KNMIStats.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gerrits gelijk\n",
    "Bij Nieuws en Co op radio 1 melde ik gisteren dat hoewel het gemiddeld over een jaar niet meer regende, er vooral meer hevige buien zijn: de buien die overstromingen veroorzaken.\n",
    "\n",
    "Toen ik de studio uitliep en op Twitter keek had weerman Gerrit Hiemstra direct gereageerd: de gemiddelde hoeveelheid regen neemt wel degelijk toe! Hij liet daarbij de in [deze tweet](https://twitter.com/GerritHiemstra/status/1001180991623491585) het kaartje zien met het verschil in jaar-gemiddelde tussen de periode 1951-1980 en 1980-2010. \n",
    "\n",
    "Oops: typisch geval van blinde vlek aan mijn kant. Voor overstromingen zijn de pieken, niet het jaargemiddelde van belang. Ik had onthouden dat de pieken waren toegenomen, maar had me moeten realiseren dat ik veel minder zeker wist of het gemiddelde ook omhoog was gegaan. Gerrit weet dat natuurlijk als meteoroloog wel.\n",
    "\n",
    "Om mezelf wat meer gevoel bij de KNMI gegevens te geven ben ik op hun website gaan spitten en wat blijkt: je kan direct vanuit de programmertaal Python, waar ik veel mee werk, KNMI data inladen. Dat doe ik in onderstaande python notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Benodigde libraries\n",
    "Allereerst laden we de KNMI library in, plus een aantal standaard libraries voor het omgaan met gegevens (pandas en numpy) en het afbeelden van gegevens (matplotlib).\n",
    "\n",
    "Tevens printen we een lijst met alle KNMI stations, zodat we daarna kunnen kiezen voor welk station we de gegevens willen bekijken."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{257: Station(number=257, longitude=4.603, latitude=52.506, altitude=8.5, name='WIJK AAN ZEE'), 258: Station(number=258, longitude=5.401, latitude=52.649, altitude=7.3, name='HOUTRIBDIJK'), 260: Station(number=260, longitude=5.18, latitude=52.1, altitude=1.9, name='DE BILT'), 263: Station(number=263, longitude=5.177, latitude=52.101, altitude=2.0, name='DE BILT (TEST)'), 8: Station(number=8, longitude=5.528, latitude=52.453, altitude=-4.2, name='LELYSTAD'), 265: Station(number=265, longitude=5.274, latitude=52.13, altitude=13.9, name='SOESTERBERG'), 266: Station(number=266, longitude=5.338, latitude=53.07, altitude=3.0, name='KORNWERDERZAND'), 267: Station(number=267, longitude=5.384, latitude=52.898, altitude=-1.3, name='STAVOREN'), 268: Station(number=268, longitude=5.436, latitude=52.531, altitude=999.9, name='HOUTRIB'), 13: Station(number=13, longitude=3.314, latitude=47.975, altitude=999.9, name='WISSEKERKE ???'), 270: Station(number=270, longitude=5.752, latitude=53.224, altitude=1.2, name='LEEUWARDEN'), 271: Station(number=271, longitude=5.356, latitude=52.888, altitude=2.6, name='STAVOREN'), 16: Station(number=16, longitude=3.314, latitude=47.975, altitude=999.9, name='HEINKENSZAND ???'), 17: Station(number=17, longitude=3.892, latitude=51.529, altitude=0.7, name='WILHELMINADORP'), 18: Station(number=18, longitude=3.314, latitude=47.975, altitude=999.9, name='PHILIPPINE ???'), 19: Station(number=19, longitude=3.314, latitude=47.975, altitude=999.9, name='NIEUWE TONGE ???'), 20: Station(number=20, longitude=6.7, latitude=51.969, altitude=34.1, name='WINTERSWIJK'), 277: Station(number=277, longitude=6.2, latitude=53.413, altitude=2.9, name='LAUWERSOOG'), 278: Station(number=278, longitude=6.259, latitude=52.435, altitude=3.6, name='HEINO'), 279: