ClimateStripes.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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "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": 2,
   "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": 2,
     "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": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfStations['COUNTRYNAME'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 4,
     "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-7.214166666666667"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfStations['LON'] = dfStations['LON'].apply(dms2dd)\n",
    "dfStations['LAT'] = dfStations['LAT'].apply(dms2dd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfStations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fcc70bb0a6da474596c39de376c70726",
       "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31d5b8749d17478e8e3da3b800c2845e",
       "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": 10,
   "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>1873-01-01</td>\n",
       "      <td>38</td>\n",
       "      <td>1873</td>\n",
       "      <td>1061</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1513</td>\n",
       "      <td>NaN</td>\n",
       "      <td>967</td>\n",
       "      <td>1855</td>\n",
       "      <td>1063</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>831</td>\n",
       "      <td>885</td>\n",
       "      <td>1184</td>\n",
       "      <td>1673</td>\n",
       "      <td>1974</td>\n",
       "      <td>1911</td>\n",
       "      <td>1431</td>\n",
       "      <td>1075</td>\n",
       "      <td>683</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1874-01-01</td>\n",
       "      <td>38</td>\n",
       "      <td>1874</td>\n",
       "      <td>1086</td>\n",
       "      <td>607.0</td>\n",
       "      <td>1614</td>\n",
       "      <td>390.0</td>\n",
       "      <td>1007</td>\n",
       "      <td>1892</td>\n",
       "      <td>1130</td>\n",
       "      <td>452</td>\n",
       "      <td>...</td>\n",
       "      <td>696</td>\n",
       "      <td>1170</td>\n",
       "      <td>1160</td>\n",
       "      <td>1750</td>\n",
       "      <td>2136</td>\n",
       "      <td>1784</td>\n",
       "      <td>1674</td>\n",
       "      <td>1142</td>\n",
       "      <td>574</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1875-01-01</td>\n",
       "      <td>38</td>\n",
       "      <td>1875</td>\n",
       "      <td>1072</td>\n",
       "      <td>498.0</td>\n",
       "      <td>1633</td>\n",
       "      <td>234.0</td>\n",
       "      <td>1041</td>\n",
       "      <td>1817</td>\n",
       "      <td>1118</td>\n",
       "      <td>501</td>\n",
       "      <td>...</td>\n",
       "      <td>545</td>\n",
       "      <td>1021</td>\n",
       "      <td>1555</td>\n",
       "      <td>1740</td>\n",
       "      <td>1764</td>\n",
       "      <td>1946</td>\n",
       "      <td>1758</td>\n",
