From 350b6c4cbd766a72df707432d29658c1f6e0a93c Mon Sep 17 00:00:00 2001
From: Jean-Luc Parouty <Jean-Luc.Parouty@grenoble-inp.fr>
Date: Sun, 1 Mar 2020 22:08:32 +0100
Subject: [PATCH] 12h Predictions with LSTM, it works !

Former-commit-id: e9ac86c9b672ea691eefadbe8c75a14fe0ee0fe5
---
 SYNOP/01-Try-a-peediction.ipynb  | 970 ------------------------------
 SYNOP/02-First-predictions.ipynb | 973 +++++++++++++++++++++++++++++++
 SYNOP/03-12h-predictions.ipynb   | 405 +++++++++++++
 fidle/pwk.py                     |  25 +-
 4 files changed, 1394 insertions(+), 979 deletions(-)
 delete mode 100644 SYNOP/01-Try-a-peediction.ipynb
 create mode 100644 SYNOP/02-First-predictions.ipynb
 create mode 100644 SYNOP/03-12h-predictions.ipynb

diff --git a/SYNOP/01-Try-a-peediction.ipynb b/SYNOP/01-Try-a-peediction.ipynb
deleted file mode 100644
index 29952ed..0000000
--- a/SYNOP/01-Try-a-peediction.ipynb
+++ /dev/null
@@ -1,970 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
-    "\n",
-    "# <!-- TITLE --> [SYNOP2] - Try a prediction\n",
-    "<!-- DESC --> Episode 1 : Data analysis and creation of a usable dataset\n",
-    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
-    "\n",
-    "## Objectives :\n",
-    " - Undestand the data\n",
-    " - cleanup a usable dataset\n",
-    "\n",
-    "\n",
-    "SYNOP meteorological data, available at: https://public.opendatasoft.com\n",
-    "\n",
-    "## What we're going to do :\n",
-    "\n",
-    " - Read the data\n",
-    " - Cleanup and build a usable dataset\n",
-    "\n",
-    "## Step 1 - Import and init\n",
-    "### 1.1 - Python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "FIDLE 2020 - Practical Work Module\n",
-      "Version              : 0.4.4\n",
-      "Run time             : Sunday 1 March 2020, 11:10:19\n",
-      "TensorFlow version   : 2.0.0\n",
-      "Keras version        : 2.2.4-tf\n"
-     ]
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from tensorflow import keras\n",
-    "from tensorflow.keras.callbacks import TensorBoard\n",
-    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
-    "\n",
-    "import numpy as np\n",
-    "import math, random\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import pandas as pd\n",
-    "import h5py, json\n",
-    "import os,time,sys\n",
-    "\n",
-    "from importlib import reload\n",
-    "\n",
-    "sys.path.append('..')\n",
-    "import fidle.pwk as ooo\n",
-    "\n",
-    "ooo.init()\n",
-    "\n",
-    "def np_print(*args):\n",
-    "    with np.printoptions(formatter={'float':'{:8.2f}'.format}, linewidth=np.inf):\n",
-    "        for a in args:\n",
-    "            print(a)    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1.2 - Where are we ? "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Well, we should be at HOME !\n",
-      "We are going to use: /home/pjluc/datasets/SYNOP\n"
-     ]
-    }
-   ],
-   "source": [
-    "place, dataset_dir = ooo.good_place( { 'GRICAD' : f'{os.getenv(\"SCRATCH_DIR\",\"\")}/PROJECTS/pr-fidle/datasets/SYNOP',\n",
-    "                                       'IDRIS'  : f'{os.getenv(\"WORK\",\"\")}/datasets/SYNOP',\n",
-    "                                       'HOME'   : f'{os.getenv(\"HOME\",\"\")}/datasets/SYNOP'} )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Read and prepare dataset\n",
-    "### 2.1 - Read it"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "<br>**Train dataset example :**"
-      ],
-      "text/plain": [
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-       "      <td>96.0</td>\n",
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-       "      <th>4</th>\n",
-       "      <td>280.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>330.0</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>278.15</td>\n",
-       "      <td>94.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>96620.0</td>\n",
-       "      <td>8.7</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>5.9</td>\n",
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-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>480.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>276.95</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>97100.0</td>\n",
-       "      <td>8.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>5.2</td>\n",
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-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>530.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>274.05</td>\n",
-       "      <td>83.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>97630.0</td>\n",
-       "      <td>7.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>450.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>340.0</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>272.15</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98080.0</td>\n",
-       "      <td>9.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>280.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>320.0</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>270.15</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98360.0</td>\n",
-       "      <td>10.3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>220.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>290.0</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>269.65</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98580.0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>100.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>270.45</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98680.0</td>\n",
-       "      <td>4.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>300.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>268.55</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98980.0</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>130.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>267.45</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>99110.0</td>\n",
-       "      <td>7.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>150.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>5.7</td>\n",
-       "      <td>267.45</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>99260.0</td>\n",
-       "      <td>8.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>140.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>268.15</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>99400.0</td>\n",
