Level2_Nedbørsoppgaven/Del2_RyddDatasett.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Del 2: Rydd datasettet\n",
    "\n",
    "*Nedbørsoppgavene Del 1 til 5 er laget som støtte-oppgaver til oppgaven \"Nedbør i Norge\". Oppgaven \"Nedbør i Norge\" er en lengre oppgave som kommer innom mange forskjellige programerings-elementer. Intensjonen er at hvis man først gjør deloppgavene 1 til 5 og så går gjennom det nokså omfattende eksempelet på starten av oppgaven \"Nedbør i Norge\" så vil det programeringstekniske i oppgaven \"Nedbør i Norge\" være repetisjon, slik at man kan fokusere på den naturvitenskaplige tolkningen av dataene. På denne måten får man både trening i programmering og erfaring med hvordan det kan brukes til å studere og forstå naturvitenskaplige prosesser.*\n",
    "\n",
    "-------------\n",
    "\n",
    "I denne oppgaven går vi gjennom et eksempel på:\n",
    "- Hvordan man kan gå frem for å få oversikten over datasettet man skal bruke, og\n",
    "- Hvordan man kan rydde i datasettet om nødvendig (og når og hvorfor det i blant er nødvendig).\n",
    "\n",
    "Når man jobber med datasett med observasjoner fra naturen er det ofte slik at man mangler et par verdier. Har man f.eks. en gammel tidsserie over snødybde på et gitt sted kan det være at en dag mangler fordi det var storm og utrygt å gå ut for å måle snødybden. I dag har vi mange metoder for å automatisk hente inn data, men hull kan likevel oppstå i et datasett. Et skydekke vil hindre noen satellitter i å ta målinger, en alge kan legge seg over en sensor i havet og bli skylt vekk av en sterk strøm dager eller uker seinere, og en is- eller sediment-kjerne kan knekke nettopp der du hadde tenkt å bore et hull for å måle temperatur. Mye kan skje, og det er viktig å sjekke om datasettet du skal bruke er \"good to go\" eller om det krever en gjennomgang først. \n",
    "\n",
    "I dette eksempelet bruker vi en tidsserie over temperatur i Bergen siden 1861. Datasettet har to kolonner: år og gjennomsnitlig års-temperatur.\n",
    "\n",
    "#### Last inn pakkene som trengs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # for matematikk, filinnlasting mm.\n",
    "import matplotlib.pyplot as plt # for figurer\n",
    "\n",
    "import sys\n",
    "# Bytt ut stien under slik at den peker på hvor på din PC du har \n",
    "# lagret mappen \"Funksjoner\".\n",
    "sys.path.append(r\"W:\\Work\\Documents\\EkteData\\EkteData\\Funksjoner\")\n",
    "from EkteDataFunk import loadData # for filinnlasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Last inn datasettet. \n",
    "\n",
    "Vi bruker funksjonen loadData som ligger i GitHub mappen til Ekte Data. Vi setter `rydd = 'N'` for at funksjonen ikke skal rydde datasettet automatisk. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file='TempBergenYearly.txt'\n",
    "data = loadData(file, rydd='N')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nå er filen lastet inn, og innholdet er lagret i variabelen `data`. Vi skriver den ut for å sjekke hvordan dataene våre er strukturert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Oppgave 1. \n",
    "**Studer outputtet og undersøk punktene:**\n",
    "- Hva beskriver kolonne en og to?\n",
    "- Hva er dimensjonene til datasettet?\n",
    "- Finnes det Nan-verdier eller andre verdier som representerer at data mangler?\n",
    "\n",
    "For å svare på punkt 1 er det lurt å åpne filen i notepad fordi det ofte er en *header* i .txt-filer som dette. I første linje til denne filen står det *Year* og *Temperature*, altså *år* og *temperatur*. Kolonne en er år, kolonne to er nedbør. Det varierer litt fra filformat til filformat hvor informasjon som dette ligger, og det er heller ikke alltid datafiler har en tydelig header. Men stort sett er dette lett tilgjengelig informasjon, og i datasettene vi bruker her vil det alltid enten være en header, eller så vil det stå i oppgaveteksten."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`data.shape` gir oss dimensjonene til `data`: `161 x 2`. Matrisen (arrayet) `data` har 161 rader og 2 kolonner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(161, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Lagre dataene til variables\n",