       "      <td>978</td>\n",
       "      <td>623</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1876-01-01</td>\n",
       "      <td>38</td>\n",
       "      <td>1876</td>\n",
       "      <td>1106</td>\n",
       "      <td>493.0</td>\n",
       "      <td>1577</td>\n",
       "      <td>216.0</td>\n",
       "      <td>970</td>\n",
       "      <td>1912</td>\n",
       "      <td>1157</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>694</td>\n",
       "      <td>1049</td>\n",
       "      <td>1170</td>\n",
       "      <td>1687</td>\n",
       "      <td>2042</td>\n",
       "      <td>2000</td>\n",
       "      <td>1500</td>\n",
       "      <td>1284</td>\n",
       "      <td>682</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1877-01-01</td>\n",
       "      <td>38</td>\n",
       "      <td>1877</td>\n",
       "      <td>1096</td>\n",
       "      <td>756.0</td>\n",
       "      <td>1512</td>\n",
       "      <td>662.0</td>\n",
       "      <td>901</td>\n",
       "      <td>1879</td>\n",
       "      <td>1036</td>\n",
       "      <td>623</td>\n",
       "      <td>...</td>\n",
       "      <td>570</td>\n",
       "      <td>998</td>\n",
       "      <td>1139</td>\n",
       "      <td>1965</td>\n",
       "      <td>1820</td>\n",
       "      <td>1855</td>\n",
       "      <td>1284</td>\n",
       "      <td>1002</td>\n",
       "      <td>824</td>\n",
       "      <td>377</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",
       "1873-01-01     38  1873    1061               NaN              1513   \n",
       "1874-01-01     38  1874    1086             607.0              1614   \n",
       "1875-01-01     38  1875    1072             498.0              1633   \n",
       "1876-01-01     38  1876    1106             493.0              1577   \n",
       "1877-01-01     38  1877    1096             756.0              1512   \n",
       "\n",
       "            WINTER (DJF)  SPRING (MAM)  SUMMER (JJA)  AUTUMN (SON)    9  ...  \\\n",
       "Date                                                                     ...   \n",
       "1873-01-01           NaN           967          1855          1063  492  ...   \n",
       "1874-01-01         390.0          1007          1892          1130  452  ...   \n",
       "1875-01-01         234.0          1041          1817          1118  501  ...   \n",
       "1876-01-01         216.0           970          1912          1157    6  ...   \n",
       "1877-01-01         662.0           901          1879          1036  623  ...   \n",
       "\n",
       "             11    12    13    14    15    16    17    18   19   20  \n",
       "Date                                                                 \n",
       "1873-01-01  831   885  1184  1673  1974  1911  1431  1075  683  306  \n",
       "1874-01-01  696  1170  1160  1750  2136  1784  1674  1142  574   39  \n",
       "1875-01-01  545  1021  1555  1740  1764  1946  1758   978  623  216  \n",