-       "      <td>5.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     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_dcd777e2_5ba4_11ea_a20b_13352ca3afbf\" ><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_dcd777e2_5ba4_11ea_a20b_13352ca3afbflevel0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow0_col0\" class=\"data row0 col0\" >25000.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow0_col1\" class=\"data row0 col1\" >25000.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow0_col2\" class=\"data row0 col2\" >25000.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow0_col3\" class=\"data row0 col3\" >25000.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow0_col4\" class=\"data row0 col4\" >25000.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow0_col5\" class=\"data row0 col5\" >25000.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow0_col6\" class=\"data row0 col6\" >25000.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow0_col7\" class=\"data row0 col7\" >25000.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow0_col8\" class=\"data row0 col8\" >25000.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow0_col9\" class=\"data row0 col9\" >25000.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow0_col10\" class=\"data row0 col10\" >25000.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow0_col11\" class=\"data row0 col11\" >25000.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbflevel0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow1_col0\" class=\"data row1 col0\" >-0.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow1_col1\" class=\"data row1 col1\" >-0.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow1_col2\" class=\"data row1 col2\" >0.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow1_col3\" class=\"data row1 col3\" >0.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow1_col5\" class=\"data row1 col5\" >0.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow1_col6\" class=\"data row1 col6\" >0.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow1_col9\" class=\"data row1 col9\" >-0.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow1_col10\" class=\"data row1 col10\" >0.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbflevel0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow2_col0\" class=\"data row2 col0\" >1.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow2_col1\" class=\"data row2 col1\" >1.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow2_col2\" class=\"data row2 col2\" >1.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow2_col3\" class=\"data row2 col3\" >1.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow2_col4\" class=\"data row2 col4\" >1.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow2_col5\" class=\"data row2 col5\" >1.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow2_col6\" class=\"data row2 col6\" >1.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow2_col7\" class=\"data row2 col7\" >1.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow2_col8\" class=\"data row2 col8\" >1.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow2_col9\" class=\"data row2 col9\" >1.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow2_col10\" class=\"data row2 col10\" >1.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow2_col11\" class=\"data row2 col11\" >1.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbflevel0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow3_col0\" class=\"data row3 col0\" >-6.80</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow3_col1\" class=\"data row3 col1\" >-1.59</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow3_col2\" class=\"data row3 col2\" >-1.75</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow3_col3\" class=\"data row3 col3\" >-1.37</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow3_col4\" class=\"data row3 col4\" >-5.18</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow3_col5\" class=\"data row3 col5\" >-3.82</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow3_col6\" class=\"data row3 col6\" >-0.52</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow3_col7\" class=\"data row3 col7\" >-4.94</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow3_col8\" class=\"data row3 col8\" >-1.64</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow3_col9\" class=\"data row3 col9\" >-0.31</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow3_col10\" class=\"data row3 col10\" >-0.27</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow3_col11\" class=\"data row3 col11\" >-3.03</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbflevel0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow4_col0\" class=\"data row4 col0\" >-0.64</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow4_col1\" class=\"data row4 col1\" >-0.85</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow4_col2\" class=\"data row4 col2\" >-0.64</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow4_col3\" class=\"data row4 col3\" >-0.76</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow4_col4\" class=\"data row4 col4\" >-0.72</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow4_col5\" class=\"data row4 col5\" >-0.71</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow4_col6\" class=\"data row4 col6\" >-0.42</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow4_col7\" class=\"data row4 col7\" >-0.55</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow4_col8\" class=\"data row4 col8\" >-0.69</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow4_col9\" class=\"data row4 col9\" >-0.15</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow4_col10\" class=\"data row4 col10\" >-0.20</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow4_col11\" class=\"data row4 col11\" >-0.75</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbflevel0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow5_col0\" class=\"data row5 col0\" >-0.00</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow5_col1\" class=\"data row5 col1\" >-0.48</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow5_col2\" class=\"data row5 col2\" >-0.12</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow5_col3\" class=\"data row5 col3\" >-0.19</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow5_col4\" class=\"data row5 col4\" >0.05</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow5_col5\" class=\"data row5 col5\" >0.18</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow5_col6\" class=\"data