diff --git a/src/models/olive_oli/olive_oil-512-60_30.ipynb b/src/models/olive_oli/olive_oil-512-60_30.ipynb new file mode 100644 index 0000000..99a7a3e --- /dev/null +++ b/src/models/olive_oli/olive_oil-512-60_30.ipynb @@ -0,0 +1,3402 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hit:1 http://archive.ubuntu.com/ubuntu jammy InRelease\n", + "Get:2 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB] \n", + "Hit:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", + "Get:4 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB] \n", + "Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease \n", + "Fetched 257 kB in 1s (369 kB/s) \n", + "Reading package lists... Done\n", + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... Done\n", + "graphviz is already the newest version (2.42.2-6ubuntu0.1).\n", + "0 upgraded, 0 newly installed, 0 to remove and 121 not upgraded.\n", + "Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.0.0)\n", + "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.6.3)\n", + "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (23.5.26)\n", + "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", + "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n", + "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.9.0)\n", + "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (16.0.6)\n", + "Requirement already satisfied: ml-dtypes==0.2.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n", + "Requirement already satisfied: numpy>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.26.0)\n", + "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.3.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow) (23.1)\n", + "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", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from tensorflow) (68.2.2)\n", + "Requirement already satisfied: six>=1.12.0 in /usr/lib/python3/dist-packages (from tensorflow) (1.16.0)\n", + "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.3.0)\n", + "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.8.0)\n", + "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.14.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.37.1)\n", + "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.58.0)\n", + "Requirement already satisfied: tensorboard<2.15,>=2.14 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n", + "Requirement already satisfied: tensorflow-estimator<2.15,>=2.14.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n", + "Requirement already satisfied: keras<2.15,>=2.14.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n", + "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", + "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.23.1)\n", + "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (1.0.0)\n", + "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", + "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", + "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", + "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", + "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", + "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", + "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", + "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", + "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", + "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", + "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", + "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", + "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.15,>=2.14->tensorflow) (2.1.3)\n", + "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.5.0)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\n", + "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "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", + "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", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.1.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.3)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow\n", + "!pip install numpy\n", + "!pip install pandas\n", + "\n", + "!pip install keras\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a467d3f0dfd9beab", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-07 09:08:36.583435: 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", + "2024-12-07 09:08:36.583487: 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", + "2024-12-07 09:08:36.583541: 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", + "2024-12-07 09:08:36.594504: 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": [ + "2024-12-07 09:08:39.452923: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:81:00.0, compute capability: 8.9\n" + ] + } + ], + "source": [ + "import 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": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-07_09-08_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": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-07_09-08_plots/variety_comparison_avg_oil_production_l_ha.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-07_09-08_plots/variety_comparison_avg_water_need_m³_ha.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-07_09-08_plots/variety_comparison_oil_efficiency_l_kg.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-07_09-08_plots/variety_comparison_water_efficiency_l_oil_m³_water.png\n", + "Plot salvato come: .//2024-12-07_09-08_plots/efficiency_vs_production.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-07_09-08_plots/water_efficiency_vs_production.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-07_09-08_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) * 0.85) # 25% validation\n", + " # Il resto rimane 15% test\n", + "\n", + " train_indices = indices[:train_idx]\n", + " val_indices = indices[train_idx:val_idx]\n", + " test_indices = indices[val_idx:]\n", + "\n", + " # Split dei dati\n", + " X_temporal_train = X_temporal[train_indices]\n", + " X_temporal_val = X_temporal[val_indices]\n", + " X_temporal_test = X_temporal[test_indices]\n", + "\n", + " X_static_train = X_static[train_indices]\n", + " X_static_val = X_static[val_indices]\n", + " X_static_test = X_static[test_indices]\n", + "\n", + " y_train = y[train_indices]\n", + " y_val = y[val_indices]\n", + " y_test = y[test_indices]\n", + "\n", + " # Standardizzazione\n", + " scaler_temporal = StandardScaler()\n", + " scaler_static = StandardScaler()\n", + " scaler_y = StandardScaler()\n", + "\n", + " # Standardizzazione dei dati 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": [ + "2024-12-07 10:14:27.438127: 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": [ + "2024-12-07 10:14:45.036044: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1b179440 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-12-07 10:14:45.036095: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-12-07 10:14:45.050382: 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", + "2024-12-07 10:14:45.136716: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n", + "2024-12-07 10:14:45.271095: 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": [ + " 5/4977 [..............................] - ETA: 3:24 - loss: 0.7884 - mae: 1.1915 WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0364s vs `on_train_batch_end` time: 0.0387s). Check your callbacks.\n", + "4977/4977 [==============================] - 336s 63ms/step - loss: 0.0565 - mae: 0.2041 - val_loss: 0.0176 - val_mae: 0.0966 - val_olive_prod_mae: 0.1012 - val_min_oil_prod_mae: 0.1038 - val_max_oil_prod_mae: 0.1024 - val_avg_oil_prod_mae: 0.0993 - val_total_water_need_mae: 0.0760 - lr: 1.0111e-04\n", + "Epoch 2/150\n", + "4977/4977 [==============================] - 309s 62ms/step - loss: 0.0253 - mae: 0.1438 - val_loss: 0.0145 - val_mae: 0.0870 - val_olive_prod_mae: 0.0950 - val_min_oil_prod_mae: 0.0960 - val_max_oil_prod_mae: 0.0948 - val_avg_oil_prod_mae: 0.0914 - val_total_water_need_mae: 0.0580 - lr: 1.0219e-05\n", + "Epoch 3/150\n", + "4977/4977 [==============================] - 306s 61ms/step - loss: 0.0243 - mae: 0.1416 - val_loss: 0.0142 - val_mae: 0.0863 - val_olive_prod_mae: 0.0946 - val_min_oil_prod_mae: 0.0952 - val_max_oil_prod_mae: 0.0941 - val_avg_oil_prod_mae: 0.0906 - val_total_water_need_mae: 0.0571 - lr: 1.0328e-06\n", + "Epoch 4/150\n", + "4977/4977 [==============================] - 304s 61ms/step - loss: 0.0243 - mae: 0.1417 - val_loss: 0.0141 - val_mae: 0.0862 - val_olive_prod_mae: 0.0944 - val_min_oil_prod_mae: 0.0951 - val_max_oil_prod_mae: 0.0941 - val_avg_oil_prod_mae: 0.0906 - val_total_water_need_mae: 0.0567 - lr: 1.0438e-07\n", + "Epoch 5/150\n", + "4977/4977 [==============================] - 307s 61ms/step - loss: 0.0243 - mae: 0.1416 - val_loss: 0.0141 - val_mae: 0.0862 - val_olive_prod_mae: 0.0944 - val_min_oil_prod_mae: 0.0951 - val_max_oil_prod_mae: 0.0941 - val_avg_oil_prod_mae: 0.0905 - val_total_water_need_mae: 0.0569 - lr: 1.0549e-08\n", + "Epoch 6/150\n", + "4977/4977 [==============================] - 302s 60ms/step - loss: 0.0243 - mae: 0.1416 - val_loss: 0.0143 - val_mae: 0.0867 - val_olive_prod_mae: 0.0948 - val_min_oil_prod_mae: 0.0954 - val_max_oil_prod_mae: 0.0944 - val_avg_oil_prod_mae: 0.0909 - val_total_water_need_mae: 0.0579 - lr: 1.0661e-09\n", + "Epoch 7/150\n", + "4977/4977 [==============================] - 307s 62ms/step - loss: 0.0243 - mae: 0.1415 - val_loss: 0.0141 - val_mae: 0.0859 - val_olive_prod_mae: 0.0942 - val_min_oil_prod_mae: 0.0947 - val_max_oil_prod_mae: 0.0937 - val_avg_oil_prod_mae: 0.0902 - val_total_water_need_mae: 0.0568 - lr: 1.0775e-10\n", + "Epoch 8/150\n", + "4977/4977 [==============================] - 307s 61ms/step - loss: 0.0242 - mae: 0.1414 - val_loss: 0.0141 - val_mae: 0.0863 - val_olive_prod_mae: 0.0944 - val_min_oil_prod_mae: 0.0951 - val_max_oil_prod_mae: 0.0941 - val_avg_oil_prod_mae: 0.0906 - val_total_water_need_mae: 0.0571 - lr: 1.0889e-11\n", + "Epoch 9/150\n", + "4977/4977 [==============================] - 307s 61ms/step - loss: 0.0242 - mae: 0.1414 - val_loss: 0.0141 - val_mae: 0.0862 - val_olive_prod_mae: 0.0942 - val_min_oil_prod_mae: 0.0950 - val_max_oil_prod_mae: 0.0940 - val_avg_oil_prod_mae: 0.0904 - val_total_water_need_mae: 0.0572 - lr: 1.1005e-12\n", + "Epoch 10/150\n", + "4977/4977 [==============================] - 309s 62ms/step - loss: 0.0243 - mae: 0.1416 - val_loss: 0.0142 - val_mae: 0.0863 - val_olive_prod_mae: 0.0944 - val_min_oil_prod_mae: 0.0951 - val_max_oil_prod_mae: 0.0941 - val_avg_oil_prod_mae: 0.0905 - val_total_water_need_mae: 0.0573 - lr: 1.1122e-13\n", + "Epoch 11/150\n", + "4977/4977 [==============================] - 305s 61ms/step - loss: 0.0243 - mae: 0.1416 - val_loss: 0.0140 - val_mae: 0.0859 - val_olive_prod_mae: 0.0942 - val_min_oil_prod_mae: 0.0948 - val_max_oil_prod_mae: 0.0938 - val_avg_oil_prod_mae: 0.0903 - val_total_water_need_mae: 0.0563 - lr: 1.1241e-14\n", + "Epoch 12/150\n", + "4977/4977 [==============================] - 307s 62ms/step - loss: 0.0242 - mae: 0.1414 - val_loss: 0.0139 - val_mae: 0.0853 - val_olive_prod_mae: 0.0937 - val_min_oil_prod_mae: 0.0942 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0896 - val_total_water_need_mae: 0.0559 - lr: 1.1361e-15\n", + "Epoch 13/150\n", + "4977/4977 [==============================] - 308s 62ms/step - loss: 0.0243 - mae: 0.1416 - val_loss: 0.0141 - val_mae: 0.0862 - val_olive_prod_mae: 0.0944 - val_min_oil_prod_mae: 0.0949 - val_max_oil_prod_mae: 0.0939 - val_avg_oil_prod_mae: 0.0904 - val_total_water_need_mae: 0.0573 - lr: 1.1482e-16\n", + "Epoch 14/150\n", + "4977/4977 [==============================] - 307s 61ms/step - loss: 0.0242 - mae: 0.1414 - val_loss: 0.0140 - val_mae: 0.0857 - val_olive_prod_mae: 0.0940 - val_min_oil_prod_mae: 0.0945 - val_max_oil_prod_mae: 0.0935 - val_avg_oil_prod_mae: 0.0899 - val_total_water_need_mae: 0.0566 - lr: 1.1604e-17\n", + "Epoch 15/150\n", + "4977/4977 [==============================] - 305s 61ms/step - loss: 0.0243 - mae: 0.1416 - val_loss: 0.0141 - val_mae: 0.0862 - val_olive_prod_mae: 0.0944 - val_min_oil_prod_mae: 0.0950 - val_max_oil_prod_mae: 0.0939 - val_avg_oil_prod_mae: 0.0904 - val_total_water_need_mae: 0.0574 - lr: 1.1727e-18\n", + "Epoch 16/150\n", + "4977/4977 [==============================] - 306s 61ms/step - loss: 0.0243 - mae: 0.1416 - val_loss: 0.0142 - val_mae: 0.0865 - val_olive_prod_mae: 0.0946 - val_min_oil_prod_mae: 0.0953 - val_max_oil_prod_mae: 0.0943 - val_avg_oil_prod_mae: 0.0907 - val_total_water_need_mae: 0.0576 - lr: 1.1852e-19\n", + "Epoch 17/150\n", + "4977/4977 [==============================] - 315s 63ms/step - loss: 0.0242 - mae: 0.1415 - val_loss: 0.0139 - val_mae: 0.0855 - val_olive_prod_mae: 0.0938 - val_min_oil_prod_mae: 0.0943 - val_max_oil_prod_mae: 0.0933 - val_avg_oil_prod_mae: 0.0897 - val_total_water_need_mae: 0.0566 - lr: 1.1978e-20\n", + "Epoch 18/150\n", + "4977/4977 [==============================] - 331s 66ms/step - loss: 0.0242 - mae: 0.1415 - val_loss: 0.0140 - val_mae: 0.0859 - val_olive_prod_mae: 0.0941 - val_min_oil_prod_mae: 0.0948 - val_max_oil_prod_mae: 0.0938 - val_avg_oil_prod_mae: 0.0903 - val_total_water_need_mae: 0.0564 - lr: 1.2106e-21\n", + "Epoch 19/150\n", + "4977/4977 [==============================] - 307s 62ms/step - loss: 0.0243 - mae: 0.1415 - val_loss: 0.0140 - val_mae: 0.0857 - val_olive_prod_mae: 0.0941 - val_min_oil_prod_mae: 0.0946 - val_max_oil_prod_mae: 0.0936 - val_avg_oil_prod_mae: 0.0901 - val_total_water_need_mae: 0.0562 - lr: 1.2235e-22\n", + "Epoch 20/150\n", + "4977/4977 [==============================] - 316s 63ms/step - loss: 0.0242 - mae: 0.1414 - val_loss: 0.0141 - val_mae: 0.0860 - val_olive_prod_mae: 0.0943 - val_min_oil_prod_mae: 0.0949 - val_max_oil_prod_mae: 0.0939 - val_avg_oil_prod_mae: 0.0904 - val_total_water_need_mae: 0.0564 - lr: 1.2365e-23\n", + "Epoch 21/150\n", + "4977/4977 [==============================] - 304s 61ms/step - loss: 0.0243 - mae: 0.1415 - val_loss: 0.0140 - val_mae: 0.0857 - val_olive_prod_mae: 0.0938 - val_min_oil_prod_mae: 0.0944 - val_max_oil_prod_mae: 0.0934 - val_avg_oil_prod_mae: 0.0898 - val_total_water_need_mae: 0.0568 - lr: 1.2497e-24\n", + "Epoch 22/150\n", + "4977/4977 [==============================] - 307s 62ms/step - loss: 0.0243 - mae: 0.1415 - val_loss: 0.0139 - val_mae: 0.0855 - val_olive_prod_mae: 0.0937 - val_min_oil_prod_mae: 0.0944 - val_max_oil_prod_mae: 0.0933 - val_avg_oil_prod_mae: 0.0898 - val_total_water_need_mae: 0.0562 - lr: 1.2630e-25\n", + "Epoch 23/150\n", + "4977/4977 [==============================] - 310s 62ms/step - loss: 0.0243 - mae: 0.1416 - val_loss: 0.0140 - val_mae: 0.0858 - val_olive_prod_mae: 0.0940 - val_min_oil_prod_mae: 0.0946 - val_max_oil_prod_mae: 0.0935 - val_avg_oil_prod_mae: 0.0900 - val_total_water_need_mae: 0.0567 - lr: 1.2764e-26\n", + "Epoch 24/150\n", + "4977/4977 [==============================] - 323s 65ms/step - loss: 0.0243 - mae: 0.1416 - val_loss: 0.0142 - val_mae: 0.0863 - val_olive_prod_mae: 0.0945 - val_min_oil_prod_mae: 0.0952 - val_max_oil_prod_mae: 0.0942 - val_avg_oil_prod_mae: 0.0907 - val_total_water_need_mae: 0.0570 - lr: 1.2900e-27\n", + "Epoch 25/150\n", + "4977/4977 [==============================] - 299s 60ms/step - loss: 0.0242 - mae: 0.1414 - val_loss: 0.0141 - val_mae: 0.0860 - val_olive_prod_mae: 0.0942 - val_min_oil_prod_mae: 0.0948 - val_max_oil_prod_mae: 0.0938 - val_avg_oil_prod_mae: 0.0903 - val_total_water_need_mae: 0.0569 - lr: 1.3038e-28\n", + "Epoch 26/150\n", + "4977/4977 [==============================] - 304s 61ms/step - loss: 0.0242 - mae: 0.1414 - val_loss: 0.0141 - val_mae: 0.0861 - val_olive_prod_mae: 0.0944 - val_min_oil_prod_mae: 0.0951 - val_max_oil_prod_mae: 0.0940 - val_avg_oil_prod_mae: 0.0905 - val_total_water_need_mae: 0.0566 - lr: 1.3177e-29\n", + "Epoch 27/150\n", + "4977/4977 [==============================] - 324s 65ms/step - loss: 0.0243 - mae: 0.1416 - val_loss: 0.0139 - val_mae: 0.0853 - val_olive_prod_mae: 0.0936 - val_min_oil_prod_mae: 0.0941 - val_max_oil_prod_mae: 0.0931 - val_avg_oil_prod_mae: 0.0895 - val_total_water_need_mae: 0.0561 - lr: 1.3317e-30\n", + "Epoch 28/150\n", + "4977/4977 [==============================] - 312s 62ms/step - loss: 0.0243 - mae: 0.1417 - val_loss: 0.0141 - val_mae: 0.0862 - val_olive_prod_mae: 0.0943 - val_min_oil_prod_mae: 0.0951 - val_max_oil_prod_mae: 0.0941 - val_avg_oil_prod_mae: 0.0905 - val_total_water_need_mae: 0.0571 - lr: 1.3459e-31\n", + "Epoch 29/150\n", + "4977/4977 [==============================] - 304s 61ms/step - loss: 0.0242 - mae: 0.1415 - val_loss: 0.0141 - val_mae: 0.0862 - val_olive_prod_mae: 0.0943 - val_min_oil_prod_mae: 0.0950 - val_max_oil_prod_mae: 0.0940 - val_avg_oil_prod_mae: 0.0904 - val_total_water_need_mae: 0.0573 - lr: 1.3602e-32\n", + "Epoch 30/150\n", + "4977/4977 [==============================] - 298s 60ms/step - loss: 0.0242 - mae: 0.1414 - val_loss: 0.0142 - val_mae: 0.0866 - val_olive_prod_mae: 0.0947 - val_min_oil_prod_mae: 0.0955 - val_max_oil_prod_mae: 0.0945 - val_avg_oil_prod_mae: 0.0910 - val_total_water_need_mae: 0.0575 - lr: 1.3747e-33\n", + "Epoch 31/150\n", + "4977/4977 [==============================] - 305s 61ms/step - loss: 0.0242 - mae: 0.1415 - val_loss: 0.0140 - val_mae: 0.0856 - val_olive_prod_mae: 0.0940 - val_min_oil_prod_mae: 0.0946 - val_max_oil_prod_mae: 0.0936 - val_avg_oil_prod_mae: 0.0901 - val_total_water_need_mae: 0.0558 - lr: 1.3893e-34\n", + "Epoch 32/150\n", + "4977/4977 [==============================] - 305s 61ms/step - loss: 0.0242 - mae: 0.1414 - val_loss: 0.0139 - val_mae: 0.0853 - val_olive_prod_mae: 0.0937 - val_min_oil_prod_mae: 0.0942 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0896 - val_total_water_need_mae: 0.0559 - lr: 1.4041e-35\n", + "\n", + "Modello salvato in: 2024-12-07_09-08_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 [==============================] - 114s 5ms/step\n", + "\n", + "Errori per target:\n", + "--------------------------------------------------\n", + "olive_prod:\n", + "MAE assoluto: 1540.62\n", + "Errore percentuale medio: 5.91%\n", + "Precisione: 94.09%\n", + "--------------------------------------------------\n", + "min_oil_prod:\n", + "MAE assoluto: 321.61\n", + "Errore percentuale medio: 6.11%\n", + "Precisione: 93.89%\n", + "--------------------------------------------------\n", + "max_oil_prod:\n", + "MAE assoluto: 385.74\n", + "Errore percentuale medio: 6.06%\n", + "Precisione: 93.94%\n", + "--------------------------------------------------\n", + "avg_oil_prod:\n", + "MAE assoluto: 338.12\n", + "Errore percentuale medio: 5.86%\n", + "Precisione: 94.14%\n", + "--------------------------------------------------\n", + "total_water_need:\n", + "MAE assoluto: 1659.84\n", + "Errore percentuale medio: 3.71%\n", + "Precisione: 96.29%\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: 0.0937\n", + "olive_prod_rmse: 0.1312\n", + "olive_prod_mape: 95.6676\n", + "min_oil_prod_mae: 0.0942\n", + "min_oil_prod_rmse: 0.1360\n", + "min_oil_prod_mape: 244.0470\n", + "max_oil_prod_mae: 0.0931\n", + "max_oil_prod_rmse: 0.1341\n", + "max_oil_prod_mape: 110.9277\n", + "avg_oil_prod_mae: 0.0896\n", + "avg_oil_prod_rmse: 0.1286\n", + "avg_oil_prod_mape: 87.8932\n", + "total_water_need_mae: 0.0559\n", + "total_water_need_rmse: 0.0806\n", + "total_water_need_mape: 175.1367\n", + "\n", + "Performance sul set validazione:\n", + "olive_prod_mae: 0.0937\n", + "olive_prod_rmse: 0.1311\n", + "olive_prod_mape: 990.4179\n", + "min_oil_prod_mae: 0.0942\n", + "min_oil_prod_rmse: 0.1360\n", + "min_oil_prod_mape: 112.9031\n", + "max_oil_prod_mae: 0.0932\n", + "max_oil_prod_rmse: 0.1342\n", + "max_oil_prod_mape: 102.9228\n", + "avg_oil_prod_mae: 0.0896\n", + "avg_oil_prod_rmse: 0.1286\n", + "avg_oil_prod_mape: 206.4427\n", + "total_water_need_mae: 0.0559\n", + "total_water_need_rmse: 0.0806\n", + "total_water_need_mape: 37.3202\n", + "\n", + "Performance sul set test:\n", + "olive_prod_mae: 0.0939\n", + "olive_prod_rmse: 0.1313\n", + "olive_prod_mape: 86.2025\n", + "min_oil_prod_mae: 0.0942\n", + "min_oil_prod_rmse: 0.1359\n", + "min_oil_prod_mape: 113.9333\n", + "max_oil_prod_mae: 0.0932\n", + "max_oil_prod_rmse: 0.1343\n", + "max_oil_prod_mape: 109.8982\n", + "avg_oil_prod_mae: 0.0896\n", + "avg_oil_prod_rmse: 0.1286\n", + "avg_oil_prod_mape: 97.3011\n", + "total_water_need_mae: 0.0559\n", + "total_water_need_rmse: 0.0806\n", + "total_water_need_mape: 45.3331\n", + "\n", + "Avvio retraining...\n", + "Epoch 1/50\n", + "13782/13782 [==============================] - 487s 34ms/step - loss: 0.0235 - mae: 0.1435 - val_loss: 0.0114 - val_mae: 0.0813 - lr: 2.3411e-05\n", + "Epoch 2/50\n", + "13782/13782 [==============================] - 447s 32ms/step - loss: 0.0216 - mae: 0.1393 - val_loss: 0.0108 - val_mae: 0.0799 - lr: 5.4801e-06\n", + "Epoch 3/50\n", + "13782/13782 [==============================] - 451s 33ms/step - loss: 0.0213 - mae: 0.1385 - val_loss: 0.0107 - val_mae: 0.0799 - lr: 1.2828e-06\n", + "Epoch 4/50\n", + "13782/13782 [==============================] - 415s 30ms/step - loss: 0.0212 - mae: 0.1383 - val_loss: 0.0107 - val_mae: 0.0795 - lr: 3.0028e-07\n", + "Epoch 5/50\n", + "13782/13782 [==============================] - 404s 29ms/step - loss: 0.0211 - mae: 0.1381 - val_loss: 0.0106 - val_mae: 0.0787 - lr: 7.0290e-08\n", + "Epoch 6/50\n", + "13782/13782 [==============================] - 412s 30ms/step - loss: 0.0211 - mae: 0.1381 - val_loss: 0.0107 - val_mae: 0.0795 - lr: 1.6454e-08\n", + "Epoch 7/50\n", + "13782/13782 [==============================] - 415s 30ms/step - loss: 0.0211 - mae: 0.1380 - val_loss: 0.0108 - val_mae: 0.0804 - lr: 3.8516e-09\n", + "Epoch 8/50\n", + "13782/13782 [==============================] - 421s 31ms/step - loss: 0.0211 - mae: 0.1380 - val_loss: 0.0106 - val_mae: 0.0790 - lr: 9.0158e-10\n", + "Epoch 9/50\n", + "13782/13782 [==============================] - 390s 28ms/step - loss: 0.0212 - mae: 0.1381 - val_loss: 0.0108 - val_mae: 0.0802 - lr: 2.1105e-10\n", + "Epoch 10/50\n", + "13782/13782 [==============================] - 396s 29ms/step - loss: 0.0211 - mae: 0.1380 - val_loss: 0.0106 - val_mae: 0.0794 - lr: 4.9402e-11\n", + "Epoch 11/50\n", + "13782/13782 [==============================] - 453s 33ms/step - loss: 0.0211 - mae: 0.1380 - val_loss: 0.0108 - val_mae: 0.0802 - lr: 1.1564e-11\n", + "Epoch 12/50\n", + "13782/13782 [==============================] - 415s 30ms/step - loss: 0.0211 - mae: 0.1381 - val_loss: 0.0109 - val_mae: 0.0813 - lr: 2.7070e-12\n", + "Epoch 13/50\n", + "13782/13782 [==============================] - 443s 32ms/step - loss: 0.0212 - mae: 0.1382 - val_loss: 0.0106 - val_mae: 0.0791 - lr: 6.3366e-13\n", + "Epoch 14/50\n", + "13782/13782 [==============================] - 415s 30ms/step - loss: 0.0212 - mae: 0.1382 - val_loss: 0.0108 - val_mae: 0.0803 - lr: 1.4833e-13\n", + "Epoch 15/50\n", + "13782/13782 [==============================] - 437s 32ms/step - loss: 0.0211 - mae: 0.1380 - val_loss: 0.0108 - val_mae: 0.0802 - lr: 3.4721e-14\n", + "\n", + "Valutazione performance finali...\n", + "\n", + "Performance sul set training:\n", + "olive_prod_mae: 0.0887\n", + "olive_prod_rmse: 0.1212\n", + "olive_prod_mape: 94.1047\n", + "min_oil_prod_mae: 0.0886\n", + "min_oil_prod_rmse: 0.1252\n", + "min_oil_prod_mape: 251.0980\n", + "max_oil_prod_mae: 0.0877\n", + "max_oil_prod_rmse: 0.1238\n", + "max_oil_prod_mape: 109.6036\n", + "avg_oil_prod_mae: 0.0840\n", + "avg_oil_prod_rmse: 0.1174\n", + "avg_oil_prod_mape: 85.1022\n", + "total_water_need_mae: 0.0452\n", + "total_water_need_rmse: 0.0637\n", + "total_water_need_mape: 111.5210\n", + "\n", + "Performance sul set validazione:\n", + "olive_prod_mae: 0.0886\n", + "olive_prod_rmse: 0.1212\n", + "olive_prod_mape: 1002.4407\n", + "min_oil_prod_mae: 0.0886\n", + "min_oil_prod_rmse: 0.1251\n", + "min_oil_prod_mape: 107.6972\n", + "max_oil_prod_mae: 0.0878\n", + "max_oil_prod_rmse: 0.1238\n", + "max_oil_prod_mape: 96.9785\n", + "avg_oil_prod_mae: 0.0840\n", + "avg_oil_prod_rmse: 0.1174\n", + "avg_oil_prod_mape: 239.2705\n", + "total_water_need_mae: 0.0452\n", + "total_water_need_rmse: 0.0637\n", + "total_water_need_mape: 31.5776\n", + "\n", + "Performance sul set test:\n", + "olive_prod_mae: 0.0888\n", + "olive_prod_rmse: 0.1214\n", + "olive_prod_mape: 83.2926\n", + "min_oil_prod_mae: 0.0885\n", + "min_oil_prod_rmse: 0.1251\n", + "min_oil_prod_mape: 108.5717\n", + "max_oil_prod_mae: 0.0878\n", + "max_oil_prod_rmse: 0.1238\n", + "max_oil_prod_mape: 103.3148\n", + "avg_oil_prod_mae: 0.0840\n", + "avg_oil_prod_rmse: 0.1174\n", + "avg_oil_prod_mape: 81.7609\n", + "total_water_need_mae: 0.0451\n", + "total_water_need_rmse: 0.0637\n", + "total_water_need_mape: 45.1097\n", + "\n", + "Modello riaddestrato salvato in: 2024-12-07_09-08_retrained_model.keras\n", + "\n", + "Miglioramenti delle performance:\n", + "\n", + "Set train:\n", + "olive_prod_mae: 5.38% di miglioramento\n", + "olive_prod_rmse: 7.57% di miglioramento\n", + "olive_prod_mape: 1.63% di miglioramento\n", + "min_oil_prod_mae: 5.94% di miglioramento\n", + "min_oil_prod_rmse: 7.96% di miglioramento\n", + "min_oil_prod_mape: -2.89% di miglioramento\n", + "max_oil_prod_mae: 5.74% di miglioramento\n", + "max_oil_prod_rmse: 7.74% di miglioramento\n", + "max_oil_prod_mape: 1.19% di miglioramento\n", + "avg_oil_prod_mae: 6.20% di miglioramento\n", + "avg_oil_prod_rmse: 8.66% di miglioramento\n", + "avg_oil_prod_mape: 3.18% di miglioramento\n", + "total_water_need_mae: 19.20% di miglioramento\n", + "total_water_need_rmse: 20.95% di miglioramento\n", + "total_water_need_mape: 36.32% di miglioramento\n", + "\n", + "Set val:\n", + "olive_prod_mae: 5.41% di miglioramento\n", + "olive_prod_rmse: 7.57% di miglioramento\n", + "olive_prod_mape: -1.21% di miglioramento\n", + "min_oil_prod_mae: 5.98% di miglioramento\n", + "min_oil_prod_rmse: 8.01% di miglioramento\n", + "min_oil_prod_mape: 4.61% di miglioramento\n", + "max_oil_prod_mae: 5.78% di miglioramento\n", + "max_oil_prod_rmse: 7.79% di miglioramento\n", + "max_oil_prod_mape: 5.78% di miglioramento\n", + "avg_oil_prod_mae: 6.26% di miglioramento\n", + "avg_oil_prod_rmse: 8.72% di miglioramento\n", + "avg_oil_prod_mape: -15.90% di miglioramento\n", + "total_water_need_mae: 19.20% di miglioramento\n", + "total_water_need_rmse: 20.93% di miglioramento\n", + "total_water_need_mape: 15.39% di miglioramento\n", + "\n", + "Set test:\n", + "olive_prod_mae: 5.41% di miglioramento\n", + "olive_prod_rmse: 7.57% di miglioramento\n", + "olive_prod_mape: 3.38% di miglioramento\n", + "min_oil_prod_mae: 6.01% di miglioramento\n", + "min_oil_prod_rmse: 7.99% di miglioramento\n", + "min_oil_prod_mape: 4.71% di miglioramento\n", + "max_oil_prod_mae: 5.80% di miglioramento\n", + "max_oil_prod_rmse: 7.80% di miglioramento\n", + "max_oil_prod_mape: 5.99% di miglioramento\n", + "avg_oil_prod_mae: 6.26% di miglioramento\n", + "avg_oil_prod_rmse: 8.70% di miglioramento\n", + "avg_oil_prod_mape: 15.97% di miglioramento\n", + "total_water_need_mae: 19.25% di miglioramento\n", + "total_water_need_rmse: 20.96% di miglioramento\n", + "total_water_need_mape: 0.49% 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=256\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a95606bc-c4bc-418a-acdb-2e24c30dfa81", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24500/24500 [==============================] - 122s 5ms/step\n", + "\n", + "Errori per target:\n", + "--------------------------------------------------\n", + "olive_prod:\n", + "MAE assoluto: 1457.21\n", + "Errore percentuale medio: 5.44%\n", + "Precisione: 94.56%\n", + "--------------------------------------------------\n", + "min_oil_prod:\n", + "MAE assoluto: 302.37\n", + "Errore percentuale medio: 5.54%\n", + "Precisione: 94.46%\n", + "--------------------------------------------------\n", + "max_oil_prod:\n", + "MAE assoluto: 363.44\n", + "Errore percentuale medio: 5.51%\n", + "Precisione: 94.49%\n", + "--------------------------------------------------\n", + "avg_oil_prod:\n", + "MAE assoluto: 316.96\n", + "Errore percentuale medio: 5.29%\n", + "Precisione: 94.71%\n", + "--------------------------------------------------\n", + "total_water_need:\n", + "MAE assoluto: 1341.23\n", + "Errore percentuale medio: 2.92%\n", + "Precisione: 97.08%\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 [==============================] - 89s 5ms/step\n", + "\n", + "Analisi per olive_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -252.482\n", + "variance: 3919206.250\n", + "std: 1979.698\n", + "min: -22682.084\n", + "max: 12285.283\n", + "median: -161.612\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 80.0%:\n", + "Range: [-2750.68, 2144.75]\n", + "\n", + "Intervallo di Confidenza 85.0%:\n", + "Range: [-2750.68, 2144.75]\n", + "\n", + "Intervallo di Confidenza 90.0%:\n", + "Range: [-3450.03, 2844.09]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-4848.73, 3543.44]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-7646.12, 4942.14]\n", + "\n", + "Analisi per min_oil_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -56.632\n", + "variance: 179151.406\n", + "std: 423.263\n", + "min: -5393.592\n", + "max: 2835.948\n", + "median: -43.304\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 80.0%:\n", + "Range: [-538.16, 449.38]\n", + "\n", + "Intervallo di Confidenza 85.0%:\n", + "Range: [-538.16, 449.38]\n", + "\n", + "Intervallo di Confidenza 90.0%:\n", + "Range: [-702.75, 613.97]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-1031.94, 778.56]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-1690.30, 1107.75]\n", + "\n", + "Analisi per max_oil_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -66.027\n", + "variance: 258418.484\n", + "std: 508.349\n", + "min: -6048.554\n", + "max: 3337.733\n", + "median: -49.297\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 80.0%:\n", + "Range: [-698.37, 427.98]\n", + "\n", + "Intervallo di Confidenza 85.0%:\n", + "Range: [-698.37, 615.71]\n", + "\n", + "Intervallo di Confidenza 90.0%:\n", + "Range: [-886.10, 803.44]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-1261.55, 991.16]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-2012.45, 1366.61]\n", + "\n", + "Analisi per avg_oil_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -61.259\n", + "variance: 192523.219\n", + "std: 438.775\n", + "min: -5699.054\n", + "max: 3080.489\n", + "median: -48.549\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 80.0%:\n", + "Range: [-519.12, 358.83]\n", + "\n", + "Intervallo di Confidenza 85.0%:\n", + "Range: [-694.71, 534.42]\n", + "\n", + "Intervallo di Confidenza 90.0%:\n", + "Range: [-870.31, 534.42]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-1045.90, 710.01]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-1748.26, 1061.19]\n", + "\n", + "Analisi per total_water_need\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -225.730\n", + "variance: 3522691.250\n", + "std: 1876.883\n", + "min: -24839.678\n", + "max: 12051.430\n", + "median: -99.665\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 80.0%:\n", + "Range: [-2336.10, 2090.83]\n", + "\n", + "Intervallo di Confidenza 85.0%:\n", + "Range: [-3073.92, 2090.83]\n", + "\n", + "Intervallo di Confidenza 90.0%:\n", + "Range: [-3073.92, 2090.83]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-4549.57, 2828.65]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-7500.86, 5042.12]\n", + "\n", + "2. IMPORTANZA DELLE FEATURE\n", + "--------------------------------------------------\n", + "18375/18375 [==============================] - 93s 5ms/step\n", + "18375/18375 [==============================] - 78s 4ms/step\n", + "18375/18375 [==============================] - 78s 4ms/step\n", + "18375/18375 [==============================] - 80s 4ms/step\n", + "18375/18375 [==============================] - 78s 4ms/step\n", + "\n", + "Importanza relativa delle feature:\n", + "ha: 0.8927\n", + "precip_sum: 0.0470\n", + "solar_energy_sum: 0.0311\n", + "temp_mean: 0.0291\n", + "\n", + "3. ANALISI DISTRIBUZIONALE\n", + "--------------------------------------------------\n", + "18375/18375 [==============================] - 79s 4ms/step\n", + "\n", + "Analisi distribuzionale per olive_prod\n", + "\n", + "Statistiche Predizioni:\n", + "mean: 29598.463\n", + "variance: 261045152.000\n", + "std: 16156.892\n", + "min: 2993.700\n", + "max: 87878.977\n", + "median: 27913.680\n", + "\n", + "Statistiche Target Reali:\n", + "mean: 29850.945\n", + "variance: 270643200.000\n", + "std: 16451.236\n", + "min: 2309.466\n", + "max: 98699.406\n", + "median: 27913.607\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analisi distribuzionale per min_oil_prod\n", + "\n", + "Statistiche Predizioni:\n", + "mean: 5857.163\n", + "variance: 11289681.000\n", + "std: 3360.012\n", + "min: 552.748\n", + "max: 19531.742\n", + "median: 5413.594\n", + "\n", + "Statistiche Target Reali:\n", + "mean: 5913.796\n", + "variance: 11652752.000\n", + "std: 3413.613\n", + "min: 412.902\n", + "max: 22359.766\n", + "median: 5417.150\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analisi distribuzionale per max_oil_prod\n", + "\n", + "Statistiche Predizioni:\n", + "mean: 7081.931\n", + "variance: 16605453.000\n", + "std: 4074.979\n", + "min: 650.012\n", + "max: 24202.312\n", + "median: 6537.561\n", + "\n", + "Statistiche Target Reali:\n", + "mean: 7147.958\n", + "variance: 17142514.000\n", + "std: 4140.352\n", + "min: 487.384\n", + "max: 28191.498\n", + "median: 6547.522\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analisi distribuzionale per avg_oil_prod\n", + "\n", + "Statistiche Predizioni:\n", + "mean: 6469.615\n", + "variance: 13817345.000\n", + "std: 3717.169\n", + "min: 603.203\n", + "max: 21864.619\n", + "median: 5975.386\n", + "\n", + "Statistiche Target Reali:\n", + "mean: 6530.874\n", + "variance: 14234416.000\n", + "std: 3772.853\n", + "min: 453.466\n", + "max: 24578.670\n", + "median: 5985.040\n" + ] + }, + { + "data": { + "image/png": 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79u0qX768Qy93YZo/f75Onz5tf5zTqOOS9Nlnnyk9PV2ffvqpQ0+7s9sCAgMD1aVLFy1ZskRTpkzRokWLdNNNNzlM6Va1alVZrVbt3bvXocd49+7deT6PqlWras2aNTp58qRDb/f27dvt611h165datOmjf3xqVOndOjQIXXq1Omy21atWjXH1zxrfdbP33//XcaYbIOzAQAKjnu6AcCFvvrqKz377LOKjo6+ZHJ0/PjxbGVZvZdZ0/ZkJUXOkuD8ePfddx3uM1+6dKkOHTpkHwlcsiWR33//vTIyMuxly5cvzza12Jo1azRu3Dg9+eST6tGjR66OHxMTIx8fH73++usOvaf//e9/lZycnK8Rpnv16qUDBw7orbfeyrbu9OnTSk1NzfM+83r8+Ph4rVq1Ktu6EydO6Ny5c5KkTp066dy5cw5TYWVmZuqNN9647DEOHTqkXr16qWXLlg6jdF9KxYoV1bBhQ82bN8/h/fP777/ryy+/zFUCl18tWrRQTEyMfblU0p3Vu37h+yE5OVlz5sxxWj8uLk4HDx7U22+/rV9++UVxcXEO67O+6JoxY4ZDeW6e54t16tRJmZmZmj59ukP51KlTZbFYHNpNYZo9e7bOnj1rfzxz5kydO3cuV8fr1KmTfvzxR8XHx9vLUlNTNXv2bEVFRemaa66x1zt48KCWLl1qr5eWlqbZs2cX4pkAwNWLnm4AKCRffPGFtm/frnPnzunIkSP66quvtHr1alWtWlWffvqp/Pz8ctz2mWee0YYNG9S5c2dVrVpViYmJmjFjhipXrmwfBKl69eoKCQnRrFmzVLp0aQUGBqpJkyZO7+XMjbJly6ply5YaOHCgjhw5omnTpqlGjRoO05rdc889Wrp0qTp06KBevXppz549ev/991W9enWHffXp00ehoaGqWbOmw3zkktSuXTun05eFhoZq7Nixmjhxojp06KBu3bppx44dmjFjhm688UaHQdNyq2/fvlq8eLHuvfderVu3Ti1atFBmZqa2b9+uxYsX2+cxdpVHH31Un376qbp06aIBAwaoUaNGSk1N1W+//aalS5dq3759Kl++vLp27aoWLVpozJgx2rdvn6655hp99NFH9oHCLmXEiBFKSkrSY489poULFzqsu+666xyuVLjQyy+/rI4dO6pZs2YaPHiwfcqw4ODgPM9X7Srt27eXj4+PunbtqmHDhunUqVN66623FBYWpkOHDmWr36lTJ5UuXVqPPPKIPD09dfvttzusb9SokW6//XZNmzZNx44ds08ZtnPnTkk53z7hTNeuXdWmTRs9+eST2rdvnxo0aKAvv/xSn3zyiUaNGpWtTRSWjIwMtW3bVr169bK3j5YtW6pbt26X3XbMmDH64IMP1LFjR40YMUJly5bVvHnztHfvXn344Yf28SKGDBmi6dOnq1+/ftq8ebMqVqyo9957TwEBAS45JwC46rhv4HQAKBmypvbJWnx8fEx4eLhp166dee211xym5cpy8TRQa9euNd27dzcRERHGx8fHREREmD59+mSbeuqTTz4x11xzjfHy8nKYEqpVq1amXr16TuPLacqwDz74wIwdO9aEhYUZf39/07lzZ/tUSxd69dVXTaVKlYyvr69p0aKF+emnn7Lt88Lzv3jJmm7o4inDskyfPt3UqVPHeHt7mwoVKpj77rvP/Pvvv9nOwdn59e/f31StWtWhLCMjw7z00kumXr16xtfX15QpU8Y0atTITJw40SQnJzt9ji7cX2Bg4CXrGGOboqlz585O1508edKMHTvW1KhRw/j4+Jjy5cub5s2bm1deecVh6qdjx46Zvn37mqCgIBMcHGz69u1rfv7558tOGZY1fZqzJWsKLGdThhljzJo1a0yLFi2Mv7+/CQoKMl27djV//vmnQ52s4108fZ2z188VU4Z9+umn5rrrrjN+fn4mKirKvPTSS+add95x+t4xxpi77rrLSDIxMTFO95eammqGDx9uypYta0qVKmV69OhhduzYYSSZF198MU+xnTx50jz00EMmIiLCeHt7m5o1a5qXX37ZWK1Wh3qFOWXY119/bYYOHWrKlCljSpUqZe666y6Had+yjpfT+3HPnj2mZ8+eJiQkxPj5+ZnGjRub5cuXZ6uXkJBgunXrZgICAkz58uXNyJEjzcqVK5kyDAAKgcWYPI6IAgAAUIxt3bpV//nPf/T+++/n6p54d5g7d64GDhyoTZs2ufTqDACA63FPNwAAKLEuHMQty7Rp0+Th4aGbb77ZDREBAK423NMNAABKrMmTJ2vz5s1q06aNvLy89MUXX+iLL77Q0KFDFRkZqczMTCUlJV1yH5ebWzwvTp8+fdl798uWLVsoxwIAFA0k3QAAoMRq3ry5Vq9erWeffVanTp1SlSpVNGHCBD355JOSpL///vuygxFeam7xvFq0aJEGDhx4yTrOpkgDABRf3NMNAACuWmfOnNG33357yTrVqlW75FRneXHo0CH98ccfl6zTqFEjlSlTplCOBwBwP5JuAAAAAABchIHUAAAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAVyWLxaIHHnig0PY3d+5cWSwW/fTTT5et27p1a7Vu3dr+eN++fbJYLJo7d669bMKECbJYLIUWH4qOi19/AEDJRtINACgyshLXrMXPz0+1atXSAw88oCNHjrg7PLd74YUXtGzZskLd5/r16+3P9/vvv++0TosWLWSxWFS/fv1CPXZhuPD9cuESHh7u1rj+/PNPTZgwQfv27XNrHAAA9/NydwAAAFzsmWeeUXR0tM6cOaNvv/1WM2fO1IoVK/T7778rICDA3eEV2JdffnnZOuPGjdOYMWMcyl544QX17NlTPXr0KPSY/Pz8tGDBAt19990O5fv27dPGjRvl5+dX6McsLO3atVO/fv0cyvz9/d0Ujc2ff/6piRMnqnXr1oqKinJYl5vXHwBQcpB0AwCKnI4dO+qGG26QJN1zzz0qV66cpkyZok8++UR9+vRxuk1qaqoCAwOvZJj55uPjc9k6Xl5e8vK6cv+mO3XqpE8//VRHjx5V+fLl7eULFixQhQoVVLNmTf37779XLJ68qFWrVrYvC4qy3Lz+AICSg8vLAQBF3i233CJJ2rt3ryRpwIABKlWqlPbs2aNOnTqpdOnSuuuuuyTZku+HH35YkZGR8vX1Ve3atfXKK6/IGON03/Pnz1ft2rXl5+enRo0aacOGDQ7rExISdP/996t27dry9/dXuXLldMcdd+R42XBaWpqGDRumcuXKKSgoSP369cuWrObmnt6L7+m2WCxKTU3VvHnz7JdQDxgwQOvWrZPFYtHHH3+cbR8LFiyQxWJRfHz8JY8lSd27d5evr6+WLFmSbR+9evWSp6dntm3mzJmjW265RWFhYfL19dU111yjmTNnZqv3008/KTY2VuXLl5e/v7+io6M1aNAghzoLFy5Uo0aNVLp0aQUFBenaa6/Va6+9dtm4L2fAgAHZepol5/fMZ93nv2zZMtWvX1++vr6qV6+eVq5cmW37AwcOaPDgwYqIiJCvr6+io6N13333KSMjQ3PnztUdd9whSWrTpo399Vq/fr0k569/YmKiBg8erAoVKsjPz08NGjTQvHnzHOpk3fv/yiuvaPbs2apevbp8fX114403atOmTfl/kgAALkVPNwCgyNuzZ48kqVy5cvayc+fOKTY2Vi1bttQrr7yigIAAGWPUrVs3rVu3ToMHD1bDhg21atUqPfroozpw4ICmTp3qsN+vv/5aixYt0ogRI+Tr66sZM2aoQ4cO+vHHH+33L2/atEkbN25U7969VblyZe3bt08zZ85U69at9eeff2a73P2BBx5QSEiIJkyYoB07dmjmzJlKSEiw3zudX++9957uueceNW7cWEOHDpUkVa9eXU2bNlVkZKTmz5+vW2+91WGb+fPnq3r16mrWrNll9x8QEKDu3bvrgw8+0H333SdJ+uWXX/THH3/o7bff1q+//pptm5kzZ6pevXrq1q2bvLy89Nlnn+n++++X1WrV8OHDJdmSyfbt2ys0NFRjxoxRSEiI9u3bp48++si+n9WrV6tPnz5q27atXnrpJUnStm3b9N1332nkyJGXjf3MmTM6evSoQ1np0qXl6+t72W0v9u233+qjjz7S/fffr9KlS+v111/X7bffrv3799vffwcPHlTjxo114sQJDR06VHXq1NGBAwe0dOlSpaWl6eabb9aIESP0+uuv64knnlDdunUlyf7zYqdPn1br1q21e/duPfDAA4qOjtaSJUs0YMAAnThxIttzsGDBAp08eVLDhg2TxWLR5MmTddttt+mvv/6St7d3ns8ZAOBiBgCAImLOnDlGklmzZo1JSkoyf//9t1m4cKEpV66c8ff3N//8848xxpj+/fsbSWbMmDEO2y9btsxIMs8995xDec+ePY3FYjG7d++2l0kyksxPP/1kL0tISDB+fn7m1ltvtZelpaVlizM+Pt5IMu+++2622Bs1amQyMjLs5ZMnTzaSzCeffGIva9WqlWnVqpX98d69e40kM2fOHHvZ+PHjzcX/pgMDA03//v2zxTN27Fjj6+trTpw4YS9LTEw0Xl5eZvz48dnqX2jdunVGklmyZIlZvny5sVgsZv/+/cYYYx599FFTrVo1e8z16tVz2NbZcxMbG2vfxhhjPv74YyPJbNq0KccYRo4caYKCgsy5c+cuGaszWa/jxUvWc9m/f39TtWrVbNs5e34lGR8fH4f3yS+//GIkmTfeeMNe1q9fP+Ph4eH0nKxWqzHGmCVLlhhJZt26ddnqXPz6T5s2zUgy77//vr0sIyPDNGvWzJQqVcqkpKQYY86/T8qVK2eOHz9ur/vJJ58YSeazzz7L+YkCALgNl5cDAIqcmJgYhYaGKjIyUr1791apUqX08ccfq1KlSg71snpks6xYsUKenp4aMWKEQ/nDDz8sY4y++OILh/JmzZqpUaNG9sdVqlRR9+7dtWrVKmVmZkpyHJDr7NmzOnbsmGrUqKGQkBBt2bIlW+xDhw516G2877775OXlpRUrVuTxWci9fv36KT09XUuXLrWXLVq0SOfOncvTvc7t27dX2bJltXDhQhljtHDhwhzvoZccn5vk5GQdPXpUrVq10l9//aXk5GRJUkhIiCRp+fLlOnv2rNP9hISEKDU1VatXr851rBfq3r27Vq9e7bDExsbma18xMTGqXr26/fF1112noKAg/fXXX5Ikq9WqZcuWqWvXrvZxBy6Un6sZVqxYofDwcIfn2tvbWyNGjNCpU6f09ddfO9SPi4tTmTJl7I9vuukmSbLHCAAoWri8HABQ5Lz55puqVauWvLy8VKFCBdWuXVseHo7fE3t5ealy5coOZQkJCYqIiFDp0qUdyrMu601ISHAor1mzZrZj16pVS2lpaUpKSlJ4eLhOnz6tSZMmac6cOTpw4IDDveFZieWl9lmqVClVrFjRpVNH1alTRzfeeKPmz5+vwYMHS7JdWt60aVPVqFEj1/vx9vbWHXfcoQULFqhx48b6+++/deedd+ZY/7vvvtP48eMVHx+vtLQ0h3XJyckKDg5Wq1atdPvtt2vixImaOnWqWrdurR49eujOO++0X/59//33a/HixerYsaMqVaqk9u3bq1evXurQoUOu4q5cubJiYmJyfZ6XUqVKlWxlZcqUsd+Xn5SUpJSUlEKdPi0hIUE1a9bM9h7P6X17cYxZCXhRHegOAK529HQDAIqcxo0bKyYmRq1bt1bdunWzJSOS5Ovr67S8sD344IN6/vnn1atXLy1evFhffvmlVq9erXLlyslqtbr8+LnVr18/ff311/rnn3+0Z88eff/99/ka0fvOO+/U1q1bNWHCBDVo0EDXXHON03p79uxR27ZtdfToUU2ZMkWff/65Vq9erYceekiS7M+NxWLR0qVLFR8frwceeEAHDhzQoEGD1KhRI506dUqSFBYWpq1bt+rTTz+135PfsWNH9e/fP5/Pxnk59TxnXclwMWcDxknKcSA+dygOMQIAziPpBgCUGFWrVtXBgwd18uRJh/Lt27fb119o165d2faxc+dOBQQEKDQ0VJK0dOlS9e/fX6+++qp69uypdu3aqWXLljpx4oTTGC7e56lTp3To0CGnI2jn1aUuXe7du7c8PT31wQcfaP78+fL29lZcXFyej9GyZUtVqVJF69evv2Qv92effab09HR9+umnGjZsmDp16qSYmJgc58du2rSpnn/+ef3000+aP3++/vjjDy1cuNC+3sfHR127dtWMGTO0Z88eDRs2TO+++652796d53O4UJkyZZy+Vhf3HudWaGiogoKC9Pvvv1+yXl4uM69atap27dqV7UucnN63AIDihaQbAFBidOrUSZmZmZo+fbpD+dSpU2WxWNSxY0eH8vj4eIf7sv/++2998sknat++vb030dPTM1sP4htvvJFjT+ns2bMd7l2eOXOmzp07l+3Y+REYGJhjsl++fHl17NhR77//vubPn68OHTo4zLedWxaLRa+//rrGjx+vvn375lgv6/m5+HL7OXPmONT7999/sz1/DRs2lCSlp6dLko4dO+aw3sPDQ9ddd51DnfyqXr26kpOTHUZfP3TokNMp1nLDw8NDPXr00Geffaaffvop2/qsc82aMz6n1+tCnTp10uHDh7Vo0SJ72blz5/TGG2+oVKlSatWqVb5iBQAUDdzTDQAoMbp27ao2bdroySef1L59+9SgQQN9+eWX+uSTTzRq1CiHAbIkqX79+oqNjXWYMkySJk6caK/TpUsXvffeewoODtY111yj+Ph4rVmzxmH6sgtlZGSobdu26tWrl3bs2KEZM2aoZcuW6tatW4HPr1GjRlqzZo2mTJmiiIgIRUdHq0mTJvb1/fr1U8+ePSVJzz77bL6P0717d3Xv3v2Sddq3b2/vnR42bJhOnTqlt956S2FhYTp06JC93rx58zRjxgzdeuutql69uk6ePKm33npLQUFB6tSpkyTpnnvu0fHjx3XLLbeocuXKSkhI0BtvvKGGDRvmOM1WbvXu3VuPP/64br31Vo0YMUJpaWmaOXOmatWq5XQgvNx44YUX9OWXX6pVq1YaOnSo6tatq0OHDmnJkiX69ttvFRISooYNG8rT01MvvfSSkpOT5evra5/T/GJDhw7V//3f/2nAgAHavHmzoqKitHTpUn333XeaNm1atjEKAADFC0k3AKDE8PDw0Keffqqnn35aixYt0pw5cxQVFaWXX35ZDz/8cLb6rVq1UrNmzTRx4kTt379f11xzjebOnWvvZZWk1157TZ6enpo/f77OnDmjFi1aaM2aNTmOjj19+nTNnz9fTz/9tM6ePas+ffro9ddfL9Ac3VmmTJmioUOHaty4cTp9+rT69+/vkHR37dpVZcqUkdVqLZQk/1Jq166tpUuXaty4cXrkkUcUHh6u++67T6GhoRo0aJC9XqtWrfTjjz9q4cKFOnLkiIKDg9W4cWPNnz9f0dHRkqS7775bs2fP1owZM3TixAmFh4crLi5OEyZMKPB9++XKldPHH3+s0aNH67HHHlN0dLQmTZqkXbt25TvprlSpkn744Qc99dRTmj9/vlJSUlSpUiV17NjRPm97eHi4Zs2apUmTJmnw4MHKzMzUunXrnCbd/v7+Wr9+vcaMGaN58+YpJSVFtWvX1pw5czRgwICCnD4AoAiwGEbdAACgRDh37pwiIiLUtWtX/fe//3V3OAAAQNzTDQBAibFs2TIlJSWpX79+7g4FAAD8Dz3dAAAUcz/88IN+/fVXPfvssypfvny+L5sGAACFj55uAACKuZkzZ+q+++5TWFiY3n33XXeHAwAALkBPNwAAAAAALkJPNwAAAAAALkLSDQAAAACAizBPtxNWq1UHDx5U6dKlC2VeVQAAAABAyWKM0cmTJxURESEPj5z7s0m6nTh48KAiIyPdHQYAAAAAoIj7+++/Vbly5RzXk3Q7Ubp0aUm2Jy8oKCjf+7FarUpKSlJoaOglv/kASjLaAWBDWwBsaAsA7aCkSElJUWRkpD1/zAlJtxNZl5QHBQUVOOk+c+aMgoKCaEy4atEOABvaAmBDWwBoByXN5W5J5hUGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEe7oBAAAA4CqRmZmps2fPujuMYsHT01NeXl4FnkaapBsAAAAArgKnTp3SP//8I2OMu0MpNgICAlSxYkX5+Pjkex8k3QAAAABQwmVmZuqff/5RQECAQkNDC9x7W9IZY5SRkaGkpCTt3btXNWvWzPdI8yTdAAAAAFDCnT17VsYYhYaGyt/f393hFAv+/v7y9vZWQkKCMjIy5Ofnl6/9MJAaAAAAAFwl6OHOm8KYR52kGwAAAAAAFyHpBgAAAADARbinGwAAAACuUlNX77yix3uoXa0reryigKQbBVIYjfRqbHgAAAAALm/AgAGaN2+eJMnb21tVqlRRv3799MQTT+jbb79VmzZtFBISokOHDjkMdLZp0yY1btxYkuxTpK1fv15t2rTJdownn3xSzz33nMvOgaQbAAAAAFBkdejQQXPmzFF6erpWrFih4cOHy9vbW82aNZMklS5dWh9//LH69Olj3+a///2vqlSpov3792fb344dOxQUFGR/XKpUKZfGzz3dAAAAAIAiy9fXV+Hh4apataruu+8+xcTE6NNPP7Wv79+/v9555x3749OnT2vhwoXq37+/0/2FhYUpPDzcvpB0AwAAAADwP/7+/srIyLA/7tu3r7755ht7r/aHH36oqKgoXX/99e4K0QFJNwAAAACgyDPGaM2aNVq1apVuueUWe3lYWJg6duyouXPnSpLeeecdDRo0KMf9VK5cWaVKlbIvx44dc2nc3NMNAAAAACiyli9frlKlSuns2bOyWq268847NWHCBG3atMleZ9CgQRo5cqTuvvtuxcfHa8mSJfrmm2+c7u+bb75R6dKl7Y/LlCnj0vhJugEAAAAARVabNm00c+ZM+fj4KCIiQl5e2dPYjh07