ClimateStripes_20191208.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "Patrick BROCKMANN - LSCE (Climate and Environment Sciences Laboratory)<br>\n",
    "<img align=\"left\" width=\"40%\" src=\"http://www.lsce.ipsl.fr/Css/img/banniere_LSCE_75.png\" \\><br><br>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read observation stations file from European Climate Assessment & Dataset\n",
    "\n",
    "https://www.ecad.eu//download/millennium/millennium.php\n",
    "\n",
    "<pre>\n",
    "wget https://www.ecad.eu//download/millennium/data/ECA_indexTG.zip\n",
    "mkdir data\n",
    "unzip ECA_indexTG.zip -d data\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "File: stations.txt\n",
    "Error at line 318 because there is an extra comma\n",
    "\n",
    "<pre>\n",
    "394  ,KISINEV                                 ,MOLDOVA, REPUBLIC OF                    ,+47:01:12,+028:52:12, 173\n",
    "</pre>\n",
    "\n",
    "stations2.txt is the same without this typo error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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>STAID</th>\n",
       "      <th>STATIONNAME</th>\n",
       "      <th>COUNTRYNAME</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>HGT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>LINKOEPING</td>\n",
       "      <td>SWEDEN</td>\n",
       "      <td>+58:24:00</td>\n",
       "      <td>+015:31:59</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>LINKOEPING-MALMSLAETT</td>\n",
       "      <td>SWEDEN</td>\n",
       "      <td>+58:24:00</td>\n",
       "      <td>+015:31:59</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>KARLSTAD</td>\n",
       "      <td>SWEDEN</td>\n",
       "      <td>+59:21:00</td>\n",
       "      <td>+013:28:00</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>KARLSTAD-AIRPORT</td>\n",
       "      <td>SWEDEN</td>\n",
       "      <td>+59:26:40</td>\n",
       "      <td>+013:20:15</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>OESTERSUND</td>\n",
       "      <td>SWEDEN</td>\n",
       "      <td>+63:10:59</td>\n",
       "      <td>+014:28:59</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6412</td>\n",
       "      <td>20062</td>\n",
       "      <td>NORDLI - SANDVIKA</td>\n",
       "      <td>NORWAY</td>\n",
       "      <td>+64:27:33</td>\n",
       "      <td>+013:35:52</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6413</td>\n",
       "      <td>20063</td>\n",
       "      <td>SKARDSBOEFJELLET</td>\n",
       "      <td>NORWAY</td>\n",
       "      <td>+61:13:00</td>\n",
       "      <td>+007:01:05</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6414</td>\n",
       "      <td>20064</td>\n",
       "      <td>SKISTUA</td>\n",
       "      <td>NORWAY</td>\n",
       "      <td>+63:25:05</td>\n",
       "      <td>+010:15:51</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6415</td>\n",
       "      <td>20065</td>\n",
       "      <td>AASTA HANSTEEN</td>\n",
       "      <td>NORWAY</td>\n",
       "      <td>+67:16:28</td>\n",
       "      <td>+007:39:15</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6416</td>\n",
       "      <td>20066</td>\n",
       "      <td>HAUKELISETER TESTFELT</td>\n",
       "      <td>NORWAY</td>\n",
       "      <td>+59:48:42</td>\n",
       "      <td>+007:12:51</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6417 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      STAID            STATIONNAME COUNTRYNAME        LAT         LON  HGT\n",
       "0         4             LINKOEPING      SWEDEN  +58:24:00  +015:31:59   93\n",
       "1         5  LINKOEPING-MALMSLAETT      SWEDEN  +58:24:00  +015:31:59   93\n",
       "2         6               KARLSTAD      SWEDEN  +59:21:00  +013:28:00   46\n",
       "3         7       KARLSTAD-AIRPORT      SWEDEN  +59:26:40  +013:20:15  107\n",
       "4         8             OESTERSUND      SWEDEN  +63:10:59  +014:28:59  376\n",
       "...     ...                    ...         ...        ...         ...  ...\n",
       "6412  20062      NORDLI - SANDVIKA      NORWAY  +64:27:33  +013:35:52  420\n",
       "6413  20063       SKARDSBOEFJELLET      NORWAY  +61:13:00  +007:01:05  713\n",
