3398 lines
1.6 MiB
3398 lines
1.6 MiB
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "initial_id",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Get:1 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]\n",
|
||
"Hit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n",
|
||
"Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease \n",
|
||
"Get:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB]\n",
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||
"Get:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB]\n",
|
||
"Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [2738 kB]\n",
|
||
"Fetched 3122 kB in 1s (2095 kB/s) \n",
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||
"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",
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||
"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",
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||
"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",
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||
"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",
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||
"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",
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||
"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",
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||
"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",
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||
"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",
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||
"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n",
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||
"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",
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||
"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",
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||
"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": [
|
||
"2024-12-06 21:10:18.211466: 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",
|
||
"2024-12-06 21:10:18.211503: 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",
|
||
"2024-12-06 21:10:18.211543: 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",
|
||
"2024-12-06 21:10:18.219524: 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": [
|
||
"2024-12-06 21:10:20.488869: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 26565 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:81:00.0, compute capability: 8.9\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import tensorflow as tf\n",
|
||
"import keras\n",
|
||
"\n",
|
||
"print(f\"Keras version: {keras.__version__}\")\n",
|
||
"print(f\"TensorFlow version: {tf.__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 configuration\n",
|
||
"import tensorflow as tf\n",
|
||
"import os\n",
|
||
"\n",
|
||
"# Limita la crescita della memoria GPU\n",
|
||
"gpus = tf.config.experimental.list_physical_devices('GPU')\n",
|
||
"if gpus:\n",
|
||
" try:\n",
|
||
" # Imposta la crescita di memoria dinamica\n",
|
||
" for gpu in gpus:\n",
|
||
" tf.config.experimental.set_memory_growth(gpu, True)\n",
|
||
" \n",
|
||
" # Opzionalmente, limita la memoria GPU massima (uncomment se necessario)\n",
|
||
" # tf.config.experimental.set_virtual_device_configuration(\n",
|
||
" # gpus[0],\n",
|
||
" # [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024*4)] # 4GB\n",
|
||
" # )\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",
|
||
"# Imposta le opzioni di logging\n",
|
||
"os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Riduce i messaggi di log\n",
|
||
" \n",
|
||
"# Configura la modalità mista di precisione\n",
|
||
"tf.keras.mixed_precision.set_global_policy('float32')\n",
|
||
"\n",
|
||
"# Imposta il seed per la riproducibilità\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": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"import tensorflow_addons as tfa\n",
|
||
"from datetime import datetime\n",
|
||
"import os\n",
|
||
"import joblib\n",
|
||
"import re\n",
|
||
"from typing import List\n",
|
||
"\n",
|
||
"random_state_value = None\n",
|
||
"execute_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n",
|
||
"\n",
|
||
"base_project_dir = './'\n",
|
||
"data_dir = '../../sources/'\n",
|
||
"models_project_dir = base_project_dir\n",
|
||
"\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",
|
||
" Rimuove caratteri speciali e spazi, converte in snake_case e abbrevia.\n",
|
||
"\n",
|
||
" Parameters\n",
|
||
" ----------\n",
|
||
" name : str\n",
|
||
" Nome della colonna da pulire\n",
|
||
"\n",
|
||
" Returns\n",
|
||
" -------\n",
|
||
" str\n",
|
||
" Nome della colonna pulito\n",
|
||
" \"\"\"\n",
|
||
" # Rimuove caratteri speciali\n",
|
||
" name = re.sub(r'[^a-zA-Z0-9\\s]', '', name)\n",
|
||
" # Converte in snake_case\n",
|
||
" name = name.lower().replace(' ', '_')\n",
|
||
"\n",
|
||
" # Abbreviazioni comuni\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",
|
||
" Pulisce tutti i nomi delle colonne in un DataFrame.\n",
|
||
"\n",
|
||
" Parameters\n",
|
||
" ----------\n",
|
||
" df : pd.DataFrame\n",
|
||
" DataFrame con le colonne da pulire\n",
|
||
"\n",
|
||
" Returns\n",
|
||
" -------\n",
|
||
" list\n",
|
||
" Lista dei nuovi nomi delle colonne puliti\n",
|
||
" \"\"\"\n",
|
||
" new_columns = []\n",
|
||
"\n",
|
||
" for col in df.columns:\n",
|
||
" # Usa regex per separare le varietà\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",
|
||
" # Trova tutte le colonne delle tecniche\n",
|
||
" tech_columns = [col for col in df.columns if col.endswith('_tech')]\n",
|
||
"\n",
|
||
" # Applica il mapping a tutte le colonne delle tecniche\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",
|
||
" # Pulisci i nomi delle colonne\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",
|
||
" # Pulisci i nomi delle colonne\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",
|
||
" # Fare predizioni\n",
|
||
" predictions = model.predict(test_data)\n",
|
||
"\n",
|
||
" # Denormalizzare predizioni e target\n",
|
||
" predictions_real = scaler_y.inverse_transform(predictions)\n",
|
||
" targets_real = scaler_y.inverse_transform(test_targets)\n",
|
||
"\n",
|
||
" # Calcolare errore percentuale per ogni 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",
|
||
" # Stampa risultati per ogni target\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": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"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: .//2024-12-06_21-10_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: .//2024-12-06_21-10_plots/variety_comparison_avg_oil_production_l_ha.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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HR0uXLlW2bNmUN29eB1ePpMxiscjHx0epUqXSkSNHNHDgQFWpUkUfffSRKlWqpNOnT+u1115Tp06dVKtWLTk7OysuLo73KOjnn3/W1KlTVaVKFVs/n5w5c6pfv34aMmSIpkyZIklq0KCBOnToICcnJ33wwQfP7LdJT7KXV+7cubVp0yblzp1bP/30k6xWq6QnPX/TpEmj77//Xjdu3LCNz5o1q/Lnz28bB0iipxSSjj9u+4yNjTWxsbFm5MiRxsnJyUyaNMlEREQkGnPt2jUzbtw4vqFBIvE75ZYuXWrSp09v+vbta44fP247P3jwYOPs7Jxox9Tx48fN+fPn7V4rki6r1WrGjBljihYtamrXrm1cXFzM3Llzbednz55tKlasaGrXrm3Onz9vzp07Zz799FOTKVOmpy5zAK5cuWIyZcpk3N3dzddff22MebKrJd6ECRNM8eLFTffu3W2Xq3N5FRJ6VnuCAwcOmBw5cphKlSoZT09P065dO7NgwQKza9cukz9/fhMcHOyocvECOHjwoMmXL58pV66c7bLOK1eumE8//dR4eXmZyZMn28YuW7bMFClSxHTq1MlR5SIJu3XrlilQoIApUaKE7bPr66+/Np6enqZHjx5mx44d5tq1a6ZPnz4mY8aMifq8AoRSSBISLrz37t1r1q9fb7Zt22Y7NmjQIOPk5GSmTZtmC6Zat25tW7gbw9ZhJLZnzx6TOnVq8/XXXz/zMoXBgwebFClSPNUwH/ijli1bGovFYmrUqJHouNVqNXPmzDGVKlUyFovFFChQwOTIkcMcPHjQQZUiKbt+/bpJnTq18fHxMa+99prtUr6El85MnDjRZMuWzfTp08dER0dzKTpsEq6Tbt26ZSIjI20hwvbt281XX31l1qxZY2sW/OjRI1O8eHGzYsUKR5SLF8jBgwdNnjx5zGuvvWabU5cvX7YFU1OmTLGN3bp1K+ttPFNMTIxZvXq1KVasWKKbKXzzzTemaNGi5pVXXjEFChQw2bJlM4cOHXJwtUhqLMb8/zUuQBLQt29frV69Wo8ePVKGDBnk4uKinTt3ymKxaPjw4RoxYoSaNWums2fP6vr16zp37hxb0JGIMUYWi0WjRo1SSEiIVq1aZTsXFxeXaIt5z549NWfOHJ0/f16pU6d2RLlIwmJiYmSxWNS3b19dvnxZ165dU8mSJfXZZ58pZcqUtnFWq1W7du2Sp6enMmbMqIwZMzqwaiRlly9f1uPHj1WrVi15enpq27ZtSpUqlaKjo22XgM6bN0/ly5dXrly5HFwtkgqr1SonpycdN8aMGaNVq1YpOjpavr6++u6775QmTRrFxsbKxcVFjx8/VmRkpFq2bKlff/1Ve/bs4dIqJBK/Tkr48+HDh9WoUSOlT59eO3bskKurq65cuaIZM2Zo8uTJ6tu3r/r06WN7zB/XU4AkPX78WFu3blXPnj2VJk0abd++Xc7Ozjp//rzu3LmjBw8eKH/+/KyT8BRCKThMwkWWJE2YMEEjRozQmjVrVKZMGY0ePVoDBgzQunXrVL16dUnS5MmTtW/fPrm6uurrr7+Wq6srH4x4pu7du+vQoUPaunVronkmPekzVapUKTk5OenWrVtKnz69g6pEUvTHBXv8sSFDhmj9+vV67bXXEgVTV69eVcaMGZ+aZ3i5xc+jW7du2T6rfHx8ZLVadfz4cTVp0kSpU6fW5s2blSpVKo0fP14PHjyw9TADpMTvRwMGDNC3336rESNG6JVXXlH//v3l5uamtWvXKkuWLIqKitIXX3yhrVu36uHDh9q5cyfrJCSScO1ttVoVHR0tDw8PSdLhw4f13nvvKUOGDLZg6urVq/riiy908uRJbdiwQRJ9W/H7+9KBAwd04MABWSwWlStXTv7+/omCqbRp02r79u2sj/AfMUPgELdu3ZKTk5OtqXlcXJyOHTumkSNHqmzZslq1apVGjx6tr7/+WtWrV1dkZKQkqXPnzpoxY4ZmzZolV1dXxcbGstCCran5lStXbH/PnDmzTp48+VRDzqioKH333Xf64YcfJIlAConEL7Q2b96szp07a+DAgdq6dassFov69OmjGjVqaN++ferbt69+++03DR48WI0aNdKjR48cXTqSkPh59MMPP6hWrVqqWLGiypUrp82bN8vJyUlFixbVokWLdP/+feXNm1dNmzZV7969Vbt2bUeXjiTiwoULkn4PADZs2KC1a9dq2bJlatu2rVxdXXX9+nXduXNHb7zxhq5cuSIPDw9Vq1ZN77zzjnbv3s06CYkkDKTGjRun999/X2XLltWYMWN08OBBFS9eXMuWLdPNmzdVsWJFxcTEKHPmzBowYIA2bNhAGAVJv3++LV++XLVr19bs2bO1ePFiVapUSZs3b5a7u7uqVKmisWPH6v79+ypevDhNzfGf2f2CQbz0hgwZYlKmTGnCwsKMMU/6JMTFxZkKFSqYr7/+2qxbt854enqaadOmGWOe9Ir68ssvzXfffZfoeei1AWN+nwcrV640RYoUMbNnz7adK1OmjClSpIg5e/asefDggYmKijL9+vUzWbNmNRcvXnRQxUjqVq9ebTw8PMxbb71lSpUqZdKmTWvmz59vjDHm4cOHZsSIEcbf399kz57d+Pn5mb179zq4YiRFq1evNp6enubzzz83u3btMoGBgSZVqlSJPstu3LhhOnbsaD766CNz4sQJB1aLpKRevXrm008/TXRs06ZNZtiwYcYYY3788UeTLl06M3XqVHP8+HGTLl06U6xYMfPzzz8negy9f2DM0+vlvn37Gh8fHzNgwADToUMHkzdvXvPOO++Y9evXG2OMOXTokMmXL5959dVXE/XkZN2NeDt27DDp06c3M2bMMMY86UtmsViMq6urWbZsmTHmSb/ElStXmnLlyrHmxn9EKAW727VrlwkICDCvvvqqbQEVGxtr+vXrZypXrmy8vb3NV199ZRsfHh5u3n77bTNp0iRHlYwk7ocffjAeHh5m4sSJif5jFxoaaipUqGB8fHyMv7+/qVixokmfPj0NFvGnbt++baZOnWpbaIWFhZlevXoZi8Vi5s2bZ4x5cse0vXv3msWLFz/1n0DAGGN++eUXU6lSJfPll18aY4y5dOmSyZUrlylYsKBxcXExQUFBiRpXxzcXBowxZvfu3bYG+OHh4bbjV65cMY8fPzZVq1Y1AwYMMMYYExERYcqXL2+cnJxM7dq1jTGEB3ha/PvN0aNHTZ48eRLdTGjbtm2mdu3apm7duuby5cvGarWakJAQ07hxY4JNPNPQoUPNwIEDjTFPmuJny5bNtGnTxrRr1864uLiYdevWGWOeBFPxN/UA/gqhFOzm+++/t/1937595q233jI5c+Y0Fy5cMMYYs3//fpM5c2ZTokQJc/LkSRMXF2euXr1qatasacqVK8cHI57p/v375q233jL9+/f/0zGzZs0yo0ePNpMnT7bNN+CPjh8/bry9vU3BggVtCypjntzpKj6Yit8xBfxRfBBw9+5dEx0dbUaOHGnu3Lljrl27ZvLnz28CAwNNdHS0adiwoUmbNq359ttvCQ/wlyZNmmTq16+f6IuUX375xWTPnt1s2LDBGGPMnTt3TOPGjc3+/fsTBZ1Anz59TL9+/RIdO3nypPH19TXbt29PdHzLli0mTZo0tnmVEOtvxH9WbdiwwZw8edKEhoaa3bt3m8jISFOuXDnz4YcfGmOe3EHdYrEYi8ViVq1a5ciS8YKhpxTsYv369WrcuLFGjBghSSpdurRGjhypPHnyqGrVqjp//rxKlSqlBQsW6Nq1a2revLny5Mmjhg0b6tatW7a7N8T3oALiRUVFKTQ0VIULF5akRNetm//vL/XBBx+oT58+6tSpE3e0wp9ydXVVgwYNdOHCBd29e1fSkzmULl069enTR3379lXz5s21bNkyB1eKpMhisWjx4sUqWbKkHj16pDZt2iht2rSaPHmycuXKpXHjxsnV1VVZs2aVk5OT+vTpo4iICEeXjSTE/OHeQ1mzZtXevXs1ZcoUHTt2TJKULVs2ZciQQX379tWSJUtUv359Xb16VSVKlEjUqxMvt99++03Xr1/X1q1b9fnnn9uOx8bGymKx6OrVq7afJaly5crKli2b9u3b99Rz0ZMMFotFO3fuVN26dXX48GHlzp1b5cuX19mzZxUdHa2uXbtKklKnTq3GjRurf//+yp07t4OrxouEUAp2UalSJU2bNk3Dhg3T8OHDJT0JpkaMGKG8efPqrbfeUmhoqN58801t3rxZAwcO1EcffaS+fftq7969NOvEn/Ly8pKPj48OHDggSYkW5QcOHNDMmTNtY/+44AcSypcvn3r16qVGjRopMDBQmzdvtjV29fHxUffu3TVo0CAVKFDAwZUiKbp69armzZunHj16yNvbW35+fpKkM2fOKHPmzPL29pb05MYec+bM0blz55Q6dWpHlowkJv795qefftLDhw9Vt25dzZw5U5s2bdL48eN19OhRSdKkSZPk7u6u4cOHy93dXVu2bJGTk5OsVivrJEh6Eg588cUXKlOmjNauXauRI0dKkvz9/fXee++pffv22rt3r1xcXCRJ9+7dkzFGmTJlcmTZSKJ++eUXrV27VgMHDlSzZs1sx+/evatDhw7p4cOHkqT58+fr7t276tevH2sl/DMO3aeFl8rjx4/NtGnTjLOzs61ZpzFPLuWrXr26yZEjhzl//rwx5ul+CGwdhjG/z4uYmBjz8OFD2/Hu3bubokWL2porxuvdu7cpV66cuXv3rj3LxAsgfi6FhYWZs2fPmmPHjtnOnTlzxrRu3dqkTZvWbNq0KdF4Lo/Bsxw4cMC8//77JiAgwISHhyf6zBo0aJBJlSqVGTVqlGnZsqVJmzatOXv2rAOrRVKT8H1l9erVxt/f34wbN85ERUUZY4xZs2aNyZYtm2nVqpU5deqUbeylS5cSfS4CxiReM2/atMk0atTI5M6d29bjzhhjmjRpYtzc3Ezv3r3N0KFDTfXq1U2RIkWYR3jKqVOnTPny5U2OHDnM119/bYz5fY49fvzYNGrUyFgsFlOqVCnj5eVljhw54shy8YKyGMPWAfx7zP/fNjReVFSUZs2apc6dO2vIkCH69NNPJUn79+/Xp59+qgsXLmjt2rXKkyePo0pGEhU/l9auXavvvvtOR48e1TvvvKOaNWuqfPnyqlevnu7cuaNixYrJ399f+/fv1/Lly7Vr1y75+/s7unwkIfFzaeXKlfr0008VGRmplClTqnr16ho/frykJ7tbPv/8c61du1Zz5sxRjRo1HFw1krJhw4Zpzpw5evjwoc6dOycvLy/FxMTI1dVVDx48UP/+/bVt2zalTZtWEyZMULFixRxdMpIIq9UqJ6cnFy7MmzdPx44d04wZM5Q2bVr16NFDbdu2lYeHh9auXauPP/5YVapU0ccff6xSpUo98zmAeD169NDRo0fl5OSkI0eOKGXKlOrQoYP69OkjSfrss8+0ZcsWRUdHK0eOHPr222/l6uqquLg4dtwhkc6dO+u7775T1apVFRQUJC8vL9ta6tdff9XatWv122+/qUaNGly2h/+OIxMxJG8Jv/mLiYlJtPtp8uTJxsnJ6akdUyVKlDDvvfeeXetE0hc/d1auXGlSpkxpPv30U/Pdd9+ZSpUqmZw5c5qLFy+ae/fumYEDB5pKlSqZIkWKmDp16iTa/QIktHbtWuPp6WmmTp1qzp07Z6ZOnWosFov56KOPbGPOnDljGjRoYHLmzGkePHhAU2r8qejoaDN27FiTOXNm06JFC3Pv3j1jTOJdv3fu3DEPHjxwVIlI4gYOHGhrgD937lxToUIFU7RoUTN+/Hjz6NEjY8yT9y13d3czYsQIB1eLpG7RokUmTZo0Zt++febRo0fmxo0bplWrVqZkyZJmzJgxtnG//fZbop1V7JTCn611evToYQoWLGiGDx9ufvvtNztXheSOnVL4VyT81m7ChAk6cuSIzp8/r/r166tu3brKlSuXpk2bpi5dumjw4MG2HVOnT59Wvnz5+MYPWrt2rbJkySJ/f38ZY3T79m01bNhQdevWVbdu3fTo0SNlz55dzZs319ixYxPNmfv378vNzU1ubm4OfAVIqm7fvq22bdvqzTff1CeffKLr16+rfPnyyps3r3bv3q0mTZrYepGFhobK09NTGTNmdHDVSCrM/387fOPGDdtOqKxZsyo2NlZffvmlli9frjJlyuizzz6Tl5eXYmNjbX1bgD8yxujq1auqWrWqPv30UzVv3lyS9ODBA7Vr10779+9X165dbTumQkJCVKZMGXay4C+NGTNGCxcu1P79+23vP1euXFGHDh108OBB9erVS927d0/0GPOHqxvw8omfA/v27VNISIjc3NyUK1cuBQQESJK6deumXbt2qV69eurcubO8vb3ZqYnnghmEf0X8m1Pfvn01cuRIlS5dWlWqVNHMmTPVvn17PXz4UIGBgZo8ebJGjBih3r17S5IKFChga9aJl9eNGzfUqVMnTZgwQadPn5bFYlHKlCl1//591axZU2FhYcqTJ4/q1q2rcePGycnJSevXr1doaKgkydPTk0AKNsYYW5P70NBQpUuXTtWqVVPt2rV18+ZNVa9eXQEBAVq1apW6d++uWbNm2f5jmCdPHgIp2MQv2IODg1WzZk2VLVtWlStX1ogRI+Ti4qKePXuqbt26OnDggAYOHKiIiAgCKTwl4ffBFotFqVKlkpOTk61ZcGxsrFKlSqW5c+fKyclJ06ZN04wZM/T48WOVK1eOuxHjT8WvnzNkyCCr1Wq7y57ValWWLFnUv39/PXz4UJMmTVJQUFCixxJIwWKxaNmyZXrrrbe0ZMkSTZs2Te+++67t/2kTJkxQuXLl9MMPP+jzzz9XZGQkgRSeC2YRnrv4xda+ffu0atUqrV69Wh07dlSFChUUFhamZs2aKWXKlHJ3d1eHDh00fPhwhYSEJFqk8Qb3cvP19dXSpUt14sQJjRs3TidOnJCzs7MePXqkbdu2qXr16qpZs6a++uorSdLFixcVFBSk8+fPO7hyJCWRkZGSniyyLBaLVq1apYoVK+rUqVNq3769cufOrSVLlsjX11dDhw6Vu7u7MmfOrJIlSyokJMS2mAfiWSwWbdq0SU2aNFHr1q01dOhQde7cWUOHDlVgYKCcnZ3Vs2dP1a5dWxs2bNCIESO46ycSSbgb5fbt25IkV1dXeXt7a9OmTZIkFxcXxcXFycXFRcWLF5ebm5uWLl2qXbt22Z6HnVKQ9NSXuPHr59KlSyssLEwTJ07Uw4cPbcdjYmL0xhtvqHv37mrZsqXd60XSFhoaqk6dOmn06NHatWuXtm/frqCgIE2ZMkV9+/aVJE2ePFmFChVSSEiIoqOjHVwxkg0HXDKIZGjw4MFm9erViY5t3brVFChQwBhjzNKlS42Xl5f56quvjDHG3L9/3wQHB5sHDx6YuLg42/XL9GxBQocOHTIlSpQwgYGB5tq1a2bKlCnGYrGYt99+O9G4/v37m8KFC5tLly45qFIkNe3atTMffPCBiY6ONsYY88svv5jGjRub6dOnJxrXoUMHU6ZMGdvPvXr1MqNGjUp0d0fAmN8/nzp06GDef//9ROe2bt1qnJyczOeff26MeXJHoi+//NKEhYXZu0wkYQl7ba5YscJUrVrVnDx50hhjzE8//WRSpkxpOnXqZOvDabVaTdOmTc3atWtN8eLFTcOGDR1VOpKghGvm6dOnm08++cQMGjTI/PLLL8aYJ2tvZ2dn0759e7NmzRpz8uRJU6NGDdOhQwfbY7m7NRLas2ePyZcvn7ly5Uqi43PmzDEpUqQw27Ztsx0LDw+3d3lIxthTjv/ZiRMntHHjRu3evVseHh6qVq2apCffBvr4+GjhwoX66KOP9Pnnn+ujjz6SJO3du1crV65UoUKFbHdpMFzLjj8oXry4Zs6cqTZt2mjQoEFq0qSJevToofHjx+uLL76QJIWFhWnevHnasWOHsmbN6uCKkRQsWrRIwcHB2rBhg1xdXXX48GFNmzZNV69eVZUqVST93veubt26mj17turVqyc3NzetX79eISEhSpEihYNfBZKK+M+mhw8fKlWqVAoLC1PatGlt52JiYlSpUiUNHz5c8+fPV6tWreTr66tPPvnEwZUjKUnYd2XLli1atmyZDh06pCFDhmjo0KEqU6aM5s2bp2bNmunw4cPy9fXVtWvXdOfOHS1YsEB79uzR1q1b6d8CSYnnU9++fTVr1iwVLVpUN2/e1KxZs7Rp0yY1aNBAq1atUq9evbRmzRo5OzsrXbp0WrVqlSwWi4wx7LhDIq6urgoNDVVoaKgyZ85s+/yrUqWKMmbMqOvXr9vG+vr6OrBSJDd8quF/VrhwYY0cOVIeHh4aM2aM1q9fL0mqXLmyfvvtNzVr1kyfffaZOnToIEmKiorSuHHjFBkZqVy5ctmeh0AKz1K8eHHNmjVLhw8f1pIlS1S9enVNmDBBc+bM0bJly3Tv3j3t2bOHW6zD5vLly/Lx8VGxYsW0bt06tWrVSjt37tSBAwcUFhYm6fdLHMqXL6/Zs2frwYMHcnJy0o4dO1SgQAFHlo8kJH5BvmnTJg0aNEiXLl1SnTp1tHXrVh04cEAWi0Wurq6SpLRp08piscjb29vBVSMpin/P+eSTT9S5c2e98soreuONN7Rjxw4NHDhQZ86cUb169XT06FEVK1ZM3t7eKlu2rE6cOCHpyY1gcuXKxeWgkPT7fLp586YePnyo9evXa+PGjVqwYIH8/f312muv6cyZM3r77be1YcMGbd68WYsXL9a+ffvk6uqq2NhY1t0vufj3ktOnT2vnzp0KCwtTiRIlVKtWLU2dOlVHjhyxzZH06dMrTZo0XK6Hfw1338P/JCYmxrYgX7Rokb777jvdv39fQ4YMUeXKlXX69GnVqVNHPj4+at++vWJjY7V48WKFh4fr8OHDcnFx4Vs//C2HDh1S+/btVaxYMQ0bNkx+fn6yWCyKioqSh4eHo8tDErJ//361aNFCmTJl0vbt27VhwwbFxMSoZ8+eypUrlwYNGqRSpUoleozValVMTIzc3d0dVDWSquXLl6t58+bq16+f3n77bXl4eKhfv36Ki4vTsGHDVLJkSUlSz549dfDgQa1atUpeXl4OrhpJ0fbt29W4cWOtWLFC5cqVkyTNnDlTQUFB8vX11YgRI1SgQAHFxcXZdrDcvHlTX3zxhYKCgrR9+3YVLFjQkS8BSci8efPUoUMHFSxYUEuXLrXtFj9//ry6du2qkJAQhYSEKF++fIkel3B+4eUWHBysFi1ayM/PT5cvX9bMmTP16NEjLVy4UN7e3mrfvr1y5MihOXPmaPbs2frpp5+UI0cOR5eN5MhBlw0imRk8eLBp1KiR8ff3N05OTqZChQpm06ZNxhhjQkNDTdWqVU3hwoXN66+/blq1amXr88K17PgnDh06ZEqXLm0aN25sTpw4YYyhDxme7eOPPzYWi8WULVvWdmzBggWmVKlSpkWLFubgwYO24wn7vAAJnT171uTMmdNMmzYt0fHg4GBTq1Yt4+PjY95++20TEBBgvL29zeHDhx1TKF4ImzdvNj4+PrbPr3iTJk0y7u7upkGDBrYeU8YYc/nyZfPZZ5+ZvHnzMrfwlC1btpiAgADj6elp6yMVvyY6f/68qVWrlrFYLOby5cuOLBNJUFxcnPn111/N66+/br7++msTGhpqhg8fblxcXMzUqVPNN998Yxo3bmycnJxM/vz5Te7cuc2hQ4ccXTaSMXZK4X82bdo09e3bV6tXr1bu3LkVEhKiKVOmyMnJSQMHDrT1cLl165ZSpkypVKlSSXpyy2NulY1/av/+/erVq5cWLlyojBkzOrocJEGPHj3Su+++q1y5cmnPnj3y9/fXwoULJUkLFizQ+PHjVbhwYXXo0EFlypRxcLVIyjZt2qSOHTtqw4YNyp49e6KdvWfOnNHBgwe1YcMGZcmSRS1atFD+/PkdXDGSIvP/l4Hu3btXzZo106RJk/TOO+/Y5lNcXJz8/f2VMmVKFSpUSKNGjVLGjBlltVp1/fp1ubi40L/lJfesqwqMMTpw4IA6dOigiIgI7d69W+nTp7fNt7Nnz2rmzJkaNWoU621I+v29KCoqSsYYjRgxQj179rT1SRw/frx69+6tsWPHqmnTpoqMjFR0dLR8fHyUIUMGB1eP5Ix3KPzP9u3bp9q1a6tixYqSpPfee0+enp7q0aOHBg0aJCcnJ1WqVEnp06e3PcYYwwck/iulS5fWunXruGQPfypFihRavXq1UqZMqVmzZmnMmDF6//33tWDBAr3//vu2wNzDw0NFixblkj38qfv37+vRo0eJjsVf+hIeHq7XX39dzZo1c1B1SKr+GCDE92V57bXXlDdvXnXt2lVZs2aVv7+/JCk8PFxFihRRgQIFNHfuXJ06dUoZM2aUk5OTMmfO7JDXgKQj4XxasWKFrl27JqvVqrfeekulS5fWjBkz1KVLF1WqVElbt25VhgwZZIxRvnz5bDeF4YtgSE/ei1auXKmvvvpKly9fltVqVePGjW2hVPfu3WWxWNS7d2/dvHlT/fv3t20mAP5NNPLB/+yVV17Rr7/+mmjhXqNGDbVs2VIHDx5Ut27d9NNPPyV6DM0V8b8gkMJ/kjJlSklSo0aN1KdPHx0+fFjvv/++JKlJkyYaPXq0evfuTSCFv1S0aFHdvn1bM2bMkPSkuXB8L5bg4GDNnj2bxq9IJGGAsGTJEg0ePFiTJ0/W9u3bJUlr165V+vTpVbt2bX322WcKCgpSq1atdP/+fQ0ePFjGGP3444+OfAlIYuLnU+/evdWxY0dt27ZNs2bN0vvvv69Zs2apRIkSGjNmjHx8fFStWjWFh4c/tc4mkIIkHThwQC1btlTOnDlVpkwZXbhwQbNmzdIvv/xiG9OtWzcNGzZM06ZNU1RUlAOrxcuEUAr/M39/f+3Zs0ebNm1KdFcYX19flS9fXu+9955Kly7twAoBvKw8PT3VqFEj9e7dW8ePH9e7774r6cmOzpw5czq4OiR1OXPm1JQpU/TFF1+od+/eOnHihE6fPq0+ffpozpw5atq0qdzc3BxdJpIIY0yiAKFbt246ePCgVqxYoV69eum7776TxWJRSEiIqlWrpjVr1mjUqFFyc3PTkiVLJEkZM2ZU3rx5HfkykAQtXLhQCxcu1KpVq7RkyRJ16dJFJ0+eVJo0aSQ9uZPs2LFjFR0drZ49ezq2WCRJFy5c0OrVq9WvXz999dVXmj17tiZOnKhly5Zp+vTpiYKpPn366Oeff5aPj48DK8bLhNgc/7PWrVtr9+7dat68uaZPn64SJUrIz89Py5cvV5UqVdS/f39ZLBbusgfAIVKlSqVGjRopKipKQUFBunr1KpfE4G9r3bq1vLy81L59ey1cuFAeHh5ydnbWli1b6CEFm4RrnKlTp+r777/XsmXL9Nprr2natGnq3r27Bg8erIcPH6p9+/aaOXOm7t27J2OM7dKZQYMGKSwsTFWrVnXkS0ESdP78eb355psqVaqUlixZom7dumnixImqX7++7t+/r5s3b6pMmTJaunSpChQo4OhykcRERESoSZMmunjxoj788EPb8Q4dOshqtWrUqFFydnZWYGCg7Qu7+MATsAcaneN/knAR1qlTJwUHBysuLk5eXl5ydnbW8ePH5eLiYmusBwCO8vDhQ8XExCh16tSOLgUvoGvXrumXX36RxWJRzpw5aTwNm4RrnIiICPXv3185cuRQz549tWrVKrVs2VJdu3ZVaGioduzYodGjR6t58+a2x4eGhmrQoEHavn271qxZo+LFizvqpSAJeNaXuH379pWzs7Nq1aqlt956S1988YU++ugjGWMUFBSkO3fuqEuXLnJ1dZX0e+87IN7hw4fVuHFjZciQQdOnT1fhwoVt56ZPn67u3burX79+6t+/P5d7wu4IpfA/S7gYCwkJ0e3bt/XgwQM1bNhQzs7OfDACAIBkaevWrbp27ZqaNWum9u3bK23atOratasePXqkuLg4vf322+rYsaO6deumFStWqGnTpnJ1ddXcuXNVr149SVJUVJQ2btyoAgUKKHfu3A5+RXCkhIHUhQsXlCJFCqVPn1779+9XhQoVJEmLFy9Ww4YNJUkPHjxQ/fr1VbhwYX355ZcOqxsvhmPHjqlVq1YqU6aMunTpokKFCtnOffvtt3rzzTeVJ08eB1aIlxWhFJ6LP7s0j0AKAAAkN8YY3b9/Xw0aNFB0dLS8vb21fft27dy503ZXvXnz5mny5MnasGGDUqdOrQ0bNujrr79WzZo19cEHH7A+QiIJv+Tt27evVq5cqVu3bqlQoUK2/nUff/yxZs2apddff10RERHq1auXbt68qX379rG7BX/L4cOH1bZtW5UoUULdu3dXwYIFHV0SQKNzPJvVan3m8T/LMOMDqT8+jgUXAABIbiwWi7y8vLRo0SKFh4frhx9+UP/+/W2BlCS5urrq0qVL2rlzpx4+fKjJkycrR44cCgwMtO0kB6Qn6+f4QGrRokWaM2eORo8erS+//FJly5ZVt27dtH//fo0ZM0aBgYEqV66cWrZsqejoaP30009ycXFhPuFvKV68uGbOnKljx45p+PDhOnPmjKNLAmh0jqcl3PV04sQJPXz4UBkyZFCOHDlksVj+dPdTwrvOnDlzRtmyZbPdlh0AACC5cXJy0quvvipfX19t3rxZWbJkUbNmzSRJBQsW1JtvvqmWLVsqTZo0SpUqlZYvXy6LxSJjDF/cwSZ+/bxt2zZt3rxZvXv3Vp06dSQ96VOWI0cO9e3bVwsXLtTJkyd1+fJleXt7q2jRonJyclJsbCw7pfC3FS9eXFOmTFGvXr3os4kkgcv3kEjCrcMDBgzQqlWrdOnSJZUtW1ZlypTRiBEjJD19WV7Cx02ePFmjRo3Snj17lCNHDru/BgAAAHsKDw9XYGCgHj16pMDAQFswdfbsWZ05c0aRkZFq2rSpnJ2dCRDwTOHh4apQoYJu3rypPn36aMCAAbZzv/76qwIDA5U1a1ZNnjw50eO4uzX+W1FRUfLw8HB0GQCX7yGx+GBpxIgRmjlzpiZOnKjz588rc+bMmjJlijp16iRJibadJwykvv76aw0ZMkTjxo0jkAIAAC8FPz8/TZkyRSlTptScOXM0a9YsxcXF6eOPP9bx48fVvHlz29qJQArP4ufnp+XLlytDhgxavny5Dh8+bDvn4+OjdOnS6cKFC089jkAK/y0CKSQVvItBUuJeUadOndKKFSs0f/58ValSRUePHtX333+v6tWra926derWrZukJ8FUTExMokCqd+/emjFjhpo0aeKIlwEAAOAQOXPm1OTJk+Xl5aWxY8cqT548unnzpnr37m0bwyV7+Cv+/v5avny54uLiNGHCBB05ckSSFBkZqdOnTytLliyOLRAA/gVcvodEO52OHj0qf39/zZw5U/Xr19eJEyfUpEkTDR8+XG3atNG7776rLVu2qH79+lqwYIHtOWbMmKHevXvr22+/VYMGDRz1UgAAABzq+vXrOnjwoG7cuKFWrVrJxcWFS/bwjxw+fFjNmzfXnTt3VKpUKbm5uSksLEx79+6Vm5tborU7ALzoCKVecn+8/ezevXu1aNEi+fr6ymKxqEOHDnJxcdG4cePk6uqqXr16af/+/SpYsKCmTJkiJycnrVq1SnXr1tXSpUtVv359B78iAACApOPPbhAD/JUTJ06odu3aypIli95//3199NFHkqSYmBi5uro6uDoAeH64fO8lFx9InTlzRiEhIRo5cqT8/Pxsx8PCwnTlyhW5uroqLi5Ov/zyi1q0aKGpU6farmF/9913tXXrVgIpAACAPyCQwn+jcOHCWr58uaKjo3Xo0CGdP39ekgikACQ77JSCRo0apW3btsnDw0Pz5s2Tl5eXrFarJGnChAn67rvvlDFjRkVEROjevXs6evSonJ2dZYyhYScAAADwLzl8+LA++ugj5cqVS4MHD1b+/PkdXRIAPFfslIIKFCigjRs3ateuXbp48aKkJ3fycHJyUtOmTdWyZUulTp1ahQoV0uHDh213j7FYLARSAAAAwL+kePHimjJliq5fv67UqVM7uhwAeO7YKfWS+bPGiJs3b1b16tXVunVr2yV8f4ZmnQAAAID9REVFycPDw9FlAMBzx06pl4jVarUFUjdv3tSlS5ds56pWrarg4GAFBQVpxIgRunHjRqLHxTPGEEgBAAAAdkQgBSC5IpR6SVitVltj8mHDhqlmzZoqXbq0atSooW3btikqKkq1atVScHCwpk+frpEjR+r69euSZHucJG4/CwAAAAAAngtCqZeAMcYWLA0ePFjTp09Xt27dFBISop9//lkDBw7U6tWrEwVTU6ZM0cKFCx1cOQAAAAAASK64DisZO336tAoUKGD7effu3Vq5cqXmzZunKlWqaOfOnbp69aqMMRo4cKCcnZ319ttv691339XOnTtVtmxZB1YPAAAAAACSM3ZKJVNjx461BU8Wi0XGGKVNm1adOnVSlSpVtHnzZtWvX19Tp05VaGiooqKiNG7cOC1evFjR0dF6/fXX5eLiotjYWEe/FAAAAAAAkAwRSiVTRYoU0ZtvvqmuXbvagqk8efKoVq1aiomJ0YQJE9SuXTu1bNlSxhjlyZNHR48e1e7du+Xm5mZ7HpqaAwAAAACAfwOhVDLzzTffSJICAgL08ccfK3fu3OrcubN27NghV1dX+fr6Kjo6Wrdv35aPj4+t11S2bNm0bds2TZ8+3ZHlAwAAAACAlwTbYJKRTZs2qX379jp69KimTJmiihUryhijadOmqUuXLpo8ebLeeOMNOTk5ycXFRUuXLlVERIR27typX3/9VcWLF5eTk5Pi4uLk7Ozs6JcDAAAAAACSMXZKJSOlS5fWjBkztHTpUnXs2FGSVKlSJX388cfKmzevOnfurG3btilFihRatmyZUqZMqd27d8vLy0sHDhyQk5OTrFYrgRQAAAAAAPjXWYwxxtFF4PmJjIzUokWLNGDAADVs2FBTp06VJG3btk3Tpk3TuXPnNG7cOFWpUkVRUVEyxsjDw0MWi0WxsbH0kAIAAAAAAHZBApEMGGNksVgkSV5eXmrYsKEkqX///pKkqVOnqlKlSpKkadOmqVevXho1apSqV6+e6DkIpAAAAAAAgL2QQrzgrFarrVm51WpVbGys0qRJo1atWkmS+vXrJ+n3YMpisWj48OFasGBBolAqPtQCAAAAAACwB0KpF1jCQOrLL7/U0aNHdejQIbVv316VK1dWu3btJEkDBgyQxWKxNT/39vZW0aJFHVk6AAAAAAB4ydFTKhno16+fvv32Ww0aNEj379/XzJkzlT9/fi1atEhxcXFasmSJBg4cqKpVq2r+/Pm2xyUMtQAAAAAAAOyJROIFt2/fPgUHB2v16tXq1KmTKlSooEuXLqlRo0by9PRU6tSp1aJFC/Xr10/37t2T1Wq1PZZACgAAAAAAOAqpxAvOarXKw8NDZcuW1ffff6+aNWtq0qRJatmypR48eKC1a9dKkj788EP98MMPcnJyShRMAQAAAAAAOAKh1AvkWWHS/fv3FRUVpUWLFunDDz/U6NGj9dFHH0mS9uzZowULFujSpUtKkSKFLBaLjDHskAIAAAAAAA5HT6kXRML+T9OnT5ckW/gUEBCgjRs3avLkyerYsaMkKSoqSu+9955SpEihxYsXE0QBAAAAAIAkhbvvvSDiQ6VevXpp8eLFatWqla5cuaIsWbLos88+02+//abx48crderUunv3rlavXq1r167pyJEjtkv2CKYAAAAAAEBSwU6pF8i8efP0ySef6Mcff1TJkiVtx61Wq86cOaNhw4bp6NGjypAhg/LkyaOvvvpKrq6uio2NlYsL+SMAAAAAAEg6CKVeIP3799fVq1c1Z84cxcXFydnZ+anA6caNG/Lx8bEdI5ACAAAAAABJEddzvUCuXr2qsLAwSZKzs7OMMXJxcVFUVJQ2bdokSfL19bWFUPHnAQAAAAAAkhpCqSToWXfZk6TixYvrxo0b2rp1q6Kjo2WxWCRJERERGjp0qH788cdE4+PPAwAAAAAAJDVcvpfEJGxIvn//flmtVjk7O6tUqVJ6/PixXn/9dUlSv3799Prrr+v+/fvq1q2b7t69qx07dsjZ2dmR5QMAAAAAAPwthFJJiDHGtrupT58+WrhwoSwWi27cuKGmTZtqzJgx8vLyUp06dXT16lWdP39eBQsWlKurq3bt2iVXV1dbrykAAAAAAICkjFAqCZoyZYqGDh2qlStXysfHR5cvX1aLFi1UtmxZzZ8/X25ubjp16pTOnj0rX19fVahQ4ZlNzwEAAAAAAJIqQqkkqFWrVkqRIoWmT59u2z115MgRvfnmm+rcubNGjhz51GPYIQUAAAAAAF4kNDp3sD9mgjExMbp69aqioqJs56Ojo1WsWDENGTJES5Ys0d27dxUXF5focQRSAAAAAADgRUIo5UBWq9XWQ+rnn3/WzZs35erqqpYtW2rp0qXavHmznJyc5OrqKklyd3dXunTplCpVKkIoAAAAAADwQiOUcqD4u+z1799ftWvXVsGCBdW7d295enqqTZs26tixo9atWyer1arffvtNP/zwgzJnzmwLqQAAAAAAAF5UdMV2AKvVaguklixZorlz52rKlCk6duyY1q1bp0uXLum1115TrVq19O677ypXrlxydnaWu7u79u/fL4vFkuhOfQAAAAAAAC8aGp070I4dO7Rs2TIVLVpUbdq0kSStWrVKkydPVtq0adWuXTtlyJBBP/30kzw9PdW4cWPusgcAAAAAAJIFQikHCQ8PV4UKFXTr1i0NHTpU3bp1s51bvXq1JkyYIG9vb/Xr109lypSxneMuewAAAAAAIDmgp5SD+Pn5afny5fLz89PatWt1/Phx27latWqpR48eOn/+vFasWJHocQRSAAAAAAAgOWCnlIMdPXpUH3zwgUqVKqWuXbuqUKFCtnN79uxR2bJlCaIAAAAAAECyQyiVBBw+fFht27ZVyZIl1a1bNxUsWDDReS7ZAwAAAAAAyQ2hVBJx+PBhtW/fXtmzZ9eYMWOUM2dOR5cEAAAAAADwr6GnVBJRvHhxTZkyRV5eXsqePbujywEAAAAAAPhXsVMqiTHGyGKxyGq1ysmJzBAAAAAAACRPhFJJUHwwBQAAAAAAkFyxFScJIpACAAAAAADJHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAJIJi8Wi4OBgR5cBAADwtxBKAQAA2FGtWrVUo0aNZ57buXOnLBaLjh079l899/Xr11WzZs2/Pb5169aqW7fuf/W7AAAA/leEUgAAAHYUGBiojRs36sqVK0+dmz17tkqVKiV/f/9/9JzR0dGSJD8/P7m7uz+XOgEAAP5thFIAAAB29O677yp9+vQKCgpKdPz+/ftasmSJ6tatq6ZNmypz5sxKmTKlihQpooULFyYaW6lSJXXq1EndunVTunTpFBAQIOnpy/cuX76sRo0aKU2aNHrllVdUp04dXbx4UZI0ZMgQzZkzRytXrpTFYpHFYtG2bdtUpUoVderUKdHvu3Xrltzc3LR58+bn/u8BAABeXoRSAAAAduTi4qKWLVsqKChIxhjb8SVLliguLk7NmzdXyZIltWbNGp04cUIffvihWrRooX379iV6njlz5sjNzU27d+/W9OnTn/o9MTExCggIkJeXl3bu3Kndu3fL09NTNWrUUHR0tHr27KlGjRqpRo0aun79uq5fv67y5curbdu2WrBggR4/fmx7rnnz5ilz5syqUqXKv/cPAwAAXjqEUgAAAHbWpk0bXbhwQdu3b7cdmz17tho0aKDs2bOrZ8+eKlasmHLlyqXOnTurRo0a+v777xM9R548eTRmzBjly5dP+fLle+p3LF68WFarVTNnzlSRIkVUoEABzZ49W5cuXdK2bdvk6empFClSyN3dXX5+fvLz85Obm5vq168vSVq5cqXtuYKCgtS6dWtZLJZ/6V8EAAC8jAilAAAA7Cx//vwqX768Zs2aJUk6f/68du7cqcDAQMXFxWn48OEqUqSIXnnlFXl6emr9+vW6dOlSoucoWbLkX/6Oo0eP6vz58/Ly8pKnp6c8PT31yiuvKCoqShcuXPjTx3l4eKhFixa22g4dOqQTJ06odevW/9uLBgAA+AMXRxcAAADwMgoMDFTnzp01depUzZ49W6+++qoqVqyozz//XBMnTtSECRNUpEgRpUqVSt26dbM1M4+XKlWqv3z++/fvq2TJkpo/f/5T59KnT/+Xj23btq2KFSumK1euaPbs2apSpYqyZ8/+z18kAADAXyCUAgAAcIBGjRqpa9euWrBggebOnasOHTrIYrFo9+7dqlOnjpo3by5JslqtOnfunAoWLPiPnr9EiRJavHixMmTIIG9v72eOcXNzU1xc3FPHixQpolKlSumbb77RggULNGXKlH/+AgEAAP4DLt8DAABwAE9PTzVu3Fj9+vXT9evXbZfH5cmTRxs3btSePXt0+vRptW/fXjdu3PjHz9+sWTOlS5dOderU0c6dOxUWFqZt27apS5cuunLliiQpR44cOnbsmM6ePavbt28rJibG9vi2bdtq9OjRMsaoXr16z+U1AwAAJEQoBQAA4CCBgYG6e/euAgIClClTJknSwIEDVaJECQUEBKhSpUry8/NT3bp1//Fzp0yZUjt27FC2bNlUv359FShQQIGBgYqKirLtnGrXrp3y5cunUqVKKX369Nq9e7ft8U2bNpWLi4uaNm0qDw+P5/J6AQAAErKYhPciBgAAACRdvHhRr776qvbv368SJUo4uhwAAJAMEUoBAADAJiYmRr/++qt69uypsLCwRLunAAAAnicu3wMAAIDN7t27lTFjRu3fv1/Tp093dDkAACAZY6cUAAAAAAAA7I6dUgAAAAAAALA7QikAAAAAAADYHaEUAAAAAAAA7I5QCgAAAAAAAHZHKAUAAAAAAAC7I5QCAAAAAACA3RFKAQAAAAAAwO4IpQAAAAAAAGB3hFIAAAAAAACwu/8DIhDgfzTDc08AAAAASUVORK5CYII=",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-06_21-10_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: .//2024-12-06_21-10_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: .//2024-12-06_21-10_plots/variety_comparison_water_efficiency_l_oil_m³_water.png\n",