aujQoRo8eLC6du2qcuXK5bi/6OhohYSEuDBiRyTdAAAAAIAiKzAwUDVq1LhkHS8vL/Xr10+TJ0/WF198cYUiyx3u6QYAAAAAFHvPPvuskpKSFBsb6+5QHNDTDQAAAABXqYfa1XJ3CIXGx8dH5cuXd3cY2ZB0w+2mrt5Z4H2UpD8WAAAAAGyyRiR3pnXr1jLG5Li+R48eDusvV99VuLwcAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAArhLuGEisOCuM54ukGwAAAABKOE9PT0lSRkaGmyMpXtLS0iRJ3t7e+d4HU4YBAAAAQAnn5eWlgIAAJSUlydvbWx4e9L9eijFGaWlpSkxMVEhIiP1Li/wg6UaJUNC5vpnnGwAAACWZxWJRxYoVtXfvXiUkJLg7nGIjJCRE4eHhBdoHSTcAAAAAXAV8fHxUs2ZNLjHPJW9v7wL1cGch6QZgV9ArBiSuGgAAACjKPDw85Ofn5+4wriok3YBINgEAAAC4BnfPAwAAAADgIvR0A4WEwdwAAAAAXIykG0ChyvblgzEKyDylNM9kyWK57PZ8+QAAAICShKQbKEEK4950AAAAAIWHpBsoIkiYAQAAgJKHgdQAAAAAAHARkm4AAAAAAFyEy8sBFCnMmQ4AAICShJ5uAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFykSIxe/uabb+rll1/W4cOH1aBBA73xxhtq3LhxjvWXLFmip556Svv27VPNmjX10ksvqVOnTk7r3nvvvfq///s/TZ06VaNGjXLRGQAoSgo6AjqjnwMAAKCwuL2ne9GiRRo9erTGjx+vLVu2qEGDBoqNjVViYqLT+hs3blSfPn00ePBg/fzzz+rRo4d69Oih33//PVvdjz/+WN9//70iIiJcfRoAAAAAAGTj9p7uKVOmaMiQIRo4cKAkadasWfr888/1zjvvaMyYMdnqv/baa+rQoYMeffRRSdKzzz6r1atXa/r06Zo1a5a93oEDB/Tggw9q1apV6ty58yVjSE9PV3p6uv1xSkqKJMlqtcpqteb73KxWq4wxBdpHkWeMuyNAUWfM+aWYKNFtFm5zVfxPAHKBtgDQDkqK3L5+bk26MzIytHnzZo0dO9Ze5uHhoZiYGMXHxzvdJj4+XqNHj3Yoi42N1bJly+yPrVar+vbtq0cffVT16tW7bByTJk3SxIkTs5UnJSXpzJkzuTyb7KxWq5KTk2WMkYeH2y8qcImAzFPuDgFFnpGvOSNZJcni7mBy5a1VPxd4H93/U6kQIkFJcjX8TwByg7YA0A5KipMnT+aqnluT7qNHjyozM1MVKlRwKK9QoYK2b9/udJvDhw87rX/48GH745deekleXl4aMWJEruIYO3asQyKfkpKiyMhIhYaGKigoKLenk43VapXFYlFoaGiJbUxpnsnuDgFFnTGSkdI8SkmW4pF0F4awsDB3h4Ai5mr4nwDkBm0BoB2UFH5+frmq5/bLywvb5s2b9dprr2nLli2y5PIDvq+vr3x9fbOVe3h4FLgRWCyWQtlPkXUVJVEoAIvl/HKVeG3t7gLvgwHdSp4S/z8ByCXaAkA7KAly+9q59RUuX768PD09deTIEYfyI0eOKDw83Ok24eHhl6z/zTffKDExUVWqVJGXl5e8vLyUkJCghx9+WFFRUS45DwAAAAAAnHFr0u3j46NGjRpp7dq19jKr1aq1a9eqWbNmTrdp1qyZQ31JWr16tb1+37599euvv2rr1q32JSIiQo8++qhWrVrlupMBAAAAAOAibr+8fPTo0erfv79uuOEGNW7cWNOmTVNqaqp9NPN+/fqpUqVKmjRpkiRp5MiRatWqlV599VV17txZCxcu1E8//aTZs2dLksqVK6dy5co5HMPb21vh4eGqXbv2lT05AAAAAMBVze1Jd1xcnJKSkvT000/r8OHDatiwoVauXGkfLG3//v0O18o3b95cCxYs0Lhx4/TEE0+oZs2aWrZsmerXr++uUwAAAAAAwCmLMcVo8twrJCUlRcHBwUpOTi7w6OWJiYkKCwsrsQMkTF29090hoKgzRgGZp5TmeXWNXl4YGEitZLka/icAuUFbAGgHJUVu80ZeYQAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEbePXg4AcK6gAxUyEBsAAID70dMNAAAAAICLkHQDAAAAAOAiJN0AAAAAALgISTcAAAAAAC5C0g0AAAAAgIuQdAMAAAAA4CJMGXaVK+iURAAAAACAnNHTDQAAAACAi5B0AwAAAADgIiTdAAAAAAC4CPd0A0AJVRhjNjzUrlYhRAIAAHD1oqcbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARL3cHAAAouqau3lmg7R9qV6uQIgEAACie6OkGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAX8XJ3AACAkmvq6p0F3sdD7WoVQiQAAADuQU83AAAAAAAuQtINAAAAAICLkHQDAAAAAOAiJN0AAAAAALgISTcAAAAAAC5C0g0AAAAAgIuQdAMAAAAA4CJFIul+8803FRUVJT8/PzVp0kQ//vjjJesvWbJEderUkZ+fn6699lqtWLHCYf2ECRNUp04dBQYGqkyZMoqJidEPP/zgylMAAAAAACAbtyfdixYt0ujRozV+/Hht2bJFDRo0UGxsrBITE53W37hxo/r06aPBgwfr559/Vo8ePdSjRw/9/vvv9jq1atXS9OnT9dtvv+nbb79VVFSU2rdvr6SkpCt1WgAAAAAAyGKMMe4MoEmTJrrxxhs1ffp0SZLValVkZKQefPBBjRkzJlv9uLg4paamavny5faypk2bqmHDhpo1a5bTY6SkpCg4OFhr1qxR27ZtLxtTVv3k5GQFBQXl88xs55KYmKiwsDB5eLj9+w2npq7e6e4QUNIZo4DMU0rzLCVZLO6OBsXQQ+1quTuEQlEc/icAVwJtAaAdlBS5zRu9rmBM2WRkZGjz5s0aO3asvczDw0MxMTGKj493uk18fLxGjx7tUBYbG6tly5bleIzZs2crODhYDRo0cFonPT1d6enp9scpKSmSbI3BarXm5ZQcWK1WGWMKtA+Xc+93LrgaGHN+AfKhSP8NzYNi8T8BuAJoCwDtoKTI7evn1qT76NGjyszMVIUKFRzKK1SooO3btzvd5vDhw07rHz582KFs+fLl6t27t9LS0lSxYkWtXr1a5cuXd7rPSZMmaeLEidnKk5KSdObMmbyckgOr1ark5GQZY4rsN1gBmafcHQJKPCNfc0ayShI93ci7nG43Km6Kw/8E4EqgLQC0g5Li5MmTuarn1qTbldq0aaOtW7fq6NGjeuutt9SrVy/98MMPCgsLy1Z37NixDr3nKSkpioyMVGhoaIEvL7dYLAoNDS2yjSnNM9ndIaCkM0YyUpoHl5cjfz74tWB/p0bG1CykSAqmOPxPAK4E2gJAOygp/Pz8clXPrUl3+fLl5enpqSNHjjiUHzlyROHh4U63CQ8Pz1X9wMBA1ahRQzVq1FDTpk1Vs2ZN/fe//3W4lD2Lr6+vfH19s5V7eHgUuBFYLJZC2Y/LkAThSrBYzi/AFVaU/v4W+f8JwBVCWwBoByVBbl+7fL3C69aty89m2fj4+KhRo0Zau3atvcxqtWrt2rVq1qyZ022aNWvmUF+SVq9enWP9C/d74X3bAAAAAAC4Wr6S7g4dOqh69ep67rnn9PfffxcogNGjR+utt97SvHnztG3bNt13331KTU3VwIEDJUn9+vVz6J0eOXKkVq5cqVdffVXbt2/XhAkT9NNPP+mBBx6QJKWmpuqJJ57Q999/r4SEBG3evFmDBg3SgQMHdMcddxQoVgAAAAAA8iJfSfeBAwf0wAMPaOnSpapWrZpiY2O1ePFiZWRk5HlfcXFxeuWVV/T000+rYcOG2rp1q1auXGkfLG3//v06dOiQvX7z5s21YMECzZ49Ww0aNNDSpUu1bNky1a9fX5Lk6emp7du36/bbb1etWrXUtWtXHTt2TN98843q1auXn9MFAAAAACBfCjxP95YtWzRnzhx98MEHkqQ777xTgwcPznF6ruKAebqBQsQ83XCzojLPd3H4nwBcCbQFgHZQUuQ2byzwK3z99ddr7NixeuCBB3Tq1Cm98847atSokW666Sb98ccfBd09AAAAAADFVr6T7rNnz2rp0qXq1KmTqlatqlWrVmn69Ok6cuSIdu/erapVq3IPNQAAAADgqpavKcMefPBBffDBBzLGqG/fvpo8ebL9nmrJNl3XK6+8ooiIiEILFAAAAACA4iZfSfeff/6pN954Q7fddpvT+a0l2xzchTW1GAAAAAAAxVG+Li8fP3687rjjjmwJ97lz57RhwwZJkpeXl1q1alXwCAEAAAAAKKbylXS3adNGx48fz1aenJysNm3aFDgoAAAAAABKgnwl3cYYWZxM/XPs2DEFBgYWOCgAAAAAAEqCPN3Tfdttt0mSLBaLBgwY4HB5eWZmpn799Vc1b968cCMEAAAAAKCYylPSHRwcLMnW0126dGn5+/vb1/n4+Khp06YaMmRI4UYIAAAAAEAxlaeke86cOZKkqKgoPfLII1xKDgAAAADAJeRryrDx48cXdhwAALjE1NU7C7yPh9rVKoRIAADA1SjXSff111+vtWvXqkyZMvrPf/7jdCC1LFu2bCmU4AAAAAAAKM5ynXR3797dPnBajx49XBUPAAAAAAAlRq6T7gsvKefycgAAAAAALi9f83QDAAAAAIDLy3VPd5kyZS55H/eFjh8/nu+AAAAAAAAoKXKddE+bNs2FYQAAAAAAUPLkOunu37+/K+MAAAAAAKDEyXXSnZKSoqCgIPvvl5JVDwAAAACAq1me7uk+dOiQwsLCFBIS4vT+bmOMLBaLMjMzCzVIAADcaerqnQXa/qF2tQopEgAAUNzkOun+6quvVLZsWUnSunXrXBYQAAAAAAAlRa6T7latWjn9HQAAAAAAOJfrpPti//77r/773/9q27ZtkqRrrrlGAwcOtPeGAwAAAABwtfPIz0YbNmxQVFSUXn/9df3777/6999/9frrrys6OlobNmwo7BgBAAAAACiW8tXTPXz4cMXFxWnmzJny9PSUJGVmZur+++/X8OHD9dtvvxVqkAAAAAAAFEf56unevXu3Hn74YXvCLUmenp4aPXq0du/eXWjBAQAAAABQnOUr6b7++uvt93JfaNu2bWrQoEGBgwIAAAAAoCTI9eXlv/76q/33ESNGaOTIkdq9e7eaNm0qSfr+++/15ptv6sUXXyz8KAEAAAAAKIYsxhiTm4oeHh6yWCy6XHWLxaLMzMxCCc5dUlJSFBwcrOTkZAUFBeV7P1arVYmJiQoLC5OHR74uKnC5qat3ujsElHTGKCDzlNI8S0kWi7ujAdyngG3hoXa1XBAUcOUVh89HgKvRDkqG3OaNue7p3rt3b6EEBgAAAADA1SLXSXfVqlVdGQcAAAAAACVOvqYMy/Lnn39q//79ysjIcCjv1q1bgYICAAAAAKAkyFfS/ddff+nWW2/Vb7/95nCft+V/96gV93u6AQAAAAAoDPm6a3/kyJGKjo5WYmKiAgIC9Mcff2jDhg264YYbtH79+kIOEQAAAACA4ilfPd3x8fH66quvVL58eXl4eMjDw0MtW7bUpEmTNGLECP3888+FHScAAAAAAMVOvnq6MzMzVbp0aUlS+fLldfDgQUm2wdZ27NhReNEBAAAAAFCM5aunu379+vrll18UHR2tJk2aaPLkyfLx8dHs2bNVrVq1wo4RAAAAAIBiKV9J97hx45SamipJeuaZZ9SlSxfddNNNKleunBYtWlSoAQIAAAAAUFzlK+mOjY21/16jRg1t375dx48fV5kyZewjmAMAAAAAcLUr0DzdkvT3339LkiIjIwscDAAAAAAAJUm+BlI7d+6cnnrqKQUHBysqKkpRUVEKDg7WuHHjdPbs2cKOEQAAAACAYilfPd0PPvigPvroI02ePFnNmjWTZJtGbMKECTp27JhmzpxZqEECAHC1m7p6Z4G2f6hdrUKKBAAA5EW+ku4FCxZo4cKF6tixo73suuuuU2RkpPr06UPSDQAAAACA8nl5ua+vr6KiorKVR0dHy8fHp6AxAQAAAABQIuQr6X7ggQf07LPPKj093V6Wnp6u559/Xg888EChBQcAAAAAQHGW68vLb7vtNofHa9asUeXKldWgQQNJ0i+//KKMjAy1bdu2cCMEAAAAAKCYynXSHRwc7PD49ttvd3jMlGEAAAAAADjKddI9Z84cV8YBAAAAAECJk6/Ry7MkJSVpx44dkqTatWsrNDS0UIICAAAAAKAkyNdAaqmpqRo0aJAqVqyom2++WTfffLMiIiI0ePBgpaWlFXaMAAAAAAAUS/lKukePHq2vv/5an332mU6cOKETJ07ok08+0ddff62HH364sGMEAAAAAKBYytfl5R9++KGWLl2q1q1b28s6deokf39/9erVSzNnziys+AAAAAAAKLby1dOdlpamChUqZCsPCwvL1+Xlb775pqKiouTn56cmTZroxx9/vGT9JUuWqE6dOvLz89O1116rFStW2NedPXtWjz/+uK699loFBgYqIiJC/fr108GDB/McFwAAAAAABZGvnu5mzZpp/Pjxevfdd+Xn5ydJOn36tCZOnKhmzZrlaV+LFi3S6NGjNWvWLDVp0kTTpk1TbGysduzYobCwsGz1N27cqD59+mjSpEnq0qWLFixYoB49emjLli2qX7++0tLStGXLFj311FNq0KCB/v33X40cOVLdunXTTz/9lJ/TBQCg2Ju6emeB9/FQu1qFEAkAAFcXizHG5HWj3377TR06dFB6eroaNGggSfrll1/k5+enVatWqV69erneV5MmTXTjjTdq+vTpkiSr1arIyEg9+OCDGjNmTLb6cXFxSk1N1fLly+1lTZs2VcOGDTVr1iynx9i0aZMaN26shIQEValS5bIxpaSkKDg4WMnJyQoKCsr1uVzMarUqMTFRYWFh8vDI10UFLlcYH8KASzJGAZmnlOZZSrJY3B0N4D4loC2QdKMwFIfPR4Cr0Q5Khtzmjfnq6b722mu1a9cuzZ8/X9u3b5ck9enTR3fddZf8/f1zvZ+MjAxt3rxZY8eOtZd5eHgoJiZG8fHxTreJj4/X6NGjHcpiY2O1bNmyHI+TnJwsi8WikJAQp+vT09OVnp5uf5ySkiLJ1hisVmsuzyY7q9UqY0yB9uFyef/OBcgbY84vwNWsBLSFIv3/DMVGsfh8BLgY7aBkyO3rl+ek++zZs6pTp46WL1+uIUOG5DmwCx09elSZmZnZ7g+vUKGCPZm/2OHDh53WP3z4sNP6Z86c0eOPP64+ffrk+O3DpEmTNHHixGzlSUlJOnPmTG5OxSmr1ark5GQZY4rsN1gBmafcHQJKPCNfc0aySlLx7N0DCkfxbwuJiYnuDgElQHH4fAS4Gu2gZDh58mSu6uU56fb29i5QInolnT17Vr169ZIx5pIjqo8dO9ah9zwlJUWRkZEKDQ0t8OXlFotFoaGhRbYxpXkmuzsElHTGSEZK8yi+l9QChaIEtAVnY60AeVUcPh8BrkY7KBmyxje7nHxdXj58+HC99NJLevvtt+Xlla9dSJLKly8vT09PHTlyxKH8yJEjCg8Pd7pNeHh4rupnJdwJCQn66quvLpk8+/r6ytfXN1u5h4dHgRuBxWIplP24TDH94IdixmI5vwBXs2LeFors/zIUO0X+8xFwBdAOir/cvnb5eoU3bdqkjz76SFWqVFFsbKxuu+02hyW3fHx81KhRI61du9ZeZrVatXbt2hxHQW/WrJlDfUlavXq1Q/2shHvXrl1as2aNypUrl8czBAAAAACg4PLVTR0SEqLbb7+9UAIYPXq0+vfvrxtuuEGNGzfWtGnTlJqaqoEDB0qS+vXrp0qVKmnSpEmSpJEjR6pVq1Z69dVX1blzZy1cuFA//fSTZs+eLcmWcPfs2VNbtmzR8uXLlZmZab/fu2zZsvLx8SmUuAEAAAAAuJw8Jd1Wq1Uvv/yydu7cqYyMDN1yyy2aMGFCnkYsv1hcXJySkpL09NNP6/Dhw2rYsKFWrlxpHyxt//79Dt32zZs314IFCzRu3Dg98cQTqlmzppYtW6b69etLkg4cOKBPP/1UktSwYUOHY61bt06tW7fOd6wAAAAAAORFnubpfvbZZzVhwgTFxMTI399fq1atUp8+ffTOO++4MsYrjnm6gUJUAuYmBgpFCWgLzNONwlAcPh8BrkY7KBlymzfm6RV+9913NWPGDK1atUrLli3TZ599pvnz5zO/HAAAAAAATuQp6d6/f786depkfxwTEyOLxaKDBw8WemAAAAAAABR3eUq6z507l20uMm9vb509e7ZQgwIAAAAAoCTI00BqxhgNGDDAYU7rM2fO6N5771VgYKC97KOPPiq8CAEAAAAAKKbylHT3798/W9ndd99daMEAAAAAAFCS5CnpnjNnjqviAAAARVxhzHjBCOgAgKsN49MDAAAAAOAiJN0AAAAAALgISTcAAAAAAC5C0g0AAAAAgIuQdAMAAAAA4CIk3QAAAAAAuAhJNwAAAAAALkLSDQAAAACAi5B0AwAAAADgIiTdAAAAAAC4CEk3AAAAAAAuQtINAAAAAICLeLk7AAAAcPWYunpngbZ/qF2tQooEAIArg55uAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFzEy90BAAAA5NbU1TsLvI+H2tUqhEgAAMgderoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFvNwdAAAAwJU0dfXOAm3/ULtahRQJAOBqQE83AAAAAAAuQtINAAAAAICLuD3pfvPNNxUVFSU/Pz81adJEP/744yXrL1myRHXq1JGfn5+uvfZarVixwmH9Rx99pPbt26tcuXKyWCzaunWrC6MHAAAAACBnbk26Fy1apNGjR2v8+PHasmWLGjRooNjYWCUmJjqtv3HjRvXp00eDBw/Wzz//rB49eqhHjx76/fff7XVSU1PVsmVLvfTSS1fqNAAAAAAAcMpijDHuOniTJk104403avr06ZIkq9WqyMhIPfjggxozZky2+nFxcUpNTdXy5cvtZU2bNlXDhg01a9Ysh7r79u1TdHS0fv75ZzVs2DBPcaWkpCg4OFjJyckKCgrK+4n9j9VqVWJiosLCwuTh4faLCpwq6GAywGUZo4DMU0rzLCVZLO6OBnAf2kKJwUBqBVMcPh8BrkY7KBlymze6bfTyjIwMbd68WWPHjrWXeXh4KCYmRvHx8U63iY+P1+jRox3KYmNjtWzZsgLFkp6ervT0dPvjlJQUSbbGYLVa871fq9UqY0yB9uFy7vvOBVcLY84vwNWMtlBiFOn/68VAsfh8BLgY7aBkyO3r57ak++jRo8rMzFSFChUcyitUqKDt27c73ebw4cNO6x8+fLhAsUyaNEkTJ07MVp6UlKQzZ87ke79Wq1XJyckyxhTZb7ACMk+5OwSUeEa+5oxklSR693A1oy2UFDndBofcKQ6fjwBXox2UDCdPnsxVPebpljR27FiHHvSUlBRFRkYqNDS0wJeXWywWhYaGFtnGlOaZ7O4QUNIZIxkpzYNLanGVoy2UGB/8WvD/nSNjahZCJMVTcfh8BLga7aBk8PPzy1U9tyXd5cuXl6enp44cOeJQfuTIEYWHhzvdJjw8PE/1c8vX11e+vr7Zyj08PArcCCwWS6Hsx2X44IcrwWI5vwBXM9oC/qfIfi64Qor85yPgCqAdFH+5fe3c9gr7+PioUaNGWrt2rb3MarVq7dq1atasmdNtmjVr5lBfklavXp1jfQAAAAAA3Mmtl5ePHj1a/fv31w033KDGjRtr2rRpSk1N1cCBAyVJ/fr1U6VKlTRp0iRJ0siRI9WqVSu9+uqr6ty5sxYuXKiffvpJs2fPtu/z+PHj2r9/vw4ePChJ2rFjhyRbL3lBe8QBAAAAAMgLtybdcXFxSkpK0tNPP63Dhw+rYcOGWrlypX2wtP379zt02Tdv3lwLFizQuHHj9MQTT6hmzZpatmyZ6tevb6/z6aef2pN2Serdu7ckafz48ZowYcKVOTEAAAAAAOTmebqLKubpBgoRcxMDNrQFXOBqnuu7OHw+AlyNdlAy5DZv5BUGAAAAAMBFmDIMAADgCivolWZXc085ABQ39HQDAAAAAOAiJN0AAAAAALgISTcAAAAAAC5C0g0AAAAAgIuQdAMAAAAA4CIk3QAAAAAAuAhJNwAAAAAALkLSDQAAAACAi3i5OwDk39TVO90dAgAAAADgEujpBgAAAADARUi6AQAAAABwEZJuAAAAAABchHu6AQAAipnCGNfloXa1CiESAMDl0NMNAAAAAICLkHQDAAAAAOAiJN0AAAAAALgISTcAAAAAAC5C0g0AAAAAgIuQdAMAAAAA4CIk3QAAAAAAuAhJNwAAAAAALkLSDQAAAACAi3i5OwAAAABceVNX7yzQ9g+1q1VIkQBAyUZPNwAAAAAALkLSDQAAAACAi3B5OQAAAPIs35enG6OAzFNK80zWQ+1rF25QAFAE0dMNAAAAAICLkHQDAAAAAOAiJN0AAAAAALgISTcAAAAAAC7CQGoAAABwC+YKB3A1oKcbAAAAAAAXIekGAAAAAMBFuLwcAAAAxVJBL0+XuEQdgOvR0w0AAAAAgIuQdAMAAAAA4CJcXg4AAICrFpeoA3A1km4AAACgAJj6DMClcHk5AAAAAAAuQk83AAAA4EZc4g6UbPR0AwAAAADgIiTdAAAAAAC4CEk3AAAAAAAuQtINAAAAAICLMJAaAAAAUMwVxmBsBcVgboBz9HQDAAAAAOAiJN0AAAAAALgIl5cDAAAAKLCCXuLO5ekoqejpBgAAAADARejpBgAAAOB2hTEYHL3lKIqKRNL95ptv6uWXX9bhw4fVoEEDvfHGG2rcuHGO9ZcsWaKnnnpK+/btU82aNfXSSy+pU6dO9vXGGI0fP15vvfWWTpw4oRYtWmjmzJmqWbPmlTgdAAAAAG7AJe4oityedC9atEijR4/WrFmz1KRJE02bNk2xsbHasWOHwsLCstXfuHGj+vTpo0mTJqlLly5asGCBevTooS1btqh+/fqSpMmTJ+v111/XvHnzFB0draeeekqxsbH6888/5efnd6VPEQAAAEAxcMWmXjNGAZmnlOaZLFksDqtI/EseizHGuDOAJk2a6MYbb9T06dMlSVarVZGRkXrwwQc1ZsyYbPXj4uKUmpqq5cuX28uaNm2qhg0batasWTLGKCIiQg8//LAeeeQRSVJycrIqVKiguXPnqnfv3peNKSUlRcHBwUpOTlZQUFC+z81qtSoxMVFhYWHy8Cj82+eLwnyMwGXZ/6mUyvZPBbiq0BYAG9oCQDvIpaL+BURu80a39nRnZGRo8+bNGjt2rL3Mw8NDMTExio+Pd7pNfHy8Ro8e7VAWGxurZcuWSZL27t2rw4cPKyYmxr4+ODhYTZo0UXx8vNOkOz09Xenp6fbHycnJkqQTJ07IarXm+/ysVqtSUlLk4+PjkqT7zKmThb5PoNAZI4v1lM54GP6p4OpGWwBsaAsA7SCXTpw44e4QLiklJUWS7fbmS3Fr0n306FFlZmaqQoUKDuUVKlTQ9u3bnW5z+PBhp/UPHz5sX59VllOdi02aNEkTJ07MVl61atXcnQgAAAAAoFA94e4AcunkyZMKDg7Ocb3b7+kuCsaOHevQe261WnX8+HGVK1dOlgJ885SSkqLIyEj9/fffBbpMHSjOaAeADW0BsKEtALSDksIYo5MnTyoiIuKS9dyadJcvX16enp46cuSIQ/mRI0cUHh7udJvw8PBL1s/6eeTIEVWsWNGhTsOGDZ3u09fXV76+vg5lISEheTmVSwoKCqIx4apHOwBsaAuADW0BoB2UBJfq4c5S+Dca54GPj48aNWqktWvX2susVqvWrl2rZs2aOd2mWbNmDvUlafXq1fb60dHRCg8Pd6iTkpKiH374Icd9AgAAAADgCm6/vHz06NHq37+/brjhBjVu3FjTpk1TamqqBg4cKEnq16+fKlWqpEmTJkmSRo4cqVatWunVV19V586dtXDhQv3000+aPXu2JMlisWjUqFF67rnnVLNmTfuUYREREerRo4e7ThMAAAAAcBVye9IdFxenpKQkPf300zp8+LAaNmyolStX2gdC279/v8PI382bN9eCBQs0btw4PfHEE6pZs6aWLVtmn6Nbkh577DGlpqZq6NChOnHihFq2bKmVK1de8Tm6fX19NX78+GyXrgNXE9oBYENbAGxoCwDt4Grj9nm6AQAAAAAoqdx6TzcAAAAAACUZSTcAAAAAAC5C0g0AAAAAgIuQdAMAAAAA4CIk3S7y5ptvKioqSn5+fmrSpIl+/PFHd4cE5NuECRNksVgcljp16tjXnzlzRsOHD1e5cuVUqlQp3X777Tpy5IjDPvbv36/OnTsrICBAYWFhevTRR3Xu3DmHOuvXr9f1118vX19f1ahRQ3Pnzr0SpwfkaMOGDeratasiIiJksVi0bNkyh/XGGD399NOqWLGi/P39FRMTo127djnUOX78uO666y4FBQUpJCREgwcP1qlTpxzq/Prrr7rpppvk5+enyMhITZ48OVssS5YsUZ06deTn56drr71WK1asKPTzBZy5XDsYMGBAtv8RHTp0cKhDO0BxN2nSJN14440qXbq0wsLC1KNHD+3YscOhzpX8PESuUbyQdLvAokWLNHr0aI0fP15btmxRgwYNFBsbq8TERHeHBuRbvXr1dOjQIfvy7bff2tc99NBD+uyzz7RkyRJ9/fXXOnjwoG677Tb7+szMTHXu3FkZGRnauHGj5s2bp7lz5+rpp5+219m7d686d+6sNm3aaOvWrRo1apTuuecerVq16oqeJ3Ch1NRUNWjQQG+++abT9ZMnT9brr7+uWbNm6YcfflBgYKBiY2N15swZe5277rpLf/zxh1avXq3ly5drw4YNGjp0qH19SkqK2rdvr6pVq2rz5s16+eWXNWHCBM2ePdteZ+PGjerTp48GDx6sn3/+WT169FCPHj30+++/u+7kgf+5XDuQpA4dOjj8j/jggw8c1tMOUNx9/fXXGj58uL7//nutXr1aZ8+eVfv27ZWammqvc6U+D5FrFEMGha5x48Zm+PDh9seZmZkmIiLCTJo0yY1RAfk3fvx406BBA6frTpw4Yby9vc2SJUvsZdu2bTOSTHx8vDHGmBUrVhgPDw9z+PBhe52ZM2eaoKAgk56ebowx5rHHHjP16tVz2HdcXJyJjY0t5LMB8keS+fjjj+2PrVarCQ8PNy+//LK97MSJE8bX19d88MEHxhhj/vzzTyPJbNq0yV7niy++MBaLxRw4cMAYY8yMGTNMmTJl7G3BGGMef/xxU7t2bfvjXr16mc6dOzvE06RJEzNs2LBCPUfgci5uB8YY079/f9O9e/cct6EdoCRKTEw0kszXX39tjLmyn4fINYoferoLWUZGhjZv3qyYmBh7mYeHh2JiYhQfH+/GyICC2bVrlyIiIlStWjXddddd2r9/vyRp8+bNOnv2rMN7vk6dOqpSpYr9PR8fH69rr71WFSpUsNeJjY1VSkqK/vjjD3udC/eRVYd2g6Jq7969Onz4sMP7Njg4WE2aNHF474eEhOiGG26w14mJiZGHh4d++OEHe52bb75ZPj4+9jqxsbHasWOH/v33X3sd2geKsvXr1yssLEy1a9fWfffdp2PHjtnX0Q5QEiUnJ0uSypYtK+nKfR4i1yieSLoL2dGjR5WZmenQmCSpQoUKOnz4sJuiAgqmSZMmmjt3rlauXKmZM2dq7969uummm3Ty5EkdPnxYPj4+CgkJcdjmwvf84cOHnbaJrHWXqpOSkqLTp0+76MyA/Mt6717q7/3hw4cVFhbmsN7Ly0tly5YtlPbB/xUUBR06dNC7776rtWvX6qWXXtLXX3+tjh07KjMzUxLtACWP1WrVqFGj1KJFC9WvX1+SrtjnIXKN4snL3QEAKPo6duxo//26665TkyZNVLVqVS1evFj+/v5ujAwA4G69e/e2/37ttdfquuuuU/Xq1bV+/Xq1bdvWjZEBrjF8+HD9/vvvDuPbAJdCT3chK1++vDw9PbONVHjkyBGFh4e7KSqgcIWEhKhWrVravXu3wsPDlZGRoRMnTjjUufA9Hx4e7rRNZK27VJ2goCASexRJWe/dS/29Dw8Pzzawzblz53T8+PFCaR/8X0FRVK1aNZUvX167d++WRDtAyfLAAw9o+fLlWrdunSpXrmwvv1Kfh8g1iieS7kLm4+OjRo0aae3atfYyq9WqtWvXqlmzZm6MDCg8p06d0p49e1SxYkU1atRI3t7eDu/5HTt2aP/+/fb3fLNmzfTbb785fOhavXq1goKCdM0119jrXLiPrDq0GxRV0dHRCg8Pd3jfpqSk6IcffnB47584cUKbN2+21/nqq69ktVrVpEkTe50NGzbo7Nmz9jqrV69W7dq1VaZMGXsd2geKi3/++UfHjh1TxYoVJdEOUDIYY/TAAw/o448/1ldffaXo6GiH9Vfq8xC5RjHl7pHcSqKFCxcaX19fM3fuXPPnn3+aoUOHmpCQEIeRCoHi5OGHHzbr1683e/fuNd99952JiYkx5cuXN4mJicYYY+69915TpUoV89VXX5mffvrJNGvWzDRr1sy+/blz50z9+vVN+/btzdatW83KlStNaGioGTt2rL3OX3/9ZQICAsyjjz5qtm3bZt58803j6elpVq5cecXPF8hy8uRJ8/PPP5uff/7ZSDJTpkwxP//8s0lISDDGGPPiiy+akJAQ88knn5hff/3VdO/e3URHR5vTp0/b99GhQwfzn//8x/zwww/m22+/NTVr1jR9+vSxrz9x4oSpUKGC6du3r/n999/NwoULTUBAgPm///s/e53vvvvOeHl5mVdeecVs27bNjB8/3nh7e5vffvvtyj0ZuGpdqh2cPHnSPPLIIyY+Pt7s3bvXrFmzxlx//fWmZs2a5syZM/Z90A5Q3N13330mODjYrF+/3hw6dMi+pKWl2etcqc9D5BrFD0m3i7zxxhumSpUqxsfHxzRu3Nh8//337g4JyLe4uDhTsWJF4+PjYypVqmTi4uLM7t277etPnz5t7r//flOmTBkTEBBgbr31VnPo0CGHfezbt8907NjR+Pv7m/Lly5uHH37YnD171qHOunXrTMOGDY2Pj4+pVq2amTNnzpU4PSBH69atM5KyLf379zfG2KYNe+qpp0yFChWMr6+vadu2rdmxY4fDPo4dO2b69OljSpUqZYKCgszAgQPNyZMnHer88ssvpmXLlsbX19dUqlTJvPjii9liWbx4salVq5bx8fEx9erVM59//rnLzhu40KXaQVpammnfvr0JDQ013t7epmrVqmbIkCHZPvzTDlDcOWsDkhw+q1zJz0PkGsWLxRhjrnTvOgAAAAAAVwPu6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAogAEDBigqKqpQ9zl37lxZLBbt27evUPeLoicqKkoDBgxwdxgAABci6QYAuN2ePXs0bNgwVatWTX5+fgoKClKLFi302muv6fTp0+4Oz2VeeOEFLVu2zN1h2GUl+xaLRd9++2229cYYRUZGymKxqEuXLm6IMGf79u2zx37x0rRpU7fGtnHjRk2YMEEnTpxwaxwAAPfwcncAAICr2+eff6477rhDvr6+6tevn+rXr6+MjAx9++23evTRR/XHH39o9uzZ7g7TJV544QX17NlTPXr0cCjv27evevfuLV9fX7fE5efnpwULFqhly5YO5V9//bX++ecft8WVG3369FGnTp0cykJDQ90Ujc3GjRs1ceJEDRgwQCEhIQ7rduzYIQ8P+kAAoCQj6QYAuM3evXvVu3dvVa1aVV999ZUqVqxoXzd8+HDt3r1bn3/+uRsjdA9PT095enq67fidOnXSkiVL9Prrr8vL6/xHhQULFqhRo0Y6evSo22K7nOuvv1533323u8PItaL8BQYAoHDw1SoAwG0mT56sU6dO6b///a9Dwp2lRo0aGjlypKTzlw/PnTs3Wz2LxaIJEybYH0+YMEEWi0U7d+7U3XffreDgYIWGhuqpp56SMUZ///23unfvrqCgIIWHh+vVV1912F9O91SvX79eFotF69evv+R5vfLKK2revLnKlSsnf39/NWrUSEuXLs0Wc2pqqubNm2e/DDrr3t6Lj9+lSxdVq1bN6bGaNWumG264waHs/fffV6NGjeTv76+yZcuqd+/e+vvvvy8Z84X69OmjY8eOafXq1fayjIwMLV26VHfeeWe+z1mSVq9erZYtWyokJESlSpVS7dq19cQTTzjUeeONN1SvXj0FBASoTJkyuuGGG7RgwYJcx5+T1q1bq3Xr1tnKL74vP+u99sorr2j27NmqXr26fH19deONN2rTpk3Ztt++fbt69eql0NBQ+fv7q3bt2nryyScl2d6Ljz76qCQpOjra/lpnvbbO7un+66+/dMcdd6hs2bIKCAhQ06ZNs335lPVeXLx4sZ5//nlVrlxZfn5+atu2rXbv3p3/JwkAUOhIugEAbvPZZ5+pWrVqat68uUv2HxcXJ6vVqhdffFFNmjTRc889p2nTpqldu3aqVKmSXnrpJdWoUUOPPPKINmzYUGjHfe211/Sf//xHzzzzjF544QV5eXnpjjvucEic3nvvPfn6+uqmm27Se++9p/fee0/Dhg3L8Tz27t2bLeFLSEjQ999/r969e9vLnn/+efXr1081a9bUlClTNGrUKK1du1Y333xzru8pjoqKUrNmzfTBBx/Yy7744gslJyc7HCuv5/zHH3+oS5cuSk9P1zPPPKNXX31V3bp103fffWev89Zbb2nEiBG65pprNG3aNE2cOFENGzbUDz/8kKvY09LSdPToUYfl7Nmzudr2YgsWLNDLL7+sYcOG6bnnntO+fft02223Oezv119/VZMmTfTVV19pyJAheu2119SjRw999tlnkqTbbrtNffr0kSRNnTrV/lrndMn7kSNH1Lx5c61atUr333+/nn/+eZ05c0bdunXTxx9/nK3+iy++qI8//liPPPKIxo4dq++//1533XVXvs4XAOAiBgAAN0hOTjaSTPfu3XNVf+/evUaSmTNnTrZ1ksz48ePtj8ePH28kmaFDh9rLzp07ZypXrmwsFot58cUX7eX//vuv8ff3N/3797eXzZkzx0gye/fudTjOunXrjCSzbt06e1n//v1N1apVHeqlpaU5PM7IyDD169c3t9xyi0N5YGCgw3FzOn5ycrLx9fU1Dz/8sEO9yZMnG4vFYhISEowxxuzbt894enqa559/3qHeb7/9Zry8vLKV53TcTZs2menTp5vSpUvbz+WOO+4wbdq0McYYU7VqVdO5c+c8n/PUqVONJJOUlJRjDN27dzf16tW7ZJzOZL0/nC1Zr1erVq1Mq1atsm178WuYta9y5cqZ48eP28s/+eQTI8l89tln9rKbb77ZlC5d2v4aZLFarfbfX375ZafvJ2Nsz+WF74FRo0YZSeabb76xl508edJER0ebqKgok5mZaYw5/16sW7euSU9Pt9d97bXXjCTz22+/XfL5AgBcOfR0AwDcIiUlRZJUunRplx3jnnvusf/u6empG264QcYYDR482F4eEhKi2rVr66+//iq04/r7+9t///fff5WcnKybbrpJW7Zsydf+goKC1LFjRy1evFjGGHv5okWL1LRpU1WpUkWS9NFHH8lqtapXr14OPb3h4eGqWbOm1q1bl+tj9urVS6dPn9by5ct18uRJLV++PMdLy6XcnXPWIGKffPKJrFar0/2EhITon3/+cXoZd24MHTpUq1evdlgaNGiQr33FxcWpTJky9sc33XSTJNnfK0lJSdqwYYMGDRpkfw2yWCyWfB1zxYoVaty4scMgdqVKldLQoUO1b98+/fnnnw71Bw4cKB8fnxxjBAC4HwOpAQDcIigoSJJ08uRJlx3j4kQoODhYfn5+Kl++fLbyY8eOFdpxly9frueee05bt25Venq6vTy/iZhkSwCXLVum+Ph4NW/eXHv27NHmzZs1bdo0e51du3bJGKOaNWs63Ye3t3eujxcaGqqYmBgtWLBAaWlpyszMVM+ePXOsn5tzjouL09tvv6177rlHY8aMUdu2bXXbbbepZ8+e9hG8H3/8ca1Zs0aNGzdWjRo11L59e915551q0aJFruKuWbOmYmJicn2el3Lx+ycrAf/3338lnU9s69evXyjHk2y3DDRp0iRbed26de3rLzze5WIEALgfSTcAwC2CgoIUERGh33//PVf1c0pYMzMzc9zG2QjgOY0KfmEPcn6OleWbb75Rt27ddPPNN2vGjBmqWLGivL29NWfOnAINBta1a1cFBARo8eLFat68uRYvXiwPDw/dcccd9jpWq1UWi0VffPGF0/MsVapUno555513asiQITp8+LA6duyYbbqrLLk9Z39/f23YsEHr1q3T559/rpUrV2rRokW65ZZb9OWXX8rT01N169bVjh07tHz5cq1cuVIffvihZsyYoaeffloTJ07MU/wXs1gsDq9zlpxe19y8V9ytOMQIAFc7km4AgNt06dJFs2fPVnx8vJo1a3bJulk9eBcPBpaQkFDocRXkWB9++KH8/Py0atUqh+mg5syZk61uXnq+AwMD1aVLFy1ZskRTpkzRokWLdNNNNykiIsJep3r16jLGKDo6WrVq1cr1vnNy6623atiwYfr++++1aNGiHOvl5Zw9PDzUtm1btW3bVlOmTNELL7ygJ598UuvWrbP3UAcGBiouLk5xcXHKyMjQbbfdpueff15jx46Vn59fvs+nTJkyTi+7zu97KGtE+ct9cZSX17lq1arasWNHtvLt27fb1wMAihfu6QYAuM1jjz2mwMBA3XPPPTpy5Ei29Xv27NFrr70mydYzXr58+WyjjM+YMaPQ46pevbokORwrMzNTs2fPvuy2np6eslgsDr2n+/bt07Jly7LVDQwMzPWI4pLt8uyDBw/q7bff1i+//KK4uDiH9bfddps8PT01ceLEbD2dxpg8X0JfqlQpzZw5UxMmTFDXrl1zrJfbcz5+/Hi2bRs2bChJ9kvSL47Rx8dH11xzjYwx+R6FPEv16tW1fft2JSUl2ct++eUXh9HT8yI0NFQ333yz3nnnHe3fv99h3YXPf2BgoKTsX+I406lTJ/3444+Kj4+3l6Wmpmr27NmKiorSNddck69YAQDuQ083AMBtqlevrgULFiguLk5169ZVv379VL9+fWVkZGjjxo1asmSJwxzG99xzj1588UXdc889uuGGG7Rhwwbt3Lmz0OOqV6+emjZtqrFjx+r48eMqW7asFi5cqHPnzl12286dO2vKlCnq0KGD7rzzTiUmJurNN99UjRo19OuvvzrUbdSokdasWaMpU6YoIiJC0dHRTu/nzdKpUyeVLl1ajzzyiDw9PXX77bc7rK9evbqee+45jR07Vvv27VOPHj1UunRp7d27Vx9//LGGDh2qRx55JE/PRf/+/QvtnJ955hlt2LBBnTt3VtWqVZWYmKgZM2aocuXK9oHD2rdvr/DwcLVo0UIVKlTQtm3bNH36dHXu3LnAg+4NGjRIU6ZMUWxsrAYPHqzExETNmjVL9erVsw/sl1evv/66WrZsqeuvv15Dhw5VdHS09u3bp88//1xbt26VZHudJenJJ59U79695e3tra5du9qT8QuNGTNGH3zwgTp27KgRI0aobNmymjdvnvbu3asPP/zQfu87AKAYcc+g6QAAnLdz504zZMgQExUVZXx8fEzp0qVNixYtzBtvvGHOnDljr5eWlmYGDx5sgoODTenSpU2vXr1MYmJijlOGXTw1Vf/+/U1gYGC247dq1SrbNFV79uwxMTExxtfX11SoUME88cQTZvXq1bmaMuy///2vqVmzpvH19TV16tQxc+bMscd0oe3bt5ubb77Z+Pv7G0n2qaNymrLMGGPuuusuI8nExMTk+Hx++OGHpmXLliYwMNAEBgaaOnXqmOHDh5sdO3bkuM2Fx920adMl6zmbMiw357x27VrTvXt3ExERYXx8fExERITp06eP2blzp73O//3f/5mbb77ZlCtXzvj6+prq1aubRx991CQnJ18ypqxpvl5++eVL1nv//fdNtWrVjI+Pj2nYsKFZtWpVjlOGOdvXxe81Y4z5/fffza233mpCQkKMn5+fqV27tnnqqacc6jz77LOmUqVKxsPDw+G1vXjKMGNs772ePXva99e4cWOzfPlyhzpZU4YtWbLE6fPgbGo9AIB7WIxhpA0AAAAAAFyBa5QAAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXMTL3QEURVarVQcPHlTp0qVlsVjcHQ4AAAAAoIgxxujkyZOKiIiQh0fO/dkk3U4cPHhQkZGR7g4DAAAAAFDE/f3336pcuXKO60m6nShdurQk25MXFBTktI7ValVSUpJCQ0Mv+a0GcLWhbQDZ0S4A52gbQHa0i+IjJSVFkZGR9vwxJyTdTmRdUh4UFHTJpPvMmTMKCgqiMQAXoG0A2dEuAOdoG0B2tIvi53K3JPMqAgAAAADgIiTdAAAAAAC4CEk3AAAAAAAuwj3d+WSMUWZmps6cOcO9Frnk6ekpLy8vpmEDAAAAcNUg6c6HjIwMHTx4UCdPnlRycjJJZB4EBASoYsWK8vHxcXcoAAAAAOByJN15ZLVatXfvXnl6eqpSpUry8/OjpzsXjDHKyMhQUlKS9u7dq5o1a/K8AQAAACjxSLrzKCMjQ1arVZUrV5aPjw+XS+eBv7+/vL29lZCQoIyMDPn5+bk7JAAAAABwqSLf1bhhwwZ17dpVERERslgsWrZs2WW3Wb9+va6//nr5+vqqRo0amjt3bqHHRS9t/vC8AQAAALiaFPkMKDU1VQ0aNNCbb76Zq/p79+5V586d1aZNG23dulWjRo3SPffco1WrVrk4UgAAAAAAHBX5y8s7duyojh075rr+rFmzFB0drVdffVWSVLduXX377beaOnWqYmNjXRUmAAAAgJLOGMlqvfxyYb2Lt7ncunPn5HXsmHTggGSxnK/j7Gdu111um0v9XpDHWc/ZxUtuy+vUkXr1cs9rXYiKfNKdV/Hx8YqJiXEoi42N1ahRo3LcJj09Xenp6fbHKSkpkmyDplmtVoe6VqtV5n9viIt/4vKMMTLGOH1uUTJktRFeX+A82gXgHG2jiDJGOntWysiQ0tOz/8z6PSPDVu/sWencuew/s37PzMy2znJhnXPnbHWcLVZrzusuXl/Q3y+zWDIzr8jT7yGp/BU5UtFnevSQ6dnT3WHkKLd/u0pc0n348GFVqFDBoaxChQpKSUnR6dOn5e/vn22bSZMmaeLEidnKk5KSdObMGYeys2fPymq16uzZs/ay4jSQ2uHDh/Xiiy/qiy++0IEDBxQWFqbrrrtOI0aM0C233KKaNWsqISFB7733nuLi4hy2bdCggbZt26a3335b/fr1kyR7/QtVqlRJe/fudXr8c+fOyWq16tixY/L29nbNScKtrFarkpOTZYzhHn7gf2gXgHO0jYv8L9m1nD59fjlzxv67sn5PS5MlPV2W/yW99t8zMhx+Ov09Pd22TU7r/7c/FA5jsUgeHrYeaw8PycPDVnbBY/t6i8Ve3yrJw9PTXi6LRebC/VxQ7rAuh/VO60gOj82F9bPWO4nN2b6zndeF+dHFdf9XZi567PD7/36erV9fpxMTXf465dfJkydzVa/EJd35MXbsWI0ePdr+OCUlRZGRkQoNDVVQUJBD3TNnzujkyZPy9vaWp6dnsUoc9+3bp5YtWyokJESTJ0/Wtddeq7Nnz2rVqlUaOXKktm3bJkmKjIzUe++9p7vuusu+7ffff68jR44oMDBQHh4e8vI6/9aZOHGihgwZYn/s6enpsP5CXl5e8vDwULly5Ri9vISyWq2yWCwKDQ3lAxTwP7QLwLli1zaMsfXupqY6LqdOOT5OS5NSU2W5uF7Wcvq0rU7WcsFjSxHs9TceHpKvr+TjY/uZ9XvW4uUleXuf/+npmb3M2c8LF09PydNT5n8/5elpS94u9djZcmGdvPx+4c/8LhaLbR8XJ6d5YJGtXRxNSsrWLopPN1/h8ZNU2t1BXEJu85kSl3SHh4fryJEjDmVHjhxRUFCQ015uSfL19ZWvr2+2cg8Pj2z/ADw8POw92/afku0P5ZUWEJCnhjx8+HBZLBb9+OOPCgwMtJfXr19fgwcPtp/PXXfdpalTp+qff/5RZGSkJGnOnDm666679O6778pisTj07gcFBalixYq5iiFrW2fPLUoOXmMgO9oF4JxL20Zmpi0hTkk5v5w65bicPJm97FLrzp0r/Did8fCQAgMlf3/bZ76AgPO/+/vblguTYGdJ8eV+v/DxxT8v+N3i6XllzllXZ2LpDP8ziofcvj4lLulu1qyZVqxY4VC2evVqNWvWzHUHTUuTSpVy3f5zcuqU7Y9xLhw/flwrV67U888/75BwZwkJCbH/XqFCBcXGxmrevHkaN26c0tLStGjRIn399dd69913Cyt6AAAA54yx9f6eOOG4XJg8X7wkJ2cvO3XKdTF6e9s+h5UqZfuZ26VUqfNJ9MXJ9IW/e3vnuZcUQNFU5JPuU6dOaffu3fbHe/fu1datW1W2bFlVqVJFY8eO1YEDB+zJ4L333qvp06frscce06BBg/TVV19p8eLF+vzzz911CkXC7t27ZYxRnTp1clV/0KBBevjhh/Xkk09q6dKlql69uho2bOi07uOPP65x48bZH7/wwgsaMWJEYYQNAACKq3PnpOPHpWPHbD8vTqBPnJDl338VcuSI7X7l5GTH9YXZo+zrKwUFSaVL25ZSpXK3OKublTwXo1sMAbhXkU+6f/rpJ7Vp08b+OOve6/79+2vu3Lk6dOiQ9u/fb18fHR2tzz//XA899JBee+01Va5cWW+//bZrpwsLCHDtN6mXOm4u5XWE9c6dO2vYsGHasGGD3nnnHQ0aNCjHuo8++qgGDBhgf1y+POMtAgBQYhhjuw/52LG8LcnJl921RbZ7NnPk6SmFhNiW4GDbEhR0frn4cU6Lk9sIAeBKKfJJd+vWrS+ZMM6dO9fpNj///LMLo7qIxZLry7zdpWbNmrJYLNq+fXuu6nt5ealv374aP368fvjhB3388cc51i1fvrxq1KhRWKECAABXMsZ26XVSkpSYaPuZtTh7fPSobYqo/AoJkcqVk8qUOZ9A/2+xBgfrlKenSlWuLI+yZc8n11l1AgO5xBpAsVfkk24UjrJlyyo2NlZvvvmmRowYke2+7hMnTjjc1y3ZLjF/5ZVXFBcXpzJlylzBaAEAQJ6dPSsdOiT984/jcuRI9iQ6IyPv+/fxsSXPeVnKlLGNTJ0Tq1VpiYkqFRZ2fooiAChhSLqvIm+++aZatGihxo0b65lnntF1112nc+fOafXq1Zo5c6Z9yrAsdevW1dGjRxWQh8vYAQCAC5w5Ix04kD2hvji5zsvtZIGBUliYFBrquDgrK1+eXmcAyCeS7qtItWrVtGXLFj3//PN6+OGHdejQIYWGhqpRo0aaOXOm023KlSt3haMEAOAqZLXaEufdu6Vdu84v+/fbyo8ezd1+vL2lypXPL5UqSeHhzhPqHKZSBQAULpLuq0zFihU1ffp0TZ8+3en6ffv2XXL7EydO5Kk+AAD4H2Okgwcdk+qsZc8eW2/2pfj7OybUzpby5blMGwCKGJJuAACAwnTsmLRtm7Rjh2NivXu3be7pnHh5SdWqSTVrnl+ios4n1GXKcHk3ABRDJN0AAAB5ZYztHuo//7Qt27ad/z0xMeftPD1tifSFiXXWUrXqpQcdAwAUS/xlBwAAyIkx0t9/Z0+st22T/v035+0iI6U6dbIn1lFRtlHAAQBXDZJuAAAASTp8WPr1V+mXX6Tff7cl1tu2SadOOa9vsdguB7/mmvNL3bq2ZLt06SsbOwCgyCLpzieTlyk5YMfzBgBwu4wMWzL9yy/nk+xff835snAvL1sv9YWJ9TXXSLVqMQI4AOCySLrzyNvbW5KUlpam4OBgN0dT/KSlpUk6/zwCAOBShw9nT663bZPOncte12KxJdcNGkj160v16tmS6xo1bFNxAQCQDyTdeeTp6amQkBAlJSXJarWqVKlS8mBqjssyxigtLU2JiYkKCQmRp6enu0MCAJQkxkh790o//CBt3nz53uvgYFtyfd1153/Wry8FBFzZuAEAJR5Jdz6Eh4fLGKMjR47o6NGjsjB9R66FhIQoPDzc3WEAAIq75GTpxx9tSfb339t+T0rKXs/D43zv9XXXnU+yIyOZfgsAcEWQdOeDxWKxJ44hISH0dOeSt7c3PdwAgLw7d842sNn339uS7B9+sF0ifjFvb+k//5FuvFFq2NCWXNerR+81AMCtSLoLwMPDQ35+fiTdAAAUpn/+OZ9cf/+97XLx/40J4qBaNalJE9vStKkt0fb1veLhAgBwKSTdAADAvfbvl9auldaskb7+WjpwIHudoKDzCXbWEhp65WMFACCPSLoBAMCV9e+/0rp1tiR7zRpp1y7H9Z6e0rXX2nqvsxLs2rVt92cDAFDMkHQDAADXOnNG2rjxfJK9ebNktZ5f7+kpNW4sxcRIt9xiuyc7MNB98QIAUIhIugEAQOHKzJS2bj2fZH/7rS3xvlDdurYkOyZGatXKNoUXAAAlEEk3AAAouP37pS++sCXZX30lHT/uuD4i4nySfcstUqVK7okTAIArjKQbAADkXWambW7s5culzz6TfvvNcX3p0lKbNucT7Tp1mBcbAHBVIukGAAC5k5wsffmlLdFesUI6evT8Og8PqVkzKTbWlmTfeKPkxccMAAD4bwgAAHK2a5ctyV6+XNqwQTp37vy64GCpY0epSxepQwepXDn3xQkAQBFF0g0AAM47e1b67jvbJePLl0s7dzqur1PHlmR36SI1by55e7snTgAAigmSbgAArnbJyeeT7JUrbY+zeHnZRhfPSrRr1HBfnAAAFEMk3QAAXI1SUmyJ9qJF0qpVUkbG+XXly0udO9uS7HbtmM4LAIACIOkGAOBqceqUrTd78WLbQGjp6efX1a0r3XqrLdFu3Fjy9HRfnAAAlCAk3QAAlGRpabYEe9Ei6fPPpdOnz6+rVUuKi5N69ZLq13dfjAAAlGAk3QAAlDRnztjuzV60yHYJeWrq+XXVqtkS7bg46brrmDsbAAAXI+kGAKAkSE+3zaG9eLH0ySfSyZPn11Wter5H+/rrSbQBALiCSLoBACiurFZp/Xrpvfekjz92HHW8cmVbkt2rl+0ebRJtAADcgqQbAIDiZscO6d13bcn233+fL69YUbrjDluvdtOmkoeH+2IEAACSSLoBACgejh+33aM9b570ww/ny4ODbUn2XXdJLVuSaAMAUMSQdAMAUFSdPSt98YWtV/uzz87Ppe3pKXXoIPXrJ3XrJvn5uTdOAACQI5JuAACKEmOkn3+2JdoLFkhJSefXNWhgS7TvvFMKD3dfjAAAINdIugEAKAoOHpTmz7cl27//fr68QgXbpeP9+tmSbgAAUKyQdAMA4C7p6bZRx+fOlVavto1GLkm+vlL37lL//lL79pIX/64BACiu+C8OAMCVtnOnNHu2Ldk+dux8efPmtkT7jjukMmXcFh4AACg8JN0AAFwJGRm20cffektat+58eeXK0sCBtsvHa9RwX3wAAMAlSLoBAHCl3btlmT1boe+8I4+sXm2LRerUSRo2TOrYkcvHAQAowfgvDwBAYTt7VvrkE+n//k9as0YWSZ6STESELIMHS/fcI1Wp4u4oAQDAFUDSDQBAYdm713b5+DvvSEeO2MosFpn27XUiLk7Bd90li4+Pe2MEAABXFEk3AAAFcfastHy5rVf7yy9t82xLtnm0Bw2ShgyRqVJF6YmJXEYOAMBViP/+AADkx4EDtkT77belQ4fOl7drZ7tXu1s3ydvbVpY1FRgAALjqkHQDAJBbxkjffSe98Yb00UfSuXO28rAw2wjkQ4ZI1au7N0YAAFCkkHQDAHA5p09LH3xgS7a3bj1ffvPN0v33S7feKnGvNgAAcMLD3QHkxptvvqmoqCj5+fmpSZMm+vHHHy9Zf9q0aapdu7b8/f0VGRmphx56SGfOnLlC0QIASoz9+6UxY6TISGnwYFvC7ednG31861bp66+luDgSbgAAkKMi39O9aNEijR49WrNmzVKTJk00bdo0xcbGaseOHQoLC8tWf8GCBRozZozeeecdNW/eXDt37tSAAQNksVg0ZcoUN5wBAKBYMUZav97Wq/3JJ+fvx65a1darPXiwVK6cW0MEAADFR5FPuqdMmaIhQ4Zo4MCBkqRZs2bp888/1zvvvKMxY8Zkq79x40a1aNFCd955pyQpKipKffr00Q8//JDjMdLT05Wenm5/nJKSIkmyWq2y5jD4jdVqlTEmx/XA1Yq2gWIrNVV6/31ZZsyQ5fff7cXmlltkhg+XunaVPD1thXl8f9MuAOdoG0B2tIviI7evUZFOujMyMrR582aNHTvWXubh4aGYmBjFx8c73aZ58+Z6//339eOPP6px48b666+/tGLFCvXt2zfH40yaNEkTJ07MVp6UlJTjZelWq1XJyckyxsjDo1hcpQ9cEbQNFDeeCQkKmDNH/gsXyiM5WZJkDQjQmTvuUNrAgTpXu7at4rFj+T4G7QJwjrYBZEe7KD5OnjyZq3pFOuk+evSoMjMzVaFCBYfyChUqaPv27U63ufPOO3X06FG1bNlSxhidO3dO9957r5544okcjzN27FiNHj3a/jglJUWRkZEKDQ1VUFCQ022sVqssFotCQ0NpDMAFaBsoFoyR1q6V5Y03pM8/l+V/c2ub6tVtvdr9+8svJER+hXQ42gXgHG0DyI52UXz4+eXuk0KRTrrzY/369XrhhRc0Y8YMNWnSRLt379bIkSP17LPP6qmnnnK6ja+vr3x9fbOVe3h4XPKNbrFYLlsHuBrRNlBkZWRICxdKr74q/frr+fLYWOnBB2Xp2FEWF71vaReAc7QNIDvaRfGQ29enSCfd5cuXl6enp44cOeJQfuTIEYWHhzvd5qmnnlLfvn11zz33SJKuvfZapaamaujQoXryySd54wLA1ejECen//k96/XXp4EFbWWCgbW7tBx6Qsi4hBwAAKGRFOgP18fFRo0aNtHbtWnuZ1WrV2rVr1axZM6fbpKWlZUusPf838I353+WDAICrxL590qhRtim/xoyxJdwVK0qTJkl//20boZyEGwAAuFCR7umWpNGjR6t///664YYb1LhxY02bNk2pqan20cz79eunSpUqadKkSZKkrl27asqUKfrPf/5jv7z8qaeeUteuXe3JNwCghNu0SXrlFWnp0vMjjdevLz3yiNSnD/NqAwCAK8YlSXdqaqoCAwMLZV9xcXFKSkrS008/rcOHD6thw4ZauXKlfXC1/fv3O/Rsjxs3ThaLRePGjdOBAwcUGhqqrl276vnnny+UeAAARZTVKi1fbrtfe8OG8+Xt2tmS7XbtJIvFffEBAICrksW44JrrUqVKqVevXho0aJBatmxZ2Lt3uZSUFAUHBys5OfmSo5cnJiYqLCyM+8SBC9A2cMWdPi29+640ZYq0c6etzNvb1qM9erTUoIF74xPtAsgJbQPIjnZRfOQmb5RcdE/3+++/r+PHj+uWW25RrVq19OKLL+pg1sA1AAAUhsREacIEqUoV6d57bQl3cLD0+OPS3r3SvHlFIuEGAABXN5ck3T169NCyZct04MAB3XvvvVqwYIGqVq2qLl266KOPPtK5c+dccVgAwNUgIUG6/36palVp4kTp6FEpKkqaNs02ONqLL0qVKrk7SgAAAEkuHr08NDRUo0eP1q+//qopU6ZozZo16tmzpyIiIvT0008rLS3NlYcHAJQkf/0lDRki1aghzZwpnTkj3XijtHixtGuXNHKkVLq0u6MEAABw4NLRy48cOaJ58+Zp7ty5SkhIUM+ePTV48GD9888/eumll/T999/ryy+/dGUIAIDibudO6YUXpPfflzIzbWVt20pPPSXdfDODowEAgCLNJUn3Rx99pDlz5mjVqlW65pprdP/99+vuu+9WSEiIvU7z5s1Vt25dVxweAFASbNsmPf+89MEH56f96tDBlmw3b+7e2AAAAHLJJUn3wIED1bt3b3333Xe68cYbndaJiIjQk08+6YrDAwCKs99+k557TlqyRMqaYKNLF1uy3bixe2MDAADII5ck3YcOHVJAQMAl6/j7+2v8+PGuODwAoDjaulV69lnpo4/Ol/XoYUu2r7/eXVEBAAAUiEsGUitdurQSExOzlR87dkyenp6uOCQAoLj66Sepe3fpP/+xJdwWi3THHdIvv0gff0zCDQAAijWX9HSbrMsBL5Keni4fHx9XHBIAUNzEx9t6tr/4wvbYw0OKi5OefFKqV8+9sQEAABSSQk26X3/9dUmSxWLR22+//f/t3XlYVdX+x/HPAQUkRPQyOU855IhimpqNJpiVWPdKDmlGVqZmomaUikOpZamlpmkOVxscumVlZimJmuN1zjJLw0wT0FTAAUHO/v2xr+cXgYZ5NucA79fz8MRae529vkdYer6ttdeSn5+f41pOTo7Wr1+v+vXrO7NLAEBRs2mTNHq0tHq1WfbwkHr2lF54QapXz6WhAQAAOJtTk+4pU6ZIMme6Z82alWspuZeXl2rUqKFZs2Y5s0sAQFGxZ480YoS0YoVZLlVK6tVLioszz94GAAAohpyadCclJUmS7rzzTn300UcqX768M28PACiKDh6URo2SFi82dyP39JQefdRcRl6zpqujAwAAsJQlz3SvXbvWitsCAIqSY8eksWOluXOlnByzrmtXs45l5AAAoIRwWtIdGxurcePG6YYbblBsbOxV206ePNlZ3QIA3M3vv0sTJ0rTp0uZmWZdx47Syy+bO5QDAACUIE5Lunft2qXs7GzH91dis9mc1SUAwJ1kZEhTpkivvWZ+L0m33iqNHy+1a+fa2AAAAFzEaUn3H5eUs7wcAEqQzExp5kwzuT550qwLCzPLkZHmudsAAAAllCXPdAMASoBLl6R//1saM0b69Vezrk4d8+ztf/3LPAoMAACghHNa0v3ggw8WuO1HH33krG4BAIXNbpc+/FAaOVL68UezrkoVKT7e3JW8FP8/FwAA4DKnfTIqV66cs24FAHBXa9ZIzz0nXd67IzBQeuEFqV8/ycfHtbEBAAC4Iacl3fPnz3fWrQAA7ub776Vhw6SVK81y2bLS0KHS4MHm9wAAAMgXawABAFeWmiqNHi3Nnm2etV26tNS/v/Tii+YsNwAAAK7KaUl38+bNlZCQoPLly6tZs2ZXPRps586dzuoWAGCFzEzpjTfMHcjT0826Ll2kV14xN0sDAABAgTgt6e7cubO8vb0lSVFRUc66LQCgMBmGtGSJ9Pzz0i+/mHXh4dLkydJtt7k2NgAAgCLIaUl3fHx8vt8DAIqIzZul2FhpyxazXKWKOdPdowfHfwEAAPxNlj7TvX37du3fv1+S1KBBA4WHh1vZHQDg70hKMme2ly41yzfcYJZjYyVfX9fGBgAAUMRZknQfPXpU3bp108aNGxUQECBJOnPmjNq0aaPFixerSpUqVnQLALgWaWnSyy+bz25nZUk2mxQTI40dK1Ws6OroAAAAigVL1gs+/vjjys7O1v79+3Xq1CmdOnVK+/fvl91u1+OPP25FlwCAgsrOlmbMkG68UZo0yUy427c3z96eM4eEGwAAwIksmelet26dNm3apHr16jnq6tWrp2nTpqldu3ZWdAkA+CuGYZ6zPXSo9MMPZt1NN0mvvSZ17GjOdAMAAMCpLEm6q1atquzs7Dz1OTk5qlSpkhVdAgCu5uBB6ZlnpC++MMuBgeYy8r59pVKWbu8BAABQolmyvHzSpEkaOHCgtm/f7qjbvn27Bg0apNdee82KLgEA+blwQRo1SmrY0Ey4vbykYcPMJLxfPxJuAAAAiznt01b58uVl+8PSxHPnzqlVq1Yq9b8PdJcuXVKpUqX02GOPcY43ABSGzz4zZ7cPHzbLHTpI06ZJdeu6NCwAAICSxGlJ99SpU511KwDA9UhKkgYNMpNuyTxve+pU6cEHeW4bAACgkDkt6e7du7ezbgUA+DsyM6VXX5UmTDC/L1VKGjJEGjFC8vNzdXQAAAAlkuUP82VmZiorKytXnb+/v9XdAkDJ8sUX0sCB0qFDZvmuu6Tp083dyQEAAOAylmykdu7cOQ0YMEDBwcG64YYbVL58+VxfAAAn+eUXc9n4vfeaCXelStLixdKaNSTcAAAAbsCSpPu5557T119/rZkzZ8rb21vvvPOOxowZo0qVKmnhwoVWdAkAJcvFi9L48WZi/fHHkqenuZT8hx+k6Gie3QYAAHATliwv/+yzz7Rw4ULdcccd6tOnj9q1a6cbb7xR1atX13vvvacePXpY0S0AlAxr1kj9+0s//miWb7tNmjFDatTItXEBAAAgD0tmuk+dOqVatWpJMp/fPnXqlCTp1ltv1fr1663oEgCKv6NHpa5dpXvuMRPukBBp0SIpMZGEGwAAwE1ZknTXqlVLSUlJkqT69etr6dKlkswZ8ICAACu6BIDiKyfHPPKrfn1p2TLJw8M8f/vAAalnT5aSAwAAuDFLlpf36dNHe/bs0e23367nn39e999/v6ZPn67s7GxNnjzZii4BoHjatUvq21fascMst2kjvfWW1LSpa+MCAABAgViSdA8ePNjxffv27bV//37t3LlTN954o5o0aWJFlwBQvJw7J8XHmzPcOTlSuXLSpElSTIw50w0AAIAiwfJzuiWpRo0aqlGjRmF0BQBF3xdfSP36mceBSeZu5FOnSqGhLg0LAAAA186y6ZKEhATdd999ql27tmrXrq377rtPa9assao7ACj6UlKk7t3NM7d/+UWqVk1ascI8d5uEGwAAoEiyJOl+6623FBkZqbJly2rQoEEaNGiQ/P39de+992rGjBlWdAkARZdhSHPnmmduf/CBuXw8Nlb67jupUydXRwcAAIDrYMny8vHjx2vKlCkaMGCAo+6ZZ55R27ZtNX78ePXv39+KbgGg6DlwQHrySWndOrPcrJk0Z44UHu7auAAAAOAUlsx0nzlzRpGRkXnqO3TooLS0tGu+34wZM1SjRg35+PioVatW2rZt21/2379/f1WsWFHe3t6qW7euVq5cec39AoBlsrKkceOkJk3MhNvXV3rtNWnbNhJuAACAYsSSpPuBBx7Qxx9/nKf+k08+0X333XdN91qyZIliY2MVHx+vnTt3qmnTpoqIiFBqamq+7bOysnTPPffo8OHD+vDDD3XgwAHNmTNHlStX/lvvBQCcbuNGc0Z71Cgz+Y6MNJeSDxkilSqU/S0BAABQSJz26e7NN990fN+gQQO9/PLLSkxMVOvWrSVJW7Zs0caNGzVkyJBruu/kyZPVt29f9enTR5I0a9Ysff7555o3b56ef/75PO3nzZunU6dOadOmTSpdurQk/eXO6RcvXtTFixcd5fT0dEmS3W6X3W7P9zV2u12GYVzxOlBSMTau4swZ2eLiZJs9W5JkBAfLmDLF3J3cZpP4Myu2GBdA/hgbQF6Mi6KjoD8jm2EYhjM6rFmzZsE6tNn0888/F6htVlaWfH199eGHHyoqKspR37t3b505c0affPJJntfce++9qlChgnx9ffXJJ58oKChI3bt31/Dhw+Xp6ZlvP6NHj9aYMWPy1P/4448qW7Zsvq+x2+1KS0tTuXLl5MGZuYADYyMfhiHvzz+X/4svyvN/q3TOd++ujBEjZJQv7+LgUBgYF0D+GBtAXoyLoiMjI0N169ZVWlqa/P39r9jOaTPdSUlJzrqVw8mTJ5WTk6OQkJBc9SEhIfrhhx/yfc3PP/+sr7/+Wj169NDKlSt18OBBPf3008rOzlZ8fHy+r4mLi1NsbKyjnJ6erqpVqyooKOiKf3h2u102m01BQUEMBuAPGBt/kpwsW//+si1fLkky6taVMWuWfG6/XT6ujQyFiHEB5I+xAeTFuCg6fHwK9mnO8ocHL0+k22w2q7uSZP6SBgcHa/bs2fL09FR4eLiOHTumSZMmXTHp9vb2lre3d556Dw+Pq/6i22y2v2wDlESMDZnHgL37rjRokHT6tPmsdlycbC+8IFsB/4JG8cK4APLH2ADyYlwUDQX9+Vj2U1y4cKEaN26sMmXKqEyZMmrSpIkWLVp0TfcIDAyUp6enUlJSctWnpKQoNDQ039dUrFhRdevWzbWU/KabblJycrKysrKu/Y0AwLU6dkx64AGpVy8z4W7WTNq+XRo7ViLhBgAAKFEsSbonT56sfv366d5779XSpUu1dOlSRUZG6qmnntKUKVMKfB8vLy+Fh4crISHBUWe325WQkODYoO3P2rZtq4MHD+Z6qP3HH39UxYoV5eXl9fffFAD8FcOQ5s6VGjSQVqyQvLykl1+Wtm6VmjZ1dXQAAABwAUuWl0+bNk0zZ85Ur169HHUPPPCAGjZsqNGjR2vw4MEFvldsbKx69+6tFi1aqGXLlpo6darOnTvn2M28V69eqly5siZMmCBJ6tevn6ZPn65BgwZp4MCB+umnnzR+/Hg988wzzn2TAPBHv/wiPfGE9NVXZrllS2nePKlhQ9fGBQAAAJeyJOk+fvy42rRpk6e+TZs2On78+DXdKzo6WidOnNCoUaOUnJyssLAwrVq1yrG52pEjR3Ktpa9ataq+/PJLDR48WE2aNFHlypU1aNAgDR8+/PreFADkx26X3n5beu456exZc/n4uHHS4MHSFU5MAAAAQMlhSdJ94403aunSpXrhhRdy1S9ZskR16tS55vsNGDBAAwYMyPdaYmJinrrWrVtry5Yt19wPAFyTn3+WYmKky38P3Xqruby8bl2XhgUAAAD3YUnSPWbMGEVHR2v9+vVq27atJGnjxo1KSEjQ0qVLregSAAqP3S5Nmya98IJ0/rzk6ytNmCANGCCxyygAAAD+wJKk+6GHHtK2bds0efJkLf/f2bQ33XSTtm3bpmbNmlnRJQAUjgMHzNntjRvN8p13Su+8I9Wq5dq4AAAA4JacnnRnZ2frySef1MiRI/Xuu+86+/YA4Bo5OdLkydKoUVJmpuTnJ02aZG6exuw2AAAArsDpnxRLly6t//znP86+LQC4znffSW3amJulZWZKHTqYdU89RcINAACAq7Lk02JUVJRjWTkAFFmXLknjx0vNm0vbtknlypkbpa1aJVWr5uroAAAAUARY8kx3nTp1NHbsWG3cuFHh4eG64YYbcl3nzGwAbm/vXqlPH2nnTrPcqZN5NFjlyq6NCwAAAEWKJUn33LlzFRAQoB07dmjHjh25rtlsNpJuAO4rO1uaONE8azs7WypfXnrzTalHD8lmc3V0AAAAKGIsSbqTkpKsuC0AWGv3bnN2e/dus9y5szRzplSxoiujAgAAQBHm9KR7y5Yt+uyzz5SVlaW7775bkZGRzu4CAJwrK0t6+WXz+e1Ll6QKFaTp06WHH2Z2GwAAANfFqUn3hx9+qOjoaJUpU0alS5fW5MmT9corr2jo0KHO7AYAnGfnTnN2e+9es/zgg9Jbb0khIa6NCwAAAMWCU3cvnzBhgvr27au0tDSdPn1aL730ksaPH+/MLgDAOS5elEaMkFq2NBPuwEBpyRLpww9JuAEAAOA0Tk26Dxw4oKFDh8rT01OSNGTIEGVkZCg1NdWZ3QDA9dm+XQoPN5eU5+RIXbtK339v/pfl5AAAAHAipybd58+fl7+/v6Ps5eUlHx8fnT171pndAMDfk5kpxcVJt9wiffedFBQkLVtmznAHBbk6OgAAABRDTt9I7Z133pGfn5+jfOnSJS1YsECBgYGOOo4MA1Dotm41n93ev98sd+tmHgX2h7+bAAAAAGdzatJdrVo1zZkzJ1ddaGioFi1a5ChzTjeAQnXhghQfL73+umS3m89rz5wpdeni6sgAAABQAjg16T58+LAzbwcA12fLFunRR6UDB8xyz57S1KnSP/7hyqgAAABQgjj1mW4AcAuZmdLw4VLbtmbCXbGi9Omn0qJFJNwAAAAoVE5/phsAXGrbNnN2+/Kz2488Ir3xhlS+vEvDAgAAQMnETDeA4uHiRXNn8tatzYQ7NFT65BNp4UISbgAAALgMM90Air7t283Z7e++M8vdu5s7k7OUHAAAAC7GTDeAouviRWnEiP8/dzs4WProI+m990i4AQAA4BYsS7oPHTqkESNGqFu3bkpNTZUkffHFF/ru8kwUAFyPnTulFi2kl1+WcnKkhx82E2+OAgMAAIAbsSTpXrdunRo3bqytW7fqo48+0tmzZyVJe/bsUXx8vBVdAigpsrLMc7dbtpT27ZOCgqQPP5Q++EAKDHR1dAAAAEAuliTdzz//vF566SWtXr1aXl5ejvq77rpLW7ZssaJLACXB7t1msj12rDm73bWrObv90EOujgwAAADIlyVJ97fffqsu+SzxDA4O1smTJ63oEkBxlp0tjRkj3XyztGePOaO9dKm0ZIk50w0AAAC4KUuS7oCAAB0/fjxP/a5du1S5cmUrugRQXO3dK7VqJY0eLV26ZM5qf/ed9K9/uToyAAAA4C9ZknQ//PDDGj58uJKTk2Wz2WS327Vx40YNHTpUvXr1sqJLAMXNpUvSSy+Zm6Xt2iVVqGA+t71smblLOQAAAFAEWJJ0jx8/XvXr11fVqlV19uxZNWjQQLfddpvatGmjESNGWNElgOLkwAGpbVtp5EhzaXlUlDm7/fDDks3m6ugAAACAAitlxU29vLw0Z84cjRw5Uvv27dPZs2fVrFkz1alTx4ruABQXdrv01lvSc89JFy5I5cpJ06dLPXqQbAMAAKBIsiTp/uabb3TrrbeqWrVqqlatmhVdAChufv1Veuwxac0as9y+vTR/vlSlimvjAgAAAK6DJcvL77rrLtWsWVMvvPCCvv/+eyu6AFBcGIb07rtS48Zmwl2mjDm7/eWXJNwAAAAo8ixJun/77TcNGTJE69atU6NGjRQWFqZJkybp6NGjVnQHoKg6edLchfyRR6S0NHOX8t27pf79JQ9L/noCAAAACpUln2oDAwM1YMAAbdy4UYcOHdK//vUv/fvf/1aNGjV01113WdElgKJmxQqpUSPpP/+RSpUydyr/5hupbl1XRwYAAAA4jSXPdP9RzZo19fzzz6tp06YaOXKk1q1bZ3WXANxZeroUGyvNnWuWGzaUFi2SmjVzbVwAAACABSxdv7lx40Y9/fTTqlixorp3765GjRrp888/t7JLAO5s/XqpaVMz4bbZpKFDpe3bSbgBAABQbFky0x0XF6fFixfrt99+0z333KM33nhDnTt3lq+vrxXdAXB3mZnSiBHS5Mnmxmk1akj//rd0222ujgwAAACwlCVJ9/r16zVs2DB17dpVgYGBVnQBoKjYuVPq1Uv67juz/PjjZvJdtqxr4wIAAAAKgSVJ98aNG624LYCi5NIl6dVXpTFjzO9DQqR33pHuu8/VkQEAAACFxmlJ96effqqOHTuqdOnS+vTTT6/a9oEHHnBWtwDckOfPP8sWFSVt3WpWPPSQNGuWxMoXAAAAlDBOS7qjoqKUnJys4OBgRUVFXbGdzWZTTk6Os7oF4E4MQ5o9W/+IjZXtwgWpXDlp+nSpRw9z4zQAAACghHFa0m232/P9HkAJkZIiPf64PFaskCQZd94p24IFUrVqro0LAAAAcCFLjgxbuHChLl68mKc+KytLCxcutKJLAK706adS48bSihUyvLyUHh8v46uvSLgBAABQ4lmSdPfp00dpaWl56jMyMtSnTx8rugTgCmfPSn37Sp07SydOSI0by9i2TeefekrysOSvFwAAAKBIseRTsWEYsuXz/ObRo0dVrlw5K7oEUNg2b5bCwswdyW02aehQ6b//NWe8AQAAAEhy8pFhzZo1k81mk81m0913361Spf7/9jk5OUpKSlJkZKQzuwRQ2LKzpXHjpJdflux2qWpVaeFC6Y47zOvs6QAAAAA4ODXpvrxr+e7duxURESE/Pz/HNS8vL9WoUUMPPfTQNd93xowZmjRpkpKTk9W0aVNNmzZNLVu2/MvXLV68WN26dVPnzp21fPnya+4XwJ8cOCA98og5oy2Zu5JPny4FBLg0LAAAAMBdOTXpjo+PlyTVqFFD0dHR8vHxue57LlmyRLGxsZo1a5ZatWqlqVOnKiIiQgcOHFBwcPAVX3f48GENHTpU7dq1u+4YgBLPMMxztocMkS5ckMqXl2bOlKKjXR0ZAAAA4NacmnRf1rt3b6fda/Lkyerbt69jA7ZZs2bp888/17x58/T888/n+5qcnBz16NFDY8aM0YYNG3TmzJmr9nHx4sVcu62np6dLMo8+u9LxZ3a7XYZhcDwair/kZNliYmRbtUqSZNx9t4z586XKlfNdSs7YAPJiXAD5Y2wAeTEuio6C/owsSbpzcnI0ZcoULV26VEeOHFFWVlau66dOnSrQfbKysrRjxw7FxcU56jw8PNS+fXtt3rz5iq8bO3asgoODFRMTow0bNvxlPxMmTNCYMWPy1J84cUKZmZn5vsZutystLU2GYciDXZpRTHmvXKlyQ4fKdvq0DG9vZbz4os7HxJg7k6em5vsaxgaQF+MCyB9jA8iLcVF0ZGRkFKidJUn3mDFj9M4772jIkCEaMWKEXnzxRR0+fFjLly/XqFGjCnyfkydPKicnRyEhIbnqQ0JC9MMPP+T7mm+++UZz587V7t27C9xPXFycYmNjHeX09HRVrVpVQUFB8vf3z/c1drtdNptNQUFBDAYUPxkZsj37rGwLFkiSjLAwGQsXyq9hQ/ld/ZWMDSAfjAsgf4wNIC/GRdFR0MepLUm633vvPc2ZM0edOnXS6NGj1a1bN9WuXVtNmjTRli1b9Mwzz1jRrTIyMvTII49ozpw5CgwMLPDrvL295e3tnafew8Pjqr/oNpvtL9sARc6WLeYGaT//bB4F9txzso0dK5uXV4FvwdgA8mJcAPljbAB5MS6KhoL+fCxJupOTk9X4f2f1+vn5KS0tTZJ03333aeTIkQW+T2BgoDw9PZWSkpKrPiUlRaGhoXnaHzp0SIcPH9b999/vqLu8zr5UqVI6cOCAateufc3vBygRLl2Sxo+Xxo6VcnKk6tXNo8Buu83VkQEAAABFliX/66RKlSo6fvy4JKl27dr66quvJEn//e9/851RvhIvLy+Fh4crISHBUWe325WQkKDWrVvnaV+/fn19++232r17t+PrgQce0J133qndu3eratWq1/nOgGIqKUm6/XYpPt5MuLt3l/bsIeEGAAAArpMlM91dunRRQkKCWrVqpYEDB6pnz56aO3eujhw5osGDB1/TvWJjY9W7d2+1aNFCLVu21NSpU3Xu3DnHbua9evVS5cqVNWHCBPn4+KhRo0a5Xh/wv/OD/1wP4H/efVd6+mkpI0Py95feestcXg4AAADgulmSdE+cONHxfXR0tKpVq6bNmzerTp06uZZ+F0R0dLROnDihUaNGKTk5WWFhYVq1apVjc7UjR47wrAPwd5w5I/XvL73/vllu21ZatEiqWdOlYQEAAADFic0wDMPVQbib9PR0lStXTmlpaVfdvTw1NVXBwcEk/Sh6NmyQHnlE+uUXydPTXFYeFyeVuv7/D8fYAPJiXAD5Y2wAeTEuio6C5I2SE2e6P/300wK3feCBB5zVLYBrkZ1tbpQ2frxkt0u1aknvvSfdcourIwMAAACKJacl3VFRUQVqZ7PZlJOT46xuARTUwYPms9rbtpnl3r2ladOksmVdGxcAAABQjDkt6b58NBcAN2MY0oIF0sCB0rlzUkCANGuWFB3t6sgAAACAYs+SjdQAuIlTp6SnnpKWLTPLt99unr1drZpr4wIAAABKCEuS7rFjx171+qhRo6zoFsAfrV0r9eolHT1qbpA2bpw0bJi5cRoAAACAQmFJ0v3xxx/nKmdnZyspKUmlSpVS7dq1SboBK2VlSaNGSa++ai4tr1PHPBasRQtXRwYAAACUOJYk3bt27cpTl56erkcffVRdunSxoksAkvT991LPntLlMfj449LUqdINN7g0LAAAAKCkKrSD3/z9/TVmzBiNHDmysLoESg67XXrzTSk83Ey4K1SQ/vMfac4cEm4AAADAhQp1I7W0tDSlpaUVZpdA8Xf0qNSnj7RmjVmOjJTmzZMqVnRtXAAAAACsSbrffPPNXGXDMHT8+HEtWrRIHTt2tKJLoGRassTcnfzMGalMGen1182yzebqyAAAAADIoqR7ypQpucoeHh4KCgpS7969FRcXZ0WXQMly+rQ0YIC5QZok3XyztGiRVK+ea+MCAAAAkIslSXdSUpIVtwUgSQkJ0qOPmsvKPT2lESOkF1+USpd2dWQAAAAA/qRQn+kGcB0yM6UXXpAuryS58Ubp3XelVq1cGxcAAACAK7Ik6c7MzNS0adO0du1apaamym6357q+c+dOK7oFiq/du82jwL77ziw/9ZT02mvsTA4AAAC4OUuS7piYGH311Vf65z//qZYtW8rGpk7A35OTYybXI0dK2dlSSIg0d67UqZOrIwMAAABQAJYk3StWrNDKlSvVtm1bK24PlAxJSVKvXtI335jlqChp9mwpKMilYQEAAAAoOA8rblq5cmWVLVvWilsDxZ9hSAsWSE2bmgm3n5957vZHH5FwAwAAAEWMJUn366+/ruHDh+uXX36x4vZA8XXypPTQQ1KfPlJGhtS2rbR3r1nmMQ0AAACgyLFkeXmLFi2UmZmpWrVqydfXV6X/dJTRqVOnrOgWKNo+/1yKiZFSUszjv8aOlYYNM48FAwAAAFAkWZJ0d+vWTceOHdP48eMVEhLCRmrA1Zw7Jw0dKs2aZZYbNDCPAmvWzLVxAQAAALhuliTdmzZt0ubNm9W0aVMrbg8UH1u3mkeBHTxolp99Vho/XipTxqVhAQAAAHAOS57prl+/vi5cuGDFrYHiITtbio83n9k+eFCqUkVas0aaMoWEGwAAAChGLEm6J06cqCFDhigxMVG///670tPTc30BJdqBA1KbNuYz2zk5Uo8e0rffSnff7erIAAAAADiZJcvLIyMjJUl3/ymJMAxDNptNOTk5VnQLuDfDkN56y9wc7cIFKSDAfI47OtrVkQEAAACwiCVJ99q1a624LVB0/fab9Nhj0pdfmuV77pHmz5cqV3ZtXAAAAAAsZUnSffvtt1txW6BoWrZMeuop6dQpycdHevVVqX9/ycOSpzsAAAAAuBFLku7169df9fptt91mRbeAezlzRho40Dz+S5KaNze/v+kml4YFAAAAoPBYknTfcccdeer+eFY3z3Sj2EtMlHr1kn791ZzRfuEFaeRIycvL1ZEBAAAAKESWrG89ffp0rq/U1FStWrVKN998s7766isrugTcQ2amNHSodNddZsJdu7b0zTfSuHEk3AAAAEAJZMlMd7ly5fLU3XPPPfLy8lJsbKx27NhhRbeAa+3dax7/tW+fWX7iCen11yU/P9fGBQAAAMBlCnUnp5CQEB04cKAwuwSsZ7ebyfXNN5sJd3Cw9Nln0ttvk3ADAAAAJZwlM9179+7NVTYMQ8ePH9fEiRMVFhZmRZeAa/z6q9S7t3T5mLwHHpDmzDETbwAAAAAlniVJd1hYmGw2mwzDyFV/yy23aN68eVZ0CRS+JUvMo8DOnJF8faWpU6XHH5f+sGkgAAAAgJLNkqQ7KSkpV9nDw0NBQUHy8fGxojugcKWlSQMG/P9RYC1bmt/XqePauAAAAAC4HUuS7urVq1txW8D11q+XHnlEOnLEPApsxAjzq3RpV0cGAAAAwA05dSO1r7/+Wg0aNFB6enqea2lpaWrYsKE2bNjgzC6BwpGVJcXFSXfcYSbctWqZR4GNGUPCDQAAAOCKnJp0T506VX379pW/v3+ea+XKldOTTz6pyZMnO7NLwHr790u33CJNnCgZhvTYY9Lu3VLr1q6ODAAAAICbc2rSvWfPHkVGRl7xeocOHTijG0WHYUgzZkjNm0u7dkn/+If00UfS3LlS2bKujg4AAABAEeDUZ7pTUlJU+ipLbUuVKqUTJ044s0vAGsnJUp8+0qpVZjkiQpo/X6pY0bVxAQAAAChSnDrTXblyZe3bt++K1/fu3auKJC1wd8uXS40bmwm3j4/05pvSF1+QcAMAAAC4Zk5Nuu+9916NHDlSmZmZea5duHBB8fHxuu+++5zZJeA8Z89KfftKXbpIJ09KYWHS9u3SwIGcvQ0AAADgb3Hq8vIRI0boo48+Ut26dTVgwADVq1dPkvTDDz9oxowZysnJ0YsvvujMLgHn2LpV6tFDOnTITLCHDZPGjpW8vV0dGQAAAIAizKlJd0hIiDZt2qR+/fopLi5OhmFIkmw2myIiIjRjxgyFhIQ4s0vg+ly6JE2YYB79lZMjVa0qLVxoHg0GAAAAANfJqUm3JFWvXl0rV67U6dOndfDgQRmGoTp16qh8+fLO7gq4PklJUs+e0qZNZrlbN+mtt6SAAJeGBQAAAKD4cOoz3X9Uvnx53XzzzWrZsuV1J9wzZsxQjRo15OPjo1atWmnbtm1XbDtnzhy1a9dO5cuXV/ny5dW+ffurtkcJZBjmbHbTpmbC7e8vvfuu9P77JNwAAAAAnMqypNtZlixZotjYWMXHx2vnzp1q2rSpIiIilJqamm/7xMREdevWTWvXrtXmzZtVtWpVdejQQceOHSvkyOGWTp82Z7R795YyMqRbb5X27DGf5wYAAAAAJ3P7pHvy5Mnq27ev+vTpowYNGmjWrFny9fXVvHnz8m3/3nvv6emnn1ZYWJjq16+vd955R3a7XQkJCYUcOdxOYqI5u71kieTpKb30kllXo4aLAwMAAABQXDn9mW5nysrK0o4dOxQXF+eo8/DwUPv27bV58+YC3eP8+fPKzs5WhQoVrtjm4sWLunjxoqOcnp4uSbLb7bLb7fm+xm63yzCMK16HG8nKki0+Xpo0STbDkHHjjTIWLZJatjSv8zN0KsYGkBfjAsgfYwPIi3FRdBT0Z+TWSffJkyeVk5OTZ8fzkJAQ/fDDDwW6x/Dhw1WpUiW1b9/+im0mTJigMWPG5Kk/ceJEvmeOS+YfcFpamgzDkIeH2y8YKLE8f/pJAf37q/S330qSznfvroyxY2XccIN0hUcUcH0YG0BejAsgf4wNIC/GRdGRkZFRoHZunXRfr4kTJ2rx4sVKTEyUj4/PFdvFxcUpNjbWUU5PT1fVqlUVFBQkf3//fF9jt9tls9kUFBTEYHBHhiG9/bZsQ4fKduGCjAoVZMyeLZ8uXXTl3wQ4A2MDyItxAeSPsQHkxbgoOq6WY/6RWyfdgYGB8vT0VEpKSq76lJQUhYaGXvW1r732miZOnKg1a9aoSZMmV23r7e0tb2/vPPUeHh5X/UW32Wx/2QYukJoqPf649NlnZvmee2RbsEC2SpVcG1cJwtgA8mJcAPljbAB5MS6KhoL+fNz6p+jl5aXw8PBcm6Bd3hStdevWV3zdq6++qnHjxmnVqlVq0aJFYYQKd/HFF1KTJmbC7eUlTZkirVolkXADAAAAcAG3numWpNjYWPXu3VstWrRQy5YtNXXqVJ07d059+vSRJPXq1UuVK1fWhAkTJEmvvPKKRo0apffff181atRQcnKyJMnPz09+fn4uex+w2IUL0nPPSdOnm+VGjaT33jMTcAAAAABwEbdPuqOjo3XixAmNGjVKycnJCgsL06pVqxybqx05ciTXtP7MmTOVlZWlf/7zn7nuEx8fr9GjRxdm6Cgse/eaZ29//71ZHjRImjhRKuAzFgAAAABgFbdPuiVpwIABGjBgQL7XEhMTc5UPHz5sfUBwD4YhvfmmOcOdlSWFhkoLFkgREa6ODAAAAAAkFZGkG8gjJUV69FHzeW1Juv9+ae5cKSjIpWEBAAAAwB+59UZqQL5WrjSf1V61ylxCPmOG9MknJNwAAAAA3A4z3Sg6MjPNpeTTppnlJk2k99+XGjZ0bVwAAAAAcAXMdKNo2LdPatny/xPuQYOkrVtJuAEAAAC4NZJuuDfDMI8Ba9FC+vZbKTjYXF4+dSq7kwMAAABweywvh/tKTZViYqQVK8xyx47S/PnS/46LAwAAAAB3x0w33NOXX5rPbK9YIXl7m0eDff45CTcAAACAIoWZbriXixeluDhpyhSz3LCh9MEHUuPGro0LAAAAAP4Gkm64j++/l7p3l/bsMcsDBkivviqVKePauAAAAADgb2J5OVzPMKRZs6TwcDPhDgyUPvvM3KmchBsAAABAEcZMN1zr1Clzs7Tly81yhw7SggVSxYqujAoAAAAAnIKZbrjON99IYWFmwu3lJU2eLH3xBQk3AAAAgGKDpBuFLydHeukl6fbbpV9/lerUkTZvlgYPljz4lQQAAABQfLC8HIXr+HGpZ0/p66/Ncs+e0ltvSWXLujYuAAAAALAA04ooPKtWSU2bmgm3r6/57PaiRSTcAAAAAIotkm5YLztbGj5c6thROnFCatJE2rFD6t3b1ZEBAAAAgKVYXg5rJSVJ3bpJW7ea5f79pddek3x8XBsXAAAAABQCkm5Y58MPpccfl9LSpIAAae5c6cEHXR0VAAAAABQalpfD+S5ckPr1k/71LzPhbt1a2rWLhBsAAABAiUPSDefav19q1UqaNcssP/+8tG6dVKOGS8MCAAAAAFdgeTmcwzCk+fOlgQOl8+el4GBzZ/IOHVwdGQAAAAC4DEk3rl96urmc/P33zXL79mbCHRrq2rgAAAAAwMVYXo7rs2OHFB5uJtyentL48dKXX5JwAwAAAICY6cbfZRjmc9vPPitlZUnVqkkffCC1aePqyAAAAADAbZB049qdPSs98YSZZEvSAw+Yz3NXqODauAAAAADAzbC8HNfm+++lli3NhNvTU5o0SVq+nIQbAAAAAPLBTDcK7t13pSefNHcnr1RJWrJEuvVWV0cFAAAAAG6LmW78