       "1876-01-01  694  1049  1170  1687  2042  2000  1500  1284  682  688  \n",
       "1877-01-01  570   998  1139  1965  1820  1855  1284  1002  824  377  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "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/indexTG000038.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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcf36b3eef0>"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date start: 1873\n",
      "Date end: 2018\n",
      "Size: 146\n",
      "Missing: 0\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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcf3225e7b8>"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.531917808219184 14.04 8.76\n"
     ]
    }
   ],
   "source": [
    "print((df1['ANNUAL']/100).mean(), (df1['ANNUAL']/100).max(), (df1['ANNUAL']/100).min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.958947368421052"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[ df1['YEAR'] >= 2000]['ANNUAL'].mean()/100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.405666666666667"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[(df1['YEAR'] >= 1980) & (df1['YEAR'] < 2010)]['ANNUAL'].mean()/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcf321a7a58>"
      ]
     },
     "execution_count": 17,
     "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "min=-1.6\n",
    "max=1.6\n",
    "delta=0.2\n",
    "levels = np.arange(min,max,delta)\n",
    "cmap = plt.get_cmap('RdBu_r')\n",
    "colors = cmap(np.linspace(0, 1, len(levels)+1))\n",
    "cmap, norm = matplotlib.colors.from_levels_and_colors(levels, colors, extend='both')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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": [
    "barlist=plt.bar([1,2,3,4], [1,1,1,1], width=0.8)\n",
    "barlist[0].set_color(cmap(norm(-1.5)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anomalies = (df1['ANNUAL']-df1[(df1['YEAR'] >= 1980) & (df1['YEAR'] < 2010)]['ANNUAL'].mean())/100\n",
    "\n",
    "min=-1.6\n",
    "max=1.6\n",
    "delta=0.2\n",
    "allLevels = np.arange(min,max+delta,delta)\n",
    "cmap = plt.get_cmap('RdBu_r')\n",
    "colors = cmap(np.linspace(0, 1, len(allLevels)+1))\n",
    "cmap, norm = matplotlib.colors.from_levels_and_colors(allLevels, colors, extend='both')\n",
    "    \n",
    "dateStart = 1870\n",
    "dateEnd = 2018\n",
    "    \n",
    "x=[]\n",
    "y=[]\n",
    "for i in range(dateStart, dateEnd):\n",
    "    x.append(i)\n",
    "\n",
    "    try: \n",
    "        if (not np.isnan(anomalies.loc[str(i)][0])):\n",
    "                c = cmap(norm(anomalies.loc[str(i)][0]))\n",
    "        else:\n",
    "                c = (0,1,0)               # missing \n",
    "    except:\n",
    "        c = (1,1,1)\n",
    "            \n",
    "    y.append(c)\n",
    "    \n",
    "    \n",
    "fig, axes = plt.subplots(figsize=(8, 1), dpi=100)\n",
    "fig.subplots_adjust(bottom=0.3)\n",
