diff --git a/.gitignore b/.gitignore
index 70dac46c81b04b2e0b6c14216f0028874dbcc5b0..f2ae1102ba8b2bf060abea69901ef2a583f271d6 100755
--- a/.gitignore
+++ b/.gitignore
@@ -2,7 +2,7 @@
*/.ipynb_checkpoints/*
__pycache__
*/__pycache__/*
-*==[0-9]*==.ipynb
+*==*==.ipynb
stderr.txt
stdout.txt
fidle/log/finished.json
diff --git a/AE/01-AE-with-MNIST.ipynb b/AE/01-AE-with-MNIST.ipynb
index 5fdf595ad4f3999174ecc5c9be02df24564e57bf..ebf58252fb0b21d499c447b889e8bb1a05c619bf 100644
--- a/AE/01-AE-with-MNIST.ipynb
+++ b/AE/01-AE-with-MNIST.ipynb
@@ -6,8 +6,8 @@
"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
- "# <!-- TITLE --> [AE1] - AutoEncoder (AE) with MNIST\n",
- "<!-- DESC --> Episode 1 : Model construction and Training\n",
+ "# <!-- TITLE --> [AE1] - Building and training an AE denoiser model\n",
+ "<!-- DESC --> Episode 1 : After construction, the model is trained with noisy data from the MNIST dataset.\n",
"\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
diff --git a/AE/02-AE-with-MNIST-post.ipynb b/AE/02-AE-with-MNIST-post.ipynb
index 6bbac6ea92c2805e480b7c01539fd57e91ce6023..abc1369d929abf78c6c65dc305811cd3c156100e 100644
--- a/AE/02-AE-with-MNIST-post.ipynb
+++ b/AE/02-AE-with-MNIST-post.ipynb
@@ -6,8 +6,8 @@
"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
- "# <!-- TITLE --> [AE2] - AutoEncoder (AE) with MNIST - Analysis\n",
- "<!-- DESC --> Episode 2 : Exploring our denoiser\n",
+ "# <!-- TITLE --> [AE2] - Exploring our denoiser model\n",
+ "<!-- DESC --> Episode 2 : Using the previously trained autoencoder to denoise data\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
diff --git a/GTSRB/00-Test.ipynb b/GTSRB/00-Test.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..89bf5775b89e16754a8c43896feac8d652412bbe
--- /dev/null
+++ b/GTSRB/00-Test.ipynb
@@ -0,0 +1,233 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# TEST"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>\n",
+ "\n",
+ "div.warn { \n",
+ " background-color: #fcf2f2;\n",
+ " border-color: #dFb5b4;\n",
+ " border-left: 5px solid #dfb5b4;\n",
+ " padding: 0.5em;\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;;\n",
+ " }\n",
+ "\n",
+ "\n",
+ "\n",
+ "div.nota { \n",
+ " background-color: #DAFFDE;\n",
+ " border-left: 5px solid #92CC99;\n",
+ " padding: 0.5em;\n",
+ " }\n",
+ "\n",
+ "div.todo:before { content:url(data:image/svg+xml;base64,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);\n",
+ " float:left;\n",
+ " margin-right:20px;\n",
+ " margin-top:-20px;\n",
+ " margin-bottom:20px;\n",
+ "}\n",
+ "div.todo{\n",
+ " font-weight: bold;\n",
+ " font-size: 1.1em;\n",
+ " margin-top:40px;\n",
+ "}\n",
+ "div.todo ul{\n",
+ " margin: 0.2em;\n",
+ "}\n",
+ "div.todo li{\n",
+ " margin-left:60px;\n",
+ " margin-top:0;\n",
+ " margin-bottom:0;\n",
+ "}\n",
+ "\n",
+ "div .comment{\n",
+ " font-size:0.8em;\n",
+ " color:#696969;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "</style>\n",
+ "\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<br>**FIDLE 2020 - Practical Work Module**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Version : 1.2b1 DEV\n",
+ "Notebook id : GTSRB0\n",
+ "Run time : Saturday 9 January 2021, 18:01:32\n",
+ "TensorFlow version : 2.2.0\n",
+ "Keras version : 2.3.0-tf\n",
+ "Datasets dir : /home/pjluc/datasets/fidle\n",
+ "Run dir : ./run/GTSRB0\n",
+ "Update keras cache : False\n",
+ "Save figs : True\n",
+ "Path figs : ./run/GTSRB0/figs\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os, time, sys\n",
+ "import csv\n",
+ "import math, random\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import h5py\n",
+ "\n",
+ "from skimage.morphology import disk\n",
+ "from skimage.util import img_as_ubyte\n",
+ "from skimage.filters import rank\n",
+ "from skimage import io, color, exposure, transform\n",
+ "\n",
+ "from importlib import reload\n",
+ "\n",
+ "sys.path.append('..')\n",
+ "import fidle.pwk as pwk\n",
+ "\n",
+ "run_dir='./run/GTSRB0'\n",
+ "datasets_dir = pwk.init('GTSRB0', run_dir)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ---- For smart tests :\n",
+ "#\n",
+ "scale = 1\n",
+ "output_dir = './data' \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Override parameters (batch mode) - Just forget this cell"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pwk.override('scale', 'output_dir')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "scale : 1\n",
+ "output_dir : ./data\n",
+ "run_dir : ./run/GTSRB0\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('scale : ', scale)\n",
+ "print('output_dir : ', output_dir)\n",
+ "print('run_dir : ', run_dir)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'/home/pjluc/dev/fidle/GTSRB'"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import os\n",
+ "os.getcwd()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.ipynb b/README.ipynb
index 2adbfbf320ec5aca0066e6d6a808d9299271f028..1edfa1efcfe55cb58b6da6d7bf4889a00314a927 100644
--- a/README.ipynb
+++ b/README.ipynb
@@ -5,10 +5,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2021-01-08T21:53:24.359601Z",
- "iopub.status.busy": "2021-01-08T21:53:24.359126Z",
- "iopub.status.idle": "2021-01-08T21:53:24.367752Z",
- "shell.execute_reply": "2021-01-08T21:53:24.368077Z"
+ "iopub.execute_input": "2021-01-09T18:32:21.410112Z",
+ "iopub.status.busy": "2021-01-09T18:32:21.409590Z",
+ "iopub.status.idle": "2021-01-09T18:32:21.417639Z",
+ "shell.execute_reply": "2021-01-09T18:32:21.417225Z"
},
"jupyter": {
"source_hidden": true
@@ -85,6 +85,8 @@
"An example of classification using a dense neural network for the famous MNIST dataset\n",
"\n",
"### Images classification with Convolutional Neural Networks (CNN)\n",
+ "- **[??](GTSRB/00-Test.ipynb)** - [??](GTSRB/00-Test.ipynb) \n",
+ "??\n",
"- **[GTSRB1](GTSRB/01-Preparation-of-data.ipynb)** - [Dataset analysis and preparation](GTSRB/01-Preparation-of-data.ipynb) \n",
"Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset\n",
"- **[GTSRB2](GTSRB/02-First-convolutions.ipynb)** - [First convolutions](GTSRB/02-First-convolutions.ipynb) \n",
@@ -113,36 +115,36 @@
"Still the same problem, but with a network combining embedding and LSTM\n",
"\n",
"### Time series with Recurrent Neural Network (RNN)\n",
- "- **[SYNOP1](SYNOP/01-Preparation-of-data.ipynb)** - [Time series with RNN - Preparation of data](SYNOP/01-Preparation-of-data.ipynb) \n",
- "Episode 1 : Data analysis and creation of a usable dataset\n",
- "- **[SYNOP2](SYNOP/02-First-predictions.ipynb)** - [Time series with RNN - Try a prediction](SYNOP/02-First-predictions.ipynb) \n",
- "Episode 2 : Training session and first predictions\n",
- "- **[SYNOP3](SYNOP/03-12h-predictions.ipynb)** - [Time series with RNN - 12h predictions](SYNOP/03-12h-predictions.ipynb) \n",
- "Episode 3: Attempt to predict in the longer term \n",
+ "- **[SYNOP1](SYNOP/01-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/01-Preparation-of-data.ipynb) \n",
+ "Episode 1 : Data analysis and preparation of a meteorological dataset (SYNOP)\n",
+ "- **[SYNOP2](SYNOP/02-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/02-First-predictions.ipynb) \n",
+ "Episode 2 : Learning session and weather prediction attempt at 3h\n",
+ "- **[SYNOP3](SYNOP/03-12h-predictions.ipynb)** - [12h predictions](SYNOP/03-12h-predictions.ipynb) \n",
+ "Episode 3: Attempt to predict in a more longer term \n",
"\n",
"### Unsupervised learning with an autoencoder neural network (AE)\n",
- "- **[AE1](AE/01-AE-with-MNIST.ipynb)** - [AutoEncoder (AE) with MNIST](AE/01-AE-with-MNIST.ipynb) \n",
- "Episode 1 : Model construction and Training\n",
- "- **[AE2](AE/02-AE-with-MNIST-post.ipynb)** - [AutoEncoder (AE) with MNIST - Analysis](AE/02-AE-with-MNIST-post.ipynb) \n",
- "Episode 2 : Exploring our denoiser\n",
+ "- **[AE1](AE/01-AE-with-MNIST.ipynb)** - [Building and training an AE denoiser model](AE/01-AE-with-MNIST.ipynb) \n",
+ "Episode 1 : After construction, the model is trained with noisy data from the MNIST dataset.\n",
+ "- **[AE2](AE/02-AE-with-MNIST-post.ipynb)** - [Exploring our denoiser model](AE/02-AE-with-MNIST-post.ipynb) \n",
+ "Episode 2 : Using the previously trained autoencoder to denoise data\n",
"\n",
"### Generative network with Variational Autoencoder (VAE)\n",
- "- **[VAE1](VAE/01-VAE-with-MNIST.ipynb)** - [Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb) \n",
- "Building a simple model with the MNIST dataset\n",
- "- **[VAE2](VAE/02-VAE-with-MNIST-post.ipynb)** - [Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb) \n",
- "Visualization and analysis of latent space\n",
- "- **[VAE3](VAE/05-About-CelebA.ipynb)** - [About the CelebA dataset](VAE/05-About-CelebA.ipynb) \n",
- "Presentation of the CelebA dataset and problems related to its size\n",
- "- **[VAE6](VAE/06-Prepare-CelebA-datasets.ipynb)** - [Preparation of the CelebA dataset](VAE/06-Prepare-CelebA-datasets.ipynb) \n",
- "Preparation of a clustered dataset, batchable\n",
- "- **[VAE7](VAE/07-Check-CelebA.ipynb)** - [Checking the clustered CelebA dataset](VAE/07-Check-CelebA.ipynb) \n",
- "Check the clustered dataset\n",
- "- **[VAE8](VAE/08-VAE-with-CelebA==1090048==.ipynb)** - [Variational AutoEncoder (VAE) with CelebA (small)](VAE/08-VAE-with-CelebA==1090048==.ipynb) \n",
- "Variational AutoEncoder (VAE) with CelebA (small res. 128x128)\n",
- "- **[VAE9](VAE/09-VAE-withCelebA-post.ipynb)** - [Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/09-VAE-withCelebA-post.ipynb) \n",
- "Exploring latent space of our trained models\n",
+ "- **[VAE1](VAE/01-VAE-with-MNIST.ipynb)** - [First VAE, with a small dataset (MNIST)](VAE/01-VAE-with-MNIST.ipynb) \n",
+ "Construction and training of a VAE with a latent space of small dimension.\n",
+ "- **[VAE2](VAE/02-VAE-with-MNIST-post.ipynb)** - [Analysis of the associated latent space](VAE/02-VAE-with-MNIST-post.ipynb) \n",
+ "Visualization and analysis of the VAE's latent space\n",
+ "- **[VAE5](VAE/05-About-CelebA.ipynb)** - [Another game play : About the CelebA dataset](VAE/05-About-CelebA.ipynb) \n",
+ "Episode 1 : Presentation of the CelebA dataset and problems related to its size\n",
+ "- **[VAE6](VAE/06-Prepare-CelebA-datasets.ipynb)** - [Generation of a clustered dataset](VAE/06-Prepare-CelebA-datasets.ipynb) \n",
+ "Episode 2 : Analysis of the CelebA dataset and creation of an clustered and usable dataset\n",
+ "- **[VAE7](VAE/07-Check-CelebA.ipynb)** - [Checking the clustered dataset](VAE/07-Check-CelebA.ipynb) \n",
+ "Episode : 3 Clustered dataset verification and testing of our datagenerator\n",
+ "- **[VAE8](VAE/08-VAE-with-CelebA.ipynb)** - [Training session for our VAE](VAE/08-VAE-with-CelebA.ipynb) \n",
+ "Episode 4 : Training with our clustered datasets in notebook or batch mode\n",
+ "- **[VAE9](VAE/09-VAE-withCelebA-post.ipynb)** - [Data generation from latent space](VAE/09-VAE-withCelebA-post.ipynb) \n",
+ "Episode 5 : Exploring latent space to generate new data\n",
"- **[VAE10](VAE/batch_slurm.sh)** - [SLURM batch script](VAE/batch_slurm.sh) \n",
- "Bash script for SLURM batch submission of VAE notebooks \n",
+ "Bash script for SLURM batch submission of VAE8 notebooks \n",
"\n",
"### Miscellaneous\n",
"- **[ACTF1](Misc/Activation-Functions.ipynb)** - [Activation functions](Misc/Activation-Functions.ipynb) \n",
diff --git a/README.md b/README.md
index 457cc4e345126d4d41f8cdc0fffc1ee8c8ea43ea..95fce7bd865bfa59af79f571d295cd4d4fd9c35d 100644
--- a/README.md
+++ b/README.md
@@ -65,6 +65,8 @@ A more advanced implementation of the precedent example
An example of classification using a dense neural network for the famous MNIST dataset
### Images classification with Convolutional Neural Networks (CNN)
+- **[??](GTSRB/00-Test.ipynb)** - [??](GTSRB/00-Test.ipynb)
+??
- **[GTSRB1](GTSRB/01-Preparation-of-data.ipynb)** - [Dataset analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)
Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset
- **[GTSRB2](GTSRB/02-First-convolutions.ipynb)** - [First convolutions](GTSRB/02-First-convolutions.ipynb)
@@ -93,36 +95,36 @@ Retrieving a saved model to perform a sentiment analysis (movie review)
Still the same problem, but with a network combining embedding and LSTM
### Time series with Recurrent Neural Network (RNN)
-- **[SYNOP1](SYNOP/01-Preparation-of-data.ipynb)** - [Time series with RNN - Preparation of data](SYNOP/01-Preparation-of-data.ipynb)
-Episode 1 : Data analysis and creation of a usable dataset
-- **[SYNOP2](SYNOP/02-First-predictions.ipynb)** - [Time series with RNN - Try a prediction](SYNOP/02-First-predictions.ipynb)
-Episode 2 : Training session and first predictions
-- **[SYNOP3](SYNOP/03-12h-predictions.ipynb)** - [Time series with RNN - 12h predictions](SYNOP/03-12h-predictions.ipynb)
-Episode 3: Attempt to predict in the longer term
+- **[SYNOP1](SYNOP/01-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/01-Preparation-of-data.ipynb)
+Episode 1 : Data analysis and preparation of a meteorological dataset (SYNOP)
+- **[SYNOP2](SYNOP/02-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/02-First-predictions.ipynb)
+Episode 2 : Learning session and weather prediction attempt at 3h
+- **[SYNOP3](SYNOP/03-12h-predictions.ipynb)** - [12h predictions](SYNOP/03-12h-predictions.ipynb)
+Episode 3: Attempt to predict in a more longer term
### Unsupervised learning with an autoencoder neural network (AE)
-- **[AE1](AE/01-AE-with-MNIST.ipynb)** - [AutoEncoder (AE) with MNIST](AE/01-AE-with-MNIST.ipynb)
-Episode 1 : Model construction and Training
-- **[AE2](AE/02-AE-with-MNIST-post.ipynb)** - [AutoEncoder (AE) with MNIST - Analysis](AE/02-AE-with-MNIST-post.ipynb)
-Episode 2 : Exploring our denoiser
+- **[AE1](AE/01-AE-with-MNIST.ipynb)** - [Building and training an AE denoiser model](AE/01-AE-with-MNIST.ipynb)
+Episode 1 : After construction, the model is trained with noisy data from the MNIST dataset.
+- **[AE2](AE/02-AE-with-MNIST-post.ipynb)** - [Exploring our denoiser model](AE/02-AE-with-MNIST-post.ipynb)
+Episode 2 : Using the previously trained autoencoder to denoise data
### Generative network with Variational Autoencoder (VAE)
-- **[VAE1](VAE/01-VAE-with-MNIST.ipynb)** - [Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb)
-Building a simple model with the MNIST dataset
-- **[VAE2](VAE/02-VAE-with-MNIST-post.ipynb)** - [Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb)
-Visualization and analysis of latent space
-- **[VAE3](VAE/05-About-CelebA.ipynb)** - [About the CelebA dataset](VAE/05-About-CelebA.ipynb)
-Presentation of the CelebA dataset and problems related to its size
-- **[VAE6](VAE/06-Prepare-CelebA-datasets.ipynb)** - [Preparation of the CelebA dataset](VAE/06-Prepare-CelebA-datasets.ipynb)
-Preparation of a clustered dataset, batchable
-- **[VAE7](VAE/07-Check-CelebA.ipynb)** - [Checking the clustered CelebA dataset](VAE/07-Check-CelebA.ipynb)
-Check the clustered dataset
-- **[VAE8](VAE/08-VAE-with-CelebA==1090048==.ipynb)** - [Variational AutoEncoder (VAE) with CelebA (small)](VAE/08-VAE-with-CelebA==1090048==.ipynb)
-Variational AutoEncoder (VAE) with CelebA (small res. 128x128)
-- **[VAE9](VAE/09-VAE-withCelebA-post.ipynb)** - [Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/09-VAE-withCelebA-post.ipynb)
-Exploring latent space of our trained models
+- **[VAE1](VAE/01-VAE-with-MNIST.ipynb)** - [First VAE, with a small dataset (MNIST)](VAE/01-VAE-with-MNIST.ipynb)
+Construction and training of a VAE with a latent space of small dimension.
+- **[VAE2](VAE/02-VAE-with-MNIST-post.ipynb)** - [Analysis of the associated latent space](VAE/02-VAE-with-MNIST-post.ipynb)
+Visualization and analysis of the VAE's latent space
+- **[VAE5](VAE/05-About-CelebA.ipynb)** - [Another game play : About the CelebA dataset](VAE/05-About-CelebA.ipynb)
+Episode 1 : Presentation of the CelebA dataset and problems related to its size
+- **[VAE6](VAE/06-Prepare-CelebA-datasets.ipynb)** - [Generation of a clustered dataset](VAE/06-Prepare-CelebA-datasets.ipynb)
+Episode 2 : Analysis of the CelebA dataset and creation of an clustered and usable dataset
+- **[VAE7](VAE/07-Check-CelebA.ipynb)** - [Checking the clustered dataset](VAE/07-Check-CelebA.ipynb)
+Episode : 3 Clustered dataset verification and testing of our datagenerator
+- **[VAE8](VAE/08-VAE-with-CelebA.ipynb)** - [Training session for our VAE](VAE/08-VAE-with-CelebA.ipynb)
+Episode 4 : Training with our clustered datasets in notebook or batch mode
+- **[VAE9](VAE/09-VAE-withCelebA-post.ipynb)** - [Data generation from latent space](VAE/09-VAE-withCelebA-post.ipynb)
+Episode 5 : Exploring latent space to generate new data
- **[VAE10](VAE/batch_slurm.sh)** - [SLURM batch script](VAE/batch_slurm.sh)
-Bash script for SLURM batch submission of VAE notebooks
+Bash script for SLURM batch submission of VAE8 notebooks
### Miscellaneous
- **[ACTF1](Misc/Activation-Functions.ipynb)** - [Activation functions](Misc/Activation-Functions.ipynb)
diff --git a/SYNOP/01-Preparation-of-data.ipynb b/SYNOP/01-Preparation-of-data.ipynb
index 765cb5950f8c9a4589e699bead79acd22670dc60..ebbeb9083b8fb3ec8e83bb7c63a1d2e2c6577d33 100644
--- a/SYNOP/01-Preparation-of-data.ipynb
+++ b/SYNOP/01-Preparation-of-data.ipynb
@@ -6,8 +6,8 @@
"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
- "# <!-- TITLE --> [SYNOP1] - Time series with RNN - Preparation of data\n",
- "<!-- DESC --> Episode 1 : Data analysis and creation of a usable dataset\n",
+ "# <!-- TITLE --> [SYNOP1] - Preparation of data\n",
+ "<!-- DESC --> Episode 1 : Data analysis and preparation of a meteorological dataset (SYNOP)\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
@@ -15,7 +15,9 @@
" - cleanup a usable dataset\n",
"\n",
"\n",
- "SYNOP meteorological data, available at: https://public.opendatasoft.com\n",
+ "SYNOP meteorological data, available at: https://public.opendatasoft.com \n",
+ "This dataset contains a set of measurements (temperature, pressure, ...) made every 3 hours at the LYS airport. \n",
+ "The objective will be to predict the evolution of the weather !\n",
"\n",
"## What we're going to do :\n",
"\n",
@@ -93,7 +95,7 @@
{
"data": {
"text/markdown": [
- "**FIDLE 2020 - Practical Work Module**"
+ "<br>**FIDLE 2020 - Practical Work Module**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
@@ -106,13 +108,13 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Version : 0.6.1 DEV\n",
+ "Version : 1.2b1 DEV\n",
"Notebook id : SYNOP1\n",
- "Run time : Saturday 19 December 2020, 10:43:18\n",
- "TensorFlow version : 2.0.0\n",
- "Keras version : 2.2.4-tf\n",
+ "Run time : Saturday 9 January 2021, 10:04:28\n",
+ "TensorFlow version : 2.2.0\n",
+ "Keras version : 2.3.0-tf\n",
"Datasets dir : /home/pjluc/datasets/fidle\n",
- "Running mode : full\n",
+ "Run dir : ./run\n",
"Update keras cache : False\n",
"Save figs : True\n",
"Path figs : ./run/figs\n"
@@ -599,499 +601,499 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
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+ " }</style><table id=\"T_ade5a_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >Code</th> <th class=\"col_heading level0 col1\" >Description</th> <th class=\"col_heading level0 col2\" >Na</th> </tr></thead><tbody>\n",
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- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row0_col2\" class=\"data row0 col2\" >0</td>\n",
+ " <th id=\"T_ade5a_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_ade5a_row0_col0\" class=\"data row0 col0\" >numer_sta</td>\n",
+ " <td id=\"T_ade5a_row0_col1\" class=\"data row0 col1\" >ID OMM station</td>\n",
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- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row1_col2\" class=\"data row1 col2\" >0</td>\n",
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+ " <td id=\"T_ade5a_row1_col1\" class=\"data row1 col1\" >Date</td>\n",
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" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row2_col0\" class=\"data row2 col0\" >pmer</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row2_col1\" class=\"data row2 col1\" >Pression au niveau mer</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row2_col2\" class=\"data row2 col2\" >17</td>\n",
+ " <th id=\"T_ade5a_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_ade5a_row2_col0\" class=\"data row2 col0\" >pmer</td>\n",
+ " <td id=\"T_ade5a_row2_col1\" class=\"data row2 col1\" >Pression au niveau mer</td>\n",
+ " <td id=\"T_ade5a_row2_col2\" class=\"data row2 col2\" >17</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row3_col0\" class=\"data row3 col0\" >tend</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row3_col1\" class=\"data row3 col1\" >Variation de pression en 3 heures</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row3_col2\" class=\"data row3 col2\" >2</td>\n",
+ " <th id=\"T_ade5a_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_ade5a_row3_col0\" class=\"data row3 col0\" >tend</td>\n",
+ " <td id=\"T_ade5a_row3_col1\" class=\"data row3 col1\" >Variation de pression en 3 heures</td>\n",
+ " <td id=\"T_ade5a_row3_col2\" class=\"data row3 col2\" >2</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row4_col0\" class=\"data row4 col0\" >cod_tend</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row4_col1\" class=\"data row4 col1\" >Type de tendance barométrique</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row4_col2\" class=\"data row4 col2\" >2</td>\n",
+ " <th id=\"T_ade5a_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_ade5a_row4_col0\" class=\"data row4 col0\" >cod_tend</td>\n",
+ " <td id=\"T_ade5a_row4_col1\" class=\"data row4 col1\" >Type de tendance barométrique</td>\n",
+ " <td id=\"T_ade5a_row4_col2\" class=\"data row4 col2\" >2</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row5_col0\" class=\"data row5 col0\" >dd</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row5_col1\" class=\"data row5 col1\" >Direction du vent moyen 10 mn</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row5_col2\" class=\"data row5 col2\" >3</td>\n",
+ " <th id=\"T_ade5a_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+ " <td id=\"T_ade5a_row5_col0\" class=\"data row5 col0\" >dd</td>\n",
+ " <td id=\"T_ade5a_row5_col1\" class=\"data row5 col1\" >Direction du vent moyen 10 mn</td>\n",
+ " <td id=\"T_ade5a_row5_col2\" class=\"data row5 col2\" >3</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row6_col0\" class=\"data row6 col0\" >ff</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row6_col1\" class=\"data row6 col1\" >Vitesse du vent moyen 10 mn</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row6_col2\" class=\"data row6 col2\" >2</td>\n",
+ " <th id=\"T_ade5a_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+ " <td id=\"T_ade5a_row6_col0\" class=\"data row6 col0\" >ff</td>\n",
+ " <td id=\"T_ade5a_row6_col1\" class=\"data row6 col1\" >Vitesse du vent moyen 10 mn</td>\n",
+ " <td id=\"T_ade5a_row6_col2\" class=\"data row6 col2\" >2</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row7_col0\" class=\"data row7 col0\" >t</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row7_col1\" class=\"data row7 col1\" >Température</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row7_col2\" class=\"data row7 col2\" >14</td>\n",
+ " <th id=\"T_ade5a_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+ " <td id=\"T_ade5a_row7_col0\" class=\"data row7 col0\" >t</td>\n",
+ " <td id=\"T_ade5a_row7_col1\" class=\"data row7 col1\" >Température</td>\n",
+ " <td id=\"T_ade5a_row7_col2\" class=\"data row7 col2\" >14</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row8_col0\" class=\"data row8 col0\" >td</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row8_col1\" class=\"data row8 col1\" >Point de rosée</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row8_col2\" class=\"data row8 col2\" >17</td>\n",
+ " <th id=\"T_ade5a_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+ " <td id=\"T_ade5a_row8_col0\" class=\"data row8 col0\" >td</td>\n",
+ " <td id=\"T_ade5a_row8_col1\" class=\"data row8 col1\" >Point de rosée</td>\n",
+ " <td id=\"T_ade5a_row8_col2\" class=\"data row8 col2\" >17</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row9_col0\" class=\"data row9 col0\" >u</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row9_col1\" class=\"data row9 col1\" >Humidité</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row9_col2\" class=\"data row9 col2\" >17</td>\n",
