diff --git a/notebooks/c07_Recurrent_Neural_Networks/03_mike-seq2seq_timeseries.ipynb b/notebooks/c07_Recurrent_Neural_Networks/03_mike-seq2seq_timeseries.ipynb
index 8dd2ede..bd15109 100644
--- a/notebooks/c07_Recurrent_Neural_Networks/03_mike-seq2seq_timeseries.ipynb
+++ b/notebooks/c07_Recurrent_Neural_Networks/03_mike-seq2seq_timeseries.ipynb
@@ -34,8 +34,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:56:19.225337Z",
-     "start_time": "2020-10-11T10:56:18.130950Z"
+     "end_time": "2020-10-16T04:36:15.235547Z",
+     "start_time": "2020-10-16T04:36:14.522810Z"
     }
    },
    "outputs": [],
@@ -73,8 +73,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:56:19.245427Z",
-     "start_time": "2020-10-11T10:56:19.227501Z"
+     "end_time": "2020-10-16T04:36:15.249766Z",
+     "start_time": "2020-10-16T04:36:15.237079Z"
     }
    },
    "outputs": [
@@ -111,8 +111,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:56:19.261886Z",
-     "start_time": "2020-10-11T10:56:19.247936Z"
+     "end_time": "2020-10-16T04:36:15.295873Z",
+     "start_time": "2020-10-16T04:36:15.251348Z"
     },
     "lines_to_next_cell": 0
    },
@@ -127,6 +127,8 @@
     "    # Load csv files\n",
     "    csv_files = sorted((indir/'halfhourly_dataset').glob('*.csv'))[:1]\n",
     "    \n",
+    "#     import pdb; pdb.set_trace() # you can use debugging in jupyter to interact with variables inside a function\n",
+    "    \n",
     "    # concatendate them\n",
     "    df = pd.concat([pd.read_csv(f, parse_dates=[1], na_values=['Null']) for f in csv_files])\n",
     "\n",
@@ -139,7 +141,7 @@
     "           'pressure', 'apparentTemperature', 'windSpeed', \n",
     "           'humidity']\n",
     "    df_weather = df_weather[use_cols].set_index('time')\n",
-    "    df_weather = df_weather.resample(freq).ffill()  # Resample to match energy data    \n",
+    "    df_weather = df_weather.resample(freq).first().ffill()  # Resample to match energy data    \n",
     "\n",
     "    # Join weather and energy data\n",
     "    df = pd.concat([df, df_weather], 1).dropna()    \n",
@@ -149,7 +151,10 @@
     "    holidays = set(df_hols['Bank holidays'].dt.round('D'))  \n",
     "\n",
     "    time = df.index.to_series()\n",
-    "    df['holiday'] = time.apply(lambda dt:dt.floor('D') in holidays).astype(int)\n",
+    "    def is_holiday(dt):\n",
+    "        return dt.floor('D') in holidays\n",
+    "    df['holiday'] = time.apply(is_holiday).astype(int)\n",
+    "\n",
     "\n",
     "    # Add time features    \n",
     "    df[\"month\"] = time.dt.month\n",
@@ -194,18 +199,11 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:56:26.728571Z",
-     "start_time": "2020-10-11T10:56:19.265728Z"
+     "end_time": "2020-10-16T04:36:20.888572Z",
+     "start_time": "2020-10-16T04:36:15.297303Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/wassname/anaconda/envs/deep_ml_curriculum/lib/python3.7/site-packages/ipykernel_launcher.py:36: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -257,13 +255,13 @@
        "      <td>11.00</td>\n",
        "      <td>5.99</td>\n",
        "      <td>0.87</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>3</td>\n",
+       "      <td>48</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-12-03 09:30:00</th>\n",
@@ -276,13 +274,13 @@
        "      <td>11.00</td>\n",
        "      <td>5.99</td>\n",
        "      <td>0.87</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>3</td>\n",
+       "      <td>48</td>\n",
+       "      <td>9</td>\n",
+       "      <td>30</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-12-03 10:00:00</th>\n",
@@ -295,13 +293,13 @@
        "      <td>11.42</td>\n",
        "      <td>6.10</td>\n",
        "      <td>0.77</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>3</td>\n",
+       "      <td>48</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-12-03 10:30:00</th>\n",
@@ -314,13 +312,13 @@
        "      <td>11.42</td>\n",
        "      <td>6.10</td>\n",
        "      <td>0.77</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>3</td>\n",
+       "      <td>48</td>\n",
+       "      <td>10</td>\n",
+       "      <td>30</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-12-03 11:00:00</th>\n",
@@ -333,13 +331,13 @@
        "      <td>11.41</td>\n",
        "      <td>6.20</td>\n",
        "      <td>0.71</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>3</td>\n",
+       "      <td>48</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -371,13 +369,13 @@
        "      <td>1.41</td>\n",
        "      <td>3.02</td>\n",
        "      <td>0.84</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>27</td>\n",
+       "      <td>9</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-27 22:30:00</th>\n",
@@ -390,13 +388,13 @@
        "      <td>1.41</td>\n",
        "      <td>3.02</td>\n",
        "      <td>0.84</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>27</td>\n",
+       "      <td>9</td>\n",
+       "      <td>22</td>\n",
+       "      <td>30</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-27 23:00:00</th>\n",
@@ -409,13 +407,13 @@
        "      <td>1.42</td>\n",
        "      <td>2.75</td>\n",
        "      <td>0.85</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>27</td>\n",
+       "      <td>9</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-27 23:30:00</th>\n",
@@ -428,13 +426,13 @@
        "      <td>1.42</td>\n",
        "      <td>2.75</td>\n",
        "      <td>0.85</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>27</td>\n",
+       "      <td>9</td>\n",
+       "      <td>23</td>\n",
+       "      <td>30</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-28 00:00:00</th>\n",
@@ -447,17 +445,17 @@
        "      <td>1.47</td>\n",
        "      <td>2.53</td>\n",
        "      <td>0.85</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>28</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>39225 rows × 16 columns</p>\n",
+       "<p>39247 rows × 16 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -487,33 +485,33 @@
        "2014-02-27 23:30:00      1.61   1004.62                 1.42       2.75   \n",
        "2014-02-28 00:00:00      1.53   1003.57                 1.47       2.53   \n",
        "\n",
-       "                     humidity  holiday  month   day  week  hour  minute  \\\n",
-       "2011-12-03 09:00:00      0.87      0.0   12.0   3.0  48.0   9.0     0.0   \n",
-       "2011-12-03 09:30:00      0.87      0.0   12.0   3.0  48.0   9.0    30.0   \n",
-       "2011-12-03 10:00:00      0.77      0.0   12.0   3.0  48.0  10.0     0.0   \n",
-       "2011-12-03 10:30:00      0.77      0.0   12.0   3.0  48.0  10.0    30.0   \n",
-       "2011-12-03 11:00:00      0.71      0.0   12.0   3.0  48.0  11.0     0.0   \n",
-       "...                       ...      ...    ...   ...   ...   ...     ...   \n",
-       "2014-02-27 22:00:00      0.84      0.0    2.0  27.0   9.0  22.0     0.0   \n",
-       "2014-02-27 22:30:00      0.84      0.0    2.0  27.0   9.0  22.0    30.0   \n",
-       "2014-02-27 23:00:00      0.85      0.0    2.0  27.0   9.0  23.0     0.0   \n",
-       "2014-02-27 23:30:00      0.85      0.0    2.0  27.0   9.0  23.0    30.0   \n",
-       "2014-02-28 00:00:00      0.85      0.0    2.0  28.0   9.0   0.0     0.0   \n",
+       "                     humidity  holiday  month  day  week  hour  minute  \\\n",
+       "2011-12-03 09:00:00      0.87        0     12    3    48     9       0   \n",
+       "2011-12-03 09:30:00      0.87        0     12    3    48     9      30   \n",
+       "2011-12-03 10:00:00      0.77        0     12    3    48    10       0   \n",
+       "2011-12-03 10:30:00      0.77        0     12    3    48    10      30   \n",
+       "2011-12-03 11:00:00      0.71        0     12    3    48    11       0   \n",
+       "...                       ...      ...    ...  ...   ...   ...     ...   \n",
+       "2014-02-27 22:00:00      0.84        0      2   27     9    22       0   \n",
+       "2014-02-27 22:30:00      0.84        0      2   27     9    22      30   \n",
+       "2014-02-27 23:00:00      0.85        0      2   27     9    23       0   \n",
+       "2014-02-27 23:30:00      0.85        0      2   27     9    23      30   \n",
+       "2014-02-28 00:00:00      0.85        0      2   28     9     0       0   \n",
        "\n",
        "                     dayofweek  \n",
-       "2011-12-03 09:00:00        5.0  \n",
-       "2011-12-03 09:30:00        5.0  \n",
-       "2011-12-03 10:00:00        5.0  \n",
-       "2011-12-03 10:30:00        5.0  \n",
-       "2011-12-03 11:00:00        5.0  \n",
+       "2011-12-03 09:00:00          5  \n",
+       "2011-12-03 09:30:00          5  \n",
+       "2011-12-03 10:00:00          5  \n",
+       "2011-12-03 10:30:00          5  \n",
+       "2011-12-03 11:00:00          5  \n",
        "...                        ...  \n",
-       "2014-02-27 22:00:00        3.0  \n",
-       "2014-02-27 22:30:00        3.0  \n",
-       "2014-02-27 23:00:00        3.0  \n",
-       "2014-02-27 23:30:00        3.0  \n",
-       "2014-02-28 00:00:00        4.0  \n",
+       "2014-02-27 22:00:00          3  \n",
+       "2014-02-27 22:30:00          3  \n",
+       "2014-02-27 23:00:00          3  \n",
+       "2014-02-27 23:30:00          3  \n",
+       "2014-02-28 00:00:00          4  \n",
        "\n",
-       "[39225 rows x 16 columns]"
+       "[39247 rows x 16 columns]"
       ]
      },
      "execution_count": 4,
@@ -524,7 +522,7 @@
    "source": [
     "df = get_smartmeter_df()\n",
     "\n",
-    "df = df.resample(freq).mean().dropna() # Where empty we will backfill, this will respect causality, and mostly maintain the mean\n",
+    "df = df.resample(freq).first().dropna() # Where empty we will backfill, this will respect causality, and mostly maintain the mean\n",
     "\n",
     "df = df.tail(int(max_rows)) # Just use last X rows\n",
     "df"
@@ -535,8 +533,284 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:56:27.445657Z",
-     "start_time": "2020-10-11T10:56:26.730375Z"
+     "end_time": "2020-10-16T04:36:20.962200Z",
+     "start_time": "2020-10-16T04:36:20.889920Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>energy(kWh/hh)</th>\n",
+       "      <th>visibility</th>\n",
+       "      <th>windBearing</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>dewPoint</th>\n",
+       "      <th>pressure</th>\n",
+       "      <th>apparentTemperature</th>\n",
+       "      <th>windSpeed</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>holiday</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day</th>\n",
+       "      <th>week</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>minute</th>\n",
+       "      <th>dayofweek</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>39247.000000</td>\n",
+       "      <td>39247.000000</td>\n",
+       "      <td>39247.000000</td>\n",
+       "      <td>39247.000000</td>\n",
+       "      <td>39247.000000</td>\n",
+       "      <td>39247.000000</td>\n",
+       "      <td>39247.000000</td>\n",
+       "      <td>39247.000000</td>\n",
+       "      <td>39247.000000</td>\n",
+       "      <td>39247.000000</td>\n",
+       "      <td>39247.000000</td>\n",
+       "      <td>39247.000000</td>\n",
+       "      <td>39247.000000</td>\n",
+       "      <td>39247.000000</td>\n",
+       "      <td>39247.000000</td>\n",
+       "      <td>39247.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.449280</td>\n",
+       "      <td>11.289506</td>\n",
+       "      <td>197.401840</td>\n",
+       "      <td>10.533910</td>\n",
+       "      <td>6.550560</td>\n",
+       "      <td>1013.987087</td>\n",
+       "      <td>9.272663</td>\n",
+       "      <td>3.927207</td>\n",
+       "      <td>0.779764</td>\n",
+       "      <td>0.022014</td>\n",
+       "      <td>6.353046</td>\n",
+       "      <td>15.742095</td>\n",
+       "      <td>25.771779</td>\n",
+       "      <td>11.503147</td>\n",
+       "      <td>14.999618</td>\n",
+       "      <td>2.997885</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.218968</td>\n",
+       "      <td>2.975889</td>\n",
+       "      <td>90.833443</td>\n",
+       "      <td>5.924281</td>\n",
+       "      <td>5.155743</td>\n",
+       "      <td>11.457274</td>\n",
+       "      <td>7.109520</td>\n",
+       "      <td>2.041219</td>\n",
+       "      <td>0.140156</td>\n",
+       "      <td>0.146732</td>\n",
+       "      <td>3.673233</td>\n",
+       "      <td>8.769535</td>\n",
+       "      <td>15.991587</td>\n",
+       "      <td>6.921932</td>\n",
+       "      <td>15.000191</td>\n",
+       "      <td>2.000922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.023000</td>\n",
+       "      <td>0.270000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-5.640000</td>\n",
+       "      <td>-9.980000</td>\n",
+       "      <td>975.740000</td>\n",
+       "      <td>-8.880000</td>\n",
+       "      <td>0.040000</td>\n",
+       "      <td>0.230000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.293000</td>\n",
+       "      <td>10.360000</td>\n",
+       "      <td>127.000000</td>\n",
+       "      <td>6.410000</td>\n",
+       "      <td>2.720000</td>\n",
+       "      <td>1007.320000</td>\n",
+       "      <td>3.740000</td>\n",
+       "      <td>2.430000</td>\n",
+       "      <td>0.700000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.436375</td>\n",
+       "      <td>12.310000</td>\n",
+       "      <td>219.000000</td>\n",
+       "      <td>9.980000</td>\n",
+       "      <td>6.620000</td>\n",
+       "      <td>1014.500000</td>\n",
+       "      <td>9.770000</td>\n",
+       "      <td>3.710000</td>\n",
+       "      <td>0.810000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.583882</td>\n",
+       "      <td>13.080000</td>\n",
+       "      <td>257.000000</td>\n",
+       "      <td>14.630000</td>\n",
+       "      <td>10.470000</td>\n",
+       "      <td>1021.850000</td>\n",
+       "      <td>14.630000</td>\n",
+       "      <td>5.110000</td>\n",
+       "      <td>0.890000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>40.000000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.981000</td>\n",
+       "      <td>16.090000</td>\n",
+       "      <td>359.000000</td>\n",
+       "      <td>32.400000</td>\n",
+       "      <td>19.880000</td>\n",
+       "      <td>1043.320000</td>\n",
+       "      <td>32.420000</td>\n",
+       "      <td>14.800000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       energy(kWh/hh)    visibility   windBearing   temperature      dewPoint  \\\n",
+       "count    39247.000000  39247.000000  39247.000000  39247.000000  39247.000000   \n",
+       "mean         0.449280     11.289506    197.401840     10.533910      6.550560   \n",
+       "std          0.218968      2.975889     90.833443      5.924281      5.155743   \n",
+       "min          0.023000      0.270000      0.000000     -5.640000     -9.980000   \n",
