{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "8adcbe0819b88578", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", "Hit:2 http://security.ubuntu.com/ubuntu jammy-security InRelease\n", "Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease\n", "Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\n", "Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\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 (18.0.0)\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 (2024.11.0)\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 (3.0.2)\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", "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": [ "from opt_einsum.paths import branch_1\n", "!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": 11, "id": "7a813e3cbca057b7", "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras.layers import Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, LayerNormalization, Input, Activation, Lambda, Bidirectional, Add, MaxPooling1D, SpatialDropout1D, GlobalAveragePooling1D, \\\n", " GlobalMaxPooling1D, Concatenate\n", "from tensorflow.keras import regularizers\n", "from tensorflow.keras.models import Model\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import RobustScaler\n", "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", "from tensorflow.keras.optimizers import AdamW\n", "import json\n", "from datetime import datetime\n", "import matplotlib.pyplot as plt\n", "from tensorflow.keras.utils import plot_model\n", "import tensorflow_addons as tfa\n", "import os\n", "import joblib\n", "import seaborn as sns\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, confusion_matrix\n", "from scipy import stats\n", "\n", "folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", "\n", "random_state_value = None" ] }, { "cell_type": "code", "execution_count": 3, "id": "b3f525e19f78a1da", "metadata": {}, "outputs": [], "source": [ "def get_season(date):\n", " month = date.month\n", " day = date.day\n", " if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n", " return 'Winter'\n", " elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n", " return 'Spring'\n", " elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n", " return 'Summer'\n", " elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n", " return 'Autumn'\n", " else:\n", " return 'Unknown'\n", "\n", "\n", "def get_time_period(hour):\n", " if 5 <= hour < 12:\n", " return 'Morning'\n", " elif 12 <= hour < 17:\n", " return 'Afternoon'\n", " elif 17 <= hour < 21:\n", " return 'Evening'\n", " else:\n", " return 'Night'\n", "\n", "\n", "def add_time_features(df):\n", " \"\"\"\n", " Add time-based features to the DataFrame.\n", " Works with both 'datetime' as column or index.\n", " \"\"\"\n", " # Se datetime è l'indice, lo usiamo direttamente\n", " if isinstance(df.index, pd.DatetimeIndex):\n", " datetime_col = df.index\n", " else:\n", " # Se datetime è una colonna, la convertiamo\n", " if 'datetime' in df.columns:\n", " datetime_col = pd.to_datetime(df['datetime'])\n", " else:\n", " raise ValueError(\"No datetime column or index found in DataFrame\")\n", "\n", " # Creazione delle feature temporali\n", " df['timestamp'] = datetime_col.astype(np.int64) // 10 ** 9\n", " df['year'] = datetime_col.year\n", " df['month'] = datetime_col.month\n", " df['day'] = datetime_col.day\n", " df['hour'] = datetime_col.hour\n", " df['minute'] = datetime_col.minute\n", " df['hour_sin'] = np.sin(datetime_col.hour * (2 * np.pi / 24))\n", " df['hour_cos'] = np.cos(datetime_col.hour * (2 * np.pi / 24))\n", " df['day_of_week'] = datetime_col.dayofweek\n", " df['day_of_year'] = datetime_col.dayofyear\n", " df['week_of_year'] = datetime_col.isocalendar().week.astype(int)\n", " df['quarter'] = datetime_col.quarter\n", " df['is_month_end'] = datetime_col.is_month_end.astype(int)\n", " df['is_quarter_end'] = datetime_col.is_quarter_end.astype(int)\n", " df['is_year_end'] = datetime_col.is_year_end.astype(int)\n", " df['month_sin'] = np.sin(datetime_col.month * (2 * np.pi / 12))\n", " df['month_cos'] = np.cos(datetime_col.month * (2 * np.pi / 12))\n", " df['day_of_year_sin'] = np.sin(datetime_col.dayofyear * (2 * np.pi / 365.25))\n", " df['day_of_year_cos'] = np.cos(datetime_col.dayofyear * (2 * np.pi / 365.25))\n", " df['season'] = datetime_col.map(get_season)\n", " df['time_period'] = datetime_col.hour.map(get_time_period)\n", "\n", " return df\n", "\n", "\n", "def add_solar_features(df):\n", " # Solar angle calculation\n", " df['solar_angle'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n", "\n", " # Interactions between relevant features\n", " df['cloud_temp_interaction'] = df['cloudcover'] * df['temp']\n", " df['visibility_cloud_interaction'] = df['visibility'] * (100 - df['cloudcover'])\n", "\n", " # Derived features\n", " df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n", " df['temp_gradient'] = df['temp'] - df['tempmin']\n", "\n", " return df\n", "\n", "\n", "def add_solar_specific_features(df):\n", " \"\"\"\n", " Aggiunge feature specifiche per la predizione della radiazione solare\n", " combinando caratteristiche astronomiche e meteorologiche\n", " \"\"\"\n", " # Caratteristiche astronomiche\n", " df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n", " df['solar_noon'] = 12 - df['hour']\n", " df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n", "\n", " # Angolo solare teorico\n", " df['solar_angle'] = np.sin(df['hour_sin']) * np.sin(df['day_of_year_sin'])\n", "\n", " # Interazioni con condizioni atmosferiche\n", " df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n", " df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n", " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", "\n", " # Indici di chiarezza e trasmissione\n", " df['clearness_index'] = (100 - df['cloudcover']) * df['visibility'] / 10000\n", " df['atmospheric_attenuation'] = (df['pressure'] / 1013.25) * (1 - (df['humidity'] / 100) * 0.6)\n", "\n", " # Radiazione teorica e attenuazione\n", " df['theoretical_radiation'] = df['solar_angle'].clip(0, 1) * 1000\n", " df['expected_radiation'] = df['theoretical_radiation'] * df['clearness_index']\n", "\n", " # Rolling features\n", " df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n", " df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n", " df['uv_rolling_12h'] = df['uvindex'].rolling(window=12).mean()\n", "\n", " # Interazioni temperatura-radiazione\n", " df['temp_radiation_potential'] = df['temp'] * df['solar_elevation']\n", "\n", " return df\n", "\n", "\n", "def add_radiation_energy_features(df):\n", " \"\"\"Adds specific features based on solarenergy and uvindex\"\"\"\n", "\n", " # Assicuriamoci che l'indice sia di tipo datetime\n", " if not isinstance(df.index, pd.DatetimeIndex):\n", " df.index = pd.to_datetime(df['datetime'])\n", "\n", " # Solar energy to UV ratio (independent from solarradiation)\n", " df['energy_uv_ratio'] = df['solarenergy'] / (df['uvindex'] + 1e-6)\n", "\n", " # Time aggregations\n", " # Moving averages\n", " windows = [3, 6, 12, 24] # hours\n", " for w in windows:\n", " df[f'energy_rolling_mean_{w}h'] = df['solarenergy'].rolling(window=w).mean()\n", " df[f'uv_rolling_mean_{w}h'] = df['uvindex'].rolling(window=w).mean()\n", "\n", " # Daily aggregations utilizzando datetime\n", " df['energy_daily_sum'] = df.groupby(df.index.date)['solarenergy'].transform('sum')\n", " df['uv_daily_max'] = df.groupby(df.index.date)['uvindex'].transform('max')\n", "\n", " # Changes\n", " df['energy_change'] = df['solarenergy'].diff()\n", " df['uv_change'] = df['uvindex'].diff()\n", "\n", " # Lag features\n", " lags = [1, 2, 3, 6, 12, 24] # hours\n", " for lag in lags:\n", " df[f'energy_lag_{lag}h'] = df['solarenergy'].shift(lag)\n", " df[f'uv_lag_{lag}h'] = df['uvindex'].shift(lag)\n", "\n", " # Peak indicators\n", " df['is_energy_peak'] = (df['solarenergy'] > df['energy_rolling_mean_6h'] * 1.2).astype(int)\n", " df['is_uv_peak'] = (df['uvindex'] > df['uv_rolling_mean_6h'] * 1.2).astype(int)\n", "\n", " # Aggiungiamo alcune metriche di volatilità\n", " df['energy_volatility'] = df['energy_change'].rolling(window=24).std()\n", " df['uv_volatility'] = df['uv_change'].rolling(window=24).std()\n", "\n", " # Indice di intensità solare composito\n", " df['solar_intensity_index'] = (df['solarenergy'] * df['uvindex']) / (df['cloudcover'] + 1e-6)\n", "\n", " # Interazioni\n", " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", " df['energy_temp_interaction'] = df['solarenergy'] * df['temp']\n", "\n", " return df\n", "\n", "\n", "def add_advanced_features(df):\n", " \"\"\"\n", " Add all advanced features to the DataFrame\n", " Assumes df has a DatetimeIndex\n", " \"\"\"\n", " # Verifichiamo che abbiamo un DatetimeIndex\n", " if not isinstance(df.index, pd.DatetimeIndex):\n", " raise ValueError(\"DataFrame must have a DatetimeIndex\")\n", "\n", " # Existing features\n", " df = add_time_features(df)\n", " df = add_solar_features(df)\n", " df = add_solar_specific_features(df)\n", " df = add_radiation_energy_features(df)\n", "\n", " # Weather variable interactions\n", " df['temp_humidity'] = df['temp'] * df['humidity']\n", " df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n", " df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n", "\n", " # Derived features\n", " df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n", " df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n", " df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n", "\n", " # Rolling means\n", " df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n", " df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n", "\n", " # Lag features\n", " df['temp_1h_lag'] = df['temp'].shift(1)\n", " df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n", " df['humidity_1h_lag'] = df['humidity'].shift(1)\n", "\n", " # Extreme conditions indicator\n", " df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n", " (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n", "\n", " # One-hot encoding for categorical features\n", " df = pd.get_dummies(df, columns=['season', 'time_period'])\n", "\n", " return df\n", "\n", "\n", "def prepare_advanced_data(df):\n", " \"\"\"\n", " Prepare data for advanced modeling with proper datetime handling\n", " \"\"\"\n", " # Assicuriamoci che abbiamo una copia del DataFrame\n", " df = df.copy()\n", "\n", " # Verifichiamo se datetime è già l'indice\n", " if not isinstance(df.index, pd.DatetimeIndex):\n", " if 'datetime' in df.columns:\n", " df['datetime'] = pd.to_datetime(df['datetime'])\n", " df.set_index('datetime', inplace=True)\n", " else:\n", " raise ValueError(\"No datetime column or index found in DataFrame\")\n", "\n", " # Ordiniamo il DataFrame per datetime\n", " df = df.sort_index()\n", "\n", " # Apply feature engineering functions\n", " df = add_advanced_features(df)\n", "\n", " #all_columns = list(df.columns)\n", " #print(all_columns)\n", "\n", " features = {\n", " # Primary Features (strong direct correlation)\n", " 'primary_features': [\n", " 'uvindex', # Direct radiation indicator\n", " 'cloudcover', # Cloud coverage\n", " 'visibility', # Atmospheric transparency\n", " 'temp', # Temperature\n", " 'pressure', # Atmospheric pressure\n", " 'humidity', # Humidity\n", " ],\n", "\n", " # Astronomical and Temporal Features\n", " 'astronomical_features': [\n", " 'solar_elevation', # Solar elevation\n", " 'solar_angle', # Solar angle\n", " 'day_length', # Day length\n", " 'hour_sin', # Daily cycle\n", " 'hour_cos',\n", " 'day_of_year_sin', # Annual cycle\n", " 'day_of_year_cos',\n", " 'month_sin', # Monthly cycle\n", " 'month_cos',\n", " ],\n", "\n", " # Key Indices and Interactions\n", " 'key_interactions': [\n", " 'clear_sky_index', # Clear sky index\n", " 'atmospheric_attenuation', # Atmospheric attenuation\n", " 'theoretical_radiation', # Theoretical radiation\n", " 'expected_radiation', # Expected radiation\n", " 'cloud_elevation', # Cloud-elevation interaction\n", " 'visibility_elevation', # Visibility-elevation interaction\n", " 'uv_cloud_interaction', # UV-cloud interaction\n", " 'temp_radiation_potential', # Temperature-radiation potential\n", " ],\n", "\n", " # Rolling Features (temporal trends)\n", " 'rolling_features': [\n", " 'cloud_rolling_12h', # Cloud coverage moving average\n", " 'temp_rolling_12h', # Temperature moving average\n", " 'uv_rolling_12h', # UV moving average\n", " 'cloudcover_rolling_mean_6h',\n", " 'temp_rolling_mean_6h',\n", " ],\n", "\n", " # Lag Features (most recent)\n", " 'lag_features': [\n", " 'temp_1h_lag', # 1-hour temperature lag\n", " 'cloudcover_1h_lag', # 1-hour cloud coverage lag\n", " 'humidity_1h_lag', # 1-hour humidity lag\n", " 'uv_lag_1h', # 1-hour UV lag\n", " ],\n", "\n", " # Categorical Features\n", " 'categorical_features': [\n", " 'season_Spring', # Seasons\n", " 'season_Summer',\n", " 'season_Autumn',\n", " 'season_Winter',\n", " 'time_period_Morning', # Time periods\n", " 'time_period_Afternoon',\n", " 'time_period_Evening',\n", " 'time_period_Night',\n", " ]\n", " }\n", "\n", " final_features = [feature for group in features.values() for feature in group]\n", "\n", " # Handle missing values\n", " target_variables = ['solarradiation', 'solarenergy', 'uvindex']\n", " for column in final_features + target_variables:\n", " if column in df.columns:\n", " df[column] = df[column].interpolate(method='time')\n", " df.fillna(0, inplace=True)\n", "\n", " # Temporal split\n", " data_after_2010 = df[df['year'] >= 2010].copy()\n", " data_before_2010 = df[df['year'] < 2010].copy()\n", "\n", " X = data_after_2010[final_features]\n", " y = data_after_2010['solarradiation']\n", " X_to_predict = data_before_2010[final_features]\n", "\n", " # Train-test split\n", " X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=0.2, random_state=random_state_value, shuffle=False\n", " )\n", "\n", " # Scaling\n", " scaler_X = RobustScaler()\n", " X_train_scaled = scaler_X.fit_transform(X_train)\n", " X_test_scaled = scaler_X.transform(X_test)\n", " X_to_predict_scaled = scaler_X.transform(X_to_predict)\n", "\n", " scaler_y = RobustScaler()\n", " y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1))\n", " y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1))\n", "\n", " # Print info about selected features\n", " print(\"\\nSelected features:\")\n", " print(f\"Number of features: {len(final_features)}\")\n", " print(\"Features list:\", final_features)\n", "\n", " return X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, final_features, X_to_predict_scaled\n", "\n", "\n", "def create_sequence_data(X, sequence_length=24):\n", " \"\"\"\n", " Converts data into sequences for LSTM input\n", " sequence_length represents how many previous hours to consider\n", " \"\"\"\n", " sequences = []\n", " for i in range(len(X) - sequence_length + 1):\n", " sequences.append(X[i:i + sequence_length])\n", " return np.array(sequences)\n", "\n", "\n", "def prepare_hybrid_data(df):\n", " X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, features, X_to_predict_scaled = prepare_advanced_data(df)\n", "\n", " # Convert data into sequences\n", " sequence_length = 24 # 24 hours of historical data\n", "\n", " X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n", " X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n", "\n", " # Adjust y by removing the first (sequence_length-1) elements\n", " y_train = y_train_scaled[sequence_length - 1:]\n", " y_test = y_test_scaled[sequence_length - 1:]\n", "\n", " X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n", "\n", " return X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq" ] }, { "cell_type": "code", "execution_count": 4, "id": "9dff3259-b376-4cfc-89d8-ab2ea18aaa5e", "metadata": { "jupyter": { "is_executing": true } }, "outputs": [], "source": [ "def create_residual_lstm_layer(x, units, dropout_rate, l2_reg=0.01, return_sequences=True, survival_probability=0.8):\n", " \"\"\"\n", " Creates a bidirectional LSTM layer with residual connections and regularization.\n", "\n", " Parameters:\n", " x: Input tensor\n", " units: Number of LSTM units\n", " dropout_rate: Dropout rate for regularization\n", " l2_reg: L2 regularization factor\n", " return_sequences: Whether to return sequences or just the last output\n", " survival_probability: Probability of layer survival for stochastic depth\n", " \"\"\"\n", " residual = x\n", " x = Bidirectional(LSTM(units, return_sequences=return_sequences, kernel_regularizer=regularizers.l2(l2_reg)))(x)\n", " x = LayerNormalization()(x)\n", " x = Dropout(dropout_rate)(x)\n", "\n", " if return_sequences:\n", " if int(residual.shape[-1]) != 2 * units:\n", " residual = Dense(2 * units, activation='linear')(residual)\n", " x = tfa.layers.StochasticDepth(survival_probability)([x, residual])\n", " return x\n", "\n", "\n", "def attention_block(x, units, num_heads=8, survival_probability=0.8):\n", " \"\"\"\n", " Creates a multi-head attention block with residual connections.\n", "\n", " Parameters:\n", " x: Input tensor\n", " units: Dimension of the key space\n", " num_heads: Number of attention heads\n", " survival_probability: Probability of layer survival for stochastic depth\n", " \"\"\"\n", " attention = MultiHeadAttention(num_heads=num_heads, key_dim=units)(x, x)\n", " x = tfa.layers.StochasticDepth(survival_probability)([x, attention])\n", " x = LayerNormalization()(x)\n", " return x\n", "\n", "\n", "def create_solarradiation_model(input_shape, folder_name, l2_lambda=0.005, min_output=0, max_output=1):\n", " \"\"\"\n", " Creates a deep learning model for solar radiation prediction using LSTM and attention mechanisms.\n", "\n", " Parameters:\n", " input_shape: Shape of input data\n", " folder_name: Directory to save model architecture visualization\n", " l2_lambda: L2 regularization factor\n", " \"\"\"\n", " inputs = Input(shape=input_shape)\n", "\n", " # Progressive hyperparameters for model architecture\n", " survival_probs = [0.9, 0.8, 0.7, 0.6] # Decreasing survival probabilities for deeper layers\n", " attention_survival_probs = [0.85, 0.75, 0.65, 0.55] # Survival probabilities for attention blocks\n", " lstm_units = [256, 128, 64, 32] # Decreasing number of units for LSTM layers\n", " dropout_rates = [0.4, 0.3, 0.2, 0.2] # Decreasing dropout rates\n", " attention_heads = [32, 24, 16, 8] # Decreasing number of attention heads\n", "\n", " lstm_blocks = 4\n", " # Main network architecture\n", " x = inputs\n", " for i in range(lstm_blocks):\n", " # LSTM layer with residual connections\n", " x = create_residual_lstm_layer(\n", " x,\n", " units=lstm_units[i],\n", " dropout_rate=dropout_rates[i],\n", " l2_reg=l2_lambda,\n", " return_sequences=True,\n", " survival_probability=survival_probs[i]\n", " )\n", " # Attention block\n", " x = attention_block(\n", " x,\n", " units=lstm_units[i],\n", " num_heads=attention_heads[i],\n", " survival_probability=attention_survival_probs[i]\n", " )\n", " if i < lstm_blocks - 1: # No pooling after last LSTM layer\n", " x = MaxPooling1D()(x)\n", "\n", " # Final LSTM layer for sequence aggregation\n", " x = create_residual_lstm_layer(\n", " x,\n", " units=32,\n", " dropout_rate=0.1,\n", " l2_reg=l2_lambda,\n", " return_sequences=False,\n", " survival_probability=0.6\n", " )\n", "\n", " # Dense layers for final prediction\n", " dense_units = [128, 64, 32]\n", " dense_dropout = [0.2, 0.1, 0.05]\n", "\n", " for units, dropout in zip(dense_units, dense_dropout):\n", " x = Dense(units, kernel_regularizer=regularizers.l2(l2_lambda))(x)\n", " x = BatchNormalization()(x)\n", " x = Activation('swish')(x)\n", " x = Dropout(dropout)(x)\n", "\n", " # Output layer with value clipping\n", " outputs = Dense(1)(x)\n", " outputs = Lambda(lambda x: tf.clip_by_value(x, min_output, max_output))(outputs)\n", "\n", " # Model compilation\n", " model = Model(inputs=inputs, outputs=outputs, name=\"SolarRadiationModel\")\n", "\n", " # Improved loss function\n", " def hybrid_focal_loss(y_true, y_pred):\n", " # MSE with focal weighting\n", " mse = tf.square(y_true - y_pred)\n", " error_ratio = tf.abs(y_true - y_pred) / (tf.abs(y_true) + 1.0)\n", " focal_weight = tf.pow(error_ratio, 2)\n", " weighted_mse = focal_weight * mse\n", "\n", " # MAE component\n", " mae = tf.abs(y_true - y_pred)\n", "\n", " return tf.reduce_mean(0.7 * weighted_mse + 0.3 * mae)\n", "\n", " # Custom metrics\n", " def rmse(y_true, y_pred):\n", " return tf.sqrt(tf.reduce_mean(tf.square(y_true - y_pred)))\n", "\n", " def custom_mape(y_true, y_pred):\n", " epsilon = 1e-7\n", " diff = tf.abs((y_true - y_pred) / (y_true + epsilon))\n", " diff = tf.clip_by_value(diff, 0, 1)\n", " return tf.reduce_mean(diff) * 100\n", "\n", " # Optimizer\n", " optimizer = AdamW(\n", " learning_rate=0.0003,\n", " beta_1=0.9,\n", " beta_2=0.999,\n", " epsilon=1e-7,\n", " weight_decay=0.001,\n", " amsgrad=True\n", " )\n", "\n", " model.compile(\n", " optimizer=optimizer,\n", " loss=hybrid_focal_loss,\n", " metrics=['mse', 'mae', rmse, custom_mape]\n", " )\n", "\n", " model.summary()\n", "\n", " plot_model(model,\n", " to_file=f'{folder_name}_model_architecture.png',\n", " show_shapes=True,\n", " show_layer_names=True,\n", " dpi=150,\n", " show_layer_activations=True)\n", "\n", " return model\n", "\n", "\n", "def evaluate_solarradiation_predictions(y_true, y_pred, hour=None, folder_name=None):\n", " \"\"\"\n", " Comprehensive evaluation of solar radiation predictions with detailed analysis and visualizations.