+ " <th id=\"T_ade5a_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+ " <td id=\"T_ade5a_row9_col0\" class=\"data row9 col0\" >u</td>\n",
+ " <td id=\"T_ade5a_row9_col1\" class=\"data row9 col1\" >Humidité</td>\n",
+ " <td id=\"T_ade5a_row9_col2\" class=\"data row9 col2\" >17</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row10_col0\" class=\"data row10 col0\" >vv</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row10_col1\" class=\"data row10 col1\" >Visibilité horizontale</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row10_col2\" class=\"data row10 col2\" >31</td>\n",
+ " <th id=\"T_ade5a_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+ " <td id=\"T_ade5a_row10_col0\" class=\"data row10 col0\" >vv</td>\n",
+ " <td id=\"T_ade5a_row10_col1\" class=\"data row10 col1\" >Visibilité horizontale</td>\n",
+ " <td id=\"T_ade5a_row10_col2\" class=\"data row10 col2\" >31</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row11_col0\" class=\"data row11 col0\" >ww</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row11_col1\" class=\"data row11 col1\" >Temps présent</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row11_col2\" class=\"data row11 col2\" >1</td>\n",
+ " <th id=\"T_ade5a_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+ " <td id=\"T_ade5a_row11_col0\" class=\"data row11 col0\" >ww</td>\n",
+ " <td id=\"T_ade5a_row11_col1\" class=\"data row11 col1\" >Temps présent</td>\n",
+ " <td id=\"T_ade5a_row11_col2\" class=\"data row11 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row12_col0\" class=\"data row12 col0\" >w1</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row12_col1\" class=\"data row12 col1\" >Temps passé 1</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row12_col2\" class=\"data row12 col2\" >542</td>\n",
+ " <th id=\"T_ade5a_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+ " <td id=\"T_ade5a_row12_col0\" class=\"data row12 col0\" >w1</td>\n",
+ " <td id=\"T_ade5a_row12_col1\" class=\"data row12 col1\" >Temps passé 1</td>\n",
+ " <td id=\"T_ade5a_row12_col2\" class=\"data row12 col2\" >542</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row13_col0\" class=\"data row13 col0\" >w2</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row13_col1\" class=\"data row13 col1\" >Temps passé 2</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row13_col2\" class=\"data row13 col2\" >552</td>\n",
+ " <th id=\"T_ade5a_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+ " <td id=\"T_ade5a_row13_col0\" class=\"data row13 col0\" >w2</td>\n",
+ " <td id=\"T_ade5a_row13_col1\" class=\"data row13 col1\" >Temps passé 2</td>\n",
+ " <td id=\"T_ade5a_row13_col2\" class=\"data row13 col2\" >552</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row14_col0\" class=\"data row14 col0\" >n</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row14_col1\" class=\"data row14 col1\" >Nebulosité totale</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row14_col2\" class=\"data row14 col2\" >801</td>\n",
+ " <th id=\"T_ade5a_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+ " <td id=\"T_ade5a_row14_col0\" class=\"data row14 col0\" >n</td>\n",
+ " <td id=\"T_ade5a_row14_col1\" class=\"data row14 col1\" >Nebulosité totale</td>\n",
+ " <td id=\"T_ade5a_row14_col2\" class=\"data row14 col2\" >801</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row15_col0\" class=\"data row15 col0\" >nbas</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row15_col1\" class=\"data row15 col1\" >Nébulosité des nuages de l' étage inférieur</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row15_col2\" class=\"data row15 col2\" >2381</td>\n",
+ " <th id=\"T_ade5a_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
+ " <td id=\"T_ade5a_row15_col0\" class=\"data row15 col0\" >nbas</td>\n",
+ " <td id=\"T_ade5a_row15_col1\" class=\"data row15 col1\" >Nébulosité des nuages de l' étage inférieur</td>\n",
+ " <td id=\"T_ade5a_row15_col2\" class=\"data row15 col2\" >2381</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row16_col0\" class=\"data row16 col0\" >hbas</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row16_col1\" class=\"data row16 col1\" >Hauteur de la base des nuages de l'étage inférieur</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row16_col2\" class=\"data row16 col2\" >8861</td>\n",
+ " <th id=\"T_ade5a_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
+ " <td id=\"T_ade5a_row16_col0\" class=\"data row16 col0\" >hbas</td>\n",
+ " <td id=\"T_ade5a_row16_col1\" class=\"data row16 col1\" >Hauteur de la base des nuages de l'étage inférieur</td>\n",
+ " <td id=\"T_ade5a_row16_col2\" class=\"data row16 col2\" >8861</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row17_col0\" class=\"data row17 col0\" >cl</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row17_col1\" class=\"data row17 col1\" >Type des nuages de l'étage inférieur</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row17_col2\" class=\"data row17 col2\" >3377</td>\n",
+ " <th id=\"T_ade5a_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
+ " <td id=\"T_ade5a_row17_col0\" class=\"data row17 col0\" >cl</td>\n",
+ " <td id=\"T_ade5a_row17_col1\" class=\"data row17 col1\" >Type des nuages de l'étage inférieur</td>\n",
+ " <td id=\"T_ade5a_row17_col2\" class=\"data row17 col2\" >3377</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row18_col0\" class=\"data row18 col0\" >cm</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row18_col1\" class=\"data row18 col1\" >Type des nuages de l'étage moyen</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row18_col2\" class=\"data row18 col2\" >6912</td>\n",
+ " <th id=\"T_ade5a_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
+ " <td id=\"T_ade5a_row18_col0\" class=\"data row18 col0\" >cm</td>\n",
+ " <td id=\"T_ade5a_row18_col1\" class=\"data row18 col1\" >Type des nuages de l'étage moyen</td>\n",
+ " <td id=\"T_ade5a_row18_col2\" class=\"data row18 col2\" >6912</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row19_col0\" class=\"data row19 col0\" >ch</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row19_col1\" class=\"data row19 col1\" >Type des nuages de l'étage supérieur</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row19_col2\" class=\"data row19 col2\" >8494</td>\n",
+ " <th id=\"T_ade5a_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
+ " <td id=\"T_ade5a_row19_col0\" class=\"data row19 col0\" >ch</td>\n",
+ " <td id=\"T_ade5a_row19_col1\" class=\"data row19 col1\" >Type des nuages de l'étage supérieur</td>\n",
+ " <td id=\"T_ade5a_row19_col2\" class=\"data row19 col2\" >8494</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row20_col0\" class=\"data row20 col0\" >pres</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row20_col1\" class=\"data row20 col1\" >Pression station</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row20_col2\" class=\"data row20 col2\" >0</td>\n",
+ " <th id=\"T_ade5a_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
+ " <td id=\"T_ade5a_row20_col0\" class=\"data row20 col0\" >pres</td>\n",
+ " <td id=\"T_ade5a_row20_col1\" class=\"data row20 col1\" >Pression station</td>\n",
+ " <td id=\"T_ade5a_row20_col2\" class=\"data row20 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row21_col0\" class=\"data row21 col0\" >niv_bar</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row21_col1\" class=\"data row21 col1\" >Niveau barométrique</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row21_col2\" class=\"data row21 col2\" >29165</td>\n",
+ " <th id=\"T_ade5a_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
+ " <td id=\"T_ade5a_row21_col0\" class=\"data row21 col0\" >niv_bar</td>\n",
+ " <td id=\"T_ade5a_row21_col1\" class=\"data row21 col1\" >Niveau barométrique</td>\n",
+ " <td id=\"T_ade5a_row21_col2\" class=\"data row21 col2\" >29165</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row22_col0\" class=\"data row22 col0\" >geop</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row22_col1\" class=\"data row22 col1\" >Géopotentiel</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row22_col2\" class=\"data row22 col2\" >29165</td>\n",
+ " <th id=\"T_ade5a_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
+ " <td id=\"T_ade5a_row22_col0\" class=\"data row22 col0\" >geop</td>\n",
+ " <td id=\"T_ade5a_row22_col1\" class=\"data row22 col1\" >Géopotentiel</td>\n",
+ " <td id=\"T_ade5a_row22_col2\" class=\"data row22 col2\" >29165</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row23_col0\" class=\"data row23 col0\" >tend24</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row23_col1\" class=\"data row23 col1\" >Variation de pression en 24 heures</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row23_col2\" class=\"data row23 col2\" >14443</td>\n",
+ " <th id=\"T_ade5a_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
+ " <td id=\"T_ade5a_row23_col0\" class=\"data row23 col0\" >tend24</td>\n",
+ " <td id=\"T_ade5a_row23_col1\" class=\"data row23 col1\" >Variation de pression en 24 heures</td>\n",
+ " <td id=\"T_ade5a_row23_col2\" class=\"data row23 col2\" >14443</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row24_col0\" class=\"data row24 col0\" >tn12</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row24_col1\" class=\"data row24 col1\" >Température minimale sur 12 heures</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row24_col2\" class=\"data row24 col2\" >21883</td>\n",
+ " <th id=\"T_ade5a_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
+ " <td id=\"T_ade5a_row24_col0\" class=\"data row24 col0\" >tn12</td>\n",
+ " <td id=\"T_ade5a_row24_col1\" class=\"data row24 col1\" >Température minimale sur 12 heures</td>\n",
+ " <td id=\"T_ade5a_row24_col2\" class=\"data row24 col2\" >21883</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row25_col0\" class=\"data row25 col0\" >tn24</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row25_col1\" class=\"data row25 col1\" >Température minimale sur 24 heures</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row25_col2\" class=\"data row25 col2\" >29165</td>\n",
+ " <th id=\"T_ade5a_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
+ " <td id=\"T_ade5a_row25_col0\" class=\"data row25 col0\" >tn24</td>\n",
+ " <td id=\"T_ade5a_row25_col1\" class=\"data row25 col1\" >Température minimale sur 24 heures</td>\n",
+ " <td id=\"T_ade5a_row25_col2\" class=\"data row25 col2\" >29165</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row26_col0\" class=\"data row26 col0\" >tx12</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row26_col1\" class=\"data row26 col1\" >Température maximale sur 12 heures</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row26_col2\" class=\"data row26 col2\" >21883</td>\n",
+ " <th id=\"T_ade5a_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
+ " <td id=\"T_ade5a_row26_col0\" class=\"data row26 col0\" >tx12</td>\n",
+ " <td id=\"T_ade5a_row26_col1\" class=\"data row26 col1\" >Température maximale sur 12 heures</td>\n",
+ " <td id=\"T_ade5a_row26_col2\" class=\"data row26 col2\" >21883</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row27_col0\" class=\"data row27 col0\" >tx24</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row27_col1\" class=\"data row27 col1\" >Température maximale sur 24 heures</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row27_col2\" class=\"data row27 col2\" >29165</td>\n",
+ " <th id=\"T_ade5a_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
+ " <td id=\"T_ade5a_row27_col0\" class=\"data row27 col0\" >tx24</td>\n",
+ " <td id=\"T_ade5a_row27_col1\" class=\"data row27 col1\" >Température maximale sur 24 heures</td>\n",
+ " <td id=\"T_ade5a_row27_col2\" class=\"data row27 col2\" >29165</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row28_col0\" class=\"data row28 col0\" >tminsol</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row28_col1\" class=\"data row28 col1\" >Température minimale du sol sur 12 heures</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row28_col2\" class=\"data row28 col2\" >27364</td>\n",
+ " <th id=\"T_ade5a_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
+ " <td id=\"T_ade5a_row28_col0\" class=\"data row28 col0\" >tminsol</td>\n",
+ " <td id=\"T_ade5a_row28_col1\" class=\"data row28 col1\" >Température minimale du sol sur 12 heures</td>\n",
+ " <td id=\"T_ade5a_row28_col2\" class=\"data row28 col2\" >27364</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row29_col0\" class=\"data row29 col0\" >sw</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row29_col1\" class=\"data row29 col1\" >Méthode de mesure Température du thermomètre mouillé</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row29_col2\" class=\"data row29 col2\" >29165</td>\n",
+ " <th id=\"T_ade5a_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
+ " <td id=\"T_ade5a_row29_col0\" class=\"data row29 col0\" >sw</td>\n",
+ " <td id=\"T_ade5a_row29_col1\" class=\"data row29 col1\" >Méthode de mesure Température du thermomètre mouillé</td>\n",
+ " <td id=\"T_ade5a_row29_col2\" class=\"data row29 col2\" >29165</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row30_col0\" class=\"data row30 col0\" >tw</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row30_col1\" class=\"data row30 col1\" >Température du thermomètre mouillé</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row30_col2\" class=\"data row30 col2\" >29165</td>\n",
+ " <th id=\"T_ade5a_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
+ " <td id=\"T_ade5a_row30_col0\" class=\"data row30 col0\" >tw</td>\n",
+ " <td id=\"T_ade5a_row30_col1\" class=\"data row30 col1\" >Température du thermomètre mouillé</td>\n",
+ " <td id=\"T_ade5a_row30_col2\" class=\"data row30 col2\" >29165</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row31_col0\" class=\"data row31 col0\" >raf10</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row31_col1\" class=\"data row31 col1\" >Rafale sur les 10 dernières minutes</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row31_col2\" class=\"data row31 col2\" >14127</td>\n",
+ " <th id=\"T_ade5a_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
+ " <td id=\"T_ade5a_row31_col0\" class=\"data row31 col0\" >raf10</td>\n",
+ " <td id=\"T_ade5a_row31_col1\" class=\"data row31 col1\" >Rafale sur les 10 dernières minutes</td>\n",
+ " <td id=\"T_ade5a_row31_col2\" class=\"data row31 col2\" >14127</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row32_col0\" class=\"data row32 col0\" >rafper</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row32_col1\" class=\"data row32 col1\" >Rafales sur une période</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row32_col2\" class=\"data row32 col2\" >9</td>\n",
+ " <th id=\"T_ade5a_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
+ " <td id=\"T_ade5a_row32_col0\" class=\"data row32 col0\" >rafper</td>\n",
+ " <td id=\"T_ade5a_row32_col1\" class=\"data row32 col1\" >Rafales sur une période</td>\n",
+ " <td id=\"T_ade5a_row32_col2\" class=\"data row32 col2\" >9</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row33_col0\" class=\"data row33 col0\" >per</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row33_col1\" class=\"data row33 col1\" >Periode de mesure de la rafale</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row33_col2\" class=\"data row33 col2\" >8</td>\n",
+ " <th id=\"T_ade5a_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
+ " <td id=\"T_ade5a_row33_col0\" class=\"data row33 col0\" >per</td>\n",
+ " <td id=\"T_ade5a_row33_col1\" class=\"data row33 col1\" >Periode de mesure de la rafale</td>\n",
+ " <td id=\"T_ade5a_row33_col2\" class=\"data row33 col2\" >8</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row34_col0\" class=\"data row34 col0\" >etat_sol</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row34_col1\" class=\"data row34 col1\" >Etat du sol</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row34_col2\" class=\"data row34 col2\" >12278</td>\n",
+ " <th id=\"T_ade5a_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
+ " <td id=\"T_ade5a_row34_col0\" class=\"data row34 col0\" >etat_sol</td>\n",
+ " <td id=\"T_ade5a_row34_col1\" class=\"data row34 col1\" >Etat du sol</td>\n",
+ " <td id=\"T_ade5a_row34_col2\" class=\"data row34 col2\" >12278</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row35_col0\" class=\"data row35 col0\" >ht_neige</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row35_col1\" class=\"data row35 col1\" >Hauteur totale de la couche de neige, glace, autre au sol</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row35_col2\" class=\"data row35 col2\" >12083</td>\n",
+ " <th id=\"T_ade5a_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
+ " <td id=\"T_ade5a_row35_col0\" class=\"data row35 col0\" >ht_neige</td>\n",
+ " <td id=\"T_ade5a_row35_col1\" class=\"data row35 col1\" >Hauteur totale de la couche de neige, glace, autre au sol</td>\n",
+ " <td id=\"T_ade5a_row35_col2\" class=\"data row35 col2\" >12083</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row36_col0\" class=\"data row36 col0\" >ssfrai</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row36_col1\" class=\"data row36 col1\" >Hauteur de la neige fraîche</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row36_col2\" class=\"data row36 col2\" >2914</td>\n",
+ " <th id=\"T_ade5a_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
+ " <td id=\"T_ade5a_row36_col0\" class=\"data row36 col0\" >ssfrai</td>\n",
+ " <td id=\"T_ade5a_row36_col1\" class=\"data row36 col1\" >Hauteur de la neige fraîche</td>\n",
+ " <td id=\"T_ade5a_row36_col2\" class=\"data row36 col2\" >2914</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row37_col0\" class=\"data row37 col0\" >perssfrai</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row37_col1\" class=\"data row37 col1\" >Periode de mesure de la neige fraiche</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row37_col2\" class=\"data row37 col2\" >4489</td>\n",
+ " <th id=\"T_ade5a_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
+ " <td id=\"T_ade5a_row37_col0\" class=\"data row37 col0\" >perssfrai</td>\n",
+ " <td id=\"T_ade5a_row37_col1\" class=\"data row37 col1\" >Periode de mesure de la neige fraiche</td>\n",
+ " <td id=\"T_ade5a_row37_col2\" class=\"data row37 col2\" >4489</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row38_col0\" class=\"data row38 col0\" >rr1</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row38_col1\" class=\"data row38 col1\" >Précipitations dans la dernière heure</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row38_col2\" class=\"data row38 col2\" >95</td>\n",
+ " <th id=\"T_ade5a_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
+ " <td id=\"T_ade5a_row38_col0\" class=\"data row38 col0\" >rr1</td>\n",
+ " <td id=\"T_ade5a_row38_col1\" class=\"data row38 col1\" >Précipitations dans la dernière heure</td>\n",
+ " <td id=\"T_ade5a_row38_col2\" class=\"data row38 col2\" >95</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row39_col0\" class=\"data row39 col0\" >rr3</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row39_col1\" class=\"data row39 col1\" >Précipitations dans les 3 dernières heures</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row39_col2\" class=\"data row39 col2\" >73</td>\n",
+ " <th id=\"T_ade5a_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
+ " <td id=\"T_ade5a_row39_col0\" class=\"data row39 col0\" >rr3</td>\n",
+ " <td id=\"T_ade5a_row39_col1\" class=\"data row39 col1\" >Précipitations dans les 3 dernières heures</td>\n",
+ " <td id=\"T_ade5a_row39_col2\" class=\"data row39 col2\" >73</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row40_col0\" class=\"data row40 col0\" >rr6</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row40_col1\" class=\"data row40 col1\" >Précipitations dans les 6 dernières heures</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row40_col2\" class=\"data row40 col2\" >10869</td>\n",
+ " <th id=\"T_ade5a_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
+ " <td id=\"T_ade5a_row40_col0\" class=\"data row40 col0\" >rr6</td>\n",
+ " <td id=\"T_ade5a_row40_col1\" class=\"data row40 col1\" >Précipitations dans les 6 dernières heures</td>\n",
+ " <td id=\"T_ade5a_row40_col2\" class=\"data row40 col2\" >10869</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row41_col0\" class=\"data row41 col0\" >rr12</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row41_col1\" class=\"data row41 col1\" >Précipitations dans les 12 dernières heures</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row41_col2\" class=\"data row41 col2\" >10919</td>\n",
+ " <th id=\"T_ade5a_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
+ " <td id=\"T_ade5a_row41_col0\" class=\"data row41 col0\" >rr12</td>\n",
+ " <td id=\"T_ade5a_row41_col1\" class=\"data row41 col1\" >Précipitations dans les 12 dernières heures</td>\n",
+ " <td id=\"T_ade5a_row41_col2\" class=\"data row41 col2\" >10919</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row42_col0\" class=\"data row42 col0\" >rr24</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row42_col1\" class=\"data row42 col1\" >Précipitations dans les 24 dernières heures</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row42_col2\" class=\"data row42 col2\" >12730</td>\n",
+ " <th id=\"T_ade5a_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
+ " <td id=\"T_ade5a_row42_col0\" class=\"data row42 col0\" >rr24</td>\n",
+ " <td id=\"T_ade5a_row42_col1\" class=\"data row42 col1\" >Précipitations dans les 24 dernières heures</td>\n",
+ " <td id=\"T_ade5a_row42_col2\" class=\"data row42 col2\" >12730</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row43_col0\" class=\"data row43 col0\" >phenspe1</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row43_col1\" class=\"data row43 col1\" >Phénomène spécial 1</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row43_col2\" class=\"data row43 col2\" >14818</td>\n",
+ " <th id=\"T_ade5a_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
+ " <td id=\"T_ade5a_row43_col0\" class=\"data row43 col0\" >phenspe1</td>\n",
+ " <td id=\"T_ade5a_row43_col1\" class=\"data row43 col1\" >Phénomène spécial 1</td>\n",
+ " <td id=\"T_ade5a_row43_col2\" class=\"data row43 col2\" >14818</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row44_col0\" class=\"data row44 col0\" >phenspe2</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row44_col1\" class=\"data row44 col1\" >Phénomène spécial 2</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row44_col2\" class=\"data row44 col2\" >14826</td>\n",
+ " <th id=\"T_ade5a_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
+ " <td id=\"T_ade5a_row44_col0\" class=\"data row44 col0\" >phenspe2</td>\n",
+ " <td id=\"T_ade5a_row44_col1\" class=\"data row44 col1\" >Phénomène spécial 2</td>\n",
+ " <td id=\"T_ade5a_row44_col2\" class=\"data row44 col2\" >14826</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row45_col0\" class=\"data row45 col0\" >phenspe3</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row45_col1\" class=\"data row45 col1\" >Phénomène spécial 3</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row45_col2\" class=\"data row45 col2\" >15515</td>\n",
+ " <th id=\"T_ade5a_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
+ " <td id=\"T_ade5a_row45_col0\" class=\"data row45 col0\" >phenspe3</td>\n",
+ " <td id=\"T_ade5a_row45_col1\" class=\"data row45 col1\" >Phénomène spécial 3</td>\n",
+ " <td id=\"T_ade5a_row45_col2\" class=\"data row45 col2\" >15515</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row46_col0\" class=\"data row46 col0\" >phenspe4</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row46_col1\" class=\"data row46 col1\" >Phénomène spécial 4</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row46_col2\" class=\"data row46 col2\" >28869</td>\n",
+ " <th id=\"T_ade5a_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
+ " <td id=\"T_ade5a_row46_col0\" class=\"data row46 col0\" >phenspe4</td>\n",
+ " <td id=\"T_ade5a_row46_col1\" class=\"data row46 col1\" >Phénomène spécial 4</td>\n",
