3939 lines
1.6 MiB
3939 lines
1.6 MiB
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "initial_id",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Hit:1 http://archive.ubuntu.com/ubuntu jammy InRelease\n",
|
||
"Get:2 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB] \n",
|
||
"Hit:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n",
|
||
"Get:4 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB] \n",
|
||
"Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease \n",
|
||
"Fetched 257 kB in 1s (347 kB/s) \n",
|
||
"Reading package lists... Done\n",
|
||
"Reading package lists... Done\n",
|
||
"Building dependency tree... Done\n",
|
||
"Reading state information... Done\n",
|
||
"graphviz is already the newest version (2.42.2-6ubuntu0.1).\n",
|
||
"0 upgraded, 0 newly installed, 0 to remove and 121 not upgraded.\n",
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"Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.14.0)\n",
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"Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.0.0)\n",
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"Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.6.3)\n",
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"Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (23.5.26)\n",
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"Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.5.4)\n",
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"Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n",
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"Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.9.0)\n",
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"Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (16.0.6)\n",
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"Requirement already satisfied: ml-dtypes==0.2.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n",
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"Requirement already satisfied: numpy>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.26.0)\n",
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"Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.3.0)\n",
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"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow) (23.1)\n",
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"Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.24.3)\n",
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"Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from tensorflow) (68.2.2)\n",
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"Requirement already satisfied: six>=1.12.0 in /usr/lib/python3/dist-packages (from tensorflow) (1.16.0)\n",
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"Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.3.0)\n",
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"Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.8.0)\n",
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"Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.14.1)\n",
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"Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.37.1)\n",
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"Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.58.0)\n",
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"Requirement already satisfied: tensorboard<2.15,>=2.14 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n",
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"Requirement already satisfied: tensorflow-estimator<2.15,>=2.14.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n",
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"Requirement already satisfied: keras<2.15,>=2.14.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n",
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"Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from astunparse>=1.6.0->tensorflow) (0.41.2)\n",
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"Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.23.1)\n",
|
||
"Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (1.0.0)\n",
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||
"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (3.4.4)\n",
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||
"Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.31.0)\n",
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||
"Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (0.7.1)\n",
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||
"Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.3.7)\n",
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||
"Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (5.3.1)\n",
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||
"Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.3.0)\n",
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||
"Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (4.9)\n",
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||
"Requirement already satisfied: urllib3>=2.0.5 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (2.0.5)\n",
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||
"Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (1.3.1)\n",
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||
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (3.4)\n",
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||
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (2023.7.22)\n",
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||
"Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.15,>=2.14->tensorflow) (2.1.3)\n",
|
||
"Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.5.0)\n",
|
||
"Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n",
|
||
"\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
|
||
"\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n",
|
||
"Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.0)\n",
|
||
"\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
|
||
"\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n",
|
||
"Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\n",
|
||
"Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.0)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n",
|
||
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n",
|
||
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n",
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||
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
|
||
"\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
|
||
"\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n",
|
||
"Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (2.14.0)\n",
|
||
"\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
|
||
"\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n",
|
||
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.5.2)\n",
|
||
"Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.0)\n",
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||
"Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.14.1)\n",
|
||
"Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n",
|
||
"Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n",
|
||
"\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
|
||
"\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n",
|
||
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n",
|
||
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n",
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||
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n",
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||
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n",
|
||
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n",
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||
"Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n",
|
||
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n",
|
||
"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n",
|
||
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n",
|
||
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
|
||
"\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
|
||
"\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n",
|
||
"Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n",
|
||
"\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
|
||
"\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n",
|
||
"Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.1.0)\n",
|
||
"\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
|
||
"\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n",
|
||
"Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n",
|
||
"Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n",
|
||
"Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n",
|
||
"Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n",
|
||
"Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n",
|
||
"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n",
|
||
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n",
|
||
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n",
|
||
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n",
|
||
"\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
|
||
"\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n",
|
||
"Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n",
|
||
"Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n",
|
||
"\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
|
||
"\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n",
|
||
"Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n",
|
||
"Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n",
|
||
"Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n",
|
||
"Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n",
|
||
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n",
|
||
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n",
|
||
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n",
|
||
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n",
|
||
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n",
|
||
"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n",
|
||
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n",
|
||
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n",
|
||
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n",
|
||
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
|
||
"\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
|
||
"\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n",
|
||
"Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n",
|
||
"\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
|
||
"\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n",
|
||
"Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.3)\n",
|
||
"Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n",
|
||
"\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
|
||
"\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n",
|
||
"Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n",
|
||
"Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n",
|
||
"\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
|
||
"\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n",
|
||
"Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n",
|
||
"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n",
|
||
"Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n",
|
||
"\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n",
|
||
"\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n",
|
||
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!apt-get update\n",
|
||
"!apt-get install graphviz -y\n",
|
||
"\n",
|
||
"!pip install tensorflow\n",
|
||
"!pip install numpy\n",
|
||
"!pip install pandas\n",
|
||
"\n",
|
||
"!pip install keras\n",
|
||
"!pip install scikit-learn\n",
|
||
"!pip install matplotlib\n",
|
||
"!pip install joblib\n",
|
||
"!pip install pyarrow\n",
|
||
"!pip install fastparquet\n",
|
||
"!pip install scipy\n",
|
||
"!pip install seaborn\n",
|
||
"!pip install tqdm\n",
|
||
"!pip install pydot\n",
|
||
"!pip install tensorflow-io\n",
|
||
"!pip install tensorflow-addons"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "a467d3f0dfd9beab",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-08 14:47:22.527266: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
|
||
"2024-12-08 14:47:22.527310: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
|
||
"2024-12-08 14:47:22.527375: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
|
||
"2024-12-08 14:47:22.536734: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
|
||
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Keras version: 2.14.0\n",
|
||
"TensorFlow version: 2.14.0\n",
|
||
"TensorFlow version: 2.14.0\n",
|
||
"CUDA available: True\n",
|
||
"GPU devices: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n",
|
||
"1 Physical GPUs, 1 Logical GPUs\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-08 14:47:25.041282: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:81:00.0, compute capability: 8.9\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import os\n",
|
||
"import tensorflow as tf\n",
|
||
"import keras\n",
|
||
"\n",
|
||
"# Print versions and system information\n",
|
||
"print(f\"Keras version: {keras.__version__}\")\n",
|
||
"print(f\"TensorFlow version: {tf.__version__}\")\n",
|
||
"print(f\"CUDA available: {tf.test.is_built_with_cuda()}\")\n",
|
||
"print(f\"GPU devices: {tf.config.list_physical_devices('GPU')}\")\n",
|
||
"\n",
|
||
"# GPU memory configuration\n",
|
||
"gpus = tf.config.experimental.list_physical_devices('GPU')\n",
|
||
"if gpus:\n",
|
||
" try:\n",
|
||
" for gpu in gpus:\n",
|
||
" tf.config.experimental.set_memory_growth(gpu, True)\n",
|
||
"\n",
|
||
" logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
|
||
" print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n",
|
||
" except RuntimeError as e:\n",
|
||
" print(e)\n",
|
||
"\n",
|
||
"# Reduce TensorFlow logging verbosity\n",
|
||
"os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
|
||
"\n",
|
||
"# Set global precision policy\n",
|
||
"tf.keras.mixed_precision.set_global_policy('float32')\n",
|
||
"\n",
|
||
"# Uncomment to set seed for reproducibility\n",
|
||
"#tf.random.set_seed(42)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "c0155cde4740b0a3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
|
||
"\n",
|
||
"TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
|
||
"TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
|
||
"Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
|
||
"\n",
|
||
"For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
|
||
"\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from datetime import datetime\n",
|
||
"import os\n",
|
||
"from typing import List\n",
|
||
"\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"import tensorflow_addons as tfa\n",
|
||
"import joblib\n",
|
||
"import re\n",
|
||
"\n",
|
||
"# Set random state value (None for non-deterministic behavior)\n",
|
||
"random_state_value = None\n",
|
||
"\n",
|
||
"# Create execution timestamp for model versioning and logging\n",
|
||
"execute_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n",
|
||
"\n",
|
||
"# Define directory paths\n",
|
||
"base_project_dir = './'\n",
|
||
"data_dir = '../../sources/'\n",
|
||
"models_project_dir = base_project_dir\n",
|
||
"\n",
|
||
"# Create required directories if they don't exist\n",
|
||
"os.makedirs(base_project_dir, exist_ok=True)\n",
|
||
"os.makedirs(models_project_dir, exist_ok=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "1347fb59-50cc-4aa8-b805-ca9403037af5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def clean_column_name(name: str) -> str:\n",
|
||
" \"\"\"\n",
|
||
" Cleans column names by removing special characters and converting to snake_case with abbreviations.\n",
|
||
"\n",
|
||
" Parameters\n",
|
||
" ----------\n",
|
||
" name : str\n",
|
||
" Column name to clean\n",
|
||
"\n",
|
||
" Returns\n",
|
||
" -------\n",
|
||
" str\n",
|
||
" Cleaned column name\n",
|
||
" \"\"\"\n",
|
||
" # Remove special characters\n",
|
||
" name = re.sub(r'[^a-zA-Z0-9\\s]', '', name)\n",
|
||
"\n",
|
||
" # Convert to snake_case\n",
|
||
" name = name.lower().replace(' ', '_')\n",
|
||
"\n",
|
||
" # Common abbreviations mapping\n",
|
||
" abbreviations = {\n",
|
||
" 'production': 'prod',\n",
|
||
" 'percentage': 'pct',\n",
|
||
" 'hectare': 'ha',\n",
|
||
" 'tonnes': 't',\n",
|
||
" 'litres': 'l',\n",
|
||
" 'minimum': 'min',\n",
|
||
" 'maximum': 'max',\n",
|
||
" 'average': 'avg'\n",
|
||
" }\n",
|
||
"\n",
|
||
" for full, abbr in abbreviations.items():\n",
|
||
" name = name.replace(full, abbr)\n",
|
||
"\n",
|
||
" return name\n",
|
||
"\n",
|
||
"\n",
|
||
"def clean_column_names(df: pd.DataFrame) -> List[str]:\n",
|
||
" \"\"\"\n",
|
||
" Cleans all column names in a DataFrame.\n",
|
||
"\n",
|
||
" Parameters\n",
|
||
" ----------\n",
|
||
" df : pd.DataFrame\n",
|
||
" DataFrame whose columns need cleaning\n",
|
||
"\n",
|
||
" Returns\n",
|
||
" -------\n",
|
||
" list\n",
|
||
" List of cleaned column names\n",
|
||
" \"\"\"\n",
|
||
" new_columns = []\n",
|
||
"\n",
|
||
" for col in df.columns:\n",
|
||
" # Extract variety patterns using regex\n",
|
||
" varieties = re.findall(r'([a-z]+)_([a-z_]+)', col)\n",
|
||
" if varieties:\n",
|
||
" new_columns.append(f\"{varieties[0][0]}_{varieties[0][1]}\")\n",
|
||
" else:\n",
|
||
" new_columns.append(col)\n",
|
||
"\n",
|
||
" return new_columns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "4da1f1bb67343e3e",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def save_plot(plt, title, output_dir=f'{base_project_dir}/{execute_name}_plots'):\n",
|
||
" os.makedirs(output_dir, exist_ok=True)\n",
|
||
" filename = \"\".join(x for x in title if x.isalnum() or x in [' ', '-', '_']).rstrip()\n",
|
||
" filename = filename.replace(' ', '_').lower()\n",
|
||
" filepath = os.path.join(output_dir, f\"{filename}.png\")\n",
|
||
" plt.savefig(filepath, bbox_inches='tight', dpi=300)\n",
|
||
" print(f\"Plot salvato come: {filepath}\")\n",
|
||
"\n",
|
||
"\n",
|
||
"def encode_techniques(df, mapping_path=f'{data_dir}technique_mapping.joblib'):\n",
|
||
" if not os.path.exists(mapping_path):\n",
|
||
" raise FileNotFoundError(f\"Mapping not found at {mapping_path}. Run create_technique_mapping first.\")\n",
|
||
"\n",
|
||
" technique_mapping = joblib.load(mapping_path)\n",
|
||
"\n",
|
||
" tech_columns = [col for col in df.columns if col.endswith('_tech')]\n",
|
||
"\n",
|
||
" # Mapping apply to all tech columns\n",
|
||
" for col in tech_columns:\n",
|
||
" df[col] = df[col].str.lower().map(technique_mapping).fillna(0).astype(int)\n",
|
||
"\n",
|
||
" return df\n",
|
||
"\n",
|
||
"\n",
|
||
"def decode_single_technique(technique_value, mapping_path=f'{data_dir}technique_mapping.joblib'):\n",
|
||
" if not os.path.exists(mapping_path):\n",
|
||
" raise FileNotFoundError(f\"Mapping not found at {mapping_path}\")\n",
|
||
"\n",
|
||
" technique_mapping = joblib.load(mapping_path)\n",
|
||
" reverse_mapping = {v: k for k, v in technique_mapping.items()}\n",
|
||
" reverse_mapping[0] = ''\n",
|
||
"\n",
|
||
" return reverse_mapping.get(technique_value, '')\n",
|
||
"\n",
|
||
"\n",
|
||
"def prepare_comparison_data(simulated_data, olive_varieties):\n",
|
||
"\n",
|
||
" df = simulated_data.copy()\n",
|
||
"\n",
|
||
" df.columns = clean_column_names(df)\n",
|
||
" df = encode_techniques(df)\n",
|
||
"\n",
|
||
" all_varieties = olive_varieties['Varietà di Olive'].unique()\n",
|
||
" varieties = [clean_column_name(variety) for variety in all_varieties]\n",
|
||
" comparison_data = []\n",
|
||
"\n",
|
||
" for variety in varieties:\n",
|
||
" olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n",
|
||
" oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n",
|
||
" tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n",
|
||
" water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n",
|
||
"\n",
|
||
" if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n",
|
||
" variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n",
|
||
" variety_data = variety_data[variety_data[tech_col] != 0] # Esclude le righe dove la tecnica è 0\n",
|
||
"\n",
|
||
" if not variety_data.empty:\n",
|
||
" avg_olive_prod = pd.to_numeric(variety_data[olive_prod_col], errors='coerce').mean()\n",
|
||
" avg_oil_prod = pd.to_numeric(variety_data[oil_prod_col], errors='coerce').mean()\n",
|
||
" avg_water_need = pd.to_numeric(variety_data[water_need_col], errors='coerce').mean()\n",
|
||
" efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n",
|
||
" water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n",
|
||
"\n",
|
||
" comparison_data.append({\n",
|
||
" 'Variety': variety,\n",
|
||
" 'Avg Olive Production (kg/ha)': avg_olive_prod,\n",
|
||
" 'Avg Oil Production (L/ha)': avg_oil_prod,\n",
|
||
" 'Avg Water Need (m³/ha)': avg_water_need,\n",
|
||
" 'Oil Efficiency (L/kg)': efficiency,\n",
|
||
" 'Water Efficiency (L oil/m³ water)': water_efficiency\n",
|
||
" })\n",
|
||
"\n",
|
||
" return pd.DataFrame(comparison_data)\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_variety_comparison(comparison_data, metric):\n",
|
||
" plt.figure(figsize=(12, 6))\n",
|
||
" bars = plt.bar(comparison_data['Variety'], comparison_data[metric])\n",
|
||
" plt.title(f'Comparison of {metric} across Olive Varieties')\n",
|
||
" plt.xlabel('Variety')\n",
|
||
" plt.ylabel(metric)\n",
|
||
" plt.xticks(rotation=45, ha='right')\n",
|
||
"\n",
|
||
" for bar in bars:\n",
|
||
" height = bar.get_height()\n",
|
||
" plt.text(bar.get_x() + bar.get_width() / 2., height,\n",
|
||
" f'{height:.2f}',\n",
|
||
" ha='center', va='bottom')\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" save_plot(plt, f'variety_comparison_{metric.lower().replace(\" \", \"_\").replace(\"/\", \"_\").replace(\"(\", \"\").replace(\")\", \"\")}')\n",
|
||
" plt.close()\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_efficiency_vs_production(comparison_data):\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
"\n",
|
||
" plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n",
|
||
" comparison_data['Oil Efficiency (L/kg)'],\n",
|
||
" s=100)\n",
|
||
"\n",
|
||
" for i, row in comparison_data.iterrows():\n",
|
||
" plt.annotate(row['Variety'],\n",
|
||
" (row['Avg Olive Production (kg/ha)'], row['Oil Efficiency (L/kg)']),\n",
|
||
" xytext=(5, 5), textcoords='offset points')\n",
|
||
"\n",
|
||
" plt.title('Oil Efficiency vs Olive Production by Variety')\n",
|
||
" plt.xlabel('Average Olive Production (kg/ha)')\n",
|
||
" plt.ylabel('Oil Efficiency (L oil / kg olives)')\n",
|
||
" plt.tight_layout()\n",
|
||
" save_plot(plt, 'efficiency_vs_production')\n",
|
||
" plt.close()\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_water_efficiency_vs_production(comparison_data):\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
"\n",
|
||
" plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n",
|
||
" comparison_data['Water Efficiency (L oil/m³ water)'],\n",
|
||
" s=100)\n",
|
||
"\n",
|
||
" for i, row in comparison_data.iterrows():\n",
|
||
" plt.annotate(row['Variety'],\n",
|
||
" (row['Avg Olive Production (kg/ha)'], row['Water Efficiency (L oil/m³ water)']),\n",
|
||
" xytext=(5, 5), textcoords='offset points')\n",
|
||
"\n",
|
||
" plt.title('Water Efficiency vs Olive Production by Variety')\n",
|
||
" plt.xlabel('Average Olive Production (kg/ha)')\n",
|
||
" plt.ylabel('Water Efficiency (L oil / m³ water)')\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" save_plot(plt, 'water_efficiency_vs_production')\n",
|
||
" plt.close()\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_water_need_vs_oil_production(comparison_data):\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
"\n",
|
||
" plt.scatter(comparison_data['Avg Water Need (m³/ha)'],\n",
|
||
" comparison_data['Avg Oil Production (L/ha)'],\n",
|
||
" s=100)\n",
|
||
"\n",
|
||
" for i, row in comparison_data.iterrows():\n",
|
||
" plt.annotate(row['Variety'],\n",
|
||
" (row['Avg Water Need (m³/ha)'], row['Avg Oil Production (L/ha)']),\n",
|
||
" xytext=(5, 5), textcoords='offset points')\n",
|
||
"\n",
|
||
" plt.title('Oil Production vs Water Need by Variety')\n",
|
||
" plt.xlabel('Average Water Need (m³/ha)')\n",
|
||
" plt.ylabel('Average Oil Production (L/ha)')\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" save_plot(plt, 'water_need_vs_oil_production')\n",
|
||
" plt.close()\n",
|
||
"\n",
|
||
"\n",
|
||
"def analyze_by_technique(simulated_data, olive_varieties):\n",
|
||
"\n",
|
||
" df = simulated_data.copy()\n",
|
||
"\n",
|
||
" df.columns = clean_column_names(df)\n",
|
||
" df = encode_techniques(df)\n",
|
||
" all_varieties = olive_varieties['Varietà di Olive'].unique()\n",
|
||
" varieties = [clean_column_name(variety) for variety in all_varieties]\n",
|
||
"\n",
|
||
" technique_data = []\n",
|
||
"\n",
|
||
" for variety in varieties:\n",
|
||
" olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n",
|
||
" oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n",
|
||
" tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n",
|
||
" water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n",
|
||
"\n",
|
||
" if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n",
|
||
" variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n",
|
||
" variety_data = variety_data[variety_data[tech_col] != 0]\n",
|
||
"\n",
|
||
" if not variety_data.empty:\n",
|
||
" for tech in variety_data[tech_col].unique():\n",
|
||
" tech_data = variety_data[variety_data[tech_col] == tech]\n",
|
||
"\n",
|
||
" avg_olive_prod = pd.to_numeric(tech_data[olive_prod_col], errors='coerce').mean()\n",
|
||
" avg_oil_prod = pd.to_numeric(tech_data[oil_prod_col], errors='coerce').mean()\n",
|
||
" avg_water_need = pd.to_numeric(tech_data[water_need_col], errors='coerce').mean()\n",
|
||
"\n",
|
||
" efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n",
|
||
" water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n",
|
||
"\n",
|
||
" technique_data.append({\n",
|
||
" 'Variety': variety,\n",
|
||
" 'Technique': tech,\n",
|
||
" 'Technique String': decode_single_technique(tech),\n",
|
||
" 'Avg Olive Production (kg/ha)': avg_olive_prod,\n",
|
||
" 'Avg Oil Production (L/ha)': avg_oil_prod,\n",
|
||
" 'Avg Water Need (m³/ha)': avg_water_need,\n",
|
||
" 'Oil Efficiency (L/kg)': efficiency,\n",
|
||
" 'Water Efficiency (L oil/m³ water)': water_efficiency\n",
|
||
" })\n",
|
||
"\n",
|
||
" return pd.DataFrame(technique_data)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "9aa4bf176c4affb9",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def calculate_real_error(model, test_data, test_targets, scaler_y):\n",
|
||
"\n",
|
||
" predictions = model.predict(test_data)\n",
|
||
"\n",
|
||
" # Denormalize predictions and target values\n",
|
||
" predictions_real = scaler_y.inverse_transform(predictions)\n",
|
||
" targets_real = scaler_y.inverse_transform(test_targets)\n",
|
||
"\n",
|
||
" # Calculate percentage error for each target\n",
|
||
" percentage_errors = []\n",
|
||
" absolute_errors = []\n",
|
||
"\n",
|
||
" for i in range(predictions_real.shape[1]):\n",
|
||
" mae = np.mean(np.abs(predictions_real[:, i] - targets_real[:, i]))\n",
|
||
" mape = np.mean(np.abs((predictions_real[:, i] - targets_real[:, i]) / targets_real[:, i])) * 100\n",
|
||
" percentage_errors.append(mape)\n",
|
||
" absolute_errors.append(mae)\n",
|
||
"\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n",
|
||
"\n",
|
||
" print(\"\\nErrori per target:\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" for i, target in enumerate(target_names):\n",
|
||
" print(f\"{target}:\")\n",
|
||
" print(f\"MAE assoluto: {absolute_errors[i]:.2f}\")\n",
|
||
" print(f\"Errore percentuale medio: {percentage_errors[i]:.2f}%\")\n",
|
||
" print(f\"Precisione: {100 - percentage_errors[i]:.2f}%\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
"\n",
|
||
" return percentage_errors, absolute_errors"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "b3ba2b96ba678389",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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A6Onpibt370rlixcvVvqf6NwvQQMHDpTKcnJyRMuWLYWOjo548uSJVP7q1SuFeDIzM0W1atXE999/r1AOQGhoaIgrV64o7dv7X16MjY0VvtS+LzMzU1hYWIhq1aqJ9PR0qXz37t0CgBg/frzSvkycOFGhD1dX149+8Xz8+LHQ0dERzZs3V0jazZs3TwAQK1askMpyv5S9e2w+ZPPmzQKAuHnzphBCiJSUFKGrq6uQhMn9bC5duqSwbZUqVRSO78CBA4VMJpOSfEK8/cJlamr60aRU7nn17heZvDg7OwtTU1PpvSpJqYCAAKGtrS2SkpKksoyMDFGyZEnh5+cnlfn7+wtra2vx9OlThf58fHyEsbGx0jn2PltbW9G8eXPx5MkT8eTJE3Hx4kXh4+OjcA7nJjGMjIzE48ePFbZ3cXERFhYW4tmzZ1LZxYsXhYaGhvSFVQghvL29ha6ursLvz9WrV4WmpuYnJ6W6d+8uNDQ0xLlz55Ta5iZMNm3alGeiRgjlpNSsWbMEAIUEbmZmpqhbt64wMDAQKSkpCjGWKlVK4fPZsWOHACB27dqlNNa7li1blue5mbuPub+/w4cPFxoaGmLlypUKbWbMmCEAiO3bt0tl6enpwtHRMc997datm8J+qhN/XufPunXrBABx4sQJpbo+ffoIPT29D+5/fv1OnjxZyGQyhXOkYcOGwtDQUKFMCKGQEMv9fercubNCG1Wv+9u2bRMA8jyPcg0ePFgYGRmJN2/efHTf3uXt7S0AfDD5s3XrVgFAzJkzRyr7WFIqOTlZyOVyMXz4cIW+pk2bpnAM79y5IzQ1NcXvv/+u0O7SpUtCS0tLqfx9b968EdbW1qJu3boK5YsWLRIAxIEDB4QQb5Pv717nhXh7nsnlcoV/P3ITTw4ODkrnwPtJqZycHFGxYkXh4eGh8Hm/evVK2Nvbi2bNmkllwcHB+V6v37+2Tpo0Sejr64sbN24otBs9erTQ1NQU9+7dE0J8+mdORFTQePseEdF/UEpKCgCovLD13r17AQDDhg1TKM9d3Pn9taeqVKmCunXrSu9r164NAPj+++9Rrlw5pfLbt28rjfnuY91zb7/LzMzEoUOHpHI9PT3p5+fPnyM5ORkNGjRQutUOABo1aoQqVap8ZE/frst05swZPHjwIM/6yMhIPH78GP369VNYv6Nly5ZwdHTMcx2u99cVatCgQZ77/K5Dhw4hMzMTQ4YMUVhktnfv3jAyMvpX632FhYWhZs2a0qLohoaGaNmypcItfO3atYOWlhY2bNgglV2+fBlXr15VeFrf/v37UbduXbi4uEhlpqamKq2TkvtUqo+dh4aGhtI5q6pOnTohKysLW7dulcoOHjyIFy9eSPELIbBlyxa0bt0aQgg8ffpUenl4eCA5OTnPc+l9Bw8ehLm5OczNzVGjRg1s2rQJ3bp1w9SpUxXatW/fHubm5tL7hw8fIjo6Gj169ICpqalU7uzsjGbNmkm/d9nZ2Thw4AC8vb0Vfn+cnJzg4eGh1nHJlZOTg+3bt6N169ZKtyoB+KS1k/bu3QsrKyt07txZKtPW1sagQYOQmpqK48ePK7Tv1KmTwjphDRo0AJD39eBdube+5bfGmBACAwYMwOzZs7FmzRqltdL279+P0qVLK9wOpquri969eyv1lZOTg/379+d5654q8b97jXr9+jWePn2KOnXqAECe55aJiQnS09Px6tWrPPctr37T0tLw9OlT1KtXD0II6ZboJ0+e4MSJE/Dz81M4b4C8P9/3r1OqXvdz17LbvXs3srKy8oy3ZMmSSEtLQ3h4+Af3632qXCNy69S5RuTeHrdx40aF28I3bNiAOnXqSMdr69atyMnJQceOHRWuD1ZWVqhYsSKOHj36wXE0NTXh4+ODiIgIhVuZ165dC0tLSzRt2hTA23Wbcq/z2dnZePbsGQwMDFC5cuU8zxNfX1+FcyAv0dHRuHnzJrp06YJnz55JsaelpaFp06Y4ceKE0u12qti0aRMaNGgAExMThWPi7u6O7OxsnDhxAsCnf+ZERAWNSSkiov8gIyMjAPjgo6rfdffuXWhoaCg92c3KygolS5ZUehrV+1+AjI2NAQBly5bNs/z58+cK5RoaGkrrfVSqVAkAFP7Hfvfu3ahTpw50dXVhamoKc3NzLFy4EMnJyUr7YG9v/7HdBPB2ra3Lly+jbNmy+PbbbxEYGKjwJTN3XytXrqy0raOjo9Kx0NXVVUhEAG+/eL6/z+/LbxwdHR04ODh88hPAXrx4gb1796JRo0aIi4uTXvXr10dkZCRu3LgBADAzM0PTpk2xceNGadsNGzZAS0sL7dq1U4gzryf+qfIUwNwvkx87Dz/lyYA1atSAo6OjQlJtw4YNMDMzkxaRfvLkCV68eIElS5ZISaXcV8+ePQEgz7Vg3le7dm2Eh4fj0KFDOH36NJ4+fYrVq1crfWl8/xz80Lnk5OQkfYF88uQJ0tPTUbFiRaV2eW2riidPniAlJQXVqlX7pO3zcvfuXVSsWFHpSV1OTk5S/bvev07kJng+9ruR691kwrtWr16N+fPnY+7cuQoJsnfjLF++vFJiJq9z9ty5c3jy5EmeSSlV4k9KSsLgwYNhaWkJPT09mJubS+dBXtep3H36WFLw3r17UjIzd626Ro0aKfSbe91S9TPO6/xU5brfqFEjtG/fHkFBQTAzM0Pbtm0RGhqqsO5Uv379UKlSJXh5eaFMmTLw8/OT1vX7EFWuEaomt9/XqVMn/PPPP4iIiAAA3Lp1C1FRUQpJ95s3b0IIgYoVKypdI65du6bS9SE3Qb927VoAwP3793Hy5En4+PhIi6/n5OQgJCQEFStWhFwuh5mZGczNzRETE/PJ/57dvHkTwNsE1vuxL1u2DBkZGXn2rUq/+/fvV+ozd4233GPyqZ85EVFB49P3iIj+g4yMjGBjY4PLly+rtZ2qsyfye8pRfuX5fbn8kJMnT6JNmzZo2LAhFixYAGtra2hrayM0NFT6n/93feyvyrk6duyIBg0aYNu2bTh48CCCg4MxdepUbN26FV5eXmrHWdRPfPqYTZs2ISMjAzNmzMCMGTOU6sPCwqQFzn18fNCzZ09ER0fDxcUFGzduRNOmTWFmZlYgsVSoUAFaWlqIiYnJt01GRgZiY2PznM3zMZ06dcLvv/+Op0+fwtDQEDt37kTnzp2hpfX2f09yZwn8+OOP+T550NnZ+aPjmJmZqbTotqrn4L+R3++oqgvrF6VPvR6UKlUKwNvkT5kyZZTq69evj+joaMybNw8dO3ZUmIWmrr1798LOzi7PWZaqxN+xY0ecPn0aI0aMgIuLCwwMDJCTkwNPT888Z6k8f/4cJUqU+OC5kp2djWbNmiEpKQmjRo2Co6Mj9PX1kZCQgB49enzS7Bcg//PzY9d9mUyGzZs34++//8auXbtw4MAB+Pn5YcaMGfj7779hYGAACwsLREdH48CBA9i3bx/27duH0NBQdO/ePc8HEuRycnLC9u3bERMTg4YNG+bZJvf6ocpM2He1bt0aJUqUwMaNG1GvXj1s3LgRGhoa0uL+wNtrhEwmw759+/L8vA0MDD46jpubGxwdHbFu3TqMGTMG69atgxBCYTbpH3/8gV9//RV+fn6YNGkSTE1NoaGhgSFDhuT5eapyLcndLjg4WGEmq7rx59Vvs2bNMHLkyDzrc/+A9KmfORFRQWNSiojoP6pVq1ZYsmQJIiIiFG61y4utrS1ycnJw8+ZNadYD8PYpbS9evICtrW2BxpaTk4Pbt29L/3MLQJrBY2dnBwDYsmULdHV1ceDAAYVHVoeGhv7r8a2trdGvXz/069cPjx8/xjfffIPff/8dXl5e0r7GxsYqPbY9Nja2wI7Fu+O8O2ssMzMT8fHxKj957H1hYWGoVq2a9MS+dy1evBhr166VklLe3t746aefpNlGN27cQEBAgFKccXFxSn3lVfY+fX19NGnSBEeOHMHdu3fzPHYbN25ERkYGWrVqpdL+vatTp04ICgrCli1bYGlpiZSUFPj4+Ej15ubmMDQ0RHZ29icfz3/j3c/4fdevX4eZmRn09fWhq6sLPT09aebDu97fNne2zrtPaASUZymZm5vDyMjoo4lpdW7js7W1RUxMDHJychRmS12/fl2qLwiOjo4AgPj4eFSvXl2pvkKFCpg2bRoaN24MT09PHD58WGEWja2tLa5evQohhML+5XXO7tmzBy1atPikOJ8/f47Dhw8jKCgI48ePl8rz+hxzxcfHK1xj83Lp0iXcuHEDq1atQvfu3aXy92+Tyr1uqPvHh1zqXvfr1KmDOnXq4Pfff8fatWvRtWtXrF+/Hr169QLwdpZn69at0bp1a+Tk5KBfv35YvHgxfv3113xnVrZq1QqTJ0/G6tWr80xKZWdnY+3atTAxMUH9+vXV2j99fX20atUKmzZtwsyZM7FhwwY0aNAANjY2Upvy5ctDCAF7e3uFf4/U1bVrV/z666+IiYnB2rVrUbFiRdSqVUuq37x5M5o0aYLly5crbPfixYtP/iNA+fLlAbz9I9THrm/q/J6XL18eqampKl0zP+UzJyIqaLx9j4joP2rkyJHQ19dHr1698OjRI6X6W7duSY9uzv1SNmvWLIU2M2fOBIA8b235t+bNmyf9LITAvHnzoK2tLa3BoampCZlMpjAD5M6dO9i+ffsnj5mdna10O4OFhQVsbGykW1Fq1qwJCwsLLFq0SOH2lH379uHatWsFdizc3d2ho6ODOXPmKMy8WL58OZKTkz9pnH/++QcnTpxAx44d8cMPPyi9evbsibi4OJw5cwbA2zVBPDw8sHHjRqxfvx46Ojrw9vZW6NPDwwMRERGIjo6WypKSkhTWp/qQcePGQQiBHj16KD3SPT4+HiNHjoS1tTV++ukntffXyckJ1atXx4YNG7BhwwZYW1srfLHV1NRE+/btsWXLljy/uD958kTtMdVhbW0NFxcXrFq1SiGJdPnyZRw8eFD6vdPU1ISHhwe2b9+Oe/fuSe2uXbuGAwcOKPRpZGQEMzMzaV2XXAsWLFB4r6GhAW9vb+zatQuRkZFKseWec/r6+gCUk1x5adGiBRITExVumXzz5g3mzp0LAwMD6fayf8vNzQ06Ojp5xp3L2dkZe/fuxbVr19C6dWuFc8vDwwMJCQnYuXOnVPb69WssXbpUoY9Hjx7h/Pnzn/w7nTuz5v2ZX+9fR991/vx51KtXT+1+hRDS9TqXubk5GjZsiBUrViicN3nFlBdVr/vPnz9X6i93Zk7uNTJ3HbBcGhoa0izEd6+j76tXrx7c3d0RGhqK3bt3K9WPHTsWN27cwMiRIz9pJmKnTp3w4MEDLFu2DBcvXlS4dQ94u7aepqYmgoKClPZRCKG0X/nJnRU1fvx4REdHK625p6mpqdT/pk2bkJCQoO4uSdzc3FC+fHlMnz4dqampSvXvXt/U+T3v2LEjIiIilK49udu/efMGwKd/5kREBY0zpYiI/qPKly+PtWvXolOnTnByckL37t1RrVo1ZGZm4vTp09i0aRN69OgB4O36PL6+vliyZAlevHiBRo0a4ezZs1i1ahW8vb3RpEmTAo1NV1cX+/fvh6+vL2rXro19+/Zhz549GDNmjLQ+U8uWLTFz5kx4enqiS5cuePz4MebPn48KFSp88HawD3n58iXKlCmDH374ATVq1ICBgQEOHTqEc+fOSbe6aWtrY+rUqejZsycaNWqEzp0749GjR5g9ezbs7OwwdOjQAjkG5ubmCAgIQFBQEDw9PdGmTRvExsZiwYIFqFWrFn788Ue1+1y7di2EEAoLPL+rRYsW0NLSQlhYmLQIfadOnfDjjz9iwYIF8PDwkBY1zjVy5EisWbMGzZo1w8CBA6Gvr49ly5ahXLlySEpK+uhf4Bs2bIjp06dj2LBhcHZ2Ro8ePWBtbY3r169j6dKlyMnJwd69e/Nd1PpjOnXqhPHjx0NXVxf+/v5K6x1NmTIFR48eRe3atdG7d29UqVIFSUlJOH/+PA4dOoSkpKRPGldVwcHB8PLyQt26deHv74/09HTMnTsXxsbGCAwMlNoFBQVh//79aNCgAfr16ycle6pWrap0vvfq1QtTpkxBr169ULNmTZw4cUKaafiuP/74AwcPHkSjRo3Qp08fODk54eHDh9i0aRNOnTqFkiVLwsXFBZqampg6dSqSk5Mhl8vx/fffw8LCQqm/Pn36YPHixejRoweioqJgZ2eHzZs346+//sKsWbPUXvMnP7q6umjevDkOHTqEiRMn5tuuTp062LFjB1q0aIEffvgB27dvh7a2Nn766SfMmzcPnTt3xuDBg2FtbY2wsDDpwQW55+zevXuhq6v7ydc3IyMjNGzYENOmTUNWVhZKly6NgwcPIj4+Ps/2UVFRSEpKQtu2bT/Yr6OjI8qXL49ffvkFCQkJMDIywpYtW/Jci2vOnDn47rvv8M0336BPnz6wt7fHnTt3sGfPHoVEcl5Uve6vWrUKCxYswP/+9z+UL18eL1++xNKlS2FkZCQltnr16oWkpCR8//33KFOmDO7evYu5c+fCxcXlozPDVq9ejaZNm6Jt27bo0qULGjRogIyMDGzduhXHjh1Dp06dMGLEiA/2kZ8WLVrA0NAQv/zyi5Skflf58uXx22+/ISAgAHfu3IG3tzcMDQ0RHx+Pbdu2oU+fPvjll18+Oo69vT3q1auHHTt2AIBSUqpVq1aYOHEievbsiXr16uHSpUsICwtTWltRHRoaGli2bBm8vLxQtWpV9OzZE6VLl0ZCQgKOHj0KIyMj7Nq1C8DbBBbwNsnn4+MDbW1ttG7dWkpWvWvEiBHYuXMnWrVqhR49esDNzQ1paWm4dOkSNm/ejDt37sDMzOxffeZERAWqiJ7yR0REn+jGjRuid+/ews7OTujo6AhDQ0NRv359MXfuXPH69WupXVZWlggKChL29vZCW1tblC1bVgQEBCi0EeLt46NbtmypNA7eeVR7rtxHqwcHB0tlvr6+Ql9fX9y6dUs0b95clChRQlhaWooJEyYoPTJ7+fLlomLFikIulwtHR0cRGhoqPd78Y2O/W5f76PCMjAwxYsQIUaNGDWFoaCj09fVFjRo1xIIFC5S227Bhg3B1dRVyuVyYmpqKrl27ivv37yu0yd2X9+UVY37mzZsnHB0dhba2trC0tBQ///yz0uPRc/t78uTJB/uqXr26KFeu3AfbNG7cWFhYWIisrCwhhBApKSlCT09PABBr1qzJc5sLFy6IBg0aCLlcLsqUKSMmT54s5syZIwCIxMRElfbzxIkTom3btsLMzExoa2uLcuXKid69e4s7d+4otc3r+L3/2PJcN2/eFAAEAHHq1Kk8x3706JHo37+/KFu2rNDW1hZWVlaiadOmYsmSJR+NO7/z/V15nefvOnTokKhfv77Q09MTRkZGonXr1uLq1atK7Y4fPy7c3NyEjo6OcHBwEIsWLcrzWLx69Ur4+/sLY2NjYWhoKDp27CgeP36scK7nunv3rujevbswNzcXcrlcODg4iP79+4uMjAypzdKlS4WDg4PQ1NRUeOR8o0aNRKNGjRT6e/TokejZs6cwMzMTOjo6onr16iI0NFTl45FXjHnZunWrkMlk0uPn393+/d/1HTt2CC0tLdGpUyfpGnL79m3RsmVLoaenJ8zNzcXw4cPFli1bBADx999/CyGE+OGHH0SLFi2UxlYn/vv374v//e9/omTJksLY2Fh06NBBPHjwIM/9HDVqlChXrpzIycn56P5fvXpVuLu7CwMDA2FmZiZ69+4tLl68KAAoHe/Lly9LMejq6orKlSuLX3/9Var/0PVDlev++fPnRefOnUW5cuWEXC4XFhYWolWrViIyMlJqs3nzZtG8eXNhYWEhdHR0RLly5cRPP/0kHj58+NF9FUKIly9fisDAQFG1alWhp6cn/Tu1cuXKPI/X+8c3NDRUABDx8fFKbbt27SoACHd393zH37Jli/juu++Evr6+0NfXF46OjqJ///4iNjZWpfiFEGL+/PkCgPj222+V6l6/fi2GDx8urK2thZ6enqhfv76IiIhQ+h07evSoACA2bdqk1EduXe7vZ64LFy6Idu3aiVKlSgm5XC5sbW1Fx44dxeHDhxXaTZo0SZQuXVpoaGgoHKu8rq0vX74UAQEBokKFCkJHR0eYmZmJevXqienTp4vMzEwhxL//zImICopMiE9YvZaIiL5aPXr0wObNm/O83YC+HEOGDMHixYuRmpr6n1vsnb582dnZqFKlCjp27IhJkyYVSJ+zZs3C0KFDcf/+fVhaWqJUqVKYPHky+vXrVyD9f0hGRgbs7OwwevRoDB48uNDHIyIi+lpwTSkiIqJi7v21oJ49e4Y///wT3333HRNSVCg0NTUxceJEzJ8//5MS2O+fs69fv8bixYtRsWJFlC5dGklJSRg6dCj+97//FVTIHxQaGgptbW307du3SMYjIiL6WnCmFBERqYUzpb48Li4uaNy4MZycnPDo0SMsX74cDx48wOHDh/N9jDvR5+Tl5YVy5crBxcUFycnJWLNmDa5cuYKwsDB06dLlc4dHREREBYQLnRMRERVzLVq0wObNm7FkyRLIZDJ88803WL58ORNS9J/l4eGBZcuWISwsTLoVcP369UpPXyMiIqIvG2dKERERERERERFRkeOaUkREREREREREVOSYlCIiIiIiIiIioiLHNaUKSE5ODh48eABDQ0PIZLLPHQ4RERERERER0WchhMDLly9hY2MDDY3850MxKVVAHjx4gLJly37uMIiIiIiIiIiI/hP++ecflClTJt96JqUKiKGhIYC3B9zIyOgzR0NERERERERE9HmkpKSgbNmyUq4kP0xKFZDcW/aMjIyYlCIiIiIiIiKir97HljfiQudERERERERERFTkmJQiIiIiIiIiIqIix6QUEREREREREREVOSaliIiIiIiIiIioyDEpRURE9P+dOHECrVu3ho2NDWQyGbZv365Q/+jRI/To0QM2NjYoUaIEPD09cfPmTaV+IiIi8P3330NfXx9GRkZo2LAh0tPTldplZGTAxcUFMpkM0dHRCnUHDhxAnTp1YGhoCHNzc7Rv3x537tz56D7s2bMHtWvXhp6eHkxMTODt7S3VPXv2DJ6enrCxsYFcLkfZsmUxYMAApKSkqHJ4iIiIiIgKFJNSRERE/19aWhpq1KiB+fPnK9UJIeDt7Y3bt29jx44duHDhAmxtbeHu7o60tDSpXUREBDw9PdG8eXOcPXsW586dw4ABA6ChofxP7siRI2FjY6NUHh8fj7Zt2+L7779HdHQ0Dhw4gKdPn6Jdu3YfjH/Lli3o1q0bevbsiYsXL+Kvv/5Cly5dpHoNDQ20bdsWO3fuxI0bN7By5UocOnQIffv2VecwEREREREVDPEZ/fHHH6JmzZrCwMBAmJubi7Zt24rr168rtElPTxf9+vUTpqamQl9fX7Rr104kJiYqtLl7965o0aKF0NPTE+bm5uKXX34RWVlZCm2OHj0qXF1dhY6OjihfvrwIDQ1VimfevHnC1tZWyOVy8e2334ozZ86ovC/JyckCgEhOTlb9ABAR0X8WALFt2zbpfWxsrAAgLl++LJVlZ2cLc3NzsXTpUqmsdu3aYty4cR/tf+/evcLR0VFcuXJFABAXLlyQ6jZt2iS0tLREdna2VLZz504hk8lEZmZmnv1lZWWJ0qVLi2XLlqmxl0LMnj1blClTRq1tiIiIiIg+RNUcyWedKXX8+HH0798ff//9N8LDw5GVlYXmzZsr/MV56NCh2LVrFzZt2oTjx4/jwYMHCn8pzs7ORsuWLZGZmYnTp09j1apVWLlyJcaPHy+1iY+PR8uWLdGkSRNER0djyJAh6NWrFw4cOCC12bBhA4YNG4YJEybg/PnzqFGjBjw8PPD48eOiORhERPSflpGRAQDQ1dWVyjQ0NCCXy3Hq1CkAwOPHj3HmzBlYWFigXr16sLS0RKNGjaT6XI8ePULv3r3x559/okSJEkpjubm5QUNDA6GhocjOzkZycjL+/PNPuLu7Q1tbO8/4zp8/j4SEBGhoaMDV1RXW1tbw8vLC5cuX892nBw8eYOvWrWjUqJHax4OIiIiI6F8roiSZSh4/fiwAiOPHjwshhHjx4oXQ1tYWmzZtktpcu3ZNABARERFCiLd/adbQ0FCYPbVw4UJhZGQkMjIyhBBCjBw5UlStWlVhrE6dOgkPDw/p/bfffiv69+8vvc/OzhY2NjZi8uTJKsXOmVJERMUL3psplZmZKcqVKyc6dOggkpKSREZGhpgyZYoAIJo3by6EECIiIkIAEKampmLFihXi/PnzYsiQIUJHR0fcuHFDCCFETk6O8PT0FJMmTRJCCBEfH680U0oIIY4dOyYsLCyEpqamACDq1q0rnj9/nm+869atEwBEuXLlxObNm0VkZKTo3LmzKFWqlHj27JlCWx8fH6GnpycAiNatW4v09PR/f8CIiIiIiP6/L2Km1PuSk5MBAKampgCAqKgoZGVlwd3dXWrj6OiIcuXKISIiAsDbtTuqV68OS0tLqY2HhwdSUlJw5coVqc27feS2ye0jMzMTUVFRCm00NDTg7u4utXlfRkYGUlJSFF5ERFR8aWtrY+vWrbhx4wZMTU1RokQJHD16FF5eXtJ6UTk5OQCAn376CT179oSrqytCQkJQuXJlrFixAgAwd+5cvHz5EgEBAfmOlZiYiN69e8PX1xfnzp3D8ePHoaOjgx9++AFCiDy3yR177NixaN++Pdzc3BAaGgqZTIZNmzYptA0JCcH58+exY8cO3Lp1C8OGDfvXx4eIiIiISF1anzuAXDk5ORgyZAjq16+PatWqAXj7P+U6OjooWbKkQltLS0skJiZKbd5NSOXW59Z9qE1KSgrS09Px/PlzZGdn59nm+vXrecY7efJkBAUFfdrOEhHRF8nNzQ3R0dFITk5GZmYmzM3NUbt2bdSsWRMAYG1tDQCoUqWKwnZOTk64d+8eAODIkSOIiIiAXC5XaFOzZk107doVq1atwvz582FsbIxp06ZJ9WvWrEHZsmVx5swZ1KlTRym2vMaWy+VwcHCQxs5lZWUFKysrODo6wtTUFA0aNMCvv/4q9UFEREREVBT+MzOl+vfvj8uXL2P9+vWfOxSVBAQEIDk5WXr9888/nzskIiIqIsbGxjA3N8fNmzcRGRmJtm3bAgDs7OxgY2OD2NhYhfY3btyAra0tAGDOnDm4ePEioqOjER0djb179wJ4u7bh77//DgB49eqV0tP6NDU1AfzfjKj3ubm5QS6XK4ydlZWFO3fuSGPnJbe/3DWziIiIiIiKyn9iptSAAQOwe/dunDhxAmXKlJHKrayskJmZiRcvXijMlnr06BGsrKykNmfPnlXo79GjR1Jd7n9zy95tY2RkBD09PWhqakJTUzPPNrl9vE8ulyv9lZuIiL5sqampiIuLk97Hx8cjOjoapqamKFeuHDZt2gRzc3OUK1cOly5dwuDBg+Ht7Y3mzZsDAGQyGUaMGIEJEyagRo0acHFxwapVq3D9+nVs3rwZAFCuXDmFMQ0MDAAA5cuXl/4NbNmyJUJCQjBx4kR07twZL1++xJgxY2BrawtXV1cAwNmzZ9G9e3ccPnwYpUuXhpGREfr27YsJEyagbNmysLW1RXBwMACgQ4cOAIC9e/fi0aNHqFWrFgwMDHDlyhWMGDEC9evXh52dXeEdWCIiIiKiPHzWmVJCCAwYMADbtm3DkSNHYG9vr1Dv5uYGbW1tHD58WCqLjY3FvXv3ULduXQBA3bp1cenSJYWn5IWHh8PIyEi6haFu3boKfeS2ye1DR0cHbm5uCm1ycnJw+PBhqQ0RERV/kZGRcHV1lRI/w4YNg6urq/RE14cPH6Jbt25wdHTEoEGD0K1bN6xbt06hjyFDhiAgIABDhw5FjRo1cPjwYYSHh6N8+fIqx/H9999j7dq12L59O1xdXeHp6Qm5XI79+/dDT08PwNvZVLGxscjKypK2Cw4Oho+PD7p164ZatWrh7t27OHLkCExMTAAAenp6WLp0Kb777js4OTlh6NChaNOmDXbv3v2vjhsRERER0aeQifxWTC0C/fr1w9q1a7Fjxw5UrlxZKjc2Npb+p/vnn3/G3r17sXLlShgZGWHgwIEAgNOnTwMAsrOz4eLiAhsbG0ybNg2JiYno1q0bevXqhT/++APA2790V6tWDf3794efnx+OHDmCQYMGYc+ePfDw8ADw9rYJX19fLF68GN9++y1mzZqFjRs34vr160prTeUlJSUFxsbGSE5OhpGRUYEeJyIiIiIiIiKiL4WqOZLPmpSSyWR5loeGhqJHjx4AgNevX2P48OFYt24dMjIy4OHhgQULFijcVnf37l38/PPPOHbsGPT19eHr64spU6ZAS+v/7k48duwYhg4diqtXr6JMmTL49ddfpTFyzZs3D8HBwUhMTISLiwvmzJmD2rVrq7QvTEoREREREREREX0hSanihEkpIiIiIiIiIiLVcyT/iYXOiYiI8mM3es/nDoEKwZ0pLT93CERERET0mX3Whc6JiIiIiIiIiOjrxKQUEREREREREREVOSaliIiIiIiIiIioyDEpRURERERERERERY5JKSIiIiIiIiIiKnJMShERERERERERUZFjUoqIiIiIiIiIiIock1JERERERERERFTkmJQiIiIiIiIiIqIix6QUEREREREREREVOSaliIiIiIiIiIioyDEpRURERERERERERY5JKSIiIiIiIiIiKnJMShERERERERERUZFjUoqIiIiIiIiIiIock1JERERERERERFTkmJQiIiIiIiIiIqIix6QUEREREREREREVOSaliIiIiIiIiIioyDEpRURERERERERERY5JKSIiIiIiIiIiKnJMShERERERERERUZFjUoqIiIiIiIiIiIock1JERERERERERFTkmJQiIiIiIiIiIqIix6QUEREREREREREVOSaliIiIiIiIiIioyDEpRURERERERERERY5JKSIiIiIiIiIiKnJMShERERERERERUZFjUoqIiIiIiIiIiIock1JERERERERERFTkmJQiIiIiIiIiIqIix6QUEREREREREREVOSaliIiIiIiIiIioyDEpRURERERERERERY5JKSIiIiIiIiIiKnJMShERERERERERUZH7rEmpEydOoHXr1rCxsYFMJsP27dsV6mUyWZ6v4OBgqY2dnZ1S/ZQpUxT6iYmJQYMGDaCrq4uyZcti2rRpSrFs2rQJjo6O0NXVRfXq1bF3795C2WciIiIiIiIiIvrMSam0tDTUqFED8+fPz7P+4cOHCq8VK1ZAJpOhffv2Cu0mTpyo0G7gwIFSXUpKCpo3bw5bW1tERUUhODgYgYGBWLJkidTm9OnT6Ny5M/z9/XHhwgV4e3vD29sbly9fLpwdJyIiIiIiIiL6yml9zsG9vLzg5eWVb72VlZXC+x07dqBJkyZwcHBQKDc0NFRqmyssLAyZmZlYsWIFdHR0ULVqVURHR2PmzJno06cPAGD27Nnw9PTEiBEjAACTJk1CeHg45s2bh0WLFv2bXSQiIiIiIiIiojx8MWtKPXr0CHv27IG/v79S3ZQpU1CqVCm4uroiODgYb968keoiIiLQsGFD6OjoSGUeHh6IjY3F8+fPpTbu7u4KfXp4eCAiIqKQ9oaIiIiIiIiI6Ov2WWdKqWPVqlUwNDREu3btFMoHDRqEb775Bqampjh9+jQCAgLw8OFDzJw5EwCQmJgIe3t7hW0sLS2lOhMTEyQmJkpl77ZJTEzMN56MjAxkZGRI71NSUv7V/hERERERERERfU2+mKTUihUr0LVrV+jq6iqUDxs2TPrZ2dkZOjo6+OmnnzB58mTI5fJCi2fy5MkICgoqtP6JiIiIiIiIiIqzL+L2vZMnTyI2Nha9evX6aNvatWvjzZs3uHPnDoC361I9evRIoU3u+9x1qPJrk986VQAQEBCA5ORk6fXPP/+os0tERERERERERF+1LyIptXz5cri5uaFGjRofbRsdHQ0NDQ1YWFgAAOrWrYsTJ04gKytLahMeHo7KlSvDxMREanP48GGFfsLDw1G3bt18x5HL5TAyMlJ4ERERERERERGRaj5rUio1NRXR0dGIjo4GAMTHxyM6Ohr37t2T2qSkpGDTpk15zpKKiIjArFmzcPHiRdy+fRthYWEYOnQofvzxRynh1KVLF+jo6MDf3x9XrlzBhg0bMHv2bIXb/gYPHoz9+/djxowZuH79OgIDAxEZGYkBAwYU7gEgIiIiIiIiIvpKfdY1pSIjI9GkSRPpfW6iyNfXFytXrgQArF+/HkIIdO7cWWl7uVyO9evXIzAwEBkZGbC3t8fQoUMVEk7GxsY4ePAg+vfvDzc3N5iZmWH8+PHo06eP1KZevXpYu3Ytxo0bhzFjxqBixYrYvn07qlWrVkh7TkRERERERET0dZMJIcTnDqI4SElJgbGxMZKTk3krHxFRAbIbvedzh0CF4M6Ulp87BCIiIiIqJKrmSL6INaWIiIiIiIiIiKh4YVKKiIiIiIiIiIiKHJNSRERERERERERU5JiUIiIiIiIiIiKiIsekFBERERERERERFTkmpYiIiIiIiIiIqMgxKUVEREREREREREWOSSkiIiIiIiIiIipyTEoREREREREREVGRY1KKiIiIiIiIiIiKHJNSRERERERERERU5JiUIiIiIiIiIiKiIsekFBEVCydOnEDr1q1hY2MDmUyG7du3K9T36NEDMplM4eXp6anQ5vz582jWrBlKliyJUqVKoU+fPkhNTZXqV65cqdRH7uvx48cAgIcPH6JLly6oVKkSNDQ0MGTIEJXiHzRoENzc3CCXy+Hi4pJnGyEEpk+fjkqVKkEul6N06dL4/fffVT5GRFR0/ivXpHf99ddf0NLSyvcak+v169fo0aMHqlevDi0tLXh7e+fZLiMjA2PHjoWtrS3kcjns7OywYsUKlY4PEREREcCkFBEVE2lpaahRowbmz5+fbxtPT088fPhQeq1bt06qe/DgAdzd3VGhQgWcOXMG+/fvx5UrV9CjRw+pTadOnRS2f/jwITw8PNCoUSNYWFgAePslzdzcHOPGjUONGjXU2gc/Pz906tQp3/rBgwdj2bJlmD59Oq5fv46dO3fi22+/VWsMIioa/5VrUq4XL16ge/fuaNq06Udjz87Ohp6eHgYNGgR3d/d823Xs2BGHDx/G8uXLERsbi3Xr1qFy5cof7Z+IiIgol9bnDoCIqCB4eXnBy8vrg23kcjmsrKzyrNu9eze0tbUxf/58aGi8zdcvWrQIzs7OiIuLQ4UKFaCnpwc9PT1pmydPnuDIkSNYvny5VGZnZ4fZs2cDgFozBubMmSP1GRMTo1R/7do1LFy4EJcvX5a+9Nnb26vcPxEVrf/KNSlX37590aVLF2hqairN2nqfvr4+Fi5cCODt7KoXL14otdm/fz+OHz+O27dvw9TUFMDb6x8RERGROjhTioi+GseOHYOFhQUqV66Mn3/+Gc+ePZPqMjIyoKOjI335AyB92Tt16lSe/a1evRolSpTADz/8ULiBA9i1axccHBywe/du2Nvbw87ODr169UJSUlKhj01EhaOorkmhoaG4ffs2JkyYUGCx79y5EzVr1sS0adNQunRpVKpUCb/88gvS09MLbAwiIiIq/piUIqKvgqenJ1avXo3Dhw9j6tSpOH78OLy8vJCdnQ0A+P7775GYmIjg4GBkZmbi+fPnGD16NIC360TlZfny5ejSpYvCTIXCcvv2bdy9exebNm3C6tWrsXLlSkRFRRVJQoyICl5RXZNu3ryJ0aNHY82aNdDSKrgJ8rdv38apU6dw+fJlbNu2DbNmzcLmzZvRr1+/AhuDiIiIij/evkdEXwUfHx/p5+rVq8PZ2Rnly5fHsWPH0LRpU1StWhWrVq3CsGHDEBAQAE1NTQwaNAiWlpYKMxVyRURE4Nq1a/jzzz+LJP6cnBxkZGRg9erVqFSpEoC3X0Dd3NwQGxvLdVyIvjBFcU3Kzs5Gly5dEBQUJF03CkpOTg5kMhnCwsJgbGwMAJg5cyZ++OEHLFiwoEiS9URERPTl40wpIvoqOTg4wMzMDHFxcVJZly5dkJiYiISEBDx79gyBgYF48uQJHBwclLZftmwZXFxc4ObmViTxWltbQ0tLS+GLpZOTEwDg3r17RRIDERWewrgmvXz5EpGRkRgwYAC0tLSgpaWFiRMn4uLFi9DS0sKRI0c+OV5ra2uULl1aSkgBb69JQgjcv3//k/slIiKirwuTUkT0Vbp//z6ePXsGa2trpTpLS0sYGBhgw4YN0NXVRbNmzRTqU1NTsXHjRvj7+xdVuKhfvz7evHmDW7duSWU3btwAANja2hZZHERUOArjmmRkZIRLly4hOjpaevXt2xeVK1dGdHQ0ateu/cnx1q9fHw8ePEBqaqpUduPGDWhoaKBMmTKf3C8RERF9XXj7HhEVC6mpqQozDOLj4xEdHQ1TU1OYmpoiKCgI7du3h5WVFW7duoWRI0eiQoUK8PDwkLaZN28e6tWrBwMDA4SHh2PEiBGYMmUKSpYsqTDWhg0b8ObNG/z44495xhIdHS3F9OTJE0RHR0NHRwdVqlQBAGzbtg0BAQG4fv26tE1cXBxSU1ORmJiI9PR0qY8qVapAR0cH7u7u+Oabb+Dn54dZs2YhJycH/fv3R7NmzQr8thwi+vf+C9ckDQ0NVKtWTaHMwsICurq6CuXz5s3Dtm3bcPjwYans6tWryMzMRFJSEl6+fCldk1xcXAC8ncU1adIk9OzZE0FBQXj69ClGjBgBPz8/3rpHREREKmNSioiKhcjISDRp0kR6P2zYMACAr68vFi5ciJiYGKxatQovXryAjY0NmjdvjkmTJkEul0vbnD17FhMmTEBqaiocHR2xePFidOvWTWms5cuXo127dkpfDHO5urpKP0dFRWHt2rWwtbXFnTt3AADJycmIjY1V2KZXr144fvy4Uh/x8fGws7ODhoYGdu3ahYEDB6Jhw4bQ19eHl5cXZsyYod6BIqIi8V+6Jn3M06dPFWZhAkCLFi1w9+5d6X3uNUkIAQBSomzgwIGoWbMmSpUqhY4dO+K33377pBiIiIjo6yQTuf93Qf9KSkoKjI2NkZycDCMjo88dDhFRsWE3es/nDoEKwZ0pLT93CERERERUSFTNkXBNKSIiIiIiIiIiKnJMShERERERERERUZHjmlJEVGh421Xxw1uu6EvGa1Lxw2sSERHRl40zpYiIiIiIiIiIqMgxKUVEREREREREREWOSSkiIiIiIiIiIipyTEoREREREREREVGRY1KKiIiIiIiIiIiKHJNSRERERERERERU5JiUIiIiIiIiIiKiIsekFBERERERERERFTkmpYiIiIiIiIiIqMgxKUVEREREREREREWOSSkiIiIiIiIiIipyTEoREREREREREVGRY1KKiIiIiIiIiIiKHJNSRERERERExdiJEyfQunVr2NjYQCaTYfv27fm27du3L2QyGWbNmqVUt2fPHtSuXRt6enowMTGBt7e3Qv2gQYPg5uYGuVwOFxcXpe0DAwMhk8mUXvr6+irtx7Nnz1CmTBnIZDK8ePFCKj916hTq16+PUqVKQU9PD46OjggJCVGpTyL6vD5rUupjF8cePXooXbA8PT0V2iQlJaFr164wMjJCyZIl4e/vj9TUVIU2MTExaNCgAXR1dVG2bFlMmzZNKZZNmzbB0dERurq6qF69Ovbu3Vvg+0tERERERFTU0tLSUKNGDcyfP/+D7bZt24a///4bNjY2SnVbtmxBt27d0LNnT1y8eBF//fUXunTpotTOz88PnTp1yrP/X375BQ8fPlR4ValSBR06dFBpP/z9/eHs7KxUrq+vjwEDBuDEiRO4du0axo0bh3HjxmHJkiUq9UtEn4/W5xw89+Lo5+eHdu3a5dnG09MToaGh0nu5XK5Q37VrVzx8+BDh4eHIyspCz5490adPH6xduxYAkJKSgubNm8Pd3R2LFi3CpUuX4Ofnh5IlS6JPnz4AgNOnT6Nz586YPHkyWrVqhbVr18Lb2xvnz59HtWrVCmnviYiIiIiICp+Xlxe8vLw+2CYhIQEDBw7EgQMH0LJlS4W6N2/eYPDgwQgODoa/v79UXqVKFYV2c+bMAQA8efIEMTExSmMYGBjAwMBAen/x4kVcvXoVixYt+ug+LFy4EC9evMD48eOxb98+hTpXV1e4urpK7+3s7LB161acPHlS+s5HRP9N/yoplZGRoZQkUocqF0e5XA4rK6s8665du4b9+/fj3LlzqFmzJgBg7ty5aNGiBaZPnw4bGxuEhYUhMzMTK1asgI6ODqpWrYro6GjMnDlTukDNnj0bnp6eGDFiBABg0qRJCA8Px7x581S6QBIREREREX2pcnJy0K1bN4wYMQJVq1ZVqj9//jwSEhKgoaEBV1dXJCYmwsXFBcHBwf/qj/jLli1DpUqV0KBBgw+2u3r1KiZOnIgzZ87g9u3bH+33woULOH36NH777bdPjo2IioZat+/t27cPvr6+cHBwgLa2NkqUKAEjIyM0atQIv//+Ox48eFDgAR47dgwWFhaoXLkyfv75Zzx79kyqi4iIQMmSJaWEFAC4u7tDQ0MDZ86ckdo0bNgQOjo6UhsPDw/Exsbi+fPnUht3d3eFcT08PBAREZFvXBkZGUhJSVF4ERERERERfWmmTp0KLS0tDBo0KM/63ERQYGAgxo0bh927d8PExASNGzdGUlLSJ435+vVrhIWFKcy8yktGRgY6d+6M4OBglCtX7oNty5QpA7lcjpo1a6J///7o1avXJ8VGREVHpaTUtm3bUKlSJfj5+UFLSwujRo3C1q1bceDAASxbtgyNGjXCoUOH4ODggL59++LJkycFEpynpydWr16Nw4cPY+rUqTh+/Di8vLyQnZ0NAEhMTISFhYXCNlpaWjA1NUViYqLUxtLSUqFN7vuPtcmtz8vkyZNhbGwsvcqWLfvvdpaIiIiIiKiIRUVFYfbs2Vi5ciVkMlmebXJycgAAY8eORfv27eHm5obQ0FDIZDJs2rTpk8bdtm0bXr58CV9f3w+2CwgIgJOTE3788ceP9nny5ElERkZi0aJFmDVrFtatW/dJsRFR0VHp9r1p06YhJCQEXl5e0NBQzmN17NgRwNv7kOfOnYs1a9Zg6NCh/zo4Hx8f6efq1avD2dkZ5cuXx7Fjx9C0adN/3f+/ERAQgGHDhknvU1JSmJgiIiIiIqIvysmTJ/H48WOFWUjZ2dkYPnw4Zs2ahTt37sDa2hqA4hpScrkcDg4OuHfv3ieNu2zZMrRq1UppcsD7jhw5gkuXLmHz5s0AACEEAMDMzAxjx45FUFCQ1Nbe3h7A2++Ojx49QmBgIDp37vxJ8RFR0VApKfWh29jeVbp0aUyZMuVfBfQhDg4OMDMzQ1xcHJo2bQorKys8fvxYoc2bN2+QlJQkrUNlZWWFR48eKbTJff+xNvmtZQW8vQj/m/W0iIiIiIiIPrdu3brluZRJ7pP2AMDNzQ1yuRyxsbH47rvvAABZWVm4c+cObG1t1R4zPj4eR48exc6dOz/adsuWLUhPT5fenzt3Dn5+fjh58iTKly+f73Y5OTnIyMhQOzYiKlqf9el76rp//z6ePXsmZerr1q2LFy9eICoqCm5ubgDeZtJzcnJQu3Ztqc3YsWORlZUFbW1tAEB4eDgqV64MExMTqc3hw4cxZMgQaazw8HDUrVu3CPeOiIiIiIio4KWmpiIuLk56Hx8fj+joaJiamqJcuXIoVaqUQnttbW1YWVmhcuXKAAAjIyP07dsXEyZMQNmyZWFra4vg4GAAQIcOHaTt4uLikJqaisTERKSnpyM6OhrA2xlW767xu2LFClhbW+f50Ktt27YhICAA169fBwClxNPTp08BAE5OTihZsiQAYP78+ShXrhwcHR0BACdOnMD06dPzXSOLiP47Pikpdf/+fezcuRP37t1DZmamQt3MmTNV7udDF0dTU1MEBQWhffv2sLKywq1btzBy5EhUqFABHh4eAN5eiDw9PdG7d28sWrQIWVlZGDBgAHx8fGBjYwMA6NKlC4KCguDv749Ro0bh8uXLmD17NkJCQqRxBw8ejEaNGmHGjBlo2bIl1q9fj8jISCxZsuRTDg8REREREdF/RmRkJJo0aSK9z12GxNfXFytXrlSpj+DgYGhpaaFbt25IT09H7dq1ceTIEekP/QDQq1cvHD9+XHrv6uoK4O33PDs7OwBvZzCtXLkSPXr0gKamptI4ycnJiI2NVWv/cnJyEBAQgPj4eGhpaaF8+fKYOnUqfvrpJ7X6IaKiJxO5N+Wq6PDhw2jTpg0cHBxw/fp1VKtWDXfu3IEQAt988w2OHDmicl/Hjh1TuDjm8vX1xcKFC+Ht7Y0LFy7gxYsXsLGxQfPmzTFp0iSF+46TkpIwYMAA7Nq1CxoaGmjfvj3mzJkDAwMDqU1MTAz69++Pc+fOwczMDAMHDsSoUaMUxty0aRPGjRuHO3fuoGLFipg2bRpatGih8r6kpKTA2NgYycnJMDIyUnk7ouLMbvSezx0CFbA7U1oW+Zg8j4onnktUED7HeUREREQfp2qORO2ZUgEBAfjll18QFBQEQ0NDbNmyBRYWFujatSs8PT3V6qtx48b4UE7swIEDH+3D1NQUa9eu/WAbZ2dnnDx58oNtOnTooDD1lIiIiIiIiIiICo/yo/Q+4tq1a+jevTsAQEtLC+np6TAwMMDEiRMxderUAg+QiIiIiIiIiIiKH7VnSunr60vrSFlbW+PWrVuoWrUqgP9bdI6IiIiIiIjyx1uKiyfeVkykHrWTUnXq1MGpU6fg5OSEFi1aYPjw4bh06RK2bt2KOnXqFEaMRERERERERERUzKidlJo5cyZSU1MBAEFBQUhNTcWGDRtQsWJFtZ68R0REREREREREXy+1k1IODg7Sz/r6+li0aFGBBkRERERERERERMWf2kmpXJmZmXj8+DFycnIUysuVK/evgyIiIiIiIiIiouJN7aTUjRs34O/vj9OnTyuUCyEgk8mQnZ1dYMEREREREREREVHxpKHuBj179oSGhgZ2796NqKgonD9/HufPn8eFCxdw/vz5woiRirETJ06gdevWsLGxgUwmw/bt2xXqAwMD4ejoCH19fZiYmMDd3R1nzpxR6mfPnj2oXbs29PT0YGJiAm9vb4X6w4cPo169ejA0NISVlRVGjRqFN2/eKLQRQmD69OmoVKkS5HI5Spcujd9///2j+/Cxsc+dO4emTZuiZMmSMDExgYeHBy5evKjS8SEiIiIiIiIqrtSeKRUdHY2oqCg4OjoWRjz0lUlLS0ONGjXg5+eHdu3aKdVXqlQJ8+bNg4ODA9LT0xESEoLmzZsjLi4O5ubmAIAtW7agd+/e+OOPP/D999/jzZs3uHz5stTHxYsX0aJFC4wdOxarV69GQkIC+vbti+zsbEyfPl1qN3jwYBw8eBDTp09H9erVkZSUhKSkpA/G/7GxU1NT4enpiTZt2mDBggV48+YNJkyYAA8PD/zzzz/Q1tb+t4eQiIiIiIiI6IukdlKqSpUqePr0aWHEQl8hLy8veHl55VvfpUsXhfczZ87E8uXLERMTg6ZNm+LNmzcYPHgwgoOD4e/vL7WrUqWK9POGDRvg7OyM8ePHAwAqVKiAadOmoWPHjpgwYQIMDQ1x7do1LFy4EJcvX0blypUBAPb29h+MXZWxr1+/jqSkJEycOBFly5YFAEyYMAHOzs64e/cuKlSo8LFDRERERERERFQsqXT7XkpKivSaOnUqRo4ciWPHjuHZs2cKdSkpKYUdL33FMjMzsWTJEhgbG6NGjRoAgPPnzyMhIQEaGhpwdXWFtbU1vLy8FGYrZWRkQFdXV6EvPT09vH79GlFRUQCAXbt2wcHBAbt374a9vT3s7OzQq1evD86UUmXsypUro1SpUli+fDkyMzORnp6O5cuXw8nJCXZ2dgV4dIiIiIiIiIi+LColpXLXwjExMUGzZs3w999/o2nTprCwsJDKc9sQFbTdu3fDwMAAurq6CAkJQXh4OMzMzAAAt2/fBvB27alx48Zh9+7dMDExQePGjaWEkoeHB06fPo1169YhOzsbCQkJmDhxIgDg4cOHUj93797Fpk2bsHr1aqxcuRJRUVH44Ycf8o1LlbENDQ1x7NgxrFmzBnp6ejAwMMD+/fuxb98+aGl98sMviYiIiIiIiL54Kn0rPnr0aGHHQZSvJk2aIDo6Gk+fPsXSpUvRsWNHnDlzBhYWFsjJyQEAjB07Fu3btwcAhIaGokyZMti0aRN++uknNG/eHMHBwejbty+6desGuVyOX3/9FSdPnoSGxtu8bE5ODjIyMrB69WpUqlQJALB8+XK4ubkhNjZWuqXvXaqMnZ6eDn9/f9SvX19Kik2fPh0tW7bEuXPnoKenV+jHj4iIiIiIiOi/SKWkVKNGjbBixQq0bt1aWlyaqKjo6+ujQoUKqFChAurUqYOKFSti+fLlCAgIgLW1NQDFdZzkcjkcHBxw7949qWzYsGEYOnQoHj58CBMTE9y5cwcBAQFwcHAAAFhbW0NLS0tKSAGAk5MTAODevXt5JqVUGXvt2rW4c+cOIiIipATY2rVrYWJigh07dsDHx6dAjhERERERERHRl0al2/cAYM2aNShbtizq1auHqVOn4tq1a4UZF1G+cmc1AYCbmxvkcjliY2Ol+qysLNy5cwe2trYK28lkMtjY2EBPTw/r1q1D2bJl8c033wAA6tevjzdv3uDWrVtS+xs3bgCAUj+5VBn71atX0NDQgEwmk9rkvs+daUVERERERET0NVI5KXXkyBE8fPgQ/fr1Q1RUFGrXro2KFSti+PDhOHHiBL9g0ydJTU1FdHQ0oqOjAQDx8fGIjo7GvXv3kJaWhjFjxuDvv//G3bt3ERUVBT8/PyQkJKBDhw4AACMjI/Tt2xcTJkzAwYMHERsbi59//hkApDYAEBwcjEuXLuHKlSuYNGkSpkyZgjlz5kBTUxMA4O7ujm+++QZ+fn64cOECoqKi8NNPP6FZs2bS7KmzZ8/C0dERCQkJKo/drFkzPH/+HP3798e1a9dw5coV9OzZE1paWmjSpEnhH2AiIiIiIiKi/yiVk1IAYGJigh9//BEbN27E06dPMXfuXKSnp6Nr166wsLBA9+7dsXnzZqSlpRVWvFTMREZGwtXVFa6urgDe3mbn6uqK8ePHQ1NTE9evX0f79u1RqVIltG7dGs+ePcPJkydRtWpVqY/g4GD4+PigW7duqFWrFu7evYsjR44oLLy/b98+NGjQADVr1sSePXuwY8cOeHt7S/UaGhrYtWsXzMzM0LBhQ7Rs2RJOTk5Yv3691ObVq1eIjY1FVlaWymM7Ojpi165diImJQd26ddGgQQM8ePAA+/fvl27/IyIiIiIi+hKcOHECrVu3ho2NDWQyGbZv365QHxgYCEdHR+jr68PExATu7u44c+aMVH/nzh34+/vD3t4eenp6KF++PCZMmIDMzEypTWxsLJo0aQJLS0vo6urCwcEB48aNU/getnTpUjRo0EB68Jq7uzvOnj370fjnz58PJycn6OnpoXLlyli9erVSmxcvXqB///6wtraGXC5HpUqVsHfv3k84WqSKT378l46ODjw9PeHp6YkFCxYgMjISO3fuxKRJk3Dt2jX8+uuvBRknFVONGzeGECLf+q1bt360D21tbUyfPh3Tp0/Pt82RI0c+2o+NjQ22bNmSb31esaoydrNmzdCsWbOPjk9ERERERPRflpaWhho1asDPzw/t2rVTqq9UqRLmzZsHBwcHpKenIyQkBM2bN0dcXBzMzc1x/fp15OTkYPHixahQoQIuX76M3r17Iy0tTfpOpa2tje7du+Obb75ByZIlcfHiRfTu3Rs5OTn4448/AADHjh1D586dUa9ePejq6mLq1Klo3rw5rly5gtKlS+cZ+8KFCxEQEIClS5eiVq1aOHv2LHr37g0TExO0bt0aAJCZmYlmzZrBwsICmzdvRunSpXH37l2ULFmycA4oQSY+lBH4RFlZWdDW1i7obv/TUlJSYGxsjOTkZBgZGX3ucIj+E+xG7/ncIVABuzOlZZGPyfOoeOK5RAXhc5xHRAWF16Ti6Wu6LslkMmzbtk3hDpT35X5PPnToEJo2bZpnm+DgYCxcuBC3b9/Ot59hw4bh3LlzOHnyZJ712dnZMDExwbx589C9e/c829SrVw/169dHcHCwVDZ8+HCcOXMGp06dAgAsWrQIwcHBuH79+leX0yhoquZI1J4pNWzYsDzLZTIZdHV1UbFiRbRp0wampqbqdk1ERERERERExUBmZiaWLFkCY2Nj1KhRI992ycnJH8wfxMXFYf/+/XnOzMr16tUrZGVlfbCfjIwM6OrqKpTp6enh7Nmz0sSanTt3om7duujfvz927NgBc3NzdOnSBaNGjZLWI6aCpXZS6sKFCzh//jyys7NRuXJlAG+fUqapqQlHR0csWLAAw4YNw6lTp1ClSpUCD5gKH/9qU/x8TX+xISIiIiKiz2f37t3w8fHBq1evYG1tjfDwcJiZmeXZNi4uDnPnzs1zOZR69erh/PnzyMjIQJ8+fTBx4sR8xxw1ahRsbGzg7u6ebxsPDw8sW7YM3t7e+OabbxAVFYVly5YhKysLT58+hbW1NW7fvo0jR46ga9eu2Lt3L+Li4tCvXz9kZWVhwoQJ6h8M+ii1FjoHgLZt28Ld3R0PHjxAVFQUoqKicP/+fTRr1gydO3dGQkICGjZsiKFDhxZGvERERERERET0H9WkSRNER0fj9OnT8PT0RMeOHfH48WOldgkJCfD09ESHDh3Qu3dvpfoNGzbg/PnzWLt2Lfbs2ZPvOr5TpkzB+vXrsW3bNqWZUO/69ddf4eXlhTp16kBbWxtt27aFr68vgLcPvgKAnJwcWFhYYMmSJXBzc0OnTp0wduxYLFq06FMOBalA7aRUcHAwJk2apHBPoLGxMQIDAzFt2jSUKFEC48ePR1RUVIEGSkRERERERET/bfr6+qhQoQLq1KmD5cuXQ0tLC8uXL1do8+DBAzRp0gT16tXDkiVL8uynbNmyqFKlCjp37owpU6YgMDAQ2dnZCm2mT5+OKVOm4ODBg3B2dv5gXHp6elixYgVevXqFO3fu4N69e7Czs4OhoSHMzc0BANbW1qhUqZLCrXpOTk5ITExUeEIgFRy1k1LJycl5ZjmfPHmClJQUAEDJkiX5gRERERERERF95XJycpCRkSG9T0hIQOPGjeHm5obQ0FBpltLH+sjKykJOTo5UNm3aNEyaNAn79+9HzZo1VY5HW1sbZcqUgaamJtavX49WrVpJMdSvXx9xcXEK49y4cQPW1tbQ0dFReQxSndprSrVt2xZ+fn6YMWMGatWqBQA4d+4cfvnlF2nV/bNnz6JSpUoFGigRERERERERfT6pqamIi4uT3sfHxyM6OhqmpqYoVaoUfv/9d7Rp0wbW1tZ4+vQp5s+fj4SEBHTo0AHA/yWkbG1tMX36dDx58kTqy8rKCgAQFhYGbW1tVK9eHXK5HJGRkQgICECnTp2kJ+JNnToV48ePx9q1a2FnZ4fExEQAgIGBAQwMDAAAAQEBSEhIwOrVqwG8TS6dPXsWtWvXxvPnzzFz5kxcvnwZq1atkmL4+eefMW/ePAwePBgDBw7EzZs38ccff2DQoEGFeFS/bmonpRYvXoyhQ4fCx8cHb968eduJlhZ8fX0xc+ZMAICjoyOWLVtWsJESERERERER0WcTGRmJJk2aSO+HDRsGAPD19cWiRYtw/fp1rFq1Ck+fPkWpUqVQq1YtnDx5ElWrVgUAhIeHIy4uDnFxcShTpoxC30IIAG/zC1OnTsWNGzcghICtrS0GDBigsG71woULkZmZiR9++EGhjwkTJiAwMBAA8PDhQ9y7d0+qy87OxowZMxAbGwttbW00adIEp0+fhp2dndSmbNmyOHDgAIYOHQpnZ2eULl0agwcPxqhRo/79waM8yUTuJ6+m1NRU3L59GwDg4OAgZSO/VikpKTA2NkZycrLCeltfIj59r/j5XE/f47lU/HyOc4nnUfHEc4kKAp8uS18yXpOKJ16XiN5SNUei9ppSR48eBfB2WpyzszOcnZ2lhNT8+fM/MVwiIiIiIiIiIvqaqJ2UateuXZ5P1ps9ezYCAgIKJCgiIiIiIiIiIire1F5TKjg4GF5eXjhx4gQcHR0BADNmzMDEiROxZw+noBIREREREREVFd4KWvx8TbeBqp2U6tWrF5KSkuDu7o5Tp05hw4YN+OOPP7B3717Ur1+/MGIkIiIiIiIiIqJiRu2kFACMHDkSz549Q82aNZGdnY0DBw6gTp06BR0bEREREREREREVUyolpebMmaNUVrp0aZQoUQINGzbE2bNncfbsWQDAoEGDCjZCIiIiIiIiIiIqdlRKSoWEhORZrqmpib/++gt//fUXAEAmkzEpRUREREREREREH6VSUio+Pr6w4yAiIiIiIiIioq+IxucOgIiIiIiIiIiIvj4qJaWmTJmCV69eqdThmTNnsGcPH0lJRERERERERET5UykpdfXqVdja2qJfv37Yt28fnjx5ItW9efMGMTExWLBgAerVq4dOnTrB0NCw0AImIiIiIiIiIqIvn0prSq1evRoXL17EvHnz0KVLF6SkpEBTUxNyuVyaQeXq6opevXqhR48e0NXVLdSgiYiIiIiIiIjoy6ZSUgoAatSogaVLl2Lx4sWIiYnB3bt3kZ6eDjMzM7i4uMDMzKww4yQiIiIiIiIiomJE7YXONTQ04OLigrZt28LHxwfu7u6fnJA6ceIEWrduDRsbG8hkMmzfvl2qy8rKwqhRo1C9enXo6+vDxsYG3bt3x4MHDxT6sLOzg0wmU3hNmTJFoU1MTAwaNGgAXV1dlC1bFtOmTVOKZdOmTXB0dISuri6qV6+OvXv3ftI+ERERERERERHRx33Wp++lpaWhRo0amD9/vlLdq1evcP78efz66684f/48tm7ditjYWLRp00ap7cSJE/Hw4UPpNXDgQKkuJSUFzZs3h62tLaKiohAcHIzAwEAsWbJEanP69Gl07twZ/v7+uHDhAry9veHt7Y3Lly8Xzo4TEREREREREX3lVL59rzB4eXnBy8srzzpjY2OEh4crlM2bNw/ffvst7t27h3LlyknlhoaGsLKyyrOfsLAwZGZmYsWKFdDR0UHVqlURHR2NmTNnok+fPgCA2bNnw9PTEyNGjAAATJo0CeHh4Zg3bx4WLVpUELtKRERERERERETv+KwzpdSVnJwMmUyGkiVLKpRPmTIFpUqVgqurK4KDg/HmzRupLiIiAg0bNoSOjo5U5uHhgdjYWDx//lxq4+7urtCnh4cHIiIi8o0lIyMDKSkpCi8iIiIiIiIiIlLNZ50ppY7Xr19j1KhR6Ny5M4yMjKTyQYMG4ZtvvoGpqSlOnz6NgIAAPHz4EDNnzgQAJCYmwt7eXqEvS0tLqc7ExASJiYlS2bttEhMT841n8uTJCAoKKqjdIyIiIiIiIiL6qnwRSamsrCx07NgRQggsXLhQoW7YsGHSz87OztDR0cFPP/2EyZMnQy6XF1pMAQEBCmOnpKSgbNmyhTYeEREREREREVFxonZSKi0tDVOmTMHhw4fx+PFj5OTkKNTfvn27wIID/i8hdffuXRw5ckRhllReateujTdv3uDOnTuoXLkyrKys8OjRI4U2ue9z16HKr01+61QBgFwuL9SkFxERERERERFRcaZ2UqpXr144fvw4unXrBmtra8hkssKIC8D/JaRu3ryJo0ePolSpUh/dJjo6GhoaGrCwsAAA1K1bF2PHjkVWVha0tbUBAOHh4ahcuTJMTEykNocPH8aQIUOkfsLDw1G3bt2C3ykiIiIiIiIiIlI/KbVv3z7s2bMH9evX/9eDp6amIi4uTnofHx+P6OhomJqawtraGj/88APOnz+P3bt3Izs7W1rjydTUFDo6OoiIiMCZM2fQpEkTGBoaIiIiAkOHDsWPP/4oJZy6dOmCoKAg+Pv7Y9SoUbh8+TJmz56NkJAQadzBgwejUaNGmDFjBlq2bIn169cjMjISS5Ys+df7SEREREREREREytROSpmYmMDU1LRABo+MjESTJk2k97lrNPn6+iIwMBA7d+4EALi4uChsd/ToUTRu3BhyuRzr169HYGAgMjIyYG9vj6FDhyqs9WRsbIyDBw+if//+cHNzg5mZGcaPH48+ffpIberVq4e1a9di3LhxGDNmDCpWrIjt27ejWrVqBbKfRERERERERESkSO2k1KRJkzB+/HisWrUKJUqU+FeDN27cGEKIfOs/VAcA33zzDf7++++PjuPs7IyTJ09+sE2HDh3QoUOHj/ZFRERERERERET/ntpJqRkzZuDWrVuwtLSEnZ2dtE5TrvPnzxdYcEREREREREREVDypnZTy9vYuhDCIiIiIiIiIiOhroqHuBhMmTPjgi4iIiIiICsaJEyfQunVr2NjYQCaTYfv27Qr1W7duRfPmzVGqVCnIZDJER0fn25cQAl5eXnn2M2jQILi5uUEulyut5woAx44dQ9u2bWFtbQ19fX24uLggLCxMpX1YuXIlnJ2doaurCwsLC/Tv3z/PdnFxcTA0NETJkiVV6peIiL58aielckVFRWHNmjVYs2YNLly4UJAxERERERERgLS0NNSoUQPz58/Pt/67777D1KlTP9rXrFmzIJPJ8q338/NDp06d8qw7ffo0nJ2dsWXLFsTExKBnz57o3r07du/e/cExZ86cibFjx2L06NG4cuUKDh06BA8PD6V2WVlZ6Ny5Mxo0aPDR/SAiouJD7dv3Hj9+DB8fHxw7dkz6K8aLFy/QpEkTrF+/Hubm5gUdIxERERHRV8nLywteXl751nfr1g0AcOfOnQ/2Ex0djRkzZiAyMhLW1tZK9XPmzAEAPHnyBDExMUr1Y8aMUXg/ePBgHDx4EFu3bkWrVq3yHPP58+cYN24cdu3ahaZNm0rlzs7OSm3HjRsHR0dHNG3aFKdPn/7gvhARUfGh9kypgQMH4uXLl7hy5QqSkpKQlJSEy5cvIyUlBYMGDSqMGImIiIiI6BO9evUKXbp0wfz582FlZVVg/SYnJ8PU1DTf+vDwcOTk5CAhIQFOTk4oU6YMOnbsiH/++Ueh3ZEjR7Bp06Z8Z4MREVHxpXZSav/+/ViwYAGcnJyksipVqmD+/PnYt29fgQZHRERERET/ztChQ1GvXj20bdu2wPrcuHEjzp07h549e+bb5vbt28jJycEff/yBWbNmYfPmzUhKSkKzZs2QmZkJAHj27Bl69OiBlStXwsjIqMDiIyKiL4Pat+/l5ORAW1tbqVxbWxs5OTkFEhQREREREf17O3fuxJEjRwp0DdijR4+iZ8+eWLp0KapWrZpvu5ycHGRlZWHOnDlo3rw5AGDdunWwsrLC0aNH4eHhgd69e6NLly5o2LBhgcVHRERfDrVnSn3//fcYPHgwHjx4IJUlJCRg6NChCveKExERERHR53XkyBHcunULJUuWhJaWFrS03v5Nun379mjcuLHa/R0/fhytW7dGSEgIunfv/sG2uWtXValSRSozNzeHmZkZ7t27J8U3ffp0KTZ/f38kJydDS0sLK1asUDs+IiL6sqg9U2revHlo06YN7OzsULZsWQDAP//8g2rVqmHNmjUFHiAREREREX2a0aNHo1evXgpl1atXR0hICFq3bq1WX8eOHUOrVq0wdepU9OnT56Pt69evDwCIjY1FmTJlAABJSUl4+vQpbG1tAQARERHIzs6WttmxYwemTp2K06dPo3Tp0mrFR0REXx61k1Jly5bF+fPncejQIVy/fh0A4OTkBHd39wIPjoiIiIjoa5aamoq4uDjpfXx8PKKjo2Fqaopy5cohKSkJ9+7dk+5iiI2NBQBYWVkpvN5Xrlw52NvbS+/j4uKQmpqKxMREpKenIzo6GsDbWU46Ojo4evQoWrVqhcGDB6N9+/ZITEwEAOjo6EiLnW/btg0BAQHSd4RKlSqhbdu2GDx4MJYsWQIjIyMEBATA0dERTZo0AQCFdWoBIDIyEhoaGqhWrVpBHD4iIvqPUzspBQAymQzNmjVDs2bNCjoeIiIiIiL6/yIjI6UEDgAMGzYMAODr64uVK1di586dCouN+/j4AAAmTJiAwMBAlcfp1asXjh8/Lr13dXUF8DYJZmdnh1WrVuHVq1eYPHkyJk+eLLVr1KgRjh07BuDt0/hyk2K5Vq9ejaFDh6Jly5bQ0NBAo0aNsH///jzXqCUioq+PTAghPtZozpw56NOnD3R1dTFnzpwPth00aFCBBfclSUlJgbGxMZKTk7/4J4fYjd7zuUOgAnZnSsvPMi7PpeLnc5xLPI+KJ55LVBA+179vRAWB16Tiif++UUEoDv++qZojUWmmVEhICLp27QpdXV2EhITk204mk321SSkiIiIiIiIiIlKdSkmp+Pj4PH8mIiIiIiIiIiL6FGqvKTVx4kT88ssvKFGihEJ5eno6goODMX78+AILjoiIiIjov4a3yhQ/xeFWGSKiL5GGuhsEBQUhNTVVqfzVq1cICgoqkKCIiIiIiIiIiKh4UzspJYSATCZTKr948aL0OFgiIiIiIiIiIqIPUfn2PRMTE8hkMshkMlSqVEkhMZWdnY3U1FT07du3UIIkIiIiIiIiIqLiReWk1KxZsyCEgJ+fH4KCgmBsbCzV6ejowM7ODnXr1i2UIImIiIiIiIiIqHhROSnl6+sLALC3t0f9+vWhpaX2GulEREREREREREQAPmFNqbS0NBw+fFip/MCBA9i3b1+BBEVERERERERERMWb2kmp0aNHIzs7W6lcCIHRo0cXSFBERERERERERFS8qZ2UunnzJqpUqaJU7ujoiLi4uAIJioiIiIiIiIiIije1k1LGxsa4ffu2UnlcXBz09fULJCgiIiIiIiIiIire1E5KtW3bFkOGDMGtW7eksri4OAwfPhxt2rQp0OCIiIiIiIiIiKh4UjspNW3aNOjr68PR0RH29vawt7eHk5MTSpUqhenTpxdGjEREREREREREVMxoqbuBsbExTp8+jfDwcFy8eBF6enpwdnZGw4YNCyM+IiIiIiIiIiIqhtROSgGATCZD8+bN0bx584KOh4iIiIiIiIiIvgJqJ6UmTpz4wfrx48d/cjBERERERERERPR1UDsptW3bNoX3WVlZiI+Ph5aWFsqXL8+kFBERERERERERfZTaSakLFy4olaWkpKBHjx743//+VyBBERERERERERFR8ab20/fyYmRkhKCgIPz6668F0R0RERERERERERVzBZKUAoDk5GQkJycXVHdERERERERERFSMqX373pw5cxTeCyHw8OFD/Pnnn/Dy8iqwwIiIiIiIiIiIqPhSOykVEhKi8F5DQwPm5ubw9fVFQEBAgQVGRERERERERETFl9pJqfj4+MKIg4iIiIiIiIiIviIFtqYUERERERERERGRqlSaKdWuXTuVO9y6desnB0NERERERERERF8HlWZKGRsbSy8jIyMcPnwYkZGRUn1UVBQOHz4MY2NjtQY/ceIEWrduDRsbG8hkMmzfvl2hXgiB8ePHw9raGnp6enB3d8fNmzcV2iQlJaFr164wMjJCyZIl4e/vj9TUVIU2MTExaNCgAXR1dVG2bFlMmzZNKZZNmzbB0dERurq6qF69Ovbu3avWvhARERERERERkepUSkqFhoZKL0tLS3Ts2BHx8fHYunUrtm7ditu3b8PHxwdmZmZqDZ6WloYaNWpg/vz5edZPmzYNc+bMwaJFi3DmzBno6+vDw8MDr1+/ltp07doVV65cQXh4OHbv3o0TJ06gT58+Un1KSgqaN28OW1tbREVFITg4GIGBgViyZInU5vTp0+jcuTP8/f1x4cIFeHt7w9vbG5cvX1Zrf4iIiIiIiIiISDVqL3S+YsUKnDp1CpqamlKZpqYmhg0bhnr16iE4OFjlvry8vODl5ZVnnRACs2bNwrhx49C2bVsAwOrVq2FpaYnt27fDx8cH165dw/79+3Hu3DnUrFkTADB37ly0aNEC06dPh42NDcLCwpCZmYkVK1ZAR0cHVatWRXR0NGbOnCklr2bPng1PT0+MGDECADBp0iSEh4dj3rx5WLRokbqHiIiIiIiIiIiIPkLthc7fvHmD69evK5Vfv34dOTk5BRIU8PYpf4mJiXB3d5fKjI2NUbt2bURERAAAIiIiULJkSSkhBQDu7u7Q0NDAmTNnpDYNGzaEjo6O1MbDwwOxsbF4/vy51ObdcXLb5I5DREREREREREQFS+2ZUj179oS/vz9u3bqFb7/9FgBw5swZTJkyBT179iywwBITEwEAlpaWCuWWlpZSXWJiIiwsLBTqtbS0YGpqqtDG3t5eqY/cOhMTEyQmJn5wnLxkZGQgIyNDep+SkqLO7hERERERERERfdXUTkpNnz4dVlZWmDFjBh4+fAgAsLa2xogRIzB8+PACD/C/avLkyQgKCvrcYRARERERERERfZHUvn1PQ0MDI0eOREJCAl68eIEXL14gISEBI0eOVFhn6t+ysrICADx69Eih/NGjR1KdlZUVHj9+rFD/5s0bJCUlKbTJq493x8ivTW59XgICApCcnCy9/vnnH3V3kYiIiIiIiIjoq6V2UirXkydPEBMTg5iYGDx9+rQgYwIA2Nvbw8rKCocPH5bKUlJScObMGdStWxcAULduXbx48QJRUVFSmyNHjiAnJwe1a9eW2pw4cQJZWVlSm/DwcFSuXBkmJiZSm3fHyW2TO05e5HI5jIyMFF5ERERERERERKQatZNSaWlp8PPzg7W1NRo2bIiGDRvC2toa/v7+ePXqlVp9paamIjo6GtHR0QDeLm4eHR2Ne/fuQSaTYciQIfjtt9+wc+dOXLp0Cd27d4eNjQ28vb0BAE5OTvD09ETv3r1x9uxZ/PXXXxgwYAB8fHxgY2MDAOjSpQt0dHTg7++PK1euYMOGDZg9ezaGDRsmxTF48GDs378fM2bMwPXr1xEYGIjIyEgMGDBA3cNDREREREREREQqUDspNWzYMBw/fhy7du2Sbt/bsWMHjh8/rvaaUpGRkXB1dYWrq6vUt6urK8aPHw8AGDlyJAYOHIg+ffqgVq1aSE1Nxf79+6Grqyv1ERYWBkdHRzRt2hQtWrTAd999hyVLlkj1xsbGOHjwIOLj4+Hm5obhw4dj/Pjx6NOnj9SmXr16WLt2LZYsWYIaNWpg8+bN2L59O6pVq6bu4SEiIiIiIiIiIhWovdD5li1bsHnzZjRu3Fgqa9GiBfT09NCxY0csXLhQ5b4aN24MIUS+9TKZDBMnTsTEiRPzbWNqaoq1a9d+cBxnZ2ecPHnyg206dOiADh06fDhgIiIiIiIiIiIqEGrPlHr16hUsLS2Vyi0sLNS+fY+IiIiIiIiIiL5Oaiel6tatiwkTJuD169dSWXp6OoKCgj64MDgREREREREREVEutW/fmzVrFjw9PVGmTBnUqFEDAHDx4kXo6uriwIEDBR4gEREREREREREVP2onpapXr46bN28iLCwM169fBwB07twZXbt2hZ6eXoEHSERERERERERExY9aSamsrCw4Ojpi9+7d6N27d2HFRERERERERERExZxaa0ppa2srrCVFRERERERERET0KdRe6Lx///6YOnUq3rx5UxjxEBERERERERHRV0DtNaXOnTuHw4cP4+DBg6hevTr09fUV6rdu3VpgwRERERERERERUfGkdlKqZMmSaN++fWHEQkREREREREREXwm1k1KhoaGFEQcREREREREREX1FVF5TKicnB1OnTkX9+vVRq1YtjB49Gunp6YUZGxERERERERERFVMqJ6V+//13jBkzBgYGBihdujRmz56N/v37F2ZsRERERERERERUTKmclFq9ejUWLFiAAwcOYPv27di1axfCwsKQk5NTmPEREREREREREVExpHJS6t69e2jRooX03t3dHTKZDA8ePCiUwIiIiIiIiIiIqPhSOSn15s0b6OrqKpRpa2sjKyurwIMiIiIiIiIiIqLiTeWn7wkh0KNHD8jlcqns9evX6Nu3L/T19aWyrVu3FmyERERERERERERU7KiclPL19VUq+/HHHws0GCIiIiIiIiIi+jqonJQKDQ0tzDiIiIiIiIiIiOgrovKaUkRERERERERERAWFSSkiIiIiIiIiIipyTEoREREREREREVGRY1KKiIiIiIiIiIiKHJNSRERERERERERU5D4pKfXnn3+ifv36sLGxwd27dwEAs2bNwo4dOwo0OCIiIiIiIiIiKp7UTkotXLgQw4YNQ4sWLfDixQtkZ2cDAEqWLIlZs2YVdHxERERERERERFQMqZ2Umjt3LpYuXYqxY8dCU1NTKq9ZsyYuXbpUoMEREREREREREVHxpHZSKj4+Hq6urkrlcrkcaWlpBRIUEREREREREREVb2onpezt7REdHa1Uvn//fjg5ORVETEREREREREREVMxpqbvBsGHD0L9/f7x+/RpCCJw9exbr1q3D5MmTsWzZssKIkYiIiIiIiIiIihm1k1K9evWCnp4exo0bh1evXqFLly6wsbHB7Nmz4ePjUxgxEhERERERERFRMaN2UgoAunbtiq5du+LVq1dITU2FhYVFQcdFRERERERERETFmNprSv3222+Ij48HAJQoUYIJKSIiIiIiIiIiUpvaSalNmzahQoUKqFevHhYsWICnT58WRlxERERERERERFSMqZ2UunjxImJiYtC4cWNMnz4dNjY2aNmyJdauXYtXr14VRoxERERERERERFTMqJ2UAoCqVavijz/+wO3bt3H06FHY2dlhyJAhsLKyKuj4iIiIiIiIiIioGPqkpNS79PX1oaenBx0dHWRlZRVETEREREREREREVMx9UlIqPj4ev//+O6pWrYqaNWviwoULCAoKQmJiYkHHR0RERERERERExZCWuhvUqVMH586dg7OzM3r27InOnTujdOnShREbEREREREREREVU2o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|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-08_14-47_plots/variety_comparison_avg_olive_production_kg_ha.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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QkNXr+/rrrxsWi8U4dOiQ2fbnn38aOXPmNCQZp06demAdyefVG2+88dB6S5UqZeTMmdN8nHy89/P39zfCwsIeOo5h3Hu/u3TpYvzxxx/GpUuXjD179hh16tQxJBkfffSRYRiGsWnTJkOSUahQIatzLD3vfZkyZQw/Pz/j6tWrZtvatWsNSYa/v7/Zlryv+z8DhmEYp06dMiQZs2bNMtuqV69ueHh4WH2GDOPe5yPZ2LFjH/i6//016tOnjyHJ2LZtm9l27do1IyAgwChYsKB53iXXGBgYaMTHx5t9J02alOo58ndDhw41JBnXrl1LsU3SQz9vyapVq2ZVe3pq+vvPCcMwjJEjRxoWiyXFa2kYhlG/fn0jMDDwkTWlNm737t2NbNmyGbdv3zYMwzDu3r1rBAQEGP7+/sZff/1l1ff+9y35Z8WgQYOs+nz77beGJOP999+3am/VqpVhsViMEydOGIZhGBMmTHjkz4BmzZoZxYsXf+Rx/V2ZMmUMLy+vh/YZP368IclYsWKFYRipn79//9xGR0cbkozJkydbjfXaa68Z7u7u5uu7bds2Q5Ixd+5cq36rV69Otf3vrly5Yjg7Oxvt2rWzah80aJAhyYiOjjYMI/X3c9euXYYk48svvzTbZs2aZUgyqlataty9e9eqf/K25M/ftWvXjOzZsxvdunWz6hcTE2N4eXlZtYeHh6f4uZbs77+nunTpYvj5+RmXL1+26te2bVvDy8vLPJbHfc8B4L+AS/kA4D8u+ZIkDw+PNPVftWqVJKlv375W7ckzbv5+6U9QUJCCg4PNx8mXf9SuXVsFChRI0f7rr7+m2Of9d+pK/lfhhIQErV+/3mx3dXU1v//rr78UGxuratWqpbjsTpJq1KihoKCgRxzpvXWa9uzZo/Pnz6e6ff/+/bp06ZJee+01q3U/QkNDVaxYsVTX5erRo4fV42rVqqV6zPdbv369EhIS1KdPH6vZZN26dZOnp+cTrf81d+5cVahQwZzZkHxpyf2X87Vo0UIODg5asGCB2XbkyBEdO3bMah2Z1atXKzg4WGXKlDHbcubMmaY1W5IXXH/Ueejh4WGesxlh5syZypMnj7y9vVWxYkXt2LFDffv2NWcZJAsLC7M6x9L63l+4cEFRUVEKCwuTl5eX2a9evXppOgdT88cff2jr1q3q3Lmz1WdI0mNf1rhq1Sq98MILqlq1qtnm7u6uV199VadPn9axY8es+nfq1ElOTk7m42rVqklK/fN7vz///FMODg5yd3d/rDqvXr2qXbt2pZgFmdaa7n8Pb9y4ocuXL6ty5coyDCPFpcSSlCNHDl2+fPmRdd0/7rVr13T58mVVq1ZNN2/e1E8//SRJOnTokE6dOqU+ffqkWAMutfft77MoV61aJXt7e/Xu3duq/c0335RhGOaC/cljL1++XElJSanWmz17dv3+++/at2/fI4/tfteuXUvTZ1RSuj6nzz33nMqUKWP1MyYxMVGLFy9WkyZNzNd30aJF8vLyUr169XT58mXzq3z58nJ3d9emTZseup8cOXKoUaNGWrFihTmzyTAMzZ8/XxUqVNBzzz0nyfr9vHPnjv78808VKVJE2bNnT/V3Srdu3R65ntS6det09epVtWvXzqp2e3t7VaxY8ZG1p8YwDC1ZskRNmjSRYRhW44aEhCg2Ntas93HfcwD4LyCYAoD/OE9PT0lK853YfvvtN9nZ2aW4RMPX11fZs2fXb7/9ZtX+9z+ck/84z58/f6rtf18nxM7OLsXCw8l/PNy/dkdkZKQqVapk3ro8T548mj59umJjY1Mcw4MuFfu7MWPG6MiRI8qfP79eeOEFDR8+3OqP3ORjLVq0aIrnFitWLMVr4eLikuJSqhw5cjxybZQH7cfJyUmFChVKsZ+0unr1qlatWqUaNWroxIkT5leVKlW0f/9+/fzzz5Kk3Llzq06dOlq4cKH53AULFsjBwUEtWrSwqjO1S3fScjlP8h+zjzoP0/KHcXo0a9ZM69at0/r167Vnzx5dvnxZH330UYqFzf9+zqT1vU/+77PPPpuiX2rPTYvkc7BEiRKP9fzU/Pbbb6nWExgYaG6/398/18mL0T/qXH5Sa9askSTVr18/xba01HTmzBl17NhROXPmNNd5q1GjhiSl+rPCMIw0hX1Hjx7V//73P3l5ecnT01N58uQxF9VPHvfkyZOS0va+OTg4pLjU87ffflPevHlTnP9/f4/atGmjKlWqqGvXrvLx8VHbtm21cOFCq5Bq4MCBcnd31wsvvKBnn31W4eHhVpf6PYiHh0eaPqPJfdOjTZs22rFjh7lG2+bNm3Xp0iWr8PuXX35RbGysvL29lSdPHquv69evmzd/eJj27dvrxo0b5tphO3fu1OnTp60C9Fu3bmnYsGHmWl65c+dWnjx5dPXq1cf+nfLLL79IuvePMn+vfe3atWmq/e/++OMPXb16VZ9++mmKMTt16iTp/26I8bjvOQD8F7DGFAD8x3l6eipv3rw6cuRIup6X1pkZD/pX5Ae1G39bxDcttm3bpqZNm6p69eqaNm2a/Pz85OjoqFmzZqW6sOv9/xr+MK1bt1a1atW0bNkyrV27VmPHjtXo0aO1dOnSB96x7WEy4w5NT2LRokWKj4/XRx99pI8++ijF9rlz55qLnrdt21adOnVSVFSUypQpo4ULF6pOnTrm2kVPqkiRInJwcNCPP/74wD7x8fGKjo5OsbbQk8iXL1+aFuJO6znzJB70mUrr4vj/pMf9/ObKlUt379597IBx1apVqlKlitXss7TWlJiYqHr16unKlSsaOHCgihUrJjc3N507d04dO3ZMdXbRX3/99chz/OrVq6pRo4Y8PT01YsQIFS5cWC4uLjp48KAGDhz4wFlLD+Ps7PzYd310dXXV1q1btWnTJq1cuVKrV6/WggULVLt2ba1du1b29vYKDAxUdHS0IiMjtXr1ai1ZskTTpk3TsGHDUr3RQbLAwEBFRUXpzJkzKYLAZMmf4fTOCGzTpo0GDx6sRYsWqU+fPlq4cKG8vLzUoEEDs09SUpK8vb2tZnTe70FrqN2vcePG8vLy0rx58/TSSy9p3rx5sre3t1rH6vXXX9esWbPUp08fBQcHy8vLSxaLRW3btk31/UzLz4fk53311VdW62gle5y7FCaP+fLLLz/wTqrJ6ww+7nsOAP8FBFMAkAU0btxYn376qXbt2mV12V1q/P39lZSUpF9++cX8l3rp3gKsV69elb+/f4bWlpSUpF9//dWcJSXJnMmTvGj5kiVL5OLiojVr1lgttD1r1qwn3r+fn59ee+01vfbaa7p06ZLKlSunDz74QA0bNjSPNTo6WrVr17Z6XnR0dIa9Fvfv5/7ZYwkJCTp16tRj3+Fs7ty5KlGiRIpFzSXpk08+0bx588w/WJo3b67u3bubl9r8/PPPGjx4cIo6U7szW1ru1ubm5qZatWpp48aN+u2331J97RYuXKj4+HirRYttJa3vffJ/k2dL/L3f/ZJn+Nx/p0Up5Wyl5HPgUWFyei7r8/f3T1GPJPMytIw6l4sVKybp3t35HrUw/98ZhqHVq1erX79+j7Xvw4cP6+eff9acOXOsFu9et27dA59z6tQplS5d+qHjbt68WX/++aeWLl2q6tWrWz33foULF5Z07317nM+sv7+/1q9fnyLUS+09srOzU506dVSnTh2NHz9eH374od566y1t2rTJ3Lebm5vatGmjNm3aKCEhQS1atNAHH3ygwYMHW12eer/GjRvrm2++0ZdffqmhQ4em2B4XF6fly5erWLFiaV74PFlAQIBeeOEFLViwQL169dLSpUvVvHlzq5/phQsX1vr161WlSpXHDoudnZ3VqlUrffnll7p48aIWLVqk2rVrW4VFixcvVlhYmFVgf/v27RSfzfRIfv+9vb0f+f6n9bObJ08eeXh4KDExMU3n1OO85wDwX8ClfACQBQwYMEBubm7q2rWrLl68mGL7yZMnzduZN2rUSJI0ceJEqz7jx4+XpFTXfnlSU6ZMMb83DENTpkyRo6Oj6tSpI+neTAmLxWI1s+T06dP69ttvH3ufiYmJKS7Z8Pb2Vt68eRUfHy9JqlChgry9vTVjxgyzTbp3O/Djx49n2GtRt25dOTk56eOPP7aakTJz5kzFxsY+1n7Onj2rrVu3qnXr1mrVqlWKr06dOunEiRPmHc2yZ8+ukJAQLVy4UPPnz5eTk5OaN29uNWZISIh27dqlqKgos+3KlSsPnN3wd0OHDpVhGOrYsaNu3bplte3UqVMaMGCA/Pz81L1793Qfb0ZL63vv5+enMmXKaM6cOVbn07p161Ks2+Tv7y97e3tt3brVqn3atGlWj/PkyaPq1avriy++0JkzZ6y23X9+uLm5SUoZdKWmUaNG2rt3r3bt2mW23bhxQ59++qkKFiz42Oth/V1y8L1///50P3ffvn26dOnSY3+ukmdU3f8aGYZh/mz7u9jYWJ08efKRdytNbdyEhIQU71u5cuUUEBCgiRMnpnhP0jJTtFGjRkpMTLT6eShJEyZMkMViMWdxXrlyJcVzk9d9Sz5X//zzT6vtTk5OCgoKkmEYunPnzgNraNWqlYKCgjRq1KgU72FSUpJ69uypv/76K9WwOy3atGmj3bt364svvtDly5etLuOT7s1iTUxM1HvvvZfiuXfv3k1zcNS+fXvduXNH3bt31x9//JFiHTx7e/sU78nkyZOfaPZiSEiIPD099eGHH6b6Gv/xxx/m92n97Nrb26tly5ZasmRJqkH1/WM+7nsOAP8FzJgCgCygcOHCmjdvntq0aaPAwEB16NBBJUqUUEJCgnbu3KlFixapY8eOkqTSpUsrLCxMn376qXkJy969ezVnzhw1b95ctWrVytDaXFxctHr1aoWFhalixYr6/vvvtXLlSg0ZMsS8bCM0NFTjx49XgwYN9NJLL+nSpUuaOnWqihQp8tBLwx7m2rVrypcvn1q1aqXSpUvL3d1d69ev1759+8x/RXd0dNT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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-08_14-47_plots/variety_comparison_avg_oil_production_l_ha.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-08_14-47_plots/variety_comparison_avg_water_need_m³_ha.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-08_14-47_plots/variety_comparison_oil_efficiency_l_kg.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-08_14-47_plots/variety_comparison_water_efficiency_l_oil_m³_water.png\n",
|
||
"Plot salvato come: .//2024-12-08_14-47_plots/efficiency_vs_production.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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5eHioe/fuVz3Wnj17KiUlxXyJsHTxMvL8/Hz17NlT0sXvhKurq+Li4iwu172W8+fPS5J8fHyu2s/Hx0dpaWlWj1tQd25urr755htz288//6xz586Z6y7OZ1KUn3/+WYGBgQoMDFSjRo20cOFC9enTR++9955Fv8u/y8nJydq6dav69+9vcYVGw4YNdc8992jp0qWSLl45sXz5cnXt2lVVq1Y196tfv775jLyt8vPz9e233+rBBx9Us2bNCq0vzuOkli5dqooVK1r8rLm4uOiFF15Qenp6odsOevbsaXGf/eXf72sZNGiQxRnvwYMHq0yZMub3zcnJSb1799b3339v/q5JF7/7rVq1uuLVEwUaNGigJk2aaP78+ea2jIwMff/993rggQfk6+sryfLvxL///lunT5/WP/7xD0kq8vtjzWR433zzjfLz8/XYY49ZfC8rVqyo2rVrF/kUiMtFRkaqUqVK+uKLL8xt27dv159//nnNeSsA4HoQsgHcdkwmk1q1amW+9zo+Pl4VKlQwz6Z9acgu+O+lITs+Pl6RkZHm+0cDAwP16quvSpJVIXvfvn1atmyZOZQULAWzfBdMPlbgWv8QvlxAQIAiIyMLLUXNEFxcBw4cUN26dW2aqOjUqVM6d+6cZs2aVejYo6KiJBU+9ksDlSRzICkIsgX3fxdcsn0l99xzj4KDg83/2M7Pz9d///tfPfzww9cMuPfdd5/8/Py0YMECc9uCBQvUuHFj1alTR9LFScfee+89/fTTTwoKCtLdd9+t8ePH68SJE1cdu2Dflwagopw/f/6adV6uUaNGqlevXqG6AwIC1KFDB0nF+0yKUvCLnZUrV2rdunU6ffq0Pv30U4vwJRX+Lh85ckSSirw3uH79+jp9+rQyMjJ06tQpZWVlqXbt2oX6Xe2+4qs5deqU0tLSrvndscWRI0dUu3btQrO8F1xeXnC8Ba71/b6Wy98Pb29vBQcHW8wD0LdvX2VlZZkfqbZnzx4lJCSoT58+Vu2jd+/eOnTokNatWydJ+vbbb5WZmWlxqfWZM2c0bNgwBQUFycPDQ4GBgebPuqi/E635O23fvn0yDEO1a9cu9N3ctWuXVd/Lgl8yFNQsXfwFg7u7u/leewAoCdyTDeC21KZNG/3www/atm2b+X7sAq1atdLLL7+spKQkrV27VpUqVVKNGjUkXQx1HTt2VL169TRp0iSFhITI1dVVS5cu1QcffGAxcdmV5Ofn65577tHIkSOLXF8Q3ApcHlRKq4L35sknnyx0n3CBy+8Bv/Q+yksZV7mPsyjOzs564oknNHv2bH300UeKj4/X8ePHrTqb5ebmpq5du2rx4sX66KOPdPLkScXHx+vdd9+16Dd8+HA9+OCD+vbbb7V8+XK9/vrrGjt2rH755ZdC94IXqFWrlsqUKaM///zzivvPzs7Wnj17ijzbei09e/bUO++8o9OnT8vHx0fff/+9evXqZf7lSHE+k6IU/GLnWm7Ed/lKZ6Dz8vJKfN+2stf3+2oaNGigsLAwff755+rbt68+//xzubq66rHHHrNq+169emnkyJH68ssv1apVK3355Zfy9/dX586dzX0ee+wxrVu3Ti+//LIaN24sb29v5efn67777ivy70Rrvgf5+fkymUz66aefinyfvL29raq/b9++ev/99/Xtt9+qV69e+vLLL/XAAw/Iz8/Pqu0BoDgI2QBuS5c+L7vgEUoFwsLC5Obmpri4OPOs1QV++OEHZWdn6/vvv7c4C1XUpYtX+sd+zZo1lZ6eblUouVnVrFlTGzZsUG5urlUTNEkXZyz38fFRXl6e3Y69YIK17du3X3PMvn37auLEifrhhx/0008/KTAw0OpLjXv27Kl58+YpNjZWu3btkmEY5kuuL6/npZde0ksvvaR9+/apcePGmjhxoj7//PMix/Xy8lL79u31yy+/XPF5xF999ZWys7MtJq6yVs+ePfXmm29q0aJFCgoKUlpamsUjl0riM7FFwfHu2bOn0Lrdu3crICBAXl5ecnd3l4eHh/bt21eo3+XbFpwNPnfunEX75WeRAwMD5evra54d/kpsuWy8WrVq+vPPP5Wfn29xNnv37t3m9fa0b98+tW/f3vw6PT1dycnJFn9nSRe/+9HR0UpOTtaXX36pLl26XPVxcJeqVKmS2rdvr4ULF+r111/XihUr1L9/f/NtHGfPnlVsbKzefPNNjR492qK261GzZk0ZhqHq1asX+sXj5a72Gd15551q0qSJvvjiC1WpUkWJiYmaOnXqddUGANfC5eIAbkvNmjWTu7u7vvjiCyUlJVmcyXZzc1PTpk01ffp0ZWRkWFwqXnBG5dIzTampqZozZ06hfXh5eRX6h7508azP+vXrtXz58kLrzp07pwsXLlzPod0Q3bt31+nTpzVt2rRC6650Fs7Z2Vndu3fXokWLigw2p06dsrmOpk2bqnr16po8eXKh9/ryOho2bKiGDRvq3//+txYtWqTHH3/c6svdIyMjVa5cOS1YsEALFixQixYtLC55zczM1N9//22xTc2aNeXj46Ps7Oyrjv3Pf/5ThmGof//+Fo+Vk6RDhw5p5MiRCg4O1jPPPGNVrZeqX7++7rrrLnPdwcHBuvvuu83rS+IzsUVwcLAaN26sefPmWXx+27dv188//2wOi87OzurUqZO+/fZbJSYmmvvt2rWr0M+Rr6+vAgICCs1t8NFHH1m8dnJyUteuXfXDDz/o999/L1RbwffHy8tLUuHQXpTOnTvrxIkTFpfoX7hwQVOnTpW3t3ehGeqv16xZsywezzVjxgxduHBB999/v0W/Xr16yWQyadiwYTp48KDN9yP37t1bKSkpeuaZZ5Sbm2txqXhRfydKKvSUBFt169ZNzs7OevPNNwuNbRiGxePBvLy8rnqrTp8+ffTzzz9r8uTJKl++fKH3BwDsjTPZAG5Lrq6uat68uX799Ve5ubkpLCzMYn2rVq00ceJESZb3Y997771ydXXVgw8+qGeeeUbp6emaPXu2KlSooOTkZIsxwsLCNGPGDL399tuqVauWKlSooA4dOujll182TxzUv39/hYWFKSMjQ9u2bdPXX3+tw4cPKyAgoNjHlpSUVOSZU29vb3Xt2rXY416qb9+++vTTTxUdHa2NGzeqbdu2ysjI0MqVK/Xcc8/p4YcfLnK7cePGadWqVQoPD9fAgQPVoEEDnTlzRps3b9bKlSt15swZm+pwcnLSjBkz9OCDD6px48aKiopScHCwdu/erR07dhQKYH379tWIESMkyaag4eLiom7dumn+/PnKyMjQhAkTLNbv3btXHTt21GOPPaYGDRqoTJkyWrx4sU6ePGlx5rgod999tyZMmKDo6Gg1bNhQ/fv3Nx/D7NmzlZ+fr6VLl1p95vFyPXv21OjRo+Xu7q4BAwYUul/Y3p+Jrd5//33df//9atmypQYMGGB+hJefn5/FM63ffPNNLVu2TG3bttVzzz1nDq933HFHocvtn376aY0bN05PP/20mjVrpjVr1mjv3r2F9v3uu+/q559/VkREhAYNGqT69esrOTlZCxcu1Nq1a1W2bFk1btxYzs7Oeu+995Samio3Nzd16NBBFSpUKDTeoEGD9PHHH6t///5KSEhQaGiovv76a8XHx2vy5Mk231d/LTk5Oebv3Z49e/TRRx+pTZs2euihhyz6BQYG6r777tPChQtVtmxZdenSxab9dO/eXc8995y+++47hYSEWPyixtfX1zwHQW5uripXrqyff/7Z/Iz04qpZs6befvttxcTE6PDhw+ratat8fHx06NAhLV68WIMGDTL/LIeFhWnBggWKjo5W8+bN5e3trQcffNA81hNPPKGRI0dq8eLFGjx4sNVX3wBAsd3w+cwB4CYRExNjSDJatWpVaN0333xjSDJ8fHwsHt1kGIbx/fffGw0bNjTc3d2N0NBQ47333jM/0urQoUPmfidOnDC6dOli+Pj4GJIsHhlz/vx5IyYmxqhVq5bh6upqBAQEGK1atTImTJhgfiRPwWN+bHks1NUe4XXpI26u9xFehnHxsT2vvfaaUb16dcPFxcWoWLGi8eijjxoHDhww99Flj0wyDMM4efKk8fzzzxshISHm7Tp27GjMmjXL3KfgEV4LFy602PZKj2dau3atcc899xg+Pj6Gl5eX0bBhQ2Pq1KmF3p/k5GTD2dnZqFOnzpXewitasWKFIckwmUzG0aNHLdadPn3aeP7554169eoZXl5ehp+fnxEeHm589dVXVo+/Zs0a4+GHHzYCAgIMFxcXo2rVqsbAgQONw4cPF+przSO8Cuzbt8/8HVi7dm2R+7bmM7mSatWqGV26dLlqn2t9l1euXGm0bt3a8PDwMHx9fY0HH3zQ2LlzZ6F+q1evNsLCwgxXV1ejRo0axsyZM4t8LzIzM40BAwYYfn5+ho+Pj/HYY48ZKSkpRX4fjxw5YvTt29cIDAw03NzcjBo1ahjPP/+8xaPjZs+ebdSoUcP8uLCCx3kV9XNx8uRJIyoqyggICDBcXV2Nu+66q9D39WrvR1E1Xq7g53f16tXGoEGDDH9/f8Pb29vo3bu3xaPQLvXVV18ZkoxBgwZddewr6dGjhyHJGDlyZKF1x44dMx555BGjbNmyhp+fn9GjRw/j+PHjhY7lSn+/XLrucosWLTLatGljeHl5GV5eXka9evWM559/3tizZ4+5T3p6uvHEE08YZcuWLfR3XYHOnTsbkox169YV6/gBwBYmw7Dj7BoAANzETp8+reDgYI0ePVqvv/66o8sBbpjvvvtOXbt21Zo1a8yPCrudPPLII9q2bZv279/v6FIA3Aa4JxsAcNuYO3eu8vLyrH58EXCrmD17tmrUqGFx+8vtIjk5WT/++CM/9wBuGO7JBgDc8n755Rft3LlT77zzjrp27arQ0FBHlwTcEPPnz9eff/6pH3/8UVOmTLFptvTS7tChQ4qPj9e///1vubi4FGvyQAAoDi4XBwDc8tq1a6d169apdevW+vzzz1W5cmVHlwTcECaTSd7e3urZs6dmzpxp9Yz6t4K5c+cqKipKVatW1cSJE/Xoo486uiQAtwlCNgAAAAAAdsI92QAAAAAA2AkhGwAAAAAAO7l9bsyxQX5+vo4fPy4fH5/baoIQAAAAACjtDMPQ+fPnValSJTk5OeC8sgOf0W02bdo0o1q1aoabm5vRokULY8OGDVfsu337dqNbt25GtWrVDEnGBx98UKhPwbrLl+eee86qeo4ePVrk9iwsLCwsLCwsLCwsLCylYzl69GhxI+p1cfiZ7AULFig6OlozZ85UeHi4Jk+erE6dOmnPnj2qUKFCof6ZmZmqUaOGevTooRdffLHIMTdt2qS8vDzz6+3bt+uee+5Rjx49rKrJx8dHknT06FH5+voW46gAAAAAAI6QlpamkJAQc6670Rw+u3h4eLiaN2+uadOmSbp4qXZISIiGDh2qUaNGXXXb0NBQDR8+XMOHD79qv+HDh2vJkiXat2+fVZd/p6Wlyc/PT6mpqYRsAAAAAChFHJ3nHDrxWU5OjhISEhQZGWluc3JyUmRkpNavX2+3fXz++ed66qmnuL8aAAAAAFCiHHq5+OnTp5WXl6egoCCL9qCgIO3evdsu+/j222917tw59e/f/4p9srOzlZ2dbX6dlpZml30DAAAAAG4vt/wjvP7zn//o/vvvV6VKla7YZ+zYsfLz8zMvISEhN7BCAAAAALi1GIahQYMGqVy5cjKZTNq6daujSyqkf//+6tq1q93HdeiZ7ICAADk7O+vkyZMW7SdPnlTFihWve/wjR45o5cqV+uabb67aLyYmRtHR0ebXBTfKAwAAAABst2zZMs2dO1dxcXGqUaOGAgICSmxf/fv317lz5/Ttt9/atN2UKVNUElOUOfRMtqurq8LCwhQbG2tuy8/PV2xsrFq2bHnd48+ZM0cVKlRQly5drtrPzc1Nvr6+FgsAAAAAoHgOHDig4OBgtWrVShUrVlSZMpbnd3NychxU2f/4+fmpbNmydh/X4ZeLR0dHa/bs2Zo3b5527dqlwYMHKyMjQ1FRUZKkvn37KiYmxtw/JydHW7du1datW5WTk6OkpCRt3bpV+/fvtxg3Pz9fc+bMUb9+/Qp9oAAAAACAktG/f38NHTpUiYmJMplMCg0NVbt27TRkyBANHz5cAQEB6tSpkyRp0qRJuuuuu+Tl5aWQkBA999xzSk9PN481d+5clS1bVsuXL1f9+vXl7e2t++67T8nJyZKkN954Q/PmzdN3330nk8kkk8mkX3/9VZK0Y8cOdejQQR4eHipfvrwGDRpkMfbll4tnZ2frhRdeUIUKFeTu7q42bdpo06ZNNh+/w0N2z549NWHCBI0ePVqNGzfW1q1btWzZMvNkaImJieY3UJKOHz+uJk2aqEmTJkpOTtaECRPUpEkTPf300xbjrly5UomJiXrqqadu6PEAAAAAwO1sypQp+te//qUqVaooOTnZHFTnzZsnV1dXxcfHa+bMmZIuPl3qww8/1I4dOzRv3jz98ssvGjlypMV4mZmZmjBhgj777DOtWbNGiYmJGjFihCRpxIgReuyxx8zBOzk5WeHh4ZKkbt26yd/fX5s2bdLChQu1cuVKDRky5Ip1jxw5UosWLdK8efO0efNm1apVS506ddKZM2dsOn6HPyf7ZuTo56oBAAAAQGk2efJkTZ48WYcPH5YktWvXTmlpadq8efNVt/v666/17LPP6vTp05IunsmOiorS/v37VbNmTUnSRx99pH/96186ceKEpML3ZBfkubJly+rYsWPy8vKSJC1dulQPPvigjh8/rqCgIIvtMjIy5O/vr7lz5+qJJ56QJOXm5io0NFTDhw/Xyy+/bPWxO/xMNgAAAADg1hcWFlaobeXKlerYsaMqV64sHx8f9enTR3/99ZcyMzPNfTw9Pc0BW5KCg4OVkpJyzf0VXIZeoHXr1srPz9eePXsK9T1w4IByc3PVunVrc5uLi4tatGihXbt2WX2MEiEbAAAAAHADXBp4Jenw4cN64IEH1LBhQy1atEgJCQmaPn26JMuJ0VxcXCy2M5lMJTIruL0QsgEAAAAAN1xCQoLy8/M1ceJE/eMf/1CdOnV0/Phxm8dxdXVVXl5eofZt27YpIyPD/Do+Pl5OTk6qW7duob41a9Y03y9eIDc3V5s2bVKDBg1sqoeQDQAAAAC44WrVqqXc3FxNnTpVBw8e1GeffWaeEM0WoaGh+vPPP7Vnzx6dPn1aubm5kiR3d3f169dP27dv16pVqzR06FD16dPHPMn2pby8vDR48GC9/PLLWrZsmXbu3KmBAwcqMzNTAwYMsKkeQjYAAAAAwCqGYehMRo6OnsnUmYyc67psu1GjRpo0aZLee+893Xnnnfriiy80duxYm8cZOHCg6tatq2bNmikwMFC//fabJOmbb77RmTNn1Lx5cz366KPq2LGjpk2bdsVxxo0bp+7du6tPnz5q2rSp9u/fr+XLl8vf39+mephdvAjMLg4AAAAA/5OalatFCcc0b91hHTnzv0nJqpXzVL9WoeoeVkV+Hi5XGeHGcXSeI2QXwdEfCgAAAADcLFbvPaXBnycoK+fifc+XBkjT///Xw9VZM54MU0SdwBte3+Ucnee4XBwAAAAAUKTVe08pas5GZeXmyZBlwNb/vzYkZeXmKWrORq3ee+rGF3mTIWQDAAAAAApJzcrV4M8TLgbpa1z/bBgXw/bgzxOUmpV7I8q7aRGyAQAAAACFLEo4pqycvGsG7AKGIWXl5OmbzcdKtrCbHCEbAAAAAGDBMAzNW3e4WNvOjT98XbOOl3aEbAAAAACAhbOZuTpyJrPQPdjXYkg6ciZT5zJv30vGCdkAAAAAAAsZ2Reua/v069y+NCNkAwAAAAAseLmVua7tva9z+9KMkA0AAAAAsODv6aJq5TzNz8G2lklStXKeKuvpUhJllQqEbAAAAACABZPJpH6tQou1bf/WoTKZbI3ntw5CNgAAAACgkO5hVeTh6ixr87KTSfJwdVa3plVKtrCbHCEbAAAAAFCIn4eLZjwZJpN0zaBdsH7mk2Hy87h9LxWXCNkAAAAAgCuIqBOoOVEt5OHifDFsX7a+oM3DxVlzo1ro7jqBN77Im8ztO+UbAAAAAOCaIuoEan1MR32z+Zjmxh/WkTOZ5nVVy3mqf+tQdQ+rIl/32/sMdgGTYRi2Pl/8lpeWliY/Pz+lpqbK19fX0eUAAAAAwE3BMAydy8xVevYFebuVUVlPl5tukjNH5znOZAMAAAAArGIymeTv5Sp/L1dHl3LT4p5sAAAAAADshJANAAAAAICdELIBAAAAALATQjYAAAAAAHZCyAYAAAAAwE4I2QAAAAAA2AkhGwAAAAAAOyFkAwAAAABgJ4RsAAAAAADshJANAAAAAICdELIBAAAAALATQjYAAAAAAHZCyAYAAAAAwE4I2QAAAAAA2AkhGwAAAAAAOyFkAwAAAABgJ4RsAAAAAADshJANAAAAAICdELIBAAAAALATQjYAAAAAAHZCyAYAAAAAwE4I2QAAAAAA2AkhGwAAAAAAOyFkAwAAAABgJ4RsAAAAAADsxOEhe/r06QoNDZW7u7vCw8O1cePGK/bdsWOHunfvrtDQUJlMJk2ePLnIfklJSXryySdVvnx5eXh46K677tLvv/9eQkcAAAAAAMBFDg3ZCxYsUHR0tMaMGaPNmzerUaNG6tSpk1JSUorsn5mZqRo1amjcuHGqWLFikX3Onj2r1q1by8XFRT/99JN27typiRMnyt/fvyQPBQAAAAAAmQzDMBy18/DwcDVv3lzTpk2TJOXn5yskJERDhw7VqFGjrrptaGiohg8fruHDh1u0jxo1SvHx8fr111+LXVdaWpr8/PyUmpoqX1/fYo8DAAAAALixHJ3nHHYmOycnRwkJCYqMjPxfMU5OioyM1Pr164s97vfff69mzZqpR48eqlChgpo0aaLZs2fbo2QAAAAAAK7KYSH79OnTysvLU1BQkEV7UFCQTpw4UexxDx48qBkzZqh27dpavny5Bg8erBdeeEHz5s274jbZ2dlKS0uzWAAAAAAAsFUZRxdgb/n5+WrWrJneffddSVKTJk20fft2zZw5U/369Stym7Fjx+rNN9+8kWUCAAAAAG5BDjuTHRAQIGdnZ508edKi/eTJk1ec1MwawcHBatCggUVb/fr1lZiYeMVtYmJilJqaal6OHj1a7P0DAAAAAG5fDgvZrq6uCgsLU2xsrLktPz9fsbGxatmyZbHHbd26tfbs2WPRtnfvXlWrVu2K27i5ucnX19diAQAAAADAVg69XDw6Olr9+vVTs2bN1KJFC02ePFkZGRmKioqSJPXt21eVK1fW2LFjJV2cLG3nzp3mPyclJWnr1q3y9vZWrVq1JEkvvviiWrVqpXfffVePPfaYNm7cqFmzZmnWrFmOOUgAAAAAwG3DoY/wkqRp06bp/fff14kTJ9S4cWN9+OGHCg8PlyS1a9dOoaGhmjt3riTp8OHDql69eqExIiIiFBcXZ369ZMkSxcTEaN++fapevbqio6M1cOBAq2ty9JTvAAAAAIDicXSec3jIvhk5+kMBAAAAABSPo/Ocw+7JBgAAAADgVkPIBgAAAADATgjZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOyEkA0AAAAAgJ0QsgEAAAAAsBNCNgAAAAAAdkLIBgAAAADATgjZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOyEkA0AAAAAgJ0QsgEAAAAAsBNCNgAAAAAAdkLIBgAAAADATgjZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOyEkA0AAAAAgJ0QsgEAAAAAsBNCNgAAAAAAdkLIBgAAAADATgjZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOyEkA0AAAAAgJ0QsgEAAAAAsBNCNgAAAAAAdkLIBgAAAADATgjZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOyEkA0AAAAAgJ0QsgEAAAAAsBNCNgAAAAAAdkLIBgAAAADATgjZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOyEkA0AAAAAgJ3cFCF7+vTpCg0Nlbu7u8LDw7Vx48Yr9t2xY4e6d++u0NBQmUwmTZ48uVCfN954QyaTyWKpV69eCR4BAAAAAAA3QchesGCBoqOjNWbMGG3evFmNGjVSp06dlJKSUmT/zMxM1ahRQ+PGjVPFihWvOO4dd9yh5ORk87J27dqSOgQAAAAAACTdBCF70qRJGjhwoKKiotSgQQPNnDlTnp6e+uSTT4rs37x5c73//vt6/PHH5ebmdsVxy5Qpo4oVK5qXgICAkjoEAAAAAAAkOThk5+TkKCEhQZGRkeY2JycnRUZGav369dc19r59+1SpUiXVqFFDvXv3VmJi4vWWCwAAAADAVTk0ZJ8+fVp5eXkKCgqyaA8KCtKJEyeKPW54eLjmzp2rZcuWacaMGTp06JDatm2r8+fPF9k/OztbaWlpFgsAAAAAALYq4+gCSsL9999v/nPDhg0VHh6uatWq6auvvtKAAQMK9R87dqzefPPNG1kiAAAAAOAW5NAz2QEBAXJ2dtbJkyct2k+ePHnVSc1sVbZsWdWpU0f79+8vcn1MTIxSU1PNy9GjR+22bwAAAADA7cOhIdvV1VVhYWGKjY01t+Xn5ys2NlYtW7a0237S09N14MABBQcHF7nezc1Nvr6+FgsAAAAAALZy+OXi0dHR6tevn5o1a6YWLVpo8uTJysjIUFRUlCSpb9++qly5ssaOHSvp4mRpO3fuNP85KSlJW7dulbe3t2rVqiVJGjFihB588EFVq1ZNx48f15gxY+Ts7KxevXo55iABAAAAALcFh4fsnj176tSpUxo9erROnDihxo0ba9myZebJ0BITE+Xk9L8T7sePH1eTJk3MrydMmKAJEyYoIiJCcXFxkqRjx46pV69e+uuvvxQYGKg2bdrot99+U2Bg4A09NgAAAADA7cVkGIbh6CJuNmlpafLz81NqaiqXjgMAAABAKeLoPOfQe7IBAAAAALiVELIBAAAAALATQjYAAAAAAHZCyAYAAAAAwE4I2QAAAAAA2AkhGwAAAAAAOyFkAwAAAABgJ4RsAAAAAADshJANAAAAAICdELIBAAAAALATQjYAAAAAAHZSxpbOu3bt0vz58/Xrr7/qyJEjyszMVGBgoJo0aaJOnTqpe/fucnNzK6laAQAAAAC4qZkMwzCu1Wnz5s0aOXKk1q5dq9atW6tFixaqVKmSPDw8dObMGW3fvl2//vqr0tLSNHLkSA0fPrxUh+20tDT5+fkpNTVVvr6+ji4HAAAAAGAlR+c5q85kd+/eXS+//LK+/vprlS1b9or91q9frylTpmjixIl69dVX7VUjAAAAAAClglVnsnNzc+Xi4mL1oLb2v9k4+jcfAAAAAIDicXSes2ris4LAnJubq44dO2rfvn1W9QcAAAAA4HZi0+ziLi4u+vPPP0uqFgAAAAAASjWbH+H15JNP6j//+U9J1AIAAAAAQKlm0yO8JOnChQv65JNPtHLlSoWFhcnLy8ti/aRJk+xWHAAAAAAApYnNIXv79u1q2rSpJGnv3r0W60wmk32qAgAAAACgFLI5ZK9ataok6gAAAAAAoNSz+Z7sAvv379fy5cuVlZUlSbLiSWAAAAAAANzSbA7Zf/31lzp27Kg6deqoc+fOSk5OliQNGDBAL730kt0LBAAAAACgtLA5ZL/44otycXFRYmKiPD09ze09e/bUsmXL7FocAAAAAAClic33ZP/8889avny5qlSpYtFeu3ZtHTlyxG6FAQAAAABQ2th8JjsjI8PiDHaBM2fOyM3NzS5FAQAAAABQGtkcstu2batPP/3U/NpkMik/P1/jx49X+/bt7VocAAAAAAClic2Xi48fP14dO3bU77//rpycHI0cOVI7duzQmTNnFB8fXxI1AgAAAABQKth8JvvOO+/U3r171aZNGz388MPKyMhQt27dtGXLFtWsWbMkagQAAAAAoFQwGTY+4DoxMVEhISEymUxFrqtatardinOUtLQ0+fn5KTU1Vb6+vo4uBwAAAABgJUfnOZvPZFevXl2nTp0q1P7XX3+pevXqdikKAAAAAIDSyOaQbRhGkWex09PT5e7ubpeiAAAAAAAojaye+Cw6OlrSxdnEX3/9dYvHeOXl5WnDhg1q3Lix3QsEAAAAAKC0sDpkb9myRdLFM9nbtm2Tq6ureZ2rq6saNWqkESNG2L9CAAAAAABKCatD9qpVqyRJUVFRmjJlChOCAQAAAABwGZufkz1nzpySqAMAAAAAgFLP5pAtSb///ru++uorJSYmKicnx2LdN998Y5fCAAAAAAAobWyeXXz+/Plq1aqVdu3apcWLFys3N1c7duzQL7/8Ij8/v5KoEQAAAACAUsHmkP3uu+/qgw8+0A8//CBXV1dNmTJFu3fv1mOPPaaqVauWRI0AAAAAAJQKNofsAwcOqEuXLpIuziqekZEhk8mkF198UbNmzbJ7gQAAAAAAlBY2h2x/f3+dP39eklS5cmVt375dknTu3DllZmbatzoAAAAAAEoRmyc+u/vuu7VixQrddddd6tGjh4YNG6ZffvlFK1asUMeOHUuiRgAAAAAASgWbQ/a0adP0999/S5Jee+01ubi4aN26derevbv++c9/2r1AAAAAAABKC5NhGIaji7jZpKWlyc/PT6mpqfL19XV0OQAAAAAAKzk6z9l8T3bfvn01Z84cHThwoCTqAQAAAACg1LI5ZLu6umrs2LGqXbu2QkJC9OSTT+rf//639u3bVxL1AQAAAABQahT7cvGkpCStWbNGq1ev1urVq7V3714FBwfr2LFj9q7xhnP05QUAAAAAgOJxdJ6z+Ux2AX9/f5UvX17+/v4qW7asypQpo8DAwGKNNX36dIWGhsrd3V3h4eHauHHjFfvu2LFD3bt3V2hoqEwmkyZPnnzVsceNGyeTyaThw4cXqzYAAAAAAKxlc8h+9dVX1apVK5UvX16jRo3S33//rVGjRunEiRPasmWLzQUsWLBA0dHRGjNmjDZv3qxGjRqpU6dOSklJKbJ/ZmamatSooXHjxqlixYpXHXvTpk36+OOP1bBhQ5vrAgAAAADAVjZfLu7k5KTAwEC9+OKL6tatm+rUqXNdBYSHh6t58+aaNm2aJCk/P18hISEaOnSoRo0addVtQ0NDNXz48CLPUqenp6tp06b66KOP9Pbbb6tx48bXPOtdwNGXFwAAAAAAisfRec7mM9lbtmzRa6+9po0bN6p169aqXLmynnjiCc2aNUt79+61aaycnBwlJCQoMjLyfwU5OSkyMlLr16+3tTQLzz//vLp06WIxNgAAAAAAJamMrRs0atRIjRo10gsvvCBJ+uOPP/TBBx/o+eefV35+vvLy8qwe6/Tp08rLy1NQUJBFe1BQkHbv3m1raWbz58/X5s2btWnTJqv6Z2dnKzs72/w6LS2t2PsGAAAAANy+bA7ZhmFoy5YtiouLU1xcnNauXau0tDQ1bNhQERERJVGjTY4ePaphw4ZpxYoVcnd3t2qbsWPH6s033yzhygAAAAAAtzqbQ3a5cuWUnp6uRo0aKSIiQgMHDlTbtm1VtmxZm3ceEBAgZ2dnnTx50qL95MmT15zU7EoSEhKUkpKipk2bmtvy8vK0Zs0aTZs2TdnZ2XJ2drbYJiYmRtHR0ebXaWlpCgkJKdb+AQAAAAC3L5tD9ueff662bdva5QZyV1dXhYWFKTY2Vl27dpV0ceKz2NhYDRkypFhjduzYUdu2bbNoi4qKUr169fTKK68UCtiS5ObmJjc3t2LtDwAAAACAAjaH7C5duti1gOjoaPXr10/NmjVTixYtNHnyZGVkZCgqKkqS1LdvX1WuXFljx46VdHGytJ07d5r/nJSUpK1bt8rb21u1atWSj4+P7rzzTot9eHl5qXz58oXaAQAAAACwJ5tDtr317NlTp06d0ujRo3XixAk1btxYy5YtM0+GlpiYKCen/02Cfvz4cTVp0sT8esKECZowYYIiIiIUFxd3o8sHAAAAAMDM5udk3w4c/Vw1AAAAAEDxODrP2fycbAAAAAAAUDRCNgAAAAAAdmJ1yF60aJH69eunpUuXqmnTpvrmm29Ksi4AAAAAAEodq0P2zJkz9eOPP6pMmTIaN26c3njjjRIsCwAAAACA0sfq2cVdXFzUuHFj3XvvvZIkf3//EisKAAAAAIDSyOoz2QEBAZozZ475dW5ubokUBAAAAABAaWX1meyZM2fK09NTkpSdna0pU6aUWFEAAAAAAJRGVp/JLgjYkuTm5qbmzZuXSEEAAAAAAJRWVp/JLvD3339r6tSpWrVqlVJSUpSfn2+xfvPmzXYrDgAAAACA0sTmkD1gwAD9/PPPevTRR9WiRQuZTKaSqAsAAAAAgFLH5pC9ZMkSLV26VK1bty6JegAAAAAAKLWsvie7QOXKleXj41MStQAAAAAAUKrZHLInTpyoV155RUeOHCmJegAAAAAAKLVsvly8WbNm+vvvv1WjRg15enrKxcXFYv2ZM2fsVhwAAAAAAKWJzSG7V69eSkpK0rvvvqugoCAmPgMAAAAA4P/ZHLLXrVun9evXq1GjRiVRDwAAAAAApZbN92TXq1dPWVlZJVELAAAAAAClms0he9y4cXrppZcUFxenv/76S2lpaRYLAAAAAAC3K5NhGIYtGzg5Xczll9+LbRiGTCaT8vLy7Fedg6SlpcnPz0+pqany9fV1dDkAAAAAACs5Os/ZfE/2qlWrSqIOAAAAAABKPZtDdkREREnUAQAAAABAqWfzPdkAAAAAAKBohGwAAAAAAOyEkA0AAAAAgJ1YHbIzMzNLsg4AAAAAAEo9q0N2QECAHnjgAc2aNUsnTpwoyZoAAAAAACiVrA7Zu3fvVqdOnfTVV18pNDRU4eHheuedd7Rt27aSrA8AAAAAgFLDZBiGYetGqampWrp0qb777jstW7ZM5cqV00MPPaSHHnpIERERcnZ2LolabxhHP7wcAAAAAFA8js5zxZr4zM/PT7169dL8+fN16tQpffzxx8rLy1NUVJQCAwP1xRdf2LtOAAAAAABuesU6k301W7Zs0YULF9S8eXN7DntDOfo3HwAAAACA4nF0nitj7wGbNGli7yEBAAAAACgVeE42AAAAAAB2QsgGAAAAAMBOCNkAAAAAANgJIRsAAAAAADuxauKzJk2ayGQyWTXg5s2br6sgAAAAAABKK6tCdteuXUu4DAAAAAAASj+7Pyf7VuDo56oBAAAAAIrH0XmOe7IBAAAAALATqy4XL1eunPbu3auAgAD5+/tf9f7sM2fO2K04AAAAAABKE6tC9gcffCAfHx9J0uTJk0uyHgAAAAAASi3uyS6Co6/hBwAAAAAUj6PznFVnsi+Xl5enb7/9Vrt27ZIk3XHHHXrooYfk7Oxs1+IAAAAAAChNbA7Z+/fvV+fOnZWUlKS6detKksaOHauQkBD9+OOPqlmzpt2LBAAAAACgNLB5dvEXXnhBNWvW1NGjR7V582Zt3rxZiYmJql69ul544YWSqBEAAAAAgFLB5jPZq1ev1m+//aZy5cqZ28qXL69x48apdevWdi0OAAAAAIDSxOYz2W5ubjp//nyh9vT0dLm6utqlKAAAAAAASiObQ/YDDzygQYMGacOGDTIMQ4Zh6LffftOzzz6rhx56qFhFTJ8+XaGhoXJ3d1d4eLg2btx4xb47duxQ9+7dFRoaKpPJVOQjxWbMmKGGDRvK19dXvr6+atmypX766adi1QYAAAAAgLVsDtkffvihatasqZYtW8rd3V3u7u5q3bq1atWqpSlTpthcwIIFCxQdHa0xY8Zo8+bNatSokTp16qSUlJQi+2dmZqpGjRoaN26cKlasWGSfKlWqaNy4cUpISNDvv/+uDh066OGHH9aOHTtsrg8AAAAAAGsV+znZ+/fvNz/Cq379+qpVq1axCggPD1fz5s01bdo0SVJ+fr5CQkI0dOhQjRo16qrbhoaGavjw4Ro+fPg191OuXDm9//77GjBgwDX7Ovq5agAAAACA4nF0nivWc7IlqVatWsUO1gVycnKUkJCgmJgYc5uTk5MiIyO1fv366xq7QF5enhYuXKiMjAy1bNnSLmMCAAAAAFCUYodsezh9+rTy8vIUFBRk0R4UFKTdu3df19jbtm1Ty5Yt9ffff8vb21uLFy9WgwYNiuybnZ2t7Oxs8+u0tLTr2jcAAAAA4PZk8z3ZpUXdunW1detWbdiwQYMHD1a/fv20c+fOIvuOHTtWfn5+5iUkJOQGVwsAAAAAuBU4NGQHBATI2dlZJ0+etGg/efLkFSc1s5arq6tq1aqlsLAwjR07Vo0aNbrixGwxMTFKTU01L0ePHr2ufQMAAAAAbk92C9nnzp3Tl19+adM2rq6uCgsLU2xsrLktPz9fsbGxdr9/Oj8/3+KS8Eu5ubmZH/dVsAAAAAAAYCu73ZN95MgR9enTR0888YRN20VHR6tfv35q1qyZWrRoocmTJysjI0NRUVGSpL59+6py5coaO3aspIuTpRVc9p2Tk6OkpCRt3bpV3t7e5onYYmJidP/996tq1ao6f/68vvzyS8XFxWn58uX2OlwAAAAAAApx6MRnktSzZ0+dOnVKo0eP1okTJ9S4cWMtW7bMPBlaYmKinJz+d8L9+PHjatKkifn1hAkTNGHCBEVERCguLk6SlJKSor59+yo5OVl+fn5q2LChli9frnvuueeGHhsAAAAA4PZS7OdkX+6PP/5Q06ZNlZeXZ4/hHMrRz1UDAAAAABSPo/PcLTu7OAAAAAAAN5rVl4t/+OGHV12flJR03cUAAAAAAFCaWR2yP/jgg2v2qVq16nUVAwAAYKt27dqpcePGmjx5conu5/Dhw6pevbq2bNmixo0bl+i+AACll9Uh+9ChQyVZBwAAwE0tJCREycnJCggIcHQpAICbmMNnFwcAACgNnJ2dVbFiRUeXAQC4yVk18dn8+fOtHvDo0aOKj48vdkEAAADFlZ2drREjRqhy5cry8vJSeHi4+RGfBeLj49WuXTt5enrK399fnTp10tmzZyVJ+fn5Gj9+vGrVqiU3NzdVrVpV77zzjqSLl4ubTCZt3bpVkhQXFyeTyaTY2Fg1a9ZMnp6eatWqlfbs2WOxvxkzZqhmzZpydXVV3bp19dlnn5X4+wAAcByrQvaMGTNUv359jR8/Xrt27Sq0PjU1VUuXLtUTTzyhpk2b6q+//rJ7oQAAANcyZMgQrV+/XvPnz9eff/6pHj166L777tO+ffskSVu3blXHjh3VoEEDrV+/XmvXrtWDDz5ofgRpTEyMxo0bp9dff107d+7Ul19+qaCgoKvu87XXXtPEiRP1+++/q0yZMnrqqafM6xYvXqxhw4bppZde0vbt2/XMM88oKipKq1atKrk3AQDgUFY/J/v777/X1KlT9csvv8jLy0tBQUFyd3fX2bNndeLECQUEBKh///568cUXr/k/o5udo5+rBgAArFcw8Vl0dLRq1KihxMREVapUybw+MjJSLVq00LvvvqsnnnhCiYmJWrt2baFxzp8/r8DAQE2bNk1PP/10ofWXT3wWFxen9u3ba+XKlerYsaMkaenSperSpYuysrLk7u6u1q1b64477tCsWbPM4zz22GPKyMjQjz/+WALvBgDA0XnO6nuyH3roIT300EM6ffq01q5dqyNHjigrK0sBAQFq0qSJmjRpIicnHrsNAAAcY9u2bcrLy1OdOnUs2rOzs1W+fHlJF89k9+jRo8jtd+3apezsbHNgtlbDhg3Nfw4ODpYkpaSkqGrVqtq1a5cGDRpk0b9169aaMmWKTfsAAJQeNk98FhAQoK5du5ZAKQAAAMWXnp4uZ2dnJSQkyNnZ2WKdt7e3JMnDw+OK219t3dW4uLiY/2wymSRdvLcbAHB74tQzAAC4JTRp0kR5eXlKSUlRrVq1LJaCWcEbNmyo2NjYIrevXbu2PDw8rri+OOrXr19oQtj4+Hg1aNDAbvsAANxceIQXAAC4JdSpU0e9e/dW3759NXHiRDVp0kSnTp1SbGysGjZsqC5duigmJkZ33XWXnnvuOT377LNydXXVqlWr1KNHDwUEBOiVV17RyJEj5erqqtatW+vUqVPasWOHBgwYUKyaXn75ZT322GNq0qSJIiMj9cMPP+ibb77RypUr7Xz0AICbBSEbAADclAzD0NnMXGVkX5CXWxn5e7qYL8e+kjlz5ujtt9/WSy+9pKSkJAUEBOgf//iHHnjgAUkXg/jPP/+sV199VS1atJCHh4fCw8PVq1cvSdLrr7+uMmXKaPTo0Tp+/LiCg4P17LPPFvsYunbtqilTpmjChAkaNmyYqlevrjlz5qhdu3bFHhMAcHOzenbx24mjZ6MDAOB2lpqVq0UJxzRv3WEdOZNpbq9WzlP9WoWqe1gV+Xm4XGUEAMDtzNF5zuaQvWrVKrVv376k6rkpOPpDAQDgdrV67ykN/jxBWTkXn1t96T9SCs5he7g6a8aTYYqoE3jD6wMA3PwcnedsnvjsvvvuU82aNfX222/r6NGjJVETAAC4Da3ee0pRczYqKzdPhiwDtv7/tSEpKzdPUXM2avXeUze+SAAArsHmkJ2UlKQhQ4bo66+/Vo0aNdSpUyd99dVXysnJKYn6AADAbSA1K1eDP0+4GKSvcY2dYVwM24M/T1BqVu6NKA8AAKvZHLIDAgL04osvauvWrdqwYYPq1Kmj5557TpUqVdILL7ygP/74oyTqBAAAt7BFCceUlZN3zYBdwDCkrJw8fbP5WMkWBgCAja7rOdlNmzZVTEyMhgwZovT0dH3yyScKCwtT27ZttWPHDnvVCAAAbmGGYWjeusPF2nZu/GExhysA4GZSrJCdm5urr7/+Wp07d1a1atW0fPlyTZs2TSdPntT+/ftVrVo19ejRw961AgCAW9DZzFwdOZNZ6B7sazEkHTmTqXOZXDIOALh52Pyc7KFDh+q///2vDMNQnz59NH78eN15553m9V5eXpowYYIqVapk10IBAMCtKSP7wnVtn559Qf5ernaqBgCA62NzyN65c6emTp2qbt26yc3Nrcg+AQEBWrVq1XUXBwAAbn1ebjb/c8SC93VuDwCAPdn8f6XY2NhrD1qmjCIiIopVEAAAuL34e7qoWjlPJdp4ybhJUtVynirr6VJSpQEAYDOb78keO3asPvnkk0Ltn3zyid577z27FAUAAG4fJpNJ/VqFFmvb/q1DZTKZ7FsQAADXweaQ/fHHH6tevXqF2u+44w7NnDnTLkUBAIDbS/ewKvJwdZa1ednJJHm4Oqtb0yolWxgAADayOWSfOHFCwcHBhdoDAwOVnJxsl6IAAMDtxc/DRTOeDJNJumbQLlg/88kw+XlwqTgA4OZic8gOCQlRfHx8ofb4+HhmFAcAAMUWUSdQc6JayMPF+WLYvmx9QZuHi7PmRrXQ3XUCb3yRAABcg80Tnw0cOFDDhw9Xbm6uOnToIOniZGgjR47USy+9ZPcCAQDA7SOiTqDWx3TUN5uPaW78YR05k2leV7Wcp/q3DlX3sCrydecMNgDg5mQyDMOWiTxlGIZGjRqlDz/8UDk5OZIkd3d3vfLKKxo9enSJFHmjpaWlyc/PT6mpqfL19XV0OQAA3JYMw9C5zFylZ1+Qt1sZlfV0YZIzAMA1OTrP2RyyC6Snp2vXrl3y8PBQ7dq1r/jM7NLI0R8KAAAAAKB4HJ3nbL5cvIC3t7eaN29uz1oAAAAAACjVbA7ZGRkZGjdunGJjY5WSkqL8/HyL9QcPHrRbcQAAAAAAlCY2h+ynn35aq1evVp8+fRQcHMy9UQAAAAAA/D+bQ/ZPP/2kH3/8Ua1bty6JegAAAAAAKLVsfk62v7+/ypUrVxK1AAAAAABQqtkcst966y2NHj1amZmZ1+4MAAAAAMBtxObLxSdOnKgDBw4oKChIoaGhcnFxsVi/efNmuxUHAAAAAEBpYnPI7tq1awmUAQAAAABA6WcyDMNwdBE3G0c/vBwAAAAAUDyOznM235MtSefOndO///1vxcTE6MyZM5IuXiaelJRk1+IAAAAAAChNbL5c/M8//1RkZKT8/Px0+PBhDRw4UOXKldM333yjxMREffrppyVRJwAAAAAANz2bz2RHR0erf//+2rdvn9zd3c3tnTt31po1a+xaHAAAAAAApYnNIXvTpk165plnCrVXrlxZJ06csEtRAAAAAACURjaHbDc3N6WlpRVq37t3rwIDA+1SFAAAAAAApZHNIfuhhx7Sv/71L+Xm5kqSTCaTEhMT9corr6h79+52LxAAAAAAgNLC5pA9ceJEpaenq0KFCsrKylJERIRq1aolHx8fvfPOOyVRIwAAAAAApYLNs4v7+flpxYoVWrt2rf7880+lp6eradOmioyMLIn6AAAAAAAoNYr1nGxJatOmjZ577jmNHDnyugP29OnTFRoaKnd3d4WHh2vjxo1X7Ltjxw51795doaGhMplMmjx5cqE+Y8eOVfPmzeXj46MKFSqoa9eu2rNnz3XVCAAAAADAtVh1JvvDDz/UoEGD5O7urg8//PCqfV944QWbCliwYIGio6M1c+ZMhYeHa/LkyerUqZP27NmjChUqFOqfmZmpGjVqqEePHnrxxReLHHP16tV6/vnn1bx5c124cEGvvvqq7r33Xu3cuVNeXl421QcAAAAAgLVMhmEY1+pUvXp1/f777ypfvryqV69+5cFMJh08eNCmAsLDw9W8eXNNmzZNkpSfn6+QkBANHTpUo0aNuuq2oaGhGj58uIYPH37VfqdOnVKFChW0evVq3X333desKS0tTX5+fkpNTZWvr6/VxwIAAAAAcCxH5zmrzmQfOnSoyD9fr5ycHCUkJCgmJsbc5uTkpMjISK1fv95u+0lNTZUklStXzm5jAgAAAABwOZsnPrOn06dPKy8vT0FBQRbtQUFB2r17t132kZ+fr+HDh6t169a68847i+yTnZ2t7Oxs8+uingMOAAAAAMC12DzxWffu3fXee+8Vah8/frx69Ohhl6Ls6fnnn9f27ds1f/78K/YZO3as/Pz8zEtISMgNrBAAAAAAcKuwOWSvWbNGnTt3LtR+//33a82aNTaNFRAQIGdnZ508edKi/eTJk6pYsaKtpRUyZMgQLVmyRKtWrVKVKlWu2C8mJkapqanm5ejRo9e9bwAAAADA7cfmkJ2eni5XV9dC7S4uLjZfZu3q6qqwsDDFxsaa2/Lz8xUbG6uWLVvaWpqZYRgaMmSIFi9erF9++eWqk7VJkpubm3x9fS0WAAAAAABsZXPIvuuuu7RgwYJC7fPnz1eDBg1sLiA6OlqzZ8/WvHnztGvXLg0ePFgZGRmKioqSJPXt29diYrScnBxt3bpVW7duVU5OjpKSkrR161bt37/f3Of555/X559/ri+//FI+Pj46ceKETpw4oaysLJvrAwAAAADAWjZPfPb666+rW7duOnDggDp06CBJio2N1X//+18tXLjQ5gJ69uypU6dOafTo0Tpx4oQaN26sZcuWmSdDS0xMlJPT/34XcPz4cTVp0sT8esKECZowYYIiIiIUFxcnSZoxY4YkqV27dhb7mjNnjvr3729zjQAAAAAAWMOq52Rf7scff9S7776rrVu3ysPDQw0bNtSYMWMUERFREjXecI5+rhoAAAAAoHgcneeKFbJvdY7+UAAAAAAAxePoPGfzPdkAAAAAAKBoVt2TXa5cOe3du1cBAQHy9/eXyWS6Yt8zZ87YrTgAAAAAAEoTq0L2Bx98IB8fH0nS5MmTS7IeAAAAAABKLatC9h9//KFHH31Ubm5uql69ulq1aqUyZWyemBwAAAAAgFuaVfdkT506Venp6ZKk9u3bc0k4AAAAAABFsOp0dGhoqD788EPde++9MgxD69evl7+/f5F97777brsWCAAAAABAaWHVI7y+/fZbPfvss0pJSZHJZNKVNjGZTMrLy7N7kTeao6d8BwAAAAAUj6PznE3PyU5PT5evr6/27NmjChUqFNnHz8/PbsU5iqM/FAAAAABA8Tg6z1l1T3Z0dLQyMjLk7e2tVatWqXr16vLz8ytyAQAAAADgdmXzxGcdOnRg4jMAAAAAAIrAxGcAAAAAANgJE58VwdHX8AMAAAAAisfReY6Jz4rg6A8FAAAAAFA8js5zVl0uXuDSic/KlLFpUwAAAAAAbnlWTXwmSV999ZVycnIUERGhMmXK6NixY8rPzzevz8zM1Pjx40ukSAAAAAAASgOrQ3avXr107tw58+sGDRro8OHD5tfnz59XTEyMPWsDAAAAAKBUsTpkX37rtg23cgMAAAAAcFuwOmQDAAAAAICrI2QDAAAAAGAnNk0Rvnz5cvMjuvLz8xUbG6vt27dLksX92gAAAAAA3I6sfk62k9O1T3qbTCbl5eVdd1GO5ujnqgEAAAAAisfRec7qM9mXPq4LAAAAAAAUxj3ZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOzEppCdl5enNWvW8LguAAAAAACKYFPIdnZ21r333quzZ8+WVD0AAAAAAJRaNl8ufuedd+rgwYMlUQsAAAAAAKWazSH77bff1ogRI7RkyRIlJycrLS3NYgEAAAAA4HZlMgzDsGUDJ6f/5XKTyWT+s2EYMplMysvLs191DpKWliY/Pz+lpqbK19fX0eUAAAAAAKzk6DxXxtYNVq1aVRJ1AAAAAABQ6tkcsiMiIkqiDgAAAAAASr1iPSf7119/1ZNPPqlWrVopKSlJkvTZZ59p7dq1di0OAAAAAIDSxOaQvWjRInXq1EkeHh7avHmzsrOzJUmpqal699137V4gAAAAAAClRbFmF585c6Zmz54tFxcXc3vr1q21efNmuxYHAAAAAEBpYnPI3rNnj+6+++5C7X5+fjp37pw9agIAAAAAoFSyOWRXrFhR+/fvL9S+du1a1ahRwy5FAQAAAABQGtkcsgcOHKhhw4Zpw4YNMplMOn78uL744guNGDFCgwcPLokaAQAAAAAoFWx+hNeoUaOUn5+vjh07KjMzU3fffbfc3Nw0YsQIDR06tCRqBAAAAACgVDAZhmEUZ8OcnBzt379f6enpatCggby9ve1dm8OkpaXJz89Pqamp8vX1dXQ5AAAAAAArOTrP2Xy5+FNPPaXz58/L1dVVDRo0UIsWLeTt7a2MjAw99dRTJVEjAAAAAAClgs0he968ecrKyirUnpWVpU8//dQuRQEAAAAAUBpZfU92WlqaDMOQYRg6f/683N3dzevy8vK0dOlSVahQoUSKBAAAAACgNLA6ZJctW1Ymk0kmk0l16tQptN5kMunNN9+0a3EAAAAAAJQmVofsVatWyTAMdejQQYsWLVK5cuXM61xdXVWtWjVVqlSpRIoEAAAAAKA0sPqe7IiICLVr106HDh3Sww8/rIiICPPSsmXLYgfs6dOnKzQ0VO7u7goPD9fGjRuv2HfHjh3q3r27QkNDZTKZNHny5EJ91qxZowcffFCVKlWSyWTSt99+W6y6AAAAAACwlc0Tn1WrVk1OTk7KzMzU7t279eeff1ostliwYIGio6M1ZswYbd68WY0aNVKnTp2UkpJSZP/MzEzVqFFD48aNU8WKFYvsk5GRoUaNGmn69Om2HhoAAAAAANfF5udknzp1SlFRUfrpp5+KXJ+Xl2f1WOHh4WrevLmmTZsmScrPz1dISIiGDh2qUaNGXXXb0NBQDR8+XMOHD79iH5PJpMWLF6tr165W1yQ5/rlqAAAAAIDicXSes/lM9vDhw3Xu3Dlt2LBBHh4eWrZsmebNm6fatWvr+++/t3qcnJwcJSQkKDIy8n/FODkpMjJS69evt7UsAAAAAAAczuqJzwr88ssv+u6779SsWTM5OTmpWrVquueee+Tr66uxY8eqS5cuVo1z+vRp5eXlKSgoyKI9KChIu3fvtrWs65Kdna3s7Gzz67S0tBu6fwAAcOto166dGjduXOTcMQCAW5/NZ7IzMjLMz8P29/fXqVOnJEl33XWXNm/ebN/qbpCxY8fKz8/PvISEhDi6JAAAcJOLi4uTyWTSuXPnLNq/+eYbvfXWW44pCgDgcDaH7Lp162rPnj2SpEaNGunjjz9WUlKSZs6cqeDgYKvHCQgIkLOzs06ePGnRfvLkyStOalZSYmJilJqaal6OHj16Q/cPAABuHjk5Ode1fbly5eTj42OnagAApY3NIXvYsGFKTk6WJI0ZM0Y//fSTqlatqg8//FDvvvuu1eO4uroqLCxMsbGx5rb8/HzFxsaqZcuWtpZ1Xdzc3OTr62uxAACA0iM/P1/jx49XrVq15ObmpqpVq+qdd96RJG3btk0dOnSQh4eHypcvr0GDBik9Pd28bf/+/dW1a1e98847qlSpkurWrStJ+uyzz9SsWTP5+PioYsWKeuKJJ8xPQDl8+LDat28v6eKVfSaTSf3795d08XLxSydmDQ0N1bvvvqunnnpKPj4+qlq1qmbNmmVR/yuvvKI6derI09NTNWrU0Ouvv67c3NySersAACXI6nuyDx06pOrVq+vJJ580t4WFhenIkSPavXu3qlatqoCAAJt2Hh0drX79+qlZs2Zq0aKFJk+erIyMDEVFRUmS+vbtq8qVK2vs2LGSLv5meefOneY/JyUlaevWrfL29latWrUkSenp6dq/f79F3Vu3blW5cuVUtWpVm+oDAAClQ0xMjGbPnq0PPvhAbdq0UXJysnbv3q2MjAx16tRJLVu21KZNm5SSkqKnn35aQ4YM0dy5c83bx8bGytfXVytWrDC35ebm6q233lLdunWVkpKi6Oho9e/fX0uXLlVISIgWLVqk7t27a8+ePfL19ZWHh8cV65s4caLeeustvfrqq/r66681ePBgRUREmAO9j4+P5s6dq0qVKmnbtm0aOHCgfHx8NHLkyBJ7zwAAJcSwkslkMkJDQ42oqCjjs88+M44ePWrtplc1depUo2rVqoarq6vRokUL47fffjOvi4iIMPr162d+fejQIUNSoSUiIsLcZ9WqVUX2uXSca0lNTTUkGampqXY4QgAAUJLS0tIMNzc3Y/bs2YXWzZo1y/D39zfS09PNbT/++KPh5ORknDhxwjAMw+jXr58RFBRkZGdnX3U/mzZtMiQZ58+fNwzjf//mOHv2rEW/iIgIY9iwYebX1apVM5588knz6/z8fKNChQrGjBkzrriv999/3wgLC7tqPQCAojk6z1l9JvuXX35RXFyc4uLi9N///lc5OTmqUaOGOnTooPbt26t9+/aFZgq3xpAhQzRkyJAi18XFxVm8Dg0NlXGNx3q3a9fumn0AAMCtY9euXcrOzlbHjh2LXNeoUSN5eXmZ21q3bq38/Hzt2bPH/G+Xu+66S66urhbbJiQk6I033tAff/yhs2fPKj8/X5KUmJioBg0a2FRjw4YNzX82mUyqWLGi+dJzSVqwYIE+/PBDHThwQOnp6bpw4QK3rwFAKWX1Pdnt2rXTG2+8obi4OJ09e1YrVqxQr169tGvXLvXv31+VKlXSHXfcUZK1AgAAFHK1y7StdWkIl2S+zNzX11dffPGFNm3apMWLF0sq3sRoLi4uFq9NJpM5tK9fv169e/dW586dtWTJEm3ZskWvvfbadU/ABgBwDJufky1J7u7u6tChg9q0aaP27dvrp59+0scff3zDn28NAABQu3ZteXh4KDY2Vk8//bTFuvr162vu3LnKyMgwB+n4+Hg5OTmZ74cuyu7du/XXX39p3Lhx5kd7/v777xZ9Cs585+XlXVf969atU7Vq1fTaa6+Z244cOXJdYwIAHMem2cVzcnK0Zs0avfnmm2rfvr3Kli2rZ599VmfPntW0adN06NChkqoTAACgSO7u7nrllVc0cuRIffrppzpw4IB+++03/ec//1Hv3r3l7u6ufv36afv27Vq1apWGDh2qPn36XPU2t6pVq8rV1VVTp07VwYMH9f333xd69nW1atVkMpm0ZMkSnTp1ymLGclvUrl1biYmJmj9/vg4cOKAPP/zQfNYcAFD6WB2yO3ToIH9/fz333HNKSUnRM888owMHDmjPnj2aPXu2+vTpw+zdAADAbgzD0JmMHB09k6kzGTlXnXPl9ddf10svvaTRo0erfv366tmzp1JSUuTp6anly5frzJkzat68uR599FF17NhR06ZNu+q+AwMDNXfuXC1cuFANGjTQuHHjNGHCBIs+lStX1ptvvqlRo0YpKCjoinPMXMtDDz2kF198UUOGDFHjxo21bt06vf7668UaCwDgeCbDylnCXFxcFBwcrK5du6pdu3aKiIhQ+fLlS7o+h0hLS5Ofn59SU1OZdAQAgBssNStXixKOad66wzpyJtPcXq2cp/q1ClX3sCry83C5yggAgNuZo/Oc1SE7IyNDv/76q+Li4rRq1Spt3bpVderUUUREhDl0BwYGlnS9N4SjPxQAAG5Xq/ee0uDPE5SVc/E+50v/kWL6//96uDprxpNhiqhza/y7AwBgX47Oc1aH7MudP39ea9eu1apVqxQXF6c//vhDtWvX1vbt2+1d4w3n6A8FAIDb0eq9pxQ1Z6MMSVf714nJdDFwz4lqQdAGABTi6Dxn08Rnl/Ly8lK5cuVUrlw5+fv7q0yZMtq1a5c9awMAALeJ1KxcDf484ZoBW/+/3pA0+PMEpWbl3ojyAACwmtWP8MrPz9fvv/9uvlw8Pj5eGRkZqly5stq3b6/p06erffv2JVkrAAC4RS1KOKasnDxZe3mdYUhZOXn6ZvMxRbWuXqK1AQBgC6tDdtmyZZWRkaGKFSuqffv2+uCDD9SuXTvVrFmzJOsDAAC3OMMwNG/d4WJtOzf+sPq3CpXJZLp2ZwAAbgCrQ/b777+v9u3bq06dOiVZDwAAuM2czcy1mEXcWoakI2cydS4zV/5ervYvDACAYrA6ZD/zzDMlWQcAALhNZWRfuK7t07MvELIBADeNYk98BgAAYA9eblb/zr9I3te5PQAA9kTIBgAADuXv6aJq5Txl613VJknVynmqrKdLSZQFAECxELIBAIBDmUwm9WsVWqxt+7dm0jMAwM2FkA0AAByue1gVebg6y9q87GSSPFyd1a1plZItDAAAGxGyAQCAw/l5uGjGk2EySdcM2gXrZz4ZJj8PLhUHANxcCNkAAOCmEFEnUHOiWsjDxfli2L5sfUGbh4uz5ka10N11Am98kQAAXAPTcQIAgJtGRJ1ArY/pqG82H9Pc+MMWz8+uWs5T/VuHqntYFfm6cwYbAHBzMhmGYTi6iJtNWlqa/Pz8lJqaKl9fX0eXAwDAbckwDJ3LzFV69gV5u5VRWU8XJjkDAFyTo/McZ7IBAMBNyWQyyd/LVf5ero4uBQAAq3FPNgAAAAAAdkLIBgAAAADATgjZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOyEkA0AAAAAgJ0QsgEAAAAAsBNCNgAAAAAAdkLIBgDASv3791fXrl2t7h8XFyeTyaRz585JkubOnauyZcuWSG0AAODmQMgGAOAG6dmzp/bu3evoMgAAQAkq4+gCAAC4XXh4eMjDw8PRZQAAgBLEmWwAQKnTrl07DRkyREOGDJGfn58CAgL0+uuvyzAMSVJ2drZGjBihypUry8vLS+Hh4YqLizNvX3DZ9vLly1W/fn15e3vrvvvuU3JysrlPXl6eoqOjVbZsWZUvX14jR440j18gOztbL7zwgipUqCB3d3e1adNGmzZtumLdl18u/sYbb6hx48b67LPPFBoaKj8/Pz3++OM6f/58sfcBAAAci5ANACiV5s2bpzJlymjjxo2aMmWKJk2apH//+9+SpCFDhmj9+vWaP3++/vzzT/Xo0UP33Xef9u3bZ94+MzNTEyZM0GeffaY1a9YoMTFRI0aMMK+fOHGi5s6dq08++URr167VmTNntHjxYosaRo4cqUWLFmnevHnavHmzatWqpU6dOunMmTNWH8eBAwf07bffasmSJVqyZIlWr16tcePG2XUfAADgBjJQSGpqqiHJSE1NdXQpAIAiREREGPXr1zfy8/PNba+88opRv35948iRI4azs7ORlJRksU3Hjh2NmJgYwzAMY86cOYYkY//+/eb106dPN4KCgsyvg4ODjfHjx5tf5+bmGlWqVDEefvhhwzAMIz093XBxcTG++OILc5+cnByjUqVK5u1WrVplSDLOnj1r3q+fn5+5/5gxYwxPT08jLS3N3Pbyyy8b4eHhVu8DAABYcnSe455sAECp9I9//EMmk8n8umXLlpo4caK2bdumvLw81alTx6J/dna2ypcvb37t6empmjVrml8HBwcrJSVFkpSamqrk5GSFh4eb15cpU0bNmjUzXzJ+4MAB5ebmqnXr1uY+Li4uatGihXbt2mX1cYSGhsrHx6fIOuy1DwAAcOMQsgEAt5T09HQ5OzsrISFBzs7OFuu8vb3Nf3ZxcbFYZzKZCt1zfSMUVUd+fv4NrwMAANgH92QDAEqlDRs2WLz+7bffVLt2bTVp0kR5eXlKSUlRrVq1LJaKFStaNbafn5+Cg4Mt9nHhwgUlJCSYX9esWVOurq6Kj483t+Xm5mrTpk1q0KDBdR7djdsHAACwL85kAwBKpcTEREVHR+uZZ57R5s2bNXXqVE2cOFF16tRR79691bdvX02cOFFNmjTRqVOnFBsbq4YNG6pLly5WjT9s2DCNGzdOtWvXVr169TRp0iSdO3fOvN7Ly0uDBw/Wyy+/rHLlyqlq1aoaP368MjMzNWDAALsc443YBwAAsC9CNgDgpmEYhs5m5ioj+4K83MrI39PF4r7rS/Xt21dZWVlq0aKFnJ2dNWzYMA0aNEiSNGfOHL399tt66aWXlJSUpICAAP3jH//QAw88YHUtL730kpKTk9WvXz85OTnpqaee0iOPPKLU1FRzn3Hjxik/P199+vTR+fPn1axZMy1fvlz+/v7X90Zc4kbsAwAA2I/JcMQNaDe5tLQ0+fn5KTU1Vb6+vo4uBwBuealZuVqUcEzz1h3WkTOZ5vZq5TzVr1WouodVkZ/H/+5dbteunRo3bqzJkyc7oFoAAHAzc3Se40w2AMChVu89pcGfJygrJ6/QusQzmXpryU5N+HmPZjwZpog6gQ6oEAAAwHpMfAYAcJjVe08pas5GZeXmyZB0+aVVBW1ZuXmKmrNRq/eeuvFFAgAA2IDLxYvg6MsLAOB2kJqVq5ZjYy8GbCv+T2QySR4uzlof09Hi0nEAAIBLOTrPcSYbAOAQixKOKSvHuoAtSYYhZeXk6ZvNx0q2MAAAgOtAyAYA3HCGYWjeusPF2nZu/GFxERYAALhZEbIBADfc2cxcHTmTWege7GsxJB05k6lzmbklURYAAMB1uylC9vTp0xUaGip3d3eFh4dr48aNV+y7Y8cOde/eXaGhoTKZTFd8fIstYwIAbqyM7AvXtX36dW4PAABQUhweshcsWKDo6GiNGTNGmzdvVqNGjdSpUyelpKQU2T8zM1M1atTQuHHjVLFiRbuMCQC4sbzcru8Jkt7XuT0AAEBJcXjInjRpkgYOHKioqCg1aNBAM2fOlKenpz755JMi+zdv3lzvv/++Hn/8cbm5udllTADAjeXv6aJq5TxlsnE7k6Rq5TxV1pPZxQEAwM3JoSE7JydHCQkJioyMNLc5OTkpMjJS69evv2FjZmdnKy0tzWIBAJQck8mkfq1Ci7Vt/9YXbxcCAAC4GTk0ZJ8+fVp5eXkKCgqyaA8KCtKJEydu2Jhjx46Vn5+feQkJCSnWvgEA1useVkUers6yNi87mSQPV2d1a1qlZAsDAAC4Dg6/XPxmEBMTo9TUVPNy9OhRR5cEALc8Pw8XzXgyTCbpmkG7YP3MJ8Pk58Gl4gAA4Obl0JAdEBAgZ2dnnTx50qL95MmTV5zUrCTGdHNzk6+vr8UCACh5EXUCNSeqhTxcnC+G7cvWF7R5uDhrblQL3V0n8MYXCQAAYAOHhmxXV1eFhYUpNjbW3Jafn6/Y2Fi1bNnyphkTAFByIuoEan1MR41+sIGqlvO0WFe1nKdGP9hAv73akYANAABKBYc/AyU6Olr9+vVTs2bN1KJFC02ePFkZGRmKioqSJPXt21eVK1fW2LFjJV2c2Gznzp3mPyclJWnr1q3y9vZWrVq1rBoTAHBz8fNwUVTr6urfKlTnMnOVnn1B3m5lVNbThUnOAABAqeLwkN2zZ0+dOnVKo0eP1okTJ9S4cWMtW7bMPHFZYmKinJz+d8L9+PHjatKkifn1hAkTNGHCBEVERCguLs6qMQEANyeTySR/L1f5e7k6uhQAAIBiMRmGYTi6iJtNWlqa/Pz8lJqayv3ZAAAAAFCKODrPMbs4AAAAAAB2QsgGAAAAAMBOCNkAAAAAANgJIRsAAAAAADshZAMAAAAAYCeEbAAAAAAA7ISQDQAAAACAnRCyAQAAAACwE0I2AAAAAAB2QsgGAAAAAMBOCNkAAAAAANgJIRsAAAAAADshZAMAAAAAYCeEbAAAAAAA7ISQDQAAAACAnRCyAQAAAACwE0I2AAAAAAB2QsgGAAAAAMBOCNkAAAAAANgJIRsAAAAAADshZAMAAAAAYCeEbAAAAAAA7ISQDQAAAACAnRCyAQAAAACwE0I2AAAAAAB2QsgGAAAAAMBOCNkAAAAAANgJIRsAAAAAADshZAMAAAAAYCeEbAAAAAAA7ISQDQAAAACAnRCyAeAWMnfuXJUtW9b8+o033lDjxo0dVs/V9O/fX127drW6f1xcnEwmk86dOyep8LECAADcDAjZAIDbhslk0rfffuvoMgAAwC2MkA0AuKrc3FxHlwAAAFBqELIBwI7atWunF154QSNHjlS5cuVUsWJFvfHGG+b1iYmJevjhh+Xt7S1fX1899thjOnnypMUYP/zwg5o3by53d3cFBATokUceMa/Lzs7WiBEjVLlyZXl5eSk8PFxxcXFW17dp0ybdc889CggIkJ+fnyIiIrR582aLPiaTSTNmzNBDDz0kLy8vvfPOO8rLy9OAAQNUvXp1eXh4qG7dupoyZYrV+83Ly1N0dLTKli2r8uXLa+TIkTIMw6JPfn6+xo4da95Ho0aN9PXXX1u9D0n67rvv1LRpU7m7u6tGjRp68803deHCBUlSaGioJOmRRx6RyWQyvy64pP6zzz5TaGio/Pz89Pjjj+v8+fPmcZctW6Y2bdqY63/ggQd04MABm2oDAAC3B0I2ANjZvHnz5OXlpQ0bNmj8+PH617/+pRUrVig/P18PP/ywzpw5o9WrV2vFihU6ePCgevbsad72xx9/1COPPKLOnTtry5Ytio2NVYsWLczrhwwZovXr12v+/Pn6888/1aNHD913333at2+fVbWdP39e/fr109q1a/Xbb7+pdu3a6ty5s0WglC4Gz0ceeUTbtm3TU089pfz8fFWpUkULFy7Uzp07NXr0aL366qv66quvrNrvxIkTNXfuXH3yySdau3atzpw5o8WLF1v0GTt2rD799FPNnDlTO3bs0Isvvqgnn3xSq1evtmofv/76q/r27athw4Zp586d+vjjjzV37ly98847ki7+gkGS5syZo+TkZPNrSTpw4IC+/fZbLVmyREuWLNHq1as1btw48/qMjAxFR0fr999/V2xsrJycnPTII48oPz/fqtoAAMBtxEAhqamphiQjNTXV0aUAKGUiIiKMNm3aWLQ1b97ceOWVV4yff/7ZcHZ2NhITE83rduzYYUgyNm7caBiGYbRs2dLo3bt3kWMfOXLEcHZ2NpKSkizaO3bsaMTExBiGYRhz5swx/Pz8zOvGjBljNGrU6Ir15uXlGT4+PsYPP/xgbpNkDB8+/JrH+vzzzxvdu3e/Zj/DMIzg4GBj/Pjx5te5ublGlSpVjIcfftgwDMP4+++/DU9PT2PdunUW2w0YMMDo1auXYRiGsWrVKkOScfbsWcMwCh9rx44djXfffddi+88++8wIDg62OLbFixdb9BkzZozh6elppKWlmdtefvllIzw8/IrHc+rUKUOSsW3btmseOwAAuLEcnefKODDfA8AtqWHDhhavg4ODlZKSol27dikkJEQhISHmdQ0aNFDZsmW1a9cuNW/eXFu3btXAgQOLHHfbtm3Ky8tTnTp1LNqzs7NVvnx5q2o7efKk/vnPfyouLk4pKSnKy8tTZmamEhMTLfo1a9as0LbTp0/XJ598osTERGVlZSknJ8eqmctTU1OVnJys8PBwc1uZMmXUrFkz8yXj+/fvV2Zmpu655x6LbXNyctSkSROrju2PP/5QfHy8+cy1dPEy9b///luZmZny9PS84rahoaHy8fExvy74zArs27dPo0eP1oYNG3T69GnzGezExETdeeedVtUHAABuD4RsALAzFxcXi9cmk8nqy4o9PDyuuC49PV3Ozs5KSEiQs7OzxTpvb2+rxu/Xr5/++usvTZkyRdWqVZObm5tatmypnJwci35eXl4Wr+fPn68RI0Zo4sSJatmypXx8fPT+++9rw4YNVu33WtLT0yVdvFy+cuXKFuvc3NysHuPNN99Ut27dCq1zd3e/6rbX+swefPBBVatWTbNnz1alSpWUn5+vO++8s9D7BgAAQMgGgBukfv36Onr0qI4ePWo+m71z506dO3dODRo0kHTxLHhsbKyioqIKbd+kSRPl5eUpJSVFbdu2LVYN8fHx+uijj9S5c2dJ0tGjR3X69GmrtmvVqpWee+45c5u1E3/5+fkpODhYGzZs0N133y1JunDhghISEtS0aVNJF8/ou7m5KTExUREREbYeliSpadOm2rNnj2rVqnXFPi4uLsrLy7Np3L/++kt79uzR7Nmzze/72rVri1UjAAC49RGyAcAKhmHobGauMrIvyMutjPw9XWQymWwaIzIyUnfddZd69+6tyZMn68KFC3ruuecUERFhvjx7zJgx6tixo2rWrKnHH39cFy5c0NKlS/XKK6+oTp066t27t/r27auJEyeqSZMmOnXqlGJjY9WwYUN16dLlmjXUrl1bn332mZo1a6a0tDS9/PLLVz17ful2n376qZYvX67q1avrs88+06ZNm1S9enWrjn3YsGEaN26cateurXr16mnSpEk6d+6ceb2Pj49GjBihF198Ufn5+WrTpo1SU1MVHx8vX19f9evX75r7GD16tB544AFVrVpVjz76qJycnPTHH39o+/btevvttyVdvCw8NjZWrVu3lpubm/z9/a85rr+/v8qXL69Zs2YpODhYiYmJGjVqlFXHDQAAbj/MLg4AV5GalatP1h5SvZ6jFFC+nNqOX6Wmb61Qu/fj9MnaQ0rNuvIzpE0mk7799luL19999538/f119913KzIyUjVq1NCCBQvMfdq1a6eFCxfq+++/V+PGjdWhQwdt3LjRvH7OnDnq27evXnrpJdWtW1ddu3bVpk2bVLVqVauO5z//+Y/Onj2rpk2bqk+fPnrhhRdUoUKFa273zDPPqFu3burZs6fCw8P1119/WZzVvpaXXnpJffr0Ub9+/cyXm1/6aDJJeuutt/T6669r7Nixql+/vu677z79+OOPVgf5Tp06acmSJfr555/VvHlz/eMf/9AHH3ygatWqmftMnDhRK1asUEhIiNX3ejs5OWn+/PlKSEjQnXfeqRdffFHvv/++1ccOAABuLybDuOxBpVBaWpr8/PyUmpoqX19fR5cDwEFW7z2lwZ8nKCsnT/m52crPyZKzV1lJUsE5bA9XZ814MkwRdQILbW8ymbR48WJ17dr1htUMAABwu3N0nuNMNoDb2pUmrlq995Si5mxUVm6eDEkmFzdzwJYk4/+XrNw8Rc3ZqNV7T92IcgEAAHCTI2QDuGm1a9dOQ4cO1fDhw+Xv76+goCDNnj1bGRkZioqKko+Pj2rVqqWffvrJvM3q1avVokULubm5KTg4WKNGjdKFCxcsxhwyZIiGDx+ugIAAderUSZI0adIk3XXXXfLy8lKVKiHq+kSU8nKyVHCtT/q2lUqc3NOivvNblurYzKd1aHxX3duyiT7+z5yrHs+2bdvUoUMHeXh4qHz58ho0aJB5Vu3Sztvb+4rLr7/+6ujyAAAAbhhCNoCb2rx58xQQEKCNGzdq6NChGjx4sHr06KFWrVpp8+bNuvfee9WnTx9lZmYqKSlJnTt3VvPmzfXHH39oxowZ+s9//mOe9OrSMV1dXRUfH6+ZM2dKunjf7YcffqgdO3ao18vjlH7oD51ZdeXQnLl3nc6snCXf5o+o0oDp8mp0n557ZqBWrVpVZP+MjAx16tRJ/v7+2rRpkxYuXKiVK1dqyJAh9nuzHGjr1q1XXIp65jYAAMCtinuyi+Doa/hvBaGhoRo+fLiGDx/u6FJQirVr1055eXnmM6F5eXny8/NTt27d9Omnn0qSTpw4oeDgYK1fv14//PCDFi1apF27dpln/v7oo4/0yiuvKDU1VU5OTmrXrp3S0tK0efPmIvdpGIbavR+nXet+1l8/f6SQF76UdPFM9pnY2ao6/OIkZSc+f1kuAVVV/r6hki7eo52+9H21CPHSjz/+eLHtknuyZ8+erVdeeUVHjx41P4N66dKlevDBB3X8+HEFBQWVzJsIAABwm3F0nrspzmRPnz5doaGhcnd3V3h4uMVMukVZuHCh6tWrJ3d3d911111aunSpxfqTJ0+qf//+qlSpkjw9PXXfffdp3759JXkIN6V27do5LORu2rRJgwYNcsi+cWtp2LCh+c/Ozs4qX7687rrrLnNbQThNSUnRrl271LJlS4tHa7Vu3Vrp6ek6duyYuS0sLKzQflauXKmOHTuqUuUq+vX1Ljr94yTlZ6UpP/fvIuvK/euo3Co3ML82JOVVqKMdO3cW2X/Xrl1q1KiROWAX1Jafn689e/Zc410AAABAaeHwkL1gwQJFR0drzJgx2rx5sxo1aqROnTopJSWlyP7r1q1Tr169NGDAAG3ZskVdu3ZV165dtX37dkkXz0J17dpVBw8e1HfffactW7aoWrVqioyMVEZGxo08tNtaYGCgPD09HV0GbgEuLi4Wr00mk0VbQaDOz8+3esxLg64kHT58WA888IAaNmyomXO+UHC/ySp3z7MXV+ZdKGKEK8vn2iAAAIDbmsND9qRJkzRw4EBFRUWpQYMGmjlzpjw9PfXJJ58U2X/KlCm677779PLLL6t+/fp666231LRpU02bNk2StG/fPv3222+aMWOGmjdvrrp162rGjBnKysrSf//73xt5aA7Vv39/rV69WlOmTJHJZJLJZNKBAwc0YMAAVa9eXR4eHqpbt66mTJlisd2FCxf0wgsvqGzZsipfvrxeeeUV9evXz+IRROfPn1fv3r3l5eWl4OBgffDBB4XOmoeGhmry5Mnm15dOKhUSEqLnnnvOYsKnI0eO6MEHH5S/v7+8vLx0xx13WFyhsGPHDj3wwAPy9fWVj4+P2rZtqwMHDki6eNb8nnvuUUBAgPz8/BQREXHFS4Fxa6tfv77Wr1+vS++CiY+Pl4+Pj6pUqXLF7RISEpSfn6+JEyeqbZtWcilXWRfSz1x1Xy7lQ5SdZHnWOvvYLtWvX/+Ktf3xxx8Wv+yLj4+Xk5OT6tata83hAQAAoBRwaMjOyclRQkKCIiMjzW1OTk6KjIzU+vXri9xm/fr1Fv0lqVOnTub+2dnZkiR3d3eLMd3c3LR27doix8zOzlZaWprFUtpNmTJFLVu21MCBA5WcnKzk5GRVqVJFVapU0cKFC7Vz506NHj1ar776qr766ivzdu+9956++OILzZkzR/Hx8UpLS9O3335rMXZ0dLTi4+P1/fffa8WKFfr111+vGWovnVRq3rx5+uWXXzRy5Ejz+ueff17Z2dlas2aNtm3bpvfee0/e3t6SpKSkJN19991yc3PTL7/8ooSEBD311FPmGaPPnz+vfv36ae3atfrtt99Uu3Ztde7cWefPn7fTu4nS4rnnntPRo0c1dOhQ7d69W999953GjBmj6OhoOTld+a+7WrVqKTc3V1OnTtXZE0flfnit0rf8dMX+kuTbopvSt8Xq/Jalyj2TpLSNi5W5b51GjXy5yP69e/eWu7u7+vXrp+3bt2vVqlUaOnSo+vTpw/3YAAAAt5Ayjtz56dOnlZeXV+gfmEFBQdq9e3eR25w4caLI/idOnJAk1atXT1WrVlVMTIw+/vhjeXl56YMPPtCxY8eUnJxc5Jhjx47Vm2++aYcjunn4+fnJ1dVVnp6eqlixorn90uOsXr261q9fr6+++kqPPfaYJGnq1KmKiYnRI488IkmaNm2axRnl8+fPa968efryyy/VsWNHSdKcOXNUqVKlq9Zz+Vnut99+W88++6w++ugjSVJiYqK6d+9uvte2Ro0a5v7Tp0+Xn5+f5s+fb75MuE6dOub1HTp0sNjXrFmzVLZsWa1evVoPPPDANd4pOIJhGDqbmauM7Avycisjf08Xi/uoi6ty5cpaunSpXn75ZTVq1EjlypXTgAED9M9//vOq2zVq1EiTJk3Se++9p5iYGNW8q7n8I/rp9I+TrriNZ52WKhc5SGkbv9GZlbNUpmyQnn71fbVv377o/p6eWr58uYYNG6bmzZvL09NT3bt316RJV94HAAAASh+HhuyS4OLiom+++UYDBgxQuXLl5OzsrMjISN1///260kTqMTExio6ONr9OS0tTSEjIjSr5hpo+fbo++eQTJSYmKisrSzk5OWrcuLEkKTU1VSdPnlSLFi3M/Z2dnRUWFma+3/XgwYPKzc216OPn53fNy11XrlypsWPHavfu3UpLS9OFCxf0999/KzMzU56ennrhhRc0ePBg/fzzz4qMjFT37t3NE15t3bpVbdu2LXRvboGTJ0/qn//8p+Li4pSSkqK8vDxlZmYqMTHxet4qlIDUrFwtSjimeesO68iZTHN7tXKe6tcqVN3DqsjP43+fc1xcXKExDh8+XKjt0p/tiIiIq06eWNSYkvTiiy/qxRdfNNfZcmysvO/qYH5OtvddkfK+y/IqGp8mneXTpLOcTJK7i7MmxHS8Yl2SdNddd+mXX365Ym0AAAAo/Rx6uXhAQICcnZ118uRJi/aTJ09anH29VMWKFa/ZPywsTFu3btW5c+eUnJysZcuW6a+//rI4O3opNzc3+fr6Wiy3ovnz52vEiBEaMGCAfv75Z23dulVRUVHKyckp0f1eOqnUokWLlJCQoOnTp0uSed9PP/20Dh48qD59+mjbtm1q1qyZpk6dKkny8PC46vj9+vXT1q1bNWXKFK1bt05bt25V+fLlS/y4YJvVe0+p5dhYvbVkpxIvCdiSlHgmU28t2amWY2O1eu8pB1X4P34eLprxZJhMkq51gr1g/cwnwyx+QQAAAIDbk0NDtqurq8LCwhQbG2tuy8/PV2xsrFq2bFnkNi1btrToL0krVqwosr+fn58CAwO1b98+/f7773r44YftewA3OVdXV+Xl5Zlfx8fHq1WrVvq/9u48rKpq/x/4+zAdDrOJqAiCiqiY4myCydVUNC7m8E1C1DLLUDAtQTNzuHXLBlHLTLJropVpllPmrKCoODE4IALhgIVGXQdG9QCf3x9c9s8toJKHIXq/nofnce/12Wutvd2r/LD2XnvSpEno3Lkz3NzclMXDgNLr1bhxYxw/flzZV1xcrHrfumXLljA1NVXF3Lx5E2lpaZX24+5FpZ544gm4u7sjKyurXJyzszOCg4OxYcMGTJs2DV988QWA0k84xcbGQq/XV1j/oUOH8Oqrr+Lpp59G+/btodVq8ccffzzEFaKasj/td4xbeQyF+mIISj93dbeyfYX6YoxbeaxOJNo+7o2wclwP6EyNS5Pte8rL9ulMjRE1rgf6uDeq+U4SERERUZ1T66uLv/766/jiiy+watUqpKSkYOLEicjPz8e4ceMAAGPHjsXMmTOV+ClTpmDHjh2IiIjAuXPnMG/ePJw4cQKhoaFKzPr16xETE6N8xmvAgAEYOnQoBg4cWOPnV5tcXV1x9OhRXLx4EX/88Qdat26NEydOYOfOnUhLS8Ps2bNVyTIATJ48GfPnz8fmzZuRmpqKKVOm4Pr168r7stbW1nj++ecRHh6O6OhoJCcnY/z48TAyMqr0ndq7F5U6f/48vvrqK0RGRqpipk6dip07d+LChQtISEhAdHS0skpzaGgocnJy8Nxzz+HEiRNIT0/HV199pXxbuHXr1vjqq6+QkpKCo0ePIigo6IGz31RzbhbqMfHr+NJE+gGftxIpTbYnfh2Pm4UV/1KlJvm4N0LczKcwx98DzR9Tf5Ku+WMWmOPvgSNvPsUEm4iIiIgUtf5OdkBAAH7//XfMmTMHV69eRadOnbBjxw5lcbPMzEzVqsBeXl5Ys2YN3nrrLbz55pto3bo1Nm3ahMcff1yJuXLlCl5//XX89ttvaNq0KcaOHYvZs2fX+LlVh6osGBUWFobnn38eHh4eKCwsxLlz55CYmIiAgABoNBoEBgZi0qRJ2L79/6+iPGPGDFy9ehVjx46FsbExJkyYAF9fXxgbGysxCxcuRHBwsPJJrenTp+Py5cuqFd3vdu+iUn369MH8+fMxduxYJaa4uBghISH45ZdfYGNjg0GDBmHRokUAgIYNG2Lfvn0IDw+Hj48PjI2N0alTJ3h7ewMAVqxYgQkTJqBLly5wdnbGe++9h7CwsEe+1mQYP8T/gsI7xeVmrysjAhTeKcaGhF8wzrtFtfbtYdjqTDHOuwVe8HLFjQI98m4XwUprAjsDLdZGRERERPWLRipbDexvLCcnB7a2trh582adeT+7qgtGGUpJSQnatWuHkSNH4p133qkwJj8/H82aNUNERATGjx9v8D7QX5eI4B8fxSDzWsFDJ9lA6WPYzR+zQEz4P5jIEhEREVGV1HY+V+sz2fRg+9N+x8Sv41F4p7hcWdmCUQt2pWLZ6K7wecTHVi9duoRdu3bBx8cHt2/fxqeffooLFy5g1KhRSkxiYiLOnTuHHj164ObNm3j77bcB4G/3zjs92PUCveqXQg9LAFy6VoAbBXo0sDQzfMeIiIiIiKpJrb+TTfdX0wtGGRkZISoqCt27d4e3tzdOnz6NPXv2KO9Hl1mwYAE8PT3Rv39/5OfnIzY2Fvb29o/UNtU/+beLHun4vEc8noiIiIiopnEmuw6r6oJR0JQuGBU386k//ei4s7MzDh06dN+Yzp07Iz4+/k/VT38vltpH+0+M1SMeT0RERERU0ziTXYcpC0Y95Musdy8YRVQXNLAwhctjFuU+f/UgGpSuN2Bnwe9OExEREdFfC5PsOkpEsOrwxT91bNShi+B6dlQXaDQaPO/l+qeOfcHblYueEREREdFfDpPsOqpswaiqpsp3LxhFVBeM6OoEnZkxHjZfNtIAOjNjDO/iVL0dIyIiIiKqBkyy6yguGEX1ha3OFMtGd4UGeGCiXVYeObprtXySjoiIiIioujHJrqO4YBTVJz7ujbByXA/oTI1Lk+17ysv26UyNETWuB/o84qfoiIiIiIhqCzOxOqpswajMKj4yrgHQnAtGUR3k494IcTOfwoaEXxB16KLq+9nNH7PAC96uGNHVCTbmvHeJiIiI6K+LSXYdVbZg1Dtbz1b5WC4YRXWVrc4U47xb4AUvV9wo0CPvdhGstCawszDlPUtERERE9QIfF6/DuGAU1VcajQYNLM3g/JgFGliaMcEmIiIionqDSXYdxgWjiIiIiIiI/lqYZNdxXDCKiIiIiIjor4PvZP8FcMEoIiIiIiKivwaNiFRl8eq/hZycHNja2uLmzZuwsbGp7e6oiAgXjCIiIiIiIqpEbedznMn+iylbMKqBpVltd4WIiIiIiIjuwXeyiYiIiIiIiAyESTYRERERERGRgTDJJiIiIiIiIjIQJtlEREREREREBsIkm4iIiIiIiMhAmGQTERERERERGQiTbCIiIiIiIiIDYZJNREREREREZCBMsomIiIiIiIgMhEk2ERERERERkYEwySYiIiIiIiIyECbZRERERERERAZiUtsdqItEBACQk5NTyz0hIiIiIiKiqijL48ryuprGJLsCubm5AABnZ+da7gkRERERERH9Gbm5ubC1ta3xdjVSW+l9HVZSUoKsrCxYW1tDo9HUdnf+tnJycuDs7IzLly/DxsamtrtDVGs4FohKcSwQleJYICpV2VgQEeTm5sLR0RFGRjX/hjRnsitgZGQEJyen2u4G/Y+NjQ3/B0IEjgWiMhwLRKU4FohKVTQWamMGuwwXPiMiIiIiIiIyECbZRERERERERAbCJJvqLK1Wi7lz50Kr1dZ2V4hqFccCUSmOBaJSHAtEperqWODCZ0REREREREQGwplsIiIiIiIiIgNhkk1ERERERERkIEyyiYiIiIiIiAyESTYZzIEDB+Dv7w9HR0doNBps2rRJKdPr9ZgxYwY6dOgAS0tLODo6YuzYscjKylLVce3aNQQFBcHGxgZ2dnYYP3488vLyVDGnTp3Ck08+CXNzczg7O+PDDz8s15f169ejbdu2MDc3R4cOHbBt27ZqOWeiitxvLNwrODgYGo0GixcvVu3nWKD64GHGQkpKCoYMGQJbW1tYWlqie/fuyMzMVMpv3bqFkJAQNGzYEFZWVhgxYgR+++03VR2ZmZnw8/ODhYUFHBwcEB4ejqKiIlVMTEwMunTpAq1WCzc3N0RFRVXHKRNV6EFjIS8vD6GhoXBycoJOp4OHhwciIyNVMRwLVB/Mnz8f3bt3h7W1NRwcHDB06FCkpqaqYmryXl+6dClcXV1hbm6Onj174tixYwY5TybZZDD5+fnw9PTE0qVLy5UVFBQgISEBs2fPRkJCAjZs2IDU1FQMGTJEFRcUFITk5GTs3r0bW7duxYEDBzBhwgSlPCcnBwMHDoSLiwvi4+Px0UcfYd68eVi+fLkSc/jwYQQGBmL8+PFITEzE0KFDMXToUJw5c6b6Tp7oLvcbC3fbuHEjjhw5AkdHx3JlHAtUHzxoLGRkZKB3795o27YtYmJicOrUKcyePRvm5uZKzGuvvYYff/wR69evx/79+5GVlYXhw4cr5cXFxfDz88OdO3dw+PBhrFq1ClFRUZgzZ44Sc+HCBfj5+aFv375ISkrC1KlT8dJLL2Hnzp3Vd/JEd3nQWHj99dexY8cOfP3110hJScHUqVMRGhqKLVu2KDEcC1Qf7N+/HyEhIThy5Ah2794NvV6PgQMHIj8/X4mpqXt93bp1eP311zF37lwkJCTA09MTvr6+yM7OfvQTFaJqAEA2btx435hjx44JALl06ZKIiJw9e1YAyPHjx5WY7du3i0ajkV9//VVERD777DNp0KCB3L59W4mZMWOGtGnTRtkeOXKk+Pn5qdrq2bOnvPLKK496WkRVVtlY+OWXX6RZs2Zy5swZcXFxkUWLFillHAtUH1U0FgICAmT06NGVHnPjxg0xNTWV9evXK/tSUlIEgMTFxYmIyLZt28TIyEiuXr2qxCxbtkxsbGyU8TF9+nRp3759ubZ9fX0f9bSIqqyisdC+fXt5++23Vfu6dOkis2bNEhGOBaq/srOzBYDs379fRGr2Xu/Ro4eEhIQo28XFxeLo6Cjz589/5PPiTDbVmps3b0Kj0cDOzg4AEBcXBzs7O3Tr1k2J6d+/P4yMjHD06FElpk+fPjAzM1NifH19kZqaiuvXrysx/fv3V7Xl6+uLuLi4aj4joodTUlKCMWPGIDw8HO3bty9XzrFAfwclJSX46aef4O7uDl9fXzg4OKBnz56qx2jj4+Oh1+tV93Hbtm3RvHlz5T6Oi4tDhw4d0LhxYyXG19cXOTk5SE5OVmI4Fqgu8/LywpYtW/Drr79CRBAdHY20tDQMHDgQAMcC1V83b94EADz22GMAau5ev3PnDuLj41UxRkZG6N+/v0HGA5NsqhW3bt3CjBkzEBgYCBsbGwDA1atX4eDgoIozMTHBY489hqtXryoxdw8oAMr2g2LKyolq2wcffAATExO8+uqrFZZzLNDfQXZ2NvLy8vD+++9j0KBB2LVrF4YNG4bhw4dj//79AErvYTMzM+WXsWXuvo8fZSzk5OSgsLCwOk6PqEqWLFkCDw8PODk5wczMDIMGDcLSpUvRp08fABwLVD+VlJRg6tSp8Pb2xuOPPw6g5u71P/74A8XFxdX27ySTR66BqIr0ej1GjhwJEcGyZctquztENSo+Ph4ff/wxEhISoNFoars7RLWmpKQEAPDMM8/gtddeAwB06tQJhw8fRmRkJHx8fGqze0Q1asmSJThy5Ai2bNkCFxcXHDhwACEhIXB0dCw3G0dUX4SEhODMmTM4ePBgbXfF4DiTTTWqLMG+dOkSdu/ercxiA0CTJk3KLTRQVFSEa9euoUmTJkrMvasLlm0/KKasnKg2xcbGIjs7G82bN4eJiQlMTExw6dIlTJs2Da6urgA4Fujvwd7eHiYmJvDw8FDtb9eunbK6eJMmTXDnzh3cuHFDFXP3ffwoY8HGxgY6nc5g50T0ZxQWFuLNN9/EwoUL4e/vj44dOyI0NBQBAQFYsGABAI4Fqn9CQ0OxdetWREdHw8nJSdlfU/e6vb09jI2Nq+3fSUyyqcaUJdjp6enYs2cPGjZsqCrv1asXbty4gfj4eGXfvn37UFJSgp49eyoxBw4cgF6vV2J2796NNm3aoEGDBkrM3r17VXXv3r0bvXr1qq5TI3poY8aMwalTp5CUlKT8ODo6Ijw8XFnxkmOB/g7MzMzQvXv3cp9uSUtLg4uLCwCga9euMDU1Vd3HqampyMzMVO7jXr164fTp06pfTJX9ErcsgedYoLpMr9dDr9fDyEj9z3JjY2PliQ+OBaovRAShoaHYuHEj9u3bhxYtWqjKa+peNzMzQ9euXVUxJSUl2Lt3r2HGwyMvnUb0P7m5uZKYmCiJiYkCQBYuXCiJiYly6dIluXPnjgwZMkScnJwkKSlJrly5ovzcvTryoEGDpHPnznL06FE5ePCgtG7dWgIDA5XyGzduSOPGjWXMmDFy5swZWbt2rVhYWMjnn3+uxBw6dEhMTExkwYIFkpKSInPnzhVTU1M5ffp0jV4P+vu631ioyL2ri4twLFD98KCxsGHDBjE1NZXly5dLenq6LFmyRIyNjSU2NlapIzg4WJo3by779u2TEydOSK9evaRXr15KeVFRkTz++OMycOBASUpKkh07dkijRo1k5syZSsz58+fFwsJCwsPDJSUlRZYuXSrGxsayY8eOmrsY9Lf2oLHg4+Mj7du3l+joaDl//rysXLlSzM3N5bPPPlPq4Fig+mDixIlia2srMTExqnygoKBAiampe33t2rWi1WolKipKzp49KxMmTBA7OzvVquV/FpNsMpjo6GgBUO7n+eeflwsXLlRYBkCio6OVOv773/9KYGCgWFlZiY2NjYwbN05yc3NV7Zw8eVJ69+4tWq1WmjVrJu+//365vnz33Xfi7u4uZmZm0r59e/npp5+q+/SJFPcbCxWpKMnmWKD64GHGwooVK8TNzU3Mzc3F09NTNm3apKqjsLBQJk2aJA0aNBALCwsZNmyYXLlyRRVz8eJFGTx4sOh0OrG3t5dp06aJXq8v15dOnTqJmZmZtGzZUlauXFldp01UzoPGwpUrV+SFF14QR0dHMTc3lzZt2khERISUlJQodXAsUH1QWT5w931Yk/f6kiVLpHnz5mJmZiY9evSQI0eOGOQ8Nf87WSIiIiIiIiJ6RHwnm4iIiIiIiMhAmGQTERERERERGQiTbCIiIiIiIiIDYZJNREREREREZCBMsomIiIiIiIgMhEk2ERERERERkYEwySYiIiIiIiIyECbZRERERERERAbCJJuIiMiAYmJioNFocOPGDQBAVFQU7OzsarVPhnTv+VWXefPmoVOnTtXaRpm9e/eiXbt2KC4urta2XV1dsXjx4keqIzIyEv7+/obpEBERVQsm2URE9KfExcXB2NgYfn5+td2VGrN161b4+PjA2toaFhYW6N69O6Kiou57TEBAANLS0qq1X2WJb9lP48aNMWLECJw/f75a2zUUjUaDTZs2qfaFhYVh7969NdL+9OnT8dZbb8HY2Njgda9atQq9e/c2WH0vvvgiEhISEBsba7A6iYjIsJhkExHRn7JixQpMnjwZBw4cQFZWVrW2JSIoKiqq1jYeZMmSJXjmmWfg7e2No0eP4tSpU3juuecQHByMsLCwSo/T6XRwcHCokT6mpqYiKysL69evR3JyMvz9/ZXZ2bvVhev5IFZWVmjYsGG1t3Pw4EFkZGRgxIgR1VL/5s2bMWTIEIPVZ2ZmhlGjRuGTTz4xWJ1ERGRYTLKJiKjK8vLysG7dOkycOBF+fn6q2dxRo0YhICBAFa/X62Fvb4/Vq1cDAEpKSjB//ny0aNECOp0Onp6e+P7775X4spnZ7du3o2vXrtBqtUoy9Mwzz6Bx48awsrJC9+7dsWfPHlVbV65cgZ+fH3Q6HVq0aIE1a9aUe0z3xo0beOmll9CoUSPY2NigX79+OHnyZKXne/nyZUybNg1Tp07Fe++9Bw8PD7i5uWHatGn46KOPEBERgaNHj1Z47N2Pi6elpUGj0eDcuXOqmEWLFqFVq1bK9pkzZzB48GBYWVmhcePGGDNmDP74449K+1fGwcEBTZs2RZ8+fTBnzhycPXsWP//8c6XX8/bt23j11Vfh4OAAc3Nz9O7dG8ePH1fVuW3bNri7u0On06Fv3764ePGiqryiR6sXL14MV1dX1b4vv/wS7du3h1arRdOmTREaGgoAStywYcOg0WiU7XvrLSkpwdtvvw0nJydotVp06tQJO3bsUMovXrwIjUaDDRs2oG/fvrCwsICnpyfi4uLue83Wrl2LAQMGwNzcvNKYjIwMtGzZEqGhoRARAMAXX3wBZ2dnWFhYYNiwYVi4cGG51wJu3bqFXbt2qZLsgoICvPjii7C2tkbz5s2xfPly1TEzZsyAu7s7LCws0LJlS8yePRt6vV4V4+/vjy1btqCwsPC+50ZERLWDSTYREVXZd999h7Zt26JNmzYYPXo0vvzySyX5CAoKwo8//oi8vDwlfufOnSgoKMCwYcMAAPPnz8fq1asRGRmJ5ORkvPbaaxg9ejT279+vaueNN97A+++/j5SUFHTs2BF5eXl4+umnsXfvXiQmJmLQoEHw9/dHZmamcszYsWORlZWFmJgY/PDDD1i+fDmys7NV9T777LPIzs7G9u3bER8fjy5duuCpp57CtWvXKjzf77//Hnq9vsIZ61deeQVWVlb49ttvH3jd3N3d0a1bN3zzzTeq/d988w1GjRoFoPQXAP369UPnzp1x4sQJ7NixA7/99htGjhz5wPrvptPpAAB37txR9t17PadPn44ffvgBq1atQkJCAtzc3ODr66tch8uXL2P48OHw9/dHUlISXnrpJbzxxhtV6gcALFu2DCEhIZgwYQJOnz6NLVu2wM3NDQCUpH7lypW4cuVKuSS/zMcff4yIiAgsWLAAp06dgq+vL4YMGYL09HRV3KxZsxAWFoakpCS4u7sjMDDwvrP2sbGx6NatW6Xlp06dQu/evTFq1Ch8+umn0Gg0OHToEIKDgzFlyhQkJSVhwIABePfdd8sdu3fvXjRr1gxt27ZV9kVERKBbt25ITEzEpEmTMHHiRKSmpirl1tbWiIqKwtmzZ/Hxxx/jiy++wKJFi1T1duvWDUVFRZX+YoeIiGqZEBERVZGXl5csXrxYRET0er3Y29tLdHS0anv16tVKfGBgoAQEBIiIyK1bt8TCwkIOHz6sqnP8+PESGBgoIiLR0dECQDZt2vTAvrRv316WLFkiIiIpKSkCQI4fP66Up6enCwBZtGiRiIjExsaKjY2N3Lp1S1VPq1at5PPPP6+wjeDgYLG1ta20Dx07dpTBgwer+n79+nUREVm5cqXq2EWLFkmrVq2U7dTUVAEgKSkpIiLyzjvvyMCBA1X1X758WQBIampqhe3f22ZWVpZ4eXlJs2bN5Pbt2xVez7y8PDE1NZVvvvlG2Xfnzh1xdHSUDz/8UEREZs6cKR4eHqq2ZsyYoWpr7ty54unpqYpZtGiRuLi4KNuOjo4ya9asCvsuIgJANm7cqNp3b72Ojo7y7rvvqmK6d+8ukyZNEhGRCxcuCAD5z3/+o5QnJyerrm1FbG1tVffq3W0fOnRIGjRoIAsWLFCVBwQEiJ+fn2pfUFBQuXvk5ZdflrCwMGXbxcVFRo8erWyXlJSIg4ODLFu2rNL+ffTRR9K1a9dy+xs0aCBRUVGVHkdERLWHM9lERFQlqampOHbsGAIDAwEAJiYmCAgIwIoVK5TtkSNHKrO1+fn52Lx5M4KCggAAP//8MwoKCjBgwABYWVkpP6tXr0ZGRoaqrXtnGPPy8hAWFoZ27drBzs4OVlZWSElJUWayU1NTYWJigi5duijHuLm5oUGDBsr2yZMnkZeXh4YNG6rav3DhQrn2q8Nzzz2Hixcv4siRIwBKZ7G7dOmizHaePHkS0dHRqr6VlT2of05OTrC0tISjoyPy8/Pxww8/wMzMTCm/+3pmZGRAr9fD29tb2WdqaooePXogJSUFAJCSkoKePXuq2ujVq1eVzjc7OxtZWVl46qmnqnTc3XJycpCVlaXqKwB4e3srfS3TsWNH5c9NmzZV+lCZwsLCCh8Vz8zMxIABAzBnzhxMmzZNVZaamooePXqo9t27LSL48ccfy72PfXf/NBoNmjRpourfunXr4O3tjSZNmsDKygpvvfWW6kmNMjqdDgUFBZWeFxER1R6T2u4AERH9taxYsQJFRUVwdHRU9okItFotPv30U9ja2iIoKAg+Pj7Izs7G7t27odPpMGjQIABQHiP/6aef0KxZM1XdWq1WtW1paanaDgsLw+7du7FgwQK4ublBp9Ph//7v/1SPRD9IXl4emjZtipiYmHJllX1qy93dHTdv3kRWVpbqvIHSx7EzMjLQt2/fh2q/SZMm6NevH9asWYMnnngCa9aswcSJE1X98/f3xwcffFDu2LKksTKxsbGwsbGBg4MDrK2ty5Xfez0NwcjISHlVoMzd7xCXPbZeU0xNTZU/azQaAKXvc1fG3t4e169fL7e/UaNGcHR0xLfffosXX3wRNjY2VerHsWPHUFRUBC8vr0r7V9bHsv7FxcUhKCgI//rXv+Dr6wtbW1usXbsWERER5eq/du0aGjVqVKU+ERFRzeBMNhERPbSioiKsXr0aERERSEpKUn5OnjypJCQA4OXlBWdnZ6xbtw7ffPMNnn32WSW58PDwgFarRWZmJtzc3FQ/zs7O923/0KFDeOGFFzBs2DB06NABTZo0US3E1aZNGxQVFSExMVHZ9/PPP6uSqC5duuDq1aswMTEp1769vX2F7Y4YMQKmpqYVJjuRkZHIz89XZvYfRlBQENatW4e4uDicP38ezz33nKp/ycnJcHV1Lde/ByXJLVq0QKtWrSpMsO/VqlUrmJmZ4dChQ8o+vV6P48ePw8PDAwDQrl07HDt2THVc2Qx8mUaNGuHq1auqRDspKUn5s7W1NVxdXe/7OS5TU9MKV0EvY2NjA0dHR1VfgdL7oayvf1bnzp1x9uzZcvt1Oh22bt0Kc3Nz+Pr6Ijc3Vylr06ZNuXfH793evHkz/Pz8qvRZsMOHD8PFxQWzZs1Ct27d0Lp1a1y6dKlcXEZGBm7duoXOnTs/dN1ERFRzmGQTEdFD27p1K65fv47x48fj8ccfV/2MGDFCeWQcKF1lPDIyErt371YeFQdKk66wsDC89tprWLVqFTIyMpCQkIAlS5Zg1apV922/devW2LBhg5LYjxo1SjVL2bZtW/Tv3x8TJkzAsWPHkJiYiAkTJkCn0ymzmv3790evXr0wdOhQ7Nq1CxcvXsThw4cxa9YsnDhxosJ2mzdvjg8//BCLFy/GrFmzcO7cOWRkZGDhwoWYPn06pk2bVu6x6vsZPnw4cnNzMXHiRPTt21c1Ox4SEoJr164hMDAQx48fR0ZGBnbu3Ilx48bdNxGtKktLS0ycOBHh4eHYsWMHzp49i5dffhkFBQUYP348ACA4OBjp6ekIDw9Hamoq1qxZU+674P/4xz/w+++/48MPP0RGRgaWLl2K7du3q2LmzZuHiIgIfPLJJ0hPT1f+vsuUJeFXr16tcFYZAMLDw/HBBx9g3bp1SE1NxRtvvIGkpCRMmTLlka6Dr68vDh48WGGZpaUlfvrpJ5iYmGDw4MHKUxiTJ0/Gtm3bsHDhQqSnp+Pzzz/H9u3blXsMALZs2VLlT3e1bt0amZmZWLt2LTIyMvDJJ59g48aN5eJiY2PRsmVL1Yr0RERUh9TuK+FERPRX8s9//lOefvrpCsuOHj0qAOTkyZMiInL27FkBIC4uLlJSUqKKLSkpkcWLF0ubNm3E1NRUGjVqJL6+vrJ//34RKb+QV5kLFy5I3759RafTibOzs3z66afi4+MjU6ZMUWKysrJk8ODBotVqxcXFRdasWSMODg4SGRmpxOTk5MjkyZPF0dFRTE1NxdnZWYKCgiQzM/O+579582Z58sknxdLSUszNzaVr167y5ZdfqmIetPBZmZEjRwqAcseLiKSlpcmwYcPEzs5OdDqdtG3bVqZOnVruOlbW5sOWFxYWyuTJk8Xe3l60Wq14e3vLsWPHVDE//vijuLm5iVarlSeffFK+/PLLcnUtW7ZMnJ2dxdLSUsaOHSvvvvuuauEzEZHIyEjl77tp06YyefJkpWzLli3i5uYmJiYmynH3LnxWXFws8+bNk2bNmompqal4enrK9u3blfKyhc8SExOVfdevXxcAyqJ8Ffnvf/8r5ubmcu7cOWXfvW3n5uaKl5eX9OnTR/Ly8kREZPny5dKsWTPR6XQydOhQ+fe//y1NmjQREZGff/5ZtFqtElvGxcVFWYCvjKenp8ydO1fZDg8Pl4YNG4qVlZUEBATIokWLyt0/AwcOlPnz51d6TkREVLs0Ive8SEVERFSP/PLLL3B2dsaePXseafEtqr/Cw8ORk5ODzz///E/X8fLLL+PcuXOIjY3FwoULsWfPHmzbts2AvSyVnJyMfv36IS0tDba2tgavn4iIHh0XPiMionpl3759yMvLQ4cOHXDlyhVMnz4drq6u6NOnT213jeqoWbNm4bPPPkNJSQmMjB7uTboFCxZgwIABsLS0xPbt27Fq1Sp89tlnAEpXeZ85c2a19PXKlStYvXo1E2wiojqMM9lERFSv7Ny5E9OmTcP58+dhbW0NLy8vLF68GC4uLrXdNapHRo4ciZiYGOTm5qJly5aYPHkygoODa7tbRERUBzDJJiIiIiIiIjIQri5OREREREREZCBMsomIiIiIiIgMhEk2ERERERERkYEwySYiIiIiIiIyECbZRERERERERAbCJJuIiIiIiIjIQJhkExERERERERkIk2wiIiIiIiIiA2GSTURERERERGQg/w/xz0qvpcnGYAAAAABJRU5ErkJggg==",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-08_14-47_plots/water_efficiency_vs_production.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-08_14-47_plots/water_need_vs_oil_production.png\n",
|
||
" Variety Technique Technique String \\\n",
|
||
"0 nocellara_delletna 3 tradizionale \n",
|
||
"1 nocellara_delletna 1 intensiva \n",
|
||
"2 nocellara_delletna 2 superintensiva \n",
|
||
"3 leccino 1 intensiva \n",
|
||
"4 leccino 2 superintensiva \n",
|
||
"5 leccino 3 tradizionale \n",
|
||
"6 frantoio 2 superintensiva \n",
|
||
"7 frantoio 3 tradizionale \n",
|
||
"8 frantoio 1 intensiva \n",
|
||
"9 coratina 1 intensiva \n",
|
||
"10 coratina 2 superintensiva \n",
|
||
"11 coratina 3 tradizionale \n",
|
||
"12 taggiasca 3 tradizionale \n",
|
||
"13 taggiasca 2 superintensiva \n",
|
||
"14 taggiasca 1 intensiva \n",
|
||
"15 pendolino 1 intensiva \n",
|
||
"16 pendolino 2 superintensiva \n",
|
||
"17 pendolino 3 tradizionale \n",
|
||
"18 moraiolo 2 superintensiva \n",
|
||
"19 moraiolo 1 intensiva \n",
|
||
"20 moraiolo 3 tradizionale \n",
|
||
"\n",
|
||
" Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n",
|
||
"0 9564.638687 2088.362004 \n",
|
||
"1 13699.079622 2991.183032 \n",
|
||
"2 17826.710664 3892.059753 \n",
|
||
"3 16432.379678 3229.053194 \n",
|
||
"4 20528.499013 4033.942398 \n",
|
||
"5 10937.982122 2149.449585 \n",
|
||
"6 24621.040119 6047.876212 \n",
|
||
"7 13740.739760 3375.103688 \n",
|
||
"8 20550.900635 5047.942655 \n",
|
||
"9 16429.706879 4215.265516 \n",
|
||
"10 19164.700743 4916.649709 \n",
|
||
"11 12318.510310 3160.037128 \n",
|
||
"12 6839.506230 1381.247995 \n",
|
||
"13 16433.741502 3319.210170 \n",
|
||
"14 10968.603159 2215.371493 \n",
|
||
"15 13705.431414 2468.678455 \n",
|
||
"16 19183.689269 3455.879324 \n",
|
||
"17 10960.549241 1974.357984 \n",
|
||
"18 17793.971752 3885.415851 \n",
|
||
"19 13144.222436 2870.020002 \n",
|
||
"20 8765.195655 1913.745255 \n",
|
||
"\n",
|
||
" Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n",
|
||
"0 32997.227891 0.218342 \n",
|
||
"1 33079.012125 0.218349 \n",
|
||
"2 33118.708645 0.218327 \n",
|
||
"3 25013.303736 0.196506 \n",
|
||
"4 24989.459147 0.196504 \n",
|
||
"5 24981.219100 0.196512 \n",
|
||
"6 28874.473543 0.245639 \n",
|
||
"7 29003.452741 0.245628 \n",
|
||
"8 28921.261327 0.245631 \n",
|
||
"9 38270.638622 0.256564 \n",
|
||
"10 38264.650562 0.256547 \n",
|
||
"11 38253.676395 0.256528 \n",
|
||
"12 26219.134374 0.201951 \n",
|
||
"13 26253.317778 0.201975 \n",
|
||
"14 26284.027794 0.201974 \n",
|
||
"15 26154.359691 0.180124 \n",
|
||
"16 26153.199618 0.180147 \n",
|
||
"17 26152.823801 0.180133 \n",
|
||
"18 32561.911109 0.218356 \n",
|
||
"19 32577.899255 0.218348 \n",
|
||
"20 32594.860153 0.218335 \n",
|
||
"\n",
|
||
" Water Efficiency (L oil/m³ water) \n",
|
||
"0 0.063289 \n",
|
||
"1 0.090425 \n",
|
||
"2 0.117518 \n",
|
||
"3 0.129093 \n",
|
||
"4 0.161426 \n",
|
||
"5 0.086043 \n",
|
||
"6 0.209454 \n",
|
||
"7 0.116369 \n",
|
||
"8 0.174541 \n",
|
||
"9 0.110144 \n",
|
||
"10 0.128491 \n",
|
||
"11 0.082607 \n",
|
||
"12 0.052681 \n",
|
||
"13 0.126430 \n",
|
||
"14 0.084286 \n",
|
||
"15 0.094389 \n",
|
||
"16 0.132140 \n",
|
||
"17 0.075493 \n",
|
||
"18 0.119324 \n",
|
||
"19 0.088097 \n",
|
||
"20 0.058713 \n",
|
||
"Comparison by Variety:\n",
|
||
" Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n",
|
||
"Variety \n",
|
||
"nocellara_delletna 13696.683690 2990.507461 \n",
|
||
"leccino 15971.162702 3138.439782 \n",
|
||
"frantoio 19648.631813 4826.360700 \n",
|
||
"coratina 15974.164423 4098.136472 \n",
|
||
"taggiasca 11412.636779 2305.011278 \n",
|
||
"pendolino 14617.432649 2633.129635 \n",
|
||
"moraiolo 13232.961913 2889.399172 \n",
|
||
"\n",
|
||
" Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n",
|
||
"Variety \n",
|
||
"nocellara_delletna 33064.983905 0.218338 \n",
|
||
"leccino 24994.676451 0.196507 \n",
|
||
"frantoio 28932.932409 0.245633 \n",
|
||
"coratina 38262.995517 0.256548 \n",
|
||
"taggiasca 26252.184893 0.201970 \n",
|
||
"pendolino 26153.461822 0.180136 \n",
|
||
"moraiolo 32578.228327 0.218349 \n",
|
||
"\n",
|
||
" Water Efficiency (L oil/m³ water) \n",
|
||
"Variety \n",
|
||
"nocellara_delletna 0.090443 \n",
|
||
"leccino 0.125564 \n",
|
||
"frantoio 0.166812 \n",
|
||
"coratina 0.107104 \n",
|
||
"taggiasca 0.087803 \n",
|
||
"pendolino 0.100680 \n",
|
||
"moraiolo 0.088691 \n",
|
||
"\n",
|
||
"Best Varieties by Water Efficiency:\n",
|
||
" Variety Avg Olive Production (kg/ha) \\\n",
|
||
"2 frantoio 19648.631813 \n",
|
||
"1 leccino 15971.162702 \n",
|
||
"3 coratina 15974.164423 \n",
|
||
"5 pendolino 14617.432649 \n",
|
||
"0 nocellara_delletna 13696.683690 \n",
|
||
"\n",
|
||
" Avg Oil Production (L/ha) Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n",
|
||
"2 4826.360700 28932.932409 0.245633 \n",
|
||
"1 3138.439782 24994.676451 0.196507 \n",
|
||
"3 4098.136472 38262.995517 0.256548 \n",
|
||
"5 2633.129635 26153.461822 0.180136 \n",
|
||
"0 2990.507461 33064.983905 0.218338 \n",
|
||
"\n",
|
||
" Water Efficiency (L oil/m³ water) \n",
|
||
"2 0.166812 \n",
|
||
"1 0.125564 \n",
|
||
"3 0.107104 \n",
|
||
"5 0.100680 \n",
|
||
"0 0.090443 \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"simulated_data = pd.read_parquet(f\"{data_dir}olive_training_dataset.parquet\")\n",
|
||
"olive_varieties = pd.read_parquet(f\"{data_dir}olive_varieties.parquet\")\n",
|
||
"# Esecuzione dell'analisi\n",
|
||
"comparison_data = prepare_comparison_data(simulated_data, olive_varieties)\n",
|
||
"\n",
|
||
"# Genera i grafici\n",
|
||
"plot_variety_comparison(comparison_data, 'Avg Olive Production (kg/ha)')\n",
|
||
"plot_variety_comparison(comparison_data, 'Avg Oil Production (L/ha)')\n",
|
||
"plot_variety_comparison(comparison_data, 'Avg Water Need (m³/ha)')\n",
|
||
"plot_variety_comparison(comparison_data, 'Oil Efficiency (L/kg)')\n",
|
||
"plot_variety_comparison(comparison_data, 'Water Efficiency (L oil/m³ water)')\n",
|
||
"plot_efficiency_vs_production(comparison_data)\n",
|
||
"plot_water_efficiency_vs_production(comparison_data)\n",
|
||
"plot_water_need_vs_oil_production(comparison_data)\n",
|
||
"\n",
|
||
"technique_data = analyze_by_technique(simulated_data, olive_varieties)\n",
|
||
"\n",
|
||
"print(technique_data)\n",
|
||
"\n",
|
||
"# Stampa un sommario statistico\n",
|
||
"print(\"Comparison by Variety:\")\n",
|
||
"print(comparison_data.set_index('Variety'))\n",
|
||
"print(\"\\nBest Varieties by Water Efficiency:\")\n",
|
||
"print(comparison_data.sort_values('Water Efficiency (L oil/m³ water)', ascending=False).head())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "bbe87b415168368",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def prepare_transformer_data(df, olive_varieties_df):\n",
|
||
" # Crea una copia del DataFrame per evitare modifiche all'originale\n",
|
||
" df = df.copy()\n",
|
||
"\n",
|
||
" # Definisci le feature\n",
|
||
" temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n",
|
||
" static_features = ['ha'] # Feature statiche base\n",
|
||
" target_features = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n",
|
||
"\n",
|
||
" # Ottieni le varietà pulite\n",
|
||
" all_varieties = olive_varieties_df['Varietà di Olive'].unique()\n",
|
||
" varieties = [clean_column_name(variety) for variety in all_varieties]\n",
|
||
"\n",
|
||
" # Crea la struttura delle feature per ogni varietà\n",
|
||
" variety_features = [\n",
|
||
" 'tech', 'pct', 'prod_t_ha', 'oil_prod_t_ha', 'oil_prod_l_ha',\n",
|
||
" 'min_yield_pct', 'max_yield_pct', 'min_oil_prod_l_ha', 'max_oil_prod_l_ha',\n",
|
||
" 'avg_oil_prod_l_ha', 'l_per_t', 'min_l_per_t', 'max_l_per_t', 'avg_l_per_t'\n",
|
||
" ]\n",
|
||
"\n",
|
||
" # Prepara dizionari per le nuove colonne\n",
|
||
" new_columns = {}\n",
|
||
"\n",
|
||
" # Prepara le feature per ogni varietà\n",
|
||
" for variety in varieties:\n",
|
||
" # Feature esistenti\n",
|
||
" for feature in variety_features:\n",
|
||
" col_name = f\"{variety}_{feature}\"\n",
|
||
" if col_name in df.columns:\n",
|
||
" if feature != 'tech': # Non includere la colonna tech direttamente\n",
|
||
" static_features.append(col_name)\n",
|
||
"\n",
|
||
" # Feature binarie per le tecniche di coltivazione\n",
|
||
" for technique in ['tradizionale', 'intensiva', 'superintensiva']:\n",
|
||
" col_name = f\"{variety}_{technique}\"\n",
|
||
" new_columns[col_name] = df[f\"{variety}_tech\"].notna() & (\n",
|
||
" df[f\"{variety}_tech\"].str.lower() == technique\n",
|
||
" ).fillna(False)\n",
|
||
" static_features.append(col_name)\n",
|
||
"\n",
|
||
" # Aggiungi tutte le nuove colonne in una volta sola\n",
|
||
" df = pd.concat([df] + [pd.Series(v, name=k) for k, v in new_columns.items()], axis=1)\n",
|
||
"\n",
|
||
" # Prepara X e y\n",
|
||
" X_temporal = df[temporal_features].values\n",
|
||
" X_static = df[static_features].values\n",
|
||
" y = df[target_features].values\n",
|
||
"\n",
|
||
" print(f\"Dataset completo - Temporal: {X_temporal.shape}, Static: {X_static.shape}, Target: {y.shape}\")\n",
|
||
"\n",
|
||
" # Split dei dati (usando indici casuali per una migliore distribuzione)\n",
|
||
" indices = np.random.permutation(len(X_temporal))\n",
|
||
" train_idx = int(len(indices) * 0.65) # 65% training\n",
|
||
" val_idx = int(len(indices) * 0.85) # 20% validation\n",
|
||
" # Il resto rimane 15% test\n",
|
||
"\n",
|
||
" train_indices = indices[:train_idx]\n",
|
||
" val_indices = indices[train_idx:val_idx]\n",
|
||
" test_indices = indices[val_idx:]\n",
|
||
"\n",
|
||
" # Split dei dati\n",
|
||
" X_temporal_train = X_temporal[train_indices]\n",
|
||
" X_temporal_val = X_temporal[val_indices]\n",
|
||
" X_temporal_test = X_temporal[test_indices]\n",
|
||
"\n",
|
||
" X_static_train = X_static[train_indices]\n",
|
||
" X_static_val = X_static[val_indices]\n",
|
||
" X_static_test = X_static[test_indices]\n",
|
||
"\n",
|
||
" y_train = y[train_indices]\n",
|
||
" y_val = y[val_indices]\n",
|
||
" y_test = y[test_indices]\n",
|
||
"\n",
|
||
" # Standardizzazione\n",
|
||
" scaler_temporal = StandardScaler()\n",
|
||
" scaler_static = StandardScaler()\n",
|
||
" scaler_y = StandardScaler()\n",
|
||
"\n",
|
||
" # Standardizzazione dei dati\n",
|
||
" X_temporal_train = scaler_temporal.fit_transform(X_temporal_train)\n",
|
||
" X_temporal_val = scaler_temporal.transform(X_temporal_val)\n",
|
||
" X_temporal_test = scaler_temporal.transform(X_temporal_test)\n",
|
||
"\n",
|
||
" X_static_train = scaler_static.fit_transform(X_static_train)\n",
|
||
" X_static_val = scaler_static.transform(X_static_val)\n",
|
||
" X_static_test = scaler_static.transform(X_static_test)\n",
|
||
"\n",
|
||
" y_train = scaler_y.fit_transform(y_train)\n",
|
||
" y_val = scaler_y.transform(y_val)\n",
|
||
" y_test = scaler_y.transform(y_test)\n",
|
||
"\n",
|
||
" print(\"\\nShape dopo lo split e standardizzazione:\")\n",
|
||
" print(f\"Train - Temporal: {X_temporal_train.shape}, Static: {X_static_train.shape}, Target: {y_train.shape}\")\n",
|
||
" print(f\"Val - Temporal: {X_temporal_val.shape}, Static: {X_static_val.shape}, Target: {y_val.shape}\")\n",
|
||
" print(f\"Test - Temporal: {X_temporal_test.shape}, Static: {X_static_test.shape}, Target: {y_test.shape}\")\n",
|
||
"\n",
|
||
" # Reshape per il transformer (aggiunge la dimensione del sequence length = 1)\n",
|
||
" X_temporal_train = X_temporal_train.reshape(X_temporal_train.shape[0], 1, -1)\n",
|
||
" X_temporal_val = X_temporal_val.reshape(X_temporal_val.shape[0], 1, -1)\n",
|
||
" X_temporal_test = X_temporal_test.reshape(X_temporal_test.shape[0], 1, -1)\n",
|
||
"\n",
|
||
" # Prepara i dizionari di input\n",
|
||
" train_data = {'temporal': X_temporal_train, 'static': X_static_train}\n",
|
||
" val_data = {'temporal': X_temporal_val, 'static': X_static_val}\n",
|
||
" test_data = {'temporal': X_temporal_test, 'static': X_static_test}\n",
|
||
"\n",
|
||
" # Salva gli scaler\n",
|
||
" joblib.dump(scaler_temporal, os.path.join(base_project_dir, f'{execute_name}_scaler_temporal.joblib'))\n",
|
||
" joblib.dump(scaler_static, os.path.join(base_project_dir, f'{execute_name}_scaler_static.joblib'))\n",
|
||
" joblib.dump(scaler_y, os.path.join(base_project_dir, f'{execute_name}_scaler_y.joblib'))\n",
|
||
"\n",
|
||
" return (train_data, y_train), (val_data, y_val), (test_data, y_test), (scaler_temporal, scaler_static, scaler_y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "9c4d5f0f3fafdc2d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Dataset completo - Temporal: (4000000, 3), Static: (4000000, 113), Target: (4000000, 5)\n",
|
||
"\n",
|
||
"Shape dopo lo split e standardizzazione:\n",
|
||
"Train - Temporal: (2600000, 3), Static: (2600000, 113), Target: (2600000, 5)\n",
|
||
"Val - Temporal: (800000, 3), Static: (800000, 113), Target: (800000, 5)\n",
|
||
"Test - Temporal: (600000, 3), Static: (600000, 113), Target: (600000, 5)\n",
|
||
"Temporal data shape: (2600000, 1, 3)\n",
|
||
"Static data shape: (2600000, 113)\n",
|
||
"Target shape: (2600000, 5)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"simulated_data = pd.read_parquet(f\"{data_dir}olive_training_dataset.parquet\")\n",
|
||
"olive_varieties = pd.read_parquet(f\"{data_dir}olive_varieties.parquet\")\n",
|
||
"\n",
|
||
"(train_data, train_targets), (val_data, val_targets), (test_data, test_targets), scalers = prepare_transformer_data(simulated_data, olive_varieties)\n",
|
||
"\n",
|
||
"scaler_temporal, scaler_static, scaler_y = scalers\n",
|
||
"\n",
|
||
"print(\"Temporal data shape:\", train_data['temporal'].shape)\n",
|
||
"print(\"Static data shape:\", train_data['static'].shape)\n",
|
||
"print(\"Target shape:\", train_targets.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "604c952c7195f40c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"@keras.saving.register_keras_serializable()\n",
|
||
"class DataAugmentation(tf.keras.layers.Layer):\n",
|
||
" \"\"\"Custom layer per l'augmentation dei dati\"\"\"\n",
|
||
"\n",
|
||
" def __init__(self, noise_stddev=0.03, **kwargs):\n",
|
||
" super().__init__(**kwargs)\n",
|
||
" self.noise_stddev = noise_stddev\n",
|
||
"\n",
|
||
" def call(self, inputs, training=None):\n",
|
||
" if training:\n",
|
||
" return inputs + tf.random.normal(\n",
|
||
" shape=tf.shape(inputs),\n",
|
||
" mean=0.0,\n",
|
||
" stddev=self.noise_stddev\n",
|
||
" )\n",
|
||
" return inputs\n",
|
||
"\n",
|
||
" def get_config(self):\n",
|
||
" config = super().get_config()\n",
|
||
" config.update({\"noise_stddev\": self.noise_stddev})\n",
|
||
" return config\n",
|
||
"\n",
|
||
"\n",
|
||
"@keras.saving.register_keras_serializable()\n",
|
||
"class PositionalEncoding(tf.keras.layers.Layer):\n",
|
||
" \"\"\"Custom layer per l'encoding posizionale\"\"\"\n",
|
||
"\n",
|
||
" def __init__(self, d_model, **kwargs):\n",
|
||
" super().__init__(**kwargs)\n",
|
||
" self.d_model = d_model\n",
|
||
"\n",
|
||
" def build(self, input_shape):\n",
|
||
" _, seq_length, _ = input_shape\n",
|
||
"\n",
|
||
" # Crea la matrice di encoding posizionale\n",
|
||
" position = tf.range(seq_length, dtype=tf.float32)[:, tf.newaxis]\n",
|
||
" div_term = tf.exp(\n",
|
||
" tf.range(0, self.d_model, 2, dtype=tf.float32) *\n",
|
||
" (-tf.math.log(10000.0) / self.d_model)\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Calcola sin e cos\n",
|
||
" pos_encoding = tf.zeros((1, seq_length, self.d_model))\n",
|
||
" pos_encoding_even = tf.sin(position * div_term)\n",
|
||
" pos_encoding_odd = tf.cos(position * div_term)\n",
|
||
"\n",
|
||
" # Assegna i valori alle posizioni pari e dispari\n",
|
||
" pos_encoding = tf.concat(\n",
|
||
" [tf.expand_dims(pos_encoding_even, -1),\n",
|
||
" tf.expand_dims(pos_encoding_odd, -1)],\n",
|
||
" axis=-1\n",
|
||
" )\n",
|
||
" pos_encoding = tf.reshape(pos_encoding, (1, seq_length, -1))\n",
|
||
" pos_encoding = pos_encoding[:, :, :self.d_model]\n",
|
||
"\n",
|
||
" # Salva l'encoding come peso non trainabile\n",
|
||
" self.pos_encoding = self.add_weight(\n",
|
||
" shape=(1, seq_length, self.d_model),\n",
|
||
" initializer=tf.keras.initializers.Constant(pos_encoding),\n",
|
||
" trainable=False,\n",
|
||
" name='positional_encoding'\n",
|
||
" )\n",
|
||
"\n",
|
||
" super().build(input_shape)\n",
|
||
"\n",
|
||
" def call(self, inputs):\n",
|
||
" # Broadcast l'encoding posizionale sul batch\n",
|
||
" batch_size = tf.shape(inputs)[0]\n",
|
||
" pos_encoding_tiled = tf.tile(self.pos_encoding, [batch_size, 1, 1])\n",
|
||
" return inputs + pos_encoding_tiled\n",
|
||
"\n",
|
||
" def get_config(self):\n",
|
||
" config = super().get_config()\n",
|
||
" config.update({\"d_model\": self.d_model})\n",
|
||
" return config\n",
|
||
"\n",
|
||
"\n",
|
||
"@keras.saving.register_keras_serializable()\n",
|
||
"class WarmUpLearningRateSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n",
|
||
" \"\"\"Custom learning rate schedule with linear warmup and exponential decay.\"\"\"\n",
|
||
"\n",
|
||
" def __init__(self, initial_learning_rate=1e-3, warmup_steps=500, decay_steps=5000):\n",
|
||
" super().__init__()\n",
|
||
" self.initial_learning_rate = initial_learning_rate\n",
|
||
" self.warmup_steps = warmup_steps\n",
|
||
" self.decay_steps = decay_steps\n",
|
||
"\n",
|
||
" def __call__(self, step):\n",
|
||
" warmup_pct = tf.cast(step, tf.float32) / self.warmup_steps\n",
|
||
" warmup_lr = self.initial_learning_rate * warmup_pct\n",
|
||
" decay_factor = tf.pow(0.1, tf.cast(step, tf.float32) / self.decay_steps)\n",
|
||
" decayed_lr = self.initial_learning_rate * decay_factor\n",
|
||
" return tf.where(step < self.warmup_steps, warmup_lr, decayed_lr)\n",
|
||
"\n",
|
||
" def get_config(self):\n",
|
||
" return {\n",
|
||
" 'initial_learning_rate': self.initial_learning_rate,\n",
|
||
" 'warmup_steps': self.warmup_steps,\n",
|
||
" 'decay_steps': self.decay_steps\n",
|
||
" }\n",
|
||
"\n",
|
||
"\n",
|
||
"def create_olive_oil_transformer(temporal_shape, static_shape, num_outputs,\n",
|
||
" d_model=128, num_heads=8, ff_dim=256,\n",
|
||
" num_transformer_blocks=4, mlp_units=None,\n",
|
||
" dropout=0.2):\n",
|
||
" if mlp_units is None:\n",
|
||
" mlp_units = [256, 128, 64]\n",
|
||
"\n",
|
||
" temporal_input = tf.keras.layers.Input(shape=temporal_shape, name='temporal')\n",
|
||
" static_input = tf.keras.layers.Input(shape=static_shape, name='static')\n",
|
||
"\n",
|
||
" # === TEMPORAL PATH ===\n",
|
||
" x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(temporal_input)\n",
|
||
" x = DataAugmentation()(x)\n",
|
||
"\n",
|
||
" # Temporal projection con dimensione aumentata per compensare la sequenza corta\n",
|
||
" x = tf.keras.layers.Dense(\n",
|
||
" d_model,\n",
|
||
" activation='swish',\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(x)\n",
|
||
" x = tf.keras.layers.Dropout(dropout)(x)\n",
|
||
"\n",
|
||
" # Additional feature extraction prima del transformer\n",
|
||
" x = tf.keras.layers.Dense(\n",
|
||
" d_model * 2,\n",
|
||
" activation='swish',\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(x)\n",
|
||
"\n",
|
||
" x = PositionalEncoding(d_model * 2)(x)\n",
|
||
"\n",
|
||
" skip_connection = x\n",
|
||
" for _ in range(num_transformer_blocks):\n",
|
||
" # Multi-head self-attention con più heads per compensare la sequenza corta\n",
|
||
" attention_output = tf.keras.layers.MultiHeadAttention(\n",
|
||
" num_heads=num_heads * 2,\n",
|
||
" key_dim=d_model // num_heads,\n",
|
||
" value_dim=d_model // num_heads\n",
|
||
" )(x, x)\n",
|
||
" attention_output = tf.keras.layers.Dropout(dropout)(attention_output)\n",
|
||
"\n",
|
||
" # Residual connection con gating mechanism\n",
|
||
" gate = tf.keras.layers.Dense(d_model * 2, activation='sigmoid')(x)\n",
|
||
" x = x + gate * attention_output\n",
|
||
" x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n",
|
||
"\n",
|
||
" # Feed-forward network potenziato\n",
|
||
" ffn = tf.keras.Sequential([\n",
|
||
" tf.keras.layers.Dense(ff_dim * 2, activation=\"swish\"), # Raddoppiato\n",
|
||
" tf.keras.layers.Dropout(dropout),\n",
|
||
" tf.keras.layers.Dense(ff_dim, activation=\"swish\"),\n",
|
||
" tf.keras.layers.Dropout(dropout),\n",
|
||
" tf.keras.layers.Dense(d_model * 2)\n",
|
||
" ])\n",
|
||
" ffn_output = ffn(x)\n",
|
||
"\n",
|
||
" # Gated residual connection\n",
|
||
" gate = tf.keras.layers.Dense(d_model * 2, activation='sigmoid')(x)\n",
|
||
" x = x + gate * ffn_output\n",
|
||
" x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n",
|
||
"\n",
|
||
" # Global feature attention\n",
|
||
" x = tf.keras.layers.MultiHeadAttention(\n",
|
||
" num_heads=num_heads,\n",
|
||
" key_dim=d_model // num_heads\n",
|
||
" )(x, x)\n",
|
||
"\n",
|
||
" # Feature pooling\n",
|
||
" x = tf.keras.layers.GlobalAveragePooling1D()(x)\n",
|
||
"\n",
|
||
" # === STATIC PATH ===\n",
|
||
" s = tf.keras.layers.LayerNormalization(epsilon=1e-6)(static_input)\n",
|
||
" for units in [512, 256, 128]: # Aumentate le dimensioni\n",
|
||
" s = tf.keras.layers.Dense(\n",
|
||
" units,\n",
|
||
" activation='swish',\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(s)\n",
|
||
" s = tf.keras.layers.BatchNormalization()(s)\n",
|
||
" s = tf.keras.layers.Dropout(dropout)(s)\n",
|
||
"\n",
|
||
"\n",
|
||
" # === FEATURE FUSION con attention ===\n",
|
||
" # Project features to same dimensionality\n",
|
||
" x = tf.keras.layers.Dense(256)(x)\n",
|
||
" s = tf.keras.layers.Dense(256)(s)\n",
|
||
"\n",
|
||
" # Cross-attention between temporal and static features\n",
|
||
" combined = tf.keras.layers.Concatenate()([x, s])\n",
|
||
" combined = tf.keras.layers.Dense(256, activation='swish')(combined)\n",
|
||
"\n",
|
||
" # MLP head with residual connections\n",
|
||
" for units in mlp_units:\n",
|
||
" skip = combined\n",
|
||
" combined = tf.keras.layers.BatchNormalization()(combined)\n",
|
||
" combined = tf.keras.layers.Dense(units, activation=\"swish\")(combined)\n",
|
||
" combined = tf.keras.layers.Dropout(dropout)(combined)\n",
|
||
" if units == skip.shape[-1]: # Se le dimensioni combaciano\n",
|
||
" combined = combined + skip\n",
|
||
"\n",
|
||
" # Apply final normalization to output\n",
|
||
" outputs = tf.keras.layers.BatchNormalization()(combined)\n",
|
||
" outputs = tf.keras.layers.Dense(\n",
|
||
" num_outputs,\n",
|
||
" activation='linear',\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(outputs)\n",
|
||
"\n",
|
||
" # Create model\n",
|
||
" model = tf.keras.Model(\n",
|
||
" inputs={'temporal': temporal_input, 'static': static_input},\n",
|
||
" outputs=outputs,\n",
|
||
" name='OilTransformer'\n",
|
||
" )\n",
|
||
"\n",
|
||
" return model\n",
|
||
"\n",
|
||
"\n",
|
||
"def create_transformer_callbacks(target_names, val_data, val_targets):\n",
|
||
" \"\"\"\n",
|
||
" Crea i callbacks per il training del modello single-step.\n",
|
||
" \"\"\"\n",
|
||
" class TargetSpecificMetric(tf.keras.callbacks.Callback):\n",
|
||
" def __init__(self, validation_data, target_names):\n",
|
||
" super().__init__()\n",
|
||
" self.validation_data = validation_data\n",
|
||
" self.target_names = target_names\n",
|
||
" self.best_metrics = {name: float('inf') for name in target_names}\n",
|
||
" \n",
|
||
" def on_epoch_end(self, epoch, logs=None):\n",
|
||
" logs = logs or {}\n",
|
||
" \n",
|
||
" # Esegui il calcolo solo ogni 5 epoche\n",
|
||
" if epoch % 5 == 0:\n",
|
||
" x_val, y_val = self.validation_data\n",
|
||
" y_pred = self.model.predict(x_val, verbose=0)\n",
|
||
" \n",
|
||
" # Calcola e logga le metriche per ogni target\n",
|
||
" for i, name in enumerate(self.target_names):\n",
|
||
" mae = np.mean(np.abs(y_val[:, i] - y_pred[:, i]))\n",
|
||
" mape = np.mean(np.abs((y_val[:, i] - y_pred[:, i]) / np.clip(np.abs(y_val[:, i]), 1e-7, None))) * 100\n",
|
||
" logs[f'val_{name}_mae'] = mae\n",
|
||
" logs[f'val_{name}_mape'] = mape\n",
|
||
" \n",
|
||
" # Traccia i migliori risultati\n",
|
||
" if mae < self.best_metrics[name]:\n",
|
||
" self.best_metrics[name] = mae\n",
|
||
" logs[f'best_{name}_mae'] = mae\n",
|
||
"\n",
|
||
"\n",
|
||
" callbacks = [\n",
|
||
" # Early Stopping ottimizzato\n",
|
||
" tf.keras.callbacks.EarlyStopping(\n",
|
||
" monitor='val_loss',\n",
|
||
" patience=25, # Aumentato per dare più chance al modello\n",
|
||
" restore_best_weights=True,\n",
|
||
" min_delta=0.0001, # Più sensibile ai miglioramenti\n",
|
||
" mode='min'\n",
|
||
" ),\n",
|
||
"\n",
|
||
" # Model Checkpoint con monitoraggio multiplo\n",
|
||
" tf.keras.callbacks.ModelCheckpoint(\n",
|
||
" filepath=f'{execute_name}_best_oil_model.h5',\n",
|
||
" monitor='val_loss',\n",
|
||
" save_best_only=True,\n",
|
||
" mode='min',\n",
|
||
" save_weights_only=True\n",
|
||
" ),\n",
|
||
"\n",
|
||
" # Metric per target specifici\n",
|
||
" TargetSpecificMetric(\n",
|
||
" validation_data=(val_data, val_targets),\n",
|
||
" target_names=target_names\n",
|
||
" ),\n",
|
||
"\n",
|
||
" # LR reduction ottimizzato per single-step\n",
|
||
" tf.keras.callbacks.ReduceLROnPlateau(\n",
|
||
" monitor='val_loss',\n",
|
||
" factor=0.2, # Riduzione più aggressiva\n",
|
||
" patience=15,\n",
|
||
" min_lr=1e-7,\n",
|
||
" verbose=1,\n",
|
||
" cooldown=5 # Periodo di cool-down per stabilizzazione\n",
|
||
" ),\n",
|
||
"\n",
|
||
" # TensorBoard con più metriche\n",
|
||
" tf.keras.callbacks.TensorBoard(\n",
|
||
" log_dir=f'./logs_{execute_name}',\n",
|
||
" histogram_freq=1,\n",
|
||
" write_graph=True,\n",
|
||
" write_images=True,\n",
|
||
" update_freq='epoch',\n",
|
||
" profile_batch='500,520' # Profile per ottimizzazione\n",
|
||
" )\n",
|
||
" ]\n",
|
||
"\n",
|
||
" return callbacks\n",
|
||
"\n",
|
||
"def compile_model(model, learning_rate=5e-4): # Learning rate ridotto\n",
|
||
" \"\"\"\n",
|
||
" Compila il modello con ottimizzazioni per single-step.\n",
|
||
" \"\"\"\n",
|
||
" lr_schedule = WarmUpLearningRateSchedule(\n",
|
||
" initial_learning_rate=learning_rate,\n",
|
||
" warmup_steps=1000, # Aumentato per stabilità\n",
|
||
" decay_steps=7000 # Aumentato per permettere più esplorazione\n",
|
||
" )\n",
|
||
"\n",
|
||
" def weighted_huber_loss(y_true, y_pred):\n",
|
||
" # Pesi per diversi output\n",
|
||
" weights = tf.constant([1.0, 0.8, 0.8, 1.0, 0.6], dtype=tf.float32)\n",
|
||
" huber = tf.keras.losses.Huber(delta=1.0)\n",
|
||
" loss = huber(y_true, y_pred)\n",
|
||
" weighted_loss = tf.reduce_mean(loss * weights)\n",
|
||
" return weighted_loss\n",
|
||
"\n",
|
||
" model.compile(\n",
|
||
" optimizer=tf.keras.optimizers.AdamW(\n",
|
||
" learning_rate=lr_schedule,\n",
|
||
" weight_decay=0.01,\n",
|
||
" clipnorm=1.0, # Gradient clipping\n",
|
||
" epsilon=1e-7 # Aumentato per stabilità numerica\n",
|
||
" ),\n",
|
||
" loss=weighted_huber_loss,\n",
|
||
" metrics=['mae', 'mape']\n",
|
||
" )\n",
|
||
"\n",
|
||
" return model\n",
|
||
"\n",
|
||
"def setup_transformer_training(train_data, train_targets, val_data, val_targets):\n",
|
||
" \"\"\"\n",
|
||
" Configura il single-step transformer.\n",
|
||
" \"\"\"\n",
|
||
" # Estrai le shape dai dati\n",
|
||
" temporal_shape = (1, train_data['temporal'].shape[2])\n",
|
||
" static_shape = (train_data['static'].shape[1],)\n",
|
||
" num_outputs = train_targets.shape[1]\n",
|
||
"\n",
|
||
" print(f\"Shape rilevate:\")\n",
|
||
" print(f\"- Temporal shape: {temporal_shape}\")\n",
|
||
" print(f\"- Static shape: {static_shape}\")\n",
|
||
" print(f\"- Numero di output: {num_outputs}\")\n",
|
||
"\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n",
|
||
"\n",
|
||
" assert len(target_names) == num_outputs, \\\n",
|
||
" f\"Il numero di target names ({len(target_names)}) non corrisponde al numero di output ({num_outputs})\"\n",
|
||
"\n",
|
||
" # Crea il modello con il nuovo transformer\n",
|
||
" model = create_olive_oil_transformer(\n",
|
||
" temporal_shape=temporal_shape,\n",
|
||
" static_shape=static_shape,\n",
|
||
" num_outputs=num_outputs,\n",
|
||
" d_model=256,\n",
|
||
" num_heads=8,\n",
|
||
" ff_dim=512,\n",
|
||
" num_transformer_blocks=6,\n",
|
||
" dropout=0.1\n",
|
||
" )\n",
|
||
"\n",
|
||
" model = compile_model(model)\n",
|
||
" callbacks = create_transformer_callbacks(target_names, val_data, val_targets)\n",
|
||
"\n",
|
||
" return model, callbacks, target_names\n",
|
||
"\n",
|
||
"def train_transformer(train_data, train_targets, val_data, val_targets, epochs=200, batch_size=128, save_name='final_model'):\n",
|
||
" \"\"\"\n",
|
||
" Training ottimizzato per single-step transformer.\n",
|
||
" \"\"\"\n",
|
||
" # Dataset con augmentation\n",
|
||
" def augment(x, y):\n",
|
||
" # Ottieni il dtype dei dati originali\n",
|
||
" original_dtype = x['temporal'].dtype\n",
|
||
" # Genera il rumore con lo stesso dtype\n",
|
||
" noise = tf.random.normal(\n",
|
||
" tf.shape(x['temporal']), \n",
|
||
" mean=0.0, \n",
|
||
" stddev=0.01,\n",
|
||
" dtype=original_dtype\n",
|
||
" )\n",
|
||
" x['temporal'] += noise\n",
|
||
" return x, y\n",
|
||
"\n",
|
||
" train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_targets))\\\n",
|
||
" .map(augment, num_parallel_calls=tf.data.AUTOTUNE)\\\n",
|
||
" .cache()\\\n",
|
||
" .shuffle(buffer_size=10000)\\\n",
|
||
" .batch(batch_size)\\\n",
|
||
" .prefetch(tf.data.AUTOTUNE)\n",
|
||
"\n",
|
||
" val_dataset = tf.data.Dataset.from_tensor_slices((val_data, val_targets))\\\n",
|
||
" .cache()\\\n",
|
||
" .batch(batch_size)\\\n",
|
||
" .prefetch(tf.data.AUTOTUNE)\n",
|
||
"\n",
|
||
" strategy = tf.distribute.MirroredStrategy() if len(tf.config.list_physical_devices('GPU')) > 1 else tf.distribute.get_strategy()\n",
|
||
"\n",
|
||
" with strategy.scope():\n",
|
||
" model, callbacks, target_names = setup_transformer_training(\n",
|
||
" train_data, train_targets, val_data, val_targets\n",
|
||
" )\n",
|
||
"\n",
|
||
" model.summary()\n",
|
||
"\n",
|
||
" try:\n",
|
||
" keras.utils.plot_model(model, f\"{execute_name}_{save_name}.png\", show_shapes=True)\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"Warning: Could not create model plot: {e}\")\n",
|
||
"\n",
|
||
" # Training con gestione errori e memory saving\n",
|
||
" try:\n",
|
||
" with tf.device('/GPU:0'): # Forza l'uso della GPU principale\n",
|
||
" history = model.fit(\n",
|
||
" train_dataset,\n",
|
||
" validation_data=val_dataset,\n",
|
||
" epochs=epochs,\n",
|
||
" callbacks=callbacks,\n",
|
||
" verbose=1,\n",
|
||
" workers=8,\n",
|
||
" use_multiprocessing=True\n",
|
||
" )\n",
|
||
" except tf.errors.ResourceExhaustedError:\n",
|
||
" print(\"Memoria GPU esaurita, riprovo con batch size più piccolo...\")\n",
|
||
" batch_size = batch_size // 2\n",
|
||
" train_dataset = train_dataset.unbatch().batch(batch_size)\n",
|
||
" val_dataset = val_dataset.unbatch().batch(batch_size)\n",
|
||
" history = model.fit(\n",
|
||
" train_dataset,\n",
|
||
" validation_data=val_dataset,\n",
|
||
" epochs=epochs,\n",
|
||
" callbacks=callbacks,\n",
|
||
" verbose=1\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Salvataggio modello\n",
|
||
" try:\n",
|
||
" save_path = f'{execute_name}_{save_name}.keras'\n",
|
||
" model.save(save_path, save_format='keras')\n",
|
||
"\n",
|
||
" os.makedirs(f'{execute_name}/weights', exist_ok=True)\n",
|
||
" model.save_weights(f'{execute_name}/weights')\n",
|
||
" print(f\"\\nModello salvato in: {save_path}\")\n",
|
||
"\n",
|
||
" # Salva anche la storia del training\n",
|
||
" with open(f'{execute_name}_training_history.json', 'w') as f:\n",
|
||
" json.dump(history.history, f)\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"Warning: Could not save model: {e}\")\n",
|
||
"\n",
|
||
" return model, history"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "35490e902e494c4a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Shape rilevate:\n",