+       "25%          0.293000     10.360000    127.000000      6.410000      2.720000   \n",
+       "50%          0.436375     12.310000    219.000000      9.980000      6.620000   \n",
+       "75%          0.583882     13.080000    257.000000     14.630000     10.470000   \n",
+       "max          1.981000     16.090000    359.000000     32.400000     19.880000   \n",
+       "\n",
+       "           pressure  apparentTemperature     windSpeed      humidity  \\\n",
+       "count  39247.000000         39247.000000  39247.000000  39247.000000   \n",
+       "mean    1013.987087             9.272663      3.927207      0.779764   \n",
+       "std       11.457274             7.109520      2.041219      0.140156   \n",
+       "min      975.740000            -8.880000      0.040000      0.230000   \n",
+       "25%     1007.320000             3.740000      2.430000      0.700000   \n",
+       "50%     1014.500000             9.770000      3.710000      0.810000   \n",
+       "75%     1021.850000            14.630000      5.110000      0.890000   \n",
+       "max     1043.320000            32.420000     14.800000      1.000000   \n",
+       "\n",
+       "            holiday         month           day          week          hour  \\\n",
+       "count  39247.000000  39247.000000  39247.000000  39247.000000  39247.000000   \n",
+       "mean       0.022014      6.353046     15.742095     25.771779     11.503147   \n",
+       "std        0.146732      3.673233      8.769535     15.991587      6.921932   \n",
+       "min        0.000000      1.000000      1.000000      1.000000      0.000000   \n",
+       "25%        0.000000      3.000000      8.000000     11.000000      6.000000   \n",
+       "50%        0.000000      6.000000     16.000000     25.000000     12.000000   \n",
+       "75%        0.000000     10.000000     23.000000     40.000000     18.000000   \n",
+       "max        1.000000     12.000000     31.000000     52.000000     23.000000   \n",
+       "\n",
+       "             minute     dayofweek  \n",
+       "count  39247.000000  39247.000000  \n",
+       "mean      14.999618      2.997885  \n",
+       "std       15.000191      2.000922  \n",
+       "min        0.000000      0.000000  \n",
+       "25%        0.000000      1.000000  \n",
+       "50%        0.000000      3.000000  \n",
+       "75%       30.000000      5.000000  \n",
+       "max       30.000000      6.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " <div class=\"alert alert-success\">\n",
+    "  <h2>Exercise: Debug</h2>\n",
+    "\n",
+    "  Sometimes the best way to understand something is to interact with it. But if the code is inside a function it's difficult. Use the python debugger to play with the dataloading code.\n",
+    "    \n",
+    "   - insert the line `import pdb; pdb.set_trace()` in the function above\n",
+    "   - run the function definition\n",
+    "   - run the function again\n",
+    "   - you should be in a debugger, try pressing `?` then enter\n",
+    "   - try printing a variable\n",
+    "   - `q` to exit\n",
+    "\n",
+    "  </div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T04:36:21.409733Z",
+     "start_time": "2020-10-16T04:36:20.963445Z"
     },
     "scrolled": true
    },
@@ -583,41 +857,41 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
-       "      <td>3.922500e+04</td>\n",
-       "      <td>3.922500e+04</td>\n",
-       "      <td>3.922500e+04</td>\n",
-       "      <td>3.922500e+04</td>\n",
-       "      <td>3.922500e+04</td>\n",
-       "      <td>3.922500e+04</td>\n",
-       "      <td>3.922500e+04</td>\n",
-       "      <td>3.922500e+04</td>\n",
-       "      <td>3.922500e+04</td>\n",
-       "      <td>3.922500e+04</td>\n",
-       "      <td>3.922500e+04</td>\n",
-       "      <td>3.922500e+04</td>\n",
-       "      <td>3.922500e+04</td>\n",
-       "      <td>3.922500e+04</td>\n",
-       "      <td>3.922500e+04</td>\n",
-       "      <td>3.922500e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
+       "      <td>3.924700e+04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>-8.115313e-17</td>\n",
-       "      <td>-9.187693e-16</td>\n",
-       "      <td>-9.274643e-17</td>\n",
-       "      <td>3.477991e-17</td>\n",
-       "      <td>2.202728e-16</td>\n",
-       "      <td>-2.388221e-15</td>\n",
-       "      <td>1.970862e-16</td>\n",
-       "      <td>6.955982e-17</td>\n",
-       "      <td>6.955982e-17</td>\n",
-       "      <td>3.767824e-17</td>\n",
-       "      <td>4.057656e-17</td>\n",
-       "      <td>-7.100899e-17</td>\n",
-       "      <td>-5.796652e-17</td>\n",
-       "      <td>-1.376705e-17</td>\n",
-       "      <td>1.517376e-16</td>\n",
-       "      <td>7.789251e-17</td>\n",
+       "      <td>1.216615e-16</td>\n",
+       "      <td>-9.110126e-16</td>\n",
+       "      <td>3.476042e-17</td>\n",
+       "      <td>2.317361e-17</td>\n",
+       "      <td>-1.738021e-17</td>\n",
+       "      <td>5.086608e-15</td>\n",
+       "      <td>-6.952083e-17</td>\n",
+       "      <td>1.158681e-16</td>\n",
+       "      <td>-8.110764e-17</td>\n",
+       "      <td>-3.331207e-17</td>\n",
+       "      <td>9.848785e-17</td>\n",
+       "      <td>-1.303516e-16</td>\n",
+       "      <td>5.793403e-17</td>\n",
+       "      <td>-6.734831e-17</td>\n",
+       "      <td>-2.648162e-16</td>\n",
+       "      <td>3.584668e-17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
@@ -640,98 +914,98 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
-       "      <td>-1.946819e+00</td>\n",
-       "      <td>-3.703458e+00</td>\n",
-       "      <td>-2.173571e+00</td>\n",
-       "      <td>-2.729647e+00</td>\n",
-       "      <td>-3.206261e+00</td>\n",
-       "      <td>-3.337412e+00</td>\n",
-       "      <td>-2.552864e+00</td>\n",
-       "      <td>-1.904117e+00</td>\n",
-       "      <td>-3.921771e+00</td>\n",
-       "      <td>-1.498985e-01</td>\n",
-       "      <td>-1.456976e+00</td>\n",
-       "      <td>-1.681277e+00</td>\n",
-       "      <td>-1.548731e+00</td>\n",
-       "      <td>-1.662307e+00</td>\n",
+       "      <td>-1.946796e+00</td>\n",
+       "      <td>-3.702976e+00</td>\n",
+       "      <td>-2.173256e+00</td>\n",
+       "      <td>-2.730140e+00</td>\n",
+       "      <td>-3.206283e+00</td>\n",
+       "      <td>-3.338279e+00</td>\n",
+       "      <td>-2.553322e+00</td>\n",
+       "      <td>-1.904380e+00</td>\n",
+       "      <td>-3.922565e+00</td>\n",
+       "      <td>-1.500332e-01</td>\n",
+       "      <td>-1.457330e+00</td>\n",
+       "      <td>-1.681079e+00</td>\n",
+       "      <td>-1.549070e+00</td>\n",
+       "      <td>-1.661862e+00</td>\n",
        "      <td>-9.999745e-01</td>\n",
-       "      <td>-1.498470e+00</td>\n",
+       "      <td>-1.498271e+00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
-       "      <td>-7.138837e-01</td>\n",
-       "      <td>-3.127832e-01</td>\n",
-       "      <td>-7.752553e-01</td>\n",
-       "      <td>-6.974452e-01</td>\n",
-       "      <td>-7.425864e-01</td>\n",
-       "      <td>-5.816362e-01</td>\n",
-       "      <td>-7.778616e-01</td>\n",
-       "      <td>-7.383479e-01</td>\n",
-       "      <td>-5.687687e-01</td>\n",
-       "      <td>-1.498985e-01</td>\n",
-       "      <td>-9.125645e-01</td>\n",
-       "      <td>-8.830354e-01</td>\n",
-       "      <td>-9.234714e-01</td>\n",
-       "      <td>-7.953824e-01</td>\n",
+       "      <td>-7.137231e-01</td>\n",
+       "      <td>-3.123495e-01</td>\n",
+       "      <td>-7.750751e-01</td>\n",
+       "      <td>-6.961120e-01</td>\n",
+       "      <td>-7.429789e-01</td>\n",
+       "      <td>-5.819161e-01</td>\n",
+       "      <td>-7.782148e-01</td>\n",
+       "      <td>-7.334959e-01</td>\n",
+       "      <td>-5.691148e-01</td>\n",
+       "      <td>-1.500332e-01</td>\n",
+       "      <td>-9.128440e-01</td>\n",
+       "      <td>-8.828511e-01</td>\n",
+       "      <td>-9.237337e-01</td>\n",
+       "      <td>-7.950405e-01</td>\n",
        "      <td>-9.999745e-01</td>\n",
-       "      <td>-9.986530e-01</td>\n",
+       "      <td>-9.984948e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
-       "      <td>-5.885527e-02</td>\n",
-       "      <td>3.425008e-01</td>\n",
-       "      <td>2.376977e-01</td>\n",
-       "      <td>-9.318589e-02</td>\n",
-       "      <td>1.203535e-02</td>\n",
-       "      <td>4.404180e-02</td>\n",
-       "      <td>6.603837e-02</td>\n",
-       "      <td>-1.064817e-01</td>\n",
-       "      <td>2.159766e-01</td>\n",
-       "      <td>-1.498985e-01</td>\n",
-       "      <td>-9.594692e-02</td>\n",
-       "      <td>2.924051e-02</td>\n",
-       "      <td>-4.810793e-02</td>\n",
-       "      <td>7.154198e-02</td>\n",
+       "      <td>-5.893824e-02</td>\n",
+       "      <td>3.429252e-01</td>\n",
+       "      <td>2.377807e-01</td>\n",
+       "      <td>-9.349948e-02</td>\n",
+       "      <td>1.346874e-02</td>\n",
+       "      <td>4.476806e-02</td>\n",
+       "      <td>6.995454e-02</td>\n",
+       "      <td>-1.064117e-01</td>\n",
+       "      <td>2.157353e-01</td>\n",
+       "      <td>-1.500332e-01</td>\n",
+       "      <td>-9.611440e-02</td>\n",
+       "      <td>2.940958e-02</td>\n",
+       "      <td>-4.826216e-02</td>\n",
+       "      <td>7.178048e-02</td>\n",
        "      <td>-9.999745e-01</td>\n",
-       "      <td>9.811579e-04</td>\n",
+       "      <td>1.056932e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
-       "      <td>6.146349e-01</td>\n",
-       "      <td>6.012539e-01</td>\n",
-       "      <td>6.560913e-01</td>\n",
-       "      <td>6.916760e-01</td>\n",
-       "      <td>7.608374e-01</td>\n",
-       "      <td>6.871725e-01</td>\n",
-       "      <td>7.538168e-01</td>\n",
-       "      <td>5.792647e-01</td>\n",
-       "      <td>7.867005e-01</td>\n",
-       "      <td>-1.498985e-01</td>\n",
-       "      <td>9.928765e-01</td>\n",
-       "      <td>8.274819e-01</td>\n",
-       "      <td>8.897815e-01</td>\n",
-       "      <td>9.384663e-01</td>\n",
+       "      <td>6.147169e-01</td>\n",
+       "      <td>6.016747e-01</td>\n",
+       "      <td>6.561341e-01</td>\n",
+       "      <td>6.914159e-01</td>\n",
+       "      <td>7.602183e-01</td>\n",
+       "      <td>6.862901e-01</td>\n",
+       "      <td>7.535537e-01</td>\n",
+       "      <td>5.794616e-01</td>\n",
+       "      <td>7.865354e-01</td>\n",
+       "      <td>-1.500332e-01</td>\n",
+       "      <td>9.928584e-01</td>\n",
+       "      <td>8.276377e-01</td>\n",
+       "      <td>8.897430e-01</td>\n",
+       "      <td>9.386015e-01</td>\n",
        "      <td>1.000025e+00</td>\n",
-       "      <td>1.000615e+00</td>\n",
+       "      <td>1.000609e+00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
-       "      <td>6.994243e+00</td>\n",
-       "      <td>1.612744e+00</td>\n",
-       "      <td>1.779148e+00</td>\n",
-       "      <td>3.691030e+00</td>\n",
-       "      <td>2.586285e+00</td>\n",
-       "      <td>2.559843e+00</td>\n",
-       "      <td>3.255980e+00</td>\n",
-       "      <td>5.325609e+00</td>\n",
-       "      <td>1.571446e+00</td>\n",
-       "      <td>6.671180e+00</td>\n",
-       "      <td>1.537288e+00</td>\n",
-       "      <td>1.739758e+00</td>\n",
-       "      <td>1.640093e+00</td>\n",
-       "      <td>1.660903e+00</td>\n",
+       "      <td>6.995269e+00</td>\n",
+       "      <td>1.613150e+00</td>\n",
+       "      <td>1.779083e+00</td>\n",
+       "      <td>3.690974e+00</td>\n",
+       "      <td>2.585391e+00</td>\n",
+       "      <td>2.560233e+00</td>\n",
+       "      <td>3.255864e+00</td>\n",
+       "      <td>5.326685e+00</td>\n",
+       "      <td>1.571385e+00</td>\n",
+       "      <td>6.665191e+00</td>\n",
+       "      <td>1.537345e+00</td>\n",
+       "      <td>1.739898e+00</td>\n",
+       "      <td>1.640147e+00</td>\n",
+       "      <td>1.660952e+00</td>\n",
        "      <td>1.000025e+00</td>\n",
-       "      <td>1.500432e+00</td>\n",
+       "      <td>1.500385e+00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -739,47 +1013,47 @@
       ],
       "text/plain": [
        "       energy(kWh/hh)    visibility   windBearing   temperature      dewPoint  \\\n",
-       "count    3.922500e+04  3.922500e+04  3.922500e+04  3.922500e+04  3.922500e+04   \n",
-       "mean    -8.115313e-17 -9.187693e-16 -9.274643e-17  3.477991e-17  2.202728e-16   \n",
+       "count    3.924700e+04  3.924700e+04  3.924700e+04  3.924700e+04  3.924700e+04   \n",
+       "mean     1.216615e-16 -9.110126e-16  3.476042e-17  2.317361e-17 -1.738021e-17   \n",
        "std      1.000013e+00  1.000013e+00  1.000013e+00  1.000013e+00  1.000013e+00   \n",
-       "min     -1.946819e+00 -3.703458e+00 -2.173571e+00 -2.729647e+00 -3.206261e+00   \n",
-       "25%     -7.138837e-01 -3.127832e-01 -7.752553e-01 -6.974452e-01 -7.425864e-01   \n",
-       "50%     -5.885527e-02  3.425008e-01  2.376977e-01 -9.318589e-02  1.203535e-02   \n",
-       "75%      6.146349e-01  6.012539e-01  6.560913e-01  6.916760e-01  7.608374e-01   \n",
-       "max      6.994243e+00  1.612744e+00  1.779148e+00  3.691030e+00  2.586285e+00   \n",
+       "min     -1.946796e+00 -3.702976e+00 -2.173256e+00 -2.730140e+00 -3.206283e+00   \n",
+       "25%     -7.137231e-01 -3.123495e-01 -7.750751e-01 -6.961120e-01 -7.429789e-01   \n",
+       "50%     -5.893824e-02  3.429252e-01  2.377807e-01 -9.349948e-02  1.346874e-02   \n",
+       "75%      6.147169e-01  6.016747e-01  6.561341e-01  6.914159e-01  7.602183e-01   \n",
+       "max      6.995269e+00  1.613150e+00  1.779083e+00  3.690974e+00  2.585391e+00   \n",
        "\n",
        "           pressure  apparentTemperature     windSpeed      humidity  \\\n",
-       "count  3.922500e+04         3.922500e+04  3.922500e+04  3.922500e+04   \n",
-       "mean  -2.388221e-15         1.970862e-16  6.955982e-17  6.955982e-17   \n",
+       "count  3.924700e+04         3.924700e+04  3.924700e+04  3.924700e+04   \n",
+       "mean   5.086608e-15        -6.952083e-17  1.158681e-16 -8.110764e-17   \n",
        "std    1.000013e+00         1.000013e+00  1.000013e+00  1.000013e+00   \n",
-       "min   -3.337412e+00        -2.552864e+00 -1.904117e+00 -3.921771e+00   \n",
-       "25%   -5.816362e-01        -7.778616e-01 -7.383479e-01 -5.687687e-01   \n",
-       "50%    4.404180e-02         6.603837e-02 -1.064817e-01  2.159766e-01   \n",
-       "75%    6.871725e-01         7.538168e-01  5.792647e-01  7.867005e-01   \n",
-       "max    2.559843e+00         3.255980e+00  5.325609e+00  1.571446e+00   \n",
+       "min   -3.338279e+00        -2.553322e+00 -1.904380e+00 -3.922565e+00   \n",
+       "25%   -5.819161e-01        -7.782148e-01 -7.334959e-01 -5.691148e-01   \n",
+       "50%    4.476806e-02         6.995454e-02 -1.064117e-01  2.157353e-01   \n",
+       "75%    6.862901e-01         7.535537e-01  5.794616e-01  7.865354e-01   \n",