|
||
"Plot salvato come: .//2024-12-06_21-10_plots/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: .//2024-12-06_21-10_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: .//2024-12-06_21-10_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",
|
||
"# Analisi per tecnica\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",
|
||
" # Ordina per zona e anno\n",
|
||
" df = df.sort_values(['zone', 'year'])\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",
|
||
" new_df = pd.concat([df] + [pd.Series(v, name=k) for k, v in new_columns.items()], axis=1)\n",
|
||
"\n",
|
||
" # Ordiniamo per zona e anno per mantenere la continuità temporale\n",
|
||
" df_sorted = new_df.sort_values(['zone', 'year'])\n",
|
||
"\n",
|
||
" # Definiamo la dimensione della finestra temporale\n",
|
||
" window_size = 41\n",
|
||
"\n",
|
||
" # Liste per raccogliere i dati\n",
|
||
" temporal_sequences = []\n",
|
||
" static_features_list = []\n",
|
||
" targets_list = []\n",
|
||
"\n",
|
||
" # Iteriamo per ogni zona\n",
|
||
" for zone in df_sorted['zone'].unique():\n",
|
||
" zone_data = df_sorted[df_sorted['zone'] == zone].reset_index(drop=True)\n",
|
||
"\n",
|
||
" if len(zone_data) >= window_size: # Verifichiamo che ci siano abbastanza dati\n",
|
||
" # Creiamo sequenze temporali scorrevoli\n",
|
||
" for i in range(len(zone_data) - window_size + 1):\n",
|
||
" # Sequenza temporale\n",
|
||
" temporal_window = zone_data.iloc[i:i + window_size][temporal_features].values\n",
|
||
" # Verifichiamo che non ci siano valori NaN\n",
|
||
" if not np.isnan(temporal_window).any():\n",
|
||
" temporal_sequences.append(temporal_window)\n",
|
||
"\n",
|
||
" # Feature statiche (prendiamo quelle dell'ultimo timestep della finestra)\n",
|
||
" static_features_list.append(zone_data.iloc[i + window_size - 1][static_features].values)\n",
|
||
"\n",
|
||
" # Target (prendiamo quelli dell'ultimo timestep della finestra)\n",
|
||
" targets_list.append(zone_data.iloc[i + window_size - 1][target_features].values)\n",
|
||
"\n",
|
||
" # Convertiamo in array numpy\n",
|
||
" X_temporal = np.array(temporal_sequences)\n",
|
||
" X_static = np.array(static_features_list)\n",
|
||
" y = np.array(targets_list)\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",
|
||
"\n",
|
||
" #train_idx = int(len(indices) * 0.7) # 70% training\n",
|
||
" #val_idx = int(len(indices) * 0.85) # 15% validation\n",
|
||
" # Il resto rimane 15% test\n",
|
||
"\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_idx = int(len(indices) * 0.60) # 60% training\n",
|
||
" val_idx = int(len(indices) * 0.90) # 30% validation\n",
|
||
" # Il resto rimane 10% 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 temporali\n",
|
||
" X_temporal_train = scaler_temporal.fit_transform(X_temporal_train.reshape(-1, len(temporal_features))).reshape(X_temporal_train.shape)\n",
|
||
" X_temporal_val = scaler_temporal.transform(X_temporal_val.reshape(-1, len(temporal_features))).reshape(X_temporal_val.shape)\n",
|
||
" X_temporal_test = scaler_temporal.transform(X_temporal_test.reshape(-1, len(temporal_features))).reshape(X_temporal_test.shape)\n",
|
||
"\n",
|
||
" # Standardizzazione dei dati statici\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",
|
||
" # Standardizzazione dei target\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",
|
||
" # 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",
|
||
" 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: (3920000, 41, 3), Static: (3920000, 113), Target: (3920000, 5)\n",
|
||
"\n",
|
||
"Shape dopo lo split e standardizzazione:\n",
|
||
"Train - Temporal: (2548000, 41, 3), Static: (2548000, 113), Target: (2548000, 5)\n",
|
||
"Val - Temporal: (784000, 41, 3), Static: (784000, 113), Target: (784000, 5)\n",
|
||
"Test - Temporal: (588000, 41, 3), Static: (588000, 113), Target: (588000, 5)\n",
|
||
"Temporal data shape: (2548000, 41, 3)\n",
|
||
"Static data shape: (2548000, 113)\n",
|
||
"Target shape: (2548000, 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",
|
||
" \"\"\"\n",
|
||
" Crea un transformer per la predizione della produzione di olio d'oliva.\n",
|
||
" \"\"\"\n",
|
||
" # Input layers\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\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",
|
||
" x = tf.keras.layers.Dropout(dropout)(x)\n",
|
||
" x = tf.keras.layers.Dense(\n",
|
||
" d_model,\n",
|
||
" activation='swish',\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(x)\n",
|
||
"\n",
|
||
" # Positional encoding\n",
|
||
" x = PositionalEncoding(d_model)(x)\n",
|
||
"\n",
|
||
" # Transformer blocks\n",
|
||
" skip_connection = x\n",
|
||
" for _ in range(num_transformer_blocks):\n",
|
||
" # Self-attention\n",
|
||
" attention_output = tf.keras.layers.MultiHeadAttention(\n",
|
||
" num_heads=num_heads,\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 pesi addestrabili\n",
|
||
" residual_weights = tf.keras.layers.Dense(d_model, activation='sigmoid')(x)\n",
|
||
" x = tfa.layers.StochasticDepth(survival_probability=0.3)([x, residual_weights * attention_output])\n",
|
||
" x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n",
|
||
"\n",
|
||
" # Feed-forward network\n",
|
||
" ffn = tf.keras.layers.Dense(ff_dim, activation=\"swish\")(x)\n",
|
||
" ffn = tf.keras.layers.Dropout(dropout)(ffn)\n",
|
||
" ffn = tf.keras.layers.Dense(d_model)(ffn)\n",
|
||
" ffn = tf.keras.layers.Dropout(dropout)(ffn)\n",
|
||
"\n",
|
||
" # Second residual connection\n",
|
||
" x = tfa.layers.StochasticDepth()([x, ffn])\n",
|
||
" x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n",
|
||
"\n",
|
||
" # Add final skip connection\n",
|
||
" x = tfa.layers.StochasticDepth(survival_probability=0.5)([x, skip_connection])\n",
|
||
"\n",
|
||
" # Temporal pooling\n",
|
||
" attention_pooled = tf.keras.layers.MultiHeadAttention(\n",
|
||
" num_heads=num_heads,\n",
|
||
" key_dim=d_model // 4\n",
|
||
" )(x, x)\n",
|
||
" attention_pooled = tf.keras.layers.GlobalAveragePooling1D()(attention_pooled)\n",
|
||
"\n",
|
||
" # Additional pooling operations\n",
|
||
" avg_pooled = tf.keras.layers.GlobalAveragePooling1D()(x)\n",
|
||
" max_pooled = tf.keras.layers.GlobalMaxPooling1D()(x)\n",
|
||
"\n",
|
||
" # Combine pooling results\n",
|
||
" temporal_features = tf.keras.layers.Concatenate()(\n",
|
||
" [attention_pooled, avg_pooled, max_pooled]\n",
|
||
" )\n",
|
||
"\n",
|
||
" # === STATIC PATH ===\n",
|
||
" static_features = tf.keras.layers.LayerNormalization(epsilon=1e-6)(static_input)\n",
|
||
" for units in [256, 128, 64]:\n",
|
||
" static_features = tf.keras.layers.Dense(\n",
|
||
" units,\n",
|
||
" activation='swish',\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(static_features)\n",
|
||
" static_features = tf.keras.layers.Dropout(dropout)(static_features)\n",
|
||
"\n",
|
||
" # === FEATURE FUSION ===\n",
|
||
" combined = tf.keras.layers.Concatenate()([temporal_features, static_features])\n",
|
||
"\n",
|
||
" # === MLP HEAD ===\n",
|
||
" x = combined\n",
|
||
" for units in mlp_units:\n",
|
||
" x = tf.keras.layers.BatchNormalization()(x)\n",
|
||
" x = tf.keras.layers.Dense(\n",
|
||
" units,\n",
|
||
" activation=\"swish\",\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(x)\n",
|
||
" x = tf.keras.layers.Dropout(dropout)(x)\n",
|
||
"\n",
|
||
" # Output layer\n",
|
||
" outputs = tf.keras.layers.Dense(\n",
|
||
" num_outputs,\n",
|
||
" activation='linear',\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(x)\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.\n",
|
||
" \n",
|
||
" Parameters:\n",
|
||
" -----------\n",
|
||
" target_names : list\n",
|
||
" Lista dei nomi dei target per il monitoraggio specifico\n",
|
||
" val_data : dict\n",
|
||
" Dati di validazione\n",
|
||
" val_targets : array\n",
|
||
" Target di validazione\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" --------\n",
|
||
" list\n",
|
||
" Lista dei callbacks configurati\n",
|
||
" \"\"\"\n",
|
||
"\n",
|
||
" # Custom Metric per target specifici\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",
|
||
"\n",
|
||
" def on_epoch_end(self, epoch, logs={}):\n",
|
||
" x_val, y_val = self.validation_data\n",
|
||
" y_pred = self.model.predict(x_val, verbose=0)\n",
|
||
"\n",
|
||
" for i, name in enumerate(self.target_names):\n",
|
||
" mae = np.mean(np.abs(y_val[:, i] - y_pred[:, i]))\n",
|
||
" logs[f'val_{name}_mae'] = mae\n",
|
||
"\n",
|
||
"\n",
|
||
" callbacks = [\n",
|
||
" # Early Stopping\n",
|
||
" tf.keras.callbacks.EarlyStopping(\n",
|
||
" monitor='val_loss',\n",
|
||
" patience=20,\n",
|
||
" restore_best_weights=True,\n",
|
||
" min_delta=0.0005,\n",
|
||
" mode='min'\n",
|
||
" ),\n",
|
||
"\n",
|
||
" # Model Checkpoint\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",
|
||
" # Reduce LR on Plateau\n",
|
||
" tf.keras.callbacks.ReduceLROnPlateau(\n",
|
||
" monitor='val_loss',\n",
|
||
" factor=0.5,\n",
|
||
" patience=10,\n",
|
||
" min_lr=1e-6,\n",
|
||
" verbose=1\n",
|
||
" ),\n",
|
||
"\n",
|
||
" # TensorBoard logging\n",
|
||
" tf.keras.callbacks.TensorBoard(\n",
|
||
" log_dir=f'./logs_{execute_name}',\n",
|
||
" histogram_freq=1,\n",
|
||
" write_graph=True,\n",
|
||
" update_freq='epoch'\n",
|
||
" )\n",
|
||
" ]\n",
|
||
"\n",
|
||
" return callbacks\n",
|
||
"\n",
|
||
"\n",
|
||
"def compile_model(model, learning_rate=1e-3):\n",
|
||
" \"\"\"\n",
|
||
" Compila il modello con le impostazioni standard.\n",
|
||
" \"\"\"\n",
|
||
" lr_schedule = WarmUpLearningRateSchedule(\n",
|
||
" initial_learning_rate=learning_rate,\n",
|
||
" warmup_steps=500,\n",
|
||
" decay_steps=5000\n",
|
||
" )\n",
|
||
"\n",
|
||
" model.compile(\n",
|
||
" optimizer=tf.keras.optimizers.AdamW(\n",
|
||
" learning_rate=lr_schedule,\n",
|
||
" weight_decay=0.01\n",
|
||
" ),\n",
|
||
" loss=tf.keras.losses.Huber(),\n",
|
||
" metrics=['mae']\n",
|
||
" )\n",
|
||
"\n",
|
||
" return model\n",
|
||
"\n",
|
||
"\n",
|
||
"def setup_transformer_training(train_data, train_targets, val_data, val_targets):\n",
|
||
" \"\"\"\n",
|
||
" Configura e prepara il transformer con dimensioni dinamiche basate sui dati.\n",
|
||
" \"\"\"\n",
|
||
" # Estrai le shape dai dati\n",
|
||
" temporal_shape = (train_data['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 basati sul numero di output\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n",
|
||
"\n",
|
||
" # Assicurati che il numero di target names corrisponda al numero di output\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 le dimensioni rilevate\n",
|
||
" model = create_olive_oil_transformer(\n",
|
||
" temporal_shape=temporal_shape,\n",
|
||
" static_shape=static_shape,\n",
|
||
" num_outputs=num_outputs\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Compila il modello\n",
|
||
" model = compile_model(model)\n",
|
||
"\n",
|
||
" # Crea i callbacks\n",
|
||
" callbacks = create_transformer_callbacks(target_names, val_data, val_targets)\n",
|
||
"\n",
|
||
" return model, callbacks, target_names\n",
|
||
"\n",
|
||
"\n",
|
||
"def train_transformer(train_data, train_targets, val_data, val_targets, epochs=150, batch_size=64, save_name='final_model'):\n",
|
||
" \"\"\"\n",
|
||
" Funzione principale per l'addestramento del transformer con ottimizzazioni.\n",
|
||
" \"\"\"\n",
|
||
" # Conversione dei dati in tf.data.Dataset per una gestione più efficiente della memoria\n",
|
||
" train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_targets))\\\n",
|
||
" .cache()\\\n",
|
||
" .shuffle(buffer_size=1024)\\\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",
|
||
" # Setup del modello\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",
|
||
" # Mostra il summary del modello\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 degli errori\n",
|
||
" try:\n",
|
||
" history = model.fit(\n",
|
||
" train_dataset,\n",
|
||
" validation_data=val_dataset,\n",
|
||
" epochs=epochs,\n",
|
||
" callbacks=callbacks,\n",
|
||
" verbose=1,\n",
|
||
" workers=4,\n",
|
||
" use_multiprocessing=True\n",
|
||
" )\n",
|
||
" except tf.errors.ResourceExhaustedError:\n",
|
||
" print(\"Memoria GPU esaurita, riprovo con batch size più piccolo...\")\n",
|
||
" # Riprova 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",
|
||
" # Salva il modello finale\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",
|
||
" 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: (41, 3)\n",
|
||
"- Static shape: (113,)\n",
|
||
"- Numero di output: 5\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-06 22:13:29.329524: 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, 41, 3)] 0 [] \n",
|
||
" \n",
|
||
" layer_normalization (Layer (None, 41, 3) 6 ['temporal[0][0]'] \n",
|
||
" Normalization) \n",
|
||
" \n",
|
||
" data_augmentation (DataAug (None, 41, 3) 0 ['layer_normalization[0][0]'] \n",
|
||
" mentation) \n",
|
||
" \n",
|
||
" dense (Dense) (None, 41, 64) 256 ['data_augmentation[0][0]'] \n",
|
||
" \n",
|
||
" dropout (Dropout) (None, 41, 64) 0 ['dense[0][0]'] \n",
|
||
" \n",
|
||
" dense_1 (Dense) (None, 41, 128) 8320 ['dropout[0][0]'] \n",
|
||
" \n",
|
||
" positional_encoding (Posit (None, 41, 128) 5248 ['dense_1[0][0]'] \n",
|
||
" ionalEncoding) \n",
|
||
" \n",
|
||
" multi_head_attention (Mult (None, 41, 128) 66048 ['positional_encoding[0][0]', \n",
|
||
" iHeadAttention) 'positional_encoding[0][0]'] \n",
|
||
" \n",
|
||
" dense_2 (Dense) (None, 41, 128) 16512 ['positional_encoding[0][0]'] \n",
|
||
" \n",
|
||
" dropout_1 (Dropout) (None, 41, 128) 0 ['multi_head_attention[0][0]']\n",
|
||
" \n",
|
||
" tf.math.multiply (TFOpLamb (None, 41, 128) 0 ['dense_2[0][0]', \n",
|
||
" da) 'dropout_1[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth (Stochast (None, 41, 128) 0 ['positional_encoding[0][0]', \n",
|
||
" icDepth) 'tf.math.multiply[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_1 (Lay (None, 41, 128) 256 ['stochastic_depth[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" dense_3 (Dense) (None, 41, 256) 33024 ['layer_normalization_1[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_2 (Dropout) (None, 41, 256) 0 ['dense_3[0][0]'] \n",
|
||
" \n",
|
||
" dense_4 (Dense) (None, 41, 128) 32896 ['dropout_2[0][0]'] \n",
|
||
" \n",
|
||
" dropout_3 (Dropout) (None, 41, 128) 0 ['dense_4[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth_1 (Stocha (None, 41, 128) 0 ['layer_normalization_1[0][0]'\n",
|
||
" sticDepth) , 'dropout_3[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_2 (Lay (None, 41, 128) 256 ['stochastic_depth_1[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" multi_head_attention_1 (Mu (None, 41, 128) 66048 ['layer_normalization_2[0][0]'\n",
|
||
" ltiHeadAttention) , 'layer_normalization_2[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_5 (Dense) (None, 41, 128) 16512 ['layer_normalization_2[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_4 (Dropout) (None, 41, 128) 0 ['multi_head_attention_1[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.math.multiply_1 (TFOpLa (None, 41, 128) 0 ['dense_5[0][0]', \n",
|
||
" mbda) 'dropout_4[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth_2 (Stocha (None, 41, 128) 0 ['layer_normalization_2[0][0]'\n",
|
||
" sticDepth) , 'tf.math.multiply_1[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_3 (Lay (None, 41, 128) 256 ['stochastic_depth_2[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" dense_6 (Dense) (None, 41, 256) 33024 ['layer_normalization_3[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_5 (Dropout) (None, 41, 256) 0 ['dense_6[0][0]'] \n",
|
||
" \n",
|
||
" dense_7 (Dense) (None, 41, 128) 32896 ['dropout_5[0][0]'] \n",
|
||
" \n",
|
||
" dropout_6 (Dropout) (None, 41, 128) 0 ['dense_7[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth_3 (Stocha (None, 41, 128) 0 ['layer_normalization_3[0][0]'\n",
|
||
" sticDepth) , 'dropout_6[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_4 (Lay (None, 41, 128) 256 ['stochastic_depth_3[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" multi_head_attention_2 (Mu (None, 41, 128) 66048 ['layer_normalization_4[0][0]'\n",
|
||
" ltiHeadAttention) , 'layer_normalization_4[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_8 (Dense) (None, 41, 128) 16512 ['layer_normalization_4[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_7 (Dropout) (None, 41, 128) 0 ['multi_head_attention_2[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.math.multiply_2 (TFOpLa (None, 41, 128) 0 ['dense_8[0][0]', \n",
|
||
" mbda) 'dropout_7[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth_4 (Stocha (None, 41, 128) 0 ['layer_normalization_4[0][0]'\n",
|
||
" sticDepth) , 'tf.math.multiply_2[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_5 (Lay (None, 41, 128) 256 ['stochastic_depth_4[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" dense_9 (Dense) (None, 41, 256) 33024 ['layer_normalization_5[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_8 (Dropout) (None, 41, 256) 0 ['dense_9[0][0]'] \n",
|
||
" \n",
|
||
" dense_10 (Dense) (None, 41, 128) 32896 ['dropout_8[0][0]'] \n",
|
||
" \n",
|
||
" dropout_9 (Dropout) (None, 41, 128) 0 ['dense_10[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth_5 (Stocha (None, 41, 128) 0 ['layer_normalization_5[0][0]'\n",
|
||
" sticDepth) , 'dropout_9[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_6 (Lay (None, 41, 128) 256 ['stochastic_depth_5[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" multi_head_attention_3 (Mu (None, 41, 128) 66048 ['layer_normalization_6[0][0]'\n",
|
||
" ltiHeadAttention) , 'layer_normalization_6[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_11 (Dense) (None, 41, 128) 16512 ['layer_normalization_6[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_10 (Dropout) (None, 41, 128) 0 ['multi_head_attention_3[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.math.multiply_3 (TFOpLa (None, 41, 128) 0 ['dense_11[0][0]', \n",
|
||
" mbda) 'dropout_10[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth_6 (Stocha (None, 41, 128) 0 ['layer_normalization_6[0][0]'\n",
|
||
" sticDepth) , 'tf.math.multiply_3[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_7 (Lay (None, 41, 128) 256 ['stochastic_depth_6[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" dense_12 (Dense) (None, 41, 256) 33024 ['layer_normalization_7[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_11 (Dropout) (None, 41, 256) 0 ['dense_12[0][0]'] \n",
|
||
" \n",
|
||
" dense_13 (Dense) (None, 41, 128) 32896 ['dropout_11[0][0]'] \n",
|
||
" \n",
|
||
" static (InputLayer) [(None, 113)] 0 [] \n",
|
||
" \n",
|
||
" dropout_12 (Dropout) (None, 41, 128) 0 ['dense_13[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_9 (Lay (None, 113) 226 ['static[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" stochastic_depth_7 (Stocha (None, 41, 128) 0 ['layer_normalization_7[0][0]'\n",
|
||
" sticDepth) , 'dropout_12[0][0]'] \n",
|
||
" \n",
|
||
" dense_14 (Dense) (None, 256) 29184 ['layer_normalization_9[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" layer_normalization_8 (Lay (None, 41, 128) 256 ['stochastic_depth_7[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" dropout_13 (Dropout) (None, 256) 0 ['dense_14[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth_8 (Stocha (None, 41, 128) 0 ['layer_normalization_8[0][0]'\n",
|
||
" sticDepth) , 'positional_encoding[0][0]']\n",
|
||
" \n",
|
||
" dense_15 (Dense) (None, 128) 32896 ['dropout_13[0][0]'] \n",
|
||
" \n",
|
||
" multi_head_attention_4 (Mu (None, 41, 128) 131968 ['stochastic_depth_8[0][0]', \n",
|
||
" ltiHeadAttention) 'stochastic_depth_8[0][0]'] \n",
|
||
" \n",
|
||
" dropout_14 (Dropout) (None, 128) 0 ['dense_15[0][0]'] \n",
|
||
" \n",
|
||
" global_average_pooling1d ( (None, 128) 0 ['multi_head_attention_4[0][0]\n",
|
||
" GlobalAveragePooling1D) '] \n",
|
||
" \n",
|
||
" global_average_pooling1d_1 (None, 128) 0 ['stochastic_depth_8[0][0]'] \n",
|
||
" (GlobalAveragePooling1D) \n",
|
||
" \n",
|
||
" global_max_pooling1d (Glob (None, 128) 0 ['stochastic_depth_8[0][0]'] \n",
|
||
" alMaxPooling1D) \n",
|
||
" \n",
|
||
" dense_16 (Dense) (None, 64) 8256 ['dropout_14[0][0]'] \n",
|
||
" \n",
|
||
" concatenate (Concatenate) (None, 384) 0 ['global_average_pooling1d[0][\n",
|
||
" 0]', \n",
|
||
" 'global_average_pooling1d_1[0\n",
|
||
" ][0]', \n",
|
||
" 'global_max_pooling1d[0][0]']\n",
|
||
" \n",
|
||
" dropout_15 (Dropout) (None, 64) 0 ['dense_16[0][0]'] \n",
|
||
" \n",
|
||
" concatenate_1 (Concatenate (None, 448) 0 ['concatenate[0][0]', \n",
|
||
" ) 'dropout_15[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization (Batch (None, 448) 1792 ['concatenate_1[0][0]'] \n",
|
||
" Normalization) \n",
|
||
" \n",
|
||
" dense_17 (Dense) (None, 256) 114944 ['batch_normalization[0][0]'] \n",
|
||
" \n",
|
||
" dropout_16 (Dropout) (None, 256) 0 ['dense_17[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization_1 (Bat (None, 256) 1024 ['dropout_16[0][0]'] \n",
|
||
" chNormalization) \n",
|
||
" \n",
|
||
" dense_18 (Dense) (None, 128) 32896 ['batch_normalization_1[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_17 (Dropout) (None, 128) 0 ['dense_18[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization_2 (Bat (None, 128) 512 ['dropout_17[0][0]'] \n",
|
||
" chNormalization) \n",
|
||
" \n",
|
||
" dense_19 (Dense) (None, 64) 8256 ['batch_normalization_2[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_18 (Dropout) (None, 64) 0 ['dense_19[0][0]'] \n",
|
||
" \n",
|
||
" dense_20 (Dense) (None, 5) 325 ['dropout_18[0][0]'] \n",
|
||
" \n",
|
||
"==================================================================================================\n",
|
||
"Total params: 972077 (3.71 MB)\n",
|
||
"Trainable params: 965165 (3.68 MB)\n",
|
||
"Non-trainable params: 6912 (27.00 KB)\n",
|
||
"__________________________________________________________________________________________________\n",
|
||
"Epoch 1/150\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-06 22:13:45.462715: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x798120b6d800 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
|
||
"2024-12-06 22:13:45.462747: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n",
|
||
"2024-12-06 22:13:45.469088: 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",
|
||
"2024-12-06 22:13:45.545756: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n",
|
||
"2024-12-06 22:13:45.677401: 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": [
|
||
" 6/4977 [..............................] - ETA: 3:14 - loss: 0.7467 - mae: 1.1434 WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0344s vs `on_train_batch_end` time: 0.0364s). Check your callbacks.\n",
|
||
"4977/4977 [==============================] - 321s 60ms/step - loss: 0.0548 - mae: 0.2015 - val_loss: 0.0148 - val_mae: 0.0885 - val_olive_prod_mae: 0.0955 - val_min_oil_prod_mae: 0.0963 - val_max_oil_prod_mae: 0.0960 - val_avg_oil_prod_mae: 0.0920 - val_total_water_need_mae: 0.0630 - lr: 1.0111e-04\n",
|
||
"Epoch 2/150\n",
|
||
"4977/4977 [==============================] - 290s 58ms/step - loss: 0.0249 - mae: 0.1434 - val_loss: 0.0137 - val_mae: 0.0858 - val_olive_prod_mae: 0.0949 - val_min_oil_prod_mae: 0.0948 - val_max_oil_prod_mae: 0.0939 - val_avg_oil_prod_mae: 0.0902 - val_total_water_need_mae: 0.0554 - lr: 1.0219e-05\n",
|
||
"Epoch 3/150\n",
|
||
"4977/4977 [==============================] - 301s 60ms/step - loss: 0.0240 - mae: 0.1416 - val_loss: 0.0135 - val_mae: 0.0856 - val_olive_prod_mae: 0.0943 - val_min_oil_prod_mae: 0.0946 - val_max_oil_prod_mae: 0.0934 - val_avg_oil_prod_mae: 0.0899 - val_total_water_need_mae: 0.0560 - lr: 1.0328e-06\n",
|
||
"Epoch 4/150\n",
|
||
"4977/4977 [==============================] - 302s 61ms/step - loss: 0.0240 - mae: 0.1413 - val_loss: 0.0134 - val_mae: 0.0853 - val_olive_prod_mae: 0.0940 - val_min_oil_prod_mae: 0.0943 - val_max_oil_prod_mae: 0.0931 - val_avg_oil_prod_mae: 0.0895 - val_total_water_need_mae: 0.0553 - lr: 1.0438e-07\n",
|
||
"Epoch 5/150\n",
|
||
"4977/4977 [==============================] - 324s 65ms/step - loss: 0.0239 - mae: 0.1412 - val_loss: 0.0135 - val_mae: 0.0855 - val_olive_prod_mae: 0.0943 - val_min_oil_prod_mae: 0.0946 - val_max_oil_prod_mae: 0.0935 - val_avg_oil_prod_mae: 0.0898 - val_total_water_need_mae: 0.0553 - lr: 1.0549e-08\n",
|
||
"Epoch 6/150\n",
|
||
"4977/4977 [==============================] - 322s 64ms/step - loss: 0.0239 - mae: 0.1413 - val_loss: 0.0134 - val_mae: 0.0851 - val_olive_prod_mae: 0.0938 - val_min_oil_prod_mae: 0.0942 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0894 - val_total_water_need_mae: 0.0552 - lr: 1.0661e-09\n",
|
||
"Epoch 7/150\n",
