/Bzw9PbCzs0Wl7sRkNmE0GbGzsUciF9B2Gqgtb0KnMbDug8O0Nmnw8HHivhendmVMAcbNieZCShGNtW2c3J/N5LviMOhMZCeXcPZgHgDDZvSlorQBs8mKm5cjxUViW1yvUD/+8bE4aZk8LYErV8rJSM8RM7mzJwCioeFHH32OVqsjNDSYuXOn85+ERCLh/vvvYtmyZ7l8uRBtp4BFMGPFjEVixWIVJbQNBgsmvRk7L3vsvR3prG2nYNdFKpKLCBoXTt52kcTda2IEjgG/3DL5UzBqjZSfLQV+kMwWf8ytSJDKpNi529NR3YbFaiX1YB4SiYSAEE+m3zcYd18ntB0GMo4XUlfegtFkITS2GwXnyrEgENTfj4n3xGH7T20ueo2RE1szOXdITAQMn9kPV29Hju7IoLG2DQdnG0ZMjWTn9ycxW6yE9ulGj2BfVnwmciUTRkWzYcN2ANzcxB/0nj0Df1Xr6K9BYGA33NxcaWpqxsXNkbbWTtw8HOns1PHVqm3o9cauZW1s1cTEhoNEQlVFHZUVtRgMJspKqykrrf7JbZhMJubdNop3X9lA6slsEl6PYtjEvpw6lknBpXIUCjm3LRWvYYvF8rMVpprqBt558yuam9rw8nbDz9+TjLQ8Pv9kM6+//RCL75/E289/Q0N9G6899TULl4xlwKBQbls2loEZZWz+/ASVVxpZ+cZelj43maiEXtg6qPj+oySKs6pZ8dQupt8/hJD+/nh2c+HWV8ZRnltH5vFCCtMqqSlupKa4kePr0wkf0oOekb64+zvh6G53XUtZwblykq5Kq4+6dSA9In3Y9H4ilzN+Wl3PbLKw8/PT3PX6JIoyKim+UMX+L1K59dXxDKxqI33vJY5/fQ4nbweGLI5F06yl7nI9crUYBHn0dOfM1ovknRJlrMfdP5iAcG+aa9r5/p3jtDdqsHOyYc5TI3APcGbtq4fQa40EBHswfHYk+zacpbyoAZWNgp69fXBxd8DV00F89rDH1dMRW3tV135arVYO7DzD3m3JCAJ4+bjw0NNzuiaeNdUNvPPGl7S3a+ge6MujT9yKu8efc93+EiQSCf0H9qZXcDfWfrmT8+dy2Pb9ETIv5LP0/rn4+Ir8Hld3R7Ey9OZmaqqb+eC1jTz6/HziRvXmyLZ0aqtaCQ32o6qwCevV01uWV8eEe+K6ZPJNBjPtDZ201XXSVt9BTWEDJRlVnFqfzoI3JhE6IpiCE4Wc+uoM01+diM0vtML9VhSevsKFHVkAxC7oT8/Y7rh1c2H3qwepuVRLwYkiwq7yvmxdbBn/8iQsJgu2N6lO5h273LWunnGBxMzr3zXe0swqTqw7j6ZF9Nj6od2ypbaDTW8do7NFi6u3I/OfG42Dqy0Nla1s/vAEjdVtopz8ooHETRQnm8d2XuDAJrGiNnhcODPvHALApjVHST2Zi1QiYcnDUxkQ+8s8yMAgH+6434fZi0aQnJhF0pFMWpo7OLj7LIf2nCVyYDDjpsTQM+Rf3znyv4C/KkLX8ANH6P7777+OI+Tu7v6zvqY/h98dCD333HPMmDGD2NhY3nnnHV588UXy8vKwsbEhODgYleqXzZ/+wp8DiURCXUUrGo0BiUJKm66NhsZmrDYGekf24G+vLyL/ciHllSUYrAZM2KHXmbAKAtp2PSarFTtHG3qGeVNT3twVBI2aEUVxWQXtbR3YOyrRWjuRSCUYjHpOnz6DVCrlkUfuRaFQ/MII/zOQSCT07BlIc1MbOp0eq1VAZSPHaDYgt0qxWARWv72P8qIGbGyV3PnUhOuCIEEQUNsqmbIojo2fJXJsR4bYXnL/EIwGMwVpFfj0cKXv8J58+IxY6ene24Oyk5W4uTuSe+kyVquViL696Bnkz3vvfgHA0KHR+PiI7SXbtu3m0qUC1GoVjz12/89OKP9d8Pf3ZfbsaWzevJ36hhqUSgckFgGr0Yr5qoS21SpgsQjIVApCZg3A3KGnYPdFtI2aLl6Qc6AbPcf2/oWtXYPVYqUur5b2mnY66zvorO+gvbYds8GMg48Tzt1cKPnHSQxaE3qTFZVSTmurDkEAvcmCRC5h2Iy+jJwX1SUZLJHC4Cl9OP59JolbMlnyygTUdkp8gtzpm9DzuomwIAhcOFHEsY0ZaK4KJ4THBTJkajgtjR0cu8qRmXprPAajkeTj4uRj0sw4cnOKqK9rxsZWja29ira2Duzt7Wj7k9vi4KqS1cAoDh8+jtpGQVsr6A1iC5Reb0QqldI3MpihwwbQP7r3dVwLQRBoqG+msqKO2ppGzGYLVqsVQQDBaqWtrZPEY+c4fCCFMePjiR3Sm3PJeXz/zTGWPDiFbetPADBj/jA8vJz5dPkGLmTkET8kknETh9zAG6mqrOedN76ktbUDX18PnnnxblQqJS89+wn1dc18vWoHDz12Cw88NZtVy3dSU93Mqo930z8mmPl3jKHPgO7c/9JUVr6xl+qyJj5/fQ/3vjCFkAEB3PPGZLZ9cpL6ilY2vHOMmHFhjLt1IAqlnO4R3nSP8EbTpiP75BWyEotore/kwtHLXDgqcsKUagXu/k54XOWMpGzPFjl6Y0IYMC6EY5syuJxR2WXsau+kRm2nwsZOtCiQSGDda4eoKWnmzL5LTLh7EF8/s4/68hZStmczbF4kzVVtlFyo4sDHp5j7ynhGPzSM/BNF+PfzxdXfmawjBT9SqoulxwB/qosb2fbeCXSdBly8HJj7zCicPe1J3JJJxeV6VLYKZj8ynKKcKk7uE6/BWx4cRXh04M9eN60tnXz96V4u54mtWPHDw1lwxxhUalHlq662ib+/Lp6rgG7ePPP8Xdg7/HRb4L8KDo52PPTYLaSczuTbNbspLqzgpWc/YcGiSYwaO0is5LnY8/hLC/nH21uoKKtn+RubePiZOUTFB5F+qhCzVGRLXLlUS/dgd6oLG0k/XMDIWwYAoFDJcfN3xu1q21jk+DB2vHWU2qJGjq1OZcrjI6grbKC1qpVTX51h7LKEP43PWnGxipRvxHtk34l96D0qhMxD+djYqxg4O5JzmzJI25qJb7h3l2DDT/lQlZwv5+wm8b7Uf0Y/IqeIXDx9p4HTGzK6BECcPO0ZuWQQfmGeNFa1sfmtY2jadLj5OjH/udHYO9uQnVzCntUpGPVmHFxsmLtsBN1CPTEaTHz/RRKZqWISKmFKP6YsigNg+4YTJB3NRCKBxfdP+lVB0I/h4GjLhOlxjJ0SS1ZGEScOXaDgUjmZaYWERXT7KxC6CosgwfIrhBB+zTL/64iOjv7v4QjV1NQwZcoUZDIZU6dOZcaMGYwePfqvAOg/AL3GSHZSMR0tOjRWA03tbViUJoL7BOAfaseLL4mCBlqjBrNEglEQg6COFh1GoxmDxYiNVMmYOQMJ6OHBuRP5SCVSOgwd5F4sob1dg42LgJ1EhYODHVu37gREOezg4P9cG9evQY8e3cnKykXTqUehVCGRWtHrjNhJVIBAeVEDEgncumwMnj/iqpw6lsmOTSeZNncoCWP7c+ZoHiUFtWz45Bj3PDeZuY8mUJRZRffeXmxYcRyT0ULP3j60a8UgMqJ/D44lii0wk6clUF5exflzWUgkEmZerQYVFhazaZNYOVi69A68vf97yKGzZ09l27bdIBGQyUQuDnKwmkQJbcEqYDRaEID26jZCxoThHRXAlcOXuHI8v6sl7gcBBaPWiEwhu6FF7gd01HVwbm0qzSVNN/5TIqH3hD6UplVgNpgx6E0oHGxw9HGkurwVi9kCUrB1UDF8Vj+kUimdbTp2rDnNxTNXGDOzP3aOapprO8hPq2D8XYNu2ERVcSP715yl6qp/kLuvExPvjCWorzix3/1Natc57j+kF9s3nMBkthAU4ktIn258ulysBg0d1p+zqeLEJH7wAE4kiRXBPzMQAlE97vDh47S2im095RVV3HvvbVjMVmLi+uLsfHPFJYlEgqeXG55eN29zEgSBqsp6LheUsmvbcWYuGE3m+UKKL1fz4eub0OtNBIX4MnLCAM6kXOT8VU7UqaQMTiVl0LtPT8ZPGkLUgDAqK+p4542v6OjQdE2sHZ1EDsoDjyzkjZe/4Py5HBKPnmPU2EE89/ZiDuxI5dDuc1w4X0hBbjlzbxtF3PBw7n95Gl+8voe6yhY+f3U39704Fa9uLtzz5mSObczgzIE8zh/OpzS3hlkPD8cnUOSr2TnZEDc1nEFT+lCWU8ullFLqSptprm7HqDdRXdTY5RkF0KOfL6NvG0h2cgmnd4n7Nv3ewfQdcnNj4PGLY9m54jQntl4kLLob45bEsusfpzi79xJB/f0Ye+9gtr1xmKbKNvZ9lMSs58bSb5KoGlZ0rpzTG8RrZdCsvvRJCOLKxWp2fnwKs9GMdw835jw1AltHNWV5dZzcLgY9U+6KR66UsWmFKMwxeGyfXwyCcjKvsPbz/XR26FCpFCxcMrZLwAKgvq6Jt19fTUtLO37+Xr87CDKbzWi1epRKBWr1758LSCQShgzrT1ifHqxesZVLucWs+3oXFzLyeODhBdja2eDgaMuy5+fz6btbKSmq4eO3vmfW/JGknxJNWP283Wmu7eji22SdKGbIrL7XtUf+AKlUwph74tj80kGqCxrITSwiYelg9r5xiOrcGnIPFxAx/qclr38tGq40cuKLZFGsIL4HA2b148zWi2TsE7sLpj45Aq8QT+ou15O85izjnxqNVHrzyW1NXh2nvkoFQSBsZDD9JovXVXFaBSe/SUPbrkcigchxocTO6odCJae+vIXv3z6OtkOPR4Az858dja2jmvRjl9mzWhQ46RHhw5yHh2PnpKa5oYN1HxyiqrQJmUzCjDuGED9WvG72bDnNkX1pANx693gGDRW3bzabaWvrpK21k/a2zquvO2hv66S1tQOtVk/vPj1JGBnddT+QyaT0jwmhf0wINVVNnDyayaChf0ln/4C/KkLXsGbNmv8ejtCaNWsQBIHTp0+zZ88eHn/8caqqqhg7dizTpk1jypQpuLu7/97V/4XfgJxTV2iq7UAil9DeosNkMePiY49XDyUFl0WPnPDwYKoqG6i7VIbJasGKQHNtOzKVHIxGysrq+Pyjbdx2zwQSJkdyKauEDe8cxGgw4uiupFPfhrObmra2FsxWHd27B/yiXPZ/A34QTJDKJCgUCswWIyY92Dk4YDKaUSgVTLttMKH9Aro+k3gog83rRJ+YbeuTiIwOZs49w/nkpR1cya9lw6fHuW3ZGMJiupF9roS8DNHQcPItg3j/DXFCLJFZMRpNdA/0pU9EEJ99IirFxcZG4u8vehV98skqLBYLQ4YMYuTIYf/+g/MzUCqVeHl5UlpSAxYBq8WCVW7pqgpZrYIonGAw01rVCoi+GSFT+xE4KhTBIqC62p7RWtlK4ntHkMpl9BoZQq8RwV0ZTkEQuHK6mItbLmAxmlHYKPDq44O9pwMOnvY4eDli7+mAyl7FvrePYLUImCwCagl0dogiAVKVHInRxIBRwcgVMi4kF7FzbTKaq/4vpw/lMH56DMc2ZpC07SKRw4NQXO1ZNxnNJH6fSeq+SwiCgFItZ8ScKAZNCEMmF4O2gosVZJ8rEQUu7hyKpkPHyaOin8bEGfG0tnaQniZm9eOHRvLKyx8C4O/vhUajxcHB/k/nffXr1welUkFHRzsB/kHU1TZiMhsYPyHhD61XIpEwb+F43nhlJUmJaUyYPJTx0waxZ2syTY3tKOQybrtnAjqtnvXrRLXEkaNj0ekMnDuTTd6lK+RduoKnlyuaTh0ajY7AHr48/dy1ibUgCPQM8mfuwvFs/HY/67/ZS3BoNwK6+TBt3jAGDArl21UHKSupY93KA5xPyeO2peN54OVpfPHGXhpq2vj8td3c+8IUXNwdmLA4ll5Rfuz8PJmGqja+fGEfI+f1Z9CEsK7zLJFICOzrQ2Bfke9hMVtoqe2goaKVhoo2GipaUCjljL8rluqSJnavTAFg6PSInwyCACKH9SQnpYSizCp2rUzhzlfGEz60B7mnS9j7eQqLX5/ApGUJbH31EI3lrRz7MpXxDw6lKq+eIytTEQToOzqYgVPDKThXzp5Pk7FarQRG+DBj2TCUagXlBfVsfO84giAQlRBExOBAvnrnAB1tOrwDXJh6W/xPjs9oMLFn67UJa0B3T+5+eCpevteETRobWnj79S9pbmrDz8+Tv714LWD9ZwiCQN6lIpKSztDS0oZWo0Oj1aHV6NDpdNe3ZdqocHV1xsXFSXx2dcbV1QkPTzfCwoKwt/9lXqmbmzPPvHAXhw+ksGXTIbIyL/PWa6t56rk7cXJywM5ezaPPzuOz97ZRmF/Jvl3J+AV6UFXahJ27Dc21HZQV1uPi7UBLbQefP7ITz+4uovns1YebryMyuQwnLweGLOzPibXnObP1It36+hC7YACp354nY8dFvEM8cO/x+3lSbbXtHP34JBaTBb8IHwYvjiXv1JWuIAggae15pjw2nH1vHaGusIG8owWEj7sxAGsqb+HYZ6ewmq10HxhA7MKB6DsMnPw2jaLzYsXPxdeRUXcNwjtInIfVlTaz+e/H0Xca8OzuwvxnR2NjryIjsbArCBo0oTfjb49GKpVSfKmab5YfQdOhx95RzW3LxhLUR0wMHdx1pksZct7toxgysh+dHVr+8eF35OeV/OKxyMkqZMfWowyK78focXEE9Qroqrj5+Lkxf/Ho332c/3/EXxyh6xEZGfnfwxGSSCQMGzaMYcOG8e6775KXl8eePXtYvXo19957L4MGDWLatGksXLgQP7+/Spz/KgT190NuqwCFBK1FBworSgcr5VWV2NrZ8MKLD9OrVyBrvvqe5L1FWKxmjIIFe6kEVBKsFgG1nQKd1siqj3czaGgfcjKvIAjg080Fc00nmppWdHodWl0bbu5OPPLIvf8y/58/E0FBopqh2WzAwcmZjlY9cuTYOSuRKqQkTOnH0IkRXcsf25/Glu/ETKudvRpNp549W5K5/d4J3PHEBFa/vY/scyXs+Po0kxfFsWudKIYwYkokNbWNmExmvHxcuFwo/hgMHhZFY0Mzp0+fB2D6zHEApKSco7y8Ejs7W+67b8m/VEb898LPzxuVKg+9xoJVMGOVWLEiYBHAYhWltE0GM22Vrdd9TvlP7YXp689hNpjBYObS3mwuH8mjx9Agug8KJHdPNjXZIlfFM9SLmMWDsHW9cYLUWt1GQ3Ej2g49Sns1cpWC1mYtVouA1mxGrpARGtONdR8cJietFACfbq4YDSaa6jqwSK04e9jT2tDJuUP5DJkaQcXlenZ+nnzNhHVID8bdGo3Dj/rwzSYLO9aI53johAh8urmy+/tTGAwmugV6Eh7Zg80bDmK1WgkJDaSyshqj0YSvryf1DXUADBgQ2dWq92dBpVLRt2846emZODioqauFs2cu/OFACCAkLJCoAWFkZuSz7fsjLH1gHsmJWTQ3dTBlzhC8/dxY++VO2ts1+Pi4c+sdU1Eo5MxfNIFjh8+QePQc9XVipSooOICn/nYntnY2CILAurVbSU3J4LHH72LCpKHkZheRlXmZFR9v4uU3H0CtVuHf3ZOnX7uVo/vOs3drMpeyS/ngtU08/doiMRh6fQ+Nte2seHU3970wFTcvR3pF+vHAu9PYvSqV/LRyjm5IJ3VvLtFjQogeG3rdOQWQyWW4+zvj7u9M7x/FER0tWjZ/kIjZZCFkoD+jFwz42WMlkUiYenccnz25i4rL9Zw/VMDo26KpyK+nraGTvZ+nMPvJEUx4eBi73jlOcVolx786S3FaBVaLlaDoAIYuGkhdSTP7Pk/BarXSOz6QSffGIZPLKM6uZtP7iZgMZrqFejLpzkGcPpBDfmYFCqWMRQ+PobNDy7nkPNrbNLS3amhrFZ/b2zRotYausY4Y159Zt4y4jrje1NjKW6+tpqmxFR8fd5558W6cnG6sJprNZlKS09m75xilpT/NmfoxdDoDVVV1VFXV3fS49ezZjX6RvenbN5TQsJ4/2WItkUgYP2kIvcN78v7baygvq+HNl1fx9PNLcPdwQW2j5KGnZ/PcIytpbemkf1QYVaVNVFc3orJV0FrfSfjkPnQ0aTFojVTk1VGRd21MMrkM9wAnekT4ED8zgtILVZRerObIyhRmvziO6txayjIq2Pe2mMyRSEAikyKVSJBIJSABiVSC2l6FvbsdDh72OLjbi8+e9ti72WHQGDm8/AQGjQG3QFdG3D+UmssNJK0TfxeiJoRRdK6c9kYNl06VEDOvP6nfnid9exZ+ET44+zp1jbe9voMjH53ArDfhHebF8Lvjqciu4eiqVPQaIxKphAGTehM9LQK5UkzmlOXUsuuTU+g1Rrx7uDHvmZGo7VVknixmz6prQdCExaJHXvKhHHZ/k4LFIuDXw507nhiHi7t4XRw/kMbOzacAmLlgOKMmDMRgMLL8vW8ovFwmHlOZFEdHe5yc7XF0ssfJSfSZcnSyRyqRknL6AleKK0k+dYHkUxfoHujLmPHxxA3uh0r1y4as/9cg/MqK0O/0OP6fhJeXF++++y5KpZJly5b9ZzhCN0Pv3r3p3bs3Tz/9NPX19ezZs4fdu3cD8OSTT/6Zm/oLP0JHqw6ZWka7VofObEAn02KVWbGxsef55x+iV69AADy93BAUFswmC3qjCY9gNyqvNGASzBgtBlzd7Wlp6uTsaTG77ePnSn1rNSaTCQEL7e0anF1tiIuL7gow/tvh5+eDUqkQne6xotXocLazB6nAiFl9mTr/2izoyN7zbNtwAoCJ0+PoOyCId19eT+rJbEZNHEivcF9ueWg03318hNSjeVzJq6G1SYOblwNjZg1g1ce7AAiL6MaBQ4miMV1cX3buEKWH+/YLo1evQARBYMuWnQBMnjweB4ebZ1//0/Dz80WlVqDtMGC1mLAIZlFCm6uBkFnApDfTXtN2Ux8hgCunimguaUKuVhA5O4rik0W0VrRQeKyAwmOiN4lULqXvjEiCR4f+ZEB4+dQV8YVUilQhwyIRJ0hqRyWGditegS6sfnsvWo0RmUzC6JkDGDWjP+eO57P969OcOXaJ8bOj2fVFCqd35dDRouPsgTwEQcDBxYYpd8cTOjDghu0m7cvqEkgYO3sgWo2exENiO9PEmfFoNTqOHxFV4aZMT2D37oOAqAqYdPIEAAMH/oR+7h9EdHQU6emZaLWdAOTmFtLW1nHdRLapsYW0tCyMRhNyhRyFXI5cLkOhUKBQKrC1UWNnb4u9nS129rbY2KiRSCTMXTCOixcKOHcmm8nThvPos/MovVJL7JDeFBWWk3hM5DjccffMLgNMNzdn5i2cwLSZI0k5lUljYwtTZ4zExkYMjDdu2M3+fWKS4ZN/rOW9D55n6f1zefFvn1BVVc/6b/Zx19JZgDiRGj9tEFHRwXz63jYa6lr59J2tPP7iwqvB0F4aa9v49KWdLHp4NL0i/LB1VDP/iRFcOFHEia0XaW/SkLQ9i9O7cwiPD2TQhN74Bf10l4LJaGbj+8fpaNHh4e/MrAeH0VTXzq5vUmht7BT9s6wCgiBgsVgRrAJ2DmoWPzGesbcMZN/XZzm6KYOQgf7MXDac9a8e5srFapK3ZzN0dj8SFkeT+PU58k+LSRLfUA/GLI1H26Zj+/KTXWapk++PRyqVkn++nC0fJ2ExW+kV5cf8x0ZQX9PK3g3i9Tb11ngcXGx4/Zk1tLVqfnK/HB1tWXjXWPrHXM/faGpq5e3XV9PY0IK3tzt/e+meG1oqOzo0HDl8kkMHT9LSIkrNK5UKhicMIiSkBza2NtjZ2mBr98OzLba2aoxGEy0tbTQ3tdLcLD5++LuysoaqqjqKi8soLi5jx/aDKJUKwnr3ol+/MIYOi8HV1fmG/ejW3YfnX7mXd978itraRt54ZSVPP7cEXz9PVGolgxMiOLIvjfrmJpGn16wlPKI7ZTl11FW28ujquTRVtVNX1kJ9WTP1ZS3UlbVg1JmoK2mm7qp63Ig7Y9n0/H4ay1tJ25XD4NtjaK/voKWyFYvR/JPHWdeqo+WfkkIASCTIFDIsRjMOng6MeSSBzmYtBz89hdUiEDyoG4PnR+Hf24u9y5O4eCifWS+MxTfch+rcGk59fYbJz45FKpOia9dz5KMk9O16XLu5MOqBoTSUt3Dgk1NYzFbcA5wZdXccHlcl481GM0mbM0k/JN5rfXu5M+epkajtlGQnX2HX58kIgkDM2FAmLI7BYray/etTnEsUl+8/pBdzlw7vMko9dSyT778Vv8M/yOVbLBZW/GMThZfLsLVV87cX76Z7oO/PJvfGTRzMleJKjh1O5UxKFmWl1Xy1chubvtvP8JHRjBoz6F+iUvi/ir98hG7EnDlzmD17NitWrGDZsmXk5OSwceNG3nzzzd+8rn9ZSt/T05O77rqLu+6661+1ib9wFY1Vreh1RnRGPRYsCHIjzm6uPPf8QwSH9KCjo5MTJ06zYcMO9MZO1DhjFaxoOw0Y9CY69Bo6ta2UV8u5Y8kMEg9mYrVakdta6KjQolTJcXGzp6FRg1wuIyLi1xPg/9OQSqUEBnano12LXq9DwILaRo5OZ6CspLZrudOJWV1B0OSZ8UyZM0QkpQ8KJf1sAds3nOCRv80lMq4nmvahbP/6NHVXW8Jm3jkUg8HEpYulAFgl4o9laFggcrmMxONixu0H36Bz5zIoLS1HrVYxdeqEf8+B+B3w8/NBrVJgtnQgWOSYrSbMgqgcZxHAYDBhMVuwmq101nfg6ON03ed1rTqyd4q8hohpfek5rBc9hgZRd6mW/EOXaLhcj5O/C4PujMPJz/knx2ExWShOLcFktGC0CEgNZvQ6M0gltHeIakjtWi1ajRG/QDfm3z8C3+7ihHfg8BAObDpHY207CnsFHn5ONFS1cWa/GOxHDg9iwu0x2NyEjNzS2MHR7aLvxpRFcdjYqdi7LRmdzoivnxtR0cHs2n4cvd5IQDdv/AM8yc0Vpb6DQ7rz7XeVyGQy+vf/VwVC/Vm5ci1l5eX0CAyhsqKW8+cuEjsoijOpGaQkp3HpUtFvWqdEIsHd3YVnnr2fwUOjSD51gS0bD/H083fh5euKxWJhzeodCILAsIQB9A6/sW1MrVYxauz1PKwD+xPZueMQAPb2djQ0NLPm6y08+NDt3PvgPN558yuSjp/HxdmBabNGdlWbvXxdefiZObz38nrKS+tZuXwnDz49m/tfnsqXb++npryZlW/uZczMAYydMxCpVMqAkcFEDg8i/1w5Zw7kUXG5nqxTV8g6dYWAEE96Rfnh7uOIq48jbt4OKNUKBEFgz6pUqoubsLFXsfAp0Qri63cPUl/d+pPHq7VJw5ZVSdzz7CRyUkspy6tj96pUbn9+LOOWxLJ/ZSopO7Lx6elGn+FBtFS1kXmoADd/JyY9OhwQ2PHRKTpbtLj5OjHlgSFIpVIunipm1+fJWK0CvWO7M+eRYVgsVtb/45qJZdyY3nz1yV7aWjW4ezgxMC4URyc7HJ3tcHSyxdHZHkcnO2ztVDdMSgsLyvj0ow20tLTj6eXKMy/ehYvLNcni1tZ2tny/j6QTZzAaTQC4uDgxfsJwxo4bjoPDz7e12djIsLFR4+t7c95jU2ML2dn55GQXkJWVT2trO1kX88i6mMf2bQe49bZZjB4z5IZxe3m78fwrS3nvza+prm7grVdX8dRzS+ge6MvQkZEc2ZfGpawSRibEcO54Ae1aLRKJhKKL1bTUd3a1w4F43QqCQFt9J0UZVRxfn87ZfZcIiQkg4Y4YDn2WTMa+SwT292PqSxPQtmgRrgbCglW49loAwWJF166ns1FDR0On+GjU0FHfgdlgxmI0o3ZUM+6xESCRsG95EgatCe8gN0bdFYfFZME/3IvguO4UninjxJpzTH50OLtfP0RTaTPZB/PoPSqEIx+doKO+A3t3e8Y8moBea2L/RyexmK0ERvky4aFhXSawtVerjD94pUWNCmbELf1RqhXkpJSw/dPTCILAwNEhTFoyiPZWLd98eISywjokEpi8KI6Eyf26zsHpxCw2fH0EgLGTY5gyezCCILDmy51cSM9DoZDz2FO3X+f99HPoGeRPz/vnsmDRJE6eSOPYkbM0NrRwYO8pDuw91eU19hdAECQIv0II4dcs8/8LOjo6ePDBB/nyyy8BiIiIYP/+/f+ZQOjxxx+/6fsSiQS1Wk1wcDDTpk3D1dX1psv9hT+OAaNCyL5QytnMXCwSE/auCoYMjSGoV3c+//xrjh1Lwmw2o9HoMQtGzBITZomZloYOpGopFq0FiQokMoGtWw/y7Ev3UJBXynfr9qBQyOke5EF2disWixGwoU+fP04a/XeiZ89ACgoK6WjXoVAqkMpBp9NTXlKHIAiUFFaz6eoNftKMOKbOHdr12RkLhnMxrZBLWaXkZZfSu28gg8eF09Gq5ch2UUUuLKob+7anYLFa8e/mQX6+qK4TNziS/fsSMRpN9OrVnfCIkOuqQZMmjfuvrQaBWBFSKOWYzCZkEgErFqwSKxauGqqarFiv9iRnbskg7u4hKG2vtTVc3JqBSWvEpbsrvUaI2WiJRIJ3uA/e4T5omzWonWxuWkn6McozqzB0GjDpTSjtVVgEkFoF1I4q9M0a7JzU1NW1ABJuf2wcbl6OCIJAxtkCkhOz6d7bk/z0SpIP5TDmloFsej8Re2f1T1aBANqaNWz8LFEUSAjzZsDQYBrrWzm0W6yETJ49GIPByOEDIpdk6vQRnD59HkEQ6NOnFwUFojJZv37h/7Jz7OHhTvfuAZSVVeDu7kRlRS0b1u/iqy83YbFcyw2GhQXh4eGKyWTGbLFgMokBrMFgRKsVeTwajRaTySyqyjU0s2njHu68cx5nU7PIyS4iN7uI8L69OHwghYryWuztbZl/y8SrE0HhZ1v/kk+nsXbNVgAWLJxKnz7BvPzScpJOnCEmNpLY2EimzxrFzm3H2Ln9OBczC7jn/rn4B4iTaE9vFx58ejYfvr6JvJwyvl11kDvun8TDr89g59pkziUWcGR7BsV5NSx6eDROrnbIZFLC4wMJjw+kqriRswfzyE0tpeJyPRWX668bn4OLDfbONtSUNCOVSZm3LAFnDzvWvH+I+upWnFxtmXNPAgqFDIlUIoqHSCXodUbWvn+Iwuwq0k5eZvq9g1nx9G5Kcmq4kFjIgFEh1BQ3ceHoZfZ+nsLtr09g8IL+9IwOwD3ABblKxv4vUqkpbkRtp2TWEwmo7ZScP5zPvq9Fv6qohCCmLh2MTCZlx5pk6qtbcXSxZd7SBM4l55F+tgCZVMo9j06je89fNqEUBIEjB1PZ+N0+LBYrfn6ePPnsnbi5OXct09TUymuvLKemRvQmCgz0Z8rU0QweMvBPa4d2c3dhxMh4RoyMRxAEKiqqyc7K5/SpNIqLy1i1cgOpqRncd98iPDyvrwy4uTnz3MtLef/vaygtqeat11bz+FO3E9q7B8Fh/hTmV2KVW5BIoOJKA6HB/lQVNnL+cAET74i9bl0SiQRnLweiJ4ZRXdRI/tkyDqw+w+2vTyAkvjuXU8s4tiqVea9OxN7tt3vlCYKAvsNAZ6MGe3c7lDYKdr17nLZ60cB00qPD6WzVseG1w6jtlMx5agQV2TU0VbZRcKaMuIUDOPXVGS7uyaHiYhXN5S2oHdSMe3wEMoWMne8cR9dhwL2bM+PuG4JMLsVqsXJ27yWSt2VjtYpeaROXxhEUJQYol86Wsf3TU6JC4sheTLk7jpaGDla8upvWJg02tkpufXQMoZEBXfuw+/vTHNglViJHjI1i1i2iit62zYc5mZiGRCLhgUcWENr7t3eKODjaMXlaAhOnDCMr8zJHD6eSlXmZ0LD/ja6Tfwf+VRWhkydP8t5775Genk5NTQ07duxgxowZXf8XBIFXX32VVatW0dLSwqBBg/jss88ID78mZGEwGHjyySfZuHEjOp2O0aNHs2LFCvz9/buWaWlp4ZFHHunqEJs2bRqffPIJzs7Ov3HE1+Dp6Ul1dfV1yRK9Xv+71vWH72oXLlwgIyMDi8VCaGgogiBQWFiITCYjLCyMFStW8Pjjj3P69Gn69OnzRzf3F24CbaeBjNTL6A1GjOjx8bajX2Rvli9fQXKy+IPas2cg7e2dXM5uwGI1Y8CE0WJGai9BaLegtlNgY6NCq9Xz3ltfd5Fe590ygS1bdqHR6FDbKHFwsKdbN/+fG85/HXr27H7VMBKUaiVWTJj1Ujo7dJQU1bDyo12YLVYGxIZcFwQBeHg5kzCuP8cOpLP1u0Sef3sxUqmU8fNiiB0VhrObPa0tnRzaLR7n6MGhbNiwW5yI9Qvim6e/B2D6zPFIJBLS0y9SVHQFlUrJ9OmT/u3H4rfAz88HiUSCRCKgVMiQmK1YTVaxKgQIFgFBIUeqlFF3qZbj7xxhyAPDcPBypCanmoq0cpBIGHhrrNhL/0+4GRfoZig8fQUBMCNBCuh0JqRKOTq9mKm297Chrq2VXuG+uHk50tLUzoavj5B9QWyn8/R0QSKBwuwqpt02mIc+nIGDi81N1aPMZgvJh3I4tCUNo96MXCFl5hLxmti87hgmk5nQPt0YMCiUQweS6ezU4uXtRvSgcJ56UmyNHDZ8EHv2ikICQ4bcqFD3Z2LgwCjKyiowmkQuSGen2CIVGOjPsOGxDB48EDf3X+cDYzQaKSur5oXn3iPtfBbzF0xl1JhBHD4oEtW9fdzZsfUoAPMXTcTB0Y6PPvyK8+cvMmx4LNOmj8XP7/rJeNbFPD77dB2CIDBhYgIzZ00QzUqnj2XXzsOs/Pw7QkJ6MHPOaLx93Pjm692UXKnipWc/YdbcMUyaOlys6gb5sHTZdD5/fwdnT1/CydmeWbckMO/eEfSK8GPr6pNcyavhw2e2suCBkfTuf8100i/InVkPDmPsLaIaXH1FC03V7TTVtqPtMNDRoqPjqs/KxMUx9IjwYf+ms+RllCNXSLnjifEEBHne9JiNnxvN3vVn2fNNKk99OJ9R8/pz+Ls0Dn2XRq8oP0bdOoD6shaqChvYsfwkt74yDp9g0Q/n3N5L5CaXIJFKmP7wMFy8HDi1M5tjV+WQf+BsSCQSLqYWc+ZYHhIJLHxgJHqDkc1rxXMxZc7gXxUE6fUG1qzeQWqyKPQxKL4fd9076zp1t+bmVl5/9SNqahrw8HDlgQdvp0948L+UwyiRSOjWzY9u3fyYOGkk+/cdZ9PGPWRn5fPE46+z6NaZjBs//LoxODrZ87cX7uaj978lP6+E995ewyOPL2LY6EgK8yu5mFFIWP/u5GWUI1GJn7uQVMSo+f1R2dycizRmcTRlubU0VLRyds8lht8WTVV+Pa11naR8f4GE22N+177ZOKqxcVQjCALHVp+hplDkLk1+PAGZUsaOt4/R2aqjs1VH5vFiht4ygKOrz5C2K4d5r02gW39/yi9U0nilCblKzphHE7BztWPf8hO0VLdj52zD5GUJKNRyWuo62P9FKlWFYhAbEhPAuCWx2DqIwjX558vZ+o+TWK2i8Ma0pYNpb9HyxRt7u7z07npmIu7eYnXfZDLzzcqDnE8RBR1+3C1x7PAZdu0Q2+TuuGs6A2P+mMqbVColakAYUQPCaGxowc3d+Q+t7/8n/KtU4zQaDZGRkdx5553Mnj37hv+/++67fPjhh6xdu5aQkBDeeOMNxo4dS0FBAQ4OYhvtsmXL2LNnD5s2bcLNzY0nnniCKVOmkJ6e3mUHcsstt1BZWcnBg2Lr+NKlS7ntttvYs2fPbxvwj7B8+XIWL15MfX09mzdv5uDBg4SF/b4k/R8OhKZPn46rqytr1qzB0VEsrbe3t3PXXXcxdOhQ7rnnHm655RYee+wxDh069Ec39xdugsyUIqrKG7BKLCA3Y+eg5GTSSc6cTUMul/PUU48waNBAPv98DUf2p2HBhNFiwiPIiZqqJkyCESc7JQ8tu4XvNx6ivKwGgH5RIQSHBtDe3onBoMfOQU3v3iH/lcT+n0PPnoEAmExG7Jyc0BuMKKRi5eLTd7ei1Rjw9Xdn8X0Tu/attbWDwweSGTp8ABNnxJOalENVRSPJiVkMGx0F0EUe3bX5JEajmZ7Bvmj0Il8jvG8vzqRmoNXo8PPzIjY28rpq0Pjxo3Fy+u92z3ZwsMfe3g6VWoFUAAQBQSpgNQuisaogYBYgdkk8Fzdn0FHXzrF3jhCzeBCZ34uTueBRIbj8AUPVzkYN1Zdq0XcaQS7DZDCDXIqAaIqrUMlpaGwFRKfz4wfT2bVZFDOQy6QIAtTXt9CnVxDlhQ2cPpjN3KU3FxQoya9h+9enqSkXuQLdgj2ZfdcwfLq5cTGtiOwLV5DLpCy4cwwWi4WD+0R59ElThlFeXk1VZa1YQQ30pbS0HKlUyqBB0b97338NYmIGsH37HoqKirjv/sW0tLQTF9//hoDk10CpVBIcHEhcXH9SUzPYuf0gdy6Zz8kTaVwpruTt11ej1xsJDunO8BEDSU/PJvWqVHji8VROJJ4hOqYv06ePIyS0J8XFZbz/3krMZgvx8QO44865Xd+vefMnc+FCDuVl1az8Yj1PP3Mfg4f2p3d4EF+v2s7FCwV8v/EQ6WmXusw0I6J6cus941m38gCH957D2dWeURMGMmBIMAE9Pfnu4yNUlTbx1TsHSJjSj7GzBqL+UYXSwcWWwVOun6zpOg001bbTVNOOykZBWHQ3LiQXcXxnpjjOe0f8ZBAEMGxSPzJTi6m80siOr09z27Ix5J4ppaqokb1fnmHhU6OY/shQ1r1wkMbKVg59eZYpDw7hSmb1deat3SO8Sd6T0xUEJczqx4i5UV1B0IbPRBn2EdOiCAr3Zfkbm9HpjPQM9mXc1F8OtmtrGvnHh99RWVGHVCplwa0TGT/x+taz5uZWXnvlI6qr6/HwcOWVVx+7oRrzr4ZUKmXK1DEMjO7HFyu+Iy+viK++3MyZ1Azuve9WvH08upa1tbPhyWfv5JPl67l4oYCP3v+WpffPw85eTUtzB15DncnLKKekuAYPT2da6zv5/OndBPbxontvb7r39sLF077rGNg6qhmzOJo9nyWTujOHXgP9GX13HLvfSyTneBHdInzw7uWO1XKVI2axYrUIWK3is9JGgb2rbVdr2j8jbXcuBSmlSKQSxj84FBcfR3Z/cpqGilYUKjkmg5nz+y5x+5sT6dbXh/LsGk6sOceEh4bScKUJg8bAqAeH4dbdhaR156nIrUOulDF52XDsXGy4eLyI4+vTMRnMKNUKxiyOJnxoj679K0ivYMvHSVgtVvoN7cm0ewejadez8o29NNd34O7tyH0vTcXJRUxQaTr1fPHhDgrzK5FJpdxy11iGjOwHwLkz2XyzRszuz5wzhpFjrl2DFouF5qZWGhqaaWxspr6+iYaGJhobW2hsaKKtvZOwsCDGjhtG//7hN60m/7tMfP9XIFx9/JrlfgsmTpzIxIkTb74uQeCjjz7i+eefZ9Yskbu5bt06vLy82LBhA/feey9tbW189dVXfPvtt4wZMwYQDU8DAgI4evQo48ePJy8vj4MHD3LmzBkGDRKvk9WrVxMfH09BQQGhoaE/O8YlS5bw8ccfdwVePyAkJIR9+/axc+dOsrOziY6O5s477/yNR0DEHw6E3nvvPY4cOdIVBAE4OjryyiuvMG7cOB599FFeeuklxo0b90c39Rd+Ar0i/NBbtZhlBuxd5XRqOjhzNg2ZTMbTTz9KbKyofOTu7oJCJcFsMmG1WjHojXR2aLFgxsbenpCwQJ567k7ef3stRoORu++bw4lEsfVHpVIglQr/c21xAN27B4iZCYmAVCZBo9MjkamwWq1oNQZsbVXc9/iMLmNBvd7AB39fS1lpNbnZRbzy5oNMnBHPtg0n2Lz2GO6ezvTuGwhA2ZVaUk/mAjBn0QhWfr4JgOjYcDZsED2Cps8Yh0QiISsrl/z8yygUCmbMmPzvPxC/ERKJBF9fHwovl2MxCFglFqxSCwKihLYAmPRmkEgZ8+x4Ur44RdOVRlI+FxWFbFxsiZjW7zdt06gz0VDcSF1hA3WFDTSWNIEgIJXLuuSspWYryKVILBa8g1zJz69AJpdw+OAZykpENaigEF9uvWcCe7acJuPcZeT24mQg43QhkxbEYudo07VNTYeevevPcP6ESBC2tVcxZVEcMSNE8QaD3sjmdWL2fczkGHz83EhKTKO5qQ0XF0eGJgxkw/qdAERH9+PCBTHj/q9si/sBoaG9cHCwp6OjE28fd0aNHvKH1zlz9gRSUzNISclg7vwpTJg0lJ3bj1Nf14xMJuXOu2dgsVj4dt02AIYNi0GnN5B2Povz58RHWFgQ1dV16PVGIiJCeOiRxddNeBQKBQ8/cifPPvN30tOySTyeyqjRg3FxceTxpxdzKimd9d/so7iwghf/9glLls5k8ND+xCdE0Nbayc7Np9jy7XGcnO0YGBeGh48TD702g73rz5B8KJekvVmcPZ5H/Jg+DJ3Yt2ty98+wsVfh38sD/17iBLuypIHvV54AYMTUSAYMDf7ZYyWTSZm7NIGPn9tO9rkScs6XMv3eIax8dg+XMyo5eyCPuEl9mPbwUDa/dYy8M2XYOqnJTrqCIAhEjuzFgLEhlOfXcWyjGASNWTiAodP7AnAuMZ8tq5IQBIiKD2L83GiO7kujML8SlUrBHfdPQvYLraUZaZdYuWILOq0eZ2cHHnx04Q0tTD8OgtzdXf4jQdCP4ePjySuvPcbBg0lsXL+T3NxCnnryTRbeMo2Jk0Z2Te6VSgWPPnErKz/bwtnULL5Zs4uEYfEkHrpAWUU17t5ONNa24R/rTmeLjtaGTjKTOslMEtuXHd3s6B7mSffeXvSK9CMsrjt5qWUUZVRycPVZbn1lHP3GhJB19DL7/3HqF8ctkYCdiy0ObrY4uNvh4GaHo4c9Bq2RcztEad+E26MJCPcmdVcOBefKkcmkzHtmFGd251KcWcXRdWlMWhrH5hcOUFPYSHFaJTNem4TFZMHW2YbMQ/nknihGIoFx9w3GvbsLh74+R1aiyAcMCPNk0r3xOHlcu/dkJhWxZ3WqyC+LD2T6/UMw6Eysfnsf9dWtOLvZsfT5KV3fk8b6Vj59Zxu1Nc3Y2ChZumx6129eXu4Vvvh0M4IgMHJ0LDNmi3w6nU7P8g+/JOtiHtZfKE1kpOeQkZ6Dh4crY8YOZeSowTg7/3cnBv+TsAi/zizVcvWwt7e3X/e+SqX6zR6fJSUl1NbWXjd3V6lUJCQkkJKSwr333kt6ejomk+m6ZXx9fYmIiCAlJYXx48eTmpqKk5NTVxAEEBcXh5OTEykpKb8YCK1bt46///3vNwRCICbv5s2bx7x5837Tvv0z/nAg1NbWRn19/Q1tbw0NDV0nw9nZGaPReLOP/8cxc+ZMTpw4wejRo9m6det1/9u7dy9PPPEEVquVZ555hrvvvvs/NMqfhyCx0KRpxogBR0doaWnC28eVJ554qCsIAnBxcUaplGPEjAUznZ06DCYjglTAYNKSnZ1PTEwkr739EFar6INz8EASIGA06VCq1YSH/+8FQgqFAn9/X5qb2jEa9JgtoHKQo9cZsLO3YclDU/D0FjNQVquVLz7dTFmpKOlccqWKC+l5jJkcTWlxDelnC/jiw5088dJCAgI92XpVQSd2SG+kCqipaUSpVCAIZlpb23Fzc2boMLGlYvNmMTAaO3Ykrq7/GxkvPz8fVColnXoLWM1YpVYsVyW0zVYBk8FMS1UbAZF+JDw2ivQN5ylLFVWx+i8YiFz1624xgiCQe7iAjB0XsZqv73RW2qno0GnAYkWjNyNIJWg1euQqOWbBAoBVZqGspA61WsHMhQkMHyNm0+MTIsg4d5ni4kp8u3lTU97MmeP5jJ7RH0EQyEwpZte6ZDrbxd7iQaPCmLRwEHZXW0kA9u9IpbmpAzd3RybNjMdqtbJvVxIAEyYPRSqVcPqUKIM7fMQgvvtuIwCDB1/PR/hXQCqVMmBAJElJyaSlXaBPn5//Ufk1CAz0Z2B0X9LTstm54zB33DmXo4fP0NmpZeKUYfh382bvnqPU1DTg5OTAXfcswNbWhsrKGvbsPsqpk+e6eHKBgf48+fS9XdLIJ06c5sSJ0zzwwF107+7H/AVTWf/dTtau+Z7w8GC8vD2QSCQMHxFNeN9efLVyOzlZhaz+fCtubs6E9u7B+GmDaG3u4MSRTL76ZC/tbVpGjh+AQiln5p1DCY7wY//Gc9RXt5K4+yIn92czYGgwI6ZE4uX/09+7jjYta98/hMloISwqgEkLY2msb2X9l4cxmy3Y2qpQ26qwsVVhY6PC1k6Nm6cT/WOCGTktimM7L7BjzWmeen8+426N5sDacxzZkI5/iAcBYZ6MXDSAY9+mdSl4BYR5MmZxNLoOQ1e7Ur9hPRkyTZTzP7k/i93fXJU2HhXG7LuHUV3RyO7vxQn53NtH4entgk5n4GJGPp2dWjo7tWg0oq9PZ6eOjnYNRYXlAISEBvLgsoXXiSIAtLS08fqrH1NdXY+bm/N/PAj6ARKJhIkTRzBwQARffP4dOTmXWbtmK2VlVSy995authu5XM59D82j8HIZzU1tOLqI392czCtMnTqM4zsvUlJcxxNfzKWysJGyvFrK8uqpvtJIe5OG7OQSspNLkCtl3P78OMbdGUNFfj21JU2c359P3NxIaosbqb+qKtfFE5NJkMqkSKXis0FjxGyy0NmspbNZS01h4w37FDUhjPARvSi+UMXpraKQzJjFMfiFeHS15lXk1VFZ0EDcnH6cWp9B6pZMevT3w97VlpKMSlI2XQBg8IL+9BjgT9qBfLISi5BIJSTMiyJ6UlhX0sGgM7Hv6zNkXVXd7DOoO7MeGobZZGH13/dTVdqEg5MNS5+fgquHONEsKaphxXvb6OjQ4eLqwINPz8a/m5goKC+r4aMPvsVsthAdE87iu6YjkUiwWq384+M1ZF64dPWcyHB3d8Xd3QV3Dzc8PFyvPtxQqZUkn07jROIZGhqa2bhhN1u+30dsbBTjxg+nd59e/3NdJ/9q/NbWuICA67mvL7/8Mq+88spv2mZtrSgm5eV1veCJl5cXZWVlXcsolUpcXFxuWOaHz9fW1uLpeWNV3dPTs2uZn4Pwb9AE/1Na45YsWcIHH3xATIzYz3zu3DmefPLJLtLVuXPnCAkJ+fkV/YfwyCOPsGTJEtatW3fd+2azmccff5zExEQcHR0ZMGAAs2bN+q8Ufdi55QRWqxVkFoxmEw6OtsTHx9wwEXN2dkapkqGXmLFKLDQ1tCORCwhmC82tTSz/4Etef/NJgoK6I5PJ2PL9TpqbW7G1tUFvVKJWq7razP7X0LNnIJcLimlv0wEqHJzVeHo4M2v+SCKirilfbd5wkIw0UQEnol8wF9Lz2L7lKP0H9uaOBybR2aGj4FI5n/x9C2OnxFJYUIlCIWfmguEcOij6zURGhXL2XCYAwxPikMvlXLpUQG5uPnK5nFmzpvwHjsDvg5+fDyq1grYWDRazUZTQllgRAItFwKQz0XpVlUimkBFz+yC8+/ggWKz4RV7jkmnbdChUchQ34eVYzFbOrE+j8JQ4ebZ3t8cr2AOvEA+8gj2oKWrk2JdnsVisSJUyDAYzMqUMr0AXSoprsVqsdGo7kCokPPT0HHqFXdtun349cHK2o61Vg2+0KzXlzaQcziEqPoid65LJyxAniV7+LsxdOpzAkOtbymqqmjh61Yxy/uLRKFUKzqZmUVvbiJ2dDSPHxJKenkNbWweOjvZ4erpSUlKGVColLu63cwp+D6Kjo0hKSub8+Qxuv33Bn7LOWbMmkJ6WzamTZ5k7dxKPPL6IvEslTJ42nLa2DrZuPQDAwlumY2srVtf8/X24/4HbmDd/KgcPnKChvonFd87p+n9OTh7/+MdKrFYrH3/8BW+88QJTp40hIz2HvLwiVnz2LS+/uqxrEufm5sxTz97Jin9s4mxqFp8sX88rbz6Iu4cL8xaPxmg0k5KUw+Z1x6itamLe4tHIZFIiYnoQHh3IpfQyTuy5SElBLedPFHD+RAG9B3QjJMIfJzc7nN3scXKzw8HJBqtVYN0Hh7t4EoseHo3ZbGXl8l1UlNXf/CBdxaK7xjJm1gCyzl6hoaaNvetTmbs0gfL8enLPlLL145MsfWsKA8aFUFPcyKWUUhzd7Zj+yDCkMik7v0imvVmLm48jk5fEAXDo+/Mc2X61TW5KP6YsisNksvD1Z/swW6xEDghiyIi+GAxGXn3+M6qrG352jOMnDWH+LRNuEDtobW3ntVc/pqqqrisI8vT6eSN0o9HIpUsFaLUir+oHwQwR4rNabYOLixMuLs44OTl2BS2/B55e7rz48qMcPJjE2q+3kHg8lc5ODY8uW4JSKVbxZTIZw4YPYNeORLKzC7pEEwyCEaVKTl1lC9VlTQRH+RF8VTTAZDBTWdhA6aVaCtIrqC1rYc+qFO57Zyqjbh3IgVWpnN6WRXC0P3NeGofVInYU/NQkXRAEdO0GOpo0dDRqaG/o7Hrd0aTBJ9iD+HlRNFW3sWeFKFsdNTqYyFG9AHDysGfwzL6c/D6TxPUZLHlnMoVnyqgtbiJp3XliZ/bl8BcpCAKEj+xF5LhQynJqSbxaSRy5cADRE68lKmtKmtjycRLNtR1IJBJGzI1i2IwILGYra947SHlhPbZ2SpY+PxlPX2cALqYV8eUnezCZzAR09+TBp2fj7CJWlhrqm/ng72vRafWEhgVy38Pzu76r67/dQXpaNgqFnOdfePgXg5levQJZeMs0UpLTOXL4FIWFpaSkpJOSko6fvzfjxg1jeMIg7Oxsf3Id/5fwW8USKioqruvS+q3VoB/jn8+jIAi/GKj+8zI3W/7XrOfnPv9n4g8HQitXruSxxx5jwYIFmM2ibLBcLmfx4sUsX74cgLCwsC6Ju/82jBw5khMnTtzw/rlz5wgPD+8ygp00aRKHDh1i4cKF/+YR/jL69u9FSLgfxaUlmEydqNU2REX1vWE5FxcnlCoFVsyY5UYEmcgpkghW1DZKzGYLH334FX9/9280NDSzf5/Ylz4wJpxjx+oICwv5Qz9o/0kEBfVAqVKg13dip7bBYjHTP64ng0dcO06JR89yYK+Ybb3nvjlE9AvmiUfepbyshrRzucQMiuC+x2fw4eubqCirZ/tGsSowbkoMzq4OnEkRW6L6RoWwcqUYWA8bLk6Gt24VifQjRw7D3f0/n239tfD19UGlUmAyG8CqwGQVAyELAlYBjPprgRBcJT7HdL9uHU3lLex/+wgSmZTeI4PpMzYUG0cxa2vQGEn8/DS1+XUgkRC7oD+9R13PQ0v6VgxEUMiQGCxYrAISuRR7NxsspVbkailSiwRPbxeCQq+XbpXJpMQNC+fQnnM0Njfj4GRDW7OWdx/fhMUiIJNLGTNzACOnRyGXX39tC4LAhq8OY7GKE89+A3shCAJ7r1aDxk4YjFqtYs8uUXFw5KjBnDsnym337dsHR8cbS/n/CvTv3w+pVEpFRRX19Q14el7jUZjNZi5dKqCmppaGhh969RtpaGiivb0dR0dHPD3d8fAQH56e7vj5+RIWFkzffmFkZ+Wza9cR7r5nAWF9xITB2jVb0Gp0BAb6M2Jk3A3jcXNzZtGtM657r7GxiXff/YeYsAFyc/M5cOAIkyaN44EHb+OpJ98kL6+If3y0hnvuXdg1AZJIJNx932xqaxopK63m4w++4/lXlqJWq7ht6QS8fFzZufkkSUczqa9t4Z5Hp2FrJ3ohhUcHEh4dSFlhHSf2XCTnfAl5GeVdwe8PkEolqG2VaDsNqG0V3PnUBGzsVHy76iAVZfXYO9gw59aRmIwmtBqDaFWg1VNf18qlrFJ2bDxJVEwI8+5N4LNXdnMusYD+g3sx9Z54akqbaK7tYNfKZBY8MZIJ98TRI9KX7n28sHVUk7I3l8sZlcgVMuYuS0CplrNrXQqnD+YAMGF+DKNn9EcikbD7+1NUVzbi4GDDrUtF0YnvNxykuroBBwc7wnr3wNbeBnt7G+zsrj7sbfH2cadbd58bzlNrazuvvvIRVZW1uLk58/Irj+Hl7XHDcnC1YpubT1JSMikpZ9FotL/y6hTPoaOjI66uzri4OOPm5kpwcE9CQnrRrZv/rzIb/qE65O7uIgp0nMvirTc+5aln7uu6VoaNGMiuHYnk5hSzaNF0CvMrOZd8iQFDenP2eD7Jh3II6uPbtU6FSk6PCB96RPgwaGIfPntiJw1VbSTvyWXYjL7kp5ZSkl3DgVVnuOWlsT/J/fnxGG2d1Ng6qfHqefN7vF5jZMeHJzHqTPiHejL6toHoOg1seOcYNo4q5j6aQO7pEpqq2zi9LYuRdw1i84sHKb1YTVVBPWajhYBwb4YtGkhbfSe7Pj2NYBWIGNaTgRPEarAgCJw9kMeRDelYzFYc3eyY/dAwuvf2wmy28M1HRyjKrUaplnP3s5Px6SaONfFQBt9/c0wMtCJ7cM8j01DbiIFmVWU97731FS0t7fgHePHYU7ejVIpJrWNHk9mz5xgADz4kimv8GiiVyi7lwJKSCg4fOsnpU+epqqxlzddbaG3tYOEt037Vuv5/hyD8OrPUH5ZxdHS8LhD6PfD2FpOCtbW1+Phcu3/U19d3VYm8vb0xGo20tLRcVxWqr69n8ODBXcvU1d1oqtzQ0HBDtemnEBLyy9z05ubmX7Wum+EPB0L29vasXr2a5cuXc+WK2PccFBSEvf21/tSoqKjfte5fkvYDWLFiBe+99x41NTWEh4fz0UcfMWzYsD+wRyKqq6u7giAAf39/qqqq/vB6/xUYPCySgsICtHvqKS9vRCKREBkZccNyLi7OKBQyrOixYMbeR0FtTRtWiRkbGzFrW1fXyMrP19Pc3IrVKhAfPwC9XlSi+jPabv5T6NFDVI4TBAtqGwU6vaFLFALgUk4x674WyZ+z5o75f+ydZXgUZxeG79nsbjbuEMMJliAJGgIEd3eXoqVQoEihpV6gVCgt1hZ3l+IOgQQnCcQIECAQJcRtszbfj4Gl+YACLbSl3ZtrL2Dn3ZGVmTnvOed5aBAgeb+0aRfArh3H2bntKHXqeWNhac6493vwzScbeJCWjZ29Fa071eNa9G0y0rOxsDAnvyAHvd5AuXKl8PR049atO4SGXkEQhDcqGwTg6emOXG6GXq9HYQYGvQ6DaEAvipKMtl7kwZ2MZ87uiKLIxS1h6LV60OqJOBBN9NFYKgVWpIyfJ2dWXyQnNQe5uZzAUQ0pVbN4IJOXUUBCdCo6rZ4CnQGdzoBeNGAul5OclC5tQ24APfg38Tbug0ajZee2o5w8dpF2HZoAEBMRT+vWDQg+EIVeL1K2Ukl6jQp8ZrnU+eBoblyTMn69h7QAIOLKdeLvJKFSKWndtiGx1+KIjb2FXG5Gu/ZN+fLLb4C/pizuEdbW1lStWomoqGtcuhRGmzYtiIiI5vTps5w/f8moJPc01Oo07t9/MpswefI7dO/eloir1zhx/Azde7TF0dGe+PhEjh2VMp/D3ur1QjexGo2Gr76aT05ODmXLliYwMIDVqzeyZs0m/Pxq4upakpGj+rNo4WrOnLlMbGwc48YPxdtHqiIwN1cyccogPvlgIfF3klj203bemdAPQRBo07k+Jd0cWbl4HzGR8cz9eB3vTO1hLHUFKONVkiHvtSYtOZuLQddIT8khKz2f7Iw8cjILMBgk4Q1BgAHjW1LC3Z6zQZGEnIxAEGD4uI7G/ojfotcb+GrmWu7F32f7+pMMfbs9DVtV48yRaLYtO8V7c3vRa2JTls3cR+yle5zdH03DDt54B0j9OQk30jj6cDa/zeC6uHjas/mnk1wKkqTXuw0LIKCNdB6PjYrn6H5pQmDw6HbY2FoSGXGDI4ek0rkx4/tQvcaL3YACJCQkM3fOElJTH+DoKAVBvxUieDwuiZMngwkKCiEt7XGpl6OjgzHglpQli8/aFhQUkpWVTVZWtuTVk51NdnY2t29LJTVHjkglxRYWKry8KlCpUkUqV/aiUqUKvysiU7duTT6cOZ6v5y4hOvomn30ynw9mjsPe3pYSJZ3w9qlAVGQcWTmZRtEE51LShETkxTtkpOUaS8B+i6WNOW2H1GX7gtOc2nEV7wZlaf1WPVZM30fijTRCD1+ndps/d/0zGAzsW3KGjJQcbBwt6TrhcUbw3kOVtyun4mg1tC6bZh/lyvGb+DQpT51O1biwKxKtWoejhy1t3glAr9Ozc/4p1HlFuJZzovUwqRqnIEfNrp9DuH45AYDKdUrRZXQAljZST+zGhceJCb2LQmnG8GntKF1RKlk6dTSczaulYKZx85r0HdbS2HsWd/Me3321iry8AqPcuqXVwyxvRCzLlkqlwL37dKRhwB8ThylXrhSjxwxg4KBunD59kaNHTtOy5Z/vd/y3ICIYrSqeN+5VUa5cOVxdXTly5Ai+vr6AdC4PCgpi7ty5ANSuXRuFQsGRI0eMfTrJyclERkby9ddfA+Dv7092djYXLlygXj3punj+/Hmys7ONwdLz+Oyzz7Czs3v+wD/InwqEHjVJ/fzzz1SqVIkaNV6uMfp5PE/ab/PmzUycOJHFixcTEBDAzz//TLt27YiOjqZ0aUk+tXbt2hQVFT3x2sOHD+Pu7v7E8494Wl3iP7luNSw0itycPKxtLHBzcy02K/wIe3spI4RMh8FgoLCgCE2RFp1QhLmFLf36d2bL5r2cOyfVIKtUSgYP6cHkKTMB3kihhEeUKiXdYIsYpPLAQjV345MRRZHMzBwW/7gRg8FAw0a16NK9ufF1bdoHcPjgGRLupXLhXAT1/WtgZ2/NuzN6sWdrMI2a10BprmDXduki0qBhTc6ekW5u/j8b1LixP25uL6/m9Xfi6loCQRBQKGQoZWagM0heQqKIARANIoUPfTJsXJ4UBrh3JZGUa6nI5GY0GFCb66fieHA7nZijscQcfShO4GBJywmBOHraA6DT6EmITuHOlSTuhCciiqCwUGD2sD9ILgqUKG3PjVtJGAwGcvNzMJPLqBcg9SnevHGXZUu2GcuFQk5fpnxFN27dTEZhLaNha2/cyzhRv3mVp/6mtVodB3edM0qid+juj5OLHXq9ns0bJPnPpi3qYW1jya8Ps0GNm9RDoyni1q07f2lZ3CNq165FVNQ1tm/fw6ZNO4s1y9rZ2eHlVR5nZydcXJwoUcIZZ2dn7OxsycrKJi3tAffvp5GWls6tW3e4efMW27fvYd68WVSuXJ7Y2Fvs23OMgYO7s3rlVkRRmiCpWs3LeJ78vXKhn39exc2bt7CxsWbGjEmUKOHCpUthREVdY9GiZXz++Qc0blKPkiWdWbhgNSkpaXz+2Q907tyS3n07olAocHK25933BvLVl8u4cC6C0mXc6NytGQC16nox5ZN+LP52J6nJmXz10VpGT+xCZe/imUkXNzva9y2usGYwGMjNLiTrQR4qCyUlPR1IiL9vNI7s2CPgqUEQSNnGfm+14ptP13PudBQNm1anfb/6RIfGk56ay6EtF+k8uCFtB9dl34rzHN0QSulKJfD0cqEw72FfkN6Ad4Oy1GlZiZ0rQ7gUdB2ZTKDP202p3VgKBO/EJfPz9w/PIc1rUt2vAvl5BSxbIvW0tmzd4KWCoLCwKH74fjkFBWpcXBz56OMJTwRBMTHXWb58LTdv3jI+Z2GhomHD+gQGBlC9erUXuh4aDAZycnLJyMgkMzObrKwskpJSuH79JjduxFFYqObq1SiuXo0yvqZ27VoMHz4Id/ennyureXvxyWeTmPXFAu7cSeDjmd/x4cxxlHR1IbBZXaIi4zgTEk7DgLocPxTKtag7lK/iyq1rKXw1YSNlK7tS1bc0VWqVxrWUg/E4fBqWIzwojrirSexddpbBM1vTtJ8vR1ZdJGhzOBV8PbAv8eQ5ThSlXkm5wux3PdGCt0UQF56IXGFGt4lNsLRVEbInkthL94xjjm0KY/z33fAOKEdUyG0Or7jAgI9bk3Q9jfzMAjpMaorSQmFUm7O0VdF1YmPkSjl3olPYsfA0ORkFmMlltB5Yh3ptqjycABTZ8nMQV87dwkwuScI/yo6Fno9l40rp+96uSwM6925kfE8iI27w43frUKs1lK/gyeT3h2JjKwkqJCWl8t23S9HrDQQE1KZHz6erj70MVlaWtG0bSNu2T1f2/K/yshmhFyUvL4+bNx+bbt++fZvw8HAcHR0pXbo0EydOZPbs2Xh5eeHl5cXs2bOxtLSkf//+gHRtGT58OJMnT8bJyQlHR0emTJlC9erVjSpyVatWpW3btowcOZKff/4ZkOSzO3bs+FyhhEf07dv3qX1Gr4o/FQgpFAoiIyNfW4Dwe9J+APPmzWP48OFGEYP58+dz6NAhlixZwpw5cwC4fPnyH9q2h4dHsQxQQkJCMdWL/6eoqKhYwPX/qh2vk+Tk+6SkpJGXn4+Ti+VTs0EgNVY7ONiD7D4GUU9WVi6CDETBgFxuRvMWASgUctaslpr6+/TtRJGmiKysLORyOZUqVfjLjulVY2trg42NNSpVJgbRgLpIR35+IWn3M/hp4RZycvIpXcaNYSO7Ffs+W1lb0rZDI3ZsPcqubceoW98HmUxGCVcHho/vBEgXi5hoKSvQIKA6Bw4dQRAEGgbU4d69RM6elRrpe/R489L8SqUSFxdn7sangk4EuQG9xoABA3oERJmAVq0jKTqFyoEVi71WrzNwcWs4AD5tqlCpcQW8GpUnOSaVK3ujSL1+H6eyjrQY3wRLOwuyUnI5szmMe5Ep6LR643oUKjk6QYBCHRqdHswEdEjLrRyUFOTKqFytNDZ2lmxct5+D+yTHdHt7G/LzC0lOfkD9er7cupnMpbMxfPzNW888Z926kcTanw+QnCSl2WvV8aJlBymoOXb4PPfupmBlZUGnLk1JTEzh8iVJCapzl1acOSOZrfr4VP3LpdHr1vVjzZpNpKdL+21jY42/fz0aNWqAj0/VZ2ZupJvNxxej3Nw8RowYz507d4mMjKF7z3bMmbWII0dO4+HpRmTkdRQKOQMHdQPg++8Xc+7cRVq2bEa3bh1wcSneX3Lo0DGOHQtCEAQmTx5HyZLSxWzcuJFMmDCdiIhoDh06Rtu2LalUuTxff/sBq1Zu5fixM/z66xHCr0Tz7oRhlCrlTqUqZRk0tBMrl+1i2+bDeJYqiV8dKfgtVbYk078cyJLvdnInLoUfZm+VApNu/jg6P/uzkMlk2DlYGdWyCvLV/Dz/V7RaHd41y9G+m//vvu/lvdxp1Kwmp49fYePyw3z41VB6jGjC8rkHOH0ggsq1SlGnVWXuxKQSdfYOW+YHMearTuz++QxZaXk4lLSh00h/YsLucuawFAwMnNCSGvWlMsTbN5NZ8NVWCgqKqFjJg54DmwKwdtUeMjJycHV1pnf/tr+7j48QRZH9+46zZvUOo/HvpMkjsbMrniEJD49g9uzv0Gi0mJmZ4etbg6ZNA6hXr7axJ+dFkclk2NvbYW//5GyuwWDg7t0EYmNvEBt7k+vX40hISOTy5XCuXImkW7eO9OzZ+an9DeXKleLLWVP58osfSUlJ46OZ3/HhR+Pxq1sNKysLMtKzcXaXPverYXG8O603+WvOkZqYxa2YZG7FJLNvw3nsna2pWqsUVXxLU9Hbgw5v1ZdMcaNSuBp8i1otvLh2Lp571+6zadZRLG1VaIt0aDU6tGod2iKd8VwlCAJW9hbYOFpi6yQpx9k6WmHjZEl+tppzu6VyxzYj6uNa3on4mMdKge2G1uPS0eukJWRxYms4zQb4cTMskfvxmVw9GUfnqc2M2/it2ly3iU2wdbLiwqFrHFh1AVEUcXKzpeeEQNzKSv3MBXlFbP7pBFGX4pHJBAaMb2E0S70WGc+KhXsRRSnI/m0QdOFcBD8t3IxOp8enekXGvzcQCwvps8jNzefrr5aQn1+Al1dZ3n5n0FN7SVJT73PvXhL37iVw717iw/LdB1SpUpHWrVvg51fjhbLK/3Vel6HqpUuXaNasmfH/7733HgBDhgxh1apVTJs2jcLCQsaOHWs0VD18+HAxBbfvv/8euVxO7969jYaqq1atKtZGsX79et59912julznzp1ZuHDhC+3jX5GA+NOlcYMHD2b58uV89dVXr2J/XhiNRsPly5eZPn16sedbt27NmTNn/vT669WrR2RkJImJidja2rJ//34+/vjjZ46fM2cOn3322Z/e7h8hPOzRbJoBMzPZMwMhANeSzghCLAaDDq1Gi8JcwEKpwNbWGjs7Gzp0bEFmRjb5BYW0bdeUEyeknhkvrwovfRH8p+Hu7kbCvRQ0Gg0ymVTfvHD+Bu7cTsLSUsX4SQMwN5eO0WAwEBYWRbVqXrRu25BD+0NITLxPyOkwGgfWNq5TFEW2b5Zm05q3rE9UlFTW4lO9Mo6O9qxduwlRFKlfvzZlypTiTcTT053IiOsU6UQMSBLaeoOITq/HIMjRFmk5vzEUe3c7Sno9nlm+duIGuam5qGxVVG9XFXgoyV3NFfdqruSm5WHlaInMTEZafCZ7vj1BYa40mWDtaEnZWu6UreWBwkrJuk8OoSnSoUdEKTcjMfEBIJJXJPUqVPYpxUfv/0hyslS+06iJH/0HdWDFLzu4dDEKtaYQhUJOclIGt28mU96reDa4SK3h1y2nOXEoFFEEGxsL+gxtSe0GkoR2dnYu27dKn3Pvfm2wsbVi/fqdiKJInbo18PBwNQZCr9tE9Wl4erozYEAv7t9/gL9/XWrU8H6iMf5FsLGxplmzJhw8eJTduw/w4YeTKVeuFLdv3+Pnn9YD0LFTS1xKOHH1ahSnTknn2v37D3P48HECAwPo2bMzbm6uxMRcZ9mytQAMGtSnWN+im5srgwf3Y9myNaxatQE/v5qUKOGCSmXOmLcH4le7Oj8tXkf8nUSmT/uKse8MJqBRHZq1rM/d+BSOHTnHTws38/EXY/EsJdWZ29lb895H/Vi/7BDng6MJPnGVc6ciadS8Bm27+hubvp+FKIqs/eUgaalZODjaMGxshxe6CHfp05jwi9dJTsrg6P5LtO1cnwYtqnLuWAwbFx5n0tyedBrpT8rtDNJTcvjlg71kpeVhJpfR890maLQ6tvx0EoAmHWoUC4J+nLOFwkINFSt5MO79npirlJw/e5UzweEIgsCod3oVM0R9FjqdjqW/bOTEcamUrllzf0aO6vfEd+TSpXDmzp2PVqvFz68mEyaMeW1BvUwmo2zZ0pQtW5o2baTS08TEZJYtW0NY2FW2bt3FyZPBDB8+iPr1az/xWbi6ufD5l5OZPWshd+OT+OTjecyYMZaAxr4cPniGqMjreFX25EZsArfiEpn6XR/SU3O4Fn6XmLC7xEUnkfUgj7NHYzh7NAZrWxVjP+lM0x41OboxlENrLuJV04O2IxuwcsZ+ctLzyUl/dpmpKIrkZRaQl1lActzTx9RtXxXvgHLkZ6vZ9mMQBoNI9YBy1GtThRKe9qz+8jCXjsRSp2UlAvvW4vCKC5zeeoXK9Uph7WD5VLW525HJxiCoZpMKdHirvtEs+l7cfdbMP0Lmw+9bn7ebGr9fd+KSWfLdTqOZeL+3Whrf45PHLrBy2S5EUZR6Y8f1QaGQvis6nY7vv1tqlFqf+v4Y473BmTMXOH/+EnfvJpCYmIRGo33q+3DhQigXLoTi5ORI69bNadGiyRvVO/tX87oMVZs2bfq7qmyCIPDpp5/+ruKcSqViwYIFLFiw4JljHB0dWbdu3cvt3EPeCNU4jUbDsmXLOHLkCHXq1MHKqrhfw7x58/7sJp7KgwcP0Ov1T5X2exFJvke0adOG0NBQ8vPz8fT0ZOfOndStWxe5XM53331Hs2bNMBgMTJs2DSenZ/9QZ8yYYYymQcoI/b+E4euiYUAdtFot8+cvRhAEatSo9syxJV1dEGQCoqjDYBCRKUBlocTCwpzc3DxsbKwZNORxGeKlS1KZ3JvcH/QId3dXzFVK8nLUyB5q8t+5LclkjxnXh5Kujz/f1au2cWD/SRo2rM3E94bTvlMTtm46xOrlv+Lm7kJFL6n0MuxyDHE372FurqBj16Z8+vF3gOStcv9+GkFBUj9Fz55d/spDfaVIgglKCvI0YNAhykREw0OxBBHMbVQYdHqOLThF+xmtsHezpSiviCt7pBlQv241SL75AKWFAtcKjzMGj0rpkm+kse/7IIoKtDiXtqf58AY4l7Y3XpSDNoUDoLBUoNNosXWxJDu5AIW5GblFOZibK7gZd5vk5Ac4ONgybGQ3avlJZZz1GlTn0sUowsOu4Vu3MhfOxHA2KNIYCOn1Bs6dimTv9jNkZuQC0KCxNz0HNsPa5rHX0Ob1ByksUFO+gieBzeuSmZnNqSCpdK5Ll1akpt7n5s1bCILwl5fFgXTB6tWr6ytZV6dObTl48CiXLoWRlJRC9x5t+e7bpYiiiIODHd26t0EURdaskTyz6tXzo7BQTURENMeOBXH8+CkaN/bn6tVodDodAQH16dbtyd64Dh1aExJynpiYWBYuXMpnn80wfub16tWkYsUy/LRkHeFh0SxZvJYyZT3w9HRjwJAOJCXeJyb6FvO+Xs37Hw43/naVSjnDxnagcfOa7NkWQmz0XU4eCSfkZCRNWtakbZcG2Ng+XYnq+IHLhF28gdxMxqiJXbC2sUAURa6ExSKTCTg62ePkbG+cFX+EtY0F3fs3ZfXPB9i/4wx1/avQZUhD7t68T1J8Out/PMbomR3pNSmQZR/tJytNMl1uPaAO7uWdWD73AHk5atxKO9K+r1RDf+tGEgu+2kphoQavyp6Me78H5iolmZk5rF4ulcl17trUeB76PbKzc5n37VJiYm4iCAKDh3SnfYfmTwQW589f5ptvfkSn01Gvnh9Tp75rlD3/q/DwcOPjj6dx7twlli9fS1raA7766nv8/GoycuTgJ0qLHR0lpbu5c5YQG3uL+d8v5/3p73D44BnCLscwcGBXbsQmEHLiKu26NsCppC0BbXwIaOODpkhLXHQyMWF3ibp0m+yMAnasCGbE9HZcDb7F/XtZHF5/ia5vN2Lw521IS8hGaS5HrjSTFDAfPpQq6TlNoZacjAJy06VHTka+9Hd6AbkZBZSq7EJgn1oYDAa2LThFbmYhLh52dBrpjyAIlPNxo1r9MkSfj2f/yvMM+ag1EUG3SI57wPF1oQT0qM6eRcXV5nIzC9j24ynpucAKdBkTYCyFO3skml/XnEGvM+BYwoZBE1tRqrw0UZWalMHCr7dTVKSlindphr3TAZlMhiiK7NsdxJaNhwBo2rwuQ0d0NWZtRFFk+bLNREZeR6VS8v6MsUb/n+Dgc3z7bfGbYblcjoeHG6VLe+Lp6UGpUh44ONhz5sx5Tp4MJj09g40bt7Fp03Zq165F69bNqV275hsryvS6eF2Gqm8CjwR2Xid/OhCKjIzEz0/yqrl+/XqxZX9FSuuPSPv9lkOHDj1zWefOnenc+cXKmf6IYdWrws7OBitrc2ztrPDyqvBEMPpbHBzsUShlGIqKcHJXodYVIIp6zp2/wIwZnzN//mzjLOGtW3c4d+4SgiDQuPHvl4i8CUieOAoyNIVYW1ih0WhQKpV07trMeOMMcCU8mgP7TwJw9mwofZI70a5jI2Kv3eZq+HXmzV3NR5+PwdXNme1bpCxB63YBPHiQTlLSfZRKBfXq12LNmk0YDAZq1vTBy+vNLSv08HDFXKVAp8sHvVbKCpmJGEQRndaA0sES53KOPLidzpH5J+nwQSsiDsSgKdDg4GmP0lrFnm9PSuuqWoK6nX1wryL1Ht2LSmH/D6fQafS4eTnTYVIg5paPM4+iKBJ95jaIoC7SggBFOmmW0dxGjpAhUN23PBdCpRLY8e8NKHZzWNOvCkqlgpSUB7RtH8iFMzFcPneNngObEhl+i91bg0lNzgTAydmW/sNb412zuNlkbMxtgk+FSjeRb3VBJpNxYP9JdDo9lSqVo3KVCuzcuRf4e8riXjUeHm7UrevHxYuh7NlzkNGjh1K6jDt345PoP6ALKpU5ISHnuXnzFiqVOWPHjsDe3o6YmOts2/Yrly+HGzNFpUp5MH78qKeekwVBYPz4UUycOJ2rV6M4fPi4MTMA0k3ujA/eYfashVwJj+GH+SuYPWcaCoWCcRP78/nHS0hNSeeLj5fw3vtDKV/hsWx6xSqeTJrZh9ioeHZvDSbuehLHDlzm9LErlCnnirWtBdY2ltjYWmD9MDDasUFSA+w5qBnlKkoqSb/uOM6OrUeL7belpQonZ3scneyoULEU7Ts1oUETb84ERXDjWgJbVh/j7SndGTihJfM/2M6tmGSObr9Mm9516fBWA/b8coaq9ctQr20VQg5FcS38HgqlGQPGt0CuMCseBFXxZNw0KQgSRZEVv+wgL6+AMmXd6dLjcS9jenoWhYWFqNVFFKk1qIuKKFIXUVCgZueOg6SlZWBpqWLCpOH4+no/8VkEB5/j++8Xo9frCQioz6RJY/9QRvFVIAgC/v518fWtzrZtu9m1ax+hoVcYP/59unfvRO/eXYvtm7W1FR9+NJ63R39IenoWOTk5lCvvwe1biRRo8rG0MicjPZeoK7ep7vv4PKw0V1DVtzRVfUvTtGMNvpmyhZtRSVy9cJtOI/1Z8clBwoPiqNm4AuV83HB+2MP4LJQqBdYOlvCcU/2JLWHcjkxGYS6n96SmaIp0/PDhDqxsVPQe3ZTrYQnEx6QSfS6e1sPqsuajg1w7L5XnadSP1eb0Oj1b5weRn6OmZGkHOrzVAEEQUBdq2PbLKcLPSmkpnzpl6fN2UyyspPuTzIxcfpizhbzcQsqUK8mY97qhUMgRRZFN6w8YlVM7dW1Kzz6ti/129+87zrGjIQiCwIRJkhcYSKIaCxf+AkBgYCMaNqxHqVIelCzp8tSgpmrVSgwa1Idz5y5y6NBxo9DLpUthODo60LJlU1q1avpEqe1/Fb0B9C9wW6t//THDv5I/faY7ceLEq9iPl8bZ2RkzM7Mnsj+/lfb7LxEeLvUq/F5ZHEhqP0qlHHWRiEIlkPWgCDOlBpVKSUJCIr/+ut/Yy7Jhg9SM26hRA8qWff7M4z8dd3c3BEFAZgbWtirU6iLqNahB994tjWNyc/NZvEgq51EqFWg0Wnb/eoTRYwYwbmJ/vvpiGbfiEvhmzkpat2vIvbspWFqqaN+xMdu27QegTp0aaDQajh17eGP1BmeDQHrflEoFWq0WM/RoRS0GRAyCJJaQn62m4ZD6nFh8mtz7uRz+7gTZqVJ2pU6vWpzeGGZcV2LMfRJjjuNeyYUyNd05