+       "max    2.560233e+00         3.255864e+00  5.326685e+00  1.571385e+00   \n",
        "\n",
        "            holiday         month           day          week          hour  \\\n",
-       "count  3.922500e+04  3.922500e+04  3.922500e+04  3.922500e+04  3.922500e+04   \n",
-       "mean   3.767824e-17  4.057656e-17 -7.100899e-17 -5.796652e-17 -1.376705e-17   \n",
+       "count  3.924700e+04  3.924700e+04  3.924700e+04  3.924700e+04  3.924700e+04   \n",
+       "mean  -3.331207e-17  9.848785e-17 -1.303516e-16  5.793403e-17 -6.734831e-17   \n",
        "std    1.000013e+00  1.000013e+00  1.000013e+00  1.000013e+00  1.000013e+00   \n",
-       "min   -1.498985e-01 -1.456976e+00 -1.681277e+00 -1.548731e+00 -1.662307e+00   \n",
-       "25%   -1.498985e-01 -9.125645e-01 -8.830354e-01 -9.234714e-01 -7.953824e-01   \n",
-       "50%   -1.498985e-01 -9.594692e-02  2.924051e-02 -4.810793e-02  7.154198e-02   \n",
-       "75%   -1.498985e-01  9.928765e-01  8.274819e-01  8.897815e-01  9.384663e-01   \n",
-       "max    6.671180e+00  1.537288e+00  1.739758e+00  1.640093e+00  1.660903e+00   \n",
+       "min   -1.500332e-01 -1.457330e+00 -1.681079e+00 -1.549070e+00 -1.661862e+00   \n",
+       "25%   -1.500332e-01 -9.128440e-01 -8.828511e-01 -9.237337e-01 -7.950405e-01   \n",
+       "50%   -1.500332e-01 -9.611440e-02  2.940958e-02 -4.826216e-02  7.178048e-02   \n",
+       "75%   -1.500332e-01  9.928584e-01  8.276377e-01  8.897430e-01  9.386015e-01   \n",
+       "max    6.665191e+00  1.537345e+00  1.739898e+00  1.640147e+00  1.660952e+00   \n",
        "\n",
        "             minute     dayofweek  \n",
-       "count  3.922500e+04  3.922500e+04  \n",
-       "mean   1.517376e-16  7.789251e-17  \n",
+       "count  3.924700e+04  3.924700e+04  \n",
+       "mean  -2.648162e-16  3.584668e-17  \n",
        "std    1.000013e+00  1.000013e+00  \n",
-       "min   -9.999745e-01 -1.498470e+00  \n",
-       "25%   -9.999745e-01 -9.986530e-01  \n",
-       "50%   -9.999745e-01  9.811579e-04  \n",
-       "75%    1.000025e+00  1.000615e+00  \n",
-       "max    1.000025e+00  1.500432e+00  "
+       "min   -9.999745e-01 -1.498271e+00  \n",
+       "25%   -9.999745e-01 -9.984948e-01  \n",
+       "50%   -9.999745e-01  1.056932e-03  \n",
+       "75%    1.000025e+00  1.000609e+00  \n",
+       "max    1.000025e+00  1.500385e+00  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -799,11 +1073,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:56:27.452570Z",
-     "start_time": "2020-10-11T10:56:27.447271Z"
+     "end_time": "2020-10-16T04:36:21.414354Z",
+     "start_time": "2020-10-16T04:36:21.411122Z"
     }
    },
    "outputs": [],
@@ -826,27 +1100,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:56:27.921541Z",
-     "start_time": "2020-10-11T10:56:27.454424Z"
-    }
+     "end_time": "2020-10-16T04:36:23.402331Z",
+     "start_time": "2020-10-16T04:36:21.416369Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f2ab33be950>"
+       "<matplotlib.legend.Legend at 0x7f4d41f96f98>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -858,7 +1133,7 @@
     }
    ],
    "source": [
-    "# split data\n",
+    "# split data, with the test in the future\n",
     "n_split = -int(len(df)*0.2)\n",
     "df_train = df_norm[:n_split]\n",
     "df_test = df_norm[n_split:]\n",
@@ -870,6 +1145,19 @@
     "plt.legend()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-14T22:36:25.702374Z",
+     "start_time": "2020-10-14T22:36:25.681535Z"
+    },
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -908,11 +1196,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:56:27.945399Z",
-     "start_time": "2020-10-11T10:56:27.925353Z"
+     "end_time": "2020-10-16T04:36:23.417088Z",
+     "start_time": "2020-10-16T04:36:23.404221Z"
     }
    },
    "outputs": [],
@@ -925,6 +1213,7 @@
     "    \"\"\"\n",
     "    \n",
     "    def __init__(self, df, window_past=40, window_future=10, label_names=['energy(kWh/hh)']):\n",
+    "        # Use numpy instead of pandas, for speed\n",
     "        self.x = df.drop(columns=label_names).copy().values\n",
     "        self.y = df[label_names].copy().values\n",
     "        self.t = df[label_names].index.copy()\n",
@@ -934,8 +1223,7 @@
     "        self.label_names = label_names\n",
     "\n",
     "    def get_components(self, i):\n",
-    "        \"\"\"Get  rows.\"\"\"\n",
-    "        # Get past and future rows\n",
+    "        \"\"\"Get past and future rows.\"\"\"\n",
     "        x = self.x[i : i + (self.window_past + self.window_future)].copy()\n",
     "        y = self.y[i : i + (self.window_past + self.window_future)].copy()\n",
     "        \n",
@@ -954,12 +1242,20 @@
     "            x_future[:, :8]=0\n",
     "        return x_past, y_past, x_future, y_future\n",
     "\n",
+    "\n",
+    "    def __getitem__(self, i):\n",
+    "        \"\"\"This is how python implements square brackets\"\"\"\n",
+    "        if i<0:\n",
+    "            # Handle negative integers\n",
+    "            i = len(self)+i\n",
+    "        data = self.get_components(i)\n",
+    "        # From dataframe to torch\n",
+    "        return [d.astype(np.float32) for d in data]\n",
+    "    \n",
     "    \n",
     "    def get_rows(self, i):\n",
     "        \"\"\"\n",
-    "        A helper to put index and columns back on.\n",
-    "        \n",
-    "        We take them off originally for training speed\n",
+    "        Output pandas dataframes for display purposes.\n",
     "        \"\"\"\n",
     "        x_cols = list(self.columns)[1:] + ['tstp', 'is_past']\n",
     "        x_past, y_past, x_future, y_future = self.get_components(i)\n",
@@ -970,15 +1266,6 @@
     "        y_past = pd.DataFrame(y_past, columns=self.label_names, index=t_past)\n",
     "        y_future = pd.DataFrame(y_future, columns=self.label_names, index=t_future)\n",
     "        return x_past, y_past, x_future, y_future\n",
-    "\n",
-    "\n",
-    "    def __getitem__(self, i):\n",
-    "        if i<0:\n",
-    "            # Handle negative integers\n",
-    "            i = len(self)+i\n",
-    "        data = self.get_components(i)\n",
-    "        # From dataframe to torch\n",
-    "        return [d.astype(np.float32) for d in data]\n",
     "        \n",
     "    def __len__(self):\n",
     "        return len(self.x) - (self.window_past + self.window_future)\n",
@@ -989,11 +1276,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:56:27.967188Z",
-     "start_time": "2020-10-11T10:56:27.948327Z"
+     "end_time": "2020-10-16T04:36:23.495185Z",
+     "start_time": "2020-10-16T04:36:23.418310Z"
     }
    },
    "outputs": [
@@ -1001,8 +1288,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<SmartMeterSeq2SeqDataSet(df.shape=(31380, 15))>\n",
-      "<SmartMeterSeq2SeqDataSet(df.shape=(7845, 15))>\n"
+      "<SmartMeterSeq2SeqDataSet(df.shape=(31398, 15))>\n",
+      "<SmartMeterSeq2SeqDataSet(df.shape=(7849, 15))>\n"
      ]
     }
    ],
@@ -1015,23 +1302,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T09:16:00.661392Z",
-     "start_time": "2020-10-11T09:16:00.647301Z"
+     "end_time": "2020-10-16T04:36:23.546799Z",
+     "start_time": "2020-10-16T04:36:23.496472Z"
     }
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  0.        ,   0.        ,   0.        , ...,  -0.49871895,\n",
+       "          0.        ,   0.        ],\n",
+       "       [  0.        ,   0.        ,   0.        , ...,  -0.49871895,\n",
+       "          1.        ,   0.        ],\n",
+       "       [  0.        ,   0.        ,   0.        , ...,  -0.49871895,\n",
+       "          2.        ,   0.        ],\n",
+       "       ...,\n",
+       "       [  0.        ,   0.        ,   0.        , ...,   1.5003846 ,\n",
+       "        189.        ,   0.        ],\n",
+       "       [  0.        ,   0.        ,   0.        , ...,   1.5003846 ,\n",
+       "        190.        ,   0.        ],\n",
+       "       [  0.        ,   0.        ,   0.        , ...,   1.5003846 ,\n",
+       "        191.        ,   0.        ]], dtype=float32)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_train[0]\n",
+    "len(ds_train)\n",
+    "ds_train[0][2]"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:56:28.298342Z",
-     "start_time": "2020-10-11T10:56:27.969151Z"
+     "end_time": "2020-10-16T04:36:23.789576Z",
+     "start_time": "2020-10-16T04:36:23.548125Z"
     }
    },
    "outputs": [
@@ -1078,101 +1392,101 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2011-12-07 11:30:00</th>\n",
-       "      <td>0.597893</td>\n",
-       "      <td>0.788216</td>\n",
-       "      <td>-0.331176</td>\n",
-       "      <td>-0.915238</td>\n",
-       "      <td>-0.597344</td>\n",
-       "      <td>-0.663935</td>\n",
-       "      <td>2.499354</td>\n",
-       "      <td>-1.068152</td>\n",
-       "      <td>-0.149899</td>\n",
-       "      <td>1.537288</td>\n",
-       "      <td>-0.99707</td>\n",
-       "      <td>1.452515</td>\n",
-       "      <td>-0.072945</td>\n",
+       "      <td>0.598314</td>\n",
+       "      <td>0.788246</td>\n",
+       "      <td>-0.331506</td>\n",
+       "      <td>-0.915604</td>\n",
+       "      <td>-0.597627</td>\n",
+       "      <td>-0.664282</td>\n",
+       "      <td>2.499907</td>\n",
+       "      <td>-1.068565</td>\n",
+       "      <td>-0.150033</td>\n",
+       "      <td>1.537345</td>\n",
+       "      <td>-0.996884</td>\n",
+       "      <td>1.452546</td>\n",
+       "      <td>-0.072690</td>\n",
        "      <td>1.000025</td>\n",
-       "      <td>-0.498836</td>\n",
+       "      <td>-0.498719</td>\n",
        "      <td>-5.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-12-07 12:00:00</th>\n",
-       "      <td>0.517243</td>\n",
-       "      <td>0.854278</td>\n",
-       "      <td>-0.255222</td>\n",
-       "      <td>-1.182944</td>\n",
-       "      <td>-0.561566</td>\n",
-       "      <td>-0.609082</td>\n",
-       "      <td>2.915700</td>\n",
-       "      <td>-1.638876</td>\n",
-       "      <td>-0.149899</td>\n",
-       "      <td>1.537288</td>\n",
-       "      <td>-0.99707</td>\n",
-       "      <td>1.452515</td>\n",
-       "      <td>0.071542</td>\n",
+       "      <td>0.517665</td>\n",
+       "      <td>0.854302</td>\n",
+       "      <td>-0.255547</td>\n",
+       "      <td>-1.183270</td>\n",
+       "      <td>-0.561841</td>\n",
+       "      <td>-0.609425</td>\n",
+       "      <td>2.916330</td>\n",
+       "      <td>-1.639365</td>\n",
+       "      <td>-0.150033</td>\n",
+       "      <td>1.537345</td>\n",
+       "      <td>-0.996884</td>\n",
+       "      <td>1.452546</td>\n",
+       "      <td>0.071780</td>\n",
        "      <td>-0.999975</td>\n",
-       "      <td>-0.498836</td>\n",
+       "      <td>-0.498719</td>\n",
        "      <td>-4.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-12-07 12:30:00</th>\n",
-       "      <td>0.517243</td>\n",
-       "      <td>0.854278</td>\n",
-       "      <td>-0.255222</td>\n",
-       "      <td>-1.182944</td>\n",
-       "      <td>-0.561566</td>\n",
-       "      <td>-0.609082</td>\n",
-       "      <td>2.915700</td>\n",
-       "      <td>-1.638876</td>\n",
-       "      <td>-0.149899</td>\n",
-       "      <td>1.537288</td>\n",
-       "      <td>-0.99707</td>\n",
-       "      <td>1.452515</td>\n",
-       "      <td>0.071542</td>\n",
+       "      <td>0.517665</td>\n",
+       "      <td>0.854302</td>\n",
+       "      <td>-0.255547</td>\n",
+       "      <td>-1.183270</td>\n",
+       "      <td>-0.561841</td>\n",
+       "      <td>-0.609425</td>\n",
+       "      <td>2.916330</td>\n",
+       "      <td>-1.639365</td>\n",
+       "      <td>-0.150033</td>\n",
+       "      <td>1.537345</td>\n",
+       "      <td>-0.996884</td>\n",
+       "      <td>1.452546</td>\n",
+       "      <td>0.071780</td>\n",
        "      <td>1.000025</td>\n",
-       "      <td>-0.498836</td>\n",
+       "      <td>-0.498719</td>\n",
        "      <td>-3.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-12-07 13:00:00</th>\n",
-       "      <td>0.597893</td>\n",
-       "      <td>0.898319</td>\n",
-       "      <td>-0.260286</td>\n",
-       "      <td>-1.376934</td>\n",
-       "      <td>-0.490882</td>\n",
-       "      <td>-0.603456</td>\n",
-       "      <td>2.754060</td>\n",
-       "      <td>-1.924238</td>\n",
-       "      <td>-0.149899</td>\n",
-       "      <td>1.537288</td>\n",
-       "      <td>-0.99707</td>\n",
-       "      <td>1.452515</td>\n",
-       "      <td>0.216029</td>\n",
+       "      <td>0.598314</td>\n",
+       "      <td>0.898339</td>\n",
+       "      <td>-0.260611</td>\n",
+       "      <td>-1.377231</td>\n",
+       "      <td>-0.491143</td>\n",
+       "      <td>-0.603799</td>\n",
+       "      <td>2.754660</td>\n",
+       "      <td>-1.924765</td>\n",
+       "      <td>-0.150033</td>\n",
+       "      <td>1.537345</td>\n",
+       "      <td>-0.996884</td>\n",
+       "      <td>1.452546</td>\n",
+       "      <td>0.216251</td>\n",
        "      <td>-0.999975</td>\n",
-       "      <td>-0.498836</td>\n",
+       "      <td>-0.498719</td>\n",
        "      <td>-2.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-12-07 13:30:00</th>\n",
-       "      <td>0.597893</td>\n",
-       "      <td>0.898319</td>\n",
-       "      <td>-0.260286</td>\n",
-       "      <td>-1.376934</td>\n",
-       "      <td>-0.490882</td>\n",
-       "      <td>-0.603456</td>\n",
-       "      <td>2.754060</td>\n",
-       "      <td>-1.924238</td>\n",
-       "      <td>-0.149899</td>\n",
-       "      <td>1.537288</td>\n",
-       "      <td>-0.99707</td>\n",
-       "      <td>1.452515</td>\n",
-       "      <td>0.216029</td>\n",
+       "      <td>0.598314</td>\n",
+       "      <td>0.898339</td>\n",
+       "      <td>-0.260611</td>\n",
+       "      <td>-1.377231</td>\n",
+       "      <td>-0.491143</td>\n",
+       "      <td>-0.603799</td>\n",
+       "      <td>2.754660</td>\n",
+       "      <td>-1.924765</td>\n",
+       "      <td>-0.150033</td>\n",
+       "      <td>1.537345</td>\n",
+       "      <td>-0.996884</td>\n",
+       "      <td>1.452546</td>\n",
+       "      <td>0.216251</td>\n",
        "      <td>1.000025</td>\n",
-       "      <td>-0.498836</td>\n",
+       "      <td>-0.498719</td>\n",
        "      <td>-1.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
@@ -1182,41 +1496,41 @@
       ],
       "text/plain": [
        "                     visibility  windBearing  temperature  dewPoint  pressure  \\\n",
-       "2011-12-07 11:30:00    0.597893     0.788216    -0.331176 -0.915238 -0.597344   \n",
-       "2011-12-07 12:00:00    0.517243     0.854278    -0.255222 -1.182944 -0.561566   \n",
-       "2011-12-07 12:30:00    0.517243     0.854278    -0.255222 -1.182944 -0.561566   \n",
-       "2011-12-07 13:00:00    0.597893     0.898319    -0.260286 -1.376934 -0.490882   \n",
-       "2011-12-07 13:30:00    0.597893     0.898319    -0.260286 -1.376934 -0.490882   \n",