Station(number=279, longitude=6.574, latitude=52.75, altitude=15.8, name='HOOGEVEEN'), 280: Station(number=280, longitude=6.585, latitude=53.125, altitude=5.2, name='EELDE'), 283: Station(number=283, longitude=6.657, latitude=52.069, altitude=29.1, name='HUPSEL'), 28: Station(number=28, longitude=5.82, latitude=52.961, altitude=-0.4, name='JOURE'), 29: Station(number=29, longitude=5.044, latitude=52.65, altitude=999.9, name='HOORN (NH)'), 286: Station(number=286, longitude=7.15, latitude=53.196, altitude=-0.2, name='NIEUW BEERTA'), 32: Station(number=32, longitude=4.526, latitude=51.576, altitude=1.2, name='OUDENBOSCH'), 33: Station(number=33, longitude=5.677, latitude=51.549, altitude=13.2, name='GEMERT'), 34: Station(number=34, longitude=6.887, latitude=52.785, altitude=24.1, name='EMMEN'), 35: Station(number=35, longitude=3.314, latitude=47.975, altitude=999.9, name='HAAMSTEDE ???'), 36: Station(number=36, longitude=3.314, latitude=47.975, altitude=999.9, name='NUMANSDORP ???'), 37: Station(number=37, longitude=5.675, latitude=51.976, altitude=2.0, name='WAGENINGEN (HAARWEG)'), 38: Station(number=38, longitude=3.314, latitude=47.975, altitude=999.9, name='NAALDWIJK ???'), 39: Station(number=39, longitude=4.156, latitude=51.988, altitude=999.9, name='H. VAN HOLLAND(MOLENPAD)'), 40: Station(number=40, longitude=5.837, latitude=51.856, altitude=999.9, name='NIJMEGEN (RADBOUD)'), 41: Station(number=41, longitude=5.526, latitude=52.458, altitude=-4.0, name='LELYSTAD'), 554: Station(number=554, longitude=3.592, latitude=51.681, altitude=999.9, name='MEETPOST NOORDWIJK'), 391: Station(number=391, longitude=6.197, latitude=51.498, altitude=19.5, name='ARCEN'), 300: Station(number=300, longitude=2.567, latitude=51.65, altitude=999.9, name='LICHTSCHIP NOORDHINDER'), 385: Station(number=385, longitude=5.933, latitude=51.55, altitude=9999.9, name='DE PEEL'), 308: Station(number=308, longitude=3.379, latitude=51.381, altitude=0.0, name='CADZAND'), 310: Station(number=310, longitude=3.596, latitude=51.442, altitude=8.0, name='VLISSINGEN'), 311: Station(number=311, longitude=3.672, latitude=51.379, altitude=0.0, name='HOOFDPLAAT'), 290: Station(number=290, longitude=6.891, latitude=52.274, altitude=34.8, name='TWENTHE'), 313: Station(number=313, longitude=3.242, latitude=51.505, altitude=0.0, name='VLAKTE V.D. RAAN'), 315: Station(number=315, longitude=3.998, latitude=51.447, altitude=0.0, name='HANSWEERT'), 316: Station(number=316, longitude=3.694, latitude=51.657, altitude=0.0, name='SCHAAR'), 319: Station(number=319, longitude=3.861, latitude=51.226, altitude=1.7, name='WESTDORPE'), 320: Station(number=320, longitude=3.67, latitude=51.927, altitude=22.0, name='LICHTEILAND GOEREE'), 321: Station(number=321, longitude=3.275, latitude=51.999, altitude=19.0, name='EUROPLATFORM'), 323: Station(number=323, longitude=3.884, latitude=51.527, altitude=1.4, name='WILHELMINADORP'), 324: Station(number=324, longitude=4.006, latitude=51.596, altitude=0.0, name='STAVENISSE'), 325: Station(number=325, longitude=3.931, latitude=51.654, altitude=-4.0, name='ZIERIKZEE'), 609: Station(number=609, longitude=4.318, latitude=51.891, altitude=999.9, name=\"R'DAM-GEULHAVEN\"), 328: Station(number=328, longitude=3.821, latitude=51.668, altitude=999.9, name='ROGGENPLAAT'), 330: Station(number=330, longitude=4.122, latitude=51.992, altitude=11.9, name='HOEK VAN HOLLAND'), 331: Station(number=331, longitude=4.193, latitude=51.48, altitude=0.0, name='THOLEN'), 551: Station(number=551, longitude=2.066, latitude=56.4, altitude=999.9, name='AUK'), 269: Station(number=269, longitude=5.52, latitude=52.458, altitude=-3.7, name='LELYSTAD'), 312: Station(number=312, longitude=3.622, latitude=51.768, altitude=0.0, name='OOSTERSCHELDE'), 995: Station(number=995, longitude=3.596, latitude=51.442, altitude=8.0, name='VLISSINGEN (TEST)'), 340: Station(number=340, longitude=4.342, latitude=51.449, altitude=19.2, name='WOENSDRECHT'), 343: Station(number=343, longitude=4.313, latitude=51.893, altitude=3.5, name=\"R'DAM-GEULHAVEN\"), 344: Station(number=344, longitude=4.447, latitude=51.962, altitude=-4.3, name='ROTTERDAM'), 603: Station(number=603, longitude=6.189, latitude=52.311, altitude=999.9, name='DIEPENVEEN'), 348: Station(number=348, longitude=4.926, latitude=51.97, altitude=-0.7, name='CABAUW'), 605: Station(number=605, longitude=6.196, latitude=53.409, altitude=3.0, name='LAUWERSOOG'), 350: Station(number=350, longitude=4.936, latitude=51.566, altitude=14.9, name='GILZE-RIJEN'), 272: Station(number=272, longitude=5.85, latitude=52.616, altitude=3.6, name='RAMSPOL'), 553: Station(number=553, longitude=3.276, latitude=51.999, altitude=999.9, name='EUROPLATFORM'), 356: Station(number=356, longitude=5.146, latitude=51.859, altitude=0.7, name='HERWIJNEN'), 998: Station(number=998, longitude=5.177, latitude=52.101, altitude=2.0, name='DE BILT (TEST)'), 614: Station(number=614, longitude=6.92, latitude=53.337, altitude=999.9, name='EEMSHAVEN'), 273: Station(number=273, longitude=5.888, latitude=52.703, altitude=-3.3, name='MARKNESSE'), 616: Station(number=616, longitude=4.907, latitude=52.367, altitude=999.9, name='AMSTERDAM (COENHAVEN)'), 617: Station(number=617, longitude=6.196, latitude=51.498, altitude=19.0, name='ARCEN'), 615: Station(number=615, longitude=4.296, latitude=52.274, altitude=12.0, name='HOOGEVEEN'), 108: Station(number=108, longitude=3.314, latitude=47.975, altitude=999.9, name='HELLEVOETSLUIS ???'), 370: Station(number=370, longitude=5.377, latitude=51.451, altitude=22.6, name='EINDHOVEN'), 275: Station(number=275, longitude=5.873, latitude=52.056, altitude=48.2, name='DEELEN'), 375: Station(number=375, longitude=5.707, latitude=51.659, altitude=22.0, name='VOLKEL'), 377: Station(number=377, longitude=5.763, latitude=51.198, altitude=30.0, name='ELL'), 122: Station(number=122, longitude=6.303, latitude=52.158, altitude=9.0, name='ALMEN'), 379: Station(number=379, longitude=5.7, latitude=50.794, altitude=42.0, name='OOST-MAARLAND'), 380: Station(number=380, longitude=5.762, latitude=50.906, altitude=114.3, name='MAASTRICHT'), 125: Station(number=125, longitude=5.054, latitude=51.784, altitude=1.4, name='ANDEL'), 126: Station(number=126, longitude=4.079, latitude=51.596, altitude=1.0, name='ST ANNALAND'), 127: Station(number=127, longitude=3.314, latitude=47.975, altitude=999.9, name='BUCHTEN ???'), 128: Station(number=128, longitude=6.443, latitude=52.593, altitude=6.0, name='DEDEMSVAART'), 129: Station(number=129, longitude=5.78, latitude=52.712, altitude=-3.7, name='EMMELOORD'), 130: Station(number=130, longitude=3.314, latitude=47.975, altitude=999.9, name='EPEN ???'), 133: Station(number=133, longitude=5.342, latitude=53.398, altitude=-1.0, name='HOORN (TERSCHELLING)'), 135: Station(number=135, longitude=5.338, latitude=53.07, altitude=3.0, name='KORNWERDERZAND'), 138: Station(number=138, longitude=5.676, latitude=50.843, altitude=999.9, name='MAASTRICHT'), 139: Station(number=139, longitude=5.121, latitude=52.458, altitude=-0.8, name='MARKEN'), 142: Station(number=142, longitude=3.314, latitude=47.975, altitude=999.9, name='OUDE WETERING ???'), 143: Station(number=143, longitude=4.393, latitude=51.855, altitude=0.2, name='POORTUGAAL'), 147: Station(number=147, longitude=5.611, latitude=52.658, altitude=999.9, name='URK'), 148: Station(number=148, longitude=6.191, latitude=51.356, altitude=30.0, name='VENLO'), 152: Station(number=152, longitude=3.558, latitude=51.356, altitude=1.2, name='SCHOONDIJKE'), 153: Station(number=153, longitude=5.356, latitude=52.886, altitude=2.6, name='STAVOREN'), 158: Station(number=158, longitude=3.314, latitude=47.975, altitude=999.9, name='SPIJK (GR.) ???'), 159: Station(number=159, longitude=4.057, latitude=51.251, altitude=2.4, name='KAPELLEBRUG'), 161: Station(number=161, longitude=5.975, latitude=53.391, altitude=999.9, name='TERNAARD'), 162: Station(number=162, longitude=5.222, latitude=52.418, altitude=999.9, name='OOSTVAARDERSDIEP'), 163: Station(number=163, longitude=5.476, latitude=52.263, altitude=0.2, name='NIJKERK (SLUIS)'), 164: Station(number=164, longitude=5.909, latitude=51.092, altitude=34.1, name='ECHT'), 165: Station(number=165, longitude=3.314, latitude=47.975, altitude=999.9, name='KAAP HOOFD ???'), 166: Station(number=166, longitude=6.238, latitude=53.583, altitude=4.0, name='SCHIERMONNIKOOG'), 167: Station(number=167, longitude=6.85, latitude=53.381, altitude=999.9, name='BIERUM'), 168: Station(number=168, longitude=5.906, latitude=50.764, altitude=155.0, name='EPEN'), 169: Station(number=169, longitude=5.304, latitude=53.392, altitude=0.4, name='FORMERUM'), 170: Station(number=170, longitude=5.715, latitude=50.794, altitude=50.0, name='OOST MAARLAND'), 285: Station(number=285, longitude=6.399, latitude=53.575, altitude=0.0, name='HUIBERTGAT'), 550: Station(number=550, longitude=3.22, latitude=53.218, altitude=999.9, name='K13'), 200: Station(number=200, longitude=4.355, latitude=52.041, altitude=999.9, name='YPENBURG'), 201: Station(number=201, longitude=2.936, latitude=54.327, altitude=42.7, name='D15-FA-1'), 202: Station(number=202, longitude=4.362, latitude=52.206, altitude=9999.9, name='MP KATWIJK'), 203: Station(number=203, longitude=3.342, latitude=52.361, altitude=41.8, name='P11-B'), 204: Station(number=204, longitude=3.628, latitude=53.271, altitude=41.8, name='K14-FA-1C'), 205: Station(number=205, longitude=3.81, latitude=55.401, altitude=48.4, name='A12-CPP'), 206: Station(number=206, longitude=4.012, latitude=54.118, altitude=43.4, name='F16-A'), 207: Station(number=207, longitude=4.961, latitude=53.616, altitude=44.0, name='L9-FF-1'), 208: Station(number=208, longitude=5.942, latitude=53.493, altitude=40.5, name='AWG-1'), 209: Station(number=209, longitude=4.518, latitude=52.465, altitude=0.0, name='IJMOND'), 210: Station(number=210, longitude=4.43, latitude=52.171, altitude=-0.2, name='VALKENBURG'), 211: Station(number=211, longitude=2.945, latitude=53.825, altitude=45.7, name='J6-A'), 212: Station(number=212, longitude=4.151, latitude=52.919, altitude=50.9, name='HOORN-A'), 214: Station(number=214, longitude=6.042, latitude=54.038, altitude=42.5, name='Gemini'), 215: Station(number=215, longitude=4.437, latitude=52.141, altitude=-1.1, name='VOORSCHOTEN'), 604: Station(number=604, longitude=6.262, latitude=52.435, altitude=1.4, name='HERWIJNEN'), 225: Station(number=225, longitude=4.555, latitude=52.463, altitude=4.4, name='IJMUIDEN'), 