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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
"# <!-- TITLE --> [SYNOP1] - Preaparation of data\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": [
{
"data": {
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],
"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",
"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",
"\n",
"import numpy as np\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",
"from fidle.pwk import subtitle\n",
"pd.set_option('display.max_rows',200)\n"
]
},
{
"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"
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]
}
],
"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 the data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"data_filename = 'origine/donnees-synop-essentielles-omm-LYS.csv'\n",
"schema_filename = 'origine/schema.json'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1 - Read columns code"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"with open(f'{dataset_dir}/{schema_filename}','r') as json_file:\n",
" schema = json.load(json_file)\n",
"\n",
"synop_codes=list( schema['definitions']['donnees-synop-essentielles-omm_records']['properties']['fields']['properties'].keys() )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2 - Read data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
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