\n", "\n", " Parameters:\n", " -----------\n", " y_true : array-like\n", " Actual solar radiation values (W/m²)\n", " y_pred : array-like\n", " Predicted solar radiation values (W/m²)\n", " hour : array-like, optional\n", " Array of hours corresponding to predictions, for temporal analysis\n", " folder_name : str, optional\n", " Directory to save analysis plots\n", "\n", " Returns:\n", " --------\n", " dict\n", " Dictionary containing all calculated metrics\n", " \"\"\"\n", "\n", " # Data preparation\n", " y_true = np.array(y_true).ravel()\n", " y_pred = np.array(y_pred).ravel()\n", " errors = y_pred - y_true\n", "\n", " # Basic metrics calculation\n", " mae_raw = mean_absolute_error(y_true, y_pred)\n", " rmse_raw = np.sqrt(mean_squared_error(y_true, y_pred))\n", " r2_raw = r2_score(y_true, y_pred)\n", " \n", " # Corrected MAPE calculation\n", " mask = y_true > 10 # Consider only values above 10 W/m²\n", " if np.any(mask):\n", " mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100\n", " else:\n", " mape = np.nan\n", "\n", " # Corrected error margin accuracy\n", " within_5_percent = np.mean(np.abs(errors) <= 5) * 100 # Within 5 W/m²\n", " within_10_percent = np.mean(np.abs(errors) <= 10) * 100 # Within 10 W/m²\n", " within_20_percent = np.mean(np.abs(errors) <= 20) * 100 # Within 20 W/m²\n", "\n", " # Radiation level classification\n", " def get_radiation_level(value):\n", " if value <= 200:\n", " return 'Very Low'\n", " elif value <= 400:\n", " return 'Low'\n", " elif value <= 600:\n", " return 'Moderate'\n", " elif value <= 800:\n", " return 'High'\n", " elif value <= 1000:\n", " return 'Very High'\n", " else:\n", " return 'Extreme'\n", "\n", " # Calculate radiation levels\n", " y_true_levels = [get_radiation_level(v) for v in y_true]\n", " y_pred_levels = [get_radiation_level(v) for v in y_pred]\n", " level_accuracy = np.mean([t == p for t, p in zip(y_true_levels, y_pred_levels)])\n", "\n", " # Print main metrics\n", " print(\"\\nSolar Radiation Prediction Metrics:\")\n", " print(\"\\nAbsolute Metrics:\")\n", " print(f\"MAE: {mae_raw:.2f} W/m²\")\n", " print(f\"RMSE: {rmse_raw:.2f} W/m²\")\n", " print(f\"R² Score: {r2_raw:.3f}\")\n", " print(f\"MAPE: {mape:.2f}%\" if not np.isnan(mape) else \"MAPE: N/A (insufficient data)\")\n", "\n", " print(\"\\nAccuracy Metrics:\")\n", " print(f\"Within ±5 W/m²: {within_5_percent:.1f}%\")\n", " print(f\"Within ±10 W/m²: {within_10_percent:.1f}%\")\n", " print(f\"Within ±20 W/m²: {within_20_percent:.1f}%\")\n", "\n", " print(\"\\nLevel Accuracy:\")\n", " print(f\"Level Accuracy: {level_accuracy * 100:.1f}%\")\n", "\n", " # Confusion matrix for radiation levels\n", " cm = confusion_matrix(y_true_levels, y_pred_levels)\n", " print(\"\\nConfusion Matrix for Radiation Levels:\")\n", " cm_df = pd.DataFrame(\n", " cm,\n", " columns=['Very Low', 'Low', 'Moderate', 'High', 'Very High', 'Extreme'],\n", " index=['Very Low', 'Low', 'Moderate', 'High', 'Very High', 'Extreme']\n", " )\n", " print(cm_df)\n", "\n", " # Time period analysis\n", " if hour is not None:\n", " day_periods = {\n", " 'Morning (5-11)': (5, 11),\n", " 'Noon (11-13)': (11, 13),\n", " 'Afternoon (13-17)': (13, 17),\n", " 'Evening (17-21)': (17, 21),\n", " 'Night (21-5)': (21, 5)\n", " }\n", "\n", " print(\"\\nAnalysis by Time Period:\")\n", " for period, (start, end) in day_periods.items():\n", " if start < end:\n", " mask = (hour >= start) & (hour < end)\n", " else:\n", " mask = (hour >= start) | (hour < end)\n", "\n", " if np.any(mask):\n", " period_mae = mean_absolute_error(y_true[mask], y_pred[mask])\n", " \n", " # Corrected period MAPE calculation\n", " period_mask = mask & (y_true > 10)\n", " if np.any(period_mask):\n", " period_mape = np.mean(np.abs((y_true[period_mask] - y_pred[period_mask]) / y_true[period_mask])) * 100\n", " print(f\"\\n{period}:\")\n", " print(f\"MAE: {period_mae:.2f} W/m²\")\n", " print(f\"MAPE: {period_mape:.2f}%\")\n", " else:\n", " print(f\"\\n{period}:\")\n", " print(f\"MAE: {period_mae:.2f} W/m²\")\n", " print(\"MAPE: N/A (insufficient data)\")\n", "\n", " # Visualizations\n", " if folder_name is not None:\n", " try:\n", " # Figure 1: Main analysis plots\n", " plt.figure(figsize=(20, 15))\n", "\n", " # Plot 1: Scatter plot of actual vs predicted values\n", " plt.subplot(3, 2, 1)\n", " plt.scatter(y_true, y_pred, alpha=0.5)\n", " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", " plt.xlabel('Actual Radiation (W/m²)')\n", " plt.ylabel('Predicted Radiation (W/m²)')\n", " plt.title('Actual vs Predicted Values')\n", " plt.grid(True)\n", "\n", " # Plot 2: Absolute error distribution\n", " plt.subplot(3, 2, 2)\n", " plt.hist(errors, bins=50, alpha=0.7)\n", " plt.xlabel('Prediction Error (W/m²)')\n", " plt.ylabel('Frequency')\n", " plt.title('Error Distribution')\n", " plt.grid(True)\n", "\n", " # Plot 3: Percentage error distribution (only for values > 10 W/m²)\n", " plt.subplot(3, 2, 3)\n", " mask = y_true > 10\n", " if np.any(mask):\n", " percentage_errors = ((y_pred[mask] - y_true[mask]) / y_true[mask]) * 100\n", " plt.hist(np.clip(percentage_errors, -100, 100), bins=50, alpha=0.7)\n", " plt.xlabel('Percentage Error (%)')\n", " plt.ylabel('Frequency')\n", " plt.title('Percentage Error Distribution (for values > 10 W/m²)')\n", " plt.grid(True)\n", "\n", " # Plot 4: Errors vs actual values\n", " plt.subplot(3, 2, 4)\n", " plt.scatter(y_true, errors, alpha=0.5)\n", " plt.axhline(y=0, color='r', linestyle='--')\n", " plt.xlabel('Actual Radiation (W/m²)')\n", " plt.ylabel('Error (W/m²)')\n", " plt.title('Errors vs Actual Values')\n", " plt.grid(True)\n", "\n", " # Plot 5: Error boxplot by radiation level\n", " plt.subplot(3, 2, 5)\n", " sns.boxplot(x=[get_radiation_level(v) for v in y_true], y=errors)\n", " plt.xticks(rotation=45)\n", " plt.xlabel('Radiation Level')\n", " plt.ylabel('Error (W/m²)')\n", " plt.title('Error Distribution by Level')\n", "\n", " # Plot 6: Confusion matrix heatmap\n", " plt.subplot(3, 2, 6)\n", " sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n", " plt.title('Confusion Matrix')\n", " plt.xticks(rotation=45)\n", " plt.yticks(rotation=45)\n", "\n", " plt.tight_layout()\n", " filename = f'{folder_name}_radiation_analysis.png'\n", " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", " print(f\"\\nPlot saved as: {filename}\")\n", " plt.close()\n", "\n", " except Exception as e:\n", " print(f\"\\nError saving plots: {str(e)}\")\n", "\n", " # Additional error statistics\n", " print(\"\\nError Statistics:\")\n", " print(f\"Mean error: {np.mean(errors):.3f}\")\n", " print(f\"Error standard deviation: {np.std(errors):.3f}\")\n", " print(f\"Median error: {np.median(errors):.3f}\")\n", " print(f\"95th percentile absolute error: {np.percentile(np.abs(errors), 95):.3f}\")\n", "\n", " # Return structured metrics\n", " metrics = {\n", " 'absolute': {\n", " 'mae': mae_raw,\n", " 'rmse': rmse_raw,\n", " 'r2': r2_raw,\n", " 'mape': float(mape) if not np.isnan(mape) else None\n", " },\n", " 'accuracy': {\n", " 'within_5_wm2': float(within_5_percent),\n", " 'within_10_wm2': float(within_10_percent),\n", " 'within_20_wm2': float(within_20_percent)\n", " },\n", " 'categorical': {\n", " 'level_accuracy': float(level_accuracy)\n", " },\n", " 'error_stats': {\n", " 'mean': float(np.mean(errors)),\n", " 'std': float(np.std(errors)),\n", " 'median': float(np.median(errors)),\n", " 'p95_abs': float(np.percentile(np.abs(errors), 95))\n", " }\n", " }\n", "\n", " return metrics\n", "\n", "\n", "def plot_training_history(history, folder_name=None):\n", " \"\"\"\n", " Visualize and save training loss and metrics plots\n", "\n", " Parameters:\n", " -----------\n", " history : tensorflow.keras.callbacks.History\n", " History object returned by model training\n", " folder_name : str\n", " Directory to save the plots and metrics\n", " \"\"\"\n", "\n", " try:\n", " # Create figure\n", " plt.figure(figsize=(12, 4))\n", "\n", " # Loss plot\n", " plt.subplot(1, 2, 1)\n", " plt.plot(history.history['loss'], label='Training Loss')\n", " plt.plot(history.history['val_loss'], label='Validation Loss')\n", " plt.title('Model Loss')\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Loss')\n", " plt.legend()\n", " plt.grid(True)\n", "\n", " # MAE plot\n", " plt.subplot(1, 2, 2)\n", " plt.plot(history.history['mae'], label='Training MAE')\n", " plt.plot(history.history['val_mae'], label='Validation MAE')\n", " plt.title('Model MAE')\n", " plt.xlabel('Epoch')\n", " plt.ylabel('MAE')\n", " plt.legend()\n", " plt.grid(True)\n", "\n", " plt.tight_layout()\n", "\n", " if folder_name is not None:\n", " # Generate filename with timestamp\n", " filename = f'{folder_name}_training_history.png' # Rimossa parentesi extra\n", "\n", " # Save figure\n", " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", " print(f\"\\nTraining history plot saved as: {filename}\")\n", "\n", " # Save numerical data in CSV format\n", " history_df = pd.DataFrame({\n", " 'epoch': range(1, len(history.history['loss']) + 1),\n", " 'training_loss': history.history['loss'],\n", " 'validation_loss': history.history['val_loss'],\n", " 'training_mae': history.history['mae'],\n", " 'validation_mae': history.history['val_mae']})\n", "\n", " if folder_name is not None:\n", " csv_filename = f'{folder_name}_training_history.csv' # Rimossa parentesi extra\n", " history_df.to_csv(csv_filename, index=False)\n", " print(f\"Training history data saved as: {csv_filename}\")\n", "\n", " # Calculate and save final statistics\n", " final_stats = {\n", " 'final_training_loss': history.history['loss'][-1],\n", " 'final_validation_loss': history.history['val_loss'][-1],\n", " 'final_training_mae': history.history['mae'][-1],\n", " 'final_validation_mae': history.history['val_mae'][-1],\n", " 'best_validation_loss': min(history.history['val_loss']),\n", " 'best_validation_mae': min(history.history['val_mae']),\n", " 'epochs': len(history.history['loss']),\n", " }\n", "\n", " if folder_name is not None:\n", " # Save statistics in JSON format\n", " stats_filename = f'{folder_name}_training_stats.json' # Rimossa parentesi extra\n", " with open(stats_filename, 'w') as f:\n", " json.dump(final_stats, f, indent=4)\n", " print(f\"Final statistics saved as: {stats_filename}\")\n", "\n", " # Print main statistics\n", " print(\"\\nFinal Training Statistics:\")\n", " print(f\"Final Loss (train/val): {final_stats['final_training_loss']:.4f}/{final_stats['final_validation_loss']:.4f}\")\n", " print(f\"Final MAE (train/val): {final_stats['final_training_mae']:.4f}/{final_stats['final_validation_mae']:.4f}\")\n", " print(f\"Best validation loss: {final_stats['best_validation_loss']:.4f}\")\n", " print(f\"Best validation MAE: {final_stats['best_validation_mae']:.4f}\")\n", "\n", " plt.show()\n", "\n", " except Exception as e:\n", " print(f\"\\nError during plot creation or saving: {str(e)}\")\n", "\n", "\n", "def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='solarradiation'):\n", " \"\"\"\n", " Advanced training function for the hybrid solar radiation model\n", " \"\"\"\n", "\n", " def calculate_metrics(y_true, y_pred):\n", " \"\"\"Helper function to calculate metrics safely\"\"\"\n", " y_true = np.array(y_true).flatten()\n", " y_pred = np.array(y_pred).flatten()\n", "\n", " # Count out of range predictions\n", " out_of_range = np.sum((y_pred < 0) | (y_pred > 1500))\n", "\n", " # Calculate MAPE with clipping to avoid extreme values\n", " diff = np.abs((y_true - y_pred) / (y_true + 1e-7))\n", " diff = np.clip(diff, 0, 1) # Clip to maximum 100% error\n", " mape = np.mean(diff) * 100\n", "\n", " # Calculate accuracy within 10%\n", " within_10_percent = np.mean(diff <= 0.10) * 100\n", "\n", " # Calculate MAE and RMSE\n", " mae = np.mean(np.abs(y_true - y_pred))\n", " rmse = np.sqrt(np.mean(np.square(y_true - y_pred)))\n", "\n", " return out_of_range, mape, within_10_percent, mae, rmse\n", "\n", " callbacks = [\n", " EarlyStopping(\n", " monitor='val_loss',\n", " patience=15,\n", " restore_best_weights=True,\n", " mode='min',\n", " verbose=1,\n", " min_delta=1e-4\n", " ),\n", " ReduceLROnPlateau(\n", " monitor='val_loss',\n", " factor=0.5,\n", " patience=7,\n", " verbose=1,\n", " mode='min',\n", " min_delta=1e-4,\n", " cooldown=2,\n", " min_lr=1e-7\n", " ),\n", " tf.keras.callbacks.ModelCheckpoint(\n", " filepath=f'{folder_name}_best_model.h5',\n", " monitor='val_loss',\n", " save_best_only=True,\n", " mode='min',\n", " save_weights_only=False\n", " ),\n", " tf.keras.callbacks.TensorBoard(\n", " log_dir=f'./{folder_name}_logs',\n", " histogram_freq=1,\n", " write_graph=True,\n", " update_freq='epoch'\n", " ),\n", " tf.keras.callbacks.LambdaCallback(\n", " on_epoch_end=lambda epoch, logs: (\n", " print(f\"\\nEpoch {epoch + 1} Detailed Metrics:\") and\n", " (lambda: (\n", " y_pred := model.predict(X_test, verbose=0),\n", " metrics := calculate_metrics(y_test, y_pred),\n", " print(f\"Out of range: {metrics[0]} predictions\"),\n", " print(f\"MAPE: {metrics[1]:.2f}%\"),\n", " print(f\"Within ±10%: {metrics[2]:.2f}%\"),\n", " print(f\"MAE: {metrics[3]:.2f}\"),\n", " print(f\"RMSE: {metrics[4]:.2f}\")\n", " ))()\n", " ) if epoch % 5 == 0 else None\n", " )\n", " ]\n", "\n", " try:\n", " history = model.fit(\n", " X_train, y_train,\n", " validation_data=(X_test, y_test),\n", " epochs=epochs,\n", " batch_size=batch_size,\n", " callbacks=callbacks,\n", " verbose=1,\n", " shuffle=False\n", " )\n", "\n", " print(\"\\nTraining completed successfully!\")\n", "\n", " # Final evaluation\n", " final_pred = model.predict(X_test, verbose=0)\n", " metrics = calculate_metrics(y_test, final_pred)\n", "\n", " print(\"\\nFinal Model Performance:\")\n", " print(f\"Out of range predictions: {metrics[0]} ({metrics[0] / len(y_test) * 100:.2f}%)\")\n", " print(f\"MAPE: {metrics[1]:.2f}%\")\n", " print(f\"Predictions within ±10%: {metrics[2]:.2f}%\")\n", " print(f\"MAE: {metrics[3]:.2f}\")\n", " print(f\"RMSE: {metrics[4]:.2f}\")\n", "\n", " plot_training_history(history, folder_name=folder_name)\n", "\n", " return history\n", "\n", " except Exception as e:\n", " print(f\"\\nError during training: {str(e)}\")\n", " raise\n", "\n", " finally:\n", " tf.keras.backend.clear_session()\n", "\n", "\n", "def integrate_predictions(df, predictions, sequence_length=24):\n", " \"\"\"\n", " Integrates solar radiation predictions into the original dataset for pre-2010 data.\n", "\n", " Parameters:\n", " -----------\n", " df : pandas.DataFrame\n", " Original dataset\n", " predictions : numpy.ndarray\n", " Array of solar radiation predictions\n", " sequence_length : int\n", " Sequence length used for predictions\n", "\n", " Returns:\n", " --------\n", " pandas.DataFrame\n", " Updated dataset with solar radiation predictions\n", " \"\"\"\n", " # Convert datetime to datetime format if not already\n", " df['datetime'] = pd.to_datetime(df['datetime'])\n", "\n", " # Identify pre-2010 rows\n", " mask_pre_2010 = df['datetime'].dt.year < 2010\n", "\n", " # Create temporary DataFrame with predictions\n", " dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n", " predictions_df = pd.DataFrame({\n", " 'datetime': dates_pre_2010,\n", " 'solarradiation_predicted': predictions.flatten()})\n", "\n", " # Merge with original dataset\n", " df = df.merge(predictions_df, on='datetime', how='left')\n", "\n", " # Update solar radiation column where missing\n", " df['solarradiation'] = df['solarradiation'].fillna(df['solarradiation_predicted'])\n", "\n", " # Remove temporary column\n", " df = df.drop('solarradiation_predicted', axis=1)\n", "\n", " print(f\"Added {len(predictions)} predictions to dataset\")\n", " print(f\"Rows with solar radiation after integration: {df['solarradiation'].notna().sum()}\")\n", "\n", " return df" ] }, { "cell_type": "code", "execution_count": 5, "id": "1dd1bb91-cdf9-4304-be56-8d55bd5d1148", "metadata": {}, "outputs": [], "source": [ "def analyze_distribution(data, solar_column='solarenergy', name = 'Solar Energy'):\n", " \"\"\"\n", " Analizza dettagliatamente la distribuzione della variabile solarenergy.