|
||
"4977/4977 [==============================] - 304s 61ms/step - loss: 0.0239 - mae: 0.1413 - val_loss: 0.0134 - val_mae: 0.0853 - val_olive_prod_mae: 0.0940 - val_min_oil_prod_mae: 0.0943 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0896 - val_total_water_need_mae: 0.0553 - lr: 1.0775e-10\n",
|
||
"Epoch 8/150\n",
|
||
"4977/4977 [==============================] - 306s 61ms/step - loss: 0.0239 - mae: 0.1413 - val_loss: 0.0134 - val_mae: 0.0853 - val_olive_prod_mae: 0.0940 - val_min_oil_prod_mae: 0.0944 - val_max_oil_prod_mae: 0.0931 - val_avg_oil_prod_mae: 0.0896 - val_total_water_need_mae: 0.0555 - lr: 1.0889e-11\n",
|
||
"Epoch 9/150\n",
|
||
"4977/4977 [==============================] - 312s 62ms/step - loss: 0.0239 - mae: 0.1412 - val_loss: 0.0134 - val_mae: 0.0852 - val_olive_prod_mae: 0.0941 - val_min_oil_prod_mae: 0.0943 - val_max_oil_prod_mae: 0.0931 - val_avg_oil_prod_mae: 0.0895 - val_total_water_need_mae: 0.0551 - lr: 1.1005e-12\n",
|
||
"Epoch 10/150\n",
|
||
"4977/4977 [==============================] - 293s 59ms/step - loss: 0.0239 - mae: 0.1413 - val_loss: 0.0135 - val_mae: 0.0856 - val_olive_prod_mae: 0.0944 - val_min_oil_prod_mae: 0.0946 - val_max_oil_prod_mae: 0.0935 - val_avg_oil_prod_mae: 0.0899 - val_total_water_need_mae: 0.0557 - lr: 1.1122e-13\n",
|
||
"Epoch 11/150\n",
|
||
"4977/4977 [==============================] - 300s 60ms/step - loss: 0.0240 - mae: 0.1414 - val_loss: 0.0135 - val_mae: 0.0854 - val_olive_prod_mae: 0.0941 - val_min_oil_prod_mae: 0.0945 - val_max_oil_prod_mae: 0.0933 - val_avg_oil_prod_mae: 0.0897 - val_total_water_need_mae: 0.0555 - lr: 1.1241e-14\n",
|
||
"Epoch 12/150\n",
|
||
"4977/4977 [==============================] - 297s 60ms/step - loss: 0.0240 - mae: 0.1414 - val_loss: 0.0134 - val_mae: 0.0852 - val_olive_prod_mae: 0.0939 - val_min_oil_prod_mae: 0.0943 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0895 - val_total_water_need_mae: 0.0551 - lr: 1.1361e-15\n",
|
||
"Epoch 13/150\n",
|
||
"4977/4977 [==============================] - 304s 61ms/step - loss: 0.0239 - mae: 0.1412 - val_loss: 0.0134 - val_mae: 0.0853 - val_olive_prod_mae: 0.0941 - val_min_oil_prod_mae: 0.0944 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0896 - val_total_water_need_mae: 0.0554 - lr: 1.1482e-16\n",
|
||
"Epoch 14/150\n",
|
||
"4977/4977 [==============================] - 305s 61ms/step - loss: 0.0239 - mae: 0.1413 - val_loss: 0.0134 - val_mae: 0.0850 - val_olive_prod_mae: 0.0938 - val_min_oil_prod_mae: 0.0941 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0893 - val_total_water_need_mae: 0.0548 - lr: 1.1604e-17\n",
|
||
"Epoch 15/150\n",
|
||
"4977/4977 [==============================] - 306s 61ms/step - loss: 0.0239 - mae: 0.1413 - val_loss: 0.0134 - val_mae: 0.0852 - val_olive_prod_mae: 0.0940 - val_min_oil_prod_mae: 0.0943 - val_max_oil_prod_mae: 0.0931 - val_avg_oil_prod_mae: 0.0895 - val_total_water_need_mae: 0.0553 - lr: 1.1727e-18\n",
|
||
"Epoch 16/150\n",
|
||
"4977/4977 [==============================] - 317s 63ms/step - loss: 0.0239 - mae: 0.1412 - val_loss: 0.0134 - val_mae: 0.0853 - val_olive_prod_mae: 0.0942 - val_min_oil_prod_mae: 0.0943 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0895 - val_total_water_need_mae: 0.0552 - lr: 1.1852e-19\n",
|
||
"Epoch 17/150\n",
|
||
"4977/4977 [==============================] - 311s 62ms/step - loss: 0.0239 - mae: 0.1412 - val_loss: 0.0134 - val_mae: 0.0852 - val_olive_prod_mae: 0.0940 - val_min_oil_prod_mae: 0.0943 - val_max_oil_prod_mae: 0.0931 - val_avg_oil_prod_mae: 0.0895 - val_total_water_need_mae: 0.0550 - lr: 1.1978e-20\n",
|
||
"Epoch 18/150\n",
|
||
"4977/4977 [==============================] - 303s 61ms/step - loss: 0.0240 - mae: 0.1414 - val_loss: 0.0134 - val_mae: 0.0852 - val_olive_prod_mae: 0.0940 - val_min_oil_prod_mae: 0.0943 - val_max_oil_prod_mae: 0.0931 - val_avg_oil_prod_mae: 0.0895 - val_total_water_need_mae: 0.0552 - lr: 1.2106e-21\n",
|
||
"Epoch 19/150\n",
|
||
"4977/4977 [==============================] - 311s 62ms/step - loss: 0.0240 - mae: 0.1414 - val_loss: 0.0135 - val_mae: 0.0855 - val_olive_prod_mae: 0.0941 - val_min_oil_prod_mae: 0.0945 - val_max_oil_prod_mae: 0.0933 - val_avg_oil_prod_mae: 0.0898 - val_total_water_need_mae: 0.0559 - lr: 1.2235e-22\n",
|
||
"Epoch 20/150\n",
|
||
"4977/4977 [==============================] - 296s 59ms/step - loss: 0.0239 - mae: 0.1413 - val_loss: 0.0134 - val_mae: 0.0853 - val_olive_prod_mae: 0.0940 - val_min_oil_prod_mae: 0.0944 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0896 - val_total_water_need_mae: 0.0553 - lr: 1.2365e-23\n",
|
||
"Epoch 21/150\n",
|
||
"4977/4977 [==============================] - 298s 60ms/step - loss: 0.0239 - mae: 0.1413 - val_loss: 0.0134 - val_mae: 0.0854 - val_olive_prod_mae: 0.0941 - val_min_oil_prod_mae: 0.0944 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0896 - val_total_water_need_mae: 0.0557 - lr: 1.2497e-24\n",
|
||
"Epoch 22/150\n",
|
||
"4977/4977 [==============================] - 299s 60ms/step - loss: 0.0239 - mae: 0.1410 - val_loss: 0.0134 - val_mae: 0.0853 - val_olive_prod_mae: 0.0949 - val_min_oil_prod_mae: 0.0948 - val_max_oil_prod_mae: 0.0939 - val_avg_oil_prod_mae: 0.0902 - val_total_water_need_mae: 0.0554 - lr: 1.2630e-25\n",
|
||
"\n",
|
||
"Modello salvato in: 2024-12-06_21-10_final_model.keras\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model, history = train_transformer(train_data, train_targets, val_data, val_targets, 150, 512)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "3e2fb5a5341dac92",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"24500/24500 [==============================] - 106s 4ms/step\n",
|
||
"\n",
|
||
"Errori per target:\n",
|
||
"--------------------------------------------------\n",
|
||
"olive_prod:\n",
|
||
"MAE assoluto: 1560.68\n",
|
||
"Errore percentuale medio: 6.74%\n",
|
||
"Precisione: 93.26%\n",
|
||
"--------------------------------------------------\n",
|
||
"min_oil_prod:\n",
|
||
"MAE assoluto: 323.74\n",
|
||
"Errore percentuale medio: 6.93%\n",
|
||
"Precisione: 93.07%\n",
|
||
"--------------------------------------------------\n",
|
||
"max_oil_prod:\n",
|
||
"MAE assoluto: 388.71\n",
|
||
"Errore percentuale medio: 6.79%\n",
|
||
"Precisione: 93.21%\n",
|
||
"--------------------------------------------------\n",
|
||
"avg_oil_prod:\n",
|
||
"MAE assoluto: 340.31\n",
|
||
"Errore percentuale medio: 6.61%\n",
|
||
"Precisione: 93.39%\n",
|
||
"--------------------------------------------------\n",
|
||
"total_water_need:\n",
|
||
"MAE assoluto: 1644.70\n",
|
||
"Errore percentuale medio: 4.19%\n",
|
||
"Precisione: 95.81%\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": 14,
|
||
"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: 0.0948\n",
|
||
"olive_prod_rmse: 0.1302\n",
|
||
"olive_prod_mape: 92.8177\n",
|
||
"min_oil_prod_mae: 0.0947\n",
|
||
"min_oil_prod_rmse: 0.1338\n",
|
||
"min_oil_prod_mape: 117.7128\n",
|
||
"max_oil_prod_mae: 0.0939\n",
|
||
"max_oil_prod_rmse: 0.1325\n",
|
||
"max_oil_prod_mape: 93.7488\n",
|
||
"avg_oil_prod_mae: 0.0901\n",
|
||
"avg_oil_prod_rmse: 0.1265\n",
|
||
"avg_oil_prod_mape: 93.7298\n",
|
||
"total_water_need_mae: 0.0554\n",
|
||
"total_water_need_rmse: 0.0791\n",
|
||
"total_water_need_mape: 35.4225\n",
|
||
"\n",
|
||
"Performance sul set validazione:\n",
|
||
"olive_prod_mae: 0.0949\n",
|
||
"olive_prod_rmse: 0.1303\n",
|
||
"olive_prod_mape: 102.8539\n",
|
||
"min_oil_prod_mae: 0.0948\n",
|
||
"min_oil_prod_rmse: 0.1339\n",
|
||
"min_oil_prod_mape: 77.3828\n",
|
||
"max_oil_prod_mae: 0.0939\n",
|
||
"max_oil_prod_rmse: 0.1326\n",
|
||
"max_oil_prod_mape: 196.4722\n",
|
||
"avg_oil_prod_mae: 0.0902\n",
|
||
"avg_oil_prod_rmse: 0.1266\n",
|
||
"avg_oil_prod_mape: 100.6176\n",
|
||
"total_water_need_mae: 0.0554\n",
|
||
"total_water_need_rmse: 0.0790\n",
|
||
"total_water_need_mape: 186.4282\n",
|
||
"\n",
|
||
"Performance sul set test:\n",
|
||
"olive_prod_mae: 0.0947\n",
|
||
"olive_prod_rmse: 0.1299\n",
|
||
"olive_prod_mape: 107.6260\n",
|
||
"min_oil_prod_mae: 0.0945\n",
|
||
"min_oil_prod_rmse: 0.1332\n",
|
||
"min_oil_prod_mape: 77.8684\n",
|
||
"max_oil_prod_mae: 0.0936\n",
|
||
"max_oil_prod_rmse: 0.1322\n",
|
||
"max_oil_prod_mape: 353.2343\n",
|
||
"avg_oil_prod_mae: 0.0899\n",
|
||
"avg_oil_prod_rmse: 0.1260\n",
|
||
"avg_oil_prod_mape: 308.7326\n",
|
||
"total_water_need_mae: 0.0554\n",
|
||
"total_water_need_rmse: 0.0790\n",
|
||
"total_water_need_mape: 42.0580\n",
|
||
"\n",
|
||
"Avvio retraining...\n",
|
||
"Epoch 1/50\n",
|
||
"27563/27563 [==============================] - 865s 31ms/step - loss: 0.0250 - mae: 0.1506 - val_loss: 0.0112 - val_mae: 0.0804 - lr: 5.4806e-06\n",
|
||
"Epoch 2/50\n",
|
||
"27563/27563 [==============================] - 809s 29ms/step - loss: 0.0235 - mae: 0.1462 - val_loss: 0.0111 - val_mae: 0.0801 - lr: 3.0034e-07\n",
|
||
"Epoch 3/50\n",
|
||
"27563/27563 [==============================] - 936s 34ms/step - loss: 0.0234 - mae: 0.1461 - val_loss: 0.0111 - val_mae: 0.0801 - lr: 1.6459e-08\n",
|
||
"Epoch 4/50\n",
|
||
"27563/27563 [==============================] - 837s 30ms/step - loss: 0.0234 - mae: 0.1461 - val_loss: 0.0111 - val_mae: 0.0797 - lr: 9.0196e-10\n",
|
||
"Epoch 5/50\n",
|
||
"27563/27563 [==============================] - 831s 30ms/step - loss: 0.0233 - mae: 0.1459 - val_loss: 0.0112 - val_mae: 0.0804 - lr: 4.9428e-11\n",
|
||
"Epoch 6/50\n",
|
||
"27563/27563 [==============================] - 766s 28ms/step - loss: 0.0234 - mae: 0.1459 - val_loss: 0.0111 - val_mae: 0.0801 - lr: 2.7087e-12\n",
|
||
"Epoch 7/50\n",
|
||
"27563/27563 [==============================] - 847s 31ms/step - loss: 0.0233 - mae: 0.1459 - val_loss: 0.0112 - val_mae: 0.0802 - lr: 1.4844e-13\n",
|
||
"Epoch 8/50\n",
|
||
"27563/27563 [==============================] - 864s 31ms/step - loss: 0.0233 - mae: 0.1459 - val_loss: 0.0111 - val_mae: 0.0797 - lr: 8.1345e-15\n",
|
||
"Epoch 9/50\n",
|
||
"27563/27563 [==============================] - 823s 30ms/step - loss: 0.0233 - mae: 0.1460 - val_loss: 0.0110 - val_mae: 0.0795 - lr: 4.4578e-16\n",
|
||
"Epoch 10/50\n",
|
||
"27563/27563 [==============================] - 801s 29ms/step - loss: 0.0233 - mae: 0.1458 - val_loss: 0.0111 - val_mae: 0.0799 - lr: 2.4429e-17\n",
|
||
"Epoch 11/50\n",
|
||
"27563/27563 [==============================] - 881s 32ms/step - loss: 0.0233 - mae: 0.1458 - val_loss: 0.0110 - val_mae: 0.0792 - lr: 1.3387e-18\n",
|
||
"Epoch 12/50\n",
|
||
"27563/27563 [==============================] - 856s 31ms/step - loss: 0.0233 - mae: 0.1459 - val_loss: 0.0112 - val_mae: 0.0803 - lr: 7.3363e-20\n",
|
||
"Epoch 13/50\n",
|
||
"27563/27563 [==============================] - 861s 31ms/step - loss: 0.0234 - mae: 0.1461 - val_loss: 0.0111 - val_mae: 0.0802 - lr: 4.0203e-21\n",
|
||
"Epoch 14/50\n",
|
||
"27563/27563 [==============================] - 835s 30ms/step - loss: 0.0234 - mae: 0.1460 - val_loss: 0.0111 - val_mae: 0.0798 - lr: 2.2032e-22\n",
|
||
"Epoch 15/50\n",
|
||
"27563/27563 [==============================] - 856s 31ms/step - loss: 0.0233 - mae: 0.1458 - val_loss: 0.0110 - val_mae: 0.0798 - lr: 1.2074e-23\n",
|
||
"Epoch 16/50\n",
|
||
"27563/27563 [==============================] - 944s 34ms/step - loss: 0.0234 - mae: 0.1460 - val_loss: 0.0111 - val_mae: 0.0797 - lr: 6.6164e-25\n",
|
||
"Epoch 17/50\n",
|
||
"27563/27563 [==============================] - 873s 32ms/step - loss: 0.0234 - mae: 0.1460 - val_loss: 0.0111 - val_mae: 0.0801 - lr: 3.6258e-26\n",
|
||
"Epoch 18/50\n",
|
||
"27563/27563 [==============================] - 913s 33ms/step - loss: 0.0234 - mae: 0.1459 - val_loss: 0.0111 - val_mae: 0.0800 - lr: 1.9870e-27\n",
|
||
"Epoch 19/50\n",
|
||
"27563/27563 [==============================] - 871s 32ms/step - loss: 0.0234 - mae: 0.1460 - val_loss: 0.0111 - val_mae: 0.0800 - lr: 1.0889e-28\n",
|
||
"Epoch 20/50\n",
|
||
"27563/27563 [==============================] - 867s 31ms/step - loss: 0.0233 - mae: 0.1459 - val_loss: 0.0111 - val_mae: 0.0798 - lr: 5.9671e-30\n",
|
||
"Epoch 21/50\n",
|
||
"27563/27563 [==============================] - 827s 30ms/step - loss: 0.0233 - mae: 0.1459 - val_loss: 0.0111 - val_mae: 0.0800 - lr: 3.2700e-31\n",
|
||
"\n",
|
||
"Valutazione performance finali...\n",
|
||
"\n",
|
||
"Performance sul set training:\n",
|
||
"olive_prod_mae: 0.0885\n",
|
||
"olive_prod_rmse: 0.1203\n",
|
||
"olive_prod_mape: 92.9890\n",
|
||
"min_oil_prod_mae: 0.0887\n",
|
||
"min_oil_prod_rmse: 0.1245\n",
|
||
"min_oil_prod_mape: 117.2236\n",
|
||
"max_oil_prod_mae: 0.0879\n",
|
||
"max_oil_prod_rmse: 0.1231\n",
|
||
"max_oil_prod_mape: 92.1364\n",
|
||
"avg_oil_prod_mae: 0.0840\n",
|
||
"avg_oil_prod_rmse: 0.1166\n",
|
||
"avg_oil_prod_mape: 91.9667\n",
|
||
"total_water_need_mae: 0.0472\n",
|
||
"total_water_need_rmse: 0.0653\n",
|
||
"total_water_need_mape: 36.8083\n",
|
||
"\n",
|
||
"Performance sul set validazione:\n",
|
||
"olive_prod_mae: 0.0885\n",
|
||
"olive_prod_rmse: 0.1203\n",
|
||
"olive_prod_mape: 105.5279\n",
|
||
"min_oil_prod_mae: 0.0887\n",
|
||
"min_oil_prod_rmse: 0.1245\n",
|
||
"min_oil_prod_mape: 76.7865\n",
|
||
"max_oil_prod_mae: 0.0879\n",
|
||
"max_oil_prod_rmse: 0.1232\n",
|
||
"max_oil_prod_mape: 269.5465\n",
|
||
"avg_oil_prod_mae: 0.0841\n",
|
||
"avg_oil_prod_rmse: 0.1167\n",
|
||
"avg_oil_prod_mape: 102.2860\n",
|
||
"total_water_need_mae: 0.0471\n",
|
||
"total_water_need_rmse: 0.0652\n",
|
||
"total_water_need_mape: 226.4810\n",
|
||
"\n",
|
||
"Performance sul set test:\n",
|
||
"olive_prod_mae: 0.0883\n",
|
||
"olive_prod_rmse: 0.1201\n",
|
||
"olive_prod_mape: 122.1959\n",
|
||
"min_oil_prod_mae: 0.0885\n",
|
||
"min_oil_prod_rmse: 0.1240\n",
|
||
"min_oil_prod_mape: 75.7994\n",
|
||
"max_oil_prod_mae: 0.0877\n",
|
||
"max_oil_prod_rmse: 0.1229\n",
|
||
"max_oil_prod_mape: 372.2539\n",
|
||
"avg_oil_prod_mae: 0.0838\n",
|
||
"avg_oil_prod_rmse: 0.1162\n",
|
||
"avg_oil_prod_mape: 293.5905\n",
|
||
"total_water_need_mae: 0.0472\n",
|
||
"total_water_need_rmse: 0.0653\n",
|
||
"total_water_need_mape: 41.3984\n",
|
||
"\n",
|
||
"Modello riaddestrato salvato in: 2024-12-06_21-10_retrained_model.keras\n",
|
||
"\n",
|
||
"Miglioramenti delle performance:\n",
|
||
"\n",
|
||
"Set train:\n",
|
||
"olive_prod_mae: 6.73% di miglioramento\n",
|
||
"olive_prod_rmse: 7.61% di miglioramento\n",
|
||
"olive_prod_mape: -0.18% di miglioramento\n",
|
||
"min_oil_prod_mae: 6.37% di miglioramento\n",
|
||
"min_oil_prod_rmse: 6.91% di miglioramento\n",
|
||
"min_oil_prod_mape: 0.42% di miglioramento\n",
|
||
"max_oil_prod_mae: 6.30% di miglioramento\n",
|
||
"max_oil_prod_rmse: 7.09% di miglioramento\n",
|
||
"max_oil_prod_mape: 1.72% di miglioramento\n",
|
||
"avg_oil_prod_mae: 6.75% di miglioramento\n",
|
||
"avg_oil_prod_rmse: 7.79% di miglioramento\n",
|
||
"avg_oil_prod_mape: 1.88% di miglioramento\n",
|
||
"total_water_need_mae: 14.96% di miglioramento\n",
|
||
"total_water_need_rmse: 17.46% di miglioramento\n",
|
||
"total_water_need_mape: -3.91% di miglioramento\n",
|
||
"\n",
|
||
"Set val:\n",
|
||
"olive_prod_mae: 6.71% di miglioramento\n",
|
||
"olive_prod_rmse: 7.65% di miglioramento\n",
|
||
"olive_prod_mape: -2.60% di miglioramento\n",
|
||
"min_oil_prod_mae: 6.43% di miglioramento\n",
|
||
"min_oil_prod_rmse: 6.96% di miglioramento\n",
|
||
"min_oil_prod_mape: 0.77% di miglioramento\n",
|
||
"max_oil_prod_mae: 6.35% di miglioramento\n",
|
||
"max_oil_prod_rmse: 7.12% di miglioramento\n",
|
||
"max_oil_prod_mape: -37.19% di miglioramento\n",
|
||
"avg_oil_prod_mae: 6.80% di miglioramento\n",
|
||
"avg_oil_prod_rmse: 7.83% di miglioramento\n",
|
||
"avg_oil_prod_mape: -1.66% di miglioramento\n",
|
||
"total_water_need_mae: 14.92% di miglioramento\n",
|
||
"total_water_need_rmse: 17.45% di miglioramento\n",
|
||
"total_water_need_mape: -21.48% di miglioramento\n",
|
||
"\n",
|
||
"Set test:\n",
|
||
"olive_prod_mae: 6.71% di miglioramento\n",
|
||
"olive_prod_rmse: 7.58% di miglioramento\n",
|
||
"olive_prod_mape: -13.54% di miglioramento\n",
|
||
"min_oil_prod_mae: 6.41% di miglioramento\n",
|
||
"min_oil_prod_rmse: 6.90% di miglioramento\n",
|
||
"min_oil_prod_mape: 2.66% di miglioramento\n",
|
||
"max_oil_prod_mae: 6.30% di miglioramento\n",
|
||
"max_oil_prod_rmse: 7.07% di miglioramento\n",
|
||
"max_oil_prod_mape: -5.38% di miglioramento\n",
|
||
"avg_oil_prod_mae: 6.77% di miglioramento\n",
|
||
"avg_oil_prod_rmse: 7.78% di miglioramento\n",
|
||
"avg_oil_prod_mape: 4.90% di miglioramento\n",
|
||
"total_water_need_mae: 14.84% di miglioramento\n",
|
||
"total_water_need_rmse: 17.31% di miglioramento\n",
|
||
"total_water_need_mape: 1.57% 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=50,\n",
|
||
" batch_size=128\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "a95606bc-c4bc-418a-acdb-2e24c30dfa81",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"24500/24500 [==============================] - 107s 4ms/step\n",
|
||
"\n",
|
||
"Errori per target:\n",
|
||
"--------------------------------------------------\n",
|
||
"olive_prod:\n",
|
||
"MAE assoluto: 1455.88\n",
|
||
"Errore percentuale medio: 5.66%\n",
|
||
"Precisione: 94.34%\n",
|
||
"--------------------------------------------------\n",
|
||
"min_oil_prod:\n",
|
||
"MAE assoluto: 302.94\n",
|
||
"Errore percentuale medio: 5.75%\n",
|
||
"Precisione: 94.25%\n",
|
||
"--------------------------------------------------\n",
|
||
"max_oil_prod:\n",
|
||
"MAE assoluto: 364.04\n",
|
||
"Errore percentuale medio: 5.71%\n",
|
||
"Precisione: 94.29%\n",
|
||
"--------------------------------------------------\n",
|
||
"avg_oil_prod:\n",
|
||
"MAE assoluto: 317.18\n",
|
||
"Errore percentuale medio: 5.49%\n",
|
||
"Precisione: 94.51%\n",
|
||
"--------------------------------------------------\n",
|
||
"total_water_need:\n",
|
||
"MAE assoluto: 1399.28\n",
|
||
"Errore percentuale medio: 3.31%\n",
|
||
"Precisione: 96.69%\n",
|
||
"--------------------------------------------------\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"percentage_errors, absolute_errors = calculate_real_error(retrained_model, val_data, val_targets, scaler_y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"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": 17,
|
||
"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",
|
||
"18375/18375 [==============================] - 84s 5ms/step\n",
|
||
"\n",
|
||
"Analisi per olive_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -17.427\n",
|
||
"variance: 3898197.250\n",
|
||
"std: 1974.385\n",
|
||
"min: -20014.463\n",
|
||
"max: 15113.210\n",
|
||
"median: 73.972\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: [-2099.35, 2115.97]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 85.0%:\n",
|
||
"Range: [-2801.90, 2818.52]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 90.0%:\n",
|
||
"Range: [-3504.46, 2818.52]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-4207.01, 3521.08]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-7017.22, 5628.74]\n",
|
||
"\n",
|
||
"Analisi per min_oil_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -20.391\n",
|
||
"variance: 178912.562\n",
|
||
"std: 422.981\n",
|
||
"min: -4500.833\n",
|
||
"max: 3076.512\n",
|
||
"median: -7.863\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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//Oc/BZadOHHinKNpt2zZUmFhYZo+fbrD/s6cObPAMXd2O/l/aJk6dapDnSlTpji9T+7k5eWlG264QQsXLnR47CAtLU1z5sxR27ZtS+2OhurVqys+Pt5h8lQtW7ZUeHi4pk+f7vC6rc8//1xbtmxxyej1kpSSkqIff/zRPv/nn39q4cKFuuGGG857Bb9r166STr+J4Ez5d3Pk9zk+Pl4VKlTQlClTHM7bs9cDAJwfV7oBoJg+//xzbd26Vbm5uUpLS9OKFSu0dOlS1alTR5988onDgEpne/bZZ7V69WrdeOONqlOnjg4ePKipU6eqZs2a9nfo1qtXT6GhoZo+fbqCgoJUsWJFxcbGlvgZ2sqVK6tt27YaOHCg0tLSNHnyZNWvX9/htWb33nuvPvzwQ3Xu3Fm33367duzYoffee0/16tVzaKtv374KCwtTgwYNHN5HLknXX399oa8vCwsLU1JSksaOHavOnTurW7du2rZtm6ZOnapWrVo5DJrmrLvvvlsffPCB7r//fq1cuVLXXHON8vLytHXrVn3wwQf64osv1LJly0LXrVChgp5//nndd999uu6669S7d2/t3LlTM2bMKHBV39ntxMTEqGfPnpo8ebL++usv+yvDfvvtN0mleyvvvn37Chx76fQAdM7+IaQwzz//vP0d8g8++KC8vb3173//W9nZ2YW+Jx2nP0svvfSSBg4cqPbt26tv3772V4ZFRUVp+PDhLtlu06ZNlZCQ4PDKMEkaO3bseddt3ry5+vfvrzfffFNHjhxR+/bttXbtWs2aNUvdu3dXx44dJcn+3vLk5GTddNNN6tq1q3766Sd9/vnnBZ6jBwCcG0k3ABTTqFGjJJ0eCKxy5cpq1qyZJk+erIEDBzqMEl6Ybt26adeuXXr77bd16NAhVa1aVe3bt9fYsWPtI2dXqFBBs2bNUlJSku6//37l5uZqxowZJU66R44cqY0bNyo5OVlHjx5Vp06dNHXqVIf3NickJOiVV17RxIkT9eijj6ply5ZatGiRfUTjfPnvh+7fv3+B7axcubLId4aPGTNGYWFhev311zV8+HBVrlxZQ4YM0Ysvvujwjm5nWa1WLViwQJMmTdI777yjjz/+WAEBAapbt64eeeQRXXbZZedcf8iQIcrLy9PLL7+sJ554Qs2aNdMnn3yiZ555psTbeeeddxQREaH3339fH3/8seLj4zVv3jw1bNjwnH+IKa4NGzbo7rvvLlBep06dC0q6L7/8cn399ddKSkpScnKybDabYmNj9d577xV4Rzf+MWDAAAUEBGjcuHF68sknVbFiRd1666166aWXXPKObun06PNxcXEaO3as9uzZoyZNmmjmzJkOj4ycy3//+1/VrVtXM2fO1Mcff6yIiAglJSVp9OjRDvWef/55+fn5afr06Vq5cqViY2P15ZdfuuwKPgCUVxbjzGgjAACg2DZs2KArr7xS77333jkfOwCcZbFYNHToUL3++uvu7goAwEk80w0AQCk4ceJEgbLJkyfLarWqXbt2bugRAAAoC7i9HACAUjB+/HitX79eHTt2lLe3tz7//HN9/vnnGjJkSJl+tRgKd/jwYYeB9s7m5eV13pHCiyM1NfWcy/39/e2PoAAAPAtJNwAApaBNmzZaunSpnnvuOR07dky1a9fWmDFj9NRTT7m7ayiBHj166KuvvipyeZ06dRxGer9Q1atXP+fy/v37a+bMmaW2PQDAxcMz3QAAAGdZv379Od9H7e/vr2uuuabUtrds2bJzLo+MjFSTJk1KbXsAgIuHpBsAAAAAABdhIDUAAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAkWSwWDRs2rNTamzlzpiwWi3744Yfz1u3QoYM6dOhgn9+1a5csFotmzpxpLxszZowsFkup9Q9lx9nxBwCULyTdAIAyKz9xzZ/8/Px02WWXadiwYUpLS3N399zuxRdf1IIFC0q1zVWrVtmP93vvvVdonWuuuUYWi0VNmzYt1W2XhjM/L2dOERERbu3X5s2bNWbMGO3atcut/QAAXHze7u4AAADn8+yzzyo6OlonT57UN998o2nTpumzzz7Tr7/+qoCAAHd374J9+eWX563z9NNPa8SIEQ5lL774om677TZ179691Pvk5+enOXPm6K677nIo37Vrl7777jv5+fmV+jZLy/XXX69+/fo5lPn7+7upN6dt3rxZY8eOVYcOHRQVFeWwzJn4AwA8F0k3AKDM69Kli1q2bClJuvfee1WlShVNnDhRCxcuVN++fQtd5/jx46pYseLF7GaJ+fj4nLeOt7e3vL0v3q/trl276pNPPtGhQ4dUtWpVe/mcOXNUrVo1NWjQQH///fdF609xXHbZZQX+WFCWORN/AIDn4vZyAIDHue666yRJO3fulCQNGDBAgYGB2rFjh7p27aqgoCDdeeedkk4n34899phq1aolX19fNWzYUBMmTJAxptC2Z8+erYYNG8rPz08xMTFavXq1w/Ldu3frwQcfVMOGDeXv768qVaqoV69eRd42nJWVpfvuu09VqlRRcHCw+vXrVyBZdeaZ3rOf6bZYLDp+/LhmzZplv4V6wIABWrlypSwWiz7++OMCbcyZM0cWi0UpKSnn3JYk3XLLLfL19dX8+fMLtHH77bfLy8urwDozZszQddddp/DwcPn6+qpJkyaaNm1agXo//PCDEhISVLVqVfn7+ys6Olr33HOPQ525c+cqJiZGQUFBCg4OVrNmzfTqq6+et9/nM2DAgAJXmqXCn5nPf85/wYIFatq0qXx9fXX55ZdryZIlBdbft2+fBg0apMjISPn6+io6OloPPPCAcnJyNHPmTPXq1UuS1LFjR3u8Vq1aJanw+B88eFCDBg1StWrV5Ofnp+bNm2vWrFkOdfKf/Z8wYYLefPNN1atXT76+vmrVqpXWrVtX8oMEAChVXOkGAHicHTt2SJKqVKliL8vNzVVCQoLatm2rCRMmKCAgQMYYdevWTStXrtSgQYPUokULffHFF3riiSe0b98+TZo0yaHdr776SvPmzdPDDz8sX19fTZ06VZ07d9batWvtzy+vW7dO3333nfr06aOaNWtq165dmjZtmjp06KDNmzcXuN192LBhCg0N1ZgxY7Rt2zZNmzZNu3fvtj87XVLvvvuu7r33XrVu3VpDhgyRJNWrV09XX321atWqpdmzZ+vWW291WGf27NmqV6+e4uLiztt+QECAbrnlFr3//vt64IEHJEk///yzNm3apP/+97/auHFjgXWmTZumyy+/XN26dZO3t7c+/fRTPfjgg7LZbBo6dKik08nkDTfcoLCwMI0YMUKhoaHatWuXPvroI3s7S5cuVd++fdWpUye99NJLkqQtW7bo22+/1SOPPHLevp88eVKHDh1yKAsKCpKvr+951z3bN998o48++kgPPviggoKC9Nprr6lnz57as2eP/fO3f/9+tW7dWkeOHNGQIUPUqFEj7du3Tx9++KGysrLUrl07Pfzww3rttdc0cuRINW7cWJLs/57txIkT6tChg7Zv365hw4YpOjpa8+fP14ABA3TkyJECx2DOnDk6evSo7rvvPlksFo0fP149evTQH3/8oQoVKhR7nwEApcwAAFBGzZgxw0gyy5YtM+np6ebPP/80c+fONVWqVDH+/v5m7969xhhj+vfvbySZESNGOKy/YMECI8k8//zzDuW33XabsVgsZvv27fYySUaS+eGHH+xlu3fvNn5+fubWW2+1l2VlZRXoZ0pKipFk3nnnnQJ9j4mJMTk5Ofby8ePHG0lm4cKF9rL27dub9u3b2+d37txpJJkZM2bYy0aPHm3O/rVdsWJF079//wL9SUpKMr6+vubIkSP2soMHDxpvb28zevToAvXPtHLlSiPJzJ8/3yxatMhYLBazZ88eY4wxTzzxhKlbt669z5dffrnDuoUdm4SEBPs6xhjz8ccfG0lm3bp1RfbhkUceMcHBwSY3N/ecfS1MfhzPnvKPZf/+/U2dOnUKrFfY8ZVkfHx8HD4nP//8s5FkpkyZYi/r16+fsVqthe6TzWYzxhgzf/58I8msXLmyQJ2z4z958mQjybz33nv2spycHBMXF2cCAwNNZmamMeafz0mVKlXM4cOH7XUXLlxoJJlPP/206AMFALhouL0cAFDmxcfHKywsTLVq1VKfPn0UGBiojz/+WDVq1HCol39FNt9nn30mLy8vPfzwww7ljz32mIwx+vzzzx3K4+LiFBMTY5+vXbu2brnlFn3xxRfKy8uT5Dgg16lTp/TXX3+pfv36Cg0N1Y8//lig70OGDHG42vjAAw/I29tbn332WTGPgvP69eun7Oxsffjhh/ayefPmKTc3t1jPOt9www2qXLmy5s6dK2OM5s6dW+Qz9JLjscnIyNChQ4fUvn17/fHHH8rIyJAkhYaGSpIWLVqkU6dOFdpOaGiojh8/rqVLlzrd1zPdcsstWrp0qcOUkJBQorbi4+NVr149+/wVV1yh4OBg/fHHH5Ikm82mBQsW6Oabb7aPO3CmktzN8NlnnykiIsLhWFeoUEEPP/ywjh07pq+++sqhfu/evVWpUiX7/LXXXitJ9j4CANyL28sBAGXeG2+8ocsuu0ze3t6qVq2aGjZsKKvV8e/G3t7eqlmzpkPZ7t27FRkZqaCgIIfy/Nt6d+/e7VDeoEGDAtu+7LLLlJWVpfT0dEVEROjEiRNKTk7WjBkztG/fPodnw/MTy3O1GRgYqOrVq7v01VGNGjVSq1atNHv2bA0aNEjS6VvLr776atWvX9/pdipUqKBevXppzpw5at26tf7880/dcccdRdb/9ttvNXr0aKWkpCgrK8thWUZGhkJCQtS+fXv17NlTY8eO1aRJk9ShQwd1795dd9xxh/327wcffFAffPCBunTpoho1auiGG27Q7bffrs6dOzvV75o1ayo+Pt7p/TyX2rVrFyirVKmS/bn89PR0ZWZmlurr03bv3q0GDRoU+IwX9bk9u4/5CXhZHegOAC41XOkGAJR5rVu3Vnx8vDp06KDGjRsXSEYkydfXt9Dy0vbQQw/phRde0O23364PPvhAX375pZYuXaoqVarIZrO5fPvO6tevn7766ivt3btXO3bs0Jo1a0o0ovcdd9yhDRs2aMyYMWrevLmaNGlSaL0dO3aoU6dOOnTokCZOnKjFixdr6dKlGj58uCTZj43FYtGHH36olJQUDRs2TPv27dM999yjmJgYHTt2TJIUHh6uDRs26JNPPrE/k9+lSxf179+/hEfjH0Vdec6/k+FshQ0YJ6nIgfjcwRP6CACXMpJuAEC5VadOHe3fv19Hjx51KN+6dat9+Zl+//33Am389ttvCggIUFhYmCTpww8/VP/+/fXKK6/otttu0/XXX6+2bdvqyJEjhfbh7DaPHTumAwcOFDqCdnGd69blPn36yMvLS++//75mz56tChUqqHfv3sXeRtu2bVW7dm2tWrXqnFe5P/30U2VnZ+uTTz7Rfffdp65duyo+Pr7I92NfffXVeuGFF/TDDz9o9uzZ2rRpk+bOnWtf7uPjo5tvvllTp07Vjh07dN999+mdd97R9u3bi70PZ6pUqVKhsTr76rGzwsLCFBwcrF9//fWc9Ypzm3mdOnX0+++/F/gjTlGfWwBA2UbSDQAot7p27aq8vDy9/vrrDuWTJk2SxWJRly5dHMpTUlIcnsv+888/tXDhQt1www32q4leXl4FriBOmTKlyCulb775psOzy9OmTVNubm6BbZdExYoVi0z2q1atqi5duui9997T7Nmz1blzZ4f3bTvLYrHotdde0+jRo3X33XcXWS//+Jx9u/2MGTMc6v39998Fjl+LFi0kSdnZ2ZKkv/76y2G51WrVFVdc4VCnpOrVq6eMjAyH0dcPHDhQ6CvWnGG1WtW9e3d9+umn+uGHHwosz9/X/HfGFxWvM3Xt2lWpqamaN2+evSw3N1dTpkxRYGCg2rdvX6K+AgDcg2e6AQDl1s0336yOHTvqqaee0q5du9S8eXN9+eWXWrhwoR599FGHAbIkqWnTpkpISHB4ZZgkjR071l7npptu0rvvvquQkBA1adJEKSkpWrZsmcPry86Uk5OjTp066fbbb9e2bds0depUtW3bVt26dbvg/YuJidGyZcs0ceJERUZGKjo6WrGxsfbl/fr102233SZJeu6550q8nVtuuUW33HLLOevccMMN9qvT9913n44dO6b//Oc/Cg8P14EDB+z1Zs2apalTp+rWW29VvXr1dPToUf3nP/9RcHCwunbtKkm69957dfjwYV133XWqWbOmdu/erSlTpqhFixZFvmbLWX369NGTTz6pW2+9VQ8//LCysrI0bdo0XXbZZYUOhOeMF198UV9++aXat2+vIUOGqHHjxjpw4IDmz5+vb775RqGhoWrRooW8vLz00ksvKSMjQ76+vvZ3mp9tyJAh+ve//60BAwZo/fr1ioqK0ocffqhvv/1WkydPLjBGAQCgbCPpBgCUW1arVZ988olGjRqlefPmacaMGYqKitLLL7+sxx57rED99u3bKy4uTmPHjtWePXvUpEkTzZw5036VVZJeffVVeXl5afbs2Tp58qSuueYaLVu2rMjRsV9//XXNnj1bo0aN0qlTp9S3b1+99tprF/SO7nwTJ07UkCFD9PTTT+vEiRPq37+/Q9J98803q1KlSrLZbKWS5J9Lw4YN9eGHH+rpp5/W448/roiICD3wwAMKCwvTPffcY6/Xvn17rV27VnPnzlVaWppCQkLUunVrzZ49W9HR0ZKku+66S2+++aamTp2qI0eOKCIiQr1799aYMWMu+Ln9KlWq6OOPP1ZiYqL+9a9/KTo6WsnJyfr9999LnHTXqFFD33//vZ555hnNnj1bmZmZqlGjhrp06WJ/b3tERISmT5+u5ORkDRo0SHl5eVq5cmWhSbe/v79WrVqlESNGaNasWcrMzFTDhg01Y8YMDRgw4EJ2HwDgBhbDKBsAAJRLubm5ioyM1M0336y33nrL3d0BAOCSxDPdAACUUwsWLFB6err69evn7q4AAHDJ4ko3AADlzPfff6+NGzfqueeeU9WqVUt82zQAALhwXOkGAKCcmTZtmh544AGFh4frnXfecXd3AAC4pHGlGwAAAAAAF+FKNwAAAAAALkLSDQAAAACAi5SJ93S/8cYbevnll5WamqrmzZtrypQpat26daF1//Of/+idd97Rr7/+KkmKiYnRiy++6FDfGKPRo0frP//5j44cOaJrrrlG06ZNU4MGDex1Dh8+rIceekiffvqprFarevbsqVdffVWBgYFO9dlms2n//v0KCgoqlXetAgAAAAA8hzFGR48eVWRkpKzWc1zPNm42d+5c4+PjY95++22zadMmM3jwYBMaGmrS0tIKrX/HHXeYN954w/z0009my5YtZsCAASYkJMTs3bvXXmfcuHEmJCTELFiwwPz888+mW7duJjo62pw4ccJep3PnzqZ58+ZmzZo15uuvvzb169c3ffv2dbrff/75p5HExMTExMTExMTExMTEdAlPf/755zlzR7cPpBYbG6tWrVrp9ddfl3T6CnKtWrX00EMPacSIEeddPy8vT5UqVdLrr7+ufv36yRijyMhIPfbYY3r88cclSRkZGapWrZpmzpypPn36aMuWLWrSpInWrVunli1bSpKWLFmirl27au/evYqMjDzvdjMyMhQaGqo///xTwcHBF3AEUFw2m03p6ekKCws791+UUCYQL89CvDwL8fIsxMvzEDPPQrw8S3mIV2ZmpmrVqqUjR44oJCSkyHpuvb08JydH69evV1JSkr3MarUqPj5eKSkpTrWRlZWlU6dOqXLlypKknTt3KjU1VfHx8fY6ISEhio2NVUpKivr06aOUlBSFhobaE25Jio+Pl9Vq1ffff69bb721wHays7OVnZ1tnz969KgkKTAw0Olb0lE6bDabTpw4ocDAQI89QS8lxMuzEC/PQrw8C/HyPMTMsxAvz1Ie4mWz2STpvI8buzXpPnTokPLy8lStWjWH8mrVqmnr1q1OtfHkk08qMjLSnmSnpqba2zi7zfxlqampCg8Pd1ju7e2typUr2+ucLTk5WWPHji1Qnp6erpMnTzrVV5QOm82mjIwMGWM89gS9lBAvz0K8PAvx8izEy/MQM89CvDxLeYhX/oXY8ykTA6mV1Lhx4zR37lytWrVKfn5+Lt1WUlKSEhMT7fP5txKEhYVxe/lFZrPZZLFYPPpWlEsJ8fIsxMuzEC/PQrw8DzHzLMTLs5SHeDmbg7o16a5ataq8vLyUlpbmUJ6WlqaIiIhzrjthwgSNGzdOy5Yt0xVXXGEvz18vLS1N1atXd2izRYsW9joHDx50aC83N1eHDx8ucru+vr7y9fUtUG61Wj32Q+LJLBYLx96DEC/PQrw8C/HyLMTL8xAzz0K8PIunx8vZfrs16fbx8VFMTIyWL1+u7t27Szr9F4/ly5dr2LBhRa43fvx4vfDCC/riiy8cnsuWpOjoaEVERGj58uX2JDszM1Pff/+9HnjgAUlSXFycjhw5ovXr1ysmJkaStGLFCtlsNsXGxpb+jgIAAABAGZCXl6dTp065uxuy2Ww6deqUTp48WWaTbi8vL3l7e1/wK6Ldfnt5YmKi+vfvr5YtW6p169aaPHmyjh8/roEDB0qS+vXrpxo1aig5OVmS9NJLL2nUqFGaM2eOoqKi7M9g5w9oZrFY9Oijj+r5559XgwYNFB0drWeeeUaRkZH2xL5x48bq3LmzBg8erOnTp+vUqVMaNmyY+vTp49TI5QAAAADgaY4dO6a9e/fKzS+wkiQZY2Sz2XT06NELTmpdKSAgQNWrV5ePj0+J23B70t27d2+lp6dr1KhRSk1NVYsWLbRkyRL7QGh79uxx+MvHtGnTlJOTo9tuu82hndGjR2vMmDGSpH/96186fvy4hgwZoiNHjqht27ZasmSJwz33s2fP1rBhw9SpUydZrVb17NlTr732mut3GAAAAAAusry8PO3du1cBAQEKCwtze6JrjFFubm6pXEl2BWOMcnJylJ6erp07d6pBgwYlviLv9vd0e6rMzEyFhIQoIyODgdQuMpvNpoMHDyo8PLzM3oqCfxAvz0K8PAvx8izEy/MQM89CvM7t5MmT2rlzp6KiouTv7+/u7pT5pDtfVlaWdu/erejo6AIDpzmbE/JpBAAAAIBLRFlOcMui0vgDDkk3AAAAAAAuQtINAAAAAICLuH0gNQAAAACAe0xa+ttF3d7w6y+7qNsrC0i6AQAALoDT/2E1RgF5x5TllSGd8UzlpfgfUABw1oABAzRr1ixJUoUKFVS7dm3169dPI0eO1DfffKOOHTsqNDRUBw4ccBjobN26dWrdurUk2V+RtmrVKnXs2LHANp566ik9//zzLtsHkm4AAAAAQJnVuXNnzZgxQ9nZ2frss880dOhQVahQQXFxcZKkoKAgffzxx+rbt699nbfeeku1a9fWnj17CrS3bds2h9HGAwMDXdp/nukGAAAAAJRZvr6+ioiIUJ06dfTAAw8oPj5en3zyiX15//799fbbb9vnT5w4oblz56p///6FthceHq6IiAj7RNINAAAAAMD/8/f3V05Ojn3+7rvv1tdff22/qv2///1PUVFRuuqqq9zVRQck3QAAAACAMs8Yo2XLlumLL77QddddZy8PDw9Xly5dNHPmTEnS22+/rXvuuafIdmrWrKnAwED79Ndff7m03zzTDQAAAAAosxYtWqTAwECdOnVKNptNd9xxh8aMGaN169bZ69xzzz165JFHdNdddyklJUXz58/X119/XWh7X3/9tYKCguzzlSpVcmn/SboBAAAAAGVWx44dNW3aNPn4+CgyMlLe3gXT2C5dumjIkCEaNGiQbr75ZlWpUqXI9qKjoxUaGurCHjsi6QYAAAAAlFkVK1ZU/fr1z1nH29tb/fr10/jx4/X5559fpJ45h2e6AQAAAAAe77nnnlN6eroSEhLc3RUHXOkGAAAAgEvU8Osvc3cXSo2Pj4+qVq3q7m4UQNINAAAAACiT8kckL0yHDh1kjClyeffu3R2Wn6++q3B7OQAAAAAALkLSDQAAAACAi5B0AwAAAADgIiTdAAAAAAC4CEk3AAAAAFwi3DGQmCcrjeNF0g0AAAAA5ZyXl5ckKScnx8098SxZWVmSpAoVKpS4DV4ZBgAAAADlnLe3twICApSenq4KFSrIanXv9VdjjHJzc+Xt7S2LxeLWvhTGGKOsrCwdPHhQoaGh9j9alARJNwAAAACUcxaLRdWrV9fOnTu1e/dud3dHxhjZbDZZrdYymXTnCw0NVURExAW1QdINAAAAAJcAHx8fNWjQoEzcYm6z2fTXX3+pSpUqbr/qXpQKFSpc0BXufCTdAAAAAHCJsFqt8vPzc3c3ZLPZVKFCBfn5+ZXZpLu0lO+9AwAAAADAjUi6AQAAAABwEbcn3W+88YaioqLk5+en2NhYrV27tsi6mzZtUs+ePRUVFSWLxaLJkycXqJO/7Oxp6NCh9jodOnQosPz+++93xe4BAAAAAC5hbk26582bp8TERI0ePVo//vijmjdvroSEBB08eLDQ+llZWapbt67GjRtX5Ahy69at04EDB+zT0qVLJUm9evVyqDd48GCHeuPHjy/dnQMAAAAAXPLcmnRPnDhRgwcP1sCBA9WkSRNNnz5dAQEBevvttwut36pVK7388svq06ePfH19C60TFhamiIgI+7Ro0SLVq1dP7du3d6gXEBDgUC84OLjU9w8AAAAAcGlz2+jlOTk5Wr9+vZKSkuxlVqtV8fHxSklJKbVtvPfee0pMTCzw7rfZs2frvffeU0REhG6++WY988wzCggIKLKt7OxsZWdn2+czMzMlnR51z2azlUp/4RybzWZ/rx/KPuLlWYiXZyFeZYQxztfLn85A/MouzjHPQrw8S3mIl7N9d1vSfejQIeXl5alatWoO5dWqVdPWrVtLZRsLFizQkSNHNGDAAIfyO+64Q3Xq1FFkZKQ2btyoJ598Utu2bdNHH31UZFvJyckaO3ZsgfL09HSdPHmyVPoL59hsNmVkZMgYU+5fL1AeEC/PQrw8C/EqGwLyjjlZ08jXnJRskvTPxYCiHquD+3GOeRbi5VnKQ7yOHj3qVL1y/Z7ut956S126dFFkZKRD+ZAhQ+w/N2vWTNWrV1enTp20Y8cO1atXr9C2kpKSlJiYaJ/PzMxUrVq1FBYWxq3pF5nNZpPFYlFYWJjHnqCXEuLlWYiXZyFeZUOWV4ZzFY2RjJRlDZTOuAMvPDzcRT3DheIc8yzEy7OUh3g5+75ztyXdVatWlZeXl9LS0hzK09LSihwkrTh2796tZcuWnfPqdb7Y2FhJ0vbt24tMun19fQt9jtxqtXrsh8STWSwWjr0HIV6ehXh5FuJVBpz1CNt56+ZP/+/V5dsvuAvDr7/sgttA4TjHPAvx8iyeHi9n++22vfPx8VFMTIyWL19uL7PZbFq+fLni4uIuuP0ZM2YoPDxcN95443nrbtiwQZJUvXr1C94uAAAAAAD53Hp7eWJiovr376+WLVuqdevWmjx5so4fP66BAwdKkvr166caNWooOTlZ0umB0TZv3mz/ed++fdqwYYMCAwNVv359e7s2m00zZsxQ//795e3tuIs7duzQnDlz1LVrV1WpUkUbN27U8OHD1a5dO11xxRUXac8BAAAAAJcCtybdvXv3Vnp6ukaNGqXU1FS1aNFCS5YssQ+utmfPHodL9vv379eVV15pn58wYYImTJig9u3ba9WqVfbyZcuWac+ePbrnnnsKbNPHx0fLli2zJ/i1atVSz5499fTTT7tuRwEAAAAAlyS3D6Q2bNgwDRs2rNBlZybSkhQVFSXjxGs5brjhhiLr1apVS1999VWx+wkAAAAAQHF55hPrAAAAAAB4AJJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXcXvS/cYbbygqKkp+fn6KjY3V2rVri6y7adMm9ezZU1FRUbJYLJo8eXKBOmPGjJHFYnGYGjVq5FDn5MmTGjp0qKpUqaLAwED17NlTaWlppb1rAAAAAIBLnLc7Nz5v3jwlJiZq+vTpio2N1eTJk5WQkKBt27YpPDy8QP2srCzVrVtXvXr10vDhw4ts9/LLL9eyZcvs897ejrs5fPhwLV68WPPnz1dISIiGDRumHj166Ntvvy29nQMAAB5h0tLf3N0FAEA55take+LEiRo8eLAGDhwoSZo+fboWL16st99+WyNGjChQv1WrVmrVqpUkFbo8n7e3tyIiIgpdlpGRobfeektz5szRddddJ0maMWOGGjdurDVr1ujqq68udL3s7GxlZ2fb5zMzMyVJNptNNpvNib1FabHZbDLGcNw9BPHyLMTLsxCvUmLMxdtO/lTK+Ay4BueYZyFenqU8xMvZvrst6c7JydH69euVlJRkL7NarYqPj1dKSsoFtf37778rMjJSfn5+iouLU3JysmrXri1JWr9+vU6dOqX4+Hh7/UaNGql27dpKSUkpMulOTk7W2LFjC5Snp6fr5MmTF9RfFI/NZlNGRoaMMbJa3f6EBM6DeHkW4uVZiFfpCMg7dpG2ZORrTko2SbKUassHDx4s1fZwGueYZyFenqU8xOvo0aNO1XNb0n3o0CHl5eWpWrVqDuXVqlXT1q1bS9xubGysZs6cqYYNG+rAgQMaO3asrr32Wv36668KCgpSamqqfHx8FBoaWmC7qampRbablJSkxMRE+3xmZqZq1aqlsLAwBQcHl7i/KD6bzSaLxaKwsDCPPUEvJcTLsxAvz0K8SkeWV8bF2ZAxkpGyrIGSpXST7sIey8OF4xzzLMTLs5SHePn5+TlVz623l7tCly5d7D9fccUVio2NVZ06dfTBBx9o0KBBJW7X19dXvr6+BcqtVqvHfkg8mcVi4dh7EOLlWYiXZyFepaCUE+Dzbit/KkXE33U4xzwL8fIsnh4vZ/vttr2rWrWqvLy8CowanpaWVuTz2CURGhqqyy67TNu3b5ckRUREKCcnR0eOHHHpdgEAAAAAcFvS7ePjo5iYGC1fvtxeZrPZtHz5csXFxZXado4dO6YdO3aoevXqkqSYmBhVqFDBYbvbtm3Tnj17SnW7AAAAAAC49fbyxMRE9e/fXy1btlTr1q01efJkHT9+3D6aeb9+/VSjRg0lJydLOj342ubNm+0/79u3Txs2bFBgYKDq168vSXr88cd18803q06dOtq/f79Gjx4tLy8v9e3bV5IUEhKiQYMGKTExUZUrV1ZwcLAeeughxcXFFTmIGgAAAAAAJeHWpLt3795KT0/XqFGjlJqaqhYtWmjJkiX2wdX27NnjcJ/8/v37deWVV9rnJ0yYoAkTJqh9+/ZatWqVJGnv3r3q27ev/vrrL4WFhalt27Zas2aNwsLC7OtNmjRJVqtVPXv2VHZ2thISEjR16tSLs9MAAAAAgEuGxZiL9XLK8iUzM1MhISHKyMhg9PKLzGaz6eDBgwoPD/fYQRcuJcTLsxAvz0K8Ssekpb9dnA0Zo4C8Y8ryKv3Ry4dff1mptofTOMc8C/HyLOUhXs7mhJ65dwAAAAAAeACSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXMTtSfcbb7yhqKgo+fn5KTY2VmvXri2y7qZNm9SzZ09FRUXJYrFo8uTJBeokJyerVatWCgoKUnh4uLp3765t27Y51OnQoYMsFovDdP/995f2rgEAAAAALnFuTbrnzZunxMREjR49Wj/++KOaN2+uhIQEHTx4sND6WVlZqlu3rsaNG6eIiIhC63z11VcaOnSo1qxZo6VLl+rUqVO64YYbdPz4cYd6gwcP1oEDB+zT+PHjS33/AAAAAACXNm93bnzixIkaPHiwBg4cKEmaPn26Fi9erLffflsjRowoUL9Vq1Zq1aqVJBW6XJKWLFniMD9z5kyFh4dr/fr1ateunb08ICCgyMQdAAAAAIDS4LakOycnR+vXr1dSUpK9zGq1Kj4+XikpKaW2nYyMDElS5cqVHcpnz56t9957TxEREbr55pv1zDPPKCAgoMh2srOzlZ2dbZ/PzMyUJNlsNtlstlLrL87PZrPJGMNx9xDEy7MQL89CvEqJMRdvO/lTKeMz4BqcY56FeHmW8hAvZ/vutqT70KFDysvLU7Vq1RzKq1Wrpq1bt5bKNmw2mx599FFdc801atq0qb38jjvuUJ06dRQZGamNGzfqySef1LZt2/TRRx8V2VZycrLGjh1boDw9PV0nT54slf7COTabTRkZGTLGyGp1+7AEOA/i5VmIl2chXqUjIO/YRdqSka85KdkkyVKqLRf1aB4uDOeYZyFenqU8xOvo0aNO1XPr7eWuNnToUP3666/65ptvHMqHDBli/7lZs2aqXr26OnXqpB07dqhevXqFtpWUlKTExET7fGZmpmrVqqWwsDAFBwe7ZgdQKJvNJovForCwMI89QS8lxMuzEC/PQrxKR5ZXxsXZkDGSkbKsgZKldJPu8PDwUm0Pp3GOeRbi5VnKQ7z8/Pycque2pLtq1ary8vJSWlqaQ3laWlqpPGs9bNgwLVq0SKtXr1bNmjXPWTc2NlaStH379iKTbl9fX/n6+hYot1qtHvsh8WQWi4Vj70GIl2chXp6FeJWCUk6Az7ut/KkUEX/X4RzzLMTLs3h6vJztd4n2buXKlSVZzYGPj49iYmK0fPlye5nNZtPy5csVFxdX4naNMRo2bJg+/vhjrVixQtHR0eddZ8OGDZKk6tWrl3i7AAAAAACcrURXujt37qyaNWtq4MCB6t+/v2rVqlWijScmJqp///5q2bKlWrdurcmTJ+v48eP20cz79eunGjVqKDk5WdLpwdc2b95s/3nfvn3asGGDAgMDVb9+fUmnbymfM2eOFi5cqKCgIKWmpkqSQkJC5O/vrx07dmjOnDnq2rWrqlSpoo0bN2r48OFq166drrjiihLtBwAAAAAAhSnRle59+/Zp2LBh+vDDD1W3bl0lJCTogw8+UE5OTrHa6d27tyZMmKBRo0apRYsW2rBhg5YsWWIfXG3Pnj06cOCAvf7+/ft15ZVX6sorr9SBAwc0YcIEXXnllbr33nvtdaZNm6aMjAx16NBB1atXt0/z5s2TdPoK+7Jly3TDDTeoUaNGeuyxx9SzZ099+umnJTkUAAAAAAAUyWLMhb234scff9SMGTP0/vvvSzo9MvigQYPUvHnzUulgWZWZmamQkBBlZGQwkNpFZrPZdPDgQYWHh3vs8x+XEuLlWYiXZyFepWPS0t8uzoaMUUDeMWV5lf5AasOvv6xU28NpnGOehXh5lvIQL2dzwgveu6uuukpJSUkaNmyYjh07prffflsxMTG69tprtWnTpgttHgAAAAAAj1XipPvUqVP68MMP1bVrV9WpU0dffPGFXn/9daWlpWn79u2qU6eOevXqVZp9BQAAAADAo5RoILWHHnpI77//vowxuvvuuzV+/Hg1bdrUvrxixYqaMGGCIiMjS62jAAAAAAB4mhIl3Zs3b9aUKVPUo0ePQt9dLZ1+D3dpvFoMAAAAAABPVaLby0ePHq1evXoVSLhzc3O1evVqSZK3t7fat29/4T0EAAAAAMBDlSjp7tixow4fPlygPCMjQx07drzgTgEAAAAAUB6UKOk2xshSyKsu/vrrL1WsWPGCOwUAAAAAQHlQrGe6e/ToIUmyWCwaMGCAw+3leXl52rhxo9q0aVO6PQQAAAAAwEMVK+kOCQmRdPpKd1BQkPz9/e3LfHx8dPXVV2vw4MGl20MAAAAAADxUsZLuGTNmSJKioqL0+OOPcys5AAAAAADnUKJXho0ePbq0+wEAAIASmrT0twtaf/j1l5VSTwAAZ3M66b7qqqu0fPlyVapUSVdeeWWhA6nl+/HHH0ulcwAAAAAAeDKnk+5bbrnFPnBa9+7dXdUfAAAAAADKDaeT7jNvKef2cgAAAAAAzq9E7+kGAAAAAADn5/SV7kqVKp3zOe4zHT58uMQdAgAAAACgvHA66Z48ebILuwEAAAAAQPnjdNLdv39/V/YDAAAAAIByx+mkOzMzU8HBwfafzyW/HgAAAAAAl7JiPdN94MABhYeHKzQ0tNDnu40xslgsysvLK9VOAgAAAADgiZxOulesWKHKlStLklauXOmyDgEAAAAAUF44nXS3b9++0J8BAAAAAEDhnE66z/b333/rrbfe0pYtWyRJTZo00cCBA+1XwwEAAAAAuNRZS7LS6tWrFRUVpddee01///23/v77b7322muKjo7W6tWrS7uPAAAAAAB4pBJd6R46dKh69+6tadOmycvLS5KUl5enBx98UEOHDtUvv/xSqp0EAAAAAMATlehK9/bt2/XYY4/ZE25J8vLyUmJiorZv315qnQMAAAAAwJOVKOm+6qqr7M9yn2nLli1q3rz5BXcKAAAAAIDywOmke+PGjfbp4Ycf1iOPPKIJEybom2++0TfffKMJEyZo+PDhGj58eLE68MYbbygqKkp+fn6KjY3V2rVri6y7adMm9ezZU1FRUbJYLJo8eXKJ2jx58qSGDh2qKlWqKDAwUD179lRaWlqx+g0AAAAAwPk4/Ux3ixYtZLFYZIyxl/3rX/8qUO+OO+5Q7969nWpz3rx5SkxM1PTp0xUbG6vJkycrISFB27ZtU3h4eIH6WVlZqlu3rnr16lVkcu9Mm8OHD9fixYs1f/58hYSEaNiwYerRo4e+/fZbp/oNAAAAAIAznE66d+7cWeobnzhxogYPHqyBAwdKkqZPn67Fixfr7bff1ogRIwrUb9WqlVq1aiVJhS53ps2MjAy99dZbmjNnjq677jpJ0owZM9S4cWOtWbNGV199danvJwAAAADg0uR00l2nTp1S3XBOTo7Wr1+vpKQke5nValV8fLxSUlJc1ub69et16tQpxcfH2+s0atRItWvXVkpKSpFJd3Z2trKzs+3zmZmZkiSbzSabzVai/qJkbDabjDEcdw9BvDwL8fIsxKuUnHEXn8u3kz+VMXyGCsc55lmIl2cpD/Fytu8lemVYvs2bN2vPnj3KyclxKO/Wrdt51z106JDy8vJUrVo1h/Jq1app69atJeqPM22mpqbKx8dHoaGhBeqkpqYW2XZycrLGjh1boDw9PV0nT54sUX9RMjabTRkZGTLGyGot0ViAuIiIl2chXp6FeJWOgLxjF2lLRr7mpGSTJMtF2qZzDh486O4ulEmcY56FeHmW8hCvo0ePOlWvREn3H3/8oVtvvVW//PKLw3PeFsvpXyB5eXklabZMS0pKUmJion0+MzNTtWrVUlhYmIKDg93Ys0uPzWaTxWJRWFiYx56glxLi5VmIl2chXqUjyyvj4mzIGMlIWdZAyVK2ku7CxtIB55inIV6epTzEy8/Pz6l6JUq6H3nkEUVHR2v58uWKjo7W2rVr9ddff+mxxx7ThAkTnGqjatWq8vLyKjBqeFpamiIiIkrSLafajIiIUE5Ojo4cOeJwtft82/X19ZWvr2+BcqvV6rEfEk9msVg49h6EeHkW4uVZiFcpuJgJsMXyz1SG8PkpGueYZyFensXT4+Vsv0u0dykpKXr22WdVtWpV+0Fq27atkpOT9fDDDzvVho+Pj2JiYrR8+XJ7mc1m0/LlyxUXF1eSbjnVZkxMjCpUqOBQZ9u2bdqzZ0+JtwsAAAAAQGFKdKU7Ly9PQUFBkk5fXd6/f78aNmyoOnXqaNu2bU63k5iYqP79+6tly5Zq3bq1Jk+erOPHj9tHHu/Xr59q1Kih5ORkSacHStu8ebP953379mnDhg0KDAxU/fr1nWozJCREgwYNUmJioipXrqzg4GA99NBDiouLY+RyAAAAAECpKlHS3bRpU/3888+Kjo5WbGysxo8fLx8fH7355puqW7eu0+307t1b6enpGjVqlFJTU9WiRQstWbLEPhDanj17HC7Z79+/X1deeaV9fsKECZowYYLat2+vVatWOdWmJE2aNElWq1U9e/ZUdna2EhISNHXq1JIcCgAAAAAAimQxpvjvrfjiiy90/Phx9ejRQ9u3b9dNN92k3377TVWqVNG8efPs778uzzIzMxUSEqKMjAwGUrvIbDabDh48qPDwcI99/uNSQrw8C/HyLMSrdExa+tvF2ZAxCsg7piyvsjeQ2vDrL3N3F8okzjHPQrw8S3mIl7M5YYmudCckJNh/rl+/vrZu3arDhw+rUqVK9hHMAQAAAAC41F3Qe7ol6c8//5Qk1apV64I7AwAAAABAeVKi6/i5ubl65plnFBISoqioKEVFRSkkJERPP/20Tp06Vdp9BAAAAADAI5XoSvdDDz2kjz76SOPHj7e/ZislJUVjxozRX3/9pWnTppVqJwEAAApz0Z7HBgCghEqUdM+ZM0dz585Vly5d7GVXXHGFatWqpb59+5J0AwAAAACgEt5e7uvrq6ioqALl0dHR8vHxudA+AQAAAABQLpQo6R42bJiee+45ZWdn28uys7P1wgsvaNiwYaXWOQAAAAAAPJnTt5f36NHDYX7ZsmWqWbOmmjdvLkn6+eeflZOTo06dOpVuDwEAAAAA8FBOJ90hISEO8z179nSY55VhAAAAAAA4cjrpnjFjhiv7AQAAAABAuVOi0cvzpaena9u2bZKkhg0bKiwsrFQ6BQAAAABAeVCigdSOHz+ue+65R9WrV1e7du3Url07RUZGatCgQcrKyirtPgIAAAAA4JFKlHQnJibqq6++0qeffqojR47oyJEjWrhwob766is99thjpd1HAAAAAAA8UoluL//f//6nDz/8UB06dLCXde3aVf7+/rr99ts1bdq00uofAAAAAAAeq0RXurOyslStWrUC5eHh4dxeDgAAAADA/ytR0h0XF6fRo0fr5MmT9rITJ05o7NixiouLK7XOAQAAAADgyUp0e/nkyZPVuXNn1axZU82bN5ck/fzzz/Lz89MXX3xRqh0EAAAAAMBTlSjpbtasmX7//XfNnj1bW7dulST17dtXd955p/z9/Uu1gwAAAAAAeKpiJ92nTp1So0aNtGjRIg0ePNgVfQIAAAAAoFwo9jPdFSpUcHiWGwAAAAAAFK5EA6kNHTpUL730knJzc0u7PwAAAAAAlBsleqZ73bp1Wr58ub788ks1a9ZMFStWdFj+0UcflUrnAAAAAADwZCVKukNDQ9WzZ8/S7gsAAAAAAOVKsZJum82ml19+Wb/99ptycnJ03XXXacyYMYxYDgAAAABAIYr1TPcLL7ygkSNHKjAwUDVq1NBrr72moUOHuqpvAAAAAAB4tGIl3e+8846mTp2qL774QgsWLNCnn36q2bNny2azuap/AAAAAAB4rGIl3Xv27FHXrl3t8/Hx8bJYLNq/f3+pdwwAAAAAAE9XrKQ7NzdXfn5+DmUVKlTQqVOnLqgTb7zxhqKiouTn56fY2FitXbv2nPXnz5+vRo0ayc/PT82aNdNnn33msNxisRQ6vfzyy/Y6UVFRBZaPGzfugvYDAAAAAIAzFWsgNWOMBgwYIF9fX3vZyZMndf/99zu8Nqw4rwybN2+eEhMTNX36dMXGxmry5MlKSEjQtm3bFB4eXqD+d999p759+yo5OVk33XST5syZo+7du+vHH39U06ZNJUkHDhxwWOfzzz/XoEGDCoy4/uyzz2rw4MH2+aCgIKf7DQAAAADA+RQr6e7fv3+BsrvuuuuCOjBx4kQNHjxYAwcOlCRNnz5dixcv1ttvv60RI0YUqP/qq6+qc+fOeuKJJyRJzz33nJYuXarXX39d06dPlyRFREQ4rLNw4UJ17NhRdevWdSgPCgoqULco2dnZys7Ots9nZmZKOj2iO8+0X1w2m03GGI67hyBenoV4eRbiJckYd/fAecb8M5Uxl/Rn6Bw4xzwL8fIs5SFezva9WEn3jBkzStSZouTk5Gj9+vVKSkqyl1mtVsXHxyslJaXQdVJSUpSYmOhQlpCQoAULFhRaPy0tTYsXL9asWbMKLBs3bpyee+451a5dW3fccYeGDx8ub+/CD0lycrLGjh1boDw9PV0nT54sahfhAjabTRkZGTLGyGot1hMScAPi5VmIl2chXlJA3jF3d6EYjHzNSckmSRZ3d8bBwYMH3d2FMolzzLMQL89SHuJ19OhRp+oVK+kubYcOHVJeXp6qVavmUF6tWjVt3bq10HVSU1MLrZ+amlpo/VmzZikoKEg9evRwKH/44Yd11VVXqXLlyvruu++UlJSkAwcOaOLEiYW2k5SU5JDsZ2ZmqlatWgoLC1NwcPB59xWlx2azyWKxKCwszGNP0EsJ8fIsxMuzEC8pyyvD3V1wnjGSkbKsgZKlbCXdhT3SB84xT0O8PEt5iNfZ450Vxa1J98Xw9ttv68477yxwQM5MoK+44gr5+PjovvvuU3JyssMz6/l8fX0LLbdarR77IfFkFouFY+9BiJdnIV6e5ZKPVxlLXs/LYvlnKkMu2c+PEy75c8zDEC/P4unxcrbfbt27qlWrysvLS2lpaQ7laWlpRT5rHRER4XT9r7/+Wtu2bdO999573r7ExsYqNzdXu3btcn4HAAAAAAA4B7cm3T4+PoqJidHy5cvtZTabTcuXL1dcXFyh68TFxTnUl6SlS5cWWv+tt95STEyMmjdvft6+bNiwQVarldurAAAAAAClxu23lycmJqp///5q2bKlWrdurcmTJ+v48eP20cz79eunGjVqKDk5WZL0yCOPqH379nrllVd04403au7cufrhhx/05ptvOrSbmZmp+fPn65VXXimwzZSUFH3//ffq2LGjgoKClJKSouHDh+uuu+5SpUqVXL/TAAAAAIBLgtuT7t69eys9PV2jRo1SamqqWrRooSVLltgHS9uzZ4/DvfJt2rTRnDlz9PTTT2vkyJFq0KCBFixYYH9Hd765c+fKGKO+ffsW2Kavr6/mzp2rMWPGKDs7W9HR0Ro+fHiBUdEBAAAAALgQFmPK4MsiPUBmZqZCQkKUkZHB6OUXmc1m08GDBxUeHu6xgy5cSoiXZyFenoV4SZOW/ubuLjjPGAXkHVOWV9kbvXz49Ze5uwtlEueYZyFenqU8xMvZnNAz9w4AAAAAAA9A0g0AAAAAgIuQdAMAAAAA4CIk3QAAAAAAuAhJNwAAAAAALkLSDQAAAACAi5B0AwAAAADgIiTdAAAAAAC4CEk3AAAAAAAuQtINAAAAAICLkHQDAAAAAOAiJN0AAAAAALgISTcAAAAAAC5C0g0AAAAAgIt4u7sDAAAAcK9JS3+7oPWHX39ZKfUEAMofrnQDAAAAAOAiJN0AAAAAALgISTcAAAAAAC5C0g0AAAAAgIuQdAMAAAAA4CIk3QAAAAAAuAhJNwAAAAAALkLSDQAAAACAi5B0AwAAAADgIiTdAAAAAAC4CEk3AAAAAAAuQtINAAAAAICLeLu7A5L0xhtv6OWXX1ZqaqqaN2+uKVOmqHXr1kXWnz9/vp555hnt2rVLDRo00EsvvaSuXbvalw8YMECzZs1yWCchIUFLliyxzx8+fFgPPfSQPv30U1mtVvXs2VOvvvqqAgMDS38HAQBAoSYt/c3dXQAAwKXcfqV73rx5SkxM1OjRo/Xjjz+qefPmSkhI0MGDBwut/91336lv374aNGiQfvrpJ3Xv3l3du3fXr7/+6lCvc+fOOnDggH16//33HZbfeeed2rRpk5YuXapFixZp9erVGjJkiMv2EwAAAABw6XH7le6JEydq8ODBGjhwoCRp+vTpWrx4sd5++22NGDGiQP1XX31VnTt31hNPPCFJeu6557R06VK9/vrrmj59ur2er6+vIiIiCt3mli1btGTJEq1bt04tW7aUJE2ZMkVdu3bVhAkTFBkZWWCd7OxsZWdn2+czMzMlSTabTTabrYR7j5Kw2WwyxnDcPQTx8izEy7OUi3gZ4+4eXDzG/DOVMx79GTyHcnGOXUKIl2cpD/Fytu9uTbpzcnK0fv16JSUl2cusVqvi4+OVkpJS6DopKSlKTEx0KEtISNCCBQscylatWqXw8HBVqlRJ1113nZ5//nlVqVLF3kZoaKg94Zak+Ph4Wa1Wff/997r11lsLbDc5OVljx44tUJ6enq6TJ086vc+4cDabTRkZGTLGyGp1+80aOA/i5VmIl2cpD/EKyDvm7i5cREa+5qRkkySLuztTqoq6Q9HTlYdz7FJCvDxLeYjX0aNHnarn1qT70KFDysvLU7Vq1RzKq1Wrpq1btxa6TmpqaqH1U1NT7fOdO3dWjx49FB0drR07dmjkyJHq0qWLUlJS5OXlpdTUVIWHhzu04e3trcqVKzu0c6akpCSHZD8zM1O1atVSWFiYgoODi7XfuDA2m00Wi0VhYWEee4JeSoiXZyFenqU8xCvLK8PdXbh4jJGMlGUNlCzlK+k++/9V5UV5OMcuJcTLs5SHePn5+TlVz+23l7tCnz597D83a9ZMV1xxherVq6dVq1apU6dOJWrT19dXvr6+BcqtVqvHfkg8mcVi4dh7EOLlWYiXZ/H4eJWz5PO8LJZ/pnLEYz9/TvD4c+wSQ7w8i6fHy9l+u3XvqlatKi8vL6WlpTmUp6WlFfk8dkRERLHqS1LdunVVtWpVbd++3d7G2bdB5ebm6vDhw+dsBwAAAACA4nBr0u3j46OYmBgtX77cXmaz2bR8+XLFxcUVuk5cXJxDfUlaunRpkfUlae/evfrrr79UvXp1extHjhzR+vXr7XVWrFghm82m2NjYC9klAAAAAADs3H4dPzExUf/5z380a9YsbdmyRQ888ICOHz9uH828X79+DgOtPfLII1qyZIleeeUVbd26VWPGjNEPP/ygYcOGSZKOHTumJ554QmvWrNGuXbu0fPly3XLLLapfv74SEhIkSY0bN1bnzp01ePBgrV27Vt9++62GDRumPn36FDpyOQAAAAAAJeH2Z7p79+6t9PR0jRo1SqmpqWrRooWWLFliHyxtz549DvfKt2nTRnPmzNHTTz+tkSNHqkGDBlqwYIGaNm0qSfLy8tLGjRs1a9YsHTlyRJGRkbrhhhv03HPPOTyTPXv2bA0bNkydOnWS1WpVz5499dprr13cnQcAAAAAlGsWY8rhyyIvgszMTIWEhCgjI4PRyy8ym82mgwcPKjw83GMHXbiUEC/PQrw8S3mI16Slv7m7CxePMQrIO6Ysr/I3evnw6y9zdxdcojycY5cS4uVZykO8nM0JPXPvAAAAAADwACTdAAAAAAC4CEk3AAAAAAAuQtINAAAAAICLkHQDAAAAAOAiJN0AAAAAALgISTcAAAAAAC5C0g0AAAAAgIuQdAMAAAAA4CIk3QAAAAAAuAhJNwAAAAAALkLSDQAAAACAi5B0AwAAAADgIiTdAAAAAAC4CEk3AAAAAAAu4u3uDgAAAMCzTVr62wW3Mfz6y0qhJwBQ9nClGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFeGUYAAAokdJ4TRQAAOUdV7oBAAAAAHARkm4AAAAAAFyEpBsAAAAAABcpE0n3G2+8oaioKPn5+Sk2NlZr1649Z/358+erUaNG8vPzU7NmzfTZZ5/Zl506dUpPPvmkmjVrpooVKyoyMlL9+vXT/v37HdqIioqSxWJxmMaNG+eS/QMAAAAAXJrcnnTPmzdPiYmJGj16tH788Uc1b95cCQkJOnjwYKH1v/vuO/Xt21eDBg3STz/9pO7du6t79+769ddfJUlZWVn68ccf9cwzz+jHH3/URx99pG3btqlbt24F2nr22Wd14MAB+/TQQw+5dF8BAAAAAJcWtyfdEydO1ODBgzVw4EA1adJE06dPV0BAgN5+++1C67/66qvq3LmznnjiCTVu3FjPPfecrrrqKr3++uuSpJCQEC1dulS33367GjZsqKuvvlqvv/661q9frz179ji0FRQUpIiICPtUsWJFl+8vAAAAAODS4dZXhuXk5Gj9+vVKSkqyl1mtVsXHxyslJaXQdVJSUpSYmOhQlpCQoAULFhS5nYyMDFksFoWGhjqUjxs3Ts8995xq166tO+64Q8OHD5e3d+GHJDs7W9nZ2fb5zMxMSZLNZpPNZjvXbqKU2Ww2GWM47h6CeHkW4uVZ3B4vY9yzXU9lzD8TCiiL3ztuP8dQLMTLs5SHeDnbd7cm3YcOHVJeXp6qVavmUF6tWjVt3bq10HVSU1MLrZ+amlpo/ZMnT+rJJ59U3759FRwcbC9/+OGHddVVV6ly5cr67rvvlJSUpAMHDmjixImFtpOcnKyxY8cWKE9PT9fJkyfPuZ8oXTabTRkZGTLGyGp1+80aOA/i5VmIl2dxd7wC8o5d9G16NiNfc1KySZLF3Z0pc4p6tNCd3H2OoXiIl2cpD/E6evSoU/XcmnS72qlTp3T77bfLGKNp06Y5LDvzavkVV1whHx8f3XfffUpOTpavr2+BtpKSkhzWyczMVK1atRQWFuaQzMP1bDabLBaLwsLCPPYEvZQQL89CvDyLu+OV5ZVx0bfp0YyRjJRlDZQsJN1nCw8Pd3cXCnD3OYbiIV6epTzEy8/Pz6l6bk26q1atKi8vL6WlpTmUp6WlKSIiotB1IiIinKqfn3Dv3r1bK1asOG9iHBsbq9zcXO3atUsNGzYssNzX17fQZNxqtXrsh8STWSwWjr0HIV6ehXh5FrfGi8Sx+CyWfyY4KKvfOXwnehbi5Vk8PV7O9tute+fj46OYmBgtX77cXmaz2bR8+XLFxcUVuk5cXJxDfUlaunSpQ/38hPv333/XsmXLVKVKlfP2ZcOGDbJarWXyr6wAAAAAAM/k9tvLExMT1b9/f7Vs2VKtW7fW5MmTdfz4cQ0cOFCS1K9fP9WoUUPJycmSpEceeUTt27fXK6+8ohtvvFFz587VDz/8oDfffFPS6YT7tttu048//qhFixYpLy/P/rx35cqV5ePjo5SUFH3//ffq2LGjgoKClJKSouHDh+uuu+5SpUqV3HMgAAAAAADljtuT7t69eys9PV2jRo1SamqqWrRooSVLltgHS9uzZ4/DZfs2bdpozpw5evrppzVy5Eg1aNBACxYsUNOmTSVJ+/bt0yeffCJJatGihcO2Vq5cqQ4dOsjX11dz587VmDFjlJ2drejoaA0fPrzAqOgAAAAAAFwItyfdkjRs2DANGzas0GWrVq0qUNarVy/16tWr0PpRUVEy53kVx1VXXaU1a9YUu58AAAAAABRHmUi6AQAAcGmbtPS3C1p/+PWXlVJPAKB0eeYwcQAAAAAAeACSbgAAAAAAXITbywEAuERd6O28AADg/LjSDQAAAACAi5B0AwAAAADgIiTdAAAAAAC4CEk3AAAAAAAuQtINAAAAAICLkHQDAAAAAOAiJN0AAAAAALgISTcAAAAAAC5C0g0AAAAAgIt4u7sDAACgZF5d9rsC8o4pyytDsljc3R3ArSYt/e2C2xh+/WWl0BMAcMSVbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAX4ZVhAAC4QWm83ggAAJR9JN0AAACACvljmDEKyDumLK8MyWI57/q85xtAYbi9HAAAAAAAFyHpBgAAAADARbi9HACAEuCZbAAA4AySbgAAAKAUXOgf43gmHCifSLoBAJccrlIDAICLpUw80/3GG28oKipKfn5+io2N1dq1a89Zf/78+WrUqJH8/PzUrFkzffbZZw7LjTEaNWqUqlevLn9/f8XHx+v33393qHP48GHdeeedCg4OVmhoqAYNGqRjx46V+r4BAAAAAC5dbr/SPW/ePCUmJmr69OmKjY3V5MmTlZCQoG3btik8PLxA/e+++059+/ZVcnKybrrpJs2ZM0fdu3fXjz/+qKZNm0qSxo8fr9dee02zZs1SdHS0nnnmGSUkJGjz5s3y8/OTJN155506cOCAli5dqlOnTmngwIEaMmSI5syZc1H3HwBQfFypBlAelcZ3G7eoA2WPxRhj3NmB2NhYtWrVSq+//rokyWazqVatWnrooYc0YsSIAvV79+6t48ePa9GiRfayq6++Wi1atND06dNljFFkZKQee+wxPf7445KkjIwMVatWTTNnzlSfPn20ZcsWNWnSROvWrVPLli0lSUuWLFHXrl21d+9eRUZGnrffmZmZCgkJUUZGhoKDg0vjUMBJNptNBw8eVHh4uKzWMnGzBs6BeHkWZ+JFwluG2N8hHOjUO4ThZsTL81yCMfPkpJ3/c3iW8hAvZ3NCt17pzsnJ0fr165WUlGQvs1qtio+PV0pKSqHrpKSkKDEx0aEsISFBCxYskCTt3LlTqampio+Pty8PCQlRbGysUlJS1KdPH6WkpCg0NNSecEtSfHy8rFarvv/+e916660Ftpudna3s7Gz7fEZGhiTpyJEjstlsxd95lJjNZlNmZqZ8fHw89gS9lJRmvKat3FFKvUKRjJG/7ZhOWPdeMv/B9GjGyGI7ppNWQ7w8AfHyPJdgzJI/Xu/uLpRcGfod9kDHem7dvicoD/+nz8zMlHT68eZzcWvSfejQIeXl5alatWoO5dWqVdPWrVsLXSc1NbXQ+qmpqfbl+WXnqnP2reve3t6qXLmyvc7ZkpOTNXbs2ALlderUKWr3AAAAAFyCRrq7A7iojh49qpCQkCKXu/2Zbk+RlJTkcIXdZrPp8OHDqlKliiyXyF8+y4rMzEzVqlVLf/75J7f2ewDi5VmIl2chXp6FeHkeYuZZiJdnKQ/xMsbo6NGj53082a1Jd9WqVeXl5aW0tDSH8rS0NEVERBS6TkRExDnr5/+blpam6tWrO9Rp0aKFvc7Bgwcd2sjNzdXhw4eL3K6vr698fX0dykJDQ8+9g3Cp4OBgjz1BL0XEy7MQL89CvDwL8fI8xMyzEC/P4unxOtcV7nxuvXnex8dHMTExWr58ub3MZrNp+fLliouLK3SduLg4h/qStHTpUnv96OhoRUREONTJzMzU999/b68TFxenI0eOaP36f55ZWbFihWw2m2JjY0tt/wAAAAAAlza3316emJio/v37q2XLlmrdurUmT56s48ePa+DAgZKkfv36qUaNGkpOTpYkPfLII2rfvr1eeeUV3XjjjZo7d65++OEHvfnmm5Iki8WiRx99VM8//7waNGhgf2VYZGSkunfvLklq3LixOnfurMGDB2v69Ok6deqUhg0bpj59+jg1cjkAAAAAAM5we9Ldu3dvpaena9SoUUpNTVWLFi20ZMkS+0Boe/bscRjNrk2bNpozZ46efvppjRw5Ug0aNNCCBQvs7+iWpH/96186fvy4hgwZoiNHjqht27ZasmSJ/R3dkjR79mwNGzZMnTp1ktVqVc+ePfXaa69dvB1Hifn6+mr06NEFbvdH2US8PAvx8izEy7MQL89DzDwL8fIsl1K83P6ebgAAAAAAyivPfCEaAAAAAAAegKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm6UOdnZ2WrRooUsFos2bNjgsGzjxo269tpr5efnp1q1amn8+PEF1p8/f74aNWokPz8/NWvWTJ999pnDcmOMRo0aperVq8vf31/x8fH6/fffXblL5VK3bt1Uu3Zt+fn5qXr16rr77ru1f/9+hzrEq2zYtWuXBg0apOjoaPn7+6tevXoaPXq0cnJyHOoRr7LjhRdeUJs2bRQQEKDQ0NBC6+zZs0c33nijAgICFB4erieeeEK5ubkOdVatWqWrrrpKvr6+ql+/vmbOnFmgnTfeeENRUVHy8/NTbGys1q5d64I9gsSxdpfVq1fr5ptvVmRkpCwWixYsWOCw3JnvrcOHD+vOO+9UcHCwQkNDNWjQIB07dsyhjjPfoTi35ORktWrVSkFBQQoPD1f37t21bds2hzonT57U0KFDVaVKFQUGBqpnz55KS0tzqFNa3484t2nTpumKK65QcHCwgoODFRcXp88//9y+nFidwQBlzMMPP2y6dOliJJmffvrJXp6RkWGqVatm7rzzTvPrr7+a999/3/j7+5t///vf9jrffvut8fLyMuPHjzebN282Tz/9tKlQoYL55Zdf7HXGjRtnQkJCzIIFC8zPP/9sunXrZqKjo82JEycu5m56vIkTJ5qUlBSza9cu8+2335q4uDgTFxdnX068yo7PP//cDBgwwHzxxRdmx44dZuHChSY8PNw89thj9jrEq2wZNWqUmThxoklMTDQhISEFlufm5pqmTZua+Ph489NPP5nPPvvMVK1a1SQlJdnr/PHHHyYgIMAkJiaazZs3mylTphgvLy+zZMkSe525c+caHx8f8/bbb5tNmzaZwYMHm9DQUJOWlnYxdvOSwrF2n88++8w89dRT5qOPPjKSzMcff+yw3Jnvrc6dO5vmzZubNWvWmK+//trUr1/f9O3b177cme9QnF9CQoKZMWOG+fXXX82GDRtM165dTe3atc2xY8fsde6//35Tq1Yts3z5cvPDDz+Yq6++2rRp08a+vLS+H3F+n3zyiVm8eLH57bffzLZt28zIkSNNhQoVzK+//mqMIVZnIulGmfLZZ5+ZRo0amU2bNhVIuqdOnWoqVapksrOz7WVPPvmkadiwoX3+9ttvNzfeeKNDm7Gxsea+++4zxhhjs9lMRESEefnll+3Ljxw5Ynx9fc3777/vor26NCxcuNBYLBaTk5NjjCFeZd348eNNdHS0fZ54lU0zZswoNOn+7LPPjNVqNampqfayadOmmeDgYHsM//Wvf5nLL7/cYb3evXubhIQE+3zr1q3N0KFD7fN5eXkmMjLSJCcnl/KegGNdNpyddDvzvbV582Yjyaxbt85e5/PPPzcWi8Xs27fPGOPcdyiK7+DBg0aS+eqrr4wxp2NToUIFM3/+fHudLVu2GEkmJSXFGFN6348omUqVKpn//ve/xOos3F6OMiMtLU2DBw/Wu+++q4CAgALLU1JS1K5dO/n4+NjLEhIStG3bNv3999/2OvHx8Q7rJSQkKCUlRZK0c+dOpaamOtQJCQlRbGysvQ6K7/Dhw5o9e7batGmjChUqSCJeZV1GRoYqV65snydeniUlJUXNmjVTtWrV7GUJCQnKzMzUpk2b7HXOFa+cnBytX7/eoY7ValV8fDzxKmUc67LLme+tlJQUhYaGqmXLlvY68fHxslqt+v777+11zvcdiuLLyMiQJPvvq/Xr1+vUqVMO8WrUqJFq167tEK8L/X5E8eXl5Wnu3Lk6fvy44uLiiNVZSLpRJhhjNGDAAN1///0Ov9TOlJqa6nBSSrLPp6amnrPOmcvPXK+wOnDek08+qYoVK6pKlSras2ePFi5caF9GvMqu7du3a8qUKbrvvvvsZcTLs1xIvDIzM3XixAkdOnRIeXl5xOsi4FiXXc58b6Wmpio8PNxhube3typXrnze8+3MbaB4bDabHn30UV1zzTVq2rSppNPH0sfHp8BYF2fH60K/H+G8X375RYGBgfL19dX999+vjz/+WE2aNCFWZyHphkuNGDFCFovlnNPWrVs1ZcoUHT16VElJSe7u8iXN2Xjle+KJJ/TTTz/pyy+/lJeXl/r16ydjjBv34NJS3HhJ0r59+9S5c2f16tVLgwcPdlPPL00liRcAXKqGDh2qX3/9VXPnznV3V3AODRs21IYNG/T999/rgQceUP/+/bV582Z3d6vM8XZ3B1C+PfbYYxowYMA569StW1crVqxQSkqKfH19HZa1bNlSd955p2bNmqWIiIgCIx7mz0dERNj/LazOmcvzy6pXr+5Qp0WLFsXev/LG2Xjlq1q1qqpWrarLLrtMjRs3Vq1atbRmzRrFxcURr4uguPHav3+/OnbsqDZt2ujNN990qEe8XK+48TqXiIiIAiNfOxuv4OBg+fv7y8vLS15eXueMKUpH1apVOdZllDPfWxERETp48KDDerm5uTp8+PB5z7cztwHnDRs2TIsWLdLq1atVs2ZNe3lERIRycnJ05MgRhyuoZ/8uutDvRzjPx8dH9evXlyTFxMRo3bp1evXVV9W7d29idQaudMOlwsLC1KhRo3NOPj4+eu211/Tzzz9rw4YN2rBhg/01RPPmzdMLL7wgSYqLi9Pq1at16tQpe/tLly5Vw4YNValSJXud5cuXO/Rh6dKliouLkyRFR0crIiLCoU5mZqa+//57e51LmbPxKozNZpN0+pVvEvG6GIoTr3379qlDhw6KiYnRjBkzZLU6fv0TL9e7kPPrbHFxcfrll18cEoGlS5cqODhYTZo0sdc5V7x8fHwUExPjUMdms2n58uXEq5RxrMsuZ7634uLidOTIEa1fv95eZ8WKFbLZbIqNjbXXOd93KM7PGKNhw4bp448/1ooVKxQdHe2wPCYmRhUqVHCI17Zt27Rnzx6HeF3o9yNKzmazKTs7m1idzc0DuQGF2rlzZ4HRy48cOWKqVatm7r77bvPrr7+auXPnmoCAgAKvNPL29jYTJkwwW7ZsMaNHjy70lUahoaFm4cKFZuPGjeaWW27hlUbFtGbNGjNlyhTz008/mV27dpnly5ebNm3amHr16pmTJ08aY4hXWbJ3715Tv35906lTJ7N3715z4MAB+5SPeJUtu3fvNj/99JMZO3asCQwMND/99JP56aefzNGjR40x/7xm5YYbbjAbNmwwS5YsMWFhYYW+ZuWJJ54wW7ZsMW+88Uahrwzz9fU1M2fONJs3bzZDhgwxoaGhDiPJonRwrN3n6NGj9nNIkpk4caL56aefzO7du40xzn1vde7c2Vx55ZXm+++/N998841p0KCBwyvDnPkOxfk98MADJiQkxKxatcrhd1VWVpa9zv33329q165tVqxYYX744YcCrywtre9HnN+IESPMV199ZXbu3Gk2btxoRowYYSwWi/nyyy+NMcTqTCTdKJMKS7qNMebnn382bdu2Nb6+vqZGjRpm3LhxBdb94IMPzGWXXWZ8fHzM5ZdfbhYvXuyw3GazmWeeecZUq1bN+Pr6mk6dOplt27a5cnfKnY0bN5qOHTuaypUrG19fXxMVFWXuv/9+s3fvXod6xKtsmDFjhpFU6HQm4lV29O/fv9B4rVy50l5n165dpkuXLsbf399UrVrVPPbYY+bUqVMO7axcudK0aNHC+Pj4mLp165oZM2YU2NaUKVNM7dq1jY+Pj2ndurVZs2aNi/fu0sWxdo+VK1cWej7179/fGOPc99Zff/1l+vbtawIDA01wcLAZOHCg/Y9g+Zz5DsW5FfW76szvrhMnTpgHH3zQVKpUyQQEBJhbb73V4Y/IxpTe9yPO7Z577jF16tQxPj4+JiwszHTq1MmecBtDrM5kMYZRjwAAAAAAcAWe6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAoZQMGDFBUVFSptjlz5kxZLBbt2rWrVNtF2RMVFaUBAwa4uxsAgFJC0g0AKJN27Nih++67T3Xr1pWfn5+Cg4N1zTXX6NVXX9WJEyfc3T2XefHFF7VgwQJ3d8MuP9m3WCz65ptvCiw3xqhWrVqyWCy66aab3NDDou3atcve97Onq6++2q19++677zRmzBgdOXLErf0AALiet7s7