    "barlist = plt.bar(x, width=1, height=20)\n",
    "for i in range(len(x)):\n",
    "    barlist[i].set_color(y[i])    \n",
    "axes.get_yaxis().set_visible(False)\n",
    "axes.set_xlim([dateStart,dateEnd])\n",
    "axes.set_ylim([0,8])\n",
    "color = 'gray'\n",
    "axes.tick_params(axis='x', colors=color)\n",
    "axes.spines['bottom'].set_color(color)\n",
    "axes.spines['top'].set_color(color) \n",
    "axes.spines['right'].set_color(color)\n",
    "axes.spines['left'].set_color(color)\n",
    "\n",
    "fig.savefig('stripes2.png', bbox_inches='tight')\n",
    "#plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(file, verbose=False, displayPNG=True, levels=[-1.6,1.6,0.2]):\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     # EXIT_FAILURE\n",
    "             \n",
    "    anomalies = (df['ANNUAL']-df[(df['YEAR'] >= 1980) & (df['YEAR'] < 2010)]['ANNUAL'].mean())/100\n",
    "    \n",
    "    min=levels[0]\n",
    "    max=levels[1]\n",
    "    delta=levels[2]\n",
    "    allLevels = np.arange(min,max+delta,delta)\n",
    "    cmap = plt.get_cmap('RdBu_r')\n",
    "    colors = cmap(np.linspace(0, 1, len(allLevels)+1))\n",
    "    cmap, norm = matplotlib.colors.from_levels_and_colors(allLevels, colors, extend='both')\n",
    "    \n",
    "    dateStart = 1870\n",
    "    dateEnd = 2018\n",
    "    \n",
    "    x = []\n",
    "    y = []\n",
    "    for i in range(dateStart, dateEnd):\n",
    "        x.append(i)\n",
    "        \n",
    "        try: \n",
    "            if (not np.isnan(anomalies.loc[str(i)][0])):\n",
    "                c = cmap(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",
    "        y.append(c)\n",
    "          \n",
    "    plt.ioff()\n",
    "    fig, axes = plt.subplots(figsize=(8, 1))\n",
    "    fig.subplots_adjust(bottom=0.3)\n",
    "    barlist = plt.bar(x, height=10)\n",
    "    for i in range(len(x)):\n",
    "        barlist[i].set_color(y[i])    \n",
    "    axes.get_yaxis().set_visible(False)\n",
    "    axes.set_xlim([dateStart,dateEnd+1])\n",
    "    axes.set_ylim([0,8])\n",
    "    color = 'gray'\n",
    "    axes.tick_params(axis='x', colors=color)\n",
    "    axes.spines['bottom'].set_color(color)\n",
    "    axes.spines['top'].set_color(color) \n",
    "    axes.spines['right'].set_color(color)\n",
    "    axes.spines['left'].set_color(color)\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",
    "    fig.savefig(STATID + '.png', bbox_inches='tight')\n",
    "    \n",
    "    if not displayPNG:\n",
    "        plt.close(fig)\n",
    "        \n",
    "    return 0            # EXIT_SUCCESS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
      "FRANCE PARIS - MONTSOURIS\n",
      "Mean: 12.405667\n",
      "Date start: 1873\n",
      "Date end: 2018\n",
      "Size: 146\n",
      "Missing: 0\n",
      "Min:  -3.65\n",
      "Max:   1.63\n",
      "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "read_data('data/indexTG000032.txt', verbose=True)\n",