row5 col6\" >-0.42</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow5_col7\" class=\"data row5 col7\" >0.03</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow5_col8\" class=\"data row5 col8\" >-0.27</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow5_col9\" class=\"data row5 col9\" >-0.15</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow5_col10\" class=\"data row5 col10\" >-0.20</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow5_col11\" class=\"data row5 col11\" >-0.01</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbflevel0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow6_col0\" class=\"data row6 col0\" >0.63</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow6_col1\" class=\"data row6 col1\" >0.99</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow6_col2\" class=\"data row6 col2\" >1.08</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow6_col3\" class=\"data row6 col3\" >0.50</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow6_col4\" class=\"data row6 col4\" >0.79</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow6_col5\" class=\"data row6 col5\" >0.84</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow6_col6\" class=\"data row6 col6\" >-0.37</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow6_col7\" class=\"data row6 col7\" >0.61</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow6_col8\" class=\"data row6 col8\" >0.52</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow6_col9\" class=\"data row6 col9\" >-0.15</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow6_col10\" class=\"data row6 col10\" >-0.20</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow6_col11\" class=\"data row6 col11\" >0.72</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbflevel0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow7_col0\" class=\"data row7 col0\" >7.16</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow7_col1\" class=\"data row7 col1\" >1.36</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow7_col2\" class=\"data row7 col2\" >1.34</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow7_col3\" class=\"data row7 col3\" >6.28</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow7_col4\" class=\"data row7 col4\" >2.40</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow7_col5\" class=\"data row7 col5\" >1.62</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow7_col6\" class=\"data row7 col6\" >4.46</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow7_col7\" class=\"data row7 col7\" >3.10</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow7_col8\" class=\"data row7 col8\" >6.29</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow7_col9\" class=\"data row7 col9\" >30.36</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow7_col10\" class=\"data row7 col10\" >31.27</td>\n",
-       "                        <td id=\"T_dcd777e2_5ba4_11ea_a20b_13352ca3afbfrow7_col11\" class=\"data row7 col11\" >3.02</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fc156680c50>"
-      ]
-     },
-     "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"
-     ]
-    }
-   ],
-   "source": [
-    "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",
-    "# ---- Read dataset\n",
-    "\n",
-    "df = pd.read_csv(f'{dataset_dir}/{dataset_filename}', header=0, sep=';')\n",
-    "\n",
-    "# ---- Train / Test\n",
-    "\n",
-    "dataset_train = df.loc[ :train_len-1, features ]\n",
-    "dataset_test  = df.loc[train_len:,    features ]\n",
-    "ooo.subtitle('Train dataset example :')\n",
-    "display(dataset_train.head(15))\n",
-    "\n",
-    "# ---- Normalize, and convert to numpy array\n",
-    "\n",
-    "mean = dataset_train.mean()\n",
-    "std  = dataset_train.std()\n",
-    "dataset_train = (dataset_train - mean) / std\n",
-    "dataset_test  = (dataset_test  - mean) / std\n",
-    "\n",
-    "ooo.subtitle('After normalization :')\n",
-    "display(dataset_train.describe().style.format(\"{0:.2f}\"))\n",
-    "\n",
-    "dataset_train = dataset_train.to_numpy()\n",
-    "dataset_test  = dataset_test.to_numpy()\n",
-    "\n",
-    "\n",
-    "print('Dataset : ',df.shape)\n",
-    "print('Train dataset : ',dataset_train.shape)\n",
-    "print('Test  dataset : ',dataset_test.shape)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.2 - Prepare data generator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "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"
-     ]
-    }
-   ],
-   "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",
-    "\n",
-    "# ---- About\n",
-    "\n",
-    "x,y=train_generator[0]\n",
-    "print(f'Nombre de train batchs disponibles : ', len(train_generator))\n",
-    "print('batch x shape : ',x.shape)\n",
-    "print('batch y shape : ',y.shape)\n",
-    "\n",
-    "# x,y=train_generator[0]\n",
-    "# np_print(x[0])\n",
-    "# np_print(y[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Create a model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = keras.models.Sequential()\n",
-    "model.add( keras.layers.InputLayer(input_shape=(sequence_len, features_len)) )\n",
-    "model.add( keras.layers.LSTM(100, activation='relu') )\n",
-    "model.add( keras.layers.Dropout(0.2) )\n",
-    "model.add( keras.layers.Dense(features_len) )\n",
-    "\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Step 4 - Compile and run"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.1 - Callback"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.makedirs('./run/models',   mode=0o750, exist_ok=True)\n",
-    "save_dir = \"./run/models/best_model.h5\"\n",
-    "bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.2 - Compile"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.compile(optimizer='adam', \n",
-    "              loss='mse', \n",
-    "              metrics   = ['mae'] )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.3 - Fit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "\n",
-    "history=model.fit_generator(train_generator, \n",
-    "                            epochs=10, \n",
-    "                            verbose=1,\n",
-    "                            validation_data = test_generator,\n",