       "6414  20064                SKISTUA      NORWAY  +63:25:05  +010:15:51  433\n",
       "6415  20065         AASTA HANSTEEN      NORWAY  +67:16:28  +007:39:15   52\n",
       "6416  20066  HAUKELISETER TESTFELT      NORWAY  +59:48:42  +007:12:51  990\n",
       "\n",
       "[6417 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfStations = pd.read_csv('data/stations2.txt', header=None, skiprows=20)\n",
    "dfStations.rename(columns={\n",
    "                   0: \"STAID\", \n",
    "                   1: \"STATIONNAME\", \n",
    "                   2: \"COUNTRYNAME\", \n",
    "                   3: \"LAT\", \n",
    "                   4: \"LON\", \n",
    "                   5: \"HGT\",\n",
    "                  }, inplace=True)\n",
    "dfStations['STATIONNAME'] = dfStations['STATIONNAME'].apply(lambda x: x.strip())\n",
    "dfStations['COUNTRYNAME'] = dfStations['COUNTRYNAME'].apply(lambda x: x.strip())\n",
    "dfStations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SWEDEN', 'AUSTRIA', 'BELGIUM', 'BULGARIA', 'CROATIA', 'CYPRUS',\n",
       "       'FINLAND', 'FRANCE', 'GERMANY', 'GREECE', 'HUNGARY', 'ICELAND',\n",
       "       'RUSSIAN FEDERATION', 'DENMARK', 'IRELAND', 'ISRAEL',\n",
       "       'NETHERLANDS', 'ITALY', 'NORWAY', 'LATVIA', 'LITHUANIA',\n",
       "       'LUXEMBOURG', 'POLAND', 'PORTUGAL', 'ROMANIA', 'SLOVAKIA',\n",
       "       'SLOVENIA', 'SPAIN', 'SWITZERLAND', 'TURKEY', 'UKRAINE',\n",
       "       'UNITED KINGDOM', 'SERBIA', 'ESTONIA', 'REPUBLIC OF MACEDONIA',\n",
       "       'BOSNIA AND HERZEGOVINA', 'ALBANIA', 'ALGERIA', 'ARMENIA',\n",
       "       'GREENLAND [DENMARK]', 'EGYPT', 'TURKMENISTAN', 'KAZAKHSTAN',\n",
       "       'AZERBAIJAN', 'MOLDOVA REPUBLIC OF', 'BELARUS', 'GEORGIA',\n",
       "       'MOROCCO', 'TUNISIA', 'LIECHTENSTEIN', 'GIBRALTAR', 'UZBEKISTAN',\n",
       "       'KYRGYZSTAN', 'TAJIKISTAN', 'MONTENEGRO', 'SVALBARD AND JAN MAYEN'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfStations['COUNTRYNAME'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfStations['COUNTRYNAME'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert string to correct positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-7.214166666666667"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://stackoverflow.com/questions/50193159/converting-pandas-data-frame-with-degree-minute-second-dms-coordinates-to-deci\n",
    "\n",
    "def dms2dd(s):\n",
    "    # example: s = '+007:12:51'\n",
    "    degrees, minutes, seconds = re.split('[:]+', s)\n",
    "    #print(degrees, minutes, seconds)\n",
    "    dd = abs(float(degrees)) + float(minutes)/60 + float(seconds)/(60*60);\n",
    "    if float(degrees) < 0:\n",
    "        dd*= -1\n",
    "    return dd\n",
    "\n",
    "dms2dd('-007:12:51')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfStations['LON'] = dfStations['LON'].apply(dms2dd)\n",
    "dfStations['LAT'] = dfStations['LAT'].apply(dms2dd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>STAID</th>\n",
       "      <th>STATIONNAME</th>\n",
       "      <th>COUNTRYNAME</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>HGT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>LINKOEPING</td>\n",
       "      <td>SWEDEN</td>\n",
       "      <td>58.400000</td>\n",
       "      <td>15.533056</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>LINKOEPING-MALMSLAETT</td>\n",
       "      <td>SWEDEN</td>\n",
       "      <td>58.400000</td>\n",
       "      <td>15.533056</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>KARLSTAD</td>\n",
       "      <td>SWEDEN</td>\n",
       "      <td>59.350000</td>\n",
       "      <td>13.466667</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>KARLSTAD-AIRPORT</td>\n",
       "      <td>SWEDEN</td>\n",
       "      <td>59.444444</td>\n",
       "      <td>13.337500</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>OESTERSUND</td>\n",
       "      <td>SWEDEN</td>\n",
       "      <td>63.183056</td>\n",
       "      <td>14.483056</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6412</td>\n",
       "      <td>20062</td>\n",
       "      <td>NORDLI - SANDVIKA</td>\n",
       "      <td>NORWAY</td>\n",
       "      <td>64.459167</td>\n",