tcxMM9l+5BEz4W7fXtq1i4QbAAAAAP4CSTeu7tAhc3O02bMlm02Kj5dWrTLP4QYAAAAAXBXLy3Fly5dLjz4qpaVJgYHSe+9JHTq4OioAAAAAKDKY6UZe2dnS0KFSly5mwt2mjbmcnIQbAAAAAK4JSTdyO3pUuvNO6fXXzXJsrJSYKFWp4tKwAAAAAKAoYnk5/t/q1VL37tLJk5K/v7RggTnbDQAAAAD4W5jphpSTI40eLUVEmAl3s2bSzp0k3AAAAABwnZjpLulOnDBnt9esMctPPCG98Ybk4+PauAAAAACgGCDpLskSE6WePaVjxyRfX+ntt80yAAAAAMApWF5eEqWmSr17mxumHTsm1a8vbdtGwg0AAAAATkbSXZLY7eZsdv360sKFks0mPfmk9N//Sg0bujo6AAAAACh2WF5eUuzaJfXrJ23dapbDwqSZM6VbbnFpWAAAAABQnDHTXdylp0vPPiu1aGEm3GXLSlOnmrPbJNwAAAAAYClmuosrw5CWLpUGD5aOHzfroqOlyZOlSpVcGxsAAAAAlBAk3cXRTz9JAwZIX31llm+8UZoxQ+rQwbVxAQAAAEAJw/Ly4iQzUxo9Wmrc2Ey4vb3N8rffknADAAAAgAsw011cfPml1L+/dOiQWe7QQZo+XapTx7VxAQAAAEAJxkx3UXfsmNS1qxQZaSbcFStKS5ZIq1aRcAMAAACAixWJpHvGjBmqUaOGfHx81KpVK23btu2q7ZctW6b69evLx8dHjRs31sqVKwsp0kJ06ZK5C3n9+tKyZZKHh7lL+Q8/mEm4zebqCAEAAACgxHP7pHvJkiWKjY1VfHy8du7cqaZNmyoiIkKpqan5tt+0aZO6deummJgY7dq1S1FRUYqKitK+ffsKOXILGYZ0113mzuRnz5pHf+3YIU2ZIvn7uzo6AAAAAMD/uH3SPXnyZPXt21d9+vRRgwYNNGvWLPn6+mrevHn5tn/jjTcUGRmpYcOG6aabbtK4cePUvHlzTZ8+vZAjt5DNJnXpIpUvL739trRxoxQW5uqoAAAAAAB/4tYbqWVlZWnHjh2Ki4tz1Hl4eKh9+/bavHlzvq/ZvHmzYmNjc9VFRERo+fLlV+zn4sWLunjxoqOcnp4uSbLb7bLb7fm+xm63yzCMK163XP/+UvfuUlDQ5YBcEwfwJy4fG4AbYlwA+WNsAHkxLoqOgv6M3DrpPnnypHJychQSEpKrPiQkRD/88EO+r0lOTs63fXJy8hX7mTBhgsaMGZOn/sSJE8rMzMz3NXa7XWlpaTIMQx4eLlwwcIVl9oCruM3YANwI4wLIH2MDyItxUXRkZGQUqJ1bJ92FJS4uLtfseHp6uqpWraqgoCD5X+EZabvdLpvNpqCgIAYD8AeMDSAvxgWQP8YGkBfjoujw8fEpUDu3TroDAwPl6emplJSUXPUpKSkKDQ3N9zWhoaHX1F6SvL295e3tnafew8Pjqr/oNpvtL9sAJRFjA8iLcQHkj7EB5MW4KBoK+vNx65+il5eXwsPDlZCQ4Kiz2+1KSEhQ69at831N69atc7WXpNWrV1+xPQAAAAAAVnHrmW5Jio2NVe/evdWiRQu1bNlSU6dO1blz59SnTx9JUq9evVS5cmVNmDBBkjRo0CDdfvvtev3119WpUyctXrxY27dv1+zZs135NgAAAAAAJZDbJ93R0dE6ceKERo0apeTkZIWFhWnVqlWOzdKOHDmSa1q/TZs2ev/99zVixAi98MILqlOnjpYvX65GjRq56i0AAAAAAEoom2EYhquDcDfp6ekqV66c0tLSrrqRWmpqqoKDg3nWAvgDxgaQF+MCyB9jA8iLcVF0FCRvlNz8mW4AAAAAAIoykm4AAAAAACxC0g0AAAAAgEVIugEAAAAAsIjb717uCpf3lktPT79iG7vdroyMDPn4+LDBAfAHjA0gL8YFkD/GBpAX46LouJwv/tXe5CTd+cjIyJAkVa1a1cWRAAAAAADcWUZGhsqVK3fF6xwZlg+73a7ffvtNZcuWlc1my7dNenq6qlatql9//fWq28MDJQ1jA8iLcQHkj7EB5MW4KDoMw1BGRoYqVap01VUJzHTnw8PDQ1WqVClQW39/fwYDkA/GBpAX4wLIH2MDyItxUTRcbYb7Mh4SAAAAAADAIiTdAAAAAABYhKT7b/L29lZ8fLy8vb1dHQrgVhgbQF6MCyB/jA0gL8ZF8cNGagAAAAAAWISZbgAAAAAALELSDQAAAACARUi6AQAAAACwCEk3AAAAAAAWIen+m2bMmKEaNWrIx8dHrVq10rZt21wdEuAUo0ePls1my/VVv359x/XMzEz1799f//jHP+Tn56eHHnpIKSkpue5x5MgRderUSb6+vgoODtawYcN06dKlXG0SExPVvHlzeXt768Ybb9SCBQsK4+0BBbZ+/Xrdf//9qlSpkmw2m5YvX57rumEYGjVqlCpWrKgyZcqoffv2+umnn3K1OXXqlHr06CF/f38FBAQoJiZGZ8+ezdVm7969ateunXx8fFS1alW9+uqreWJZtmyZ6tevLx8fHzVu3FgrV650+vsFCuKvxsWjjz6a59+QyMjIXG0YFyhuJkyYoJtvvllly5ZVcHCwoqKidODAgVxtCvPzE3mK+yHp/huWLFmi2NhYxcfHa+fOnWratKkiIiKUmprq6tAAp2jYsKGOHz/u+Prmm28c1wYPHqzPPvtMy5Yt07p16/Tbb7/pwQcfdFzPyclRp06dlJWVpU2bNunf//63FixYoFGjRjnaJCUlqVOnTrrzzju1e/duPfvss3r88cf15ZdfFur7BK7m3Llzatq0qWbMmJHv9VdffVVvvvmmZs2apa1bt+qGG25QRESEMjMzHW169Oih7777TqtXr9aKFSu0fv16PfHEE47r6enp6tChg6pXr64dO3Zo0qRJGj16tGbPnu1os2nTJnXr1k0xMTHatWuXoqKiFBUVpX379ln35oEr+KtxIUmRkZG5/g354IMPcl1nXKC4Wbdunfr3768tW7Zo9erVys7OVocOHXTu3DlHm8L6/ESe4qYMXLOWLVsa/fv3d5RzcnKMSpUqGRMmTHBhVIBzxMfHG02bNs332pkzZ4zSpUsby5Ytc9Tt37/fkGRs3rzZMAzDWLlypeHh4WEkJyc72sycOdPw9/c3Ll68aBiGYTz33HNGw4YNc907OjraiIiIcPK7AZxDkvHxxx87yna73QgNDTUmTZrkqDtz5ozh7e1tfPDBB4ZhGMb3339vSDL++9//Otp88cUXhs1mM44dO2YYhmG89dZbRvny5R1jwzAMY/jw4Ua9evUc5a5duxqdOnXKFU+rVq2MJ5980qnvEbhWfx4XhmEYvXv3Njp37nzF1zAuUBKkpqYakox169YZhlG4n5/IU9wTM93XKCsrSzt27FD79u0ddR4eHmrfvr02b97swsgA5/npp59UqVIl1apVSz169NCRI0ckSTt27FB2dnau3//69eurWrVqjt//zZs3q3HjxgoJCXG0iYiIUHp6ur777jtHmz/e43IbxhCKiqSkJCUnJ+f6PS5XrpxatWqVaywEBASoRYsWjjbt27eXh4eHtm7d6mhz2223ycvLy9EmIiJCBw4c0OnTpx1tGC8oShITExUcHKx69eqpX79++v333x3XGBcoCdLS0iRJFSpUkFR4n5/IU9wXSfc1OnnypHJycnINCEkKCQlRcnKyi6ICnKdVq1ZasGCBVq1apZkzZyopKUnt2rVTRkaGkpOT5eXlpYCAgFyv+ePvf3Jycr7j4/K1q7VJT0/XhQsXLHpngPNc/l2+2r8FycnJCg4OznW9VKlSqlChglPGC//mwB1FRkZq4cKFSkhI0CuvvKJ169apY8eOysnJkcS4QPFnt9v17LPPqm3btmrUqJEkFdrnJ/IU91XK1QEAcC8dO3Z0fN+kSRO1atVK1atX19KlS1WmTBkXRgYAcHcPP/yw4/vGjRurSZMmql27thITE3X33Xe7MDKgcPTv31/79u3LtR8OwEz3NQoMDJSnp2ee3QZTUlIUGhrqoqgA6wQEBKhu3bo6ePCgQkNDlZWVpTNnzuRq88ff/9DQ0HzHx+VrV2vj7+9PYo8i4fLv8tX+LQgNDc2zcc2lS5d06tQpp4wX/s1BUVCrVi0FBgbq4MGDkhgXKN4GDBigFStWaO3atapSpYqjvrA+P5GnuC+S7mvk5eWl8PBwJSQkOOrsdrsSEhLUunVrF0YGWOPs2bM6dOiQKlasqPDwcJUuXTrX7/+BAwd05MgRx+9/69at9e233+b6ULV69Wr5+/urQYMGjjZ/vMflNowhFBU1a9ZUaGhort/j9PR0bd26NddYOHPmjHbs2OFo8/XXX8tut6tVq1aONuvXr1d2drajzerVq1WvXj2VL1/e0YbxgqLq6NGj+v3331WxYkVJjAsUT4ZhaMCAAfr444/19ddfq2bNmrmuF9bnJ/IUN+bqndyKosWLFxve3t7GggULjO+//9544oknjICAgFy7DQJF1ZAhQ4zExEQjKSnJ2Lhxo9G+fXsjMDDQSE1NNQzDMJ566imjWrVqxtdff21s377daN26tdG6dWvH6y9dumQ0atTI6NChg7F7925j1apVRlBQkBEXF+do8/PPPxu+vr7GsGHDjP379xszZswwPD09jVWrVhX6+wWuJCMjw9i1a5exa9cuQ5IxefJkY9euXcYvv/xiGIZhTJw40QgICDA++eQTY+/evUbnzp2NmjVrGhcuXHDcIzIy0mjWrJmxdetW45tvvjHq1KljdOvWzXH9zJkzRkhIiPHII48Y+/btMxYvXmz4+voab7/9tqPNxo0bjVKlShmvvfaasX//fiM+Pt4oXbq08e233xbeHwbwP1cbFxkZGcbQoUONzZs3G0lJScaaNWuM5s2bG3Xq1DEyMzMd92BcoLjp16+fUa5cOSMxMdE4fvy44+v8+fOONoX1+Yk8xT2RdP9N06ZNM6pVq2Z4eXkZLVu2NLZs2eLqkACniI6ONipWrGh4eXkZlStXNqKjo42DBw86rl+4cMF4+umnjfLlyxu+vr5Gly5djOPHj+e6x+HDh42OHTsaZcqUMQIDA40hQ4YY2dnZudqsXbvWCAsLM7y8vIxatWoZ8+fPL4y3BxTY2rVrDUl5vnr37m0Yhnls2MiRI42QkBDD29vbuPvuu40DBw7kusfvv/9udOvWzfDz8zP8/f2NPn36GBkZGbna7Nmzx7j11lsNb29vo3LlysbEiRPzxLJ06VKjbt26hpeXl9GwYUPj888/t+x9A1dztXFx/vx5o0OHDkZQUJBRunRpo3r16kbfvn3zfNhnXKC4yW9MSMr12aYwPz+Rp7gfm2EYRmHPrgMAAAAAUBLwTDcAAAAAABYh6QYAAAAAwCIk3QAAAAAAWISkGwAAAAAAi5B0AwAAAABgEZJuAAAAAAAsQtINAAAAAIBFSLoBAAAAALAISTcAALiqO+64Q88++6yrwwAAoEgi6QYAoBi7//77FRkZme+1DRs2yGazae/evYUcFQAAJQdJNwAAxVhMTIxWr16to0eP5rk2f/58tWjRQk2aNHFBZAAAlAwk3QAAFGP33XefgoKCtGDBglz1Z8+e1bJlyxQVFaVu3bqpcuXK8vX1VePGjfXBBx9c9Z42m03Lly/PVRcQEJCrj19//VVdu3ZVQECAKlSooM6dO+vw4cPOeVMAABQhJN0AABRjpUqVUq9evbRgwQIZhuGoX7ZsmXJyctSzZ0+Fh4fr888/1759+/TEE0/okUce0bZt2/52n9nZ2YqIiFDZsmW1YcMGbdy4UX5+foqMjFRWVpYz3hYAAEUGSTcAAMXcY489pkOHDmndunWOuvnz5+uhhx5S9erVNXToUIWFhalWrVoaOHCgIiMjtXTp0r/d35IlS2S32/XOO++ocePGuummmzR//nwdOXJEiYmJTnhHAAAUHSTdAAAUc/Xr11ebNm00b948SdLBgwe1YcMGxcTEKCcnR+PGjVPjxo1VoUIF+fn56csvv9SRI0f+dn979uzRwYMHVbZsWfn5+cnPz08VKlRQZmamDh065Ky3BQBAkVDK1QEAAADrxcTEaODAgZoxY4bmz5+v2rVr6/bbb9crr7yiN954Q1OnTlXjxo11ww036Nlnn73qMnCbzZZrqbpkLim/7OzZswoPD9d7772X57VBQUHOe1MAABQBJN0AAJQAXbt21aBBg/T+++9r4cKF6tevn2w2mzZu3KjOnTurZ8+ekiS73a4ff/xRDRo0uOK9goKCdPz4cUf5p59+0vnz5x3l5s2ba8mSJQoODpa/v791bwoAgCKA5eUAAJQAfn5+io6OVlxcnI4fP65HH31UklSnTh2tXr1amzZt0v79+/Xkk08qJSXlqve66667NH36dO3atUvbt2/XU089pdKlSzuu9+jRQ4GBgercubM2bNigpKQkJSYm6plnnsn36DIAAIozkm4AAEqImJgYnT59WhEREapUqZIkacSIEWrevLkiIiJ0xx13KDQ0VFFRUVe9z+uvv66qVauqXbt26t69u4YOHSpfX1/HdV9fX61fv17VqlXTgw8+qJtuukkxMTHKzMxk5hsAUOLYjD8/lAUAAAAAAJyCmW4AAAAAACxC0g0AAAAAgEVIugEAAAAAsAhJNwAAAAAAFiHpBgAAAADAIiTdAAAAAABYhKQbAAAAAACLkHQDAAAAAGARkm4AAAAAACxC0g0AAAAAgEVIugEAAAAAsMj/ARYKVBwDw8FHAAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analisi distribuzionale per total_water_need\n", + "\n", + "Statistiche Predizioni:\n", + "mean: 59881.418\n", + "variance: 854359552.000\n", + "std: 29229.430\n", + "min: 10220.502\n", + "max: 138613.984\n", + "median: 59181.953\n", + "\n", + "Statistiche Target Reali:\n", + "mean: 60107.148\n", + "variance: 880123648.000\n", + "std: 29666.877\n", + "min: 8114.031\n", + "max: 150365.750\n", + "median: 59140.652\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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Done\n", + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... Done\n", + "graphviz is already the newest version (2.42.2-6ubuntu0.1).\n", + "0 upgraded, 0 newly installed, 0 to remove and 121 not upgraded.\n", + "Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.0.0)\n", + "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.6.3)\n", + "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (23.5.26)\n", + "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", + "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n", + "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.11/dist-packages (from 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/usr/lib/python3/dist-packages (from tensorflow) (1.16.0)\n", + "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.3.0)\n", + "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.8.0)\n", + "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.14.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.37.1)\n", + "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.58.0)\n", + "Requirement already satisfied: tensorboard<2.15,>=2.14 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n", + "Requirement already satisfied: tensorflow-estimator<2.15,>=2.14.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n", + "Requirement already satisfied: keras<2.15,>=2.14.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n", + "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", + "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.23.1)\n", + "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (1.0.0)\n", + "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", + "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", + "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", + "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", + "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", + "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", + "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", + "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", + "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", + "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", + "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", + "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", + "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.15,>=2.14->tensorflow) (2.1.3)\n", + "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.5.0)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\n", + "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "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", + "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", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.1.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.3)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow\n", + "!pip install numpy\n", + "!pip install pandas\n", + "\n", + "!pip install keras\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a467d3f0dfd9beab", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-06 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": [ + "
" + ] + }, + "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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ExOjWrVtmn1mzZmn37t06efKkvv76a7344ouKiIiwmrE0ZcoUHTx4UD///LOmTp2qXr16aeTIkcqePbuke7OugoKC9Morr+iHH37QmjVrNHToUIWHh5uX5O3du1fFihXTuXPnJN1bMH3ixIn64Ycf9Ouvv2ru3LmKiIjQyy+/rBw5cpj7PnbsmKKionTlyhXFxsYqKipKUVFRmf/iAQAAAMC/xMiRI/X888/Lw8ND3t7eat68uaKjo636xMTE6JVXXpGvr6/c3NxUrlw5LVmyxKrPzz//rGbNmil37tzy9PRU1apVtWnTJqs+GzZsUOXKlc07qA8cODDNN6kyDEMNGzaUxWLRt99+a7UtLRNdkPke+1K+jPD3u/d98MEHmj59unbv3q18+fJp5syZmjdvnmrXri3pXmARGBio3bt3q1KlSlq7dq2OHTum9evXy8fHR2XKlNF7772ngQMHavjw4XJyctKMGTMUEBCgjz76SJIUGBio7du3a8KECQoJCfnHj/lpN336dElSzZo1rdpnzZqljh07SpKio6M1ePBgXblyRQULFtRbb72liIgIq/579+7VO++8o+vXr6tYsWL65JNP9Morr5jb7e3tFRkZqZ49eyo4OFhubm4KCwvTiBEjzD43b95UdHS07ty5I+neOmrz58/X8OHDFR8fr4CAAEVERFitOyVJjRo10m+//WY+Llu2rKR7P/AAAAAA4GmwZcsWhYeH6/nnn9fdu3c1ZMgQ1a9fX8eOHZObm5skqUOHDrp69apWrFih3Llza968eWrdurX2799v/h3VuHFjPfvss9q4caNcXV01ceJENW7cWCdPnpSvr69++OEHNWrUSG+99Za+/PJLnTt3Tj169FBiYqLGjRv3yDonTpwoi8WSoj15oouvr6927typCxcuqEOHDnJ0dNSHH36YsS8WHspi/Ev+mk5MTNSiRYsUFhamQ4cOKSYmRnXq1NFff/1lzoKRJH9/f/Xp00cREREaNmyYVqxYYTVb5dSpUypUqJAOHjyosmXLqnr16ipXrpwmTpxo9pk1a5b69Olj3lnwUeLi4uTl5aXY2Fh5enpm0BED/30FB620dQnIJKdHhf7j++R8yro4n5BRbHEuAQDS5o8//pC3t7e2bNmi6tWrS5Lc3d01ffp0q0kEuXLl0ujRo9W1a1ddvnxZefLk0datW1WtWjVJ0rVr1+Tp6al169apbt26GjJkiNatW6d9+/aZY3z33Xdq3bq1Ll26JA8PjwfWFBUVpcaNG2v//v3y8/PTsmXL1Lx5c0nS999/r8aNG+v8+fPmHdxnzJihgQMH6o8//pCTk1NGv0RPlfTkKI99KV9CQoJ+//13nTlzxuorvQ4fPix3d3c5OzurR48eWrZsmYKCghQTEyMnJyerUEq6t6ZQTEyMpHvTApNPoPu3J297WJ+4uDiry8fuFx8fr7i4OKsvAAAAAACQuuSJHzlz5jTbKleurAULFujKlStKSkrS/Pnzdfv2bfMKmly5cqlo0aL68ssvdePGDd29e1effPKJvL29Vb58eUn3/j53cXGx2perq6tu376tAwcOPLCemzdv6qWXXtLUqVNTvQHWrl27VLJkSau8ICQkRHFxcebyQvhnpPtSvl9++UWdO3fWzp07rdoNw5DFYlFiYmK6xitatKiioqIUGxurxYsXKywsTFu2bElvWRlq5MiRevfdd21aAwAAAAAA/wVJSUnq06ePqlSpohIlSpjtCxcuVJs2bZQrVy45ODgoW7ZsWrZsmYoUKSJJslgsWr9+vZo3by4PDw/zxlOrV6821/kNCQnRxIkT9c0336h169aKiYkxl2i5cOHCA2uKiIhQ5cqV1axZs1S3p2WiC/4Z6Q6mOnbsKAcHB0VGRsrPzy/VazXTw8nJyTwpy5cvr3379mnSpElq06aNEhISdPXqVatZUxcvXjTTTl9fX+3du9dqvOS79t3f5+938rt48aI8PT3l6uqaak2DBw+2WlcoLi5O+fPnf6Lj/Dfh8oasicsbAAAAANhCeHi4jhw5ou3bt1u1v/3227p69arWr1+v3Llz69tvv1Xr1q21bds2lSxZUoZhKDw8XN7e3tq2bZtcXV31+eefq0mTJtq3b5/8/PxUv359jR07Vj169NArr7wiZ2dnvf3229q2bZvs7FK/CGzFihXauHGjDh069E8cPp5QuoOpqKgoHThwQMWKFcuMepSUlKT4+HiVL19ejo6O2rBhg1q2bCnp3qLYZ86cUXBwsCQpODhYH3zwgS5duiRvb29J0rp16+Tp6amgoCCzz6pVq6z2sW7dOnOM1Dg7O5t3bgMAAAAAAKnr1auXIiMjtXXrVuXLl89sP3nypKZMmaIjR46oePHikqTSpUtr27Ztmjp1qmbMmKGNGzcqMjJSf/31l7kO0bRp07Ru3TrNmTNHgwYNkiT17dtXERERunDhgnLkyKHTp09r8ODBKlSoUKo1bdy4USdPnkyxNFDLli1VrVo1bd68OU0TXfDPSHcwFRQUpMuXL2fIzgcPHqyGDRuqQIECunbtmubNm6fNmzdrzZo18vLyUpcuXdS3b1/lzJlTnp6eev311xUcHKxKlSpJkurXr6+goCC98sorGjNmjGJiYjR06FCFh4ebwVKPHj00ZcoUDRgwQJ07d9bGjRu1cOFCrVzJrCEAAAAAAB6HYRh6/fXXtWzZMm3evFkBAQFW22/evClJKWY12dvbKykp6aF97OzszD7JLBaL8ubNK0n65ptvlD9/fpUrVy7V2gYNGqSuXbtatZUsWVITJkxQkyZNJKVtogv+GekOpkaPHq0BAwboww8/VMmSJeXo6Gi1PT13rbt06ZI6dOigCxcuyMvLS6VKldKaNWtUr149SdKECRNkZ2enli1bKj4+XiEhIZo2bZr5fHt7e0VGRqpnz54KDg6Wm5ubwsLCzOtNJSkgIEArV65URESEJk2apHz58unzzz9XSEhIeg8dAAAAAADo3uV78+bN0/Lly+Xh4WGuy+Tl5SVXV1cVK1ZMRYoUUffu3TVu3DjlypVL3377rdatW6fIyEhJ98KhHDlyKCwsTMOGDZOrq6s+++wznTp1SqGh/7dUydixY9WgQQPZ2dlp6dKlGjVqlBYuXCh7e3tJ0rlz51SnTh19+eWXeuGFF+Tr65vqrKcCBQqYAVpaJrrgn5HuYKpu3bqSpDp16li1P87i5zNnznzodhcXF02dOlVTp059YB9/f/8Ul+r9Xc2aNbm2FAAAAACADDJ9+nRJMu+wl2zWrFnq2LGjHB0dtWrVKg0aNEhNmjTR9evXVaRIEc2ZM0eNGjWSJOXOnVurV6/WW2+9pdq1a+vOnTsqXry4li9frtKlS5tjfv/99/rggw8UHx+v0qVLa/ny5WrYsKG5/c6dO4qOjjZnYKVFWia64J+R7mBq06ZNmVEHAAAAAAD4jzAM45F9nn32WS1ZsuShfSpUqKA1a9Y8tM/GjRsfur1gwYKPrCe17WmZ6ILMl+5gqkaNGplRBwAAAAAAAJ4y6Q6mJOnq1auaOXOmjh8/LkkqXry4OnfuLC8vrwwtDgAAAAAAAFlXuoOp/fv3KyQkRK6urnrhhRckSePHj9cHH3ygtWvXPnBVfAAAAAAAkDEKDuJO81nV6VGhj+6UhaQ7mIqIiFDTpk312WefycHh3tPv3r2rrl27qk+fPtq6dWuGFwkAAAAAAICs57FmTN0fSkmSg4ODBgwYoAoVKmRocQAAAAAAAMi67NL7BE9PT505cyZF+9mzZ+Xh4ZEhRQEAAAAAACDrS3cw1aZNG3Xp0kULFizQ2bNndfbsWc2fP19du3ZVu3btMqNGAAAAAAAAZEHpvpRv3Lhxslgs6tChg+7evStJcnR0VM+ePTVq1KgMLxAAAAAAAABZU7qDKScnJ02aNEkjR47UyZMnJUmFCxdWtmzZMrw4AAAAAAAAZF3pDqaSZcuWTSVLlszIWgAAAAAAAPAUSdMaUy1atFBcXJz5/cO+AAAAAAD/PiNHjtTzzz8vDw8PeXt7q3nz5oqOjk7Rb9euXapdu7bc3Nzk6emp6tWr69atW+b2pk2bqkCBAnJxcZGfn59eeeUVnT9/3tweHR2tWrVqycfHRy4uLipUqJCGDh2qO3fuPLS+3r17q3z58nJ2dlaZMmVSbH/ccQH8u6VpxpSXl5csFouke3flS/4eAAAAAPDfsGXLFoWHh+v555/X3bt3NWTIENWvX1/Hjh2Tm5ubpHuhVIMGDTR48GBNnjxZDg4O+uGHH2Rn939zGmrVqqUhQ4bIz89P586dU79+/dSqVSvt3LlT0r01iDt06KBy5cope/bs+uGHH9StWzclJSXpww8/fGiNnTt31p49e/Tjjz+m2PYk4wL490pTMDVr1izz+9mzZ2dWLQAAAACATLJ69Wqrx7Nnz5a3t7cOHDig6tWrS5IiIiLUu3dvDRo0yOxXtGhRq+dFRESY3/v7+2vQoEFq3ry57ty5I0dHRxUqVEiFChWy6rN582Zt27btofV9/PHHkqQ//vgj1WDqcccF8O+Wpkv57le7dm1dvXo1RXtcXJxq166dETUBAAAAADJZbGysJClnzpySpEuXLmnPnj3y9vZW5cqV5ePjoxo1amj79u0PHOPKlSuaO3euKleuLEdHx1T7nDhxQqtXr1aNGjUytP7MGhfAPyvdwdTmzZuVkJCQov327dsk1QAAAADwH5CUlKQ+ffqoSpUqKlGihCTp119/lSQNHz5c3bp10+rVq1WuXDnVqVNHv/zyi9XzBw4cKDc3N+XKlUtnzpzR8uXLU+yjcuXKcnFx0bPPPqtq1appxIgRGVJ7Zo0LwDbSHEz9+OOP5nTKY8eOmY9//PFHHTp0SDNnztQzzzyTaYUCAAAAADJGeHi4jhw5ovnz55ttSUlJkqTu3burU6dOKlu2rCZMmKCiRYvqiy++sHp+//79dejQIa1du1b29vbq0KGDDMOw6rNgwQIdPHhQ8+bN08q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", + "text/plain": [ + "
" + ] + }, + "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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", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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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", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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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", 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", + "text/plain": [ + "
" + ] + }, + "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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", 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" + ] + }, + "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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", 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", + "text/plain": [ + "
" + ] + }, + "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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", 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", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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 +} diff --git a/src/models/olive_oli/olive_oil.ipynb b/src/models/olive_oli/olive_oil.ipynb index fd89e32..44309dc 100644 --- a/src/models/olive_oli/olive_oil.ipynb +++ b/src/models/olive_oli/olive_oil.ipynb @@ -10,18 +10,217 @@ "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", - "Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n", - "Fetched 257 kB in 1s (278 kB/s)\n", + "Get:1 http://archive.ubuntu.com/ubuntu jammy InRelease [270 kB]\n", + "Get:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease [1581 B]\n", + "Get:3 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB] \n", + "Get:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 Packages [1192 kB]\n", + "Get:5 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB] \n", + "Get:6 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB] \n", + "Get:7 http://archive.ubuntu.com/ubuntu jammy/restricted amd64 Packages [164 kB]\n", + "Get:8 http://archive.ubuntu.com/ubuntu jammy/universe amd64 Packages [17.5 MB]\n", + "Get:9 http://security.ubuntu.com/ubuntu jammy-security/multiverse amd64 Packages [45.2 kB]\n", + "Get:10 http://archive.ubuntu.com/ubuntu jammy/multiverse amd64 Packages [266 kB]\n", + "Get:11 http://archive.ubuntu.com/ubuntu jammy/main amd64 Packages [1792 kB] \n", + "Get:12 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [2738 kB]\n", + "Get:13 http://security.ubuntu.com/ubuntu jammy-security/restricted amd64 Packages [3323 kB]\n", + "Get:14 http://archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 Packages [3446 kB]\n", + "Get:15 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1514 kB]\n", + "Get:16 http://archive.ubuntu.com/ubuntu jammy-updates/multiverse amd64 Packages [53.3 kB]\n", + "Get:17 http://archive.ubuntu.com/ubuntu jammy-backports/universe amd64 Packages [33.8 kB]\n", + "Get:18 http://archive.ubuntu.com/ubuntu jammy-backports/main amd64 Packages [81.4 kB]\n", + "Get:19 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [2454 kB]\n", + "Get:20 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1225 kB]\n", + "Fetched 36.5 MB in 2s (18.2 MB/s) \n", "Reading package lists... Done\n", "Reading package lists... Done\n", "Building dependency tree... Done\n", "Reading state information... Done\n", - "graphviz is already the newest version (2.42.2-6ubuntu0.1).\n", - "0 upgraded, 0 newly installed, 0 to remove and 120 not upgraded.\n", + "The following additional packages will be installed:\n", + " fontconfig fonts-liberation libann0 libcairo2 libcdt5 libcgraph6 libdatrie1\n", + " libfribidi0 libgraphite2-3 libgts-0.7-5 libgts-bin libgvc6 libgvpr2\n", + " libharfbuzz0b libice6 liblab-gamut1 libltdl7 libpango-1.0-0\n", + " libpangocairo-1.0-0 libpangoft2-1.0-0 libpathplan4 libpixman-1-0 libsm6\n", + " libthai-data libthai0 libxaw7 libxcb-render0 libxmu6 libxrender1 libxt6\n", + " x11-common\n", + "Suggested packages:\n", + " gsfonts graphviz-doc\n", + "The following NEW packages will be installed:\n", + " fontconfig fonts-liberation graphviz libann0 libcairo2 libcdt5 libcgraph6\n", + " libdatrie1 libfribidi0 libgraphite2-3 libgts-0.7-5 libgts-bin libgvc6\n", + " libgvpr2 libharfbuzz0b libice6 liblab-gamut1 libltdl7 libpango-1.0-0\n", + " libpangocairo-1.0-0 libpangoft2-1.0-0 libpathplan4 libpixman-1-0 libsm6\n", + " libthai-data libthai0 libxaw7 libxcb-render0 libxmu6 libxrender1 libxt6\n", + " x11-common\n", + "0 upgraded, 32 newly installed, 0 to remove and 121 not upgraded.\n", + "Need to get 7298 kB of archives.\n", + "After this operation, 18.3 MB of additional disk space will be used.\n", + "Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libfribidi0 amd64 1.0.8-2ubuntu3.1 [26.1 kB]\n", + "Get:2 http://archive.ubuntu.com/ubuntu jammy/main amd64 fontconfig amd64 2.13.1-4.2ubuntu5 [177 kB]\n", + "Get:3 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-liberation all 1:1.07.4-11 [822 kB]\n", + "Get:4 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libann0 amd64 1.1.2+doc-7build1 [26.0 kB]\n", + "Get:5 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libcdt5 amd64 2.42.2-6ubuntu0.1 [21.1 kB]\n", + "Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libcgraph6 amd64 2.42.2-6ubuntu0.1 [45.4 kB]\n", + "Get:7 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libgts-0.7-5 amd64 0.7.6+darcs121130-5 [164 kB]\n", + "Get:8 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpixman-1-0 amd64 0.40.0-1ubuntu0.22.04.1 [264 kB]\n", + "Get:9 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-render0 amd64 1.14-3ubuntu3 [16.4 kB]\n", + "Get:10 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxrender1 amd64 1:0.9.10-1build4 [19.7 kB]\n", + "Get:11 http://archive.ubuntu.com/ubuntu jammy/main amd64 libcairo2 amd64 1.16.0-5ubuntu2 [628 kB]\n", + "Get:12 http://archive.ubuntu.com/ubuntu jammy/main amd64 libltdl7 amd64 2.4.6-15build2 [39.6 kB]\n", + "Get:13 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgraphite2-3 amd64 1.3.14-1build2 [71.3 kB]\n", + "Get:14 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libharfbuzz0b amd64 2.7.4-1ubuntu3.1 [352 kB]\n", + "Get:15 http://archive.ubuntu.com/ubuntu jammy/main amd64 libthai-data all 0.1.29-1build1 [162 kB]\n", + "Get:16 http://archive.ubuntu.com/ubuntu jammy/main amd64 libdatrie1 amd64 0.2.13-2 [19.9 kB]\n", + "Get:17 http://archive.ubuntu.com/ubuntu jammy/main amd64 libthai0 amd64 0.1.29-1build1 [19.2 kB]\n", + "Get:18 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpango-1.0-0 amd64 1.50.6+ds-2ubuntu1 [230 kB]\n", + "Get:19 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpangoft2-1.0-0 amd64 1.50.6+ds-2ubuntu1 [54.0 kB]\n", + "Get:20 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpangocairo-1.0-0 amd64 1.50.6+ds-2ubuntu1 [39.8 kB]\n", + "Get:21 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libpathplan4 amd64 2.42.2-6ubuntu0.1 [23.4 kB]\n", + "Get:22 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libgvc6 amd64 2.42.2-6ubuntu0.1 [724 kB]\n", + "Get:23 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libgvpr2 amd64 2.42.2-6ubuntu0.1 [192 kB]\n", + "Get:24 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 liblab-gamut1 amd64 2.42.2-6ubuntu0.1 [1965 kB]\n", + "Get:25 http://archive.ubuntu.com/ubuntu jammy/main amd64 x11-common all 1:7.7+23ubuntu2 [23.4 kB]\n", + "Get:26 http://archive.ubuntu.com/ubuntu jammy/main amd64 libice6 amd64 2:1.0.10-1build2 [42.6 kB]\n", + "Get:27 http://archive.ubuntu.com/ubuntu jammy/main amd64 libsm6 amd64 2:1.2.3-1build2 [16.7 kB]\n", + "Get:28 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxt6 amd64 1:1.2.1-1 [177 kB]\n", + "Get:29 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxmu6 amd64 2:1.1.3-3 [49.6 kB]\n", + "Get:30 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxaw7 amd64 2:1.0.14-1 [191 kB]\n", + "Get:31 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 graphviz amd64 2.42.2-6ubuntu0.1 [653 kB]\n", + "Get:32 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libgts-bin amd64 0.7.6+darcs121130-5 [44.3 kB]\n", + "Fetched 7298 kB in 2s (4771 kB/s) \n", + "debconf: delaying package configuration, since apt-utils is not installed\n", + "Selecting previously unselected package libfribidi0:amd64.\n", + "(Reading database ... 20752 files and directories currently installed.)\n", + "Preparing to unpack .../00-libfribidi0_1.0.8-2ubuntu3.1_amd64.deb ...\n", + "Unpacking libfribidi0:amd64 (1.0.8-2ubuntu3.1) ...\n", + "Selecting previously unselected package fontconfig.\n", + "Preparing to unpack .../01-fontconfig_2.13.1-4.2ubuntu5_amd64.deb ...\n", + "Unpacking fontconfig (2.13.1-4.2ubuntu5) ...\n", + "Selecting previously unselected package fonts-liberation.\n", + "Preparing to unpack .../02-fonts-liberation_1%3a1.07.4-11_all.deb ...\n", + "Unpacking fonts-liberation (1:1.07.4-11) ...\n", + "Selecting previously unselected package libann0.\n", + "Preparing to unpack .../03-libann0_1.1.2+doc-7build1_amd64.deb ...\n", + "Unpacking libann0 (1.1.2+doc-7build1) ...\n", + "Selecting previously unselected package libcdt5:amd64.\n", + "Preparing to unpack .../04-libcdt5_2.42.2-6ubuntu0.1_amd64.deb ...\n", + "Unpacking libcdt5:amd64 (2.42.2-6ubuntu0.1) ...\n", + "Selecting previously unselected package libcgraph6:amd64.\n", + "Preparing to unpack .../05-libcgraph6_2.42.2-6ubuntu0.1_amd64.deb ...\n", + "Unpacking libcgraph6:amd64 (2.42.2-6ubuntu0.1) ...\n", + "Selecting previously unselected package libgts-0.7-5:amd64.\n", + "Preparing to unpack .../06-libgts-0.7-5_0.7.6+darcs121130-5_amd64.deb ...\n", + "Unpacking libgts-0.7-5:amd64 (0.7.6+darcs121130-5) ...\n", + "Selecting previously unselected package libpixman-1-0:amd64.\n", + "Preparing to unpack .../07-libpixman-1-0_0.40.0-1ubuntu0.22.04.1_amd64.deb ...\n", + "Unpacking libpixman-1-0:amd64 (0.40.0-1ubuntu0.22.04.1) ...\n", + "Selecting previously unselected package libxcb-render0:amd64.\n", + "Preparing to unpack .../08-libxcb-render0_1.14-3ubuntu3_amd64.deb ...\n", + "Unpacking libxcb-render0:amd64 (1.14-3ubuntu3) ...\n", + "Selecting previously unselected package 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determine current runlevel\n", + "invoke-rc.d: policy-rc.d denied execution of start.\n", + "Setting up libcairo2:amd64 (1.16.0-5ubuntu2) ...\n", + "Setting up libgts-0.7-5:amd64 (0.7.6+darcs121130-5) ...\n", + "Setting up libpathplan4:amd64 (2.42.2-6ubuntu0.1) ...\n", + "Setting up libann0 (1.1.2+doc-7build1) ...\n", + "Setting up libfribidi0:amd64 (1.0.8-2ubuntu3.1) ...\n", + "Setting up libltdl7:amd64 (2.4.6-15build2) ...\n", + "Setting up fonts-liberation (1:1.07.4-11) ...\n", + "Setting up libharfbuzz0b:amd64 (2.7.4-1ubuntu3.1) ...\n", + "Setting up libthai-data (0.1.29-1build1) ...\n", + "Setting up libcdt5:amd64 (2.42.2-6ubuntu0.1) ...\n", + "Setting up libcgraph6:amd64 (2.42.2-6ubuntu0.1) ...\n", + "Setting up libgts-bin (0.7.6+darcs121130-5) ...\n", + "Setting up libice6:amd64 (2:1.0.10-1build2) ...\n", + "Setting up libthai0:amd64 (0.1.29-1build1) ...\n", + "Setting up libgvpr2:amd64 (2.42.2-6ubuntu0.1) ...\n", + "Setting up libsm6:amd64 (2:1.2.3-1build2) ...\n", + "Setting up libpango-1.0-0:amd64 (1.50.6+ds-2ubuntu1) ...\n", + "Setting up libxt6:amd64 (1:1.2.1-1) ...\n", + "Setting up libpangoft2-1.0-0:amd64 (1.50.6+ds-2ubuntu1) ...\n", + "Setting up libpangocairo-1.0-0:amd64 (1.50.6+ds-2ubuntu1) ...\n", + "Setting up libxmu6:amd64 (2:1.1.3-3) ...\n", + "Setting up libxaw7:amd64 (2:1.0.14-1) ...\n", + "Setting up libgvc6 (2.42.2-6ubuntu0.1) ...\n", + "Setting up graphviz (2.42.2-6ubuntu0.1) ...\n", + "Processing triggers for libc-bin (2.35-0ubuntu3.3) ...\n", "Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.0.0)\n", "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.6.3)\n", @@ -40,7 +239,7 @@ "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.3.0)\n", "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.8.0)\n", "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.14.1)\n", - "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.34.0)\n", "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.58.0)\n", "Requirement already satisfied: tensorboard<2.15,>=2.14 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n", "Requirement already satisfied: tensorflow-estimator<2.15,>=2.14.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n", @@ -63,39 +262,72 @@ "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.15,>=2.14->tensorflow) (2.1.3)\n", "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.5.0)\n", "Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n", - "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", - "\u001B[0m\n", - "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", - "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "\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", + "\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", + "Collecting pandas\n", + " Obtaining dependency information for pandas from https://files.pythonhosted.org/packages/cd/5f/4dba1d39bb9c38d574a9a22548c540177f78ea47b32f99c0ff2ec499fac5/pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (89 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.9/89.9 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.0)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", - "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Collecting pytz>=2020.1 (from pandas)\n", + " Obtaining dependency information for pytz>=2020.1 from https://files.pythonhosted.org/packages/11/c3/005fcca25ce078d2cc29fd559379817424e94885510568bc1bc53d7d5846/pytz-2024.2-py2.py3-none-any.whl.metadata\n", + " Downloading pytz-2024.2-py2.py3-none-any.whl.metadata (22 kB)\n", + "Collecting tzdata>=2022.7 (from pandas)\n", + " Obtaining dependency information for tzdata>=2022.7 from https://files.pythonhosted.org/packages/a6/ab/7e5f53c3b9d14972843a647d8d7a853969a58aecc7559cb3267302c94774/tzdata-2024.2-py2.py3-none-any.whl.metadata\n", + " Downloading tzdata-2024.2-py2.py3-none-any.whl.metadata (1.4 kB)\n", "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", + "Downloading pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.1 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.1/13.1 MB\u001b[0m \u001b[31m74.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m:01\u001b[0m\n", + "\u001b[?25hDownloading pytz-2024.2-py2.py3-none-any.whl (508 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m508.0/508.0 kB\u001b[0m \u001b[31m106.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading tzdata-2024.2-py2.py3-none-any.whl (346 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m346.6/346.6 kB\u001b[0m \u001b[31m103.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: pytz, tzdata, pandas\n", + "Successfully installed pandas-2.2.3 pytz-2024.2 tzdata-2024.2\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: 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", + "\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", + "Collecting scikit-learn\n", + " Obtaining dependency information for scikit-learn from https://files.pythonhosted.org/packages/49/21/3723de321531c9745e40f1badafd821e029d346155b6c79704e0b7197552/scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n", "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.0)\n", - "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", + "Collecting scipy>=1.6.0 (from scikit-learn)\n", + " Obtaining dependency information for scipy>=1.6.0 from https://files.pythonhosted.org/packages/93/6b/701776d4bd6bdd9b629c387b5140f006185bd8ddea16788a44434376b98f/scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.8/60.8 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting joblib>=1.2.0 (from scikit-learn)\n", + " Obtaining dependency information for joblib>=1.2.0 from https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl.metadata\n", + " Downloading joblib-1.4.2-py3-none-any.whl.metadata (5.4 kB)\n", + "Collecting threadpoolctl>=3.1.0 (from scikit-learn)\n", + " Obtaining dependency information for threadpoolctl>=3.1.0 from https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl.metadata\n", + " Downloading threadpoolctl-3.5.0-py3-none-any.whl.metadata (13 kB)\n", + "Downloading scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m78.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hDownloading joblib-1.4.2-py3-none-any.whl (301 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m301.8/301.8 kB\u001b[0m \u001b[31m104.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (41.2 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.2/41.2 MB\u001b[0m \u001b[31m55.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m:00:01\u001b[0m\n", + "\u001b[?25hDownloading threadpoolctl-3.5.0-py3-none-any.whl (18 kB)\n", + "Installing collected packages: threadpoolctl, scipy, joblib, scikit-learn\n", + "Successfully installed joblib-1.4.2 scikit-learn-1.5.2 scipy-1.14.1 threadpoolctl-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", "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", @@ -104,44 +336,66 @@ "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib) (2.4.7)\n", "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", - "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", - "\u001B[0m\n", - "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", - "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "\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 (18.0.0)\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 (2024.5.0)\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", + "Collecting pyarrow\n", + " Obtaining dependency information for pyarrow from https://files.pythonhosted.org/packages/5e/b5/9e14e9f7590e0eaa435ecea84dabb137284a4dbba7b3c337b58b65b76d95/pyarrow-18.1.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata\n", + " Downloading pyarrow-18.1.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n", + "Downloading pyarrow-18.1.0-cp311-cp311-manylinux_2_28_x86_64.whl (40.1 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.1/40.1 MB\u001b[0m \u001b[31m58.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hInstalling collected packages: pyarrow\n", + "Successfully installed pyarrow-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", + "Collecting fastparquet\n", + " Obtaining dependency information for fastparquet from https://files.pythonhosted.org/packages/8d/e8/e1ede861bea68394a755d8be1aa2e2d60a3b9f6b551bfd56aeca74987e2e/fastparquet-2024.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading fastparquet-2024.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)\n", "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", - "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", - "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Collecting cramjam>=2.3 (from fastparquet)\n", + " Obtaining dependency information for cramjam>=2.3 from https://files.pythonhosted.org/packages/79/1d/180f2ca168625073f0df80b16c795926deed91b7e89dbfc045263ba7444b/cramjam-2.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading cramjam-2.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.9 kB)\n", + "Collecting fsspec (from fastparquet)\n", + " Obtaining dependency information for fsspec from https://files.pythonhosted.org/packages/c6/b2/454d6e7f0158951d8a78c2e1eb4f69ae81beb8dca5fee9809c6c99e9d0d0/fsspec-2024.10.0-py3-none-any.whl.metadata\n", + " Downloading fsspec-2024.10.0-py3-none-any.whl.metadata (11 kB)\n", "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", - "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", - "\u001B[0m\n", - "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", - "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Downloading fastparquet-2024.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hDownloading cramjam-2.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.4 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m66.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m\n", + "\u001b[?25hDownloading fsspec-2024.10.0-py3-none-any.whl (179 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.6/179.6 kB\u001b[0m \u001b[31m37.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: fsspec, cramjam, fastparquet\n", + "Successfully installed cramjam-2.9.0 fastparquet-2024.11.0 fsspec-2024.10.0\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", + "\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", + "Collecting seaborn\n", + " Obtaining dependency information for seaborn from https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl.metadata\n", + " Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\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", @@ -151,39 +405,86 @@ "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: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.4.7)\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 (4.67.0)\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: pydot in /usr/local/lib/python3.11/dist-packages (3.0.2)\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", + "Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hInstalling collected packages: seaborn\n", + "Successfully installed seaborn-0.13.2\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. 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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", + "Collecting tensorflow-addons\n", + " Obtaining dependency information for tensorflow-addons from https://files.pythonhosted.org/packages/24/94/80165946ec4986505cbfac29b5ae79544bfe2200d9d7883e1ad7c7342a55/tensorflow_addons-0.23.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading tensorflow_addons-0.23.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.8 kB)\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. 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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" ] } ], @@ -219,10 +520,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-11-12 13:23:10.750385: 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", - "2024-11-12 13:23:10.750440: 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", - "2024-11-12 13:23:10.750502: 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", - "2024-11-12 13:23:10.761720: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "2024-12-06 10:36:10.368632: 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 10:36:10.368679: 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 10:36:10.368726: 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 10:36:10.377750: 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" ] }, @@ -242,7 +543,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-11-12 13:23:13.297044: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:01:00.0, compute capability: 8.9\n" + "2024-12-06 10:36:13.233242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:81:00.0, compute capability: 8.9\n" ] } ], @@ -662,32 +963,272 @@ "id": "b3ba2b96ba678389", "metadata": {}, "outputs": [ - { - "ename": "KeyError", - "evalue": "'Variety'", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mKeyError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[7], line 7\u001B[0m\n\u001B[1;32m 4\u001B[0m comparison_data \u001B[38;5;241m=\u001B[39m prepare_comparison_data(simulated_data, olive_varieties)\n\u001B[1;32m 6\u001B[0m \u001B[38;5;66;03m# Genera i grafici\u001B[39;00m\n\u001B[0;32m----> 7\u001B[0m \u001B[43mplot_variety_comparison\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcomparison_data\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mAvg Olive Production (kg/ha)\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[1;32m 8\u001B[0m plot_variety_comparison(comparison_data, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mAvg Oil Production (L/ha)\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m 9\u001B[0m plot_variety_comparison(comparison_data, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mAvg Water Need (m³/ha)\u001B[39m\u001B[38;5;124m'\u001B[39m)\n", - "Cell \u001B[0;32mIn[5], line 79\u001B[0m, in \u001B[0;36mplot_variety_comparison\u001B[0;34m(comparison_data, metric)\u001B[0m\n\u001B[1;32m 77\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mplot_variety_comparison\u001B[39m(comparison_data, metric):\n\u001B[1;32m 78\u001B[0m plt\u001B[38;5;241m.\u001B[39mfigure(figsize\u001B[38;5;241m=\u001B[39m(\u001B[38;5;241m12\u001B[39m, \u001B[38;5;241m6\u001B[39m))\n\u001B[0;32m---> 79\u001B[0m bars \u001B[38;5;241m=\u001B[39m plt\u001B[38;5;241m.\u001B[39mbar(\u001B[43mcomparison_data\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mVariety\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m, comparison_data[metric])\n\u001B[1;32m 80\u001B[0m plt\u001B[38;5;241m.\u001B[39mtitle(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mComparison of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mmetric\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m across Olive Varieties\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m 81\u001B[0m plt\u001B[38;5;241m.\u001B[39mxlabel(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mVariety\u001B[39m\u001B[38;5;124m'\u001B[39m)\n", - "File \u001B[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/frame.py:4102\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m 4100\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[1;32m 4101\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[0;32m-> 4102\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 4103\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[1;32m 4104\u001B[0m indexer \u001B[38;5;241m=\u001B[39m [indexer]\n", - "File \u001B[0;32m/usr/local/lib/python3.11/dist-packages/pandas/core/indexes/range.py:417\u001B[0m, in \u001B[0;36mRangeIndex.get_loc\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m 415\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[1;32m 416\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(key, Hashable):\n\u001B[0;32m--> 417\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key)\n\u001B[1;32m 418\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n\u001B[1;32m 419\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key)\n", - "\u001B[0;31mKeyError\u001B[0m: 'Variety'" - ] - }, { "data": { + "image/png": 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" + "
" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_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": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_avg_oil_production_l_ha.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_avg_water_need_m³_ha.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_oil_efficiency_l_kg.png\n" + ] + }, + { + "data": { + "image/png": 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OlsVi0b333qtdu3bp448/liR5eHhIktzc3OTq6qoKFSrwfXcNIZSCU134w1RiYqLWrFlj/VLz9fVVZmam9u7dq1q1asnFxUXlypWTq6ur1q1bp2+++caZpeMKZf7/5Ilr167VjBkzNG3aNP3zzz9q3ry5nnvuOb399tuaOHGivvvuOx06dEjjx49XWlqamjdv7uzScYUxxqhWrVqqW7eutm3bpgEDBmjs2LGSpIiICPXr10933XWX1qxZo8zMTEnnJ4AtV66cNVAA/i1vctfg4GD16NFDwcHBWrp0qc6cOSMPDw+1a9dOn332mWbPnq0nn3xSS5cuVc+ePVWtWjVnl44rUN7PUM8//7xGjBihHTt26OTJk9b2I0eOKDMzU2lpaTpx4oQ++eQTde7cWe+++65cXFz4gwzy+fc+4erqKkkKDQ3Vtm3bFBsbK0kqV+78RdzPnTun5s2by8vLy7GF4oqX90eXxYsX6+abb9bgwYOVkJCgc+fOqXXr1rrzzjv19ttva/78+ZKk7Oxs/fDDD/L29pa7u7szS4eDcfU9OI254Codzz77rObNm6fc3FzVrVtXAwcOtJ5PPGDAAK1Zs0bh4eFau3atMjMz9fPPP8vV1fWil6fFtWvp0qUaNGiQrrvuOh08eFBVqlTRihUrVL9+fc2dO1dTp07V/v375evrq6ysLC1btkxt27Z1dtm4gh04cEAvv/yydu7cqTvuuEMvvPCCzpw5o4ceekhLlizR9ddfr8qVK2vbtm1KTExkf4KNC7/vtm3bprvuukuPPPKIgoKC9MYbb+jvv//Wo48+qoiICO3atUuxsbFau3atqlevrtmzZ7M/4aLeffddvfjii/r666/VokULa1ggSb///rtuvPFGeXp6KicnR97e3tqwYYPKly/vxIpxpbrws2rOnDnatWuX6tSpo969e6tRo0b68MMPFRUVpUcffVT9+/dX1apV9cILLygzM1Nr1qzhZ3Lk888//1g/kwYMGKBvvvlGH3zwgfr27atffvlF06ZN05IlS1S7dm1VqVJFv/32m1auXMn33jWGUApO8e8f0B944AHNmTNHp0+f1jfffKO5c+cqPDzcelTCoEGD9Ndff6lKlSr68MMPVb58eQIp2Mjbp86ePathw4bpxhtvVP/+/bV//35FRUVp//79WrlypRo1aqQ//vhDhw8fVlZWlpo2bapatWo5u3xcBfbv32+d2+7ee+/Vc889J0latGiRDh48KGOM7rjjDjVq1MjJleJKcezYMZsjnH7++WetWrVK+/fv1+TJkyWd/4F90KBB2rVrl5544gk9+OCDcnV1VVpamtzd3VW1alVnlY+rxJNPPqnTp09r9uzZysnJyfdHuz/++MM6sXlkZKTKlStn84siINmevTBixAjNmTNHTZo00YkTJ1ShQgW99957atGihZYtW6bHH39cFotFFSpUkJ+fn1atWsXP5iiSe+65RytXrtSHH36ovn376ujRo9qxY4f++9//qn79+rrlllvUpEkTZ5cJByOUglO9++67WrlypWrWrKm33npLkpSWlqb3339fb7/9tgYPHqxXX31V0vl5EPJOieGHKUjS+vXr1bFjR+v97777TkOHDlVAQIBef/11tWrVSpJ06NAhDRw4UPv27dOqVavUsGFDZ5WMq0BewLlx40Zt2bJF//zzj/5fe3ceV2P6/gH885wWSdmy1NhihCxR9mVGYiwzdmMnRoydDCrJnmEwthYxJuugsddoMCKSKCTLWMpEJLKTpOVcvz98e6YzzPxmvt/hFJ/3X+NZjuvM63mdc/uc+77uZs2awdbWFikpKZg7dy6io6PRo0cPuLu767tcyqcWLVqE1NRUzJ07V10m9dFHHyE6Ohpt2rTRWYKemZkJZ2dnJCYmwtnZGUOGDEHhwoX1WD3lV3l/1ANejoccHR1Rvnx5bNmyBcDv4UJmZibOnz8PBwcHndfIDa6IcuUNk65evYr58+dj9OjRqFevHg4dOoQlS5YgPj4eQUFBsLOzQ0pKCu7evYvs7GzUq1dP7THFsTkBv39OnTlzBpcuXYKxsTEqVaqE+vXrA9ANptq3b88ewcSeUvR25c1As7KyEBUVhYMHDyIpKUk9bmlpiS+++AKjRo3Chg0b4OrqCgBqICUi/NIj7N27F+3atdPZucPc3BwajQb79+9XB1darRZWVlYICgrChx9+CAcHB1y7dk1PVVNBoCgKtm3bhrZt22Lx4sVYsGAB6tati4CAAFhZWWHq1Klo2LAhQkJCMHPmTH2XS/mUqakpXFxcYGRkhBcvXsDIyAihoaFo37494uPjsWPHDrXfhrGxMdavXw8LCwts374dL1680HP1lB9ptVo1kPrtt9/w9OlTGBoaonv37oiLi0NYWBiA3/tMJSUlYd68eYiNjdV5HQZSlGvnzp0Afn9mtmzZovaOqlChAoCXO8u6u7vDxsYGffr0QVxcHKysrGBnZwcHBwc1dOfYnHLl9k5s1aoVlixZgqFDh+KLL77A9OnTAbzsMdWmTRsMHToUu3fv5uYwBAjRW3L58mVJT08XEZGZM2fK+fPn5bfffpPx48eLmZmZ+Pn56Vx/+/Zt8fT0lG7duolWq9VHyZTPpaSkiIjIjRs31GNnzpyR2rVri4ODgzx79kxERH1+kpOT5bPPPpP4+Pi3XywVGOfOnZPSpUtLYGCgPH36VJ48eSJz5swRQ0ND+e6770REJCkpSZydnaVNmzZy//59PVdM+VlERISMGTNGLl68KCIi9+/flxYtWkizZs0kJCREcnJy1GtfvHghN2/e1FeplI/lfU6mT58uHTt2lLCwMBERiYqKko8++kh69Oghe/bsERGRa9euSefOnaV58+aSnZ2tl5opf1u1apU0bdpUcnJy1Gdk/fr10rJlSylRooQkJyfrXB8ZGSldu3aVkiVLytWrV/VRMuVjeT9nzp07J6VKlRJ/f3/JyMiQy5cvi7e3t1SsWFFmzJihXte+fXuxtraWp0+f6qFiyk8YStEbp9Vq5dy5c6IoiqxevVpGjx4thQsXll9//VVERK5evSrjx4+XGjVqSEBAgM69Dx48UAMFBlP0OleuXBFFUWTFihXqsTNnzkiNGjWkYcOGahCa+/xwcE5/9MfPlrCwMKlVq5akpKTonJs5c6aYmpqqoWZKSooajBLlDQ0yMzPV//7uu+/kww8/lAkTJsjly5dFROTevXvSvHlzadasmezZs0fnXqK/4uHhIWXKlJEdO3bI3bt31eMHDx6UTp06iYWFhVSoUEFq1qwp9evXV59FPmP0R7dv31bHRDExMerxXbt2Sf369aVFixZy/fp1nXsOHjwobm5uHEuRavny5a98vuzYsUNq1qwpjx49Uo/duXNHZs6cKfXr11d/pBER/hBDIsJQit6iBQsWiImJiZiamkpUVJSI/P6PwStXrqjB1KpVq165l4EU/ZVJkyZJ4cKF5fvvv1eP5QZTTZs2VWdMEeXKHUDlHUjdu3dPtFqthISEiEajUX8lfvHihYiI3Lp1S6ytrWX79u1vv2AqEK5duyZpaWkiIrJz506ZNWuWiIgsW7ZM7O3tZdy4cTrBVMuWLaVmzZqyb98+vdVMBceRI0ekYsWKcuLECRERycjIkGvXrsnevXslNTVVMjMzJSoqSnx8fCQ4OFgNDrKysvRZNuUzHh4e6veaiEh4eLgoiiLLli1Tj23btk2cnJzE0dFRkpKSXvs6DKbo4sWL0qhRo1dWIBw4cEAsLS3l5MmTOsfj4uKkcOHCsn///rdZJhUA7ClFb1xOTg4AwNraGllZWcjIyMC5c+fw5MkTtTeCjY0NRo8ejQ4dOmDy5MnYvXu3zmvkbepJ7zf5T1+y6OhoBAUFQavVYuHChXBzc8OXX36JwMBAAEDdunURFBSEq1evolOnTvosmfIhjUaD69evw8vLCwCwfft2dOjQAffv30erVq3QrFkzjB07FqmpqWoDTmNjY5iYmLBvBr1WRkYG+vTpg4YNG2LDhg3o3r27uhPjuHHjMGjQIERERMDPzw9XrlyBhYUFtm3bhooVK6JatWp6rp4KAkVRYGFhgSJFiuDMmTPw8vKCk5MTRowYAUdHR1y8eBFNmjTBmDFj0KlTJxgYGCAnJ4efWaT67bff4OfnBycnJ7WPT+XKlTFlyhTMnDkTvr6+AIAePXpg5MiR0Gg0+OKLL17bi5O9yahq1ao4cOAAqlatihMnTkCr1QJ42R+4ePHi+PHHH3Hnzh31+goVKqBGjRrqdUQqfadi9O7641TO7Oxsyc7Olrlz54pGo5Hly5fLkydPdK65deuWLF68mL++0Gvlzpjbtm2blC5dWjw8POTcuXPq+RkzZoiBgYHOjKlz585JQkLCW6+V8jetVisLFiyQunXrSufOncXQ0FDWr1+vnl+zZo20bNlSOnfuLAkJCXLlyhWZNm2afPDBB68sZyDKdfPmTfnggw+kUKFCsnLlShF5OZsl19KlS8Xe3l4mTJigLmHnsip6nde1Ljh58qRYW1uLo6OjmJmZybBhw2TTpk1y9OhRqVGjhuzatUtf5VIBcurUKalevbo0bdpUXd558+ZNmTZtmpibm4uPj4967fbt26VOnToyZswYfZVLBcDdu3fF1tZWHBwc1O+0lStXipmZmUycOFGOHDkit27dEnd3d7GystLpBUskwuV79IbkHWQfP35c9u3bJ+Hh4eqx6dOni0ajEX9/fzWYGjx4sDpIF+G0YHq9Y8eOSbFixWTlypWvXZIwY8YMKVy48CuN84lex9nZWRRFkfbt2+sc12q1sm7dOnF0dBRFUcTW1lasra3l1KlTeqqUCoKUlBQpVqyYWFhYSJMmTdSlfHmXyixbtkwqVqwo7u7ukpmZyeXp9Iq8Y6i7d+/K06dP1fDg8OHDsmLFCtmzZ4/aHPj58+dib28vO3fu1Ee5VACdOnVKbGxspEmTJuqzdePGDTWY8vX1Va89dOgQx+T0l7KysiQkJETq1auns7nCd999J3Xr1pWSJUuKra2tVKxYUU6fPq3naik/UkT+sxaG6A3w8PBASEgInj9/jjJlysDQ0BARERFQFAVz5syBt7c3+vfvj8uXLyMlJQVXrlzhNHN6LRGBoiiYN28eoqKiEBwcrJ7LycnRmUY+adIkrFu3DgkJCShWrJg+yqV8LisrC4qiwMPDAzdu3MCtW7dQv359fP311zA1NVWv02q1OHr0KMzMzGBlZQUrKys9Vk0FwY0bN/DixQt06tQJZmZmCA8PR5EiRZCZmakuBd24cSOaNWuGKlWq6Llaym+0Wi00mpfdNRYsWIDg4GBkZmaibNmy2LBhA4oXL47s7GwYGhrixYsXePr0KZydnXH//n0cO3aMS6rotXLHUHn/HBsbi169eqF06dI4cuQIjIyMcPPmTaxatQo+Pj7w8PCAu7u7es8fx1pEeb148QKHDh3CpEmTULx4cRw+fBgGBgZISEjAgwcP8OzZM9SoUYPjKHothlL0r8k7kAKApUuXwtvbG3v27EGjRo0wf/58TJ06FXv37kXbtm0BAD4+PoiOjoaRkRFWrlwJIyMjfunRX5owYQJOnz6NQ4cO6TxvwMs+Uw0aNIBGo8Hdu3dRunRpPVVJ+dUfB+a5x2bOnIl9+/ahSZMmOsFUcnIyrKysXnnWiIDfn6e7d++q318WFhbQarU4d+4c+vTpg2LFiiEsLAxFihTBkiVL8OzZM7WXGVFeeT+fpk6diu+//x7e3t4oWbIkPD09YWxsjNDQUJQvXx4ZGRlYuHAhDh06hPT0dERERHAMRa+Vd3yu1WqRmZkJExMTAEBsbCw+//xzlClTRg2mkpOTsXDhQly4cAH79+8HwN6u9Lvcz6mTJ0/i5MmTUBQFTZs2hZ2dnU4wVaJECRw+fJjjJ/pb+JTQv+Lu3bvQaDRqU/OcnBycPXsWc+fORePGjREcHIz58+dj5cqVaNu2LZ4+fQoAGDt2LFatWoXAwEAYGRkhOzubgylS5WbmN2/eVP+7XLlyuHDhwitNNzMyMrBhwwb89NNPAMBAil6RO5AKCwvD2LFj4eXlhUOHDkFRFLi7u6N9+/aIjo6Gh4cHHj9+jBkzZqBXr154/vy5vkunfCj3efrpp5/QqVMntGzZEk2bNkVYWBg0Gg3q1q2LLVu2IC0tDdWqVUPfvn3h5uaGzp0767t0ymeuXr0K4Pd/+O/fvx+hoaHYvn07hg4dCiMjI6SkpODBgwf46KOPcPPmTZiYmKBNmzb47LPPEBkZyTEUvVbeQGrx4sXo168fGjdujAULFuDUqVOwt7fH9u3bkZqaipYtWyIrKwvlypXD1KlTsX//foZRpCP3e2/Hjh3o3Lkz1qxZg6CgIDg6OiIsLAyFChWCk5MTFi1ahLS0NNjb27OpOf09b33BIL1zZs6cKaamppKYmCgiL3sh5OTkSIsWLWTlypWyd+9eMTMzE39/fxF52Svq22+/lQ0bNui8DvtqUF65z8Pu3bulTp06smbNGvVco0aNpE6dOnL58mV59uyZZGRkyJQpU6RChQpy7do1PVVMBUFISIiYmJjIJ598Ig0aNJASJUrIDz/8ICIi6enp4u3tLXZ2dlKpUiWxtLSU48eP67liys9CQkLEzMxMvvnmGzl69Ki4uLhIkSJFdL7f7ty5I6NHj5YRI0bI+fPn9Vgt5UfdunWTadOm6Rw7cOCAzJ49W0REfv75ZylVqpT4+fnJuXPnpFSpUlKvXj357bffdO5hzx/K649jag8PD7GwsJCpU6fKyJEjpVq1avLZZ5/Jvn37RETk9OnTUr16dfnwww91+nVybE5/dOTIESldurSsWrVKRF72J1MURYyMjGT79u0i8rKP4u7du6Vp06Ycl9PfwlCK/mdHjx6Vdu3ayYcffqgOkrKzs2XKlCnSqlUrKVq0qKxYsUK9/vbt2/Lpp5/K8uXL9VUyFRA//fSTmJiYyLJly3T+MRcfHy8tWrQQCwsLsbOzk5YtW0rp0qXZPJH+0r1798TPz08dSCUmJsrkyZNFURTZuHGjiLzcKe348eMSFBT0yj/6iPK6fv26ODo6yrfffisiIklJSVKlShWpWbOmGBoaytq1a3UaVuc2EybKKzIyUm2Ef/v2bfX4zZs35cWLF9K6dWuZOnWqiIg8efJEmjVrJhqNRjp37iwiDA3oz+V+/sTFxYmNjY3OhkPh4eHSuXNn6dq1q9y4cUO0Wq1ERUVJ7969GXDSX5o1a5Z4eXmJyMvm+BUrVpQhQ4bIsGHDxNDQUPbu3SsiL4Op3M0+iP4/DKXov/bjjz+q/x0dHS2ffPKJVK5cWa5evSoiIjExMVKuXDlxcHCQCxcuSE5OjiQnJ0uHDh2kadOm/NKjv5SWliaffPKJeHp6/uk1gYGBMn/+fPHx8VGfO6LXOXfunBQtWlRq1qypDphEXu5slRtM5c6YIvozuQHAw4cPJTMzU+bOnSsPHjyQW7duSY0aNcTFxUUyMzOlZ8+eUqJECfn+++8ZGtDfsnz5cunevbvOjyvXr1+XSpUqyf79+0VE5MGDB9K7d2+JiYnRCTyJcrm7u8uUKVN0jl24cEHKli0rhw8f1jl+8OBBKV68uPp85cUxOuXK/Q7bv3+/XLhwQeLj4yUyMlKePn0qTZs2lS+//FJEXu62riiKKIoiwcHB+iyZCiD2lKL/yr59+9C7d294e3sDABo2bIi5c+fCxsYGrVu3RkJCAho0aIBNmzbh1q1bGDBgAGxsbNCzZ0/cvXtX3ZEhtwcV0R9lZGQgPj4etWvXBgCdNenyn/5SX3zxBdzd3TFmzBjuYkV/ycjICD169MDVq1fx8OFDAC+fo1KlSsHd3R0eHh4YMGAAtm/frudKKT9TFAVBQUGoX78+nj9/jiFDhqBEiRLw8fFBlSpVsHjxYhgZGaFChQrQaDRwd3fHkydP9F025UPyh32GKlSogOPHj8PX1xdnz54FAFSsWBFlypSBh4cHtm7diu7duyM5ORkODg46fTyJAODx48dISUnBoUOH8M0336jHs7OzoSgKkpOT1T8DQKtWrVCxYkVER0e/8lrsTUa5FEVBREQEunbtitjYWFStWhXNmjXD5cuXkZmZifHjxwMAihUrht69e8PT0xNVq1bVc9VU0DCUov+Ko6Mj/P39MXv2bMyZMwfAy2DK29sb1apVwyeffIL4+Hh8/PHHCAsLg5eXF0aMGAEPDw8cP36cDTnp/2Vubg4LCwucPHkSAHQG4CdPnsTq1avVa/84uCf6o+rVq2Py5Mno1asXXFxcEBYWpjZwtbCwwIQJEzB9+nTY2trquVLKz5KTk7Fx40ZMnDgRRYsWhaWlJQDg0qVLKFeuHIoWLQrg5WYf69atw5UrV1CsWDF9lkz5VO7nz4kTJ5Ceno6uXbti9erVOHDgAJYsWYK4uDgAwPLly1GoUCHMmTMHhQoVwsGDB6HRaKDVajmGIh3FihXDwoUL0ahRI4SGhmLu3LkAADs7O3z++ecYPnw4jh8/DkNDQwDAo0ePICL44IMP9Fk25XPXr19HaGgovLy80L9/f/X4w4cPcfr0aaSnpwMAfvjhBzx8+BBTpkzhWIr+Ob3O06IC7cWLF+Lv7y8GBgZqQ06Rl0v52rZtK9bW1pKQkCAir/Y84LRgyiv3+cjKypL09HT1+IQJE6Ru3bpq48Rcbm5u0rRpU3n48OHbLJMKiNznKTExUS5fvixnz55Vz126dEkGDx4sJUqUkAMHDuhcz+Uw9FdOnjwp/fr1k3bt2snt27d1vsemT58uRYoUkXnz5omzs7OUKFFCLl++rMdqKb/K+zkTEhIidnZ2snjxYsnIyBARkT179kjFihVl0KBB8uuvv6rXJiUl6XxXEuWV9/PowIED0qtXL6latara805EpE+fPmJsbCxubm4ya9Ysadu2rdSpU4fPE/2pX3/9VZo1aybW1taycuVKEfn9WXvx4oX06tVLFEWRBg0aiLm5uZw5c0af5VIBpohwigH9ffKfrUBzZWRkIDAwEGPHjsXMmTMxbdo0AEBMTAymTZuGq1evIjQ0FDY2NvoqmfK53GcqNDQUGzZsQFxcHD777DN06NABzZo1Q7du3fDgwQPUq1cPdnZ2iImJwY4dO3D06FHY2dnpu3zKZ3Kfp927d2PatGl4+vQpTE1N0bZtWyxZsgTAy1kt33zzDUJDQ7Fu3Tq0b99ez1VTQTB79mysW7cO6enpuHLlCszNzZGVlQUjIyM8e/YMnp6eCA8PR4kSJbB06VLUq1dP3yVTPqPVaqHRvFyksHHjRpw9exarVq1CiRIlMHHiRAwdOhQmJiYIDQ3FqFGj4OTkhFGjRqFBgwavfQ2iP5o4cSLi4uKg0Whw5swZmJqaYuTIkXB3dwcAfP311zh48CAyMzNhbW2N77//HkZGRsjJyeHMO3qtsWPHYsOGDWjdujXWrl0Lc3Nzdax1//59hIaG4vHjx2jfvj2X7dF/T5+JGBUseX/dy8rK0pn95OPjIxqN5pUZUw4ODvL555+/1Tqp4Mh9hnbv3i2mpqYybdo02bBhgzg6OkrlypXl2rVr8ujRI/Hy8hJHR0epU6eOdOnSRWfmC9EfhYaGipmZmfj5+cmVK1fEz89PFEWRESNGqNdcunRJevToIZUrV5Znz56xGTX9vzIzM2XRokVSrlw5GThwoDx69EhEdGcCP3jwQJ49e6avEqmA8PLyUhvhr1+/Xlq0aCF169aVJUuWyPPnz0Xk5edYoUKFxNvbW8/VUkGxZcsWKV68uERHR8vz58/lzp07MmjQIKlfv74sWLBAve7x48c6M6s4U4py/dlYaOLEiVKzZk2ZM2eOPH78+C1XRe8DzpSivyXvL3NLly7FmTNnkJCQgO7du6Nr166oUqUK/P39MW7cOMyYMUOdMXXx4kVUr16dv+qRKjQ0FOXLl4ednR1EBPfu3UPPnj3RtWtXuLq64vnz56hUqRIGDBiARYsW6Tw7aWlpMDY2hrGxsR7fAeVn9+7dw9ChQ/Hxxx/jq6++QkpKCpo1a4Zq1aohMjISffr0UfuRxcfHw8zMDFZWVnqumvIb+c+vwHfu3FFnQlWoUAHZ2dn49ttvsWPHDjRq1Ahff/01zM3NkZ2drfZpIfozIoLk5GS0bt0a06ZNw4ABAwAAz549w7BhwxATE4Px48erM6aioqLQqFEjzmChv2XBggXYvHkzYmJi1M+jmzdvYuTIkTh16hQmT56MCRMm6Nwjf1gBQe+v3GchOjoaUVFRMDY2RpUqVdCuXTsAgKurK44ePYpu3bph7NixKFq0KGdu0r+GTxH9LbkfOB4eHpg7dy4aNmwIJycnrF69GsOHD0d6ejpcXFzg4+MDb29vuLm5AQBsbW3VhpxEd+7cwZgxY7B06VJcvHgRiqLA1NQUaWlp6NChAxITE2FjY4OuXbti8eLF0Gg02LdvH+Lj4wEAZmZmDKRIh4ioje7j4+NRqlQptGnTBp07d0Zqairatm2Ldu3aITg4GBMmTEBgYKD6D0EbGxsGUvSK3IH5rl270KFDBzRu3BitWrWCt7c3DA0NMWnSJHTt2hUnT56El5cXnjx5wkCK/lTe334VRUGRIkWg0WjU5sDZ2dkoUqQI1q9fD41GA39/f6xatQovXrxA06ZNuVMx/b9yx9hlypSBVqtVd9nTarUoX748PD09kZ6ejuXLl2Pt2rU69zKQolyKomD79u345JNPsHXrVvj7+6Njx47qv+mWLl2Kpk2b4qeffsI333yDp0+fMpCifw2fJPp/5Q6ooqOjERwcjJCQEIwePRotWrRAYmIi+vfvD1NTUxQqVAgjR47EnDlzEBUVpTMQ44cWAUDZsmWxbds2nD9/HosXL8b58+dhYGCA58+fIzw8HG3btkWHDh2wYsUKAMC1a9ewdu1aJCQk6Llyym+ePn0K4OUgSlEUBAcHo2XLlvj1118xfPhwVK1aFVu3bkXZsmUxa9YsFCpUCOXKlUP9+vURFRWlDtqJ/khRFBw4cAB9+vTB4MGDMWvWLIwdOxazZs2Ci4sLDAwMMGnSJHTu3Bn79++Ht7c3dwCl18o7C+XevXsAACMjIxQtWhQHDhwAABgaGiInJweGhoawt7eHsbExtm3bhqNHj6qvw5lSlNcff+jNHWM3bNgQiYmJWLZsGdLT09XjWVlZ+OijjzBhwgQ4Ozu/9XqpYIiPj8eYMWMwf/58HD16FIcPH8batWvh6+sLDw8PAICPjw9q1aqFqKgoZGZm6rlieqfoYckgFQAzZsyQkJAQnWOHDh0SW1tbERHZtm2bmJuby4oVK0REJC0tTXbt2iXPnj2TnJwcdU0y+7TQ65w+fVocHBzExcVFbt26Jb6+vqIoinz66ac613l6ekrt2rUlKSlJT5VSfjRs2DD54osvJDMzU0RErl+/Lr1795aAgACd60aOHCmNGjVS/zx58mSZN2+ezg6PRHnlfmeNHDlS+vXrp3Pu0KFDotFo5JtvvhGRlzsPffvtt5KYmPi2y6QCIG8fzp07d0rr1q3lwoULIiJy4sQJMTU1lTFjxqg9OrVarfTt21dCQ0PF3t5eevbsqa/SKR/LO64OCAiQr776SqZPny7Xr18XkZfjcwMDAxk+fLjs2bNHLly4IO3bt5eRI0eq93IHbHqdY8eOSfXq1eXmzZs6x9etWyeFCxeW8PBw9djt27ffdnn0juN8c3rF+fPn8csvvyAyMhImJiZo06YNgJe/+FlYWGDz5s0YMWIEvvnmG4wYMQIAcPz4cezevRu1atVSd14QrlOnP2Fvb4/Vq1djyJAhmD59Ovr06YOJEydiyZIlWLhwIQAgMTERGzduxJEjR1ChQgU9V0z5xZYtW7Br1y7s378fRkZGiI2Nhb+/P5KTk+Hk5ATg9x54Xbt2xZo1a9CtWzcYGxtj3759iIqKQuHChfX8Lii/yf2+Sk9PR5EiRZCYmIgSJUqo57KysuDo6Ig5c+bghx9+wKBBg1C2bFl89dVXeq6c8qO8fVYOHjyI7du34/Tp05g5cyZmzZqFRo0aYePGjejfvz9iY2NRtmxZ3Lp1Cw8ePMCmTZtw7NgxHDp0iP1aSEfe58HDwwOBgYGoW7cuUlNTERgYiAMHDqBHjx4IDg7G5MmTsWfPHhgYGKBUqVIIDg6GoigQEc68o9cyMjJCfHw84uPjUa5cOfV70cnJCVZWVkhJSVGvLVu2rB4rpXcRv+noFbVr18bcuXNhYmKCBQsWYN++fQCAVq1a4fHjx+jfvz++/vprjBw5EgCQkZGBxYsX4+nTp6hSpYr6Ogyk6K/Y29sjMDAQsbGx2Lp1K9q2bYulS5di3bp12L59Ox49eoRjx45xW3XScePGDVhYWKBevXrYu3cvBg0ahIiICJw8eRKJiYkAfl/K0KxZM6xZswbPnj2DRqPBkSNHYGtrq8/yKR/KHXgfOHAA06dPR1JSErp06YJDhw7h5MmTUBQFRkZGAIASJUpAURQULVpUz1VTfpb7GfTVV19h7NixKFmyJD766CMcOXIEXl5euHTpErp164a4uDjUq1cPRYsWRePGjXH+/HkALzeJqVKlCpeFko7c5yo1NRXp6enYt28ffvnlF2zatAl2dnZo0qQJLl26hE8//RT79+9HWFgYgoKCEB0dDSMjI2RnZ3NsTgB+b81y8eJFREREIDExEQ4ODujUqRP8/Pxw5swZ9VkpXbo0ihcvzuV69EZx9z3SkZWVpQ6+t2zZgg0bNiAtLQ0zZ85Eq1atcPHiRXTp0gUWFhYYPnw4srOzERQUhNu3byM2NhaGhob8ZY/+kdOnT2P48OGoV68eZs+eDUtLSyiKgoyMDJiYmOi7PMpnYmJiMHDgQHzwwQc4fPgw9u/fj6ysLEyaNAlVqlTB9OnT0aBBA517tFotsrKyUKhQIT1VTfndjh07MGDAAEyZMgWffvopTExMMGXKFOTk5GD27NmoX78+AGDSpEk4deoUgoODYW5urueqKT87fPgwevfujZ07d6Jp06YAgNWrV2Pt2rUoW7YsvL29YWtri5ycHHXmSmpqKhYuXIi1a9fi8OHDqFmzpj7fAuVDGzduxMiRI1GzZk1s27ZNnUmekJCA8ePHIyoqClFRUahevbrOfXmfMyIA2LVrFwYOHAhLS0vcuHEDq1evxvPnz7F582YULVoUw4cPh7W1NdatW4c1a9bgxIkTsLa21nfZ9K7S07JByudmzJghvXr1Ejs7O9FoNNKiRQs5cOCAiIjEx8dL69atpXbt2tK8eXMZNGiQ2tuF69Tpv3H69Glp2LCh9O7dW86fPy8i7EdGf27UqFGiKIo0btxYPbZp0yZp0KCBDBw4UE6dOqUez9vXheh1Ll++LJUrVxZ/f3+d47t27ZJOnTqJhYWFfPrpp9KuXTspWrSoxMbG6qdQKlDCwsLEwsJC/U7LtXz5cilUqJD06NFD7TElInLjxg35+uuvpVq1anzG6E8dPHhQ2rVrJ2ZmZmofqdzxUkJCgnTq1EkURZEbN27os0zKx3JycuT+/fvSvHlzWblypcTHx8ucOXPE0NBQ/Pz85LvvvpPevXuLRqORGjVqSNWqVeX06dP6LpvecZwpRa/w9/eHh4cHQkJCULVqVURFRcHX1xcajQZeXl5q35a7d+/C1NQURYoUAfByW2Nui03/rZiYGEyePBmbN2+GlZWVvsuhfOr58+fo2LEjqlSpgmPHjsHOzg6bN28GAGzatAlLlixB7dq1MXLkSDRq1EjP1VJBcODAAYwePRr79+9HpUqVdGb7Xrp0CadOncL+/ftRvnx5DBw4EDVq1NBzxZSfyX+Wgx4/fhz9+/fH8uXL8dlnn6nPVU5ODuzs7GBqaopatWph3rx5sLKyglarRUpKCgwNDdmvhQDgtSsPRAQnT57EyJEj8eTJE0RGRqJ06dLqc3f58mWsXr0a8+bN45icdOQ+IxkZGRAReHt7Y9KkSWr/xCVLlsDNzQ2LFi1C37598fTpU2RmZsLCwgJlypTRc/X0ruOnFb0iOjoanTt3RsuWLQEAn3/+OczMzDBx4kRMnz4dGo0Gjo6OKF26tHqPiPDLj/4nDRs2xN69e7lkj/5S4cKFERISAlNTUwQGBmLBggXo168fNm3ahH79+qnhuYmJCerWrcsle/T/SktLw/Pnz3WO5S51uX37Npo3b47+/fvrqTrK7/4YHOT2YWnSpAmqVauG8ePHo0KFCrCzswMA3L59G3Xq1IGtrS3Wr1+PX3/9FVZWVtBoNChXrpxe3gPlP3mfq507d+LWrVvQarX45JNP0LBhQ6xatQrjxo2Do6MjDh06hDJlykBEUL16dXXDGP5YTHkpioLdu3djxYoVuHHjBrRaLXr37q2GUhMmTICiKHBzc0Nqaio8PT3ViQdEbxob/9ArSpYsifv37+sM0tu3bw9nZ2ecOnUKrq6uOHHihM49bJxI/wYGUvR3mJqaAgB69eoFd3d3xMbGol+/fgCAPn36YP78+XBzc2MgRX9L3bp1ce/ePaxatQrAy2bCub1Xdu3ahTVr1rDBK71W3uBg69atmDFjBnx8fHD48GEAQGhoKEqXLo3OnTvj66+/xtq1azFo0CCkpaVhxowZEBH8/PPP+nwLlE/lPldubm4YPXo0wsPDERgYiH79+iEwMBAODg5YsGABLCws0KZNG9y+ffuVsTgDKcrr5MmTcHZ2RuXKldGoUSNcvXoVgYGBuH79unqNq6srZs+eDX9/f2RkZOixWnrfMJSiV9jZ2eHYsWM4cOCAzs4vZcuWRbNmzfD555+jYcOGeqyQiAgwMzNDr1694ObmhnPnzqFjx44AXs7urFy5sp6ro4KicuXK8PX1xcKFC+Hm5obz58/j4sWLcHd3x7p169C3b18YGxvru0zKZ0REJzhwdXXFqVOnsHPnTkyePBkbNmyAoiiIiopCmzZtsGfPHsybNw/GxsbYunUrAMDKygrVqlXT59ugfGzz5s3YvHkzgoODsXXrVowbNw4XLlxA8eLFAbzcYXbRokXIzMzEpEmT9Fss5WtXr15FSEgIpkyZghUrVmDNmjVYtmwZtm/fjoCAAJ1gyt3dHb/99hssLCz0WDG9bxih0ysGDx6MyMhIDBgwAAEBAXBwcIClpSV27NgBJycneHp6QlEU7rJHRHpXpEgR9OrVCxkZGVi7di2Sk5O5BIb+scGDB8Pc3BzDhw/H5s2bYWJiAgMDAxw8eJA9pOgVecc/fn5++PHHH7F9+3Y0adIE/v7+mDBhAmbMmIH09HQMHz4cq1evxqNHjyAi6lKZ6dOnIzExEa1bt9bnW6F8LCEhAR9//DEaNGiArVu3wtXVFcuWLUP37t2RlpaG1NRUNGrUCNu2bYOtra2+y6V86smTJ+jTpw+uXbuGL7/8Uj0+cuRIaLVazJs3DwYGBnBxcVF/0MsNPoneFjY6Jx15B1pjxozBrl27kJOTA3NzcxgYGODcuXMwNDRUm+UREeUH6enpyMrKQrFixfRdChVgt27dwvXr16EoCipXrsyG0/SKvOOfJ0+ewNPTE9bW1pg0aRKCg4Ph7OyM8ePHIz4+HkeOHMH8+fMxYMAA9f74+HhMnz4dhw8fxp49e2Bvb6+vt0L5yOt+6PXw8ICBgQE6deqETz75BAsXLsSIESMgIli7di0ePHiAcePGwcjICMDvvfCI/ig2Nha9e/dGmTJlEBAQgNq1a6vnAgICMGHCBEyZMgWenp5c9kl6wVCKXpF3wBUVFYV79+7h2bNn6NmzJwwMDPilR0RERO+dQ4cO4datW+jfvz+GDx+OEiVKYPz48Xj+/DlycnLw6aefYvTo0XB1dcXOnTvRt29fGBkZYf369ejWrRsAICMjA7/88gtsbW1RtWpVPb8jyg/yBlJXr15F4cKFUbp0acTExKBFixYAgKCgIPTs2RMA8OzZM3Tv3h21a9fGt99+q7e6qWA5e/YsBg0ahEaNGmHcuHGoVauWeu7777/Hxx9/DBsbGz1WSO8zhlL0Wn+2NI+BFBEREb1PRARpaWno0aMHMjMzUbRoURw+fBgRERHqrnobN26Ej48P9u/fj2LFimH//v1YuXIlOnTogC+++IJjJ3qtvD8Ee3h4YPfu3bh79y5q1aql9rMbNWoUAgMD0bx5czx58gSTJ09GamoqoqOjOauF/pHY2FgMHToUDg4OmDBhAmrWrKnvkogAsNH5e0Or1b72+J9lkrmB1B/v46CKiIiI3ieKosDc3BxbtmzB7du38dNPP8HT01MNpADAyMgISUlJiIiIQHp6Onx8fGBtbQ0XFxd1ljlRXlqtVg2ktmzZgnXr1mH+/Pn49ttv0bhxY7i6uiImJgYLFiyAi4sLmjZtCmdnZ2RmZuLEiRMwNDTkc0X/iL29PVavXo2zZ89izpw5uHTpkr5LIgLARufvhbyzns6fP4/09HSUKVMG1tbWUBTlT2c/5d1Z5tKlS6hYsaK6FTsRERHR+0Sj0eDDDz9E2bJlERYWhvLly6N///4AgJo1a+Ljjz+Gs7MzihcvjiJFimDHjh1QFAUiwh/16BW5Y+zw8HCEhYXBzc0NXbp0AfCyX5m1tTU8PDywefNmXLhwATdu3EDRokVRt25daDQaZGdnc6YU/WP29vbw9fXF5MmT2YeT8g0u33vH5Z0WPHXqVAQHByMpKQmNGzdGo0aN4O3tDeDVZXl57/Px8cG8efNw7NgxWFtbv/X3QERERJRf3L59Gy4uLnj+/DlcXFzUYOry5cu4dOkSnj59ir59+8LAwIDBAf2l27dvo0WLFkhNTYW7uzumTp2qnrt//z5cXFxQoUIF+Pj46NzHHbDpf5WRkQETExN9l0EEgMv33nm5wZK3tzdWr16NZcuWISEhAeXKlYOvry/GjBkDADpTy/MGUitXrsTMmTOxePFiBlJERET03rO0tISvry9MTU2xbt06BAYGIicnB6NGjcK5c+cwYMAAdVzFQIr+iqWlJXbs2IEyZcpgx44diI2NVc9ZWFigVKlSuHr16iv3MZCi/xUDKcpP+In2jso7Ae7XX3/Fzp078cMPP8DJyQlxcXH48ccf0bZtW+zduxeurq4AXgZTWVlZOoGUm5sbVq1ahT59+ujjbRARERHlO5UrV4aPjw/Mzc2xaNEi2NjYIDU1FW5ubuo1XLJHf4ednR127NiBnJwcLF26FGfOnAEAPH36FBcvXkT58uX1WyAR0RvG5XvvoLwzneLi4mBnZ4fVq1eje/fuOH/+PPr06YM5c+ZgyJAh6NixIw4ePIju3btj06ZN6musWrUKbm5u+P7779GjRw99vRUiIiKifCslJQWnTp3CnTt3MGjQIBgaGnLJHv1XYmNjMWDAADx48AANGjSAsbExEhMTcfz4cRgbG+uM74mI3iUMpd4xf9xa9vjx49iyZQvKli0LRVEwcuRIGBoaYvHixTAyMsLkyZMRExODmjVrwtfXFxqNBsHBwejatSu2bduG7t276/kdERERERUMf7Z5DNHfcf78eXTu3Bnly5dHv379MGLECABAVlYWjIyM9FwdEdGbweV775jcQOrSpUuIiorC3LlzYWlpqR5PTEzEzZs3YWRkhJycHFy/fh0DBw6En5+fuj69Y8eOOHToEAMpIiIion+AgRT9L2rXro0dO3YgMzMTp0+fRkJCAgAwkCKidxpnSr2D5s2bh/DwcJiYmGDjxo0wNzeHVqsFACxduhQbNmyAlZUVnjx5gkePHiEuLg4GBgYQETblJCIiIiLSo9jYWIwYMQJVqlTBjBkzUKNGDX2XRET0xnCm1DvI1tYWv/zyC44ePYpr164BeLlLh0ajQd++feHs7IxixYqhVq1aiI2NVXeIURSFgRQRERERkR7Z29vD19cXKSkpKFasmL7LISJ6ozhTqoD7s6aHYWFhaNu2LQYPHqwu4fszbMhJRERERJS/ZGRkwMTERN9lEBG9UZwpVYBptVo1kEpNTUVSUpJ6rnXr1ti1axfWrl0Lb29v3LlzR+e+XCLCQIqIiIiIKJ9hIEVE7wOGUgWUVqtVG5PPnj0bHTp0QMOGDdG+fXuEh4cjIyMDnTp1wq5duxAQEIC5c+ciJSUFANT7AHBrWSIiIiIiIiLSC4ZSBZCIqMHSjBkzEBAQAFdXV0RFReG3336Dl5cXQkJCdIIpX19fbN68Wc+VExERERERERG9xHVbBcjFixdha2ur/jkyMhK7d+/Gxo0b4eTkhIiICCQnJ0NE4OXlBQMDA3z66afo2LEjIiIi0LhxYz1WT0RERERERET0O86UKiAWLVqkBk+KokBEUKJECYwZMwZOTk4ICwtD9+7d4efnh/j4eGRkZGDx4sUICgpCZmYmmjdvDkNDQ2RnZ+v7rRARERERERERMZQqKOrUqYOPP/4Y48ePV4MpGxsbdOrUCVlZWVi6dCmGDRsGZ2dniAhsbGwQFxeHyMhIGBsbq6/DpuZERERERERElB8wlMrnvvvuOwBAu3btMGrUKFStWhVjx47FkSNHYGRkhLJlyyIzMxP37t2DhYWF2muqYsWKCA8PR0BAgD7LJyIiIiIiIiJ6LU6byccOHDiA4cOHIy4uDr6+vmjZsiVEBP7+/hg3bhx8fHzw0UcfQaPRwNDQENu2bcOTJ08QERGB+/fvw97eHhqNBjk5OTAwMND32yEiIiIiIiIiUnGmVD7WsGFDrFq1Ctu2bcPo0aMBAI6Ojhg1ahSqVauGsWPHIjw8HIULF8b27dthamqKyMhImJub4+TJk9BoNNBqtQykiIiIiIiIiCjfUURE9F0E/bmnT59iy5YtmDp1Knr27Ak/Pz8AQHh4OPz9/XHlyhUsXrwYTk5OyMjIgIjAxMQEiqIgOzubPaSIiIiIiIiIKF9iYpEPiQgURQEAmJubo2fPngAAT09PAICfnx8cHR0BAP7+/pg8eTLmzZuHtm3b6rwGAykiIiIiIiIiyq+YWuQzWq1WbVau1WqRnZ2N4sWLY9CgQQCAKVOmAPg9mFIUBXPmzMGmTZt0QqncUIuIiIiIiIiIKD9iKJWP5A2kvv32W8TFxeH06dMYPnw4WrVqhWHDhgEApk6dCkVR1ObnRYsWRd26dfVZOhERERERERHRP8KeUvnQlClT8P3332P69OlIS0vD6tWrUaNGDWzZsgU5OTnYunUrvLy80Lp1a/zwww/qfXlDLSIiIiIiIiKi/IwJRj4THR2NXbt2ISQkBGPGjEGLFi2QlJSEXr16wczMDMWKFcPAgQMxZcoUPHr0CFqtVr2XgRQRERERERERFRRMMfIZrVYLExMTNG7cGD/++CM6dOiA5cuXw9nZGc+ePUNoaCgA4Msvv8RPP/0EjUajE0wRERERERERERUEDKX06HVhUlpaGjIyMrBlyxZ8+eWXmD9/PkaMGAEAOHbsGDZt2oSkpCQULlwYiqJARDhDioiIiIiIiIgKHPaU0pO8/Z8CAgIAQA2f2rVrh19++QU+Pj4YPXo0ACAjIwOff/45ChcujKCgIAZRRERERERERFSgcfc9PckNlSZPnoygoCAMGjQIN2/eRPny5fH111/j8ePHWLJkCYoVK4aHDx8iJCQEt27dwpkzZ9QlewymiIiIiIiIiKig4kwpPdq4cSO++uor/Pzzz6hfv756XKvV4tKlS5g9ezbi4uJQpkwZ2NjYYMWKFTAyMkJ2djYMDZknEhEREREREVHBxVBKjzw9PZGcnIx169YhJycHBgYGrwROd+7cgYWFhXqMgRQRERERERERvQu4/kuPkpOTkZiYCAAwMDCAiMDQ0BAZGRk4cOAAAKBs2bJqCJV7noiIiIiIiIiooGMo9Ra8bpc9ALC3t8edO3dw6NAhZGZmQlEUAMCTJ08wa9Ys/PzzzzrX554nIiIiIiIiIirouHzvDcvbkDwmJgZarRYGBgZo0KABXrx4gebNmwMApkyZgubNmyMtLQ2urq54+PAhjhw5AgMDA32WT0RERERERET0RjCUeoNERJ3d5O7ujs2bN0NRFNy5cwd9+/bFggULYG5uji5duiA5ORkJCQmoWbMmjIyMcPToURgZGam9poiIiIiIiIiI3iUMpd4CX19fzJo1C7t374aFhQVu3LiBgQMHonHjxvjhhx9gbGyMX3/9FZcvX0bZsmXRokWL1zY9JyIiIiIiIiJ6VzCUegsGDRqEwoULIyAgQJ09debMGXz88ccYO3Ys5s6d+8o9nCFFRERERERERO8yNjr/l/0x48vKykJycjIyMjLU85mZmahXrx5mzpyJrVu34uHDh8jJydG5j4EUEREREREREb3LGEr9i7RardpD6rfffkNqaiqMjIzg7OyMbdu2ISwsDBqNBkZGRgCAQoUKoVSpUihSpAhDKCIiIiIiIiJ6rzCU+hfl7rLn6emJzp07o2bNmnBzc4OZmRmGDBmC0aNHY+/evdBqtXj8+DF++uknlCtXTg2piIiIiIiIiIjeF+yi/S/QarVqILV161asX78evr6+OHv2LPbu3YukpCQ0adIEnTp1QseOHVGlShUYGBigUKFCiImJgaIoOjv1ERERERERERG969jo/F905MgRbN++HXXr1sWQIUMAAMHBwfDx8UGJEiUwbNgwlClTBidOnICZmRl69+7NXfaIiIiIiIiI6L3EUOpfcvv2bbRo0QJ3797FrFmz4Orqqp4LCQnB0qVLUbRoUUyZMgWNGjVSz3GXPSIiIiIiIiJ6H7Gn1L/E0tISO3bsgKWlJUJDQ3Hu3Dn1XKdOnTBx4kQkJCRg586dOvcxkCIiIiIiIiKi9xFnSv3L4uLi8MUXX6BBgwYYP348atWqpZ47duwYGjduzCCKiIiIiIiIiN57DKXegNjYWAwdOhT169eHq6sratasqXOeS/aIiIiIiIiI6H3HUOoNiY2NxfDhw1GpUiUsWLAAlStX1ndJRERERERERET5BntKvSH29vbw9fWFubk5KlWqpO9yiIiIiIiIiIjyFc6UesNEBIqiQKvVQqNhBkhEREREREREBDCUeitygykiIiIiIiIiInqJU3feAgZSRERERERERES6GEoREREREREREdFbx1CKiIiIiIiIiIjeOoZSRERERERERET01jGUIiIiIiIiIiKit46hFBERERERERERvXUMpYiIiIgKGEVRsGvXLn2XQURERPQ/YShFRERE9AZ06tQJ7du3f+25iIgIKIqCs2fP/levnZKSgg4dOvzt6wcPHoyuXbv+V38XERER0ZvCUIqIiIjoDXBxccEvv/yCmzdvvnJuzZo1aNCgAezs7P7Ra2ZmZgIALC0tUahQoX+lTiIiIiJ9YShFRERE9AZ07NgRpUuXxtq1a3WOp6WlYevWrejatSv69u2LcuXKwdTUFHXq1MHmzZt1rnV0dMSYMWPg6uqKUqVKoV27dgBeXb5348YN9OrVC8WLF0fJkiXRpUsXXLt2DQAwc+ZMrFu3Drt374aiKFAUBeHh4XBycsKYMWN0/r67d+/C2NgYYWFh//r/DyIiIqI/YihFRERE9AYYGhrC2dkZa9euhYiox7du3YqcnBwMGDAA9evXx549e3D+/Hl8+eWXGDhwIKKjo3VeZ926dTA2NkZkZCQCAgJe+XuysrLQrl07mJubIyIiApGRkTAzM0P79u2RmZmJSZMmoVevXmjfvj1SUlKQkpKCZs2aYejQodi0aRNevHihvtbGjRtRrlw5ODk5vbn/MURERET/wVCKiIiI6A0ZMmQIrl69isOHD6vH1qxZgx49eqBSpUqYNGkS6tWrhypVqmDs2LFo3749fvzxR53XsLGxwYIFC1C9enVUr179lb8jKCgIWq0Wq1evRp06dWBra4s1a9YgKSkJ4eHhMDMzQ+HChVGoUCFYWlrC0tISxsbG6N69OwBg9+7d6mutXbsWgwcPhqIob+j/CBEREdHvGEoRERERvSE1atRAs2bNEBgYCABISEhAREQEXFxckJOTgzlz5qBOnTooWbIkzMzMsG/fPiQlJem8Rv369f/y74iLi0NCQgLMzc1hZmYGMzMzlCxZEhkZGbh69eqf3mdiYoKBAweqtZ0+fRrnz5/H4MGD/7c3TURERPQ3Geq7ACIiIqJ3mYuLC8aOHQs/Pz+sWbMGH374IVq2bIlvvvkGy5Ytw9KlS1GnTh0UKVIErq6uajPzXEWKFPnL109LS0P9+vXxww8/vHKudOnSf3nv0KFDUa9ePdy8eRNr1qyBk5MTKlWq9M/fJBEREdF/gaEUERER0RvUq1cvjB8/Hps2bcL69esxcuRIKIqCyMhIdOnSBQMGDAAAaLVaXLlyBTVr1vxHr+/g4ICgoCCUKVMGRYsWfe01xsbGyMnJeeV4nTp10KBBA3z33XfYtGkTfH19//kbJCIiIvovcfkeERER0RtkZmaG3r17Y8qUKUhJSVGXx9nY2OCXX37BsWPHcPHiRQwfPhx37tz5x6/fv39/lCpVCl26dEFERAQSExMRHh6OcePG4ebNmwAAa2trnD17FpcvX8a9e/eQlZWl3j906FDMnz8fIoJu3br9K++ZiIiI6O9gKEVERET0hrm4uODhw4do164dPvjgAwCAl5cXHBwc0K5dOzg6OsLS0hJdu3b9x69tamqKI0eOoGLFiujevTtsbW3h4uKCjIwMdebUsGHDUL16dTRo0AClS5dGZGSken/fvn1haGiIvn37wsTE5F95v0RERER/hyJ59ygmIiIiovfKtWvX8OGHHyImJgYODg76LoeIiIjeIwyliIiIiN5DWVlZuH//PiZNmoTExESd2VNEREREbwOX7xERERG9hyIjI2FlZYWYmBgEBATouxwiIiJ6D3GmFBERERERERERvXWcKUVERERERERERG8dQykiIiIiIiIiInrrGEoREREREREREdFbx1CKiIiIiIiIiIjeOoZSRERERERERET01jGUIiIiIiIiIiKit46hFBERERERERERvXUMpYiIiIiIiIiI6K1jKEVERERERERERG/d/wGlU1SEziQW4AAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_water_efficiency_l_oil_m³_water.png\n", + "Plot salvato come: .//2024-12-06_10-36_plots/efficiency_vs_production.png\n" + ] + }, + { + "data": { + "image/png": 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5eHioe/fuVz3Wnj17KiUlxXyJsHTxMvL8/Hz17NlT0sXvhKurq+Li4iwu172W8+fPS5J8fHyu2s/Hx0dpaWlWj1tQd25urr755htz288//6xz586Z6y7OZ1KUn3/+WYGBgQoMDFSjRo20cOFC9enTR++9955Fv8u/y8nJydq6dav69+9vcYVGw4YNdc8992jp0qWSLl45sXz5cnXt2lVVq1Y196tfv775jLyt8vPz9e233+rBBx9Us2bNCq0vzuOkli5dqooVK1r8rLm4uOiFF15Qenp6odsOevbsaXGf/eXf72sZNGiQxRnvwYMHq0yZMub3zcnJSb1799b3339v/q5JF7/7rVq1uuLVEwUaNGigJk2aaP78+ea2jIwMff/993rggQfk6+sryfLvxL///lunT5/WP/7xD0kq8vtjzWR433zzjfLz8/XYY49ZfC8rVqyo2rVrF/kUiMtFRkaqUqVK+uKLL8xt27dv159//nnNeSsA4HoQsgHcdkwmk1q1amW+9zo+Pl4VKlQwz6Z9acgu+O+lITs+Pl6RkZHm+0cDAwP16quvSpJVIXvfvn1atmyZOZQULAWzfBdMPlbgWv8QvlxAQIAiIyMLLUXNEFxcBw4cUN26dW2aqOjUqVM6d+6cZs2aVejYo6KiJBU+9ksDlSRzICkIsgX3fxdcsn0l99xzj4KDg83/2M7Pz9d///tfPfzww9cMuPfdd5/8/Py0YMECc9uCBQvUuHFj1alTR9LFScfee+89/fTTTwoKCtLdd9+t8ePH68SJE1cdu2Dflwagopw/f/6adV6uUaNGqlevXqG6AwIC1KFDB0nF+0yKUvCLnZUrV2rdunU6ffq0Pv30U4vwJRX+Lh85ckSSirw3uH79+jp9+rQyMjJ06tQpZWVlqXbt2oX6Xe2+4qs5deqU0tLSrvndscWRI0dUu3btQrO8F1xeXnC8Ba71/b6Wy98Pb29vBQcHW8wD0LdvX2VlZZkfqbZnzx4lJCSoT58+Vu2jd+/eOnTokNatWydJ+vbbb5WZmWlxqfWZM2c0bNgwBQUFycPDQ4GBgebPuqi/E635O23fvn0yDEO1a9cu9N3ctWuXVd/Lgl8yFNQsXfwFg7u7u/leewAoCdyTDeC21KZNG/3www/atm2b+X7sAq1atdLLL7+spKQkrV27VpUqVVKNGjUkXQx1HTt2VL169TRp0iSFhITI1dVVS5cu1QcffGAxcdmV5Ofn65577tHIkSOLXF8Q3ApcHlRKq4L35sknnyx0n3CBy+8Bv/Q+yksZV7mPsyjOzs564oknNHv2bH300UeKj4/X8ePHrTqb5ebmpq5du2rx4sX66KOPdPLkScXHx+vdd9+16Dd8+HA9+OCD+vbbb7V8+XK9/vrrGjt2rH755ZdC94IXqFWrlsqUKaM///zzivvPzs7Wnj17ijzbei09e/bUO++8o9OnT8vHx0fff/+9evXqZf7lSHE+k6IU/GLnWm7Ed/lKZ6Dz8vJKfN+2stf3+2oaNGigsLAwff755+rbt68+//xzubq66rHHHrNq+169emnkyJH68ssv1apVK3355Zfy9/dX586dzX0ee+wxrVu3Ti+//LIaN24sb29v5efn67777ivy70Rrvgf5+fkymUz66aefinyfvL29raq/b9++ev/99/Xtt9+qV69e+vLLL/XAAw/Iz8/Pqu0BoDgI2QBuS5c+L7vgEUoFwsLC5Obmpri4OPOs1QV++OEHZWdn6/vvv7c4C1XUpYtX+sd+zZo1lZ6eblUouVnVrFlTGzZsUG5urlUTNEkXZyz38fFRXl6e3Y69YIK17du3X3PMvn37auLEifrhhx/0008/KTAw0OpLjXv27Kl58+YpNjZWu3btkmEY5kuuL6/npZde0ksvvaR9+/apcePGmjhxoj7//PMix/Xy8lL79u31yy+/XPF5xF999ZWys7MtJq6yVs+ePfXmm29q0aJFCgoKUlpamsUjl0riM7FFwfHu2bOn0Lrdu3crICBAXl5ecnd3l4eHh/bt21eo3+XbFpwNPnfunEX75WeRAwMD5evra54d/kpsuWy8WrVq+vPPP5Wfn29xNnv37t3m9fa0b98+tW/f3vw6PT1dycnJFn9nSRe/+9HR0UpOTtaXX36pLl26XPVxcJeqVKmS2rdvr4ULF+r111/XihUr1L9/f/NtHGfPnlVsbKzefPNNjR492qK261GzZk0ZhqHq1asX+sXj5a72Gd15551q0qSJvvjiC1WpUkWJiYmaOnXqddUGANfC5eIAbkvNmjWTu7u7vvjiCyUlJVmcyXZzc1PTpk01ffp0ZWRkWFwqXnBG5dIzTampqZozZ06hfXh5eRX6h7508azP+vXrtXz58kLrzp07pwsXLlzPod0Q3bt31+nTpzVt2rRC6650Fs7Z2Vndu3fXokWLigw2p06dsrmOpk2bqnr16po8eXKh9/ryOho2bKiGDRvq3//+txYtWqTHH3/c6svdIyMjVa5cOS1YsEALFixQixYtLC55zczM1N9//22xTc2aNeXj46Ps7Oyrjv3Pf/5ThmGof//+Fo+Vk6RDhw5p5MiRCg4O1jPPPGNVrZeqX7++7rrrLnPdwcHBuvvuu83rS+IzsUVwcLAaN26sefPmWXx+27dv188//2wOi87OzurUqZO+/fZbJSYmmvvt2rWr0M+Rr6+vAgICCs1t8NFHH1m8dnJyUteuXfXDDz/o999/L1RbwffHy8tLUuHQXpTOnTvrxIkTFpfoX7hwQVOnTpW3t3ehGeqv16xZsywezzVjxgxduHBB999/v0W/Xr16yWQyadiwYTp48KDN9yP37t1bKSkpeuaZZ5Sbm2txqXhRfydKKvSUBFt169ZNzs7OevPNNwuNbRiGxePBvLy8rnqrTp8+ffTzzz9r8uTJKl++fKH3BwDsjTPZAG5Lrq6uat68uX799Ve5ubkpLCzMYn2rVq00ceJESZb3Y997771ydXXVgw8+qGeeeUbp6emaPXu2KlSooOTkZIsxwsLCNGPGDL399tuqVauWKlSooA4dOujll182TxzUv39/hYWFKSMjQ9u2bdPXX3+tw4cPKyAgoNjHlpSUVOSZU29vb3Xt2rXY416qb9+++vTTTxUdHa2NGzeqbdu2ysjI0MqVK/Xcc8/p4YcfLnK7cePGadWqVQoPD9fAgQPVoEEDnTlzRps3b9bKlSt15swZm+pwcnLSjBkz9OCDD6px48aKiopScHCwdu/erR07dhQKYH379tWIESMkyaag4eLiom7dumn+/PnKyMjQhAkTLNbv3btXHTt21GOPPaYGDRqoTJkyWrx4sU6ePGlx5rgod999tyZMmKDo6Gg1bNhQ/fv3Nx/D7NmzlZ+fr6VLl1p95vFyPXv21OjRo+Xu7q4BAwYUul/Y3p+Jrd5//33df//9atmypQYMGGB+hJefn5/FM63ffPNNLVu2TG3bttVzzz1nDq933HFHocvtn376aY0bN05PP/20mjVrpjVr1mjv3r2F9v3uu+/q559/VkREhAYNGqT69esrOTlZCxcu1Nq1a1W2bFk1btxYzs7Oeu+995Samio3Nzd16NBBFSpUKDTeoEGD9PHHH6t///5KSEhQaGiovv76a8XHx2vy5Mk231d/LTk5Oebv3Z49e/TRRx+pTZs2euihhyz6BQYG6r777tPChQtVtmxZdenSxab9dO/eXc8995y+++47hYSEWPyixtfX1zwHQW5uripXrqyff/7Z/Iz04qpZs6befvttxcTE6PDhw+ratat8fHx06NAhLV68WIMGDTL/LIeFhWnBggWKjo5W8+bN5e3trQcffNA81hNPPKGRI0dq8eLFGjx4sNVX3wBAsd3w+cwB4CYRExNjSDJatWpVaN0333xjSDJ8fHwsHt1kGIbx/fffGw0bNjTc3d2N0NBQ47333jM/0urQoUPmfidOnDC6dOli+Pj4GJIsHhlz/vx5IyYmxqhVq5bh6upqBAQEGK1atTImTJhgfiRPwWN+bHks1NUe4XXpI26u9xFehnHxsT2vvfaaUb16dcPFxcWoWLGi8eijjxoHDhww99Flj0wyDMM4efKk8fzzzxshISHm7Tp27GjMmjXL3KfgEV4LFy602PZKj2dau3atcc899xg+Pj6Gl5eX0bBhQ2Pq1KmF3p/k5GTD2dnZqFOnzpXewitasWKFIckwmUzG0aNHLdadPn3aeP7554169eoZXl5ehp+fnxEeHm589dVXVo+/Zs0a4+GHHzYCAgIMFxcXo2rVqsbAgQONw4cPF+przSO8Cuzbt8/8HVi7dm2R+7bmM7mSatWqGV26dLlqn2t9l1euXGm0bt3a8PDwMHx9fY0HH3zQ2LlzZ6F+q1evNsLCwgxXV1ejRo0axsyZM4t8LzIzM40BAwYYfn5+ho+Pj/HYY48ZKSkpRX4fjxw5YvTt29cIDAw03NzcjBo1ahjPP/+8xaPjZs+ebdSoUcP8uLCCx3kV9XNx8uRJIyoqyggICDBcXV2Nu+66q9D39WrvR1E1Xq7g53f16tXGoEGDDH9/f8Pb29vo3bu3xaPQLvXVV18ZkoxBgwZddewr6dGjhyHJGDlyZKF1x44dMx555BGjbNmyhp+fn9GjRw/j+PHjhY7lSn+/XLrucosWLTLatGljeHl5GV5eXka9evWM559/3tizZ4+5T3p6uvHEE08YZcuWLfR3XYHOnTsbkox169YV6/gBwBYmw7Dj7BoAANzETp8+reDgYI0ePVqvv/66o8sBbpjvvvtOXbt21Zo1a8yPCrudPPLII9q2bZv279/v6FIA3Aa4JxsAcNuYO3eu8vLyrH58EXCrmD17tmrUqGFx+8vtIjk5WT/++CM/9wBuGO7JBgDc8n755Rft3LlT77zzjrp27arQ0FBHlwTcEPPnz9eff/6pH3/8UVOmTLFptvTS7tChQ4qPj9e///1vubi4FGvyQAAoDi4XBwDc8tq1a6d169apdevW+vzzz1W5cmVHlwTcECaTSd7e3urZs6dmzpxp9Yz6t4K5c+cqKipKVatW1cSJE/Xoo486uiQAtwlCNgAAAAAAdsI92QAAAAAA2AkhGwAAAAAAO7l9bsyxQX5+vo4fPy4fH5/baoIQAAAAACjtDMPQ+fPnValSJTk5OeC8sgOf0W02bdo0o1q1aoabm5vRokULY8OGDVfsu337dqNbt25GtWrVDEnGBx98UKhPwbrLl+eee86qeo4ePVrk9iwsLCwsLCwsLCwsLCylYzl69GhxI+p1cfiZ7AULFig6OlozZ85UeHi4Jk+erE6dOmnPnj2qUKFCof6ZmZmqUaOGevTooRdffLHIMTdt2qS8vDzz6+3bt+uee+5Rjx49rKrJx8dHknT06FH5+voW46gAAAAAAI6QlpamkJAQc6670Rw+u3h4eLiaN2+uadOmSbp4qXZISIiGDh2qUaNGXXXb0NBQDR8+XMOHD79qv+HDh2vJkiXat2+fVZd/p6Wlyc/PT6mpqYRsAAAAAChFHJ3nHDrxWU5OjhISEhQZGWluc3JyUmRkpNavX2+3fXz++ed66qmnuL8aAAAAAFCiHHq5+OnTp5WXl6egoCCL9qCgIO3evdsu+/j222917tw59e/f/4p9srOzlZ2dbX6dlpZml30DAAAAAG4vt/wjvP7zn//o/vvvV6VKla7YZ+zYsfLz8zMvISEhN7BCAAAAALi1GIahQYMGqVy5cjKZTNq6daujSyqkf//+6tq1q93HdeiZ7ICAADk7O+vkyZMW7SdPnlTFihWve/wjR45o5cqV+uabb67aLyYmRtHR0ebXBTfKAwAAAABst2zZMs2dO1dxcXGqUaOGAgICSmxf/fv317lz5/Ttt9/atN2UKVNUElOUOfRMtqurq8LCwhQbG2tuy8/PV2xsrFq2bHnd48+ZM0cVKlRQly5drtrPzc1Nvr6+FgsAAAAAoHgOHDig4OBgtWrVShUrVlSZMpbnd3NychxU2f/4+fmpbNmydh/X4ZeLR0dHa/bs2Zo3b5527dqlwYMHKyMjQ1FRUZKkvn37KiYmxtw/JydHW7du1datW5WTk6OkpCRt3bpV+/fvtxg3Pz9fc+bMUb9+/Qp9oAAAAACAktG/f38NHTpUiYmJMplMCg0NVbt27TRkyBANHz5cAQEB6tSpkyRp0qRJuuuuu+Tl5aWQkBA999xzSk9PN481d+5clS1bVsuXL1f9+vXl7e2t++67T8nJyZKkN954Q/PmzdN3330nk8kkk8mkX3/9VZK0Y8cOdejQQR4eHipfvrwGDRpkMfbll4tnZ2frhRdeUIUKFeTu7q42bdpo06ZNNh+/w0N2z549NWHCBI0ePVqNGzfW1q1btWzZMvNkaImJieY3UJKOHz+uJk2aqEmTJkpOTtaECRPUpEkTPf300xbjrly5UomJiXrqqadu6PEAAAAAwO1sypQp+te//qUqVaooOTnZHFTnzZsnV1dXxcfHa+bMmZIuPl3qww8/1I4dOzRv3jz98ssvGjlypMV4mZmZmjBhgj777DOtWbNGiYmJGjFihCRpxIgReuyxx8zBOzk5WeHh4ZKkbt26yd/fX5s2bdLChQu1cuVKDRky5Ip1jxw5UosWLdK8efO0efNm1apVS506ddKZM2dsOn6HPyf7ZuTo56oBAAAAQGk2efJkTZ48WYcPH5YktWvXTmlpadq8efNVt/v666/17LPP6vTp05IunsmOiorS/v37VbNmTUnSRx99pH/96186ceKEpML3ZBfkubJly+rYsWPy8vKSJC1dulQPPvigjh8/rqCgIIvtMjIy5O/vr7lz5+qJJ56QJOXm5io0NFTDhw/Xyy+/bPWxO/xMNgAAAADg1hcWFlaobeXKlerYsaMqV64sHx8f9enTR3/99ZcyMzPNfTw9Pc0BW5KCg4OVkpJyzf0VXIZeoHXr1srPz9eePXsK9T1w4IByc3PVunVrc5uLi4tatGihXbt2WX2MEiEbAAAAAHADXBp4Jenw4cN64IEH1LBhQy1atEgJCQmaPn26JMuJ0VxcXCy2M5lMJTIruL0QsgEAAAAAN1xCQoLy8/M1ceJE/eMf/1CdOnV0/Phxm8dxdXVVXl5eofZt27YpIyPD/Do+Pl5OTk6qW7duob41a9Y03y9eIDc3V5s2bVKDBg1sqoeQDQAAAAC44WrVqqXc3FxNnTpVBw8e1GeffWaeEM0WoaGh+vPPP7Vnzx6dPn1aubm5kiR3d3f169dP27dv16pVqzR06FD16dPHPMn2pby8vDR48GC9/PLLWrZsmXbu3KmBAwcqMzNTAwYMsKkeQjYAAAAAwCqGYehMRo6OnsnUmYyc67psu1GjRpo0aZLee+893Xnnnfriiy80duxYm8cZOHCg6tatq2bNmikwMFC//fabJOmbb77RmTNn1Lx5cz366KPq2LGjpk2bdsVxxo0bp+7du6tPnz5q2rSp9u/fr+XLl8vf39+mephdvAjMLg4AAAAA/5OalatFCcc0b91hHTnzv0nJqpXzVL9WoeoeVkV+Hi5XGeHGcXSeI2QXwdEfCgAAAADcLFbvPaXBnycoK+fifc+XBkjT///Xw9VZM54MU0SdwBte3+Ucnee4XBwAAAAAUKTVe08pas5GZeXmyZBlwNb/vzYkZeXmKWrORq3ee+rGF3mTIWQDAAAAAApJzcrV4M8TLgbpa1z/bBgXw/bgzxOUmpV7I8q7aRGyAQAAAACFLEo4pqycvGsG7AKGIWXl5OmbzcdKtrCbHCEbAAAAAGDBMAzNW3e4WNvOjT98XbOOl3aEbAAAAACAhbOZuTpyJrPQPdjXYkg6ciZT5zJv30vGCdkAAAAAAAsZ2Reua/v069y+NCNkAwAAAAAseLmVua7tva9z+9KMkA0AAAAAsODv6aJq5TzNz8G2lklStXKeKuvpUhJllQqEbAAAAACABZPJpH6tQou1bf/WoTKZbI3ntw5CNgAAAACgkO5hVeTh6ixr87KTSfJwdVa3plVKtrCbHCEbAAAAAFCIn4eLZjwZJpN0zaBdsH7mk2Hy87h9LxWXCNkAAAAAgCuIqBOoOVEt5OHifDFsX7a+oM3DxVlzo1ro7jqBN77Im8ztO+UbAAAAAOCaIuoEan1MR32z+Zjmxh/WkTOZ5nVVy3mqf+tQdQ+rIl/32/sMdgGTYRi2Pl/8lpeWliY/Pz+lpqbK19fX0eUAAAAAwE3BMAydy8xVevYFebuVUVlPl5tukjNH5znOZAMAAAAArGIymeTv5Sp/L1dHl3LT4p5sAAAAAADshJANAAAAAICdELIBAAAAALATQjYAAAAAAHZCyAYAAAAAwE4I2QAAAAAA2AkhGwAAAAAAOyFkAwAAAABgJ4RsAAAAAADshJANAAAAAICdELIBAAAAALATQjYAAAAAAHZCyAYAAAAAwE4I2QAAAAAA2AkhGwAAAAAAOyFkAwAAAABgJ4RsAAAAAADshJANAAAAAICdELIBAAAAALATQjYAAAAAAHZCyAYAAAAAwE4I2QAAAAAA2AkhGwAAAAAAOyFkAwAAAABgJ4RsAAAAAADsxOEhe/r06QoNDZW7u7vCw8O1cePGK/bdsWOHunfvrtDQUJlMJk2ePLnIfklJSXryySdVvnx5eXh46K677tLvv/9eQkcAAAAAAMBFDg3ZCxYsUHR0tMaMGaPNmzerUaNG6tSpk1JSUorsn5mZqRo1amjcuHGqWLFikX3Onj2r1q1by8XFRT/99JN27typiRMnyt/fvyQPBQAAAAAAmQzDMBy18/DwcDVv3lzTpk2TJOXn5yskJERDhw7VqFGjrrptaGiohg8fruHDh1u0jxo1SvHx8fr111+LXVdaWpr8/PyUmpoqX1/fYo8DAAAAALixHJ3nHHYmOycnRwkJCYqMjPxfMU5OioyM1Pr164s97vfff69mzZqpR48eqlChgpo0aaLZs2fbo2QAAAAAAK7KYSH79OnTysvLU1BQkEV7UFCQTpw4UexxDx48qBkzZqh27dpavny5Bg8erBdeeEHz5s274jbZ2dlKS0uzWAAAAAAAsFUZRxdgb/n5+WrWrJneffddSVKTJk20fft2zZw5U/369Stym7Fjx+rNN9+8kWUCAAAAAG5BDjuTHRAQIGdnZ508edKi/eTJk1ec1MwawcHBatCggUVb/fr1lZiYeMVtYmJilJqaal6OHj1a7P0DAAAAAG5fDgvZrq6uCgsLU2xsrLktPz9fsbGxatmyZbHHbd26tfbs2WPRtnfvXlWrVu2K27i5ucnX19diAQAAAADAVg69XDw6Olr9+vVTs2bN1KJFC02ePFkZGRmKioqSJPXt21eVK1fW2LFjJV2cLG3nzp3mPyclJWnr1q3y9vZWrVq1JEkvvviiWrVqpXfffVePPfaYNm7cqFmzZmnWrFmOOUgAAAAAwG3DoY/wkqRp06bp/fff14kTJ9S4cWN9+OGHCg8PlyS1a9dOoaGhmjt3riTp8OHDql69eqExIiIiFBcXZ369ZMkSxcTEaN++fapevbqio6M1cOBAq2ty9JTvAAAAAIDicXSec3jIvhk5+kMBAAAAABSPo/Ocw+7JBgAAAADgVkPIBgAAAADATgjZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOyEkA0AAAAAgJ0QsgEAAAAAsBNCNgAAAAAAdkLIBgAAAADATgjZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOyEkA0AAAAAgJ0QsgEAAAAAsBNCNgAAAAAAdkLIBgAAAADATgjZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOyEkA0AAAAAgJ0QsgEAAAAAsBNCNgAAAAAAdkLIBgAAAADATgjZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOyEkA0AAAAAgJ0QsgEAAAAAsBNCNgAAAAAAdkLIBgAAAADATgjZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOyEkA0AAAAAgJ0QsgEAAAAAsBNCNgAAAAAAdkLIBgAAAADATgjZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOyEkA0AAAAAgJ3cFCF7+vTpCg0Nlbu7u8LDw7Vx48Yr9t2xY4e6d++u0NBQmUwmTZ48uVCfN954QyaTyWKpV69eCR4BAAAAAAA3QchesGCBoqOjNWbMGG3evFmNGjVSp06dlJKSUmT/zMxM1ahRQ+PGjVPFihWvOO4dd9yh5ORk87J27dqSOgQAAAAAACTdBCF70qRJGjhwoKKiotSgQQPNnDlTnp6e+uSTT4rs37x5c73//vt6/PHH5ebmdsVxy5Qpo4oVK5qXgICAkjoEAAAAAAAkOThk5+TkKCEhQZGRkeY2JycnRUZGav369dc19r59+1SpUiXVqFFDvXv3VmJi4vWWCwAAAADAVTk0ZJ8+fVp5eXkKCgqyaA8KCtKJEyeKPW54eLjmzp2rZcuWacaMGTp06JDatm2r8+fPF9k/OztbaWlpFgsAAAAAALYq4+gCSsL9999v/nPDhg0VHh6uatWq6auvvtKAAQMK9R87dqzefPPNG1kiAAAAAOAW5NAz2QEBAXJ2dtbJkyct2k+ePHnVSc1sVbZsWdWpU0f79+8vcn1MTIxSU1PNy9GjR+22bwAAAADA7cOhIdvV1VVhYWGKjY01t+Xn5ys2NlYtW7a0237S09N14MABBQcHF7nezc1Nvr6+FgsAAAAAALZy+OXi0dHR6tevn5o1a6YWLVpo8uTJysjIUFRUlCSpb9++qly5ssaOHSvp4mRpO3fuNP85KSlJW7dulbe3t2rVqiVJGjFihB588EFVq1ZNx48f15gxY+Ts7KxevXo55iABAAAAALcFh4fsnj176tSpUxo9erROnDihxo0ba9myZebJ0BITE+Xk9L8T7sePH1eTJk3MrydMmKAJEyYoIiJCcXFxkqRjx46pV69e+uuvvxQYGKg2bdrot99+U2Bg4A09NgAAAADA7cVkGIbh6CJuNmlpafLz81NqaiqXjgMAAABAKeLoPOfQe7IBAAAAALiVELIBAAAAALATQjYAAAAAAHZCyAYAAAAAwE4I2QAAAAAA2AkhGwAAAAAAOyFkAwAAAABgJ4RsAAAAAADshJANAAAAAICdELIBAAAAALATQjYAAAAAAHZSxpbOu3bt0vz58/Xrr7/qyJEjyszMVGBgoJo0aaJOnTqpe/fucnNzK6laAQAAAAC4qZkMwzCu1Wnz5s0aOXKk1q5dq9atW6tFixaqVKmSPDw8dObMGW3fvl2//vqr0tLSNHLkSA0fPrxUh+20tDT5+fkpNTVVvr6+ji4HAAAAAGAlR+c5q85kd+/eXS+//LK+/vprlS1b9or91q9frylTpmjixIl69dVX7VUjAAAAAAClglVnsnNzc+Xi4mL1oLb2v9k4+jcfAAAAAIDicXSes2ris4LAnJubq44dO2rfvn1W9QcAAAAA4HZi0+ziLi4u+vPPP0uqFgAAAAAASjWbH+H15JNP6j//+U9J1AIAAAAAQKlm0yO8JOnChQv65JNPtHLlSoWFhcnLy8ti/aRJk+xWHAAAAAAApYnNIXv79u1q2rSpJGnv3r0W60wmk32qAgAAAACgFLI5ZK9ataok6gAAAAAAoNSz+Z7sAvv379fy5cuVlZUlSbLiSWAAAAAAANzSbA7Zf/31lzp27Kg6deqoc+fOSk5OliQNGDBAL730kt0LBAAAAACgtLA5ZL/44otycXFRYmKiPD09ze09e/bUsmXL7FocAAAAAAClic33ZP/8889avny5qlSpYtFeu3ZtHTlyxG6FAQAAAABQ2th8JjsjI8PiDHaBM2fOyM3NzS5FAQAAAABQGtkcstu2batPP/3U/NpkMik/P1/jx49X+/bt7VocAAAAAAClic2Xi48fP14dO3bU77//rpycHI0cOVI7duzQmTNnFB8fXxI1AgAAAABQKth8JvvOO+/U3r171aZNGz388MPKyMhQt27dtGXLFtWsWbMkagQAAAAAoFQwGTY+4DoxMVEhISEymUxFrqtatardinOUtLQ0+fn5KTU1Vb6+vo4uBwAAAABgJUfnOZvPZFevXl2nTp0q1P7XX3+pevXqdikKAAAAAIDSyOaQbRhGkWex09PT5e7ubpeiAAAAAAAojaye+Cw6OlrSxdnEX3/9dYvHeOXl5WnDhg1q3Lix3QsEAAAAAKC0sDpkb9myRdLFM9nbtm2Tq6ureZ2rq6saNWqkESNG2L9CAAAAAABKCatD9qpVqyRJUVFRmjJlChOCAQAAAABwGZufkz1nzpySqAMAAAAAgFLP5pAtSb///ru++uorJSYmKicnx2LdN998Y5fCAAAAAAAobWyeXXz+/Plq1aqVdu3apcWLFys3N1c7duzQL7/8Ij8/v5KoEQAAAACAUsHmkP3uu+/qgw8+0A8//CBXV1dNmTJFu3fv1mOPPaaqVauWRI0AAAAAAJQKNofsAwcOqEuXLpIuziqekZEhk8mkF198UbNmzbJ7gQAAAAAAlBY2h2x/f3+dP39eklS5cmVt375dknTu3DllZmbatzoAAAAAAEoRmyc+u/vuu7VixQrddddd6tGjh4YNG6ZffvlFK1asUMeOHUuiRgAAAAAASgWbQ/a0adP0999/S5Jee+01ubi4aN26derevbv++c9/2r1AAAAAAABKC5NhGIaji7jZpKWlyc/PT6mpqfL19XV0OQAAAAAAKzk6z9l8T3bfvn01Z84cHThwoCTqAQAAAACg1LI5ZLu6umrs2LGqXbu2QkJC9OSTT+rf//639u3bVxL1AQAAAABQahT7cvGkpCStWbNGq1ev1urVq7V3714FBwfr2LFj9q7xhnP05QUAAAAAgOJxdJ6z+Ux2AX9/f5UvX17+/v4qW7asypQpo8DAwGKNNX36dIWGhsrd3V3h4eHauHHjFfvu2LFD3bt3V2hoqEwmkyZPnnzVsceNGyeTyaThw4cXqzYAAAAAAKxlc8h+9dVX1apVK5UvX16jRo3S33//rVGjRunEiRPasmWLzQUsWLBA0dHRGjNmjDZv3qxGjRqpU6dOSklJKbJ/ZmamatSooXHjxqlixYpXHXvTpk36+OOP1bBhQ5vrAgAAAADAVjZfLu7k5KTAwEC9+OKL6tatm+rUqXNdBYSHh6t58+aaNm2aJCk/P18hISEaOnSoRo0addVtQ0NDNXz48CLPUqenp6tp06b66KOP9Pbbb6tx48bXPOtdwNGXFwAAAAAAisfRec7mM9lbtmzRa6+9po0bN6p169aqXLmynnjiCc2aNUt79+61aaycnBwlJCQoMjLyfwU5OSkyMlLr16+3tTQLzz//vLp06WIxNgAAAAAAJamMrRs0atRIjRo10gsvvCBJ+uOPP/TBBx/o+eefV35+vvLy8qwe6/Tp08rLy1NQUJBFe1BQkHbv3m1raWbz58/X5s2btWnTJqv6Z2dnKzs72/w6LS2t2PsGAAAAANy+bA7ZhmFoy5YtiouLU1xcnNauXau0tDQ1bNhQERERJVGjTY4ePaphw4ZpxYoVcnd3t2qbsWPH6s033yzhygAAAAAAtzqbQ3a5cuWUnp6uRo0aKSIiQgMHDlTbtm1VtmxZm3ceEBAgZ2dnnTx50qL95MmT15zU7EoSEhKUkpKipk2bmtvy8vK0Zs0aTZs2TdnZ2XJ2drbYJiYmRtHR0ebXaWlpCgkJKdb+AQAAAAC3L5tD9ueff662bdva5QZyV1dXhYWFKTY2Vl27dpV0ceKz2NhYDRkypFhjduzYUdu2bbNoi4qKUr169fTKK68UCtiS5ObmJjc3t2LtDwAAAACAAjaH7C5duti1gOjoaPXr10/NmjVTixYtNHnyZGVkZCgqKkqS1LdvX1WuXFljx46VdHGytJ07d5r/nJSUpK1bt8rb21u1atWSj4+P7rzzTot9eHl5qXz58oXaAQAAAACwJ5tDtr317NlTp06d0ujRo3XixAk1btxYy5YtM0+GlpiYKCen/02Cfvz4cTVp0sT8esKECZowYYIiIiIUFxd3o8sHAAAAAMDM5udk3w4c/Vw1AAAAAEDxODrP2fycbAAAAAAAUDRCNgAAAAAAdmJ1yF60aJH69eunpUuXqmnTpvrmm29Ksi4AAAAAAEodq0P2zJkz9eOPP6pMmTIaN26c3njjjRIsCwAAAACA0sfq2cVdXFzUuHFj3XvvvZIkf3//EisKAAAAAIDSyOoz2QEBAZozZ475dW5ubokUBAAAAABAaWX1meyZM2fK09NTkpSdna0pU6aUWFEAAAAAAJRGVp/JLgjYkuTm5qbmzZuXSEEAAAAAAJRWVp/JLvD3339r6tSpWrVqlVJSUpSfn2+xfvPmzXYrDgAAAACA0sTmkD1gwAD9/PPPevTRR9WiRQuZTKaSqAsAAAAAgFLH5pC9ZMkSLV26VK1bty6JegAAAAAAKLWsvie7QOXKleXj41MStQAAAAAAUKrZHLInTpyoV155RUeOHCmJegAAAAAAKLVsvly8WbNm+vvvv1WjRg15enrKxcXFYv2ZM2fsVhwAAAAAAKWJzSG7V69eSkpK0rvvvqugoCAmPgMAAAAA4P/ZHLLXrVun9evXq1GjRiVRDwAAAAAApZbN92TXq1dPWVlZJVELAAAAAAClms0he9y4cXrppZcUFxenv/76S2lpaRYLAAAAAAC3K5NhGIYtGzg5Xczll9+LbRiGTCaT8vLy7Fedg6SlpcnPz0+pqany9fV1dDkAAAAAACs5Os/ZfE/2qlWrSqIOAAAAAABKPZtDdkREREnUAQAAAABAqWfzPdkAAAAAAKBohGwAAAAAAOyEkA0AAAAAgJ1YHbIzMzNLsg4AAAAAAEo9q0N2QECAHnjgAc2aNUsnTpwoyZoAAAAAACiVrA7Zu3fvVqdOnfTVV18pNDRU4eHheuedd7Rt27aSrA8AAAAAgFLDZBiGYetGqampWrp0qb777jstW7ZM5cqV00MPPaSHHnpIERERcnZ2LolabxhHP7wcAAAAAFA8js5zxZr4zM/PT7169dL8+fN16tQpffzxx8rLy1NUVJQCAwP1xRdf2LtOAAAAAABuesU6k301W7Zs0YULF9S8eXN7DntDOfo3HwAAAACA4nF0nitj7wGbNGli7yEBAAAAACgVeE42AAAAAAB2QsgGAAAAAMBOCNkAAAAAANgJIRsAAAAAADuxauKzJk2ayGQyWTXg5s2br6sgAAAAAABKK6tCdteuXUu4DAAAAAAASj+7Pyf7VuDo56oBAAAAAIrH0XmOe7IBAAAAALATqy4XL1eunPbu3auAgAD5+/tf9f7sM2fO2K04AAAAAABKE6tC9gcffCAfHx9J0uTJk0uyHgAAAAAASi3uyS6Co6/hBwAAAAAUj6PznFVnsi+Xl5enb7/9Vrt27ZIk3XHHHXrooYfk7Oxs1+IAAAAAAChNbA7Z+/fvV+fOnZWUlKS6detKksaOHauQkBD9+OOPqlmzpt2LBAAAAACgNLB5dvEXXnhBNWvW1NGjR7V582Zt3rxZiYmJql69ul544YWSqBEAAAAAgFLB5jPZq1ev1m+//aZy5cqZ28qXL69x48apdevWdi0OAAAAAIDSxOYz2W5ubjp//nyh9vT0dLm6utqlKAAAAAAASiObQ/YDDzygQYMGacOGDTIMQ4Zh6LffftOzzz6rhx56qFhFTJ8+XaGhoXJ3d1d4eLg2btx4xb47duxQ9+7dFRoaKpPJVOQjxWbMmKGGDRvK19dXvr6+atmypX766adi1QYAAAAAgLVsDtkffvihatasqZYtW8rd3V3u7u5q3bq1atWqpSlTpthcwIIFCxQdHa0xY8Zo8+bNatSokTp16qSUlJQi+2dmZqpGjRoaN26cKlasWGSfKlWqaNy4cUpISNDvv/+uDh066OGHH9aOHTtsrg8AAAAAAGsV+znZ+/fvNz/Cq379+qpVq1axCggPD1fz5s01bdo0SVJ+fr5CQkI0dOhQjRo16qrbhoaGavjw4Ro+fPg191OuXDm9//77GjBgwDX7Ovq5agAAAACA4nF0nivWc7IlqVatWsUO1gVycnKUkJCgmJgYc5uTk5MiIyO1fv366xq7QF5enhYuXKiMjAy1bNnSLmMCAAAAAFCUYodsezh9+rTy8vIUFBRk0R4UFKTdu3df19jbtm1Ty5Yt9ffff8vb21uLFy9WgwYNiuybnZ2t7Oxs8+u0tLTr2jcAAAAA4PZk8z3ZpUXdunW1detWbdiwQYMHD1a/fv20c+fOIvuOHTtWfn5+5iUkJOQGVwsAAAAAuBU4NGQHBATI2dlZJ0+etGg/efLkFSc1s5arq6tq1aqlsLAwjR07Vo0aNbrixGwxMTFKTU01L0ePHr2ufQMAAAAAbk92C9nnzp3Tl19+adM2rq6uCgsLU2xsrLktPz9fsbGxdr9/Oj8/3+KS8Eu5ubmZH/dVsAAAAAAAYCu73ZN95MgR9enTR0888YRN20VHR6tfv35q1qyZWrRoocmTJysjI0NRUVGSpL59+6py5coaO3aspIuTpRVc9p2Tk6OkpCRt3bpV3t7e5onYYmJidP/996tq1ao6f/68vvzyS8XFxWn58uX2OlwAAAAAAApx6MRnktSzZ0+dOnVKo0eP1okTJ9S4cWMtW7bMPBlaYmKinJz+d8L9+PHjatKkifn1hAkTNGHCBEVERCguLk6SlJKSor59+yo5OVl+fn5q2LChli9frnvuueeGHhsAAAAA4PZS7OdkX+6PP/5Q06ZNlZeXZ4/hHMrRz1UDAAAAABSPo/PcLTu7OAAAAAAAN5rVl4t/+OGHV12flJR03cUAAAAAAFCaWR2yP/jgg2v2qVq16nUVAwAAYKt27dqpcePGmjx5conu5/Dhw6pevbq2bNmixo0bl+i+AACll9Uh+9ChQyVZBwAAwE0tJCREycnJCggIcHQpAICbmMNnFwcAACgNnJ2dVbFiRUeXAQC4yVk18dn8+fOtHvDo0aOKj48vdkEAAADFlZ2drREjRqhy5cry8vJSeHi4+RGfBeLj49WuXTt5enrK399fnTp10tmzZyVJ+fn5Gj9+vGrVqiU3NzdVrVpV77zzjqSLl4ubTCZt3bpVkhQXFyeTyaTY2Fg1a9ZMnp6eatWqlfbs2WOxvxkzZqhmzZpydXVV3bp19dlnn5X4+wAAcByrQvaMGTNUv359jR8/Xrt27Sq0PjU1VUuXLtUTTzyhpk2b6q+//rJ7oQAAANcyZMgQrV+/XvPnz9eff/6pHj166L777tO+ffskSVu3blXHjh3VoEEDrV+/XmvXrtWDDz5ofgRpTEyMxo0bp9dff107d+7Ul19+qaCgoKvu87XXXtPEiRP1+++/q0yZMnrqqafM6xYvXqxhw4bppZde0vbt2/XMM88oKipKq1atKrk3AQDgUFY/J/v777/X1KlT9csvv8jLy0tBQUFyd3fX2bNndeLECQUEBKh///568cUXr/k/o5udo5+rBgAArFcw8Vl0dLRq1KihxMREVapUybw+MjJSLVq00LvvvqsnnnhCiYmJWrt2baFxzp8/r8DAQE2bNk1PP/10ofWXT3wWFxen9u3ba+XKlerYsaMkaenSperSpYuysrLk7u6u1q1b64477tCsWbPM4zz22GPKyMjQjz/+WALvBgDA0XnO6nuyH3roIT300EM6ffq01q5dqyNHjigrK0sBAQFq0qSJmjRpIicnHrsNAAAcY9u2bcrLy1OdOnUs2rOzs1W+fHlJF89k9+jRo8jtd+3apezsbHNgtlbDhg3Nfw4ODpYkpaSkqGrVqtq1a5cGDRpk0b9169aaMmWKTfsAAJQeNk98FhAQoK5du5ZAKQAAAMWXnp4uZ2dnJSQkyNnZ2WKdt7e3JMnDw+OK219t3dW4uLiY/2wymSRdvLcbAHB74tQzAAC4JTRp0kR5eXlKSUlRrVq1LJaCWcEbNmyo2NjYIrevXbu2PDw8rri+OOrXr19oQtj4+Hg1aNDAbvsAANxceIQXAAC4JdSpU0e9e/dW3759NXHiRDVp0kSnTp1SbGysGjZsqC5duigmJkZ33XWXnnvuOT377LNydXXVqlWr1KNHDwUEBOiVV17RyJEj5erqqtatW+vUqVPasWOHBgwYUKyaXn75ZT322GNq0qSJIiMj9cMPP+ibb77RypUr7Xz0AICbBSEbAADclAzD0NnMXGVkX5CXWxn5e7qYL8e+kjlz5ujtt9/WSy+9pKSkJAUEBOgf//iHHnjgAUkXg/jPP/+sV199VS1atJCHh4fCw8PVq1cvSdLrr7+uMmXKaPTo0Tp+/LiCg4P17LPPFvsYunbtqilTpmjChAkaNmyYqlevrjlz5qhdu3bFHhMAcHOzenbx24mjZ6MDAOB2lpqVq0UJxzRv3WEdOZNpbq9WzlP9WoWqe1gV+Xm4XGUEAMDtzNF5zuaQvWrVKrVv376k6rkpOPpDAQDgdrV67ykN/jxBWTkXn1t96T9SCs5he7g6a8aTYYqoE3jD6wMA3PwcnedsnvjsvvvuU82aNfX222/r6NGjJVETAAC4Da3ee0pRczYqKzdPhiwDtv7/tSEpKzdPUXM2avXeUze+SAAArsHmkJ2UlKQhQ4bo66+/Vo0aNdSpUyd99dVXysnJKYn6AADAbSA1K1eDP0+4GKSvcY2dYVwM24M/T1BqVu6NKA8AAKvZHLIDAgL04osvauvWrdqwYYPq1Kmj5557TpUqVdILL7ygP/74oyTqBAAAt7BFCceUlZN3zYBdwDCkrJw8fbP5WMkWBgCAja7rOdlNmzZVTEyMhgwZovT0dH3yyScKCwtT27ZttWPHDnvVCAAAbmGGYWjeusPF2nZu/GExhysA4GZSrJCdm5urr7/+Wp07d1a1atW0fPlyTZs2TSdPntT+/ftVrVo19ejRw961AgCAW9DZzFwdOZNZ6B7sazEkHTmTqXOZXDIOALh52Pyc7KFDh+q///2vDMNQnz59NH78eN15553m9V5eXpowYYIqVapk10IBAMCtKSP7wnVtn559Qf5ernaqBgCA62NzyN65c6emTp2qbt26yc3Nrcg+AQEBWrVq1XUXBwAAbn1ebjb/c8SC93VuDwCAPdn8f6XY2NhrD1qmjCIiIopVEAAAuL34e7qoWjlPJdp4ybhJUtVynirr6VJSpQEAYDOb78keO3asPvnkk0Ltn3zyid577z27FAUAAG4fJpNJ/VqFFmvb/q1DZTKZ7FsQAADXweaQ/fHHH6tevXqF2u+44w7NnDnTLkUBAIDbS/ewKvJwdZa1ednJJHm4Oqtb0yolWxgAADayOWSfOHFCwcHBhdoDAwOVnJxsl6IAAMDtxc/DRTOeDJNJumbQLlg/88kw+XlwqTgA4OZic8gOCQlRfHx8ofb4+HhmFAcAAMUWUSdQc6JayMPF+WLYvmx9QZuHi7PmRrXQ3XUCb3yRAABcg80Tnw0cOFDDhw9Xbm6uOnToIOniZGgjR47USy+9ZPcCAQDA7SOiTqDWx3TUN5uPaW78YR05k2leV7Wcp/q3DlX3sCrydecMNgDg5mQyDMOWiTxlGIZGjRqlDz/8UDk5OZIkd3d3vfLKKxo9enSJFHmjpaWlyc/PT6mpqfL19XV0OQAA3JYMw9C5zFylZ1+Qt1sZlfV0YZIzAMA1OTrP2RyyC6Snp2vXrl3y8PBQ7dq1r/jM7NLI0R8KAAAAAKB4HJ3nbL5cvIC3t7eaN29uz1oAAAAAACjVbA7ZGRkZGjdunGJjY5WSkqL8/HyL9QcPHrRbcQAAAAAAlCY2h+ynn35aq1evVp8+fRQcHMy9UQAAAAAA/D+bQ/ZPP/2kH3/8Ua1bty6JegAAAAAAKLVsfk62v7+/ypUrVxK1AAAAAABQqtkcst966y2NHj1amZmZ1+4MAAAAAMBtxObLxSdOnKgDBw4oKChIoaGhcnFxsVi/efNmuxUHAAAAAEBpYnPI7tq1awmUAQAAAABA6WcyDMNwdBE3G0c/vBwAAAAAUDyOznM235MtSefOndO///1vxcTE6MyZM5IuXiaelJRk1+IAAAAAAChNbL5c/M8//1RkZKT8/Px0+PBhDRw4UOXKldM333yjxMREffrppyVRJwAAAAAANz2bz2RHR0erf//+2rdvn9zd3c3tnTt31po1a+xaHAAAAAAApYnNIXvTpk165plnCrVXrlxZJ06csEtRAAAAAACURjaHbDc3N6WlpRVq37t3rwIDA+1SFAAAAAAApZHNIfuhhx7Sv/71L+Xm5kqSTCaTEhMT9corr6h79+52LxAAAAAAgNLC5pA9ceJEpaenq0KFCsrKylJERIRq1aolHx8fvfPOOyVRIwAAAAAApYLNs4