-    "                            callbacks = [bestmodel_callback])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ooo.plot_history(history,plot={'loss':['loss','val_loss'], 'mae':['mae','val_mae']})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - Predict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 5.1 - Load model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 5.2 Make a prediction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x1152 with 12 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "s=random.randint(0,len(dataset_test)-sequence_len)\n",
-    "\n",
-    "sequence      = dataset_test[s:s+sequence_len]\n",
-    "sequence_true = dataset_test[s:s+sequence_len+1]\n",
-    "\n",
-    "pred = loaded_model.predict( np.array([sequence]) )\n",
-    "\n",
-    "# ---- Show result\n",
-    "ooo.plot_multivariate_serie(sequence_true, prediction=pred, labels=features)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 5.3 Full prediction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gap between prediction and reality : 1.72 °C\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 3024x2304 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def denormalize(mean,std,seq):\n",
-    "    nseq = seq.copy()\n",
-    "    for i,s in enumerate(nseq):\n",
-    "        s = s*std + mean\n",
-    "        nseq[i]=s\n",
-    "    return nseq\n",
-    "\n",
-    "\n",
-    "# ---- Get a sequence\n",
-    "\n",
-    "i=random.randint(0,len(dataset_test)-sequence_len)\n",
-    "sequence      = dataset_test[i:i+sequence_len]\n",
-    "sequence_true = dataset_test[i:i+sequence_len+1]\n",
-    "\n",
-    "# ---- Prediction\n",
-    "\n",
-    "pred = loaded_model.predict( np.array([sequence]) )\n",
-    "\n",
-    "# ---- De-normalization\n",
-    "\n",
-    "sequence_true = denormalize(mean,std, sequence_true)\n",
-    "pred          = denormalize(mean,std, pred)\n",
-    "\n",
-    "# ---- Show it\n",
-    "feat=11\n",
-    "\n",
-    "delta_deg=abs(sequence_true[-1][feat]-pred[-1][feat])\n",
-    "print(f'Gap between prediction and reality : {delta_deg:.2f} °C')\n",
-    "\n",
-    "reload(ooo)\n",
-    "ooo.plot_multivariate_serie(sequence_true, prediction=pred, labels=features, only_features=[feat],width=14, height=8)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
-   ]
-  }
- ],
- "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": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/SYNOP/02-First-predictions.ipynb b/SYNOP/02-First-predictions.ipynb
new file mode 100644
index 0000000..01c7ebb
--- /dev/null
+++ b/SYNOP/02-First-predictions.ipynb
@@ -0,0 +1,973 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+    "\n",
+    "# <!-- TITLE --> [SYNOP2] - Try a prediction\n",
+    "<!-- DESC --> Episode 2 : Training session and first predictions\n",
+    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+    "\n",
+    "## Objectives :\n",
+    " - Make a simple prediction (3h)\n",
+    " - Understanding the use of a recurrent neural network\n",
+    "\n",
+    "\n",
+    "SYNOP meteorological data, available at: https://public.opendatasoft.com\n",
+    "\n",
+    "## What we're going to do :\n",
+    "\n",
+    " - Read our dataset\n",
+    " - Select our data and normalize it\n",
+    " - Doing our training\n",
+    " - Making simple predictions\n",
+    "\n",
+    "## Step 1 - Import and init\n",
+    "### 1.1 - Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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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+       "    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",
+       "\n",
+       "</style>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "FIDLE 2020 - Practical Work Module\n",
+      "Version              : 0.5.0\n",
+      "Run time             : Sunday 1 March 2020, 21:17:19\n",
+      "TensorFlow version   : 2.0.0\n",
+      "Keras version        : 2.2.4-tf\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras.callbacks import TensorBoard\n",
+    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
+    "\n",
+    "import numpy as np\n",
+    "import math, random\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd\n",
+    "import h5py, json\n",
+    "import os,time,sys\n",
+    "\n",
+    "from importlib import reload\n",
+    "\n",
+    "sys.path.append('..')\n",
+    "import fidle.pwk as ooo\n",
+    "\n",
+    "ooo.init()\n",
+    "\n",
+    "def np_print(*args):\n",
+    "    with np.printoptions(formatter={'float':'{:8.2f}'.format}, linewidth=np.inf):\n",
+    "        for a in args:\n",
+    "            print(a)    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 - Where are we ? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Well, we should be at HOME !\n",
+      "We are going to use: /home/pjluc/datasets/SYNOP\n"
+     ]
+    }
+   ],
+   "source": [
+    "place, dataset_dir = ooo.good_place( { 'GRICAD' : f'{os.getenv(\"SCRATCH_DIR\",\"\")}/PROJECTS/pr-fidle/datasets/SYNOP',\n",
+    "                                       'IDRIS'  : f'{os.getenv(\"WORK\",\"\")}/datasets/SYNOP',\n",
+    "                                       'HOME'   : f'{os.getenv(\"HOME\",\"\")}/datasets/SYNOP'} )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Read and prepare dataset\n",
+    "### 2.1 - Read it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "<br>**Train dataset example :**"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tend</th>\n",
+       "      <th>cod_tend</th>\n",
+       "      <th>dd</th>\n",
+       "      <th>ff</th>\n",
+       "      <th>td</th>\n",
+       "      <th>u</th>\n",
+       "      <th>ww</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>rafper</th>\n",
+       "      <th>rr1</th>\n",
+       "      <th>rr3</th>\n",