|
||
"- Temporal shape: (1, 3)\n",
|
||
"- Static shape: (113,)\n",
|
||
"- Numero di output: 5\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-08 14:50:03.536829: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Model: \"OilTransformer\"\n",
|
||
"__________________________________________________________________________________________________\n",
|
||
" Layer (type) Output Shape Param # Connected to \n",
|
||
"==================================================================================================\n",
|
||
" temporal (InputLayer) [(None, 1, 3)] 0 [] \n",
|
||
" \n",
|
||
" layer_normalization (Layer (None, 1, 3) 6 ['temporal[0][0]'] \n",
|
||
" Normalization) \n",
|
||
" \n",
|
||
" data_augmentation (DataAug (None, 1, 3) 0 ['layer_normalization[0][0]'] \n",
|
||
" mentation) \n",
|
||
" \n",
|
||
" dense (Dense) (None, 1, 256) 1024 ['data_augmentation[0][0]'] \n",
|
||
" \n",
|
||
" dropout (Dropout) (None, 1, 256) 0 ['dense[0][0]'] \n",
|
||
" \n",
|
||
" dense_1 (Dense) (None, 1, 512) 131584 ['dropout[0][0]'] \n",
|
||
" \n",
|
||
" positional_encoding (Posit (None, 1, 512) 512 ['dense_1[0][0]'] \n",
|
||
" ionalEncoding) \n",
|
||
" \n",
|
||
" multi_head_attention (Mult (None, 1, 512) 1050624 ['positional_encoding[0][0]', \n",
|
||
" iHeadAttention) 'positional_encoding[0][0]'] \n",
|
||
" \n",
|
||
" dense_2 (Dense) (None, 1, 512) 262656 ['positional_encoding[0][0]'] \n",
|
||
" \n",
|
||
" dropout_1 (Dropout) (None, 1, 512) 0 ['multi_head_attention[0][0]']\n",
|
||
" \n",
|
||
" tf.math.multiply (TFOpLamb (None, 1, 512) 0 ['dense_2[0][0]', \n",
|
||
" da) 'dropout_1[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add (TFOp (None, 1, 512) 0 ['positional_encoding[0][0]', \n",
|
||
" Lambda) 'tf.math.multiply[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_1 (Lay (None, 1, 512) 1024 ['tf.__operators__.add[0][0]']\n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" dense_6 (Dense) (None, 1, 512) 262656 ['layer_normalization_1[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" sequential (Sequential) (None, 1, 512) 1312768 ['layer_normalization_1[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" tf.math.multiply_1 (TFOpLa (None, 1, 512) 0 ['dense_6[0][0]', \n",
|
||
" mbda) 'sequential[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_1 (TF (None, 1, 512) 0 ['layer_normalization_1[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_1[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_2 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_1[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" multi_head_attention_1 (Mu (None, 1, 512) 1050624 ['layer_normalization_2[0][0]'\n",
|
||
" ltiHeadAttention) , 'layer_normalization_2[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_7 (Dense) (None, 1, 512) 262656 ['layer_normalization_2[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_4 (Dropout) (None, 1, 512) 0 ['multi_head_attention_1[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.math.multiply_2 (TFOpLa (None, 1, 512) 0 ['dense_7[0][0]', \n",
|
||
" mbda) 'dropout_4[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_2 (TF (None, 1, 512) 0 ['layer_normalization_2[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_2[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_3 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_2[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" dense_11 (Dense) (None, 1, 512) 262656 ['layer_normalization_3[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" sequential_1 (Sequential) (None, 1, 512) 1312768 ['layer_normalization_3[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" tf.math.multiply_3 (TFOpLa (None, 1, 512) 0 ['dense_11[0][0]', \n",
|
||
" mbda) 'sequential_1[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_3 (TF (None, 1, 512) 0 ['layer_normalization_3[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_3[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_4 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_3[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" multi_head_attention_2 (Mu (None, 1, 512) 1050624 ['layer_normalization_4[0][0]'\n",
|
||
" ltiHeadAttention) , 'layer_normalization_4[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_12 (Dense) (None, 1, 512) 262656 ['layer_normalization_4[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_7 (Dropout) (None, 1, 512) 0 ['multi_head_attention_2[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.math.multiply_4 (TFOpLa (None, 1, 512) 0 ['dense_12[0][0]', \n",
|
||
" mbda) 'dropout_7[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_4 (TF (None, 1, 512) 0 ['layer_normalization_4[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_4[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_5 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_4[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" dense_16 (Dense) (None, 1, 512) 262656 ['layer_normalization_5[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" sequential_2 (Sequential) (None, 1, 512) 1312768 ['layer_normalization_5[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" tf.math.multiply_5 (TFOpLa (None, 1, 512) 0 ['dense_16[0][0]', \n",
|
||
" mbda) 'sequential_2[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_5 (TF (None, 1, 512) 0 ['layer_normalization_5[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_5[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_6 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_5[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" multi_head_attention_3 (Mu (None, 1, 512) 1050624 ['layer_normalization_6[0][0]'\n",
|
||
" ltiHeadAttention) , 'layer_normalization_6[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_17 (Dense) (None, 1, 512) 262656 ['layer_normalization_6[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_10 (Dropout) (None, 1, 512) 0 ['multi_head_attention_3[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.math.multiply_6 (TFOpLa (None, 1, 512) 0 ['dense_17[0][0]', \n",
|
||
" mbda) 'dropout_10[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_6 (TF (None, 1, 512) 0 ['layer_normalization_6[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_6[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_7 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_6[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" dense_21 (Dense) (None, 1, 512) 262656 ['layer_normalization_7[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" sequential_3 (Sequential) (None, 1, 512) 1312768 ['layer_normalization_7[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" tf.math.multiply_7 (TFOpLa (None, 1, 512) 0 ['dense_21[0][0]', \n",
|
||
" mbda) 'sequential_3[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_7 (TF (None, 1, 512) 0 ['layer_normalization_7[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_7[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_8 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_7[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" multi_head_attention_4 (Mu (None, 1, 512) 1050624 ['layer_normalization_8[0][0]'\n",
|
||
" ltiHeadAttention) , 'layer_normalization_8[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_22 (Dense) (None, 1, 512) 262656 ['layer_normalization_8[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_13 (Dropout) (None, 1, 512) 0 ['multi_head_attention_4[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.math.multiply_8 (TFOpLa (None, 1, 512) 0 ['dense_22[0][0]', \n",
|
||
" mbda) 'dropout_13[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_8 (TF (None, 1, 512) 0 ['layer_normalization_8[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_8[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_9 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_8[0][0]\n",
|
||
" erNormalization) '] \n",
|
||
" \n",
|
||
" dense_26 (Dense) (None, 1, 512) 262656 ['layer_normalization_9[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" sequential_4 (Sequential) (None, 1, 512) 1312768 ['layer_normalization_9[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" tf.math.multiply_9 (TFOpLa (None, 1, 512) 0 ['dense_26[0][0]', \n",
|
||
" mbda) 'sequential_4[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_9 (TF (None, 1, 512) 0 ['layer_normalization_9[0][0]'\n",
|
||
" OpLambda) , 'tf.math.multiply_9[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_10 (La (None, 1, 512) 1024 ['tf.__operators__.add_9[0][0]\n",
|
||
" yerNormalization) '] \n",
|
||
" \n",
|
||
" multi_head_attention_5 (Mu (None, 1, 512) 1050624 ['layer_normalization_10[0][0]\n",
|
||
" ltiHeadAttention) ', \n",
|
||
" 'layer_normalization_10[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" static (InputLayer) [(None, 113)] 0 [] \n",
|
||
" \n",
|
||
" dense_27 (Dense) (None, 1, 512) 262656 ['layer_normalization_10[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dropout_16 (Dropout) (None, 1, 512) 0 ['multi_head_attention_5[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" layer_normalization_13 (La (None, 113) 226 ['static[0][0]'] \n",
|
||
" yerNormalization) \n",
|
||
" \n",
|
||
" tf.math.multiply_10 (TFOpL (None, 1, 512) 0 ['dense_27[0][0]', \n",
|
||
" ambda) 'dropout_16[0][0]'] \n",
|
||
" \n",
|
||
" dense_32 (Dense) (None, 512) 58368 ['layer_normalization_13[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.__operators__.add_10 (T (None, 1, 512) 0 ['layer_normalization_10[0][0]\n",
|
||
" FOpLambda) ', \n",
|
||
" 'tf.math.multiply_10[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization (Batch (None, 512) 2048 ['dense_32[0][0]'] \n",
|
||
" Normalization) \n",
|
||
" \n",
|
||
" layer_normalization_11 (La (None, 1, 512) 1024 ['tf.__operators__.add_10[0][0\n",
|
||
" yerNormalization) ]'] \n",
|
||
" \n",
|
||
" dropout_19 (Dropout) (None, 512) 0 ['batch_normalization[0][0]'] \n",
|
||
" \n",
|
||
" dense_31 (Dense) (None, 1, 512) 262656 ['layer_normalization_11[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" sequential_5 (Sequential) (None, 1, 512) 1312768 ['layer_normalization_11[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_33 (Dense) (None, 256) 131328 ['dropout_19[0][0]'] \n",
|
||
" \n",
|
||
" tf.math.multiply_11 (TFOpL (None, 1, 512) 0 ['dense_31[0][0]', \n",
|
||
" ambda) 'sequential_5[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization_1 (Bat (None, 256) 1024 ['dense_33[0][0]'] \n",
|
||
" chNormalization) \n",
|
||
" \n",
|
||
" tf.__operators__.add_11 (T (None, 1, 512) 0 ['layer_normalization_11[0][0]\n",
|
||
" FOpLambda) ', \n",
|
||
" 'tf.math.multiply_11[0][0]'] \n",
|
||
" \n",
|
||
" dropout_20 (Dropout) (None, 256) 0 ['batch_normalization_1[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" layer_normalization_12 (La (None, 1, 512) 1024 ['tf.__operators__.add_11[0][0\n",
|
||
" yerNormalization) ]'] \n",
|
||
" \n",
|
||
" dense_34 (Dense) (None, 128) 32896 ['dropout_20[0][0]'] \n",
|
||
" \n",
|
||
" multi_head_attention_6 (Mu (None, 1, 512) 525568 ['layer_normalization_12[0][0]\n",
|
||
" ltiHeadAttention) ', \n",
|
||
" 'layer_normalization_12[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" batch_normalization_2 (Bat (None, 128) 512 ['dense_34[0][0]'] \n",
|
||
" chNormalization) \n",
|
||
" \n",
|
||
" global_average_pooling1d ( (None, 512) 0 ['multi_head_attention_6[0][0]\n",
|
||
" GlobalAveragePooling1D) '] \n",
|
||
" \n",
|
||
" dropout_21 (Dropout) (None, 128) 0 ['batch_normalization_2[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dense_35 (Dense) (None, 256) 131328 ['global_average_pooling1d[0][\n",
|
||
" 0]'] \n",
|
||
" \n",
|
||
" dense_36 (Dense) (None, 256) 33024 ['dropout_21[0][0]'] \n",
|
||
" \n",
|
||
" concatenate (Concatenate) (None, 512) 0 ['dense_35[0][0]', \n",
|
||
" 'dense_36[0][0]'] \n",
|
||
" \n",
|
||
" dense_37 (Dense) (None, 256) 131328 ['concatenate[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization_3 (Bat (None, 256) 1024 ['dense_37[0][0]'] \n",
|
||
" chNormalization) \n",
|
||
" \n",
|
||
" dense_38 (Dense) (None, 256) 65792 ['batch_normalization_3[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_22 (Dropout) (None, 256) 0 ['dense_38[0][0]'] \n",
|
||
" \n",
|
||
" tf.__operators__.add_12 (T (None, 256) 0 ['dropout_22[0][0]', \n",
|
||
" FOpLambda) 'dense_37[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization_4 (Bat (None, 256) 1024 ['tf.__operators__.add_12[0][0\n",
|
||
" chNormalization) ]'] \n",
|
||
" \n",
|
||
" dense_39 (Dense) (None, 128) 32896 ['batch_normalization_4[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_23 (Dropout) (None, 128) 0 ['dense_39[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization_5 (Bat (None, 128) 512 ['dropout_23[0][0]'] \n",
|
||
" chNormalization) \n",
|
||
" \n",
|
||
" dense_40 (Dense) (None, 64) 8256 ['batch_normalization_5[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_24 (Dropout) (None, 64) 0 ['dense_40[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization_6 (Bat (None, 64) 256 ['dropout_24[0][0]'] \n",
|
||
" chNormalization) \n",
|
||
" \n",
|
||
" dense_41 (Dense) (None, 5) 325 ['batch_normalization_6[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
"==================================================================================================\n",
|
||
"Total params: 18635373 (71.09 MB)\n",
|
||
"Trainable params: 18631661 (71.07 MB)\n",
|
||
"Non-trainable params: 3712 (14.50 KB)\n",
|
||
"__________________________________________________________________________________________________\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-08 14:50:05.929229: I tensorflow/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.\n",
|
||
"2024-12-08 14:50:05.929268: I tensorflow/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.\n",
|
||
"2024-12-08 14:50:05.929313: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1694] Profiler found 1 GPUs\n",
|
||
"2024-12-08 14:50:05.963777: I tensorflow/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.\n",
|
||
"2024-12-08 14:50:05.963883: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1828] CUPTI activity buffer flushed\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/200\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-08 14:50:28.629786: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x773b6d05ca80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
|
||
"2024-12-08 14:50:28.629849: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n",
|
||
"2024-12-08 14:50:28.638389: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
|
||
"2024-12-08 14:50:28.698504: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n",
|
||
"2024-12-08 14:50:28.846260: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"80/80 [==============================] - 336s 4s/step - loss: 0.6468 - mae: 1.1652 - mape: 1425.9640 - val_loss: 0.3750 - val_mae: 0.8167 - val_mape: 357.5072 - val_olive_prod_mae: 0.8317 - val_olive_prod_mape: 540.9344 - best_olive_prod_mae: 0.8317 - val_min_oil_prod_mae: 0.8388 - val_min_oil_prod_mape: 440.9449 - best_min_oil_prod_mae: 0.8388 - val_max_oil_prod_mae: 0.7774 - val_max_oil_prod_mape: 197.5839 - best_max_oil_prod_mae: 0.7774 - val_avg_oil_prod_mae: 0.7770 - val_avg_oil_prod_mape: 158.8580 - best_avg_oil_prod_mae: 0.7770 - val_total_water_need_mae: 0.8589 - val_total_water_need_mape: 449.2141 - best_total_water_need_mae: 0.8589 - lr: 3.9500e-05\n",
|
||
"Epoch 2/200\n",
|
||
"80/80 [==============================] - 37s 447ms/step - loss: 0.2883 - mae: 0.6758 - mape: 853.8861 - val_loss: 0.3493 - val_mae: 0.7684 - val_mape: 759.7661 - lr: 7.9500e-05\n",
|
||
"Epoch 3/200\n",
|
||
"80/80 [==============================] - 37s 450ms/step - loss: 0.1705 - mae: 0.4817 - mape: 538.0516 - val_loss: 0.2085 - val_mae: 0.5459 - val_mape: 613.3998 - lr: 1.1950e-04\n",
|
||
"Epoch 4/200\n",
|
||
"80/80 [==============================] - 37s 448ms/step - loss: 0.1249 - mae: 0.3872 - mape: 533.9152 - val_loss: 0.1381 - val_mae: 0.4314 - val_mape: 558.7706 - lr: 1.5950e-04\n",
|
||
"Epoch 5/200\n",
|
||
"80/80 [==============================] - 37s 449ms/step - loss: 0.0947 - mae: 0.3178 - mape: 355.3927 - val_loss: 0.0832 - val_mae: 0.3333 - val_mape: 487.2971 - lr: 1.9950e-04\n",
|
||
"Epoch 6/200\n",
|
||
"80/80 [==============================] - 236s 3s/step - loss: 0.0720 - mae: 0.2658 - mape: 324.9050 - val_loss: 0.0496 - val_mae: 0.2558 - val_mape: 360.3815 - val_olive_prod_mae: 0.3133 - val_olive_prod_mape: 210.0914 - best_olive_prod_mae: 0.3133 - val_min_oil_prod_mae: 0.2730 - val_min_oil_prod_mape: 291.2299 - best_min_oil_prod_mae: 0.2730 - val_max_oil_prod_mae: 0.2761 - val_max_oil_prod_mape: 889.4504 - best_max_oil_prod_mae: 0.2761 - val_avg_oil_prod_mae: 0.2060 - val_avg_oil_prod_mape: 174.9547 - best_avg_oil_prod_mae: 0.2060 - val_total_water_need_mae: 0.2104 - val_total_water_need_mape: 236.1803 - best_total_water_need_mae: 0.2104 - lr: 2.3950e-04\n",
|
||
"Epoch 7/200\n",
|
||
"19/80 [======>.......................] - ETA: 20s - loss: 0.0607 - mae: 0.2401 - mape: 225.5450"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-08 15:02:15.855854: I tensorflow/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.\n",
|
||
"2024-12-08 15:02:15.855911: I tensorflow/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"39/80 [=============>................] - ETA: 14s - loss: 0.0594 - mae: 0.2364 - mape: 234.2973"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-08 15:02:23.478578: I tensorflow/tsl/profiler/lib/profiler_session.cc:70] Profiler session collecting data.\n",
|
||
"2024-12-08 15:02:23.647243: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1828] CUPTI activity buffer flushed\n",
|
||
"2024-12-08 15:02:27.754232: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_collector.cc:541] GpuTracer has collected 110980 callback api events and 111545 activity events. \n",
|
||
"2024-12-08 15:03:20.421265: I tensorflow/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"80/80 [==============================] - 96s 1s/step - loss: 0.0577 - mae: 0.2319 - mape: 262.5403 - val_loss: 0.1514 - val_mae: 0.4553 - val_mape: 361.8612 - lr: 2.7950e-04\n",
|
||
"Epoch 8/200\n",
|
||
"80/80 [==============================] - 36s 437ms/step - loss: 0.0537 - mae: 0.2191 - mape: 247.8973 - val_loss: 0.0820 - val_mae: 0.3394 - val_mape: 311.3165 - lr: 3.1950e-04\n",
|
||
"Epoch 9/200\n",
|
||
"80/80 [==============================] - 36s 439ms/step - loss: 0.0457 - mae: 0.1994 - mape: 220.3584 - val_loss: 0.0261 - val_mae: 0.1699 - val_mape: 192.3138 - lr: 3.5950e-04\n",
|
||
"Epoch 10/200\n",
|
||
"80/80 [==============================] - 35s 436ms/step - loss: 0.0398 - mae: 0.1852 - mape: 238.8312 - val_loss: 0.0632 - val_mae: 0.3179 - val_mape: 472.3796 - lr: 3.9950e-04\n",
|
||
"Epoch 11/200\n",
|
||
"80/80 [==============================] - 242s 3s/step - loss: 0.0380 - mae: 0.1785 - mape: 226.0223 - val_loss: 0.0411 - val_mae: 0.2068 - val_mape: 289.5609 - val_olive_prod_mae: 0.2628 - val_olive_prod_mape: 305.8365 - best_olive_prod_mae: 0.2628 - val_min_oil_prod_mae: 0.1618 - val_min_oil_prod_mape: 150.3432 - best_min_oil_prod_mae: 0.1618 - val_max_oil_prod_mae: 0.1877 - val_max_oil_prod_mape: 491.9082 - best_max_oil_prod_mae: 0.1877 - val_avg_oil_prod_mae: 0.2413 - val_avg_oil_prod_mape: 259.4505 - val_total_water_need_mae: 0.1803 - val_total_water_need_mape: 240.2667 - best_total_water_need_mae: 0.1803 - lr: 4.3950e-04\n",
|
||
"Epoch 12/200\n",
|
||
"80/80 [==============================] - 36s 440ms/step - loss: 0.0367 - mae: 0.1737 - mape: 186.3838 - val_loss: 0.0223 - val_mae: 0.1476 - val_mape: 164.0920 - lr: 4.7950e-04\n",
|
||
"Epoch 13/200\n",
|
||
"80/80 [==============================] - 36s 441ms/step - loss: 0.0327 - mae: 0.1630 - mape: 184.1294 - val_loss: 0.0191 - val_mae: 0.1254 - val_mape: 148.7276 - lr: 3.5526e-04\n",
|
||
"Epoch 14/200\n",
|
||
"80/80 [==============================] - 35s 436ms/step - loss: 0.0390 - mae: 0.1759 - mape: 202.7457 - val_loss: 0.0271 - val_mae: 0.1711 - val_mape: 249.5935 - lr: 3.4603e-04\n",
|
||
"Epoch 15/200\n",
|
||
"80/80 [==============================] - 36s 442ms/step - loss: 0.0312 - mae: 0.1579 - mape: 155.0903 - val_loss: 0.0202 - val_mae: 0.1389 - val_mape: 188.7654 - lr: 3.3704e-04\n",
|
||
"Epoch 16/200\n",
|
||
"80/80 [==============================] - 260s 3s/step - loss: 0.0300 - mae: 0.1540 - mape: 163.7010 - val_loss: 0.0186 - val_mae: 0.1330 - val_mape: 187.7290 - val_olive_prod_mae: 0.1273 - val_olive_prod_mape: 106.2623 - best_olive_prod_mae: 0.1273 - val_min_oil_prod_mae: 0.1385 - val_min_oil_prod_mape: 108.2680 - best_min_oil_prod_mae: 0.1385 - val_max_oil_prod_mae: 0.1391 - val_max_oil_prod_mape: 505.4183 - best_max_oil_prod_mae: 0.1391 - val_avg_oil_prod_mae: 0.1402 - val_avg_oil_prod_mape: 103.5567 - best_avg_oil_prod_mae: 0.1402 - val_total_water_need_mae: 0.1197 - val_total_water_need_mape: 115.1407 - best_total_water_need_mae: 0.1197 - lr: 3.2829e-04\n",
|
||
"Epoch 17/200\n",
|
||
"80/80 [==============================] - 36s 441ms/step - loss: 0.0287 - mae: 0.1509 - mape: 154.7843 - val_loss: 0.0143 - val_mae: 0.0994 - val_mape: 142.0511 - lr: 3.1976e-04\n",
|
||
"Epoch 18/200\n",
|
||
"80/80 [==============================] - 36s 437ms/step - loss: 0.0285 - mae: 0.1517 - mape: 147.2274 - val_loss: 0.0308 - val_mae: 0.1751 - val_mape: 184.6624 - lr: 3.1146e-04\n",
|
||
"Epoch 19/200\n",
|
||
"80/80 [==============================] - 35s 435ms/step - loss: 0.0296 - mae: 0.1522 - mape: 166.7331 - val_loss: 0.0153 - val_mae: 0.1091 - val_mape: 158.7550 - lr: 3.0337e-04\n",
|
||
"Epoch 20/200\n",
|
||
"80/80 [==============================] - 36s 439ms/step - loss: 0.0291 - mae: 0.1503 - mape: 166.9374 - val_loss: 0.0157 - val_mae: 0.1154 - val_mape: 130.2295 - lr: 2.9549e-04\n",
|
||
"Epoch 21/200\n",
|
||
"80/80 [==============================] - 251s 3s/step - loss: 0.0296 - mae: 0.1512 - mape: 157.1642 - val_loss: 0.0269 - val_mae: 0.1741 - val_mape: 309.7536 - val_olive_prod_mae: 0.2191 - val_olive_prod_mape: 276.2847 - val_min_oil_prod_mae: 0.1626 - val_min_oil_prod_mape: 218.7291 - val_max_oil_prod_mae: 0.1893 - val_max_oil_prod_mape: 669.6759 - val_avg_oil_prod_mae: 0.1389 - val_avg_oil_prod_mape: 156.0846 - best_avg_oil_prod_mae: 0.1389 - val_total_water_need_mae: 0.1608 - val_total_water_need_mape: 227.9932 - lr: 2.8781e-04\n",
|
||
"Epoch 22/200\n",
|
||
"80/80 [==============================] - 35s 435ms/step - loss: 0.0259 - mae: 0.1422 - mape: 166.8822 - val_loss: 0.0144 - val_mae: 0.1019 - val_mape: 137.3515 - lr: 2.8034e-04\n",
|
||
"Epoch 23/200\n",
|
||
"80/80 [==============================] - 35s 434ms/step - loss: 0.0248 - mae: 0.1394 - mape: 143.1400 - val_loss: 0.0165 - val_mae: 0.1251 - val_mape: 158.9414 - lr: 2.7306e-04\n",
|
||
"Epoch 24/200\n",
|
||
"80/80 [==============================] - 35s 433ms/step - loss: 0.0247 - mae: 0.1388 - mape: 164.7200 - val_loss: 0.0146 - val_mae: 0.1058 - val_mape: 129.0920 - lr: 2.6597e-04\n",
|
||
"Epoch 25/200\n",
|
||
"80/80 [==============================] - 35s 435ms/step - loss: 0.0241 - mae: 0.1368 - mape: 149.9482 - val_loss: 0.0144 - val_mae: 0.1076 - val_mape: 127.7412 - lr: 2.5906e-04\n",
|
||
"Epoch 26/200\n",
|
||
"80/80 [==============================] - 239s 3s/step - loss: 0.0233 - mae: 0.1353 - mape: 166.1661 - val_loss: 0.0153 - val_mae: 0.1085 - val_mape: 116.0930 - val_olive_prod_mae: 0.1049 - val_olive_prod_mape: 89.0422 - best_olive_prod_mae: 0.1049 - val_min_oil_prod_mae: 0.0967 - val_min_oil_prod_mape: 100.7810 - best_min_oil_prod_mae: 0.0967 - val_max_oil_prod_mae: 0.1026 - val_max_oil_prod_mape: 147.8183 - best_max_oil_prod_mae: 0.1026 - val_avg_oil_prod_mae: 0.0993 - val_avg_oil_prod_mape: 78.5883 - best_avg_oil_prod_mae: 0.0993 - val_total_water_need_mae: 0.1389 - val_total_water_need_mape: 164.2348 - lr: 2.5233e-04\n",
|
||
"Epoch 27/200\n",
|
||
"80/80 [==============================] - 35s 433ms/step - loss: 0.0248 - mae: 0.1380 - mape: 152.2872 - val_loss: 0.0155 - val_mae: 0.1181 - val_mape: 110.0643 - lr: 2.4578e-04\n",
|
||
"Epoch 28/200\n",
|
||
"80/80 [==============================] - 36s 440ms/step - loss: 0.0230 - mae: 0.1342 - mape: 143.7230 - val_loss: 0.0139 - val_mae: 0.0997 - val_mape: 108.3232 - lr: 2.3939e-04\n",
|
||
"Epoch 29/200\n",
|
||
"80/80 [==============================] - 37s 449ms/step - loss: 0.0227 - mae: 0.1332 - mape: 143.9378 - val_loss: 0.0137 - val_mae: 0.0994 - val_mape: 123.0778 - lr: 2.3318e-04\n",
|
||
"Epoch 30/200\n",
|
||
"80/80 [==============================] - 36s 439ms/step - loss: 0.0248 - mae: 0.1380 - mape: 162.3151 - val_loss: 0.0210 - val_mae: 0.1404 - val_mape: 200.2853 - lr: 2.2712e-04\n",
|
||
"Epoch 31/200\n",
|
||
"80/80 [==============================] - 235s 3s/step - loss: 0.0224 - mae: 0.1323 - mape: 156.1841 - val_loss: 0.0191 - val_mae: 0.1358 - val_mape: 241.1420 - val_olive_prod_mae: 0.1327 - val_olive_prod_mape: 126.8244 - val_min_oil_prod_mae: 0.1761 - val_min_oil_prod_mape: 250.3756 - val_max_oil_prod_mae: 0.1593 - val_max_oil_prod_mape: 618.0728 - val_avg_oil_prod_mae: 0.1147 - val_avg_oil_prod_mape: 96.8866 - val_total_water_need_mae: 0.0962 - val_total_water_need_mape: 113.5497 - best_total_water_need_mae: 0.0962 - lr: 2.2122e-04\n",
|
||
"Epoch 32/200\n",
|
||
"80/80 [==============================] - 35s 434ms/step - loss: 0.0220 - mae: 0.1309 - mape: 150.9560 - val_loss: 0.0188 - val_mae: 0.1264 - val_mape: 163.6075 - lr: 2.1548e-04\n",
|
||
"Epoch 33/200\n",
|
||
"80/80 [==============================] - 36s 436ms/step - loss: 0.0229 - mae: 0.1333 - mape: 166.5528 - val_loss: 0.0165 - val_mae: 0.1219 - val_mape: 138.8618 - lr: 2.0988e-04\n",
|
||
"Epoch 34/200\n",
|
||
"80/80 [==============================] - 36s 440ms/step - loss: 0.0231 - mae: 0.1332 - mape: 166.1271 - val_loss: 0.0155 - val_mae: 0.1149 - val_mape: 156.8145 - lr: 2.0443e-04\n",
|
||
"Epoch 35/200\n",
|
||
"80/80 [==============================] - 37s 449ms/step - loss: 0.0208 - mae: 0.1283 - mape: 162.5032 - val_loss: 0.0134 - val_mae: 0.1000 - val_mape: 129.9323 - lr: 1.9912e-04\n",
|
||
"Epoch 36/200\n",
|
||
"80/80 [==============================] - 255s 3s/step - loss: 0.0206 - mae: 0.1278 - mape: 157.6071 - val_loss: 0.0129 - val_mae: 0.0970 - val_mape: 124.6722 - val_olive_prod_mae: 0.1012 - val_olive_prod_mape: 78.9201 - best_olive_prod_mae: 0.1012 - val_min_oil_prod_mae: 0.1006 - val_min_oil_prod_mape: 102.2447 - val_max_oil_prod_mae: 0.0998 - val_max_oil_prod_mape: 267.4592 - best_max_oil_prod_mae: 0.0998 - val_avg_oil_prod_mae: 0.0957 - val_avg_oil_prod_mape: 76.1857 - best_avg_oil_prod_mae: 0.0957 - val_total_water_need_mae: 0.0878 - val_total_water_need_mape: 98.5509 - best_total_water_need_mae: 0.0878 - lr: 1.9395e-04\n",
|
||
"Epoch 37/200\n",
|
||
"80/80 [==============================] - 36s 444ms/step - loss: 0.0205 - mae: 0.1267 - mape: 152.8490 - val_loss: 0.0119 - val_mae: 0.0886 - val_mape: 128.2448 - lr: 1.8891e-04\n",
|
||
"Epoch 38/200\n",
|
||
"80/80 [==============================] - 36s 441ms/step - loss: 0.0202 - mae: 0.1257 - mape: 136.0964 - val_loss: 0.0129 - val_mae: 0.0986 - val_mape: 124.0754 - lr: 1.8400e-04\n",
|
||
"Epoch 39/200\n",
|
||
"80/80 [==============================] - 37s 451ms/step - loss: 0.0201 - mae: 0.1259 - mape: 149.9923 - val_loss: 0.0116 - val_mae: 0.0882 - val_mape: 101.0983 - lr: 1.7923e-04\n",
|
||
"Epoch 40/200\n",
|
||
"80/80 [==============================] - 36s 438ms/step - loss: 0.0202 - mae: 0.1252 - mape: 162.2166 - val_loss: 0.0129 - val_mae: 0.0950 - val_mape: 103.8257 - lr: 1.7457e-04\n",
|
||
"Epoch 41/200\n",
|
||
"80/80 [==============================] - 249s 3s/step - loss: 0.0201 - mae: 0.1254 - mape: 167.0735 - val_loss: 0.0126 - val_mae: 0.0987 - val_mape: 110.8911 - val_olive_prod_mae: 0.1091 - val_olive_prod_mape: 76.9292 - val_min_oil_prod_mae: 0.1120 - val_min_oil_prod_mape: 92.9203 - val_max_oil_prod_mae: 0.0993 - val_max_oil_prod_mape: 259.1275 - best_max_oil_prod_mae: 0.0993 - val_avg_oil_prod_mae: 0.1080 - val_avg_oil_prod_mape: 77.5547 - val_total_water_need_mae: 0.0651 - val_total_water_need_mape: 47.9241 - best_total_water_need_mae: 0.0651 - lr: 1.7004e-04\n",
|
||
"Epoch 42/200\n",
|
||
"80/80 [==============================] - 36s 448ms/step - loss: 0.0196 - mae: 0.1243 - mape: 157.7031 - val_loss: 0.0140 - val_mae: 0.1126 - val_mape: 122.0974 - lr: 1.6562e-04\n",
|
||
"Epoch 43/200\n",
|
||
"80/80 [==============================] - 36s 444ms/step - loss: 0.0192 - mae: 0.1233 - mape: 137.9573 - val_loss: 0.0118 - val_mae: 0.0918 - val_mape: 119.4805 - lr: 1.6132e-04\n",
|
||
"Epoch 44/200\n",
|
||
"80/80 [==============================] - 36s 442ms/step - loss: 0.0192 - mae: 0.1232 - mape: 154.7663 - val_loss: 0.0117 - val_mae: 0.0897 - val_mape: 103.2779 - lr: 1.5713e-04\n",
|
||
"Epoch 45/200\n",
|
||
"80/80 [==============================] - 36s 442ms/step - loss: 0.0190 - mae: 0.1226 - mape: 153.6464 - val_loss: 0.0118 - val_mae: 0.0905 - val_mape: 117.5741 - lr: 1.5305e-04\n",
|
||
"Epoch 46/200\n",
|
||
"80/80 [==============================] - 252s 3s/step - loss: 0.0191 - mae: 0.1233 - mape: 150.4706 - val_loss: 0.0131 - val_mae: 0.0959 - val_mape: 114.9800 - val_olive_prod_mae: 0.1016 - val_olive_prod_mape: 76.1410 - val_min_oil_prod_mae: 0.1050 - val_min_oil_prod_mape: 98.5945 - val_max_oil_prod_mae: 0.1024 - val_max_oil_prod_mape: 273.3916 - val_avg_oil_prod_mae: 0.1005 - val_avg_oil_prod_mape: 77.1865 - val_total_water_need_mae: 0.0697 - val_total_water_need_mape: 49.5867 - lr: 1.4907e-04\n",
|
||
"Epoch 47/200\n",
|
||
"80/80 [==============================] - 36s 438ms/step - loss: 0.0187 - mae: 0.1218 - mape: 145.5018 - val_loss: 0.0117 - val_mae: 0.0899 - val_mape: 108.3948 - lr: 1.4520e-04\n",
|
||
"Epoch 48/200\n",
|
||
"80/80 [==============================] - 36s 444ms/step - loss: 0.0185 - mae: 0.1212 - mape: 136.5238 - val_loss: 0.0115 - val_mae: 0.0881 - val_mape: 106.7443 - lr: 1.4143e-04\n",
|
||
"Epoch 49/200\n",
|
||
"80/80 [==============================] - 36s 444ms/step - loss: 0.0184 - mae: 0.1209 - mape: 143.3521 - val_loss: 0.0110 - val_mae: 0.0855 - val_mape: 101.2103 - lr: 1.3776e-04\n",
|
||
"Epoch 50/200\n",
|
||
"80/80 [==============================] - 36s 440ms/step - loss: 0.0182 - mae: 0.1199 - mape: 127.5113 - val_loss: 0.0113 - val_mae: 0.0887 - val_mape: 111.8407 - lr: 1.3418e-04\n",
|
||
"Epoch 51/200\n",
|
||
"80/80 [==============================] - 245s 3s/step - loss: 0.0180 - mae: 0.1196 - mape: 154.3594 - val_loss: 0.0114 - val_mae: 0.0906 - val_mape: 105.7354 - val_olive_prod_mae: 0.0969 - val_olive_prod_mape: 73.9075 - best_olive_prod_mae: 0.0969 - val_min_oil_prod_mae: 0.0960 - val_min_oil_prod_mape: 94.2868 - best_min_oil_prod_mae: 0.0960 - val_max_oil_prod_mae: 0.0943 - val_max_oil_prod_mape: 226.3041 - best_max_oil_prod_mae: 0.0943 - val_avg_oil_prod_mae: 0.0927 - val_avg_oil_prod_mape: 68.3263 - best_avg_oil_prod_mae: 0.0927 - val_total_water_need_mae: 0.0730 - val_total_water_need_mape: 65.8522 - lr: 1.3069e-04\n",
|
||
"Epoch 52/200\n",
|
||
"80/80 [==============================] - 36s 442ms/step - loss: 0.0179 - mae: 0.1191 - mape: 134.7333 - val_loss: 0.0108 - val_mae: 0.0833 - val_mape: 102.4398 - lr: 1.2730e-04\n",
|
||
"Epoch 53/200\n",
|
||
"80/80 [==============================] - 36s 439ms/step - loss: 0.0179 - mae: 0.1194 - mape: 140.6877 - val_loss: 0.0113 - val_mae: 0.0904 - val_mape: 108.1014 - lr: 1.2399e-04\n",
|
||
"Epoch 54/200\n",
|
||
"80/80 [==============================] - 36s 442ms/step - loss: 0.0176 - mae: 0.1187 - mape: 139.4526 - val_loss: 0.0112 - val_mae: 0.0914 - val_mape: 99.9837 - lr: 1.2077e-04\n",
|
||
"Epoch 55/200\n",
|
||
"80/80 [==============================] - 36s 437ms/step - loss: 0.0173 - mae: 0.1176 - mape: 155.5683 - val_loss: 0.0109 - val_mae: 0.0872 - val_mape: 102.1552 - lr: 1.1764e-04\n",
|
||
"Epoch 56/200\n",
|
||
"80/80 [==============================] - 233s 3s/step - loss: 0.0173 - mae: 0.1174 - mape: 147.7777 - val_loss: 0.0110 - val_mae: 0.0888 - val_mape: 94.7351 - val_olive_prod_mae: 0.0954 - val_olive_prod_mape: 75.4792 - best_olive_prod_mae: 0.0954 - val_min_oil_prod_mae: 0.0942 - val_min_oil_prod_mape: 95.7656 - best_min_oil_prod_mae: 0.0942 - val_max_oil_prod_mae: 0.0956 - val_max_oil_prod_mape: 188.4087 - val_avg_oil_prod_mae: 0.0902 - val_avg_oil_prod_mape: 69.8961 - best_avg_oil_prod_mae: 0.0902 - val_total_water_need_mae: 0.0685 - val_total_water_need_mape: 44.1256 - lr: 1.1458e-04\n",
|
||
"Epoch 57/200\n",
|
||
"80/80 [==============================] - 36s 443ms/step - loss: 0.0171 - mae: 0.1171 - mape: 142.1757 - val_loss: 0.0109 - val_mae: 0.0869 - val_mape: 99.5790 - lr: 1.1161e-04\n",
|
||
"Epoch 58/200\n",
|
||
"80/80 [==============================] - 36s 444ms/step - loss: 0.0170 - mae: 0.1168 - mape: 138.0693 - val_loss: 0.0106 - val_mae: 0.0847 - val_mape: 96.3503 - lr: 1.0871e-04\n",
|
||
"Epoch 59/200\n",
|
||
"80/80 [==============================] - 247s 3s/step - loss: 0.0167 - mae: 0.1159 - mape: 136.2257 - val_loss: 0.0115 - val_mae: 0.0938 - val_mape: 98.5057 - val_olive_prod_mae: 0.1011 - val_olive_prod_mape: 72.6107 - val_min_oil_prod_mae: 0.0996 - val_min_oil_prod_mape: 90.0922 - val_max_oil_prod_mae: 0.1000 - val_max_oil_prod_mape: 218.1897 - val_avg_oil_prod_mae: 0.0983 - val_avg_oil_prod_mape: 65.7479 - val_total_water_need_mae: 0.0699 - val_total_water_need_mape: 45.8883 - lr: 1.0045e-04\n",
|
||
"Epoch 62/200\n",
|
||
"80/80 [==============================] - 36s 439ms/step - loss: 0.0167 - mae: 0.1166 - mape: 122.0693 - val_loss: 0.0107 - val_mae: 0.0880 - val_mape: 100.1896 - lr: 9.7846e-05\n",
|
||
"Epoch 63/200\n",
|
||
"80/80 [==============================] - 37s 453ms/step - loss: 0.0165 - mae: 0.1158 - mape: 135.5000 - val_loss: 0.0104 - val_mae: 0.0846 - val_mape: 95.0203 - lr: 9.5304e-05\n",
|
||
"Epoch 64/200\n",
|
||
"80/80 [==============================] - 36s 438ms/step - loss: 0.0163 - mae: 0.1150 - mape: 142.8906 - val_loss: 0.0107 - val_mae: 0.0892 - val_mape: 98.5729 - lr: 9.2829e-05\n",
|
||
"Epoch 65/200\n",
|
||
"80/80 [==============================] - 36s 440ms/step - loss: 0.0162 - mae: 0.1147 - mape: 139.8213 - val_loss: 0.0108 - val_mae: 0.0894 - val_mape: 112.6549 - lr: 9.0418e-05\n",
|
||
"Epoch 66/200\n",
|
||
"80/80 [==============================] - 237s 3s/step - loss: 0.0161 - mae: 0.1148 - mape: 140.7891 - val_loss: 0.0104 - val_mae: 0.0866 - val_mape: 100.1666 - val_olive_prod_mae: 0.0930 - val_olive_prod_mape: 76.0337 - best_olive_prod_mae: 0.0930 - val_min_oil_prod_mae: 0.0930 - val_min_oil_prod_mape: 93.2880 - best_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0929 - val_max_oil_prod_mape: 222.5383 - best_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0891 - val_avg_oil_prod_mape: 67.5470 - best_avg_oil_prod_mae: 0.0891 - val_total_water_need_mae: 0.0648 - val_total_water_need_mape: 41.4260 - best_total_water_need_mae: 0.0648 - lr: 8.8070e-05\n",
|
||
"Epoch 67/200\n",
|
||
"80/80 [==============================] - 36s 439ms/step - loss: 0.0161 - mae: 0.1147 - mape: 132.5854 - val_loss: 0.0104 - val_mae: 0.0870 - val_mape: 95.3056 - lr: 8.5782e-05\n",
|
||
"Epoch 68/200\n",
|
||
"80/80 [==============================] - 36s 441ms/step - loss: 0.0159 - mae: 0.1139 - mape: 133.2579 - val_loss: 0.0104 - val_mae: 0.0879 - val_mape: 96.8682 - lr: 8.3555e-05\n",
|
||
"Epoch 69/200\n",
|
||
"80/80 [==============================] - 36s 437ms/step - loss: 0.0159 - mae: 0.1142 - mape: 134.3016 - val_loss: 0.0110 - val_mae: 0.0905 - val_mape: 102.5002 - lr: 8.1384e-05\n",
|
||
"Epoch 70/200\n",
|
||
"80/80 [==============================] - 36s 441ms/step - loss: 0.0158 - mae: 0.1136 - mape: 127.2419 - val_loss: 0.0104 - val_mae: 0.0881 - val_mape: 104.7385 - lr: 7.9271e-05\n",
|
||
"Epoch 71/200\n",
|
||
"80/80 [==============================] - 37s 450ms/step - loss: 0.0155 - mae: 0.1132 - mape: 129.8317 - val_loss: 0.0103 - val_mae: 0.0860 - val_mape: 99.2322 - lr: 7.3253e-05\n",
|
||
"Epoch 74/200\n",
|
||
"80/80 [==============================] - 37s 450ms/step - loss: 0.0155 - mae: 0.1131 - mape: 138.3970 - val_loss: 0.0103 - val_mae: 0.0895 - val_mape: 110.4572 - lr: 7.1351e-05\n",
|
||
"Epoch 75/200\n",
|
||
"80/80 [==============================] - 37s 456ms/step - loss: 0.0153 - mae: 0.1125 - mape: 134.7098 - val_loss: 0.0099 - val_mae: 0.0838 - val_mape: 101.4078 - lr: 6.9498e-05\n",
|
||
"Epoch 76/200\n",
|
||
"80/80 [==============================] - 209s 3s/step - loss: 0.0153 - mae: 0.1127 - mape: 133.3882 - val_loss: 0.0103 - val_mae: 0.0884 - val_mape: 101.1207 - val_olive_prod_mae: 0.0948 - val_olive_prod_mape: 73.1592 - val_min_oil_prod_mae: 0.0959 - val_min_oil_prod_mape: 92.9563 - val_max_oil_prod_mae: 0.0954 - val_max_oil_prod_mape: 230.6032 - val_avg_oil_prod_mae: 0.0924 - val_avg_oil_prod_mape: 66.7822 - val_total_water_need_mae: 0.0637 - val_total_water_need_mape: 42.1024 - lr: 6.7693e-05\n",
|
||
"Epoch 77/200\n",
|
||
"80/80 [==============================] - 37s 454ms/step - loss: 0.0151 - mae: 0.1123 - mape: 129.7830 - val_loss: 0.0099 - val_mae: 0.0844 - val_mape: 100.0219 - lr: 6.5935e-05\n",
|
||
"Epoch 78/200\n",
|
||
"80/80 [==============================] - 37s 448ms/step - loss: 0.0151 - mae: 0.1121 - mape: 128.3232 - val_loss: 0.0098 - val_mae: 0.0846 - val_mape: 104.9378 - lr: 6.4222e-05\n",
|
||
"Epoch 79/200\n",
|
||
"80/80 [==============================] - 36s 443ms/step - loss: 0.0150 - mae: 0.1119 - mape: 136.6634 - val_loss: 0.0098 - val_mae: 0.0831 - val_mape: 94.8757 - lr: 6.2554e-05\n",
|
||
"Epoch 80/200\n",
|
||
"80/80 [==============================] - 229s 3s/step - loss: 0.0149 - mae: 0.1113 - mape: 154.3307 - val_loss: 0.0097 - val_mae: 0.0837 - val_mape: 100.8595 - val_olive_prod_mae: 0.0925 - val_olive_prod_mape: 74.3520 - best_olive_prod_mae: 0.0925 - val_min_oil_prod_mae: 0.0903 - val_min_oil_prod_mape: 93.5097 - best_min_oil_prod_mae: 0.0903 - val_max_oil_prod_mae: 0.0895 - val_max_oil_prod_mape: 224.7449 - best_max_oil_prod_mae: 0.0895 - val_avg_oil_prod_mae: 0.0861 - val_avg_oil_prod_mape: 65.9836 - best_avg_oil_prod_mae: 0.0861 - val_total_water_need_mae: 0.0603 - val_total_water_need_mape: 45.7072 - lr: 5.9347e-05\n",
|
||
"Epoch 82/200\n",
|
||
"80/80 [==============================] - 36s 437ms/step - loss: 0.0148 - mae: 0.1116 - mape: 142.9829 - val_loss: 0.0095 - val_mae: 0.0829 - val_mape: 104.9523 - lr: 5.7806e-05\n",
|
||
"Epoch 83/200\n",
|
||
"80/80 [==============================] - 36s 436ms/step - loss: 0.0148 - mae: 0.1117 - mape: 137.5172 - val_loss: 0.0101 - val_mae: 0.0874 - val_mape: 100.8047 - lr: 5.6304e-05\n",
|
||
"Epoch 84/200\n",
|
||
"80/80 [==============================] - 36s 436ms/step - loss: 0.0147 - mae: 0.1109 - mape: 149.5698 - val_loss: 0.0097 - val_mae: 0.0862 - val_mape: 99.6029 - lr: 5.4842e-05\n",
|
||
"Epoch 85/200\n",
|
||
"80/80 [==============================] - 230s 3s/step - loss: 0.0146 - mae: 0.1108 - mape: 138.4030 - val_loss: 0.0096 - val_mae: 0.0849 - val_mape: 104.8251 - val_olive_prod_mae: 0.0921 - val_olive_prod_mape: 72.9428 - best_olive_prod_mae: 0.0921 - val_min_oil_prod_mae: 0.0908 - val_min_oil_prod_mape: 93.9297 - val_max_oil_prod_mae: 0.0915 - val_max_oil_prod_mape: 243.2711 - val_avg_oil_prod_mae: 0.0882 - val_avg_oil_prod_mape: 68.0993 - val_total_water_need_mae: 0.0617 - val_total_water_need_mape: 45.8819 - lr: 5.2030e-05\n",
|
||
"Epoch 87/200\n",
|
||
"80/80 [==============================] - 36s 440ms/step - loss: 0.0143 - mae: 0.1101 - mape: 133.9900 - val_loss: 0.0095 - val_mae: 0.0845 - val_mape: 103.7470 - lr: 4.8081e-05\n",
|
||
"Epoch 90/200\n",
|
||
"80/80 [==============================] - 36s 437ms/step - loss: 0.0143 - mae: 0.1101 - mape: 129.2636 - val_loss: 0.0094 - val_mae: 0.0834 - val_mape: 107.9905 - lr: 4.6832e-05\n",
|
||
"Epoch 91/200\n",
|
||
"80/80 [==============================] - 264s 3s/step - loss: 0.0142 - mae: 0.1103 - mape: 138.2555 - val_loss: 0.0094 - val_mae: 0.0847 - val_mape: 102.1767 - val_olive_prod_mae: 0.0921 - val_olive_prod_mape: 73.1834 - val_min_oil_prod_mae: 0.0913 - val_min_oil_prod_mape: 93.8826 - val_max_oil_prod_mae: 0.0915 - val_max_oil_prod_mape: 236.0179 - val_avg_oil_prod_mae: 0.0877 - val_avg_oil_prod_mape: 67.3626 - val_total_water_need_mae: 0.0609 - val_total_water_need_mape: 40.4373 - lr: 4.5616e-05\n",
|
||
"Epoch 92/200\n",
|
||
"80/80 [==============================] - 36s 436ms/step - loss: 0.0142 - mae: 0.1099 - mape: 137.3376 - val_loss: 0.0094 - val_mae: 0.0840 - val_mape: 103.3979 - lr: 4.4431e-05\n",
|
||
"Epoch 93/200\n",
|
||
"80/80 [==============================] - 36s 438ms/step - loss: 0.0141 - mae: 0.1097 - mape: 131.1958 - val_loss: 0.0094 - val_mae: 0.0827 - val_mape: 99.3171 - lr: 4.3277e-05\n",
|
||
"Epoch 94/200\n",
|
||
"80/80 [==============================] - 36s 445ms/step - loss: 0.0140 - mae: 0.1094 - mape: 129.6252 - val_loss: 0.0091 - val_mae: 0.0807 - val_mape: 99.7888 - lr: 4.2153e-05\n",
|
||
"Epoch 95/200\n",
|
||
"80/80 [==============================] - 36s 446ms/step - loss: 0.0141 - mae: 0.1099 - mape: 145.5369 - val_loss: 0.0092 - val_mae: 0.0828 - val_mape: 99.6861 - lr: 4.1058e-05\n",
|
||
"Epoch 96/200\n",
|
||
"80/80 [==============================] - 260s 3s/step - loss: 0.0143 - mae: 0.1101 - mape: 133.5272 - val_loss: 0.0095 - val_mae: 0.0844 - val_mape: 94.5343 - val_olive_prod_mae: 0.0933 - val_olive_prod_mape: 72.0466 - val_min_oil_prod_mae: 0.0915 - val_min_oil_prod_mape: 92.5089 - val_max_oil_prod_mae: 0.0918 - val_max_oil_prod_mape: 200.5542 - val_avg_oil_prod_mae: 0.0883 - val_avg_oil_prod_mape: 65.6080 - val_total_water_need_mae: 0.0571 - val_total_water_need_mape: 41.9541 - best_total_water_need_mae: 0.0571 - lr: 3.9992e-05\n",
|
||
"Epoch 97/200\n",
|
||
"80/80 [==============================] - 36s 437ms/step - loss: 0.0139 - mae: 0.1095 - mape: 121.2405 - val_loss: 0.0095 - val_mae: 0.0845 - val_mape: 101.0546 - lr: 3.8953e-05\n",
|
||
"Epoch 98/200\n",
|
||
"80/80 [==============================] - 36s 438ms/step - loss: 0.0139 - mae: 0.1094 - mape: 129.6525 - val_loss: 0.0092 - val_mae: 0.0825 - val_mape: 105.1573 - lr: 3.7941e-05\n",
|
||
"Epoch 99/200\n",
|
||
"80/80 [==============================] - 36s 442ms/step - loss: 0.0139 - mae: 0.1095 - mape: 127.7979 - val_loss: 0.0093 - val_mae: 0.0841 - val_mape: 106.7909 - lr: 3.6956e-05\n",
|
||
"Epoch 100/200\n",
|
||
"80/80 [==============================] - 36s 443ms/step - loss: 0.0138 - mae: 0.1093 - mape: 132.6569 - val_loss: 0.0092 - val_mae: 0.0826 - val_mape: 101.0493 - lr: 3.5996e-05\n",
|
||
"Epoch 101/200\n",
|
||
"80/80 [==============================] - 231s 3s/step - loss: 0.0138 - mae: 0.1092 - mape: 142.6844 - val_loss: 0.0091 - val_mae: 0.0822 - val_mape: 103.1497 - val_olive_prod_mae: 0.0911 - val_olive_prod_mape: 74.2005 - best_olive_prod_mae: 0.0911 - val_min_oil_prod_mae: 0.0893 - val_min_oil_prod_mape: 95.3363 - best_min_oil_prod_mae: 0.0893 - val_max_oil_prod_mae: 0.0891 - val_max_oil_prod_mape: 236.3043 - best_max_oil_prod_mae: 0.0891 - val_avg_oil_prod_mae: 0.0856 - val_avg_oil_prod_mape: 67.7334 - best_avg_oil_prod_mae: 0.0856 - val_total_water_need_mae: 0.0560 - val_total_water_need_mape: 42.1736 - best_total_water_need_mae: 0.0560 - lr: 3.5061e-05\n",
|
||
"Epoch 102/200\n",
|
||
"80/80 [==============================] - 36s 447ms/step - loss: 0.0137 - mae: 0.1087 - mape: 120.9930 - val_loss: 0.0092 - val_mae: 0.0840 - val_mape: 102.9081 - lr: 3.4151e-05\n",
|
||
"Epoch 103/200\n",
|
||
"80/80 [==============================] - 37s 453ms/step - loss: 0.0137 - mae: 0.1090 - mape: 145.3493 - val_loss: 0.0090 - val_mae: 0.0820 - val_mape: 103.3405 - lr: 3.3264e-05\n",
|
||
"Epoch 104/200\n",
|
||
"80/80 [==============================] - 37s 447ms/step - loss: 0.0137 - mae: 0.1088 - mape: 132.0474 - val_loss: 0.0088 - val_mae: 0.0788 - val_mape: 99.5223 - lr: 3.2400e-05\n",
|
||
"Epoch 105/200\n",
|
||
"80/80 [==============================] - 36s 443ms/step - loss: 0.0136 - mae: 0.1089 - mape: 143.3882 - val_loss: 0.0090 - val_mae: 0.0820 - val_mape: 102.5941 - lr: 3.1558e-05\n",
|
||
"Epoch 106/200\n",
|
||
"80/80 [==============================] - 233s 3s/step - loss: 0.0136 - mae: 0.1085 - mape: 130.0781 - val_loss: 0.0090 - val_mae: 0.0824 - val_mape: 100.9554 - val_olive_prod_mae: 0.0908 - val_olive_prod_mape: 73.1759 - best_olive_prod_mae: 0.0908 - val_min_oil_prod_mae: 0.0893 - val_min_oil_prod_mape: 93.9239 - val_max_oil_prod_mae: 0.0895 - val_max_oil_prod_mape: 228.2562 - val_avg_oil_prod_mae: 0.0859 - val_avg_oil_prod_mape: 66.5741 - val_total_water_need_mae: 0.0565 - val_total_water_need_mape: 42.8471 - lr: 3.0739e-05\n",
|
||
"Epoch 107/200\n",
|
||
"80/80 [==============================] - 36s 440ms/step - loss: 0.0136 - mae: 0.1088 - mape: 128.6352 - val_loss: 0.0090 - val_mae: 0.0816 - val_mape: 101.1500 - lr: 2.9940e-05\n",
|
||
"Epoch 108/200\n",
|
||
"80/80 [==============================] - 36s 442ms/step - loss: 0.0135 - mae: 0.1085 - mape: 139.2546 - val_loss: 0.0091 - val_mae: 0.0820 - val_mape: 100.3162 - lr: 2.9163e-05\n",
|
||
"Epoch 109/200\n",
|
||
"80/80 [==============================] - 36s 446ms/step - loss: 0.0134 - mae: 0.1086 - mape: 148.4389 - val_loss: 0.0087 - val_mae: 0.0794 - val_mape: 102.8431 - lr: 2.8405e-05\n",
|
||
"Epoch 110/200\n",
|
||
"80/80 [==============================] - 36s 443ms/step - loss: 0.0135 - mae: 0.1084 - mape: 126.4040 - val_loss: 0.0088 - val_mae: 0.0805 - val_mape: 100.3828 - lr: 2.7668e-05\n",
|
||
"Epoch 111/200\n",
|
||
"80/80 [==============================] - 245s 3s/step - loss: 0.0134 - mae: 0.1080 - mape: 138.5028 - val_loss: 0.0088 - val_mae: 0.0802 - val_mape: 103.2501 - val_olive_prod_mae: 0.0886 - val_olive_prod_mape: 73.6275 - best_olive_prod_mae: 0.0886 - val_min_oil_prod_mae: 0.0870 - val_min_oil_prod_mape: 94.9520 - best_min_oil_prod_mae: 0.0870 - val_max_oil_prod_mae: 0.0869 - val_max_oil_prod_mape: 239.6956 - best_max_oil_prod_mae: 0.0869 - val_avg_oil_prod_mae: 0.0836 - val_avg_oil_prod_mape: 67.5310 - best_avg_oil_prod_mae: 0.0836 - val_total_water_need_mae: 0.0550 - val_total_water_need_mape: 40.4444 - best_total_water_need_mae: 0.0550 - lr: 2.6949e-05\n",
|
||
"Epoch 112/200\n",
|
||
"80/80 [==============================] - 35s 434ms/step - loss: 0.0133 - mae: 0.1080 - mape: 140.4691 - val_loss: 0.0088 - val_mae: 0.0800 - val_mape: 100.8578 - lr: 2.6249e-05\n",
|
||
"Epoch 113/200\n",
|
||
"80/80 [==============================] - 36s 445ms/step - loss: 0.0133 - mae: 0.1082 - mape: 128.5066 - val_loss: 0.0087 - val_mae: 0.0808 - val_mape: 102.7916 - lr: 2.5567e-05\n",
|
||
"Epoch 114/200\n",
|
||
"80/80 [==============================] - 36s 443ms/step - loss: 0.0133 - mae: 0.1083 - mape: 148.3104 - val_loss: 0.0088 - val_mae: 0.0817 - val_mape: 100.1457 - lr: 2.4903e-05\n",
|
||
"Epoch 115/200\n",
|
||
"80/80 [==============================] - 247s 3s/step - loss: 0.0132 - mae: 0.1079 - mape: 145.9036 - val_loss: 0.0086 - val_mae: 0.0797 - val_mape: 102.3304 - val_olive_prod_mae: 0.0879 - val_olive_prod_mape: 74.0978 - best_olive_prod_mae: 0.0879 - val_min_oil_prod_mae: 0.0872 - val_min_oil_prod_mape: 94.1461 - val_max_oil_prod_mae: 0.0865 - val_max_oil_prod_mape: 235.5081 - best_max_oil_prod_mae: 0.0865 - val_avg_oil_prod_mae: 0.0833 - val_avg_oil_prod_mape: 67.5759 - best_avg_oil_prod_mae: 0.0833 - val_total_water_need_mae: 0.0535 - val_total_water_need_mape: 40.3236 - best_total_water_need_mae: 0.0535 - lr: 2.3626e-05\n",
|
||
"Epoch 117/200\n",
|
||
"80/80 [==============================] - 36s 436ms/step - loss: 0.0132 - mae: 0.1078 - mape: 141.3102 - val_loss: 0.0089 - val_mae: 0.0828 - val_mape: 104.7780 - lr: 2.3013e-05\n",
|
||
"Epoch 118/200\n",
|
||
"80/80 [==============================] - 36s 440ms/step - loss: 0.0132 - mae: 0.1081 - mape: 132.8581 - val_loss: 0.0086 - val_mae: 0.0797 - val_mape: 100.6831 - lr: 2.2415e-05\n",
|
||
"Epoch 119/200\n",
|
||
"80/80 [==============================] - 231s 3s/step - loss: 0.0131 - mae: 0.1078 - mape: 133.2317 - val_loss: 0.0086 - val_mae: 0.0792 - val_mape: 101.1063 - val_olive_prod_mae: 0.0877 - val_olive_prod_mape: 73.5447 - best_olive_prod_mae: 0.0877 - val_min_oil_prod_mae: 0.0867 - val_min_oil_prod_mape: 94.1456 - best_min_oil_prod_mae: 0.0867 - val_max_oil_prod_mae: 0.0868 - val_max_oil_prod_mape: 231.5488 - val_avg_oil_prod_mae: 0.0835 - val_avg_oil_prod_mape: 66.7244 - val_total_water_need_mae: 0.0511 - val_total_water_need_mape: 39.5675 - best_total_water_need_mae: 0.0511 - lr: 2.0714e-05\n",
|
||
"Epoch 122/200\n",
|
||
"80/80 [==============================] - 36s 442ms/step - loss: 0.0131 - mae: 0.1077 - mape: 135.5140 - val_loss: 0.0085 - val_mae: 0.0784 - val_mape: 100.5666 - lr: 2.0176e-05\n",
|
||
"Epoch 123/200\n",
|
||
"80/80 [==============================] - 35s 435ms/step - loss: 0.0131 - mae: 0.1077 - mape: 137.8844 - val_loss: 0.0086 - val_mae: 0.0798 - val_mape: 102.1380 - lr: 1.9652e-05\n",
|
||
"Epoch 124/200\n",
|
||
"80/80 [==============================] - 37s 457ms/step - loss: 0.0130 - mae: 0.1074 - mape: 126.6170 - val_loss: 0.0084 - val_mae: 0.0780 - val_mape: 98.9292 - lr: 1.9141e-05\n",
|
||
"Epoch 125/200\n",
|
||
"80/80 [==============================] - 36s 447ms/step - loss: 0.0130 - mae: 0.1074 - mape: 124.3804 - val_loss: 0.0085 - val_mae: 0.0795 - val_mape: 99.5572 - lr: 1.8644e-05\n",
|
||
"Epoch 126/200\n",
|
||
"80/80 [==============================] - 258s 3s/step - loss: 0.0130 - mae: 0.1073 - mape: 140.2923 - val_loss: 0.0085 - val_mae: 0.0792 - val_mape: 101.3633 - val_olive_prod_mae: 0.0875 - val_olive_prod_mape: 73.1242 - best_olive_prod_mae: 0.0875 - val_min_oil_prod_mae: 0.0866 - val_min_oil_prod_mape: 93.7120 - best_min_oil_prod_mae: 0.0866 - val_max_oil_prod_mae: 0.0864 - val_max_oil_prod_mape: 233.8133 - best_max_oil_prod_mae: 0.0864 - val_avg_oil_prod_mae: 0.0832 - val_avg_oil_prod_mape: 66.9306 - best_avg_oil_prod_mae: 0.0832 - val_total_water_need_mae: 0.0522 - val_total_water_need_mape: 39.2365 - lr: 1.8160e-05\n",