    "For å gjøre datasettet enklere å jobbe med lagrer vi tid-kolonnen i vektoren `tid`, og temperatur-kolonnen i vektoren `temp`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tid=data[:,0]\n",
    "temp=data[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Lag en figur\n",
    "Nå som vi vet hva datasettet vårt inneholder kan vi beregne gjennomsnittstemperatur over alle årene og lage en figur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-105.0359420289855"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tid, temp)\n",
    "plt.xlabel('År')\n",
    "plt.ylabel('Temperatur')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Fyll-verdier og behandling av fyll-verdier\n",
    "Dette ser veldig rart ut. Ser vi tilbake på tabellen med dataverdier over, så ser vi at det står -999.99 flere steder. Den laveste teoretiske temperaturen man kan ha er -273.15. Dette finnes ikke naturlig, og i allefall ikke i Bergen. Disse verdiene må altså være fyll-verdier som er satt inn fordi data mangler. Ved å sette inn slike verdier blir datasettet lett å jobbe med til tross for manglende verdier. Det hadde vært mye mer knotete om disse årene bare ble tatt ut. Da kunne man plutselig ha et hopp i tid fra f.eks 1923 til 1927 som man måtte identifisere før man kunne f.eks. plotte og analysere dataene. \n",
    "\n",
    "Dette oppsettet med fyll-verdier krever imidlertid litt opprenskning det også. Det enkleste er å sette alle steder med -999.99 til `NaN`. `NaN` står for \"Not a Number\" og en er placeholder for manglende verdier. Å bruke `NaN` som fyll-verdi er praktisk fordi det er lett å utelukke NaN-verdiene i beregninger med `Numpy`-biblioteket: På samme måte som kan beregne gjennomsnitt med `np.mean(x)` eller standardavvik med `np.std(x)` dersom `x` ikke inneholder fyll-verdier, kan man bruke `np.nanmean` og `np.nanstd` for å utelukke alle steder med `NaN` som fyllverdi i beregningen av for eksempel gjennomsnitt og standaravvik. Det er også lett å lage figurer uten at man risikerer hopp i tid når man bruker `NaN` som fyllverdi.\n",
    "\n",
    "#### 5. Sett alle tilfeller av -999.99 til `NaN`\n",
    "\n",
    "**Metode 1 - np.where().**\n",
    "Husk fra forrige oppgave der vi fant alle indeksene til år mellom 1873 og 1947 som vi så ga tilbake til vektoren `tid` og `temp` for å velge ut temperatur de rette årene. Tilsvarende kan vi finne indeksen til alle verdiene i `temp` som nå er `-999.99`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0,  10,  11,  17,  34,  56,  65,  68,  69,  78,  81,  83,  99,\n",
       "        122, 141, 143, 151, 160], dtype=int64),)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id999=np.where(temp==-999.99)\n",
    "id999"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temperaturverdiene på alle disse indeksene erstatter man med `NaN` som dette:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp[id999]=np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Nå dukker plutselig variabiliteten til temperatur opp!\n",
    "plt.plot(tid, temp)\n",
    "plt.xlabel('År')\n",
    "plt.ylabel('Temperatur')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Metode 2 - Logisk indeksering.**\n",
    "Vi kan også sette -999.99 til `NaN` uten å bruke funksjonen `np.where()`: vi kan bruke *logisk indeksering*. Logisk indeksering vil si at man setter et kriterie, og sjekker om veridene i en variabel tilfredstiller kriteriet eller ikke. Svaret et alltid `True` eller `False` som tilsvarer `1` eller `0`. Et kort eksempel viser dette best:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 4 1 5 3 6]\n",
      "[False  True False False False False]\n"
     ]
    }
   ],
   "source": [
    "x=np.array([2,4,1,5,3,6]) # et array (i dette tilfellet en vektor)\n",
    "print(x)\n",
    "\n",
    "# Det doble likhetstegnet betyr at vi bruker logisk indeksering: \n",
    "# \"For alle elementer i x, sjekk om ellementet er lik 4\"\n",
    "# Resultatet er et array med like dimensjoner som x, men alle \n",
    "# elementene er \"True\" eller \"False\". I dette eksempelet er kun \n",
    "# element nr 1 \"True\", altså er x[1]=4. \n",
