weather_background.ipynb

{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.12 64-bit ('base': conda)",
   "metadata": {
    "interpreter": {
     "hash": "0993a60ed560169030d334dd0d3e855cc9ba9ed4c4d3e40f82ea61f157e274fa"
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# Temperature Background"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "from PIL import Image\n",
    "from meteostat import Point, Daily\n",
    "\n",
    "import datetime as dt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "source": [
    "# Variables\n",
    "\n",
    "# Time period for data\n",
    "start = dt.datetime(1950, 1, 1)\n",
    "end = dt.datetime.now()\n",
    "\n",
    "# Weather station. Location taken from: https://github.com/meteostat/weather-stations/search?q=berlin\n",
    "berlin = Point(52.4667, 13.4, 50)\n",
    "munich = Point(48.1333, 11.55, 520)\n",
    "\n",
    "# colour scale\n",
    "scale = {}"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 2,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Functions\n",
    "\n",
    "def df_to_list(df):\n",
    "    ls = df.values.tolist()\n",
    "    return ls\n",
    "\n",
    "\n",
    "def df_to_pv(df, value):\n",
    "    df = df.pivot(index=\"year\", columns=\"day\", values=value)\n",
    "    df = df.dropna(how=\"any\")\n",
    "    return df   \n",
    "\n",
    "\n",
    "def get_data(location):\n",
    "    data = Daily(location, start, end)\n",
    "    df = data.fetch()\n",
    "    df.to_excel('RawData.xlsx')\n",
    "    return df\n",
    "\n",
    "\n",
    "def group_data(df, value):\n",
    "    df[\"year\"] = df[\"time\"].dt.year\n",
    "    df = df.drop(columns=[\"time\"], axis=1)\n",
    "    df[\"count\"] = 1\n",
    "    df = df.dropna()\n",
    "    df = df.groupby([\"year\"]) \\\n",
    "            .agg({value:'mean', 'count':'sum'}) \\\n",
    "            .reset_index()\n",
    "    df = df.loc[df[\"count\"] >= 356]\n",
    "    df = df.drop(columns=[\"count\"], axis=1)\n",
    "    return df\n",
    "\n",
    "\n",
    "def ls_to_image(ls):\n",
    "    array = np.array(ls, dtype=np.uint8)\n",
    "    image = Image.fromarray(array)\n",
    "    return image\n",
    "\n",
    "\n",
    "def repeat_rows(df, reps):\n",
    "    df = pd.DataFrame(np.repeat(df.values, reps, axis=0))\n",
    "    return df\n",
    "\n",
    "\n",
    "def select_rel_data(df, value):\n",
    "    df = df.reset_index()\n",
    "    coloumns = [\"time\", str(value)]\n",
    "    df = df[coloumns]\n",
    "    df = df.sort_values(by=\"time\", ascending=True)\n",
    "    return df\n",
    "\n",
    "\n",
    "def set_colour(d, keys, value):\n",
    "    for k in keys:\n",
    "        d[k] = value\n",
    "\n",
    "\n",
    "def set_scale(df):\n",
    "    min = round(df.min().min())\n",
    "    max = round(df.max().max()) + 1\n",
    "    diff = max - min\n",
    "    set_colour(scale, range(int(min), int(min + diff * 1/8)), [230, 234, 233])\n",
    "    set_colour(scale, range(int(min + diff * 1/8), int(min + diff * 2/8)), [209, 219, 225])\n",
    "    set_colour(scale, range(int(min + diff * 2/8), int(min + diff * 3/8)), [218, 220, 223])\n",
    "    set_colour(scale, range(int(min + diff * 3/8), int(min + diff * 4/8)), [199, 189, 194])\n",
    "    set_colour(scale, range(int(min + diff * 4/8), int(min + diff * 5/8)), [179, 173, 178])\n",