|
||
"Epoch 127/200\n",
|
||
"80/80 [==============================] - 36s 434ms/step - loss: 0.0130 - mae: 0.1072 - mape: 136.5076 - val_loss: 0.0085 - val_mae: 0.0797 - val_mape: 100.7464 - lr: 1.7688e-05\n",
|
||
"Epoch 128/200\n",
|
||
"80/80 [==============================] - 35s 434ms/step - loss: 0.0129 - mae: 0.1075 - mape: 138.2281 - val_loss: 0.0086 - val_mae: 0.0807 - val_mape: 99.2860 - lr: 1.7229e-05\n",
|
||
"Epoch 129/200\n",
|
||
"80/80 [==============================] - 36s 440ms/step - loss: 0.0130 - mae: 0.1073 - mape: 132.3837 - val_loss: 0.0085 - val_mae: 0.0796 - val_mape: 100.0050 - lr: 1.6781e-05\n",
|
||
"Epoch 130/200\n",
|
||
"80/80 [==============================] - 36s 442ms/step - loss: 0.0128 - mae: 0.1071 - mape: 147.8209 - val_loss: 0.0085 - val_mae: 0.0797 - val_mape: 101.9830 - lr: 1.6346e-05\n",
|
||
"Epoch 131/200\n",
|
||
"80/80 [==============================] - 37s 457ms/step - loss: 0.0128 - mae: 0.1070 - mape: 132.8198 - val_loss: 0.0084 - val_mae: 0.0784 - val_mape: 100.2413 - lr: 1.5105e-05\n",
|
||
"Epoch 134/200\n",
|
||
"80/80 [==============================] - 37s 453ms/step - loss: 0.0128 - mae: 0.1071 - mape: 122.8966 - val_loss: 0.0084 - val_mae: 0.0792 - val_mape: 99.7014 - lr: 1.4712e-05\n",
|
||
"Epoch 135/200\n",
|
||
"80/80 [==============================] - 37s 451ms/step - loss: 0.0128 - mae: 0.1069 - mape: 131.0883 - val_loss: 0.0085 - val_mae: 0.0802 - val_mape: 101.1884 - lr: 1.4330e-05\n",
|
||
"Epoch 136/200\n",
|
||
"80/80 [==============================] - 194s 2s/step - loss: 0.0128 - mae: 0.1069 - mape: 125.1339 - val_loss: 0.0084 - val_mae: 0.0795 - val_mape: 100.6931 - val_olive_prod_mae: 0.0883 - val_olive_prod_mape: 72.7114 - val_min_oil_prod_mae: 0.0870 - val_min_oil_prod_mape: 93.7169 - val_max_oil_prod_mae: 0.0869 - val_max_oil_prod_mape: 230.8019 - val_avg_oil_prod_mae: 0.0836 - val_avg_oil_prod_mape: 66.5721 - val_total_water_need_mae: 0.0517 - val_total_water_need_mape: 39.6633 - lr: 1.3958e-05\n",
|
||
"Epoch 137/200\n",
|
||
"80/80 [==============================] - 37s 458ms/step - loss: 0.0128 - mae: 0.1069 - mape: 141.0860 - val_loss: 0.0083 - val_mae: 0.0783 - val_mape: 99.3577 - lr: 1.3596e-05\n",
|
||
"Epoch 138/200\n",
|
||
"80/80 [==============================] - 38s 460ms/step - loss: 0.0128 - mae: 0.1068 - mape: 126.0336 - val_loss: 0.0084 - val_mae: 0.0791 - val_mape: 103.3938 - lr: 1.3242e-05\n",
|
||
"Epoch 139/200\n",
|
||
"80/80 [==============================] - 37s 460ms/step - loss: 0.0127 - mae: 0.1068 - mape: 141.5065 - val_loss: 0.0084 - val_mae: 0.0796 - val_mape: 100.4431 - lr: 1.2899e-05\n",
|
||
"Epoch 140/200\n",
|
||
"80/80 [==============================] - 37s 456ms/step - loss: 0.0127 - mae: 0.1068 - mape: 131.7737 - val_loss: 0.0084 - val_mae: 0.0797 - val_mape: 100.3351 - lr: 1.2564e-05\n",
|
||
"Epoch 141/200\n",
|
||
"80/80 [==============================] - 198s 2s/step - loss: 0.0126 - mae: 0.1066 - mape: 135.8964 - val_loss: 0.0084 - val_mae: 0.0797 - val_mape: 100.3036 - val_olive_prod_mae: 0.0880 - val_olive_prod_mape: 73.0974 - val_min_oil_prod_mae: 0.0873 - val_min_oil_prod_mape: 93.2542 - val_max_oil_prod_mae: 0.0869 - val_max_oil_prod_mape: 228.5796 - val_avg_oil_prod_mae: 0.0839 - val_avg_oil_prod_mape: 65.8285 - val_total_water_need_mae: 0.0525 - val_total_water_need_mape: 40.7585 - lr: 1.2237e-05\n",
|
||
"Epoch 142/200\n",
|
||
"80/80 [==============================] - 36s 445ms/step - loss: 0.0127 - mae: 0.1069 - mape: 144.6937 - val_loss: 0.0083 - val_mae: 0.0784 - val_mape: 98.5027 - lr: 1.1919e-05\n",
|
||
"Epoch 143/200\n",
|
||
"80/80 [==============================] - 36s 442ms/step - loss: 0.0126 - mae: 0.1066 - mape: 140.0901 - val_loss: 0.0083 - val_mae: 0.0787 - val_mape: 100.0019 - lr: 1.1610e-05\n",
|
||
"Epoch 144/200\n",
|
||
"80/80 [==============================] - 36s 444ms/step - loss: 0.0126 - mae: 0.1066 - mape: 142.0852 - val_loss: 0.0083 - val_mae: 0.0795 - val_mape: 101.1596 - lr: 1.1308e-05\n",
|
||
"Epoch 145/200\n",
|
||
"80/80 [==============================] - 239s 3s/step - loss: 0.0126 - mae: 0.1066 - mape: 132.7490 - val_loss: 0.0083 - val_mae: 0.0790 - val_mape: 100.0930 - val_olive_prod_mae: 0.0877 - val_olive_prod_mape: 73.2577 - val_min_oil_prod_mae: 0.0865 - val_min_oil_prod_mape: 93.5152 - val_max_oil_prod_mae: 0.0862 - val_max_oil_prod_mape: 226.7579 - val_avg_oil_prod_mae: 0.0830 - val_avg_oil_prod_mape: 66.1355 - val_total_water_need_mae: 0.0514 - val_total_water_need_mape: 40.7990 - lr: 1.0729e-05\n",
|
||
"Epoch 147/200\n",
|
||
"80/80 [==============================] - 36s 447ms/step - loss: 0.0126 - mae: 0.1063 - mape: 131.2485 - val_loss: 0.0083 - val_mae: 0.0786 - val_mape: 101.2258 - lr: 1.0450e-05\n",
|
||
"Epoch 148/200\n",
|
||
"80/80 [==============================] - 36s 447ms/step - loss: 0.0126 - mae: 0.1064 - mape: 117.1725 - val_loss: 0.0082 - val_mae: 0.0786 - val_mape: 102.0448 - lr: 1.0179e-05\n",
|
||
"Epoch 149/200\n",
|
||
"80/80 [==============================] - 36s 444ms/step - loss: 0.0126 - mae: 0.1067 - mape: 155.9800 - val_loss: 0.0082 - val_mae: 0.0778 - val_mape: 98.7628 - lr: 9.9142e-06\n",
|
||
"Epoch 150/200\n",
|
||
"80/80 [==============================] - 36s 442ms/step - loss: 0.0125 - mae: 0.1064 - mape: 137.1006 - val_loss: 0.0083 - val_mae: 0.0791 - val_mape: 101.1951 - lr: 9.6567e-06\n",
|
||
"Epoch 151/200\n",
|
||
"80/80 [==============================] - 231s 3s/step - loss: 0.0125 - mae: 0.1062 - mape: 133.0278 - val_loss: 0.0082 - val_mae: 0.0780 - val_mape: 101.7752 - val_olive_prod_mae: 0.0869 - val_olive_prod_mape: 73.5762 - val_min_oil_prod_mae: 0.0858 - val_min_oil_prod_mape: 94.9374 - best_min_oil_prod_mae: 0.0858 - val_max_oil_prod_mae: 0.0854 - val_max_oil_prod_mape: 233.4457 - best_max_oil_prod_mae: 0.0854 - val_avg_oil_prod_mae: 0.0825 - val_avg_oil_prod_mape: 66.9196 - best_avg_oil_prod_mae: 0.0825 - val_total_water_need_mae: 0.0496 - val_total_water_need_mape: 39.9972 - lr: 9.4059e-06\n",
|
||
"Epoch 152/200\n",
|
||
"80/80 [==============================] - 36s 442ms/step - loss: 0.0125 - mae: 0.1063 - mape: 139.6716 - val_loss: 0.0082 - val_mae: 0.0778 - val_mape: 101.3717 - lr: 9.1616e-06\n",
|
||
"Epoch 153/200\n",
|
||
"80/80 [==============================] - 36s 436ms/step - loss: 0.0125 - mae: 0.1063 - mape: 136.4329 - val_loss: 0.0083 - val_mae: 0.0793 - val_mape: 101.8978 - lr: 8.9236e-06\n",
|
||
"Epoch 154/200\n",
|
||
"80/80 [==============================] - 37s 456ms/step - loss: 0.0125 - mae: 0.1065 - mape: 136.6036 - val_loss: 0.0082 - val_mae: 0.0774 - val_mape: 100.2120 - lr: 8.6919e-06\n",
|
||
"Epoch 155/200\n",
|
||
"80/80 [==============================] - 36s 441ms/step - loss: 0.0124 - mae: 0.1063 - mape: 121.0664 - val_loss: 0.0083 - val_mae: 0.0795 - val_mape: 100.9535 - lr: 8.4661e-06\n",
|
||
"Epoch 156/200\n",
|
||
"80/80 [==============================] - 257s 3s/step - loss: 0.0125 - mae: 0.1062 - mape: 128.6301 - val_loss: 0.0082 - val_mae: 0.0785 - val_mape: 100.7175 - val_olive_prod_mae: 0.0871 - val_olive_prod_mape: 73.1359 - val_min_oil_prod_mae: 0.0860 - val_min_oil_prod_mape: 94.4131 - val_max_oil_prod_mae: 0.0856 - val_max_oil_prod_mape: 230.1177 - val_avg_oil_prod_mae: 0.0827 - val_avg_oil_prod_mape: 66.3622 - val_total_water_need_mae: 0.0511 - val_total_water_need_mape: 39.5585 - lr: 8.2462e-06\n",
|
||
"Epoch 157/200\n",
|
||
"80/80 [==============================] - 36s 442ms/step - loss: 0.0125 - mae: 0.1066 - mape: 140.2754 - val_loss: 0.0082 - val_mae: 0.0790 - val_mape: 100.1723 - lr: 8.0321e-06\n",
|
||
"Epoch 158/200\n",
|
||
"80/80 [==============================] - 36s 446ms/step - loss: 0.0125 - mae: 0.1063 - mape: 140.8587 - val_loss: 0.0082 - val_mae: 0.0784 - val_mape: 101.4369 - lr: 7.8235e-06\n",
|
||
"Epoch 159/200\n",
|
||
"80/80 [==============================] - 36s 438ms/step - loss: 0.0124 - mae: 0.1061 - mape: 130.1687 - val_loss: 0.0082 - val_mae: 0.0785 - val_mape: 100.3944 - lr: 7.6203e-06\n",
|
||
"Epoch 160/200\n",
|
||
"80/80 [==============================] - 36s 440ms/step - loss: 0.0124 - mae: 0.1062 - mape: 125.2505 - val_loss: 0.0082 - val_mae: 0.0787 - val_mape: 100.8615 - lr: 7.4224e-06\n",
|
||
"Epoch 161/200\n",
|
||
"80/80 [==============================] - 260s 3s/step - loss: 0.0125 - mae: 0.1064 - mape: 130.2965 - val_loss: 0.0081 - val_mae: 0.0776 - val_mape: 100.5380 - val_olive_prod_mae: 0.0866 - val_olive_prod_mape: 73.4325 - best_olive_prod_mae: 0.0866 - val_min_oil_prod_mae: 0.0857 - val_min_oil_prod_mape: 94.5307 - best_min_oil_prod_mae: 0.0857 - val_max_oil_prod_mae: 0.0854 - val_max_oil_prod_mape: 226.6531 - best_max_oil_prod_mae: 0.0854 - val_avg_oil_prod_mae: 0.0824 - val_avg_oil_prod_mape: 66.6267 - best_avg_oil_prod_mae: 0.0824 - val_total_water_need_mae: 0.0479 - val_total_water_need_mape: 41.4470 - best_total_water_need_mae: 0.0479 - lr: 7.2296e-06\n",
|
||
"Epoch 162/200\n",
|
||
"80/80 [==============================] - 36s 440ms/step - loss: 0.0124 - mae: 0.1062 - mape: 130.7567 - val_loss: 0.0082 - val_mae: 0.0791 - val_mape: 100.3720 - lr: 7.0418e-06\n",
|
||
"Epoch 163/200\n",
|
||
"80/80 [==============================] - 36s 439ms/step - loss: 0.0124 - mae: 0.1060 - mape: 123.9180 - val_loss: 0.0081 - val_mae: 0.0782 - val_mape: 101.3976 - lr: 6.8589e-06\n",
|
||
"Epoch 164/200\n",
|
||
"80/80 [==============================] - 36s 443ms/step - loss: 0.0124 - mae: 0.1060 - mape: 143.9072 - val_loss: 0.0081 - val_mae: 0.0784 - val_mape: 102.0180 - lr: 6.6808e-06\n",
|
||
"Epoch 165/200\n",
|
||
"80/80 [==============================] - 37s 453ms/step - loss: 0.0124 - mae: 0.1061 - mape: 118.9328 - val_loss: 0.0081 - val_mae: 0.0780 - val_mape: 100.5574 - lr: 6.5073e-06\n",
|
||
"Epoch 166/200\n",
|
||
"80/80 [==============================] - 249s 3s/step - loss: 0.0123 - mae: 0.1060 - mape: 135.2133 - val_loss: 0.0082 - val_mae: 0.0787 - val_mape: 102.0016 - val_olive_prod_mae: 0.0872 - val_olive_prod_mape: 72.9689 - val_min_oil_prod_mae: 0.0864 - val_min_oil_prod_mape: 94.5919 - val_max_oil_prod_mae: 0.0861 - val_max_oil_prod_mape: 234.7237 - val_avg_oil_prod_mae: 0.0830 - val_avg_oil_prod_mape: 66.6378 - val_total_water_need_mae: 0.0507 - val_total_water_need_mape: 41.0858 - lr: 6.3383e-06\n",
|
||
"Epoch 167/200\n",
|
||
"80/80 [==============================] - 36s 438ms/step - loss: 0.0123 - mae: 0.1060 - mape: 146.5321 - val_loss: 0.0081 - val_mae: 0.0780 - val_mape: 101.1286 - lr: 6.1736e-06\n",
|
||
"Epoch 168/200\n",
|
||
"80/80 [==============================] - 36s 445ms/step - loss: 0.0124 - mae: 0.1060 - mape: 134.5835 - val_loss: 0.0081 - val_mae: 0.0778 - val_mape: 100.2120 - lr: 6.0133e-06\n",
|
||
"Epoch 169/200\n",
|
||
"80/80 [==============================] - ETA: 0s - loss: 0.0123 - mae: 0.1061 - mape: 133.2897\n",
|
||
"Epoch 169: ReduceLROnPlateau reducing learning rate to 1.1714245374605527e-06.\n",
|
||
"80/80 [==============================] - 36s 446ms/step - loss: 0.0123 - mae: 0.1061 - mape: 133.2897 - val_loss: 0.0081 - val_mae: 0.0780 - val_mape: 100.6940 - lr: 5.8571e-06\n",
|
||
"Epoch 170/200\n",
|
||
"80/80 [==============================] - 36s 441ms/step - loss: 0.0123 - mae: 0.1060 - mape: 139.9322 - val_loss: 0.0081 - val_mae: 0.0781 - val_mape: 101.5473 - lr: 5.7050e-06\n",
|
||
"Epoch 171/200\n",
|
||
"80/80 [==============================] - 260s 3s/step - loss: 0.0124 - mae: 0.1061 - mape: 126.8107 - val_loss: 0.0080 - val_mae: 0.0774 - val_mape: 100.5322 - val_olive_prod_mae: 0.0860 - val_olive_prod_mape: 73.5066 - best_olive_prod_mae: 0.0860 - val_min_oil_prod_mae: 0.0851 - val_min_oil_prod_mape: 95.2965 - best_min_oil_prod_mae: 0.0851 - val_max_oil_prod_mae: 0.0851 - val_max_oil_prod_mape: 224.2950 - best_max_oil_prod_mae: 0.0851 - val_avg_oil_prod_mae: 0.0818 - val_avg_oil_prod_mape: 66.9162 - best_avg_oil_prod_mae: 0.0818 - val_total_water_need_mae: 0.0489 - val_total_water_need_mape: 42.6473 - lr: 5.5568e-06\n",
|
||
"Epoch 172/200\n",
|
||
"80/80 [==============================] - 36s 437ms/step - loss: 0.0123 - mae: 0.1060 - mape: 128.4193 - val_loss: 0.0081 - val_mae: 0.0784 - val_mape: 101.6377 - lr: 5.4125e-06\n",
|
||
"Epoch 173/200\n",
|
||
"80/80 [==============================] - 36s 437ms/step - loss: 0.0123 - mae: 0.1061 - mape: 125.6502 - val_loss: 0.0081 - val_mae: 0.0782 - val_mape: 101.5257 - lr: 5.2719e-06\n",
|
||
"Epoch 174/200\n",
|
||
"80/80 [==============================] - 241s 3s/step - loss: 0.0123 - mae: 0.1059 - mape: 133.7707 - val_loss: 0.0081 - val_mae: 0.0782 - val_mape: 102.2110 - val_olive_prod_mae: 0.0868 - val_olive_prod_mape: 72.7026 - val_min_oil_prod_mae: 0.0861 - val_min_oil_prod_mape: 94.5739 - val_max_oil_prod_mae: 0.0856 - val_max_oil_prod_mape: 236.4975 - val_avg_oil_prod_mae: 0.0826 - val_avg_oil_prod_mape: 66.7356 - val_total_water_need_mae: 0.0498 - val_total_water_need_mape: 40.5453 - lr: 4.8717e-06\n",
|
||
"Epoch 177/200\n",
|
||
"80/80 [==============================] - 36s 438ms/step - loss: 0.0123 - mae: 0.1059 - mape: 144.8284 - val_loss: 0.0081 - val_mae: 0.0781 - val_mape: 100.9244 - lr: 4.7452e-06\n",
|
||
"Epoch 178/200\n",
|
||
"80/80 [==============================] - 36s 442ms/step - loss: 0.0123 - mae: 0.1060 - mape: 135.6987 - val_loss: 0.0080 - val_mae: 0.0775 - val_mape: 100.0468 - lr: 4.6220e-06\n",
|
||
"Epoch 179/200\n",
|
||
"80/80 [==============================] - 37s 450ms/step - loss: 0.0123 - mae: 0.1059 - mape: 136.3290 - val_loss: 0.0080 - val_mae: 0.0775 - val_mape: 100.4255 - lr: 4.5019e-06\n",
|
||
"Epoch 180/200\n",
|
||
"80/80 [==============================] - 36s 446ms/step - loss: 0.0123 - mae: 0.1058 - mape: 154.4108 - val_loss: 0.0080 - val_mae: 0.0772 - val_mape: 99.4628 - lr: 4.3850e-06\n",
|
||
"Epoch 181/200\n",
|
||
"80/80 [==============================] - 225s 3s/step - loss: 0.0123 - mae: 0.1057 - mape: 124.7869 - val_loss: 0.0080 - val_mae: 0.0775 - val_mape: 100.7030 - val_olive_prod_mae: 0.0861 - val_olive_prod_mape: 73.0007 - val_min_oil_prod_mae: 0.0853 - val_min_oil_prod_mape: 94.6533 - val_max_oil_prod_mae: 0.0850 - val_max_oil_prod_mape: 227.6605 - best_max_oil_prod_mae: 0.0850 - val_avg_oil_prod_mae: 0.0820 - val_avg_oil_prod_mape: 66.6156 - val_total_water_need_mae: 0.0488 - val_total_water_need_mape: 41.5849 - lr: 4.2711e-06\n",
|
||
"Epoch 182/200\n",
|
||
"80/80 [==============================] - 36s 444ms/step - loss: 0.0123 - mae: 0.1059 - mape: 131.7903 - val_loss: 0.0080 - val_mae: 0.0776 - val_mape: 100.5481 - lr: 4.1602e-06\n",
|
||
"Epoch 183/200\n",
|
||
"80/80 [==============================] - 36s 442ms/step - loss: 0.0123 - mae: 0.1059 - mape: 130.2979 - val_loss: 0.0081 - val_mae: 0.0781 - val_mape: 101.2592 - lr: 4.0521e-06\n",
|
||
"Epoch 184/200\n",
|
||
"80/80 [==============================] - 35s 433ms/step - loss: 0.0123 - mae: 0.1059 - mape: 145.6990 - val_loss: 0.0080 - val_mae: 0.0776 - val_mape: 99.8872 - lr: 3.9469e-06\n",
|
||
"Epoch 185/200\n",
|
||
"80/80 [==============================] - 36s 446ms/step - loss: 0.0122 - mae: 0.1057 - mape: 132.5820 - val_loss: 0.0080 - val_mae: 0.0783 - val_mape: 102.3656 - lr: 3.8444e-06\n",
|
||
"Epoch 186/200\n",
|
||
"80/80 [==============================] - 255s 3s/step - loss: 0.0122 - mae: 0.1058 - mape: 142.5915 - val_loss: 0.0080 - val_mae: 0.0776 - val_mape: 100.3732 - val_olive_prod_mae: 0.0863 - val_olive_prod_mape: 72.9255 - val_min_oil_prod_mae: 0.0857 - val_min_oil_prod_mape: 94.1567 - val_max_oil_prod_mae: 0.0853 - val_max_oil_prod_mape: 226.7281 - val_avg_oil_prod_mae: 0.0823 - val_avg_oil_prod_mape: 66.1140 - val_total_water_need_mae: 0.0487 - val_total_water_need_mape: 41.9421 - lr: 3.7445e-06\n",
|
||
"Epoch 187/200\n",
|
||
"80/80 [==============================] - 36s 439ms/step - loss: 0.0122 - mae: 0.1058 - mape: 151.9291 - val_loss: 0.0080 - val_mae: 0.0774 - val_mape: 101.3301 - lr: 3.6473e-06\n",
|
||
"Epoch 188/200\n",
|
||
"80/80 [==============================] - ETA: 0s - loss: 0.0122 - mae: 0.1055 - mape: 142.8741\n",
|
||
"Epoch 188: ReduceLROnPlateau reducing learning rate to 7.105123586370611e-07.\n",
|
||
"80/80 [==============================] - 36s 445ms/step - loss: 0.0122 - mae: 0.1055 - mape: 142.8741 - val_loss: 0.0080 - val_mae: 0.0771 - val_mape: 100.6780 - lr: 3.5526e-06\n",
|
||
"Epoch 189/200\n",
|
||
"80/80 [==============================] - 36s 445ms/step - loss: 0.0122 - mae: 0.1058 - mape: 136.2120 - val_loss: 0.0080 - val_mae: 0.0775 - val_mape: 101.6683 - lr: 3.4603e-06\n",
|
||
"Epoch 190/200\n",
|
||
"80/80 [==============================] - 36s 444ms/step - loss: 0.0122 - mae: 0.1055 - mape: 140.9985 - val_loss: 0.0080 - val_mae: 0.0777 - val_mape: 101.3500 - lr: 3.3704e-06\n",
|
||
"Epoch 191/200\n",
|
||
"80/80 [==============================] - 256s 3s/step - loss: 0.0122 - mae: 0.1058 - mape: 134.9611 - val_loss: 0.0080 - val_mae: 0.0780 - val_mape: 101.2254 - val_olive_prod_mae: 0.0866 - val_olive_prod_mape: 73.1756 - val_min_oil_prod_mae: 0.0859 - val_min_oil_prod_mape: 94.5778 - val_max_oil_prod_mae: 0.0855 - val_max_oil_prod_mape: 230.5076 - val_avg_oil_prod_mae: 0.0825 - val_avg_oil_prod_mape: 66.5401 - val_total_water_need_mae: 0.0495 - val_total_water_need_mape: 41.3259 - lr: 3.2829e-06\n",
|
||
"Epoch 192/200\n",
|
||
"80/80 [==============================] - 36s 438ms/step - loss: 0.0122 - mae: 0.1056 - mape: 131.2589 - val_loss: 0.0080 - val_mae: 0.0773 - val_mape: 100.6354 - lr: 3.1976e-06\n",
|
||
"Epoch 193/200\n",
|
||
"80/80 [==============================] - 36s 438ms/step - loss: 0.0122 - mae: 0.1057 - mape: 138.6146 - val_loss: 0.0080 - val_mae: 0.0779 - val_mape: 101.6130 - lr: 3.1146e-06\n",
|
||
"Epoch 194/200\n",
|
||
"80/80 [==============================] - 36s 438ms/step - loss: 0.0122 - mae: 0.1059 - mape: 121.4555 - val_loss: 0.0080 - val_mae: 0.0775 - val_mape: 100.8018 - lr: 3.0337e-06\n",
|
||
"Epoch 195/200\n",
|
||
"80/80 [==============================] - 36s 440ms/step - loss: 0.0122 - mae: 0.1058 - mape: 126.8764 - val_loss: 0.0080 - val_mae: 0.0776 - val_mape: 100.0004 - lr: 2.9549e-06\n",
|
||
"Epoch 196/200\n",
|
||
"80/80 [==============================] - 226s 3s/step - loss: 0.0122 - mae: 0.1057 - mape: 139.6645 - val_loss: 0.0080 - val_mae: 0.0776 - val_mape: 100.6636 - val_olive_prod_mae: 0.0860 - val_olive_prod_mape: 73.5066 - val_min_oil_prod_mae: 0.0851 - val_min_oil_prod_mape: 95.2965 - val_max_oil_prod_mae: 0.0851 - val_max_oil_prod_mape: 224.2950 - val_avg_oil_prod_mae: 0.0818 - val_avg_oil_prod_mape: 66.9162 - val_total_water_need_mae: 0.0489 - val_total_water_need_mape: 42.6473 - lr: 2.8781e-06\n",
|
||
"\n",
|
||
"Modello salvato in: 2024-12-08_14-47_final_model.keras\n",
|
||
"Warning: Could not save model: name 'json' is not defined\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model, history = train_transformer(train_data, train_targets, val_data, val_targets, 200, 32768)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "3e2fb5a5341dac92",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"25000/25000 [==============================] - 211s 8ms/step\n",
|
||
"\n",
|
||
"Errori per target:\n",
|
||
"--------------------------------------------------\n",
|
||
"olive_prod:\n",
|
||
"MAE assoluto: 1413.43\n",
|
||
"Errore percentuale medio: 5.47%\n",
|
||
"Precisione: 94.53%\n",
|
||
"--------------------------------------------------\n",
|
||
"min_oil_prod:\n",
|
||
"MAE assoluto: 290.38\n",
|
||
"Errore percentuale medio: 5.54%\n",
|
||
"Precisione: 94.46%\n",
|
||
"--------------------------------------------------\n",
|
||
"max_oil_prod:\n",
|
||
"MAE assoluto: 352.04\n",
|
||
"Errore percentuale medio: 5.57%\n",
|
||
"Precisione: 94.43%\n",
|
||
"--------------------------------------------------\n",
|
||
"avg_oil_prod:\n",
|
||
"MAE assoluto: 308.69\n",
|
||
"Errore percentuale medio: 5.37%\n",
|
||
"Precisione: 94.63%\n",
|
||
"--------------------------------------------------\n",
|
||
"total_water_need:\n",
|
||
"MAE assoluto: 1450.19\n",
|
||
"Errore percentuale medio: 3.24%\n",
|
||
"Precisione: 96.76%\n",
|
||
"--------------------------------------------------\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"percentage_errors, absolute_errors = calculate_real_error(model, val_data, val_targets, scaler_y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "4af58aa9bbc156f5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def evaluate_model_performance(model, data, targets, set_name=\"\"):\n",
|
||
" \"\"\"\n",
|
||
" Valuta le performance del modello su un set di dati specifico.\n",
|
||
" \"\"\"\n",
|
||
" predictions = model.predict(data, verbose=0)\n",
|
||
"\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n",
|
||
" metrics = {}\n",
|
||
"\n",
|
||
" for i, name in enumerate(target_names):\n",
|
||
" mae = np.mean(np.abs(targets[:, i] - predictions[:, i]))\n",
|
||
" mse = np.mean(np.square(targets[:, i] - predictions[:, i]))\n",
|
||
" rmse = np.sqrt(mse)\n",
|
||
" mape = np.mean(np.abs((targets[:, i] - predictions[:, i]) / (targets[:, i] + 1e-7))) * 100\n",
|
||
"\n",
|
||
" metrics[f\"{name}_mae\"] = mae\n",
|
||
" metrics[f\"{name}_rmse\"] = rmse\n",
|
||
" metrics[f\"{name}_mape\"] = mape\n",
|
||
"\n",
|
||
" if set_name:\n",
|
||
" print(f\"\\nPerformance sul set {set_name}:\")\n",
|
||
" for metric, value in metrics.items():\n",
|
||
" print(f\"{metric}: {value:.4f}\")\n",
|
||
"\n",
|
||
" return metrics\n",
|
||
"\n",
|
||
"\n",
|
||
"def retrain_model(base_model, train_data, train_targets,\n",
|
||
" val_data, val_targets,\n",
|
||
" test_data, test_targets,\n",
|
||
" epochs=50, batch_size=128):\n",
|
||
" \"\"\"\n",
|
||
" Implementa il retraining del modello con i dati combinati.\n",
|
||
" \"\"\"\n",
|
||
" print(\"Valutazione performance iniziali del modello...\")\n",
|
||
" initial_metrics = {\n",
|
||
" 'train': evaluate_model_performance(base_model, train_data, train_targets, \"training\"),\n",
|
||
" 'val': evaluate_model_performance(base_model, val_data, val_targets, \"validazione\"),\n",
|
||
" 'test': evaluate_model_performance(base_model, test_data, test_targets, \"test\")\n",
|
||
" }\n",
|
||
"\n",
|
||
" # Combina i dati per il retraining\n",
|
||
" combined_data = {\n",
|
||
" 'temporal': np.concatenate([train_data['temporal'], val_data['temporal'], test_data['temporal']]),\n",
|
||
" 'static': np.concatenate([train_data['static'], val_data['static'], test_data['static']])\n",
|
||
" }\n",
|
||
" combined_targets = np.concatenate([train_targets, val_targets, test_targets])\n",
|
||
"\n",
|
||
" # Crea una nuova suddivisione per la validazione\n",
|
||
" indices = np.arange(len(combined_targets))\n",
|
||
" np.random.shuffle(indices)\n",
|
||
"\n",
|
||
" split_idx = int(len(indices) * 0.9)\n",
|
||
" train_idx, val_idx = indices[:split_idx], indices[split_idx:]\n",
|
||
"\n",
|
||
" # Prepara i dati per il retraining\n",
|
||
" retrain_data = {k: v[train_idx] for k, v in combined_data.items()}\n",
|
||
" retrain_targets = combined_targets[train_idx]\n",
|
||
" retrain_val_data = {k: v[val_idx] for k, v in combined_data.items()}\n",
|
||
" retrain_val_targets = combined_targets[val_idx]\n",
|
||
"\n",
|
||
" # Configura callbacks\n",
|
||
" callbacks = [\n",
|
||
" tf.keras.callbacks.EarlyStopping(\n",
|
||
" monitor='val_loss',\n",
|
||
" patience=10,\n",
|
||
" restore_best_weights=True,\n",
|
||
" min_delta=0.0001\n",
|
||
" ),\n",
|
||
" tf.keras.callbacks.ReduceLROnPlateau(\n",
|
||
" monitor='val_loss',\n",
|
||
" factor=0.2,\n",
|
||
" patience=5,\n",
|
||
" min_lr=1e-6,\n",
|
||
" verbose=1\n",
|
||
" ),\n",
|
||
" tf.keras.callbacks.ModelCheckpoint(\n",
|
||
" filepath=f'{execute_name}_retrained_best_oil_model.h5',\n",
|
||
" monitor='val_loss',\n",
|
||
" save_best_only=True,\n",
|
||
" mode='min',\n",
|
||
" save_weights_only=True\n",
|
||
" )\n",
|
||
" ]\n",
|
||
"\n",
|
||
" # Imposta learning rate per il fine-tuning\n",
|
||
" optimizer = tf.keras.optimizers.AdamW(\n",
|
||
" learning_rate=tf.keras.optimizers.schedules.ExponentialDecay(\n",
|
||
" initial_learning_rate=1e-4,\n",
|
||
" decay_steps=1000,\n",
|
||
" decay_rate=0.9\n",
|
||
" ),\n",
|
||
" weight_decay=0.01\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Ricompila il modello con il nuovo optimizer\n",
|
||
" base_model.compile(\n",
|
||
" optimizer=optimizer,\n",
|
||
" loss=tf.keras.losses.Huber(),\n",
|
||
" metrics=['mae']\n",
|
||
" )\n",
|
||
"\n",
|
||
" print(\"\\nAvvio retraining...\")\n",
|
||
" history = base_model.fit(\n",
|
||
" retrain_data,\n",
|
||
" retrain_targets,\n",
|
||
" validation_data=(retrain_val_data, retrain_val_targets),\n",
|
||
" epochs=epochs,\n",
|
||
" batch_size=batch_size,\n",
|
||
" callbacks=callbacks,\n",
|
||
" verbose=1\n",
|
||
" )\n",
|
||
"\n",
|
||
" print(\"\\nValutazione performance finali...\")\n",
|
||
" final_metrics = {\n",
|
||
" 'train': evaluate_model_performance(base_model, train_data, train_targets, \"training\"),\n",
|
||
" 'val': evaluate_model_performance(base_model, val_data, val_targets, \"validazione\"),\n",
|
||
" 'test': evaluate_model_performance(base_model, test_data, test_targets, \"test\")\n",
|
||
" }\n",
|
||
"\n",
|
||
" # Salva il modello finale\n",
|
||
" save_path = f'{execute_name}_retrained_model.keras'\n",
|
||
" os.makedirs(f'{execute_name}_retrained/weights', exist_ok=True)\n",
|
||
" \n",
|
||
" base_model.save_weights(f'{execute_name}_retrained/weights')\n",
|
||
" base_model.save(save_path, save_format='keras')\n",
|
||
" print(f\"\\nModello riaddestrato salvato in: {save_path}\")\n",
|
||
"\n",
|
||
" # Report miglioramenti\n",
|
||
" print(\"\\nMiglioramenti delle performance:\")\n",
|
||
" for dataset in ['train', 'val', 'test']:\n",
|
||
" print(f\"\\nSet {dataset}:\")\n",
|
||
" for metric in initial_metrics[dataset].keys():\n",
|
||
" initial = initial_metrics[dataset][metric]\n",
|
||
" final = final_metrics[dataset][metric]\n",
|
||
" improvement = ((initial - final) / initial) * 100\n",
|
||
" print(f\"{metric}: {improvement:.2f}% di miglioramento\")\n",
|
||
"\n",
|
||
" return base_model, history, final_metrics\n",
|
||
"\n",
|
||
"\n",
|
||
"def start_retraining(model_path, train_data, train_targets,\n",
|
||
" val_data, val_targets,\n",
|
||
" test_data, test_targets,\n",
|
||
" epochs=50, batch_size=128):\n",
|
||
" \"\"\"\n",
|
||
" Avvia il processo di retraining in modo sicuro.\n",
|
||
" \"\"\"\n",
|
||
" try:\n",
|
||
" print(\"Caricamento del modello...\")\n",
|
||
" base_model = tf.keras.models.load_model(model_path, compile=False)\n",
|
||
" print(\"Modello caricato con successo!\")\n",
|
||
"\n",
|
||
" return retrain_model(\n",
|
||
" base_model=base_model,\n",
|
||
" train_data=train_data,\n",
|
||
" train_targets=train_targets,\n",
|
||
" val_data=val_data,\n",
|
||
" val_targets=val_targets,\n",
|
||
" test_data=test_data,\n",
|
||
" test_targets=test_targets,\n",
|
||
" epochs=epochs,\n",
|
||
" batch_size=batch_size\n",
|
||
" )\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"Errore durante il retraining: {str(e)}\")\n",
|
||
" raise"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "588c7e49371f4a0c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Caricamento del modello...\n",
|
||
"Modello caricato con successo!\n",
|
||
"Valutazione performance iniziali del modello...\n",
|
||
"\n",
|
||
"Performance sul set training:\n",
|
||
"olive_prod_mae: 0.0860\n",
|
||
"olive_prod_rmse: 0.1161\n",
|
||
"olive_prod_mape: 78.0493\n",
|
||
"min_oil_prod_mae: 0.0851\n",
|
||
"min_oil_prod_rmse: 0.1188\n",
|
||
"min_oil_prod_mape: 97.5077\n",
|
||
"max_oil_prod_mae: 0.0852\n",
|
||
"max_oil_prod_rmse: 0.1187\n",
|
||
"max_oil_prod_mape: 153.0588\n",
|
||
"avg_oil_prod_mae: 0.0819\n",
|
||
"avg_oil_prod_rmse: 0.1132\n",
|
||
"avg_oil_prod_mape: 133.1723\n",
|
||
"total_water_need_mae: 0.0489\n",
|
||
"total_water_need_rmse: 0.0645\n",
|
||
"total_water_need_mape: 49.3876\n",
|
||
"\n",
|
||
"Performance sul set validazione:\n",
|
||
"olive_prod_mae: 0.0860\n",
|
||
"olive_prod_rmse: 0.1160\n",
|
||
"olive_prod_mape: 73.4536\n",
|
||
"min_oil_prod_mae: 0.0851\n",
|
||
"min_oil_prod_rmse: 0.1186\n",
|
||
"min_oil_prod_mape: 96.1195\n",
|
||
"max_oil_prod_mae: 0.0851\n",
|
||
"max_oil_prod_rmse: 0.1186\n",
|
||
"max_oil_prod_mape: 284.3090\n",
|
||
"avg_oil_prod_mae: 0.0818\n",
|
||
"avg_oil_prod_rmse: 0.1130\n",
|
||
"avg_oil_prod_mape: 66.9595\n",
|
||
"total_water_need_mae: 0.0489\n",
|
||
"total_water_need_rmse: 0.0644\n",
|
||
"total_water_need_mape: 41.5436\n",
|
||
"\n",
|
||
"Performance sul set test:\n",
|
||
"olive_prod_mae: 0.0859\n",
|
||
"olive_prod_rmse: 0.1158\n",
|
||
"olive_prod_mape: 85.4890\n",
|
||
"min_oil_prod_mae: 0.0851\n",
|
||
"min_oil_prod_rmse: 0.1187\n",
|
||
"min_oil_prod_mape: 147.1340\n",
|
||
"max_oil_prod_mae: 0.0850\n",
|
||
"max_oil_prod_rmse: 0.1184\n",
|
||
"max_oil_prod_mape: 181.8315\n",
|
||
"avg_oil_prod_mae: 0.0818\n",
|
||
"avg_oil_prod_rmse: 0.1130\n",
|
||
"avg_oil_prod_mape: 83.6362\n",
|
||
"total_water_need_mae: 0.0490\n",
|
||
"total_water_need_rmse: 0.0645\n",
|
||
"total_water_need_mape: 40.1340\n",
|
||
"\n",
|
||
"Avvio retraining...\n",
|
||
"Epoch 1/100\n",
|
||
"220/220 [==============================] - 65s 158ms/step - loss: 0.0145 - mae: 0.1079 - val_loss: 0.0098 - val_mae: 0.0878 - lr: 9.7719e-05\n",
|
||
"Epoch 2/100\n",
|
||
"220/220 [==============================] - 34s 154ms/step - loss: 0.0141 - mae: 0.1066 - val_loss: 0.0094 - val_mae: 0.0835 - lr: 9.5480e-05\n",
|
||
"Epoch 3/100\n",
|
||
"220/220 [==============================] - 34s 154ms/step - loss: 0.0138 - mae: 0.1059 - val_loss: 0.0095 - val_mae: 0.0857 - lr: 9.3292e-05\n",
|
||
"Epoch 4/100\n",
|
||
"220/220 [==============================] - 34s 155ms/step - loss: 0.0135 - mae: 0.1055 - val_loss: 0.0097 - val_mae: 0.0856 - lr: 9.1155e-05\n",
|
||
"Epoch 5/100\n",
|
||
"220/220 [==============================] - 34s 156ms/step - loss: 0.0133 - mae: 0.1049 - val_loss: 0.0096 - val_mae: 0.0856 - lr: 8.9066e-05\n",
|
||
"Epoch 6/100\n",
|
||
"220/220 [==============================] - 35s 158ms/step - loss: 0.0131 - mae: 0.1046 - val_loss: 0.0091 - val_mae: 0.0834 - lr: 8.7025e-05\n",
|
||
"Epoch 7/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0129 - mae: 0.1040 - val_loss: 0.0092 - val_mae: 0.0827 - lr: 8.5031e-05\n",
|
||
"Epoch 8/100\n",
|
||
"220/220 [==============================] - 34s 154ms/step - loss: 0.0127 - mae: 0.1037 - val_loss: 0.0090 - val_mae: 0.0821 - lr: 8.3083e-05\n",
|
||
"Epoch 9/100\n",
|
||
"220/220 [==============================] - 33s 152ms/step - loss: 0.0125 - mae: 0.1034 - val_loss: 0.0093 - val_mae: 0.0860 - lr: 8.1179e-05\n",
|
||
"Epoch 10/100\n",
|
||
"220/220 [==============================] - 34s 155ms/step - loss: 0.0124 - mae: 0.1030 - val_loss: 0.0088 - val_mae: 0.0818 - lr: 7.9319e-05\n",
|
||
"Epoch 11/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0122 - mae: 0.1026 - val_loss: 0.0091 - val_mae: 0.0821 - lr: 7.7502e-05\n",
|
||
"Epoch 12/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0121 - mae: 0.1023 - val_loss: 0.0093 - val_mae: 0.0856 - lr: 7.5726e-05\n",
|
||
"Epoch 13/100\n",
|
||
"220/220 [==============================] - 35s 157ms/step - loss: 0.0120 - mae: 0.1020 - val_loss: 0.0086 - val_mae: 0.0823 - lr: 7.3991e-05\n",
|
||
"Epoch 14/100\n",
|
||
"220/220 [==============================] - 33s 152ms/step - loss: 0.0118 - mae: 0.1017 - val_loss: 0.0092 - val_mae: 0.0862 - lr: 7.2296e-05\n",
|
||
"Epoch 15/100\n",
|
||
"220/220 [==============================] - 34s 155ms/step - loss: 0.0117 - mae: 0.1015 - val_loss: 0.0086 - val_mae: 0.0804 - lr: 7.0639e-05\n",
|
||
"Epoch 16/100\n",
|
||
"220/220 [==============================] - 34s 154ms/step - loss: 0.0117 - mae: 0.1013 - val_loss: 0.0083 - val_mae: 0.0800 - lr: 6.9021e-05\n",
|
||
"Epoch 17/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0116 - mae: 0.1011 - val_loss: 0.0084 - val_mae: 0.0809 - lr: 6.7439e-05\n",
|
||
"Epoch 18/100\n",
|
||
"220/220 [==============================] - 34s 154ms/step - loss: 0.0114 - mae: 0.1008 - val_loss: 0.0083 - val_mae: 0.0809 - lr: 6.5894e-05\n",
|
||
"Epoch 19/100\n",
|
||
"220/220 [==============================] - 34s 156ms/step - loss: 0.0114 - mae: 0.1006 - val_loss: 0.0081 - val_mae: 0.0804 - lr: 6.4384e-05\n",
|
||
"Epoch 20/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0113 - mae: 0.1004 - val_loss: 0.0088 - val_mae: 0.0837 - lr: 6.2909e-05\n",
|
||
"Epoch 21/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0112 - mae: 0.1002 - val_loss: 0.0084 - val_mae: 0.0812 - lr: 6.1468e-05\n",
|
||
"Epoch 22/100\n",
|
||
"220/220 [==============================] - 34s 156ms/step - loss: 0.0111 - mae: 0.1000 - val_loss: 0.0080 - val_mae: 0.0806 - lr: 6.0059e-05\n",
|
||
"Epoch 23/100\n",
|
||
"220/220 [==============================] - 34s 154ms/step - loss: 0.0110 - mae: 0.0999 - val_loss: 0.0084 - val_mae: 0.0808 - lr: 5.8683e-05\n",
|
||
"Epoch 24/100\n",
|
||
"220/220 [==============================] - 34s 155ms/step - loss: 0.0110 - mae: 0.0997 - val_loss: 0.0079 - val_mae: 0.0803 - lr: 5.7338e-05\n",
|
||
"Epoch 25/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0109 - mae: 0.0995 - val_loss: 0.0081 - val_mae: 0.0806 - lr: 5.6025e-05\n",
|
||
"Epoch 26/100\n",
|
||
"220/220 [==============================] - 34s 154ms/step - loss: 0.0108 - mae: 0.0993 - val_loss: 0.0078 - val_mae: 0.0788 - lr: 5.4741e-05\n",
|
||
"Epoch 27/100\n",
|
||
"220/220 [==============================] - 33s 152ms/step - loss: 0.0108 - mae: 0.0992 - val_loss: 0.0082 - val_mae: 0.0820 - lr: 5.3487e-05\n",
|
||
"Epoch 28/100\n",
|
||
"220/220 [==============================] - 33s 152ms/step - loss: 0.0107 - mae: 0.0992 - val_loss: 0.0079 - val_mae: 0.0792 - lr: 5.2261e-05\n",
|
||
"Epoch 29/100\n",
|
||
"220/220 [==============================] - 34s 154ms/step - loss: 0.0107 - mae: 0.0990 - val_loss: 0.0082 - val_mae: 0.0804 - lr: 5.1064e-05\n",
|
||
"Epoch 30/100\n",
|
||
"220/220 [==============================] - 34s 155ms/step - loss: 0.0106 - mae: 0.0988 - val_loss: 0.0077 - val_mae: 0.0787 - lr: 4.9894e-05\n",
|
||
"Epoch 31/100\n",
|
||
"220/220 [==============================] - 33s 151ms/step - loss: 0.0106 - mae: 0.0987 - val_loss: 0.0078 - val_mae: 0.0806 - lr: 4.8751e-05\n",
|
||
"Epoch 32/100\n",
|
||
"220/220 [==============================] - 35s 159ms/step - loss: 0.0105 - mae: 0.0986 - val_loss: 0.0073 - val_mae: 0.0774 - lr: 4.7634e-05\n",
|
||
"Epoch 33/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0105 - mae: 0.0985 - val_loss: 0.0075 - val_mae: 0.0794 - lr: 4.6542e-05\n",
|
||
"Epoch 34/100\n",
|
||
"220/220 [==============================] - 35s 158ms/step - loss: 0.0104 - mae: 0.0983 - val_loss: 0.0072 - val_mae: 0.0776 - lr: 4.5476e-05\n",
|
||
"Epoch 35/100\n",
|
||
"220/220 [==============================] - 35s 157ms/step - loss: 0.0104 - mae: 0.0982 - val_loss: 0.0073 - val_mae: 0.0767 - lr: 4.4434e-05\n",
|
||
"Epoch 36/100\n",
|
||
"220/220 [==============================] - 34s 155ms/step - loss: 0.0103 - mae: 0.0982 - val_loss: 0.0073 - val_mae: 0.0782 - lr: 4.3416e-05\n",
|
||
"Epoch 37/100\n",
|
||
"220/220 [==============================] - 34s 155ms/step - loss: 0.0103 - mae: 0.0981 - val_loss: 0.0071 - val_mae: 0.0770 - lr: 4.2421e-05\n",
|
||
"Epoch 38/100\n",
|
||
"220/220 [==============================] - 33s 152ms/step - loss: 0.0103 - mae: 0.0979 - val_loss: 0.0071 - val_mae: 0.0770 - lr: 4.1449e-05\n",
|
||
"Epoch 39/100\n",
|
||
"220/220 [==============================] - ETA: 0s - loss: 0.0102 - mae: 0.0978\n",
|
||
"Epoch 39: ReduceLROnPlateau reducing learning rate to 8.099838305497542e-06.\n",
|
||
"220/220 [==============================] - 33s 152ms/step - loss: 0.0102 - mae: 0.0978 - val_loss: 0.0073 - val_mae: 0.0779 - lr: 4.0499e-05\n",
|
||
"Epoch 40/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0102 - mae: 0.0977 - val_loss: 0.0071 - val_mae: 0.0772 - lr: 3.9571e-05\n",
|
||
"Epoch 41/100\n",
|
||
"220/220 [==============================] - 34s 154ms/step - loss: 0.0101 - mae: 0.0976 - val_loss: 0.0069 - val_mae: 0.0756 - lr: 3.8665e-05\n",
|
||
"Epoch 42/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0101 - mae: 0.0976 - val_loss: 0.0073 - val_mae: 0.0773 - lr: 3.7779e-05\n",
|
||
"Epoch 43/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0101 - mae: 0.0974 - val_loss: 0.0071 - val_mae: 0.0760 - lr: 3.6913e-05\n",
|
||
"Epoch 44/100\n",
|
||
"220/220 [==============================] - 33s 152ms/step - loss: 0.0101 - mae: 0.0974 - val_loss: 0.0070 - val_mae: 0.0762 - lr: 3.6067e-05\n",
|
||
"Epoch 45/100\n",
|
||
"220/220 [==============================] - 34s 154ms/step - loss: 0.0100 - mae: 0.0972 - val_loss: 0.0071 - val_mae: 0.0774 - lr: 3.5241e-05\n",
|
||
"Epoch 46/100\n",
|
||
"220/220 [==============================] - 34s 155ms/step - loss: 0.0100 - mae: 0.0972 - val_loss: 0.0068 - val_mae: 0.0748 - lr: 3.4433e-05\n",
|
||
"Epoch 47/100\n",
|
||
"220/220 [==============================] - 34s 152ms/step - loss: 0.0100 - mae: 0.0972 - val_loss: 0.0074 - val_mae: 0.0790 - lr: 3.3644e-05\n",
|
||
"Epoch 48/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0099 - mae: 0.0971 - val_loss: 0.0069 - val_mae: 0.0759 - lr: 3.2874e-05\n",
|
||
"Epoch 49/100\n",
|
||
"220/220 [==============================] - 34s 152ms/step - loss: 0.0099 - mae: 0.0970 - val_loss: 0.0069 - val_mae: 0.0768 - lr: 3.2120e-05\n",
|
||
"Epoch 50/100\n",
|
||
"220/220 [==============================] - 33s 151ms/step - loss: 0.0099 - mae: 0.0969 - val_loss: 0.0070 - val_mae: 0.0758 - lr: 3.1384e-05\n",
|
||
"Epoch 51/100\n",
|
||
"220/220 [==============================] - ETA: 0s - loss: 0.0099 - mae: 0.0969\n",
|
||
"Epoch 51: ReduceLROnPlateau reducing learning rate to 6.133050192147493e-06.\n",
|
||
"220/220 [==============================] - 33s 151ms/step - loss: 0.0099 - mae: 0.0969 - val_loss: 0.0068 - val_mae: 0.0757 - lr: 3.0665e-05\n",
|
||
"Epoch 52/100\n",
|
||
"220/220 [==============================] - 34s 154ms/step - loss: 0.0099 - mae: 0.0968 - val_loss: 0.0068 - val_mae: 0.0753 - lr: 2.9963e-05\n",
|
||
"Epoch 53/100\n",
|
||
"220/220 [==============================] - 33s 152ms/step - loss: 0.0098 - mae: 0.0967 - val_loss: 0.0069 - val_mae: 0.0761 - lr: 2.9276e-05\n",
|
||
"Epoch 54/100\n",
|
||
"220/220 [==============================] - 33s 152ms/step - loss: 0.0098 - mae: 0.0966 - val_loss: 0.0068 - val_mae: 0.0754 - lr: 2.8605e-05\n",
|
||
"Epoch 55/100\n",
|
||
"220/220 [==============================] - 34s 155ms/step - loss: 0.0098 - mae: 0.0966 - val_loss: 0.0067 - val_mae: 0.0750 - lr: 2.7950e-05\n",
|
||
"Epoch 56/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0097 - mae: 0.0965 - val_loss: 0.0066 - val_mae: 0.0748 - lr: 2.7309e-05\n",
|
||
"Epoch 57/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0097 - mae: 0.0964 - val_loss: 0.0066 - val_mae: 0.0746 - lr: 2.6684e-05\n",
|
||
"Epoch 58/100\n",
|
||
"220/220 [==============================] - 33s 152ms/step - loss: 0.0097 - mae: 0.0964 - val_loss: 0.0067 - val_mae: 0.0748 - lr: 2.6072e-05\n",
|
||
"Epoch 59/100\n",
|
||
"220/220 [==============================] - 33s 151ms/step - loss: 0.0097 - mae: 0.0964 - val_loss: 0.0066 - val_mae: 0.0740 - lr: 2.5475e-05\n",
|
||
"Epoch 60/100\n",
|
||
"220/220 [==============================] - 34s 152ms/step - loss: 0.0097 - mae: 0.0963 - val_loss: 0.0070 - val_mae: 0.0760 - lr: 2.4891e-05\n",
|
||
"Epoch 61/100\n",
|
||
"220/220 [==============================] - ETA: 0s - loss: 0.0097 - mae: 0.0962\n",
|
||
"Epoch 61: ReduceLROnPlateau reducing learning rate to 4.864184666075744e-06.\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0097 - mae: 0.0962 - val_loss: 0.0067 - val_mae: 0.0754 - lr: 2.4321e-05\n",
|
||
"Epoch 62/100\n",
|
||
"220/220 [==============================] - 33s 152ms/step - loss: 0.0096 - mae: 0.0962 - val_loss: 0.0067 - val_mae: 0.0751 - lr: 2.3764e-05\n",
|
||
"Epoch 63/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0096 - mae: 0.0962 - val_loss: 0.0065 - val_mae: 0.0746 - lr: 2.3219e-05\n",
|
||
"Epoch 64/100\n",
|
||
"220/220 [==============================] - 33s 151ms/step - loss: 0.0096 - mae: 0.0960 - val_loss: 0.0067 - val_mae: 0.0754 - lr: 2.2687e-05\n",
|
||
"Epoch 65/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0096 - mae: 0.0960 - val_loss: 0.0065 - val_mae: 0.0743 - lr: 2.2167e-05\n",
|
||
"Epoch 66/100\n",
|
||
"220/220 [==============================] - 34s 153ms/step - loss: 0.0096 - mae: 0.0961 - val_loss: 0.0066 - val_mae: 0.0744 - lr: 2.1659e-05\n",
|
||
"Epoch 67/100\n",
|
||
"220/220 [==============================] - 34s 155ms/step - loss: 0.0096 - mae: 0.0960 - val_loss: 0.0064 - val_mae: 0.0739 - lr: 2.1163e-05\n",
|
||
"Epoch 68/100\n",
|
||
"220/220 [==============================] - ETA: 0s - loss: 0.0095 - mae: 0.0959\n",
|
||
"Epoch 68: ReduceLROnPlateau reducing learning rate to 4.135647031944245e-06.\n",
|
||
"220/220 [==============================] - 34s 152ms/step - loss: 0.0095 - mae: 0.0959 - val_loss: 0.0066 - val_mae: 0.0742 - lr: 2.0678e-05\n",
|
||
"Epoch 69/100\n",
|
||
"220/220 [==============================] - 34s 152ms/step - loss: 0.0095 - mae: 0.0959 - val_loss: 0.0065 - val_mae: 0.0750 - lr: 2.0204e-05\n",
|
||
"Epoch 70/100\n",
|
||
"220/220 [==============================] - 33s 152ms/step - loss: 0.0095 - mae: 0.0959 - val_loss: 0.0066 - val_mae: 0.0749 - lr: 1.9742e-05\n",
|
||
"Epoch 71/100\n",
|
||
"220/220 [==============================] - 33s 152ms/step - loss: 0.0095 - mae: 0.0959 - val_loss: 0.0065 - val_mae: 0.0745 - lr: 1.9289e-05\n",
|
||
"Epoch 72/100\n",
|
||
"220/220 [==============================] - 34s 154ms/step - loss: 0.0095 - mae: 0.0958 - val_loss: 0.0064 - val_mae: 0.0741 - lr: 1.8847e-05\n",
|
||
"Epoch 73/100\n",
|
||
"220/220 [==============================] - ETA: 0s - loss: 0.0095 - mae: 0.0957\n",
|
||
"Epoch 73: ReduceLROnPlateau reducing learning rate to 3.683072645799257e-06.\n",
|
||
"220/220 [==============================] - 34s 152ms/step - loss: 0.0095 - mae: 0.0957 - val_loss: 0.0065 - val_mae: 0.0736 - lr: 1.8415e-05\n",
|
||
"\n",
|
||
"Valutazione performance finali...\n",
|
||
"\n",
|
||
"Performance sul set training:\n",
|
||
"olive_prod_mae: 0.0840\n",
|
||
"olive_prod_rmse: 0.1148\n",
|
||
"olive_prod_mape: 77.0417\n",
|
||
"min_oil_prod_mae: 0.0818\n",
|
||
"min_oil_prod_rmse: 0.1149\n",
|
||
"min_oil_prod_mape: 96.4133\n",
|
||
"max_oil_prod_mae: 0.0816\n",
|
||
"max_oil_prod_rmse: 0.1144\n",
|
||
"max_oil_prod_mape: 149.8516\n",
|
||
"avg_oil_prod_mae: 0.0798\n",
|
||
"avg_oil_prod_rmse: 0.1114\n",
|
||
"avg_oil_prod_mape: 125.5588\n",
|
||
"total_water_need_mae: 0.0456\n",
|
||
"total_water_need_rmse: 0.0610\n",
|
||
"total_water_need_mape: 45.2614\n",
|
||
"\n",
|
||
"Performance sul set validazione:\n",
|
||
"olive_prod_mae: 0.0839\n",
|
||
"olive_prod_rmse: 0.1146\n",
|
||
"olive_prod_mape: 72.7076\n",
|
||
"min_oil_prod_mae: 0.0817\n",
|
||
"min_oil_prod_rmse: 0.1147\n",
|
||
"min_oil_prod_mape: 95.6358\n",
|
||
"max_oil_prod_mae: 0.0815\n",
|
||
"max_oil_prod_rmse: 0.1141\n",
|
||
"max_oil_prod_mape: 271.0537\n",
|
||
"avg_oil_prod_mae: 0.0798\n",
|
||
"avg_oil_prod_rmse: 0.1112\n",
|
||
"avg_oil_prod_mape: 64.6259\n",
|
||
"total_water_need_mae: 0.0456\n",
|
||
"total_water_need_rmse: 0.0609\n",
|
||
"total_water_need_mape: 39.7287\n",
|
||
"\n",
|
||
"Performance sul set test:\n",
|
||
"olive_prod_mae: 0.0838\n",
|
||
"olive_prod_rmse: 0.1145\n",
|
||
"olive_prod_mape: 82.7762\n",
|
||
"min_oil_prod_mae: 0.0817\n",
|
||
"min_oil_prod_rmse: 0.1147\n",
|
||
"min_oil_prod_mape: 146.3828\n",
|
||
"max_oil_prod_mae: 0.0815\n",
|
||
"max_oil_prod_rmse: 0.1141\n",
|
||
"max_oil_prod_mape: 171.8919\n",
|
||
"avg_oil_prod_mae: 0.0797\n",
|
||
"avg_oil_prod_rmse: 0.1112\n",
|
||
"avg_oil_prod_mape: 78.1842\n",
|
||
"total_water_need_mae: 0.0456\n",
|
||
"total_water_need_rmse: 0.0610\n",
|
||
"total_water_need_mape: 38.6316\n",
|
||
"\n",
|
||
"Modello riaddestrato salvato in: 2024-12-08_14-47_retrained_model.keras\n",
|
||
"\n",
|
||
"Miglioramenti delle performance:\n",
|
||
"\n",
|
||
"Set train:\n",
|
||
"olive_prod_mae: 2.37% di miglioramento\n",
|
||
"olive_prod_rmse: 1.12% di miglioramento\n",
|
||
"olive_prod_mape: 1.29% di miglioramento\n",
|
||
"min_oil_prod_mae: 3.93% di miglioramento\n",
|
||
"min_oil_prod_rmse: 3.30% di miglioramento\n",
|
||
"min_oil_prod_mape: 1.12% di miglioramento\n",
|
||
"max_oil_prod_mae: 4.13% di miglioramento\n",
|
||
"max_oil_prod_rmse: 3.68% di miglioramento\n",
|
||
"max_oil_prod_mape: 2.10% di miglioramento\n",
|
||
"avg_oil_prod_mae: 2.51% di miglioramento\n",
|
||
"avg_oil_prod_rmse: 1.57% di miglioramento\n",
|
||
"avg_oil_prod_mape: 5.72% di miglioramento\n",
|
||
"total_water_need_mae: 6.84% di miglioramento\n",
|
||
"total_water_need_rmse: 5.40% di miglioramento\n",
|
||
"total_water_need_mape: 8.35% di miglioramento\n",
|
||
"\n",
|
||
"Set val:\n",
|
||
"olive_prod_mae: 2.39% di miglioramento\n",
|
||
"olive_prod_rmse: 1.17% di miglioramento\n",
|
||
"olive_prod_mape: 1.02% di miglioramento\n",
|
||
"min_oil_prod_mae: 3.93% di miglioramento\n",
|
||
"min_oil_prod_rmse: 3.33% di miglioramento\n",
|
||
"min_oil_prod_mape: 0.50% di miglioramento\n",
|
||
"max_oil_prod_mae: 4.18% di miglioramento\n",
|
||
"max_oil_prod_rmse: 3.75% di miglioramento\n",
|
||
"max_oil_prod_mape: 4.66% di miglioramento\n",
|
||
"avg_oil_prod_mae: 2.54% di miglioramento\n",
|
||
"avg_oil_prod_rmse: 1.64% di miglioramento\n",
|
||
"avg_oil_prod_mape: 3.49% di miglioramento\n",
|
||
"total_water_need_mae: 6.85% di miglioramento\n",
|
||
"total_water_need_rmse: 5.38% di miglioramento\n",
|
||
"total_water_need_mape: 4.37% di miglioramento\n",
|
||
"\n",
|
||
"Set test:\n",
|
||
"olive_prod_mae: 2.39% di miglioramento\n",
|
||
"olive_prod_rmse: 1.12% di miglioramento\n",
|
||
"olive_prod_mape: 3.17% di miglioramento\n",
|
||
"min_oil_prod_mae: 3.98% di miglioramento\n",
|
||
"min_oil_prod_rmse: 3.38% di miglioramento\n",
|
||
"min_oil_prod_mape: 0.51% di miglioramento\n",
|
||
"max_oil_prod_mae: 4.14% di miglioramento\n",
|
||
"max_oil_prod_rmse: 3.69% di miglioramento\n",
|
||
"max_oil_prod_mape: 5.47% di miglioramento\n",
|
||
"avg_oil_prod_mae: 2.54% di miglioramento\n",
|
||
"avg_oil_prod_rmse: 1.60% di miglioramento\n",
|
||
"avg_oil_prod_mape: 6.52% di miglioramento\n",
|
||
"total_water_need_mae: 6.87% di miglioramento\n",
|
||
"total_water_need_rmse: 5.43% di miglioramento\n",
|
||
"total_water_need_mape: 3.74% di miglioramento\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model_path = f'{execute_name}_final_model.keras'\n",
|
||
"\n",
|
||
"retrained_model, retrain_history, final_metrics = start_retraining(\n",
|
||
" model_path=model_path,\n",
|
||
" train_data=train_data,\n",
|
||
" train_targets=train_targets,\n",
|
||
" val_data=val_data,\n",
|
||
" val_targets=val_targets,\n",
|
||
" test_data=test_data,\n",
|
||
" test_targets=test_targets,\n",
|
||
" epochs=100,\n",
|
||
" batch_size=16384\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "a95606bc-c4bc-418a-acdb-2e24c30dfa81",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"25000/25000 [==============================] - 247s 10ms/step\n",
|
||
"\n",
|
||
"Errori per target:\n",
|
||
"--------------------------------------------------\n",
|
||
"olive_prod:\n",
|
||
"MAE assoluto: 1379.63\n",
|
||
"Errore percentuale medio: 5.07%\n",
|
||
"Precisione: 94.93%\n",
|
||
"--------------------------------------------------\n",
|
||
"min_oil_prod:\n",
|
||
"MAE assoluto: 278.96\n",
|
||
"Errore percentuale medio: 5.09%\n",
|
||
"Precisione: 94.91%\n",
|
||
"--------------------------------------------------\n",
|
||
"max_oil_prod:\n",
|
||
"MAE assoluto: 337.33\n",
|
||
"Errore percentuale medio: 5.13%\n",
|
||
"Precisione: 94.87%\n",
|
||
"--------------------------------------------------\n",
|
||
"avg_oil_prod:\n",
|
||
"MAE assoluto: 300.85\n",
|
||
"Errore percentuale medio: 4.98%\n",
|
||
"Precisione: 95.02%\n",
|
||
"--------------------------------------------------\n",
|
||
"total_water_need:\n",
|
||
"MAE assoluto: 1350.83\n",
|
||
"Errore percentuale medio: 2.90%\n",
|
||
"Precisione: 97.10%\n",
|
||
"--------------------------------------------------\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"percentage_errors, absolute_errors = calculate_real_error(retrained_model, val_data, val_targets, scaler_y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "e6f357cb-56a4-4f19-a4e8-77b73a28329d",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import tensorflow as tf\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from typing import List, Dict, Tuple, Union\n",
|
||
"\n",
|
||
"def analyze_feature_importance(model: tf.keras.Model, \n",
|
||
" test_data: dict, \n",
|
||
" feature_names: List[str]) -> Dict[str, float]:\n",
|
||
" \"\"\"\n",
|
||
" Analizza l'importanza delle feature usando perturbazione.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" model: Modello TensorFlow addestrato\n",
|
||
" test_data: Dizionario con chiavi 'temporal' e 'static' contenenti i dati\n",
|
||
" feature_names: Lista dei nomi delle feature\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" dict: Dizionario con l'importanza relativa di ogni feature\n",
|
||
" \"\"\"\n",
|
||
" # Estrai i dati temporali e statici\n",
|
||
" temporal_data = test_data['temporal']\n",
|
||
" static_data = test_data['static']\n",
|
||
" \n",
|
||
" # Ottieni la predizione base\n",
|
||
" base_prediction = model.predict(test_data)\n",
|
||
" feature_importance = {}\n",
|
||
" \n",
|
||
" # Per ogni feature temporale\n",
|
||
" for i, feature in enumerate(feature_names):\n",
|
||
" if feature in ['temp_mean', 'precip_sum', 'solar_energy_sum']:\n",
|
||
" # Crea copia perturbata dei dati\n",
|
||
" perturbed_data = {\n",