+       "2011-12-07 11:30:00    0.598314     0.788246    -0.331506 -0.915604 -0.597627   \n",
+       "2011-12-07 12:00:00    0.517665     0.854302    -0.255547 -1.183270 -0.561841   \n",
+       "2011-12-07 12:30:00    0.517665     0.854302    -0.255547 -1.183270 -0.561841   \n",
+       "2011-12-07 13:00:00    0.598314     0.898339    -0.260611 -1.377231 -0.491143   \n",
+       "2011-12-07 13:30:00    0.598314     0.898339    -0.260611 -1.377231 -0.491143   \n",
        "\n",
        "                     apparentTemperature  windSpeed  humidity   holiday  \\\n",
-       "2011-12-07 11:30:00            -0.663935   2.499354 -1.068152 -0.149899   \n",
-       "2011-12-07 12:00:00            -0.609082   2.915700 -1.638876 -0.149899   \n",
-       "2011-12-07 12:30:00            -0.609082   2.915700 -1.638876 -0.149899   \n",
-       "2011-12-07 13:00:00            -0.603456   2.754060 -1.924238 -0.149899   \n",
-       "2011-12-07 13:30:00            -0.603456   2.754060 -1.924238 -0.149899   \n",
-       "\n",
-       "                        month      day      week      hour    minute  \\\n",
-       "2011-12-07 11:30:00  1.537288 -0.99707  1.452515 -0.072945  1.000025   \n",
-       "2011-12-07 12:00:00  1.537288 -0.99707  1.452515  0.071542 -0.999975   \n",
-       "2011-12-07 12:30:00  1.537288 -0.99707  1.452515  0.071542  1.000025   \n",
-       "2011-12-07 13:00:00  1.537288 -0.99707  1.452515  0.216029 -0.999975   \n",
-       "2011-12-07 13:30:00  1.537288 -0.99707  1.452515  0.216029  1.000025   \n",
+       "2011-12-07 11:30:00            -0.664282   2.499907 -1.068565 -0.150033   \n",
+       "2011-12-07 12:00:00            -0.609425   2.916330 -1.639365 -0.150033   \n",
+       "2011-12-07 12:30:00            -0.609425   2.916330 -1.639365 -0.150033   \n",
+       "2011-12-07 13:00:00            -0.603799   2.754660 -1.924765 -0.150033   \n",
+       "2011-12-07 13:30:00            -0.603799   2.754660 -1.924765 -0.150033   \n",
+       "\n",
+       "                        month       day      week      hour    minute  \\\n",
+       "2011-12-07 11:30:00  1.537345 -0.996884  1.452546 -0.072690  1.000025   \n",
+       "2011-12-07 12:00:00  1.537345 -0.996884  1.452546  0.071780 -0.999975   \n",
+       "2011-12-07 12:30:00  1.537345 -0.996884  1.452546  0.071780  1.000025   \n",
+       "2011-12-07 13:00:00  1.537345 -0.996884  1.452546  0.216251 -0.999975   \n",
+       "2011-12-07 13:30:00  1.537345 -0.996884  1.452546  0.216251  1.000025   \n",
        "\n",
        "                     dayofweek  tstp  is_past  \n",
-       "2011-12-07 11:30:00  -0.498836  -5.0      1.0  \n",
-       "2011-12-07 12:00:00  -0.498836  -4.0      1.0  \n",
-       "2011-12-07 12:30:00  -0.498836  -3.0      1.0  \n",
-       "2011-12-07 13:00:00  -0.498836  -2.0      1.0  \n",
-       "2011-12-07 13:30:00  -0.498836  -1.0      1.0  "
+       "2011-12-07 11:30:00  -0.498719  -5.0      1.0  \n",
+       "2011-12-07 12:00:00  -0.498719  -4.0      1.0  \n",
+       "2011-12-07 12:30:00  -0.498719  -3.0      1.0  \n",
+       "2011-12-07 13:00:00  -0.498719  -2.0      1.0  \n",
+       "2011-12-07 13:30:00  -0.498719  -1.0      1.0  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1235,6 +1549,7 @@
     "y_past['energy(kWh/hh)'].plot(label='past')\n",
     "y_future['energy(kWh/hh)'].plot(ax=plt.gca(), label='future')\n",
     "plt.legend()\n",
+    "plt.ylabel('energy(kWh/hh)')\n",
     "\n",
     "# Notice we've added on two new columns tsp (time since present) and is_past\n",
     "x_past.tail()"
@@ -1242,11 +1557,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:56:28.323567Z",
-     "start_time": "2020-10-11T10:56:28.300038Z"
+     "end_time": "2020-10-16T04:36:23.809966Z",
+     "start_time": "2020-10-16T04:36:23.790927Z"
     }
    },
    "outputs": [
@@ -1301,13 +1616,13 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.149899</td>\n",
-       "      <td>1.537288</td>\n",
-       "      <td>-0.540932</td>\n",
-       "      <td>1.452515</td>\n",
-       "      <td>-0.072945</td>\n",
+       "      <td>-0.150033</td>\n",
+       "      <td>1.537345</td>\n",
+       "      <td>-0.540753</td>\n",
+       "      <td>1.452546</td>\n",
+       "      <td>-0.072690</td>\n",
        "      <td>1.000025</td>\n",
-       "      <td>1.500432</td>\n",
+       "      <td>1.500385</td>\n",
        "      <td>187.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
@@ -1321,13 +1636,13 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.149899</td>\n",
-       "      <td>1.537288</td>\n",
-       "      <td>-0.540932</td>\n",
-       "      <td>1.452515</td>\n",
-       "      <td>0.071542</td>\n",
+       "      <td>-0.150033</td>\n",
+       "      <td>1.537345</td>\n",
+       "      <td>-0.540753</td>\n",
+       "      <td>1.452546</td>\n",
+       "      <td>0.071780</td>\n",
        "      <td>-0.999975</td>\n",
-       "      <td>1.500432</td>\n",
+       "      <td>1.500385</td>\n",
        "      <td>188.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
@@ -1341,13 +1656,13 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.149899</td>\n",
-       "      <td>1.537288</td>\n",
-       "      <td>-0.540932</td>\n",
-       "      <td>1.452515</td>\n",
-       "      <td>0.071542</td>\n",
+       "      <td>-0.150033</td>\n",
+       "      <td>1.537345</td>\n",
+       "      <td>-0.540753</td>\n",
+       "      <td>1.452546</td>\n",
+       "      <td>0.071780</td>\n",
        "      <td>1.000025</td>\n",
-       "      <td>1.500432</td>\n",
+       "      <td>1.500385</td>\n",
        "      <td>189.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
@@ -1361,13 +1676,13 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.149899</td>\n",
-       "      <td>1.537288</td>\n",
-       "      <td>-0.540932</td>\n",
-       "      <td>1.452515</td>\n",
-       "      <td>0.216029</td>\n",
+       "      <td>-0.150033</td>\n",
+       "      <td>1.537345</td>\n",
+       "      <td>-0.540753</td>\n",
+       "      <td>1.452546</td>\n",
+       "      <td>0.216251</td>\n",
        "      <td>-0.999975</td>\n",
-       "      <td>1.500432</td>\n",
+       "      <td>1.500385</td>\n",
        "      <td>190.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
@@ -1381,13 +1696,13 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.149899</td>\n",
-       "      <td>1.537288</td>\n",
-       "      <td>-0.540932</td>\n",
-       "      <td>1.452515</td>\n",
-       "      <td>0.216029</td>\n",
+       "      <td>-0.150033</td>\n",
+       "      <td>1.537345</td>\n",
+       "      <td>-0.540753</td>\n",
+       "      <td>1.452546</td>\n",
+       "      <td>0.216251</td>\n",
        "      <td>1.000025</td>\n",
-       "      <td>1.500432</td>\n",
+       "      <td>1.500385</td>\n",
        "      <td>191.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
@@ -1404,28 +1719,28 @@
        "2011-12-11 13:30:00         0.0          0.0          0.0       0.0       0.0   \n",
        "\n",
        "                     apparentTemperature  windSpeed  humidity   holiday  \\\n",
-       "2011-12-11 11:30:00                  0.0        0.0       0.0 -0.149899   \n",
-       "2011-12-11 12:00:00                  0.0        0.0       0.0 -0.149899   \n",
-       "2011-12-11 12:30:00                  0.0        0.0       0.0 -0.149899   \n",
-       "2011-12-11 13:00:00                  0.0        0.0       0.0 -0.149899   \n",
-       "2011-12-11 13:30:00                  0.0        0.0       0.0 -0.149899   \n",
+       "2011-12-11 11:30:00                  0.0        0.0       0.0 -0.150033   \n",
+       "2011-12-11 12:00:00                  0.0        0.0       0.0 -0.150033   \n",
+       "2011-12-11 12:30:00                  0.0        0.0       0.0 -0.150033   \n",
+       "2011-12-11 13:00:00                  0.0        0.0       0.0 -0.150033   \n",
+       "2011-12-11 13:30:00                  0.0        0.0       0.0 -0.150033   \n",
        "\n",
        "                        month       day      week      hour    minute  \\\n",
-       "2011-12-11 11:30:00  1.537288 -0.540932  1.452515 -0.072945  1.000025   \n",
-       "2011-12-11 12:00:00  1.537288 -0.540932  1.452515  0.071542 -0.999975   \n",
-       "2011-12-11 12:30:00  1.537288 -0.540932  1.452515  0.071542  1.000025   \n",
-       "2011-12-11 13:00:00  1.537288 -0.540932  1.452515  0.216029 -0.999975   \n",
-       "2011-12-11 13:30:00  1.537288 -0.540932  1.452515  0.216029  1.000025   \n",
+       "2011-12-11 11:30:00  1.537345 -0.540753  1.452546 -0.072690  1.000025   \n",
+       "2011-12-11 12:00:00  1.537345 -0.540753  1.452546  0.071780 -0.999975   \n",
+       "2011-12-11 12:30:00  1.537345 -0.540753  1.452546  0.071780  1.000025   \n",
+       "2011-12-11 13:00:00  1.537345 -0.540753  1.452546  0.216251 -0.999975   \n",
+       "2011-12-11 13:30:00  1.537345 -0.540753  1.452546  0.216251  1.000025   \n",
        "\n",
        "                     dayofweek   tstp  is_past  \n",
-       "2011-12-11 11:30:00   1.500432  187.0      0.0  \n",
-       "2011-12-11 12:00:00   1.500432  188.0      0.0  \n",
-       "2011-12-11 12:30:00   1.500432  189.0      0.0  \n",
-       "2011-12-11 13:00:00   1.500432  190.0      0.0  \n",
-       "2011-12-11 13:30:00   1.500432  191.0      0.0  "
+       "2011-12-11 11:30:00   1.500385  187.0      0.0  \n",
+       "2011-12-11 12:00:00   1.500385  188.0      0.0  \n",
+       "2011-12-11 12:30:00   1.500385  189.0      0.0  \n",
+       "2011-12-11 13:00:00   1.500385  190.0      0.0  \n",
+       "2011-12-11 13:30:00   1.500385  191.0      0.0  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1437,11 +1752,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:56:28.336080Z",
-     "start_time": "2020-10-11T10:56:28.325477Z"
+     "end_time": "2020-10-16T04:36:23.862674Z",
+     "start_time": "2020-10-16T04:36:23.811251Z"
     }
    },
    "outputs": [
@@ -1449,7 +1764,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "30996\n"
+      "31014\n"
      ]
     },
     {
@@ -1458,7 +1773,7 @@
        "(192, 17)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1469,6 +1784,18 @@
     "ds_train[0][0].shape"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-14T06:56:02.750012Z",
+     "start_time": "2020-10-14T06:56:02.690554Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1481,7 +1808,15 @@
     "  - get the 2nd to last element of the dataset\n",
     "  - get the shape of each of the 4 returned elements of ds_train[0]\n",
     "  - get the type of each of the 4 returned elements of ds_train[0]\n",
+    "  <details>\n",
+    "  <summary>\n",
+    "    <b>→ Hints</b>\n",
+    "  </summary>\n",
+    "      \n",
+    "  - `type(x)`\n",
+    "  - `x.shape`\n",
     "\n",
+    "  </details>\n",
     "  <br/>\n",
     "  <br/>\n",
     "  <details>\n",
@@ -1490,16 +1825,38 @@
     "  </summary>\n",
     "\n",
     "  ```python\n",
-    "  ds_train[-2]\n",
+    "  # x_past, y_past, x_future, y_future\n",
+    "  print(ds_train[-2])\n",
     "  print([x.shape for x in ds_train[0]])\n",
     "  print([type(x) for x in ds_train[0]])\n",
     "  ```\n",
+    "  or \n",
     "\n",
+    "  ```python\n",
+    "  print(ds_train[-2])\n",
+    "  for x in ds_train[0]:\n",
+    "      print(x.shape)\n",
+    "      print(type(x))\n",
+    "  ```\n",
+    "\n",
+    "      \n",
     "  </details>\n",
     "\n",
     "  </div>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-14T06:56:02.797942Z",
+     "start_time": "2020-10-14T06:56:02.751303Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1531,7 +1888,7 @@
     "\n",
     "To understand more see these visualisations:\n",
     "\n",
-    "- [distill.pub memorization in rnns](memorization-in-rnns)\n",
+    "- [distill.pub memorization in rnns](https://distill.pub/2019/memorization-in-rnns/)\n",
     "- [Chris Olah Understanding LSTMs](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)\n",
     "\n",
     "And see these chapters:\n",
@@ -1549,65 +1906,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-10-11T10:56:28.349838Z",
-     "start_time": "2020-10-11T10:56:28.337912Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "class Seq2SeqNet(nn.Module):\n",
-    "    def __init__(self, input_size, output_size, hidden_size=32, lstm_layers=2, lstm_dropout=0, _min_std = 0.05):\n",
-    "        super().__init__()\n",
-    "        self._min_std = _min_std\n",
-    "\n",
-    "        self.encoder = nn.LSTM(\n",
-    "            input_size=input_size + output_size,\n",
-    "            hidden_size=hidden_size,\n",
-    "            batch_first=True,\n",
-    "            num_layers=lstm_layers,\n",
-    "            dropout=lstm_dropout,\n",
-    "        )\n",
-    "        self.mean = nn.Linear(hidden_size, output_size)\n",
-    "        self.std = nn.Linear(hidden_size, output_size)\n",
-    "\n",
-    "    def forward(self, past_x, past_y, future_x, future_y=None):\n",
-    "        past = torch.cat([past_x, past_y], -1)\n",
-    "        \n",
-    "        # Placeholder\n",
-    "        B, S, _ = future_x.shape\n",
-    "        future_y_fake = torch.zeros((B, S, 1)).to(device)\n",
-    "\n",
-    "        future = torch.cat([future_x, future_y_fake], -1)\n",
-    "        x = torch.cat([past, future], 1).detach()\n",
-    "        \n",
-    "        outputs, _ = self.encoder(x)        \n",
-    "        \n",
-    "        # We only want the future\n",
-    "        outputs = outputs[:, -S:]\n",
-    "        \n",
-    "        # outputs: [B, T, num_direction * H]\n",
-    "        mean = self.mean(outputs)      \n",
-    "        \n",
-    "        log_sigma = self.std(outputs)\n",
-    "        log_sigma = torch.clamp(log_sigma, np.log(self._min_std), -np.log(self._min_std))\n",
-    "\n",
-    "        sigma = torch.exp(log_sigma)\n",
-    "        y_dist = torch.distributions.Normal(mean, sigma)\n",
-    "        return y_dist\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:56:28.363752Z",
-     "start_time": "2020-10-11T10:56:28.351558Z"
-    }
+     "end_time": "2020-10-16T04:36:23.905143Z",
+     "start_time": "2020-10-16T04:36:23.863988Z"
+    },
+    "lines_to_end_of_cell_marker": 2,
+    "lines_to_next_cell": 0
    },
    "outputs": [],
    "source": [
@@ -1652,8 +1958,7 @@
     "\n",
     "        sigma = torch.exp(log_sigma)\n",
     "        y_dist = torch.distributions.Normal(mean, sigma)\n",
-    "        return y_dist\n",
-    "\n"
+    "        return y_dist"
    ]
   },
   {
@@ -1663,19 +1968,21 @@
     "ExecuteTime": {
      "end_time": "2020-10-11T10:56:42.836934Z",
      "start_time": "2020-10-11T10:56:42.832646Z"
-    }
+    },
+    "lines_to_next_cell": 2
    },
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:57:04.790622Z",
-     "start_time": "2020-10-11T10:57:01.125911Z"
-    }
+     "end_time": "2020-10-16T04:36:25.472238Z",
+     "start_time": "2020-10-16T04:36:23.906383Z"
+    },
+    "lines_to_next_cell": 0
    },