+ " <td id=\"T_ade5a_row46_col2\" class=\"data row46 col2\" >28869</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row47_col0\" class=\"data row47 col0\" >nnuage1</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row47_col1\" class=\"data row47 col1\" >Nébulosité couche nuageuse 1</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row47_col2\" class=\"data row47 col2\" >4753</td>\n",
+ " <th id=\"T_ade5a_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
+ " <td id=\"T_ade5a_row47_col0\" class=\"data row47 col0\" >nnuage1</td>\n",
+ " <td id=\"T_ade5a_row47_col1\" class=\"data row47 col1\" >Nébulosité couche nuageuse 1</td>\n",
+ " <td id=\"T_ade5a_row47_col2\" class=\"data row47 col2\" >4753</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row48_col0\" class=\"data row48 col0\" >ctype1</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row48_col1\" class=\"data row48 col1\" >Type nuage 1</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row48_col2\" class=\"data row48 col2\" >5699</td>\n",
+ " <th id=\"T_ade5a_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
+ " <td id=\"T_ade5a_row48_col0\" class=\"data row48 col0\" >ctype1</td>\n",
+ " <td id=\"T_ade5a_row48_col1\" class=\"data row48 col1\" >Type nuage 1</td>\n",
+ " <td id=\"T_ade5a_row48_col2\" class=\"data row48 col2\" >5699</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row49_col0\" class=\"data row49 col0\" >hnuage1</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row49_col1\" class=\"data row49 col1\" >Hauteur de base 1</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row49_col2\" class=\"data row49 col2\" >5439</td>\n",
+ " <th id=\"T_ade5a_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
+ " <td id=\"T_ade5a_row49_col0\" class=\"data row49 col0\" >hnuage1</td>\n",
+ " <td id=\"T_ade5a_row49_col1\" class=\"data row49 col1\" >Hauteur de base 1</td>\n",
+ " <td id=\"T_ade5a_row49_col2\" class=\"data row49 col2\" >5439</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row50_col0\" class=\"data row50 col0\" >nnuage2</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row50_col1\" class=\"data row50 col1\" >Nébulosité couche nuageuse 2</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row50_col2\" class=\"data row50 col2\" >16112</td>\n",
+ " <th id=\"T_ade5a_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
+ " <td id=\"T_ade5a_row50_col0\" class=\"data row50 col0\" >nnuage2</td>\n",
+ " <td id=\"T_ade5a_row50_col1\" class=\"data row50 col1\" >Nébulosité couche nuageuse 2</td>\n",
+ " <td id=\"T_ade5a_row50_col2\" class=\"data row50 col2\" >16112</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row51_col0\" class=\"data row51 col0\" >ctype2</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row51_col1\" class=\"data row51 col1\" >Type nuage 2</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row51_col2\" class=\"data row51 col2\" >16643</td>\n",
+ " <th id=\"T_ade5a_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
+ " <td id=\"T_ade5a_row51_col0\" class=\"data row51 col0\" >ctype2</td>\n",
+ " <td id=\"T_ade5a_row51_col1\" class=\"data row51 col1\" >Type nuage 2</td>\n",
+ " <td id=\"T_ade5a_row51_col2\" class=\"data row51 col2\" >16643</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row52_col0\" class=\"data row52 col0\" >hnuage2</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row52_col1\" class=\"data row52 col1\" >Hauteur de base 2</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row52_col2\" class=\"data row52 col2\" >16317</td>\n",
+ " <th id=\"T_ade5a_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
+ " <td id=\"T_ade5a_row52_col0\" class=\"data row52 col0\" >hnuage2</td>\n",
+ " <td id=\"T_ade5a_row52_col1\" class=\"data row52 col1\" >Hauteur de base 2</td>\n",
+ " <td id=\"T_ade5a_row52_col2\" class=\"data row52 col2\" >16317</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row53_col0\" class=\"data row53 col0\" >nnuage3</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row53_col1\" class=\"data row53 col1\" >Nébulosité couche nuageuse 3</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row53_col2\" class=\"data row53 col2\" >25387</td>\n",
+ " <th id=\"T_ade5a_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
+ " <td id=\"T_ade5a_row53_col0\" class=\"data row53 col0\" >nnuage3</td>\n",
+ " <td id=\"T_ade5a_row53_col1\" class=\"data row53 col1\" >Nébulosité couche nuageuse 3</td>\n",
+ " <td id=\"T_ade5a_row53_col2\" class=\"data row53 col2\" >25387</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row54_col0\" class=\"data row54 col0\" >ctype3</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row54_col1\" class=\"data row54 col1\" >Type nuage 3</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row54_col2\" class=\"data row54 col2\" >25642</td>\n",
+ " <th id=\"T_ade5a_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
+ " <td id=\"T_ade5a_row54_col0\" class=\"data row54 col0\" >ctype3</td>\n",
+ " <td id=\"T_ade5a_row54_col1\" class=\"data row54 col1\" >Type nuage 3</td>\n",
+ " <td id=\"T_ade5a_row54_col2\" class=\"data row54 col2\" >25642</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row55_col0\" class=\"data row55 col0\" >hnuage3</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row55_col1\" class=\"data row55 col1\" >Hauteur de base 3</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row55_col2\" class=\"data row55 col2\" >25431</td>\n",
+ " <th id=\"T_ade5a_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
+ " <td id=\"T_ade5a_row55_col0\" class=\"data row55 col0\" >hnuage3</td>\n",
+ " <td id=\"T_ade5a_row55_col1\" class=\"data row55 col1\" >Hauteur de base 3</td>\n",
+ " <td id=\"T_ade5a_row55_col2\" class=\"data row55 col2\" >25431</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row56_col0\" class=\"data row56 col0\" >nnuage4</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row56_col1\" class=\"data row56 col1\" >Nébulosité couche nuageuse 4</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row56_col2\" class=\"data row56 col2\" >28850</td>\n",
+ " <th id=\"T_ade5a_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
+ " <td id=\"T_ade5a_row56_col0\" class=\"data row56 col0\" >nnuage4</td>\n",
+ " <td id=\"T_ade5a_row56_col1\" class=\"data row56 col1\" >Nébulosité couche nuageuse 4</td>\n",
+ " <td id=\"T_ade5a_row56_col2\" class=\"data row56 col2\" >28850</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row57_col0\" class=\"data row57 col0\" >ctype4</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row57_col1\" class=\"data row57 col1\" >Type nuage 4</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row57_col2\" class=\"data row57 col2\" >28780</td>\n",
+ " <th id=\"T_ade5a_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
+ " <td id=\"T_ade5a_row57_col0\" class=\"data row57 col0\" >ctype4</td>\n",
+ " <td id=\"T_ade5a_row57_col1\" class=\"data row57 col1\" >Type nuage 4</td>\n",
+ " <td id=\"T_ade5a_row57_col2\" class=\"data row57 col2\" >28780</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row58_col0\" class=\"data row58 col0\" >hnuage4</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row58_col1\" class=\"data row58 col1\" >Hauteur de base 4</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row58_col2\" class=\"data row58 col2\" >28850</td>\n",
+ " <th id=\"T_ade5a_level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
+ " <td id=\"T_ade5a_row58_col0\" class=\"data row58 col0\" >hnuage4</td>\n",
+ " <td id=\"T_ade5a_row58_col1\" class=\"data row58 col1\" >Hauteur de base 4</td>\n",
+ " <td id=\"T_ade5a_row58_col2\" class=\"data row58 col2\" >28850</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row59_col0\" class=\"data row59 col0\" >coordonnees</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row59_col1\" class=\"data row59 col1\" >Coordonnees</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row59_col2\" class=\"data row59 col2\" >0</td>\n",
+ " <th id=\"T_ade5a_level0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
+ " <td id=\"T_ade5a_row59_col0\" class=\"data row59 col0\" >coordonnees</td>\n",
+ " <td id=\"T_ade5a_row59_col1\" class=\"data row59 col1\" >Coordonnees</td>\n",
+ " <td id=\"T_ade5a_row59_col2\" class=\"data row59 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row60\" class=\"row_heading level0 row60\" >60</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row60_col0\" class=\"data row60 col0\" >nom</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row60_col1\" class=\"data row60 col1\" >Nom</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row60_col2\" class=\"data row60 col2\" >0</td>\n",
+ " <th id=\"T_ade5a_level0_row60\" class=\"row_heading level0 row60\" >60</th>\n",
+ " <td id=\"T_ade5a_row60_col0\" class=\"data row60 col0\" >nom</td>\n",
+ " <td id=\"T_ade5a_row60_col1\" class=\"data row60 col1\" >Nom</td>\n",
+ " <td id=\"T_ade5a_row60_col2\" class=\"data row60 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row61\" class=\"row_heading level0 row61\" >61</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row61_col0\" class=\"data row61 col0\" >type_de_tendance_barometrique</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row61_col1\" class=\"data row61 col1\" >Type de tendance barométrique.1</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row61_col2\" class=\"data row61 col2\" >2</td>\n",
+ " <th id=\"T_ade5a_level0_row61\" class=\"row_heading level0 row61\" >61</th>\n",
+ " <td id=\"T_ade5a_row61_col0\" class=\"data row61 col0\" >type_de_tendance_barometrique</td>\n",
+ " <td id=\"T_ade5a_row61_col1\" class=\"data row61 col1\" >Type de tendance barométrique.1</td>\n",
+ " <td id=\"T_ade5a_row61_col2\" class=\"data row61 col2\" >2</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row62\" class=\"row_heading level0 row62\" >62</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row62_col0\" class=\"data row62 col0\" >temps_passe_1</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row62_col1\" class=\"data row62 col1\" >Temps passé 1.1</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row62_col2\" class=\"data row62 col2\" >542</td>\n",
+ " <th id=\"T_ade5a_level0_row62\" class=\"row_heading level0 row62\" >62</th>\n",
+ " <td id=\"T_ade5a_row62_col0\" class=\"data row62 col0\" >temps_passe_1</td>\n",
+ " <td id=\"T_ade5a_row62_col1\" class=\"data row62 col1\" >Temps passé 1.1</td>\n",
+ " <td id=\"T_ade5a_row62_col2\" class=\"data row62 col2\" >542</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row63\" class=\"row_heading level0 row63\" >63</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row63_col0\" class=\"data row63 col0\" >temps_present</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row63_col1\" class=\"data row63 col1\" >Temps présent.1</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row63_col2\" class=\"data row63 col2\" >1</td>\n",
+ " <th id=\"T_ade5a_level0_row63\" class=\"row_heading level0 row63\" >63</th>\n",
+ " <td id=\"T_ade5a_row63_col0\" class=\"data row63 col0\" >temps_present</td>\n",
+ " <td id=\"T_ade5a_row63_col1\" class=\"data row63 col1\" >Temps présent.1</td>\n",
+ " <td id=\"T_ade5a_row63_col2\" class=\"data row63 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row64\" class=\"row_heading level0 row64\" >64</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row64_col0\" class=\"data row64 col0\" >tc</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row64_col1\" class=\"data row64 col1\" >Température (°C)</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row64_col2\" class=\"data row64 col2\" >14</td>\n",
+ " <th id=\"T_ade5a_level0_row64\" class=\"row_heading level0 row64\" >64</th>\n",
+ " <td id=\"T_ade5a_row64_col0\" class=\"data row64 col0\" >tc</td>\n",
+ " <td id=\"T_ade5a_row64_col1\" class=\"data row64 col1\" >Température (°C)</td>\n",
+ " <td id=\"T_ade5a_row64_col2\" class=\"data row64 col2\" >14</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row65\" class=\"row_heading level0 row65\" >65</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row65_col0\" class=\"data row65 col0\" >tn12c</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row65_col1\" class=\"data row65 col1\" >Température minimale sur 12 heures (°C)</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row65_col2\" class=\"data row65 col2\" >21883</td>\n",
+ " <th id=\"T_ade5a_level0_row65\" class=\"row_heading level0 row65\" >65</th>\n",
+ " <td id=\"T_ade5a_row65_col0\" class=\"data row65 col0\" >tn12c</td>\n",
+ " <td id=\"T_ade5a_row65_col1\" class=\"data row65 col1\" >Température minimale sur 12 heures (°C)</td>\n",
+ " <td id=\"T_ade5a_row65_col2\" class=\"data row65 col2\" >21883</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row66\" class=\"row_heading level0 row66\" >66</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row66_col0\" class=\"data row66 col0\" >tn24c</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row66_col1\" class=\"data row66 col1\" >Température minimale sur 24 heures (°C)</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row66_col2\" class=\"data row66 col2\" >29165</td>\n",
+ " <th id=\"T_ade5a_level0_row66\" class=\"row_heading level0 row66\" >66</th>\n",
+ " <td id=\"T_ade5a_row66_col0\" class=\"data row66 col0\" >tn24c</td>\n",
+ " <td id=\"T_ade5a_row66_col1\" class=\"data row66 col1\" >Température minimale sur 24 heures (°C)</td>\n",
+ " <td id=\"T_ade5a_row66_col2\" class=\"data row66 col2\" >29165</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row67\" class=\"row_heading level0 row67\" >67</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row67_col0\" class=\"data row67 col0\" >tx12c</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row67_col1\" class=\"data row67 col1\" >Température maximale sur 12 heures (°C)</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row67_col2\" class=\"data row67 col2\" >21883</td>\n",
+ " <th id=\"T_ade5a_level0_row67\" class=\"row_heading level0 row67\" >67</th>\n",
+ " <td id=\"T_ade5a_row67_col0\" class=\"data row67 col0\" >tx12c</td>\n",
+ " <td id=\"T_ade5a_row67_col1\" class=\"data row67 col1\" >Température maximale sur 12 heures (°C)</td>\n",
+ " <td id=\"T_ade5a_row67_col2\" class=\"data row67 col2\" >21883</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row68\" class=\"row_heading level0 row68\" >68</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row68_col0\" class=\"data row68 col0\" >tx24c</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row68_col1\" class=\"data row68 col1\" >Température maximale sur 24 heures (°C)</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row68_col2\" class=\"data row68 col2\" >29165</td>\n",
+ " <th id=\"T_ade5a_level0_row68\" class=\"row_heading level0 row68\" >68</th>\n",
+ " <td id=\"T_ade5a_row68_col0\" class=\"data row68 col0\" >tx24c</td>\n",
+ " <td id=\"T_ade5a_row68_col1\" class=\"data row68 col1\" >Température maximale sur 24 heures (°C)</td>\n",
+ " <td id=\"T_ade5a_row68_col2\" class=\"data row68 col2\" >29165</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row69\" class=\"row_heading level0 row69\" >69</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row69_col0\" class=\"data row69 col0\" >tminsolc</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row69_col1\" class=\"data row69 col1\" >Température minimale du sol sur 12 heures (en °C)</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row69_col2\" class=\"data row69 col2\" >27364</td>\n",
+ " <th id=\"T_ade5a_level0_row69\" class=\"row_heading level0 row69\" >69</th>\n",
+ " <td id=\"T_ade5a_row69_col0\" class=\"data row69 col0\" >tminsolc</td>\n",
+ " <td id=\"T_ade5a_row69_col1\" class=\"data row69 col1\" >Température minimale du sol sur 12 heures (en °C)</td>\n",
+ " <td id=\"T_ade5a_row69_col2\" class=\"data row69 col2\" >27364</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row70\" class=\"row_heading level0 row70\" >70</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row70_col0\" class=\"data row70 col0\" >altitude</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row70_col1\" class=\"data row70 col1\" >Altitude</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row70_col2\" class=\"data row70 col2\" >0</td>\n",
+ " <th id=\"T_ade5a_level0_row70\" class=\"row_heading level0 row70\" >70</th>\n",
+ " <td id=\"T_ade5a_row70_col0\" class=\"data row70 col0\" >altitude</td>\n",
+ " <td id=\"T_ade5a_row70_col1\" class=\"data row70 col1\" >Altitude</td>\n",
+ " <td id=\"T_ade5a_row70_col2\" class=\"data row70 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row71\" class=\"row_heading level0 row71\" >71</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row71_col0\" class=\"data row71 col0\" >longitude</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row71_col1\" class=\"data row71 col1\" >Longitude</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row71_col2\" class=\"data row71 col2\" >0</td>\n",
+ " <th id=\"T_ade5a_level0_row71\" class=\"row_heading level0 row71\" >71</th>\n",
+ " <td id=\"T_ade5a_row71_col0\" class=\"data row71 col0\" >longitude</td>\n",
+ " <td id=\"T_ade5a_row71_col1\" class=\"data row71 col1\" >Longitude</td>\n",
+ " <td id=\"T_ade5a_row71_col2\" class=\"data row71 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row72\" class=\"row_heading level0 row72\" >72</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row72_col0\" class=\"data row72 col0\" >latitude</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row72_col1\" class=\"data row72 col1\" >Latitude</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row72_col2\" class=\"data row72 col2\" >0</td>\n",
+ " <th id=\"T_ade5a_level0_row72\" class=\"row_heading level0 row72\" >72</th>\n",
+ " <td id=\"T_ade5a_row72_col0\" class=\"data row72 col0\" >latitude</td>\n",
+ " <td id=\"T_ade5a_row72_col1\" class=\"data row72 col1\" >Latitude</td>\n",
+ " <td id=\"T_ade5a_row72_col2\" class=\"data row72 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row73\" class=\"row_heading level0 row73\" >73</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row73_col0\" class=\"data row73 col0\" >libgeo</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row73_col1\" class=\"data row73 col1\" >communes (name)</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row73_col2\" class=\"data row73 col2\" >0</td>\n",
+ " <th id=\"T_ade5a_level0_row73\" class=\"row_heading level0 row73\" >73</th>\n",
+ " <td id=\"T_ade5a_row73_col0\" class=\"data row73 col0\" >libgeo</td>\n",
+ " <td id=\"T_ade5a_row73_col1\" class=\"data row73 col1\" >communes (name)</td>\n",
+ " <td id=\"T_ade5a_row73_col2\" class=\"data row73 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row74\" class=\"row_heading level0 row74\" >74</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row74_col0\" class=\"data row74 col0\" >codegeo</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row74_col1\" class=\"data row74 col1\" >communes (code)</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row74_col2\" class=\"data row74 col2\" >0</td>\n",
+ " <th id=\"T_ade5a_level0_row74\" class=\"row_heading level0 row74\" >74</th>\n",
+ " <td id=\"T_ade5a_row74_col0\" class=\"data row74 col0\" >codegeo</td>\n",
+ " <td id=\"T_ade5a_row74_col1\" class=\"data row74 col1\" >communes (code)</td>\n",
+ " <td id=\"T_ade5a_row74_col2\" class=\"data row74 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row75\" class=\"row_heading level0 row75\" >75</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row75_col0\" class=\"data row75 col0\" >nom_epci</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row75_col1\" class=\"data row75 col1\" >EPCI (name)</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row75_col2\" class=\"data row75 col2\" >0</td>\n",
+ " <th id=\"T_ade5a_level0_row75\" class=\"row_heading level0 row75\" >75</th>\n",
+ " <td id=\"T_ade5a_row75_col0\" class=\"data row75 col0\" >nom_epci</td>\n",
+ " <td id=\"T_ade5a_row75_col1\" class=\"data row75 col1\" >EPCI (name)</td>\n",
+ " <td id=\"T_ade5a_row75_col2\" class=\"data row75 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row76\" class=\"row_heading level0 row76\" >76</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row76_col0\" class=\"data row76 col0\" >code_epci</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row76_col1\" class=\"data row76 col1\" >EPCI (code)</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row76_col2\" class=\"data row76 col2\" >0</td>\n",
+ " <th id=\"T_ade5a_level0_row76\" class=\"row_heading level0 row76\" >76</th>\n",
+ " <td id=\"T_ade5a_row76_col0\" class=\"data row76 col0\" >code_epci</td>\n",
+ " <td id=\"T_ade5a_row76_col1\" class=\"data row76 col1\" >EPCI (code)</td>\n",
+ " <td id=\"T_ade5a_row76_col2\" class=\"data row76 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row77\" class=\"row_heading level0 row77\" >77</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row77_col0\" class=\"data row77 col0\" >nom_dept</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row77_col1\" class=\"data row77 col1\" >department (name)</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row77_col2\" class=\"data row77 col2\" >0</td>\n",
+ " <th id=\"T_ade5a_level0_row77\" class=\"row_heading level0 row77\" >77</th>\n",
+ " <td id=\"T_ade5a_row77_col0\" class=\"data row77 col0\" >nom_dept</td>\n",
+ " <td id=\"T_ade5a_row77_col1\" class=\"data row77 col1\" >department (name)</td>\n",
+ " <td id=\"T_ade5a_row77_col2\" class=\"data row77 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row78\" class=\"row_heading level0 row78\" >78</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row78_col0\" class=\"data row78 col0\" >code_dep</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row78_col1\" class=\"data row78 col1\" >department (code)</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row78_col2\" class=\"data row78 col2\" >0</td>\n",
+ " <th id=\"T_ade5a_level0_row78\" class=\"row_heading level0 row78\" >78</th>\n",
+ " <td id=\"T_ade5a_row78_col0\" class=\"data row78 col0\" >code_dep</td>\n",
+ " <td id=\"T_ade5a_row78_col1\" class=\"data row78 col1\" >department (code)</td>\n",
+ " <td id=\"T_ade5a_row78_col2\" class=\"data row78 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row79\" class=\"row_heading level0 row79\" >79</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row79_col0\" class=\"data row79 col0\" >nom_reg</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row79_col1\" class=\"data row79 col1\" >region (name)</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row79_col2\" class=\"data row79 col2\" >0</td>\n",
+ " <th id=\"T_ade5a_level0_row79\" class=\"row_heading level0 row79\" >79</th>\n",
+ " <td id=\"T_ade5a_row79_col0\" class=\"data row79 col0\" >nom_reg</td>\n",
+ " <td id=\"T_ade5a_row79_col1\" class=\"data row79 col1\" >region (name)</td>\n",
+ " <td id=\"T_ade5a_row79_col2\" class=\"data row79 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3level0_row80\" class=\"row_heading level0 row80\" >80</th>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row80_col0\" class=\"data row80 col0\" >code_reg</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row80_col1\" class=\"data row80 col1\" >region (code)</td>\n",
- " <td id=\"T_a0099504_41de_11eb_aa99_e3cf125b8da3row80_col2\" class=\"data row80 col2\" >0</td>\n",
+ " <th id=\"T_ade5a_level0_row80\" class=\"row_heading level0 row80\" >80</th>\n",
+ " <td id=\"T_ade5a_row80_col0\" class=\"data row80 col0\" >code_reg</td>\n",
+ " <td id=\"T_ade5a_row80_col1\" class=\"data row80 col1\" >region (code)</td>\n",
+ " <td id=\"T_ade5a_row80_col2\" class=\"data row80 col2\" >0</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f79fa772ed0>"
+ "<pandas.io.formats.style.Styler at 0x7f1756123e90>"
]
},
"metadata": {},
@@ -2422,97 +2424,97 @@
"data": {
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+ "#T_efd4e_row0_col0,#T_efd4e_row0_col1,#T_efd4e_row0_col2,#T_efd4e_row1_col0,#T_efd4e_row1_col1,#T_efd4e_row1_col2,#T_efd4e_row2_col0,#T_efd4e_row2_col1,#T_efd4e_row2_col2,#T_efd4e_row3_col0,#T_efd4e_row3_col1,#T_efd4e_row3_col2,#T_efd4e_row4_col0,#T_efd4e_row4_col1,#T_efd4e_row4_col2,#T_efd4e_row5_col0,#T_efd4e_row5_col1,#T_efd4e_row5_col2,#T_efd4e_row6_col0,#T_efd4e_row6_col1,#T_efd4e_row6_col2,#T_efd4e_row7_col0,#T_efd4e_row7_col1,#T_efd4e_row7_col2,#T_efd4e_row8_col0,#T_efd4e_row8_col1,#T_efd4e_row8_col2,#T_efd4e_row9_col0,#T_efd4e_row9_col1,#T_efd4e_row9_col2,#T_efd4e_row10_col0,#T_efd4e_row10_col1,#T_efd4e_row10_col2,#T_efd4e_row11_col0,#T_efd4e_row11_col1,#T_efd4e_row11_col2,#T_efd4e_row12_col0,#T_efd4e_row12_col1,#T_efd4e_row12_col2,#T_efd4e_row13_col0,#T_efd4e_row13_col1,#T_efd4e_row13_col2{\n",