|
||
" 'temporal': temporal_data.copy(),\n",
|
||
" 'static': static_data.copy()\n",
|
||
" }\n",
|
||
" \n",
|
||
" # Trova l'indice della feature temporale\n",
|
||
" temp_idx = ['temp_mean', 'precip_sum', 'solar_energy_sum'].index(feature)\n",
|
||
" \n",
|
||
" # Crea rumore per la feature temporale\n",
|
||
" feature_values = temporal_data[..., temp_idx]\n",
|
||
" noise = np.random.normal(0, np.std(feature_values) * 0.1, \n",
|
||
" size=feature_values.shape)\n",
|
||
" \n",
|
||
" # Applica il rumore alla feature temporale\n",
|
||
" perturbed_temporal = perturbed_data['temporal'].copy()\n",
|
||
" perturbed_temporal[..., temp_idx] = feature_values + noise\n",
|
||
" perturbed_data['temporal'] = perturbed_temporal\n",
|
||
" \n",
|
||
" else: # Feature statiche\n",
|
||
" # Crea copia perturbata dei dati\n",
|
||
" perturbed_data = {\n",
|
||
" 'temporal': temporal_data.copy(),\n",
|
||
" 'static': static_data.copy()\n",
|
||
" }\n",
|
||
" \n",
|
||
" # Trova l'indice della feature statica\n",
|
||
" static_idx = ['ha'].index(feature)\n",
|
||
" \n",
|
||
" # Crea rumore per la feature statica\n",
|
||
" feature_values = static_data[..., static_idx]\n",
|
||
" noise = np.random.normal(0, np.std(feature_values) * 0.1, \n",
|
||
" size=feature_values.shape)\n",
|
||
" \n",
|
||
" # Applica il rumore alla feature statica\n",
|
||
" perturbed_static = perturbed_data['static'].copy()\n",
|
||
" perturbed_static[..., static_idx] = feature_values + noise\n",
|
||
" perturbed_data['static'] = perturbed_static\n",
|
||
" \n",
|
||
" # Calcola nuova predizione\n",
|
||
" perturbed_prediction = model.predict(perturbed_data)\n",
|
||
" \n",
|
||
" # Calcola impatto della perturbazione\n",
|
||
" impact = np.mean(np.abs(perturbed_prediction - base_prediction))\n",
|
||
" feature_importance[feature] = float(impact)\n",
|
||
" \n",
|
||
" # Normalizza le importanze\n",
|
||
" total_importance = sum(feature_importance.values())\n",
|
||
" feature_importance = {k: v/total_importance \n",
|
||
" for k, v in feature_importance.items()}\n",
|
||
" \n",
|
||
" return feature_importance\n",
|
||
"\n",
|
||
"class ProbabilityFunctions:\n",
|
||
" @staticmethod\n",
|
||
" def calculate_statistics(data: Union[np.ndarray, tf.Tensor]) -> Dict[str, float]:\n",
|
||
" \"\"\"\n",
|
||
" Calcola statistiche di base usando TensorFlow.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Tensor o array dei dati\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" dict: Dizionario con le statistiche\n",
|
||
" \"\"\"\n",
|
||
" if not isinstance(data, tf.Tensor):\n",
|
||
" data = tf.convert_to_tensor(data, dtype=tf.float32)\n",
|
||
" \n",
|
||
" mean = tf.reduce_mean(data)\n",
|
||
" # Calcola varianza manualmente\n",
|
||
" squared_deviations = tf.square(data - mean)\n",
|
||
" variance = tf.reduce_mean(squared_deviations)\n",
|
||
" std = tf.sqrt(variance)\n",
|
||
" \n",
|
||
" # Ordina il tensor per il calcolo della mediana\n",
|
||
" sorted_data = tf.sort(data)\n",
|
||
" size = tf.size(data)\n",
|
||
" mid_index = size // 2\n",
|
||
" median = sorted_data[mid_index]\n",
|
||
" \n",
|
||
" return {\n",
|
||
" 'mean': mean.numpy(),\n",
|
||
" 'variance': variance.numpy(),\n",
|
||
" 'std': std.numpy(),\n",
|
||
" 'min': tf.reduce_min(data).numpy(),\n",
|
||
" 'max': tf.reduce_max(data).numpy(),\n",
|
||
" 'median': median.numpy()\n",
|
||
" }\n",
|
||
"\n",
|
||
" @staticmethod\n",
|
||
" def calculate_pmf(data: np.ndarray, bins: int = 50) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n",
|
||
" \"\"\"\n",
|
||
" Calcola la Probability Mass Function (PMF) dei dati.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Array di dati\n",
|
||
" bins: Numero di bin per l'istogramma\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" tuple: (bin_centers, pmf, bin_edges)\n",
|
||
" \"\"\"\n",
|
||
" # Calcola l'istogramma\n",
|
||
" hist, bin_edges = np.histogram(data, bins=bins, density=True)\n",
|
||
" \n",
|
||
" # Calcola i centri dei bin\n",
|
||
" bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n",
|
||
" \n",
|
||
" # Normalizza per ottenere la PMF\n",
|
||
" pmf = hist / np.sum(hist)\n",
|
||
" \n",
|
||
" return bin_centers, pmf, bin_edges\n",
|
||
"\n",
|
||
" @staticmethod\n",
|
||
" def calculate_cmf(pmf: np.ndarray) -> np.ndarray:\n",
|
||
" \"\"\"\n",
|
||
" Calcola la Cumulative Mass Function (CMF) dalla PMF.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" pmf: Probability Mass Function\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" array: Cumulative Mass Function\n",
|
||
" \"\"\"\n",
|
||
" return np.cumsum(pmf)\n",
|
||
"\n",
|
||
" def plot_distributions(self, data: np.ndarray, \n",
|
||
" bins: int = 50, \n",
|
||
" title: str = \"Distribuzione\") -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n",
|
||
" \"\"\"\n",
|
||
" Calcola e visualizza PMF e CMF delle distribuzioni.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Array di dati da analizzare\n",
|
||
" bins: Numero di bin per l'istogramma\n",
|
||
" title: Titolo del grafico\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" tuple: (bin_centers, pmf, cmf)\n",
|
||
" \"\"\"\n",
|
||
" # Calcola PMF e CMF\n",
|
||
" bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)\n",
|
||
" cmf = self.calculate_cmf(pmf)\n",
|
||
" \n",
|
||
" # Crea il plot\n",
|
||
" fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n",
|
||
" \n",
|
||
" # Plot PMF\n",
|
||
" width = np.diff(bin_edges)\n",
|
||
" ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')\n",
|
||
" ax1.set_title('Probability Mass Function')\n",
|
||
" ax1.set_ylabel('Probability')\n",
|
||
" ax1.grid(True, alpha=0.3)\n",
|
||
" ax1.legend()\n",
|
||
" \n",
|
||
" # Plot CMF\n",
|
||
" ax2.plot(bin_centers, cmf, 'r-', label='CMF')\n",
|
||
" ax2.set_title('Cumulative Mass Function')\n",
|
||
" ax2.set_xlabel('Value')\n",
|
||
" ax2.set_ylabel('Cumulative Probability')\n",
|
||
" ax2.grid(True, alpha=0.3)\n",
|
||
" ax2.legend()\n",
|
||
" \n",
|
||
" # Imposta il titolo generale\n",
|
||
" fig.suptitle(title, y=1.02)\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" \n",
|
||
" return bin_centers, pmf, cmf\n",
|
||
"\n",
|
||
"def analyze_model_predictions(model: tf.keras.Model, \n",
|
||
" test_data: np.ndarray,\n",
|
||
" test_targets: np.ndarray,\n",
|
||
" scaler_y) -> None:\n",
|
||
" \"\"\"\n",
|
||
" Analizza le distribuzioni di probabilità delle predizioni del modello.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" model: Modello TensorFlow addestrato\n",
|
||
" test_data: Dati di test\n",
|
||
" test_targets: Target di test\n",
|
||
" scaler_y: Scaler usato per denormalizzare i target\n",
|
||
" \"\"\"\n",
|
||
" # Ottieni le predizioni\n",
|
||
" predictions = model.predict(test_data)\n",
|
||
" \n",
|
||
" # Denormalizza predizioni e target\n",
|
||
" predictions_real = scaler_y.inverse_transform(predictions)\n",
|
||
" targets_real = scaler_y.inverse_transform(test_targets)\n",
|
||
" \n",
|
||
" # Inizializza la classe per l'analisi delle probabilità\n",
|
||
" prob = ProbabilityFunctions()\n",
|
||
" \n",
|
||
" # Analizza ogni target\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n",
|
||
" 'avg_oil_prod', 'total_water_need']\n",
|
||
" \n",
|
||
" for i, target in enumerate(target_names):\n",
|
||
" print(f\"\\nAnalisi per {target}\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" \n",
|
||
" # Calcola errori\n",
|
||
" errors = predictions_real[:, i] - targets_real[:, i]\n",
|
||
" \n",
|
||
" # Calcola statistiche degli errori\n",
|
||
" error_stats = prob.calculate_statistics(errors)\n",
|
||
" print(\"\\nStatistiche degli Errori:\")\n",
|
||
" for key, value in error_stats.items():\n",
|
||
" print(f\"{key}: {value:.3f}\")\n",
|
||
" \n",
|
||
" # Visualizza le distribuzioni degli errori\n",
|
||
" bin_centers, pmf, cmf = prob.plot_distributions(\n",
|
||
" errors, \n",
|
||
" bins=50,\n",
|
||
" title=f\"Distribuzione degli Errori - {target}\"\n",
|
||
" )\n",
|
||
" \n",
|
||
" # Calcola intervalli di confidenza\n",
|
||
" confidence_levels = [0.68, 0.95, 0.99] # 1σ, 2σ, 3σ\n",
|
||
" for level in confidence_levels:\n",
|
||
" lower_idx = np.searchsorted(cmf, (1 - level) / 2)\n",
|
||
" upper_idx = np.searchsorted(cmf, (1 + level) / 2)\n",
|
||
" \n",
|
||
" print(f\"\\nIntervallo di Confidenza {level*100}%:\")\n",
|
||
" print(f\"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]\")\n",
|
||
"\n",
|
||
"def run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y):\n",
|
||
" \"\"\"\n",
|
||
" Esegue un'analisi completa del modello includendo errori,\n",
|
||
" importanza delle feature e distribuzioni.\n",
|
||
" \"\"\"\n",
|
||
" print(\"=== ANALISI COMPLETA DEL MODELLO ===\")\n",
|
||
" \n",
|
||
" # 1. Analisi degli errori\n",
|
||
" print(\"\\n1. ANALISI DEGLI ERRORI\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" analyze_model_predictions(retrained_model, test_data, test_targets, scaler_y)\n",
|
||
" \n",
|
||
" # 2. Analisi dell'importanza delle feature\n",
|
||
" print(\"\\n2. IMPORTANZA DELLE FEATURE\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" \n",
|
||
" # Definisci i nomi delle feature\n",
|
||
" temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n",
|
||
" static_features = ['ha']\n",
|
||
" \n",
|
||
" all_features = temporal_features + static_features\n",
|
||
" importance = analyze_feature_importance(retrained_model, test_data, all_features)\n",
|
||
" \n",
|
||
" print(\"\\nImportanza relativa delle feature:\")\n",
|
||
" for feature, imp in sorted(importance.items(), key=lambda x: x[1], reverse=True):\n",
|
||
" print(f\"{feature}: {imp:.4f}\")\n",
|
||
" \n",
|
||
" # 3. Analisi distribuzionale\n",
|
||
" print(\"\\n3. ANALISI DISTRIBUZIONALE\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" \n",
|
||
" prob = ProbabilityFunctions()\n",
|
||
" predictions = retrained_model.predict(test_data)\n",
|
||
" predictions_real = scaler_y.inverse_transform(predictions)\n",
|
||
" targets_real = scaler_y.inverse_transform(test_targets)\n",
|
||
" \n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n",
|
||
" 'avg_oil_prod', 'total_water_need']\n",
|
||
" \n",
|
||
" for i, target in enumerate(target_names):\n",
|
||
" print(f\"\\nAnalisi distribuzionale per {target}\")\n",
|
||
" \n",
|
||
" # Statistiche\n",
|
||
" stats_pred = prob.calculate_statistics(predictions_real[:, i])\n",
|
||
" stats_true = prob.calculate_statistics(targets_real[:, i])\n",
|
||
" \n",
|
||
" print(\"\\nStatistiche Predizioni:\")\n",
|
||
" for key, value in stats_pred.items():\n",
|
||
" print(f\"{key}: {value:.3f}\")\n",
|
||
" \n",
|
||
" print(\"\\nStatistiche Target Reali:\")\n",
|
||
" for key, value in stats_true.items():\n",
|
||
" print(f\"{key}: {value:.3f}\")\n",
|
||
" \n",
|
||
" # Visualizza distribuzioni\n",
|
||
" prob.plot_distributions(predictions_real[:, i], bins=50,\n",
|
||
" title=f\"Distribuzione Predizioni - {target}\")\n",
|
||
" prob.plot_distributions(targets_real[:, i], bins=50,\n",
|
||
" title=f\"Distribuzione Target Reali - {target}\")\n",
|
||
"\n",
|
||
"def analyze_model_predictions(model, test_data, test_targets, scaler_y):\n",
|
||
" \"\"\"\n",
|
||
" Analizza le distribuzioni di probabilità delle predizioni del modello.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" model: Modello TensorFlow addestrato\n",
|
||
" test_data: Dati di test\n",
|
||
" test_targets: Target di test\n",
|
||
" scaler_y: Scaler usato per denormalizzare i target\n",
|
||
" \"\"\"\n",
|
||
" # Ottieni le predizioni\n",
|
||
" predictions = model.predict(test_data)\n",
|
||
" \n",
|
||
" # Denormalizza predizioni e target\n",
|
||
" predictions_real = scaler_y.inverse_transform(predictions)\n",
|
||
" targets_real = scaler_y.inverse_transform(test_targets)\n",
|
||
" \n",
|
||
" # Inizializza la classe per l'analisi delle probabilità\n",
|
||
" prob = ProbabilityFunctions()\n",
|
||
" \n",
|
||
" # Analizza ogni target\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n",
|
||
" 'avg_oil_prod', 'total_water_need']\n",
|
||
" \n",
|
||
" for i, target in enumerate(target_names):\n",
|
||
" print(f\"\\nAnalisi per {target}\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" \n",
|
||
" # Calcola errori\n",
|
||
" errors = predictions_real[:, i] - targets_real[:, i]\n",
|
||
" \n",
|
||
" # Calcola statistiche degli errori\n",
|
||
" error_stats = prob.calculate_statistics(errors)\n",
|
||
" print(\"\\nStatistiche degli Errori:\")\n",
|
||
" for key, value in error_stats.items():\n",
|
||
" print(f\"{key}: {value:.3f}\")\n",
|
||
" \n",
|
||
" # Visualizza le distribuzioni degli errori\n",
|
||
" bin_centers, pmf, cmf = prob.plot_distributions(\n",
|
||
" errors, \n",
|
||
" bins=50,\n",
|
||
" title=f\"Distribuzione degli Errori - {target}\"\n",
|
||
" )\n",
|
||
" \n",
|
||
" # Calcola intervalli di confidenza\n",
|
||
" confidence_levels = [0.80,0.85, 0.90, 0.95, 0.99] # 1σ, 2σ, 3σ\n",
|
||
" for level in confidence_levels:\n",
|
||
" lower_idx = np.searchsorted(cmf, (1 - level) / 2)\n",
|
||
" upper_idx = np.searchsorted(cmf, (1 + level) / 2)\n",
|
||
" \n",
|
||
" print(f\"\\nIntervallo di Confidenza {level*100}%:\")\n",
|
||
" print(f\"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]\")\n",
|
||
"\n",
|
||
"class ProbabilityFunctions:\n",
|
||
" @staticmethod\n",
|
||
" def calculate_statistics(data):\n",
|
||
" \"\"\"\n",
|
||
" Calcola statistiche di base usando TensorFlow.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Tensor dei dati\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" dict: Dizionario con le statistiche\n",
|
||
" \"\"\"\n",
|
||
" if not isinstance(data, tf.Tensor):\n",
|
||
" data = tf.convert_to_tensor(data, dtype=tf.float32)\n",
|
||
" \n",
|
||
" mean = tf.reduce_mean(data)\n",
|
||
" # Calculate variance manually\n",
|
||
" squared_deviations = tf.square(data - mean)\n",
|
||
" variance = tf.reduce_mean(squared_deviations)\n",
|
||
" std = tf.sqrt(variance)\n",
|
||
" \n",
|
||
" # Sort the tensor for median calculation\n",
|
||
" sorted_data = tf.sort(data)\n",
|
||
" size = tf.size(data)\n",
|
||
" mid_index = size // 2\n",
|
||
" median = sorted_data[mid_index]\n",
|
||
" \n",
|
||
" return {\n",
|
||
" 'mean': mean.numpy(),\n",
|
||
" 'variance': variance.numpy(),\n",
|
||
" 'std': std.numpy(),\n",
|
||
" 'min': tf.reduce_min(data).numpy(),\n",
|
||
" 'max': tf.reduce_max(data).numpy(),\n",
|
||
" 'median': median.numpy()\n",
|
||
" }\n",
|
||
"\n",
|
||
" @staticmethod\n",
|
||
" def calculate_pmf(data, bins=50):\n",
|
||
" \"\"\"\n",
|
||
" Calcola la Probability Mass Function (PMF) dei dati.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Array di dati\n",
|
||
" bins: Numero di bin per l'istogramma\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" tuple: (bin_centers, pmf, bin_edges)\n",
|
||
" \"\"\"\n",
|
||
" # Calcola l'istogramma\n",
|
||
" hist, bin_edges = np.histogram(data, bins=bins, density=True)\n",
|
||
" \n",
|
||
" # Calcola i centri dei bin\n",
|
||
" bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n",
|
||
" \n",
|
||
" # Normalizza per ottenere la PMF\n",
|
||
" pmf = hist / np.sum(hist)\n",
|
||
" \n",
|
||
" return bin_centers, pmf, bin_edges\n",
|
||
"\n",
|
||
" @staticmethod\n",
|
||
" def calculate_cmf(pmf):\n",
|
||
" \"\"\"\n",
|
||
" Calcola la Cumulative Mass Function (CMF) dalla PMF.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" pmf: Probability Mass Function\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" array: Cumulative Mass Function\n",
|
||
" \"\"\"\n",
|
||
" return np.cumsum(pmf)\n",
|
||
"\n",
|
||
" def plot_distributions(self, data, bins=50, title=\"Distribuzione\"):\n",
|
||
" \"\"\"\n",
|
||
" Calcola e visualizza PMF e CMF delle distribuzioni.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Array di dati da analizzare\n",
|
||
" bins: Numero di bin per l'istogramma\n",
|
||
" title: Titolo del grafico\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" tuple: (bin_centers, pmf, cmf)\n",
|
||
" \"\"\"\n",
|
||
" # Calcola PMF e CMF\n",
|
||
" bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)\n",
|
||
" cmf = self.calculate_cmf(pmf)\n",
|
||
" \n",
|
||
" # Crea il plot\n",
|
||
" fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n",
|
||
" \n",
|
||
" # Plot PMF\n",
|
||
" width = np.diff(bin_edges)\n",
|
||
" ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')\n",
|
||
" ax1.set_title('Probability Mass Function')\n",
|
||
" ax1.set_ylabel('Probability')\n",
|
||
" ax1.grid(True, alpha=0.3)\n",
|
||
" ax1.legend()\n",
|
||
" \n",
|
||
" # Plot CMF\n",
|
||
" ax2.plot(bin_centers, cmf, 'r-', label='CMF')\n",
|
||
" ax2.set_title('Cumulative Mass Function')\n",
|
||
" ax2.set_xlabel('Value')\n",
|
||
" ax2.set_ylabel('Cumulative Probability')\n",
|
||
" ax2.grid(True, alpha=0.3)\n",
|
||
" ax2.legend()\n",
|
||
" \n",
|
||
" # Set overall title\n",
|
||
" fig.suptitle(title, y=1.02)\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" \n",
|
||
" return bin_centers, pmf, cmf"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "b98f2a45-ba6f-4519-acaa-0138b4ee7812",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"=== ANALISI COMPLETA DEL MODELLO ===\n",
|
||
"\n",
|
||
"1. ANALISI DEGLI ERRORI\n",
|
||
"--------------------------------------------------\n",
|
||
"18750/18750 [==============================] - 191s 10ms/step\n",
|
||
"\n",
|
||
"Analisi per olive_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: 16.634\n",
|
||
"variance: 3545753.250\n",
|
||
"std: 1883.017\n",
|
||
"min: -15594.572\n",
|
||
"max: 14395.641\n",
|
||
"median: 192.294\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Intervallo di Confidenza 80.0%:\n",
|
||
"Range: [-2098.98, 2099.65]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 85.0%:\n",
|
||
"Range: [-2698.78, 2099.65]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 90.0%:\n",
|
||
"Range: [-3298.58, 2699.46]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-4498.19, 3299.26]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-6297.61, 5098.67]\n",
|
||
"\n",
|
||
"Analisi per min_oil_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -10.703\n",
|
||
"variance: 153119.078\n",
|
||
"std: 391.304\n",
|
||
"min: -3452.054\n",
|
||
"max: 3142.031\n",
|
||
"median: 27.116\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Intervallo di Confidenza 80.0%:\n",
|
||
"Range: [-484.72, 438.46]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 85.0%:\n",
|
||
"Range: [-616.60, 438.46]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 90.0%:\n",
|
||
"Range: [-748.48, 570.34]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-880.36, 702.22]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-1407.89, 1097.86]\n",
|
||
"\n",
|
||
"Analisi per max_oil_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -3.161\n",
|
||
"variance: 222900.141\n",
|
||
"std: 472.123\n",
|
||
"min: -4621.081\n",
|
||
"max: 3967.894\n",
|
||
"median: 42.622\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Intervallo di Confidenza 80.0%:\n",
|
||
"Range: [-584.26, 446.41]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 85.0%:\n",
|
||
"Range: [-756.04, 618.19]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 90.0%:\n",
|
||
"Range: [-756.04, 618.19]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-1099.60, 961.75]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-1614.94, 1305.31]\n",
|
||
"\n",
|
||
"Analisi per avg_oil_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -7.018\n",
|
||
"variance: 175727.656\n",
|
||
"std: 419.199\n",
|
||
"min: -3742.384\n",
|
||
"max: 3360.436\n",
|
||
"median: 33.824\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Intervallo di Confidenza 80.0%:\n",
|
||
"Range: [-546.11, 448.28]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 85.0%:\n",
|
||
"Range: [-546.11, 448.28]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 90.0%:\n",
|
||
"Range: [-688.17, 590.34]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-972.28, 732.39]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-1540.51, 1158.56]\n",
|
||
"\n",
|
||
"Analisi per total_water_need\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: 65.956\n",
|
||
"variance: 3269755.500\n",
|
||
"std: 1808.246\n",
|
||
"min: -15266.289\n",
|
||
"max: 11478.280\n",
|
||
"median: 354.974\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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hp2/fvlq1apUeeughxcTEKCAgQDabTVdeeaVTMdlsNlksFn3++eeFfpbyryXPd7a5cKbPq7Px5K/bCy+8UORjFAMCApy+eSIAVGUU3gDOaW+//bYknfF0Rw8PD1122WW67LLLNGHCBD377LN67LHHtGzZMsXFxZ1VIVGY/CNJ+Ywx2rp1q8PzxmvWrFngDuPSyaOKpx8FHjdunBYtWqQPPvhATZs2PePy69evL0natGmTw7xyc3O1bds2p47enq5hw4b65ZdfdNlll5V4e+XHs2XLFvsp49LJm5Zt27ZNrVq1KvFy6tevL5vNpm3btjkcidu6dWuJYnOXkJAQ+fv7a9OmTQXGbdy4UR4eHmX2I0N8fLyWLFnidP+itnv+Nt+yZYuaNWtmb09PT9fBgwft73Nx81iwYIG6deum//3vfw7tBw8etN+w7Gx06tRJzz33nL766isFBweradOmslgsatGihb755ht98803uvrqq+39Dxw4oKVLlyolJUWjRo2yt5/+GS5unRo2bChjjKKjo3X++eef9TqcLWfjadiwoSQpMDCw2H1CSEiI/Pz8Ct0mheUvAFRFXOMN4Jz19ddf66mnnlJ0dLT9sU2F+ffffwu05R/dyT+Sk/+s5cIK4dJ46623HK47X7Bggfbs2WO/Q7h08j+933//vXJzc+1tn3zySYHH/Xz11Vd6/PHH9dhjj6lXr15OLT8uLk4+Pj56+eWXHY5s/u9//1NmZmap7vzdt29f7dq1S6+99lqBcUePHi32Lttt2rRRSEiIpk+f7rC+s2bNKrDNnV1O/o8tU6dOdegzefJkp9fJnTw9PXXFFVfoww8/dLgEIT09XXPmzFHHjh3L7MyGOnXqKC4uzuFVnKI+D1dddZWkk3ctP1X+2Qmn5lW1atUK/Tx5enoWONo+f/587dq1y5lVOaNOnTopJydHkyZNUseOHe3FcqdOnfT2229r9+7dDtd35x8RPj2m09dRKnq79O7dW56enkpJSSkwH2OMw2PJyoOz8bRu3VoNGzbU+PHjCzw5QTp5OYR0chvFx8dr0aJF2rlzp338H3/8oS+++MKFawIAFQdHvAGcEz7//HNt3LhRJ06cUHp6ur7++mstWbJE9evX10cffSRfX98ip33yySe1cuVK9ezZU/Xr19fevXs1depU1atXz36dZ8OGDRUUFKTp06erevXqqlatmtq3b1/qa2pr1aqljh07auDAgUpPT9ekSZPUqFEjh0ee3XHHHVqwYIGuvPJK9e3bV3/++afeeecd+1GofP369VNISIgaN27s8LxySbr88ssLfZxPSEiIkpOTlZKSoiuvvFLXXnutNm3apKlTp6pt27YON1Jz1m233ab33ntPd999t5YtW6ZLL71UeXl52rhxo9577z198cUXatOmTaHTent76+mnn9Zdd92l7t27KyEhQdu2bdPMmTMLHN13djmtW7fWDTfcoEmTJmn//v32x4lt3rxZ0tmdDn26Xbt2Fdj20snTcJ39MaQwTz/9tP0Z80OGDJGXl5deffVV5eTkFPoc9fLSunVrSdJjjz2mm266Sd7e3rrmmmvUqlUrJSYmasaMGTp48KC6dOmiH3/8UW+++aZ69eqlbt26Ocxj2rRpevrpp9WoUSOFhoaqe/fuuvrqq/Xkk09q4MCB6tChg3777TfNnj27QB6UVmxsrLy8vLRp0yYNHjzY3t65c2dNmzZNkhwK78DAQHXu3FnPP/+8jh8/rrp16+rLL7/Utm3bnN4uDRs21NNPP63k5GRt375dvXr1UvXq1bVt2zYtXLhQgwcP1oMPPlgm6+cMZ+Px8PDQ66+/rh49eqhFixYaOHCg6tatq127dmnZsmUKDAzUxx9/LElKSUnR4sWL1alTJw0ZMkQnTpzQ5MmT1aJFC4d7QQBAleWGO6kDQLnJf1RS/svHx8eEh4ebyy+/3Lz00ksOj+zKd/rjxJYuXWquu+46ExERYXx8fExERITp16+f2bx5s8N0H374oWnevLnx8vJyeIxRly5dTIsWLQqNr6jHib377rsmOTnZhIaGGj8/P9OzZ0+Hx/Dke/HFF03dunWN1Wo1l156qfnpp58KzFOFPMYq/7Vs2TKH7XT6Y45eeeUV07RpU+Pt7W3CwsLMPffcYw4cOFBgHQpbv8TERFO/fn2HttzcXPPcc8+ZFi1aGKvVamrWrGlat25tUlJSTGZmZqHb6FRTp0410dHRxmq1mjZt2piVK1cW+lgvZ5dz5MgRM3ToUFOrVi0TEBBgevXqZTZt2mQkmXHjxtn7uepxYqdun8TERFOtWrVC5yHJDB06tNBxa9euNfHx8SYgIMD4+/ubbt26mVWrVjn0Ke6xeq54nJgxxjz11FOmbt26xsPDw2H+x48fNykpKSY6Otp4e3ubyMhIk5ycbI4dO+YwfVpamunZs6epXr26wyPjjh07ZkaMGGHq1Klj/Pz8zKWXXmpSU1MLvB+leZxYvrZt2xpJ5ocffrC3/fPPP0aSiYyMLND/n3/+Mddff70JCgoyNWrUMH369DG7d+8u8Fi64raLMca8//77pmPHjqZatWqmWrVqpmnTpmbo0KFm06ZN9j7F7U/OJH/fdvqjEYvKAWfiMcaYn3/+2fTu3dvUrl3bWK1WU79+fdO3b1+zdOlSh34rVqwwrVu3Nj4+PqZBgwZm+vTpBfa3AFBVWYw5i7ujAABQxaxbt04XXXSR3nnnnWIvQQAAAHAW13gDAM5ZR48eLdA2adIkeXh4qHPnzm6ICAAAVEVc4w0AOGc9//zzWrNmjbp16yYvLy99/vnn+vzzzzV48OAK/dgxlExubm6hN0k8VY0aNVzy2L/ycPjw4UJvbnaqkJAQpx59CABwDU41BwCcs5YsWaKUlBRt2LBBhw8f1nnnnafbbrtNjz32mLy8+G26qli+fLnDjdsKM3PmTA0YMKB8AipjY8aMUUpKSrF9tm3bpqioqPIJCABQAIU3AACo0g4cOKA1a9YU26dFixaqU6dOOUVUtv766y/99ddfxfbp2LFjsU9vAAC4FoU3AAAAAAAuxM3VAAAAAABwIQpvAAAAAABciMIbAAAAAAAXovAGAAAAAMCFKLwBAAAAAHAhCm8AAAAAAFyIwhsAAAAAABei8AYAAAAAwIUovAEAAAAAcCEKbwAAAAAAXIjCGwAAAAAAF6LwBgAAAADAhSi8AQAAAABwIQpvAAAAAABciMIbAAAAAAAXovAGAAAAAMCFKLwBAAAAAHAhCm8AAAAAAFyIwhsAAAAAABei8AYAAAAAwIUovAEAAAAAcCEKbwAAAAAAXIjCGwAAAAAAF6LwBgAAAADAhSi8AQAAAABwIQpvAAAAAABciMIbAAAAAAAXovAGAAAAAMCFKLwBAAAAAHAhCm8AAAAAAFyIwhsAAAAAABei8AYAAAAAwIUovAEAAAAAcCEKbwAAAAAAXIjCGwAAAAAAF6LwBgAAAADAhSi8AQAAAABwIQpvAAAAAABciMIbAAAAAAAXovAGAAAAAMCFKLwBAAAAAHAhCm8AAAAAAFyIwhsAAAAAABei8AYAAAAAwIUovAEA5ySLxaJhw4aV2fxmzZoli8Win3766Yx9u3btqq5du9qHt2/fLovFolmzZtnbxowZI4vFUmbxoeI4/f0HAFR9FN4AgAojv3jNf/n6+ur888/XsGHDlJ6e7u7w3O7ZZ5/VokWLynSey5cvt2/vd955p9A+l156qSwWi1q2bFmmyy4Lp+bLqa/w8HC3xrVhwwaNGTNG27dvd2scAICKwcvdAQAAcLonn3xS0dHROnbsmL799ltNmzZNn332mX7//Xf5+/u7O7yz9uWXX56xz+OPP66RI0c6tD377LO68cYb1atXrzKPydfXV3PmzNGtt97q0L59+3atWrVKvr6+Zb7MsnL55Zerf//+Dm1+fn5uiuakDRs2KCUlRV27dlVUVJTDOGfefwBA1ULhDQCocHr06KE2bdpIku644w7Vrl1bEyZM0Icffqh+/foVOs2RI0dUrVq18gyz1Hx8fM7Yx8vLS15e5fc1fdVVV+mjjz7Svn37FBwcbG+fM2eOwsLC1LhxYx04cKDc4imJ888/v8APBhWZM+8/AKBq4VRzAECF1717d0nStm3bJEkDBgxQQECA/vzzT1111VWqXr26brnlFkknC/ARI0YoMjJSVqtVTZo00fjx42WMKXTes2fPVpMmTeTr66vWrVtr5cqVDuN37NihIUOGqEmTJvLz81Pt2rXVp0+fIk8hzs7O1l133aXatWsrMDBQ/fv3L1CwOnON7+nXeFssFh05ckRvvvmm/XTqAQMGaNmyZbJYLFq4cGGBecyZM0cWi0WpqanFLkuSrrvuOlmtVs2fP7/APPr27StPT88C08ycOVPdu3dXaGiorFarmjdvrmnTphXo99NPPyk+Pl7BwcHy8/NTdHS0br/9doc+c+fOVevWrVW9enUFBgbqggsu0EsvvXTGuM9kwIABBY44S4VfQ59/3f+iRYvUsmVLWa1WtWjRQosXLy4w/a5duzRo0CBFRETIarUqOjpa99xzj3JzczVr1iz16dNHktStWzf7+7V8+XJJhb//e/fu1aBBgxQWFiZfX1+1atVKb775pkOf/HsBjB8/XjNmzFDDhg1ltVrVtm1brV69uvQbCQDgchzxBgBUeH/++ackqXbt2va2EydOKD4+Xh07dtT48ePl7+8vY4yuvfZaLVu2TIMGDVJMTIy++OILPfTQQ9q1a5cmTpzoMN8VK1Zo3rx5Gj58uKxWq6ZOnaorr7xSP/74o/165tWrV2vVqlW66aabVK9ePW3fvl3Tpk1T165dtWHDhgKnvg8bNkxBQUEaM2aMNm3apGnTpmnHjh32a6lL6+2339Ydd9yhdu3aafDgwZKkhg0b6pJLLlFkZKRmz56t66+/3mGa2bNnq2HDhoqNjT3j/P39/XXdddfp3Xff1T333CNJ+uWXX7R+/Xq9/vrr+vXXXwtMM23aNLVo0ULXXnutvLy89PHHH2vIkCGy2WwaOnSopJMF5RVXXKGQkBCNHDlSQUFB2r59uz744AP7fJYsWaJ+/frpsssu03PPPSdJ+uOPP/Tdd9/pvvvuO2Psx44d0759+xzaqlevLqvVesZpT/ftt9/qgw8+0JAhQ1S9enW9/PLLuuGGG7Rz5057/u3evVvt2rXTwYMHNXjwYDVt2lS7du3SggULlJ2drc6dO2v48OF6+eWX9eijj6pZs2aSZP/3dEePHlXXrl21detWDRs2TNHR0Zo/f74GDBiggwcPFtgGc+bM0aFDh3TXXXfJYrHo+eefV+/evfXXX3/J29u7xOsMACgHBgCACmLmzJlGkvnqq69MRkaG+fvvv83cuXNN7dq1jZ+fn/nnn3+MMcYkJiYaSWbkyJEO0y9atMhIMk8//bRD+4033mgsFovZunWrvU2SkWR++ukne9uOHTuMr6+vuf766+1t2dnZBeJMTU01ksxbb71VIPbWrVub3Nxce/vzzz9vJJkPP/zQ3talSxfTpUsX+/C2bduMJDNz5kx72+jRo83pX9PVqlUziYmJBeJJTk42VqvVHDx40N62d+9e4+XlZUaPHl2g/6mWLVtmJJn58+ebTz75xFgsFrNz505jjDEPPfSQadCggT3mFi1aOExb2LaJj4+3T2OMMQsXLjSSzOrVq4uM4b777jOBgYHmxIkTxcZamPz38fRX/rZMTEw09evXLzBdYdtXkvHx8XHIk19++cVIMpMnT7a39e/f33h4eBS6TjabzRhjzPz5840ks2zZsgJ9Tn//J02aZCSZd955x96Wm5trYmNjTUBAgMnKyjLG/JcntWvXNv/++6+974cffmgkmY8//rjoDQUAcCtONQcAVDhxcXEKCQlRZGSkbrrpJgUEBGjhwoWqW7euQ7/8I7P5PvvsM3l6emr48OEO7SNGjJAxRp9//rlDe2xsrFq3bm0fPu+883Tdddfpiy++UF5eniTHm3QdP35c+/fvV6NGjRQUFKS1a9cWiH3w4MEORx3vueceeXl56bPPPivhVnBe//79lZOTowULFtjb5s2bpxMnTpTo2ucrrrhCtWrV0ty5c2WM0dy5c4u8pl5y3DaZmZnat2+funTpor/++kuZmZmSpKCgIEnSJ598ouPHjxc6n6CgIB05ckRLlixxOtZTXXfddVqyZInDKz4+vlTziouLU8OGDe3DF154oQIDA/XXX39Jkmw2mxYtWqRrrrnGfh+CU5XmrIbPPvtM4eHhDtva29tbw4cP1+HDh7VixQqH/gkJCapZs6Z9uFOnTpJkjxEAUPFwqjkAoMKZMmWKzj//fHl5eSksLExNmjSRh4fjb8VeXl6qV6+eQ9uOHTsUERGh6tWrO7Tnn+K7Y8cOh/bGjRsXWPb555+v7OxsZWRkKDw8XEePHtXYsWM1c+ZM7dq1y+Fa8fzisrh5BgQEqE6dOi59rFTTpk3Vtm1bzZ49W4MGDZJ08jTzSy65RI0aNXJ6Pt7e3urTp4/mzJmjdu3a6e+//9bNN99cZP/vvvtOo0ePVmpqqrKzsx3GZWZmqkaNGurSpYtuuOEGpaSkaOLEieratat69eqlm2++2X4q+JAhQ/Tee++pR48eqlu3rq644gr17dtXV155pVNx16tXT3FxcU6vZ3HOO++8Am01a9a0X6efkZGhrKysMn202o4dO9S4ceMCOV5U3p4eY34RXlFvfgcA4OZqAIAKqF27doqLi1PXrl3VrFmzAgWJJFmt1kLby9q9996rZ555Rn379tV7772nL7/8UkuWLFHt2rVls9lcvnxn9e/fXytWrNA///yjP//8U99//32p7vR98803a926dRozZoxatWql5s2bF9rvzz//1GWXXaZ9+/ZpwoQJ+vTTT7VkyRI98MADkmTfNhaLRQsWLFBqaqqGDRumXbt26fbbb1fr1q11+PBhSVJoaKjWrVunjz76yH6Nfo8ePZSYmFjKrfGfoo5A55/RcLrCbiInqcib87lDZYgRAOCIwhsAUGXUr19fu3fv1qFDhxzaN27caB9/qi1bthSYx+bNm+Xv76+QkBBJ0oIFC5SYmKgXX3xRN954oy6//HJ17NhRBw8eLDSG0+d5+PBh7dmzp9A7a5dUcacx33TTTfL09NS7776r2bNny9vbWwkJCSVeRseOHXXeeedp+fLlxR7t/vjjj5WTk6OPPvpId911l6666irFxcUV+fzsSy65RM8884x++uknzZ49W+vXr9fcuXPt4318fHTNNddo6tSp+vPPP3XXXXfprbfe0tatW0u8DqeqWbNmoe/V6UeRnRUSEqLAwED9/vvvxfYrySnn9evX15YtWwr8kFNU3gIAKh8KbwBAlXHVVVcpLy9Pr7zyikP7xIkTZbFY1KNHD4f21NRUh+u0//77b3344Ye64oor7EcVPT09CxxJnDx5cpFHTGfMmOFwLfO0adN04sSJAssujWrVqhVZ8AcHB6tHjx565513NHv2bF155ZUOz+N2lsVi0csvv6zRo0frtttuK7Jf/vY5/dT7mTNnOvQ7cOBAge0XExMjScrJyZEk7d+/32G8h4eHLrzwQoc+pdWwYUNlZmY63JV9z549hT5+zRkeHh7q1auXPv74Y/30008Fxueva/4z5Yt6v0511VVXKS0tTfPmzbO3nThxQpMnT1ZAQIC6dOlSqlgBABUH13gDAKqMa665Rt26ddNjjz2m7du3q1WrVvryyy/14Ycf6v7773e4aZYktWzZUvHx8Q6PE5OklJQUe5+rr75ab7/9tmrUqKHmzZsrNTVVX331lcOjzU6Vm5uryy67TH379tWmTZs0depUdezYUddee+1Zr1/r1q311VdfacKECYqIiFB0dLTat29vH9+/f3/deOONkqSnnnqq1Mu57rrrdN111xXb54orrrAfpb7rrrt0+PBhvfbaawoNDdWePXvs/d58801NnTpV119/vRo2bKhDhw7ptddeU2BgoK666ipJ0h133KF///1X3bt3V7169bRjxw5NnjxZMTExRT6Cy1k33XSTHnnkEV1//fUaPny4srOzNW3aNJ1//vmF3hzPGc8++6y+/PJLdenSRYMHD1azZs20Z88ezZ8/X99++62CgoIUExMjT09PPffcc8rMzJTVarU/8/x0gwcP1quvvqoBAwZozZo1ioqK0oIFC/Tdd99p0qRJBe5ZAACofCi8AQBVhoeHhz766CONGjVK8+bN08yZMxUVFaUXXnhBI0aMKNC/S5cuio2NVUpKinbu3KnmzZtr1qxZ9qOtkvTSSy/J09NTs2fP1rFjx3TppZfqq6++KvKu2a+88opmz56tUaNG6fjx4+rXr59efvnls3qGd74JEyZo8ODBevzxx3X06FElJiY6FN7XXHONatasKZvNViaFfnGaNGmiBQsW6PHHH9eDDz6o8PBw3XPPPQoJCdHtt99u79elSxf9+OOPmjt3rtLT01WjRg21a9dOs2fPVnR0tCTp1ltv1YwZMzR16lQdPHhQ4eHhSkhI0JgxY876Ov7atWtr4cKFSkpK0sMPP6zo6GiNHTtWW7ZsKXXhXbduXf3www964oknNHv2bGVlZalu3brq0aOH/bnu4eHhmj59usaOHatBgwYpLy9Py5YtK7Tw9vPz0/LlyzVy5Ei9+eabysrKUpMmTTRz5kwNGDDgbFYfAFBBWAx34gAAoEo4ceKEIiIidM011+h///ufu8MBAAD/j2u8AQCoIhYtWqSMjAz179/f3aEAAIBTcMQbAIBK7ocfftCvv/6qp556SsHBwaU+hRoAALgGR7wBAKjkpk2bpnvuuUehoaF666233B0OAAA4DUe8AQAAAABwIY54AwAAAADgQhTeAAAAAAC4EM/xLoTNZtPu3btVvXr1MnnuKgAAAACgajHG6NChQ4qIiJCHR/HHtCm8C7F7925FRka6OwwAAAAAQAX3999/q169esX2ofAuRPXq1SWd3ICBgYFujqb0bDabMjIyFBIScsZfYIDSIs9QXsg1lBdyDeWBPEN5IddcJysrS5GRkfb6sTgU3oXIP708MDCw0hfex44dU2BgIB8yuAx5hvJCrqG8kGsoD+QZygu55nrOXJ7MlgcAAAAAwIUovAEAAAAAcCEKbwAAAAAAXMjt13hPmTJFL7zwgtLS0tSqVStNnjxZ7dq1K7Tv+vXrNWrUKK1Zs0Y7duzQxIkTdf/99xfot2vXLj3yyCP6/PPPlZ2drUaNGmnmzJlq06aNi9cGAAAAACquvLw8HT9+3N1hVAqenp7y8vIqk0dMu7XwnjdvnpKSkjR9+nS1b99ekyZNUnx8vDZt2qTQ0NAC/bOzs9WgQQP16dNHDzzwQKHzPHDggC699FJ169ZNn3/+uUJCQrRlyxbVrFnT1asDAAAAABXW4cOH9c8//8gY4+5QKg1/f3/VqVNHPj4+ZzUftxbeEyZM0J133qmBAwdKkqZPn65PP/1Ub7zxhkaOHFmgf9u2bdW2bVtJKnS8JD333HOKjIzUzJkz7W3R0dEuiB4AAAAAKoe8vDz9888/8vf3V0hISJkcxa3KjDHKzc1VRkaGtm3bpsaNG5/VXeHdVnjn5uZqzZo1Sk5Otrd5eHgoLi5OqamppZ7vRx99pPj4ePXp00crVqxQ3bp1NWTIEN15551FTpOTk6OcnBz7cFZWlqSTt9632WyljsXdbDabjDGVeh1Q8ZFnKC/kGsoLuYbyQJ6hvOTnWm5urmw2m4KDg+Xr6+vusCoFX19feXl5aceOHcrJyZHVanUYX5LPr9sK73379ikvL09hYWEO7WFhYdq4cWOp5/vXX39p2rRpSkpK0qOPPqrVq1dr+PDh8vHxUWJiYqHTjB07VikpKQXaMzIydOzYsVLH4m42m02ZmZkyxvDMPrgMeYbyQq6hvJBrKA/kGcpLfq4dP35cNptNeXl5OnHihLvDqjTyD8bu27dP3t7eDuMOHTrk9HzcfnO1smaz2dSmTRs9++yzkqSLLrpIv//+u6ZPn15k4Z2cnKykpCT7cFZWliIjIxUSEqLAwMByidsVbDabLBaLQkJC2KHDZcgzlBdyDeWFXEN5IM9QXvJzrXr16jp8+LC8vLzk5VXlykCX8fLykoeHh2rXrl3gTIGSnDngti0eHBwsT09PpaenO7Snp6crPDy81POtU6eOmjdv7tDWrFkzvf/++0VOY7VaC5w2IJ089b2y7wgtFkuVWA9UbOQZygu5hvJCrqE8kGcoL/m5ZrFY7C84J397FfZZLcln122Ft4+Pj1q3bq2lS5eqV69ekk7+GrN06VINGzas1PO99NJLtWnTJoe2zZs3q379+mcTLgAAAABUOROXbC7X5T1w+fnluryKwq3nGCQlJSkxMVFt2rRRu3btNGnSJB05csR+l/P+/furbt26Gjt2rKSTN2TbsGGD/e9du3Zp3bp1CggIUKNGjSRJDzzwgDp06KBnn31Wffv21Y8//qgZM2ZoxowZ7llJAABQpZTFf1LP1f94AkBpDBgwQG+++aYkydvbW+edd5769++vRx99VN9++626deumoKAg7dmzx+H079WrV6tdu3aSZH+E2vLly9WtW7cCy3jsscf09NNPu2wd3Fp4JyQkKCMjQ6NGjVJaWppiYmK0ePFi+w3Xdu7c6XD4fvfu3brooovsw+PHj9f48ePVpUsXLV++XNLJR44tXLhQycnJevLJJxUdHa1JkybplltuKdd1AwAAAACUjSuvvFIzZ85UTk6OPvvsMw0dOlTe3t6KjY2VJFWvXl0LFy5Uv3797NP873//03nnnaedO3cWmN+mTZsc7ucVEBDg0vjdflX9sGHDijy1PL+YzhcVFeXUw96vvvpqXX311WURHgAAAADAzaxWq/1eYPfcc48WLlyojz76yF54JyYm6o033rAX3kePHtXcuXM1fPhwPfXUUwXmFxoaqqCgoHKLnzs5AAAAAAAqFT8/P+Xm5tqHb7vtNn3zzTf2o9vvv/++oqKidPHFF7srRAcU3gAAAACASsEYo6+++kpffPGFunfvbm8PDQ1Vjx49NGvWLEnSG2+8odtvv73I+dSrV08BAQH21/79+10at9tPNQcAAAAAoDiffPKJAgICdPz4cdlsNt18880aM2aMVq9ebe9z++2367777tOtt96q1NRUzZ8/X998802h8/vmm29UvXp1+3DNmjVdGj+FNwAAAACgQuvWrZumTZsmHx8fRUREyMurYCnbo0cPDR48WIMGDdI111yj2rVrFzm/6Ojocr3Gm8IbAACgnJ3tI8l4HBmAc021atXsj5AuipeXl/r376/nn39en3/+eTlF5hyu8QYAAAAAVAlPPfWUMjIyFB8f7+5QHHDEGwAAAADOUVXtDBofHx8FBwe7O4wCKLwBAAAAABVW/p3KC9O1a1cZY4oc36tXL4fxZ+rvKpxqDgAAAACAC1F4AwAAAADgQhTeAAAAAAC4EIU3AAAAAAAuROENAAAAAOcId9xYrDIrq+1F4Q0AAAAAVZynp6ckKTc3182RVC7Z2dmSJG9v77OaD48TAwAAAIAqzsvLS/7+/srIyJC3t7c8PDgGWxxjjLKzs7V3714FBQXZf7goLQpvAAAAAKjiLBaL6tSpo23btmnHjh3uDqfSCAoKUnh4+FnPh8IbAAAAAM4BPj4+aty4MaebO8nb2/usj3Tno/AGAAAAgHOEh4eHfH193R3GOYcT+wEAAAAAcCEKbwAAAAAAXIjCGwAAAAAAF6LwBgAAAADAhSi8AQAAAABwIQpvAAAAAABciMIbAAAAAAAXovAGAAAAAMCFKkThPWXKFEVFRcnX11ft27fXjz/+WGTf9evX64YbblBUVJQsFosmTZpU7LzHjRsni8Wi+++/v2yDBgAAAADACW4vvOfNm6ekpCSNHj1aa9euVatWrRQfH6+9e/cW2j87O1sNGjTQuHHjFB4eXuy8V69erVdffVUXXnihK0IHAAAAAOCM3F54T5gwQXfeeacGDhyo5s2ba/r06fL399cbb7xRaP+2bdvqhRde0E033SSr1VrkfA8fPqxbbrlFr732mmrWrOmq8AEAAAAAKJaXOxeem5urNWvWKDk52d7m4eGhuLg4paamntW8hw4dqp49eyouLk5PP/10sX1zcnKUk5NjH87KypIk2Ww22Wy2s4rDnWw2m4wxlXodUPGRZygv5BrKyxlzzZjyDagQfA4qP/ZpKC/kmuuUZJu6tfDet2+f8vLyFBYW5tAeFhamjRs3lnq+c+fO1dq1a7V69Wqn+o8dO1YpKSkF2jMyMnTs2LFSx+FuNptNmZmZMsbIw8PtJzegiiLPUF7INZSXM+Waf95hN0TlqKhL8lB5sE9DeSHXXOfQoUNO93Vr4e0Kf//9t+677z4tWbJEvr6+Tk2TnJyspKQk+3BWVpYiIyMVEhKiwMBAV4XqcjabTRaLRSEhIXzI4DLkGcoLuYbycqZcy/bMdENUjkJDQ90dAs4S+zSUF3LNdZytNyU3F97BwcHy9PRUenq6Q3t6evoZb5xWlDVr1mjv3r26+OKL7W15eXlauXKlXnnlFeXk5MjT09NhGqvVWuj14h4eHpU+OS0WS5VYD1Rs5BnKC7mG8lJsrlks5R/QafgMVA3s01BeyDXXKMn2dOuW9/HxUevWrbV06VJ7m81m09KlSxUbG1uqeV522WX67bfftG7dOvurTZs2uuWWW7Ru3boCRTcAAAAAAK7k9lPNk5KSlJiYqDZt2qhdu3aaNGmSjhw5ooEDB0qS+vfvr7p162rs2LGSTt6QbcOGDfa/d+3apXXr1ikgIECNGjVS9erV1bJlS4dlVKtWTbVr1y7QDgAAAACAq7m98E5ISFBGRoZGjRqltLQ0xcTEaPHixfYbru3cudPhEP7u3bt10UUX2YfHjx+v8ePHq0uXLlq+fHl5hw8AAAAAQLHcXnhL0rBhwzRs2LBCx51eTEdFRcmU8DEeFOQAAAAAAHfh6noAAAAAAFyIwhsAAAAAABei8AYAAAAAwIUovAEAAAAAcCEKbwAAAAAAXIjCGwAAAAAAF6LwBgAAAADAhSi8AQAAAABwIQpvAAAAAABciMIbAAAAAAAXovAGAAAAAMCFKLwBAAAAAHAhCm8AAAAAAFyIwhsAAAAAABfycncAAAAA5Wniks3FdzBG/nmHle2ZKVks5RMUAKBK44g3AAAAAAAuROENAAAAAIALUXgDAAAAAOBCFN4AAAAAALgQhTcAAAAAAC5E4Q0AAAAAgAtReAMAAAAA4EIU3gAAAAAAuBCFNwAAAAAALkThDQAAAACAC1F4AwAAAADgQhWi8J4yZYqioqLk6+ur9u3b68cffyyy7/r163XDDTcoKipKFotFkyZNKtBn7Nixatu2rapXr67Q0FD16tVLmzZtcuEaAAAAAABQOLcX3vPmzVNSUpJGjx6ttWvXqlWrVoqPj9fevXsL7Z+dna0GDRpo3LhxCg8PL7TPihUrNHToUH3//fdasmSJjh8/riuuuEJHjhxx5aoAAAAAAFCAl7sDmDBhgu68804NHDhQkjR9+nR9+umneuONNzRy5MgC/du2bau2bdtKUqHjJWnx4sUOw7NmzVJoaKjWrFmjzp07F+ifk5OjnJwc+3BWVpYkyWazyWazlW7FKgCbzSZjTKVeB1R85BnKC7mGMmPMmcfnvyooPgeVH/s0lBdyzXVKsk3dWnjn5uZqzZo1Sk5Otrd5eHgoLi5OqampZbaczMxMSVKtWrUKHT927FilpKQUaM/IyNCxY8fKLI7yZrPZlJmZKWOMPDzcfnIDqijyDOWFXENZ8c87fIYeRlZzTLJJkqUcIiq5os4MROXBPg3lhVxznUOHDjnd162F9759+5SXl6ewsDCH9rCwMG3cuLFMlmGz2XT//ffr0ksvVcuWLQvtk5ycrKSkJPtwVlaWIiMjFRISosDAwDKJwx1sNpssFotCQkL4kMFlyDOUF3INZSXbM7P4DsZIRsr2CJAsFbPwDg0NdXcIOEvs01BeyDXX8fX1dbqv2081d7WhQ4fq999/17fffltkH6vVKqvVWqDdw8Oj0ienxWKpEuuBio08Q3kh11AmnCmmLZb/XhUQn4GqgX0aygu55hol2Z5uLbyDg4Pl6emp9PR0h/b09PQib5xWEsOGDdMnn3yilStXql69emc9PwAAAAAASsqtP3n4+PiodevWWrp0qb3NZrNp6dKlio2NLfV8jTEaNmyYFi5cqK+//lrR0dFlES4AAAAAACXm9lPNk5KSlJiYqDZt2qhdu3aaNGmSjhw5Yr/Lef/+/VW3bl2NHTtW0skbsm3YsMH+965du7Ru3ToFBASoUaNGkk6eXj5nzhx9+OGHql69utLS0iRJNWrUkJ+fnxvWEgAAAABwrnJ74Z2QkKCMjAyNGjVKaWlpiomJ0eLFi+03XNu5c6fDufO7d+/WRRddZB8eP368xo8fry5dumj58uWSpGnTpkmSunbt6rCsmTNnasCAAS5dHwAAAAAATuX2wls6eS32sGHDCh2XX0zni4qKkjnDczXPNB4AAAAAgPLCbe0AAAAAAHAhCm8AAAAAAFyIwhsAAAAAABei8AYAAAAAwIUovAEAAAAAcCEKbwAAAAAAXIjCGwAAAAAAF6LwBgAAAADAhSi8AQAAAABwIQpvAAAAAABcyMvdAQAAAKBkJi7ZfNbzeODy88sgEgCAMzjiDQAAAACAC1F4AwAAAADgQhTeAAAAAAC4EIU3AAAAAAAuROENAAAAAIALUXgDAAAAAOBCFN4AAAAAALgQhTcAAAAAAC5E4Q0AAAAAgAtReAMAAAAA4EIU3gAAAAAAuBCFNwAAAAAALkThDQAAAACAC1F4AwAAAADgQhTeAAAAAAC4UIUovKdMmaKoqCj5+vqqffv2+vHHH4vsu379et1www2KioqSxWLRpEmTznqeAAAAAAC4itsL73nz5ikpKUmjR4/W2rVr1apVK8XHx2vv3r2F9s/OzlaDBg00btw4hYeHl8k8AQAAAABwFa/STLRs2TJ169atTAKYMGGC7rzzTg0cOFCSNH36dH366ad64403NHLkyAL927Ztq7Zt20pSoeNLM8+cnBzl5OTYh7OysiRJNptNNpvt7FbQjWw2m4wxlXodUPGRZygv5BrKjDFnHp//qsL4LLkX+zSUF3LNdUqyTUtVeF955ZWqV6+eBg4cqMTEREVGRpZmNsrNzdWaNWuUnJxsb/Pw8FBcXJxSU1PLbZ5jx45VSkpKgfaMjAwdO3asVHFUBDabTZmZmTLGyMPD7Sc3oIoiz1BeyDWUFf+8w2foYWQ1xySbJFnKISL34ExA92KfhvJCrrnOoUOHnO5bqsJ7165devvtt/Xmm28qJSVF3bt316BBg9SrVy/5+Pg4PZ99+/YpLy9PYWFhDu1hYWHauHFjaUIr1TyTk5OVlJRkH87KylJkZKRCQkIUGBhYqjgqApvNJovFopCQED5kcBnyDOWFXENZyfbMLL6DMZKRsj0CJEvVLbxDQ0PdHcI5jX0aygu55jq+vr5O9y1V4R0cHKwHHnhADzzwgNauXauZM2dqyJAhGjJkiG6++WYNGjRIrVq1Ks2s3cJqtcpqtRZo9/DwqPTJabFYqsR6oGIjz1BeyDWUCWeKaYvlv1cVxefI/dinobyQa65Rku151lv+4osvVnJysoYNG6bDhw/rjTfeUOvWrdWpUyetX7++2GmDg4Pl6emp9PR0h/b09PQib5x2Jq6YJwAAAAAApVXqwvv48eNasGCBrrrqKtWvX19ffPGFXnnlFaWnp2vr1q2qX7+++vTpU+w8fHx81Lp1ay1dutTeZrPZtHTpUsXGxpYqLlfMEwAAAACA0irVqeb33nuv3n33XRljdNttt+n5559Xy5Yt7eOrVaum8ePHKyIi4ozzSkpKUmJiotq0aaN27dpp0qRJOnLkiP2O5P3791fdunU1duxYSSdvnrZhwwb737t27dK6desUEBCgRo0aOTVPAAAAAADKS6kK7w0bNmjy5Mnq3bt3oddGSydP+V62bNkZ55WQkKCMjAyNGjVKaWlpiomJ0eLFi+03R9u5c6fDufO7d+/WRRddZB8eP368xo8fry5dumj58uVOzRMAAAAAgPJiMabkD6lcuXKlOnToIC8vx7r9xIkTWrVqlTp37lxmAbpDVlaWatSooczMzEp/V/O9e/cqNDSUGynAZcgzlBdyDWVl4pLNxXcwRv55h5XtWbXvav7A5ee7O4RzGvs0lBdyzXVKUjeWast369ZN//77b4H2zMxMdevWrTSzBAAAAACgSirVqebGGFkK+QV4//79qlat2lkHBQAAUJgzHq0GAKACKlHh3bt3b0knnwM3YMAAh+u78/Ly9Ouvv6pDhw5lGyEAAAAAAJVYiQrvGjVqSDp5xLt69ery8/Ozj/Px8dEll1yiO++8s2wjBAAAAACgEitR4T1z5kxJUlRUlB588EFOKwcAAAAA4AxKdY336NGjyzoOAAAAAACqJKcL74svvlhLly5VzZo1ddFFFxV6c7V8a9euLZPgAAAAAACo7JwuvK+77jr7zdR69erlqngAAAAAAKhSnC68Tz29nFPNAQAAAABwjoe7AwAAAAAAoCpz+oh3zZo1i72u+1T//vtvqQMCAAAAAKAqcbrwnjRpkgvDAAAAAACganK68E5MTHRlHAAAAAAAVElOF95ZWVkKDAy0/12c/H4AAAAAAJzrSnSN9549exQaGqqgoKBCr/c2xshisSgvL69MgwQAAAAAoLJyuvD++uuvVatWLUnSsmXLXBYQAAAAAABVidOFd5cuXQr9GwAAAAAAFM3pwvt0Bw4c0P/+9z/98ccfkqTmzZtr4MCB9qPiAAAAAABA8ijNRCtXrlRUVJRefvllHThwQAcOHNDLL7+s6OhorVy5sqxjBAAAAACg0irVEe+hQ4cqISFB06ZNk6enpyQpLy9PQ4YM0dChQ/Xbb7+VaZAAAAAAAFRWpTrivXXrVo0YMcJedEuSp6enkpKStHXr1jILDgAAAACAyq5UhffFF19sv7b7VH/88YdatWp11kEBAAAAAFBVOH2q+a+//mr/e/jw4brvvvu0detWXXLJJZKk77//XlOmTNG4cePKPkoAAAAAACoppwvvmJgYWSwWGWPsbQ8//HCBfjfffLMSEhLKJjoAAAAAACo5pwvvbdu2uTIOAAAAAACqJKcL7/r167syDgAAAAAAqqRS3Vwt34YNG7R48WJ99NFHDq+SmjJliqKiouTr66v27dvrxx9/LLb//Pnz1bRpU/n6+uqCCy7QZ5995jD+8OHDGjZsmOrVqyc/Pz81b95c06dPL3FcAAAAAACcrVI9x/uvv/7S9ddfr99++83hum+LxSLp5DO9nTVv3jwlJSVp+vTpat++vSZNmqT4+Hht2rRJoaGhBfqvWrVK/fr109ixY3X11Vdrzpw56tWrl9auXauWLVtKkpKSkvT111/rnXfeUVRUlL788ksNGTJEERERuvbaa0uzygAAAAAAlIrFnHq3NCddc8018vT01Ouvv67o6Gj9+OOP2r9/v0aMGKHx48erU6dOTs+rffv2atu2rV555RVJks1mU2RkpO69916NHDmyQP+EhAQdOXJEn3zyib3tkksuUUxMjP2odsuWLZWQkKAnnnjC3qd169bq0aOHnn766QLzzMnJUU5Ojn04KytLkZGROnDggAIDA51el4rGZrMpIyNDISEh8vA4q5MbgCKRZygv5Bok6aWvtrh+IcbIP++wsj0DpP8/qFAV3RfX2N0hnNPYp6G8kGuuk5WVpZo1ayozM/OMdWOpjninpqbq66+/VnBwsDw8POTh4aGOHTtq7NixGj58uH7++Wen5pObm6s1a9YoOTnZ3ubh4aG4uDilpqYWueykpCSHtvj4eC1atMg+3KFDB3300Ue6/fbbFRERoeXLl2vz5s2aOHFiofMcO3asUlJSCrRnZGTo2LFjTq1LRWSz2ZSZmSljDB8yuAx5hvJCrkGS/PMOl8NSjKzmmGSTpKpbeO/du9fdIZzT2KehvJBrrnPo0CGn+5aq8M7Ly1P16tUlScHBwdq9e7eaNGmi+vXra9OmTU7PZ9++fcrLy1NYWJhDe1hYmDZu3FjoNGlpaYX2T0tLsw9PnjxZgwcPVr169eTl5SUPDw+99tpr6ty5c6HzTE5Odijm8494h4SEVPoj3haLhV+34FLkGcoLuQZJyvbMdP1CjJGMlO1RtY94F3ZJH8oP+zSUF3LNdXx9fZ3uW6rCu2XLlvrll18UHR2t9u3b6/nnn5ePj49mzJihBg0alGaWZWry5Mn6/vvv9dFHH6l+/fpauXKlhg4dqoiICMXFxRXob7VaZbVaC7TnH82vzCwWS5VYD1Rs5BnKC7mGciuELZb/XlUUnyP3Y5+G8kKuuUZJtmepCu/HH39cR44ckSQ9+eSTuvrqq9WpUyfVrl1b8+bNc3o+wcHB8vT0VHp6ukN7enq6wsPDC50mPDy82P5Hjx7Vo48+qoULF6pnz56SpAsvvFDr1q3T+PHjCy28AQAAAABwlVL95BEfH6/evXtLkho1aqSNGzdq37592rt3r7p37+70fHx8fNS6dWstXbrU3maz2bR06VLFxsYWOk1sbKxDf0lasmSJvf/x48d1/PjxAr8+eHp6ymazOR0bAAAAAABloVRHvE/1999/S5IiIyNLNX1SUpISExPVpk0btWvXTpMmTdKRI0c0cOBASVL//v1Vt25djR07VpJ03333qUuXLnrxxRfVs2dPzZ07Vz/99JNmzJghSQoMDFSXLl300EMPyc/PT/Xr19eKFSv01ltvacKECWe7ugAAAAAAlEipjnifOHFCTzzxhGrUqKGoqChFRUWpRo0aevzxx3X8+PESzSshIUHjx4/XqFGjFBMTo3Xr1mnx4sX2G6jt3LlTe/bssffv0KGD5syZoxkzZqhVq1ZasGCBFi1aZH+GtyTNnTtXbdu21S233KLmzZtr3LhxeuaZZ3T33XeXZnUBAAAAACi1Uh3xvvfee/XBBx/o+eeft5/inZqaqjFjxmj//v2aNm1aieY3bNgwDRs2rNBxy5cvL9DWp08f9enTp8j5hYeHa+bMmSWKAQAAAAAAVyhV4T1nzhzNnTtXPXr0sLddeOGFioyMVL9+/UpceAMAAAAAUFWV6lRzq9WqqKioAu3R0dHy8fE525gAAAAAAKgySlV4Dxs2TE899ZRycnLsbTk5OXrmmWeKPGUcAAAAAIBzkdOnmuc/PizfV199pXr16qlVq1aSpF9++UW5ubm67LLLyjZCAAAAAAAqMacL7xo1ajgM33DDDQ7DpX2cGAAAAAAAVZnThTd3CQcAAAAAoORKdVfzfBkZGdq0aZMkqUmTJgoJCSmToAAAAAAAqCpKdXO1I0eO6Pbbb1edOnXUuXNnde7cWRERERo0aJCys7PLOkYAAAAAACqtUhXeSUlJWrFihT7++GMdPHhQBw8e1IcffqgVK1ZoxIgRZR0jAAAAAACVVqlONX///fe1YMECde3a1d521VVXyc/PT3379tW0adPKKj4AAAAAACq1Uh3xzs7OVlhYWIH20NBQTjUHAAAAAOAUpTriHRsbq9GjR+utt96Sr6+vJOno0aNKSUlRbGxsmQYIAACqjolLNrs7BAAAyl2pCu9JkybpyiuvVL169dSqVStJ0i+//CJfX1998cUXZRogAAAAAACVWakK7wsuuEBbtmzR7NmztXHjRklSv379dMstt8jPz69MAwQAAAAAoDIrceF9/PhxNW3aVJ988onuvPNOV8QEAAAAAECVUeKbq3l7e+vYsWOuiAUAAAAAgCqnVHc1Hzp0qJ577jmdOHGirOMBAAAAAKBKKdU13qtXr9bSpUv15Zdf6oILLlC1atUcxn/wwQdlEhwAAAAAAJVdqQrvoKAg3XDDDWUdCwAAAAAAVU6JCm+bzaYXXnhBmzdvVm5urrp3764xY8ZwJ3MAAAAAAIpQomu8n3nmGT366KMKCAhQ3bp19fLLL2vo0KGuig0AAAAAgEqvRIX3W2+9palTp+qLL77QokWL9PHHH2v27Nmy2Wyuig8AAAAAgEqtRIX3zp07ddVVV9mH4+LiZLFYtHv37jIPDAAAAACAqqBEhfeJEyfk6+vr0Obt7a3jx4+XaVAAAAAAAFQVJbq5mjFGAwYMkNVqtbcdO3ZMd999t8MjxXicGAAAAAAAJ5Wo8E5MTCzQduutt5ZZMAAAAAAAVDUlKrxnzpzpkiCmTJmiF154QWlpaWrVqpUmT56sdu3aFdl//vz5euKJJ7R9+3Y1btxYzz33nMO155L0xx9/6JFHHtGKFSt04sQJNW/eXO+//77OO+88l6wDAAAAAACFKdE13q4wb948JSUlafTo0Vq7dq1atWql+Ph47d27t9D+q1atUr9+/TRo0CD9/PPP6tWrl3r16qXff//d3ufPP/9Ux44d1bRpUy1fvly//vqrnnjiiQLXpwMAAAAA4GoWY4xxZwDt27dX27Zt9corr0iSbDabIiMjde+992rkyJEF+ickJOjIkSP65JNP7G2XXHKJYmJiNH36dEnSTTfdJG9vb7399ttOxZCTk6OcnBz7cFZWliIjI3XgwAEFBgaezeq5lc1mU0ZGhkJCQuTh4fbfWFBFkWcoL+Ra1fDSV1vcHcKZGSP/vMPK9gyQLBZ3R+My98U1dncI5zT2aSgv5JrrZGVlqWbNmsrMzDxj3ViiU83LWm5urtasWaPk5GR7m4eHh+Li4pSamlroNKmpqUpKSnJoi4+P16JFiySdTKxPP/1UDz/8sOLj4/Xzzz8rOjpaycnJ6tWrV6HzHDt2rFJSUgq0Z2Rk6NixY6VbuQrAZrMpMzNTxhg+ZHAZ8gzlhVyrGvzzDrs7BCcYWc0xySZJVbfwLursQpQP9mkoL+Sa6xw6dMjpvm4tvPft26e8vDyFhYU5tIeFhWnjxo2FTpOWllZo/7S0NEknv0QOHz6scePG6emnn9Zzzz2nxYsXq3fv3lq2bJm6dOlSYJ7JyckOxXz+Ee+QkJBKf8TbYrHw6xZcijxDeSHXqoZsz0x3h3BmxkhGyvao2ke8Q0ND3R3COY19GsoLueY6JbmU2a2FtyvYbDZJ0nXXXacHHnhAkhQTE6NVq1Zp+vTphRbeVqvV4RFp+Tw8PCp9closliqxHqjYyDOUF3KtCqgshazF8t+riuJz5H7s01BeyDXXKMn2dOuWDw4Olqenp9LT0x3a09PTFR4eXug04eHhxfYPDg6Wl5eXmjdv7tCnWbNm2rlzZxlGDwAAAADAmbm18Pbx8VHr1q21dOlSe5vNZtPSpUsVGxtb6DSxsbEO/SVpyZIl9v4+Pj5q27atNm3a5NBn8+bNql+/fhmvAQAAAAAAxXP7qeZJSUlKTExUmzZt1K5dO02aNElHjhzRwIEDJUn9+/dX3bp1NXbsWEnSfffdpy5duujFF19Uz549NXfuXP3000+aMWOGfZ4PPfSQEhIS1LlzZ3Xr1k2LFy/Wxx9/rOXLl7tjFQEAAAAA5zC3F94JCQnKyMjQqFGjlJaWppiYGC1evNh+A7WdO3c6nDvfoUMHzZkzR48//rgeffRRNW7cWIsWLVLLli3tfa6//npNnz5dY8eO1fDhw9WkSRO9//776tixY7mvHwAAQEU0ccnms5r+gcvPL6NIAKDqc/tzvCuirKws1ahRw6nnsVVkNptNe/fuVWhoKDdSgMuQZygv5FrVcLbFXrk4R57jfbYovM8O+zSUF3LNdUpSN7LlAQAAAABwIQpvAAAAAABciMIbAAAAAAAXovAGAAAAAMCFKLwBAAAAAHAhCm8AAAAAAFyIwhsAAAAAABei8AYAAAAAwIUovAEAAAAAcCEvdwcAAAAqh4lLNrs7BAAAKiWOeAMAAAAA4EIU3gAAAAAAuBCFNwAAAAAALkThDQAAAACAC1F4AwAAAADgQhTeAAAAAAC4EIU3AAAAAAAuROENAAAAAIALUXgDAAAAAOBCFN4AAAAAALgQhTcAAAAAAC5E4Q0AAAAAgAtReAMAAAAA4EIU3gAAAAAAuBCFNwAAAAAALlQhCu8pU6YoKipKvr6+at++vX788cdi+8+fP19NmzaVr6+vLrjgAn322WdF9r377rtlsVg0adKkMo4aAAAAAIAzc3vhPW/ePCUlJWn06NFau3atWrVqpfj4eO3du7fQ/qtWrVK/fv00aNAg/fzzz+rVq5d69eql33//vUDfhQsX6vvvv1dERISrVwMAAAAAgEK5vfCeMGGC7rzzTg0cOFDNmzfX9OnT5e/vrzfeeKPQ/i+99JKuvPJKPfTQQ2rWrJmeeuopXXzxxXrllVcc+u3atUv33nuvZs+eLW9v7/JYFQAAAAAACvBy58Jzc3O1Zs0aJScn29s8PDwUFxen1NTUQqdJTU1VUlKSQ1t8fLwWLVpkH7bZbLrtttv00EMPqUWLFmeMIycnRzk5OfbhrKws+3xsNltJVqlCsdlsMsZU6nVAxUeeobyQaxWAMe6OoHwY898LReKzeHbYp6G8kGuuU5Jt6tbCe9++fcrLy1NYWJhDe1hYmDZu3FjoNGlpaYX2T0tLsw8/99xz8vLy0vDhw52KY+zYsUpJSSnQnpGRoWPHjjk1j4rIZrMpMzNTxhh5eLj95AZUUeQZygu55n7+eYfdHUI5MbKaY5JNkizuDqbCKuqyQDiHfRrKC7nmOocOHXK6r1sLb1dYs2aNXnrpJa1du1YWi3NflsnJyQ5H0bOyshQZGamQkBAFBga6KlSXs9lsslgsCgkJ4UMGlyHPUF7INffL9sx0dwjlwxjJSNkeAZKT/5c4F4WGhro7hEqNfRrKC7nmOr6+vk73dWvhHRwcLE9PT6Wnpzu0p6enKzw8vNBpwsPDi+3/zTffaO/evTrvvPPs4/Py8jRixAhNmjRJ27dvLzBPq9Uqq9VaoN3Dw6PSJ6fFYqkS64GKjTxDeSHX3OxcKkItlv9eKBSfw7PHPg3lhVxzjZJsT7dueR8fH7Vu3VpLly61t9lsNi1dulSxsbGFThMbG+vQX5KWLFli73/bbbfp119/1bp16+yviIgIPfTQQ/riiy9ctzIAAAAAABTC7aeaJyUlKTExUW3atFG7du00adIkHTlyRAMHDpQk9e/fX3Xr1tXYsWMlSffdd5+6dOmiF198UT179tTcuXP1008/acaMGZKk2rVrq3bt2g7L8Pb2Vnh4uJo0aVK+KwcAAAAAOOe5vfBOSEhQRkaGRo0apbS0NMXExGjx4sX2G6jt3LnT4RB+hw4dNGfOHD3++ON69NFH1bhxYy1atEgtW7Z01yoAAAAAAFAkizE8K+N0WVlZqlGjhjIzMyv9zdX27t2r0NBQrueAy5BnKC/k2tmbuGSzu0OoHIyRf95hZXtyc7XiPHD5+e4OoVJjn4byQq65TknqRrY8AAAAAAAuROENAAAAAIALUXgDAAAAAOBCFN4AAAAAALgQhTcAAAAAAC5E4Q0AAAAAgAtReAMAAAAA4EIU3gAAAAAAuBCFNwAAAAAALkThDQAAAACAC1F4AwAAAADgQhTeAAAAAAC4EIU3AAAAAAAuROENAAAAAIALUXgDAAAAAOBCFN4AAAAAALgQhTcAAAAAAC5E4Q0AAAAAgAtReAMAAAAA4EJe7g4AAACc2cQlm90dAgAAKCUKbwAAAJRYWfwY9MDl55dBJABQ8XGqOQAAAAAALkThDQAAAACAC1F4AwAAAADgQhTeAAAAAAC4EIU3AAAAAAAuVCEK7ylTpigqKkq+vr5q3769fvzxx2L7z58/X02bNpWvr68uuOACffbZZ/Zxx48f1yOPPKILLrhA1apVU0REhPr376/du3e7ejUAAAAAACjA7YX3vHnzlJSUpNGjR2vt2rVq1aqV4uPjtXfv3kL7r1q1Sv369dOgQYP0888/q1evXurVq5d+//13SVJ2drbWrl2rJ554QmvXrtUHH3ygTZs26dprry3P1QIAAAAAQJJkMcYYdwbQvn17tW3bVq+88ookyWazKTIyUvfee69GjhxZoH9CQoKOHDmiTz75xN52ySWXKCYmRtOnTy90GatXr1a7du20Y8cOnXfeeWeMKSsrSzVq1FBmZqYCAwNLuWbuZ7PZtHfvXoWGhsrDw+2/saCKIs9QXs71XCuLZybDScbIP++wsj0DJIvF3dFUaefyc7zP9X0ayg+55jolqRu9yimmQuXm5mrNmjVKTk62t3l4eCguLk6pqamFTpOamqqkpCSHtvj4eC1atKjI5WRmZspisSgoKKjQ8Tk5OcrJybEPZ2VlSTqZpDabzcm1qXhsNpuMMZV6HVDxkWcoL+d8rrn3d/JzizH/veBS5+znWezTUH7INdcpyTZ1a+G9b98+5eXlKSwszKE9LCxMGzduLHSatLS0QvunpaUV2v/YsWN65JFH1K9fvyJ/hRg7dqxSUlIKtGdkZOjYsWPOrEqFZLPZlJmZKWMMv27BZcgzlJdzPdf88w67O4RziJHVHJNsksQRb1cq6tLCc8G5vk9D+SHXXOfQoUNO93Vr4e1qx48fV9++fWWM0bRp04rsl5yc7HAUPSsrS5GRkQoJCan0p5pbLBaFhITwIYPLkGcoL+d6rmV7Zro7hHOHMZKRsj041dzVQkND3R2C25zr+zSUH3LNdXx9fZ3u69bCOzg4WJ6enkpPT3doT09PV3h4eKHThIeHO9U/v+jesWOHvv7662ILaKvVKqvVWqDdw8Oj0ienxWKpEuuBio08Q3mprLlWJtdnUwCWL4vlvxdcprJ9lstaZd2nofIh11yjJNvTrVvex8dHrVu31tKlS+1tNptNS5cuVWxsbKHTxMbGOvSXpCVLljj0zy+6t2zZoq+++kq1a9d2zQoAAAAAAHAGbj/VPCkpSYmJiWrTpo3atWunSZMm6ciRIxo4cKAkqX///qpbt67Gjh0rSbrvvvvUpUsXvfjii+rZs6fmzp2rn376STNmzJB0sui+8cYbtXbtWn3yySfKy8uzX/9dq1Yt+fj4uGdFAQAAAADnJLcX3gkJCcrIyNCoUaOUlpammJgYLV682H4DtZ07dzocwu/QoYPmzJmjxx9/XI8++qgaN26sRYsWqWXLlpKkXbt26aOPPpIkxcTEOCxr2bJl6tq1a7msFwAAAAAAUgUovCVp2LBhGjZsWKHjli9fXqCtT58+6tOnT6H9o6Ki5OZHkwMAAAAAYMfV9QAAAAAAuBCFNwAAAAAALkThDQAAAACAC1F4AwAAAADgQhTeAAAAAAC4UIW4qzkAABXZxCWb3R0CAACoxDjiDQAAAACAC1F4AwAAAADgQpxqDgAAALcoi8s4Hrj8/DKIBABciyPeAAAAAAC4EIU3AAAAAAAuROENAAAAAIALcY03AKBK41FgAADA3TjiDQAAAACAC1F4AwAAAADgQhTeAAAAAAC4EIU3AAAAAAAuxM3VAAAVmv3maMbIP++wsj0zJYvFvUEBAACUAIU3AAAAKq2zfXLBA5efX0aRAEDRONUcAAAAAAAXovAGAAAAAMCFONUcAOAyZ3sKKAAAQFXAEW8AAAAAAFyII94AAAA4Z5XFmTncoA3AmVB4AwCKxKniAAAAZ49TzQEAAAAAcKEKccR7ypQpeuGFF5SWlqZWrVpp8uTJateuXZH958+fryeeeELbt29X48aN9dxzz+mqq66yjzfGaPTo0Xrttdd08OBBXXrppZo2bZoaN25cHqsDABUCR6sBoHyUan9rjPzzDivbM1MPXNGk7IMCUKG4vfCeN2+ekpKSNH36dLVv316TJk1SfHy8Nm3apNDQ0AL9V61apX79+mns2LG6+uqrNWfOHPXq1Utr165Vy5YtJUnPP/+8Xn75Zb355puKjo7WE088ofj4eG3YsEG+vr7lvYoAUCoUzgBwbuA6c6DqsxhjjDsDaN++vdq2batXXnlFkmSz2RQZGal7771XI0eOLNA/ISFBR44c0SeffGJvu+SSSxQTE6Pp06fLGKOIiAiNGDFCDz74oCQpMzNTYWFhmjVrlm666aYzxpSVlaUaNWooMzNTgYGBZbSm5c9ms2nv3r0KDQ2VhwdXFcA1qmqeUfRWQPajQwGSxeLuaFCVkWsoDxUszyjcq66q+n+1iqAkdaNbj3jn5uZqzZo1Sk5Otrd5eHgoLi5OqamphU6TmpqqpKQkh7b4+HgtWrRIkrRt2zalpaUpLi7OPr5GjRpq3769UlNTCy28c3JylJOTYx/OzMyUJB08eFA2m63U6+duNptNWVlZ8vHx4UN2jpi27M/yX6gx8rMd1lGPfyrEfxxQhRkji+2wjnkYcg2uRa6hPFSwPBu7cI27QygT93Rr6O4QKhxqAtfJysqSdPJS5zNxa+G9b98+5eXlKSwszKE9LCxMGzduLHSatLS0QvunpaXZx+e3FdXndGPHjlVKSkqB9vr16zu3IgAAAADc7lF3B4Bz0qFDh1SjRo1i+7j9Gu+KIDk52eEous1m07///qvatWvLUgF+gSytrKwsRUZG6u+//67Up8yjYiPPUF7INZQXcg3lgTxDeSHXXMcYo0OHDikiIuKMfd1aeAcHB8vT01Pp6ekO7enp6QoPDy90mvDw8GL75/+bnp6uOnXqOPSJiYkpdJ5Wq1VWq9WhLSgoqCSrUqEFBgbyIYPLkWcoL+Qaygu5hvJAnqG8kGuucaYj3fncepK/j4+PWrduraVLl9rbbDabli5dqtjY2EKniY2NdegvSUuWLLH3j46OVnh4uEOfrKws/fDDD0XOEwAAAAAAV3H7qeZJSUlKTExUmzZt1K5dO02aNElHjhzRwIEDJUn9+/dX3bp1NXbsWEnSfffdpy5duujFF19Uz549NXfuXP3000+aMWOGJMlisej+++/X008/rcaNG9sfJxYREaFevXq5azUBAAAAAOcotxfeCQkJysjI0KhRo5SWlqaYmBgtXrzYfnO0nTt3Otx9r0OHDpozZ44ef/xxPfroo2rcuLEWLVpkf4a3JD388MM6cuSIBg8erIMHD6pjx45avHjxOfcMb6vVqtGjRxc4jR4oS+QZygu5hvJCrqE8kGcoL+RaxeD253gDAAAAAFCV8SA3AAAAAABciMIbAAAAAAAXovAGAAAAAMCFKLwBAAAAAHAhCu8K7plnnlGHDh3k7++voKCgQvtYLJYCr7lz5zr0Wb58uS6++GJZrVY1atRIs2bNKjCfKVOmKCoqSr6+vmrfvr1+/PFHh/HHjh3T0KFDVbt2bQUEBOiGG25Qenp6Wa0q3MyZXNu5c6d69uwpf39/hYaG6qGHHtKJEycc+pBrKKmoqKgC+7Bx48Y59Pn111/VqVMn+fr6KjIyUs8//3yB+cyfP19NmzaVr6+vLrjgAn322WcO440xGjVqlOrUqSM/Pz/FxcVpy5YtLl03VD5n2j8BpxozZkyB/VfTpk3t4535Piur71ZULStXrtQ111yjiIgIWSwWLVq0yGG8M99p//77r2655RYFBgYqKChIgwYN0uHDhx36lMX3K5xkUKGNGjXKTJgwwSQlJZkaNWoU2keSmTlzptmzZ4/9dfToUfv4v/76y/j7+5ukpCSzYcMGM3nyZOPp6WkWL15s7zN37lzj4+Nj3njjDbN+/Xpz5513mqCgIJOenm7vc/fdd5vIyEizdOlS89NPP5lLLrnEdOjQwWXrjvJ1plw7ceKEadmypYmLizM///yz+eyzz0xwcLBJTk629yHXUBr169c3Tz75pMM+7PDhw/bxmZmZJiwszNxyyy3m999/N++++67x8/Mzr776qr3Pd999Zzw9Pc3zzz9vNmzYYB5//HHj7e1tfvvtN3ufcePGmRo1aphFixaZX375xVx77bUmOjraYX+Jc5sz+yfgVKNHjzYtWrRw2H9lZGTYx5/p+6ysvltR9Xz22WfmscceMx988IGRZBYuXOgw3pnvtCuvvNK0atXKfP/99+abb74xjRo1Mv369bOPL6vvVziHwruSmDlzZrGF9+kfxlM9/PDDpkWLFg5tCQkJJj4+3j7crl07M3ToUPtwXl6eiYiIMGPHjjXGGHPw4EHj7e1t5s+fb+/zxx9/GEkmNTW1FGuEiqqoXPvss8+Mh4eHSUtLs7dNmzbNBAYGmpycHGMMuYbSqV+/vpk4cWKR46dOnWpq1qxpzzNjjHnkkUdMkyZN7MN9+/Y1PXv2dJiuffv25q677jLGGGOz2Ux4eLh54YUX7OMPHjxorFareffdd8toTVDZnWn/BJxu9OjRplWrVoWOc+b7rKy+W1G1nf5/fWe+0zZs2GAkmdWrV9v7fP7558ZisZhdu3YZY8rm+xXO41TzKmLo0KEKDg5Wu3bt9MYbb8ic8nj21NRUxcXFOfSPj49XamqqJCk3N1dr1qxx6OPh4aG4uDh7nzVr1uj48eMOfZo2barzzjvP3gdVW2pqqi644AKFhYXZ2+Lj45WVlaX169fb+5BrKI1x48apdu3auuiii/TCCy84nGaZmpqqzp07y8fHx94WHx+vTZs26cCBA/Y+xeXetm3blJaW5tCnRo0aat++PXkFSc7tn4DCbNmyRREREWrQoIFuueUW7dy5U5Jz32dl8d2Kc48z32mpqakKCgpSmzZt7H3i4uLk4eGhH374wd7nbL9f4TwvdweAs/fkk0+qe/fu8vf315dffqkhQ4bo8OHDGj58uCQpLS3NYYcuSWFhYcrKytLRo0d14MAB5eXlFdpn48aN9nn4+PgUuPY3LCxMaWlprls5VBhF5VH+uOL6kGsozvDhw3XxxRerVq1aWrVqlZKTk7Vnzx5NmDBB0smciI6Odpjm1NyrWbNmkbl3am6eOl1hfXBu27dv3xn3T8Dp2rdvr1mzZqlJkybas2ePUlJS1KlTJ/3+++9OfZ+VxXern5+fi9YOFZUz32lpaWkKDQ11GO/l5aVatWo59Dnb71c4j8LbDUaOHKnnnnuu2D5//PGHw805ivPEE0/Y/77ooot05MgRvfDCC/bCG+euss41wFklyb2kpCR724UXXigfHx/dddddGjt2rKxWq6tDBYBS69Gjh/3vCy+8UO3bt1f9+vX13nvvURADcEDh7QYjRozQgAEDiu3ToEGDUs+/ffv2euqpp5STkyOr1arw8PACd9BMT09XYGCg/Pz85OnpKU9Pz0L7hIeHS5LCw8OVm5urgwcPOvxye2ofVDxlmWvh4eEF7u6bnzOn5gm5Bunscq99+/Y6ceKEtm/friZNmhSZV9KZc+/U8fltderUcegTExPj9Hqh6goODj7j/gk4k6CgIJ1//vnaunWrLr/88jN+n5XFdyvOPc58p4WHh2vv3r0O0504cUL//vvvGXPr1GWc6fsVzuMabzcICQlR06ZNi32deq1FSa1bt041a9a0HymKjY3V0qVLHfosWbJEsbGxkiQfHx+1bt3aoY/NZtPSpUvtfVq3bi1vb2+HPps2bdLOnTvtfVDxlGWuxcbG6rfffnPYiS9ZskSBgYFq3ry5vQ+5Bunscm/dunXy8PCwnyIXGxurlStX6vjx4/Y+S5YsUZMmTVSzZk17n+JyLzo6WuHh4Q59srKy9MMPP5BXkOTc/gk4k8OHD+vPP/9UnTp1nPo+K4vvVpx7nPlOi42N1cGDB7VmzRp7n6+//