       "      <td>13.597778</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6413</td>\n",
       "      <td>20063</td>\n",
       "      <td>SKARDSBOEFJELLET</td>\n",
       "      <td>NORWAY</td>\n",
       "      <td>61.216667</td>\n",
       "      <td>7.018056</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6414</td>\n",
       "      <td>20064</td>\n",
       "      <td>SKISTUA</td>\n",
       "      <td>NORWAY</td>\n",
       "      <td>63.418056</td>\n",
       "      <td>10.264167</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6415</td>\n",
       "      <td>20065</td>\n",
       "      <td>AASTA HANSTEEN</td>\n",
       "      <td>NORWAY</td>\n",
       "      <td>67.274444</td>\n",
       "      <td>7.654167</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6416</td>\n",
       "      <td>20066</td>\n",
       "      <td>HAUKELISETER TESTFELT</td>\n",
       "      <td>NORWAY</td>\n",
       "      <td>59.811667</td>\n",
       "      <td>7.214167</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6417 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      STAID            STATIONNAME COUNTRYNAME        LAT        LON  HGT\n",
       "0         4             LINKOEPING      SWEDEN  58.400000  15.533056   93\n",
       "1         5  LINKOEPING-MALMSLAETT      SWEDEN  58.400000  15.533056   93\n",
       "2         6               KARLSTAD      SWEDEN  59.350000  13.466667   46\n",
       "3         7       KARLSTAD-AIRPORT      SWEDEN  59.444444  13.337500  107\n",
       "4         8             OESTERSUND      SWEDEN  63.183056  14.483056  376\n",
       "...     ...                    ...         ...        ...        ...  ...\n",
       "6412  20062      NORDLI - SANDVIKA      NORWAY  64.459167  13.597778  420\n",
       "6413  20063       SKARDSBOEFJELLET      NORWAY  61.216667   7.018056  713\n",
       "6414  20064                SKISTUA      NORWAY  63.418056  10.264167  433\n",
       "6415  20065         AASTA HANSTEEN      NORWAY  67.274444   7.654167   52\n",
       "6416  20066  HAUKELISETER TESTFELT      NORWAY  59.811667   7.214167  990\n",
       "\n",
       "[6417 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfStations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b51bb3b2e16e42249eea5a6d5e8736c1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(basemap={'url': 'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', 'max_zoom': 19, 'attribution': 'Map …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import HTML\n",
    "from ipyleaflet import Map, Marker, Popup, CircleMarker\n",
    "\n",
    "center = (45, 3)\n",
    "\n",
    "m = Map(center=center, zoom=5)\n",
    "\n",
    "for index, row in dfStations.iterrows():\n",
    "  \n",
    "    if row['COUNTRYNAME'] in ['FRANCE', 'GERMANY', 'ITALY']: \n",
    "        circle_marker = CircleMarker()\n",
    "        circle_marker.location = (row['LAT'], row['LON'])\n",
    "        circle_marker.radius = 5\n",
    "        circle_marker.weight = 0\n",
    "        circle_marker.fill_opacity = 0.4\n",
    "\n",
    "        if row['COUNTRYNAME'] == 'FRANCE' :  \n",
    "            circle_marker.color = \"red\"\n",
    "            circle_marker.fill_color = \"red\"\n",
    "        elif row['COUNTRYNAME'] == 'GERMANY' :\n",
    "            circle_marker.color = \"blue\"\n",
    "            circle_marker.fill_color = \"blue\"\n",
    "        elif row['COUNTRYNAME'] == 'ITALY' :\n",
    "            circle_marker.color = \"green\"\n",
    "            circle_marker.fill_color = \"green\"     \n",
    "        m.add_layer(circle_marker)\n",
    "\n",
    "display(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f789569f03c1491cacce4791baafdec7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(basemap={'url': 'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', 'max_zoom': 19, 'attribution': 'Map …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import HTML\n",
    "from ipyleaflet import Map, Marker, Popup, CircleMarker\n",
    "\n",
    "center = (45, 3)\n",
    "\n",
    "m = Map(center=center, zoom=5)\n",
    "\n",
    "for index, row in dfStations.iterrows():\n",
    "  \n",
    "    if row['COUNTRYNAME'] in ['FRANCE']: \n",
    "        circle_marker = CircleMarker()\n",