227: Station(number=227, longitude=4.655, latitude=52.774, altitude=0.5, name='PETTEN'), 229: Station(number=229, longitude=4.72, latitude=52.996, altitude=10.0, name='TEXELHORS MM'), 230: Station(number=230, longitude=4.759, latitude=52.967, altitude=999.9, name='DEN HELDER'), 999: Station(number=999, longitude=6.575, latitude=52.75, altitude=15.6, name='HOOGEVEEN TEST'), 235: Station(number=235, longitude=4.781, latitude=52.928, altitude=1.2, name='DE KOOY'), 239: Station(number=239, longitude=4.696, latitude=54.855, altitude=50.6, name='F3-FB-1'), 240: Station(number=240, longitude=4.79, latitude=52.318, altitude=-3.3, name='SCHIPHOL'), 552: Station(number=552, longitude=3.213, latitude=56.547, altitude=999.9, name='EKOFISK'), 242: Station(number=242, longitude=4.921, latitude=53.241, altitude=10.8, name='VLIELAND'), 244: Station(number=244, longitude=5.044, latitude=52.65, altitude=999.9, name='HOORN (NH)'), 247: Station(number=247, longitude=4.555, latitude=52.422, altitude=18.0, name='BLOEMENDAAL'), 248: Station(number=248, longitude=5.174, latitude=52.634, altitude=0.8, name='WIJDENES'), 249: Station(number=249, longitude=4.979, latitude=52.644, altitude=-2.4, name='BERKHOUT'), 250: Station(number=250, longitude=5.147, latitude=53.36, altitude=31.4, name='TERSCHELLING'), 251: Station(number=251, longitude=5.346, latitude=53.392, altitude=0.7, name='HOORN (TERSCHELLING)'), 252: Station(number=252, longitude=3.22, latitude=53.219, altitude=37.7, name='K13'), 253: Station(number=253, longitude=2.067, latitude=56.4, altitude=999.9, name='AUK-ALFA'), 254: Station(number=254, longitude=4.296, latitude=52.274, altitude=999.9, name='MEETPOST NOORDWIJK'), 255: Station(number=255, longitude=1.16, latitude=61.234, altitude=999.9, name='NORTH CORMORANT')}\n"
     ]
    }
   ],
   "source": [
    "import knmi\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "print(knmi.stations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### kiezen stationnummer\n",
    "Uit de lijst hierboven kies ik station 210: Valkenburg. Die ligt dicht bij de overstroomde gebieden in Limburg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "stationNummer = 210"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### inladen data\n",
    "Vervolgens laad ik de data in, ik kies voor de periode 1951-heden. Ik print voor de zekerheid ook de KNMI disclaimer uit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BRON: KONINKLIJK NEDERLANDS METEOROLOGISCH INSTITUUT (KNMI)\n",
      "Opmerking: door stationsverplaatsingen en veranderingen in waarneemmethodieken zijn deze tijdreeksen van dagwaarden mogelijk inhomogeen! Dat betekent dat deze reeks van gemeten waarden niet geschikt is voor trendanalyse. Voor studies naar klimaatverandering verwijzen we naar de gehomogeniseerde reeks maandtemperaturen van De Bilt <http://www.knmi.nl/kennis-en-datacentrum/achtergrond/gehomogeniseerde-reeks-maandtemperaturen-de-bilt> of de Centraal Nederland Temperatuur <http://www.knmi.nl/kennis-en-datacentrum/achtergrond/centraal-nederland-temperatuur-cnt>.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rwhut/anaconda/lib/python3.5/site-packages/knmi/knmi.py:100: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  df.legend = legend\n",
      "/Users/rwhut/anaconda/lib/python3.5/site-packages/knmi/knmi.py:101: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  df.stations = stations\n"
     ]
    }
   ],
   "source": [
