weather time series.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tutorial: https://www.tensorflow.org/tutorials/structured_data/time_series#split_the_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>989.212842</td>\n",
       "      <td>8.358886</td>\n",
       "      <td>913.60</td>\n",
       "      <td>984.20</td>\n",
       "      <td>989.57</td>\n",
       "      <td>994.720</td>\n",
       "      <td>1015.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T (degC)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>9.450482</td>\n",
       "      <td>8.423384</td>\n",
       "      <td>-22.76</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.41</td>\n",
       "      <td>15.480</td>\n",
       "      <td>37.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tpot (K)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>283.493086</td>\n",
       "      <td>8.504424</td>\n",
       "      <td>250.85</td>\n",
       "      <td>277.44</td>\n",
       "      <td>283.46</td>\n",
       "      <td>289.530</td>\n",
       "      <td>311.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>4.956471</td>\n",
       "      <td>6.730081</td>\n",
       "      <td>-24.80</td>\n",
       "      <td>0.24</td>\n",
       "      <td>5.21</td>\n",
       "      <td>10.080</td>\n",
       "      <td>23.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rh (%)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>76.009788</td>\n",
       "      <td>16.474920</td>\n",
       "      <td>13.88</td>\n",
       "      <td>65.21</td>\n",
       "      <td>79.30</td>\n",
       "      <td>89.400</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>13.576576</td>\n",
       "      <td>7.739883</td>\n",
       "      <td>0.97</td>\n",
       "      <td>7.77</td>\n",
       "      <td>11.82</td>\n",
       "      <td>17.610</td>\n",
       "      <td>63.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>9.533968</td>\n",
       "      <td>4.183658</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6.22</td>\n",
       "      <td>8.86</td>\n",
       "      <td>12.360</td>\n",
       "      <td>28.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>4.042536</td>\n",
       "      <td>4.898549</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2.19</td>\n",
       "      <td>5.300</td>\n",
       "      <td>46.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>6.022560</td>\n",
       "      <td>2.655812</td>\n",
       "      <td>0.51</td>\n",
       "      <td>3.92</td>\n",
       "      <td>5.59</td>\n",
       "      <td>7.800</td>\n",
       "      <td>18.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>9.640437</td>\n",
       "      <td>4.234862</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6.29</td>\n",
       "      <td>8.96</td>\n",
       "      <td>12.490</td>\n",
       "      <td>28.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>1216.061232</td>\n",
       "      <td>39.974263</td>\n",
       "      <td>1059.45</td>\n",
       "      <td>1187.47</td>\n",
       "      <td>1213.80</td>\n",
       "      <td>1242.765</td>\n",
       "      <td>1393.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wv (m/s)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>1.702567</td>\n",
       "      <td>65.447512</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.860</td>\n",
       "      <td>14.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>2.963041</td>\n",
       "      <td>75.597657</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.98</td>\n",
       "      <td>4.740</td>\n",
       "      <td>23.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wd (deg)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>174.789095</td>\n",
       "      <td>86.619431</td>\n",
       "      <td>0.00</td>\n",
       "      <td>125.30</td>\n",
       "      <td>198.10</td>\n",
       "      <td>234.000</td>\n",
       "      <td>360.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean        std      min      25%      50%  \\\n",
       "p (mbar)         70091.0   989.212842   8.358886   913.60   984.20   989.57   \n",
       "T (degC)         70091.0     9.450482   8.423384   -22.76     3.35     9.41   \n",
       "Tpot (K)         70091.0   283.493086   8.504424   250.85   277.44   283.46   \n",
       "Tdew (degC)      70091.0     4.956471   6.730081   -24.80     0.24     5.21   \n",
       "rh (%)           70091.0    76.009788  16.474920    13.88    65.21    79.30   \n",
       "VPmax (mbar)     70091.0    13.576576   7.739883     0.97     7.77    11.82   \n",
       "VPact (mbar)     70091.0     9.533968   4.183658     0.81     6.22     8.86   \n",
       "VPdef (mbar)     70091.0     4.042536   4.898549     0.00     0.87     2.19   \n",
       "sh (g/kg)        70091.0     6.022560   2.655812     0.51     3.92     5.59   \n",
       "H2OC (mmol/mol)  70091.0     9.640437   4.234862     0.81     6.29     8.96   \n",
       "rho (g/m**3)     70091.0  1216.061232  39.974263  1059.45  1187.47  1213.80   \n",
       "wv (m/s)         70091.0     1.702567  65.447512 -9999.00     0.99     1.76   \n",
       "max. wv (m/s)    70091.0     2.963041  75.597657 -9999.00     1.76     2.98   \n",
       "wd (deg)         70091.0   174.789095  86.619431     0.00   125.30   198.10   \n",
       "\n",
       "                      75%      max  \n",
       "p (mbar)          994.720  1015.29  \n",
       "T (degC)           15.480    37.28  \n",