\n", "\n", " Parameters:\n", " -----------\n", " data : pandas.DataFrame\n", " DataFrame contenente la colonna solarenergy\n", " solar_column : str, default='solarenergy'\n", " Nome della colonna da analizzare\n", "\n", " Returns:\n", " --------\n", " dict\n", " Dizionario contenente le statistiche principali\n", " \"\"\"\n", "\n", " # Creiamo una figura con più subplot\n", " fig = plt.figure(figsize=(20, 12))\n", "\n", " # 1. Statistiche di base\n", " stats_dict = {\n", " 'count': len(data[solar_column]),\n", " 'missing': data[solar_column].isnull().sum(),\n", " 'zeros': (data[solar_column] == 0).sum(),\n", " 'mean': data[solar_column].mean(),\n", " 'median': data[solar_column].median(),\n", " 'std': data[solar_column].std(),\n", " 'min': data[solar_column].min(),\n", " 'max': data[solar_column].max(),\n", " 'skewness': stats.skew(data[solar_column].dropna()),\n", " 'kurtosis': stats.kurtosis(data[solar_column].dropna())\n", " }\n", "\n", " # Calcolo dei percentili\n", " percentiles = [1, 5, 10, 25, 50, 75, 90, 95, 99]\n", " for p in percentiles:\n", " stats_dict[f'percentile_{p}'] = np.percentile(data[solar_column].dropna(), p)\n", "\n", " # 2. Visualizzazioni\n", "\n", " # 2.1 Distribuzione\n", " plt.subplot(2, 2, 1)\n", " sns.histplot(data=data, x=solar_column, kde=True)\n", " plt.title(f'Distribuzione di {name}')\n", " plt.xlabel(f'{name}')\n", " plt.ylabel('Frequenza')\n", "\n", " # 2.2 Box Plot\n", " plt.subplot(2, 2, 2)\n", " sns.boxplot(y=data[solar_column])\n", " plt.title(f'Box Plot di {name}')\n", "\n", " # 2.3 QQ Plot\n", " plt.subplot(2, 2, 3)\n", " stats.probplot(data[solar_column].dropna(), dist=\"norm\", plot=plt)\n", " plt.title(f'Q-Q Plot di {name}')\n", "\n", " # 2.4 Distribuzione Log-trasformata\n", " plt.subplot(2, 2, 4)\n", " sns.histplot(data=np.log1p(data[solar_column]), kde=True)\n", " plt.title(f'Distribuzione Log-trasformata di {name}')\n", " plt.xlabel(f'Log({name} + 1)')\n", " plt.ylabel('Frequenza')\n", "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", " # 3. Analisi temporale se disponibile\n", " if 'timestamp' in data.columns or 'datetime' in data.columns:\n", " time_col = 'timestamp' if 'timestamp' in data.columns else 'datetime'\n", " if isinstance(data[time_col].iloc[0], (int, float)):\n", " data['temp_datetime'] = pd.to_datetime(data[time_col], unit='s')\n", " else:\n", " data['temp_datetime'] = pd.to_datetime(data[time_col])\n", "\n", " # Plot temporale\n", " plt.figure(figsize=(15, 6))\n", " plt.plot(data['temp_datetime'], data[solar_column])\n", " plt.title(f'Serie Temporale di {name}')\n", " plt.xlabel('Data')\n", " plt.ylabel(f'{name}')\n", " plt.xticks(rotation=45)\n", " plt.tight_layout()\n", " plt.show()\n", "\n", " # Analisi stagionale\n", " data['month'] = data['temp_datetime'].dt.month\n", " seasonal_stats = data.groupby('month')[solar_column].agg(['mean', 'std', 'median'])\n", "\n", " plt.figure(figsize=(12, 6))\n", " seasonal_stats['mean'].plot(kind='bar')\n", " plt.title(f'Media Mensile di {name}')\n", " plt.xlabel('Mese')\n", " plt.ylabel(f'{name} Media')\n", " plt.tight_layout()\n", " plt.show()\n", "\n", " # 4. Stampa delle statistiche principali\n", " print(f\"\\nStatistiche principali di {name}:\")\n", " print(\"-\" * 50)\n", " for key, value in stats_dict.items():\n", " print(f\"{key:15}: {value:,.4f}\")\n", "\n", " # 5. Suggerimenti per la normalizzazione\n", " print(\"\\nSuggerimenti per la normalizzazione:\")\n", " print(\"-\" * 50)\n", "\n", " skewness = abs(stats_dict['skewness'])\n", " if skewness > 1:\n", " print(\"- La distribuzione è fortemente asimmetrica (skewness > 1)\")\n", " print(\"- Considerare una trasformazione logaritmica: np.log1p(x)\")\n", "\n", " range_ratio = stats_dict['max'] / stats_dict['std']\n", " if range_ratio > 10:\n", " print(\"- La variabile ha una scala molto ampia\")\n", " print(\"- Considerare RobustScaler o StandardScaler per la normalizzazione\")\n", "\n", " zero_ratio = stats_dict['zeros'] / stats_dict['count']\n", " if zero_ratio > 0.1:\n", " print(f\"- Alta presenza di zeri ({zero_ratio:.2%})\")\n", " print(\"- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\")\n", "\n", " return stats_dict" ] }, { "cell_type": "code", "execution_count": 6, "id": "initial_id", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initializing solar radiation model training...\n", "\n", "1. Preparing data...\n", "\n", "Selected features:\n", "Number of features: 40\n", "Features list: ['uvindex', 'cloudcover', 'visibility', 'temp', 'pressure', 'humidity', 'solar_elevation', 'solar_angle', 'day_length', 'hour_sin', 'hour_cos', 'day_of_year_sin', 'day_of_year_cos', 'month_sin', 'month_cos', 'clear_sky_index', 'atmospheric_attenuation', 'theoretical_radiation', 'expected_radiation', 'cloud_elevation', 'visibility_elevation', 'uv_cloud_interaction', 'temp_radiation_potential', 'cloud_rolling_12h', 'temp_rolling_12h', 'uv_rolling_12h', 'cloudcover_rolling_mean_6h', 'temp_rolling_mean_6h', 'temp_1h_lag', 'cloudcover_1h_lag', 'humidity_1h_lag', 'uv_lag_1h', 'season_Spring', 'season_Summer', 'season_Autumn', 'season_Winter', 'time_period_Morning', 'time_period_Afternoon', 'time_period_Evening', 'time_period_Night']\n", "Training data shape: (103798, 24, 40)\n", "Test data shape: (25933, 24, 40)\n", "Saving scaler X to: 2024-11-25_13-52_scale_X.joblib\n", "Saving scaler X to: 2024-11-25_13-52_scale_y.joblib\n", "Saving features to: 2024-11-25_13-52_features.json\n" ] } ], "source": [ "df = pd.read_parquet('../../sources/weather_data_uvindex.parquet')\n", "\n", "\n", "print(\"Initializing solar radiation model training...\")\n", "\n", "\n", "# Data preparation\n", "print(\"\\n1. Preparing data...\")\n", "X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq = prepare_hybrid_data(df)\n", "\n", "print(f\"Training data shape: {X_train_seq.shape}\")\n", "print(f\"Test data shape: {X_test_seq.shape}\")\n", "\n", "# Save or load scaler and features\n", "scaler_X_path = f'{folder_name}_scale_X.joblib'\n", "scaler_y_path = f'{folder_name}_scale_y.joblib'\n", "features_path = f'{folder_name}_features.json'\n", "model_path = f'{folder_name}_best_model.h5'\n", "history_path = f'{folder_name}_training_history.json'\n", "\n", "if os.path.exists(scaler_X_path):\n", " print(f\"Loading existing scaler X from: {scaler_X_path}\")\n", " scaler = joblib.load(scaler_X_path)\n", "else:\n", " print(f\"Saving scaler X to: {scaler_X_path}\")\n", " joblib.dump(scaler_X, scaler_X_path)\n", "\n", "if os.path.exists(scaler_y_path):\n", " print(f\"Loading existing scaler X from: {scaler_y_path}\")\n", " scaler = joblib.load(scaler_y_path)\n", "else:\n", " print(f\"Saving scaler X to: {scaler_y_path}\")\n", " joblib.dump(scaler_y, scaler_y_path)\n", "\n", "if os.path.exists(features_path):\n", " print(f\"Loading existing features from: {features_path}\")\n", " with open(features_path, 'r') as f:\n", " features = json.load(f)\n", "else:\n", " print(f\"Saving features to: {features_path}\")\n", " with open(features_path, 'w') as f:\n", " json.dump(features, f)\n", "\n", "# Data quality verification\n", "if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n", " raise ValueError(\"Found NaN values in training data\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "44749f6a-8941-41e8-8105-c36417245ed5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "2. Creating model...\n", "\n", "Max dataset solar radiation : 1113.0 - Scaled Version : 3.2535460992907805\n", "Max dataset solar radiation increased by 15% : 1279.9499999999998 - Scaled Version : 3.7415780141843973\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2024-11-25 13:52:27.432588: 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", "2024-11-25 13:52:28.267692: 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: \"SolarRadiationModel\"\n", "__________________________________________________________________________________________________\n", " Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", " input_1 (InputLayer) [(None, 24, 40)] 0 [] \n", " \n", " bidirectional (Bidirection (None, 24, 512) 608256 ['input_1[0][0]'] \n", " al) \n", " \n", " layer_normalization (Layer (None, 24, 512) 1024 ['bidirectional[0][0]'] \n", " Normalization) \n", " \n", " dropout (Dropout) (None, 24, 512) 0 ['layer_normalization[0][0]'] \n", " \n", " dense (Dense) (None, 24, 512) 20992 ['input_1[0][0]'] \n", " \n", " stochastic_depth (Stochast (None, 24, 512) 0 ['dropout[0][0]', \n", " icDepth) 'dense[0][0]'] \n", " \n", " multi_head_attention (Mult (None, 24, 512) 1680230 ['stochastic_depth[0][0]', \n", " iHeadAttention) 4 'stochastic_depth[0][0]'] \n", " \n", " stochastic_depth_1 (Stocha (None, 24, 512) 0 ['stochastic_depth[0][0]', \n", " sticDepth) 'multi_head_attention[0][0]']\n", " \n", " layer_normalization_1 (Lay (None, 24, 512) 1024 ['stochastic_depth_1[0][0]'] \n", " erNormalization) \n", " \n", " max_pooling1d (MaxPooling1 (None, 12, 512) 0 ['layer_normalization_1[0][0]'\n", " D) ] \n", " \n", " bidirectional_1 (Bidirecti (None, 12, 256) 656384 ['max_pooling1d[0][0]'] \n", " onal) \n", " \n", " layer_normalization_2 (Lay (None, 12, 256) 512 ['bidirectional_1[0][0]'] \n", " erNormalization) \n", " \n", " dropout_1 (Dropout) (None, 12, 256) 0 ['layer_normalization_2[0][0]'\n", " ] \n", " \n", " dense_1 (Dense) (None, 12, 256) 131328 ['max_pooling1d[0][0]'] \n", " \n", " stochastic_depth_2 (Stocha (None, 12, 256) 0 ['dropout_1[0][0]', \n", " sticDepth) 'dense_1[0][0]'] \n", " \n", " multi_head_attention_1 (Mu (None, 12, 256) 3155200 ['stochastic_depth_2[0][0]', \n", " ltiHeadAttention) 'stochastic_depth_2[0][0]'] \n", " \n", " stochastic_depth_3 (Stocha (None, 12, 256) 0 ['stochastic_depth_2[0][0]', \n", " sticDepth) 'multi_head_attention_1[0][0]\n", " '] \n", " \n", " layer_normalization_3 (Lay (None, 12, 256) 512 ['stochastic_depth_3[0][0]'] \n", " erNormalization) \n", " \n", " max_pooling1d_1 (MaxPoolin (None, 6, 256) 0 ['layer_normalization_3[0][0]'\n", " g1D) ] \n", " \n", " bidirectional_2 (Bidirecti (None, 6, 128) 164352 ['max_pooling1d_1[0][0]'] \n", " onal) \n", " \n", " layer_normalization_4 (Lay (None, 6, 128) 256 ['bidirectional_2[0][0]'] \n", " erNormalization) \n", " \n", " dropout_2 (Dropout) (None, 6, 128) 0 ['layer_normalization_4[0][0]'\n", " ] \n", " \n", " dense_2 (Dense) (None, 6, 128) 32896 ['max_pooling1d_1[0][0]'] \n", " \n", " stochastic_depth_4 (Stocha (None, 6, 128) 0 ['dropout_2[0][0]', \n", " sticDepth) 'dense_2[0][0]'] \n", " \n", " multi_head_attention_2 (Mu (None, 6, 128) 527488 ['stochastic_depth_4[0][0]', \n", " ltiHeadAttention) 'stochastic_depth_4[0][0]'] \n", " \n", " stochastic_depth_5 (Stocha (None, 6, 128) 0 ['stochastic_depth_4[0][0]', \n", " sticDepth) 'multi_head_attention_2[0][0]\n", " '] \n", " \n", " layer_normalization_5 (Lay (None, 6, 128) 256 ['stochastic_depth_5[0][0]'] \n", " erNormalization) \n", " \n", " max_pooling1d_2 (MaxPoolin (None, 3, 128) 0 ['layer_normalization_5[0][0]'\n", " g1D) ] \n", " \n", " bidirectional_3 (Bidirecti (None, 3, 64) 41216 ['max_pooling1d_2[0][0]'] \n", " onal) \n", " \n", " layer_normalization_6 (Lay (None, 3, 64) 128 ['bidirectional_3[0][0]'] \n", " erNormalization) \n", " \n", " dropout_3 (Dropout) (None, 3, 64) 0 ['layer_normalization_6[0][0]'\n", " ] \n", " \n", " dense_3 (Dense) (None, 3, 64) 8256 ['max_pooling1d_2[0][0]'] \n", " \n", " stochastic_depth_6 (Stocha (None, 3, 64) 0 ['dropout_3[0][0]', \n", " sticDepth) 'dense_3[0][0]'] \n", " \n", " multi_head_attention_3 (Mu (None, 3, 64) 66368 ['stochastic_depth_6[0][0]', \n", " ltiHeadAttention) 'stochastic_depth_6[0][0]'] \n", " \n", " stochastic_depth_7 (Stocha (None, 3, 64) 0 ['stochastic_depth_6[0][0]', \n", " sticDepth) 'multi_head_attention_3[0][0]\n", " '] \n", " \n", " layer_normalization_7 (Lay (None, 3, 64) 128 ['stochastic_depth_7[0][0]'] \n", " erNormalization) \n", " \n", " bidirectional_4 (Bidirecti (None, 64) 24832 ['layer_normalization_7[0][0]'\n", " onal) ] \n", " \n", " layer_normalization_8 (Lay (None, 64) 128 ['bidirectional_4[0][0]'] \n", " erNormalization) \n", " \n", " dropout_4 (Dropout) (None, 64) 0 ['layer_normalization_8[0][0]'\n", " ] \n", " \n", " dense_4 (Dense) (None, 128) 8320 ['dropout_4[0][0]'] \n", " \n", " batch_normalization (Batch (None, 128) 512 ['dense_4[0][0]'] \n", " Normalization) \n", " \n", " activation (Activation) (None, 128) 0 ['batch_normalization[0][0]'] \n", " \n", " dropout_5 (Dropout) (None, 128) 0 ['activation[0][0]'] \n", " \n", " dense_5 (Dense) (None, 64) 8256 ['dropout_5[0][0]'] \n", " \n", " batch_normalization_1 (Bat (None, 64) 256 ['dense_5[0][0]'] \n", " chNormalization) \n", " \n", " activation_1 (Activation) (None, 64) 0 ['batch_normalization_1[0][0]'\n", " ] \n", " \n", " dropout_6 (Dropout) (None, 64) 0 ['activation_1[0][0]'] \n", " \n", " dense_6 (Dense) (None, 32) 2080 ['dropout_6[0][0]'] \n", " \n", " batch_normalization_2 (Bat (None, 32) 128 ['dense_6[0][0]'] \n", " chNormalization) \n", " \n", " activation_2 (Activation) (None, 32) 0 ['batch_normalization_2[0][0]'\n", " ] \n", " \n", " dropout_7 (Dropout) (None, 32) 0 ['activation_2[0][0]'] \n", " \n", " dense_7 (Dense) (None, 1) 33 ['dropout_7[0][0]'] \n", " \n", " lambda (Lambda) (None, 1) 0 ['dense_7[0][0]'] \n", " \n", "==================================================================================================\n", "Total params: 22263425 (84.93 MB)\n", "Trainable params: 22262977 (84.93 MB)\n", "Non-trainable params: 448 (1.75 KB)\n", "__________________________________________________________________________________________________\n", "\n", "4. Starting training...\n", "Epoch 1/100\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2024-11-25 13:52:50.667967: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n", "2024-11-25 13:52:52.254189: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x759e08d2de90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "2024-11-25 13:52:52.254217: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", "2024-11-25 13:52:52.258602: 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", "2024-11-25 13:52:52.401237: 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": [ "540/541 [============================>.] - ETA: 0s - loss: 8.3573 - mse: 0.6705 - mae: 0.5175 - rmse: 0.7610 - custom_mape: 62.5211" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n", " saving_api.save_model(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Epoch 1 Detailed Metrics:\n", "541/541 [==============================] - 70s 79ms/step - loss: 8.3541 - mse: 0.6698 - mae: 0.5171 - rmse: 0.7602 - custom_mape: 62.5082 - val_loss: 5.5029 - val_mse: 0.1555 - val_mae: 0.2236 - val_rmse: 0.3853 - val_custom_mape: 33.0501 - lr: 1.3120e-04\n", "Epoch 2/100\n", "541/541 [==============================] - 37s 69ms/step - loss: 4.8835 - mse: 0.1649 - mae: 0.2351 - rmse: 0.3909 - custom_mape: 34.9535 - val_loss: 4.0981 - val_mse: 0.1782 - val_mae: 0.2380 - val_rmse: 0.3937 - val_custom_mape: 32.7671 - lr: 2.6891e-04\n", "Epoch 3/100\n", "541/541 [==============================] - 37s 69ms/step - loss: 2.8253 - mse: 0.1180 - mae: 0.1955 - rmse: 0.3321 - custom_mape: 30.7958 - val_loss: 2.0010 - val_mse: 0.0838 - val_mae: 0.1619 - val_rmse: 0.2713 - val_custom_mape: 28.0167 - lr: 2.1053e-04\n", "Epoch 4/100\n", "541/541 [==============================] - 37s 69ms/step - loss: 1.6423 - mse: 0.0603 - mae: 0.1361 - rmse: 0.2385 - custom_mape: 24.5993 - val_loss: 1.4116 - val_mse: 0.0688 - val_mae: 0.1494 - val_rmse: 0.2560 - val_custom_mape: 26.3951 - lr: 1.0081e-04\n", "Epoch 5/100\n", "541/541 [==============================] - 38s 70ms/step - loss: 1.3057 - mse: 0.0424 - mae: 0.1127 - rmse: 0.2007 - custom_mape: 21.8917 - val_loss: 1.2564 - val_mse: 0.0442 - val_mae: 0.1149 - val_rmse: 0.2065 - val_custom_mape: 22.3474 - lr: 1.4334e-05\n", "Epoch 6/100\n", "540/541 [============================>.] - ETA: 0s - loss: 1.1928 - mse: 0.0425 - mae: 0.1130 - rmse: 0.2005 - custom_mape: 21.7451\n", "Epoch 6 Detailed Metrics:\n", "541/541 [==============================] - 39s 72ms/step - loss: 1.1925 - mse: 0.0425 - mae: 0.1129 - rmse: 0.2003 - custom_mape: 21.7690 - val_loss: 1.0131 - val_mse: 0.0528 - val_mae: 0.1265 - val_rmse: 0.2226 - val_custom_mape: 22.8960 - lr: 2.4076e-04\n", "Epoch 7/100\n", "541/541 [==============================] - 39s 72ms/step - loss: 0.8364 - mse: 0.0590 - mae: 0.1341 - rmse: 0.2334 - custom_mape: 24.2004 - val_loss: 0.6962 - val_mse: 0.0741 - val_mae: 0.1525 - val_rmse: 0.2596 - val_custom_mape: 25.3537 - lr: 2.2058e-04\n", "Epoch 8/100\n", "541/541 [==============================] - 38s 70ms/step - loss: 0.5898 - mse: 0.0509 - mae: 0.1241 - rmse: 0.2155 - custom_mape: 23.1834 - val_loss: 0.5431 - val_mse: 0.0950 - val_mae: 0.2095 - val_rmse: 0.3062 - val_custom_mape: 37.3372 - lr: 1.8277e-04\n", "Epoch 9/100\n", "541/541 [==============================] - 40s 74ms/step - loss: 0.4529 - mse: 0.0402 - mae: 0.1098 - rmse: 0.1931 - custom_mape: 21.1223 - val_loss: 0.4038 - val_mse: 0.0302 - val_mae: 0.0935 - val_rmse: 0.1677 - val_custom_mape: 20.0386 - lr: 1.3408e-04\n", "Epoch 10/100\n", "541/541 [==============================] - 37s 68ms/step - loss: 0.3761 - mse: 0.0310 - mae: 0.0958 - rmse: 0.1704 - custom_mape: 19.7314 - val_loss: 0.3586 - val_mse: 0.0451 - val_mae: 0.1141 - val_rmse: 0.1979 - val_custom_mape: 20.7993 - lr: 8.3141e-05\n", "Epoch 11/100\n", "540/541 [============================>.] - ETA: 0s - loss: 0.3347 - mse: 0.0242 - mae: 0.0840 - rmse: 0.1510 - custom_mape: 18.0393\n", "Epoch 11 Detailed Metrics:\n", "541/541 [==============================] - 37s 69ms/step - loss: 0.3347 - mse: 0.0242 - mae: 0.0840 - rmse: 0.1509 - custom_mape: 18.0607 - val_loss: 0.3301 - val_mse: 0.0397 - val_mae: 0.1035 - val_rmse: 0.1812 - val_custom_mape: 18.6451 - lr: 3.9028e-05\n", "Epoch 12/100\n", "541/541 [==============================] - 37s 69ms/step - loss: 0.3175 - mse: 0.0224 - mae: 0.0806 - rmse: 0.1453 - custom_mape: 17.6096 - val_loss: 0.3190 - val_mse: 0.0345 - val_mae: 0.0967 - val_rmse: 0.1716 - val_custom_mape: 18.2344 - lr: 9.5830e-06\n", "Epoch 13/100\n", "541/541 [==============================] - 39s 72ms/step - loss: 0.3131 - mse: 0.0225 - mae: 0.0811 - rmse: 0.1450 - custom_mape: 17.6923 - val_loss: 0.3130 - val_mse: 0.0311 - val_mae: 0.0966 - val_rmse: 0.1710 - val_custom_mape: 20.7828 - lr: 2.1869e-04\n", "Epoch 14/100\n", "541/541 [==============================] - 38s 71ms/step - loss: 0.2933 - mse: 0.0562 - mae: 0.1310 - rmse: 0.2245 - custom_mape: 24.0791 - val_loss: 0.2675 - val_mse: 0.0672 - val_mae: 0.1489 - val_rmse: 0.2424 - val_custom_mape: 29.0105 - lr: 2.1594e-04\n", "Epoch 15/100\n", "541/541 [==============================] - 37s 69ms/step - loss: 0.2300 - mse: 0.0400 - mae: 0.1099 - rmse: 0.1920 - custom_mape: 21.2963 - val_loss: 0.2111 - val_mse: 0.0450 - val_mae: 0.1232 - val_rmse: 0.2018 - val_custom_mape: 25.0584 - lr: 2.0840e-04\n", "Epoch 16/100\n", "541/541 [==============================] - ETA: 0s - loss: 0.1924 - mse: 0.0436 - mae: 0.1148 - rmse: 0.1990 - custom_mape: 21.9478\n", "Epoch 16 Detailed Metrics:\n", "541/541 [==============================] - 40s 73ms/step - loss: 0.1924 - mse: 0.0436 - mae: 0.1148 - rmse: 0.1990 - custom_mape: 21.9478 - val_loss: 0.1791 - val_mse: 0.0464 - val_mae: 0.1232 - val_rmse: 0.2052 - val_custom_mape: 24.7895 - lr: 1.9641e-04\n", "Epoch 17/100\n", "541/541 [==============================] - 39s 72ms/step - loss: 0.1588 - mse: 0.0344 - mae: 0.1019 - rmse: 0.1778 - custom_mape: 20.1684 - val_loss: 0.1435 - val_mse: 0.0285 - val_mae: 0.0913 - val_rmse: 0.1615 - val_custom_mape: 19.9306 - lr: 1.8050e-04\n", "Epoch 18/100\n", "541/541 [==============================] - 38s 69ms/step - loss: 0.1359 - mse: 0.0305 - mae: 0.0962 - rmse: 0.1677 - custom_mape: 19.8060 - val_loss: 0.1478 - val_mse: 0.0771 - val_mae: 0.1585 - val_rmse: 0.2474 - val_custom_mape: 27.4152 - lr: 1.6139e-04\n", "Epoch 19/100\n", "541/541 [==============================] - 39s 71ms/step - loss: 0.1209 - mse: 0.0312 - mae: 0.0959 - rmse: 0.1679 - custom_mape: 19.2837 - val_loss: 0.1135 - val_mse: 0.0329 - val_mae: 0.0929 - val_rmse: 0.1644 - val_custom_mape: 19.0066 - lr: 1.3994e-04\n", "Epoch 20/100\n", "541/541 [==============================] - 37s 69ms/step - loss: 0.1055 - mse: 0.0236 - mae: 0.0839 - rmse: 0.1472 - custom_mape: 18.0202 - val_loss: 0.1032 - val_mse: 0.0308 - val_mae: 0.0911 - val_rmse: 0.1598 - val_custom_mape: 19.2490 - lr: 1.1712e-04\n", "Epoch 21/100\n", "541/541 [==============================] - ETA: 0s - loss: 0.0994 - mse: 0.0271 - mae: 0.0897 - rmse: 0.1563 - custom_mape: 18.7032\n", "Epoch 21 Detailed Metrics:\n", "541/541 [==============================] - 38s 70ms/step - loss: 0.0994 - mse: 0.0271 - mae: 0.0897 - rmse: 0.1563 - custom_mape: 18.7032 - val_loss: 0.1014 - val_mse: 0.0343 - val_mae: 0.1057 - val_rmse: 0.1742 - val_custom_mape: 24.6971 - lr: 9.3944e-05\n", "Epoch 22/100\n", "541/541 [==============================] - 38s 70ms/step - loss: 0.0895 - mse: 0.0197 - mae: 0.0768 - rmse: 0.1354 - custom_mape: 17.1486 - val_loss: 0.0911 - val_mse: 0.0286 - val_mae: 0.0896 - val_rmse: 