" text-align: left;\n",
- " }</style><table id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >Columns</th> <th class=\"col_heading level0 col1\" >Description</th> <th class=\"col_heading level0 col2\" >Na</th> </tr></thead><tbody>\n",
+ " }</style><table id=\"T_efd4e_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >Columns</th> <th class=\"col_heading level0 col1\" >Description</th> <th class=\"col_heading level0 col2\" >Na</th> </tr></thead><tbody>\n",
" <tr>\n",
- " <th id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row0_col0\" class=\"data row0 col0\" >date</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row0_col1\" class=\"data row0 col1\" >Date</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row0_col2\" class=\"data row0 col2\" >0</td>\n",
+ " <th id=\"T_efd4e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_efd4e_row0_col0\" class=\"data row0 col0\" >date</td>\n",
+ " <td id=\"T_efd4e_row0_col1\" class=\"data row0 col1\" >Date</td>\n",
+ " <td id=\"T_efd4e_row0_col2\" class=\"data row0 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row1_col0\" class=\"data row1 col0\" >pmer</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row1_col1\" class=\"data row1 col1\" >Pression au niveau mer</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row1_col2\" class=\"data row1 col2\" >0</td>\n",
+ " <th id=\"T_efd4e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_efd4e_row1_col0\" class=\"data row1 col0\" >pmer</td>\n",
+ " <td id=\"T_efd4e_row1_col1\" class=\"data row1 col1\" >Pression au niveau mer</td>\n",
+ " <td id=\"T_efd4e_row1_col2\" class=\"data row1 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row2_col0\" class=\"data row2 col0\" >tend</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row2_col1\" class=\"data row2 col1\" >Variation de pression en 3 heures</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row2_col2\" class=\"data row2 col2\" >0</td>\n",
+ " <th id=\"T_efd4e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_efd4e_row2_col0\" class=\"data row2 col0\" >tend</td>\n",
+ " <td id=\"T_efd4e_row2_col1\" class=\"data row2 col1\" >Variation de pression en 3 heures</td>\n",
+ " <td id=\"T_efd4e_row2_col2\" class=\"data row2 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row3_col0\" class=\"data row3 col0\" >cod_tend</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row3_col1\" class=\"data row3 col1\" >Type de tendance barométrique</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row3_col2\" class=\"data row3 col2\" >0</td>\n",
+ " <th id=\"T_efd4e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+ " <td id=\"T_efd4e_row3_col0\" class=\"data row3 col0\" >cod_tend</td>\n",
+ " <td id=\"T_efd4e_row3_col1\" class=\"data row3 col1\" >Type de tendance barométrique</td>\n",
+ " <td id=\"T_efd4e_row3_col2\" class=\"data row3 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row4_col0\" class=\"data row4 col0\" >dd</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row4_col1\" class=\"data row4 col1\" >Direction du vent moyen 10 mn</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row4_col2\" class=\"data row4 col2\" >0</td>\n",
+ " <th id=\"T_efd4e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+ " <td id=\"T_efd4e_row4_col0\" class=\"data row4 col0\" >dd</td>\n",
+ " <td id=\"T_efd4e_row4_col1\" class=\"data row4 col1\" >Direction du vent moyen 10 mn</td>\n",
+ " <td id=\"T_efd4e_row4_col2\" class=\"data row4 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row5_col0\" class=\"data row5 col0\" >ff</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row5_col1\" class=\"data row5 col1\" >Vitesse du vent moyen 10 mn</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row5_col2\" class=\"data row5 col2\" >0</td>\n",
+ " <th id=\"T_efd4e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+ " <td id=\"T_efd4e_row5_col0\" class=\"data row5 col0\" >ff</td>\n",
+ " <td id=\"T_efd4e_row5_col1\" class=\"data row5 col1\" >Vitesse du vent moyen 10 mn</td>\n",
+ " <td id=\"T_efd4e_row5_col2\" class=\"data row5 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row6_col0\" class=\"data row6 col0\" >td</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row6_col1\" class=\"data row6 col1\" >Point de rosée</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row6_col2\" class=\"data row6 col2\" >0</td>\n",
+ " <th id=\"T_efd4e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+ " <td id=\"T_efd4e_row6_col0\" class=\"data row6 col0\" >td</td>\n",
+ " <td id=\"T_efd4e_row6_col1\" class=\"data row6 col1\" >Point de rosée</td>\n",
+ " <td id=\"T_efd4e_row6_col2\" class=\"data row6 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row7_col0\" class=\"data row7 col0\" >u</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row7_col1\" class=\"data row7 col1\" >Humidité</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row7_col2\" class=\"data row7 col2\" >0</td>\n",
+ " <th id=\"T_efd4e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+ " <td id=\"T_efd4e_row7_col0\" class=\"data row7 col0\" >u</td>\n",
+ " <td id=\"T_efd4e_row7_col1\" class=\"data row7 col1\" >Humidité</td>\n",
+ " <td id=\"T_efd4e_row7_col2\" class=\"data row7 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row8_col0\" class=\"data row8 col0\" >ww</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row8_col1\" class=\"data row8 col1\" >Temps présent</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row8_col2\" class=\"data row8 col2\" >0</td>\n",
+ " <th id=\"T_efd4e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+ " <td id=\"T_efd4e_row8_col0\" class=\"data row8 col0\" >ww</td>\n",
+ " <td id=\"T_efd4e_row8_col1\" class=\"data row8 col1\" >Temps présent</td>\n",
+ " <td id=\"T_efd4e_row8_col2\" class=\"data row8 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row9_col0\" class=\"data row9 col0\" >pres</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row9_col1\" class=\"data row9 col1\" >Pression station</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row9_col2\" class=\"data row9 col2\" >0</td>\n",
+ " <th id=\"T_efd4e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+ " <td id=\"T_efd4e_row9_col0\" class=\"data row9 col0\" >pres</td>\n",
+ " <td id=\"T_efd4e_row9_col1\" class=\"data row9 col1\" >Pression station</td>\n",
+ " <td id=\"T_efd4e_row9_col2\" class=\"data row9 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row10_col0\" class=\"data row10 col0\" >rafper</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row10_col1\" class=\"data row10 col1\" >Rafales sur une période</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row10_col2\" class=\"data row10 col2\" >0</td>\n",
+ " <th id=\"T_efd4e_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+ " <td id=\"T_efd4e_row10_col0\" class=\"data row10 col0\" >rafper</td>\n",
+ " <td id=\"T_efd4e_row10_col1\" class=\"data row10 col1\" >Rafales sur une période</td>\n",
+ " <td id=\"T_efd4e_row10_col2\" class=\"data row10 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row11_col0\" class=\"data row11 col0\" >rr1</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row11_col1\" class=\"data row11 col1\" >Précipitations dans la dernière heure</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row11_col2\" class=\"data row11 col2\" >0</td>\n",
+ " <th id=\"T_efd4e_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+ " <td id=\"T_efd4e_row11_col0\" class=\"data row11 col0\" >rr1</td>\n",
+ " <td id=\"T_efd4e_row11_col1\" class=\"data row11 col1\" >Précipitations dans la dernière heure</td>\n",
+ " <td id=\"T_efd4e_row11_col2\" class=\"data row11 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row12_col0\" class=\"data row12 col0\" >rr3</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row12_col1\" class=\"data row12 col1\" >Précipitations dans les 3 dernières heures</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row12_col2\" class=\"data row12 col2\" >0</td>\n",
+ " <th id=\"T_efd4e_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+ " <td id=\"T_efd4e_row12_col0\" class=\"data row12 col0\" >rr3</td>\n",
+ " <td id=\"T_efd4e_row12_col1\" class=\"data row12 col1\" >Précipitations dans les 3 dernières heures</td>\n",
+ " <td id=\"T_efd4e_row12_col2\" class=\"data row12 col2\" >0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row13_col0\" class=\"data row13 col0\" >tc</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row13_col1\" class=\"data row13 col1\" >Température (°C)</td>\n",
- " <td id=\"T_a028327a_41de_11eb_aa99_e3cf125b8da3row13_col2\" class=\"data row13 col2\" >0</td>\n",
+ " <th id=\"T_efd4e_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+ " <td id=\"T_efd4e_row13_col0\" class=\"data row13 col0\" >tc</td>\n",
+ " <td id=\"T_efd4e_row13_col1\" class=\"data row13 col1\" >Température (°C)</td>\n",
+ " <td id=\"T_efd4e_row13_col2\" class=\"data row13 col2\" >0</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f79f9511390>"
+ "<pandas.io.formats.style.Styler at 0x7f1755d6ae90>"
]
},
"metadata": {},
@@ -3011,7 +3013,7 @@
},
{
"data": {
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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1152x1440 with 13 Axes>"
]
@@ -3082,8 +3084,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "End time is : Saturday 19 December 2020, 10:43:22\n",
- "Duration is : 00:00:04 776ms\n",
+ "End time is : Saturday 9 January 2021, 10:04:33\n",
+ "Duration is : 00:00:05 236ms\n",
"This notebook ends here\n"
]
}
@@ -3117,7 +3119,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.7"
+ "version": "3.7.9"
}
},
"nbformat": 4,
diff --git a/SYNOP/02-First-predictions.ipynb b/SYNOP/02-First-predictions.ipynb
index 3583ca09484da4c8f1a8023146170dc37fe34792..904b93e4331dd3f9f12a188ec9250a53db20fcfa 100644
--- a/SYNOP/02-First-predictions.ipynb
+++ b/SYNOP/02-First-predictions.ipynb
@@ -6,8 +6,8 @@
"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
- "# <!-- TITLE --> [SYNOP2] - Time series with RNN - Try a prediction\n",
- "<!-- DESC --> Episode 2 : Training session and first predictions\n",
+ "# <!-- TITLE --> [SYNOP2] - First predictions at 3h\n",
+ "<!-- DESC --> Episode 2 : Learning session and weather prediction attempt at 3h\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
@@ -30,97 +30,9 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
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- "div.todo:before { 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- {
- "data": {
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- "**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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- "text": [
- "Version : 0.6.1 DEV\n",
- "Notebook id : SYNOP2\n",
- "Run time : Saturday 19 December 2020, 11:22:47\n",
- "TensorFlow version : 2.0.0\n",
- "Keras version : 2.2.4-tf\n",
- "Datasets dir : /home/pjluc/datasets/fidle\n",
- "Running mode : full\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras\n",
@@ -147,513 +59,72 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Step 2 - Read and prepare dataset\n",
- "### 2.1 - Read it"
+ "### 1.2 - Parameters"
]
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**Train dataset example :**"
- ],
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- " tend cod_tend dd ff td u ww pres rafper rr1 \\\n",
- "0 -120.0 6.0 0.0 0.0 278.75 88.0 60.0 96250.0 4.1 0.0 \n",
- "1 -150.0 6.0 60.0 1.0 278.65 93.0 61.0 96100.0 2.6 0.2 \n",
- "2 10.0 3.0 280.0 2.1 278.85 95.0 58.0 96110.0 2.6 0.0 \n",
- "3 230.0 3.0 310.0 2.6 279.15 96.0 50.0 96340.0 5.7 0.0 \n",
- "4 280.0 1.0 330.0 4.6 278.15 94.0 21.0 96620.0 8.7 0.4 \n",
- "5 480.0 3.0 350.0 5.1 276.95 91.0 60.0 97100.0 8.2 0.2 \n",
- "6 530.0 2.0 350.0 3.1 274.05 83.0 21.0 97630.0 7.2 0.0 \n",
- "7 450.0 2.0 340.0 6.2 272.15 81.0 2.0 98080.0 9.3 0.0 \n",
- "8 280.0 1.0 320.0 6.2 270.15 74.0 2.0 98360.0 10.3 0.0 \n",
- "9 220.0 1.0 290.0 2.6 269.65 72.0 2.0 98580.0 5.1 0.0 \n",
- "10 100.0 1.0 350.0 3.1 270.45 79.0 2.0 98680.0 4.1 0.0 \n",
- "11 300.0 3.0 350.0 5.1 268.55 70.0 2.0 98980.0 6.7 0.0 \n",
- "12 130.0 1.0 10.0 4.6 267.45 60.0 2.0 99110.0 7.7 0.0 \n",
- "13 150.0 3.0 10.0 5.7 267.45 59.0 2.0 99260.0 8.7 0.0 \n",
- "14 140.0 1.0 50.0 2.6 268.15 70.0 2.0 99400.0 5.7 0.0 \n",
- "\n",
- " rr3 tc \n",
- "0 0.0 7.5 \n",
- "1 0.6 6.6 \n",
- "2 0.4 6.4 \n",
- "3 3.0 6.6 \n",
- "4 0.8 5.9 \n",
- "5 0.4 5.2 \n",
- "6 0.0 3.5 \n",
- "7 0.0 1.9 \n",
- "8 0.0 1.1 \n",
- "9 0.0 1.0 \n",
- "10 0.0 0.5 \n",
- "11 0.0 -0.3 \n",
- "12 0.0 1.2 \n",
- "13 0.0 1.5 \n",
- "14 0.0 -0.8 "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**After normalization :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\" >\n",
- "</style><table id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >tend</th> <th class=\"col_heading level0 col1\" >cod_tend</th> <th class=\"col_heading level0 col2\" >dd</th> <th class=\"col_heading level0 col3\" >ff</th> <th class=\"col_heading level0 col4\" >td</th> <th class=\"col_heading level0 col5\" >u</th> <th class=\"col_heading level0 col6\" >ww</th> <th class=\"col_heading level0 col7\" >pres</th> <th class=\"col_heading level0 col8\" >rafper</th> <th class=\"col_heading level0 col9\" >rr1</th> <th class=\"col_heading level0 col10\" >rr3</th> <th class=\"col_heading level0 col11\" >tc</th> </tr></thead><tbody>\n",
- " <tr>\n",
- " <th id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row0_col0\" class=\"data row0 col0\" >25000.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row0_col1\" class=\"data row0 col1\" >25000.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row0_col2\" class=\"data row0 col2\" >25000.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row0_col3\" class=\"data row0 col3\" >25000.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row0_col4\" class=\"data row0 col4\" >25000.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row0_col5\" class=\"data row0 col5\" >25000.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row0_col6\" class=\"data row0 col6\" >25000.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row0_col7\" class=\"data row0 col7\" >25000.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row0_col8\" class=\"data row0 col8\" >25000.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row0_col9\" class=\"data row0 col9\" >25000.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row0_col10\" class=\"data row0 col10\" >25000.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row0_col11\" class=\"data row0 col11\" >25000.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row1_col0\" class=\"data row1 col0\" >-0.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row1_col1\" class=\"data row1 col1\" >-0.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row1_col9\" class=\"data row1 col9\" >-0.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row3_col0\" class=\"data row3 col0\" >-6.80</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row3_col1\" class=\"data row3 col1\" >-1.59</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row3_col2\" class=\"data row3 col2\" >-1.75</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row3_col3\" class=\"data row3 col3\" >-1.37</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row3_col4\" class=\"data row3 col4\" >-5.18</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row3_col5\" class=\"data row3 col5\" >-3.82</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row3_col6\" class=\"data row3 col6\" >-0.52</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row3_col7\" class=\"data row3 col7\" >-4.94</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row3_col8\" class=\"data row3 col8\" >-1.64</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row3_col9\" class=\"data row3 col9\" >-0.31</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row3_col10\" class=\"data row3 col10\" >-0.27</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row3_col11\" class=\"data row3 col11\" >-3.03</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row4_col0\" class=\"data row4 col0\" >-0.64</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row4_col1\" class=\"data row4 col1\" >-0.85</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row4_col2\" class=\"data row4 col2\" >-0.64</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row4_col3\" class=\"data row4 col3\" >-0.76</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row4_col4\" class=\"data row4 col4\" >-0.72</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row4_col5\" class=\"data row4 col5\" >-0.71</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row4_col6\" class=\"data row4 col6\" >-0.42</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row4_col7\" class=\"data row4 col7\" >-0.55</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row4_col8\" class=\"data row4 col8\" >-0.69</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row4_col9\" class=\"data row4 col9\" >-0.15</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row4_col10\" class=\"data row4 col10\" >-0.20</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row4_col11\" class=\"data row4 col11\" >-0.75</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row5_col0\" class=\"data row5 col0\" >-0.00</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row5_col1\" class=\"data row5 col1\" >-0.48</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row5_col2\" class=\"data row5 col2\" >-0.12</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row5_col3\" class=\"data row5 col3\" >-0.19</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row5_col4\" class=\"data row5 col4\" >0.05</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row5_col5\" class=\"data row5 col5\" >0.18</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row5_col6\" class=\"data row5 col6\" >-0.42</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row5_col7\" class=\"data row5 col7\" >0.03</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row5_col8\" class=\"data row5 col8\" >-0.27</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row5_col9\" class=\"data row5 col9\" >-0.15</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row5_col10\" class=\"data row5 col10\" >-0.20</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row5_col11\" class=\"data row5 col11\" >-0.01</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row6_col0\" class=\"data row6 col0\" >0.63</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row6_col1\" class=\"data row6 col1\" >0.99</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row6_col2\" class=\"data row6 col2\" >1.08</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row6_col3\" class=\"data row6 col3\" >0.50</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row6_col4\" class=\"data row6 col4\" >0.79</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row6_col5\" class=\"data row6 col5\" >0.84</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row6_col6\" class=\"data row6 col6\" >-0.37</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row6_col7\" class=\"data row6 col7\" >0.61</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row6_col8\" class=\"data row6 col8\" >0.52</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row6_col9\" class=\"data row6 col9\" >-0.15</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row6_col10\" class=\"data row6 col10\" >-0.20</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row6_col11\" class=\"data row6 col11\" >0.72</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row7_col0\" class=\"data row7 col0\" >7.16</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row7_col1\" class=\"data row7 col1\" >1.36</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row7_col2\" class=\"data row7 col2\" >1.34</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row7_col3\" class=\"data row7 col3\" >6.28</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row7_col4\" class=\"data row7 col4\" >2.40</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row7_col5\" class=\"data row7 col5\" >1.62</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row7_col6\" class=\"data row7 col6\" >4.46</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row7_col7\" class=\"data row7 col7\" >3.10</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row7_col8\" class=\"data row7 col8\" >6.29</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row7_col9\" class=\"data row7 col9\" >30.36</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row7_col10\" class=\"data row7 col10\" >31.27</td>\n",
- " <td id=\"T_0b3f8a90_41e4_11eb_8992_174cdf497e22row7_col11\" class=\"data row7 col11\" >3.02</td>\n",
- " </tr>\n",
- " </tbody></table>"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f3858235d50>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Shapes :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset : (29165, 14)\n",
- "Train dataset : (25000, 12)\n",
- "Test dataset : (4165, 12)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
+ "# ---- About dataset\n",
+ "#\n",
"dataset_dir = './data'\n",
"dataset_filename = 'synop-LYS.csv'\n",
"schema_filename = 'synop.json'\n",
- "train_len = 25000\n",
"features = ['tend', 'cod_tend', 'dd', 'ff', 'td', 'u', 'ww', 'pres', 'rafper', 'rr1', 'rr3', 'tc']\n",
"features_len = len(features)\n",
"\n",
+ "# ---- About training\n",
+ "#\n",
+ "scale = 1 # Percentage of dataset to be used (1=all)\n",
+ "train_prop = .8 # Percentage for train (the rest being for the test)\n",
+ "sequence_len = 16\n",
+ "batch_size = 32\n",
+ "epochs = 10"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Override parameters (batch mode) - Just forget this cell"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pwk.override('scale', 'train_prop', 'sequence_len', 'batch_size', 'epochs')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Read and prepare dataset\n",
+ "### 2.1 - Read it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
"# ---- Read dataset from ./data\n",
"\n",
"df = pd.read_csv(f'{dataset_dir}/{dataset_filename}', header=0, sep=';')\n",
"\n",
- "# ---- Train / Test\n",
+ "# ---- Scaling\n",
"\n",
+ "df = df[:int(scale*len(df))]\n",
+ "train_len=int(train_prop*len(df))\n",
+ "\n",
+ "# ---- Train / Test\n",
"dataset_train = df.loc[ :train_len-1, features ]\n",
"dataset_test = df.loc[train_len:, features ]\n",
"pwk.subtitle('Train dataset example :')\n",
@@ -687,88 +158,10 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**About the splitting of our dataset :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Nombre de train batchs disponibles : 781\n",
- "batch x shape : (32, 16, 12)\n",
- "batch y shape : (32, 12)\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**What a batch looks like (x) :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[-1.089 0.623 -1.753 -1.371 -0.22 0.952 2.56 -3.502 -0.56 -0.154 -0.199 -0.64 ]\n",
- " [-1.361 0.623 -1.237 -0.964 -0.237 1.229 2.611 -3.702 -0.954 0.167 0.221 -0.75 ]\n",
- " [ 0.089 -0.482 0.654 -0.517 -0.203 1.34 2.457 -3.688 -0.954 -0.154 0.081 -0.775]\n",
- " [ 2.082 -0.482 0.912 -0.314 -0.153 1.396 2.046 -3.382 -0.14 -0.154 1.899 -0.75 ]\n",
- " [ 2.535 -1.218 1.084 0.5 -0.321 1.285 0.557 -3.009 0.648 0.488 0.36 -0.836]\n",
- " [ 4.347 -0.482 1.256 0.704 -0.522 1.118 2.56 -2.37 0.516 0.167 0.081 -0.921]\n",
- " [ 4.801 -0.85 1.256 -0.11 -1.01 0.675 0.557 -1.664 0.254 -0.154 -0.199 -1.129]\n",
- " [ 4.076 -0.85 1.17 1.151 -1.329 0.564 -0.418 -1.065 0.805 -0.154 -0.199 -1.324]\n",
- " [ 2.535 -1.218 0.998 1.151 -1.665 0.176 -0.418 -0.692 1.068 -0.154 -0.199 -1.422]\n",
- " [ 1.992 -1.218 0.74 -0.314 -1.749 0.065 -0.418 -0.399 -0.298 -0.154 -0.199 -1.434]\n",
- " [ 0.904 -1.218 1.256 -0.11 -1.615 0.453 -0.418 -0.266 -0.56 -0.154 -0.199 -1.495]\n",
- " [ 2.717 -0.482 1.256 0.704 -1.934 -0.046 -0.418 0.134 0.122 -0.154 -0.199 -1.593]\n",
- " [ 1.176 -1.218 -1.667 0.5 -2.119 -0.601 -0.418 0.307 0.385 -0.154 -0.199 -1.41 ]\n",
- " [ 1.357 -0.482 -1.667 0.948 -2.119 -0.656 -0.418 0.507 0.648 -0.154 -0.199 -1.373]\n",
- " [ 1.267 -1.218 -1.323 -0.314 -2.001 -0.046 -0.418 0.693 -0.14 -0.154 -0.199 -1.654]\n",