       "Tpot (K)          289.530   311.21  \n",
       "Tdew (degC)        10.080    23.06  \n",
       "rh (%)             89.400   100.00  \n",
       "VPmax (mbar)       17.610    63.77  \n",
       "VPact (mbar)       12.360    28.25  \n",
       "VPdef (mbar)        5.300    46.01  \n",
       "sh (g/kg)           7.800    18.07  \n",
       "H2OC (mmol/mol)    12.490    28.74  \n",
       "rho (g/m**3)     1242.765  1393.54  \n",
       "wv (m/s)            2.860    14.01  \n",
       "max. wv (m/s)       4.740    23.50  \n",
       "wd (deg)          234.000   360.00  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import datetime\n",
    "\n",
    "import IPython\n",
    "import IPython.display\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (8, 6)\n",
    "mpl.rcParams['axes.grid'] = False\n",
    "\n",
    "zip_path = tf.keras.utils.get_file(\n",
    "    origin='https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip',\n",
    "    fname='jena_climate_2009_2016.csv.zip',\n",
    "    extract=True)\n",
    "csv_path, _ = os.path.splitext(zip_path)\n",
    "\n",
    "df = pd.read_csv(csv_path)\n",
    "# Slice [start:stop:step], starting from index 5 take every 6th record.\n",
    "df = df[5::6]\n",
    "\n",
    "date_time = pd.to_datetime(df.pop('Date Time'), format='%d.%m.%Y %H:%M:%S')\n",
    "\n",
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>989.212842</td>\n",
       "      <td>8.358886</td>\n",
       "      <td>913.60</td>\n",
       "      <td>984.20</td>\n",
       "      <td>989.57</td>\n",
       "      <td>994.720</td>\n",
       "      <td>1015.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T (degC)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>9.450482</td>\n",
       "      <td>8.423384</td>\n",
       "      <td>-22.76</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.41</td>\n",
       "      <td>15.480</td>\n",
       "      <td>37.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tpot (K)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>283.493086</td>\n",
       "      <td>8.504424</td>\n",
       "      <td>250.85</td>\n",
       "      <td>277.44</td>\n",
       "      <td>283.46</td>\n",
       "      <td>289.530</td>\n",
       "      <td>311.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>4.956471</td>\n",
       "      <td>6.730081</td>\n",
       "      <td>-24.80</td>\n",
       "      <td>0.24</td>\n",
       "      <td>5.21</td>\n",
       "      <td>10.080</td>\n",
       "      <td>23.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rh (%)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>76.009788</td>\n",
       "      <td>16.474920</td>\n",
       "      <td>13.88</td>\n",
       "      <td>65.21</td>\n",
       "      <td>79.30</td>\n",
       "      <td>89.400</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>13.576576</td>\n",
       "      <td>7.739883</td>\n",
       "      <td>0.97</td>\n",
       "      <td>7.77</td>\n",
       "      <td>11.82</td>\n",
       "      <td>17.610</td>\n",
       "      <td>63.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>9.533968</td>\n",
       "      <td>4.183658</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6.22</td>\n",
       "      <td>8.86</td>\n",
       "      <td>12.360</td>\n",
       "      <td>28.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>4.042536</td>\n",
       "      <td>4.898549</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2.19</td>\n",
       "      <td>5.300</td>\n",
       "      <td>46.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>6.022560</td>\n",
       "      <td>2.655812</td>\n",
       "      <td>0.51</td>\n",
       "      <td>3.92</td>\n",
       "      <td>5.59</td>\n",
       "      <td>7.800</td>\n",
       "      <td>18.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>9.640437</td>\n",
       "      <td>4.234862</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6.29</td>\n",
       "      <td>8.96</td>\n",
       "      <td>12.490</td>\n",
       "      <td>28.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>1216.061232</td>\n",
       "      <td>39.974263</td>\n",
       "      <td>1059.45</td>\n",
       "      <td>1187.47</td>\n",
       "      <td>1213.80</td>\n",
       "      <td>1242.765</td>\n",
       "      <td>1393.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wv (m/s)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>2.130539</td>\n",
       "      <td>1.543098</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.860</td>\n",
       "      <td>14.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>3.533671</td>\n",
       "      <td>2.343417</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.98</td>\n",
       "      <td>4.740</td>\n",
       "      <td>23.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wd (deg)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>174.789095</td>\n",
       "      <td>86.619431</td>\n",
       "      <td>0.00</td>\n",
       "      <td>125.30</td>\n",
       "      <td>198.10</td>\n",
       "      <td>234.000</td>\n",
       "      <td>360.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean        std      min      25%      50%  \\\n",
       "p (mbar)         70091.0   989.212842   8.358886   913.60   984.20   989.57   \n",
       "T (degC)         70091.0     9.450482   8.423384   -22.76     3.35     9.41   \n",
       "Tpot (K)         70091.0   283.493086   8.504424   250.85   277.44   283.46   \n",