AADA2RYvXqxevXrJ19dX/fr1U9OmTZWTk6NvvvlGTzzxhDZt2qQ333zT3d10iRdffFG33Xabunfv7lB+9913q0+fPvL19XVLv/z8/DRnzhy1bdvWofyrr77S3r173dYvZ/Tt21ddu3Z1KAsLC3NTb0777rvvNHbsWA0YMEChoaEOy7Zt2yarlesiAFBekHQDAMqUnTt3qk+fPqpTp45WrFih6tWr25cNHTpU27dv1+LFi93YQ/fw8vKSl5eX27bftWtXzZ8/X6+99pq8vf/578OcOXMUExOjQ4cOua1v53PVVVfprrvucnc3nFaW/4ABACg+/owKAChTxo8fr2PHjumtt95ySLjz1a9fX4888oikf24fnjlzZoF6FotFY8aMsc+PGTNGFotFv/32m+666y6FhIQoLCxMzzzzjIwx+vPPP3XLLbcoODhYEREReuWVVxzaK+qZ6lWrVslisWjVqlXn3K8JEyaoTZs2qlKlivz9/RUTE6MPP/ywQJ+PHz+uWbNm2W+Dzn+29+zt33TTTapbt26h24qLi1PLli0dyt577z3FxMTI399flStXVp8+ffTnn3+es89n6tu3r/766y8tXbrUXpaTk6MPP/xQd9xxR4n3WZKWLl2qtm3bKjQ0VIGBgWrYsKFGjhzpUGfKlCm6/PLLFRAQoEqVKqlly5aaM2eO0/0vSocOHdShQ4cC5Wc/l5//WZswYYLefPNN1atXT76+vmrVqpXWrVtXYP2tW7fq9ttvV1hYmPz9/dWwYUM99dRTkk5/Fp944glJUnR0tD3W+bEt7JnuP/74Q7169VLlypUVEBCgq6++usAfn/I/ix988IFeeOEF1axZU35+furUqZO2b99e8oMEALggJN0AgDLl008/Vd26ddWmTRuXtN+7d2/ZbDaNGzdOsbGxev755zV58mRdf/31qlGjhl566SXVr19fjz/+uFavXl1q23311Vd15ZVX6tlnn9WLL74ob29v9erVyyFxevfdd+Xr66trr71W7777rt59913dd999Re7Hzp07CyR8u3fv1po1a9SnTx972QsvvKB+/fqpQYMGmjhxoh599FEtX75c7dq1c/qZ4qioKMXFxen999+3l33++efKyMhw2FZx93nTpk266aablJ2drWeffVavvPKKunXrpm+//dZe5z//+Y8efvhhNWnSRJMnT9bYsWPVokULff/99071PSsrS4cOHXKYTp065dS6Z5szZ45efvll3XfffXr++ee1a9cu9ejRw6G9jRs3KjY2VitWrNDgwYP16quvqnv37vr0008lST169FDfvn0lSZMmTbLHuqhb3tPS0tSmTRt98cUXevDBB/XCCy/o5MmT6tatmz7++OMC9ceNG6ePP/5Yjz/+uJKSkrRmzRrdeeedJdpfAEApMAAAlBEZGRlGkrnlllucqr9z504jycyYMaPAMklm9OjR9vnRo0cbSWbIkCH2stzcXFOzZk1jsVjMuHHj7OV///238ff3N/3797eXzZgxw0gyO3fudNjOypUrjSSzcuVKe1n//v1NnTp1HOplZWU5zOfk5JimTZua6667zqG8YsWKDtstavsZGRnG19fXPPbYYw71xo8fbywWi9m9e7cxxphdu3YZLy8v88ILLzjU++WXX4y3t3eB8qK2u27dOvP666+boKAg+7706tXLdOzY0RhjTJ06dcyNN95Y7H2eNGmSkWTS09OL7MMtt9xiLr/88nP2szD5n4/Cpvx4tW/f3rRv377AumfHML+tKlWqmMOHD9vLFy5caCSZTz/91F7Wrl07ExQUZI9BPpvNZv/55ZdfLvTzZMzpY3nmZ+DRRx81kszXX39tLzt69KiJjo42UVFRJi8vzxjzz2excePGJjs721731VdfNZLML7/8cs7jBQBwDa50AwDKjMzMTElSUFCQy7Zx77332n/28vJSy5YtZYzRoEGD7OWhoaFq2LCh/vjjj1Lbrr+/v/3nv//+WxkZGbr22mv1448/lqi94OBgdenSRR988IGMMfbyefPm6eqrr1bt2rUlSR999JFsNptuv/12hyu9ERERatCggVauXOn0Nm+//XadOHFCixYt0tGjR7Vo0aIiby2XnNvn/EHEFi5cKJvNVmg7oaGh2rt3b6G3cTtjyJAhWrp0qcPUvHnzErXVu3dvVapUyT5/7bXXSpL9s5Kenq7Vq1frnnvusccgn8ViKdE2P/vsM7Vu3dphELvAwEANGTJEu3bt0ubNmx3qDxw4UD4+PkX2EQBwcTGQGgCgzAgODpYkHT161GXbODsRCgkJkZ+fn6pWrVqg/K+//iq17S5atEjPP/+8NmzYoOzsbHt5SRMx6XQCuGDBAqWkpKhNmzbasWOH1q9fr8mTJ9vr/P777zLGqEGDBoW2UaFCBae3FxYWpvj4eM2ZM0dZWVnKy8vTbbfdVmR9Z/a5d+/e+u9//6t7771XI0aMUKdOndSjRw/ddttt9hG8n3zySS1btkytW7dW/fr1dcMNN+iOO+7QNddc41S/GzRooPj4eKf381zO/vzkJ+B///23pH8S26ZNm5bK9qTTjwzExsYWKG/cuLF9+ZnbO18fAQAXF0k3AKDMCA4OVmRkpH799Ven6heVsObl5RW5TmEjgBc1KviZV5BLsq18X3/9tbp166Z27dpp6tSpql69uipUqKAZM2Zc0GBgN998swICAvTBBx+oTZs2+uCDD2S1WtWrVy97HZvNJovFos8//7zQ/QwMDCzWNu+44w4NHjxYqamp6tKlS4HXXeVzdp/9/f21evVqrVy5UosXL9aSJUs0b948XXfddfryyy/l5eWlxo0ba9u2bVq0aJGWLFmi//3vf5o6dapGjRqlsWPHFqv/Z7NYLA5xzldUXJ35rLibJ/QRAC4lJN0AgDLlpptu0ptvvqmUlBTFxcWds27+FbyzBwPbvXt3qffrQrb1v//9T35+fvriiy8cXgc1Y8aMAnWLc+W7YsWKuummmzR//nxNnDhR8+bN07XXXqvIyEh7nXr16skYo+joaF122WVOt12UW2+9Vffdd5/WrFmjefPmFVmvOPtstVrVqVMnderUSRMnTtSLL76op556SitXrrRfoa5YsaJ69+6t3r17KycnRz169NALL7ygpKQk+fn5lXh/KlWqVOht1yX9DOWPKH++PxwVJ8516tTRtm3bCpRv3brVvhwAUHbxTDcAoEz517/+pYoVK+ree+9VWlpageU7duzQq6++Kun0lfGqVasWGGV86tSppd6vevXqSZLDtvLy8vTmm2+ed10vLy9ZLBaHq6e7du3SggULCtStWLGi0yOKS6dvz96/f7/++9//6ueff1bv3r0dlvfo0UNeXl4aO3ZsgSudxphi30IfGBioadOmacyYMbr55puLrOfsPh8+fLjAui1atJAk+y3pZ/fRx8dHTZo0kTGmxKOQ56tXr562bt2q9PR0e9nPP//sMHp6cYSFhaldu3Z6++23tWfPHodlZx7/ihUrSir4R5zCdO3aVWvXrlVKSoq97Pjx43rzzTcVFRWlJk2alKivAICLgyvdAIAypV69epozZ4569+6txo0bq1+/fmratKlycnL03Xffaf78+Q7vML733ns1btw43XvvvWrZsqVWr16t3377rdT7dfnll+vqq69WUlKSDh8+rMqVK2vu3LnKzc0977o33nijJk6cqM6dO+uOO+7QwYMH9cYbb6h+/frauHGjQ92YmBgtW7ZMEydOVGRkpKKjowt9njdf165dFRQUpMcff1xeXl7q2bOnw/J69erp+eefV1JSknbt2qXu3bsrKChIO3fu1Mcff6whQ4bo8ccfL9ax6N+/f6nt87PPPqvVq1frxhtvVJ06dXTw4EFNnTpVNWvWtA8cdsMNNygiIkLXXHONqlWrpi1btuj111/XjTfeeMGD7t1zzz2aOHGiEhISNGjQIB08eFDTp0/X5Zdfbh/Yr7hee+01tW3bVldddZWGDBmi6Oho7dq1S4sXL9aGDRsknY6zJD311FPq06ePKlSooJtvvtmejJ9pxIgRev/999WlSxc9/PDDqly5smbNmqWdO3fqf//7n/3ZdwBAGeWeQdMBADi33377zQwePNhERUUZHx8fExQUZK655hozZcoUc/LkSXu9rKwsM2jQIBMSEmKCgoLM7bffbg4ePFjkK8POfjVV//79TcWKFQtsv3379gVeU7Vjxw4THx9vfH19TbVq1czIkSPN0qVLnXpl2FtvvWUaNGhgfH19TaNGjcyMGTPsfTrT1q1bTbt27Yy/v7+RZH91VFGvLDPGmDvvvNNIMvHx8UUez//973+mbdu2pmLFiqZixYqmUaNGZujQoWbbtm1FrnPmdtetW3fOeoW9MsyZfV6+fLm55ZZbTGRkpPHx8TGRkZGmb9++5rfffrPX+fe//23atWtnqlSpYnx9fU29evXME088YTIyMs7Zp/zXfL388svnrPfee++ZunXrGh8fH9OiRQvzxRdfFPnKsMLaOvuzZowxv/76q7n11ltNaGio8fPzMw0bNjTPPPOMQ53nnnvO1KhRw1itVofYnv3KMGNOf/Zuu+02e3utW7c2ixYtcqiT/8qw+fPnF3ocCnu1HgDA9SzGMKoGAAAAAACuwP1IAAAAAAC4CEk3AAAAAAAuQtINAAAAAICLkHQDAAAAAOAiJN0AAAAAALgISTcAAAAAAC7i7e4OeCqbzab9+/crKChIFovF3d0BAAAAAFxExhgdPXpUkZGRslqLvp5N0l1C+/fvV61atdzdDQAAAACAG/3555+qWbNmkctJuksoKChI0ukDHBwc7ObelD82m03p6ekKCws751+NULYQN89E3DwXsfNMxM0zETfPRew8kyfELTMzU7Vq1bLnhkUh6S6h/FvKg4ODSbpdwGaz6eTJkwoODi6zJxkKIm6eibh5LmLnmYibZyJunovYeSZPitv5Hjcu270HAAAAAMCDkXQDAAAAAOAiJN0AAAAAALgIz3S7kDFGubm5ysvLc3dXPEaFChXk5eXl7m4AAAAAQKkg6XaRnJwcHThwQFlZWe7uikexWCyqWbOmAgIC3N0VAAAAALhgJN0uYLPZtHPnTnl5eSkyMlI+Pj7nHdEOp+8MSE9P1969e1WvXj13dwcAAAAALhhJtwvk5OTIZrOpVq1aXLEtprCwMO3atUunTp1yd1cAAAAA4IKVi4HUVq9erZtvvlmRkZGyWCxasGDBeddZtWqVrrrqKvn6+qp+/fqaOXNmqferrL9PrizijgAAAAAA5Um5yAqPHz+u5s2b64033nCq/s6dO3XjjTeqY8eO2rBhgx599FHde++9+uKLL1zcUwAAAADApaRc3F7epUsXdenSxen606dPV3R0tF555RVJUuPGjfXNN99o0qRJSkhIcFU3AQAAgLLJGMlmk/LyCp9yc4teZrP9M+W3k/9vYWVF1SntKX+/8vLkl5kpBQZKVmvhdc6eP7O8JGVF/Xy+5Wf/fK6y0qhb3DacXV4ajFHFY8ekUaNOx86DlYuku7hSUlIUHx/vUJaQkKBHH320yHWys7OVnZ1tn8/MzJR0etA0m83mUNdms8kYY5/gvPxjln8Mzz62KNuIm2cibp6L2Hkm4uaZSjVuxkgnTkgZGaf/PXFCOnnyn5/Pns/Olk6ckKWoZadOnZ5yc09P+fNnlp9ddtY6lnL8ebRKCnV3J1BsVklBknITE6UyOk6Ws98Hl2TSnZqaqmrVqjmUVatWTZmZmTpx4oT8/f0LrJOcnKyxY8cWKE9PT9fJkycdyk6dOiWbzabc3Fzl5uaWbucvgtTUVI0bN06ff/659u3bp/DwcF1xxRV6+OGHdd1116lBgwbavXu33n33XfXu3dth3ebNm2vLli3673//q379+kmSvf6ZatSooZ07dxbYdm5urmw2mw4fPqzjx4/LGMOz8R7EZrMpIyODuHkY4ua5iJ1nIm6eqUDcTp6UNSNDlowMWTMz//k3M1PWI0dO/5uR4fjvmfU8bNBYY7FI3t6Sl5eM1Sp5eZ2erNZ/5i2W01eSpX/K88ssFvvkUJ7/s+RY54yfHaaz6jnUzd/2mfX+v++5ubny9vEpvI1C1ilQ76z2zlnvTGeWnb3OOX4252vrfOXFHSepNNpwtl0nGWOUk5Ojk0ePqqyO+nT06FGn6l2SSXdJJCUlKTEx0T6fmZmpWrVqKSwsTMHBwQ51T548qaNHj8rb21ve3p51iHft2qW2bdsqNDRU48ePV7NmzXTq1Cl98cUXeuSRR7RlyxZJUq1atfTuu+/qzjvvtK+7Zs0apaWlqWLFirJarQ77PnbsWA0ePNg+7+XlVeix8fb2ltVqVeXKlVWhQgWFhYXxHxIPYrPZZLFYiJuHIW6ei9h5JuJWxp06JaWlSfv32yfL/v0y+/apyq5d8vnrr9Nlf/99wZsyFovk7+84+fk5/nzm/BnLTf68r+/pZLhChX/+PXsqqjx/2f8n0+eczpE8FbWkrCRKNptNf6enc855GJvNpswyHjc/Pz+n6nlWRlhKIiIilJaW5lCWlpam4ODgQq9yS5Kvr698fX0LlFut1gIfAqvVKovFYp9kjJSVVXo7UBwBAcX6C9PQoUNlsVi0du1aVaxY0V7etGlTDRo0yD66+J133qlJkyZp7969qlWrliRpxowZuvPOO/XOO+/8s+//Lzg4WNWrVz/v9vPXyz+GhR1flG3EzTMRN89F7DwTcXOjv/6SNmyQdu48nVTv2+eQYCstrdDnVS2SCvxP0GqVQkKkSpWk0NBiT5bAwBJfCSwrCa2n4JzzTGU9bs7265JMuuPi4vTZZ585lC39v/buPc6mev/j+HvPfcaYi+aGxl2k3BrR0D01KtfTRbqQpFTSMXSYcvmp49LloFCdCOlUqONSkWjkEoPcUyhFDplBmBmGmTF7/f7YzWY3gxlm9tprz+v5eOzHXuu7vmuvz94fe/Oxvuu7lixRYmJi+RwwJ8e8i/+PH5fOKp7P58iRI1q0aJFGjhzpUnAXioiIcC7HxsYqKSlJ77//voYMGaKcnBzNmjVLy5cv14wZM8oqegAAgItjGNLevY4Ce9OmM4///e/C+/r6SlWrStWrS9WqSdWqyV61qrIqV1ZYgwbyufxyR3t4+JkhzQBwDl5RdB8/fly7du1yru/evVubN29WlSpVVKNGDaWkpGj//v3OYrBPnz6aOHGi/vGPf+ixxx7T0qVLNXv2bC1YsMCst+ARdu3aJcMw1LBhwxL1f+yxxzRgwAC9+OKL+vTTT1W3bl01a9as2L6DBg3SkCFDnOujRo1Sv379yiJsAABQ0Z0+Le3c6Vpgb94sHTlSfP86daQGDc4U1WcV16peXYqKchTeZ7PbdergQYXFxFBoAygVryi6169fr1tuucW5XnjtdY8ePTR9+nQdOHBAe/fudW6vXbu2FixYoP79++uNN97Q5ZdfrilTppTf7cJCQhxnnM1Qipn+SjvT+t13360nn3xSK1as0NSpU/XYY4+ds+/zzz+vRx991LkeFRVVqmMBAABIkvLyzhTVhQX21q2OGb3/ys9PatRIat5catbszHN4uJuDBlCReUXRffPNN5+3YJw+fXqx+2zatKkcozqLzVbiId5mql+/vmw2m3bs2FGi/n5+fnrkkUc0fPhwrV27VnPnzj1n36ioKNWrV6+sQgUAABXJ6dPS0qXSrFnS3LlScZOYVaokNW3qKKwLi+urrnJMPAYAJvKKohtlo0qVKkpKStKkSZPUr1+/Itd1Hzt2zOW6bskxxPz1119X165dFRkZ6cZoAQCAVysokFascBTa//2vdPjwmW2XXSYlJLgW2PXqFR0SDgAegKIbLiZNmqQ2bdqoZcuWeumll9SkSROdPn1aS5Ys0dtvv+28ZVihK6+8UocPH1aIh96wHgAAWIjdLq1e7Si0P/1USk8/sy0qSrr3XqlrV+mGGyiwAVgGRTdc1KlTRxs3btTIkSM1YMAAHThwQNHR0UpISNDbb79d7D6XXXaZm6MEAABewzCktWsdhfYnnzhu4VUoMlL6298chfYttziu0QYAi+GXC0VUrVpVEydO1MSJE4vdvmfPnvPuf+zYsVL1BwAAFYxhSBs3Ogrt2bOl3347sy0sTOrc2VFot20rBQSYFiYAlAWKbgAAALhHero0caI0c6b0yy9n2itVkjp2dBTaSUlMfgbAq1B0AwAAoHzl5UlvvCG9/LKUne1oCw6W7r7bUWjfdVepbnMKAFZC0Q0AAIDys2CB1L+/9PPPjvVrr3Wsd+gghYaaGxsAuAFFNwAAAMrezp2O4vrLLx3rsbHSmDFS9+6Sj4+5sQGAG/GLV44MwzA7BMvhMwMAwOKysqTnn5caN3YU3P7+0sCB0k8/SY8+SsENoMLhTHc58Pf3lyTl5OQoODjY5GisJS8vT5Lky703AQCwFrtdmjFDGjxYyshwtN11lzRunHTFFebGBgAmouguB76+voqIiNDBgwclSSEhIbLZbCZH5fnsdrsOHTqkkJAQ+XEfTgAArGPtWqlfP2ndOsd6/frS+PGOohsAKjgqm3ISFxcnSc7CGyXj4+OjGjVq8J8UAABYwYEDjjPbM2Y41itXloYNcxTg3F8bACRRdJcbm82mqlWrKiYmRvn5+WaHYxkBAQHy8fGR3W43OxQAAHAuubmOM9n//Kd0/Lij7dFHpdGjpT9PPAAAHCi6y5mvry/XJwMAAO9gGGduAbZrl6OtVSvpzTelli3NjQ0APBRFNwAAAC7s6FHp4YelhQsd63Fx0iuvONqYkRwAzomiGwAAAOd37Jh0xx3S+vWOW4AlJ0svvui4hhsAcF4U3QAAADi3zEwpKclRcEdFSV9/LTVtanZUAGAZFN0AAAAoXlaWo+Bet0667DIpNVVq0sTsqADAUrgABwAAAEVlZUnt2jnuwV2liuMMNwU3AJQaRTcAAABcZWdLd94ppaVJkZGOgrtZM7OjAgBLougGAADAGcePS3fdJa1eLUVEOAru5s3NjgoALIuiGwAAAA6FBfe3354puK+5xuyoAMDSKLoBAAAgnTghtW8vrVwphYdLixdLCQlmRwUAlkfRDQAAUNHl5DgK7uXLpbAw6auvpGuvNTsqAPAKFN0AAAAVWU6O1KGDtGyZVLmyo+Bu1crsqADAa1B0AwAAVFQnT0qdOklLl0qhoY6C+7rrzI4KALwKRTcAAEBFVFhwf/21VKmStGiRlJhodlQA4HUougEAACqaU6ekLl2kJUscBfeXX0pt2pgdFQB4JYpuAACAiqSw4P7qKykkRFq4ULrhBrOjAgCvRdENAABQUeTmSvfc4xhKXlhw33ij2VEBgFej6AYAAKgICgvuhQul4GDpiy+km24yOyoA8HoU3QAAAN7OMKSHH5YWLJCCgqTPP5duucXsqACgQqDoBgAA8HZLlkiffioFBDgK7ttuMzsiAKgwKLoBAAC8md0upaQ4lp95Rmrb1tx4AKCCoegGAADwZp9+Km3cKFWufKb4BgC4DUU3AACAt8rPl4YMcSwPHChFR5sbDwBUQBTdAAAA3mr6dOnnnx3Fdv/+ZkcDABUSRTcAAIA3OnlS+r//cywPGeIYXg4AcDuKbgAAAG80caL0++9SzZrSk0+aHQ0AVFgU3QAAAN7m2DFp9GjH8ogRUmCgqeEAQEVG0Q0AAOBtXntNOnpUatRIevhhs6MBgAqNohsAAMCbpKdL48c7lkeNknx9TQ0HACo6im4AAABv8s9/Sjk50nXXSR07mh0NAFR4FN0AAADe4tdfpX//27E8Zoxks5kbDwCAohsAAMBrDBsmnT4tJSVJN91kdjQAAHlR0T1p0iTVqlVLQUFBatWqldatW3fe/uPHj1eDBg0UHBys+Ph49e/fX6dOnXJTtAAAAGVsyxbpo48cy6NGmRsLAMDJK4ruWbNmKTk5WcOHD9fGjRvVtGlTJSUl6eDBg8X2/+ijjzR48GANHz5c27dv13vvvadZs2bphRdecHPkAAAAZeTFFyXDkLp2la65xuxoAAB/8jM7gLIwduxY9e7dWz179pQkvfPOO1qwYIGmTp2qwYMHF+m/evVqtWnTRg8++KAkqVatWurWrZvWrl17zmPk5uYqNzfXuZ6VlSVJstvtstvtZfl2IMfnahgGn63FkDdrIm/WRe6sqVzy9u238lmwQIavr4wRIyT+TJQ5vm/WRe6syQp5K2lsli+68/LytGHDBqWkpDjbfHx81LZtW6WlpRW7T+vWrfWf//xH69atU8uWLfXrr79q4cKFeuSRR855nNGjR2vEiBFF2g8dOsSw9HJgt9uVmZkpwzDk4+MVAzIqBPJmTeTNusidNZV53gxDVQYOVICkkw8+qKzwcOkco/1w8fi+WRe5syYr5C07O7tE/SxfdB8+fFgFBQWKjY11aY+NjdWOHTuK3efBBx/U4cOHdf3118swDJ0+fVp9+vQ57/DylJQUJScnO9ezsrIUHx+v6OhohYWFlc2bgZPdbpfNZlN0dLTHfslQFHmzJvJmXeTOmso8bwsWyOe772QEBSlo5EgFxcRc+muiCL5v1kXurMkKeQsKCipRP8sX3Rdj2bJlGjVqlN566y21atVKu3bt0nPPPaeXX35ZQ4cOLXafwMBABQYGFmn38fHx2D8EVmez2fh8LYi8WRN5sy5yZ01llje73XEttyRbv36yxceXQXQ4F75v1kXurMnT81bSuCxfdEdFRcnX11cZGRku7RkZGYqLiyt2n6FDh+qRRx7R448/Lklq3LixTpw4oSeeeEIvvviixyYVAADAxccfS99/L4WHS4MGmR0NAKAYlq8uAwIClJCQoNTUVGeb3W5XamqqEhMTi90nJyenSGHt6+srSTIMo/yCBQAAKCt5eVLhCL1Bg6QqVcyNBwBQLMuf6Zak5ORk9ejRQy1atFDLli01fvx4nThxwjmbeffu3VW9enWNHj1aktShQweNHTtWzZs3dw4vHzp0qDp06OAsvgEAADza5MnS7t1SXJzUr5/Z0QAAzsGUovvEiROqVKlSmb1e165ddejQIQ0bNkzp6elq1qyZFi1a5Jxcbe/evS5ntocMGSKbzaYhQ4Zo//79io6OVocOHTRy5MgyiwkAAKDcnDghvfyyY3nYMKkM/10FAChbNsOE8dShoaG6//779dhjj+n666939+HLRFZWlsLDw5WZmcns5eXAbrfr4MGDiomJ4Rp7CyFv1kTerIvcWVOZ5G3UKMcEanXqSNu3SwEBZRskiuD7Zl3kzpqskLeS1oSmRP+f//xHR44c0a233qorrrhCY8aM0e+//25GKAAAANbyxx/SK684ll9+mYIbADycKUV3586dNW/ePO3fv199+vTRRx99pJo1a6p9+/aaM2eOTp8+bUZYAAAAnu+VV6SsLKlJE+mBB8yOBgBwAaaep4+OjlZycrK2bt2qsWPH6uuvv9a9996ratWqadiwYcrJyTEzPAAAAM+yb580YYJjefRoyUOHXAIAzjB19vKMjAy9//77mj59un777Tfde++96tWrl/bt26dXXnlFa9as0eLFi80MEQAAwHO89JJ06pR0ww3SnXeaHQ0AoARMKbrnzJmjadOm6auvvlKjRo309NNP6+GHH1ZERISzT+vWrXXllVeaER4AAIDn2blTmjrVsTx6tGSzmRsPAKBETCm6e/bsqQceeECrVq3StddeW2yfatWq6cUXX3RzZAAAAB5q6FCpoEBq315q08bsaAAAJWRK0X3gwAGFhISct09wcLCGDx/upogAAAA82IYN0iefOM5ujxxpdjQAgFIwZfaNypUr6+DBg0Xa//jjD/n6+poQEQAAgAcbNcrx/NBDjlnLAQCWYUrRbRhGse25ubkK4F6TAAAAZxw7Jn3xhWP5+edNDQUAUHpuHV7+5ptvSpJsNpumTJmi0NBQ57aCggKtWLFCDRs2dGdIAAAAnm3uXCkvT7rqKs5yA4AFubXoHjdunCTHme533nnHZSh5QECAatWqpXfeecedIQEAAHi2jz92PHfrZm4cAICL4taie/fu3ZKkW265RXPmzFFkZKQ7Dw8AAGAtGRlSaqpj+YEHzI0FAHBRTJm9/JtvvjHjsAAAANbyySeS3S61bCnVrWt2NACAi+C2ojs5OVkvv/yyKlWqpOTk5PP2HTt2rJuiAgAA8GAMLQcAy3Nb0b1p0ybl5+c7l8/FZrO5KyQAAADP9dtv0urVjntz33+/2dEAAC6S24rus4eUM7wcAADgAmbOdDzffLNUrZqpoQAALp4p9+kGAADABTC0HAC8gtvOdP/tb38rcd85c+aUYyQAAAAebvt2acsWyd9fuuces6MBAFwCtxXd4eHh7joUAACAtRWe5U5KkqpUMTcWAMAlcVvRPW3aNHcdCgAAwLoMg6HlAOBFuKYbAADAk2zYIO3aJQUHSx07mh0NAOASue1M9zXXXKPU1FRFRkaqefPm57012MaNG90VFgAAgGcpPMvdsaMUGmpuLACAS+a2ortTp04KDAyUJHXu3NldhwUAALAOu12aNcuxzNByAPAKbiu6hw8fXuwyAAAA/rRypbR/vxQeLrVrZ3Y0AIAy4Laiuzjr16/X9u3bJUmNGjVSQkKCmeEAAACYq3Bo+d/+Jv05QhAAYG2mFN379u1Tt27dtGrVKkVEREiSjh07ptatW2vmzJm6/PLLzQgLAADAPPn50qefOpYZWg4AXsOU2csff/xx5efna/v27Tpy5IiOHDmi7du3y2636/HHHzcjJAAAAHMtWSL98YcUEyPdcovZ0QAAyogpZ7qXL1+u1atXq0GDBs62Bg0aaMKECbrhhhvMCAkAAMBchUPL779f8jP1CkAAQBky5Ux3fHy88vPzi7QXFBSoWrVqJkQEAABgopwcad48xzJDywHAq5hSdL/22mt69tlntX79emfb+vXr9dxzz+n11183IyQAAADzLFggHT8u1awpJSaaHQ0AoAy5bexSZGSkbDabc/3EiRNq1aqV/P4cPnX69Gn5+fnpscce4z7eAACgYikcWv7AA9JZ/14CAFif24ru8ePHu+tQAAAA1pGZKS1c6FhmaDkAeB23Fd09evRw16EAAACsY+5cKTdXuvJKqUkTs6MBAJQx06fGPHXqlPLy8lzawsLCTIoGAADAzQqHlnfrxtByAPBCpkykduLECfXt21cxMTGqVKmSIiMjXR4AAAAVwsGDUmqqY5mh5QDglUwpuv/xj39o6dKlevvttxUYGKgpU6ZoxIgRqlatmmbMmGFGSAAAAO73ySdSQYHUooVUr57Z0QAAyoEpw8s///xzzZgxQzfffLN69uypG264QfXq1VPNmjX14Ycf6qGHHjIjLAAAAPc6e2g5AMArmXKm+8iRI6pTp44kx/XbR44ckSRdf/31WrFihRkhAQAAuNfevdKqVY7ruLt2NTsaAEA5MaXorlOnjnbv3i1JatiwoWbPni3JcQY8IiLCjJAAAADca9Ysx/ONN0rVq5sbCwCg3JhSdPfs2VNbtmyRJA0ePFiTJk1SUFCQ+vfvr+eff96MkAAAANzKNnOmY4Gh5QDg1Uy5prt///7O5bZt22r79u3auHGj6tWrpybcnxIAAHg5359/lm3zZsnPT7r3XrPDAQCUI9Pv0y1JtWrVUq1atcwOAwAAwC2C581zLNxxh3TZZabGAgAoX6YML5ek1NRUtW/fXnXr1lXdunXVvn17ff3112aFAwAA4B6GoaDCopuh5QDg9Uwput966y21a9dOlStX1nPPPafnnntOYWFhuuuuuzRp0iQzQgIAAHCPjRvl9+uvMoKCpE6dzI4GAFDOTCm6R40apXHjxunjjz9Wv3791K9fP3300UcaN26cRo0adVGvOWnSJNWqVUtBQUFq1aqV1q1bd97+x44d0zPPPKOqVasqMDBQV1xxhRYuXHhRxwYAACgp5wRq7dtLlSubGwwAoNyZUnQfO3ZM7dq1K9J+xx13KDMzs9SvN2vWLCUnJ2v48OHauHGjmjZtqqSkJB08eLDY/nl5ebr99tu1Z88effrpp9q5c6cmT56s6tyuAwAAlCe7XfrzVqnGAw+YHAwAwB1MmUitY8eOmjt3bpHbg82fP1/t27cv9euNHTtWvXv3Vs+ePSVJ77zzjhYsWKCpU6dq8ODBRfpPnTpVR44c0erVq+Xv7y9JF5zILTc3V7m5uc71rKwsSZLdbpfdbi91zDg/u90uwzD4bC2GvFkTebMucmdBK1bIZ98+2StXlj0pyVGEwxL4vlkXubMmK+StpLG5reh+8803ncuNGjXSyJEjtWzZMiUmJkqS1qxZo1WrVmnAgAGlet28vDxt2LBBKSkpzjYfHx+1bdtWaWlpxe7z2WefKTExUc8884zmz5+v6OhoPfjggxo0aJB8fX2L3Wf06NEaMWJEkfZDhw7p1KlTpYoZF2a325WZmSnDMOTjY9p8fygl8mZN5M26yJ31hE2bphBJWbfdppysLPkcP252SCghvm/WRe6syQp5y87OLlE/txXd48aNc1mPjIzUjz/+qB9//NHZFhERoalTp2rIkCElft3Dhw+roKBAsbGxLu2xsbHasWNHsfv8+uuvWrp0qR566CEtXLhQu3bt0tNPP638/HwNHz682H1SUlKUnJzsXM/KylJ8fLyio6MVFhZW4nhRMna7XTabTdHR0R77JUNR5M2ayJt1kTuLyc+XbcECSVLB/fcrJiaGvFkI3zfrInfWZIW8BQUFlaif24ru3bt3u+tQF2S32xUTE6N3331Xvr6+SkhI0P79+/Xaa6+ds+gODAxUYGBgkXYfHx+P/UNgdTabjc/XgsibNZE36yJ3FrJ0qfTHHzJiYpR/ww3kzYL4vlkXubMmT89bSeMy5ZrusxmGIcnxgV6MqKgo+fr6KiMjw6U9IyNDcXFxxe5TtWpV+fv7uwwlv/LKK5Wenq68vDwFBARcVCwAAADn9PHHjud775X8TP8nGADATUz7L4MZM2aocePGCg4OVnBwsJo0aaIPPvig1K8TEBCghIQEpaamOtvsdrtSU1Od14v/VZs2bbRr1y6XC99/+uknVa1alYIbAACUvZMnpblzJTFrOQBUNKYU3WPHjtVTTz2lu+66S7Nnz9bs2bPVrl079enTp8i13yWRnJysyZMn6/3339f27dv11FNP6cSJE87ZzLt37+4y0dpTTz2lI0eO6LnnntNPP/2kBQsWaNSoUXrmmWfK7D0CAAA4LVggHT8u1aghneOkAADAO5kytmnChAl6++231b17d2dbx44dddVVV+n//u//1L9//1K9XteuXXXo0CENGzZM6enpatasmRYtWuScXG3v3r0u4+3j4+P11VdfqX///mrSpImqV6+u5557ToMGDSqbNwgAAHC2wqHlDzwgeei1iQCA8mFK0X3gwAG1bt26SHvr1q114MCBi3rNvn37qm/fvsVuW7ZsWZG2xMRErVmz5qKOBQAAUGKZmY4z3ZLUrZu5sQAA3M6U/2qtV6+eZs+eXaR91qxZql+/vgkRAQAAlJPPPpNyc6WGDaWmTc2OBgDgZqac6R4xYoS6du2qFStWqE2bNpKkVatWKTU1tdhiHAAAwLLmzHE833efZLNJf965BQBQMZhypvuee+7RunXrFBUVpXnz5mnevHmKiorSunXr1KVLFzNCAgAAKHs5OdJXXzmW+TcOAFRIbj/TnZ+fryeffFJDhw7Vf/7zH3cfHgAAwH2WLHHcLqxmTalZM7OjAQCYwO1nuv39/fXf//7X3YcFAABwv3nzHM+dOzuGlgMAKhxThpd37txZ8wr/EgIAAPBGp09Ln3/uWO7c2dRQAADmMWUitfr16+ull17SqlWrlJCQoEqVKrls79evnxlhAQAAlJ1vv5X++EOqUkW6/nqzowEAmMSUovu9995TRESENmzYoA0bNrhss9lsFN0AAMD6Ckf1degg+ZnyTy4AgAcw5W+A3bt3m3FYAAAA9zCMM0U3s5YDQIXm9qJ7zZo1+vzzz5WXl6fbbrtN7dq1c3cIAAAA5WvLFum336TgYOn2282OBgBgIrcW3Z9++qm6du2q4OBg+fv7a+zYsXrllVc0cOBAd4YBAABQvgrPciclSSEhpoYCADCXW2cvHz16tHr37q3MzEwdPXpU//znPzVq1Ch3hgAAAFD+5s51PDNrOQBUeG4tunfu3KmBAwfK19dXkjRgwABlZ2fr4MGD7gwDAACg/Pz6q7R1q+TrK7Vvb3Y0AACTubXozsnJUVhYmHM9ICBAQUFBOn78uDvDAAAAKD/z5zueb7xRuuwyc2MBAJjO7ROpTZkyRaGhoc7106dPa/r06YqKinK2ccswAABgWYXXczO0HAAgNxfdNWrU0OTJk13a4uLi9MEHHzjXuU83AACwrEOHpG+/dSx36mRuLAAAj+DWonvPnj3uPBwAAIB7ff65ZLdLzZtLNWuaHQ0AwAO49ZpuAAAAr8bQcgDAX1B0AwAAlIXjx6XFix3LXbqYGwsAwGNQdAMAAJSFxYul3FypTh3p6qvNjgYA4CEougEAAMrC2UPLbTYzIwEAeBCKbgAAgEuVn++YRE3iem4AgAvTiu5ffvlFQ4YMUbdu3XTw4EFJ0pdffqkffvjBrJAAAAAuzooV0rFjUnS01Lq12dEAADyIKUX38uXL1bhxY61du1Zz5szR8ePHJUlbtmzR8OHDzQgJAADg4hUOLe/YUfL1NTUUAIBnMaXoHjx4sP75z39qyZIlCggIcLbfeuutWrNmjRkhAQAAXBzD4FZhAIBzMqXo/v7779WlmFtpxMTE6PDhwyZEBAAAcJE2bpT27ZMqVZJuu83saAAAHsaUojsiIkIHDhwo0r5p0yZVr17dhIgAAAAuUuFZ7nbtpOBgU0MBAHgeU4ruBx54QIMGDVJ6erpsNpvsdrtWrVqlgQMHqnv37maEBAAAcHHmznU8M7QcAFAMU4ruUaNGqWHDhoqPj9fx48fVqFEj3XjjjWrdurWGDBliRkgAAACl9/PP0g8/SH5+0t13mx0NAMAD+Zlx0ICAAE2ePFlDhw7Vtm3bdPz4cTVv3lz169c3IxwAAICLM3++4/nmm6XISFNDAQB4JlOK7m+//VbXX3+9atSooRo1apgRAgAAwKVj1nIAwAWYMrz81ltvVe3atfXCCy/oxx9/NCMEAACAS5ORIa1e7Vju2NHcWAAAHsuUovv333/XgAEDtHz5cl199dVq1qyZXnvtNe3bt8+McAAAAErvs88c9+hu0UKKjzc7GgCAhzKl6I6KilLfvn21atUq/fLLL7rvvvv0/vvvq1atWrr11lvNCAkAAKB0CoeWd+liahgAAM9mStF9ttq1a2vw4MEaM2aMGjdurOXLl5sdEgAAwPllZ0tff+1Y5npuAMB5mFp0r1q1Sk8//bSqVq2qBx98UFdffbUWLFhgZkgAAAAXtmiRlJcn1a8vXXml2dEAADyYKbOXp6SkaObMmfr99991++2364033lCnTp0UEhJiRjgAAAClc/as5TabmZEAADycKUX3ihUr9Pzzz+v+++9XVFSUGSEAAABcnLw8qXBkHkPLAQAXYErRvWrVKjMOCwAAcOmWLZMyM6XYWOm668yOBgDg4dxWdH/22We688475e/vr88+++y8fTtyr0sAAOCpCoeWd+ok+Zg+Jy0AwMO5reju3Lmz0tPTFRMTo87nGYpls9lUUFDgrrAAAABKzm6X5s93LDO0HABQAm4ruu12e7HLAAAAlrF+vfT771JoqHTrrWZHAwCwAFPGRM2YMUO5ublF2vPy8jRjxgwTIgIAACiBuXMdz3fdJQUGmhsLAMASTCm6e/bsqczMzCLt2dnZ6tmzpwkRAQAAlEDh9dxdupgaBgDAOkwpug3DkK2Ye1ru27dP4eHhF/WakyZNUq1atRQUFKRWrVpp3bp1Jdpv5syZstls573OHAAAQDt2OB7+/tKdd5odDQDAItx6y7DmzZvLZrPJZrPptttuk5/fmcMXFBRo9+7dateuXalfd9asWUpOTtY777yjVq1aafz48UpKStLOnTsVExNzzv327NmjgQMH6oYbbrio9wMAACqQwgnUbr1VusiTBACAisetRXfh2eTNmzcrKSlJoaGhzm0BAQGqVauW7rnnnlK/7tixY9W7d2/n0PR33nlHCxYs0NSpUzV48OBi9ykoKNBDDz2kESNGaOXKlTp27FipjwsAACqQwqHljI4DAJSCW4vu4cOHS5Jq1aqlrl27Kigo6JJfMy8vTxs2bFBKSoqzzcfHR23btlVaWto593vppZcUExOjXr16aeXKlRc8Tm5ursvkb1lZWZIcM7EzG3vZs9vtMgyDz9ZiyJs1kTfrIndu9Pvv8lmzRpJkb9/eceuwi0TerIm8WRe5syYr5K2ksbm16C7Uo0ePMnutw4cPq6CgQLGxsS7tsbGx2rFjR7H7fPvtt3rvvfe0efPmEh9n9OjRGjFiRJH2Q4cO6dSpU6WKGRdmt9uVmZkpwzDk42PK1AO4COTNmsibdZE79wn+8EOFS8pLSNARPz/p4MGLfi3yZk3kzbrInTVZIW/Z2dkl6mdK0V1QUKBx48Zp9uzZ2rt3r/Ly8ly2HzlypNyOnZ2drUceeUSTJ09WVFRUifdLSUlRcnKycz0rK0vx8fGKjo5WWFhYeYRaodntdtlsNkVHR3vslwxFkTdrIm/WRe7cx7Z0qSTJ7957zztfTEmQN2sib9ZF7qzJCnkr6chtU4ruESNGaMqUKRowYICGDBmiF198UXv27NG8efM0bNiwUr1WVFSUfH19lZGR4dKekZGhuLi4Iv1/+eUX7dmzRx06dHC2FQ4L8PPz086dO1W3bt0i+wUGBiqwmPtx+vj4eOwfAquz2Wx8vhZE3qyJvFkXuXODzEzpm28kST5dukhl8FmTN2sib9ZF7qzJ0/NW0rhMif7DDz/U5MmTNWDAAPn5+albt26aMmWKhg0bpjV/Xi9VUgEBAUpISFBqaqqzzW63KzU1VYmJiUX6N2zYUN9//702b97sfHTs2FG33HKLNm/erPj4+Et+fwAAwIt8+aWUny81bCg1aGB2NAAAizHlTHd6eroaN24sSQoNDVVmZqYkqX379ho6dGipXy85OVk9evRQixYt1LJlS40fP14nTpxwzmbevXt3Va9eXaNHj1ZQUJCuvvpql/0jIiIkqUg7AAAAs5YDAC6FKUX35ZdfrgMHDqhGjRqqW7euFi9erGuuuUbfffddsUO4L6Rr1646dOiQhg0bpvT0dDVr1kyLFi1yTq62d+9ejx2SAAAAPFhurrRwoWO5SxdzYwEAWJIpRXeXLl2UmpqqVq1a6dlnn9XDDz+s9957T3v37lX//v0v6jX79u2rvn37Frtt2bJl5913+vTpF3VMAADg5ebNk7Kzpcsvl1q0MDsaAIAFmVJ0jxkzxrnctWtX1ahRQ2lpaapfv77LBGcAAACmevddx/Njj5XJBGoAgIrHlKL7rxITE4ud9AwAAMA0u3ZJS5dKNpvUq5fZ0QAALMptRfdnn31W4r4dO3Ysx0gAAABKYMoUx3O7dlKNGubGAgCwLLcV3Z1LOOOnzWZTQUFB+QYDAABwPnl50rRpjuUnnjA3FgCApbmt6Lbb7e46FAAAwKX57DPp4EGpalXp7rvNjgYAYGHMCAIAAPBXkyc7nnv2lPz9zY0FAGBppkyk9tJLL513+7Bhw9wUCQAAwF/s3i0tXuxYfvxxc2MBAFieKUX33LlzXdbz8/O1e/du+fn5qW7duhTdAADAPIUTqN1xh1S7trmxAAAsz5Sie9OmTUXasrKy9Oijj6pLly4mRAQAACApP1+aOtWx3Lu3ubEAALyCx1zTHRYWphEjRmjo0KFmhwIAACqqBQuk9HQpJkbiFqYAgDLgMUW3JGVmZiozM9PsMAAAQEX17ruO5549pYAAc2MBAHgFU4aXv/nmmy7rhmHowIED+uCDD3TnnXeaERIAAKjofvtNWrTIscwEagCAMmJK0T1u3DiXdR8fH0VHR6tHjx5KSUkxIyQAAFDRTZ0qGYZ0661SvXpmRwMA8BKmFN27d+8247AAAADFO31aeu89x/ITT5gbCwDAq3jUNd0AAACm+PJLaf9+KSpK6tzZ7GgAAF7ElDPdp06d0oQJE/TNN9/o4MGDstvtLts3btxoRlgAAKCiKpxArUcPKTDQ3FgAAF7FlKK7V69eWrx4se699161bNlSNpvNjDAAAACkffukhQsdy9ybGwBQxkwpur/44gstXLhQbdq0MePwAAAAZ0ydKtnt0k03SQ0amB0NAMDLmHJNd/Xq1VW5cmUzDg0AAHBGQYE0ZYpjmbPcAIByYErR/a9//UuDBg3Sb7/9ZsbhAQAAHBYvlv73PykyUrrnHrOjAQB4IVOGl7do0UKnTp1SnTp1FBISIn9/f5ftR44cMSMsAABQ0Zw9gVpQkLmxAAC8kilFd7du3bR//36NGjVKsbGxTKQGAADc78AB6fPPHcsMLQcAlBNTiu7Vq1crLS1NTZs2NePwAAAA0rRpjmu627SRGjUyOxoAgJcy5Zruhg0b6uTJk2YcGgAAwDFb+eTJjuUnnjA3FgCAVzOl6B4zZowGDBigZcuW6Y8//lBWVpbLAwAAoFx9/bW0Z48UESHdd5/Z0QAAvJgpw8vbtWsnSbrttttc2g3DkM1mU0FBgRlhAQCAiqJwArWHH5aCg82NBQDg1Uwpur/55hszDgsAACBlZEjz5zuWGVoOAChnphTdN910kxmHBQAAkKZPl06flq67Tmrc2OxoAABezpSie8WKFefdfuONN7opEgAAUKGcPYEatwkDALiBKUX3zTffXKTt7Ht1c003AAAoF8uWSb/8IlWuLHXtanY0AIAKwJTZy48ePeryOHjwoBYtWqRrr71WixcvNiMkAABQEZw9gVqlSubGAgCoEEw50x0eHl6k7fbbb1dAQICSk5O1YcMGE6ICAABe7dAhac4cxzJDywEAbmLKme5ziY2N1c6dO80OAwAAeKP335fy86UWLaTmzc2OBgBQQZhypnvr1q0u64Zh6MCBAxozZoyaNWtmRkgAAMCbGcaZCdS4TRgAwI1MKbqbNWsmm80mwzBc2q+77jpNnTrVjJAAAIA3W7FC+uknKTRUeuABs6MBAFQgphTdu3fvdln38fFRdHS0goKCzAgHAAB4u8IJ1Lp1c8xcDgCAm5hSdNesWdOMwwIAgIrojz+k//7XsczQcgCAm7l1IrWlS5eqUaNGysrKKrItMzNTV111lVauXOnOkAAAgLf74AMpN9cxeVpCgtnRAAAqGLcW3ePHj1fv3r0VFhZWZFt4eLiefPJJjR071p0hAQAAb2YYZ4aW9+4t2WzmxgMAqHDcWnRv2bJF7dq1O+f2O+64g3t0AwCAsrNqlbR9uxQSIj34oNnRAAAqILcW3RkZGfL39z/ndj8/Px06dMiNEQEAAK/27387nh94QAoPNzcWAECF5Naiu3r16tq2bds5t2/dulVVq1Z1Y0QAAMBrrVkjffihY/nJJ82NBQBQYbm16L7rrrs0dOhQnTp1qsi2kydPavjw4Wrfvr07QwIAAN4oN1fq1ctxTfcjj0gtW5odEQCggnLrLcOGDBmiOXPm6IorrlDfvn3VoEEDSdKOHTs0adIkFRQU6MUXX3RnSAAAwBuNHi39+KMUHS2NG2d2NACACsytRXdsbKxWr16tp556SikpKTIMQ5Jks9mUlJSkSZMmKTY21p0hAQAAb7NtmzRqlGN5wgTpssvMjQcAUKG5dXi5JNWsWVMLFy7U4cOHtXbtWq1Zs0aHDx/WwoULVbt27Yt+3UmTJqlWrVoKCgpSq1attG7dunP2nTx5sm644QZFRkYqMjJSbdu2PW9/AABgEQUF0uOPS/n5UseO0v33mx0RAKCCc3vRXSgyMlLXXnutWrZsqcjIyEt6rVmzZik5OVnDhw/Xxo0b1bRpUyUlJengwYPF9l+2bJm6deumb775RmlpaYqPj9cdd9yh/fv3X1IcAADAZBMmSGvXSmFh0ltvcV9uAIDpbEbhGG8La9Wqla699lpNnDhRkmS32xUfH69nn31WgwcPvuD+BQUFioyM1MSJE9W9e/di++Tm5io3N9e5npWVpfj4eB09elRhYWFl80bgZLfbdejQIUVHR8vHx7T/G0IpkTdrIm/WRe7+4tdfZWvaVLacHNnfflt64gmzIyoWebMm8mZd5M6arJC3rKwsRUZGKjMz87w1oVuv6S4PeXl52rBhg1JSUpxtPj4+atu2rdLS0kr0Gjk5OcrPz1eVKlXO2Wf06NEaMWJEkfZDhw4VOxs7Lo3dbldmZqYMw/DYLxmKIm/WRN6si9ydxTAU2auXAnNylNu6tY527CidY8Sb2cibNZE36yJ31mSFvGVnZ5eon+WL7sOHD6ugoKDIBGyxsbHasWNHiV5j0KBBqlatmtq2bXvOPikpKUpOTnauF57pjo6O5kx3ObDb7bLZbB79P1soirxZE3mzLnJ3lmnT5LNihYygIPlPm6aYuDizIzon8mZN5M26yJ01WSFvQUFBJepn+aL7Uo0ZM0YzZ87UsmXLzvuhBQYGKjAwsEi7j4+Px/4hsDqbzcbna0HkzZrIm3WRO0kHDkgDB0qSbC+9JNsVV5gc0IWRN2sib9ZF7qzJ0/NW0rgsX3RHRUXJ19dXGRkZLu0ZGRmKu8D/cr/++usaM2aMvv76azVp0qQ8wwQAAOXl2WelY8ekhASpf3+zowEAwIVn/pdBKQQEBCghIUGpqanONrvdrtTUVCUmJp5zv1dffVUvv/yyFi1apBYtWrgjVAAAUNbmzJH++1/Jz0967z3HMwAAHsQr/mZKTk5Wjx491KJFC7Vs2VLjx4/XiRMn1LNnT0lS9+7dVb16dY0ePVqS9Morr2jYsGH66KOPVKtWLaWnp0uSQkNDFRoaatr7AAAApXD0qPTMM47lQYOkpk3NjQcAgGJ4RdHdtWtXHTp0SMOGDVN6erqaNWumRYsWOSdX27t3r8t4+7ffflt5eXm69957XV5n+PDh+r//+z93hg4AAC7WwIFSerrUoIE0ZIjZ0QAAUCyvKLolqW/fvurbt2+x25YtW+ayvmfPnvIPCAAAlJ+vv5amTpVsNsew8hLOIAsAgLtZ/ppuAABQwZw4IT3xhGP56aelNm3MjQcAgPOg6AYAANYydKi0e7cUHy/9OV8LAACeiqIbAABYx9q10htvOJb//W+pcmVz4wEA4AIougEAgDXk5Um9ekl2u/Tww9Kdd5odEQAAF0TRDQAArGH0aOmHH6ToaGncOLOjAQCgRCi6AQCA5/vhB2nkSMfym29KUVHmxgMAQAlRdAMAAM9WUOAYVp6fL3XoIHXtanZEAACUGEU3AADwbBMnOiZQCwuT3nrLcW9uAAAsgqIbAAB4rt27pRdecCy/+qp0+eXmxgMAQClRdAMAAM9kGNKTT0o5OdJNN0m9e5sdEQAApUbRDQAAPNPrr0tLlkhBQdLkyZIP/2wBAFiPn9kBAAAAuCgokAYOlMaPd6y//LJUv76pIQEAcLEougEAgOfIyZEeekiaN8+xPmaMNGCAqSEBAHApKLoBAIBnyMhw3BLsu++kwEDp/fe5PRgAwPIougEAgPm2b5fuukvas0e67DJp/nypTRuzowIA4JIxIwkAADDXN99IrVs7Cu569aS0NApuAIDXoOgGAADmmTFDSkqSjh1zFNppaUyaBgDwKhTdAADA/QxDGjFC6tFDys93XLv99ddSVJTZkQEAUKa4phsAALhXXp7Uu7fjLLckDR4sjRzJfbgBAF6JohsAALjPsWPS3/7muI7b11d6+21HAQ4AgJei6AYAAO6xZ49jhvLt26XQUOnTTx3XcwMA4MUougEAQPn77jvHPbgzMqTq1aUFC6SmTc2OCgCAcsfFUwAAoHzNny/ddJOj4G7WTFq7loIbAFBhUHQDAIDy88YbUpcu0smT0p13SitWOM50AwBQQVB0AwCAspeTI/XrJ/39747bg/XpI332mVS5stmRAQDgVlzTDQAAyk5OjvTvf0uvvOIYTi5Jr74qDRwo2WzmxgYAgAkougEAwKUrrtiuVUsaN07q3NnMyAAAMBVFNwAAuHjnKraHDJG6d5f8/U0NDwAAs1F0AwCA0qPYBgCgRCi6AQBAyVFsAwBQKhTdAADgwii2AQC4KBTdAADg3Ci2AQC4JBTdAACgKIptAADKBEU3AABwOHRIWrhQ+vxz6auvpOPHHe0U2wAAXDSKbgAAKirDkLZvdxTZn38upaVJdvuZ7bVrSy++SLENAMAloOgGAKAiyc+XVq48U2j/8ovr9mbNpA4dHI+EBMnHx5QwAQDwFhTdAAB4u6NHpS+/dBTZX34pZWae2RYQIN16q6PIbt9eqlHDvDgBAPBCFN0AAHijn38+czZ75UqpoODMtuho6e67HYX27bdLlSubFycAAF6OohsAACsrKJB27pS2bpU2b3Y8Nm06M+N4oauuOjNsvFUrydfXjGgBAKhwKLoBALCKkyel7793Fte2zZsVs2WLfHJyivb185NuuulMoV2njtvDBQAAFN0AAHimQ4fOnLkufOzY4TK7uO3PhxEcLFvTpo5J0AofjRtLISHujxsAALig6AYAwAwnTki//XbmsXfvmeVff5UOHCh+v+hoqXlzqXlz2Zs00R/x8brsuutk45ZeAAB4JIpuAADKmmFIf/xRtJg++/HHHxd+nfr1HWetmzc/cwY7Lk6y2Rzb7XYVHDzI9dkAAHgwim4AAC4kP186csRRKB8+7Ppc3PL+/Y4z2RcSHi7VrOn6qFFDqlVLuvJKZhUHAMALeE3RPWnSJL322mtKT09X06ZNNWHCBLVs2fKc/T/55BMNHTpUe/bsUf369fXKK6/orrvucmPEAIByZxjSqVPS8eNSdvaZ53MtHz8uHTtWtJA++77WpREbW7SoPvsRHl6mbxcAAHgeryi6Z82apeTkZL3zzjtq1aqVxo8fr6SkJO3cuVMxMTFF+q9evVrdunXT6NGj1b59e3300Ufq3LmzNm7cqKuvvtqEdwAAXqygwHGmOC/vzPPZy2e3nTp15nHyZNHlkrT9tZA++/7Ul8JmkyIjpcsuk6KiHM/nWq5a1XHGOiiobI4NAAAsy2YYhmF2EJeqVatWuvbaazVx4kRJkt1uV3x8vJ599lkNHjy4SP+uXbvqxIkT+uKLL5xt1113nZo1a6Z33nmnRMfMyspSeHi4MjMzFRYWVjZvpCxlZkp79lz665j0x8Nut+vIkSOqUqWKfHx8TImhiLL4LDzh63auGIprL2Vfu92uo0ePKjIy8kzeLvS6pVk+X9vF9DGMM48LrV+oj91etP+FHn/dp3DdbnddPtfzX5cLHwUF514vZptRUKBTOTkK8vOTzW6XTp929CsoKH75XNvz84svqs+abdtUISGO4dqVK0uhoedeDg8vvqCOjPS4a6ftdrsOHjyomJgYz/mtxAWRN2sib9ZF7qzJCnkraU1o+TPdeXl52rBhg1JSUpxtPj4+atu2rdLS0ordJy0tTcnJyS5tSUlJmjdv3jmPk5ubq9zcXOd6VlaWJMcfBrun/IPybMuWyadzZ7OjuGg+kqLMDgKl5iPpMrODQKnZJAW78XiGj48UEOB4+Pufefb3l4KDHWeHz36c3XbWslG4HBh4pj04uPiCulKlsimYPez33m63y/jzP7xgHeTNmsibdZE7a7JC3koam+WL7sOHD6ugoECxsbEu7bGxsdqxY0ex+6SnpxfbPz09/ZzHGT16tEaMGFGk/dChQzp16tRFRF6+Ak6eVPhf3qPV2O32sv1frcLZfs1+DU+OobjXPsfxjHO0O/N2oTjP3l64fKG2c2wz/tr3fK9zrj5ntRt/WXd5FLePj8+Zff7a9y9tzn5nf0ZnrRuF7YXPf+lvnKvd1/fMPn8uGz4+jrY/H4av75l9zupj2Gw6mZenoEqVZPPzc2zz9XX0L1z283Pud/ay/PwcxylcDghwbA8IkPFnIf3XZ7efLS4ceu6F7Ha7MjMzZRiGx54FQFHkzZrIm3WRO2uyQt6ys7NL1M/yRbe7pKSkuJwdz8rKUnx8vKKjoz1zePn99zseFmW323X40CFFR0d77JesoiuupLbb7fqDvFmO3W5XzqFDCiVvlmO322Wz2fjOWQx5sybyZl3kzpqskLegEs7dYvmiOyoqSr6+vsrIyHBpz8jIUFxcXLH7xMXFlaq/JAUGBiowMLBIu4+Pj8f+IbA6m83G52tB5M2ayJt1kTtrIm/WRN6si9xZk6fnraRxeWb0pRAQEKCEhASlpqY62+x2u1JTU5WYmFjsPomJiS79JWnJkiXn7A8AAAAAwMWw/JluSUpOTlaPHj3UokULtWzZUuPHj9eJEyfUs2dPSVL37t1VvXp1jR49WpL03HPP6aabbtK//vUv3X333Zo5c6bWr1+vd99918y3AQAAAADwMl5RdHft2lWHDh3SsGHDlJ6ermbNmmnRokXOydL27t3rcuq/devW+uijjzRkyBC98MILql+/vubNm8c9ugEAAAAAZcorim5J6tu3r/r27VvstmXLlhVpu++++3TfffeVc1QAAAAAgIrM8td0AwAAAADgqSi6AQAAAAAoJxTdAAAAAACUE4puAAAAAADKCUU3AAAAAADlhKIbAAAAAIBy4jW3DHM3wzAkSVlZWSZH4p3sdruys7MVFBTkco91eDbyZk3kzbrInTWRN2sib9ZF7qzJCnkrrAULa8Nzoei+SNnZ2ZKk+Ph4kyMBAAAAAJglOztb4eHh59xuMy5UlqNYdrtdv//+uypXriybzWZ2OF4nKytL8fHx+t///qewsDCzw0EJkTdrIm/WRe6sibxZE3mzLnJnTVbIm2EYys7OVrVq1c57Np4z3RfJx8dHl19+udlheL2wsDCP/ZLh3MibNZE36yJ31kTerIm8WRe5syZPz9v5znAX8szB8QAAAAAAeAGKbgAAAAAAyglFNzxSYGCghg8frsDAQLNDQSmQN2sib9ZF7qyJvFkTebMucmdN3pQ3JlIDAAAAAKCccKYbAAAAAIByQtENAAAAAEA5oegGAAAAAKCcUHQDAAAAAFBOKLphitzcXDVr1kw2m02bN2922bZ161bdcMMNCgoKUnx8vF599dUi+3/yySdq2LChgoKC1LhxYy1cuNBlu2EYGjZsmKpWrarg4GC1bdtWP//8c3m+Ja/WsWNH1ahRQ0FBQapataoeeeQR/f777y59yJtn2bNnj3r16qXatWsrODhYdevW1fDhw5WXl+fSj7x5ppEjR6p169YKCQlRREREsX327t2ru+++WyEhIYqJidHzzz+v06dPu/RZtmyZrrnmGgUGBqpevXqaPn16kdeZNGmSatWqpaCgILVq1Urr1q0rh3eEQnze5lqxYoU6dOigatWqyWazad68eS7bS/J7duTIET300EMKCwtTRESEevXqpePHj7v0KclvK0pu9OjRuvbaa1W5cmXFxMSoc+fO2rlzp0ufU6dO6ZlnntFll12m0NBQ3XPPPcrIyHDpU1a/myi5t99+W02aNFFYWJjCwsKUmJioL7/80rm9wuTNAEzQr18/48477zQkGZs2bXK2Z2ZmGrGxscZDDz1kbNu2zfj444+N4OBg49///rezz6pVqwxfX1/j1VdfNX788UdjyJAhhr+/v/H99987+4wZM8YIDw835s2bZ2zZssXo2LGjUbt2bePkyZPufJteY+zYsUZaWpqxZ88eY9WqVUZiYqKRmJjo3E7ePM+XX35pPProo8ZXX31l/PLLL8b8+fONmJgYY8CAAc4+5M1zDRs2zBg7dqyRnJxshIeHF9l++vRp4+qrrzbatm1rbNq0yVi4cKERFRVlpKSkOPv8+uuvRkhIiJGcnGz8+OOPxoQJEwxfX19j0aJFzj4zZ840AgICjKlTpxo//PCD0bt3byMiIsLIyMhwx9uscPi8zbdw4ULjxRdfNObMmWNIMubOneuyvSS/Z+3atTOaNm1qrFmzxli5cqVRr149o1u3bs7tJfltRekkJSUZ06ZNM7Zt22Zs3rzZuOuuu4waNWoYx48fd/bp06ePER8fb6Smphrr1683rrvuOqN169bO7WX1u4nS+eyzz4wFCxYYP/30k7Fz507jhRdeMPz9/Y1t27YZhlFx8kbRDbdbuHCh0bBhQ+OHH34oUnS/9dZbRmRkpJGbm+tsGzRokNGgQQPn+v3332/cfffdLq/ZqlUr48knnzQMwzDsdrsRFxdnvPbaa87tx44dMwIDA42PP/64nN5VxTJ//nzDZrMZeXl5hmGQN6t49dVXjdq1azvXyZvnmzZtWrFF98KFCw0fHx8jPT3d2fb2228bYWFhznz+4x//MK666iqX/bp27WokJSU511u2bGk888wzzvWCggKjWrVqxujRo8v4ncAw+Lw9zV+L7pL8nv3444+GJOO7775z9vnyyy8Nm81m7N+/3zCMkv224tIcPHjQkGQsX77cMAxHnvz9/Y1PPvnE2Wf79u2GJCMtLc0wjLL73cSli4yMNKZMmVKh8sbwcrhVRkaGevfurQ8++EAhISFFtqelpenGG29UQECAsy0pKUk7d+7U0aNHnX3atm3rsl9SUpLS0tIkSbt371Z6erpLn/DwcLVq1crZBxfvyJEj+vDDD9W6dWv5+/tLIm9WkZmZqSpVqjjXyZt1paWlqXHjxoqNjXW2JSUlKSsrSz/88IOzz/lyl5eXpw0bNrj08fHxUdu2bcldOeDz9nwl+T1LS0tTRESEWrRo4ezTtm1b+fj4aO3atc4+F/ptxaXJzMyUJOffaRs2bFB+fr5L7ho2bKgaNWq45O5SfzdxaQoKCjRz5kydOHFCiYmJFSpvFN1wG8Mw9Oijj6pPnz4uf1mdLT093eVLJcm5np6eft4+Z28/e7/i+qD0Bg0apEqVKumyyy7T3r17NX/+fOc28ub5du3apQkTJujJJ590tpE367qU3GVlZenkyZM6fPiwCgoKyJ2b8Hl7vpL8nqWnpysmJsZlu5+fn6pUqXLB797Zx8DFs9vt+vvf/642bdro6quvluT4XAMCAorMgfHX3F3q7yYuzvfff6/Q0FAFBgaqT58+mjt3rho1alSh8kbRjUs2ePBg2Wy28z527NihCRMmKDs7WykpKWaHDJU8b4Wef/55bdq0SYsXL5avr6+6d+8uwzBMfAcVU2nzJkn79+9Xu3btdN9996l3794mRY6LyR0AwNUzzzyjbdu2aebMmWaHghJq0KCBNm/erLVr1+qpp55Sjx499OOPP5odllv5mR0ArG/AgAF69NFHz9unTp06Wrp0qdLS0hQYGOiyrUWLFnrooYf0/vvvKy4ursiMhYXrcXFxzufi+py9vbCtatWqLn2aNWtW6vfnrUqat0JRUVGKiorSFVdcoSuvvFLx8fFas2aNEhMTyZsblTZvv//+u2655Ra1bt1a7777rks/8uZepc3d+cTFxRWZ9bqkuQsLC1NwcLB8fX3l6+t73vyi7ERFRfF5e7iS/J7FxcXp4MGDLvudPn1aR44cueB37+xj4OL07dtXX3zxhVasWKHLL7/c2R4XF6e8vDwdO3bM5azpX/++utTfTVycgIAA1atXT5KUkJCg7777Tm+88Ya6du1aYfLGmW5csujoaDVs2PC8j4CAAL355pvasmWLNm/erM2bNztvOzRr1iyNHDlSkpSYmKgVK1YoPz/f+fpLlixRgwYNFBkZ6eyTmprqEsOSJUuUmJgoSapdu7bi4uJc+mRlZWnt2rXOPih53opjt9slOW79JpE3dypN3vbv36+bb75ZCQkJmjZtmnx8XH/yyZt7Xcp37q8SExP1/fffu/zjf8mSJQoLC1OjRo2cfc6Xu4CAACUkJLj0sdvtSk1NJXflgM/b85Xk9ywxMVHHjh3Thg0bnH2WLl0qu92uVq1aOftc6LcVpWMYhvr27au5c+dq6dKlql27tsv2hIQE+fv7u+Ru586d2rt3r0vuLvV3E2XDbrcrNze3YuXN5IncUIHt3r27yOzlx44dM2JjY41HHnnE2LZtmzFz5kwjJCSkyC2M/Pz8jNdff93Yvn27MXz48GJvYRQREWHMnz/f2Lp1q9GpUyduYXSR1qxZY0yYMMHYtGmTsWfPHiM1NdVo3bq1UbduXePUqVOGYZA3T7Rv3z6jXr16xm233Wbs27fPOHDggPNRiLx5rt9++83YtGmTMWLECCM0NNTYtGmTsWnTJiM7O9swjDO3ULnjjjuMzZs3G4sWLTKio6OLvYXK888/b2zfvt2YNGlSsbcMCwwMNKZPn278+OOPxhNPPGFERES4zBKLssPnbb7s7Gzn90mSMXbsWGPTpk3Gb7/9ZhhGyX7P2rVrZzRv3txYu3at8e233xr169d3uWVYSX5bUTpPPfWUER4ebixbtszl77OcnBxnnz59+hg1atQwli5daqxfv77I7U3L6ncTpTN48GBj+fLlxu7du42tW7cagwcPNmw2m7F48WLDMCpO3ii6YZriim7DMIwtW7YY119/vREYGGhUr17dGDNmTJF9Z8+ebVxxxRVGQECAcdVVVxkLFixw2W63242hQ4casbGxRmBgoHHbbbcZO3fuLM+347W2bt1q3HLLLUaVKlWMwMBAo1atWkafPn2Mffv2ufQjb55l2rRphqRiH2cjb56pR48exebum2++cfbZs2ePceeddxrBwcFGVFSUMWDAACM/P9/ldb755hujWbNmRkBAgFGnTh1j2rRpRY41YcIEo0aNGkZAQIDRsmVLY82aNeX87io2Pm9zffPNN8V+t3r06GEYRsl+z/744w+jW7duRmhoqBEWFmb07NnT+R9ihUry24qSO9ffZ2f/pp08edJ4+umnjcjISCMkJMTo0qWLy380G0bZ/W6i5B577DGjZs2aRkBAgBEdHW3cdtttzoLbMCpO3myGwUxIAAAAAACUB67pBgAAAACgnFB0AwAAAABQTii6AQAAAAAoJxTdAAAAAACUE4puAAAAAADKCUU3AAAAAADlhKIbAAAAAIByQtENAAAAAEA5oegGAAClcvPNN+vvf/+72WEAAGAJFN0AAFQgHTp0ULt27YrdtnLlStlsNm3dutXNUQEA4L0ougEAqEB69eqlJUuWaN++fUW2TZs2TS1atFCTJk1MiAwAAO9E0Q0AQAXSvn17RUdHa/r06S7tx48f1yeffKLOnTurW7duql69ukJCQtS4cWN9/PHH531Nm82mefPmubRFRES4HON///uf7r//fkVERKhKlSrq1KmT9uzZUzZvCgAAD0bRDQBABeLn56fu3btr+vTpMgzD2f7JJ5+ooKBADz/8sBISErRgwQJt27ZNTzzxhB555BGtW7fuoo+Zn5+vpKQkVa5cWStXrtSqVasUGhqqdu3aKS8vryzeFgAAHouiGwCACuaxxx7TL7/8ouXLlzvbpk2bpnvuuUc1a9bUwIED1axZM9WpU0fPPvus2rVrp9mzZ1/08WbNmiW73a4pU6aocePGuvLKKzVt2jTt3btXy5YtK4N3BACA56LoBgCggmnYsKFat26tqVOnSpJ27dqllStXqlevXiooKNDLL7+sxo0bq0qVKgoNDdVXX32lvXv3XvTxtmzZol27dqly5coKDQ1VaGioqlSpolOnTumXX34pq7cFAIBH8jM7AAAA4H69evXSs88+q0mTJmnatGmqW7eubrrpJr3yyit64403NH78eDVu3FiVKlXS3//+9/MOA7fZbC5D1SXHkPJCx48fV0JCgj788MMi+0ZHR5fdmwIAwANRdAMAUAHdf//9eu655/TRRx9pxowZeuqpp2Sz2bRq1Sp16tRJDz/8sCTJbrfrp59+UqNGjc75WtHR0Tpw4IBz/eeff1ZOTo5z/ZprrtGsWbMUExOjsLCw8ntTAAB4IIaXAwBQAYWGhqpr165KSUnRgQMH9Oijj0qS6tevryVLlmj16tXavn27nnzySWVkZJz3tW699VZNnDhRmzZt0vr169WnTx/5+/s7tz/00EOKiopSp06dtHLlSu3evVvLli1Tv379ir11GQAA3oSiGwCACqpXr146evSokpKSVK1aNUnSkCFDdM011ygpKUk333yz4uLi1Llz5/O+zr/+9S/Fx8frhhtu0IMPPqiBAwcqJCTEuT0kJEQrVqxQjRo19Le//U1XXnmlevXqpVOnTnHmGwDg9WzGXy/CAgAAAAAAZYIz3QAAAAAAlBOKbgAAAAAAyglFNwAAAAAA5YSiGwAAAACAckLRDQAAAABAOaHoBgAAAACgnFB0AwAAAABQTii6AQAAAAAoJxTdAAAAAACUE4puAAAAAADKCUU3AAAAAADl5P8BN5crO0oth6EAAAAASUVORK5CYII=",
|
||
"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: [-484.84, 424.44]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 85.0%:\n",
|
||
"Range: [-636.39, 575.99]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 90.0%:\n",
|
||
"Range: [-787.93, 575.99]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-939.48, 879.08]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-1545.67, 1333.72]\n",
|
||
"\n",
|
||
"Analisi per max_oil_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -23.877\n",
|
||
"variance: 258261.500\n",
|
||
"std: 508.194\n",
|
||
"min: -6101.242\n",
|
||
"max: 3861.077\n",
|
||
"median: -9.072\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: [-621.97, 573.51]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 85.0%:\n",
|
||
"Range: [-621.97, 573.51]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 90.0%:\n",
|
||
"Range: [-821.21, 772.76]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-1219.71, 972.00]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-1817.44, 1569.74]\n",
|
||
"\n",
|
||
"Analisi per avg_oil_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -22.163\n",
|
||
"variance: 191787.047\n",
|
||
"std: 437.935\n",
|
||
"min: -4815.025\n",
|
||
"max: 3367.829\n",
|
||
"median: -10.699\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: [-478.11, 503.83]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 85.0%:\n",
|
||
"Range: [-641.77, 503.83]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 90.0%:\n",
|
||
"Range: [-805.43, 667.49]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-969.08, 831.14]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-1623.71, 1322.12]\n",
|
||
"\n",
|
||
"Analisi per total_water_need\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: 194.517\n",
|
||
"variance: 3724558.250\n",
|
||
"std: 1929.911\n",
|
||
"min: -23049.143\n",
|
||
"max: 13846.180\n",
|
||
"median: 267.719\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: [-2018.81, 2408.63]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 85.0%:\n",
|
||
"Range: [-2018.81, 2408.63]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 90.0%:\n",
|
||
"Range: [-2756.72, 3146.54]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-4232.53, 3884.44]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-7184.15, 5360.26]\n",
|
||
"\n",
|
||
"2. IMPORTANZA DELLE FEATURE\n",
|
||
"--------------------------------------------------\n",
|
||
"18375/18375 [==============================] - 76s 4ms/step\n",
|
||
"18375/18375 [==============================] - 74s 4ms/step\n",
|
||
"18375/18375 [==============================] - 75s 4ms/step\n",
|
||
"18375/18375 [==============================] - 80s 4ms/step\n",
|
||
"18375/18375 [==============================] - 75s 4ms/step\n",
|
||
"\n",
|
||
"Importanza relativa delle feature:\n",
|
||
"ha: 0.8955\n",
|
||
"precip_sum: 0.0472\n",
|
||
"temp_mean: 0.0290\n",
|
||
"solar_energy_sum: 0.0284\n",
|
||
"\n",
|
||
"3. ANALISI DISTRIBUZIONALE\n",
|
||
"--------------------------------------------------\n",
|
||
"18375/18375 [==============================] - 74s 4ms/step\n",
|
||
"\n",
|
||
"Analisi distribuzionale per olive_prod\n",
|
||
"\n",
|
||
"Statistiche Predizioni:\n",
|
||
"mean: 29841.928\n",
|
||
"variance: 262459312.000\n",
|
||
"std: 16200.596\n",
|
||
"min: 3735.242\n",
|
||
"max: 92215.945\n",
|
||
"median: 28023.080\n",
|
||
"\n",
|
||
"Statistiche Target Reali:\n",
|
||
"mean: 29859.352\n",
|
||
"variance: 270111936.000\n",
|
||
"std: 16435.082\n",
|
||
"min: 2191.779\n",
|
||
"max: 98752.773\n",
|
||
"median: 27918.707\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: 5897.439\n",
|
||
"variance: 11426750.000\n",
|
||
"std: 3380.348\n",
|
||
"min: 712.364\n",
|
||
"max: 20253.113\n",
|
||
"median: 5420.939\n",
|
||
"\n",
|
||
"Statistiche Target Reali:\n",
|
||
"mean: 5917.830\n",
|
||
"variance: 11648225.000\n",
|
||
"std: 3412.950\n",
|
||
"min: 395.257\n",
|
||
"max: 22385.047\n",
|
||
"median: 5423.035\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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4ONhulLs4zZ8/X6dPn7bNX/i+cSU1atTQtm3bZLVa7Ua7t2/fblvuCH///bfdKPbOnTtltVpz3PzuQiEhIfLz88sz725ubrY/RNWoUSPH/wlSwd+TAIC8cU03ADjQ119/reeff15RUVH5FkdHjx7N0ZY9epn9qKvsoii3IrgoPvjgA7vrzJcsWaKDBw/a7gQuZRWRP/74o9LT021ty5cvz/FosdWrV2vMmDF6+umn1a1btwLtPyYmRl5eXnrrrbfsRhvff/99JScnq1OnToU+pp49e2r//v169913cyw7ffq0Tp06VehtFnb/8fHxWrVqVY5lx48fV0ZGhiSpY8eOysjI0PTp023LMzMz9fbbb190HwcPHlTPnj3VqlUrvfbaawWKq3LlymratKnmzp1r9/757bff9NVXX6ljx44F2k5RXH/99YqJibFNrlx0d+zYUQkJCVq0aJGtLSMjQ2+//bbKly+vNm3aOGS/U6dOtZvPfp+c/1nNjbu7u2655RZ99tlndqeiHzp0SAsWLFCrVq1sZ6R07NhRP/74ozZs2GDrl5SUpPnz5xfTUQDA5YuRbgAoJv/73/+0fft2ZWRk6NChQ/r6668VFxenGjVq6PPPP5ePj0+e6z733HP69ttv1alTJ9WoUUOJiYmaNm2aqlatqlatWknKKoADAwM1Y8YMVahQQeXKlVOLFi1yvba4ICpVqqRWrVppwIABOnTokCZPnqzatWvbPdbsvvvu05IlS9S+fXv17NlTu3bt0rx581SrVi27bfXp00chISGqU6eO3fPIJenmm2/O9fFlISEhGjVqlMaPH6/27durS5cu2rFjh6ZNm6Zrr73W7qZpBXXPPffo448/1v3336+1a9fq+uuvV2ZmprZv366PP/7Y9lxtR3niiSf0+eefq3Pnzurfv7+aNWumU6dO6ddff9WSJUu0Z88eBQcH69Zbb9X111+vkSNHas+ePbriiiv06aefKjk5+aL7GDZsmJKSkvTkk09q4cKFdsuuvPJKuzMVzvfaa6+pQ4cOio6O1sCBA22PDAsICLjk50lfLgYPHqx33nlH/fv31+bNmxUZGaklS5bohx9+0OTJk+1u8lacdu/erS5duqh9+/aKj4/XvHnzdOedd6pJkyYXXfeFF15QXFycWrVqpQcffFAeHh565513lJaWZvec+yeffFIffvih2rdvr+HDh9seGZY9ug8AKDqKbgAoJs8++6ykrBuBVapUSY0bN9bkyZM1YMCAi34Z79Kli/bs2aNZs2bp8OHDCg4OVps2bTR+/HjbzaI8PT01d+5cjRo1Svfff78yMjI0e/bsIhfdo0eP1rZt2zRhwgSdOHFC7dq107Rp0+Tn52frExsbqzfeeEMTJ07UI488omuuuUbLly/XY489Zretw4cPS1Kuz5leu3Ztns8MHzdunEJCQjRlyhQ9+uijqlSpkgYPHqyXXnqpwM9OPp+bm5uWLVumSZMm6YMPPtDSpUvl5+enmjVravjw4cV6TXVu/Pz89M033+ill17S4sWL9cEHH8jf319169a1y6Wbm5s+//xzPfLII5o3b54sFou6dOmiN954Q1dddVW++8i+MdmIESNyLBs7dmyeRXdMTIxWrlypsWPH6tlnn5Wnp6fatGmjV155pcjvocuNr6+v1q1bp5EjR2ru3LlKSUlRvXr1NHv2bPXv399h+120aJGeffZZjRw5Uh4eHho6dGiBz3Jo2LChvvvuO40aNUoTJkyQ1WpVixYtNG/ePLsbp1WuXFlr167Vww8/rJdffllBQUG6//77FRERoYEDBzrq0ADgsmAxrngHEQAAgDJu3LhxGj9+vJKSkhQcHOzscAAARcQ13QAAAAAAOAinlwMAgMtaenp6rjczPF9AQECxPZLr5MmTOnnyZL598nrWOwDA9VB0AwCAy9r69et144035tunOK/bfv311zV+/Ph8++zevbtY9gUAcD6u6QYAAJe1Y8eOafPmzfn2adiwoSpXrlws+/vnn3/0zz//5NunVatW+T7xAADgOii6AQAAAABwEG6kBgAAAACAg1B0AwAAAADgIBTdAAAAAAA4CEU3AAAAAAAOQtENAAAAAICDUHQDAAAAAOAgFN0AAAAAADgIRTcAAAAAAA5C0Q0AAAAAgINQdAMAAAAA4CAU3QAAAAAAOAhFNwAAAAAADkLRDQAAAACAg1B0AwAAAADgIBTdAAAAAAA4CEU3AAAAAAAOQtENAAAAAICDUHQDAAAAAOAgFN0AAAAAADgIRTcAAAAAAA5C0Q0AAAAAgINQdAMAAAAA4CAU3QAAAAAAOAhFNwAAAAAADkLRDQAAAACAg1B0AwAAAADgIBTdAAAAAAA4CEU3AAAAAAAOQtENAAAAAICDUHQDAAAAAOAgFN0AAAAAADgIRTcAAAAAAA5C0Q0AAAAAgINQdAMAAAAA4CAU3QAAAAAAOAhFNwAAAAAADkLRDQAAAACAg1B0AwAAAADgIBTdAAAAAAA4CEU3AAAAAAAOQtENAAAAAICDUHQDAAAAAOAgFN0AAAAAADgIRTcAAAAAAA5C0Q0AuCxZLBYNHTq02LY3Z84cWSwWbdq06aJ927Ztq7Zt29rm9+zZI4vFojlz5tjaxo0bJ4vFUmzxofS4MP8AgLKNohsAUGpkF67Zk4+Pj+rWrauhQ4fq0KFDzg7P6V566SUtW7asWLe5bt062+s9b968XPtcf/31slgsatSoUbHuuzic/345fwoPD3dqXH/88YfGjRunPXv2ODUOAIDzeTg7AAAALvTcc88pKipKZ86c0ffff6/p06drxYoV+u233+Tn5+fs8C7ZV199ddE+Y8aM0ciRI+3aXnrpJd1xxx3q1q1bscfk4+OjBQsW6O6777Zr37Nnj9avXy8fH59i32dxufnmm9W3b1+7Nl9fXydFk+WPP/7Q+PHj1bZtW0VGRtotK0j+AQBlB0U3AKDU6dChg6655hpJ0n333aegoCBNnDhRn332mfr06ZPrOqdOnVK5cuVKMswi8/LyumgfDw8PeXiU3K/pjh076vPPP9fhw4cVHBxsa1+wYIHCwsJUp04dHTt2rMTiKYy6devm+GNBaVaQ/AMAyg5OLwcAlHo33XSTJGn37t2SpP79+6t8+fLatWuXOnbsqAoVKuiuu+6SlFV8P/bYY6pWrZq8vb1Vr149vf766zLG5Lrt+fPnq169evLx8VGzZs307bff2i3fu3evHnzwQdWrV0++vr4KCgpSjx498jxtODU1VUOGDFFQUJD8/f3Vt2/fHMVqQa7pvfCabovFolOnTmnu3Lm2U6j79++vtWvXymKxaOnSpTm2sWDBAlksFsXHx+e7L0nq2rWrvL29tXjx4hzb6Nmzp9zd3XOsM3v2bN10000KDQ2Vt7e3rrjiCk2fPj1Hv02bNik2NlbBwcHy9fVVVFSU7r33Xrs+CxcuVLNmzVShQgX5+/urcePGevPNNy8a98X0798/x0izlPs189nX+S9btkyNGjWSt7e3GjZsqJUrV+ZYf//+/Ro4cKAiIiLk7e2tqKgoPfDAA0pPT9ecOXPUo0cPSdKNN95oy9e6desk5Z7/xMREDRw4UGFhYfLx8VGTJk00d+5cuz7Z1/6//vrrmjlzpmrVqiVvb29de+212rhxY9FfJACAQzHSDQAo9Xbt2iVJCgoKsrVlZGQoNjZWrVq10uuvvy4/Pz8ZY9SlSxetXbtWAwcOVNOmTbVq1So98cQT2r9/vyZNmmS33W+++UaLFi3SsGHD5O3trWnTpql9+/basGGD7frljRs3av369erdu7eqVq2qPXv2aPr06Wrbtq3++OOPHKe7Dx06VIGBgRo3bpx27Nih6dOna+/evbZrp4vqww8/1H333afmzZtr8ODBkqRatWrpuuuuU7Vq1TR//nzddtttduvMnz9ftWrVUnR09EW37+fnp65du+qjjz7SAw88IEn65Zdf9Pvvv+u9997Ttm3bcqwzffp0NWzYUF26dJGHh4e++OILPfjgg7JarXrooYckZRWTt9xyi0JCQjRy5EgFBgZqz549+vTTT23biYuLU58+fdSuXTu98sorkqQ///xTP/zwg4YPH37R2M+cOaPDhw/btVWoUEHe3t4XXfdC33//vT799FM9+OCDqlChgt566y3dfvvt2rdvn+39d+DAATVv3lzHjx/X4MGDVb9+fe3fv19LlixRamqqWrdurWHDhumtt97S6NGj1aBBA0my/Xuh06dPq23bttq5c6eGDh2qqKgoLV68WP3799fx48dzvAYLFizQiRMnNGTIEFksFr366qvq3r27/vnnH3l6ehb6mAEADmYAACglZs+ebSSZ1atXm6SkJPPvv/+ahQsXmqCgIOPr62v+++8/Y4wx/fr1M5LMyJEj7dZftmyZkWReeOEFu/Y77rjDWCwWs3PnTlubJCPJbNq0yda2d+9e4+PjY2677TZbW2pqao444+PjjSTzwQcf5Ii9WbNmJj093db+6quvGknms88+s7W1adPGtGnTxja/e/duI8nMnj3b1jZ27Fhz4a/pcuXKmX79+uWIZ9SoUcbb29scP37c1paYmGg8PDzM2LFjc/Q/39q1a40ks3jxYrN8+XJjsVjMvn37jDHGPPHEE6ZmzZq2mBs2bGi3bm6vTWxsrG0dY4xZunSpkWQ2btyYZwzDhw83/v7+JiMjI99Yc5Odxwun7NeyX79+pkaNGjnWy+31lWS8vLzs3ie//PKLkWTefvttW1vfvn2Nm5tbrsdktVqNMcYsXrzYSDJr167N0efC/E+ePNlIMvPmzbO1paenm+joaFO+fHmTkpJijDn3PgkKCjJHjx619f3ss8+MJPPFF1/k/UIBAJyG08sBAKVOTEyMQkJCVK1aNfXu3Vvly5fX0qVLVaVKFbt+2SOy2VasWCF3d3cNGzbMrv2xxx6TMUb/+9//7Nqjo6PVrFkz23z16tXVtWtXrVq1SpmZmZLsb8h19uxZHTlyRLVr11ZgYKC2bNmSI/bBgwfbjTY+8MAD8vDw0IoVKwr5KhRc3759lZaWpiVLltjaFi1apIyMjEJd63zLLbeoUqVKWrhwoYwxWrhwYZ7X0Ev2r01ycrIOHz6sNm3a6J9//lFycrIkKTAwUJK0fPlynT17NtftBAYG6tSpU4qLiytwrOfr2rWr4uLi7KbY2NgibSsmJka1atWyzV955ZXy9/fXP//8I0myWq1atmyZbr31Vtt9B85XlLMZVqxYofDwcLvX2tPTU8OGDdPJkyf1zTff2PXv1auXKlasaJu/4YYbJMkWIwCgdOH0cgBAqTN16lTVrVtXHh4eCgsLU7169eTmZv93Yg8PD1WtWtWube/evYqIiFCFChXs2rNP6927d69de506dXLsu27dukpNTVVSUpLCw8N1+vRpTZgwQbNnz9b+/fvtrg3PLizz22b58uVVuXJlhz46qn79+rr22ms1f/58DRw4UFLWqeXXXXedateuXeDteHp6qkePHlqwYIGaN2+uf//9V3feeWee/X/44QeNHTtW8fHxSk1NtVuWnJysgIAAtWnTRrfffrvGjx+vSZMmqW3bturWrZvuvPNO2+nfDz74oD7++GN16NBBVapU0S233KKePXuqffv2BYq7atWqiomJKfBx5qd69eo52ipWrGi7Lj8pKUkpKSnF+vi0vXv3qk6dOjne43m9by+MMbsAL603ugOAyx0j3QCAUqd58+aKiYlR27Zt1aBBgxzFiCR5e3vn2l7cHn74Yb344ovq2bOnPv74Y3311VeKi4tTUFCQrFarw/dfUH379tU333yj//77T7t27dKPP/5YpDt633nnndq6davGjRunJk2a6Iorrsi1365du9SuXTsdPnxYEydO1Jdffqm4uDg9+uijkmR7bSwWi5YsWaL4+HgNHTpU+/fv17333qtmzZrp5MmTkqTQ0FBt3bpVn3/+ue2a/A4dOqhfv35FfDXOyWvkOftMhgvldsM4SXneiM8ZXCFGAMA5FN0AgDKjRo0aOnDggE6cOGHXvn37dtvy8/399985tvHXX3/Jz89PISEhkqQlS5aoX79+euONN3THHXfo5ptvVqtWrXT8+PFcY7hwmydPntTBgwdzvYN2YeV36nLv3r3l7u6ujz76SPPnz5enp6d69epV6H20atVK1atX17p16/Id5f7iiy+Ulpamzz//XEOGDFHHjh0VExOT5/Oxr7vuOr344ovatGmT5s+fr99//10LFy60Lffy8tKtt96qadOmadeuXRoyZIg++OAD7dy5s9DHcL6KFSvmmqsLR48LKiQkRP7+/vrtt9/y7VeY08xr1Kihv//+O8cfcfJ63wIAXAtFNwCgzOjYsaMyMzM1ZcoUu/ZJkybJYrGoQ4cOdu3x8fF212X/+++/+uyzz3TLLbfYRhPd3d1zjCC+/fbbeY6Uzpw50+7a5enTpysjIyPHvouiXLlyeRb7wcHB6tChg+bNm6f58+erffv2ds/bLiiLxaK33npLY8eO1T333JNnv+zX58LT7WfPnm3X79ixYzlev6ZNm0qS0tLSJElHjhyxW+7m5qYrr7zSrk9R1apVS8nJyXZ3Xz948GCuj1grCDc3N3Xr1k1ffPGFNm3alGN59rFmPzM+r3ydr2PHjkpISNCiRYtsbRkZGXr77bdVvnx5tWnTpkixAgBKB67pBgCUGbfeeqtuvPFGPf3009qzZ4+aNGmir776Sp999pkeeeQRuxtkSVKjRo0UGxtr98gwSRo/frytT+fOnfXhhx8qICBAV1xxheLj47V69Wq7x5edLz09Xe3atVPPnj21Y8cOTZs2Ta1atVKXLl0u+fiaNWum1atXa+LEiYqIiFBUVJRatGhhW963b1/dcccdkqTnn3++yPvp2rWrunbtmm+fW265xTY6PWTIEJ08eVLvvvuuQkNDdfDgQVu/uXPnatq0abrttttUq1YtnThxQu+++678/f3VsWNHSdJ9992no0eP6qabblLVqlW1d+9evf3222ratGmej9kqqN69e+upp57SbbfdpmHDhik1NVXTp09X3bp1c70RXkG89NJL+uqrr9SmTRsNHjxYDRo00MGDB7V48WJ9//33CgwMVNOmTeXu7q5XXnlFycnJ8vb2tj3T/EKDBw/WO++8o/79+2vz5s2KjIzUkiVL9MMPP2jy5Mk57lEAAHAtFN0AgDLDzc1Nn3/+uZ599lktWrRIs2fPVmRkpF577TU99thjOfq3adNG0dHRGj9+vPbt26crrrhCc+bMsY2yStKbb74pd3d3zZ8/X2fOnNH111+v1atX53l37ClTpmj+/Pl69tlndfbsWfXp00dvvfXWJT2jO9vEiRM1ePBgjRkzRqdPn1a/fv3siu5bb71VFStWlNVqLZYiPz/16tXTkiVLNGbMGD3++OMKDw/XAw88oJCQEN177722fm3atNGGDRu0cOFCHTp0SAEBAWrevLnmz5+vqKgoSdLdd9+tmTNnatq0aTp+/LjCw8PVq1cvjRs37pKv2w8KCtLSpUs1YsQIPfnkk4qKitKECRP0999/F7norlKlin766Sc988wzmj9/vlJSUlSlShV16NDB9tz28PBwzZgxQxMmTNDAgQOVmZmptWvX5lp0+/r6at26dRo5cqTmzp2rlJQU1atXT7Nnz1b//v0v5fABAKWAxXDXDQAAyoSMjAxFRETo1ltv1fvvv+/scAAAgLimGwCAMmPZsmVKSkpS3759nR0KAAD4f4x0AwDg4n766Sdt27ZNzz//vIKDg4t82jQAACh+jHQDAODipk+frgceeEChoaH64IMPnB0OAAA4DyPdAAAAAAA4CCPdAAAAAAA4CEU3AAAAAAAOwnO6c2G1WnXgwAFVqFChWJ6rCgAAAAAoW4wxOnHihCIiIuTmlvd4NkV3Lg4cOKBq1ao5OwwAAAAAQCn377//qmrVqnkup+jORYUKFSRlvXj+/v759rVarUpKSlJISEi+f92Ac5En10CeXAN5cg3kqfQjR66BPLkG8uQaylqeUlJSVK1aNVv9mBeK7lxkn1Lu7+9foKL7zJkz8vf3LxNvnLKKPLkG8uQayJNrIE+lHzlyDeTJNZAn11BW83SxS5LLzpECAAAAAFDKUHQDAAAAAOAgpaLonjp1qiIjI+Xj46MWLVpow4YN+fZfvHix6tevLx8fHzVu3FgrVqywW96/f39ZLBa7qX379o48BAAAAAAAcnD6Nd2LFi3SiBEjNGPGDLVo0UKTJ09WbGysduzYodDQ0Bz9169frz59+mjChAnq3LmzFixYoG7dumnLli1q1KiRrV/79u01e/Zs27y3t3eJHA8AAAAAlFaZmZk6e/asU/ZttVp19uxZnTlzxiWu6XZ3d5eHh8clP0ba6UX3xIkTNWjQIA0YMECSNGPGDH355ZeaNWuWRo4cmaP/m2++qfbt2+uJJ56QJD3//POKi4vTlClTNGPGDFs/b29vhYeHl8xBAAAAAEApd/LkSf33338yxjhl/8YYWa1WnThx4pIL2ZLi5+enypUry8vLq8jbcGrRnZ6ers2bN2vUqFG2Njc3N8XExCg+Pj7XdeLj4zVixAi7ttjYWC1btsyubd26dQoNDVXFihV100036YUXXlBQUFCu20xLS1NaWpptPiUlRVLWX2KsVmu+x2C1Wm1vHpRe5Mk1kCfXQJ5cA3kq/ciRayBProE8XVxmZqb+++8/+fn5KTg42GlF79mzZ+Xp6emUfReGMUZnz55VUlKS/vnnH9WuXTvH6HxB329OLboPHz6szMxMhYWF2bWHhYVp+/btua6TkJCQa/+EhATbfPv27dW9e3dFRUVp165dGj16tDp06KD4+Hi5u7vn2OaECRM0fvz4HO1JSUk6c+ZMvsdgtVqVnJwsY4xLnCJxuSJProE8uQby5BrIU+lHjlwDeXIN5Onizp49q4yMDAUGBjqt6M0eYXd3d3eJkW5PT0+FhoZq3759SkhIkIeHffl84sSJAm3H6aeXO0Lv3r1tPzdu3FhXXnmlatWqpXXr1qldu3Y5+o8aNcpu9Dz7IechISEFek63xWIpMw94L6vIk2sgT66BPLkG8lT6kSPXQJ5cA3m6uDNnzujEiRPy9PTMUTyWNFcY6c7m6ekpNzc3VapUST4+PnbLLpzPi1Nf7eDgYLm7u+vQoUN27YcOHcrzeuzw8PBC9ZekmjVrKjg4WDt37sy16Pb29s71Rmtubm4F+tBaLJYC94XzkCfXQJ5cA3lyDeSp9CNHroE8uQbylD83Nze7pzs5gzHGtm9XGOmWZHu9cntvFfS95tR3pJeXl5o1a6Y1a9bY2qxWq9asWaPo6Ohc14mOjrbrL0lxcXF59pek//77T0eOHFHlypWLJ3AAAAAAAArA6aeXjxgxQv369dM111yj5s2ba/LkyTp16pTtbuZ9+/ZVlSpVNGHCBEnS8OHD1aZNG73xxhvq1KmTFi5cqE2bNmnmzJmSsu7IN378eN1+++0KDw/Xrl279OSTT6p27dqKjY112nECAAAAQGkzKe6vEtyb0dC2NUtwf6WD04vuXr16KSkpSc8++6wSEhLUtGlTrVy50naztH379tkN27ds2VILFizQmDFjNHr0aNWpU0fLli2zPaPb3d1d27Zt09y5c3X8+HFFRETolltu0fPPP8+zuh2gOD6kj95ctxgiAQAAAFDW9O/fX3PnzpWUdX119erV1bdvX40ePVrff/+9brzxRgUGBurgwYN211hv3LhRzZs3l3TuBm7r1q3TjTfemGMfTz/9tF544QWHHYPTi25JGjp0qIYOHZrrsnXr1uVo69Gjh3r06JFrf19fX61atao4wwMAAAAAOEn79u01e/ZspaWlacWKFXrooYfk6elpu8S4QoUKWrp0qfr06WNb5/3331f16tW1b9++HNvbsWOH3Q2zy5cv79D4ucsAAAAAAKDU8vb2Vnh4uGrUqKEHHnhAMTEx+vzzz23L+/Xrp1mzZtnmT58+rYULF6pfv365bi80NFTh4eG2iaIbAAAAAID/5+vrq/T0dNv8Pffco++++842qv3JJ58oMjJSV199tbNCtEPRDQAAAAAo9YwxWr16tVatWqWbbrrJ1h4aGqoOHTpozpw5kqRZs2bp3nvvzXM7VatWVfny5W3TkSNHHBp3qbimGwAAAACA3Cxfvlzly5fX2bNnZbVadeedd2rcuHHauHGjrc+9996r4cOH6+6771Z8fLwWL16s7777Ltftfffdd6pQoYJtvmLFig6Nn6IbAAAAAFBq3XjjjZo+fbq8vLwUEREhD4+cZWyHDh00ePBgDRw4ULfeequCgoLy3F5UVJQCAwMdGLE9im4AAAAAQKlVrlw51a5dO98+Hh4e6tu3r1599VX973//K6HICoZrugEAAAAALu/5559XUlKSYmNjnR2KHUa6AQAAAOAy9ejNdUtsX8YYZWRkOGz7Xl5eCg4Odtj2i4qiGwAAAABQKmXfkTw3bdu2lTEmz+XdunWzW36x/o7C6eUAAAAAADgIRTcAAAAAAA5C0Q0AAAAAgINQdAMAAAAA4CAU3QAAAABwmXDGjcRcWXG8XhTdAAAAAFDGubu7S5LS09OdHIlrSU1NlSR5enoWeRs8MgwAAAAAyjgPDw/5+fkpKSlJnp6ecnMr+fHX7Od0e3h4yGKxlPj+C8MYo9TUVCUmJiowMND2R4uioOgGAAAAgDLOYrGocuXK2r17t/bu3euUGIwxslqtcnNzK/VFd7bAwECFh4df0jYoui9zk+L+cnYIAAAAAEqAl5eX6tSp47RTzK1Wq44cOaKgoCCnjLQXlqen5yWNcGej6AYAAACAy4Sbm5t8fHycsm+r1SpPT0/5+Pi4RNFdXCi64XTFMdr+6M11iyESAAAAAChel8+fFwAAAAAAKGEU3QAAAAAAOAhFNwAAAAAADkLRDQAAAACAg1B0AwAAAADgIBTdAAAAAAA4CEU3AAAAAAAOwnO6gVKC55UDAAAAZQ9FN8qEixasxsgv86RS3ZMli8UhMVDwAgAAALgQRTdQTIpjpBoAAABA2ULRDZQhl1r4M1oPAAAAFC9upAYAAAAAgINQdAMAAAAA4CAU3QAAAAAAOAhFNwAAAAAADkLRDQAAAACAg3D3cgA2xfHYM+6ADgAAAJzDSDcAAAAAAA7CSDeAYnVJo+XGqM+VAcUXDAAAAOBkFN0ASpXPft6vVPdkyWIp8jY4xR0AAAClBaeXAwAAAADgIIx0AyhzLvWGcIyUAwAAoLgw0g0AAAAAgINQdAMAAAAA4CAU3QAAAAAAOAjXdAPABS71mnCJ68IBAACQhZFuAAAAAAAchKIbAAAAAAAHKRVF99SpUxUZGSkfHx+1aNFCGzZsyLf/4sWLVb9+ffn4+Khx48ZasWJFnn3vv/9+WSwWTZ48uZijBgAAAAAgf04vuhctWqQRI0Zo7Nix2rJli5o0aaLY2FglJibm2n/9+vXq06ePBg4cqJ9//lndunVTt27d9Ntvv+Xou3TpUv3444+KiIhw9GEAAAAAAJCD04vuiRMnatCgQRowYICuuOIKzZgxQ35+fpo1a1au/d988021b99eTzzxhBo0aKDnn39eV199taZMmWLXb//+/Xr44Yc1f/58eXp6lsShAAAAAABgx6l3L09PT9fmzZs1atQoW5ubm5tiYmIUHx+f6zrx8fEaMWKEXVtsbKyWLVtmm7darbrnnnv0xBNPqGHDhheNIy0tTWlpabb5lJQU23asVmu+61qtVhljLtqv1DLG2RGUDGPOTSi9ylCeXPb/hAJw+f/3LhPkqfQjR66BPLkG8uQaylqeCnocTi26Dx8+rMzMTIWFhdm1h4WFafv27bmuk5CQkGv/hIQE2/wrr7wiDw8PDRs2rEBxTJgwQePHj8/RnpSUpDNnzuS7rtVqVXJysowxcnNz+okDheaXedLZIZQQI29zRrJKksXZwSBPZSdPeV0iUxa4+v97lwvyVPqRI9dAnlwDeXINZS1PJ06cKFC/Mvec7s2bN+vNN9/Uli1bZLEU7Ev7qFGj7EbPU1JSVK1aNYWEhMjf3z/fda1WqywWi0JCQlzyjZPqnuzsEEqGMZKRUt3KSwV8X8AJylCePtp26Z+t4TF1iiGS4ufq/+9dLshT6UeOXAN5cg3kyTWUtTz5+PgUqJ9Ti+7g4GC5u7vr0KFDdu2HDh1SeHh4ruuEh4fn2/+7775TYmKiqlevbluemZmpxx57TJMnT9aePXtybNPb21ve3t452t3c3Ar0ZrBYLAXuW+q4eGFTKBbLuQmlF3myKc3/p7j0/3uXEfJU+pEj10CeXAN5cg1lKU8FPQanHqmXl5eaNWumNWvW2NqsVqvWrFmj6OjoXNeJjo626y9JcXFxtv733HOPtm3bpq1bt9qmiIgIPfHEE1q1apXjDgYAAAAAgAs4/fTyESNGqF+/frrmmmvUvHlzTZ48WadOndKAAQMkSX379lWVKlU0YcIESdLw4cPVpk0bvfHGG+rUqZMWLlyoTZs2aebMmZKkoKAgBQUF2e3D09NT4eHhqlevXskeHAAAAADgsub0ortXr15KSkrSs88+q4SEBDVt2lQrV6603Sxt3759dsP2LVu21IIFCzRmzBiNHj1aderU0bJly9SoUSNnHQIAAAAAALlyetEtSUOHDtXQoUNzXbZu3bocbT169FCPHj0KvP3cruMGAAAAAMDRXP/qdQAAAAAASimKbgAAAAAAHISiGwAAAAAAB6HoBgAAAADAQSi6AQAAAABwEIpuAAAAAAAchKIbAAAAAAAHoegGAAAAAMBBKLoBAAAAAHAQD2cHAADI3aS4vy5p/UdvrltMkQAAAKCoGOkGAAAAAMBBKLoBAAAAAHAQim4AAAAAAByEohsAAAAAAAeh6AYAAAAAwEEougEAAAAAcBCKbgAAAAAAHISiGwAAAAAAB6HoBgAAAADAQSi6AQAAAABwEIpuAAAAAAAchKIbAAAAAAAHoegGAAAAAMBBKLoBAAAAAHAQim4AAAAAAByEohsAAAAAAAeh6AYAAAAAwEE8nB0AAMAxJsX9dcnbePTmusUQCQAAwOWLkW4AAAAAAByEohsAAAAAAAeh6AYAAAAAwEEougEAAAAAcBCKbgAAAAAAHISiGwAAAAAAB6HoBgAAAADAQSi6AQAAAABwEIpuAAAAAAAchKIbAAAAAAAHoegGAAAAAMBBKLoBAAAAAHAQim4AAAAAAByEohsAAAAAAAeh6AYAAAAAwEE8nB0AAKD0mhT3V85GY+SXeVKp7smSxZLv+o/eXNdBkQEAALiGIo10r127trjjAAAAAACgzClS0d2+fXvVqlVLL7zwgv7999/ijgkAAAAAgDKhSEX3/v37NXToUC1ZskQ1a9ZUbGysPv74Y6Wnpxd3fAAAAAAAuKwiFd3BwcF69NFHtXXrVv3000+qW7euHnzwQUVERGjYsGH65ZdfijtOAAAAAABcziXfvfzqq6/WqFGjNHToUJ08eVKzZs1Ss2bNdMMNN+j3338vjhgBAAAAAHBJRS66z549qyVLlqhjx46qUaOGVq1apSlTpujQoUPauXOnatSooR49ehRoW1OnTlVkZKR8fHzUokULbdiwId/+ixcvVv369eXj46PGjRtrxYoVdsvHjRun+vXrq1y5cqpYsaJiYmL0008/FfVQAQAAAAAokiIV3Q8//LAqV66sIUOGqG7duvr5558VHx+v++67T+XKlVNkZKRef/11bd++/aLbWrRokUaMGKGxY8dqy5YtatKkiWJjY5WYmJhr//Xr16tPnz4aOHCgfv75Z3Xr1k3dunXTb7/9ZutTt25dTZkyRb/++qu+//57RUZG6pZbblFSUlJRDhcAAAAAgCKxGGNMYVdq166d7rvvPnXv3l3e3t659snIyNAPP/ygNm3a5LutFi1a6Nprr9WUKVMkSVarVdWqVdPDDz+skSNH5ujfq1cvnTp1SsuXL7e1XXfddWratKlmzJiR6z5SUlIUEBCg1atXq127djmWp6WlKS0tza5/tWrVdOzYMfn7++cbv9VqVVJSkkJCQuTmdsln65e4N1f/7ewQSobtucLlL/pcYTgReXINhcjT8Jg6JRQULuTqv58uB+TINZAn10CeXENZy1NKSooqVqyo5OTkfOtGj6JsfOzYsWrZsqU8POxXz8jI0Pr169W6dWt5eHhctOBOT0/X5s2bNWrUKFubm5ubYmJiFB8fn+s68fHxGjFihF1bbGysli1bluc+Zs6cqYCAADVp0iTXPhMmTND48eNztCclJenMmTP5HoPValVycrKMMS75xvHLPOnsEEqIkbc5I1kliWKu9CJPrqHgecrrrCU4nqv/frockCPXQJ5cA3lyDWUtTydOnChQvyIV3TfeeKMOHjyo0NBQu/bk5GTdeOONyszMLNB2Dh8+rMzMTIWFhdm1h4WF5XlqekJCQq79ExIS7NqWL1+u3r17KzU1VZUrV1ZcXJyCg4Nz3eaoUaPsCvnske6QkJACjXRbLBaX/WtNqnuys0MoGcZIRkp1YwS1VCNPrqEQebrw9wRKjqv/frockCPXQJ5cA3lyDWUtTz4+PgXqV6Si2xgjSy5ftI4cOaJy5coVZZPF7sYbb9TWrVt1+PBhvfvuu+rZs6d++umnXL8Aent753qavJubW4HeDBaLpcB9S53LqbCxWM5NKL3Ik2soYJ7eXLPzknf16M11L3kblyuX/v10mSBHroE8uQby5BrKUp4KegyFKrq7d+8uKeuF6t+/v12hmpmZqW3btqlly5YF3l5wcLDc3d116NAhu/ZDhw4pPDw813XCw8ML1L9cuXKqXbu2ateureuuu0516tTR+++/b3cqOwAAAAAAjlSoPy8EBAQoICBAxhhVqFDBNh8QEKDw8HANHjxY8+bNK/D2vLy81KxZM61Zs8bWZrVatWbNGkVHR+e6TnR0tF1/SYqLi8uz//nbPf9maQAAAAAAOFqhRrpnz54tSYqMjNTjjz9eLKeSjxgxQv369dM111yj5s2ba/LkyTp16pQGDBggSerbt6+qVKmiCRMmSJKGDx+uNm3a6I033lCnTp20cOFCbdq0STNnzpQknTp1Si+++KK6dOmiypUr6/Dhw5o6dar2799f4OeGAwAAAABQHIp89/Li0qtXLyUlJenZZ59VQkKCmjZtqpUrV9pulrZv3z67c+VbtmypBQsWaMyYMRo9erTq1KmjZcuWqVGjRpIkd3d3bd++XXPnztXhw4cVFBSka6+9Vt99950aNmxYbHEDAAAAAHAxBS66r776aq1Zs0YVK1bUVVddleuN1LJt2bKlUEEMHTpUQ4cOzXXZunXrcrT16NEjz1FrHx8fffrpp4XaPwAAAAAAjlDgortr1662G6d169bNUfEAAAAAAFBmFLjoPv+U8uI8vRwAAAAAgLLK9R+OBgAAAABAKVXgke6KFSvmex33+Y4ePVrkgAAAAAAAKCsKXHRPnjzZgWEAAAAAAFD2FLjo7tevnyPjAAAgV5Pi/rqk9R+9uW4xRQIAAFB4BS66U1JS5O/vb/s5P9n9AAAAAAC4nBXqmu6DBw8qNDRUgYGBuV7fbYyRxWJRZmZmsQYJAAAAAIArKnDR/fXXX6tSpUqSpLVr1zosIAAAAAAAyooCF91t2rTJ9WcAAAAAAJC7AhfdFzp27Jjef/99/fnnn5KkK664QgMGDLCNhgMAAAAAcLlzK8pK3377rSIjI/XWW2/p2LFjOnbsmN566y1FRUXp22+/Le4YAQAAAABwSUUa6X7ooYfUq1cvTZ8+Xe7u7pKkzMxMPfjgg3rooYf066+/FmuQAAAAAAC4oiKNdO/cuVOPPfaYreCWJHd3d40YMUI7d+4stuAAAAAAAHBlRRrpvvrqq/Xnn3+qXr16du1//vmnmjRpUiyBAQBQHCbF/XXJ23j05rrFEAkAALgcFbjo3rZtm+3nYcOGafjw4dq5c6euu+46SdKPP/6oqVOn6uWXXy7+KAEAAAAAcEEFLrqbNm0qi8UiY4yt7cknn8zR784771SvXr2KJzoAAAAAAFxYgYvu3bt3OzIOAAAAAADKnAIX3TVq1HBkHAAAAAAAlDlFupFatj/++EP79u1Tenq6XXuXLl0uKSgAAAAAAMqCIhXd//zzj2677Tb9+uuvdtd5WywWSVnP7AYAAAAA4HJXpKJ7+PDhioqK0po1axQVFaUNGzboyJEjeuyxx/T6668Xd4wAADjVpT52jEeOAQBw+SpS0R0fH6+vv/5awcHBcnNzk5ubm1q1aqUJEyZo2LBh+vnnn4s7TgAAAAAAXI5bUVbKzMxUhQoVJEnBwcE6cOCApKybre3YsaP4ogMAAAAAwIUVaaS7UaNG+uWXXxQVFaUWLVro1VdflZeXl2bOnKmaNWsWd4wAAAAAALikIhXdY8aM0alTpyRJzz33nDp37qwbbrhBQUFBWrRoUbEGCAAAAACAqypS0R0bG2v7uXbt2tq+fbuOHj2qihUr2u5gDgAAAADA5e6SntMtSf/++68kqVq1apccDAAAAAAAZUmRiu6MjAyNHz9eb731lk6ePClJKl++vB5++GGNHTtWnp6exRokAACu7FIfOSbx2DEAAFxVkYruhx9+WJ9++qleffVVRUdHS8p6jNi4ceN05MgRTZ8+vViDBAAAAADAFRWp6F6wYIEWLlyoDh062NquvPJKVatWTX369KHoBgAAAABARXxOt7e3tyIjI3O0R0VFycvL61JjAgAAAACgTChS0T106FA9//zzSktLs7WlpaXpxRdf1NChQ4stOAAAAAAAXFmBTy/v3r273fzq1atVtWpVNWnSRJL0yy+/KD09Xe3atSveCAEAAAAAcFEFLroDAgLs5m+//Xa7eR4ZBgAAAACAvQIX3bNnz3ZkHAAAAAAAlDlFunt5tqSkJO3YsUOSVK9ePYWEhBRLUAAAAAAAlAVFupHaqVOndO+996py5cpq3bq1WrdurYiICA0cOFCpqanFHSMAAAAAAC6pSEX3iBEj9M033+iLL77Q8ePHdfz4cX322Wf65ptv9NhjjxV3jAAAAAAAuKQinV7+ySefaMmSJWrbtq2trWPHjvL19VXPnj01ffr04ooPAAAAAACXVaSR7tTUVIWFheVoDw0N5fRyAAAAAAD+X5FGuqOjozV27Fh98MEH8vHxkSSdPn1a48ePV3R0dLEGCAAApElxfxVuBWPkl3lSqe7JksWiR2+u65jAAABAvopUdE+ePFnt27dX1apV1aRJE0nSL7/8Ih8fH61atapYAwQAAAAAwFUVqehu3Lix/v77b82fP1/bt2+XJPXp00d33XWXfH19izVAAAAAAABcVaGL7rNnz6p+/fpavny5Bg0a5IiYAAAAAAAoEwp9IzVPT0+dOXPGEbEAAAAAAFCmFOnu5Q899JBeeeUVZWRkFHc8AAAAAACUGUW6pnvjxo1as2aNvvrqKzVu3FjlypWzW/7pp58WS3AAAAAAALiyIo10BwYG6vbbb1dsbKwiIiIUEBBgNxXW1KlTFRkZKR8fH7Vo0UIbNmzIt//ixYtVv359+fj4qHHjxlqxYoVt2dmzZ/XUU0/Z/hgQERGhvn376sCBA4WOCwAAAACAS1GokW6r1arXXntNf/31l9LT03XTTTdp3Lhxl3TH8kWLFmnEiBGaMWOGWrRoocmTJys2NlY7duxQaGhojv7r169Xnz59NGHCBHXu3FkLFixQt27dtGXLFjVq1EipqanasmWLnnnmGTVp0kTHjh3T8OHD1aVLF23atKnIcQIAAAAAUFgWY4wpaOfnn39e48aNU0xMjHx9fbVq1Sr16dNHs2bNKnIALVq00LXXXqspU6ZIyirsq1WrpocfflgjR47M0b9Xr146deqUli9fbmu77rrr1LRpU82YMSPXfWzcuFHNmzfX3r17Vb169RzL09LSlJaWZptPSUlRtWrVdOzYMfn7++cbv9VqVVJSkkJCQuTmVqQTB5zqzdV/OzuEkmGM/DJPKtW9vGSxODsa5IU8uQby5BouyNPwmDrOjggXcPXvEJcL8uQayJNrKGt5SklJUcWKFZWcnJxv3Vioke4PPvhA06ZN05AhQyRJq1evVqdOnfTee+8V6UVLT0/X5s2bNWrUKFubm5ubYmJiFB8fn+s68fHxGjFihF1bbGysli1blud+kpOTZbFYFBgYmOvyCRMmaPz48Tnak5KSLnqndqvVquTkZBljXPKN45d50tkhlBAjb3NGskoSRULpRZ5cA3lyDfZ5SkxMdHZAuICrf4e4XJAn10CeXENZy9OJEycK1K9QRfe+ffvUsWNH23xMTIwsFosOHDigqlWrFi5CSYcPH1ZmZqbCwsLs2sPCwrR9+/Zc10lISMi1f0JCQq79z5w5o6eeekp9+vTJ868Po0aNsivks0e6Q0JCCjTSbbFYXPavNanuyc4OoWQYIxkp1Y2RuVKNPLkG8uQaLshTbpdswblc/TvE5YI8uQby5BrKWp58fHwK1K9QRXdGRkaODXt6eurs2bOF2UyJOXv2rHr27CljjKZPn55nP29vb3l7e+dod3NzK9CbwWKxFLhvqXM5fWG2WM5NKL3Ik2sgT67hvDy55O+oy4BLf4e4jJAn10CeXENZylNBj6FQRbcxRv3797crUM+cOaP777/f7rFhBX1kWHBwsNzd3XXo0CG79kOHDik8PDzXdcLDwwvUP7vg3rt3r77++uuLjlgDAFCWTYr765K38ejNdYshEgAALi+F+vNCv379FBoaavd4sLvvvjvHY8MKysvLS82aNdOaNWtsbVarVWvWrFF0dHSu60RHR9v1l6S4uDi7/tkF999//63Vq1crKCioMIcJAAAAAECxKNRI9+zZs4s9gBEjRqhfv3665ppr1Lx5c02ePFmnTp3SgAEDJEl9+/ZVlSpVNGHCBEnS8OHD1aZNG73xxhvq1KmTFi5cqE2bNmnmzJmSsgruO+64Q1u2bNHy5cuVmZlpu967UqVK8vLyKvZjAAAAAAAgN4Uquh2hV69eSkpK0rPPPquEhAQ1bdpUK1eutN0sbd++fXbnyrds2VILFizQmDFjNHr0aNWpU0fLli1To0aNJEn79+/X559/Lklq2rSp3b7Wrl2rtm3blshxAQAAAADg9KJbkoYOHaqhQ4fmumzdunU52nr06KEePXrk2j8yMlKFePQ4AAAAAAAO4/q3jAMAAAAAoJSi6AYAAAAAwEEougEAAAAAcBCKbgAAAAAAHISiGwAAAAAAB6HoBgAAAADAQSi6AQAAAABwEIpuAAAAAAAchKIbAAAAAAAHoegGAAAAAMBBPJwdAAAAcA2T4v665G08enPdYogEAADXwUg3AAAAAAAOQtENAAAAAICDUHQDAAAAAOAgFN0AAAAAADgIRTcAAAAAAA5C0Q0AAAAAgINQdAMAAAAA4CAU3QAAAAAAOAhFNwAAAAAADkLRDQAAAACAg1B0AwAAAADgIBTdAAAAAAA4CEU3AAAAAAAOQtENAAAAAICDUHQDAAAAAOAgFN0AAAAAADgIRTcAAAAAAA5C0Q0AAAAAgINQdAMAAAAA4CAU3QAAAAAAOIiHswMAAACXj0lxf13S+o/eXLeYIgEAoGQw0g0AAAAAgINQdAMAAAAA4CAU3QAAAAAAOAhFNwAAAAAADkLRDQAAAACAg1B0AwAAAADgIBTdAAAAAAA4CEU3AAAAAAAOQtENAAAAAICDUHQDAAAAAOAgFN0AAAAAADiIh7MDAAAAKKhJcX9d8jYevbluMUQCAEDBMNINAAAAAICDUHQDAAAAAOAgFN0AAAAAADgIRTcAAAAAAA7i9BupTZ06Va+99poSEhLUpEkTvf3222revHme/RcvXqxnnnlGe/bsUZ06dfTKK6+oY8eOtuWffvqpZsyYoc2bN+vo0aP6+eef1bRp0xI4kpJXHDeTAQAAAAA4jlNHuhctWqQRI0Zo7Nix2rJli5o0aaLY2FglJibm2n/9+vXq06ePBg4cqJ9//lndunVTt27d9Ntvv9n6nDp1Sq1atdIrr7xSUocBAAAAAECunDrSPXHiRA0aNEgDBgyQJM2YMUNffvmlZs2apZEjR+bo/+abb6p9+/Z64oknJEnPP/+84uLiNGXKFM2YMUOSdM8990iS9uzZU+A40tLSlJaWZptPSUmRJFmtVlmt1nzXtVqtMsZctJ9DGFPy+3RVxpybUHqRJ9dAnlwDecqTU35n58Kp3yFQYOTJNZAn11DW8lTQ43Ba0Z2enq7Nmzdr1KhRtjY3NzfFxMQoPj4+13Xi4+M1YsQIu7bY2FgtW7bskmKZMGGCxo8fn6M9KSlJZ86cyXddq9Wq5ORkGWPk5layJw74ZZ4s0f25NiNvc0aySpLF2cEgT+TJNZAn10Ce8pLXGXUlzZnfIVBw5Mk1kCfXUNbydOLEiQL1c1rRffjwYWVmZiosLMyuPSwsTNu3b891nYSEhFz7JyQkXFIso0aNsivmU1JSVK1aNYWEhMjf3z/fda1WqywWi0JCQkr8jZPqnlyi+3NpxkhGSnUrL1n48llqkSfXQJ5cA3nK00fbLu335/CYOsUShzO/Q6DgyJNrIE+uoazlycfHp0D9nH4jtdLA29tb3t7eOdrd3NwK9GawWCwF7lus+BJVOBbLuQmlF3lyDeTJNZAnhyjO3/dO+w6BQiFProE8uYaylKeCHoPTjjQ4OFju7u46dOiQXfuhQ4cUHh6e6zrh4eGF6g8AAAAAgDM5rej28vJSs2bNtGbNGlub1WrVmjVrFB0dnes60dHRdv0lKS4uLs/+AAAAAAA4k1NPLx8xYoT69euna665Rs2bN9fkyZN16tQp293M+/btqypVqmjChAmSpOHDh6tNmzZ644031KlTJy1cuFCbNm3SzJkzbds8evSo9u3bpwMHDkiSduzYISlrlJwRcQAAAABASXJq0d2rVy8lJSXp2WefVUJCgpo2baqVK1fabpa2b98+u/PkW7ZsqQULFmjMmDEaPXq06tSpo2XLlqlRo0a2Pp9//rmtaJek3r17S5LGjh2rcePGlcyBAQAAAAAgyWIMD/C8UEpKigICApScnFygu5cnJiYqNDS0xG8GMCnurxLdn0szRn6ZJ5Xqzl18SzXy5BrIk2sgTw7z6M11i2U7zvwOgYIjT66BPLmGspangtaN3L0cAACgEIrjj97FVbgDAEo/1//zAgAAAAAApRRFNwAAAAAADkLRDQAAAACAg1B0AwAAAADgIBTdAAAAAAA4CEU3AAAAAAAOwiPDAAAAStikuL/Oe5Z6cqGfpc4jxwDAdTDSDQAAAACAg1B0AwAAAADgIBTdAAAAAAA4CEU3AAAAAAAOQtENAAAAAICDUHQDAAAAAOAgFN0AAAAAADgIRTcAAAAAAA5C0Q0AAAAAgINQdAMAAAAA4CAU3QAAAAAAOAhFNwAAAAAADkLRDQAAAACAg3g4OwAAAAAUzqS4vy55G4/eXLcYIgEAXAwj3QAAAAAAOAhFNwAAAAAADsLp5QAAAJehSz1FndPTAaBgGOkGAAAAAMBBKLoBAAAAAHAQim4AAAAAAByEa7oBAABQaDy2DAAKhpFuAAAAAAAchJFuAAAAOAV3UAdwOWCkGwAAAAAAB6HoBgAAAADAQSi6AQAAAABwEIpuAAAAAAAchBupAQAAwCUVx2PLLsoY+WWeVKp7smSx5NqFG7oByA8j3QAAAAAAOAhFNwAAAAAADkLRDQAAAACAg3BNNwAAAHAJLvXacq4JB8o2im4AAADAiYrjhnAU7kDpRdENAAAAuDhG24HSi2u6AQAAAABwEIpuAAAAAAAchKIbAAAAAAAH4ZpuAAAA4DLHzdwAx2GkGwAAAAAAB2GkGwAAAMAl4w7qQO5KRdE9depUvfbaa0pISFCTJk309ttvq3nz5nn2X7x4sZ555hnt2bNHderU0SuvvKKOHTvalhtjNHbsWL377rs6fvy4rr/+ek2fPl116tQpicMBAAAAUEiXVLQbI7/MkxoUG1p8AQHFxOlF96JFizRixAjNmDFDLVq00OTJkxUbG6sdO3YoNDTnh2b9+vXq06ePJkyYoM6dO2vBggXq1q2btmzZokaNGkmSXn31Vb311luaO3euoqKi9Mwzzyg2NlZ//PGHfHx8SvoQAQAAAJSAN1f/LVkszg7jkjDiX/ZYjDHGmQG0aNFC1157raZMmSJJslqtqlatmh5++GGNHDkyR/9evXrp1KlTWr58ua3tuuuuU9OmTTVjxgwZYxQREaHHHntMjz/+uCQpOTlZYWFhmjNnjnr37n3RmFJSUhQQEKDk5GT5+/vn29dqtSoxMVGhoaFycyvZS+SL44YXl43//+tnqnt5l/+PuEwjT66BPLkG8lT6kSPXQJ5cA3kqdXL744EzaydHKGjd6NSR7vT0dG3evFmjRo2ytbm5uSkmJkbx8fG5rhMfH68RI0bYtcXGxmrZsmWSpN27dyshIUExMTG25QEBAWrRooXi4+NzLbrT0tKUlpZmm09OTpYkHT9+XFarNd9jsFqtSklJkZeXV4m/cc6cPFGi+3NpxshiPakzbob/iEsz8uQayJNrIE+lHzlyDeTJNZCnUmfC0s05G42Rr/WkTrv9V6A8PXBjLQdEVnxSUlIkZV3enB+nFt2HDx9WZmamwsLC7NrDwsK0ffv2XNdJSEjItX9CQoJteXZbXn0uNGHCBI0fPz5He40aNQp2IAAAAACAYjXa2QEU0IkTJxQQEJDncqdf010ajBo1ym703Gq16ujRowoKCpLlIn+BSUlJUbVq1fTvv/9e9FR0OA95cg3kyTWQJ9dAnko/cuQayJNrIE+uoazlyRijEydOKCIiIt9+Ti26g4OD5e7urkOHDtm1Hzp0SOHh4bmuEx4enm//7H8PHTqkypUr2/Vp2rRprtv09vaWt7e3XVtgYGBhDkX+/v5l4o1T1pEn10CeXAN5cg3kqfQjR66BPLkG8uQaylKe8hvhzubUq9e9vLzUrFkzrVmzxtZmtVq1Zs0aRUdH57pOdHS0XX9JiouLs/WPiopSeHi4XZ+UlBT99NNPeW4TAAAAAABHcPrp5SNGjFC/fv10zTXXqHnz5po8ebJOnTqlAQMGSJL69u2rKlWqaMKECZKk4cOHq02bNnrjjTfUqVMnLVy4UJs2bdLMmTMlSRaLRY888oheeOEF1alTx/bIsIiICHXr1s1ZhwkAAAAAuAw5veju1auXkpKS9OyzzyohIUFNmzbVypUrbTdC27dvn91dwVu2bKkFCxZozJgxGj16tOrUqaNly5bZntEtSU8++aROnTqlwYMH6/jx42rVqpVWrlzpkGd0e3t7a+zYsTlOT0fpQp5cA3lyDeTJNZCn0o8cuQby5BrIk2u4XPPk9Od0AwAAAABQVrn+E8kBAAAAACilKLoBAAAAAHAQim4AAAAAAByEohsAAAAAAAeh6L5EU6dOVWRkpHx8fNSiRQtt2LDB2SGVWRMmTNC1116rChUqKDQ0VN26ddOOHTvs+rRt21YWi8Vuuv/+++367Nu3T506dZKfn59CQ0P1xBNPKCMjw67PunXrdPXVV8vb21u1a9fWnDlzHH14ZcK4ceNyvP7169e3LT9z5oweeughBQUFqXz58rr99tt16NAhu22QH8eLjIzMkSeLxaKHHnpIEp8jZ/n222916623KiIiQhaLRcuWLbNbbozRs88+q8qVK8vX11cxMTH6+++/7focPXpUd911l/z9/RUYGKiBAwfq5MmTdn22bdumG264QT4+PqpWrZpeffXVHLEsXrxY9evXl4+Pjxo3bqwVK1YU+/G6qvzydPbsWT311FNq3LixypUrp4iICPXt21cHDhyw20Zun8GXX37Zrg95ujQX+zz1798/Rw7at29v14fPk+NdLE+5/a6yWCx67bXXbH34PDlWQb5/l+T3O5etvQyKbOHChcbLy8vMmjXL/P7772bQoEEmMDDQHDp0yNmhlUmxsbFm9uzZ5rfffjNbt241HTt2NNWrVzcnT5609WnTpo0ZNGiQOXjwoG1KTk62Lc/IyDCNGjUyMTEx5ueffzYrVqwwwcHBZtSoUbY+//zzj/Hz8zMjRowwf/zxh3n77beNu7u7WblyZYkerysaO3asadiwod3rn5SUZFt+//33m2rVqpk1a9aYTZs2meuuu860bNnStpz8lIzExES7HMXFxRlJZu3atcYYPkfOsmLFCvP000+bTz/91EgyS5cutVv+8ssvm4CAALNs2TLzyy+/mC5dupioqChz+vRpW5/27dubJk2amB9//NF89913pnbt2qZPnz625cnJySYsLMzcdddd5rfffjMfffSR8fX1Ne+8846tzw8//GDc3d3Nq6++av744w8zZswY4+npaX799VeHvwauIL88HT9+3MTExJhFixaZ7du3m/j4eNO8eXPTrFkzu23UqFHDPPfcc3afsfN/l5GnS3exz1O/fv1M+/bt7XJw9OhRuz58nhzvYnk6Pz8HDx40s2bNMhaLxezatcvWh8+TYxXk+3dJfb9z5dqLovsSNG/e3Dz00EO2+czMTBMREWEmTJjgxKguH4mJiUaS+eabb2xtbdq0McOHD89znRUrVhg3NzeTkJBga5s+fbrx9/c3aWlpxhhjnnzySdOwYUO79Xr16mViY2OL9wDKoLFjx5omTZrkuuz48ePG09PTLF682Nb2559/GkkmPj7eGEN+nGX48OGmVq1axmq1GmP4HJUGF375tFqtJjw83Lz22mu2tuPHjxtvb2/z0UcfGWOM+eOPP4wks3HjRluf//3vf8ZisZj9+/cbY4yZNm2aqVixoi1Pxhjz1FNPmXr16tnme/bsaTp16mQXT4sWLcyQIUOK9RjLgtyKhAtt2LDBSDJ79+61tdWoUcNMmjQpz3XIU/HKq+ju2rVrnuvweSp5Bfk8de3a1dx00012bXyeStaF379L8vudK9denF5eROnp6dq8ebNiYmJsbW5uboqJiVF8fLwTI7t8JCcnS5IqVapk1z5//nwFBwerUaNGGjVqlFJTU23L4uPj1bhxY4WFhdnaYmNjlZKSot9//93W5/y8ZvchrwXz999/KyIiQjVr1tRdd92lffv2SZI2b96ss2fP2r229evXV/Xq1W2vLfkpeenp6Zo3b57uvfdeWSwWWzufo9Jl9+7dSkhIsHtNAwIC1KJFC7vPT2BgoK655hpbn5iYGLm5uemnn36y9WndurW8vLxsfWJjY7Vjxw4dO3bM1ofcFZ/k5GRZLBYFBgbatb/88ssKCgrSVVddpddee83uNEvyVDLWrVun0NBQ1atXTw888ICOHDliW8bnqfQ5dOiQvvzySw0cODDHMj5PJefC798l9f3O1WsvD2cH4KoOHz6szMxMuzePJIWFhWn79u1OiuryYbVa9cgjj+j6669Xo0aNbO133nmnatSooYiICG3btk1PPfWUduzYoU8//VSSlJCQkGvOspfl1yclJUWnT5+Wr6+vIw/NpbVo0UJz5sxRvXr1dPDgQY0fP1433HCDfvvtNyUkJMjLyyvHF8+wsLCLvvbZy/LrQ36KZtmyZTp+/Lj69+9va+NzVPpkv665vabnv+ahoaF2yz08PFSpUiW7PlFRUTm2kb2sYsWKeeYuexsouDNnzuipp55Snz595O/vb2sfNmyYrr76alWqVEnr16/XqFGjdPDgQU2cOFESeSoJ7du3V/fu3RUVFaVdu3Zp9OjR6tChg+Lj4+Xu7s7nqRSaO3euKlSooO7du9u183kqObl9/y6p73fHjh1z6dqLohsu6aGHHtJvv/2m77//3q598ODBtp8bN26sypUrq127dtq1a5dq1apV0mFedjp06GD7+corr1SLFi1Uo0YNffzxxxRZpdT777+vDh06KCIiwtbG5wi4dGfPnlXPnj1ljNH06dPtlo0YMcL285VXXikvLy8NGTJEEyZMkLe3d0mHelnq3bu37efGjRvryiuvVK1atbRu3Tq1a9fOiZEhL7NmzdJdd90lHx8fu3Y+TyUnr+/fuDhOLy+i4OBgubu757gz36FDhxQeHu6kqC4PQ4cO1fLly7V27VpVrVo1374tWrSQJO3cuVOSFB4enmvOspfl18ff35/CsZACAwNVt25d7dy5U+Hh4UpPT9fx48ft+pz/mSE/JWvv3r1avXq17rvvvnz78TlyvuzXNb/fOeHh4UpMTLRbnpGRoaNHjxbLZ4zfbQWXXXDv3btXcXFxdqPcuWnRooUyMjK0Z88eSeTJGWrWrKng4GC7/+f4PJUe3333nXbs2HHR31cSnydHyev7d0l9v3P12ouiu4i8vLzUrFkzrVmzxtZmtVq1Zs0aRUdHOzGysssYo6FDh2rp0qX6+uuvc5wqlJutW7dKkipXrixJio6O1q+//mr3izT7C9EVV1xh63N+XrP7kNfCO3nypHbt2qXKlSurWbNm8vT0tHttd+zYoX379tleW/JTsmbPnq3Q0FB16tQp3358jpwvKipK4eHhdq9pSkqKfvrpJ7vPz/Hjx7V582Zbn6+//lpWq9X2h5Po6Gh9++23Onv2rK1PXFyc6tWrp4oVK9r6kLuiyy64//77b61evVpBQUEXXWfr1q1yc3Oznc5Mnkref//9pyNHjtj9P8fnqfR4//331axZMzVp0uSiffk8Fa+Lff8uqe93Ll97OflGbi5t4cKFxtvb28yZM8f88ccfZvDgwSYwMNDuznwoPg888IAJCAgw69ats3ssRGpqqjHGmJ07d5rnnnvObNq0yezevdt89tlnpmbNmqZ169a2bWQ/suCWW24xW7duNStXrjQhISG5PrLgiSeeMH/++aeZOnUqjzoqoMcee8ysW7fO7N692/zwww8mJibGBAcHm8TERGNM1iMlqlevbr7++muzadMmEx0dbaKjo23rk5+Sk5mZaapXr26eeuopu3Y+R85z4sQJ8/PPP5uff/7ZSDITJ040P//8s+2u1y+//LIJDAw0n332mdm2bZvp2rVrro8Mu+qqq8xPP/1kvv/+e1OnTh27RxwdP37chIWFmXvuucf89ttvZuHChcbPzy/Ho3M8PDzM66+/bv78808zduxYHp1znvzylJ6ebrp06WKqVq1qtm7dave7KvsOvevXrzeTJk0yW7duNbt27TLz5s0zISEhpm/fvrZ9kKdLl1+eTpw4YR5//HETHx9vdu/ebVavXm2uvvpqU6dOHXPmzBnbNvg8Od7F/t8zJuuRX35+fmb69Ok51ufz5HgX+/5tTMl9v3Pl2oui+xK9/fbbpnr16sbLy8s0b97c/Pjjj84OqcySlOs0e/ZsY4wx+/btM61btzaVKlUy3t7epnbt2uaJJ56we76wMcbs2bPHdOjQwfj6+prg4GDz2GOPmbNnz9r1Wbt2rWnatKnx8vIyNWvWtO0D+evVq5epXLmy8fLyMlWqVDG9evUyO3futC0/ffq0efDBB03FihWNn5+fue2228zBgwfttkF+SsaqVauMJLNjxw67dj5HzrN27dpc/4/r16+fMSbrsWHPPPOMCQsLM97e3qZdu3Y58nfkyBHTp08fU758eePv728GDBhgTpw4Ydfnl19+Ma1atTLe3t6mSpUq5uWXX84Ry8cff2zq1q1rvLy8TMOGDc2XX37psON2Nfnlaffu3Xn+rlq7dq0xxpjNmzebFi1amICAAOPj42MaNGhgXnrpJbtizxjydKnyy1Nqaqq55ZZbTEhIiPH09DQ1atQwgwYNyvHFnc+T413s/z1jjHnnnXeMr6+vOX78eI71+Tw53sW+fxtTst/vXLX2shhjjIMG0QEAAAAAuKxxTTcAAAAAAA5C0Q0AAAAAgINQdAMAAAAA4CAU3QAAAAAAOAhFNwAAAAAADkLRDQAAAACAg1B0AwAAAADgIBTdAAAAAAA4CEU3AAAAAAAOQtENAAAAAICDUHQDAAAAAOAgFN0AAAAAADgIRTcAAAAAAA5C0Q0AAAAAgINQdAMAAAAA4CAU3QAAAAAAOAhFNwAAAAAADkLRDQDAJejfv78iIyOLdZtz5syRxWLRnj17inW7KH0iIyPVv39/Z4cBAHAgim4AgNPt2rVLQ4YMUc2aNeXj4yN/f39df/31evPNN3X69Glnh+cwL730kpYtW+bsMGyyi32LxaLvv/8+x3JjjKpVqyaLxaLOnTs7IcK87dmzxxb7hdN1113n1NjWr1+vcePG6fjx406NAwDgHB7ODgAAcHn78ssv1aNHD3l7e6tv375q1KiR0tPT9f333+uJJ57Q77//rpkzZzo7TId46aWXdMcdd6hbt2527ffcc4969+4tb29vp8Tl4+OjBQsWqFWrVnbt33zzjf777z+nxVUQffr0UceOHe3aQkJCnBRNlvXr12v8+PHq37+/AgMD7Zbt2LFDbm6MgQBAWUbRDQBwmt27d6t3796qUaOGvv76a1WuXNm27KGHHtLOnTv15ZdfOjFC53B3d5e7u7vT9t+xY0ctXrxYb731ljw8zn1VWLBggZo1a6bDhw87LbaLufrqq3X33Xc7O4wCK81/wAAAFA/+tAoAcJpXX31VJ0+e1Pvvv29XcGerXbu2hg8fLunc6cNz5szJ0c9isWjcuHG2+XHjxsliseivv/7S3XffrYCAAIWEhOiZZ56RMUb//vuvunbtKn9/f4WHh+uNN96w215e11SvW7dOFotF69aty/e4Xn/9dbVs2VJBQUHy9fVVs2bNtGTJkhwxnzp1SnPnzrWdBp19be+F++/cubNq1qyZ676io6N1zTXX2LXNmzdPzZo1k6+vrypVqqTevXvr33//zTfm8/Xp00dHjhxRXFycrS09PV1LlizRnXfeWeRjlqS4uDi1atVKgYGBKl++vOrVq6fRo0fb9Xn77bfVsGFD+fn5qWLFirrmmmu0YMGCAsefl7Zt26pt27Y52i+8Lj/7vfb6669r5syZqlWrlry9vXXttddq48aNOdbfvn27evbsqZCQEPn6+qpevXp6+umnJWW9F5944glJUlRUlC3X2bnN7Zruf/75Rz169FClSpXk5+en6667Lscfn7Lfix9//LFefPFFVa1aVT4+PmrXrp127txZ9BcJAFDsKLoBAE7zxRdfqGbNmmrZsqVDtt+rVy9ZrVa9/PLLatGihV544QVNnjxZN998s6pUqaJXXnlFtWvX1uOPP65vv/222Pb75ptv6qqrrtJzzz2nl156SR4eHurRo4dd4fThhx/K29tbN9xwgz788EN9+OGHGjJkSJ7HsXv37hwF3969e/Xjjz+qd+/etrYXX3xRffv2VZ06dTRx4kQ98sgjWrNmjVq3bl3ga4ojIyMVHR2tjz76yNb2v//9T8nJyXb7Kuwx//777+rcubPS0tL03HPP6Y033lCXLl30ww8/2Pq8++67GjZsmK644gpNnjxZ48ePV9OmTfXTTz8VKPbU1FQdPnzYbjp79myB1r3QggUL9Nprr2nIkCF64YUXtGfPHnXv3t1ue9u2bVOLFi309ddfa9CgQXrzzTfVrVs3ffHFF5Kk7t27q0+fPpKkSZMm2XKd1ynvhw4dUsuWLbVq1So9+OCDevHFF3XmzBl16dJFS5cuzdH/5Zdf1tKlS/X4449r1KhR+vHHH3XXXXcV6XgBAA5iAABwguTkZCPJdO3atUD9d+/ebSSZ2bNn51gmyYwdO9Y2P3bsWCPJDB482NaWkZFhqlataiwWi3n55Zdt7ceOHTO+vr6mX79+trbZs2cbSWb37t12+1m7dq2RZNauXWtr69evn6lRo4Zdv9TUVLv59PR006hRI3PTTTfZtZcrV85uv3ntPzk52Xh7e5vHHnvMrt+rr75qLBaL2bt3rzHGmD179hh3d3fz4osv2vX79ddfjYeHR472vPa7ceNGM2XKFFOhQgXbsfTo0cPceOONxhhjatSoYTp16lToY540aZKRZJKSkvKMoWvXrqZhw4b5xpmb7PdHblN2vtq0aWPatGmTY90Lc5i9raCgIHP06FFb+2effWYkmS+++MLW1rp1a1OhQgVbDrJZrVbbz6+99lqu7ydjsl7L898DjzzyiJFkvvvuO1vbiRMnTFRUlImMjDSZmZnGmHPvxQYNGpi0tDRb3zfffNNIMr/++mu+rxcAoOQw0g0AcIqUlBRJUoUKFRy2j/vuu8/2s7u7u6655hoZYzRw4EBbe2BgoOrVq6d//vmn2Pbr6+tr+/nYsWNKTk7WDTfcoC1bthRpe/7+/urQoYM+/vhjGWNs7YsWLdJ1112n6tWrS5I+/fRTWa1W9ezZ026kNzw8XHXq1NHatWsLvM+ePXvq9OnTWr58uU6cOKHly5fneWq5VLBjzr6J2GeffSar1ZrrdgIDA/Xff//lehp3QQwePFhxcXF2U5MmTYq0rV69eqlixYq2+RtuuEGSbO+VpKQkffvtt7r33nttOchmsViKtM8VK1aoefPmdjexK1++vAYPHqw9e/bojz/+sOs/YMAAeXl55RkjAMD5uJEaAMAp/P39JUknTpxw2D4uLIQCAgLk4+Oj4ODgHO1Hjhwptv0uX75cL7zwgrZu3aq0tDRbe1ELMSmrAFy2bJni4+PVsmVL7dq1S5s3b9bkyZNtff7++28ZY1SnTp1ct+Hp6Vng/YWEhCgmJkYLFixQamqqMjMzdccdd+TZvyDH3KtXL7333nu67777NHLkSLVr107du3fXHXfcYbuD91NPPaXVq1erefPmql27tm655Rbdeeeduv766wsUd506dRQTE1Pg48zPhe+f7AL82LFjks4Vto0aNSqW/UlZlwy0aNEiR3uDBg1sy8/f38ViBAA4H0U3AMAp/P39FRERod9++61A/fMqWDMzM/NcJ7c7gOd1V/DzR5CLsq9s3333nbp06aLWrVtr2rRpqly5sjw9PTV79uxLuhnYrbfeKj8/P3388cdq2bKlPv74Y7m5ualHjx62PlarVRaLRf/73/9yPc7y5csXap933nmnBg0apISEBHXo0CHH466yFfSYfX199e2332rt2rX68ssvtXLlSi1atEg33XSTvvrqK7m7u6tBgwbasWOHli9frpUrV+qTTz7RtGnT9Oyzz2r8+PGFiv9CFovFLs/Z8sprQd4rzuYKMQLA5Y6iGwDgNJ07d9bMmTMVHx+v6OjofPtmj+BdeDOwvXv3Fntcl7KvTz75RD4+Plq1apXd46Bmz56do29hRr7LlSunzp07a/HixZo4caIWLVqkG264QREREbY+tWrVkjFGUVFRqlu3boG3nZfbbrtNQ4YM0Y8//qhFixbl2a8wx+zm5qZ27dqpXbt2mjhxol566SU9/fTTWrt2rW2Euly5curVq5d69eql9PR0de/eXS+++KJGjRolHx+fIh9PxYoVcz3tuqjvoew7yl/sD0eFyXONGjW0Y8eOHO3bt2+3LQcAuBau6QYAOM2TTz6pcuXK6b777tOhQ4dyLN+1a5fefPNNSVkj48HBwTnuMj5t2rRij6tWrVqSZLevzMxMzZw586Lruru7y2Kx2I2e7tmzR8uWLcvRt1y5cgW+o7iUdXr2gQMH9N577+mXX35Rr1697JZ3795d7u7uGj9+fI6RTmNMoU+hL1++vKZPn65x48bp1ltvzbNfQY/56NGjOdZt2rSpJNlOSb8wRi8vL11xxRUyxhT5LuTZatWqpe3btyspKcnW9ssvv9jdPb0wQkJC1Lp1a82aNUv79u2zW3b+61+uXDlJOf+Ik5uOHTtqw4YNio+Pt7WdOnVKM2fOVGRkpK644ooixQoAcB5GugEATlOrVi0tWLBAvXr1UoMGDdS3b181atRI6enpWr9+vRYvXmz3DOP77rtPL7/8su677z5dc801+vbbb/XXX38Ve1wNGzbUddddp1GjRuno0aOqVKmSFi5cqIyMjIuu26lTJ02cOFHt27fXnXfeqcTERE2dOlW1a9fWtm3b7Po2a9ZMq1ev1sSJExUREaGoqKhcr+fN1rFjR1WoUEGPP/643N3ddfvtt9str1Wrll544QWNGjVKe/bsUbdu3VShQgXt3r1bS5cu1eDBg/X4448X6rXo169fsR3zc889p2+//VadOnVSjRo1lJiYqGnTpqlq1aq2G4fdcsstCg8P1/XXX6+wsDD9+eefmjJlijp16nTJN9279957NXHiRMXGxmrgwIFKTEzUjBkz1LBhQ9uN/QrrrbfeUqtWrXT11Vdr8ODBioqK0p49e/Tll19q69atkrLyLElPP/20evfuLU9PT9166622Yvx8I0eO1EcffaQOHTpo2LBhqlSpkubOnavdu3frk08+sV37DgBwIc65aToAAOf89ddfZtCgQSYyMtJ4eXmZChUqmOuvv968/fbb5syZM7Z+qampZuDAgSYgIMBUqFDB9OzZ0yQmJub5yLALH03Vr18/U65cuRz7b9OmTY7HVO3atcvExMQYb29vExYWZkaPHm3i4uIK9Miw999/39SpU8d4e3ub+vXrm9mzZ9tiOt/27dtN69atja+vr5Fke3RUXo8sM8aYu+66y0gyMTExeb6en3zyiWnVqpUpV66cKVeunKlfv7556KGHzI4dO/Jc5/z9bty4Md9+uT0yrCDHvGbNGtO1a1cTERFhvLy8TEREhOnTp4/566+/bH3eeecd07p1axMUFGS8vb1NrVq1zBNPPGGSk5PzjSn7MV+vvfZavv3mzZtnatasaby8vEzTpk3NqlWr8nxkWG7buvC9Zowxv/32m7nttttMYGCg8fHxMfXq1TPPPPOMXZ/nn3/eVKlSxbi5udnl9sJHhhmT9d674447bNtr3ry5Wb58uV2f7EeGLV68ONfXIbdH6wEAnMNiDHfaAAAAAADAEThHCQAAAAAAB6HoBgAAAADAQSi6AQAAAABwEIpuAAAAAAAchKIbAAAAAAAHoegGAAAAAMBBPJwdQGlktVp14MABVahQQRaLxdnhAAAAAABKGWOMTpw4oYiICLm55T2eTdGdiwMHDqhatWrODgMAAAAAUMr9+++/qlq1ap7LKbpzUaFCBUnS3r17lZaWppCQkHz/coHSwWq1KikpiXy5CPLlWsiXayFfroV8uRby5VrIl2txtXylpKSoWrVqtvoxLxTducg+pdzf319nzpyRv7+/SyT9cme1WsmXCyFfroV8uRby5VrIl2shX66FfLkWV83XxS5Jdp0jAQAAAADAxVB0AwAAAADgIBTdAAAAAAA4CNd0F5ExRhkZGcrMzHR2KC7D3d1dHh4ePIYNAAAAwGWDorsI0tPTdfDgQaWmpjo7FJfj5+enypUry8vLy9mhAAAAAIDDUXQXktVq1e7du+Xu7q6IiAh5eXkxclsAxhilp6crKSlJu3fvVp06dVzqjoQAAAAAUBQU3YWUnp4uq9WqatWqyc/Pz9nhuBRfX195enpq7969Sk9Pl4+Pj7NDAgAAAACHKvVDjd9++61uvfVWRUREyGKxaNmyZRddZ926dbr66qvl7e2t2rVra86cOcUeF6O0RcPrBgAAAOByUuoroFOnTqlJkyaaOnVqgfrv3r1bnTp10o033qitW7fqkUce0X333adVq1Y5OFIAAAAAAOyV+tPLO3TooA4dOhS4/4wZMxQVFaU33nhDktSgQQN9//33mjRpkmJjYx0VJgAAAICyzhjJai3cVNB1zu9nzLn58/8tyM+FbSvM/goTS/brlf1vXj+f32aMyp04IZUvn9Vev77Us2fJ5ddBSn3RXVjx8fGKiYmxa4uNjdUjjzyS5zppaWlKS0uzzaekpEjKummaMUbW7DfNeW3ZEwon+3WzWq12r2txyC1fKL3Il2shX66FfLkW8uVaykS+MjOltDQpPV06e/bSpoyMrO1Zred+zsw893NGhiwXtl2w3LZ+9rLc5s9vu/Df7J9zmSyZmQo+e1YWi0XmwuX5rJfrtlw55y7CTVKF8+ZNt24yd9zhrHAuqqD/D5S5ojshIUFhYWF2bWFhYUpJSdHp06fl6+ubY50JEyZo/PjxOdqTkpJs/7FmX4t89uxZWa1WZWRkKCMjwzEH4UAJCQl6+eWX9b///U/79+9XaGiorrzySg0bNkw33XST6tSpo7179+rDDz9Ur1697NZt0qSJ/vzzT7333nvq27evJNn6n69KlSravXt3rvvPyMiQ1WrVkSNH5OnpWazHZrValZycbJcvlF7ky7WQL9dCvlwL+XItxZIvq1U6c0aWtDRZzpzJmtLSstpOn869/fwpLU3K7pOeLqWnZ63z/9P587n+7ILfYS+FMwse4+YmnTcZi+XcfPbPFsu5fue1ZU/m/PkLlsliObfN8/tI57adx3rZfXMsP3/dfLab5/Lc5qWs/Zy//LxlttfLYlF6enrWE6Lc3HS2YUOdTkx0VHou2YkTJwrUr8wV3UUxatQojRgxwjafkpKiatWqKSQkRGlpaQoJCbH9p3rmzBmdOHFCHh4e8vBwrZdvz549atWqlQIDA/Xqq6+qcePGOnv2rFatWqXhw4frzz//lCRVq1ZNH374oe666y7buj/++KMOHTqkcuXKyc3Nze7Yx48fr0GDBtnm3d3d83xtPDw85ObmpqCgoGK/e7nVapXFYrHLF0ov8uVayJdrIV+uhXyVMunp0smTWdOpU+d+/v95c/KkyiUmqryHR1bRm5oqnT59bsqeP3Pm3M/nt/9/UV2aGDc3ydNT8vLK+rcgk4fHuX/d3bOmIvxssn92czvXfn7b+e159bmw/3nrWS0WHU9OVmClSnLL3ncu/WSx5L7s/CL5YtOF2yjEY4Xz63k5PZzYarUqJSlJFf7//0Mf2Y98lzYFrWdcq2osgPDwcB06dMiu7dChQ/L39891lFuSvL295e3tnaPdzc1NFotFbm5utl+C2W3Zk6Ss6w9SU4v3QArCz69QH+aHHnpIFotFGzZsULly5WztjRo10sCBA23Hc9ddd2nSpEn677//VK1aNUnS7Nmzddddd+mDDz6wP3ZJ/v7+qly5coFiyF73/Ne0ODly2yh+5Mu1kC/XQr5cC/kqguzvX+cXxdnTiRN5t+VSSNvNF2AUOKA4j8PDQ/LxkXx9s6aC/pw9eXtnTdk/F7TN29tWZFuc+L5zeEFptSojMVFuoaF8vlyEK/1/WNAYy1zRHR0drRUrVti1xcXFKTo62nE7TU09d7F/STp5UjqveM7P0aNHtXLlSr344ot2BXe2wMBA289hYWGKjY3V3LlzNWbMGKWmpmrRokX65ptv9MEHHxRX9AAA4HKVkSGlpEjHj0vJyVn/FuTnCwtpR95fx9s76/td+fJZ37f+/2fj66sz7u7yCQyUpVy5c4Wwr2/WgEh+89lt5xfQLnbmJIDCK/Wf8pMnT2rnzp22+d27d2vr1q2qVKmSqlevrlGjRmn//v22YvD+++/XlClT9OSTT+ree+/V119/rY8//lhffvmlsw6hVNi5c6eMMapfv36B+t9777167LHH9PTTT2vJkiWqVauWmjZtmmvfp556SmPGjLHNv/TSSxo2bFhxhA0AAEqr9HTp2LFz09Gjuf+cPWUX0NnFc3HKLo6zpwoVcrblNZ1XUNu15VEMG6tVyYmJ8g4NdeoIMQDXUeqL7k2bNunGG2+0zWdfe92vXz/NmTNHBw8e1L59+2zLo6Ki9OWXX+rRRx/Vm2++qapVq+q9995z7OPC/PyK/5dHQfdbQIW903qnTp00ZMgQffvtt5o1a5buvffePPs+8cQT6t+/v20+ODi4UPsCAABOZLVmFcKHD+c+HTlyrog+v5g+derS9+3nJwUGZk0BAed+vnA+ICBrOr+Yzv7Z1/fcDZsAoBQq9UV327Zt8y0Y58yZk+s6P//8swOjuoDFUuDTvJ2lTp06slgs2r59e4H6e3h46J577tHYsWP1008/aenSpXn2DQ4OVu3atYsrVAAAcClOn5aSkqRDh6TExLyL6eyC+siRc8/ULSyLJasYrlhRqlQp698Lfz5/urCQLuYnmQBAaVTqi24Uj0qVKik2NlZTp07VsGHDclzXffz4cbvruqWsU8xff/119erVSxUrVizBaAEAgI3VmjXCnJh4bsouqHObL+AjbHLw95eCg+2noKBzU27FdEBA1t2aAQB5oui+jEydOlXXX3+9mjdvrueee05XXnmlMjIyFBcXp+nTp9seGZatQYMGOnz4sPwKcRo7AAAopOPH5fHbb1nF8r590p49WdPevdLBg1mj1pmZhduml5cUFiaFhEihoVlF84UF9flTpUpZ6wAAih1F92WkZs2a2rJli1588UU99thjOnjwoEJCQtSsWTNNnz4913WCgoJKOEoAAMoQY7Kuf84uorML6vPm3ZKTVaC7oVSsmFVAh4Vl/Zs9nT+f/bO/f6EeKwoAcByK7stM5cqVNWXKFE2ZMiXX5Xv27Ml3/ePHjxeqPwAAZZ7VKu3fL/39t/20e3dWcV2A070zg4LkFhUlS2SklD3VqCFVqZJVRIeEMBINAC6KohsAAOBijJESEnIW1n/9Je3alXXzsvyEhWUV0RcW1ZGRslarpqRTpxTKI6gAoEyi6AYAAMiWnCz9+ae0fbt9cb1zZ/6PB3V3l2rWlOrUOTfVqpVVXFevnv9jPq3W4nn8FgCgVKLoBgAAl5+jR6U//sg57d+f9zpublmj0+cX1tlTZCSPvwIA5IqiGwAAlF1JSbkX1wkJea8TESHVr5+zsK5ZU/L2LrnYAQBlAkV3ERljnB2CS+J1AwA4xMmT0q+/Sj//nPVvdnF9+HDe61SvLl1xhf3UoIEUGFhiYQMAyj6K7kLy/P9Tx1JTU+Xr6+vkaFxPamqqpHOvIwAAhXboUFZxvXVr1vTzz1nXXef2h12LRYqKyllc168vVahQ0pEDAC5DFN2F5O7ursDAQCUmJkqS/Pz8ZOE5mBdljFFqaqoSExMVGBgod3d3Z4cEACjtrNasO4NfWGDndWp45crSVVdJV14pNWqUVVzXq5f/TcwAAHAwiu4iCA8PlyRb4Y2CCwwMtL1+AADYZGZKv/8ubdhwrsD+5Zfc7xhusWQV002bZk1XXSU1aZL1WC4AAEoZiu4isFgsqly5skJDQ3X27Flnh+MyPD09GeEGAGQ5dkz68UcpPj5r+ukn6cSJnP18fLJGrrOL66ZNpcaNpXLlSjpiAACKhKL7Eri7u1NEAgBwMVZr1nOvswvs9euznoV9ofLlpebNpauvPldg160refB1BQDguvgtBgAAildKStZp4uvXZxXZP/4oHT+es1+dOlJ0dNbUsqXUsKHEH7MBAGUMRTcAALg0x49Lq1dnTfHxWY/suvBO4n5+0rXXZhXX0dHSdddJISFOCRcAgJJE0Q0AAArHapW2bJFWrsyafvwx60Zo54uMPFdgt2yZdR02j4sEAFyGKLoBAMDFJSVJq1ZlFdlffZU1f74GDaTYWKl166xCmydVAAAgiaIbAADkJiMj647i2aPZmzfbnzJeoYIUEyO1b59VbNeo4bxYAQAoxSi6AQBAlv37zxXZcXFScrL98qZNs4rs9u2zRrO9vJwSJgAAroSiGwCAy5Ux0i+/SJ9+Ki1blnUDtPNVrCjdcovUoUPWv5UrOyVMAABcGUU3AACXE6tV2rhR+uSTrOmff84ts1iynpOdPZp97bU8wgsAgEtE0Q0AQFmXmSl9/31Wkf3pp1mnkWfz8ckqsLt3lzp2lIKCnBcnAABlEEU3AABl0dmzWXcbX7Ysa0pMPLesfHmpUyfp9tuzTh0vX95ZUQIAUOZRdAMAUFacPi3FxcmyZIlCP/9cbuffCK1iRalLl6xC++abs0a4AQCAw1F0AwDgylJTpS+/lJYsyfr31ClZJFkkmdBQWW67LavQbttW8vR0crAAAFx+KLoBAHA1mZnSunXSvHlZ12mfOHFuWbVqMrfdpqNt26pi586yUGgDAOBUFN0AALiKX37JKrQXLJAOHDjXXqOG1KtX1oj2tdfKGKOziYnceRwAgFKAohsAgNLs33+ljz7KKrbPf452xYpSz57S3XdLLVtKbm7nlhlT8nECAIBcUXQDAFDaJCdnnTY+b17WaeTZRbSXl3TrrVmFdocOkre3U8MEAAAXR9ENAEBpkJ4urVyZVWh//rmUlnZuWevWWYX2HXdkjXADAACXQdENAICzGCP99JP0wQfSokXS0aPnljVoIN1zj3TnnVnXbAMAAJdE0Q0AQEk7ckT68EPp3XelP/441x4enlVk33231LSpZLE4LUQAAFA8KLoBACgJxkjffJNVaH/yybnTx319s04bv+ce6aabuOM4AABlDEU3AACOlJgozZ0rvfee9Ndf59qbNpUGD84a2Q4IcFp4AADAsSi6AQAoblartGaNNHOm9Nln0tmzWe3ly0t9+mQV282acfo4AACXAYpuAACKy8GD0uzZWaPau3efa7/22qxCu1cvqUIF58UHAABKHEU3AACXIjNTWrUqa1R7+fKseUny98+6TnvQIKlJE+fGCAAAnIaiGwCAojhwIOumaO+/L/3777n2li2zRrV79JD8/JwXHwAAKBUougEAKKjsO5BPnSotXXpuVLtiRalfP+m++6SGDZ0bIwAAKFUougEAuJiUlKznak+bZv9c7VatpPvvl26/XfLxcV58AACg1KLoBgAgL7//njWq/eGH0smTWW1+ftLdd0sPPsi12gAA4KIougEAON/Zs1mnjk+blnUqebb69bMK7b59ea42AAAoMIpuAAAkaf/+rDuQv/tu1qO/JMndXeraVXroIenGG3muNgAAKDSKbgDA5csYad26rFHt82+MFhaWdQfywYOlqlWdGiIAAHBtbs4OoCCmTp2qyMhI+fj4qEWLFtqwYUO+/SdPnqx69erJ19dX1apV06OPPqozZ86UULQAgFLv5Mmsa7UbNpRuuklasiSr4G7dWlq4UNq3T3ruOQpuAABwyUr9SPeiRYs0YsQIzZgxQy1atNDkyZMVGxurHTt2KDQ0NEf/BQsWaOTIkZo1a5Zatmypv/76S/3795fFYtHEiROdcAQAgFJjzx5pyhTpvfek5OSstvLlpXvukR54QGrc2KnhAQCAsqfUF90TJ07UoEGDNGDAAEnSjBkz9OWXX2rWrFkaOXJkjv7r16/X9ddfrzvvvFOSFBkZqT59+uinn34q0bgBAKWEMdJ330mTJ0uffSZZrVntdetKDz+cdWM0f3+nhggAAMquUl10p6ena/PmzRo1apStzc3NTTExMYqPj891nZYtW2revHnasGGDmjdvrn/++UcrVqzQPffck+d+0tLSlJaWZptPSUmRJFmtVv1fe/cdHVW19nH8NwkpRAwBE0KRIkgRaQISaWJBCYqAFQEFERGQIBhAjFIElaAoYEG4IkWuheJF8F4QxUgAKSJN5FIUBLkoCaEl1CRk9vvHeZlrbkLTOZk5yfezFmtl77Pn7GfyrIF5OOfsbYyR+9wXNPg18uUs5MtZHJmvzExp9my53npLrs2bPd3mjjtknn5aio2VAv7/KSsnva9L4Mh8FWHky1nIl7OQL2dxWr4uNU6/LroPHTqknJwcRUdH5+qPjo7Wjh078n1Nly5ddOjQIbVo0ULGGJ09e1Z9+vTR888/f955EhMTNWrUqDz9aWlpnsQHBDji8fcize12Kz09nXw5BPlyFiflK+DgQYXNmqXiH3ygwEOHJEkmNFSnH3xQJ3v2VE7NmtbA/z9WGDkpXyBfTkO+nIV8OYvT8nX8+PFLGufXRfefkZycrDFjxujdd99VTEyMdu3apQEDBuill17S8OHD831NQkKC4uPjPe2MjAxVrFhRUVFRyszMVFRUlCOSXtS53W65XC7y5RDky1kcka+NG+V66y3r6nZ2tiTJXH21TL9+0hNPKLR0aYX6OMSC4oh8wYN8OQv5chby5SxOy