    "        circle_marker.location = (row['LAT'], row['LON'])\n",
    "        circle_marker.radius = 5\n",
    "        circle_marker.weight = 1\n",
    "        circle_marker.fill_opacity = 0.4\n",
    "        circle_marker.color = \"red\"\n",
    "        circle_marker.fill_color = \"red\"\n",
    "        message = HTML()\n",
    "        message.value = \"%s %s %s\" %(row['STAID'], row['COUNTRYNAME'], row['STATIONNAME'])       \n",
    "        circle_marker.popup = message   \n",
    "        \n",
    "        m.add_layer(circle_marker)\n",
    "\n",
    "display(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading a data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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>SOUID</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>ANNUAL</th>\n",
       "      <th>WINTER HALF YEAR</th>\n",
       "      <th>SUMMER HALF YEAR</th>\n",
       "      <th>WINTER (DJF)</th>\n",
       "      <th>SPRING (MAM)</th>\n",
       "      <th>SUMMER (JJA)</th>\n",
       "      <th>AUTUMN (SON)</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1893-01-01</td>\n",
       "      <td>35</td>\n",
       "      <td>1893</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1889.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1433</td>\n",
       "      <td>2121.0</td>\n",
       "      <td>1231</td>\n",
       "      <td>-94</td>\n",
       "      <td>...</td>\n",
       "      <td>1088</td>\n",
       "      <td>1586</td>\n",
       "      <td>1630</td>\n",
       "      <td>2007</td>\n",
       "      <td>2102.0</td>\n",
       "      <td>2249.0</td>\n",
       "      <td>1750</td>\n",
       "      <td>1366</td>\n",
       "      <td>572</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1894-01-01</td>\n",
       "      <td>35</td>\n",
       "      <td>1894</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>1645.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>1886.0</td>\n",
       "      <td>1216</td>\n",
       "      <td>341</td>\n",
       "      <td>...</td>\n",
       "      <td>860</td>\n",
       "      <td>1285</td>\n",
       "      <td>1322</td>\n",
       "      <td>1749</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>1903.0</td>\n",
       "      <td>1603</td>\n",
       "      <td>1212</td>\n",
       "      <td>832</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1895-01-01</td>\n",
       "      <td>35</td>\n",
       "      <td>1895</td>\n",
       "      <td>1158.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>1784.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1108</td>\n",
       "      <td>1946.0</td>\n",
       "      <td>1427</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>585</td>\n",
       "      <td>1194</td>\n",
       "      <td>1549</td>\n",
       "      <td>1887</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>1963.0</td>\n",
       "      <td>2123</td>\n",
       "      <td>1089</td>\n",
       "      <td>1079</td>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1896-01-01</td>\n",
       "      <td>35</td>\n",
       "      <td>1896</td>\n",
       "      <td>1111.0</td>\n",
       "      <td>718.0</td>\n",
       "      <td>1641.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1162</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>1043</td>\n",
       "      <td>227</td>\n",
       "      <td>...</td>\n",
       "      <td>1014</td>\n",
       "      <td>1028</td>\n",
       "      <td>1438</td>\n",
       "      <td>1880</td>\n",
       "      <td>2087.0</td>\n",
       "      <td>1791.0</td>\n",
       "      <td>1609</td>\n",
       "      <td>1045</td>\n",
       "      <td>475</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1897-01-01</td>\n",
       "      <td>35</td>\n",
       "      <td>1897</td>\n",
       "      <td>1205.0</td>\n",
       "      <td>675.0</td>\n",
       "      <td>1668.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>1194</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1138</td>\n",
       "      <td>282</td>\n",
       "      <td>...</td>\n",
       "      <td>1021</td>\n",
       "      <td>1178</td>\n",
       "      <td>1381</td>\n",
       "      <td>1982</td>\n",