vyMCg95AKW9X2r3bGMVvDFhFUSTmbDy5GQXo9QYMoohCKSftQTYGg4G0TCmAsXOyoKhIS4mSjlSo+DgLm52dS0jwRbwqlSYqMo4H6Q9wcrYl/UEOH7+3jOwsqczFylpF284NCGzti1JZ/JSo1+tZs1JSE2zavC7lK3hSWKjm8OFTAHTuKtU8h4RI2aG/Ui3uddK5czsuXgzl+PEgBgzoxYwPxpFwL4kaNaui1+tZv37rw3Htjf0fVatW4qOPphIXd5sdO/aQmprG5MnvoFKpnrkdd3dXBg7sw4oV61i1agM1angXm/UXBIG3xw5i6uRZxN9JZMP6XxkytCc2tlZ8+OlovvtqFfF3kvjqi6W8O3kgPtWLiwZU9i7DlGqlib56h91bg4m/lcKN2IRn7k9d/yoEtpIUkq5F32LnNkkExcOjBJmZORQUqKXH3RTu3U3hSlgsWq2OXn3b0O+tVsyavporoXFcuXSTmnUq0mtkIOsXHOPozlDKVXXDt2lFKtcuhYW1ktSETPaul0rVOvRvgGspR27dSOLHOVtQq7XFgiCQypWuhMWiUMgZ/U5v5HLp5vWH71dw5szv98K6urowbfoYPD2flNEOCgph/vwliKJIYGAA7747+h8xI69SqRg4sDfNmjVm6dLVhIdHsGXLTm7evMXUqeONSqfSWHMCGtXh8KFTnDxxlsBmdbl9K5GQoFAaNK7J8YOXCT52pVgg9FscS9jSopsfBzdfZM+as0yb14c6LStx8Ugse5efY8zcTiiUfz4wvHklkVM7pWt1p5H+OHvYsXzuAVITswC4duUejbtU58TWcA6vv8y477rg17oSlw/Fkp9daFSbM5ObcXjdJe7G3sfcUkHvSU1RmMtJvpvOmu+PkJacjZmZQPv+9WnSvoYxmMnLLWTBnK1kpOdS0s2Bce/3RGUhBdnLf9nBqROSMmG/ge1p17G4+u6FC1eM/cODh3Sndm2p1FWtVvPVV/NRq4vw8anKu++OeqHvj1KppEmTAJo0CSAxMZnDh49z/PhpMjIy2bJlJwqF/JVlud90XlePkAmJV9Kldvr0aQYOHEjDhg2NAgNr164lODj4Vaz+qSiVSmrXrs2RI0eKPX/kyJEXluT7tyCKIleuSH1CzwuE7O3tUCrlCDIRjUaLTqdDq9UY/QK2bNlJWtoDYmKuc+lSGDKZjH79er72Y/gr8PSUyqEMBklCu/+QdkyaOrhY2n/ZLxvJzMzG3b0E094fA0DQyXNkZGShUpnz3rQhlHR14kFaJhvW7AOgXcfGqCzMCT4tiSI0DqxHUFAIWq2WihXL4+NT9W842leHh4cbMpmAIDOATMQg6tGJkoKbKIBWraMgR02riYGobFRkJWUj6g141nAnP6eI9IRs6WL9WVt8mldEZiYj6XoaZ7dewaA3UKGOJx0mNjEGQRkpOQRvu8ov7+1m72KptFBprUSQCdiWsJRU5FRm6EU9Do42JCZLMugN/GsaL/jhYdFMee9LVq3cRtoDyRDz4vlI6jeSykazs/JRqRR06hnAl/NH0apj3SeCIICjh85x724K1taW9OrbBoDjx0IoyC/E3b0EdevW4P79NGNZnL//vyMQ8vGpSrlyZdBotBw6dAwnJ3tq1pKUwo4ePUlSUjK2trZ07dr+iddWqFCOqVPf5dtvv3ghlcSOHdtQubIXhYVqpk37hIsXw4otd3S0Z+y4wQDs23ucK+GSe729vQ0zPh6Jt08F1GoN8+au5mxI+BPrFwQB75rlmP7FQKZ9NoAR4zvRd2gLOnRvSGDLWtSuX5nK1UpTu35lBo5sgyAI5OUW8NPCzYiiSONAP+Z8N4mfVnzCzys/Zc63k5gyYxjdekoTKAf2niYp8T7uns607CDJB29efZQitQbfgIrUb14FUYQNC46RnZmPpY05ep2B9QuOodMaqFKrFAFtvLkTl/zMICg1JZ2N66SJlp59Wxv7oo4cPs2ZM5cRBAFrayucnR3w8HSlQoUyeHt74Vfbh3btmzJrzrSnBkFHj540BkHNmzdhwoQx/4gg6Ld4eLjxySfvM3nyOyiVCkJDr/DBB1+QkZFZbNwjT6sLF65QvZYXSqWCxMT7lCovZRUiwm4Zy1+fRtOONXFxsyM3u5CDWy7Soq8f1vYWpCfnELwr4k8fR3Z6PtsXnEYURWq3qESNRuUJ2neVa+GPVeMOb7tEzaYVsXexJic9n+BfI2nUowa2zlYozOVGtbmYC/Gc2Std87uMDsDJzZYLJ6/x48ydpCVnY+9kxdsfdyaww+NzYpFaw+Jvd5CUmI6dvRXvTu9lLBPdtP4Ap05cMvp2/X8QdP58OPPnLUMURVq2akT7DlI2UhRFFi1aRkJCIg4O9kyZMv4PfX88PNwYNmwAK1YsYMqUcdSs6UOLFibluEc86hF6kYeJl+dPB0Lbt2+nTZs2WFhYEBoaalROy83NZfbs2X9q3Xl5eYSHhxMeHg48lva7e1cyxHvvvfdYtmwZK1asICYmhkmTJnH37l3GjBnzp7b7pnHnzl1ycnJQqcypUuX3ZVSl0jg5IiKFhWpkZlCoVqNSKVEqFRQVaVixYh3r128BJAPQZ8mYvmm4uZVEEATMzAS0Wh1paenF0v4hwZc4ezYUmUxg3LtDqVGzKpUrl0en03Ngn5T5tLWzZsr0Ydg+lBC1sbGidbsAwsOjyc7Oxdraipo1qxpFJlq2DPxHy66/CE5OjqhU5ijN5chkkmCCXjSgNxgQZQKaIh3ntl/BytGKFuObYKYwQyaX4de9Jud3SI29tdpVxaWMA4GD6zLo207UaFUJpYUC76YVaP12AGYKM+7fzWTdp4dYNmUPZ3ZFkJ2Wh8JcTrWAcmgf1glrDJJps0Em/V2rbkUirtwAoEHDGmi1Wlav2sbsWQvJzpZuelIfpKFQyElNScfL2wPful606VSPL38YTYfuDbGwfHpJa2ZmDju2SWVRvfu1wdrGEp1Ox96H5oEdO0nNxfv3S5Mx1atXe6o61puIIAh06SIFOfv2HTaaZRcVFbFpkzQr3Lt3Vywt/7zzu0wmY+rU8VSoUI7c3DxmzfqWFSvWGbcJULt2dVq3kfygFi1cY/xsLS1VvPf+UOr710Cn07NkwWYO7n/6BJwgCJT3cqeOfxWatvajU88A+r3VipETOjNpZh9GTuhsLEFb+tM2MjJycHNzZtCwx+XRFhbmeHiWoEbNSnTt0ZxaflUkk8oVuxFFkfbd/HFytiUjPZf9O6VsT9ehAbiVdiQvR83GhccxGAzs33SB5LsZWNuq6D2mKVqtnhWL9qFWa6lUtVSxICgvt4Af50kSxlWqlqNt+0YApKaksW6t9FkMGdqDFau+YfFPs/h+/sfMmfs+n3w2iekzxjLsrd7Y2DxZLn3gwBEWLpR6v9q0acH48aP+sQpeUnl2Q7788iNsbW25fTue99//lHv3Hiu7VqhQBg9PVzQaLVevxFC3vjQpGB19k4qVPDCIIl9MW8m6pYeIjb77RBO2XGFG97ek9/bM4SjSUrNpN0Sa2AjeHUlaQpZxrCiKaNRastPzSb2byZ3oFBJupJGTkf/Unga9Ts+2+UEU5hXhVs6RtkPqEn8jlQMbpUxyt2HSd6Qgr4igveG0GSQF1Gf2RpGfW8TQWe0ZNa8zruWdyEjJYddP0gSRf4dqVKtfhn0bz7PlpyC0Gj1VapVi0pwelK38+Lqt0+n55Yfd3LqRhKWVORM+6I2Ti3SuOrQ/xFgON2psL5o0Le4FdP5cGPPnLUOn0xMQUJvhI/oYr2kHDhzh9OmzD3/D7/7p859CoaBRI38++2wGjo5P93f7LyK+xMPEy/Onz3pffvklP/30E0uXLi3WWNmwYUNCQ0P/1LovXbqEr6+v0czpvffew9fX16je1qdPH+bPn8/nn39OrVq1OHXqFPv376dMmTK/t9p/HVeuSE3p3t5Vn1vX/chUVURPTk4elpbmiKKImZmMUaOGIpPJOHv2IpGRMcjlcvr27f5XHMJfgkKhwMXFGZVKgVqtJuHeY0PVB2kZLFsmNX/37NWBihXLAtC1u5QFOHz4lNFNvaSrE5OnD6VS5bIMGd4FCwtz9jz0k2narAH37iVy585d5HI5AQEN/sIjfD0IgoCbmysqcwWCTMQg02PAgE6vRy+XIRpEUuPSOb/jKi7lnej8SVs6fdSGxNg0ctLysbRVUbPVY7ENawdLGg+ozYjFPWg6tB4yMxkPErPZPPsYSTcfIMgEytVwp+PYAN5Z1J2qjcqiLtCgVMm5n5qFTqcj86E8vbmVGTqdHg/PkiAT+WDG1+zbexyA1m2aYG1tRZG6CA9PqUk44up1Rk/qSrd+gcXEEP4ftbqIVct2UVigpkLFUgQ2lwQQzoRcJj09Czs7G5oE1qOwsNBoDtmhQ5vX8v7/XTRq1AAHB3syM7MIDj4HwJ49B8nMzKJECRfatGn+nDW8OM7OTnz11Sd06iRJQe/efYAZMz4nNfW+cczgId3x8HQlKyuHn5asM97IKhRyxr7blzbtJAPGDWv2sWn9AfR6/ZMbegGOHDxL2OUY5HIzxk7o90xVNkEQGDS0E0qlguioOM6fuYq5SknvwVKf05G9F7l1IwmFUs6gia1QquTcjEpi9bzDnNonTRD0HtMUW3tLDuw8y/2UTOzsrRjzXldjEJSfV8DcWcu5dzcFe3sbRo3tZWyGX7J4LWq1hmrVKtKufbOn7uPTEEWR9eu38vPPqwDo2LEtY8YMeyMmbCpVqsDcuZ/g5uZKWtoDpk//lMjIGED6PJo2lc63UnmcdEN//swVOvUOwMHRhoKCIoJPXOX7Lzfzwfif2b7+JPfupBq/S17VPanVsAKiCDuWn6ZKvVJU8vNErzOw+svDLJi0k29GbeaLgeuYPXQD37+zjSXTdrPq80Ms+2g/88Zu44sB65g3divLPtrP5nknObDqAtt+PM29G2moLJX0mtgUnVbPuh+PoteL1Khfjoatvek8SOrDPXM4Ckd3G8pXd0On1XNo7UVUVkqs7CzQanRs+f4kRQVaSlUqQct+fkRevM2JX8MBaNunLsPfb4eV7eNzW25OAT/M3kLUldsoFHLGTe2Bu6eUJbtwLoINa6XKhl592xDQ2LfY+332zGW+n7cMvd5A48Z1GffuUGPGJzb2JitWSEqSQ4b0+1cIKv1TMWWEXi9/OhCKjY2lSZMmTzxva2tLVlbWn1r3I2m//3+sWrXKOGbs2LHcuXOHoqIiLl++/NR9+bfTsWMbvvxy5gv1ojg4SKVxyLSUcLPB3tECmUz69TRsWI8OHVobx7Zp0+Jf16zo6emOuUqJWl1EYqJkqKrVavl+3jIK8gupWLEM3bo/vqH18/OhVGl3CguLOHzolPH5cuU9mfnZaOo1qE7stTiio28il5vRoWMLTp6UZqTr1vXFxub3JXvfFDw93SUzXkREQY9BkHp2tBo99mUkv4qw/THEX03CztUWaydrLj30zajTxRuEJ9VfHt14ZabmsmXOMQrziihZzpG3f+hKr2nNqNawLEqVgsgztwGwd7XGYBARBRG5yoxyFd24HisZPpYqXYLp074i/k4iNjZWvD/jbUaM7EvNmpIIhrmFNElz4VzEc+U479xO5JMZCwm7HINMJmPQsM4IgkBRkYbNm/YA0K59U5RKJUePBpGfX4C7uxv16vm9gnf6n4NcLqd9+1YA/PrrfnJz84yiEAMG9HrlimIKhYLhwwcxY8Z7WFlZcuNGHO+996FRllypVDJh4lvI5WZcvhRR7PcoCAL9B3egdz/pt7t/zylmvr+Aq1euP3VbzyL+ThKb1kslaP0GtqdM2WebbgO4lHA0GrtuWLuPgvxCatapSF3/KhhEkZWL91Gk1lDC3Z6eI6RrU9SleAAatqpGNb8yJNxN4/Ae6Rj7DmuJpZXUU1WQX8g3c1YSfycJW1sr3p85AmcXaZZ8/77jREffRKVS8vbYJz1cnoVer2fhwqVs3bpL2l7fHgwfPvCNCIIe4ebmyty5n1K5shf5+QV8+ulXnD4tiXM0blIPQRCIjb2Frb0Vrq7OqNUaUlLTmPXjKCZ92JuGgdWxtDQnMyOXI/suMuuDNXw+dQXHD1xCFEU6DfJHZangXlwaF07E0n5YfZQqOXlZhaQn55Cfo8bwsCtdZibDylaFk5sttk5WyGRSkJqTUUDCjTRiLsRz/mAMMRekz7zr2AAcSlizbekpMtPycHCxptcoqWrAq7on3nXKoNeL7F1/jnZDpEmi2Ev3uHlFynztX3melPhMrGxV9JrYhJysAqP0emDHGrTs5lfss7x7O5U5H67hxrUEzM0VvD25K+UrSQIHsTG3+XnRFkRRpEWrBnTsUrwULST4EvO/X4HBINIksD7vjB9iDIJycnL5+usf0Ol0+PvXpXPnP2+mauLZiAgv/DDx8vzp7j83Nzdu3rxJ2bJliz0fHBxM+fLl/+zqTbwAcrn8hftQHBzskSvkiKIBa1sL1Go1KgslDg72WFpa0q9fDy5cCKWwUE2vXm92k//TeNT4n5tdgFqt4UFaBrt3H+HGjTtYWVkycdLwYjXOUolQKxYuWM2+vcfp0LH5E35KO3dKyoNNAutjb29DUJB0UW7atHid9ZuM9L7J0RsKkAl6RDMRvUFErzegVuto0KYSkcdvcvSXc/T5oi2xIbcpyFZj62yFW5WSLHl3J3KFGf5dfageWAG5QnqPs9Py2DTrKHlZhbiUsqf3+82xsH48A6/X6Yk5L5XCFukltTjRTJrp965Vjq1bpdnM5NRkNBotPj6VGPfuUBwd7QGo5etNSMhlMjIeoFQquJ+aQfydJMqW83jiGEVR5MDe02zbfBidTo+Dgy2j3+ltVCPb/esR0tIycHKyp0PHFuj1enbvPgBA585t36ibyRelbduWbN26i9u34/nqq+/Jzy+gbNnSNGny+vow69evzfffz+bbbxdy/fpNvv76BwYN6kOPHp0pW9aTAQO7snrVdtau2YG3TyVj74sgCHTs0hQnZ3vWrtxDYkIq385ZSfWaleg3oB2epX+/xFetLmLR/I3odHr86lSlZZsXU8ps17ExIadCSU5+wI6tRxk4tBP93mrFzdhE0lKz2Lz6OINHt8WvkRdx0UmcP36NEu72dBzYAIPBwNpfDqI3GKhVxwvfupUAKChQ881XK7kVl4C1tSXTZ47Aw1Myo01KSmXjBknAY9DgHpR0dXnmvhU/PjXffruQS5fCJBGKt9+idetXl9X7K7G1teGLLz7g++8Xc/bsRb77bhEPHmTQtWsHataqSnhYNKeCztOkWW22bDzEqROXCGxWh8reZajsXYa+w1oSdeUWF4KjiQy7RXJSBlvWnsBMbkZgK1/a9KrLr6vPcGDjearXLceo2R15kJSNylKJhbU5KislFlZKFObyYr97g8FAfraanIyCh75DBeRmFZCTUUCZKiWpUqc0Z49Gc+XcLczMBAZNaGVUcwPoOMCfa+H3iAm7R0CbHOq1qcK5/dEcWHUB/w7VCDshSaD3GN8EKzsLlny2m4J8DaUquNCub/H+xPPB0axbegitVkcJVwfentwNNw9JZj4x4T7zv12LVqvDr05VBg3rVOw4TgWdZ9HCNYiiSNNm/ox5e4CxbNJgMDBv3iLS0zNwd3d7piqkiVeHyItle0wJoT/Gn84IjR49mgkTJnD+/HkEQSApKYn169czZcoUxo4d+yr20cQrRKVSGftbtFot6kKpP+iRkIClpSU//DCHJUu+/df0O/wWDw9XZDIBuVz66m/etIdDB6WZ5fEThlKi5JMZsIYBtXF2diAnJ8849hF37iQQejlSCpi6tiY8PJKsrCxsbGyoXbvm6z+gvwgPD1cUCjkGgw6DQQ8yAzxUjSvMLaJ668o4l7ZHnVfEoUUhhO2XylXqda/Bhb3RqPM15GUVcmTVRZZN2cOVEzfJTstj85xj5GYU4OhmS58ZLYoFQaIocuX0LdQFGixtzElOykCr1RlNVHV6NaIoUrqMK3FxdwAYNry3MQgCqFlLEke4l5BMpSpSyeyFc082PmdkZDP3y+VsWn8AnU5PnbrezPp6AtV8JJWptPvp/LrrMACDh/TA3FzJ2bMXSUt78NCE9N8T9P6WRwarAFFR1wAYNKjva7/xKVHChdmzP6Jr1w6ApGB5+7Y0q96+Q3Nq1KyKRqNl3nfLyM0tbnLpH1CLb+ZPpl3HxsjlZkRcuc6H7//IyqU7jb1FT2Ptyt2kpDzA0cmOEaN7vvAxKhRyBr0l9REdOXSW+DtJWFqpGPZOBwQBzgRFEHZRykx1G9aI3qMDGfVhB5TmCk4eCiX+VgoWFkr6DpPEF9TqIubNXU3cjXtYWVnw/szhxiDOYDCwaMFqNBotNWpWpWWrRi+0jzk5uXz88RwuXQpDqVQwffqkNzYIeoRSqWTq1Hfp2FEqp1y9eiMbNmwzlscFnTxHw8a+yGQyblyPJzHh/m9eK8e3biVGT+rK3CVjadNJCiJ2bw0mL7eQhq298SjrREG+hr0bzuHsbkeVOqUpW82VkqUdsHOyQqlSPPEdkclk2DhY4lHBmar1ylC/XVVa9qtN93caU7tFJZLvprN7jTRR1q5ffUpXLIFOp2fl4n1sXXscZ1dbGrWVepv2rD1Loy4+WNmqSE/OYe8yqTy1We9alK/uxuGtl7hzPRWVpYKB77ZELpcml/R6A9vWnWDl4n1otTp8aklCIY+CoMzMHL79aiX5+YVU8CrF2+P7FusNO3nirDEIat6iIW+PHVhs+aZNOwgPj8DcXMn77094JX2CJn4fU2nc6+VPB0LTpk2ja9euNGvWjLy8PJo0acKIESMYPXo048aNexX7aOIVU6KEC4IgoNVqKVQXobJQ4uHxuAREpVL9rhfRm4y7+yPlJOmMceqUVJLSo2c7/PweK+5lZmbxyy+rjb0+XR7KJK9bu5Pz5x6rWv36MBvk7++Hm1sJTp6Umk6bNGn4t/lwvA7c3d1QKORodBr06NBjwCAzgExAq9GRfT+P1m8HoDCXk3LzAUUFWpw87XAp70hUiFTaVr+jN9b2FuSk53No+Xl+eW83WffzsC9hTZ8ZLbC0lUqCRFHk2qW7LP/4ALt/lm4a7F2tMehFZGaSYly5im5EREgiCc4l7NDp9Li5uTyhjGVvb0v58pL0u629dME+f/aqsTwuPT2Ljev2M2Py90RHxWFuruCtUd0Z/94ArG0eX+DXrN5uzDg18PdDFEVjNqht25Z/m4fYX8Fvy158fKri51fjL9muXC5n6ND+NGxYD71ez6JFyzAYDAiCwNh3BmFnZ0PCvWS+/PxH8vKKB0NW1pb0G9ieOd9Oom59H0RR5MSxC0yd8C1ffbGM779Zw+IfN7Fy6U42rN3HyqU7OR0keUW9Pa6P8bNPSU7j45nfMXvWQtav3cnpUxeIj08sJuQA4FPdi/r+NaTS7eW7EEWRSlVL0bqjdIO9bukhsjLzkCvMqNesCvZO1qSnZfPrFqmMtlu/QOwdrCkq0vD912u4HnsHS0sV0z58q1h53u5fpey1paWKt99+sZK2+/fTmD79M65fv4mNjTWfffYB9evX/lOfzT8FmUzGiBGDGDZsAAC7du2jarWKWFpZkJ6eRWJCMrX8pN6VUycvPXUdllYqOvdujLuHE/l5avZuC8bMTEb34Y0RBLgUdJ246KQ/va9Fai1r5x81ChoEdpB+R3u2BnM+OJpjBy4TGXaLlt38sLJRkZqYRfjZOFr2f/xZefl60Lhrda5HJHD8V+k61HNEE5xKSt5lebmFLPhqK0f3S8farksDxk7p/rjcskDNd1+tIv1BFq6uzrw3dQjm5o8rHI4fO8OSxeuM6nCjxwwo9h27fPmKsazy7beHU6bMX2Ma/1/HJJbwenklEjGzZs3iwYMHXLhwgXPnzpGWlsYXX3zxKlZt4jXg6GiPXGGGVqulqKgIc3MlHh5PSqv+G3l0nFqdBlGU6rxr1KxKr94dio1bvHg5+/cfZvHi5YDUeN+0mb/k2zF/BWFhUaQkp3HmjCQI0rV7GwoKCjh/XroANW0a8Fcd0l+Ch4cbZnIZBlEPGNAZJC8hZKAp0nH21yjsXW1oOrSu8TUNetbk3O4oRINIuRruBPatxah5nWk+oDaWNipEUcTG0ZI+M1pg42iJKIpEhNxiybTdbPr2BAk30pArzKjbqjIyq4enKqX4UP3LjRvX4xEEgcJCKUNUr16tYhdttVpNUFAIlatIJbq5uTmYmytIu59JyOkwflm8lSnvfsOBvacpLCyifAVPPp8znqbN6xZbz9UrMZw/H45MJjD0LalZ/dq1G1y/frNYH82/FQ8PNwIDG6FSmTN0aP+/vAxm5MghWFlZcvPmLfbulSYeHB3t+eiTd7GxseL27XvM+mKhUczkt5R0dWL8pAHM/HQ05St4olZriI6KI+xyDOfOXOHEsQsc3BfMiWPShEi3ni2oXFUy1NXpdPwwfwXXrsURHhbNr78eYcGPq5g6eRaDB05iyuQvWb1qG2q1pJTaf3AHLCzMibtxj6CHfiydejWidNkS5OepWb1kvzEAF0WRDSuOUFSkpWIlDxq3qIlGo2X+N2uIib6FhYU5U2YMo1z5xyaxd+8msmWz1KM1dFgvnJylfqH799M4cuQEhw4d4+DBoxw4cIQDB46wb99hdu8+wLRpn5KUlIyzsxNz5nxC1aqVXsfH9LfSuXM7PD090Gq1XL4cTsOGUvAQdPKcUQUt5FToEwHsI8zMZPQZKolcBB0NJ+FuGmW8SlK/uVRyvmNFMDrdHxPfeMSuVSHcT8rC1sGSvm83QxAE4mITObz3gnHMjg0nUaoUtO0t7fPhbZfw8vOgar0yeHq50G1sY/JyCtm48DiiCPWbV6FWw4oAJMTfZ86Ha7gWdRdzcwWjJnSmS5/GxmyOTqdj4ffruRufjK2tFVNmDMXG9vGE59EjwUYRktZtmjByVL9iv/X799OYP38xoijStm1LmjZ9sWykiT+PKSP0enllU9aWlpbUqVPn+QNN/O04ONijVMjRanUIMpDJhP9MIOTk5Ii5uRKVSpIK9/R0Y8LEt4ql/i9fvsLFi1KAc/36TWJjb1K5ckXGvD2AInURZ8+G8t03v1CxYhlEUcTXz5uyZT05evQkGo0WT08PKlb8d/XHWVhYPDTjvYdBK/kIaQUdKGXodQYSYu9z5cRNajX3oqhAg6ZQh52rDVHBUjYooLtkvidXyqnTrgo1mlUgLjyJUpVdsHaQgqD9K85z8UgsAEoLBfVaV6Z+u2oozM34dNRq9Ho9uZo85CoztAY1AF6VSnMtVnpNvfq1jPubkJDEV1/NJyEhkRrVpW1HRV+ndi1fLp6P4pfFW41jq1QtR/tOTajpW/mJm3ydTsfKldLYNm0DKV1a6i3avVtqqA8MDMDBwf5VvtX/SCZOHINWO/yJ/ri/AgcHe4YO7c+iRctYv34L9evXpmTJEpQu7cHHn07ks0/mExcXz6wvFvDhR+OxsnqyVKdSlbJ88uVYYmNuk5WZi1pdhLpIg7qwCLVaQ5Fag72DDZ26NjW+ZueOQ8TFxWNpZUGfPh1JTEwl/k4Cd+8mUlhYxN34JO7GJyETBAYN6YGDgy3derVkw5p9bNlwkNp1qmFja8Vb73Rk1gdriImM58TByzRvV4dLZ68RdeU2cjOZ0bto3ao9REXGoVIpmTJ9GBW9HptY63Q6Fi1YI/Uv1fYh8GH5V2FhIR9++CVpaQ9+9z0sU6YUn3zy/r9WklgQBAIDG7J+/VaCgkIYPKg/R48Ec+5cGEOG9cTBwZbMzBw2rt1P+85NcHKyf2Idlb3L4FevEqEXrrNl9TEmzexDu771iLhwm9SETPatP4dbGSfysgvJzSogN7uw2L/lCjNsHSyxc7TCzsEKWwdLbB2tsHO04n5iFhdPxiII0H9cC6ztLChSa1i1ZB+iCH71KhEbfZfkpAzOnYqkQXMfzhyJJvluBkd3hNLnvaaAFEAvnbOf3OxCSno60GWI1Kt39fJNlv64B61Wh0sJO0a/1w3P0o97x0RRZPnPO4iMkAQ2Jk8fSomSTsbl58+Hs/SXjYAkBDN0WK9i58KcnFw+//wbcnPzqFixPMOHD3wNn6KJZ6EXRfTPEfl5NM7Ey/OHAqGrV6/i4+Pzwp4DUVFRVK5c+V9VKvQmY29vL5U5aTSIBmmW61GP0L8dQRBwd3dDrS6ic6eWdO3WvpjHhk6nY9myNQBYWKgoLFSzd+9BKlceh0wmY/yEoRRpNIRejiQ6+iYA3btLNeqPvIOaNWv0r2we9fR0R6GMQqfVY5CK49DodDh6SmUZJzeGUaGWB9VbSDPOB5aek7JB1d1wr1i890qpUlC1wWOZ+3MHYrh4JBZBEGjSvQb+7auhspJuusNCbqLTGhDMwEwuw93DiZgYSS2upJsjV6OKcHCwo6JXWQCCg8+xcOEvxpn6ewn3sLa2JC+vgNJlXLl4PgpBEKhb34d2HRtToeKzyzsOHQwiMSEFGxsrY9YwOTmFc+ekGf9HXjv/dgRB+FuCoEe0bNmUoKAQIiNjWLx4OZ9+Oh1BEChTxoOPP53A55/+wM2b8cz+ciEffjQeS8snpdEFQaBKtReboLh58w7bt0nB7siR/Qho9HiSTxRFHqRlcDk0khXLNrN//wmatWiIp6cbrdr4c/rkZe7dTWHzxoOMGN0DVw8neg5sysaVR9m58RSeZUuyZbXkRdWumz+uHk7cvHGXk8clQ+Z3Jw/Eq3JxC4gtm/dx+/Y9rKwsGTX6cVZu5coNpKU9wN7ensqVKyAIgnHZo387OzvRu3fXf2258yOaNJECocjIGByd7HB3L0FS0n0uXrhKyzb+bN10iCOHznL08Dl8a1ehecv6VK9Zqdi5useApkSE3eJ6zD1Cz8dSu0EVOgyoz5afgjh9IPK5+5CTWUDCrWcHpS2716ait3St3bbuJGn3s3F0smHgyDacDYpk67oT7N4aTB3/KnQe5M/Ps/Zx5nAU/i2rUdLTgRO7w7l+NQGF0oxBE1qiNFeQlprF8oV70Wp1VKtelrfGdSxmDaDT6Vi5dCchpyWD9HET+xfLNMbFxbPgh5XGcrj/D4IKCwv5/POvSUhIxMnJkenTJ75yxUgTv8+Llr2ZwqA/xh+KTHx9fUlJScHF5cXUavz9/QkPDzepyP1DcHCww8JSSW5OPnb2FiiVin+dTPbv4eHhxu3b8Tg52z0xM7h79wGSk1Ows7Nj6tTxzJz5JWfOXGDo0AycnByRy+W8N3kEX81eTGTkdapWrUjlKhW4fz+NqKhrD2cm/11lcY94pBynKxRBMGAwGNAU6RDlAm7lnUiOS+fIqot0m9SEnAf5RJ2WskENH2aDnkXs5XscXisFFq0G1KZhR+9iy6+el4IeM5WAoBEo6+XK0eOnMTOTUVCYB0DdejXR6/WsXr2RPXsOAuDtXYXr1+PIzMzCz9eLyIjrFKgL+PCTUTg42habEX0aWVk5bN0iqdL1H9AVa2vpRnLPnkOIooifX01Kl/b8vVWYeEUIgsDYsSOYMGE6V65EcvJksFGgomxZTz765F0+//QHbty4w5xZi/hg5jgsLFR/aFtFRRoW/rgKg0GkYcPaxYKgR/viUsKJtm0DuXolhksXr7Ji2WY++mQCZmZmDB3elS8++YlTJy5Rr74PNWpVpknLWkSG3yIi7Bbff7kJUQQ3d0fadK6PwWBgzYpfAWgc6IdP9eKm2Pv3HWfXw17EESP7GsVAwsMjOHxY8syaPPkdqlev9oeO999CyZIlqFKlEteuXSc4+ByBTRuwccNugk6e49PPJ1GipCMnjl4gOiqO0EsxhF6KwdnFgeYt69G4aW3s7GxwcrGjdad67Ntxhm3rTlLdtwJ1AysTfz2VhFtp2NhbYm1ngY2dxcO/H/9fpzOQk5lPTkY+2RkFZGfmk52RLz2XWUCFau606iFJ7EeExnH6+BUAhoxph6WViiatanHiUCgP0rI5fvAy7br6412nDFGX4tmz7iwtu/txcLMULHcdGoBrKUf0egOrluwzlli+M60HZmaPJ6gfmfFei7kt9VON6UGNWo/9ftIfZPL1Vz8ZxTd+a5YKoNFomD17Hjdv3sLGxobPPpuBs/PvnzdNvHpetOzNVBr3x/hDgZAoinz00UcvrBai0Wj+yGZMvCYcHOxxdrHH1laHuUqOu7vbvzKD8SweCSYkJBRvgM3IyGTLlp0ADBnSFx+fqlSrVpno6FgOHDjKwIG9AUmtaNr0tzkVdJ7ataWb/IMHpRne6tWr/WsvFB4ekmCCKOoQzB76ehlECnKLaNy7FtvmnuBmaALXL9zjdmQyBoOBMj6u2Jew4afpezBXKWjcrToVargbv2/JdzLYvuAUoihSu0Ul/DsUv5krUmu5Fn4Xg8FAXl4uMqWAVi9leqr5VCAiQlIy8/b2YubMWVy7Jqlzde/eiQEDevHJJ3OIjIzB0vJhdik0kn79Oz/3WPV6PSuXb6GgQE358qVp1lySUs7NzePYsZMAJu+Mvxh3d1f69u3O2rWbWbFiHX5+NbGzk7KR5cqVkoKhz34gNvYWs79cyOSpo7C3t33p7WxYv4ukpPs4ONgxfGTf3x07bFgvrl6JITLyOmdCLhPQqA5elcvQum1DDh88w/KfdzDr6wlY21gyaFRbvpi2ktzcQgQBBo5si1xuxrHD57hzOwlLSxV9+hf/Th0/doZVK7cB0LtPR2NQVlhYyMKFSwFo167Vfz4IekRgYEOuXbtOUFAIH34whU0b9xATc5PUlAfU969Bff8aJCXe58SxC5w+eZkHaZls2XiIHVuP4h9QkyHDu9KmUz3OBkWQkZ7Lod3n6dSrEb1GBT5/4wA8f3I4L6eAtb9IkzUt2tWmsreU/VMo5HTp05jlC/dyaPd5GjWvaZTTvhZ+j7s3UjEYRGo1rEC9ZpI/2uE954m7noRKpWDYOx2KBUHJSWnM+3o1qSnpWFiqeGdCP2rUfNwfplYX8dVXi8nMzMazlBuT3ituHaHX6/n22wVERERjYaHik0+m/WcqR/5piKL0eJFxJl6ePySW0KRJE2JjYwkLC3uhh7+/PxYWz3ZxN/HX4uBgj0wmoLKQ5D//aye3R8piSUnJxZ5fvXoTanURlSpVNM42P5JmPXToWLGAXqUyp3WbJjg5O1BUVGScme3QoQ3/VpycHFEq5YgYEAUDmIkgE9FqdMjkMhp0ljI5R1ZdJDJIyuI06l6DcweiSbmTQfy1VNbNOcqKTw4SF5FEdno+G+YeRaPWUb66G+2H1SsWkIuiyIUT19Bq9MjkAoICnJxtuX3nHgCubk7k5ORhZWXJ1YhIrl27jqWlBdOnT2Lw4L6YmZlRo4a0T4WFkqrYnTsJZGZm/+5x5ucX8NWcxZw9K6mIvTW8t7EM+ODBoxQVaShTphQ1a/r87npMvHq6dGlP2bKlyc3NM5awPqJ8+dJ89PG7WFpZEBt7i0kTPuf4sTPPNdD9LVevxHBg/0kAxowdWKxs9mm4lHCiazfpN79m9XYKC6Xetd792+Lu7kJmZo5RRc7Wzoohb7dHoZDTumM9KlT2ICc7j22bJVn2Hr1bYWv32ID5TMglfv5pPQCdOrWgR8/HQdLKlRt48CCdEiVcGDLk94O1/xIBAQ0wMzPj1q07FKoLqV5DChiCgs4Zx7h7lGDA4I78+NMHjHy7JxW8SqHT6TkdFMq+3adQmivoOVAyyT285wIP7me9sv0TRZH1yw+Tk1OAm7sjXfpI0vS34hJITLhPHf8qlCnvilqtZd/2EFzc7Ixy2gX5GpxK2tBzRBMEQeBOXDJ7t0mqmn2HtcLJ5bHdRXRkHJ/NXExqSjrOLg589NmYYkGQwWBg/vfLib+TiK2tNTNmjC3WWyeKIgsXLuXChVAUCgUffDD5X9f3+iZheImHiZfnDwVCJ0+e5MSJEy/1cHP7bzTjvwn8f3P3b6Wz/wu4uUmeHAkJjwOhiIhogoKCEQSBkSMHG2/IGzSog4uLM7m5eQQFhTx1fSdPBpOXl0+JEi7Urev7+g/gb8LR0QGFwkzyEkKPKBMRZaAt0nE99B4Nunjj6GZLQa7amA1yKW3PpYcCCJX8PJErzbh3/T5rZx1h0eRd5GYW4uJpT++JTTGTP56NTE3IZOmc/fy6WrrQK63NEASBilU9uBufjCAIxrI4v9reXLokiVuMHz+aBg0elzJVry4FQtdvxFG+vNQLdCU8+pnHmJSUyoczvuZKeAxKpYKJk96iUmXpBiArK5sdO/YAUsbpv5RF/acgl8t5550RCILA6dNnuXgxrNjyChXK8Nnn71G2rCf5+QX8tGQdn386n6Sk1OeuOz+/gCWL1wKSSqSvr/dzXiHRuUtLXF1dyMzMZvtWqa9IqVQw6p3emJnJuHAugnNnpDIon1rl+XHVRLr1kzIMWzYdIj+/kDJl3Wneqr5xnZcvR7Dgx1XGvo2Bg7sbv2+/LYkbP34UKtUfKwH8N2Jra4OvryRLferUGaOn0Kmg808ExEqlgsaBtfnki7GMGtsLgP17gkhPz8K3XiUqVyuNVqdn+/qTr2z/zgdHE3bxBmYyGcPe6YhSKefmjbt8NnMxn81cRE5OHt37ScHR6WNXSU3KoGU3P2wdLJErZAx8tyUqSyVFag0rF+9DbzBQu35l6jd6nBEMOnGJb+asoKBATUWv0nz65Vg8S5Usth9r1+wg9HIkSqWC96e/jUuJx1UMoiiyfPk6Tpw4jUwmY8qU8aaM49+MSTXu9fJK5LNNvFn8fyD0/94r/3YeKeTl5OSQn59PZmYW3323CJCasr28KhjHymQyOnSQPIT27Dn4xMVUFEX27JHq9zt2bPPCAiJvIk5ODigUcvQGPXqDDgMGMBfRaHScOxBDYb6GdiMbGMcHdKtO6PEbFOZrcHK1pe+UZkz4oTv121ZFrjBDo9ZhZatiwPstjMII+blqdq4M5rtpW7l+NQEzuYwmHapToJOkkRXm0s1gRa9SXL0qmbaWKeNBWtoDFAoFvr7F+5G8vMqjUpmTm5uLZykpAA4LjXrq8V0Jj+aDGV+TlHQfJyd7Pv9yMv4NH3t4rFu3hcJCNRUrlqdJk4av6F018bJ4eVUwliUuXryM9PSMYsvLlPFgztz3GTS4O+bmSqKibjDlvS/ZtnX/M+WTAVYs20x6ehZubi4MHNTthfdHqVQyZFhPAPbtO26cYClfwZMu3SXT0tXLfyU9PQvAGNDcvHGXUw9ltgcP62wsS4qMiGXet0vR6w00alSHESMfG9gWFBSYSuKeQ2Cg9NsMCgqhTt0aWFqqSEvLIDrqxjNfE9DYF69KZSgq0rJt02EEQaDX4ObIBIGwize4Fhn/p/crMz3HKJLRoUdDSpcriVarY/lP2xFFEbVaw56dJ6nsXYbqvuXRGwzs2nwKCytzJs7pwbR5fSlVoQQA29efJDU5E3sHa/q91QpBEBBFkU3rD7D85+3o9QYaNKzJ9I9GFMsygiQAs2+vFEi/M24wXpXKFVu+Zcsu9u6VSvfefXf0v8Zz6o1GfFwe93sPk1rCH+Pfe9dm4pnY2toUqwX+r2WELC0tjcHg3buJfPvtArKysihVyuOpsqCtWjVFpTLn7t0Erl4tfhMdHh5BQkIiFhYqWrZ80TryNxMHB3sUD0vjdHoNelGLTtCjslai0+g5vfMqHpVc6DyuEe1G+eNWwYmz+6Xsi3/HakbX9XZD6zHhx+60HliHYZ+2xd5FulDHRScxd9ImQg5FYTCI+NQpy9Rve+NVyx21WoONjQVJKdLMvkfpkqSlZaBUKoyZoerVqz0xOy6Xy6lWTSqPMZNLN5NXrsag1z/2BCksVLNr5yFmz1pEQX4hlSqVY87c6UYjVoBbt+5w7FgQAMOHDzJlg/5m+vXrgbu7G5mZWXz++dcUFBT3EDIzM6NT55Z8N28mtXyrodPp2bJ5L1OnzGb9ul1s3rSH7dsO8Ouuw+zbe4z163Zx+vRFBEFg3PihqFSSQW5eXh7Llq1l+/bd3Lp155lldrVrV6dO3Rro9QZWLNtsHNepa1MqVCxFQYHaeMMLPCGQ8Egl7sb123w9dwlarY46dWswdtzgYpMrq1ZtNJXEPYe6df1QqcxJTb1PfPw942TGyZPnnvkaQRAYMKQjACGnw7gVl4BnaRcCW0sZ/i2rj/2uj5Aoir9bgimKIquWHKCgoIhyFd1o01nK/u3ZeYLExPuoVNJE0PGj50m7n0HXvk0QBAi7eINb1xOxtbfE0cUGkIQWTh2TMoxD326PtY0FanURP363jv17TgHQtUcL3h7fB6WyuLpbWFgUK1dsAaBvv07FJnoA9u49xMaNUk/a8OGDTF5B/xBMpXGvF5Oe9X8QQRCws7MlIyMTkJqQ/2t4eEg3UYsWLSMhIRGVypzp0yc9tczEysqKZs2acODAERYuXMqsWTMpUUJqin1k8Ni8eeALi4e8qSgUiof9ZXcxoEer16HV6nDwtMaQK3L52HUadvSmykNZ7IiQW2Q/yMfKVkXNJhWKrcvGwbKYOlzmg1zWfH+Egrwi3Eo70mVwQyr6SJ49h/edB6BazXKcPnMWAK1W6sWo5VuNK1ckWdtnlSXWqOFNaOgV0tMfYG1tRV5ePrHXbqHRaDkVdI4LF66g0WgBCGzagFGj+xWTh5VKRdYiiiKNG/v/Kw0p3zRUKql5e+rUT4iPv8fcuT/w0UdTn7BoKFHSmRkfvMOZkMusXLGFxIQUEhNSnrnebt3bFJshX7p0jbEkdu3azdja2uLrWwNf3+rUqlUde/vHfRlDh/Y0CiecOxuKf8PamJmZMWpsLz6avoDIiJscOXiW1u0acuLohScEEu7eTWT2rEWo1Rp8qldm4qS3ih2PqSTuxVCpVNSvX4egoBCCgkJo2rQRx46GcP5cKMNH9DEGuf9P+QqeNGriR/CpUNav3svMz0bTsUcAF0KiSUpMZ++2EEqVLUFWRh5ZmbnS3xl5ZGbkkp2ZhyATsHOwxt7BGntHG+wdrHFwtMbOwZrEu2nERt9FqZQz9O32mJnJuHc3mT2/ngRg+OgeBB2/SGTETXZuO8aosb3wb1KdM0ERbN9wkimfSJLpuTkFrPn5AAAt29ehio/kZbdw/gauhl9HLjdj5Ns98Q+o9cTx3b2byPx5yzAYRAKbNqDbQ9uHR5w8GWzsu+vTpzudOrV9Yh0m/h5MYgmvF1Mg9B/F0dGBjIxMnJ2d/pMXVHd3NyIjY0hISASk3pLfM5Xt06cbYWFXSUlJZebMWcyaNRONRnIxFwSBjh1b/1W7/rfi5OSAXGmGqDVgEHXo9QYKizRU8SnN7chkTm6/QtcxAYiiSMhuKUCp364qCuWzTzVajY413x8hP1eNR1knxn3e1TheFEWuXJL8mixtFOh0elxdnYm9HgeAj09lfv5F8m+qXbvWU9f/qHwoOvoades04OyZUL784sdiM7zu7iXo0LEFLVs96QF19uxFoqKuoVQqGDzYNAv/T6FkyRJ89NEUPvpoFleuRLJw4TImTBj9xOcnCAIBjepQo2ZVjh0NJicnD51Oh06nR6vVoX/4b2cXR3r2euwLdfnyFYKCQhAEAV/fGkRHXyMnJ4egoGCCgoIBqFatMlOnvouDgz0lSjrTpWtrtm7Zx+pV2/H180GlMsfN3YW+A9qxZuVutmw8SNly7mzdJE2g9OzTGls7a0RRZMmideTnF1CpUjmmvT+mmG+TqSTu5WjSJICgoBCCg8/x1lsDcHV1ISUljZ+XrKNP3864uj1d3a1n39ZcPB/BjevxXDgXQX3/GnTt05j1y49wcPf55243LTWLtNSsZy7vMaApJd0c0ev1LP95B3q9gdp1qlGvQXVcSjgSGXGTkNNhtO/UhM69G3HxTAxx15MIv3SDWnW8WPvLQXJzC/Eo5Uzn3pKgz8ljF7gafl3q9/lw+BMeVAAZGVnMnbOEwsIiqlWryOgx/Yv9TiIjY1iw4BdAEvzp27f7c4/VxF+HyVD19WIKhP6jPCoN+68pxj3it0FPly7tCQio/zujwd7eji+//JCZM2cZg6EKFcoCUibikQDDv51HynEGA5KEtkEkOzOfgM7e3I5M5kpQHI06+ZCdnk9KfCZKlZw6LSuj1xuQyYSnlpTtWhXCvbg0LK3NGTK5TbGg6db1JLKz87GwUJKRKfWClKvowYmTpySpWEGPKIqUKVPKmKX7f8qVK2PMBLm6Sn5ZOp0ea2srAhrVpklgAypWLPPUfdNoNKxatQGArl07/qf8tt4EvLwqMGXKu8ye/R0nT57GxcWJAQN6PXWsjY2VUeHteajVan76aQUg9f4NHz4InU7HtWs3CAu7SljYVW7dukN0dCwrVqxj8uRxAHTp2opTQedJTX3A9q37GfCw16hF6waEXo4h8uoN5nwh9f/8ViAh+PRF4uLiUamUTJ46qljWQhRFfv55takk7iWoWdMbW1tbcnJyuHIlik6dW7L0l42EhFzmzJlQ/P396NKtNeXKFTdTdnS0o0PnQHZsPcrmDQfxrV2VgGY1iL56h7u3U7F3tMbewQYHp4cZHycb7B1ssHe0Rq83kJWZR3ZmXvGsUWYu2Zn5VKpWmiYtawFw+MAZbsUlYGmpYvDwLgiCQPkKntSt58PFC5Fs33yYCVMG0apDHfbvOseuTafIzszjamgccrPHQgsP0jLZuE4S6OjZt/VTg6CkpFRmfbGAtLQM3NxcmDx1dLFMY1raA77++kf0ej0BAfUZMcJU+vtPw+Qj9HoxBUL/UR4FQv+1/qBHeHtLfSPVq1dj0KA+L/QaZ2enYsFQaup94LHE9n8BBwd7FAoziopEBDMAEY1Gi4WtOZXrlCL20j1ObA2nMF/y+vFr5kVRkYavJ29CYS6nZTc/ajephPyhQty5YzGcP34NQYAB77Yw1sEDJCems36ZNHPuXbMcoVelungEKZNTzbsSUVGSYEKdOs9W65PJZPj4VOXcuUsolAIjR/XDzt4WPz/vJ0qp/p89ew5y/34aDg72dO/e8Y+8ZSZeM3Xq1OLtt99i0aJlbN26C2dnR9q0afGn1rl+/VbS0h7g4uJsDKzkcjk+PlXx8anKoEF9uHEjjmnTPuH06bO0atWMGjW8USqVDH2rF3PnLGHv3mP4B9SmfPnSCILAiDE9+HDqD+TnFwKSQIJMJqOoSMOG9bsA6Na9LQ4OdsX2Zc2aTQQFBSOTyXj33dH/yQz+yyKXywkIqM+BA0c4deoMkya9jbt7SX799TDhYdGcOXOZM2cuU6NmVbp2bY23TyXjzX+7jo05efwiD9IyObQ/hE5dmzJ6UtcX2m4JV4fnjklNSWf7liMA9BvUAQeHx15X3Xu34tLFKC5fiubmjbu06liP08eukJqcyaZVktBCt36BeJZ2QRRFlv20HbVaQ+UqZWnT7kkj7xvXbzNn9uKHk0AufPDhuGKS8BqNhjlzvicnJ4dy5crw7rtPZlRN/P28qA7Cy8ZBOp2OTz/9lPXr15OSkoKbmxtDhw5l5syZxt5EURT57LPP+OWXX8jMzKR+/fosWrQIb+/Hpe1FRUVMmTKFjRs3UlhYSIsWLVi8eDGenm+G4bhJLOE/SqtWTfH1rUGrVk3/7l35W6hYsTzLly/g00+nP/dm+Lc8CoZKlpTUe8qUKfWfKlNxcnJEqZCDgBSQPPQRSr6bTvPevgiCQNS5O9yKSEYmE2jQvhqnD0SSl6MmMy2Prb+c4uv3NnPh5DXuXE9h50qpxKhtn3pUriHNzoqiSMiJq8z+YA1JienY2VlRpXop8vIKsLKyIDlZ6vHw8/MhNFQKjn4vEILHMtoREdG0at2YevVqPvdzz8zMYuvWXQAMGdLPdAP6D6ZVq2b07i1lX37+edUTstovw40bccbevzFj3nrm5+7lVYG2bVsC8Msvq42KdLVrV8ff3w+93sDiRWvQaqX+M0dHO4aN7IZMJqNl6wbG2ft9e4+Rnp6Fs7MDHTo2L7aNXbv2sXPnXgDGjRuJj0/VP3xc/zUCA6XA4Pz5i6jVarx9KvHBh+OY+80MAgJqIwgCV6/E8PlnP/DB9K+5fDkCAHNzJb37SZnDPbtOkJWV+8r2SRRFlv+8HY1Gi0/1ijRpWlyswMOzBI2aSOeybZsOo7JQ0qHHY4XKKt6lad5Oes2xw+eIjorD3FzBiDE9nwhgLl+O4PPPfiAvL58KFcrwxawplHR9nDUXRZFFi5Zx69YdbGxsmDFjEubmT++fMvH38rrks+fOnctPP/3EwoULiYmJ4euvv+abb75hwYIFxjFff/018+bNY+HChVy8eBFXV1datWpFbu7j38XEiRPZuXMnmzZtIjg4mLy8PDp27FhMlOifjCkQ+o/i5VWBTz55n7JlSz9/8L8UJyfHYup5L4qzsxOzZs2kbduWvPPOyP/UDJqj40PlOFGPQTAgKEFTpCPkUBQlStnj07Cscay3f1ksbMy5eOIaAPWbV8HGzoKM+7ls+SmIhR//il5nwKdOWZp3qQWAulDDikX7WLv0EFqtjmrVyzJz7lDuJUqSxNV8KnDjxm0ArG1U5OcXYGNjTeXKFX93vx8Zq8bExBpvTH8PURRZtWoDanURXl4VjDdVJv659OvXg2bNGmMwGPj22x85c+bCS69Dp9OxcOFSRFGkSZOG1K5d83fHDxjQE1tbWxISEo3BE8BbI/pga2vN3fgktm09YHy+XoPqLF46k0HDOgOQmZnNrp3S6wYM7FasL+j48VPGsswhQ/rRvHmTlz6e/zKVK1ekZMkSqNVFXLwYany+XLlSTJg0nB8XfErrNk1QKhXExcUzd84SYzDkH1CLChVLoVZrjIa3r4KTxy5wLeY25uYKho3sZrx2REVe5+bNOwB069kSudyM6Kg4oiJv0qh5TcqUK4m9gzVDxrRDEARSU9LZvEH6XvXp346Srk7FtnPi+Bm+mfsTRUUaavlW45PPJmJnZ1NszO7dBwgKCkEmkzF16vhnlhab+Pt5XYHQ2bNn6dKlCx06dKBs2bL07NmT1q1bc+mSJOsviiLz58/nww8/pHv37vj4+LB69WoKCgrYsEE6N2VnZ7N8+XK+++47WrZsia+vL+vWrSMiIoKjR4++6rfitfDaAqHU1FQKCwtf1+pNmPhbcXZ2YsyYYVSqVOH5g/9FODhIXkIGUY9B1COY69Hp9dyLSyMuKolmvWohM5NOKw07+XApKJbCAg3Ornb0HNmEGT/2o+PABljbSrPsLm529B3bDEEQuJ+SyawPVnPxTAwyQaBr3yaMn94TG1tLwi9LJXDWNir0egNubi7cvn0HAD+/Ws/1b/L0dMfOzg6NRkts7M3fHSv1ZKwyqoW99dbA/1Sw+6YiCALvvDMCX98aUjnm1z/w008r0Wg0L7yOX3/dT3z8PWxsrBk+fNBzx1tbWzN0aD8ANm3abvQ0srOzYeQo6flfdx0y3uQCWFpZGL9PmzftQa3W4OVVloYBj7MDFy+GGcURunRpT7duprLMl0UQBKPfV1DQmSeWl3R1YcTIvixa8iUBD9/7dWt3otfrEQSB/oM7AHD65GXi7yT96f1JT89i0wbJn6dnnza4lHAE4HrsLT77dD6ffvw9GRlZOLs40KKV5Me2bdNhzMxkvP/FQL78YRQOTraIosjSJdsoKtJSzbsCLVo/9m4TRZEd2w+wZPE6DAaRJoH1mfb+mCeU8sLDI4xB9ltvDTROFJn4ZyKVxr3IH4mcnJxij6Kioqeut1GjRhw7dozr168DcOXKFYKDg2nfXhKNuX37NikpKbRu/VgMytzcnMDAQM6ckX5Tly9fRqvVFhvj7u6Oj4+Pccw/ndcSCPn5+dG/f3/8/Px47733nvkhmDBh4s3C0dH+oamqDr1eh1avw8ZZCmqO7w7H0dWWgdNb0ndKM1zLOBB8UFKOa9TWB0EQUJoraNqxJjN+7M+gCS0Z+2lnVJZKdDo9y37cTVpqFo5ONkz+uB9tO9dHEASSk9JITn6AXG5GXr6Ujq/l682lS+HAs2Wzf4sgCNSoIZUwRkREP3OcwWBg4cKlHDx41HhjbZLLfnOQy+V8+OFkY+Bw8OBRpk79mHv3Ep/72qSkFDZt2gFIN4d2drbPeYVEs2aNqVzZC7W6yHhzCVC/gS8BAbUxGEQWLVzzREAWH5/IieOSHPzgIT2MwVF0dCzffPMDBoOBZs0aM3Ro/xfaDxNP8igQCgu7Sk7O00vc7OxsGDGqH9bWViQmpHD8mHTz5lWpDA0a1kQURTas2fe7PkHPQxRFVi//lcICNRW8StGqrT8Aer2eZUs3AaDRaI3ZwU5dm6JSKbkVl8ClC1HIZDJjX+WhAyFcj72DSqVkxJjH3xuDwcDyZZvZtHEPAF27teGdcYOfKAFOSUnl228XIIoizZo1NhqGm/jn8rIZoVKlSmFnZ2d8zJkz56nrff/99+nXrx9VqlR5aEjuy8SJE+nXT5rESUmRytBLlixZ7HUlS5Y0LktJSUGpVOLg4PDMMf90XksgdOHCBY4dO0Z0dDQeHh60adOGrKys17EpEyZM/IU4OTmiUJo9DIK0aDValNYyBBlcv5pA4p0HlK/uRpU6pYm9co+05GxUlgrqBBYPJsxVCmr6V8DGTvJe2rsthLt37mNlrWLa5wOpUNnDODbsYTaoStVyREdLM1eepVxJSEjEzMwMX9/qL7Tvj2Y9IyKinrpcr9czf/4Sjh0LQiaTMXHi27Rq1ezl3iATfztyuZwhQ/rxySfvY2trS3z8PSZP/pAjR04882ZWFEWWLFmOVqulZk2flzKSFASB0aOHIggCp0+fLWa6/NaIvtjZ2ZCYkMLWLfuLbW/Naslg1d/fj8pVpMzynTt3mTXrWzQaLXXq+PLOOyNM2cg/QalSHpQrVwa9Xk9IyLMNVa2sLOnZS/Jz2rJ5L2q1NHnbu18blEoFMdG3uHg+kvQHWcTG3OZMcBh7dp1k5dKdfPvVSmZMmc+H037gu7mrWLl0J7u2H+PUyUtERtwgMeE+p4MuEx56DbncjBGjexoz2IcPneLOnQSj8enRI8GkP8jE1s6atu2l7+D2zYeNvRbJSWls2ySV6vUb2B5nF+nmU6PR8P13yzh86BSCIDBseG/6D+jyxHdHrVYze/Y88vLy8fKqwNtvv2X6fr0BPPIRepEHwL1798jOzjY+ZsyY8dT1bt68mXXr1rFhwwZCQ0NZvXo13377LatXry427v+/I6IoPvd78yJj/im8FtW4RzMQeXl5dOrUiYyMDJo3b05oaOhzXmnChIl/Mvb2dpLZqCBiECUfFgMGKtcqxbXQe5zYHc7Ad6UG8tMHpHr7ek2roLJQPnOdN67d49AeyaNjwIg22DtYF1seekkKhDxLl+BS2GWUSgX5DzND1apVxsrKihfhkajF9etxqNXqYk3wOp2O775byNmzFzEzM2Py5HE0bFjvhdZr4p+Jr28NfvzxK+bPX0J4eASLFi3jypVIAgMbodVqpWBeq0On03H3biIREdEolYo/dHNYvnxZ2rVrxf79h/nll9XMnz8buVyOjY0Vo8b055u5P7P71yPUq1cTr0rlCAuLIuKqdGM8YGBXAO7fT+Ozz+aSn19AlSqVmDp1/EsJuZh4OoGBAdy+Hc+pU2dp167VM8e1btOEgweCSElJY/evR+jdpyPOLg60bd+I3btOsHD+hme+9hH37v7+DHiX7s3x8JSEdrKycti8ScreDBrcnbNnLhMdfZOdOw8xYmRf2nZoxNHD50hKSiPkdBiNmvjxy5KtktBCDS+atpDOT6IosuCHVZw/H45cbsa7E4bRwN/viW2LosgPP/zE3bsJ2NvbM336xGI9aSb+uYiIGF5AE+5RcZytrS22ts/PaE+dOpXp06fTt68kyV+9enXi4+OZM2cOQ4YMwdVVsgV5pCj3iPv37xuzRK6urmg0GjIzM4tlhe7fv0/Dhg15E3gtZ1kvLy8yMjKwtrbGzc0NV1dX6tf/fZ8WEyZM/PORyWQPBRPMEEQRmZlU0uFTrwzXQu9x5WwcbXvXRa8zEHslAUGAgDY+z1xfYUERqxbvRxTBv4k3fvWKZ45yc/K5cT0eAAOPZLO9CAu7CjzbRPVplCxZAhcXZ9LSHhATc50aNby5c+cusbE3OH36HDExscjlct5/f+ILlduZ+Odjb2/HJ5+8z86de1m/fivBwecIDn52ZqB//164upZ85vLfY8CAngQHnzMKJ3TtKvWY1K1bkyZN6nHq1AUWLVzNnLnTWbt6OwAdOjSnREnJm+qnn1aSmZlF6dKezJw52aTg9Ypo3Nif1as3EhMTy/37ac8UBZDL5fTr35nv5y1nz+6jtGzVCEdHezp2DeTsmXDS7mcil5vh6GSHk5M9zi72ODnZ4+TigKOTHaLBQEZGDhnp2WRkZJP58N+ZGdkUPCyJ69D5seDFujU7pOcrlKFV68aUKuXOp598z/FjIXTp0gqXEk506tqUjev2s2PrUTLSs4m7cQ8LSxXDR3c3BuvnzoYag6APZ47H2+fppbzbtv3K2bMXH57jJuDk5Pjq32wTrwW9CLIXqMzUv2T1ZkFBwRP9tWZmZhgMBgDKlSuHq6srR44cwddXuiZqNBqCgoKYO3cuALVr10ahUHDkyBF69+4NQHJyMpGRkXz99dcvt0N/E68lEDp37hxOTk7PH2jChIk3jkcS2oIeBDPQarUUaTVUqVWKa+H3OLXvqrEEqVrtMihUZnw08ReU5gpadaxH3YZVJTNUYMuaY6Q/yMHZxY7eg5/0frl4PhJRFCldxo1bcXekdVbzYt16aXa2bt0nZz6fhSAIVK9ejePHT7Fw4VLy8vIoKnrct6FUKvjgg8nUqvVipXYm3gwEQaB79054e1dlw4at5OcXIJfLUSjkxf52d3ejU6c/7glmZWXF0KH9+PHHn9m0aTuNGjXA2Vm6Dg4Z1ouIiFiSku7z4QffkJiYKhm8dpdkmqOjYwkNvYKZmRkzZkzC2tr69zZl4iVwcnLEx6cqERHRzJr1HUOH9sfXt8ZTxzbw98PL6xg3btxh65Z9jB4zAJXKnNnfTKSgQI29vc0fKvdRq4swN1c+7gOLusGpUxcQBIHhI/sgk8mo5u2FT/XKREbEsmPHQUaPGUCL1g04tD+YjPRsdmyVFLgGDumIk5M9ALm5+SxfthmQeoKeFQSdPn2GDRu2ATBq1BBT3+MbhiiKL9Sj9rJ9bJ06dWLWrFmULl0ab29vwsLCmDdvHm+99RYgnTsnTpzI7Nmz8fLywsvLi9mzZ2NpaUn//lLvop2dHcOHD2fy5Mk4OTnh6OjIlClTqF69Oi1btnz5g/0b+MOB0K1btyhXrtxTTwqmIMiEiX8vjo72yBVyDAaQm4NGo+N8cDQ9+jTjWvg9LgbFGs8LjdtWJ/j4VdLuZwOwasl+9m0PoXWneijNlZw9FYUgwLCxHbCwLD4DHnI6jLWrdgNQy68KO3ZKZSRmZlIpm6trSdzdXV9q3319q3P8+CmjupeVlSWVK3tRubIXDRvWo1Qpj+eswcSbSuXKFfnss6fXyr8qmjVrzOHDJ7h27TorV65n6tR3AYwlcnPnLCHhniQF37tPR6ysLBFFkfXrtwDQokUgbm4v95028Xz69u1BXNxt4uPv8dlnc6lZ04fBg/tSoUK5YuMEQWDwkB58NPM7jh87Q/sOzShVyh1zcyXm5n+8jOy3qm06nY7ly6XgpUXLACpWLGtc1qdPRyIjYjl54izdurWhRElnuvZowYqlOwHpPNioyePJnzWrt5OTk4eHpyvdHgbV/8+xY0FGSfi2bVvSunXzp44z8c/lRaWxX1Y+e8GCBXz00UeMHTuW+/fv4+7uzujRo/n444+NY6ZNm0ZhYSFjx441GqoePnwYG5vHcuzff/89crmc3r17Gw1VV61a9YfsSf4O/rBYgpeXF2lpacb/9+nTh9TU1FeyUyZMmPjn4uj4yFRVRG4OOq2OWzeSsLRVUtqrBFqNHk2RDrfSjpSr6krICamMzbeuF9Y2FqTdz2b98iOsXLwPgLadGxQTRxBFkb2/nuTnRVvQ6w3U96+BR2lndDo9rq4u3ImXSuXq1PF96dnZgIAGjBgxmLFjh/Pjj3NZt+4XPv54Gn36dDMFQSb+NIIgMGrUEARBICTkfDFj19q1qxPYVJI69vB0pWUrqRk+PDyCqKhrKBQKevfu+nfs9r8eb+8q/PTT93Tq1Ba5XM6VK5FMnjyTefMWkZp6v9jYylUqUK++pBa3ft2uV74vhw4Gce9uEtbWVvTr3+WJbdeoWRW93sD27ZJPUOOmtSlfwRNHJ7ti3kNXwqMJOnkOQRB4++2BUu/m/7Fv32EWLPgFURRp3bo5o0cPfeXHY+L1Y3jYI/Qij5fBxsaG+fPnEx8fT2FhIXFxcXz55ZfFescEQeDTTz8lOTkZtVpNUFAQPj7Fy91VKhULFiwgPT2dgoIC9uzZQ6lSpV7Jsf8V/OFA6P9TcPv37yc/P/9P75AJEyb+2TzqERJFAzqDDgdnSazg3OkomnWqZRzXuF11YiLiSX+Qg6WVOcPe6cisH0bRa2Az7Oyl15QpV7KYc7rBYGDNit1s2SjJyLbr2Jix7/Yl4qpkylqzVjXCwyURBj+/p5e3/B4ymYyOHdvQunVzSpf2fGNUbUy8OZQvX5bOnSUFsiVLlhe7Lg4f0YcBA7syddpozMzMHmaDtgLQtm0LYymdiVePra0Nw4cPYtGib4wGyadOneGdd6aybNnaYvLa/Qd0xcxMRujlSCIjYl/ZPmRkZLFl814ABgzsio3Nk0IvffpI0u9BJ8+RkpyGmZkZn3w5lm9/mIKDg9QAr1YX8cvPUnlwu/ZNqVS5/BPr2b59N0uXSupfnTq1NSnEvcGIvKBq3N+9o28or81Q1YQJE/9OHB0fm6pqNVrsnCQJ7HOnoqjqV5oqtUpRxqskvgEVjdmg+o28USrlmKuUtGhfhy9/GMW4aT2Y8EFvoz+GRqNlwbz1HDsizXIOGNyRfgMlY7cr4ZL3j4dHCdLTM1AoFHh7V/kbjt6EiefTv39PXF1LkpGRyerVm4zPq1TmdOnaGnd3SZDh/PnL3Lx5C5XKnJ49uzxrdSZeISVLlmDSpLHMmzeLWrWqo9Pp2Lv3IO+8M4XkZEn1zd29pDFjt3bNjj/lIfRb1q7eTmFhEV5eZWneQpoAEkWRQ4eOGUU8vCqVw9fPG4NBZNs2SXJdEIRiCoKbNu4mLS0DFxdH+vbrXGwbj4LrtWul8rvevbuZTKHfcF5XRsiExB8OhARBeOKHZfqhmTDx78cYCBl0aDRaBIUBaxsLsrPzibl6hxHT2zP+i67k56u5ejkOgEbNi2dvFAo5PrXKY2n1WMJ649r9XL4UjUIh550J/WjTXpq1TUxMIS0tA4VCTqG6AJBks02qWib+qZibmzNu3EgADh8+Xsxb6BEGg4ENG6RsUMeObV/YwNXEq6F8+bJ8+ul0Pv10Op6eHuTm5hmzcwA9e3XAwsKc27fvEXz64p/eXmRELCEhlx8KJPQ13i+Fhl5lyZIVfPfdQhITpf6xXr0lxcHTpy6QlFS85eDG9dsc2H8SgFGj+xfrPxJFkRUr1rF16y4ABg3qQ//+PU33Zm84L+sjZOLl+FOlcUOHDqV79+50794dtVrNmDFjjP9/9DBhwsS/i0eBkE6vQ6vVkpWZQ70AyaPnTFCEcdy5U5HoDQYqVHLHo9TTJWsfcSM2nuNHJS+hCZMHUq/BY+W28DDpJrJqNS+ioiRPIZOym4l/Oj4+VWnbVlJNWrRoGWq1utjy4OCz3L2bgJWVJV27tv87dtEE0rlkypRxCIJAcPA54uJuA2BnZ0OXrq0B2LjhVzQazVNfL4oi+fkFRhPWp/FbgYTWbRpTvnxpQAqGV6/eYFzP9u2SOEzFimWpXac6oiiybcu+YutZsmQdoijSJLA+NWtVMy4zGAwsXrycPXsOApI6XI8exbNFJt5MTBmh18sfVo0bMmRIsf8PHDjwT++MCRMm/vk4OTmiUJqh0+swGAwUFWnw8S3L8YOXiQiNIy+nACsbC2NZXKNmNX93fTqdjhVLd0oX96a1qVGrcrHlYWFSWVyNGlXYsHEjYAqETLwZDB7ch4sXQ0lNvc/69VsZPnwQAHq9ng0bJC+hrl07mOSy/2bKli1NkyYNCQoKYe3azXz66XQAOnRsweFDp3nwIJPNG/dQvkIZ0tLSefAg0/j3g7R0CgulIMjW1hoXFydKlHDC2dkBl5LOuLg4EnvtFokJKdjaWtOnbyfjdo8fP8XduwmYmyspKtIQFBRC377dKVHChd59OnL5UgQhIZfp3rMdnp5u7NxxiIR7ydjaWjNkaE/jevR6PT/++DNBQSEIgsD48aNo3rwJJv4dGMQXC3IMppTQH+IPB0IrV658lfthwoSJNwRrayvMzc1RKMyQyQQ0Gi1KlZzSZUtw9859LoRE4+bpTNr9bCwtzandoPLvrm//ntMkJqRia2tF3wHFZ8bV6iJiom8AYGNjgVpdhK2tLWXLln5tx2fCxKvC0tKSsWNH8MUXX7N37yEaNfKncuWKnDhxmpSUVGxtbf+Ud5GJV0e/fj0ICTlPeHgEV69GUaOGN+bmSvr268TiRWvZs+fYc9eRk5NHTk4ecXHxT10+cFA3rK0lgQS1Wm0sxRswoDeXLoVx9WoUO3bsZcyYYZQrV4p69Wty4fwVtm3ZT49e7di5Q8r2DB/Rp5jQwpIlKwgKCsHMzIxJk96mUSP/P/t2mPgHYUBE/yKBkCkj9Id4LYaqJkyY+PciCAIODvYPAyHQarSkJD/AP7A6d+8c40xQJCXdJNfyegFVUZo/Kev6iOSkNH7dcRyAAUM6Ym1jWWx5VNR1dDo9JUo4kZCYCEheQKaadxNvCrVr1yQwsBFBQcEsWPALX3/9KZs27QCgZ8/OqFSq56zBxF+Bq2tJWrduzv79h1m7djNff/0ZgiDQJLA+ly9FcOvWXZydHXFxccTJ2VHK+Lg44VLCEScnB/R6PWlpGaSlpZN2P9347/upUubI29vLKJ8O8OuvB8jMzKJECRfatWtJuXJluHo1imPHgujduyuOjg706t2RC+evcPZsKPHxCeh0eurUrUED/8deQlevRnH06EkEQWDatAnUr1/773j7TLxGXrTszRQI/TFMgZAJEyZemkd9QnKZDK1WR9jlGIaN6M72dSdIuJtG4j3JY6xR85rkZOcx+7NfUJor6NA5kLr1fZDJZIiiyKplu9BqddSoVYkGDYuX0OXnF7Bxw68A1PL15sqVSOnfprI4E28YI0YMIizsKgkJicyY8TkPHqTj6OhA27Yt/u5dM/EbevfuyvHjQdy4EcfZsxdp2LAeMpmMyVNHvdDrrawsKVvW87njsrKy2fnQIHrw4D4oFAp8fKpSpUolrl27zq5d+3jrrYGUKeOBv78fZ8+GkpiYiqWliuEjHgstaLVafvpJqs5p06aFKQj6lyKKIuILBDmvSt3wv4ZJPtuECRMvjZOTAwqlHIW5DI1Gy9XwWOQKGTXreAGSek3ZCq54lilByOkwkpLSuHM7iUU/bOSDKfMJOR3GyWMXiIm+hbm5giFvdSmW5Skq0jB3zhLuxidhb29L8xb+xibmmjV9nrpPJkz8U7GxsTaaWcbH3wOgT59uxYwLTfz92Nvb0bmzVJ67fv1W9Hr9a9nOxo3bUauLqFixPAEBUpZIEAR69eoKwKFDx4y+Rj17tzeeGwcM7IaTk71xPTt27CUpKRk7OzsGDer9WvbVxN+PSSzh9WIKhEyYMPHSODhIGSHQozQ3o6hIS8SVG/gHPg5SGjeXMjzBp0IBqFGrEpaWKpKS0vh50RZWLtsFQPderXAp4Wh8nU6n4/t5y7h2LQ5LKws+/Gg8SUlJiKJImTKlcHR0+MuO04SJV0XDhvVo0KAOIHnZtGgR+DfvkYmn0aVLO2xsrElMTOLEidOvfP0JCUkcOXICgKFD+xebAPLzq0H58mUpKtKwe/cBAEqVcmf0mAH07dfJ6G0EkJycwrZtUsZ8xIiBWFk9ac5q4t+BKRB6vZgCIRMmTLw0Tk4OKBRmaHU6HJwk/5OL5yOpVqMsZcq74lLSnjr+Vbgbn8y9uynI5Wa8Pa4P8xa+T+9+bYyNvmXLudO6XUPjekVR5KfF6wi9HIlSqWDGjLGUKeNBWJgky20qizPxpiIIAu+8M5KOHdsyefK4YgaZJv45WFlZGc1tN27c/kzZ7D/KmjUbMRgM1Kvnh49P1WLLfpsV2r//MPn5+QA0b9GQ7j3aGYMmURT5+edVaLVaatb0MYkj/Mt58TDI8Hfv6huJ6UxswoSJl+ZRj5BWq8bCSkFRPoSHxqDT6Zn+hSSlLwgCIafDAPD1q4qVtSSE0LFLU1q28Sfy6k2qVC2HmZkZIF3cV63cyqlTFzAzk/HelJFUrlIBURQJDzcFQibefGxsrBkxYtDfvRsmnkO7di3ZvfsA6ekZ7N9/hK5dO7yS9UZGxnDhQigymYzBg/s9dUyDBnUoVcqDe/cS2b//iDEw+i3BwWcJD49AoVAwevQwk3jMvxyTWMLrxZQRMmHCxEvj4GCPQilHo9Gi0Rbh6GSHWq0h8uoNBEFAEAT0ej1ng8MBCGjiW+z1KpU5dep5F1OJO3Y0xOiYPvadwfj5SWV2CQlJpKdnoFAo8Pau8pccnwkTJv67KJVK+veXfHq2b99tzMz8GURRZPVqyQetVatmeHq6P3WclBWSMlK7dx94wog3Pz+f5cvXA9CzZxfc3V3/9L6Z+GdjKo17vfznA6Fu3brh4OBAz549iz1/7949mjZtSrVq1ahRowZbt279m/bQhIl/Hk5Ojg8zQloKC9T4+kkBysXzkcYxUZFxZGXlYm1tSY1alX53fb9ViOs/oAuNm9QzLnukFletWmVTc7kJEyb+Epo2bYSnpwe5uXns3LnvT68vJOQcN27EoVKZ069fj98dGxDQAFfXkuTm5nHo0PFiy9at20pWVhbu7m50797xT++XiX8+hpf4Y+Ll+c8HQu+++y5r1qx54nm5XM78+fOJjo7m6NGjTJo06ZXMCpkw8W/A0dEeMzMZIKLX66lYqRQAoZdj0Gp1AJx5WBbXoGGN5/ZD7Nh2gNzcfDw8XenUuWWxZY/K4nx9a7ziozBhwoSJp2NmZsbAgZIS2549kufP/yOKInl5ecTH3yM5OQWD4ek3olqtljVrNgPQrVsn7O3tnrvtnj07A7Bz5z5jn9L163EcPHgUgDFjhqFQPNujzcS/B1EQEQXDCzxMGaH/tXfvcVGV+R/AP8MducmAMNwEb3gJRAQ1QRfwAlpiaakliXlpNU1DUjdzU7us7NpPxTRJXVetDE1bzH2lCZmAihdEMETDGyq6EIKsXOQmc35/EEdHbjPIMMB83r3Oy+aZZ875zjN4nC/Pc76nObT+GqGAgADEx8fXabezs4OdnR0AwMbGBlKpFPfv32dlFiIAxsbGMDY2EmeFzC1MYGlpjsLCImSkX0Pvvt2QkpwBAPAZ7tnovnJz7uHw4XgAwLTQieI1Q0DNF4j09Jr9sGw2EbWmIUO84OraE1euXMOmTdvg7OyEgoJCFBQUiH9WVlaJ/fX09GBnZwtHR3s4ONjDwcEO9vZ2+PXXi8jLuwdLy8546aWxSh3b338YoqO/R0HBfRw9moDAwBGIitoOQRDg5zcM/fs/p663TW1MNaoBNF3KvVqJPlRXm06EEhMT8dlnnyElJQU5OTmIiYnByy+/rNBn8+bN+Oyzz5CTk4PnnnsOkZGRGD58eIvGce7cOcjlcjg5ObXofonaM0vLmnsJVVVW4c6dHAwa4obYn5KQfCYdxcWlqKiogp2dNXr0bPzvze5vYvDoUTX6e/SFp6fiP+6//XYVFRWVsLCwgItLV3W+HSIiBRKJBNOmTcGHH/4NKSlpSElJq7efmZkpyssrUFVVhezsu8jOvltvv6lTX4WRkZFSx9bT08PEicHYtm0Xvv/+P6ioqERW1i2YmHTCzJkhzX1L1A4pWxGOVeOap00nQqWlpfDw8MCMGTPwyit119Tu3bsXYWFh2Lx5M3x9fbFlyxaMHTsWly5dQteuNV+avLy8UFFRUee1sbGxsLev/2LFJxUUFCA0NBT//Oc/n/0NEXUgVlaWMDU1QmVVFdLTMzE+OBCxPyUhJfkS7uUVAqiZDWqsotHlS1dx5kwaJBIJQqdPrNO39n4bAwa4szISEbU6d/d+mDZtCq5duwGpVApraymsrCwhldb8aWUlhYGBAeRyOe7dy8edOzm4e/e/uHv38Z+Fhf+Dq2tPle8dNXq0P7777gDy8wuwc+e3AIDQ0NdgYWGujrdKbZRcIodE0nSSw2uEmqdNJ0Jjx47F2LENTyOvW7cOs2bNwuzZswEAkZGROHLkCKKiohAREQEASElJafbxKyoqMGHCBCxbtgw+Pj5N9n0y4SoqKmr2cYnaA6lUClOzTih/WIWL6b8hbNFMWFiY4sGDEvx2OQsA4DvcE1VVVfjntj0wMjLEiy+OgI2tNYA/qijt/B4AMHKUL7p2dVDY/y+/JCIxMQkSiQRjxoxs3TdHRPSHV14Z32QfHR0d2NrawNbWBl5eHgrPlZeXw9DQUOVf5hgYGGDChBfFJKh3714IDByh0j6o/ZNDDokSSQ4ToeZpt8USKisrkZKSgsDAQIX2wMBAJCUlPfP+BUHAm2++iREjRmDatKbv+xAREQELCwtx4zI66uik0s4wNjaAjo4E5eWVuH7tFrwHP76Op0/fbrDuYomzZy7g2C+ncPhQPBYuWIlNn+/EnTs5SEw4gxs3bsPY2BBTXgtW2Hd29l1s2bIDAPD666+gb9/Gq84REbVVRkZGzZ7RHjNmJCwsLKCnp4e3357JmXEtxKpx6tWmZ4Qak5+fj+rqatja2iq029raIjc3V+n9BAUF4fz58ygtLYWjoyNiYmIwaNAgnDx5Env37kX//v1x4MABAMDXX38Nd/f6b+i4bNkyhIeHi4+LioqYDFGHJpVaQiKRwFJas0zjwoXLGDTEDUfjTgMAfP80EABw+tR5AIClpQUKCx8gMfEsEhPPwtCwphT2hIljYGFhJu63oqICa9ZsQEVFJTw83MS7vBMRaRsjIyOsXfsJKioq4eBgp+lwSAN4jZB6tdtEqNbTvx0RBEGl35gcOXKk3vZhw4Y1WAqzPoaGhjA0NFS6P1F7Z2nZGQBgYmKEygrg1wuXMWnyi3BwsEFZWTkGDX4O5eUVSE2tqfr2/gfzUF1djR9iYnHmTBoqKirRpYsUL7wYoLDfrVt3ITv7Ljp37oxFi+ZBR6fdTlwTET0za2srTYdAGiRHNSRKVISTs2pcs7TbbxjW1tbQ1dWtM/uTl5dXZ5aIiFqelZUUAKD7x69Trl+/jYcPy7Fq9Xz8fV04OpkY43xKOiorqyCTdYGLiyN69nTBe0v+jHWRH2LiK2Ow9P23FW6SmpBwEkePJkAikSA8fF6T99sgIiLqyAQI4qxQ45vq9xG6e/cu3njjDVhZWaFTp04YMGCAwrX1giBg1apVsLe3h7GxMfz9/ZGRkaGwj4qKCixYsADW1tYwMTHB+PHjcefOnWd+362l3SZCBgYG8PLyQlxcnEJ7XFxck4UNiOjZWVlZAgAePnwIR0cZBEHAxfTfYGhoACOjmtnR06dqbqo65HnF6nGOjnZ47fXxcHZ+XCDhv//NRVTUdgDApEkv8z4ZRESk9eQSudKbKgoLC+Hr6wt9fX0cPnwYly5dwtq1a9G5c2exz5o1a7Bu3Tps2rQJycnJkMlkGD16NIqLi8U+YWFhiImJwZ49e3DixAmUlJRg3LhxqK5uHzNUbXppXElJCa5duyY+zsrKQlpaGqRSKbp27Yrw8HBMmzYN3t7eGDp0KLZu3Yrbt29j7ty5GoyaSDvULo2rqqpC7z7dcedOLi5cuIyhPl4AgPLyCpw/fxEAMNRnYJP7+/rrPSgvr4CbW1+89tpEtcVNRETUXshR1XQnFfrV+sc//gEnJyfs2LFDbHNxcRH/XxAEREZGYvny5Zg4sebf5F27dsHW1hbffvst5syZgwcPHmD79u34+uuvMWrUKADAN998AycnJ/z8888ICgpSKSZNaNMzQufOnYOnpyc8PWvuTB8eHg5PT0+sWLECADBlyhRERkbi448/xoABA5CYmIhDhw7B2dlZk2ETaQV9fX2YmZkCAJycai7i/fXCZQhCzfR86vmLqKysgo2NFbp1a7xwSFbWLZw6lQyJRII5c2bwuiAiIiIAcqFa6Q2oKdb15FbfvTQB4ODBg/D29sakSZNgY2MDT09PbNu2TXw+KysLubm5CtWZDQ0N4efnJ1ZnTklJQVVVlUIfe3t7uLm5tUgF59bQpr9t+Pv7QxCEOtvOnTvFPvPmzcPNmzdRUVGBlJQU/OlPf9JcwERaRiqtWR4ntbKAnp4u8vMLkZOTB+DxsrihQwc2WcDku+9iAAC+vkPg5OTQaF8iIiJtodz1QY8ryzk5OSnczqX2vppPu3HjBqKiotCrVy8cOXIEc+fOxcKFC/HVV18BgHgNfmPVmXNzc2FgYABLS8sG+7R1bXppHBG1bTY2XXDrVjbu3ctHn749cTE9ExfSLsHKylJcFjdkqGej+7h587Y4GzR58oTWCJuIiKhdEFANQYl5C+GPqnHZ2dkwNzcX2xuqaCyXy+Ht7Y3Vq1cDADw9PZGRkYGoqCiEhoaK/ZpTnVnVCs6a1KZnhIiobXN17QEAyMy8Cg+PvgBq7ieUlpohlsfu0aPxpap79/4bAODjMxhduzqqN2AiIqJ2RNUbqpqbmytsDSVCdnZ26Nevn0Jb3759cfv2bQCATCYDgEarM8tkMlRWVqKwsLDBPm0dEyEiarbevXsBAC5fvoL+fyRClzKu4MTxZADA809Vi3saZ4OIiIgapq7y2b6+vsjMzFRou3Llinidfbdu3SCTyRSqM1dWViIhIUGszuzl5QV9fX2FPjk5Obh48WK7qeDMpXFE1Gyurj0gkUhQUHAfpqbGMDc3RVFRCc6cSQMAPP9HBbmG1F4b5OMzGM7OjRdUICIi0jaCUA0BTS8zEwTVylUvWrQIPj4+WL16NSZPnoyzZ89i69at2Lp1K4CaJXFhYWFYvXo1evXqhV69emH16tXo1KkTpk6dCgCwsLDArFmz8N5778HKygpSqRSLFy+Gu7u7WEWurWMiRETNZmRkBBeXrsjKuoXMzGvw8OiL43/MBllbW6JnT2c8eFCERYs+gIGBAYKDx2DkyD/ByMgIt25lIynpLABwNoiIiKgeNUvemr5HkFyJPk8aNGgQYmJisGzZMnz88cfo1q0bIiMjERISIvZZunQpysrKMG/ePBQWFmLIkCGIjY2FmZmZ2Gf9+vXQ09PD5MmTUVZWhpEjR2Lnzp3Q1dVVKR5NkQi1tW6pRRUVFcHCwgIPHjxQuGiNqKPZsmUnDh+Ow7hxY9CrZy98samm4sy44JEInf4KDh+Ow5YtO8X+ZmamGDNmFLKybuHcuVT4+g7BkiULNRQ9ERFps7b6fa02LlsLX+hImp63kAuP8PuDk23ufbR1vEaIiJ5J37411wn99tsVuPfvI7YPHVpzE9WUlAsAgIEDPWBra4Pi4hLs23cA587VlNeeNOnl1g2YiIionagWHim9keq4NI6InkmfPq4Aam6KamraCdNCJ6Kk5CF69nJBRUUFfv21pox2aOhr6NrVEadOJePAgR9x9ep1+PsPh4tLV02GT0RE1Gap6xohqsFEiIieSZcu1rC07IzCwv/h6tUbCB7/+ALJX3+9hMrKKlhbW8HZ2QkSiQS+vkPg4zMY9+7lizdkJSIiorpqq8Yp049Ux6VxRPRMJBIJ+vR5vDzuSSkpaQAAb2/FMtoSiQQ2Nl2gp8ffxRARETVEEORKb6Q6JkJE9Mxql8dlZl4V2wRBQHLyeQCAl9cATYRFRETUzlVDUGIDuDSuOfjrWCJ6Zo9vrHoVgiBAIpHg1q1sFBTch4GBPjw8ntNwhERERO1PzUyPMtcIcUaoOTgjRETPrEcPF+jp6aG4uBg5Ob8DeLwsrn9/NxgYGGgwOiIiovaJS+PUi4kQET0zfX199OzZHcDj64SSk2vKY3t7D9BUWERERO2aXIX/SHVMhIioRfTu3RMA8NtvV1FcXCJeL8Trg4iIiJqHM0LqxUSIiFpEbcGE3367gtTUCxAEAc7OTujSxVrDkREREbVPcuER5EKVEhtvqNocLJZARC2itoR2dvZdJCQkAagpm01ERETNo+xMD2eEmoczQkTUIiwtO8PW1gaCICjcP4iIiIiah0vj1IuJEBG1mNrlcQBgZmYqXjdEREREqhMgV3oj1TERIqIW06fP48Rn4EAP6OjwFENERNRcnBFSL14jREQtpvbGqgDLZhMRET0rXiOkXkyEiKjFODs7wcamC8rKyuHp2V/T4RAREbVzyiY4TISag4kQEbUYXV1d/N//fYLq6mqYmppqOhwiIqJ2jTNC6sVEiIhalLm5maZDICIi6hCULYLAYgnNw0SIiIiIiKgNEgQByix7q+lHqmIiRERERETUBglCFQCJEv2YCDUHEyEiIiIiojZJDmUSIYCJUHMwESIiIiIiaosEJRMhzgg1C+92SERERETUBgkq/NdcERERkEgkCAsLe3xcQcCqVatgb28PY2Nj+Pv7IyMjQ+F1FRUVWLBgAaytrWFiYoLx48fjzp07zY5DE5gIERERERG1SXIVNtUlJydj69at6N9f8d5/a9aswbp167Bp0yYkJydDJpNh9OjRKC4uFvuEhYUhJiYGe/bswYkTJ1BSUoJx48ahurq6WbFoAhMhIiIiIqI2SahZ9tbU1owZoZKSEoSEhGDbtm2wtLR8fERBQGRkJJYvX46JEyfCzc0Nu3btwsOHD/Htt98CAB48eIDt27dj7dq1GDVqFDw9PfHNN98gPT0dP//8c0u9ebXjNUJqUlu9o6ioSMOREBEREVF9ar+ntd2qa6ote3v6e6ehoSEMDQ3r7Tt//ny8+OKLGDVqFD799FOxPSsrC7m5uQgMDFTYj5+fH5KSkjBnzhykpKSgqqpKoY+9vT3c3NyQlJSEoKAgpWPWJCZCalI7dejk5KThSIiIiIioMcXFxbCwsNB0GCIDAwPIZDLk5uYq/RpTU9M63ztXrlyJVatW1em7Z88enD9/HsnJyXWeqz2mra2tQrutrS1u3bol9jEwMFCYSarto0rMmsZESE3s7e2RnZ0NMzMzSCTKlD3sWIqKiuDk5ITs7GyYm5trOpw2g+NSF8ekfhyX+nFc6sdxqYtjUj+OiyJBEFBcXAx7e3tNh6LAyMgIWVlZqKysVPo1giDU+c5Z32xQdnY23n33XcTGxsLIyKjB/T29r/r2r0wMbRkTITXR0dGBo6OjpsPQOHNzc55o68FxqYtjUj+OS/04LvXjuNTFMakfx+WxtjQT9CQjI6NGE5XmSklJQV5eHry8vMS26upqJCYmYtOmTcjMzARQM+tjZ2cn9snLyxNniWQyGSorK1FYWKgwK5SXlwcfH58Wj1ldWCyBiIiIiEhLjBw5Eunp6UhLSxM3b29vhISEIC0tDd27d4dMJkNcXJz4msrKSiQkJIhJjpeXF/T19RX65OTk4OLFi+0qEeKMEBERERGRljAzM4Obm5tCm4mJCaysrMT2sLAwrF69Gr169UKvXr2wevVqdOrUCVOnTgVQM4s2a9YsvPfee7CysoJUKsXixYvh7u6OUaNGtfp7ai4mQqQWhoaGWLlyZYOVSrQVx6Uujkn9OC7147jUj+NSF8ekfhwXUsbSpUtRVlaGefPmobCwEEOGDEFsbCzMzMzEPuvXr4eenh4mT56MsrIyjBw5Ejt37oSurq4GI1eNRGi79QKJiIiIiIjUgtcIERERERGR1mEiREREREREWoeJEBERERERaR0mQkREREREpHWYCJHKEhMTERwcDHt7e0gkEhw4cKDJ1yQkJMDLywtGRkbo3r07vvzyS/UH2spUHZd///vfGD16NLp06QJzc3MMHToUR44caZ1gW1Fzfl5qnTx5Enp6ehgwYIDa4tOU5oxLRUUFli9fDmdnZxgaGqJHjx7417/+pf5gW0lzxmT37t3w8PBAp06dYGdnhxkzZqCgoED9wbaiiIgIDBo0CGZmZrCxscHLL78s3vCwMR35vNucMdGGc25zf1ZqdeRzLlF9mAiRykpLS+Hh4YFNmzYp1T8rKwsvvPAChg8fjtTUVHzwwQdYuHAhvv/+ezVH2rpUHZfExESMHj0ahw4dQkpKCgICAhAcHIzU1FQ1R9q6VB2XWg8ePEBoaChGjhyppsg0qznjMnnyZBw9ehTbt29HZmYmoqOj0adPHzVG2bpUHZMTJ04gNDQUs2bNQkZGBvbt24fk5GTMnj1bzZG2roSEBMyfPx+nT59GXFwcHj16hMDAQJSWljb4mo5+3m3OmGjDObc541Kro59zieolED0DAEJMTEyjfZYuXSr06dNHoW3OnDnC888/r8bINEuZcalPv379hI8++qjlA2ojVBmXKVOmCH/961+FlStXCh4eHmqNS9OUGZfDhw8LFhYWQkFBQesEpWHKjMlnn30mdO/eXaHt888/FxwdHdUYmebl5eUJAISEhIQG+2jbeVeZMalPRz/nqjIu2nTOJarFGSFSu1OnTiEwMFChLSgoCOfOnUNVVZWGomp75HI5iouLIZVKNR2Kxu3YsQPXr1/HypUrNR1Km3Hw4EF4e3tjzZo1cHBwgKurKxYvXoyysjJNh6YxPj4+uHPnDg4dOgRBEPD7779j//79ePHFFzUdmlo9ePAAABo9V2jbeVeZMXmaNpxzlR0XnnNJW+lpOgDq+HJzc2Fra6vQZmtri0ePHiE/Px92dnYaiqxtWbt2LUpLSzF58mRNh6JRV69exfvvv4/jx49DT4+nqFo3btzAiRMnYGRkhJiYGOTn52PevHm4f/9+h7pOSBU+Pj7YvXs3pkyZgvLycjx69Ajjx4/Hxo0bNR2a2giCgPDwcAwbNgxubm4N9tOm866yY/K0jn7OVXZceM4lbcYZIWoVEolE4bEgCPW2a6vo6GisWrUKe/fuhY2NjabD0Zjq6mpMnToVH330EVxdXTUdTpsil8shkUiwe/duDB48GC+88ALWrVuHnTt3au2s0KVLl7Bw4UKsWLECKSkp+Omnn5CVlYW5c+dqOjS1eeedd/Drr78iOjq6yb7act5VZUxqacM5V5lx4TmXtB1Tf1I7mUyG3Nxchba8vDzo6enByspKQ1G1HXv37sWsWbOwb98+jBo1StPhaFRxcTHOnTuH1NRUvPPOOwBqEgBBEKCnp4fY2FiMGDFCw1Fqhp2dHRwcHGBhYSG29e3bF4Ig4M6dO+jVq5cGo9OMiIgI+Pr6YsmSJQCA/v37w8TEBMOHD8enn37aoWY9AGDBggU4ePAgEhMT4ejo2GhfbTnvqjImtbThnKvsuPCcS9qOiRCp3dChQ/Gf//xHoS02Nhbe3t7Q19fXUFRtQ3R0NGbOnIno6OgOf12DMszNzZGenq7QtnnzZvzyyy/Yv38/unXrpqHINM/X1xf79u1DSUkJTE1NAQBXrlyBjo6O0l8AO5qHDx/WWcqjq6sL4PHsR0cgCAIWLFiAmJgYxMfHK/X3oKOfd5szJkDHP+eqOi4855K2YyJEKispKcG1a9fEx1lZWUhLS4NUKkXXrl2xbNky3L17F1999RUAYO7cudi0aRPCw8Px1ltv4dSpU9i+fbtKyxjaA1XHJTo6GqGhodiwYQOef/558be3xsbGCr/1b+9UGRcdHZ06a9ltbGxgZGSk0tr/9kDVn5epU6fik08+wYwZM/DRRx8hPz8fS5YswcyZM2FsbKypt9GiVB2T4OBgvPXWW4iKikJQUBBycnIQFhaGwYMHw97eXlNvo8XNnz8f3377LX744QeYmZmJ5woLCwvxs9e2825zxkQbzrmqjos2nXOJ6qWRWnXUrh07dkwAUGebPn26IAiCMH36dMHPz0/hNfHx8YKnp6dgYGAguLi4CFFRUa0fuJqpOi5+fn6N9u8omvPz8qSOWsq1OeNy+fJlYdSoUYKxsbHg6OgohIeHCw8fPmz94NWkOWPy+eefC/369ROMjY0FOzs7ISQkRLhz507rB69G9Y0JAGHHjh1iH2077zZnTLThnNvcn5UnddRzLlF9JILQgdYPEBERERERKYFV44iIiIiISOswESIiIiIiIq3DRIiIiIiIiLQOEyEiIiIiItI6TISIiIiIiEjrMBEiIiIiIiKtw0SIiIiIiIi0DhONgK3EAAALfElEQVQhIiIiIiLSOkyEiIiIiIhI6zARIiIiIiIircNEiIionSkoKICNjQ1u3ryp6VCa5O/vj7CwME2HIWpOPP7+/pBIJJBIJEhLS1NLXE968803xeMdOHAAAPDqq69i3bp1aj82EZE2YSJERO3Gk18Qn9yuXbvWYsdoa1/c6xMREYHg4GC4uLjgyy+/hJmZGR49eiQ+X1JSAn19fQwfPlzhdcePH4dEIsGVK1daO+RW19Kf41tvvYWcnBy4ubm12D4bsmHDBuTk5Ci0rVixAn/7299QVFSk9uMTEWkLJkJE1K6MGTMGOTk5Clu3bt00HVYdlZWVatlvWVkZtm/fjtmzZwMAAgICUFJSgnPnzol9jh8/DplMhuTkZDx8+FBsj4+Ph729PVxdXdUSW0fWqVMnyGQy6Onpqf1YFhYWkMlkCm39+/eHi4sLdu/erfbjExFpCyZCRNSuGBoaQiaTKWy6uroQBAFr1qxB9+7dYWxsDA8PD+zfv1/htT/99BOGDRuGzp07w8rKCuPGjcP169fF5998800kJCRgw4YN4mzTzZs34eLigsjISIV9DRgwAKtWrRIf+/v745133kF4eDisra0xevRoAGgyrv3798Pd3R3GxsawsrLCqFGjUFpa2uD7P3z4MPT09DB06FAAQO/evWFvb4/4+HixT3x8PF566SX06NEDSUlJCu0BAQFKjceWLVvg4OAAuVyucPzx48dj+vTp4mNlxv1JyvT39/fHwoULsXTpUkilUshkMoWxLi4uRkhICExMTGBnZ4f169crzAA19DnWksvlDe5bWSdOnIC+vj4qKirEtqysLEgkEty6dUt8HwsWLEBYWBgsLS1ha2uLrVu3orS0FDNmzICZmRl69OiBw4cPK3XM8ePHIzo6WuVYiYiofkyEiKhD+Otf/4odO3YgKioKGRkZWLRoEd544w0kJCSIfUpLSxEeHo7k5GQcPXoUOjo6mDBhgvhlf8OGDRg6dKi4DConJwdOTk5Kx7Br1y7o6enh5MmT2LJlS5Nx5eTk4PXXX8fMmTNx+fJlxMfHY+LEiRAEocFjJCYmwtvbW6HN398fx44dEx8fO3YM/v7+8PPzE9srKytx6tQphUSosfGYNGkS8vPzFfZbWFiII0eOICQkRKVxf5Ky/Xft2gUTExOcOXMGa9aswccff4y4uDgAQHh4OE6ePImDBw8iLi4Ox48fx/nz58XXNvU5NrZvZaWlpaFv374wNDRUaOvcuTOcnZ0VjmVtbY2zZ89iwYIFePvttzFp0iT4+Pjg/PnzCAoKwrRp0xRm7hoyePBgnD17ViH5IiKiZyAQEbUT06dPF3R1dQUTExNxe/XVV4WSkhLByMhISEpKUug/a9Ys4fXXX29wf3l5eQIAIT09XWzz8/MT3n33XYV+zs7Owvr16xXaPDw8hJUrVyq8bsCAAQp9moorJSVFACDcvHlTiXdf46WXXhJmzpyp0LZ161bBxMREqKqqEoqKigQ9PT3h999/F/bs2SP4+PgIgiAICQkJAgDh+vXrDe776fEYP368wrG2bNkiyGQy4dGjR0q9v9pxqR1PZT8nPz8/YdiwYQp9Bg0aJPzlL38RioqKBH19fWHfvn3ic//73/+ETp06KXxu9X2OTe27IfXta/bs2UJoaKhC24oVKwQ/P78Gj/Xo0SPBxMREmDZtmtiWk5MjABBOnTpV57gAhJiYGPHxhQsXVP55ISKihql/sTMRUQsKCAhAVFSU+NjExASXLl1CeXm5uBytVmVlJTw9PcXH169fx4cffojTp08jPz9fnAm6fft2i1wE//RMTVNxeXh4YOTIkXB3d0dQUBACAwPx6quvwtLSssFjlJWVwcjISKEtICAApaWlSE5ORmFhIVxdXWFjYwM/Pz9MmzYNpaWliI+PR9euXdG9e3fxdU2NR0hICP785z9j8+bNMDQ0xO7du/Haa69BV1dXqff3NFX69+/fX+GxnZ0d8vLycOPGDVRVVWHw4MHicxYWFujdu3eDY/a0hvatirS0NEydOlWhLTU1FR4eHg0eS1dXF1ZWVnB3dxfbbG1tAUCp4xsbGwOAUrNHRETUNCZCRNSumJiYoGfPngptt2/fBgD8+OOPcHBwUHjuyaVLwcHBcHJywrZt22Bvbw+5XA43N7cmCxvo6OjUWa5WVVVVb2xPqk0sGopLV1cXcXFxSEpKQmxsLDZu3Ijly5fjzJkzDRaAsLa2RmFhoUJbz5494ejoiGPHjqGwsBB+fn4AAJlMhm7duuHkyZM4duwYRowYofC6psYjODgYcrkcP/74IwYNGoTjx48rlHBu6v09TZX++vr6Co8lEgnkcrn4OUgkEoXnn/58GtPQvpVVXV2NjIyMOsnb+fPnMWHChCaP9WRb7ftQ5vj3798HAHTp0kXpWImIqGFMhIio3evXrx8MDQ1x+/ZtMQl4WkFBAS5fvowtW7aIZaVPnDhRp5+BgQGqq6sV2rp06aJQzrioqAhZWVktEpdEIoGvry98fX2xYsUKODs7IyYmBuHh4fX29/T0xDfffFOnPSAgAPHx8SgsLMSSJUvEdj8/Pxw5cgSnT5/GjBkzxHZlxsPY2BgTJ07E7t27ce3aNbi6usLLy0ul96fqeDSlR48e0NfXx9mzZ8XrfoqKinD16lWFfdb3ObaUzMxMlJWVwd7eXmw7deoU7t69W2dGqCVdvHgRjo6OsLa2VtsxiIi0CRMhImr3zMzMsHjxYixatAhyuRzDhg1DUVERkpKSYGpqiunTp8PS0hJWVlbYunUr7OzscPv2bbz//vt19uXi4oIzZ87g5s2bMDU1hVQqxYgRI7Bz504EBwfD0tISH374obg87Fni6tOnD44ePYrAwEDY2NjgzJkzuHfvHvr27dvgPoOCgrBs2TIUFhYqLKELCAjA/PnzUVVVpZAQ+Pn54e2330Z5eblCoQRlxyMkJATBwcHIyMjAG2+8ofK4P0v/hsZ0+vTpWLJkCaRSKWxsbLBy5Uro6OgozBLV9znq6LRMfaDam6pu3LgRCxcuxLVr17Bw4UIAUGshg+PHjyMwMFBt+yci0jZMhIioQ/jkk09gY2ODiIgI3LhxA507d8bAgQPxwQcfAKhZ3rZnzx4sXLgQbm5u6N27Nz7//HP4+/sr7Gfx4sWYPn06+vXrh7KyMmRlZWHZsmW4ceMGxo0bBwsLC3zyySdKzQg1FZe5uTkSExMRGRmJoqIiODs7Y+3atRg7dmyD+3N3d4e3tze+++47zJkzR2wPCAhAWVkZ+vTpI153AtQkQsXFxejRo4dC5TRlx2PEiBGQSqXIzMysc02MMuP+rP3rs27dOsydOxfjxo2Dubk5li5diuzsbIVrp+r7HF1cXJQ+RmPS0tIwevRoZGVlwc3NDf369cPf//53zJw5E1988YVY2rwllZeXIyYmBkeOHGnxfRMRaSuJoMrCaiIi0rhDhw5h8eLFuHjxYovNcrRnpaWlcHBwwNq1azFr1qwW37+/vz8GDBgg3ksqKCgIAwcORERERIsf60kSiQQxMTF4+eWX8cUXX+CHH35AbGysWo9JRKRN+C8oEVE788ILL2DOnDm4e/eupkPRiNTUVERHR+P69es4f/68eF+jl156SW3H3Lx5M0xNTZGeno4LFy7UqTzXkubOnQtTU1OFNn19fWzcuFFtxyQi0kacESIionYlNTUVs2fPRmZmJgwMDODl5YV169YplKVuSXfv3kVZWRmAmiIMzs7OuHjxIp577jm1HC8vLw9FRUUAakp7P12NkIiIWgYTISIiIiIi0jpcGkdERERERFqHiRAREREREWkdJkJERERERKR1mAgREREREZHWYSJERERERERah4kQERERERFpHSZCRERERESkdZgIERERERGR1mEiREREREREWoeJEBERERERaZ3/B80rj7Rhj+bPAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the filter bounds\n", + "filter_bounds = {'gravity': (5.,5), \n", + " 'c_o_ratio' : (1,1),\n", + " 'metallicity' : (0.0,0.0),\n", + " 'temperature': (400, 1800)}\n", + "\n", + "# Call the function to filter the dataset\n", + "plot_filtered_dataframe(train_BD[train_BD['is_augmented'].isin(\n", + " ['no'])], \n", + " filter_bounds,\n", + " 'temperature',\n", + " title_label = '[log$g$='+str(filter_bounds['gravity'][0])+\n", + " ', C/O ='+str(filter_bounds['c_o_ratio'][0])+\n", + " ', [M/H]='+str(filter_bounds['metallicity'][0])+\n", + " ']',\n", + " wl_synthetic = wl_synthetic.wl,\n", + " __reference_data__ = __reference_data_path__)" + ] + }, + { + "cell_type": "markdown", + "id": "945b671b-fb5e-4a47-8eaa-73306d28702a", + "metadata": {}, + "source": [ + "### 1.4.2 Gravity\n", + "\n", + "Plot the brown dwarf datasets for log$g$= 3-5.5 while other paremeters are constnat ($T_{eff}$=800, C/O=1, [M/E]=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1049f635-9bbe-48c6-978b-501802e8f8de", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the filter bounds\n", + "filter_bounds = {'gravity': (3,5.5), \n", + " 'c_o_ratio' : (1,1),\n", + " 'metallicity' : (0.0,0.0),\n", + " 'temperature': (800, 800)}\n", + "\n", + "# Call the function to filter the dataset\n", + "plot_filtered_dataframe(train_BD[train_BD['is_augmented'].isin(\n", + " ['no'])], \n", + " filter_bounds,\n", + " 'gravity', \n", + " title_label = '[T='+str(filter_bounds['temperature'][0])+\n", + " ', C/O ='+str(filter_bounds['c_o_ratio'][0])+\n", + " ', [M/H]='+str(filter_bounds['metallicity'][0])+\n", + " ']',\n", + " wl_synthetic = wl_synthetic.wl,\n", + " __reference_data__ = __reference_data_path__)" + ] + }, + { + "cell_type": "markdown", + "id": "3d625d83-bf05-4eed-a7df-b18d4b72513a", + "metadata": {}, + "source": [ + "### 1.4.3 Carbon-to-Oxygen ratio\n", + "\n", + "Plot the brown dwarf datasets for C/O= 0.25-2.5 while other paremeters are constnat ($T_{eff}$=800, log$g$=5.0, [M/E]=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "731d9bd0-edb6-4957-b947-456f7552887c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the filter bounds\n", + "filter_bounds = {'gravity': (5.,5), \n", + " 'c_o_ratio' : (0.25,2.5),\n", + " 'metallicity' : (0.0,0.0),\n", + " 'temperature': (800, 800)}\n", + "\n", + "# Call the function to filter the dataset\n", + "plot_filtered_dataframe(train_BD[train_BD['is_augmented'].isin(\n", + " ['no'])], \n", + " filter_bounds,\n", + " 'c_o_ratio', \n", + " title_label = '[T='+str(filter_bounds['temperature'][0])+\n", + " ', log$g$='+str(filter_bounds['gravity'][0])+\n", + " ', [M/H]='+str(filter_bounds['metallicity'][0])+\n", + " ']',\n", + " wl_synthetic = wl_synthetic.wl,\n", + " __reference_data__ = __reference_data_path__)" + ] + }, + { + "cell_type": "markdown", + "id": "80fd8be3-a13b-4449-99f5-b2f77357900c", + "metadata": {}, + "source": [ + "### 1.4.4 Metallicity\n", + "\n", + "Plot the brown dwarf datasets for [M/H]= -1.0 - 2.0 while other paremeters are constnat ($T_{eff}$=800, log$g$=5.0, C/O=1.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "580a0a1e-b1aa-4859-9190-6658ea2603fc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the filter bounds\n", + "filter_bounds = {'gravity': (5.,5), \n", + " 'c_o_ratio' : (1.,1.),\n", + " 'metallicity' : (-1,2),\n", + " 'temperature': (800, 800)}\n", + "\n", + "# Call the function to filter the dataset\n", + "plot_filtered_dataframe(train_BD[train_BD['is_augmented'].isin(\n", + " ['no'])], \n", + " filter_bounds,\n", + " 'metallicity', \n", + " title_label = '[T='+str(filter_bounds['temperature'][0])+\n", + " ', log$g$='+str(filter_bounds['gravity'][0])+\n", + " ', C/O='+str(filter_bounds['c_o_ratio'][0])+\n", + " ']',\n", + " wl_synthetic = wl_synthetic.wl,\n", + " __reference_data__ = __reference_data_path__)" + ] + }, + { + "cell_type": "markdown", + "id": "d2efe3ec-4560-407f-9b61-354d8dd38017", + "metadata": {}, + "source": [ + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/2__Deploy_ML_Models_Predict_BrownDwarf_Parameters.ipynb b/docs/tutorials/2__Deploy_ML_Models_Predict_BrownDwarf_Parameters.ipynb new file mode 100644 index 00000000..38c7a0b0 --- /dev/null +++ b/docs/tutorials/2__Deploy_ML_Models_Predict_BrownDwarf_Parameters.ipynb @@ -0,0 +1,5766 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "723c394d-987d-4f2d-8bd6-378a23b1218e", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "# 2. Deploy CNN Model to Predict Brown Dwarf Atmospheric Parameters \n", + "\n", + "\n", + "In the following steps, you will: \n", + "\n", + "- Load the brown dwarf dataset used to train the ML models\n", + "- Prepare the X and y variables to deploy the trained ML models\n", + "- Visualize them for few cases\n", + "\n", + "We will need the following modules from `TelescopeML`:\n", + "\n", + "- *DeepBuilder*: to prepare the syntheric brown dwarf dataset, load the trained machine learning (ML) models\n", + "- *Predictor*: to prepare the observatinal brown dwarf dataset and deploy the trained ML models\n", + "- *StatVisAnalyzer*: provide statistical-test and plotting function \n", + "- *IO_utils*: provide functions to load the trained ML models" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "dab6ba1b-87cf-482b-9bad-7ae019708a27", + "metadata": {}, + "outputs": [], + "source": [ + "from TelescopeML.DeepBuilder import *\n", + "from TelescopeML.Predictor import *\n", + "from TelescopeML.StatVisAnalyzer import *\n", + "from TelescopeML.IO_utils import load_or_dump_trained_model_CNN" + ] + }, + { + "cell_type": "markdown", + "id": "98309fb6-0ed0-4f20-a65e-45b54910a8c8", + "metadata": { + "tags": [] + }, + "source": [ + "## 2.1 Data Preperation \n", + "\n", + "### 2.1.1 Load the Synthetic spectra - training dataset\n", + "\n", + "We computed a low-resolution spectrum ($R$=200) utilizing atmopshric brown dwarfs grid model, [*Sonora-Bobcat*](https://arxiv.org/pdf/2107.07434.pdf) for spectral range $\\sim$0.9-2.4 $\\mu m$. An open-source atmospheric radiative transfer Python package, [*PICASO*](https://natashabatalha.github.io/picaso/) was employed for generating these datasets. This dataset encompass 30,888 synthetic spectra (or instances or rows). \n", + "\n", + "Each spectrum has 104 features (or wavelengths in the astrophysics context, i.e., 0.897, 0.906, ..., 2.512~$\\mu$m) and 4 parameters:\n", + "- gravity ( log$g$ ) \n", + "- temperature ( T$_{eff}$ )\n", + "- carbon-to-oxygen ratio ( C/O )\n", + "- metallicity ( [M/H] )" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "94ab3753-16a7-40be-8832-06a8837649db", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/egharibn/RESEARCH/ml/projects/TelescopeML_project/reference_data/'" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read the `TelescopeML_reference_data` path\n", + "\n", + "import os # to check the path \n", + "\n", + "__reference_data_path__ = os.getenv(\"TelescopeML_reference_data\")\n", + "__reference_data_path__" + ] + }, + { + "cell_type": "markdown", + "id": "440fbe88-e7b4-496f-81ab-7c7fa9db75d3", + "metadata": {}, + "source": [ + " Load the dataset and check few instances " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "5332f129-5253-4067-ba57-165f5249e252", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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        " + ], + "text/plain": [ + " wl\n", + "0 2.511960\n", + "1 2.486966\n", + "2 2.462220" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wl_synthetic = pd.read_csv(os.path.join(__reference_data_path__, \n", + " 'training_datasets', \n", + " 'wl.csv'))\n", + "wl_synthetic.head(3)" + ] + }, + { + "cell_type": "markdown", + "id": "8bb511a7-5a5f-4b52-a6d9-e8533ece247e", + "metadata": { + "tags": [] + }, + "source": [ + "### 2.1.3 Prepare Inputs and outputs for ML models (X,y)\n", + "- X: 104 column variables or fluxes\n", + "- y: output variables: 'gravity', 'temperature', 'c_o_ratio', 'metallicity'\n", + "\n", + "Note: one of the columns, *is_augmented*, is indicating whether this is a native dataset or an augmented one. let's just ignore it for now!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "e90e78b1-4f64-40b6-b92e-6027c8ace483", + "metadata": {}, + "outputs": [], + "source": [ + "# to assure we are only training the module with the native non-augmented BD training dataset\n", + "train_BD = train_BD[train_BD['is_augmented'].isin(['no'])] \n", + "\n", + "\n", + "# Training feature variables\n", + "X = train_BD.drop(\n", + " columns=['gravity', \n", + " 'temperature', \n", + " 'c_o_ratio', \n", + " 'metallicity', \n", + " 'is_augmented'])#.astype(np.float32)\n", + "\n", + "\n", + "# Target/Output feature variables\n", + "y = train_BD[['gravity', 'c_o_ratio', 'metallicity', 'temperature', ]]#.astype(np.float32)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6932555b-26d2-4140-b5d9-f03b7ba86337", + "metadata": {}, + "source": [ + "log-transform the 'temperature' variable toreduce the skewness of the data, making it more symmetric and normal-like for the ML model\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "07998d41-e101-410c-9a35-e5c6da1800b0", + "metadata": {}, + "outputs": [], + "source": [ + "y.loc[:, 'temperature'] = np.log10(y['temperature'])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "37b9edab-f6dd-406e-b4c4-aba7ef45634c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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        " + ], + "text/plain": [ + " gravity c_o_ratio metallicity temperature\n", + "0 5.0 0.25 -1.0 3.041393\n", + "1 5.0 0.25 -0.7 3.041393\n", + "2 5.0 0.25 -0.5 3.041393\n", + "3 5.0 0.25 -0.3 3.041393\n", + "4 5.0 0.25 0.0 3.041393" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check the output variables\n", + "y.head()" + ] + }, + { + "cell_type": "markdown", + "id": "6108f702-bb40-49f4-9c38-306e0e27ad4f", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "c9cc267d-85a9-44c6-9081-5067d0bdd8e6", + "metadata": { + "tags": [] + }, + "source": [ + "## 2.2 Build the CNN model and Processing the Data\n", + "\n", + "Here we instintiate BuildRegressorCNN class from DeepBuilder module to prepare the datasets and take the trained CNN (Convolutional Neural Networks) for us:\n", + "\n", + "- Take the synthetic datasets\n", + "- Process them, e.g.\n", + " - Divide them to three sets: train, validation, and test sets\n", + " - Scale y variables\n", + " - Scale X variables\n", + " - Create new features \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "60932dc5-bd2b-4479-9126-da6f7b13735b", + "metadata": { + "tags": [] + }, + "source": [ + "### 2.2.1 Instintiate BuildRegressorCNN class from DeepBuilder module" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "bc38d0e4-a4c2-4288-9d33-ab15fb25093b", + "metadata": {}, + "outputs": [], + "source": [ + "build_regressor_CNN = BuildRegressorCNN( \n", + " trained_model = None,\n", + " trained_model_history = None,\n", + " feature_values=X.to_numpy(),\n", + " feature_names=X.columns,\n", + " target_values=y.to_numpy(),\n", + " target_name=['gravity', 'c_o_ratio', 'metallicity', 'temperature'],\n", + " is_tuned='yes',\n", + " param_grid=None,\n", + " spectral_resolution=100,\n", + " is_feature_improved='no',\n", + " is_augmented='no',\n", + " ml_model=None,\n", + " ml_model_str='CNN',\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "ffbeb92b-46ad-4ebb-8edc-f29d5d56e7e2", + "metadata": {}, + "source": [ + "### 2.2.2 Split the dataset into train, validate and test sets" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "3a00b374-192b-4a7f-9606-a9015f6db53d", + "metadata": {}, + "outputs": [], + "source": [ + "build_regressor_CNN.split_train_validation_test(test_size=0.1, \n", + " val_size=0.1, \n", + " random_state_=42,)" + ] + }, + { + "cell_type": "markdown", + "id": "bf565480-2818-48ad-859d-dd5153250007", + "metadata": {}, + "source": [ + "### 2.2.3 Standardize X Variables Row-wise " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "a5401e9b-1a1f-4914-838f-cac6fa26ee1b", + "metadata": {}, + "outputs": [], + "source": [ + "# Scale the X features using MinMax Scaler\n", + "build_regressor_CNN.standardize_X_row_wise()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "7bd0840e-399b-46e7-93d5-3e850d28651a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "build_regressor_CNN.plot_boxplot_scaled_features(scaled_feature= build_regressor_CNN.X_train_standardized_rowwise, \n", + " xticks_list = training_features_labels,\n", + " title = 'Scaled main 104 Features',\n", + " fig_size=(14, 3),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "99348432-ec3b-445d-922f-dea2eef0a605", + "metadata": {}, + "source": [ + "### 2.2.4 Standardize y Variables Column-wise " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "ba7f068d-9503-4b77-b516-03d432309423", + "metadata": {}, + "outputs": [], + "source": [ + "# Standardize the y features using Standard Scaler\n", + "build_regressor_CNN.standardize_y_column_wise()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "cec99f30-00f2-4f5c-a640-01e9d237c2d5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "build_regressor_CNN.plot_boxplot_scaled_features(scaled_feature= build_regressor_CNN.y_train_standardized_columnwise, \n", + " xticks_list = ['','$\\log g$', 'T$_{eff}$', 'C/O ratio', '[M/H]'],\n", + " title = 'Scaled target Features - ColumnWise',\n", + " fig_size=(4, 3),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "9a4e7224-b203-448e-b531-78925f44e645", + "metadata": {}, + "source": [ + "### 2.2.5 Feature engeenering: Take Min and Max of each row (BD spectra) " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "b4dec506-00b2-478e-b7d5-3733f1e4efcf", + "metadata": {}, + "outputs": [], + "source": [ + "# train\n", + "build_regressor_CNN.X_train_min = build_regressor_CNN.X_train.min(axis=1)\n", + "build_regressor_CNN.X_train_max = build_regressor_CNN.X_train.max(axis=1)\n", + "\n", + "# validation\n", + "build_regressor_CNN.X_val_min = build_regressor_CNN.X_val.min(axis=1)\n", + "build_regressor_CNN.X_val_max = build_regressor_CNN.X_val.max(axis=1)\n", + "\n", + "# test\n", + "build_regressor_CNN.X_test_min = build_regressor_CNN.X_test.min(axis=1)\n", + "build_regressor_CNN.X_test_max = build_regressor_CNN.X_test.max(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "ab93721b-c0d0-4d4d-969a-d36bed701a67", + "metadata": {}, + "outputs": [], + "source": [ + "df_MinMax_train = pd.DataFrame((build_regressor_CNN.X_train_min, build_regressor_CNN.X_train_max)).T\n", + "df_MinMax_val = pd.DataFrame((build_regressor_CNN.X_val_min, build_regressor_CNN.X_val_max)).T\n", + "df_MinMax_test = pd.DataFrame((build_regressor_CNN.X_test_min, build_regressor_CNN.X_test_max)).T" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "915c7c1f-2e5e-46ea-bcb9-29c6b4203f8c", + "metadata": {}, + "outputs": [], + "source": [ + "df_MinMax_train.rename(columns={0:'min', 1:'max'}, inplace=True)\n", + "df_MinMax_val.rename(columns={0:'min', 1:'max'}, inplace=True)\n", + "df_MinMax_test.rename(columns={0:'min', 1:'max'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "1aad7765-9e8e-436a-9760-a3b2210c4c7a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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        " + ], + "text/plain": [ + " min max\n", + "0 8.265340e-12 3.445259e-08\n", + "1 8.080712e-22 8.397132e-14\n", + "2 2.734403e-07 8.632182e-06\n", + "3 4.414951e-16 3.373262e-10\n", + "4 3.722576e-07 6.859888e-06\n", + "... ... ...\n", + "25014 3.537783e-10 2.404020e-07\n", + "25015 9.266921e-07 8.640541e-06\n", + "25016 3.583572e-12 1.047753e-07\n", + "25017 3.782887e-13 8.244911e-09\n", + "25018 7.391600e-16 8.305618e-10\n", + "\n", + "[25019 rows x 2 columns]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_MinMax_train.head()" + ] + }, + { + "cell_type": "markdown", + "id": "8fb8ee2c-2127-4ca7-b1b1-619f6b5a37bc", + "metadata": {}, + "source": [ + "### 2.2.6 Scale Min Max features - ColumnWise" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "1ba420c3-13b0-418b-ab72-3d0dc45e156b", + "metadata": {}, + "outputs": [], + "source": [ + "build_regressor_CNN.standardize_X_column_wise(\n", + " X_train = df_MinMax_train.to_numpy(),\n", + " X_val = df_MinMax_val.to_numpy(),\n", + " X_test = df_MinMax_test.to_numpy(),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "c39656a6-ca83-46e4-8532-2d8e5b383216", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "build_regressor_CNN.plot_boxplot_scaled_features(scaled_feature= build_regressor_CNN.X_test_standardized_columnwise, \n", + " xticks_list = ['','Min','Max'],\n", + " title = 'Scaled Min Max Features - ColumnWise',\n", + " fig_size=(4, 3),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "cebec7ae-7d8f-4b76-a523-2280fd4d35a5", + "metadata": { + "tags": [] + }, + "source": [ + "## 2.3 Load the Trained CNN model and check the performance\n", + "\n", + "We will perform the following tasks in the follow:\n", + "\n", + "1. Load the trained CNN model\n", + "2. Plot the archeticture of the trained ML model\n", + "3. Check the model summary \n", + "4. Check the Loss function from the training history\n", + "5. Plot the regression reports to evaluate the trained model performace using R$^2$ " + ] + }, + { + "cell_type": "markdown", + "id": "2d2e166f-bcdb-4074-bfbd-d159cb691ac1", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "### 2.3.1 Load the trained CNN model\n", + "\n", + "- Load the trained CNN model, which includes:\n", + " - The model parameters to apply it to a given datasets \n", + " - The training history to check the performance " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "fde89b23-8885-48c8-8248-0b62afbd1d6c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-03 14:30:09.029136: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], + "source": [ + "loaded_model, history = load_or_dump_trained_model_CNN(indicator='Toy_parameters_v4_1000epoch_v2',\n", + " load_or_dump = 'load')" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "cbbfc24d-f767-41a4-b213-db4fe7d6bd60", + "metadata": {}, + "outputs": [], + "source": [ + "build_regressor_CNN.trained_model = loaded_model\n", + "build_regressor_CNN.trained_model_history = history" + ] + }, + { + "cell_type": "markdown", + "id": "4eeeab05-4ed2-4594-83ed-dbadd85a03af", + "metadata": { + "tags": [] + }, + "source": [ + "### 2.3.2 Check the Trained CNN Archeticture and Summary\n", + "\n", + "Checking the architecture of a CNN and its summary is importnat because it provide insights about: \n", + "- Model Design and Structure\n", + "- Model complexity\n", + "- Hyperparameter Tuning:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "9239fc9c-3fa9-4833-8999-04132087ecda", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# model architecture\n", + "tf.keras.utils.plot_model(\n", + " build_regressor_CNN.trained_model,\n", + " # to_file=\"model.png\",\n", + " show_shapes=True,\n", + " show_dtype=False,\n", + " show_layer_names=True,\n", + " rankdir=\"TB\",\n", + " expand_nested=True,\n", + " dpi=90,\n", + " layer_range=None,\n", + "# show_layer_activations=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5740190d-a077-4a0b-8ca4-9ed4026c4a0d", + "metadata": {}, + "source": [ + "Trained Model summary" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "a7199a91-1b68-4d43-af65-13855060db49", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_4\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_9 (InputLayer) [(None, 104, 1)] 0 \n", + "__________________________________________________________________________________________________\n", + "conv1d_20 (Conv1D) (None, 102, 32) 128 input_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_21 (Conv1D) (None, 100, 64) 6208 conv1d_20[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_22 (Conv1D) (None, 98, 128) 24704 conv1d_21[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling1d_8 (MaxPooling1D) (None, 49, 128) 0 conv1d_22[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_23 (Conv1D) (None, 47, 256) 98560 max_pooling1d_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1d_24 (Conv1D) (None, 45, 512) 393728 conv1d_23[0][0] \n", + "__________________________________________________________________________________________________\n", + "max_pooling1d_9 (MaxPooling1D) (None, 22, 512) 0 conv1d_24[0][0] \n", + "__________________________________________________________________________________________________\n", + "flatten_4 (Flatten) (None, 11264) 0 max_pooling1d_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "input_10 (InputLayer) [(None, 2)] 0 \n", + "__________________________________________________________________________________________________\n", + "Concatenated_Layer (Concatenate (None, 11266) 0 flatten_4[0][0] \n", + " input_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_8 (Dense) (None, 128) 1442176 Concatenated_Layer[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_9 (Dense) (None, 512) 66048 dense_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "gravity (Dense) (None, 1) 513 dense_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "c_o_ratio (Dense) (None, 1) 513 dense_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "metallicity (Dense) (None, 1) 513 dense_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "temperature (Dense) (None, 1) 513 dense_9[0][0] \n", + "==================================================================================================\n", + "Total params: 2,033,604\n", + "Trainable params: 2,033,604\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "build_regressor_CNN.trained_model.