       "Tdew (degC)      70091.0     4.956471   6.730081   -24.80     0.24     5.21   \n",
       "rh (%)           70091.0    76.009788  16.474920    13.88    65.21    79.30   \n",
       "VPmax (mbar)     70091.0    13.576576   7.739883     0.97     7.77    11.82   \n",
       "VPact (mbar)     70091.0     9.533968   4.183658     0.81     6.22     8.86   \n",
       "VPdef (mbar)     70091.0     4.042536   4.898549     0.00     0.87     2.19   \n",
       "sh (g/kg)        70091.0     6.022560   2.655812     0.51     3.92     5.59   \n",
       "H2OC (mmol/mol)  70091.0     9.640437   4.234862     0.81     6.29     8.96   \n",
       "rho (g/m**3)     70091.0  1216.061232  39.974263  1059.45  1187.47  1213.80   \n",
       "wv (m/s)         70091.0     2.130539   1.543098     0.00     0.99     1.76   \n",
       "max. wv (m/s)    70091.0     3.533671   2.343417     0.00     1.76     2.98   \n",
       "wd (deg)         70091.0   174.789095  86.619431     0.00   125.30   198.10   \n",
       "\n",
       "                      75%      max  \n",
       "p (mbar)          994.720  1015.29  \n",
       "T (degC)           15.480    37.28  \n",
       "Tpot (K)          289.530   311.21  \n",
       "Tdew (degC)        10.080    23.06  \n",
       "rh (%)             89.400   100.00  \n",
       "VPmax (mbar)       17.610    63.77  \n",
       "VPact (mbar)       12.360    28.25  \n",
       "VPdef (mbar)        5.300    46.01  \n",
       "sh (g/kg)           7.800    18.07  \n",
       "H2OC (mmol/mol)    12.490    28.74  \n",
       "rho (g/m**3)     1242.765  1393.54  \n",
       "wv (m/s)            2.860    14.01  \n",
       "max. wv (m/s)       4.740    23.50  \n",
       "wd (deg)          234.000   360.00  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data set preparation\n",
    "\n",
    "#as you can see in the min column we have outliers we need to remove.\n",
    "\n",
    "#bad_wv its self is erroneous data recorded in the dataset.\n",
    "#to be clear the \"wv (m/s)\" is the minimum wind speed\n",
    "wv = df['wv (m/s)']\n",
    "bad_wv = wv == -9999.0\n",
    "wv[bad_wv] = 0.0\n",
    "\n",
    "#the same story as wv column, except the max wind velocity is also recorded erroneously.\n",
    "max_wv = df['max. wv (m/s)']\n",
    "bad_max_wv = max_wv == -9999.0\n",
    "max_wv[bad_max_wv] = 0.0\n",
    "\n",
    "# The above inplace edits are reflected in the DataFrame.\n",
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-11.305513973134667, 8.24469928549079, -8.27438540335515, 7.7338312955467785)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#feature engineering\n",
    "\n",
    "#Our wind data is represented as a speed and a degree which it blows. Because degrees go from 0 to 360 then wrap around\n",
    "#our model will struggle to accept this data in its current form. Instead, to simplify loading the data, we make it into\n",
    "#a x vector and a y vector for velocity using trig and, the velocity, and the degrees:\n",
    "\n",
    "#.pop removes the entire column\n",
    "wv = df.pop('wv (m/s)')\n",
    "max_wv = df.pop('max. wv (m/s)')\n",
    "\n",
    "# Convert to radians.\n",
    "wd_rad = df.pop('wd (deg)')*np.pi / 180\n",
    "\n",
    "# Calculate the wind x and y components.\n",
    "df['Wx'] = wv*np.cos(wd_rad)\n",
    "df['Wy'] = wv*np.sin(wd_rad)\n",
    "\n",
    "# Calculate the max wind x and y components.\n",
    "df['max Wx'] = max_wv*np.cos(wd_rad)\n",
    "df['max Wy'] = max_wv*np.sin(wd_rad)\n",
    "\n",
    "plt.hist2d(df['Wx'], df['Wy'], bins=(50, 50), vmax=400)\n",
    "plt.colorbar()\n",
    "plt.xlabel('Wind X [m/s]')\n",
    "plt.ylabel('Wind Y [m/s]')\n",
    "ax = plt.gca()\n",
    "ax.axis('tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Time of day signal')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8FK7v2YKrzmpezQG6lzED4hjaviH/+WUTC5My7A5HKauT4/c3QlQHGP62z1zRXFlOSQwiMlREtohIsog8Wsbzb4jIasdtq4gcLvVcUannnHs+aHXxD4QrJkLNCJg8EnLSy119fWoWD3+3lh4x4Tx1UTsXBek+/PyE167sTHyDOoyZtJJdmcfsDkn5srxsa7JZ/GHEVxBUy+6I3E6VE4OI+APvABcA7YCrReSEbz9jzH3GmC7GmC7AeOCHUk8fL3nOGHNJVeNxmdqR1j+qY5nWaW5FZZeByMjJY/TnidSvFcS713bzuKuanaVWcAATru+OMTD6i0SO5rlHeXPlY4qL4YdbISPJ6v1er4XdEbklZ3xL9QCSjTEpxph8YDJQXqHyq4FJTnhf+zXuApeMh52LrLLdJykoKuaOr1aSeTSfCdcnEFE72PUxupEW9Wvx9jVd2Xogm4e+W6OT0cr15r9ilc8+/z8Q09fuaNyWMxJDE6B0O689jmV/IyItgBhgdqnFISKSKCJLROTSU72JiIx2rJeYnl7+oRuX6nQFnHsXLP8IVkw84annpm9k2faDvHJ5J22F6dAnPpLHLmjLL+v2886cZLvDUb5k03SrtE3na6zy2eqUXH1cYwTwnTGm9EntLRz1wa8BxolIbFkbGmMmGGMSjDEJkZGRroi18gb/G2IHws8PwK6lAPxv+S4+X7yT0X1bMrxLmXnSZ93cJ4bLujbhtZlbmbXpgN3hKF+Qthmm3ApNusNFb+hkcwWckRhSgWalHjd1LCvLCE46jGSMSXX8TAHmAl2dEJNr+flbXd/CmsI317Fu0yae+nEDfeIjeGRoG7ujczsiwov/6Ej7xqHcO3k1yWk5doekvNnxQ1bFgqBa1kWqgd59/ZAzOCMxLAfiRSRGRIKwvvz/dnaRiLQB6gGLSy2rJyLBjvsRQC/AOcWIXK1GPbh6EsV52cg319EszJ/xV3fF30//MilLSKA/H1yXQFCAH6O/SCQ7V3s4qGpQXAzf3wyHd8OVX0Co51YwdqUqJwZjTCEwBpgBbAK+McZsEJFnRaT0WUYjgMnmxBnHtkCiiKwB5gAvGWM8MzEANGjLR/UfpoNJYnKHROrWDLI7IrfWpG4N3r22G9szjvLa71vtDkd5o1WfQ/IfcMFL0Pxsu6PxGNrz2YnmbE7jX58t54+mHxN3eBHcsRjCW9odltt7Zup6Pl+yk6l39qJT07p2h6O8RU46vJ0ADTvCqJ90XgHt+exyx/OLeGrqeuIa1Kb5NW+BXyD8/KDLekZ7sgfOb01k7WAen7KOwiLt/Kac5PcnrLa8F76uSeE0aWJwkrdmJ7Hn0HFeuLQDQeFNYeCTsG0WbJhid2huLzQkkKcvbsf61CN8sUSb+ygnSJkHa/8Hve+FyFZ2R+NxNDE4wZb92Xw4P4Urujfl7JaOTmw9boFGXawL33KzbI3PE1zYsRH9WkXy2u9b2Z+Va3c4ypMV5sHP90O9GOjzgN3ReCRNDFVUXGx48sd11AkJ4LFhbf96ws/fOl/6aDrMft6+AD2EiPDc8A4UFBXz7HTtF62qYOE4yEy2SuQH1rA7Go+kiaGKvl2xm+U7DvHYsLaE1zrpLKQm3awS3cs+