- " [-0.183 0.255 0.654 -0.964 -2.052 0.453 -0.418 0.666 -1.243 -0.154 -0.199 -1.861]]\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**What a batch looks like (y) :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[ 0.18 -1.218 0.568 -0.761 -2.052 0.675 -0.418 0.693 -1.243 -0.154 -0.199 -1.935]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "sequence_len = 16\n",
- "batch_size = 32\n",
- "\n",
"# ---- Train generator\n",
"train_generator = TimeseriesGenerator(dataset_train, dataset_train, length=sequence_len, batch_size=batch_size)\n",
"test_generator = TimeseriesGenerator(dataset_test, dataset_test, length=sequence_len, batch_size=batch_size)\n",
@@ -784,9 +177,9 @@
"\n",
"x,y=train_generator[0]\n",
"pwk.subtitle('What a batch looks like (x) :')\n",
- "np_print2(x[0] )\n",
+ "pwk.np_print(x[0] )\n",
"pwk.subtitle('What a batch looks like (y) :')\n",
- "np_print2(y[0])"
+ "pwk.np_print(y[0])"
]
},
{
@@ -798,30 +191,9 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "lstm (LSTM) (None, 100) 45200 \n",
- "_________________________________________________________________\n",
- "dropout (Dropout) (None, 100) 0 \n",
- "_________________________________________________________________\n",
- "dense (Dense) (None, 12) 1212 \n",
- "=================================================================\n",
- "Total params: 46,412\n",
- "Trainable params: 46,412\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"model = keras.models.Sequential()\n",
"model.add( keras.layers.InputLayer(input_shape=(sequence_len, features_len)) )\n",
@@ -848,7 +220,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -866,7 +238,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -886,102 +258,26 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/10\n",
- "781/781 [==============================] - 39s 49ms/step - loss: 0.5982 - mae: 0.5025 - val_loss: 0.4670 - val_mae: 0.4259\n",
- "Epoch 2/10\n",
- "781/781 [==============================] - 38s 49ms/step - loss: 0.5033 - mae: 0.4368 - val_loss: 0.4431 - val_mae: 0.4058\n",
- "Epoch 3/10\n",
- "781/781 [==============================] - 38s 48ms/step - loss: 0.4817 - mae: 0.4189 - val_loss: 0.4261 - val_mae: 0.3870\n",
- "Epoch 4/10\n",
- "781/781 [==============================] - 38s 48ms/step - loss: 0.4699 - mae: 0.4088 - val_loss: 0.4176 - val_mae: 0.3818\n",
- "Epoch 5/10\n",
- "781/781 [==============================] - 38s 49ms/step - loss: 0.4604 - mae: 0.4013 - val_loss: 0.4106 - val_mae: 0.3738\n",
- "Epoch 6/10\n",
- "781/781 [==============================] - 38s 49ms/step - loss: 0.4512 - mae: 0.3962 - val_loss: 0.4073 - val_mae: 0.3690\n",
- "Epoch 7/10\n",
- "781/781 [==============================] - 38s 49ms/step - loss: 0.4454 - mae: 0.3923 - val_loss: 0.4077 - val_mae: 0.3703\n",
- "Epoch 8/10\n",
- "781/781 [==============================] - 38s 48ms/step - loss: 0.4395 - mae: 0.3899 - val_loss: 0.4099 - val_mae: 0.3703\n",
- "Epoch 9/10\n",
- "781/781 [==============================] - 38s 48ms/step - loss: 0.4350 - mae: 0.3865 - val_loss: 0.4125 - val_mae: 0.3709\n",
- "Epoch 10/10\n",
- "781/781 [==============================] - 37s 48ms/step - loss: 0.4314 - mae: 0.3854 - val_loss: 0.4090 - val_mae: 0.3749\n",
- "CPU times: user 8min 37s, sys: 1min 38s, total: 10min 15s\n",
- "Wall time: 6min 19s\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "%%time\n",
+ "pwk.chrono_start()\n",
"\n",
- "history=model.fit_generator(train_generator, \n",
- " epochs=10, \n",
- " verbose=1,\n",
- " validation_data = test_generator,\n",
- " callbacks = [bestmodel_callback])"
+ "history=model.fit(train_generator, \n",
+ " epochs=epochs, \n",
+ " verbose=1,\n",
+ " validation_data = test_generator,\n",
+ " callbacks = [bestmodel_callback])\n",
+ "\n",
+ "pwk.chrono_show()"
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/SYNOP2-01-history_0</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"pwk.plot_history(history,plot={'loss':['loss','val_loss'], 'mae':['mae','val_mae']}, save_as='01-history')"
]
@@ -1002,7 +298,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1019,34 +315,9 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/SYNOP2-02-prediction-norm</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1080x1152 with 12 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"s=random.randint(0,len(dataset_test)-sequence_len)\n",
"\n",
@@ -1069,41 +340,9 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/SYNOP2-03-prediction</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 3024x2304 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Gap between prediction and reality : 0.40 °C\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"def denormalize(mean,std,seq):\n",
" nseq = seq.copy()\n",
@@ -1139,19 +378,9 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Saturday 19 December 2020, 11:24:13\n",
- "Duration is : 00:01:25 329ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"pwk.end()"
]
@@ -1181,7 +410,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.7"
+ "version": "3.7.9"
}
},
"nbformat": 4,
diff --git a/SYNOP/03-12h-predictions.ipynb b/SYNOP/03-12h-predictions.ipynb
index 4eb838846698e3fa44a1b80ee4c85389341ee8b3..698df743df729fd6af8ca063da0cf14392efc429 100644
--- a/SYNOP/03-12h-predictions.ipynb
+++ b/SYNOP/03-12h-predictions.ipynb
@@ -6,8 +6,8 @@
"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
- "# <!-- TITLE --> [SYNOP3] - Time series with RNN - 12h predictions\n",
- "<!-- DESC --> Episode 3: Attempt to predict in the longer term \n",
+ "# <!-- TITLE --> [SYNOP3] - 12h predictions\n",
+ "<!-- DESC --> Episode 3: Attempt to predict in a more longer term \n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
@@ -28,97 +28,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "div.todo:before { 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- " float:left;\n",
- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
- "}\n",
- "div.todo ul{\n",
- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
- " margin-top:0;\n",
- " margin-bottom:0;\n",
- "}\n",
- "\n",
- "div .comment{\n",
- " font-size:0.8em;\n",
- " color:#696969;\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 0.6.1 DEV\n",
- "Notebook id : SYNOP3\n",
- "Run time : Saturday 19 December 2020, 11:37:54\n",
- "TensorFlow version : 2.0.0\n",
- "Keras version : 2.2.4-tf\n",
- "Datasets dir : /home/pjluc/datasets/fidle\n",
- "Running mode : full\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras\n",
@@ -145,36 +57,73 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Step 2 - Read and prepare dataset\n",
- "As before, in episode 2... ;-)"
+ "### 1.2 - Parameters"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset : (29165, 14)\n",
- "Train dataset : (25000, 12)\n",
- "Test dataset : (4165, 12)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
+ "# ---- About dataset\n",
+ "#\n",
"dataset_dir = './data'\n",
"dataset_filename = 'synop-LYS.csv'\n",
"schema_filename = 'synop.json'\n",
- "train_len = 25000\n",
"features = ['tend', 'cod_tend', 'dd', 'ff', 'td', 'u', 'ww', 'pres', 'rafper', 'rr1', 'rr3', 'tc']\n",
"features_len = len(features)\n",
"\n",
+ "# ---- About training\n",
+ "#\n",
+ "iterations = 4 # number of iterations for prediction (1 iteration = 3h)\n",
+ "\n",
+ "scale = 1 # Percentage of dataset to be used (1=all)\n",
+ "train_prop = .8 # Percentage for train (the rest being for the test)\n",
+ "sequence_len = 16\n",
+ "batch_size = 32\n",
+ "epochs = 10"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Override parameters (batch mode) - Just forget this cell"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pwk.override('iterations', 'scale', 'train_prop', 'sequence_len', 'batch_size', 'epochs')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Read and prepare dataset\n",
+ "As before, in episode 2... ;-)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
"# ---- Read dataset\n",
+ "\n",
"df = pd.read_csv(f'{dataset_dir}/{dataset_filename}', header=0, sep=';')\n",
"\n",
+ "# ---- Scaling\n",
+ "\n",
+ "df = df[:int(scale*len(df))]\n",
+ "train_len=int(train_prop*len(df))\n",
+ "\n",
"# ---- Train / Test\n",
"dataset_train = df.loc[ :train_len-1, features ]\n",
"dataset_test = df.loc[train_len:, features ]\n",
@@ -206,7 +155,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -223,37 +172,10 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/SYNOP3-01-prediction-norm</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1080x1152 with 12 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
- "sequence_len = 16\n",
- "iterations = 4\n",
"\n",
"# ---- Initial sequence\n",
"\n",
@@ -290,7 +212,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -341,34 +263,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/SYNOP3-02-prediction</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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wJbciwWHhujKeXLKVksp6RmYkcP6cfBZMznU7rJCm5DaAxMdEkpEYQ0llw4Dn+InI0LW0tfPwfzaxbMt+fvmNGRSNPPKFYFPz0nninWIPRCciblu4roxHF27m2tOnMHlkGutKqrnr5TUASnBdpOG/AFOQk8yWvbVuhyES9HZX1HHNn9+jur6ZP14+1yOJLUDRyDS27TtIc0ubR/YnIu55cslWrj19CtPyMogID2NaXgbXnj6FJ5dsdTu0kKbkNsB0teEVEe+w1vLqyt38+LH3+dqsPH5x9gwSYyM9tv+YyHDys5NZX1LjsX2KiDtKKutJjo3inlfX8tiizXRYy+SRaZRU1rsdWkhTchtgCnLUqUzEW+qaWrn12ZW8tGInd150HKf20ZThSEwdrVa8IsEgOzWO/3l8GRNzU3lrbRl7qhtYV1LNyIwEt0MLaUpuA0x+VhI7yutoUwtPEY9aX1LN1Q+9S2pCNPdcegKjvdgsZWpeOquU3IoEtNqGQxxsagUDmckxjBuexH/WlHLXy2s4f06+2+GFNC0oCzCxUREMT45lV0Ud47KS3Q5HJOC1d1ieWrKVlz/cxX+ddhTHjR/u9WNOHJHCroo6Gg61Eh/tuSkPIuIb7R2W5Lgobjl/FntrGrnv9fXsrqhn3e5qrvpKkRaTuUzJbQAq6OxUpuRW5MhUHGziNy+swhjDHy6bQ0ZSjE+OGxURzoTcFNbtrmZ2gfeTaRHxnP21jdz41IfcdsExTMhNZUJuKgsm53KwqYVwY4iP0QdWt2laQgAqVKcykSO2dNM+fvDwEmaMzeTXF8z2WWLbRVMTRAJP+YEmfva35ZwyYyTpiZ99z0iMiWT1ziqstS5FJ12U3AagguxkilUxQWRIDrW284fX1vHAmxv432/O5PwjbMowVFPz0lmj5FYkoNzx4ipOnzmarx8z5nM/M8bwxzfWs6+2yYXI5HCalhCAxmUls6uijpa2dqIiBt7XXiRU7a1p5ManVlBaVU94WBjTx6Rz/+VzXb18WJiTwp7qRg42tZAUG+VaHCLSv9qGQ8THRPK/35xJQh/vGwVZzpXV7NQ4H0Yn3WnkNgDFRIaTkxbPrgrV0RMZiBufWkFJZT0dFtraO9hX2+T6vLjI8DAmjkxl7a5qV+MQkb7V1B/iJ4+9z/ub9/eZ2ALkZyezVdMGXafkNkDlZyezZU+t22GIBITSqga6ZsHZzu/9gerdivi32oZD/M/jy5hflMOJk7L73X7B5BzmDmA78S4ltwGqsLNigoj0b0R6/Cf/Nuaz37tpap6SWxF/tnDdHuZMyOKCEwsGtH1uWjwp8VFaVOYyJbcBqjBHlz5EBuqHp04mzBjCjGFkegI3nTfL7ZAAKMhOouJgE7UNh9wORUQOc7CphQ2lNXz9mDy+M79wUJ0Kr/nze1QcbPZidNIfryS3xpjhxpgHjDElxpgWY8xuY8zvjTEp3jheKBozLImSynpa2trdDkXE71XXHeLYwmG8dsOpPHTlPL9Z7BEeFsbkUWkavRXxI3VNrfz8b8tZvmU/xphBt+DOz0rS4JPLPJ7cGmOGAcuBS4AXgB8CLwJXAguNMf7xVyXARUeGMyI9ge3769wORcTvbSyrYeKIVLfD6NHUvHRW71JyK+IPGppbuf7vyzlqdDrfXTB+SPvIz1Yterd5Y+T2emA0cJG19ofW2j9Za38IXARMA/7bC8cMSQU5yRTvrXU7DBG/t6HUf5PbaZp3K+I3Dja1ckz+ML538sRBj9h2mV0wnLHDkzwcmQyGN5LbBUAT8FS3+/8BNAMXe+GYIakwO5ktauYg0qdDre3sKq+jINs/21WPGZ7EgcYWquo0R0/ELU0tbTy2cDMZSTF8e97g5th2NyE3hTkTs7SozEXeSG6jgWbb7X/VWtuBk/SONcZkeOG4IadAlz5E+lW89wCjMhOJifTPhidhxjBF825FXNPc0sYvn1xBdcMhj3Ur/P6f3qW6XgtF3eKN5HY9kGqMmXb4nZ3fd10XHOWF44acvGGJ7KluoLlVi8pEerOxtIaJI1LcDqNPKgkm4o7W9g5u/MeHZKfG8V+nHUXYEYzYHi49MVqDTy7yRnJ7N9AB/NMYc6oxZpQx5hScaQmtndt8blGZMeYKY8yHXognaEVFhDMqM5Ht+w+6HYqI39pYWsPEXP+cb9tlal6GFpWJuCAizHD6zNH86KtTPJbYgjqVuc3jya219l3gPCAReAXYBbwMLAT+1bnZ57Ixa+2D1tqZno4n2BVkJ1OsTmUiPbLWsrGslkl+upisy+jMBJpa2thf2+h2KCIhoaWtnZue/ojt++uYOzHbY9MRukwZnU5khH9OhQoFXqlza619GhgBTAdOBHKstd/vvK8N2OqN44aiwhx1KhPpTfmBJqyF4SmxbofSJ2MMU0arJJiIL7S0tXPz0x8RHmbIG5bglWPMHJfJuSeM88q+pX9e61BmrW231q6y1r5rrS03xmThJLuLrbUanvAQVUwQ6d3G0lomjkg5opXPvqKSYCK+cf8bG4iKCOdnX59GeJj3GrX+5oVV6j7okghfHMQYEwbcA4QDt/rimKFidGYi+w800dTSRmyUT/47RQLGxrIaJvj5fNsuU/PS+fuSrVhrAyIZFwk0be0dWOBbc/NJiY8mItx7iS1AVV0zxXsPMCt/mFePI5/njQ5lCcaYDcaYW40xlxljfgx8AHwDuMFau9DTxwxlEeFh5GUmsnWfFpWJdLehtIZJfl4poUtuWjzWWvbU6MKWiKe1d3Rw+/Mf88z728lMiiXSy4ktqFynm7wx1NcCrAG+BWQDjcAK4CvW2je8cLyQV5jjLCo7alSa26GI+I1PmjfkpLgdyoAYY5g62pmakJsW73Y4IkGjvaOD37ywmubWds4+dozPjpuflcyqnZU+O558yuPJrbW2BadagvhIQXYyq3boBSRyOH9v3tCTaWMyWLm9klNnqBS4iKe8t2k/B5ta+L9zZxLlwwoG84qymT85x2fHk095f1xevK4wWxUTRLoLhOYN3XWN3Kptp8iRa++w7Nh/kLkTs7j5vFk+TWzBuRrz9PvbqGtq7X9j8Sglt0FgVGYClQebaTikF5BIl41ltX7fvKG7rNQ4oiLCKKmsdzsUkYDWYS2/f2UND7+1CcDri8d6s3xLuebdukDL64NAeFgYY4cnsXXvQabmpbsdjojrrLVsLK3hii9OdDuUQZua59S7HZWZ6HYoIgFj4boynlyylZLKekZmJJAaH01rewe3fusYV6uPdC0qmzE2w7UYQpFGboNEQXYyW/bWuh2GiF8IlOYNPZmal86qHap3KzJQC9eV8ejCzVz1lSJe/vkpfGdeIZvKavny1JGul8jMz0pi6z6N3Pqaktsg4bTh1QtIBJzmDRNyA6N5Q3dT89JZs6uKDs27FRmQJ5ds5drTpzB1dDpvrinlmIJh/N95M3l2+Xa3Q+O48Vlc/ZUit8MIOUpug0RhTjLF+nQoAjjNGyaOCKz5tl0yk2JJiI1kZ3md26GIBISSynomj0xj8fq9vPjBTlrbOpg8Ms0v5q7HRUewbf9Bmlra3A4lpCi5DRIj0hOoqT+kVZkiBFbzhp5My8tglVrxigzIyIwE1pVUs2TTPs6cPYb4mEjWlVQzMiPB7dAA+NviYrboyqpPKbkNEuFhhnFZyZrbIyEv0Jo39KSrJJiI9O/8Ofnc9fIaVu2oZPqYdFbtrOSul9dw/px8t0MDID87SRUTfEzJbRApzE7Wp0MJeVv3BV7zhu6m5KWxbncV7R2adyvSnwWTc/nugvGkJkRz0b0Lue/19Xx3wXgWTM51OzTAWROjgSffUimwIFKQnczSzfvcDkPEVRsCsHlDd2kJMaQlxLB9/0EKspPdDkfE71kLN5wzg9F+WELv6LGZZKeqpbYvaeQ2iBSoU5kIG0sDr3lDT5ySYGqrLdIfay1/XbyFtnb/vNKRnhjDyPR4Wts73A4lZCi5DSK56fHUNbVysLHF7VBEXNHVvGFSgFZKOFxXMwcR6VtJZT1t7R2MHe5/o7ZdbnzqQzaX1bodRshQchtEwowhP0sT1yV0lR9oosPagGze0N2U0emsL6mhTaM9In1asa2C2QXD/LqudX62mjn4kpLbIFOYk6KpCRKyuqYk+PMfuYFKjosiKyVOr2eRfnz9mDwuPmmC22H0KT8rWQNPPqTkNsg4ncpq3Q5DxBWB3LyhJ1PzVBJMpC8Hm1pYuG4PCTGRbofSp6mj0ykameZ2GCFDyW2QKdSiMglhG0trA7p5Q3eqdyvStxXF5SzZ6P9VgnLT4zl1xii3wwgZSm6DTHZqHE0tbdTUH3I7FBGfamlrZ2dFYDdv6O6o0WlsKquhpa3d7VBE/NLy4nKOLRzmdhgDcsOTH7CxtMbtMEKCktsgY4whP1tzeyT0FO89wKiMhIBu3tBdQkwkI9MTtMpapAdt7R18tL2CWfmBkdymJ8RoUZmPKLkNQoXZKUpuJeQEQ/OGnmjerUjPwsMM91w6h/TEGLdDGRANPPmOktsgpHm3EoqCpXlDd6p3K9Kz1TurAupKzcTcFOKi/XvhW7BQchuECnKSKd5b63YYIj7T1bwhmColdCkamcaWPQc41Kp5tyJdrLX8/tW1AbW+JD87me9/aZLbYYQEJbdBaHhyLK1tHVTVNbsdiohPdDVvyAqC5g3dxUVHMGZ4Ihu0EEXkEyVVDbS0dTAuK8ntUAblwTc3sH3/QbfDCHpKboOQMcapd6upCRIiNpYFT/OGnqgkmMhnLS/e7/ddyXpysLGVTVog6nVKboNUQXYyW/YouZXQEKxTErpMG5Oh5FbkMCdNzuWbx49zO4xBy89O0sCTDyi5DVKFOSmadyshI9iaN3Q3aUQq2/cfpKmlze1QRFzX0NxKxcEmslLi3A5l0Aqykyk/0OR2GEFPyW2QcqYlHMRa63YoIl4VjM0buouODKcgO5l1u6vdDkXEdcuLy3ny3a1uhzEkk0akcuu3jnE7jKCn5DZIZSbFYLFUalGZBLlgbN7QE9W7FXEsLy5nduFwt8MYEmMMb6wqYU91g9uhBDUlt0HKGENhdjLFmncrQW5DaQ0TclPcDsPrpim5FaGtvYMPt1UwuyAwupL1ZNWOStbqKoxXKbkNYgXZKWrmIEHPmW8bvIvJuozPTaGkqp6G5la3QxFxjQWuO3NawHQl64k6lXmfktsgVqBOZRLkgrl5Q3dREeGMz03RiI+EtL01jUwZne52GEekIDuZrfv0t9mblNwGscKcZIr31GpRmQStioPNQdu8oSfT8jJYpakJEsL+7x8fsruy3u0wjsiE3BR+ec7RbocR1JTcBrH0xBgiwsPYr7IjEqQ2lNYEdfOG7rSoTEJZaVU9Ta1t5AdYV7LuoiLC2X+gKaBaBwcaryS3xpgEY8z1xpi1xpg6Y0ylMWapMea7JlT+CvmJQs3tkSAWKlMSuhRmJ7OvppGDjS1uhyLic8uLy5ldMDwoPsy+sHwHH22vcDuMoOXx5NYYEwa8BtwMrAB+DNwChAOPALd7+pjSu4KcFFVMkKAV7M0buosID2PSyFTW7NLorYSeyaPS+NrM0W6H4RH52cls3XfQ7TCCljdGbmcDc4B7rLWXWGsftNbeDcwFdgDf88IxpReFWlQmQSoUmjf0ZFpeuubdSshpbm0nOyWOMcMDe0pClwJdVfUqbyS3XWfensPvtNa2AJWAKhf7kPMC0qIyCT7Few8wMj0+6Js3dKd5txKK3t+8j9++tNrtMDymMCeZ804Y53YYQSvCC/v8AKgF/scYsxNYDsQC3wWOBr7vhWNKL1IToomJimBvTSM5afFuhyPiMRtCbL5tl3FZyVTXN1NTf4jUhGi3wxHxiUDuStaT+OhIJuSm0tzSRkyUN1Kx0ObxkVtrbQ3wNaAa+CewC9gEXA2cba19qKfHGWOuMMZ86Ol4RFMTJDhtCpHmDd2Fhxkmj9LorYSO9g6nK9kx+YHblawnd/9rDe9v2e92GEHJW6XA6oF1wJ3AWcBlwFbg78aYk3t6QOfc3JleiiekFWQns1XJrQQRay0by0Jz5BY6pyZoUZmEiJa2Dr4zr5CMpMDtStYTLSrzHm9USzgKWAq8aa39qbX2eWvtn3EWme0DHjLGhNYkOZcV5qgNrwSXioPNtHeETvOG7qaO1sithI4Oa/narDy3w/A4LSrzHm+M3F4LxABPH36ntbYReAUYDeR54bjSi64XUIcWlUmQCLXmDd2NGZ5IXVMLFQfVoEWC3zV/fo/t+4NvhLMwO5mjx2a6HUZQ8kZym9t529PobES3W/GB5LgoEmMj2VOtQhUSHEKteUN3YcYwRaO3EgLKqhpoPNRG3rBEt0PxuKS4KM5VxQSv8EZyu6Hz9ruH32mMSQHOAGqAbV44rvShICuZLWrmIEFiY2ktE0OoeUNPVBJMQsHy4v0cUzCMsCC9SvP7V9by7oa9bocRdLyR3N6NUynhdmPM48aY7xtjrgc+BrKBG6y1bV44rvShMEdzeyQ4dDVvKMxOdjsUV2lRmYSCYcmxfGnqCLfD8Jr0xBj9bfYCb5QC2wUcAzwOLADuBa4DSnBKgd3n6WNK/wqytahMgsMnzRtCvDbkqIwEWlo72FfT6HYoIl7R3tHBCROyKBqZ5nYoXlOQnUTxPv1t9jSv/HWw1m4DLvLGvmVoCrKT2b7vIO0dlvCw4Ly8I6HBmZIQuvNtuxhjPhm9zUqNczscEY97d+M+Pigu53++Ps3tULymIDuZpNgot8MIOt6qcyt+JjE2kuT4KMqq6t0OReSIbCytCcnmDT3RvFsJZsu37GfSyOB+raclxPDzs6a7HUbQUXIbQtSpTAJdqDdv6K6r3q1VmT8JMu0dHazYVsHsguDqStaTp5du4+MdlW6HEVSU3IYQFYyWQBfqzRu6y0mLAwNlKvMnQaauqZUvTR1BZlLwv9YbW9pYoyswHqXkNoQU5KgcmAS2UG/e0J0xRt3KJCglx0VxxcmT3A7DJwqykrWozMOU3IaQgqxktu8/SHtHh9uhiAyJ07whxe0w/Irm3UowuvaRpSHTeCg/O5n9teo26ElKbkNIfEwkGYkx7K7QojIJTJvKVCmhu2mdFRM071aCxZ7qBvYfaAqZKiCZSTE8+P0T3Q4jqCi5DTEFObr8IYGppa2dHeVq3tDd8JQ4YiLD2aUPrRIklhWXc0x+8HYl684Yw9LN+7UmxoOU3IaYwmzNu5XApOYNvVO3Mgk2JxZlux2CT23eU8vyLfvdDiNoKLkNMQU5Kfp0KAFJzRt6N3V0OqtVSkiCxFmzx3D02Ey3w/ApZ1HZQbfDCBpKbkNMflYSO8rraGvXojIJLGre0LupeRms2V1Nh+bdSoBbumkfjy7c7HYYPleQncxWDTx5jJLbEBMbFcHw5Fh2VdS5HYrIgKl5Q98ykmJIjo1ix36N/EhgW7JpH+mJ0YN/4BNPQF4ehIU5t0884enQvGp4Six3XnSc22EEDSW3IahAncokwKh5Q/+mqCSYBLj2DsuH2yqYXTB8cA984gm44grYtQusdW6vuCKgElxjDE0tbeyraXQ7lKCg5DYEFeaoU5kElo2lNUxQ84Y+TctLZ5WSWwlgVXXNTBmdxrDkQX6I/cUvoLFbUtjY6NzfFDj1Y99eW8bb68rcDiMoKLkNQQXZyRSrYoIEkI1ltUxS84Y+Tc1LZ93uajVpkYA1LDmWG845evAP3L279/uHD4dJk+Db34bnnnPub2sbepBelJ+VzFYtKvMIJbchaFxWMrsq6mhpa3c7FJEBcTqTab5tX1Lio8lIitEfRwlYtz27kqq65sE/cNSo3u+vqoK//x3mz3emLACccMKnCe+DDzr3+cFiTC0q8xwltyEoJjKcnLR4FX2XgKDmDQM3NS+dVTs0NUECz96aRlbvqiI1YQiLyS691FlIdri4OLj1VoiMhGnTnG3OPtv52ZIlnya8dZ2Lq7///U8T3rvvhuYhJNlHKDstjsu+OFHdBj1AyW2Iys9OZsueWrfDEOmXmjcM3LS8DDVzkIC0vHj/0LqStbfDiy/CZZfB6NFgjHP74INwwQU9P+bwhPfHP3bu+8MfPk14t21ztnnwQZg4ES68EO66C/bt63l/HqrUEGYMs/IzaTjkn9MmAon+WoSows6KCae5HYhIP9S8YeCOGp3GnS+upq29g4hwjV1I4NhX28Rx4wdZJQHggQecUdoHHnAS26HqSninTfv0vosvhtmz4aOPnK+GBuf2wgvh6KOdr44OuPHGTxe0dVVqgN6T6z78ddEWUuKjOfeEcUP/XUTJbagqzEnm9Y9L3A5DpF8bS2s4YUKW22EEhKTYKLJT49i8p5aikWluhyMyYN//0qShPXDmTDjppCNLbHsTGQlTpzpfl1zi3NfaCk899WnC+8wzvVdqGEJym5+VxLLicg8EH9r00T5EjRmWRGlVvRaVid/bVFbLhNwUt8MIGFPHqN6tBJaPtlXw6speKh705amn4KijnKkDvtKV8F5yCfzxj1BR0fN2vVVw6EdBtkp1eoKS2xAVHRnOiPQEtu9XpzLxX+UHmmht7yA7Nc7tUALG1NFKbiWwvL2ujNbBtoR/+2342c/cr3LQW6WGkSOHtLvc9AROnJitVtpHSMltCCvISaZ4b63bYYj0qqsEmJo3DNxRo9LYVFarqzISENo7LCu2VnBswbCBP6ilBa66Cn7/e4iP915wA3Hrrc6c38PFxUFWFjz22KB3Fx5muOQLEwa/sE4+Q8ltCCvITmaLmjmIH1PzhsGLj4lkVGYCG0tr3Q5FpF9lVfVkpcQxPGUQV2fefhvGj4czzvBeYAN1wQVOVYXulRoefRRuucWpxtA+uA+af3+3mOeW7/BOvCFCyW0IK9TcHvFzat4wNNPyMjQ1QQLCqMxE7r7k+IE/wFr4ylecbmP+Mrp5wQWwc6dTOWHnTuf7iRNh+XJYswb+/e9B7S4jKUalOo+QktsQljcskT3VDTS36vKl+B81bxi6qXnpqncrAeEvb22icaB1Xa11GjG8/TaEh3s3ME9IS4M33oBTToFnn4XNmwf0sIIsDTwdKSW3Iayq7hAdwJn/73Uuv38xe2sa+32MiK+oecPQFY1MZeveA/rgKn5tX20jb6wuIXagr/GXXoKNG2HOHO8G5kldndMOHIC5c51ktx+jMhMYnhJHe4cWlQ2VktsQduNTK2ht66DDQklVPTc+tcLtkEQ+salMzRuGKjYqgnFZSawvqXY7FJFeLd+yn1n5wwgPG8D0goYGuOYap/xWVJT3g/O0Sy5xRm+/+1345z/73DQ8LIzbvnXMwJ4X6ZGS2xBWWtXwyb+t/ez3Im7bWFrDRNW3HTKVBBN/t3pnFbMHWiWhowNuuslp2BCo5s6FZctgwQKorIRDh3rd9M3Vpby9tsyHwQUXJbchbER6/Cfz8U3n9yL+Qm13j8zUvHTWKLkVP/aLc2ZwXOEAWu5u3gw7dsBFF3k/KG8bPRoyM+EPf3CS3H37etysraODj7b30iBC+qXkNoTddN4sRqYnYAyEhRluOGeG2yGJAGre4AkTR6Syo7xu4It1RHxofUk1K7dXEhHeTxpiLVxxBbz7rm8C85Ubb3SqPhxzjNPGt5t8LSo7IkpuQ1h2ahwPXTmP1284jWMLhvHepp4/QYr4mpo3HLnoyHAKc5JZt1vzbsX/vPZxCXuqBzAV7q9/debbfv/73g/Kl8LCnAT37rth69bP/Xh0ZgIHG1sH37lNAC8kt8aYXxljbB9frZ4+phy5y0+exHPLd1BV1+x2KCJq3uAhU/MyVBJM/E57h+WD4nJmF/QzJcFapwvZ/fcHRumvoTjrLDj3XKeb2S9+4cwtBqIiwnny2i8Q2d/ItvTIG8/ac8C3e/i6o/PnL3vhmHKEslPjOGX6KB55e2B1+ES8Sc0bPGNqXjqrdlS6HYbIZxTvPUBKfBRZA5l2tGwZzJrl/aDcdsopztSLs86CujoAVu+q0ut3iDye3Fpr11hr/9b9C+iqxP5nTx9TPOP8Ofl8tL2CzeqMIi5S8wbPmZCbQll1A3VNumAm/mN8TjJ3fOe4vjdatsxp2BCIZb+GYtgw+M9/nMVm110HOBWM3l6niglD4ZPxbmNMHHAeUAa87otjyuDFRUfw3QXjeeCNDVir4tHijq37Dqp5g4dUHmzGWvjGb/+tRi3iN15fVUJMZB/TDNra4MornVHMUBIVBQ8+CL/9LZSUcNSOdWzde9DtqAKSryZzfBNIAh6x1qpljh87eeoIWtraWbR+j9uhSIjSlATPufGpFTS3tmPVqEX8xP7aRh55e3PfVRLuuw9SUuCCC3wWl98wBmJioKSEUVdfwlGv/IOWNqVNg+Wr5PZSwAJ/6W0DY8wVxpgPfRSP9CLMGL7/5SL+/NYmte4UV6h5g+eoUYv4m+XF5czKz+y7+9bo0U6CG8rVUo4/HvPee1y+6S0if/Y/bkcTcLye3BpjxgNzgLettTt6285a+6C1dqa345H+HTUqjQm5qTyzdJvboUgIUvMGz1GjFvE3y4rLObavKgn//jd89aswcaLvgvJX+fmUv/4W++d/yfl02rnQTPrni5HbSztvH/bBscRDLvviBF5YsZPyA01uhyIhRM0bPOuTRi1AYmwkN50XAqvOxa9d+9WjmNVby90334Tvfa/PtrShZkXFIf4RlgPvvQdTp8K6dW6HFBC8mtwaYyKA7wDVwPPePJZ4VlZKHF89ejR/eXuT26FICFHzBs/qatTyx8vnEBsVQVZKrNshSQjbWV5HfVNrz4vJDh2Cq6+Ge+6BOH247ZKfnczWvQdgzhy4+WanZe+LL7odlt/z9sjt6cBw4HFrrT6KBZhzTxjH2l3VbCitcTsUCRFq3uAdY4cnERURxqayWrdDkRD23PLtvTcVeeYZmDQJTj/dt0H5uXHDk9hVUed0KrvgAnjlFXj1VWeagvTK28lt15QE1bYNQLFRn5YG69ALSXxgkyoleIUxhvlFOaqCIq7psJYPiit670r2rW/B3//u26ACQExUBNedNf3T8pzHHAN/+hPs3u20JH7kEcjLc9r55uXBE0+4Ga7f8Fpya4zJAb4CfGCtXeut44h3fWFKLgBvr1UhafGulrZ2tqt5g9ecWJTDOxv20t6hD6rie1v21JIYG/n5+fTWOontxx9rOkIvZo7LpLmlW/WiYcNg7Vq49FLYtct5HnftgiuuUIKLd0duvwuEo4VkAc0pDTaJR97eTFNLm9vhSBBT8wbvGpWRQGp8NOt2V7sdStDbW9PI5fcv5pRbXlXzjE6jMhL5+VnTP/+D556D1ath8mTfBxUg/vXhLv66eMtn74yNhdLSz09PaGyEX/zCd8H5Ka8lt9ba26y1xlr7kLeOIb4xaUQqR41O45/vqTSYeI+aN3jfPE1N8Ikbn1pBSVU9HdaqeUanLXtrGZ2Z8Nk76+rgRz9yatqGSpvdIfhkUVl3JSU9P2D3bu8GFAB81cRBAtwlJ03g5Y92sb9WIxDiHWre4H3zirJ5b9M+2to73A4lqJVW1X8yoKbmGU6Jv1ufWYlTbfkwjY3w05/CvHmuxBUoxg1PYnt5He0d3V63o0b1/IC4ONgW2oNRSm5lQIYlx/L1WXk8/JZKg4l3qHmD92WlxJGTGsfHOyrdDiVotXdYoiI+W+rKGPjHe1s52NjiUlTucrqSDftsV7Jt26CjA665xr3AAkR8TCTfPH7c57uG3nrr5+cpx8bCl78My5c7z+/27b4L1I8ouZUBO+f4cWwsrWGt5uyJh6l5g+/Mn6ypCd705LvFjM5MYFRGPGHGMCojgRvPOZqSygYu/uNCfv/KWnZVhFanqeXF+5l9eOOGjg646CJ46SX3ggowF55YQHx05GfvvOACePBBp12xMc7tQw/Bs886i/Q2b3aqK1x0ERQXuxO4S7RyQwYsJjKcS06awANvrOfey+YQpkL74iGbymrVvMFH5k7M5vHFW2hpa//cCKMcmY+2VfDKyt384bI5pCfGfOZnx44fTk39BF75aBfX/W05ecMSOfOYMczMzwz699Jzjh1LQc5hVVAeewxaW+Hyy90LKsC8saqE3ZX1XP7Fbm2JL7jA+erJxInOCPk998Dxx8Mbb8CMGd4P1g9o5FYGZcHkHCIjwnhzdanboUgQ2Vhao+YNPpKeGMO4rGRWbK1wO5SgUn6giTteXM11Z07/XGLbJTUhmgvnFfLYDxdw0uRcHlu0mcvvW8xLK3YGbTWa8gNNjB2e9OmoY1sb3HILPPCAU5tVBmRYcuzQmrAkJ8Mvf+kkudOnw1/+At/5DmzZ0v9jA5jOLBkUYwxXfrmIRxdupuFQq9vhSJDYWFrDhFzNt/UVNXTwrLb2Dm57biVnzs5jal56v9tHRYRz8tQR/OGyOfzo9Cms2lnFt+95mwff3MC+IFu0+8Q7xfzn8DrpERGwcqWTaMmAjctKYvu+g0NvqJSU5ExdOPtsKCyEE05wktxDwdk8VsmtDNr4nBRmjM3gH0tCezWmeEZX84bxOWre4CsnTMjiw20VQTta6Gt/fmsTSbFRfOP4cYN6nDGGo0alceM3juYPl80B4AcPL+Gmpz9i7a6qT7tSBagOa/lgaznHds23XbrUWUCWrNf6YCXFRnH0uAzqmo5wUCk5GW64AbZuhS98AaKjnbnPmzd7JlA/oeRWhuSSkybw2se7A7I4uQqs+xc1b/C95Lgoikam8v7m/W6HEvDe3biX9zbv4ydnTD2iubNZKXFccfIkHr/mJKblpXP3v9byg4eX8ObqUlra2vvfgR8q3nuA+OgIctLinekIV14Jxx3ndlgB64ZzjiY5zkP1gJOTnYVm4HQ2mzsXLrwwaJJcJbcyJOmJMZw5ewwP/Wej26EMmgqs+xc1b3DHvEk5LNbUhCNSVtXAva+u44azZ5AU65mkIzYqgq/NyuOhq+Zx0fzxLFxXxkX3LuRvi7dQUx9Yl5BT46O58stFzjf33guZmXDeee4GFcA+KC7nhQ92eH7HP/yhM5I7aRL8/vfOfdWBXRVJya0M2dnHjmXrvgOs3lnldiiDUlrVoALrfkTNG9xx/IThrNldfeSXOUNUc2s7Nz/zEd+eV0hhTorH9x9mDMcUDOO2C2Zz+4Wzqao/xGX3L+KOF1dR3FO3Kj8UZgxHj8t0vomKgj/+0Zn3KUMSFmZY6q2rLUlJcP31Tre4lhanqsIFF8CmwKxtr+RWhiw6MpzLvjCRB/69gfaOwJgb1mEt8TGfXv42wIj0ePcCEjaWqXmDG+KjI5k+JoOlm/e5HUpA+uNr68gblshXj+6lS5QHjc5M5L9OO4pHrl7AqIwEfvXPD/nJY++zZONev33vrTjYxJUPvuPEt3IlXH01jB/vdlgBLT8ria17D3h/LnZUFKxdC5Mnw4knOhUWAoySWzkicydmERcdwRureulx7UcOtbZz27MryUmNY0R6PAaIi4ngpvNmuR1ayKo42ERrm5o3uEVVE4bmjVUlbCqr5b9OO8qntZmT4qI494R8HvvBAk6fOZpnlm3n4j8u5Jn3t1Pf7F8j8MuLy5k5LpPwN/8N55zjjAbKEUmJjyY1IZqqOh9MT0lMhJ//3CkhdsYZThOIb30LNmzw/rE9QMmtHBFjDN//0iT+umgLDX725nq42oZD/OxvywgPC+POi47jz1fN56/XnESYMWQk9VyTUryvq+Wumje445iCYWwuq6W2IbDmcrpp274D/PmtTfzynBnEurQIMiI8jHlFOdx98Qlcf9YMtu47wEX3LuQPr63j4+0VfrFgdnlxOceNTnZGbP/wB2c0UI7Yw1fO8+3frMRESE+H7GyYOhUWLHDmTZd1lnd74gnIy3NqFuflOd/7ASW3csQKspM5piCTJ971z/Z+ZVUNXPvoUqaMTudnZ077pCvTsORYRmUksHK7itm7RfNt3RUTGc4xBcN4Z8Net0MJCA3Nrdzy7Eq+/6VJjMpMdDscACbkpnDdmdP50/dOJCEmkuv//gG7KzsXzFbWc/3fl7sydWF2wTBmL3rJSYhOPdXnxw9WW/cdZOkmF6YSJSTAz37mjOQefTTExsIdd8BllznVFqx1bq+4wi8SXCW34hHfXTCeN1eXUuZni7PWl1Tz48fe5xvHjeOSkyZ8rlTP/KIcFq/XH3a3qFKC+zQ1YWCstfz25TUcPTaTk47KdTucz8lIiuG7C8bjrCRwWGBPdSNf/3+vc/n9i/nVPz7kof9s5NWVu1m1s5KKg01DbwrQh6aWNk6bMYroa34ADz/s8f2Hsur6Zl7+aJd7ASQkwE9/Cmlp8JvfQHPzZ3/e2Ai/+IU7sR1GhSXFI9ISYjjnuHE8+J+N/N+5M90OB4B3N+zl3tfW8dMzpjIrf1iP28ydmM2jCzfT3NpOTGS4jyMMbWre4B9mjM3gzpdWU36giWHJsW6H47eeX76DigNNXHfmNLdD6dOI9HhKquqx1ilMMDI9gXsvm8Oe6gbKqhvYU93AxtIa/rOmlD3VjTS2tJGTGkduWrzzlR5PTlo8uWlxpMZHD2nK0ANvrOesP/+a0XfeAvn5XvgtQ1d+VvIni8pcn85V1UulpN27fRtHD5TcisecOTuP1z7ezcrtlcwYm+FaHNZanl22g+eX7+C2bx1DfnbvyVNqQjSFOSmsKC5n7qRsH0Ypat7gH6IiwjlhfBbvbNjLOceNdTscv7S+pJp/LN3G7y854ZNpTf7qpvNmceNTKyitamBEejw3nTeLmMhwxg5PYuzwpM9t33ColT3VjZRVOcnv6p1VvLZyN2XVDbS1W3LSPk18czqT3xFp8ST10Exg4boynlyylVELXyf8gw9YVBfBfB/8zqEkPTGGiPAwyg80MTzF5YW4o0Y5UxF6ut9l+qsiHhMVEc7lX5zIA/9ez/1XzCU8zPezXto7LPe/sZ61u6q56+LjBzQSNb8om0Xr9yi59bFNmpLgN+YV5fDI25uU3PagtuEQtz33Mdd+dQpZbicTA5CdGsdDV84b8Pbx0ZEUZCdT0MMgwMGmFmfEt6qBsupGPtxWwUsrdlJa3UCYMZ1Jr5P8Hmhs4Z2Ne4ltrud7rz/EA9+9gc3vbMNGRrJgsv9N4whkd198vH8shL71VmeObeNhixbj4pz7XabkVjzq+PHDeXHFTl5duZvTZ+b59NjNLW38+rmPOdTWwe++exzxMZEDetzxE7L405sbaTjUSnz0wB4jR25DaS3HFfY8XUR8a2peGuUHmyirbiA3TXWfu7R3WG5/fhVfOCqXYwuHux2OzyXFRpGUG8WE3M9+CLXWcqCxhbLOqQ5lVQ38Z20Zh1rbiauo5F+zTuO9zAIygSeXbFVy62FhYYbt++t6/EDiUxdc4Nz+4hfOVIRRo5zEtut+F2lBmXhUV2mwv71T7NPOR9X1zfz0r8tIjIvi5vNnDTixBecN/KhRaSzzVucX6dHGMo3c+ovwsDDmTsxWO95unninmPaODi6aX+h2KH7FGENKfDRFI9P40tSRXHzSBJpb2ujosOxJz+Gped/EWqg42ExJZb3b4Qad4r0HeGzRZrfDcFxwAezcCR0dzq0fJLag5Fa8YOzwJI4fn8Xf3tnik+PtrqjjR48sZXbhcH58+hQiwwd/Ws8vymGRyiH5jJo3+B9VTfisD7dV8Pqq3fz8rOmuTLEKNCMzEshMivmku64xkJkUw8iMBHcDC0L5WckU+6JTWQDTK1a84qL5hSxct4fdXv7UvmZXFT99fBnfPrGQC08sGPLq0ePGD2fd7moONqqLji9sLK1lYm6K+6t95ROTRqbScKiNneV1bofiuvIDTdz54mp+fuZ00hL8YG5jADh/Tj4WyEyMwXTe2s77xbMyk2KwFt90KgtQSm7FK1Lio/nm8eN48E3vtep7e20ZtzyzkuvOnM7JU0cc0b5ioyI4emwmS9wojh2CVN/W/4QZw7xJ2SE/etva3sFtz67krGPHcNTodLfDCRgLJudy6RcmEBsdgTEQGx3BpV+YoPm2XmCM4adnTCUmyr8rd7hJC8rEa844Jo9XV+5mxdbyXuvMDoW1lqfe28arK3fzm28fS94wz3QKml+Uzcsf7uLUGe6XMQl2G0truPikCW6HId0smJzLrc+u5KL5hSE7qv7wfzaSHBelyhFDsGByrpJZH5k+JoN6P2557zaN3IrXRIaHccXJE/nTvzfQ1t7hkX22tXdw9ytrWbJxL3dffLzHEluAWfnD2LrvAFV1zf1vLEOm5g3+Kz8rCWOcBSuh6J0Ne1m2ZT8/OWPa57oZiviT97fs566X17gdht9SciteNbtgGJnJsfzLA+0CGw+1ceM/PqS6rpk7LzqO9ETPzoWLjgxndsFwlmzUwjJv2qbmDX7LGMP8SaG5sKyksp4/vLaOG845msRYlQQU/1aQlczWfQfdDsNvKbkVrzLG8L2TJ/H3d7ce0WKtyoPN/Pix9xmeHMuvzp1JrJcSI2fFuJJbb9J8W/82ryiHxRv20hFCK7GbW9u55RlnOobrtUNFBmB4SizNre3U1GtRWU+U3IrX5Q1L5MRJ2fx18dBKg+3Yf5AfPfIe84tyuObUyV4tyzNjbAZl1Q3sr23sf2MZkg2dlRLEP+UNSyQhOpL1JTVuh+IT1lrufXUt47KSNN9eAoYxhgtOLKCtwzNT/oKNklvxie/MK+SdDXsHXWboo+0V/Oxvy7nsCxM594RxXl/kEhEexgkTslismrdes7GshgkaufVr84pCp6HD66tKKN57gGtOnRyyi+gkMJ01ewyZSf23mA9FSm7FJ5LiovjW3Hz+9OaGAReefmNVCXe8sJpffuNo5k/O8XKEnwqlP+y+1tW8IUfNG/zavKIc3t24l/YgHxXatu8Aj7y9mRvOOVpzwCXgfLStgtuf/9jtMPySklvxma8ePZrKg80sLy7vcztrLY8t2syTS7Zyx3eO5ahRaT6K0HHUqHSq6w+pbaSH7a1p5Ed/WcqBxhaueOAd9tZo6oe/yk2LZ1hyLKt2VrkditfUN7dy8zMruerLRYxSFy0JQFkpcWwIkelDg+W15NYYk2aMudMYs9UY02yMqTDGLDTGzPXWMcW/RXSWBnvwzY209lIarLW9gzteXM1H2yq5++LjXWndGB5mOHFStqYmeNiNT62gsrPMWklVPTc+tcLliKQv84tygvYKhrWW3760mpnjMn16VUjEk7LT4qhrblVnzR54Jbk1xowGPgIuAp4BrgJuA3YCqvAcwmblDyM3LY4XP9j5uZ/VN7dyw98/oKmljd9851hS4qN9H2CneUU5LFpXpt7dHlRa9elIuLVQWtXgYjTSnxMnZbN0835a2trdDsXjnl22g8q6Zq44eaLboYgMWZgxLJicQ62S28/x1iSjv3Xue4q1VsNf8hmXnzyJHz+6lC9Oyf0kgd1f28gvn1rB9DEZXHHyJMLD3F3YMTE3hZa2DnaU1zF2eJKrsQSDDmuJjAjjUKszYm8MjEiPdzkq6UtmUiyjMxNZub2SYwuHux2Ox6zbXc3T72/jnktOICpC7UslsF1z6lFuh+CXPD5ya4w5EZgD/MZau9cYE2mM0eoR+cSojAROOiqXxxY5pcGK9x7gvx99n1Omj+LKLxe5ntiCU2ZlXlFoFrP3hhc/2MnI9ARGZSQQZgwj0xO46bxZbocl/ZhflM3CdcHzGqipP8Svn/uYH58+leEp+rMkgW/znloeW7TZ7TD8jjdGbk/tvN1tjHkZOAUIN8YUAzdZa//mhWNKgDl5ygh++OclvLZyN8bA1V+ZzFdnjnY7rM+YX5TNTU9/xMULxqtE0BHYXVHHk0u2cvfFx5OTptHaQDJ3YjZ/eXszzS1tAV9NoL3DcvvzH3Py1BEcUzDM7XBEPCI2MpyF6/Zw0fzxbofiV7wx57brGX4ISMOZd3sp0AI8boy52AvHlADz/15YhbVgceZfvrhip9shfc7Y4UlEhIexeU+t26EErLb2Dn7z4mouml+oxDYApcRHMyE3pd8KJ4Hgb51NZL49r9DlSEQ8Jzc9gdqGQ9Q3t7odil/xRnKb2HlbByyw1j5hrf0LMBeoBW4zxnzuuMaYK4wxH3ohHvFDpVUNdC3Vsvjn4iJjjNrxHqEnl2wlOS5KnZ8CWDBUTVixtZx/ry7lujOn+8W0JxFPCQ8zFOak+OXfUDd5I7lt6rx90lr7yRI+a20N8BKQxaejuxz28wettTO9EI/4oRHp8XRd6ffnxUXzJmXzzoY9tHeoasJgbd5Ty78+2sV/nz5F0zoC2AkTsvh4ZxUNAToyVH6gid++tIbrzppOaoJ7FVhEvGHhujJqGw5x7SPvccUDi1m4rsztkPyCNyZRlXbe7uvhZ11DYOq9GeJuOm8WNz61gtKqBkakx/vt4qJRmYkkx0WzvqSaKaPT3Q4nYDS3tvObF1Zx1ZeLSE+McTscOQIJMZFMGZ3O0s37OXnqCLfDGbC9NY388qkPKKlsICU+igydhxJkFq4r49GFm/n2vELa2i1ZqbHc9fIaABZMDu2qq94Yuf2g87and8Gu+wJ/ApcckezUOB66ch6v3XAqD105j2w/bsc6vyhbVRMG6ZG3N5Gflcy8IhXIDwbzi7JZvCGwXgM3PrWCkkrnUu2BxhY1DZGg8+SSrVx7+hTGDU/i6fe3MS0vg2tPn8KTS7a6HZrrvJHcvoAz3/ZCY8wn7aWMMdnA14Fia62eeQkY84pyWLJxH229dFWTz/p4RyVLNu3j6lOK3A5FPOTYwuGsL6nhQAAViy9R0xAJciWV9UwemcaozAQONrZQfqCJySPT1DoeLyS3nXNrf4LTiWyZMea/jTHXAcuAKOAHnj6miDdlpcSRkxrHxzsq3Q7F79U3t/K7l9fw31+dQlJslNvhiIfERkUwc1wmSzYGxuLK3ZX1GKBrprc/z+sXGaqRGQmsK6kmPCyMEyZksWTjXtaVVLvStt7feKX9rrX2QeBsoB64GfgFsBmnesK/vXFMEW+aV5TDYlVN6Nd9r69ndsEwjh6X6XYo4mELAqSpSXNLG7c88xHfmT+ekWoaIkHs/Dn53PXyGlbtrOSi+ePJG5bIXS+v4fw5+W6H5jqvVeW21j4HPOet/Yv40omTsvnbO1toaZuslp29eHfjXjaW1XD/5XPdDkW8YGZ+Jr99eTVVdc1+u0jQWss9r66jIDuZ804Ypz/yEtS6Fo3d9/p6SirrSU+M4ezjxob8YjLw0sitSLBJT4xh7PAkPtxa4XYofqm6vpk/vrae/zljWsB3spKeRUWEc1xhFu9s8N8rGK99XMK2fQf54SmTVX5OQsKCybk8+P15vHbDaRw/PovG5ja3Q/ILSm5FBmh+gFyW9TVrLXf/ay1fmT6SiSNU5S+YzSvK9tuGDsV7D/Dows3ccM4MfcCSkDSvs7KPtarLruRWZIDmTMzmw20VNLfok/Hh3lhVQuXBZi44scDtUMTLpo/JYE9NI/tqGt0O5TPqmlq59dmVXPWVIi2mkZA1cUQqza3t7KpQtQQltyIDlBwXxaSRqSzbojLNXfbVNPKXtzfzP1+fRmS43k6CXUS4syrbn2reWmu586XVHJM/jPmqqywhLMwY7rnkBEZn6gOe/hqJDMK8STks9NPLsr7W3mG546XVfOP4seQNS3Q7HPERZ3qO/8y7feb97dQ2HOLykye6HYqI6xJjI3npw10hPzVBya3IIBw/fjhrdlVR19Tqdiiue275dgxw1uyxbociPjR5VBoHGg+xu6LO7VBYu6uKZ5ft4Bdnz9CVAxEgPMzw0gc72bL3gNuhuErvBiKDEB8TyfS8dJZu3ud2KK7aWV7H00u385OvTSU8TKvSQ0l4mOHESTksdrlqQk39IW5/fhU//toUhiXHuhqLiL8wxjBPi5+V3IoMltPQIXTfOFrbO/jNC6u45KTxZKXGuR2OuGC+y6uy2zssv37+Y740bQSz8oe5EoOIv5pXlM07G/aG9NQEJbcigzS7YBibymqpbTjkdiiu+NviLWQmxfDlaSPdDkVcMj4nhdb2DrbvP+jK8R9fvAVj4MITC105vog/G52ZyD2XnBDStZ6V3IoMUkxUBLPyh/HuxtCbmrChtIY3VpXyo69OCek3zlBnjGH+pBwWrvP9FYzlxft5c00pPz9zuqbEiPSiw1re3eg/Cz99TcmtyBAsmBx6UxOaW9q448VVXH1KEakJ0W6HIy6bV+TMu/Xlpc/9tY387uU1/PzM6aTE6xwU6U17u+XeV9fR3tHhdiiuUHIrMgQzxmaws6KOioNNbofiMw/9ZyMTc1OZOzHb7VDED4wdnkh0RBgby2p9cryWtnZueXYl3zhuHJNHpfnkmCKBKis1juzUOFbtqHI7FFcouRUZgqiIcI4fP5x3XF4x7isfbqvgg60VXPWVIrdDET9hjGH+5FyfXcF48M2NZCTGcPaxY3xyPJFA51xdCa0rjF2U3IoMUaiUWznY1MJdL6/hv0+fQkJMpNvhiB+ZN8lZld3e4d2pCYvW7eHDbRX8+GtTNddbZIC+PG0E3zt5ktthuELJrcgQTctLp/xAE3uqG9wOxav++Np65kzMYvqYDLdDET8zMiOBtIRo1u723qXP3RV13PfGen55zgx9uBIZhPjoSHZX1rNlT63bofickluRIQoPC2PuxGzXi9l706J1e9i27wCXnDTB7VDETzl1n73zGmhuaePmZ1ZyyUnjGZeV7JVjiASzbfsP8uyyHW6H4XNKbkWOQDA3dKiqa+b+f6/np1+fRnRkuNvhiJ86cVI2SzbupbXds6uyrbX8/pW1jM9NUU1lkSGaMyGLD7aW09zS5nYoPqXkVuQIFI1Mpa65lZ3ldW6H4lHWWn738hq+evRoxuekuB2O+LGslDhy0+P5eHulR/f76srd7Civ4wenTNY8W5EhSomPZuKIVJYVl7sdik8puRU5AmHGMG9SdtCN3r6ycjcHG1s4f06+26FIAJjv4cWVxXsP8NiiLdxwzgxidNVA5Ij8+PQpzJmQ5XYYPqXkVuQIzS/KYdGGPUHTx7usuoHHFm7mp2dMJSJcbxHSvxMnZbO8eD+HWtuPeF91Ta3c8sxH/OCUyYxIT/BAdCKhLT0xhnc37qWhudXtUHxGf7lEjlBBdjLWwtZ9B90O5Yi1d1jueHEV35pbwKjMRLfDkQCRlhBDflYyK7Ye2aXPDmu588VVHFs4nBMnqVmIiKe8s2Ev720OnZbxSm5FjpAxxuOXZd3y9NJtREeEc8YxeW6HIgHGqft8ZFUTnnl/OwcaW7jsixM9FJWIgGden4FEya2IB8zvrJrQEcBTE7btO8Bzy3fw469NJUwLeGSQ5kzI4qPtFTQeGtqq7DW7qnhu2Q6uP3sGkZoOI+JRxxYMY2NpDbUNh9wOxSf0DiLiAXnDEomLjmBjaY3boQxJS1s7v3lhNZd/cSLDkmPdDkcCUFJcFJNHprJsy/5BP7a6vpnbn/+Yn5wxVeefiBfEREVw/+VzSY6LcjsUn1ByK+Ih84tyWLguMKcm/HXRFnLT4vjilFy3Q5EANpSW1O0dHfz6uY/5yrRRzByX6aXIRCQlPipg/0YNlpJbEQ+ZV5TDuxv30t7h2WL23rZ2dzVvrS3jmtOOUj1ROSLHjR/O2t3VHGxqGfBjHlu0hfCwMC44scCLkYlIWJjhj6+vp/Jgs9uheJ2SWxEPyU2LZ1hSLKt3VrsdyoA1HmrjzhdXcc2pR5ESH+12OBLg4qMjmTEmg6WbBrYqe3nxft5aW8Z1Z04jPEwfrES8KSoinOPHD+edjcG/sEzJrYgHBVo73gff3MDUvHSOGz/c7VAkSMwf4KrsfbWN/O7lNVx/1nR9sBLxkflFOSwKgakJSm5FPOjESdm8t3kfre3+PzVh2Zb9rNxRyfe+NMntUCSIzCoYxpY9tdTU974qu6WtnVufWcm5x4+jaGSaD6MTCW3TxqRz/dnT3Q7D65TcinjQsORYRmUk8NG2CrdD6dOBxhZ+/8pafvK1qcRHR7odjgSRmMhwZhcM490+Ln0++OZGMpNjOXP2GB9GJiLhYWFgYdXOSrdD8SoltyIeNt+PpybsrWnk8vsX883fvklreweZSSq7JJ7XV9WEt9eW8dH2Cn58+hQtYBRxQUVdMw+8scHtMLzKK8mtMcb28lXvjeOJ+JO5E7NZXlxOc2u726F8zo1PraCk0nkZ1je3cuNTK1yOSILR0eMy2V1ZT/mBps/cv6uijgf+vYEbzj6a+BhdMRBxQ9HIVOqaWtlVUed2KF7jzZHbd4Fvd/u61IvHE/ELqQnRjM9N4YPicrdD+QxrLSVV9dhPvofSqgZXY5LgFBkexgnjs1i84dPR26aWNm55ZiWXfmEC47KSXIxOJLSFGcOJRdlB0TK+NxFe3Pd2a+3fvLh/Eb81v/Oy7ImTst0OBYDahkP89uU1RIaH0dregbVgDIxIj3c7NAlS8yfn8Oe3NvGN48ZhreWeV9YyITeFL08b6XZoIiHv3OPHERHEba69+psZY6KMMQnePIaIPzp+fBYf76ik4VCr26GwcnslVz30LnmZidx3+VxGpicQZgwj0xO46bxZbocnQWrK6HQqDzZTVtXAKyt3s6O8jqtPmex2WCICpMRHs7O8jj3VwXn1zpsjt+cAFwLhxpgK4B/ADdbaA148pohfSIyNZMqoNN7fvJ8vThnhSgxt7R08tmgLb60t5Sdfm8aMsRkAPHTlPFfikdBSfqCJ1vYOLrlvEWEGbvvWbGIiw90OS0Q6rdhaTnuH5bIvTnQ7FI/z1sjtB8CvcBLci4C3gR8A72okV0KFmw0d9lQ38N+Pvs/O8oPcd/ncTxJbEV+58akVNDQ7Vy6shfveWO9yRCJyuPlFOSzesJcOa/vfOMB4Jbm11s621t5prX3BWvtXa+15wC+Ao4D/6ukxxpgrjDEfeiMeETccN34460pqONjY4tPjvr22jB89spQFk3O46bxZ6v4kriitavh08SJavCjib/KGJRITGc7G0hq3Q/E4X84mvgNoAU7r6YfW2gettTN9GI+IV8VGRXD02EyWbNrnk+M1HmrjzhdX88S7xdz2rWM4c/YY1REV14xIj6fr9NPiRRH/Y4zh/86dSWFOituheJzPkltrbSuwB9D1UQkZCyb3Xszek4r3HuAHDy8hLAz+eNkc8rOTvX5Mkb7cdN4sLV4U8XPZqXG8t3Ef7R3+3zJ+MLy5oOwzjDExwAhgma+OKeK2WfmZ/O7l1VTVNZOeGOPx/XdYy3PLdvDPpdu46itFzC/K8fgxRIYiOzVOixdF/JwxhmeWbSc5PorpY4Jn7NHjI7fGmPRefnQzTjL9sqePKeKvoiLCObZwOO9u3OvxfdfUH+KGJ1ewZONe7rnkBCW2IiIyaPOCsKGDN6Yl3GCMed8Yc5sx5vvGmJ8YY94GfgIsB+71wjFF/FZXQwdP+nBbBVc99C6F2cncedFxZKXGeXT/IiISGuZNyuG9TftobQ+eqQnemJawCJiEUwIsHWgHinGqJfzOWtvshWOK+K3pYzL4zQur2FfbSFbKkSWhre0dPLpwM4vW7eFnZ05jWl7wXEYSERHfG5Ycy53fOY6IsOBZgOzx5NZa+yLwoqf3KxKoIsLDmDMxm3c27OWbx48b8n7Kqhr49fMfk54QzX1XzCU5LsqDUYqISKjKSIrhw20VzMof5nYoHhG8jYVF/Mi8omwWrRv61IT/rCnl2keXcvLUEfzq3JlKbEVExGNa2zq4/fmPOdTa7nYoHuGzagkioeyoUenUNBxid2U9ozIG3qSv4VArf3xtPVv21PLrC2YzLivJi1GKiEgoSk2IpiA7hQ+2ljN3Yrbb4RwxjdyK+EB4mOHESdm8M4iFZZv31HL1Q0uIjgznD5fPVWIrIiJeM78o27WW8Z6mkVsRH5lflMNvX1rNBScW9Nk5rMNannl/O8+8v50fnjKZuZMC/1O0iIj4txMn5TA1SBYpK7kV8ZEJuSm0tHWwfX9dr6OwVXXN3PHialra2rn30hMYfoTVFURERAYiLjqCmvpDbN17IOC7XGpagoiPGGOYV5TT62WfD4rL+cHDS5g0IpU7vnOsElsREfGpzXtqeWzxFrfDOGJKbkV8aH5RNos27MFa+8l9LW3t/OnfG7jn1bX8/KzpfGd+IeFhemmKiIhvHVs4nHW7qznY1OJ2KEdEf0FFfGjs8CQiw8PYVFYLQEllPdc+spR9tY3cd/lcpozurXu1iIiId8VFR3D02Eze27TP7VCOiObciviQMYaZYzP55ZMrqD/UigEuOLGQC+bm97nITERExBcu+8IE4mICOz3UyK2Ijy3fWk5dcyvWggUWr9+jxFZERPxCVmocu8rrqG045HYoQ6bkVsTH9tc2ffJva6G0qsHFaERERD7rtY9LWLxhr9thDJmSWxEfG5EeT9dArTHO9yIiIv5ifh+VfQKBklsRH7vpvFmMTE8gzBhGpidw03mz3A5JRETkE9PHZlBSWU/5gab+N/ZDgT1jWCQAZafG8dCV89wOQ0REpEeR4WHcfuGxpMRHuR3KkGjkVkREREQ+Y3RmAh/vqHQ7jCFRcisiIiIin2GM4e5/raW0qt7tUAZNya2IiIiIfEZ4mOHESdksWh94VROU3IqIiIjI58wrymHRurLPtIwPBEpuRURERORzJuam8JMzprodxqApuRURERGRzzHGkJMaz4bSGrdDGRQltyIiIiLSo/IDTfzmhVUBNTVBya2IiIiI9GhcVhIRYWFs3nPA7VAGTMmtiIiIiPTIGOMsLAugdrzqUCYiIiIivfrqzFE0tbS7HcaAaeRWRERERHqVlhBDR4dlT3WD26EMiJJbEREREenT0s37eGbZdrfDGBAltyIiIiLSp3mTcliycR9t7R1uh9IvJbciIiIi0qes1DiyU+P4eEel26H0SwvKRERERKRfPzrtKNKTYtwOo18auRURERGRfo0ZnsSu8jpa2vy7coKSWxEREREZkL8u3sKH2yrcDqNPSm5FREREZEDmF+WwaJ1/N3TwSXJrjIkzxuwwxlhjzB98cUwRERER8aw5E7NZsa2C5pY2t0Ppla9Gbm8CMnx0LBERERHxguS4KG4+bxbh4f578d/rkRljZgA/Av7X28cSEREREe8qzElmU1mt22H0yqvJrTEmHHgIeB14zpvHEhERERHva23r4ManVlDX1Op2KD3y9sjttcAE4AdePo6IiIiI+EB8TCTT89JZunmf26H0yGvJrTFmDPB/wE3W2p0D2P4KY8yH3opHRERERDxjXlEOi9f7Z9UEb47c3g/sAH43kI2ttQ9aa2d6MR4RERER8YDZhcO54MQCt8PokVfa7xpjLgS+BJxorfXPCRkiIiIiMiQxkeHsLK/jzhdXs6+2kZEZCZw/J58Fk3PdDs3zya0xJhpntPZVYJ8xJr/zR12/bXLnfZXW2lpPH19EREREvGvhujL+ungLqfHRvPzzU1hXUs1dL68BcD3B9ca0hFggEzgNKD7sa1Hnzy/s/P4yLxxbRERERLzsySVb+ekZUynMScYYw7S8DK49fQpPLtnqdmhemZbQAHyjh/szgftwyoL9GVjjhWOLiIiIiJeVVNYzLS+DmeOGfXLf5JFplFTWuxiVw+PJbecc22e632+Myev85zZr7ed+LiIiIiKBYWRGAutKqpmW92kD2nUl1YzMSHAxKof/9k4TEREREb90/px87np5Dat2VtLW3sGqnZXc9fIazp+T3/+Dvcwr1RJ60lnr1vjqeCIiIiLiHV2Lxu57fT0llfWMzEjguwvGu76YDHyY3IqIiIhI8FgwOdcvktnuNC1BRERERIKGklsRERERCRpKbkVEREQkaCi5FREREZGgoeRWRERERIKGklsRERERCRpKbkVEREQkaCi5FREREZGgoeRWRERERIKGklsRERERCRpKbkVEREQkaES4HUBvrrrqKrdDEBERERH/ZO+77z7T0w80cisiIiIiQcNYa92OwW8YYz601s50O45Ao+dtaPS8DZ6es6HR8zY0et6GRs/b0Oh58xyN3IqIiIhI0FByKyIiIiJBQ8ntZz3odgABSs/b0Oh5Gzw9Z0Oj521o9LwNjZ63odHz5iGacysiIiIiQUMjtyIiIiISNJTcioiIiEjQCOrk1hgTZoy51hizyRjTbIwpMcb81hgTP4h9nGqMWWqMaTDGVBtjnjbGjPFm3G4yxhQaY24yxiwzxlQYY+qMMauMMb8Y6PNmjFlkjLG9fAVtmZM+fuf6Qewj1M63X/XxvFljTOsA9hG055sx5ued58D2zt9nZz/bjzfGvGCMqek8h941xpw0yGMmG2PuNcaUdb5vrjfGXGmM6bFYuj8a6PNmHBcaY54yxmw1xjQaY3YbY14yxswexPHm93EO/stjv5iXDeZ86+e1+5NBHDNkzrfObft6v7PGmF8M4HhBcb55k992KPOQu4BrgOeB3wITO7+fboz5orW2o68HG2POAp4BVgM/BZKBHwHvGWNmWmv3eDF2t1wCXA28BDwBtAILgFuAbxpjjrXWNg1gP5XAtT3cv91Tgfqpd/n8ooB+EzQI2fPtOWBrD/dPwXkOXh7gfoL1fLsNqAZWAil9bWiMGQcsBdqA3wAHgMuBN4wxp1hr/9PfwYwxUcCbwHTgXmAjcApwHzAc+NUQfw9fG+jzFg08DqwCngJ2ANnA94H3jTHfsdb+bRDHfRDnPeBwpYN4vNsGfL4d5lqc19/hPhrIA0PwfAP4di/3/woYx8Df8yDwzzfvsdYG5RdQBHQAz3a7/4eABb7Vz+MjgTJgF5Bw2P3TgHbgQbd/Ry89bzOB5B7uv6XzefvBAPaxCNjp9u/iwnNngUeH+NiQPN/6eD7+1Pl8njaAbYP2fAPGHvbvdX39nsA/O8+VaYfdl9B5Tm2mcwFxP8e7qvN5/2G3+58FWoDRbj8nnnzecAZ45vVw/3CchG0/EDaA483vfN6+6/bv7ovnrfPnv+r8nfOO4Hghdb718fgRna/dFQPcPijON29+BfO0hPMBA9zd7f6HgEbgwn4ePw/IAR621n5yWdlauwrnj+m5xphID8XqN6y1H1prD/Two3903k4e6L6MMy0kKZAuL3mCMSbKGJMwyIeF5PnWE2NMHHAeTrL/+iAeF3Tnm7V2QCPPxpky9DVgUec50/X4euBhoBCYNYBdfQvn/fGhbvffjfMB7NyBxOO2gT5v1to2a+3iHu7fDywGhnV+DZgxJt4YEzOYx/iLgT5v3XW+7oZyJTikzrc+XIwzTfThwT4wkM83bwrm5HYWzsjtB4ffaa1txrkE1d8bfdfP3+/hZ8uAJJw/GKFiROft/gFunwvU41warTfGPGeMmeCVyPzLOThv1nXGmPLOuWTJA3iczrdPfRPn933EWts+wMeE6vnWZQrOJfbezh/o5z3PGBMGzAA+7nyfPNwHOO+nA0mQg8UInNHD2kE85vc452GTMWaLMea/gunDVi/W4Lzumo2zXuCUgTxI55uj8/y4GOfvxpODfHgonm8DEsxzbnOASmvtoR5+VgYcb4yJsta29PH4rm17ejw4f1DXH1mY/s8YEw7ciDOX7+8DeMgO4D2cN712YDbwA+ALxpg51tq13orVZR8AT+PMIU0CTsX5vecZY44/fES2BzrfPnUpziW3vwxw+1A93w430POnL6lAbE/7sNYeMsZUDWAfQcEYcypwDPB4D4lXT1px1im8CuzB+f+4FGcEchpO8hJsanHmfC4FaoDxOGsEXjHGXGKtfbSfx+t8c5wEjMGZ0nZwgI8JxfNtUII5uY0DekpsAZoP26a35Dau87anfTR32ybY3Q0cC1xvrd3c38bW2u4vrGeMMS/hXF7/HXCypwP0B9ba7qur/2qMWQPcCvxX521vdL7hrPYH5gBvWWt3DOQxoXq+deOJ86evfXTtJxTOwQKcRWZlwI8H8hhr7XvAGd328xBO8vFdY8yfrbVLPB2rm6y1d3e/zxjzF5w5p3cZY57p5wO9zjfHZZ23fx7oA0LxfBusYJ6W0Ihzma4nMYdt09fj6WUfA3l8UDDG3IwzCvagtfbXQ92PtfZd4B1ggTEm1lPxBYA7cD5AndbPdjrfHJd23g567tnhQvB888T509c+uvYT1OegccruvYVz5eAUa23FUPdlnWo8Xe+Zp3ogPL9nra0CHsCpGHB8P5vrfDMmFTgT2HSkyWgonm99Cebkdg+QYYzp6YWTizNlobdR267Hd23b0+Oh50uAQcMY8yvgBuARnNI4R2onEI5zOSokWGtb6TwX+9lU55uzIOU7OCV1nvfALncSOuebJ86fGqCpp310vo+mD2AfAcsYkwcsxKkwcbKHprPs7Lzt7/UfTHZ23vb3O4f0+dbpQpzkfsCjtv3Y2XkbSudbj4I5uV2B8/sdc/idnasKpwEfDuDxAMf18LNjgYPAliML0X8ZY/4X+F/gr8BltrP+yBEqwJm3W+2BfQWEzvNtBP0vxAvp863T6TglmB7vZa78YIXS+bYW5/Jub+cP9POe1znysxKnDnj3QYFjcN5P+3vfDEjGmNE4iW0yTmL7sYd2XdB5O9CFuMFgQL9zKJ9vh7kUZ/7sXz20v1A833oUzMntP3AuLf2o2/2X48zjeaLrDmNMtjFmQmcJoi6Lgb3AZYeXdTLGTMWpMfd056hc0DHG3IhTw/Bx4GLbS7OLnp4343SbCe9h29OAE4A3B7hAI6AYY9J7+dHNOHPbXz5sW51vPeuaktDjKIbOt951zm18GZjfec4A0HkuXQYUc1jlGGNMZOdzOarbrp7EeX+8otv9P8L5oPBPz0fvrs7EdhHOCP+XrLW9NiDo7Xnr6fXfmbD9qvPbwRTm93vGmIieqsAYY0YCVwJVOAvNuu7X+daNcbonTgVettaW97KNzrchMp4ZkPNPxph7ceaLPo8z0bqrQ9l7wEldSZsx5lHgImCBtXbRYY//Bk6SvBqnDl8STjcWCxxtrQ26SybGmKuBPwC7gV/ilGM53H5r7Zud2z5Kt+fNGPN1nEU8L+N0h2rD+RR+Ic4I2gnW2qAbgTTG3IUzQrYQ57lLwJn3tABYjvMcNXVu+yg63z7DGJOD87x91MPCvK5tHiXEzjdjzLeB0Z3f/hCIwum2CLDLWvv4Ydvm4ySwrTjdGQ/ifJg/CqcZxhuHbZuHU2VisbV2/mH3R+EkJVOBe3A6Rp2KMy/wFmvtLz3+S3rBQJ83Y0wizuttDE6HrA/4vDc769729bytwJka8hGfrl6/EGck7V5r7TUe/PW8ZhDPWwrO8/ACzjnSVS3hMpz3vvOttU8ftt88dL51f8z9ONP9TrXWvtbLfvMI4vPNq9zsIOHtL5z5dj/G6c5zCGf+zu84rANU53aP4iQQ83vYx1dx6kQ24ryAnwHGuf27efE563ouevta1NfzhvMB4mlgG079vUOd//4jkOv27+fF5+0M4I3Oc6wZaMCpp3w9EKPzrd/n7/rO5+TyAZybIXO+4Ywo9vta7PZ8vIhTpqkRWAJ8sYft8vrYRwrOB9w9nc/nBpxBgn47nPnL10Cft8Oeh76+5vew/aJux/sZTo3hCpwPF7U4H3TPd/u58NLzFo2z6HNt5/tUK86Vp2eAY3S+9fs6je08R0roowNesJ9v3vwK6pFbEREREQktwTznVkRERERCjJJbEREREQkaSm5FREREJGgouRURERGRoKHkVkRERESChpJbEREREQkaSm5FREREJGgouRURERGRoKHkVkRERESChpJbEREREQka/x+19UIK2TJAfwAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 3024x2304 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
" \n",
"sequence_true, pred = get_prediction(dataset_test, loaded_model,iterations=4)\n",
@@ -380,19 +277,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Saturday 19 December 2020, 11:37:57\n",
- "Duration is : 00:00:03 329ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"pwk.end()"
]
@@ -435,7 +322,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.7"
+ "version": "3.7.9"
}
},
"nbformat": 4,
diff --git a/VAE/01-VAE-with-MNIST.ipynb b/VAE/01-VAE-with-MNIST.ipynb
index 88d5a0f5217b09dfa7b2912e0979b52f6ae1cfa3..294b1d8cfb609e3239482da70320c63c77f6e14b 100644
--- a/VAE/01-VAE-with-MNIST.ipynb
+++ b/VAE/01-VAE-with-MNIST.ipynb
@@ -6,8 +6,8 @@
"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
- "# <!-- TITLE --> [VAE1] - Variational AutoEncoder (VAE) with MNIST\n",
- "<!-- DESC --> Building a simple model with the MNIST dataset\n",
+ "# <!-- TITLE --> [VAE1] - First VAE, with a small dataset (MNIST)\n",
+ "<!-- DESC --> Construction and training of a VAE with a latent space of small dimension.\n",
"\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
diff --git a/VAE/02-VAE-with-MNIST-post.ipynb b/VAE/02-VAE-with-MNIST-post.ipynb
index 6a0a89a39e1ea038f37f154b812644acf162f352..011d0410d21840280c3051a478f00a8813ea4b01 100644
--- a/VAE/02-VAE-with-MNIST-post.ipynb
+++ b/VAE/02-VAE-with-MNIST-post.ipynb
@@ -6,8 +6,8 @@
"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
- "# <!-- TITLE --> [VAE2] - Variational AutoEncoder (VAE) with MNIST - Analysis\n",
- "<!-- DESC --> Visualization and analysis of latent space\n",
+ "# <!-- TITLE --> [VAE2] - Analysis of the associated latent space\n",
+ "<!-- DESC --> Visualization and analysis of the VAE's latent space\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
diff --git a/VAE/05-About-CelebA.ipynb b/VAE/05-About-CelebA.ipynb
index 78cd59577e6b62b05ac328116801d9ebd75060b3..298188ce635ab83278350032c9003a4edb11473a 100644
--- a/VAE/05-About-CelebA.ipynb
+++ b/VAE/05-About-CelebA.ipynb
@@ -6,8 +6,8 @@
"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
- "# <!-- TITLE --> [VAE3] - About the CelebA dataset\n",
- "<!-- DESC --> Presentation of the CelebA dataset and problems related to its size\n",
+ "# <!-- TITLE --> [VAE5] - Another game play : About the CelebA dataset\n",
+ "<!-- DESC --> Episode 1 : Presentation of the CelebA dataset and problems related to its size\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
diff --git a/VAE/06-Prepare-CelebA-datasets.ipynb b/VAE/06-Prepare-CelebA-datasets.ipynb
index 4fbd5797f5b410b4a658afa061227715e4792041..36520fe91e60638d1c8b4785c4712e1519ee1335 100644
--- a/VAE/06-Prepare-CelebA-datasets.ipynb
+++ b/VAE/06-Prepare-CelebA-datasets.ipynb
@@ -6,8 +6,8 @@
"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
- "# <!-- TITLE --> [VAE6] - Preparation of the CelebA dataset\n",
- "<!-- DESC --> Preparation of a clustered dataset, batchable\n",
+ "# <!-- TITLE --> [VAE6] - Generation of a clustered dataset\n",
+ "<!-- DESC --> Episode 2 : Analysis of the CelebA dataset and creation of an clustered and usable dataset\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
diff --git a/VAE/07-Check-CelebA.ipynb b/VAE/07-Check-CelebA.ipynb
index aae386cef50aa10926cab014687a2a6153eb2d8d..bb909950d21b69bd2804ff064029bc1c1e3919df 100644
--- a/VAE/07-Check-CelebA.ipynb
+++ b/VAE/07-Check-CelebA.ipynb
@@ -6,8 +6,8 @@
"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
- "# <!-- TITLE --> [VAE7] - Checking the clustered CelebA dataset\n",
- "<!-- DESC --> Check the clustered dataset\n",
+ "# <!-- TITLE --> [VAE7] - Checking the clustered dataset\n",
+ "<!-- DESC --> Episode : 3 Clustered dataset verification and testing of our datagenerator\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
diff --git a/VAE/08-VAE-with-CelebA.ipynb b/VAE/08-VAE-with-CelebA.ipynb
index 260efa9964cc1677f5c295651a7883536fec2bea..27e1684fcdabe9d3af2a8b325ba8760a90e28f8e 100644
--- a/VAE/08-VAE-with-CelebA.ipynb
+++ b/VAE/08-VAE-with-CelebA.ipynb
@@ -6,8 +6,8 @@
"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
- "# <!-- TITLE --> [VAE8] - Variational AutoEncoder (VAE) with CelebA\n",
- "<!-- DESC --> Building a VAE and train it, using a data generator\n",
+ "# <!-- TITLE --> [VAE8] - Training session for our VAE\n",
+ "<!-- DESC --> Episode 4 : Training with our clustered datasets in notebook or batch mode\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
diff --git a/VAE/09-VAE-withCelebA-post.ipynb b/VAE/09-VAE-withCelebA-post.ipynb
index 32bbfa8793b20fcead5717c86ec88a1f6f404482..b96248f1cadd6abf07bb6f948335c1fc6bf01934 100644
--- a/VAE/09-VAE-withCelebA-post.ipynb
+++ b/VAE/09-VAE-withCelebA-post.ipynb
@@ -6,8 +6,8 @@
"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
- "# <!-- TITLE --> [VAE9] - Variational AutoEncoder (VAE) with CelebA - Analysis\n",
- "<!-- DESC --> Exploring latent space of our trained models\n",
+ "# <!-- TITLE --> [VAE9] - Data generation from latent space\n",
+ "<!-- DESC --> Episode 5 : Exploring latent space to generate new data\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
diff --git a/VAE/batch_slurm.sh b/VAE/batch_slurm.sh
index ad89ff96184fb55e0e06dd202b403baf968740d7..bd27856fc401703539adf6e6aef03227a37240b6 100755
--- a/VAE/batch_slurm.sh
+++ b/VAE/batch_slurm.sh
@@ -9,7 +9,7 @@
# -----------------------------------------------
#
# <!-- TITLE --> [VAE10] - SLURM batch script
-# <!-- DESC --> Bash script for SLURM batch submission of VAE notebooks
+# <!-- DESC --> Bash script for SLURM batch submission of VAE8 notebooks
# <!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->
#
# Soumission : sbatch /(...)/fidle/VAE/batch_slurm.sh
diff --git a/fidle/01 - Set and reset.ipynb b/fidle/01 - Set and reset.ipynb
index 7fbc4dfcd52f7edf8f4289e65afc357d29958a48..b5065b10011a140e508522e9e078c6681263d453 100644
--- a/fidle/01 - Set and reset.ipynb
+++ b/fidle/01 - Set and reset.ipynb
@@ -33,6 +33,8 @@
"from IPython.display import display,Image,Markdown,HTML\n",
"import re\n",
"import sys, os, glob\n",
+ "import datetime, time\n",
+ "\n",
"import json\n",
"from collections import OrderedDict\n",
"from importlib import reload\n",
@@ -87,7 +89,7 @@
"output_type": "stream",
"text": [
"Catalog saved as ../fidle/log/catalog.json\n",
- "Entries : 36\n"
+ "Entries : 37\n"
]
}
],
@@ -159,6 +161,8 @@
"An example of classification using a dense neural network for the famous MNIST dataset\n",
"\n",
"### Images classification with Convolutional Neural Networks (CNN)\n",
+ "- **[??](GTSRB/00-Test.ipynb)** - [??](GTSRB/00-Test.ipynb) \n",
+ "??\n",
"- **[GTSRB1](GTSRB/01-Preparation-of-data.ipynb)** - [Dataset analysis and preparation](GTSRB/01-Preparation-of-data.ipynb) \n",
"Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset\n",
"- **[GTSRB2](GTSRB/02-First-convolutions.ipynb)** - [First convolutions](GTSRB/02-First-convolutions.ipynb) \n",
@@ -187,36 +191,36 @@
"Still the same problem, but with a network combining embedding and LSTM\n",
"\n",
"### Time series with Recurrent Neural Network (RNN)\n",
- "- **[SYNOP1](SYNOP/01-Preparation-of-data.ipynb)** - [Time series with RNN - Preparation of data](SYNOP/01-Preparation-of-data.ipynb) \n",
- "Episode 1 : Data analysis and creation of a usable dataset\n",
- "- **[SYNOP2](SYNOP/02-First-predictions.ipynb)** - [Time series with RNN - Try a prediction](SYNOP/02-First-predictions.ipynb) \n",
- "Episode 2 : Training session and first predictions\n",
- "- **[SYNOP3](SYNOP/03-12h-predictions.ipynb)** - [Time series with RNN - 12h predictions](SYNOP/03-12h-predictions.ipynb) \n",
- "Episode 3: Attempt to predict in the longer term \n",
+ "- **[SYNOP1](SYNOP/01-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/01-Preparation-of-data.ipynb) \n",
+ "Episode 1 : Data analysis and preparation of a meteorological dataset (SYNOP)\n",
+ "- **[SYNOP2](SYNOP/02-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/02-First-predictions.ipynb) \n",
+ "Episode 2 : Learning session and weather prediction attempt at 3h\n",
+ "- **[SYNOP3](SYNOP/03-12h-predictions.ipynb)** - [12h predictions](SYNOP/03-12h-predictions.ipynb) \n",
+ "Episode 3: Attempt to predict in a more longer term \n",
"\n",
"### Unsupervised learning with an autoencoder neural network (AE)\n",
- "- **[AE1](AE/01-AE-with-MNIST.ipynb)** - [AutoEncoder (AE) with MNIST](AE/01-AE-with-MNIST.ipynb) \n",
- "Episode 1 : Model construction and Training\n",
- "- **[AE2](AE/02-AE-with-MNIST-post.ipynb)** - [AutoEncoder (AE) with MNIST - Analysis](AE/02-AE-with-MNIST-post.ipynb) \n",
- "Episode 2 : Exploring our denoiser\n",
+ "- **[AE1](AE/01-AE-with-MNIST.ipynb)** - [Building and training an AE denoiser model](AE/01-AE-with-MNIST.ipynb) \n",
+ "Episode 1 : After construction, the model is trained with noisy data from the MNIST dataset.\n",
+ "- **[AE2](AE/02-AE-with-MNIST-post.ipynb)** - [Exploring our denoiser model](AE/02-AE-with-MNIST-post.ipynb) \n",
+ "Episode 2 : Using the previously trained autoencoder to denoise data\n",
"\n",
"### Generative network with Variational Autoencoder (VAE)\n",
- "- **[VAE1](VAE/01-VAE-with-MNIST.ipynb)** - [Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb) \n",
- "Building a simple model with the MNIST dataset\n",
- "- **[VAE2](VAE/02-VAE-with-MNIST-post.ipynb)** - [Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb) \n",
- "Visualization and analysis of latent space\n",
- "- **[VAE3](VAE/05-About-CelebA.ipynb)** - [About the CelebA dataset](VAE/05-About-CelebA.ipynb) \n",
- "Presentation of the CelebA dataset and problems related to its size\n",
- "- **[VAE6](VAE/06-Prepare-CelebA-datasets.ipynb)** - [Preparation of the CelebA dataset](VAE/06-Prepare-CelebA-datasets.ipynb) \n",
- "Preparation of a clustered dataset, batchable\n",
- "- **[VAE7](VAE/07-Check-CelebA.ipynb)** - [Checking the clustered CelebA dataset](VAE/07-Check-CelebA.ipynb) \n",
- "Check the clustered dataset\n",
- "- **[VAE8](VAE/08-VAE-with-CelebA==1090048==.ipynb)** - [Variational AutoEncoder (VAE) with CelebA (small)](VAE/08-VAE-with-CelebA==1090048==.ipynb) \n",
- "Variational AutoEncoder (VAE) with CelebA (small res. 128x128)\n",
- "- **[VAE9](VAE/09-VAE-withCelebA-post.ipynb)** - [Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/09-VAE-withCelebA-post.ipynb) \n",