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "e253c8be-bae8-4bd5-a799-2c4dc3b50e43", + "metadata": { + "tags": [] + }, + "source": [ + "### 2.3.3 Check the training history through *Loss* metric" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "49106f2d-7f8d-4066-81d0-fd13a4b39b11", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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"### 2.3.4 Plot the Performance of the trained CNN models - Regression metrics\n", + "- Plot predicted against actual scatter plots for all parameters \n", + "- Plot Residual histograms (predicted - Actual)\n", + "- Report regression metrics: R$^2$ and skewness for training and test sets\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "bf27744b-88a2-4f38-827b-660e5456c4f1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-10-31 20:57:00.746462: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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+ "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 1\n", + "regression_report(\n", + " trained_model = build_regressor_CNN,\n", + " \n", + " Xtrain = [build_regressor_CNN.X_train_standardized_rowwise[::i],\n", + " build_regressor_CNN.X_train_standardized_columnwise[::i]],\n", + " \n", + " Xtest = [build_regressor_CNN.X_test_standardized_rowwise[::i], \n", + " build_regressor_CNN.X_test_standardized_columnwise[::i]],\n", + " \n", + " ytrain = build_regressor_CNN.y_train_standardized_columnwise[::i],\n", + " \n", + " ytest = build_regressor_CNN.y_test_standardized_columnwise[::i],\n", + " \n", + " target_i = 4,\n", + " \n", + " xy_top = [0.05, 0.7],\n", + " xy_bottom= [0.05, 0.85],\n", + " __print_results__ = False, \n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "3b63f8b7-b0f1-44bc-8612-5666e1d76bd7", + "metadata": { + "tags": [] + }, + "source": [ + "## 2.4 Predict Brown Dwarf Atmosphric Parameters of HD 3651 B\n", + "\n", + "In the following, we will deploy the trained CNN models to predict atmospheric parameters. Major steps are: \n", + "\n", + "1. Define the literature reported data for the object \n", + "\n", + "2. Instintiate *ObsParameterPredictor* class from *Predictor* module\n", + "\n", + "3. Load the observational Telescope data\n", + " - Replace negative Flam with zero\n", + " - Take the average of neibougrs for Flam_error <= 0\n", + " - Plot_observational_spectra_errorbar_\n", + " \n", + " \n", + "4. Process the observational Telescope data\n", + " - Instintiate ProcessObservationalDataset class\n", + " - Define feature_names_synthetic and feature_names_obs\n" + ] + }, + { + "cell_type": "markdown", + "id": "fadee9b4-cecc-4dc7-973f-db2498ed730b", + "metadata": { + "tags": [] + }, + "source": [ + "\n", + "### 2.4.1 Load the data and process\n", + "\n", + "Define the literature data reported for this object\n", + "\n", + "This data will be used later to: \n", + "- Compare the Literature vs ML predicted results\n", + "- Convert the unit of the real observational dataset or fluxes from $F_\\lambda$ to $F_\\nu$ and $F_\\nu^{absolute}$\n", + "- Revised the object radius" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "d8491eae-99ea-4b64-86de-bf0d3d275b13", + "metadata": {}, + "outputs": [], + "source": [ + "HD3651B_BD_literature_info = {\n", + " 'bd_name':'HD3651B',\n", + " 'bd_Teff':818,\n", + " 'bd_logg':3.94,\n", + " 'bd_met': -0.22, \n", + " 'bd_distance_pc' : 11.134, \n", + " 'bd_radius_Rjup' : .81,\n", + " 'bd_radius_Rjup_tuned': .81} #0.81\n", + "\n", + "brown_dwarf_literature_info = HD3651B_BD_literature_info" + ] + }, + { + "cell_type": "markdown", + "id": "bf1c0860-4064-49d7-b1cb-1c6035da6960", + "metadata": {}, + "source": [ + " Instintiate the *ObserveParameterPredictor* class from *Predictor* module \n", + "\n", + "*ObserveParameterPredictor* class performs the following tasks:\n", + "- Load the observational datasets\n", + "- Process the data including converting their units\n", + "- Visualize the data" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "93cd4e86-4b53-44b8-8bcb-ec9f8b7c0675", + "metadata": {}, + "outputs": [], + "source": [ + "# brown_dwarf_literature_info \n", + "predict = ObserveParameterPredictor(object_name = brown_dwarf_literature_info['bd_name'], #Ross458C, HD3651B, GJ570D\n", + " training_dataset_df = train_BD,\n", + " wl_synthetic = wl_synthetic, \n", + " BuildRegressorCNN_class = build_regressor_CNN,\n", + " bd_literature_dic = brown_dwarf_literature_info\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "6c6b28d3-4b95-40a5-be12-24a0890fdb5a", + "metadata": {}, + "source": [ + " Load the observational Telescope data \n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "8eac3bbc-9943-40b2-b147-9f6606c1d2a3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------- Observational DataFrame Example ---------\n", + " wl F_lambda F_lambda_error F_lambda_obs F_lambda_obs_err\n", + "0 0.650857 8.606238e-17 4.358173e-17 8.606238e-17 4.358173e-17\n", + "1 0.653073 1.771977e-17 3.360256e-17 1.771977e-17 3.360256e-17\n", + "2 0.655278 7.300979e-17 3.132892e-17 7.300979e-17 3.132892e-17\n", + "3 0.657472 2.569511e-17 2.837585e-17 2.569511e-17 2.837585e-17\n", + "4 0.659659 3.786160e-18 2.497690e-17 3.786160e-18 2.497690e-17\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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+ "source": [ + "### 2.4.2 Process the observational Telescope data" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "87054ecd-00c6-4401-a610-05df2f8ffe8f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------ Interpolated Observational Spectra: Absolute F𝜈 ------------\n", + " 0.897 0.906 0.915 0.924 0.933 \\\n", + "0 5.997174e-09 8.576299e-09 6.022747e-09 1.077888e-08 9.085077e-09 \n", + "\n", + " 0.943 0.952 0.962 0.971 0.981 ... \\\n", + "0 1.302502e-08 1.262613e-08 1.206215e-08 1.860160e-08 2.463705e-08 ... \n", + "\n", + " 2.296 2.319 2.342 2.366 2.389 \\\n", + "0 7.905764e-09 6.980955e-09 6.795411e-09 4.822663e-09 7.304593e-09 \n", + "\n", + " 2.413 2.438 2.462 2.487 2.512 \n", + "0 1.166870e-08 8.334444e-09 1.709369e-08 1.834442e-08 2.162885e-08 \n", + "\n", + "[1 rows x 104 columns]\n", + "------------ df_MinMax Single Observational Spectrum ------------\n", + " 0 1\n", + "0 1.059763e-09 3.096774e-07\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-06 12:44:09.724066: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "******************************\n", + "\n", + "Radius, BD_mean, BD_std:\n", + "\n", + "{ 'BD_mean': 5.6044032080948946e-08,\n", + " 'BD_std': 6.905598259831068e-08,\n", + " 'bd_radius_Rjup': 0.81}\n", + "******************************\n", + "\n", + "******************************\n", + "\n", + "Predicted Targets from the Signle Observational Spectrum:\n", + "\n", + "{ 'c_o_ratio': 1.24,\n", + " 'gravity': 4.97,\n", + " 'metallicity': -0.44,\n", + " 'temperature': 830.89}\n", + "******************************\n", + "\n" + ] + } + ], + "source": [ + "predict.Process_Observational_Dataset(\n", + " __print_results__=True,\n", + " F_lambda_obs=None,\n", + " F_lambda_obs_err=None )" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "4776160e-f2d2-4ab8-963b-b9dabe5c9b61", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['wl', 'F_lambda', 'F_lambda_error', 'F_lambda_obs', 'F_lambda_obs_err',\n", + " 'Fnu_obs', 'Fnu_obs_err', 'Fnu_obs_absolute', 'Fnu_obs_absolute_err'],\n", + " dtype='object')" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict.obs_data_df.columns" + ] + }, + { + "cell_type": "markdown", + "id": "8ee9939f-d328-41cc-9da0-a18f30df1835", + "metadata": {}, + "source": [ + " Check the processed Telescope dataset \n", + "\n", + "This dataframe includes the following columns:\n", + "\n", + "- **wl (wavelengths in $\\mu m$)**: Wavelengths from the observational dataset.\n", + "\n", + "- **F_lambda ($F_\\lambda$)**: Original observational spectral density flux along the wavelength axis.\n", + "- **F_lambda_error ($F_\\lambda^{error}$)**: Original observational spectral density flux uncertainty along the wavelength axis.\n", + "- **F_lambda_obs ($F_\\lambda^{obs}$)**: Same as the original F_lambda but with a more descriptive label.\n", + "- **F_lambda_obs_err ($F_\\lambda^{error, obs}$)**: Same as the original F_lambda_error but with a more descriptive label.\n", + "- **Fnu_obs ($F_\\nu^{obs}$)**: Observational spectral density flux along the frequency axis (converted from the F_lambda column).\n", + "- **Fnu_obs_err ($F_\\nu^{error}$)**: Observational spectral density flux uncertainty along the frequency axis (converted from the F_lambda column).\n", + "- **Fnu_obs_absolute ($F_\\nu^{obs, abs}$)**: Observational absolute spectral density flux along the frequency axis (converted from the F_lambda column).\n", + "- **Fnu_obs_absolute_err ($F_\\nu^{obs, abs, error}$)**: Observational absolute spectral density flux uncertainty along the frequency axis (converted from the F_lambda column).\n", + 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        " + ], + "text/plain": [ + " wl F_lambda F_lambda_error F_lambda_obs F_lambda_obs_err \\\n", + "0 0.650857 8.606238e-17 4.358173e-17 8.606238e-17 4.358173e-17 \n", + "1 0.653073 1.771977e-17 3.360256e-17 1.771977e-17 3.360256e-17 \n", + "2 0.655278 7.300979e-17 3.132892e-17 7.300979e-17 3.132892e-17 \n", + "3 0.657472 2.569511e-17 2.837585e-17 2.569511e-17 2.837585e-17 \n", + "4 0.659659 3.786160e-18 2.497690e-17 3.786160e-18 2.497690e-17 \n", + "5 0.661837 1.798661e-17 2.475434e-17 1.798661e-17 2.475434e-17 \n", + "\n", + " Fnu_obs Fnu_obs_err Fnu_obs_absolute Fnu_obs_absolute_err \n", + "0 1.216085e-27 6.158216e-28 4.280381e-08 2.167572e-08 \n", + "1 2.520930e-28 4.780520e-28 8.873182e-09 1.682650e-08 \n", + "2 1.045711e-27 4.487205e-28 3.680698e-08 1.579409e-08 \n", + "3 3.704967e-28 4.091501e-28 1.304076e-08 1.440129e-08 \n", + "4 5.495628e-29 3.625408e-28 1.934354e-09 1.276073e-08 \n", + "5 2.628032e-28 3.616868e-28 9.250161e-09 1.273067e-08 " + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict.obs_data_df.head(6)" + ] + }, + { + "cell_type": "markdown", + "id": "2d6f81ac-541d-4b1d-8277-caedc218465b", + "metadata": { + "tags": [] + }, + "source": [ + "### 2.4.3 Deploy the Trained ML Models for Prediction: \n", + "\n", + " *predict_from_random_spectra* function from *Predictor* class to perform the following tasks: \n", + " \n", + "- Generate random spectra based on the observational data\n", + "\n", + "- Deploy the trained ML models to predict the target outputs: \n", + " - gravity ( log$g$ )\n", + " - temperature ( T$_{eff}$ )\n", + " - carbon-to-oxygen ratio ( C/O )\n", + " - metallicity ( [M/H] )\n", + "\n", + "- Print the results, including:\n", + " - Predicted Targets from Randomly Generated Spectra.\n", + " - Print a summary of descriptive statistics, including the mean, standard deviation, and the shape of a dataset's distribution (percentiles).\n", + "\n", + "- Visualize the results to gain insights about:\n", + " - *Histograms* representing the distribution of the predicted target outputs by the trained ML models.\n", + " - *Boxplots* displaying the first quartile, median, third quartile, and maximum. In a box plot, the box is drawn from the first quartile to the third quartile. A vertical black line goes through the box at the median, and a vertical white line goes through the box to indicate the mean.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "1592ba02-1de8-4d2e-acbf-0db2339e6563", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "******************************\n", + "\n", + "Predicted Targets from Randomly Generated Spectra:\n", + "\n", + "{ 'c_o_ratio': 1.3979592323303223,\n", + " 'gravity': 5.028677463531494,\n", + " 'metallicity': -0.35800063610076904,\n", + " 'temperature': 816.0519409179688}\n", + "******************************\n", + "\n", + " logg c_o met T\n", + "count 30.000000 30.000000 30.000000 30.000000\n", + "mean 5.028677 1.397959 -0.358001 816.051941\n", + "std 0.158367 0.242653 0.093348 40.158440\n", + "min 4.754249 0.947547 -0.450389 735.295654\n", + "25% 4.902417 1.206631 -0.428000 789.810074\n", + "50% 5.027622 1.374340 -0.396786 818.078217\n", + "75% 5.131451 1.530977 -0.312748 828.568527\n", + "max 5.389977 1.933057 -0.149096 907.883240\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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const render_items = [{\"docid\":\"1344b6e7-6c82-4183-824b-d8713347b7cf\",\"roots\":{\"p1179\":\"bb54e8a0-b511-4670-9d6d-c896cccd4377\"},\"root_ids\":[\"p1179\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p1179" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
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zC1A/hOSyCwCEAvMLUD/w3S4AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrYsNdAGBDdu4qecpcdV7PntkjHKgG0aLdtLcuOMYdYzTnaudeY4BTAnn9BsPJ+Y8zHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsCpk4WPevHnKyspS48aN1bNnT73//vuh2hSABob5BYhuIQkfS5Ys0eTJk/X4449r69atuu666zR8+HDt27cvFJsD0IAwvwDRLzYUK33mmWc0ceJE/ehHP5IkzZ07V6tWrdL8+fOVl5fnN9bj8cjj8fjunzx5UpJ07Ngxeb3eGrcTe/Z00LXFlhuVlpYr1ttIZeWuoJ8frejbmb6PHj3qQFX/pzav4epU1Ob1elVaWqqjR48qLi7Ob0xJSYkkyRjj2HZtC2Z+kUI/x9SX9xZ9RI5I7eFC819Q84txmMfjMTExMWbZsmV+yx966CFz/fXXVxo/Y8YMI4kbN24Wb/v373f6rW9FsPOLMcwx3LjZvgUyvzh+5uPIkSMqKytTy5Yt/Za3bNlSRUVFlcZPnz5dU6ZM8d0vLy/XsWPHlJqaKpfL+cRXXFyszMxM7d+/X8nJyY6vP1LRd8Ppu6aejTEqKSlRRkZGmKqrm2DnFyn0c0x9eY3RR+SI1h6CmV9CctlFUqU3tTGmyje62+2W2+32W3bxxReHqiyf5OTkqDqoTqHvhqO6nlNSUsJQjbMCnV8ke3NMfXmN0UfkiMYeAp1fHP/AabNmzRQTE1Ppp5DDhw9X+mkFAILB/ALUD46Hj/j4ePXs2VOFhYV+ywsLC9WvXz+nNwegAWF+AeqHkFx2mTJlisaNG6devXqpb9++ys/P1759+3TfffeFYnNBcbvdmjFjRqXTsPUdfTecvut7z5E2v9SX/U0fkaM+9HAhLmNC8zt38+bN05w5c3To0CFlZ2fr2Wef1fXXXx+KTQFoYJhfgOgWsvABAABQFb7bBQAAWEX4AAAAVhE+AACAVYQPAABgVb0OH3l5eXK5XJo8eXKN41555RX16NFDiYmJSk9P1z333OP4F4iFUm5urlwul98tLS2txuesWbNGPXv2VOPGjdW+fXv97ne/s1Stc4Lte9myZRoyZIiaN2+u5ORk9e3bV6tWrbJYcd3V5lhX+Nvf/qbY2FhdccUVoS0yiq1du1Y33nijMjIy5HK59Oc///mCzwnkvbR06VJ16dJFbrdbXbp00fLly0NQ/f8Jto9A3hsLFy6s9NpzuVz69ttvI6aP1atXV1nj559/7jfO5vEItocJEyZU2UPXrl19Y8JxLJxWb8PH5s2blZ+fr+7du9c4bt26dbr77rs1ceJE7dixQ6+//ro2b97s+8bMaNG1a1cdOnTId9u+fXu1Y3fv3q3vfe97uu6667R161Y99thjeuihh7R06VKLFTsjmL7Xrl2rIUOGaMWKFdqyZYsGDhyoG2+8UVu3brVYcd0F03OFkydP6u6779agQYMsVBi9Tp8+rR49euj5558PaHwg76UNGzZo9OjRGjdunD7++GONGzdOo0aN0qZNm0LVRtB9BPreSE5O9nvtHTp0SI0bNw5FC5KC76PCzp07/Wrs2LGj7zHbxyPYHn7zm9/41b5//341bdpUt99+u98428fCcXX6iskIVVJSYjp27GgKCwtN//79zcMPP1zt2F/96lemffv2fsuee+4507p16xBX6ZwZM2aYHj16BDz+Zz/7mencubPfsnvvvdf06dPH4cpCK9i+q9KlSxczc+ZMZwqyoLY9jx492jzxxBOO7LOGQpJZvnx5jWMCeS+NGjXKDBs2zG/M0KFDzR133OFYrTUJpI+qnP/eWLBggUlJSXGusCAF0sd7771nJJnjx49XOyacx6M2x2L58uXG5XKZPXv2+JaF+1g4oV6e+Zg0aZJGjBihwYMHX3Bsv379dODAAa1YsULGGH399df605/+pBEjRlio1Dm7du1SRkaGsrKydMcdd+irr76qduyGDRuUk5Pjt2zo0KH68MMP5fV6Q12qo4Lp+3zl5eUqKSlR06ZNQ1ih84LtecGCBfryyy81Y8YMSxU2HIG8l6obs379emt1Bqu698apU6fUtm1btW7dWiNHjozYs4ZXXnml0tPTNWjQIL333nt+j0Xb8XjxxRc1ePBgtW3b1m95tByL6tS78PHaa6/po48+Ul5eXkDj+/Xrp1deeUWjR49WfHy80tLSdPHFF+u//uu/Qlypc3r37q0//OEPWrVqlV544QUVFRWpX79+1X5upaioqMqvJD979qyOHDlio2RHBNv3+X7961/r9OnTGjVqVIgrdU6wPe/atUvTpk3TK6+8otjYkH2JdYMVyHupujHnfzleJKnqvdG5c2ctXLhQb7zxhl599VU1btxY11xzjXbt2hXGSv2lp6crPz9fS5cu1bJly3TZZZdp0KBBWrt2rW9MNB2PQ4cO6e233670MYBoOBYXFO5TL07at2+fadGihdm2bZtv2YUuu+zYscOkp6ebOXPmmI8//tisXLnSdOvWzfzwhz+0UHFonDp1yrRs2dL8+te/rvLxjh07mlmzZvktW7dunZFkDh06ZKPEkLhQ3+davHixSUxMNIWFhRYqC52aej579qzp1auXmT9/vm8Zl10CpwBOkQfyXoqLizOLFy/2G/Pyyy8bt9vtaL3VCaSPcwX63igrKzM9evQwDz74YB0rDEywfVQYOXKkufHGG333w3k8gu1h1qxZJjU11Xg8nhrH2T4WTqhXPwpt2bJFhw8fVs+ePX3LysrKtHbtWj3//PPyeDyKiYnxe05eXp6uueYa/fSnP5Ukde/eXU2aNNF1112np556Sunp6VZ7cEKTJk3UrVu3alNwWlpalV9JHhsbq9TUVBslhsSF+q6wZMkSTZw4Ua+//npAl+YiWU09l5SU6MMPP9TWrVv1wAMPSPrudLoxRrGxsSooKNANN9xgu+R6JZD3UnVjzv/pOxIE895o1KiRrrrqqoj/abtPnz56+eWXffej5XgYY/TSSy9p3Lhxio+Pr3FstByLc9Wryy6DBg3S9u3btW3bNt+tV69eGjt2rLZt21YpeEhSaWmpGjXy3w0V40yUfu2Nx+PRZ599Vm1w6tu3b6WvJC8oKFCvXr0UFxdno8SQuFDfkvTqq69qwoQJWrx4cdR9rqcqNfWcnJxc6f1w33336bLLLtO2bdvUu3fvMFRcvwTyXqpuTL9+/azVGYhg3xvGGG3bti3if0DbunWrX43RcjzWrFmjf/zjH5o4ceIFx0bLsfAT1vMuFpx/2WXatGlm3LhxvvsLFiwwsbGxZt68eebLL78069atM7169TJXX311GKqtnUcffdSsXr3afPXVV2bjxo1m5MiRJikpyffp6PN7/uqrr0xiYqJ55JFHzN///nfz4osvmri4OPOnP/0pXC3USrB9L1682MTGxprf/va35tChQ77biRMnwtVC0ILt+XxcdqlZSUmJ2bp1q9m6dauRZJ555hmzdetWs3fvXmNM7d5Lf/vb30xMTIyZPXu2+eyzz8zs2bNNbGys2bhxY8T0Ech7Izc316xcudJ8+eWXZuvWreaee+4xsbGxZtOmTRHTx7PPPmuWL19uvvjiC/Ppp5+aadOmGUlm6dKlvjG2j0ewPVS46667TO/evatcZziOhdMaXPgYP3686d+/v9+Y5557znTp0sUkJCSY9PR0M3bsWHPgwAG7hdbB6NGjTXp6uomLizMZGRnmlltuMTt27PA9XlXPq1evNldeeaWJj4837dq18/tcQLQItu/+/fsbSZVu48ePt198LdXmWJ+L8FGzil/VrO41Utv30uuvv24uu+wyExcXZzp37uz3j2Ek9BHIe2Py5MmmTZs2Jj4+3jRv3tzk5OSY9evXR1QfTz/9tLn00ktN48aNzSWXXGKuvfZa89Zbb1Var83jUZvX1IkTJ0xCQoLJz8+vcp3hOBZOcxkTpdcWAABAVKpXn/kAAACRj/ABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifMCqWbNm6c9//nO4ywAQoVwuV0C31atXh7tU1AF/5wNWXXTRRbrtttu0cOHCcJcCIAJt3LjR7/4vfvELvffee3r33Xf9lnfp0kXJyck2S4OD6tUXywEAolufPn387jdv3lyNGjWqtBzRjcsukCTl5ubK5XLpk08+0e23366UlBQ1bdpUU6ZM0dmzZ7Vz504NGzZMSUlJateunebMmeP3/OLiYv3kJz9RVlaW4uPj1apVK02ePFmnT5/2jXG5XDp9+rQWLVrkO3U6YMAAy50CAMKNMx/wM2rUKN1111269957VVhYqDlz5sjr9eqdd97R/fffr5/85CdavHixpk6dqg4dOuiWW25RaWmp+vfvrwMHDuixxx5T9+7dtWPHDv3Hf/yHtm/frnfeeUcul0sbNmzQDTfcoIEDB+rnP/+5JHHaFAAaIMIH/Pzbv/2bpkyZIkkaPHiwCgoK9Pzzz2vZsmX6wQ9+IEkaMGCA3nzzTb3yyiu65ZZb9Nxzz+mTTz7Rpk2b1KtXL0nSoEGD1KpVK912221auXKlhg8frj59+qhRo0Zq3rw5p1ABoAHjsgv8jBw50u/+5ZdfLpfLpeHDh/uWxcbGqkOHDtq7d68k6c0331R2drauuOIKnT171ncbOnQon0oHAFTCmQ/4adq0qd/9+Ph4JSYmqnHjxpWWFxcXS5K+/vpr/eMf/1BcXFyV6zxy5EhoigUARCXCB+qsWbNmSkhI0EsvvVTt4wAAVCB8oM5GjhypWbNmKTU1VVlZWTWOdbvd+uabbyxVBgCIRIQP1NnkyZO1dOlSXX/99XrkkUfUvXt3lZeXa9++fSooKNCjjz6q3r17S5K6deum1atX63//93+Vnp6upKQkXXbZZWHuAABgE+EDddakSRO9//77mj17tvLz87V7924lJCSoTZs2Gjx4sNq1a+cb+5vf/EaTJk3SHXfc4fsVXT6QCgANC39eHQAAWMWv2gIAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAqoj7Ox/l5eU6ePCgkpKS5HK5wl0OUK8YY1RSUqKMjAw1atQwf/ZgjgFCI5j5JeLCx8GDB5WZmRnuMoB6bf/+/WrdunW4ywgL5hggtAKZXyIufCQlJUn6rvjk5OQwV1Mzr9ergoIC5eTkVPuNrvUd+yC69kFxcbEyMzN977OGyNYcE02vi9qix/rBqR6DmV8iLnxUnAZNTk6OivCRmJio5OTkevuivBD2QXTug4Z8ucHWHBONr4tg0WP94HSPgcwvDfOiLwAACBvCBwAAsIrwAQAArCJ8AAAAqyLuA6fBaDftLUfXt2f2CEfXBwANkdNzs5OY5yMDZz4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwBR5Z///KfuuusupaamKjExUVdccYW2bNkS7rIABCE23AUAQKCOHz+ua665RgMHDtTbb7+tFi1a6Msvv9TFF18c7tIABIHwASBqPP3008rMzNSCBQt8y9q1axe+ggDUCuEDQNR44403NHToUN1+++1as2aNWrVqpfvvv18//vGPq32Ox+ORx+Px3S8uLpYkeb1eeb3ekNVase5QbiPcquvRHWPCUU5Agj0eDfk41nY9gQg6fKxdu1a/+tWvtGXLFh06dEjLly/XzTff7HvcGKOZM2cqPz9fx48fV+/evfXb3/5WXbt2DXZTAODnq6++0vz58zVlyhQ99thj+uCDD/TQQw/J7Xbr7rvvrvI5eXl5mjlzZqXlBQUFSkxMDHXJKiwsDPk2wu38HudcHaZCArBixYpaPa8hHsdglZaWBjw26PBx+vRp9ejRQ/fcc49uvfXWSo/PmTNHzzzzjBYuXKhOnTrpqaee0pAhQ7Rz504lJSUFuzkA8CkvL1evXr00a9YsSdKVV16pHTt2aP78+dWGj+nTp2vKlCm++8XFxcrMzFROTo6Sk5NDVqvX61VhYaGGDBmiuLi4kG0nnKrrMTt3VRirqtmnuUODGt+Qj2OwKs4qBiLo8DF8+HANHz68yseMMZo7d64ef/xx3XLLLZKkRYsWqWXLllq8eLHuvffeYDcHAD7p6enq0qWL37LLL79cS5curfY5brdbbre70vK4uDgr/5jY2k44nd+jp8wVxmpqVttj0RCPY22eHyhHP/Oxe/duFRUVKScnx7fM7Xarf//+Wr9+fZXhoy7XY52+rsi1wOCxD6JrH0RDjTW55pprtHPnTr9lX3zxhdq2bRumigDUhqPho6ioSJLUsmVLv+UtW7bU3r17q3xOXa7HOn1dkWuBtcc+iI59EMw12Uj0yCOPqF+/fpo1a5ZGjRqlDz74QPn5+crPzw93aQCCEJLfdnG5/E+5GWMqLatQl+uxTl9X5Fpg8NgH0bUPgrkmG4muuuoqLV++XNOnT9eTTz6prKwszZ07V2PHjg13aQCC4Gj4SEtLk/TdGZD09HTf8sOHD1c6G1KhLtdjnb6uyLXA2mMfRMc+iPT6AjFy5EiNHDky3GUAqANH/7x6VlaW0tLS/E4/nzlzRmvWrFG/fv2c3BQAAIhSQZ/5OHXqlP7xj3/47u/evVvbtm1T06ZN1aZNG02ePFmzZs1Sx44d1bFjR82aNUuJiYm68847HS0cAABEp6DDx4cffqiBAwf67ld8XmP8+PFauHChfvazn+mbb77R/fff7/sjYwUFBfyNDwAAIKkW4WPAgAEypvpfcXW5XMrNzVVubm5d6gIAAPWUo5/5AAAAuBDCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwK+rtdAADh127aW46ta8/sEY6tCwgEZz4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwBRKy8vTy6XS5MnTw53KQCCQPgAEJU2b96s/Px8de/ePdylAAgS4QNA1Dl16pTGjh2rF154QZdcckm4ywEQpNhwFwAAwZo0aZJGjBihwYMH66mnnqpxrMfjkcfj8d0vLi6WJHm9Xnm93pDVWLHuUG3DHWMcW1dta6yuRydrc1qwvYb6OEYCp3oM5vmEDwBR5bXXXtNHH32kzZs3BzQ+Ly9PM2fOrLS8oKBAiYmJTpdXSWFhYUjWO+dq59a1YsWKOj3//B6drM1pte01VMcxktS1x9LS0oDHEj4ARI39+/fr4YcfVkFBgRo3bhzQc6ZPn64pU6b47hcXFyszM1M5OTlKTk4OVanyer0qLCzUkCFDFBcX5/j6s3NXObauT3OH1up51fXoZG1OC7bXUB/HSOBUjxVnFQNB+AAQNbZs2aLDhw+rZ8+evmVlZWVau3atnn/+eXk8HsXExPg9x+12y+12V1pXXFyclX9MQrUdT5nLsXXVtb7ze3SyNqfVtldbr5dwqmuPwTyX8AEgagwaNEjbt2/3W3bPPfeoc+fOmjp1aqXgASAyET4ARI2kpCRlZ2f7LWvSpIlSU1MrLQcQufhVWwAAYBVnPgBEtdWrV4e7BABB4swHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwyvHwkZubK5fL5XdLS0tzejMAACBKxYZipV27dtU777zjux8TExOKzQAAgCgUkvARGxvL2Q4AAILQbtpbjq5vz+wRjq7PSSEJH7t27VJGRobcbrd69+6tWbNmqX379lWO9Xg88ng8vvvFxcWSJK/XK6/XW+N23DHGuaL//zZrMz7Y59Un7IPo2gfRUCOA+s/x8NG7d2/94Q9/UKdOnfT111/rqaeeUr9+/bRjxw6lpqZWGp+Xl6eZM2dWWl5QUKDExMQatzXnasfKliStWLGiVs8rLCx0tpAoxD6Ijn1QWloa7hIAwPnwMXz4cN//d+vWTX379tWll16qRYsWacqUKZXGT58+3W95cXGxMjMzlZOTo+Tk5Bq3lZ27yrnCJX2aOzSo8V6vV4WFhRoyZIji4uIcrSVasA+iax9UnFkEgHAKyWWXczVp0kTdunXTrl27qnzc7XbL7XZXWh4XF3fBidxT5nKkxnO3WdvnRfo/OqHGPoiOfRDp9QFoGEL+dz48Ho8+++wzpaenh3pTAAAgCjgePn7yk59ozZo12r17tzZt2qTbbrtNxcXFGj9+vNObAgAAUcjxyy4HDhzQmDFjdOTIETVv3lx9+vTRxo0b1bZtW6c3BQAAopDj4eO1115zepUAAKAe4btdAACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVIf8Lp3BGQ/q2QwBA/caZDwAAYBXhAwAAWEX4AAAAVhE+AESNvLw8XXXVVUpKSlKLFi108803a+fOneEuC0CQCB8AosaaNWs0adIkbdy4UYWFhTp79qxycnJ0+vTpcJcGIAj8tguAqLFy5Uq/+wsWLFCLFi20ZcsWXX/99WGqCkCwCB8AotbJkyclSU2bNq12jMfjkcfj8d0vLi6WJHm9Xnm93pDVVrHuUG3DHWMcW1dta6yuRydrc1qwvYb6OJ7L6f0WaM1O9RjM8wkfAKKSMUZTpkzRtddeq+zs7GrH5eXlaebMmZWWFxQUKDExMZQlSpIKCwtDst45Vzu3rhUrVtTp+ef36GRtTqttr6E6judyer8F22tdeywtLQ14LOEDQFR64IEH9Mknn2jdunU1jps+fbqmTJniu19cXKzMzEzl5OQoOTm5xudm566qdX3uRka/6FWun3/YSJ5ylz7NHVrrdVWlLrU55fweo0Gwx8Hr9aqwsFBDhgxRXFxciKr6jtPHNNBeneqx4qxiIAgfAKLOgw8+qDfeeENr165V69ataxzrdrvldrsrLY+Li7vgROspq/s/qJ5ylzxlLsf/4XKiNqdU9BgNanscAnm91JXT+zDYeuvaYzDPJXwAiBrGGD344INavny5Vq9eraysrHCXBKAWCB8AosakSZO0ePFi/eUvf1FSUpKKiookSSkpKUpISAhzdQACRfg4R7Bf3uaOMZpz9XfX6c4/XcYXtyEQTn5hYEN4zc2fP1+SNGDAAL/lCxYs0IQJE+wXBKBWCB8AooYxkfsrnAACx184BQAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXf7RIiTn5hWCg4VV/Fl+tFMr68DQAiC2c+AACAVYQPAABgFZddAMCCSL8U21AEexwqLi1n566Sp8wVoqpCI9BeA+3RycvOnPkAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVvEXTuGIaPzrfwCA8ODMBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrQhY+5s2bp6ysLDVu3Fg9e/bU+++/H6pNAWhgmF+A6BaS8LFkyRJNnjxZjz/+uLZu3arrrrtOw4cP1759+0KxOQANCPMLEP1CEj6eeeYZTZw4UT/60Y90+eWXa+7cucrMzNT8+fNDsTkADQjzCxD9HP/z6mfOnNGWLVs0bdo0v+U5OTlav359pfEej0cej8d3/+TJk5KkY8eOyev11rit2LOnHai49mLLjUpLyxXrbaSy8ob5p8Ub2j44evRopWVer1elpaU6evSo4uLiglqfk6/hqmo7X0lJiSTJGOPYdm0Kdn6RwjfHNIT3Bj3WD4H2eKE5Jqj5xTjsn//8p5Fk/va3v/kt/+Uvf2k6depUafyMGTOMJG7cuFm87d+/3+m3vhXBzi/GMMdw42b7Fsj8ErIvlnO5/NOTMabSMkmaPn26pkyZ4rtfXl6uY8eOKTU1tcrxkaS4uFiZmZnav3+/kpOTw11OWLAPomsfGGNUUlKijIyMcJdSJ4HOL1L45phoel3UFj3WD071GMz84nj4aNasmWJiYlRUVOS3/PDhw2rZsmWl8W63W26322/ZxRdf7HRZIZWcnFxvX5SBYh9Ezz5ISUkJdwm1Fuz8IoV/jomW10Vd0GP94ESPgc4vjn/gND4+Xj179lRhYaHf8sLCQvXr18/pzQFoQJhfgPohJJddpkyZonHjxqlXr17q27ev8vPztW/fPt13332h2ByABoT5BYh+IQkfo0eP1tGjR/Xkk0/q0KFDys7O1ooVK9S2bdtQbC5s3G63ZsyYUemUbkPCPmAf2BYt80tDeF3QY/0Qjh5dxkTp79wBAICoxHe7AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB9BOn78uMaNG6eUlBSlpKRo3LhxOnHiRMDPv/fee+VyuTR37tyQ1RhqtdkHubm56ty5s5o0aaJLLrlEgwcP1qZNm+wUHALB7gOv16upU6eqW7duatKkiTIyMnT33Xfr4MGD9opGnbVr104ul6vSbdKkSZKkCRMmVHqsT58+fuvweDx68MEH1axZMzVp0kTf//73deDAgXC0U6WzZ8/qiSeeUFZWlhISEtS+fXs9+eSTKi8v940xxig3N1cZGRlKSEjQgAEDtGPHDr/1RHKfgfQY7ceypKREkydPVtu2bZWQkKB+/fpp8+bNvsfDfgzr9C1PDdCwYcNMdna2Wb9+vVm/fr3Jzs42I0eODOi5y5cvNz169DAZGRnm2WefDW2hIVSbffDKK6+YwsJC8+WXX5pPP/3UTJw40SQnJ5vDhw9bqtpZwe6DEydOmMGDB5slS5aYzz//3GzYsMH07t3b9OzZ02LVqKvDhw+bQ4cO+W6FhYVGknnvvfeMMcaMHz/eDBs2zG/M0aNH/dZx3333mVatWpnCwkLz0UcfmYEDB5oePXqYs2fPhqGjyp566imTmppq3nzzTbN7927z+uuvm4suusjMnTvXN2b27NkmKSnJLF261Gzfvt2MHj3apKenm+LiYt+YSO4zkB6j/ViOGjXKdOnSxaxZs8bs2rXLzJgxwyQnJ5sDBw4YY8J/DAkfQfj73/9uJJmNGzf6lm3YsMFIMp9//nmNzz1w4IBp1aqV+fTTT03btm2jNnzUZR+c6+TJk0aSeeedd0JRZkg5tQ8++OADI8ns3bs3FGXCgocffthceumlpry83Bjz3T9YN910U7XjT5w4YeLi4sxrr73mW/bPf/7TNGrUyKxcuTLU5QZkxIgR5oc//KHfsltuucXcddddxhhjysvLTVpampk9e7bv8W+//dakpKSY3/3ud8aYyO/zQj0aE93HsrS01MTExJg333zTb3mPHj3M448/HhHHkMsuQdiwYYNSUlLUu3dv37I+ffooJSVF69evr/Z55eXlGjdunH7605+qa9euNkoNmdrug3OdOXNG+fn5SklJUY8ePUJVasg4sQ8k6eTJk3K5XFH3RYr4zpkzZ/Tyyy/rhz/8od+3465evVotWrRQp06d9OMf/1iHDx/2PbZlyxZ5vV7l5OT4lmVkZCg7Ozuo104oXXvttfrrX/+qL774QpL08ccfa926dfre974nSdq9e7eKior8enC73erfv7+vh0jv80I9VojWY3n27FmVlZWpcePGfssTEhK0bt26iDiGIfnz6vVVUVGRWrRoUWl5ixYtKn3L5rmefvppxcbG6qGHHgpleVbUdh9I0ptvvqk77rhDpaWlSk9PV2FhoZo1axaqUkOmLvugwrfffqtp06bpzjvvrPfflFlf/fnPf9aJEyc0YcIE37Lhw4fr9ttvV9u2bbV79279/Oc/1w033KAtW7bI7XarqKhI8fHxuuSSS/zW1bJly4BfO6E2depUnTx5Up07d1ZMTIzKysr0y1/+UmPGjJEkX53nf4twy5YttXfvXt+YSO7zQj1K0X0sk5KS1LdvX/3iF7/Q5ZdfrpYtW+rVV1/Vpk2b1LFjx4g4hpz50HcfhqzqQ2Tn3j788ENJ8vsJp4Ixpsrl0nfp8Te/+Y0WLlxY7ZhIEMp9UGHgwIHatm2b1q9fr2HDhmnUqFF+P0mEm419IH334dM77rhD5eXlmjdvnuN9wI4XX3xRw4cPV0ZGhm/Z6NGjNWLECGVnZ+vGG2/U22+/rS+++EJvvfVWjesK9LVjw5IlS/Tyyy9r8eLF+uijj7Ro0SL953/+pxYtWuQ37vx6A+khUvoMpMdoP5Z//OMfZYxRq1at5Ha79dxzz+nOO+9UTEyMb0w4jyFnPiQ98MADuuOOO2oc065dO33yySf6+uuvKz32r3/9q1KCrPD+++/r8OHDatOmjW9ZWVmZHn30Uc2dO1d79uypU+1OCeU+qNCkSRN16NBBHTp0UJ8+fdSxY0e9+OKLmj59ep1qd4qNfeD1ejVq1Cjt3r1b7777Lmc9otTevXv1zjvvaNmyZTWOS09PV9u2bbVr1y5JUlpams6cOaPjx4/7/UR5+PBh9evXL6Q1B+qnP/2ppk2b5nsvdOvWTXv37lVeXp7Gjx+vtLQ0Sd/9ZJyenu573uHDh32v/0jv80I9ViXajuWll16qNWvW6PTp0youLlZ6erpGjx6trKysyDiGdf7USANS8UHDTZs2+ZZt3Lixxg8aHjlyxGzfvt3vlpGRYaZOnRrUhxMjRW32QXUuvfRSM2PGDIcrDL3a7oMzZ86Ym2++2XTt2jVqf8sH35kxY4ZJS0szXq+3xnFHjhwxbrfbLFq0yBjzfx/iW7JkiW/MwYMHI+JDihWaNm1q5s2b57ds1qxZpmPHjsaY//vA6dNPP+173OPxVPlhxUjt80I9ViUaj+W5jh07ZlJSUszvf//7iDiGhI8gDRs2zHTv3t1s2LDBbNiwwXTr1q3Sr1hedtllZtmyZdWuI5p/28WY4PfBqVOnzPTp082GDRvMnj17zJYtW8zEiRON2+02n376aThaqLNg94HX6zXf//73TevWrc22bdv8fn3P4/GEowXUUllZmWnTpo2ZOnWq3/KSkhLz6KOPmvXr15vdu3eb9957z/Tt29e0atWq0q8vtm7d2rzzzjvmo48+MjfccEPE/HqmMd/9lkerVq18v4a6bNky06xZM/Ozn/3MN2b27NkmJSXFLFu2zGzfvt2MGTOmyl/TjNQ+L9RjfTiWK1euNG+//bb56quvTEFBgenRo4e5+uqrzZkzZ4wx4T+GhI8gHT161IwdO9YkJSWZpKQkM3bsWHP8+HG/MZLMggULql1HtIePYPfBN998Y37wgx+YjIwMEx8fb9LT0833v/9988EHH9gv3iHB7oPdu3cbSVXeKv5GBKLDqlWrjCSzc+dOv+WlpaUmJyfHNG/e3MTFxZk2bdqY8ePHm3379vmN++abb8wDDzxgmjZtahISEszIkSMrjQmn4uJi8/DDD5s2bdqYxo0bm/bt25vHH3/cLySXl5f7zv643W5z/fXXm+3bt/utJ5L7vFCP9eFYLlmyxLRv397Ex8ebtLQ0M2nSJHPixAnf4+E+hi5jjKn7xRsAAIDA8NsuAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArPp/UvSTFYKCwLcAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\"},\"shape\":[564],\"dtype\":\"float64\",\"order\":\"little\"}],[\"lower\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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{\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p1603" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "predict.predict_from_random_spectra(random_spectra_num= 30,\n", + " __print_results__= True,\n", + " __plot_randomly_generated_spectra__= True,\n", + " __plot_histogram__= True,\n", + " __plot_boxplot_hist__= True,\n", + " __plot_pred_vs_obs_errorbar__= True,\n", + " __plot_pred_vs_obs_errorbar_stat__ = True,\n", + " __calculate_confidence_intervals_std_df__ = True,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "49761b48-0852-4376-bb1f-e7598c249a0b", + "metadata": { + "tags": [] + }, + "source": [ + "### 2.4.4 Tune the Radius" + ] + }, + { + "cell_type": "markdown", + "id": "e945e753-4c02-41a7-80b8-86ac168553dc", + "metadata": {}, + "source": [ + "What is the 𝛘2 value for the predicted ML vs. the observational spectra?" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "a988b139-ed86-4690-b3ba-315dd08a636b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Radius = 0.81 R_Jup: 𝛘2 = 80.03, p-value = 9.55e-01\n" + ] + }, + { + "data": { + "text/plain": [ + "(80.03, '9.55e-01')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chi_square_test(x_obs = predict.obs_data_df['wl'], \n", + " y_obs = predict.obs_data_df['Fnu_obs_absolute'], \n", + " yerr_obs = predict.obs_data_df['Fnu_obs_absolute_err'],\n", + " \n", + " x_pre = predict.confidence_intervals_std_df['wl'].values[::-1], \n", + " y_pre = predict.confidence_intervals_std_df['mean'], \n", + " yerr_pre = predict.confidence_intervals_std_df['std_values'],\n", + " radius = brown_dwarf_literature_info['bd_radius_Rjup'],\n", + " __plot_results__ = False,\n", + " __print_results__ = True)" + ] + }, + { + "cell_type": "markdown", + "id": "84e1ea29-d6bf-4832-a3fa-6aebb95ca91e", + "metadata": {}, + "source": [ + " What is the 𝛘2 value (and p-value) for a range of $R_{Jup}$ radius? " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "05c2e685-296b-40bd-a787-c99446b5e5e5", + "metadata": {}, + "outputs": [], + "source": [ + "def tune_radius(Rmin = brown_dwarf_literature_info['bd_radius_Rjup']-0.1, \n", + " Rmax = brown_dwarf_literature_info['bd_radius_Rjup']+0.1, \n", + " dR=0.01,\n", + " brown_dwarf_literature_info = None,\n", + " __plot_results__ = False,\n", + " __print_results__ = False,\n", + " __plot_stat_results__ = False):\n", + " \n", + " R_list = []\n", + " chi_square_list = []\n", + " p_value_list = []\n", + " random_pred_mean_list = []\n", + " \n", + "\n", + " for radius in list( np.linspace(Rmin, Rmax, num=int((Rmax-Rmin)/dR) )):\n", + " \n", + " brown_dwarf_literature_info['bd_radius_Rjup_tuned'] = radius\n", + " \n", + " # brown_dwarf_literature_info = Ross458C_BD_literature_info\n", + " predict = ObserveParameterPredictor(object_name = brown_dwarf_literature_info['bd_name'], #Ross458C, HD3651B, GJ570D\n", + " training_dataset_df = train_BD,\n", + " wl_synthetic = wl_synthetic, \n", + " BuildRegressorCNN_class = build_regressor_CNN,\n", + " bd_literature_dic = brown_dwarf_literature_info\n", + " )\n", + "\n", + " predict.load_observational_spectra(\n", + " __plot_observational_spectra_errorbar__=False,\n", + " __replace_zeros_negatives_with_mean__=True,\n", + " __print_results__=False,\n", + " )\n", + " \n", + " predict.Process_Observational_Dataset(\n", + " __print_results__=False,\n", + " F_lambda_obs=None,\n", + " F_lambda_obs_err=None \n", + " )\n", + "\n", + " predict.predict_from_random_spectra(random_spectra_num= 3,\n", + " __print_results__= False,\n", + " __plot_randomly_generated_spectra__= False,\n", + " __plot_histogram__= False,\n", + " __plot_boxplot_hist__= False,\n", + " __plot_pred_vs_obs_errorbar__= False,\n", + " __plot_pred_vs_obs_errorbar_stat__ = False,\n", + " __calculate_confidence_intervals_std_df__ = True,\n", + " )\n", + "\n", + " chi_square, p_value = chi_square_test(x_obs = predict.obs_data_df['wl'], \n", + " y_obs = predict.obs_data_df['Fnu_obs_absolute'], \n", + " yerr_obs = predict.obs_data_df['Fnu_obs_absolute_err'],\n", + "\n", + " x_pre = predict.confidence_intervals_std_df['wl'].values[::-1], \n", + " y_pre = predict.confidence_intervals_std_df['mean'], \n", + " yerr_pre = predict.confidence_intervals_std_df['std_values'],\n", + " radius = brown_dwarf_literature_info['bd_radius_Rjup'],\n", + " __plot_results__ = False,\n", + " __print_results__ = False)\n", + " \n", + " \n", + " if chi_square >= 0.:\n", + "\n", + " R_list.append(radius)\n", + " chi_square_list.append(chi_square)\n", + " p_value_list.append(np.float64(p_value))\n", + " random_pred_mean_list.append(list( predict.dic_random_pred_mean.values() ))\n", + " else:\n", + " pass\n", + "\n", + " if __print_results__:\n", + " print( f\"Radius = {'{:.2f}'.format(radius)} R_Jup: 𝛘2 = {chi_square}, p-value = {p_value}\")\n", + " \n", + "# if __plot_stat_results__:\n", + "# plot_chi_square_p_value(R_list, chi_square_list, p_value_list)\n", + " \n", + " tuned_ML_R_param_df = pd.DataFrame(columns=['radius','chi_square','p_value','gravity','temperature','c_o_ratio','metallicity'])\n", + " tuned_ML_R_param_df['radius'] = R_list\n", + " tuned_ML_R_param_df['chi_square'] = chi_square_list\n", + " tuned_ML_R_param_df['p_value'] = p_value_list\n", + " tuned_ML_R_param_df['gravity'] = np.array(random_pred_mean_list).T[0]\n", + " tuned_ML_R_param_df['c_o_ratio'] = np.array(random_pred_mean_list).T[1]\n", + " tuned_ML_R_param_df['metallicity'] = np.array(random_pred_mean_list).T[2]\n", + " tuned_ML_R_param_df['temperature'] = np.array(random_pred_mean_list).T[3]\n", + "\n", + " return tuned_ML_R_param_df" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "49017667-f00f-4c2a-8159-d3a85a8cc412", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "******************************\n", + "\n", + "HD3651B: Literature reported Info\n", + "\n", + "{ 'bd_Teff': 818,\n", + " 'bd_distance_pc': 11.134,\n", + " 'bd_logg': 3.94,\n", + " 'bd_met': -0.22,\n", + " 'bd_name': 'HD3651B',\n", + " 'bd_radius_Rjup': 0.81,\n", + " 'bd_radius_Rjup_tuned': 0.81}\n", + "******************************\n", + "\n" + ] + } + ], + "source": [ + "print_results_fun(brown_dwarf_literature_info, print_title= 'HD3651B: Literature reported Info')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "b5119eb2-56f3-48ea-8bea-bf472dd0e55e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tuned_ML_R_param_df = tune_radius(Rmin = brown_dwarf_literature_info['bd_radius_Rjup']- 0.2, \n", + " Rmax = brown_dwarf_literature_info['bd_radius_Rjup']+ 0.2, \n", + " dR = 0.01,\n", + " brown_dwarf_literature_info = brown_dwarf_literature_info,\n", + " __plot_results__=False,\n", + " __print_results__=False,\n", + " __plot_stat_results__=False,)" + ] + }, + { + "cell_type": "code", + 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" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p1697" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "datuned_ML_R_param_df_filtered = tuned_ML_R_param_df[tuned_ML_R_param_df['p_value'].between(.04, 1.)]\n", + "\n", + "plot_scatter_x_y( x = datuned_ML_R_param_df_filtered['radius'],\n", + " y = datuned_ML_R_param_df_filtered['chi_square'],\n", + " plot_title='Scatter Plot',\n", + " x_label='Radius (RJupiter)',\n", + " y_label='𝛘2 value',\n", + " plot_width=800,\n", + " plot_height=300,)" + ] + }, + { + "cell_type": "markdown", + "id": "994d449f-b685-458f-b237-3aabe796ba34", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "2d52f4cf-681e-4b3e-a9a1-53b909cdfe52", + "metadata": { + "tags": [] + }, + "source": [ + "## 2.5 Predict Brown Dwarf Atmosphric Parameters of Ross 458C \n", + "\n", + "In the following, we will deploy the trained CNN models to predict atmospheric parameters. Major steps are: \n", + "\n", + "1. Define the literature reported data for the object \n", + "\n", + "2. Instintiate *ObsParameterPredictor* class from *Predictor* module\n", + "\n", + "3. Load the observational Telescope data\n", + " - Replace negative Flam with zero\n", + " - Take the average of neibougrs for Flam_error <= 0\n", + " - Plot_observational_spectra_errorbar_\n", + " \n", + " \n", + "4. Process the observational Telescope data\n", + " - Instintiate ProcessObservationalDataset class\n", + " - Define feature_names_synthetic and feature_names_obs\n" + ] + }, + { + "cell_type": "markdown", + "id": "d2295c06-b03f-47b7-99d8-a613f2cd129c", + "metadata": {}, + "source": [ + "\n", + "### 2.5.1 Load the data and process\n", + "\n", + "Define the literature data reported for this object\n", + "\n", + "This data will be used later to: \n", + "- Compare the Literature vs ML predicted results\n", + "- Convert the unit of the real observational dataset or fluxes from $F_\\lambda$ to $F_\\nu$ and $F_\\nu^{absolute}$\n", + "- Revised the object radius" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "22323161-2847-4cd6-9b2f-f418ae88df28", + "metadata": {}, + "outputs": [], + "source": [ + "Ross458C_BD_literature_info = {'bd_name':'Ross458C',\n", + " 'bd_Teff':804,\n", + " 'bd_logg':4.09,\n", + " 'bd_met': 0.23, \n", + " 'bd_distance_pc' : 11.509, \n", + " 'bd_radius_Rjup' : 0.68,\n", + " 'bd_radius_Rjup_tuned': None } #1.0 * 0.68 \n", + "\n", + "brown_dwarf_literature_info = Ross458C_BD_literature_info" + ] + }, + { + "cell_type": "markdown", + "id": "0f0a6260-4237-4a5c-b560-e98d41feeb9d", + "metadata": {}, + "source": [ + " Instintiate the *ObserveParameterPredictor* class from *Predictor* module \n", + "\n", + "*ObserveParameterPredictor* class performs the following tasks:\n", + "- Load the observational datasets\n", + "- Process the data including converting their units\n", + "- Visualize the data" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "5ffa756c-a180-42c0-92d6-341dc3281e5b", + "metadata": {}, + "outputs": [], + "source": [ + "# brown_dwarf_literature_info \n", + "predict = ObserveParameterPredictor(object_name = brown_dwarf_literature_info['bd_name'], #Ross458C, HD3651B, GJ570D\n", + " training_dataset_df = train_BD,\n", + " wl_synthetic = wl_synthetic, \n", + " BuildRegressorCNN_class = build_regressor_CNN,\n", + " bd_literature_dic = brown_dwarf_literature_info\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "e430a6b3-b24d-4490-ae70-ead890e6590f", + "metadata": {}, + "source": [ + " Load the observational Telescope data \n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "075c64c5-3119-4f32-935b-4ff550dc1b84", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------- Observational DataFrame Example ---------\n", + " wl F_lambda F_lambda_error F_lambda_obs F_lambda_obs_err\n", + "0 0.692196 1.896532e-18 3.679267e-18 1.896532e-18 3.679267e-18\n", + "1 0.693680 -8.169787e-18 3.527262e-18 1.025778e-18 3.527262e-18\n", + "2 0.695165 1.550241e-19 3.538419e-18 1.550241e-19 3.538419e-18\n", + "3 0.696652 2.584982e-18 3.294942e-18 2.584982e-18 3.294942e-18\n", + "4 0.698140 2.829340e-18 3.251767e-18 2.829340e-18 3.251767e-18\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(\"b59b4b76-7cdc-4f68-ae97-734f0e4018c9\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
        \\n\"+\n \"