+       "      <th>tc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-120.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>278.75</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>96250.0</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-150.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>278.65</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>96100.0</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>6.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>278.85</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>96110.0</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>6.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>230.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>310.0</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>279.15</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>96340.0</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>280.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>330.0</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>278.15</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>96620.0</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>5.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>480.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>276.95</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>97100.0</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>5.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>530.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>274.05</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>97630.0</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>450.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>340.0</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>272.15</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98080.0</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>280.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>320.0</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>270.15</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98360.0</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>220.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>269.65</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98580.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>100.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>270.45</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98680.0</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>300.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>268.55</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>98980.0</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>130.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>267.45</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>99110.0</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>267.45</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>99260.0</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>140.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>268.15</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>99400.0</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     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_a7c146fa_5bf9_11ea_a502_85a858c76468\" ><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_a7c146fa_5bf9_11ea_a502_85a858c76468level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row0_col0\" class=\"data row0 col0\" >25000.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row0_col1\" class=\"data row0 col1\" >25000.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row0_col2\" class=\"data row0 col2\" >25000.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row0_col3\" class=\"data row0 col3\" >25000.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row0_col4\" class=\"data row0 col4\" >25000.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row0_col5\" class=\"data row0 col5\" >25000.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row0_col6\" class=\"data row0 col6\" >25000.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row0_col7\" class=\"data row0 col7\" >25000.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row0_col8\" class=\"data row0 col8\" >25000.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row0_col9\" class=\"data row0 col9\" >25000.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row0_col10\" class=\"data row0 col10\" >25000.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row0_col11\" class=\"data row0 col11\" >25000.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row1_col0\" class=\"data row1 col0\" >-0.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row1_col1\" class=\"data row1 col1\" >-0.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row1_col7\" class=\"data row1 col7\" >-0.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row1_col8\" class=\"data row1 col8\" >-0.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row1_col9\" class=\"data row1 col9\" >-0.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row2_col0\" class=\"data row2 col0\" >1.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row2_col4\" class=\"data row2 col4\" >1.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row2_col5\" class=\"data row2 col5\" >1.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row2_col6\" class=\"data row2 col6\" >1.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row2_col7\" class=\"data row2 col7\" >1.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row2_col8\" class=\"data row2 col8\" >1.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row2_col9\" class=\"data row2 col9\" >1.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row2_col10\" class=\"data row2 col10\" >1.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row2_col11\" class=\"data row2 col11\" >1.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row3_col0\" class=\"data row3 col0\" >-6.80</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row3_col1\" class=\"data row3 col1\" >-1.59</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row3_col2\" class=\"data row3 col2\" >-1.75</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row3_col3\" class=\"data row3 col3\" >-1.37</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row3_col4\" class=\"data row3 col4\" >-5.18</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row3_col5\" class=\"data row3 col5\" >-3.82</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row3_col6\" class=\"data row3 col6\" >-0.52</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row3_col7\" class=\"data row3 col7\" >-4.94</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row3_col8\" class=\"data row3 col8\" >-1.64</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row3_col9\" class=\"data row3 col9\" >-0.31</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row3_col10\" class=\"data row3 col10\" >-0.27</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row3_col11\" class=\"data row3 col11\" >-3.03</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row4_col0\" class=\"data row4 col0\" >-0.64</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row4_col1\" class=\"data row4 col1\" >-0.85</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row4_col2\" class=\"data row4 col2\" >-0.64</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row4_col3\" class=\"data row4 