3_Weather_Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from lib.utils import *\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weather Data Analysis\n",
    "\n",
    "As we are not using grasshopper/Rhino to display data it seemed a good idea to use some well established python tools to run our data analysis. In this case famous pandas/seaborn duo seems like a good starting point. Pandas will be useful for quick and efficient data manipulation (as epws are really slightly borked looking csvs) and seaborn will be great for one line data viz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "      <th>Remove</th>\n",
       "      <th>Dry_Bulb_Temperature</th>\n",
       "      <th>Dew_Point_Temperature</th>\n",
       "      <th>Relative_Humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>Present_Weather_Codes</th>\n",
       "      <th>Precipitable_Water</th>\n",
       "      <th>Aerosol_Optical_Depth</th>\n",
       "      <th>Snow_Depth</th>\n",
       "      <th>Days_Since_Last_Snowfall</th>\n",
       "      <th>Albedo</th>\n",
       "      <th>Liquid_Precipitation_Depth</th>\n",
       "      <th>Liquid_Precipitation_Quantity</th>\n",
       "      <th>datetime</th>\n",
       "      <th>doy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2001</td>\n",
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       "      <td>200</td>\n",
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       "      <td>88</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
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       "      <th>1</th>\n",
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       "      <td>8.9</td>\n",
       "      <td>7.4</td>\n",
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       "      <td>160</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>93</td>\n",
       "      <td>...</td>\n",
       "      <td>999999999</td>\n",
       "      <td>139</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2017-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
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       "      <td>9.8</td>\n",
       "      <td>8.6</td>\n",
       "      <td>92</td>\n",
       "      <td>...</td>\n",
       "      <td>999999999</td>\n",
       "      <td>170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>93</td>\n",
       "      <td>...</td>\n",
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       "      <td>139</td>\n",
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       "      <td>2017-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
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       "   Index  Year  Month  Day  Hour  Minute  \\\n",
       "0      0  2001      1    1     1       0   \n",
       "1      1  2001      1    1     2       0   \n",
       "2      2  2001      1    1     3       0   \n",
       "3      3  2001      1    1     4       0   \n",
       "4      4  2001      1    1     5       0   \n",
       "\n",
       "                                              Remove  Dry_Bulb_Temperature  \\\n",
       "0  ?9?9?9?9E0?9?9?9?9*9?9?9?9?9?9?9?9?9?9*_*9*9*9...                  11.6   \n",
       "1  ?9?9?9?9E0?9?9?9?9*9?9?9?9?9?9?9?9?9?9*_*9*9*9...                   8.9   \n",
       "2  ?9?9?9?9E0?9?9?9?9*9?9?9?9?9?9?9?9?9?9*_*9*9*9...                   7.0   \n",
       "3  ?9?9?9?9E0?9?9?9?9*9?9?9?9?9?9?9?9?9?9*_*9*9*9...                   9.8   \n",
       "4  ?9?9?9?9E0?9?9?9?9*9?9?9?9?9?9?9?9?9?9*_*9*9*9...                   7.0   \n",
       "\n",
       "   Dew_Point_Temperature  Relative_Humidity ...   Present_Weather_Codes  \\\n",
       "0                   10.7                 94 ...               999999999   \n",
       "1                    7.4                 90 ...               999999999   \n",
       "2                    6.0                 93 ...               999999999   \n",
       "3                    8.6                 92 ...               999999999   \n",
       "4                    6.0                 93 ...               999999999   \n",
       "\n",
       "   Precipitable_Water  Aerosol_Optical_Depth  Snow_Depth  \\\n",
       "0                 200                    0.0           0   \n",
       "1                 160                    0.0           0   \n",
       "2                 139                    0.0           0   \n",
       "3                 170                    0.0           0   \n",
       "4                 139                    0.0           0   \n",
       "\n",
       "   Days_Since_Last_Snowfall  Albedo  Liquid_Precipitation_Depth  \\\n",