    "outputs": [
     {
@@ -1689,7 +1996,7 @@
        ")"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1707,11 +2014,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:57:05.431884Z",
-     "start_time": "2020-10-11T10:57:05.428082Z"
+     "end_time": "2020-10-16T04:36:25.475887Z",
+     "start_time": "2020-10-16T04:36:25.473549Z"
     }
    },
    "outputs": [],
@@ -1747,8 +2063,7 @@
     "  <details>\n",
     "  <summary><b>→ Hints</b></summary>\n",
     "\n",
-    "  * One\n",
-    "  * Two\n",
+    "  * `x_past = torch.rand((batch_size, window_past, input_size)).to(device)`\n",
     "\n",
     "  </details>\n",
     "\n",
@@ -1766,6 +2081,10 @@
     "    future_y = torch.rand((batch_size, window_future, output_size)).to(device)\n",
     "    output = model(past_x, past_y, future_x, future_y)  \n",
     "    print(output)\n",
+    "    \n",
+    "    # We can also use torchsummaryX to summarise the model size\n",
+    "    from deep_ml_curriculum.torchsummaryX import summary\n",
+    "    summary(model, past_x, past_y, future_x, future_y)\n",
     "  ```\n",
     "\n",
     "  </details>\n",
@@ -1775,15 +2094,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T01:06:03.848966Z",
-     "start_time": "2020-10-11T01:06:03.764868Z"
+     "end_time": "2020-10-16T04:36:25.608259Z",
+     "start_time": "2020-10-16T04:36:25.477144Z"
     }
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normal(loc: torch.Size([64, 192, 1]), scale: torch.Size([64, 192, 1]))\n",
+      "========================================================\n",
+      "          Kernel Shape   Output Shape  Params  Mult-Adds\n",
+      "Layer                                                   \n",
+      "0_encoder            -  [64, 192, 32]   15104      14592\n",
+      "1_decoder            -  [64, 192, 32]   14976      14464\n",
+      "2_mean         [32, 1]   [64, 192, 1]      33         32\n",
+      "3_std          [32, 1]   [64, 192, 1]      33         32\n",
+      "--------------------------------------------------------\n",
+      "                      Totals\n",
+      "Total params           30146\n",
+      "Trainable params       30146\n",
+      "Non-trainable params       0\n",
+      "Mult-Adds              29120\n",
+      "========================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "past_x = torch.rand((batch_size, window_past, input_size)).to(device)\n",
+    "future_x = torch.rand((batch_size, window_future, input_size)).to(device)\n",
+    "past_y = torch.rand((batch_size, window_past, output_size)).to(device)\n",
+    "future_y = torch.rand((batch_size, window_future, output_size)).to(device)\n",
+    "output = model(past_x, past_y, future_x, future_y)  \n",
+    "print(output)\n",
+    "\n",
+    "from deep_ml_curriculum.torchsummaryX import summary\n",
+    "summary(model, past_x, past_y, future_x, future_y )\n",
+    "1"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1816,8 +2179,8 @@
    "execution_count": 19,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:57:06.664243Z",
-     "start_time": "2020-10-11T10:57:06.275499Z"
+     "end_time": "2020-10-16T04:36:25.825576Z",
+     "start_time": "2020-10-16T04:36:25.609500Z"
     }
    },
    "outputs": [
@@ -1825,13 +2188,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/wassname/anaconda/envs/deep_ml_curriculum/lib/python3.7/site-packages/torch/distributions/distribution.py:134: UserWarning: sample_n will be deprecated. Use .sample((n,)) instead\n",
+      "/anaconda/envs/py37_pytorch/lib/python3.7/site-packages/torch/distributions/distribution.py:134: UserWarning: sample_n will be deprecated. Use .sample((n,)) instead\n",
       "  warnings.warn('sample_n will be deprecated. Use .sample((n,)) instead', UserWarning)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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5slJJ/mu7KioqdOzYMS8PEWi1OBUIAPXk2788ryM73pM7Xqrcp6coMuFf1e7KW1W0dbVKcj6RQkMVEhGpmB88ENwm7zf3q9sdT6ns0EEd2vBbhXWI1f4XfiIpcPow4VqVfPVp4E5A85/mu+ZeSVKby4er5MttyntuusxMEb4r1KbHkOC+i7a+ocj4qxUSHqHwTj5JTnnPTdfNd96sdu3aNeSvBqizwsJCLV26VNOmTWvsUk5CsAKAetJ+1J1qP+rOKu0dRv/vGrfpdsdTkqSw6I669D9WV9unba8r1bbXlVXaLSRUMUn31bjvyqcTzUydxs+QJP2SmeDRDBUWFmrhwoVVglV5eblCQ0MbqSpOBQIAgGYoNTVV2dnZGjBggAYNGqRRo0Zp4sSJ6tevn3JychQfHx/sm56ertmzZ0uSsrOzlZSUpIEDB2rEiBHauXNnDZ9QO4xYAQCAZictLU2ZmZnKyMjQunXrNHbsWGVmZsrn8yknJ6fG7aZOnapnnnlGPXv21Icffqhp06Zp7dq1ntVFsAIAAHU3cqS3+zvHhzoPHjxYPp/vtH0OHz6s9evXKzk5OdhWWlpam+pqRLACAADNXtu2bYPvw8LCVFFREVwuKSmR5L8Dtl27dsrIyKi3OghWAACg7s5xhKmuoqKiVFRUVO26Ll26KD8/XwUFBYqMjNTq1auVlJSk6Oho+Xw+rVy5UsnJyXLOadu2bUpISPCsLoIVAABodmJiYjR8+HDFx8frggsuUJcuXYLrwsPDNWvWLA0ZMkQ+n0+9evUKrluyZInuvfdezZ07V8ePH9eECRMIVgCajmXLluk///M/ZWbq1q2bXnnlFXXs2LFKv/nz5+u5555TaGioFixYoGuvvVaSdOzYMd13331at26dQkJCNG/ePP3bv/2bJOnIZ3/Tdx8slWQK7+xTp/EP6XhBrg6+/rhcRblirp2u87v3lqsoV/6KWer0b48oJDyiIQ8fQCNaunRpjetSUlKUkpJSpd3n82nNmjX1VhPBCkCtlZWV6Sc/+Yl27Nihjh07asaMGfrVr34VvK35hB07dmj58uXavn278vLyNHr0aO3evVuhoaGaN2+eOnfurN27d6uiokLffPONJP/s5N9tXKkutz2u0IhIlR8plCQVZfxJ7f7ldoVd2FmF772oTj/sraKP31TbvlcRqgA0OuaxAlBrzjk553TkyBE553To0CF161b18SyrVq3ShAkTdP7558vn86lHjx7atGmTJOn555/Xww8/LEkKCQkJjnYd/uQtRV0xVqERkZKk0Lb+mcEtNEyu7Jj/kSwhoaooOayjn29S2/irGuKQAeC0CFYAai08PFyLFi1Sv3791K1bN+3YsUN33XVXlX779u3TxRf/45l1sbGx2rdvnwoL/aNQjzzyiK644golJyfr66+/liQd/zZPZd/s099feUj7X/p3Hf1iiyQp6oqxOvTRH1Tw1tO6cNhNKvxgmS4cdpPMrAGOGABOj2AFoNaOHz+uRYsW6eOPP1ZeXp769++v+fPnV+nnnKvSZmYqKytTbm6uhg8frq1bt2rYsGF68MEH/R0qynX82zx1uWW+Oo5/SAV/WqCKksMKi+6siyamqeukJ2Th56v88DcKj4nVwdVP6MCqX570gGMAaGgEKwC1dmIumMsuu0xmpptuuknr16+v0i82NlZ79+4NLufm5qpbt26KiYlRmzZt9MMf/lCSlJycrK1bt0qSQqNi1KbnUFlomMLbXaTwmO46/m3eSfst/OvLajfiNh3a8rra9hmpdldOVOEHNV/MCgD1jWAFoNa6d++uHTt26MCBA5Kkd955R717967Sb/z48Vq+fLlKS0u1Z88eZWVlafDgwTIzXXfddVoXmP/m3XffVZ8+fSRJbXoOU8lX2yRJ5cXf6fg3eQprd1FwnyVffarQyA4K79Bd7nipZCZZiP89ADQS7goEUGvdunXTL37xC33/+99XeHi4Lr30Ur3wwguSpNdee02bN2/Wo48+qr59++qmm25Snz59FBYWpqeffjr49Plf/vKXmjRpkn7605+qU6dO+s1vfiNJivBdoaN7tirvf+6VLETtR96h0AuiJflPLX63/rfqeEOqJCkqIUkHV6fLVZSrw79Oq1oogBansLBQS5cu1bRpTetv3qq79qGhJSYmus2bN9fvh5x4hlEDzwyLetScv9PmWnsD1h2X+ka9f4aXli/1h7wJE9MauZJzl5M2trFLQDP02WefVTtC3VBycnI0btw4ZWZmntReXl4e/IebF6o7TjPb4pxLrK4/pwIBAECzk5qaquzsbA0YMECDBg3SqFGjNHHiRPXr1085OTmKj48P9k1PTw/Or5edna2kpCQNHDhQI0aM0M6dOz2ti2AFNBFJSUlKSEhQ3759dc8996i8vLxKn02bNmnAgAEaMGCAEhIS9Ic//CG4bubMmbr44osVGRlZZbsVK1aoT58+6tu3ryZOnChJ2rVrlwYOHKiEhARt2LBBkn/Cz9GjR6u4uLiejhIAvJGWlqbLLrtMGRkZevzxx7Vp0ybNmzdPO3bsOO12U6dO1VNPPaUtW7YoPT3d81OJXGMFNBErVqxQdHS0nHO68cYbtXLlSk2YMOGkPvHx8dq8ebPCwsK0f/9+JSQk6LrrrlNYWJiuu+463XffferZs+dJ22RlZWn+/Pn64IMP1L59e+Xn50uSnn32WaWlpSkuLk6pqan63e9+p0WLFmnSpElq06ZNgx03gBbixKUCXjnHSw4GDx4sn8932j6HDx/W+vXrlZycHGwrLfX2hheCFdBEREf7L8wuKyvTsWPHqp3wsnLgKSkpOanP0KFDq93vr3/9a02fPl3t27eXJHXu3FmSf3LPo0ePqri4WOHh4SosLNTrr7+ut956y7NjAoCG0rZt2+D7sLAwVVRUBJdLSkokSRUVFWrXrl1wqpj6QLACmpBrr71WmzZt0pgxY3TjjTdW2+fDDz/UnXfeqS+//FIvv/yywsJO/2e8e/duSdLw4cNVXl6u2bNnKykpSdOnT9fkyZNVWlqqZ599Vo8++qhmzpzJDOYAaqeBb8aJiopSUVFRteu6dOmi/Px8FRQUKDIyUqtXr1ZSUpKio6Pl8/m0cuVKJScnyzmnbdu2KSEhwbO6uMYKaELeeust7d+/X6WlpVq7dm21fYYMGaLt27fro48+0vz584P/EqtJWVmZsrKytG7dOi1btkx33323CgsLdckll2jdunXasGGD2rRpo7y8PPXq1UuTJk3SzTffHAxkANAUxcTEaPjw4YqPj9dDDz100rrw8HDNmjVLQ4YM0bhx49SrV6/guiVLlui5554LXtO6atUqT+tixApoYiIiIjR+/HitWrVK11xzTY39evfurbZt2yozM1OJidXe9SvJP+v50KFDFR4eLp/Pp8svv1xZWVkaNGhQsM/MmTM1d+5cLViwQLfeeqvi4uI0Z84cLVmyxNNjAwAvLV1a85MWUlJSlJKSUqXd5/NpzZo19VYTI1ZAE3D48GHt379fkn+E6c033zzpX1gn7NmzR2VlZZKkL7/8Urt27VJcXNxp933DDTfoL3/5iyTp4MGD2r17t773ve8F17/33nvq3r27evbsqeLiYoWEhCg0NJQ7AwGgFhixApqAI0eOaPz48SotLVV5ebmuuuoq3XPPPZJOnsH8/fffV1pamsLDwxUSEqKFCxeqY8eOkqQZM2Zo6dKlKi4uVmxsrO6++27Nnj1b1157rd5++2316dNHoaGhevzxxxUTEyPJP4P53LlztWLFCkn+25BvvfVWlZWVadGiRY3zywCAZoyZ19F8NefvtLnWzszrNWLmdbQ2jT3zekNh5nUAAIBGQrBCi7Jlyxb169dPPXr0UEpKiqobkT1+/LimTJmifv36qXfv3po/f74kqaioKDir+YABA9SxY0f99Kc/PWnbV199VWamEyOszF4OAKiMYIUW5d5779XixYuVlZWlrKysau/8WLlypUpLS/Xpp59qy5YtevbZZ5WTk6OoqChlZGQEfy699FL96Ec/Cm5XVFSkBQsWaMiQIcG2E7OXv/rqq0pPT5ckZi8HgFaMYIUWY//+/Tp06JCGDRsmM9PkyZP1xz/+sUo/M9ORI0dUVlamo0eP6rzzzgvOen5CVlaW8vPzNWLEiGDbI488ohkzZigiIiLYVtPs5ZMnT66/AwUAqLCwUAsXLmzsMqogWKHF2Ldvn2JjY4PLsbGx2rdvX5V+N954o9q2bauuXbvqkksu0YMPPqgOHTqc1GfZsmW6+eabg7OQf/zxx9q7d6/GjRt3Ur/p06frySef1D333KOf//znzF4OAA2kpmBV3QPsG1KdgpWZPWBm280s08yWmVmEmXUws3fMLCvw2t6rYoHTqe56quoCzqZNmxQaGqq8vDzt2bNHTzzxhL744ouT+ixfvly33HKLJP+zpR544AE98cQTVfbF7OUA0DhSU1OVnZ2tAQMGaNCgQRo1apQmTpyofv36KScnR/Hx8cG+6enpmj17tiQpOztbSUlJGjhwoEaMGKGdO3d6Wletg5WZdZeUIinRORcvKVTSBEmpkt51zvWU9G5gGah3sbGxys3NDS7n5uaqW7duVfotXbpUSUlJCg8PV+fOnTV8+HBVnu7jk08+UVlZmQYOHCjJf21VZmamRo4cqbi4OG3cuFHjx4/XqVOEzJw5U4899lhw9vI5c+Zozpw59XS0ANC6paWl6bLLLlNGRoYef/xxbdq0SfPmzdOOHTtOu93UqVP11FNPacuWLUpPT9e0adM8rauuE4SGSbrAzI5LaiMpT9LDkkYG1r8oaZ2k/6jj5wBn1LVrV0VFRWnjxo0aMmSIXnrpJd1///1V+l1yySVau3atbrvtNhUXF2vjxo0n3f23bNmy4GiVJF144YU6ePBgcHnkyJFKT08/6TEyzF4OoNU7Mc+dV85xvrzBgwfL5/Odts/hw4e1fv16JScnB9tKS0trU12Nah2snHP7zCxd0leSjkp62zn3tpl1cc7tD/TZb2adPaoVOKNFixbp9ttv19GjRzVmzBiNGTNG0smzl0+fPl133HGH4uPj5ZzTHXfcof79+wf3sWLFCr355ptn/ZnMXg4Aja9t27bB92FhYaqoqAgun3hYfUVFhdq1a6eMjIx6q6PWwSpw7dT1knySCiWtNLPbzmH7qZKmSv4RBMALiYmJyszMrNI+fvx4jR8/XpIUGRmplStX1riPU6+3OtW6U/4VZWZ65513gsu9e/fW1q1bz6FqAGgBGvhJElFRUSoqKqp2XZcuXZSfn6+CggJFRkZq9erVSkpKUnR0tHw+n1auXKnk5GQ557Rt2zYlJCR4VlddLl4fLWmPc+6Ac+64pN9L+l+SvjazrpIUeM2vbmPn3GLnXKJzLrFTp051KAMAALQ2MTExGj58uOLj4/XQQw+dtC48PFyzZs3SkCFDNG7cuJMear9kyRI999xzSkhIUN++fbVq1SpP66rLNVZfSRpqZm3kPxV4taTNko5ImiIpLfDqbcUAAADy34xUk5SUFKWkpFRp9/l81U4e7ZW6XGP1oZm9KmmrpDJJH0taLClS0gozu0v+8JVc814AAABajjrdFeic+4WkX5zSXCr/6BUAAECrwszrAAAAHiFYAQAAeIRgBQAA4BGCFQAAgEcIVgAAoNkpLCzUwoULG7uMKghWAACg2akpWJWXlzdCNf9AsAIAAM1OamqqsrOzNWDAAA0aNEijRo3SxIkT1a9fP+Xk5Cg+Pj7YNz09XbNnz5YkZWdnKykpSQMHDtSIESO0c+dOT+uq0zxWAAAAjSEtLU2ZmZnKyMjQunXrNHbsWGVmZsrn8yknJ6fG7aZOnapnnnlGPXv21Icffqhp06Zp7dq1ntVFsAIAAHU3cqS3+zvHhzoPHjxYPp/vtH0OHz6s9evXKzn5Hw+FKS0trU11NSJYAQCAZq9t27bB92FhYaqoqAgul5SUSJIqKirUrl07ZWRk1FsdBCsAAFB35zjCVFdRUVEqKiqqdl2XLl2Un5+vgoICRUZGavXq1UpKSlJ0dLR8Pp9Wrlyp5ORkOee0bds2JSQkeFZXq754fcuWLerXr5969OihlJQUOeeq7Td//iVGEQEAAA6sSURBVHz16NFDl19+ud56662z3v7VV1+VmWnz5s2SpF27dmngwIFKSEjQhg0bJEllZWUaPXq0iouL6+koAQBoeWJiYjR8+HDFx8froYceOmldeHi4Zs2apSFDhmjcuHHq1atXcN2SJUv03HPPKSEhQX379tWqVas8ratVj1jde++9Wrx4sYYOHaof/OAHWrNmjcaMGXNSnx07dmj58uXavn278vLyNHr0aO3evVuhoaGn3b6oqEgLFizQkCFDgvt69tlnlZaWpri4OKWmpup3v/udFi1apEmTJqlNmzYNeuxAfYlLfaOxSwDQSixdurTGdSkpKUpJSanS7vP5tGbNmnqrqdWOWO3fv1+HDh3SsGHDZGaaPHmy/vjHP1bpt2rVKk2YMEHnn3++fD6fevTooU2bNp1x+0ceeUQzZsxQREREsC08PFxHjx5VcXGxwsPDVVhYqNdff12TJ09ukGMGAAD1q9WOWO3bt0+xsbHB5djYWO3bt6/afkOHDq3SLzw8vMbtP/74Y+3du1fjxo1Tenp6sM/06dM1efJklZaW6tlnn9Wjjz6qmTNnyszq4xABAEADa7XBqrrrqaoLODX1q6m9oqJCDzzwgF544YUq6y+55BKtC1zc9/nnnysvL0+9evXSpEmTdOzYMT322GP6p3/6p3M/GAAA0CS02lOBsbGxys3NDS7n5uaqW7du1fbbu3dvlX41bV9UVKTMzEyNHDlScXFx2rhxo8aPHx+8gP2EmTNn6rHHHtOCBQt06623as6cOZozZ049HCkAAPWjppu+WoraHF+rHbHq2rWroqKitHHjRg0ZMkQvvfSS7r///ir9xo8fr4kTJ+pnP/uZ8vLylJWVpcGDBys0NLTa7S+88EIdPHgwuP3IkSOVnp6uxMTEYNt7772n7t27q2fPniouLlZISIhCQ0O5MxBAo/DqhoOctLGe7AfNQ0REhAoKChQTE9MiL2lxzqmgoOCka6XPRqsNVpK0aNEi3X777Tp69KjGjBkTvKPvtdde0+bNm/Xoo4+qb9++uummm9SnTx+FhYXp6aefVmho6Gm3Px3nnObOnasVK1ZI8k+tf+utt6qsrEyLFi2qv4MFAMBDJ87cHDhwoLFLqTcREREnXU99NqwpDOMlJia6U0+Vee7EVPsNPIEZ6lFz/k6ba+1nUXdrnW5h+dJUSdKEiWmNXEnjYcQKrYWZbXHOJVa3rtVeYwUAAOA1ghUAAIBHCFYAAAAeIVgBAAB4hGAFAADgEYIVAACARwhWAAAAHiFYAQAAeIRgBQAA4BGCFQAAgEcIVgAAAB4hWAEAAHiEYAUAAOARghUAAIBHCFYAAAAeIVgBAAB4JKyxCwAAtAxxqW94sp+ctLGe7AdoDIxYAQAAeIRgBQAA4BGCFQAAgEfqFKzMrJ2ZvWpmO83sMzMbZmYdzOwdM8sKvLb3qlgAAICmrK4jVv8taY1zrpekBEmfSUqV9K5zrqekdwPLAAAALV6tg5WZRUv6vqTnJMk5d8w5VyjpekkvBrq9KOmGuhYJAADQHNRlxOp7kg5I+o2ZfWxm/2NmbSV1cc7tl6TAa2cP6gQAAGjy6hKswiRdIWmRc+6fJR3ROZz2M7OpZrbZzDYfOHCgDmUAAAA0DXUJVrmScp1zHwaWX5U/aH1tZl0lKfCaX93GzrnFzrlE51xip06d6lAGAABA01DrYOWc+7ukvWZ2eaDpakk7JL0maUqgbYqkVXWqEAAAoJmo6yNt7pe0xMzOk/SFpDvkD2srzOwuSV9JSq7jZwAAADQLdQpWzrkMSYnVrLq6LvsFAABojngIMwBJZ/cA3eVfFEiSJnj0sF0AaGl4pA0AAIBHCFYAAAAeIVgBAAB4hGAFAADgEYIVAACARwhWAAAAHiFYAQAAeIRgBQAA4BGCFQAAgEcIVgAAAB4hWAEAAHiEYAUAAOARghUAAIBHCFYAAAAeIVgBAAB4hGAFAADgkbDGLgBA3cSlvtHYJQAAAhixAgAA8AjBCgAAwCMEKwAAAI8QrAAAADxCsAIAAPAIwQoAAMAjBCsAAACPEKwAAAA8QrACAADwCMEKAADAIwQrAAAAjxCsAAAAPEKwAgAA8AjBCgAAwCNhjV0AAACVxaW+4cl+ctLGerIf4FwwYgUAAOARghUAAIBHCFYAAAAeIVgBAAB4hGAFAADgEYIVAACARwhWAAAAHqlzsDKzUDP72MxWB5Y7mNk7ZpYVeG1f9zIBAACaPi9GrH4i6bNKy6mS3nXO9ZT0bmAZAACgxatTsDKzWEljJf1PpebrJb0YeP+ipBvq8hkAAADNRV1HrP6fpBmSKiq1dXHO7ZekwGvnOn4GAABAs1DrYGVm4yTlO+e21HL7qWa22cw2HzhwoLZlAAAANBl1GbEaLmm8meVIWi7pKjN7RdLXZtZVkgKv+dVt7Jxb7JxLdM4ldurUqQ5lAAAANA21DlbOuYedc7HOuThJEyStdc7dJuk1SVMC3aZIWlXnKgEAAJqB+pjHKk3SNWaWJemawDIAAECLF+bFTpxz6yStC7wvkHS1F/sFAABoTph5HQAAwCMEKwAAAI8QrAAAADxCsAIAAPAIwQoAAMAjBCsAAACPEKwAAAA8QrACAADwCMEKAADAIwQrAAAAjxCsAAAAPEKwAgAA8AjBCgAAwCMEKwAAAI8QrAAAADxCsAIAAPAIwQoAAMAjYY1dAAAA9SEu9Q1P9pOTNtaT/aB1YMQKAADAIwQrAAAAj3AqEGgkG78o0ASPTlUAAJoGRqwAAAA8QrACAADwCMEKAADAIwQrAAAAjxCsAAAAPEKwAgAA8AjTLQAAcBrM4I5zwYgVAACARwhWAAAAHiFYAQAAeIRgBQAA4BGCFQAAgEcIVgAAAB4hWAEAAHiEYAUAAOARghUAAIBHCFYAAAAeIVgBAAB4hGAFAADgEYIVAACAR2odrMzsYjP7i5l9ZmbbzewngfYOZvaOmWUFXtt7Vy4AAEDTVZcRqzJJ/+6c6y1pqKTpZtZHUqqkd51zPSW9G1gGAABo8WodrJxz+51zWwPviyR9Jqm7pOslvRjo9qKkG+paJAAAQHPgyTVWZhYn6Z8lfSipi3Nuv+QPX5I617DNVDPbbGabDxw44EUZAAAAjarOwcrMIiX9TtJPnXOHznY759xi51yicy6xU6dOdS0DAACg0dUpWJlZuPyhaolz7veB5q/NrGtgfVdJ+XUrEQAAoHkIq+2GZmaSnpP0mXPuyUqrXpM0RVJa4HVVnSoEmpi41DfqvI/lXxR4UAkAoKmpdbCSNFzSJEmfmllGoO3n8geqFWZ2l6SvJCXXrUQAAIDmodbByjn3viSrYfXVtd0vAABAc8XM6wAAAB4hWAEAAHiEYAUAAOARghUAAIBHCFYAAAAeIVgBAAB4hGAFAADgEYIVAACARwhWAAAAHiFYAQAAeIRgBQAA4BGCFQAAgEcIVgAAAB4hWAEAAHiEYAUAAOARghUAAIBHCFYAAAAeIVgBAAB4hGAFAADgEYIVAACARwhWAAAAHiFYAQAAeIRgBQAA4BGCFQAAgEfCGrsAAABag7jUNzzZT07aWE/2g/rBiBUAAIBHCFYAAAAeIVgBAAB4hGAFAADgEYIVAACARwhWAAAAHmG6BTR5Nd2ivPyLAknShLO8hZlblAEA9Y0RKwAAAI8QrAAAADzCqUC0Gl7NegwALQEzwdcPRqwAAAA8wogVqmBkBwCaLv4b3bQxYgUAAOARghUAAIBHOBXYgjA8DABoaE3t/z2NfTF9vY1YmVmSme0ys8/NLLW+PgcAAKCpqJdgZWahkp6WNEZSH0m3mFmf+vgsAACApqK+RqwGS/rcOfeFc+6YpOWSrq+nzwIAAGgS6itYdZe0t9JybqANAACgxTLnnPc7NUuWdK1z7u7A8iRJg51z91fqM1XS1MDi5ZJ2eV5IVR0lHWyAz0HD4TttefhOWx6+05antX+nlzrnOlW3or7uCsyVdHGl5VhJeZU7OOcWS1pcT59fLTPb7JxLbMjPRP3iO215+E5bHr7TlofvtGb1dSrwI0k9zcxnZudJmiDptXr6LAAAgCahXkasnHNlZnafpLckhUp63jm3vT4+CwAAoKmotwlCnXNvSnqzvvZfSw166hENgu+05eE7bXn4TlsevtMa1MvF6wAAAK0RzwoEAADwSIsPVmaWbGbbzazCzBJPWfdw4JE7u8zs2saqEXVjZrPNbJ+ZZQR+ftDYNeHc8RislsnMcszs08Df5ubGrgfnzsyeN7N8M8us1NbBzN4xs6zAa/vGrLEpafHBSlKmpB9J+mvlxsAjdiZI6ispSdLCwKN40Dz9X+fcgMBPU7u2D2fAY7BavFGBv01uz2+eXpD//5OVpUp61znXU9K7gWWoFQQr59xnzrnqJh+9XtJy51ypc26PpM/lfxQPgIbHY7CAJso591dJ35zSfL2kFwPvX5R0Q4MW1YS1+GB1Gjx2p2W5z8y2BYasGZJufvh7bLmcpLfNbEvgiRtoGbo45/ZLUuC1cyPX02TU23QLDcnM/izpompWzXTOrapps2rauEWyiTrddyxpkaTH5P/+HpP0hKQ7G646eIC/x5ZruHMuz8w6S3rHzHYGRkCAFqlFBCvn3OhabHbGx+6g6Tjb79jMfi1pdT2XA+/x99hCOefyAq/5ZvYH+U/7Eqyav6/NrKtzbr+ZdZWU39gFNRWt+VTga5ImmNn5ZuaT1FPSpkauCbUQ+KM+4Yfy37CA5oXHYLVAZtbWzKJOvJf0r+Lvs6V4TdKUwPspkmo6O9TqtIgRq9Mxsx9KekpSJ0lvmFmGc+5a59x2M1shaYekMknTnXPljVkrau2/zGyA/KeOciT978YtB+eKx2C1WF0k/cHMJP//b5Y659Y0bkk4V2a2TNJISR3NLFfSLySlSVphZndJ+kpScuNV2LQw8zoAAIBHWvOpQAAAAE8RrAAAADxCsAIAAPAIwQoAAMAjBCsAAACPEKwAAAA8QrACAADwCMEKAADAI/8fthRUS1LUj+oAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -1869,7 +2232,7 @@