- "Exploring latent space of our trained models\n",
+ "- **[VAE1](VAE/01-VAE-with-MNIST.ipynb)** - [First VAE, with a small dataset (MNIST)](VAE/01-VAE-with-MNIST.ipynb) \n",
+ "Construction and training of a VAE with a latent space of small dimension.\n",
+ "- **[VAE2](VAE/02-VAE-with-MNIST-post.ipynb)** - [Analysis of the associated latent space](VAE/02-VAE-with-MNIST-post.ipynb) \n",
+ "Visualization and analysis of the VAE's latent space\n",
+ "- **[VAE5](VAE/05-About-CelebA.ipynb)** - [Another game play : About the CelebA dataset](VAE/05-About-CelebA.ipynb) \n",
+ "Episode 1 : Presentation of the CelebA dataset and problems related to its size\n",
+ "- **[VAE6](VAE/06-Prepare-CelebA-datasets.ipynb)** - [Generation of a clustered dataset](VAE/06-Prepare-CelebA-datasets.ipynb) \n",
+ "Episode 2 : Analysis of the CelebA dataset and creation of an clustered and usable dataset\n",
+ "- **[VAE7](VAE/07-Check-CelebA.ipynb)** - [Checking the clustered dataset](VAE/07-Check-CelebA.ipynb) \n",
+ "Episode : 3 Clustered dataset verification and testing of our datagenerator\n",
+ "- **[VAE8](VAE/08-VAE-with-CelebA.ipynb)** - [Training session for our VAE](VAE/08-VAE-with-CelebA.ipynb) \n",
+ "Episode 4 : Training with our clustered datasets in notebook or batch mode\n",
+ "- **[VAE9](VAE/09-VAE-withCelebA-post.ipynb)** - [Data generation from latent space](VAE/09-VAE-withCelebA-post.ipynb) \n",
+ "Episode 5 : Exploring latent space to generate new data\n",
"- **[VAE10](VAE/batch_slurm.sh)** - [SLURM batch script](VAE/batch_slurm.sh) \n",
- "Bash script for SLURM batch submission of VAE notebooks \n",
+ "Bash script for SLURM batch submission of VAE8 notebooks \n",
"\n",
"### Miscellaneous\n",
"- **[ACTF1](Misc/Activation-Functions.ipynb)** - [Activation functions](Misc/Activation-Functions.ipynb) \n",
@@ -255,7 +259,6 @@
" description = about['description']\n",
"\n",
" link=f'{dirname}/{basename}'.replace(' ','%20')\n",
- "# md = f'- [{id}]({link}) - {description}'\n",
" md = f'- **[{id}]({link})** - [{title}]({link}) \\n'\n",
" md += f'{description}'\n",
" html = f\"\"\"<div class=\"fid_line\">\n",
diff --git a/fidle/02 - Running test.ipynb b/fidle/02 - Running test.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0076fa2d43c32e7663e3f99b5ad0f02010d4dfa1
--- /dev/null
+++ b/fidle/02 - Running test.ipynb
@@ -0,0 +1,280 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "\n",
+ "## Continuous integration report\n",
+ "\n",
+ "Each notebook indicates its start and end of execution:\n",
+ " - at the beginning, with `pwk.init()`\n",
+ " - at the end, with `pwk.end()` \n",
+ " \n",
+ "All of theses informations are saved in a json file - Final report is below and in a HTML file. \n",
+ "See ./log"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys,os\n",
+ "import json\n",
+ "import datetime, time\n",
+ "import nbformat\n",
+ "from nbconvert.preprocessors import ExecutePreprocessor\n",
+ "import re\n",
+ "import yaml\n",
+ "from collections import OrderedDict\n",
+ "from IPython.display import display\n",
+ "sys.path.append('..')\n",
+ "import fidle.config as config\n",
+ "import fidle.cooker as cooker"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "./ci/smart.yml loaded.\n",
+ "Entries : 4\n",
+ "\n",
+ "Notebook : LINR1\n",
+ " Run notebook.....done.\n",
+ " Duration : 0:00:04\n",
+ " Saved as : 01-Linear-Regression==done==.ipynb\n",
+ "\n",
+ "Notebook : GTSRB0\n",
+ " set overrides :\n",
+ " FIDLE_OVERRIDE_GTSRB0_scale = 0.7\n",
+ " FIDLE_OVERRIDE_GTSRB0_output_dir = ./data/foobar\n",
+ " FIDLE_OVERRIDE_GTSRB0_run_dir = ./run/xxx\n",
+ " Run notebook.....done.\n",
+ " Duration : 0:00:02\n",
+ " Saved as : 00-Test==done==.ipynb\n",
+ "\n",
+ "Notebook : GTSRB1\n",
+ " set overrides :\n",
+ " FIDLE_OVERRIDE_GTSRB1_scale = 0.1\n",
+ " FIDLE_OVERRIDE_GTSRB1_output_dir = ./data\n",
+ " Run notebook.....done.\n",
+ " Duration : 0:02:00\n",
+ " Saved as : 01-Preparation-of-data==done==.ipynb\n",
+ "\n",
+ "Notebook : GTSRB2\n",
+ " set overrides :\n",
+ " FIDLE_OVERRIDE_GTSRB2_run_dir = ./run/GTSRB2_ci\n",
+ " FIDLE_OVERRIDE_GTSRB2_dataset_name = set-24x24-L\n",
+ " FIDLE_OVERRIDE_GTSRB2_batch_size = 32\n",
+ " FIDLE_OVERRIDE_GTSRB2_epochs = 3\n",
+ " FIDLE_OVERRIDE_GTSRB2_scale = 1\n",
+ " Run notebook.....done.\n",
+ " Duration : 0:03:02\n",
+ " Saved as : 02-First-convolutions==done==.ipynb\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "_start_time=None\n",
+ "\n",
+ "def get_profile(existant_config=None, output_tag='==done==', save_figs=True):\n",
+ " \n",
+ " catalog = cooker.read_catalog()\n",
+ "\n",
+ " config = {'version':'1.0', 'output_tag':output_tag, 'save_figs':save_figs}\n",
+ " profile = { 'config':config }\n",
+ " for id, about in catalog.items():\n",
+ " \n",
+ " id = about['id']\n",
+ " title = about['title']\n",
+ " dirname = about['dirname']\n",
+ " basename = about['basename']\n",
+ " overrides = about.get('overrides',None)\n",
+ " \n",
+ " notebook = {}\n",
+ " notebook['notebook_dir'] = dirname\n",
+ " notebook['notebook_src'] = basename\n",
+ " notebook['notebook_out'] = 'default'\n",
+ " if len(overrides)>0:\n",
+ " notebook['overrides']={ name:'default' for name in overrides }\n",
+ " \n",
+ " profile[id]=notebook\n",
+ " \n",
+ " return profile\n",
+ "\n",
+ "\n",
+ "def save_profile(profile, filename):\n",
+ " with open(filename,'wt') as fp:\n",
+ " yaml.dump(profile, fp, sort_keys=False)\n",
+ " print(f'Catalog saved as {filename}')\n",
+ " print('Entries : ',len(profile)-1)\n",
+ "\n",
+ "def load_profile(filename):\n",
+ " with open(filename,'r') as fp:\n",
+ " profile=yaml.load(fp, Loader=yaml.FullLoader)\n",
+ " print(f'{filename} loaded.')\n",
+ " print('Entries : ',len(profile)-1)\n",
+ " return profile\n",
+ " \n",
+ "def chrono_start():\n",
+ " global _start_time\n",
+ " _start_time = time.time()\n",
+ "\n",
+ "def chrono_hdelay():\n",
+ " global _start_time\n",
+ " delay=int( time.time() - _start_time )\n",
+ " return str(datetime.timedelta(seconds=delay))\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "def run_profile(profile, top_dir='..'):\n",
+ "\n",
+ " # ---- My place\n",
+ " #\n",
+ " home = os.getcwd()\n",
+ " \n",
+ " # ---- Read profile\n",
+ " #\n",
+ " config = profile['config']\n",
+ " notebooks = profile\n",
+ " del notebooks['config']\n",
+ " \n",
+ " # ---- Save figs or not ?\n",
+ " #\n",
+ " os.environ['FIDLE_SAVE_FIGS']=str(config['save_figs'])\n",
+ "\n",
+ " # ---- For each notebook\n",
+ " #\n",
+ " for id,about in notebooks.items():\n",
+ " \n",
+ " print(f'\\nNotebook : {id}')\n",
+ "\n",
+ " # ---- Get notebook infos ---------------------------------------------\n",
+ " #\n",
+ " notebook_dir = about['notebook_dir']\n",
+ " notebook_src = about['notebook_src']\n",
+ " notebook_out = about['notebook_out']\n",
+ " overrides = about.get('overrides',None)\n",
+ "\n",
+ " # ---- notebook_out (Default)\n",
+ " #\n",
+ " if notebook_out=='default':\n",
+ " notebook_out = notebook_src.replace('.ipynb', config['output_tag']+'.ipynb')\n",
+ " \n",
+ " # ---- Override ------------------------------------------------------- \n",
+ " \n",
+ " to_unset=[]\n",
+ " if isinstance(overrides,dict):\n",
+ " print(' set overrides :')\n",
+ " for name,value in overrides.items():\n",
+ " # ---- Default : no nothing\n",
+ " if value=='default' : continue\n",
+ " # ---- Set env\n",
+ " env_name = f'FIDLE_OVERRIDE_{id.upper()}_{name}'\n",
+ " env_value = str(value)\n",
+ " os.environ[env_name] = env_value\n",
+ " # ---- For cleaning\n",
+ " to_unset.append(env_name)\n",
+ " # ---- Fine :(-)\n",
+ " print(f' {env_name:38s} = {env_value}')\n",
+ " \n",
+ " # ---- Run it ! -------------------------------------------------------\n",
+ " \n",
+ " # ---- Go to the right place\n",
+ " #\n",
+ " os.chdir(f'{top_dir}/{notebook_dir}')\n",
+ " notebook = nbformat.read( f'{notebook_src}', nbformat.NO_CONVERT)\n",
+ "\n",
+ " print(' Run notebook...',end='')\n",
+ " ep = ExecutePreprocessor(timeout=600, kernel_name=\"python3\")\n",
+ " chrono_start()\n",
+ " ep.preprocess(notebook)\n",
+ " print('..done.')\n",
+ " print(' Duration : ',chrono_hdelay() )\n",
+ " \n",
+ " # ---- Save it\n",
+ " #\n",
+ " with open( f'{notebook_out}', mode=\"w\", encoding='utf-8') as fp:\n",
+ " nbformat.write(notebook, fp)\n",
+ " print(' Saved as : ',notebook_out)\n",
+ " \n",
+ " # ---- Back to home and clean\n",
+ " #\n",
+ " os.chdir(home)\n",
+ " for env_name in to_unset:\n",
+ " del os.environ[env_name] \n",
+ " \n",
+ " \n",
+ "# ---- Save profile\n",
+ " \n",
+ "# profile = get_profile()\n",
+ "# save_profile(profile, './ci/default.yml')\n",
+ "# profile=load_profile('./ci/default.yml')\n",
+ " \n",
+ " \n",
+ "# ---- Run profile \n",
+ " \n",
+ "profile = load_profile('./ci/smart.yml')\n",
+ "\n",
+ "run_profile(profile)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/fidle/02 - Finished report.ipynb b/fidle/03 - Finished report.ipynb
similarity index 99%
rename from fidle/02 - Finished report.ipynb
rename to fidle/03 - Finished report.ipynb
index 64617441579a6065afdfc0acb72af127365c5354..0745d5dc4f814b4a0538f754ff52b2d2d6dab5e1 100644
--- a/fidle/02 - Finished report.ipynb
+++ b/fidle/03 - Finished report.ipynb
@@ -20,11 +20,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "jupyter": {
- "source_hidden": true
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"import sys\n",
diff --git a/fidle/ci/default.yml b/fidle/ci/default.yml
new file mode 100644
index 0000000000000000000000000000000000000000..31d334b8c5bb4e5ea191f4d22c4a8ffc8bba872c
--- /dev/null
+++ b/fidle/ci/default.yml
@@ -0,0 +1,232 @@
+config:
+ version: '1.0'
+ output_tag: ==done==
+ save_figs: true
+LINR1:
+ notebook_dir: LinearReg
+ notebook_src: 01-Linear-Regression==ci==.ipynb
+ notebook_out: default
+GRAD1:
+ notebook_dir: LinearReg
+ notebook_src: 02-Gradient-descent.ipynb
+ notebook_out: default
+POLR1:
+ notebook_dir: LinearReg
+ notebook_src: 03-Polynomial-Regression.ipynb
+ notebook_out: default
+LOGR1:
+ notebook_dir: LinearReg
+ notebook_src: 04-Logistic-Regression.ipynb
+ notebook_out: default
+PER57:
+ notebook_dir: IRIS
+ notebook_src: 01-Simple-Perceptron.ipynb
+ notebook_out: default
+BHPD1:
+ notebook_dir: BHPD
+ notebook_src: 01-DNN-Regression.ipynb
+ notebook_out: default
+BHPD2:
+ notebook_dir: BHPD
+ notebook_src: 02-DNN-Regression-Premium.ipynb
+ notebook_out: default
+MNIST1:
+ notebook_dir: MNIST
+ notebook_src: 01-DNN-MNIST.ipynb
+ notebook_out: default
+??:
+ notebook_dir: GTSRB
+ notebook_src: 00-Test==done==.ipynb
+ notebook_out: default
+ overrides:
+ run_dir: default
+ scale: default
+ output_dir: default
+GTSRB1:
+ notebook_dir: GTSRB
+ notebook_src: 01-Preparation-of-data==ci==.ipynb
+ notebook_out: default
+ overrides:
+ scale: default
+ output_dir: default
+GTSRB2:
+ notebook_dir: GTSRB
+ notebook_src: 02-First-convolutions.ipynb
+ notebook_out: default
+ overrides:
+ run_dir: default
+ dataset_name: default
+ batch_size: default
+ epochs: default
+ scale: default
+GTSRB3:
+ notebook_dir: GTSRB
+ notebook_src: 03-Tracking-and-visualizing.ipynb
+ notebook_out: default
+ overrides:
+ run_dir: default
+ dataset_name: default
+ batch_size: default
+ epochs: default
+ scale: default
+GTSRB4:
+ notebook_dir: GTSRB
+ notebook_src: 04-Data-augmentation.ipynb
+ notebook_out: default
+ overrides:
+ run_dir: default
+ dataset_name: default
+ batch_size: default
+ epochs: default
+ scale: default
+GTSRB5:
+ notebook_dir: GTSRB
+ notebook_src: 05-Full-convolutions.ipynb
+ notebook_out: default
+ overrides:
+ run_dir: default
+ datasets: default
+ models: default
+ batch_size: default
+ epochs: default
+ scale: default
+ with_datagen: default
+ verbose: default
+GTSRB6:
+ notebook_dir: GTSRB
+ notebook_src: 06-Notebook-as-a-batch.ipynb
+ notebook_out: default
+GTSRB7:
+ notebook_dir: GTSRB
+ notebook_src: 07-Show-report.ipynb
+ notebook_out: default
+ overrides:
+ run_dir: default
+ report_dir: default
+GTSRB10:
+ notebook_dir: GTSRB
+ notebook_src: batch_oar.sh
+ notebook_out: default
+GTSRB11:
+ notebook_dir: GTSRB
+ notebook_src: batch_slurm.sh
+ notebook_out: default
+IMDB1:
+ notebook_dir: IMDB
+ notebook_src: 01-Embedding-Keras.ipynb
+ notebook_out: default
+IMDB2:
+ notebook_dir: IMDB
+ notebook_src: 02-Prediction.ipynb
+ notebook_out: default
+IMDB3:
+ notebook_dir: IMDB
+ notebook_src: 03-LSTM-Keras.ipynb
+ notebook_out: default
+SYNOP1:
+ notebook_dir: SYNOP
+ notebook_src: 01-Preparation-of-data.ipynb
+ notebook_out: default
+SYNOP2:
+ notebook_dir: SYNOP
+ notebook_src: 02-First-predictions.ipynb
+ notebook_out: default
+ overrides:
+ scale: default
+ train_prop: default
+ sequence_len: default
+ batch_size: default
+ epochs: default
+SYNOP3:
+ notebook_dir: SYNOP
+ notebook_src: 03-12h-predictions.ipynb
+ notebook_out: default
+ overrides:
+ iterations: default
+ scale: default
+ train_prop: default
+ sequence_len: default
+ batch_size: default
+ epochs: default
+AE1:
+ notebook_dir: AE
+ notebook_src: 01-AE-with-MNIST.ipynb
+ notebook_out: default
+AE2:
+ notebook_dir: AE
+ notebook_src: 02-AE-with-MNIST-post.ipynb
+ notebook_out: default
+VAE1:
+ notebook_dir: VAE
+ notebook_src: 01-VAE-with-MNIST.ipynb
+ notebook_out: default
+ overrides:
+ run_dir: default
+ scale: default
+ latent_dim: default
+ r_loss_factor: default
+ batch_size: default
+ epochs: default
+VAE2:
+ notebook_dir: VAE
+ notebook_src: 02-VAE-with-MNIST-post.ipynb
+ notebook_out: default
+ overrides:
+ run_dir: default
+VAE5:
+ notebook_dir: VAE
+ notebook_src: 05-About-CelebA.ipynb
+ notebook_out: default
+VAE6:
+ notebook_dir: VAE
+ notebook_src: 06-Prepare-CelebA-datasets.ipynb
+ notebook_out: default
+ overrides:
+ scale: default
+ image_size: default
+ output_dir: default
+VAE7:
+ notebook_dir: VAE
+ notebook_src: 07-Check-CelebA.ipynb
+ notebook_out: default
+ overrides:
+ image_size: default
+ enhanced_dir: default
+VAE8:
+ notebook_dir: VAE
+ notebook_src: 08-VAE-with-CelebA.ipynb
+ notebook_out: default
+ overrides:
+ run_dir: default
+ scale: default
+ image_size: default
+ enhanced_dir: default
+ latent_dim: default
+ r_loss_factor: default
+ batch_size: default
+ epochs: default
+VAE9:
+ notebook_dir: VAE
+ notebook_src: 09-VAE-withCelebA-post.ipynb
+ notebook_out: default
+ overrides:
+ run_dir: default
+ scale: default
+ image_size: default
+ enhanced_dir: default
+VAE10:
+ notebook_dir: VAE
+ notebook_src: batch_slurm.sh
+ notebook_out: default
+ACTF1:
+ notebook_dir: Misc
+ notebook_src: Activation-Functions.ipynb
+ notebook_out: default
+NP1:
+ notebook_dir: Misc
+ notebook_src: Numpy.ipynb
+ notebook_out: default
+TSB1:
+ notebook_dir: Misc
+ notebook_src: Using-Tensorboard.ipynb
+ notebook_out: default
diff --git a/fidle/ci/smart.yml b/fidle/ci/smart.yml
new file mode 100644
index 0000000000000000000000000000000000000000..57ff0f4adb2a7af93bf81d9dce05cef14fd5fe3c
--- /dev/null
+++ b/fidle/ci/smart.yml
@@ -0,0 +1,33 @@
+config:
+ version: '1.0'
+ output_tag: ==done==
+ save_figs: true
+LINR1:
+ notebook_dir: LinearReg
+ notebook_src: 01-Linear-Regression.ipynb
+ notebook_out: default
+GTSRB0:
+ notebook_dir: GTSRB
+ notebook_src: 00-Test.ipynb
+ notebook_out: default
+ overrides:
+ scale: 0.7
+ output_dir: ./data/foobar
+ run_dir: ./run/xxx
+GTSRB1:
+ notebook_dir: GTSRB
+ notebook_src: 01-Preparation-of-data.ipynb
+ notebook_out: default
+ overrides:
+ scale: 0.1
+ output_dir: ./data
+GTSRB2:
+ notebook_dir: GTSRB
+ notebook_src: 02-First-convolutions.ipynb
+ notebook_out: default
+ overrides:
+ run_dir: ./run/GTSRB2_ci
+ dataset_name: set-24x24-L
+ batch_size: 32
+ epochs: 3
+ scale: 1
\ No newline at end of file
diff --git a/fidle/config.py b/fidle/config.py
index ff769c9d0affa5f2577037d8986d6e27037919d5..464daf3dc5c202b9488f65624eec8d1fe3229cae 100644
--- a/fidle/config.py
+++ b/fidle/config.py
@@ -28,7 +28,7 @@ FIDLE_CSSFILE = '../fidle/css/custom.css'
# ---- Save figs or not (yes|no)
# Overided by env : FIDLE_SAVE_FIGS
#
-DEFAULT_SAVE_FIGS = 'yes'
+SAVE_FIGS = False
# ---- Catalog file, a json description of all notebooks ------------
#
diff --git a/fidle/cooker.py b/fidle/cooker.py
index e3e5c36cf44641e40268f4ccc8acf1db9cb630db..55aa23d3d097235eac6b9d3c9502837d92153b9b 100644
--- a/fidle/cooker.py
+++ b/fidle/cooker.py
@@ -42,7 +42,7 @@ def get_files(directories, top_dir='..'):
files : filenames list (without top_dir prefix)
'''
files = []
- regex = re.compile('.*==\d+==.*')
+ regex = re.compile('.*==.+?==.*')
for d in directories:
notebooks = glob.glob( f'{top_dir}/{d}/*.ipynb')
@@ -52,7 +52,7 @@ def get_files(directories, top_dir='..'):
files.extend(notebooks)
files.extend(scripts)
-# files = [x for x in files if not regex.match(x)]
+ files = [x for x in files if not regex.match(x)]
files = [ x.replace(f'{top_dir}/','') for x in files]
return files
@@ -73,6 +73,7 @@ def get_notebook_infos(filename, top_dir='..'):
about['basename'] = os.path.basename(filename)
about['title'] = '??'
about['description'] = '??'
+ about['overrides'] = None
# ---- Read notebook
#
@@ -80,8 +81,11 @@ def get_notebook_infos(filename, top_dir='..'):
# ---- Get id, title and desc tags
#
+ overrides=[]
for cell in notebook.cells:
-
+
+ # ---- Find Index informations
+ #
if cell['cell_type'] == 'markdown':
find = re.findall(r'<\!-- TITLE -->\s*\[(.*)\]\s*-\s*(.*)\n',cell.source)
@@ -93,7 +97,21 @@ def get_notebook_infos(filename, top_dir='..'):
if find:
about['description'] = find[0]
+ # ---- Find override informations
+ #
+ if cell['cell_type'] == 'code':
+
+ # Try to find : override(...) call
+ for m in re.finditer('override\((.+?)\)', cell.source):
+ overrides.extend ( re.findall(r'\w+', m.group(1)) )
+
+ # Try to find : run_dir=
+ if re.search(r"\s*run_dir\s*?=", cell.source):
+ overrides.append('run_dir')
+
+ about['overrides']=overrides
return about
+
def get_txtfile_infos(filename, top_dir='..'):
@@ -112,6 +130,7 @@ def get_txtfile_infos(filename, top_dir='..'):
about['basename'] = os.path.basename(filename)
about['title'] = '??'
about['description'] = '??'
+ about['overrides'] = []
# ---- Read file
#
@@ -130,7 +149,7 @@ def get_txtfile_infos(filename, top_dir='..'):
return about
-def get_catalog(files_list, top_dir='..'):
+def get_catalog(files_list=None, top_dir='..'):
'''
Return an OrderedDict of files attributes.
Keys are file id.
@@ -140,7 +159,7 @@ def get_catalog(files_list, top_dir='..'):
return:
OrderedDict : {<file id> : { description} }
'''
-
+
catalog = OrderedDict()
# ---- Build catalog
@@ -165,6 +184,11 @@ def tag(tag, text, document):
return output
+def read_catalog():
+ with open(config.CATALOG_FILE) as fp:
+ catalog = json.load(fp)
+ return catalog
+
# -----------------------------------------------------------------------------
# To built : CI Report
# -----------------------------------------------------------------------------
diff --git a/fidle/log/catalog.json b/fidle/log/catalog.json
index 7d6b0abbf3a4cad142f855bc9a16c5b7d20b0063..95b1e63865209aa908ebd0f49a8be390ac6b655d 100644
--- a/fidle/log/catalog.json
+++ b/fidle/log/catalog.json
@@ -4,251 +4,383 @@
"dirname": "LinearReg",
"basename": "01-Linear-Regression.ipynb",
"title": "Linear regression with direct resolution",
- "description": "Low-level implementation, using numpy, of a direct resolution for a linear regression"
+ "description": "Low-level implementation, using numpy, of a direct resolution for a linear regression",
+ "overrides": []
},
"GRAD1": {
"id": "GRAD1",
"dirname": "LinearReg",
"basename": "02-Gradient-descent.ipynb",
"title": "Linear regression with gradient descent",
- "description": "Low level implementation of a solution by gradient descent. Basic and stochastic approach."
+ "description": "Low level implementation of a solution by gradient descent. Basic and stochastic approach.",
+ "overrides": []
},
"POLR1": {
"id": "POLR1",
"dirname": "LinearReg",
"basename": "03-Polynomial-Regression.ipynb",
"title": "Complexity Syndrome",
- "description": "Illustration of the problem of complexity with the polynomial regression"
+ "description": "Illustration of the problem of complexity with the polynomial regression",
+ "overrides": []
},
"LOGR1": {
"id": "LOGR1",
"dirname": "LinearReg",
"basename": "04-Logistic-Regression.ipynb",
"title": "Logistic regression",
- "description": "Simple example of logistic regression with a sklearn solution"
+ "description": "Simple example of logistic regression with a sklearn solution",
+ "overrides": []
},
"PER57": {
"id": "PER57",
"dirname": "IRIS",
"basename": "01-Simple-Perceptron.ipynb",
"title": "Perceptron Model 1957",
- "description": "Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !"
+ "description": "Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !",
+ "overrides": []
},
"BHPD1": {
"id": "BHPD1",
"dirname": "BHPD",
"basename": "01-DNN-Regression.ipynb",
"title": "Regression with a Dense Network (DNN)",
- "description": "Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)"
+ "description": "Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)",
+ "overrides": []
},
"BHPD2": {
"id": "BHPD2",
"dirname": "BHPD",
"basename": "02-DNN-Regression-Premium.ipynb",
"title": "Regression with a Dense Network (DNN) - Advanced code",
- "description": "A more advanced implementation of the precedent example"
+ "description": "A more advanced implementation of the precedent example",
+ "overrides": []
},
"MNIST1": {
"id": "MNIST1",
"dirname": "MNIST",
"basename": "01-DNN-MNIST.ipynb",
"title": "Simple classification with DNN",
- "description": "An example of classification using a dense neural network for the famous MNIST dataset"
+ "description": "An example of classification using a dense neural network for the famous MNIST dataset",
+ "overrides": []
+ },
+ "??": {
+ "id": "??",
+ "dirname": "GTSRB",
+ "basename": "00-Test.ipynb",
+ "title": "??",
+ "description": "??",
+ "overrides": [
+ "run_dir",
+ "scale",
+ "output_dir"
+ ]
},
"GTSRB1": {
"id": "GTSRB1",
"dirname": "GTSRB",
"basename": "01-Preparation-of-data.ipynb",
"title": "Dataset analysis and preparation",
- "description": "Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset"
+ "description": "Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset",
+ "overrides": [
+ "scale",
+ "output_dir"
+ ]
},
"GTSRB2": {
"id": "GTSRB2",
"dirname": "GTSRB",
"basename": "02-First-convolutions.ipynb",
"title": "First convolutions",
- "description": "Episode 2 : First convolutions and first classification of our traffic signs"
+ "description": "Episode 2 : First convolutions and first classification of our traffic signs",
+ "overrides": [
+ "run_dir",
+ "dataset_name",
+ "batch_size",
+ "epochs",
+ "scale"
+ ]
},
"GTSRB3": {
"id": "GTSRB3",
"dirname": "GTSRB",
"basename": "03-Tracking-and-visualizing.ipynb",
"title": "Training monitoring",
- "description": "Episode 3 : Monitoring, analysis and check points during a training session"
+ "description": "Episode 3 : Monitoring, analysis and check points during a training session",
+ "overrides": [
+ "run_dir",
+ "dataset_name",
+ "batch_size",
+ "epochs",
+ "scale"
+ ]
},
"GTSRB4": {
"id": "GTSRB4",
"dirname": "GTSRB",
"basename": "04-Data-augmentation.ipynb",
"title": "Data augmentation ",
- "description": "Episode 4 : Adding data by data augmentation when we lack it, to improve our results"
+ "description": "Episode 4 : Adding data by data augmentation when we lack it, to improve our results",
+ "overrides": [
+ "run_dir",
+ "dataset_name",
+ "batch_size",
+ "epochs",
+ "scale"
+ ]
},
"GTSRB5": {
"id": "GTSRB5",
"dirname": "GTSRB",
"basename": "05-Full-convolutions.ipynb",
"title": "Full convolutions",
- "description": "Episode 5 : A lot of models, a lot of datasets and a lot of results."
+ "description": "Episode 5 : A lot of models, a lot of datasets and a lot of results.",
+ "overrides": [
+ "run_dir",
+ "datasets",
+ "models",
+ "batch_size",
+ "epochs",
+ "scale",
+ "with_datagen",
+ "verbose"
+ ]
},
"GTSRB6": {
"id": "GTSRB6",
"dirname": "GTSRB",
"basename": "06-Notebook-as-a-batch.ipynb",
"title": "Full convolutions as a batch",
- "description": "Episode 6 : To compute bigger, use your notebook in batch mode"
+ "description": "Episode 6 : To compute bigger, use your notebook in batch mode",
+ "overrides": []
},
"GTSRB7": {
"id": "GTSRB7",
"dirname": "GTSRB",
"basename": "07-Show-report.ipynb",
"title": "Batch reportss",
- "description": "Episode 7 : Displaying our jobs report, and the winner is..."