        \\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

        \\n\"+\n \"
          \\n\"+\n \"
        • re-rerun `output_notebook()` to attempt to load from CDN again, or
        • \\n\"+\n \"
        • use INLINE resources instead, as so:
        • \\n\"+\n \"
        \\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
        \"}};\n\n function display_loaded() {\n const el = document.getElementById(\"b59b4b76-7cdc-4f68-ae97-734f0e4018c9\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"b59b4b76-7cdc-4f68-ae97-734f0e4018c9\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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{\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p1737" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "predict.load_observational_spectra(\n", + " __plot_observational_spectra_errorbar__=True,\n", + " __replace_zeros_negatives_with_mean__=True,\n", + " __print_results__=True,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "dddd0f88-81a2-4e65-8d85-4ef13d5c7726", + "metadata": {}, + "source": [ + "### 2.5.2 Process the observational Telescope data" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "7454c366-f618-4280-aa23-69f40d6261c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------ Interpolated Observational Spectra: Absolute F𝜈 ------------\n", + " 0.897 0.906 0.915 0.924 0.933 \\\n", + "0 7.431980e-09 6.772274e-09 7.564100e-09 9.779585e-09 6.589324e-09 \n", + "\n", + " 0.943 0.952 0.962 0.971 0.981 ... \\\n", + "0 1.186165e-08 1.168911e-08 1.217457e-08 1.491817e-08 1.867662e-08 ... \n", + "\n", + " 2.296 2.319 2.342 2.366 2.389 \\\n", + "0 1.675178e-08 2.010950e-08 7.911809e-09 1.281873e-08 1.298326e-08 \n", + "\n", + " 2.413 2.438 2.462 2.487 2.512 \n", + "0 2.857164e-08 4.066384e-08 4.169449e-08 1.412377e-07 1.428379e-07 \n", + "\n", + "[1 rows x 104 columns]\n", + "------------ df_MinMax Single Observational Spectrum ------------\n", + " 0 1\n", + "0 2.687596e-09 2.890186e-07\n", + "******************************\n", + "\n", + "Radius, BD_mean, BD_std:\n", + "\n", + "{ 'BD_mean': 6.724862374473837e-08,\n", + " 'BD_std': 7.044146438046857e-08,\n", + " 'bd_radius_Rjup': 0.68}\n", + "******************************\n", + "\n", + "******************************\n", + "\n", + "Predicted Targets from the Signle Observational Spectrum:\n", + "\n", + "{ 'c_o_ratio': 1.5,\n", + " 'gravity': 4.76,\n", + " 'metallicity': 0.61,\n", + " 'temperature': 933.87}\n", + "******************************\n", + "\n" + ] + } + ], + "source": [ + "predict.Process_Observational_Dataset(\n", + " __print_results__=True,\n", + " F_lambda_obs=None,\n", + " F_lambda_obs_err=None )" + ] + }, + { + "cell_type": "markdown", + "id": "e0cbf61d-58d4-4b8a-a056-29a5eb88e2ad", + "metadata": {}, + "source": [ + " Check the processed Telescope dataset \n", + "\n", + "This dataframe includes the following columns:\n", + "\n", + "- **wl (wavelengths in $\\mu m$)**: Wavelengths from the observational dataset.\n", + "\n", + "- **F_lambda ($F_\\lambda$)**: Original observational spectral density flux along the wavelength axis.\n", + "- **F_lambda_error ($F_\\lambda^{error}$)**: Original observational spectral density flux uncertainty along the wavelength axis.\n", + "- **F_lambda_obs ($F_\\lambda^{obs}$)**: Same as the original F_lambda but with a more descriptive label.\n", + "- **F_lambda_obs_err ($F_\\lambda^{error, obs}$)**: Same as the original F_lambda_error but with a more descriptive label.\n", + "- **Fnu_obs ($F_\\nu^{obs}$)**: Observational spectral density flux along the frequency axis (converted from the F_lambda column).\n", + "- **Fnu_obs_err ($F_\\nu^{error}$)**: Observational spectral density flux uncertainty along the frequency axis (converted from the F_lambda column).\n", + "- **Fnu_obs_absolute ($F_\\nu^{obs, abs}$)**: Observational absolute spectral density flux along the frequency axis (converted from the F_lambda column).\n", + "- **Fnu_obs_absolute_err ($F_\\nu^{obs, abs, error}$)**: Observational absolute spectral density flux uncertainty along the frequency axis (converted from the F_lambda column).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "5a1fabed-9a09-45b4-93bb-ec3bf3e91154", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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        " + ], + "text/plain": [ + " wl F_lambda F_lambda_error F_lambda_obs F_lambda_obs_err \\\n", + "0 0.692196 1.896532e-18 3.679267e-18 1.896532e-18 3.679267e-18 \n", + "1 0.693680 -8.169787e-18 3.527262e-18 1.025778e-18 3.527262e-18 \n", + "2 0.695165 1.550241e-19 3.538419e-18 1.550241e-19 3.538419e-18 \n", + "3 0.696652 2.584982e-18 3.294942e-18 2.584982e-18 3.294942e-18 \n", + "4 0.698140 2.829340e-18 3.251767e-18 2.829340e-18 3.251767e-18 \n", + ".. ... ... ... ... ... \n", + "804 2.535954 -2.561479e-17 6.676713e-17 4.611883e-17 6.676713e-17 \n", + "805 2.537791 4.631278e-17 5.670232e-17 4.631278e-17 5.670232e-17 \n", + "806 2.539632 4.883466e-17 4.699071e-17 4.883466e-17 4.699071e-17 \n", + "807 2.541476 -1.191826e-16 4.816838e-16 1.981941e-16 4.816838e-16 \n", + "808 2.543324 3.475536e-16 1.165061e-15 3.475536e-16 1.165061e-15 \n", + "\n", + " Fnu_obs Fnu_obs_err Fnu_obs_absolute Fnu_obs_absolute_err \n", + "0 3.031078e-29 5.880282e-29 1.617486e-09 3.137919e-09 \n", + "1 1.646459e-29 5.661549e-29 8.786066e-10 3.021195e-09 \n", + "2 2.498932e-30 5.703804e-29 1.333515e-10 3.043744e-09 \n", + "3 4.184739e-29 5.334067e-29 2.233119e-09 2.846440e-09 \n", + "4 4.599908e-29 5.286686e-29 2.454668e-09 2.821156e-09 \n", + ".. ... ... ... ... \n", + "804 9.893281e-27 1.432269e-26 5.279392e-07 7.643078e-07 \n", + "805 9.949287e-27 1.218125e-26 5.309279e-07 6.500331e-07 \n", + "806 1.050628e-26 1.010957e-26 5.606509e-07 5.394813e-07 \n", + "807 4.270140e-26 1.037799e-25 2.278692e-06 5.538051e-06 \n", + "808 7.499016e-26 2.513803e-25 4.001730e-06 1.341451e-05 \n", + "\n", + "[809 rows x 9 columns]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predict.obs_data_df" + ] + }, + { + "cell_type": "markdown", + "id": "39bb0ba1-edaf-413f-a671-704a12b9422b", + "metadata": {}, + "source": [ + "### 2.5.3 Deploy the Trained ML Models for Prediction: \n", + "\n", + " *predict_from_random_spectra* function from *Predictor* class to perform the following tasks: \n", + " \n", + "- Generate random spectra based on the observational data\n", + "\n", + "- Deploy the trained ML models to predict the target outputs: \n", + " - gravity ( log$g$ )\n", + " - temperature ( T$_{eff}$ )\n", + " - carbon-to-oxygen ratio ( C/O )\n", + " - metallicity ( [M/H] )\n", + "\n", + "- Print the results, including:\n", + " - Predicted Targets from Randomly Generated Spectra.\n", + " - Print a summary of descriptive statistics, including the mean, standard deviation, and the shape of a dataset's distribution (percentiles).\n", + "\n", + "- Visualize the results to gain insights about:\n", + " - *Histograms* representing the distribution of the predicted target outputs by the trained ML models.\n", + " - *Boxplots* displaying the first quartile, median, third quartile, and maximum. In a box plot, the box is drawn from the first quartile to the third quartile. A vertical black line goes through the box at the median, and a vertical white line goes through the box to indicate the mean.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "6508c998-87ba-40f6-ba1b-bc070fb9d054", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "******************************\n", + "\n", + "Predicted Targets from Randomly Generated Spectra:\n", + "\n", + "{ 'c_o_ratio': 1.16231107711792,\n", + " 'gravity': 4.712645053863525,\n", + " 'metallicity': 0.8085116744041443,\n", + " 'temperature': 926.8609619140625}\n", + "******************************\n", + "\n", + " logg c_o met T\n", + "count 30.000000 30.000000 30.000000 30.000000\n", + "mean 4.712645 1.162311 0.808512 926.860962\n", + "std 0.149738 0.157524 0.079663 41.823452\n", + "min 4.374824 0.916235 0.661885 841.351196\n", + "25% 4.613415 1.036935 0.748673 898.174149\n", + "50% 4.736428 1.171796 0.808830 933.706940\n", + "75% 4.811934 1.258477 0.849938 956.195648\n", + "max 5.012516 1.565088 0.983340 1011.545288\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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const render_items = [{\"docid\":\"2153e8e8-a9f2-4096-909a-2bbdcf19f9f7\",\"roots\":{\"p1790\":\"e849f078-615e-47f0-9bbf-48594b095f1b\"},\"root_ids\":[\"p1790\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p1790" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
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\n", 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\n", 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\n", 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\n", 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\n", 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\"},\"shape\":[809],\"dtype\":\"float64\",\"order\":\"little\"}],[\"upper\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[809],\"dtype\":\"float64\",\"order\":\"little\"}],[\"lower\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[809],\"dtype\":\"float64\",\"order\":\"little\"}],[\"upper\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[809],\"dtype\":\"float64\",\"order\":\"little\"}],[\"lower\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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{\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p2214" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "predict.predict_from_random_spectra(random_spectra_num= 30,\n", + " __print_results__= True,\n", + " __plot_randomly_generated_spectra__= True,\n", + " __plot_histogram__= True,\n", + " __plot_boxplot_hist__= True,\n", + " __plot_pred_vs_obs_errorbar__= True,\n", + " __plot_pred_vs_obs_errorbar_stat__ = True,\n", + " __calculate_confidence_intervals_std_df__ = True,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "541aba8f-99f3-4718-90f1-0bf88d8f23b0", + "metadata": {}, + "source": [ + "Comennet from natasha: >2.2 mincron - Is not missing any thing!\n", + "> 1 sigma error \n", + "> training set\n", + "> trusing the posterior space " + ] + }, + { + "cell_type": "markdown", + "id": "ea82de65-bbc6-403c-8134-b005a4bffc77", + "metadata": { + "tags": [] + }, + "source": [ + "### 2.5.4 Tune the Radius" + ] + }, + { + "cell_type": "markdown", + "id": "925179e1-a276-4959-8d5f-89820d4318be", + "metadata": {}, + "source": [ + " What is the 𝛘2 value for the predicted ML vs. the observational spectra?" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "8b7e6eeb-4448-4d5f-9b38-81421c009256", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Radius = 0.68 R_Jup: 𝛘2 = 130.57, p-value = 3.46e-02\n" + ] + }, + { + "data": { + "text/plain": [ + "(130.57, '3.46e-02')" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chi_square_test(x_obs = predict.obs_data_df['wl'], \n", + " y_obs = predict.obs_data_df['Fnu_obs_absolute'], \n", + " yerr_obs = predict.obs_data_df['Fnu_obs_absolute_err'],\n", + " \n", + " x_pre = predict.confidence_intervals_std_df['wl'].values[::-1], \n", + " y_pre = predict.confidence_intervals_std_df['mean'], \n", + " yerr_pre = predict.confidence_intervals_std_df['std_values'],\n", + " radius = brown_dwarf_literature_info['bd_radius_Rjup'],\n", + " __plot_results__ = False,\n", + " __print_results__ = True)" + ] + }, + { + "cell_type": "markdown", + "id": "061979fc-8ea6-458a-9202-f68348a6ace9", + "metadata": {}, + "source": [ + " What is the 𝛘2 value (and p-value) for a range of $R_{Jup}$ radius?" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "c5480456-14fc-4cdd-b94b-da43b88f25d0", + "metadata": {}, + "outputs": [], + "source": [ + "def tune_radius(Rmin = brown_dwarf_literature_info['bd_radius_Rjup']-0.1, \n", + " Rmax = brown_dwarf_literature_info['bd_radius_Rjup']+0.1, \n", + " dR=0.01,\n", + " brown_dwarf_literature_info = None,\n", + " __plot_results__ = False,\n", + " __print_results__ = False,\n", + " __plot_stat_results__ = False):\n", + " \n", + " R_list = []\n", + " chi_square_list = []\n", + " p_value_list = []\n", + " random_pred_mean_list = []\n", + " \n", + "\n", + " for radius in list( np.linspace(Rmin, Rmax, num=int((Rmax-Rmin)/dR) )):\n", + " \n", + " brown_dwarf_literature_info['bd_radius_Rjup_tuned'] = radius\n", + " \n", + " # brown_dwarf_literature_info = Ross458C_BD_literature_info\n", + " predict = ObserveParameterPredictor(object_name = brown_dwarf_literature_info['bd_name'], #Ross458C, HD3651B, GJ570D\n", + " training_dataset_df = train_BD,\n", + " wl_synthetic = wl_synthetic, \n", + " BuildRegressorCNN_class = build_regressor_CNN,\n", + " bd_literature_dic = brown_dwarf_literature_info\n", + " )\n", + "\n", + " predict.load_observational_spectra(\n", + " __plot_observational_spectra_errorbar__=False,\n", + " __replace_zeros_negatives_with_mean__=True,\n", + " __print_results__=False,\n", + " )\n", + " \n", + " predict.Process_Observational_Dataset(\n", + " __print_results__=False,\n", + " F_lambda_obs=None,\n", + " F_lambda_obs_err=None \n", + " )\n", + "\n", + " predict.predict_from_random_spectra(random_spectra_num= 3,\n", + " __print_results__= False,\n", + " __plot_randomly_generated_spectra__= False,\n", + " __plot_histogram__= False,\n", + " __plot_boxplot_hist__= False,\n", + " __plot_pred_vs_obs_errorbar__= False,\n", + " __plot_pred_vs_obs_errorbar_stat__ = False,\n", + " __calculate_confidence_intervals_std_df__ = True,\n", + " )\n", + "\n", + " chi_square, p_value = chi_square_test(x_obs = predict.obs_data_df['wl'], \n", + " y_obs = predict.obs_data_df['Fnu_obs_absolute'], \n", + " yerr_obs = predict.obs_data_df['Fnu_obs_absolute_err'],\n", + "\n", + " x_pre = predict.confidence_intervals_std_df['wl'].values[::-1], \n", + " y_pre = predict.confidence_intervals_std_df['mean'], \n", + " yerr_pre = predict.confidence_intervals_std_df['std_values'],\n", + " radius = brown_dwarf_literature_info['bd_radius_Rjup'],\n", + " __plot_results__ = False,\n", + " __print_results__ = False)\n", + " \n", + " \n", + " if chi_square >= 0.:\n", + "\n", + " R_list.append(radius)\n", + " chi_square_list.append(chi_square)\n", + " p_value_list.append(np.float64(p_value))\n", + " random_pred_mean_list.append(list( predict.dic_random_pred_mean.values() ))\n", + " else:\n", + " pass\n", + "\n", + " if __print_results__:\n", + " print( f\"Radius = {'{:.2f}'.format(radius)} R_Jup: 𝛘2 = {chi_square}, p-value = {p_value}\")\n", + " \n", + "# if __plot_stat_results__:\n", + "# plot_chi_square_p_value(R_list, chi_square_list, p_value_list)\n", + " \n", + " tuned_ML_R_param_df = pd.DataFrame(columns=['radius','chi_square','p_value','gravity','temperature','c_o_ratio','metallicity'])\n", + " tuned_ML_R_param_df['radius'] = R_list\n", + " tuned_ML_R_param_df['chi_square'] = chi_square_list\n", + " tuned_ML_R_param_df['p_value'] = p_value_list\n", + " tuned_ML_R_param_df['gravity'] = np.array(random_pred_mean_list).T[0]\n", + " tuned_ML_R_param_df['c_o_ratio'] = np.array(random_pred_mean_list).T[1]\n", + " tuned_ML_R_param_df['metallicity'] = np.array(random_pred_mean_list).T[2]\n", + " tuned_ML_R_param_df['temperature'] = np.array(random_pred_mean_list).T[3]\n", + "\n", + " return tuned_ML_R_param_df" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "7401b0a1-ba7f-4af6-a59f-a9850657994d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "******************************\n", + "\n", + "Ross458C: Literature reported Info\n", + "\n", + "{ 'bd_Teff': 804,\n", + " 'bd_distance_pc': 11.509,\n", + " 'bd_logg': 4.09,\n", + " 'bd_met': 0.23,\n", + " 'bd_name': 'Ross458C',\n", + " 'bd_radius_Rjup': 0.68,\n", + " 'bd_radius_Rjup_tuned': None}\n", + "******************************\n", + "\n" + ] + } + ], + "source": [ + "print_results_fun(brown_dwarf_literature_info, print_title= 'Ross458C: Literature reported Info')" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "7bbeac2e-2466-47b0-bf19-1c8e0cfa447c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tuned_ML_R_param_df = tune_radius(Rmin = brown_dwarf_literature_info['bd_radius_Rjup']- 0.2, \n", + " Rmax = brown_dwarf_literature_info['bd_radius_Rjup']+ 0.2, \n", + " dR = 0.01,\n", + " brown_dwarf_literature_info = brown_dwarf_literature_info,\n", + " __plot_results__=False,\n", + " __print_results__=False,\n", + " __plot_stat_results__=False,)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "010c5f3a-65e3-4a12-91d6-7f171d6fa547", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p2308" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "datuned_ML_R_param_df_filtered = tuned_ML_R_param_df[tuned_ML_R_param_df['p_value'].between(.04, 1.)]\n", + "\n", + "plot_scatter_x_y( x = datuned_ML_R_param_df_filtered['radius'],\n", + " y = datuned_ML_R_param_df_filtered['chi_square'],\n", + " plot_title='Scatter Plot',\n", + " x_label='Radius (RJupiter)',\n", + " y_label='𝛘2 value',\n", + " plot_width=800,\n", + " plot_height=300,)" + ] + }, + { + "cell_type": "markdown", + "id": "c44287ab-e348-447e-9e83-f1bb0538eb44", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "8b4cb690-6379-4daa-b0db-3eb217f72eb4", + "metadata": { + "tags": [] + }, + "source": [ + "## 2.6 Predict Brown Dwarf Atmosphric Parameters of GJ 570D\n", + "\n", + "In the following, we will deploy the trained CNN models to predict atmospheric parameters. Major steps are: \n", + "\n", + "1. Define the literature reported data for the object \n", + "\n", + "2. Instintiate *ObsParameterPredictor* class from *Predictor* module\n", + "\n", + "3. Load the observational Telescope data\n", + " - Replace negative Flam with zero\n", + " - Take the average of neibougrs for Flam_error <= 0\n", + " - Plot_observational_spectra_errorbar_\n", + " \n", + " \n", + "4. Process the observational Telescope data\n", + " - Instintiate ProcessObservationalDataset class\n", + " - Define feature_names_synthetic and feature_names_obs\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "8e6c1db5-6573-4cf0-a212-c21a3b7b3e3c", + "metadata": {}, + "source": [ + "\n", + "### 2.6.1 Load the data and process\n", + "\n", + "Define the literature data reported for this object\n", + "\n", + "This data will be used later to: \n", + "- Compare the Literature vs ML predicted results\n", + "- Convert the unit of the real observational dataset or fluxes from $F_\\lambda$ to $F_\\nu$ and $F_\\nu^{absolute}$\n", + "- Revised the object radius" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "dadf8679-a0ac-45c4-8920-8f3925fa6a0b", + "metadata": {}, + "outputs": [], + "source": [ + "GJ570D_BD_literature_info = {'bd_name':'GJ570D',\n", + " 'bd_Teff':828,\n", + " 'bd_logg':3.90,\n", + " 'bd_met': -0.33, \n", + " 'bd_distance_pc' : 5.884, \n", + " 'bd_radius_Rjup' : 0.79,\n", + " 'bd_radius_Rjup_tuned': None } # 0.79\n", + "\n", + "brown_dwarf_literature_info = GJ570D_BD_literature_info" + ] + }, + { + "cell_type": "markdown", + "id": "9088e2fb-fba8-4d98-a8fd-83e4370566d9", + "metadata": {}, + "source": [ + " Instintiate the *ObserveParameterPredictor* class from *Predictor* module \n", + "\n", + "*ObserveParameterPredictor* class performs the following tasks:\n", + "- Load the observational datasets\n", + "- Process the data including converting their units\n", + "- Visualize the data" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "2702febf-4ae7-4d2a-b6db-53c47eed4b46", + "metadata": {}, + "outputs": [], + "source": [ + "# brown_dwarf_literature_info \n", + "predict = ObserveParameterPredictor(object_name = brown_dwarf_literature_info['bd_name'], #Ross458C, HD3651B, GJ570D\n", + " training_dataset_df = train_BD,\n", + " wl_synthetic = wl_synthetic, \n", + " BuildRegressorCNN_class = build_regressor_CNN,\n", + " bd_literature_dic = brown_dwarf_literature_info\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "594a9427-c19d-4657-bc5c-1f6383c3604c", + "metadata": {}, + "source": [ + " Load the observational Telescope data \n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "0db1a138-b322-4235-8628-19e9672922b7", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------- Observational DataFrame Example ---------\n", + " wl F_lambda F_lambda_error F_lambda_obs F_lambda_obs_err\n", + "0 0.657971 4.569383e-17 7.119990e-17 4.569383e-17 7.119990e-17\n", + "1 0.660155 -2.299240e-17 0.000000e+00 4.581152e-17 7.541037e-17\n", + "2 0.662332 -4.419588e-17 0.000000e+00 4.592921e-17 7.962084e-17\n", + "3 0.664501 -9.488424e-18 0.000000e+00 4.604690e-17 8.383131e-17\n", + "4 0.666665 4.616459e-17 8.804178e-17 4.616459e-17 8.804178e-17\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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+ " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p2348" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "predict.load_observational_spectra(\n", + " __plot_observational_spectra_errorbar__=True,\n", + " __replace_zeros_negatives_with_mean__=True,\n", + " __print_results__=True,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "e97b1e8f-d9bd-4dcb-891e-78c9cb5a5c08", + "metadata": {}, + "source": [ + "### 2.6.2 Process the observational Telescope data" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "9f585c83-0a22-473c-b2f0-c5c95970af36", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------ Interpolated Observational Spectra: Absolute F𝜈 ------------\n", + " 0.897 0.906 0.915 0.924 0.933 \\\n", + "0 7.280711e-09 8.611113e-09 1.062637e-08 1.170489e-08 7.329417e-09 \n", + "\n", + " 0.943 0.952 0.962 0.971 0.981 ... \\\n", + "0 1.371097e-08 1.299396e-08 1.443783e-08 1.943760e-08 2.911763e-08 ... \n", + "\n", + " 2.296 2.319 2.342 2.366 2.389 \\\n", + "0 7.612835e-09 6.693866e-09 8.187037e-09 8.760881e-09 8.572828e-09 \n", + "\n", + " 2.413 2.438 2.462 2.487 2.512 \n", + "0 1.231363e-08 1.863393e-08 1.684466e-08 2.206102e-08 1.915624e-08 \n", + "\n", + "[1 rows x 104 columns]\n", + "------------ df_MinMax Single Observational Spectrum ------------\n", + " 0 1\n", + "0 1.670937e-09 3.343356e-07\n", + "******************************\n", + "\n", + "Radius, BD_mean, BD_std:\n", + "\n", + "{ 'BD_mean': 5.960877683707767e-08,\n", + " 'BD_std': 7.325304103971966e-08,\n", + " 'bd_radius_Rjup': 0.79}\n", + "******************************\n", + "\n", + "******************************\n", + "\n", + "Predicted Targets from the Signle Observational Spectrum:\n", + "\n", + "{ 'c_o_ratio': 1.58,\n", + " 'gravity': 5.2,\n", + " 'metallicity': -0.42,\n", + " 'temperature': 882.22}\n", + "******************************\n", + "\n" + ] + } + ], + "source": [ + "predict.Process_Observational_Dataset(\n", + " __print_results__=True,\n", + " F_lambda_obs=None,\n", + " F_lambda_obs_err=None )" + ] + }, + { + "cell_type": "markdown", + "id": "79eb161e-7517-43c5-ab9d-ebfecb8a98d9", + "metadata": {}, + "source": [ + " Check the processed Telescope dataset \n", + "\n", + "This dataframe includes the following columns:\n", + "\n", + "- **wl (wavelengths in $\\mu m$)**: Wavelengths from the observational dataset.\n", + "\n", + "- **F_lambda ($F_\\lambda$)**: Original observational spectral density flux along the wavelength axis.\n", + "- **F_lambda_error ($F_\\lambda^{error}$)**: Original observational spectral density flux uncertainty along the wavelength axis.\n", + "- **F_lambda_obs ($F_\\lambda^{obs}$)**: Same as the original F_lambda but with a more descriptive label.\n", + "- **F_lambda_obs_err ($F_\\lambda^{error, obs}$)**: Same as the original F_lambda_error but with a more descriptive label.\n", + "- **Fnu_obs ($F_\\nu^{obs}$)**: Observational spectral density flux along the frequency axis (converted from the F_lambda column).\n", + "- **Fnu_obs_err ($F_\\nu^{error}$)**: Observational spectral density flux uncertainty along the frequency axis (converted from the F_lambda column).\n", + "- **Fnu_obs_absolute ($F_\\nu^{obs, abs}$)**: Observational absolute spectral density flux along the frequency axis (converted from the F_lambda column).\n", + "- **Fnu_obs_absolute_err ($F_\\nu^{obs, abs, error}$)**: Observational absolute spectral density flux uncertainty along the frequency axis (converted from the F_lambda column).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "53fff9b9-c0e0-4bf9-82b2-fd4a789458ca", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'ObserveParameterPredictor' object has no attribute 'obs_data_df'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [62], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpredict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobs_data_df\u001b[49m\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m6\u001b[39m)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'ObserveParameterPredictor' object has no attribute 'obs_data_df'" + ] + } + ], + "source": [ + "predict.obs_data_df.head(6)" + ] + }, + { + "cell_type": "markdown", + "id": "2d932707-726b-4083-a0b6-cfc893eb3cf5", + "metadata": {}, + "source": [ + "### 2.6.3 Deploy the Trained ML Models for Prediction: \n", + "\n", + " *predict_from_random_spectra* function from *Predictor* class to perform the following tasks: \n", + " \n", + "- Generate random spectra based on the observational data\n", + "\n", + "- Deploy the trained ML models to predict the target outputs: \n", + " - gravity ( log$g$ )\n", + " - temperature ( T$_{eff}$ )\n", + " - carbon-to-oxygen ratio ( C/O )\n", + " - metallicity ( [M/H] )\n", + "\n", + "- Print the results, including:\n", + " - Predicted Targets from Randomly Generated Spectra.\n", + " - Print a summary of descriptive statistics, including the mean, standard deviation, and the shape of a dataset's distribution (percentiles).\n", + "\n", + "- Visualize the results to gain insights about:\n", + " - *Histograms* representing the distribution of the predicted target outputs by the trained ML models.\n", + " - *Boxplots* displaying the first quartile, median, third quartile, and maximum. In a box plot, the box is drawn from the first quartile to the third quartile. A vertical black line goes through the box at the median, and a vertical white line goes through the box to indicate the mean.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "e8232ce0-9065-4a71-8299-c12f95de63e3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "******************************\n", + "\n", + "Predicted Targets from Randomly Generated Spectra:\n", + "\n", + "{ 'c_o_ratio': 1.58674156665802,\n", + " 'gravity': 5.180192947387695,\n", + " 'metallicity': -0.3848172426223755,\n", + " 'temperature': 871.9872436523438}\n", + "******************************\n", + "\n", + " logg c_o met T\n", + "count 30.000000 30.000000 30.000000 30.000000\n", + "mean 5.180193 1.586742 -0.384817 871.987244\n", + "std 0.155322 0.306846 0.048721 37.302635\n", + "min 4.858307 1.114380 -0.458606 791.445129\n", + "25% 5.079404 1.408313 -0.412241 848.342346\n", + "50% 5.174807 1.534234 -0.392759 874.133850\n", + "75% 5.287008 1.656186 -0.357554 889.841217\n", + "max 5.444837 2.661638 -0.253799 948.316406\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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const render_items = [{\"docid\":\"468d506f-b169-4922-a472-89514f3e9454\",\"roots\":{\"p2401\":\"f8782b14-bd80-4fa6-8158-cddc936dc615\"},\"root_ids\":[\"p2401\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p2401" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
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\n", 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\n", 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\n", 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3WHbXrl0YP348XFxcIBKJEBER0WSe/5aWtrt/WT5CSM9g0gSXnJyM2NhYLF26FJmZmYiKisKkSZNanDDy2LFjGD9+PFJSUpCRkYFHHnkEMTExyMzM1CknEolQUFCgszUuy0cI6TlM+hR1zZo1mDNnDubOnQsASExMxKFDh7Bx48Zm50RLTEzU+fmjjz7Cnj17sG/fPp35yFgslnb6HkJIz2WyFpxKpUJGRgaio6N19kdHRyMtLU2vOjQaDSorK+Ho6Kizv6qqCr6+vvDy8sLUqVObtPAepFQqIZfLdTZCSPdnsgQnlUqhVqvh5uams9/NzQ2FhYV61bF69WpUV1dj2rRp2n1BQUFISkrC3r17sW3bNgiFQowcORLXrl1rsZ6EhASIxWLtpu+qWIQQ82byhwwPLurC6LlK0rZt2/DBBx8gOTkZrq6u2v0jRozAjBkzMHDgQERFReGnn35Cnz59sH79+hbrio+Ph0wm0265ubmGfyBCiNkw2T04Z2dncDicJq214uLiJq26ByUnJ2POnDnYsWNHi/P8N2Kz2Rg6dGirLTiBQKBdnJgQYjlM1oLj8/kICwtDamqqzv7U1FRERka2eNy2bdvwwgsvYOvWrZgyZUqb78MwjHZJO0JIz2LSp6hxcXGYOXMmwsPDERERgc2bN0MikWD+/PkAGi4d8/Pz8f333wNoSG6zZs3CunXrMGLECG3rz8rKSrs+6PLlyzFixAgEBgZCLpfjs88+Q1ZWFr744gvTfEhCiMmYNMFNnz4dpaWlWLFiBQoKChAaGoqUlBT4+voCAAoKCnT6xG3atAn19fVYuHAhFi5cqN0/e/ZsJCUlAWhYCPjll19GYWEhxGIxBg8ejGPHjmHYsGFd+tkIIaZHi840o7MXnTGFM2fOICwsDHFf7DLabCJ51y5izcKnkJGRgSFDhhilTkKMyeRPUQkhpLNQgiOEWCxKcIQQi0Uz+popYy8Qk52drXdZhmEgV9SjRlUPNosFsRUPQh7HaLEQ0lUowZmhzlogBmgYp9uSsmoVsnIrcL24CrV1ap3XXOwECHa3Q2gvMXgcaviT7oESnBnqjAVisv86ioPfrWt2Xrw6tQYnrktxPk+GxkfqHBYLNgIO1BoG1So1SiqVKKlU4vTtcozp44JAV1ujxEVIZ6IEZ8aMuUBMkeRGs/tltXXYf+4OpFUqAEBvZxsM9LZHL3srcNgNY4JrVPW4VlyFM7fLIVfU4+CFQuT1EuMhasgRM0cJrgcrlCmwJysfinoNrHgcTAx1h4+jdZNy1nwuBnrZI9RTjL9yyvDXrTKcz5ehWMgFi29lgsgJ0Q8luB6qpFKJX7LyoazXwE0kwJT+HrAT8lo9hsNmISLACa4iAX69UIgiBRuuT/8fVGrqK07ME11k9EAVNSptcvMQC/H0EK82k9v9Alxs8XSYF7gsBkKf/lh3qgJqDSU5Yn4owfUwagbYf74ANSo1nG35eHygp0FPRd1FQkS41IOpr0N6ngIfH9S/GwohXYUSXA9zXSVGaZUKVjwOHh/UC4IO9G9zFTKQHlgDAPjqeA7+l11krDAJMQpKcD2ITb9HUKi2AQvApFB32Ao6fgu25vJxTAlseDDxxo6zKJDVdrhOQoyFElwPoQIXjuNeAQAM7+0I72aelhpq1gARQnuJUFFTh7jks6AJaoi5oATXQ9yAG9hCW9ixVRjq69j2Ae3A47Dw+T+HQMhjI/1mKXZk5Bm1fkIMRQmuB7heXIVSiMCo69GHXwE2u+1FfdrLz9kGceP7AAD+cyAb0iql0d+DkPaiBGfh6tQaHL1aAgCQn9oJW3Z9p73XSyP9EeIhgqy2Dh/uv9Rp70OIvijBWbhMSQWqlPUQoA6y9OROfS8uh42Pn+4PFgvYk3UHGbfLOvX9CGkLJTgLVq2sx+m7ScYPxWDqVZ3+ngO87DEtrGHh7H8fyKYHDsSkKMFZsPSbpahTM3AXCeEMeZe97xvRfWDN5yBTUoH95wq67H0JeRAlOAtVXqPCpTsNSS0q0BnGf6zQMleREK+MbpjmaeWvl6Gq13ThuxNyj8kT3IYNG+Dv7w+hUIiwsDAcP368xbK7du3C+PHj4eLiApFIhIiICBw6dKhJuZ07dyIkJAQCgQAhISHYvXt3Z34Es/RXThkYAP7ONvC07/oZP+aN9oernQB55bXYkZHb5e9PCGDiBJecnIzY2FgsXboUmZmZiIqKwqRJk3TWQr3fsWPHMH78eKSkpCAjIwOPPPIIYmJikJmZqS2Tnp6O6dOnY+bMmTh79ixmzpyJadOm4dSpU131sUyuvFqFK4WVAIDh/sbt86Yvaz4XCx5uaMV98cd1KOvVbRxBiPGZNMGtWbMGc+bMwdy5cxEcHIzExER4e3tj48aNzZZPTEzEW2+9haFDhyIwMBAfffQRAgMDsW/fPp0y48ePR3x8PIKCghAfH4+xY8ciMTGxiz6V6Z2623rr7WwDN5HQZHE8O8wH7iIh7sgU+Ok0df4lXc9k88GpVCpkZGTgnXfe0dkfHR2NtLQ0verQaDSorKyEo+O9Vkp6ejqWLFmiU27ChAmtJjilUgml8l7HVLm8627IG1tFjQpXiu623np3TeuttQVtYgL4+CpTgcRDl9CHIwWP0/bdQGdnZ/j4+BgzRNJDmSzBSaVSqNVquLm56ex3c3NDYWGhXnWsXr0a1dXVmDZtmnZfYWFhu+tMSEjA8uXL2xG9+TojqQAA+DlZw9Wuc1tv8rKGDsQzZsxouRCHi16v/BelcMa4ufGoOpfaZr1W1ta4nJ1NSY50mMln9GWxdP9HZximyb7mbNu2DR988AH27NkDV1fXDtUZHx+PuLg47c9yuRze3t76hG9WalT1uFTQ0PoM83Xo9PerrWp4rymvLEXfAWEtlrsqZ+N8BeAX8zrGv/wqWvvnLZLcwJaVb0IqlVKCIx1msgTn7OwMDofTpGVVXFzcpAX2oOTkZMyZMwc7duzAuHHjdF5zd3dvd50CgQACgaCdn8D8nMuTQa1h4GonQK8ufHLq5Onb6uI4LvVqXDlxC5X1GtQ5+KK3C63IRbqGyR4y8Pl8hIWFITVV95IlNTUVkZGRLR63bds2vPDCC9i6dSumTJnS5PWIiIgmdf7222+t1mkJ6tQanMuTAWhovenTCu4qAi4H/b3EAO5dQhPSFUx6iRoXF4eZM2ciPDwcERER2Lx5MyQSCebPnw+g4dIxPz8f33//PYCG5DZr1iysW7cOI0aM0LbUrKysIBY3/AEtXrwYo0ePxsqVK/H4449jz549+P3333HixAnTfMgukl0gR22dGiIhFw+ZYQtpkJc9MiXlyK+oRaFMAXex6Z7ukp7DoBZcUlISaoyw6vr06dORmJiIFStWYNCgQTh27BhSUlLg6+sLACgoKNDpE7dp0ybU19dj4cKF8PDw0G6LFy/WlomMjMT27dvx7bffYsCAAUhKSkJycjKGDx/e4XjNlYZhtC2jwT4OnTIdUkfZCrno624HAMiQlJs4GtJTGNSCi4+Px+uvv45//OMfmDNnTocu/xYsWIAFCxY0+1pSUpLOz0eOHNGrzmeeeQbPPPOMwTF1N7ek1ZDV1kHAZaOfp8jU4bRoiI8DsgsqcaO4ChU1Kthb800dErFwBrXg8vLy8OOPP6K8vByPPPIIgoKCsHLlSr27dxDjarz3FuopNmiFrK7ibCuAr5M1GNyLmZDOZNBfA4fDwWOPPYZdu3YhNzcXL7/8MrZs2QIfHx889thj2LNnDzQaGmDdFcprVLhd1nC7oPFGvjkb6GUPALhUIEedmn5HSOfq8H/3rq6uGDlyJCIiIsBms3H+/Hm88MILCAgI0PuSkhjufH5DS8jPyRpiK/0XbzYVPydriIRcKOs12vGyhHQWgxNcUVERVq1ahX79+uHhhx+GXC7H/v37kZOTgzt37uCpp57C7NmzjRkreUCdWqOdEmnA3ZaRuWOxWNpYz+XJaEJM0qkMSnAxMTHw9vZGUlIS5s2bh/z8fGzbtk3b6dbKygpvvPEGcnNpmpzOdLWoEsp6DURCLnydjLcMYGcL8RSBw2ahpEqJApnC1OEQC2bQU1RXV1ccPXoUERERLZbx8PBATk6OwYGR1jEMo71R399LDLYZdextixWPgz5utsguqMS5fJlJ5qsjPYNBLbgxY8ZgyJAhTfarVCptp1wWi6Xtz0aMr0iuRHGlEhw2C/08zP/hwoMaHzZcL6pCjarzVvoiPZtBCe7FF1+ETNb0MX9lZSVefPHFDgdF2nbhTsP5D3S1hRWfY+Jo2s9NJISbSAA1w+DCne47PRUxbwYluJZm58jLy9MOmSKdp06twbWiKgAw6469bRnQyx4AcOmOnB42kE7RrntwgwcPBovFAovFwtixY8Hl3jtcrVYjJycHEydONHqQRNf14iqo1BqIrXhdOmuIsQW62eLo1RLIauuQV14Lb8fu86CEdA/tSnBPPPEEACArKwsTJkyAre29Qd18Ph9+fn54+umnjRogaapxzrdgDzuzmjWkvXgcNvq42+JCvhwX78gpwRGja1eCW7ZsGQDAz88P06dPh1BIM0J0tcbWDgAEe3Tfy9NGoZ5iXMiX43pJFRR1tDANMS6D7sHNnj2bkpuJNLbevB2tIBKa/8iFtrjaCeBsy4daw+AyjWwgRqZ3C87R0RFXr16Fs7MzHBxan1CxrKzMKMERXQzDIPtugguxgNYb0NCdqJ+nGEevluDiHRlGd/5M66QH0TvBrV27FnZ2dtrvu/O9n+4qt7wWlYp68Llss5zU0lBB7nY4cV0KaZUKFTb0e0WMR+8Ed/+40hdeeKEzYiFtaLw87eNmC64ZT4vUXkIeBw+52OJKUSVyqi3ncxHT0zvBtWetUJHIMi6fzImyXo3rxXf7vnXDkQtt6ecpwpWiSuRWs8Hidf8FgIh50DvB2dvbt3lZ2tgBWK2mp2HGdrWoCmoNA0cbPtxElpcAvBysILbiQVZbB+u+I00dDrEQeie4w4cPd2YcpA2N0yKFeIgs8v4ni8VCiKcI6TdKYTtggqnDIRZC7wQ3ZsyYzoyDtKKsWoVCuQIsVsMNeUsV4i5C+g0phN79cKeyHk2ncyCkffROcOfOnUNoaCjYbDbOnTvXatkBAwZ0ODByT+PDBT8nG9gITLrSY6eyFXLhJmRQpGDhj5waTKX/U0kH6f3IatCgQZBKpdrvBw8ejEGDBjXZBg8e3K4ANmzYAH9/fwiFQoSFheH48eMtli0oKMBzzz2Hvn37gs1mIzY2tkmZpKQk7XjZ+zeFontOrKjRWF7ft9b42Tbcvz1yuxZqDQ3AJx2jd3MgJycHLi4u2u+NITk5GbGxsdiwYQNGjhyJTZs2YdKkSbh06RJ8fHyalFcqlXBxccHSpUuxdu3aFusViUS4cuWKzr7uOvLiVlk1alRqWPE48He2MXU4nc7DioG6Vo4yiHD8Wgke7utq6pBIN6Z3grt/8kpjTWS5Zs0azJkzB3PnzgUAJCYm4tChQ9i4cSMSEhKalPfz88O6desAAN98802L9bJYLLi7uxslRlNrfLjQ190OHDNc0NnYOCyg+uIRiMIfw46MPEpwpEMM7lV55coVLFq0CGPHjsW4ceOwaNGiJq2m1qhUKmRkZCA6Olpnf3R0NNLS0gwNCwBQVVUFX19feHl5YerUqcjMzGy1vFKphFwu19nMgVIN5EirAXTved/aq+r87wCA1ItFqKhRmTga0p0ZlOB+/vlnhIaGIiMjAwMHDsSAAQNw5swZhIaGYseOHXrVIZVKoVar4ebmprPfzc2tQwtIBwUFISkpCXv37sW2bdsgFAoxcuRIXLt2rcVjEhISIBaLtZu3t7fB729Mkmo2NEzjgHTL6/vWkrrim/Cz50Kl1mBP1h1Th0O6MYMS3FtvvYX4+Hikp6djzZo1WLNmDdLS0vDuu+/i7bffblddD/bpamm2YH2NGDECM2bMwMCBAxEVFYWffvoJffr0wfr161s8Jj4+HjKZTLuZy2pgt+8OW+oJDxceNNavYW64HRnm8W9BuieDElxhYSFmzZrVZP+MGTP0bn05OzuDw+E0KV9cXNykVdcRbDYbQ4cObbUFJxAIIBKJdDZT47n2hqyODQ6Lhb4W3PetJVG+VuBxWLiQL9fehySkvQxKcA8//HCz3TlOnDiBqKgoverg8/kICwtDamqqzv7U1FRERkYaElazGIZBVlYWPDw8jFZnV7Dt37DGbG8XGwh53W9RmY4SCdgYF9zwHx214oih9H6KunfvXu33jz32GN5++21kZGRgxIgRAICTJ09ix44dWL58ud5vHhcXh5kzZyI8PBwRERHYvHkzJBIJ5s+fD6Dh0jE/P1+7FCHQMF060PAgoaSkBFlZWeDz+QgJCQEALF++HCNGjEBgYCDkcjk+++wzZGVl4YsvvtA7LlOrUzOw6fcwgIZFknuqf4R74eCFQuzJuoP4ScHgc2mmEdI+eie4xvUY7rdhwwZs2LBBZ9/ChQu1Caot06dPR2lpKVasWIGCggKEhoYiJSVF2w2loKAAEolE55j7OxJnZGRg69at8PX1xa1btwAAFRUVePnll1FYWAixWIzBgwfj2LFjGDZsmL4f1eT+vqMAx0oEKw4Dnx68TsHoQBe42glQXKnEH5eLMDG0e7XCienpneA0Gk2nBLBgwQIsWLCg2deSkpKa7Gtrebm1a9e22gm4O/gjp2HNBR8bTbdasd7YuBw2nhrihS+P3sBPp/MowZF2oza/mSmUKZBVpAQA+NrQtFP/CPcCABy5UoxiefccbkdMx+CR29XV1Th69CgkEglUKt3OmK+//nqHA+updmXmQcMAityLsPMJNHU4JhfgYoswXwdk3C7Hrsx8zB8TYOqQSDdiUILLzMzE5MmTUVNTg+rqajg6OkIqlcLa2hqurq6U4AzEMAx+Pp0H4G5v/pGU4ADgH2FeyLhdjh2nc/HK6N4WOR8e6RwGJbglS5YgJiYGGzduhL29PU6ePAkej4cZM2Zg8eLFxo6xxzgjKcdNaTUEHBZqrpwA8KqpQzKZ7Oxs7fdejAZ8DnCjpBrbU0+hrzPfoDqdnZ2bncSBWC6DElxWVhY2bdoEDocDDocDpVKJ3r1745NPPsHs2bPx1FNPGTvOHmHH3dZbpLcQV1W1Jo7GNORlJQAaOo3fz2lKHGxDH8WiNVtQduhzg+q2srbG5exsSnI9iEEJjsfjaS8T3NzcIJFIEBwcDLFY3KRbB9FPjaoe+88VAAAe9bNCkmnDMZnaqoZRC1NeWYq+A8K0+0sULBwrBhwGT8CMqY+ivV3iiiQ3sGXlm5BKpZTgehCDEtzgwYNx+vRp9OnTB4888gj+7//+D1KpFD/88AP69+9v7Bh7hF8vFKJKWQ8fR2uEuBh2CWZJnDx94RXYT/tzL4ZBVtotyBX1qBV5I7gHjs8l7WdQN5GPPvpIO/Tpww8/hJOTE1599VUUFxdj8+bNRg2wp2i8PH0mzItuojeDxWJpJx1onMKdkLYY1IILDw/Xfu/i4oKUlBSjBdQT5ZbVIP1mKVgs4OkwLxTdzG77oB4o2EOEkzllyCuvhay2DmIrnqlDImauQx19i4uLcfz4cZw4cQIlJSXGiqnH+el0w2DyUQ85o5e9lYmjMV8iKx68HRvOTza14ogeDEpwcrkcM2fORK9evTBmzBiMHj0anp6emDFjBmQymbFjtGhqDYOfMxouT6eFm8dEm+bs/svUtobtEWJQgps7dy5OnTqF/fv3o6KiAjKZDPv378fp06cxb948Y8do0Y5dK0GBTAF7ax6i+xlvHjxL9ZCLLfhcNioV9cgt75ldaYj+DLoHd+DAARw6dAijRo3S7pswYQK++uorTJw40WjB9QQ//d1wefrk4F4QcHvevG/txeWw0dfNDufzZbhUIO/Rs62QthnUgnNycoJYLG6yXywWw8HBocNB9RSlVUr8nl0EAJg+lC5P9dV4mXq9uArKOpqQgLTMoAT33nvvIS4uDgUFBdp9hYWFePPNN/H+++8bLThLtzszH3VqBgO9xAhyp35d+nITCeBow4daw+BqUZWpwyFmTO9L1MGDB+v0z7p27Rp8fX21vcIlEgkEAgFKSkrwyiuvGD9SC8MwDJLvXp5Oo9Zbu7BYLPTzEOH4dSkuFcjR36vp1QQhQAdn9CWGy8ytwLXiKgh5bMQM9DR1ON1OX3c7nLghRaFcgdIqJZx60LKKRH96J7hly5Z1Zhw9TuPDhcn9PSASUofV9rIRcOHvZIOb0mpcKpAjKtDF1CERM2TwhJdAw5oI2dnZDcNoQkJ01ksgLatW1mPf2YYFjadT3zeDhXiKcFNajcuFlYgMcAaHTUPciC6DElxxcTGeffZZHDlyBPb29mAYBjKZDI888gi2b98OFxf637Q1B84VoFqlhr+zDYb5O5o6nG7Lz8kGVjwOalRq3C6tRm8XW1OHRMyMQU9RX3vtNcjlcly8eBFlZWUoLy/HhQsXIJfLaTZfPfx46jaAhq4hNLDecBw2C8EeDYtin8+nETSkKYMS3K+//oqNGzciODhYuy8kJARffPEFDh482K66NmzYAH9/fwiFQoSFhTW7oHSjgoICPPfcc+jbty/YbDZiY2ObLbdz506EhIRAIBAgJCQEu3fvbldMnelsbgXO5cnA57JpaJYRhPZqeIJ6q7QGsto6E0dDzI1BCU6j0YDHa3pjnMfjtWt5weTkZMTGxmLp0qXIzMxEVFQUJk2a1OKkmUqlEi4uLli6dCkGDhzYbJn09HRMnz4dM2fOxNmzZzFz5kxMmzYNp06d0juuzvTjyYbW25T+HnC0oXnfOsrBmq8dzUCtOPIggxLco48+isWLF+POnTvaffn5+ViyZAnGjh2rdz1r1qzBnDlzMHfuXAQHByMxMRHe3t7YuHFjs+X9/Pywbt06zJo1q9mRFACQmJiI8ePHIz4+HkFBQYiPj8fYsWORmJjYrs/YGWQ1ddh3ruGczRhBs8oay4C7/eAu3pGhXt056/eS7smgBPf555+jsrISfn5+CAgIwEMPPQR/f39UVlZi/fr1etWhUqmQkZGB6Ohonf3R0dFIS0szJCwADS24B+ucMGFCq3UqlUrI5XKdrTP8fCYPijoNgtztMMSHhrQZi7+zDeyEXCjqNLhWTCMbyD0GPUX19vbGmTNnkJqaisuXL4NhGISEhGDcuHF61yGVSqFWq+HmpjuDhpubGwoLCw0JC0DDkLH21pmQkIDly5cb/J76YBgGW+4+XJgxwpceLhgRm8VC/15ipN0oxdm8CprOnGi1O8HV19dDKBQiKysL48ePx/jx4zsUwIN/6AzDdPiPv711xsfHIy4uTvuzXC6Ht7dxHwCk3yjFzZJq2PA5eGJwL6PWTYB+niKculmGIrkSRXIF3ERCU4dEzEC7L1G5XC58fX2hVndsFgdnZ2dwOJwmLavi4uImLbD2cHd3b3edAoEAIpFIZzO2xq4hTw7pBVtBh/pXk2ZY87l4yK2hH9zZvArTBkPMhsGzicTHx6OsrMzgN+bz+QgLC0NqaqrO/tTUVERGRhpcb0RERJM6f/vttw7V2VFFcgUOXWyYFmnGCF+TxWHpBnnZAwCuFlahWllv2mCIWTCoKfHZZ5/h+vXr8PT0hK+vL2xsbHReP3PmjF71xMXFYebMmQgPD0dERAQ2b94MiUSC+fPnA2i4dMzPz8f333+vPSYrKwsAUFVVhZKSEmRlZYHP5yMkJAQAsHjxYowePRorV67E448/jj179uD333/HiRMnDPmoRrH9r1yoNQzCfR1oWqRO5C4WwkMsRIFMgbN5FYgMcDZ1SMTEDEpwTzzxBFgsVofnxJ8+fTpKS0uxYsUKFBQUIDQ0FCkpKfD1bWjlFBQUNOkTd/9414yMDGzduhW+vr64desWACAyMhLbt2/He++9h/fffx8BAQFITk7G8OHDOxSroZT1au3l6cwIar11tjBfB+w/V4BzeTIM9XMEj9OhdZVIN9euBFdTU4M333wTv/zyC+rq6jB27FisX78ezs6G/0+5YMECLFiwoNnXkpKSmuzTJ6k+88wzeOaZZwyOyZj2ny1ASaUSbiIBJoV6mDoci+fvbAOxFQ+y2jpcuiPHQG97U4dETKhd/70tW7YMSUlJmDJlCv75z3/i999/x6uvvtpZsXV7DMPg6xM5AIBZEX7gc6k10dnYLBaG+NgDAM5IyqGhlbd6tHa14Hbt2oWvv/4azz77LADg+eefx8iRI6FWq8Hh0IIpDzp5swyXCuQQ8th4bhiNXOgqwR4ipN8shVxRjxvFVQh0szN1SMRE2tWkyM3NRVRUlPbnYcOGgcvl6gzZIvd882dD6+2pIV5woHGnXYbHYWPA3SeqGZJyWj+1B2tXglOr1eDzdf9QuVwu6uvpkfyDbkmrtStmvTTS38TR9DwDvcTgslkokitxu6zG1OEQE2nXJSrDMHjhhRcgENyb/16hUGD+/Pk6XUV27dplvAi7qaS0W2AY4OG+LnjIlSZi7GrWfC76e4mRKanAyZulGEnr0vRI7Upws2fPbrJvxowZRgvGUpRVq7QrZlHrzXTCfBxwPk/WMHxLQGN/e6J2Jbhvv/22s+KwKElpt1Bbp0Y/TxGiAqmzqanYCLgY4CXGGUkFLsnoIVhPRP0WjKxKWY+kuw8XFjz8EM0aYmJhvg7gslkoV7Fh1Tvc1OGQLkYJzsi2nroNuaIevZ1tMDHU3dTh9HjWfK52QkzxqOfpiWoPQwnOiBR1avz3eEPrbf6YAFrGzkyE+TqAw2Ig8AhEWp7C1OGQLkQJzoh2nslDcaUSHmIhzflmRqz5XPSxa5jK/MdzlVDWd2yqL9J9UIIzkjq1BpuO3gQAzIvqTcOyzEwfkRr1laUoqlbju7Rbpg6HdBH6KzSSXWfyICmrgZMNH88Oo+UAzQ2XDVQc/wEAsP6P6yitUpo4ItIVaGpZI7iecwufHrwMAHjsIQEuXzjXofqys7ONERZ5QPWFPzD0uX8hp6IeCQcvY9U/ml96sj0kEgmkUqkRorvH2dkZPj40dtkYKMF1kEQiwfB/xsHu4TmoryzF8llP4YN6lVHqrqqiFaKMitHg5TAx3v2jFD9n5OGZMC+M6O1kcHUSiQRBwcGorTHuUDAra2tczs6mJGcElOA6KL+wBFbhTwIAwn3ECFi3vcN1Zv91FAe/WweFgp74GVtfJz7+OcwHW09J8N4vF5DyepTB90ulUilqa2rw/Nufws0nwCjxFUluYMvKNyGVSinBGQEluA769UY1uLaOsOYwGDUoyChdQ4okN4wQGWnJ2xOCcOhCIa4XV2H9H9fwRnTfDtXn5hMAr8B+RoqOGBM9ZOiAKmU9dl+uBgAEi9XU762bEFvzsOLxUADAF4ev44yk3MQRkc5CCa4DDl8uhlypQV1ZPnxsNKYOh7TDlAEeeHyQJzQM8MZPZ1Gjoim/LBEluA6IGeiJleOcUHroc1DjrftZ8VgoPMRC5Eir8f4vF2kYlwWiBNdBgY58KCXnTR0GMYDYmoc10waBzWoYhbL1L0nbB5FuxeQJbsOGDfD394dQKERYWBiOHz/eavmjR48iLCwMQqEQvXv3xpdffqnzelJSElgsVpONnkiS5kQEOOHtiUEAgA/2XqT7cRbGpAkuOTkZsbGxWLp0KTIzMxEVFYVJkyY1WQu1UU5ODiZPnoyoqChkZmbi3Xffxeuvv46dO3fqlBOJRCgoKNDZhEJhV3wk0g29PLo3JvZzR52awdzvTiNHWm3qkIiRmDTBrVmzBnPmzMHcuXMRHByMxMREeHt7Y+PGjc2W//LLL+Hj44PExEQEBwdj7ty5eOmll7Bq1SqdciwWC+7u7jobIS1hsVhYPW0g+vcSo6xahdnf/IWSShrKZQlMluBUKhUyMjIQHR2tsz86OhppaWnNHpOent6k/IQJE3D69GnU1dVp91VVVcHX1xdeXl6YOnUqMjMzW41FqVRCLpfrbKRnsRFw8c0LQ+HrZA1JWQ1m/PcUJTkLYLIEJ5VKoVar4ebmprPfzc0NhYWFzR5TWFjYbPn6+nrteMCgoCAkJSVh79692LZtG4RCIUaOHIlr1661GEtCQgLEYrF28/amwfI9kYudAN+9OAyudgJcKarE9M3pKJTRvdvuzOQPGR6c0pthmFan+W6u/P37R4wYgRkzZmDgwIGIiorCTz/9hD59+mD9+vUt1hkfHw+ZTKbdcnNzDf04pJvzc7bBT69EwFMsxM2Sajy9MQ3ZBdSi765MluCcnZ3B4XCatNaKi4ubtNIaubu7N1uey+XCyan5QdNsNhtDhw5ttQUnEAggEol0NtJz+TnbIPmVCPg72yC/ohZPb0zDrxeav6og5s1kCY7P5yMsLAypqak6+1NTUxEZGdnsMREREU3K//bbbwgPDwePx2v2GIZhkJWVBQ8PD+METnoEb0dr/LJgJEY95IwalRrzf8zA0t3nUaui2YC7E5NeosbFxeG///0vvvnmG2RnZ2PJkiWQSCSYP38+gIZLx1mzZmnLz58/H7dv30ZcXByys7PxzTff4Ouvv8a//vUvbZnly5fj0KFDuHnzJrKysjBnzhxkZWVp6yREX2JrHr59cSjmRTWsbbvllASTPzuOE9eMO/8b6TwmnU1k+vTpKC0txYoVK1BQUIDQ0FCkpKTA19cXAFBQUKDTJ87f3x8pKSlYsmQJvvjiC3h6euKzzz7D008/rS1TUVGBl19+GYWFhRCLxRg8eDCOHTuGYcOGdfnnI90fj8PG0ikhGN3HBf/acRY50mrM+PoUJvd3x6Re1JozdyafLmnBggVYsGBBs68lJSU12TdmzBicOXOmxfrWrl2LtWvXGis8QgAAUYEu+G3JGKxNvYrv028h5Xwhfr0AOE19A5V1bR9PTMPkT1EJ6S7EVjx88Fg/7H8tCuND3KBhANt+j+C3Ah72nr2DW9JqGrBvZijBEdJOIZ4ifDUrHJ+Od0bNtZMAWMiRVmPP2Tv4Lv02/sopg7yWmnXmwOSXqIR0VwEOPJTs+jfmJe5CMdcVlwrkkNXWIf1mKdJvlqKXvRWCPezwkKstBFyOqcM1uu6w4A4lOEI6yI4HBAe6IDLACdeKqpBdKEdeeS3yKxq2w1dKEOBig2B3EXwcrcG2gMkDu8uCO5TgCDESHoeNEE8RQjxFqFTU4XJhJS4XVKKsRoWrRVW4WlQFaz4HIR4i9PMUwd6ab+qQDdZdFtyhBEdIJ7AT8jDUzxHhvg4orlTicmElrhRWokalxunb5Th9uxxeDlYI9RQjwMUGXE73vB1u7gvuUIIjpBOxWCy4iYRwEwkx6iFn5EirceGODLdLa5BXXou88loIuWz09xJjoJe9qcO1OJTgCOkiHDYLD7na4iFXW8gVdbh0R46Ld+SoUtbj71vlyLhdDm9rDngufqYO1WJ0z3YxId2cSMjDiN5OeHGkH6b094CHWAgNA9yu5sDzpc/xwdFSHLtaQv3qOohacISYEJt1r1VXKFPgz0u3kFsFnCtSYdY3f6GfpwjzxwRgcn8PWnfXANSCI8RMuIuFGO6sRv6meZgSaA0rHgcX78jx2rZMPLr6CLacug1FHY1/bQ9KcISYGbW8GHMGi5H2zqOIHRcIe2sebpfWYOnuCxi18jA2HrkBuYJGSuiDEhwhZsrBho/YcX2Q9s6j+L+pIfAUCyGtUmLlr5cxMuEPfHzwMooraUr11lCCI8TMWfO5eGmUP46+9QhW/2MgAl1tUamsx5dHb2DUysN4d/d53KKlDptFCY6QboLHYePpMC8cih2Nr2aFY4iPPVT1Gmw9JcGjq49g4dYzuJAvM3WYZoWeohLSzbDZLIwPccO4YFf8fascG49cx+ErJThwrgAHzhVgoLc9/hHmhZgBnhBbNz+Vf1diGAa1dWrIausgq6lDtUqN2jo1au9+ravXQM0wUNRy4fHCOtwor8MQI703JThCuikWi4Vh/o4Y5j8Ml+7IsenYDew/V4CzuRU4m1uBFfsvYXSgM8aHuGFssBucbQWdGo9aw6C8RoWy6nubrLYOFTV1UKk1etTABt8tADUqfcrqhxIcIRYgxFOEdc8OxntTQrAnKx87TufhSlElfs8uxu/ZxWCxzqOfpwjhvo4I93PAgF726OVg1e6+dQzDQF5bj5vldbAOHo2LFRxknbuDsmoVKmrr0Fq/ZFsBF/ZWPNgIubDicWDF58CaxwGfywabxUJ5oQS7v/gQfo9v6uDZuIcSHCEWxMVOgLlRvTFnlD+yCyqReqkIqdmFuJAv125JabcAAHwOG75O1vBxtIa9NR/21jzYCu6lBA3DoFJRf7cVpkKBTIH88lpUKusb3uuxt3BZDgD3HnDwOWw42vC1m4M1D2Krhq2tCQXyZAwUOWdgJzDeowFKcIRYIBaLpZ26afG4QBTKFPj7Vhkybpfj71tluFZcBVW9BteKq3CtuKrd9YsFbBTdvIigPn3g7ekGp7sJzVbAbXXh9q5m8gS3YcMGfPrppygoKEC/fv2QmJiIqKioFssfPXoUcXFxuHjxIjw9PfHWW281WRJw586deP/993Hjxg0EBATgP//5D5588snO/iiEmC13sRAxAz0RM9ATQMP9sjsVtbgprcadilrIautQXqNClaKhdcZiASywYCfkaltg7mIhvBys0cveCtkXziIsbDKe/2IXvHwcTPnRWmXSBJecnIzY2Fhs2LABI0eOxKZNmzBp0iRcunSp2QnvcnJyMHnyZMybNw8//vgj/vzzTyxYsAAuLi7apQPT09Mxffp0fPjhh3jyySexe/duTJs2DSdOnMDw4cO7+iMSYpY4bBa8Ha3h7Wht6lA6lUn7wa1ZswZz5szB3LlzERwcjMTERHh7e2Pjxo3Nlv/yyy/h4+ODxMREBAcHY+7cuXjppZewatUqbZnExESMHz8e8fHxCAoKQnx8PMaOHYvExMQu+lSEEHNhsgSnUqmQkZGB6Ohonf3R0dFIS0tr9pj09PQm5SdMmIDTp0+jrq6u1TIt1UkIsVwmu0SVSqVQq9Vwc3PT2e/m5obCwsJmjyksLGy2fH19PaRSKTw8PFos01KdAKBUKqFUKrU/y2QNvcHlcnmbn6OqquEGbd61i1DWGmcBjiLJDQBA4a2ruGFjnEuInlwnAJTk5QAAMjIytP9mHXXlyhUAxv2374w4AYDNZkOjMV7/ss787FVVVXr97QGAnZ1d6w81GBPJz89nADBpaWk6+//9738zffv2bfaYwMBA5qOPPtLZd+LECQYAU1BQwDAMw/B4PGbr1q06ZX788UdGIBC0GMuyZcsYALTRRls322QyWat5xmQtOGdnZ3A4nCYtq+Li4iYtsEbu7u7NludyuXBycmq1TEt1AkB8fDzi4uK0P2s0GpSVlcHJycmsHnl3FblcDm9vb+Tm5kIkEpk6HJOj86HLnM6HnZ1dq6+bLMHx+XyEhYUhNTVVpwtHamoqHn/88WaPiYiIwL59+3T2/fbbbwgPDwePx9OWSU1NxZIlS3TKREZGthiLQCCAQKA7jMXe3r69H8niiEQik/8CmxM6H7q6xflotX3XybZv387weDzm66+/Zi5dusTExsYyNjY2zK1btxiGYZh33nmHmTlzprb8zZs3GWtra2bJkiXMpUuXmK+//prh8XjMzz//rC3z559/MhwOh/n444+Z7Oxs5uOPP2a4XC5z8uTJLv983ZVMJtOr+d9T0PnQ1Z3Oh0kTHMMwzBdffMH4+voyfD6fGTJkCHP06FHta7Nnz2bGjBmjU/7IkSPM4MGDGT6fz/j5+TEbN25sUueOHTuYvn37MjwejwkKCmJ27tzZ2R/DonSnX+CuQOdDV3c6HyyGoWV7iC6lUomEhATEx8c3uXTvieh86OpO54MSHCHEYtGMvoQQi0UJjhBisSjBEUIsFiU4QojFogTXwxw7dgwxMTHw9PQEi8XCL7/80mr5I0eOgMViNdkuX77cNQF3soSEBAwdOhR2dnZwdXXFE088oR1n2ZqjR48iLCwMQqEQvXv3xpdfftkF0XY+Q86HOf+OUILrYaqrqzFw4EB8/vnn7TruypUrKCgo0G6BgYGdFGHXOnr0KBYuXIiTJ08iNTUV9fX1iI6ORnV1y+uMNs5LGBUVhczMTLz77rt4/fXXsXPnzi6MvHMYcj4ameXviGm74RFTAsDs3r271TKHDx9mADDl5eVdEpOpFRcXMwB0Opw/6K233mKCgoJ09r3yyivMiBEjOju8LqfP+TDn3xFqwRG9DB48GB4eHhg7diwOHz5s6nA6TeNUWY6Oji2W0WdeQkuhz/loZI6/I5TgSKs8PDywefNm7Ny5E7t27ULfvn0xduxYHDt2zNShGR3DMIiLi8OoUaMQGhraYrm25iW0FPqeD3P+HTH5ojPEvPXt2xd9+/bV/hwREYHc3FysWrUKo0ePNmFkxrdo0SKcO3cOJ06caLPsg9NoMXcHBFnS9Fr6ng9z/h2hFhxptxEjRuDatWumDsOoXnvtNezduxeHDx+Gl5dXq2X1mZewu2vP+WiOufyOUIIj7ZaZmQkPDw9Th2EUDMNg0aJF2LVrF/744w/4+/u3eUzjnIP3e3Bewu7KkPPRHHP5HaFL1B6mqqoK169f1/6ck5ODrKwsODo6wsfHB/Hx8cjPz8f3338PoGGVMj8/P/Tr1w8qlQo//vgjdu7caRFdIgBg4cKF2Lp1K/bs2QM7Oztty0wsFsPKygoAmpyT+fPn4/PPP0dcXBzmzZuH9PR0fP3119i2bZvJPoexGHI+zPp3xKTPcEmXa3yk/+A2e/ZshmGazsG3cuVKJiAggBEKhYyDgwMzatQo5sCBA6YJvhM0dy4AMN9++622jKHzEnZHhpwPc/4doemSCCEWi+7BEUIsFiU4QojFogRHCLFYlOAIIRaLEhwhxGJRgiOEWCxKcIQQi0UJjnRb586dw4svvgh/f38IhULY2tpiyJAh+OSTT1BWVqZTdu/eveByuSgpKdHuy83NxaJFixAQEAChUAgHBwc8/PDD2LJlC6h7qGWgoVqkW/rqq6+wYMEC9O3bF2+++SZCQkJQV1eH06dP48svv0R6ejp2796tLb9z506MHj0aLi4uAIA///wTU6dOha2tLd58800MGDAAMpkMP/30E2bMmIF9+/Zh69atYLOpDdCtmXgkBSHtlpaWxnA4HGbixImMQqFo8rpSqWT27Nmj/VmlUjH29vbM559/zjAMw5SXlzOurq6Mr68vU1hY2OT4jz/+mAHAJCQkdN6HIF2CEhzpdqZOncpwuVxGIpHoVf7gwYMMi8Vi8vPzGYZhmE8//ZQBwGzbtq3Z8hqNhgkKCmIcHR0ZlUpltLhJ16P2N+lW1Go1/vjjD4SFhcHb21uvY3bu3ImIiAh4enoCAFJTU8HhcBATE9NseRaLhcceewxlZWXIyMgwWuyk61GCI92KVCpFTU2N3vOUqdVq/PLLL3j66ae1+yQSCVxcXGBjY9PicY31SySSjgVMTIoSHLFoR48ehVQqxVNPPdWu4xgLnIK8J6IER7oVZ2dnWFtbIycnR6/yP//8M8LCwuDn56fd5+Pjg5KSklbX+rx16xYA6H0ZTMwTJTjSrXA4HIwdOxYZGRnIy8trtaxGo8Hu3bt1Lk8BYPz48VCr1di3b1+zxzEMg71798LR0RFhYWFGi510PUpwpNuJj48HwzCYN28eVCpVk9fr6uqwb98+pKWlobCwsEmCmzt3LlxdXREfH4/i4uImx3/yySe4fPky3nrrrW6/xkJPRzP6km6psaNvUFAQXn31VfTr1w91dXXIzMzE5s2bERoaCl9fX/zvf//D+fPnmxz/YEffgQMHQi6XIzk5GVu2bMH06dOpo68FoARHuq2zZ89i7dq1OHz4MAoLC8Hj8dCnTx/ExMRg0aJFCAsLw0svvYQPPvig2eNzc3OxcuVKHDx4EHl5ebCyssLAgQPx8ssv47nnnqMHDBaAEhyxSH/99ReGDx+Oc+fOoX///qYOh5gIJThCiMWiGwyEEItFCY4QYrEowRFCLBYlOEKIxaIERwixWJTgCCEWixIcIcRiUYIjhFgsSnCEEItFCY4QYrEowRFCLNb/A20UnqwI1/cmAAAAAElFTkSuQmCC\n", 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\n", 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{\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p2825" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "predict.predict_from_random_spectra(random_spectra_num= 30,\n", + " __print_results__= True,\n", + " __plot_randomly_generated_spectra__= True,\n", + " __plot_histogram__= True,\n", + " __plot_boxplot_hist__= True,\n", + " __plot_pred_vs_obs_errorbar__= True,\n", + " __plot_pred_vs_obs_errorbar_stat__ = True,\n", + " __calculate_confidence_intervals_std_df__ = True,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "bc2ceb36-a932-475c-9ad7-e126be3e0497", + "metadata": {}, + "source": [ + "Comennet from natasha: >2.2 mincron - Is not missing any thing!\n", + "> 1 sigma error \n", + "> training set\n", + "> trusing the posterior space " + ] + }, + { + "cell_type": "markdown", + "id": "aae1e1c5-86e2-42c8-8e7d-a4a0b67302a9", + "metadata": {}, + "source": [ + "### 2.6.4 Tune the Radius" + ] + }, + { + "cell_type": "markdown", + "id": "4ee14018-45b2-4823-8816-646d1c59dd43", + "metadata": {}, + "source": [ + " What is the 𝛘2 value for the predicted ML vs. the observational spectra?" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "93fc8831-a0c8-4aa5-9ff4-1f60ac03adb7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Radius = 0.79 R_Jup: 𝛘2 = 297.24, p-value = 0.00e+00\n" + ] + }, + { + "data": { + "text/plain": [ + "(297.24, '0.00e+00')" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chi_square_test(x_obs = predict.obs_data_df['wl'], \n", + " y_obs = predict.obs_data_df['Fnu_obs_absolute'], \n", + " yerr_obs = predict.obs_data_df['Fnu_obs_absolute_err'],\n", + " \n", + " x_pre = predict.confidence_intervals_std_df['wl'].values[::-1], \n", + " y_pre = predict.confidence_intervals_std_df['mean'], \n", + " yerr_pre = predict.confidence_intervals_std_df['std_values'],\n", + " radius = brown_dwarf_literature_info['bd_radius_Rjup'],\n", + " __plot_results__ = False,\n", + " __print_results__ = True)" + ] + }, + { + "cell_type": "markdown", + "id": "8148e86d-313c-4ebd-83a1-929b280e039a", + "metadata": {}, + "source": [ + " What is the 𝛘2 value (and p-value) for a range of $R_{Jup}$ radius?" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "c5a95970-fc44-4d09-9b2e-bb976fdc44c8", + "metadata": {}, + "outputs": [], + "source": [ + "def tune_radius(Rmin = brown_dwarf_literature_info['bd_radius_Rjup']-0.1, \n", + " Rmax = brown_dwarf_literature_info['bd_radius_Rjup']+0.1, \n", + " dR=0.01,\n", + " brown_dwarf_literature_info = None,\n", + " __plot_results__ = False,\n", + " __print_results__ = False,\n", + " __plot_stat_results__ = False):\n", + " \n", + " R_list = []\n", + " chi_square_list = []\n", + " p_value_list = []\n", + " random_pred_mean_list = []\n", + " \n", + "\n", + " for radius in list( np.linspace(Rmin, Rmax, num=int((Rmax-Rmin)/dR) )):\n", + " \n", + " brown_dwarf_literature_info['bd_radius_Rjup_tuned'] = radius\n", + " \n", + " # brown_dwarf_literature_info = Ross458C_BD_literature_info\n", + " predict = ObserveParameterPredictor(object_name = brown_dwarf_literature_info['bd_name'], #Ross458C, HD3651B, GJ570D\n", + " training_dataset_df = train_BD,\n", + " wl_synthetic = wl_synthetic, \n", + " BuildRegressorCNN_class = build_regressor_CNN,\n", + " bd_literature_dic = brown_dwarf_literature_info\n", + " )\n", + "\n", + " predict.load_observational_spectra(\n", + " __plot_observational_spectra_errorbar__=False,\n", + " __replace_zeros_negatives_with_mean__=True,\n", + " __print_results__=False,\n", + " )\n", + " \n", + " predict.Process_Observational_Dataset(\n", + " __print_results__=False,\n", + " F_lambda_obs=None,\n", + " F_lambda_obs_err=None \n", + " )\n", + "\n", + " predict.predict_from_random_spectra(random_spectra_num= 3,\n", + " __print_results__= False,\n", + " __plot_randomly_generated_spectra__= False,\n", + " __plot_histogram__= False,\n", + " __plot_boxplot_hist__= False,\n", + " __plot_pred_vs_obs_errorbar__= False,\n", + " __plot_pred_vs_obs_errorbar_stat__ = False,\n", + " __calculate_confidence_intervals_std_df__ = True,\n", + " )\n", + "\n", + " chi_square, p_value = chi_square_test(x_obs = predict.obs_data_df['wl'], \n", + " y_obs = predict.obs_data_df['Fnu_obs_absolute'], \n", + " yerr_obs = predict.obs_data_df['Fnu_obs_absolute_err'],\n", + "\n", + " x_pre = predict.confidence_intervals_std_df['wl'].values[::-1], \n", + " y_pre = predict.confidence_intervals_std_df['mean'], \n", + " yerr_pre = predict.confidence_intervals_std_df['std_values'],\n", + " radius = brown_dwarf_literature_info['bd_radius_Rjup'],\n", + " __plot_results__ = False,\n", + " __print_results__ = False)\n", + " \n", + " \n", + " if chi_square >= 0.:\n", + "\n", + " R_list.append(radius)\n", + " chi_square_list.append(chi_square)\n", + " p_value_list.append(np.float64(p_value))\n", + " random_pred_mean_list.append(list( predict.dic_random_pred_mean.values() ))\n", + " else:\n", + " pass\n", + "\n", + " if __print_results__:\n", + " print( f\"Radius = {'{:.2f}'.format(radius)} R_Jup: 𝛘2 = {chi_square}, p-value = {p_value}\")\n", + " \n", + "# if __plot_stat_results__:\n", + "# plot_chi_square_p_value(R_list, chi_square_list, p_value_list)\n", + " \n", + " tuned_ML_R_param_df = pd.DataFrame(columns=['radius','chi_square','p_value','gravity','temperature','c_o_ratio','metallicity'])\n", + " tuned_ML_R_param_df['radius'] = R_list\n", + " tuned_ML_R_param_df['chi_square'] = chi_square_list\n", + " tuned_ML_R_param_df['p_value'] = p_value_list\n", + " tuned_ML_R_param_df['gravity'] = np.array(random_pred_mean_list).T[0]\n", + " tuned_ML_R_param_df['c_o_ratio'] = np.array(random_pred_mean_list).T[1]\n", + " tuned_ML_R_param_df['metallicity'] = np.array(random_pred_mean_list).T[2]\n", + " tuned_ML_R_param_df['temperature'] = np.array(random_pred_mean_list).T[3]\n", + "\n", + " return tuned_ML_R_param_df" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "c0b8186c-34e6-4ce1-9701-6f9bac6b8ace", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "******************************\n", + "\n", + "GJ570D: Literature reported Info\n", + "\n", + "{ 'bd_Teff': 828,\n", + " 'bd_distance_pc': 5.884,\n", + " 'bd_logg': 3.9,\n", + " 'bd_met': -0.33,\n", + " 'bd_name': 'GJ570D',\n", + " 'bd_radius_Rjup': 0.79,\n", + " 'bd_radius_Rjup_tuned': None}\n", + "******************************\n", + "\n" + ] + } + ], + "source": [ + "print_results_fun(brown_dwarf_literature_info, print_title= 'GJ570D: Literature reported Info')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fae3b40d-a0bf-4269-919e-c9a4e4e62e1b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tuned_ML_R_param_df = tune_radius(Rmin = brown_dwarf_literature_info['bd_radius_Rjup']- 0.2, \n", + " Rmax = brown_dwarf_literature_info['bd_radius_Rjup']+ 0.2, \n", + " dR = 0.01,\n", + " brown_dwarf_literature_info = brown_dwarf_literature_info,\n", + " __plot_results__=False,\n", + " __print_results__=False,\n", + " __plot_stat_results__=False,)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5dd2a985-1907-4946-b6a5-52e5c245cb51", + "metadata": {}, + "outputs": [], + "source": [ + "tuned_ML_R_param_df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2177776-11ab-4eaa-bb2a-f42ca94719e4", + "metadata": {}, + "outputs": [], + "source": [ + "tuned_ML_R_param_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f007db9-932a-4c11-b703-c07f49e9162b", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "datuned_ML_R_param_df_filtered = tuned_ML_R_param_df[tuned_ML_R_param_df['p_value'].between(.04, 1.)]\n", + "\n", + "plot_scatter_x_y( x = datuned_ML_R_param_df_filtered['radius'],\n", + " y = datuned_ML_R_param_df_filtered['chi_square'],\n", + " plot_title='Scatter Plot',\n", + " x_label='Radius (RJupiter)',\n", + " y_label='𝛘2 value',\n", + " plot_width=800,\n", + " plot_height=300,)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/3__Train_CNN_Model.ipynb b/docs/tutorials/3__Train_CNN_Model.ipynb new file mode 100644 index 00000000..ea8ce884 --- /dev/null +++ b/docs/tutorials/3__Train_CNN_Model.ipynb @@ -0,0 +1,2625 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "723c394d-987d-4f2d-8bd6-378a23b1218e", + "metadata": { + "tags": [] + }, + "source": [ + "# 3. Train a Regression ConvNN Model using the BOHB Tuned Hyperparameters\n", + "\n", + "\n", + "In the following steps, you will: \n", + "\n", + "- Load the brown dwarf dataset used to train the ML models\n", + "- Prepare the X and y variables to deploy the trained ML models\n", + "- Visualize them for few cases\n", + "\n", + "We will need the following modules from `TelescopeML`:\n", + "\n", + "- *DeepBuilder*: to prepare the syntheric brown dwarf dataset, load the trained machine learning (ML) models\n", + "- *Predictor*: to prepare the observatinal brown dwarf dataset and deploy the trained ML models\n", + "- *StatVisAnalyzer*: provide statistical-test and plotting function \n", + "- *IO_utils*: provide functions to load the trained ML models" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6a0e9b08-df67-4377-9ad7-61865ed8f859", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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        \n", + " \n", + " Loading BokehJS ...\n", + "
        \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
        \\n\"+\n", + " \"

        \\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

        \\n\"+\n", + " \"
          \\n\"+\n", + " \"
        • re-rerun `output_notebook()` to attempt to load from CDN again, or
        • \\n\"+\n", + " \"
        • use INLINE resources instead, as so:
        • \\n\"+\n", + " \"
        \\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
        \"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(\"df244045-bd92-4e0d-b173-5c56901c2f34\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(\"df244045-bd92-4e0d-b173-5c56901c2f34\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
        \\n\"+\n \"

        \\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

        \\n\"+\n \"
          \\n\"+\n \"
        • re-rerun `output_notebook()` to attempt to load from CDN again, or
        • \\n\"+\n \"
        • use INLINE resources instead, as so:
        • \\n\"+\n \"
        \\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
        \"}};\n\n function display_loaded() {\n const el = document.getElementById(\"df244045-bd92-4e0d-b173-5c56901c2f34\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"df244045-bd92-4e0d-b173-5c56901c2f34\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
        \n", + " \n", + " Loading BokehJS ...\n", + "
        \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
        \\n\"+\n", + " \"