       "0                        88   999.0                         0.0   \n",
       "1                        88   999.0                         0.0   \n",
       "2                        88   999.0                         0.0   \n",
       "3                        88   999.0                         0.0   \n",
       "4                        88   999.0                         0.0   \n",
       "\n",
       "   Liquid_Precipitation_Quantity            datetime  doy  \n",
       "0                            1.0 2017-01-01 00:00:00    1  \n",
       "1                            1.0 2017-01-01 01:00:00    1  \n",
       "2                            1.0 2017-01-01 02:00:00    1  \n",
       "3                            1.0 2017-01-01 03:00:00    1  \n",
       "4                            1.0 2017-01-01 04:00:00    1  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epw_path = 'data/ESP_MALAGA-AP_084820_IW2.epw'\n",
    "\n",
    "df = epw_to_df(epw_path)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>93</td>\n",
       "      <td>...</td>\n",
       "      <td>999999999</td>\n",
       "      <td>139</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>92</td>\n",
       "      <td>...</td>\n",
       "      <td>999999999</td>\n",
       "      <td>170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>999.0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
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       "      <td>?9?9?9?9E0?9?9?9?9*9?9?9?9?9?9?9?9?9?9*_*9*9*9...</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>93</td>\n",
       "      <td>...</td>\n",
       "      <td>999999999</td>\n",
       "      <td>139</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "   Index  Year  Month  Day  Hour  Minute  \\\n",
       "0      0  2001      1    1     1       0   \n",
       "1      1  2001      1    1     2       0   \n",
       "2      2  2001      1    1     3       0   \n",
       "3      3  2001      1    1     4       0   \n",
       "4      4  2001      1    1     5       0   \n",
       "\n",
       "                                              Remove  Dry_Bulb_Temperature  \\\n",
       "0  ?9?9?9?9E0?9?9?9?9*9?9?9?9?9?9?9?9?9?9*_*9*9*9...                  11.6   \n",
       "1  ?9?9?9?9E0?9?9?9?9*9?9?9?9?9?9?9?9?9?9*_*9*9*9...                   8.9   \n",
       "2  ?9?9?9?9E0?9?9?9?9*9?9?9?9?9?9?9?9?9?9*_*9*9*9...                   7.0   \n",
       "3  ?9?9?9?9E0?9?9?9?9*9?9?9?9?9?9?9?9?9?9*_*9*9*9...                   9.8   \n",
       "4  ?9?9?9?9E0?9?9?9?9*9?9?9?9?9?9?9?9?9?9*_*9*9*9...                   7.0   \n",
       "\n",
       "   Dew_Point_Temperature  Relative_Humidity ...   Present_Weather_Codes  \\\n",
       "0                   10.7                 94 ...               999999999   \n",
       "1                    7.4                 90 ...               999999999   \n",
       "2                    6.0                 93 ...               999999999   \n",
       "3                    8.6                 92 ...               999999999   \n",
       "4                    6.0                 93 ...               999999999   \n",
       "\n",
       "   Precipitable_Water  Aerosol_Optical_Depth  Snow_Depth  \\\n",
       "0                 200                    0.0           0   \n",
       "1                 160                    0.0           0   \n",
       "2                 139                    0.0           0   \n",
       "3                 170                    0.0           0   \n",
       "4                 139                    0.0           0   \n",
       "\n",
       "   Days_Since_Last_Snowfall  Albedo  Liquid_Precipitation_Depth  \\\n",
       "0                        88   999.0                         0.0   \n",
       "1                        88   999.0                         0.0   \n",
       "2                        88   999.0                         0.0   \n",
       "3                        88   999.0                         0.0   \n",
       "4                        88   999.0                         0.0   \n",
       "\n",
       "   Liquid_Precipitation_Quantity            datetime  doy  \n",
       "0                            1.0 2017-01-01 00:00:00    1  \n",
       "1                            1.0 2017-01-01 01:00:00    1  \n",
       "2                            1.0 2017-01-01 02:00:00    1  \n",
       "3                            1.0 2017-01-01 03:00:00    1  \n",
       "4                            1.0 2017-01-01 04:00:00    1  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['datetime'] = df.apply(lambda row: get_datetime(row), axis=1)\n",