+ "description": "Episode 7 : Displaying our jobs report, and the winner is...",
+ "overrides": [
+ "run_dir",
+ "report_dir"
+ ]
},
"GTSRB10": {
"id": "GTSRB10",
"dirname": "GTSRB",
"basename": "batch_oar.sh",
"title": "OAR batch script submission",
- "description": "Bash script for an OAR batch submission of an ipython code"
+ "description": "Bash script for an OAR batch submission of an ipython code",
+ "overrides": []
},
"GTSRB11": {
"id": "GTSRB11",
"dirname": "GTSRB",
"basename": "batch_slurm.sh",
"title": "SLURM batch script",
- "description": "Bash script for a Slurm batch submission of an ipython code"
+ "description": "Bash script for a Slurm batch submission of an ipython code",
+ "overrides": []
},
"IMDB1": {
"id": "IMDB1",
"dirname": "IMDB",
"basename": "01-Embedding-Keras.ipynb",
"title": "Sentiment alalysis with text embedding",
- "description": "A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)"
+ "description": "A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)",
+ "overrides": []
},
"IMDB2": {
"id": "IMDB2",
"dirname": "IMDB",
"basename": "02-Prediction.ipynb",
"title": "Reload and reuse a saved model",
- "description": "Retrieving a saved model to perform a sentiment analysis (movie review)"
+ "description": "Retrieving a saved model to perform a sentiment analysis (movie review)",
+ "overrides": []
},
"IMDB3": {
"id": "IMDB3",
"dirname": "IMDB",
"basename": "03-LSTM-Keras.ipynb",
"title": "Sentiment analysis with a LSTM network",
- "description": "Still the same problem, but with a network combining embedding and LSTM"
+ "description": "Still the same problem, but with a network combining embedding and LSTM",
+ "overrides": []
},
"SYNOP1": {
"id": "SYNOP1",
"dirname": "SYNOP",
"basename": "01-Preparation-of-data.ipynb",
- "title": "Time series with RNN - Preparation of data",
- "description": "Episode 1 : Data analysis and creation of a usable dataset"
+ "title": "Preparation of data",
+ "description": "Episode 1 : Data analysis and preparation of a meteorological dataset (SYNOP)",
+ "overrides": []
},
"SYNOP2": {
"id": "SYNOP2",
"dirname": "SYNOP",
"basename": "02-First-predictions.ipynb",
- "title": "Time series with RNN - Try a prediction",
- "description": "Episode 2 : Training session and first predictions"
+ "title": "First predictions at 3h",
+ "description": "Episode 2 : Learning session and weather prediction attempt at 3h",
+ "overrides": [
+ "scale",
+ "train_prop",
+ "sequence_len",
+ "batch_size",
+ "epochs"
+ ]
},
"SYNOP3": {
"id": "SYNOP3",
"dirname": "SYNOP",
"basename": "03-12h-predictions.ipynb",
- "title": "Time series with RNN - 12h predictions",
- "description": "Episode 3: Attempt to predict in the longer term "
+ "title": "12h predictions",
+ "description": "Episode 3: Attempt to predict in a more longer term ",
+ "overrides": [
+ "iterations",
+ "scale",
+ "train_prop",
+ "sequence_len",
+ "batch_size",
+ "epochs"
+ ]
},
"AE1": {
"id": "AE1",
"dirname": "AE",
"basename": "01-AE-with-MNIST.ipynb",
- "title": "AutoEncoder (AE) with MNIST",
- "description": "Episode 1 : Model construction and Training"
+ "title": "Building and training an AE denoiser model",
+ "description": "Episode 1 : After construction, the model is trained with noisy data from the MNIST dataset.",
+ "overrides": []
},
"AE2": {
"id": "AE2",
"dirname": "AE",
"basename": "02-AE-with-MNIST-post.ipynb",
- "title": "AutoEncoder (AE) with MNIST - Analysis",
- "description": "Episode 2 : Exploring our denoiser"
+ "title": "Exploring our denoiser model",
+ "description": "Episode 2 : Using the previously trained autoencoder to denoise data",
+ "overrides": []
},
"VAE1": {
"id": "VAE1",
"dirname": "VAE",
"basename": "01-VAE-with-MNIST.ipynb",
- "title": "Variational AutoEncoder (VAE) with MNIST",
- "description": "Building a simple model with the MNIST dataset"
+ "title": "First VAE, with a small dataset (MNIST)",
+ "description": "Construction and training of a VAE with a latent space of small dimension.",
+ "overrides": [
+ "run_dir",
+ "scale",
+ "latent_dim",
+ "r_loss_factor",
+ "batch_size",
+ "epochs"
+ ]
},
"VAE2": {
"id": "VAE2",
"dirname": "VAE",
"basename": "02-VAE-with-MNIST-post.ipynb",
- "title": "Variational AutoEncoder (VAE) with MNIST - Analysis",
- "description": "Visualization and analysis of latent space"
- },
- "VAE3": {
- "id": "VAE3",
+ "title": "Analysis of the associated latent space",
+ "description": "Visualization and analysis of the VAE's latent space",
+ "overrides": [
+ "run_dir"
+ ]
+ },
+ "VAE5": {
+ "id": "VAE5",
"dirname": "VAE",
"basename": "05-About-CelebA.ipynb",
- "title": "About the CelebA dataset",
- "description": "Presentation of the CelebA dataset and problems related to its size"
+ "title": "Another game play : About the CelebA dataset",
+ "description": "Episode 1 : Presentation of the CelebA dataset and problems related to its size",
+ "overrides": []
},
"VAE6": {
"id": "VAE6",
"dirname": "VAE",
"basename": "06-Prepare-CelebA-datasets.ipynb",
- "title": "Preparation of the CelebA dataset",
- "description": "Preparation of a clustered dataset, batchable"
+ "title": "Generation of a clustered dataset",
+ "description": "Episode 2 : Analysis of the CelebA dataset and creation of an clustered and usable dataset",
+ "overrides": [
+ "scale",
+ "scale",
+ "image_size",
+ "image_size",
+ "output_dir",
+ "output_dir"
+ ]
},
"VAE7": {
"id": "VAE7",
"dirname": "VAE",
"basename": "07-Check-CelebA.ipynb",
- "title": "Checking the clustered CelebA dataset",
- "description": "Check the clustered dataset"
+ "title": "Checking the clustered dataset",
+ "description": "Episode : 3 Clustered dataset verification and testing of our datagenerator",
+ "overrides": [
+ "image_size",
+ "image_size",
+ "enhanced_dir",
+ "enhanced_dir"
+ ]
},
"VAE8": {
"id": "VAE8",
"dirname": "VAE",
- "basename": "08-VAE-with-CelebA==1090048==.ipynb",
- "title": "Variational AutoEncoder (VAE) with CelebA (small)",
- "description": "Variational AutoEncoder (VAE) with CelebA (small res. 128x128)"
+ "basename": "08-VAE-with-CelebA.ipynb",
+ "title": "Training session for our VAE",
+ "description": "Episode 4 : Training with our clustered datasets in notebook or batch mode",
+ "overrides": [
+ "run_dir",
+ "scale",
+ "image_size",
+ "enhanced_dir",
+ "latent_dim",
+ "r_loss_factor",
+ "batch_size",
+ "epochs"
+ ]
},
"VAE9": {
"id": "VAE9",
"dirname": "VAE",
"basename": "09-VAE-withCelebA-post.ipynb",
- "title": "Variational AutoEncoder (VAE) with CelebA - Analysis",
- "description": "Exploring latent space of our trained models"
+ "title": "Data generation from latent space",
+ "description": "Episode 5 : Exploring latent space to generate new data",
+ "overrides": [
+ "run_dir",
+ "scale",
+ "scale",
+ "image_size",
+ "image_size",
+ "enhanced_dir",
+ "enhanced_dir"
+ ]
},
"VAE10": {
"id": "VAE10",
"dirname": "VAE",
"basename": "batch_slurm.sh",
"title": "SLURM batch script",
- "description": "Bash script for SLURM batch submission of VAE notebooks "
+ "description": "Bash script for SLURM batch submission of VAE8 notebooks ",
+ "overrides": []
},
"ACTF1": {
"id": "ACTF1",
"dirname": "Misc",
"basename": "Activation-Functions.ipynb",
"title": "Activation functions",
- "description": "Some activation functions, with their derivatives."
+ "description": "Some activation functions, with their derivatives.",
+ "overrides": []
},
"NP1": {
"id": "NP1",
"dirname": "Misc",
"basename": "Numpy.ipynb",
"title": "A short introduction to Numpy",
- "description": "Numpy is an essential tool for the Scientific Python."
+ "description": "Numpy is an essential tool for the Scientific Python.",
+ "overrides": []
},
"TSB1": {
"id": "TSB1",
"dirname": "Misc",
"basename": "Using-Tensorboard.ipynb",
"title": "Tensorboard with/from Jupyter ",
- "description": "4 ways to use Tensorboard from the Jupyter environment"
+ "description": "4 ways to use Tensorboard from the Jupyter environment",
+ "overrides": []
}
}
\ No newline at end of file
diff --git a/fidle/log/ci_report.html b/fidle/log/ci_report.html
index 0afd20ccc4bedf9da5f4043b288c82e60a995f60..483396e5ceb34e38d8bd92e001443572cc9a20f8 100644
--- a/fidle/log/ci_report.html
+++ b/fidle/log/ci_report.html
@@ -24,90 +24,130 @@
<body>
<br>Hi,
<p>Below is the result of the continuous integration tests of the Fidle project:</p>
- <div class="header"><b>Report date :</b> Thursday 7 January 2021, 18:18:53</div>
+ <div class="header"><b>Report date :</b> Saturday 9 January 2021, 11:20:19</div>
<div class="result">
<style type="text/css" >
- #T_84eff_ td {
+ #T_fa3d5_ td {
font-size: 110%;
text-align: left;
- } #T_84eff_ th {
+ } #T_fa3d5_ th {
font-size: 110%;
text-align: left;
- }#T_84eff_row0_col5,#T_84eff_row1_col5{
+ }#T_fa3d5_row0_col5,#T_fa3d5_row1_col5{
background-color: OrangeRed;
color: white;
- }</style><table id="T_84eff_" ><thead> <tr> <th class="col_heading level0 col0" >id</th> <th class="col_heading level0 col1" >repo</th> <th class="col_heading level0 col2" >name</th> <th class="col_heading level0 col3" >start</th> <th class="col_heading level0 col4" >end</th> <th class="col_heading level0 col5" >duration</th> </tr></thead><tbody>
+ }</style><table id="T_fa3d5_" ><thead> <tr> <th class="col_heading level0 col0" >id</th> <th class="col_heading level0 col1" >repo</th> <th class="col_heading level0 col2" >name</th> <th class="col_heading level0 col3" >start</th> <th class="col_heading level0 col4" >end</th> <th class="col_heading level0 col5" >duration</th> </tr></thead><tbody>
<tr>
- <td id="T_84eff_row0_col0" class="data row0 col0" ><a href="../VAE/08-VAE-with-CelebA==1090048==.ipynb">VAE8</a></td>
- <td id="T_84eff_row0_col1" class="data row0 col1" >VAE</td>
- <td id="T_84eff_row0_col2" class="data row0 col2" ><a href="../VAE/08-VAE-with-CelebA==1090048==.ipynb"><b>08-VAE-with-CelebA==1090048==.ipynb</b></a></td>
- <td id="T_84eff_row0_col3" class="data row0 col3" >Wednesday 6 January 2021, 22:17:12</td>
- <td id="T_84eff_row0_col4" class="data row0 col4" ></td>
- <td id="T_84eff_row0_col5" class="data row0 col5" >Unfinished...</td>
+ <td id="T_fa3d5_row0_col0" class="data row0 col0" ><a href="../VAE/08-VAE-with-CelebA.ipynb">VAE8</a></td>
+ <td id="T_fa3d5_row0_col1" class="data row0 col1" >VAE</td>
+ <td id="T_fa3d5_row0_col2" class="data row0 col2" ><a href="../VAE/08-VAE-with-CelebA.ipynb"><b>08-VAE-with-CelebA.ipynb</b></a></td>
+ <td id="T_fa3d5_row0_col3" class="data row0 col3" >Wednesday 6 January 2021, 22:17:12</td>
+ <td id="T_fa3d5_row0_col4" class="data row0 col4" ></td>
+ <td id="T_fa3d5_row0_col5" class="data row0 col5" >Unfinished...</td>
</tr>
<tr>
- <td id="T_84eff_row1_col0" class="data row1 col0" ><a href="../VAE/01-VAE-with-MNIST.ipynb">VAE1</a></td>
- <td id="T_84eff_row1_col1" class="data row1 col1" >VAE</td>
- <td id="T_84eff_row1_col2" class="data row1 col2" ><a href="../VAE/01-VAE-with-MNIST.ipynb"><b>01-VAE-with-MNIST.ipynb</b></a></td>
- <td id="T_84eff_row1_col3" class="data row1 col3" >Thursday 7 January 2021, 09:37:44</td>
- <td id="T_84eff_row1_col4" class="data row1 col4" ></td>
- <td id="T_84eff_row1_col5" class="data row1 col5" >Unfinished...</td>
+ <td id="T_fa3d5_row1_col0" class="data row1 col0" ><a href="../VAE/01-VAE-with-MNIST.ipynb">VAE1</a></td>
+ <td id="T_fa3d5_row1_col1" class="data row1 col1" >VAE</td>
+ <td id="T_fa3d5_row1_col2" class="data row1 col2" ><a href="../VAE/01-VAE-with-MNIST.ipynb"><b>01-VAE-with-MNIST.ipynb</b></a></td>
+ <td id="T_fa3d5_row1_col3" class="data row1 col3" >Thursday 7 January 2021, 09:37:44</td>
+ <td id="T_fa3d5_row1_col4" class="data row1 col4" ></td>
+ <td id="T_fa3d5_row1_col5" class="data row1 col5" >Unfinished...</td>
</tr>
<tr>
- <td id="T_84eff_row2_col0" class="data row2 col0" ><a href="../GTSRB/01-Preparation-of-data.ipynb">GTSRB1</a></td>
- <td id="T_84eff_row2_col1" class="data row2 col1" >GTSRB</td>
- <td id="T_84eff_row2_col2" class="data row2 col2" ><a href="../GTSRB/01-Preparation-of-data.ipynb"><b>01-Preparation-of-data.ipynb</b></a></td>
- <td id="T_84eff_row2_col3" class="data row2 col3" >Thursday 7 January 2021, 10:09:55</td>
- <td id="T_84eff_row2_col4" class="data row2 col4" >Thursday 7 January 2021, 10:11:51</td>
- <td id="T_84eff_row2_col5" class="data row2 col5" >00:01:56 358ms</td>
+ <td id="T_fa3d5_row2_col0" class="data row2 col0" ><a href="../GTSRB/01-Preparation-of-data.ipynb">GTSRB1</a></td>
+ <td id="T_fa3d5_row2_col1" class="data row2 col1" >GTSRB</td>
+ <td id="T_fa3d5_row2_col2" class="data row2 col2" ><a href="../GTSRB/01-Preparation-of-data.ipynb"><b>01-Preparation-of-data.ipynb</b></a></td>
+ <td id="T_fa3d5_row2_col3" class="data row2 col3" >Thursday 7 January 2021, 10:09:55</td>
+ <td id="T_fa3d5_row2_col4" class="data row2 col4" >Thursday 7 January 2021, 10:11:51</td>
+ <td id="T_fa3d5_row2_col5" class="data row2 col5" >00:01:56 358ms</td>
</tr>
<tr>
- <td id="T_84eff_row3_col0" class="data row3 col0" ><a href="../GTSRB/02-First-convolutions.ipynb">GTSRB2</a></td>
- <td id="T_84eff_row3_col1" class="data row3 col1" >GTSRB</td>
- <td id="T_84eff_row3_col2" class="data row3 col2" ><a href="../GTSRB/02-First-convolutions.ipynb"><b>02-First-convolutions.ipynb</b></a></td>
- <td id="T_84eff_row3_col3" class="data row3 col3" >Thursday 7 January 2021, 13:14:32</td>
- <td id="T_84eff_row3_col4" class="data row3 col4" >Thursday 7 January 2021, 13:14:59</td>
- <td id="T_84eff_row3_col5" class="data row3 col5" >00:00:27 021ms</td>
+ <td id="T_fa3d5_row3_col0" class="data row3 col0" ><a href="../GTSRB/02-First-convolutions.ipynb">GTSRB2</a></td>
+ <td id="T_fa3d5_row3_col1" class="data row3 col1" >GTSRB</td>
+ <td id="T_fa3d5_row3_col2" class="data row3 col2" ><a href="../GTSRB/02-First-convolutions.ipynb"><b>02-First-convolutions.ipynb</b></a></td>
+ <td id="T_fa3d5_row3_col3" class="data row3 col3" >Thursday 7 January 2021, 13:14:32</td>
+ <td id="T_fa3d5_row3_col4" class="data row3 col4" >Thursday 7 January 2021, 13:14:59</td>
+ <td id="T_fa3d5_row3_col5" class="data row3 col5" >00:00:27 021ms</td>
</tr>
<tr>
- <td id="T_84eff_row4_col0" class="data row4 col0" ><a href="../GTSRB/03-Tracking-and-visualizing.ipynb">GTSRB3</a></td>
- <td id="T_84eff_row4_col1" class="data row4 col1" >GTSRB</td>
- <td id="T_84eff_row4_col2" class="data row4 col2" ><a href="../GTSRB/03-Tracking-and-visualizing.ipynb"><b>03-Tracking-and-visualizing.ipynb</b></a></td>
- <td id="T_84eff_row4_col3" class="data row4 col3" >Thursday 7 January 2021, 12:15:52</td>
- <td id="T_84eff_row4_col4" class="data row4 col4" >Thursday 7 January 2021, 12:20:01</td>
- <td id="T_84eff_row4_col5" class="data row4 col5" >00:04:09 711ms</td>
+ <td id="T_fa3d5_row4_col0" class="data row4 col0" ><a href="../GTSRB/03-Tracking-and-visualizing.ipynb">GTSRB3</a></td>
+ <td id="T_fa3d5_row4_col1" class="data row4 col1" >GTSRB</td>
+ <td id="T_fa3d5_row4_col2" class="data row4 col2" ><a href="../GTSRB/03-Tracking-and-visualizing.ipynb"><b>03-Tracking-and-visualizing.ipynb</b></a></td>
+ <td id="T_fa3d5_row4_col3" class="data row4 col3" >Thursday 7 January 2021, 12:15:52</td>
+ <td id="T_fa3d5_row4_col4" class="data row4 col4" >Thursday 7 January 2021, 12:20:01</td>
+ <td id="T_fa3d5_row4_col5" class="data row4 col5" >00:04:09 711ms</td>
</tr>
<tr>
- <td id="T_84eff_row5_col0" class="data row5 col0" ><a href="../GTSRB/04-Data-augmentation.ipynb">GTSRB4</a></td>
- <td id="T_84eff_row5_col1" class="data row5 col1" >GTSRB</td>
- <td id="T_84eff_row5_col2" class="data row5 col2" ><a href="../GTSRB/04-Data-augmentation.ipynb"><b>04-Data-augmentation.ipynb</b></a></td>
- <td id="T_84eff_row5_col3" class="data row5 col3" >Thursday 7 January 2021, 13:40:52</td>
- <td id="T_84eff_row5_col4" class="data row5 col4" >Thursday 7 January 2021, 13:41:31</td>
- <td id="T_84eff_row5_col5" class="data row5 col5" >00:00:39 161ms</td>
+ <td id="T_fa3d5_row5_col0" class="data row5 col0" ><a href="../GTSRB/04-Data-augmentation.ipynb">GTSRB4</a></td>
+ <td id="T_fa3d5_row5_col1" class="data row5 col1" >GTSRB</td>
+ <td id="T_fa3d5_row5_col2" class="data row5 col2" ><a href="../GTSRB/04-Data-augmentation.ipynb"><b>04-Data-augmentation.ipynb</b></a></td>
+ <td id="T_fa3d5_row5_col3" class="data row5 col3" >Thursday 7 January 2021, 13:40:52</td>
+ <td id="T_fa3d5_row5_col4" class="data row5 col4" >Thursday 7 January 2021, 13:41:31</td>
+ <td id="T_fa3d5_row5_col5" class="data row5 col5" >00:00:39 161ms</td>
</tr>
<tr>
- <td id="T_84eff_row6_col0" class="data row6 col0" ><a href="../GTSRB/05-Full-convolutions.ipynb">GTSRB5</a></td>
- <td id="T_84eff_row6_col1" class="data row6 col1" >GTSRB</td>
- <td id="T_84eff_row6_col2" class="data row6 col2" ><a href="../GTSRB/05-Full-convolutions.ipynb"><b>05-Full-convolutions.ipynb</b></a></td>
- <td id="T_84eff_row6_col3" class="data row6 col3" >Thursday 7 January 2021, 14:47:12</td>
- <td id="T_84eff_row6_col4" class="data row6 col4" >Thursday 7 January 2021, 14:48:44</td>
- <td id="T_84eff_row6_col5" class="data row6 col5" >00:01:32 269ms</td>
+ <td id="T_fa3d5_row6_col0" class="data row6 col0" ><a href="../GTSRB/05-Full-convolutions.ipynb">GTSRB5</a></td>
+ <td id="T_fa3d5_row6_col1" class="data row6 col1" >GTSRB</td>
+ <td id="T_fa3d5_row6_col2" class="data row6 col2" ><a href="../GTSRB/05-Full-convolutions.ipynb"><b>05-Full-convolutions.ipynb</b></a></td>
+ <td id="T_fa3d5_row6_col3" class="data row6 col3" >Thursday 7 January 2021, 14:47:12</td>
+ <td id="T_fa3d5_row6_col4" class="data row6 col4" >Thursday 7 January 2021, 14:48:44</td>
+ <td id="T_fa3d5_row6_col5" class="data row6 col5" >00:01:32 269ms</td>
</tr>
<tr>
- <td id="T_84eff_row7_col0" class="data row7 col0" ><a href="../GTSRB/06-Notebook-as-a-batch.ipynb">GTSRB6</a></td>
- <td id="T_84eff_row7_col1" class="data row7 col1" >GTSRB</td>
- <td id="T_84eff_row7_col2" class="data row7 col2" ><a href="../GTSRB/06-Notebook-as-a-batch.ipynb"><b>06-Notebook-as-a-batch.ipynb</b></a></td>
- <td id="T_84eff_row7_col3" class="data row7 col3" >Thursday 7 January 2021, 15:41:17</td>
- <td id="T_84eff_row7_col4" class="data row7 col4" >Thursday 7 January 2021, 15:41:18</td>
- <td id="T_84eff_row7_col5" class="data row7 col5" >00:00:01 101ms</td>
+ <td id="T_fa3d5_row7_col0" class="data row7 col0" ><a href="../GTSRB/06-Notebook-as-a-batch.ipynb">GTSRB6</a></td>
+ <td id="T_fa3d5_row7_col1" class="data row7 col1" >GTSRB</td>
+ <td id="T_fa3d5_row7_col2" class="data row7 col2" ><a href="../GTSRB/06-Notebook-as-a-batch.ipynb"><b>06-Notebook-as-a-batch.ipynb</b></a></td>
+ <td id="T_fa3d5_row7_col3" class="data row7 col3" >Thursday 7 January 2021, 15:41:17</td>
+ <td id="T_fa3d5_row7_col4" class="data row7 col4" >Thursday 7 January 2021, 15:41:18</td>
+ <td id="T_fa3d5_row7_col5" class="data row7 col5" >00:00:01 101ms</td>
</tr>
<tr>
- <td id="T_84eff_row8_col0" class="data row8 col0" ><a href="../GTSRB/07-Show-report.ipynb">GTSRB7</a></td>
- <td id="T_84eff_row8_col1" class="data row8 col1" >GTSRB</td>
- <td id="T_84eff_row8_col2" class="data row8 col2" ><a href="../GTSRB/07-Show-report.ipynb"><b>07-Show-report.ipynb</b></a></td>
- <td id="T_84eff_row8_col3" class="data row8 col3" >Thursday 7 January 2021, 15:16:05</td>
- <td id="T_84eff_row8_col4" class="data row8 col4" >Thursday 7 January 2021, 15:16:06</td>
- <td id="T_84eff_row8_col5" class="data row8 col5" >00:00:00 116ms</td>
+ <td id="T_fa3d5_row8_col0" class="data row8 col0" ><a href="../GTSRB/07-Show-report.ipynb">GTSRB7</a></td>
+ <td id="T_fa3d5_row8_col1" class="data row8 col1" >GTSRB</td>
+ <td id="T_fa3d5_row8_col2" class="data row8 col2" ><a href="../GTSRB/07-Show-report.ipynb"><b>07-Show-report.ipynb</b></a></td>
+ <td id="T_fa3d5_row8_col3" class="data row8 col3" >Thursday 7 January 2021, 15:16:05</td>
+ <td id="T_fa3d5_row8_col4" class="data row8 col4" >Thursday 7 January 2021, 15:16:06</td>
+ <td id="T_fa3d5_row8_col5" class="data row8 col5" >00:00:00 116ms</td>
+ </tr>
+ <tr>
+ <td id="T_fa3d5_row9_col0" class="data row9 col0" ><a href="../BHPD/01-DNN-Regression.ipynb">BHPD1</a></td>
+ <td id="T_fa3d5_row9_col1" class="data row9 col1" >BHPD</td>
+ <td id="T_fa3d5_row9_col2" class="data row9 col2" ><a href="../BHPD/01-DNN-Regression.ipynb"><b>01-DNN-Regression.ipynb</b></a></td>
+ <td id="T_fa3d5_row9_col3" class="data row9 col3" >Friday 8 January 2021, 01:09:13</td>
+ <td id="T_fa3d5_row9_col4" class="data row9 col4" >Friday 8 January 2021, 01:09:24</td>
+ <td id="T_fa3d5_row9_col5" class="data row9 col5" >00:00:11 984ms</td>
+ </tr>
+ <tr>
+ <td id="T_fa3d5_row10_col0" class="data row10 col0" ><a href="../BHPD/02-DNN-Regression-Premium.ipynb">BHPD2</a></td>
+ <td id="T_fa3d5_row10_col1" class="data row10 col1" >BHPD</td>
+ <td id="T_fa3d5_row10_col2" class="data row10 col2" ><a href="../BHPD/02-DNN-Regression-Premium.ipynb"><b>02-DNN-Regression-Premium.ipynb</b></a></td>
+ <td id="T_fa3d5_row10_col3" class="data row10 col3" >Friday 8 January 2021, 01:10:28</td>
+ <td id="T_fa3d5_row10_col4" class="data row10 col4" >Friday 8 January 2021, 01:10:39</td>
+ <td id="T_fa3d5_row10_col5" class="data row10 col5" >00:00:12 582ms</td>
+ </tr>
+ <tr>
+ <td id="T_fa3d5_row11_col0" class="data row11 col0" ><a href="../SYNOP/01-Preparation-of-data.ipynb">SYNOP1</a></td>
+ <td id="T_fa3d5_row11_col1" class="data row11 col1" >SYNOP</td>
+ <td id="T_fa3d5_row11_col2" class="data row11 col2" ><a href="../SYNOP/01-Preparation-of-data.ipynb"><b>01-Preparation-of-data.ipynb</b></a></td>
+ <td id="T_fa3d5_row11_col3" class="data row11 col3" >Saturday 9 January 2021, 10:04:28</td>
+ <td id="T_fa3d5_row11_col4" class="data row11 col4" >Saturday 9 January 2021, 10:04:34</td>
+ <td id="T_fa3d5_row11_col5" class="data row11 col5" >00:00:05 236ms</td>
+ </tr>
+ <tr>
+ <td id="T_fa3d5_row12_col0" class="data row12 col0" ><a href="../SYNOP/02-First-predictions.ipynb">SYNOP2</a></td>
+ <td id="T_fa3d5_row12_col1" class="data row12 col1" >SYNOP</td>
+ <td id="T_fa3d5_row12_col2" class="data row12 col2" ><a href="../SYNOP/02-First-predictions.ipynb"><b>02-First-predictions.ipynb</b></a></td>
+ <td id="T_fa3d5_row12_col3" class="data row12 col3" >Saturday 9 January 2021, 10:30:55</td>
+ <td id="T_fa3d5_row12_col4" class="data row12 col4" >Saturday 9 January 2021, 10:31:11</td>
+ <td id="T_fa3d5_row12_col5" class="data row12 col5" >00:00:16 248ms</td>
+ </tr>
+ <tr>
+ <td id="T_fa3d5_row13_col0" class="data row13 col0" ><a href="../SYNOP/03-12h-predictions.ipynb">SYNOP3</a></td>
+ <td id="T_fa3d5_row13_col1" class="data row13 col1" >SYNOP</td>
+ <td id="T_fa3d5_row13_col2" class="data row13 col2" ><a href="../SYNOP/03-12h-predictions.ipynb"><b>03-12h-predictions.ipynb</b></a></td>
+ <td id="T_fa3d5_row13_col3" class="data row13 col3" >Saturday 9 January 2021, 10:27:54</td>
+ <td id="T_fa3d5_row13_col4" class="data row13 col4" >Saturday 9 January 2021, 10:27:59</td>
+ <td id="T_fa3d5_row13_col5" class="data row13 col5" >00:00:05 249ms</td>
</tr>
</tbody></table>
</div>
diff --git a/fidle/pwk.py b/fidle/pwk.py
index 9ebde8698ac050a1888daed4faf20fbc266adb6b..a4778682fbae7883ed6a1c1b8a694cf234b131bc 100644
--- a/fidle/pwk.py
+++ b/fidle/pwk.py
@@ -113,8 +113,8 @@ def init(name=None, run_directory='./run'):
# ---- Save figs or not
#
- save_figs = os.getenv('FIDLE_SAVE_FIGS', config.DEFAULT_SAVE_FIGS)
- if save_figs.lower() == 'yes':
+ save_figs = os.getenv('FIDLE_SAVE_FIGS', str(config.SAVE_FIGS) )
+ if save_figs.lower() == 'true':
set_save_fig(save=True, figs_dir=f'{run_dir}/figs', figs_name='fig_', figs_id=0)
@@ -226,49 +226,7 @@ def override(*names, module_name='__main__', verbose=True, return_attributes=Fal
if return_attributes:
return overrides
-
-
-
-# -------------------------------------------------------------
-# param_override
-# -------------------------------------------------------------
-# Try to override a given parameter 'param'.
-#
-# def override(name, value):
-# '''
-# Try to override a given parameter (name,value) with an environment variable.
-# Env variable name is : FIDLE_OVERRIDE_<NOTEBOOK-ID>_<NAME>
-# If no env variable is available, return the given value.
-# If type is str, substitution is done with notebook_id and datasets_dir
-# params:
-# name : parameter name
-# value: parameter value
-# return :
-# eval(env variable), if it env variable exist, or given value
-# '''
-# # ---- Environment variable name
-# #
-# env_name = f'FIDLE_OVERRIDE_{notebook_id}_{name}'
-# env_value = os.environ.get(env_name)
-
-# # ---- Doesn't exist ?
-# #
-# if env_value is None:
-# return value
-
-# # ---- Exist
-# #
-# if isinstance(value, str) :
-# new_value = env_value.format(datasets_dir=datasets_dir, notebook_id=notebook_id)
-
-# if type(value) in [ tuple, int, float]:
-# new_value = eval(env_value)
-
-# # ---- Return
-# #
-# print(f'Override : Parameter [{name}={value}] set to [{new_value}]')
-# return new_value
-
+
# -------------------------------------------------------------
# Folder cooking