1do6KV9s/DrojsyMlKBgYFKTU3N1Z+amqqyZcvm+5rhw4fr0Ucf1RNPPCFJqlu3rk6ePKknn3xSL7zwQr7JCwkJUUhISJ7+gIAAuVwuBQQEOCLpEPlyGPLlLH6Zr7Nnree0J06Uvv32v/1Nm0oDB8p1771yBQX5LDxf8st84bzIl7OQL2chX87ipHxdaox+/U6Cg4PVqFEjJSUlefrcbreSkpLUtGnTfF9z6tSpPG8+MDBQkmSMsS9YAEDBOXZMev11qVo16YEHrIK7WDGpSxfpu++k1aulhx6SimjBDQAA/IdfX+mWpPj4eHXv3l2NGzdWkyZNNHHiRJ08edKzmnm3bt1UoUIFJSYmSpLuuecejR8/XjfccIPn9vLhw4frnnvu8RTfAACH2r9fmjBBeu89a69tSYqMlHr3lp56Sipf3rfxAQAA/A9biu6TJ0/qiiuu8Mq5OnXqpLS0NI0YMUIpKSlq0KCBlixZ4llcbd++fbmubA8bNkwul0vDhg3Tb7/9pqioKN1zzz165ZVXvBIPAMAHtm2TXntN+ugj65ZySapTRxo40Lq6Xby4T8MDAAA4H5ex4Z7rEiVK6KGHHtLjjz+uFi1aePv0tsvIyFDJkiV19OhRnTlzRmXKlHHEMwVFndvt1sGDB8mXQ5AvZ/FZvr79Vnr1Velf//pvX6tW0tCh1pZfLlfBxeIgfL6chXw5C/lyFvLlLE7L17m6MT09XeHh4ecdZ8s7+fDDD3XkyBHddtttqlGjhsaOHavff//djqkAAIWN220tjta8udSypVVwu1zSffdJa9dKyclS27YU3AAAwBFsKbo7duyoBQsW6LffflOfPn308ccfq3LlymrXrp3mz5+vs+duDQQA4JzMTGn6dOn666WOHa3F0IKDpV69pB07pH/8Q4qJ8XWUAAAAl8XWa/ZRUVGKj4/Xli1bNH78eH399dd64IEHVL58eY0YMUKnTp2yc3oAgBNkZEjjxklVq0o9e1oFdsmS0nPPSXv3Woum1ajh6ygBAAD+FFtXL09NTdUHH3ygmTNn6tdff9UDDzygnj17av/+/Xr11Ve1du1affXVV3aGAADwVwcOSG++KU2ebBXekrX6+DPPSE8+KV3g2SgAAACnsKXonj9/vmbMmKEvv/xStWvX1lNPPaVHHnlEERERnjHNmjXTddddZ8f0AAB/9vPP1pXtDz6QsrKsvlq1pGeflbp2tW4pBwAAKCRsKbp79Oihhx9+WKtWrdKNN96Y75jy5cvrhRdesGN6AIA/2rFDeuUV6eOPrcXSJKlZM2sl8nbtJAesUgoAAHC5bCm6Dxw4oLCwsAuOKV68uEaOHGnH9AAAf7Jtm/Tyy9Ls2dK5XSrvvltKSLBWKAcAACjEbLmscOWVV+rgwYN5+g8fPqzAwEA7pgQA+Jsff5QeekiqU0f65BOr4O7QQVq/3toGjIIbAAAUAbZc6TbnrmT8j8zMTAXzrB4AFG6bN0svvSTNn//fvvvvl4YNkxo08FVUAAAAPuHVovutt96SJLlcLr3//vsqUaKE51hOTo5WrFihWrVqeXNKAIC/2LBBGj1a+vxzq+1ySQ8+aBXbdev6NjYAAAAf8WrRPWHCBEnWle4pU6bkupU8ODhYVapU0ZQpU7w5JQDA19ats4rtRYustsslPfyw9MIL0vXX+zY2AAAAH/Nq0b1nzx5J0q233qr58+erVKlS3jw9AMCfrFkjjRolffml1Q4IsLb8ev55awswAAAA2PNM97Jly+w4LQDAH6xcaV3Z/vprqx0YKD36qFVsV6/u29gAAAD8jNeK7vj4eL300ku64oorFB8ff8Gx48eP99a0AIACErRpk1xvvCElJVkdxYpJ3btbxXbVqr4NDgAAwE95rejetGmTsrOzPT+fj8vl8taUAICC8OOPcg0bpqvOLZAWFCT16GHts12lik9DAwAA8HdeK7r/eEs5t5cDQCGwa5f04ovSxx/LZYxMQID06KNyvfgixTYAAMAlsuWZbgCAg+3fb+2zPX26dPasJMncf78OPf20rmrRQq6AAB8HCAAA4BxeK7rvu+++Sx47f/58b00LAPCWtDRp7Fhp0iQpM9Pqa9tWevllmQYNlHPwoG/jAwAAcCCvFd0lS5b01qkAAAUpPV164w1pwgTpxAmrr0ULacwYqWVLq+12+y4+AAAAB/Na0T1jxgxvnQoAUBBOnZLeece6un30qNXXsKH0yitSmzYSC18CAAD8ZTzTDQBFTVaWNHWq9PLLUkqK1VerltW+7z6KbQAAAC/yWtHdsGFDJSUlqVSpUrrhhhsuuDXYxo0bvTUtAOBS5eRIH35orUi+d6/VV6WK1X7kESkw0HexAQAAFFJeK7o7dOigkJAQSVLHjh29dVoAgDckJUnx8dKWLVa7bFlp+HDpiSek4GDfxgYAAFCIea3oHjlyZL4/AwB86KefpCFDpM8/t9oREdJzz0n9+0thYT4NDQAAoCiw9Znu9evXa/v27ZKk2rVrq1GjRnZOBwA45+hRafRoa6G0s2etW8f79pVGjpQiI30dHQAAQJFhS9G9f/9+de7cWatWrVJERIQk6dixY2rWrJlmz56tq6++2o5pAQDZ2dLf/mYV10eOWH133SW9/rp03XW+jQ0AAKAICrDjpE888YSys7O1fft2HTlyREeOHNH27dvldrv1xBNP2DElAOCLL6R69axbx48ckWrXlpYskRYtouAGAADwEVuudC9fvlyrV69WzZo1PX01a9bU22+/rZYtW9oxJQAUXf/+tzRokPTll1Y7MtK6tbxXL6kYO0MCAAD4ki3fxipWrKjs7Ow8/Tk5OSpfvrwdUwJA0ZOWZt1G/re/SW63FBQkDRggvfCCtWAaAAAAfM6W28vHjRun/v37a/369Z6+9evXa8CAAXr99dftmBIAio7MTOsZ7erVpcmTrYL73nulbdukceMouAEAAPyI1650lypVSi6Xy9M+efKkYmJiVOz/b208e/asihUrpscff5x9vAHgzzBGWrDA2gJs926r74YbpPHjpVtu8WVkAAAAOA+vFd0TJ0701qkAAP9r61ZrgbTkZKtdtqw0ZozUrZu1HRgAAAD8kteK7u7du3vrVACAc06dshZFe+MNa7/t0FBr0bShQ6Urr/R1dAAAALgI25e1PXPmjLKysnL1hYeH2z0tADjfokVSXJy0d6/V7thRmjhRqlzZh0EBAADgctiykNrJkycVFxenMmXK6IorrlCpUqVy/QEAXMD+/dL990vt2lkFd8WK0sKF0mefUXADAAA4jC1F97PPPqtvvvlGkydPVkhIiN5//32NGjVK5cuX16xZs+yYEgCc7+xZ6c03peuuk+bPt57VHjzYWpW8fXtfRwcAAIA/wZbby//5z39q1qxZuuWWW9SjRw+1bNlS1157rSpXrqyPPvpIXbt2tWNaAHCu77+XeveWNm2y2jfdZO2/Xa+eb+MCAADAX2LLle4jR46oatWqkqznt48cOSJJatGihVasWGHHlADgTOnp1nPbMTFWwR0RIU2ZIq1aRcENAABQCNhSdFetWlV79uyRJNWqVUtz586VZF0Bj4iIsGNKAHAWY6Q5c6RataRJk6x2167Sjh3WFe8AW/56BgAAQAGz5Vtdjx499MMPP0iSnnvuOU2aNEmhoaF65plnNGTIEDumBADn2L1battWevhhKSVFql5d+vpr6cMPpehoX0cHAAAAL7Llme5nnnnG83Pr1q21fft2bdy4Uddee63qcbskgKIqM1N6/XXp5ZelM2ek4GApIUF67jlr/20AAAAUOrbv0y1JVapUUZUqVQpiKgDwT8uXS336WLePS9Ltt0vvvivVqOHbuAAAAGAr2x4aTEpKUrt27VStWjVVq1ZN7dq109dff23XdADgnzIyrGL7llusgrtMGes28qVLKbgBAACKAFuK7nfffVexsbG68sorNWDAAA0YMEDh4eG66667NGnSJDumBAD/89VXUp061tZfkvTkk1bh3bWr5HL5NjYAAAAUCFtuLx8zZowmTJiguLg4T9/TTz+t5s2ba8yYMerXr58d0wKAf0hPlwYNkqZNs9rXXGP9fOutvo0LAAAABc6WK93Hjh1TbGxsnv4777xT6enpl32+SZMmqUqVKgoNDVVMTIzWrVt30fn79euncuXKKSQkRDVq1NDixYsve14AuGyLF0vXX//fgrt/f2nLFgpuAACAIsqWort9+/b67LPP8vQvXLhQ7dq1u6xzzZkzR/Hx8Ro5cqQ2btyo+vXrq02bNjp48GC+47OysnTHHXdo7969+vTTT7Vz505NnTpVFSpU+FPvBQAuydGj0mOPSXffLf32m1StmrV42ltvSSVK+Do6AAAA+IjXbi9/6623PD/Xrl1br7zyipKTk9W0aVNJ0tq1a7Vq1SoNGjToss47fvx49erVSz169JAkTZkyRYsWLdL06dP13HPP5Rk/ffp0HTlyRKtXr1ZQUJAksXI6AHt9/rm1WNqBA9az2gMHWtuChYX5OjIAAAD4mMsYY7xxomuuuebSJnS59Msvv1zS2KysLIWFhenTTz9Vx44dPf3du3fXsWPHtHDhwjyvueuuu1S6dGmFhYVp4cKFioqKUpcuXTR06FAFBgbmO09mZqYyMzM97YyMDFWsWFGHDx9WZmamoqKiFBBg20Lv8BK32620tDTy5RCFIl+HD8s1cKBcH38sSTI1ashMmyY1a+bjwLyvUOSrCCFfzkK+nIV8OQv5chan5SsjI0OlSpVSenq6wsPDzzvOa1e69+zZ461TeRw6dEg5OTmKjo7O1R8dHa0d5/a6/R+//PKLvvnmG3Xt2lWLFy/Wrl279NRTTyk7O1sjR47M9zWJiYkaNWpUnv60tDS53W4ZYxyR9KLO7XYrPT2dfDmE0/MVsmiRwhMSFJCWJhMQoJN9+ujE4MFS8eLSeR5/cTKn56uoIV/OQr6chXw5C/lyFqfl6/jx45c0zpbVy//o3IV0VwFtj+N2u1WmTBm99957CgwMVKNGjfTbb79p3Lhx5y26ExISFB8f72mfu9IdFRXFlW4Hcbvdcrlc5MshHJuvtDS5+veXa948SZKpXVvm/fcVFhOjwnwzuWPzVUSRL2chX85CvpyFfDmL0/IVGhp6SeNsK7pnzZqlcePG6eeff5Yk1ahRQ0OGDNGjjz56yeeIjIxUYGCgUlNTc/WnpqaqbNmy+b6mXLlyCgoKynUr+XXXXaeUlBRlZWUpODg4z2tCQkIUEhKSpz8gIEAul0sBAQGOSDpEvhzGUfkyRpo3T+rXTzp0SAoMlIYOlWvECLny+fujMHJUvkC+HIZ8OQv5chby5SxOytelxmjLOxk/frz69u2ru+66S3PnztXcuXMVGxurPn36aMKECZd8nuDgYDVq1EhJSUmePrfbraSkJM8Cbf+refPm2rVrl9xut6fvp59+Urly5fItuAHgolJTpQcekDp1sgruOnWk776TXnlFKiIFNwAAAP4cW650v/3225o8ebK6devm6Wvfvr2uv/56vfjii3rmmWcu+Vzx8fHq3r27GjdurCZNmmjixIk6efKkZzXzbt26qUKFCkpMTJQk9e3bV++8844GDBig/v376+eff9aYMWP09NNPe/dNAiga5s2T+vaVDh+WihWTnn9eeuEFif/EAwAAwCWwpeg+cOCAmuWzem+zZs104MCByzpXp06dlJaWphEjRiglJUUNGjTQkiVLPIur7du3L9dl/YoVK+rLL7/UM888o3r16qlChQoaMGCAhg4d+tfeFICi5cgR61by2bOtdv360syZUoMGvowKAAAADmNL0X3ttddq7ty5ev7553P1z5kzR9WrV7/s88XFxSkuLi7fY8nJyXn6mjZtqrVr1172PAAgSfriC6lnT2vf7cBAKSFBGj6cq9sAAAC4bLYU3aNGjVKnTp20YsUKNW/eXJK0atUqJSUlae7cuXZMCQB/3YkT0qBB0nvvWe2aNaVZs6QmTXwbFwAAABzLloXU7r//fq1bt06RkZFasGCBFixYoMjISK1bt0733nuvHVMCwF+zcqVUr95/C+4BA6SNGym4AQAA8Jd4/Up3dna2evfureHDh+vDDz/09ukBwLvOnLFuHX/jDWtbsEqVrGe3b73V15EBAACgEPD6le6goCD94x//8PZpAcD7NmyQGjWSXn/dKrgff1z68UcKbgAAAHiNLbeXd+zYUQsWLLDj1ADw12VnS6NHSzfdJG3bJkVHS59/Lk2bJoWH+zo6AAAAFCK2LKRWvXp1jR49WqtWrVKjRo10xRVX5DrOntkAfGb7dqlbN2n9eqv9wAPS5MlSZKRv4wIAAEChZEvRPW3aNEVERGjDhg3asGFDrmMul4uiG0DBc7ulN9+Unn/eeo47IkKaNEnq3FlyuXwdHQAAAAopW4ruPXv22HFaAPhz9u6VHntMWr7casfGSu+/L1Wo4MuoAAAAUAR4veheu3at/vnPfyorK0u33367YmNjvT0FAFwaY6Tp06WBA609uK+4wlql/MknuboNAACAAuHVovvTTz9Vp06dVLx4cQUFBWn8+PF69dVXNXjwYG9OAwAXl5oq9eol/fOfVrtFC2srsGrVfBoWAAAAihavrl6emJioXr16KT09XUePHtXLL7+sMWPGeHMKALi4BQukunWtgjs4WHrtNSk5mYIbAAAABc6rRffOnTs1ePBgBQYGSpIGDRqk48eP6+DBg96cBgDyl5Eh9ewp3XuvlJYm1atnrVI+ZIj0/38vAQAAAAXJq0X3qVOnFP6HPW6Dg4MVGhqqEydOeHMaAMhr5Uqpfn3rGW6XSxo6VFq3zrriDQAAAPiI1xdSe//991WiRAlP++zZs5o5c6Yi/7AHLluGAfCazExpxAhp3Dhr4bQqVaRZs6SWLX0dGQAAAODdortSpUqaOnVqrr6yZcvq73//u6fNPt0AvObHH6VHHpG2bLHajz8uTZgg/eGOGwAAAMCXvFp0792715unA4D85eRYxfULL0hZWVJUlDR1qtShg68jAwAAAHLx+u3lAGCrvXul7t2lFSus9j33WAV3dLRPwwIAAADy49WF1ADANsZY+2zXq2cV3CVKWMX2woUU3AAAAPBbXOkG4P/S0qTevaXPPrPazZtbi6VVrerbuAAAAICL4Eo3AP/2r39Z23599pkUFCQlJkrLl1NwAwAAwBG40g3AP504IQ0aJL33ntW+/nrpww+lBg18GhYAAABwOWy70r17924NGzZMnTt31sGDByVJX3zxhf7973/bNSWAwmLtWumGG6yC2+WS4uOl9espuAEAAOA4thTdy5cvV926dfXdd99p/vz5OnHihCTphx9+0MiRI+2YEkBhkJ0tjRwptWgh7dolVawoJSVJb7whhYb6OjoAAADgstlSdD/33HN6+eWXtXTpUgUHB3v6b7vtNq1du9aOKQE43U8/WQukjR5t7cPdtau0ZYt0662+jgwAAAD402wpun/88Ufde++9efrLlCmjQ4cO2TElAKcyRpoyxbqd/PvvpYgIafZs6/ntiAhfRwcAAAD8JbYU3RERETpw4ECe/k2bNqlChQp2TAnAgQIOHpSrfXupb1/p1Cnp9tulH3+UOnXydWgAAACAV9hSdD/88MMaOnSoUlJS5HK55Ha7tWrVKg0ePFjdunWzY0oATrNwoSJvvVWuxYulkBBpwgTpq6+kq6/2dWQAAACA19hSdI8ZM0a1atVSxYoVdeLECdWuXVs333yzmjVrpmHDhtkxJQCnOH5ceuIJBdx3nwKOHJGpX99amXzgQCnAtg0VAAAAAJ+wZZ/u4OBgTZ06VcOHD9fWrVt14sQJ3XDDDapevbod0wFwitWrpUcflX75Rcbl0sl+/RT22mtyFS/u68gAAAAAW9hSdH/77bdq0aKFKlWqpEqVKtkxBQAnyc6WRo2SEhMlt1uqXFlm5kydqFVLYSEhvo4OAAAAsI0t93Ledtttuuaaa/T8889r27ZtdkwBwCl27JCaNpVeecUquLt1k374Qbr5Zl9HBgAAANjOlqL7999/16BBg7R8+XLVqVNHDRo00Lhx47R//347pgPgj4yRJk2SGjaUNmyQSpeW5s2TPvhAKlnS19EBAAAABcKWojsyMlJxcXFatWqVdu/erQcffFAffPCBqlSpottuu82OKQH4kwMHpLvukuLipNOnpTvvtLYCe+ABX0cGAAAAFCjblwq+5ppr9Nxzz2ns2LGqW7euli9fbveUAHxpwQKpbl1pyRIpNFR66y3piy+k8uV9HRkAAABQ4GwtuletWqWnnnpK5cqVU5cuXVSnTh0tWrTIzikB+MqJE1KvXtK990qHD0sNGli3lffvz1ZgAAAAKLJsWb08ISFBs2fP1u+//6477rhDb775pjp06KCwsDA7pgPga2vXSo88Iu3eLblc0rPPSqNHS8HBvo4MAAAA8Clbiu4VK1ZoyJAheuihhxQZGWnHFAD8wdmz1qrkL70k5eRIlSpJs2ZJrVr5OjIAAADAL9hSdK9atcqO0wLwJ7t2WVe3v/vOanftKr3zjhQR4dOwAAAAAH/itaL7888/V9u2bRUUFKTPP//8gmPbt2/vrWkBFDRjpOnTpQEDpJMnre2/Jk+WOnf2dWQAAACA3/Fa0d2xY0elpKSoTJky6tix43nHuVwu5eTkeGtaAAXp0CFrsbQFC6x2q1bW7eSVKvk0LAAAAMBfea3odrvd+f4MoJBYskTq0UNKSZGCgqxnuePjpcBAX0cGAAAA+C1b9vGZNWuWMjMz8/RnZWVp1qxZdkwJwC6nT1vbfrVtaxXctWtL69ZJQ4ZQcAMAAAAXYUvR3aNHD6Wnp+fpP378uHr06GHHlADssGmT1KiRtUCaZBXf69dbe3ADAAAAuChbim5jjFwuV57+/fv3q2TJknZMCcCbcnKkV1+VYmKk7dulsmWlL76Q3npLKl7c19EBAAAAjuHVLcNuuOEGuVwuuVwu3X777SpW7L+nz8nJ0Z49exQbG+vNKQF42759Urdu0vLlVrtjR2nqVCky0qdhAQAAAE7k1aL73KrlmzdvVps2bVSiRAnPseDgYFWpUkX333//ZZ930qRJGjdunFJSUlS/fn29/fbbatKkyUVfN3v2bHXu3FkdOnTQgnOrLQM4vzlzpN69pfR06YorpDfflB5/XMrnzhUAAAAAF+fVonvkyJGSpCpVqqhTp04KDQ39y+ecM2eO4uPjNWXKFMXExGjixIlq06aNdu7cqTJlypz3dXv37tXgwYPVsmXLvxwDUOhlZFjPa59b6LBJE+mjj6Rrr/VtXAAAAIDD2fJMd/fu3b1ScEvS+PHj1atXL/Xo0UO1a9fWlClTFBYWpunTp5/3NTk5OeratatGjRqlqlWreiUOoNBas8ZaGG3WLCkgQBo+XPr2WwpuAAAAwAu8eqX7nJycHE2YMEFz587Vvn37lJWVlev4kSNHLuk8WVlZ2rBhgxISEjx9AQEBat26tdasWXPe140ePVplypRRz549tXLlyovOk5mZmWuLs4yMDEnWfuPGGPYddwjydZnOnpXrlVekV16RKydHpnJlmVmzpBYtrOM2/x7Jl7OQL2chX85CvpyFfDkL+XIWp+XrUuO0pegeNWqU3n//fQ0aNEjDhg3TCy+8oL1792rBggUaMWLEJZ/n0KFDysnJUXR0dK7+6Oho7dixI9/XfPvtt5o2bZo2b958yfMkJiZq1KhRefrT0tI8iQ8IsOWmAHiR2+1Weno6+boEgb/+qpJxcQpev16SdPq++5SRmCgTHi4dPFggMZAvZyFfzkK+nIV8OQv5chby5SxOy9fx48cvaZwtRfdHH32kqVOn6u6779aLL76ozp07q1q1aqpXr57Wrl2rp59+2o5pdfz4cT366KOaOnWqIi9jpeWEhATFx8d72hkZGapYsaKioqKUmZmpqKgoRyS9qHO73XK5XOTrQoyR/v53ufr3l+vECZnwcJlJkxTSpYuiCjgU8uUs5MtZyJezkC9nIV/OQr6cxWn5utRHqm0pulNSUlS3bl1JUokSJZSeni5JateunYYPH37J54mMjFRgYKBSU1Nz9aempqps2bJ5xu/evVt79+7VPffc4+k7d8m/WLFi2rlzp6pVq5bndSEhIQoJCcnTHxAQIJfLpYCAAEckHSJfF3L0qNSnjzR3rtVu2VKuv/9drsqVfRYS+XIW8uUs5MtZyJezkC9nIV/O4qR8XWqMtryTq6++WgcOHJAkVatWTV999ZUk6fvvv8+3uD2f4OBgNWrUSElJSZ4+t9utpKQkNW3aNM/4WrVq6ccff9TmzZs9f9q3b69bb71VmzdvVsWKFf/iOwMcKjlZqlfPKriLFZNeeUVatkzyYcENAAAAFAW2XOm+9957lZSUpJiYGPXv31+PPPKIpk2bpn379umZZ565rHPFx8ere/fuaty4sZo0aaKJEyfq5MmT6tGjhySpW7duqlChghITExUaGqo6derken1ERIQk5ekHioSsLGnkSOnVV61by6tXt7YCu/FGX0cGAAAAFAm2FN1jx471/NypUydVqlRJa9asUfXq1XPd+n0pOnXqpLS0NI0YMUIpKSlq0KCBlixZ4llcbd++fY649QAocDt3Sl26SBs3Wu0nnpAmTJBKlPBtXAAAAEARYkvR/b+aNm2a7+3glyouLk5xcXH5HktOTr7ga2fOnPmn5wUcyRhp6lRp4EDp9GmpdGmrfd99vo4MAAAAKHK8VnR//vnnlzy2ffv23poWwB8dOSL17CktWGC1W7eWZs6UKlTwZVQAAABAkeW1ortjx46XNM7lciknJ8db0wI4Z/ly6ZFHpP37paAgKTFReuYZiccvAAAAAJ/xWtF9bmsuAAXs7Flp9GhrRXK3W6pRQ/rkE6lhQ19HBgAAABR5BfJMNwCb/PqrtVja6tVWu0cP6a23WCwNAAAA8BO2FN2jR4++4PERI0bYMS1QtMybJ/XqJaWnS+Hh0pQpUufOvo4KAAAAwB/YUnR/9tlnudrZ2dnas2ePihUrpmrVqlF0A3/FyZPWs9pTp1rtm26SPv5YuuYa38YFAAAAIA9biu5Nmzbl6cvIyNBjjz2me++9144pgaLhhx+khx+WduyQXC4pIUF68UVr4TQAAAAAfqfAljUODw/XqFGjNHz48IKaEig8jJHefluKibEK7nLlpK+/thZPo+AGAAAA/FaBLqSWnp6u9PT0gpwScL5Dh6THH5f++U+r3a6dNGOGFBnp27gAAAAAXJQtRfdbb72Vq22M0YEDB/T3v/9dbdu2tWNKoHD65hvp0Uel33+XgoOl11+X4uKsW8sBAAAA+D1biu4JEybkagcEBCgqKkrdu3dXQkKCHVMChUt2tvWsdmKidWt5rVrS7NlS/fq+jgwAAADAZbCl6N6zZ48dpwWKhj17rL2316612k88IU2cKF1xhU/DAgAAAHD5CmwhNQCXYM4cqUEDq+AuWdJqT51KwQ0AAAA4lC1Xus+cOaO3335by5Yt08GDB+V2u3Md37hxox3TAs516pQ0YID0/vtWu2lTa+/tKlV8GhYAAACAv8aWortnz5766quv9MADD6hJkyZysegTcH4//ih16iRt324tkPb889bz3MUKdHMBAAAAADaw5Vv9v/71Ly1evFjNmze34/RA4WCMNGWK9MwzUmamtff2hx9Kt93m68gAAAAAeIktRXeFChV05ZVX2nFqoHA4etRaIG3+fKvdtq30wQdSVJRv4wIAAADgVbYspPbGG29o6NCh+vXXX+04PeBsq1ZZi6XNny8FBUlvvCH9618U3AAAAEAhZMuV7saNG+vMmTOqWrWqwsLCFBQUlOv4kSNH7JgW8G85OdKrr0ojRlg/V6tm7b3duLGvIwMAAABgE1uK7s6dO+u3337TmDFjFB0dzUJqwIED0iOPSN98Y7W7dJEmT5bCw30bFwAAAABb2VJ0r169WmvWrFH9+vXtOD3gLF98IXXvLqWlSWFh0qRJVpv/jAIAAAAKPVue6a5Vq5ZOnz5tx6kB58jKkgYPlu66yyq469eXNmyQHnuMghsAAAAoImwpuseOHatBgwYpOTlZhw8fVkZGRq4/QKG3e7fUvLm1SJokxcVJa9dKtWr5Ni4AAAAABcqW28tjY2MlSbfffnuufmOMXC6XcnJy7JgW8A+ffCL17i0dPy6VKiVNny517OjrqAAAAAD4gC1F97Jly+w4LeDfTp6Unn7aKrIlqUUL6eOPpYoVfRsXAAAAAJ+xpehu1aqVHacF/NfmzdLDD0s7d1rPaw8fbv0pZstHDAAAAIBD2FIRrFix4oLHb775ZjumBQqeMdJbb0nPPmstnFa+vPThh9Ktt/o6MgAAAAB+wJai+5ZbbsnT98e9unmmG4VCWprUo4e0aJHVbt9emjZNioz0bVwAAAAA/IYtq5cfPXo015+DBw9qyZIluvHGG/XVV1/ZMSVQsJKSrC3AFi2SQkKkd96RFiyg4AYAAACQiy1XukuWLJmn74477lBwcLDi4+O1YcMGO6YF7JedLY0YIb36qnVr+XXXSbNnS/Xq+ToyAAAAAH6oQFd5io6O1s6dOwtySsB7fvlF6txZWrfOaj/5pDRhghQW5tu4AAAAAPgtW4ruLVu25GobY3TgwAGNHTtWDRo0sGNKwF5/3Hs7IkKaOlV64AFfRwUAAADAz9lSdDdo0EAul0vGmFz9N910k6af28MYcIITJ6y9t2fMsNrNm0sffSRVruzbuAAAAAA4gi1F9549e3K1AwICFBUVpdDQUDumA+yxcaN1O/lPP0kBAdKwYey9DQAAAOCy2FI9VOYqIJzMGGniRGnoUGvhtKuvtvbebtXK15EBAAAAcBivbhn2zTffqHbt2srIyMhzLD09Xddff71WrlzpzSkB7zp4UGrXToqPtwrujh2lzZspuAEAAAD8KV4tuidOnKhevXopPDw8z7GSJUuqd+/eGj9+vDenBLzn66+tvbcXL7b23n73XWn+fOmqq3wdGQAAAACH8mrR/cMPPyg2Nva8x++880726Ib/yc6WnntOuvNOKSVFuv566fvvpb59JZfL19EBAAAAcDCvPtOdmpqqoKCg809WrJjS0tK8OSXw1+zZYy2W9t13Vrt3b2n8ePbeBgAAAOAVXr3SXaFCBW3duvW8x7ds2aJy5cp5c0rgz5szR2rQwCq4IyKkTz+Vpkyh4AYAAADgNV4tuu+66y4NHz5cZ86cyXPs9OnTGjlypNq1a+fNKYHLd/Kk9MQT0sMPSxkZ1t7bmzdL99/v68gAAAAAFDJevb182LBhmj9/vmrUqKG4uDjVrFlTkrRjxw5NmjRJOTk5euGFF7w5JXB5fvjBKrZ37LCe137hBWnkSPbeBgAAAGALr1Ya0dHRWr16tfr27auEhAQZYyRJLpdLbdq00aRJkxQdHe3NKYFLY4w0aZI0eLCUmSmVL2/tvX3rrb6ODAAAAEAh5vXLe5UrV9bixYt19OhR7dq1S8YYVa9eXaVKlfL2VMClOXxY6tlTWrjQardrJ82YIUVG+jYuAAAAAIWebffUlipVSjfeeKNdpwcuzYoVUteu0v79UnCw9Npr0tNPsxUYAAAAgALh1YXU7DJp0iRVqVJFoaGhiomJ0bp16847durUqWrZsqVKlSqlUqVKqXXr1hccj0Lq7FnpxRet28f375eqV5fWrpUGDKDgBgAAAFBg/L7onjNnjuLj4zVy5Eht3LhR9evXV5s2bXTw4MF8xycnJ6tz585atmyZ1qxZo4oVK+rOO+/Ub7/9VsCRw2f+8x/pttukUaMkt1vq3l3auFG64QZfRwYAAACgiPH7onv8+PHq1auXevToodq1a2vKlCkKCwvT9OnT8x3/0Ucf6amnnlKDBg1Uq1Ytvf/++3K73UpKSirgyOELIV98IdcNN0grV0pXXmktljZzplSihK9DAwAAAFAE+fU+SVlZWdqwYYMSEhI8fQEBAWrdurXWrFlzSec4deqUsrOzVbp06fOOyczMVGZmpqedkZEhSXK73TLGyO12/8l3gAJz5ow0aJBKTZkiSTKNG8t8/LFUrZp1tRt+h8+Xs5AvZyFfzkK+nIV8OQv5chan5etS4/TrovvQoUPKycnJs81YdHS0duzYcUnnGDp0qMqXL6/WrVufd0xiYqJGjRqVpz8tLc2T+IAAv78poMgK/OknRfTtq6Bt2yRJJ/r00YmEBGvhtPM8hgDfc7vdSk9P5/PlEOTLWciXs5AvZyFfzkK+nMVp+Tp+/PgljfProvuvGjt2rGbPnq3k5GSFhoaed1xCQoLi4+M97YyMDFWsWFFRUVHKzMxUVFSUI5Je5BgjTZsm18CBcp0+LRMVpaMTJyr8oYcURr78ntvtlsvl4vPlEOTLWciXs5AvZyFfzkK+nMVp+bpQjflHfl10R0ZGKjAwUKmpqbn6U1NTVbZs2Qu+9vXXX9fYsWP19ddfq169ehccGxISopCQkDz9AQEBcrlcCggIcETSi5T0dKl3b2nOHKvdurXMBx8o6/9zRb6cgc+Xs5AvZyFfzkK+nIV8OQv5chYn5etSY/TrdxIcHKxGjRrlWgTt3KJoTZs2Pe/rXnvtNb300ktasmSJGjduXBChoiB99521EvmcOVKxYtLYsdKXX0oX+Y8YAAAAAChofn2lW5Li4+PVvXt3NW7cWE2aNNHEiRN18uRJ9ejRQ5LUrVs3VahQQYmJiZKkV199VSNGjNDHH3+sKlWqKCUlRZJUokQJlWAFa2dzu6Vx46Rhw6x9uKtUkT75RLrppv8eBwAAAAA/4vdFd6dOnZSWlqYRI0YoJSVFDRo00JIlSzyLq+3bty/XZf3JkycrKytLDzzwQK7zjBw5Ui+++GJBhg5vSkmRunWTli612p06SX/7m1SypG/jAgAAAIAL8PuiW5Li4uIUFxeX77Hk5ORc7b1799ofEArWl19aBffBg1Lx4tLbb0uPPy65XL6ODAAAAAAuyK+f6UYRl5UlDRkixcZaBXe9etKGDVLPnhTcAAAAABzBEVe6UQTt3i117ix9/73V7tdPev116RKX5QcAAAAAf0DRDf/z8cdSnz7S8eNSqVLS9OlSx46+jgoAAAAALhtFN/zHiRNS//7SzJlWu2VL6aOPpIoVfRoWAAAAAPxZPNMN/7B5s9S4sVVwBwRII0dK33xDwQ0AAADA0bjSDd8yRnrnHWnwYGvhtAoVrKvbrVr5OjIAAAAA+MsouuE76enWSuT/+IfVvuceacYM6aqrfBsXAAAAAHgJt5fDNzZtkho1sgruoCBp4kRp4UIKbgAAAACFCle6UbCMkd57TxowQMrMlCpXlubOlZo08XVkAAAAAOB1FN0oOCdOSL17W1uCSVK7dtIHH0ilS/s2LgAAAACwCbeXo2Bs3SrdeKNVcAcGSq+9Zt1OTsENAAAAoBDjSjfsN3Om9NRT0unT1urkc+ZIzZv7OioAAAAAsB1XumGfU6ekxx+XevSwCu4777QWUKPgBgAAAFBEUHTDHjt3SjEx1hZgAQHSSy9JX3whRUX5OjIAAAAAKDDcXg7v++QT6cknrYXToqOt57hvu83XUQEAAABAgeNKN7znzBmpb1+pSxer4L7lFmnzZgpuAAAAAEUWRTe8Y/duqVkzacoUqz1smLR0qVS2rG/jAgAAAAAf4vZy/HXz51uLpWVkSFddJX34oRQb6+uoAAAAAMDnuNKNP+/sWSk+Xrr/fqvgbtbMWp2cghsAAAAAJFF04886fNgqridMsNqDB0vJyVLFij4NCwAAAAD8CbeX4/L9+99S+/bSL79IYWHSrFnW1W4AAAAAQC4U3bg8CxdKjzxirU5epYrVrlfP11EBAAAAgF/i9nJcGmOkl16SOnb873Zg339PwQ0AAAAAF8CVblzciRPW6uSffmq14+Kk8eOloCDfxgUAAAAAfo6iGxe2d6/UoYO0ZYtVZL/7rvTEE76OCgAAAAAcgaIb55ecLD3wgLVSeZky1n7czZv7OioAAAAAcAye6UZexlhXtO+4wyq4GzaU1q+n4AYAAACAy0TRjdyysqQ+faR+/aSzZ6UuXaSVK9l/GwAAAAD+BG4vx3+lplr7ba9aJblc0tix0pAh1s8AAAAAgMtG0Q3Lxo3WdmD/+Y8UHi598ol0112+jgoAAAAAHI3byyHNni21aGEV3DVqSOvWUXADAAAAgBdQdBdlOTlSQoLUubN0+rTUtq303XdSzZq+jgwAAAAACgWK7qLq8GGpXTvruW1JevZZ6Z//lCIifBoWAAAAABQmPNNdFK1dKz30kHU7eWioNG2atUo5AAAAAMCruNJdlBgjvfmm1LKlVXBXr27dTk7BDQAAAAC24Ep3UZGeLvXsKf3jH1b7wQel99+3VioHAAAAANiCorso2LzZKrJ37ZKCgqTx46V+/dh/GwAAAABsRtFdmBljPa8dFydlZkqVKknz5klNmvg6MgAAAAAoEnimu7A6eVJ67DGpVy+r4L77bmnTJgpuAAAAAChAFN2F0Y4dUkyMNGuWFBAgJSZKn38ulS7t68gAAAAAoEjh9vLC5pNPrKvbJ09KZctKs2dLrVr5OioAAAAAKJK40l1YZGZKTz1lbf918qR0663W7eQU3AAAAADgMxTdhcGePVLz5tLkyVZ72DBp6VLrSjcAAAAAwGe4vdzpPv9c6t5dOnZMuuoq6cMPpdhYX0cFAAAAABBXup0rO1t69lmpQwer4L7pJut2cgpuAAAAAPAbjii6J02apCpVqig0NFQxMTFat27dBcfPmzdPtWrVUmhoqOrWravFixcXUKQFxO22iutx46z2wIHS8uVSxYo+DQsAAAAAkJvfF91z5sxRfHy8Ro4cqY0bN6p+/fpq06aNDh48mO/41atXq3PnzurZs6c2bdqkjh07qmPHjtq6dWsBR26jgACpfXspPFz69FNpwgQpONjXUQEAAAAA/offF93jx49Xr1691KNHD9WuXVtTpkxRWFiYpk+fnu/4N998U7GxsRoyZIiuu+46vfTSS2rYsKHeeeedAo7cZk8/LW3bJt1/v68jAQAAAACch18vpJaVlaUNGzYoISHB0xcQEKDWrVtrzZo1+b5mzZo1io+Pz9XXpk0bLViw4LzzZGZmKjMz09POyMiQJLndbhlj5Ha7/8K7sFG5ctat5pDkgHwhF/LlLOTLWciXs5AvZyFfzkK+nMVp+brUOP266D506JBycnIUHR2dqz86Olo7duzI9zUpKSn5jk9JSTnvPImJiRo1alSe/rS0NE/iAwL8/qaAIs/tdis9PZ18OQT5chby5Szky1nIl7OQL2chX87itHwdP378ksb5ddFdUBISEnJdHc/IyFDFihUVFRWlzMxMRUVFOSLpRZ3b7ZbL5SJfDkG+nIV8OQv5chby5Szky1nIl7M4LV+hoaGXNM6vi+7IyEgFBgYqNTU1V39qaqrKli2b72vKli17WeMlKSQkRCEhIXn6AwIC5HK5FBAQ4IikQ+TLYciXs5AvZyFfzkK+nIV8OQv5chYn5etSY/TrdxIcHKxGjRopKSnJ0+d2u5WUlKSmTZvm+5qmTZvmGi9JS5cuPe94AAAAAADs4tdXuiUpPj5e3bt3V+PGjdWkSRNNnDhRJ0+eVI8ePSRJ3bp1U4UKFZSYmChJGjBggFq1aqU33nhDd999t2bPnq3169frvffe8+XbAAAAAAAUQX5fdHfq1ElpaWkaMWKEUlJS1KBBAy1ZssSzWNq+fftyXdZv1qyZPv74Yw0bNkzPP/+8qlevrgULFqhOnTq+egsAAAAAgCLK74tuSYqLi1NcXFy+x5KTk/P0Pfjgg3rwwQdtjgoAAAAAgAvz62e6AQAAAABwMopuAAAAAABsQtENAAAAAIBNHPFMd0EzxkiSMjIylJmZqdDQUEfsE1fUud1uHT9+nHw5BPlyFvLlLOTLWciXs5AvZyFfzuK0fGVkZEj6b/14PhTd+Th+/LgkqXLlyj6OBAAAAADgz44fP66SJUue97jLXKwsL4Lcbrd+//13GWNUqVIl/ec//1F4eLivw8JFZGRkqGLFiuTLIciXs5AvZyFfzkK+nIV8OQv5chan5csYo+PHj6t8+fIXvDLPle58BAQE6Oqrr/bcLhAeHu6IpMNCvpyFfDkL+XIW8uUs5MtZyJezkC9ncVK+LnSF+xz/v1EeAAAAAACHougGAAAAAMAmFN0XEBISopEjRyokJMTXoeASkC9nIV/OQr6chXw5C/lyFvLlLOTLWQprvlhIDQAAAAAAm3ClGwAAAAAAm1B0AwAAAABgE4puAAAAAABsQtENAAAAAIBNKLovYNKkSapSpYpCQ0MVExOjdevW+TqkQi8xMVE33nijrrzySpUpU0YdO3bUzp07c4255ZZb5HK5cv3p06dPrjH79u3T3XffrbCwMJUpU0ZDhgzR2bNnc41JTk5Ww4YNFRISomuvvVYzZ860++0VOi+++GKeXNSqVctz/MyZM+rXr5+uuuoqlShRQvfff79SU1NznYNcFZwqVarkyZfL5VK/fv0k8dnytRUrVuiee+5R+fLl5XK5tGDBglzHjTEaMWKEypUrp+LFi6t169b6+eefc405cuSIunbtqvDwcEVERKhnz546ceJErjFbtmxRy5YtFRoaqooVK+q1117LE8u8efNUq1YthYaGqm7dulq8eLHX36/TXShf2dnZGjp0qOrWrasrrrhC5cuXV7du3fT777/nOkd+n8mxY8fmGkO+vONin6/HHnssTy5iY2NzjeHzVXAulq/8/i1zuVwaN26cZwyfr4JxKd/dC/L7oN/Wbwb5mj17tgkODjbTp083//73v02vXr1MRESESU1N9XVohVqbNm3MjBkzzNatW83mzZvNXXfdZSpVqmROnDjhGdOqVSvTq1cvc+DAAc+f9PR0z/GzZ8+aOnXqmNatW5tNmzaZxYsXm8jISJOQkOAZ88svv5iwsDATHx9vtm3bZt5++20TGBholixZUqDv1+lGjhxprr/++ly5SEtL8xzv06ePqVixoklKSjLr1683N910k2nWrJnnOLkqWAcPHsyVq6VLlxpJZtmyZcYYPlu+tnjxYvPCCy+Y+fPnG0nms88+y3V87NixpmTJkmbBggXmhx9+MO3btzfXXHONOX36tGdMbGysqV+/vlm7dq1ZuXKlufbaa03nzp09x9PT0010dLTp2rWr2bp1q/nkk09M8eLFzd/+9jfPmFWrVpnAwEDz2muvmW3btplhw4aZoKAg8+OPP9r+O3CSC+Xr2LFjpnXr1mbOnDlmx44dZs2aNaZJkyamUaNGuc5RuXJlM3r06FyfuT/+e0e+vOdin6/u3bub2NjYXLk4cuRIrjF8vgrOxfL1xzwdOHDATJ8+3bhcLrN7927PGD5fBeNSvrsX1PdBf67fKLrPo0mTJqZfv36edk5OjilfvrxJTEz0YVRFz8GDB40ks3z5ck9fq1atzIABA877msWLF5uAgACTkpLi6Zs8ebIJDw83mZmZxhhjnn32WXP99dfnel2nTp1MmzZtvPsGCrmRI0ea+vXr53vs2LFjJigoyMybN8/Tt337diPJrFmzxhhDrnxtwIABplq1asbtdhtj+Gz5k//9kul2u03ZsmXNuHHjPH3Hjh0zISEh5pNPPjHGGLNt2zYjyXz//feeMV988YVxuVzmt99+M8YY8+6775pSpUp58mWMMUOHDjU1a9b0tB966CFz991354onJibG9O7d26vvsTDJryj4X+vWrTOSzK+//urpq1y5spkwYcJ5X0O+7HG+ortDhw7nfQ2fL9+5lM9Xhw4dzG233Zarj8+Xb/zvd/eC/D7oz/Ubt5fnIysrSxs2bFDr1q09fQEBAWrdurXWrFnjw8iKnvT0dElS6dKlc/V/9NFHioyMVJ06dZSQkKBTp055jq1Zs0Z169ZVdHS0p69NmzbKyMjQv//9b8+YP+b33Bjye/l+/vlnlS9fXlWrVlXXrl21b98+SdKGDRuUnZ2d6/dcq1YtVapUyfN7Jle+k5WVpQ8//FCPP/64XC6Xp5/Pln/as2ePUlJScv1uS5YsqZiYmFyfp4iICDVu3NgzpnXr1goICNB3333nGXPzzTcrODjYM6ZNmzbauXOnjh496hlDDr0vPT1dLpdLERERufrHjh2rq666SjfccIPGjRuX63ZK8lWwkpOTVaZMGdWsWVN9+/bV4cOHPcf4fPmv1NRULVq0SD179sxzjM9Xwfvf7+4F9X3Q3+u3Yr4OwB8dOnRIOTk5uRIvSdHR0dqxY4ePoip63G63Bg4cqObNm6tOnTqe/i5duqhy5coqX768tmzZoqFDh2rnzp2aP3++JCklJSXf3J07dqExGRkZOn36tIoXL27nWys0YmJiNHPmTNWsWVMHDhzQqFGj1LJlS23dulUpKSkKDg7O8wUzOjr6onk4d+xCY8jVX7NgwQIdO3ZMjz32mKePz5b/Ovf7ze93+8fffZkyZXIdL1asmEqXLp1rzDXXXJPnHOeOlSpV6rw5PHcOXL4zZ85o6NCh6ty5s8LDwz39Tz/9tBo2bKjSpUtr9erVSkhI0IEDBzR+/HhJ5KsgxcbG6r777tM111yj3bt36/nnn1fbtm21Zs0aBQYG8vnyYx988IGuvPJK3Xfffbn6+XwVvPy+uxfU98GjR4/6df1G0Q2/1a9fP23dulXffvttrv4nn3zS83PdunVVrlw53X777dq9e7eqVatW0GEWaW3btvX8XK9ePcXExKhy5cqaO3cuxZWfmzZtmtq2bavy5ct7+vhsAd6XnZ2thx56SMYYTZ48Odex+Ph4z8/16tVTcHCwevfurcTERIWEhBR0qEXaww8/7Pm5bt26qlevnqpVq6bk5GTdfvvtPowMFzN9+nR17dpVoaGhufr5fBW88313B6uX5ysyMlKBgYF5VtVLTU1V2bJlfRRV0RIXF6d//etfWrZsma6++uoLjo2JiZEk7dq1S5JUtmzZfHN37tiFxoSHh1Ms/gURERGqUaOGdu3apbJlyyorK0vHjh3LNeaPnyNy5Ru//vqrvv76az3xxBMXHMdny3+c+/1e6N+lsmXL6uDBg7mOnz17VkeOHPHKZ45//y7fuYL7119/1dKlS3Nd5c5PTEyMzp49q71790oiX75UtWpVRUZG5vr7j8+X/1m5cqV27tx50X/PJD5fdjvfd/eC+j7o7/UbRXc+goOD1ahRIyUlJXn63G63kpKS1LRpUx9GVvgZYxQXF6fPPvtM33zzTZ7bfvKzefNmSVK5cuUkSU2bNtWPP/6Y6x/Hc192ateu7Rnzx/yeG0N+/5oTJ05o9+7dKleunBo1aqSgoKBcv+edO3dq3759nt8zufKNGTNmqEyZMrr77rsvOI7Plv+45pprVLZs2Vy/24yMDH333Xe5Pk/Hjh3Thg0bPGO++eYbud1uz3+gNG3aVCtWrFB2drZnzNKlS1WzZk2VKlXKM4Yc/nXnCu6ff/5ZX3/9ta666qqLvmbz5s0KCAjw3MZMvnxn//79Onz4cK6///h8+Z9p06apUaNGql+//kXH8vmyx8W+uxfU90G/r998vJCb35o9e7YJCQkxM2fONNu2bTNPPvmkiYiIyLWqHryvb9++pmTJkiY5OTnXFg+nTp0yxhiza9cuM3r0aLN+/XqzZ88es3DhQlO1alVz8803e85xbtuBO++802zevNksWbLEREVF5bvtwJAhQ8z27dvNpEmT2NboTxg0aJBJTk42e/bsMatWrTKtW7c2kZGR5uDBg8YYa4uISpUqmW+++casX7/eNG3a1DRt2tTzenJV8HJyckylSpXM0KFDc/Xz2fK948ePm02bNplNmzYZSWb8+PFm06ZNntWux44dayIiIszChQvNli1bTIcOHfLdMuyGG24w3333nfn2229N9erVc21pdOzYMRMdHW0effRRs3XrVjN79mwTFhaWZ4ucYsWKmddff91s377djBw5ki1y8nGhfGVlZZn27dubq6++2mzevDnXv2fnVuJdvXq1mTBhgtm8ebPZvXu3+fDDD01UVJTp1q2bZw7y5T0Xytfx48fN4MGDzZo1a8yePXvM119/bRo2bGiqV69uzpw54zkHn6+Cc7G/D42xtvwKCwszkydPzvN6Pl8F52Lf3Y0puO+D/ly/UXRfwNtvv20qVapkgoODTZMmTczatWt9HVKhJynfPzNmzDDGGLNv3z5z8803m9KlS5uQkBBz7bXXmiFDhuTaS9gYY/bu3Wvatm1rihcvbiIjI82gQYNMdnZ2rjHLli0zDRo0MMHBwaZq1aqeOXDpOnXqZMqVK2eCg4NNhQoVTKdOncyuXbs8x0+fPm2eeuopU6pUKRMWFmbuvfdec+DAgVznIFcF68svvzSSzM6dO3P189nyvWXLluX791/37t2NMda2YcOHDzfR0dEmJCTE3H777XnyePjwYdO5c2dTokQJEx4ebnr06GGOHz+ea8wPP/xgWrRoYUJCQkyFChXM2LFj88Qyd+5cU6NGDRMcHGyuv/56s2jRItvet1NdKF979uw5779ny5YtM8YYs2HDBhMTE2NKlixpQkNDzXXXXWfGjBmTq8gzhnx5y4XyderUKXPnnXeaqKgoExQUZCpXrmx69eqV54s6n6+Cc7G/D40x5m9/+5spXry4OXbsWJ7X8/kqOBf77m5MwX4f9Nf6zWWMMTZdRAcAAAAAoEjjmW4AAAAAAGxC0Q0AAAAAgE0ougEAAAAAsAlFNwAAAAAANqHoBgAAAADAJhTdAAAAAADYhKIbAAAAAACbUHQDAAAAAGATim4AAHBBt9xyiwYOHOjrMAAAcCSKbgAACrF77rlHsbGx+R5buXKlXC6XtmzZUsBRAQBQdFB0AwBQiPXs2VNLly7V/v378xybMWOGGjdurHr16vkgMgAAigaKbgAACrF27dopKipKM2fOzNV/4sQJzZs3Tx07dlTnzp1VoUIFhYWFqW7duvrkk08ueE6Xy6UFCxbk6ouIiMg1x3/+8x899NBDioiIUOnSpdWhQwft3bvXO28KAAAHoegGAKAQK1asmLp166aZM2fKGOPpnzdvnnJycvTII4+oUaNGWrRokbZu3aonn3xSjz76qNatW/en58zOzlabNm105ZVXauXKlVq1apVKlCih2NhYZWVleeNtAQDgGBTdAAAUco8//rh2796t5cuXe/pmzJih+++/X5UrV9bgwYPVoEEDVa1aVf3791dsbKzmzp37p+ebM2eO3G633n//fdWtW1fXXXedZsyYoX379ik5OdkL7wgAAOeg6AYAoJCrVauWmjVrpunTp0uSdu3apZUrV6pnz57KycnRSy+9pLp166p06dIqUaKEvvzyS+3bt+9Pz/fDDz9o165duvLKK1WiRAmVKFFCpUuX1pkzZ7R7925vvS0AAByhmK8DAAAA9uvZs6f69++vSZMmacaMGapWrZpatWqlV199VW+++aYmTpyounXr6oorrtDAgQMveBu4y+XKdau6ZN1Sfs6JEyfUqFEjffTRR3leGxUV5b03BQCAA1B0AwBQBDz00EMaMGCAPv74Y82aNUt9+/aVy+XSqlWr1KFDBz3yyCOSJLfbrZ9++km1a9c+77mioqJ04MABT/vnn3/WqVOnPO2GDRtqzpw5KlOmjMLDw+17UwAAOAC3lwMAUASUKFFCnTp1UkJCgg4cOKDHHntMklS9enUtXbpUq1ev1vbt29W7d2+lpqZe8Fy33Xab3nnnHW3atEnr169Xnz59FBQU5DnetWtXRUZGqkOHDlq5cqX27Nmj5ORkPf300/luXQYAQGFG0Q0AQBHRs2dPHT16VG3atFH58uUlScOGDVPDhg3Vpk0b3XLLLSpbtqw6dux4wfO88cYbqlixolq2bKkuXbpo8ODBCgsL8xwPCwvTihUrVKlSJd1333267rrr1LNnT505c4Yr3wCAIsdl/vehLAAAAAAA4BVc6QYAAAAAwCYU3QAAAAAA2ISiGwAAAAAAm1B0AwAAAABgE4puAAAAAABsQtENAAAAAIBNKLoBAAAAALAJRTcAAAAAADah6AYAAAAAwCYU3QAAAAAA2ISiGwAAAAAAm/wfvTxHF0h3l5oAAAAASUVORK5CYII=",
|
||
"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: 7128.346\n",
|
||
"variance: 16790598.000\n",
|
||
"std: 4097.633\n",
|
||
"min: 839.628\n",
|
||
"max: 25132.535\n",
|
||
"median: 6553.444\n",
|
||
"\n",
|
||
"Statistiche Target Reali:\n",
|
||
"mean: 7152.223\n",
|
||
"variance: 17124484.000\n",
|
||
"std: 4138.174\n",
|
||
"min: 470.396\n",
|
||
"max: 27987.883\n",
|
||
"median: 6554.293\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 avg_oil_prod\n",
|
||
"\n",
|
||
"Statistiche Predizioni:\n",
|
||
"mean: 6512.859\n",
|
||
"variance: 13977679.000\n",
|
||
"std: 3738.673\n",
|
||
"min: 775.591\n",
|
||
"max: 22695.547\n",
|
||
"median: 5988.284\n",
|
||
"\n",
|
||
"Statistiche Target Reali:\n",
|
||
"mean: 6535.022\n",
|
||
"variance: 14223723.000\n",
|
||
"std: 3771.435\n",
|
||
"min: 443.685\n",
|
||
"max: 24585.691\n",
|
||
"median: 5992.125\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: 60335.375\n",
|
||
"variance: 852123200.000\n",
|
||
"std: 29191.148\n",
|
||
"min: 11271.769\n",
|
||
"max: 141681.609\n",
|
||
"median: 59481.145\n",
|
||
"\n",
|
||
"Statistiche Target Reali:\n",
|
||
"mean: 60140.859\n",
|
||
"variance: 880151488.000\n",
|
||
"std: 29667.348\n",
|
||
"min: 8168.099\n",
|
||
"max: 151610.656\n",
|
||
"median: 59167.406\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
|
||
}
|