       "      <td>2054.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>1492</td>\n",
       "      <td>1111</td>\n",
       "      <td>813</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            SOUID  YEAR  ANNUAL  WINTER HALF YEAR  SUMMER HALF YEAR  \\\n",
       "Date                                                                  \n",
       "1893-01-01     35  1893  1283.0               NaN            1889.0   \n",
       "1894-01-01     35  1894  1180.0             690.0            1645.0   \n",
       "1895-01-01     35  1895  1158.0             474.0            1784.0   \n",
       "1896-01-01     35  1896  1111.0             718.0            1641.0   \n",
       "1897-01-01     35  1897  1205.0             675.0            1668.0   \n",
       "\n",
       "            WINTER (DJF)  SPRING (MAM)  SUMMER (JJA)  AUTUMN (SON)    9  ...  \\\n",
       "Date                                                                     ...   \n",
       "1893-01-01           NaN          1433        2121.0          1231  -94  ...   \n",
       "1894-01-01         437.0          1154        1886.0          1216  341  ...   \n",
       "1895-01-01          61.0          1108        1946.0          1427   44  ...   \n",
       "1896-01-01         371.0          1162        1920.0          1043  227  ...   \n",
       "1897-01-01         496.0          1194        1982.0          1138  282  ...   \n",
       "\n",
       "              11    12    13    14      15      16    17    18    19   20  \n",
       "Date                                                                       \n",
       "1893-01-01  1088  1586  1630  2007  2102.0  2249.0  1750  1366   572  362  \n",
       "1894-01-01   860  1285  1322  1749  2001.0  1903.0  1603  1212   832  391  \n",
       "1895-01-01   585  1194  1549  1887  1987.0  1963.0  2123  1089  1079  598  \n",
       "1896-01-01  1014  1028  1438  1880  2087.0  1791.0  1609  1045   475  415  \n",
       "1897-01-01  1021  1178  1381  1982  2054.0  1910.0  1492  1111   813  400  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df1 = pd.read_csv('data/indexTG011322.txt', header=None, delim_whitespace=True, skiprows=30, na_values=['-999999'])\n",
    "df1 = pd.read_csv('data/indexTG000035.txt', header=None, delim_whitespace=True, skiprows=30, na_values=['-999999'])\n",
    "df1.rename(columns={\n",
    "                   0: \"SOUID\", \n",
    "                   1: \"YEAR\", \n",
    "                   2: \"ANNUAL\", \n",
    "                   3: \"WINTER HALF YEAR\", \n",
    "                   4: \"SUMMER HALF YEAR\", \n",
    "                   5: \"WINTER (DJF)\",\n",
    "                   6: \"SPRING (MAM)\",\n",
    "                   7: \"SUMMER (JJA)\",\n",
    "                   8: \"AUTUMN (SON)\"\n",
    "                  }, inplace=True)\n",
    "df1['Date'] =  pd.to_datetime(df1['YEAR'], format='%Y')\n",
    "df1.set_index('Date', inplace=True)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff613783eb8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df1[['ANNUAL', 'WINTER (DJF)', 'SUMMER (JJA)']]/100).plot(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date start: 1893\n",
      "Date end: 2018\n",
      "Size: 126\n",
      "Missing: 1\n"
     ]
    }
   ],
   "source": [
    "print('Date start: %d\\nDate end: %d\\nSize: %d\\nMissing: %d' \\\n",
    "          %(df1['YEAR'].iloc[0], df1['YEAR'].iloc[-1], df1['YEAR'].size, df1['YEAR'].size-df1['ANNUAL'].count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff60ef07be0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df1['ANNUAL']/100).plot(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.485200000000003 12.83 9.52\n"
     ]
    }
   ],
   "source": [
    "print((df1['ANNUAL']/100).mean(), (df1['ANNUAL']/100).max(), (df1['ANNUAL']/100).min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.143888888888888"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[ df1['YEAR'] >= 2000]['ANNUAL'].mean()/100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.823333333333332"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[(df1['YEAR'] >= 1980) & (df1['YEAR'] < 2010)]['ANNUAL'].mean()/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff60ee93be0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "anomalies = (df1['ANNUAL']-df1[(df1['YEAR'] >= 1980) & (df1['YEAR'] < 2010)]['ANNUAL'].mean())/100\n",