     " <div class=\"alert alert-success\">\n",
     "  <h2>Exercise Likelihood</h2>\n",
     "\n",
-    "  If you have a normal distribution with mean 1 and std 3. What is the liklihood of 2?\n",
+    "  If you have a normal distribution with mean 1 and std 3. What is the likelihood of 2?\n",
     "    \n",
     "    \n",
     "      \n",
@@ -1923,8 +2286,8 @@
    "execution_count": 20,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:57:06.934767Z",
-     "start_time": "2020-10-11T10:57:06.930937Z"
+     "end_time": "2020-10-16T04:36:25.829742Z",
+     "start_time": "2020-10-16T04:36:25.826919Z"
     }
    },
    "outputs": [],
@@ -1953,8 +2316,8 @@
    "execution_count": 21,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T10:57:07.468360Z",
-     "start_time": "2020-10-11T10:57:07.451562Z"
+     "end_time": "2020-10-16T04:36:25.874531Z",
+     "start_time": "2020-10-16T04:36:25.831061Z"
     }
    },
    "outputs": [],
@@ -1974,11 +2337,16 @@
     "    )\n",
     "\n",
     "    for batch in tqdm(load_train, leave=False, desc='train'):\n",
-    "        # make it a pytorch gpu variable\n",
+    "        # Send data to gpu\n",
     "        x_past, y_past, x_future, y_future = [d.to(device) for d in batch]\n",
     "\n",
+    "        # Discard previous gradients\n",
     "        optimizer.zero_grad()\n",
+    "        \n",
+    "        # Run model\n",
     "        y_dist = model(x_past, y_past, x_future, y_future)\n",
+    "        \n",
+    "        # Get loss, it's Negative Log Likelihood\n",
     "        loss = -y_dist.log_prob(y_future).mean()\n",
     "\n",
     "        # Backprop\n",
@@ -2000,9 +2368,12 @@
     "                                                       pin_memory=False,\n",
     "                                                       num_workers=num_workers)\n",
     "    for batch in tqdm(load_test, leave=False, desc='test'):\n",
+    "        # Send data to gpu\n",
     "        x_past, y_past, x_future, y_future = [d.to(device) for d in batch]\n",
     "        with torch.no_grad():\n",
+    "            # Run model\n",
     "            y_dist = model(x_past, y_past, x_future, y_future)\n",
+    "            # Get loss, it's Negative Log Likelihood\n",
     "            loss = -y_dist.log_prob(y_future).mean()\n",
     "\n",
     "        test_loss.append(loss.item())\n",
@@ -2044,8 +2415,8 @@
    "execution_count": 22,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T11:02:09.352395Z",
-     "start_time": "2020-10-11T10:57:07.669238Z"
+     "end_time": "2020-10-16T04:42:55.508891Z",
+     "start_time": "2020-10-16T04:36:25.875735Z"
     }
    },
    "outputs": [
@@ -2067,13 +2438,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Start: Test Loss = 1.27\n"
+      "Start: Test Loss = 1.23\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a4f870e2670c413f84d0a1737d270f1f",
+       "model_id": "943cf67e6c454fe889296d6c458e0415",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2102,7 +2473,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/8: Training Loss = 0.83\n"
+      "Epoch 1/8: Training Loss = 0.99\n"
      ]
     },
     {
@@ -2123,7 +2494,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/8: Test Loss = 0.10\n",
+      "Epoch 1/8: Test Loss = 0.75\n",
       "--------------------------------------------------\n"
      ]
     },
@@ -2145,7 +2516,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 2/8: Training Loss = 0.45\n"
+      "Epoch 2/8: Training Loss = 0.59\n"
      ]
     },
     {
@@ -2166,7 +2537,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 2/8: Test Loss = 0.18\n",
+      "Epoch 2/8: Test Loss = 0.19\n",
       "--------------------------------------------------\n"
      ]
     },
@@ -2188,7 +2559,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 3/8: Training Loss = 0.42\n"
+      "Epoch 3/8: Training Loss = 0.45\n"
      ]
     },
     {
@@ -2209,7 +2580,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 3/8: Test Loss = 0.06\n",
+      "Epoch 3/8: Test Loss = 0.13\n",
       "--------------------------------------------------\n"
      ]
     },
@@ -2231,7 +2602,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 4/8: Training Loss = 0.32\n"
+      "Epoch 4/8: Training Loss = 0.39\n"
      ]
     },
     {
@@ -2252,7 +2623,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 4/8: Test Loss = 0.20\n",
+      "Epoch 4/8: Test Loss = 0.12\n",
       "--------------------------------------------------\n"
      ]
     },
@@ -2274,7 +2645,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 5/8: Training Loss = 0.33\n"
+      "Epoch 5/8: Training Loss = 0.36\n"
      ]
     },
     {
@@ -2295,7 +2666,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 5/8: Test Loss = 0.07\n",
+      "Epoch 5/8: Test Loss = 0.14\n",
       "--------------------------------------------------\n"
      ]
     },
@@ -2317,7 +2688,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 6/8: Training Loss = 0.31\n"
+      "Epoch 6/8: Training Loss = 0.29\n"
      ]
     },
     {
@@ -2338,7 +2709,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 6/8: Test Loss = 0.13\n",
+      "Epoch 6/8: Test Loss = 0.06\n",
       "--------------------------------------------------\n"
      ]
     },
@@ -2360,7 +2731,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 7/8: Training Loss = 0.26\n"
+      "Epoch 7/8: Training Loss = 0.29\n"
      ]
     },
     {
@@ -2381,14 +2752,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 7/8: Test Loss = 0.15\n",
+      "Epoch 7/8: Test Loss = 0.13\n",
       "--------------------------------------------------\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d48e0997a82544c9b7f50d0635f725a6",
+       "model_id": "",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2403,6 +2774,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Epoch 8/8: Training Loss = 0.27\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, description='test', max=15.0, style=ProgressStyle(description_width='i…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8/8: Test Loss = 0.07\n",
+      "--------------------------------------------------\n",
       "\n"
      ]
     },
@@ -2418,7 +2812,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2445,7 +2839,7 @@
     "\n",
     "But we also care about how far we were predicting into the future, so we have 3 dimensions: source time, target time, time ahead.\n",
     "\n",
-    "It's hard to use pandas for data with more than 2 dimensions, so we will use xarray. Xarray has an interface similar to pandas but can have N dimensions."
+    "It's hard to use pandas for data with virtual dimensions so we will use xarray. Xarray has an interface similar to pandas but can have N dimensions. It also allow coordinates which are virtual dimensions."
    ]
   },
   {
@@ -2453,8 +2847,8 @@
    "execution_count": 23,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T11:02:09.909097Z",
-     "start_time": "2020-10-11T11:02:09.355529Z"
+     "end_time": "2020-10-16T04:42:56.136515Z",
+     "start_time": "2020-10-16T04:42:55.511479Z"
     }
    },
    "outputs": [],
@@ -2516,15 +2910,15 @@
    "execution_count": 24,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T11:02:11.543542Z",
-     "start_time": "2020-10-11T11:02:09.911819Z"
+     "end_time": "2020-10-16T04:42:57.535661Z",
+     "start_time": "2020-10-16T04:42:56.138208Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "44e80e59ad6942d9b2a6d214e20a5aec",
+       "model_id": "d6aa7dc536e946bb94eff61e69a9d951",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2540,7 +2934,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "NLL mean over <=6 hours: 0.09597 (lower is better)\n"
+      "NLL mean over <=6 hours: 0.08013 (lower is better)\n"
      ]
     }
    ],
@@ -2553,8 +2947,8 @@
    "execution_count": 25,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T11:02:11.662644Z",
-     "start_time": "2020-10-11T11:02:11.546145Z"
+     "end_time": "2020-10-16T04:42:57.593546Z",
+     "start_time": "2020-10-16T04:42:57.537033Z"
     }
    },
    "outputs": [
@@ -2912,23 +3306,23 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:        (t_ahead: 192, t_behind: 192, t_source: 7461)\n",
+       "Dimensions:        (t_ahead: 192, t_behind: 192, t_source: 7465)\n",
        "Coordinates:\n",
-       "  * t_source       (t_source) datetime64[ns] 2013-09-17T14:00:00 ... 2013-09-...\n",
+       "  * t_source       (t_source) datetime64[ns] 2013-09-17T12:00:00 ... 2013-09-...\n",
        "  * t_ahead        (t_ahead) timedelta64[ns] 00:00:00 ... 3 days 23:30:00\n",
        "  * t_behind       (t_behind) timedelta64[ns] -4 days +00:00:00 ... -1 days +...\n",
-       "    t_target       (t_source, t_ahead) datetime64[ns] 2013-09-17T14:00:00 ......\n",
-       "    t_past         (t_source, t_behind) datetime64[ns] 2013-09-13T14:00:00 .....\n",
+       "    t_target       (t_source, t_ahead) datetime64[ns] 2013-09-17T12:00:00 ......\n",
+       "    t_past         (t_source, t_behind) datetime64[ns] 2013-09-13T12:00:00 .....\n",
        "    t_ahead_hours  (t_ahead) float64 0.0 0.0 1.0 1.0 2.0 ... 94.0 94.0 95.0 95.0\n",
        "Data variables:\n",
-       "    y_past         (t_source, t_behind) float32 0.34478724 ... 0.42559525\n",
-       "    nll            (t_source, t_ahead) float32 0.4811635 ... -0.26418364\n",
-       "    y_pred         (t_source, t_ahead) float32 0.39915967 ... 0.4406219\n",
-       "    y_pred_std     (t_source, t_ahead) float64 0.04404 0.04372 ... 0.06705\n",
-       "    y_true         (t_source, t_ahead) float32 0.46641305 ... 0.44257143</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0c238417-f224-4805-8a2d-0d522563bdba' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0c238417-f224-4805-8a2d-0d522563bdba' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>t_ahead</span>: 192</li><li><span class='xr-has-index'>t_behind</span>: 192</li><li><span class='xr-has-index'>t_source</span>: 7461</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3f2b90bd-4ebb-431d-8358-dbe09907c76f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f2b90bd-4ebb-431d-8358-dbe09907c76f' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t_source</span></div><div class='xr-var-dims'>(t_source)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-09-17T14:00:00 ... 2013-09-...</div><input id='attrs-5fa34d38-9359-40b8-b27a-f74d21bc3e96' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5fa34d38-9359-40b8-b27a-f74d21bc3e96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92d80c35-5472-413f-841e-23609e6b0150' class='xr-var-data-in' type='checkbox'><label for='data-92d80c35-5472-413f-841e-23609e6b0150' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2013-09-17T14:00:00.000000000&#x27;, &#x27;2013-09-17T14:30:00.000000000&#x27;,\n",
-       "       &#x27;2013-09-17T15:00:00.000000000&#x27;, ..., &#x27;2013-09-21T08:30:00.000000000&#x27;,\n",
+       "    y_past         (t_source, t_behind) float32 0.34182978 ... 0.42559525\n",
+       "    nll            (t_source, t_ahead) float32 -0.39930427 ... -0.5875193\n",
+       "    y_pred         (t_source, t_ahead) float32 0.4066002 ... 0.43110877\n",
+       "    y_pred_std     (t_source, t_ahead) float64 0.0581 0.05431 ... 0.04713\n",
+       "    y_true         (t_source, t_ahead) float32 0.39902174 ... 0.44257143</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-273eb36a-a585-495b-ac60-5ffb218677fd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-273eb36a-a585-495b-ac60-5ffb218677fd' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>t_ahead</span>: 192</li><li><span class='xr-has-index'>t_behind</span>: 192</li><li><span class='xr-has-index'>t_source</span>: 7465</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-80480814-5c7b-448a-a4f2-7843ae194735' class='xr-section-summary-in' type='checkbox'  checked><label for='section-80480814-5c7b-448a-a4f2-7843ae194735' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t_source</span></div><div class='xr-var-dims'>(t_source)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-09-17T12:00:00 ... 2013-09-...</div><input id='attrs-e82f791d-925a-484d-8668-eac1c315f1f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e82f791d-925a-484d-8668-eac1c315f1f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37cf9374-3fe9-4fe4-9780-ccdce4dd615c' class='xr-var-data-in' type='checkbox'><label for='data-37cf9374-3fe9-4fe4-9780-ccdce4dd615c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2013-09-17T12:00:00.000000000&#x27;, &#x27;2013-09-17T12:30:00.000000000&#x27;,\n",
+       "       &#x27;2013-09-17T13:00:00.000000000&#x27;, ..., &#x27;2013-09-21T08:30:00.000000000&#x27;,\n",
        "       &#x27;2013-09-21T09:00:00.000000000&#x27;, &#x27;2013-09-21T09:30:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t_ahead</span></div><div class='xr-var-dims'>(t_ahead)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00 ... 3 days 23:30:00</div><input id='attrs-69ebb898-9f41-4fe7-a29a-73061ad9ee0d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69ebb898-9f41-4fe7-a29a-73061ad9ee0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ea848d6-9639-4217-9e03-59278cacdd60' class='xr-var-data-in' type='checkbox'><label for='data-5ea848d6-9639-4217-9e03-59278cacdd60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([              0,   1800000000000,   3600000000000,   5400000000000,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t_ahead</span></div><div class='xr-var-dims'>(t_ahead)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00 ... 3 days 23:30:00</div><input id='attrs-8008975e-535e-4b78-b767-537f3d4c72f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8008975e-535e-4b78-b767-537f3d4c72f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1294f80a-d9b6-4f51-bbeb-04e79c017148' class='xr-var-data-in' type='checkbox'><label for='data-1294f80a-d9b6-4f51-bbeb-04e79c017148' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([              0,   1800000000000,   3600000000000,   5400000000000,\n",
        "         7200000000000,   9000000000000,  10800000000000,  12600000000000,\n",
        "        14400000000000,  16200000000000,  18000000000000,  19800000000000,\n",
        "        21600000000000,  23400000000000,  25200000000000,  27000000000000,\n",
@@ -2976,7 +3370,7 @@
        "       324000000000000, 325800000000000, 327600000000000, 329400000000000,\n",
        "       331200000000000, 333000000000000, 334800000000000, 336600000000000,\n",
        "       338400000000000, 340200000000000, 342000000000000, 343800000000000],\n",
-       "      dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t_behind</span></div><div class='xr-var-dims'>(t_behind)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>-4 days +00:00:00 ... -1 days +2...</div><input id='attrs-de516506-2f84-4b90-9a8e-5fa3d6d9753e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de516506-2f84-4b90-9a8e-5fa3d6d9753e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8c5e92c-9814-4005-b11a-ba4f66e3f6c6' class='xr-var-data-in' type='checkbox'><label for='data-f8c5e92c-9814-4005-b11a-ba4f66e3f6c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-345600000000000, -343800000000000, -342000000000000, -340200000000000,\n",
+       "      dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t_behind</span></div><div class='xr-var-dims'>(t_behind)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>-4 days +00:00:00 ... -1 days +2...</div><input id='attrs-1150c033-6629-4df5-a410-43161a194db9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1150c033-6629-4df5-a410-43161a194db9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acc19fce-b404-4e0f-a353-f3a2edbee77d' class='xr-var-data-in' type='checkbox'><label for='data-acc19fce-b404-4e0f-a353-f3a2edbee77d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-345600000000000, -343800000000000, -342000000000000, -340200000000000,\n",
        "       -338400000000000, -336600000000000, -334800000000000, -333000000000000,\n",
        "       -331200000000000, -329400000000000, -327600000000000, -325800000000000,\n",
        "       -324000000000000, -322200000000000, -320400000000000, -318600000000000,\n",
@@ -3024,18 +3418,18 @@
        "        -21600000000000,  -19800000000000,  -18000000000000,  -16200000000000,\n",
        "        -14400000000000,  -12600000000000,  -10800000000000,   -9000000000000,\n",
        "         -7200000000000,   -5400000000000,   -3600000000000,   -1800000000000],\n",
-       "      dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_target</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-09-17T14:00:00 ... 2013-09-...</div><input id='attrs-c7738813-6d9f-46dc-909b-7389d97b669f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c7738813-6d9f-46dc-909b-7389d97b669f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc07521c-0f25-43f5-992f-cc957620b41b' class='xr-var-data-in' type='checkbox'><label for='data-bc07521c-0f25-43f5-992f-cc957620b41b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2013-09-17T14:00:00.000000000&#x27;, &#x27;2013-09-17T14:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-09-17T15:00:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-09-21T12:30:00.000000000&#x27;, &#x27;2013-09-21T13:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-09-21T13:30:00.000000000&#x27;],\n",
-       "       [&#x27;2013-09-17T14:30:00.000000000&#x27;, &#x27;2013-09-17T15:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-09-17T15:30:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-09-21T13:00:00.000000000&#x27;, &#x27;2013-09-21T13:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-09-21T14:00:00.000000000&#x27;],\n",
-       "       [&#x27;2013-09-17T15:00:00.000000000&#x27;, &#x27;2013-09-17T15:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-09-17T16:00:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-09-21T13:30:00.000000000&#x27;, &#x27;2013-09-21T14:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-09-21T14:30:00.000000000&#x27;],\n",
+       "      dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_target</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-09-17T12:00:00 ... 2013-09-...</div><input id='attrs-3c9c50c5-dade-456e-8867-8e8cb1fdbcde' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c9c50c5-dade-456e-8867-8e8cb1fdbcde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7abe1526-29e8-4ca7-8dcc-394678521bd6' class='xr-var-data-in' type='checkbox'><label for='data-7abe1526-29e8-4ca7-8dcc-394678521bd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2013-09-17T12:00:00.000000000&#x27;, &#x27;2013-09-17T12:30:00.000000000&#x27;,\n",
+       "        &#x27;2013-09-17T13:00:00.000000000&#x27;, ...,\n",
+       "        &#x27;2013-09-21T10:30:00.000000000&#x27;, &#x27;2013-09-21T11:00:00.000000000&#x27;,\n",
+       "        &#x27;2013-09-21T11:30:00.000000000&#x27;],\n",
+       "       [&#x27;2013-09-17T12:30:00.000000000&#x27;, &#x27;2013-09-17T13:00:00.000000000&#x27;,\n",
+       "        &#x27;2013-09-17T13:30:00.000000000&#x27;, ...,\n",
+       "        &#x27;2013-09-21T11:00:00.000000000&#x27;, &#x27;2013-09-21T11:30:00.000000000&#x27;,\n",
+       "        &#x27;2013-09-21T12:00:00.000000000&#x27;],\n",
+       "       [&#x27;2013-09-17T13:00:00.000000000&#x27;, &#x27;2013-09-17T13:30:00.000000000&#x27;,\n",
+       "        &#x27;2013-09-17T14:00:00.000000000&#x27;, ...,\n",
+       "        &#x27;2013-09-21T11:30:00.000000000&#x27;, &#x27;2013-09-21T12:00:00.000000000&#x27;,\n",
+       "        &#x27;2013-09-21T12:30:00.000000000&#x27;],\n",
        "       ...,\n",
        "       [&#x27;2013-09-21T08:30:00.000000000&#x27;, &#x27;2013-09-21T09:00:00.000000000&#x27;,\n",
        "        &#x27;2013-09-21T09:30:00.000000000&#x27;, ...,\n",
@@ -3048,18 +3442,18 @@
        "       [&#x27;2013-09-21T09:30:00.000000000&#x27;, &#x27;2013-09-21T10:00:00.000000000&#x27;,\n",
        "        &#x27;2013-09-21T10:30:00.000000000&#x27;, ...,\n",
        "        &#x27;2013-09-25T08:00:00.000000000&#x27;, &#x27;2013-09-25T08:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-09-25T09:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_past</span></div><div class='xr-var-dims'>(t_source, t_behind)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-09-13T14:00:00 ... 2013-09-...</div><input id='attrs-36142117-2046-40e0-b664-f8128ad29158' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36142117-2046-40e0-b664-f8128ad29158' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-addc6dec-dbff-48f4-a03e-7f8b330d5a5c' class='xr-var-data-in' type='checkbox'><label for='data-addc6dec-dbff-48f4-a03e-7f8b330d5a5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2013-09-13T14:00:00.000000000&#x27;, &#x27;2013-09-13T14:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-09-13T15:00:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-09-17T12:30:00.000000000&#x27;, &#x27;2013-09-17T13:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-09-17T13:30:00.000000000&#x27;],\n",
-       "       [&#x27;2013-09-13T14:30:00.000000000&#x27;, &#x27;2013-09-13T15:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-09-13T15:30:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-09-17T13:00:00.000000000&#x27;, &#x27;2013-09-17T13:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-09-17T14:00:00.000000000&#x27;],\n",
-       "       [&#x27;2013-09-13T15:00:00.000000000&#x27;, &#x27;2013-09-13T15:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-09-13T16:00:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-09-17T13:30:00.000000000&#x27;, &#x27;2013-09-17T14:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-09-17T14:30:00.000000000&#x27;],\n",
+       "        &#x27;2013-09-25T09:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_past</span></div><div class='xr-var-dims'>(t_source, t_behind)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-09-13T12:00:00 ... 2013-09-...</div><input id='attrs-a208028e-10c3-47e0-89d8-f5495f6a00f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a208028e-10c3-47e0-89d8-f5495f6a00f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7a43be4-21f3-4a15-aad7-f40c4af9947d' class='xr-var-data-in' type='checkbox'><label for='data-e7a43be4-21f3-4a15-aad7-f40c4af9947d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2013-09-13T12:00:00.000000000&#x27;, &#x27;2013-09-13T12:30:00.000000000&#x27;,\n",
+       "        &#x27;2013-09-13T13:00:00.000000000&#x27;, ...,\n",
+       "        &#x27;2013-09-17T10:30:00.000000000&#x27;, &#x27;2013-09-17T11:00:00.000000000&#x27;,\n",
+       "        &#x27;2013-09-17T11:30:00.000000000&#x27;],\n",
+       "       [&#x27;2013-09-13T12:30:00.000000000&#x27;, &#x27;2013-09-13T13:00:00.000000000&#x27;,\n",
+       "        &#x27;2013-09-13T13:30:00.000000000&#x27;, ...,\n",
+       "        &#x27;2013-09-17T11:00:00.000000000&#x27;, &#x27;2013-09-17T11:30:00.000000000&#x27;,\n",
+       "        &#x27;2013-09-17T12:00:00.000000000&#x27;],\n",
+       "       [&#x27;2013-09-13T13:00:00.000000000&#x27;, &#x27;2013-09-13T13:30:00.000000000&#x27;,\n",
+       "        &#x27;2013-09-13T14:00:00.000000000&#x27;, ...,\n",
+       "        &#x27;2013-09-17T11:30:00.000000000&#x27;, &#x27;2013-09-17T12:00:00.000000000&#x27;,\n",
+       "        &#x27;2013-09-17T12:30:00.000000000&#x27;],\n",
        "       ...,\n",
        "       [&#x27;2013-09-17T08:30:00.000000000&#x27;, &#x27;2013-09-17T09:00:00.000000000&#x27;,\n",
        "        &#x27;2013-09-17T09:30:00.000000000&#x27;, ...,\n",
@@ -3072,7 +3466,7 @@
        "       [&#x27;2013-09-17T09:30:00.000000000&#x27;, &#x27;2013-09-17T10:00:00.000000000&#x27;,\n",
        "        &#x27;2013-09-17T10:30:00.000000000&#x27;, ...,\n",
        "        &#x27;2013-09-21T08:00:00.000000000&#x27;, &#x27;2013-09-21T08:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-09-21T09:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ahead_hours</span></div><div class='xr-var-dims'>(t_ahead)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 1.0 1.0 ... 94.0 95.0 95.0</div><input id='attrs-677e7048-732a-4505-a787-d1ecfd14b987' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-677e7048-732a-4505-a787-d1ecfd14b987' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbc40bb0-d585-41fa-b93a-9454bce40229' class='xr-var-data-in' type='checkbox'><label for='data-bbc40bb0-d585-41fa-b93a-9454bce40229' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.,  0.,  1.,  1.,  2.,  2.,  3.,  3.,  4.,  4.,  5.,  5.,  6.,\n",
+       "        &#x27;2013-09-21T09:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ahead_hours</span></div><div class='xr-var-dims'>(t_ahead)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 1.0 1.0 ... 94.0 95.0 95.0</div><input id='attrs-ce2be652-6cf0-4b22-bf86-1faf45797914' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce2be652-6cf0-4b22-bf86-1faf45797914' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad918656-dfbd-417f-9668-7fd33c490376' class='xr-var-data-in' type='checkbox'><label for='data-ad918656-dfbd-417f-9668-7fd33c490376' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.,  0.,  1.,  1.,  2.,  2.,  3.,  3.,  4.,  4.,  5.,  5.,  6.,\n",
        "        6.,  7.,  7.,  8.,  8.,  9.,  9., 10., 10., 11., 11., 12., 12.,\n",
        "       13., 13., 14., 14., 15., 15., 16., 16., 17., 17., 18., 18., 19.,\n",
        "       19., 20., 20., 21., 21., 22., 22., 23., 23., 24., 24., 25., 25.,\n",
@@ -3086,84 +3480,84 @@
        "       71., 72., 72., 73., 73., 74., 74., 75., 75., 76., 76., 77., 77.,\n",
        "       78., 78., 79., 79., 80., 80., 81., 81., 82., 82., 83., 83., 84.,\n",
        "       84., 85., 85., 86., 86., 87., 87., 88., 88., 89., 89., 90., 90.,\n",
-       "       91., 91., 92., 92., 93., 93., 94., 94., 95., 95.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c0ba88a8-83f8-4111-ada9-c1ed2e7dc2be' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c0ba88a8-83f8-4111-ada9-c1ed2e7dc2be' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_past</span></div><div class='xr-var-dims'>(t_source, t_behind)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.34478724 ... 0.42559525</div><input id='attrs-48dfc966-3342-42b4-93d8-e874d31c9461' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48dfc966-3342-42b4-93d8-e874d31c9461' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42f8f22a-206e-426c-a0fb-1f408ce4eaf2' class='xr-var-data-in' type='checkbox'><label for='data-42f8f22a-206e-426c-a0fb-1f408ce4eaf2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.34478724, 0.35642552, 0.36070213, ..., 0.3688913 , 0.38763043,\n",
-       "        0.37802175],\n",
-       "       [0.35642552, 0.36070213, 0.33378723, ..., 0.38763043, 0.37802175,\n",
-       "        0.46641305],\n",
-       "       [0.36070213, 0.33378723, 0.3802553 , ..., 0.37802175, 0.46641305,\n",
-       "        0.4613913 ],\n",
+       "       91., 91., 92., 92., 93., 93., 94., 94., 95., 95.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b52e4d8e-2eb4-4e95-82ec-1f4626898cc3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b52e4d8e-2eb4-4e95-82ec-1f4626898cc3' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_past</span></div><div class='xr-var-dims'>(t_source, t_behind)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.34182978 0.3743617 ... 0.42559525</div><input id='attrs-47b93b55-12fe-4d48-8864-7113e1e2b632' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47b93b55-12fe-4d48-8864-7113e1e2b632' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-416a08ca-f000-4e3c-8541-05c17b5d8434' class='xr-var-data-in' type='checkbox'><label for='data-416a08ca-f000-4e3c-8541-05c17b5d8434' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.34182978, 0.3743617 , 0.35202128, ..., 0.45023912, 0.37195653,\n",
+       "        0.41715217],\n",
+       "       [0.3743617 , 0.35202128, 0.341     , ..., 0.37195653, 0.41715217,\n",
+       "        0.39902174],\n",
+       "       [0.35202128, 0.341     , 0.34478724, ..., 0.41715217, 0.39902174,\n",
+       "        0.3688913 ],\n",
        "       ...,\n",
        "       [0.48439535, 0.4035349 , 0.2892093 , ..., 0.63783336, 0.7064286 ,\n",
        "        0.5955476 ],\n",
        "       [0.4035349 , 0.2892093 , 0.2788372 , ..., 0.7064286 , 0.5955476 ,\n",
        "        0.44709525],\n",
        "       [0.2892093 , 0.2788372 , 0.28755814, ..., 0.5955476 , 0.44709525,\n",
-       "        0.42559525]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nll</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.4811635 ... -0.26418364</div><input id='attrs-9aebe1d9-5a58-4b59-af1e-1242e3f4567c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9aebe1d9-5a58-4b59-af1e-1242e3f4567c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2327a4f-2d73-4ec9-a410-502dd6ab547e' class='xr-var-data-in' type='checkbox'><label for='data-f2327a4f-2d73-4ec9-a410-502dd6ab547e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.4811635 ,  0.42480052, -0.6300602 , ...,  0.3413955 ,\n",
-       "         1.9772846 ,  0.150496  ],\n",
-       "       [-0.14486027, -0.5012256 , -0.52454865, ...,  2.0219655 ,\n",
-       "         0.18210459,  1.4224399 ],\n",
-       "       [-0.17290866, -0.36061275, -0.0468756 , ...,  0.17795575,\n",
-       "         1.4165425 ,  0.04167783],\n",
+       "        0.42559525]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nll</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.39930427 ... -0.5875193</div><input id='attrs-f8dea2b7-a273-473d-8ec2-6d0753186515' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8dea2b7-a273-473d-8ec2-6d0753186515' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68ad0ca6-37b8-41d6-8ec8-40e12e7e4d0a' class='xr-var-data-in' type='checkbox'><label for='data-68ad0ca6-37b8-41d6-8ec8-40e12e7e4d0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.39930427, -0.4066378 , -0.5434241 , ..., -0.70005894,\n",
+       "         0.34137225,  0.25325173],\n",
+       "       [-0.30783594, -0.46890533, -0.53692436, ...,  0.3190887 ,\n",