    "df['doy'] = df.apply(lambda row: get_doy(row), axis=1)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1728x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_heatmap_plot(df, 'Dry_Bulb_Temperature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_heatmap_plot(df, 'Relative_Humidity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "epw_path = 'data/ESP_MALAGA-AP_084820_IW2.epw'\n",
    "new_epw_path = 'data/baseline.epw'\n",
    "\n",
    "# epw_path = 'urban_heat_island/ESP_MALAGA-AP_084820_IW2.epw'\n",
    "# new_epw_path = 'urban_heat_island/ESP_MALAGA-AP_084820_IW2_URBAN.epw'\n",
    "\n",
    "df_new = epw_to_df(new_epw_path)\n",
    "df_original = epw_to_df(epw_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Wind Speed difference from UHI')"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dIwAAAAAAKKj2xoWIeH1EHB4RiyVNSvpsRLy47jgAAAAA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\n/wPo1pLFlN15MAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17283d79048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# value = 'Dry_Bulb_Temperature'\n",
    "# value = 'Dew_Point_Temperature'\n",
    "# value = 'Relative_Humidity'\n",
    "value = 'Wind_Speed'\n",
    "# value = 'Dew_Point_Temperature'\n",
    "\n",
    "diff = df_new\n",
    "dump = df_new\n",
    "\n",
    "diff[value] = dump[value] - df_original[value]\n",
    "\n",
    "diff = diff.pivot(index='Hour', columns='doy')[value]\n",
    "diff = diff.reindex(index=diff.index[::-1])\n",
    "\n",
    "plt.figure(figsize = (20,5))\n",
    "\n",
    "\n",
    "ax = sns.heatmap(diff, cmap= cmap, cbar_kws={'label': 'm/s'})\n",
    "\n",
    "ax.set_title('{} difference from UHI'.format(' '.join(value.split('_'))), fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Parametric Simulation Post Processing\n",
    "\n",
    "Now that we have looked at how the baseline neighborhood design impacts the local climate it's time to analyse the results from the parametric study. In order to do this we will have to:\n",
    "1. Read all the epws saved in the data folder and merge them into one big dataframe inspirationally called `df` (be sure to keep the design parameter values in each row)\n",
    "2. Remove the impact of seasonality on the dataset by focusing on one month of averaged data\n",
    "3. Run a quick correlation and make a fancy heat map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Read all the things\n",
    "\n",
    "The step below reads and merges all the simulated EPWs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4257360, 45)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "\n",
    "folder = os.path.join(os.getcwd(), 'data')\n",
    "\n",
    "df_list = []\n",
    "\n",
    "regex = r\"([a-z]+_[a-z]+_\\d.?\\d?_)\"\n",
    "\n",
    "for file_path in os.listdir(folder)[2:]:\n",
    "    matches = re.finditer(regex, file_path, re.MULTILINE)\n",
    "    params = []\n",
    "\n",
    "    for matchNum, match in enumerate(matches):\n",
    "        param = match.group().split('_')\n",
    "        params.append((' '.join(param[:2]), param[2]))\n",
    "    \n",
    "    iter_df = epw_to_df(os.path.join(folder, file_path))\n",
    "    \n",
    "    for param in params:\n",
    "        iter_df[param[0]] = param[1]\n",
    "        \n",
    "    df_list.append(iter_df)\n",
    "    \n",
    "df_total = pd.concat(df_list)\n",
    "\n",
    "df_total.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df_total = pd.read_csv('malaga_parametric_epw.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dump all the things to a csv so we don't have to read and merge again if we break something."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_total.to_csv('malaga_parametric_epw.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean the df for human use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>doy</th>\n",
       "      <th>glz ratio</th>\n",
       "      <th>grass cov</th>\n",
       "      <th>pav albedo</th>\n",
       "      <th>roof albedo</th>\n",
       "      <th>traffic heat</th>\n",
       "      <th>tree cov</th>\n",
       "      <th>wall albedo</th>\n",
       "      <th>Dry_Bulb_Temperature</th>\n",
       "      <th>Dew_Point_Temperature</th>\n",
       "      <th>Relative_Humidity</th>\n",
       "      <th>Wind_Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.8</td>\n",
       "      <td>10.7</td>\n",
       "      <td>92.6</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.3</td>\n",