    "print(x==4) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dette er veldig nyttig når man skal gjøre noe med elementer som tilfredstiller et kriterie. Vil vi for eksempel at alle elementer som er 4 skal være 7 i stedet kan vi bruke den logiske rekken vår og si at der denne er \"True\" skal verdien byttes til 7. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 7, 1, 5, 3, 6])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind = x==4 # Lagre den logiske vektoren som en variabel \"ind\" (for indeks)\n",
    "x[ind]=7 # Sett elementene i x der ind=True til 7\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 7, 1, 5, 3, 6])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dette kan også komprimeres til en linje\n",
    "x=np.array([2,4,1,5,3,6]) \n",
    "x[x==4]=7 # \"der x=4 skal verdien byttes til 7\"\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hvis man vil ha tak i selve indeksen kan man igjen bruke `np.where()`. \n",
    "I dette tilfelle er det imidlertid en litt mer knotete metode, men det viser at mye kan gjøres på mange ulike måter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 7, 1, 5, 3, 6])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.array([2,4,1,5,3,6]) \n",
    "ind=np.where(x==4)\n",
    "x[ind[0][0]]=7\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vi må bruke `ind[0][0]` av samme grunn som notatet i Oppgave 7 fra Del 1: Indeksering, altså for å komme på innsiden av parantesene som er rundt `ind`. Kjør cellen under for å se tydeligere hva som er forskjellen på `ind`, `ind[0]`, og `ind[0][0]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([1], dtype=int64),)\n",
      "[1]\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(ind)\n",
    "print(ind[0])\n",
    "print(ind[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sett fyll-verdien `-999.99` til `NaN`\n",
    "Vi kan nå bruke logisk indeksering på datasettet vårt og si at \"der `x=-999.99` skal verdien byttes til `NaN`\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[data==-999.99]=np.nan\n",
    "temp=data[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hvis vi nå plotter dataene igjen får vi mye mer informasjon. All variabiliteten (høye of lave verdier, trender etc.) vi ser her fantes også i den første figuren, men det ble overskygget av -999.99 verdiene. Hadde vi zoomet inn på y-aksen, f.eks. satt `plt.ylim([6,10])`, ville vi fått noe som lignet på figuren under. \n",
    "\n",
    "Det er noen hull i figuren under, spesielt rundt 1940, men slik er det i blant - man har ikke alltid et fullstendig datasett - og dette gir oss likevel et godt inntrykk av temperatur mellom 1860 og 2020. Man kan estimere verdier som mangler med interpolasjon - sjekk oppgaven [Del6: databehandling av egne data](https://github.com/irendundas/EkteData/blob/main/Level1_Temperaturoppgaven/Del6_databehandling_av_egne_data.ipynb) for et eksempel på dette. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(tid, temp)\n",
    "plt.xlim([1860,2021])\n",
    "plt.xlabel('År')\n",
    "plt.ylabel('Temperatur')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Datasettet er nå klart til at vi kan sette igang å jobbe med det. I tillegg har vi fått oversikten over dimensjonene til datasettet og hva kolonnene inneholder.\n",
    "__________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Oppgave 2.\n",
    "\n",
    "Gitt arrayet x under. Gjør slik at det istedet står 100 alle de stedene hvor det nå står 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.array([2,4,2,1,5,7,7,3,6,0,5,2,4,6,5,2,5,78,6,4,2,1,3,2,4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Oppgave 3.\n",
    "Gitt arrayet x under. Gjør slik at det står 100 alle stedene det står 7 i den andre kolonnen. Kolonne nr 1 skal ikke endre seg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.array([[2,1,3,5,7,3,6,8,0,7],[2,7,8,6,5,6,8,8,7,7]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Oppgave 4.\n",
    "Prøv deg frem med datasettet: `PrecipVestlandYearly.txt`. Det ligner, men er ikke helt likt som det i eksempelet over. Lag en figur og beregn gjennomsnittet. Hva er fyll-verdien i dette datasettet? Erstatt fyll-verdiene med `NaN` og lag figuren og beregn gjennomsnittet på nytt. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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