hNQV9gToQZrXr8ndg+L5Zd1+5mxOszsc5Ykyt8GC16DDPyFukN3ReCxNDFWQmZPHi79upkdMOFd0b1r2SgOfhNpR8NO9Z9z1zZfc0qclcQ1q89TU9RzP165v6jQYYx1CCgiB81+0OxqPpomhCv7zy2aO5hXyQnl9m0NCrXOo96+F5R+6NkAPFBTgxwuXdmDPoeO8NTvJ7nCUJ1n3HaTMhUFPQZ0ou6PxaJoYztDibZl8v3IPo/u2JD6qTvkrt7sU4s6z5hqyTlUtRJU4u2V9Lu/elA/np7Blf7bd4ShPcPwQzHjMqoWUcKPd0Xg8TQxnIK+wiCd+XEez8BqMGRBf8QYiMOxVKC4sszy3+rvHh7WldkgAT/64juJivRZEVWDWs1ZvlIvesE78UFWiieEMTJiXQkr6UZ4b3oEaQZX8RxgeA/0ehk3TYOuM6g3QC4TXCuLxC9qyfMchvl2xu+INlO/avRwSP4Wzb4dGne2OxitoYjhNOzKOMn5OMhd2akT/1g1Ob+Oed0FkG+uK6Hxtb1mRKxKa0iM6nBd/3UxmTp7d4Sh3VFQI0++1iuMNeMzuaLyGJobTYIzhqanrCfb34+kz6d0cEGQNdbN2wbyXnR+glxERXrisAzm5hfznl812h6Pc0dL34MB6uOBlCK5grk9VmiaG0/DT2n0sSMrgwfNbExV6hjXdW5wLXUfC4rfhgOcWknWV+Kg6jO7bku9X7mHxtky7w1Hu5PBumPMfaHUBtLnI7mi8iiaGSso6XsBz0zfSqWkYI8+pYgPxwc9CcChMv8+qF6/KddfAeJqF1+CJH9eRV6jXNiiHXx+xfg57RYvkOZkmhkr674wtZObk8Z/LOla9+U6t+jDkedi9BFZ94ZwAvViNIH+eHd6BlPSjTJiXYnc4yh1s/hm2/Az9H4W6ze2OxutoYqiE1bsP8+XSnYw6N5oOTcKc86JdroEWvWHm03A0wzmv6cUGtG7AhR0bMX5OMjsyjtodjrJTXg788jA0aA/n3GF3NF7JKYlBRIaKyBYRSRaRv52oLyI3iEi6iKx23G4u9dwoEUly3EY5Ix5nKiwq5vEf1tGgTjD3n+fE8r0icNHrVr3435903ut6sacvbkeQvx9PTV2PJzaYUk4y90U4ssc6kcM/0O5ovFKVE4OI+APvABcA7YCrRaSsU3b+Z4zp4rh95Ng2HHgGOBvoATwjIvWqGpMzffbnDjbuO8LYi9tTJ8TJ/wgjW0Ove2DNJNg+37mv7YWiQkN4cEgrFiRl8NPafXaHo+ywfx0seQ+636CtOquRM0YMPYBkY0yKMSYfmAwMr+S25wMzjTEHjTGHgJnAUCfE5BTp2Xm8MXMrA1pHMrRDw+p5k74PQr1o69oGLbJXoet6RtOxSRjPTd/IsXz9vHyKMfDLQ1CjHgx6xu5ovJozEkMToPSlqXscy072TxFZKyLfiUiz09wWERktIokikpienu6EsCv29uwkcguLeeqidqcukldVgTWsieiMLdbIQZXL30945uJ2pGfn8emiHXaHo1xp62+wazEMfAJqhtsdjVdz1eTzT0C0MaYT1qhg4um+gDFmgjEmwRiTEBkZ6fQAT7Yr8xhfL9vFVWc1o2Vk7ep9szYXWcW/5r4IBdq9rCIJ0eEMbtuA9+dt4/CxfLvDUa5QXGTVQwqPha7X2R2N13NGYkgFmpV63NSx7P8ZYzKNMSU1DT4Culd2W7u88cdW/ES4Z1AliuRVlQgMHgtHUmH5R9X/fl7gwfNbk5NXyHtzt9kdinKFdd9C2karv4lOOFc7ZySG5UC8iMSISBAwAphWegURaVTq4SXAJsf9GcAQEannmHQe4lhmq037jvDj6lT+1SvmzK9wPl0xfSF2oNV9SntEV6hNw1Au69KEz/7cwb6s43aHo6pTYR7MecEqkNfuUruj8QlVTgzGmEJgDNYX+ibgG2PMBhF5VkQucax2t4hsEJE1wN3ADY5tDwLPYSWX5cCzjmW2+u+MLdQJDuD2frGufeNBz8Dxg/Dn2659Xw9133mtKDaGt2ZpQx+vtuIzOLzL+v/hp5deuYJTPmVjzC/GmFbGmFhjzAuOZU8bY6Y57j9mjGlvjOlsjBlgjNlcattPjDFxjtunzoinKpbvOMiszWnc3j+OsJouHrI27gLt/wGL34Ec7XlckWbhNbn27BZ8k7iHbek5doejqkNeNsx75a8RtXIJTb+lGGN4+dfNNKgTzA3nRtsTxMAnoTAX5r9qz/t7mDED4wgO8OO137fYHYqqDovfhWMZMGis1kNyIU0MpczenEbizkPcMzi+8g14nK1+LHS73mo8cnC7PTF4kIjawdzcpyW/rNvPmt2H7Q5HOdPRDPhzPLS9GJp2r3h95TSaGByKig2v/LaFmIhaXJnQrOINqlO/R6z2hHNftDcOD3FLnxjCawXx6gwdNXiVBa9DwVEY+JTdkfgcTQwOU1ensuVANg8MaUWgv80fS2gjOPs2WPsN7F9vbyweoE5IIHcOiGNhcgYLk7QgoVc4vBuWf2gVm4xsbXc0PkcTA5BXWMTrM7fSoUkowzo0qngDV+h9L4SEWhf1qApde3ZzmtStwSszNmuBPW8w90VAoL+267SDJgZg0tJd7Dl0nIfPb4NfVXstOEuNetD7PkiaATv/tDsatxcS6M+9g+NZuyeL39bvtzscVRVpm6zyMD1ugbCmdkfjk3w+MeTkFTJ+djI9W9anT3yE3eGcqMetULsh/PFvq4CYKtc/ujUlvkFtXv19C4VF2hnPY81+HoJqQ58H7I7EZ/l8Yvhk4XYyj+bz8NDW1Vco70wF1YT+j1id3rbafkG42/P3Ex48vzUp6Uf5bsUeu8NRZ2L3ctg8Hc69Wwvl2cinE0NmTh4T5qcwtH1DujZ3qzYQf+l6HYS3hFn/tgqJqXINaRdF1+Z1GfdHErkF+nl5FGPgj7FQKxLOud3uaHyaTyeGd+du41h+IQ+e78TObM7mH2hd9Ja20SokpsolIjwytA37j+Ty+eIddoejTkfyLNi5EPo+DMHVXNFYlctnE0Pq4eN8sXgnl3dvSlyDOnaHU752l0HDTlYhscK8itf3cee0rE+/VpG8M2cbWccL7A5HVUZxMcwaC3VbWN3ZlK18NjGMm7kVBO4d7MajhRJ+flZZ7sO7rIJiqkIPnd+arOMFfDg/xe5QVGVs+MFq2znwSQgIsjsan+eTiSHpQDbfr9zDqJ4taFy3ht3hVE7sQIjuYxUUy8u2Oxq316FJGJd0bszHC7eTdkSbH7m1ogLrTKSoDtDhcrujUfhoYnh1xhZqBQVwR/84u0OpvJJmPscyrMJiqkL3n9eKgqJixs9OtjsUVZ6VE+HQdhj0tJbVdhM+91tYuesQv288wOi+LalXy8OGrE0TrDagf463CoypckVH1GJEj2ZMWraLnZlH7Q5HlSX/qDUKbt4T4ofYHY1ycEpiEJGhIrJFRJJF5NEynr9fRDaKyFoRmSUiLUo9VyQiqx23aSdv60wlZbUjagdxY++Y6nyr6jPwKauw2ILX7Y7EI9w9MJ4Af+H1mVvtDkWVZen7kHPAasLjbtcR+bAqJwYR8QfeAS4A2gFXi0i7k1ZbBSQYYzoB3wGvlHruuDGmi+N2CdVoflIGS7cf5K6B8dQKDqjOt6o+DdpA52usAmOHd9sdjdtrEBrCjb1imLp6Lxv2astUt3LsICx8E1oNhRY97Y7G7eUWFDFnS5pLaoE5Y8TQA0g2xqQYY/KBycDw0isYY+YYY445Hi4BbCmAMn5WEs3Ca3B1j+Z2vL3z9H8UEJj3st2ReIRb+8USViOQ13/XUYNb+XM85B2x5hZUhb5euot/fbqcNXuq/w8cZySGJkDpP133OJadyk3Ar6Ueh4hIoogsEZFLT7WRiIx2rJeYnp5+RoG+c2033hzRlaAAD59aqdvMauazZhIc2ml3NG4vrEYgN/eOYdbmNNa54D+VqoRjB2HZBGh/KUS1tzsat5dbUMT787ZxTstwujSrW+3v59JvSBEZCSQApftWtjDGJADXAONEJLasbY0xE4wxCcaYhMjIyDN6/6jQELq5a+mL09X7PhA/WKhzDZUxqlc0oSEBvDU7ye5QFFh9zfNzrKucVYUmL9tFWnYe9wxyzXVXzkgMqUDplmdNHctOICKDgSeAS4wx/3/5rjEm1fEzBZgLdHVCTN4vrIlVR2nVVzrXUAmhIYHc1LslMzce0LkGux07CEs/gHbDIerk6Uh1styCIt6bt40eMeH0jK3vkvd0RmJYDsSLSIyIBAEjgBPOLhKRrsAHWEkhrdTyeiIS7LgfAfQCNjohJt/Q+z7rp44aKuWGXtHUCQngrVk6arDVkvcgP9tqYasq9E3ibg4cyePeQfEue88qJwZjTCEwBpgBbAK+McZsEJFnRaTkLKNXgdrAtyedltoWSBSRNcAc4CVjjCaGyqrbDLqOhJVfQJaWma5IWI1AbuwVw4wNB9i074jd4fim44esU1TbXqxzC5WQV1jEe3O3cVZ0PZeNFsBJcwzGmF+MMa2MMbHGmBccy542xkxz3B9sjIk6+bRUY8yfxpiOxpjOjp8fOyMen9LnfuvnwjfsjcND3NgrhjrBOmqwzZL3rTORdLRQKd8k7mFfVi53D4p3ab8YDz89R1G3udUwfeXnkPW3qR11krCagfyrVzS/rt/P5v06anCp44etw0htLoKGHe2Oxu3lFRbx3pxkujWvS+8413aX1MTgDfo8AKYYFo2zOxKPcGPvGGoHBzB+ltZQcqmlH0BeFvTTM5Eq47sVe9iblcs9g1u5vLukJgZvUK8FdL4aVkyEI/vsjsbt1a0ZxA3nRvPL+n1sPaCVal0iNwuWvAOth0GjznZH4/byC4t5d842ujSrS18betFrYvAWfR6A4kIdNVTSTb1jqBnor3MNrrJ0gpUcdLRQKT+s3EPq4ePcM9i1cwslNDF4i/AYx6jhM8jeb3c0bq9erSCuPzean9ftI0lHDdUr9wgsftuqidRYL1OqSEFRMW/PSaZz0zD6tzqzi3mrShODN+n7gNX0ZNFbdkfiEW7p05Iagf7ar6G6LZsAuYf1TKRKmrIylT2H7BstgCYG7xLeEjpdBYmfQE5axev7uPBaQVzXswU/rd1LclqO3eF4p7xsa7QQPwSadLM7GrdXMlro2CSMAa0b2BaHJgZv0/dBKMqDRW/aHYlHGN2nJSEB/rytNZSqx7IPrYva+v2tTYsqw4+rUtl18JjLr1s4mSYGb1M/FjpeCcs/hpwzq0LrS+rXDua6ni2YtmYvKek6anCqvBxrtBA3GJp2tzsat1foGC20bxzK4Lb2jRZAE4N3Khk1/KlzDZVxS5+WBAX48fYcnWtwqsSP4Vimzi1U0tTVe9mZaf9oATQxeKeIeOjwT1j+kfaGroTIOsGMPLsFU1fvZUeG9oZ2ivyj1kkQLQdAsx52R+P2SkYLbRuFMqRdlN3haGLwWn0fgoLjVpcsVaHR/VoS4Cc6anCWxE/gWIaj26CqyPS1+9iecZR7BsXZPloATQzeK7I1dPiHNfl3NNPuaNxegzohXHt2C6asSmVnpo4aqiT/mHXyQ0w/aH6O3dG4vaJiw1uzk2jTsA5D2jW0OxxAE4N36/swFByzJgBVhW7r1xJ/P+EdHTVUzYpP4Wi6jhYqafravaSkH+XuQfH4+dk/WgBNDN6tQRurp+6yCVbXLFWuBqEhXNOjOT+sTGX3wWN2h+OZCo5bo4XoPtDiXLujcXtFxYbxs5NpFVWboe3dY7QAmhi8X9+Hrd66i9+xOxKPcFu/WPxERw1nbMVnkHNARwuV9Mu6fSSn5XDXQPcZLYCTEoOIDBWRLSKSLCJ/+xchIsEi8j/H80tFJLrUc485lm8RkfOdEY8qJaqd1Vt36QfWhUaqXA3DQhjRoxnfrdijo4bTVZALC8dBi94Q3dvuaNxecbFh/Owk4hrUZljHRnaHc4IqJwYR8QfeAS4A2gFXi8jJHb5vAg4ZY+KAN4CXHdu2w+oR3R4YCrzreD3lTP0esXrsLnnP7kg8wu39rVHDu3O32R2KZ1k5EXL2Q3+9bqEyfl2/n60HcrhrYBz+bjRaAOeMGHoAycaYFGNMPjAZGH7SOsOBiY773wGDxDonazgw2RiTZ4zZDiQ7Xk85U1R7q8fukvetLlqqXI3CanDlWU35bsVuUg8ftzscz1CQa7WXbX6uNb+gylUyWoiNrMVFnRrbHc7fOCMxNAF2l3q8x7GszHWMMYVAFlC/ktsCICKjRSRRRBLT07XUw2nr94jVPWvp+3ZH4hHu6B8HwLs611A5q76A7H3WaMENzsN3dzM27Gfz/mzuGhjvdqMF8KDJZ2PMBGNMgjEmITLSnhrlHq1hR6vX7pJ3rYYpqlyN69bgioRmfJO4m706aihfYZ41Wmh2jnXtgipXcbHhzVlJtIyoxcWd3W+0AM5JDKlAs1KPmzqWlbmOiAQAYUBmJbdVztLvYSspLP3A7kg8wh39YwF4f57ONZRr1ZdwJFVHC5X0+8YDbN6fzRg3nFso4YzEsByIF5EYEQnCmkyedtI604BRjvuXA7ONMcaxfITjrKUYIB5Y5oSYVFkadbZ67i5+x+qqpcrVtF5NLu/elMnLdrM/K9fucNxTYb41Wmjaw6qLpMpljOGtWUlE16/JJW46WgAnJAbHnMEYYAawCfjGGLNBRJ4VkUscq30M1BeRZOB+4FHHthuAb4CNwG/AncaYoqrGpMrR72Grm9YyHTVUxh394yg2RkcNp7L6K8jaraOFSvpjUxob9x1hzMB4Avzd90i+WH+4e5aEhASTmJhodxie6+urYPdSuHcdBNexOxq398h3a5myOpUFDw8gKjTE7nDcR2E+jO8OtSPh5lmaGCpgjOHitxdy5Hghsx/oZ0tiEJEVxpiEitZz35Slqk+/h62L3ZZNsDsSj3DngDiKinXU8DdrJkHWLqs7myaFCs3enMb61COMGRDn1qMF0MTgm5p0h7jz4M+3rS5bqlzN69fksq5N+HrpLtKO6FwDAEUFsOC/0LgrxJ9ndzRuzxjrTKRm4TW4rFuZZ+S7FU0Mvqr/o3D8ICz/0O5IPMKYAXEUFhs+mJ9idyjuYc1kOKyjhcqauyWdtXuyuLN/HIFuPloATQy+q2kCxA6yGvnka/+BikRH1GJ4l8Z8tXQn6dl5dodjr5LRQqMu0ErLm1XEGMO4WUk0qVuDf3Rranc4laKJwZf1f9Tqybv8Y7sj8Qh3DYwnv7CYCfN9fK5h7TdwaId1Nb2OFio0b2s6a3Yf5s4BcQQFeMZXrmdEqapHM8e553++ZXXdUuWKiajF8C5N+GLJTjJyfHTUUFQI81+Fhp2g9QV2R+P2SuYWmtStweXdPWO0AJoYVP9HrW5biZ/YHYlHGDMwjvzCYj5c4KNzDeu/g0PbdbRQSQuSMli16zC394/1mNECaGJQzR31bRa9qaOGSoiNrM3FnRvzxeKdHDyab3c4rlVcZI0WojpYV9CrcpWMFhqFhXBFgueMFkATgwLHqCHN6r6lKnTXwDiOFxT53qhh/feQmWxdB+OnXx0VWZScyYqdh7ijfyzBAZ7VZkZ/u8rqzRvdBxaNs3r2qnLFNajDRZ0a8/mfOzjkK6OG4iKY9wo0aAdtLrY7GrdnjRa20jA0hCvPalbxBm5GE4Oy9H/U6tW7YmLF6yruHhjHsYIiPlroI6OGDVMgM0lHC5W0eFsmy3cc4nYPHC2AJgZVIrq31at30TirG5cqV3xUHYZ1bMTEP3dy+JiXjxpKRguRbaHtyc0ZVVnGzUoiKjSYqzxwtACaGFRp/R+xunCt/NzuSDzC3QPjyckr5OOF2+0OpXpt/BEytkC/h3S0UAmLt2WybPtBbusXS0ig540WQBODKi26j9Wzd+EbVlcuVa7WDeswrGNDPlu0g6xjBXaHUz2Ki2HeqxDRGtpdanc0HuHNWVuJrBPM1T2a2x3KGdPEoP4iYh1Dzt6ro4ZKumtgPNl5hXy8yEtHDZumQvomx9yCZ/7160pLUzJZknKQW/u29NjRAmhiUCdr2R+ana2jhkpq2yiU89tH8emi7WQd97JRQ3GxNbdQPx7aX2Z3NB7hzVlJRNQO5tqzW9gdSpVUKTGISLiIzBSRJMfPemWs00VEFovIBhFZKyJXlXruMxHZLiKrHbcuVYlHOYGIdVXrkVSrO5eq0N2D4snOLeRTbxs1bP4J0jZC34d0tFAJy3cc5M9tmdzatyU1gjz786rqiOFRYJYxJh6Y5Xh8smPA9caY9sBQYJyI1C31/EPGmC6O2+oqxqOcIXYgND0LFrxudelS5WrfOIzz2kXxycLtHMn1klFDyWghPBY6/NPuaDzCW7OSqF8riGvP8dy5hRJVTQzDgZIT3ycCl568gjFmqzEmyXF/L5AGRFbxfVV1ErHq7GfthjVf2x2NR7hnUDxHcguZuGiH3aE4x5Zf4MB6a7TgH2B3NG5vxc5DLEjKYHTfltQM8vzPq6qJIcoYs89xfz8QVd7KItIDCAJK1y1+wXGI6Q0RCS5n29Eikigiienp6VUMW1UobpDV6W3+azrXUAkdmoQxuG0DPlroBXMNxcUw9yUIbwkdr7A7Go8w7o+thNcK4rqenj23UKLCxCAif4jI+jJuJ1zpYowxgCnndRoBXwD/MsYUOxY/BrQBzgLCgUdOtb0xZoIxJsEYkxAZqQOOaicCA5+0evomfmp3NB7hvvNakXW8wPP7NWz4AQ6ss0aNOlqo0KLkDBYkZXB7v1ivGC1AJRKDMWawMaZDGbepwAHHF37JF39aWa8hIqHAz8ATxpglpV57n7HkAZ8CPZyxU8pJWg6AmL5WRc28bLujcXvtG4dxSefGfLJwh+f2hi7Mh9nPWRVUdbRQIWMMr/y2mUZhIV4zWoCqH0qaBoxy3B8FTD15BREJAqYAnxtjvjvpuZKkIljzE+urGI9yJhEYNBaOZcDid+2OxiPcf14rCoqKGT872e5Qzsyqz63ubIOe1qucK2HGhv2s2ZPFfYNbefR1Cyer6m/+JeA8EUkCBjseIyIJIvKRY50rgb7ADWWclvqViKwD1gERwPNVjEc5W9Pu0PZiqzf00Qy7o3F70RG1uLpHcyYt28XOTA/rpZ1/1DoTqfm5ED/E7mjcXmFRMa/O2EJcg9r8o1sTu8NxqiolBmNMpjFmkDEm3nHI6aBjeaIx5mbH/S+NMYGlTkn9/9NSjTEDjTEdHYemRhpjcqq8R8r5Bj4NBUet01dVhe4aGEegvx+v/b7V7lBOz9L3rQq7g5/R7myV8MPKVLalH+XBIa0J8Peu0ZV37Y2qHpGtoMu1sPxDOLzL7mjcXoPQEG7sHc20NXvZsDfL7nAq59hBWPgmtLrA6uqnypVbUMQbf2ylc7O6nN++3JMxPZImBlU5/R8FxDqNUVVodN9YwmoE8uqMLXaHUjkL34C8IzDoKbsj8QhfLN7JvqxcHhnaGvHC0ZUmBlU5YU2hxy2wZhKkbbI7GrcXViOQO/rHMndLOktSMu0Op3xZqbBsAnS6CqLa2x2N2zuSW8A7c5PpEx/BubERdodTLTQxqMrr8wAE1YbZeo5AZYw6N5qGoSG8/NtmrMt83NS8l61mPAMetzsSj/Dh/BQOHyvgkaFt7A6l2mhiUJVXMxx63Q2bp8Pu5XZH4/ZCAv25d3A8q3YdZubGA3aHU7aMJFj1JZx1E9TznvPwq0t6dh4fLdjORZ0a0aFJmN3hVBtNDOr0nH071GoAf4wFd/4r2E1c3r0pLSNq8eqMLRQVu+HnNft5CAiBPg/aHYlHeHt2EvlFxTwwpLXdoVQrTQzq9ATXtpq27FwIybPsjsbtBfj78eD5rUlKy2HKqlS7wzlR6kqrbee5Y6C2lpmpyK7MY3y9bBdXndWMmIhadodTrTQxqNPXbRTUbQGzxloF11S5LujQkE5Nw3hj5lbyCovsDucvs/4NNetDzzF2R+IRXp+5BX8/4Z5B8XaHUu00MajTFxBkFdjbv84quKbKJSI8MrQNqYeP8+USN7kOZNscSJlrHUIKCbU7Gre3ce8Rpq7Zy796xRAVGmJ3ONVOE4M6Mx0utwqtzX4eijy8zLQL9IqLoHdcBO/MSSbb7mY+xlijhbBmkHCjvbF4iP/+voU6wQHc1jfW7lBcQhODOjN+fjDoGTi0HVZ+bnc0HuGh81tz8Gg+Hy2wuQXopmmwdxX0fwwCvf+v36patv0gszencXv/OMJqBtodjktoYlBnLv48q+DavJch/5jd0bi9zs3qMqxjQz5akEJGjk3Nj4oKYdZzENkGOo+wJwYPUlJWOyo0mBvOjbY7HJfRxKDOnIhVcC3nACx9z+5oPMIDQ1qTW1jMO3NsKsu9+ivITHKU1faeMtHVZdamNBJ3HuKeQa2oEeQ7n5cmBlU1zc+xCq8tfNMqxKbKFRtZmysTmvLVkl3sPujiUVbBcavWVdOzoPUw1763ByoqNrw6YwsxEbW4IqGp3eG4lCYGVXWDnrIKsC0aZ3ckHuHuQfGIwBt/uLgs97IPIXsvDB6rZbUrYerqVLYcyOaBIa0I9LKy2hWp0t6KSLiIzBSRJMfPeqdYr6hUk55ppZbHiMhSEUkWkf85ur0pTxPV3irAtvQDOLLX7mjcXqOwGtxwbjRTVqWyZb+LWqYePwwLXoO4wRDd2zXv6cHyCot4feZWOjQJZViHRnaH43JVTYOPArOMMfHALMfjshwv1aTnklLLXwbeMMbEAYeAm6oYj7LLgMetQmzzXrY7Eo9we/9YagcHuK4s95/jIfewNbegKjRp6S72HDrOw+e3wc/P90ZXVU0Mw4GJjvsTsfo2V4qjz/NAoKQP9Gltr9xMvRZWIbaVX0CGh/Y7dqG6NYO4rV8sf2w6wIqd1Tw3k30AlrxrXXvSqHP1vpcXyMkrZPzsZM6NrU+feO8sq12RqiaGKGPMPsf9/cCpWhmFiEiiiCwRkUsdy+oDh40xhY7He4BTNk4VkdGO10hMT