v7+flpxYoVWrt2rf7880+lp6eradOmioyMLIn6AAAAAAAoNYr1nGxJatOmjZ577jmNHDnyugP29OnTFRoaKnd3d4WHh2vjxo1X7Ltjxw51795doaGhMplMmjx5cqE+Y8eOVfPmzeXj46MKFSqoa9eu2rNnz3XVCAAAAADAtVh1JvvDDz/UoEGD5O7urg8//PCqfV944QWbCliwYIGio6M1c+ZMhYeHa/LkyerUqZP27NmjChUqFOqfmZmpGjVqqEePHnrxxReLHHP16tV6/vnn1bx5c124cEGvvvqq7r33Xu3cuVNeXl421QcAAAAAgLVMhmEY1+pUvXp1/f777ypfvryqV69+5cFMJh08eNCmAsLDw9W8eXNNmzZNkpSfn6+QkBANHTpUo0aNuuq2oaGhGj58uIYPH37VfqdOnVKFChW0evVq3X333desKS0tTX5+fkpNTZWvr6/VxwIAAAAAcCxH5zmrzmQfOnSoyD9fr5ycHCUkJCgmJsbc5uTkpMjISK1fv95u+0lNTZUklStXzm5jAgAAAABwOZsnPrOn06dPKy8vT0FBQRbtQUFB2r17t132kZ+fr+HDh6t169a68847i+yTnZ2t7Oxs8+uingMOAAAAAMC12DzxWffu3fXee+8Vah8/frx69Ohhl6Ls6fnnn9f27ds1f/78K/YZO3as/Pz8zEtISMgNrBAAAAAAcKuwOWSvWbNGnTt3LtR+//33a82aNTaNFRAQIGdnZ508edKi/eTJk6pYsaKtpRUyZMgQLVmyRKtWrVKVKlWu2C8mJkapqanm5ejRo9e9bwAAAADA7cfmkJ2eni5XV9dC7S4uLjZfZu3q6qqwsDDFxsaa2/Lz8xUbG6uWLVvaWpqZYRgaMmSIFi9erF9++eWqk7VJkpubm3x9fS0WAAAAAABsZXPIvuuuu7RgwYJC7fPnz1eDBg1sLiA6OlqzZ8/WvHnztGvXLg0ePFgZGRmKioqSJPXt29diYrScnBxt3bpVW7duVU5OjpKSkrR161bt37/f3Of555/X559/ri+//FI+Pj46ceKETpw4oaysLJvrAwAAAADAWjZPfPb666+rW7duOnDggDp06CBJio2N1X//+18tXLjQ5gJ69uypU6dOafTo0Tpx4oQaN26sZcuWmSdDS0xMlJPT/34XcPz4cTVp0sT8esKECZowYYIiIiIUFxcnSZoxY4YkqV27dhb7mjNnjvr3729zjQAAAAAAWMOq52Rf7scff9S7776rrVu3ysPDQw0bNtSYMWMUERFREjXecI5+rhoAAAAAoHgcneeKFbJvdY7+UAAAAAAAxePoPGfzPdkAAAAAAKBoVt2TXa5cOe3du1cBAQHy9/eXyWS6Yt8zZ87YrTgAAAAAAEoTq0L2Bx98IB8fH0nS5MmTS7IeAAAAAABKLatC9h9//KFHH31Ubm5uql69ulq1aqUyZWyemBwAAAAAgFuaVfdkT506Venp6ZKk9u3bc0k4AAAAAABFsOp0dGhoqD788EPde++9MgxD69evl7+/f5F97777brsWCAAAAABAaWHVI7y+/fZbPfvss0pJSZHJZNKVNjGZTMrLy7N7kTeao6d8BwAAAAAUj6PznE3PyU5PT5evr6/27NmjChUqFNnHz8/PbsU5iqM/FAAAAABA8Tg6z1l1T3Z0dLQyMjLk7e2tVatWqXr16vLz8ytyAQAAAADgdmXzxGcdOnRg4jMAAAAAAIrAxGcAAAAAANgJE58VwdHX8AMAAAAAisfReY6Jz4rg6A8FAAAAAFA8js5zVl0uXuDSic/KlLFpUwAAAAAAbnlWTXwmSV999ZVycnIUERGhMmXK6NixY8rPzzevz8zM1Pjx40ukSAAAAAAASgOrQ3avXr107tw58+sGDRro8OHD5tfnz59XTEyMPWsDAAAAAKBUsTpkX37rtg23cgMAAAAAcFuwOmQDAAAAAICrI2QDAAAAAGAnNk0Rvnz5cvMjuvLz8xUbG6vt27dLksX92gAAAAAA3I6sfk62k9O1T3qbTCbl5eVdd1GO5ujnqgEAAAAAisfRec7qM9mXPq4LAAAAAAAUxj3ZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOzEppCdl5enNWvW8LguAAAAAACKYFPIdnZ21r333quzZ8+WVD0AAAAAAJRaNl8ufuedd+rgwYMlUQsAAAAAAKWazSH77bff1ogRI7RkyRIlJycrLS3NYgEAAAAA4HZlMgzDsGUDJ6f/5XKTyWT+s2EYMplMysvLs191DpKWliY/Pz+lpqbK19fX0eUAAAAAAKzk6DxXxtYNVq1aVRJ1AAAAAABQ6tkcsiMiIkqiDgAAAAAASr1iPSf7119/1ZNPPqlWrVopKSlJkvTZZ59p7dq1di0OAAAAAIDSxOaQvWjRInXq1EkeHh7avHmzsrOzJUmpqal699137V4gAAAAAAClRbFmF585c6Zmz54tFxcXc3vr1q21efNmuxYHAAAAAEBpYnPI3rNnj+6+++5C7X5+fjp37pw9agIAAAAAoFSyOWRXrFhR+/fvL9S+du1a1ahRwy5FAQAAAABQGtkcsgcOHKhhw4Zpw4YNMplMOn78uL744guNGDFCgwcPLokaAQAAAAAoFWx+hNeoUaOUn5+vjh07KjMzU3fffbfc3Nw0YsQIDR06tCRqBAAAAACgVDAZhmEUZ8OcnBzt379f6enpatCggby9ve1dm8OkpaXJz89Pqamp8vX1dXQ5AAAAAAArOTrP2Xy5+FNPPaXz58/L1dVVDRo0UIsWLeTt7a2MjAw99dRTJVEjAAAAAAClgs0he968ecrKyirUnpWVpU8//dQuRQEAAAAAUBpZfU92WlqaDMOQYRg6f/683N3dzevy8vK0dOlSVahQoUSKBAAAAACgNLA6ZJctW1Ymk0kmk0l16tQptN5kMunNN9+0a3EAAAAAAJQmVofsVatWyTAMdejQQYsWLVK5cuXM61xdXVWtWjVVqlSpRIoEAAAAAKA0sPqe7IiICLVr106HDh3Sww8/rIiICPPSsmXLYgfs6dOnKzQ0VO7u7goPD9fGjRuv2HfHjh3q3r27QkNDZTKZNHny5EJ91qxZowcffFCVKlWSyWTSt99+W6y6AAAAAACwlc0Tn1WrVk1OTk7KzMzU7t279eeff1ostliwYIGio6M1ZswYbd68WY0aNVKnTp2UkpJSZP/MzEzVqFFD48aNU8WKFYvsk5GRoUaNGmn69Om2HhoAAAAAANfF5udknzp1SlFRUfrpp5+KXJ+Xl2f1WOHh4WrevLmmTZsmScrPz1dISIiGDh2qUaNGXXXb0NBQDR8+XMOHD79iH5PJpMWLF6tr165W1yQ5/rlqAAAAAIDicXSes/lM9vDhw3Xu3Dlt2LBBHh4eWrZsmebNm6fatWvr+++/t3qcnJwcJSQkKDIy8n/FODkpMjJS69evt7UsAAAAAAAczuqJzwr88ssv+u6779SsWTM5OTmpWrVquueee+Tr66uxY8eqS5cuVo1z+vRp5eXlKSgoyKI9KChIu3fvtrWs65Kdna3s7Gzz67S0tBu6fwAAcOto166dGjduXOTcMQCAW5/NZ7IzMjLMz8P29/fXqVOnJEl33XWXNm/ebN/qbpCxY8fKz8/PvISEhDi6JAAAcJOLi4uTyWTSuXPnLNq/+eYbvfXWW44pCgDgcDaH7Lp162rPnj2SpEaNGunjjz9WUlKSZs6cqeDgYKvHCQgIkLOzs06ePGnRfvLkyStOalZSYmJilJqaal6OHj16Q/cPAABuHjk5Ode1fbly5eTj42OnagAApY3NIXvYsGFKTk6WJI0ZM0Y//fSTqlatqg8//FDvvvuu1eO4uroqLCxMsbGx5rb8/HzFxsaqZcuWtpZ1Xdzc3OTr62uxAACA0iM/P1/jx49XrVq15ObmpqpVq+qdd96RJG3btk0dOnSQh4eHypcvr0GDBik9Pd28bf/+/dW1a1e98847qlSpkurWrStJ+uyzz9SsWTP5+PioYsWKeuKJJ8xPQDl8+LDat28v6eKVfSaTSf3795d08XLxSydmDQ0N1bvvvqunnnpKPj4+qlq1qmbNmmVR/yuvvKI6derI09NTNWrU0Ouvv67c3NySersAACXI6nuyDx06pOrVq+vJJ580t4WFhenIkSPavXu3qlatqoCAAJt2Hh0drX79+qlZs2Zq0aKFJk+erIyMDEVFRUmS+vbtq8qVK2vs2LGSLv5meefOneY/JyUlaevWrfL29latWrUkSenp6dq/f79F3Vu3blW5cuVUtWpVm+oDAAClQ0xMjGbPnq0PPvhAbdq0UXJysnbv3q2MjAx16tRJLVu21KZNm5SSkqKnn35aQ4YM0dy5c83bx8bGytfXVytWrDC35ebm6q233lLdunWVkpKi6Oho9e/fX0uXLlVISIgWLVqk7t27a8+ePfL19ZWHh8cV65s4caLeeustvfrqq/r66681ePBgRUREmAO9j4+P5s6dq0qVKmnbtm0aOHCgfHx8NHLkyBJ7zwAAJcSwkslkMkJDQ42oqCjjs88+M44ePWrtplc1depUo2rVqoarq6vRokUL47fffjOvi4iIMPr162d+fejQIUNSoSUiIsLcZ9WqVUX2uXSca0lNTTUkGampqXY4QgAAUJLS0tIMNzc3Y/bs2YXWzZo1y/D39zfS09PNbT/++KPh5ORknDhxwjAMw+jXr58RFBRkZGdnX3U/mzZtMiQZ58+fNwzjf//mOHv2rEW/iIgIY9iwYebX1apVM5588knz6/z8fKNChQrGjBkzrriv999/3wgLC7tqPQCAojk6z1l9JvuXX35RXFyc4uLi9N///lc5OTmqUaOGOnTooPbt26t9+/aFZgq3xpAhQzRkyJAi18XFxVm8Dg0NlXGNx3q3a9fumn0AAMCtY9euXcrOzlbHjh2LXNeoUSN5eXmZ21q3bq38/Hzt2bPH/G+Xu+66S66urhbbJiQk6I033tAff/yhs2fPKj8/X5KUmJioBg0a2FRjw4YNzX82mUyqWLGi+dJzSVqwYIE+/PBDHThwQOnp6bpw4QK3rwFAKWX1Pdnt2rXTG2+8obi4OJ09e1YrVqxQr169tGvXLvXv31+VKlXSHXfcUZK1AgAAFHK1y7StdWkIl2S+zNzX11dffPGFNm3apMWLF0sq3sRoLi4uFq9NJpM5tK9fv169e/dW586dtWTJEm3ZskWvvfbadU/ABgBwDJufky1J7u7u6tChg9q0aaP27dvrp59+0scff3zDn28NAABQu3ZteXh4KDY2Vk8//bTFuvr162vu3LnKyMgwB+n4+Hg5OTmZ74cuyu7du/XXX39p3Lhx5kd7/v777xZ9Cs585+XlXVf969atU7Vq1fTaa6+Z244cOXJdYwIAHMem2cVzcnK0Zs0avfnmm2rfvr3Kli2rZ599VmfPntW0adN06NChkqoTAACgSO7u7nrllVc0cuRIffrppzpw4IB+++03/ec//1Hv3r3l7u6ufv36afv27Vq1apWGDh2qPn36XPU2t6pVq8rV1VVTp07VwYMH9f333xd69nW1atVkMpm0ZMkSnTp1ymLGclvUrl1biYmJmj9/vg4cOKAPP/zQfNYcAFD6WB2yO3ToIH9/fz333HNKSUnRM888owMHDmjPnj2aPXu2+vTpw+zdAADAbgzD0JmMHB09k6kzGTlXnXPl9ddf10svvaTRo0erfv366tmzp1JSUuTp6anly5frzJkzat68uR599FF17NhR06ZNu+q+AwMDNXfuXC1cuFANGjTQuHHjNGHCBIs+lStX1ptvvqlRo0YpKCjoinPMXMtDDz2kF198UUOGDFHjxo21bt06vf7668UaCwDgeCbDylnCXFxcFBwcrK5du6pdu3aKiIhQ+fLlS7o+h0hLS5Ofn59SU1OZdAQAgBssNStXixKOad66wzpyJtPcXq2cp/q1ClX3sCry83C5yggAgNuZo/Oc1SE7IyNDv/76q+Li4rRq1Spt3bpVderUUUREhDl0BwYGlnS9N4SjPxQAAG5Xq/ee0uDPE5SVc/E+50v/kWL6//96uDprxpNhiqhza/y7AwBgX47Oc1aH7MudP39ea9eu1apVqxQXF6c//vhDtWvX1vbt2+1d4w3n6A8FAIDb0eq9pxQ1Z6MMSVf714nJdDFwz4lqQdAGABTi6Dxn08Rnl/Ly8lK5cuVUrlw5+fv7q0yZMtq1a5c9awMAALeJ1KxcDf484ZoBW/+/3pA0+PMEpWbl3ojyAACwmtWP8MrPz9fvv/9uvlw8Pj5eGRkZqly5stq3b6/p06erffv2JVkrAAC4RS1KOKasnDxZe3mdYUhZOXn6ZvMxRbWuXqK1AQBgC6tDdtmyZZWRkaGKFSuqffv2+uCDD9SuXTvVrFmzJOsDAAC3OMMwNG/d4WJtOzf+sPq3CpXJZLp2ZwAAbgCrQ/b777+v9u3bq06dOiVZDwAAuM2czcy1mEXcWoakI2cydS4zV/5ervYvDACAYrA6ZD/zzDMlWQcAALhNZWRfuK7t07MvELIBADeNYk98BgAAYA9eblb/zr9I3te5PQAA9kTIBgAADuXv6aJq5Txl613VJknVynmqrKdLSZQFAECxELIBAIBDmUwm9WsVWqxt+7dm0jMAwM2FkA0AAByue1gVebg6y9q87GSSPFyd1a1plZItDAAAGxGyAQCAw/l5uGjGk2EySdcM2gXrZz4ZJj8PLhUHANxcCNkAAOCmEFEnUHOiWsjDxfli2L5sfUGbh4uz5ka10N11Am98kQAAXAPTcQIAgJtGRJ1ArY/pqG82H9Pc+MMWz8+uWs5T/VuHqntYFfm6cwYbAHBzMhmGYTi6iJtNWlqa/Pz8lJqaKl9fX0eXAwDAbckwDJ3LzFV69gV5u5VRWU8XJjkDAFyTo/McZ7IBAMBNyWQyyd/LVf5ero4uBQAAq3FPNgAAAAAAdkLIBgAAAADATgjZAAAAAADYCSEbAAAAAAA7IWQDAAAAAGAnhGwAAAAAAOyEkA0AAAAAgJ0QsgEAAAAAsBNCNgAAAAAAdkLIBgDASv3791fXrl2t7h8XFyeTyaRz585JkubOnauyZcuWSG0AAODmQMgGAOAG6dmzp/bu3evoMgAAQAkq4+gCAAC4XXh4eMjDw8PRZQAAgBLEmWwAQKnTrl07DRkyREOGDJGfn58CAgL0+uuvyzAMSVJ2drZGjBihypUry8vLS+Hh4YqLizNvX3DZ9vLly1W/fn15e3vrvvvuU3JysrlPXl6eoqOjVbZsWZUvX14jR440j18gOztbL7zwgipUqCB3d3e1adNGmzZtumLdl18u/sYbb6hx48b67LPPFBoaKj8/Pz3++OM6f/58sfcBAAAci5ANACiV5s2bpzJlymjjxo2aMmWKJk2apH//+9+SpCFDhmj9+vWaP3++/vzzT/Xo0UP33Xef9u3bZ94+MzNTEyZM0GeffaY1a9YoMTFRI0aMMK+fOHGi5s6dq08++URr167VmTNntHjxYosaRo4cqUWLFmnevHnavHmzatWqpU6dOunMmTNWH8eBAwf07bffasmSJVqyZIlWr16tcePG2XUfAADgBjJQSGpqqiHJSE1NdXQpAIAiREREGPXr1zfy8/PNba+88opRv35948iRI4azs7ORlJRksU3Hjh2NmJgYwzAMY86cOYYkY//+/eb106dPN4KCgsyvg4ODjfHjx5tf5+bmGlWqVDEefvhhwzAMIz093XBxcTG++OILc5+cnByjUqVK5u1WrVplSDLOnj1r3q+fn5+5/5gxYwxPT08jLS3N3Pbyyy8b4eHhVu8DAABYcnSe455sAECp9I9//EMmk8n8umXLlpo4caK2bdumvLw81alTx6J/dna2ypcvb37t6empmjVrml8HBwcrJSVFkpSamqrk5GSFh4eb15cpU0bNmjUzXzJ+4MAB5ebmqnXr1uY+Li4uatGihXbt2mX1cYSGhsrHx6fIOuy1DwAAcOMQsgEAt5T09HQ5OzsrISFBzs7OFuu8vb3Nf3ZxcbFYZzKZCt1zfSMUVUd+fv4NrwMAANgH92QDAEqlDRs2WLz+7bffVLt2bTVp0kR5eXlKSUlRrVq1LJaKFStaNbafn5+Cg4Mt9nHhwgUlJCSYX9esWVOurq6Kj483t+Xm5mrTpk1q0KDBdR7djdsHAACwL85kAwBKpcTEREVHR+uZZ57R5s2bNXXqVE2cOFF16tRR79691bdvX02cOFFNmjTRqVOnFBsbq4YNG6pLly5WjT9s2DCNGzdOtWvXVr169TRp0iSdO3fOvN7Ly0uDBw/Wyy+/rHLlyqlq1aoaP368MjMzNWDAALsc443YBwAAsC9CNgDgpmEYhs5m5ioj+4K83MrI39PF4r7rS/Xt21dZWVlq0aKFnJ2dNWzYMA0aNEiSNGfOHL399tt66aWXlJSUpICAAP3jH//QAw88YHUtL730kpKTk9WvXz85OTnpqaee0iOPPKLU1FRzn3Hjxik/P199+vTR+fPn1axZMy1fvlz+/v7X90Zc4kbsAwAA2I/JcMQNaDe5tLQ0+fn5KTU1Vb6+vo4uBwBuealZuVqUcEzz1h3WkTOZ5vZq5TzVr1WouodVkZ/H/+5dbteunRo3bqzJkyc7oFoAAHAzc3Se40w2AMChVu89pcGfJygrJ6/QusQzmXpryU5N+HmPZjwZpog6gQ6oEAAAwHpMfAYAcJjVe08pas5GZeXmyZB0+aVVBW1ZuXmKmrNRq/eeuvFFAgAA2IDLxYvg6MsLAOB2kJqVq5ZjYy8GbCv+T2QySR4uzlof09Hi0nEAAIBLOTrPcSYbAOAQixKOKSvHuoAtSYYhZeXk6ZvNx0q2MAAAgOtAyAYA3HCGYWjeusPF2nZu/GFxERYAALhZEbIBADfc2cxcHTmTWege7GsxJB05k6lzmbklURYAAMB1uylC9vTp0xUaGip3d3eFh4dr48aNV+y7Y8cOde/eXaGhoTKZTFd8fIstYwIAbqyM7AvXtX36dW4PAABQUhweshcsWKDo6GiNGTNGmzdvVqNGjdSpUyelpKQU2T8zM1M1atTQuHHjVLFiRbuMCQC4sbzcru8Jkt7XuT0AAEBJcXjInjRpkgYOHKioqCg1aNBAM2fOlKenpz755JMi+zdv3lzvv/++Hn/8cbm5udllTADAjeXv6aJq5TxlsnE7k6Rq5TxV1pPZxQEAwM3JoSE7JydHCQkJioyMNLc5OTkpMjJS69evv2FjZmdnKy0tzWIBAJQck8mkfq1Ci7Vt/9YXbxcCAAC4GTk0ZJ8+fVp5eXkKCgqyaA8KCtKJEydu2Jhjx46Vn5+feQkJCSnWvgEA1useVkUers6yNi87mSQPV2d1a1qlZAsDAAC4Dg6/XPxmEBMTo9TUVPNy9OhRR5cEALc8Pw8XzXgyTCbpmkG7YP3MJ8Pk58Gl4gAA4Obl0JAdEBAgZ2dnnTx50qL95MmTV5zUrCTGdHNzk6+vr8UCACh5EXUCNSeqhTxcnC+G7cvWF7R5uDhrblQL3V0n8MYXCQAAYAOHhmxXV1eFhYUpNjbW3Jafn6/Y2Fi1bNnyphkTAFByIuoEan1MR41+sIGqlvO0WFe1nKdGP9hAv73akYANAABKBYc/AyU6Olr9+vVTs2bN1KJFC02ePFkZGRmKioqSJPXt21eVK1fW2LFjJV2c2Gznzp3mPyclJWnr1q3y9vZWrVq1rBoTAHBz8fNwUVTr6urfKlTnMnOVnn1B3m5lVNbThUnOAABAqeLwkN2zZ0+dOnVKo0eP1okTJ9S4cWMtW7bMPHFZYmKinJz+d8L9+PHjatKkifn1hAkTNGHCBEVERCguLs6qMQEANyeTySR/L1f5e7k6uhQAAIBiMRmGYTi6iJtNWlqa/Pz8lJqayv3ZAAAAAFCKODrPMbs4AAAAAAB2QsgGAAAAAMBOCNkAAAAAANgJIRsAAAAAADshZAMAAAAAYCeEbAAAAAAA7ISQDQAAAACAnRCyAQAAAACwE0I2AAAAAAB2QsgGAAAAAMBOCNkAAAAAANgJIRsAAAAAADshZAMAAAAAYCeEbAAAAAAA7ISQDQAAAACAnRCyAQAAAACwE0I2AAAAAAB2QsgGAAAAAMBOCNkAAAAAANgJIRsAAAAAADshZAMAAAAAYCeEbAAAAAAA7ISQDQAAAACAnRCyAQAAAACwE0I2AAAAAAB2QsgGAAAAAMBOCNkAAAAAANgJIRsAAAAAADshZAMAAAAAYCeEbAAAAAAA7ISQDQAAAACAnRCyAeAWMnfuXJUtW9b8+o033lDjxo0dVs/V9O/fX127drW6f1xcnEwmk86dOyep8LECAADcDAjZAIDbhslk0rfffuvoMgAAwC2MkA0AuKrc3FxHlwAAAFBqELIBwI7atWunF154QSNHjlS5cuVUsWJFvfHGG+b1iYmJevjhh+Xt7S1fX1899thjOnnypMUYP/zwg5o3by53d3cFBATokUceMa/Lzs7WiBEjVLlyZXl5eSk8PFxxcXFW17dp0ybdc889CggIkJ+fnyIiIrR582aLPiaTSTNmzNBDDz0kLy8vvfPOO8rLy9OAAQNUvXp1eXh4qG7dupoyZYrV+83Ly1N0dLTKli2r8uXLa+TIkTIMw6JPfn6+xo4da95Ho0aN9PXXX1u9D0n67rvv1LRpU7m7u6tGjRp68803deHCBUlSaGioJOmRRx6RyWQyvy64pP6zzz5TaGio/Pz89Pjjj+v8+fPmcZctW6Y2bdqY63/ggQd04MABm2oDAAC3B0I2ANjZvHnz5OXlpQ0bNmj8+PH617/+pRUrVig/P18PP/ywzpw5o9WrV2vFihU6ePCgevbsad72xx9/1COPPKLOnTtry5Ytio2NVYsWLczrhwwZovXr12v+/Pn6888/1aNHD913333at2+fVbWdP39e/fr109q1a/Xbb7+pdu3a6ty5s0WglC4Gz0ceeUTbtm3TU089pfz8fFWpUkULFy7Uzp07NXr0aL366qv66quvrNrvxIkTNXfuXH3yySdau3atzpw5o8WLF1v0GTt2rD799FPNnDlTO3bs0Isvvqgnn3xSq1evtmofv/76q/r27athw4Zp586d+vjjjzV37ly98847ki7+gkGS5syZo+TkZPNrSTpw4IC+/fZbLVmyREuWLNHq1as1btw48/qMjAxFR0fr999/V2xsrJycnPTII48oPz/fqtoAAMBtxEAhqamphiQjNTXV0aUAKGUiIiKMNm3aWLQ1b97ceOWVV4yff/7ZcHZ2NhITE83rduzYYUgyNm7caBiGYbRs2dLo3bt3kWMfOXLEcHZ2NpKSkizaO3bsaMTExBiGYRhz5swx/Pz8zOvGjBljNGrU6Ir15uXlGT4+PsYPP/xgbpNkDB8+/JrH+vzzzxvdu3e/Zj/DMIzg4GBj/Pjx5te5ublGlSpVjIcfftgwDMP4+++/DU9PT2PdunUW2w0YMMDo1auXYRiGsWrVKkOScfbsWcMwCh9rx44djXfffddi+88++8wIDg62OLbFixdb9BkzZozh6elppKWlmdtefvllIzw8/IrHc+rUKUOSsW3btmseOwAAuLEcnefKODDfA8AtqWHDhhavg4ODlZKSol27dikkJEQhISHmdQ0aNFDZsmW1a9cuNW/eXFu3btXAgQOLHHfbtm3Ky8tTnTp1LNqzs7NVvnx5q2o7efKk/vnPfyouLk4pKSnKy8tTZmamEhMTLfo1a9as0LbTp0/XJ598osTERGVlZSknJ8eqmctTU1OVnJys8PBwc1uZMmXUrFkz8yXj+/fvV2Zmpu655x6LbXNyctSkSROrju2PP/5QfHy8+cy1dPEy9b///luZmZny9PS84rahoaHy8fExvy74zArs27dPo0eP1oYNG3T69GnzGezExETdeeedVtUHAABuD4RsALAzFxcXi9cmk8nqy4o9PDyuuC49PV3Ozs5KSEiQs7OzxTpvb2+rxu/Xr5/++usvTZkyRdWqVZObm5tatmypnJwci35eXl4Wr+fPn68RI0Zo4sSJatmypXx8fPT+++9rw4YNVu33WtLT0yVdvFy+cuXKFuvc3NysHuPNN99Ut27dCq1zd3e/6rbX+swefPBBVatWTbNnz1alSpWUn5+vO++8s9D7BgAAQMgGgBukfv36Onr0qI4ePWo+m71z506dO3dODRo0kHTxLHhsbKyioqIKbd+kSRPl5eUpJSVFbdu2LVYN8fHx+uijj9S5c2dJ0tGjR3X69GmrtmvVqpWee+45c5u1E3/5+fkpODhYGzZs0N133y1JunDhghISEtS0aVNJF8/ou7m5KTExUREREbYeliSpadOm2rNnj2rVqnXFPi4uLsrLy7Np3L/++kt79uzR7Nmzze/72rVri1UjAAC49RGyAcAKhmHobGauMrIvyMutjPw9XWQymWwaIzIyUnfddZd69+6tyZMn68KFC3ruuecUERFhvjx7zJgx6tixo2rWrKnHH39cFy5c0NKlS/XKK6+oTp066t27t/r27auJEyeqSZMmOnXqlGJjY9WwYUN16dLlmjXUrl1bn332mZo1a6a0tDS9/PLLVz17ful2n376qZYvX67q1avrs88+06ZNm1S9enWrjn3YsGEaN26cateurXr16mnSpEk6d+6ceb2Pj49GjBihF198Ufn5+WrTpo1SU1MVHx8vX19f9evX75r7GD16tB544AFVrVpVjz76qJycnPTHH39o+/btevvttyVdvCw8NjZWrVu3lpubm/z9/a85rr+/v8qXL69Zs2YpODhYiYmJGjVqlFXHDQAAbj/MLg4AV5GalatP1h5SvZ6jFFC+nNqOX6Wmb61Qu/fj9MnaQ0rNuvIzpE0mk7799luL19999538/f119913KzIyUjVq1NCCBQvMfdq1a6eFCxfq+++/V+PGjdWhQwdt3LjRvH7OnDnq27evXnrpJdWtW1ddu3bVpk2bVLVqVauO5z//+Y/Onj2rpk2bqk+fPnrhhRdUoUKFa273zDPPqFu3burZs6fCw8P1119/WZzVvpaXXnpJffr0Ub9+/cyXm1/6aDJJeuutt/T6669r7Nixql+/vu677z79+OOPVgf5Tp06acmSJfr555/VvHlz/eMf/9AHH3ygatWqmftMnDhRK1asUEhIiNX3ejs5OWn+/PlKSEjQnXfeqRdffFHvv/++1ccOAABuLybDuOxBpVBaWpr8/PyUmpoqX19fR5cDwEFW7z2lwZ8nKCsnT/m52crPyZKzV1lJUsE5bA9XZ814MkwRdQILbW8ymbR48WJ17dr1htUMAABwu3N0nuNMNoDb2pUmrlq995Si5mxUVm6eDEkmFzdzwJYk4/+XrNw8Rc3ZqNV7T92IcgEAAHCTI2QDuGm1a9dOQ4cO1fDhw+Xv76+goCDNnj1bGRkZioqKko+Pj2rVqqWffvrJvM3q1avVokULubm5KTg4WKNGjdKFCxcsxhwyZIiGDx+ugIAAderUSZI0adIk3XXXXfLy8lKVKiHq+kSU8nKyVHCtT/q2lUqc3NOivvNblurYzKd1aHxX3duyiT7+z5yrHs+2bdvUoUMHeXh4qHz58ho0aJB5Vu3Sztvb+4rLr7/+6ujyAAAAbhhCNoCb2rx58xQQEKCNGzdq6NChGjx4sHr06KFWrVpp8+bNuvfee9WnTx9lZmYqKSlJnTt3VvPmzfXHH39oxowZ+s9//mOe9OrSMV1dXRUfH6+ZM2dKunjf7YcffqgdO3ao18vjlH7oD51ZdeXQnLl3nc6snCXf5o+o0oDp8mp0n557ZqBWrVpVZP+MjAx16tRJ/v7+2rRpkxYuXKiVK1dqyJAh9nuzHGjr1q1XXIp65jYAAMCtinuyi+Doa/hvBaGhoRo+fLiGDx/u6FJQirVr1055eXnmM6F5eXny8/NTt27d9Omnn0qSTpw4oeDgYK1fv14//PCDFi1apF27dpln/v7oo4/0yiuvKDU1VU5OTmrXrp3S0tK0efPmIvdpGIbavR+nXet+1l8/f6SQF76UdPFM9pnY2ao6/OIkZSc+f1kuAVVV/r6hki7eo52+9H21CPHSjz/+eLHtknuyZ8+erVdeeUVHjx41P4N66dKlevDBB3X8+HEFBQWVzJsIAABwm3F0nrspzmRPnz5doaGhcnd3V3h4uMVMukVZuHCh6tWrJ3d3d911111aunSpxfqTJ0+qf//+qlSpkjw9PXXfffdp3759JXkIN6V27do5LORu2rRJgwYNcsi+cWtp2LCh+c/Ozs4qX7687rrrLnNbQThNSUnRrl271LJlS4tHa7Vu3Vrp6ek6duyYuS0sLKzQflauXKmOHTuqUuUq+vX1Ljr94yTlZ6UpP/fvIuvK/euo3Co3ML82JOVVqKMdO3cW2X/Xrl1q1KiROWAX1Jafn689e/Zc410AAABAaeHwkL1gwQJFR0drzJgx2rx5sxo1aqROnTopJSWlyP7r1q1Tr169NGDAAG3ZskVdu3ZV165dtX37dkkXz0J17dpVBw8e1HfffactW7aoWrVqioyMVEZGxo08tNtaYGCgPD09HV0GbgEuLi4Wr00mk0VbQaDOz8+3esxLg64kHT58WA888IAaNmyomXO+UHC/ySp3z7MXV+ZdKGKEK8vn2iAAAIDbmsND9qRJkzRw4EBFRUWpQYMGmjlzpjw9PfXJJ58U2X/KlCm677779PLLL6t+/fp666231LRpU02bNk2StG/fPv3222+aMWOGmjdvrrp162rGjBnKysrSf//73xt5aA7Vv39/rV69WlOmTJHJZJLJZNKBAwc0YMAAVa9eXR4eHqpbt66mTJlisd2FCxf0wgsvqGzZsipfvrxeeeUV9evXz+IRROfPn1fv3r3l5eWl4OBgffDBB4XOmoeGhmry5Mnm15dOKhUSEqLnnnvOYsKnI0eO6MEHH5S/v7+8vLx0xx13WFyhsGPHDj3wwAPy9fWVj4+P2rZtqwMHDki6eNb8nnvuUUBAgPz8/BQREXHFS4Fxa6tfv77Wr1+vS++CiY+Pl4+Pj6pUqXLF7RISEpSfn6+JEyeqbZtWcilXWRfSz1x1Xy7lQ5SdZHnWOvvYLtWvX/+Ktf3xxx8Wv+yLj4+Xk5OT6tata83hAQAAoBRwaMjOyclRQkKCIiMjzW1OTk6KjIzU+vXri9xm/fr1Fv0lqVOnTub+2dnZkiR3d3eLMd3c3LR27doix8zOzlZaWprFUtpNmTJFLVu21MCBA5WcnKzk5GRVqVJFVapU0cKFC7Vz506NHj1ar776qr766ivzdu+9956++OILzZkzR/Hx8UpLS9O3335rMXZ0dLTi4+P1/fffa8WKFfr111+vGWovnVRq3rx5+uWXXzRy5Ejz+ueff17Z2dlas2aNtm3bpvfee0/e3t6SpKSkJN19991yc3PTL7/8ooSEBD311FPmGaPPnz+vfv36ae3atfrtt99Uu3Ztde7cWefPn7fTu4nS4rnnntPRo0c1dOhQ7d69W999953GjBmj6OhoOTld+a+7WrVqKTc3V1OnTtXZE0flfnit0rf8dMX+kuTbopvSt8Xq/Jalyj2TpLSNi5W5b51GjXy5yP69e/eWu7u7+vXrp+3bt2vVqlUaOnSo+vTpw/3YAAAAt5Ayjtz56dOnlZeXV+gfmEFBQdq9e3eR25w4caLI/idOnJAk1atXT1WrVlVMTIw+/vhjeXl56YMPPtCxY8eUnJxc5Jhjx47Vm2++aYcjunn4+fnJ1dVVnp6eqlixorn90uOsXr261q9fr6+++kqPPfaYJGnq1KmKiYnRI488IkmaNm2axRnl8+fPa968efryyy/VsWNHSdKcOXNUqVKlq9Zz+Vnut99+W88++6w++ugjSVJiYqK6d+9uvte2Ro0a5v7Tp0+Xn5+f5s+fb75MuE6dOub1HTp0sNjXrFmzVLZsWa1evVoPPPDANd4pOIJhGDqbmauM7Avycisjf08Xi/uoi6ty5cpaunSpXn75ZTVq1EjlypXTgAED9M9//vOq2zVq1EiTJk3Se++9p5iYGNW8q7n8I/rp9I+TrriNZ52WKhc5SGkbv9GZlbNUpmyQnn71fbVv377o/p6eWr58uYYNG6bmzZvL09NT3bt316RJV94HAAAASh+HhuyS4OLiom+++UYDBgxQuXLl5OzsrMjISN1///260kTqMTExio6ONr9OS0tTSEjIjSr5hpo+fbo++eQTJSYmKisrSzk5OWrcuLEkKTU1VSdPnlSLFi3M/Z2dnRUWFma+3/XgwYPKzc216OPn53fNy11XrlypsWPHavfu3UpLS9OFCxf0999/KzMzU56ennrhhRc0ePBg/fzzz4qMjFT37t3NE15t3bpVbdu2LXRvboGTJ0/qn//8p+Li4pSSkqK8vDxlZmYqMTHxet4qlIDUrFwtSjimeesO68iZTHN7tXKe6tcqVN3DqsjP43+fc1xcXKExDh8+XKjt0p/tiIiIq06eWNSYkvTiiy/qxRdfNNfZcmysvO/qYH5OtvddkfK+y/IqGp8mneXTpLOcTJK7i7MmxHS8Yl2SdNddd+mXX365Ym0AAAAo/Rx6uXhAQICcnZ118uRJi/aTJ09anH29VMWKFa/ZPywsTFu3btW5c+eUnJysZcuW6a+//rI4O3opNzc3+fr6Wiy3ovnz52vEiBEaMGCAfv75Z23dulVRUVHKyckp0f1eOqnUokWLlJCQoOnTp0uSed9PP/20Dh48qD59+mjbtm1q1qyZpk6dKkny8PC46vj9+vXT1q1bNWXKFK1bt05bt25V+fLlS/y4YJvVe0+p5dhYvbVkpxIvCdiSlHgmU28t2amWY2O1eu8pB1X4P34eLprxZJhMkq51gr1g/cwnwyx+QQAAAIDbk0NDtqurq8LCwhQbG2tuy8/PV2xsrFq2bFnkNi1btrToL0krVqwosr+fn58CAwO1b98+/f7773r44YftewA3OVdXV+Xl5Zlfx8fHq1WrVvq/9u48rKpq/x/4+zAdDrOJqAiCiqiY4myCydVUNC7m8E1C1DLLUDAtQTNzuHXLBlHLTLJropVpllPmrKCoODE4IALhgIVGXQdG9QCf3x9c9s8toJKHIXq/nofnce/12Wutvd2r/LD2XnvSpEno3Lkz3NzclMXDgNLr1bhxYxw/flzZV1xcrHrfumXLljA1NVXF3Lx5E2lpaZX24+5FpZ544gm4u7sjKyurXJyzszOCg4OxYcMGTJs2DV988QWA0k84xcbGQq/XV1j/oUOH8Oqrr+Lpp59G+/btodVq8ccffzzEFaKasj/td4xbeQyF+mIISj93dbeyfYX6YoxbeaxOJNo+7o2wclwP6EyNS5Pte8rL9ulMjRE1rgf6uDeq+U4SERERUZ1T66uLv/766/jiiy+watUqpKSkYOLEicjPz8e4ceMAAGPHjsXMmTOV+ClTpmDHjh2IiIjAuXPnMG/ePJw4cQKhoaFKzPr16xETE6N8xmvAgAEYOnQoBg4cWOPnV5tcXV1x9OhRXLx4EX/88Qdat26NEydOYOfOnUhLS8Ps2bNVyTIATJ48GfPnz8fmzZuRmpqKKVOm4Pr168r7stbW1nj++ecRHh6O6OhoJCcnY/z48TAyMqr0ndq7F5U6f/48vvrqK0RGRqpipk6dip07d+LChQtISEhAdHS0skpzaGgocnJy8Nxzz+HEiRNIT0/HV199pXxbuHXr1vjqq6+QkpKCo0ePIigo6IGz31RzbhbqMfHr+NJE+gGftxIpTbYnfh2Pm4UV/1KlJvm4N0LczKcwx98DzR9Tf5Ku+WMWmOPvgSNvPsUEm4iIiIgUtf5OdkBAAH7//XfMmTMHV69eRadOnbBjxw5lcbPMzEzVqsBeXl5Ys2YN3nrrLbz55pto3bo1Nm3ahMcff1yJuXLlCl5//XX89ttvaNq0KcaOHYvZs2fX+LlVh6osGBUWFobnn38eHh4eKCwsxLlz55CYmIiAgABoNBoEBgZi0qRJ2L79/6+iPGPGDFy9ehVjx46FsbExJkyYAF9fXxgbGysxCxcuRHBwsPJJrenTp+Py5cuqFd3vdu+iUn369MH8+fMxduxYJaa4uBghISH45ZdfYGNjg0GDBmHRokUAgIYNG2Lfvn0IDw+Hj48PjI2N0alTJ3h7ewMAVqxYgQkTJqBLly5wdnbGe++9h7CwsEe+1mQYP8T/gsI7xeVmrysjAhTeKcaGhF8wzrtFtfbtYdjqTDHOuwVe8HLFjQI98m4XwUprAjsDLdZGRERERPWLRipbDexvLCcnB7a2trh582adeT+7qgtGGUpJSQnatWuHkSNH4p133qkwJj8/H82aNUNERATGjx9v8D7QX5eI4B8fxSDzWsFDJ9lA6WPYzR+zQEz4P5jIEhEREVGV1HY+V+sz2fRg+9N+x8Sv41F4p7hcWdmCUQt2pWLZ6K7wecTHVi9duoRdu3bBx8cHt2/fxqeffooLFy5g1KhRSkxiYiLOnTuHHj164ObNm3j77bcB4G/3zjs92PUCveqXQg9LAFy6VoAbBXo0sDQzfMeIiIiIiKpJrb+TTfdX0wtGGRkZISoqCt27d4e3tzdOnz6NPXv2KO9Hl1mwYAE8PT3Rv39/5OfnIzY2Fvb29o/UNtU/+beLHun4vEc8noiIiIiopnEmuw6r6oJR0JQuGBU386k//ei4s7MzDh06dN+Yzp07Iz4+/k/VT38vltpH+0+M1SMeT0RERERU0ziTXYcpC0Y95Musdy8YRVQXNLAwhctjFuU+f/UgGpSuN2Bnwe9OExEREdFfC5PsOkpEsOrwxT91bNShi+B6dlQXaDQaPO/l+qeOfcHblYueEREREdFfDpPsOqpswaiqpsp3LxhFVBeM6OoEnZkxHjZfNtIAOjNjDO/iVL0dIyIiIiKqBkyy6yguGEX1ha3OFMtGd4UGeGCiXVYeObprtXySjoiIiIioujHJrqO4YBTVJz7ujbByXA/oTI1Lk+17ysv26UyNETWuB/o84qfoiIiIiIhqCzOxOqpswajMKj4yrgHQnAtGUR3k494IcTOfwoaEXxB16KLq+9nNH7PAC96uGNHVCTbmvHeJiIiI6K+LSXYdVbZg1Dtbz1b5WC4YRXWVrc4U47xb4AUvV9wo0CPvdhGstCawszDlPUtERERE9QIfF6/DuGAU1VcajQYNLM3g/JgFGliaMcEmIiIionqDSXYdxgWjiIiIiIiI/lqYZNdxXDCKiIiIiIjor4PvZP8FcMEoIiIiIiKivwaNiFRl8eq/hZycHNja2uLmzZuwsbGp7e6oiAgXjCIiIiIiIqpEbedznMn+iylbMKqBpVltd4WIiIiIiIjuwXeyiYiIiIiIiAyESTYRERERERGRgTDJJiIiIiIiIjIQJtlEREREREREBsIkm4iIiIiIiMhAmGQTERERERERGQiTbCIiIiIiIiIDYZJNREREREREZCBMsomIiIiIiIgMhEk2ERERERERkYEwySYiIiIiIiIyECbZRERERERERAZiUtsdqItEBACQk5NTyz0hIiIiIiKiqijL48ryuprGJLsCubm5AABnZ+da7gkRERERERH9Gbm5ubC1ta3xdjVSW+l9HVZSUoKsrCxYW1tDo9HUdnf+tnJycuDs7IzLly/DxsamtrtDVGs4FohKcSwQleJYICpV2VgQEeTm5sLR0RFGRjX/hjRnsitgZGQEJyen2u4G/Y+NjQ3/B0IEjgWiMhwLRKU4FohKVTQWamMGuwwXPiMiIiIiIiIyECbZRERERERERAbCJJvqLK1Wi7lz50Kr1dZ2V4hqFccCUSmOBaJSHAtEperqWODCZ0REREREREQGwplsIiIiIiIiIgNhkk1ERERERERkIEyyiYiIiIiIiAyESTYZzIEDB+Dv7w9HR0doNBps2rRJKdPr9ZgxYwY6dOgAS0tLODo6YuzYscjKylLVce3aNQQFBcHGxgZ2dnYYP3488vLyVDGnTp3Ck08+CXNzczg7O+PDDz8s15f169ejbdu2MDc3R4cOHbBt27ZqOWeiitxvLNwrODgYGo0GixcvVu3nWKD64GHGQkpKCoYMGQJbW1tYWlqie/fuyMzMVMpv3bqFkJAQNGzYEFZWVhgxYgR+++03VR2ZmZnw8/ODhYUFHBwcEB4ejqKiIlVMTEwMunTpAq1WCzc3N0RFRVXHKRNV6EFjIS8vD6GhoXBycoJOp4OHhwciIyNVMRwLVB/Mnz8f3bt3h7W1NRwcHDB06FCkpqaqYmryXl+6dClcXV1hbm6Onj174tixYwY5TybZZDD5+fnw9PTE0qVLy5UVFBQgISEBs2fPRkJCAjZs2IDU1FQMGTJEFRcUFITk5GTs3r0bW7duxYEDBzBhwgSlPCcnBwMHDoSLiwvi4+Px0UcfYd68eVi+fLkSc/jwYQQGBmL8+PFITEzE0KFDMXToUJw5c6b6Tp7oLvcbC3fbuHEjjhw5AkdHx3JlHAtUHzxoLGRkZKB3795o27YtYmJicOrUKcyePRvm5uZKzGuvvYYff/wR69evx/79+5GVlYXhw4cr5cXFxfDz88OdO3dw+PBhrFq1ClFRUZgzZ44Sc+HCBfj5+aFv375ISkrC1KlT8dJLL2Hnzp3Vd/JEd3nQWHj99dexY8cOfP3110hJScHUqVMRGhqKLVu2KDEcC1Qf7N+/HyEhIThy5Ah2794NvV6PgQMHIj8/X4mpqXt93bp1eP311zF37lwkJCTA09MTvr6+yM7OfvQTFaJqAEA2btx435hjx44JALl06ZKIiJw9e1YAyPHjx5WY7du3i0ajkV9//VVERD777DNp0KCB3L59W4mZMWOGtGnTRtkeOXKk+Pn5qdrq2bOnvPLKK496WkRVVtlY+OWXX6RZs2Zy5swZcXFxkUWLFillHAtUH1U0FgICAmT06NGVHnPjxg0xNTWV9evXK/tSUlIEgMTFxYmIyLZt28TIyEiuXr2qxCxbtkxsbGyU8TF9+nRp3759ubZ9fX0f9bSIqqyisdC+fXt5++23Vfu6dOkis2bNEhGOBaq/srOzBYDs379fRGr2Xu/Ro4eEhIQo28XFxeLo6Cjz589/5PPiTDbVmps3b0Kj0cDOzg4AEBcXBzs7O3Tr1k2J6d+/P4yMjHD06FElpk+fPjAzM1NifH19kZqaiuvXrysx/fv3V7Xl6+uLuLi4aj4joodTUlKCMWPGIDw8HO3bty9XzrFAfwclJSX46aef4O7uDl9fXzg4OKBnz56qx2jj4+Oh1+tV93Hbtm3RvHlz5T6Oi4tDhw4d0LhxYyXG19cXOTk5SE5OVmI4Fqgu8/LywpYtW/Drr79CRBAdHY20tDQMHDgQAMcC1V83b94EADz22GMAau5ev3PnDuLj41UxRkZG6N+/v0HGA5NsqhW3bt3CjBkzEBgYCBsbGwDA1atX4eDgoIozMTHBY489hqtXryoxdw8oAMr2g2LKyolq2wcffAATExO8+uqrFZZzLNDfQXZ2NvLy8vD+++9j0KBB2LVrF4YNG4bhw4dj//79AErvYTMzM+WXsWXuvo8fZSzk5OSgsLCwOk6PqEqWLFkCDw8PODk5wczMDIMGDcLSpUvRp08fABwLVD+VlJRg6tSp8Pb2xuOPPw6g5u71P/74A8XFxdX27ySTR66BqIr0ej1GjhwJEcGyZctquztENSo+Ph4ff/wxEhISoNFoars7RLWmpKQEAPDMM8/gtddeAwB06tQJhw8fRmRkJHx8fGqze0Q1asmSJThy5Ai2bNkCFxcXHDhwACEhIXB0dCw3G0dUX4SEhODMmTM4ePBgbXfF4DiTTTWqLMG+dOkSdu/ercxiA0CTJk3KLTRQVFSEa9euoUmTJkrMvasLlm0/KKasnKg2xcbGIjs7G82bN4eJiQlMTExw6dIlTJs2Da6urgA4Fujvwd7eHiYmJvDw8FDtb9eunbK6eJMmTXDnzh3cuHFDFXP3ffwoY8HGxgY6nc5g50T0ZxQWFuLNN9/EwoUL4e/vj44dOyI0NBQBAQFYsGABAI4Fqn9CQ0OxdetWREdHw8nJSdlfU/e6vb09jI2Nq+3fSUyyqcaUJdjp6enYs2cPGjZsqCrv1asXbty4gfj4eGXfvn37UFJSgp49eyoxBw4cgF6vV2J2796NNm3aoEGDBkrM3r17VXXv3r0bvXr1qq5TI3poY8aMwalTp5CUlKT8ODo6Ijw8XFnxkmOB/g7MzMzQvXv3cp9uSUtLg4uLCwCga9euMDU1Vd3HqampyMzMVO7jXr164fTp06pfTJX9ErcsgedYoLpMr9dDr9fDyEj9z3JjY2PliQ+OBaovRAShoaHYuHEj9u3bhxYtWqjKa+peNzMzQ9euXVUxJSUl2Lt3r2HGwyMvnUb0P7m5uZKYmCiJiYkCQBYuXCiJiYly6dIluXPnjgwZMkScnJwkKSlJrly5ovzcvTryoEGDpHPnznL06FE5ePCgtG7dWgIDA5XyGzduSOPGjWXMmDFy5swZWbt2rVhYWMjnn3+uxBw6dEhMTExkwYIFkpKSInPnzhVTU1M5ffp0jV4P+vu631ioyL2ri4twLFD98KCxsGHDBjE1NZXly5dLenq6LFmyRIyNjSU2NlapIzg4WJo3by779u2TEydOSK9evaRXr15KeVFRkTz++OMycOBASUpKkh07dkijRo1k5syZSsz58+fFwsJCwsPDJSUlRZYuXSrGxsayY8eOmrsY9Lf2oLHg4+Mj7du3l+joaDl//rysXLlSzM3N5bPPPlPq4Fig+mDixIlia2srMTExqnygoKBAiampe33t2rWi1WolKipKzp49KxMmTBA7OzvVquV/FpNsMpjo6GgBUO7n+eeflwsXLlRYBkCio6OVOv773/9KYGCgWFlZiY2NjYwbN05yc3NV7Zw8eVJ69+4tWq1WmjVrJu+//365vnz33Xfi7u4uZmZm0r59e/npp5+q+/SJFPcbCxWpKMnmWKD64GHGwooVK8TNzU3Mzc3F09NTNm3apKqjsLBQJk2aJA0aNBALCwsZNmyYXLlyRRVz8eJFGTx4sOh0OrG3t5dp06aJXq8v15dOnTqJmZmZtGzZUlauXFldp01UzoPGwpUrV+SFF14QR0dHMTc3lzZt2khERISUlJQodXAsUH1QWT5w931Yk/f6kiVLpHnz5mJmZiY9evSQI0eOGOQ8Nf87WSIiIiIiIiJ6RHwnm4iIiIiIiMhAmGQTERERERERGQiTbCIiIiIiIiIDYZJNREREREREZCBMsomIiIiIiIgMhEk2ERERERERkYEwySYiIiIiIiIyECbZRERERERERAbCJJuIiMiAYmJioNFocOPGDQBAVFQU7OzsarVPhnTv+VWXefPmoVOnTtXaRpm9e/eiXbt2KC4urta2XV1dsXjx4keqIzIyEv7+/obpEBERVQsm2URE9KfExcXB2NgYfn5+td2VGrN161b4+PjA2toaFhYW6N69O6Kiou57TEBAANLS0qq1X2WJb9lP48aNMWLECJw/f75a2zUUjUaDTZs2qfaFhYVh7969NdL+9OnT8dZbb8HY2Njgda9atQq9e/c2WH0vvvgiEhISEBsba7A6iYjIsJhkExHRn7JixQpMnjwZBw4cQFZWVrW2JSIoKiqq1jYeZMmSJXjmmWfg7e2No0eP4tSpU3juuecQHByMsLCwSo/T6XRwcHCokT6mpqYiKysL69evR3JyMvz9/ZXZ2bvVhev5IFZWVmjYsGG1t3Pw4EFkZGRgxIgR1VL/5s2bMWTIEIPVZ2ZmhlGjRuGTTz4xWJ1ERGRYTLKJiKjK8vLysG7dOkycOBF+fn6q2dxRo0YhICBAFa/X62Fvb4/Vq1cDAEpKSjB//ny0aNECOp0Onp6e+P7775X4spnZ7du3o2vXrtBqtUoy9Mwzz6Bx48awsrJC9+7dsWfPHlVbV65cgZ+fH3Q6HVq0aIE1a9aUe0z3xo0beOmll9CoUSPY2NigX79+OHnyZKXne/nyZUybNg1Tp07Fe++9Bw8PD7i5uWHatGn46KOPEBERgaNHj1Z47N2Pi6elpUGj0eDcuXOqmEWLFqFVq1bK9pkzZzB48GBYWVmhcePGGDNmDP74449K+1fGwcEBTZs2RZ8+fTBnzhycPXsWP//8c6XX8/bt23j11Vfh4OAAc3Nz9O7dG8ePH1fVuW3bNri7u0On06Fv3764ePGiqryiR6sXL14MV1dX1b4vv/wS7du3h1arRdOmTREaGgoAStywYcOg0WiU7XvrLSkpwdtvvw0nJydotVp06tQJO3bsUMovXrwIjUaDDRs2oG/fvrCwsICnpyfi4uLue83Wrl2LAQMGwNzcvNKYjIwMtGzZEqGhoRARAMAXX3wBZ2dnWFhYYNiwYVi4cGG51wJu3bqFXbt2qZLsgoICvPjii7C2tkbz5s2xfPly1TEzZsyAu7s7LCws0LJlS8yePRt6vV4V4+/vjy1btqCwsPC+50ZERLWDSTYREVXZd999h7Zt26JNmzYYPXo0vvzySyX5CAoKwo8//oi8vDwlfufOnSgoKMCwYcMAAPPnz8fq1asRGRmJ5ORkvPbaaxg9ejT279+vaueNN97A+++/j5SUFHTs2BF5eXl4+umnsXfvXiQmJmLQoEHw9/dHZmamcszYsWORlZWFmJgY/PDDD1i+fDmys7NV9T777LPIzs7G9u3bER8fjy5duuCpp57CtWvXKjzf77//Hnq9vsIZ61deeQVWVlb49ttvH3jd3N3d0a1bN3zzzTeq/d988w1GjRoFoPQXAP369UPnzp1x4sQJ7NixA7/99htGjhz5wPrvptPpAAB37txR9t17PadPn44ffvgBq1atQkJCAtzc3ODr66tch8uXL2P48OHw9/dHUlISXnrpJbzxxhtV6gcALFu2DCEhIZgwYQJOnz6NLVu2wM3NDQCUpH7lypW4cuVKuSS/zMcff4yIiAgsWLAAp06dgq+vL4YMGYL09HRV3KxZsxAWFoakpCS4u7sjMDDwvrP2sbGx6NatW6Xlp06dQu/evTFq1Ch8+umn0Gg0OHToEIKDgzFlyhQkJSVhwIABePfdd8sdu3fvXjRr1gxt27ZV9kVERKBbt25ITEzEpEmTMHHiRKSmpirl1tbWiIqKwtmzZ/Hxxx/jiy++wKJFi1T1duvWDUVFRZX+YoeIiGqZEBERVZGXl5csXrxYRET0er3Y29tLdHS0anv16tVKfGBgoAQEBIiIyK1bt8TCwkIOHz6sqnP8+PESGBgoIiLR0dECQDZt2vTAvrRv316WLFkiIiIpKSkCQI4fP66Up6enCwBZtGiRiIjExsaKjY2N3Lp1S1VPq1at5PPPP6+wjeDgYLG1ta20Dx07dpTBgwer+n79+nUREVm5cqXq2EWLFkmrVq2U7dTUVAEgKSkpIiLyzjvvyMCBA1X1X758WQBIampqhe3f22ZWVpZ4eXlJs2bN5Pbt2xVez7y8PDE1NZVvvvlG2Xfnzh1xdHSUDz/8UEREZs6cKR4eHqq2ZsyYoWpr7ty54unpqYpZtGiRuLi4KNuOjo4ya9asCvsuIgJANm7cqNp3b72Ojo7y7rvvqmK6d+8ukyZNEhGRCxcuCAD5z3/+o5QnJyerrm1FbG1tVffq3W0fOnRIGjRoIAsWLFCVBwQEiJ+fn2pfUFBQuXvk5ZdflrCwMGXbxcVFRo8erWyXlJSIg4ODLFu2rNL+ffTRR9K1a9dy+xs0aCBRUVGVHkdERLWHM9lERFQlqampOHbsGAIDAwEAJiYmCAgIwIoVK5TtkSNHKrO1+fn52Lx5M4KCggAAP//8MwoKCjBgwABYWVkpP6tXr0ZGRoaqrXtnGPPy8hAWFoZ27drBzs4OVlZWSElJUWayU1NTYWJigi5duijHuLm5oUGDBsr2yZMnkZeXh4YNG6rav3DhQrn2q8Nzzz2Hixcv4siRIwBKZ7G7dOmizHaePHkS0dHRqr6VlT2of05OTrC0tISjoyPy8/Pxww8/wMzMTCm/+3pmZGRAr9fD29tb2WdqaooePXogJSUFAJCSkoKePXuq2ujVq1eVzjc7OxtZWVl46qmnqnTc3XJycpCVlaXqKwB4e3srfS3TsWNH5c9NmzZV+lCZwsLCCh8Vz8zMxIABAzBnzhxMmzZNVZaamooePXqo9t27LSL48ccfy72PfXf/NBoNmjRpourfunXr4O3tjSZNmsDKygpvvfWW6kmNMjqdDgUFBZWeFxER1R6T2u4AERH9taxYsQJFRUVwdHRU9okItFotPv30U9ja2iIoKAg+Pj7Izs7G7t27odPpMGjQIABQHiP/6aef0KxZM1XdWq1WtW1paanaDgsLw+7du7FgwQK4ublBp9Ph//7v/1SPRD9IXl4emjZtipiYmHJllX1qy93dHTdv3kRWVpbqvIHSx7EzMjLQt2/fh2q/SZMm6NevH9asWYMnnngCa9aswcSJE1X98/f3xwcffFDu2LKksTKxsbGwsbGBg4MDrK2ty5Xfez0NwcjISHlVoMzd7xCXPbZeU0xNTZU/azQaAKXvc1fG3t4e169fL7e/UaNGcHR0xLfffosXX3wRNjY2VerHsWPHUFRUBC8vr0r7V9bHsv7FxcUhKCgI//rXv+Dr6wtbW1usXbsWERER5eq/du0aGjVqVKU+ERFRzeBMNhERPbSioiKsXr0aERERSEpKUn5OnjypJCQA4OXlBWdnZ6xbtw7ffPMNnn32WSW58PDwgFarRWZmJtzc3FQ/zs7O923/0KFDeOGFFzBs2DB06NABTZo0US3E1aZNGxQVFSExMVHZ9/PPP6uSqC5duuDq1aswMTEp1769vX2F7Y4YMQKmpqYVJjuRkZHIz89XZvYfRlBQENatW4e4uDicP38ezz33nKp/ycnJcHV1Lde/ByXJLVq0QKtWrSpMsO/VqlUrmJmZ4dChQ8o+vV6P48ePw8PDAwDQrl07HDt2THVc2Qx8mUaNGuHq1auqRDspKUn5s7W1NVxdXe/7OS5TU9MKV0EvY2NjA0dHR1VfgdL7oayvf1bnzp1x9uzZcvt1Oh22bt0Kc3Nz+Pr6Ijc3Vylr06ZNuXfH793evHkz/Pz8qvRZsMOHD8PFxQWzZs1Ct27d0Lp1a1y6dKlcXEZGBm7duoXOnTs/dN1ERFRzmGQTEdFD27p1K65fv47x48fj8ccfV/2MGDFCeWQcKF1lPDIyErt371YeFQdKk66wsDC89tprWLVqFTIyMpCQkIAlS5Zg1apV922/devW2LBhg5LYjxo1SjVL2bZtW/Tv3x8TJkzAsWPHkJiYiAkTJkCn0ymzmv3790evXr0wdOhQ7Nq1CxcvXsThw4cxa9YsnDhxosJ2mzdvjg8//BCLFy/GrFmzcO7cOWRkZGDhwoWYPn06pk2bVu6x6vsZPnw4cnNzMXHiRPTt21c1Ox4SEoJr164hMDAQx48fR0ZGBnbu3Ilx48bdNxGtKktLS0ycOBHh4eHYsWMHzp49i5dffhkFBQUYP348ACA4OBjp6ekIDw9Hamoq1qxZU+674P/4xz/w+++/48MPP0RGRgaWLl2K7du3q2LmzZuHiIgIfPLJJ0hPT1f+vsuUJeFXr16tcFYZAMLDw/HBBx9g3bp1SE1NxRtvvIGkpCRMmTLlka6Dr68vDh48WGGZpaUlfvrpJ5iYmGDw4MHKUxiTJ0/Gtm3bsHDhQqSnp+Pzzz/H9u3blXsMALZs2VLlT3e1bt0amZmZWLt2LTIyMvDJJ59g48aN5eJiY2PRsmVL1Yr0RERUh9TuK+FERPRX8s9//lOefvrpCsuOHj0qAOTkyZMiInL27FkBIC4uLlJSUqKKLSkpkcWLF0ubNm3E1NRUGjVqJL6+vrJ//34RKb+QV5kLFy5I3759RafTibOzs3z66afi4+MjU6ZMUWKysrJk8ODBotVqxcXFRdasWSMODg4SGRmpxOTk5MjkyZPF0dFRTE1NxdnZWYKCgiQzM/O+579582Z58sknxdLSUszNzaVr167y5ZdfqmIetPBZmZEjRwqAcseLiKSlpcmwYcPEzs5OdDqdtG3bVqZOnVruOlbW5sOWFxYWyuTJk8Xe3l60Wq14e3vLsWPHVDE//vijuLm5iVarlSeffFK+/PLLcnUtW7ZMnJ2dxdLSUsaOHSvvvvuuauEzEZHIyEjl77tp06YyefJkpWzLli3i5uYmJiYmynH3LnxWXFws8+bNk2bNmompqal4enrK9u3blfKyhc8SExOVfdevXxcAyqJ8Ffnvf/8r5ubmcu7cOWXfvW3n5uaKl5eX9OnTR/Ly8kREZPny5dKsWTPR6XQydOhQ+fe//y1NmjQREZGff/5ZtFqtElvGxcVFWYCvjKenp8ydO1fZDg8Pl4YNG4qVlZUEBATIokWLyt0/AwcOlPnz51d6TkREVLs0Ive8SEVERFSP/PLLL3B2dsaePXseafEtqr/Cw8ORk5ODzz///E/X8fLLL+PcuXOIjY3FwoULsWfPHmzbts2AvSyVnJyMfv36IS0tDba2tgavn4iIHh0XPiMionpl3759yMvLQ4cOHXDlyhVMnz4drq6u6NOnT213jeqoWbNm4bPPPkNJSQmMjB7uTboFCxZgwIABsLS0xPbt27Fq1Sp89tlnAEpXeZ85c2a19PXKlStYvXo1E2wiojqMM9lERFSv7Ny5E9OmTcP58+dhbW0NLy8vLF68GC4uLrXdNapHRo4ciZiYGOTm5qJly5aYPHkygoODa7tbRERUBzDJJiIiIiIiIjIQri5OREREREREZCBMsomIiIiIiIgMhEk2ERERERERkYEwySYiIiIiIiIyECbZRERERERERAbCJJuIiIiIiIjIQJhkExERERERERkIk2wiIiIiIiIiA2GSTURERERERGQg/w/xz0qvpcnGYAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/water_efficiency_vs_production.png\n" + ] + }, + { + "data": { + "image/png": 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665YBXrr6C4aWLVta7r0ODw9XuXLl5O/vL8k6ZGf/99qQHR4ernbt2lnusffy8tJrr70mSXkK2ceOHdOPP/5o+cVN9qtdu3aSrt73fq3rf67yKnsRwM8//1wxMTE52i9cuKD4+HjL+gbXvvr27WtVy7Fjx3Tw4MEc/bLDaHa/kydPqkSJEqpevbrVufL7c3z9nwWTySR/f3+r+5q7du0qR0dHS9BMSEjQmjVr8hwyu3fvrqSkJH333XeSpB07digqKspqwbOUlBRNnDjRcu+8p6envLy8FB8fn+v3Oi/fq2PHjkmSHnvssRyf54YNG3J8/2+kV69eCg8Pt/y5W758udLT09WzZ8887Q8ARR33ZAPAXWA2m+Xj46N9+/bdtN++fftUsWJFeXh42OS8np6elgB0M9eH7oJwo/CQmZlZ4OfOrxuN6Bk3eT5xtq5du2rKlCm6ePGi3N3d9f333+uFF16weqTS888/b3lm+YYNG/Tee+9p+vTpWrVqlZ588smbHv/hhx/W6tWrtX//fsv92NlatmypsWPH6syZM9q+fbsqVKigatWqSbq6un3btm31wAMPaNasWfL19ZWDg4PWrVunDz74wGrhshvJysrS448/rnHjxuXanh1cs93Jz9V//vMfhYaGavr06ercuXOOOiSpR48eOe5/z5Z9X3pWVpbq16+vWbNm5drP19f3tmu8XaVLl1aHDh20ePFiTZw4UStWrFBqamqe70fu0KGDzGazlixZohdffFFLliyRnZ2dunXrZukzfPhwLViwQKNGjVJAQIDMZrNMJpO6deuW6/c6L9+r7P1CQ0Pl7e2doz2vjw3r1q2bRo8ercWLF+u1117TokWL1LRp09v+xRwAFDWEbAC4Szp06KD58+dr+/btuT5S6eeff1ZUVFSRWF03+7m4R44cydH2xx9/yNPTU66urnJycpKzs7NlhOta1++bPRocHx9vtf36UWQvLy95eHjowIEDN60xP9NKq1Spon379ikrK8tqNPuPP/6wtNtK165dNWnSJK1cuVLly5dXYmKiVfjJ5uPjoyFDhmjIkCGKjY1V48aNNWXKlDyFbOnqiu7h4eFW05mbNGkiR0dHbdu2TTt37lT79u0tbatXr1Zqaqq+//57q5H63Kb43uizrV69uq5cuZKnX9zcqerVq6tHjx767LPP1KJFC6u27NW/MzMzb1lL9erVtXfvXrVt2/amPzNVqlRRVlaWjh8/bhX2cvszcDPX/1kwDEN//vmnJfRn69Wrl5566int2rVLixcv1oMPPqi6devm6RyOjo569tln9dVXX+n8+fNavny5HnvsMavgu2LFCvXu3VszZ860bPvnn39y/PnLj+xR/nLlyt3yc7/ZZ12mTBkFBwdr8eLF6t69u8LDw3MsmgcAxRnTxQHgLhk7dqycnZ01cOBAXbp0yaotLi5OgwYNkouLi8aOHVtIFf5/Pj4+atSokRYuXGj1j/IDBw5ow4YNlvBmZ2enoKAgffvtt4qOjrb0O3z4sNavX291TA8PD3l6eua4b/fTTz+1el+iRAl17txZq1ev1m+//ZajtuzRZFdXV0k5Q3tu2rdvr3PnzllN487IyNBHH30kNze3W94Hmx+1a9dW/fr19fXXX+vrr7+Wj4+PWrVqZWnPzMzMMV23XLlyqlChQo5HSuWmadOmcnJy0uLFi3XmzBmrkWxHR0c1btxYn3zyiZKSkqx+mZM9On/taHxCQoIWLFiQ4xyurq65fq7PP/+8IiIicnxvpavfh4yMjFvWnx+vv/660tPTrR5rJ129li5dumjlypW5/jLm2sdjPf/88zpz5ozmz5+fo19KSorlvubsX25cv6J5fsPfV199ZXVbyIoVKxQTE5PjlydPPvmkPD09NX36dIWFheV7Ve3u3bsrPT1dAwcO1IULF3I8G9vOzi7HzIuPPvrojmaOBAUFycPDQ++++26ujxC79nO/1Z/Pnj176tChQxo7dmyOUXgAKO4YyQaAu6RGjRpauHChunfvrvr166tfv36qWrWqoqKi9OWXX+rixYv63//+l+Oe0MLy3nvv6cknn1RAQID69etneYSX2Wy2eqb1pEmT9OOPP+qRRx7RkCFDLOG1bt26OabHv/zyy5o2bZpefvllNW3aVD/99JOOHj2a49zvvvuuNmzYoNatW1seuxQTE6Ply5dr+/btKlWqlBo1aiQ7OztNnz5dCQkJcnR0tDwD+noDBgzQZ599pj59+igyMlJ+fn5asWKFZQTNVvfAZ+vatasmTpwoJycn9evXz2r0/PLly6pUqZKeffZZNWzYUG5ubtq0aZN27dplNep4Iw4ODmrWrJl+/vlnOTo6qkmTJlbtLVu2tBzn2pD9xBNPyMHBQR07dtTAgQN15coVzZ8/X+XKlctx33OTJk00d+5cTZ48Wf7+/ipXrpwee+wxjR07Vt9//706dOigPn36qEmTJkpKStL+/fu1YsUKRUVFydPT804+OivZo9m5PXt82rRp2rp1q1q0aKH+/furTp06iouL0+7du7Vp0ybFxcVJuhrmli1bpkGDBmnr1q0KDAxUZmam/vjjDy1btkzr169X06ZN1ahRI73wwgv69NNPlZCQoJYtW2rz5s36888/81VzmTJl9PDDD6tv3746f/68Zs+eLX9/f/Xv39+qn729vbp166aPP/5YdnZ2Vovy5UXr1q1VqVIlfffdd3J2dtYzzzxj1d6hQweFhobKbDarTp06ioiI0KZNm3I8vi8/PDw8NHfuXPXs2VONGzdWt27d5OXlpejoaK1du1aBgYH6+OOPJcnyczlixAgFBQXlCNLBwcEqW7asli9frieffDLXP7cAUGwV3sLmAHB/2rdvn/HCCy8YPj4+hr29veHt7W288MILxv79+3P0vdNHeGU/EulGsh+19N577+XavmnTJiMwMNBwdnY2PDw8jI4dOxqHDh3K0S8sLMxo0qSJ4eDgYFSrVs2YN2+e8eabb+Z4hE9ycrLRr18/w2w2G+7u7sbzzz9vxMbG5vqIpJMnTxq9evUyvLy8DEdHR6NatWrG0KFDjdTUVEuf+fPnG9WqVbM8Liz78Ue5fUbnz583+vbta3h6ehoODg5G/fr1czxO7GafR2413sixY8cMSYYkY/v27VZtqampxtixY42GDRsa7u7uhqurq9GwYUPj008/zdOxDePqo8IkGS1btszRlv0oKHd39xyPZPr++++NBg0aGE5OToafn58xffp047///W+On7Fz584ZwcHBhru7uyHJ6rO8fPmyMWHCBMPf399wcHAwPD09jZYtWxrvv/++kZaWZhjGrX+ucnOjn9djx45Zvr/XPsLLMK5+T4cOHWr4+vpa/iy1bdvW+Pzzz636paWlGdOnTzfq1q1rODo6GqVLlzaaNGliTJo0yUhISLD0S0lJMUaMGGGULVvWcHV1NTp27GicOnUqX4/w+t///mdMmDDBKFeunOHs7GwEBwdbPd7uWr/++qshyXjiiSfy+ClZGzt2rCHJeP7553O0/f3335afdzc3NyMoKMj4448/jCpVqhi9e/e29Mv+Oya3x+Xl9vdP9rUGBQUZZrPZcHJyMqpXr2706dPH+O233yx9MjIyjOHDhxteXl6GyWTK9XFeQ4YMMSQZS5Ysua3rB4CiymQYeVjFBQAAADa1d+9eNWrUSF999dV9ubL26NGj9eWXX+rcuXNycXEp7HIAwGa4JxsAAKAQzJ8/X25ubjmmet8P/vnnHy1atEhdunQhYAO453BPNgAAwF20evVqHTp0SJ9//rmGDRtmWSTsfhAbG6tNmzZpxYoVunTpkkaOHFnYJQGAzTFdHAAA4C7y8/PT+fPnFRQUpNDQUJsvvFeUbdu2TY8++qjKlSunN954Q8OGDSvskgDA5gjZAAAAAADYCPdkAwAAAABgI4RsAAAAAABshIXP8iArK0tnz56Vu7u7TCZTYZcDAAAAALiGYRi6fPmyKlSooBIlCncsmZCdB2fPnpWvr29hlwEAAAAAuIlTp06pUqVKhVoDITsPslf9PHXqlDw8PAq5GgAAAADAtRITE+Xr61sknthAyM6D7CniHh4ehGwAAAAAKKKKwu29LHwGAAAAAICNELIBAAAAALARQjYAFEGGYWjAgAEqU6aMTCaT9uzZU9gl5dCnTx917ty5sMsAAAAoUrgnGwCKoB9//FEhISHatm2bqlWrJk9PzwI7V58+fRQfH69vv/02X/vNmTNHhmEUTFEAAADFFCEbAIqg48ePy8fHRy1btsy1PS0tTQ4ODne5Kmtms7lQzw8AAFAUMV0cAIqYPn36aPjw4YqOjpbJZJKfn5/atGmjYcOGadSoUfL09FRQUJAkadasWapfv75cXV3l6+urIUOG6MqVK5ZjhYSEqFSpUlq/fr1q164tNzc3/etf/1JMTIwk6a233tLChQv13XffyWQyyWQyadu2bZKk/fv367HHHpOzs7PKli2rAQMGWB37+uniqampGjFihMqVKycnJyc9/PDD2rVrV8F/YAAAAEUIIRsAipg5c+bo7bffVqVKlRQTE2MJqgsXLpSDg4PCw8M1b948SVKJEiX04Ycf6uDBg1q4cKG2bNmicePGWR0vOTlZ77//vkJDQ/XTTz8pOjpaY8aMkSSNGTNGzz//vCV4x8TEqGXLlkpKSlJQUJBKly6tXbt2afny5dq0aZOGDRt2w7rHjRunlStXauHChdq9e7f8/f0VFBSkuLi4AvqkAAAAih6miwNAEWM2m+Xu7i47Ozt5e3tbtteoUUMzZsyw6jtq1CjL135+fpo8ebIGDRqkTz/91LI9PT1d8+bNU/Xq1SVJw4YN09tvvy1JcnNzk7Ozs1JTU63OtXDhQv3zzz/66quv5OrqKkn6+OOP1bFjR02fPl3ly5e3qiMpKUlz585VSEiInnzySUnS/PnztXHjRn355ZcaO3asDT4ZAACAoo+RbAAoJpo0aZJj26ZNm9S2bVtVrFhR7u7u6tmzpy5duqTk5GRLHxcXF0vAliQfHx/Fxsbe9FyHDx9Ww4YNLQFbkgIDA5WVlaUjR47k6H/8+HGlp6crMDDQss3e3l7NmzfX4cOH83WdAAAAxRkhGwCKiWsDryRFRUWpQ4cOatCggVauXKnIyEh98sknkq4ujJbN3t7eaj+TycSq4AAAAAWEkA0AxVRkZKSysrI0c+ZMPfTQQ6pZs6bOnj2b7+M4ODgoMzPTalvt2rW1d+9eJSUlWbaFh4erRIkSqlWrVo5jVK9e3XK/eLb09HTt2rVLderUyXdNAAAAxRUhGwCKKX9/f6Wnp+ujjz7SiRMnFBoaalkQLT/8/Py0b98+HTlyRBcvXlR6erq6d+8uJycn9e7dWwcOHNDWrVs1fPhw9ezZM8f92NLVUfbBgwdr7Nix+vHHH3Xo0CH1799fycnJ6tevny0uFwAAoFggZAPAXWQYhuKS0nQqLllxSWl3NG27YcOGmjVrlqZPn6569epp8eLFmjp1ar6P079/f9WqVUtNmzaVl5eXwsPD5eLiovXr1ysuLk7NmjXTs88+q7Zt2+rjjz++4XGmTZumLl26qGfPnmrcuLH+/PNPrV+/XqVLl77tawQAAChuTAY35t1SYmKizGazEhIS5OHhUdjlACiGElLStTLytBbuiNLJuP+/KFmVMi7q3dJPXZpUktnZ/iZHAAAAwI0UpcxGyM6DovQNA1D8hB29oMGLIpWSdvW+52v/0jX933+dHew0t0cTta7pddfrAwAAKO6KUmZjujgAFKCwoxfUd8GvSknPlCHrgK3/e29ISknPVN8Fvyrs6IW7XyQAAABshpANAAUkISVdgxdFXg3St5gzZBhXw/bgRZFKSEm/G+UBAACgABCyAaCArIw8rZS0zFsG7GyGIaWkZWrV7tMFWxgAAAAKDCEbAAqAYRhauCPqtvYNCY+6o1XHAQAAUHgI2QBQAP5OTtfJuOQc92DfiiHpZFyy4pOZMg4AAFAcEbIBoAAkpWbc0f5X7nB/AAAAFA5CNgAUAFfHkne0v9sd7g8AAIDCQcgGgAJQ2sVeVcq4WJ6DnVcmSVXKuKiUi31BlAUAAIACRsgGgAJgMpnUu6Xfbe3bJ9BPJlN+4zkAAACKAkI2ABSQLk0qydnBTnnNyyVMkrODnZ5pXKlgCwMAAECBIWQDQAExO9trbo8mMkm3DNrZ7fN6NJHZmaniAAAAxRUhGwAKUOuaXlrQt7mc7e2uhu3r2rO3OdvbKaRvc7Wq6XX3iwQAAIDNsHwtABSw1jW9FDGhrVbtPq2Q8CidjEu2tFUu46I+gX7q0qSSPJwYwQYAACjuTIZhGIVdRFGXmJgos9mshIQEeXh4FHY5AIoxwzAUn5yuK6kZcnMsqVIu9ixyBgAAcIeKUmZjJBsA7iKTyaTSrg4q7epQ2KUAAACgAHBPNgAAAAAANkLIBgAAAADARgjZAAAAAADYCCEbAAAAAAAbIWQDAAAAAGAjhGwAAAAAAGyEkA0AAAAAgI0QsgEAAAAAsBFCNgAAAAAANkLIBgAAAADARgjZAAAAAADYCCEbAAAAAAAbIWQDAAAAAGAjhGwAAAAAAGyEkA0AAAAAgI0QsgEAAAAAsBFCNgAAAAAANkLIBgAAAADARgjZAAAAAADYCCEbAAAAAAAbIWQDAAAAAGAjhGwAAAAAAGyEkA0AAAAAgI0QsgEAAAAAsBFCNgAAAAAANkLIBgAAAADARgjZAAAAAADYCCEbAAAAAAAbIWQDAAAAAGAjhGwAAAAAAGyEkA0AAAAAgI0QsgEAAAAAsBFCNgAAAAAANkLIBgAAAADARgjZAAAAAADYCCEbAAAAAAAbIWQDAAAAAGAjhGwAAAAAAGyEkA0AAAAAgI0QsgEAAAAAsBFCNgAAAAAANkLIBgAAAADARgjZAAAAAADYCCEbAAAAAAAbIWQDAAAAAGAjRSZkT5s2TSaTSaNGjbJsa9OmjUwmk9Vr0KBBVvtFR0crODhYLi4uKleunMaOHauMjAyrPtu2bVPjxo3l6Ogof39/hYSE3IUrAgAAAADcb0oWdgGStGvXLn322Wdq0KBBjrb+/fvr7bfftrx3cXGxfJ2Zmang4GB5e3trx44diomJUa9evWRvb693331XkvTXX38pODhYgwYN0uLFi7V582a9/PLL8vHxUVBQUMFfHAAAAADgvlHoI9lXrlxR9+7dNX/+fJUuXTpHu4uLi7y9vS0vDw8PS9uGDRt06NAhLVq0SI0aNdKTTz6pd955R5988onS0tIkSfPmzVPVqlU1c+ZM1a5dW8OGDdOzzz6rDz744K5dIwAAAADg/lDoIXvo0KEKDg5Wu3btcm1fvHixPD09Va9ePU2YMEHJycmWtoiICNWvX1/ly5e3bAsKClJiYqIOHjxo6XP9sYOCghQREVEAVwMAAAAAuJ8V6nTxpUuXavfu3dq1a1eu7S+++KKqVKmiChUqaN++fRo/fryOHDmiVatWSZLOnTtnFbAlWd6fO3fupn0SExOVkpIiZ2fnHOdNTU1Vamqq5X1iYuLtXyQAAAAA4L5RaCH71KlTGjlypDZu3CgnJ6dc+wwYMMDydf369eXj46O2bdvq+PHjql69eoHVNnXqVE2aNKnAjg8AAAAAuDcV2nTxyMhIxcbGqnHjxipZsqRKliypsLAwffjhhypZsqQyMzNz7NOiRQtJ0p9//ilJ8vb21vnz5636ZL/39va+aR8PD49cR7ElacKECUpISLC8Tp06dWcXCwAAAAC4LxTaSHbbtm21f/9+q219+/bVAw88oPHjx8vOzi7HPnv27JEk+fj4SJICAgI0ZcoUxcbGqly5cpKkjRs3ysPDQ3Xq1LH0WbdundVxNm7cqICAgBvW5ujoKEdHx9u+NgAAAADA/anQQra7u7vq1atntc3V1VVly5ZVvXr1dPz4cS1ZskTt27dX2bJltW/fPo0ePVqtWrWyPOrriSeeUJ06ddSzZ0/NmDFD586d0+uvv66hQ4daQvKgQYP08ccfa9y4cXrppZe0ZcsWLVu2TGvXrr3r1wwAAAAAuLcV+uriN+Lg4KBNmzbpiSee0AMPPKBXX31VXbp00erVqy197OzstGbNGtnZ2SkgIEA9evRQr169rJ6rXbVqVa1du1YbN25Uw4YNNXPmTH3xxRc8IxsAAAAAYHMmwzCMwi6iqEtMTJTZbFZCQoLVc7oBAAAAAIWvKGW2IjuSDQAAAABAcUPIBgAAAADARgjZAAAAAADYCCEbAAAAAAAbIWQDAAAAAGAjhGwAAAAAAGyEkA0AAAAAgI0QsgEAAAAAsBFCNgAAAAAANkLIBgAAAADARgjZAAAAAADYCCEbAAAAAAAbIWQDAAAAAGAjhGwAAAAAAGyEkA0AAAAAgI0QsgEAAAAAsBFCNgAAAAAANkLIBgAAAADARgjZAAAAAADYCCEbAAAAAAAbIWQDAAAAAGAjhGwAAAAAAGyEkA0AAAAAgI0QsgEAAAAAsBFCNgAAAAAANkLIBgAAAADARgjZAAAAAADYCCEbAAAAAAAbIWQDAAAAAGAjhGwAAAAAAGyEkA0AAAAAgI0QsgEAAAAAsBFCNgAAAAAANkLIBgAAAADARgjZAAAAAADYCCEbAAAAAAAbIWQDAAAAAGAjhGwAAAAAAGyEkA0AAAAAgI0QsgEAAAAAsBFCNgAAAAAANkLIBgAAAADARgjZAAAAAADYCCEbAAAAAAAbIWQDAAAAAGAjhGwAAAAAAGyEkA0AAAAAgI0QsgEAAAAAsBFCNgAAAAAANkLIBgAAAADcc9q0aaNRo0bd9fOWvOtnBAAAAADARrZt26ZHH300x/ZVq1bJ3t7+rtdDyAYAAAAAFDlpaWlycHC47f3LlCljw2ryjuniAAAAAACbyMrK0owZM+Tv7y9HR0dVrlxZU6ZMkSTt379fjz32mJydnVW2bFkNGDBAV65csezbp08fde7cWVOmTFGFChVUq1YtSVJoaKiaNm0qd3d3eXt768UXX1RsbKwkKSoqymoU22w2q0+fPpJyThf38/PTu+++q5deeknu7u6qXLmyPv/8c6v6x48fr5o1a8rFxUXVqlXTG2+8ofT09Hx9BoRsAAAAAIBNTJgwQdOmTdMbb7yhQ4cOacmSJSpfvrySkpIUFBSk0qVLa9euXVq+fLk2bdqkYcOGWe2/efNmHTlyRBs3btSaNWskSenp6XrnnXe0d+9effvtt4qKirIEaV9fX61cudKy/9GjRzVnzpwb1jdz5kw1bdpUv//+u4YMGaLBgwfryJEjlnZ3d3eFhITo0KFDmjNnjubPn68PPvggX5+ByTAMI1973IcSExNlNpuVkJAgDw+Pwi4HAAAAAIqcy5cvy8vLSx9//LFefvllq7b58+dr/PjxOnXqlFxdXSVJ69atU8eOHXX27FmVL19effr00Y8//qjo6OibThP/7bff1KxZM12+fFlubm5W92Rfm9natGmjRo0aafbs2ZKujmQ/8sgjCg0NlSQZhiFvb29NmjRJgwYNyvVc77//vpYuXarffvstz58DI9kAAAAAgDt2+PBhpaamqm3btrm2NWzY0BKwJSkwMFBZWVlWI8n169fPEbAjIyPVsWNHVa5cWe7u7mrdurUkKTo6Ot81NmjQwPK1yWSSt7e3Zeq5JH399dcKDAyUt7e33Nzc9Prrr+f7PIRsAAAAAMAdc3Z2vuNjXBvCJVmmmXt4eGjx4sXatWuXvvnmG0lXF0bLr+tXGzeZTMrKypIkRUREqHv37mrfvr3WrFmj33//Xf/5z3/yfR5CNgAAAADgjtWoUUPOzs7avHlzjrbatWtr7969SkpKsmwLDw9XiRIlLAuc5eaPP/7QpUuXNG3aND3yyCN64IEHrEaeJd3RCuTX2rFjh6pUqaL//Oc/atq0qWrUqKGTJ0/m+ziEbAAAAADAHXNyctL48eM1btw4ffXVVzp+/Lh++eUXffnll+revbucnJzUu3dvHThwQFu3btXw4cPVs2dPlS9f/obHrFy5shwcHPTRRx/pxIkT+v777/XOO+9Y9alSpYpMJpMk6eLFi1YrludHjRo1FB0draVLl+r48eP68MMPLaPm+UHIBgAAAADckGEYiktK06m4ZMUlpelma2e/8cYbevXVVzVx4kTVrl1bXbt2VWxsrFxcXLR+/XrFxcWpWbNmevbZZ9W2bVt9/PHHNz23l5eXQkJCtHz5ctWpU0fTpk3T+++/b9WnYsWKeu211yRJ/v7+OVYsz6tOnTpp9OjRGjZsmBo1aqQdO3bojTfeyPdxWF08D1hdHAAAAMD9JiElXSsjT2vhjiidjEu2bK9SxkW9W/qpS5NKMjvb3+QId09RymyE7DwoSt8wAAAAAChoYUcvaPCiSKWkZUqSrg2Npv/7r7ODneb2aKLWNb3uen3XK0qZjeniAAAAAACLsKMX1HfBr0pJz5Qh64Ct/3tvSEpJz1TfBb8q7OiFu19kEVYyvzukpqZq586dOnnypJKTk+Xl5aUHH3xQVatWLYj6AAAAAAB3SUJKugYvirwapG8x59kwJJmkwYsiFTGhbZGZOl7Y8hyyw8PDNWfOHK1evVrp6ekym81ydnZWXFycUlNTVa1aNQ0YMECDBg2Su7t7QdYMAAAAACgAKyNPKyUtM8fo9Y0YhpSSlqlVu0+rbyADr1Iep4t36tRJXbt2lZ+fnzZs2KDLly/r0qVLOn36tJKTk3Xs2DG9/vrr2rx5s2rWrKmNGzcWdN0AAAAAABsyDEMLd0Td1r4h4VE3XXX8fpKnkezg4GCtXLlS9va5D/9Xq1ZN1apVU+/evXXo0CHFxMTYtEgAAAAAQMH6OzndahXxvDIknYxLVnxyukq7Oti+sGImTyF74MCBeT5gnTp1VKdOndsuCAAAAABw9yWlZtzR/ldSMwjZYnVxAAAAAIAkV8d8r4ttxe0O979X5DtkZ2Zm6v3331fz5s3l7e2tMmXKWL0AAAAAAMVPaRd7VSnjYnkOdl6ZJFUp46JSLqwuLt1GyJ40aZJmzZqlrl27KiEhQa+88oqeeeYZlShRQm+99VYBlAgAAAAAKGgmk0m9W/rd1r59Av1kMuU3nt+b8h2yFy9erPnz5+vVV19VyZIl9cILL+iLL77QxIkT9csvvxREjQAAAACAu6BLk0pydrBTXvNyCZPk7GCnZxpXKtjCipF8h+xz586pfv36kiQ3NzclJCRIkjp06KC1a9fatjoAAAAAwF1jdrbX3B5NZJJuGbSz2+f1aCKzM1PFs+U7ZFeqVMnyiK7q1atrw4YNkqRdu3bJ0dHRttUBAAAAAO6q1jW9tKBvcznb210N29e1Z29ztrdTSN/malXT6+4XWYTle/m3p59+Wps3b1aLFi00fPhw9ejRQ19++aWio6M1evTogqgRAAAAAHAXta7ppYgJbbVq92mFhEdZPT+7chkX9Qn0U5cmleThxAj29UyGYRh3coCIiAhFRESoRo0a6tixo63qKlISExNlNpuVkJAgDw+Pwi4HAAAAAO4awzAUn5yuK6kZcnMsqVIu9kVukbOilNnuOGTfD4rSNwwAAAAAYK0oZbbbelr4sWPHtHXrVsXGxiorK8uqbeLEiTYpDAAAAACA4ibfIXv+/PkaPHiwPD095e3tbTVNwGQyEbIBAAAAAPetfIfsyZMna8qUKRo/fnxB1AMAAAAAQLGV70d4/f3333ruuedsXsi0adNkMpk0atQoy7Z//vlHQ4cOVdmyZeXm5qYuXbro/PnzVvtFR0crODhYLi4uKleunMaOHauMjAyrPtu2bVPjxo3l6Ogof39/hYSE2Lx+AAAAAADyHbKfe+45y7OxbWXXrl367LPP1KBBA6vto0eP1urVq7V8+XKFhYXp7NmzeuaZZyztmZmZCg4OVlpamnbs2KGFCxcqJCTEasr6X3/9peDgYD366KPas2ePRo0apZdfflnr16+36TUAAAAAAJCn1cU//PBDy9dJSUmaNWuWgoODVb9+fdnbWz8XbcSIEfkq4MqVK2rcuLE+/fRTTZ48WY0aNdLs2bOVkJAgLy8vLVmyRM8++6wk6Y8//lDt2rUVERGhhx56SD/88IM6dOigs2fPqnz58pKkefPmafz48bpw4YIcHBw0fvx4rV27VgcOHLCcs1u3boqPj9ePP/6YpxqL0kp1AAAAAABrRSmz5eme7A8++MDqvZubm8LCwhQWFma13WQy5TtkDx06VMHBwWrXrp0mT55s2R4ZGan09HS1a9fOsu2BBx5Q5cqVLSE7IiJC9evXtwRsSQoKCtLgwYN18OBBPfjgg4qIiLA6Rnafa6elAwAAAABgC3kK2X/99VeBnHzp0qXavXu3du3alaPt3LlzcnBwUKlSpay2ly9fXufOnbP0uTZgZ7dnt92sT2JiolJSUuTs7Jzj3KmpqUpNTbW8T0xMzP/FAQAAAADuO3m+J7tVq1aaOXOmjh07ZpMTnzp1SiNHjtTixYvl5ORkk2PaytSpU2U2my0vX1/fwi4JAAAAAFAM5Dlk9+vXTzt27FDjxo1Vu3ZtjR8/XuHh4crDLd25ioyMVGxsrBo3bqySJUuqZMmSCgsL04cffqiSJUuqfPnySktLU3x8vNV+58+fl7e3tyTJ29s7x2rj2e9v1cfDwyPXUWxJmjBhghISEiyvU6dO3dY1AgAAAADuL3kO2b1799bKlSt18eJFzZw5U/Hx8Xruuefk7e2tl156Sd9++61SUlLyfOK2bdtq//792rNnj+XVtGlTde/e3fK1vb29Nm/ebNnnyJEjio6OVkBAgCQpICBA+/fvV2xsrKXPxo0b5eHhoTp16lj6XHuM7D7Zx8iNo6OjPDw8rF4AAAAAANxKnlYXv5mdO3fq+++/1/fff6/jx4/rscce04QJExQYGJjvY7Vp08ayurgkDR48WOvWrVNISIg8PDw0fPhwSdKOHTskXX2EV6NGjVShQgXNmDFD586dU8+ePfXyyy/r3XfflXT1fvJ69epp6NCheumll7RlyxaNGDFCa9euVVBQUJ7qKkor1QEAAAAArBWlzJanhc9upkWLFmrRooUmTZqkHTt2KDIyUjExMbaoTR988IFKlCihLl26KDU1VUFBQfr0008t7XZ2dlqzZo0GDx6sgIAAubq6qnfv3nr77bctfapWraq1a9dq9OjRmjNnjipVqqQvvvgizwEbAAAAAIC8uuOR7Gx79+5V48aNlZmZaYvDFSlF6bciAAAAAABrRSmz5fmebAAAAAAAcHOEbAAAAAAAbISQDQAAAACAjeR54bN9+/bdtP3IkSN3XAwAAAAAAMVZnkN2o0aNZDKZlNs6adnbTSaTTYsDAAAAAKA4yXPI/uuvvwqyDgAAAAAAir08h+wqVaoUZB0AAAAAABR7eVr4LDo6Ol8HPXPmzG0VAwAAAABAcZankN2sWTMNHDhQu3btumGfhIQEzZ8/X/Xq1dPKlSttViAAAAAAAMVFnqaLHzp0SFOmTNHjjz8uJycnNWnSRBUqVJCTk5P+/vtvHTp0SAcPHlTjxo01Y8YMtW/fvqDrBgAAAACgyDEZuS0XfgMpKSlau3attm/frpMnTyolJUWenp568MEHFRQUpHr16hVkrYUmMTFRZrNZCQkJ8vDwKOxyAAAAAADXKEqZLV8h+35VlL5hAAAAAABrRSmz5emebAAAAAAAcGuEbAAAAAAAbISQDQAAAACAjRCyAQAAAACwEUI2AAAAAAA2kqfnZF/v2LFj2rp1q2JjY5WVlWXVNnHiRJsUBgAAAABAcZPvkD1//nwNHjxYnp6e8vb2lslksrSZTCZCNgAAAADgvpXvkD158mRNmTJF48ePL4h6AAAAAAAotvJ9T/bff/+t5557riBqAQAAAACgWMt3yH7uuee0YcOGgqgFAAAAAIBiLd/Txf39/fXGG2/ol19+Uf369WVvb2/VPmLECJsVBwAAAABAcWIyDMPIzw5Vq1a98cFMJp04ceKOiypqEhMTZTablZCQIA8Pj8IuBwAAAABwjaKU2fI9kv3XX38VRB0AAAAAABR7+b4n+1qGYSifA+EAAAAAANyzbitkf/XVV6pfv76cnZ3l7OysBg0aKDQ01Na1AQAAAABQrOR7uvisWbP0xhtvaNiwYQoMDJQkbd++XYMGDdLFixc1evRomxcJAAAAAEBxcFsLn02aNEm9evWy2r5w4UK99dZb9+Q920XpJnoAAAAAgLWilNnyPV08JiZGLVu2zLG9ZcuWiomJsUlRAAAAAAAUR/kO2f7+/lq2bFmO7V9//bVq1Khhk6IAAAAAACiO8n1P9qRJk9S1a1f99NNPlnuyw8PDtXnz5lzDNwAAAAAA94t8j2R36dJFO3fulKenp7799lt9++238vT01K+//qqnn366IGoEAAAAAKBYyPfCZ/ejonQTPQAAAADAWlHKbHmaLp6YmGgpNDEx8aZ9C/uCAAAAAAAoLHkK2aVLl1ZMTIzKlSunUqVKyWQy5ehjGIZMJpMyMzNtXiQAAAAAAMVBnkL2li1bVKZMGUnS1q1bC7QgAAAAAACKqzyF7NatW1u+rlq1qnx9fXOMZhuGoVOnTtm2OgAAAAAAipF8ry5etWpVXbhwIcf2uLg4Va1a1SZFAQAAAABQHOU7ZGffe329K1euyMnJySZFAQAAAABQHOVpurgkvfLKK5Ikk8mkN954Qy4uLpa2zMxM7dy5U40aNbJ5gQAAAAAAFBd5Dtm///67pKsj2fv375eDg4OlzcHBQQ0bNtSYMWNsXyEAAAAAAMVEnkN29qriffv21Zw5c3geNgAAAAAA18n3PdmzZ89WRkZGju1xcXFKTEy0SVEAAAAAABRH+Q7Z3bp109KlS3NsX7Zsmbp162aTogAAAAAAKI7yHbJ37typRx99NMf2Nm3aaOfOnTYpCgAAAACA4ijfITs1NTXX6eLp6elKSUmxSVEAAAAAABRH+Q7ZzZs31+eff55j+7x589SkSRObFAUAAAAAQHGU59XFs02ePFnt2rXT3r171bZtW0nS5s2btWvXLm3YsMHmBQIAAAAAUFzkeyQ7MDBQERER8vX11bJly7R69Wr5+/tr3759euSRRwqiRgAAAAAAigWTYRhGYRdR1CUmJspsNishIYHngwMAAABAEVOUMlu+p4tHR0fftL1y5cq3XQwAAAAAAMVZvkO2n5+fTCbTDdszMzPvqCAAAAAAAIqrfIfs33//3ep9enq6fv/9d82aNUtTpkyxWWEAAAAAABQ3+Q7ZDRs2zLGtadOmqlChgt577z0988wzNikMAAAAAIDiJt+ri99IrVq1tGvXLlsdDgAAAACAYiffI9mJiYlW7w3DUExMjN566y3VqFHDZoUBAAAAAFDc5DtklypVKsfCZ4ZhyNfXV0uXLrVZYQAAAAAAFDf5Dtlbt261el+iRAl5eXnJ399fJUvm+3AAAAAAANwz8p2KW7duXRB1AAAAAABQ7OUpZH///fd5PmCnTp1uuxgAAAAAAIqzPIXszp07W703mUwyDMPqfbbMzEzbVAYAAAAAQDGTp0d4ZWVlWV4bNmxQo0aN9MMPPyg+Pl7x8fFat26dGjdurB9//LGg6wUAAAAAoMjK9z3Zo0aN0rx58/Twww9btgUFBcnFxUUDBgzQ4cOHbVogAAAAAADFRZ5Gsq91/PhxlSpVKsd2s9msqKgoG5QEAAAAAEDxlO+Q3axZM73yyis6f/68Zdv58+c1duxYNW/e3KbFAQAAAABQnOQ7ZP/3v/9VTEyMKleuLH9/f/n7+6ty5co6c+aMvvzyy4KoEQAAAACAYiHf92T7+/tr37592rhxo/744w9JUu3atdWuXTurVcYBAAAAALjfmIxrn8WFXCUmJspsNishIUEeHh6FXQ4AAAAA4BpFKbPle7q4JIWFhaljx46W6eKdOnXSzz//bOvaAAAAAAAoVvIdshctWqR27drJxcVFI0aM0IgRI+Tk5KS2bdtqyZIlBVEjAAAAAADFQr6ni9euXVsDBgzQ6NGjrbbPmjVL8+fPvyefk12Uph4AAAAAAKwVpcyW75HsEydOqGPHjjm2d+rUSX/99ZdNigIAAAAAoDjKd8j29fXV5s2bc2zftGmTfH19bVIUAAAAAADFUb4f4fXqq69qxIgR2rNnj1q2bClJCg8PV0hIiObMmWPzAgEAAAAAKC7yHbIHDx4sb29vzZw5U8uWLZN09T7tr7/+Wk899ZTNCwQAAAAAoLjIV8jOyMjQu+++q5deeknbt28vqJoAAAAAACiW8nVPdsmSJTVjxgxlZGQUVD0AAAAAABRb+V74rG3btgoLCyuIWgAAAAAAKNbyfU/2k08+qX//+9/av3+/mjRpIldXV6v2Tp062aw4AAAAAACKk3yPZA8ZMkTnz5/XrFmz1L17d3Xu3Nnyevrpp/N1rLlz56pBgwby8PCQh4eHAgIC9MMPP1ja27RpI5PJZPUaNGiQ1TGio6MVHBwsFxcXlStXTmPHjs0xnX3btm1q3LixHB0d5e/vr5CQkPxeNgAAAAAAt5TvkeysrCybnbxSpUqaNm2aatSoIcMwtHDhQj311FP6/fffVbduXUlS//799fbbb1v2cXFxsXydmZmp4OBgeXt7a8eOHYqJiVGvXr1kb2+vd999V5L0119/KTg4WIMGDdLixYu1efNmvfzyy/Lx8VFQUJDNrgUAAAAAAJNhGEZeO0dFRWnjxo1KT09X69atLUHYlsqUKaP33ntP/fr1U5s2bdSoUSPNnj07174//PCDOnTooLNnz6p8+fKSpHnz5mn8+PG6cOGCHBwcNH78eK1du1YHDhyw7NetWzfFx8frxx9/zFNNiYmJMpvNSkhIkIeHxx1fIwAAAADAdopSZsvzdPGtW7eqbt26GjhwoIYNG6YHH3xQixYtslkhmZmZWrp0qZKSkhQQEGDZvnjxYnl6eqpevXqaMGGCkpOTLW0RERGqX7++JWBLUlBQkBITE3Xw4EFLn3bt2lmdKygoSBERETesJTU1VYmJiVYvAAAAAABuJc8h+4033tDjjz+uM2fO6NKlS+rfv7/GjRt3xwXs379fbm5ucnR01KBBg/TNN9+oTp06kqQXX3xRixYt0tatWzVhwgSFhoaqR48eln3PnTtnFbAlWd6fO3fupn0SExOVkpKSa01Tp06V2Wy2vHx9fe/4OgEAAAAA974835N94MAB7dixQz4+PpKk9957T5999pkuXbqksmXL3nYBtWrV0p49e5SQkKAVK1aod+/eCgsLU506dTRgwABLv/r168vHx0dt27bV8ePHVb169ds+561MmDBBr7zyiuV9YmIiQRsAAAAAcEt5HslOTEyUp6en5b2Li4ucnZ2VkJBwRwU4ODjI399fTZo00dSpU9WwYUPNmTMn174tWrSQJP3555+SJG9vb50/f96qT/Z7b2/vm/bx8PCQs7NzrudxdHS0rHie/QIAAAAA4Fbytbr4+vXrZTabLe+zsrK0efNmq0XF7vQ52VlZWUpNTc21bc+ePZJkGU0PCAjQlClTFBsbq3LlykmSNm7cKA8PD8uU84CAAK1bt87qOBs3brS67xsAAAAAAFvI8+riJUrcetDbZDIpMzMzzyefMGGCnnzySVWuXFmXL1/WkiVLNH36dK1fv17VqlXTkiVL1L59e5UtW1b79u3T6NGjValSJYWFhUm6ulhao0aNVKFCBc2YMUPnzp1Tz5499fLLL1s9wqtevXoaOnSoXnrpJW3ZskUjRozQ2rVr8/wIr6K0Uh0AAAAAwFpRymx5Hsm25fOxs8XGxqpXr16KiYmR2WxWgwYNtH79ej3++OM6deqUNm3apNmzZyspKUm+vr7q0qWLXn/9dcv+dnZ2WrNmjQYPHqyAgAC5urqqd+/eVs/Vrlq1qtauXavRo0drzpw5qlSpkr744guekQ0AAAAAsLl8PSf7flWUfisCAAAAALBWlDJbnhc+AwAAAAAAN0fIBgAAAADARgjZAAAAAADYCCEbAAAAAAAbIWQDAAAAAGAjeXqEV+nSpWUymfJ0wLi4uDsqCAAAAACA4ipPIXv27NkFXAYAAAAAAMVfnkJ27969C7oOAAAAAACKvTyF7MTERMsDvRMTE2/at7Af/A0AAAAAQGHJ8z3ZMTExKleunEqVKpXr/dmGYchkMikzM9PmRQIAAAAAUBzkKWRv2bJFZcqUkSRt3bq1QAsCAAAAAKC4MhmGYRR2EUVdYmKizGazEhISmA4PAAAAAEVMUcpseRrJvtauXbv0v//9T0ePHpUk1apVSy+88IKaNm1q8+IAAAAAAChOSuSn87hx49SiRQt98cUXOn36tE6fPq3PP/9cLVq00Pjx4wuqRgAAAAAAioU8h+yFCxfqo48+0ocffqhLly5pz5492rNnj+Li4vTBBx/oww8/1FdffVWQtQIAAAAAUKTl+Z7s5s2b64UXXtDo0aNzbZ81a5aWLl2qX3/91aYFFgVFaX4/AAAAAMBaUcpseR7JPnjwoJ566qkbtnfu3FkHDx60SVEAAAAAABRHeQ7ZdnZ2SktLu2F7enq67OzsbFIUAAAAAADFUZ5DduPGjbV48eIbtoeGhqpx48Y2KQoAAAAAgOIoz4/wGjNmjDp37qzU1FS9+uqrKl++vCTp3LlzmjlzpmbPnq1vvvmmwAoFAAAAAKCoy/PCZ5L00UcfacyYMcrIyJDZbJYkJSQkqGTJkpoxY4ZGjhxZYIUWpqJ0Ez0AAAAAwFpRymz5CtmSdPr0aS1fvlzHjh2TJNWsWVNdunSRr69vgRRYFBSlbxgAAAAAwFpRymz5Dtn3o6L0DQMAAAAAWCtKmS3PC58BAAAAAICbI2QDAAAAAGAjhGwAAAAAAGyEkA0AAAAAgI3cVsiOj4/XF198oQkTJiguLk6StHv3bp05c8amxQEAAAAAUJyUzO8O+/btU7t27WQ2mxUVFaX+/furTJkyWrVqlaKjo/XVV18VRJ0AAAAAABR5+R7JfuWVV9SnTx8dO3ZMTk5Olu3t27fXTz/9ZNPiAAAAAAAoTvIdsnft2qWBAwfm2F6xYkWdO3fOJkUBAAAAAFAc5TtkOzo6KjExMcf2o0ePysvLyyZFAQAAAABQHOU7ZHfq1Elvv/220tPTJUkmk0nR0dEaP368unTpYvMCAQAAAAAoLvIdsmfOnKkrV66oXLlySklJUevWreXv7y93d3dNmTKlIGoEAAAAAKBYyPfq4mazWRs3btT27du1b98+XblyRY0bN1a7du0Koj4AAAAAAIoNk2EYRmEXUdQlJibKbDYrISFBHh4ehV0OAAAAAOAaRSmz5Xsk+8MPP8x1u8lkkpOTk/z9/dWqVSvZ2dndcXEAAAAAABQn+Q7ZH3zwgS5cuKDk5GSVLl1akvT333/LxcVFbm5uio2NVbVq1bR161b5+vravGAAAAAAAIqqfC989u6776pZs2Y6duyYLl26pEuXLuno0aNq0aKF5syZo+joaHl7e2v06NEFUS8AAAAAAEVWvu/Jrl69ulauXKlGjRpZbf/999/VpUsXnThxQjt27FCXLl0UExNjy1oLTVGa3w8AAAAAsFaUMlu+R7JjYmKUkZGRY3tGRobOnTsnSapQoYIuX75859UBAAAAAFCM5DtkP/rooxo4cKB+//13y7bff/9dgwcP1mOPPSZJ2r9/v6pWrWq7KnHH2rRpo1GjRhX4eaKiomQymbRnz54CPxcAAAAAFDX5DtlffvmlypQpoyZNmsjR0VGOjo5q2rSpypQpoy+//FKS5ObmppkzZ9q8WBR9vr6+iomJUb169Qq7FAAAAAC46/K9uri3t7c2btyoP/74Q0ePHpUk1apVS7Vq1bL0efTRR21XIYoVOzs7eXt7F3YZAAAAAFAo8j2Sne2BBx5Qp06d1KlTJ6uAjaIvNTVVY8aMUcWKFeXq6qoWLVpo27ZtVn3Cw8PVpk0bubi4qHTp0goKCtLff/8tScrKytKMGTPk7+8vR0dHVa5cWVOmTJGUc7r4tm3bZDKZtHnzZjVt2lQuLi5q2bKljhw5YnW+uXPnqnr16nJwcFCtWrUUGhpa4J8DAAAAANhavkeyJen06dP6/vvvFR0drbS0NKu2WbNm2aQwFJxhw4bp0KFDWrp0qSpUqKBvvvlG//rXv7R//37VqFFDe/bsUdu2bfXSSy9pzpw5KlmypLZu3arMzExJ0oQJEzR//nx98MEHevjhhxUTE6M//vjjpuf8z3/+o5kzZ8rLy0uDBg3SSy+9pPDwcEnSN998o5EjR2r27Nlq166d1qxZo759+6pSpUrMigAAAABQrOT7EV6bN29Wp06dVK1aNf3xxx+qV6+eoqKiZBiGGjdurC1bthRUrYWmKC0Hf7vatGmjRo0a6ZVXXlG1atUUHR2tChUqWNrbtWun5s2b691339WLL76o6Ohobd++PcdxLl++LC8vL3388cd6+eWXc7RHRUWpatWq+v3339WoUSNt27ZNjz76qDZt2qS2bdtKktatW6fg4GClpKTIyclJgYGBqlu3rj7//HPLcZ5//nklJSVp7dq1BfBpAAAAALiXFKXMlu/p4hMmTNCYMWO0f/9+OTk5aeXKlTp16pRat26t5557riBqhA3t379fmZmZqlmzptzc3CyvsLAwHT9+XJIsI9m5OXz4sFJTU2/YfiMNGjSwfO3j4yNJio2NtRwzMDDQqn9gYKAOHz6cr3MAAAAAQGHL93Txw4cP63//+9/VnUuWVEpKitzc3PT222/rqaee0uDBg21eJGznypUrsrOzU2RkpOzs7Kza3NzcJEnOzs433P9mbTdjb29v+dpkMkm6em83AAAAANxL8j2S7erqarkP28fHxzL6KUkXL160XWUoEA8++KAyMzMVGxsrf39/q1f2quANGjTQ5s2bc92/Ro0acnZ2vmH77ahdu7bl/uxs4eHhqlOnjs3OAQAAAAB3Q75Hsh966CFt375dtWvXVvv27fXqq69q//79WrVqlR566KGCqBE2VLNmTXXv3l29evXSzJkz9eCDD+rChQvavHmzGjRooODgYE2YMEH169fXkCFDNGjQIDk4OGjr1q167rnn5OnpqfHjx2vcuHFycHBQYGCgLly4oIMHD6pfv363VdPYsWP1/PPP68EHH1S7du20evVqrVq1Sps2bbLx1QMAAABAwcp3yJ41a5auXLkiSZo0aZKuXLmir7/+WjVq1GBl8UJgGIb+Tk5XUmqGXB1LqrSLvWU69o0sWLBAkydP1quvvqozZ87I09NTDz30kDp06CDpahDfsGGDXnvtNTVv3lzOzs5q0aKFXnjhBUnSG2+8oZIlS2rixIk6e/asfHx8NGjQoNu+hs6dO2vOnDl6//33NXLkSFWtWlULFixQmzZtbvuYAAAAAFAY8rW6eGZmpsLDw9WgQQOVKlWqAMsqWorSSnXZElLStTLytBbuiNLJuGTL9iplXNS7pZ+6NKkks7P9TY4AAAAAAPeGopTZ8v0ILycnJx0+fFhVq1YtqJqKnKL0DZOksKMXNHhRpFLSrj63+tpvYPYYtrODneb2aKLWNb3uen0AAAAAcDcVpcyW74XP6tWrpxMnThRELciDsKMX1HfBr0pJz5Qh64Ct/3tvSEpJz1TfBb8q7OiFu18kAAAAANyn8h2yJ0+erDFjxmjNmjWKiYlRYmKi1QsFJyElXYMXRV4N0reYf2AYV8P24EWRSkhJvxvlAQAAAMB9L98Ln7Vv316S1KlTJ6sFtgzDkMlkUmZmpu2qg5WVkaeVkpaZY/T6RgxDSknL1Krdp9U38P6Z3g8AAAAAhSXfIXvr1q0FUQduwTAMLdwRdVv7hoRHqU9Lv1uuOg4AAAAAuDP5DtmtW7cuiDpwC38np1utIp5XhqSTccmKT05XaVcH2xcGAAAAALDI9z3ZkvTzzz+rR48eatmypc6cOSNJCg0N1fbt221aHP6/pNSMO9r/yh3uDwAAAAC4tXyH7JUrVyooKEjOzs7avXu3UlNTJUkJCQl69913bV4grnJ1zPekAytud7g/AAAAAODWbmt18Xnz5mn+/Pmyt7e3bA8MDNTu3bttWhz+v9Iu9qpSxkX5vavaJKlKGReVcrG/ZV8AAAAAwJ3Jd8g+cuSIWrVqlWO72WxWfHy8LWpCLkwmk3q39LutffsEsugZAAAAANwN+Q7Z3t7e+vPPP3Ns3759u6pVq2aTopC7Lk0qydnBTnnNyyVMkrODnZ5pXKlgCwMAAAAASLqNkN2/f3+NHDlSO3fulMlk0tmzZ7V48WKNGTNGgwcPLoga8X/Mzvaa26OJTNItg3Z2+7weTWR2Zqo4AAAAANwN+V4N69///reysrLUtm1bJScnq1WrVnJ0dNSYMWM0fPjwgqgR12hd00sL+jbX4EWRSknLlHT1MV3ZsrO3s72d5vVoolY1ve56jQAAAABwvzIZhmHcultOaWlp+vPPP3XlyhXVqVNHbm5utq6tyEhMTJTZbFZCQoI8PDwKuxxJUkJKulbtPq2Q8Cir52dXKeOiPoF+6tKkkjycGMEGAAAAcO8rSpkt3yF70aJFeuaZZ+Ti4lJQNRU5Rekbdj3DMBSfnK4rqRlycyypUi72LHIGAAAA4L5SlDJbvu/JHj16tMqVK6cXX3xR69atU2ZmZkHUhTwymUwq7eog3zIuKu3qQMAGAAA3FRISolKlSlnev/XWW2rUqFGh1XMzffr0UefOnfPcf9u2bTKZTJYn3lx/rQBwN+Q7ZMfExGjp0qUymUx6/vnn5ePjo6FDh2rHjh0FUR8AAABw15hMJn377beFXQaAYizfIbtkyZLq0KGDFi9erNjYWH3wwQeKiorSo48+qurVqxdEjQAAACjC0tPTC7sEACgy8h2yr+Xi4qKgoCA9+eSTqlGjhqKiomxUFgAAwL2pTZs2GjFihMaNG6cyZcrI29tbb731lqU9OjpaTz31lNzc3OTh4aHnn39e58+ftzrG6tWr1axZMzk5OcnT01NPP/20pS01NVVjxoxRxYoV5erqqhYtWmjbtm15rm/Xrl16/PHH5enpKbPZrNatW2v37t1WfUwmk+bOnatOnTrJ1dVVU6ZMUWZmpvr166eqVavK2dlZtWrV0pw5c/J83szMTL3yyisqVaqUypYtq3Hjxun6pYOysrI0depUyzkaNmyoFStW5PkckvTdd9+pcePGcnJyUrVq1TRp0iRlZGRIkvz8/CRJTz/9tEwmk+V99pT60NBQ+fn5yWw2q1u3brp8+bLluD/++KMefvhhS/0dOnTQ8ePH81UbgHvDbYXs5ORkLV68WO3bt1fFihU1e/ZsPf300zp48KCt6wMAALjnLFy4UK6urtq5c6dmzJiht99+Wxs3blRWVpaeeuopxcXFKSwsTBs3btSJEyfUtWtXy75r167V008/rfbt2+v333/X5s2b1bx5c0v7sGHDFBERoaVLl2rfvn167rnn9K9//UvHjh3LU22XL19W7969tX37dv3yyy+qUaOG2rdvbxUopavB8+mnn9b+/fv10ksvKSsrS5UqVdLy5ct16NAhTZw4Ua+99pqWLVuWp/POnDlTISEh+u9//6vt27crLi5O33zzjVWfqVOn6quvvtK8efN08OBBjR49Wj169FBYWFiezvHzzz+rV69eGjlypA4dOqTPPvtMISEhmjJliqSrv2CQpAULFigmJsbyXpKOHz+ub7/9VmvWrNGaNWsUFhamadOmWdqTkpL0yiuv6LffftPmzZtVokQJPf3008rKyspTbQDuIUY+de3a1XB1dTW8vLyMoUOHGjt27MjvIYqdhIQEQ5KRkJBQ2KUAAIBirnXr1sbDDz9sta1Zs2bG+PHjjQ0bNhh2dnZGdHS0pe3gwYOGJOPXX381DMMwAgICjO7du+d67JMnTxp2dnbGmTNnrLa3bdvWmDBhgmEYhrFgwQLDbDZb2t58802jYcOGN6w3MzPTcHd3N1avXm3ZJskYNWrULa916NChRpcuXW7ZzzAMw8fHx5gxY4blfXp6ulGpUiXjqaeeMgzDMP755x/DxcUlx789+/XrZ7zwwguGYRjG1q1bDUnG33//bRhGzmtt27at8e6771rtHxoaavj4+Fhd2zfffGPV58033zRcXFyMxMREy7axY8caLVq0uOH1XLhwwZBk7N+//5bXDuDOFaXMVjK/odzOzk7Lli1TUFCQ7OzsrNoOHDigevXq3XnyBwAAuIc1aNDA6r2Pj49iY2N1+PBh+fr6ytfX19JWp04dlSpVSocPH1azZs20Z88e9e/fP9fj7t+/X5mZmapZs6bV9tTUVJUtWzZPtZ0/f16vv/66tm3bptjYWGVmZio5OVnR0dFW/Zo2bZpj308++UT//e9/FR0drZSUFKWlpeVp5fKEhATFxMSoRYsWlm0lS5ZU06ZNLVPG//zzTyUnJ+vxxx+32jctLU0PPvhgnq5t7969Cg8Pt4xcS1enqf/zzz9KTk6+6SNq/fz85O7ubnmf/T3LduzYMU2cOFE7d+7UxYsXLSPY0dHR/PsYuM/kO2QvXrzY6v3ly5f1v//9T1988YUiIyN5pBcAAMAt2NvbW703mUx5nlbs7Ox8w7YrV67Izs5OkZGROQZD3Nzc8nT83r1769KlS5ozZ46qVKkiR0dHBQQEKC0tzaqfq6ur1fulS5dqzJgxmjlzpgICAuTu7q733ntPO3fuzNN5b+XKlSuSrk6Xr1ixolWbo6Njno8xadIkPfPMMznanJycbrrvrb5nHTt2VJUqVTR//nxVqFBBWVlZqlevXo7PDcC9L98hO9tPP/2kL7/8UitXrlSFChX0zDPP6JNPPrFlbQAAAPeV2rVr69SpUzp16pRlNPvQoUOKj49XnTp1JF0dBd+8ebP69u2bY/8HH3xQmZmZio2N1SOPPHJbNYSHh+vTTz9V+/btJUmnTp3SxYsX87Rfy5YtNWTIEMu2vC78ZTab5ePjo507d6pVq1aSpIyMDEVGRqpx48aSro7oOzo6Kjo6Wq1bt87vZUmSGjdurCNHjsjf3/+Gfezt7fM9aHTp0iUdOXJE8+fPt3zu27dvv60aARR/+QrZ586dU0hIiL788kslJibq+eefV2pqqr799lvLX/wAAAC4Pe3atVP9+vXVvXt3zZ49WxkZGRoyZIhat25tmZ795ptvqm3btqpevbq6deumjIwMrVu3TuPHj1fNmjXVvXt39erVSzNnztSDDz6oCxcuaPPmzWrQoIGCg4NvWUONGjUUGhqqpk2bKjExUWPHjr3p6Pm1+3311Vdav369qlatqtDQUO3atUtVq1bN07WPHDlS06ZNU40aNfTAAw9o1qxZio+Pt7S7u7trzJgxGj16tLKysvTwww8rISFB4eHh8vDwUO/evW95jokTJ6pDhw6qXLmynn32WZUoUUJ79+7VgQMHNHnyZElXp4Vv3rxZgYGBcnR0VOnSpW953NKlS6ts2bL6/PPP5ePjo+joaP373//O03UDuPfkeXXxjh07qlatWtq3b59mz56ts2fP6qOPPirI2gAAAIoNwzAUl5SmU3HJiktKy/H4qbwwmUz67rvvVLp0abVq1Urt2rVTtWrV9PXXX1v6tGnTRsuXL9f333+vRo0a6bHHHtOvv/5qaV+wYIF69eqlV199VbVq1VLnzp21a9cuVa5cOU81fPnll/r777/VuHFj9ezZUyNGjFC5cuVuud/AgQP1zDPPqGvXrmrRooUuXbpkNap9K6+++qp69uyp3r17W6abX/toMkl655139MYbb2jq1KmqXbu2/vWvf2nt2rV5DvJBQUFas2aNNmzYoGbNmumhhx7SBx98oCpVqlj6zJw5Uxs3bpSvr2+e7/UuUaKEli5dqsjISNWrV0+jR4/We++9l+drB3BvMRl5/D9AyZIlNWLECA0ePFg1atSwbLe3t9fevXvv6ZHsxMREmc1mJSQkyMPDo7DLAQAARUhCSrpWRp7Wwh1ROhmXbNlepYyLerf0U5cmlWR2tr/JEQAAd6ooZbY8j2Rv375dly9fVpMmTdSiRQt9/PHHebo/52bmzp2rBg0ayMPDQx4eHgoICNAPP/xgaf/nn380dOhQlS1bVm5uburSpYvOnz9vdYzo6GgFBwfLxcVF5cqV09ixY5WRkWHVZ9u2bWrcuLEcHR3l7++vkJCQO6obAABAksKOXlDA1M16Z80hRV8TsCUpOi5Z76w5pICpmxV29EIhVQgAuNvyHLIfeughzZ8/XzExMRo4cKCWLl1qWTlx48aNunz5cr5PXqlSJU2bNk2RkZH67bff9Nhjj+mpp57SwYMHJUmjR4/W6tWrtXz5coWFhens2bNWq0FmZmYqODhYaWlp2rFjhxYuXKiQkBBNnDjR0uevv/5ScHCwHn30Ue3Zs0ejRo3Syy+/rPXr1+e7XgAAgGxhRy+o74JflZKeKUPS9VMDs7elpGeq74Jf7/ug7ebmdsPXzz//XNjlAYDN5Hm6eG6OHDmiL7/8UqGhoYqPj9fjjz+u77///o4KKlOmjN577z09++yz8vLy0pIlS/Tss89Kkv744w/Vrl1bEREReuihh/TDDz+oQ4cOOnv2rMqXLy9JmjdvnsaPH68LFy7IwcFB48eP19q1a3XgwAHLObp166b4+Hj9+OOPeaqpKE09AAAAhS8hJV0BUzdfDdh5+JeUySQ529spYkLb+3bq+J9//nnDtooVK+ZpcTUAuJGilNnyPJKdm1q1amnGjBk6ffq0/ve//91RIZmZmVq6dKmSkpIUEBCgyMhIpaenq127dpY+DzzwgCpXrqyIiAhJUkREhOrXr28J2NLVBS0SExMto+ERERFWx8juk32M3KSmpioxMdHqBQAAkG1l5GmlpOUtYEuSYUgpaZlatft0wRZWhPn7+9/wRcAGcC+5o5Cdzc7OTp07d76tUez9+/fLzc1Njo6OGjRokL755hvVqVNH586dk4ODg0qVKmXVv3z58jp37pykq48UuzZgZ7dnt92sT2JiolJSUnKtaerUqTKbzZZX9nMqAQAADMPQwh1Rt7VvSHjUba06DgAoPmwSsu9ErVq1tGfPHu3cuVODBw9W7969dejQoUKtacKECUpISLC8Tp06Vaj1AACAouPv5HSdjEvOcQ/2rRiSTsYlKz45vSDKAgAUESULuwAHBwf5+/tLkpo0aaJdu3Zpzpw56tq1q9LS0hQfH281mn3+/Hl5e3tLkry9va2eC5ndnt2W/d/rVyQ/f/68PDw8bjg1ydHRUY6Ojja5PgAAcG9JSs24daebuJKaodKuDjaqBgBQ1BT6SPb1srKylJqaqiZNmsje3l6bN2+2tB05ckTR0dEKCAiQJAUEBGj//v2KjY219Nm4caM8PDwsz+0OCAiwOkZ2n+xjAAAA5Ier452NUbjdYP+QkJAct8ndislk0rfffntH9QAAbKtQR7InTJigJ598UpUrV9bly5e1ZMkSbdu2TevXr5fZbFa/fv30yiuvqEyZMvLw8NDw4cMVEBCghx56SJL0xBNPqE6dOurZs6dmzJihc+fO6fXXX9fQoUMtI9GDBg3Sxx9/rHHjxumll17Sli1btGzZMq1du7YwLx0AABRTpV3sVaWMi6LzOWXcJKlyGReVcsl9dfGuXbuqffv2NqkRAFB4CjVkx8bGqlevXoqJiZHZbFaDBg20fv16Pf7445KkDz74QCVKlFCXLl2UmpqqoKAgffrpp5b97ezstGbNGg0ePFgBAQFydXVV79699fbbb1v6VK1aVWvXrtXo0aM1Z84cVapUSV988YWCgoLu+vUCAIDiz2QyqXdLP72zJm9ryBiZ6TLZXQ3WfQL9ZDKZcu3n7OzMKtsAcA+4o+dk3y+K0jPXAACA7bRp00b169eXnZ2dFi5cKAcHB02ePFkvvviihg0bphUrVqh8+fL66KOP9OSTT0qSwsLC9MqrY/T7nj0q4eQu13ptVapVT5lK2EmSzi35txy8qkgmOyUd2iZ7ryqq8OJUJUV+J8+zOxT1118qU6aMOnbsqBkzZsjNzU3S1enio0aNUnx8vKW+uXPn6v3339epU6dUtWpVvf766+rZs6el3WQy6ZtvvlHnzp0lXX1qy8iRIxURESEXFxd16dJFs2bNspwDAO5VRSmzFbl7sgEAAO6mhQsXytPTU7/++quGDx+uwYMH67nnnlPLli21e/duPfHEE+rZs6eSk5N15swZtW/fXg+1aK6Q1WEqGzREV/ZtUMKOpVbHvHJgi0x2JeXdfYY8g4ZKkp5vWlkff/SRDh48qIULF2rLli0aN27cDev65ptvNHLkSL366qs6cOCABg4cqL59+2rr1q259k9KSlJQUJBKly6tXbt2afny5dq0aZOGDRtmuw8LAHBLjGTnQVH6rQgAALCdNm3aKDMzUz///LMkKTMzU2azWc8884y++uorSdK5c+fk4+OjiIgIrV69WitXrtThw4dlMpkUdvSCuo6apNjN/1XlUV9LphI6t+TfMtJSVKHPHEmSs4Od5vVoolY1vazOvWLFCg0aNEgXL16UlHMkOzAwUHXr1tXnn39u2ef5559XUlKSZW2Za0ey58+fr/Hjx+vUqVNydXWVJK1bt04dO3bU2bNnVb58+YL7IAGgkBWlzMZINgAAuK81aNDA8rWdnZ3Kli2r+vXrW7Zlh9PY2FgdPnxYAQEBlvuqW9f00oo3+8hIS1H5EkmWfRzKV1flMi6a2LGOfnmtrVrV9NKmTZvUtm1bVaxYUe7u7urZs6cuXbqk5OTkXOs6fPiwAgMDrbYFBgbq8OHDN+zfsGFDS8DO7p+VlaUjR47k81MBANwuQjYAALiv2dtbr/ZtMpmstmUH6qysrFz3d3e62nfZoAD9/sbjerByaXV/uKa2jW2jvoFV5eFkr6ioKHXo0EENGjTQypUrFRkZqU8++USSlJaWVhCXBQAoJIRsAACAPKpdu7YiIiJ07d124eHhcnd3l6+vr0q7OsixZAk52dtZrSIeGRmprKwszZw5Uw899JBq1qyps2fP3vJc4eHhVtvCw8NVp06dG/bfu3evkpKSrPqXKFFCtWrVup3LBQDcBkI2AABAHg0ZMkSnTp3S8OHD9ccff+i7777Tm2++qVdeeUUlStz4n1X+/v5KT0/XRx99pBMnTig0NFTz5s276bnGjh2rkJAQzZ07V8eOHdOsWbO0atUqjRkzJtf+3bt3l5OTk3r37q0DBw5o69atGj58uHr27Mn92ABwFxGyAQDAPccwDMUlpelUXLLiktJkq3VeK1asqHXr1unXX39Vw4YNNWjQIPXr10+vv/76Tfdr2LChZs2apenTp6tevXpavHixpk6detN9OnfurDlz5uj9999X3bp19dlnn2nBggVq06ZNrv1dXFy0fv16xcXFqVmzZnr22WfVtm1bffzxx7d7uQCA28Dq4nlQlFaqAwAAN5aQkq6Vkae1cEeUTsb9/wXFqpRxUe+WfurSpJLMzvY3OQIAoDgqSpmNkJ0HRekbBgAAchd29IIGL4pUSlqmJOnaf+Bk3x3t7GCnuT2aqPV1j9MCABRvRSmzMV0cAAAUe2FHL6jvgl+Vkp4pQ9YBW//33pCUkp6pvgt+VdjRC3e/SADAfYGQDQAAirWElHQNXhR5NUjfYn6eYVwN24MXRSohJf1ulAcAuM8QsgEAQLG2MvK0UtIybxmwsxmGlJKWqVW7TxdsYQCA+xIhGwAAFFuGYWjhjqjb2jckPMpmq44DAJCNkA0AAIqtv5PTdTIuOcc92LdiSDoZl6z4ZKaMAwBsi5ANAACKraTUjDva/8od7g8AwPUI2QAAoNhydSx5R/u73eH+AABcj5ANAACKrdIu9qpSxsXyHOy8MkmqUsZFpVzsC6IsAMB9jJANAACKLZPJpN4t/W5r3z6BfjKZ8hvPAQC4OUI2AAAo1ro0qSRnBzvlNS+XMEnODnZ6pnGlgi0MAHBfImQDAIBizexsr7k9msgk3TJoZ7fP69FEZmemigMAbI+QDQAAir3WNb20oG9zOdvbXQ3b17Vnb3O2t1NI3+ZqVdPr7hcJALgvsKQmAAC4J7Su6aWICW21avdphYRH6WRcsqWtchkX9Qn0U5cmleThxAg2AKDgmAzDMAq7iKIuMTFRZrNZCQkJ8vDwKOxyAADALRiGofjkdF1JzZCbY0mVcrFnkTMAuIcVpczGSDYAALjnmEwmlXZ1UGlXh8IuBQBwn+GebAAAAAAAbISQDQAAAACAjRCyAQAAAACwEUI2AAAAAAA2QsgGAAAAAMBGCNkAAAAAANgIIRsAAAAAABshZAMAAAAAYCOEbAAAAAAAbISQDQAAAACAjRCyAQAAAACwEUI2AAAAAAA2QsgGAAAAAMBGCNkAAAAAANgIIRsAAAAAABshZAMAAAAAYCOEbAAAAAAAbISQDQAAAACAjRCygULQp08fde7cOc/9t23bJpPJpPj4eElSSEiISpUqVSC1AQAAALh9hGygGOratauOHj1a2GUAAAAAuE7Jwi4AQP45OzvL2dm5sMsAAAAAcB1GsnFfa9OmjYYNG6Zhw4bJbDbL09NTb7zxhgzDkCSlpqZqzJgxqlixolxdXdWiRQtt27bNsn/2tO3169erdu3acnNz07/+9S/FxMRY+mRmZuqVV15RqVKlVLZsWY0bN85y/GypqakaMWKEypUrJycnJz388MPatWvXDeu+frr4W2+9pUaNGik0NFR+fn4ym83q1q2bLl++fNvnAAAAAJB/hGzc9xYuXKiSJUvq119/1Zw5czRr1ix98cUXkqRhw4YpIiJCS5cu1b59+/Tcc8/pX//6l44dO2bZPzk5We+//75CQ0P1008/KTo6WmPGjLG0z5w5UyEhIfrvf/+r7du3Ky4uTt98841VDePGjdPKlSu1cOFC7d69W/7+/goKClJcXFyer+P48eP69ttvtWbNGq1Zs0ZhYWGaNm2aTc8BAAAA4BYM3FJCQoIhyUhISCjsUmBjrVu3NmrXrm1kZWVZto0fP96oXbu2cfLkScPOzs44c+aM1T5t27Y1JkyYYBiGYSxYsMCQZPz555+W9k8++cQoX7685b2Pj48xY8YMy/v09HSjUqVKxlNPPWUYhmFcuXLFsLe3NxYvXmzpk5aWZlSoUMGy39atWw1Jxt9//205r9lstvR/8803DRcXFyMxMdGybezYsUaLFi3yfA4AAACguCpKmY17snHfe+ihh2QymSzvAwICNHPmTO3fv1+ZmZmqWbOmVf/U1FSVLVvW8t7FxUXVq1e3vPfx8VFsbKwkKSEhQTExMWrRooWlvWTJkmratKllyvjx48eVnp6uwMBASx97e3s1b95chw8fzvN1+Pn5yd3dPdc6bHUOAAAAADdHyAZu4MqVK7Kzs1NkZKTs7Oys2tzc3Cxf29vbW7WZTKYc91zfDbnVkZWVddfrAAAAAO5n3JON+97OnTut3v/yyy+qUaOGHnzwQWVmZio2Nlb+/v5WL29v7zwd22w2y8fHx+ocGRkZioyMtLyvXr26HBwcFB4ebtmWnp6uXbt2qU6dOnd4dXfvHAAAAAAYyQYUHR2tV155RQMHDtTu3bv10UcfaebMmapZs6a6d++uXr16aebMmXrwwQd14cIFbd68WQ0aNFBwcHCejj9y5EhNmzZNNWrU0AMPPKBZs2YpPj7e0u7q6qrBgwdr7NixKlOmjCpXrqwZM2YoOTlZ/fr1s8k13o1zAAAAACBk4x5lGIb+Tk5XUmqGXB1LqrSLvdV919fq1auXUlJS1Lx5c9nZ2WnkyJEaMGCAJGnBggWaPHmyXn31VZ05c0aenp566KGH1KFDhzzX8uqrryomJka9e/dWiRIl9NJLL+npp59WQkKCpc+0adOUlZWlnj176vLly2ratKnWr1+v0qVL39kHcY27cQ4AAADgfmcyCuPm0WImMTFRZrNZCQkJ8vDwKOxycBMJKelaGXlaC3dE6WRcsmV7lTIu6t3ST12aVJLZ+f/fu9ymTRs1atRIs2fPLoRqAQAAANhCUcpsjGTjnhF29IIGL4pUSlpmjrbouGS9s+aQ3t9wRHN7NFHrml6FUCEAAACAex0Ln+GeEHb0gvou+FUp6ZkyJF0/PSN7W0p6pvou+FVhRy/c/SIBAAAA3POYLp4HRWnqAXJKSElXwNTNVwN2Hn6aTSbJ2d5OERPaWk0dBwAAAFA8FaXMxkg2ir2VkaeVkpa3gC1JhiGlpGVq1e7TBVsYAAAAgPsOIRvFmmEYWrgj6rb2DQmPEhM5AAAAANgSIRvF2t/J6ToZl5zjHuxbMSSdjEtWfHJ6QZQFAAAA4D5FyEaxlpSacUf7X7nD/QEAAADgWoRsFGuujnf2FDq3O9wfAAAAAK5FyEaxVtrFXlXKuMiUz/1MkqqUcVEpF1YXBwAAAGA7hGwUayaTSb1b+t3Wvn0C/WQy5TeeAwAAAMCNEbJR7HVpUknODnbKa14uYZKcHez0TONKBVsYAAAAgPsOIRvFntnZXnN7NJFJumXQzm6f16OJzM5MFQcAAABgW4Rs3BNa1/TSgr7N5WxvdzVsX9eevc3Z3k4hfZurVU2vu18kAAAAgHseSyvjntG6ppciJrTVqt2nFRIepZNxyZa2ymVc1CfQT12aVJKHEyPYAAAAAAqGyTAMo7CLKOoSExNlNpuVkJAgDw+Pwi4HeWAYhuKT03UlNUNujiVVysWeRc4AAACAe1RRymyMZOOeZDKZVNrVQaVdHQq7FAAAAAD3Ee7JBgAAAADARgjZAAAAAADYCCEbAAAAAAAbIWQDAAAAAGAjhGwAAAAAAGyEkA0AAAAAgI0QsgEAAAAAsBFCNgAAAAAANkLIBgAAAADARgjZAAAAAADYSKGG7KlTp6pZs2Zyd3dXuXLl1LlzZx05csSqT5s2bWQymaxegwYNsuoTHR2t4OBgubi4qFy5cho7dqwyMjKs+mzbtk2NGzeWo6Oj/P39FRISUtCXBwAAAAC4zxRqyA4LC9PQoUP1yy+/aOPGjUpPT9cTTzyhpKQkq379+/dXTEyM5TVjxgxLW2ZmpoKDg5WWlqYdO3Zo4cKFCgkJ0cSJEy19/vrrLwUHB+vRRx/Vnj17NGrUKL388stav379XbtWAAAAAMC9z2QYhlHYRWS7cOGCypUrp7CwMLVq1UrS1ZHsRo0aafbs2bnu88MPP6hDhw46e/asypcvL0maN2+exo8frwsXLsjBwUHjx4/X2rVrdeDAAct+3bp1U3x8vH788cdb1pWYmCiz2ayEhAR5eHjc+YUCAAAAAGymKGW2InVPdkJCgiSpTJkyVtsXL14sT09P1atXTxMmTFBycrKlLSIiQvXr17cEbEkKCgpSYmKiDh48aOnTrl07q2MGBQUpIiIi1zpSU1OVmJho9QIAAAAA4FZKFnYB2bKysjRq1CgFBgaqXr16lu0vvviiqlSpogoVKmjfvn0aP368jhw5olWrVkmSzp07ZxWwJVnenzt37qZ9EhMTlZKSImdnZ6u2qVOnatKkSTa/RgAAAADAva3IhOyhQ4fqwIED2r59u9X2AQMGWL6uX7++fHx81LZtWx0/flzVq1cvkFomTJigV155xfI+MTFRvr6+BXIuAAAAAMC9o0hMFx82bJjWrFmjrVu3qlKlSjft26JFC0nSn3/+KUny9vbW+fPnrfpkv/f29r5pHw8Pjxyj2JLk6OgoDw8PqxcAAAAAALdSqCHbMAwNGzZM33zzjbZs2aKqVavecp89e/ZIknx8fCRJAQEB2r9/v2JjYy19Nm7cKA8PD9WpU8fSZ/PmzVbH2bhxowICAmx0JQAAAAAAFHLIHjp0qBYtWqQlS5bI3d1d586d07lz55SSkiJJOn78uN555x1FRkYqKipK33//vXr16qVWrVqpQYMGkqQnnnhCderUUc+ePbV3716tX79er7/+uoYOHSpHR0dJ0qBBg3TixAmNGzdOf/zxhz799FMtW7ZMo0ePLrRrBwAAAADcewr1EV4mkynX7QsWLFCfPn106tQp9ejRQwcOHFBSUpJ8fX319NNP6/XXX7eawn3y5EkNHjxY27Ztk6urq3r37q1p06apZMn/f8v5tm3bNHr0aB06dEiVKlXSG2+8oT59+uSpzqK0HDwAAAAAwFpRymxF6jnZRVVR+oYBAAAAAKwVpcxWJBY+AwAAAADgXkDIBgAAAADARgjZAAAAAADYCCEbAAAAAAAbIWQDAAAAAGAjhGwAAAAAAGyEkA0AAAAAgI0QsgEAAAAAsBFCNgAAAAAANkLIBgAAAADARgjZAAAAAADYCCEbKGL8/Pw0e/bswi4DAAAAwG0oWdgFAEVRmzZt1KhRo0IJu7t27ZKrq+tdPy8AAACAO0fIBooYLy+vwi4BAAAAwG1iujhwnT59+igsLExz5syRyWSSyWTS8ePH1a9fP1WtWlXOzs6qVauW5syZY7VfRkaGRowYoVKlSqls2bIaP368evfurc6dO1v6XL58Wd27d5erq6t8fHz0wQcfqE2bNho1apSlz/XTxWfNmqX69evL1dVVvr6+GjJkiK5cuWJpP3nypDp27KjSpUvL1dVVdevW1bp16yztBw8eVIcOHeTh4SF3d3c98sgjOn78uKSro+aPP/64PD09ZTab1bp1a+3evdu2HygAAABwHyFkA9eZM2eOAgIC1L9/f8XExCgmJkaVKlVSpUqVtHz5ch06dEgTJ07Ua6+9pmXLlln2mz59uhYvXqwFCxYoPDxciYmJ+vbbb62O/corryg8PFzff/+9Nm7cqJ9//vmWobZEiRL68MMPdfDgQS1cuFBbtmzRuHHjLO1Dhw5VamqqfvrpJ+3fv1/Tp0+Xm5ubJOnMmTNq1aqVHB0dtWXLFkVGRuqll15SRkaGpKuhv3fv3tq+fbt++eUX1ahRQ+3bt9fly5dt9GkCAAAA9xemiwPXMZvNcnBwkIuLi7y9vS3bJ02aZPm6atWqioiI0LJly/T8889Lkj766CNNmDBBTz/9tCTp448/thpRvnz5shYuXKglS5aobdu2kqQFCxaoQoUKN63n+lHuyZMna9CgQfr0008lSdHR0erSpYvq168vSapWrZql/yeffCKz2aylS5fK3t5eklSzZk1L+2OPPWZ1rs8//1ylSpVSWFiYOnTocItPCgAAAMD1GMkG8uiTTz5RkyZN5OXlJTc3N33++eeKjo6WJCUkJOj8+fNq3ry5pb+dnZ2aNGlieX/ixAmlp6db9TGbzapVq9ZNz7tp0ya1bdtWFStWlLu7u3r27KlLly4pOTlZkjRixAhNnjxZgYGBevPNN7Vv3z7Lvnv27NEjjzxiCdjXO3/+vPr3768aNWrIbDbLw8NDV65csVwXAAAAgPwhZAN5sHTpUo0ZM0b9+vXThg0btGfPHvXt21dpaWkFet6oqCh16NBBDRo00MqVKxUZGalPPvlEkiznfvnll3XixAn17NlT+/fvV9OmTfXRRx9JkpydnW96/N69e2vPnj2aM2eOduzYoT179qhs2bIFfl0AAADAvYqQDeTCwcFBmZmZlvfh4eFq2bKlhgwZogcffFD+/v6WxcOkqyPS5cuX165duyzbMjMzre63rlatmuzt7a36JCQk6OjRozesIzIyUllZWZo5c6Yeeugh1axZU2fPns3Rz9fXV4MGDdKqVav06quvav78+ZKkBg0a6Oeff1Z6enquxw8PD9eIESPUvn171a1bV46Ojrp48WIePiEAAAAAuSFkA7nw8/PTzp07FRUVpYsXL6pGjRr67bfftH79eh09elRvvPGGVViWpOHDh2vq1Kn67rvvdOTIEY0cOVJ///23TCaTJMnd3V29e/fW2LFjtXXrVh08eFD9+vVTiRIlLH2u5+/vr/T0dH300Uc6ceKEQkNDNW/ePKs+o0aN0vr16/XXX39p9+7d2rp1q2rXri1JGjZsmBITE9WtWzf99ttvOnbsmEJDQ3XkyBFJUo0aNRQaGqrDhw9r586d6t69+y1HvwEAAADcGCEb9xXDMBSXlKZTccmKS0qTYRi59hszZozs7OxUp04deXl5KSgoSM8884y6du2qFi1a6NKlSxoyZIjVPuPHj9cLL7ygXr16KSAgQG5ubgoKCpKTk5Olz6xZsxQQEKAOHTqoXbt2CgwMVO3ata36XKthw4aaNWuWpk+frnr16mnx4sWaOnWqVZ/MzEwNHTpUtWvX1r/+9S/VrFnTsiha2bJltWXLFl25ckWtW7dWkyZNNH/+fMs92l9++aX+/vtvNW7cWD179tSIESNUrly52/58AQAAgPudybhRyoBFYmKizGazEhIS5OHhUdjl4DYkpKRrZeRpLdwRpZNxyZbtVcq4qHdLP3VpUklm59wXB7tdWVlZql27tp5//nm98847ufZJSkpSxYoVNXPmTPXr18+m5wcAAADuF0Ups/EIL9zzwo5e0OBFkUpJy8zRFh2XrHfWHNL7G45obo8mal3T67bPc/LkSW3YsEGtW7dWamqqPv74Y/3111968cUXLX1+//13/fHHH2revLkSEhL09ttvS5Keeuqp2z4vAAAAgKKD6eK4p4UdvaC+C35VSnqmDEnXT9vI3paSnqm+C35V2NELt32uEiVKKCQkRM2aNVNgYKD279+vTZs2We6Pzvb++++rYcOGateunZKSkvTzzz/L09Pzts8LAAAAoOhgungeFKWpB8i7hJR0BUzdfDVg5+Gn3GSSnO3tFDGhrc2njgMAAAAoOEUpszGSjXvWysjTSknLW8CWJMOQUtIytWr36YItDAAAAMA9i5CNe5JhGFq4I+q29g0Jj7rhquMAAAAAcDOEbNyT/k5O18m45Bz3YN+KIelkXLLik9MLoiwAAAAA9zhCNu5JSakZd7T/lTvcHwAAAPh/7d15VFXl/j/w9xE4cA6cAygIDiCWikNEiorAVwlFwazrQOlV66qRYpmt682KLMuu14u3a5OEQ6UiLsuhrkOKGiIoKjmCQhAGDlhhloiCgIJ8fn/4Yy+3jOaRyfdrLdZyP/vZz/Ps8+Hg/uxnD/RgYpJNLZK15b29nc7mHrcnIiIiIqIHE5NsapHs9Rbo1FoPzV1upwHQqbUedno+XZyIiIiIiO4ek2xqkTQaDSb5uv2pbSf7uUGjudv0nIiIiIiIiEk2tWAhXh2h05qhvvlyKw2g05phTJ+O93dgRERERETUYjHJphbLVmeBpc96QQPUmWhXrl/2rBdsdbxUnIiIiIiI/hwm2dSi+XdzxKop/aGzMLuVbN+xvrJMZ2GG6Cn9MaibY8MPkoiIiIiIWgw+QplaPP9ujkh+cwj+d/xnRB84i3P5xco619Z6TPZzQ4hXRxitOINNRERERET3RiMi0tiDaOquXr0KW1tbXLlyBUajsbGHQ/dARFBQXIai6+WwsTSHnd6CDzkjIiIiImrmmlLOxplseqBoNBrYW2thb61t7KEQEREREVELxHuyiYiIiIiIiEyESTYRERERERGRiTDJJiIiIiIiIjIRJtlEREREREREJsIkm4iIiIiIiMhEmGQTERERERERmQiTbCIiIiIiIiITYZJNREREREREZCJMsomIiIiIiIhMhEk2ERERERERkYkwySYiIiIiIiIyESbZRERERERERCZi3tgDaA5EBABw9erVRh4JERERERER3akyV6vM3RoTk+x6KCwsBAC4uLg08kiIiIiIiIioJoWFhbC1tW3UMWikKaT6TVxFRQV+/fVXGAwGaDSaxh4O4daZKhcXF5w/fx5Go7Gxh0Mmxvi2bIxvy8b4tmyMb8vG+LZsLT2+IoLCwkK0b98erVo17l3RnMmuh1atWqFjx46NPQyqhtFobJF/JOgWxrdlY3xbNsa3ZWN8WzbGt2VryfFt7BnsSnzwGREREREREZGJMMkmIiIiIiIiMhEm2dQsWVpa4t1334WlpWVjD4XuA8a3ZWN8WzbGt2VjfFs2xrdlY3wbDh98RkRERERERGQinMkmIiIiIiIiMhEm2UREREREREQmwiSbiIiIiIiIyESYZFODiIiIQL9+/WAwGNC2bVuMGjUKWVlZVeolJydj8ODBsLa2htFoxKBBg1BSUqKsz8/Px8SJE2E0GmFnZ4fQ0FAUFRWp2jh58iQGDhwIKysruLi44P3336/Sz8aNG9G9e3dYWVnBw8MDsbGxpt/pB0h94nvhwgU899xzcHZ2hrW1Nfr06YNvvvlGVYfxbZqWLl2KRx99VHmvpo+PD3bs2KGsLy0txYwZM9CmTRvY2NggJCQEv/32m6qN3NxcjBgxAnq9Hm3btsVrr72G8vJyVZ3ExET06dMHlpaW6NKlC6Kjo6uMJSoqCm5ubrCysoK3tzcOHz58X/b5QVJbfPPz8zFz5ky4u7tDp9PB1dUVr7zyCq5cuaJqg/Ftuur6/lYSEQwfPhwajQabN29WrWN8m676xJfHVs1bXTHm8VUTJUQNICgoSFatWiXp6emSmpoqTzzxhLi6ukpRUZFS5+DBg2I0GiUiIkLS09Plxx9/lPXr10tpaalSJzg4WDw9PeX777+XpKQk6dKli4wfP15Zf+XKFXFycpKJEydKenq6fPXVV6LT6WT58uVKnQMHDoiZmZm8//77kpGRIW+//bZYWFhIWlpaw3wYLVB94jt06FDp16+fHDp0SHJycmT+/PnSqlUrOX78uFKH8W2atm7dKtu3b5dTp05JVlaWzJkzRywsLCQ9PV1ERKZPny4uLi4SHx8vR48elQEDBoivr6+yfXl5uTzyyCMSGBgoKSkpEhsbKw4ODvLmm28qdU6fPi16vV7+8Y9/SEZGhkRGRoqZmZns3LlTqbNu3TrRarWycuVK+eGHH2Tq1KliZ2cnv/32W8N9GC1QbfFNS0uTMWPGyNatWyU7O1vi4+Ola9euEhISomzP+DZtdX1/K3344YcyfPhwASCbNm1Syhnfpq2u+PLYqvmrK8Y8vmqamGRTo7h48aIAkL179ypl3t7e8vbbb9e4TUZGhgCQI0eOKGU7duwQjUYjv/zyi4iILFmyROzt7eX69etKnTfeeEPc3d2V5bFjx8qIESNUbXt7e0tYWNg97xfdUl18ra2tJSYmRlWvdevW8vnnn4sI49vc2NvbyxdffCEFBQViYWEhGzduVNZlZmYKAElOThYRkdjYWGnVqpVcuHBBqbN06VIxGo1KLF9//XXp1auXqo9x48ZJUFCQsty/f3+ZMWOGsnzz5k1p3769RERE3Jd9fJBVxrc6GzZsEK1WK2VlZSLC+DZHd8Y3JSVFOnToIHl5eVWSbMa3+bk9vjy2aplujzGPr5omXi5OjaLyUsPWrVsDAC5evIhDhw6hbdu28PX1hZOTE/z9/bF//35lm+TkZNjZ2aFv375KWWBgIFq1aoVDhw4pdQYNGgStVqvUCQoKQlZWFi5fvqzUCQwMVI0nKCgIycnJ92dnH0B3xhcAfH19sX79euTn56OiogLr1q1DaWkpHn/8cQCMb3Nx8+ZNrFu3DteuXYOPjw+OHTuGsrIy1WfevXt3uLq6Kp95cnIyPDw84OTkpNQJCgrC1atX8cMPPyh1aovbjRs3cOzYMVWdVq1aITAwkLE1oTvjW50rV67AaDTC3NwcAOPbnFQX3+LiYkyYMAFRUVFwdnausg3j23zcGV8eW7U81X2HeXzVNDHJpgZXUVGBv//97/Dz88MjjzwCADh9+jQAYN68eZg6dSp27tyJPn36YMiQIfjpp58A3LrnpG3btqq2zM3N0bp1a1y4cEGpc/uBAABlua46levp3lQXXwDYsGEDysrK0KZNG1haWiIsLAybNm1Cly5dADC+TV1aWhpsbGxgaWmJ6dOnY9OmTejZsycuXLgArVYLOzs7Vf3bP/N7idvVq1dRUlKCP/74Azdv3mRs75Oa4nunP/74A/Pnz8e0adOUMsa36astvrNmzYKvry9GjhxZ7baMb9NXU3x5bNVy1PYd5vFV02Te2AOgB8+MGTOQnp6uOpNaUVEBAAgLC8OUKVMAAL1790Z8fDxWrlyJiIiIRhkr3b3q4gsAc+fORUFBAXbv3g0HBwds3rwZY8eORVJSEjw8PBpptFRf7u7uSE1NxZUrV/D1119j0qRJ2Lt3b2MPi0ykpvjenmhfvXoVI0aMQM+ePTFv3rzGGyzdtZrim52djT179iAlJaWxh0j3oKb48tiq5ajtbzSPr5omJtnUoF5++WVs27YN+/btQ8eOHZXydu3aAUCVmZMePXogNzcXAODs7IyLFy+q1peXlyM/P1+5xM3Z2bnKU40rl+uqU91lcnR3aopvTk4OPv30U6Snp6NXr14AAE9PTyQlJSEqKgrLli1jfJs4rVarnBX38vLCkSNH8Mknn2DcuHG4ceMGCgoKVLPZt3/mzs7OVZ4iXN+4GY1G6HQ6mJmZwczMjLG9T2qK7/LlywEAhYWFCA4OhsFgwKZNm2BhYaFsy/g2fTXFV6fTIScnp8qVKCEhIRg4cCASExMZ32agpviGh4cD4LFVS1BTjF9//XUeXzVRvFycGoSI4OWXX8amTZuwZ88edO7cWbXezc0N7du3r/Lap1OnTqFTp04AAB8fHxQUFODYsWPK+j179qCiogLe3t5KnX379qGsrEypExcXB3d3d9jb2yt14uPjVf3ExcXVeP8h1a2u+BYXFwO4dQ/e7czMzJQz7Yxv81JRUYHr16/Dy8sLFhYWqs88KysLubm5ymfu4+ODtLQ01X/ycXFxMBqNysFfXXHTarXw8vJS1amoqEB8fDxjex9Uxhe4NYM9bNgwaLVabN26FVZWVqq6jG/zUxnf8PBwnDx5EqmpqcoPAHz00UdYtWoVAMa3OaqML4+tWq7KGPP4qglr7Cev0YPhxRdfFFtbW0lMTJS8vDzlp7i4WKnz0UcfidFolI0bN8pPP/0kb7/9tlhZWUl2drZSJzg4WHr37i2HDh2S/fv3S9euXVWvICgoKBAnJyd57rnnJD09XdatWyd6vb7KKwjMzc1l0aJFkpmZKe+++y5fQXCP6orvjRs3pEuXLjJw4EA5dOiQZGdny6JFi0Sj0cj27duVdhjfpik8PFz27t0rZ86ckZMnT0p4eLhoNBr57rvvROTWK7xcXV1lz549cvToUfHx8REfHx9l+8pXAA0bNkxSU1Nl586d4ujoWO0rgF577TXJzMyUqKioal8BZGlpKdHR0ZKRkSHTpk0TOzs71VOP6e7VFt8rV66It7e3eHh4SHZ2tur7XV5eLiKMb1NX1/f3TqjhFV6Mb9NUV3x5bNX81RZjHl81XUyyqUEAqPZn1apVqnoRERHSsWNH0ev14uPjI0lJSar1ly5dkvHjx4uNjY0YjUaZMmWKFBYWquqcOHFC/u///k8sLS2lQ4cOsnDhwirj2bBhg3Tr1k20Wq306tVL9YeI7l594nvq1CkZM2aMtG3bVvR6vTz66KNVXjnB+DZNzz//vHTq1Em0Wq04OjrKkCFDVAfoJSUl8tJLL4m9vb3o9XoZPXq05OXlqdo4e/asDB8+XHQ6nTg4OMirr76qvAKqUkJCgjz22GOi1WrloYceqvL3QUQkMjJSXF1dRavVSv/+/eX777+/L/v8IKktvgkJCTV+v8+cOaO0wfg2XXV9f+90Z5Itwvg2ZfWJL4+tmre6Yszjq6ZJIyLSsHPnRERERERERC0T78kmIiIiIiIiMhEm2UREREREREQmwiSbiIiIiIiIyESYZBMRERERERGZCJNsIiIiIiIiIhNhkk1ERERERERkIkyyiYiIiIiIiEyESTYRERERERGRiTDJJiIioibp7Nmz0Gg0SE1NrbVeVlYWnJ2dUVhY2CDjmjx5MkaNGnVPbWRkZKBjx464du2aaQZFRERNBpNsIiJqFMnJyTAzM8OIESMaeyj33YABAzB9+nRV2bJly6DRaBAdHa0qnzx5MgYOHFivdk2R7NUmOjoaGo0GwcHBqvKCggJoNBokJibet77vxptvvomZM2fCYDD86TbOnz+P8ePHIzg4GEOHDkVSUpJq/blz56DT6VBUVHSvwwUA9OzZEwMGDMCHH35okvaIiKjpYJJNRESNYsWKFZg5cyb27duHX3/99b72JSIoLy+/r33UJiAgoEpCmpCQABcXlyrliYmJGDx4cMMNDsCNGzdqXGdubo7du3cjISGhAUdUf7m5udi2bRsmT558T+24uLhg1qxZKCkpQXJyMr777jvV+i1btiAgIAA2Njb31M/tpkyZgqVLlzbq7yYREZkek2wiImpwRUVFWL9+PV588UWMGDFCNZs7YcIEjBs3TlW/rKwMDg4OiImJAQBUVFQgIiICnTt3hk6ng6enJ77++mulfmJiIjQaDXbs2AEvLy9YWlpi//79yMnJwciRI+Hk5AQbGxv069cPu3fvVvWVl5eHESNGQKfToXPnzvjyyy/h5uaGjz/+WKlTUFCAF154AY6OjjAajRg8eDBOnDhR4/4GBAQgKysLFy5cUMr27t2L8PBwVZJ95swZnDt3DgEBAbh58yZCQ0OVfXR3d8cnn3yi1J03bx5Wr16NLVu2QKPRqGaWz58/j7Fjx8LOzg6tW7fGyJEjcfbsWWXbyhnwBQsWoH379nB3d69x7NbW1nj++ecRHh5eY5369AkAX3zxBXr06AErKyt0794dS5YsUa0/fPgwevfuDSsrK/Tt2xcpKSm19gkAGzZsgKenJzp06KCURUdHw87ODtu2bYO7uzv0ej2efvppFBcXY/Xq1XBzc4O9vT1eeeUV3Lx5U9muf//+2Lt3L5YsWYK//OUvqn62bNlSpWzRokVo164d2rRpgxkzZqCsrExZt2bNGvTt2xcGgwHOzs6YMGECLl68qNp+6NChyM/Px969e+vcTyIiaj6YZBMRUYPbsGEDunfvDnd3dzz77LNYuXIlRAQAMHHiRHz77beqy3J37dqF4uJijB49GgAQERGBmJgYLFu2DD/88ANmzZqFZ599tkqyEh4ejoULFyIzMxOPPvooioqK8MQTTyA+Ph4pKSkIDg7GU089hdzcXGWbv/3tb/j111+RmJiIb775Bp999lmV5OiZZ57BxYsXsWPHDhw7dgx9+vTBkCFDkJ+fX+3++vn5wcLCQpkNzsjIQElJCUJDQ3Hp0iWcOXMGwK3ZbSsrK/j4+KCiogIdO3bExo0bkZGRgXfeeQdz5szBhg0bAACzZ8/G2LFjERwcjLy8POTl5cHX1xdlZWUICgqCwWBAUlISDhw4ABsbGwQHB6tmrOPj45GVlYW4uDhs27at1njNmzcPaWlpqhMZt6tPn2vXrsU777yDBQsWIDMzE//+978xd+5crF69GsCtEy9PPvkkevbsiWPHjmHevHmYPXt2reMCgKSkJPTt27dKeXFxMRYvXox169Zh586dSExMxOjRoxEbG4vY2FisWbMGy5cvV/aptLRU2ba0tBQLFixQlgsKCrB//35Vkp2QkICcnBwkJCRg9erViI6OVp0sKisrw/z583HixAls3rwZZ8+erTLbrtVq8dhjj1W5NJ2IiJo5ISIiamC+vr7y8ccfi4hIWVmZODg4SEJCgmo5JiZGqT9+/HgZN26ciIiUlpaKXq+XgwcPqtoMDQ2V8ePHi4hIQkKCAJDNmzfXOZZevXpJZGSkiIhkZmYKADly5Iiy/qeffhIA8tFHH4mISFJSkhiNRiktLVW18/DDD8vy5ctr7MfPz0+mTZsmIiJRUVHyxBNPiIjIsGHDZOXKlSIi8txzz0lAQECNbcyYMUNCQkKU5UmTJsnIkSNVddasWSPu7u5SUVGhlF2/fl10Op3s2rVL2c7JyUmuX79eY18iIqtWrRJbW1sREQkPD5du3bpJWVmZXL58WQAoMatPnw8//LB8+eWXqvbnz58vPj4+IiKyfPlyadOmjZSUlCjrly5dKgAkJSWlxjF6enrKP//5zyrjBiDZ2dlKWVhYmOj1eiksLFTKgoKCJCwsTERENm3aJD4+PuLn5yeenp7KvomIrF27Vvr27assT5o0STp16iTl5eVK2TPPPKP8jlbnyJEjAkDVv4jI6NGjZfLkyTVuR0REzQ9nsomIqEFlZWXh8OHDGD9+PIBb9/yOGzcOK1asUJbHjh2LtWvXAgCuXbuGLVu2YOLEiQCA7OxsFBcXY+jQobCxsVF+YmJikJOTo+rrzhnOoqIizJ49Gz169ICdnR1sbGyQmZmpzGRnZWXB3Nwcffr0Ubbp0qUL7O3tleUTJ06gqKgIbdq0UfV/5syZKv3f7vHHH1cu505MTMTjjz8OAPD391eVBwQEKNtERUXBy8sLjo6OsLGxwWeffaaada/OiRMnkJ2dDYPBoIytdevWKC0tVY3Pw8MDWq221rZu98Ybb+D333/HypUr77rPa9euIScnB6GhoarP7F//+pcypsqrDaysrJR2fXx86hxXSUmJaptKer0eDz/8sLLs5OQENzc31T3VTk5OylUKo0aNwsGDB7F//36kpqYq8QGqv1S8V69eMDMzU5bbtWunuuLh2LFjeOqpp+Dq6gqDwQB/f38AqBI/nU6H4uLiOveTiIiaD/PGHgARET1YVqxYgfLycrRv314pExFYWlri008/ha2tLSZOnAh/f39cvHgRcXFx0Ol0yhOuKy8j3759u+o+XACwtLRULVtbW6uWZ8+ejbi4OCxatAhdunSBTqfD008/XeuDv+5UVFSEdu3aVftkbTs7uxq3CwgIwIIFC/DLL78gMTFRuRTa398fy5cvR05ODs6fP6889GzdunWYPXs2PvjgA/j4+MBgMOC///0vDh06VOf4vLy8lJMUt3N0dFT+fednUxc7Ozu8+eabeO+99/Dkk0/eVZ+VMfv888/h7e2tWn97ovpnODg44PLly1XKLSwsVMsajabasoqKilrbv3HjBnbu3Ik5c+bU2X5lW9euXUNQUBCCgoKwdu1aODo6Ijc3F0FBQVV+1/Lz81UnA4iIqPljkk1ERA2mvLwcMTEx+OCDDzBs2DDVulGjRuGrr77C9OnT4evrCxcXF6xfvx47duzAM888oyQ1PXv2hKWlJXJzc5XZwfo6cOAAJk+erNzbXVRUpHo4l7u7O8rLy5GSkgIvLy8At2bOb0/i+vTpgwsXLsDc3Bxubm717tvX1xdarRZLlixBaWmp0n6/fv2UGWJra2v0799fGauvry9eeuklpY07Z8q1Wq3qwV2V41u/fj3atm0Lo9FY7/HVx8yZM7F48WLVA9jq06etrS3at2+P06dPK1ck3KlHjx5Ys2YNSktLlZnp77//vs4x9e7dGxkZGX9ib+onMTER9vb28PT0rPc2P/74Iy5duoSFCxfCxcUFAHD06NFq66anp+Ppp582yViJiKhp4OXiRETUYLZt24bLly8jNDQUjzzyiOonJCREuWQcuPWU8WXLliEuLk6VmBkMBsyePRuzZs3C6tWrkZOTg+PHjyMyMlJ5iFZNunbtiv/9739ITU3FiRMnMGHCBNVMZvfu3REYGIhp06bh8OHDSElJwbRp06DT6aDRaAAAgYGB8PHxwahRo/Ddd9/h7NmzOHjwIN56660aEyng1mXBAwYMQGRkJPz8/JQZXK1WqyqvPJnQtWtXHD16FLt27cKpU6cwd+5cHDlyRNWmm5sbTp48iaysLPzxxx8oKyvDxIkT4eDggJEjRyIpKQlnzpxBYmIiXnnlFfz888/1jFT1rKys8N5772Hx4sWq8vr0+d577yEiIgKLFy/GqVOnkJaWhlWrVinviZ4wYQI0Gg2mTp2KjIwMxMbGYtGiRXWOKSgoCMnJyVVONpjK1q1bq1wqXhdXV1dotVpERkbi9OnT2Lp1K+bPn1+l3tmzZ/HLL78gMDDQVMMlIqImgEk2ERE1mBUrViAwMBC2trZV1oWEhODo0aM4efIkgFuJW0ZGBjp06AA/Pz9V3fnz52Pu3LmIiIhAjx49EBwcjO3bt6Nz58619v/hhx/C3t4evr6+eOqppxAUFKS6/xoAYmJi4OTkhEGDBmH06NGYOnUqDAaDMruq0WgQGxuLQYMGYcqUKejWrRv++te/4ty5c3Bycqq1/4CAABQWFqru9wVuXTJeWFiouh87LCwMY8aMwbhx4+Dt7Y1Lly6pZrUBYOrUqXB3d0ffvn3h6OiIAwcOQK/XY9++fXB1dcWYMWPQo0cPhIaGorS01CQz25MmTcJDDz2kKqtPny+88AK++OILrFq1Ch4eHvD390d0dLQSMxsbG3z77bdIS0tD79698dZbb+E///lPneMZPny48i7v++HPJNmOjo6Ijo7Gxo0b0bNnTyxcuLDaEwZfffUVhg0bhk6dOplquERE1ARoRP7/O1OIiIioip9//hkuLi7YvXs3hgwZ0tjDoWpERUVh69at2LVrl0nbPX78OAYPHozff/+9yj3Y9+rGjRvo2rUrvvzyyyonkYiIqHnjPdlERES32bNnD4qKiuDh4YG8vDy8/vrrcHNzw6BBgxp7aFSDsLAwFBQUoLCwEAaDwWTtlpeXIzIy0uQJNnDrKeNz5sxhgk1E1AJxJpuIiOg2u3btwquvvorTp0/DYDDA19cXH3/8MS/pJSIionphkk1ERERERERkInzwGREREREREZGJMMkmIiIiIiIiMhEm2UREREREREQmwiSbiIiIiIiIyESYZBMRERERERGZCJNsIiIiIiIiIhNhkk1ERERERERkIkyyiYiIiIiIiEyESTYRERERERGRifw/KdoIqdA/y8IAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_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}simulated_data.parquet\")\n", + "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", @@ -716,7 +1257,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "bbe87b415168368", "metadata": {}, "outputs": [], @@ -875,12 +1416,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "9c4d5f0f3fafdc2d", "metadata": {}, - "outputs": [], + "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}simulated_data.parquet\")\n", + "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", @@ -892,15 +1449,9 @@ "print(\"Target shape:\", train_targets.shape)" ] }, - { - "cell_type": "markdown", - "id": "af0c53c96eb322cc", - "metadata": {}, - "source": [] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "604c952c7195f40c", "metadata": {}, "outputs": [], @@ -1338,35 +1889,376 @@ " return model, history" ] }, - { - "cell_type": "markdown", - "id": "33ad638941654e12", - "metadata": {}, - "source": [] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "35490e902e494c4a", "metadata": {}, - "outputs": [], + "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 11:43:09.026945: 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 11:43:25.651745: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7d7e70d1ce40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-12-06 11:43:25.651778: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-12-06 11:43:25.659099: 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 11:43:25.722749: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n", + "2024-12-06 11:43:25.861911: 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": [ + "9954/9954 [==============================] - 481s 46ms/step - loss: 0.0460 - mae: 0.1872 - val_loss: 0.0145 - val_mae: 0.0865 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0935 - val_max_oil_prod_mae: 0.0936 - val_avg_oil_prod_mae: 0.0894 - val_total_water_need_mae: 0.0598 - lr: 1.0219e-05\n", + "Epoch 2/150\n", + "9954/9954 [==============================] - 473s 47ms/step - loss: 0.0273 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0963 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0603 - lr: 1.0438e-07\n", + "Epoch 3/150\n", + "9954/9954 [==============================] - 477s 48ms/step - loss: 0.0273 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0861 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0929 - val_max_oil_prod_mae: 0.0927 - val_avg_oil_prod_mae: 0.0886 - val_total_water_need_mae: 0.0602 - lr: 1.0661e-09\n", + "Epoch 4/150\n", + "9954/9954 [==============================] - 508s 51ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0867 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0932 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0616 - lr: 1.0889e-11\n", + "Epoch 5/150\n", + "9954/9954 [==============================] - 431s 43ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0143 - val_mae: 0.0865 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0612 - lr: 1.1122e-13\n", + "Epoch 6/150\n", + "9954/9954 [==============================] - 438s 44ms/step - loss: 0.0273 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0598 - lr: 1.1361e-15\n", + "Epoch 7/150\n", + "9954/9954 [==============================] - 413s 41ms/step - loss: 0.0273 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0868 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0932 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0620 - lr: 1.1604e-17\n", + "Epoch 8/150\n", + "9954/9954 [==============================] - 433s 43ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0865 - val_olive_prod_mae: 0.0966 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0611 - lr: 1.1852e-19\n", + "Epoch 9/150\n", + "9954/9954 [==============================] - 413s 41ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0143 - val_mae: 0.0865 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0608 - lr: 1.2106e-21\n", + "Epoch 10/150\n", + "9954/9954 [==============================] - 430s 43ms/step - loss: 0.0273 - mae: 0.1508 - val_loss: 0.0143 - val_mae: 0.0864 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0607 - lr: 1.2365e-23\n", + "Epoch 11/150\n", + "9954/9954 [==============================] - 438s 44ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0928 - val_avg_oil_prod_mae: 0.0887 - val_total_water_need_mae: 0.0604 - lr: 1.2630e-25\n", + "Epoch 12/150\n", + "9954/9954 [==============================] - 430s 43ms/step - loss: 0.0273 - mae: 0.1505 - val_loss: 0.0144 - val_mae: 0.0866 - val_olive_prod_mae: 0.0968 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0891 - val_total_water_need_mae: 0.0606 - lr: 1.2900e-27\n", + "Epoch 13/150\n", + "9954/9954 [==============================] - 425s 43ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0144 - val_mae: 0.0868 - val_olive_prod_mae: 0.0966 - val_min_oil_prod_mae: 0.0932 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0619 - lr: 1.3177e-29\n", + "Epoch 14/150\n", + "9954/9954 [==============================] - 409s 41ms/step - loss: 0.0272 - mae: 0.1504 - val_loss: 0.0144 - val_mae: 0.0865 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0891 - val_total_water_need_mae: 0.0605 - lr: 1.3459e-31\n", + "Epoch 15/150\n", + "9954/9954 [==============================] - 439s 44ms/step - loss: 0.0273 - mae: 0.1509 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0929 - val_max_oil_prod_mae: 0.0926 - val_avg_oil_prod_mae: 0.0886 - val_total_water_need_mae: 0.0609 - lr: 1.3747e-33\n", + "Epoch 16/150\n", + "9954/9954 [==============================] - 421s 42ms/step - loss: 0.0273 - mae: 0.1508 - val_loss: 0.0143 - val_mae: 0.0862 - val_olive_prod_mae: 0.0963 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0928 - val_avg_oil_prod_mae: 0.0887 - val_total_water_need_mae: 0.0604 - lr: 1.4041e-35\n", + "Epoch 17/150\n", + "9954/9954 [==============================] - 429s 43ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0966 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0600 - lr: 1.4342e-37\n", + "Epoch 18/150\n", + "9954/9954 [==============================] - 414s 41ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0144 - val_mae: 0.0865 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0931 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0602 - lr: 0.0000e+00\n", + "Epoch 19/150\n", + "9954/9954 [==============================] - 441s 44ms/step - loss: 0.0272 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0864 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0928 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0608 - lr: 0.0000e+00\n", + "Epoch 20/150\n", + "9954/9954 [==============================] - 440s 44ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0862 - val_olive_prod_mae: 0.0963 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0601 - lr: 0.0000e+00\n", + "Epoch 21/150\n", + "9954/9954 [==============================] - 448s 45ms/step - loss: 0.0273 - mae: 0.1508 - val_loss: 0.0143 - val_mae: 0.0862 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0935 - val_max_oil_prod_mae: 0.0936 - val_avg_oil_prod_mae: 0.0894 - val_total_water_need_mae: 0.0598 - lr: 0.0000e+00\n", + "\n", + "Modello salvato in: 2024-12-06_10-36_final_model.keras\n" + ] + } + ], "source": [ "model, history = train_transformer(train_data, train_targets, val_data, val_targets, 150, 256)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "3e2fb5a5341dac92", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24500/24500 [==============================] - 102s 4ms/step\n", + "\n", + "Errori per target:\n", + "--------------------------------------------------\n", + "olive_prod:\n", + "MAE assoluto: 1585.45\n", + "Errore percentuale medio: 6.91%\n", + "Precisione: 93.09%\n", + "--------------------------------------------------\n", + "min_oil_prod:\n", + "MAE assoluto: 319.12\n", + "Errore percentuale medio: 6.61%\n", + "Precisione: 93.39%\n", + "--------------------------------------------------\n", + "max_oil_prod:\n", + "MAE assoluto: 387.31\n", + "Errore percentuale medio: 6.74%\n", + "Precisione: 93.26%\n", + "--------------------------------------------------\n", + "avg_oil_prod:\n", + "MAE assoluto: 337.11\n", + "Errore percentuale medio: 6.46%\n", + "Precisione: 93.54%\n", + "--------------------------------------------------\n", + "total_water_need:\n", + "MAE assoluto: 1775.48\n", + "Errore percentuale medio: 4.24%\n", + "Precisione: 95.76%\n", + "--------------------------------------------------\n" + ] + } + ], "source": [ "percentage_errors, absolute_errors = calculate_real_error(model, val_data, val_targets, scaler_y)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "4af58aa9bbc156f5", "metadata": {}, "outputs": [], @@ -1542,10 +2434,203 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "588c7e49371f4a0c", "metadata": {}, - "outputs": [], + "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: 0.0963\n", + "olive_prod_rmse: 0.1300\n", + "olive_prod_mape: 77.2491\n", + "min_oil_prod_mae: 0.0936\n", + "min_oil_prod_rmse: 0.1312\n", + "min_oil_prod_mape: 91.4612\n", + "max_oil_prod_mae: 0.0936\n", + "max_oil_prod_rmse: 0.1304\n", + "max_oil_prod_mape: 88.9396\n", + "avg_oil_prod_mae: 0.0895\n", + "avg_oil_prod_rmse: 0.1238\n", + "avg_oil_prod_mape: 89.5317\n", + "total_water_need_mae: 0.0598\n", + "total_water_need_rmse: 0.0808\n", + "total_water_need_mape: 44.4531\n", + "\n", + "Performance sul set validazione:\n", + "olive_prod_mae: 0.0964\n", + "olive_prod_rmse: 0.1301\n", + "olive_prod_mape: 133.2427\n", + "min_oil_prod_mae: 0.0935\n", + "min_oil_prod_rmse: 0.1310\n", + "min_oil_prod_mape: 120.7693\n", + "max_oil_prod_mae: 0.0936\n", + "max_oil_prod_rmse: 0.1304\n", + "max_oil_prod_mape: 86.2224\n", + "avg_oil_prod_mae: 0.0894\n", + "avg_oil_prod_rmse: 0.1237\n", + "avg_oil_prod_mape: 83.8138\n", + "total_water_need_mae: 0.0598\n", + "total_water_need_rmse: 0.0809\n", + "total_water_need_mape: 53.9347\n", + "\n", + "Performance sul set test:\n", + "olive_prod_mae: 0.0962\n", + "olive_prod_rmse: 0.1298\n", + "olive_prod_mape: 77.9806\n", + "min_oil_prod_mae: 0.0935\n", + "min_oil_prod_rmse: 0.1312\n", + "min_oil_prod_mape: 95.5886\n", + "max_oil_prod_mae: 0.0934\n", + "max_oil_prod_rmse: 0.1301\n", + "max_oil_prod_mape: 76.3217\n", + "avg_oil_prod_mae: 0.0893\n", + "avg_oil_prod_rmse: 0.1237\n", + "avg_oil_prod_mape: 111.2211\n", + "total_water_need_mae: 0.0596\n", + "total_water_need_rmse: 0.0806\n", + "total_water_need_mape: 38.1699\n", + "\n", + "Avvio retraining...