lo2m03t27e39znb71eUgLvv7obi7dixw/z8888mJSXFBAQEmJ9//tn8/PPP5tChQ8YYYz766CPz2muvmd9++81s2bLFTJ061fj7+5tRo0bZ55H/GIqHHnrI/PHHH2bKlCmFPuLJarWaWbNmmQ0bNpjBgweboKAgh7ts3n333ea8884zX3/9tfnpp59MbGysiY2NLb+NAZc6U67lP/LkiiuuMOvWrTOLFy82ISEhhT7yhFyDs1atWmUmTpxo1q1bZ/7880/zzjvvmJCQENO/f397n4MHD5qwsDBz2223md9//93MnTvX+Pv7F3jciZeXlxk/frz5448/zOjRowt9nFhQUJD58MMPza+//mquu+46HicGB87sn4BTjRgxwixfvtxs27bNfPfddyYuLs4EBwebvXv3GmPO/H1WVt+tqHoOHTpk/7+YJDNhwgTz888/mx07dhhjnPtOu/LKK81FF11kfvjhB/Ptt9+axo0bOzxOrKy+X+EcCu8KLjEx0Ugq8Fq2bJkx5uRjAWJiYkxAQICpVq2aadWqlZk+fbrJy8tzmM+yZctMTEyM8fHxMQ0aNDAzZ84ssKzJkyeb8847z/j4+Jh27dqZ77//3mH80aNHzZAhQ0zNmjWNv7+/uf76682ePXtcteooZ2fKNWOM2b59u+nRo4fx8/MzwcHBZsSIEeb48eMO8yHXUBJr1qwx7du3NzVq1DC+vr6mWbNm5tlnnzXHjh1z6PfLL7+Yjh07GqvVaurWrWvGjRtXYF7vvfeeOf/8842Pj49p0aKF+fTTTx3G22w288QTT5iwsDBjtVrNZZddZjZt2uTS9UPlc6b9E3CqhIQEU6dOHePj42Pq1q1rEhISzNatW+3jnfk+K6vvVlQty5YtK/T/ZYmJicYY577T9u/fb/r162cCAgJMYGCgGThwoP2ASr6y+H6FcyzGnPLcKQAAAAAAUKa4xhsAAAAAABei8AYAAAAAwIUovAEAAAAAcCEKbwAAAAAAXIjCGwAAAAAAF6LwBgAAAADAhSi8AQAAAABwIQpvAAAAAABciMIbAAAAAAAXovAGAAAAAMCFKLwBAAAAAHAhCm8AAAAAAFyIwhsAAAAAABei8AYAAAAAwIUovAEAAAAAcCEKbwAAAAAAXIjCGwAAAAAAF6LwBgDgLAwYMEBRUVFlOs9Zs2bJYrFo+/btZTpfVDxRUVEaMGCAu8MAALgYhTcAwO3+/PNP3XXXXWrQoIF8fX0VGBioSy+9VC+99JKOHj3q7vBc5tlnn9WiRYvcHYZdfsFvsVj07bffFhhvjFFkZKQsFouuvvpqN0RYtO3bt9tjP/11ySWXuDW2VatWacyYMTp48KBb4wAAuI+XuwMAAJzbPv30U/Xp00dWq1X9+/dXy5YtlZubq2+//VYPPfSQ1q9frxkzZrg7TJd49tlndeONN6pXr14O7bfddptuuukmWa1Wt8Tl6+urOXPmqGPHjg7tK1as0D///OO2uJzRr18/XXXVVQ5tISEhbormpFWrViklJUUDBgxQUFCQw7hNmzbJw4PjIABQ1VF4AwDcZtu2bbrppptUv359ff3116pTp4593NChQ7V161Z9+umnbozQPTw9PeXp6em25V911VWaP3++Xn75ZXl5/fdfhTlz5qh169bat2+f22I7k4svvli33nqru8NwWkX+EQMAUHb4iRUA4DbPP/+8Dh8+rP/9738ORXe+Ro0a6b777pP036nEs2bNKtDPYrFozJgx9uExY8bIYrFo8+bNuvXWW1WjRg2FhIToiSeekDFGf//9t6677joFBgYqPDxcL774osP8irrGevny5bJYLFq+fHmx6zV+/Hh16NBBtWvXlp+fn1q3bq0FCxYUiPnIkSN688037adE51/re/ryr776ajVo0KDQZcXGxqpNmzYObe+8845at24tPz8/1apVSzfddJP+/vvvYmM+Vb9+/bR//34tWbLE3pabm6sFCxbo5ptvLvU6S9KSJUvUsWNHBQUFKSAgQE2aNNGjjz7q0Gfy5Mlq0aKF/P39VbNmTbVp00Zz5sxxOv6idO3aVV27di3Qfvp1+vm5Nn78eM2YMUMNGzaU1WpV27ZttXr16gLTb9y4UX379lVISIj8/PzUpEkTPfbYY5JO5uJDDz0kSYqOjra/1/nvbWHXeP/111/q06ePatWqJX9/f11yySUFfoDKz8X33ntPzzzzjOrVqydfX19ddtll2rp1a+k3EgDAJSi8AQBu8/HHH6tBgwbq0KGDS+afkJAgm82mcePGqX379nr66ac1adIkXX755apbt66ee+45NWrUSA8++KBWrlxZZst96aWXdNFFF+nJJ5/Us88+Ky8vL/Xp08eheHr77bdltVrVqVMnvf3223r77bd11113Fbke27ZtK1D07dixQ99//71uuukme9szzzyj/v37q3HjxpowYYLuv/9+LV26VJ07d3b6GuOoqCjFxsbq3Xfftbd9/vnnyszMdFhWSdd5/fr1uvrqq5WTk6Mnn3xSL774oq699lp999139j6vvfaahg8frubNm2vSpElKSUlRTEyMfvjhB6diz87O1r59+xxex48fd2ra082ZM0cvvPCC7rrrLj399NPavn27evfu7TC/X3/9Ve3bt9fXX3+tO++8Uy+99JJ69eqljz/+WJLUu3dv9evXT5I0ceJE+3td1Onv6enp6tChg7744gsNGTJEzzzzjI4dO6Zrr71WCxcuLNB/3LhxWrhwoR588EElJyfr+++/1y233FKq9QUAuJABAMANMjMzjSRz3XXXOdV/27ZtRpKZOXNmgXGSzOjRo+3Do0ePNpLM4MGD7W0nTpww9erVMxaLxYwbN87efuDAAePn52cSExPtbTNnzjSSzLZt2xyWs2zZMiPJLFu2zN6WmJho6tev79AvOzvbYTg3N9e0bNnSdO/e3aG9WrVqDsstavmZmZnGarWaESNGOPR7/vnnjcViMTt27DDGGLN9+3bj6elpnnnmGYd+v/32m/Hy8irQXtRyV69ebV555RVTvXp1+7r06dPHdOvWzRhjTP369U3Pnj1LvM4TJ040kkxGRkaRMVx33XWmRYsWxcZZmPz8KOyV/3516dLFdOnSpcC0p7+H+fOqXbu2+ffff+3tH374oZFkPv74Y3tb586dTfXq1e3vQT6bzWb/+4UXXig0n4w5uS1PzYH777/fSDLffPONve3QoUMmOjraREVFmby8PGPMf7nYrFkzk5OTY+/70ksvGUnmt99+K3Z7AQDKF0e8AQBukZWVJUmqXr26y5Zxxx132P/29PRUmzZtZIzRoEGD7O1BQUFq0qSJ/vrrrzJbrp+fn/3vAwcOKDMzU506ddLatWtLNb/AwED16NFD7733nowx9vZ58+bpkksu0XnnnSdJ+uCDD2Sz2dS3b1+HI77h4eFq3Lixli1b5vQy+/btq6NHj+qTTz7RoUOH9MknnxR5mrnk3Drn31jsww8/lM1mK3Q+QUFB+ueffwo9pdsZgwcP1pIlSxxerVq1KtW8EhISVLNmTftwp06dJMmeKxkZGVq5cqVuv/12+3uQz2KxlGqZn332mdq1a+dwY7uAgAANHjxY27dv14YNGxz6Dxw4UD4+PkXGCACoGLi5GgDALQIDAyVJhw4dctkyTi+GatSoIV9fXwUHBxdo379/f5kt95NPPtHTTz+tdevWKScnx95e2mJMOlkELlq0SKmpqerQoYP+/PNPrVmzRpMmTbL32bJli4wxaty4caHz8Pb2dnp5ISEhiouL05w5c5Sdna28vDzdeOONRfZ3Zp0TEhL0+uuv64477tDIkSN12WWXqXfv3rrxxhvtd/Z+5JFH9NVXX6ldu3Zq1KiRrrjiCt1888269NJLnYq7cePGiouLc3o9i3N6/uQX4QcOHJD0X3HbsmXLMlmedPLygfbt2xdob9asmX38qcs7U4wAgIqBwhsA4BaBgYGKiIjQ77//7lT/oorWvLy8Iqcp7M7gRd0t/NQjyaVZVr5vvvlG1157rTp37qypU6eqTp068vb21syZM8/qBmHXXHON/P399d5776lDhw5677335OHhoT59+tj72Gw2WSwWff7554WuZ0BAQImWefPNN+vOO+9UWlqaevToUeBRWPmcXWc/Pz+tXLlSy5Yt06effqrFixdr3rx56t69u7788kt5enqqWbNm2rRpkz755BMtXrxY77//vqZOnapRo0YpJSWlRPGfzmKxOLzP+Yp6X53JFXerDDECACi8AQBudPXVV2vGjBlKTU1VbGxssX3zj+SdfoOwHTt2lHlcZ7Os999/X76+vvriiy8cHhU1c+bMAn1LcgS8WrVquvrqqzV//nxNmDBB8+bNU6dOnRQREWHv07BhQxljFB0drfPPP9/peRfl+uuv11133aXvv/9e8+bNK7JfSdbZw8NDl112mS677DJNmDBBzz77rB577DEtW7bMfqS6WrVqSkhIUEJCgnJzc9W7d28988wzSk5Olq+vb6nXp2bNmoWegl3aHMq/0/yZfjwqyftcv359bdq0qUD7xo0b7eMBAJUP13gDANzm4YcfVrVq1XTHHXcoPT29wPg///xTL730kqSTR8iDg4ML3H186tSpZR5Xw4YNJclhWXl5eZoxY8YZp/X09JTFYnE4irp9+3YtWrSoQN9q1ao5fadx6eSp2rt379brr7+uX375RQkJCQ7je/fuLU9PT6WkpBQ44mmMKfHp9AEBAZo2bZrGjBmja665psh+zq7zv//+W2DamJgYSbKfnn56jD4+PmrevLmMMaW+O3m+hg0bauPGjcrIyLC3/fLLLw53VS+JkJAQde7cWW+88YZ27tzpMO7U7V+tWjVJBX/IKcxVV12lH3/8Uampqfa2I0eOaMaMGYqKilLz5s1LFSsAwL044g0AcJuGDRtqzpw5SkhIULNmzdS/f3+1bNlSubm5WrVqlebPn+/wjOM77rhD48aN0x133KE2bdpo5cqV2rx5c5nH1aJFC11yySVKTk7Wv//+q1q1amnu3Lk6ceLEGaft2bOnJkyYoCuvvFI333yz9u7dqylTpqhRo0b69ddfHfq2bt1aX331lSZMmKCIiAhFR0cXen1vvquuukrVq1fXgw8+KE9PT91www0O4xs2bKinn35aycnJ2r59u3r16qXq1atr27ZtWrhwoQYPHqwHH3ywRNsiMTGxzNb5ySef1MqVK9WzZ0/Vr19fe/fu1dSpU1WvXj37zcSuuOIKhYeH69JLL1VYWJj++OMPvfLKK+rZs+dZ34jv9ttv14QJExQfH69BgwZp7969mj59ulq0aGG/2V9Jvfzyy+rYsaMuvvhiDR48WNHR0dq+fbs+/fRTrVu3TtLJ91mSHnvsMd10003y9vbWNddcYy/ITzVy5Ei9++676tGjh4YPH65atWrpzTff1LZt2/T+++/br4UHAFQy7rmZOgAA/9m8ebO58847TVRUlPHx8THVq1c3l156qZk8ebI5duyYvV92drYZNGiQqVGjhqlevbrp27ev2bt3b5GPEzv9sVWJiYmmWrVqBZbfpUuXAo+w+vPPP01cXJyxWq0mLCzMPProo2bJkiVOPU7sf//7n2ncuLGxWq2madOmZubMmfaYTrVx40bTuXNn4+fnZyTZHytV1OPMjDHmlltuMZJMXFxckdvz/fffNx07djTVqlUz1apVM02bNjVDhw41mzZtKnKaU5e7evXqYvsV9jgxZ9Z56dKl5rrrrjMRERHGx8fHREREmH79+pnNmzfb+7z66qumc+fOpnbt2sZqtZqGDRuahx56yGRmZhYbU/4jwF544YVi+73zzjumQYMGxsfHx8TExJgvvviiyMeJFTav03PNGGN+//13c/3115ugoCDj6+trmjRpYp544gmHPk899ZSpW7eu8fDwcHhvT3+cmDEnc+/GG2+0z69du3bmk08+ceiT/zix+fPnF7odCnvsHgDAfSzGcPcNAAAAAABchfOVAAAAAABwIQpvAAAAAABciMIbAAAAAAAXovAGAAAAAMCFKLwBAAAAAHAhCm8AAAAAAFzIy90BVEQ2m027d+9W9erVZbFY3B0OAAAAAKCCMcbo0KFDioiIkIdH8ce0KbwLsXv3bkVGRro7DAAAAABABff333+rXr16xfah8C5E9erVJZ3cgIGBgW6OpnA2m00ZGRkKCQk5468rQFkh7+AO5B3cgbyDO5B3KG/k3NnJyspSZGSkvX4sDoV3IfJPLw8MDKzQhfexY8cUGBjIhwTlhryDO5B3cAfyDu5A3qG8kXNlw5nLk9m6AAAAAAC4EIU3AAAAAAAuROENAAAAAIALcY13KRljdOLECeXl5bll+TabTcePH9exY8cqzfUYnp6e8vLy4hFtAAAAAM4pFN6lkJubqz179ig7O9ttMRhjZLPZdOjQoUpVyPr7+6tOnTry8fFxdygAAAAAUC4ovEvIZrNp27Zt8vT0VEREhHx8fNxS+OYfca8sR5CNMcrNzVVGRoa2bdumxo0bV5oj9QAAAABwNii8Syg3N1c2m02RkZHy9/d3WxyVrfCWJD8/P3l7e2vHjh3Kzc2Vr6+vu0MCAAAAAJer8IccV65cqWuuuUYRERGyWCxatGjRGadZvny5Lr74YlmtVjVq1EizZs0q87g4Wls6bDcAAAAA55oKXwUdOXJErVq10pQpU5zqv23bNvXs2VPdunXTunXrdP/99+uOO+7QF1984eJIAQAAAAAoqMKfat6jRw/16NHD6f7Tp09XdHS0XnzxRUlSs2bN9O2332rixImKj493VZgAAABA+TBGyssr/HXiRNHjTh9vszm+jCnY5kwfY5x7FdU3f50K+7e4ccX1LWzY2X7FtZWm3dnxzvYpy3kYo2qHD0sBAVL+5atlEUNZadJE6tvX3VGUiQpfeJdUamqq4uLiHNri4+N1//33FzlNTk6OcnJy7MNZWVmSTt5IzWazOfS12Wwyxthf7pS/fHfHURL5262wbYuKLz//ee9Qnsg7uAN5hwJsNikrSzp0SDp2TDp69OQr/+9T/z3lb0thfXJypOPHpdzck4Xw8ePS8eOy5Oaq9rFjshgjk5trb3d45ebKcuKEu7cGqggPSdXdHUQxzHXXydx4o7vDKFJJviOqXOGdlpamsLAwh7awsDBlZWXp6NGj8vPzKzDN2LFjlZKSUqA9IyNDx44dc2g7fvy4bDabTpw4oRNu3OkZY+zPEC/JzdXS0tI0btw4ff7559q1a5dCQ0N14YUXavjw4erevbsaN26sHTt26O2331ZCQoLDtK1atdIff/yh119/Xf3795cke/9T1a1bV9u2bSt0+SdOnJDNZtP+/fvl7e1dklVGBWCz2ZSZmSljDNfro9yQd3AH8q4KysmRx/798sjKkiUrS5ZDh+SRmXny3/9v8zh06OS4rKyTbaeOO3y4XMIsi/8dGQ8PydNT8vSU+f9/5el5st3L6+Swh8fJcR4eJ490Wiwn2zw8Trblt5/a9v/Dp/5rTp0+//+kpw5bLDKnTldEH7v//9uc2u+0cYW2ner0tqL6F9fv1BhKu6zSKsd55D95yCVPaiqD+R1v0UJH9+4tg2Bc49ChQ073rXKFd2kkJycrKSnJPpyVlaXIyEiFhIQoMDDQoe+xY8d06NAheXl5ycvL/ZuvJMXr9u3b1bFjRwUFBen555/XBRdcoOPHj+uLL77Qfffdpz/++EOSFBkZqbffflu33HKLfdrvv/9e6enpqlatmjw8PBzWPSUlRXfeead92NPTs8ht4+XlJQ8PD9WuXZu7mldCNptNFotFISEh/EcU5Ya8gzuQd5VITo60Z8/J1+7d0p49spzyd/7Lsn9/mSzOeHtLfn4nX76+jv/6+UlWa8G2///b5Lf5+Eje3o4vHx8ZLy8dPHJEQSEhshTW5//7ydv7vwI6/3VKQV1UwXN6a+V4Lg5cyWazKSsjQ9Ur6L7OVxX7iHxJ6hn3V45lLDw8XOnp6Q5t6enpCgwMLPRotyRZrVZZrdYC7R4eHgUS0MPDQxaLxf6SdPI6iOzsslkBJxljTu6Q5fwR76FDh8pisejHH39UtWrV7O0tW7bUoEGD7PO55ZZbNHHiRP3zzz+KjIyUJM2cOVO33HKL3nrrLcd1lxQYGKg6deo4FUP+tIVtW1QOvH9wB/IO7kDeVRAHDkjr1knr158spvNf+cX1v/86Py8vLykoSKpR47/X6cNnaLMU8n9GZ53pf2w2m03H9+6VJTSUvEO5YV9XeiXZZlWu8I6NjdVnn33m0LZkyRLFxsa6bqHZ2SdvSFCOLNLJL6IaNZzq/++//2rx4sV65plnHIrufEFBQfa/w8LCFB8frzfffFOPP/64srOzNW/ePK1YsUJvvfVW2awAAADA6dLSpJ9/ltauPfn6+WepiMvXHFitUp06UkRE8f/WqlU2p/ECQAlV+ML78OHD2rp1q31427ZtWrdunWrVqqXzzjtPycnJ2rVrl70gvPvuu/XKK6/o4Ycf1u23366vv/5a7733nj799FN3rUKFsHXrVhlj1LRpU6f633777RoxYoQee+wxLViwQA0bNlRMTEyhfR955BE9/vjj9uFnn31Ww4cPL4uwAQBAVWSMtGPHf8V1/r979hTePypKatVKOu+8k0X06QV1zZoU1AAqtApfeP/000/q1q2bfTj/WuzExETNmjVLe/bs0c6dO+3jo6Oj9emnn+qBBx7QSy+9pHr16un111937aPE/P2lcrrhRj5jzMlrfErSvwR69uypu+66SytXrtQbb7yh22+/vci+Dz30kAYMGGAfDg4OLtGyAABAFWaMtHmztGbNfwX2zz+fPHPvdB4eJx8fdPHF0kUXnfw3JuZkYQ0AlViFL7y7du1abNE4a9asQqf5+eefXRjVaSwWqZDTt13KmJOPn3BS48aNZbFYtHHjRqf6e3l56bbbbtPo0aP1ww8/aOHChUX2DQ4OVqNGjZyOBQAAnAOys6V335WmTj1ZcJ/O21u64IL/CuyLLpIuvLD8/08FAOWgwhfeKBu1atVSfHy8pkyZouHDhxe4zvvgwYMO13lLJ083Hz9+vBISElSTX5oBAIAzNm2Spk+XZs2SDh482ebjI7Vp81+RffHFUvPmJTp7DwAqMwrvc8iUKVN06aWXql27dnryySd14YUX6sSJE1qyZImmTZtmf5xYvmbNmmnfvn3y9/d3U8QAAKBSOHFC+ugjado06auv/mtv0EC65x5p4ECpdm33xQcAbkbhfQ5p0KCB1q5dq2eeeUYjRozQnj17FBISotatW2vatGmFTlObL0kAAFCUPXuk116TZsyQdu062WaxSFdfLQ0ZIl1xxcnrtgHgHEfhfY6pU6eOXnnlFb3yyiuFjt++fXux0x/MP2XMyf4AAKCKMUZaseLktdsLF/53z5mQEOmOO6TBg0/ehRwAYEfhDQAAgDPLzJTeeuvk6eSnXp7WsePJo9u9e598njYAoAAKbwAAABTt11+lKVOk2bOlI0dOtgUESLfeevL67QsvdG98AFAJUHgDAACgcC++KD344H/DLVqcPLp9661SYKD74gKASobCGwAAAI6MkR5/XHr22ZPDvXtL990ndep08uZpAID/a+/e42yq9z+Ov/fcZ4wxoxkzaJiUKLkUmUbXI0UldJUUSbohNRIKky4uXfBLyjmEdEMl3RzlTInDyHEpnFA5pDQ3YWZc5rrX74/VbHYzmDF7zdp75vV8PPZj1v6u71rfz54+7eUza63vqhQK79NkGIbdIfgkfm8AAHg5p1MaOtScPE2SJk2SRo60NyYA8HEU3pUUGBgoSTpy5IhCQ0Ntjsb3HDlyRNKx3yMAAPAiRUXmM7ffecc8s/3669IDD9gdFQD4PArvSvL391dkZKSysrIkSWFhYXLYcMmVYRgqLi5WQECALeNXlmEYOnLkiLKyshQZGSl/f3+7QwIAAMfLz5duv1369FMpIEB66y3pjjvsjgoAagQK79MQFxcnSa7i2w6GYcjpdMrPz88nCu9SkZGRrt8fAADwEnl5Uo8e0ooVUkiI9OGH0vXX2x0VANQYFN6nweFwqGHDhmrQoIGKiopsicHpdOqPP/7QGWecIT8/P1tiqKzAwEDOdAMA4G327ZOuu05av16qW1f67DPpiivsjgoAahQK7yrw9/e3rZB0Op0KDAxUSEiIzxTeAADAy+zdK117rfTDD1J0tLRsmdS+vd1RAUCNQ+ENAABQG+3cKV1zjbRrl3TmmdLy5VLLlnZHBQA1EoU3AABAbbN1q1l0Z2RI55wj/etfUtOmdkcFADUW1ygDAADUJt9+a97DnZEhtWkjrVpF0Q0AFqPwBgAAqC1SU6Wrr5YOHJCSksxZzHnaCABYjsIbAACgNliyxHxE2OHD5mXmy5dLUVF2RwUAtQKFNwAAQE03f750661SYaF0883Sp59KderYHRUA1BoU3gAAADXZ9OlS//5SSYk0YIC0cKEUHGx3VABQq1B4AwAA1FTPPSc98oi5/Oij0uzZUgAPtQGA6kbhDQAAUBN98ok0dqy5PH68NGWK5Mc//QDADvzJEwAAoKYpKpKeeMJcTk6Wxo2zNx4AqOX4sycAAEBNM3u2tGOHFBMjpaTYHQ0A1HoU3gAAADVJbu6xYjslRYqIsDceAACFNwAAQI3ywgtSdrZ07rnS/ffbHQ0AQBTeAAAANcdvv5mTqEnS5MlSYKC98QAAJFF4AwAA1Bxjx0pHj0qXXSb17Gl3NACAP1F4AwAA1ATffy+9+aa5/NJLksNhbzwAABcKbwAAgJrgiSckw5B695YSE+2OBgBwHApvAAAAX/fFF9KXX5r3dE+YYHc0AIC/oPAGAADwZSUl0ogR5vKQIVKzZvbGAwAog8IbAADAl82fL23ZIkVGSmPG2B0NAKAcFN4AAAC+6vDhY8X2mDFS/fr2xgMAKBeFNwAAgK+aOlX6/XcpIcG8zBwA4JUovAEAAHxRZqY0ebK5PHGiFBxsbzwAgBOi8AYAAPBFTz8tHTokXXyx+QgxAIDXovAGAADwNdu2SbNmmcsvvSQ5HPbGAwA4KQpvAAAAXzNypPkYsZ49pSuusDsaAMApUHgDAAD4km++kT79VPL3P3aPNwDAq1F4AwAA+AqnU3r8cXP5gQekFi3sjQcAUCEU3gAAAL5iwQJp/Xqpbl0pJcXuaAAAFUThDQAA4Avy86UnnzSXR46UGjSwNx4AQIVReAMAAPiCV1+VfvlFatxYeuwxu6MBAFSCTxTeM2bMUEJCgkJCQpSYmKh169adtP+0adPUokULhYaGKj4+Xo899pjy8/OrKVoAAAAP++MP6bnnzOXnnpPCwuyNBwBQKV5feC9cuFDJyclKSUnRxo0b1bZtW3Xt2lVZWVnl9n/33Xc1atQopaSkaNu2bXrjjTe0cOFCPVl6aRYAAICvee45KSdHatNGuvtuu6MBAFRSgN0BnMqUKVM0aNAgDRgwQJI0c+ZMff7555ozZ45GjRpVpv+aNWt06aWX6s4775QkJSQkqE+fPvr2229POEZBQYEKCgpc73NzcyVJTqdTTqfTkx/HY5xOpwzD8Nr4UDORd7ADeQc7eFXe7dwpx4wZckhyvvCC5HCYs5ujxvGqvEOtQM5VTWV+b15deBcWFmrDhg0aPXq0q83Pz09dunRRWlpaudt06tRJb7/9ttatW6eOHTvqf//7n5YuXaq7T/LX4YkTJ2r8+PFl2rOzs732EnWn06mcnBwZhiE/P6+/cAE1BHkHO5B3sIM35V294cMVWlSkgquu0oG2baUTXPUH3+dNeYfagZyrmry8vAr39erCe9++fSopKVFsbKxbe2xsrLZv317uNnfeeaf27dunyy67TIZhqLi4WA8++OBJLzUfPXq0kpOTXe9zc3MVHx+vmJgYRUREeObDeJjT6ZTD4VBMTAz/k6DakHewA3kHO3hN3qWlye/TT2U4HAqcOlUNmMm8RvOavEOtQc5VTUhISIX7enXhfTpWrFihCRMm6LXXXlNiYqJ+/vlnDRs2TM8++6zGjh1b7jbBwcEKDg4u0+7n5+fVCehwOLw+RtQ85B3sQN7BDrbnnWFITzxhxjJggBzt2tkTB6qV7XmHWoecO32V+Z15deEdHR0tf39/ZWZmurVnZmYqLi6u3G3Gjh2ru+++W/fdd58kqXXr1jp8+LDuv/9+PfXUUyQUAADwDR99JK1ZI4WGSs88Y3c0AIAq8OoqNCgoSO3bt1dqaqqrzel0KjU1VUlJSeVuc+TIkTLFtb+/vyTJMAzrggUAAPCU4mKpdBLZxx83n90NAPBZXn3GW5KSk5PVv39/dejQQR07dtS0adN0+PBh1yzn/fr1U+PGjTVx4kRJ0o033qgpU6bowgsvdF1qPnbsWN14442uAhwAAMCrffaZ9NNPUnS0NGKE3dEAAKrIksL78OHDqlOnjkf21bt3b2VnZ2vcuHHKyMhQu3bttGzZMteEa3v27HE7wz1mzBg5HA6NGTNGe/fuVUxMjG688UY9//zzHokHAADAcrNmmT/vvVeqW9feWAAAVeYwLLj+Ojw8XLfffrvuvfdeXXbZZZ7eveVyc3NVr1495eTkePWs5llZWWrQoAH3raPakHewA3kHO9iad3v2SAkJ5uRqP/4oNW9evePDNnzfobqRc1VTmbrRkt/u22+/rf3796tz584699xzNWnSJP3+++9WDAUAAFCzzJljFt1XXUXRDQA1hCWFd69evbRkyRLt3btXDz74oN599101bdpU3bt31+LFi1VcXGzFsAAAAL6tpMQsvCVp0CB7YwEAeIyl1xPExMQoOTlZmzdv1pQpU/Svf/1Lt956qxo1aqRx48bpyJEjVg4PAADgW774Qvr1V6l+fenmm+2OBgDgIZbOap6Zmak333xT8+bN0y+//KJbb71VAwcO1G+//abJkydr7dq1+vLLL60MAQAAwHeUTqrWr58UEmJvLAAAj7Gk8F68eLHmzp2rL774Queff74efvhh3XXXXYqMjHT16dSpk8477zwrhgcAAPA96enSp5+ay1xmDgA1iiWF94ABA3THHXdo9erVuvjii8vt06hRIz311FNWDA8AAOB75s417/Hu1Ek6/3y7owEAeJAlhXd6errCwsJO2ic0NFQpKSlWDA8AAOBbnE5p9mxzmbPdAFDjWDK5Wt26dZWVlVWm/Y8//pC/v78VQwIAAPiur76Sdu2SIiKk226zOxoAgIdZUngbhlFue0FBgYKCgqwYEgAAwHeVTqrWt69Up469sQAAPM6jl5q/8sorkiSHw6HZs2crPDzcta6kpEQrV65Uy5YtPTkkAACAb8vOlj76yFy+/357YwEAWMKjhffUqVMlmWe8Z86c6XZZeVBQkBISEjRz5kxPDgkAAODb5s+XioqkDh2kdu3sjgYAYAGPFt67du2SJP3tb3/T4sWLFRUV5cndAwAA1CyGcewycyZVA4Aay5JZzb/++msrdgsAAFCzrFol7dhh3tfdp4/d0QAALOKxwjs5OVnPPvus6tSpo+Tk5JP2nTJliqeGBQAA8F2lZ7vvuEOqW9feWAAAlvFY4b1p0yYVFRW5lk/E4XB4akgAAADfdeCA9MEH5jKXmQNAjeaxwvv4y8u51BwAAOAU3n5bys+X2rSROna0OxoAgIUseY43AAAATuKvk6pxRSAA1GgeO+N98803V7jv4sWLPTUsAACA71m3TtqyRQoJkfr2tTsaAIDFPFZ416tXz1O7AgAAqNn+8Q/z5223STx+FQBqPI8V3nPnzvXUrgAAAGqu3FxpwQJzmUnVAKBW4B5vAACA6vTee9KRI1LLltJll9kdDQCgGnjsjPdFF12k1NRURUVF6cILLzzpY8M2btzoqWEBAAB8S+mkavfdx6RqAFBLeKzw7tmzp4KDgyVJvXr18tRuAQAAao5Nm6QNG6SgIKl/f7ujAQBUE48V3ikpKeUuAwAA4E+lZ7tvukmKjrY3FgBAtfFY4V2e9evXa9u2bZKk888/X+3bt7dyOAAAAO91+LD0zjvmMpOqAUCtYknh/dtvv6lPnz5avXq1IiMjJUkHDx5Up06dtGDBAp155plWDAsAAOC9Fi0yZzRv1kz629/sjgYAUI0smdX8vvvuU1FRkbZt26b9+/dr//792rZtm5xOp+677z4rhgQAAPBux0+q5seDZQCgNrHkjPc333yjNWvWqEWLFq62Fi1aaPr06br88sutGBIAAMB7/fe/Ulqa5O8v3XOP3dEAAKqZJX9ujY+PV1FRUZn2kpISNWrUyIohAQAAvFfp2e4ePaSGDe2NBQBQ7SwpvF988UUNHTpU69evd7WtX79ew4YN00svvWTFkAAAAN4pP1966y1zmUnVAKBW8til5lFRUXI4HK73hw8fVmJiogICzCGKi4sVEBCge++9l+d8AwCA2mPxYmn/fqlJE+naa+2OBgBgA48V3tOmTfPUrgAAAGqOf/zD/HnvveY93gCAWsdjhXf//v09tSsAAICa4ccfpW++MWcxv/deu6MBANjEklnNj5efn6/CwkK3toiICKuHBQAAsN/s2ebPbt2k+Hh7YwEA2MaSydUOHz6sIUOGqEGDBqpTp46ioqLcXgAAADVeYaE0b565fP/9toYCALCXJYX3E088oa+++kqvv/66goODNXv2bI0fP16NGjXS/PnzrRgSAADAu3zyiZSdbT4+7IYb7I4GAGAjSy41//TTTzV//nxdddVVGjBggC6//HKdc845atq0qd555x317dvXimEBAAC8R+mzuwcMkAIsv7sPAODFLDnjvX//fjVr1kySeT/3/v37JUmXXXaZVq5cacWQAAAA3mPXLunLL83lgQPtjQUAYDtLCu9mzZpp165dkqSWLVtq0aJFkswz4ZGRkVYMCQAA4D3eeMP82aWL9OfJCABA7WVJ4T1gwAB9//33kqRRo0ZpxowZCgkJ0WOPPaYRI0ZYMSQAAIB3KC6W5s41lwcNsjcWAIBXsOSGo8cee8y13KVLF23btk0bN27UOeecozZt2lgxJAAAgHdYtkz6/XcpOlrq2dPuaAAAXqBaZvpISEhQQkJCdQwFAABgr9LLzO++WwoOtjcWAIBXsORSc0lKTU1V9+7ddfbZZ+vss89W9+7d9a9//cuq4QAAAOyXmSl99pm5zKRqAIA/WVJ4v/baa+rWrZvq1q2rYcOGadiwYYqIiND111+vGTNmWDEkAACA/d56y7zHOzFRatXK7mgAAF7CksJ7woQJmjp1qt577z098sgjeuSRR/Tuu+9q6tSpmjBhQqX3N2PGDCUkJCgkJESJiYlat27dSfsfPHhQgwcPVsOGDRUcHKxzzz1XS5cuPd2PAwAAcGqGcewyc852AwCOY0nhffDgQXXr1q1M+7XXXqucnJxK7WvhwoVKTk5WSkqKNm7cqLZt26pr167Kysoqt39hYaGuueYa7d69Wx988IF27NihWbNmqXHjxqf1WQAAACokLU3avl0KC5N697Y7GgCAF7FkcrUePXroo48+KvPosI8//ljdu3ev1L6mTJmiQYMGacCAAZKkmTNn6vPPP9ecOXM0atSoMv3nzJmj/fv3a82aNQoMDJSkU07sVlBQoIKCAtf73NxcSZLT6ZTT6axUvNXF6XTKMAyvjQ81E3kHO5B3sMPp5J1j9mw5JBm33iojPFwiZ1FJfN+hupFzVVOZ35vHCu9XXnnFtXz++efr+eef14oVK5SUlCRJWrt2rVavXq3hw4dXeJ+FhYXasGGDRo8e7Wrz8/NTly5dlJaWVu42n3zyiZKSkjR48GB9/PHHiomJ0Z133qmRI0fK39+/3G0mTpyo8ePHl2nPzs5Wfn5+heOtTk6nUzk5OTIMQ35+ls2RB7gh72AH8g52qGzeOQ4dUszChXJI2n/TTSo6wZV5wMnwfYfqRs5VTV5eXoX7eqzwnjp1qtv7qKgo/fDDD/rhhx9cbZGRkZozZ47GjBlToX3u27dPJSUlio2NdWuPjY3V9u3by93mf//7n7766iv17dtXS5cu1c8//6yHH35YRUVFSklJKXeb0aNHKzk52fU+NzdX8fHxiomJUURERIVirW5Op1MOh0MxMTH8T4JqQ97BDuQd7FDpvPv0U/kdOSLj3HMVdeONksNhfZCocfi+Q3Uj56omJCSkwn09Vnjv2rXLU7uqEqfTqQYNGugf//iH/P391b59e+3du1cvvvjiCQvv4OBgBZfznE0/Pz+vTkCHw+H1MaLmIe9gB/IOdqhU3s2da25z771ynOAKO6Ai+L5DdSPnTl9lfmeW3ON9PMMwJJn/QSsrOjpa/v7+yszMdGvPzMxUXFxcuds0bNhQgYGBbpeVn3feecrIyFBhYaGCgoIqHQcAAMAJbdtmTqzm7y/17293NAAAL2TZnzXmz5+v1q1bKzQ0VKGhoWrTpo3eeuutSu0jKChI7du3V2pqqqvN6XQqNTXVde/4X1166aX6+eef3W50//HHH9WwYUOKbgAA4Hlz5pg/b7hBOsGJAQBA7WZJ4T1lyhQ99NBDuv7667Vo0SItWrRI3bp104MPPljmXvBTSU5O1qxZs/Tmm29q27Zteuihh3T48GHXLOf9+vVzm3ztoYce0v79+zVs2DD9+OOP+vzzzzVhwgQNHjzYo58RAABARUXS/PnmMs/uBgCcgCWXmk+fPl2vv/66+vXr52rr0aOHWrVqpaefflqPPfZYhffVu3dvZWdna9y4ccrIyFC7du20bNky14Rre/bscbu2Pj4+Xl988YUee+wxtWnTRo0bN9awYcM0cuRIz31AAAAASfrsMykryzzTff31dkcDAPBSlhTe6enp6tSpU5n2Tp06KT09vdL7GzJkiIYMGVLuuhUrVpRpS0pK0tq1ays9DgAAQKW88Yb5s18/KcDyqXMAAD7KkkvNzznnHC1atKhM+8KFC9W8eXMrhgQAAKhee/dK//ynuXzvvfbGAgDwapb8aXb8+PHq3bu3Vq5cqUsvvVSStHr1aqWmppZbkAMAAPicN9+UnE7pssukFi3sjgYA4MUsOeN9yy23aN26dYqOjtaSJUu0ZMkSRUdHa926dbrpppusGBIAAKD6OJ3HZjNnUjUAwCl4/Ix3UVGRHnjgAY0dO1Zvv/22p3cPAABgv5UrpZ07pbp1pdtuszsaAICX8/gZ78DAQH344Yee3i0AAID3KD3bfccdUp069sYCAPB6llxq3qtXLy1ZssSKXQMAANgrJ0f64ANzmcvMAQAVYMnkas2bN9czzzyj1atXq3379qrzl78EP/LII1YMCwAAYL333pOOHpVatZI6drQ7GgCAD7Ck8H7jjTcUGRmpDRs2aMOGDW7rHA4HhTcAAPBdpc/uvvdeyeGwNxYAgE+wpPDetWuXFbsFAACw1+bN0vr1UmCgdPfddkcDAPARHi+8165dq08//VSFhYW6+uqr1a1bN08PAQAAYI/Ss909ekgxMfbGAgDwGR4tvD/44AP17t1boaGhCgwM1JQpUzR58mQ9/vjjnhwGAACg+hUUSKWPSmVSNQBAJXh0VvOJEydq0KBBysnJ0YEDB/Tcc89pwoQJnhwCAADAHkuWSPv3S2eeKV17rd3RAAB8iEcL7x07dujxxx+Xv7+/JGn48OHKy8tTVlaWJ4cBAACofqXP7r7nHunPf+sAAFARHi28jxw5ooiICNf7oKAghYSE6NChQ54cBgAAoHr98ou0fLm5PGCAvbEAAHyOxydXmz17tsLDw13vi4uLNW/ePEVHR7vaeJwYAADwKfPmSYYhde4sNWtmdzQAAB/j0cK7SZMmmjVrlltbXFyc3nrrLdd7nuMNAAB8itMpzZ1rLjOpGgDgNHi08N69e7cndwcAAGC/1FTzUvN69aSbbrI7GgCAD/LoPd4AAAA1Tumzu/v2lUJD7Y0FAOCTKLwBAABO5I8/pI8+Mpe5zBwAcJoovAEAAE7k3XelwkKpXTvpoovsjgYA4KMovAEAAMpjGHKUPrubs90AgCqg8AYAAChHwObNcmzeLAUHm/d3AwBwmiwrvHfu3KkxY8aoT58+ysrKkiT985//1H//+1+rhgQAAPCYsPfeMxduvlmKirI3GACAT7Ok8P7mm2/UunVrffvtt1q8eLEOHTokSfr++++VkpJixZAAAACec/SoQphUDQDgIZYU3qNGjdJzzz2n5cuXKygoyNXeuXNnrV271oohAQAAPOfDD+WXmysjIUH629/sjgYA4OMsKby3bNmim266qUx7gwYNtG/fPiuGBAAA8BjH3LmSJOOeeyQ/psQBAFSNJUeSyMhIpaenl2nftGmTGjdubMWQAAAAnrFzpxwrVshwOKT+/e2OBgBQA1hSeN9xxx0aOXKkMjIy5HA45HQ6tXr1aj3++OPq16+fFUMCAAB4xp+PECu88kqpSRObgwEA1ASWFN4TJkxQy5YtFR8fr0OHDun888/XFVdcoU6dOmnMmDFWDAkAAFB1BQWuwvvInXfaHAwAoKYIsGKnQUFBmjVrlsaOHautW7fq0KFDuvDCC9W8eXMrhgMAAPCMN9+UMjJknHmmCrp2tTsaAEANYUnh/e9//1uXXXaZmjRpoiZcogUAAHxBcbE0ebIkyRg+XDruySwAAFSFJZead+7cWWeddZaefPJJ/fDDD1YMAQAA4Fnvvy/973/SGWfw7G4AgEdZUnj//vvvGj58uL755htdcMEFateunV588UX99ttvVgwHAABQNYYhTZxoLj/6qFSnjq3hAABqFksK7+joaA0ZMkSrV6/Wzp07ddttt+nNN99UQkKCOnfubMWQAAAAp+/zz6UtW6TwcGnwYLujAQDUMJYU3sc766yzNGrUKE2aNEmtW7fWN998Y/WQAAAAFWcY0oQJ5vJDD0lRUfbGAwCocSwtvFevXq2HH35YDRs21J133qkLLrhAn3/+uZVDAgAAVM7KlVJamhQcLD32mN3RAABqIEtmNR89erQWLFig33//Xddcc43+7//+Tz179lRYWJgVwwEAAJy+0nu7BwyQGja0NxYAQI1kSeG9cuVKjRgxQrfffruio6OtGAIAAKDqNm6UvvhC8vOTRoywOxoAQA1lSeG9evVqK3YLAADgWaVnu/v0kZo1szcWAECN5bHC+5NPPtF1112nwMBAffLJJyft26NHD08NCwAAcHp27JA+/NBcHjXK3lgAADWaxwrvXr16KSMjQw0aNFCvXr1O2M/hcKikpMRTwwIAAJyeF14wZzS/8UbpggvsjgYAUIN5rPB2Op3lLgMAAHidX3+V5s83l5980t5YAAA1niWPE5s/f74KCgrKtBcWFmp+6UEOAADALi+/LBUXS1ddJV1yid3RAABqOEsK7wEDBignJ6dMe15engYMGGDFkAAAABWTnS3NmmUujx5tbywAgFrBksLbMAw5HI4y7b/99pvq1atX6f3NmDFDCQkJCgkJUWJiotatW1eh7RYsWCCHw3HSe84BAEAt88or0pEjUvv20jXX2B0NAKAW8OjjxC688EI5HA45HA5dffXVCgg4tvuSkhLt2rVL3bp1q9Q+Fy5cqOTkZM2cOVOJiYmaNm2aunbtqh07dqhBgwYn3G737t16/PHHdfnll5/25wEAADVMbq706qvm8ujRUjknCgAA8DSPFt6lZ5a/++47de3aVeHh4a51QUFBSkhI0C233FKpfU6ZMkWDBg1yXaI+c+ZMff7555ozZ45GneDRHyUlJerbt6/Gjx+vVatW6eDBg6f1eQAAQA3z979LBw9KLVtKN91kdzQAgFrCo4V3SkqKJCkhIUG9e/dWSEhIlfZXWFioDRs2aPRx91/5+fmpS5cuSktLO+F2zzzzjBo0aKCBAwdq1apVpxynoKDAbTK43NxcSebs7N46Q7vT6ZRhGF4bH2om8g52IO/gMfn5ckyZIock54gRZtsJ8oq8gx3IO1Q3cq5qKvN782jhXap///4e2c++fftUUlKi2NhYt/bY2Fht37693G3+/e9/64033tB3331X4XEmTpyo8ePHl2nPzs5Wfn5+pWKuLk6nUzk5OTIMQ35+ltyqD5RB3sEO5B08JfTNN1UvI0MljRopu0sXKSvrhH3JO9iBvEN1I+eqJi8vr8J9LSm8S0pKNHXqVC1atEh79uxRYWGh2/r9+/dbMazy8vJ09913a9asWYqOjq7wdqNHj1ZycrLrfW5uruLj4xUTE6OIiAgrQq0yp9Mph8OhmJgY/idBtSHvYAfyDh5RXCzH3/8uSXKMHKkGZ5550u7kHexA3qG6kXNVU5krvC0pvMePH6/Zs2dr+PDhGjNmjJ566int3r1bS5Ys0bhx4yq8n+joaPn7+yszM9OtPTMzU3FxcWX679y5U7t379aNN97oais9/R8QEKAdO3bo7LPPLrNdcHCwgoODy7T7+fl5dQI6HA6vjxE1D3kHO5B3qLL335d27ZJiYuR3331SBXKJvIMdyDtUN3Lu9FXmd2bJb/edd97RrFmzNHz4cAUEBKhPnz6aPXu2xo0bp7Vr11Z4P0FBQWrfvr1SU1NdbU6nU6mpqUpKSirTv2XLltqyZYu+++4716tHjx7629/+pu+++07x8fEe+XwAAMCHOJ3SpEnm8rBhUliYvfEAAGodS854Z2RkqHXr1pKk8PBw5eTkSJK6d++usWPHVmpfycnJ6t+/vzp06KCOHTtq2rRpOnz4sGuW8379+qlx48aaOHGiQkJCdMEFF7htHxkZKUll2gEAQC3x2WfS1q1S3brS4MF2RwMAqIUsKbzPPPNMpaenq0mTJjr77LP15Zdf6qKLLtJ//vOfci/pPpnevXsrOztb48aNU0ZGhtq1a6dly5a5Jlzbs2cPl0UAAIDyGYY0caK5/PDD0p9/kAcAoDpZUnjfdNNNSk1NVWJiooYOHaq77rpLb7zxhvbs2aPHHnus0vsbMmSIhgwZUu66FStWnHTbefPmVXo8AABQQ3zzjbR2rRQcLD36qN3RAABqKUsK70ml91HJPGPdpEkTpaWlqXnz5m4TnwEAAFhqwgTz58CBUjkTswIAUB0sKbz/KikpqdzJ0AAAACyzYYO0fLnk7y+NGGF3NACAWsxjhfcnn3xS4b49evTw1LAAAADlK723u08fKSHB1lAAALWbxwrvXr16Vaifw+FQSUmJp4YFAAAoa/t2afFic3nUKHtjAQDUeh4rvJ1Op6d2BQAAUDWTJ5szmvfsKbVqZXc0AIBajudwAQCAmmXPHuntt83l0aPtjQUAAFk0udozzzxz0vXjxo2zYlgAAADppZek4mKpc2cpMdHuaAAAsKbw/uijj9zeFxUVadeuXQoICNDZZ59N4Q0AAKyRlSXNnm0uc7YbAOAlLCm8N23aVKYtNzdX99xzj2666SYrhgQAAJCmTZOOHpU6dJCuvtruaAAAkFSN93hHRERo/PjxGjt2bHUNCQAAapMff5SmTDGXn3xScjjsjQcAgD9V6+RqOTk5ysnJqc4hAQBAbeB0SvffLxUUSNdeK1XwMacAAFQHSy41f+WVV9zeG4ah9PR0vfXWW7ruuuusGBIAANRmb7whffONFBYmzZzJ2W4AgFexpPCeOnWq23s/Pz/FxMSof//+Gs1EJwAAwJPS06URI8zl556TzjrL3ngAAPgLSwrvXbt2WbFbAACAsoYOlXJypIsvlh55xO5oAAAoo1rv8QYAAPCojz6SPvxQCgiQZs2S/P3tjggAgDIsOeOdn5+v6dOn6+uvv1ZWVpacTqfb+o0bN1oxLAAAqE0OHpQGDzaXR4yQ2ra1NRwAAE7EksJ74MCB+vLLL3XrrbeqY8eOcjDBCQAA8LRRo8z7u5s3l3hcKQDAi1lSeH/22WdaunSpLr30Uit2DwAAaruVK6W//91cnjVLCg21Nx4AAE7Cknu8GzdurLp161qxawAAUNvl55vP7JakQYOkK6+0Nx4AAE7BksL75Zdf1siRI/XLL79YsXsAAFCbPf+8tGOHFBcnvfCC3dEAAHBKllxq3qFDB+Xn56tZs2YKCwtTYGCg2/r9+/dbMSwAAKjptmyRJk0yl2fMkCIjbQ0HAICKsKTw7tOnj/bu3asJEyYoNjaWydUAAEDVlZRI990nFRdLvXpJN99sd0QAAFSIJYX3mjVrlJaWprY81gMAAHjKq69K69ZJERHmMgAAPsKSe7xbtmypo0ePWrFrAABQG/3yi/TUU+byCy9IjRvbGw8AAJVgSeE9adIkDR8+XCtWrNAff/yh3NxctxcAAECFGYb00EPS4cPS5ZebM5kDAOBDLLnUvFu3bpKkq6++2q3dMAw5HA6VlJRYMSwAAKiJ3ntP+uc/paAg85ndfpacNwAAwDKWFN5ff/21FbsFAAC1zb590rBh5vK4cVKLFvbGAwDAabCk8L7yyiut2C0AAKhtkpPN4vuCC6QRI+yOBgCA02JJ4b1y5cqTrr/iiiusGBYAANQkX34pvfWW5HBIs2ebl5oDAOCDLCm8r7rqqjJtxz/Lm3u8AQDASR0+LD3wgLn8yCNSYqK98QAAUAWWzE5y4MABt1dWVpaWLVumiy++WF9++aUVQwIAgJpk3Dhp926pSRPpuefsjgYAgCqx5Ix3vXr1yrRdc801CgoKUnJysjZs2GDFsAAAoCb4z3+kadPM5ZkzpfBwW8MBAKCqqvV5HLGxsdqxY0d1DgkAAHxJUZF0332S0yn17Stdd53dEQEAUGWWnPHevHmz23vDMJSenq5JkyapXbt2VgwJAABqgpdfljZvls44Q5o61e5oAADwCEsK73bt2snhcMgwDLf2Sy65RHPmzLFiSAAA4Ot++kl6+mlzeepUKSbG1nAAAPAUSwrvXbt2ub338/NTTEyMQkJCrBgOAAD4utxc6bbbpIIC6dprpbvusjsiAAA8xpLCu2nTplbsFgAA1ERFRWbR/f33UmysNGuW+exuAABqCI9OrvbVV1/p/PPPV25ubpl1OTk5atWqlVatWuXJIQEAgC8zDOmhh6Qvv5TCwqTPPjMfIQYAQA3i0cJ72rRpGjRokCIiIsqsq1evnh544AFNmTLFk0MCAABf9vzz0htvSH5+0sKFUocOdkcEAIDHebTw/v7779WtW7cTrr/22mt5hjcAADC99ZY0dqy5PGOG1L27vfEAAGARjxbemZmZCgwMPOH6gIAAZWdne3JIAADgi776Sho40Fx+4gnpwQftjQcAAAt5tPBu3Lixtm7desL1mzdvVsOGDT05JAAA8DVbt0o332xOqta7tzRxot0RAQBgKY8W3tdff73Gjh2r/Pz8MuuOHj2qlJQUdecyMgAAaq/ff5euv17KyZEuu0yaN8+8vxsAgBrMo48TGzNmjBYvXqxzzz1XQ4YMUYsWLSRJ27dv14wZM1RSUqKnnnrKk0MCAABfkZdn3sf9669SixbSxx9LISF2RwUAgOU8WnjHxsZqzZo1euihhzR69GgZhiFJcjgc6tq1q2bMmKHY2FhPDgkAAHxBcbF0++3Spk1SgwbS0qVS/fp2RwUAQLXw+LVdTZs21dKlS7Vv3z59++23Wrt2rfbt26elS5fqrLPOOq19zpgxQwkJCQoJCVFiYqLWrVt3wr6zZs3S5ZdfrqioKEVFRalLly4n7Q8AACxmGNLDD0vLlkmhoeazups1szsqAACqjWU3VUVFReniiy9Wx44dFRUVddr7WbhwoZKTk5WSkqKNGzeqbdu26tq1q7Kyssrtv2LFCvXp00dff/210tLSFB8fr2uvvVZ79+497RgAAEAVTJokzZpl3su9YIF08cV2RwQAQLVyGKXXg3upxMREXXzxxXr11VclSU6nU/Hx8Ro6dKhGjRp1yu1LSkoUFRWlV199Vf369Su3T0FBgQoKClzvc3NzFR8frwMHDigiIsIzH8TDnE6nsrOzFRMTIz8mpUE1Ie9gB/LOx73zjvz+PP46X3lFGjzY5oAqhryDHcg7VDdyrmpyc3MVFRWlnJycU9aNHr3H29MKCwu1YcMGjR492tXm5+enLl26KC0trUL7OHLkiIqKilT/JPeRTZw4UePHjy/Tnp2dXe4M7d7A6XQqJydHhmHwPwmqDXkHO5B3vitozRpF/fms7sMPPqi8226TTnDFmrch72AH8g7VjZyrmry8vAr39erCe9++fSopKSkzIVtsbKy2b99eoX2MHDlSjRo1UpcuXU7YZ/To0UpOTna9Lz3jHRMT49VnvB0OB3+dQrUi72AH8s5H/fCDHAMHylFUJOOWWxQ6fbpCfei/H3kHO5B3qG7kXNWEVOLJHF5deFfVpEmTtGDBAq1YseKkv5Tg4GAFBweXaffz8/PqBHQ4HF4fI2oe8g52IO98THq6+diwgwelTp3kePttOQJ8758c5B3sQN6hupFzp68yvzOvPgpGR0fL399fmZmZbu2ZmZmKi4s76bYvvfSSJk2apH/9619q06aNlWECAIBShw6ZRfcvv0jNm/OsbgAAZOGs5p4QFBSk9u3bKzU11dXmdDqVmpqqpKSkE273wgsv6Nlnn9WyZcvUoUOH6ggVAAAUF0t33CFt3ChFR0v//Kf5EwCAWs6rz3hLUnJysvr3768OHTqoY8eOmjZtmg4fPqwBAwZIkvr166fGjRtr4sSJkqTJkydr3Lhxevfdd5WQkKCMjAxJUnh4uMLDw237HAAA1GiGIQ0dKn3+uXmG+9NPpbPPtjsqAAC8gtcX3r1791Z2drbGjRunjIwMtWvXTsuWLXNNuLZnzx63a+tff/11FRYW6tZbb3XbT0pKip5++unqDB0AgNqhpER64glp5kzJ4ZDefVe65BK7owIAwGt4feEtSUOGDNGQIUPKXbdixQq397t377Y+IAAAYMrLk+68U/rsM/P9//2fdNNN9sYEAICX8YnCGwAAeKFffpFuvFHassW8vHzuXPMebwAA4IbCGwAAVN6aNeaZ7awsKS7OnL28Y0e7owIAwCt59azmAADAC73zjvS3v5lFd7t20rp1FN0AAJwEhTcAAKgYp1MaM0a66y6psFDq1UtatUqKj7c7MgAAvBqXmgMAgFM7fFjq31/68EPz/ahR0vPPS378DR8AgFOh8AYAACe3d6/Uo4e0caMUGCjNmmUW4QAAoEIovAEAwImtX28W3enpUnS09NFH0mWX2R0VAAA+hevDAABA+T74QLriCrPobtXKnESNohsAgEqj8AYAAO4MQ3ruOem226SjR6XrrjMfH3bWWXZHBgCAT6LwBgAAx+Tnm7OWjx1rvn/0UemTT6SICFvDAgDAl3GPNwAAMGVkmI8I+/ZbKSBAevVV6YEH7I4KAACfR+ENAACkzZul7t2lX3+VoqLM+7s7d7Y7KgAAagQuNQcAoDYrKZFee03q1Mksus89V1q7lqIbAAAP4ow3AAC11dq10sMPS5s2me87dzbPdEdF2RsXAAA1DGe8AQCobbKzpfvuk5KSzKK7Xj3zfu4vv6ToBgDAApzxBgCgtigpkWbNkp58UjpwwGy75x5p8mSpQQNbQwMAoCaj8AYAoDb4z3/My8rXrzfft20rzZghXXqpvXEBAFALcKk5AAA12R9/mI8ES0w0i+6ICOmVV8xlim4AAKoFZ7wBAKiJnE7pjTekUaOk/fvNtrvvll54QYqLszc2AABqGQpvAABqmg0bzMvK160z37dubU6edsUV9sYFAEAtxaXmAADUFPv3mwX3xRebRXfdutLUqdLGjRTdAADYiDPeAAD4OqdTmjdPGjlS2rfPbOvbV3rxRalhQ1tDAwAAFN4AAPiu4mLp00/Nx4F9+63Z1qqVOVv5lVfaGxsAAHCh8AYAwNfs2yfNni29/rq0Z4/ZFh4ujR8vDR0qBQbaGx8AAHBD4Q0AgK/YuFGaPl167z2poMBsi46WBg0yC24uKwcAwCtReAMA4M0KC6UPPzQL7rS0Y+3t25vFdu/eUkiIffEBAIBTovAGAMAbpadLf/+7+crIMNsCA6XbbjML7sREyeGwN0YAAFAhFN4AAHgLwzDPak+fLn3wgTl5mmReQv7gg9L990txcfbGCAAAKo3CGwAAux09Ki1YYBbcmzYda7/0UvPs9s03M2EaAAA+jMIbAAA7OJ3S2rXS4sXmM7j/+MNsDwmR7rxTGjJEuvBCW0MEAACeQeENAEB1OXpUSk2VPv7YfP52ZuaxdU2bSg8/LA0cKJ1xhn0xAgAAj6PwBgDASn/8IX32mVlsf/GFdOTIsXX16knXXy/dcYd0ww2Sv799cQIAAMtQeAMA4Gn/+59ZaH/8sbRqlXlZean4eKlnT/N1xRVSUJB9cQIAgGpB4Q0AQFUZhrRhg7RkiVlsb93qvr5t22PF9oUX8hgwAABqGQpvAABOx7595uRoS5dKn3wi7d17bJ2/v3k2u7TYTkiwLUwAAGA/Cm8AAE6luFjassV8xvbatebPn39271OnjnTddWahff31Uv369sQKAAC8DoU3AAB/lZl5rMBeu1b6z3/cJ0Ur1bLlsTPbnTubjwIDAAD4CwpvAEDtVlgoff+9e6G9a1fZfvXqSYmJUlKSdMkl5nJUVPXHCwAAfA6FNwCgdjAMKT1d2r792GvjRnNStPx8974Oh9SqlVlglxbaLVtKfn72xA4AAHwahTcAoGYpKJB++knascO9yN6xQ8rLK3+b+vXN4rq00L74YvMMNwAAgAdQeAMAfI9hmLOK/7W43r7dvEz8+OdmH8/fXzr7bKlFC/MMdqtWZqHdvDmP+AIAAJah8AYAeBen0yyq9+6VfvtN2rNH4T/+KMeBA9Lvv5ttv/0mHT584n3Uq2cW1i1bHiuyW7Y0i+6goOr7LAAAAKLwBgBUF8MwL/Xet0/KyjpWWP/2W9nlwkLXZn6Swsvbn8MhNW16rKg+vtCOjeUMNgAA8BoU3gCAyjMM84zzvn3HXn/84f6+vLaioort3+Ewi+czz5TRuLGOnHGGQps3l198vNS4sXTmmebP0FBrPycAAIAH+EThPWPGDL344ovKyMhQ27ZtNX36dHXs2PGE/d9//32NHTtWu3fvVvPmzTV58mRdf/311RgxAHip0oI5L0/KzT29nzk5ZkFdUHB6MYSFSdHRZvFcWkCXLpe+GjaUAgPNkJ1O5WVlKbRBA2YVBwAAPsnrC++FCxcqOTlZM2fOVGJioqZNm6auXbtqx44datCgQZn+a9asUZ8+fTRx4kR1795d7777rnr16qWNGzfqggsusOETAMCfSkrMS6hP9SoqKr+9oMB87NWRI9LRo+6virYdPWoW354SHCzFxJiFdHS0dMYZx5bLazvjDLPwBgAAqEUchuHJf4F5XmJioi6++GK9+uqrkiSn06n4+HgNHTpUo0aNKtO/d+/eOnz4sD777DNX2yWXXKJ27dpp5syZFRozNzdX9erVU05OjiIiIjzzQTzM+dtv2r99u+rXry8/u88AeSKFTrWPioxR1X1YOUZl2o9vO9VyRdb/te+J2spb95f3zpIS5Rw8qHoREfIrvX/2+H4n2t7pPPXyqfqV9zrZuuP7lJSYL6fT/Wdl2kpKpOLiqr1ONNO2HRwOKSLCfNWte/Kf5bWVFtNhYZbfS+10OpWVlaUGDRrY/32HWoO8gx3IO1Q3cq5qKlM3evUZ78LCQm3YsEGjR492tfn5+alLly5KS0srd5u0tDQlJye7tXXt2lVLliw54TgFBQUqOO6SydzcXElmIjq96R/Kx3vlFUW/+KLdUaCW8ZMUZXcQNYgREGDOsF2RV2Cg+QoNPfYKC5NCQ2Uc3xYS4mov8yptr1vXcwXz8X9ssYjT6ZRhGN77fYwaibyDHcg7VDdyrmoq83vz6sJ73759KikpUWxsrFt7bGystm/fXu42GRkZ5fbPyMg44TgTJ07U+PHjy7RnZ2crPz//NCK3XpjDodCYGM/8ZcpbZv49VRynG+eJtiun3ajIGJ6IszL7ONHyKda7PovDcaz9RMvHb3tcu1G6fFy/YqdTAQEB5e/L4Ti2TWl7aY6WLpf2+XPZ1fe498bx60r7lm573PKp2t324+8v+fsf6+fvb/b582dpm+HvX37/gACzUPb3N5f/bFNAgLnNX9f/+dO17O8vIzBQCgoyfwYGmn3tcPjwyR/F5WWcTqdycnJkGAZ/jUe1Ie9gB/IO1Y2cq5q8vLwK9/Xqwru6jB492u0seW5uruLj4xUTE+O9l5o//7yyH31UMZ4qviFJ8pI/QXgtp9OpnOxs8g7Vyul0yuFwkHeoVuQd7EDeobqRc1UTEhJS4b5eXXhHR0fL399fmZmZbu2ZmZmKi4srd5u4uLhK9Zek4OBgBQcHl2n38/Pz6gR0OBxeHyNqHvIOdiDvYAfyDnYg71DdyLnTV5nfmVf/doOCgtS+fXulpqa62pxOp1JTU5WUlFTuNklJSW79JWn58uUn7A8AAAAAgJW8+oy3JCUnJ6t///7q0KGDOnbsqGnTpunw4cMaMGCAJKlfv35q3LixJk6cKEkaNmyYrrzySr388su64YYbtGDBAq1fv17/+Mc/7PwYAAAAAIBayusL7969eys7O1vjxo1TRkaG2rVrp2XLlrkmUNuzZ4/bKf5OnTrp3Xff1ZgxY/Tkk0+qefPmWrJkCc/wBgAAAADYwuuf420Hn3iON8/cgw3IO9iBvIMdyDvYgbxDdSPnqqYydSO/XQAAAAAALEThDQAAAACAhSi8AQAAAACwEIU3AAAAAAAWovAGAAAAAMBCFN4AAAAAAFjI65/jbYfSJ6zl5ubaHMmJOZ1O5eXlKSQkhKn/UW3IO9iBvIMdyDvYgbxDdSPnqqa0XqzIE7opvMuRl5cnSYqPj7c5EgAAAACAN8vLy1O9evVO2sdhVKQ8r2WcTqd+//131a1bVw6Hw+5wypWbm6v4+Hj9+uuvp3xYO+Ap5B3sQN7BDuQd7EDeobqRc1VjGIby8vLUqFGjU14xwBnvcvj5+enMM8+0O4wKiYiI4H8SVDvyDnYg72AH8g52IO9Q3ci503eqM92luJAfAAAAAAALUXgDAAAAAGAhCm8fFRwcrJSUFAUHB9sdCmoR8g52IO9gB/IOdiDvUN3IuerD5GoAAAAAAFiIM94AAAAAAFiIwhsAAAAAAAtReAMAAAAAYCEKbwAAAAAALEThbbPnn39enTp1UlhYmCIjI8vt43A4yrwWLFjg1mfFihW66KKLFBwcrHPOOUfz5s0rs58ZM2YoISFBISEhSkxM1Lp169zW5+fna/DgwTrjjDMUHh6uW265RZmZmZ76qPAiFcm7PXv26IYbblBYWJgaNGigESNGqLi42K0PeYeqSEhIKPPdNmnSJLc+mzdv1uWXX66QkBDFx8frhRdeKLOf999/Xy1btlRISIhat26tpUuXuq03DEPjxo1Tw4YNFRoaqi5duuinn36y9LPBt53qews4kaeffrrM91rLli1d6ytyzPPU8Rc118qVK3XjjTeqUaNGcjgcWrJkidv6ihz39u/fr759+yoiIkKRkZEaOHCgDh065NbHE8dgHMeArcaNG2dMmTLFSE5ONurVq1duH0nG3LlzjfT0dNfr6NGjrvX/+9//jLCwMCM5Odn44YcfjOnTpxv+/v7GsmXLXH0WLFhgBAUFGXPmzDH++9//GoMGDTIiIyONzMxMV58HH3zQiI+PN1JTU43169cbl1xyidGpUyfLPjvsc6q8Ky4uNi644AKjS5cuxqZNm4ylS5ca0dHRxujRo119yDtUVdOmTY1nnnnG7bvt0KFDrvU5OTlGbGys0bdvX2Pr1q3Ge++9Z4SGhhp///vfXX1Wr15t+Pv7Gy+88ILxww8/GGPGjDECAwONLVu2uPpMmjTJqFevnrFkyRLj+++/N3r06GGcddZZbt+jQKmKfG8BJ5KSkmK0atXK7XstOzvbtf5UxzxPHX9Rsy1dutR46qmnjMWLFxuSjI8++shtfUWOe926dTPatm1rrF271li1apVxzjnnGH369HGt99QxGMdQeHuJuXPnnrTw/uv/UMd74oknjFatWrm19e7d2+jatavrfceOHY3Bgwe73peUlBiNGjUyJk6caBiGYRw8eNAIDAw03n//fVefbdu2GZKMtLS00/hE8AUnyrulS5cafn5+RkZGhqvt9ddfNyIiIoyCggLDMMg7VF3Tpk2NqVOnnnD9a6+9ZkRFRblyzjAMY+TIkUaLFi1c72+//XbjhhtucNsuMTHReOCBBwzDMAyn02nExcUZL774omv9wYMHjeDgYOO9997z0CdBTXKq7y3gZFJSUoy2bduWu64ixzxPHX9Re/y1TqjIce+HH34wJBn/+c9/XH3++c9/Gg6Hw9i7d69hGJ45BsMdl5r7iMGDBys6OlodO3bUnDlzZBz3+PW0tDR16dLFrX/Xrl2VlpYmSSosLNSGDRvc+vj5+alLly6uPhs2bFBRUZFbn5YtW6pJkyauPqg90tLS1Lp1a8XGxrraunbtqtzcXP33v/919SHvUFWTJk3SGWecoQsvvFAvvvii2+WUaWlpuuKKKxQUFORq69q1q3bs2KEDBw64+pwsD3ft2qWMjAy3PvXq1VNiYiI5hjIq8r0FnMpPP/2kRo0aqVmzZurbt6/27NkjqWLHPE8cf1G7VeS4l5aWpsjISHXo0MHVp0uXLvLz89O3337r6lPVYzDcBdgdAE7tmWeeUefOnRUWFqYvv/xSDz/8sA4dOqRHHnlEkpSRkeH2BS1JsbGxys3N1dGjR3XgwAGVlJSU22f79u2ufQQFBZW53zc2NlYZGRnWfTh4pRPlVOm6k/Uh71BRjzzyiC666CLVr19fa9as0ejRo5Wenq4pU6ZIMvPjrLPOctvm+DyMioo6YR4en6fHb1deH6DUvn37Tvm9BZxMYmKi5s2bpxYtWig9PV3jx4/X5Zdfrq1bt1bomOeJ429oaKhFnw6+oCLHvYyMDDVo0MBtfUBAgOrXr+/Wp6rHYLij8LbAqFGjNHny5JP22bZtm9tkGyczduxY1/KFF16ow4cP68UXX3QV3oDk+bwDTkdl8jA5OdnV1qZNGwUFBemBBx7QxIkTFRwcbHWoAOBx1113nWu5TZs2SkxMVNOmTbVo0SIKYqCWo/C2wPDhw3XPPfectE+zZs1Oe/+JiYl69tlnVVBQoODgYMXFxZWZETMzM1MREREKDQ2Vv7+//P39y+0TFxcnSYqLi1NhYaEOHjzo9pfY4/vAu3ky7+Li4srM4luaP8fnDHmHv6pKHiYmJqq4uFi7d+9WixYtTphj0qnz8Pj1pW0NGzZ069OuXbsKfy7UDtHR0af83gIqIzIyUueee65+/vlnXXPNNac85nni+IvarSLHvbi4OGVlZbltV1xcrP37958yz44f41THYLjjHm8LxMTEqGXLlid9HX+/RGV99913ioqKcp0RSkpKUmpqqluf5cuXKykpSZIUFBSk9u3bu/VxOp1KTU119Wnfvr0CAwPd+uzYsUN79uxx9YF382TeJSUlacuWLW5fysuXL1dERITOP/98Vx/yDn9VlTz87rvv5Ofn57r8LSkpSStXrlRRUZGrz/Lly9WiRQtFRUW5+pwsD8866yzFxcW59cnNzdW3335LjqGMinxvAZVx6NAh7dy5Uw0bNqzQMc8Tx1/UbhU57iUlJengwYPasGGDq89XX30lp9OpxMREV5+qHoPxF3bP7lbb/fLLL8amTZuM8ePHG+Hh4camTZuMTZs2GXl5eYZhGMYnn3xizJo1y9iyZYvx008/Ga+99poRFhZmjBs3zrWP0sdKjBgxwti2bZsxY8aMch/rFBwcbMybN8/44YcfjPvvv9+IjIx0mzXzwQcfNJo0aWJ89dVXxvr1642kpCQjKSmp+n4ZqDanyrvSx5lce+21xnfffWcsW7bMiImJKfdxJuQdTseaNWuMqVOnGt99952xc+dO4+233zZiYmKMfv36ufocPHjQiI2NNe6++25j69atxoIFC4ywsLAyjzIJCAgwXnrpJWPbtm1GSkpKuY8Ti4yMND7++GNj8+bNRs+ePXmcGE6oIt9bwIkMHz7cWLFihbFr1y5j9erVRpcuXYzo6GgjKyvLMIxTH/M8dfxFzZaXl+f6t5skY8qUKcamTZuMX375xTCMih33unXrZlx44YXGt99+a/z73/82mjdv7vY4MU8dg3EMhbfN+vfvb0gq8/r6668NwzCn9m/Xrp0RHh5u1KlTx2jbtq0xc+ZMo6SkxG0/X3/9tdGuXTsjKCjIaNasmTF37twyY02fPt1o0qSJERQUZHTs2NFYu3at2/qjR48aDz/8sBEVFWWEhYUZN910k5Genm7VR4eNTpV3hmEYu3fvNq677jojNDTUiI6ONoYPH24UFRW57Ye8w+nasGGDkZiYaNSrV88ICQkxzjvvPGPChAlGfn6+W7/vv//euOyyy4zg4GCjcePGxqRJk8rsa9GiRca5555rBAUFGa1atTI+//xzt/VOp9MYO3asERsbawQHBxtXX321sWPHDks/H3zbqb63gBPp3bu30bBhQyMoKMho3Lix0bt3b+Pnn392ra/IMc9Tx1/UXF9//XW5/47r37+/YRgVO+798ccfRp8+fYzw8HAjIiLCGDBggOsETClPHINxjMMwjnsuFQAAAAAA8Cju8QYAAAAAwEIU3gAAAAAAWIjCGwAAAAAAC1F4AwAAAABgIQpvAAAAAAAsROENAAAAAICFKLwBAAAAALAQhTcAAAAAABai8AYAACd11VVX6dFHH7U7DAAAfBaFNwAANdiNN96obt26lbtu1apVcjgc2rx5czVHBQBA7ULhDQBADTZw4EAtX75cv/32W5l1c+fOVYcOHdSmTRsbIgMAoPag8AYAoAbr3r27YmJiNG/ePLf2Q4cO6f3331evXr3Up08fNW7cWGFhYWrdurXee++9k+7T4XBoyZIlbm2RkZFuY/z666+6/fbbFRkZqfr166tnz57avXu3Zz4UAAA+hsIbAIAaLCAgQP369dO8efNkGIar/f3331dJSYnuuusutW/fXp9//rm2bt2q+++/X3fffbfWrVt32mMWFRWpa9euqlu3rlatWqXVq1crPDxc3bp1U2FhoSc+FgAAPoXCGwCAGu7ee+/Vzp079c0337ja5s6dq1tuuUVNmzbV448/rnbt2qlZs2YaOnSounXrpkWLFp32eAsXLpTT6dTs2bPVunVrnXfeeZo7d6727NmjFStWeOATAQDgWyi8AQCo4Vq2bKlOnTppzpw5kqSff/5Zq1at0sCBA1VSUqJnn31WrVu3Vv369RUeHq4vvvhCe/bsOe3xvv/+e/3888+qW7euwsPDFR4ervr16ys/P187d+701McCAMBnBNgdAAAAsN7AgQM1dOhQzZgxQ3PnztXZZ5+tK6+8UpMnT9b//d//adq0aWrdurXq1KmjRx999KSXhDscDrfL1iXz8vJShw4dUvv27fXOO++U2TYmJsZzHwoAAB9B4Q0AQC1w++23a9iwYXr33Xc1f/58PfTQQ3I4HFq9erV69uypu+66S5LkdDr1448/6vzzzz/hvmJiYpSenu56/9NPP+nIkSOu9xdddJEWLlyoBg0aKCIiwroPBQCAj+BScwAAaoHw8HD17t1bo0ePVnp6uu655x5JUvPmzbV8+XKtWbNG27Zt0wMPPKDMzMyT7qtz58569dVXtWnTJq1fv14PPvigAgMDXev79u2r6Oho9ezZU6tWrdKuXbu0YsUKPfLII+U+1gwAgJqOwhsAgFpi4MCBOnDggLp27apGjRpJksaMGaOLLrpIXbt21VVXXaW4uDj16tXrpPt5+eWXFR8fr8svv1x33nmnHn/8cYWFhbnWh4WFaeXKlWrSpIluvvlmnXfeeRo4cKDy8/M5Aw4AqJUcxl9v0gIAAAAAAB7DGW8AAAAAACxE4Q0AAAAAgIUovAEAAAAAsBCFNwAAAAAAFqLwBgAAAADAQhTeAAAAAABYiMIbAAAAAAALUXgDAAAAAGAhCm8AAAAAACxE4Q0AAAAAgIUovAEAAAAAsND/A5RdpTXsRvl+AAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Intervallo di Confidenza 80.0%:\n",
|
||
"Range: [-2161.45, 2117.68]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 85.0%:\n",
|
||
"Range: [-2696.34, 2117.68]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 90.0%:\n",
|
||
"Range: [-3231.23, 2652.57]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-4301.02, 3187.46]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-6440.58, 4792.14]\n",
|
||
"\n",
|
||
"2. IMPORTANZA DELLE FEATURE\n",
|
||
"--------------------------------------------------\n",
|
||
"18750/18750 [==============================] - 187s 10ms/step\n",
|
||
"18750/18750 [==============================] - 186s 10ms/step\n",
|
||
"18750/18750 [==============================] - 186s 10ms/step\n",
|
||
"18750/18750 [==============================] - 189s 10ms/step\n",
|
||
"18750/18750 [==============================] - 184s 10ms/step\n",
|
||
"\n",
|
||
"Importanza relativa delle feature:\n",
|
||
"ha: 0.8679\n",
|
||
"precip_sum: 0.0541\n",
|
||
"solar_energy_sum: 0.0431\n",
|
||
"temp_mean: 0.0349\n",
|
||
"\n",
|
||
"3. ANALISI DISTRIBUZIONALE\n",
|
||
"--------------------------------------------------\n",
|
||
"18750/18750 [==============================] - 181s 10ms/step\n",
|
||
"\n",
|
||
"Analisi distribuzionale per olive_prod\n",
|
||
"\n",
|
||
"Statistiche Predizioni:\n",
|
||
"mean: 29887.219\n",
|
||
"variance: 257054016.000\n",
|
||
"std: 16032.904\n",
|
||
"min: 2732.547\n",
|
||
"max: 87850.508\n",
|
||
"median: 28077.900\n",
|
||
"\n",
|
||
"Statistiche Target Reali:\n",
|
||
"mean: 29870.586\n",
|
||
"variance: 270589536.000\n",
|
||
"std: 16449.605\n",
|
||
"min: 2030.459\n",
|
||
"max: 99272.031\n",
|
||
"median: 27900.719\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Analisi distribuzionale per min_oil_prod\n",
|
||
"\n",
|
||
"Statistiche Predizioni:\n",
|
||
"mean: 5911.217\n",
|
||
"variance: 11063664.000\n",
|
||
"std: 3326.208\n",
|
||
"min: 552.748\n",
|
||
"max: 19390.480\n",
|
||
"median: 5451.560\n",
|
||
"\n",
|
||
"Statistiche Target Reali:\n",
|
||
"mean: 5921.921\n",
|
||
"variance: 11676302.000\n",
|
||
"std: 3417.060\n",
|
||
"min: 374.026\n",
|
||
"max: 22359.766\n",
|
||
"median: 5426.787\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Analisi distribuzionale per max_oil_prod\n",
|
||
"\n",
|
||
"Statistiche Predizioni:\n",
|
||
"mean: 7153.816\n",
|
||
"variance: 16302536.000\n",
|
||
"std: 4037.640\n",
|
||
"min: 664.406\n",
|
||
"max: 24333.047\n",
|
||
"median: 6596.512\n",
|
||
"\n",
|
||
"Statistiche Target Reali:\n",
|
||
"mean: 7156.977\n",
|
||
"variance: 17168200.000\n",
|
||
"std: 4143.453\n",
|
||
"min: 458.719\n",
|
||
"max: 27636.207\n",
|
||
"median: 6558.154\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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gnn/+edWsWVN+fn7atGmTnnrqqXzfuXG+SpUqOa3Pnj3b5QB/58upvVyqHeXnerKm5Ttw4ICOHDmisLCwS11SsWLF7xYAgGsk3QCQD1nzP0dHR1+0noeHh9q3b6/27dtr4sSJeumll/TMM89o7dq1ioqKumivcX7s3LnTad0Yo127djnNJ16uXLlsI4lLmT1Y598KK2VOsbVs2TItWbJE9erVu+T5q1WrJknavn2707HS0tIUFxfnSArzombNmvrtt9/Uvn37An+9Fi9erBo1amjJkiVOxx4zZkyu9g8JCZGfn5927dqVbduFZTVr1pQxRtWrV3f0PuakIK6zS5cuatu2rV566SUNHjxYpUqVUs2aNbV69Wq1atXqoknXunXrdOTIES1ZskRt2rRxlMfFxV12XFliYmKc1hs0aFBgx75QXq9n5syZiomJ0fjx4zVhwgQNHjxYn376aa7PFxwcrICAAG3fvj3btm3btsnDw+Oy/lmUkwMHDujUqVNOvd07duyQpEuOgH/+z27WoxtZtm/f7tie9fXC3zVZ9QAA2XF7OQDk0ddff60XXnhB1atX1913351jvQunLJLkGA07a6qrrA/HrpLg/Pjggw+cnjNfvHixDh486PR8cc2aNfXjjz8qLS3NUbZ8+fJst7uuXr1ao0eP1jPPPKNu3brl6vxRUVHy8fHRm2++6dRb/e677yopKUldunTJ8zX17NlT+/fv1//+979s206fPq1Tp07l+ZhZsnpIz4/1p59+UmxsbK73j4qK0rJly3TgwAFH+a5du/Tll1861e3evbs8PT01btw4p/Nlnf/IkSOO9VKlShXILdxPPfWUjhw54njtevbsqYyMDL3wwgvZ6qanpzvaoavXJS0tTdOnT7/smLJERUU5LRf2fBekvFxPXFycRowYoTvuuEOjRo3S66+/rs8++0wffPBBns7XoUMHffrpp063dR86dEjz5s1T69atC/yuESnzPXz77bcd62lpaXr77bcVHByspk2bXnTfZs2aKSQkRDNnznSaiu/LL7/U1q1bHT+7lSpVUpMmTfT+++87tdGYmBjHFHoAAGf0dAPARXz55Zfatm2b0tPTdejQIX399deKiYlRtWrV9NlnnzkNOHSh559/XuvXr1eXLl1UrVo1JSQkaPr06apSpYpat24tKTMBDgoK0syZM1WmTBmVKlVKLVq0yPa8ZW6VL19erVu31oABA3To0CFNnjxZtWrVcprW7P7779fixYvVsWNH9ezZU7t379ZHH32kmjVrOh2rT58+Cg4OVu3atZ3mI5cyB01yNX1ZcHCwRo4cqXHjxqljx4667bbbtH37dk2fPl3XXXed06BpuXXvvfdq4cKF+u9//6u1a9eqVatWysjI0LZt27Rw4UKtWrUqT4Ncne+WW27RkiVLdPvtt6tLly6Ki4vTzJkzdfXVV2ebLi0nY8eO1VdffaVWrVrpwQcfVEZGhqZOnaqGDRtq8+bNjno1a9bUiy++qJEjR2rPnj3q1q2bypQpo7i4OC1dulSDBg3SE088IUlq2rSpFixYoOHDh+u6665T6dKldeutt+b5+jp16qSGDRtq4sSJGjJkiNq2bavBgwdrwoQJ2rx5szp06CBvb2/t3LlTixYt0pQpU3TnnXeqZcuWKleunPr166dhw4bJZrPpww8/zPbPguIit9djjNF9990nf39/zZgxQ1LmFGuffPKJHnnkEUVFRTkNmHcxL774omJiYtS6dWs99NBD8vLy0ttvv63U1FSXc9EXhPDwcL3yyivas2eP6tSpowULFmjz5s2aNWuW01R9rnh7e+uVV17RgAED1LZtW/Xp08cxZVhERIQee+wxR90JEyaoS5cuat26te677z4dPXpUb731lho0aJDrnxsAuKK4Y8h0ACjqsqbCylp8fHxMWFiYufnmm82UKVOcpuXKcuGUYWvWrDFdu3Y14eHhxsfHx4SHh5s+ffqYHTt2OO336aefmquvvtoxxVTW9GFt27Y1DRo0cBlfTlOGffzxx2bkyJEmJCTE+Pv7my5dupi9e/dm2/+NN94wlStXNr6+vqZVq1Zmw4YN2Y55/vVfuGRNb3ThlGFZpk6daurVq2e8vb1NaGioefDBB82xY8eyXYOr63M1XVJaWpp55ZVXTIMGDYyvr68pV66cadq0qRk3bpxJSkpy+Rq5cuGUYXa73bz00kumWrVqxtfX1/znP/8xy5cvzxZD1pRVr732msvjrlmzxvznP/8xPj4+pmbNmuadd94xjz/+uPHz88tW95NPPjGtW7c2pUqVMqVKlTL16tUzQ4YMMdu3b3fUOXnypLnrrrtMUFBQrqZhqlatmunSpYvLbXPmzMk2Ld2sWbNM06ZNjb+/vylTpoxp1KiRefLJJ82BAwccdb7//ntz/fXXG39/fxMeHm6efPJJs2rVqlxNbyWLpgy78PXPaveLFi1yKnc15V9urmfKlClGkvnkk0+cjrdv3z4TGBhoOnfunKe4N23aZKKjo03p0qVNQECAufHGG80PP/zg8hoKYsqwBg0amA0bNpjIyEjj5+dnqlWrZqZOneryfBe+ZlkWLFhg/vOf/xhfX19Tvnx5c/fdd5t///03W71PPvnE1K9f3/j6+pqrr77aLFmyJF9xA8CVwGZMMf23NQAARVi3bt30119/uXz2FSho7dq10+HDh/Xnn3+6OxQAwAV4phsAgMt04XRXO3fu1IoVK9SuXTv3BAQAAIoMnukGAOAy1ahRQ/3793fMRT5jxgz5+PjoySefdHdoKGAnT5685HPLwcHBuZoSMDeOHj3qNOjhhTw9PR1TvQEAiiaSbgAALlPHjh318ccfKz4+Xr6+voqMjNRLL72k2rVruzs0FLDXX39d48aNu2iduLi4S07RlVvdu3fXN998k+P2atWqOY2QDgAoenimGwAAIJf+/vtv/f333xet07p164vObJAXGzdu1LFjx3Lc7u/vr1atWhXIuQAA1iDpBgAAAADAIgykBgAAAACARUi6AQAAAACwCEk3AAAAAAAWIekGAAAAAMAiJN0AAAAAAFiEpBsAAAAAAIuQdAMAAAAAYBGSbgAAAAAALELSDQAAAACARUi6AQAAAACwCEk3AAAAAAAWIekGAAAAAMAiJN0AAAAAAFiEpBsAAAAAAIuQdAMAAAAAYBGSbgAAAAAALELSDQAAAACARUi6AQAAAACwCEk3AAAAAAAWIekGAAAAAMAiJN0AAAAAAFiEpBsAAAAAAIuQdAMAAAAAYBGSbgAAAAAALELSDQAAAACARUi6AQAAAACwCEk3AAAAAAAWIekGAAAAAMAiJN0AAAAAAFiEpBsAAAAAAIuQdAMAAAAAYBGSbgAAAAAALELSDQAAAACARUi6AQAAAACwCEk3AAAAAAAWIekGAAAAAMAiJN0AAAAAAFiEpBsAAAAAAIuQdAMAAAAAYBGSbgAAAAAALELSDQAAAACARUi6AQAAAACwCEk3AAAAAAAWIekGAAAAAMAiJN0AgCuSzWbT0KFDC+x4c+bMkc1m04YNGy5Zt127dmrXrp1jfc+ePbLZbJozZ46jbOzYsbLZbAUWH4qOC99/AEDJRtINACgyshLXrMXPz0916tTR0KFDdejQIXeH53YvvfSSli1bVqDHXLduneP1/uijj1zWadWqlWw2mxo2bFig5y4I57eX85ewsDC3xrVlyxaNHTtWe/bscWscAAD383J3AAAAXOj5559X9erVdebMGX333XeaMWOGVqxYoT///FMBAQHuDu+yffXVV5esM3r0aD399NNOZS+99JLuvPNOdevWrcBj8vPz07x583TPPfc4le/Zs0c//PCD/Pz8CvycBeXmm29W3759ncr8/f3dFE2mLVu2aNy4cWrXrp0iIiKctuXm/QcAlBwk3QCAIqdTp05q1qyZJOn+++9XhQoVNHHiRH366afq06ePy31OnTqlUqVKFWaY+ebj43PJOl5eXvLyKrw/0507d9Znn32mw4cPq2LFio7yefPmKTQ0VLVr19axY8cKLZ68qFOnTrZ/FhRluXn/AQAlB7eXAwCKvJtuukmSFBcXJ0nq37+/Spcurd27d6tz584qU6aM7r77bkmZyffjjz+uqlWrytfXV3Xr1tXrr78uY4zLY8+dO1d169aVn5+fmjZtqvXr1ztt37t3rx566CHVrVtX/v7+qlChgnr06JHjbcMpKSkaPHiwKlSooMDAQPXt2zdbspqbZ3ovfKbbZrPp1KlTev/99x23UPfv319r166VzWbT0qVLsx1j3rx5stlsio2Nvei5JKlr167y9fXVokWLsh2jZ8+e8vT0zLbP7NmzddNNNykkJES+vr66+uqrNWPGjGz1NmzYoOjoaFWsWFH+/v6qXr267rvvPqc68+fPV9OmTVWmTBkFBgaqUaNGmjJlyiXjvpT+/ftn62mWXD8zn/Wc/7Jly9SwYUP5+vqqQYMGWrlyZbb99+/fr4EDByo8PFy+vr6qXr26HnzwQaWlpWnOnDnq0aOHJOnGG290vF/r1q2T5Pr9T0hI0MCBAxUaGio/Pz81btxY77//vlOdrGf/X3/9dc2aNUs1a9aUr6+vrrvuOv3yyy/5f5EAAJaipxsAUOTt3r1bklShQgVHWXp6uqKjo9W6dWu9/vrrCggIkDFGt912m9auXauBAweqSZMmWrVqlUaMGKH9+/dr0qRJTsf95ptvtGDBAg0bNky+vr6aPn26OnbsqJ9//tnx/PIvv/yiH374Qb1791aVKlW0Z88ezZgxQ+3atdOWLVuy3e4+dOhQBQUFaezYsdq+fbtmzJihvXv3Op6dzq8PP/xQ999/v5o3b65BgwZJkmrWrKnrr79eVatW1dy5c3X77bc77TN37lzVrFlTkZGRlzx+QECAunbtqo8//lgPPvigJOm3337TX3/9pXfeeUe///57tn1mzJihBg0a6LbbbpOXl5c+//xzPfTQQ7Lb7RoyZIikzGSyQ4cOCg4O1tNPP62goCDt2bNHS5YscRwnJiZGffr0Ufv27fXKK69IkrZu3arvv/9ejzzyyCVjP3PmjA4fPuxUVqZMGfn6+l5y3wt99913WrJkiR566CGVKVNGb775pu644w7t27fP0f4OHDig5s2b6/jx4xo0aJDq1aun/fv3a/HixUpJSVGbNm00bNgwvfnmmxo1apTq168vSY6vFzp9+rTatWunXbt2aejQoapevboWLVqk/v376/jx49leg3nz5unEiRMaPHiwbDabXn31VXXv3l1///23vL2983zNAACLGQAAiojZs2cbSWb16tUmMTHR/PPPP2b+/PmmQoUKxt/f3/z777/GGGP69etnJJmnn37aaf9ly5YZSebFF190Kr/zzjuNzWYzu3btcpRJMpLMhg0bHGV79+41fn5+5vbbb3eUpaSkZIszNjbWSDIffPBBttibNm1q0tLSHOWvvvqqkWQ+/fRTR1nbtm1N27ZtHetxcXFGkpk9e7ajbMyYMebCP9OlSpUy/fr1yxbPyJEjja+vrzl+/LijLCEhwXh5eZkxY8Zkq3++tWvXGklm0aJFZvny5cZms5l9+/YZY4wZMWKEqVGjhiPmBg0aOO3r6rWJjo527GOMMUuXLjWSzC+//JJjDI888ogJDAw06enpF43Vlaz38cIl67Xs16+fqVatWrb9XL2+koyPj49TO/ntt9+MJPPWW285yvr27Ws8PDxcXpPdbjfGGLNo0SIjyaxduzZbnQvf/8mTJxtJ5qOPPnKUpaWlmcjISFO6dGmTnJxsjDnXTipUqGCOHj3qqPvpp58aSebzzz/P+YUCALgNt5cDAIqcqKgoBQcHq2rVqurdu7dKly6tpUuXqnLlyk71snpks6xYsUKenp4aNmyYU/njjz8uY4y+/PJLp/LIyEg1bdrUsX7VVVepa9euWrVqlTIyMiQ5D8h19uxZHTlyRLVq1VJQUJA2bdqULfZBgwY59TY++OCD8vLy0ooVK/L4KuRe3759lZqaqsWLFzvKFixYoPT09Dw969yhQweVL19e8+fPlzFG8+fPz/EZesn5tUlKStLhw4fVtm1b/f3330pKSpIkBQUFSZKWL1+us2fPujxOUFCQTp06pZiYmFzHer6uXbsqJibGaYmOjs7XsaKiolSzZk3H+jXXXKPAwED9/fffkiS73a5ly5bp1ltvdYw7cL783M2wYsUKhYWFOb3W3t7eGjZsmE6ePKlvvvnGqX6vXr1Urlw5x/oNN9wgSY4YAQBFC7eXAwCKnGnTpqlOnTry8vJSaGio6tatKw8P5/8Te3l5qUqVKk5le/fuVXh4uMqUKeNUnnVb7969e53Ka9eune3cderUUUpKihITExUWFqbTp09rwoQJmj17tvbv3+/0bHhWYnmxY5YuXVqVKlWydOqoevXq6brrrtPcuXM1cOBASZm3ll9//fWqVatWro/j7e2tHj16aN68eWrevLn++ecf3XXXXTnW//777zVmzBjFxsYqJSXFaVtSUpLKli2rtm3b6o477tC4ceM0adIktWvXTt26ddNdd93luP37oYce0sKFC9WpUydVrlxZHTp0UM+ePdWxY8dcxV2lShVFRUXl+jov5qqrrspWVq5cOcdz+YmJiUpOTi7Q6dP27t2r2rVrZ2vjObXbC2PMSsCL6kB3AHClo6cbAFDkNG/eXFFRUWrXrp3q16+fLRmRJF9fX5flBe3hhx/W+PHj1bNnTy1cuFBfffWVYmJiVKFCBdntdsvPn1t9+/bVN998o3///Ve7d+/Wjz/+mK8Rve+66y5t3rxZY8eOVePGjXX11Ve7rLd79261b99ehw8f1sSJE/XFF18oJiZGjz32mCQ5XhubzabFixcrNjZWQ4cO1f79+3XfffepadOmOnnypCQpJCREmzdv1meffeZ4Jr9Tp07q169fPl+Nc3Lqec66k+FCrgaMk5TjQHzuUBxiBACcQ9INACgxqlWrpgMHDujEiRNO5du2bXNsP9/OnTuzHWPHjh0KCAhQcHCwJGnx4sXq16+f3njjDd155526+eab1bp1ax0/ftxlDBce8+TJkzp48KDLEbTz6mK3Lvfu3Vuenp76+OOPNXfuXHl7e6tXr155Pkfr1q111VVXad26dRft5f7888+Vmpqqzz77TIMHD1bnzp0VFRWV4/zY119/vcaPH68NGzZo7ty5+uuvvzR//nzHdh8fH916662aPn26du/ercGDB+uDDz7Qrl278nwN5ytXrpzL9+rC3uPcCg4OVmBgoP7888+L1svLbebVqlXTzp07s/0TJ6d2CwAoXki6AQAlRufOnZWRkaGpU6c6lU+aNEk2m02dOnVyKo+NjXV6Lvuff/7Rp59+qg4dOjh6Ez09PbP1IL711ls59pTOmjXL6dnlGTNmKD09Pdu586NUqVI5JvsVK1ZUp06d9NFHH2nu3Lnq2LGj03zbuWWz2fTmm29qzJgxuvfee3Osl/X6XHi7/ezZs53qHTt2LNvr16RJE0lSamqqJOnIkSNO2z08PHTNNdc41cmvmjVrKikpyWn09YMHD7qcYi03PDw81K1bN33++efasGFDtu1Z15o1Z3xO79f5OnfurPj4eC1YsMBRlp6errfeekulS5dW27Zt8xUrAKBo4JluAECJceutt+rGG2/UM888oz179qhx48b66quv9Omnn+rRRx91GiBLkho2bKjo6GinKcMkady4cY46t9xyiz788EOVLVtWV199tWJjY7V69Wqn6cvOl5aWpvbt26tnz57avn27pk+frtatW+u222677Otr2rSpVq9erYkTJyo8PFzVq1dXixYtHNv79u2rO++8U5L0wgsv5Ps8Xbt2VdeuXS9ap0OHDo7e6cGDB+vkyZP63//+p5CQEB08eNBR7/3339f06dN1++23q2bNmjpx4oT+97//KTAwUJ07d5Yk3X///Tp69KhuuukmValSRXv37tVbb72lJk2a5DjNVm717t1bTz31lG6//XYNGzZMKSkpmjFjhurUqeNyILzceOmll/TVV1+pbdu2GjRokOrXr6+DBw9q0aJF+u677xQUFKQmTZrI09NTr7zyipKSkuTr6+uY0/xCgwYN0ttvv63+/ftr48aNioiI0OLFi/X9999r8uTJ2cYoAAAULyTdAIASw8PDQ5999pmee+45LViwQLNnz1ZERIRee+01Pf7449nqt23bVpGRkRo3bpz27dunq6++WnPmzHH0skrSlClT5Onpqblz5+rMmTNq1aqVVq9enePo2FOnTtXcuXP13HPP6ezZs+rTp4/efPPNy5qjO8vEiRM1aNAgjR49WqdPn1a/fv2cku5bb71V5cqVk91uL5Ak/2Lq1q2rxYsXa/To0XriiScUFhamBx98UMHBwbrvvvsc9dq2bauff/5Z8+fP16FDh1S2bFk1b95cc+fOVfXq1SVJ99xzj2bNmqXp06fr+PHjCgsLU69evTR27NjLfm6/QoUKWrp0qYYPH64nn3xS1atX14QJE7Rz5858J92VK1fWTz/9pGeffVZz585VcnKyKleurE6dOjnmbQ8LC9PMmTM1YcIEDRw4UBkZGVq7dq3LpNvf31/r1q3T008/rffff1/JycmqW7euZs+erf79+1/O5QMAigCbYdQNAABKhPT0dIWHh+vWW2/Vu+++6+5wAACAeKYbAIASY9myZUpMTFTfvn3dHQoAAPh/9HQDAFDM/fTTT/r999/1wgsvqGLFivm+bRoAABQ8eroBACjmZsyYoQcffFAhISH64IMP3B0OAAA4Dz3dAAAAAABYhJ5uAAAAAAAsQtINAAAAAIBFmKfbBbvdrgMHDqhMmTIFMq8qAAAAAKBkMcboxIkTCg8Pl4dHzv3ZJN0uHDhwQFWrVnV3GAAAAACAIu6ff/5RlSpVctxO0u1CmTJlJGW+eIGBgZaey263KzExUcHBwRf97wiQX7QxWIn2BSvRvmAl2hesRPu6MiQnJ6tq1aqO/DEnJN0uZN1SHhgYWChJ95kzZxQYGMgPJCxBG4OVaF+wEu0LVqJ9wUq0ryvLpR5JpgUAAAAAAGARkm4AAAAAACxC0g0AAAAAgEV4phsAAAAArhAZGRk6e/asu8MoFjw9PeXl5XXZ00iTdAMAAADAFeDkyZP6999/ZYxxdyjFRkBAgCpVqiQfH598H4OkGwAAAABKuIyMDP37778KCAhQcHDwZffelnTGGKWlpSkxMVFxcXGqXbt2vkeiJ+kGAAAAgBLu7NmzMsYoODhY/v7+7g6nWPD395e3t7f27t2rtLQ0+fn55es4DKQGAAAAAFcIerjzpiDmWSfpBgAAAADAIiTdAAAAAABYhGe6AQAAAOAKNSlmR6Ge77Gb6xTq+YoCkm6UCJf7y+JK/OEHAAAAirr+/fvr/ffflyR5e3vrqquuUt++fTVq1Ch99913uvHGGxUUFKSDBw86DXT2yy+/qHnz5pLkmCJt3bp1uvHGG7Od45lnntGLL75o2TWQdAMqmP/wXW7iXhRiAAAAAIqajh07avbs2UpNTdWKFSs0ZMgQeXt7KzIyUpJUpkwZLV26VH369HHs8+677+qqq67Svn37sh1v+/btCgwMdKyXLl3a0vhJuoECUti35uQ6BmMUkHFSKZ5J0iVGqyRpBwAAQFHj6+ursLAwSdKDDz6opUuX6rPPPnMk3f369dN7773nSLpPnz6t+fPna9iwYXrhhReyHS8kJERBQUGFFn+RGEht2rRpioiIkJ+fn1q0aKGff/75ovUXLVqkevXqyc/PT40aNdKKFSuctvfv3182m81p6dixo5WXAAAAAAAoBP7+/kpLS3Os33vvvfr2228dvdqffPKJIiIidO2117orRCduT7oXLFig4cOHa8yYMdq0aZMaN26s6OhoJSQkuKz/ww8/qE+fPho4cKB+/fVXdevWTd26ddOff/7pVK9jx446ePCgY/n4448L43IAAAAAABYwxmj16tVatWqVbrrpJkd5SEiIOnXqpDlz5kiS3nvvPd133305HqdKlSoqXbq0Yzly5Iilcbv99vKJEyfqgQce0IABAyRJM2fO1BdffKH33ntPTz/9dLb6U6ZMUceOHTVixAhJ0gsvvKCYmBhNnTpVM2fOdNQ7/xaES0lNTVVqaqpjPTk5WZJkt9tlt9vzfW25YbfbZYyx/DxF2ZTVO90dQslmzLnlEq7kdoj84XcYrET7gpVoX7BSUWxfWTFlLedc+jNiQTK5+Ex6oeXLl6t06dI6e/as7Ha77rrrLo0ZM0a//PKL45gDBgzQo48+qrvvvluxsbFauHChvv32W6dzZn1dv369ypQp4zh+UFBQjnFlvV6ucsPcvr9uTbrT0tK0ceNGjRw50lHm4eGhqKgoxcbGutwnNjZWw4cPdyqLjo7WsmXLnMrWrVunkJAQlStXTjfddJNefPFFVahQweUxJ0yYoHHjxmUrT0xM1JkzZ/J4VXljt9uVlJQkY4w8PNx+44FbBGScdHcIJZyRrzkj2SXp4s9053SHCZATfofBSrQvWIn2BSsVxfaVlbCmp6crPT3dUV7Y/xg4/9y5Ybfb1a5dO7311lvy8fFReHi4vLwy09iMjAzHMW+++WadPn1aAwcOVJcuXVS2bFmn7efXr1q1qtMz3RfrbE1PT5fdbteRI0fk7e3ttO3EiRO5uga3Jt2HDx9WRkaGQkNDncpDQ0O1bds2l/vEx8e7rB8fH+9Y79ixo7p3767q1atr9+7dGjVqlDp16qTY2Fh5enpmO+bIkSOdEvnk5GRVrVpVwcHBTqPaWcFut8tmsyk4OLjI/EAWthTPJHeHULIZIxkpxaP0JQdSCwkJKaSgUFLwOwxWon3BSrQvWKkotq8zZ87oxIkT8vLyciStkgo9vvPPnRseHh4qXbq06tWrl21bVm7n5eUlPz8/3XvvvXrttde0YsUKeXl5OW2/sH5u4/Dy8pKHh4cqVKjgNCWZpGzrOR4jV7WKmd69ezu+b9Soka655hrVrFlT69atU/v27bPV9/X1la+vb7ZyDw+PQmmENput0M5VJF0iEUQBsNnOLRdxxbZBXJYr/ncYLEX7gpVoX7BSUWtfHh4eTgNNn1O4n8Vt+fzs72q/rLKsa3rxxRf15JNPqkKFCk7X6eprbuPIquvqvczte+vWpLtixYry9PTUoUOHnMoPHTqU4/PYYWFheaovSTVq1FDFihW1a9cul0k3gEzMFQ4AAHBlKUmf3Xx8fFSxYkV3h5GNW//t4uPjo6ZNm2rNmjWOMrvdrjVr1jjmXLtQZGSkU31JiomJybG+JP377786cuSIKlWqVDCBAwAAAAAsN2fOnGzjd2Vp166djDE5zrndrVs3pwHSLlXfKm6/vXz48OHq16+fmjVrpubNm2vy5Mk6deqUYzTzvn37qnLlypowYYIk6ZFHHlHbtm31xhtvqEuXLpo/f742bNigWbNmSZJOnjypcePG6Y477lBYWJh2796tJ598UrVq1VJ0dLTbrhO4Ulxub3lJ+m8rAAAA4Paku1evXkpMTNRzzz2n+Ph4NWnSRCtXrnQMlrZv3z6ne+VbtmypefPmafTo0Ro1apRq166tZcuWqWHDhpIyH47//fff9f777+v48eMKDw9Xhw4d9MILL7h8bhsAAAAAAKu4PemWpKFDh2ro0KEut61bty5bWY8ePdSjRw+X9f39/bVq1aqCDA8AAAAAgHwpGkPpAQAAAABQApF0AwAAAMAV4vyBxXBpBfF6kXQDAAAAQAnn6ekpSUpLS3NzJMVLSkqKJMnb2zvfxygSz3QDAAAAAKzj5eWlgIAAJSYmytvb22mwamRnjFFKSooSEhIUFBTk+KdFfpB047Jc7vRQwIUKok0x7RgAAIAzm82mSpUqKS4uTnv37nV3OMVGUFCQwsLCLusYJN0AAAAAcAXw8fFR7dq1ucU8l7y9vS+rhzsLSTcAAAAAXCE8PDzk5+fn7jCuKCTdAEqcy71FndvTAQAAUFB4eh4AAAAAAIuQdAMAAAAAYBGSbgAAAAAALELSDQAAAACARUi6AQAAAACwCEk3AAAAAAAWIekGAAAAAMAiJN0AAAAAAFiEpBsAAAAAAIuQdAMAAAAAYBGSbgAAAAAALELSDQAAAACARUi6AQAAAACwiJe7AwCAomZSzI7LPsZjN9cpgEgAAABQ3NHTDQAAAACARUi6AQAAAACwCEk3AAAAAAAWIekGAAAAAMAiJN0AAAAAAFiEpBsAAAAAAIuQdAMAAAAAYBGSbgAAAAAALELSDQAAAACARUi6AQAAAACwCEk3AAAAAAAWIekGAAAAAMAiJN0AAAAAAFiEpBsAAAAAAIuQdAMAAAAAYBGSbgAAAAAALELSDQAAAACARUi6AQAAAACwCEk3AAAAAAAW8XJ3AABQEk2K2XHZx3js5joFEAkAAADciZ5uAAAAAAAsQtINAAAAAIBFSLoBAAAAALAISTcAAAAAABYpEkn3tGnTFBERIT8/P7Vo0UI///zzResvWrRI9erVk5+fnxo1aqQVK1bkWPe///2vbDabJk+eXMBRAwAAAABwcW5PuhcsWKDhw4drzJgx2rRpkxo3bqzo6GglJCS4rP/DDz+oT58+GjhwoH799Vd169ZN3bp1059//pmt7tKlS/Xjjz8qPDzc6ssAAAAAACAbt08ZNnHiRD3wwAMaMGCAJGnmzJn64osv9N577+npp5/OVn/KlCnq2LGjRowYIUl64YUXFBMTo6lTp2rmzJmOevv379fDDz+sVatWqUuXLheNITU1VampqY715ORkSZLdbpfdbr/sa7wYu90uY4zl57GMMe6OAJdizLkFxUpx+L1Q7H+HoUijfcFKtC9YifZ1Zcjt++vWpDstLU0bN27UyJEjHWUeHh6KiopSbGysy31iY2M1fPhwp7Lo6GgtW7bMsW6323XvvfdqxIgRatCgwSXjmDBhgsaNG5etPDExUWfOnMnl1eSP3W5XUlKSjDHy8HD7jQd5FpBx0t0h4JKMfM0ZyS5JNncHgzzI6Y6foqS4/w5D0Ub7gpVoX7AS7evKcOLEiVzVc2vSffjwYWVkZCg0NNSpPDQ0VNu2bXO5T3x8vMv68fHxjvVXXnlFXl5eGjZsWK7iGDlypFMin5ycrKpVqyo4OFiBgYG5vZx8sdvtstlsCg4OLpY/kCmeSe4OAZdijGSkFI/Sko2kuzgJCQlxdwiXVNx/h6Foo33BSrQvWIn2dWXw8/PLVT23315e0DZu3KgpU6Zo06ZNsuUywfD19ZWvr2+2cg8Pj0L5IbHZbIV2rgJHElc82GznFhQbxeV3QrH+HYYij/YFK9G+YCXaV8mX2/fWrS2gYsWK8vT01KFDh5zKDx06pLCwMJf7hIWFXbT+t99+q4SEBF111VXy8vKSl5eX9u7dq8cff1wRERGWXAcAAAAAAK64Nen28fFR06ZNtWbNGkeZ3W7XmjVrFBkZ6XKfyMhIp/qSFBMT46h/77336vfff9fmzZsdS3h4uEaMGKFVq1ZZdzEAAAAAAFzA7beXDx8+XP369VOzZs3UvHlzTZ48WadOnXKMZt63b19VrlxZEyZMkCQ98sgjatu2rd544w116dJF8+fP14YNGzRr1ixJUoUKFVShQgWnc3h7eyssLEx169Yt3IsDAAAAAFzR3J509+rVS4mJiXruuecUHx+vJk2aaOXKlY7B0vbt2+d0r3zLli01b948jR49WqNGjVLt2rW1bNkyNWzY0F2XAAAAAACAS25PuiVp6NChGjp0qMtt69aty1bWo0cP9ejRI9fH37NnTz4jAwAAAAAg/4pE0g0AyG5SzI7L2v+xm+sUUCQAAADIL8avBwAAAADAIiTdAAAAAABYhKQbAAAAAACLkHQDAAAAAGARkm4AAAAAACxC0g0AAAAAgEVIugEAAAAAsAhJNwAAAAAAFiHpBgAAAADAIiTdAAAAAABYhKQbAAAAAACLkHQDAAAAAGARkm4AAAAAACxC0g0AAAAAgEVIugEAAAAAsAhJNwAAAAAAFiHpBgAAAADAIiTdAAAAAABYxMvdAQAArDEpZsdlH+Oxm+sUQCQAAABXLnq6AQAAAACwCEk3AAAAAAAWIekGAAAAAMAiJN0AAAAAAFiEpBsAAAAAAIuQdAMAAAAAYBGSbgAAAAAALELSDQAAAACARUi6AQAAAACwCEk3AAAAAAAWIekGAAAAAMAiJN0AAAAAAFiEpBsAAAAAAIt4uTsAAEDRNSlmx8UrGKOAjJNK8UySbLZsmx+7uY5FkQEAABQP+erpXrt2bUHHAQAAAABAiZOvpLtjx46qWbOmXnzxRf3zzz8FHRMAAAAAACVCvpLu/fv3a+jQoVq8eLFq1Kih6OhoLVy4UGlpaQUdHwAAAAAAxVa+ku6KFSvqscce0+bNm/XTTz+pTp06euihhxQeHq5hw4bpt99+K+g4AQAAAAAodi579PJrr71WI0eO1NChQ3Xy5Em99957atq0qW644Qb99ddfBREjAAAAAADFUr6T7rNnz2rx4sXq3LmzqlWrplWrVmnq1Kk6dOiQdu3apWrVqqlHjx4FGSsAAAAAAMVKvqYMe/jhh/Xxxx/LGKN7771Xr776qho2bOjYXqpUKb3++usKDw8vsEABAAAAAChu8pV0b9myRW+99Za6d+8uX19fl3UqVqzI1GLFwCXn4AUAAAAA5Fu+bi8fM2aMevTokS3hTk9P1/r16yVJXl5eatu27eVHCAAAAABAMZWvpPvGG2/U0aNHs5UnJSXpxhtvvOygAAAAAAAoCfKVdBtjZLPZspUfOXJEpUqVuuygAAAAAAAoCfL0THf37t0lSTabTf3793e6vTwjI0O///67WrZsWbARAgAAAABQTOWpp7ts2bIqW7asjDEqU6aMY71s2bIKCwvToEGD9NFHH+U5iGnTpikiIkJ+fn5q0aKFfv7554vWX7RokerVqyc/Pz81atRIK1ascNo+duxY1atXT6VKlVK5cuUUFRWln376Kc9xAQAAAABwOfLU0z179mxJUkREhJ544okCuZV8wYIFGj58uGbOnKkWLVpo8uTJio6O1vbt2xUSEpKt/g8//KA+ffpowoQJuuWWWzRv3jx169ZNmzZtckxbVqdOHU2dOlU1atTQ6dOnNWnSJHXo0EG7du1ScHDwZccMAAAAAEBu2Iwxxp0BtGjRQtddd52mTp0qSbLb7apataoefvhhPf3009nq9+rVS6dOndLy5csdZddff72aNGmimTNnujxHcnKyypYtq9WrV6t9+/bZtqempio1NdWpftWqVXXs2DEFBgZe7iVelN1uV2JiooKDg+Xhka9H7C/LlNU7C/2cKGTGKCDjpFI8S0suxmIALkshtK9HompbclwUfe7+G4mSjfYFK9G+rgzJyckqV66ckpKSLpo35rqn+9prr9WaNWtUrlw5/ec//3E5kFqWTZs25eqYaWlp2rhxo0aOHOko8/DwUFRUlGJjY13uExsbq+HDhzuVRUdHa9myZTmeY9asWSpbtqwaN27sss6ECRM0bty4bOWJiYk6c+ZMrq4lv+x2u5KSkmSMccsPZEDGyUI/Jwqbka85I9kliaQbBc369pWQkGDJcVH0uftvJEo22hesRPu6Mpw4cSJX9XKddHft2tUxcFq3bt3yFdSFDh8+rIyMDIWGhjqVh4aGatu2bS73iY+Pd1k/Pj7eqWz58uXq3bu3UlJSVKlSJcXExKhixYoujzly5EinRD6rpzs4OLhQerptNpvb/guW4plU6OdEITNGMlKKBz3dsEAhtC9XjxrhyuDuv5Eo2WhfsBLt68rg5+eXq3q5TrrHjBnj8vui6sYbb9TmzZt1+PBh/e9//1PPnj31008/ufzw5uvr6zQSexYPD49C+SGx2WyFdi4XJy/8c6Lw2WznFqCgWdy++LByZXPr30iUeLQvWIn2VfLl9r11awuoWLGiPD09dejQIafyQ4cOKSwszOU+YWFhuapfqlQp1apVS9dff73effddeXl56d133y3YCwAAAAAA4CJynXSXK1dO5cuXz9WSWz4+PmratKnWrFnjKLPb7VqzZo0iIyNd7hMZGelUX5JiYmJyrH/+cc8fLA0AAAAAAKvl+vbyyZMnWxLA8OHD1a9fPzVr1kzNmzfX5MmTderUKQ0YMECS1LdvX1WuXFkTJkyQJD3yyCNq27at3njjDXXp0kXz58/Xhg0bNGvWLEnSqVOnNH78eN12222qVKmSDh8+rGnTpmn//v3q0aOHJdcAAAAAAIAruU66+/XrZ0kAvXr1UmJiop577jnFx8erSZMmWrlypWOwtH379jndK9+yZUvNmzdPo0eP1qhRo1S7dm0tW7bMMUe3p6entm3bpvfff1+HDx9WhQoVdN111+nbb79VgwYNLLkGAAAAAABcyfU83cnJyY6RvJOTky9a1+oRv62WNa/3peZbKwh2u10JCQkKCQlxyyALk2J2FPo5UciYpxtWKoT29djNdSw5Loo+d/+NRMlG+4KVaF9Xhtzmjbnu6S5XrpwOHjyokJAQBQUFuZyn2xgjm82mjIyM/EUNAAAAAEAJkuuk++uvv3YMkrZ27VrLAgIAAAAAoKTIddLdtm1bl98DAAAAAADXcp10X+jYsWN69913tXXrVknS1VdfrQEDBuRpyjAAAC7lcsee4JlwAADgTvl6qn/9+vWKiIjQm2++qWPHjunYsWN68803Vb16da1fv76gYwQAAAAAoFjKV0/3kCFD1KtXL82YMUOenp6SpIyMDD300EMaMmSI/vjjjwINEgAAAACA4ihfPd27du3S448/7ki4pcz5sYcPH65du3YVWHAAAAAAABRn+Uq6r732Wsez3OfbunWrGjdufNlBAQAAAABQEuT69vLff//d8f2wYcP0yCOPaNeuXbr++uslST/++KOmTZuml19+ueCjBAAAAACgGMp10t2kSRPZbDYZYxxlTz75ZLZ6d911l3r16lUw0QEAAAAAUIzlOumOi4uzMg4AAAAAAEqcXCfd1apVszIOAAAAAABKnHxNGZZly5Yt2rdvn9LS0pzKb7vttssKCgAAAACAkiBfSffff/+t22+/XX/88YfTc942m01S5pzdAAAAAABc6fI1Zdgjjzyi6tWrKyEhQQEBAfrrr7+0fv16NWvWTOvWrSvgEAEAAAAAKJ7y1dMdGxurr7/+WhUrVpSHh4c8PDzUunVrTZgwQcOGDdOvv/5a0HECAJAvk2J2XPYxHru5TgFEAgAArkT56unOyMhQmTJlJEkVK1bUgQMHJGUOtrZ9+/aCiw4AAAAAgGIsXz3dDRs21G+//abq1aurRYsWevXVV+Xj46NZs2apRo0aBR0jAAAAAADFUr6S7tGjR+vUqVOSpOeff1633HKLbrjhBlWoUEELFiwo0AABAAAAACiu8pV0R0dHO76vVauWtm3bpqNHj6pcuXKOEcwBAAAAALjSXdY83ZL0zz//SJKqVq162cEAAAAAAFCS5GsgtfT0dD377LMqW7asIiIiFBERobJly2r06NE6e/ZsQccIAAAAAECxlK+e7ocfflhLlizRq6++qsjISEmZ04iNHTtWR44c0YwZMwo0SAAAAAAAiqN8Jd3z5s3T/Pnz1alTJ0fZNddco6pVq6pPnz4k3QAAAAAAKJ+3l/v6+ioiIiJbefXq1eXj43O5MQEAAAAAUCLkK+keOnSoXnjhBaWmpjrKUlNTNX78eA0dOrTAggMAAAAAoDjL9e3l3bt3d1pfvXq1qlSposaNG0uSfvvtN6Wlpal9+/YFGyEAAAAAAMVUrpPusmXLOq3fcccdTutMGQYAAAAAgLNcJ92zZ8+2Mg4AAAAAAEqcfI1eniUxMVHbt2+XJNWtW1fBwcEFEhQAAAAAACVBvgZSO3XqlO677z5VqlRJbdq0UZs2bRQeHq6BAwcqJSWloGMEAAAAAKBYylfSPXz4cH3zzTf6/PPPdfz4cR0/flyffvqpvvnmGz3++OMFHSMAAAAAAMVSvm4v/+STT7R48WK1a9fOUda5c2f5+/urZ8+emjFjRkHFBwAAAABAsZWvpDslJUWhoaHZykNCQri9HABQ4kyK2XFZ+z92c50CigQAABQ3+bq9PDIyUmPGjNGZM2ccZadPn9a4ceMUGRlZYMEBAAAAAFCc5aune/LkyerYsaOqVKmixo0bS5J+++03+fn5adWqVQUaIAAAAAAAxVW+ku5GjRpp586dmjt3rrZt2yZJ6tOnj+6++275+/sXaIAAAAAAABRXeU66z549q3r16mn58uV64IEHrIgJAAAAAIASIc/PdHt7ezs9yw0AAAAAAFzL10BqQ4YM0SuvvKL09PSCjgcAAAAAgBIjX890//LLL1qzZo2++uorNWrUSKVKlXLavmTJkgIJDgAAAACA4ixfSXdQUJDuuOOOgo4FAAAAAIASJU9Jt91u12uvvaYdO3YoLS1NN910k8aOHcuI5QAAAAAAuJCnZ7rHjx+vUaNGqXTp0qpcubLefPNNDRkyxKrYAAAAAAAo1vKUdH/wwQeaPn26Vq1apWXLlunzzz/X3LlzZbfbrYoPAAAAAIBiK09J9759+9S5c2fHelRUlGw2mw4cOHBZQUybNk0RERHy8/NTixYt9PPPP1+0/qJFi1SvXj35+fmpUaNGWrFihWPb2bNn9dRTTzkGeAsPD1ffvn0vO0YAAAAAAPIqT0l3enq6/Pz8nMq8vb119uzZfAewYMECDR8+XGPGjNGmTZvUuHFjRUdHKyEhwWX9H374QX369NHAgQP166+/qlu3burWrZv+/PNPSVJKSoo2bdqkZ599Vps2bdKSJUu0fft23XbbbfmOEQAAAACA/LAZY0xuK3t4eKhTp07y9fV1lH3++ee66aabnKYNy8uUYS1atNB1112nqVOnSsocrK1q1ap6+OGH9fTTT2er36tXL506dUrLly93lF1//fVq0qSJZs6c6fIcv/zyi5o3b669e/fqqquuumRMycnJKlu2rJKSkhQYGJjra8kPu92uhIQEhYSEyMMjX9OmX5ZJMTsK/ZwoZMYoIOOkUjxLSzabu6NBSUP7ypXHbq7j7hCKJXf/jUTJRvuClWhfV4bc5o15Gr28X79+2cruueeevEf3/9LS0rRx40aNHDnSUebh4aGoqCjFxsa63Cc2NlbDhw93KouOjtayZctyPE9SUpJsNpuCgoJcbk9NTVVqaqpjPTk5WVLmD4vVz6vb7XYZY9z3XHzu/+eC4sqYcwtQ0GhfucLYJ/nj9r+RKNFoX7AS7evKkNv3N09J9+zZs/MVTE4OHz6sjIwMhYaGOpWHhoZq27ZtLveJj493WT8+Pt5l/TNnzuipp55Snz59cvzvw4QJEzRu3Lhs5YmJiTpz5kxuLiXf7Ha7kpKSZIxxy3/BAjJOFvo5UdiMfM0ZyS5J9ESioNG+ciOnR6Zwce7+G4mSjfYFK9G+rgwnTpzIVb08Jd3FzdmzZ9WzZ08ZYzRjxowc640cOdKp9zw5OVlVq1ZVcHBwodxebrPZFBwc7JYfyBTPpEI/JwqZMZKRUjy4/RcWoH3lSkhIiLtDKJbc/TcSJRvtC1aifV0ZLhzvLCduTborVqwoT09PHTp0yKn80KFDCgsLc7lPWFhYrupnJdx79+7V119/fdHk2dfX1+k59SweHh6F8kNis9kK7VwuTl7450Ths9nOLUBBo31dEh+48s+tfyNR4tG+YCXaV8mX2/fWrUm3j4+PmjZtqjVr1qhbt26SMv8rtGbNGg0dOtTlPpGRkVqzZo0effRRR1lMTIwiIyMd61kJ986dO7V27VpVqFDByssAAOCiCmLQSgZjAwCgeHL77eXDhw9Xv3791KxZMzVv3lyTJ0/WqVOnNGDAAElS3759VblyZU2YMEGS9Mgjj6ht27Z644031KVLF82fP18bNmzQrFmzJGUm3Hfeeac2bdqk5cuXKyMjw/G8d/ny5eXj4+OeCwUAAAAAXHHcnnT36tVLiYmJeu655xQfH68mTZpo5cqVjsHS9u3b59Rt37JlS82bN0+jR4/WqFGjVLt2bS1btkwNGzaUJO3fv1+fffaZJKlJkyZO51q7dq3atWtXKNcFAAAAAIDbk25JGjp0aI63k69bty5bWY8ePdSjRw+X9SMiIpSHqccBAAAAALAMT/UDAAAAAGARkm4AAAAAACxC0g0AAAAAgEVIugEAAAAAsAhJNwAAAAAAFiHpBgAAAADAIiTdAAAAAABYhKQbAAAAAACLkHQDAAAAAGARkm4AAAAAACxC0g0AAAAAgEW83B0AAAC4tEkxOy5r/8durlNAkQAAgLygpxsAAAAAAIuQdAMAAAAAYBGSbgAAAAAALELSDQAAAACARUi6AQAAAACwCEk3AAAAAAAWIekGAAAAAMAiJN0AAAAAAFiEpBsAAAAAAIuQdAMAAAAAYBGSbgAAAAAALELSDQAAAACARUi6AQAAAACwCEk3AAAAAAAWIekGAAAAAMAiXu4OAAAAWG9SzI7LPsZjN9cpgEgAALiy0NMNAAAAAIBFSLoBAAAAALAISTcAAAAAABYh6QYAAAAAwCIk3QAAAAAAWISkGwAAAAAAi5B0AwAAAABgEZJuAAAAAAAsQtINAAAAAIBFSLoBAAAAALAISTcAAAAAABYh6QYAAAAAwCJe7g4AAAAUD5Nidlz2MR67uU4BRAIAQPFBTzcAAAAAABYh6QYAAAAAwCIk3QAAAAAAWISkGwAAAAAAi5B0AwAAAABgEbcn3dOmTVNERIT8/PzUokUL/fzzzxetv2jRItWrV09+fn5q1KiRVqxY4bR9yZIl6tChgypUqCCbzabNmzdbGD0AAAAAADlza9K9YMECDR8+XGPGjNGmTZvUuHFjRUdHKyEhwWX9H374QX369NHAgQP166+/qlu3burWrZv+/PNPR51Tp06pdevWeuWVVwrrMgAAAAAAcMmtSffEiRP1wAMPaMCAAbr66qs1c+ZMBQQE6L333nNZf8qUKerYsaNGjBih+vXr64UXXtC1116rqVOnOurce++9eu655xQVFVVYlwEAAAAAgEte7jpxWlqaNm7cqJEjRzrKPDw8FBUVpdjYWJf7xMbGavjw4U5l0dHRWrZs2WXFkpqaqtTUVMd6cnKyJMlut8tut1/WsS/FbrfLGGP5eXJkjHvOi8JjzLkFKGi0L+RRXv7euf1vJEo02hesRPu6MuT2/XVb0n348GFlZGQoNDTUqTw0NFTbtm1zuU98fLzL+vHx8ZcVy4QJEzRu3Lhs5YmJiTpz5sxlHftS7Ha7kpKSZIyRh0fh33gQkHGy0M+Jwmbka85IdkmyuTsYlDi0L+RNTo+QueLuv5Eo2WhfsBLt68pw4sSJXNVzW9JdlIwcOdKpBz05OVlVq1ZVcHCwAgMDLT233W6XzWZTcHCwW34gUzyTCv2cKGTGSEZK8Sgt2UiKUMBoX8ijj3/Pw98dYxSQcUopnjZH+3okqrZFkeFK4+7PYCjZaF9XBj8/v1zVc1vSXbFiRXl6eurQoUNO5YcOHVJYWJjLfcLCwvJUP7d8fX3l6+ubrdzDw6NQfkhsNluhncvFyQv/nCh8Ntu5BShotC9Y6YL2xYdXFCS3fgZDiUf7Kvly+966rQX4+PioadOmWrNmjaPMbrdrzZo1ioyMdLlPZGSkU31JiomJybE+AAAAAADu5Nbby4cPH65+/fqpWbNmat68uSZPnqxTp05pwIABkqS+ffuqcuXKmjBhgiTpkUceUdu2bfXGG2+oS5cumj9/vjZs2KBZs2Y5jnn06FHt27dPBw4ckCRt375dUmYv+eX2iAMAAAAAkBduTbp79eqlxMREPffcc4qPj1eTJk20cuVKx2Bp+/btc+qyb9mypebNm6fRo0dr1KhRql27tpYtW6aGDRs66nz22WeOpF2SevfuLUkaM2aMxo4dWzgXBgAAAACAJJsxzPNyoeTkZJUtW1ZJSUmFMpBaQkKCQkJC3PK8x6SYHYV+ThQyYxSQcVIpngx0BQvQvmAlF+3rsZvruDkolBTu/gyGko32dWXIbd5ICwAAAAAAwCIk3QAAAAAAWISkGwAAAAAAi5B0AwAAAABgEbeOXg4AAJAXBTEAKIOxAQAKEz3dAAAAAABYhKQbAAAAAACLkHQDAAAAAGARkm4AAAAAACxC0g0AAAAAgEUYvRwAAFxRLncEdEY/BwDkBT3dAAAAAABYhKQbAAAAAACLcHt5MXa5t8cBAAAAAKxFTzcAAAAAABYh6QYAAAAAwCIk3QAAAAAAWISkGwAAAAAAizCQGgAAQB4UxECmzPUNAFcOeroBAAAAALAISTcAAAAAABYh6QYAAAAAwCIk3QAAAAAAWISkGwAAAAAAi5B0AwAAAABgEaYMAwAAKGSXO+0YU44BQPFBTzcAAAAAABYh6QYAAAAAwCIk3QAAAAAAWISkGwAAAAAAizCQGgAAQDFzuQOxSQzGBgCFhZ5uAAAAAAAsQtINAAAAAIBFuL0cAADgCsRc4QBQOOjpBgAAAADAIiTdAAAAAABYhNvLAQAAkGeMoA4AuUNPNwAAAAAAFqGnGwAAAG7BYG4ArgT0dAMAAAAAYBF6ugEAAFAsXVZPuTEKyDipB6JDCi4gAHCBnm4AAAAAACxCTzcAAACuWFNW75Rstss6Bs+WA7gYeroBAAAAALAIPd0AAADAZWAUdgAXQ9INAAAAuNHlJu0SiTtQlBWJpHvatGl67bXXFB8fr8aNG+utt95S8+bNc6y/aNEiPfvss9qzZ49q166tV155RZ07d3ZsN8ZozJgx+t///qfjx4+rVatWmjFjhmrXrl0YlwMAAAAUqoJI3C8XiT/gmtuT7gULFmj48OGaOXOmWrRoocmTJys6Olrbt29XSEj2KRx++OEH9enTRxMmTNAtt9yiefPmqVu3btq0aZMaNmwoSXr11Vf15ptv6v3331f16tX17LPPKjo6Wlu2bJGfn19hXyIAAABQ4nGbPeCazRhj3BlAixYtdN1112nq1KmSJLvdrqpVq+rhhx/W008/na1+r169dOrUKS1fvtxRdv3116tJkyaaOXOmjDEKDw/X448/rieeeEKSlJSUpNDQUM2ZM0e9e/e+ZEzJyckqW7askpKSFBgYWEBX6prdbldCQoJCQkLk4ZG3ce2Kwn80UQz8/zykKZ6lL3t0ViAb2hesRPuClWhfsMhjN9e5rM/4KD5ymze6tac7LS1NGzdu1MiRIx1lHh4eioqKUmxsrMt9YmNjNXz4cKey6OhoLVu2TJIUFxen+Ph4RUVFObaXLVtWLVq0UGxsrMukOzU1VampqY71pKQkSdLx48dlt9vzfX25YbfblZycLB8fnzz/QJ45ecKiqFCiGCOb/aTOeBg+VKDg0b5gJdoXrET7gkUmLN0oGSN/+0md9vj3im1fD95Y090hWC45OVlS5uPNF+PWpPvw4cPKyMhQaGioU3loaKi2bdvmcp/4+HiX9ePj4x3bs8pyqnOhCRMmaNy4cdnKq1WrlrsLAQAAAAA4jHJ3AIXoxIkTKlu2bI7b3f5Md1EwcuRIp95zu92uo0ePqkKFCrJZ/J+p5ORkVa1aVf/884/lt7LjykQbg5VoX7AS7QtWon3BSrSvK4MxRidOnFB4ePhF67k16a5YsaI8PT116NAhp/JDhw4pLCzM5T5hYWEXrZ/19dChQ6pUqZJTnSZNmrg8pq+vr3x9fZ3KgoKC8nIply0wMJAfSFiKNgYr0b5gJdoXrET7gpVoXyXfxXq4s7j1qX4fHx81bdpUa9ascZTZ7XatWbNGkZGRLveJjIx0qi9JMTExjvrVq1dXWFiYU53k5GT99NNPOR4TAAAAAAAruP328uHDh6tfv35q1qyZmjdvrsmTJ+vUqVMaMGCAJKlv376qXLmyJkyYIEl65JFH1LZtW73xxhvq0qWL5s+frw0bNmjWrFmSJJvNpkcffVQvvviiateu7ZgyLDw8XN26dXPXZQIAAAAArkBuT7p79eqlxMREPffcc4qPj1eTJk20cuVKx0Bo+/btcxrVu2XLlpo3b55Gjx6tUaNGqXbt2lq2bJljjm5JevLJJ3Xq1CkNGjRIx48fV+vWrbVy5coiOUe3r6+vxowZk+32dqCg0MZgJdoXrET7gpVoX7AS7Qvnc/s83QAAAAAAlFTM1A4AAAAAgEVIugEAAAAAsAhJNwAAAAAAFiHpBgAAAADAIiTdbjZt2jRFRETIz89PLVq00M8//+zukFDEjB07VjabzWmpV6+eY/uZM2c0ZMgQVahQQaVLl9Ydd9yhQ4cOOR1j37596tKliwICAhQSEqIRI0YoPT3dqc66det07bXXytfXV7Vq1dKcOXMK4/JQyNavX69bb71V4eHhstlsWrZsmdN2Y4yee+45VapUSf7+/oqKitLOnTud6hw9elR33323AgMDFRQUpIEDB+rkyZNOdX7//XfdcMMN8vPzU9WqVfXqq69mi2XRokWqV6+e/Pz81KhRI61YsaLArxeF71JtrH///tl+p3Xs2NGpDm0MrkyYMEHXXXedypQpo5CQEHXr1k3bt293qlOYfxP5DFfy5KaNtWvXLtvvsP/+979OdWhjyMbAbebPn298fHzMe++9Z/766y/zwAMPmKCgIHPo0CF3h4YiZMyYMaZBgwbm4MGDjiUxMdGx/b///a+pWrWqWbNmjdmwYYO5/vrrTcuWLR3b09PTTcOGDU1UVJT59ddfzYoVK0zFihXNyJEjHXX+/vtvExAQYIYPH262bNli3nrrLePp6WlWrlxZqNcK661YscI888wzZsmSJUaSWbp0qdP2l19+2ZQtW9YsW7bM/Pbbb+a2224z1atXN6dPn3bU6dixo2ncuLH58ccfzbfffmtq1apl+vTp49ielJRkQkNDzd13323+/PNP8/HHHxt/f3/z9ttvO+p8//33xtPT07z66qtmy5YtZvTo0cbb29v88ccflr8GsNal2li/fv1Mx44dnX6nHT161KkObQyuREdHm9mzZ5s///zTbN682XTu3NlcddVV5uTJk446hfU3kc9wJVNu2ljbtm3NAw884PQ7LCkpybGdNgZXSLrdqHnz5mbIkCGO9YyMDBMeHm4mTJjgxqhQ1IwZM8Y0btzY5bbjx48bb29vs2jRIkfZ1q1bjSQTGxtrjMn8AOzh4WHi4+MddWbMmGECAwNNamqqMcaYJ5980jRo0MDp2L169TLR0dEFfDUoSi5MiOx2uwkLCzOvvfaao+z48ePG19fXfPzxx8YYY7Zs2WIkmV9++cVR58svvzQ2m83s37/fGGPM9OnTTbly5RztyxhjnnrqKVO3bl3Hes+ePU2XLl2c4mnRooUZPHhwgV4j3CunpLtr16457kMbQ24lJCQYSeabb74xxhTu30Q+w10ZLmxjxmQm3Y888kiO+9DG4Aq3l7tJWlqaNm7cqKioKEeZh4eHoqKiFBsb68bIUBTt3LlT4eHhqlGjhu6++27t27dPkrRx40adPXvWqR3Vq1dPV111laMdxcbGqlGjRgoNDXXUiY6OVnJysv766y9HnfOPkVWHtnhliYuLU3x8vFNbKFu2rFq0aOHUnoKCgtSsWTNHnaioKHl4eOinn35y1GnTpo18fHwcdaKjo7V9+3YdO3bMUYc2d+Vat26dQkJCVLduXT344IM6cuSIYxttDLmVlJQkSSpfvrykwvubyGe4K8eFbSzL3LlzVbFiRTVs2FAjR45USkqKYxttDK54uTuAK9Xhw4eVkZHh9AMpSaGhodq2bZubokJR1KJFC82ZM0d169bVwYMHNW7cON1www36888/FR8fLx8fHwUFBTntExoaqvj4eElSfHy8y3aWte1idZKTk3X69Gn5+/tbdHUoSrLag6u2cH5bCQkJcdru5eWl8uXLO9WpXr16tmNkbStXrlyObS7rGCi5OnbsqO7du6t69eravXu3Ro0apU6dOik2Nlaenp60MeSK3W7Xo48+qlatWqlhw4aSVGh/E48dO8ZnuCuAqzYmSXfddZeqVaum8PBw/f7773rqqae0fft2LVmyRBJtDK6RdANFXKdOnRzfX3PNNWrRooWqVaumhQsXkgwDKHZ69+7t+L5Ro0a65pprVLNmTa1bt07t27d3Y2QoToYMGaI///xT3333nbtDQQmVUxsbNGiQ4/tGjRqpUqVKat++vXbv3q2aNWsWdpgoJri93E0qVqwoT0/PbCNqHjp0SGFhYW6KCsVBUFCQ6tSpo127diksLExpaWk6fvy4U53z21FYWJjLdpa17WJ1AgMDSeyvIFnt4WK/l8LCwpSQkOC0PT09XUePHi2QNsfvvytPjRo1VLFiRe3atUsSbQyXNnToUC1fvlxr165VlSpVHOWF9TeRz3AlX05tzJUWLVpIktPvMNoYLkTS7SY+Pj5q2rSp1qxZ4yiz2+1as2aNIiMj3RgZirqTJ09q9+7dqlSpkpo2bSpvb2+ndrR9+3bt27fP0Y4iIyP1xx9/OH2IjYmJUWBgoK6++mpHnfOPkVWHtnhlqV69usLCwpzaQnJysn766Sen9nT8+HFt3LjRUefrr7+W3W53fPCIjIzU+vXrdfbsWUedmJgY1a1bV+XKlXPUoc1Bkv79918dOXJElSpVkkQbQ86MMRo6dKiWLl2qr7/+OtsjBoX1N5HPcCXXpdqYK5s3b5Ykp99htDFk4+6R3K5k8+fPN76+vmbOnDlmy5YtZtCgQSYoKMhptEPg8ccfN+vWrTNxcXHm+++/N1FRUaZixYomISHBGJM5PcpVV11lvv76a7NhwwYTGRlpIiMjHftnTV3RoUMHs3nzZrNy5UoTHBzscuqKESNGmK1bt5pp06YxZVgJdeLECfPrr7+aX3/91UgyEydONL/++qvZu3evMSZzyrCgoCDz6aefmt9//9107drV5ZRh//nPf8xPP/1kvvvuO1O7dm2n6ZyOHz9uQkNDzb333mv+/PNPM3/+fBMQEJBtOicvLy/z+uuvm61bt5oxY8YwnVMJcbE2duLECfPEE0+Y2NhYExcXZ1avXm2uvfZaU7t2bXPmzBnHMWhjcOXBBx80ZcuWNevWrXOariklJcVRp7D+JvIZrmS6VBvbtWuXef75582GDRtMXFyc+fTTT02NGjVMmzZtHMegjcEVkm43e+utt8xVV11lfHx8TPPmzc2PP/7o7pBQxPTq1ctUqlTJ+Pj4mMqVK5tevXqZXbt2ObafPn3aPPTQQ6ZcuXImICDA3H777ebgwYNOx9izZ4/p1KmT8ff3NxUrVjSPP/64OXv2rFOdtWvXmiZNmhgfHx9To0YNM3v27MK4PBSytWvXGknZln79+hljMqcNe/bZZ01oaKjx9fU17du3N9u3b3c6xpEjR0yfPn1M6dKlTWBgoBkwYIA5ceKEU53ffvvNtG7d2vj6+prKlSubl19+OVssCxcuNHXq1DE+Pj6mQYMG5osvvrDsulF4LtbGUlJSTIcOHUxwcLDx9vY21apVMw888EC2D5G0Mbjiql1Jcvp7VZh/E/kMV/Jcqo3t27fPtGnTxpQvX974+vqaWrVqmREjRjjN020MbQzZ2YwxpvD61QEAAAAAuHLwTDcAAAAAABYh6QYAAAAAwCIk3QAAAAAAWISkGwAAAAAAi5B0AwAAAABgEZJuAAAAAAAsQtINAAAAAIBFSLoBAAAAALAISTcAAAAAABYh6QYAAAAAwCIk3QAAAAAAWISkGwAAAAAAi5B0AwAAAABgEZJuAAAAAAAsQtINAAAAAIBFSLoBAAAAALAISTcAAAAAABYh6QYA4DL0799fERERBXrMOXPmyGazac+ePQV6XBQ9ERER6t+/v7vDAABYiKQbAOB2u3fv1uDBg1WjRg35+fkpMDBQrVq10pQpU3T69Gl3h2eZl156ScuWLXN3GA5Zyb7NZtN3332XbbsxRlWrVpXNZtMtt9zihghztmfPHkfsFy7XX3+9W2P74YcfNHbsWB0/ftytcQAA3MPL3QEAAK5sX3zxhXr06CFfX1/17dtXDRs2VFpamr777juNGDFCf/31l2bNmuXuMC3x0ksv6c4771S3bt2cyu+991717t1bvr6+bonLz89P8+bNU+vWrZ3Kv/nmG/37779uiys3+vTpo86dOzuVBQcHuymaTD/88IPGjRun/v37KygoyGnb9u3b5eFBHwgAlGQk3QAAt4mLi1Pv3r1VrVo1ff3116pUqZJj25AhQ7Rr1y598cUXbozQPTw9PeXp6em283fu3FmLFi3Sm2++KS+vcx8V5s2bp6ZNm+rw4cNui+1Srr32Wt1zzz3uDiPXivI/MAAABYN/rQIA3ObVV1/VyZMn9e677zol3Flq1aqlRx55RNK524fnzJmTrZ7NZtPYsWMd62PHjpXNZtOOHTt0zz33qGzZsgoODtazzz4rY4z++ecfde3aVYGBgQoLC9Mbb7zhdLycnqlet26dbDab1q1bd9Hrev3119WyZUtVqFBB/v7+atq0qRYvXpwt5lOnTun999933Aad9Wzvhee/5ZZbVKNGDZfnioyMVLNmzZzKPvroIzVt2lT+/v4qX768evfurX/++eeiMZ+vT58+OnLkiGJiYhxlaWlpWrx4se666658X7MkxcTEqHXr1goKClLp0qVVt25djRo1yqnOW2+9pQYNGiggIEDlypVTs2bNNG/evFzHn5N27dqpXbt22covfC4/q629/vrrmjVrlmrWrClfX19dd911+uWXX7Ltv23bNvXs2VPBwcHy9/dX3bp19cwzz0jKbIsjRoyQJFWvXt3xXme9t66e6f7777/Vo0cPlS9fXgEBAbr++uuz/fMpqy0uXLhQ48ePV5UqVeTn56f27dtr165d+X+RAAAFjqQbAOA2n3/+uWrUqKGWLVtacvxevXrJbrfr5ZdfVosWLfTiiy9q8uTJuvnmm1W5cmW98sorqlWrlp544gmtX7++wM47ZcoU/ec//9Hzzz+vl156SV5eXurRo4dT4vThhx/K19dXN9xwgz788EN9+OGHGjx4cI7XERcXly3h27t3r3788Uf17t3bUTZ+/Hj17dtXtWvX1sSJE/Xoo49qzZo1atOmTa6fKY6IiFBkZKQ+/vhjR9mXX36ppKQkp3Pl9Zr/+usv3XLLLUpNTdXzzz+vN954Q7fddpu+//57R53//e9/GjZsmK6++mpNnjxZ48aNU5MmTfTTTz/lKvaUlBQdPnzYaTl79myu9r3QvHnz9Nprr2nw4MF68cUXtWfPHnXv3t3peL///rtatGihr7/+Wg888ICmTJmibt266fPPP5ckde/eXX369JEkTZo0yfFe53TL+6FDh9SyZUutWrVKDz30kMaPH68zZ87otttu09KlS7PVf/nll7V06VI98cQTGjlypH788Ufdfffd+bpeAIBFDAAAbpCUlGQkma5du+aqflxcnJFkZs+enW2bJDNmzBjH+pgxY4wkM2jQIEdZenq6qVKlirHZbObll192lB87dsz4+/ubfv36Ocpmz55tJJm4uDin86xdu9ZIMmvXrnWU9evXz1SrVs2pXkpKitN6WlqaadiwobnpppucykuVKuV03pzOn5SUZHx9fc3jjz/uVO/VV181NpvN7N271xhjzJ49e4ynp6cZP368U70//vjDeHl5ZSvP6by//PKLmTp1qilTpozjWnr06GFuvPFGY4wx1apVM126dMnzNU+aNMlIMomJiTnG0LVrV9OgQYOLxulKVvtwtWS9X23btjVt27bNtu+F72HWsSpUqGCOHj3qKP/000+NJPP55587ytq0aWPKlCnjeA+y2O12x/evvfaay/ZkTOZreX4bePTRR40k8+233zrKTpw4YapXr24iIiJMRkaGMeZcW6xfv75JTU111J0yZYqRZP7444+Lvl4AgMJDTzcAwC2Sk5MlSWXKlLHsHPfff7/je09PTzVr1kzGGA0cONBRHhQUpLp16+rvv/8usPP6+/s7vj927JiSkpJ0ww03aNOmTfk6XmBgoDp16qSFCxfKGOMoX7Bgga6//npdddVVkqQlS5bIbrerZ8+eTj29YWFhql27ttauXZvrc/bs2VOnT5/W8uXLdeLECS1fvjzHW8ul3F1z1iBin376qex2u8vjBAUF6d9//3V5G3duDBo0SDExMU5L48aN83WsXr16qVy5co71G264QZIcbSUxMVHr16/Xfffd53gPsthstnydc8WKFWrevLnTIHalS5fWoEGDtGfPHm3ZssWp/oABA+Tj45NjjAAA92MgNQCAWwQGBkqSTpw4Ydk5LkyEypYtKz8/P1WsWDFb+ZEjRwrsvMuXL9eLL76ozZs3KzU11VGe30RMykwAly1bptjYWLVs2VK7d+/Wxo0bNXnyZEednTt3yhij2rVruzyGt7d3rs8XHBysqKgozZs3TykpKcrIyNCdd96ZY/3cXHOvXr30zjvv6P7779fTTz+t9u3bq3v37rrzzjsdI3g/9dRTWr16tZo3b65atWqpQ4cOuuuuu9SqVatcxV27dm1FRUXl+jov5sL2k5WAHzt2TNK5xLZhw4YFcj4p85GBFi1aZCuvX7++Y/v557tUjAAA9yPpBgC4RWBgoMLDw/Xnn3/mqn5OCWtGRkaO+7gaATynUcHP70HOz7myfPvtt7rtttvUpk0bTZ8+XZUqVZK3t7dmz559WYOB3XrrrQoICNDChQvVsmVLLVy4UB4eHurRo4ejjt1ul81m05dffunyOkuXLp2nc95111164IEHFB8fr06dOmWb7ipLbq/Z399f69ev19q1a/XFF19o5cqVWrBggW666SZ99dVX8vT0VP369bV9+3YtX75cK1eu1CeffKLp06frueee07hx4/IU/4VsNpvT+5wlp/c1N23F3YpDjABwpSPpBgC4zS233KJZs2YpNjZWkZGRF62b1YN34WBge/fuLfC4Ludcn3zyifz8/LRq1Sqn6aBmz56drW5eer5LlSqlW265RYsWLdLEiRO1YMEC3XDDDQoPD3fUqVmzpowxql69uurUqZPrY+fk9ttv1+DBg/Xjjz9qwYIFOdbLyzV7eHioffv2at++vSZOnKiXXnpJzzzzjNauXevooS5VqpR69eqlXr16KS0tTd27d9f48eM1cuRI+fn55ft6ypUr5/K26/y2oawR5S/1j6O8vM/VqlXT9u3bs5Vv27bNsR0AULzwTDcAwG2efPJJlSpVSvfff78OHTqUbfvu3bs1ZcoUSZk94xUrVsw2yvj06dMLPK6aNWtKktO5MjIyNGvWrEvu6+npKZvN5tR7umfPHi1btixb3VKlSuV6RHEp8/bsAwcO6J133tFvv/2mXr16OW3v3r27PD09NW7cuGw9ncaYPN9CX7p0ac2YMUNjx47VrbfemmO93F7z0aNHs+3bpEkTSXLckn5hjD4+Prr66qtljMn3KORZatasqW3btikxMdFR9ttvvzmNnp4XwcHBatOmjd577z3t27fPadv5r3+pUqUkZf8njiudO3fWzz//rNjYWEfZqVOnNGvWLEVEROjqq6/OV6wAAPehpxsA4DY1a9bUvHnz1KtXL9WvX199+/ZVw4YNlZaWph9++EGLFi1ymsP4/vvv18svv6z7779fzZo10/r167Vjx44Cj6tBgwa6/vrrNXLkSB09elTly5fX/PnzlZ6efsl9u3TpookTJ6pjx4666667lJCQoGnTpqlWrVr6/fffneo2bdpUq1ev1sSJExUeHq7q1au7fJ43S+fOnVWmTBk98cQT8vT01B133OG0vWbNmnrxxRc1cuRI7dmzR926dVOZMmUUFxenpUuXatCgQXriiSfy9Fr069evwK75+eef1/r169WlSxdVq1ZNCQkJmj59uqpUqeIYOKxDhw4KCwtTq1atFBoaqq1bt2rq1Knq0qXLZQ+6d99992nixImKjo7WwIEDlZCQoJkzZ6pBgwaOgf3y6s0331Tr1q117bXXatCgQapevbr27NmjL774Qps3b5aU+T5L0jPPPKPevXvL29tbt956qyMZP9/TTz+tjz/+WJ06ddKwYcNUvnx5vf/++4qLi9Mnn3ziePYdAFCMuGfQdAAAztmxY4d54IEHTEREhPHx8TFlypQxrVq1Mm+99ZY5c+aMo15KSooZOHCgKVu2rClTpozp2bOnSUhIyHHKsAunpurXr58pVapUtvO3bds22zRVu3fvNlFRUcbX19eEhoaaUaNGmZiYmFxNGfbuu++a2rVrG19fX1OvXj0ze/ZsR0zn27Ztm2nTpo3x9/c3khxTR+U0ZZkxxtx9991GkomKisrx9fzkk09M69atTalSpUypUqVMvXr1zJAhQ8z27dtz3Of88/7yyy8XredqyrDcXPOaNWtM165dTXh4uPHx8THh4eGmT58+ZseOHY46b7/9tmnTpo2pUKGC8fX1NTVr1jQjRowwSUlJF40pa5qv11577aL1PvroI1OjRg3j4+NjmjRpYlatWpXjlGGujnVhWzPGmD///NPcfvvtJigoyPj5+Zm6deuaZ5991qnOCy+8YCpXrmw8PDyc3tsLpwwzJrPt3XnnnY7jNW/e3CxfvtypTtaUYYsWLXL5OriaWg8A4B42YxhpAwAAAAAAK3CPEgAAAAAAFiHpBgAAAADAIiTdAAAAAABYhKQbAAAAAACLkHQDAAAAAGARkm4AAAAAACzi5e4AiiK73a4DBw6oTJkystls7g4HAAAAAFDEGGN04sQJhYeHy8Mj5/5skm4XDhw4oKpVq7o7DAAAAABAEffPP/+oSpUqOW4n6XahTJkykjJfvMDAwMs6lt1uV2JiooKDgy/63w/gctHWUFhoayhMtDcUFtoaCgttreRITk5W1apVHfljTki6Xci6pTwwMLBAku4zZ84oMDCQHypYiraGwkJbQ2GivaGw0NZQWGhrJc+lHknmXQYAAAAAwCIk3QAAAAAAWISkGwAAAAAAi/BMdz4ZY5Senq6MjIyL1rPb7Tp79qzOnDlzxT+z4enpKS8vL6ZhAwAAAHDFIOnOh7S0NB08eFApKSmXrGuMkd1u14kTJ0g2JQUEBKhSpUry8fFxdygAAAAAYDmS7jyy2+2Ki4uTp6enwsPD5ePjc9FkOqtH/Erv4TXGKC0tTYmJiYqLi1Pt2rWv+J5/AAAAACUfSXcepaWlyW63q2rVqgoICLhkfZLuc/z9/eXt7a29e/cqLS1Nfn5+7g4JAAAAACxV5Lsa169fr1tvvVXh4eGy2WxatmzZJfdZt26drr32Wvn6+qpWrVqaM2dOgcdFL23+8LoBAAAAuJIU+Qzo1KlTaty4saZNm5ar+nFxcerSpYtuvPFGbd68WY8++qjuv/9+rVq1yuJIAQAAAABwVuRvL+/UqZM6deqU6/ozZ85U9erV9cYbb0iS6tevr++++06TJk1SdHS0VWECAAAAVw5jJLs9+3I55a6+z0vZhd/nZ/3CJeta81LnYuuSlJEh/xMnpNKlJZste72C+Hr+++Tq+4ttu7CeK5eqk5tj5Ea9elKPHgVzLDcq8kl3XsXGxioqKsqpLDo6Wo8++miO+6Smpio1NdWxnpycLClz0DS73e5U1263yxjjWHIjq15u65dkWa+bq9cWlyerbfK6wmq0NRQm2hsKi1Nbs9uls2czl7Q0569nz0rp6ee+Zi3nr19sW0aG87b/X7edX+7q+5zK7PbMr1nLhesXK88qy7rmi5Wd973tElPm4uI8JJV1dxDFhOnWTeaOO9wdRo5y+7epxCXd8fHxCg0NdSoLDQ1VcnKyTp8+LX9//2z7TJgwQePGjctWnpiYqDNnzjiVnT17Vna7Xenp6UpPT79kPMYYx1zeRWEgtfj4eL388sv68ssvtX//foWEhOiaa67RsGHDdNNNN6l27drau3evPvzwQ/Xq1ctp38aNG2vr1q1655131LdvX0ly1D9f5cqVFRcX5/L86enpstvtOnLkiLy9va25yCuU3W5XUlKSjDE8Ow9L0dZQmGhvVwBjpLNnZTtzRrbUVNnOnJHOnHFeT02V7fRp2dLSZEtLy1x39X1aWuY+Wd+fOeNUrrNnZfv/xNl2wfceZ88qNKucpLLAGZtN8vA4t9hsmWU2m+Tpmfn1/8sd27LWs/aRJA+P7NuyjnP+MS8od2y/sDzruBfWO3/9/O051b2wPGv9wq82m4yks+npmZ+FLzy/q/2yXOy45301F+53sWPltJ6TnOrl5pz5cLZBA51OSLjs41jlxIkTuapX4pLu/Bg5cqSGDx/uWE9OTlbVqlUVHByswMBAp7pnzpzRiRMn5OXlJS+v3L98RSHB3LNnj1q3bq2goCC9+uqratSokc6ePatVq1bpkUce0datWyVJVatW1Ycffqi7777bse+PP/6oQ4cOqVSpUvLw8HC69nHjxumBBx5wrHt6eub42nh5ecnDw0MVKlRg9PICZrfbZbPZFBwczAdTWIq2hsJEe3Mju11KSZFOnpROncr8/vTpzK8XLv9fbnO1LWv7mTOZX8//PivBLgZ3MhgfH8nb+9zi5XXua9biat3TM/t2T0/nbefXuaDMuCiTp+e5xcPDeT2v5Vnrrr5eatuF6xcm1ueX59HF9nB/N9blsdvtOp6YyO+1XPCTVMbdQVxEbvOZEpd0h4WF6dChQ05lhw4dUmBgoMtebkny9fWVr69vtnIPD49sPwgeHh6y2WyORVLmf2hTUlwe2xgjW3q6ZMWUYQEBefolNmTIENlsNv38888qVaqUo7xhw4YaOHCgI767775bkyZN0r///quqVatKkmbPnq27775bH3zwgfO1SwoMDFSlSpVyFUPWvq5eW1w+XlsUFtoaChPtLZfs9swEOSkpc0lOzvx68qR04kTm1/OXS5WdOuWe6/D3l/z8sn/NWnx9L2/JSp4v+Gr38tKR5GRVqFRJHhfW8/R02x2LxT3BhGv8XisZcvv+lbikOzIyUitWrHAqi4mJUWRkpHUnTUnJHAjBBZsky/q4T56UzkueL+bo0aNauXKlxo8f75RwZwkKCnJ8HxoaqujoaL3//vsaPXq0UlJStGDBAn3zzTf64IMPCip6AABwvrNnpWPHpKNHpSNHMr8eP549ic5p/cSJghu86Hw2W+bnjYCAc4u/v/P6pRZ//3OLq4Q6a5uPT4HckpovdrsyEhKkkJBzt/wCQAEo8kn3yZMntWvXLsd6XFycNm/erPLly+uqq67SyJEjtX//fkcy+N///ldTp07Vk08+qfvuu09ff/21Fi5cqC+++MJdl1Ak7Nq1S8YY1atXL1f177vvPj3++ON65plntHjxYtWsWVNNmjRxWfepp57S6NGjHesvvfSShg0bVhBhAwBQ/NjtmQnz4cOZX89Poi/2/f8P5HrZvL2lsmUzl8DAzKV0aeelTJnsZTmV+/u7LxEGgBKgyCfdGzZs0I033uhYz3r2ul+/fpozZ44OHjyoffv2ObZXr15dX3zxhR577DFNmTJFVapU0TvvvGPtdGEBAZm9zi4YY5Seni4vq24vz6W8jpzepUsXDR48WOvXr9d7772n++67L8e6I0aMUP/+/R3rFStWzNO5AAAo8rIS6UOHMpf4+HPfX7iekJA5snR+2GxSUJBUvnzmEhR0LoE+P5G+2LqvL0kyABQhRT7pbteu3UUTxjlz5rjc59dff7Uwqgtk3XblijGZf3i9vNz6B7B27dqy2Wzatm1brup7eXnp3nvv1ZgxY/TTTz9p6dKlOdatWLGiatWqVVChAgBQeIzJvIX7n3+cl/37nZPq/CTSQUFShQrnEuicvj9/PSgoc2AqAECJUeSTbhSM8uXLKzo6WtOmTdOwYcOyPdd9/Phxp+e6pcxbzF9//XX16tVL5cqVK8RoAQAoIKdOZU+os5Z9+zK/5mXAsPLlpdDQzCUsLOfvg4Mzn08GAFzxSLqvINOmTVOrVq3UvHlzPf/887rmmmuUnp6umJgYzZgxwzFlWJb69evr8OHDCsjDbewAABSqlBQpLk7avVv6++/MJS7uXGJ99GjujlOhglS16rmlcuXM5Pn8ZDokhEQaAJBnJN1XkBo1amjTpk0aP368Hn/8cR08eFDBwcFq2rSpZsyY4XKfChUqFHKUAACcxxh5HDok7dyZmUxnJdZ//52ZaMfHX/oYZco4J9RXXeW8XqVKnsZJAQAgL0i6rzCVKlXS1KlTNXXqVJfb9+zZc9H9jx8/nqf6AABckjHSv/9KW7dK27c79Vrb/v5bIadPX3z/oCCpZk2pRo3MpXp158S6bNlCuQwAAFwh6QYAAIUjPT0zmd66NXPZsiXz67ZtOc4CYpNkPDykq66SLSupzkqws74y7ggAoAgj6QYAAAXrzBlpxw7nxHrr1syytDTX+3h5SbVqSfXqZX79/6TaHhGhBD8/hVSpIpuHR+FeBwAABYCkGwAA5I8xmSOA//qrtGmTtHlzZpIdF5c5r7Ur/v6ZiXX9+pnL1Vdnfq1VS/L2zl7fbs+crgsAgGKKpBsAAFya3Z75rPWmTc5LTqODBwWdS6jPT7CvukqixxoAcAUh6c4nY4y7QyiWeN0AoBhIT898zvr85HrzZunEiex1vbykhg2la6+V/vMfqUGDzAQ7NFSy2Qo9dAAAihqS7jzy/v9b31JSUuTv7+/maIqflJQUSedeRwCAm9ntmc9bx8ZKGzdm3ir+22+Zz2VfyM9Patz4XIJ97bWZCbevb+HHDQBAMUHSnUeenp4KCgpSwv8/XxYQECDbRf6Tb4xRenq6vLy8LlqvpDPGKCUlRQkJCQoKCpKnp6e7QwKAK9OZM9Ivv0jffy999530ww/SsWPZ65UpIzVpkplYZy316mX2bAMAgFzjL2c+hIWFSZIj8b4YY4zsdrs8PDyu6KQ7S1BQkOP1AwAUgsOHMxPr777LTLQ3bMg+gnhAgNSihXTddecS7Jo1efYaAIACQNKdDzabTZUqVVJISIjOnj170bp2u11HjhxRhQoV5HGFf3jx9vamhxsArGSMtGvXuV7s77/PfDb7QqGhUuvWUqtWmV+bNHE9cjgAALhsJN2XwdPT85JJpN1ul7e3t/z8/K74pBsAUMCMkf76S4qJyUyyv/vO9fRa9es7J9k1ajDIGQAAhYSkGwCA4uTIEWn1amnVKumrr6T9+523+/hk3iaelWS3bClVqOCeWAEAAEk3AABF2tmz0k8/ZSbZq1ZlPpN9/vSLfn5S27bSjTdmJtpNm2aWAQCAIoGkGwCAoiYu7lxP9po1UnKy8/aGDaXo6MzlhhtIsgEAKMJIugEAcLeTJ6V16871Zu/c6by9fHnp5pszk+wOHaTKld0SJgAAyDuSbgAA3GH/fmnp0szl228zbyPP4ukpRUae682+9trMMgAAUOyQdAMAUFh275aWLMlcfvzReVtExLkk+6abpLJl3RIiAAAoWCTdAABYxRhpyxbpk08yE+3ffnPeHhkp3XGHdOutUu3aTOMFAEAJRNINAEBBMkbauPFcor1jx7ltnp6ZI43fcYfUrZsUHu62MAEAQOEg6QYA4HJlZEg//HDu1vF9+85t8/HJHAQtq0e7YkX3xQkAAAodSTcAAPmRkSGtXSstXiwtWyYdOnRuW6lSUufOUvfumV8DA90WJgAAcC+SbgAA8mLXLmnOHOmDD6R//jlXHhQk3XZbZqLdoYPk7++uCAEAQBFC0g0AwKWcOJHZoz17dub0XlmCgqQePaQ775Tatcu8lRwAAOA8JN0AALhit2cm2LNnZybcp05llttsmT3ZAwZIXbtKfn7ujRMAABRpJN0AAJxv717p/fczl7//Pldeu3Zmon3vvVKVKu6LDwAAFCsk3QAApKRIS5dm9mp//XXmtF+SVKaM1KuX1L+/1LIl82gDAIA8I+kGAFyZjJF+/DEz0V6wQEpOPrftppsyE+3u3TNHIgcAAMgnkm4AwJXl5Enpww+lqVOlLVvOlUdEZCba/fplfg8AAFAASLoBAFeG3buladOk996TkpIyywICMkceHzBAatNG8vBwb4wAAKDEIekGAJRcxkirV0tvvil98cW5Z7Vr1ZKGDs3s2S5b1q0hAgCAko2kGwBQ8pw8KX3wgfTWW9K2befKO3aUhg2ToqPp1QYAAIWCpBsAUHLs2nXuFvKsgdFKl868fXzIEKluXffGBwAArjgk3QCA4s1uP3cL+YoV524hr11bevjhzIHRAgPdGyMAALhikXQDAIqnEyfO3UK+ffu58k6dMm8h79CBW8gBAIDbkXQDAIqXf/6RJk2S3n333C3kZcqcu4W8Th33xgcAAHAekm4AQPHw11/Sa69Jc+dK6emZZXXqZN5C3rcvt5ADAIAiiaQbAFC0ffed9Mor0vLl58ratZNGjMgcjZxbyAEAQBFG0g0AKHrs9swk+5VXpB9+yCyz2aTbb5eefFJq0cK98QEAAOQSSTcAoOhIS8u8ffy116StWzPLfHwyRyB//HGm/AIAAMUOSTcAwP1OnJBmzcocIG3//syywEDpwQelRx6RKlVyb3wAAAD5RNINAHCfQ4cy59eePl06fjyzrFIl6dFHpcGDpbJl3RkdAADAZSPpBgAUvl27pNdfl+bMkVJTM8vq1s0cHO2eeyRfX7eGBwAAUFCKxZCv06ZNU0REhPz8/NSiRQv9/PPPF60/efJk1a1bV/7+/qpataoee+wxnTlzppCiBQDkaOtW6a67MhPst9/OTLhbtJCWLJG2bJEGDiThBgAAJUqR7+lesGCBhg8frpkzZ6pFixaaPHmyoqOjtX37doWEhGSrP2/ePD399NN677331LJlS+3YsUP9+/eXzWbTxIkT3XAFAABt3Sq98II0f75kTGZZ587SU09JN9yQOTI5AABACVTkk+6JEyfqgQce0IABAyRJM2fO1BdffKH33ntPTz/9dLb6P/zwg1q1aqW77rpLkhQREaE+ffrop59+yvEcqampSs26vVFScnKyJMlut8tut19W/Ha7XcaYyz4OcCm0NRSWPLW1bdtke/FFaf582f4/2Tbdusk8+6zUpElmHWPOJeLABfjdhsJCW8P/tXfncVHV+x/H3wMKSIjoZVPDfcvEPU3TVhI11yxJLU3NyqVMtKuUSmZXbTPLKO/V1BbLpWuW5bUURc01zSVzKUwjS8AVcAWZ8/vj/JwbFzTUOTMwvJ6PB4/HnO858/1+xvkwzMdzzvfrKuSa5yjse1iki+7s7Gxt27ZNcXFxjjYvLy9FRUVp48aNBT6ndevW+uijj7Rlyxa1aNFCv/zyi5YtW6ZHHnnksuNMnjxZEyZMyNd+9OjR674s3W63KyMjQ4ZhyMurWFzNj2KKXIOrFCbXvH/+WQFvvCG/JUscxfb5Dh10OjZWFxs0MA9KT3dVyCjG+GyDq5BrcBVyzXNkZWUV6rgiXXQfO3ZMubm5CgsLy9MeFhamffv2Ffic3r1769ixY2rTpo0Mw9DFixf15JNP6rnnnrvsOHFxcYqNjXVsZ2ZmKiIiQiEhIQoMDLyu12C322Wz2RQSEsIvFSxFrsFVrphrBZ3Z7tpVxrhx8mnSRBXcEC+KNz7b4CrkGlyFXPMcfn5+hTquSBfd1yIpKUmTJk3SO++8o5YtWyo5OVnDhw/XxIkTNW7cuAKf4+vrK98CJu7x8vJyyi+CzWZzWl/AlZBrcJV8ubZvn3nP9ief/PdS8W7dpPHjZWvSRNyxjevBZxtchVyDq5BrnqGw71+RLrqDg4Pl7e2ttLS0PO1paWkKDw8v8Dnjxo3TI488oscee0ySFBkZqTNnzujxxx/X888/T2IDgDPt3//fYvvSfU3/X2yrSRO3hgYAAFAUFOkK1MfHR82aNVNiYqKjzW63KzExUa1atSrwOWfPns1XWHt7e0uSDCbqAQCn8E5Olu2RR6T69aV588yCu1s36fvvpc8+o+AGAAD4f0X6TLckxcbGql+/fmrevLlatGihadOm6cyZM47ZzPv27avKlStr8uTJkqTOnTtr6tSpatKkiePy8nHjxqlz586O4hsAcI1+/lm2F15Q8Pz5sl06s921qxQfT6ENAABQAEuK7jNnzuiGG25wSl8xMTE6evSoxo8fr9TUVDVu3FjLly93TK6WkpKS58z22LFjZbPZNHbsWP3+++8KCQlR586d9Y9//MMp8QBAiZSWJr34ovSvf8l28aIkyejSRbb4eKlpUzcHBwAAUHTZDAuuuQ4ICFDPnj01YMAAtWnTxtndWy4zM1PlypVTRkaGU2YvT09PV2hoKPeTw1LkGixx+rT0+uvSa6+ZjyUZHTro+DPPqEJUFLkGy/HZBlch1+Aq5JrnKGzdaMm7/NFHH+nEiRO6++67VadOHU2ZMkV//PGHFUMBAKyQkyO9+65Uq5b0wgtmwX3LLdLq1TK+/FIXGzZ0d4QAAADFgiVFd7du3bRkyRL9/vvvevLJJ/Xxxx+ratWq6tSpkxYvXqyL/39pIgCgiDEM6d//lho0kIYMMS8rr1lTWrBA2rxZuvNOd0cIAABQrFh6PUNISIhiY2O1a9cuTZ06VStXrtQDDzygSpUqafz48Tp79qyVwwMArsa6dVLr1tIDD0g//SSFhEjTp0t79kg9e0o2VtsGAAC4WpbOXp6Wlqb3339fc+fO1a+//qoHHnhAAwcO1OHDh/Xyyy9r06ZN+uabb6wMAQDwV/bskcaMkZYuNbf9/aWRI6VRo6TrnNcCAACgpLOk6F68eLHmzJmjr7/+WvXr19eQIUP08MMPKygoyHFM69atddNNN1kxPACgMH7/3Vzqa84cc51tb2/pscfMtooV3R0dAACAR7Ck6O7fv78eeughrV+/XrfcckuBx1SqVEnPP/+8FcMDAK4kI0N6+WVp2jTp3DmzrXt3adIkqV49t4YGAADgaSwpuo8cOSJ/f/8rHlOmTBnFx8dbMTwAoCDZ2eaM5BMnSsePm2233Sa98op5LzcAAACczpKJ1MqWLav09PR87cePH5e3t7cVQwIAruSrr8wZyZ95xiy469WTliz57+RpAAAAsIQlZ7oNwyiw/cKFC/Lx8bFiSABAQfbvl0aMkP7zH3M7LEx68UVpwACplKVzaQIAAEBOLrrfeustSZLNZtOsWbMUEBDg2Jebm6u1a9eqHvcLAoD1MjPNy8jffFPKyZFKlzbPco8dy4zkAAAALuTUovuNN96QZJ7pnjFjRp5LyX18fFStWjXNmDHDmUMCAP7MbpfmzpXi4qRLt/l07Ci98YZUp45bQwMAACiJnFp0Hzx4UJJ01113afHixSpfvrwzuwcAXMmmTdLTT0vffWdu16ljFtsdO7o3LgAAgBLMkhv6Vq9ebUW3AICC/PGHNGaM9OGH5nbZstL48WYBzjwaAAAAbuW0ojs2NlYTJ07UDTfcoNjY2CseO3XqVGcNCwAl14UL5pnsl16Szpwx2/r3N9fbDg93b2wAAACQ5MSie/v27crJyXE8vhybzeasIQGgZDIMaelSKTZWOnDAbLv1Vumtt6RbbnFvbAAAAMjDaUX3ny8p5/JyALDI3r3mLOTffGNuV6wovfyy1KeP5OXl1tAAAACQH9/QAKA4yMw019tu2NAsuH18zPu49++XHnmEghsAAKCIctqZ7vvvv7/Qxy5evNhZwwKA5/viC2nIEOn3383tLl2k11+XatVyb1wAAAD4S04rusuVK+esrgAAknTkiDkD+aefmts1a0oJCVJ0tHvjAgAAQKE5reieM2eOs7oCgJLNMKT33pNGjZIyMiRvb/NxfLxUpoy7owMAAMBVsGSdbgDANfrpJ+nxx6U1a8zt5s2lmTOlxo3dGhYAAACujdOK7qZNmyoxMVHly5dXkyZNrrg02Pfff++sYQHAM2RnS6++Kk2caK6/7e9vrr/91FNSKf5/FAAAoLhy2je5rl27ytfXV5LUrVs3Z3ULAJ5v82Zp0CDphx/M7eho6d13perV3RsXAAAArpvTiu74+PgCHwMALuP0aWnsWOmtt8z7uIODpTfeMNfcvsLVQgAAACg+LL1mcevWrdq7d68kqX79+mrWrJmVwwFA8bFsmTR4sJSSYm4/8og0dapZeAMAAMBjWFJ0Hz58WL169dL69esVFBQkSTp16pRat26t+fPn68Ybb7RiWAAo+tLTpeHDpfnzze1q1aR//lNq186tYQEAAMAaXlZ0+thjjyknJ0d79+7ViRMndOLECe3du1d2u12PPfaYFUMCQNFmGNLcudJNN5kFt5eXNHKktHs3BTcAAIAHs+RM95o1a7RhwwbVrVvX0Va3bl1Nnz5dbdu2tWJIACi6fv1VeuwxaeVKc7txY2nWLIlbbgAAADyeJWe6IyIilJOTk689NzdXlSpVsmJIACh6DEOaM0eKjDQLbj8/6eWXpS1bKLgBAABKCEuK7ldffVVPPfWUtm7d6mjbunWrhg8frtdee82KIQGgaElLk7p1kwYMkLKypNatzSXB/v53qXRpd0cHAAAAF3Ha5eXly5eX7U9L3Jw5c0YtW7ZUqVLmEBcvXlSpUqU0YMAA1vEG4NkWL5aeeEI6dkzy8ZEmTjTv3/b2dndkAAAAcDGnFd3Tpk1zVlcAUDydOiU9/bT04YfmdsOG5uOGDd0aFgAAANzHaUV3v379nNUVABQ/K1dK/ftLhw+bM5OPHi3Fx0u+vu6ODAAAAG5kyezlf3b+/HllZ2fnaQsMDLR6WABwjbNnpTFjpOnTze2aNaUPPjDv4QYAAECJZ8lEamfOnNGwYcMUGhqqG264QeXLl8/zAwAeYfNmqUmT/xbcQ4ZIO3dScAMAAMDBkqL773//u1atWqV3331Xvr6+mjVrliZMmKBKlSrpgw8+sGJIAHCd7Gxp3DizuP7pJ6lSJWn5cikhQbrhBndHBwAAgCLEksvLly5dqg8++EB33nmn+vfvr7Zt26pWrVqqWrWq5s2bpz59+lgxLABY78cfpUcekbZvN7d795beflviKh4AAAAUwJIz3SdOnFCNGjUkmfdvnzhxQpLUpk0brV271oohAcBaubnS669LzZqZBXeFCtLChdK8eRTcAAAAuCxLiu4aNWro4MGDkqR69epp4cKFkswz4EFBQVYMCQDWOXhQuusuadQo6cIF6b77pN27pQcfdHdkAAAAKOIsKbr79++vnTt3SpLGjBmjhIQE+fn5acSIEXr22WetGBIArPHJJ+Y62+vWSQEB0syZ0tKlUsWK7o4MAAAAxYAl93SPGDHC8TgqKkp79+7V999/r1q1aqlhw4ZWDAkAznX2rDR8uDRrlrndpo30/vvS/986AwAAABSG5et0S1K1atVUrVo1VwwFANdvzx6pZ09z0jSbzZypfNw4qZRLPjIBAADgQSy5vFySEhMT1alTJ9WsWVM1a9ZUp06dtHLlSquGA4DrZxjS3LnSLbeYBXd4uLRihTRhAgU3AAAAroklRfc777yj9u3bq2zZsho+fLiGDx+uwMBAdezYUQkJCVYMCQDX5/RpqV8/qX9/89LyqChpxw7pnnvcHRkAAACKMUtO3UyaNElvvPGGhg0b5mh7+umnddttt2nSpEkaOnSoFcMCwLXZudO8nPynnyQvL2niRGnMGPMxAAAAcB0s+UZ56tQptW/fPl97u3btlJGRcdX9JSQkqFq1avLz81PLli21ZcuWvxx/6NChqlixonx9fVWnTh0tW7bsqscF4OEMQ5oxQ2rZ0iy4K1eWkpKk556j4AYAAIBTWPKtskuXLvrss8/ytX/++efq1KnTVfW1YMECxcbGKj4+Xt9//70aNWqk6OhopaenF3h8dna27r33Xh06dEiffvqp9u/fr5kzZ6py5crX9FoAeKiMDCkmRho8+L9rb+/YIbVt6+7IAAAA4EGcdnn5W2+95Xhcv359/eMf/1BSUpJatWolSdq0aZPWr1+vkSNHXlW/U6dO1aBBg9S/f39J0owZM/TVV19p9uzZGjNmTL7jZ8+erRMnTmjDhg0qXbq0JP3lzOkXLlzQhQsXHNuZmZmSJLvdLrvdflXx/i+73S7DMK67H+CvkGtX4bvvZOvdW7ZffpFRqpSMyZOlZ54xz27z7/eXyDW4EvkGVyHX4Crkmuco7HtoMwzDcMaA1atXL9yANpt++eWXQh2bnZ0tf39/ffrpp+rWrZujvV+/fjp16pQ+//zzfM/p2LGjKlSoIH9/f33++ecKCQlR7969NXr0aHl7exc4zgsvvKAJEybka//pp59UtmzZQsV6OXa7XRkZGSpXrpy8uFwVFiLXCsEw5D9zpsq+9JJsOTnKvfFGnfrnP5XTtKm7IytWyDW4EvkGVyHX4CrkmufIyspSnTp1lJGRocDAwMse57Qz3QcPHnRWVw7Hjh1Tbm6uwsLC8rSHhYVp3759BT7nl19+0apVq9SnTx8tW7ZMycnJGjJkiHJychQfH1/gc+Li4hQbG+vYzszMVEREhEJCQq74j1cYdrtdNptNISEh/FLBUuTaXzhxQrYBA2RbulSSZHTvLtvMmSpfvrybAyt+yDW4EvkGVyHX4Crkmufw8/Mr1HGWLzx76US6zWazeihJZhKHhobqX//6l7y9vdWsWTP9/vvvevXVVy9bdPv6+srX1zdfu5eXl1N+EWw2m9P6Aq6EXLuMDRukhx6SfvtN8vGRpk6VbcgQl30ueSJyDa5EvsFVyDW4CrnmGQr7/ln2Ln/wwQeKjIxUmTJlVKZMGTVs2FAffvjhVfURHBwsb29vpaWl5WlPS0tTeHh4gc+pWLGi6tSpk+dS8ptuukmpqanKzs6++hcCoPgyDOmVV6TbbzcL7lq1pE2bpKFDJQpuAAAAuIAlRffUqVM1ePBgdezYUQsXLtTChQvVvn17Pfnkk3rjjTcK3Y+Pj4+aNWumxMRER5vdbldiYqJjgrb/ddtttyk5OTnPTe0//fSTKlasKB8fn2t/UQCKl4wM6f77pdGjpdxc80z3tm1SkybujgwAAAAliCWXl0+fPl3vvvuu+vbt62jr0qWLbr75Zr3wwgsaMWJEofuKjY1Vv3791Lx5c7Vo0ULTpk3TmTNnHLOZ9+3bV5UrV9bkyZMlSYMHD9bbb7+t4cOH66mnntLPP/+sSZMm6emnn3buiwRQdP34o1lw//STeTn5W29Jjz/O2W0AAAC4nCVF95EjR9S6det87a1bt9aRI0euqq+YmBgdPXpU48ePV2pqqho3bqzly5c7JldLSUnJcy19RESEvv76a40YMUINGzZU5cqVNXz4cI0ePfr6XhSA4mHBAmnAAOnsWSkiQvr0U6lFC3dHBQAAgBLKkqK7Vq1aWrhwoZ577rk87QsWLFDt2rWvur9hw4Zp2LBhBe5LSkrK19aqVStt2rTpqscBUIzl5JiXkl+6heWee6RPPpFCQtwbFwAAAEo0S4ruCRMmKCYmRmvXrtVtt90mSVq/fr0SExO1cOFCK4YEUJKlpko9e0rr1pnbY8ZIEydKpSxfoAEAAAC4Iku+kfbo0UNbtmzR1KlTtWTJEknmDOJbtmxREyYxAuBM69dLDz4oHTkilS0rvf++1L27u6MCAAAAJFlQdOfk5OiJJ57QuHHj9NFHHzm7ewAwGYb09ttSbKx08aJUv760eLFUt667IwMAAAAcnL5kWOnSpfXvf//b2d0CwH+dPSv17Ss9/bRZcPfsKW3eTMENAACAIseSdbq7devmuKwcAJwqOVlq1Ur66CPJ21uaOlWaP18KCHB3ZAAAAEA+ltzTXbt2bb344otav369mjVrphtuuCHPftbMBnBNvvxSevhhKSNDCg2VFi6U7rjD3VEBAAAAl2VJ0f3ee+8pKChI27Zt07Zt2/Lss9lsFN0Ark5urjRhgjkjuWSe6V60SKpc2b1xAQAAAH/BkqL74MGDVnQLoCQ6cULq00davtzcHjZMev11ycfHvXEBAAAAheD0onvTpk1aunSpsrOzdc8996h9+/bOHgJASbF9u3T//dKhQ1KZMtK//mVeXg4AAAAUE04tuj/99FPFxMSoTJkyKl26tKZOnaqXX35Zo0aNcuYwAEqCDz6QnnhCOn9eqlHDXA6sUSN3RwUAAABcFafOXj558mQNGjRIGRkZOnnypF566SVNmjTJmUMA8HTZ2eYl5P36mQV3x47S1q0U3AAAACiWnFp079+/X6NGjZK3t7ckaeTIkcrKylJ6erozhwHgqY4cke65R0pIMLfj46WlS6Xy5d0bFwAAAHCNnHp5+dmzZxUYGOjY9vHxkZ+fn06fPq3Q0FBnDgXA02zYID3wgFl4Bwaa63B37uzuqAAAAIDr4vSJ1GbNmqWAgADH9sWLFzV37lwFBwc72lgyDICDYUgzZkjDh0s5OVL9+tKSJVLt2u6ODAAAALhuTi26q1SpopkzZ+ZpCw8P14cffujYZp1uAA7nz0tDhkhz5pjbDz4ozZ4t/ek/7gAAAIDizKlF96FDh5zZHQBPlpJiLge2bZvk5SVNmSKNGiXZbO6ODAAAAHAap19eDgB/adUqKSZGOnZM+tvfpPnzpagod0cFAAAAOJ1TZy8HgCsyDOm116R77zUL7qZNzTPdFNwAAADwUBTdAFzjzBmpVy/p2Wclu91ch/vbb6WqVd0dGQAAAGAZLi8HYL3kZKl7d2n3bqlUKenNN6XBg7l/GwAAAB6PohuAtb76SurTR8rIkMLDpU8/lW67zd1RAQAAAC5h2eXlBw4c0NixY9WrVy+lp6dLkv7zn//oxx9/tGpIAEWJ3S69+KLUubNZcLduLX3/PQU3AAAAShRLiu41a9YoMjJSmzdv1uLFi3X69GlJ0s6dOxUfH2/FkACKksxM83Ly+Hhz8rQhQ6TVq6WKFd0dGQAAAOBSlhTdY8aM0UsvvaQVK1bIx8fH0X733Xdr06ZNVgwJoKj4+Wfp1lulL76QfH2lOXOkhATpT58FAAAAQElhyT3dP/zwgz7++ON87aGhoTp27JgVQwIoCr7+WnroIenUKalyZemzz6RbbnF3VAAAAIDbWHKmOygoSEeOHMnXvn37dlWuXNmKIQG4k2FIr78udexoFtytWklbt1JwAwAAoMSzpOh+6KGHNHr0aKWmpspms8lut2v9+vUaNWqU+vbta8WQANzl3Dmpb19p1Chz8rSBA837t8PD3R0ZAAAA4HaWFN2TJk1SvXr1FBERodOnT6t+/fq6/fbb1bp1a40dO9aKIQG4w+HD0u23Sx99JHl7S9OnSzNnmvdyAwAAALDmnm4fHx/NnDlT48aN0+7du3X69Gk1adJEtWvXtmI4AO6wYYN0//1SWpr0t79JixZJd93l7qgAAACAIsWSovvbb79VmzZtVKVKFVWpUsWKIQC406xZ5jJgOTlSw4bSkiVS9erujgoAAAAociy5vPzuu+9W9erV9dxzz2nPnj1WDAHAHXJypGHDpEGDzMc9ekjr11NwAwAAAJdhSdH9xx9/aOTIkVqzZo0aNGigxo0b69VXX9Xhw4etGA6AKxw9KrVrZ665LUkTJ5qXlAcEuDcuAAAAoAizpOgODg7WsGHDtH79eh04cEAPPvig3n//fVWrVk133323FUMCsNLOnebyX0lJUtmy0uefS2PHSjabuyMDAAAAijRLiu4/q169usaMGaMpU6YoMjJSa9assXpIAM60aJHUurX0669SrVrSpk1Sly7ujgoAAAAoFiwtutevX68hQ4aoYsWK6t27txo0aKCvvvrKyiEBOIvdLo0bJ/XsKZ09a15avmWLVL++uyMDAAAAig1LZi+Pi4vT/Pnz9ccff+jee+/Vm2++qa5du8rf39+K4QA4W2am9PDD0tKl5vaoUdLkyVIpSz4yAAAAAI9lyTfotWvX6tlnn1XPnj0VHBxsxRAArJKcbF4+vnev5OtrLg/28MPujgoAAAAoliwputevX29FtwCstnKleTn5yZNSpUrm+tu33OLuqAAAAIBiy2lF9xdffKEOHTqodOnS+uKLL654bBcmYQKKFsOQpk+XYmOl3Fzp1lulxYulihXdHRkAAABQrDmt6O7WrZtSU1MVGhqqbt26XfY4m82m3NxcZw0L4HpduCANGSLNnm1u9+snzZgh+fm5Ny4AAADAAzit6Lbb7QU+BlCEpaZKPXpIGzZIXl7Sa69JzzzD+tsAAACAk1iyZNgHH3ygCxcu5GvPzs7WBx98YMWQAK7Wtm3m/dobNkhBQdKyZdKIERTcAAAAgBNZUnT3799fGRkZ+dqzsrLUv39/K4YEcDXmz5fatJEOH5bq1pU2b5aio90dFQAAAOBxLCm6DcOQrYCzZYcPH1a5cuWsGBJAYdjt0vPPS716SefPSx06mAV3nTrujgwAAADwSE5dMqxJkyay2Wyy2Wy65557VKrUf7vPzc3VwYMH1b59e2cOCaCwMjPN9baXLjW3//53adIkydvbvXEBAAAAHsypRfelWct37Nih6OhoBQQEOPb5+PioWrVq6tGjx1X3m5CQoFdffVWpqalq1KiRpk+frhYtWvzl8+bPn69evXqpa9euWrJkyVWPC3iM5GSpa1dpzx7J11eaNcsswAEAAABYyqlFd3x8vCSpWrVqiomJkZ8TlhxasGCBYmNjNWPGDLVs2VLTpk1TdHS09u/fr9DQ0Ms+79ChQxo1apTatm173TEAxdrKlVLPntLJk1KlStJnn0mF+E8rAAAAANfPqUX3Jf369XNaX1OnTtWgQYMcE7DNmDFDX331lWbPnq0xY8YU+Jzc3Fz16dNHEyZM0Lp163Tq1KkrjnHhwoU8s61nZmZKMpc+u97lz+x2uwzDYBk1WC5frhmG9Pbbso0cKVturowWLWT8+99m4U0+4jrwuQZXIt/gKuQaXIVc8xyFfQ8tKbpzc3P1xhtvaOHChUpJSVF2dnae/SdOnChUP9nZ2dq2bZvi4uIcbV5eXoqKitLGjRsv+7wXX3xRoaGhGjhwoNatW/eX40yePFkTJkzI13706FGdP3++ULFejt1uV0ZGhgzDkJeXJfPWAZL+J9dychT43HPy//hjSdK5Bx5QxquvSqVKSenpbo4UxR2fa3Al8g2uQq7BVcg1z5GVlVWo4ywpuidMmKBZs2Zp5MiRGjt2rJ5//nkdOnRIS5Ys0fjx4wvdz7Fjx5Sbm6uwsLA87WFhYdq3b1+Bz/n222/13nvvaceOHYUeJy4uTrGxsY7tzMxMRUREKCQkRIGBgYXupyB2u102m00hISH8UsFSjlyz2+Xdu7dsGzbI8PKS8fLL8h0xQqGsvw0n4XMNrkS+wVXINbgKueY5Cns7tSVF97x58zRz5kzdd999euGFF9SrVy/VrFlTDRs21KZNm/T0009bMayysrL0yCOPaObMmQoODi7083x9feXr65uv3cvLyym/CDabzWl9AVdSeu9eeffvL1tKilSunGzz58vGigGwAJ9rcCXyDa5CrsFVyDXPUNj3z5KiOzU1VZGRkZKkgIAAZWRkSJI6deqkcePGFbqf4OBgeXt7Ky0tLU97WlqawsPD8x1/4MABHTp0SJ07d3a0XbrOvlSpUtq/f79q1qx51a8HKBaWLlWF3r1lO3tWql3bXBqsbl13RwUAAACUaJb818qNN96oI0eOSJJq1qypb775RpL03XffFXhG+XJ8fHzUrFkzJSYmOtrsdrsSExPVqlWrfMfXq1dPP/zwg3bs2OH46dKli+666y7t2LFDERER1/nKgCLIMKTXXpOte3d5nT0r4+67pU2bKLgBAACAIsCSM93du3dXYmKiWrZsqaeeekoPP/yw3nvvPaWkpGjEiBFX1VdsbKz69eun5s2bq0WLFpo2bZrOnDnjmM28b9++qly5siZPniw/Pz81aNAgz/ODgoIkKV874BGys6XBg6XZs2WTdPbhh+U3a5ZsV/GfWwAAAACsY0nRPWXKFMfjmJgYValSRRs3blTt2rXzXPpdGDExMTp69KjGjx+v1NRUNW7cWMuXL3dMrpaSksK9ECiZjh2TevSQ1q6VvLxkf/11ZcbEyK90aXdHBgAAAOD/2QzDMNwdRFGTmZmpcuXKKSMjwymzl6enpys0NJT/HIDz7N0rdeok/fKLVLastGCB7NHR5Bpcgs81uBL5Blch1+Aq5JrnKGzd6LQz3V988UWhj+3SpYuzhgVKnm++kXr2lDIypOrVzQnTbr5Z+v9JAwEAAAAUHU4rurt161ao42w2m3Jzc501LFCyvP229MwzUm6u1KaNtHixFBLi7qgAAAAAXIbTim47Z9kA61y8KA0fLr3zjrndr5/0z39KTJgGAAAAFGmWTKQGwIlOnTIvJ1+xQrLZpClTpGefNR8DAAAAKNIsKbpffPHFK+4fP368FcMCnic52Zwwbf9+yd9fmjdPKuStHAAAAADcz5Ki+7PPPsuznZOTo4MHD6pUqVKqWbMmRTdQGElJ5pJgJ05IN95oTpjWuLG7owIAAABwFSwpurdv356vLTMzU48++qi6d+9uxZCAZ3nvPenJJ817uVu0kJYskSpWdHdUAAAAAK6SyxaGCwwM1IQJEzRu3DhXDQkUP7m50qhR0mOPmQX3Qw+ZZ7wpuAEAAIBiyaUTqWVkZCgjI8OVQwLFR1aW1Lu39OWX5vYLL0jjxzNhGgAAAFCMWVJ0v/XWW3m2DcPQkSNH9OGHH6pDhw5WDAkUbykpUufO0q5dkp+fNHeuFBPj7qgAAAAAXCdLiu433ngjz7aXl5dCQkLUr18/xcXFWTEkUHxt3ix17SqlpUlhYdIXX5j3cQMAAAAo9iwpug8ePGhFt4DnWbBA6tdPunBBatjQnKG8ShV3RwUAAADASVw2kRqAPzEM6cUXzYnSLlwwLy3/9lsKbgAAAMDDWHKm+/z585o+fbpWr16t9PR02e32PPu///57K4YFiofz56UBA6RPPjG3R46UXn5Z8vZ2b1wAAAAAnM6SonvgwIH65ptv9MADD6hFixayMfsyYEpLk7p1kzZtkkqVkt5911weDAAAAIBHsqTo/vLLL7Vs2TLddtttVnQPFE8//CB16mTOVF6+vPTvf0t33eXuqAAAAABYyJJ7uitXrqyyZcta0TVQPH31ldS6tVlw165tnumm4AYAAAA8niVF9+uvv67Ro0fr119/taJ7oPgwDGnaNKlLF+n0abPQ3rRJqlPH3ZEBAAAAcAFLLi9v3ry5zp8/rxo1asjf31+lS5fOs//EiRNWDAsULTk50lNPSf/8p7n92GNSQoLk4+PeuAAAAAC4jCVFd69evfT7779r0qRJCgsLYyI1lDwnT0oPPiglJko2m/Taa9KIEeZjAAAAACWGJUX3hg0btHHjRjVq1MiK7oGiLTnZnDBt/37phhvMpcE6d3Z3VAAAAADcwJKiu169ejp37pwVXQNF25o10v33SydOSBER0tKlEv/5BAAAAJRYlkykNmXKFI0cOVJJSUk6fvy4MjMz8/wAHmnOHOnee82Cu0ULacsWCm4AAACghLPkTHf79u0lSffcc0+edsMwZLPZlJuba8WwgHvk5kpjxpj3bUtSz57S3LlSmTJuDQsAAACA+1lSdK9evdqKboGi5/RpqU8f6YsvzO34ePOHCdMAAAAAyKKi+4477rCiW6BoSUkxJ0jbtUvy9TXPbj/0kLujAgAAAFCEWFJ0r1279or7b7/9diuGBVxn0yapWzcpLU0KC5M+/1xq2dLdUQEAAAAoYiwpuu+88858bX9eq5t7ulGszZ8vPfqodOGCOVHaF19IVaq4OyoAAAAARZAls5efPHkyz096erqWL1+uW265Rd98840VQwLWMwzzfu1evcyCu0sX6dtvKbgBAAAAXJYlZ7rLlSuXr+3ee++Vj4+PYmNjtW3bNiuGBaxz7px5dnvhQnP72WelyZMlb2+3hgUAAACgaLOk6L6csLAw7d+/35VDAtfvyBGpa1fpu++k0qWlGTOkAQPcHRUAAACAYsCSonvXrl15tg3D0JEjRzRlyhQ1btzYiiEBa2zfbl5GfviwVKGCtHixxOz8AAAAAArJkqK7cePGstlsMgwjT/utt96q2bNnWzEk4HxLlphrcJ89K9WrJ335pVSzprujAgAAAFCMWFJ0Hzx4MM+2l5eXQkJC5OfnZ8VwgHMZhvTKK1JcnPm4XTtpwQIpKMjdkQEAAAAoZiwpuqtWrWpFt4D1LlyQnnhCev99c3voUGnaNKmUS6c/AAAAAOAhnLpk2KpVq1S/fn1lZmbm25eRkaGbb75Z69atc+aQgPMcPSpFRZkFt7e39Pbb5g8FNwAAAIBr5NSie9q0aRo0aJACAwPz7StXrpyeeOIJTZ061ZlDAs6xZ4/UsqW57na5ctKyZeZZbgAAAAC4Dk4tunfu3Kn27dtfdn+7du1YoxtFz9dfS61aSQcPSjVqSBs3mvdxAwAAAMB1cmrRnZaWptKlS192f6lSpXT06FFnDglcn4QE6b77pMxMqW1bafNm6aab3B0VAAAAAA/h1KK7cuXK2r1792X379q1SxUrVnTmkMC1uXhReuopadgwKTdXevRRacUKKTjY3ZEBAAAA8CBOLbo7duyocePG6fz58/n2nTt3TvHx8erUqZMzhwSuXkaG1KmTOUmaJE2ZIs2eLfn6ujcuAAAAAB7HqdMyjx07VosXL1adOnU0bNgw1a1bV5K0b98+JSQkKDc3V88//7wzhwSuzi+/SJ07mxOn+ftLH30kde/u7qgAAAAAeCinFt1hYWHasGGDBg8erLi4OBmGIUmy2WyKjo5WQkKCwsLCnDkkUHjffmsW2MeOSZUqSUuXSk2bujsqAAAAAB7M6QsQV61aVcuWLdPJkyeVnJwswzBUu3ZtlS9f3tlDAYX34YfSY49J2dlmof3FF1Llyu6OCgAAAICHc+o93X9Wvnx53XLLLWrRosV1F9wJCQmqVq2a/Pz81LJlS23ZsuWyx86cOVNt27ZV+fLlVb58eUVFRV3xeHg4u10aO1bq29csuO+/X1q7loIbAAAAgEtYVnQ7y4IFCxQbG6v4+Hh9//33atSokaKjo5Wenl7g8UlJSerVq5dWr16tjRs3KiIiQu3atdPvv//u4sjhdmfPSjEx0j/+YW7HxUmLFkk33ODeuAAAAACUGEW+6J46daoGDRqk/v37q379+poxY4b8/f01e/bsAo+fN2+ehgwZosaNG6tevXqaNWuW7Ha7EhMTXRw53OqPP6Q77pA+/VQqXVp6/31p0iTJq8inPAAAAAAP4vR7up0pOztb27ZtU1xcnKPNy8tLUVFR2rhxY6H6OHv2rHJyclShQoXLHnPhwgVduHDBsZ2ZmSlJstvtstvt1xi9HH0YhnHd/eAqbN8uW9eusv3+u4y//U3Gv/8ttW1rXmruwcg1uAq5Blci3+Aq5BpchVzzHIV9D4t00X3s2DHl5ubmm/E8LCxM+/btK1Qfo0ePVqVKlRQVFXXZYyZPnqwJEybkaz969GiBa45fDbvdroyMDBmGIS/OslrO9z//UbmhQ2U7d04Xa9XSyQ8/VG61atJlbkfwJOQaXIVcgyuRb3AVcg2uQq55jqysrEIdV6SL7us1ZcoUzZ8/X0lJSfLz87vscXFxcYqNjXVsZ2ZmKiIiQiEhIQoMDLyuGOx2u2w2m0JCQvilspJhSK+9JltcnGyGISMqSl4LFuhvQUHujsxlyDW4CrkGVyLf4CrkGlyFXPMcV6ox/6xIF93BwcHy9vZWWlpanva0tDSFh4df8bmvvfaapkyZopUrV6phw4ZXPNbX11e+vr752r28vJzyi2Cz2ZzWFwqQnS0NHixdus9/yBDZ3nxTtlJFOr0tQa7BVcg1uBL5Blch1+Aq5JpnKOz7V6TfZR8fHzVr1izPJGiXJkVr1arVZZ/3yiuvaOLEiVq+fLmaN2/uilDhLsePS+3amQW3l5f01ltSQoJUAgtuAAAAAEVPka9MYmNj1a9fPzVv3lwtWrTQtGnTdObMGfXv31+S1LdvX1WuXFmTJ0+WJL388ssaP368Pv74Y1WrVk2pqamSpICAAAUEBLjtdcAC+/dLnTpJyclS2bLSggVShw7ujgoAAAAAHIp80R0TE6OjR49q/PjxSk1NVePGjbV8+XLH5GopKSl5Tuu/++67ys7O1gMPPJCnn/j4eL3wwguuDB1WWrVK6tFDOnVKqlZNWrpUatDA3VEBAAAAQB5FvuiWpGHDhmnYsGEF7ktKSsqzfejQIesDgnvNmmXew33xotSqlbRkiRQa6u6oAAAAACCfIn1PN5BHbq40apQ0aJBZcPfubZ7xpuAGAAAAUEQVizPdgE6flvr0kb74wtyeMEEaN06y2dwbFwAAAABcAUU3ir7ffpM6d5Z27pR8faW5c6WHHnJ3VAAAAADwlyi6UbR9953UpYuUmmpeRv7559Ktt7o7KgAAAAAoFO7pRtH16afS7bebBXdkpLRlCwU3AAAAgGKFohtFj2FIkyZJDz4onT8vdewoffutVLWquyMDAAAAgKtC0Y2i5cIFqV8/6fnnze1nnjEnTwsMdGtYAAAAAHAtuKcbRcexY1L37uZZbW9v6e23pSefdHdUAAAAAHDNKLpRNOzdK3XqJP3yi1SunLRokXTvve6OCgAAAACuC5eXw/1WrJBatTIL7ho1pI0bKbgBAAAAeASKbrhXQoLUoYOUkSG1aSNt3izddJO7owIAAAAAp6Dohnvk5EhDhkjDhkm5uVLfvtLKlVJwsLsjAwAAAACn4Z5uuN6JE+ZyYKtWSTab9PLL0qhR5mMAAAAA8CAU3XCtffukzp2l5GQpIECaN0/q0sXdUQEAAACAJSi64TrffCP17Gnev121qrR0qRQZ6e6oAAAAAMAy3NMN6xmGNH36fydMu+02acsWCm4AAAAAHo+iG9bKyZEGD5aeflqy26VHH5USE6XQUHdHBgAAAACW4/JyWOf4cemBB6SkJHOStFdekUaOZMI0AAAAACUGRTessXevOWHagQPmhGmffCJ16uTuqAAAAADApSi64XzLl0sxMVJmplStmjlhWoMG7o4KAAAAAFyOe7rhPIYhvfmmdN99ZsHdtq05YRoFNwAAAIASiqIbzpGdLT3xhPTMM+aEaQMGSCtXSiEh7o4MAAAAANyGy8tx/Y4fl3r0kNaskby8pFdflUaMYMI0AAAAACUeRTeuz5495oRpv/wilS0rzZ8vdezo7qgAAAAAoEjg8nJcu88+k1q2NAvu6tWljRspuAEAAADgTyi6cfVyc6Xnn5fuv186fVq6805zwrSbb3Z3ZAAAAABQpHB5Oa7OiRNS797S11+b2yNGSK+8IpUilQAAAADgf1EpofB27jTPbv/yi1SmjDRrllmAAwAAAAAKRNGNwvnkE2ngQOncOfP+7c8+kxo1cndUAAAAAFCkcU83ruziRSk21jyjfe6c1K6dtHUrBTcAAAAAFAJFNy4vPV26917pjTfM7bg4adkyqUIF98YFAAAAAMUEl5ejYN99J/XoIf32mxQQIL3/vnk/NwAAAACg0DjTjfxmz5batjUL7jp1pM2bKbgBAAAA4BpQdOO/srOlIUPMCdMuXJC6dDHX365f392RAQAAAECxRNEN0x9/SHfdJb37rmSzSS++aM5QXq6cuyMDAAAAgGKLe7ohrV8vPfCAlJpqFtnz5kn33efuqAAAAACg2ONMd0lmGNI770h33mkW3DffbE6gRsENAAAAAE5B0V1S/fij1L27NHSouRb3gw9KmzZJtWu7OzIAAAAA8BgU3SVNcrL08MNSZKT0+eeSl5f0yivSggXm0mAAAAAAAKfhnu6S4rffpIkTzeXAcnPNtvvvNydMu/lm98YGAAAAAB6KotvTpaVJkyZJM2aYS4JJUvv20ksvSc2auTc2AAAAAPBwFN2e6sQJ6dVXpbfeks6eNdvuuMMsttu0cW9sAAAAAFBCUHR7msxMado06fXXzceS1KKFWWxHRZlrcAMAAAAAXIKi21OcPWsu/zVlinT8uNkWGWkW2507U2wDAAAAgBtQdBd32dnSzJnSP/4hHTlittWpI02YIPXsac5ODgAAAABwi2JRkSUkJKhatWry8/NTy5YttWXLlisev2jRItWrV09+fn6KjIzUsmXLXBSpC128KM2ZYxbYw4aZBXfVqubs5D/+KD30EAU3AAAAALhZka/KFixYoNjYWMXHx+v7779Xo0aNFB0drfT09AKP37Bhg3r16qWBAwdq+/bt6tatm7p166bdu3e7OHILGYZ0553SgAHSr79K4eHS229L+/dL/ftLpbiAAQAAAACKgiJfdE+dOlWDBg1S//79Vb9+fc2YMUP+/v6aPXt2gce/+eabat++vZ599lnddNNNmjhxopo2baq3337bxZFbyGaTunWT/vY3c4byAwekoUMlX193RwYAAAAA+JMifUo0Oztb27ZtU1xcnKPNy8tLUVFR2rhxY4HP2bhxo2JjY/O0RUdHa8mSJZcd58KFC7pw4YJjO/P/Z/222+2y2+3X8QrMPgzDuO5+8hk8WHrsMSkw8NJAzu0fxY5luQb8D3INrkS+wVXINbgKueY5CvseFumi+9ixY8rNzVVYWFie9rCwMO3bt6/A56SmphZ4fGpq6mXHmTx5siZMmJCv/ejRozp//vw1RP5fdrtdGRkZMgxDXlbcY32d8cFzWJ5rwP8j1+BK5BtchVyDq5BrniMrK6tQxxXpottV4uLi8pwdz8zMVEREhEJCQhR46UzyNbLb7bLZbAoJCeGXCpYi1+Aq5BpciXyDq5BrcBVyzXP4+fkV6rgiXXQHBwfL29tbaWlpedrT0tIUHh5e4HPCw8Ov6nhJ8vX1lW8B90N7eXk55RfBZrM5rS/gSsg1uAq5Blci3+Aq5BpchVzzDIV9/4r0u+zj46NmzZopMTHR0Wa325WYmKhWrVoV+JxWrVrlOV6SVqxYcdnjAQAAAACwSpE+0y1JsbGx6tevn5o3b64WLVpo2rRpOnPmjPr37y9J6tu3rypXrqzJkydLkoYPH6477rhDr7/+uu677z7Nnz9fW7du1b/+9S93vgwAAAAAQAlU5IvumJgYHT16VOPHj1dqaqoaN26s5cuXOyZLS0lJyXNav3Xr1vr44481duxYPffcc6pdu7aWLFmiBg0auOslAAAAAABKKJthGIa7gyhqMjMzVa5cOWVkZDhlIrX09HSFhoZyzwYsRa7BVcg1uBL5Blch1+Aq5JrnKGzdyLsMAAAAAIBFKLoBAAAAALAIRTcAAAAAABah6AYAAAAAwCJFfvZyd7g0t1xmZuZ192W325WVlSU/Pz8mSoClyDW4CrkGVyLf4CrkGlyFXPMcl+rFv5qbnKK7AFlZWZKkiIgIN0cCAAAAACjKsrKyVK5cucvuZ8mwAtjtdv3xxx8qW7asbDbbdfWVmZmpiIgI/fbbb9e9/BhwJeQaXIVcgyuRb3AVcg2uQq55DsMwlJWVpUqVKl3xqgXOdBfAy8tLN954o1P7DAwM5JcKLkGuwVXINbgS+QZXIdfgKuSaZ7jSGe5LuIkAAAAAAACLUHQDAAAAAGARim6L+fr6Kj4+Xr6+vu4OBR6OXIOrkGtwJfINrkKuwVXItZKHidQAAAAAALAIZ7oBAAAAALAIRTcAAAAAABah6AYAAAAAwCIU3QAAAAAAWISi20IJCQmqVq2a/Pz81LJlS23ZssXdIaGIe+GFF2Sz2fL81KtXz7H//PnzGjp0qP72t78pICBAPXr0UFpaWp4+UlJSdN9998nf31+hoaF69tlndfHixTzHJCUlqWnTpvL19VWtWrU0d+5cV7w8uNHatWvVuXNnVapUSTabTUuWLMmz3zAMjR8/XhUrVlSZMmUUFRWln3/+Oc8xJ06cUJ8+fRQYGKigoCANHDhQp0+fznPMrl271LZtW/n5+SkiIkKvvPJKvlgWLVqkevXqyc/PT5GRkVq2bJnTXy/c569y7dFHH833Ode+ffs8x5BrKIzJkyfrlltuUdmyZRUaGqpu3bpp//79eY5x5d9Nvvd5rsLk2p133pnvs+3JJ5/Mcwy5VoIZsMT8+fMNHx8fY/bs2caPP/5oDBo0yAgKCjLS0tLcHRqKsPj4eOPmm282jhw54vg5evSoY/+TTz5pREREGImJicbWrVuNW2+91WjdurVj/8WLF40GDRoYUVFRxvbt241ly5YZwcHBRlxcnOOYX375xfD39zdiY2ONPXv2GNOnTze8vb2N5cuXu/S1wrWWLVtmPP/888bixYsNScZnn32WZ/+UKVOMcuXKGUuWLDF27txpdOnSxahevbpx7tw5xzHt27c3GjVqZGzatMlYt26dUatWLaNXr16O/RkZGUZYWJjRp08fY/fu3cYnn3xilClTxvjnP//pOGb9+vWGt7e38corrxh79uwxxo4da5QuXdr44YcfLP83gGv8Va7169fPaN++fZ7PuRMnTuQ5hlxDYURHRxtz5swxdu/ebezYscPo2LGjUaVKFeP06dOOY1z1d5PvfZ6tMLl2xx13GIMGDcrz2ZaRkeHYT66VbBTdFmnRooUxdOhQx3Zubq5RqVIlY/LkyW6MCkVdfHy80ahRowL3nTp1yihdurSxaNEiR9vevXsNScbGjRsNwzC/7Hp5eRmpqamOY959910jMDDQuHDhgmEYhvH3v//duPnmm/P0HRMTY0RHRzv51aCo+t9CyG63G+Hh4carr77qaDt16pTh6+trfPLJJ4ZhGMaePXsMScZ3333nOOY///mPYbPZjN9//90wDMN45513jPLlyztyzTAMY/To0UbdunUd2z179jTuu+++PPG0bNnSeOKJJ5z6GlE0XK7o7tq162WfQ67hWqWnpxuSjDVr1hiG4dq/m3zvK1n+N9cMwyy6hw8fftnnkGslG5eXWyA7O1vbtm1TVFSUo83Ly0tRUVHauHGjGyNDcfDzzz+rUqVKqlGjhvr06aOUlBRJ0rZt25STk5Mnr+rVq6cqVao48mrjxo2KjIxUWFiY45jo6GhlZmbqxx9/dBzz5z4uHUNullwHDx5UampqnrwoV66cWrZsmSe3goKC1Lx5c8cxUVFR8vLy0ubNmx3H3H777fLx8XEcEx0drf379+vkyZOOY8g/JCUlKTQ0VHXr1tXgwYN1/Phxxz5yDdcqIyNDklShQgVJrvu7yfe+kud/c+2SefPmKTg4WA0aNFBcXJzOnj3r2EeulWyl3B2AJzp27Jhyc3Pz/FJJUlhYmPbt2+emqFActGzZUnPnzlXdunV15MgRTZgwQW3bttXu3buVmpoqHx8fBQUF5XlOWFiYUlNTJUmpqakF5t2lfVc6JjMzU+fOnVOZMmUsenUoqi7lRkF58ee8CQ0NzbO/VKlSqlChQp5jqlevnq+PS/vKly9/2fy71Ac8X/v27XX//ferevXqOnDggJ577jl16NBBGzdulLe3N7mGa2K32/XMM8/otttuU4MGDSTJZX83T548yfe+EqSgXJOk3r17q2rVqqpUqZJ27dql0aNHa//+/Vq8eLEkcq2ko+gGipAOHTo4Hjds2FAtW7ZU1apVtXDhQophAB7hoYcecjyOjIxUw4YNVbNmTSUlJemee+5xY2QozoYOHardu3fr22+/dXco8HCXy7XHH3/c8TgyMlIVK1bUPffcowMHDqhmzZquDhNFDJeXWyA4OFje3t75ZsdMS0tTeHi4m6JCcRQUFKQ6deooOTlZ4eHhys7O1qlTp/Ic8+e8Cg8PLzDvLu270jGBgYEU9iXUpdy40mdWeHi40tPT8+y/ePGiTpw44ZT847Ox5KpRo4aCg4OVnJwsiVzD1Rs2bJi+/PJLrV69WjfeeKOj3VV/N/neV3JcLtcK0rJlS0nK89lGrpVcFN0W8PHxUbNmzZSYmOhos9vtSkxMVKtWrdwYGYqb06dP68CBA6pYsaKaNWum0qVL58mr/fv3KyUlxZFXrVq10g8//JDnC+uKFSsUGBio+vXrO475cx+XjiE3S67q1asrPDw8T15kZmZq8+bNeXLr1KlT2rZtm+OYVatWyW63O75YtGrVSmvXrlVOTo7jmBUrVqhu3boqX7684xjyD392+PBhHT9+XBUrVpRErqHwDMPQsGHD9Nlnn2nVqlX5bjlw1d9Nvvd5vr/KtYLs2LFDkvJ8tpFrJZi7Z3LzVPPnzzd8fX2NuXPnGnv27DEef/xxIygoKM+MhcD/GjlypJGUlGQcPHjQWL9+vREVFWUEBwcb6enphmGYS59UqVLFWLVqlbF161ajVatWRqtWrRzPv7QcRbt27YwdO3YYy5cvN0JCQgpcjuLZZ5819u7dayQkJLBkWAmQlZVlbN++3di+fbshyZg6daqxfft249dffzUMw1wyLCgoyPj888+NXbt2GV27di1wybAmTZoYmzdvNr799lujdu3aeZZxOnXqlBEWFmY88sgjxu7du4358+cb/v7++ZZxKlWqlPHaa68Ze/fuNeLj41nGycNcKdeysrKMUaNGGRs3bjQOHjxorFy50mjatKlRu3Zt4/z5844+yDUUxuDBg41y5coZSUlJeZZpOnv2rOMYV/3d5HufZ/urXEtOTjZefPFFY+vWrcbBgweNzz//3KhRo4Zx++23O/og10o2im4LTZ8+3ahSpYrh4+NjtGjRwti0aZO7Q0IRFxMTY1SsWNHw8fExKleubMTExBjJycmO/efOnTOGDBlilC9f3vD39ze6d+9uHDlyJE8fhw4dMjp06GCUKVPGCA4ONkaOHGnk5OTkOWb16tVG48aNDR8fH6NGjRrGnDlzXPHy4EarV682JOX76devn2EY5rJh48aNM8LCwgxfX1/jnnvuMfbv35+nj+PHjxu9evUyAgICjMDAQKN///5GVlZWnmN27txptGnTxvD19TUqV65sTJkyJV8sCxcuNOrUqWP4+PgYN998s/HVV19Z9rrhelfKtbNnzxrt2rUzQkJCjNKlSxtVq1Y1Bg0alO/LIrmGwigozyTl+Zvmyr+bfO/zXH+VaykpKcbtt99uVKhQwfD19TVq1aplPPvss3nW6TYMcq0ksxmGYbjuvDoAAAAAACUH93QDAAAAAGARim4AAAAAACxC0Q0AAAAAgEUougEAAAAAsAhFNwAAAAAAFqHoBgAAAADAIhTdAAAAAABYhKIbAAAAAACLUHQDAIAruvPOO/XMM8+4OwwAAIolim4AADxY586d1b59+wL3rVu3TjabTbt27XJxVAAAlBwU3QAAeLCBAwdqxYoVOnz4cL59c+bMUfPmzdWwYUM3RAYAQMlA0Q0AgAfr1KmTQkJCNHfu3Dztp0+f1qJFi9StWzf16tVLlStXlr+/vyIjI/XJJ59csU+bzaYlS5bkaQsKCsozxm+//aaePXsqKChIFSpUUNeuXXXo0CHnvCgAAIoRim4AADxYqVKl1LdvX82dO1eGYTjaFy1apNzcXD388MNq1qyZvvrqK+3evVuPP/64HnnkEW3ZsuWax8zJyVF0dLTKli2rdevWaf369QoICFD79u2VnZ3tjJcFAECxQdENAICHGzBggA4cOKA1a9Y42ubMmaMePXqoatWqGjVqlBo3bqwaNWroqaeeUvv27bVw4cJrHm/BggWy2+2aNWuWIiMjddNNN2nOnDlKSUlRUlKSE14RAADFB0U3AAAerl69emrdurVmz54tSUpOTta6des0cOBA5ebmauLEiYqMjFSFChUUEBCgr7/+WikpKdc83s6dO5WcnKyyZcsqICBAAQEBqlChgs6fP68DBw4462UBAFAslHJ3AAAAwHoDBw7UU089pYSEBM2ZM0c1a9bUHXfcoZdffllvvvmmpk2bpsjISN1www165plnrngZuM1my3OpumReUn7J6dOn1axZM82bNy/fc0NCQpz3ogAAKAYougEAKAF69uyp4cOH6+OPP9YHH3ygwYMHy2azaf369eratasefvhhSZLdbtdPP/2k+vXrX7avkJAQHTlyxLH9888/6+zZs47tpk2basGCBQoNDVVgYKB1LwoAgGKAy8sBACgBAgICFBMTo7i4OB05ckSPPvqoJKl27dpasWKFNmzYoL179+qJJ55QWlraFfu6++679fbbb2v79u3aunWrnnzySZUuXdqxv0+fPgoODlbXrl21bt06HTx4UElJSXr66acLXLoMAABPRtENAEAJMXDgQJ08eVLR0dGqVKmSJGns2LFq2rSpoqOjdeeddyo8PFzdunW7Yj+vv/66IiIi1LZtW/Xu3VujRo2Sv7+/Y7+/v7/Wrl2rKlWq6P7779dNN92kgQMH6vz585z5BgCUODbjf2/KAgAAAAAATsGZbgAAAAAALELRDQAAAACARSi6AQAAAACwCEU3AAAAAAAWoegGAAAAAMAiFN0AAAAAAFiEohsAAAAAAItQdAMAAAAAYBGKbgAAAAAALELRDQAAAACARSi6AQAAAACwyP8B1CM+Us5MKHMAAAAASUVORK5CYII=",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Analisi distribuzionale per avg_oil_prod\n",
|
||
"\n",
|
||
"Statistiche Predizioni:\n",
|
||
"mean: 6532.427\n",
|
||
"variance: 13545224.000\n",
|
||
"std: 3680.384\n",
|
||
"min: 608.335\n",
|
||
"max: 21836.357\n",
|
||
"median: 6024.207\n",
|
||
"\n",
|
||
"Statistiche Target Reali:\n",
|
||
"mean: 6539.446\n",
|
||
"variance: 14259229.000\n",
|
||
"std: 3776.139\n",
|
||
"min: 415.672\n",
|
||
"max: 24818.859\n",
|
||
"median: 5996.325\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Analisi distribuzionale per total_water_need\n",
|
||
"\n",
|
||
"Statistiche Predizioni:\n",
|
||
"mean: 60131.293\n",
|
||
"variance: 836238848.000\n",
|
||
"std: 28917.795\n",
|
||
"min: 10634.454\n",
|
||
"max: 142663.500\n",
|
||
"median: 59327.203\n",
|
||
"\n",
|
||
"Statistiche Target Reali:\n",
|
||
"mean: 60065.336\n",
|
||
"variance: 879319232.000\n",
|
||
"std: 29653.316\n",
|
||
"min: 8061.355\n",
|
||
"max: 151159.875\n",
|
||
"median: 59136.551\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y)"
|
||
]
|
||
}
|
||
],
|
||
"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": "3.11.0rc1"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|