0+vYtiqWvR5EAJrwO9PaIG9SvhXr2gi6wTz7PRN1Vtgb/azUJSvZbUr6d05yWQezeeh872zCU9lVJgYROQPEVlfxm146fWMVXD+VP+6WxhjEoBrgHEictqXDxpjJhhjEowxCZGRWvDLLdWOtDq9bf3NKs2tylUzKIAnhrVlze7DfL2smkpl7PzTKqvdcwzU942rdqti64FsJsxP4fLuTenavMwpU59QYWIwxgw2xnQo4zYVOCAijQAcP9NO8Rqpjp8pwFygK5AJ1BWRAMdqTQE3Kz+pTtvZt0NUR/jlYchz0cSqBxvepTG94urzym+bScvOde6LF+bDT/dC3ebQ7xHnvrYXKi42PDFlHbVDAnh8WFu7w7FVVQ8lTQNGOe6PAqaevIKI1BORYMf9CKAXsNExwpgDXF7e9srD+AfARW9A9j6Y86Ld0bg9EeG54R3IKyjm+elObpm6eDxkbIFh/4Wgms59bS/03Yo9LN9xiMcvaEt4Ld8+QbKqieEl4DwRSQIGOx4jIgki8pFjnbZAooiswUoELxljNjqeewS4X0SSseYcPq5iPModNDsLEv5lXQ29b43d0bi9lpG1uWNALNPW7GX+VifNnx3cDvNegbaXQKvznfOaXiwzJ4///LqJHtHhXN7dty5mK4u4dS/aU0hISDCJiYl2h6HKc/wQvH2WVcHz5j+0/EIFcguKuODNBRQbw4x7+xISWIXPyxj46nLYtQTuXAZhpzynQzk8+O0aflyVyi/39KFVVB27w6k2IrLCMd9bLt+6nE+5To16cP6LsHclJH5idzRuLyTQn+cv7cDOzGO8W9U6Sht/hOQ/rJ4ZmhQqtCQlk+9W7GF035ZenRROhyYGVX06Xg4t+8OsZyF7v93RuL1ecRFc2qUx783bRnJazpm9SG4W/Pqodb3CWbc4N0AvlFdYxBNT1tEsvAZ3DfT+zmyVpYlBVR8RuPB1KMyDGXoOfWU8cWE7agT68+SP6zijw7yzX7Cq3V70hnUigCrXh/NT2JZ+lGeHd/Cp6qkV0cSgqlf9WOjzAKz/HpJn2R2N24usE8wjF7RhScpBflh5mmdvp66EZROgxy3QpHv1BOhFdmYeZfzsZC7s2IgBrRvYHY5b0cSgql/ve6F+HPx8v1X6WZXr6rOa07V5XV74ZROHjuZXbqOiQph+L9SOsuYWVLmMMTz543oC/f14+uJ2dofjdjQxqOoXEGwd2ji0w6rwqcrl5yf857KOZB0v4OXfNlduo+UfWacGD30RQsKqN0AvMH3tPhYkZfDgkFZEhWp705NpYlCuEdMXOo2AheMg3UUVRT1Y20ah3NQ7hsnLd7N8RwVF9o7shdnPWyW121/mmgA9WNbxAp6dvpGOTcK4rme03eG4JU0MynWGPA9BtWD6/VpkrxLuHRxPk7o1eGLKOgqKik+94m+PQnGBdYWzjxZ9Ox2v/b7FuqDtso74+2BJ7crQxKBcp3YknPdv2LkQ1kyyOxq3VzMogLGXtGfrgRw+WrC97JW2/g4bp0LfhyA8xrUBeqDVuw/zxZKdXN8zmo5N9ZDbqWhiUK7V9Xpodjb8/qT2iK6E89pFMaRdFG/O2vr3HtH5x+CXByCiNZx7tz0BepDComIe/2EdDeoE88CQVnaH49Y0MSjX8vOzJqKPH4aZ2k2sMsZe0h4/EZ6euv7EaxvmvwKHd1mfZ4BvF32rjImLd7Jx3xGeubg9dUIC7Q7HrWliUK4X1R563gmrvrD6BahyNa5bg/vPa8WcLen8tt5xBfmBjVa7zi7XQnQvewP0APuyjvP671vo3zqSCzo0tDsct6eJQdmj/6MQ1hym32f1DVDluuHcaNo2CmXsTxvIPp5nfW7BoXDec3aH5hHGTttAkTE8N7yDz3ZlOx2aGJQ9gmrBsFchfTMsftvuaNxegL8f/7msA2nZecyZ/AbsXgJDnoNa9e0Oze39sfEAMzYc4O5B8TQL174UlaGJQdmn9VBoc5HVNyAjye5o3F7X5vW4tWtN+uwYT3ZUD+swkipX1vECnpm2gfgGtbm5d0u7w/EYmhiUvYa9anUXm3wt5B6xOxr3VpDLg4eeI1gKufngSNJy8uyOyK0VFRvumbyKtOxcXvpnJ4IC9Ouusqr0SYlIuIjMFJEkx8+/dc8WkQEisrrULVdELnU895mIbC/1XJeqxKM8UGhjuGIiZCbDD6OhuJwLuXyZMfDzAwTsW0HmeW+xNjeK279cSV5hkd2Rua3//r6FuVvSGXtJe7q3+NtXkypHVVPoo8AsY0w8MMvx+ATGmDnGmC7GmC7AQOAY8HupVR4qed4Ys7qK8ShPFNMHhr4EW3+FeS/ZHY17WvYhrP4S+j5Ms15X8eoVnVix8xBjp22seFsfNH3tXt6bu42rezTn2rNb2B2Ox6lqYhgOTHTcnwhcWsH6lwO/GmOOVbCe8jU9boEuI2Hey7Bxmt3RuJftC6yyF62HQf/HALioU2Nu7x/LpGW7+HLJTpsDdC8b9x7hoW/XktCiHv++pL3d4XikqiaGKGPMPsf9/UBUBeuPAE6uhfCCiKwVkTdEJPhUG4rIaBFJFJHE9HQnNUxX7kMELnodmiTAlNus8/SVdQHbt6OsvhaXfWBdIOjw4JDW9G8dydhpG1i2Xa8iBzh4NJ9bPk8krEYg747spvMKZ6jCT01E/hCR9WXchpdez1iXZJ6yMpqINAI6AjNKLX4MaAOcBYQDj5xqe2PMBGNMgjEmITIysqKwlScKCIarvoTg2jD5ai2ZkX8MJl9j9VoYMQlCQk942t9PeHNEV5qF1+SOr1aw97Bv97ooLCrmzq9Wkp6TxwfXdadBHS2nfaYqTAzGmMHGmA5l3KYCBxxf+CVf/GnlvNSVwBRjTEGp195nLHnAp0CPqu2O8nihjazkcGQvfH+T9aXoi4yBaWNg/3r450cQEVfmamE1Avnw+u7kFhRz6xcryC3w3cnoF37ZxOKUTF68rCOdm9W1OxyPVtVx1jRglOP+KGBqOetezUmHkUolFcGan1hfxXiUN2jWAy58DbbNhllj7Y7GHovetNqhDnoaWg0pd9W4BnV446ourEvN4rEfzrBXtIf7bsUePl20gxt7xfDP7k3tDsfjVTUxvAScJyJJwGDHY0QkQUQ+KllJRKKBZsC8k7b/SkTWAeuACOD5KsajvEW36+GsW6x6QGu/tTsa10r6A/4YazXd6X1fpTY5r10U95/XiimrUvl44SlKdHup1bsP8/iUdZwbW5/Hh7WxOxyvIJ7410VCQoJJTEy0OwxV3YoK4PPhkLoCbpwBjbvYHVH1y9wGHw6w6kjdNMMqHVJJxcWGO75aye8b9/P5jWfTOz6iGgN1D2nZuVwyfhEB/sK0Mb0Jr6VVZssjIiuMMQkVradT9sp9+QdaF7/VjLCujM7x8rPR8rKtyWbxhxFfnVZSAKtX9H+v7Excg9qMmbSSXZnefVZ4XmERt3+5kqzjBUy4LkGTghNpYlDurXak9SV5LNM6bbOooOJtPFFxsXWabkYSXPEZ1Duzi7JqBwfw4fUJGAOjv0jkaJ73Tt6PnbaRFTsP8eoVnWjXOLTiDVSlaWJQ7q9xF7hkPOxcBL89Znc01WP+K7B5Opz/ArTsV6WXalG/FuOv7srWA9k89N0ar5yM/nLJTiYt28Ud/WO5qFNju8PxOpoYlGfodAWcexcs/xBWfm53NM61aTrMfRE6XwNn3+aUl+zbKpJHL2jDL+v2886cZKe8prtYtv0gY6dtYEDrSB4Y0trucLySJgblOQb/G2IHws8PwO5ldkfjHGmbYcqt0Lib1aLTiU1kbunTkku7NOa1mVuZtemA017XTnsPH+eOr1bQPLwm40Z0xd9Pm+5UB00MynP4+cPln0BoE/j6KkiZa3dEVbNnBXz5DwisaV3UF+jcK3VFhJf+2Yn2jUO5a9Iqfl23r+KN3FhyWjYjP15KbkExE67vTlgN7dtcXTQxKM9Sox6M/B5qN4AvLoMFr1tXCXsSY2D5x/DpUCvZjfwewppUy1uFBPrz8aizaBVVh9u/Wsl/ftlEYZHnlTafvnYvl7y9iCPHC/jkhrOIa1DH7pC8miYG5Xnqx8LNs6wLwGb929HkJ8vuqCon/xj8eAf8fD/E9IPR86BRp2p9y6jQEP536zlcd04LJsxPYeTHS0nP9owmPwVFxTz700bGfL2Kto1C+fnuPvSICbc7LK+nF7gpz2UMLP0Afn8C6ja3DsdEuXGZ5YMp8L/r4cB66P8o9H34hGqprvDDyj08PmWdVX302m50b+G+X7JpR3K58+uVLN9xiBvOjebxYW21WmoV6QVuyvuJwDm3wQ0/W3+JfzgI1n5jd1Rl2/IbfNAfsnbDtd9aicHFSQHgH92aMuWOXoQE+nPVB0v4dNF2tzydddn2g1w4fiHrU4/w5ogujL2kvSYFF9JPWnm+5ufArfOhSTf44Rb4+UEozLc7KktxEcx+HiZdBeHRcOs8iD/P1pDaNgpl2pje9G8dyb9/2sg9k1dzLN89LoQzxvDRghSu/nAJdYID+PHOXgzvUj3zL+rU9FCS8h5FBVbxucVvQ9OzrHIa1TSpWylHM+GHm60qsV1HwrDXnH7mUVUUFxvem7eN137fQlyD2rw/sjstI2vbFk9OXiGPfL+Wn9fu4/z2Ufz3is7UCdEzj5ypsoeSNDEo77PhR5h6JwSEWKe3VvFK4jOSugK+GQU5aTDsVeg+quJtbLIwKYO7J68iv7CY/17RmaEdGro8huS0bG77ciUp6Tk8PLQNt/ZtiTjxmg5l0TkG5bvaXwq3zIGa9eGLS2HhG647pdUYSPwUPhkKCNz4m1snBYDe8RH8dFdvYiNrcduXK3jxV9ee0vrLun0Mf3sRh47m8+VNZ3Nbv1hNCjbTxKC8U2QruGU2tBtuHV7630g4mlG975mbBVPHwPR7IbqPNZ/QpFv1vqeTNKlbg29u68m1Zzfng3kpXPfxMvZlVW+r0OP5Rbzw80bu+GolrRrWYfrdvTk3zvtLhXsCPZSkvJsxsORd+P0pMMVWQb6W/a1bs3Oqdsy/MB/2LIft86yrsPckgimCfo9YNz9/5+yDi323Yg9PTFlHXmExbRrWoXdcBL3iI+gRHU6t4IAzft3ComLWpWaxKDmDhckZrNx5mPyiYkb1bMETF7bTs45cwCVzDCJyBTAWaAv0MMaU+W0tIkOBNwF/4CNjTEmntxhgMlAfWAFcZ4yp8HQSTQzqtB3YYBWrS5kLe5ZBcaE1B9H8nL8SRcNO5X+ZG2O9Tspc67bzTyg4CuJn1Tpq2R/aDIMm3V2xR9Vqe8ZRfl2/j0XJGSzfcYj8wmIC/YWuzetZiSIugs5NwwjwP/WXuTGGbelH/z8RLEnJJDvXOvupfeNQesdFMLBNA85uWd9Vu+XzXJUY2gLFwAfAg2UlBhHxB7YC5wF7gOXA1caYjSLyDfCDMWayiLwPrDHGvFfR+2piUFWSl2N9qZd8wadtsJbXqAcxff9KFPViIGvPX+ttnwdHHc2C6sf/tV50b6hR1+W74Sq5BUUk7jjEwuQMFiVnsH5vFsZYvR/OaVmf3nH16R0fQWxkbdKz81i0LYOFSZksSs5g/5FcAJrWq0GfeCuh9GxZn/q1g23eK9/k0rOSRGQup04MPYGxxpjzHY9LCuq/BKQDDY0xhSevVx5NDMqpsg/A9vl/JYAje6zlIXUh97B1v3aUlQRi+llnOYX5bsP5Q0fzWZyS+f+JYqejU1xYjUCyjluNlOrWDKRXrJUIesdF0Lx+TTtDVg6VTQxnfsCw8poAu0s93gOcjXX46LAxprDU8lOedC4io4HRAM2bN6+eSJVvqhNl9XvodIV1uChzG6TMgb2roWEHKyFEtnFqSWxPVq9WEMM6NmJYx0YA7D54jEXJGazcdYiWkbXpHRdBu0ah+GlJbI9VYWIQkT+Ask5sfsIYM9X5IZXNGDMBmADWiMFV76t8jAhExFk3VSnNwmsyokdzRvTQP9i8RYWJwRgzuIrvkQo0K/W4qWNZJlBXRAIco4aS5UoppWzkivPDlgPxIhIjIkHACGCasSY35gCXO9YbBbhsBKKUUqpsVUoMInKZiOwBegI/i8gMx/LGIvILgGM0MAaYAWwCvjHGOE4D4RHgfhFJxppz+Lgq8SillKo6vcBNKaV8hNZKUkopdUY0MSillDqBJgallFIn0MSglFLqBB45+Swi6cDOM9w8Aqjm+stuy5f3HXx7/31538G397/0vrcwxkRWtIFHJoaqEJHEyszKeyNf3nfw7f335X0H397/M9l3PZSklFLqBJoYlFJKncAXE8MEuwOwkS/vO/j2/vvyvoNv7/9p77vPzTEopZQqny+OGJRSSpVDE4NSSqkT+FRiEJGhIrJFRJJF5FG743ElEdkhIutEZLWIeH0FQhH5RETSRGR9qWXhIjJTRJIcP+vZGWN1OcW+jxWRVMfvf7WIDLMzxuoiIs1EZI6IbBSRDSJyj2O51//uy9n30/7d+8wcg4j4A1uB87DaiC4HrjbGbLQ1MBcRkR1AgjHGJy7yEZG+QA7wuTGmg2PZK8BBY8xLjj8M6hljHrEzzupwin0fC+QYY/5rZ2zVTUQaAY2MMStFpA6wArgUuAEv/92Xs+9Xcpq/e18aMfQAko0xKcaYfGAyMNzmmFQ1McbMBw6etHg4MNFxfyLWfxqvc4p99wnGmH3GmJWO+9lYPWCa4AO/+3L2/bT5UmJoAuwu9XgPZ/iheSgD/C4iK0RktN3B2CTKGLPPcX8/EGVnMDYYIyJrHYeavO5QyslEJBroCizFx373J+07nObv3pcSg6/rbYzpBlwA3Ok43OCzHK1lfeM4quU9IBboAuwDXrM1mmomIrWB74F7jTFHSj/n7b/7Mvb9tH/3vpQYUoFmpR43dSzzCcaYVMfPNGAK1qE1X3PAcRy25Hhsms3xuIwx5oAxpsgYUwx8iBf//kUkEOuL8StjzA+OxT7xuy9r38/kd+9LiWE5EC8iMSISBIwAptkck0uISC3HZBQiUgsYAqwvfyuvNA0Y5bg/CphqYywuVfKl6HAZXvr7FxHB6h2/yRjzeqmnvP53f6p9P5Pfvc+clQTgOE1rHOAPfGKMecHeiFxDRFpijRIAAoCvvX3fRWQS0B+r5PAB4BngR+AboDlW2fYrjTFeN0l7in3vj3UowQA7gFtLHXP3GiLSG1gArAOKHYsfxzrW7tW/+3L2/WpO83fvU4lBKaVUxXzpUJJSSqlK0MSglFLqBJoYlFJKnUATg1JKqRNoYlBKKXUCTQxKKaVOoIlB+SwRqV+qFPH+UqWJc0Tk3Wp4v89EZLuI3Fbq8eVlrBdbEoezY1CqMgLsDkApuxhjMrEu/HFlWeqHjDHfVRDXNqCLJgZlFx0xKHUSEekvItMd98eKyEQRWSAiO0XkHyLyiqPp0W+O2jSISHcRmeeoXjvjpDIE5ekrIn+KSEpZowel7KCJQamKxQIDgUuAL4E5xpiOwHHgQkdyGA9cbozpDnwCVLbkSCOgN3AR8JKzA1fqTOihJKUq9qsxpkBE1mHV2frNsXwdEA20BjoAM606ZvhjlTeujB8dVS83iohX9whQnkMTg1IVywMwxhSLSIH5q8BYMdb/IQE2GGN6nulrO0jVwlTKOfRQklJVtwWIFJGeYNXEF5H2Nsek1BnTxKBUFTl6iF8OvCwia4DVwLm2BqVUFWjZbaVcREQ+A6ZXdLpqqfVzjDG1qzcqpf5ORwxKuU4W8FzJBW6nUnKBG1aTHaVcTkcMSimlTqAjBqWUUifQxKCUUuoEmhiUUkqdQBODUkqpE/wfyQLhRDxDCecAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#feature engineering pt2\n",
    "\n",
    "#our time is also in an unusable format. The reason for this is we want the model to understand that time is cyclical.\n",
    "#in other words, 11:59.59 is very nearly the same time as 00:00.00. However, literally as it pertains to their numerical values these do not\n",
    "#translate well since it goes from being a large number to a small one after 11:59.59.\n",
    "#By applying sine and cosine transforms we can transform time into a continuous value no matter what the original time was.\n",
    "\n",
    "#\n",
    "timestamp_s = date_time.map(pd.Timestamp.timestamp)\n",
    "\n",
    "day = 24*60*60\n",
    "year = (365.2425)*day\n",
    "\n",
    "df['Day sin'] = np.sin(timestamp_s * (2 * np.pi / day))\n",
    "df['Day cos'] = np.cos(timestamp_s * (2 * np.pi / day))\n",
    "df['Year sin'] = np.sin(timestamp_s * (2 * np.pi / year))\n",
    "df['Year cos'] = np.cos(timestamp_s * (2 * np.pi / year))\n",
    "\n",
    "plt.plot(np.array(df['Day sin'])[:25])\n",
    "plt.plot(np.array(df['Day cos'])[:25])\n",
    "plt.xlabel('Time [h]')\n",
    "plt.title('Time of day signal')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Making the training, validation, and test data from our single dataset.\n",
    "#the tutorial split the dataset into 70% for training, 20% for validation, and 10% for testing.\n",
    "column_indices = {name: i for i, name in enumerate(df.columns)}\n",
    "\n",
    "n = len(df)\n",
    "train_df = df[0:int(n*0.7)]\n",
    "val_df = df[int(n*0.7):int(n*0.9)]\n",
    "test_df = df[int(n*0.9):]\n",
    "\n",
    "num_features = df.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Normalizing the data\n",
    "\n",
    "#subtract the mean and divide by the standard deviation of each feature to normalize each feature.\n",
    "\n",
    "#we derive the mean and standard deviation through the training set.\n",
    "#NOTE WE DO NOT DERIVE ANY DATA FROM THE VALIDATION OR TEST DATA TO PRESERVE THEIR INTEGRITY AS TEST DATA!\n",
    "train_mean = train_df.mean()\n",
    "train_std = train_df.std()\n",
    "\n",
    "train_df = (train_df - train_mean) / train_std\n",
    "val_df = (val_df - train_mean) / train_std\n",
    "test_df = (test_df - train_mean) / train_std\n",
    "\n",
    "df_std = (df - train_mean) / train_std\n",
    "df_std = df_std.melt(var_name='Column', value_name='Normalized')\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax = sns.violinplot(x='Column', y='Normalized', data=df_std)\n",
    "_ = ax.set_xticklabels(df.keys(), rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data windowing\n",
    "\n",
    "#We should probably define this earlier like during initialization so that we dont need to run it seperately. \n",
    "\n",
    "#this section is slightly complex to write out so I suggest you re-read the URL at the top for Data Windowing. Data Windowing is\n",
    "#how we tell the model how much historical data (some var x) to use for our prediction how far into the future (some var y)\n",
    "#Im still learning but the way we design our model limits the inputs and outputs.\n",