0.1566 - val_custom_mape: 19.0230 - lr: 7.1464e-05\n", "Epoch 23/100\n", "541/541 [==============================] - 37s 68ms/step - loss: 0.0833 - mse: 0.0167 - mae: 0.0708 - rmse: 0.1252 - custom_mape: 16.4218 - val_loss: 0.0898 - val_mse: 0.0347 - val_mae: 0.0968 - val_rmse: 0.1657 - val_custom_mape: 17.8852 - lr: 5.0687e-05\n", "Epoch 24/100\n", "541/541 [==============================] - 38s 69ms/step - loss: 0.0798 - mse: 0.0159 - mae: 0.0689 - rmse: 0.1217 - custom_mape: 16.0625 - val_loss: 0.0820 - val_mse: 0.0250 - val_mae: 0.0791 - val_rmse: 0.1418 - val_custom_mape: 16.7331 - lr: 3.2548e-05\n", "Epoch 25/100\n", "541/541 [==============================] - 37s 69ms/step - loss: 0.0767 - mse: 0.0140 - mae: 0.0647 - rmse: 0.1146 - custom_mape: 15.6304 - val_loss: 0.0798 - val_mse: 0.0229 - val_mae: 0.0763 - val_rmse: 0.1377 - val_custom_mape: 16.6235 - lr: 1.7863e-05\n", "Epoch 26/100\n", "540/541 [============================>.] - ETA: 0s - loss: 0.0757 - mse: 0.0140 - mae: 0.0646 - rmse: 0.1144 - custom_mape: 15.5670\n", "Epoch 26 Detailed Metrics:\n", "541/541 [==============================] - 39s 71ms/step - loss: 0.0757 - mse: 0.0140 - mae: 0.0646 - rmse: 0.1143 - custom_mape: 15.5871 - val_loss: 0.0793 - val_mse: 0.0233 - val_mae: 0.0766 - val_rmse: 0.1381 - val_custom_mape: 16.4359 - lr: 7.2916e-06\n", "Epoch 27/100\n", "541/541 [==============================] - 35s 64ms/step - loss: 0.0754 - mse: 0.0142 - mae: 0.0650 - rmse: 0.1150 - custom_mape: 15.6099 - val_loss: 0.0796 - val_mse: 0.0245 - val_mae: 0.0785 - val_rmse: 0.1406 - val_custom_mape: 16.5037 - lr: 1.3093e-06\n", "Epoch 28/100\n", "541/541 [==============================] - 35s 65ms/step - loss: 0.0753 - mse: 0.0146 - mae: 0.0660 - rmse: 0.1168 - custom_mape: 15.6841 - val_loss: 0.0847 - val_mse: 0.0333 - val_mae: 0.1012 - val_rmse: 0.1737 - val_custom_mape: 20.9143 - lr: 1.9679e-04\n", "Epoch 29/100\n", "541/541 [==============================] - 35s 66ms/step - loss: 0.0901 - mse: 0.0467 - mae: 0.1176 - rmse: 0.2031 - custom_mape: 22.1549 - val_loss: 0.1183 - val_mse: 0.1009 - val_mae: 0.1867 - val_rmse: 0.2930 - val_custom_mape: 30.4890 - lr: 1.9593e-04\n", "Epoch 30/100\n", "541/541 [==============================] - 36s 66ms/step - loss: 0.0798 - mse: 0.0364 - mae: 0.1057 - rmse: 0.1827 - custom_mape: 21.1398 - val_loss: 0.0919 - val_mse: 0.0843 - val_mae: 0.1507 - val_rmse: 0.2669 - val_custom_mape: 23.3218 - lr: 1.9398e-04\n", "Epoch 31/100\n", "541/541 [==============================] - ETA: 0s - loss: 0.0720 - mse: 0.0336 - mae: 0.1008 - rmse: 0.1748 - custom_mape: 19.9648\n", "Epoch 31 Detailed Metrics:\n", "541/541 [==============================] - 36s 67ms/step - loss: 0.0720 - mse: 0.0336 - mae: 0.1008 - rmse: 0.1748 - custom_mape: 19.9648 - val_loss: 0.0618 - val_mse: 0.0238 - val_mae: 0.0780 - val_rmse: 0.1408 - val_custom_mape: 16.9390 - lr: 1.9095e-04\n", "Epoch 32/100\n", "541/541 [==============================] - 36s 67ms/step - loss: 0.0655 - mse: 0.0299 - mae: 0.0960 - rmse: 0.1657 - custom_mape: 19.8846 - val_loss: 0.0781 - val_mse: 0.0562 - val_mae: 0.1387 - val_rmse: 0.2230 - val_custom_mape: 25.8359 - lr: 1.8687e-04\n", "Epoch 33/100\n", "541/541 [==============================] - 36s 67ms/step - loss: 0.0630 - mse: 0.0322 - mae: 0.0984 - rmse: 0.1708 - custom_mape: 19.9807 - val_loss: 0.0745 - val_mse: 0.0661 - val_mae: 0.1394 - val_rmse: 0.2340 - val_custom_mape: 22.5655 - lr: 1.8180e-04\n", "Epoch 34/100\n", "541/541 [==============================] - 36s 67ms/step - loss: 0.0694 - mse: 0.0437 - mae: 0.1127 - rmse: 0.1919 - custom_mape: 21.6774 - val_loss: 0.0740 - val_mse: 0.0535 - val_mae: 0.1283 - val_rmse: 0.2119 - val_custom_mape: 21.3689 - lr: 1.7579e-04\n", "Epoch 35/100\n", "541/541 [==============================] - 37s 68ms/step - loss: 0.0605 - mse: 0.0311 - mae: 0.0963 - rmse: 0.1663 - custom_mape: 19.5287 - val_loss: 0.0686 - val_mse: 0.0554 - val_mae: 0.1297 - val_rmse: 0.2094 - val_custom_mape: 21.9974 - lr: 1.6890e-04\n", "Epoch 36/100\n", "541/541 [==============================] - ETA: 0s - loss: 0.0568 - mse: 0.0311 - mae: 0.0973 - rmse: 0.1681 - custom_mape: 19.8627\n", "Epoch 36 Detailed Metrics:\n", "541/541 [==============================] - 39s 71ms/step - loss: 0.0568 - mse: 0.0311 - mae: 0.0973 - rmse: 0.1681 - custom_mape: 19.8627 - val_loss: 0.0517 - val_mse: 0.0246 - val_mae: 0.0877 - val_rmse: 0.1521 - val_custom_mape: 19.8935 - lr: 1.6122e-04\n", "Epoch 37/100\n", "541/541 [==============================] - 40s 73ms/step - loss: 0.0503 - mse: 0.0251 - mae: 0.0868 - rmse: 0.1509 - custom_mape: 18.7631 - val_loss: 0.0632 - val_mse: 0.0557 - val_mae: 0.1318 - val_rmse: 0.2154 - val_custom_mape: 22.4402 - lr: 1.5284e-04\n", "Epoch 38/100\n", "541/541 [==============================] - 36s 67ms/step - loss: 0.0522 - mse: 0.0322 - mae: 0.0988 - rmse: 0.1711 - custom_mape: 19.8555 - val_loss: 0.0561 - val_mse: 0.0366 - val_mae: 0.1139 - val_rmse: 0.1888 - val_custom_mape: 23.3929 - lr: 1.4383e-04\n", "Epoch 39/100\n", "541/541 [==============================] - 38s 70ms/step - loss: 0.0458 - mse: 0.0236 - mae: 0.0847 - rmse: 0.1476 - custom_mape: 18.3107 - val_loss: 0.0417 - val_mse: 0.0218 - val_mae: 0.0746 - val_rmse: 0.1359 - val_custom_mape: 17.6771 - lr: 1.3432e-04\n", "Epoch 40/100\n", "541/541 [==============================] - 36s 67ms/step - loss: 0.0430 - mse: 0.0217 - mae: 0.0815 - rmse: 0.1418 - custom_mape: 18.0985 - val_loss: 0.0502 - val_mse: 0.0404 - val_mae: 0.1058 - val_rmse: 0.1835 - val_custom_mape: 20.7356 - lr: 1.2440e-04\n", "Epoch 41/100\n", "540/541 [============================>.] - ETA: 0s - loss: 0.0426 - mse: 0.0228 - mae: 0.0833 - rmse: 0.1452 - custom_mape: 18.0141\n", "Epoch 41 Detailed Metrics:\n", "541/541 [==============================] - 36s 66ms/step - loss: 0.0426 - mse: 0.0228 - mae: 0.0832 - rmse: 0.1451 - custom_mape: 18.0266 - val_loss: 0.0439 - val_mse: 0.0246 - val_mae: 0.0896 - val_rmse: 0.1506 - val_custom_mape: 20.5262 - lr: 1.1419e-04\n", "Epoch 42/100\n", "541/541 [==============================] - 36s 67ms/step - loss: 0.0398 - mse: 0.0200 - mae: 0.0783 - rmse: 0.1361 - custom_mape: 17.6098 - val_loss: 0.0454 - val_mse: 0.0335 - val_mae: 0.0979 - val_rmse: 0.1629 - val_custom_mape: 18.9381 - lr: 1.0381e-04\n", "Epoch 43/100\n", "541/541 [==============================] - 39s 71ms/step - loss: 0.0390 - mse: 0.0205 - mae: 0.0789 - rmse: 0.1381 - custom_mape: 17.4761 - val_loss: 0.0389 - val_mse: 0.0225 - val_mae: 0.0795 - val_rmse: 0.1419 - val_custom_mape: 18.9360 - lr: 9.3356e-05\n", "Epoch 44/100\n", "541/541 [==============================] - 37s 69ms/step - loss: 0.0357 - mse: 0.0165 - mae: 0.0713 - rmse: 0.1243 - custom_mape: 16.7496 - val_loss: 0.0473 - val_mse: 0.0405 - val_mae: 0.1093 - val_rmse: 0.1787 - val_custom_mape: 19.7927 - lr: 8.2964e-05\n", "Epoch 45/100\n", "541/541 [==============================] - 39s 71ms/step - loss: 0.0371 - mse: 0.0196 - mae: 0.0776 - rmse: 0.1349 - custom_mape: 17.3625 - val_loss: 0.0400 - val_mse: 0.0258 - val_mae: 0.0872 - val_rmse: 0.1522 - val_custom_mape: 19.9462 - lr: 7.2747e-05\n", "Epoch 46/100\n", "541/541 [==============================] - ETA: 0s - loss: 0.0330 - mse: 0.0143 - mae: 0.0665 - rmse: 0.1162 - custom_mape: 16.2204\n", "Epoch 46 Detailed Metrics:\n", "541/541 [==============================] - 41s 77ms/step - loss: 0.0330 - mse: 0.0143 - mae: 0.0665 - rmse: 0.1162 - custom_mape: 16.2204 - val_loss: 0.0364 - val_mse: 0.0220 - val_mae: 0.0783 - val_rmse: 0.1340 - val_custom_mape: 17.0947 - lr: 6.2818e-05\n", "Epoch 47/100\n", "541/541 [==============================] - 41s 76ms/step - loss: 0.0333 - mse: 0.0154 - mae: 0.0690 - rmse: 0.1205 - custom_mape: 16.3305 - val_loss: 0.0363 - val_mse: 0.0243 - val_mae: 0.0790 - val_rmse: 0.1431 - val_custom_mape: 17.9395 - lr: 5.3291e-05\n", "Epoch 48/100\n", "541/541 [==============================] - 41s 77ms/step - loss: 0.0307 - mse: 0.0126 - mae: 0.0622 - rmse: 0.1091 - custom_mape: 15.5196 - val_loss: 0.0337 - val_mse: 0.0201 - val_mae: 0.0724 - val_rmse: 0.1290 - val_custom_mape: 16.5807 - lr: 4.4272e-05\n", "Epoch 49/100\n", "541/541 [==============================] - 39s 72ms/step - loss: 0.0302 - mse: 0.0125 - mae: 0.0617 - rmse: 0.1083 - custom_mape: 15.3759 - val_loss: 0.0353 - val_mse: 0.0231 - val_mae: 0.0786 - val_rmse: 0.1369 - val_custom_mape: 17.0512 - lr: 3.5864e-05\n", "Epoch 50/100\n", "541/541 [==============================] - 41s 75ms/step - loss: 0.0289 - mse: 0.0113 - mae: 0.0588 - rmse: 0.1033 - custom_mape: 15.0117 - val_loss: 0.0328 - val_mse: 0.0194 - val_mae: 0.0715 - val_rmse: 0.1260 - val_custom_mape: 16.4202 - lr: 2.8161e-05\n", "Epoch 51/100\n", "540/541 [============================>.] - ETA: 0s - loss: 0.0283 - mse: 0.0110 - mae: 0.0576 - rmse: 0.1014 - custom_mape: 14.7889\n", "Epoch 51 Detailed Metrics:\n", "541/541 [==============================] - 43s 80ms/step - loss: 0.0283 - mse: 0.0110 - mae: 0.0576 - rmse: 0.1014 - custom_mape: 14.8090 - val_loss: 0.0329 - val_mse: 0.0200 - val_mae: 0.0725 - val_rmse: 0.1267 - val_custom_mape: 16.1412 - lr: 2.1251e-05\n", "Epoch 52/100\n", "541/541 [==============================] - 41s 76ms/step - loss: 0.0279 - mse: 0.0106 - mae: 0.0570 - rmse: 0.1001 - custom_mape: 14.7309 - val_loss: 0.0326 - val_mse: 0.0199 - val_mae: 0.0721 - val_rmse: 0.1267 - val_custom_mape: 16.1540 - lr: 1.5210e-05\n", "Epoch 53/100\n", "541/541 [==============================] - 36s 67ms/step - loss: 0.0277 - mse: 0.0105 - mae: 0.0567 - rmse: 0.0993 - custom_mape: 14.6780 - val_loss: 0.0329 - val_mse: 0.0206 - val_mae: 0.0734 - val_rmse: 0.1285 - val_custom_mape: 16.2771 - lr: 1.0107e-05\n", "Epoch 54/100\n", "541/541 [==============================] - 36s 67ms/step - loss: 0.0275 - mse: 0.0103 - mae: 0.0562 - rmse: 0.0986 - custom_mape: 14.6906 - val_loss: 0.0322 - val_mse: 0.0195 - val_mae: 0.0713 - val_rmse: 0.1258 - val_custom_mape: 16.1401 - lr: 5.9992e-06\n", "Epoch 55/100\n", "541/541 [==============================] - 38s 70ms/step - loss: 0.0275 - mse: 0.0105 - mae: 0.0565 - rmse: 0.0993 - custom_mape: 14.7210 - val_loss: 0.0325 - val_mse: 0.0202 - val_mae: 0.0724 - val_rmse: 0.1279 - val_custom_mape: 16.1922 - lr: 2.9336e-06\n", "Epoch 56/100\n", "540/541 [============================>.] - ETA: 0s - loss: 0.0276 - mse: 0.0105 - mae: 0.0568 - rmse: 0.0996 - custom_mape: 14.7010\n", "Epoch 56 Detailed Metrics:\n", "541/541 [==============================] - 37s 68ms/step - loss: 0.0276 - mse: 0.0105 - mae: 0.0568 - rmse: 0.0996 - custom_mape: 14.7231 - val_loss: 0.0320 - val_mse: 0.0194 - val_mae: 0.0710 - val_rmse: 0.1254 - val_custom_mape: 16.0903 - lr: 9.4439e-07\n", "Epoch 57/100\n", "541/541 [==============================] - 36s 66ms/step - loss: 0.0274 - mse: 0.0103 - mae: 0.0562 - rmse: 0.0985 - custom_mape: 14.6804 - val_loss: 0.0318 - val_mse: 0.0191 - val_mae: 0.0703 - val_rmse: 0.1246 - val_custom_mape: 16.0085 - lr: 5.4016e-08\n", "Epoch 58/100\n", "541/541 [==============================] - 34s 63ms/step - loss: 0.0322 - mse: 0.0176 - mae: 0.0715 - rmse: 0.1250 - custom_mape: 16.7714 - val_loss: 0.0412 - val_mse: 0.0307 - val_mae: 0.0991 - val_rmse: 0.1727 - val_custom_mape: 21.0792 - lr: 1.7709e-04\n", "Epoch 59/100\n", "541/541 [==============================] - 37s 69ms/step - loss: 0.0563 - mse: 0.0543 - mae: 0.1225 - rmse: 0.2110 - custom_mape: 22.3851 - val_loss: 0.0627 - val_mse: 0.0621 - val_mae: 0.1421 - val_rmse: 0.2244 - val_custom_mape: 26.3625 - lr: 1.7679e-04\n", "Epoch 60/100\n", "541/541 [==============================] - 37s 68ms/step - loss: 0.0465 - mse: 0.0347 - mae: 0.1009 - rmse: 0.1759 - custom_mape: 19.8500 - val_loss: 0.0514 - val_mse: 0.0437 - val_mae: 0.1161 - val_rmse: 0.1923 - val_custom_mape: 23.5605 - lr: 1.7624e-04\n", "Epoch 61/100\n", "540/541 [============================>.] - ETA: 0s - loss: 0.0416 - mse: 0.0273 - mae: 0.0918 - rmse: 0.1597 - custom_mape: 19.2484\n", "Epoch 61 Detailed Metrics:\n", "541/541 [==============================] - 38s 70ms/step - loss: 0.0415 - mse: 0.0273 - mae: 0.0917 - rmse: 0.1597 - custom_mape: 19.2691 - val_loss: 0.0561 - val_mse: 0.0538 - val_mae: 0.1366 - val_rmse: 0.2197 - val_custom_mape: 25.7023 - lr: 1.7545e-04\n", "Epoch 62/100\n", "541/541 [==============================] - ETA: 0s - loss: 0.0425 - mse: 0.0304 - mae: 0.0970 - rmse: 0.1667 - custom_mape: 20.2351\n", "Epoch 62: ReduceLROnPlateau reducing learning rate to 3.4881295869126915e-05.\n", "541/541 [==============================] - 39s 72ms/step - loss: 0.0425 - mse: 0.0304 - mae: 0.0970 - rmse: 0.1667 - custom_mape: 20.2351 - val_loss: 0.0451 - val_mse: 0.0344 - val_mae: 0.1058 - val_rmse: 0.1754 - val_custom_mape: 22.0628 - lr: 1.7441e-04\n", "Epoch 63/100\n", "541/541 [==============================] - 38s 70ms/step - loss: 0.0409 - mse: 0.0288 - mae: 0.0934 - rmse: 0.1608 - custom_mape: 19.4842 - val_loss: 0.0553 - val_mse: 0.0597 - val_mae: 0.1402 - val_rmse: 0.2277 - val_custom_mape: 24.8888 - lr: 1.7312e-04\n", "Epoch 64/100\n", "541/541 [==============================] - 37s 69ms/step - loss: 0.0392 - mse: 0.0268 - mae: 0.0898 - rmse: 0.1561 - custom_mape: 18.8321 - val_loss: 0.0483 - val_mse: 0.0360 - val_mae: 0.1207 - val_rmse: 0.1853 - val_custom_mape: 30.6992 - lr: 1.7160e-04\n", "Epoch 65/100\n", "541/541 [==============================] - 36s 67ms/step - loss: 0.0367 - mse: 0.0234 - mae: 0.0850 - rmse: 0.1474 - custom_mape: 18.5001 - val_loss: 0.0611 - val_mse: 0.0787 - val_mae: 0.1592 - val_rmse: 0.2557 - val_custom_mape: 27.1302 - lr: 1.6985e-04\n", "Epoch 66/100\n", "540/541 [============================>.] - ETA: 0s - loss: 0.0354 - mse: 0.0231 - mae: 0.0835 - rmse: 0.1455 - custom_mape: 18.0659\n", "Epoch 66 Detailed Metrics:\n", "541/541 [==============================] - 38s 70ms/step - loss: 0.0355 - mse: 0.0231 - mae: 0.0835 - rmse: 0.1456 - custom_mape: 18.1011 - val_loss: 0.0308 - val_mse: 0.0148 - val_mae: 0.0698 - val_rmse: 0.1188 - val_custom_mape: 19.3388 - lr: 1.6787e-04\n", "Epoch 67/100\n", "541/541 [==============================] - 34s 63ms/step - loss: 0.0379 - mse: 0.0272 - mae: 0.0913 - rmse: 0.1574 - custom_mape: 19.2562 - val_loss: 0.0670 - val_mse: 0.0740 - val_mae: 0.1756 - val_rmse: 0.2696 - val_custom_mape: 35.2934 - lr: 1.6566e-04\n", "Epoch 68/100\n", "541/541 [==============================] - 36s 66ms/step - loss: 0.0367 - mse: 0.0258 - mae: 0.0874 - rmse: 0.1516 - custom_mape: 18.6506 - val_loss: 0.0364 - val_mse: 0.0239 - val_mae: 0.0878 - val_rmse: 0.1462 - val_custom_mape: 22.8155 - lr: 1.6323e-04\n", "Epoch 69/100\n", "541/541 [==============================] - 36s 67ms/step - loss: 0.0351 - mse: 0.0232 - mae: 0.0840 - rmse: 0.1458 - custom_mape: 18.4729 - val_loss: 0.0440 - val_mse: 0.0429 - val_mae: 0.1105 - val_rmse: 0.1929 - val_custom_mape: 22.5552 - lr: 1.6060e-04\n", "Epoch 70/100\n", "541/541 [==============================] - 37s 69ms/step - loss: 0.0373 - mse: 0.0266 - mae: 0.0880 - rmse: 0.1523 - custom_mape: 18.6093 - val_loss: 0.0342 - val_mse: 0.0220 - val_mae: 0.0808 - val_rmse: 0.1415 - val_custom_mape: 18.8001 - lr: 1.5776e-04\n", "Epoch 71/100\n", "540/541 [============================>.] - ETA: 0s - loss: 0.0285 - mse: 0.0140 - mae: 0.0653 - rmse: 0.1143 - custom_mape: 15.8762\n", "Epoch 71 Detailed Metrics:\n", "541/541 [==============================] - 38s 69ms/step - loss: 0.0285 - mse: 0.0140 - mae: 0.0653 - rmse: 0.1142 - custom_mape: 15.8894 - val_loss: 0.0261 - val_mse: 0.0135 - val_mae: 0.0598 - val_rmse: 0.1085 - val_custom_mape: 16.6281 - lr: 1.5473e-04\n", "Epoch 72/100\n", "541/541 [==============================] - 39s 71ms/step - loss: 0.0314 - mse: 0.0208 - mae: 0.0773 - rmse: 0.1348 - custom_mape: 17.4662 - val_loss: 0.1001 - val_mse: 0.1225 - val_mae: 0.2531 - val_rmse: 0.3373 - val_custom_mape: 45.8354 - lr: 1.5151e-04\n", "Epoch 73/100\n", "541/541 [==============================] - 37s 68ms/step - loss: 0.0338 - mse: 0.0232 - mae: 0.0844 - rmse: 0.1457 - custom_mape: 18.5363 - val_loss: 0.0489 - val_mse: 0.0599 - val_mae: 0.1301 - val_rmse: 0.2211 - val_custom_mape: 21.9077 - lr: 1.4811e-04\n", "Epoch 74/100\n", "541/541 [==============================] - 39s 72ms/step - loss: 0.0353 - mse: 0.0258 - mae: 0.0890 - rmse: 0.1551 - custom_mape: 18.6775 - val_loss: 0.0340 - val_mse: 0.0259 - val_mae: 0.0851 - val_rmse: 0.1516 - val_custom_mape: 19.9781 - lr: 1.4454e-04\n", "Epoch 75/100\n", "541/541 [==============================] - 39s 73ms/step - loss: 0.0317 - mse: 0.0205 - mae: 0.0790 - rmse: 0.1372 - custom_mape: 17.6368 - val_loss: 0.0631 - val_mse: 0.0685 - val_mae: 0.1722 - val_rmse: 0.2580 - val_custom_mape: 38.4844 - lr: 1.4082e-04\n", "Epoch 76/100\n", "540/541 [============================>.] - ETA: 0s - loss: 0.0397 - mse: 0.0317 - mae: 0.0969 - rmse: 0.1681 - custom_mape: 19.9562\n", "Epoch 76: ReduceLROnPlateau reducing learning rate to 2.738986222539097e-05.\n", "\n", "Epoch 76 Detailed Metrics:\n", "541/541 [==============================] - 38s 71ms/step - loss: 0.0397 - mse: 0.0317 - mae: 0.0969 - rmse: 0.1679 - custom_mape: 19.9689 - val_loss: 0.0408 - val_mse: 0.0323 - val_mae: 0.1042 - val_rmse: 0.1719 - val_custom_mape: 20.9799 - lr: 1.3695e-04\n", "Epoch 77/100\n", "541/541 [==============================] - 38s 69ms/step - loss: 0.0328 - mse: 0.0208 - mae: 0.0798 - rmse: 0.1392 - custom_mape: 17.8401 - val_loss: 0.0362 - val_mse: 0.0293 - val_mae: 0.0922 - val_rmse: 0.1596 - val_custom_mape: 19.6859 - lr: 1.3294e-04\n", "Epoch 78/100\n", "541/541 [==============================] - 37s 69ms/step - loss: 0.0295 - mse: 0.0172 - mae: 0.0724 - rmse: 0.1262 - custom_mape: 16.6377 - val_loss: 0.0507 - val_mse: 0.0652 - val_mae: 0.1377 - val_rmse: 0.2239 - val_custom_mape: 21.8391 - lr: 1.2881e-04\n", "Epoch 79/100\n", "541/541 [==============================] - 39s 73ms/step - loss: 0.0313 - mse: 0.0205 - mae: 0.0789 - rmse: 0.1380 - custom_mape: 17.4685 - val_loss: 0.0287 - val_mse: 0.0193 - val_mae: 0.0717 - val_rmse: 0.1272 - val_custom_mape: 17.1353 - lr: 1.2456e-04\n", "Epoch 80/100\n", "541/541 [==============================] - 40s 75ms/step - loss: 0.0291 - mse: 0.0180 - mae: 0.0739 - rmse: 0.1289 - custom_mape: 16.8870 - val_loss: 0.0446 - val_mse: 0.0404 - val_mae: 0.1205 - val_rmse: 0.1947 - val_custom_mape: 24.6887 - lr: 1.2022e-04\n", "Epoch 81/100\n", "540/541 [============================>.] - ETA: 0s - loss: 0.0313 - mse: 0.0210 - mae: 0.0805 - rmse: 0.1399 - custom_mape: 17.9034\n", "Epoch 81 Detailed Metrics:\n", "541/541 [==============================] - 39s 72ms/step - loss: 0.0313 - mse: 0.0210 - mae: 0.0805 - rmse: 0.1398 - custom_mape: 17.9156 - val_loss: 0.0343 - val_mse: 0.0271 - val_mae: 0.0901 - val_rmse: 0.1555 - val_custom_mape: 22.6929 - lr: 1.1578e-04\n", "Epoch 82/100\n", "541/541 [==============================] - 37s 68ms/step - loss: 0.0279 - mse: 0.0161 - mae: 0.0706 - rmse: 0.1234 - custom_mape: 16.5048 - val_loss: 0.0382 - val_mse: 0.0389 - val_mae: 0.1040 - val_rmse: 0.1719 - val_custom_mape: 18.8728 - lr: 1.1127e-04\n", "Epoch 83/100\n", "540/541 [============================>.] - ETA: 0s - loss: 0.0283 - mse: 0.0175 - mae: 0.0729 - rmse: 0.1278 - custom_mape: 16.6226\n", "Epoch 83: ReduceLROnPlateau reducing learning rate to 2.133804955519736e-05.