        \\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

        \\n\"+\n", + " \"
          \\n\"+\n", + " \"
        • re-rerun `output_notebook()` to attempt to load from CDN again, or
        • \\n\"+\n", + " \"
        • use INLINE resources instead, as so:
        • \\n\"+\n", + " \"
        \\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
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An open-source atmospheric radiative transfer Python package, [*PICASO*](https://natashabatalha.github.io/picaso/) was employed for generating these datasets. This dataset encompass 30,888 synthetic spectra (or instances or rows). \n", + "\n", + "Each spectrum has 104 features (or wavelengths in the astrophysics context, i.e., 0.897, 0.906, ..., 2.512~$\\mu$m) and 4 parameters:\n", + "- gravity ( log$g$ ) \n", + "- temperature ( T$_{eff}$ )\n", + "- carbon-to-oxygen ratio ( C/O )\n", + "- metallicity ( [M/H] )" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f0207e12-7f50-4abe-97bf-4d1229d5afd8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/egharibn/RESEARCH/ml/projects/TelescopeML_project/reference_data/'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read the `TelescopeML_reference_data` path\n", + "\n", + "import os # to check the path \n", + "\n", + "__reference_data_path__ = os.getenv(\"TelescopeML_reference_data\")\n", + "__reference_data_path__" + ] + }, + { + "cell_type": "markdown", + "id": "0e6447ec-26d9-4ad6-ac14-ccf292a25a0f", + "metadata": {}, + "source": [ + " Load the dataset and check few instances " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "68bfc730-ea01-488a-ae40-ad33704a34b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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        " + ], + "text/plain": [ + " gravity temperature c_o_ratio metallicity\n", + "0 5.0 1100 0.25 -1.0\n", + "1 5.0 1100 0.25 -0.7\n", + "2 5.0 1100 0.25 -0.5\n", + "3 5.0 1100 0.25 -0.3\n", + "4 5.0 1100 0.25 0.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "target_features = ['gravity', 'temperature', 'c_o_ratio', 'metallicity']\n", + "train_BD[target_features].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "74a42327-8bfb-4846-88b1-ad711c8da56e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['2.512', '2.487', '2.462', '2.438', '2.413']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# training_features_labels: they are Wavelengths variables in string format\n", + "\n", + "training_features_labels = [item for item in train_BD.columns.to_list() if item not in target_features+['is_augmented']]\n", + "training_features_labels[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "21aa48e5-b6ab-4db8-a2c9-b19ef458aabc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[2.512, 2.487, 2.462, 2.438, 2.413]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# training_features_wl: they are Wavelengths variables \n", + "\n", + "training_features_wl = [float(item) for item in train_BD.columns.to_list() if item not in target_features+['is_augmented']]\n", + "training_features_wl[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d56d2d6b-63c2-4005-94cb-68815ace5ff6", + "metadata": {}, + "outputs": [ + { + "data": { + "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", + "
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        " + ], + "text/plain": [ + " wl\n", + "0 2.511960\n", + "1 2.486966\n", + "2 2.462220" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wl_synthetic = pd.read_csv(os.path.join(__reference_data_path__, \n", + " 'training_datasets', \n", + " 'wl.csv'))\n", + "wl_synthetic.head(3)" + ] + }, + { + "cell_type": "markdown", + "id": "8bb511a7-5a5f-4b52-a6d9-e8533ece247e", + "metadata": { + "tags": [] + }, + "source": [ + "### 3.1.3 Prepare Inputs and outputs for ML models (X,y)\n", + "- X: 104 column variables or fluxes\n", + "- y: output variables: 'gravity', 'temperature', 'c_o_ratio', 'metallicity'\n", + "\n", + "Note: one of the columns, *is_augmented*, is indicating whether this is a native dataset or an augmented one. let's just ignore it for now!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e90e78b1-4f64-40b6-b92e-6027c8ace483", + "metadata": {}, + "outputs": [], + "source": [ + "# to assure we are only training the module with the native non-augmented BD training dataset\n", + "train_BD = train_BD[train_BD['is_augmented'].isin(['no'])] \n", + "\n", + "\n", + "# Training feature variables\n", + "X = train_BD.drop(\n", + " columns=['gravity', \n", + " 'temperature', \n", + " 'c_o_ratio', \n", + " 'metallicity', \n", + " 'is_augmented'])#.astype(np.float32)\n", + "\n", + "\n", + "# Target/Output feature variables\n", + "y = train_BD[['gravity', 'c_o_ratio', 'metallicity', 'temperature', ]]#.astype(np.float32)\n" + ] + }, + { + "cell_type": "markdown", + "id": "deff6ab8-a62a-4a2a-9a07-43757c6bb6a8", + "metadata": {}, + "source": [ + "log-transform the 'temperature' variable toreduce the skewness of the data, making it more symmetric and normal-like for the ML model\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "07998d41-e101-410c-9a35-e5c6da1800b0", + "metadata": {}, + "outputs": [], + "source": [ + "y.loc[:, 'temperature'] = np.log10(y['temperature'])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "37b9edab-f6dd-406e-b4c4-aba7ef45634c", + "metadata": {}, + "outputs": [ + { + "data": { + "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", + "
        gravityc_o_ratiometallicitytemperature
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        " + ], + "text/plain": [ + " gravity c_o_ratio metallicity temperature\n", + "0 5.0 0.25 -1.0 3.041393\n", + "1 5.0 0.25 -0.7 3.041393\n", + "2 5.0 0.25 -0.5 3.041393\n", + "3 5.0 0.25 -0.3 3.041393\n", + "4 5.0 0.25 0.0 3.041393" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check the output variables\n", + "y.head()" + ] + }, + { + "cell_type": "markdown", + "id": "6108f702-bb40-49f4-9c38-306e0e27ad4f", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "c9cc267d-85a9-44c6-9081-5067d0bdd8e6", + "metadata": { + "tags": [] + }, + "source": [ + "## 3.2 Build the CNN model and Processing the Data\n", + "\n", + "Here we instintiate BuildRegressorCNN class from DeepBuilder module to prepare the datasets and take the trained CNN (Convolutional Neural Networks) for us:\n", + "\n", + "- Take the synthetic datasets\n", + "- Process them, e.g.\n", + " - Divide them to three sets: train, validation, and test sets\n", + " - Scale y variables\n", + " - Scale X variables\n", + " - Create new features \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "107dde69-813d-4145-8765-7915321b704c", + "metadata": {}, + "source": [ + "### 3.2.1 Instintiate BuildRegressorCNN class from DeepBuilder module" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bc38d0e4-a4c2-4288-9d33-ab15fb25093b", + "metadata": {}, + "outputs": [], + "source": [ + "build_cnn_regression = BuildRegressorCNN( \n", + " feature_values=X.to_numpy(),\n", + " feature_names=X.columns,\n", + " target_values=y.to_numpy(),\n", + " target_name=['gravity', 'c_o_ratio', 'metallicity', 'temperature'],\n", + " is_tuned='yes',\n", + " param_grid=None,\n", + " spectral_resolution=100,\n", + " is_feature_improved='no',\n", + " is_augmented='no',\n", + " ml_model=None,\n", + " ml_model_str='CNN',\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "1559d83a-d54c-4d72-86d0-516f578e379d", + "metadata": {}, + "source": [ + "### 3.2.2 Split the dataset into train, validate and test sets" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3a00b374-192b-4a7f-9606-a9015f6db53d", + "metadata": {}, + "outputs": [], + "source": [ + "build_cnn_regression.split_train_validation_test(test_size=0.1, \n", + " val_size=0.1, \n", + " random_state_=100,)" + ] + }, + { + "cell_type": "markdown", + "id": "e0aa6f88-a1eb-4a8c-9965-ae92a84e6070", + "metadata": {}, + "source": [ + "### 3.2.3 Standardize X Variables Row-wise " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a5401e9b-1a1f-4914-838f-cac6fa26ee1b", + "metadata": {}, + "outputs": [], + "source": [ + "# Scale the X features using MinMax Scaler\n", + "build_cnn_regression.standardize_X_row_wise()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7bd0840e-399b-46e7-93d5-3e850d28651a", + "metadata": {}, + "outputs": [ + { + "data": { 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\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "build_cnn_regression.plot_boxplot_scaled_features(scaled_feature= build_cnn_regression.X_train_standardized_rowwise, \n", + " xticks_list = training_features_labels,\n", + " title = 'Scaled main 104 Features',\n", + " fig_size=(14, 3),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "f35db1f3-ca36-4fb2-b040-7dbfbdf4dded", + "metadata": {}, + "source": [ + "### 3.2.4 Standardize y Variables Column-wise " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ba7f068d-9503-4b77-b516-03d432309423", + "metadata": {}, + "outputs": [], + "source": [ + "# Standardize the y features using Standard Scaler\n", + "build_cnn_regression.standardize_y_column_wise()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "502c120a-ed2c-4da3-b7d6-88d08c82d404", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "build_cnn_regression.plot_boxplot_scaled_features(scaled_feature= build_cnn_regression.y_train_standardized_columnwise, \n", + " xticks_list = ['','$\\log g$', 'T$_{eff}$', 'C/O ratio', '[M/H]'],\n", + " title = 'Scaled target Features - ColumnWise',\n", + " fig_size=(4, 3),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "8352d53a-e91c-421f-a321-25a43156f5be", + "metadata": {}, + "source": [ + "### 3.2.5 Feature engeenering: Take Min and Max of each row (BD spectra) " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0c15fbc3-74eb-4417-b536-fe2f1a6686f6", + "metadata": {}, + "outputs": [], + "source": [ + "# train\n", + "build_cnn_regression.X_train_min = build_cnn_regression.X_train.min(axis=1)\n", + "build_cnn_regression.X_train_max = build_cnn_regression.X_train.max(axis=1)\n", + "\n", + "# validation\n", + "build_cnn_regression.X_val_min = build_cnn_regression.X_val.min(axis=1)\n", + "build_cnn_regression.X_val_max = build_cnn_regression.X_val.max(axis=1)\n", + "\n", + "# test\n", + "build_cnn_regression.X_test_min = build_cnn_regression.X_test.min(axis=1)\n", + "build_cnn_regression.X_test_max = build_cnn_regression.X_test.max(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ab93721b-c0d0-4d4d-969a-d36bed701a67", + "metadata": {}, + "outputs": [], + "source": [ + "df_MinMax_train = pd.DataFrame((build_cnn_regression.X_train_min, build_cnn_regression.X_train_max)).T\n", + "df_MinMax_val = pd.DataFrame((build_cnn_regression.X_val_min, build_cnn_regression.X_val_max)).T\n", + "df_MinMax_test = pd.DataFrame((build_cnn_regression.X_test_min, build_cnn_regression.X_test_max)).T" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "915c7c1f-2e5e-46ea-bcb9-29c6b4203f8c", + "metadata": {}, + "outputs": [], + "source": [ + "df_MinMax_train.rename(columns={0:'min', 1:'max'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1aad7765-9e8e-436a-9760-a3b2210c4c7a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "build_cnn_regression.plot_boxplot_scaled_features(scaled_feature= build_cnn_regression.X_test_standardized_columnwise, \n", + " xticks_list = ['','Min','Max'],\n", + " title = 'Scaled Min Max Features - ColumnWise',\n", + " fig_size=(4, 3),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "cebec7ae-7d8f-4b76-a523-2280fd4d35a5", + "metadata": { + "tags": [] + }, + "source": [ + "## 3.3 Train CNN model\n", + "\n", + "### 3.3.1 Instintiate *TrainRegressorCNN* class from *DeepTrainer* Module" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2e823b8a-af2a-4025-815d-24c1d43fd6e7", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'build_cnn_regression' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [2], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m train_cnn_model \u001b[38;5;241m=\u001b[39m TrainRegressorCNN(\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# input dataset: StandardScaled instances \u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m X1_train \u001b[38;5;241m=\u001b[39m \u001b[43mbuild_cnn_regression\u001b[49m\u001b[38;5;241m.\u001b[39mX_train_standardized_rowwise,\n\u001b[1;32m 4\u001b[0m X1_val \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39mX_val_standardized_rowwise,\n\u001b[1;32m 5\u001b[0m X1_test \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39mX_test_standardized_rowwise,\n\u001b[1;32m 6\u001b[0m \n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# input dataset: Min Max of each instance \u001b[39;00m\n\u001b[1;32m 8\u001b[0m X2_train \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39mX_train_standardized_columnwise,\n\u001b[1;32m 9\u001b[0m X2_val \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39mX_val_standardized_columnwise,\n\u001b[1;32m 10\u001b[0m X2_test \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39mX_test_standardized_columnwise,\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# X2_train = build_cnn_regression.X_train_normalized_columnwise,\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# X2_val = build_cnn_regression.X_val_normalized_columnwise,\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# X2_test = build_cnn_regression.X_test_normalized_columnwise,\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# 1st target\u001b[39;00m\n\u001b[1;32m 16\u001b[0m y1_train \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39my_train_standardized_columnwise[:,\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 17\u001b[0m y1_val \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39my_val_standardized_columnwise[:,\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 18\u001b[0m y1_test \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39my_test_standardized_columnwise[:,\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 19\u001b[0m \n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# 2nd target\u001b[39;00m\n\u001b[1;32m 21\u001b[0m y2_train \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39my_train_standardized_columnwise[:,\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m 22\u001b[0m y2_val \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39my_val_standardized_columnwise[:,\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m 23\u001b[0m y2_test \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39my_test_standardized_columnwise[:,\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m 24\u001b[0m \n\u001b[1;32m 25\u001b[0m \u001b[38;5;66;03m# 3rd target\u001b[39;00m\n\u001b[1;32m 26\u001b[0m y3_train \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39my_train_standardized_columnwise[:,\u001b[38;5;241m2\u001b[39m],\n\u001b[1;32m 27\u001b[0m y3_val \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39my_val_standardized_columnwise[:,\u001b[38;5;241m2\u001b[39m],\n\u001b[1;32m 28\u001b[0m y3_test \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39my_test_standardized_columnwise[:,\u001b[38;5;241m2\u001b[39m],\n\u001b[1;32m 29\u001b[0m \n\u001b[1;32m 30\u001b[0m \u001b[38;5;66;03m# 4th target\u001b[39;00m\n\u001b[1;32m 31\u001b[0m y4_train \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39my_train_standardized_columnwise[:,\u001b[38;5;241m3\u001b[39m],\n\u001b[1;32m 32\u001b[0m y4_val \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39my_val_standardized_columnwise[:,\u001b[38;5;241m3\u001b[39m],\n\u001b[1;32m 33\u001b[0m y4_test \u001b[38;5;241m=\u001b[39m build_cnn_regression\u001b[38;5;241m.\u001b[39my_test_standardized_columnwise[:,\u001b[38;5;241m3\u001b[39m],\n\u001b[1;32m 34\u001b[0m )\n", + "\u001b[0;31mNameError\u001b[0m: name 'build_cnn_regression' is not defined" + ] + } + ], + "source": [ + "train_cnn_model = TrainRegressorCNN(\n", + " # input dataset: StandardScaled instances \n", + " X1_train = build_cnn_regression.X_train_standardized_rowwise,\n", + " X1_val = build_cnn_regression.X_val_standardized_rowwise,\n", + " X1_test = build_cnn_regression.X_test_standardized_rowwise,\n", + "\n", + " # input dataset: Min Max of each instance \n", + " X2_train = build_cnn_regression.X_train_standardized_columnwise,\n", + " X2_val = build_cnn_regression.X_val_standardized_columnwise,\n", + " X2_test = build_cnn_regression.X_test_standardized_columnwise,\n", + "# X2_train = build_cnn_regression.X_train_normalized_columnwise,\n", + "# X2_val = build_cnn_regression.X_val_normalized_columnwise,\n", + "# X2_test = build_cnn_regression.X_test_normalized_columnwise,\n", + "\n", + " # 1st target\n", + " y1_train = build_cnn_regression.y_train_standardized_columnwise[:,0],\n", + " y1_val = build_cnn_regression.y_val_standardized_columnwise[:,0],\n", + " y1_test = build_cnn_regression.y_test_standardized_columnwise[:,0],\n", + "\n", + " # 2nd target\n", + " y2_train = build_cnn_regression.y_train_standardized_columnwise[:,1],\n", + " y2_val = build_cnn_regression.y_val_standardized_columnwise[:,1],\n", + " y2_test = build_cnn_regression.y_test_standardized_columnwise[:,1],\n", + "\n", + " # 3rd target\n", + " y3_train = build_cnn_regression.y_train_standardized_columnwise[:,2],\n", + " y3_val = build_cnn_regression.y_val_standardized_columnwise[:,2],\n", + " y3_test = build_cnn_regression.y_test_standardized_columnwise[:,2],\n", + "\n", + " # 4th target\n", + " y4_train = build_cnn_regression.y_train_standardized_columnwise[:,3],\n", + " y4_val = build_cnn_regression.y_val_standardized_columnwise[:,3],\n", + " y4_test = build_cnn_regression.y_test_standardized_columnwise[:,3],\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "e65c79d2-9123-45a8-937f-62d7fcd06c2d", + "metadata": {}, + "source": [ + "### 3.3.2 Define the Hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c3c592ab-534e-41b4-acd8-1cd90096b62a", + "metadata": {}, + "outputs": [], + "source": [ + "hyperparameters = {'Conv__MaxPooling1D': 2,\n", + " 'Conv__num_blocks': 1,\n", + " 'Conv__num_layers_per_block': 3,\n", + " 'Conv__num_filters': 4,\n", + " 'Conv__kernel_size': 6,\n", + " \n", + " 'FC2__num_blocks': 4,\n", + " 'FC2_num_layers_per_block': 2,\n", + " 'FC__dropout': 0.0024609140719442646,\n", + " 'FC2__dropout': 64,\n", + " 'FC2__units': 64,\n", + "# 'FC__units_gravity': 16,\n", + "# 'FC__units_metallicity': 256,\n", + "# 'FC__units_temperature': 8,\n", + " \n", + " 'FC1__num_blocks': 1,\n", + " 'FC1_num_layers_per_block': 4,\n", + " 'FC2__dropout': 0.09889223768186726,\n", + " 'FC1__units': 128,\n", + " 'FC1__dropout': 0.002185843015008406,\n", + " \n", + " 'learning_rate': 4.9946842008422193e-05}" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "fb308eec-87ba-490a-b9f5-4da783c7f331", + "metadata": {}, + "outputs": [], + "source": [ + "# hyperparameters = {\n", + "# 'Conv__MaxPooling1D': 2,\n", + "# 'Conv__num_blocks': 1,\n", + "# 'Conv__num_layers_per_block': 3,\n", + "# 'Conv__num_filters': 4,\n", + "# 'Conv__kernel_size': 6,\n", + "# 'FC__NumberLayers': 4,\n", + " \n", + "# 'FC1__num_blocks' : 1,\n", + "# 'FC1_num_layers_per_block': 2,\n", + "# 'FC1__dropout': 0.09889223768186726,\n", + "# 'FC1__units': 128,\n", + "\n", + "# 'FC2__num_blocks' : 1,\n", + "# 'FC2_num_layers_per_block':2,\n", + "# 'FC2__dropout': 0.0024609140719442646,\n", + "# 'FC2__units': 64,\n", + "\n", + " \n", + "# 'learning_rate': 4.9946842008422193e-05}" + ] + }, + { + "cell_type": "markdown", + "id": "4e4adeb0-42fd-451c-98c2-79272adbdbfc", + "metadata": {}, + "source": [ + "### 3.3.3 Build a CNN model" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "5ffd7ce9-8ef4-4b3a-94d7-7f78a30e839e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-10-31 21:09:40.107252: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_1 (InputLayer) [(None, 104, 1)] 0 \n", + "__________________________________________________________________________________________________\n", + "Conv__B1_L1 (Conv1D) (None, 104, 16) 112 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "Conv__B1_L2 (Conv1D) (None, 104, 64) 6208 Conv__B1_L1[0][0] \n", + "__________________________________________________________________________________________________\n", + "Conv__B1_L3 (Conv1D) (None, 104, 144) 55440 Conv__B1_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "MaxPool1D__B1_L3 (MaxPooling1D) (None, 52, 144) 0 Conv__B1_L3[0][0] \n", + "__________________________________________________________________________________________________\n", + "flatten (Flatten) (None, 7488) 0 MaxPool1D__B1_L3[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC1__B1_L1 (Dense) (None, 512) 3834368 flatten[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC1__B1_L2 (Dense) (None, 2048) 1050624 FC1__B1_L1[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC1__B1_L3 (Dense) (None, 4608) 9441792 FC1__B1_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC1__B1_L4 (Dense) (None, 8192) 37756928 FC1__B1_L3[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC1__Dropout__B1_L4 (Dropout) (None, 8192) 0 FC1__B1_L4[0][0] \n", + "__________________________________________________________________________________________________\n", + "input_2 (InputLayer) [(None, 2)] 0 \n", + "__________________________________________________________________________________________________\n", + "Concatenated_Layer (Concatenate (None, 8194) 0 FC1__Dropout__B1_L4[0][0] \n", + " input_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B1_L1 (Dense) (None, 256) 2097920 Concatenated_Layer[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B1_L2 (Dense) (None, 1024) 263168 FC2__B1_L1[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__Dropout__B1_L2 (Dropout) (None, 1024) 0 FC2__B1_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B2_L1 (Dense) (None, 1024) 1049600 FC2__Dropout__B1_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B2_L2 (Dense) (None, 2304) 2361600 FC2__B2_L1[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__Dropout__B2_L2 (Dropout) (None, 2304) 0 FC2__B2_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B3_L1 (Dense) (None, 2304) 5310720 FC2__Dropout__B2_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B3_L2 (Dense) (None, 4096) 9441280 FC2__B3_L1[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__Dropout__B3_L2 (Dropout) (None, 4096) 0 FC2__B3_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B4_L1 (Dense) (None, 4096) 16781312 FC2__Dropout__B3_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B4_L2 (Dense) (None, 6400) 26220800 FC2__B4_L1[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__Dropout__B4_L2 (Dropout) (None, 6400) 0 FC2__B4_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "gravity (Dense) (None, 1) 6401 FC2__Dropout__B4_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "c_o_ratio (Dense) (None, 1) 6401 FC2__Dropout__B4_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "metallicity (Dense) (None, 1) 6401 FC2__Dropout__B4_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "temperature (Dense) (None, 1) 6401 FC2__Dropout__B4_L2[0][0] \n", + "==================================================================================================\n", + "Total params: 115,697,476\n", + "Trainable params: 115,697,476\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n", + "None\n" + ] + } + ], + "source": [ + "train_cnn_model.build_model(hyperparameters = hyperparameters, \n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "2ebd6cd2-e25b-49a7-b207-7eba0486952e", + "metadata": {}, + "source": [ + "### 3.3.4 Train the CNN model using the datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "e23b745d-a0c5-429f-8f69-d127e66f6705", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/4\n", + "13/13 [==============================] - 50s 4s/step - loss: 1.1899 - gravity_loss: 0.4159 - c_o_ratio_loss: 0.3609 - metallicity_loss: 0.3453 - temperature_loss: 0.0678 - gravity_mae: 0.8074 - c_o_ratio_mae: 0.7477 - metallicity_mae: 0.7057 - temperature_mae: 0.2830 - val_loss: 0.8550 - val_gravity_loss: 0.3475 - val_c_o_ratio_loss: 0.2397 - val_metallicity_loss: 0.2215 - val_temperature_loss: 0.0463 - val_gravity_mae: 0.7277 - val_c_o_ratio_mae: 0.5690 - val_metallicity_mae: 0.5463 - val_temperature_mae: 0.2555\n", + "Epoch 2/4\n", + "13/13 [==============================] - 49s 4s/step - loss: 0.7556 - gravity_loss: 0.3385 - c_o_ratio_loss: 0.1810 - metallicity_loss: 0.2053 - temperature_loss: 0.0307 - gravity_mae: 0.7093 - c_o_ratio_mae: 0.4857 - metallicity_mae: 0.5239 - temperature_mae: 0.1944 - val_loss: 0.6194 - val_gravity_loss: 0.3149 - val_c_o_ratio_loss: 0.1138 - val_metallicity_loss: 0.1760 - val_temperature_loss: 0.0146 - val_gravity_mae: 0.6731 - val_c_o_ratio_mae: 0.3733 - val_metallicity_mae: 0.4800 - val_temperature_mae: 0.1321\n", + "Epoch 3/4\n", + "13/13 [==============================] - 52s 4s/step - loss: 0.5974 - gravity_loss: 0.3090 - c_o_ratio_loss: 0.1044 - metallicity_loss: 0.1665 - temperature_loss: 0.0175 - gravity_mae: 0.6649 - c_o_ratio_mae: 0.3563 - metallicity_mae: 0.4678 - temperature_mae: 0.1459 - val_loss: 0.5203 - val_gravity_loss: 0.2759 - val_c_o_ratio_loss: 0.0785 - val_metallicity_loss: 0.1546 - val_temperature_loss: 0.0112 - val_gravity_mae: 0.6116 - val_c_o_ratio_mae: 0.3085 - val_metallicity_mae: 0.4470 - val_temperature_mae: 0.1166\n", + "Epoch 4/4\n", + "13/13 [==============================] - 71s 5s/step - loss: 0.5084 - gravity_loss: 0.2663 - c_o_ratio_loss: 0.0823 - metallicity_loss: 0.1445 - temperature_loss: 0.0153 - gravity_mae: 0.6052 - c_o_ratio_mae: 0.3122 - metallicity_mae: 0.4354 - temperature_mae: 0.1363 - val_loss: 0.4271 - val_gravity_loss: 0.2252 - val_c_o_ratio_loss: 0.0601 - val_metallicity_loss: 0.1297 - val_temperature_loss: 0.0121 - val_gravity_mae: 0.5540 - val_c_o_ratio_mae: 0.2608 - val_metallicity_mae: 0.4115 - val_temperature_mae: 0.1195\n" + ] + } + ], + "source": [ + "history, model = train_cnn_model.fit_cnn_model(batch_size=2**11,\n", + " budget=4)" + ] + }, + { + "cell_type": "markdown", + "id": "7c218955-42c5-4e84-b9fd-f5dccf128881", + "metadata": {}, + "source": [ + "### 3.3.5 Check the Trained CNN Archeticture and Summary\n", + "\n", + "Checking the architecture of a CNN and its summary is importnat because it provide insights about: \n", + "- Model Design and Structure\n", + "- Model complexity\n", + "- Hyperparameter Tuning:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "d54365d5-70d1-445b-84ee-13a145a1893d", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'model' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m build_cnn_regression\u001b[38;5;241m.\u001b[39mtrained_model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\n\u001b[1;32m 2\u001b[0m build_cnn_regression\u001b[38;5;241m.\u001b[39mhistory \u001b[38;5;241m=\u001b[39m history\n", + "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined" + ] + } + ], + "source": [ + "build_cnn_regression.trained_model = model\n", + "build_cnn_regression.history = history" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "34c9c0e3-f5eb-4e8e-8723-0a11f4d16637", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'train_cnn_model' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [23], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39mplot_model(\n\u001b[0;32m----> 2\u001b[0m \u001b[43mtrain_cnn_model\u001b[49m\u001b[38;5;241m.\u001b[39mmodel,\n\u001b[1;32m 3\u001b[0m to_file\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel.png\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 4\u001b[0m show_shapes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 5\u001b[0m show_dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 6\u001b[0m show_layer_names\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 7\u001b[0m rankdir\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTB\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 8\u001b[0m expand_nested\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 9\u001b[0m dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m90\u001b[39m,\n\u001b[1;32m 10\u001b[0m layer_range\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# show_layer_activations=False,\u001b[39;00m\n\u001b[1;32m 12\u001b[0m )\n", + "\u001b[0;31mNameError\u001b[0m: name 'train_cnn_model' is not defined" + ] + } + ], + "source": [ + "tf.keras.utils.plot_model(\n", + " train_cnn_model.model,\n", + " to_file=\"model.png\",\n", + " show_shapes=True,\n", + " show_dtype=False,\n", + " show_layer_names=True,\n", + " rankdir=\"TB\",\n", + " expand_nested=True,\n", + " dpi=90,\n", + " layer_range=None,\n", + "# show_layer_activations=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d362f2b2-e53e-40e7-9fd7-f94c9dcafc06", + "metadata": {}, + "source": [ + "### 3.3.6 Trained Model Outcomes" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "5476bc74-ed25-4793-b9ea-8d153b85674b", + "metadata": {}, + "outputs": [], + "source": [ + "load_or_dump_trained_model_CNN( trained_model = build_cnn_regression,\n", + " indicator='Toy_parameters_v4_1000epoch_v2_test',\n", + " load_or_dump = 'dump')" + ] + }, + { + "cell_type": "markdown", + "id": "497859c9-57b5-4960-9929-98cd0fb1404a", + "metadata": { + "tags": [] + }, + "source": [ + "## 3.4 Check the performance of the Trained Model" + ] + }, + { + "cell_type": "markdown", + "id": "97852f63-61d5-4908-9e76-1d9aaf4eb6bd", + "metadata": {}, + "source": [ + "### 3.4.1 Load the Saved Trained CNN Models" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "9df88951-7795-4645-b692-cc42fba2492d", + "metadata": {}, + "outputs": [], + "source": [ + "loaded_model, history = load_or_dump_trained_model_CNN(indicator='Toy_parameters_v4_1000epoch_v2_test',\n", + " load_or_dump = 'load')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "cbbfc24d-f767-41a4-b213-db4fe7d6bd60", + "metadata": {}, + "outputs": [], + "source": [ + "build_cnn_regression.trained_model = model#loaded_model\n", + "build_cnn_regression.trained_model_history = history" + ] + }, + { + "cell_type": "markdown", + "id": "e359714e-5622-426f-b999-8a63769c32e5", + "metadata": {}, + "source": [ + "### 3.4.2 Double-check the Trained CNN Archeticture and Summary" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "9239fc9c-3fa9-4833-8999-04132087ecda", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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b/Pnz9dNPP2nixImaM2eOQkJC1LVrV61Zs0bS/d+pJAUHB6tOnTqaP3++fvzxR6Wnp6tbt24PdNp6u3btFH4LmxgAACAASURBVBYWpujoaPn6+srf31+TJk3K8/23twT47rvvrNqvXr0qwzAkScePH1d0dLT+8Y9/3Hc8X19f7d69O8/PBwAAyAkhKAAAAB6Ku7u7GjZsaNXm7e2t6tWrq3Tp0goODlb58uXVr18/2dnZ6Y8//lCNGjXk6+srNzc3TZkyRe3atVNERITq1aun0NBQGYaR7XPr4sWL56kef39/RUVF5brCsKAkJibq5s2b8vLyyrVfpUqVFBERoeTkZHXp0kUpKSlW19PS0jRq1CgFBQVZ2jp27KhGjRpp7Nixku7/Ti9cuKBFixYpJSVFy5Yt04oVK+Tu7q7ff/9dR48efaB5vfTSS/rb3/6mzMxMTZw4UTExMXm+t2LFiho4cKB27dql6dOn6/Lly4qIiNCePXtUpUoVZWZm6r333tOMGTPyNJ6Hh4f279//QPUDAADcjRAUAAAADy2nvS3t7e1lMpksP7u4uKhEiRKW/R3t7Ozk5uZmuW4ymRQUFKSUlBSdO3fu0RddAG6fmO7h4XHfvi1bttS8efN06NAh9e/f32o/z127dik1NVUVKlSwuqdp06b6448/LKtfc3unhw8flrOzs9q3b6/AwEAFBgZq0aJFOnnypKpWrZrnOR05ckR9+/bVkiVLtGPHDhUtWlSvvvqqTp06lecxFi5cqEmTJikqKkqjR4/W5cuXdfHiRQUFBenjjz9WlSpVtH//fn333Xf67rvvlJKSotOnT+u7777TjRs3rMby8PDQiRMn2BcUAAA8lJx3YgcAAAAe0p1hXV55enrKZDJZhaNPstvhZ14DuiFDhig2NlZz587VhAkTLO1//vmnJGnfvn0KCAiwtFeoUEFOTk6Wk9pze6cpKSlKSkqSm5tbjvuT5tV//vMftWjRQqVLl1bp0qUVFRWlgIAALV++XB988EGexnBwcNB7772n9957T5LUv39/lSpVSoMHD9bbb7+trVu3KiwszNL/2rVrSkxM1I8//qh9+/bJ19fXajyTyaQiRYrke04AAACsBAUAAIDN3H2C+e7du+Xr66tixYrJxcUl22fjUvYT0W/vM2kLfn5+KlasmC5cuJDne2bOnKmXXnpJEydOtLTVr19fkrRjxw6rvlevXlWjRo0sIWhuatSoIenWgVF3+vrrr3X27Nk81/frr79a7adau3Zt9erVS/v27cvzGHeKi4tTZGSkNm3aJE9PT61Zs0ZXrlyx+lWnTh0NHjxYV65cyRaAJiYmysvLS05OTvl6PgAAgEQICgAAgAJw8+ZNSVJqaqqlLTk5WWlpaVb9zGazpa8kJSQkKDMz0/Lz5s2b9eGHH0q6tbdkbGyszp8/r8zMTM2aNUuSrALHUqVKKSEhQenp6dqzZ4+kW5+ojxo1ynJK+aNkMplUv379HEPQ1NRU/fXXX9naHRwctGrVKlWpUsXSVqdOHXXu3Flbtmyxumfbtm2W9yHl/k79/PzUvn17TZ06Ve+++6527typsLAw/fLLLypfvrykWwHspEmTcj0oqW/fvlq7dq1V25kzZ9SxY0erttsB9p1/nnc7cuSIOnbsqDVr1qhx48b37JebM2fOZDuZHgAA4EERggIAAOChnDx5UnPnzpUkhYWF6dixY9qwYYO2bNmikydPat68eTp//rymTJmia9euacOGDdq2bZukWwHa4MGD9f/+3/9Thw4dNGjQIPXr10+S9Oabb6p06dKqUqWKmjdvrnbt2kmStm7dqq1bt0qSOnfuLLPZLH9/f9nZ3fpP25iYGM2aNUt79+59LPPv1KmTJYC9bf/+/Ro6dKh+//13ffjhh4qPj7e6Xrp0aa1bt07FihWztK1YsUIBAQFq06aNPv74Y3Xt2lWDBw9Whw4dJClP73Tp0qV64YUXNH36dAUEBGjhwoUaPny4pFsraMePH6+QkJBsJ7ffafjw4apYsaKCgoK0cuVK/c///I+ee+45vfHGG5Y+3333nebNmydJmjFjhn7++WfLNcMw9PPPP2vq1Kn697//rfDwcLVq1Spf7zYtLU3Hjh3TW2+9la/7AQAAbjMZtvx+CACAZ1R4eLjGjx+vhIQEW5cCSLq1ojE6OlqtW7d+bM/s1KmTzGazvvzyS8XHx6tq1arZToBPT09XXFycatasKTs7O/3++++qVauWVZ+bN2/KyclJ9vb2lrbjx49n+6w6rw4cOKCGDRsqKSlJJUuWvG//jIwMNW7cWN988428vb0f6FmXL19WqVKlrNqSkpJ0/vx51axZ84HGutOpU6eUmpoqPz8/q31EExMTFRUVJTc3N3Xv3j3XMZKTk5WYmKgqVarI2dk5z882m82WfT3LlCmT7zlI0n//+19NnjxZsbGxedpjNiYmRoGBgTbdIgEAADyZWAkKAAAAmypevLgaNmyYLQCVJCcnJ9WuXduyyvPuAFS6dUL6nQGopHwHoPnh6OioxYsXWx10lFd3B6CS5O7u/lABqCRVqlRJ1atXzxYcenh4aM+ePerUqdN9xyhevLhq1ar1QAGodOtz/yZNmjx0ACrdCkFXr16dr0O2AAAA7kQICgAAAJtITk5WcnKyrcsoEI0aNVJgYKA+//xzW5dyTxkZGVq+fLmmTJnyxJ+0npWVpXHjxmn48OGWA58AAAAeBiEoAAAAHiuz2ayFCxeqSJEicnV11YIFC6wOR3pa9enTRy1atNDRo0dtXUqOHB0dNWjQoBxX3D5pdu7cqREjRujll1+2dSkAAKCQcLB1AQAAIHc//vijNm7cqCtXruR43c3NTdOmTbNqu3btmqKjo/X999/r6tWrKlq0qAICAtSkSRNFRUVp+PDhysrK0oIFCxQRESHDMPTKK69o5MiRcnR0LLC6161bpxs3bljanJyc5OHhoWrVqunvf/97tk+YJenw4cOaPHmyJk2apMqVK+f5ebt379bq1auVkpIiSfL29lbbtm3VpEmTXO/L7/O2b9+utWvXWs3P2dlZ7u7uqlixogIDA+Xj45Pn8Z4lDg4OGjJkiIYMGWLrUgrc4/wMvzBr0aKFrUsAAACFDCEoAABPuBdeeEFubm5q2LCh3nzzTc2YMUOSlJKSol27dumf//ynVQiamJioF154QT179tQHH3wgT09PXblyRevXr1fr1q0tK6vCwsL02WefqXXr1tqwYYNGjx6tXbt26auvviqwuh0cHBQQEKAePXooLCxMqamp+vbbb/XZZ59pwoQJCgsLswo7Vq5cqVmzZumXX35RSEjIAz2vSZMm8vLyUsWKFVW3bl3t379fDg65/6fOwzyvZcuWKlWqlOrUqaMOHTpo2rRpKlWqlA4fPqwvv/xSgwYN0r///W9NmjTpgcYFAAAAUPAIQQEAeApUqlRJ0q3PWYsWLSpJKlq0qDp37qwjR45Y+l28eFFt27bVc889p6lTp1raS5Ysqf79+6tWrVqaPHmysrKydPLkSR08eFCSNGvWLL3yyitavXq1Dh8+rNq1axdI3XXr1pUklShRwnLK9uDBg9WvXz/VqVNHI0eO1J49eyz9+/TpIzc3N3Xu3Dlfz/P29padnZ38/PzuG4AWxPNurxz19vaWv7+/5fcvv/yy2rdvr169euns2bMKCwvLcdUrAAAAgMeDPUEBAHgK5BagjRkzxvL7kSNH6siRIwoNDc2xb+PGjdWkSRNdunRJwcHBVuPf/jT58uXLBVS17vlpvZOTk+rXr6+9e/fqwoULVtdcXV0f6pl2dnZycnLKc//cnpeVlXXfZ91Lz5491bt3by1ZskQrVqzIsU9GRka2tvT09Dw9//YemmazOU/jAgAAAM8yVoICAPCUSktL05w5c/Tuu+9a2qKjo1W+fHmVLVv2nvcFBwfnGBJ6eHjI0dFRjRo1eiT13unatWvasWOHatWqJQ8PD6truQWLj8K9nhcZGalevXpp48aNatmyZb7Gfvvtt/XFF19o2bJl6t+/v6RbAeWECRP0119/6ddff5WdnZ0WL16sc+fOad68eYqPj9eaNWs0ePBg7dixQz169LA6cTwqKkq7d+/W1atXdeDAAdWrV08zZsy457g1a9bMc72bNm1SQkJCvuZamJw6dUrSre0SHjaUx+N158p4AACAOxGCAgDwFPntt98UFhYms9msqKgo1alTx3Lt2LFjOnfunNq2bZvrGPdaJblt2zaNGjVKLi4uBVqzJMXHx2vTpk0ym82Ki4vTkiVLVKFCBS1ZsqTAn1VQvL291bx5c5UrVy7fY9SoUUOSrE4LDw4OVvPmzfXaa6/JbDarefPm6tatmyIjI3Xp0iWdOXNGERERWr58uTZs2KChQ4eqb9++6tChgwzD0NixY/XLL7/I3t5e8fHxmj59eq7jxsbG5mlrAEkKDw+Xs7NzvudbWNxejTt16tTHHsrj4aSmptq6BAAA8IQiBAUA4Cni6uqqcuXKKSsrSzVr1pTJZLJcO3TokCSpQoUKDzxuUlKSYmJitHHjxgKr9U5ms1lJSUlKTk5WfHy8kpKSVKNGDV28ePGRPK8g+Pv7Kyoq6qHGKFGihKT/+2T9woULWrRokerVq6dly5ZJktzd3bVnzx5dv35d1atX1/nz5y1bFfTr109vvfWW/vjjD3Xo0EHXrl3TwYMHNXXqVL3//vuqWrWq+vTpk+u4R48etQrLc/PFF1+odevWDzXnwuDAgQNq2LChYmNjLXvZ4ukQExOjwMBAW5cBAACeQISgAAA8RapUqaIuXbpIkoKCgiyBl/R/hyfl53PQMWPGaPbs2Q+0l+aDqFGjhl577TXLzyEhIWrfvr3atm2rDRs26NVXX30kz7W1uLg4SVKtWrUkSYcPH5azs7Pat29v6XM7sPHw8JC9vb1VsO3i4qISJUro2rVrkqTixYtrwIABGjdunL744gtNmjRJXbp0UUxMTK7jAgAAAM86vu8BAOApNmDAAMvva9WqJTc3Nx08ePC+B/rcadq0aerYsWOeVwsWBA8PD7311luSbq0+fFwMw7AEio/Dtm3bJP1fIJmSkqKkpCS5ubmpUqVKVr+cnZ2tAtB7WbRokb744gtdv35dXbt21XvvvXffcQEAAIBnHSEoAACFRNGiRfXOO+/oxo0bCgsLu2e/lStXyjAMSdLSpUvl6empoKAgy/XH9Yn67ZPjq1ev/lieJ0krVqzQlStXHsuzrly5oo8++kg+Pj6Ww6tu7xH6ySefWPX9+uuvdfbs2fuOeenSJe3YsUO9evXSkSNH9Prrr2vGjBkqXbr0Q40LAAAAFHaEoAAAPAUuXLggSUpOTs6133vvvadOnTppxIgRioyMzHZ9/vz5qlq1qkwmk0JDQ/Xtt9+qbNmyioyM1Pr16xUWFqYxY8YUeN13i42N1f/+7//K09NT/fr1s7qWkZEhSbp58+YDP+/KlSsym805Brn79u3TqlWrVLFixTw97+TJkxo1alSuofC9rh0+fFiBgYHy9PTU2rVrLSeM+/n5qX379po6dareffdd7dy5U2FhYfrll19Uvnx5JScnKy0tzWoss9lsqe3mzZsaO3aspFsHXI0ZM0aGYahWrVq5jgsAAAA869gTFACAJ9y2bds0Y8YMSdKmTZs0f/58de/eXWXKlMnWt2jRolq7dq1CQkL05ptvytnZWW3atJGLi4vs7OzUr18/NWzYUEuWLNHbb78tSYqIiLAaY+HChQVS948//qhPP/1UkrRmzRolJCSoTJkyOnDggK5fv64XXnhB06dPl6enp+We7777TvPmzZMkzZgxQ2+//baaNWuWp+ft2rXLclJ6dHS0mjdvrqJFiyo9PV1nzpzRyZMnNWHCBKt7cnteTEyMZs2apfbt26tDhw7Znrd9+3bLysuvv/5ap0+flpeXl9LS0pSenq7XX39d//znPy0rXm9bunSpevfurenTp2v69Ol6/vnntW7dOm3YsEFbtmzRxYsXNW/ePHXv3l1LlizRtWvXtGHDBrVr106+vr7au3ev/va3v6l3797auXOn5s+fr5IlS95zXAAAAACSybj9PRwAAHhswsPDNX78eCUkJDzS55w5c0YXLlyQv7+/7Oz4AORBHT9+XL6+vo9k7FOnTik1NVV+fn552gtUkjIzM2U2m3Xjxg2dPn1adevWzfbnmp9xJclkMik6OprT4fV/p8MnJSVxOvxT5vbp8PwTBwAA3I2VoAAAFGJeXl7y8vKydRlPrUcVgEpSpUqVHvgee3t72dvbq0iRInJ3dy+wcQEAAIDCjiUhAAAAwCNkNpttXcITjfcDAAAeB1aCAgAAK2fOnNHgwYNz7XPz5k25uLjk2sfd3V0rV658bDUV5POAgpCamqq5c+eqWbNm2rdvn44cOSKTyaSQkBCrvXAlaffu3Vq9erVSUlLk6empAQMGyMfHx0aVW8vKytKCBQsUEREhwzD0yiuvaOTIkdn2uw0NDdXBgwctPwcFBaldu3bZxktNTdX8+fN1/fp1VatWTRUqVFBiYqJ69er1yOcCAACeXYSgAADAipeXlzZu3GjrMqw8iTWhYMTFxalatWpP/JgPKiMjQwMHDtR//vMfVa9eXS1bttTChQs1dOhQHThwQNHR0XJycrL0b9KkicqVK6eAgACdOHHC6pqthYWF6bPPPlPr1q21YcMGjR49Wrt27dJXX31l6XPq1ClNmjRJzs7Okm7tMfv+++9nG2vnzp0aPXq0xowZo86dO1vaf/jhBw0fPlxz58599BMCAADPJD6HBwAAgE2YzeYcg7Inbcz8GDFihAICAlS9enVL25AhQ1S8eHH99NNPeuutt7LdU7FiRfn6+j5RAWhWVpZOnjypgwcPas6cOYqPj1e7du20evVqHT582NLv008/VUxMjOLj4xUfH6+4uDhVrFjRaqzdu3erbdu2mj59ulUAKkkvvviiHBwcFBkZ+VjmBQAAnj2EoAAAAHgomZmZ+bpv5MiROnbsmFVbVlbWQ42b05h3j/uo7d27V8uWLVO/fv2yXatfv7569OihJUuWaPbs2VbX7Ozs5OCQ84da93oX6enplt/fb44ZGRn3Kz2bS5cuKTg42PKzvb29hgwZIkm6fPmyJCkxMVHffvut9u3bp6SkpBzHSUlJUZ8+fTR27Fg1bdo0xz5vvvmmhg0bpmvXrj1wnQAAAPdDCAoAAIB8mTt3rkJCQvTOO++oefPmWr9+vSQpPDxcTZs21cCBAyVJFy5cUHBwsOrVq6dVq1ZJkj755BOtWbNGf/75p4YOHaq+ffuqV69eCgoK0q5du1SvXj0VLVpUffv21fXr1/M07t1jLl68WJIUGRmpEiVKaPv27Y/lvcyZM0dt27aVu7t7tmsmk0nh4eFq0KCBgoODtXXr1lzHutc7jomJUY8ePdSsWTMlJCSoTZs2cnV1zRa8ZmRkaNy4cRo2bJhatWqlFi1a6MiRI3meS9myZVWmTBmrNg8PDzk6OqpRo0aSpJkzZ+r48ePq3bu3vLy8NGvWLBmGYXVPSEiITp06pTfeeEN79uzRjh07lJaWZtWndu3aMgxDoaGhea4PAAAgrwhBAQAA8MDmz5+vn376SRMnTtScOXMUEhKirl27as2aNRo4cKDKlSun48ePS7oVmn300Uc6dOiQZfXg6NGj1bVrV3l7e2vBggX68MMPFRsbq4SEBK1du1YzZsxQx44dtXLlSk2YMEGS7jvu3WPePkzL29tbzZs3V7ly5R75e8nKytKqVatyPdTI1dVV69atU6lSpdSzZ0/LfO6W2zuuVq2aLl26pDNnzigiIkLLly/X7NmztXz5cm3atMkyRnBwsOrUqaP58+frxx9/VHp6urp16/ZQJ7Jv27ZNo0aNshyO9sknnyguLk5fffWVKlSooFGjRmXbkmDz5s0qU6aM5s6dq2+++Ub9+/dXgwYNlJCQYNXP19dXu3fvzndtAAAA90IICgAAgAeSlpamUaNGKSgoyNLWsWNHNWrUSGPHjpV0K+i7U/HixXMds0aNGvL19ZWbm5umTJmidu3aKSIiQvXq1VNoaKhlZeGDjitJ/v7+ioqKkp+fX57m9zASExN18+ZNeXl55dqvUqVKioiIUHJysrp06aKUlBSr6/d7x97e3qpevbpKly6t4OBglS9fXv369ZOdnZ3++OMPSbdWyi5atEgpKSlatmyZVqxYIXd3d/3+++86evRovuaXlJSkmJgYTZw40dJmMplUtWpVde/eXfv27VOnTp30ySefWALO5ORkHT16VIMGDdKUKVM0fvx4bd26VXFxcRoxYoTV+B4eHtq/f3++agMAAMgNISgAAAAeyK5du5SamqoKFSpYtTdt2lR//PGHZbXng7Kzs5Obm5vlZ5PJpKCgIKWkpOjcuXMPVfPjcvLkSUm3wrz7admypebNm6dDhw6pf//+Vnt65uUd29vby2QyWa65uLioRIkSlj01Dx8+LGdnZ7Vv316BgYEKDAzUokWLdPLkSVWtWjVf8xszZoxmz559z8ObihcvrtmzZysrK8uyojMuLk5ZWVlWwbCPj48aNGig77//3mreHh4eOnHiBPuCAgCAAkcICgAAgAfy559/SpL27dtn1V6hQgU5OTlZPpMuCJ6enjKZTFbh6JPsdviZ1xBvyJAhGj58uL7++mvLZ/9S3t7xnQFoTlJSUpSUlCQ3NzdVqlTJ6pezs/ODTEuSNG3aNHXs2FF16tTJtV/lypXVoEEDlS5dWpIsgeuNGzes+gUEBCgjIyPbp/kmk0lFihR54PoAAAByQwgKAACAB1K/fn1J0o4dO6zar169qkaNGsnFxUUuLi7ZPvGWsp9gfvcBOnefYL579275+vqqWLFikpSnce8e83Hy8/NTsWLFdOHChTzfM3PmTL300ktWn5jn5R3fT40aNSTd2rPzTl9//bXOnj2b5/okaenSpfL09LT6PP/ixYs59r169aouXLigJk2aSJJKlCihmjVr6uDBg1b9zGaz/P39rVaVJiYmysvL654rTQEAAPKLEBQAAAAPpE6dOurcubO2bNmiv/76y9K+bds2ffjhh5KkihUrKjY2VufPn1dmZqZmzZolSVbhYKlSpZSQkKD09HTt2bNHkpSQkKDMzExLn82bN1vGzMu4OY158uRJjRo16p6hXUEymUyqX79+jiFoamqq1fu6zcHBQatWrVKVKlUsbXl5x8nJydlOWDebzbp586akW4Fs+/btNXXqVL377rvauXOnwsLC9Msvv6h8+fKaOXOmJk2adN9DkkJDQ/Xtt9+qbNmyioyM1Pr16xUWFqYxY8bIbDbr448/1rFjxyRJ169f14QJE/Tpp59ard6dM2eOIiIirA5CioyM1EcffWT1rDNnzui5557LtR4AAIB8MQAAwGO3dOlSw8fHx9ZlABaSjOjo6Dz3v3btmtGlSxfD39/fmDZtmtGlSxdj0aJFluunT582PDw8DBcXF6Nx48bGoUOHDEnGCy+8YGzZssUwDMP4+eefDRcXF6NGjRrG3r17jVdffdXw8vIyBgwYYCxbtsxo37698cEHHxhZWVl5HvfuMQ3DMMLDww1JxsaNG/M0t/379xuSjKSkpDy/jztNnTrV8Pf3t2rbt2+f0b9/f8POzs4ICQkx4uList138OBB49VXX7X8nNs7Xr9+veHl5WU4Ojoac+fONc6dO2dMnjzZkGTUr1/f+OGHHwzDMIwzZ84YrVq1MiQZkoznn3/eSExMNDIzM43ixYsbkozIyMh7zmXx4sWWe+/+tXDhQuPmzZtG3bp1DZPJZDRr1sxo3bq1sWnTphzHWrBggdG7d29j8eLFRo8ePYxly5ZZXU9NTTVKlCiR5z+nnERHRxv8EwcAAOTEZBg2/F4IAIBnVHh4uMaPH2+1KgqwJZPJpOjoaLVu3fqB7ktKStL58+dVs2bNbNfS09MVFxenmjVrys7OTr///rtq1apl1efmzZtycnKSvb29OnXqJLPZrC+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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.keras.utils.plot_model(\n", + " build_cnn_regression.trained_model,\n", + " # to_file=\"model.png\",\n", + " show_shapes=True,\n", + " show_dtype=False,\n", + " show_layer_names=True,\n", + " rankdir=\"TB\",\n", + " expand_nested=True,\n", + " dpi=90,\n", + " layer_range=None,\n", + "# show_layer_activations=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "a7199a91-1b68-4d43-af65-13855060db49", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_1 (InputLayer) [(None, 104, 1)] 0 \n", + "__________________________________________________________________________________________________\n", + "Conv__B1_L1 (Conv1D) (None, 104, 16) 112 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "Conv__B1_L2 (Conv1D) (None, 104, 64) 6208 Conv__B1_L1[0][0] \n", + "__________________________________________________________________________________________________\n", + "Conv__B1_L3 (Conv1D) (None, 104, 144) 55440 Conv__B1_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "MaxPool1D__B1_L3 (MaxPooling1D) (None, 52, 144) 0 Conv__B1_L3[0][0] \n", + "__________________________________________________________________________________________________\n", + "flatten (Flatten) (None, 7488) 0 MaxPool1D__B1_L3[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC1__B1_L1 (Dense) (None, 512) 3834368 flatten[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC1__B1_L2 (Dense) (None, 2048) 1050624 FC1__B1_L1[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC1__B1_L3 (Dense) (None, 4608) 9441792 FC1__B1_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC1__B1_L4 (Dense) (None, 8192) 37756928 FC1__B1_L3[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC1__Dropout__B1_L4 (Dropout) (None, 8192) 0 FC1__B1_L4[0][0] \n", + "__________________________________________________________________________________________________\n", + "input_2 (InputLayer) [(None, 2)] 0 \n", + "__________________________________________________________________________________________________\n", + "Concatenated_Layer (Concatenate (None, 8194) 0 FC1__Dropout__B1_L4[0][0] \n", + " input_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B1_L1 (Dense) (None, 256) 2097920 Concatenated_Layer[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B1_L2 (Dense) (None, 1024) 263168 FC2__B1_L1[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__Dropout__B1_L2 (Dropout) (None, 1024) 0 FC2__B1_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B2_L1 (Dense) (None, 1024) 1049600 FC2__Dropout__B1_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B2_L2 (Dense) (None, 2304) 2361600 FC2__B2_L1[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__Dropout__B2_L2 (Dropout) (None, 2304) 0 FC2__B2_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B3_L1 (Dense) (None, 2304) 5310720 FC2__Dropout__B2_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B3_L2 (Dense) (None, 4096) 9441280 FC2__B3_L1[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__Dropout__B3_L2 (Dropout) (None, 4096) 0 FC2__B3_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B4_L1 (Dense) (None, 4096) 16781312 FC2__Dropout__B3_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__B4_L2 (Dense) (None, 6400) 26220800 FC2__B4_L1[0][0] \n", + "__________________________________________________________________________________________________\n", + "FC2__Dropout__B4_L2 (Dropout) (None, 6400) 0 FC2__B4_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "gravity (Dense) (None, 1) 6401 FC2__Dropout__B4_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "c_o_ratio (Dense) (None, 1) 6401 FC2__Dropout__B4_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "metallicity (Dense) (None, 1) 6401 FC2__Dropout__B4_L2[0][0] \n", + "__________________________________________________________________________________________________\n", + "temperature (Dense) (None, 1) 6401 FC2__Dropout__B4_L2[0][0] \n", + "==================================================================================================\n", + "Total params: 115,697,476\n", + "Trainable params: 115,697,476\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "build_cnn_regression.trained_model.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "36499814-0ceb-40c9-a65b-b579bf5bcd45", + "metadata": {}, + "source": [ + "### 3.4.3 Check the training history through *Loss* metric" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "400d2e76-4bf6-4c69-a12b-ce3099d4ebfd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p1001" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "build_cnn_regression.plot_model_loss(title = 'RandState=100: X:StandRow, y:StandCol - MinMax:StandCol, ToyCNNmodel')" + ] + }, + { + "cell_type": "markdown", + "id": "691d18a6-63a6-4576-8048-5c77d2c3579c", + "metadata": {}, + "source": [ + "### 3.4.4 Plot the Performance of the trained CNN models - Regression metrics\n", + "- Plot predicted against actual scatter plots for all parameters \n", + "- Plot Residual histograms (predicted - Actual)\n", + "- Report regression metrics: R$^2$ and skewness for training and test sets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "348e061c-dd4a-47e8-8f71-a0807c8af462", + "metadata": {}, + "outputs": [], + "source": [ + "i = 1\n", + "regression_report(\n", + " trained_model = build_cnn_regression,\n", + " \n", + " Xtrain = [build_cnn_regression.X_train_standardized_rowwise[::i],\n", + " build_cnn_regression.X_train_standardized_columnwise[::i]],\n", + " \n", + " Xtest = [build_cnn_regression.X_test_standardized_rowwise[::i], \n", + " build_cnn_regression.X_test_standardized_columnwise[::i]],\n", + " \n", + " ytrain = build_cnn_regression.y_train_standardized_columnwise[::i],\n", + " \n", + " ytest = build_cnn_regression.y_test_standardized_columnwise[::i],\n", + " \n", + " target_i = 4,\n", + " \n", + " xy_top = [0.05, 0.7],\n", + " xy_bottom= [0.05, 0.85],\n", + "# print_results = False, \n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ad4cf87-0874-4b68-a007-174e7211b1e7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/Concepts_ML_knowledge_Train_CNN_Model.ipynb b/docs/tutorials/Concepts_ML_knowledge_Train_CNN_Model.ipynb new file mode 100644 index 00000000..1c5e1d72 --- /dev/null +++ b/docs/tutorials/Concepts_ML_knowledge_Train_CNN_Model.ipynb @@ -0,0 +1,1984 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "723c394d-987d-4f2d-8bd6-378a23b1218e", + "metadata": { + "tags": [] + }, + "source": [ + "# Train a Regression ConvNN Model Using BOHB Tuned Hyperparameters" + ] + }, + { + "cell_type": "markdown", + "id": "e0a28b4f", + "metadata": {}, + "source": [ + "In this tutorial, we will be going over how to set-up, train and interpret a TelescopeML model using the built-in functions and also go over some of the important techniques and methods being applied behind the scenes in terms of: setup, pre-processing and training/predicting\n", + "\n", + "For the demonstration, are going to be importing the DeepRegTrainer, DeepRegBuilder files from the **TelescopeML folder** responsible for the class that houses the model architecture and its supporting functions as well as the StatVisAnalyzer's regression_report function to get an in-depth visualization about how the model performed for the 4 distinct outputs within the given data " + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "6a0e9b08-df67-4377-9ad7-61865ed8f859", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '..\\\\telescopeML\\\\telescopeML')\n", + "\n", + "\n", + "from DeepTrainer import *\n", + "from DeepBuilder import *\n", + "from StatVisAnalyzer import regression_report, engineered_feature_visualizer" + ] + }, + { + "cell_type": "markdown", + "id": "d60d60e1", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "98309fb6-0ed0-4f20-a65e-45b54910a8c8", + "metadata": { + "tags": [] + }, + "source": [ + "# Step 1: Load the training dataset (syntheric spectra)" + ] + }, + { + "cell_type": "markdown", + "id": "df8e887f", + "metadata": {}, + "source": [ + "The first step is going to be loading the dataset. In the example below, we have data collected on around 30,888 unique brown-dwarf spectra that spans from 0.897 micron to 2.512 micron with 104 features (instances) recorded in between. The goal of our model is to be able to predict 4 extra features accompanying the provided data that is:-\n", + "* Gravity\n", + "* Temperature \n", + "* C/O Ratio \n", + "* Metallicity \n", + "\n", + "of a given instance/example." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "68bfc730-ea01-488a-ae40-ad33704a34b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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        " + ], + "text/plain": [ + " gravity temperature c_o_ratio metallicity 2.512 2.487 \\\n", + "0 5.0 1100 0.25 -1.0 9.103045e-08 1.181658e-07 \n", + "1 5.0 1100 0.25 -0.7 9.103045e-08 1.181658e-07 \n", + "2 5.0 1100 0.25 -0.5 9.103045e-08 1.181658e-07 \n", + "\n", + " 2.462 2.438 2.413 2.389 ... 0.971 \\\n", + "0 1.307868e-07 1.269229e-07 1.159179e-07 8.925110e-08 ... 9.640859e-08 \n", + "1 1.307868e-07 1.269229e-07 1.159179e-07 8.925110e-08 ... 9.640859e-08 \n", + "2 1.307868e-07 1.269229e-07 1.159179e-07 8.925110e-08 ... 9.640859e-08 \n", + "\n", + " 0.962 0.952 0.943 0.933 0.924 \\\n", + "0 7.612550e-08 6.901364e-08 6.247359e-08 4.112384e-08 5.127995e-08 \n", + "1 7.612550e-08 6.901364e-08 6.247359e-08 4.112384e-08 5.127995e-08 \n", + "2 7.612550e-08 6.901364e-08 6.247359e-08 4.112384e-08 5.127995e-08 \n", + "\n", + " 0.915 0.906 0.897 is_augmented \n", + "0 4.897355e-08 4.087795e-08 2.791689e-08 no \n", + "1 4.897355e-08 4.087795e-08 2.791689e-08 no \n", + "2 4.897355e-08 4.087795e-08 2.791689e-08 no \n", + "\n", + "[3 rows x 109 columns]" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# original trainign dataset astronomers use to predict the parameters of the telescope brown dwarf spectra\n", + "\n", + "train_BD = pd.read_csv('../datasets/browndwarf_R100_v4_newWL_v2.csv.zip', compression='zip')\n", + "train_BD.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "413d1cbe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(30888, 109)\n" + ] + } + ], + "source": [ + "print(train_BD.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "d8829707-f03e-4fad-94d7-7f7529f68f59", + "metadata": {}, + "source": [ + "### 1.1: Check the columns\n", + "note: one of the columns, *is_augmented*, is indicating whether this is a native dataset or an augmented one. For now, we don't need it so drop it, but all modules will take this into account! " + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "a6de4600-1aaf-4996-bf68-74eb4104573e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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        " + ], + "text/plain": [ + " gravity temperature c_o_ratio metallicity\n", + "0 5.0 1100 0.25 -1.0\n", + "1 5.0 1100 0.25 -0.7\n", + "2 5.0 1100 0.25 -0.5\n", + "3 5.0 1100 0.25 -0.3\n", + "4 5.0 1100 0.25 0.0" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "target_features = ['gravity', 'temperature', 'c_o_ratio', 'metallicity']\n", + "train_BD[target_features].head()" + ] + }, + { + "cell_type": "markdown", + "id": "ed7be6b8", + "metadata": {}, + "source": [ + "In the following blocks below, we are eliminating the augmented rows (atrifically created data instances) as well as loading in the flux values (X_labels) as integers seperately" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "74a42327-8bfb-4846-88b1-ad711c8da56e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['2.512', '2.487', '2.462', '2.438', '2.413']" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "training_features_labels = [item for item in train_BD.columns.to_list() if item not in target_features+['is_augmented']]\n", + "training_features_labels[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "21aa48e5-b6ab-4db8-a2c9-b19ef458aabc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[2.512, 2.487, 2.462, 2.438, 2.413]" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "training_features_wl = [float(item) for item in train_BD.columns.to_list() if item not in target_features+['is_augmented']]\n", + "training_features_wl[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "d56d2d6b-63c2-4005-94cb-68815ace5ff6", + "metadata": {}, + "outputs": [ + { + "data": { + "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", + "
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        " + ], + "text/plain": [ + " wl\n", + "0 2.511960\n", + "1 2.486966\n", + "2 2.462220" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wl = pd.read_csv('../datasets/wl.csv')\n", + "wl.head(3)" + ] + }, + { + "cell_type": "markdown", + "id": "8bb511a7-5a5f-4b52-a6d9-e8533ece247e", + "metadata": { + "tags": [] + }, + "source": [ + "### 1.2: Prepare feature variables (X) and targets (y)" + ] + }, + { + "cell_type": "markdown", + "id": "2ca64cca", + "metadata": {}, + "source": [ + "The next step is splitting the data into X and y sets. The X (variable) subset consists of all the features that contain the spectral flux values. This information is used to train the model on finding a pattern/similarities between these independent features. The y (target) subset contains the 4 distinct features (gravity, temperature, C/O ratio and metallicity) that the model is trying to predict based on the particular set of X values for a given observation." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "e90e78b1-4f64-40b6-b92e-6027c8ace483", + "metadata": {}, + "outputs": [], + "source": [ + "# to assure we are only training the module with the native non-augmented BD training dataset\n", + "train_BD = train_BD[train_BD['is_augmented'].isin(['no'])] \n", + "\n", + "\n", + "# Training feature variables\n", + "X = train_BD.drop(\n", + " columns=['gravity', \n", + " 'temperature', \n", + " 'c_o_ratio', \n", + " 'metallicity', \n", + " 'is_augmented'])#.astype(np.float32)\n", + "\n", + "\n", + "# Target feature variables\n", + "y = train_BD[['gravity', 'c_o_ratio', 'metallicity', 'temperature', ]]#.astype(np.float32)\n" + ] + }, + { + "cell_type": "markdown", + "id": "8ebb7c3e", + "metadata": {}, + "source": [ + "We are also scaling the temperature on a $\\log_{10}$ scale to reduce the magnitude of the variable while still keeping the similar variance amongst them. This also eliminates the model skewing towards larger value changes from the differences generated by the temperature " + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "07998d41-e101-410c-9a35-e5c6da1800b0", + "metadata": {}, + "outputs": [], + "source": [ + "# Eliminates the Setting With Copy Warning\n", + "y_copy = y.copy()\n", + "y_copy['temperature'] = np.log10(y_copy['temperature'])\n", + "y = y_copy" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "37b9edab-f6dd-406e-b4c4-aba7ef45634c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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        " + ], + "text/plain": [ + " gravity c_o_ratio metallicity temperature\n", + "0 5.0 0.25 -1.0 3.041393\n", + "1 5.0 0.25 -0.7 3.041393\n", + "2 5.0 0.25 -0.5 3.041393\n", + "3 5.0 0.25 -0.3 3.041393\n", + "4 5.0 0.25 0.0 3.041393" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y.head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "6108f702-bb40-49f4-9c38-306e0e27ad4f", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "c9cc267d-85a9-44c6-9081-5067d0bdd8e6", + "metadata": { + "tags": [] + }, + "source": [ + "# Step 2: Build the CNN model / Data processing" + ] + }, + { + "cell_type": "markdown", + "id": "4b3a5dcf", + "metadata": {}, + "source": [ + "In the following steps, we are going to initialize the model from the BuildCNN class. Some of the uncommon parameters required here are:\n", + "* Feature names/values: This corresponds to the flux names (string converted numerical values) and their integer counterparts.\n", + "* param_grid: This is a dictionary that is used for identifying the hyperparameters to be tuned when dealing with a pre-trained model\n", + "* spectral_resolution: This value indicates the resolution of the synthetic spectra used to generate the dataset\n" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "bc38d0e4-a4c2-4288-9d33-ab15fb25093b", + "metadata": {}, + "outputs": [], + "source": [ + "build_cnn_regression = BuildRegressorCNN(feature_values=X.to_numpy(),\n", + " feature_names=X.columns,\n", + " target_values=y.to_numpy(),\n", + " target_name=['gravity', 'c_o_ratio', 'metallicity', 'temperature'],\n", + " is_tuned='yes',\n", + " param_grid=None,\n", + " spectral_resolution=100,\n", + " is_feature_improved='no',\n", + " is_augmented='no',\n", + " ml_model=None,\n", + " ml_model_str='CNN')" + ] + }, + { + "cell_type": "markdown", + "id": "1559d83a-d54c-4d72-86d0-516f578e379d", + "metadata": {}, + "source": [ + "#### 2.1: Split the dataset into train, validate and test sets" + ] + }, + { + "cell_type": "markdown", + "id": "337219fd", + "metadata": {}, + "source": [ + "After initializing our ML model, we split the processed dataset into 3 separate parts: Train, Validation and Test. \n", + "\n", + "* The training set is used to provide the model with data to analyze and understand the relationship/s between the required output and provided input information. This makes up the majority of the dataset.\n", + "\n", + "* the test set is used to evaluate the model. This means that we aren't allowed to touch this data until we finish completely training the model. This is to ensure that the model is not biased towards just the data collected and is still able to accurately provide an output when given a new instance.\n", + "\n", + "* Since we can encounter a bias while training the model repeatedly with the training data, we further split the training data into a validation set. This is used to evaluate the model during training and to fine tune the hyperparameters of the model. This is done to ensure that the model is not overfitting the training data and is still able to provide an accurate output when given a new instance." + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "3a00b374-192b-4a7f-9606-a9015f6db53d", + "metadata": {}, + "outputs": [], + "source": [ + "build_cnn_regression.split_train_validation_test(test_size=0.1, \n", + " val_size=0.1, \n", + " random_state_=100,)" + ] + }, + { + "cell_type": "markdown", + "id": "3821bf22", + "metadata": {}, + "source": [ + "### 2.2: Scale X features - Row-wise" + ] + }, + { + "cell_type": "markdown", + "id": "77c92dae", + "metadata": {}, + "source": [ + "Another crucial step in Data Preprocessing involves standardizing the data. To address this concern, we will perform scaling on the data at the level of individual rows (per-example), ensuring that each row has a mean of 0 and a standard deviation of 1. This choice of row-wise scaling is driven by the nature of the data, which consists of a collection of time-series observations where the order of features holds significant importance. Therefore, opting for row-wise scaling is essential to maintain the inherent relationships between the features, as scaling on a per-feature basis (column-wise) could disrupt the direct associations between features. This is because the resulting scaled value would correspond to the mean and standard deviation of the feature column, potentially affecting the contextual relationships between neighboring features.\n", + "\n", + "Our scaling is done using the StandardScaler() method from sklearn.preprocessing. X is transposed before applying StandardScaler() method.\n", + "\n", + "The formula used to normalize the data is $$z :=\\frac{x - \\mu}{\\sigma}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "a5401e9b-1a1f-4914-838f-cac6fa26ee1b", + "metadata": {}, + "outputs": [], + "source": [ + "# Scale the X features using MinMax Scaler\n", + "build_cnn_regression.standardize_X_row_wise()" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "id": "c1a6eb47", + "metadata": {}, + "source": [ + "The boxplot below shows us a visual reference of the data (feature) ranges after scaling them using the StandardScaler(). We can interpret the boxplot as follows:\n", + "\n", + "- The orange lines mark that Median which marks the middle of the quartile ranges within the data\n", + "- The box itself marks the interquartile range (IQR) which is the range between the upper and lower quartiles. This is calculated by the formula: $IQR := Q3 - Q1$\n", + "- The line above the box marks the upper quartile range where 75% of the scores fall\n", + "- The line below the box marks the lower quartile range where 25% of the scores fall\n", + "\n", + "##### The picture below gives us an example of a visually zoomed-in description for each of the scaled feature followed by the collective box-plot:-\n", + "\n", + "![Inter-Quartile Range](attachment:image.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "7bd0840e-399b-46e7-93d5-3e850d28651a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "build_cnn_regression.plot_boxplot_scaled_features(scaled_feature= build_cnn_regression.X_train_standardized_rowwise, \n", + " xticks_list = training_features_labels,\n", + " title = 'Scaled main 104 Features',\n", + " fig_size=(14, 3),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "f35db1f3-ca36-4fb2-b040-7dbfbdf4dded", + "metadata": {}, + "source": [ + "### 2.3: Scale y features - Column-wise" + ] + }, + { + "cell_type": "markdown", + "id": "e4eece4c", + "metadata": {}, + "source": [ + "Based on the similar challenge of normalization, we are going to be scaling our y features. Here, we follow the standard approach of scaling each feature column-wise as each of the output (features) are independent of each-other. If not scaled, thier magnitudes would incentivize the model to favor one output over the other based on their face values" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "ba7f068d-9503-4b77-b516-03d432309423", + "metadata": {}, + "outputs": [], + "source": [ + "# Standardize the y features using Standard Scaler\n", + "build_cnn_regression.standardize_y_column_wise()" + ] + }, + { + "cell_type": "markdown", + "id": "38da871f", + "metadata": {}, + "source": [ + "The boxplot below follows a similar interpretation to the one above:" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "502c120a-ed2c-4da3-b7d6-88d08c82d404", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "build_cnn_regression.plot_boxplot_scaled_features(scaled_feature= build_cnn_regression.y_train_standardized_columnwise, \n", + " xticks_list = ['','$\\log g$', 'T$_{eff}$', 'C/O ratio', '[M/E]'],\n", + " title = 'Scaled target Features - ColumnWise',\n", + " fig_size=(4, 3),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "289b3cd2-07e6-4ce6-9dad-ab1e9feb161a", + "metadata": {}, + "outputs": [], + "source": [ + "# build_cnn_regression.y_train_standardized_columnwise" + ] + }, + { + "cell_type": "markdown", + "id": "8352d53a-e91c-421f-a321-25a43156f5be", + "metadata": {}, + "source": [ + "### 2.3: Feature engeenering: Take Min and Max of each row (BD spectra) " + ] + }, + { + "cell_type": "markdown", + "id": "09f2971f", + "metadata": {}, + "source": [ + "In our CNN model, just before the concatenated layer, we add a Min-Max layer. This keeps the range of the flux, which is important for understanding the variability in the time series. It also improves the CNN's ability to learn patterns in time, leading to better overall understanding of critical points for better predictions.\n", + "\n", + "We calculate our generated feature by taking the minimum and maximum values of each example within the Train, Validation and Test sets. This is combined into a resulting array of shape (num_examples, 2) which is then concatenated with the output of the CNN model. This is then fed into the next dense layer which outputs the predicted parameters.\n", + "\n", + "For example, let's see the first example from our collective set X (Before standardization):" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "87495c03", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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build_cnn_regression.X_test.min(axis=1)\n", + "build_cnn_regression.X_test_max = build_cnn_regression.X_test.max(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "ab93721b-c0d0-4d4d-969a-d36bed701a67", + "metadata": {}, + "outputs": [], + "source": [ + "df_MinMax_train = pd.DataFrame((build_cnn_regression.X_train_min, build_cnn_regression.X_train_max)).T\n", + "df_MinMax_val = pd.DataFrame((build_cnn_regression.X_val_min, build_cnn_regression.X_val_max)).T\n", + "df_MinMax_test = pd.DataFrame((build_cnn_regression.X_test_min, build_cnn_regression.X_test_max)).T" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "915c7c1f-2e5e-46ea-bcb9-29c6b4203f8c", + "metadata": {}, + "outputs": [], + "source": [ + "df_MinMax_train.rename(columns={0:'min', 1:'max'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "1aad7765-9e8e-436a-9760-a3b2210c4c7a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ 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        minmax