col3\" >-0.76</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row4_col4\" class=\"data row4 col4\" >-0.72</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row4_col5\" class=\"data row4 col5\" >-0.71</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row4_col6\" class=\"data row4 col6\" >-0.42</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row4_col7\" class=\"data row4 col7\" >-0.55</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row4_col8\" class=\"data row4 col8\" >-0.69</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row4_col9\" class=\"data row4 col9\" >-0.15</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row4_col10\" class=\"data row4 col10\" >-0.20</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row4_col11\" class=\"data row4 col11\" >-0.75</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row5_col0\" class=\"data row5 col0\" >-0.00</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row5_col1\" class=\"data row5 col1\" >-0.48</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row5_col2\" class=\"data row5 col2\" >-0.12</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row5_col3\" class=\"data row5 col3\" >-0.19</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row5_col4\" class=\"data row5 col4\" >0.05</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row5_col5\" class=\"data row5 col5\" >0.18</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row5_col6\" class=\"data row5 col6\" >-0.42</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row5_col7\" class=\"data row5 col7\" >0.03</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row5_col8\" class=\"data row5 col8\" >-0.27</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row5_col9\" class=\"data row5 col9\" >-0.15</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row5_col10\" class=\"data row5 col10\" >-0.20</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row5_col11\" class=\"data row5 col11\" >-0.01</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row6_col0\" class=\"data row6 col0\" >0.63</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row6_col1\" class=\"data row6 col1\" >0.99</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row6_col2\" class=\"data row6 col2\" >1.08</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row6_col3\" class=\"data row6 col3\" >0.50</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row6_col4\" class=\"data row6 col4\" >0.79</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row6_col5\" class=\"data row6 col5\" >0.84</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row6_col6\" class=\"data row6 col6\" >-0.37</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row6_col7\" class=\"data row6 col7\" >0.61</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row6_col8\" class=\"data row6 col8\" >0.52</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row6_col9\" class=\"data row6 col9\" >-0.15</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row6_col10\" class=\"data row6 col10\" >-0.20</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row6_col11\" class=\"data row6 col11\" >0.72</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row7_col0\" class=\"data row7 col0\" >7.16</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row7_col1\" class=\"data row7 col1\" >1.36</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row7_col2\" class=\"data row7 col2\" >1.34</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row7_col3\" class=\"data row7 col3\" >6.28</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row7_col4\" class=\"data row7 col4\" >2.40</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row7_col5\" class=\"data row7 col5\" >1.62</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row7_col6\" class=\"data row7 col6\" >4.46</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row7_col7\" class=\"data row7 col7\" >3.10</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row7_col8\" class=\"data row7 col8\" >6.29</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row7_col9\" class=\"data row7 col9\" >30.36</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row7_col10\" class=\"data row7 col10\" >31.27</td>\n",
+       "                        <td id=\"T_a7c146fa_5bf9_11ea_a502_85a858c76468row7_col11\" class=\"data row7 col11\" >3.02</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f3c9dde0d50>"
+      ]
+     },
+     "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"
+     ]
+    }
+   ],
+   "source": [
+    "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",
+    "# ---- Read dataset\n",
+    "\n",
+    "df = pd.read_csv(f'{dataset_dir}/{dataset_filename}', header=0, sep=';')\n",
+    "\n",
+    "# ---- Train / Test\n",
+    "\n",
+    "dataset_train = df.loc[ :train_len-1, features ]\n",
+    "dataset_test  = df.loc[train_len:,    features ]\n",
+    "ooo.subtitle('Train dataset example :')\n",
+    "display(dataset_train.head(15))\n",
+    "\n",
+    "# ---- Normalize, and convert to numpy array\n",
+    "\n",
+    "mean = dataset_train.mean()\n",
+    "std  = dataset_train.std()\n",
+    "dataset_train = (dataset_train - mean) / std\n",
+    "dataset_test  = (dataset_test  - mean) / std\n",
+    "\n",
+    "ooo.subtitle('After normalization :')\n",
+    "display(dataset_train.describe().style.format(\"{0:.2f}\"))\n",
+    "\n",
+    "dataset_train = dataset_train.to_numpy()\n",
+    "dataset_test  = dataset_test.to_numpy()\n",
+    "\n",
+    "\n",
+    "print('Dataset : ',df.shape)\n",
+    "print('Train dataset : ',dataset_train.shape)\n",
+    "print('Test  dataset : ',dataset_test.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2 - Prepare data generator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "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"
+     ]
+    }
+   ],
+   "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",
+    "\n",
+    "# ---- About\n",
+    "\n",
+    "x,y=train_generator[0]\n",
+    "print(f'Nombre de train batchs disponibles : ', len(train_generator))\n",
+    "print('batch x shape : ',x.shape)\n",
+    "print('batch y shape : ',y.shape)\n",
+    "\n",
+    "# x,y=train_generator[0]\n",
+    "# np_print(x[0])\n",
+    "# np_print(y[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Create a model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = keras.models.Sequential()\n",
+    "model.add( keras.layers.InputLayer(input_shape=(sequence_len, features_len)) )\n",
+    "model.add( keras.layers.LSTM(100, activation='relu') )\n",