    "read_data('data/indexTG000038.txt', verbose=True)\n",
    "read_data('data/indexTG000033.txt', verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a map with the climate stripes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b19ab22f02746b1be0449b602352d77",
       "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 index > 40:\n",
    "#        break\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, displayPNG=False, levels=[-1.6,1.6,0.2])\n",
    "        dfStations.loc[index, 'startDate'] = read_date(file)\n",
    "        \n",
    "        if (a != -1):\n",
    "            message = HTML()\n",
    "            STATID = file[-10:-4]\n",
    "            message.value = \"%s (%s)\" %(row['STATIONNAME'],file)  + '<br><img style=\"width: 400px;\" src=\"' + STATID + '.png\">'\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": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Stripes'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2896\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2897\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Stripes'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-1b16cf10df8a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdfStations\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdfStations\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'COUNTRYNAME'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'FRANCE'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdfStations\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Stripes'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Stripes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2978\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2979\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2980\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2981\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2982\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2899\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Stripes'"
     ]
    }
   ],
   "source": [
    "a = dfStations[(dfStations['COUNTRYNAME'] == 'FRANCE') & (dfStations['Stripes'] == 0)].copy()\n",
    "a.drop(columns=['Stripes'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.to_json(\"data.json\", orient='values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "! mv *.png stripes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAABBCAYAAABcgpQ0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAARKElEQVR4nO2deZBc1XWHv9/07FpHM0jISEJIYDahncXYgBYkljg2BBJDiFkC5S04UK5QxomJg+0qg2NM4aJiE+M44NgOBRGLWQxCIOSAhNG+IBaJRQwILUgaI4FG0szJH/cO0xp6Zt7M9OtuKeer6prX7913zzmve97v3aXvkZnhOI7jOE7XlBXbAcdxHMc5EHDBdBzHcZwEuGA6juM4TgJcMB3HcRwnAS6YjuM4jpOA8kIYkVQGnAd8GagthE3HcRzn/wWbgB+Z2aK0DSnNn5VIKsuMnt7SunkVqm1g8KQLKKvqn5o9gLqRY1Ktv4364QNStzF2xMDUbYwbMSh1GwBH1vdL3cYRg2tStwEwtF/6z5kDWnambiOz4+3UbQC0vL0udRvNb7ySuo0dL29I3QbAey+9k7qNLS+9l7qN9Zt3pW4D4Ln3d7GUJvbSyhQG8YC9q7RspfKfH4RyWotqG7Cd75IZPQNV9iPTb0ga5vajrKIqdRsAmcrq1G