\n", "541/541 [==============================] - 36s 67ms/step - loss: 0.0283 - mse: 0.0175 - mae: 0.0729 - rmse: 0.1278 - custom_mape: 16.6437 - val_loss: 0.0371 - val_mse: 0.0295 - val_mae: 0.1006 - val_rmse: 0.1693 - val_custom_mape: 21.9720 - lr: 1.0669e-04\n", "Epoch 84/100\n", "541/541 [==============================] - 36s 66ms/step - loss: 0.0280 - mse: 0.0170 - mae: 0.0721 - rmse: 0.1260 - custom_mape: 17.0158 - val_loss: 0.0318 - val_mse: 0.0262 - val_mae: 0.0849 - val_rmse: 0.1485 - val_custom_mape: 17.9378 - lr: 1.0206e-04\n", "Epoch 85/100\n", "541/541 [==============================] - 38s 71ms/step - loss: 0.0278 - mse: 0.0171 - mae: 0.0723 - rmse: 0.1257 - custom_mape: 17.1040 - val_loss: 0.0441 - val_mse: 0.0515 - val_mae: 0.1231 - val_rmse: 0.1994 - val_custom_mape: 20.8031 - lr: 9.7396e-05\n", "Epoch 86/100\n", "540/541 [============================>.] - ETA: 0s - loss: 0.0282 - mse: 0.0176 - mae: 0.0734 - rmse: 0.1280 - custom_mape: 16.8326Restoring model weights from the end of the best epoch: 71.\n", "\n", "Epoch 86 Detailed Metrics:\n", "541/541 [==============================] - 36s 67ms/step - loss: 0.0282 - mse: 0.0176 - mae: 0.0734 - rmse: 0.1279 - custom_mape: 16.8449 - val_loss: 0.0324 - val_mse: 0.0245 - val_mae: 0.0870 - val_rmse: 0.1518 - val_custom_mape: 19.8808 - lr: 9.2705e-05\n", "Epoch 86: early stopping\n", "\n", "Training completed successfully!\n", "\n", "Final Model Performance:\n", "Out of range predictions: 14036 (54.12%)\n", "MAPE: 16.61%\n", "Predictions within ±10%: 68.85%\n", "MAE: 0.06\n", "RMSE: 0.12\n", "\n", "Training history plot saved as: 2024-11-25_13-52_training_history.png\n", "Training history data saved as: 2024-11-25_13-52_training_history.csv\n", "Final statistics saved as: 2024-11-25_13-52_training_stats.json\n", "\n", "Final Training Statistics:\n", "Final Loss (train/val): 0.0282/0.0324\n", "Final MAE (train/val): 0.0734/0.0870\n", "Best validation loss: 0.0261\n", "Best validation MAE: 0.0598\n" ] }, { "data": { "image/png": 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Usif1ST9fIiLSHOn/n9SXuvjZUlBKREREREREREQanIJSlXjgy4089LuFL9ceCPRQRESkGUlKSuL555/3e/0lS5ZgMpk0c6E0ev9asJWHf7cwd8WeQA9FRESaEZ1bNV4KSlUiM89BeoGJY7kFgR6KiIg0QiaTqdKvhx9+uEbbXblyJTfffLPf65988skcOHCA2NjYGu3PXzpBk9rKzHdwrMBEeq76eomISFnN9dyqZcuW5Ofnl3hu5cqVvvddnt69e2Oz2UhNTS3z3MiRI8v9/v31r3+tl/dRG5p9rxKRNuPbk1vgDPBIRESkMTpwoCiT9sMPP+TBBx9ky5YtvmVRUVG++263G6fTidVa9b/e1q1bV2scoaGhtG3btlqvEQmEUKsxM4/doXMrEREpq7meW0VHR/PVV19xww03+Ja99dZbdOrUiZSUlDLr//TTT+Tl5XHJJZfw9ttvc++995ZZZ/LkyTzyyCMlljXGSYWUKVWJiFDjxCnHrhMnEREpq23btr6v2NhYTCaT7/HmzZuJjo7mm2++YciQIdhsNn766Se2b9/OhAkTSEhIICoqihNOOIFFixaV2G7pFHOTycSbb77JhRdeSEREBD169ODLL7/0PV86g2nOnDnExcWxYMEC+vTpQ1RUFGeffXaJE73CwkJuv/124uLiaNmyJffeey/XXXcdF1xwQY2/H8eOHWPixInEx8cTERHBOeecw59//ul7fvfu3YwfP574+HgiIyPp168f8+fP97326quvpnXr1oSHh9OjRw9mz55d47FI4xRmNU497YWuAI9EREQao+Z6bjVx4kTmzp3re5yXl8e8efO47rrryl3/rbfe4qqrruLaa69l1qxZ5a4TERFR4vvZtm1bYmJiqhxLQ1NQqhKRoUbENaegMMAjERFpftxuN7kFhQH5crvddfY+7rvvPmbOnMmmTZs47rjjyM7OZty4cSxevJjVq1dz9tlnM378+HKvghU3ffp0LrvsMv744w/GjRvH1VdfzdGjRytcPzc3l6effpp3332XH374gZSUFO666y7f808++SRz585l9uzZ/Pzzz2RmZvLFF1/U6r1ef/31/Pbbb3z55ZcsX74ct9vNuHHjfNME33rrrdjtdn744QfWrVvHk08+6bvi+cADD7Bx40a++eYbNm3axKuvvkqrVq1qNR5pfMJCjFPP/EJd8BMRaWg6tyqpMZ1bXXPNNSxfvtw35k8//ZSkpCSOP/74MutmZWXx8ccfc8011zB69GgyMjL48ccf/dpPY6TyvUr4MqVUvici0uDyHE76PrggIPve+MhYIkLr5l/kI488wujRo32PW7RowcCBA32PH330UT7//HO+/PJLpkyZUuF2rr/+eq688koAnnjiCV544QVWrFjB2WefXe76DoeD1157jW7dugEwZcqUEincL774ItOmTePCCy8E4KWXXvJlLdXEn3/+yZdffsnPP//MySefDMDcuXNJTEzkiy++4NJLLyUlJYWLL76YAQMGANC1a1ff61NSUhg8eDBDhw4FjCuaEnxs3vI9ZUqJiDQ4nVuV1JjOrdq0acOoUaN4++23eeihh5g1a1aJUr7i5s2bR48ePejXrx8AV1xxBW+99RannnpqifVeeeUV3nzzzRLL/vOf/3D11Vf7NaaGokypSkTZvOV7ypQSEZGa8QZZvLKzs7nrrrvo06cPcXFxREVFsWnTpiqv5h133HG++5GRkcTExJCWllbh+hEREb6TJoB27dr51s/IyODgwYMMGzbM97zFYmHIkCHVem/Fbdq0CavVyvDhw33LWrZsSa9evdi0aRMAt99+O4899hgjRozgoYce4o8//vCt+7e//Y158+YxaNAg7rnnHpYtW1bjsUjjZfNmSjkUlBIRkZoJ1nOrq6++mrfffpsdO3awfPnyCoNHs2bN4pprrvE9vuaaa/j444/Jysoqs701a9aU+Dr//PP9Hk9DUaZUJbyNztVTSkSk4YWHWNj4yNiA7buuREZGlnh81113kZyczNNPP0337t0JDw/nkksuoaCg8pleQ0JCSjw2mUy4XBV/sC9v/bpMna+Jm266ibFjx/L111+zcOFCZsyYwTPPPMNtt93GOeecw+7du5k/fz7JycmcddZZ3HrrrTz99NMBHXOgvfzyyzz11FOkpqYycOBAXnzxxRInvMXNmTOHSZMmlVhms9nKzOYTSDZvTyk1OhcRaXA6tyqpsZ1bjR49mqlTp3LjjTcyfvx4WrZsWWadjRs38ssvv7BixYoSzc2dTifz5s1j8uTJvmWxsbF07969zsZXXwKaKeV0OnnggQfo0qUL4eHhdOvWjUcffTTgJ81e3vK9XPWUEhFpcCaTiYhQa0C+Kpp6ty78/PPPXH/99Vx44YUMGDCAtm3bsmvXrnrbX3liY2NJSEhg5cqVvmVOp5NVq1bVeJt9+vShsLCQX3/91bfsyJEjbNmyhb59+/qWJSYm8te//pXPPvuMO++8kzfeeMP3XOvWrbnuuut47733eP7553n99ddrPJ5g8OGHHzJ16lQeeughVq1axcCBAxk7dmylV3FjYmI4cOCA72v37t0NOOKqhal8T0QkYHRuVX/q4tzKarVy7bXXsmTJkgpL99566y1OO+001q5dWyIDaurUqbz11lu1fh+BENBMqSeffJJXX32Vt99+m379+vHbb78xadIkYmNjuf322wM5NECZUiIiUvd69OjBZ599xvjx4zGZTDzwwAOVXpWrL7fddhszZsyge/fu9O7dmxdffJFjx475ddK4bt06zGYzkZGRmM1mTCYTAwcOZMKECUyePJn//Oc/REdHc99999GhQwcmTJgAwB133ME555xDz549OXbsGN9//z19+vQB4MEHH2TIkCH069cPu93OV1995XuuuXr22WeZPHmyL/vptdde4+uvv2bWrFncd9995b7GO0tRY1XU6FxBKRERqRvBcG7l9cgjj3DPPfeUmyXlcDh49913eeSRR+jfv3+J52666SaeffZZNmzY4Os1lZubS2pqaon1bDYb8fHxNXh39SegQally5YxYcIEzj33XMBoavrBBx+wYsWKQA7LJ9LX6FyZUiIiUjeeffZZbrjhBk4++WRatWrFvffeS2ZmZoOP49577yU1NZWJEydisVi4+eabGTt2LBZL1en1I0eOLPHYYrFQWFjI7Nmz+fvf/855551HQUEBp512GvPnz/eluzudTm699Vb27t1LTEwMZ599Ns899xwAoaGhTJs2jV27dhEeHs6pp57KvHnz6vx9NxUFBQX8/vvvTJs2zbfMbDYzatQoli9fXuHrsrOz6dy5My6Xi+OPP54nnnjCd3JaHrvdjt1u9z32/iw6HA7frIl1yYKRDZ9fUFgv25eG4T12OoZNm45jcKjsODocDtxuNy6XKyBBmtryjrm82+Lv5+mnn+amm27ynVvdc889ZGZm+t67V+nH5X1fvMtK76v0GMob1913382BAwd851aTJ09mzJgxWCyWCr//3uXearGQkBBatGiB2+0uMV6Xy8UXX3zBkSNHmDBhQpnt9erViz59+vDmm2/yzDPPAPDGG2+UyEgHGDNmDN988025Y6kJl8uF2+3G4XCUOYf092+LyR3AWrknnniC119/nYULF9KzZ0/Wrl3LmDFjePbZZ/3qCJ+ZmUlsbCwZGRnExMTU+fg27j3GuJeWER8RwuoHx9T59qVhOBwO5s+fz7hx48rUAUvToeMYHCo6jvn5+ezcuZMuXboQFhYWwBE2Xy6Xiz59+nDZZZfx6KOPVrluZmYmMTExmM1NZ86Uyn7O6vucorr2799Phw4dWLZsGSeddJJv+T333MPSpUtLlEl6LV++nD///JPjjjuOjIwMnn76aX744Qc2bNhAx44dy93Pww8/zPTp08ssf//994mIiKi7N+SxJd3EK5sstItwc99AZaKLiNQnq9VK27ZtSUxMJDQ0NNDDaXZcLhfDhw/nggsu4P777w/0cOpFQUEBe/bsITU1lcLCksk8ubm5XHXVVVWeWwU0U+q+++4jMzOT3r17Y7FYcDqdPP744xUGpBr6al6oxYjXZdt1Na8p01Wg4KDjGBwqOo5N/UpeU7R7924WLlzI6aefjt1u5+WXX2bnzp1cccUVVR4D7/Ws0lccG7u6uJrXmJ100kklAlgnn3wyffr04T//+U+FgcZp06YxdepU3+PMzEwSExMZM2ZMvQTnWmw/xCubVhMSFsG4cadW/QJplBwOB8nJyYwePVoXipowHcfgUNlxzM/PZ8+ePURFRemiXwMo79xq9+7dXH/99VX+T3W73WRlZREdHV2v/bfqWn5+PuHh4Zx22mnlXvDzR0CDUh999BFz587l/fffp1+/fqxZs4Y77riD9u3bc91115VZf8aMGeVezVu4cGG9XM3LLQSw4nC6+fKr+VibzsVgKUdycnKghyB1QMcxOJQ+jt4rednZ2VXOlCJ1Iycnh1mzZnH33XcD0Lt3bz7//HM6dOjg90lE6amHG7uCggLy8vL44Ycfyr2a15i0atUKi8XCwYMHSyw/ePCg3z2jQkJCGDx4MNu2batwHZvNhs1mK/e19fEhNSrM2FdBoUsfgoNAff2cSMPScQwO5R1Hp9OJyWTCbDY3qczmpspqtfLOO+9wzz334Ha76d+/P4sWLaq0jN7Le5HPe7yaCm9v0fJ+/vz9uxLQoNTdd9/NfffdxxVXXAHAgAED2L17NzNmzCg3KNXQV/Ny8+1MW7kUgNPOHE1chP5YN0W6ChQcdByDQ0XHUVfyGl7fvn0r7U1UmeZ8Na+hhIaGMmTIEBYvXswFF1wAGCesixcvZsqUKX5tw+l0sm7dOsaNG1ePI60em6fRuWbfExGRYJOYmMjPP/8c6GE0OQENSuXm5paJAlbWBKyhr+ZFAFaTm0K3CbvL/0ifNE66ChQcdByDQ+njqCt5TUtzvprXkKZOncp1113H0KFDGTZsGM8//zw5OTm+2fgmTpxIhw4dmDFjBmDM2HPiiSfSvXt30tPTeeqpp9i9ezc33XRTIN9GCTZP2nm+Q/2kREREJMBBqfHjx/P444/TqVMn+vXrx+rVq32zEjUWNgsUFkKOXSdPIiIi0nAuv/xyDh06xIMPPkhqaiqDBg3i22+/JSEhAYCUlJQSQcFjx44xefJkUlNTiY+PZ8iQISxbtoy+ffsG6i2UERZi9PKyFxr9vZpSpp2IiIjUvYAGpV588UUeeOABbrnlFtLS0mjfvj1/+ctfePDBBwM5rBJsFsgphJyCwqpXFhEREalDU6ZMqbBcb8mSJSUeP/fcczz33HMNMKqaC/NkSrnc4HC6CbUqKCUiItKcBTQoFR0dzfPPP8/zzz8fyGFUyuaZnCfHrqCUiIiISG3Yis0aYy90EqpZZERERJo1nQlUIUxBKREREZE6UTwIle9Qs3MREZHmTkGpKtjMbkA9pURERERqy2QyEWIyzq3U7FxEREQUlKqCr3xPPaVERKSejBw5kjvuuMP3OCkpqcrSdpPJxBdffFHrfdfVdkT8FeI5+7QXKlNKRETqh86tmg4FpargDUplq3xPRERKGT9+PGeffXa5z/3444+YTCb++OOPam935cqV3HzzzbUdXgkPP/wwgwYNKrP8wIEDnHPOOXW6r9LmzJlDXFxcve5Dmg5vUEqZUiIiUprOrfwzZ84cTCYTffr0KfPcxx9/jMlkIikpqcxzeXl5tGjRglatWmG328s8n5SUhMlkKvM1c+bM+ngbgIJSVfIGpXJVviciIqXceOONJCcns3fv3jLPzZ49m6FDh3LcccdVe7utW7cmIiKiLoZYpbZt22Kz2RpkXyIAVmVKiYhIBXRu5b/IyEjS0tJYvnx5ieVvvfUWnTp1Kvc1n376Kf369aN3794VZnM98sgjHDhwoMTXbbfdVtfD91FQqgrKlBIRkYqcd955tG7dmjlz5pRYnp2dzccff8yNN97IkSNHuPLKK+nQoQMREREMGDCADz74oNLtlk4x//PPPznttNMICwujb9++JCcnl3nNvffeS8+ePYmIiKBr16488MADOBwOwLiaNn36dNauXeu74uUdc+kU83Xr1nHmmWcSHh5Oy5Ytufnmm8nOzvY9f/3113PhhRfy4osv0qFDB1q2bMmtt97q21dNpKSkMGHCBKKiooiJieGyyy7j4MGDvufXrl3LGWecQXR0NDExMQwZMoTffvsNgN27dzN+/Hji4+OJjIykX79+zJ8/v8ZjkfrnK99TppSIiJSicyv/z62sVitXXXUVs2bN8i3bu3cvS5Ys4aqrrir3NW+99RbXXHMN11xzDW+99Va560RHR9O2bdsSX5GRkZWOpTas9bblIOFtdJ6rnlIiIg3L7QZHbmD2HRIBJlOVq1mtViZOnMicOXO4//77MXle8/HHH+N0OrnyyivJzs5myJAh3HvvvcTExPD1119z7bXX0q1bN4YNG1blPlwuFxdddBEJCQn8+uuvZGRklOiR4BUdHc2cOXNo374969atY/LkyURHR3PPPfdw+eWXs379er799lsWLVoEQGxsbJlt5OTkMHbsWE466SRWrlxJWloaN910E1OmTClxcrhkyRJatmzJ4sWL2bFjB5dffjmDBg1i8uTJVb6f8t6fNyC1dOlSCgsLufXWW7n88stZsmQJAFdffTWDBw/m1VdfxWKxsGbNGkJCQgC49dZbKSgo4IcffiAyMpKNGzcSFRVV7XFIw/GV7xUqKCUi0qB0bgUE17nVDTfcwMiRI/n3v/9NREQEc+bM4eyzzyYhIaHMutu3b2f58uV89tlnuN1u/vGPf7B79246d+5c5fesPikoVYUwb6Nzle+JiDQsRy480T4w+/6//RDq3xWhG264gaeeeoqlS5cycuRIwEgvv/jii4mNjSU2Npa77rrLt/5tt93GggUL+Oijj/w6cVq0aBGbN29mwYIFtG9vfD+eeOKJMr0K/vnPf/ruJyUlcddddzFv3jzuuecewsPDiYqKwmq10rZt2wr39f7775Ofn88777zjuyL20ksvMX78eJ588knfCU58fDxPPfUU8fHx9O3bl3PPPZfFixfXKCi1ePFi1q1bx86dO0lMTATgnXfeoV+/fqxcuZITTjiBlJQU7r77bnr37g1Ajx49fK9PSUnh4osvZsCAAQB07dq12mOQhlWUKaXyPRGRBqVzKyC4zq0GDx5M165d+eSTT7j22muZM2cOzz77LDt27Ciz7qxZszjnnHOIj48HYOzYscyePZuHH364xHr33ntvifcO8M0333DqqadWOpaaUvleFVS+JyIilenduzcnn3yyL3V627Zt/Pjjj9x4440AOJ1OHn30UQYMGECLFi2IiopiwYIFpKSk+LX9TZs2kZiY6DtpAjjppJPKrPfhhx8yYsQI2rZtS1RUFP/85z/93kfxfQ0cOLBEivaIESNwuVxs2bLFt6xv375YLBbf43bt2pGWllatfRXfZ2Jioi8g5d1+XFwcmzZtAmDq1KncdNNNjBo1ipkzZ7J9+3bfurfffjuPPfYYI0aM4KGHHqpR81NpWCGeLHRlSomISHl0blW9c6sbbriB2bNns3TpUnJychg3blyZdZxOJ2+//TbXXHONb9k111zDnDlzcLlKXiS6++67WbNmTYmvoUOH+v2eq0uZUlXwNTpX+Z6ISMMKiTCuqgVq39Vw4403ctttt/Hyyy8ze/ZsunXrxumnnw7AU089xb///W+ef/55BgwYQGRkJHfccQcFBQV1Ntzly5dz9dVXM336dMaOHUtsbCzz5s3jmWeeqbN9FOctnfMymUxlTmjq0sMPP8xVV13F119/zTfffMNDDz3EvHnzuPDCC7npppsYO3YsX3/9NQsXLmTGjBk888wz9dqQU2qnaPY9ZUqJiDQonVv5rSmdW1199dXcc889PPzww1x77bVYrWXDPAsWLGDfvn1cfvnlJZY7nU4WL17M6NGjfctatWpF9+7da/AuakaZUlUoypTS1TwRkQZlMhlp3oH48qPnQXGXXXYZZrOZ999/n3feeYcbbrjB1wPh559/ZsKECVxzzTUMHDiQrl27snXrVr+33adPH/bs2cOBAwd8y3755ZcS6yxbtozOnTtz//33M3ToUHr06MHu3btLrBMaGorTWfn/sj59+rB27VpycnJ8y37++WfMZjO9evXye8zV4X1/e/bs8S3buHEj6enp9O3b17esZ8+e/OMf/2DhwoVcdNFFzJ492/dcYmIif/3rX/nss8+48847eeONN+plrFI31OhcRCRAdG4FBN+5VYsWLTj//PNZunQpN9xwQ7nrvPXWW1xxxRVlMqCuuOKKChueNxQFpaoQZjFSzHNUviciIhWIiori8ssvZ9q0aRw4cIDrr7/e91yPHj1ITk5m2bJlbNq0ib/85S8lZparyqhRo+jZsyfXXXcda9eu5ccff+T+++8vsU6PHj1ISUlh3rx5bN++nRdeeIHPP/+8xDpJSUns3LmTNWvWcPjwYex2e5l9XX311YSFhXHdddexfv16vv/+e2677TauvfbachtmVofT6SxzIrRp0yZGjRrFgAEDuPrqq1m1ahUrVqxg4sSJnH766QwdOpS8vDymTJnCkiVL2L17Nz///DMrV66kT58+ANxxxx0sWLCAnTt3smrVKr7//nvfc9I4FTU6V6aUiIiUT+dW1TNnzhwOHz7s679Z3KFDh/jf//7HddddR//+/Ut8TZw4kS+++IKjR4/61s/KyiI1NbXEV2ZmZp2NtTQFpaoQ6vkOqXxPREQqc+ONN3Ls2DHGjh1bokfBP//5T44//njGjh3LyJEjadu2LRdccIHf2zWbzXz++efk5eUxbNgwbrrpJh5//PES65x//vn84x//YMqUKQwaNIhly5bxwAMPlFjn4osv5uyzz+aMM86gdevW5U6dHBERwYIFCzh69CgnnHACl1xyCWeddRYvvfRS9b4Z5cjOzmbw4MElvsaPH4/JZOK///0v8fHxnHbaaYwaNYquXbvy4YcfAmCxWDhy5AgTJ06kZ8+eXHbZZZxzzjlMnz4dMIJdt956K3369OHss8+mZ8+evPLKK7Uer9Qfqxqdi4iIH3Ru5b/w8HBatmxZ7nPeJutnnXVWmefOOusswsPDee+993zLHnzwQdq1a1fi65577qnT8RZncrvd7nrbej3LzMwkNjaWjIwMYmJi6nz7DoeDdz+bzyOrrYSFmNn86DlVv0gaHYfDwfz58xk3blyZWl1pOnQcg0NFxzE/P5+dO3fSpUsXwsLCAjhC8YfL5SIzM5OYmBjM5qZzfauyn7P6PqdoKhri3OqmV75l6QEzfxvZjXvPLntFVxo//U8ODjqOwaGy46jzq6ajOZ9bNZ13GyDenlL5DheFTl3RExEREamNEE9bkXz1lBIREWn2FJSqgq1oVkZydfIkIiIiUishZiNJ366eUiIiIs2eglJVsJrAajYu6anZuYiIiEjt+Bqd62KfiIhIs6egVBVMJoj0pEspKCUiIiJSO96glDKlREREREEpP0SEWgHIseuKnoiIiEht+IJSypQSERFp9hSU8kNkqDKlREQagsulzAmpP/r5ahysvvI9HQ8RkYbgdrsDPQQJUnVxbmWtg3EEvUib8W3KVlBKRKRehIaGYjab2b9/P61btyY0NBSTyRToYUkFXC4XBQUF5OfnN4lpi91uNwUFBRw6dAiz2UxoaGigh9SsFZXvKVNKRKQ+hYSEYDKZOHToEK1bt9a5VSPWnM+tFJTygzdTKrdAJ08iIvXBbDbTpUsXDhw4wP79+wM9HKmC2+0mLy+P8PDwJnWCGxERQadOnZrEyV4wC1GmlIhIg7BYLHTs2JG9e/eya9euQA9HKtGcz60UlPKDMqVEROpfaGgonTp1orCwEKdTFwEaM4fDwQ8//MBpp51GSEhIoIfjF4vFgtVqbVInesFKs++JiDScqKgoevTogcPhCPRQpBLN+dxKQSk/RKinlIhIgzCZTISEhDSZf8bNlcViobCwkLCwMB0rqbYQs9HbRLPviYg0DIvFgsViCfQwpBLN+dxK+et+iLR5glIq3xMRERGpFWVKiYiIiJeCUn6IDDUSypQpJSIiIlI7RY3OlSklIiLS3Cko5QeV74mIiIjUDWVKiYiIiJeCUn7wNjpX+Z6IiIhI7Vg9/VDthS7cbndgByMiIiIBpaCUH6JsypQSERERqQuhxc4+VcInIiLSvCko5YcI9ZQSERERqRMhxYNSDgWlREREmjMFpfxQNPueglIiIiIitWE2GV8A9kK1RhAREWnOFJTyQ9HsezpxEhEREakNkwnCQowLfvnKlBIREWnWFJTyg2bfExEREak7NqtxCpqvTCkREZFmTUEpP0TZ1FNKREREpK54g1LqKSUiItK8KSjlh6KeUk5cLk1dLCIiIlIbvvI9ZUqJiIg0awpK+cFbvgeQ59DJk4iIiEht+Mr3dF4lIiLSrCko5YfwEItvlhiV8ImIiIjUji1E5XsiIiKioJRfTCaTbwa+bAWlRERERGolzKryPREREVFQym8Rnr5SuQU6eRIRERGpDTU6FxEREVBQym+RNmVKiYiIiNQFNToXERERUFDKb97yPfWUEhEREamdUF+jc2VKiYiINGcKSvkp0lO+l6PyPREREZFaCfM2OlemlIiISLOmoJSfomzKlBIRERGpC75G58qUEhERadYUlPJThMr3REREROqEr9G5MqVERESaNQWl/BTpy5TSyZOIiIhIbdhCNPueiIiIKCjltyhfTyllSomIiIjURlH5ni72iYiINGcKSvnJW76XrfI9ERERkVrxZUoVKlNKRESkOVNQyk/eRue5CkqJiIiI1EqYp6eUMqVERESaNwWl/OTtKZWtnlIiIiLSQF5++WWSkpIICwtj+PDhrFixwq/XzZs3D5PJxAUXXFC/A6yhUJXviYiICApK+S3S21NKmVIiIiLSAD788EOmTp3KQw89xKpVqxg4cCBjx44lLS2t0tft2rWLu+66i1NPPbWBRlp9YSrfExERERSU8lukp6dUrhqdi4iISAN49tlnmTx5MpMmTaJv37689tprREREMGvWrApf43Q6ufrqq5k+fTpdu3ZtwNFWjxqdi4iICIA10ANoKorK9xSUEhERkfpVUFDA77//zrRp03zLzGYzo0aNYvny5RW+7pFHHqFNmzbceOON/Pjjj1Xux263Y7fbfY8zMzMBcDgcOByOWryD8nm3aTEZGVL5Dme97Efql/eY6dg1bTqOwUHHMTgE43H0970oKOUnb/leboGu6ImIiEj9Onz4ME6nk4SEhBLLExIS2Lx5c7mv+emnn3jrrbdYs2aN3/uZMWMG06dPL7N84cKFREREVGvM1bF+zSrAypH0TObPn19v+5H6lZycHOghSB3QcQwOOo7BIZiOY25url/rKSjlJ2VKiYiISGOVlZXFtddeyxtvvEGrVq38ft20adOYOnWq73FmZiaJiYmMGTOGmJiYOh+nw+EgOTmZk08cxosbV2G1hTNu3Gl1vh+pX97jOHr0aEJCQgI9HKkhHcfgoOMYHILxOHqzr6uioJSfojxBqRx7IW63G5PJFOARiYiISLBq1aoVFouFgwcPllh+8OBB2rZtW2b97du3s2vXLsaPH+9b5nIZJXJWq5UtW7bQrVu3Mq+z2WzYbLYyy0NCQur1pDgq3NinvdAdNCffzVF9/5xIw9BxDA46jsEhmI6jv+9Djc79FBFqlO+53JopRkREROpXaGgoQ4YMYfHixb5lLpeLxYsXc9JJJ5VZv3fv3qxbt441a9b4vs4//3zOOOMM1qxZQ2JiYkMOv0q+2ffU6FxERKRZC3hQat++fVxzzTW0bNmS8PBwBgwYwG+//RboYZXhnX0PVMInIiIi9W/q1Km88cYbvP3222zatIm//e1v5OTkMGnSJAAmTpzoa4QeFhZG//79S3zFxcURHR1N//79CQ0NDeRbKcPmmX1PF/pERESat4CW7x07dowRI0Zwxhln8M0339C6dWv+/PNP4uPjAzmscpnNJiJCLeQWOMmxF9Iqqmyqu4iIiEhdufzyyzl06BAPPvggqampDBo0iG+//dbX/DwlJQWzOeDXF2vEZjXGXeB04XS5sZjVFkFERKQ5CmhQ6sknnyQxMZHZs2f7lnXp0iWAI6pcRKjVE5RSqrmIiIjUvylTpjBlypRyn1uyZEmlr50zZ07dD6iOeMv3AOyFTiJC1eZURESkOQroGcCXX37J2LFjufTSS1m6dCkdOnTglltuYfLkyeWub7fbsdvtvsfebu4OhwOHw1Hn4/Nu03sbGWrhMJCRm4/DEV7n+5P6Ufo4StOk4xgcdByDQzAex2B6L02Bt3wPwO5wEdG4qgtFRESkgQQ0KLVjxw5effVVpk6dyv/93/+xcuVKbr/9dkJDQ7nuuuvKrD9jxgymT59eZvnChQuJiIiot3EmJycDUJhvAUws+ekX0ja4621/Uj+8x1GaNh3H4KDjGByC6Tjm5uYGegjNisVsIsRiwuF0k1+oDHQREZHmKqBBKZfLxdChQ3niiScAGDx4MOvXr+e1114rNyg1bdo0pk6d6nucmZlJYmIiY8aMISYmps7H53A4SE5OZvTo0YSEhPDu/hXs251Ov4GDOad/2emYpXEqfRyladJxDA46jsEhGI+jN/taGo7NasHhLCTfoWbnIiIizVVAg1Lt2rWjb9++JZb16dOHTz/9tNz1bTYbNlvZBuMhISH1elLs3X50mLGP/EKC5iS8OanvnxNpGDqOwUHHMTgE03EMlvfRlISFmMm2Gz2lREREpHkK6JQtI0aMYMuWLSWWbd26lc6dOwdoRJWLsBkxvGx7YYBHIiIiItK0eftKKVNKRESk+QpoUOof//gHv/zyC0888QTbtm3j/fff5/XXX+fWW28N5LAqFOWZGSa3QEEpERERkdqweWbgszuUKSUiItJcBTQodcIJJ/D555/zwQcf0L9/fx599FGef/55rr766kAOq0KRvkwpnTyJiIiI1EaYN1OqUJlSIiIizVVAe0oBnHfeeZx33nmBHoZfIm3GyVOOyvdEREREasWbKZWvTCkREZFmK6CZUk2NN1MqR+V7IiIiIrXizZSyK1NKRESk2VJQqhp8QSllSomIiIjUSpgypURERJo9BaWqITLUW76nkycRERGR2rApU0pERKTZU1CqGlS+JyIiIlI3wjT7noiISLOnoFQ1RKl8T0RERKROhIV4Zt9TUEpERKTZUlCqGiJUviciIiJSJ2xWT6aUyvdERESaLQWlqiFK5XsiIiIidUKZUiIiIqKgVDVo9j0RERGRuuHNlMp3KFNKRESkuVJQqjKOXKLy90HeMQAiQ42glMPpxl6oq3oiIiIiNWUL8c6+p3MqERGR5kpBqUpYPr6WszZNw/TnQgAibBbfc7nqKyUiIiJSY0Xle8qUEhERaa4UlKpMTEcATBkpAIRYzIR6Us2zVcInIiIiUmNFjc51oU9ERKS5UlCqEu7YRABMGXt9y7zNznMLdAIlIiIiUlPKlBIREREFpSrhDUqRsce3LNJTwqdMKREREZGaK2p0rgt9IiIizZWCUpWJ82ZKFQtKhWoGPhEREZHaCvM1OlemlIiISHOloFQlfJlSmfvAZZwwRfrK9xSUEhEREampsBBlSomIiDR3CkpVJrodLsyYnAWQfRAoCkpla/Y9ERERkRqzWY1MqQJlSomIiDRbCkpVxmwlPyTeuO8p4YsMNU6gVL4nIiIiUnPKlBIREREFpaqQa2tl3ElPAYoypXJUviciIiJSY77Z95QpJSIi0mwpKFWFvJCSQakomxqdi4iIiNSWd/Y9uzKlREREmi0FpaqQG9rSuOMp34vwle/pBEpERESkppQpJSIiIgpKVSE31Jsp5ekppUwpERERkVrzZko5XW4cTgWmREREmiMFpaqQF1pB+Z56SomIiIjUmDdTCsCubCkREZFmSUGpKvgypTL2gNvtK9/LVvmeiIiISI15M6VAM/CJiIg0VwpKVSEvtIVxx5ELuUd9mVK5Kt8TERERqTGTyUSot9m5MqVERESaJQWlquAyh+KObGM8yEjx9ZTKVlBKREREpFbCPEEpZUqJiIg0TwpK+cEd18m4k55CpM0z+556SomIiIjUis07A5+CUiIiIs2SglL+iO1o3Kbv8WVK5aqnlIiIiEithIWofE9ERKQ5U1DKD+7YRONOxh4iQ1W+JyIiIlIXwqzKlBIREWnOFJTyR4wnKFUsU8pe6KLQqat6IiIiIjVlU6aUiIhIs6aglB/ccd6gVFFPKYCcAl3VExEREakpb6aUXZlSIiIizZKCUn4oKt9LIdRiJsRiAiAr3xHAUYmIiIg0bWG+RufKlBIREWmOFJTyh7fReX4GJnsW8RGhABzLUVBKREREpKZsVm/5njKlREREmiMFpfwRGgXh8cb9jD20jLIBcCTHHsBBiYiIiDRtypQSERFp3hSU8ldcJ+M2PYWWkUam1JHsggAOSERERKRp82ZKafY9ERGR5klBKX/FFs3A18ITlDqao6CUiIiISE3ZPJlSmn1PRESkeVJQyl/eTKmMFFpGeTKlFJQSERERqbGwEGVKiYiINGcKSvmrWKZUUfmeekqJiIiI1JTNqkwpERGR5kxBKX8V6ynVItJodK7yPREREZGaU6aUiIhI86aglL/iPJlSGXtUviciIiJSB7yZUpp9T0REpHlSUMpf3vK9nEO0shknTsqUEhEREak5b6aUvVCZUiIiIs2RglL+Co+H0CgA2rjSAPWUEhEREamNsBBlSomIiDRnCkr5y2Ty9ZVq4TgIQE6BUz0QREREpN68/PLLJCUlERYWxvDhw1mxYkWF63722WcMHTqUuLg4IiMjGTRoEO+++24Djrb6bFZlSomIiDRnNQpK7dmzh7179/oer1ixgjvuuIPXX3+9zgbWKHlK+CLy9hNiMQEq4RMRERHDihUrcDorDq7Y7XY++ugjv7f34YcfMnXqVB566CFWrVrFwIEDGTt2LGlpaeWu36JFC+6//36WL1/OH3/8waRJk5g0aRILFiyo9ntpKN5MKbsypURERJqlGgWlrrrqKr7//nsAUlNTGT16NCtWrOD+++/nkUceqdMBNiqeZuemjD3ER3ianWcrKCUiIiJw0kknceTIEd/jmJgYduzY4Xucnp7OlVde6ff2nn32WSZPnsykSZPo27cvr732GhEREcyaNavc9UeOHMmFF15Inz596NatG3//+9857rjj+Omnn2r+puqZb/Y9ZUqJiIg0SzUKSq1fv55hw4YB8NFHH9G/f3+WLVvG3LlzmTNnTl2Or3HxNjtP30PLKBsAR3LUV0pERETA7XZX+riiZeUpKCjg999/Z9SoUb5lZrOZUaNGsXz5cr/GsnjxYrZs2cJpp53m1z4DwTv7njKlREREmidrTV7kcDiw2YygzKJFizj//PMB6N27NwcOHKi70TU2np5SpKfQMtLIlFL5noiIiPjLZDL5td7hw4dxOp0kJCSUWJ6QkMDmzZsrfF1GRgYdOnTAbrdjsVh45ZVXGD16dIXr2+127PaiC2yZmZmAca7ncDj8Gmt1eLfpvbWajCBdnqOwXvYn9aP0cZSmSccxOOg4BodgPI7+vpcaBaX69evHa6+9xrnnnktycjKPPvooAPv376dly5Y12WTT4A1KZeyhRTuV74mIiEjjEh0dzZo1a8jOzmbx4sVMnTqVrl27MnLkyHLXnzFjBtOnTy+zfOHChURERNTbOJOTkwHYkw1gJSMrl/nz59fb/qR+eI+jNG06jsFBxzE4BNNxzM3N9Wu9GgWlnnzySS688EKeeuoprrvuOgYOHAjAl19+6SvrC0re8r2sA7TuZlzpPKJMKREREfHYuHEjqampgFFCt3nzZrKzswEj+8lfrVq1wmKxcPDgwRLLDx48SNu2bSt8ndlspnv37gAMGjSITZs2MWPGjAqDUtOmTWPq1Km+x5mZmSQmJjJmzBhiYmL8Hq+/HA4HycnJjB49mpCQELalZfP0umWYrKGMG3dGne9P6kfp4yhNk45jcNBxDA7BeBy92ddVqVFQauTIkRw+fJjMzEzi4+N9y2+++eZ6vaoWcJGtwWIDp53O1mMAHFVPKREREfE466yzSvSNOu+88wCjbM/tdvtdvhcaGsqQIUNYvHgxF1xwAQAul4vFixczZcoUv8fjcrlKlOeVZrPZfC0ZigsJCanXk2Lv9qPCjX3nFzqD5iS8OanvnxNpGDqOwUHHMTgE03H0933UKCiVl5eH2+32BaR2797N559/Tp8+fRg7dmxNNtk0mM3GDHxHttHedAQIV/meiIiIALBz58463d7UqVO57rrrGDp0KMOGDeP5558nJyeHSZMmATBx4kQ6dOjAjBkzAKMUb+jQoXTr1g273c78+fN59913efXVV+t0XHXJ5pl9z17oqlbQTkRERIJDjYJSEyZM4KKLLuKvf/0r6enpDB8+nJCQEA4fPsyzzz7L3/72t7oeZ+MRawSlEtxpQGeV74mIiAgAnTt3rnKd9evX+729yy+/nEOHDvHggw+SmprKoEGD+Pbbb33Nz1NSUjCbiyZSzsnJ4ZZbbmHv3r2Eh4fTu3dv3nvvPS6//PLqv5kGEhZizL7ndkOB0+WbjU9ERESaB3PVq5S1atUqTj31VAA++eQTEhIS2L17N++88w4vvPBCnQ6w0Ykz+kq1cBg9HjT7noiIiFQmKyuL119/nWHDhvn6cPprypQp7N69G7vdzq+//srw4cN9zy1ZsoQ5c+b4Hj/22GP8+eef5OXlcfToUZYtW9aoA1IANmvRqWi+wxXAkYiIiEgg1CgolZubS3R0NGDMznLRRRdhNps58cQT2b17d50OsNGJNWbgi8nfD8CRbPWUEhERkbJ++OEHrrvuOtq1a8fTTz/NmWeeyS+//BLoYTUqoRYz3oo9e6EzsIMRERGRBlejoFT37t354osv2LNnDwsWLGDMmDEApKWl1ctMLY1KnBGUCs81glI5BU7yHTqJEhEREUhNTWXmzJn06NGDSy+9lJiYGOx2O1988QUzZ87khBNOCPQQGxWTyUSYp2TPrkwpERGRZqdGQakHH3yQu+66i6SkJIYNG8ZJJ50EGFlTgwcPrtMBNjqe8j1L1l5CLMalPZXwiYiIyPjx4+nVqxd//PEHzz//PPv37+fFF18M9LAavaJm57rIJyIi0tzUKCh1ySWXkJKSwm+//caCBQt8y8866yyee+65Gg1k5syZmEwm7rjjjhq9vsHEGkEpU8Y+WoYbV/Y0A5+IiIh888033HjjjUyfPp1zzz0Xi0VNu/3hzZRSTykREZHmp0ZBKYC2bdsyePBg9u/fz969ewEYNmwYvXv3rva2Vq5cyX/+8x+OO+64mg6n4US3A5MFXA66R+YCcCRHfaVERESau59++omsrCyGDBnC8OHDeemllzh8+HCgh9XohXkypdQOQUREpPmpUVDK5XLxyCOPEBsbS+fOnencuTNxcXE8+uijuFzVu8qVnZ3N1VdfzRtvvEF8fHxNhtOwLFaIaAlAJ1seoPI9ERERgRNPPJE33niDAwcO8Je//IV58+bRvn17XC4XycnJZGVlBXqIjZLN21OqUJlSIiIizU2NglL3338/L730EjNnzmT16tWsXr2aJ554ghdffJEHHnigWtu69dZbOffccxk1alRNhhIYka0A6BCaA6h8T0RERIpERkZyww038NNPP7Fu3TruvPNOZs6cSZs2bTj//PMDPbxGR5lSIiIizZe1Ji96++23efPNN0ucWB133HF06NCBW265hccff9yv7cybN49Vq1axcuVKv9a32+3Y7UWlcpmZmQA4HA4cDkc13oF/vNssvW1LeAvMQIIlE2jPoay8etm/1I2KjqM0LTqOwUHHMTgE43Gsr/fSq1cv/vWvfzFjxgy++uorZs2aVS/7acps6iklIiLSbNUoKHX06NFye0f17t2bo0eP+rWNPXv28Pe//53k5GTCwsL8es2MGTOYPn16meULFy4kIiLCr23URHJyconHQzMK6AC4D+8AerN28w7mF26rt/1L3Sh9HKVp0nEMDjqOwSGYjmNubm6tt3HDDTdUuU7Lli1rvZ9go9n3REREmq8aBaUGDhzISy+9xAsvvFBi+UsvveR3s/Lff/+dtLQ0jj/+eN8yp9PJDz/8wEsvvYTdbi8za820adOYOnWq73FmZiaJiYmMGTOGmJiYmryVSjkcDpKTkxk9ejQhISG+5eZvl8LvK+jdJgyOQESLNowbd3wlW5JAqug4StOi4xgcdByDQzAeR2/2dW3MmTOHzp07M3jwYNxud7nrmEymWu8n2ISFKFNKRESkuapRUOpf//oX5557LosWLeKkk04CYPny5ezZs4f58+f7tY2zzjqLdevWlVg2adIkevfuzb333lvuNMo2mw2bzVZmeUhISL2eFJfZfnQbAOJMRsPSY7mFQXNSHszq++dEGoaOY3DQcQwOwXQc6+J9/O1vf+ODDz5g586dTJo0iWuuuYYWLVrUweiCm82qTCkREZHmqkaNzk8//XS2bt3KhRdeSHp6Ounp6Vx00UVs2LCBd999169tREdH079//xJfkZGRtGzZkv79+9dkWA3HM/teVGE6oNn3REREBF5++WUOHDjAPffcw//+9z8SExO57LLLWLBgQYWZU6JMKRERkeasRplSAO3bty/T0Hzt2rW89dZbvP7667UeWKPmmX0vzHEMUFBKREREDDabjSuvvJIrr7yS3bt3M2fOHG655RYKCwvZsGEDUVFRgR5io+PNlNLseyIiIs1PjYNS9WHJkiWBHoJ/IoygVKjdaOqebS8k3+H0XekTERERMZvNmEwm3G43TqcCLhXxnj/ZC5UpJSIi0tzUqHyv2fNkSpnzjhBiMRqWKltKRERE7HY7H3zwAaNHj6Znz56sW7eOl156iZSUFGVJVSAsRJlSIiIizVWjypRqMjw9pUx5x2gVbuFAdiFHcwpoHxce4IGJiIhIoNxyyy3MmzePxMREbrjhBj744ANatWoV6GE1ejarMqVERESaq2oFpS666KJKn09PT6/NWJqO8KKZdJIi7RzItnA42x7AAYmIiEigvfbaa3Tq1ImuXbuydOlSli5dWu56n332WQOPrHHzZkrZlSklIiLS7FQrKBUbG1vl8xMnTqzVgJoEixXC4yHvGJ3DcllOtMr3REREmrmJEydiMpkCPYwmxzf7XqGCUiIiIs1NtYJSs2fPrq9xND0RrSDvGB1Cc0FBKRERkWZvzpw5gR5Ck+Sdfc/uUPmeiIhIc6NG5zXlaXbeNiQHgMPZCkqJiIiIVJcypURERJovBaVqytPsvLU5C4CjOeopJSIiIlJd3kypfGVKiYhIdRzZDoe3BXoUUkuafa+mPJlSLfAGpZQpJSIiIlJdthDv7HvKlBIRET85C+HNs8Dtgrv+BKst0COSGlKmVE1FGEGpOHcGoPI9ERERkZoIs3rK95QpJSIi/rJnQt4xyM+AnEOBHo3UgoJSNeXJlIpypgPKlBIRERGpCVuIp9G5MqVERMRf9qyi+7lHAjcOqTUFpWrKkykV7jgGKCglIiIiUhPKlBIRkWoryC66n3s0cOOQWlNQqqYijUbnoXYjKJVtLyTfoSt8IiIiItURFuJtdK7zKBER8VNBTtF9ZUo1aQpK1ZQnU8qcd4QQiwlQtpSIiIhIdRU1OlemlIiI+KlE+Z4ypZoyBaVqytNTypR7hBbhxiSGCkqJiIiIVE+Y1TgdLSh04XK5AzwaERFpEpQpFTQUlKqpCKN8D7eTTpGFABzOtgdwQCIiIiJNjzdTCqDAqWwpERHxQ/GeUnnKlGrKFJSqKasNbDEAJIXlAsqUEhEREamuMKuZGLLpwCH1lRIREf8oUypoWAM9gCYtoiXYM+kQmgOEKyglIiIiUk1WRxZf2+6nNelkpp8JEZ0CPSQREWnsSvSUUlCqKVOmVG14+kq1CzGitIezFZQSERERqZaF/yTRdIgwkwP3wfWBHo2IiDQFxcv3FJRq0hSUqg3PDHxtLEaU9miOekqJiIiI+G3bYlj1ju+h/fCuwI1FRESajhLle8cCNw6pNQWlasPT7LwF3qCUMqVERERE/JKfCf/7OwAFplAAju7bFsgRiYhIU2FXplSwUFCqNiKNoFQsmYDK90RERET8lvwgZOyBuM6s7XQ9oEwpERHxU/HyvcI8KMgN3FikVhSUqg1P+V60Mx1QppSIiIiIP0y7foDfZxsPJrxEiy7HAWDL3ovb7Q7gyEREpEkoHpQCZUs1YQpK1Yan0Xm4Ix1QUEpERESkKhZnPpav7jAeDL0RupxGp659AGjrTmP7oeyKXywiIgIly/cA8o4GZhxSawpK1YYnU8pWYPwCZNsLyXc4AzkiERERkUat7/6PMGWkQGwnGD0dgJCWSQC0NR3jt20HAzg6ERFpEoo3OgdlSjVhCkrVhqenlDnvKCEWE6BsKREREZGKmHb/TNfDi4wH578AtmjjfkRLHOYwALZt2xSg0YmISJNRYEw2htX430GuMqWaKgWlasOTKWXKOUx8eAigoJSIiIhIudxuzN8ZmVGuQddCtzOKnjOZKIhOBODQHs3AJyIiVfBmSsV1Mm6VKdVkKShVG56eUjjtdIxyAXBEQSkRERGRskwmnJe+x86WZ+A8a3qZp8NaJRm3ufvYe0yzKImISCW8PaV8QSllSjVVCkrVRmgkWMMB6ByWB8CRbHsgRyQiIiLSeEW14Y9OkyAspsxTlhadAehoOsTKXfpwISIiFXA5odD4/K1MqaZPQana8mRLdQw10gdVviciIiJSA54PFh1Nh1ix81iAByMiIo1WQbGZ9+KMCxoKSjVdCkrVVoTR7LxdiBGUUvmeiIiISA2UCErpw4WIiFTAW7pntkJ0O+O+glJNloJSteXJlEqwGN3/Vb4nIiIiUgO+oNRhth/K4bDOqUREpDzeJuehUb4kEfJU9t1UKShVW54Z+FqYjKCUyvdEREREasBTgpFgOkYoDn5TXykRESlPgfHZG1s0RLQw7qvReZOloFRteTKl4twZABzOVlBKREREpNoiWkJIBGbctDMdCUxfKZcLsg81/H5FRMR/vkypyKJMqdwj4HYHbkxSYwpK1ZbnlyDOnQnA3mN5gRyNiIiISNNkMkFsIhDAGfi+uQee7gF7Vjb8vkVExD/enlKhUUWZUoX54MgN3JikxhSUqi1PplS0Kx2Aw9l2svIdARyQiIiISBNVrK/Uhv0ZDX9OtWMJ4IYDaxp2vyIi4r/imVKhUWAJNR6r2XmTpKBUbXl6SlnzjtIqyvhl2HVYEVoRERGpvZdffpmkpCTCwsIYPnw4K1asqHDdN954g1NPPZX4+Hji4+MZNWpUpes3Sp6gVL/wdFxuWJWS3nD7LrTD0R3G/ZzDDbdfERGpnuI9pUymYiV86ivVFCkoVVvFaliTWkYCsPNITgAHJCIiIsHgww8/ZOrUqTz00EOsWrWKgQMHMnbsWNLS0spdf8mSJVx55ZV8//33LF++nMTERMaMGcO+ffsaeOS14A1KRaYDsGJnA171PvwnuJ3G/VwFpUREGi1f+Z7x+btEXylpchSUqi1P+R65R0hqZfxS7DqsoJSIiIjUzrPPPsvkyZOZNGkSffv25bXXXiMiIoJZs2aVu/7cuXO55ZZbGDRoEL179+bNN9/E5XKxePHiBh55LXiCUp3MRlBoZUM2Oz+0uei+MqVERBovX/lelHGrGfiaNAWlassblS3IpnsLK6CglIiIiNROQUEBv//+O6NGjfItM5vNjBo1iuXLl/u1jdzcXBwOBy1atKivYda9uM4AxBekArBmTzr5DmfD7Lt4UEpX20VEGq8CT6aUzROUCvcGpfS3uymyBnoATV5YLJhDwOWgZ5QdUPmeiIiI1M7hw4dxOp0kJCSUWJ6QkMDmzZsreFVJ9957L+3bty8R2CrNbrdjt9t9jzMzjdmEHQ4HDkfdNxn3brPCbUe1IwSw5KTSNtJEao6LVbuOcEJSfJ2PpTTLwY2+q7XunEMU1sP7DxZVHkdpEnQcg0NzPI7m/EwsgNMSjsvhwBwWbzzOPoSriX4fgvE4+vteFJSqLW9jtexUksLzAGVKiYiISGDNnDmTefPmsWTJEsLCwipcb8aMGUyfPr3M8oULFxIREVFv40tOTi7/Cbebc02hWN0FDArdz7c57Zi78BcOdXTX21i8zty1imjPffux/SyYP7/e99nUVXgcpUnRcQwOzek4Dtm1lY7Axu0p7MiaT+8DR+kFpGxezR85TftvdzAdx9xc/yaAU1CqLkS2guxU2ocawahjuQ7ScwuIiwgN8MBERESkKWrVqhUWi4WDBw+WWH7w4EHatm1b6WuffvppZs6cyaJFizjuuOMqXXfatGlMnTrV9zgzM9PXID0mJqbmb6ACDoeD5ORkRo8eTUhISLnrWPYmweGtnN87gm+XQ6atDePGDanzsZRQaMe6pqiBvM2Zw7hzzgaTOl2Ux5/jKI2fjmNwaI7H0fLRXDgGfQaeQO/B4zCv3Aup/6Vz6yg6jhsX6OHVSDAeR2/2dVUUlKoLnr5SYfZjJMS05GCmnZ2HcxjcSUEpERERqb7Q0FCGDBnC4sWLueCCCwB8TcunTJlS4ev+9a9/8fjjj7NgwQKGDh1a5X5sNhs2m63M8pCQkHo9Ka50+3Gd4fBWBkZlAbGs359Z/yfoR7caM++FRkFBNia3k5DCnKLmuVKu+v45kYah4xgcmtVxdBgZONaIWAgJgajWAJjzj2Fu4t+DYDqO/r4PXf6pC74Z+A6T1NIzA5/6SomIiEgtTJ06lTfeeIO3336bTZs28be//Y2cnBwmTZoEwMSJE5k2bZpv/SeffJIHHniAWbNmkZSURGpqKqmpqWRnZwfqLdSMZwa+BNdBTCYjA/1Itr2KF9VS2ibjtk1fsMUa9zUDn4hI42TPMm5DPUXXvtn3GnDGVqkzCkrVhQhPUCrnMF1aGUGpnYf9q58UERERKc/ll1/O008/zYMPPsigQYNYs2YN3377ra/5eUpKCgcOHPCt/+qrr1JQUMAll1xCu3btfF9PP/10oN5CzXiCUtasvXSMDwdgW1o9B9a8M++16Q2RnpmVcxWUEhFplAo8CSChxmdvb+WSZt9rmlS+VxeKZ0p5glJqdi4iIiK1NWXKlArL9ZYsWVLi8a5du+p/QA3BE5QiPYXuraPYczSPbYeyGd61Zf3t0xuUat0H0jbD0R3KlBIRaawKPBcqbFHGbfGglNttTEYmTYYypeqC95cg54jK90RERERqI66zcZueQrfWxgeOes+USvMGpXqVuNgoQWb3Mkj5NdCjEJHa8mVKeYJS4Z7yPae96DlpMhSUqgvFTl6KyvdycLvrf/piERERkaDizZTK3E/PVkYT9noNShXajcwogDZ9VAYSrOxZ8O5F8M4EyEsP9GhEpKbc7qJMKW9QKjQSLJ5JO/KOBmZcUmMKStWFYj2lOreMwGSCrPxCjuYUBHZcIiIiIk1NZCuwhgNu+kQY00lvr8+g1JFtxsx7tliIblciA16CSPoeKMwzvnb/HOjRiEhNOXLB7TLue3tKmUy6oNCEKShVF4plSoWFWGgfazTl3Km+UiIiIiLVYzL5sqW6hBgfLvZn5JNjL6yf/Xln3mvdy9i3yveCU+b+ovs7lgRsGCJSS77yPFNRUAoUlGrCFJSqC95MqfwMcDpIahUBKCglIiIiUiNxiQBE5e2nVVQoANsP1VO21KEtxm2b3sZtsQx4CSKZe4vu71gauHGISO3Ys4zb0KiSDc0j4o3bXJXvNTUKStWF8Hgweb6VuUfV7FxERESkNorNwFfvzc4PeTOl+hi3ypQKTsUzpQ5vKflYRJoOX5PzyJLLfZlSCko1NQpK1QWzuajjf7Fm57sO5wZwUCIiIiJNVLGgVPc29RyUKj7zHqinVLDK2Ffy8c4fAjMOEakdb5NzW1TJ5Srfa7IUlKorkUWp3t5MKZXviYiIiNSALyi1xxeUqpfyvdIz70HJTCnNpBw8Mj1Bqej2xq36SonUrdR1xgyX+1bV736qzJRSUKqpUVCqrvh+CQ6T1KqofM+tkxkRERGR6onrbNzWd6ZU6Zn3oKinlLOgqHeJNH3ecr2BVxi3O5Yq6ChSl9bOg+2LYfW79bsfX0+p6JLLfZVLCko1NQpK1ZViqd6dWkRgNkFugZNDWfbAjktERESkqfFmSmXtp3tLo9H57iO5OJyuut1P6Zn3AEIjIMSYtEZ9pYKE212UKdX/IrDYIGu/EZQUkbqRfdC4re9+bd7yvYoypfKq2VPq5xfgy9vAVcf/X8RvAQ1KzZgxgxNOOIHo6GjatGnDBRdcwJYtWwI5pJorluodajXTIT4cUAmfiIiISLVFtgZrGLhdtOUokaEWCl1udtf1JDKlZ97z8s3ApyvuQcGeWfRBtkU36DTcuK8SPpG6k51m3Gbuq3y92vKW75XpKeXNlKpGUMrlgu8eg1XvwMH1dTM+qbaABqWWLl3Krbfeyi+//EJycjIOh4MxY8aQk9MEAzmlpg/u0sr4JVFQSkRERKSaTCZftpQpPYVu9VXC55t5r1RQKrKoLYMEAW+T8/B4IxOuy+nGYwWlROpOziHjNvNA/e7H7s2UqoNG57lHwOmpbDq2s/ZjkxoJaFDq22+/5frrr6dfv34MHDiQOXPmkJKSwu+//x7IYdVMqemDu7Q00r531vUVPREREZHmoPgMfK3rKSjlm3mvokwpBaWCgrecKKaDcdv1DON214/gcgZmTCLBxpsplXsYHPn1t5+CioJSxXpK+dsvLqtYqaF30gtpcNZAD6C4jIwMAFq0aFHu83a7Hbu9qEdTZmYmAA6HA4fDUefj8W7Tn22bbHFYAVf2IZwOB4nxYQDsSMuul7GJ/6pzHKXx0nEMDjqOwSEYj2MwvZegUSwo1a3NSUAdB6XKm3nPq9TFRmniMvcat96gVPtBRnP7/Aw4sAY6DAnUyESCg7OwZIZS1gFo0aV+9uUNSpUp3/NkSjkLjBK/0s+XJ1NBqcag0QSlXC4Xd9xxByNGjKB///7lrjNjxgymT59eZvnChQuJiIiot7ElJydXuU6L7BROBRz71/Ht119xKN0CWFi/6yDz58+vt7GJ//w5jtL46TgGBx3H4BBMxzE3NzfQQ5DSimdK9fJkSh2qw6CUb+a9mKKZ97x8E9goKBUUfJlS7Y1bswW6nAqbvzJm4VNQSqR2co8AxbKT6jUo5alEKt3oPCTC6EVYmG+Mx6+gVLH+V0dVvhcojSYodeutt7J+/Xp++umnCteZNm0aU6dO9T3OzMwkMTGRMWPGEBMTU+djcjgcJCcnM3r0aEJCQipf2TkK97PPYyvI4txB7ekX1pv/bP6Jow4LZ589BrPZVOfjE/9U6zhKo6XjGBx0HINDMB5Hb/a1NCLFg1KenlLb03Jwudx1c151qFjpnqnU9nyZUmp0HhS8Hzy9mVJg9JXa/JXRV+rUqeW+TET8lJNW8nF9zsBnzzJuS5fvmUzGBYXMfcbf7vjOVW+rRKaUglKB0iiCUlOmTOGrr77ihx9+oGPHjhWuZ7PZsNlsZZaHhITU60mxX9sPCYFuZ8KmL7Hu/I6k007AajZhL3RxJM9J+7jwehuf+Ke+f06kYeg4Bgcdx+AQTMcxWN5HUGnZ3bjd9xud7VsJsZjIczjZn5FHx/g6yJD39pMqPfMeqKdUsPE2Oo8tFpTq6ml2nvILOPIgROfqIjWWXTooVY8z8FXUUwqMvlKZ+/yfga94UCpzr/4WBEhAG5273W6mTJnC559/znfffUeXLvWU4tdQeowxbv9cgNViJrGFccK0SzPwiYiIiFRP2+Og93ngLMD6yfX0b2GUhtRZX6mKZt6D4O4plbYZlr8MzmbUR610+R5Aq55G2abTDnt+Dcy4RBq7L2+Ht8ZU3bjcO/OeV33OwOct3yuvPC/c05s6z9+gVKng2bHdNR+X1FhAg1K33nor7733Hu+//z7R0dGkpqaSmppKXl5eIIdVcz1GG7f7V0N2GkmeGfh2KCglIiIiUj0mE0x4GeI6Q/puHnK9DLjrMCi1xbgtLyjly5QKwvK9b++FBf9nlK41B253sfK9YhUZJpNRwgdGXykRKSn3KKx62wjaHlxf+boNmSllryxTytMP0N/Sa1+mlKeEW83OAyKgQalXX32VjIwMRo4cSbt27XxfH374YSCHVXPRbaHdQOP+n8l0aWX8oihTSkRERKQGwuPgsrfBEsqgnJ+ZbPma7Yfq4Lyq0A5Hthv3S8+8ByWnFg823mCct3wx2Nkzi8p9Yko1tO860rjdsaQhRyTSNOxeVnS/qh5R3p5S8Un+rV8bvkbntQxKud1F42x3nHF7TH2lAiHg5XvlfV1//fWBHFbt9Bhr3P65kC6tPOV7RxSUEhEREamR9oPh7JkA3Gudh3VvHZRaVTbzHhSV7zlyjB4jwaIgx5gVC+Do9sCOpaF4P3SGxZWdrcvbV+rAGshLb8BBiTQBu38uup9VRTletqd8z5ugUa9BKU+j8/LK96oTlMpPB4dn5t3Opxi3ypQKiIAGpYKSt6/U9u/o0iIUgJ3KlBIRERGpuaE3kN7tAqwmF1OOPl77BuRpxfpJlZ55D4xgldnT/D6Ymp0Xn13qSDMJSvmanJczmVJMe6O3lNsFuyqeAVykWSr+O1FVOZ43U6rdIOM2+yA4C+tlWEWZUpFln/NlufrRU8obOAtvUZQxq6BUQCgoVdc6HG9EaO2Z9LRvBGDP0TycLneAByYiIiLSRJlMhF74b7a52pPAURwf3QguZ823t3WBcdt+cIX7C8pm58U/cB3dbpSvBDtfP6n25T/v6yu1pEGGI9Ik5KVD6rqix1U1LvdmSiX0B7PVyETNSav8NTVRWADOAuN+bcv3vO8ppgO06GrcP6ryvUBQUKqumS3QfRQArQ4sJdRipsDpYn96EKV+i4iIiDSwiKg4Hg67l1y3jZDdS2DlWzXbkD0LNv3PuH/cZZXsMIDNzg9uhHcvggNr63a7xYNS+Rn+T5velPmCUh3Kf77zScbtgTUNMhyRJiHlF6BY0Lqq8j1vACo6AaLaGvfro4SvoNhEF+UGpaqTKeX929CuKCiVntK8ZiZtJBSUqg+eEj7ztmQ6eWbgUwmfiIiISO1Y2vblmcJLjAcbPq/ZRjZ+CYV50LI7dBhS8XqR3ivuAciU+vU12L4YFj1ct9stXZrSHPpKVRWU8n4Y1VTwIkV2e0r3WnY3bisLMLlcRWXOkW2KshLrMyhlDQOLtezz1cqU8owvpr0xYZk13MjwSk+pm7GK3xSUqg/dzgSTGQ5t4vgY4xdHQSkRERGR2uneJooFrhOMB3tXQH5m9TfyxzzjduAV5feT8vJlSgUgKOWdfn3Hkrr9YFc6KNUc+kplVFG+F9fZuM1Jg4LchhmTSGO3y9PkfMClxm3WgYrLffOOGsEcMMqe6zMoZfcEpcrLkgKjP5RvTFWUJxcPWJtM0KKL8VglfA1OQan6ENECEocDcLbNSLv+dNVe3M2hbl9ERESknnRvE8VedxtSrR3AVQg7f6jeBtL3wM4fjfsDKindg8D1lHI5ixqxu13wx0d1t23vh62E/p7HzSAo5f1gHFtBplR4PNhijfvKkBAxgv3e0uH+nsxUR64xW115sj2le+EtwBJSLChVRXP0mqisyTkUZUo5C0qW+pWneKYUFMuaVFCqoSkoVV96jAbgFPcqIkIt/LE3g0Wb6qHZm4iIiEgz0b2NcXX8J7dn2vHt31VvA+s+AtzG9N/xnStfN1CZUsd2FU1TDrD2g7ppSO7Ig8y9xn1P/9NmkSnl++BZQVDKZIL4Tsb9Y7saZEgilXK74bvH4df/BGb/e341Mp/iOkOr7hAWZyyvqNm5t59UVBvj1hvkqaoPVU0UZBm3tujynw+NMMrwoOoSvjJBKW+mlGbga2gKStWXHmMBCE35iRtPNJq9PZu8FZdm4RMRERGpke6tjaDUN3me6bu3L/b/xW43rP3QuD/wiqrXj6xGb5K65J3xqmV3o2/Koc1104TbG3Cxxfgy+jmyrfbbbczyM4o+xFZUvgcQn2Tcptewr5Sz0Gis7HLV7PUixR1cDz/8C765p+6z91xO+OVVOLih4nV2efpJJZ1i3HoDulkVlON5Z96LbO1ZvyHK9yrIlAL/+0qVDljHKygVKApK1ZeEfsYPeGEeN3c6QJTNyqYDmSzYkBrokYmIiIg0SfGRobSMDGW5qx8uc4gRaPH3A8T+1XB4ixHo6Tuh6vUDlSnl/bDY6UToNc64v3Ze7bfr/T616AItuxUtC+b2Et4PnWFxlX+I9faV8idTavV7MPdSeONM+PcgmNkJHm0J/+oC711kTFkvUht7Vxbdr8vyXYD1n8K398H7V1Q8y9xuTz+pziOM25h2xq2/mVLRDVG+V0FPKYCIeOM291jF69izwJ5h3C9dvqegVINTUKq+mEy+Er7olO+4YUQSAM8tUraUiIiISE11ax1FLmEciR9kLNjmZ7aUN7DT+1wIi6l6/UD1lPI2OU8YAIOuMu6v+7j2wQ5fUKqbkRlkMhs9V7KDuL2Et8l5bMfK1/NmSlU1A5/TAV/9A/5cCPt+N3rP5GcUPb/je/jqjuAO9En921MsKLV2XtU/TzmHaJe+0r+fuz8XGrcZKfDHh2WfL8gxAvgASZ6gVLQnKFVROZ73b0hkqfK9zEqao9eUt0+UrbKglB+ZUt4Amy2mqBTQ11Nql5FRVh1f3AJPdS/KGpNqUVCqPnlK+PhzITeO6EJ0mJWtB7P5al091NeKiIiINAPdPH2lNoQPNRZs/77qFzkdsP4T4/7AK/3bkS9TqoHL93xBqX7Q9QyISjA+XG1bVLvt+oJSXcFqKwrUBHOz88wqZt7z8gWldlW+3tGdRgPlkEi44n2Y9C3cugLu2gZXfWQE+tbMhZ+ere3IpTkrnil15E/Yv6ridd1uLJ9cz7CdL2Ja/3Hl23W5Sv69/PGZssGXPb8ak0jEdCzKIKyqHC/HE4iJ8pTveYNYTrtR1lqXCqqYfQ/8DEqV87chtiOYQ4zf8eqUHhYWwLpPjO+DN8tMqkVBqfrU5TSwhMKxXcTm7mLyqUb09flFW3EqW0pERESk2oZ3Mab8fvug56r2zh8qLkPx2rbI+IAS2Qa6nsHG/Zmc+fQS3vyxkjINb6aUPaPhSrLyM4p6yCT0A4u1aEr2te/XbtvFg1JgZExBcDc7L93IuCLeD9/puyvP7Di81bht1cPIuOt8ErTuZXwY7zkWzvmX8fziR2DD57UbuzRPuUeNQBRAtzON27XlZDR5pfyCee+vAJg3/bfybaf+YWR+hkYZs04e3QHrPyu5zi5PUCXpFKPyB4qCTBUFakpnSllDi/pL1XUJn72uglLl/G0wW4omwKhOCV/aRiMAB8Ed5K9HCkrVJ1tUUYO4pU8y6aSOxEWEsONQDl+urYcaWxEREZEgd3b/tsRFhLAkqx0FtnijkXXxzILyeEv3BlwKFivPJm9hx+EcHvt6E7N/rmD677A4MFmM+w3V7PzgRuM2pgOuME9fFG8J35Zva5d1UDoo5esrFcQforyzDcZUUb4X55l9ryC78u+xLyjVs/znh02G4X8z7n/+15JlWDVlz4ZlL0LWwdpvSxq/fb8bty27w4m3GPfXf1Jx4P3n5313TTuXQkFu+etB0cQQXU6DE2817v/4dMkG/d5MH2/pHlTd6Lx0Tymovxn4fD2lKukRF25cuCCvkt/lrAoC1jXpK1U8k+2I+lHVhIJS9W3434xU3vWfEP3F9dwywog0/3vRnxQ6NUOHiIiISHWEhVi45PiOuDGzyjrIWFhZX6m8Y7DlG+P+wCvYfiibRZuK+ihN/99GPlq5p+zrzGaI8Hy4aai+Up7SvbSI7vT85zd8vnqvkTHVdgC4HEaT4pootEOGJ0DjC0p1N26VKQUhYUXZIJWV8B32ZLBUFJQCGPs49DwHCvNh3pVl+1TlZxqBh0NbKx+T16+vwsJ/wic3qFdVc7BnhXHb8QSjfDeyTcXlu2mbYOu3uDFht0ZjKsw3+ppVxFu61+1MGH4z2GKN2T03/89Y7sgrCop1Lh6UqqLReenZ96AokFXXmVLe2TRr3VOq1Mx7Xr6+UhVcrCiP93sGapJeQwpK1beeY+DyucZML1u/5cYd/yApws6uI7l8tlrZUiIiIiLVdeVwI7Plk/RexoLt31W88oYvjNKKNkZw562fjA8bo/q0YfKpxhTg9372B1+uLScLoKFn4PMEpVYXdKDQ5ebpBVuNi5gDPdlSaz+o2XaP7Qa3yyh58WYztCg2A1+w8jU671D5elDUVyp9V8XrFC/fq4jZAhe/aQQScw7B3Evgf3fAnPPg6V4wM9GYue/Vk/wLCKb8Ytzu/qko00WClzfrs+MJnvLdS4zH5c3A+fMLALh7n8fe+JOMZZvnl79de3bRz1K3MyEsFob/xXj8w1NGwHPvSqOfUnS7ouAMFM2ml3vYCHAX53YX6ylVLFOqqpK/mvKnfM9bel1REA2KxuUdp1dNMqX2rS66H8yZp/VIQamG0HscTPwvhMVh2beSz8MeoR1HeGHxnxzOtlf9ehERERHx6dY6ipO6tuQH5wBjwf7VFTck984wNfAKjuQU8OnvRsbQTad25f/G9eGq4Z1wu2Hqh2tI3liqRMqfK+516eAGAFbkGh+U9qXnMX99qvHB1GSpXoZNcb7SvS5FfWJaFgtKBWsGTkXZEOXx9pWqKFPK7fYvUwqMLI4rPzQ+8B7eCr/Phl0/Qnaq8bzJYjSTriyrxbvP4lkYix8pWWolwcXlKjreHU8wbo+73Ljd8g3kpRetm7EP1n1kvOzEKaTGHm8s3/pt+TPH7frRyLaMTyr63T/xb0ZwJ3UdbF1Q1E+q84iivxNgZIxabMb9rNSS2807ZmwXSmVKVdEcvaZ85XuVBKUS+hu3B9eDs7D8dXyNzkv9bYg3LlRw1M9MqYIcOLSp6HH2QbBn+fda8VFQqqF0OhFu+BZiOhCfu5Mvwh4mPP1Pxjz3A/9bux93sJ4MiIiIiNSDq4Z3Io14/qQT4IadS8qutHk+pCw3WikMuJT3fknBXuhiQIdYhndpgclk4rEJ/blwsJGZdOvcVfz0Z7GsqEhPUKohMqVcLl9PqaWZbX2L3/xxB+7I1tBjtLGgJtlSpftJgdFHyWQBR27d931pDPIzikp9qirfg2Iz8O0u//nsNKPpvclc8vtYkdgOcO3nMOgaOPVOuPA/cNN3cF8KnHa3sU5VPaeO7jA+9FtsEBoNB9bCxi+q3rc0TYe3gD3TmN2xTV9jWbuB0LqPke25sVgj819eMQKbnU/B3WEIR6J64g6LNbKZyuux580m7XZW0bKIFnDCjcb9H54qv58UGAGqaM/fpNJBJm+WVFisMaunl698r66DUp5MqcrK91p2B1uM8bft0Oby16motNeXKbXTv2D9gbVGFmp0u6KLGMGcfVpPFJRqSG36wI0LoVUvEjjCZ2GPEJW7h9s+WM1f3/udtKz8QI9QREREpEkY268tLSND+b7Qky21rVQJX9om+GyycX/YzeSHt+HdX3YBcNOpXTB5MgHMZhNPXXIcY/slUOB0Mfmd39iwP8N4nbd8ryEypY7tBEcOLksoO11GM3eb1cwfezNYsfMoDLzCWG/tPFj1TtkvbxZPecoLSllCimaaCsa+Ut4PnWFxlTdF9oqvIlPKW7oX19noQeWPNn3ggpfhrAeN49dxiPHhPXGY8fyeXyt/vTe40G4gnDzFuP/dY1XPNilNk/d4dzjeKN0DIyA00JMt5S3hyzsGv88x7p9yBwBukxV3d0/gevPXZbft7bvnndHP66QpYA2Hfb/Brp+MZZ1PKft6X+PyUkGm0jPv+davp/I9b1Cqst9psxnaDzbuF8809HLkF/1NLx2UiutkBJ4dOUXvrTL7PE3OOwxp3DOaZqX6n/0VAApKNbTYjkbGVPvBRLuzmddqFjaziwUbDjL62R/4bNVeZU2JiIiIVCHUauayExL5wXWcsWD7d0VXtnOPwgdXGh9gkk6FMY/x3zX7OJxdQPvYMMYNKNlHxGox88KVgzm1RyvyHE7u+vgPCgpdRb1JGqLRuad071hkN5xYGJwYx0XHG7PGvfHjTqN5dlis8aHwy9vKfs0eV3GpSnlBKSjWV6oRfoiqrYrKcyriLd9LryBTqqqZ96qj41DAZAQiK/vgu/e3ovVPutXIxDi6HdbMrf0YpPHxNDl3th/Kla//wq1zVxmfCwdcBpggZZmRyffbLONvW5t+0H2U7+WuHmcbd7aU6it1bJfxc2O2GjPvFRfVBoZc73ngNoJL5fVM85XjlcqqLG/mPai/TClfT6noytfrMMS4LS8o5Q2sWcMhPL7kc9ZQ4/M6+Jfx5J15r/3gxjujqcsFs8bCa6c02lk8FZQKhIgWcNk7YIuhfdY6fjxpNf3ax5CR52DqR2uZ9tm6QI9QREREpNG78oROrHT1It8dYnzQOLTZCMx8fL3xgT+uE1z6Nm6zlTd/NK4SXz8iiRBL2VNgm9XCc5cPIj4ihE0HMnllybaGbXTuaXK+02L0NOnXPpYbTzHuL958kB3phXD+S9BrnBGgKv4VGmV8OKwo86aioJT3Q9SRbXX+dgKuOk3OoVij8z3lB/d8/aQqaXLur7BYI4sKimZbK8++YkEpWzScepfxeMmTxkxpElw8QcjdEf1YvuMIX687wPLtR4yfYW8wadU78Mtrxv0Rfy/R+8nd7Swwhxi/z8UzJ71ZUh2HQVhM2f2OuB0socb9zieX7Cfl5W0IXrrUt7yZ94qvX5BlzDhZV3w9parIfvQFpVaVfa546V5577U6zc69Qa8OxxcL8leRkZR1ED65sfyx1Ye0DUZgsiC76j52AaKgVKDEdYJznwGgzarn+e8FNu4a0xOzCeat3MO6vRkBHqCIiIhI49apZQTDe3bgV5fnA/62xbDwfti51OjLcuU8iGzJkq2H+DMtmyiblSuGdapwe62ibDwywWiS+9J329hbEGE80RDle55MqdV2I4jSr30M3dtEcVbvNrjdGLMG9j0frvwArppX8qv3ecY2tn5bdrtOB6SnGPcrypQ6EoQ9UCrqGVOR6HbGB3O3s/xp7OsyUwqqLuFz5EOqEaikw1DjdugNENPRCMCufLNuxiGNQ36Gr//Rald33+JZP3sCHN7y3Z+eMwLQMR2h/0Ult2GLLgpeFS/h8/WTKlW65xXTHobdbNzve37F60A5PaUqyJSyRYEt1rhflz3r/OkpBUVBqbSNRYEsr6r+Nnj/Th6rIriUe7So3Lf9YGMiCai6fG/lG7D+E0h+sPL16oq3gT3AjqUNs89qUlAqkAZcCv0vBrcT6xd/YcqIdkwYZJyIvPR9JX0BRERERASAq4Z14geX0VfK/fPz8Ksni+Ci/0BCP8BoFg5w+QmJxISFVLq9845rx9h+CRS63Ly8It1Y2BCZUqlGpvwPWUZD4b7tjYyGm041PiB98vtejuYUlP/anmON260Lyj6XnmIEWqzhZac/b+nNCGhk5SZ1IdOYZZEYoxTH6XJz1Ru/cNl/lpNtLycTymw2LhpD+SV8/s6856/E4cZtRZlSqX8Ys5pFti4aV0gYnDHNuP/jM0YgQ4LDvt8BN8Qnsfqo1bd48eY0dh7OgT7jjd9ht2dmvZNuNfrCldZ7nHHrLeFzOmDnD8b97hUEpQBGPwq3r4Z+F5X/fIWZUhX0lIJigaxygrw14XIazcuh6vK9mHYQ3d74fh1YW/K5qkp7/c2U8pbutehmlAH6W77nHc/un43AVn3b9WPR/Z1LG+VsqwpKBZLJZGRLxXQ0fugXTOOWkcYP84INB9l6UNNJioiIiFTmrD5t2BBuZJKYvDNBjZxmfIgDNuzP4OdtR7CYTUwakVTl9kwmE49e0J+4iBBWHbYYC+u7p1R+pi8Qss7RkWiblcR4I0vrxK4t6N8hBnuhi/d+KRsscbvdrAsbgttsNWbvKv1BqnjpXulSleLlJi5Xnb6lgCuVDbHjUDbLth9hxc6j3P3x2vJ7uMZV0Oy8IBcyPNlmdR2U2r8aCssJNnr7SXUYWvK4HXeFMYa8Y7DspboZiwSedybGjiew6YDxGTAy1ILbDW8v22VkQXn+phEWB8dPLH87Pc/xbG+FUVq39zdjRr/wFtBuUMX7N5vL/xvhVVGAyfs3N6pU+V6J19RRplTxjCd/Ji/ocLxxW7qvlHc8FWVKxXsynqoKSvmanHv24/17mnOo8pJFb1DK7So/u7UuuVywe1nR48x9jbJcW0GpQAuPhwtfBUyw6h16HF3K2f2MK2SvfN/4fmBEREREGpMQi5lhJ5zMAXcLAOw9zmPfwNvYcSibTQcyedlzPnVO/7Z09AR6qtImOozp5/fjqNvIVnLnHjWu0heXsRfeOMsop6mttE0A5IYlkE40fdrHYDYbHw5NJhOTPdlS7yzfRb6jaBwrdx3l8td/Yfyb61np6mUsLJ0t5QtKdSm739hEoweN016UWRQsSgWlNh4o+pD4zfpUXl1aTjaDt6/UsVLBP++HuPAWENmybsbXoqvRuNxpN7KiSvP1kxpScrnFCmc+YNxf/nJRTx9p2jwz77k6nMAmz8/qXWON3+mPfttDRp7D6P0U1wlGT6+4fC22gyf45DYCHt7Sva4jwWyp+fh8s++llsy0qTRTqo5n4POW7pmtYLVVvX5Fzc59mVJVlO/5HZTy7Ccspqi3VkXZUlmpkF2s2Xh5MyXWpUObIO+oUc7e6SRj2Y4l9bvPGlBQqjHochqcfJtx/8vb+PtwIx3xy7X72X0kp5IXioiIiMjlwzszzTGZ1wrPY/C6ixjx5BLOfGYp5/z7R+avSwWKyuD8df7A9gzpY1z5NuGmMLtUX6kfnzECB4sehl//U7s3cNAo3dsXaoyxb7uSzYjHDWhHu9gwDmcX8OWa/fyxN53rZq3g0teWs2KnUf6xwDEIANeWUlfeK2pyDkaAwxuIaYzTmNeGr9G5Ub7nzT7pGB8OwFMLtrBkS6mZ7+IryJSqYT+pub/upuf93/C/teV8KDeZjMbTUH5fqeKZUqX1GQ9tBxjT1m/+qlpjkkbI5fIFpVKjB5Bb4CTUauaaEzvTMyGK3AInH63cYxzzO9YVmy2vAr3PNW63zIftnibn3c+q3RijjKQJnAUle+z5MqXKC0p5Z+Cro/K94k3OK8roKq7CoJQ3YF1B+Z73b2J+RsXldW530XbbH1+03Nenr4K/pwc8AWhv+eG2xUYmZn3Z9ZNx22l40UyNOxtfXykFpRqLM/9p/KHJO0qflf9kZK/WuNzwWnlXcURERETEp0NcOO2Gjmdm4VXkEkao1Ux0mJVWUTY6xIVz3UmdGZQYV61tmkwmHrlwEBkYZSIf/bCm6MnsNFg9t+jxN/fChs9r/gY8Tc43uhIBo8l5cSEWs6/0cPr/NnD+Sz+zdOshrGYTVw7rxMd/PYlfQ04AwL3rp5KlI5UFpaDxTmNeG/mZxqxf4MuG8Gaf/PX0blw5rBNuN9z+weqSF4B9M/CVypSqwcx72w9l88j/NlLgdPHwlxvIzHeUXamiZuc5hz1jMBWVBhVnMhWVaaUs93tM0kgd3Q756WANY22hEUTt3TaaEIuZG0YYGY5zlu2i0OlniW0vT1+p7d8VZfNU1OTcX9bQoiwgb1DH7S6WKVVJ+V55jc5dLvjucfh9jv9jsHt+p6vqJ+XVfhBgMvrqFc8o9AWl2pX3KgiNMPpRQcUz6WXuM5q8myzGZ3ivlt0qf523dK/XORDbCQrz6ndGPG9QKukUI1sOjB5jpTN/A0xBqcbCaoOL3jRSqP9cwLRexlW9T37fy/50TfkqIiIiUpkZFw1g62PnsOOJcWx97BzWPTyW3/45ip/vO5Ppnhn1qqtNTBhmz4etr39Zx5ZUz4eiX/9jlF11GAIn3AS44bObYeePFW+sMp5Z1pZll2xyXtwVwzoRZbOSU+DEZIKLBndg8Z2nM+OiAZyQ1IJbLj6bHa62WNyFbPzpv0UvrCooFYwz8HkzM8LifL1nvEGpPu1iePj8vgzuFEdmfiE3v/M7Od7G5xX1lKpmppTT5ebuj9diLzSCCEdyCnhxcTmTGBVvdl68JMqbJdWqJ4TFlr+Tzicbt8X7xQTChs/hX92KysSk+jxZUrQfzIZU43Nfn7bG34ALBnegRWQo+9LzSN54sKItlJTQzyjzK8wH3NC6j/+zUFamdLNze6bxdxDKD0pFV9LofFsy/PAv+Oof/k8k4S3f86efFBi/O97fWW9TcqejqHyuokwpKCp3rqiEzxvsS+hrBLHKvK6iTKk1xm27gUUZbfVVwudyGc3UATqfYpR12mKMDLDSzd8DTEGpxqRNbzjhRgB6rXuaE5PicDjdvP5DEJ0kiIiIiNSTUKvZ14uprkS1MAJFMa4Mpn60BkdepjGlN8CIO+CcfxnlVM4CmHeVL8DkN5fLmLYcWGXvSIjFRI82ZTMBYsJCeO7yQVx/chIL7ziNZy8fROeWRR/Oxg1ox/42pwOw7edPScvKB2dhUYDFewW/tGCcga/U7FqHs+2kZdkxmYwMFJvVwmvXDKF1tI0tB7O459M/jMbn3kypnEMlmypXMyj11k87WJWSTrTNysyLjCyK2T/vYvuh7JIrth9s9MfJOgAZe4qW+/pJlVO655U4zHhtxh4jEyRQlr1oTASw+JFGOatXk+CdgbHjUF/w1BuYDguxcPVwY/bFWT9XkH1TmslUlC0FtS/d8yrd7NybfRQaVTIwU2b9cspXV3j+hrpdsOUb//bv/Z2sqJ9WeUqX8GWlAm4jESSiVcWv8waXjlXwPfcGudqXymT0t3yveFBqyzfG3+q6dmizUWoZEmH8rbFYIelU47lGVsKnoFRjc9rdRkrigbU83G0LAPNWpnA42x7ggYmIiEhDe/nll0lKSiIsLIzhw4ezYkUF08cDGzZs4OKLLyYpKQmTycTzzz/fcAMNYqZI44NLh9AcNuzPZNlHzxlXmlt0Mz5UmC1Gtnunk43MgbmXVC9IkL4bCrJxmUPY4W5Hz4RoQq3ln6KP7pvAw+f3o0dC+eUrw8ZeCcDJrt+5c95qXOl7wFUIFltR1kJpVX2IKodp4+ecvvlBSF3n92saVEbJRsbeD/qdW0QQabMCkBATxqtXH4/