\n", + "Epoch 1/50\n", + "27563/27563 [==============================] - 851s 30ms/step - loss: 0.0261 - mae: 0.1520 - val_loss: 0.0118 - val_mae: 0.0804 - lr: 5.4806e-06\n", + "Epoch 2/50\n", + "27563/27563 [==============================] - 852s 31ms/step - loss: 0.0245 - mae: 0.1478 - val_loss: 0.0117 - val_mae: 0.0803 - lr: 3.0034e-07\n", + "Epoch 3/50\n", + "27563/27563 [==============================] - 836s 30ms/step - loss: 0.0244 - mae: 0.1476 - val_loss: 0.0117 - val_mae: 0.0807 - lr: 1.6459e-08\n", + "Epoch 4/50\n", + "27563/27563 [==============================] - 863s 31ms/step - loss: 0.0244 - mae: 0.1476 - val_loss: 0.0118 - val_mae: 0.0807 - lr: 9.0196e-10\n", + "Epoch 5/50\n", + "27563/27563 [==============================] - 854s 31ms/step - loss: 0.0243 - mae: 0.1474 - val_loss: 0.0119 - val_mae: 0.0812 - lr: 4.9428e-11\n", + "Epoch 6/50\n", + "27563/27563 [==============================] - 869s 32ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0118 - val_mae: 0.0807 - lr: 2.7087e-12\n", + "Epoch 7/50\n", + "27563/27563 [==============================] - 867s 31ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0118 - val_mae: 0.0806 - lr: 1.4844e-13\n", + "Epoch 8/50\n", + "27563/27563 [==============================] - 899s 33ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0117 - val_mae: 0.0803 - lr: 8.1345e-15\n", + "Epoch 9/50\n", + "27563/27563 [==============================] - 966s 35ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0117 - val_mae: 0.0804 - lr: 4.4578e-16\n", + "Epoch 10/50\n", + "27563/27563 [==============================] - 930s 34ms/step - loss: 0.0244 - mae: 0.1474 - val_loss: 0.0118 - val_mae: 0.0807 - lr: 2.4429e-17\n", + "Epoch 11/50\n", + "27563/27563 [==============================] - 921s 33ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0118 - val_mae: 0.0809 - lr: 1.3387e-18\n", + "\n", + "Valutazione performance finali...\n", + "\n", + "Performance sul set training:\n", + "olive_prod_mae: 0.0901\n", + "olive_prod_rmse: 0.1222\n", + "olive_prod_mape: 75.7735\n", + "min_oil_prod_mae: 0.0886\n", + "min_oil_prod_rmse: 0.1245\n", + "min_oil_prod_mape: 91.0646\n", + "max_oil_prod_mae: 0.0888\n", + "max_oil_prod_rmse: 0.1243\n", + "max_oil_prod_mape: 89.5375\n", + "avg_oil_prod_mae: 0.0845\n", + "avg_oil_prod_rmse: 0.1171\n", + "avg_oil_prod_mape: 86.3355\n", + "total_water_need_mae: 0.0495\n", + "total_water_need_rmse: 0.0678\n", + "total_water_need_mape: 41.0436\n", + "\n", + "Performance sul set validazione:\n", + "olive_prod_mae: 0.0901\n", + "olive_prod_rmse: 0.1222\n", + "olive_prod_mape: 138.3196\n", + "min_oil_prod_mae: 0.0885\n", + "min_oil_prod_rmse: 0.1243\n", + "min_oil_prod_mape: 126.9523\n", + "max_oil_prod_mae: 0.0888\n", + "max_oil_prod_rmse: 0.1243\n", + "max_oil_prod_mape: 82.7593\n", + "avg_oil_prod_mae: 0.0843\n", + "avg_oil_prod_rmse: 0.1169\n", + "avg_oil_prod_mape: 84.3605\n", + "total_water_need_mae: 0.0495\n", + "total_water_need_rmse: 0.0679\n", + "total_water_need_mape: 48.6941\n", + "\n", + "Performance sul set test:\n", + "olive_prod_mae: 0.0899\n", + "olive_prod_rmse: 0.1219\n", + "olive_prod_mape: 77.0356\n", + "min_oil_prod_mae: 0.0886\n", + "min_oil_prod_rmse: 0.1243\n", + "min_oil_prod_mape: 96.3498\n", + "max_oil_prod_mae: 0.0885\n", + "max_oil_prod_rmse: 0.1238\n", + "max_oil_prod_mape: 76.4509\n", + "avg_oil_prod_mae: 0.0843\n", + "avg_oil_prod_rmse: 0.1167\n", + "avg_oil_prod_mape: 87.8912\n", + "total_water_need_mae: 0.0494\n", + "total_water_need_rmse: 0.0677\n", + "total_water_need_mape: 30.6997\n", + "\n", + "Modello riaddestrato salvato in: 2024-12-06_10-36_retrained_model.keras\n", + "\n", + "Miglioramenti delle performance:\n", + "\n", + "Set train:\n", + "olive_prod_mae: 6.48% di miglioramento\n", + "olive_prod_rmse: 6.00% di miglioramento\n", + "olive_prod_mape: 1.91% di miglioramento\n", + "min_oil_prod_mae: 5.29% di miglioramento\n", + "min_oil_prod_rmse: 5.12% di miglioramento\n", + "min_oil_prod_mape: 0.43% di miglioramento\n", + "max_oil_prod_mae: 5.11% di miglioramento\n", + "max_oil_prod_rmse: 4.70% di miglioramento\n", + "max_oil_prod_mape: -0.67% di miglioramento\n", + "avg_oil_prod_mae: 5.58% di miglioramento\n", + "avg_oil_prod_rmse: 5.45% di miglioramento\n", + "avg_oil_prod_mape: 3.57% di miglioramento\n", + "total_water_need_mae: 17.16% di miglioramento\n", + "total_water_need_rmse: 15.99% di miglioramento\n", + "total_water_need_mape: 7.67% di miglioramento\n", + "\n", + "Set val:\n", + "olive_prod_mae: 6.51% di miglioramento\n", + "olive_prod_rmse: 6.04% di miglioramento\n", + "olive_prod_mape: -3.81% di miglioramento\n", + "min_oil_prod_mae: 5.33% di miglioramento\n", + "min_oil_prod_rmse: 5.16% di miglioramento\n", + "min_oil_prod_mape: -5.12% di miglioramento\n", + "max_oil_prod_mae: 5.13% di miglioramento\n", + "max_oil_prod_rmse: 4.70% di miglioramento\n", + "max_oil_prod_mape: 4.02% di miglioramento\n", + "avg_oil_prod_mae: 5.62% di miglioramento\n", + "avg_oil_prod_rmse: 5.48% di miglioramento\n", + "avg_oil_prod_mape: -0.65% di miglioramento\n", + "total_water_need_mae: 17.23% di miglioramento\n", + "total_water_need_rmse: 16.08% di miglioramento\n", + "total_water_need_mape: 9.72% di miglioramento\n", + "\n", + "Set test:\n", + "olive_prod_mae: 6.52% di miglioramento\n", + "olive_prod_rmse: 6.09% di miglioramento\n", + "olive_prod_mape: 1.21% di miglioramento\n", + "min_oil_prod_mae: 5.32% di miglioramento\n", + "min_oil_prod_rmse: 5.22% di miglioramento\n", + "min_oil_prod_mape: -0.80% di miglioramento\n", + "max_oil_prod_mae: 5.22% di miglioramento\n", + "max_oil_prod_rmse: 4.83% di miglioramento\n", + "max_oil_prod_mape: -0.17% di miglioramento\n", + "avg_oil_prod_mae: 5.64% di miglioramento\n", + "avg_oil_prod_rmse: 5.59% di miglioramento\n", + "avg_oil_prod_mape: 20.98% di miglioramento\n", + "total_water_need_mae: 17.22% di miglioramento\n", + "total_water_need_rmse: 16.03% di miglioramento\n", + "total_water_need_mape: 19.57% di miglioramento\n" + ] + } + ], "source": [ "model_path = f'{execute_name}_final_model.keras'\n", "\n", @@ -1564,87 +2649,148 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "a95606bc-c4bc-418a-acdb-2e24c30dfa81", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24500/24500 [==============================] - 137s 6ms/step\n", + "\n", + "Errori per target:\n", + "--------------------------------------------------\n", + "olive_prod:\n", + "MAE assoluto: 1482.22\n", + "Errore percentuale medio: 5.77%\n", + "Precisione: 94.23%\n", + "--------------------------------------------------\n", + "min_oil_prod:\n", + "MAE assoluto: 302.12\n", + "Errore percentuale medio: 5.68%\n", + "Precisione: 94.32%\n", + "--------------------------------------------------\n", + "max_oil_prod:\n", + "MAE assoluto: 367.45\n", + "Errore percentuale medio: 5.78%\n", + "Precisione: 94.22%\n", + "--------------------------------------------------\n", + "avg_oil_prod:\n", + "MAE assoluto: 318.15\n", + "Errore percentuale medio: 5.49%\n", + "Precisione: 94.51%\n", + "--------------------------------------------------\n", + "total_water_need:\n", + "MAE assoluto: 1469.51\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": null, + "execution_count": 34, "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", - " \"\"\"\n", - " Classe per calcolare e visualizzare PMF e CMF usando TensorFlow.\n", - " \"\"\"\n", - " \n", " @staticmethod\n", - " def calculate_pmf(data, bins=50, range_limits=None):\n", - " \"\"\"\n", - " Calcola la PMF (Probability Mass Function) usando TensorFlow.\n", - " \n", - " Args:\n", - " data: Tensor dei dati\n", - " bins: Numero di bin per l'istogramma\n", - " range_limits: Tuple (min, max) per i limiti dell'istogramma\n", - " \n", - " Returns:\n", - " bin_edges: Bordi dei bin\n", - " pmf: Valori della PMF normalizzati\n", - " \"\"\"\n", - " # Converti i dati in tensor se non lo sono già\n", - " if not isinstance(data, tf.Tensor):\n", - " data = tf.convert_to_tensor(data, dtype=tf.float32)\n", - " \n", - " # Calcola i limiti se non specificati\n", - " if range_limits is None:\n", - " range_limits = (tf.reduce_min(data), tf.reduce_max(data))\n", - " \n", - " # Calcola l'istogramma\n", - " counts = tf.histogram_fixed_width(data, range_limits, bins)\n", - " \n", - " # Normalizza per ottenere la PMF\n", - " pmf = tf.cast(counts, tf.float32) / tf.reduce_sum(tf.cast(counts, tf.float32))\n", - " \n", - " # Calcola i bordi dei bin\n", - " bin_width = (range_limits[1] - range_limits[0]) / bins\n", - " bin_edges = tf.range(range_limits[0], range_limits[1] + bin_width, bin_width)\n", - " \n", - " return bin_edges.numpy(), pmf.numpy()\n", - " \n", - " @staticmethod\n", - " def calculate_cmf(pmf):\n", - " \"\"\"\n", - " Calcola la CMF (Cumulative Mass Function) dalla PMF.\n", - " \n", - " Args:\n", - " pmf: Array della PMF\n", - " \n", - " Returns:\n", - " cmf: Array della CMF\n", - " \"\"\"\n", - " # Converti in tensor se non lo è già\n", - " if not isinstance(pmf, tf.Tensor):\n", - " pmf = tf.convert_to_tensor(pmf, dtype=tf.float32)\n", - " \n", - " # Calcola la CMF usando cumsum\n", - " cmf = tf.cumsum(pmf)\n", - " \n", - " return cmf.numpy()\n", - " \n", - " @staticmethod\n", - " def calculate_statistics(data):\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 dei dati\n", + " data: Tensor o array dei dati\n", " \n", " Returns:\n", " dict: Dizionario con le statistiche\n", @@ -1653,91 +2799,110 @@ " data = tf.convert_to_tensor(data, dtype=tf.float32)\n", " \n", " mean = tf.reduce_mean(data)\n", - " variance = tf.reduce_variance(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': tf.raw_ops.TopKV2(input=data, k=tf.size(data)//2)[0][-1].numpy()\n", + " 'median': median.numpy()\n", " }\n", - " \n", + "\n", " @staticmethod\n", - " def plot_distributions(bin_edges, pmf, cmf, title=\"Probability and Cumulative Distributions\"):\n", + " def calculate_pmf(data: np.ndarray, bins: int = 50) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n", " \"\"\"\n", - " Visualizza PMF e CMF.\n", + " Calcola la Probability Mass Function (PMF) dei dati.\n", " \n", " Args:\n", - " bin_edges: Bordi dei bin\n", - " pmf: Array della PMF\n", - " cmf: Array della CMF\n", - " title: Titolo del plot\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", - " import matplotlib.pyplot as plt\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", - " ax1.bar(bin_edges[:-1], pmf, width=np.diff(bin_edges), alpha=0.5, \n", - " align='edge', label='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)\n", + " ax1.grid(True, alpha=0.3)\n", " ax1.legend()\n", " \n", " # Plot CMF\n", - " ax2.plot(bin_edges[:-1], cmf, 'r-', label='CMF')\n", + " ax2.plot(bin_centers, cmf, 'r-', label='CMF')\n", " ax2.set_title('Cumulative Mass Function')\n", - " ax2.set_ylabel('Cumulative Probability')\n", " ax2.set_xlabel('Value')\n", - " ax2.grid(True)\n", + " ax2.set_ylabel('Cumulative Probability')\n", + " ax2.grid(True, alpha=0.3)\n", " ax2.legend()\n", " \n", - " plt.suptitle(title)\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", - "# Esempio di utilizzo\n", - "def demonstrate_probability_functions():\n", - " \"\"\"\n", - " Dimostra l'uso delle funzioni di probabilità.\n", - " \"\"\"\n", - " # Genera dati di esempio\n", - " tf.random.set_seed(42)\n", - " data = tf.concat([\n", - " tf.random.normal([1000], mean=0, stddev=1),\n", - " tf.random.normal([500], mean=3, stddev=0.5)\n", - " ], axis=0)\n", - " \n", - " # Inizializza la classe\n", - " prob = ProbabilityFunctions()\n", - " \n", - " # Calcola PMF e CMF\n", - " bin_edges, pmf = prob.calculate_pmf(data, bins=50)\n", - " cmf = prob.calculate_cmf(pmf)\n", - " \n", - " # Calcola statistiche\n", - " stats = prob.calculate_statistics(data)\n", - " \n", - " print(\"Statistiche di Base:\")\n", - " for key, value in stats.items():\n", - " print(f\"{key}: {value:.3f}\")\n", - " \n", - " # Visualizza le distribuzioni\n", - " prob.plot_distributions(bin_edges, pmf, cmf, \"Distribuzioni di Probabilità\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2022b594-6a8b-47ae-b207-112dfd9e3e95", - "metadata": {}, - "outputs": [], - "source": [ - "def analyze_model_predictions(model, test_data, test_targets, scaler_y):\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", @@ -1758,7 +2923,8 @@ " prob = ProbabilityFunctions()\n", " \n", " # Analizza ogni target\n", - " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\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", @@ -1773,13 +2939,12 @@ " for key, value in error_stats.items():\n", " print(f\"{key}: {value:.3f}\")\n", " \n", - " # Calcola PMF e CMF degli errori\n", - " bin_edges, pmf = prob.calculate_pmf(errors, bins=50)\n", - " cmf = prob.calculate_cmf(pmf)\n", - " \n", " # Visualizza le distribuzioni degli errori\n", - " prob.plot_distributions(bin_edges, pmf, cmf, \n", - " f\"Distribuzione degli Errori - {target}\")\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", @@ -1788,51 +2953,8 @@ " upper_idx = np.searchsorted(cmf, (1 + level) / 2)\n", " \n", " print(f\"\\nIntervallo di Confidenza {level*100}%:\")\n", - " print(f\"Range: [{bin_edges[lower_idx]:.2f}, {bin_edges[upper_idx]:.2f}]\")\n", - " \n", - "def analyze_feature_importance(model, test_data, feature_names):\n", - " \"\"\"\n", - " Analizza l'importanza delle feature usando perturbazione.\n", - " \n", - " Args:\n", - " model: Modello TensorFlow addestrato\n", - " test_data: Dati di test\n", - " feature_names: Lista dei nomi delle feature\n", - " \"\"\"\n", - " base_prediction = model.predict(test_data)\n", - " feature_importance = {}\n", - " \n", - " # Per ogni feature\n", - " for i, feature in enumerate(feature_names):\n", - " # Crea copia perturbata dei dati\n", - " perturbed_data = test_data.copy()\n", - " noise = tf.random.normal(shape=perturbed_data[feature].shape,\n", - " mean=0, stddev=0.1)\n", - " perturbed_data[feature] = perturbed_data[feature] + noise\n", - " \n", - " # Calcola nuova predizione\n", - " perturbed_prediction = model.predict(perturbed_data)\n", - " \n", - " # Calcola impatto della perturbazione\n", - " impact = tf.reduce_mean(tf.abs(perturbed_prediction - base_prediction))\n", - " feature_importance[feature] = impact.numpy()\n", - " \n", - " # Normalizza e ordina le importanze\n", - " total_importance = sum(feature_importance.values())\n", - " feature_importance = {k: v/total_importance for k, v in feature_importance.items()}\n", - " feature_importance = dict(sorted(feature_importance.items(), \n", - " key=lambda x: x[1], reverse=True))\n", - " \n", - " return feature_importance" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c1150506-d853-4d00-a27c-266ac9c51f01", - "metadata": {}, - "outputs": [], - "source": [ + " 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", @@ -1851,7 +2973,7 @@ " \n", " # Definisci i nomi delle feature\n", " temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n", - " static_features = ['ha'] # Aggiungi le tue feature statiche\n", + " static_features = ['ha']\n", " \n", " all_features = temporal_features + static_features\n", " importance = analyze_feature_importance(retrained_model, test_data, all_features)\n", @@ -1875,14 +2997,6 @@ " for i, target in enumerate(target_names):\n", " print(f\"\\nAnalisi distribuzionale per {target}\")\n", " \n", - " # Analizza predizioni\n", - " bin_edges_pred, pmf_pred = prob.calculate_pmf(predictions_real[:, i])\n", - " cmf_pred = prob.calculate_cmf(pmf_pred)\n", - " \n", - " # Analizza target reali\n", - " bin_edges_true, pmf_true = prob.calculate_pmf(targets_real[:, i])\n", - " cmf_true = prob.calculate_cmf(pmf_true)\n", - " \n", " # Statistiche\n", " stats_pred = prob.calculate_statistics(predictions_real[:, i])\n", " stats_true = prob.calculate_statistics(targets_real[:, i])\n", @@ -1896,18 +3010,609 @@ " print(f\"{key}: {value:.3f}\")\n", " \n", " # Visualizza distribuzioni\n", - " prob.plot_distributions(bin_edges_pred, pmf_pred, cmf_pred,\n", - " f\"Distribuzione Predizioni - {target}\")\n", - " prob.plot_distributions(bin_edges_true, pmf_true, cmf_true,\n", - " f\"Distribuzione Target Reali - {target}\")" + " 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": null, + "execution_count": 35, "id": "b98f2a45-ba6f-4519-acaa-0138b4ee7812", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== ANALISI COMPLETA DEL MODELLO ===\n", + "\n", + "1. ANALISI DEGLI ERRORI\n", + "--------------------------------------------------\n", + "18375/18375 [==============================] - 78s 4ms/step\n", + "\n", + "Analisi per olive_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -71.944\n", + "variance: 4009595.000\n", + "std: 2002.397\n", + "min: -18637.889\n", + "max: 12871.579\n", + "median: 48.672\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 68.0%:\n", + "Range: [-1937.87, 1843.27]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-4458.63, 3733.83]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-6979.39, 5624.40]\n", + "\n", + "Analisi per min_oil_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -32.785\n", + "variance: 179026.016\n", + "std: 423.115\n", + "min: -4439.664\n", + "max: 3453.714\n", + "median: -12.655\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 68.0%:\n", + "Range: [-414.04, 375.30]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-1045.51, 848.90]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-1519.11, 1164.63]\n", + "\n", + "Analisi per max_oil_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -34.971\n", + "variance: 261409.344\n", + "std: 511.282\n", + "min: -5732.709\n", + "max: 4274.197\n", + "median: -11.391\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 68.0%:\n", + "Range: [-429.05, 371.50]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-1229.60, 971.92]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-1830.02, 1572.33]\n", + "\n", + "Analisi per avg_oil_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -33.549\n", + "variance: 192810.531\n", + "std: 439.102\n", + "min: -4876.229\n", + "max: 3813.953\n", + "median: -13.710\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 68.0%:\n", + "Range: [-444.24, 250.98]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-965.65, 772.39]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-1487.06, 1293.80]\n", + "\n", + "Analisi per total_water_need\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -216.226\n", + "variance: 3987062.750\n", + "std: 1996.763\n", + "min: -22812.350\n", + "max: 13374.520\n", + "median: -119.823\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 68.0%:\n", + "Range: [-2185.83, 1432.85]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-4357.05, 3604.06]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-7252.00, 5051.54]\n", + "\n", + "2. IMPORTANZA DELLE FEATURE\n", + "--------------------------------------------------\n", + "18375/18375 [==============================] - 79s 4ms/step\n", + "18375/18375 [==============================] - 80s 4ms/step\n", + "18375/18375 [==============================] - 99s 5ms/step\n", + "18375/18375 [==============================] - 96s 5ms/step\n", + "18375/18375 [==============================] - 89s 5ms/step\n", + "\n", + "Importanza relativa delle feature:\n", + "ha: 0.8936\n", + "precip_sum: 0.0472\n", + "temp_mean: 0.0300\n", + "solar_energy_sum: 0.0292\n", + "\n", + "3. ANALISI DISTRIBUZIONALE\n", + "--------------------------------------------------\n", + "18375/18375 [==============================] - 80s 4ms/step\n", + "\n", + "Analisi distribuzionale per olive_prod\n", + "\n", + "Statistiche Predizioni:\n", + "mean: 29777.369\n", + "variance: 260371152.000\n", + "std: 16136.021\n", + "min: 2640.763\n", + "max: 93252.961\n", + "median: 27940.055\n", + "\n", + "Statistiche Target Reali:\n", + "mean: 29849.314\n", + "variance: 270000992.000\n", + "std: 16431.707\n", + "min: 1997.078\n", + "max: 98911.648\n", + "median: 27894.504\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analisi distribuzionale per min_oil_prod\n", + "\n", + "Statistiche Predizioni:\n", + "mean: 5883.382\n", + "variance: 11284409.000\n", + "std: 3359.227\n", + "min: 591.848\n", + "max: 20596.607\n", + "median: 5403.938\n", + "\n", + "Statistiche Target Reali:\n", + "mean: 5916.168\n", + "variance: 11638207.000\n", + "std: 3411.482\n", + "min: 382.403\n", + "max: 22385.047\n", + "median: 5419.380\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analisi distribuzionale per max_oil_prod\n", + "\n", + "Statistiche Predizioni:\n", + "mean: 7114.477\n", + "variance: 16623633.000\n", + "std: 4077.209\n", + "min: 744.739\n", + "max: 25426.498\n", + "median: 6533.670\n", + "\n", + "Statistiche Target Reali:\n", + "mean: 7149.448\n", + "variance: 17105002.000\n", + "std: 4135.819\n", + "min: 439.250\n", + "max: 27600.529\n", + "median: 6552.075\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analisi distribuzionale per avg_oil_prod\n", + "\n", + "Statistiche Predizioni:\n", + "mean: 6499.278\n", + "variance: 13822488.000\n", + "std: 3717.861\n", + "min: 665.561\n", + "max: 23014.055\n", + "median: 5969.562\n", + "\n", + "Statistiche Target Reali:\n", + "mean: 6532.827\n", + "variance: 14209578.000\n", + "std: 3769.559\n", + "min: 417.208\n", + "max: 24570.369\n", + "median: 5988.624\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analisi distribuzionale per total_water_need\n", + "\n", + "Statistiche Predizioni:\n", + "mean: 59916.789\n", + "variance: 840571520.000\n", + "std: 28992.611\n", + "min: 11901.837\n", + "max: 141196.922\n", + "median: 59064.434\n", + "\n", + "Statistiche Target Reali:\n", + "mean: 60133.016\n", + "variance: 879985088.000\n", + "std: 29664.543\n", + "min: 8188.121\n", + "max: 152673.438\n", + "median: 59195.098\n" + ] + }, + { + "data": { + "image/png": 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muc4NN9xw1e3m1MSJE/Xpp5/qk08+Ud26dfNtuxlycr5z4pZbbrH7A8rIkSNtA75dTydPnlTLli0VEBCgl156SZUqVZKPj4+2bNmi5557Ls93mGRo1qyZLly4oLi4OK1fv9427kLz5s21fv16bd++XQkJCbZyKb3nu0OHDmrRooWmTp2qMmXKyNPTUzNnztScOXOuus+MmB944IFsxwO48cYb7d5fy++Jq10TuYknPz6rAOCKSLoBIAsff/yxJGW6Fflybm5uat26tVq3bq1x48bp1Vdf1fPPP681a9YoOjo6Rz1fufH333/bvTfGaNeuXXZf0kuUKJHliNr79u2zu81Xkl577TUtXrxYCxcuVLVq1a66/4z5unfs2GG3rZSUFO3ZsydHie/lKlWqpF9//VWtW7fO9/OV0/2fPn36qrFXqFBBq1at0unTp+16fXfs2JGj/axfv17PPPOMnnzySd1///1XrX/pub7c9u3bVapUKbte7vw0e/Zsu3nGL79uLpdVu4WEhMjPzy/b+N3c3GxJWnbtvnbtWh07dkwLFy60G8hsz549OTqOq2nQoIG8vLy0fv16rV+/XkOGDJEktWjRQu+++65WrVple5/h888/l4+Pj5YvX243/drMmTMzbT+781K8eHGlpaXl6fOS33ITT04/qxUqVJDVatXu3bvterdz+lkBgKKGZ7oB4DKrV6/Wyy+/rKioqCsmR8ePH89UltF7mXHLaUZSlFUSnBcfffSR3XPmCxYs0KFDh2wjgUvpX4x/+OEHpaSk2MqWLFmSaWqxlStXasSIEXr++efVqVOnHO0/OjpaXl5emjRpkl3v6fvvv6/ExMQ8jU7cpUsXHThwQO+++26mZWfPntWZM2dyvc3c7j8uLk7Lly/PtOzkyZNKTU2VJLVt21apqamaNm2abXlaWpreeeedq+7j0KFD6tKli5o1a5bjEbfLlCmjunXr6sMPP7S7frZt26Zvv/1Wbdu2zdF28qJp06aKjo62va6WdPv7+2e6xt3d3XXHHXfoiy++sHv84ciRI5ozZ46aNWtmu7Miu89JRi/tpddaSkqKpk6dmscjs+fj46NbbrlFn376qfbv32/X03327FlNmjRJlSpVUpkyZexislgsdrff7927N8tR9rM7L/fee68+//xzbdu2LdM6CQkJ+XJsOZWbeHL6Wc34fTRp0iS7OhkzAQCAq6GnG4BL++abb7R9+3alpqbqyJEjWr16tVasWKEKFSroyy+/lI+PT7brvvTSS1q3bp3atWunChUqKD4+XlOnTlW5cuXUrFkzSekJcFBQkKZPn67ixYvL399fDRs2zPMzmiVLllSzZs3Up08fHTlyRBMmTFDlypXtpjV76KGHtGDBAt15553q0qWLdu/erU8++STT85Xdu3dXSEiIqlSpYjcfuSTdfvvtWU5fFhISomHDhmn06NG688471aFDB+3YsUNTp07VLbfcYjdoWk717NlTn332mR599FGtWbNGTZs2VVpamrZv367PPvvMNq+2owwZMkRffvml7rrrLvXu3Vv16tXTmTNn9Pvvv2vBggXau3evSpUqpfbt26tp06YaOnSo9u7dqxo1amjhwoU5eq544MCBSkhI0LPPPqu5c+faLbvxxhsz3U6c4c0331SbNm3UuHFj9e3b1zZlWGBgoFNu985OvXr1tHLlSo0bN07h4eGKiopSw4YN9corr9jmsn/88cfl4eGh//3vfzp//rzdfO1169aVu7u7Xn/9dSUmJsrb21u33XabmjRpohIlSig2NlYDBw6UxWLRxx9/nOvb5a+kefPmeu211xQYGKjatWtLkkqXLq2qVatqx44dmeY0b9euncaNG6c777xTPXr0UHx8vKZMmaLKlSvbjVVwpfPy2muvac2aNWrYsKEefvhh1ahRQ8ePH9eWLVu0cuXKLP+g50g5jSenn9W6deuqe/fumjp1qhITE9WkSROtWrVKu3btuq7HBQAFhnMGTQcA58qYCivj5eXlZcLCwsztt99uJk6caDctV4bLp4FatWqV6dixowkPDzdeXl4mPDzcdO/ePdPUU1988YWpUaOG8fDwsJsSqmXLlqZmzZpZxpfdlGGffvqpGTZsmCldurTx9fU17dq1M/v27cu0/ttvv23Kli1rvL29TdOmTc3mzZszbfPS47/8lTF10+VThmWYPHmyqVatmvH09DShoaHmscces5tS6UrHl9XUTikpKeb11183NWvWNN7e3qZEiRKmXr16ZvTo0SYxMTHLc3Tp9vz9/a9Yx5j0qbLatWuX5bJTp06ZYcOGmcqVKxsvLy9TqlQp06RJE/PWW2+ZlJQUW71jx46Znj17moCAABMYGGh69uxpfvnll6tOGZYxfVpWr4zpk7KaMswYY1auXGmaNm1qfH19TUBAgGnfvr35888/7epk7O/y6euyaj9HTBm2fft206JFC+Pr62sk2W1/y5YtJiYmxhQrVsz4+fmZW2+91WzcuDHTNt59911TsWJF4+7ubncNfv/996ZRo0bG19fXhIeHm2effdYsX7480xRjuZ0yLMPXX39tJJk2bdrYlT/00ENGknn//fczrfP++++bKlWqGG9vb1OtWjUzc+bMLKeJu9J5OXLkiOnfv7+JiIgwnp6eJiwszLRu3drMmDHDVifjcz9//vxcH5cx6Z/x/v37ZyrP6hrISTzG5PyzevbsWTNw4EATHBxs/P39Tfv27c2///7LlGEAXJLFmHz8czEAAAAAALDhmW4AAAAAAByEZ7oBAECRcvz4cbuBBC/n7u6e7VzThcHhw4evuNzX11eBgYHXKRoAwNVwezkAAChSWrVqpe+++y7b5RUqVLAbUb2wudrUerGxsZo1a9b1CQYAcFX0dAMAgCLl7bff1okTJ7Jd7uvrex2jyX8rVqy44vLw8PDrFAkAICfo6QYAAAAAwEEYSA0AAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHISkGwAAAAAAByHpBgAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHAQkm4AAAAAAByEpBsAAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHISkGwAAAAAAByHpBgAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHAQkm4AAAAAAByEpBsAAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHISkGwAAAAAAByHpBgAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHAQkm4AAAAAAByEpBsAAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHISkGwAAAAAAByHpBgAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAOCSLBaLBgwYkG/bmzVrliwWizZv3nzVuq1atVKrVq1s7/fu3SuLxaJZs2bZykaNGiWLxZJv8aHguLz9AQBFG0k3AKDAyEhcM14+Pj664YYbNGDAAB05csTZ4Tndq6++qsWLF+frNteuXWs735988kmWdZo2bSqLxaJatWrl677zw6XXy6WvsLAwp8b1559/atSoUdq7d69T4wAAOJ+HswMAAOByL730kqKionTu3Dlt2LBB06ZN09KlS7Vt2zb5+fk5O7xr9u233161zogRIzR06FC7sldffVX33XefOnXqlO8x+fj4aM6cOXrggQfsyvfu3auNGzfKx8cn3/eZX26//Xb16tXLrszX19dJ0aT7888/NXr0aLVq1UqRkZF2y3LS/gCAooOkGwBQ4LRp00b169eXJD300EMKDg7WuHHj9MUXX6h79+5ZrnPmzBn5+/tfzzDzzMvL66p1PDw85OFx/f6bbtu2rb788ksdPXpUpUqVspXPmTNHoaGhqlKlik6cOHHd4smNG264IdMfCwqynLQ/AKDo4PZyAECBd9ttt0mS9uzZI0nq3bu3ihUrpt27d6tt27YqXry47r//fknpyffTTz+tiIgIeXt7q2rVqnrrrbdkjMly27Nnz1bVqlXl4+OjevXqad26dXbL9+3bp8cff1xVq1aVr6+vgoOD1blz52xvG05OTtYjjzyi4OBgBQQEqFevXpmS1Zw803v5M90Wi0VnzpzRhx9+aLuFunfv3lqzZo0sFosWLVqUaRtz5syRxWJRXFzcFfclSR07dpS3t7fmz5+faRtdunSRu7t7pnVmzpyp2267TaVLl5a3t7dq1KihadOmZaq3efNmxcTEqFSpUvL19VVUVJQefPBBuzpz585VvXr1VLx4cQUEBKh27dqaOHHiVeO+mt69e2fqaZayfmY+4zn/xYsXq1atWvL29lbNmjW1bNmyTOsfOHBAffv2VXh4uLy9vRUVFaXHHntMKSkpmjVrljp37ixJuvXWW23ttXbtWklZt398fLz69u2r0NBQ+fj4qE6dOvrwww/t6mQ8+//WW29pxowZqlSpkry9vXXLLbfop59+yvtJAgA4FD3dAIACb/fu3ZKk4OBgW1lqaqpiYmLUrFkzvfXWW/Lz85MxRh06dNCaNWvUt29f1a1bV8uXL9eQIUN04MABjR8/3m673333nebNm6eBAwfK29tbU6dO1Z133qlNmzbZnl/+6aeftHHjRnXr1k3lypXT3r17NW3aNLVq1Up//vlnptvdBwwYoKCgII0aNUo7duzQtGnTtG/fPtuz03n18ccf66GHHlKDBg3Ur18/SVKlSpXUqFEjRUREaPbs2br77rvt1pk9e7YqVaqkxo0bX3X7fn5+6tixoz799FM99thjkqRff/1Vf/zxh9577z399ttvmdaZNm2aatasqQ4dOsjDw0NfffWVHn/8cVmtVvXv319SejJ5xx13KCQkREOHDlVQUJD27t2rhQsX2razYsUKde/eXa1bt9brr78uSfrrr7/0/fffa9CgQVeN/dy5czp69KhdWfHixeXt7X3VdS+3YcMGLVy4UI8//riKFy+uSZMm6d5779X+/ftt19/BgwfVoEEDnTx5Uv369VO1atV04MABLViwQMnJyWrRooUGDhyoSZMmafjw4apevbok2f693NmzZ9WqVSvt2rVLAwYMUFRUlObPn6/evXvr5MmTmc7BnDlzdOrUKT3yyCOyWCx64403dM899+iff/6Rp6dnro8ZAOBgBgCAAmLmzJlGklm5cqVJSEgw//77r5k7d64JDg42vr6+5r///jPGGBMbG2skmaFDh9qtv3jxYiPJvPLKK3bl9913n7FYLGbXrl22MklGktm8ebOtbN++fcbHx8fcfffdtrLk5ORMccbFxRlJ5qOPPsoUe7169UxKSoqt/I033jCSzBdffGEra9mypWnZsqXt/Z49e4wkM3PmTFvZyJEjzeX/Tfv7+5vY2NhM8QwbNsx4e3ubkydP2sri4+ONh4eHGTlyZKb6l1qzZo2RZObPn2+WLFliLBaL2b9/vzHGmCFDhpiKFSvaYq5Zs6bdulmdm5iYGNs6xhizaNEiI8n89NNP2cYwaNAgExAQYFJTU68Ya1Yy2vHyV8a5jI2NNRUqVMi0XlbnV5Lx8vKyu05+/fVXI8m88847trJevXoZNze3LI/JarUaY4yZP3++kWTWrFmTqc7l7T9hwgQjyXzyySe2spSUFNO4cWNTrFgxk5SUZIy5eJ0EBweb48eP2+p+8cUXRpL56quvsj9RAACn4fZyAECBEx0drZCQEEVERKhbt24qVqyYFi1apLJly9rVy+iRzbB06VK5u7tr4MCBduVPP/20jDH65ptv7MobN26sevXq2d6XL19eHTt21PLly5WWlibJfkCuCxcu6NixY6pcubKCgoK0ZcuWTLH369fPrrfxsccek4eHh5YuXZrLs5BzvXr10vnz57VgwQJb2bx585SampqrZ53vuOMOlSxZUnPnzpUxRnPnzs32GXrJ/twkJibq6NGjatmypf755x8lJiZKkoKCgiRJS5Ys0YULF7LcTlBQkM6cOaMVK1bkONZLdezYUStWrLB7xcTE5Glb0dHRqlSpku39jTfeqICAAP3zzz+SJKvVqsWLF6t9+/a2cQculZe7GZYuXaqwsDC7c+3p6amBAwfq9OnT+u677+zqd+3aVSVKlLC9b968uSTZYgQAFCzcXg4AKHCmTJmiG264QR4eHgoNDVXVqlXl5mb/d2IPDw+VK1fOrmzfvn0KDw9X8eLF7cozbuvdt2+fXXmVKlUy7fuGG25QcnKyEhISFBYWprNnz2rs2LGaOXOmDhw4YPdseEZieaVtFitWTGXKlHHo1FHVqlXTLbfcotmzZ6tv376S0m8tb9SokSpXrpzj7Xh6eqpz586aM2eOGjRooH///Vc9evTItv7333+vkSNHKi4uTsnJyXbLEhMTFRgYqJYtW+ree+/V6NGjNX78eLVq1UqdOnVSjx49bLd/P/744/rss8/Upk0blS1bVnfccYe6dOmiO++8M0dxlytXTtHR0Tk+zispX758prISJUrYnstPSEhQUlJSvk6ftm/fPlWpUiXTNZ7ddXt5jBkJeEEd6A4AXB093QCAAqdBgwaKjo5Wq1atVL169UzJiCR5e3tnWZ7fnnjiCY0ZM0ZdunTRZ599pm+//VYrVqxQcHCwrFarw/efU7169dJ3332n//77T7t379YPP/yQpxG9e/Tooa1bt2rUqFGqU6eOatSokWW93bt3q3Xr1jp69KjGjRunr7/+WitWrNBTTz0lSbZzY7FYtGDBAsXFxWnAgAE6cOCAHnzwQdWrV0+nT5+WJJUuXVpbt27Vl19+aXsmv02bNoqNjc3j2bgou57njDsZLpfVgHGSsh2IzxkKQ4wAgItIugEARUaFChV08OBBnTp1yq58+/bttuWX+vvvvzNtY+fOnfLz81NISIgkacGCBYqNjdXbb7+t++67T7fffruaNWumkydPZhnD5ds8ffq0Dh06lOUI2rl1pVuXu3XrJnd3d3366aeaPXu2PD091bVr11zvo1mzZipfvrzWrl17xV7ur776SufPn9eXX36pRx55RG3btlV0dHS282M3atRIY8aM0ebNmzV79mz98ccfmjt3rm25l5eX2rdvr6lTp2r37t165JFH9NFHH2nXrl25PoZLlShRIsu2urz3OKdCQkIUEBCgbdu2XbFebm4zr1Chgv7+++9Mf8TJ7roFABQuJN0AgCKjbdu2SktL0+TJk+3Kx48fL4vFojZt2tiVx8XF2T2X/e+//+qLL77QHXfcYetNdHd3z9SD+M4772TbUzpjxgy7Z5enTZum1NTUTPvOC39//2yT/VKlSqlNmzb65JNPNHv2bN155512823nlMVi0aRJkzRy5Ej17Nkz23oZ5+fy2+1nzpxpV+/EiROZzl/dunUlSefPn5ckHTt2zG65m5ubbrzxRrs6eVWpUiUlJibajb5+6NChLKdYywk3Nzd16tRJX331lTZv3pxpecaxZswZn117Xapt27Y6fPiw5s2bZytLTU3VO++8o2LFiqlly5Z5ihUAUDDwTDcAoMho3769br31Vj3//PPau3ev6tSpo2+//VZffPGFnnzySbsBsiSpVq1aiomJsZsyTJJGjx5tq3PXXXfp448/VmBgoGrUqKG4uDitXLnSbvqyS6WkpKh169bq0qWLduzYoalTp6pZs2bq0KHDNR9fvXr1tHLlSo0bN07h4eGKiopSw4YNbct79eql++67T5L08ssv53k/HTt2VMeOHa9Y54477rD1Tj/yyCM6ffq03n33XZUuXVqHDh2y1fvwww81depU3X333apUqZJOnTqld999VwEBAWrbtq0k6aGHHtLx48d12223qVy5ctq3b5/eeecd1a1bN9tptnKqW7dueu6553T33Xdr4MCBSk5O1rRp03TDDTdkORBeTrz66qv69ttv1bJlS/Xr10/Vq1fXoUOHNH/+fG3YsEFBQUGqW7eu3N3d9frrrysxMVHe3t62Oc0v169fP/3vf/9T79699fPPPysyMlILFizQ999/rwkTJmQaowAAULiQdAMAigw3Nzd9+eWXevHFFzVv3jzNnDlTkZGRevPNN/X0009nqt+yZUs1btxYo0eP1v79+1WjRg3NmjXL1ssqSRMnTpS7u7tmz56tc+fOqWnTplq5cmW2o2NPnjxZs2fP1osvvqgLFy6oe/fumjRp0jXN0Z1h3Lhx6tevn0aMGKGzZ88qNjbWLulu3769SpQoIavVmi9J/pVUrVpVCxYs0IgRI/TMM88oLCxMjz32mEJCQvTggw/a6rVs2VKbNm3S3LlzdeTIEQUGBqpBgwaaPXu2oqKiJEkPPPCAZsyYoalTp+rkyZMKCwtT165dNWrUqGt+bj84OFiLFi3S4MGD9eyzzyoqKkpjx47V33//neeku2zZsvrxxx/1wgsvaPbs2UpKSlLZsmXVpk0b27ztYWFhmj59usaOHau+ffsqLS1Na9asyTLp9vX11dq1azV06FB9+OGHSkpKUtWqVTVz5kz17t37Wg4fAFAAWAyjbgAAUCSkpqYqPDxc7du31/vvv+/scAAAgHimGwCAImPx4sVKSEhQr169nB0KAAD4f/R0AwBQyP3444/67bff9PLLL6tUqVJ5vm0aAADkP3q6AQAo5KZNm6bHHntMpUuX1kcffeTscAAAwCXo6QYAAAAAwEHo6QYAAAAAwEFIugEAAAAAcBDm6c6C1WrVwYMHVbx48XyZVxUAAAAAULQYY3Tq1CmFh4fLzS37/myS7iwcPHhQERERzg4DAAAAAFDA/fvvvypXrly2y0m6s1C8eHFJ6ScvICDAydG4BqvVqoSEBIWEhFzxr0Qommh/10b7uzba37XR/q6N9ndtRaH9k5KSFBERYcsfs0PSnYWMW8oDAgJIuq8Tq9Wqc+fOKSAgoNB+6JB3tL9ro/1dG+3v2mh/10b7u7ai1P5XeyS5cB8dAAAAAAAFGEk3AAAAAAAOQtINAAAAAICD8Ew3AAAAALiItLQ0XbhwwdlhyGq16sKFCzp37lyBfabb3d1dHh4e1zyNNEk3AAAAALiA06dP67///pMxxtmhyBgjq9WqU6dOXXNS60h+fn4qU6aMvLy88rwNkm4AAAAAKOLS0tL033//yc/PTyEhIU5PdI0xSk1NzZeeZEcwxiglJUUJCQnas2ePqlSpkuceeZJuAAAAACjiLly4IGOMQkJC5Ovr6+xwCnzSLUm+vr7y9PTUvn37lJKSIh8fnzxtp2DePA8AAAAAyHcFNcEtqPLjeXOSbgAAAAAAHISkGwAAAAAAB+GZbgAAAABwUeNX7Lyu+3vq9huu6/4KApJuF3etHzJX/NAAAAAAuD569+6tDz/8UJLk6emp8uXLq1evXho+fLg2bNigW2+9VUFBQTp06JDdQGc//fSTGjRoIEm2KdLWrl2rW2+9NdM+nn/+eb3yyisOOwaSbgAAAABAgXXnnXdq5syZOn/+vJYuXar+/fvL09NTjRs3liQVL15cixYtUvfu3W3rvP/++ypfvrz279+faXs7duxQQECA7X2xYsUcGj/PdAMAAAAACixvb2+FhYWpQoUKeuyxxxQdHa0vv/zStjw2NlYffPCB7f3Zs2c1d+5cxcbGZrm90qVLKywszPYi6QYAAAAA4P/5+voqJSXF9r5nz55av369rVf7888/V2RkpG6++WZnhWiHpBsAAAAAUOAZY7Ry5UotX75ct912m628dOnSatOmjWbNmiVJ+uCDD/Tggw9mu51y5cqpWLFittexY8ccGjfPdAMAAAAACqwlS5aoWLFiunDhgqxWq3r06KFRo0bpp59+stV58MEHNWjQID3wwAOKi4vT/PnztX79+iy3t379ehUvXtz2vkSJEg6Nn6QbAAAAAFBg3XrrrZo2bZq8vLwUHh4uD4/MaWybNm3Ur18/9e3bV+3bt1dwcHC224uKilJQUJADI7ZH0g0AAAAAKLD8/f1VuXLlK9bx8PBQr1699MYbb+ibb765TpHlDM90AwAAAAAKvZdfflkJCQmKiYlxdih26OkGAAAAABf11O03ODuEfOPl5aVSpUo5O4xMSLoBAAAAAAVSxojkWWnVqpWMMdku79Spk93yq9V3FG4vBwAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAAAAAAAHIekGAAAAABfhjIHECrP8OF8k3QAAAABQxLm7u0uSUlJSnBxJ4ZKcnCxJ8vT0zPM2mDIMAAAAAIo4Dw8P+fn5KSEhQZ6ennJzc27/qzFGqamp8vDwkMVicWosWTHGKDk5WfHx8QoKCrL90SIvSLoBAAAAoIizWCwqU6aM9uzZo3379jk7HBljZLVa5ebmViCT7gxBQUEKCwu7pm2QdAMAAACAC/Dy8lKVKlUKxC3mVqtVx44dU3BwsNN73bPj6el5TT3cGUi6AQAAAMBFuLm5ycfHx9lhyGq1ytPTUz4+PgU26c4vRfvoAAAAAABwIpJuAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHCQApF0T5kyRZGRkfLx8VHDhg21adOmK9afP3++qlWrJh8fH9WuXVtLly7Ntu6jjz4qi8WiCRMm5HPUAAAAAABcmdOT7nnz5mnw4MEaOXKktmzZojp16igmJkbx8fFZ1t+4caO6d++uvn376pdfflGnTp3UqVMnbdu2LVPdRYsW6YcfflB4eLijDwMAAAAAgEycPk/3uHHj9PDDD6tPnz6SpOnTp+vrr7/WBx98oKFDh2aqP3HiRN15550aMmSIJOnll1/WihUrNHnyZE2fPt1W78CBA3riiSe0fPlytWvX7ooxnD9/XufPn7e9T0pKkpQ+d5zVar3mYyzQjLmm1fPr/FitVhljiv75RpZof9dG+7s22t+10f6ujfZ3bUWh/XMau1OT7pSUFP38888aNmyYrczNzU3R0dGKi4vLcp24uDgNHjzYriwmJkaLFy+2vbdarerZs6eGDBmimjVrXjWOsWPHavTo0ZnKExISdO7cuRweTeHkl3b6mtbP7o6E3LJarUpMTJQxRm5uTr8BA9cZ7e/aaH/XRvu7NtrftdH+rq0otP+pU6dyVM+pSffRo0eVlpam0NBQu/LQ0FBt3749y3UOHz6cZf3Dhw/b3r/++uvy8PDQwIEDcxTHsGHD7BL5pKQkRUREKCQkRAEBATk9nEIp2T3xmtYvXbp0vsRhtVplsVgUEhJSaD90yDva37XR/q6N9ndttL9ro/1dW1Fofx8fnxzVc/rt5fnt559/1sSJE7VlyxZZLJYcrePt7S1vb+9M5W5uboX2AsixHJ6j7OTn+bFYLK5xzpEl2t+10f6ujfZ3bbS/a6P9XVthb/+cxu3UoytVqpTc3d115MgRu/IjR44oLCwsy3XCwsKuWH/9+vWKj49X+fLl5eHhIQ8PD+3bt09PP/20IiMjHXIcAAAAAABkxalJt5eXl+rVq6dVq1bZyqxWq1atWqXGjRtnuU7jxo3t6kvSihUrbPV79uyp3377TVu3brW9wsPDNWTIEC1fvtxxBwMAAAAAwGWcfnv54MGDFRsbq/r166tBgwaaMGGCzpw5YxvNvFevXipbtqzGjh0rSRo0aJBatmypt99+W+3atdPcuXO1efNmzZgxQ5IUHBys4OBgu314enoqLCxMVatWvb4HBwAAAABwaU5Purt27aqEhAS9+OKLOnz4sOrWratly5bZBkvbv3+/3b3yTZo00Zw5czRixAgNHz5cVapU0eLFi1WrVi1nHQIAAAAAAFlyetItSQMGDNCAAQOyXLZ27dpMZZ07d1bnzp1zvP29e/fmMTIAAAAAAPKucA4TBwAAAABAIUDSDQAAAACAg5B0AwAAAADgICTdAAAAAAA4CEk3AAAAAAAOQtINAAAAAICDkHQDAAAAAOAgJN0AAAAAADgISTcAAAAAAA5C0g0AAAAAgIOQdAMAAAAA4CAk3QAAAAAAOAhJNwAAAAAADkLSDQAAAACAg5B0AwAAAADgICTdAAAAAAA4CEk3AAAAAAAOQtINAAAAAICDkHQDAAAAAOAgJN0AAAAAADgISTcAAAAAAA5C0g0AAAAAgIOQdAMAAAAA4CAk3QAAAAAAOAhJNwAAAAAADkLSDQAAAACAg5B0AwAAAADgICTdAAAAAAA4CEk3AAAAAAAOQtINAAAAAICDkHQDAAAAAOAgJN0AAAAAADiIh7MDQOE2fsXOa97GU7ffkA+RAAAAAEDBQ083AAAAAAAOQtINAAAAAICDkHQDAAAAAOAgJN0AAAAAADgISTcAAAAAAA5C0g0AAAAAgIOQdAMAAAAA4CAk3QAAAAAAOAhJNwAAAAAADkLSDQAAAACAg5B0AwAAAADgICTdAAAAAAA4CEk3AAAAAAAOQtINAAAAAICDkHQDAAAAAOAgJN0AAAAAADgISTcAAAAAAA5C0g0AAAAAgIOQdAMAAAAA4CAk3QAAAAAAOAhJNwAAAAAADlIgku4pU6YoMjJSPj4+atiwoTZt2nTF+vPnz1e1atXk4+Oj2rVra+nSpXbLR40apWrVqsnf318lSpRQdHS0fvzxR0ceAgAAAAAAmTg96Z43b54GDx6skSNHasuWLapTp45iYmIUHx+fZf2NGzeqe/fu6tu3r3755Rd16tRJnTp10rZt22x1brjhBk2ePFm///67NmzYoMjISN1xxx1KSEi4XocFAAAAAIDzk+5x48bp4YcfVp8+fVSjRg1Nnz5dfn5++uCDD7KsP3HiRN15550aMmSIqlevrpdfflk333yzJk+ebKvTo0cPRUdHq2LFiqpZs6bGjRunpKQk/fbbb9frsAAAAAAAkIczd56SkqKff/5Zw4YNs5W5ubkpOjpacXFxWa4TFxenwYMH25XFxMRo8eLF2e5jxowZCgwMVJ06dbKsc/78eZ0/f972PikpSZJktVpltVpzc0iFjzHOjsB2no0xRf98I0u0v2uj/V0b7e/aaH/XRvu7tqLQ/jmN3alJ99GjR5WWlqbQ0FC78tDQUG3fvj3LdQ4fPpxl/cOHD9uVLVmyRN26dVNycrLKlCmjFStWqFSpUlluc+zYsRo9enSm8oSEBJ07dy43h1To+KWddnYIio+Pl9VqVWJioowxcnNz+g0YuM5of9dG+7s22t+10f6ujfZ3bUWh/U+dOpWjek5Nuh3p1ltv1datW3X06FG9++676tKli3788UeVLl06U91hw4bZ9Z4nJSUpIiJCISEhCggIuJ5hX3fJ7onODkGlS5eW1WqVxWJRSEhIof3QIe9of9dG+7s22t+10f6ujfZ3bUWh/X18fHJUz6lJd6lSpeTu7q4jR47YlR85ckRhYWFZrhMWFpaj+v7+/qpcubIqV66sRo0aqUqVKnr//fftbmXP4O3tLW9v70zlbm5uhfYCyDGLxdkR2M6xxWJxjXOOLNH+ro32d220v2uj/V0b7e/aCnv75zRupx6dl5eX6tWrp1WrVtnKrFarVq1apcaNG2e5TuPGje3qS9KKFSuyrX/pdi99bhsAAAAAAEdz+u3lgwcPVmxsrOrXr68GDRpowoQJOnPmjPr06SNJ6tWrl8qWLauxY8dKkgYNGqSWLVvq7bffVrt27TR37lxt3rxZM2bMkCSdOXNGY8aMUYcOHVSmTBkdPXpUU6ZM0YEDB9S5c2enHScAAAAAwPU4Penu2rWrEhIS9OKLL+rw4cOqW7euli1bZhssbf/+/Xbd9k2aNNGcOXM0YsQIDR8+XFWqVNHixYtVq1YtSZK7u7u2b9+uDz/8UEePHlVwcLBuueUWrV+/XjVr1nTKMQIAAAAAXJPTk25JGjBggAYMGJDlsrVr12Yq69y5c7a91j4+Plq4cGF+hgcAAAAAQJ4UzifWAQAAAAAoBEi6AQAAAABwkAJxezlc2/gVOyVj5Jd2On3e8DxMY/bU7Tc4IDIAAAAAuDZ56ules2ZNfscBAAAAAECRk6ek+84771SlSpX0yiuv6N9//83vmAAAAAAAKBLylHQfOHBAAwYM0IIFC1SxYkXFxMTos88+U0pKSn7HBwAAAABAoZWnpLtUqVJ66qmntHXrVv3444+64YYb9Pjjjys8PFwDBw7Ur7/+mt9xAgAAAABQ6Fzz6OU333yzhg0bpgEDBuj06dP64IMPVK9ePTVv3lx//PFHfsQIAAAAAEChlOfRyy9cuKAvvvhCH3zwgVasWKH69etr8uTJ6t69uxISEjRixAh17txZf/75Z37GCwBwMeNX7Lym9ZndAAAAOFOeku4nnnhCn376qYwx6tmzp9544w3VqlXLttzf319vvfWWwsPD8y1QAMgpkrR013oepKJzLgAAAJwlT0n3n3/+qXfeeUf33HOPvL29s6xTqlQpphYDgGuQH0kzAAAAnCtPSffIkSPVpEkTeXjYr56amqqNGzeqRYsW8vDwUMuWLfMlSACuoyAkmgUhhqIiR+fSGPmlnVaye6JksTgnBgfjjgEAAFxXnpLuW2+9VYcOHVLp0qXtyhMTE3XrrbcqLS0tX4IDAAC4FI9NAAAKmzwl3cYYWbLojTh27Jj8/f2vOSgAQMFQEHqJi4KCcB7zI9EsKglvUWkPAEDhkKuk+5577pEkWSwW9e7d2+557rS0NP32229q0qRJ/kYIAACuWUFINAEAcEW5SroDAwMlpfd0Fy9eXL6+vrZlXl5eatSokR5++OH8jRDAdeO0L+UOfqYXQMGR6fcMn38AQBGXq6R75syZkqTIyEg988wz3EoOAAAAAMAV5Hn0cgAAAAAAcGU5TrpvvvlmrVq1SiVKlNBNN92U5UBqGbZs2ZIvwQE5da23RReVAW14ZhMAAAAoWHKcdHfs2NE2cFqnTp0cFQ8AAAAAAEVGjpPuS28p5/ZyAAAAAACuzs3ZAQAAAAAAUFTluKe7RIkSV3yO+1LHjx/Pc0CAM+THs9BF5blwAAAAAPknx0n3hAkTHBgGAAZBAwAAAIqeHCfdsbGxjowDKPRImgEAOcWsGwDgOnKcdCclJSkgIMD285Vk1AMAAAAAwJXl6pnuQ4cOqXTp0goKCsry+W5jjCwWi9LS0vI1SAAAAAAACqMcJ92rV69WyZIlJUlr1qxxWEAAAAAAABQVOU66W7ZsmeXPAAAAAAAgazlOui934sQJvf/++/rrr78kSTVq1FCfPn1sveEAAAAAALg6t7ystG7dOkVGRmrSpEk6ceKETpw4oUmTJikqKkrr1q3L7xgBAAAAACiU8tTT3b9/f3Xt2lXTpk2Tu7u7JCktLU2PP/64+vfvr99//z1fgwQAAAAAoDDKU0/3rl279PTTT9sSbklyd3fX4MGDtWvXrnwLDgAAAACAwixPSffNN99se5b7Un/99Zfq1KlzzUEBAAAAAFAU5Pj28t9++83288CBAzVo0CDt2rVLjRo1kiT98MMPmjJlil577bX8jxIAAAAAgEIox0l33bp1ZbFYZIyxlT377LOZ6vXo0UNdu3bNn+gAAAAAACjEcpx079mzx5FxAAAAAABQ5OQ46a5QoYIj4wAAAEAOjV+x85q38dTtN+RDJACAq8nTlGEZ/vzzT+3fv18pKSl25R06dLimoAAAAAAAKArylHT/888/uvvuu/X777/bPedtsVgkpc/ZDQAAAACAq8vTlGGDBg1SVFSU4uPj5efnpz/++EPr1q1T/fr1tXbt2nwOEQAAAACAwilPPd1xcXFavXq1SpUqJTc3N7m5ualZs2YaO3asBg4cqF9++SW/4wQAAAAAoNDJU093WlqaihcvLkkqVaqUDh48KCl9sLUdO3bkX3QAAAAAABRieerprlWrln799VdFRUWpYcOGeuONN+Tl5aUZM2aoYsWK+R0jAAAAAACFUp6S7hEjRujMmTOSpJdeekl33XWXmjdvruDgYM2bNy9fAwQAAAAAoLDKU9IdExNj+7ly5cravn27jh8/rhIlSthGMAcAAAAAwNVd0zzdkvTvv/9KkiIiIq45GAAAAAAAipI8DaSWmpqqF154QYGBgYqMjFRkZKQCAwM1YsQIXbhwIb9jBAAAAACgUMpTT/cTTzyhhQsX6o033lDjxo0lpU8jNmrUKB07dkzTpk3L1yABAAAAACiM8pR0z5kzR3PnzlWbNm1sZTfeeKMiIiLUvXt3km4AAAAAAJTHpNvb21uRkZGZyqOiouTl5XWtMQEAAMDBxq/YeU3rP3X7DfkUCQAUbXl6pnvAgAF6+eWXdf78eVvZ+fPnNWbMGA0YMCDfggMAAAAAoDDLcU/3PffcY/d+5cqVKleunOrUqSNJ+vXXX5WSkqLWrVvnb4QAAAAAABRSOU66AwMD7d7fe++9du+ZMgwAAAAAAHs5TrpnzpzpsCCmTJmiN998U4cPH1adOnX0zjvvqEGDBtnWnz9/vl544QXt3btXVapU0euvv662bdtKki5cuKARI0Zo6dKl+ueffxQYGKjo6Gi99tprCg8Pd9gxAAAAAABwuTw9050hISFBGzZs0IYNG5SQkJCnbcybN0+DBw/WyJEjtWXLFtWpU0cxMTGKj4/Psv7GjRvVvXt39e3bV7/88os6deqkTp06adu2bZKk5ORkbdmyRS+88IK2bNmihQsXaseOHerQoUOejxMAAAAAgLywGGNMblc6c+aMnnjiCX300UeyWq2SJHd3d/Xq1UvvvPOO/Pz8crythg0b6pZbbtHkyZMlSVarVREREXriiSc0dOjQTPW7du2qM2fOaMmSJbayRo0aqW7dupo+fXqW+/jpp5/UoEED7du3T+XLl79qTElJSQoMDFRiYqICAgJyfCyF0bWOXJpvjJFf2mkluxeTLBZnR4PrjfZ3bbS/a6P9C638GL3carUqPj5epUuXlpvbNfUFoRCi/V1bUWj/nOaNeZoybPDgwfruu+/01VdfqWnTppKkDRs2aODAgXr66adzPE93SkqKfv75Zw0bNsxW5ubmpujoaMXFxWW5TlxcnAYPHmxXFhMTo8WLF2e7n8TERFksFgUFBWW5/Pz583YjsSclJUlKvxAy/qhQZOX+by6OYczFF1wP7e/aaH/XRvsXWvnxHclqtcoYU/S/byFLtL9rKwrtn9PY85R0f/7551qwYIFatWplK2vbtq18fX3VpUuXHCfdR48eVVpamkJDQ+3KQ0NDtX379izXOXz4cJb1Dx8+nGX9c+fO6bnnnlP37t2z/evD2LFjNXr06EzlCQkJOnfuXE4OpdDySzvt7BD+n5G3OSdZJYmeDtdD+7s22t+10f6FVXaPAuaG1WpVYmKijDGFtqcLeUf7u7ai0P6nTp3KUb08Jd3JycmZEl9JKl26tJKTk/OySYe4cOGCunTpImPMFf8QMGzYMLve86SkJEVERCgkJKTI316e7J7o7BDSGSMZKdmN2wtdEu3v2mh/10b7F1qlS5e+5m1YrVZZLBaFhIQU2i/dyDva37UVhfb38fHJUb08Jd2NGzfWyJEj9dFHH9l2dPbsWY0ePVqNGzfO8XZKlSold3d3HTlyxK78yJEjCgsLy3KdsLCwHNXPSLj37dun1atXXzF59vb2lre3d6ZyNze3QnsB5FhB+oJjsVx8wfXQ/q6N9ndttH+hlF/fkSwWi2t850KWaH/XVtjbP6dx5+noJkyYoO+//17lypVT69at1bp1a0VERGjjxo2aOHFijrfj5eWlevXqadWqVbYyq9WqVatWZZu8N27c2K6+JK1YscKufkbC/ffff2vlypUKDg7O5RECAAAAAHDt8tTTXbt2bf3999+aPXu27dnr7t276/7775evr2+utjV48GDFxsaqfv36atCggSZMmKAzZ86oT58+kqRevXqpbNmyGjt2rCRp0KBBatmypd5++221a9dOc+fO1ebNmzVjxgxJ6Qn3fffdpy1btmjJkiVKS0uzPe9dsmRJeXl55eWQAQAAAADItVwn3RcuXFC1atW0ZMkSPfzww9ccQNeuXZWQkKAXX3xRhw8fVt26dbVs2TLbM+P79++367Zv0qSJ5syZoxEjRmj48OGqUqWKFi9erFq1akmSDhw4oC+//FKSVLduXbt9rVmzxm7wNwAAAAAAHCnXSbenp2e+j+g9YMAADRgwIMtla9euzVTWuXNnde7cOcv6kZGRysPU4wAAAAAA5Ls83V7ev39/vf7663rvvffk4ZGnTQAAAKAQG79i5zVvY1DryvkQCQAUbHnKmH/66SetWrVK3377rWrXri1/f3+75QsXLsyX4AAAAAAAKMzylHQHBQXp3nvvze9YAAAAAAAoUnKVdFutVr355pvauXOnUlJSdNttt2nUqFG5HrEcAAAAAABXkKt5useMGaPhw4erWLFiKlu2rCZNmqT+/fs7KjYAAAAAAAq1XCXdH330kaZOnarly5dr8eLF+uqrrzR79mxZrVZHxQcAAAAAQKGVq6R7//79atu2re19dHS0LBaLDh48mO+BAQAAAABQ2OUq6U5NTZWPj49dmaenpy5cuJCvQQEAAAAAUBTkaiA1Y4x69+4tb29vW9m5c+f06KOP2k0bxpRhAAAAAADkMumOjY3NVPbAAw/kWzAAAAAAABQluUq6Z86c6ag4AAAAAAAocnL1TDcAAAAAAMg5km4AAAAAAByEpBsAAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHCRXU4YBAAAA+WXiyr/ll3Zaye6JksWS6/Wfuv0GB0QFAPmLnm4AAAAAAByEpBsAAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHISkGwAAAAAAByHpBgAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBPJwdAPJu/Iqdzg4BAAAAAHAF9HQDAAAAAOAgJN0AAAAAADgISTcAAAAAAA5C0g0AAAAAgIMwkBoAAAAKpfwYVPap22/Ih0gAIHv0dAMAAAAA4CAk3QAAAAAAOAhJNwAAAAAADkLSDQAAAACAg5B0AwAAAADgIIxeDgAAAJfFCOgAHI2ebgAAAAAAHISkGwAAAAAAByHpBgAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHAQkm4AAAAAAByEpBsAAAAAAAch6QYAAAAAwEE8nB0AAAAAUJiNX7HzmtZ/6vYb8ikSAAURPd0AAAAAADgISTcAAAAAAA7i9KR7ypQpioyMlI+Pjxo2bKhNmzZdsf78+fNVrVo1+fj4qHbt2lq6dKnd8oULF+qOO+5QcHCwLBaLtm7d6sDoAQAAAADInlOT7nnz5mnw4MEaOXKktmzZojp16igmJkbx8fFZ1t+4caO6d++uvn376pdfflGnTp3UqVMnbdu2zVbnzJkzatasmV5//fXrdRgAAAAAAGTJYowxztp5w4YNdcstt2jy5MmSJKvVqoiICD3xxBMaOnRopvpdu3bVmTNntGTJEltZo0aNVLduXU2fPt2u7t69exUVFaVffvlFdevWvWIc58+f1/nz523vk5KSFBERoRMnTiggIOAajtCxJq7829kh5B9j5Jd2WsnuxSSLxdnR4Hqj/V0b7e/aaH/XRvtLkgZFV3F2CE5htVqVkJCgkJAQubk5/QZcXGdFof2TkpJUokQJJSYmXjFvdNro5SkpKfr55581bNgwW5mbm5uio6MVFxeX5TpxcXEaPHiwXVlMTIwWL158TbGMHTtWo0ePzlSekJCgc+fOXdO2Hckv7bSzQ8hHRt7mnGSVJNf9T9d10f6ujfZ3bbS/a6P9JWV7l2dRZ7ValZiYKGNMoU26kHdFof1PnTqVo3pOS7qPHj2qtLQ0hYaG2pWHhoZq+/btWa5z+PDhLOsfPnz4mmIZNmyYXTKf0dMdEhJSoHu6k90TnR1C/jFGMlKym2v/pdtl0f6ujfZ3bbS/a6P9JUmlS5d2dghOYbVaZbFYCnVPJ/KuKLS/j49PjuoxT7ckb29veXt7Zyp3c3Mr2BdAUfvPyWK5+ILrof1dG+3v2mh/10b7F+zvmw5msVgK/nduOExhb/+cxu20oytVqpTc3d115MgRu/IjR44oLCwsy3XCwsJyVR8AAAAAAGdyWk+3l5eX6tWrp1WrVqlTp06S0m8xWLVqlQYMGJDlOo0bN9aqVav05JNP2spWrFihxo0bX4eIAQAAgPw3fsXOa97GU7ffkA+RAHAEp95ePnjwYMXGxqp+/fpq0KCBJkyYoDNnzqhPnz6SpF69eqls2bIaO3asJGnQoEFq2bKl3n77bbVr105z587V5s2bNWPGDNs2jx8/rv379+vgwYOSpB07dkhK7yWnRxwAAAAAcD05Nenu2rWrEhIS9OKLL+rw4cOqW7euli1bZhssbf/+/Xb3yTdp0kRz5szRiBEjNHz4cFWpUkWLFy9WrVq1bHW+/PJLW9IuSd26dZMkjRw5UqNGjbo+BwYAAAAAgJw8T3dBlZSUpMDAwKvOt+Zs+XErUoHBPJ2ujfZ3bbS/a6P9XRvtn28K4+3lVqtV8fHxKl26dKEdSAt5VxTaP6d5Y+E8OgAAAAAACgGSbgAAAAAAHISkGwAAAAAAByHpBgAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAAAAAAAH8XB2AAAAAACuzfgVO69p/cI4zzdQWNDTDQAAAACAg5B0AwAAAADgICTdAAAAAAA4CEk3AAAAAAAOQtINAAAAAICDkHQDAAAAAOAgJN0AAAAAADgISTcAAAAAAA5C0g0AAAAAgIOQdAMAAAAA4CAezg4AAAAAgHONX7Hzmrfx1O035EMkQNFDTzcAAAAAAA5C0g0AAAAAgIOQdAMAAAAA4CAk3QAAAAAAOAhJNwAAAAAADkLSDQAAAACAg5B0AwAAAADgIMzTDQAAAOCa5Xqub2Pkl3Zaye6JksXCPN8osujpBgAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHAQkm4AAAAAAByEeboBAAAAOF2u5/nOAnN9oyCipxsAAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHISB1AAAAAAUCdc6GBsDscER6OkGAAAAAMBBSLoBAAAAAHAQkm4AAAAAAByEZ7oBAAAAQNf+TLjEc+HIjJ5uAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBeKYbAAAAAPIJc4XjcvR0AwAAAADgICTdAAAAAAA4CEk3AAAAAAAOwjPdAAAAAFBAMFd40UNPNwAAAAAADkLSDQAAAACAgxSI28unTJmiN998U4cPH1adOnX0zjvvqEGDBtnWnz9/vl544QXt3btXVapU0euvv662bdvalhtjNHLkSL377rs6efKkmjZtqmnTpqlKlSrX43AAAAAAwGm4Rb1gcXpP97x58zR48GCNHDlSW7ZsUZ06dRQTE6P4+Pgs62/cuFHdu3dX37599csvv6hTp07q1KmTtm3bZqvzxhtvaNKkSZo+fbp+/PFH+fv7KyYmRufOnbtehwUAAAAAgCzGGOPMABo2bKhbbrlFkydPliRZrVZFREToiSee0NChQzPV79q1q86cOaMlS5bYyho1aqS6detq+vTpMsYoPDxcTz/9tJ555hlJUmJiokJDQzVr1ix169btqjElJSUpMDBQiYmJCggIyKcjzX/58ResAsMY+aWdVrJ7MclicXY0uN5of9dG+7s22t+10f6ujfYv0q7WU261WhUfH6/SpUvLzc3pfcF5ktO80am3l6ekpOjnn3/WsGHDbGVubm6Kjo5WXFxcluvExcVp8ODBdmUxMTFavHixJGnPnj06fPiwoqOjbcsDAwPVsGFDxcXFZZl0nz9/XufPn7e9T0xMlCSdPHlSVqs1z8fnaOdOn3J2CPnHGFmsp3XOzfBL1xXR/q6N9ndttL9ro/1dG+1fpI1d9POVKxgjX+tpnXX7L9v2f+zWSg6ILP8kJSVJSn+8+UqcmnQfPXpUaWlpCg0NtSsPDQ3V9u3bs1zn8OHDWdY/fPiwbXlGWXZ1Ljd27FiNHj06U3mFChVydiAAAAAAgHw13NkB5NCpU6cUGBiY7fICMZCasw0bNsyu99xqter48eMKDg6Whb+6XRdJSUmKiIjQv//+W6Bv6Ydj0P6ujfZ3bbS/a6P9XRvt79qKQvsbY3Tq1CmFh4dfsZ5Tk+5SpUrJ3d1dR44csSs/cuSIwsLCslwnLCzsivUz/j1y5IjKlCljV6du3bpZbtPb21ve3t52ZUFBQbk5FOSTgICAQvuhw7Wj/V0b7e/aaH/XRvu7NtrftRX29r9SD3cGpz6x7uXlpXr16mnVqlW2MqvVqlWrVqlx48ZZrtO4cWO7+pK0YsUKW/2oqCiFhYXZ1UlKStKPP/6Y7TYBAAAAAHAEp99ePnjwYMXGxqp+/fpq0KCBJkyYoDNnzqhPnz6SpF69eqls2bIaO3asJGnQoEFq2bKl3n77bbVr105z587V5s2bNWPGDEmSxWLRk08+qVdeeUVVqlRRVFSUXnjhBYWHh6tTp07OOkwAAAAAgAtyetLdtWtXJSQk6MUXX9Thw4dVt25dLVu2zDYQ2v79++2GkG/SpInmzJmjESNGaPjw4apSpYoWL16sWrVq2eo8++yzOnPmjPr166eTJ0+qWbNmWrZsmXx8fK778SFnvL29NXLkyEy3+cM10P6ujfZ3bbS/a6P9XRvt79pcqf2dPk83AAAAAABFVeGchRwAAAAAgEKApBsAAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbuTJ2LFjdcstt6h48eIqXbq0OnXqpB07dtjVOXfunPr376/g4GAVK1ZM9957r44cOWJXZ//+/WrXrp38/PxUunRpDRkyRKmpqXZ11q5dq5tvvlne3t6qXLmyZs2alSmeKVOmKDIyUj4+PmrYsKE2bdqU78eM7L322mu26foy0P5F24EDB/TAAw8oODhYvr6+ql27tjZv3mxbbozRiy++qDJlysjX11fR0dH6+++/7bZx/Phx3X///QoICFBQUJD69u2r06dP29X57bff1Lx5c/n4+CgiIkJvvPFGpljmz5+vatWqycfHR7Vr19bSpUsdc9CQJKWlpemFF15QVFSUfH19ValSJb388su6dFxW2r/oWLdundq3b6/w8HBZLBYtXrzYbnlBauucxILcuVL7X7hwQc8995xq164tf39/hYeHq1evXjp48KDdNmj/wutqn/9LPfroo7JYLJowYYJdOe3//wyQBzExMWbmzJlm27ZtZuvWraZt27amfPny5vTp07Y6jz76qImIiDCrVq0ymzdvNo0aNTJNmjSxLU9NTTW1atUy0dHR5pdffjFLly41pUqVMsOGDbPV+eeff4yfn58ZPHiw+fPPP80777xj3N3dzbJly2x15s6da7y8vMwHH3xg/vjjD/Pwww+boKAgc+TIketzMlzcpk2bTGRkpLnxxhvNoEGDbOW0f9F1/PhxU6FCBdO7d2/z448/mn/++ccsX77c7Nq1y1bntddeM4GBgWbx4sXm119/NR06dDBRUVHm7Nmztjp33nmnqVOnjvnhhx/M+vXrTeXKlU337t1tyxMTE01oaKi5//77zbZt28ynn35qfH19zf/+9z9bne+//964u7ubN954w/z5559mxIgRxtPT0/z+++/X52S4oDFjxpjg4GCzZMkSs2fPHjN//nxTrFgxM3HiRFsd2r/oWLp0qXn++efNwoULjSSzaNEiu+UFqa1zEgty50rtf/LkSRMdHW3mzZtntm/fbuLi4kyDBg1MvXr17LZB+xdeV/v8Z1i4cKGpU6eOCQ8PN+PHj7dbRvunI+lGvoiPjzeSzHfffWeMSf9F7OnpaebPn2+r89dffxlJJi4uzhiT/kF2c3Mzhw8fttWZNm2aCQgIMOfPnzfGGPPss8+amjVr2u2ra9euJiYmxva+QYMGpn///rb3aWlpJjw83IwdOzb/DxR2Tp06ZapUqWJWrFhhWrZsaUu6af+i7bnnnjPNmjXLdrnVajVhYWHmzTfftJWdPHnSeHt7m08//dQYY8yff/5pJJmffvrJVuebb74xFovFHDhwwBhjzNSpU02JEiVs10PGvqtWrWp736VLF9OuXTu7/Tds2NA88sgj13aQyFa7du3Mgw8+aFd2zz33mPvvv98YQ/sXZZd/6S5IbZ2TWHBtrpR0Zdi0aZORZPbt22eMof2Lkuza/7///jNly5Y127ZtMxUqVLBLumn/i7i9HPkiMTFRklSyZElJ0s8//6wLFy4oOjraVqdatWoqX7684uLiJElxcXGqXbu2QkNDbXViYmKUlJSkP/74w1bn0m1k1MnYRkpKin7++We7Om5uboqOjrbVgeP0799f7dq1y9RGtH/R9uWXX6p+/frq3LmzSpcurZtuuknvvvuubfmePXt0+PBhu3YJDAxUw4YN7do/KChI9evXt9WJjo6Wm5ubfvzxR1udFi1ayMvLy1YnJiZGO3bs0IkTJ2x1rnSNIP81adJEq1at0s6dOyVJv/76qzZs2KA2bdpIov1dSUFq65zEAsdLTEyUxWJRUFCQJNq/qLNarerZs6eGDBmimjVrZlpO+19E0o1rZrVa9eSTT6pp06aqVauWJOnw4cPy8vKy/dLNEBoaqsOHD9vqXJpwZSzPWHalOklJSTp79qyOHj2qtLS0LOtkbAOOMXfuXG3ZskVjx47NtIz2L9r++ecfTZs2TVWqVNHy5cv12GOPaeDAgfrwww8lXWy/K7XL4cOHVbp0abvlHh4eKlmyZL5cI7S/4wwdOlTdunVTtWrV5OnpqZtuuklPPvmk7r//fkm0vyspSG2dk1jgWOfOndNzzz2n7t27KyAgQBLtX9S9/vrr8vDw0MCBA7NcTvtf5OHsAFD49e/fX9u2bdOGDRucHQquk3///VeDBg3SihUr5OPj4+xwcJ1ZrVbVr19fr776qiTppptu0rZt2zR9+nTFxsY6OTo42meffabZs2drzpw5qlmzprZu3aonn3xS4eHhtD/goi5cuKAuXbrIGKNp06Y5OxxcBz///LMmTpyoLVu2yGKxODucAo+eblyTAQMGaMmSJVqzZo3KlStnKw8LC1NKSopOnjxpV//IkSMKCwuz1bl8NOuM91erExAQIF9fX5UqVUru7u5Z1snYBvLfzz//rPj4eN18883y8PCQh4eHvvvuO02aNEkeHh4KDQ2l/YuwMmXKqEaNGnZl1atX1/79+yVdbL8rtUtYWJji4+Ptlqempur48eP5co3Q/o4zZMgQW2937dq11bNnTz311FO2u15of9dRkNo6J7HAMTIS7n379mnFihW2Xm6J9i/K1q9fr/j4eJUvX972XXDfvn16+umnFRkZKYn2vxRJN/LEGKMBAwZo0aJFWr16taKiouyW16tXT56enlq1apWtbMeOHdq/f78aN24sSWrcuLF+//13uw9jxi/rjC/0jRs3tttGRp2MbXh5ealevXp2daxWq1atWmWrg/zXunVr/f7779q6davtVb9+fd1///22n2n/oqtp06aZpgjcuXOnKlSoIEmKiopSWFiYXbskJSXpxx9/tGv/kydP6ueff7bVWb16taxWqxo2bGirs27dOl24cMFWZ8WKFapatapKlChhq3OlawT5Lzk5WW5u9l8f3N3dZbVaJdH+rqQgtXVOYkH+y0i4//77b61cuVLBwcF2y2n/oqtnz5767bff7L4LhoeHa8iQIVq+fLkk2t+Os0dyQ+H02GOPmcDAQLN27Vpz6NAh2ys5OdlW59FHHzXly5c3q1evNps3bzaNGzc2jRs3ti3PmDLqjjvuMFu3bjXLli0zISEhWU4ZNWTIEPPXX3+ZKVOmZDlllLe3t5k1a5b5888/Tb9+/UxQUJDdqNhwvEtHLzeG9i/KNm3aZDw8PMyYMWPM33//bWbPnm38/PzMJ598Yqvz2muvmaCgIPPFF1+Y3377zXTs2DHLaYRuuukm8+OPP5oNGzaYKlWq2E0jcvLkSRMaGmp69uxptm3bZubOnWv8/PwyTSPi4eFh3nrrLfPXX3+ZkSNHMmWUg8XGxpqyZcvapgxbuHChKVWqlHn22WdtdWj/ouPUqVPml19+Mb/88ouRZMaNG2d++eUX2+jUBamtcxILcudK7Z+SkmI6dOhgypUrZ7Zu3Wr3ffDSkahp/8Lrap//y10+erkxtH8Gkm7kiaQsXzNnzrTVOXv2rHn88cdNiRIljJ+fn7n77rvNoUOH7Lazd+9e06ZNG+Pr62tKlSplnn76aXPhwgW7OmvWrDF169Y1Xl5epmLFinb7yPDOO++Y8uXLGy8vL9OgQQPzww8/OOKwcQWXJ920f9H21VdfmVq1ahlvb29TrVo1M2PGDLvlVqvVvPDCCyY0NNR4e3ub1q1bmx07dtjVOXbsmOnevbspVqyYCQgIMH369DGnTp2yq/Prr7+aZs2aGW9vb1O2bFnz2muvZYrls88+MzfccIPx8vIyNWvWNF9//XX+HzBskpKSzKBBg0z58uWNj4+PqVixonn++eftvmTT/kXHmjVrsvz/PjY21hhTsNo6J7Egd67U/nv27Mn2++CaNWts26D9C6+rff4vl1XSTfunsxhjzPXoUQcAAAAAwNXwTDcAAAAAAA5C0g0AAAAAgIOQdAMAAAAA4CAk3QAAAAAAOAhJNwAAAAAADkLSDQAAAACAg5B0AwAAAADgICTdAAAAAAA4CEk3AAAAAAAOQtINAAAAAICDkHQDAAAAAOAgJN0AAAAAADgISTcAAAAAAA5C0g0AAAAAgIOQdAMAAAAA4CAk3QAAAAAAOAhJNwAAAAAADkLSDQDANejdu7ciIyPzdZuzZs2SxWLR3r1783W7KHgiIyPVu3dvZ4cBAHAgkm4AgNPt3r1bjzzyiCpWrCgfHx8FBASoadOmmjhxos6ePevs8Bzm1Vdf1eLFi50dhk1Gsm+xWLRhw4ZMy40xioiIkMVi0V133eWECLO3d+9eW+yXvxo1auTU2DZu3KhRo0bp5MmTTo0DAOAcHs4OAADg2r7++mt17txZ3t7e6tWrl2rVqqWUlBRt2LBBQ4YM0R9//KEZM2Y4O0yHePXVV3XfffepU6dOduU9e/ZUt27d5O3t7ZS4fHx8NGfOHDVr1syu/LvvvtN///3ntLhyonv37mrbtq1dWUhIiJOiSbdx40aNHj1avXv3VlBQkN2yHTt2yM2NPhAAKMpIugEATrNnzx5169ZNFSpU0OrVq1WmTBnbsv79+2vXrl36+uuvnRihc7i7u8vd3d1p+2/btq3mz5+vSZMmycPj4leFOXPmqF69ejp69KjTYruam2++WQ888ICzw8ixgvwHDABA/uBPqwAAp3njjTd0+vRpvf/++3YJd4bKlStr0KBBki7ePjxr1qxM9SwWi0aNGmV7P2rUKFksFu3cuVMPPPCAAgMDFRISohdeeEHGGP3777/q2LGjAgICFBYWprfffttue9k9U7127VpZLBatXbv2isf11ltvqUmTJgoODpavr6/q1aunBQsWZIr5zJkz+vDDD223QWc823v5/u+66y5VrFgxy301btxY9evXtyv75JNPVK9ePfn6+qpkyZLq1q2b/v333yvGfKnu3bvr2LFjWrFiha0sJSVFCxYsUI8ePfJ8zJK0YsUKNWvWTEFBQSpWrJiqVq2q4cOH29V55513VLNmTfn5+alEiRKqX7++5syZk+P4s9OqVSu1atUqU/nlz+VnXGtvvfWWZsyYoUqVKsnb21u33HKLfvrpp0zrb9++XV26dFFISIh8fX1VtWpVPf/885LSr8UhQ4ZIkqKiomxtndG2WT3T/c8//6hz584qWbKk/Pz81KhRo0x/fMq4Fj/77DONGTNG5cqVk4+Pj1q3bq1du3bl/SQBAPIdSTcAwGm++uorVaxYUU2aNHHI9rt27Sqr1arXXntNDRs21CuvvKIJEybo9ttvV9myZfX666+rcuXKeuaZZ7Ru3bp82+/EiRN100036aWXXtKrr74qDw8Pde7c2S5x+vjjj+Xt7a3mzZvr448/1scff6xHHnkk2+PYs2dPpoRv3759+uGHH9StWzdb2ZgxY9SrVy9VqVJF48aN05NPPqlVq1apRYsWOX6mODIyUo0bN9ann35qK/vmm2+UmJhot6/cHvMff/yhu+66S+fPn9dLL72kt99+Wx06dND3339vq/Puu+9q4MCBqlGjhiZMmKDRo0erbt26+vHHH3MUe3Jyso4ePWr3unDhQo7WvdycOXP05ptv6pFHHtErr7yivXv36p577rHb3m+//aaGDRtq9erVevjhhzVx4kR16tRJX331lSTpnnvuUffu3SVJ48ePt7V1dre8HzlyRE2aNNHy5cv1+OOPa8yYMTp37pw6dOigRYsWZar/2muvadGiRXrmmWc0bNgw/fDDD7r//vvzdLwAAAcxAAA4QWJiopFkOnbsmKP6e/bsMZLMzJkzMy2TZEaOHGl7P3LkSCPJ9OvXz1aWmppqypUrZywWi3nttdds5SdOnDC+vr4mNjbWVjZz5kwjyezZs8duP2vWrDGSzJo1a2xlsbGxpkKFCnb1kpOT7d6npKSYWrVqmdtuu82u3N/f326/2e0/MTHReHt7m6efftqu3htvvGEsFovZt2+fMcaYvXv3Gnd3dzNmzBi7er///rvx8PDIVJ7dfn/66SczefJkU7x4cduxdO7c2dx6663GGGMqVKhg2rVrl+tjHj9+vJFkEhISso2hY8eOpmbNmleMMysZ10dWr4z2atmypWnZsmWmdS9vw4xtBQcHm+PHj9vKv/jiCyPJfPXVV7ayFi1amOLFi9vaIIPVarX9/Oabb2Z5PRmTfi4vvQaefPJJI8msX7/eVnbq1CkTFRVlIiMjTVpamjHm4rVYvXp1c/78eVvdiRMnGknm999/v+L5AgBcP/R0AwCcIikpSZJUvHhxh+3joYcesv3s7u6u+vXryxijvn372sqDgoJUtWpV/fPPP/m2X19fX9vPJ06cUGJiopo3b64tW7bkaXsBAQFq06aNPvvsMxljbOXz5s1To0aNVL58eUnSwoULZbVa1aVLF7ue3rCwMFWpUkVr1qzJ8T67dOmis2fPasmSJTp16pSWLFmS7a3lUs6OOWMQsS+++EJWqzXL7QQFBem///7L8jbunOjXr59WrFhh96pTp06ettW1a1eVKFHC9r558+aSZLtWEhIStG7dOj344IO2NshgsVjytM+lS5eqQYMGdoPYFStWTP369dPevXv1559/2tXv06ePvLy8so0RAOB8DKQGAHCKgIAASdKpU6ccto/LE6HAwED5+PioVKlSmcqPHTuWb/tdsmSJXnnlFW3dulXnz5+3lec1EZPSE8DFixcrLi5OTZo00e7du/Xzzz9rwoQJtjp///23jDGqUqVKltvw9PTM8f5CQkIUHR2tOXPmKDk5WWlpabrvvvuyrZ+TY+7atavee+89PfTQQxo6dKhat26te+65R/fdd59tBO/nnntOK1euVIMGDVS5cmXdcccd6tGjh5o2bZqjuKtUqaLo6OgcH+eVXH79ZCTgJ06ckHQxsa1Vq1a+7E9Kf2SgYcOGmcqrV69uW37p/q4WIwDA+Ui6AQBOERAQoPDwcG3bti1H9bNLWNPS0rJdJ6sRwLMbFfzSHuS87CvD+vXr1aFDB7Vo0UJTp05VmTJl5OnpqZkzZ17TYGDt27eXn5+fPvvsMzVp0kSfffaZ3Nzc1LlzZ1sdq9Uqi8Wib775JsvjLFasWK722aNHDz388MM6fPiw2rRpk2m6qww5PWZfX1+tW7dOa9as0ddff61ly5Zp3rx5uu222/Ttt9/K3d1d1atX144dO7RkyRItW7ZMn3/+uaZOnaoXX3xRo0ePzlX8l7NYLHbtnCG7ds3JteJshSFGAHB1JN0AAKe56667NGPGDMXFxalx48ZXrJvRg3f5YGD79u3L97iuZV+ff/65fHx8tHz5crvpoGbOnJmpbm56vv39/XXXXXdp/vz5GjdunObNm6fmzZsrPDzcVqdSpUoyxigqKko33HBDjrednbvvvluPPPKIfvjhB82bNy/berk5Zjc3N7Vu3VqtW7fWuHHj9Oqrr+r555/XmjVrbD3U/v7+6tq1q7p27aqUlBTdc889GjNmjIYNGyYfH588H0+JEiWyvO06r9dQxojyV/vDUW7auUKFCtqxY0em8u3bt9uWAwAKF57pBgA4zbPPPit/f3899NBDOnLkSKblu3fv1sSJEyWl94yXKlUq0yjjU6dOzfe4KlWqJEl2+0pLS9OMGTOuuq67u7ssFotd7+nevXu1ePHiTHX9/f1zPKK4lH579sGDB/Xee+/p119/VdeuXe2W33PPPXJ3d9fo0aMz9XQaY3J9C32xYsU0bdo0jRo1Su3bt8+2Xk6P+fjx45nWrVu3riTZbkm/PEYvLy/VqFFDxpg8j0KeoVKlStq+fbsSEhJsZb/++qvd6Om5ERISohYtWuiDDz7Q/v377ZZdev79/f0lZf4jTlbatm2rTZs2KS4uzlZ25swZzZgxQ5GRkapRo0aeYgUAOA893QAAp6lUqZLmzJmjrl27qnr16urVq5dq1aqllJQUbdy4UfPnz7ebw/ihhx7Sa6+9poceekj169fXunXrtHPnznyPq2bNmmrUqJGGDRum48ePq2TJkpo7d65SU1Ovum67du00btw43XnnnerRo4fi4+M1ZcoUVa5cWb/99ptd3Xr16mnlypUaN26cwsPDFRUVleXzvBnatm2r4sWL65lnnpG7u7vuvfdeu+WVKlXSK6+8omHDhmnv3r3q1KmTihcvrj179mjRokXq16+fnnnmmVydi9jY2Hw75pdeeknr1q1Tu3btVKFCBcXHx2vq1KkqV66cbeCwO+64Q2FhYWratKlCQ0P1119/afLkyWrXrt01D7r34IMPaty4cYqJiVHfvn0VHx+v6dOnq2bNmraB/XJr0qRJatasmW6++Wb169dPUVFR2rt3r77++mtt3bpVUno7S9Lzzz+vbt26ydPTU+3bt7cl45caOnSoPv30U7Vp00YDBw5UyZIl9eGHH2rPnj36/PPPbc++AwAKEecMmg4AwEU7d+40Dz/8sImMjDReXl6mePHipmnTpuadd94x586ds9VLTk42ffv2NYGBgaZ48eKmS5cuJj4+Ptspwy6fmio2Ntb4+/tn2n/Lli0zTVO1e/duEx0dbby9vU1oaKgZPny4WbFiRY6mDHv//fdNlSpVjLe3t6lWrZqZOXOmLaZLbd++3bRo0cL4+voaSbapo7KbsswYY+6//34jyURHR2d7Pj///HPTrFkz4+/vb/z9/U21atVM//79zY4dO7Jd59L9/vTTT1esl9WUYTk55lWrVpmOHTua8PBw4+XlZcLDw0337t3Nzp07bXX+97//mRYtWpjg4GDj7e1tKlWqZIYMGWISExOvGFPGNF9vvvnmFet98sknpmLFisbLy8vUrVvXLF++PNspw7La1uXXmjHGbNu2zdx9990mKCjI+Pj4mKpVq5oXXnjBrs7LL79sypYta9zc3Oza9vIpw4xJv/buu+8+2/YaNGhglixZYlcnY8qw+fPnZ3kesppaDwDgHBZjGGkDAAAAAABH4B4lAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHAQkm4AAAAAAByEpBsAAAAAAAfxcHYABZHVatXBgwdVvHhxWSwWZ4cDAAAAAChgjDE6deqUwsPD5eaWfX82SXcWDh48qIiICGeHAQAAAAAo4P7991+VK1cu2+Uk3VkoXry4pPSTFxAQ4ORoig6r1aqEhASFhIRc8S9BKNq4DpCBawEZuBYgcR3gIq4FSIXjOkhKSlJERIQtf8wOSXcWMm4pDwgIIOnOR1arVefOnVNAQECB/eDA8bgOkIFrARm4FiBxHeAirgVIhes6uNojyQU7egAAAAAACjGSbgAAAAAAHISkGwAAAAAAB+GZ7jwyxig1NVVpaWnODqXQsFgsMsY4OwwAAAAAuG5IuvMgJSVFhw4dUnJysrNDKVSMMbJYLCpRooR8fHycHQ4AAAAAOBxJdy5ZrVbt2bNH7u7uCg8Pl5eX11VHq0N6wp2SkqIjR45o7969uuGGGwr8KIQAAAAAcK1IunMpJSVFVqtVERER8vPzc3Y4hYqPj48sFov+++8/paSk0NsNAAAAoMgr8F2N69atU/v27RUeHi6LxaLFixdfdZ21a9fq5ptvlre3typXrqxZs2ble1z00uYNdwUAAAAAcCUFPnM8c+aM6tSpoylTpuSo/p49e9SuXTvdeuut2rp1q5588kk99NBDWr58uYMjBQAAAADAXoG/vbxNmzZq06ZNjutPnz5dUVFRevvttyVJ1atX14YNGzR+/HjFxMQ4KkwAAAAAyF9Wq5SaKl24kP7K+Dm7fy9ckNLS0l9W65X/vVodY+xfOS3LKL/c5bMYZTWr0aVlxsi7TBmpb9/8PadOUOCT7tyKi4tTdHS0XVlMTIyefPLJbNc5f/68zp8/b3uflJQkKX3QNOtlF4zVapUxxvZC7mScs6zOLVxDxmeI9gfXAjJwLUDiOsBFBeZauHBBSk5Of509m/XPGe///2U5f17K7nXuXPq/KSnZ10lJsUukLc4+B07kJsm3TRtZ+/RxdijZyuk1WuSS7sOHDys0NNSuLDQ0VElJSTp79qx8fX0zrTN27FiNHj06U3lCQoLOnTtnV3bhwgVZrValpqYqNTU1f4O/Dg4fPqzXXntN33zzjQ4cOKDSpUvrxhtv1MCBA3XbbbepSpUq2rdvnz7++GN17drVbt06deror7/+0nvvvadevXpJkq3+pcqWLas9e/Zk2nfGL0+r1apjx47J09PTcQeKAstqtSoxMVHGGMZGcHFcC8jAtQCJ6wAX5fpasFplSU6W5fRpWc6csf3rduaM3fuMl9ul9S5d79w5Wc6eTX+dOydLAf2ubywWydNTxt1d8vSU3N1lPD0lDw8ZDw/J3V1yc0tf7uZme29X9v/lJmO5xZL+PmO5xWL3MlmUScpU11gsF8uycnl5VvX+v8wYo6SKFWXi4wvs74RTp07lqF6RS7rzYtiwYRo8eLDtfVJSkiIiIhQSEqKAgAC7uufOndOpU6fk4eEhD4/Cdfr27t2rZs2aKSgoSG+88YZq166tCxcuaPny5Ro0aJD++usvSVJERIQ+/vhj3X///bZ1f/jhBx05ckT+/v5yc3OzO/bRo0fr4Ycftr13d3fP9ty4ubnJzc1NwcHBjF7uoqxWqywWi0JCQgrsL1BcH1wLyMC1AInrwKWdOyedPCklJkonT8qcOCHff/9VgNUqy6lTslyyTImJ9j+fPClLDhOfvDIWi+Tnd/Hl62v/c8Z7H5/0l7e37WUu+VleXnbLsnx5etq/PDwy/+vunilGy2X/FgVWq1VKSFDpAvw7Iaf5TOHKGnMgLCxMR44csSs7cuSIAgICsuzlliRvb295e3tnKs9IEC8vs1gstpek9GcPkpPz5wByw88v+78iZaF///6yWCzatGmT/P39beW1atVS3759bcdz//33a/z48frvv/8UEREhSZo5c6buv/9+ffTRR/bHLikgIEBlypS56v6NMbb1sjq3cB0Wi4VrAJK4FnAR1wIkroMiwWqVTpyQEhIyv44ezbrsksc8M5TIy77d3aVixaTixa/8b3Zl2STWFi+vXH3nvlRRSoKdoaD/TshpXEUu6W7cuLGWLl1qV7ZixQo1btzYcTtNTk7/oF5vp09LlyTPV3L8+HEtW7ZMY8aMsUu4MwQFBdl+Dg0NVUxMjD788EONGDFCycnJmjdvnr777jt99NFH+RU9AAAACgNj0hPpgwelAwfS/814xcfbJ9DHjqUPxJUXgYFSUJBMYKAu+PnJMyRElqAgKeP1/8tt/2b8HBgoBQSk9xQzPS0KoAKfdJ8+fVq7du2yvd+zZ4+2bt2qkiVLqnz58ho2bJgOHDhgSwYfffRRTZ48Wc8++6wefPBBrV69Wp999pm+/vprZx1CgbBr1y4ZY1StWrUc1X/wwQf19NNP6/nnn9eCBQtUqVIl1a1bN8u6zz33nEaMGGF7/+qrr2rgwIH5ETYAAAAc6dQp+yQ6q8T64MEse6OvKDBQCgmRSpVK//fS16VlpUpJJUum9zj/f6+hsVp1PD5epUuXlqWA9nACuVHgk+7Nmzfr1ltvtb3PePY6NjZWs2bN0qFDh7R//37b8qioKH399dd66qmnNHHiRJUrV07vvfeeY6cL8/NL73W+3vz8clw1tyOtt2vXTo888ojWrVunDz74QA8++GC2dYcMGaLevXvb3pcqVSpX+wIAAIADGCMdPy7t2SPt3Zv+b8Zr717pv/9y9x02OFgKD5fKlk3/t0wZKTQ0czJdqlT688sAJBWCpLtVq1ZXTBhnzZqV5Tq//PKLA6O6jMWS49u8naVKlSqyWCzavn17jup7eHioZ8+eGjlypH788UctWrQo27qlSpVS5cqV8ytUAAAA5NTp05mT6Uvf52SQsYCA9CT68ldGch0eLoWFpQ8SBiDXCnzSjfxRsmRJxcTEaMqUKRo4cGCm57pPnjxp91y3lH6L+VtvvaWuXbuqRIk8DWcBAACAa5WcLP39t7R9+8XXrl3pCfbRo1dfPyxMiopKf0VGXvw5IiI9oXbG2ESACyHpdiFTpkxR06ZN1aBBA7300ku68cYblZqaqhUrVmjatGm2KcMyVK9eXUePHpVfLm5jBwAAQB4Ykz4o2aWJdcZr37705dkpWdI+mb40wY6MTB+JG4DTkHS7kIoVK2rLli0aM2aMnn76aR06dEghISGqV6+epk2bluU6wcHB1zlKAACAIiw1Vdq9O+vk+uTJ7NcrUUKqXl2qVi39VaXKxcQ6MPB6RQ8gD0i6XUyZMmU0efJkTZ48Ocvle/fuveL6Jy/7z+Bq9QEAAFzWsWPSr7+mv377Lf3fP/6QUlKyrm+xpCfSGYn1pa9SpZgOCyikSLoBAACAa5GaKu3caZ9c//pr+lRbWfHzyzqxrlKFwcqAIoikGwAAAMip48czJ9d//JH9PNYVK0p16kg33pj+b5066beEM/804DJIugEAAICsJCdLW7ZIP/4obdqU/u++fVnX9feXate+mFjfeGP6+4CA6xszgAKHpBsAAABIS0sfzOzHHy8m2b//nl5+uchI++S6Tp30Hm16rwFkgaQ7j8yVpm1AtjhvAACgQDhw4GLv9aZN0ubN0qlTmeuFhUkNG6a/GjSQ6tWTgoKue7gACi+S7lzy9PSUJCUnJ8uXOQ9z7dy5c5IunkcAAACHS05OT6rj4i4m2QcOZK7n5yfVr2+fZJcrx6jhAK4JSXcuubu7KygoSPHx8ZIkPz8/WfhFfFXGGJ05c0bx8fEqWbKk3N3dnR0SAAAoqg4dkjZulL7/Pv21ZUv6COOXcnOTatVKT6wzEuwaNSQPvh4DyF/8VsmDsLAwSbIl3sgZY4y8vb0VGhrq7FAAAEBRkZaWPnr4999fTLT37Mlcr0wZqUmTi73YN98sFSt2/eMF4HJIuvPAYrGoTJkyKl26tC5cuODscAoNd3d3HTt2jDsDAABA3p0+nX6LeEaSHRcnJSXZ17FY0kcOb9o0/dWkSfrgZ3wHAeAEJN3XwN3dndukc8FqtTo7BAAAUNicPCmtWSOtXp2eZP/6a+YRxYsVkxo1Sk+umzZN/5mpugAUECTdAAAAKDhSU9MHPfv22/TXpk2Zk+zy5e17sWvX5llsAAUWv50AAADgXLt3S8uWKWjJElk2bsx8u3jVqlJ0tNSiRXqSXa6cc+IEgDwg6QYAAMD1deJE+u3iK1ak92bv2SM3ST4Zy0uWTE+y77hDuv329J5tACikSLoBAADgWBcupA9+lpFkb9okXTrWi6enTJMmOt24sfzvvltu9epJjJsDoIgg6QYAAED+S0qSli6VFi2Sli3LfMt4tWrpPdl33CG1bCnj56cz8fHyL106fQ5tACgiSLoBAACQP44ckb74Ij3RXrUqvYc7Q3Cw/S3jERH26zLLCYAiiqQbAAAAebd7d3qSvWhR+pzZxlxcVrWqdPfdUqdO0i230IMNwCWRdAMAACDnjJG2br2YaG/bZr/8llsuJtrVqzsjQgAoUEi6AQAAcGWpqdL336cn2YsXS/v2XVzm7i61apWeaHfsyHReAHAZkm4AAABklpoqrVkjzZ0rffmldPToxWW+vtKdd6Yn2u3apU/xBQDIEkk3AAAA0hkj/fSTNHu2NG9e+sBoGUqWlNq3T0+0b79d8vNzXpwAUIiQdAMAALi67dulOXPSX7t3XywvWVLq0kXq3Flq0ULy4KsjAOQWvzkBAABc0YED6beOz5kjbdlysdzPL30QtB490nu0vbycFiIAFAUk3QAAAK7ixAnp88/TE+21ay9O7+XhIcXEpCfaHTtK/v5ODRMAihKSbgAAgKLs7FlpyZL057SXLpUuXLi4rFkz6f77pfvuk0qVcl6MAFCEkXQDAAAUNcZI69ZJM2dKCxdKp05dXHbjjek92t26SRUqOC9GAHARJN0AAABFxYED0ocfSh98YD8gWoUK6Yl2jx5SrVrOiw8AXBBJNwAAQGGWkpJ++/j770vLlklWa3p58eLpvdmxsVKTJpLF4tw4AcBFkXQDAAAURn/+md6j/dFHUkLCxfJmzaS+fdOn+WJANABwOpJuAACAwuLUKWnevPRe7R9+uFgeFpbeo/3gg9INNzgvPgBAJiTdAAAABZkx0vffpyfan30mJSenl7u7S3fdld6r3aZN+rRfAIACh9/OAAAABdHhwxcHRdu582J51arpiXbPnuk93ACAAo2kGwAAoKAwRtqwQZo8OX2qr9TU9HJ/f6lLl/Rkm0HRAKBQIekGAABwttOnpdmzpSlTpN9/v1jeqJH00EPpCXfx4s6LDwCQZyTdAAAAzrJzpzR1qjRrlpSYmF7m6ys98IDUv79Up45TwwMAXDuSbgAAgOspLU36+uv0Xu1vv71YXqlSeqLdu7dUooTTwgMA5C+SbgAAgOvh6NH0EcinTZP27Usvs1ikdu3Sk+077pDc3JwbIwAg35F0AwAAONJPP6X3as+dK50/n15WsmT6oGiPPipVrOjc+AAADkXSDQAAkN/OnUufU3vKFGnTpovlN98sDRggdeuW/uw2AKDII+kGAADIL/Hx6bePT5kiJSSkl3l5pY8+3r+/1LAh030BgIsh6QYAALhWf/0ljR8vffTRxVvIy5WTHnssfcqv0qWdGx8AwGkKxWgdU6ZMUWRkpHx8fNSwYUNtuvQ2rSxMmDBBVatWla+vryIiIvTUU0/p3Llz1ylaAADgEoyRVq1KHwitRg3p3XfTE+769aVPP5X27JGGDyfhBgAXV+B7uufNm6fBgwdr+vTpatiwoSZMmKCYmBjt2LFDpbP4T2zOnDkaOnSoPvjgAzVp0kQ7d+5U7969ZbFYNG7cOCccAQAAKFJSUtIHRRs3Tvr11/Qyi0Xq2FF6+mmpaVNuIQcA2BT4nu5x48bp4YcfVp8+fVSjRg1Nnz5dfn5++uCDD7Ksv3HjRjVt2lQ9evRQZGSk7rjjDnXv3v2qveMAAABXdPy4NHasFBkpxcamJ9x+funPau/cKS1aJDVrRsINALBToHu6U1JS9PPPP2vYsGG2Mjc3N0VHRysuLi7LdZo0aaJPPvlEmzZtUoMGDfTPP/9o6dKl6tmzZ7b7OX/+vM5nPH8lKSkpSZJktVpltVrz6WhgtVpljOGcujiuA2TgWkCGAn8t7Noly8SJ0qxZsiQnS5JMmTIyAwZI/fqlT/8lSQU1/kKiwF8HuG64FiAVjusgp7EV6KT76NGjSktLU2hoqF15aGiotm/fnuU6PXr00NGjR9WsWTMZY5SamqpHH31Uw4cPz3Y/Y8eO1ejRozOVJyQk8Cx4PrJarUpMTJQxRm5uBf4mCzgI1wEycC0gQ4G8FoyR56ZN8v/f/+S9bJksxkiSLtSooTOPPKJznTqlj0qempo+YjmuWYG8DuAUXAuQCsd1cOrUqRzVK9BJd16sXbtWr776qqZOnaqGDRtq165dGjRokF5++WW98MILWa4zbNgwDR482PY+KSlJERERCgkJUUBAwPUKvcizWq2yWCwKCQkpsB8cOB7XATJwLSBDgboWUlOlzz+XZcIEWS55NM3ceafM4MFyv+02BVgs4ttB/itQ1wGcimsBUuG4Dnx8fHJUr0An3aVKlZK7u7uOHDliV37kyBGFhYVluc4LL7ygnj176qGHHpIk1a5dW2fOnFG/fv30/PPPZ9lg3t7e8vb2zlTu5uZWYBu4sLJYLJxXcB3AhmsBGZx+LSQlSe+/L02cKO3bl17m7S317Ck99ZQsNWqIJ7Udz+nXAQoMrgVIBf86yGlcBTP6/+fl5aV69epp1apVtjKr1apVq1apcePGWa6TnJyc6eDd3d0lSeb/bw0DAACQJO3fLz3zjBQRIQ0enJ5wlyoljRyZvuzdd9OnAwMAII8KdE+3JA0ePFixsbGqX7++GjRooAkTJujMmTPq06ePJKlXr14qW7asxo4dK0lq3769xo0bp5tuusl2e/kLL7yg9u3b25JvAADg4jZvTp/y67PPpLS09LJq1dIT7wcekHx9nRsfAKDIcEjSfebMGfn7++fLtrp27aqEhAS9+OKLOnz4sOrWratly5bZBlfbv3+/Xc/2iBEjZLFYNGLECB04cEAhISFq3769xowZky/xAACAQspqlZYskd5+W1q37mL5/7V353E21v0fx99ndmOMscwMw4yRJfsejWidorK1kYQkaRiGQXIXfuouJFvWkO2OLN2Syq1kyRKJLCnZsmSZQZixz5hz/f64mjMmQyPnmjNz5vV8PDwezuf6nuv7Oc6H5tN1Xd/vAw+Y+2s/+qiUS29hBADkXTbDgnuuAwIC1Lp1a7344otq1KiRs09vueTkZBUuXFhJSUkspOZEdrtdJ06cUEhISK59LgPWow6QjlpAOstr4eJFafZsafRocz9tSfLyktq0Ma9s16nj/Dlxy/g3AemoBUh5ow6y2zdakv1HH32k06dP68EHH1TFihU1bNgwHTt2zIqpAAAAspaYKA0aJEVESDExZsNduLD06qvSgQPSRx/RcAMALGdJ092qVSstXrxYR48e1SuvvKK5c+eqTJkyatasmRYtWqSrV69aMS0AAID0889S585ms/3WW9Iff0iRkdKYMdLvv0vDh0ulS7s6SwBAPmHpdfrg4GDFx8drx44dGjVqlL755hs9/fTTCgsL06BBg3Tx4kUrpwcAAPmFYUjffGM+l12tmjR9upSSIjVoYC6WtnevFBcnFSrk6kwBAPmMpauXJyYmatasWZo5c6YOHTqkp59+Wp07d9aRI0c0fPhwbdy4UV9//bWVKQAAAHeWkiLNm2cujrZjhxmz2aQnnjAXR2vY0LX5AQDyPUua7kWLFmnGjBn66quvVKVKFXXr1k3PP/+8goKCHGMaNmyoypUrWzE9AABwd2fOSB98II0bJ6WvG+PvL734otSrl1SunEvTAwAgnSVNd6dOnfTss89q/fr1uuuuu7IcExYWptdff92K6QEAgLv67Tfz2ezp06ULF8xYyZJSjx5S165S0aIuTQ8AgL+ypOk+fvy4/P39bzqmQIECGjx4sBXTAwAAd7Nxo3kL+aJF5n7bklS9unkL+bPPSr6+rs0PAIAbsGQhtUKFCunEiRPXxf/44w95enpaMSUAAHA3aWlmk33PPVJUlPTJJ2bD3aSJ9PXX0vbtUseONNwAgFzNkivdhmFkGb9y5Yp8fHysmBIAALiLCxekGTPM28j37zdj3t5Su3ZSfLx5hRsAgDzCqU33+++/L0my2WyaNm2aAgICHMfS0tK0Zs0aVapUyZlTAgAAd3H8uDRxojRpkrlQmmQ+ox0TI3Xvbj67DQBAHuPUpnv06NGSzCvdkydPznQruY+PjyIjIzV58mRnTgkAAPK6rVtVeNgw2T77TEpNNWPlykm9e0svvCAVLOjS9AAAuB1ObboPHDggSXrggQe0aNEiFSlSxJmnBwAA7sJul774Qho9Wh6rV6tAevyee8zF0Vq0kFgHBgDgBix5pnvVqlVWnBYAAOR1Fy5IM2dKY8dKe/dKkgxPT11u0UK+/fvLo0ED1+YHAICTOa3pjo+P11tvvaWCBQsqPj7+pmNHjRrlrGkBAEBecOSING6cNGWKdPasGQsKkl5+WUa3bkry9VVISIgrMwQAwBJOa7q3bt2q1D+fw9q6desNx9lsNmdNCQAAcrsffpBGj5YWLpSuXjVj5ctLvXqZ230FBJi3mmex1SgAAO7AaU33tbeUc3s5AAD5WFqa9NlnZrO9bl1G/P77zcXRmjWTPDxclh4AADnJkme6AQBAPpScLE2fLr3/vvTn4qry9paefdZstmvXdm1+AAC4gNOa7ieffDLbYxctWuSsaQEAgKsdPmw22lOnmo23lLG/drduUliYa/MDAMCFnNZ0Fy5c2FmnAgAAecHmzdLIkebz2mlpZqxSJfN57fbtJX9/l6YHAEBu4LSme8aMGc46FQAAyK3S99ceOVJasyYj/uCDUny89OijPK8NAMA1eKYbAAD8vYsXpVmzzMXR/txfW15eUtu2ZrNdq5ZL0wMAILdyWtNdp04drVixQkWKFFHt2rVvujXYjz/+6KxpAQCAlRISpAkTpEmTpD/+MGNBQVLXrlKPHlKpUi5NDwCA3M5pTXfLli3l6+srSWrVqpWzTgsAAFxh505p1ChpzhwpJcWMlS1rrkLeqZO5vzYAAPhbTmu6Bw8enOXvAQBAHmEY0vLl5vPaX3+dEY+Kkvr0kVq1kjw9XZYeAAB5kaXPdG/evFm7du2SJFWpUkV169a1cjoAAPBPXLkiffyxeWX7p5/MmIeH9OST5vPaUVGuzQ8AgDzMkqb7yJEjatu2rdavX6+goCBJ0tmzZ9WwYUPNmzdPpUuXtmJaAABwK06dkiZPlsaPlxITzVjBgtJLL0lxcebt5AAA4LZYsqfHSy+9pNTUVO3atUunT5/W6dOntWvXLtntdr300ktWTAkAALJr924pJkaKiJAGDjQb7lKlpOHDpSNHpDFjaLgBAHASS650f/vtt/ruu+905513OmJ33nmnxo0bp8aNG1sxJQAAuBnDkFavNm8h/+KLjHidOubz2s88I3l7uyw9AADclSVNd3h4uFJTU6+Lp6WlKSwszIopAQBAVlJSpPnzzWZ72zYzZrNJzZubzXbjxuZrAABgCUtuLx8xYoR69OihzZs3O2KbN29WXFyc3nvvPSumBAAA1zp9Who6VIqMlDp0MBvuAgWkbt2kX3+VPvtMuvdeGm4AACzmtCvdRYoUke2a/3BfuHBBDRo0kJeXOcXVq1fl5eWlF198kX28AQCwyt690tix0owZ0sWLZqxkSalHD+nll6VixVybHwAA+YzTmu4xY8Y461QAAOBWGIa0dq15C/mSJeZrSapZ07yFvE0bycfHtTkCAJBPOa3p7tixo7NOBQAAsiM1VfrkE7PZvuaRLj3+uLm/9gMPcPs4AAAuZslCate6fPmyUlJSMsUCAwOtnhYAAPd19qw0bZr0/vvS77+bMT8/qWNHqVcvqVIlV2YHAACuYUnTfeHCBfXv318LFizQH3/8cd3xtLQ0K6YFAMC9HThgPq/94YfS+fNmLCREio2VXnlFCg52bX4AAOA6lqxe/uqrr2rlypWaNGmSfH19NW3aNA0ZMkRhYWGaPXu2FVMCAOC+Nmww99EuX95sus+fl6pWNZvvQ4ekgQNpuAEAyKUsudL9+eefa/bs2br//vvVqVMnNW7cWOXLl1eZMmU0Z84ctWvXzoppAQBwH2lp0qefms9rb9iQEX/kEXNxtIcf5nltAADyAEua7tOnT+uOO+6QZD6/ffr0aUlSo0aNFBMTY8WUAAC4h3PnpOnTzSvaBw6YMR8f6fnnpd69pWrVXJsfAAC4JZY03XfccYcOHDigiIgIVapUSQsWLFD9+vX1+eefKygoyIopAQDI237/XRo3TpoyRUpKMmPFikndupm/SpRwbX4AAOAfsaTp7tSpk7Zv36777rtPr732mpo3b67x48crNTVVo0aNsmJKAADypi1bpJEjpQULzFvKJenOO82r2u3bS/7+rs0PAADcFkua7t69ezt+Hx0drV27dunHH39U+fLlVaNGDSumBAAg77DbpS++MJvtNWsy4g88YO6v/dhjkocla50CAIAcZvk+3ZIUGRmpyMjInJgKAIDc6+JFafZsafRoac8eM+blJbVpYzbbdeq4Nj8AAOB0lv1v9BUrVqhZs2YqV66cypUrp2bNmumbb76xajoAAHKvxERp0CApIkKKiTEb7sKFpVdfNRdL++gjGm4AANyUJU33xIkT1bRpUxUqVEhxcXGKi4tTYGCgHnvsMU2YMMGKKQEAyH1+/ll66SWpTBnprbekP/6QIiOlMWPMhdOGD5dKl3Z1lgAAwEKW3F7+zjvvaPTo0YqNjXXEevbsqXvuuUfvvPOOunfvbsW0AAC4nmFIK1aYz2svW5YRv/tuc3/tVq3MW8oBAEC+YMmV7rNnz6pp06bXxR955BElpW+DAgCAO0lJMZ/Xrl1bevhhs+G22aQnn5TWr5c2bJCefpqGGwCAfMaSprtFixb69NNPr4t/9tlnatas2S2fb8KECYqMjJSfn58aNGigTZs23XT82bNn1b17d5UsWVK+vr6qWLGili5desvzAgDwt86ckYYNk8qWlTp2lLZvlwoWlHr0kPbulf77X6lhQ1dnCQAAXMRp/7v9/fffd/y+SpUqevvtt7V69WpFRUVJkjZu3Kj169erT58+t3Te+fPnKz4+XpMnT1aDBg00ZswYNWnSRLt371ZISMh141NSUvTwww8rJCREn3zyiUqVKqVDhw4pKCjotj4fAACZ/Pab+Wz29OnShQtmrGRJqWdPqWtXqUgRl6YHAAByB5thGIYzTlS2bNnsTWiz6bfffsv2eRs0aKC77rpL48ePlyTZ7XaFh4erR48eeu21164bP3nyZI0YMUK//vqrvL29szXHlStXdOXKFcfr5ORkhYeH68yZMwoMDMx2rrg5u92ukydPKjg4WB7sP5tvUQdIl2dr4fvvZRs5Uvr0U9nsdkmSUaOGjN69pWeflXx8XJxg3pNnawFORR0gHbUAKW/UQXJysooUKaKkpKSb9o1Ou9J94MABZ53KISUlRVu2bNGAAQMcMQ8PD0VHR2vDhg1ZvmfJkiWKiopS9+7d9dlnnyk4OFjPPfec+vfvL09PzyzfM3ToUA0ZMuS6+MmTJ3X58mXnfBjIbrcrKSlJhmHk2r84sB51gHR5qhbsdvl+/bUKTpokn2secbrywAO68MorSmnc2Hx+++xZ1+WYh+WpWoBlqAOkoxYg5Y06OHfuXLbGWb6aS/qFdJvNdsvvPXXqlNLS0hQaGpopHhoaql9//TXL9/z2229auXKl2rVrp6VLl2rfvn3q1q2bUlNTNXjw4CzfM2DAAMXHxztep1/pDg4O5kq3E9ntdtlstlz9f6tgPeoA6fJELVy6JM2aJduYMbLt3StJMry9peeek9G7t7yrV1eQazN0C3miFmA56gDpqAVIeaMO/Pz8sjXOsqZ79uzZGjFihPb++UNKxYoV1a9fP7Vv396qKSWZX05ISIimTJkiT09P1a1bV0ePHtWIESNu2HT7+vrK19f3uriHh0eu/YLzKpvNxp8rqAM45NpaOHFCmjhRmjBBOnXKjAUFSTExssXGSmFhuvX/lYybybW1gBxFHSAdtQAp99dBdvOypOkeNWqUBg4cqNjYWN1zzz2SpHXr1umVV17RqVOn1Lt372ydp3jx4vL09FRiYmKmeGJiokqUKJHle0qWLClvb+9Mt5JXrlxZCQkJSklJkQ/P2gEAbmT3bmnUKHPrr/THiyIjpd69pRdflAICXJoeAADIeyxpuseNG6dJkyapQ4cOjliLFi1UtWpV/d///V+2m24fHx/VrVtXK1asUKtWrSSZV7JXrFih2NjYLN9zzz33aO7cubLb7Y7/87Bnzx6VLFmShhsAcD3DkNatk957T1qyJCN+111S377mPtvsrQ0AAP4hS67THz9+XA2z2JO0YcOGOn78+C2dKz4+XlOnTtWsWbO0a9cuxcTE6MKFC+rUqZMkqUOHDpkWWouJidHp06cVFxenPXv26Msvv9Q777yj7t27396HAgC4l6tXpQULpLvvlu69N6PhbtFCWrNG+v57qXVrGm4AAHBbLPlJonz58lqwYIH+9a9/ZYrPnz9fFSpUuKVztWnTRidPntSgQYOUkJCgWrVqadmyZY7F1Q4fPpzpXvrw8HB99dVX6t27t2rUqKFSpUopLi5O/fv3v/0PBgDI+86fl2bMkEaPltJ33vD1lTp2lOLjpTvvdG1+AADArVjSdA8ZMkRt2rTRmjVrHM90r1+/XitWrNCCBQtu+XyxsbE3vJ189erV18WioqK0cePGW54HAODGjh+Xxo+XJk2SzpwxY8WKSd27m79CQlybHwAAcEuWNN1PPfWUNm3apFGjRmnx4sWSzMXMNm3apNq1a1sxJQAAWfvlF2nkSOmjj6SUFDNWvrzUp4/UoYPk7+/a/AAAgFtzetOdmpqqrl27auDAgfroo4+cfXoAAP6eYUirV5uLoy1dmhFv2FDq109q3ly6ZpcLAAAAqzh9ITVvb2/997//dfZpAQD4e1evSvPmmSuPP/ig2XDbbOYK5OvXm79ataLhBgAAOcaS1ctbtWrluK0cAADLnTsnjRkjlSsntW0rbdkiFSggdesm7dkj/fe/5lVuAACAHGbJM90VKlTQm2++qfXr16tu3boqWLBgpuM9e/a0YloAQH5z7Jj0/vvS5MlSUpIZCw6WevSQYmKk4sVdmx8AAMj3LGm6P/zwQwUFBWnLli3asmVLpmM2m42mGwBwe3buNBdHmzNHSk01YxUrmoujtW9vXuUGAADIBSxpug+k73sKAICzGIb07bfSiBGZF0dr3Fjq21dq1kzysOSpKQAAgH/M6U33xo0b9fnnnyslJUUPPfSQmjZt6uwpAAD5SVqa9Omn0rvvSj/8YMY8PMzF0fr2lRo0cG1+AAAAN+HUpvuTTz5RmzZtVKBAAXl7e2vUqFEaPny4+vbt68xpAAD5waVL0qxZ5rZf+/ebMT8/qVMnKT7e3GsbAAAgl3PqfXhDhw5Vly5dlJSUpDNnzujf//633nnnHWdOAQBwd3/8Ib31llSmjLkY2v79UtGi0sCB0qFD0sSJNNwAACDPcGrTvXv3bvXt21eef+5/2qdPH507d04nTpxw5jQAAHd08KAUFydFREiDBkknT5qN9/vvS4cPS2++KYWEuDpLAACAW+LU28svXryowMBAx2sfHx/5+fnp/PnzCuEHJQBAVrZtM1cinz/ffH5bkmrVkl59VXrmGcnLkjU/AQAAcoTTf5KZNm2aAgICHK+vXr2qmTNnqvg1e6WyZRgA5HOGIX3zjYq8/bY81qzJiEdHm812dLRks7kuPwAAACdxatMdERGhqVOnZoqVKFFC//nPfxyv2acbAPKx1FTpk0+kESPksXWrfCUZnp6ytW4t9esn1a7t6gwBAACcyqlN98GDB515OgCAuzh3Tpo2TRozxnw+W5Lh76+LbduqwIABspUr59r8AAAALMKDcgAA6xw5Yi6ENmWKlJRkxoKDpdhYGTExOpeWpgKs+QEAANwYTTcAwPm2bzcXR/v4Y+nqVTNWqZLUp4/0/PPmftt2u8TuFgAAwM3RdAMAnMMwpK+/Npvt5csz4vfdJ/XtKz32mOTh1J0qAQAAcj2abgDA7UlJMa9ojxwp/fSTGfP0NLf76tNHqlfPtfkBAAC4EE03AOCfOXtW+uAD85ntY8fMWMGCUpcuUlycFBnpyuwAAAByBcua7v3792vGjBnav3+/xo4dq5CQEP3vf/9TRESEqlatatW0AACrHTxorkL+4YfS+fNmLCxM6tlTevllqUgRV2YHAACQq1jycN23336r6tWr6/vvv9eiRYt0/s8fyrZv367BgwdbMSUAwGqbNklt2kjlykljx5oNd/Xq0qxZ0oEDUv/+NNwAAAB/YUnT/dprr+nf//63li9fLh8fH0f8wQcf1MaNG62YEgBghbQ06dNPpcaNpQYNpAULzFXHH35Y+uorc5XyDh2ka/6tBwAAQAZLbi//6aefNHfu3OviISEhOnXqlBVTAgCc6cIFaeZM8zbyffvMmLe39NxzUny8VKOGK7MDAADIMyxpuoOCgnT8+HGVLVs2U3zr1q0qVaqUFVMCAJzh2DFp/Hhp8mTpzBkzVqSIFBMjde9uPrsNAACAbLOk6X722WfVv39/LVy4UDabTXa7XevXr1ffvn3VoUMHK6YEANyO7dulUaPMrb9SU81YuXJS797SCy+Yq5IDAADgllnSdL/zzjvq3r27wsPDlZaWpipVqigtLU3PPfec3njjDSumBADcKsOQli0zm+1vvsmIN2pk7q/dvLm53zYAAAD+MUuabh8fH02dOlUDBw7Uzp07df78edWuXVsVKlSwYjoAwK24fFmaM8dstn/5xYx5ekpPP20+r12/vmvzAwAAcCOWNN3r1q1To0aNFBERoYiICCumAADcqlOnpIkTpQkTpBMnzFihQtJLL5l7bEdGujQ9AAAAd2RJ0/3ggw+qVKlSatu2rZ5//nlVqVLFimkAANmxf795VXvGDOnSJTMWHi7FxZkNd+HCrs0PAADAjVmyT/exY8fUp08fffvtt6pWrZpq1aqlESNG6MiRI1ZMBwDIyg8/SK1bSxUrmle4L12S6tSR5s41G/E+fWi4AQAALGZJ0128eHHFxsZq/fr12r9/v5555hnNmjVLkZGRevDBB62YEgAgmYujLV0qPfCA+Wz2woWS3S41bSqtWCFt3iy1bWvuuQ0AAADLWXJ7+bXKli2r1157TTVr1tTAgQP17bffWj0lAOQ/KSnmdl/vvSft3GnGvLzMBrtvX6lGDdfmBwAAkE9Z2nSvX79ec+bM0SeffKLLly+rZcuWGjp0qJVTAkD+kpQkTZkijR0rHT1qxgICpK5dzWe2w8Ndmx8AAEA+Z0nTPWDAAM2bN0/Hjh3Tww8/rLFjx6ply5by9/e3YjoAyH+OHjUb7Q8+kJKTzVjJkmaj3bWrFBTk0vQAAABgsqTpXrNmjfr166fWrVurePHiVkwBAPnTzz+bt5DPmSOlppqxypWlfv2k556TfH1dmx8AAAAysaTpXr9+vRWnBYD8yTCkb7+VRowwF0lLd++9ZrP92GOShyXrYgIAAOA2Oa3pXrJkiR599FF5e3tryZIlNx3bokULZ00LAO7r6lVp0SKz2d682YzZbNKTT5rNdoMGrs0PAAAAf8tpTXerVq2UkJCgkJAQtWrV6objbDab0tLSnDUtALifCxek6dOl0aOlAwfMmJ+f1KmT1Lu3VKGCa/MDAABAtjmt6bbb7Vn+HgCQTYmJ0rhx0sSJ0pkzZqx4cSk2VurWTQoOdm1+AAAAuGWWPAQ4e/ZsXbly5bp4SkqKZs+ebcWUAJB37d4tvfyyVKaM9PbbZsNdrpzZfB86JA0eTMMNAACQR1nSdHfq1ElJSUnXxc+dO6dOnTpZMSUA5C2GIa1bJ7VsKVWqJE2dKl25It19t/Tf/5qNeEyMxFaLAAAAeZolq5cbhiGbzXZd/MiRIypcuLAVUwJA3pCWJi1ebG77tXGjGbPZpBYtzMXRGjY0XwMAAMAtOLXprl27tmw2m2w2mx566CF5eWWcPi0tTQcOHFDTpk2dOSUA5A0XL0qzZkmjRkn79pkxX1+pY0cpPl66807X5gcAAABLOLXpTl+1fNu2bWrSpIkCAgIcx3x8fBQZGamnnnrKmVMCQO524oT5bPaECdKpU2asaFFzYbTYWCk01LX5AQAAwFJObboHDx4sSYqMjFSbNm3k5+fnlPNOmDBBI0aMUEJCgmrWrKlx48apfv36f/u+efPmqW3btmrZsqUWL17slFwAIFt27zavas+eLV2+bMbKljWvanfqJBUs6Nr8AAAAkCMsWUitY8eOTmu458+fr/j4eA0ePFg//vijatasqSZNmujEiRM3fd/BgwfVt29fNW7c2Cl5AMDfMgxp7dqMxdGmTDEb7rvukhYskPbsMa9u03ADAADkG5YspJaWlqbRo0drwYIFOnz4sFJSUjIdP336dLbPNWrUKHXp0sWx6vnkyZP15Zdfavr06XrttdduOH+7du00ZMgQrV27VmfPnr3pHFeuXMm0xVlycrIkc79x9hx3HrvdLsMw+DPN59yyDq5elT79VLZRo2TbtEmSZNhsUvPmMuLjpUaNMhZHc6fPfZvcshbwj1ALkKgDZKAWIOWNOshubpY03UOGDNG0adPUp08fvfHGG3r99dd18OBBLV68WIMGDcr2eVJSUrRlyxYNGDDAEfPw8FB0dLQ2bNhww/e9+eabCgkJUefOnbV27dq/nWfo0KEaMmTIdfGTJ0/qcvptobhtdrtdSUlJMgxDHh6W3GSBPMCd6sB24YIKfPyx/KdOldfhw5Ikw9dXl1q31oWXX1Za+fLmwJMnXZhl7uVOtYDbQy1Aog6QgVqAlDfq4Ny5c9kaZ0nTPWfOHE2dOlWPP/64/u///k9t27ZVuXLlVKNGDW3cuFE9e/bM1nlOnTqltLQ0hf5loaHQ0FD9+uuvWb5n3bp1+vDDD7Vt27Zs5ztgwADFx8c7XicnJys8PFzBwcEKDAzM9nlwc3a7XTabTcHBwbn2Lw6s5xZ1cPy4bOPHS5Mny/bnnTRG8eJSt24yYmLkFxIi5zxg497cohbgFNQCJOoAGagFSHmjDrL7SLUlTXdCQoKqV68uSQoICFBSUpIkqVmzZho4cKAVU0oy/09D+/btNXXqVBUvXjzb7/P19ZWvr+91cQ8Pj1z7BedVNpuNP1fk3Tr4+Wdp5Ehpzhwp/bGZChWk+HjZOnSQ/P3FDtu3Js/WApyOWoBEHSADtQAp99dBdvOypOkuXbq0jh8/roiICJUrV05ff/216tSpox9++CHL5vZGihcvLk9PTyUmJmaKJyYmqkSJEteN379/vw4ePKjmzZs7Yun32Xt5eWn37t0qV67cP/xUAPIlw5BWrZLee0/63/8y4vfcI/XtKzVvLnl6ui4/AAAA5GqW/C+DJ554QitWrJAk9ejRQwMHDlSFChXUoUMHvfjii9k+j4+Pj+rWres4l2Q20StWrFBUVNR14ytVqqSffvpJ27Ztc/xq0aKFHnjgAW3btk3h4eG3/+EA5A+pqdLcuVLdutJDD5kNt4eH9PTT0oYN0rp1UqtWNNwAAAC4KUuudA8bNszx+zZt2igiIkIbNmxQhQoVMl2Fzo74+Hh17NhR9erVU/369TVmzBhduHDBsZp5hw4dVKpUKQ0dOlR+fn6qVq1apvcHBQVJ0nVxAMhScrI0bZo0Zoz0++9mzN/f3Fu7d2+Ju2UAAABwCyxpuv8qKioqyyvT2dGmTRudPHlSgwYNUkJCgmrVqqVly5Y5Flc7fPhwrr3HH0AecuSI9P770gcfmI23JIWGSj16SK+8IhUr5tr8AAAAkCc5relesmRJtse2aNHils4dGxur2NjYLI+tXr36pu+dOXPmLc0FIJ/Zvt1cHO3jj839tiWpUiXzee127aRsrkoJAAAAZMVpTXerVq2yNc5msyktLc1Z0wLArTMMaflyc3G05csz4vffbzbbjz5qPr8NAAAA3CanNd3pq4QDQK6VkiLNm2c22z/9ZMY8PaVnnpH69JHq1XNtfgAAAHA7OfJMNwC41Nmz0pQp0tix0rFjZqxgQalLFykuToqMdGV2AAAAcGOWNN1vvvnmTY8PGjTIimkBILPDh81VyKdOlc6fN2MlS5qN9ssvS0WKuDQ9AAAAuD9Lmu5PP/000+vU1FQdOHBAXl5eKleuHE03AGtt327eQj5vXsbiaNWqmc9rt20r+fi4Nj8AAADkG5Y03Vu3br0ulpycrBdeeEFPPPGEFVMCyO8MQ1q5Unr3XenrrzPiDz4o9esnNWki2Wyuyw8AAAD5Uo4tzxsYGKghQ4Zo4MCBOTUlgPzg6lVzu6+6daXoaLPh9vCQnn1W2rxZWrFCatqUhhsAAAAukaMLqSUlJSkpKSknpwTgri5ckD78UBo9Wjp40Iz5+0udO0u9e0tly7o0PQAAAECyqOl+//33M702DEPHjx/Xf/7zHz366KNWTAkgv0hMlMaPlyZMkM6cMWPBwVKPHlK3blKxYq7NDwAAALiGJU336NGjM7328PBQcHCwOnbsqAEDBlgxJQB3t2ePNHKkNGuWdOWKGStf3lwcrUMHqUAB1+YHAAAAZMGSpvvAgQNWnBZAfrRhgzRihLR4sblYmiQ1aCC9+qrUsqXk6enS9AAAAICbydFnugEgW+x26fPPzW2/1q3LiDdvbq5E3qgRC6MBAAAgT7Ck6b58+bLGjRunVatW6cSJE7Lb7ZmO//jjj1ZMCyCvu3RJmj1bGjXKvJ1cMvfUfv55qU8fqUoV1+YHAAAA3CJLmu7OnTvr66+/1tNPP6369evLxhUpADdz8qQ0caK5QNqpU2YsKEiKiZFiY6WwMJemBwAAAPxTljTdX3zxhZYuXap77rnHitMDcBd795pbfs2YIV2+bMYiI80tv158UQoIcGl6AAAAwO2ypOkuVaqUChUqZMWpAbgB7x9+kG36dOmzzzIWR6tb13xe+6mnJC+WmwAAAIB78LDipCNHjlT//v116NAhK04PIC9KS5MWLZKtUSMVa9FCtvTVyJs1k1avln74QWrThoYbAAAAbsWSn27r1auny5cv64477pC/v7+8vb0zHT99+rQV0wLIjS5elGbONBdH279fNknGn4uj2VgcDQAAAG7Okqa7bdu2Onr0qN555x2FhoaykBqQH504IU2YYP764w8zVqSIjJgYnWzTRsWrVZPNw5KbbQAAAIBcw5Km+7vvvtOGDRtUs2ZNK04PIDfbuVMaM0b66CPpyhUzVrasY3E0o0AB2U+ccGmKAAAAQE6xpOmuVKmSLl26ZMWpAeRGhiF99ZW5EvnXX2fE77rLXBztiScyntW2212TIwAAAOACljTdw4YNU58+ffT222+revXq1z3THRgYaMW0AHLapUvSnDlms/3LL2bMw8Nssnv3lho2lHi8BAAAAPmYJU1306ZNJUkPPfRQprhhGLLZbEpLS7NiWgA5JTFRmjjR/HXqlBkLCJBeeknq2dO8nRwAAACANU33qlWrrDgtAFf76SfzqvacOVJKihmLiDAb7ZdekgoXdm1+AAAAQC5jSdN93333WXFaAK5gt5vPa48aJX3zTUa8QQMpPl568kn21gYAAABuwJKflNesWXPT4/fee68V0wJwpkuXzBXIR4+Wdu0yYx4eZpMdHy9FRbk2PwAAACAPsKTpvv/++6+LXbtXN890A7lYQoK5t/bkyRnPaxcqlPG8dmSkS9MDAAAA8hJLmu4zZ85kep2amqqtW7dq4MCBevvtt62YEsDt2rbNvKr98cdSaqoZK1NGiouTOneW2HUAAAAAuGWWNN2Fs1hM6eGHH5aPj4/i4+O1ZcsWK6YFcKvsdumLL8xme/XqjHhUlLnl17X7awMAAAC4ZTn603RoaKh2796dk1MCyMr589KsWdKYMdK+fWbM01N6+mmz2W7QwKXpAQAAAO7CkqZ7x44dmV4bhqHjx49r2LBhqlWrlhVTAsiO33+Xxo+XpkyRzp41Y4ULSy+/LMXGmtt/AQAAAHAaS5ruWrVqyWazyTCMTPG7775b06dPt2JKADezaZN5C/nChVL6Qobly5vPa7/wghQQ4NL0AAAAAHdlSdN94MCBTK89PDwUHBwsPz8/K6YDkJWrV6XFi81m+7vvMuL332/eQv744+Yt5QAAAAAsY0nTXaZMGStOCyA7kpKkDz+U3n9fOnTIjHl7S23bSr16SbVruzQ9AAAAID/xcObJVq5cqSpVqig5Ofm6Y0lJSapatarWrl3rzCkBpNu719xHu3RpqU8fs+EuVkx6/XXz97Nm0XADAAAAOcypV7rHjBmjLl26KDCL/XwLFy6srl27atSoUWrcuLEzpwXyL8OQVq40VyH/8kvztSRVrmxe1W7fXipQwJUZAgAAAPmaU690b9++XU2bNr3h8UceeYQ9ugFnuHRJmjZNqlFDio4299o2DOmxx6Svv5Z+/tlckZyGGwAAAHApp17pTkxMlLe3940n8/LSyZMnnTklkL8cOyZNnChNniz98YcZ8/eXOnWSevSQ7rzTtfkBAAAAyMSpTXepUqW0c+dOlS9fPsvjO3bsUMmSJZ05JZA/bNokjR0rLVhgrkouSWXKmHtrd+4sFSni2vwAAAAAZMmpt5c/9thjGjhwoC5fvnzdsUuXLmnw4MFq1qyZM6cE3NfVq2aT3bCh1KCBNHeuGWvcWPrkE2nfPqlvXxpuAAAAIBdz6pXuN954Q4sWLVLFihUVGxurO/+81fXXX3/VhAkTlJaWptdff92ZUwLu5/RpaepUafx46cgRM5a+5VdcnFSnjmvzAwAAAJBtTm26Q0ND9d133ykmJkYDBgyQ8edKyjabTU2aNNGECRMUGhrqzCkB97Fjh9lof/SRuVCaJAUHSzEx5q8SJVybHwAAAIBb5tSmW5LKlCmjpUuX6syZM9q3b58Mw1CFChVUhFtggeulpkqLF5vN9po1GfFatcyr2s8+K/n5uSo7AAAAALfJ6U13uiJFiuiuu+6y6vRA3paYaN5CPnmydPSoGfP0lJ580lwcrXFjyWZzbY4AAAAAbptlTTeAvzAM6fvvzavaCxaYV7klKSRE6trV/FWqlGtzBAAAAOBUTl293CoTJkxQZGSk/Pz81KBBA23atOmGY6dOnarGjRurSJEiKlKkiKKjo286HrDc5cvSrFlS/fpSVJQ0Z47ZcN99t/n89uHD0ptv0nADAAAAbijXN93z589XfHy8Bg8erB9//FE1a9ZUkyZNdOLEiSzHr169Wm3bttWqVau0YcMGhYeH65FHHtHR9Ft4gZxy+LD0r39J4eHSCy9ImzdLvr5Sx47SDz9IGzZI7dqZMQAAAABuKdc33aNGjVKXLl3UqVMnValSRZMnT5a/v7+mT5+e5fg5c+aoW7duqlWrlipVqqRp06bJbrdrxYoVOZw58iXDkFauNJ/NLltWGjpUOnXKbLyHDpV+/12aOVOqV8/VmQIAAADIAbn6me6UlBRt2bJFAwYMcMQ8PDwUHR2tDRs2ZOscFy9eVGpqqooWLXrDMVeuXNGVK1ccr5OTkyVJdrtddrv9H2aPv7Lb7TIMwz3/TM+dkz76SLaJE2X75RdH2HjwQRnduknNm0tef/51c8fPfwvcug5wS6gFpKMWIFEHyEAtQMobdZDd3HJ1033q1CmlpaVdt7d3aGiofv3112ydo3///goLC1N0dPQNxwwdOlRDhgy5Ln7y5Eldvnz51pLGDdntdiUlJckwDHl45PqbLLLFc+9e+c+cqQILFsjj/HlJkt3fX5efeUYXO3XS1TvvNAeePu3CLHMXd6wD/DPUAtJRC5CoA2SgFiDljTo4d+5ctsbl6qb7dg0bNkzz5s3T6tWr5XeTvY4HDBig+Ph4x+vk5GSFh4crODhYgYGBOZFqvmC322Wz2RQcHJxr/+Jky9Wr0hdfmFe1r3lswahY0byq3aGD/AoXFrtrZ81t6gC3jVpAOmoBEnWADNQCpLxRBzfrMa+Vq5vu4sWLy9PTU4mJiZniiYmJKlGixE3f+95772nYsGH65ptvVKNGjZuO9fX1lW8Wi1l5eHjk2i84r7LZbHn3z/XkSenDD6VJk8xF0iTJw0Nq1kyKjZXtoYdky4ufywXydB3AqagFpKMWIFEHyEAtQMr9dZDdvHJn9n/y8fFR3bp1My2Clr4oWlRU1A3f9+677+qtt97SsmXLVI8Fq3C7Nm0yVxwvXVoaMMBsuIsVk/r3l/bvlz77THr4YbMBBwAAAIBr5Oor3ZIUHx+vjh07ql69eqpfv77GjBmjCxcuqFOnTpKkDh06qFSpUho6dKgkafjw4Ro0aJDmzp2ryMhIJSQkSJICAgIUEBDgss+BPObyZWnBAmn8eHN7r3T16kmxsVKbNlI2bycBAAAAkH/l+qa7TZs2OnnypAYNGqSEhATVqlVLy5Ytcyyudvjw4UyX9SdNmqSUlBQ9/fTTmc4zePBg/d///V9Opo686NAhafJkado0c6svSfLxMZvs2Fipfn3X5gcAAAAgT8n1TbckxcbGKjY2Nstjq1evzvT64MGD1icE95K+t/b48dKSJRlbeoWHSzEx0ksvScHBrs0RAAAAQJ6UJ5puwBLnzkn/+Y/ZbO/alRGPjpa6dzcXSPPirwgAAACAf46OAvnPnj3ShAnSzJlScrIZCwiQXnhB6tZNqlzZldkBAAAAcCM03cgf0tKk//3PvKr91VcZ8YoVzWe1O3aU2JMdAAAAgJPRdMO9nTkjTZ8uTZwo/fabGbPZHHtrKzqarb4AAAAAWIamG+5pxw7zqvZHH0mXLpmxIkWkzp3NxdHuuMO1+QEAAADIF2i64T5SU6XPPpPGjZPWrMmI16gh9eghPfec5O/vuvwAAAAA5Ds03cj7Tp6UpkyRJk2Sjh41Y56e0pNPmreQN25s3lIOAAAAADmMpht5108/SWPHmreQX7lixkJCpJdflrp2lUqXdm1+AAAAAPI9mm7kLWlp0hdfmM32qlUZ8Xr1pJ49pdatJV9f1+UHAAAAANeg6UbekJxsrkI+blzGKuTpt5DHxUkNG3ILOQAAAIBch6YbudvevWajPWOGdP68GStSxLyFvFs3KSLCtfkBAAAAwE3QdCP3MQxpxQrzFvIvvzRfS1LlyuZV7eeflwoWdG2OAAAAAJANNN3IPS5elObMMZvtn3/OiD/+uNlsR0dzCzkAAACAPIWmG6539Kg0YYL0wQfS6dNmrGBBqVMnc3/tihVdmx8AAAAA/EM03XCdPXuk4cOl//xHSk01Y5GR5irkL74oFS7s0vQAAAAA4HbRdCPnbd1qNtuffJLxvPa990q9e0vNm5urkgMAAACAG6DpRs5Zu1ZFhgyRx7X7azdvLg0YIEVFuS4vAAAAALAITTesZRjS//4nvfOOPNavl68kw8NDtmeflV57Tape3dUZAgAAAIBlaLphjbQ08/bxoUOl7dslSYaPjy61aSO/gQNlq1DBxQkCAAAAgPVouuFcV66YC6MNHy7t22fGChaUYmJkxMUp2ctLfiEhrs0RAAAAAHIITTec4/x5aepUaeRIcwswSSpa1NxfOzbW/L3dLp044do8AQAAACAH0XTj9pw5I40bJ40dm7HHdliY1Lev1KWLFBDg2vwAAAAAwIVouvHPpKRIEydKb75pNt6SVK6cuTha+/aSr69r8wMAAACAXICmG7fGMKQlS6R+/aS9e81Y1arSG29ITz8teVFSAAAAAJCODgnZt3Wr1KePlL7PdkiI9O9/Sy++KHl6ujY3AAAAAMiFaLrx944dM69kz5xpXun29TWb79dekwoVcnV2AAAAAJBr0XTjxi5eNFcjHz5cunDBjLVta+69XaaMa3MDAAAAgDyAphvXs9uluXOlAQOkI0fMWFSUNGqUdPfdrs0NAAAAAPIQmm5ktm6dFB8v/fCD+bpMGfNKd+vWks3m2twAAAAAII+h6Ybpt9+k/v2lTz4xXxcqJP3rX1KvXpKfn0tTAwAAAIC8iqY7v0tKkt5+Wxo71tx728ND6tJFGjJECg11dXYAAAAAkKfRdOdXhiH95z/mKuSnTpmxhx82F06rXt21uQEAAACAm6Dpzo/On5e6dTObbkmqVMlsth99lOe2AQAAAMCJaLrzm23bpDZtpD17JE9P8zbyV1+VvL1dnRkAAAAAuB2a7vzCMKRJk8yVya9ckUqXlj7+WGrUyNWZAQAAAIDbounOD86elV56Sfrvf83XzZpJM2dKxYq5MisAAAAAcHserk4AFtu0Sapd22y4vb2l0aOlJUtouAEAAAAgB3Cl213Z7WaD/dpr0tWr0h13SPPnS/XquTozAAAAAMg3aLrd0alT0gsvSF9+ab5u3VqaMkUqXNilaQEAAABAfsPt5e5mzRqpVi2z4fbzkyZPlubNo+EGAAAAABeg6XYXaWnSv/8tPfCAdPSouff2999LXbuy9zYAAAAAuAi3l7uD48el55+XVq40X3fsKI0fLwUEuDYvAAAAAMjnaLrzuuXLzYb7xAmpYEFzL+727V2dFQAAAABA3F6ed129Kv3rX1KTJmbDXaOGtGULDTcAAAAA5CJc6c6L7Haz2U6/nTwmRho5UipQwLV5AQAAAAAyyRNXuidMmKDIyEj5+fmpQYMG2rRp003HL1y4UJUqVZKfn5+qV6+upUuX5lCmOcTDQ2rRQgoMlBYulCZOpOEGAAAAgFwo1zfd8+fPV3x8vAYPHqwff/xRNWvWVJMmTXTixIksx3/33Xdq27atOnfurK1bt6pVq1Zq1aqVdu7cmcOZW6xnT2nXLunpp12dCQAAAADgBnJ90z1q1Ch16dJFnTp1UpUqVTR58mT5+/tr+vTpWY4fO3asmjZtqn79+qly5cp66623VKdOHY0fPz6HM7eYzSaFhbk6CwAAAADATeTqZ7pTUlK0ZcsWDRgwwBHz8PBQdHS0NmzYkOV7NmzYoPj4+EyxJk2aaPHixTec58qVK7py5YrjdXJysiTJbrfLbrffxifAtex2uwzD4M80n6MOkI5aQDpqARJ1gAzUAqS8UQfZzS1XN92nTp1SWlqaQkNDM8VDQ0P166+/ZvmehISELMcnJCTccJ6hQ4dqyJAh18VPnjypy5cv/4PMkRW73a6kpCQZhiEPj1x/kwUsQh0gHbWAdNQCJOoAGagFSHmjDs6dO5etcbm66c4pAwYMyHR1PDk5WeHh4QoODlZgYKALM3MvdrtdNptNwcHBufYvDqxHHSAdtYB01AIk6gAZqAVIeaMO/Pz8sjUuVzfdxYsXl6enpxITEzPFExMTVaJEiSzfU6JEiVsaL0m+vr7y9fW9Lu7h4ZFrv+C8ymaz8ecK6gAO1ALSUQuQqANkoBYg5f46yG5euTP7P/n4+Khu3bpasWKFI2a327VixQpFRUVl+Z6oqKhM4yVp+fLlNxwPAAAAAIBVcvWVbkmKj49Xx44dVa9ePdWvX19jxozRhQsX1KlTJ0lShw4dVKpUKQ0dOlSSFBcXp/vuu08jR47U448/rnnz5mnz5s2aMmWKKz8GAAAAACAfyvVNd5s2bXTy5EkNGjRICQkJqlWrlpYtW+ZYLO3w4cOZLus3bNhQc+fO1RtvvKF//etfqlChghYvXqxq1aq56iMAAAAAAPKpXN90S1JsbKxiY2OzPLZ69errYs8884yeeeYZi7MCAAAAAODmcvUz3QAAAAAA5GU03QAAAAAAWCRP3F6e0wzDkGTu1w3nsdvtOnfunPz8/HLtsv+wHnWAdNQC0lELkKgDZKAWIOWNOkjvF9P7xxuh6c7CuXPnJEnh4eEuzgQAAAAAkJudO3dOhQsXvuFxm/F3bXk+ZLfbdezYMRUqVEg2m83V6biN5ORkhYeH6/fff1dgYKCr04GLUAdIRy0gHbUAiTpABmoBUt6oA8MwdO7cOYWFhd30ajxXurPg4eGh0qVLuzoNtxUYGJhr/+Ig51AHSEctIB21AIk6QAZqAVLur4ObXeFOlztvjgcAAAAAwA3QdAMAAAAAYBGabuQYX19fDR48WL6+vq5OBS5EHSAdtYB01AIk6gAZqAVI7lUHLKQGAAAAAIBFuNINAAAAAIBFaLoBAAAAALAITTcAAAAAABah6QYAAAAAwCI03bihoUOH6q677lKhQoUUEhKiVq1aaffu3ZnGXL58Wd27d1exYsUUEBCgp556SomJiZnGHD58WI8//rj8/f0VEhKifv366erVq5nGrF69WnXq1JGvr6/Kly+vmTNnXpfPhAkTFBkZKT8/PzVo0ECbNm1y+mfG3xs2bJhsNpt69erliFEH+cfRo0f1/PPPq1ixYipQoICqV6+uzZs3O44bhqFBgwapZMmSKlCggKKjo7V3795M5zh9+rTatWunwMBABQUFqXPnzjp//nymMTt27FDjxo3l5+en8PBwvfvuu9flsnDhQlWqVEl+fn6qXr26li5das2HxnXS0tI0cOBAlS1bVgUKFFC5cuX01ltv6dq1WakF97NmzRo1b95cYWFhstlsWrx4cabjuek7z04u+OduVgupqanq37+/qlevroIFCyosLEwdOnTQsWPHMp2DWnAPf/fvwrVeeeUV2Ww2jRkzJlM8X9SCAdxAkyZNjBkzZhg7d+40tm3bZjz22GNGRESEcf78eceYV155xQgPDzdWrFhhbN682bj77ruNhg0bOo5fvXrVqFatmhEdHW1s3brVWLp0qVG8eHFjwIABjjG//fab4e/vb8THxxu//PKLMW7cOMPT09NYtmyZY8y8efMMHx8fY/r06cbPP/9sdOnSxQgKCjISExNz5g8DhmEYxqZNm4zIyEijRo0aRlxcnCNOHeQPp0+fNsqUKWO88MILxvfff2/89ttvxldffWXs27fPMWbYsGFG4cKFjcWLFxvbt283WrRoYZQtW9a4dOmSY0zTpk2NmjVrGhs3bjTWrl1rlC9f3mjbtq3jeFJSkhEaGmq0a9fO2Llzp/Hxxx8bBQoUMD744APHmPXr1xuenp7Gu+++a/zyyy/GG2+8YXh7exs//fRTzvxh5HNvv/22UaxYMeOLL74wDhw4YCxcuNAICAgwxo4d6xhDLbifpUuXGq+//rqxaNEiQ5Lx6aefZjqem77z7OSCf+5mtXD27FkjOjramD9/vvHrr78aGzZsMOrXr2/UrVs30zmoBffwd/8upFu0aJFRs2ZNIywszBg9enSmY/mhFmi6kW0nTpwwJBnffvutYRjmP6re3t7GwoULHWN27dplSDI2bNhgGIb5F9HDw8NISEhwjJk0aZIRGBhoXLlyxTAMw3j11VeNqlWrZpqrTZs2RpMmTRyv69evb3Tv3t3xOi0tzQgLCzOGDh3q/A+KLJ07d86oUKGCsXz5cuO+++5zNN3UQf7Rv39/o1GjRjc8brfbjRIlShgjRoxwxM6ePWv4+voaH3/8sWEYhvHLL78YkowffvjBMeZ///ufYbPZjKNHjxqGYRgTJ040ihQp4qiN9LnvvPNOx+vWrVsbjz/+eKb5GzRoYHTt2vX2PiSy5fHHHzdefPHFTLEnn3zSaNeunWEY1EJ+8NcfrnPTd56dXOA8N2u00m3atMmQZBw6dMgwDGrBXd2oFo4cOWKUKlXK2Llzp1GmTJlMTXd+qQVuL0e2JSUlSZKKFi0qSdqyZYtSU1MVHR3tGFOpUiVFRERow4YNkqQNGzaoevXqCg0NdYxp0qSJkpOT9fPPPzvGXHuO9DHp50hJSdGWLVsyjfHw8FB0dLRjDKzXvXt3Pf7449d9V9RB/rFkyRLVq1dPzzzzjEJCQlS7dm1NnTrVcfzAgQNKSEjI9B0VLlxYDRo0yFQLQUFBqlevnmNMdHS0PDw89P333zvG3HvvvfLx8XGMadKkiXbv3q0zZ844xtysXmCthg0basWKFdqzZ48kafv27Vq3bp0effRRSdRCfpSbvvPs5IKclZSUJJvNpqCgIEnUQn5it9vVvn179evXT1WrVr3ueH6pBZpuZIvdblevXr10zz33qFq1apKkhIQE+fj4OP4BTRcaGqqEhATHmGsbrfTj6cduNiY5OVmXLl3SqVOnlJaWluWY9HPAWvPmzdOPP/6ooUOHXneMOsg/fvvtN02aNEkVKlTQV199pZiYGPXs2VOzZs2SlPFd3uw7SkhIUEhISKbjXl5eKlq0qFPqhVrIGa+99pqeffZZVapUSd7e3qpdu7Z69eqldu3aSaIW8qPc9J1nJxfknMuXL6t///5q27atAgMDJVEL+cnw4cPl5eWlnj17Znk8v9SCl+UzwC10795dO3fu1Lp161ydCnLY77//rri4OC1fvlx+fn6uTgcuZLfbVa9ePb3zzjuSpNq1a2vnzp2aPHmyOnbs6OLskJMWLFigOXPmaO7cuapataq2bdumXr16KSwsjFoA4JCamqrWrVvLMAxNmjTJ1ekgh23ZskVjx47Vjz/+KJvN5up0XIor3fhbsbGx+uKLL7Rq1SqVLl3aES9RooRSUlJ09uzZTOMTExNVokQJx5i/rmKd/vrvxgQGBqpAgQIqXry4PD09sxyTfg5YZ8uWLTpx4oTq1KkjLy8veXl56dtvv9X7778vLy8vhYaGUgf5RMmSJVWlSpVMscqVK+vw4cOSMr7Lm31HJUqU0IkTJzIdv3r1qk6fPu2UeqEWcka/fv0cV7urV6+u9u3bq3fv3o67YaiF/Cc3fefZyQXWS2+4Dx06pOXLlzuuckvUQn6xdu1anThxQhEREY6fIQ8dOqQ+ffooMjJSUv6pBZpu3JBhGIqNjdWnn36qlStXqmzZspmO161bV97e3lqxYoUjtnv3bh0+fFhRUVGSpKioKP3000+Z/jKl/8Ob/sN7VFRUpnOkj0k/h4+Pj+rWrZtpjN1u14oVKxxjYJ2HHnpIP/30k7Zt2+b4Va9ePbVr187xe+ogf7jnnnuu2zZwz549KlOmjCSpbNmyKlGiRKbvKDk5Wd9//32mWjh79qy2bNniGLNy5UrZ7XY1aNDAMWbNmjVKTU11jFm+fLnuvPNOFSlSxDHmZvUCa128eFEeHpl/hPD09JTdbpdELeRHuek7z04usFZ6w71371598803KlasWKbj1EL+0L59e+3YsSPTz5BhYWHq16+fvvrqK0n5qBYsX6oNeVZMTIxRuHBhY/Xq1cbx48cdvy5evOgY88orrxgRERHGypUrjc2bNxtRUVFGVFSU43j6VlGPPPKIsW3bNmPZsmVGcHBwlltF9evXz9i1a5cxYcKELLeK8vX1NWbOnGn88ssvxssvv2wEBQVlWg0bOefa1csNgzrILzZt2mR4eXkZb7/9trF3715jzpw5hr+/v/HRRx85xgwbNswICgoyPvvsM2PHjh1Gy5Yts9wyqHbt2sb3339vrFu3zqhQoUKmrUHOnj1rhIaGGu3btzd27txpzJs3z/D3979uaxAvLy/jvffeM3bt2mUMHjyYbaJyUMeOHY1SpUo5tgxbtGiRUbx4cePVV191jKEW3M+5c+eMrVu3Glu3bjUkGaNGjTK2bt3qWJE6N33n2ckF/9zNaiElJcVo0aKFUbp0aWPbtm2Zfoa8dvVpasE9/N2/C3/119XLDSN/1AJNN25IUpa/ZsyY4Rhz6dIlo1u3bkaRIkUMf39/44knnjCOHz+e6TwHDx40Hn30UaNAgQJG8eLFjT59+hipqamZxqxatcqoVauW4ePjY9xxxx2Z5kg3btw4IyIiwvDx8THq169vbNy40YqPjWz4a9NNHeQfn3/+uVGtWjXD19fXqFSpkjFlypRMx+12uzFw4EAjNDTU8PX1NR566CFj9+7dmcb88ccfRtu2bY2AgAAjMDDQ6NSpk3Hu3LlMY7Zv3240atTI8PX1NUqVKmUMGzbsulwWLFhgVKxY0fDx8TGqVq1qfPnll87/wMhScnKyERcXZ0RERBh+fn7GHXfcYbz++uuZfqCmFtzPqlWrsvy5oGPHjoZh5K7vPDu54J+7WS0cOHDghj9Drlq1ynEOasE9/N2/C3+VVdOdH2rBZhiGYf31dAAAAAAA8h+e6QYAAAAAwCI03QAAAAAAWISmGwAAAAAAi9B0AwAAAABgEZpuAAAAAAAsQtMNAAAAAIBFaLoBAAAAALAITTcAAAAAABah6QYAADd1//33q1evXq5OAwCAPImmGwAAN9a8eXM1bdo0y2Nr166VzWbTjh07cjgrAADyD5puAADcWOfOnbV8+XIdOXLkumMzZsxQvXr1VKNGDRdkBgBA/kDTDQCAG2vWrJmCg4M1c+bMTPHz589r4cKFatWqldq2batSpUrJ399f1atX18cff3zTc9psNi1evDhTLCgoKNMcv//+u1q3bq2goCAVLVpULVu21MGDB53zoQAAyENougEAcGNeXl7q0KGDZs6cKcMwHPGFCxcqLS1Nzz//vOrWrasvv/xSO3fu1Msvv6z27dtr06ZN/3jO1NRUNWnSRIUKFdLatWu1fv16BQQEqGnTpkpJSXHGxwIAIM+g6QYAwM29+OKL2r9/v7799ltHbMaMGXrqqadUpkwZ9e3bV7Vq1dIdd9yhHj16qGnTplqwYME/nm/+/Pmy2+2aNm2aqlevrsqVK2vGjBk6fPiwVq9e7YRPBABA3kHTDQCAm6tUqZIaNmyo6dOnS5L27duntWvXqnPnzkpLS9Nbb72l6tWrq2jRogoICNBXX32lw4cP/+P5tm/frn379qlQoUIKCAhQQECAihYtqsuXL2v//v3O+lgAAOQJXq5OAAAAWK9z587q0aOHJkyYoBkzZqhcuXK67777NHz4cI0dO1ZjxoxR9erVVbBgQfXq1eumt4HbbLZMt6pL5i3l6c6fP6+6detqzpw51703ODjYeR8KAIA8gKYbAIB8oHXr1oqLi9PcuXM1e/ZsxcTEyGazaf369WrZsqWef/55SZLdbteePXtUpUqVG54rODhYx48fd7zeu3evLl686Hhdp04dzZ8/XyEhIQoMDLTuQwEAkAdwezkAAPlAQECA2rRpowEDBuj48eN64YUXJEkVKlTQ8uXL9d1332nXrl3q2rWrEhMTb3quBx98UOPHj9fWrVu1efNmvfLKK/L29nYcb9eunYoXL66WLVtq7dq1OnDggFavXq2ePXtmuXUZAADujKYbAIB8onPnzjpz5oyaNGmisLAwSdIbb7yhOnXqqEmTJrr//vtVokQJtWrV6qbnGTlypMLDw9W4cWM999xz6tu3r/z9/R3H/f39tWbNGkVEROjJJ59U5cqV1blzZ12+fJkr3wCAfMdm/PWhLAAAAAAA4BRc6QYAAAAAwCI03QAAAAAAWISmGwAAAAAAi9B0AwAAAABgEZpuAAAAAAAsQtMNAAAAAIBFaLoBAAAAALAITTcAAAAAABah6QYAAAAAwCI03QAAAAAAWISmGwAAAAAAi/w//UytJdM5q70AAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y)" ] diff --git a/src/olive_oil_train_dataset/create_train_dataset.py b/src/olive_oil_train_dataset/create_train_dataset.py index 735973f..b3cd45a 100755 --- a/src/olive_oil_train_dataset/create_train_dataset.py +++ b/src/olive_oil_train_dataset/create_train_dataset.py @@ -434,7 +434,7 @@ def parse_arguments(): parser.add_argument( '--random-seed', type=int, - default=42, + default=None, help='Seed per la riproducibilità dei risultati' ) @@ -484,7 +484,7 @@ if __name__ == "__main__": # Carica dati try: - weather_data = pd.read_parquet('./sources/weather_data_complete.parquet') + weather_data = pd.read_parquet('./sources/weather_data_solarenergy.parquet') olive_varieties = pd.read_parquet('./sources/olive_varieties.parquet') except Exception as e: print(f"Errore nel caricamento dei dati: {str(e)}")