2UV6UvAJXVhRGZ6pr0OxZqagsTS21t+oJZ29Kauo1Mc2GuV0t1+v+TmaqK1G00VxSkQ46a8kzqNqrL0h+Nq1RhRvwaqGQ2h/Aee1hCEyNVY2kJZ16/AZLKModPa1FNA7ZzM5kjZqIK74F1HMdx0qU+SziXRuGczCAezKNw5kUwJWUyh5+xTzUN2AebyYxxoXQcx3EKTz2VzOIQtsUW54gonA/lQTj7JJhBKE/fp5p67IOtLpSO4zhOSTAkCuf2PApnrwRTUiYz6vR91NRjH2wjM2YWqijMeIjjOI7jJKWOSs7kELazl6Xs4DBV22QG8zCbyqyHs157JJiSyjOjTttLTT22exvlLpSO4zjOAUAdFcz8SDib+ARVrZ/VMB5hc2LhTCSY7UI5BNu9g/Kxs1F5+rNEHcdxHCefBOFsYEcUzuE9EM4uBVNSeWbkZ6JQNlE+9iwXSsdxHOeAZzAVzKCBpizh/DMN49EuhLPrFmbVwL2tO991oXQcx3EOSgZRwfQonPN5jyOpbQVyTgzq8pelmeFTobmJ1o3LsD3przziOI7jOIVmC80sZDuVlHECna+w1mULc9/r8yRJZcNOaG15Yz6qGULZsPGoMt3l7RzHcRwnbbbQzBKaMGAKg5hjG7v8yUm3k35iX24UzvGtLW/Mh5o6MkPHo6r011N1HMdxnHyyOQqlgMbMnpP27dv3QpLzEv+sJFs4M8MmtLa8+QxU15EZ5sLpOI7jlD6baGZpFMoNfDjVzJb05PweL1ywn3AeOqG1ZcMCqBpEZtgEF07HcRyn5NhEM0vYQQaxgQ+nmNnS3tTT66XxPtbi3LAAqgZG4Uw/LZXjOI7jdMW77GYJTZQj3mL3JDNb3pf6+rz4+v7CObG1ZcP/QuUAMoeOR1WFybXoOI7jOG1sZDdLaaKCMhrZPdHMVuSj3ryl9/p4i/NZqOwfWpzVLpyO4zhOumyMLcqqIJQTzGxlPuvPe0bU/cc4J7a2vPUcVNaSGTYh36Ycx3Ech3dii7KKMt5m9wlmtjoNO+rhYu09NyApM/as1tZNK6joX09ZyhODqvoXZuJRdb/K1G0MLICN+v7p2wAYVF2Ruo0BlelnqgeoqUg/k3yF7U3dRlnzrtRtALTuakrfxp92pG6jeUdhFm/ZvSP9z2X39t2p22j6cF/qNgBe37ubWjKs54NxZrYmTVupC+ZHhiQBNwCPFMRg+pwP3F9sJ/KEx1J6HCxxgMdSqhwsscwys5sKYahgggkgabGZTS2YwRTxWEqTgyWWgyUO8FhKlYMllkLGkX7fkuM4juMcBLhgOo7jOE4CCi2Y/15ge2nisZQmB0ssB0sc4LGUKgdLLAWLo6BjmI7jOI5zoOJdso7jOI6TgFQFU9JfSlojqVVSp7OYJA2WdJ+klyStlfSpNP3qDUlikTRS0tMxhjWSrim0n0mQNETSXEmvxr91nZT7YYxjraSfxJ8GlRRJY4llB0p6W9LthfSxKySdLellSeskXZ/jeJWke+Lx5yWNLryXyegulqxyF0qyru4JxSbB5zIq/q8vk7RS0rnF8LM7JP2HpM2Scv6QX9Il0f+Vkp6TVJIrzHQXRywzTdLyeM96JhVHzCy1F3AscDQwH5jaRbm7gKvidiUwOE2/0ooFGA5MjtsDgFeA44rtew4/fwhcH7evB27OUeZU4FkgE18LgWnF9r03sWSVvQ34DXB7sf2O/mSA9cCY+L1f0fH7AnwN+Fncvgi4p9h+9zaWWG4AsABY1NU9odRjIYybfTVuHwe8UWy/O4nldGAysLqT46cCdXH7HOD5YvvcyzgGAy8Co+L7oWn4kWoL08zWmtnLXZWRNDBejF/Ec/aYWfrLdvSQJLGY2UaLaWPM7H1gLXBYIfzrIZ8nPKQQ/56Xo4wB1YQbRhVQAWwqiHc9I0ksSJoCDAOeKJBfSTgJWGdmr5nZHuC/CfFkkx3ffcDMUmzpkywWgO8RHnLSX2qm9ySJxYC2tEyDgHcK6F9izGwBsK2L48+Z2fb4dhEwoiCO9ZDu4gD+GphjZhti+c1p+FEKY5hjgC3AL2P3xp2S+hXbqb4Su84mAc8X15OcDDOzjRBEHhjasYCZLQSeBjbG1+NmtragXiaj21gklQG3ANcV2LfuOAx4K+t9Ix9/wPqojJntA5qA+oJ41zO6jUXSJGCkmT1cSMd6QZLP5V+Av5HUCDwKfL0wrqXKlcBjxXail3wSqJM0X9ISSZemYaTPi69LehI4NMehfzKzBxP6MBn4upk9L+k2QtfaDX31rafkIZa2evoD/wNca2Z/ypd/PaGrWBKefyShG7rtiXOupNPjk15B6WsshG7NR83srRJrnOVypuO09SRlSoEu/YwPLbcClxfKoT6Q5JpfDPynmd0S51z8StI4M2tN3738I2k6QTA/U2xfekk5MAWYCdQACyUtMrNX8m2kT5jZmX2sohFoNLO2lth9BMEsOHmIBUkVBLH8tZnN6btXvaOrWCRtkjTczDZKGg7k6r44H1hkZjvjOY8BpxDGnwpKHmL5FHCapK8B/YFKSTvNrCjfsywagZFZ70fw8a69tjKNksoJ3X9ddU0Vi+5iGQCMA+bHh5ZDgYckfc7MFhfMy2Qk+VyuBM6G0BsjqRpoIPf3r6SRNB64EzjHzN4rtj+9pBHYama7gF2SFgATCPNI8kbRu2TN7F3gLUlHx10zCYO3BxxxbOkXwFoz+3Gx/emCh4DL4vZlQK7W8wbgDEnl8SHgDMKYbKnRbSxmdomZjTKz0cA/AHeXgFgCvAAcJekISZWEST0PdSiTHd+FwFMWZzWUGF3GYmZNZtZgZqPj57AIKEWxhGSfywbCvQpJxxLG+7cU1Ms8IGkUMAf4Yr5bYwXmQcJDcbmkWuBk0rhfpTyz6XyC8jcTJow8Hvd/gtBF1lZuIrAYWAk8QJy1VUqvJLEQujMsxrE8vs4ttu85YqkH5gGvxr9D4v6pwJ1xOwPcEb90LwI/LrbfvY2lQ/nLKZFZstGfcwlPwesJXf8A3yWICYQb8b3AOuCPwJhi+9zbWDqUnU+JzpJN+LkcR5hFviL+n88uts+dxPFbwhyEvfH+dSXwFeAr8fidwPas+9XiYvvcmzhimevivWo1YTgs7374Sj+O4ziOk4Cid8k6juM4zoGAC6bjOI7jJMAF03Ecx3ES4ILpOI7jOAlwwXQcx3GcBLhgOo7jOE4CXDCdVJHUkpVyZ4Wkb8Rl0pA0VdJP4naVpCdj2S9IOi2es1xSTXGjyI2kY6J/yySNTdnW7yXtkPRwh/0zJC2VtFrSXXE1ICQNkvS7eM3XSLoi65zLFNKhvSrpso62YplZcU3OVfHvjKxjU+L+dcpK+6ZOUq1JqpN0f0wh9UdJ4zqxKUlPxYQMSLooxnZtVpn+ku6QtD7GtUDSyZIq43afVy9znM5wwXTS5kMzm2hmxwOzCD8I/w6AmS02s7+P5SYBFbHsPcAlwI/i+w+7MxJvtoX+Pp8HPGhmk8xsfcq+/Cvwxewd0cZdwEVmNg54k/ZVgf4OeNHMJgDTgFuiqAwhXP+TCVk5vqPcOUS3An9uZifEOn+VdeynwJeAo+Lr7Lj/emCemR1FWESibTWlfwSWm9l44FJCmrVcnAussPb1ly8CTgROUVifGcIP7bcBR8Xv1OVAg4WsIvOAL3RSt+P0GRdMp2BYSLnzJeDqKCrTJD0saSjwX8DE2GL7MvBXwD9L+jWApOskvRBbKTfGfaMVklv/G7AUGClptqSFsWVyb9uNVtIbkm6M+1dJOibu7y/pl3HfSkkXxP0562lDIWHwtcBVComEc/lycax3taSbs87dKenm2HJ7UtJJClkWXpP0uU6u3Tzg/Q6764Fma1/SbC5wQdspwIDY+utPEJl9wFnAXDPbZiGt01zaBS/b3jIza1s/dQ1QrdALMBwYaGYLLax6cjftKdU6S7V2HEHMMLOXgNGShuUI8xL2X9qwbRF0IzyHjCUI/bctLnJuIQXXI7HcA7EOx0kFF0ynoJjZa4Tv3dCsfZuBq4A/xBblHYS1O68zs0skzSa0ZE4iLKM4RdLp8fSjCWvDTgJ2Ad8GzjSzyYTlFr+RZX5r3P9TwpqyELLiNJnZCbEF9JSkhm7qwcweBX4G3Gpm03P4she4GZgRfT5RUpuA9APmm9kUggh+n9D6Pp+w/FpStgIVkqbG9xfSvmj47YRsM+8Aq4BrosgkSV3VkQuAZWbWHMs2dnJ+Z6nWVgB/ASDpJOBwcudd/DSwJOv9HMK1X2whv+zxhJZqSyd+ria0SB0nFby/3ykGPc2xNTu+lsX3/QkCugF408wWxf2nENf4jMNqlcDCrHrasscsId7AgTMJXX8AmNl2SZ/tpp7OyPblRIIobgGILeXTCa2gPcDvY7lVhFbiXkmrgNEJ7LT5apIuAm6VVEVIjr0vHj6LsDboDGAsIT3bH+hhujBJxxOEf3bbrp6cH7kJuE3SckK8y7L8zGZIFMZQqdldtLdYu8XMWiTtkTQgux7HyRcumE5BkTQGaCGkQTo26WnAD2LLM7uu0YRWZXa5uWZ2cSf1NMe/LbR/90XuHJRd1dMZHX3pjL3Wvohza5tfZtba00krFhJ9nwahG5mQSBfgCuCmaGedpNeBYwgtwmlZVYwgpNw6nzi2DFxlZosljQDuBy7NGqNtZP/WYXbqq5yp1uKY5BXRRwGvx1dH9kkqs85zSq4BJnRTpgrY3ckxx+kT3iXrFAxJhxC6MW/PEowkPA78bdZ45GFx3LMji4BPKyS/RlKtpE/mKJfNE8DVWT7W9bKejjxPSI/WIClDSDj8TA/r6Ja26xBbmN8kXF/YP/3UMEJ38WuEazlbYeZqHaHl+LiZ3R+7wydGsRwMPAJ8y8yebbMXu1rfl3RKFL9LaR93zJlqTdJghTRZELreF1juxOovA2M6izWK9mLgxmgbSUdJ+nzcrge2mNne7q+c4/QcF0wnbWriRJ41wJMEgbqxJxWY2RPAbwhZ1FcRkowPyFFuC2HW5G8lrSQI3zHdVP99oC5OzFkBTO9lPR192Qh8C3iaMIa31Mxy5R1NROxOvReYKalR0lnx0HWS1hJSyv3OzJ6K+78HnBqv1zzgm2a21cy2xWMvxNd3476OXA0cCdwQP7/lWQ8pXyXMVl1HSH/1WNx/EzBL0quEMdmb4v5jgTWSXgLOAa7pJMxH2L/1m4urCMmn18XYfk57C3c68Gg35ztOr/H0Xo7jlASxG/duM5vVy/PnEFrEL+fXM8cJeAvTcZySILbKf664cEFPiF2+D7hYOmniLUzHcRzHSYC3MB3HcRwnAS6YjuM4jpMAF0zHcRzHSYALpuM4juMkwAXTcRzHcRLwf4Jo46qxcL99AAAAAElFTkSuQmCC\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",
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 1))\n",
    "fig.subplots_adjust(bottom=0.6)\n",
    "\n",
    "min=-1.6\n",
    "max=1.6\n",
    "delta=0.2\n",
    "allLevels = np.arange(min,max+delta,delta)\n",
    "cmap = plt.get_cmap('RdBu_r')\n",
    "colors = cmap(np.linspace(0, 1, len(allLevels)+1))\n",
    "cmap, norm = matplotlib.colors.from_levels_and_colors(allLevels, colors, extend='both')\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\n",
    "\n",
    "fig.savefig('legend.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}