    "    set_colour(scale, range(int(min + diff * 5/8), int(min + diff * 6/8)), [161, 154, 158])\n",
    "    set_colour(scale, range(int(min + diff * 6/8), int(min + diff * 7/8)), [136, 117, 123])\n",
    "    set_colour(scale, range(int(min + diff * 7/8), int(min + diff)), [96, 79, 91])\n",
    "    return scale\n",
    "\n",
    "\n",
    "def temp_to_pixel(ls):\n",
    "    for x in range(0, len(ls)):\n",
    "        for y in range(0, len(ls[x])):\n",
    "            ls[x][y] = np.round(ls[x][y])\n",
    "            ls[x][y] = scale[ls[x][y]]\n",
    "    return ls\n",
    "\n",
    "\n",
    "def transform_columns(df):\n",
    "    df[\"date\"] = pd.to_datetime(df[\"time\"])\n",
    "    df[\"year\"] = df[\"date\"].dt.year\n",
    "    df[\"day\"] = df[\"date\"].dt.day_of_year\n",
    "    # exclude day 366 for leap years\n",
    "    df = df[(df[\"day\"] <= 365)]\n",
    "    return df\n",
    "\n",
    "\n",
    "def transform_data(df, value):\n",
    "    df[value] = df[value] * 10\n",
    "    df[value] = df[value].astype(int)\n",
    "    return df\n",
    "\n",
    "\n",
    "def transpose_data(df):\n",
    "    df = df.drop(columns=[\"year\"], axis=0)\n",
    "    df = df.T\n",
    "    return df"
   ]
  },
  {
   "source": [
    "## Create & show final image"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def horizonral_image(name, location, value, reps):\n",
    "    df = get_data(location)\n",
    "    df = select_rel_data(df, value)\n",
    "    df = group_data(df, value)\n",
    "    df = transform_data(df, value)\n",
    "    df = transpose_data(df)\n",
    "    df = repeat_rows(df, reps)\n",
    "    ls = df_to_list(df)\n",
    "    scale = set_scale(df)\n",
    "    ls = temp_to_pixel(ls)\n",
    "    image = ls_to_image(ls)\n",
    "    image.save(name + \".png\")\n",
    "    return image\n",
    "\n",
    "\n",
    "def pixel_image(name, location, value, reps):\n",
    "    df = get_data(location)\n",
    "    df = select_rel_data(df, value)\n",
    "    df = transform_columns(df)\n",
    "    df = df_to_pv(df, value)\n",
    "    df = repeat_rows(df, reps)\n",
    "    ls = df_to_list(df)\n",
    "    scale = set_scale(df)\n",
    "    ls = temp_to_pixel(ls)\n",
    "    image = ls_to_image(ls)\n",
    "    image.save(name + \".png\")\n",
    "    return image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "horizontal_background = horizonral_image(name = 'horizontal_image', location = munich, value = \"tmax\", reps = 50)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pixel_background = pixel_image(name= \"pixel_image\", location = munich, value = \"tmin\", reps = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=72x50 at 0x7FA3BB2226A0>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEgAAAAyCAIAAABwNgq3AAABH0lEQVR4nO3PoU7DABSF4bMpthBIKIiJrqLdJsioBNQyEFuCROB4KFwTngENAgEImECUhoS2a7JuooK2GQZQA9GEdzjkfO7m3tzkrz3e3gNotq1GbbW+0Xz/+N7ZXEuD0LDM0A96bj/0g/aeC6BRW3391D/nab5c9tw+gGo0LBNAkS4AGJZZpIvq298xgOq+Ws1ffABlkXdtJ0qmXdu5u74ZjEdWv/f8MCmLfMvYfn2a7B7sl0WexclgPAIQJdMsTlodG8Dh0fDKu2x17K7teBcegOHJcRYnb9EMAIDT87M6/imFsVEYG4WxURgbhbFRGBuFsVEYG4WxURgbhbFRGBuFsVEYG4WxURgbhbFRGBuFsVEYG4WxURgbhbFRGJtf2GVcdmCCZ4YAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "horizontal_background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=365x213 at 0x7FA3BB131CA0>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "pixel_background"
   ]
  }
 ]
}