VbOLrPw7wf5+v47AzvCgzyXsMXc6iRud+lO9tS8vm6YVGEOuB8/pyxbBOnNm7DYUuN49/vankyqERRaU/e4r9bvv6SZVqcl7itZHGB1uoOlvq8DZ4cQj8+nqV46+WjL1FH/b3ry7K+JHq8R7vjsN8DfmLZ0tec2JnQiwmVu46xh970/3bZvGgVG1L97y8mVLe2etyKindg6KgVO4RcOQXLT+y3ciU8tr0P//2b/dmSlUnKFVqBr7ipXvmSkIhVTU7926vdHltZeXQuUeLZvFsO8BoPB4ebzQi3/NL5e+jJrxZUonDjPJLgK7GhYvG1uxcQanGJrIVnPJ3AHpteJ4hHSPJd7h46yc/I+MiIiISFD788EOmTp3KQw89xKpVqxg4cCBjx44lLS2t3PVzc3Pp2rUrM2fOpG3btg082iAWYcy2dnHvMKwU0mPH28byk28rms0qJAyufN8ok8k6AO9dDFl+ltocNDKrDoV3xYmlTJPz6gjtMgJnaDStTJlk7VjBV0s8/URadKn4A1jL7sbtsV3+Xa1PT8Hy9R3E5e3C8vUdja43CVD0wTPWyJQqXrpX3NCkFjx8fj8APlixh1Of/J6DZs/vjjfDLGOPUQZlCS0q76uA0+Xm7k/WUlDo4vSerbl0qNEf6J/n9sFqNvHd5jS+L91cvXgJHxiZc97SoMoypaBYCd/Pla/3+2wjsLbw/qpnFKuO0mVHv75Wd9tuLuxZkGb0lEtvOYgDGUbwpnfbosBzQkwY5x1nBHhm+fuZsPPJ0KqXkf3n/TmpLW+5W5bn98vbT6q8JudgBFysYZ7XFOsr9dss47aVZ8bQHd+X7INXkYKaBKWKZUq53WWyKCsU78mUOri+bAagywX715Tcvpc3mJV7BPLSSz7nnWUzvguExxmZS97ecBWV8DkL4b9T4P3LYe28osCcP3Z5ysmTTila1sUTlEr5pWSgMMAUlGqMTrwFotpiSt/N4x2NKw7vLt9NRm45DRJFREQkKD377LNMnjyZSZMm0bdvX1577TUiIiKYNWtWueufcMIJPPXUU1xxxRXYbH5Mly3+8WRK9Y62c3/nTbQ3HeGoKQ57/8tKrhceD9d8anzYObwVXhsBfy6qevueJufbTEbAo3ST82qxhmLxlOqcaVnFb6s9V/Mr6icFxnitYeByFF3Fr4jbDV/9A5OnjMacuhZWv1vz8daXUh88vTPvlRfwu+bEzrx74zAGdowlz+Hk9ywjU2rJryvJyncUNTlv0c34EFmJN3/cwWpv2d7FAzB5Zgjr2jrK16j+0a82UlBYrGF16WbnR7aBPQOs4dCmX+Xvs/MI47ayTCm3u+gDr7MAFtxf+Tarw5vhMtDI0GPjf8sv1ZKK7V9tlLDFJrIxyyiB69QiguiwkBKreRuef/XHAQ5m+hFMsITAX36AW36FkPC6GWtM6UwpT/leRZlSJlPZEr6C3KK/GWMeNYLizgL4c2HV+/cGpapTvpfQ3wgo5x0zSvF8mVJV9NjqONQo8UtdB8teKPnc0e1GVqw13LgQUZwtGqISitYrztvHyZvhCCX7SpVX/vrz88b3a+u38Plf4Knu8MmNsHWB0R+rIm437PIEq70lewCtexkzKRbmlz/rZ4AoKNUYhUbCyPsA6LX1VY5PsJBtL+S1H4Ko1l9EREQqVFBQwO+//86oUaN8y8xmM6NGjWL5cs221aA8fUdMuYe51vkFAG8WjOWFpXvKrhvbASZ+aQQTcg7B3Ivh22lQWEkbBk8J3G95RgClX4cKGlv7q+fZAFwUuZ5OGBPnLDkUxaGsCsZgNhdlBVTV7PyPD2HbItwWGztaeX42Fz9ifOBrTLxZTjGVZ0p5ndqjNV/cOoI3Jg4lN8J4zY6tGzj1X9/z0y+egE8VpXvb0rJ4JrmobK9dbMlAwG1n9aBlZCg7DuXwzvJdRU94M6VS1xk9c7z9pNoPqjIIRqcTAZMRyKooM+/wVuPDuDnE6EG1Zb4xDX1t5RwpytAaOc0oX3UVwsq3ar/t5qRYP6mNvp/TsuW5AzrGMiypBYUuN88lbzV6oFUlJMz4qiulG51XlSkFRRlJ3mDQ+k+M8uf4JOg+2ujHB/6V8Hl7Svnb6ByMsrW2xxn3963yPygV1wnOmWncX/RwyXI3b+leu+PK/x31lf6VymrzBaWOK1rW7UwjuJW+29db0Cd1HSzxjGHAZcZ2C/OM7+H7l8EzvWD9p+WP/9AWo9ebNbxk3yuTqaiErxH1lariL50EzOBrYfnLmI78yVN9vuesg6fx+g87GN03geM7xQd6dCIiIlKPDh8+jNPpJCEhocTyhIQENm/eXGf7sdvt2O1FwYrMTONDkcPhwOGo+wxt7zbrY9v1xWSLxQq4ty3Cas+i0BrBe/lnkb1kO2f0bMXAjqWCSLGdYdICzIunY/ntDfjlFdw7fqDwwtfLbZRtPbgBE/BrnvGBr1vL8Np9f7qcgRUTHe3bGBdlgnxYdDCS255ZwtRR3bnyhEQspZrBW+K7YD60CeehrbiSTi9/u9lpWL+9DxPgGDGV9Zk9SHKnYD6yFefix3CNnVnzMdeljL1Yd/9sjLPtIOx5dralGRkWPVpX/r0d2aMFjDwJFnxOL9tR0nMcpGxZA1ZYmd2S7tl5RNlKfnxyOF2s25fJY/M3G2V7PVpxwcCEMvsJt8DUUd25/78b+ffiPzmvfxtaRtkgIgFrdHtMWfspTFmBKeVXLICz3WBclYz163WpLNlyiKda9SHk8EYKd/6Eu8/5ZdYzb/oKC+Dqcjrult2wrPgP7m/vo/CmpTg8CVtlvieFdiOToqKZ/wDTxv9hdbtwJwygMKo9phMmY01Zhvv32RSefEdR2ZZUypLyK2bA2X4IG/akA9ArIarcn9ObT+3Mil1HmbdyD1YzPDCuN2azqeH+rka0IQRwZ+6n0OHAnJVq/KyGt6zwZ9USlWC8v/Q9uAoKsP76OibAefz1uJxOTD3OwfrTc7j/TKYwN7PSrC5zXoaxP2tEpb8bZV7XbjCWfb/h3LMCU1aqMZ7ItlVvY+BELHt+w/zHB7g/nkThjYshNhHznt88v6ODyt2GJa4L5pTlxt/TYs9b96/BBBS27o/bu9wUgqXrSMxbv8G96StggHEcC+1YP7sZk8uBq+c5OMe/bKy+fzWmDZ9g3vg5ppxDuP97G4XthpYJspm3LzV+7zuegNNtgmLjMHU6BesfH+LavgTnadP8/j7WhL8/kwpKNVYWK4x6CD68hm5/vs21/Ufz7no7Uz9cw/y/n0pEqA6diIiI1M6MGTOYPn16meULFy4kIqKc2ZTqSHJyctUrNRKtM3dwMmCyGyVgu+JPo7stglVH4NZ3fuHu45yElFt7cCoJXaMZnPIGtrT1mF4fyfaEc7Bbo3FjBpMZNzDIM7vTZlcnWoW5+fE7P8pYqnBKZHda5vxJ+3yj9Czb1pas3EKmf7WZt77fxAWdXbQOc2M1Q4gZBqVDT2D36u9Yd6j8XitDd75Eh7xjpId34ofMHrhNVpbHX8SIIzMx//YWP2QnkRneqczrTO5COhz7lYzwJLLCq+jjUgf67P+Ynm4Xh6L6sGzFNvbmbKPQZSXc4mb1z9+zporJGdtkpnES0D9kP9d2d9J7r5FZ8d72cJJnLubM9i56xLjZnmniz0wT2zNNFLiMjYZb3JwZnco335Q/m1iEGzpGWtibU8jf3vye/vFujhXAtfkdGc5+Zs19n7HmX+kMrDpoZv/8+eVuZ3smvLTRgsttYnRUB85lIyk/fsC6nWU/H5yy9QNaAuvsHdibN4hR1mhsh7ey+d072dHGyKor/vsYn7ON4Tuew42JJb0fwx4SV+4Yhm+fRVtgs7knW+fPx+SGUSEtiMg9wroPprOn5anlvk6KmF0FnLNjKWbgp5RCfv1zP2Aid99W5s/fUu5rLu9q4qMdZt77dQ/bdu7m8q4uvDHm4scx3wk/p5rId5oY0spF2zr4c24tzOFcwJSfzoKvPmfIrvW0A9bvTGNXdvk/q33T8ugB7Fq3nH17XZx2cB1OUwgL0trgmD8f3G7GhLQg3HGUVZ88Q2rs8eVuB2BgyiaSgK279rO1gt+N8nQ8amYIkL7hOwBaAr9vS+XA4Yq34XLDzwdNdA4/i2vDlxOXt4ucty7kx57/ZMSf39ECWH3QxL5yxtHjkIO+wP51P7IqyyjBtTrzONdTzpe8Po2CLUWv65TfgcFA7qqPoPcAkpOT6bP/I3qmbcRujea70HMpKPE35RRMPU5ixJ9P0DLnT9LevYnfukwpMYahOz+hA7DF3qrM9yqsoJCxgGn/KhZ++TGF1mpknlVTbm6uX+spstGY9T7PSOnd8yv/jPwvi2IvYNeRXB7/ehOPXzgg0KMTERGRetKqVSssFgsHD5YsyTl48GCdNjGfNm0aU6dO9T3OzMwkMTGRMWPGEBNTi95GFXA4HCQnJzN69GhCQkKqfkFjcKADbH8aALfZSucrnuQ/1jaMe/FnDmYXsM7clX+O613Bi8dB1g24vroN647v6ZX633LXyg5tzdH8GM7ulsC4cQPLXac6zLFbYcljvsdP/uUSBv1p4plF29ibU8hLGy0l1r/C0p6ZIZCVeYzTzxrjm6HOy7RlPtbVK3CbLEReOZtRrfqSnJzM4Iv+juvLjZg3f8npuV/jvOhLozzEK2Mvls9vwrzvN9yRrSm8ZWX1mhRXV6Ed64vGz3P82HsY13scn63eB39sYEBiC84994Sqt3GkJ2x/mhjnMR6ceDbW56dCLuTHdCU33cRXKZYyL4mPCGFYUjw3npLE4MS4Sjfftv8xrnxzJauPmFl9xFjWxtKL4SEr6Fe4gQ7mPWCCQefdxKDYjmVefzAzn0df/QWXuwCAr3P7cG5oMl1MB0gcN67kyjmHsa42Zg7se+Gd9I1pjykxD+ZPpf/h/9HtgmkkL1vt+300bZmP5YunMBXmATA6bB2usTPKvgl7FtY/bgKg+/ipdG9tNKw2x++E7x9hsP0XBpzzRMmfBSnDtPlrrGvtuGM6MnTC3zj0+HeAm2vOG0mHuPIzhsYBQ9fs597P1vNLmpnWbdvz2PheLPluMaNHj8ZktvDR7/t44bvtHMkxfkYW7jMzsGMslxzfgXMHJJTpV+U3txv35rswOXIYe/JxWA6bIQP6DT+Dvr3HlfsS88r9sPBrurQIpYvVKE8zDbiU0eOLevKZQ36Gla9zQlQqztI/w8VYvvgvHIGe/QfTfXjF65VxpAe89h9a2Pf4sv+OP/083JXMbvnhb3v55JeNxITFcOWNH+H+4BzicndxrnMBJvteAAaOm8RA7+ylxZg2FcJnH9Mh3E5bz/sx7fkF/gB3dHtGTbii5AtyhuH+9yzi8nYTXnCY0wb1wLbGCCRZJrzIqN7nlT/Ig11wv3UmHdJXkNA7HHfXM4zlbjfWf98JQI9Rk+je6aQyL3UfeAnT0e2M7RWJu1c1vpfV5M2+roqCUo2ZyQSjpsPss7H9MZeXz7uGiz7OZ+6vKYzqk8AZvSup3xUREZEmKzQ0lCFDhrB48WIuuOACAFwuF4sXL2bKlCmVv7gabDZbuU3RQ0JC6jVoVN/br1OxRUFA04DLCGmZRGtg5sXHcePbv/H28hSO79yCCYMqyAJqkQjXfAar3oZdPxlNjd1OY9Y6txvcLj7KPhEyYUDHuLr5vvQZVxSUModga92F6xMsnDeoI09+s5kFG1LJd7gocBr1W7vcxnuMy0vh/v9u5KWrjvc16SYvHb69x3j/I24npNNQXylISEgI5rOfgG3JmFOWY97yJQy4xHjd1gVGY15PvylTziFCVr4OI++t/furyKbPjT4q0e2x9j0fLFa2phlX6vu2j/Xve9uqK2DC5MghJHM35BrNnF+54wr+tymTV5Zs42CmnROS4jmpWytO6tqS3m2jMZv9C8Cc1L0Nt4zsxoINqSTEhNE2Nowu5jNg/buMsBhN79Pccfxvk5sbTy053oJCF7d/+AeHswvo3TaaU7q34r8/pRtPpm0gpDDbaLjvtdMIctBuICEtPTMHDr0eVr+N6cBawn5+EkxjjN/H1XPgm3uMn892A+HAWiyr38Zyyt8hLrHkm9j8ndGcumUPQtr1Kwo+nTAJfnwK08F1hBz4re5mfQtWW78CwNTvAnal23E43cSEWencKrro968cl57QmQhbKH+ft5r//WH8Lp8dAz/uSOephX/6ylW7tIqkW+solmxJY+3eDNbuzeDxbzYzrn87Lh2ayPAuLfz+ufWJaQdHthGSe8jX6Nwa2w4q+t2KNwKr5rQNkGEEc8wn3oy5+Pr9JsDK1zFv/daYJNRSwbYcxu+yJTwWS3X+TrbpDbZYTPYMyDaaxFtbdKpwzPkOJy99b/TXy8wv5OU1hTx0ySx490LM6z82VgqLJaRNr/IDr22MMm3zsR1F79Mzw6Kp/aCyf4fi2kGnk2D3z3Q8uhzbNy9hcrvguMuxDriw4vfVcTAM+wv8+irWBffBLcvBajP6SeUcAmsY1s7DwVrO++w6Eo5ux5ryM/SfUPE+asnf/2cKSjV2nU+CXuNgy3yO//mv3Dn0SZ75zcHdn/zBwn+cRovI0ECPUEREROrB1KlTue666xg6dCjDhg3j+eefJycnh0mTJgEwceJEOnTowIwZRiZDQUEBGzdu9N3ft28fa9asISoqiu7duwfsfTR5ka2NZrGFeXDybb7FZ/VJ4JaR3XhlyXbu+eQPurWOon9FTcrNZhg6yfgqx/vPLgWy6VubmfeKa9MXYhMhY4/RUNhsZPe0irLx1KUDeepSIxvL6XJTUOii4Fh/ePUxOpvTeGLreex/oR8d+pwI7QYZwaX/b+++46Oq0sePf+6dPpOeQCohNOkl0kRQRFBEV8WKflHRda3gWnZ1dV3b+rOXddVdWXd13VXW3rEiCioivYfeIQXSSJk+9/z+uJOEmISikMbzfnFfM7lz586ZnEk498lznlNVaK6SNbqRgFJCJzjpNvjmIfjyHug+Dr5/Gub91Xw843joOxFm3WuuYjX06toVDY+4hf80b4dcVVuAuKbIeWMr7zXK6jDrs1Tsho3R6VBxmVicsUzMjWVi7i+fgnjHGb2444z9suvCfWCd06zjBCw3uvHgJ2vpEOfknIF1tWIenJnH0h3lxDqtTL9sMBkJLr7fVMzm0nS66QWoHT+i9ZxQd96aKUL7Z0LoFpjwOLw8Hm35DBJ6dkf/+s8wP7rC2PFT4Kyn4dWJ5pLy3z4O5zxX/w2sM4Mp9P5V/YtydxIMuNgMwC6YLkGpAwn5Yf3n5v0+59auENk7Pe6AAakaZw1Ix2HVuXHGUmat3cMCm4WKBcsAM3PvlnHH8X/Ds7FZdPZWBvhg2W7eXLyTTXuqeG/Zbt5btpvsJDcXDc7igsFZZDSRmdVAbHq0sH5Bg9X3lFJsK/GSneSuq1tXU+soOk2ZzCGQkVv/nNkjzAUlvMVm4L7bmMZfu2b1vcMpdA7m79/M3Lpi5ZqlboW8Rvx3/jYKK/zEOq1U+sO8On87V4wYTZdx95u/x8D8vdZUP9UUOveVgbfU/LlobOW9/fU6C7bPo1fBu2gYEJth/pwezJi7YM175kp/PzwLJ99ufg8Bsoaav88a0/UUWPxS/QLuLUhW32sLJjxmrgBQuoVpW2/krOQCiqsC3PXeykNbeUEIIYQQbc6kSZN48sknuffeexk0aBDLly/n888/ry1+vmPHDgoKCmqPz8/PJzc3l9zcXAoKCnjyySfJzc3lN7/5TUu9hfbB6oBLZsD/vQWpfeo99LvTezKmZwcCYYNr/7uY4qoDrLLXBF8wwpa95sVW30MNnByMpsFx4837NRdIjbDoGi67hfiO2dDvQiKalXjNS2bZIvjhOXj3alj1lnnwOc/VFiEOhg32+PY70Ym/hYTOUJkPzx1fF5Aafj38+gsYcZMZ4ApWwbdPHpn3+FP5y2HXQnOVueOnAOZF8sFW3mtUQjSraFM0KHWQlfd+Mau93gpZetYQAH731nLmbSoG4J0lu3j1x+0A/PWSQeSkeLBbdZ68aCCLlRng2rx4v3pkIT9sNmvosH+gCsxV+/pfhIZi5MZHsdQEpMb8Cc7+qxnQO/VP5r5lM6Bkc/3zboi+Ts3Kafsbfp15u3YmlDeyQqUwbf4agpXm6nSZQ8jLjwZPDyMwPa5PKi9dOQSnTacipGG36lw/uhtzbh/DlBNzsFnMS/0OsQ6uObkrs249mfdvPJFLh3UixmFlR6mXp2ZtYORjX3P5Swv4eEU+oWj2ZJNqgkzFG2szl4jpSHUgzPWvLWHMk3M4+fFv+Ns3m8wVP+N+EsQddk3Dc+oWqJn+d6BV+GqCUo6GqxMe1P5T9WJSawP1P1XhD/H3Oebn/Z5f9eGUnh0IG4rHPltn/p7rG81c6npK069l90BMNMO2NLqiaU1QqmYlwJ+KBo51ot//c58HV8JB3hTmdMTTHzLvf/sUlG2vC0rlHKCuW84oQIPi9VBR0PRxzUSCUm1BQjZc/RWkDUCr3suzgXs41bqCL9YU8c6SXS3dOiGEEEIcJdOmTWP79u0EAgEWLFjA8OHDax+bM2cOr7zySu3XOTk5KKUabHPmzGn+hrc33cfWBXn2Y9E1nrkkl64pHvL3+blxxtKDX9T9xLrCCgxlZjF1jDuCK5aNmGpelNQECQ5E0+DCl9Dvzufprv/ijtA1vKWNJ5g2GBzxcNLvarNevtu4l7Oe/4GHllt58bto9oPNCeMfNu97S8ARBxf/1/zDqtVuZiqMu998fPFL5oXTkbYomiXV51yINQO3hRV+yrwhLLpGj9TDqGWVGA1K1VzcNbJq4hHXaVjt3THjzuSs/umEIorrXl3CW4t3cvf7qwC4eWwPTu1Vl+XRLzOe+N6nAODd+B17Kv3Rtn9nBg3iMhu/EB73AMrmxmr4UboVzv07jL69Lvsj+wTofpo51XTuY3XP2/INhKrN8+6/1HyN1L7m505FYNG/ftG3pF3L+8C87X0O6Dp5BfvMLw8zMH1Sjw7M+PVQxmcafPHbkdw5oRfxrsanTGmaRm52Io+cP4BFd4/j6YsHMqJrMkrBdxuLuen1ZYx7ei7vLNlFuKnfY7HmKqG1QRari51VOhe88ANfrDFrIO4u9/HEF+s58dHZ3PzxLgwtOjnLnQx9JjZ+3ujKkWrdTNYX7Gv892iw2rw93EwpoCqlLkOp2tl0ltQ/v91CuTdE944xnJ+byR/P7I2uwedrClm4rQzO/xdc9TmccOOBXzA5WmuqdAuEfLA3umpuU5lSSV1QHfsBEDn+KvP/nEPV/0LzZy7sg8/+ANvnmftzRjb9HHdSXVu2fnvor3WUSFCqrYhNhas+ha5jsIS9/NP2JBfo3/LAx3ks21HW0q0TQgghhDgmxbtsvHjFYGIcVhZuLeXBmXmH9fw10QyJvkdq6l6NpK5w5czDurjRrA5uvPR88tLO5Q7fFC4I/Rn/77fC2HspqvAz9X9LufylhWwrMTMknv5qE4u3lZpP7nUWjJgGx02A6+aawaH9dRtjZhdEgvDNw0foTUZ5S2HVO+b9/TIxarKkunXw4LQ1nhnRqMQc8zZiFopu3qCUhiVrME9dPJATuiZRFQhzxzsrCYQNxvTswM1jG2ZtjR0/EYDeagt/fneROZOiZurecWc0Ps0oPpPImU9R5u5KZNLrkDu54TGn3m3ernwL9kQvqmsyWXqf3fT0pRNuMG+X/geqSw7+3o814QCsj66m1ndiNKPPnL53yNNM9zMgK54zsw2yEg9xCh7gsls4//gsXr/2BL69fQy/PbU7yR4720u8/P7tFZz2l295f9kuIsZPZuXUZEpFg1J+RzLn/G0e6worSYlx8L9rhvPURQMZ1CmBUETx4coiCgxzWvPSlHPIr258ls++1BEELTFoVUXc9ezLjf8eDdRM3zv0APPeygCPfLaWs96tWwXuh7121hU2LMC9tzLAS9+bgfbfn34cVovOcamxTBpqrir60Cd5GJrFLK9jPUgJnZoM1ZLNsCfPDNK6U+q+f40In/0seekXYYz78yG/P8D8OTzzSdCtsOEzqCoCi8OcKnkgNdlerWAKnwSl2hJHrJk6PmASFhXhKft0rgi/y6R/zOfNRTtaunVCCCGEEMek7h1jeWbSIAD+O3/7YY3L1vyMaTtHk9NmYfplg0ny2Fm1ex9/+mA1L3+/lbFPzeWTlQXoGlw5IpvcZIOIofjt68so9wbNC6PxD8H/vdH0lMGabKmVb0Lh6iPX6OUzzHpMqf3Nlauj9q/Tc1hqpu/VONrT9wC6nAxp/WHQZHDE4rRZePGKIbVt75zs5plJuY0WprYl5xCKycCmRSjbMI93l+yqC3ocYGUt1e8ivu15f92qXT+VkWuuBo6COQ9DJFwX7Gps6l6N484wPwO+MnjlzFYxPahV2fwNBCrMrKOsYeTv87PPF8J6uBl9R0h2spvbTu/Jd38Yw10TepHotrG1uJpb31zBaX+Zy4wF21m2o4x9vlBdUKWqEIB1lU7KvCH6Z8bz8U0jObFbChcMzuKDqSOZedMoLhnaiffUGNYYnbluwxBOfPRrJv1jPq8v3ME+b4j1hZXc9d4qTnjiez4Nmhl9Z1gW8eainZRFVw+sFTz0oFTBPh/3f7SGUY99zT/mbmF7MI69mlnLbmc4kcv+tbB22nSNv32zCW8wwsCseMb3rVvg4rbTjsNjt7Bi1z4+Xpl/aN/U2kypzfXrSR2oXljaADamnV07TfqwdOxlZsfW6DTMzGA9kK6jzdutc81FN1qQBKXaGqsdJk6HkTcDcIftTR7R/8697y7h7vdXEQwfXsq4EEIIIYT45cb1SeW208yMmns+WMPjn69jY1HlQZ+XV3CUMqV+gaxEN89dmouumbWM/jwzj6pAmNzsBD6+aRR3n9mLS7oZ5CS7yd/n5/dvH2Kd04zcaE0WBbMfODKNNYy6aWLDflPvoi/v59STgrpMqRrNkSnliIXrv4eJf6vdFee08erVw/jDGb147erhxLubXsnK1mUUAMP0dbzy7gdQWUDQ4mZnfOPL3u/zhViwtZQVJRq+YKTpdo25G9Ag70NY8IIZaHInm8Wpm6Jb4NI3zKDL3nXw7zOgbNsB3vwxJu9D8zY6dW9tNDDdvWMMDuthZPQdYW67letGd+O7P5zK7eN7kuC2sWVvNXe/v5rz/v4DAx/4kinv1K8TtlfFc87ADN6+fgTp8fWDKf0y43n0ggFccdd0Vv5qJl27mIGaBVtLueu9VQz+f7MY/8y3vL5wB75QhFWxJwNwtn0JgXCEt5fs91pK7VdTqumglFKKV+ZtZfQTc3jlh20EwgYDOyXwryuGkNLb/BkJxXaiuCrAZf9awO5yszjezlIvMxaY04r/cEavesXmO8Q6uOEUs+2Pf74ef+gAPy81kqJBqZLNBy9yfqScfEddDa/OB5i6VyN7hFkn65xnWzwoJavvtUW6Dqf9GeKyUJ/fyQWW7+im5XPdgltZV1jJC5OPP7I1CYQQQgghxEFNG9OddYUVfLqqkL/P2czf52ymT3oc5+VmcvbADNLi68ZnSin8IYN1h7s6XDMZ2T2FOyf04uFP1xHvsvGHM3pxydBO6LpGKBTCaYFnLh7AxS8u5Ku1RbzywzauGtnl4Cc+9R5zCtjGL2HbvAPXPTkUm74yAx7OeOh/Ub2HflaRc6irKQVmVkZNHZ0WkBJTd0F8QJ1PhFVvcVbcVrRK84L6q2A/bnz6B3KzE5jQLw1vMEJefgV5BRXsKqupVG9h5jPfc9Op3Zk0NBu79Sc5C6l9zJo1q96uW3ms55lNFoqu1aEn/Ppz+O+5Zv+8fAZc/oGZ0XEsCwdh/Sfm/b4TgV8QPD1KYhxWpo7pzhUjOvPf+dv5fmMxW4qrKKoIsK46Fva7zMzIyuavlww64IqB8S4blw7L5tJh2eSX+/hoRT4fLNvNusJKLLrG+L6pTBmRw7DM0fDEM6SHC+mt7eC1Hz38ZlRXMzsw5AMVTb5oIlNqnzfE7e+s4Ms8s7bVsJwkfju2ByO7J5vtS70XOvbhwn5X8eZ/8tiyt5rJ//yRt64fwV9mbSAUUYzqnsKJ3RuuDnr1qK689uMOdpf7+Pe8bQf/mazJFi3dXNfu9CaKnB8pjhiY9CoseQWGXXvw420uOP3Bo9umQyRBqbZs+LVoKT3g7SsZ5N/Mx857uG7HLfzqOS/PXZrL8K7JLd1CIYQQQohjhq5rPHtJLmf2L+SDZfnMWb+HvAIzCPDwZ2vpkuzBH4pQFQjjDUYIR+u1eOwWcpIPv3jv0Xbtyd04oWsy2UluEtwNa6j0zYjjj2f24v6P83jk03UM6ZxE/6z4A580uZu5Ot7il+Cr++DqWQee0nIwNQXOB11WrwCyLxhhW7FZGLl3+mGu1hWTZtZkiQTMqXu/pH3NJZoZ0T2wlhs7+qAEtiWfjFYEy3aUs2xHeYOnZCU4qfT62FMZ4J4P1/CPb7dw89genJebidWyX3Bq9J2w+j2zLg7UFqU+qMQcsyj0qxOjGVMT4PL3zIy5Y9XWueDfZ64AF51qWrvyXisJStWIddqYOqY7U8d0B6AqEGZr0T7UyzpadJW4vj26H9bPR0aCi+tHd+P60d3YXlKNy26hY+x+Ua7u42DdTM5zLObh0s7M3bCXMb061hU5B7C5G5x3yfYyfvv6MnaX+7BbdP54Zi+mnJhTP1iW3A1O+QPJwIzfxHLR9PlsK/Ey6R8/sq3EPP/t43s22m6X3cLt43vyu7dX8PdvNnHxkCySYxxNv9GaoJR/HxSaixQcKFNKKcW7S3fz4WadoZUBMpKazoo8oMzB9VcabCMkKNXWdRsD134Db0ym45483nQ8yN3eXzPpxQAndE3iN6O6cmqvjo3OPxdCCCGEEEeW1aLzqwEZ/GpABmXVQT5ZVcCHy3ezaFsZW4qrG33ORUM6tdqx2oCshAM+PuXEHH7YXMKXeUVMe30pM28aRazzIBdUo/8AK16HXYvg3avN+kRdxxzaEuhgTtkr3QI7F8DGWea+oVfXO2R9UWV0VUN7/YveQ6Hr5urXJRubZ+rekZDSwyyk7C3GWrIeNJ0br7mRCyMePl1VwLcbi0l02+mbEUefjDh6p8XhtsFHMz+lokM//j53K7vKfNz+zkqmz93MebmZ+EMGlf4Qlf4w53lO46SqzwlaPNi6nMwhf1rj0uHKT2HGBZC/DP5zjln7JlgF3jLwlZqF6iMByL0chvz6wEGOkA8W/MOc7jj4KrOv2pI1H5i3vc+pzTZbW9i66so1JcZhpX92srkAV2W0Tpin488+X+fGAvF9z4N1M7nUNocn/efw3/nbokGp6FRoe0y9PjcMxYvfbeGJL9YTMRQ5yW6e/7/j6Zd54OB4eryLGb8ZzkXT57M1+nt5Qr80BnZKaPI55+Vm8vK8razJr+CKlxcy/bLBdEpqGCAz2+kmHJOOtaoAVATDHoee2Hgm6d7KAHe+u5LZ6/YAOlNeWcyb151IkucgxdTbEQlKtQdJXc2/Mr1/HfZ1M3nC9iIn66v4fNtQbt/Sh8SUdH49qgsXHJ+Fy95y85SFEEIIIY4liR47l53QmctO6MyuMi87Sr147FY8DisxDisehwW33YqllQakDoWmaTx+4QDWPPs920u83PnuKv4yaVDDaWD7i02Fk34HXz8Iq981N80C2SdAj9PNv/QbIQj5zWXOQ34IeevqsxSurKsvA9BtbF1h4aifPXWvRmKOGZRKboYi50eCpplT+NZ+ZH7daTh4kukIXDmyC1c2MrUyFAph1eGy4dlcMiyH/87fxgtzN7N5bzVPfrmh3rE/cBZ/t2/i89BQ8t/O4/ELB+C2N34puaPEy30fraZgn59/XjGETknJcMVH8Pol5nL1cx5p/D3kLzMLtJ/7N/Mz0uDEC+DDqWa/gFl0/bx/gKfhdKtWKRKCdTPN+9HVKSv9IbZHV7NsLdP3Diouoy4oFdPhyJ679zkQm05sZQHnWubxzoZT2FHiJTsUDejvlw0Zihjc8NpSvlprTtc7e2AGD5/X7+BB8ajOyR5m/GY4k178kapAmN+d3niWVA1d13jsggFc8fJC1uRXcPbz3/PsJbmcfFz974FSineW7KJzZRLDNPP7tNCfxVefrOXGMd3rBZu+WFPIXe+torQ6iM2iYdcMNu6p5vKXFvC/a04g3vUzM6baGAlKtReOGLj4Vfj2CZjzMGdb5nO2ZT4Aayo68/3Mfvz+i4F06HkC/Xp0Y3iXJLISXQec/yuEEEIIIY6MrEQ3WYlN/FW9jUtw23n20kFc/I8f+WRVAZv2VPHw+f0Z3Dmx6Sed9DszcLLhczPbqXi9GbDYPu/QXtTqgrR+kD4ITpzW4OFfHJQadq25ol//C3/e81tC55F1QameEw7rqS67hetGd+PS4dm8On87m/dUEeu0Euu01d7OqxrBy7M3El5p9rEZcKr7TBuG4tUft/PoZ+vwRYtBX/XKIt694UTiXXFw2bvw3VOwbze4k8CVaN66k83Mt28egU2z4O8nmMWXa1b4C3rNAOaPLwDKzM4JVJj1xKafBBe+DJ0PUHi9tdg6F/zl4OlgBhCB9YVmBlBanLPtZMbsX2PtF2RKNcpqh+HXw1f3cbP7S96uHM2MBdu5q2/Dlfce/WwdX60twmHVeeCcvkwa2umwr217pMby5a0n4w1EyE4++O/nfpnxfHzTKG54bQkrd+1jyr8X8vvTe3LjKd3QNI2SqgB/fH8VX6wp4mFrR4ZZ1wCwysjhX99v5c1FO7n25K5MGtqJJ75Yz9tLdgHQKy2WJy/oxw/zvmP6Rhdr8iu48t8LefXq4cQ42n/Ipv2/w2OJrsMpf4Cup5irOmyZA3vW0FffTl99O6hPYB0Ur41jo5HFAls2Rkov4jv1IS6pIwlJHUhO6UhSYhJWq3w0hBBCCCHEoRncOYnnL83l7g9Ws76okgun/8Blwztz+xk9iWssc0HToMtJ5jb+ISjdagYZNnxhBiisTnNJc6sreuuE+CwzCJU+0JxWZ2l6vLr2lxaQP+50c2tLooEOwCxG/jPEResINWV412RunLGEdYWVnP389zx/6fGM6pHC9pJq7nhnJQu2lgIwrEsSO0q8bNpTxY0zlvDKVcOw2Vxw6p+afvEe4+G9a6FoFbx5mVknrM+58NkdULbVPGbg/8EZD0NFPrw1xcyaeuUs87wjb/l50/n8FVC+3SzIXr7TnHKYNdRcyexI/gG/dtW9s2un7tUVOT/MumctKS6j7n7MEQ5KAQy+Er59gqzgNkbrK3lzsZ3fdTGwQ+3Kex8u381L35ufib9ekssZ/dJ+9sulxDig6QX9GshMcPHWdSO4/6M1vLFoJ098sZ4VO8s5Z1AG93+UR3FVAJtFI+e4AbDlGwBGnTSWPuviyCuo4KlZG3hqlpmJqGlw3cnduPW0HujKYLML/nPlEC57eTHLdpTz61cW8Z+rhjWY7aSUIn+fH6uukeC2HfKqjUopiquC7C73sbvMR365D7fDwuThnQ/+5KNIIg/tUfZwcwOo2gNbv8XY/A2BTXNxVe0kRasgxZIHRh7s+Rz21H96RGns0zz4NDchzU5QdxDSHER0OyHdSamnG6WpJ0KnE0hNjicjwUVGvEumBgohhBBCHMMm9E9neNdkHvpkLe8u3cWrP27ny7xCHjinL+P7ph04iyGpCwy7xtx+IaUU6wrMDJQ2MyXqSEjtZ9ZlsnvMGlNHwbAuSXx80yiuf3UJK3bt44qXF3D+8Vl8srIAXyiC227hzgm9uGx4Z9YWVnDR9PnM21TCPR+s5pHz+x/4M5DaB66ZDd88DPP+CstfMzcwA0Rn/xV6nGZ+7UqEa+fAzFth1Vsw+wEzy67bqebUsooCqCw07/vKokFOl1kk2+Yyt2CVGYjyljTenth0yBpiBqg69jWPq9gF+2q23eY006SukNQNkrtCcnfzfmx6/aBpJAxra6buTazdXVvkvJXXk6qnXqbUEZ6+B2ZtueOnwI9/Y5rzMy7yDmTJxgJGANhjWFdYwZ3vmsXDbzyl2y8KSP1cTpuFRy8YwMBOCdz34Rq+zCuqXfWvR8cY/jJpEP0qIrDFPL537ihmntaTj1fm89SXG9hR6iUr0cXTFw9iWJckAEIhs3h8r7RYXr16GJP/uYCFW0u59tXFvHj5ELYUV7FoaykLt5WycGsZxVWB2vbEOKwkuG0keezEOW0oFOGIwlCKiKGIKKjwhdhd7iMYNuq9l56psRKUEkdZTEfofyF6/wtxgblyQfEGggV57NmygmD+GpxVO3CGK/GoKhyEsGiKeKqIV1WgAOMn5/T9CMUz8K+2scjoyTtGP35Q/UjsNpTzBndifN80nDYJUAkhhBBCHGuSPHaeungg5x+fyd3vr2JbiZfrX1vKcakxDOqUwMBOCQzMSqBnWiw2y9EpUr2rzEdlIIzdotO1Q+tb1fCo0XU49/mj/jLp8S7evG4Ed7+/mneX7uKd6BSkEV2TeeyCAbXToPpmxPPcpblc89/FvLFoJzkpHq4f3e1ApwarA057AI4bD+9dB/t2mAGK0x8E50+KVzti4PwXzWy7T283M+02ffXz3pQ72awjFpdpBqqK1pgBrbUfm9uBFG9ofL8jHtyJ4EoCi90s6u5Orl0pMRg2mL/FDIi1qeBpTaaUxd6wT46UE66HBdMZaqykj7aNRRuKGAGErG6ue3UJvlCEk3qkHLQO1NF26bBseqfHccNrSyjY5+c3o7rw+/E9zWthe7Rt9hhI6YGua5w7KJMJ/dJZvK2UgZ0S8DQxNW9AVgKv/Hool7+0kO82FjPggS8IRVS9Y6y6hgIihqIqEKYqEGZXme+gbdY0SI11kpnoIjPBRfeOh5EmdpRIUOpYY/dARi72jFyyBk9u8HAk6KOsZA+lxXvwV1cQCXkxgj5U0IcK+8BfRczepWSULiA+XMxJltWcZFkNQP6OJD7bOpxr3h9BZr/RnD8km6E5iVK3SgghhBDiGDOyewqf33Iyz3+9ielzN7OhqIoNRVW8tdgMYDisOv0y45mYm8kFx2c2WTT751gTzT7pkRpz1AJfxzqnzcKTFw1gYKd4/rdgB5NP6MzkYdkNVpEc2zuVe37Vhwc+zuPRz9bROcnNhP5mps0+b4j5W0qYt6mYrcXVnDsogwsHZ5nXDp1PhGmLoKoIEg+QxaFpcPwVZnH8754GlJnJE5sOsWnmrTsJwgFz5b6QN3rrM6eFJuZAQmdw/iQoFKyG/OXmCpG7FplF9j0pEN/JnEYan2neahZzumnJZijdDCWbzKCWEYbAPnMr21Z33j4TazOoXvx2M9tLvKTE2BsUy27V4jLN25i0Izu9cX8J2dB3Iqx+l+tsn7KsrCvYYFlBiO2lXjITXPz1ktxWsUjEoE4JzP7daEqrg/XrBnY4DiY8br4XvS5hw27VObH7wYvzD+6cxL+mDOGqfy8iEDaIcVg5vnMiw7skMTQniQFZ8dgtOpX+MKXeIKXVQcqqg1QGQuiahq5pWHUNXdewaBoeh5WsRBdp8c5W93tRglKiHovdRUp6Z1LSD5LCp5T5V4Etc1Bb5qC2zCUjVMrV1s+4ms8oWp3AZyuG8ZrzBJydj+e4nM7kZifSNyNOsqiEEEIIIY4BTpuF34/vyZQTc1i6o4yVu8pZuWsfK3aWU+EPs2R7GUu2l/HkF+v5v+HZTBmRQ1q88xe/7i8uci4OiaZpXDEihytG5BzwuKtGdmFbcTX/mb+dW95czuLtZSzeXsaqXeUY+yV/fL+pmDkb9vLwxP7Eu23RoNEhTitK7QsXvvTz38xP2T2QM9LcDqbbmPpfR8JmQXNvqZkd5Ssz70eC0Pc8ALYVV/Pc15sA+NNZfRqvu9ZaZY8ws9dyRh3d1xkxDVa/y6/0+VQaDgA2V2jYrTr/uHxwqyoM77ZbGw+sD7/uF533xG4pfHXbaPb5QvRKi8XaSDAp3m0j3m2jS0rbzQqVoJT4eTQNOvSEDj3Rhl+HFvLD5q9Ra94nsu5TUkPlXGn9kivDX8Jm2L0pmTyjMy/RmcrE3jiycsns0ot+mQnyVywhhBBCiHasQ6yD8X3TGN/XrP2ilGJbiZdv1u3hlR+2saPUywtzNvPPb7dw1oB0zj8+C4/dgqZp6BromoZF1whGDHzBCL5gBG8ogj8YoSoQZk9lgKIKP4X7/BRV+NlVbk5hkaBU63HPr/qws8zH1+v21BaoBujWwcPI7il4HFb++e0WPllZwPId5fxlUl2tnTbHYjWzqjyNZ8Mopbjnw9UEwgajuqdw7qCMRo9rtSxWc3XEoy3zeMg5Ccu277jI8i0AXpw8NLEf/TKP0rTBVqhTkptOLd2Io0yCUuLIsDmh15lovc7EGg7AljmEV71PeOs8nFU7yNRKyLSUcBpLoeJ9yIOSNbGsNLrytdadsoR+WDMHkZUcR6d4K1lxFtJjdDwWAywOs4DgAVZYEUIIIYQQbYOmaXRJ8dBlVBemnJjDV2uLeOn7rSzcWsqHy/P5cHn+L36NGIeVU3q2oSlR7ZzVovPspbnc+8FqFOb0zpHdk0mPd9UeM75vGje/sYztJV4ueXE+08Z057djezSaHdKWfbQin+82FmO36jw4sZ+UOjmQE2+Cbd/h0EIAdMlM5dQh7T1Ec+yRq3xx5FkdcNx4rMeNNz9g/gooWoMqXEn19uWEdy8ntmIDyVQyxrKCMayAinehoulTBjQHBc7ulMT1oSq5H5G0QTg69iA5IZYOMQ4S3fYGc9jbNCNizk3XLeY8dwnICSGEEKIdsuhabRbV6t37eHneVpbvLEcpaleOUsos5muzarhtVpx2C26bBZfd3DrEOEiLd5IW56x3KyUjWpcYh5WnJw1q8vFBnRL45Lcncd+Ha3h36S6e/XoTn68ppFuHGDwOKx67BXf0tmuHGE7qkUJsW5r2hllH68GZawGYNqZ7m55y1Sy6nwYpx9UWkx/dr0sLN0gcDa3iSvdvf/sbTzzxBIWFhQwcOJDnnnuOYcOGtXSzxJHijIPOI9A6jyBmeHRfOACFq1G7l1C9dRFq91JiKjejoQhhJYSFgLIRwooHHx4C5PjWkONbA0VvQ555moCyUoGbbXjw6R4ClhjQraBb0KIbmk4Hf5BVu95DOePRnfHorngsrnhsTg82qwW7zYrNam4OmxWLw4XFEYvVFYNm95jzym1u89xH4q8ZSkEkBGG/Ob+8shAKVqDylxHevRzLntXoYTP13NBtROJzUMk9sHbsgd6hJ3TsBR16g919kBcSQrQKFfmw/H/mz36P0yB94NErDiqEEG1Uv8x4nr54UEs3Q7SgGIeVpy4eyMnHpfCn91fXFshvjN2iM6JbMqf1SeW0Pqmkxv3yemRH22NfrKO4KkDXDh6uG921pZvT+um6WVvq498CYHG2/Epx4shr8aDUm2++yW233cb06dMZPnw4zzzzDOPHj2f9+vV07NixpZsnjharA7IGo2UNJmb4tea+SBg0HZuuYwPC/hD5pV5WlHkJFq3HVrSS2LLVdKhcS6Z/Iy7lw6GF6UAFHagABYQP8Jp7j0zTI+gY9TYN0FBoKI3a+zX39r8FsKkQDoKNnlsDav7e41UONBQuI4hethHKNsKmumMNNAr0NHbaulDg6EqlI50EKkhSZcRHSomLlBIbKsWmAhiaFaXbzFuLDaVZzbYrRUSBEf1rpKE0DKvTnDJpc6JZneh2JzjiiLg7EPGkYXg6omLSIKYjTiu4IpW4IpU4Q5XYQhXooSrz+XY32FwYVjcRqwvNEYs1toO5LKpcjLd9SkF1MZGy7ZQXbEYZCmdcEs7YZKyeRHAmmMsEG5G64Gs4AIFq3IEi8JZATErTWYCGAYEKCFaBbgObywwM73+8UuYx3tK6YqIWO3TsAzFNTNlQCsq3w+4l5mo5iV3M4qjJ3cFyhP/aqpS5Ys+C6ZD3obkSD8A3/89cDei48XDcGdBl9MEDzEbEXL1nz1rze5nQ2VwxyJ0kP09CCCHanXMHZXJC12TmbSqmOhCmKhDBGwxTHYhQ6Q+xZHsZW4qrmbthL3M37OVPH6xmQFY8fTPiyExwRZe7d5OZ6CI11tEqpgAu2V7G/xbsAODh8/rjsEom3yEZMAm+fhCq94Lj2KkldSxp8aDU008/zTXXXMNVV10FwPTp0/nkk094+eWXufPOO1u4daJZ/eTiNM5po29GPH0z4qFvOnBK3YOGAcFK8O8j5C2nsryEyvISvJVlhENBQuEw4XCEcDhMKBSkqCCfDnEOLMFKLKFK7KFKHJFKLJEASilQhnkBqQw0DJwEcWsBPPhx48eqGXXNxMCCQaNU47sPpkK5WWPksFrlsNrIYYejB5UxnQENl7+IjoEdZBm76arl013L5zh9Jx20CjKNAjIDBRD44ee9cDPQo1uNgLJRpsVTrsdTqceDpuPGh0v5cCm/GWxUfiKalaDmqLd1DSm25j2BRTPQUbWb0nRCupOQbo/eOglrdmyEcCo/DsOLI+LFbviwqSAh3VF7XEh3EdSdRHQHWGwoiw3NYgM9equZA4Z6Xatp5n7dgmaxmptuBU1HUwYaCj36WdIwQNOjx1ujm27eotWLJ2j7fV23X6v5Z1L1P2RmHQIt+gStiQDFTz+Y+7+o1sjXoAwDAlWoQAUEKtGCVWjBSnRvMY6qXcQFCnCoABYguZFXbIoNOA0g73YA/JoLrx5DtR4TDcJ6cRtVuJS30eeHNRsh3UFEs+IKV2Ih0uhx1bZkSjzdKPX0oMzTBU9gLx0qVpNauQZ3uLzR85a4cih2d6fKkYpd+bFHfNgMHzbDjy3iw9CsBKwxBHQPfosHvyUGv25mUlqcMVhccdhcsdhdscRXbSYt79/ElK6qfY2SlCEEbfF02PMD1soCWPIKLHmFiG6nypmBz55kbrYkvLZEIrqNJO9Wkqo3E1+9FasRaNDuoO6mzJFOiS3dzBS1OsDqQLc60GwOrBYLzlA5zmAZzmAp9kApjkApKAO/JxOfJwuvJwuvOwuvOwNNGTgDxbgCxTgCxTj8JdiCZUQsTkK2OMLWGIK2GPx6DM6CvWz+cAV2LYJVhbEQxqpCmJ83HaXp1HwmlaajdDuGxYGyODAsdpTFgdJ0rMFyrIF9WP1lWAPlWILloCDiiCPsSCBsjydiTyDsiMeS2JlOQyYcxidOCCFEW5Ua5+T847OafHzTnipm5RUxK6+QZTvNlR1X7trX4DhNg1iHlTiXjfjoFue0EeO04rDqOKwWHDa99r7HYcFjtxLjtBLjsOJxWHHbLQTDBv5QBH8oehuOEAgZBCMGwbBBIBwhGDbvO2wW4pzma8Y5bcS5rNz9/moALhycxQldD2f0dIyzOeG86bDiDeh5Rku3RhwFLRqUCgaDLFmyhLvuuqt2n67rjBs3jvnz57dgy0Srp+tmFoYzHltCNkkZ0NT6HKFQiE8//ZRRZ56JzXbwTIhQxIhuimDEwBs2CAX9GMFqIuEwkXCIcCRCJBLCCIeJRCIozHQjBShloJSKZkuZtzX5UigwLOaFmaE7UBY7hm7DarWQ7HFwboydKz32RlcjDEUMKv1hKv0h9gYi5FcUou1Zi61kLc7S9dh9hXitiVRYk9hnSaZMT6RES8Sr7OhGGGWE0CNBMMJokRA2i5n2bLdo2K0adouOVVNEQn7CQT9GyI8KmbfOcAUJkVISjFKSjDKSVSlu/AD4lY19eNinPJQTQ7VyYiOMWwvgIoiLAC4tQCw+3FoAhxYijWLSjGKaiu0B0ThKZcP9Da/LRQsxlEYhieSrDihNJ0ZVEa9VE081Hq1hR4WUhWD0v52ax53KhzPiIynSeCpjQFmxEUHXzJ8iqwphjYTqHeNVDsqIoVzF4MFPtrYHT6gET3kJ2eULG5wzqCzkqc5sURlka3voqe0kFh+p3o2kejf+ou9Jw/bb+DByIq9ExpO3KwcAB5czXF/LWH0pYy3LyDKKifduI9677YDn8isbm1Qmfux00vaQqpVjN7yk+jaT6tt82G2zlVcQW772Z7wrU2+A8p/99J9lg60XSFBKCCEE0L1jDN07xnDDKd3YWxng+0172V7iZVeZj91lPnaX+yjY5yMUUVT4w1T4w+wq87VomxPdNv54Zu8WbUOb1H2cuYl2qUWDUsXFxUQiEVJTU+vtT01NZd26dQ2ODwQCBAJ1FzoVFWZl7FAoRCgUanD8L1VzzqNxbtF8fk4/2jSwWQGrDg4dPDFAM81hNiKEjMazP2LtGrF2u/lFhy7QrQtwZvO06ydCwSrQrVisTuIiBvawQVwogj9soEeXcLboWnQ5Z40qFCXeKkKVe4hUFmNU7UVV78UwFEGrm6DuIqi78VvcBHChqzCWiA9LxI8e8aOFfOzesZmMzGyUphNWGobSCCkNzQhjiQSwGH6sET9Ww48lEiCk2fBrLnyaG5/mxKu5CCobdoI4DD8OZW52w3wORhiMEETC6EYIzQjWJhnVy2hSRjQjKoJmRNBUBF2FQSkMTTenQWoahopmiyiFTgRdRbCo6ARQZfbxT3OY6hKh1H75S2q/r1S9R2oniyq138RRBfvd2/9s2n7PNsOlar9z7dcOwK+7a7eAxUPQ4iFsj8eS1Bl3xy4kZ3Qlu0MC/eKd5lLdYYOqQJjCQJiqai++qnKCho5fWfEZVoIG+AIhVq3Jo0/PHrjx4QhXRrcKFBp+S6z5WtYYfJYYQthQhoFmBNFDXixhn/l5iISotsZSpccRwE7EULWb1fDSwbeVNN8WUv1b6BjYRpU1kd3uPuyO6cNedw80qwNN08gDPjMMYgOFdPRtItW7CVe4nKDmxK85CepOAtH7FhXGrapxG15cRjVOVY0zUo01bLbLFs3Gsxs+AtiZZR/LF44zqLYlYNc1hujmkuYWXQM9nW+0sXynQapRQHKkhHijnLhIGbFGOXGRcmwqQJE1k522HHZassnX0wga5s+Tx24h3hYhg72kGUWkRIqwhatR4UB0C0IkiBEJU6HFUq7FU0Y8pcRRQhxKQbraQ5pRRJqxhzRVRMdIEWGslOmJlGkJlGgJlJLAPi0OJwFiNS8xyksM1XhUNVqgCuxuQlgJKgsBZSWkLNEpzapeNqOGgZ0wNhXCRhAHQWwqjI5BpeahghgqtFgqtBgqiAUUsVQTq6qJU1XEUkWcqqLa05UuR+n/ZPm/Xggh2q4OsQ7Oy22YVRUxFKXVQfb5Quzzhajwh6iI3q8ORAiEIwTCBoGQmenkDxl4g2GqAuHotEFzyqAvFMFu0XHYdJxWC06bjsNmqcu0surYrWa2lc2iEwhHqPCFzdfzh6jwhQmGDe47uw9JHnsLfIeEaL1afPre4XjkkUd44IEHGuz/8ssvcbuPXsHnWbNmHbVzi+Yj/djaOIDo4KFeDM4b3WrogBs0NzGdk5tcpLHxMF7dGTzRrTHB6CaaZo9utbz7KN+0jPJNsPIwzzEqDdi3oXa/+f2P9k4kgoUKXFTgauQctSwQr8ohUl63TzP3Y8GsPxXXj1L6Ubrf0zKADHY1zNKzA/YMquIz+Gk51Z++d190O5gk4FIMqNeCxmhASnQzkwH3zxvLiG5NF8brQDVNL3teM8Wy4USBOKA7AEXRbX8HCsVXN7KvwWekEaHo9tPJmRYgMbo1xh/dAD799NODvMrP4/U2PmVUCCFE22XRNTrEOugQ62jppgghmtCiQamUlBQsFgtFRfWHwkVFRaSlpTU4/q677uK2226r/bqiooJOnTpx+umnExcXd8TbFwqFmDVrFqeddtohTfsSrZP0Y/sg/dg+SD+2D+2xH2uyr4UQQgghRPNp0aCU3W5n8ODBzJ49m4kTJwJgGAazZ89m2rRpDY53OBw4HA2j3Dab7agOio/2+UXzkH5sH6Qf2wfpx/ahPfVje3kfQgghhBBtSYtP37vtttuYMmUKQ4YMYdiwYTzzzDNUV1fXrsYnhBBCCCGEEEIIIdqfFg9KTZo0ib1793LvvfdSWFjIoEGD+PzzzxsUPxdCCCGEEEIIIYQQ7UeLB6UApk2b1uh0PSGEEEIIIYQQQgjRPukt3QAhhBBCCCGEEEIIceyRoJQQQgghhBBCCCGEaHYSlBJCCCGEEEIIIYQQzU6CUkIIIYQQQgghhBCi2UlQSgghhBBCCCGEEEI0OwlKCSGEEEIIIYQQQohmZ23pBvwSSikAKioqjsr5Q6EQXq+XiooKbDbbUXkNcfRJP7YP0o/tg/Rj+9Ae+7FmLFEztjhWydhKHArpx/ZB+rF9kH5sH9pjPx7q2KpNB6UqKysB6NSpUwu3RAghhBDtQWVlJfHx8S3djBYjYyshhBBCHEkHG1tpqg3/SdAwDPLz84mNjUXTtCN+/oqKCjp16sTOnTuJi4s74ucXzUP6sX2QfmwfpB/bh/bYj0opKisrycjIQNeP3eoGMrYSh0L6sX2QfmwfpB/bh/bYj4c6tmrTmVK6rpOVlXXUXycuLq7dfDCOZdKP7YP0Y/sg/dg+tLd+PJYzpGrI2EocDunH9kH6sX2Qfmwf2ls/HsrY6tj9U6AQQgghhBBCCCGEaDESlBJCCCGEEEIIIYQQzU6CUgfgcDi47777cDgcLd0U8QtIP7YP0o/tg/Rj+yD9KH4u+ey0D9KP7YP0Y/sg/dg+HMv92KYLnQshhBBCCCGEEEKItkkypYQQQgghhBBCCCFEs5OglBBCCCGEEEIIIYRodhKUEkIIIYQQQgghhBDNToJSB/C3v/2NnJwcnE4nw4cPZ+HChS3dJHEAjzzyCEOHDiU2NpaOHTsyceJE1q9fX+8Yv9/P1KlTSU5OJiYmhgsuuICioqIWarE4mEcffRRN07jllltq90kftg27d+/msssuIzk5GZfLRf/+/Vm8eHHt40op7r33XtLT03G5XIwbN46NGze2YIvFT0UiEe655x66dOmCy+WiW7duPPjgg+xfilL6URwuGVu1LTK2an9kbNW2yfiqbZOxVeMkKNWEN998k9tuu4377ruPpUuXMnDgQMaPH8+ePXtaummiCXPnzmXq1Kn8+OOPzJo1i1AoxOmnn051dXXtMbfeeisff/wxb7/9NnPnziU/P5/zzz+/BVstmrJo0SL+8Y9/MGDAgHr7pQ9bv7KyMkaOHInNZuOzzz4jLy+Pp556isTExNpjHn/8cZ599lmmT5/OggUL8Hg8jB8/Hr/f34ItF/t77LHHeOGFF3j++edZu3Ytjz32GI8//jjPPfdc7THSj+JwyNiq7ZGxVfsiY6u2TcZXbZ+MrZqgRKOGDRumpk6dWvt1JBJRGRkZ6pFHHmnBVonDsWfPHgWouXPnKqWUKi8vVzabTb399tu1x6xdu1YBav78+S3VTNGIyspK1aNHDzVr1iw1evRodfPNNyulpA/bij/84Q9q1KhRTT5uGIZKS0tTTzzxRO2+8vJy5XA41Ouvv94cTRSH4KyzzlK//vWv6+07//zz1eTJk5VS0o/i8MnYqu2TsVXbJWOrtk/GV22fjK0aJ5lSjQgGgyxZsoRx48bV7tN1nXHjxjF//vwWbJk4HPv27QMgKSkJgCVLlhAKher1a69evcjOzpZ+bWWmTp3KWWedVa+vQPqwrfjoo48YMmQIF110ER07diQ3N5d//vOftY9v3bqVwsLCev0YHx/P8OHDpR9bkRNPPJHZs2ezYcMGAFasWMH333/PhAkTAOlHcXhkbNU+yNiq7ZKxVdsn46u2T8ZWjbO2dANao+LiYiKRCKmpqfX2p6amsm7duhZqlTgchmFwyy23MHLkSPr16wdAYWEhdrudhISEesempqZSWFjYAq0UjXnjjTdYunQpixYtavCY9GHbsGXLFl544QVuu+02/vjHP7Jo0SJ++9vfYrfbmTJlSm1fNfY7Vvqx9bjzzjupqKigV69eWCwWIpEIDz30EJMnTwaQfhSHRcZWbZ+MrdouGVu1DzK+avtkbNU4CUqJdmnq1KmsXr2a77//vqWbIg7Dzp07ufnmm5k1axZOp7OlmyN+JsMwGDJkCA8//DAAubm5rF69munTpzNlypQWbp04VG+99RYzZszgf//7H3379mX58uXccsstZGRkSD8KcQySsVXbJGOr9kPGV22fjK0aJ9P3GpGSkoLFYmmw6kRRURFpaWkt1CpxqKZNm8bMmTP55ptvyMrKqt2flpZGMBikvLy83vHSr63HkiVL2LNnD8cffzxWqxWr1crcuXN59tlnsVqtpKamSh+2Aenp6fTp06fevt69e7Njxw6A2r6S37Gt2+23386dd97JJZdcQv/+/bn88su59dZbeeSRRwDpR3F4ZGzVtsnYqu2SsVX7IeOrtk/GVo2ToFQj7HY7gwcPZvbs2bX7DMNg9uzZjBgxogVbJg5EKcW0adN4//33+frrr+nSpUu9xwcPHozNZqvXr+vXr2fHjh3Sr63E2LFjWbVqFcuXL6/dhgwZwuTJk2vvSx+2fiNHjmywZPiGDRvo3LkzAF26dCEtLa1eP1ZUVLBgwQLpx1bE6/Wi6/WHCRaLBcMwAOlHcXhkbNU2ydiq7ZOxVfsh46u2T8ZWTWjpSuut1RtvvKEcDod65ZVXVF5enrr22mtVQkKCKiwsbOmmiSbccMMNKj4+Xs2ZM0cVFBTUbl6vt/aY66+/XmVnZ6uvv/5aLV68WI0YMUKNGDGiBVstDmb/FWKUkj5sCxYuXKisVqt66KGH1MaNG9WMGTOU2+1Wr732Wu0xjz76qEpISFAffvihWrlypTr33HNVly5dlM/na8GWi/1NmTJFZWZmqpkzZ6qtW7eq9957T6WkpKg77rij9hjpR3E4ZGzV9sjYqn2SsVXbJOOrtk/GVo2ToNQBPPfccyo7O1vZ7XY1bNgw9eOPP7Z0k8QBAI1u//73v2uP8fl86sYbb1SJiYnK7Xar8847TxUUFLRco8VB/XTgJH3YNnz88ceqX79+yuFwqF69eqkXX3yx3uOGYah77rlHpaamKofDocaOHavWr1/fQq0VjamoqFA333yzys7OVk6nU3Xt2lXdfffdKhAI1B4j/SgOl4yt2hYZW7VPMrZqu2R81bbJ2KpxmlJKtUyOlhBCCCGEEEIIIYQ4VklNKSGEEEIIIYQQQgjR7CQoJYQQQgghhBBCCCGanQSlhBBCCCGEEEIIIUSzk6CUEEIIIYQQQgghhGh2EpQSQgghhBBCCCGEEM1OglJCCCGEEEIIIYQQotlJUEoIIYQQQgghhBBCNDsJSgkhhBBCCCGEEEKIZidBKSGEaIKmaXzwwQct3QwhhBBCiHZBxlZCiJ+SoJQQolW68sor0TStwXbGGWe0dNOEEEIIIdocGVsJIVoja0s3QAghmnLGGWfw73//u94+h8PRQq0RQgghhGjbZGwlhGhtJFNKCNFqORwO0tLS6m2JiYmAmf79wgsvMGHCBFwuF127duWdd96p9/xVq1Zx6qmn4nK5SE5O5tprr6WqqqreMS+//DJ9+/bF4XCQnp7OtGnT6j1eXFzMeeedh9vtpkePHnz00UdH900LIYQQQhwlMrYSQrQ2EpQSQrRZ99xzDxdccAErVqxg8uTJXHLJJaxduxaA6upqxo8fT2JiIosWLeLtt9/mq6++qjcweuGFF5g6dSrXXnstq1at4qOPPqJ79+71XuOBBx7g4osvZuXKlZx55plMnjyZ0tLSZn2fQgghhBDNQcZWQohmp4QQohWaMmWKslgsyuPx1NseeughpZRSgLr++uvrPWf48OHqhhtuUEop9eKLL6rExERVVVVV+/gnn3yidF1XhYWFSimlMjIy1N13391kGwD1pz/9qfbrqqoqBajPPvvsiL1PIYQQQojmIGMrIURrJDWlhBCt1pgxY3jhhRfq7UtKSqq9P2LEiHqPjRgxguXLlwOwdu1aBg4ciMfjqX185MiRGIbB+vXr0TSN/Px8xo4de8A2DBgwoPa+x+MhLi6OPXv2/Ny3JIQQQgjRYmRsJYRobSQoJYRotTweT4OU7yPF5XId0nE2m63e15qmYRjG0WiSEEIIIcRRJWMrIURrIzWlhBBt1o8//tjg6969ewPQu3dvVqxYQXV1de3j8+bNQ9d1evbsSWxsLDk5OcyePbtZ2yyEEEII0VrJ2EoI0dwkU0oI0WoFAgEKCwvr7bNaraSkpADw9ttvM2TIEEaNGsWMGTNYuHAhL730EgCTJ0/mvvvuY8qUKdx///3s3buXm266icsvv5zU1FQA7r//fq6//no6duzIhAkTqKysZN68edx0003N+0aFEEIIIZqBjK2EEK2NBKWEEK3W559/Tnp6er19PXv2ZN26dYC5essbb7zBjTfeSHp6Oq+//jp9+vQBwO1288UXX3DzzTczdOhQ3G43F1xwAU8//XTtuaZMmYLf7+cvf/kLv//970lJSeHCCy9svjcohBBCCNGMZGwlhGhtNKWUaulGCCHE4dI0jffff5+JEye2dFOEEEIIIdo8GVsJIVqC1JQSQgghhBBCCCGEEM1OglJCCCGEEEIIIYQQotnJ9D0hhBBCCCGEEEII0ewkU0oIIYQQQgghhBBCNDsJSgkhhBBCCCGEEEKIZidBKSGEEEIIIYQQQgjR7CQoJYQQQgghhBBCCCGanQSlhBBCCCGEEEIIIUSzk6CUEEIIIYQQQgghhGh2EpQSQgghhBBCCCGEEM1OglJCCCGEEEIIIYQQotlJUEoIIYQQQgghhBBCNLv/D9uuC7yEcevFAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Model creation\n", "print(\"\\n2. Creating model...\")\n", "input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n", "\n", "max_val = df['solarradiation'].max()\n", "\n", "min_val_scaled = scaler_y.transform([[0]])[0][0]\n", "max_val_scaled = scaler_y.transform([[max_val]])[0][0]\n", "\n", "print(f\"\\nMax dataset solar radiation : {max_val} - Scaled Version : {max_val_scaled}\")\n", "\n", "increase_percentage = 15\n", "\n", "max_val = max_val * (1 + increase_percentage / 100)\n", "max_val_scaled = max_val_scaled * (1 + increase_percentage / 100)\n", "\n", "print(f\"Max dataset solar radiation increased by {increase_percentage}% : {max_val} - Scaled Version : {max_val_scaled}\")\n", "\n", "model = create_solarradiation_model(input_shape=input_shape, folder_name=folder_name, min_output=min_val_scaled, max_output=max_val_scaled)\n", "\n", "print(\"\\n4. Starting training...\")\n", "history = train_hybrid_model(\n", " model=model,\n", " X_train=X_train_seq,\n", " y_train=y_train,\n", " X_test=X_test_seq,\n", " y_test=y_test,\n", " epochs=100,\n", " batch_size=192,\n", " folder_name=folder_name\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "id": "ad6226ea-ab01-47aa-9571-52ea9e654c01", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "5. Generating predictions...\n", "811/811 [==============================] - 9s 10ms/step\n", "\n", "6. Evaluating model...\n", "\n", "Solar Radiation Prediction Metrics:\n", "\n", "Absolute Metrics:\n", "MAE: 25.05 W/m²\n", "RMSE: 39.70 W/m²\n", "R² Score: 0.977\n", "MAPE: 20.66%\n", "\n", "Accuracy Metrics:\n", "Within ±5 W/m²: 7.5%\n", "Within ±10 W/m²: 13.3%\n", "Within ±20 W/m²: 68.6%\n", "\n", "Level Accuracy:\n", "Level Accuracy: 89.0%\n", "\n", "Confusion Matrix for Radiation Levels:\n", " Very Low Low Moderate High Very High Extreme\n", "Very Low 0 10 0 0 0 0\n", "Low 0 1710 0 111 0 0\n", "Moderate 0 0 2171 434 0 256\n", "High 0 374 171 1751 0 0\n", "Very High 0 1136 0 1 0 0\n", "Extreme 0 0 352 0 0 17456\n", "\n", "Plot saved as: 2024-11-25_13-52_radiation_analysis.png\n", "\n", "Error Statistics:\n", "Mean error: 1.543\n", "Error standard deviation: 39.674\n", "Median error: 12.000\n", "95th percentile absolute error: 86.014\n" ] } ], "source": [ "print(\"\\n5. Generating predictions...\")\n", "predictions = model.predict(X_test_seq)\n", "predictions = np.clip(predictions, 0, 11)\n", "\n", "predictions_original = scaler_y.inverse_transform(predictions)\n", "y_test_original = scaler_y.inverse_transform(y_test)\n", "\n", "print(\"\\n6. Evaluating model...\")\n", "metrics = evaluate_solarradiation_predictions(y_test_original, predictions_original, folder_name=folder_name)\n", "\n", "# Create results dictionary\n", "training_results = {\n", " 'model_params': {\n", " 'input_shape': input_shape,\n", " 'n_features': len(features),\n", " 'sequence_length': X_train_seq.shape[1]\n", " },\n", " 'training_params': {\n", " 'batch_size': 192,\n", " 'total_epochs': len(history.history['loss']),\n", " 'best_epoch': np.argmin(history.history['val_loss']) + 1\n", " },\n", " 'performance_metrics': {\n", " 'final_loss': float(history.history['val_loss'][-1]),\n", " 'final_mae': float(history.history['val_mae'][-1]),\n", " 'best_val_loss': float(min(history.history['val_loss'])),\n", " 'out_of_range_predictions': int(np.sum((predictions < 0) | (predictions > 11)))\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": 12, "id": "2c42461d-b189-4dc0-81da-4eb4879b9135", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "7. Predicting missing data...\n", "7122/7122 [==============================] - 70s 10ms/step\n", "\n", "8. Integrating predictions into original dataset...\n", "Added 227879 predictions to dataset\n", "Rows with solar radiation after integration: 357615\n", "\n", "Training completed successfully!\n" ] } ], "source": [ "print(\"\\n7. Predicting missing data...\")\n", "to_predict_predictions = model.predict(X_to_predict_seq)\n", "to_predict_predictions = np.clip(to_predict_predictions, 0, 11)\n", "\n", "to_predict_predictions = scaler_y.inverse_transform(to_predict_predictions)\n", "\n", "print(\"\\n8. Integrating predictions into original dataset...\")\n", "df_updated = integrate_predictions(df.copy(), to_predict_predictions)\n", "\n", "df_updated.to_parquet('../../sources/weather_data_solarradiation.parquet')\n", "\n", "# Add prediction statistics to training_results\n", "training_results['prediction_stats'] = {\n", " 'n_predictions_added': len(to_predict_predictions),\n", " 'mean_predicted_solarradiation': float(to_predict_predictions.mean()),\n", " 'min_predicted_solarradiation': float(to_predict_predictions.min()),\n", " 'max_predicted_solarradiation': float(to_predict_predictions.max()),\n", "}\n", "\n", "print(\"\\nTraining completed successfully!\")\n", "\n", "tf.keras.backend.clear_session()" ] }, { "cell_type": "code", "execution_count": 13, "id": "914f7330-d1b9-474d-8605-c0de2aefe087", "metadata": {}, "outputs": [ { "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", "Statistiche principali di Solar Radiation:\n", "--------------------------------------------------\n", "count : 357,679.0000\n", "missing : 64.0000\n", "zeros : 59,357.0000\n", "mean : 181.9066\n", "median : 12.0000\n", "std : 249.6366\n", "min : 0.0000\n", "max : 1,113.0000\n", "skewness : 1.2630\n", "kurtosis : 0.2932\n", "percentile_1 : 0.0000\n", "percentile_5 : 0.0000\n", "percentile_10 : 0.0000\n", "percentile_25 : 12.0000\n", "percentile_50 : 12.0000\n", "percentile_75 : 316.1503\n", "percentile_90 : 630.4008\n", "percentile_95 : 727.4549\n", "percentile_99 : 862.0000\n", "\n", "Suggerimenti per la normalizzazione:\n", "--------------------------------------------------\n", "- La distribuzione è fortemente asimmetrica (skewness > 1)\n", "- Considerare una trasformazione logaritmica: np.log1p(x)\n", "- Alta presenza di zeri (16.60%)\n", "- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\n" ] }, { "data": { "text/plain": [ "{'count': 357679,\n", " 'missing': 64,\n", " 'zeros': 59357,\n", " 'mean': 181.90659489328456,\n", " 'median': 12.0,\n", " 'std': 249.63664428001422,\n", " 'min': 0.0,\n", " 'max': 1113.0,\n", " 'skewness': 1.262984127717114,\n", " 'kurtosis': 0.2932425990070442,\n", " 'percentile_1': 0.0,\n", " 'percentile_5': 0.0,\n", " 'percentile_10': 0.0,\n", " 'percentile_25': 12.0,\n", " 'percentile_50': 12.0,\n", " 'percentile_75': 316.15032958984375,\n", " 'percentile_90': 630.4008203125001,\n", " 'percentile_95': 727.4549438476562,\n", " 'percentile_99': 862.0}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analyze_distribution(df_updated, 'solarradiation', 'Solar Radiation')" ] }, { "cell_type": "code", "execution_count": 14, "id": "08fd4208-0afb-4bf1-bdef-b10b4065fe55", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Plot saved as: 2024-11-25_14-57_error_analysis.png\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Error Statistics:\n", "MAE: 0.0740\n", "MSE: 0.0138\n", "RMSE: 0.1173\n", "Mean error: 0.0046\n", "Error std: 0.1172\n", "Predictions within ±0.5: 99.2%\n", "Predictions within ±1.0: 100.0%\n", "Predictions within ±1.5: 100.0%\n", "Predictions within ±2.0: 100.0%\n" ] } ], "source": [ "def plot_error_analysis(y_true, y_pred, folder_name=None):\n", " \"\"\"\n", " Function to visualize prediction error analysis\n", "\n", " Parameters:\n", " -----------\n", " y_true : array-like\n", " Actual values\n", " y_pred : array-like\n", " Predicted values\n", " folder_name : str, optional\n", " Directory to save plots. If None, plots are only displayed\n", "\n", " Generates:\n", " ----------\n", " - Error distribution histogram\n", " - Actual vs Predicted scatter plot\n", " - Errors vs Actual Values scatter plot\n", " - Comprehensive error statistics\n", " \"\"\"\n", "\n", " # Convert to 1D numpy arrays if needed\n", " if isinstance(y_true, pd.Series):\n", " y_true = y_true.values\n", " if isinstance(y_pred, pd.Series):\n", " y_pred = y_pred.values\n", "\n", " y_true = y_true.ravel()\n", " y_pred = y_pred.ravel()\n", "\n", " # Calculate errors\n", " errors = y_pred - y_true\n", "\n", " # Create main figure\n", " fig = plt.figure(figsize=(15, 5))\n", "\n", " # Plot 1: Error Distribution\n", " plt.subplot(1, 3, 1)\n", " plt.hist(errors, bins=50, alpha=0.7)\n", " plt.title('Prediction Error Distribution')\n", " plt.xlabel('Error')\n", " plt.ylabel('Frequency')\n", "\n", " # Plot 2: Actual vs Predicted\n", " plt.subplot(1, 3, 2)\n", " plt.scatter(y_true, y_pred, alpha=0.5)\n", " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", " plt.title('Actual vs Predicted Values')\n", " plt.xlabel('Actual Values')\n", " plt.ylabel('Predicted Values')\n", "\n", " # Plot 3: Errors vs Actual Values\n", " plt.subplot(1, 3, 3)\n", " plt.scatter(y_true, errors, alpha=0.5)\n", " plt.axhline(y=0, color='r', linestyle='--')\n", " plt.title('Errors vs Actual Values')\n", " plt.xlabel('Actual Values')\n", " plt.ylabel('Error')\n", "\n", " plt.tight_layout()\n", "\n", " # Save plot if directory is specified\n", " if folder_name is not None:\n", " try:\n", " # Create directory if it doesn't exist\n", " filename = f'{folder_name}_error_analysis.png'\n", "\n", " # Save figure\n", " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", " print(f\"\\nPlot saved as: {filename}\")\n", " except Exception as e:\n", " print(f\"\\nError saving plot: {str(e)}\")\n", "\n", " plt.show()\n", "\n", " # Print error statistics\n", " print(\"\\nError Statistics:\")\n", " print(f\"MAE: {np.mean(np.abs(errors)):.4f}\")\n", " print(f\"MSE: {np.mean(errors ** 2):.4f}\")\n", " print(f\"RMSE: {np.sqrt(np.mean(errors ** 2)):.4f}\")\n", " print(f\"Mean error: {np.mean(errors):.4f}\")\n", " print(f\"Error std: {np.std(errors):.4f}\")\n", "\n", " # Calculate percentage of errors within thresholds\n", " thresholds = [0.5, 1.0, 1.5, 2.0]\n", " for threshold in thresholds:\n", " within_threshold = np.mean(np.abs(errors) <= threshold) * 100\n", " print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n", "\n", "\n", "# Example usage\n", "plot_error_analysis(y_test, predictions, folder_name=folder_name)" ] }, { "cell_type": "code", "execution_count": null, "id": "846f15d4-d1b2-4a90-a702-b9a85f4e2945", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "f982c92c-ba99-4df6-b3c8-df92426679db", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.0rc1" } }, "nbformat": 4, "nbformat_minor": 5 }