       "      <td>7.4</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>76.5</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>90.2</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>81.7</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  doy  glz ratio  grass cov  pav albedo  roof albedo  \\\n",
       "0  2017-01-01 00:00:00    1        0.2        0.1         NaN          0.2   \n",
       "1  2017-01-01 01:00:00    1        0.2        0.1         NaN          0.2   \n",
       "2  2017-01-01 02:00:00    1        0.2        0.1         NaN          0.2   \n",
       "3  2017-01-01 03:00:00    1        0.2        0.1         NaN          0.2   \n",
       "4  2017-01-01 04:00:00    1        0.2        0.1         NaN          0.2   \n",
       "\n",
       "   traffic heat  tree cov  wall albedo  Dry_Bulb_Temperature  \\\n",
       "0             2       NaN          NaN                  11.8   \n",
       "1             2       NaN          NaN                  11.3   \n",
       "2             2       NaN          NaN                   9.9   \n",
       "3             2       NaN          NaN                  10.1   \n",
       "4             2       NaN          NaN                   8.9   \n",
       "\n",
       "   Dew_Point_Temperature  Relative_Humidity  Wind_Speed  \n",
       "0                   10.7               92.6         2.1  \n",
       "1                    7.4               76.5         2.6  \n",
       "2                    6.0               76.5         3.1  \n",
       "3                    8.6               90.2         3.6  \n",
       "4                    6.0               81.7         3.6  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_total[['datetime','doy','glz ratio','grass cov','pav albedo',\n",
    "          'roof albedo','traffic heat','tree cov','wall albedo',\n",
    "          'Dry_Bulb_Temperature',\n",
    "          'Dew_Point_Temperature',\n",
    "          'Relative_Humidity',\n",
    "          'Wind_Speed']]\n",
    "\n",
    "df.to_csv('malaga_clean_epws.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick example of multi-indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>doy</th>\n",
       "      <th>glz ratio</th>\n",
       "      <th>grass cov</th>\n",
       "      <th>pav albedo</th>\n",
       "      <th>roof albedo</th>\n",
       "      <th>traffic heat</th>\n",
       "      <th>tree cov</th>\n",
       "      <th>wall albedo</th>\n",
       "      <th>Dry_Bulb_Temperature</th>\n",
       "      <th>Dew_Point_Temperature</th>\n",
       "      <th>Relative_Humidity</th>\n",
       "      <th>Wind_Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1576800</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.8</td>\n",
       "      <td>10.7</td>\n",
       "      <td>92.6</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1576801</th>\n",
       "      <td>2017-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>76.1</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1576802</th>\n",
       "      <td>2017-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>73.8</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1576803</th>\n",
       "      <td>2017-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>90.3</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1576804</th>\n",
       "      <td>2017-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>79.1</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    datetime  doy  glz ratio  grass cov  pav albedo  \\\n",
       "1576800  2017-01-01 00:00:00    1        0.3        0.3         NaN   \n",
       "1576801  2017-01-01 01:00:00    1        0.3        0.3         NaN   \n",
       "1576802  2017-01-01 02:00:00    1        0.3        0.3         NaN   \n",
       "1576803  2017-01-01 03:00:00    1        0.3        0.3         NaN   \n",
       "1576804  2017-01-01 04:00:00    1        0.3        0.3         NaN   \n",
       "\n",
       "         roof albedo  traffic heat  tree cov  wall albedo  \\\n",
       "1576800          0.2             2       NaN          NaN   \n",
       "1576801          0.2             2       NaN          NaN   \n",
       "1576802          0.2             2       NaN          NaN   \n",
       "1576803          0.2             2       NaN          NaN   \n",
       "1576804          0.2             2       NaN          NaN   \n",
       "\n",
       "         Dry_Bulb_Temperature  Dew_Point_Temperature  Relative_Humidity  \\\n",
       "1576800                  11.8                   10.7               92.6   \n",
       "1576801                  11.4                    7.4               76.1   \n",