    "\n",
    "#For my sake I will implemenmt the recurrent neural network and skip the other models.\n",
    "class WindowGenerator():\n",
    "  def __init__(self, input_width, label_width, shift,\n",
    "               train_df=train_df, val_df=val_df, test_df=test_df,\n",
    "               label_columns=None):\n",
    "    # Store the raw data.\n",
    "    self.train_df = train_df\n",
    "    self.val_df = val_df\n",
    "    self.test_df = test_df\n",
    "\n",
    "    # Work out the label column indices.\n",
    "    self.label_columns = label_columns\n",
    "    if label_columns is not None:\n",
    "      self.label_columns_indices = {name: i for i, name in\n",
    "                                    enumerate(label_columns)}\n",
    "    self.column_indices = {name: i for i, name in\n",
    "                           enumerate(train_df.columns)}\n",
    "\n",
    "    # Work out the window parameters.\n",
    "    self.input_width = input_width\n",
    "    self.label_width = label_width\n",
    "    self.shift = shift\n",
    "\n",
    "    self.total_window_size = input_width + shift\n",
    "\n",
    "    self.input_slice = slice(0, input_width)\n",
    "    self.input_indices = np.arange(self.total_window_size)[self.input_slice]\n",
    "\n",
    "    self.label_start = self.total_window_size - self.label_width\n",
    "    self.labels_slice = slice(self.label_start, None)\n",
    "    self.label_indices = np.arange(self.total_window_size)[self.labels_slice]\n",
    "\n",
    "  def __repr__(self):\n",
    "    return '\\n'.join([\n",
    "        f'Total window size: {self.total_window_size}',\n",
    "        f'Input indices: {self.input_indices}',\n",
    "        f'Label indices: {self.label_indices}',\n",
    "        f'Label column name(s): {self.label_columns}'])\n",
    "\n",
    "#Here is code to create the 2 windows shown in the diagrams at the start of this section:\n",
    "\n",
    "#This window predicts the next day using the previous day's data.\n",
    "w1 = WindowGenerator(input_width=24, label_width=1, shift=24,\n",
    "                     label_columns=['T (degC)'])\n",
    "w1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This window preicts one hour into the future using 6 hours of history previously.\n",
    "w2 = WindowGenerator(input_width=6, label_width=1, shift=1,\n",
    "                     label_columns=['T (degC)'])\n",
    "w2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#subroutine to make data windowing easier.\n",
    "\n",
    "def split_window(self, features):\n",
    "  inputs = features[:, self.input_slice, :]\n",
    "  labels = features[:, self.labels_slice, :]\n",
    "  if self.label_columns is not None:\n",
    "    labels = tf.stack(\n",
    "        [labels[:, :, self.column_indices[name]] for name in self.label_columns],\n",
    "        axis=-1)\n",
    "\n",
    "  # Slicing doesn't preserve static shape information, so set the shapes\n",
    "  # manually. This way the `tf.data.Datasets` are easier to inspect.\n",
    "  inputs.set_shape([None, self.input_width, None])\n",
    "  labels.set_shape([None, self.label_width, None])\n",
    "\n",
    "  return inputs, labels\n",
    "\n",
    "WindowGenerator.split_window = split_window\n",
    "\n",
    "# Stack three slices, the length of the total window.\n",
    "example_window = tf.stack([np.array(train_df[:w2.total_window_size]),\n",
    "                           np.array(train_df[100:100+w2.total_window_size]),\n",
    "                           np.array(train_df[200:200+w2.total_window_size])])\n",
    "\n",
    "example_inputs, example_labels = w2.split_window(example_window)\n",
    "\n",
    "print('All shapes are: (batch, time, features)')\n",
    "print(f'Window shape: {example_window.shape}')\n",
    "print(f'Inputs shape: {example_inputs.shape}')\n",
    "print(f'Labels shape: {example_labels.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_dataset(self, data):\n",
    "  data = np.array(data, dtype=np.float32)\n",
    "  ds = tf.keras.utils.timeseries_dataset_from_array(\n",
    "      data=data,\n",
    "      targets=None,\n",
    "      sequence_length=self.total_window_size,\n",
    "      sequence_stride=1,\n",
    "      shuffle=True,\n",
    "      batch_size=32,)\n",
    "\n",
    "  ds = ds.map(self.split_window)\n",
    "\n",
    "  return ds\n",
    "\n",
    "WindowGenerator.make_dataset = make_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@property\n",
    "def train(self):\n",
    "  return self.make_dataset(self.train_df)\n",
    "\n",
    "@property\n",
    "def val(self):\n",
    "  return self.make_dataset(self.val_df)\n",
    "\n",
    "@property\n",
    "def test(self):\n",
    "  return self.make_dataset(self.test_df)\n",
    "\n",
    "@property\n",
    "def example(self):\n",
    "  \"\"\"Get and cache an example batch of `inputs, labels` for plotting.\"\"\"\n",
    "  result = getattr(self, '_example', None)\n",
    "  if result is None:\n",
    "    # No example batch was found, so get one from the `.train` dataset\n",
    "    result = next(iter(self.train))\n",
    "    # And cache it for next time\n",
    "    self._example = result\n",
    "  return result\n",
    "\n",
    "WindowGenerator.train = train\n",
    "WindowGenerator.val = val\n",
    "WindowGenerator.test = test\n",
    "WindowGenerator.example = example"
   ]
  }
 ],
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