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        " + ], + "text/plain": [ + " min max\n", + "0 2.984493e-18 4.560593e-11\n", + "1 6.833671e-15 2.317328e-09\n", + "2 2.870361e-14 5.564836e-09\n", + "3 5.696652e-19 5.071436e-11\n", + "4 1.321553e-10 1.636513e-07" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_MinMax_train.head()" + ] + }, + { + "cell_type": "markdown", + "id": "9f6890bd-f469-4282-9e73-7a22df6e6d05", + "metadata": {}, + "source": [ + "### 2.4: Scale Min Max features - ColumnWise" + ] + }, + { + "cell_type": "markdown", + "id": "7f57c6de", + "metadata": {}, + "source": [ + "Similar to Standardizing the data for X and y, we ensure that the magnitude does not create a bias while learning from the data provided. Hence, these values are scaled column-wise as min and max are independent of each other. We also do this using the StandardScaler() similar to previous applications " + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "1ba420c3-13b0-418b-ab72-3d0dc45e156b", + "metadata": {}, + "outputs": [], + "source": [ + "build_cnn_regression.standardize_X_column_wise(\n", + " X_train = df_MinMax_train.values,\n", + " X_val = df_MinMax_val.values,\n", + " X_test = df_MinMax_test.values,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "c3a28c5e", + "metadata": {}, + "source": [ + "The box-plot below, depicts a visualization of the range of scaled features for the min and max feature values " + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "c2a88179-5415-43cb-b3ce-c8bc96fff450", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "build_cnn_regression.plot_boxplot_scaled_features(scaled_feature= build_cnn_regression.X_test_standardized_columnwise, \n", + " xticks_list = ['','Min','Max'],\n", + " title = 'Scaled Min Max Features - ColumnWise',\n", + " fig_size=(4, 3),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "272342b0", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "cebec7ae-7d8f-4b76-a523-2280fd4d35a5", + "metadata": { + "tags": [] + }, + "source": [ + "# Step 3: Train CNN model" + ] + }, + { + "cell_type": "markdown", + "id": "b25e3e9c", + "metadata": {}, + "source": [ + "The next important step after the pre-processing is training the newly instantiated model. The technique we are using is a Convolutional Neural Network (CNN) which is able to predict the outputs based on dependencies/patterns established from the input data. There are many different ways of constructing a CNN but here, we are going to be focusing on the current architecture used in this example.\n", + "\n", + "Our CNN can be divided into a few important layers:\n", + "\n", + "* The 1-Dimension Convolutional Layer (Conv1D): The main purpose of this layer is to extract low-level local patterns within our time-series data to assist with identifying more complex trends by passing the recorded information down to other layers of the models. Each filter gathers a different feature set using a kernel/window that slides over the input data pefroming a dot product with the filter instantiated window values and the input data. This is stored into a group of sub-layers called a feature map which can be further passed down to other Convolution layers or concentrated through MaxPooling to prepare for Dense Layers. Overall, it's application in our model helps isolate local regularities between multiple spectral examples that can be further processed through FC/Dense layers.\n", + "\n", + "* The Max-Pooling Layer(Max-Pool): Following the feature map generated by our Conv1D layer, we then apply Max-Pool onto those layers to shrink their individual sizes. It achieves this picking the maximum value amongst a kernel/window area applied on the every layer. This helps in selectively highlighting characterstics that stand-out amongst a given regularity/pattern. A crude example of this would be: If we observe a drop amongst multiple spectra, max-pooling eliminates the subtle characteristics of the drop like the muinute slope change and instead signifies the drop as the similarity itself. \n", + "\n", + "* Flatten Layer: This layer follows a Conv Block (Multiple Conv layers followed by a Max-Pool) which transforms the multi-dimensional feature map into one dimensional array. This makes it simpler to extract all the features into the Fully Connected layer. This layer does not change outputs form the previous layer but instead simply re-arranges them \n", + "\n", + "* The Fully Connected Layer(FC/Dense Layer): One of the main tasks of the FC layer is to be able to link the multiple regularities generated by different layers of the Conv1D layer to obtain a generality (trends amongst groups of spectra) from the provided set. To achieve this, the layer receives all the data from the previous layers (Flattened from the Flatten layer) to every single one of its nodes (units). This way, we are able to cummulate all the feature information accquired from previous higher-level layers to find correlation amongst similar trends within all the data instances (i.e. the general pattern).\n", + "\n", + "* Dropout Layer: The dropout layer usually follows a FC layer to mitigate memorization and overfitting. This is achieved by randomly deactivating a certain percentage of individual nodes within the layer (i.e. sets the neuron's outputs to zero during the iteration therby rendering the calculation useless). This percentage is determined when configuring the layer as a hyperparamenter. The main problem it addresses is curbing overfitting the data which is 'remembering' every single detail extracted from the every example. This overall leads to a more accurate assessment and prediction of a given instance." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "2e823b8a-af2a-4025-815d-24c1d43fd6e7", + "metadata": {}, + "outputs": [], + "source": [ + "train_cnn_model = TrainRegressorCNN(\n", + " # input dataset: StandardScaled instances \n", + " X1_train = build_cnn_regression.X_train_standardized_rowwise,\n", + " X1_val = build_cnn_regression.X_val_standardized_rowwise,\n", + " X1_test = build_cnn_regression.X_test_standardized_rowwise,\n", + "\n", + " # input dataset: Min Max of each instance \n", + " X2_train = build_cnn_regression.X_train_standardized_columnwise,\n", + " X2_val = build_cnn_regression.X_val_standardized_columnwise,\n", + " X2_test = build_cnn_regression.X_test_standardized_columnwise,\n", + "\n", + " # 1st target\n", + " y1_train = build_cnn_regression.y_train_standardized_columnwise[:,0],\n", + " y1_val = build_cnn_regression.y_val_standardized_columnwise[:,0],\n", + " y1_test = build_cnn_regression.y_test_standardized_columnwise[:,0],\n", + "\n", + " # 2nd target\n", + " y2_train = build_cnn_regression.y_train_standardized_columnwise[:,1],\n", + " y2_val = build_cnn_regression.y_val_standardized_columnwise[:,1],\n", + " y2_test = build_cnn_regression.y_test_standardized_columnwise[:,1],\n", + "\n", + " # 3rd target\n", + " y3_train = build_cnn_regression.y_train_standardized_columnwise[:,2],\n", + " y3_val = build_cnn_regression.y_val_standardized_columnwise[:,2],\n", + " y3_test = build_cnn_regression.y_test_standardized_columnwise[:,2],\n", + "\n", + " # 4th target\n", + " y4_train = build_cnn_regression.y_train_standardized_columnwise[:,3],\n", + " y4_val = build_cnn_regression.y_val_standardized_columnwise[:,3],\n", + " y4_test = build_cnn_regression.y_test_standardized_columnwise[:,3],\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "35303edb", + "metadata": {}, + "source": [ + "The dictionary below is used to pass into the model which specifies the architecture of the model being used to train the given data. As mentioned in the layer description above, we are using the Conv, FC, Max-pool and Flatten layers in this CNN. We also specify details like the kernel size and the number of units in each layer using the hyperparamenters dictionary. The format for our current example (model) is described below:\n", + "\n", + "- We start-off with the input layer with each instance being (104,) dimensions\n", + "- This is followed by a Conv-Block that has 3 Conv-layers of shapes (104,16), (104,64), (104,144) including a Max-Pool and Flatten Layer\n", + "- That connects onto 4 other Dense Layers of shapes (None,512), (None,2048), (None,4608), (None,8192)\n", + "- This is passed onto a Dropout FC layer of the last shape which is concatenated with the engineered features in the following Concatenated Layer that is os shape (None,8194)\n", + "- Passing this through 2 more Dense Layers that are (None,256), (None,1024) and another Dropout FC Layer(None,1024), we then reach the simultaneous 4 output layers\n", + "- Each output layer is an FC layer of shape (None,1) that predicts one of the 4 different outputs\n", + "\n", + "The learning rate component below incdicates how much the weights vary with respect to the data through each ephoch (cycle).\n", + "\n", + "Note: The 'None' in shapes (None, Num-Units) indicate the batch-size which varies dynamically depending on the network's flexibility." + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "fb308eec-87ba-490a-b9f5-4da783c7f331", + "metadata": {}, + "outputs": [], + "source": [ + "hyperparameters = {\n", + " 'Conv__MaxPooling1D': 2,\n", + " 'Conv__num_blocks': 1,\n", + " 'Conv__num_layers_per_block': 3,\n", + " 'Conv__num_filters': 4,\n", + " 'Conv__kernel_size': 6,\n", + " 'FC__NumberLayers': 4,\n", + " \n", + " 'FC1__num_blocks' : 1,\n", + " 'FC1_num_layers_per_block': 4,\n", + " 'FC1__dropout': 0.09889223768186726,\n", + " 'FC1__units': 128,\n", + "\n", + " 'FC2__num_blocks' : 1,\n", + " 'FC2_num_layers_per_block':2,\n", + " 'FC2__dropout': 0.0024609140719442646,\n", + " 'FC2__units': 64,\n", + "\n", + " \n", + " 'learning_rate': 4.9946842008422193e-05}" + ] + }, + { + "cell_type": "markdown", + "id": "94d16e7e", + "metadata": {}, + "source": [ + "This provides us with a summary of the layers within our configured CNN" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "5ffd7ce9-8ef4-4b3a-94d7-7f78a30e839e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_2\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " input_5 (InputLayer) [(None, 104, 1)] 0 [] \n", + " \n", + " Conv__B1_L1 (Conv1D) (None, 104, 16) 112 ['input_5[0][0]'] \n", + " \n", + " Conv__B1_L2 (Conv1D) (None, 104, 64) 6208 ['Conv__B1_L1[0][0]'] \n", + " \n", + " Conv__B1_L3 (Conv1D) (None, 104, 144) 55440 ['Conv__B1_L2[0][0]'] \n", + " \n", + " MaxPool1D__B1_L3 (MaxPooli (None, 52, 144) 0 ['Conv__B1_L3[0][0]'] \n", + " ng1D) \n", + " \n", + " flatten_2 (Flatten) (None, 7488) 0 ['MaxPool1D__B1_L3[0][0]'] \n", + " \n", + " FC1__B1_L1 (Dense) (None, 512) 3834368 ['flatten_2[0][0]'] \n", + " \n", + " FC1__B1_L2 (Dense) (None, 2048) 1050624 ['FC1__B1_L1[0][0]'] \n", + " \n", + " FC1__B1_L3 (Dense) (None, 4608) 9441792 ['FC1__B1_L2[0][0]'] \n", + " \n", + " FC1__B1_L4 (Dense) (None, 8192) 3775692 ['FC1__B1_L3[0][0]'] \n", + " 8 \n", + " \n", + " FC1__Dropout__B1_L4 (Dropo (None, 8192) 0 ['FC1__B1_L4[0][0]'] \n", + " ut) \n", + " \n", + " input_6 (InputLayer) [(None, 2)] 0 [] \n", + " \n", + " Concatenated_Layer (Concat (None, 8194) 0 ['FC1__Dropout__B1_L4[0][0]', \n", + " enate) 'input_6[0][0]'] \n", + " \n", + " FC2__B1_L1 (Dense) (None, 256) 2097920 ['Concatenated_Layer[0][0]'] \n", + " \n", + " FC2__B1_L2 (Dense) (None, 1024) 263168 ['FC2__B1_L1[0][0]'] \n", + " \n", + " FC2__Dropout__B1_L2 (Dropo (None, 1024) 0 ['FC2__B1_L2[0][0]'] \n", + " ut) \n", + " \n", + " gravity (Dense) (None, 1) 1025 ['FC2__Dropout__B1_L2[0][0]'] \n", + " \n", + " c_o_ratio (Dense) (None, 1) 1025 ['FC2__Dropout__B1_L2[0][0]'] \n", + " \n", + " metallicity (Dense) (None, 1) 1025 ['FC2__Dropout__B1_L2[0][0]'] \n", + " \n", + " temperature (Dense) (None, 1) 1025 ['FC2__Dropout__B1_L2[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 54510660 (207.94 MB)\n", + "Trainable params: 54510660 (207.94 MB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "__________________________________________________________________________________________________\n", + "None\n" + ] + } + ], + "source": [ + "train_cnn_model.build_model(hyperparameters = hyperparameters, \n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "6f8c6a04", + "metadata": {}, + "source": [ + "The .fit_cnn_model() invokes the training procedure of the model. The budget represents the number of epochs specified by us for this session. This can be changed by editing the variable within the cell block" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "e23b745d-a0c5-429f-8f69-d127e66f6705", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/3\n", + "782/782 [==============================] - 479s 609ms/step - loss: 0.2587 - gravity_loss: 0.1004 - c_o_ratio_loss: 0.0573 - metallicity_loss: 0.0925 - temperature_loss: 0.0085 - gravity_mae: 0.3313 - c_o_ratio_mae: 0.2440 - metallicity_mae: 0.3355 - temperature_mae: 0.0879 - val_loss: 0.1147 - val_gravity_loss: 0.0295 - val_c_o_ratio_loss: 0.0220 - val_metallicity_loss: 0.0609 - val_temperature_loss: 0.0024 - val_gravity_mae: 0.1907 - val_c_o_ratio_mae: 0.1603 - val_metallicity_mae: 0.2731 - val_temperature_mae: 0.0508\n", + "Epoch 2/3\n", + "782/782 [==============================] - 559s 715ms/step - loss: 0.1013 - gravity_loss: 0.0271 - c_o_ratio_loss: 0.0180 - metallicity_loss: 0.0534 - temperature_loss: 0.0028 - gravity_mae: 0.1742 - c_o_ratio_mae: 0.1478 - metallicity_mae: 0.2552 - temperature_mae: 0.0573 - val_loss: 0.0967 - val_gravity_loss: 0.0274 - val_c_o_ratio_loss: 0.0170 - val_metallicity_loss: 0.0484 - val_temperature_loss: 0.0038 - val_gravity_mae: 0.1861 - val_c_o_ratio_mae: 0.1420 - val_metallicity_mae: 0.2417 - val_temperature_mae: 0.0713\n", + "Epoch 3/3\n", + "782/782 [==============================] - 587s 751ms/step - loss: 0.0812 - gravity_loss: 0.0173 - c_o_ratio_loss: 0.0145 - metallicity_loss: 0.0472 - temperature_loss: 0.0022 - gravity_mae: 0.1390 - c_o_ratio_mae: 0.1338 - metallicity_mae: 0.2377 - temperature_mae: 0.0509 - val_loss: 0.0780 - val_gravity_loss: 0.0141 - val_c_o_ratio_loss: 0.0132 - val_metallicity_loss: 0.0487 - val_temperature_loss: 0.0020 - val_gravity_mae: 0.1089 - val_c_o_ratio_mae: 0.1270 - val_metallicity_mae: 0.2398 - val_temperature_mae: 0.0487\n" + ] + } + ], + "source": [ + "history, model = train_cnn_model.fit_cnn_model(budget=3)" + ] + }, + { + "cell_type": "markdown", + "id": "ef6ca8ca", + "metadata": {}, + "source": [ + "### 3.1: Post Training of the CNN model" + ] + }, + { + "cell_type": "markdown", + "id": "5fe6abd3", + "metadata": {}, + "source": [ + "We assign the trained-model and history parameters from previously initialized class to be the newly trained model and it's history" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "d54365d5-70d1-445b-84ee-13a145a1893d", + "metadata": {}, + "outputs": [], + "source": [ + "build_cnn_regression.trained_model = model\n", + "build_cnn_regression.history = history" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "740041c9-18ba-401f-8fff-1ab48c78ac5a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "cell_type": "markdown", + "id": "c67e9d94", + "metadata": {}, + "source": [ + "The history parameter of the trained model gives us the losses recorded for each epoch from the last training session\n", + "\n", + "The following graph below shows us the graphical performance of the model throughout each epoch. There are two distinct lines presented within this plot. The solid line represents the loss at each point in the Epoch for the training dataset where as the dotted line gives us the loss at each point in the Epoch for our Validation Dataset. The various colors depict:\n", + "\n", + "- Red: The average loss across all outputs i.e. Total Loss\n", + "- Orange: The loss calculated for the output gravity\n", + "- Green: The loss calculated for the output C_O_ratio\n", + "- Purple: The loss calculated for the output metallicity\n", + "- Brown: The loss calculated for the output temperature\n", + "\n", + "One key observation from this graph is that we have the least loss encountered for Temperature where as metallicity has the highest loss calculated individually. The total loss from all the training data still falls well below $10^{-1}$" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "b61fd912-9bfa-4769-8eba-9e195aac7b72", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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described in the previous steps. This also helps us better understand the flow of the layers between each other while also specifying their individual details." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34c9c0e3-f5eb-4e8e-8723-0a11f4d16637", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.keras.utils.plot_model(\n", + " train_cnn_model.model,\n", + " to_file=\"model.png\",\n", + " show_shapes=True,\n", + " show_dtype=False,\n", + " show_layer_names=True,\n", + " rankdir=\"TB\",\n", + " expand_nested=True,\n", + " dpi=90,\n", + " layer_range=None,\n", + "# show_layer_activations=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d362f2b2-e53e-40e7-9fd7-f94c9dcafc06", + "metadata": {}, + "source": [ + "## Save Trained Model" + ] + }, + { + "cell_type": "markdown", + "id": "334392f5", + "metadata": {}, + "source": [ + "Finally, once we finish running and model, we are going to save it using the model.save() built-in method as a '.h5' file. Here, we are going to be saving 3 separate files:\n", + "* Model Architecture: This information includes the type of layers, their activation functions, connections between layers, and any other relevant parameters and hyperparameters that define the network's structure.\n", + "* Model Weights: These weights represent the neural network's learned parameters that allow it to make predictions. \n", + "* Training History: The training history, including metrics such as loss and accuracy at each epoch, is usually stored as well. This information helps visualize the progress of the model's training over time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4376ab8d-a15b-4aa0-85e9-f378e954b68e", + "metadata": {}, + "outputs": [], + "source": [ + "build_cnn_regression.trained_model.save('../../outputs/trained_models/trained_CNN_architecture_Toy_parameters__newbuild_random100_v2.h5')\n", + "build_cnn_regression.trained_model.save_weights('../../outputs/trained_models/trained_CNN_weights_Toy_parameters__newbuild_random100_v2.h5')\n", + "\n", + "with open('../../outputs/trained_models/trained_CNN_history_Toy_parameters__newbuild_random100_v2.pkl', 'wb') as file:\n", + " pk.dump(build_cnn_regression.history.history, file)" + ] + }, + { + "cell_type": "markdown", + "id": "97852f63-61d5-4908-9e76-1d9aaf4eb6bd", + "metadata": {}, + "source": [ + "## Load Saved Models" + ] + }, + { + "cell_type": "markdown", + "id": "0e5e1ef0", + "metadata": {}, + "source": [ + "Similarly, following the storing process, we load one of the previously trained models the we've stored. This includes it's weights and the history both in separate variables. In this step we are could also load it using the keras.models.load_model() and tf.keras.models.load_weights() functions. To load the history, we un-pickle the file using the pk.load() function after opening the file through python's open() function to read a file." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "2675563e-8cfc-401d-884d-b59f9e7e5a41", + "metadata": {}, + "outputs": [], + "source": [ + "model = tf.keras.models.load_model('../outputs/trained_models/trained_CNN_architecture_Toy_parameters__newbuild_random100_v2.h5')\n", + "model.load_weights('../outputs/trained_models/trained_CNN_weights_Toy_parameters__newbuild_random100_v2.h5')\n", + "\n", + "# Loading the saved history object\n", + "with open('../outputs/trained_models/trained_CNN_history_Toy_parameters__newbuild_random100_v2.pkl', 'rb') as file:\n", + " history = pk.load(file) " + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "cbbfc24d-f767-41a4-b213-db4fe7d6bd60", + "metadata": {}, + "outputs": [], + "source": [ + "build_cnn_regression.trained_model = model\n", + "build_cnn_regression.trained_model_history = history" + ] + }, + { + "cell_type": "markdown", + "id": "5928c61b-b4d5-4734-b080-dbc967d7a2a7", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "497859c9-57b5-4960-9929-98cd0fb1404a", + "metadata": { + "tags": [] + }, + "source": [ + "# Step 4: Check Trained Model" + ] + }, + { + "cell_type": "markdown", + "id": "a3b07471", + "metadata": {}, + "source": [ + "In the following Step, we are going to load the previously trained model and check the summary, architecture and history from the '.h5' extension file that it was stored into locally" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "9239fc9c-3fa9-4833-8999-04132087ecda", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.keras.utils.plot_model(\n", + " build_cnn_regression.trained_model,\n", + " # to_file=\"model.png\",\n", + " show_shapes=True,\n", + " show_dtype=False,\n", + " show_layer_names=True,\n", + " rankdir=\"TB\",\n", + " expand_nested=True,\n", + " dpi=90,\n", + " layer_range=None,\n", + "# show_layer_activations=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "a7199a91-1b68-4d43-af65-13855060db49", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " input_1 (InputLayer) [(None, 104, 1)] 0 [] \n", + " \n", + " Conv__B1_L1 (Conv1D) (None, 104, 16) 112 ['input_1[0][0]'] \n", + " \n", + " Conv__B1_L2 (Conv1D) (None, 104, 64) 6208 ['Conv__B1_L1[0][0]'] \n", + " \n", + " Conv__B1_L3 (Conv1D) (None, 104, 144) 55440 ['Conv__B1_L2[0][0]'] \n", + " \n", + " MaxPool1D__B1_L3 (MaxPooli (None, 52, 144) 0 ['Conv__B1_L3[0][0]'] \n", + " ng1D) \n", + " \n", + " flatten (Flatten) (None, 7488) 0 ['MaxPool1D__B1_L3[0][0]'] \n", + " \n", + " FC1__B1_L1 (Dense) (None, 512) 3834368 ['flatten[0][0]'] \n", + " \n", + " FC1__B1_L2 (Dense) (None, 2048) 1050624 ['FC1__B1_L1[0][0]'] \n", + " \n", + " FC1__B1_L3 (Dense) (None, 4608) 9441792 ['FC1__B1_L2[0][0]'] \n", + " \n", + " FC1__B1_L4 (Dense) (None, 8192) 3775692 ['FC1__B1_L3[0][0]'] \n", + " 8 \n", + " \n", + " FC1__Dropout__B1_L4 (Dropo (None, 8192) 0 ['FC1__B1_L4[0][0]'] \n", + " ut) \n", + " \n", + " input_2 (InputLayer) [(None, 2)] 0 [] \n", + " \n", + " Concatenated_Layer (Concat (None, 8194) 0 ['FC1__Dropout__B1_L4[0][0]', \n", + " enate) 'input_2[0][0]'] \n", + " \n", + " FC2__B1_L1 (Dense) (None, 256) 2097920 ['Concatenated_Layer[0][0]'] \n", + " \n", + " FC2__B1_L2 (Dense) (None, 1024) 263168 ['FC2__B1_L1[0][0]'] \n", + " \n", + " FC2__Dropout__B1_L2 (Dropo (None, 1024) 0 ['FC2__B1_L2[0][0]'] \n", + " ut) \n", + " \n", + " gravity (Dense) (None, 1) 1025 ['FC2__Dropout__B1_L2[0][0]'] \n", + " \n", + " c_o_ratio (Dense) (None, 1) 1025 ['FC2__Dropout__B1_L2[0][0]'] \n", + " \n", + " metallicity (Dense) (None, 1) 1025 ['FC2__Dropout__B1_L2[0][0]'] \n", + " \n", + " temperature (Dense) (None, 1) 1025 ['FC2__Dropout__B1_L2[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 54510660 (207.94 MB)\n", + "Trainable params: 54510660 (207.94 MB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "build_cnn_regression.trained_model.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "dc7dfe01", + "metadata": {}, + "source": [ + "Here, we are going to be printing the model's history which is the losses recorded when training it. This features of the graph are identical to the history graph that was displayed before. Here's a reminder of what the graph represented before and all of it's features:" + ] + }, + { + "cell_type": "markdown", + "id": "1385ed7e", + "metadata": {}, + "source": [ + "There are two distinct lines presented within this plot. The solid line represents the loss at each point in the Epoch for the training dataset where as the dotted line gives us the loss at each point in the Epoch for our Validation Dataset. The various colors depict:\n", + "\n", + "- Red: The average loss across all outputs i.e. Total Loss\n", + "- Orange: The loss calculated for the output gravity\n", + "- Green: The loss calculated for the output C_O_ratio\n", + "- Purple: The loss calculated for the output metallicity\n", + "- Brown: The loss calculated for the output temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "400d2e76-4bf6-4c69-a12b-ce3099d4ebfd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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"e94d9a8a", + "metadata": {}, + "source": [ + "In the code below, we are visualizing the model's performance for each of y's outputs. Since we have 4 different outputs where each one represents a different parameter, we will have 4 different sets of plots. \n", + "\n", + "The first plot shows us the Histogram plot of the predicted and the actual outcome overlayed on top of each other for each of the 4 values. This signifies where the probability of the prediction's magnitude of difference fromt the ground truth. If we consider our model to have good performance, we should expect to see a Gaussian Distribution of the labels. We could break down the Histogram attributes into:\n", + "\n", + "- $\\mu_{3\\text{ train}}, \\mu_{3\\text{ test}}$ - Skewness of the data which helps determine whether the data has a normal or a skewed distribution\n", + "- X-Axis labels: which represent difference between the predicted and actual values i.e. residual values\n", + "- Y-Axis labels: are the logarithimic scaled values of the probability distribution for each residual instance\n", + "\n", + "The second plot shows us a scatter plot between the predicted vs actual values for each of the 4 parameters. If we consider our model to have good performance, we should expect to see a continuous line with a slope of 1. Any variation from the center (i.e. Ground Truth) is the error produced by the prediction of the model. Some of the plot's attributes are:\n", + "- $R^{2}_{train} \\text{ and } R^{2}_{test}$ = $R^2$ value (representing the goodness of the fit) [$mean$ $\\pm$ $Standard $ $Deviation$ of the data]\n", + "- The X and Y axis labels represending the Scaled feature values for each of the output features\n" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "bf27744b-88a2-4f38-827b-660e5456c4f1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "782/782 [==============================] - 23s 29ms/step\n", + "97/97 [==============================] - 3s 31ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
        " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 1\n", + "regression_report(\n", + " trained_model = build_cnn_regression,\n", + " \n", + " Xtrain = [build_cnn_regression.X_train_standardized_rowwise[::i],\n", + " build_cnn_regression.X_train_standardized_columnwise[::i]],\n", + " \n", + " Xtest = [build_cnn_regression.X_test_standardized_rowwise[::i], \n", + " build_cnn_regression.X_test_standardized_columnwise[::i]],\n", + " \n", + " ytrain = build_cnn_regression.y_train_standardized_columnwise[::i],\n", + " \n", + " ytest = build_cnn_regression.y_test_standardized_columnwise[::i],\n", + " \n", + " target_i = 4,\n", + " \n", + " xy_top = [0.05, 0.7],\n", + " xy_bottom= [0.05, 0.85],\n", + " print_results = False, \n", + " )" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From e4baa614688c754b69f3e9b485d53903c0e748d6 Mon Sep 17 00:00:00 2001 From: EhsanGharibNezhad Date: Wed, 8 Nov 2023 12:31:58 -0800 Subject: [PATCH 2/3] mv the bohb_model_tuner to STORE - I'm not using it right now --- .../bohb_model_tuner}/Tune_CNN_hyperparameters_BOHB.py | 0 .../bohb_model_tuner}/check_bohb_results.ipynb | 0 {bohb_model_tuner => store/bohb_model_tuner}/clean_file.py | 0 {bohb_model_tuner => store/bohb_model_tuner}/cnn_bohb_tuning6.py | 0 {bohb_model_tuner => store/bohb_model_tuner}/submit.sh | 0 .../bohb_model_tuner}/test_cnn_bohb_tuner.ipynb | 0 6 files changed, 0 insertions(+), 0 deletions(-) rename {bohb_model_tuner => store/bohb_model_tuner}/Tune_CNN_hyperparameters_BOHB.py (100%) rename {bohb_model_tuner => store/bohb_model_tuner}/check_bohb_results.ipynb (100%) rename {bohb_model_tuner => store/bohb_model_tuner}/clean_file.py (100%) rename {bohb_model_tuner => store/bohb_model_tuner}/cnn_bohb_tuning6.py (100%) rename {bohb_model_tuner => store/bohb_model_tuner}/submit.sh (100%) rename {bohb_model_tuner => store/bohb_model_tuner}/test_cnn_bohb_tuner.ipynb (100%) diff --git a/bohb_model_tuner/Tune_CNN_hyperparameters_BOHB.py b/store/bohb_model_tuner/Tune_CNN_hyperparameters_BOHB.py similarity index 100% rename from bohb_model_tuner/Tune_CNN_hyperparameters_BOHB.py rename to store/bohb_model_tuner/Tune_CNN_hyperparameters_BOHB.py diff --git a/bohb_model_tuner/check_bohb_results.ipynb b/store/bohb_model_tuner/check_bohb_results.ipynb similarity index 100% rename from bohb_model_tuner/check_bohb_results.ipynb rename to store/bohb_model_tuner/check_bohb_results.ipynb diff --git a/bohb_model_tuner/clean_file.py b/store/bohb_model_tuner/clean_file.py similarity index 100% rename from bohb_model_tuner/clean_file.py rename to store/bohb_model_tuner/clean_file.py diff --git a/bohb_model_tuner/cnn_bohb_tuning6.py b/store/bohb_model_tuner/cnn_bohb_tuning6.py similarity index 100% rename from bohb_model_tuner/cnn_bohb_tuning6.py rename to store/bohb_model_tuner/cnn_bohb_tuning6.py diff --git a/bohb_model_tuner/submit.sh b/store/bohb_model_tuner/submit.sh similarity index 100% rename from bohb_model_tuner/submit.sh rename to store/bohb_model_tuner/submit.sh diff --git a/bohb_model_tuner/test_cnn_bohb_tuner.ipynb b/store/bohb_model_tuner/test_cnn_bohb_tuner.ipynb similarity index 100% rename from bohb_model_tuner/test_cnn_bohb_tuner.ipynb rename to store/bohb_model_tuner/test_cnn_bohb_tuner.ipynb From be668dfd9a5c4925722a57f018192cd879ae2cd6 Mon Sep 17 00:00:00 2001 From: "Ehsan (Sam) Gharib-Nezhad" Date: Wed, 8 Nov 2023 12:33:40 -0800 Subject: [PATCH 3/3] Delete store/bohb_model_tuner directory I do not want STORE dir to appear in the remote repo --- .../Tune_CNN_hyperparameters_BOHB.py | 627 ----- .../bohb_model_tuner/check_bohb_results.ipynb | 2237 ----------------- store/bohb_model_tuner/clean_file.py | 19 - store/bohb_model_tuner/cnn_bohb_tuning6.py | 657 ----- store/bohb_model_tuner/submit.sh | 31 - .../test_cnn_bohb_tuner.ipynb | 1659 ------------ 6 files changed, 5230 deletions(-) delete mode 100644 store/bohb_model_tuner/Tune_CNN_hyperparameters_BOHB.py delete mode 100644 store/bohb_model_tuner/check_bohb_results.ipynb delete mode 100644 store/bohb_model_tuner/clean_file.py delete mode 100644 store/bohb_model_tuner/cnn_bohb_tuning6.py delete mode 100755 store/bohb_model_tuner/submit.sh delete mode 100644 store/bohb_model_tuner/test_cnn_bohb_tuner.ipynb diff --git a/store/bohb_model_tuner/Tune_CNN_hyperparameters_BOHB.py b/store/bohb_model_tuner/Tune_CNN_hyperparameters_BOHB.py deleted file mode 100644 index 1c91f90e..00000000 --- a/store/bohb_model_tuner/Tune_CNN_hyperparameters_BOHB.py +++ /dev/null @@ -1,627 +0,0 @@ -import os -os.environ['NUMEXPR_MAX_THREADS'] = '42' -os.environ['NUMEXPR_NUM_THREADS'] = '40' -import numexpr as ne - -ip_address = '127.0.0.1' - - -#import logging -#logging.basicConfig(level=logging.INFO) - -import logging -logging.basicConfig(level=logging.INFO) -import sys -sys.path.insert(0, '../telescopeML/') - - -from DeepRegTrainer import TrainRegression -from StatVisAnalyzer import * - - -from bohb_optimizer_2 import * -from search_space import * - - - -from bohb_optimizer_2 import * -from search_space import * -# from bohb_worker import * - -# Libraries for BOHB Package -import argparse - -import hpbandster.core.nameserver as hpns -import hpbandster.core.result as hpres - -from hpbandster.optimizers import BOHB as BOHB -# from hpbandster.examples.commons import MyWorker - -#import logging -#logging.basicConfig(level=logging.ERROR) # Change the level to INFO or higher -#import argparse - -#import hpbandster.core.nameserver as hpns -#import hpbandster.core.result as hpres - -#from hpbandster.optimizers import BOHB as BOHB -# from hpbandster.examples.commons import MyWorker - -import numpy -import time - -import ConfigSpace as CS -from hpbandster.core.worker import Worker - - - - - -# Import functions from other modules ============================ -from io_funs import LoadSave - -# Import python libraries ======================================== -import numpy as np -import matplotlib.pyplot as plt - - -# Import python libraries ======================================== -import numpy as np -import matplotlib.pyplot as plt - -# Import BOHB libraries ======================================== - - -import ConfigSpace.hyperparameters as CSH - -#import logging -#logging.basicConfig(level=logging.DEBUG) - - -import tensorflow as tf -from tensorflow import keras -from tensorflow.keras.models import Sequential -from tensorflow.keras.layers import Dense, Flatten -from tensorflow.keras.layers import Conv1D, Reshape -from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau -from tensorflow.keras.utils import plot_model - -from tensorflow.keras.layers import Input, Conv1D, Concatenate, Dense, MaxPooling1D -from tensorflow.keras.models import Model - -# mlp for multi-output regression -from numpy import mean -from numpy import std -from keras.models import Sequential -from keras.layers import Dense - -import warnings -import logging - -from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, UniformFloatHyperparameter, CategoricalHyperparameter - - -# =============================================================================== -# ================== ================== -# ================== BOHB Optimizer ================== -# ================== ================== -# =============================================================================== - - - - -class KerasWorker(Worker): - def __init__(self, - X1_train, X1_val, X1_test, # Row-StandardScaled input spectra - X2_train, X2_val, X2_test, # Col-StandardScaled Mix Max of all rows of input spetra - y1_train, y1_val, y1_test, # Col-StandardScaled target feature 1 - y2_train, y2_val, y2_test, # Col-StandardScaled target feature 2 - y3_train, y3_val, y3_test, # Col-StandardScaled target feature 3 - y4_train, y4_val, y4_test, # Col-StandardScaled target feature 4 - *args, sleep_interval=0, **kwargs): - - super().__init__(**kwargs) - self.sleep_interval = sleep_interval - - # self.batch_size = 2**9 - - - # train, val, test sets for input 1 (main 104 spectral features) - self.X1_train, self.X1_val, self.X1_test = X1_train, X1_val, X1_test - - # train, val, test sets for input 2 (Min and Max 2 features) - self.X2_train, self.X2_val, self.X2_test = X2_train, X2_val, X2_test - - # train, val, test sets for target features - self.y1_train, self.y1_val, self.y1_test = y1_train, y1_val, y1_test - self.y2_train, self.y2_val, self.y2_test = y2_train, y2_val, y2_test - self.y3_train, self.y3_val, self.y3_test = y3_train, y3_val, y3_test - self.y4_train, self.y4_val, self.y4_test = y4_train, y4_val, y4_test - - - # self.input_shape = (104,1) - # print(np.shape(self.x_train), np.shape(self.y_train)) - - - def compute(self, config, budget, working_directory, *args, **kwargs): - """ - Convolution Neural Networks to be optimized by BOHB package. - The input parameter "config" (dictionary) contains the sampled configurations passed by the bohb optimizer - """ - - Conv__filters = config['Conv__filters'] - Conv__kernel_size = config['Conv__kernel_size'] - Conv__MaxPooling1D = config['Conv__MaxPooling1D'] - Conv__NumberLayers = config['Conv__NumberLayers'] - Conv__NumberBlocks = config['Conv__NumberBlocks'] - - FC__units = config['FC__units'] - # FC__units_temperature = config['FC__units_temperature'] - # FC__units_c_o_ratio = config['FC__units_c_o_ratio'] - # FC__units_gravity = config['FC__units_gravity'] - # FC__units_metallicity = config['FC__units_metallicity'] - FC__NumberLayers = config['FC__NumberLayers'] - -# FC__dropout = config['FC__dropout'] - -# FC_out_dropout = config['FC_out_dropout'] - -# FC_in_Conv__units = config['FC_in_Conv__units'] -# FC_in_Conv__dropout = config['FC_in_Conv__dropout'] -# FC_in_Conv__NumberLayers = config['FC_in_Conv__NumberLayers'] - - - lr = config['lr'] - - - - ######### Shape of the inputs - input_1 = tf.keras.layers.Input(shape=(104, 1)) - input_2 = tf.keras.layers.Input(shape=(2,)) - - - ######### Conv Blocks #################################### - # model = input_1 - for b in range(0, Conv__NumberBlocks): - for l in range(0, Conv__NumberLayers): - model = Conv1D(filters = Conv__filters*(b+l+1)**2, - kernel_size = Conv__kernel_size, - # strides = 1, - # padding ='same', - activation = 'relu', - kernel_initializer = 'he_normal', - # kernel_regularizer=tf.keras.regularizers.l2(Conv__regularizer), - name = 'Conv__B'+str(b+1)+'_L'+str(l+1))(model if l!= 0 and b!= 0 else input_1) - - model = MaxPooling1D(pool_size=(Conv__MaxPooling1D), - name = 'MaxPooling1D__B'+str(b+1)+'_L'+str(l+1))(model) - - ######### Flatten Layer #################################### - model = Flatten()(model) - - -# ######### FC Layer before the Concatenation ################ -# for l in range(FC_in_Conv__NumberLayers): -# model = Dense(FC_in_Conv__units*(l+1)**2, -# activation = 'relu', -# kernel_initializer = 'he_normal', -# # kernel_regularizer=tf.keras.regularizers.l2(Conv__regularizer), -# name = 'FC_in_Conv__B'+str(b+1)+'_L'+str(l+1))(model) - -# model= Dropout(FC__dropout, -# name = 'FC_in_Conv__Dropout__B'+str(1)+'_L'+str(l+1))(model) - - - ######### Concatenation Layer ############################### - # Concatenate the outputs from the convolutional layers and dense layer - model = tf.keras.layers.concatenate([model, input_2], - name='Concatenated_Layer') - - ######### FC Block #################################### - for l in range(0, FC__NumberLayers): - model = Dense(FC__units*(l+1)**2, - activation = 'relu', - kernel_initializer = 'he_normal', - # kernel_regularizer=tf.keras.regularizers.l2(Conv__regularizer), - name = 'FC__B'+str(1)+'_L'+str(l+1))(model) - - # model= Dropout(FC__dropout, - # name = 'FC__Dropout__B'+str(1)+'_L'+str(l+1))(model) - - ######### 3rd FC Block: gravity ############################## - -# model2 = Dense(FC__units_gravity, -# activation = 'relu', -# kernel_initializer = 'he_normal', -# # kernel_regularizer=tf.keras.regularizers.l2(0.003/2), -# name = 'FC_block3_gravity')(model) - -# model2= Dropout(FC_out_dropout, -# name = 'FC_block3_gravity__Dropout')(model2) - - out__gravity = Dense(1, - activation = 'linear', - # kernel_initializer = 'he_normal', - name = 'gravity')(model) - - - - ######### 3rd FC Block: c_o_ratio ############################## -# model2 = Dense(FC__units_c_o_ratio, -# activation = 'relu', -# kernel_initializer = 'he_normal', -# # kernel_regularizer=tf.keras.regularizers.l2(0.003/2), -# name = 'FC_block3_c_o_ratio')(model) - -# model2= Dropout(FC_out_dropout, -# name = 'FC_block3_c_o_ratio__Dropout')(model2) - - - out__c_o_ratio = Dense(1, - activation = 'linear', - # kernel_initializer = 'he_normal', - # kernel_regularizer=tf.keras.regularizers.l2(0.003/2), - name='c_o_ratio')(model) - - - ######### 3rd FC Block: metallicity ############################## -# model2 = Dense(FC__units_metallicity, -# activation = 'relu', -# kernel_initializer = 'he_normal', -# # kernel_regularizer=tf.keras.regularizers.l2(0.003/2), -# name = 'FC_block3_metallicity')(model) - -# model2= Dropout(FC_out_dropout, -# name = 'FC_block3_metallicity__Dropout')(model2) - - - out__metallicity = Dense(1, - activation = 'linear', - # kernel_initializer = 'he_normal', - name='metallicity')(model) - - - - ######### 3rd FC Block: temperature ############################## -# model2 = Dense(FC__units_temperature, -# activation = 'relu', -# kernel_initializer = 'he_normal', -# # kernel_regularizer=tf.keras.regularizers.l2(0.003/2), -# name = 'FC_block3_temperature')(model) - -# model2= Dropout(FC_out_dropout, -# name = 'FC_block3_temperature__Dropout')(model2) - - - out__temperature = Dense(1, - activation = 'linear', - name='temperature')(model) - - - ######### OUTPUT ################################################ - # Create the model with two inputs and two outputs - model = tf.keras.Model(inputs=[input_1, input_2], - outputs=[out__gravity, out__c_o_ratio, out__metallicity, out__temperature]) - - # Compile the model with an optimizer, loss function, and metrics - model.compile(loss='huber_loss', - optimizer=keras.optimizers.Adam(learning_rate = lr), - metrics=['mae']) - - early_stop = EarlyStopping(monitor='loss', min_delta=4e-4, patience=50, mode='auto', \ - restore_best_weights=True) - - - - # YOU CAN ADD FUNCTION HERE TO ADD NOISE - history = model.fit(x = [self.X1_train, self.X2_train], - y = [self.y1_train, self.y2_train, self.y3_train, self.y4_train], #self.x_train, self.y_train, - batch_size = 32, #config['batch_size'], # self.batch_size, - validation_data=([self.X1_val, self.X2_val], [self.y1_val, self.y2_val, self.y3_val, self.y4_val]), - #validation_split=0.2, - epochs=int(budget), - verbose=1, - callbacks=[early_stop], - ) - - - train_score = model.evaluate(x = [self.X1_train, self.X2_train], - y = [self.y1_train, self.y2_train, self.y3_train, self.y4_train], - verbose=0) - val_score = model.evaluate(x = [self.X1_val, self.X2_val], - y = [self.y1_val, self.y2_val, self.y3_val, self.y4_val], - verbose=0) - test_score = model.evaluate(x = [self.X1_test, self.X2_test], - y = [self.y1_test, self.y2_test, self.y3_test, self.y4_test], - verbose=0) - - #print(train_score, val_score, test_score) -# #import IPython; IPython.embed() - return ({ - 'loss': val_score[0], # remember: HpBandSter always minimizes! - 'info': { 'test accuracy': test_score, - 'train accuracy': train_score, - 'validation accuracy': val_score, - 'number of parameters': model.count_params(), - }, - # 'model' : model, - # 'history' : history, - - }) - - @staticmethod - def get_configspace(): - """ - It builds the configuration space with the needed hyperparameters. - It is easily possible to implement different types of hyperparameters. - Beside float-hyperparameters on a log scale, it is also able to handle categorical input parameter. - :return: ConfigurationsSpace-Object - """ - cs = CS.ConfigurationSpace() - - # Conv hyperparameters - Conv__filters = CategoricalHyperparameter(name='Conv__filters', choices=[16, 32, 64]) # NOTE: Apply the same categorical method for other unit and - Conv__kernel_size = UniformIntegerHyperparameter(name='Conv__kernel_size', lower=1, upper=8, default_value=3, log=False) # ok - Conv__MaxPooling1D = UniformIntegerHyperparameter(name='Conv__MaxPooling1D', lower=1, upper=8, default_value=2, log=False) # ok - Conv__NumberLayers = UniformIntegerHyperparameter(name='Conv__NumberLayers', lower=1, upper=4, default_value=2, log=False) # ok - Conv__NumberBlocks = UniformIntegerHyperparameter(name='Conv__NumberBlocks', lower=1, upper=4, default_value=2, log=False) # ok - - # FC hyperparameters - FC__units = CategoricalHyperparameter(name='FC__units', choices=[16, 32 , 64, 128, 256]) # NOTE: Apply the same categorical method for other unit and - - # FC__units_temperature = CategoricalHyperparameter(name='FC__units_temperature', choices=[8, 16, 32 , 64, 128, 256]) # the same - # FC__units_metallicity = CategoricalHyperparameter(name='FC__units_metallicity', choices=[8, 16, 32 , 64, 128, 256]) # the same - # FC__units_c_o_ratio = CategoricalHyperparameter(name='FC__units_c_o_ratio', choices=[8, 16, 32 , 64, 128, 256]) # the same - # FC__units_gravity = CategoricalHyperparameter(name='FC__units_gravity', choices=[8, 16, 32 , 64, 128, 256]) # same - - FC__NumberLayers = UniformIntegerHyperparameter(name='FC__NumberLayers', lower=1, upper=4, default_value=2, log=False) - # FC__NumberBlocks = UniformIntegerHyperparameter(name='FC__NumberBlocks', lower=1, upper=5, default_value=1, log=False) # DELETE - No blocks for FC -# FC__dropout = UniformFloatHyperparameter(name='FC__dropout', lower=0.001, upper=0.4, default_value=0.02, log=True) -# FC_out_dropout = UniformFloatHyperparameter(name='FC_out_dropout', lower=0.001, upper=0.4, default_value=0.02, log=True) - -# FC_in_Conv__units = CategoricalHyperparameter(name='FC_in_Conv__units', choices=[8, 16, 32 , 64, 128, 256]) # same -# #FC_in_Conv__NumberBlocks = UniformIntegerHyperparameter(name='FC_in_Conv__NumberBlocks', lower=1, upper=5, default_value=1, log=False) ## DELETE, -# FC_in_Conv__NumberLayers = UniformIntegerHyperparameter(name='FC_in_Conv__NumberLayers', lower=1, upper=4, default_value=1, log=False) ### DELETE -# FC_in_Conv__dropout = UniformFloatHyperparameter(name='FC_in_Conv__dropout', lower=0.001, upper=0.4, default_value=0.02, log=True) - - # Other hyperparameters - lr = UniformFloatHyperparameter(name='lr', lower=1e-5, upper=1e-2, default_value=1e-4, log=True) - - - - - cs.add_hyperparameters([ - Conv__filters, - Conv__kernel_size, - Conv__MaxPooling1D, - Conv__NumberLayers, - Conv__NumberBlocks, - # - FC__units, - # FC__units_temperature, - # FC__units_c_o_ratio, - # FC__units_gravity, - # FC__units_metallicity, - FC__NumberLayers, - # FC__dropout, - # FC_out_dropout, - - # FC_in_Conv__NumberLayers, - # FC_in_Conv__units, - # FC_in_Conv__dropout, - - - lr, - ]) - - - return cs - - - -# Step 1: Load the dataset -# original dataset -df=pd.read_csv('../../datasets/browndwarf_R100_v4_newWL_v2.csv.bz2', compression='bz2') -wl = pd.read_csv('../../datasets/wl.csv') - - -## Prepare feature variables (X) and targets (y) -df = df[df['is_augmented'].isin(['no'])] - -#df = df[::28] # Warning!! delete this for the main run!! - -X = df.drop( - columns=['gravity', - 'temperature', - 'c_o_ratio', - 'metallicity', - 'is_augmented'])#.astype(np.float32) - -y = df[['gravity', 'c_o_ratio', 'metallicity', 'temperature', ]]#.astype(np.float32) - - - -# Log-Transform -y['temperature'] = np.log10(y['temperature']) -# df['temperature'] = df['temperature'].apply(lambda x: np.log10(x)) - -# Create an instance of TrainCNNRegression -train_cnn_regression = TrainRegression(feature_values=X, - feature_names=X.columns, - target_values=y.to_numpy(), - target_name=['gravity', 'c_o_ratio', 'metallicity', 'temperature'], - is_tuned='yes', - param_grid=None, - spectral_resolution=100, - is_feature_improved='no', - n_jobs=4, - cv=5, - is_augmented='no', - ml_model=None, - ml_model_str='CNN') - - - -# Split the dataset into train and test sets -train_cnn_regression.split_train_validation_test(test_size=0.1, val_size=0.1) - -# normalize the X features using MinMax Scaler -train_cnn_regression.standardize_X_row_wise() - -# Standardize the y features using Standard Scaler -train_cnn_regression.standardize_y_column_wise() - -# Create Xmin and Xmax -train_cnn_regression.X_train_min = train_cnn_regression.X_train.min(axis=1) -train_cnn_regression.X_train_max = train_cnn_regression.X_train.max(axis=1) - -train_cnn_regression.X_val_min = train_cnn_regression.X_val.min(axis=1) -train_cnn_regression.X_val_max = train_cnn_regression.X_val.max(axis=1) - -train_cnn_regression.X_test_min = train_cnn_regression.X_test.min(axis=1) -train_cnn_regression.X_test_max = train_cnn_regression.X_test.max(axis=1) - -df_MinMax_train = pd.DataFrame((train_cnn_regression.X_train_min, train_cnn_regression.X_train_max)).T -df_MinMax_val = pd.DataFrame((train_cnn_regression.X_val_min, train_cnn_regression.X_val_max)).T -df_MinMax_test = pd.DataFrame((train_cnn_regression.X_test_min, train_cnn_regression.X_test_max)).T - - -df_MinMax_train.rename(columns={0:'min', 1:'max'}, inplace=True) - - -train_cnn_regression.standardize_X_column_wise( - X_train = df_MinMax_train.values, - X_val = df_MinMax_val.values, - X_test = df_MinMax_test.values, - ) - - - - - - - -import argparse -import hpbandster.core.nameserver as hpns -import hpbandster.core.result as hpres - -from hpbandster.optimizers import BOHB as BOHB -# from hpbandster.examples.commons import MyWorker - - - -# Create the optimization object with the desired queue size - - -parser = argparse.ArgumentParser(description='Example 3 - Local and Parallel Execution.') -parser.add_argument('--min_budget', type=float, help='Minimum budget used during the optimization.', default=10) -parser.add_argument('--max_budget', type=float, help='Maximum budget used during the optimization.', default=30) -parser.add_argument('--n_iterations', type=int, help='Number of iterations performed by the optimizer', default=10000) -parser.add_argument('--n_workers', type=int, help='Number of workers to run in parallel.', default = 40 ) -parser.add_argument('--worker', help='Flag to turn this into a worker process', action='store_true') - -args=parser.parse_args() -# args = parser.parse_args(args=[]) - - - -if args.worker: - w = KerasWorker( - # input dataset: StandardScaled instances - X1_train = train_cnn_regression.X_train_standardized_rowwise, - X1_val = train_cnn_regression.X_val_standardized_rowwise, - X1_test = train_cnn_regression.X_test_standardized_rowwise, - - # input dataset: Min Max of each instance - X2_train = train_cnn_regression.X_train_standardized_columnwise, - X2_val = train_cnn_regression.X_val_standardized_columnwise, - X2_test = train_cnn_regression.X_test_standardized_columnwise, - - # 1st target - y1_train = train_cnn_regression.y_train_standardized_columnwise[:,0], - y1_val = train_cnn_regression.y_val_standardized_columnwise[:,0], - y1_test = train_cnn_regression.y_test_standardized_columnwise[:,0], - - # 2nd target - y2_train = train_cnn_regression.y_train_standardized_columnwise[:,1], - y2_val = train_cnn_regression.y_val_standardized_columnwise[:,1], - y2_test = train_cnn_regression.y_test_standardized_columnwise[:,1], - - # 3rd target - y3_train = train_cnn_regression.y_train_standardized_columnwise[:,2], - y3_val = train_cnn_regression.y_val_standardized_columnwise[:,2], - y3_test = train_cnn_regression.y_test_standardized_columnwise[:,2], - - # 4th target - y4_train = train_cnn_regression.y_train_standardized_columnwise[:,3], - y4_val = train_cnn_regression.y_val_standardized_columnwise[:,3], - y4_test = train_cnn_regression.y_test_standardized_columnwise[:,3], - - - - sleep_interval = 0.5, nameserver=ip_address,run_id='CNNtrain') - - w.run(background=False) - exit(0) - -# Start a nameserver (see example_1) -NS = hpns.NameServer(run_id='CNNtrain', host=ip_address, port=None) -NS.start() - - -# Run an optimizer (see example_2) - -result_logger = hpres.json_result_logger(directory='/data2/ehsan_storage/telescopeML_project/outputs/bohb_outputs/out4', overwrite=True) - -bohb = BOHB( configspace = KerasWorker.get_configspace(), - run_id = 'CNNtrain', - min_budget=args.min_budget, - max_budget=args.max_budget, - result_logger = result_logger, - ) - - - -res = bohb.run(n_iterations=args.n_iterations, min_n_workers=args.n_workers) - - - -# Step 4: Shutdown -# After the optimizer run, we must shutdown the master and the nameserver. -bohb.shutdown(shutdown_workers=True) -NS.shutdown() - - -# Step 5: Analysis -# Each optimizer returns a hpbandster.core.result.Result object. -# It holds informations about the optimization run like the incumbent (=best) configuration. -# For further details about the Result object, see its documentation. -# Here we simply print out the best config and some statistics about the performed runs. -# id2config = res.get_id2config_mapping() -# incumbent = res.get_incumbent_id() - -# all_runs = res.get_all_runs() - -# print('Best found configuration:', id2config[incumbent]['config']) -# print('A total of %i unique configurations where sampled.' % len(id2config.keys())) -# print('A total of %i runs where executed.' % len(res.get_all_runs())) -# print('Total budget corresponds to %.1f full function evaluations.'%(sum([r.budget for r in all_runs])/args.max_budget)) -# print('Total budget corresponds to %.1f full function evaluations.'%(sum([r.budget for r in all_runs])/args.max_budget)) -##print('The run took %.1f seconds to complete.'%(all_runs[-1].time_stamps['finished'] - all_runs[0].time_stamps['started'])): - -# Step 5: Analysis -# Each optimizer returns a hpbandster.core.result.Result object. -# It holds informations about the optimization run like the incumbent (=best) configuration. -# For further details about the Result object, see its documentation. -# Here we simply print out the best config and some statistics about the performed runs. -id2config = res.get_id2config_mapping() -incumbent = res.get_incumbent_id() - -all_runs = res.get_all_runs() - -print('Best found configuration:', id2config[incumbent]['config']) -print('A total of %i unique configurations where sampled.' % len(id2config.keys())) -print('A total of %i runs where executed.' % len(res.get_all_runs())) -print('Total budget corresponds to %.1f full function evaluations.'%(sum([r.budget for r in all_runs])/args.max_budget)) -print('Total budget corresponds to %.1f full function evaluations.'%(sum([r.budget for r in all_runs])/args.max_budget)) - diff --git a/store/bohb_model_tuner/check_bohb_results.ipynb b/store/bohb_model_tuner/check_bohb_results.ipynb deleted file mode 100644 index 723ced06..00000000 --- a/store/bohb_model_tuner/check_bohb_results.ipynb +++ /dev/null @@ -1,2237 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "85b659ef-308b-428a-89bd-28c48e2552e5", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ab380d64-53f2-438c-9f86-85607501df19", - "metadata": {}, - "outputs": [], - "source": [ - "# Step 1: Load JSON data from the file\n", - "with open('../../outputs/bohb_outputs/out2/configs.json', 'r') as file:\n", - " loaded_data = file.readlines()\n", - "\n", - "# Step 2: Extract values from each row\n", - "rows = []\n", - "for row in loaded_data:\n", - " row_data = json.loads(row)\n", - " values = row_data[0] + list ( row_data[1].values() ) + list( row_data[2].values() )\n", - " rows.append(values)\n", - "\n", - "# # Step 3: Create a DataFrame\n", - "configs = pd.DataFrame(rows, columns=['i', 'j', 'k']+list ( row_data[1].keys() ) + list( row_data[2].keys() ))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3c9aa72f-9b0b-4515-b1d8-2f97e0c098c8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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        " - ], - "text/plain": [ - " i j k iteration submitted started finished loss \\\n", - "1 0 0 0 10.0 1.689444e+09 1.689444e+09 1.689483e+09 0.264784 \n", - "6 0 0 1 10.0 1.689444e+09 1.689444e+09 1.689498e+09 0.155014 \n", - "34 0 0 2 10.0 1.689444e+09 1.689444e+09 1.689600e+09 0.286202 \n", - "32 1 0 1 30.0 1.689444e+09 1.689444e+09 1.689585e+09 0.241843 \n", - "\n", - " test_loss test_gravity_loss ... FC__units_c_o_ratio FC__units_gravity \\\n", - "1 0.268992 0.106316 ... 32 64 \n", - "6 0.157586 0.059480 ... 128 8 \n", - "34 0.283402 0.128489 ... 8 64 \n", - "32 0.250244 0.101575 ... 8 256 \n", - "\n", - " FC__units_metallicity FC__units_temperature FC_in_Conv__NumberLayers \\\n", - "1 8 128 2 \n", - "6 32 8 1 \n", - "34 64 32 4 \n", - "32 8 128 1 \n", - "\n", - " FC_in_Conv__dropout FC_in_Conv__units FC_out_dropout lr \\\n", - "1 0.007461 16 0.155113 0.000195 \n", - "6 0.024349 32 0.003902 0.000273 \n", - "34 0.003502 128 0.141180 0.000849 \n", - "32 0.025452 256 0.003764 0.000183 \n", - "\n", - " model_based_pick \n", - "1 False \n", - "6 False \n", - "34 False \n", - "32 False \n", - "\n", - "[4 rows x 53 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df.sort_values(by='submitted').head(4)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "5f14ce1f-958a-417c-992f-7ee43b64b74d", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from bokeh.plotting import figure, show\n", - "from bokeh.io import output_notebook\n", - "from bokeh.models import LogTicker, LogTickFormatter\n", - "\n", - "def plot_CumulativeBudget_loss(loss_df_list, print_results=True, plot_results=True):\n", - " output_notebook()\n", - "\n", - " p = figure(title='Cumulative Budget vs. Validation Huber Loss', \n", - " x_axis_label='Cumulative Budget',\n", - " y_axis_label='Validation Huber Loss', \n", - " y_axis_type=\"log\",\n", - " width=800, height=400,\n", - " x_range=(1,2001), y_range=(0.04, 1))\n", - "\n", - " for loss_df in loss_df_list:\n", - " list_loss_smaller = []\n", - " budget_list = []\n", - "\n", - " budget = merged_df.sort_values(by='submitted')['iteration']\n", - "\n", - " smallest_value = 1\n", - " b = 0\n", - " for i in range(len(loss_df)):\n", - " b += int(budget[i])\n", - " if val_loss[i] < smallest_value:\n", - " smallest_value = loss_df[i]\n", - " list_loss_smaller.append(smallest_value)\n", - " budget_list.append(b)\n", - "\n", - " if print_results:\n", - " print(budget_list, list_loss_smaller)\n", - "\n", - " if plot_results:\n", - " p.scatter(budget_list, list_loss_smaller, marker='square', size=8, alpha=0.9, legend_label='Data Points')\n", - " p.line(budget_list, list_loss_smaller, line_dash='dashed', line_alpha=0.5, legend_label='Interpolated Line')\n", - "\n", - " # p.x_range.start = 1\n", - " # p.x_range.end = 301\n", - " # p.y_range.start = 0.04\n", - " # p.y_range.end = 1\n", - " # # p.legend.title = 'Data Points and Interpolated Line'\n", - "\n", - " # Set logarithmic scale for both x and y axes\n", - " # p.xaxis[0].ticker = LogTicker()\n", - " # p.xaxis[0].formatter = LogTickFormatter()\n", - " # p.yaxis[0].ticker = LogTicker()\n", - " # p.yaxis[0].formatter = LogTickFormatter()\n", - "\n", - " # Set custom y-axis ticks\n", - " yticks = [0.04, 0.05, 0.1, 0.5, 1.]\n", - " p.yaxis.ticker = yticks\n", - " p.yaxis.major_label_overrides = {tick: str(tick) for tick in yticks}\n", - "\n", - " show(p)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d078a2fc-42dd-4215-b520-a0007c0c64c8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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*/\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " const events = require('base/js/events');\n", - " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " const NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
        \\n\"+\n", - " \"