    "df = knmi.get_day_data_dataframe(stations=[stationNummer], start=\"19510101\", end=\"20180528\")\n",
    "print(df.disclaimer)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### bewerken en selecteren data\n",
    "Uit de KNMI gegevens kies ik totale hoeveelheid regen (RH). Die deel ik door 10 omdat het standaard in \"tiende millimeters\" genoteerd wordt. Ik maak alle negatieve getallen gelijk aan nul en split de data op in drie verschillende stukken: 1951-1980, 1981-2010 en 2010-heden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rain=df['RH']/10\n",
    "rain[rain<0]=0;\n",
    "rainOld=rain[(rain.index >= '1951') & (rain.index < '1980') & pd.notna(rain)]\n",
    "rainNew=rain[(rain.index >= '1980') & (rain.index < '2010') & pd.notna(rain)]\n",
    "rainLatest=rain[(rain.index >= '2010') & (rain.index < '2019') & pd.notna(rain)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gerrits gelijk: gemiddelde\n",
    "Hier reken ik per periode uit wat de gemiddelde hoeveelheid regen per jaar is. Daar zie je inderdaad een flinke stijging!\n",
    "\n",
    "Maar ik wil natuurlijk ook even weten hoe het met de hoosbuien zit, dus we gaan nog even door."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1951-1980: 726.286147804 mm per jaar\n",
      "1981-2010: 835.333044351 mm per jaar\n",
      "2011-heden: 844.295159896 mm per jaar\n"
     ]
    }
   ],
   "source": [
    "print(\"1951-1980: \" + str(rainOld.sum(axis=0)/(len(rainOld)/365)) + \" mm per jaar\")\n",
    "print(\"1981-2010: \" + str(rainNew.sum(axis=0)/(len(rainNew)/365)) + \" mm per jaar\")\n",
    "print(\"2011-heden: \" + str(rainLatest.sum(axis=0)/(len(rainLatest)/365)) + \" mm per jaar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### x-assen\n",
    "Hier maak ik assen aan die van 1 tot nul lopen en precies even lang zijn als de regen-data selecties. Hiermee kan ik hieronder cummulatieve verdelingen plotten."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "xOld=np.arange(1,0,-1/len(rainOld))\n",
    "xNew=np.arange(1,0,-1/len(rainNew))\n",
    "xLatest=np.arange(1,0,-1/len(rainLatest))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### meer plensbuien?\n",
    "Vervolgens sorteer ik de regendata op volgorde van intensiteit en plot dat langs de gemaakte assen. Je krijgt dan zogenaamde \"cummulatieve verdelingen\". Als je op de x-as bij 5 mm omhoog gaat komt je als eerst de zwarte lijn van de periode 1951-1980 tegen, rond de 60 dagen, op de Y-as. Dat betekend dat in de periode 1951-1980 het gemiddeld 60 dagen per jaar meer dan 5 mm regende. De lijnen voor de periodes 1981-2010 en 2011-heden liggen allebei hoger: er zijn dus tegenwoordig meer dagen per jaar waarin het meer dan 5 mm per dag regent.\n",
    "\n",
    "Deze grafiek laat zien dat het inderdaad vaker plenst, gelukkig dat ik dat wel goed onthouden had."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'KNMI station 210: Valkenburg')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rainOld.sort_values(),xOld*365,'k',rainNew.sort_values(),xNew*365,'r',rainLatest.sort_values(),xLatest*365,'b')\n",
    "plt.xlim(1,20)\n",
    "plt.ylim(0,0.4*365)\n",
    "plt.xlabel('regen in mm per dag')\n",
    "plt.ylabel('gemiddeld aantal dagen per jaar met meer regen')\n",
    "plt.legend(['1951-1980','1981-2010','2011-heden'])\n",
    "plt.title('KNMI station 210: Valkenburg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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