    "anomalies2 = (df1['ANNUAL']-df1[(df1['YEAR'] >= 2000)]['ANNUAL'].mean())/100\n",
    "anomalies.plot(grid=True)\n",
    "anomalies2.plot(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from IPython.display import SVG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"100\" style=\"border:1px solid lightgray\" width=\"592\">\n",
       "<rect fill=\"rgb(255,255,255)\" height=\"100\" width=\"4\" x=\"0\"/>\n",
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       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size = 4\n",
    "height = 100\n",
    "\n",
    "anomalies = (df1['ANNUAL']-df1[(df1['YEAR'] >= 1980) & (df1['YEAR'] < 2010)]['ANNUAL'].mean())/100\n",
    "    \n",
    "norm = mpl.colors.Normalize(vmin=np.nanmin(anomalies),vmax=np.nanmax(anomalies))\n",
    "    \n",
    "dateStart = 1870\n",
    "dateEnd = 2018\n",
    "    \n",
    "width = (dateEnd-dateStart)*size\n",
    "a='<svg width=\"%d\" height=\"%d\" style=\"border:1px solid lightgray\">\\n' \\\n",
    "            %(width, height)\n",
    "for i in range(dateStart, dateEnd):\n",
    "        \n",
    "    try: \n",
    "        if (not np.isnan(anomalies.loc[str(i)][0])):\n",
    "                c = plt.cm.RdBu_r(norm(anomalies.loc[str(i)][0]))\n",
    "        else:\n",
    "                c = (0,1,0)               # missing \n",
    "    except:\n",
    "        c = (1,1,1)\n",
    "            \n",
    "    c = '<rect x=\"%d\" width=\"%d\" height=\"%d\" fill=\"rgb(%d,%d,%d)\"></rect>\\n' \\\n",
    "                % ((i-dateStart)*size,size,height,c[0]*255,c[1]*255,c[2]*255)\n",
    "    a = a + c\n",
    "    \n",
    "a = a + '</svg>'\n",
    "\n",
    "SVG(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cairosvg import svg2png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "svg2png(bytestring=a, write_to='output.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_date(file):\n",
    "    \n",
    "    df = pd.read_csv(file, header=None, delim_whitespace=True, skiprows=30, na_values=['-999999'])\n",
    "    df.rename(columns={\n",
    "        0: \"SOUID\", \n",
    "        1: \"YEAR\",\n",
    "        2: \"ANNUAL\", \n",
    "        3: \"WINTER HALF YEAR\", \n",
    "        4: \"SUMMER HALF YEAR\", \n",
    "        5: \"WINTER (DJF)\",\n",
    "        6: \"SPRING (MAM)\",\n",
    "        7: \"SUMMER (JJA)\",\n",
    "        8: \"AUTUMN (SON)\"\n",
    "        }, inplace=True)\n",
    "    df['Date'] =  pd.to_datetime(df['YEAR'], format='%Y')\n",
    "    df.set_index('Date', inplace=True)\n",
    "    \n",
    "    return df['YEAR'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(file, size=2, height=40, verbose=False, displaySVG=True, localNorm=[]):\n",
    "    \n",
    "    s = pd.read_csv(file, skiprows=8, nrows=1)\n",
    "    countryName = s.iloc[0][0][33:74].strip()\n",
    "    stationName = s.iloc[0][0][74:115].strip()\n",
    "    \n",
    "    df = pd.read_csv(file, header=None, delim_whitespace=True, skiprows=30, na_values=['-999999'])\n",
    "    df.rename(columns={\n",
    "        0: \"SOUID\", \n",
    "        1: \"YEAR\",\n",
    "        2: \"ANNUAL\", \n",
    "        3: \"WINTER HALF YEAR\", \n",
    "        4: \"SUMMER HALF YEAR\", \n",
    "        5: \"WINTER (DJF)\",\n",
    "        6: \"SPRING (MAM)\",\n",
    "        7: \"SUMMER (JJA)\",\n",
    "        8: \"AUTUMN (SON)\"\n",
    "        }, inplace=True)\n",
    "    df['Date'] =  pd.to_datetime(df['YEAR'], format='%Y')\n",
    "    df.set_index('Date', inplace=True)\n",
    "    \n",
    "    nbGood1 = df[(df['YEAR'] >= 1980) & (df['YEAR'] < 2010)]['YEAR'].count()\n",
    "    if (nbGood1 != 30):\n",
    "        return -1\n",
    "             \n",
    "    anomalies = (df['ANNUAL']-df[(df['YEAR'] >= 1980) & (df['YEAR'] < 2010)]['ANNUAL'].mean())/100\n",
    "    \n",
    "    if not localNorm:\n",
    "        norm = mpl.colors.Normalize(vmin=np.nanmin(anomalies),vmax=np.nanmax(anomalies))\n",
    "    else:\n",
    "        norm = mpl.colors.Normalize(vmin=localNorm[0],vmax=localNorm[1])\n",
    "    \n",
    "    dateStart = 1870\n",
    "    dateEnd = 2018\n",
    "    \n",
    "    width = (dateEnd-dateStart)*size\n",
    "    a='<svg width=\"%d\" height=\"%d\" style=\"border:1px solid lightgray\">\\n' \\\n",
    "            %(width, height)\n",
    "    for i in range(dateStart, dateEnd):\n",
    "        \n",
    "        try: \n",
    "            if (not np.isnan(anomalies.loc[str(i)][0])):\n",
    "                c = plt.cm.RdBu_r(norm(anomalies.loc[str(i)][0]))\n",
    "            else:\n",
    "                c = (0.8,0.8,0.8)               # missing \n",
    "        except:\n",
    "            c = (1,1,1)\n",
    "            \n",
    "        c = '<rect x=\"%d\" width=\"%d\" height=\"%d\" fill=\"rgb(%d,%d,%d)\"></rect>\\n' \\\n",
    "                % ((i-dateStart)*size,size,height,c[0]*255,c[1]*255,c[2]*255)\n",
    "        a = a + c\n",
    "    \n",
    "    a = a + '</svg>'\n",
    "    \n",
    "    if verbose:\n",
    "        print(countryName, stationName)\n",
    "        print('Mean: %f' %(df[(df['YEAR'] >= 1980) & (df['YEAR'] < 2010)]['ANNUAL'].mean()/100))\n",
    "        print('Date start: %d\\nDate end: %d\\nSize: %d\\nMissing: %d' \\\n",
    "          %(df['YEAR'].iloc[0], df['YEAR'].iloc[-1], df['YEAR'].size, df['YEAR'].size-df['ANNUAL'].count()))\n",
    "        print('Min: %6.2f\\nMax: %6.2f' %(np.nanmin(anomalies),np.nanmax(anomalies)))\n",
    "        \n",
    "    STATID = file[-10:-4]\n",
    "    #print('stripes/' + STATID + '.png')\n",
    "    svg2png(bytestring=a, write_to=STATID + '.png')\n",
    "\n",
    "    if displaySVG:\n",
    "        display(SVG(a))\n",
    "        return 0\n",
    "    else:     \n",
    "        return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FRANCE BOURGES\n",
      "Mean: 11.710333\n",
      "Date start: 1945\n",
      "Date end: 2018\n",
      "Size: 74\n",
      "Missing: 2\n",
      "Min:  -1.91\n",
      "Max:   1.45\n"
     ]
    },
    {
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      "FRANCE LYON - ST EXUPERY\n",
      "Mean: 12.143667\n",
      "Date start: 1975\n",
      "Date end: 2018\n",
      "Size: 44\n",
      "Missing: 1\n",
      "Min:  -1.80\n",
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      "FRANCE TOULOUSE-BLAGNAC\n",
      "Mean: 13.750667\n",
      "Date start: 1947\n",
      "Date end: 2018\n",
      "Size: 72\n",
      "Missing: 1\n",
      "Min:  -2.28\n",
      "Max:   1.34\n"
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      "FRANCE CHATEAUROUX DEOLS\n",
      "Mean: 11.823333\n",
      "Date start: 1893\n",
      "Date end: 2018\n",
      "Size: 126\n",
      "Missing: 1\n",
      "Min:  -2.30\n",
      "Max:   1.01\n"
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      "Date end: 2019\n",
      "Size: 120\n",
      "Missing: 3\n",
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      "FRANCE BESANCON\n",
      "Mean: 10.930000\n",
      "Date start: 1884\n",
      "Date end: 2018\n",
      "Size: 135\n",
      "Missing: 2\n",
      "Min:  -1.91\n",
      "Max:   1.42\n"
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      "Mean: 15.419667\n",
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      "Date end: 2018\n",
      "Size: 70\n",
      "Missing: 2\n",
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      "Date start: 1948\n",
      "Date end: 2018\n",
      "Size: 71\n",
      "Missing: 1\n",
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      "Date start: 1873\n",
      "Date end: 2018\n",
      "Size: 146\n",
      "Missing: 0\n",
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      "FRANCE MEYTHET\n",
      "Mean: 10.869444\n",
      "Date start: 1970\n",
      "Date end: 2018\n",
      "Size: 49\n",
      "Missing: 15\n",
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       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "localNorm = [-2.5,2.5]\n",
    "\n",
    "read_data('data/indexTG000032.txt', verbose=True)\n",
    "read_data('data/indexTG000032.txt', verbose=False, localNorm=localNorm)\n",
    "\n",
    "read_data('data/indexTG000037.txt', verbose=True)\n",
    "read_data('data/indexTG000037.txt', verbose=False, localNorm=localNorm)\n",
    "\n",