       "1576802                  10.4                    6.0               73.8   \n",
       "1576803                  10.1                    8.6               90.3   \n",
       "1576804                   9.4                    6.0               79.1   \n",
       "\n",
       "         Wind_Speed  \n",
       "1576800         2.1  \n",
       "1576801         2.6  \n",
       "1576802         3.1  \n",
       "1576803         3.6  \n",
       "1576804         3.6  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['grass cov'] == 0.3)\n",
    "   & (df['glz ratio'] == 0.3)\n",
    "   & (df['roof albedo'] == 0.2)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Remove seasonality\n",
    "Arguably focusing on just one month is a really lazy way of removing seasonality. I'm sure you're a better/less time bound individual who will think of a better way to do this. In the meanwhile let's look at July in Malaga!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>glz ratio</th>\n",
       "      <th>grass cov</th>\n",
       "      <th>pav albedo</th>\n",
       "      <th>roof albedo</th>\n",
       "      <th>traffic heat</th>\n",
       "      <th>tree cov</th>\n",
       "      <th>wall albedo</th>\n",
       "      <th>Dry_Bulb_Temperature</th>\n",
       "      <th>Dew_Point_Temperature</th>\n",
       "      <th>Relative_Humidity</th>\n",
       "      <th>Wind_Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>12.306989</td>\n",
       "      <td>7.040457</td>\n",
       "      <td>71.174866</td>\n",
       "      <td>3.960349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>12.309812</td>\n",
       "      <td>7.040457</td>\n",
       "      <td>71.160484</td>\n",
       "      <td>3.960349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>12.306452</td>\n",
       "      <td>7.040457</td>\n",
       "      <td>71.179301</td>\n",
       "      <td>3.960349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>12.308737</td>\n",
       "      <td>7.040457</td>\n",
       "      <td>71.165457</td>\n",
       "      <td>3.960349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>12.305780</td>\n",
       "      <td>7.040457</td>\n",
       "      <td>71.184543</td>\n",
       "      <td>3.960349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   datetime  glz ratio  grass cov  pav albedo  roof albedo  traffic heat  \\\n",
       "0         1        0.2        0.0         0.1          0.2             2   \n",
       "1         1        0.2        0.0         0.1          0.2             4   \n",
       "2         1        0.2        0.0         0.2          0.2             2   \n",
       "3         1        0.2        0.0         0.2          0.2             4   \n",
       "4         1        0.2        0.1         0.1          0.2             2   \n",
       "\n",
       "   tree cov  wall albedo  Dry_Bulb_Temperature  Dew_Point_Temperature  \\\n",
       "0       0.1          0.2             12.306989               7.040457   \n",
       "1       0.1          0.2             12.309812               7.040457   \n",
       "2       0.1          0.2             12.306452               7.040457   \n",
       "3       0.1          0.2             12.308737               7.040457   \n",
       "4       0.1          0.2             12.305780               7.040457   \n",
       "\n",
       "   Relative_Humidity  Wind_Speed  \n",
       "0          71.174866    3.960349  \n",
       "1          71.160484    3.960349  \n",
       "2          71.179301    3.960349  \n",
       "3          71.165457    3.960349  \n",
       "4          71.184543    3.960349  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby(by=[df.datetime.dt.month,\n",
    "               'glz ratio','grass cov','pav albedo',\n",
    "               'roof albedo','traffic heat','tree cov','wall albedo']).mean().reset_index()\n",
    "\n",
    "grouped[['glz ratio','grass cov','pav albedo',\n",
    "         'roof albedo','traffic heat','tree cov','wall albedo']] = grouped[['glz ratio','grass cov','pav albedo',\n",
    "                                                                     'roof albedo','traffic heat','tree cov','wall albedo']].apply(pd.to_numeric)\n",
    "\n",
    "# grouped = grouped.drop('datetime', axis=1)\n",
    "\n",
    "grouped = grouped.drop('doy', axis=1)\n",
    "grouped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Correlate all the things\n",
    "\n",
    "All we've got left to do is run a correlation between our environmental factors and the parameters of our study and BOOM we're done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>glz ratio</th>\n",