+    "model.add( keras.layers.Dropout(0.2) )\n",
+    "model.add( keras.layers.Dense(features_len) )\n",
+    "\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 4 - Compile and run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1 - Callback"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.makedirs('./run/models',   mode=0o750, exist_ok=True)\n",
+    "save_dir = \"./run/models/best_model.h5\"\n",
+    "bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2 - Compile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer='adam', \n",
+    "              loss='mse', \n",
+    "              metrics   = ['mae'] )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.3 - Fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "\n",
+    "history=model.fit_generator(train_generator, \n",
+    "                            epochs=10, \n",
+    "                            verbose=1,\n",
+    "                            validation_data = test_generator,\n",
+    "                            callbacks = [bestmodel_callback])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ooo.plot_history(history,plot={'loss':['loss','val_loss'], 'mae':['mae','val_mae']})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Predict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.1 - Load model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.2 Make a prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1152 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "s=random.randint(0,len(dataset_test)-sequence_len)\n",
+    "\n",
+    "sequence      = dataset_test[s:s+sequence_len]\n",
+    "sequence_true = dataset_test[s:s+sequence_len+1]\n",
+    "\n",
+    "pred = loaded_model.predict( np.array([sequence]) )\n",
+    "\n",
+    "# ---- Show result\n",
+    "reload(ooo)\n",
+    "ooo.plot_multivariate_serie(sequence_true, predictions=pred, labels=features)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.3 Full prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gap between prediction and reality : 1.27 °C\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 3024x2304 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def denormalize(mean,std,seq):\n",
+    "    nseq = seq.copy()\n",
+    "    for i,s in enumerate(nseq):\n",
+    "        s = s*std + mean\n",
+    "        nseq[i]=s\n",
+    "    return nseq\n",
+    "\n",
+    "\n",
+    "# ---- Get a sequence\n",
+    "\n",
+    "i=random.randint(0,len(dataset_test)-sequence_len)\n",
+    "sequence      = dataset_test[i:i+sequence_len]\n",
+    "sequence_true = dataset_test[i:i+sequence_len+1]\n",
+    "\n",
+    "# ---- Prediction\n",
+    "\n",
+    "pred = loaded_model.predict( np.array([sequence]) )\n",
+    "\n",
+    "# ---- De-normalization\n",
+    "\n",
+    "sequence_true = denormalize(mean,std, sequence_true)\n",
+    "pred          = denormalize(mean,std, pred)\n",
+    "\n",
+    "# ---- Show it\n",
+    "feat=11\n",
+    "\n",
+    "delta_deg=abs(sequence_true[-1][feat]-pred[-1][feat])\n",
+    "print(f'Gap between prediction and reality : {delta_deg:.2f} °C')\n",
+    "\n",
+    "reload(ooo)\n",
+    "ooo.plot_multivariate_serie(sequence_true, predictions=pred, labels=features, only_features=[feat],width=14, height=8)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+   ]
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/SYNOP/03-12h-predictions.ipynb b/SYNOP/03-12h-predictions.ipynb
new file mode 100644
index 0000000..ab0d1ee
--- /dev/null
+++ b/SYNOP/03-12h-predictions.ipynb
@@ -0,0 +1,405 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+    "\n",
+    "# <!-- TITLE --> [SYNOP3] - 12h predictions\n",
+    "<!-- DESC --> Episode 3: Attempt to predict in the longer term \n",
+    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+    "\n",
+    "## Objectives :\n",
+    " - Prediction at 12:00\n",
+    " - Understand the principle of using reccurent neurons... and the limitations of our example !\n",
+    "\n",
+    "\n",
+    "SYNOP meteorological data, available at: https://public.opendatasoft.com\n",
+    "\n",
+    "## What we're going to do :\n",
+    "\n",
+    " - Read the data\n",
+    " - Make a reccurent prediction\n",
+    "\n",
+    "## Step 1 - Import and init\n",
+    "### 1.1 - Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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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+       "    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",
+       "\n",
+       "</style>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "FIDLE 2020 - Practical Work Module\n",
+      "Version              : 0.5.0\n",
+      "Run time             : Sunday 1 March 2020, 20:18:54\n",
+      "TensorFlow version   : 2.0.0\n",
+      "Keras version        : 2.2.4-tf\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras.callbacks import TensorBoard\n",
+    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
+    "\n",
+    "import numpy as np\n",
+    "import math, random\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd\n",
+    "import h5py, json\n",
+    "import os,time,sys\n",
+    "\n",
+    "from importlib import reload\n",
+    "\n",
+    "sys.path.append('..')\n",
+    "import fidle.pwk as ooo\n",
+    "\n",
+    "ooo.init()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 - Where are we ? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Well, we should be at HOME !\n",
+      "We are going to use: /home/pjluc/datasets/SYNOP\n"
+     ]
+    }
+   ],
+   "source": [
+    "place, dataset_dir = ooo.good_place( { 'GRICAD' : f'{os.getenv(\"SCRATCH_DIR\",\"\")}/PROJECTS/pr-fidle/datasets/SYNOP',\n",
+    "                                       'IDRIS'  : f'{os.getenv(\"WORK\",\"\")}/datasets/SYNOP',\n",
+    "                                       'HOME'   : f'{os.getenv(\"HOME\",\"\")}/datasets/SYNOP'} )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Read and prepare dataset\n",
+    "As before, in episode 2... ;-)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset :  (29165, 14)\n",
+      "Train dataset :  (25000, 12)\n",
+      "Test  dataset :  (4165, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "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",
+    "# ---- Read dataset\n",
+    "df = pd.read_csv(f'{dataset_dir}/{dataset_filename}', header=0, sep=';')\n",
+    "\n",
+    "# ---- Train / Test\n",
+    "dataset_train = df.loc[ :train_len-1, features ]\n",