    "read_data('data/indexTG000033.txt', verbose=True)\n",
    "read_data('data/indexTG000033.txt', verbose=False, localNorm=localNorm)\n",
    "\n",
    "read_data('data/indexTG000035.txt', verbose=True)\n",
    "read_data('data/indexTG000035.txt', verbose=False, localNorm=localNorm)\n",
    "\n",
    "read_data('data/indexTG011310.txt', verbose=True)\n",
    "read_data('data/indexTG011310.txt', verbose=False, localNorm=localNorm)\n",
    "\n",
    "read_data('data/indexTG019101.txt', verbose=True)\n",
    "read_data('data/indexTG019101.txt', verbose=False, localNorm=localNorm)\n",
    "\n",
    "read_data('data/indexTG000747.txt', verbose=True)\n",
    "read_data('data/indexTG000747.txt', verbose=False, localNorm=localNorm)\n",
    "\n",
    "read_data('data/indexTG002209.txt', verbose=True)\n",
    "read_data('data/indexTG002209.txt', verbose=False, localNorm=localNorm)\n",
    "\n",
    "read_data('data/indexTG000782.txt', verbose=True)\n",
    "read_data('data/indexTG000782.txt', verbose=False, localNorm=localNorm)\n",
    "\n",
    "read_data('data/indexTG000038.txt', verbose=True)\n",
    "read_data('data/indexTG000038.txt', verbose=False, localNorm=localNorm)\n",
    "\n",
    "read_data('data/indexTG011322.txt', verbose=True)\n",
    "read_data('data/indexTG011322.txt', verbose=False, localNorm=localNorm)\n",
    "\n",
    "read_data('data/indexTG011310.txt', verbose=True)\n",
    "read_data('data/indexTG011310.txt', verbose=False, localNorm=localNorm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a map with the climate stripes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38140a1090fe45768ff6a7d9cb85a375",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(basemap={'url': 'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', 'max_zoom': 19, 'attribution': 'Map …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import HTML\n",
    "from ipyleaflet import Map, Marker, Popup, CircleMarker\n",
    "\n",
    "center = (45, 3)\n",
    "\n",
    "m = Map(center=center, zoom=5, close_popup_on_click=False)\n",
    "\n",
    "dfStations['Stripes'] = 0       # will stay to 0 if no error\n",
    "dfStations['startDate'] = 0\n",
    "for index, row in dfStations.iterrows():\n",
    "        \n",
    "    if row['COUNTRYNAME'] == 'FRANCE' :\n",
    "        circle_marker = CircleMarker()\n",
    "        circle_marker.location = (row['LAT'], row['LON'])\n",
    "        circle_marker.radius = 5\n",
    "        circle_marker.color = \"royalblue\"\n",
    "        circle_marker.weight = 1\n",
    "        circle_marker.fill_color = \"royalblue\"\n",
    "        circle_marker.fill_opacity = 0.4\n",
    "        \n",
    "        file = 'data/indexTG0%05d.txt' % row['STAID']\n",
    "        \n",
    "        a = read_data(file, displaySVG=False, localNorm=[-2.5,2.5])\n",
    "        dfStations.loc[index, 'startDate'] = read_date(file)\n",
    "        \n",
    "        if (a != -1):\n",
    "            message = HTML()\n",
    "            message.value = \"%s (%s)\" %(row['STATIONNAME'],file)  + a\n",
    "            circle_marker.popup = message\n",
    "            m.add_layer(circle_marker)\n",
    "            \n",
    "        else:\n",
    "            dfStations.loc[index, 'Stripes'] = 1\n",
    "\n",
    "display(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = dfStations[(dfStations['COUNTRYNAME'] == 'FRANCE') & (dfStations['Stripes'] == 0)].copy()\n",
    "a.drop(columns=['Stripes'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.to_json(\"data.json\", orient='values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "! mv *.png stripes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 1))\n",
    "fig.subplots_adjust(bottom=0.6)\n",
    "\n",
    "cmap = mpl.cm.RdBu_r\n",
    "norm = mpl.colors.Normalize(vmin=-2.5, vmax=2.5)\n",
    "\n",
    "cb1 = mpl.colorbar.ColorbarBase(ax, \n",
    "                                cmap=cmap,\n",
    "                                norm=norm,\n",
    "                                orientation='horizontal',\n",
    "                                extend='both')\n",
    "cb1.set_label('Difference from 1980-2009 (°C)')\n",
    "\n",
    "fig.savefig('legend.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}