        \\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

        \\n\"+\n", - " \"
          \\n\"+\n", - " \"
        • re-rerun `output_notebook()` to attempt to load from CDN again, or
        • \\n\"+\n", - " \"
        • use INLINE resources instead, as so:
        • \\n\"+\n", - " \"
        \\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
        \"}};\n", - "\n", - " function display_loaded() {\n", - " const el = document.getElementById(\"b1930723-00c6-4341-b6e2-02fe0537cd09\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error(url) {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (let i = 0; i < css_urls.length; i++) {\n", - " const url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (let i = 0; i < js_urls.length; i++) {\n", - " const url = js_urls[i];\n", - " const element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.1.min.js\"];\n", - " const css_urls = [];\n", - "\n", - " const inline_js = [ function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - "function(Bokeh) {\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " if (root.Bokeh !== undefined || force === true) {\n", - " for (let i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - "if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " const cell = $(document.getElementById(\"b1930723-00c6-4341-b6e2-02fe0537cd09\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
        \\n\"+\n \"

        \\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

        \\n\"+\n \"
          \\n\"+\n \"
        • re-rerun `output_notebook()` to attempt to load from CDN again, or
        • \\n\"+\n \"
        • use INLINE resources instead, as so:
        • \\n\"+\n \"
        \\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
        \"}};\n\n function display_loaded() {\n const el = document.getElementById(\"b1930723-00c6-4341-b6e2-02fe0537cd09\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"b1930723-00c6-4341-b6e2-02fe0537cd09\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
        \n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " const docs_json = {\"d15a1672-8ff1-4dd5-a333-5215139d1dc9\":{\"version\":\"3.2.1\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1001\",\"attributes\":{\"width\":800,\"height\":400,\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1011\",\"attributes\":{\"start\":1,\"end\":2001}},\"y_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1012\",\"attributes\":{\"start\":0.04}},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1013\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LogScale\",\"id\":\"p1014\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1004\",\"attributes\":{\"text\":\"Cumulative Budget vs. Validation Huber Loss\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1038\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1032\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1033\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1034\"},\"data\":{\"type\":\"map\",\"entries\":[[\"x\",[10,20,50,80,120,160,390,470,1560]],[\"y\",[0.7744809985160828,0.264783650636673,0.11827833950519562,0.11075330525636673,0.09743142873048782,0.06754410266876221,0.06498707085847855,0.050968050956726074,0.04973980039358139]]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1039\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1040\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1035\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"size\":{\"type\":\"value\",\"value\":8},\"line_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.9},\"fill_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.9},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.9},\"marker\":{\"type\":\"value\",\"value\":\"square\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1036\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"size\":{\"type\":\"value\",\"value\":8},\"line_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1},\"marker\":{\"type\":\"value\",\"value\":\"square\"}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1037\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"size\":{\"type\":\"value\",\"value\":8},\"line_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2},\"marker\":{\"type\":\"value\",\"value\":\"square\"}}}}},{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1049\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1043\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1044\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1045\"},\"data\":{\"type\":\"map\",\"entries\":[[\"x\",[10,20,50,80,120,160,390,470,1560]],[\"y\",[0.7744809985160828,0.264783650636673,0.11827833950519562,0.11075330525636673,0.09743142873048782,0.06754410266876221,0.06498707085847855,0.050968050956726074,0.04973980039358139]]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1050\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1051\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1046\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#1f77b4\",\"line_alpha\":0.5,\"line_dash\":[6]}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1047\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#1f77b4\",\"line_alpha\":0.1,\"line_dash\":[6]}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p1048\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#1f77b4\",\"line_alpha\":0.2,\"line_dash\":[6]}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1010\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p1025\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p1026\"},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p1027\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1028\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"bottom_units\":\"canvas\",\"top_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p1029\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p1030\"},{\"type\":\"object\",\"name\":\"HelpTool\",\"id\":\"p1031\"}]}},\"left\":[{\"type\":\"object\",\"name\":\"LogAxis\",\"id\":\"p1020\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"FixedTicker\",\"id\":\"p1053\",\"attributes\":{\"ticks\":[0.04,0.05,0.1,0.5,1.0],\"minor_ticks\":[]}},\"formatter\":{\"type\":\"object\",\"name\":\"LogTickFormatter\",\"id\":\"p1022\"},\"axis_label\":\"Validation Huber Loss\",\"major_label_overrides\":{\"type\":\"map\",\"entries\":[[0.04,\"0.04\"],[0.05,\"0.05\"],[0.1,\"0.1\"],[0.5,\"0.5\"],[1.0,\"1.0\"]]},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1023\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1015\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1016\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1017\"},\"axis_label\":\"Cumulative Budget\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1018\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1019\",\"attributes\":{\"axis\":{\"id\":\"p1015\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1024\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1020\"}}},{\"type\":\"object\",\"name\":\"Legend\",\"id\":\"p1041\",\"attributes\":{\"items\":[{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1042\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"Data Points\"},\"renderers\":[{\"id\":\"p1038\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1052\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"Interpolated Line\"},\"renderers\":[{\"id\":\"p1049\"}]}}]}}]}}]}};\n", - " const render_items = [{\"docid\":\"d15a1672-8ff1-4dd5-a333-5215139d1dc9\",\"roots\":{\"p1001\":\"f5e64415-caa0-4cf0-9d19-833fa54f4b54\"},\"root_ids\":[\"p1001\"]}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " let attempts = 0;\n", - " const timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " clearInterval(timer);\n", - " embed_document(root);\n", - " } else {\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " clearInterval(timer);\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " }\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "p1001" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "val_loss = merged_df.sort_values(by='submitted')['val_loss']\n", - "train_loss = merged_df.sort_values(by='submitted')['train_loss']\n", - "test_loss = merged_df.sort_values(by='submitted')['test_loss']\n", - "\n", - "plot_CumulativeBudget_loss([val_loss],\n", - " print_results=False,)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "cbe06ac7-3fd2-432c-89d2-51afc4dd7f55", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_CumulativeBudget_loss(loss_df_list,\n", - " print_results = True,\n", - " plot_results = True,\n", - " ):\n", - " for loss_df in loss_df_list:\n", - " list_loss_smaller = []\n", - " budget_list = []\n", - "\n", - " budget = merged_df.sort_values(by='submitted')['iteration']\n", - "\n", - " smallest_value = 1\n", - " b = 0\n", - " for i in range(len(loss_df)):\n", - " b += int(budget[i])\n", - " if val_loss[i] < smallest_value:\n", - " smallest_value = loss_df[i]\n", - " print(smallest_value)\n", - " list_loss_smaller.append(smallest_value)\n", - " budget_list.append(b)\n", - "\n", - "\n", - "\n", - " if print_results:\n", - " print( budget_list, list_loss_smaller)\n", - "\n", - "\n", - "\n", - " if plot_results:\n", - " import seaborn as sns\n", - " import matplotlib.pyplot as plt\n", - "\n", - " sns.set_style(\"whitegrid\", {\"grid.color\": \".6\", \"grid.linestyle\": \"-\"})\n", - " sns.scatterplot(x=budget_list, y=list_loss_smaller, marker='s', s=50, alpha=0.9)\n", - " plt.plot(budget_list, list_loss_smaller, '--', alpha=0.5)\n", - " plt.xlabel('Cumulative Budget', fontsize=12)\n", - " plt.ylabel('Validation Huber Loss', fontsize=12)\n", - "\n", - " sns.set(style=\"ticks\")\n", - " plt.xscale('log')\n", - " plt.yscale('log')\n", - " plt.xlim((1, 10000))\n", - " plt.ylim((0.04, .6))\n", - " plt.grid(True, which='both', linestyle='-', linewidth=0.5, color='lightgray')\n", - " plt.grid(True, which='major', axis='both', linestyle='-', linewidth=1, color='darkgrey')\n", - "\n", - " # Set custom y-axis ticks\n", - " yticks = [0.04, 0.05, 0.1, 0.5, 1.]\n", - " plt.yticks(yticks, [str(tick) for tick in yticks])\n", - " plt.legend()\n", - "\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "fdc391b5-b34a-4cbc-b0db-e94495aa701a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7744809985160828\n", - "0.264783650636673\n", - "0.11827833950519562\n", - "0.11075330525636673\n", - "0.09743142873048782\n", - "0.06754410266876221\n", - "0.06498707085847855\n", - "0.050968050956726074\n", - "0.04973980039358139\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "val_loss = merged_df.sort_values(by='submitted')['val_loss']\n", - "train_loss = merged_df.sort_values(by='submitted')['train_loss']\n", - "test_loss = merged_df.sort_values(by='submitted')['test_loss']\n", - "\n", - "plot_CumulativeBudget_loss([val_loss],\n", - " print_results=False,)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "b6748f88-6a6e-4355-a89a-56a2aebd0808", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "i 7\n", - "j 0\n", - "k 0\n", - "iteration 30.0\n", - "submitted 1689443983.154186\n", - "started 1689443983.154705\n", - "finished 1689857588.292075\n", - "loss 0.04974\n", - "test_loss 0.049046\n", - "test_gravity_loss 0.003907\n", - "test_c_o_ratio_loss 0.00721\n", - "test_metallicity_loss 0.036967\n", - "test_temperature_loss 0.000962\n", - "test_gravity_mae 0.068561\n", - "test_c_o_ratio_mae 0.093363\n", - "test_metallicity_mae 0.202011\n", - "test_temperature_mae 0.032354\n", - "val_loss 0.04974\n", - "val_gravity_loss 0.004015\n", - "val_c_o_ratio_loss 0.007022\n", - "val_metallicity_loss 0.037795\n", - "val_temperature_loss 0.000908\n", - "val_gravity_mae 0.068664\n", - "val_c_o_ratio_mae 0.092139\n", - "val_metallicity_mae 0.203758\n", - "val_temperature_mae 0.031545\n", - "train_loss 0.045853\n", - "train_gravity_loss 0.003421\n", - "train_c_o_ratio_loss 0.006241\n", - "train_metallicity_loss 0.035297\n", - "train_temperature_loss 0.000894\n", - "train_gravity_mae 0.064821\n", - "train_c_o_ratio_mae 0.086403\n", - "train_metallicity_mae 0.196734\n", - "train_temperature_mae 0.031017\n", - "Conv__MaxPooling1D 2\n", - "Conv__NumberBlocks 1\n", - "Conv__NumberLayers 3\n", - "Conv__filters 4\n", - "Conv__kernel_size 6\n", - "FC__NumberLayers 4\n", - "FC__dropout 0.002461\n", - "FC__units 64\n", - "FC__units_c_o_ratio 64\n", - "FC__units_gravity 16\n", - "FC__units_metallicity 256\n", - "FC__units_temperature 8\n", - "FC_in_Conv__NumberLayers 4\n", - "FC_in_Conv__dropout 0.098892\n", - "FC_in_Conv__units 128\n", - "FC_out_dropout 0.002186\n", - "lr 0.00005\n", - "model_based_pick False\n", - "Name: 83, dtype: object" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df.iloc[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "a40a1e3f-9cee-4365-8d5c-e500aed2af82", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Conv__MaxPooling1D': 2,\n", - " 'Conv__NumberBlocks': 1,\n", - " 'Conv__NumberLayers': 3,\n", - " 'Conv__filters': 4,\n", - " 'Conv__kernel_size': 6,\n", - " 'FC__NumberLayers': 4,\n", - " 'FC__dropout': 0.0024609140719442646,\n", - " 'FC__units': 64,\n", - " 'FC__units_c_o_ratio': 64,\n", - " 'FC__units_gravity': 16,\n", - " 'FC__units_metallicity': 256,\n", - " 'FC__units_temperature': 8,\n", - " 'FC_in_Conv__NumberLayers': 4,\n", - " 'FC_in_Conv__dropout': 0.09889223768186726,\n", - " 'FC_in_Conv__units': 128,\n", - " 'FC_out_dropout': 0.002185843015008406,\n", - " 'lr': 4.9946842008422193e-05}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dict(merged_df.iloc[0, 35:-1])" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "01545299-e360-47ad-8216-7d2c90c10d06", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['i', 'j', 'k', 'iteration', 'submitted', 'started', 'finished', 'loss',\n", - " 'test_loss', 'test_gravity_loss', 'test_c_o_ratio_loss',\n", - " 'test_metallicity_loss', 'test_temperature_loss', 'test_gravity_mae',\n", - " 'test_c_o_ratio_mae', 'test_metallicity_mae', 'test_temperature_mae',\n", - " 'val_loss', 'val_gravity_loss', 'val_c_o_ratio_loss',\n", - " 'val_metallicity_loss', 'val_temperature_loss', 'val_gravity_mae',\n", - " 'val_c_o_ratio_mae', 'val_metallicity_mae', 'val_temperature_mae',\n", - " 'train_loss', 'train_gravity_loss', 'train_c_o_ratio_loss',\n", - " 'train_metallicity_loss', 'train_temperature_loss', 'train_gravity_mae',\n", - 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"dict(merged_df2.sort_values('val_loss').iloc[0, 35:-1])" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "40412ee9-8cd1-4a4c-8a36-8620a68c2c64", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.ticker import MaxNLocator, FixedLocator, LogLocator\n", - "\n", - "# Set the style\n", - "sns.set_style(\"darkgrid\")\n", - "\n", - "data = merged_df[[\n", - " 'Conv__NumberBlocks',\n", - " 'Conv__NumberLayers',\n", - " 'Conv__filters',\n", - " 'Conv__kernel_size',\n", - " 'Conv__MaxPooling1D',\n", - " 'val_loss']]\n", - "\n", - "# Find configurations with the lowest loss\n", - "min_loss = data['val_loss'].min()\n", - "\n", - "# Define the pairplot\n", - "g = sns.PairGrid(data, corner=True, diag_sharey=True)\n", - "\n", - "# Plot scatterplots on the lower triangle\n", - "g.map_lower(sns.scatterplot, color='black', s=15)\n", - "\n", - "# Plot the distributions on the diagonal\n", - "g.map_diag(sns.histplot, kde=False)\n", - "\n", - "# Customize the x-axis tick labels for each subplot\n", - "labels = [\n", - " 'NumberBlocks',\n", - " 'NumberLayers',\n", - " 'filters',\n", - " 'kernel_size',\n", - " 'MaxPooling1D',\n", - " 'Total val_loss']\n", - "\n", - "for i, label in enumerate(labels):\n", - " g.axes[-1, i].set_xlabel(label, fontsize=12)\n", - " g.axes[i, 0].set_ylabel(label, fontsize=12)\n", - "\n", - "# Set the number of ticks on the x-axis for each subplot\n", - "num_ticks = 5\n", - "for i in range(len(labels)):\n", - " for j in range(len(labels)):\n", - " if i > j:\n", - " if labels[j] in [ 'NumberBlocks','NumberLayers']:\n", - " # Set integer ticks for specific subplots\n", - " g.axes[i, j].xaxis.set_major_locator(FixedLocator(np.arange(1, num_ticks + 1, dtype=int)))\n", - " else:\n", - " g.axes[i, j].xaxis.set_major_locator(MaxNLocator(num_ticks))\n", - " g.axes[i, j].yaxis.set_major_locator(MaxNLocator(num_ticks))\n", - " # elif i == 0 and j==5:\n", - " # # Set logarithmic scale for the diagonal subplots\n", - " # g.axes[i, j].xaxis.set_major_locator(LogLocator(base=10))\n", - " # g.axes[i, j].xaxis.set_minor_locator(LogLocator(base=10, subs=[0.02,0.05,0.01, 0.5, 1.], numticks=10))\n", - "\n", - "# Mark the configurations with the lowest loss with a star inside the lower left half\n", - "lower_indices = np.tril_indices_from(g.axes, k=-1)\n", - "for i, j in zip(*lower_indices):\n", - " x = data.iloc[:, j]\n", - " y = data.iloc[:, i]\n", - " mask = (data['val_loss'] == min_loss)\n", - " \n", - " if i >= j:\n", - " g.axes[i, j].plot(x[mask], y[mask], marker='*', color='red', markersize=12, alpha=0.5)\n", - "\n", - "# Customize the plot\n", - "g.fig.suptitle(\"BOHB-tuned Hyperparameters: Convolutional Component\", fontweight='bold', fontsize=12)\n", - "\n", - "# Set the size of the figure to 10 inches by 10 inches\n", - "g.fig.set_size_inches(8, 8)\n", - "\n", - "# plt.tight_layout()\n", - "\n", - "# Increase the size of xticks and yticks\n", - "for ax in g.axes.flatten():\n", - " if ax is not None:\n", - " ax.tick_params(axis='x', labelsize=10)\n", - " ax.tick_params(axis='y', labelsize=10)\n", - "\n", - "for ax in g.axes[-1, :]:\n", - " if ax is not None:\n", - " plt.setp(ax.get_xticklabels(), rotation=45)\n", - "\n", - "plt.tight_layout()\n", - "\n", - "# Save the plot\n", - "plt.savefig(f'../../outputs/figures/BOHB_tuned_hyperparameters_convolution.pdf', format='pdf')\n", - "\n", - "# Show the plot\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "b4adc12b-d807-45d2-a798-efcb4b44efe1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.ticker import MaxNLocator, FixedLocator\n", - "from matplotlib.patches import Rectangle\n", - "\n", - "# Set the style\n", - "sns.set_style(\"darkgrid\")\n", - "\n", - "data = merged_df[[\n", - " # 'FC_in_Conv__units',\n", - " # 'FC_in_Conv__dropout',\n", - " ###\n", - " 'FC__NumberLayers',\n", - " 'FC__units', \n", - " 'FC__dropout', \n", - " ###\n", - " 'FC__units_c_o_ratio',\n", - " 'FC__units_gravity', \n", - " 'FC__units_metallicity', \n", - " 'FC__units_temperature',\n", - " 'FC_out_dropout',\n", - " # # ###\n", - " 'lr',\n", - " # ###\n", - " 'val_loss']]\n", - "\n", - "# display(data)\n", - "# Find configurations with the lowest loss\n", - "min_loss = data['val_loss'].min()\n", - "\n", - "# Define the pairplot\n", - "g = sns.PairGrid(data, corner=True, diag_sharey=True)\n", - "\n", - "# Plot scatterplots on the lower triangle\n", - "g.map_lower(sns.scatterplot, color='black', s=15)\n", - "\n", - "# Plot the distributions on the diagonal\n", - "g.map_diag(sns.histplot, kde=False)\n", - "\n", - "# Customize the x-axis tick labels for each subplot\n", - "# Customize the x-axis tick labels for each subplot\n", - "labels = [\n", - " # # ###\n", - " # 'Units ($FC1$)',\n", - " # 'Dropout ($FC1$)',\n", - " ###\n", - " 'NumberLayers ($FC2$)',\n", - " 'Units ($FC2$)', \n", - " 'Dropout ($FC2$)', \n", - " ###\n", - " 'Units_C/O ($FC3$)',\n", - " 'Units_$\\log g$ ($FC3$)', \n", - " 'Units_[M/H] ($FC3$)', \n", - " 'Units_T$_{eff}$ ($FC3$)',\n", - " 'Dropout ($FC3$)',\n", - " # # ###\n", - " 'lr',\n", - " # ###\n", - " 'Total val_loss'\n", - " ]\n", - "\n", - "for i, label in enumerate(labels):\n", - " g.axes[-1, i].set_xlabel(label, fontsize=16)\n", - " g.axes[i, 0].set_ylabel(label, fontsize=16)\n", - "\n", - "# Set the number of ticks on the x-axis for each subplot\n", - "num_ticks = 5\n", - "for i in range(len(labels)):\n", - " for j in range(len(labels)):\n", - " if i > j:\n", - " if labels[j] in ['FC__NumberLayers','FC__units', 'FC__units_c_o_ratio']:\n", - " # Set integer ticks for specific subplots\n", - " g.axes[i, j].xaxis.set_major_locator(FixedLocator([4,8,16,32,64,128,256]))\n", - " # g.axes[i, j].xaxis.set_major_locator(FixedLocator(np.arange(1, num_ticks + 1, dtype=int)))\n", - " else:\n", - " g.axes[i, j].xaxis.set_major_locator(MaxNLocator(num_ticks))\n", - " g.axes[i, j].yaxis.set_major_locator(MaxNLocator(num_ticks))\n", - "\n", - "# Mark the configurations with the lowest loss with a star inside the lower left half\n", - "lower_indices = np.tril_indices_from(g.axes, k=-1)\n", - "for i, j in zip(*lower_indices):\n", - " x = data.iloc[:, j]\n", - " y = data.iloc[:, i]\n", - " mask = (data['val_loss'] == min_loss)\n", - " \n", - " if i >= j:\n", - " g.axes[i, j].plot(x[mask], y[mask], marker='*', color='red', markersize=14, alpha=0.5)\n", - "\n", - "# Customize the plot\n", - "g.fig.suptitle(\"BOHB-tuned Hyperparameters: Fully-Connected Component\", fontweight='bold', fontsize=26)\n", - "\n", - "# Set the size of the figure to 10 inches by 10 inches\n", - "# g.fig.set_size_inches(16, 16)\n", - "plt.tight_layout()\n", - "\n", - "# Increase the size of xticks and yticks\n", - "for ax in g.axes.flatten():\n", - " if ax is not None:\n", - " ax.tick_params(axis='x', labelsize=16)\n", - " ax.tick_params(axis='y', labelsize=16)\n", - "\n", - "for ax in g.axes[-1, :]:\n", - " if ax is not None:\n", - " plt.setp(ax.get_xticklabels(), rotation=45)\n", - "\n", - "plt.tight_layout()\n", - "\n", - "plt.savefig(f'../../outputs/figures/BOHB_tuned_hyperparameters_FullyConnected.pdf', format='pdf')\n", - "\n", - "# Show the plot\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c728c400-ba59-4d3c-be22-e5df11683e3c", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/store/bohb_model_tuner/clean_file.py b/store/bohb_model_tuner/clean_file.py deleted file mode 100644 index 995a6dd9..00000000 --- a/store/bohb_model_tuner/clean_file.py +++ /dev/null @@ -1,19 +0,0 @@ -input_file = 'out__cnn' -output_file = 'cleaned_out__cnn' - - - - -with open(input_file, 'r') as file: - lines = file.readlines() - -filtered_lines = [line for line in lines if not (line.startswith('DEBUG') or line.startswith('INFO'))] - -with open(output_file, 'w') as file: - file.writelines(filtered_lines) - - - - - - diff --git a/store/bohb_model_tuner/cnn_bohb_tuning6.py b/store/bohb_model_tuner/cnn_bohb_tuning6.py deleted file mode 100644 index 87390397..00000000 --- a/store/bohb_model_tuner/cnn_bohb_tuning6.py +++ /dev/null @@ -1,657 +0,0 @@ -import os -os.environ['NUMEXPR_MAX_THREADS'] = '4' -os.environ['NUMEXPR_NUM_THREADS'] = '3' -import numexpr as ne - -ip_address = '127.0.0.1' - - -#import logging -#logging.basicConfig(level=logging.INFO) - -import logging -logging.basicConfig(level=logging.INFO) -import sys -sys.path.insert(0, '../telescopeML/') - - -from DeepRegTrainer import TrainRegression -from StatVisAnalyzer import * - - -from bohb_optimizer_2 import * -from search_space import * - - - -from bohb_optimizer_2 import * -from search_space import * -# from bohb_worker import * - -# Libraries for BOHB Package -import argparse - -import hpbandster.core.nameserver as hpns -import hpbandster.core.result as hpres - -from hpbandster.optimizers import BOHB as BOHB -# from hpbandster.examples.commons import MyWorker - -#import logging -#logging.basicConfig(level=logging.ERROR) # Change the level to INFO or higher -#import argparse - -#import hpbandster.core.nameserver as hpns -#import hpbandster.core.result as hpres - -#from hpbandster.optimizers import BOHB as BOHB -# from hpbandster.examples.commons import MyWorker - -import numpy -import time - -import ConfigSpace as CS -from hpbandster.core.worker import Worker - - - - - -# Import functions from other modules ============================ -from io_funs import LoadSave - -# Import python libraries ======================================== -import numpy as np -import matplotlib.pyplot as plt - - -# Import python libraries ======================================== -import numpy as np -import matplotlib.pyplot as plt - -# Import BOHB libraries ======================================== - - -import ConfigSpace.hyperparameters as CSH - -#import logging -#logging.basicConfig(level=logging.DEBUG) - - -import tensorflow as tf -from tensorflow import keras -from tensorflow.keras.models import Sequential -from tensorflow.keras.layers import Dense, Flatten -from tensorflow.keras.layers import Conv1D, Reshape -from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau -from tensorflow.keras.utils import plot_model - -from tensorflow.keras.layers import Input, Conv1D, Concatenate, Dense, MaxPooling1D -from tensorflow.keras.models import Model - -# mlp for multi-output regression -from numpy import mean -from numpy import std -from keras.models import Sequential -from keras.layers import Dense - -import warnings -import logging - -from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, UniformFloatHyperparameter, CategoricalHyperparameter -# Set the logging level to CRITICAL -#logging.basicConfig(level=logging.CRITICAL) -# Libraries for BOHB Package -#import logging -#logging.basicConfig(level=logging.WARNING) - -#import argparse - -#import hpbandster.core.nameserver as hpns -#import hpbandster.core.result as hpres - - -# =============================================================================== -# ================== ================== -# ================== BOHB Optimizer ================== -# ================== ================== -# =============================================================================== - - - - -class KerasWorker(Worker): - def __init__(self, - X1_train, X1_val, X1_test, # Row-StandardScaled input spectra - X2_train, X2_val, X2_test, # Col-StandardScaled Mix Max of all rows of input spetra - y1_train, y1_val, y1_test, # Col-StandardScaled target feature 1 - y2_train, y2_val, y2_test, # Col-StandardScaled target feature 2 - y3_train, y3_val, y3_test, # Col-StandardScaled target feature 3 - y4_train, y4_val, y4_test, # Col-StandardScaled target feature 4 - *args, sleep_interval=0, **kwargs): - - super().__init__(**kwargs) - self.sleep_interval = sleep_interval - - # self.batch_size = 2**9 - - - # train, val, test sets for input 1 (main 104 spectral features) - self.X1_train, self.X1_val, self.X1_test = X1_train, X1_val, X1_test - - # train, val, test sets for input 2 (Min and Max 2 features) - self.X2_train, self.X2_val, self.X2_test = X2_train, X2_val, X2_test - - # train, val, test sets for target features - self.y1_train, self.y1_val, self.y1_test = y1_train, y1_val, y1_test - self.y2_train, self.y2_val, self.y2_test = y2_train, y2_val, y2_test - self.y3_train, self.y3_val, self.y3_test = y3_train, y3_val, y3_test - self.y4_train, self.y4_val, self.y4_test = y4_train, y4_val, y4_test - - - # self.input_shape = (104,1) - # print(np.shape(self.x_train), np.shape(self.y_train)) - - - - - def compute(self, config, budget, working_directory, *args, **kwargs): - """ - Convolution Neural Networks to be optimized by BOHB package. - The input parameter "config" (dictionary) contains the sampled configurations passed by the bohb optimizer - """ - Conv__filters = config['Conv__filters'] - Conv__kernel_size = config['Conv__kernel_size'] - Conv__MaxPooling1D = config['Conv__MaxPooling1D'] - Conv__NumberLayers = config['Conv__NumberLayers'] - Conv__NumberBlocks = config['Conv__NumberBlocks'] - - FC__units = config['FC__units'] - FC__units_temperature = config['FC__units_temperature'] - FC__units_c_o_ratio = config['FC__units_c_o_ratio'] - FC__units_gravity = config['FC__units_gravity'] - FC__units_metallicity = config['FC__units_metallicity'] - FC__NumberBlocks = config['FC__NumberBlocks'] - FC__NumberLayers = config['FC__NumberLayers'] - - FC_in_Conv__units = config['FC_in_Conv__units'] - FC_in_Conv__NumberLayers = config['FC_in_Conv__NumberLayers'] - FC_in_Conv__NumberBlocks = config['FC_in_Conv__NumberBlocks'] - - - FC__dropout = config['FC__dropout'] - FC_out_dropout = config['FC_out_dropout'] - - - lr = config['lr'] - LeakyReLU_alpha = config['LeakyReLU_alpha'] - kernel_initializer_list = config['kernel_initializer_list'] - - - ######### Shape of the inputs - input_1 = tf.keras.layers.Input(shape=(104, 1)) - input_2 = tf.keras.layers.Input(shape=(2,)) - - - ######### Conv Blocks #################################### - model = input_1 - for b in range(0, Conv__NumberBlocks): - for l in range(0, Conv__NumberLayers): - model = Conv1D(filters = Conv__filters*(b+l+1)*2, - kernel_size = Conv__kernel_size, - strides = 1, - padding ='same', - activation = LeakyReLU(alpha=LeakyReLU_alpha), - kernel_initializer = 'he_normal', - # kernel_regularizer=tf.keras.regularizers.l2(Conv__regularizer), - name = 'Conv__B'+str(b+1)+'_L'+str(l+1))(model) #(model if l != 0 and b != 0 else input_1) - - model = MaxPooling1D(pool_size=(Conv__MaxPooling1D), - name = 'MaxPooling1D__B'+str(b+1)+'_L'+str(l+1))(model) - - - ######### Flatten Layer #################################### - model = Flatten()(model) - - - ######### FC Layer before the Concatenation ################ - for b in range(FC_in_Conv__NumberBlocks): - for l in range(FC_in_Conv__NumberLayers): - model = Dense(FC_in_Conv__units*(b+l+1)*4, - activation = LeakyReLU(alpha=LeakyReLU_alpha), - kernel_initializer = 'he_normal', - # kernel_regularizer=tf.keras.regularizers.l2(Conv__regularizer), - name = 'FC_in_Conv__B'+str(b+1)+'_L'+str(l+1))(model) - - model= Dropout(FC__dropout, - name = 'FC_in_Conv__Dropout__B'+str(b+1)+'_L'+str(l+1))(model) - ######### Concatenation Layer ############################### - # Concatenate the outputs from the convolutional layers and dense layer - model = tf.keras.layers.concatenate([model, input_2], - name='Concatenated_Layer') - - ######### FC Block #################################### - for b in range(FC__NumberBlocks): - for l in range(FC__NumberLayers): - model = Dense(FC__units*(b+l+1)*4, - activation = LeakyReLU(alpha=LeakyReLU_alpha), - kernel_initializer = 'he_normal', - # kernel_regularizer=tf.keras.regularizers.l2(Conv__regularizer), - name = 'FC__B'+str(b+1)+'_L'+str(l+1))(model) - - model= Dropout(FC__dropout, - name = 'FC__Dropout__B'+str(b+1)+'_L'+str(l+1))(model) - - - - ######### 3rd FC Block: gravity ############################## - # FC2 = FC__Drop - - model2 = Dense(FC__units_gravity, - activation = LeakyReLU(alpha=LeakyReLU_alpha), - kernel_initializer = 'he_normal', - # kernel_regularizer=tf.keras.regularizers.l2(0.003/2), - name = 'FC_block3_gravity')(model) - - model2= Dropout(FC_out_dropout, - name = 'FC_block3_gravity__Dropout')(model2) - - out__gravity = Dense(1, - activation = 'linear', - kernel_initializer = 'he_normal', - name = 'gravity')(model2) - - - - ######### 3rd FC Block: c_o_ratio ############################## - model2 = Dense(FC__units_c_o_ratio, - activation = LeakyReLU(alpha=LeakyReLU_alpha), - kernel_initializer = 'he_normal', - # kernel_regularizer=tf.keras.regularizers.l2(0.003/2), - name = 'FC_block3_c_o_ratio')(model) - - model2= Dropout(FC_out_dropout, - name = 'FC_block3_c_o_ratio__Dropout')(model2) - - - out__c_o_ratio = Dense(1, - activation = 'linear', - kernel_initializer = 'he_normal', - # kernel_regularizer=tf.keras.regularizers.l2(0.003/2), - name='c_o_ratio')(model2) - - - ######### 3rd FC Block: metallicity ############################## - model2 = Dense(FC__units_metallicity, - activation = LeakyReLU(alpha=LeakyReLU_alpha), - kernel_initializer = 'he_normal', - # kernel_regularizer=tf.keras.regularizers.l2(0.003/2), - name = 'FC_block3_metallicity')(model) - - model2= Dropout(FC_out_dropout, - name = 'FC_block3_metallicity__Dropout')(model2) - - - out__metallicity = Dense(1, - activation = 'linear', - kernel_initializer = 'he_normal', - name='metallicity')(model2) - - - - ######### 3rd FC Block: temperature ############################## - model2 = Dense(FC__units_temperature, - activation = LeakyReLU(alpha=LeakyReLU_alpha), - kernel_initializer = 'he_normal', - # kernel_regularizer=tf.keras.regularizers.l2(0.003/2), - name = 'FC_block3_temperature')(model) - - model2= Dropout(FC_out_dropout, - name = 'FC_block3_temperature__Dropout')(model2) - - - out__temperature = Dense(1, - activation = 'linear', - name='temperature')(model2) - - - ######### OUTPUT ################################################ - # Create the model with two inputs and two outputs - model = tf.keras.Model(inputs=[input_1, input_2], - outputs=[out__gravity, out__c_o_ratio, out__metallicity, out__temperature]) - - # Compile the model with an optimizer, loss function, and metrics - model.compile(loss='huber_loss', - optimizer=keras.optimizers.Adam(lr = lr), - metrics=['mae']) - - - - - - - early_stop = EarlyStopping(monitor='loss', min_delta=4e-4, patience=50, mode='auto', \ - restore_best_weights=True) - - - - # YOU CAN ADD FUNCTION HERE TO ADD NOISE - history = model.fit(x = [self.X1_train, self.X2_train], - y = [self.y1_train, self.y2_train, self.y3_train, self.y4_train], #self.x_train, self.y_train, - batch_size = 32, #config['batch_size'], # self.batch_size, - validation_data=([self.X1_val, self.X2_val], [self.y1_val, self.y2_val, self.y3_val, self.y4_val]), - #validation_split=0.2, - epochs=int(budget), - verbose=1, - callbacks=[early_stop], - ) - - - train_score = model.evaluate(x = [self.X1_train, self.X2_train], - y = [self.y1_train, self.y2_train, self.y3_train, self.y4_train], - verbose=0) - val_score = model.evaluate(x = [self.X1_val, self.X2_val], - y = [self.y1_val, self.y2_val, self.y3_val, self.y4_val], - verbose=0) - test_score = model.evaluate(x = [self.X1_test, self.X2_test], - y = [self.y1_test, self.y2_test, self.y3_test, self.y4_test], - verbose=0) - - #print(train_score, val_score, test_score) -# #import IPython; IPython.embed() - return ({ - 'loss': val_score[1], # remember: HpBandSter always minimizes! - 'info': { 'test accuracy': test_score[1], - 'train accuracy': train_score[1], - 'validation accuracy': val_score[1], - 'number of parameters': model.count_params(), - }, - # 'model' : model, - # 'history' : history, - - }) - - @staticmethod - def get_configspace(): - """ - It builds the configuration space with the needed hyperparameters. - It is easily possible to implement different types of hyperparameters. - Beside float-hyperparameters on a log scale, it is also able to handle categorical input parameter. - :return: ConfigurationsSpace-Object - """ - cs = CS.ConfigurationSpace() - - - # Conv hyperparameters - Conv__filters = UniformIntegerHyperparameter(name='Conv__filters', lower=4, upper=64, default_value=16, log=False) - Conv__kernel_size = UniformIntegerHyperparameter(name='Conv__kernel_size', lower=1, upper=6, default_value=1, log=False) - Conv__MaxPooling1D = UniformIntegerHyperparameter(name='Conv__MaxPooling1D', lower=1, upper=4, default_value=1, log=False) - Conv__NumberLayers = UniformIntegerHyperparameter(name='Conv__NumberLayers', lower=2, upper=4, default_value=2, log=False) - Conv__NumberBlocks = UniformIntegerHyperparameter(name='Conv__NumberBlocks', lower=2, upper=4, default_value=2, log=False) - - # FC hyperparameters - FC__units = UniformIntegerHyperparameter(name='FC__units', lower=32, upper=256, default_value=32, log=False) - FC__units_temperature = UniformIntegerHyperparameter(name='FC__units_temperature', lower=32, upper=256, default_value=32, log=False) - FC__units_metallicity = UniformIntegerHyperparameter(name='FC__units_metallicity', lower=32, upper=256, default_value=32, log=False) - FC__units_c_o_ratio = UniformIntegerHyperparameter(name='FC__units_c_o_ratio', lower=32, upper=256, default_value=32, log=False) - FC__units_gravity = UniformIntegerHyperparameter(name='FC__units_gravity', lower=32, upper=256, default_value=32, log=False) - - FC_in_Conv__units = UniformIntegerHyperparameter(name='FC_in_Conv__units', lower=32, upper=256, default_value=32, log=False) - FC__NumberLayers = UniformIntegerHyperparameter(name='FC__NumberLayers', lower=2, upper=4, default_value=2, log=False) - FC__NumberBlocks = UniformIntegerHyperparameter(name='FC__NumberBlocks', lower=2, upper=4, default_value=2, log=False) - FC__dropout = UniformFloatHyperparameter(name='FC__dropout', lower=0.001, upper=0.4, default_value=0.02, log=True) - FC_out_dropout = UniformFloatHyperparameter(name='FC_out_dropout', lower=0.001, upper=0.4, default_value=0.02, log=True) - - FC_in_Conv__NumberBlocks = UniformIntegerHyperparameter(name='FC_in_Conv__NumberBlocks', lower=2, upper=4, default_value=2, log=False) - FC_in_Conv__NumberLayers = UniformIntegerHyperparameter(name='FC_in_Conv__NumberLayers', lower=2, upper=4, default_value=2, log=False) - - # Other hyperparameters - lr = UniformFloatHyperparameter(name='lr', lower=1e-5, upper=1e-2, default_value=1e-4, log=True) - LeakyReLU_alpha = UniformFloatHyperparameter(name='LeakyReLU_alpha', lower=0.01, upper=0.3, default_value=0.01, log=True) - kernel_initializer_list = CategoricalHyperparameter(name='kernel_initializer_list', choices=['he_normal', 'glorot_uniform']) - - - # cs = ConfigurationSpace() - # notes from Hamed: - # Conv1Dinp__kernel_size: # this operator - # Kenrel size is the same, Stride not - # Conv1Dinp__filters: 1 filter, 2*, 4*, .... Optimize this once - # Stride = 1 - # Optimize size - # kernel_regularizer = Default - # MaxPooling1D: - # Gaus = keras.layers.GaussianNoise(0.01,)(input_1) - - - cs.add_hyperparameters([ - Conv__filters, - Conv__kernel_size, - Conv__MaxPooling1D, - Conv__NumberLayers, - Conv__NumberBlocks, - - FC__units, - FC__units_temperature, - FC__units_c_o_ratio, - FC__units_gravity, - FC__units_metallicity, - FC__NumberLayers, - FC__NumberBlocks, - FC__dropout, - FC_out_dropout, - - FC_in_Conv__units, - FC_in_Conv__NumberBlocks, - FC_in_Conv__NumberLayers, - - - lr, - LeakyReLU_alpha, - kernel_initializer_list - ]) - - return cs - - - -# Step 1: Load the dataset -df=pd.read_csv('../../datasets/browndwarf_R100_v4_newWL_v2.csv.bz2', compression='bz2') -wl = pd.read_csv('../../datasets/wl.csv') - - -## Prepare feature variables (X) and targets (y) -df = df[df['is_augmented'].isin(['no'])] - -df = df.iloc[::10] # Should be deleted for the main run - -X = df.drop( - columns=['gravity', - 'temperature', - 'c_o_ratio', - 'metallicity', - 'is_augmented'])#.astype(np.float32) - -y = df[['gravity', 'c_o_ratio', 'metallicity', 'temperature', ]]#.astype(np.float32) - -# Log-Transform -df['temperature'] = df['temperature'].apply(lambda x: np.log10(x)) -#y.loc[:, 'temperature'] = np.log10(y['temperature']) -##y['temperature'] = np.log10(y['temperature']) - -# Create an instance of TrainCNNRegression -train_cnn_regression = TrainRegression(feature_values=X, - feature_names=X.columns, - target_values=y.to_numpy(), - target_name=['gravity', 'c_o_ratio', 'metallicity', 'temperature'], - is_tuned='yes', - param_grid=None, - spectral_resolution=100, - is_feature_improved='no', - n_jobs=4, - cv=5, - is_augmented='no', - ml_model=None, - ml_model_str='CNN') - - - -# Split the dataset into train and test sets -train_cnn_regression.split_train_validation_test(test_size=0.1, val_size=0.1) - - -# normalize the X features using MinMax Scaler -train_cnn_regression.normalize_X_row_wise() - - -# Standardize the y features using Standard Scaler -train_cnn_regression.standardize_y_column_wise() - - -# Create Xmin and Xmax -train_cnn_regression.X_train_min = train_cnn_regression.X_train.min(axis=1) -train_cnn_regression.X_train_max = train_cnn_regression.X_train.max(axis=1) - -train_cnn_regression.X_val_min = train_cnn_regression.X_val.min(axis=1) -train_cnn_regression.X_val_max = train_cnn_regression.X_val.max(axis=1) - -train_cnn_regression.X_test_min = train_cnn_regression.X_test.min(axis=1) -train_cnn_regression.X_test_max = train_cnn_regression.X_test.max(axis=1) - -df_MinMax_train = pd.DataFrame((train_cnn_regression.X_train_min, train_cnn_regression.X_train_max)).T -df_MinMax_val = pd.DataFrame((train_cnn_regression.X_val_min, train_cnn_regression.X_val_max)).T -df_MinMax_test = pd.DataFrame((train_cnn_regression.X_test_min, train_cnn_regression.X_test_max)).T - -df_MinMax_train.rename(columns={0:'min', 1:'max'}, inplace=True) - - - -train_cnn_regression.standardize_X_column_wise( - X_train = df_MinMax_train.values, - X_val = df_MinMax_val.values, - X_test = df_MinMax_test.values, - ) - - - - - - - -import argparse -import hpbandster.core.nameserver as hpns -import hpbandster.core.result as hpres - -from hpbandster.optimizers import BOHB as BOHB -# from hpbandster.examples.commons import MyWorker - - - -# Create the optimization object with the desired queue size - - -parser = argparse.ArgumentParser(description='Example 3 - Local and Parallel Execution.') -parser.add_argument('--min_budget', type=float, help='Minimum budget used during the optimization.', default=2) -parser.add_argument('--max_budget', type=float, help='Maximum budget used during the optimization.', default=3) -parser.add_argument('--n_iterations', type=int, help='Number of iterations performed by the optimizer', default=10) -parser.add_argument('--n_workers', type=int, help='Number of workers to run in parallel.', default = 3 ) -parser.add_argument('--worker', help='Flag to turn this into a worker process', action='store_true') - -args=parser.parse_args() -# args = parser.parse_args(args=[]) - - - -if args.worker: - upper_index = 20_0 - w = KerasWorker( - # input dataset: StandardScaled instances - X1_train = train_cnn_regression.X_train_normalized_rowwise, - X1_val = train_cnn_regression.X_val_normalized_rowwise, - X1_test = train_cnn_regression.X_test_normalized_rowwise, - - # input dataset: Min Max of each instance - X2_train = train_cnn_regression.X_train_standardized_columnwise, - X2_val = train_cnn_regression.X_val_standardized_columnwise, - X2_test = train_cnn_regression.X_test_standardized_columnwise, - - # 1st target - y1_train = train_cnn_regression.y_train_standardized_columnwise[:,0], - y1_val = train_cnn_regression.y_val_standardized_columnwise[:,0], - y1_test = train_cnn_regression.y_test_standardized_columnwise[:,0], - - # 2nd target - y2_train = train_cnn_regression.y_train_standardized_columnwise[:,1], - y2_val = train_cnn_regression.y_val_standardized_columnwise[:,1], - y2_test = train_cnn_regression.y_test_standardized_columnwise[:,1], - - # 3rd target - y3_train = train_cnn_regression.y_train_standardized_columnwise[:,2], - y3_val = train_cnn_regression.y_val_standardized_columnwise[:,2], - y3_test = train_cnn_regression.y_test_standardized_columnwise[:,2], - - # 4th target - y4_train = train_cnn_regression.y_train_standardized_columnwise[:,3], - y4_val = train_cnn_regression.y_val_standardized_columnwise[:,3], - y4_test = train_cnn_regression.y_test_standardized_columnwise[:,3], - - - - sleep_interval = 0.5, nameserver=ip_address,run_id='example3') - - w.run(background=False) - exit(0) - -# Start a nameserver (see example_1) -NS = hpns.NameServer(run_id='example3', host=ip_address, port=None) -NS.start() - - -# Run an optimizer (see example_2) - -result_logger = hpres.json_result_logger(directory='/data2/ehsan_storage/telescopeML_project/outputs/bohb_outputs/out1/', overwrite=True) - -bohb = BOHB( configspace = KerasWorker.get_configspace(), - run_id = 'example3', - min_budget=args.min_budget, - max_budget=args.max_budget, - result_logger = result_logger, - ) - -res = bohb.run(n_iterations=args.n_iterations, min_n_workers=args.n_workers) - - -#scores = res.get_all_runs(only_largest_budget=False) -#df = res.get_pandas_dataframe()[0] - -#df['config_id']= [ scores[i].config_id for i in range(len(scores)) ] -#df['loss']= [ scores[i].loss for i in range(len(scores)) ] -#df[['test_accuracy', 'train_accuracy', 'validation_accuracy', 'number_parameters']] = [ list(scores[i].info.values()) for i in range(len(scores)) ] -#df['time_stamps']= [ scores[i].time_stamps['finished'] - scores[i].time_stamps['started'] for i in range(len(scores)) ] -#df.sort_values(by='loss', ascending=False) -#df.to_csv('df_output.csv', columns=df.columns.tolist(), index=False) - - - -# Step 4: Shutdown -# After the optimizer run, we must shutdown the master and the nameserver. -bohb.shutdown(shutdown_workers=True) -NS.shutdown() - - -# Step 5: Analysis -# Each optimizer returns a hpbandster.core.result.Result object. -# It holds informations about the optimization run like the incumbent (=best) configuration. -# For further details about the Result object, see its documentation. -# Here we simply print out the best config and some statistics about the performed runs. -id2config = res.get_id2config_mapping() -incumbent = res.get_incumbent_id() - -all_runs = res.get_all_runs() - -print('Best found configuration:', id2config[incumbent]['config']) -print('A total of %i unique configurations where sampled.' % len(id2config.keys())) -print('A total of %i runs where executed.' % len(res.get_all_runs())) -print('Total budget corresponds to %.1f full function evaluations.'%(sum([r.budget for r in all_runs])/args.max_budget)) -print('Total budget corresponds to %.1f full function evaluations.'%(sum([r.budget for r in all_runs])/args.max_budget)) -##print('The run took %.1f seconds to complete.'%(all_runs[-1].time_stamps['finished'] - all_runs[0].time_stamps['started'])): - diff --git a/store/bohb_model_tuner/submit.sh b/store/bohb_model_tuner/submit.sh deleted file mode 100755 index df76581a..00000000 --- a/store/bohb_model_tuner/submit.sh +++ /dev/null @@ -1,31 +0,0 @@ -#!/bin/bash - -# How to Run: -# 1. check the name of the *.py bohb codes -# 2. make sure to have the dir ready for the output -# 3. import the bohb enviroemnt: conda activate bohb -# 4. run: ./submit.sh -# 5. wait until all workers get submitted (~about 3 mins) -# 6. go grap a coffee :) - -# Check if the directory parameter is provided - -# conda activate bohb - -if [ -z "$1" ]; then - echo "Please provide the directory parameter." - exit 1 -fi - -directory="$1" - -# Run the first instance of the Python script -python3 Tune_CNN_hyperparameters_BOHB.py &> "$directory/out1" & -sleep 10 # Add a 10-second delay - -# Run the subsequent instances with different arguments -for i in {2..41}; do - python3 Tune_CNN_hyperparameters_BOHB.py --worker &> "$directory/out$i" & - sleep 3 # Add a 3-second delay -done - diff --git a/store/bohb_model_tuner/test_cnn_bohb_tuner.ipynb b/store/bohb_model_tuner/test_cnn_bohb_tuner.ipynb deleted file mode 100644 index 1779b15d..00000000 --- a/store/bohb_model_tuner/test_cnn_bohb_tuner.ipynb +++ /dev/null @@ -1,1659 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "id": "6fe76be1-e60f-4015-a5ab-62f63ebd20e4", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.environ['NUMEXPR_MAX_THREADS'] = '12'\n", - 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        • \\n\"+\n \"
        \\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
        \"}};\n\n function display_loaded() {\n const el = document.getElementById(\"p1001\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.0.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"p1001\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
        \n", - " \n", - " Loading BokehJS ...\n", - "
        \n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - "const JS_MIME_TYPE = 'application/javascript';\n", - " const HTML_MIME_TYPE = 'text/html';\n", - " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " const CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " const script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " const cell = handle.cell;\n", - "\n", - " const id = cell.output_area._bokeh_element_id;\n", - " const server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd_clean, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " const id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd_destroy);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " const output_area = handle.output_area;\n", - " const output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " const bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " const script_attrs = bk_div.children[0].attributes;\n", - " for (let i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " const toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " const events = require('base/js/events');\n", - " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " const NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
        \\n\"+\n", - " \"

        \\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

        \\n\"+\n", - " \"
          \\n\"+\n", - " \"
        • re-rerun `output_notebook()` to attempt to load from CDN again, or
        • \\n\"+\n", - " \"
        • use INLINE resources instead, as so:
        • \\n\"+\n", - " \"
        \\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
        \"}};\n", - "\n", - " function display_loaded() {\n", - " const el = document.getElementById(\"p1002\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error(url) {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (let i = 0; i < css_urls.length; i++) {\n", - " const url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (let i = 0; i < js_urls.length; i++) {\n", - " const url = js_urls[i];\n", - " const element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.0.min.js\"];\n", - " const css_urls = [];\n", - "\n", - " const inline_js = [ function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - "function(Bokeh) {\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " if (root.Bokeh !== undefined || force === true) {\n", - " for (let i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - "if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " const cell = $(document.getElementById(\"p1002\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
        \\n\"+\n \"

        \\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

        \\n\"+\n \"
          \\n\"+\n \"
        • re-rerun `output_notebook()` to attempt to load from CDN again, or
        • \\n\"+\n \"
        • use INLINE resources instead, as so:
        • \\n\"+\n \"
        \\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
        \"}};\n\n function display_loaded() {\n const el = document.getElementById(\"p1002\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.0.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"p1002\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
        \n", - " \n", - " Loading BokehJS ...\n", - "
        \n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - "const JS_MIME_TYPE = 'application/javascript';\n", - " const HTML_MIME_TYPE = 'text/html';\n", - " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " const CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " const script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " const cell = handle.cell;\n", - "\n", - " const id = cell.output_area._bokeh_element_id;\n", - " const server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd_clean, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " const id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd_destroy);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " const output_area = handle.output_area;\n", - " const output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " const bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " const script_attrs = bk_div.children[0].attributes;\n", - " for (let i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " const toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " const events = require('base/js/events');\n", - " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " const NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
        \\n\"+\n", - " \"

        \\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

        \\n\"+\n", - " \"
          \\n\"+\n", - " \"
        • re-rerun `output_notebook()` to attempt to load from CDN again, or
        • \\n\"+\n", - " \"
        • use INLINE resources instead, as so:
        • \\n\"+\n", - " \"
        \\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
        \"}};\n", - "\n", - " function display_loaded() {\n", - " const el = document.getElementById(\"p1003\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error(url) {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (let i = 0; i < css_urls.length; i++) {\n", - " const url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (let i = 0; i < js_urls.length; i++) {\n", - " const url = js_urls[i];\n", - " const element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.0.min.js\"];\n", - " const css_urls = [];\n", - "\n", - " const inline_js = [ function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - "function(Bokeh) {\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " if (root.Bokeh !== undefined || force === true) {\n", - " for (let i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - "if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " const cell = $(document.getElementById(\"p1003\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
        \\n\"+\n \"

        \\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

        \\n\"+\n \"
          \\n\"+\n \"
        • re-rerun `output_notebook()` to attempt to load from CDN again, or
        • \\n\"+\n \"
        • use INLINE resources instead, as so:
        • \\n\"+\n \"
        \\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
        \"}};\n\n function display_loaded() {\n const el = document.getElementById(\"p1003\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.0.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"p1003\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ip_address = '127.0.0.1'\n", - "\n", - "\n", - "#import logging\n", - "#logging.basicConfig(level=logging.INFO)\n", - "import sys \n", - "sys.path.insert(0, '../../codes/')\n", - "\n", - "from predict_observational_dataset_v2 import ProcessObservationalDataset\n", - "from train_regression import *\n", - "\n", - "# import sys\n", - "# sys.path.insert(0, '../../codes/')\n", - "\n", - "# from predict_observational_dataset_v2 import ProcessObservationalDataset\n", - "# from check_results_regression_2 import *\n", - "# from bohb_optimizer_2 import *\n", - "# # from train_ml_regression_2 import *\n", - "# from train_cnn_regression_3 import *\n", - "# from search_space import *\n", - "# from bohb_worker import *\n", - "\n", - "# Libraries for BOHB Package\n", - "import argparse\n", - "\n", - "import hpbandster.core.nameserver as hpns\n", - "import hpbandster.core.result as hpres\n", - "\n", - "from hpbandster.optimizers import BOHB as BOHB\n", - "# from hpbandster.examples.commons import MyWorker\n", - "\n", - "#import logging\n", - "#logging.basicConfig(level=logging.ERROR) # Change the level to INFO or higher\n", - "#import argparse\n", - "\n", - "#import hpbandster.core.nameserver as hpns\n", - "#import hpbandster.core.result as hpres\n", - "\n", - "#from hpbandster.optimizers import BOHB as BOHB\n", - "# from hpbandster.examples.commons import MyWorker\n", - "\n", - "import numpy\n", - "import time\n", - "\n", - "import ConfigSpace as CS\n", - "from hpbandster.core.worker import Worker\n", - "from sklearn.preprocessing import PowerTransformer\n", - "\n", - "\n", - "\n", - "\n", - "# Import functions from other modules ============================\n", - "from io_funs import LoadSave\n", - "\n", - "# Import python libraries ========================================\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "# Import python libraries ========================================\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Import BOHB libraries ========================================\n", - "\n", - "\n", - "import ConfigSpace.hyperparameters as CSH\n", - "\n", - "#import logging\n", - "#logging.basicConfig(level=logging.DEBUG)\n", - "\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow import keras \n", - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import Dense, Flatten\n", - "from tensorflow.keras.layers import Conv1D, Reshape\n", - "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", - "from tensorflow.keras.utils import plot_model\n", - "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense\n", - "\n", - "\n", - "\n", - "from tensorflow.keras.layers import Input, Conv1D, Concatenate, Dense, MaxPooling1D\n", - "from tensorflow.keras.models import Model\n", - "\n", - "# mlp for multi-output regression\n", - "from numpy import mean\n", - "from numpy import std\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense\n", - "\n", - "import warnings\n", - "import logging\n", - " \n", - " \n", - "from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, UniformFloatHyperparameter, CategoricalHyperparameter\n", - "# Set the logging level to CRITICAL\n", - "#logging.basicConfig(level=logging.CRITICAL)\n", - "# Libraries for BOHB Package \n", - "#import logging\n", - "#logging.basicConfig(level=logging.WARNING)\n", - "\n", - "#import argparse\n", - "\n", - "#import hpbandster.core.nameserver as hpns\n", - "#import hpbandster.core.result as hpres" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "87f0d8a6-3561-41c9-9142-cd85c3c382ff", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, UniformFloatHyperparameter, CategoricalHyperparameter\n", - "# Set the logging level to CRITICAL\n", - "logging.basicConfig(level=logging.CRITICAL)\n", - " \n", - "class KerasWorker(Worker):\n", - " def __init__(self, \n", - " X1_train, X1_val, X1_test, # Row-StandardScaled input spectra\n", - " X2_train, X2_val, X2_test, # Col-StandardScaled Mix Max of all rows of input spetra\n", - " y1_train, y1_val, y1_test, # Col-StandardScaled target feature 1\n", - " y2_train, y2_val, y2_test, # Col-StandardScaled target feature 2\n", - " y3_train, y3_val, y3_test, # Col-StandardScaled target feature 3\n", - " y4_train, y4_val, y4_test, # Col-StandardScaled target feature 4 \n", - " *args, sleep_interval=0, **kwargs):\n", - "\n", - " \n", - " super().__init__(**kwargs)\n", - " \n", - " self.sleep_interval = sleep_interval\n", - "\n", - " # self.batch_size = 2**9\n", - "\n", - "\n", - " # train, val, test sets for input 1 (main 104 spectral features)\n", - " self.X1_train, self.X1_val, self.X1_test = X1_train, X1_val, X1_test\n", - "\n", - " # train, val, test sets for input 2 (Min and Max 2 features)\n", - " self.X2_train, self.X2_val, self.X2_test = X2_train, X2_val, X2_test\n", - " \n", - " # train, val, test sets for target features \n", - " self.y1_train, self.y1_val, self.y1_test = y1_train, y1_val, y1_test\n", - " self.y2_train, self.y2_val, self.y2_test = y2_train, y2_val, y2_test\n", - " self.y3_train, self.y3_val, self.y3_test = y3_train, y3_val, y3_test \n", - " self.y4_train, self.y4_val, self.y4_test = y4_train, y4_val, y4_test \n", - "\n", - "\n", - " # self.input_shape = (104,1)\n", - " # print(np.shape(self.x_train), np.shape(self.y_train))\n", - "\n", - "\n", - "\n", - "\n", - " def compute(self, config, budget, working_directory, *args, **kwargs):\n", - " \"\"\"\n", - " Convolution Neural Networks to be optimized by BOHB package.\n", - " The input parameter \"config\" (dictionary) contains the sampled configurations passed by the bohb optimizer\n", - " \"\"\"\n", - " Conv__filters = config['Conv__filters']\n", - " Conv__kernel_size = config['Conv__kernel_size']\n", - " Conv__MaxPooling1D = config['Conv__MaxPooling1D']\n", - " Conv__NumberLayers = config['Conv__NumberLayers']\n", - " Conv__NumberBlocks = config['Conv__NumberBlocks']\n", - " \n", - " FC__units = config['FC__units']\n", - " FC__units_temperature = config['FC__units_temperature']\n", - " FC__units_c_o_ratio = config['FC__units_c_o_ratio']\n", - " FC__units_gravity = config['FC__units_gravity']\n", - " FC__units_metallicity = config['FC__units_metallicity']\n", - " #FC__NumberBlocks = config['FC__NumberBlocks']\n", - " #FC__NumberLayers = config['FC__NumberLayers']\n", - " \n", - " FC_in_Conv__units = config['FC_in_Conv__units']\n", - " # FC_in_Conv__NumberLayers = config['FC_in_Conv__NumberLayers']\n", - " # FC_in_Conv__NumberBlocks = config['FC_in_Conv__NumberBlocks']\n", - " \n", - " \n", - " FC__dropout = config['FC__dropout']\n", - " FC_in_Conv__dropout = config['FC_in_Conv__dropout']\n", - " FC_out_dropout = config['FC_out_dropout']\n", - " \n", - "\n", - " lr = config['lr']\n", - " # LeakyReLU_alpha = config['LeakyReLU_alpha']\n", - " # kernel_initializer_list = config['kernel_initializer_list'] \n", - " \n", - "\n", - " # Define the model architecture\n", - " input_1 = tf.keras.layers.Input(shape=(104, 1))\n", - " input_2 = tf.keras.layers.Input(shape=(2,))\n", - "\n", - " model = Conv1D(Conv__filters, 3, \n", - " kernel_initializer = 'he_normal', \n", - " activation='relu', \n", - " input_shape=(104, 1))(input_1)\n", - "\n", - " model = Conv1D(64, 3, \n", - " kernel_initializer = 'he_normal', \n", - " activation='relu')(model)\n", - "\n", - " model = Conv1D(128, 3, \n", - " kernel_initializer = 'he_normal', \n", - " activation='relu')(model)\n", - "\n", - " model = MaxPooling1D(2)(model)\n", - "\n", - " model = Conv1D(128*2, 3, \n", - " kernel_initializer = 'he_normal', \n", - " activation='relu')(model)\n", - "\n", - " model = Conv1D(128*4, 3, \n", - " kernel_initializer = 'he_normal', \n", - " activation='relu')(model)\n", - "\n", - "\n", - " model = MaxPooling1D(2)(model)\n", - " model = Flatten()(model)\n", - "\n", - " model = tf.keras.layers.concatenate([model, input_2], \n", - " name='Concatenated_Layer')\n", - "\n", - "\n", - " model = Dense(128, \n", - " kernel_initializer = 'he_normal',\n", - " activation='relu')(model)\n", - "\n", - " model = Dense(128*4, \n", - " kernel_initializer = 'he_normal',\n", - " activation='relu')(model)\n", - "\n", - " out__gravity = Dense(1, \n", - " activation='linear',\n", - " name='gravity')(model) # No activation function for regression\n", - "\n", - " out__c_o_ratio = Dense(1, \n", - " activation='linear',\n", - " name='c_o_ratio')(model) # No activation function for regression\n", - "\n", - " out__metallicity = Dense(1, \n", - " activation='linear',\n", - " name='metallicity')(model) # No activation function for regression\n", - "\n", - " out__temperature = Dense(1, \n", - " activation='linear',\n", - " name='temperature')(model) # No activation function for regression\n", - "\n", - " model = tf.keras.Model(inputs=[input_1, input_2], \n", - " outputs=[out__gravity, out__c_o_ratio, out__metallicity, out__temperature])\n", - "\n", - "\n", - " # Compile the model with an optimizer, loss function, and metrics\n", - " model.compile(loss='huber_loss', \n", - " optimizer=keras.optimizers.Adam(learning_rate=0.0001), \n", - " metrics=['mae'])\n", - "\n", - "\n", - " \n", - " early_stop = EarlyStopping(monitor='loss', min_delta=4e-4, patience=50, mode='auto', \\\n", - " restore_best_weights=True)\n", - "\n", - "\n", - "\n", - " # YOU CAN ADD FUNCTION HERE TO ADD NOISE\n", - " model.fit(x = [self.X1_train, self.X2_train], \n", - " y = [self.y1_train, self.y2_train, self.y3_train, self.y4_train], #self.x_train, self.y_train,\n", - " batch_size = 32, #config['batch_size'], # self.batch_size,\n", - " validation_data=([self.X1_val, self.X2_val], \n", - " [self.y1_val, self.y2_val, self.y3_val, self.y4_val]),\n", - " # validation_split=0.2,\n", - " epochs=int(budget),\n", - " verbose=1,\n", - " callbacks=[early_stop],\n", - " )\n", - "\n", - "\n", - " train_score = model.evaluate(x = [self.X1_train, self.X2_train], \n", - " y = [self.y1_train, self.y2_train, self.y3_train, self.y4_train],\n", - " verbose=0)\n", - " val_score = model.evaluate(x = [self.X1_val, self.X2_val], \n", - " y = [self.y1_val, self.y2_val, self.y3_val, self.y4_val],\n", - " verbose=0)\n", - " test_score = model.evaluate(x = [self.X1_test, self.X2_test], \n", - " y = [self.y1_test, self.y2_test, self.y3_test, self.y4_test],\n", - " verbose=0)\n", - "\n", - " print(train_score, val_score, test_score)\n", - "# #import IPython; IPython.embed()\n", - " return ({\n", - " 'loss': val_score[1], # remember: HpBandSter always minimizes!\n", - " 'info': { 'test accuracy': test_score[1],\n", - " 'train accuracy': train_score[1],\n", - " 'validation accuracy': val_score[1],\n", - " 'number of parameters': model.count_params(),\n", - " },\n", - " #'model' : model,\n", - " #'history' : history,\n", - "\n", - " })\n", - " \n", - " \n", - " \n", - " @staticmethod\n", - " def get_configspace():\n", - " \"\"\"\n", - " It builds the configuration space with the needed hyperparameters.\n", - " It is easily possible to implement different types of hyperparameters.\n", - " Beside float-hyperparameters on a log scale, it is also able to handle categorical input parameter.\n", - " :return: ConfigurationsSpace-Object\n", - " \"\"\"\n", - " cs = CS.ConfigurationSpace()\n", - "\n", - " \n", - " \n", - "\n", - " # Conv hyperparameters\n", - " Conv__filters = CategoricalHyperparameter(name='Conv__filters', choices=[4 , 8, 16, 32]) # NOTE: Apply the same categorical method for other unit and \n", - " Conv__kernel_size = UniformIntegerHyperparameter(name='Conv__kernel_size', lower=1, upper=8, default_value=1, log=False) # ok\n", - " Conv__MaxPooling1D = UniformIntegerHyperparameter(name='Conv__MaxPooling1D', lower=1, upper=8, default_value=1, log=False) # ok\n", - " Conv__NumberLayers = UniformIntegerHyperparameter(name='Conv__NumberLayers', lower=1, upper=6, default_value=1, log=False) # ok\n", - " Conv__NumberBlocks = UniformIntegerHyperparameter(name='Conv__NumberBlocks', lower=1, upper=4, default_value=1, log=False) # ok\n", - "\n", - " # FC hyperparameters FC__units\n", - " FC__units = CategoricalHyperparameter(name='FC__units', choices=[8, 16, 32 , 64, 128, 256]) # NOTE: Apply the same categorical method for other unit and \n", - " FC__units_temperature = CategoricalHyperparameter(name='FC__units_temperature', choices=[8, 16, 32 , 64, 128, 256]) # the same\n", - " FC__units_metallicity = CategoricalHyperparameter(name='FC__units_metallicity', choices=[8, 16, 32 , 64, 128, 256]) # the same\n", - " FC__units_c_o_ratio = CategoricalHyperparameter(name='FC__units_c_o_ratio', choices=[8, 16, 32 , 64, 128, 256]) # the same\n", - " FC__units_gravity = CategoricalHyperparameter(name='FC__units_gravity', choices=[8, 16, 32 , 64, 128, 256]) # same\n", - "\n", - " # FC_in_Conv\n", - " FC_in_Conv__units = CategoricalHyperparameter(name='FC_in_Conv__units', choices=[8, 16, 32 , 64, 128, 256]) # same\n", - " FC__NumberLayers = UniformIntegerHyperparameter(name='FC__NumberLayers', lower=1, upper=5, default_value=1, log=False) \n", - "\n", - " FC_in_Conv__dropout = UniformFloatHyperparameter(name='FC_in_Conv__dropout', lower=0.001, upper=0.4, default_value=0.02, log=True)\n", - " FC__dropout = UniformFloatHyperparameter(name='FC__dropout', lower=0.001, upper=0.4, default_value=0.02, log=True)\n", - " FC_out_dropout = UniformFloatHyperparameter(name='FC_out_dropout', lower=0.001, upper=0.4, default_value=0.02, log=True)\n", - " \n", - " #FC_in_Conv__NumberBlocks = UniformIntegerHyperparameter(name='FC_in_Conv__NumberBlocks', lower=1, upper=5, default_value=1, log=False) ## DELETE, \n", - " #FC_in_Conv__NumberLayers = UniformIntegerHyperparameter(name='FC_in_Conv__NumberLayers', lower=1, upper=5, default_value=1, log=False) ### DELETE\n", - " \n", - " # Other hyperparameters\n", - " lr = UniformFloatHyperparameter(name='lr', lower=1e-5, upper=1e-2, default_value=1e-4, log=True)\n", - " #LeakyReLU_alpha = UniformFloatHyperparameter(name='LeakyReLU_alpha', lower=0.01, upper=0.3, default_value=0.01, log=True) ## RELU\n", - " #kernel_initializer_list = CategoricalHyperparameter(name='kernel_initializer_list', choices=['he_normal', 'glorot_uniform']) ## CHECK\n", - "\n", - "\n", - " # cs = ConfigurationSpace()\n", - " # notes from Hamed: \n", - " # Conv1Dinp__kernel_size: # this operator \n", - " # Kenrel size is the same, Stride not \n", - " # Conv1Dinp__filters: 1 filter, 2*, 4*, .... Optimize this once\n", - " # Stride = 1\n", - " # Optimize size\n", - " # kernel_regularizer = Default\n", - " # MaxPooling1D: \n", - " # Gaus = keras.layers.GaussianNoise(0.01,)(input_1)\n", - " \n", - " \n", - " cs.add_hyperparameters([\n", - " Conv__filters,\n", - " Conv__kernel_size,\n", - " Conv__MaxPooling1D,\n", - " Conv__NumberLayers,\n", - " Conv__NumberBlocks,\n", - " \n", - " FC__units,\n", - " FC__units_temperature,\n", - " FC__units_c_o_ratio,\n", - " FC__units_gravity,\n", - " FC__units_metallicity,\n", - " FC__NumberLayers,\n", - " #FC__NumberBlocks,\n", - " \n", - " FC_in_Conv__dropout,\n", - " FC__dropout,\n", - " FC_out_dropout,\n", - " \n", - " FC_in_Conv__units,\n", - " # FC_in_Conv__NumberBlocks,\n", - " # FC_in_Conv__NumberLayers,\n", - " \n", - " \n", - " lr,\n", - " # LeakyReLU_alpha,\n", - " # kernel_initializer_list\n", - " ]) \n", - " \n", - " return cs\n", - " \n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "648a27e7-b5cb-449a-815d-4c1531b68635", - "metadata": {}, - "outputs": [], - "source": [ - " \n", - "\n", - "# Step 1: Load the dataset\n", - "df=pd.read_csv('../../datasets/browndwarf_R100_v4_newWL_v2.csv.bz2', compression='bz2')\n", - "wl = pd.read_csv('../../datasets/wl.csv')\n", - "\n", - "\n", - "## Prepare feature variables (X) and targets (y)\n", - "df = df[df['is_augmented'].isin(['no'])]\n", - "X = df.drop(\n", - " columns=['gravity', \n", - " 'temperature', \n", - " 'c_o_ratio', \n", - " 'metallicity', \n", - " 'is_augmented'])#.astype(np.float32)\n", - "\n", - "y = df[['gravity', 'c_o_ratio', 'metallicity', 'temperature', ]]#.astype(np.float32)\n", - "\n", - "# Log-Transform\n", - "df['temperature'] = df['temperature'].apply(lambda x: np.log10(x))\n", - "#y.loc[:, 'temperature'] = np.log10(y['temperature'])\n", - "##y['temperature'] = np.log10(y['temperature'])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d3bde96c-e51d-43b0-ae11-545600977742", - "metadata": {}, - "outputs": [], - "source": [ - "# Create an instance of TrainCNNRegression\n", - "train_cnn_regression = TrainRegression(feature_values=X,\n", - " feature_names=X.columns,\n", - " target_values=y.to_numpy(),\n", - " target_name=['gravity', 'c_o_ratio', 'metallicity', 'temperature'],\n", - " is_tuned='yes',\n", - " param_grid=None,\n", - " spectral_resolution=100,\n", - " is_feature_improved='no',\n", - " n_jobs=4,\n", - " cv=5,\n", - " is_augmented='no',\n", - " ml_model=None,\n", - " ml_model_str='CNN')\n", - "\n", - "\n", - "\n", - "# Split the dataset into train and test sets\n", - "train_cnn_regression.split_train_validation_test(test_size=0.1, val_size=0.1)\n", - "\n", - "\n", - "# normalize the X features using MinMax Scaler\n", - "train_cnn_regression.normalize_X_row_wise()\n", - "\n", - "\n", - "# Standardize the y features using Standard Scaler\n", - "train_cnn_regression.standardize_y_column_wise()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d82180ed-5ab1-4bf0-8758-780777d1b684", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# Create Xmin and Xmax\n", - "train_cnn_regression.X_train_min = train_cnn_regression.X_train.min(axis=1)\n", - "train_cnn_regression.X_train_max = train_cnn_regression.X_train.max(axis=1)\n", - "\n", - "train_cnn_regression.X_val_min = train_cnn_regression.X_val.min(axis=1)\n", - "train_cnn_regression.X_val_max = train_cnn_regression.X_val.max(axis=1)\n", - "\n", - "train_cnn_regression.X_test_min = train_cnn_regression.X_test.min(axis=1)\n", - "train_cnn_regression.X_test_max = train_cnn_regression.X_test.max(axis=1)\n", - "\n", - "df_MinMax_train = pd.DataFrame((train_cnn_regression.X_train_min, train_cnn_regression.X_train_max)).T\n", - "df_MinMax_val = pd.DataFrame((train_cnn_regression.X_val_min, train_cnn_regression.X_val_max)).T\n", - "df_MinMax_test = pd.DataFrame((train_cnn_regression.X_test_min, train_cnn_regression.X_test_max)).T\n", - "\n", - "df_MinMax_train.rename(columns={0:'min', 1:'max'}, inplace=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "bf6d709d-6de9-4c90-9458-cbc986013baf", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "train_cnn_regression.standardize_X_column_wise(\n", - " X_train = df_MinMax_train.values,\n", - " X_val = df_MinMax_val.values,\n", - " X_test = df_MinMax_test.values,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "302038ec-584a-4577-9ba7-8a47e7a0aa81", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "\n", - "import argparse\n", - "import hpbandster.core.nameserver as hpns\n", - "import hpbandster.core.result as hpres\n", - "\n", - "from hpbandster.optimizers import BOHB as BOHB\n", - "# from hpbandster.examples.commons import MyWorker\n", - "\n", - "\n", - "\n", - "# Create the optimization object with the desired queue size\n", - "\n", - "\n", - "parser = argparse.ArgumentParser(description='Example 3 - Local and Parallel Execution.')\n", - "parser.add_argument('--min_budget', type=float, help='Minimum budget used during the optimization.', default=2)\n", - "parser.add_argument('--max_budget', type=float, help='Maximum budget used during the optimization.', default=7)\n", - "parser.add_argument('--n_iterations', type=int, help='Number of iterations performed by the optimizer', default=50) #1000\n", - "parser.add_argument('--n_workers', type=int, help='Number of workers to run in parallel.', default = 10 )\n", - "parser.add_argument('--worker', help='Flag to turn this into a worker process', action='store_true')\n", - "\n", - "# args=parser.parse_args()\n", - "args = parser.parse_args(args=[])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2925b7bb-033f-4b9d-bc51-fb3856a68ed9", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "if args.worker:\n", - " \n", - " w = KerasWorker( \n", - " # input dataset: StandardScaled instances \n", - " X1_train = train_cnn_regression.X_train_normalized_rowwise,\n", - " X1_val = train_cnn_regression.X_val_normalized_rowwise,\n", - " X1_test = train_cnn_regression.X_test_normalized_rowwise,\n", - "\n", - " # input dataset: Min Max of each instance \n", - " X2_train = train_cnn_regression.X_train_standardized_columnwise,\n", - " X2_val = train_cnn_regression.X_val_standardized_columnwise,\n", - " X2_test = train_cnn_regression.X_test_standardized_columnwise,\n", - "\n", - " # 1st target\n", - " y1_train = train_cnn_regression.y_train_standardized_columnwise[:,0],\n", - " y1_val = train_cnn_regression.y_val_standardized_columnwise[:,0],\n", - " y1_test = train_cnn_regression.y_test_standardized_columnwise[:,0],\n", - "\n", - " # 2nd target\n", - " y2_train = train_cnn_regression.y_train_standardized_columnwise[:,1],\n", - " y2_val = train_cnn_regression.y_val_standardized_columnwise[:,1],\n", - " y2_test = train_cnn_regression.y_test_standardized_columnwise[:,1],\n", - "\n", - " # 3rd target\n", - " y3_train = train_cnn_regression.y_train_standardized_columnwise[:,2],\n", - " y3_val = train_cnn_regression.y_val_standardized_columnwise[:,2],\n", - " y3_test = train_cnn_regression.y_test_standardized_columnwise[:,2],\n", - "\n", - " # 4th target\n", - " y4_train = train_cnn_regression.y_train_standardized_columnwise[:,3],\n", - " y4_val = train_cnn_regression.y_val_standardized_columnwise[:,3],\n", - " y4_test = train_cnn_regression.y_test_standardized_columnwise[:,3],\n", - "\n", - "\n", - " sleep_interval = 0.5, nameserver=ip_address,run_id='example3')\n", - "\n", - " w.run(background=False)\n", - " exit(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d8d016d8-4173-45d0-9f59-ed84bbc650dc", - "metadata": {}, - "outputs": [ - { - "ename": "OSError", - "evalue": "[Errno 98] Address already in use", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[11], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Start a nameserver (see example_1)\u001b[39;00m\n\u001b[1;32m 2\u001b[0m NS \u001b[38;5;241m=\u001b[39m hpns\u001b[38;5;241m.\u001b[39mNameServer(run_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mexample3\u001b[39m\u001b[38;5;124m'\u001b[39m, host\u001b[38;5;241m=\u001b[39mip_address, port\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mNS\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/bohb/lib/python3.9/site-packages/hpbandster/core/nameserver.py:64\u001b[0m, in \u001b[0;36mNameServer.start\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 61\u001b[0m \t\u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 62\u001b[0m \t\t\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost \u001b[38;5;241m=\u001b[39m nic_name_to_host(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnic_name)\n\u001b[0;32m---> 64\u001b[0m uri, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpyro_ns, _ \u001b[38;5;241m=\u001b[39m \u001b[43mPyro4\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnaming\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstartNS\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mport\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mport \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpyro_ns\u001b[38;5;241m.\u001b[39mlocationStr\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mport \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mport)\n", - "File \u001b[0;32m~/anaconda3/envs/bohb/lib/python3.9/site-packages/Pyro4/naming.py:513\u001b[0m, in \u001b[0;36mstartNS\u001b[0;34m(host, port, enableBroadcast, bchost, bcport, unixsocket, nathost, natport, storage, hmac)\u001b[0m\n\u001b[1;32m 509\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstartNS\u001b[39m(host\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, port\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, enableBroadcast\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, bchost\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, bcport\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 510\u001b[0m unixsocket\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, nathost\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, natport\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, storage\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, hmac\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 511\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"utility fuction to quickly get a Name server daemon to be used in your own event loops.\u001b[39;00m\n\u001b[1;32m 512\u001b[0m \u001b[38;5;124;03m Returns (nameserverUri, nameserverDaemon, broadcastServer).\"\"\"\u001b[39;00m\n\u001b[0;32m--> 513\u001b[0m daemon \u001b[38;5;241m=\u001b[39m \u001b[43mNameServerDaemon\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munixsocket\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnathost\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnathost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnatport\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnatport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstorage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 514\u001b[0m daemon\u001b[38;5;241m.\u001b[39m_pyroHmacKey \u001b[38;5;241m=\u001b[39m hmac\n\u001b[1;32m 515\u001b[0m bcserver \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/bohb/lib/python3.9/site-packages/Pyro4/naming.py:269\u001b[0m, in \u001b[0;36mNameServerDaemon.__init__\u001b[0;34m(self, host, port, unixsocket, nathost, natport, storage)\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m existing_count \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 268\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumber of existing entries in storage: \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, existing_count)\n\u001b[0;32m--> 269\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mNameServerDaemon\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munixsocket\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnathost\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnathost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnatport\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnatport\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mregister(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnameserver, constants\u001b[38;5;241m.\u001b[39mNAMESERVER_NAME)\n\u001b[1;32m 271\u001b[0m metadata \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclass:Pyro4.naming.NameServer\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n", - "File \u001b[0;32m~/anaconda3/envs/bohb/lib/python3.9/site-packages/Pyro4/core.py:1141\u001b[0m, in \u001b[0;36mDaemon.__init__\u001b[0;34m(self, host, port, unixsocket, nathost, natport, interface, connected_socket)\u001b[0m\n\u001b[1;32m 1139\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1140\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mPyroError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minvalid server type \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m config\u001b[38;5;241m.\u001b[39mSERVERTYPE)\n\u001b[0;32m-> 1141\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransportServer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munixsocket\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1142\u001b[0m \u001b[38;5;66;03m#: The location (str of the form ``host:portnumber``) on which the Daemon is listening\u001b[39;00m\n\u001b[1;32m 1143\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlocationStr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransportServer\u001b[38;5;241m.\u001b[39mlocationStr\n", - "File \u001b[0;32m~/anaconda3/envs/bohb/lib/python3.9/site-packages/Pyro4/socketserver/threadpoolserver.py:129\u001b[0m, in \u001b[0;36mSocketServer_Threadpool.init\u001b[0;34m(self, daemon, host, port, unixsocket)\u001b[0m\n\u001b[1;32m 127\u001b[0m sslContext \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 128\u001b[0m log\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnot using SSL\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 129\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m \u001b[43msocketutil\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreateSocket\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbind_location\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 130\u001b[0m \u001b[43m \u001b[49m\u001b[43mreuseaddr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSOCK_REUSE\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 131\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCOMMTIMEOUT\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 132\u001b[0m \u001b[43m \u001b[49m\u001b[43mnoinherit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[43m \u001b[49m\u001b[43mnodelay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSOCK_NODELAY\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 134\u001b[0m \u001b[43m \u001b[49m\u001b[43msslContext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msslContext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_socketaddr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock\u001b[38;5;241m.\u001b[39mgetsockname()\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m unixsocket \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_socketaddr[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m127.\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", - "File \u001b[0;32m~/anaconda3/envs/bohb/lib/python3.9/site-packages/Pyro4/socketutil.py:300\u001b[0m, in \u001b[0;36mcreateSocket\u001b[0;34m(bind, connect, reuseaddr, keepalive, timeout, noinherit, ipv6, nodelay, sslContext)\u001b[0m\n\u001b[1;32m 298\u001b[0m bindOnUnusedPort(sock, bind[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 300\u001b[0m \u001b[43msock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbind\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbind\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 302\u001b[0m sock\u001b[38;5;241m.\u001b[39mlisten(\u001b[38;5;241m100\u001b[39m)\n", - "\u001b[0;31mOSError\u001b[0m: [Errno 98] Address already in use" - ] - } - ], - "source": [ - "\n", - "# Start a nameserver (see example_1)\n", - "NS = hpns.NameServer(run_id='example3', host=ip_address, port=None)\n", - "NS.start()" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "26db6c4a-01a4-4661-b3c7-a7ae44dda91e", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'hpres' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Run an optimizer (see example_2)\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m result_logger \u001b[38;5;241m=\u001b[39m \u001b[43mhpres\u001b[49m\u001b[38;5;241m.\u001b[39mjson_result_logger(directory\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/data2/ehsan_storage/telescopeML/core_codes/bohb_tuning/out22/\u001b[39m\u001b[38;5;124m'\u001b[39m, overwrite\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 5\u001b[0m bohb \u001b[38;5;241m=\u001b[39m BOHB( configspace \u001b[38;5;241m=\u001b[39m KerasWorker\u001b[38;5;241m.\u001b[39mget_configspace(),\n\u001b[1;32m 6\u001b[0m run_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mexample3\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 7\u001b[0m min_budget\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39mmin_budget, \n\u001b[1;32m 8\u001b[0m \t\t max_budget\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39mmax_budget,\n\u001b[1;32m 9\u001b[0m \t\t \u001b[38;5;66;03m# result_logger = result_logger,\u001b[39;00m\n\u001b[1;32m 10\u001b[0m )\n\u001b[1;32m 11\u001b[0m res \u001b[38;5;241m=\u001b[39m bohb\u001b[38;5;241m.\u001b[39mrun(n_iterations\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39mn_iterations, min_n_workers\u001b[38;5;241m=\u001b[39margs\u001b[38;5;241m.\u001b[39mn_workers)\n", - "\u001b[0;31mNameError\u001b[0m: name 'hpres' is not defined" - ] - } - ], - "source": [ - "\n", - "# Run an optimizer (see example_2)\n", - "result_logger = hpres.json_result_logger(directory='/data2/ehsan_storage/telescopeML/core_codes/bohb_tuning/out22/', overwrite=True)\n", - "\n", - "\n", - "bohb = BOHB( configspace = KerasWorker.get_configspace(),\n", - " run_id = 'example3',\n", - " min_budget=args.min_budget, \n", - "\t\t max_budget=args.max_budget,\n", - "\t\t # result_logger = result_logger,\n", - " )\n", - "res = bohb.run(n_iterations=args.n_iterations, min_n_workers=args.n_workers)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "d74a1388-62d9-4e43-8b5e-1d6cb028d7d8", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'bohb' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[12], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Step 4: Shutdown\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# After the optimizer run, we must shutdown the master and the nameserver.\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mbohb\u001b[49m\u001b[38;5;241m.\u001b[39mshutdown(shutdown_workers\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 4\u001b[0m NS\u001b[38;5;241m.\u001b[39mshutdown()\n", - "\u001b[0;31mNameError\u001b[0m: name 'bohb' is not defined" - ] - } - ], - "source": [ - "# Step 4: Shutdown\n", - "# After the optimizer run, we must shutdown the master and the nameserver.\n", - "bohb.shutdown(shutdown_workers=True)\n", - "NS.shutdown()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ff5b8287-430f-4b44-bab2-e1db5361473e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:bohb] *", - "language": "python", - "name": "conda-env-bohb-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}

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