       "      <th>grass cov</th>\n",
       "      <th>pav albedo</th>\n",
       "      <th>roof albedo</th>\n",
       "      <th>traffic heat</th>\n",
       "      <th>tree cov</th>\n",
       "      <th>wall albedo</th>\n",
       "      <th>Dry_Bulb_Temperature</th>\n",
       "      <th>Dew_Point_Temperature</th>\n",
       "      <th>Relative_Humidity</th>\n",
       "      <th>Wind_Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>glz ratio</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-3.540989e-17</td>\n",
       "      <td>4.028859e-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.028859e-16</td>\n",
       "      <td>4.028859e-16</td>\n",
       "      <td>-2.594600e-02</td>\n",
       "      <td>3.777055e-16</td>\n",
       "      <td>5.380259e-02</td>\n",
       "      <td>7.554111e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grass cov</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.890653e-17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.890653e-17</td>\n",
       "      <td>9.890653e-17</td>\n",
       "      <td>-9.901551e-01</td>\n",
       "      <td>1.236332e-17</td>\n",
       "      <td>9.906424e-01</td>\n",
       "      <td>-1.087972e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pav albedo</th>\n",
       "      <td>-3.540989e-17</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.775558e-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.775558e-16</td>\n",
       "      <td>2.775558e-16</td>\n",
       "      <td>2.522002e-02</td>\n",
       "      <td>2.775558e-16</td>\n",
       "      <td>-3.606164e-02</td>\n",
       "      <td>3.330669e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roof albedo</th>\n",
       "      <td>4.028859e-16</td>\n",
       "      <td>9.890653e-17</td>\n",
       "      <td>2.775558e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-1.026595e-12</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.798811e-13</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>traffic heat</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.139945e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.137581e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 glz ratio     grass cov    pav albedo   roof albedo  \\\n",
       "glz ratio     1.000000e+00  0.000000e+00 -3.540989e-17  4.028859e-16   \n",
       "grass cov     0.000000e+00  1.000000e+00  0.000000e+00  9.890653e-17   \n",
       "pav albedo   -3.540989e-17  0.000000e+00  1.000000e+00  2.775558e-16   \n",
       "roof albedo   4.028859e-16  9.890653e-17  2.775558e-16  1.000000e+00   \n",
       "traffic heat  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "\n",
       "              traffic heat      tree cov   wall albedo  Dry_Bulb_Temperature  \\\n",
       "glz ratio              0.0  4.028859e-16  4.028859e-16         -2.594600e-02   \n",
       "grass cov              0.0  9.890653e-17  9.890653e-17         -9.901551e-01   \n",
       "pav albedo             0.0  2.775558e-16  2.775558e-16          2.522002e-02   \n",
       "roof albedo            0.0  1.000000e+00  1.000000e+00         -1.026595e-12   \n",
       "traffic heat           1.0  0.000000e+00  0.000000e+00          1.139945e-01   \n",
       "\n",
       "              Dew_Point_Temperature  Relative_Humidity    Wind_Speed  \n",
       "glz ratio              3.777055e-16       5.380259e-02  7.554111e-17  \n",
       "grass cov              1.236332e-17       9.906424e-01 -1.087972e-16  \n",
       "pav albedo             2.775558e-16      -3.606164e-02  3.330669e-16  \n",
       "roof albedo            1.000000e+00       7.798811e-13  1.000000e+00  \n",
       "traffic heat           0.000000e+00      -1.137581e-01  0.000000e+00  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = grouped[grouped['datetime']==8].corr()\n",
    "\n",
    "corr = corr.drop('datetime', axis=1)\n",
    "corr = corr.drop('datetime', axis=0)\n",
    "\n",
    "corr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Correlation between simulation parameters and climatic outcomes in July')"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OL+dfPXR3/ZOAB4DTIuLJmtZxN7uZmVlzc8vczMysyfmauXXFXTdmVquePIdgDh2tV9d8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1729238cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.heatmap(corr, cmap= cmap, linewidths=.5)\n",
    "\n",
    "ax.set_title('Correlation between simulation parameters and climatic outcomes in July')"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}