+    "dataset_test  = df.loc[train_len:,    features ]\n",
+    "\n",
+    "# ---- Normalize, and convert to numpy array\n",
+    "mean = dataset_train.mean()\n",
+    "std  = dataset_train.std()\n",
+    "dataset_train = np.array( (dataset_train - mean) / std )\n",
+    "dataset_test  = np.array( (dataset_test  - mean) / std )\n",
+    "\n",
+    "print('Dataset : ',df.shape)\n",
+    "print('Train dataset : ',dataset_train.shape)\n",
+    "print('Test  dataset : ',dataset_test.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Predict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1 - Load model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2 Make a 12h prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1152 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sequence_len = 16\n",
+    "iterations   = 4\n",
+    "\n",
+    "# ---- Initial sequence\n",
+    "\n",
+    "s=random.randint(0,len(dataset_test)-sequence_len-iterations)\n",
+    "\n",
+    "sequence_pred = dataset_test[s:s+sequence_len].copy()\n",
+    "sequence_true = dataset_test[s:s+sequence_len+iterations].copy()\n",
+    "\n",
+    "# ---- Iterate on 4 predictions\n",
+    "\n",
+    "sequence_pred=list(sequence_pred)\n",
+    "\n",
+    "for i in range(iterations):\n",
+    "    sequence=sequence_pred[-sequence_len:]\n",
+    "    pred = loaded_model.predict( np.array([sequence]) )\n",
+    "    sequence_pred.append(pred[0])\n",
+    "\n",
+    "# ---- Extract the predictions    \n",
+    "\n",
+    "pred=np.array(sequence_pred[-iterations:])\n",
+    "       \n",
+    "# ---- Show result\n",
+    "\n",
+    "reload(ooo)\n",
+    "ooo.plot_multivariate_serie(sequence_true, predictions=pred, labels=features)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3 Full prediction\n",
+    "#### Some cool functions that do the job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def denormalize(mean,std,seq):\n",
+    "    nseq = seq.copy()\n",
+    "    for i,s in enumerate(nseq):\n",
+    "        s = s*std + mean\n",
+    "        nseq[i]=s\n",
+    "    return nseq\n",
+    "\n",
+    "\n",
+    "def get_prediction(dataset, model, iterations=4,sequence_len=16):\n",
+    "\n",
+    "    # ---- Initial sequence\n",
+    "\n",
+    "    s=random.randint(0,len(dataset)-sequence_len-iterations)\n",
+    "\n",
+    "    sequence_pred = dataset[s:s+sequence_len].copy()\n",
+    "    sequence_true = dataset[s:s+sequence_len+iterations].copy()\n",
+    "\n",
+    "    # ---- Iterate\n",
+    "\n",
+    "    sequence_pred=list(sequence_pred)\n",
+    "\n",
+    "    for i in range(iterations):\n",
+    "        sequence=sequence_pred[-sequence_len:]\n",
+    "        pred = model.predict( np.array([sequence]) )\n",
+    "        sequence_pred.append(pred[0])\n",
+    "\n",
+    "    # ---- Extract the predictions    \n",
+    "\n",
+    "    pred=np.array(sequence_pred[-iterations:])\n",
+    "\n",
+    "    # ---- De-normalization\n",
+    "\n",
+    "    sequence_true = denormalize(mean,std, sequence_true)\n",
+    "    pred          = denormalize(mean,std, pred)\n",
+    "\n",
+    "    return sequence_true,pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### And the result is..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 3024x2304 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "  \n",
+    "sequence_true, pred = get_prediction(dataset_test, loaded_model,iterations=4)\n",
+    "\n",
+    "feat=11\n",
+    "\n",
+    "reload(ooo)\n",
+    "ooo.plot_multivariate_serie(sequence_true, predictions=pred, labels=features, only_features=[feat],width=14, height=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"todo\">\n",
+    "    What you can do:\n",
+    "    <ul>\n",
+    "        <li>Trying to increase the forecasting time</li>\n",
+    "        <li>What could we do to try to improve our forecasts?</li>\n",
+    "    </ul>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+   ]
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/fidle/pwk.py b/fidle/pwk.py
index 8841bce..aefa87c 100644
--- a/fidle/pwk.py
+++ b/fidle/pwk.py
@@ -384,16 +384,22 @@ def plot_donut(values, labels, colors=["lightsteelblue","coral"], figsize=(6,6),
     
 
     
-def plot_multivariate_serie(sequence, labels=None, prediction=None, only_features=None,
+def plot_multivariate_serie(sequence, labels=None, predictions=None, only_features=None,
                             columns=3, width=5,height=4,wspace=0.3,hspace=0.2,
-                            save_as='auto'):
+                            save_as='auto', time_dt=1):
     
     sequence_len = len(sequence)
     features_len = sequence.shape[1]
     if only_features is None : only_features=range(features_len)
     if labels is None        : labels=range(features_len)
-        
-    t=np.arange(sequence_len)
+    
+    t  = np.arange(sequence_len)    
+    if predictions is None:
+        dt = 0
+    else:
+        dt = len(predictions)
+        sequence_with_pred = sequence.copy()
+        sequence_with_pred[-dt:]=predictions
 
     rows = math.ceil(features_len/columns)
     fig  = plt.figure(figsize=(columns*width, rows*height))
@@ -401,11 +407,12 @@ def plot_multivariate_serie(sequence, labels=None, prediction=None, only_feature
     n=1
     for i in only_features:
         ax=fig.add_subplot(rows, columns, n)
-        ax.plot(t,       sequence[:,i],    '-',  linewidth=1,   color='steelblue', label=labels[i])
-        ax.plot(t,       sequence[:,i],    'o',  markersize=4, color='steelblue')
-        ax.plot(t[-1],   sequence[-1:,i],  'o',  fillstyle='full',  markersize=8, color='steelblue')
-        if prediction is not None:
-            ax.plot(t[-1],   [prediction[0][i]],     'o',  fillstyle='full',  markersize=8, color='red')
+        ax.plot(t[:-dt],       sequence[:-dt,i],    '-',  linewidth=1,  color='steelblue', label=labels[i])
+        ax.plot(t[:-dt],       sequence[:-dt,i],    'o',  markersize=4, color='steelblue')
+        ax.plot(t[-dt-1:], sequence[-dt-1:,i],'--o', linewidth=1, fillstyle='none',  markersize=6, color='steelblue')
+        if predictions is not None:
+            ax.plot(t[-dt-1:],     sequence_with_pred[-dt-1:,i],     '--',  linewidth=1, fillstyle='full',  markersize=6, color='red')
+            ax.plot(t[-dt:],       predictions[:,i],     'o',  linewidth=1, fillstyle='full',  markersize=6, color='red')
 
         ax.legend(loc="upper left")
         n+=1
-- 
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