+       "         0.23320925, -0.26906586],\n",
+       "       [-0.4261266 , -0.49373317,  1.63804   , ...,  0.21017814,\n",
+       "        -0.29087424,  0.7156215 ],\n",
        "       ...,\n",
-       "       [ 0.18067932, -0.2901914 , -0.1263206 , ..., -0.31996548,\n",
-       "         0.20850474,  0.98635066],\n",
-       "       [ 0.05589932, -0.39409316,  0.9730953 , ...,  0.19511038,\n",
-       "         0.8826847 ,  0.04325378],\n",
-       "       [-0.48566675,  0.64664865,  1.4173292 , ...,  0.718758  ,\n",
-       "        -0.03885388, -0.26418364]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_pred</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.39915967 0.39604387 ... 0.4406219</div><input id='attrs-1b2cb5bb-222c-4b83-b164-0b811f6e83ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b2cb5bb-222c-4b83-b164-0b811f6e83ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4eeb7432-2c9c-4ee8-be00-a4f150e77cc4' class='xr-var-data-in' type='checkbox'><label for='data-4eeb7432-2c9c-4ee8-be00-a4f150e77cc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.39915967, 0.39604387, 0.39427644, ..., 0.34928963, 0.34547558,\n",
-       "        0.34342968],\n",
-       "       [0.41556203, 0.41070288, 0.4129912 , ..., 0.34632754, 0.34424052,\n",
-       "        0.34255713],\n",
-       "       [0.43122843, 0.4306855 , 0.43051592, ..., 0.34412974, 0.3424234 ,\n",
-       "        0.345708  ],\n",
+       "       [ 1.1717722 ,  1.0530639 ,  0.2793969 , ..., -0.45947552,\n",
+       "        -0.11476445,  0.7189827 ],\n",
+       "       [ 0.9254643 ,  0.27463275, -0.58350575, ..., -0.09927058,\n",
+       "         0.7557026 , -0.08390903],\n",
+       "       [ 0.2534623 , -0.55001223, -0.80816877, ...,  0.7782191 ,\n",
+       "        -0.0669713 , -0.5875193 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_pred</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.4066002 0.38901886 ... 0.43110877</div><input id='attrs-08990f5e-673b-4a96-b289-e12ff78a5877' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08990f5e-673b-4a96-b289-e12ff78a5877' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef94bcd7-5331-483f-af57-def38b5ad222' class='xr-var-data-in' type='checkbox'><label for='data-ef94bcd7-5331-483f-af57-def38b5ad222' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.4066002 , 0.38901886, 0.3836266 , ..., 0.35888395, 0.35464624,\n",
+       "        0.35680822],\n",
+       "       [0.3938792 , 0.3774008 , 0.37440446, ..., 0.3537852 , 0.35594296,\n",
+       "        0.35910505],\n",
+       "       [0.37239552, 0.3650774 , 0.36374143, ..., 0.35499245, 0.35813135,\n",
+       "        0.36522728],\n",
        "       ...,\n",
-       "       [0.5092936 , 0.43061727, 0.33515006, ..., 0.66642237, 0.613157  ,\n",
-       "        0.5495886 ],\n",
-       "       [0.4792067 , 0.35560718, 0.280221  , ..., 0.6140604 , 0.5523728 ,\n",
-       "        0.4911341 ],\n",
-       "       [0.38628143, 0.28513035, 0.25171283, ..., 0.55784625, 0.5001565 ,\n",
-       "        0.4406219 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_pred_std</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.04404 0.04372 ... 0.06708 0.06705</div><input id='attrs-79a3e1b5-399b-4a69-ab7c-91d2386b80ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79a3e1b5-399b-4a69-ab7c-91d2386b80ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a033d9c-a59d-4060-ba33-f8015084d715' class='xr-var-data-in' type='checkbox'><label for='data-6a033d9c-a59d-4060-ba33-f8015084d715' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.04403544, 0.04371839, 0.04598843, ..., 0.03799878, 0.03539199,\n",
-       "        0.03416502],\n",
-       "       [0.04663819, 0.04664564, 0.0499321 , ..., 0.03548961, 0.03426702,\n",
-       "        0.03339754],\n",
-       "       [0.04966827, 0.05054267, 0.05369781, ..., 0.03424804, 0.03337231,\n",
-       "        0.03428287],\n",
+       "       [0.60592777, 0.5684328 , 0.45174578, ..., 0.67859375, 0.63273644,\n",
+       "        0.56903   ],\n",
+       "       [0.55782264, 0.45016435, 0.35072467, ..., 0.6316794 , 0.5678074 ,\n",
+       "        0.49656454],\n",
+       "       [0.44697702, 0.34681633, 0.30443627, ..., 0.5670123 , 0.49567437,\n",
+       "        0.43110877]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_pred_std</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0581 0.05431 ... 0.05086 0.04713</div><input id='attrs-458d66bf-2ac3-4abb-8463-2318d220b88d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-458d66bf-2ac3-4abb-8463-2318d220b88d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe740a3a-4fcf-4228-a9aa-776e6c602291' class='xr-var-data-in' type='checkbox'><label for='data-fe740a3a-4fcf-4228-a9aa-776e6c602291' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.05809981, 0.05430707, 0.05057289, ..., 0.03990596, 0.04132949,\n",
+       "        0.04317982],\n",
+       "       [0.05863498, 0.05367276, 0.05093383, ..., 0.04102965, 0.04281238,\n",
+       "        0.04410928],\n",
+       "       [0.05488988, 0.05166907, 0.04870024, ..., 0.04244705, 0.04368677,\n",
+       "        0.04446527],\n",
        "       ...,\n",
-       "       [0.07227182, 0.06516515, 0.04808651, ..., 0.06035801, 0.06489231,\n",
-       "        0.06337953],\n",
-       "       [0.06721438, 0.05184645, 0.03734122, ..., 0.06594639, 0.06490071,\n",
-       "        0.06349072],\n",
-       "       [0.05356707, 0.03891621, 0.03169011, ..., 0.06732977, 0.0670791 ,\n",
-       "        0.06705275]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_true</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.46641305 0.4613913 ... 0.44257143</div><input id='attrs-681d46fd-ddf5-48c5-a1ab-84025c15b0b2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-681d46fd-ddf5-48c5-a1ab-84025c15b0b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a56e267-27c5-4773-b6a1-81a3f4622f73' class='xr-var-data-in' type='checkbox'><label for='data-5a56e267-27c5-4773-b6a1-81a3f4622f73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.46641305, 0.4613913 , 0.38726085, ..., 0.29106522, 0.26052174,\n",
-       "        0.293     ],\n",
-       "       [0.4613913 , 0.38726085, 0.39980435, ..., 0.26052174, 0.293     ,\n",
-       "        0.26963043],\n",
-       "       [0.38726085, 0.39980435, 0.38015217, ..., 0.293     , 0.26963043,\n",
-       "        0.29778263],\n",
+       "       [0.12378779, 0.1136273 , 0.07299946, ..., 0.05473997, 0.05548793,\n",
+       "        0.05332477],\n",
+       "       [0.11952963, 0.07925848, 0.04873188, ..., 0.05541608, 0.053298  ,\n",
+       "        0.05082954],\n",
+       "       [0.08220471, 0.05017719, 0.03436997, ..., 0.05333869, 0.05086022,\n",
+       "        0.04712841]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_true</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.39902174 0.3688913 ... 0.44257143</div><input id='attrs-7c45a10e-059e-461e-978e-cef037b2dc46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c45a10e-059e-461e-978e-cef037b2dc46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33314178-755c-4a45-ac93-00c36d54ae57' class='xr-var-data-in' type='checkbox'><label for='data-33314178-755c-4a45-ac93-00c36d54ae57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.39902174, 0.3688913 , 0.38763043, ..., 0.34258696, 0.29363042,\n",
+       "        0.29704347],\n",
+       "       [0.3688913 , 0.38763043, 0.37802175, ..., 0.29363042, 0.29704347,\n",
+       "        0.31895652],\n",
+       "       [0.38763043, 0.37802175, 0.46641305, ..., 0.29704347, 0.31895652,\n",
+       "        0.29106522],\n",
        "       ...,\n",
        "       [0.44709525, 0.42559525, 0.38180953, ..., 0.6854762 , 0.6784286 ,\n",
        "        0.6520714 ],\n",
        "       [0.42559525, 0.38180953, 0.35152382, ..., 0.6784286 , 0.6520714 ,\n",
        "        0.54519045],\n",
        "       [0.38180953, 0.35152382, 0.32159525, ..., 0.6520714 , 0.54519045,\n",
-       "        0.44257143]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a10b6971-e195-49ab-aa07-e7109907215c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a10b6971-e195-49ab-aa07-e7109907215c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "        0.44257143]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba30709c-8f6f-46e1-a1ea-5f788639ff73' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ba30709c-8f6f-46e1-a1ea-5f788639ff73' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
-       "Dimensions:        (t_ahead: 192, t_behind: 192, t_source: 7461)\n",
+       "Dimensions:        (t_ahead: 192, t_behind: 192, t_source: 7465)\n",
        "Coordinates:\n",
-       "  * t_source       (t_source) datetime64[ns] 2013-09-17T14:00:00 ... 2013-09-...\n",
+       "  * t_source       (t_source) datetime64[ns] 2013-09-17T12:00:00 ... 2013-09-...\n",
        "  * t_ahead        (t_ahead) timedelta64[ns] 00:00:00 ... 3 days 23:30:00\n",
        "  * t_behind       (t_behind) timedelta64[ns] -4 days +00:00:00 ... -1 days +...\n",
-       "    t_target       (t_source, t_ahead) datetime64[ns] 2013-09-17T14:00:00 ......\n",
-       "    t_past         (t_source, t_behind) datetime64[ns] 2013-09-13T14:00:00 .....\n",
+       "    t_target       (t_source, t_ahead) datetime64[ns] 2013-09-17T12:00:00 ......\n",
+       "    t_past         (t_source, t_behind) datetime64[ns] 2013-09-13T12:00:00 .....\n",
        "    t_ahead_hours  (t_ahead) float64 0.0 0.0 1.0 1.0 2.0 ... 94.0 94.0 95.0 95.0\n",
        "Data variables:\n",
-       "    y_past         (t_source, t_behind) float32 0.34478724 ... 0.42559525\n",
-       "    nll            (t_source, t_ahead) float32 0.4811635 ... -0.26418364\n",
-       "    y_pred         (t_source, t_ahead) float32 0.39915967 ... 0.4406219\n",
-       "    y_pred_std     (t_source, t_ahead) float64 0.04404 0.04372 ... 0.06705\n",
-       "    y_true         (t_source, t_ahead) float32 0.46641305 ... 0.44257143"
+       "    y_past         (t_source, t_behind) float32 0.34182978 ... 0.42559525\n",
+       "    nll            (t_source, t_ahead) float32 -0.39930427 ... -0.5875193\n",
+       "    y_pred         (t_source, t_ahead) float32 0.4066002 ... 0.43110877\n",
+       "    y_pred_std     (t_source, t_ahead) float64 0.0581 0.05431 ... 0.04713\n",
+       "    y_true         (t_source, t_ahead) float32 0.39902174 ... 0.44257143"
       ]
      },
      "execution_count": 25,
@@ -3186,17 +3580,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 26,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T11:04:40.668624Z",
-     "start_time": "2020-10-11T11:04:39.888599Z"
+     "end_time": "2020-10-16T04:42:57.971613Z",
+     "start_time": "2020-10-16T04:42:57.594840Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3208,7 +3602,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3290,27 +3684,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T11:02:13.579139Z",
-     "start_time": "2020-10-11T11:02:13.325301Z"
+     "end_time": "2020-10-16T04:42:58.121923Z",
+     "start_time": "2020-10-16T04:42:57.972985Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0.5, 1.0, 'NLL vs time (no. samples=7461)')"
+       "Text(0.5, 1.0, 'NLL vs time (no. samples=7465)')"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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/HdLXZSm1fbgq1R7UqHaiwr9nX2/PWm0czRqlXm5FuXWJJunbTNImvL6Iz0bAqMwjcy4D1YwjyGt94NAYofwo5+S25DxB63aP4JXXVrHKsllT+rcPP1vV9uGq0V1bSylVUmlGlV25RuqPAacRJYN7E9uXEk214dywU804gsL6wM1OENXEGNqYXJwwspTnBa0ZCsk2bZwC1N61tZQ8G6zLtUGcZ2Zjgc+a2djEY1czOz/zyJzLQLWL4RTWB25mdVNojPW2N2SlVSeea4SQDgONvnBPmTSO7qIqwu4RqrutKERIN9dLJJ0OjDGzkyRtD4wzs19kHJtzDVNpzqFymjVRWjUxNqIxOSutOvFcQT3VbbXMetsQxU1ITRowF9JIfQnwKrB3/HoxcE5mETnXYPUuIA/ZNwqGxiga15iclWQPHBFdeIvlNRV4cc+1oRVRcoDXJzvcduqNbDftJk6ftWCN/Saee2vFWW+z+HeZMXthameKXBupE7Yzs6MkHQNgZkNSp0zB5dpBSJ1+b083L7/62lr/EZOyrEMP7cpa7QC2vLTaxHMhq+UlFZLFT+76W9n1HQoqzXpbjzzbdEJKEK9K6uH12Vy3A17JNCrnGiikm+iZh+5UcRxBlhOlVfrP3spVSpXkPS6inhJkaIek0Flva5HnPFchCeIM4GZgtKSfAL8hmrzPuWEhrYqjIFktUOh3vkmJfuxZlpvL9Z1v9SqlSvIeF1HLDLjVyrLDQJ7zXIWsSf0rSfcCexFVgZ5qZs9lEo1zDRKyPjCkdxMtNSHaC8tXMHbqjRUXf6lFqRvQ3p7uYVOtVEre4yLqaXsKkXXpLs95rkLaIADeDbyTqFTTTbQ+hHMtKXSgWam7vlL9zoHVU3I0qhdOs/vU5yGPOvTkDUKWQlfuq1ehhLttifacrJJgyHTf3wNOBhYAfwY+JskHyrmW1Yj1gUsV6Qsa0Qsnjz71eWh2G0Ty91qpZaCvt4dvHzWeJ849mOP2GlO2OjKpe4T49lHj655zqVql4guNu1ohbRDvBiaZ2aVmdilwELBvJtE41wDlLrgh3USLu2mWUu/daW596pus2TOThrY5FHqFFf4Ozpm8M49OP4gnzj14rYQhYP2RXav/fmZ8cNdc2oRKNYavNFura24jhFQxLQTGAE/Gr0cD9zc0CucaoNJAsy6JR6cfFHSsZDfN8Wfdksnyj+USTLOqLpqhuA49bWbS5OdqVU1X1pDke87knRs6R1Uj9JWp/ix0zQUaFne59SBukHQ9sCnwkKTbJP0WeAhor2kZ29xwWPOgXiHVNbV2Rcxq+cdSPZc2Wa+76VUXWct6ZtLkALhyhsNAw3JCqj+vuntRw76vXAni6w37Ftd0pe6m8pylNEuhA81q0ejlHyutDpdhl/rcZdVgfdYND5Qd5Fjw+LkH1/U9eavUowkaOyaj3GR9t5d7NCwC13CV7qYLs5S2g5ApEKD+5R/T1DJQKaSk0w49l0ppdIN14d8/ZCnWVp3csFqF0lgzGqxDGqndMBNyN12qW+VwElqtkMXyjwXVDlQK+bdph55LpTRyZtJqRki3S4N/0jF7jq5qey1Cx0G4YSS0uJ7VoK9mCa1WqHegWSMHKmVZ0hk2GjQzaTW9lYbr33g5hYboq+5exEqzTBZ/qipBxCvLjTYz78XUwnrX6w4qcjd60FezVKrDT2pUtUK9A5VCpvJu1wtZUrmZSUPPO7S3UvcI5dYdtVmy7mlVMUFIug04NP7sfOBZSbeb2aczi8rVpdo2qiyH6jdaoVoppOSQxd14l5TaCFiu3jd0ZPdwn1IjRL2N1NWMkm/3ZNsMIW0QG5vZUuBw4FIz2w14b7ZhuXrU0sjZ6stBFhojT5s5Pyg5ZNWVsdxApVIN1ln2sBpu6m3wD61WarduwnkJSRDrSNoK+BDgq8gNA6X+E/b19pSdf6hVVdMYmfUUCOUu5KUarNt5Ku9q1drgH9pbDTon2TZDSII4G5gNPGJm90h6E/DXbMNytUj+Jyqu8ChchPJc37ZW1TRGZl3nXGmgUtqgr3aeyrtayWlM0qT9/kJ7q0FnJdtmCJnu+2rg6sTrx4AjsgzKVa+4btZg9ZQGyfrYWfMGclvftpJSawWHaNa02CEDlZYMDgXNJtoOU3nXotDgP3bqjamT6a31+1NYu5q3OzReyQQh6XNm9jVJ3yVlYSUzOyXTyFxV0u6yC8kheRFqRC+SLBQ3Pg+tWBW8b/cIceahO2UV2loKF7hSczQZcNrM+RWP084D4kJs3NOd+vtbt3vEmg3RgcmhE5Nt1sqVIB6Kf85pRiCuPqG9Q/Jc37ac0DENxfK8a6x3wGort/s0Q6nfXzU3B+DVSlkqmSDM7Ib452XNC8fVqtQiN8UXodDPNUs1YxqK5X3XWOtcTOAXNajv91fg1UrZynSqDUkHSFoo6RFJU1Pe30HSHyW9IumzRe89IWmBpPmSvBRTQVrjadpFqNlz85dTz2LyrXCBrSWpDvfZRBupnpuSvBbs6TSZJQhJXcAFwIHAjsAxknYs+tjzwCmUnjl2PzMbb2b9WcXZLiZP6OOI3fpWD9jqkjhit761/vMU9yJJm5u/WUmi2sXkkwu2tMIFNmTq5aTenm4eP/dgv6jFqv39Abkv2NNpyvZiii/yp5jZt2o49h5EXWMfi4/1U+Aw4MHCB8zsGeAZScN7Dt4WMGveAD+fO7B6INdKM34+d4D+bd6YmiQmT+hL7VfezFHVoSWHLOaYaYSQHk0FzW5IHw6q+f1B/lWKnahsgjCzlZIOA2pJEH1AcuWKxcCeVexvwC2SDPiBmV2U9iFJJwEnAYwZM6aGMNtD2t14pYt93g3WpaatgOFTt5xcee70WQtWT5wmYL2RXSx/deWwnhAxa4Xf34SzbynbDtUKVYqdKGSyvjsknQ/MBF4ubDSzeyvsl9ZHoZpuKhPNbImkLYBfSXrYzH631gGjxHERQH9/fxsvtVJeLRf7vBqsCw3T5RY2GY53iq24ROVwccYhO5WcY2u43Cy0o5AEsXf88+zENgMq/Q9eTLR+dcHWwJLQwMxsSfzzGUnXEVVZrZUgXKSWi/2USePWmvgs6zu1kMnWfKqEzpOsbloyOOSlrhYRMpJ6vxqPfQ+wvaSxwABwNHBsyI6S1gdGmNmy+Pn7WTNBuSK1XOwnT+hjzpPPrzGffFrDdiNVapj2qoTOlayuc60hZLrvLYGvAKPM7MC4J9I7zOx/y+1nZq9J+gTRPE5dwCVm9oCkk+P3L5T0L0QD8TYCVkk6jajH02bAdYp65KwDXGlmN9d6ku0sOSXBxj3drNs9gsHlK4LuwKpp2G6Uco2RXpXgXGuRVZjkRNIvgUuBL5rZrpLWAeaZWctVtvb399ucOZ0zZCKtuqanuyu4C2ip2TGz6C1SaZGXLolHpx/U0O90zlUmaW6poQQh4yA2M7OfAasgKhkA4Z3XXWbK9VwK0axeTCED4so1WDvn8hGSIF6WtClxDyRJewEvZhqVC1LvBb5UA3ajezH5gjnODU8hCeIzwPXAdpLuAC4HPplpVC5IvRf40Ok56lVpEJQ3TDvXmiomCDObC7ybqLvrx4CdzOz+rANzlU2ZNI7urqLFf7rCF/9JTrshoqkg1u0ewadmzq97XqbC4kVjp95Y9nOtMm2Gc25tIb2Yfk80/uD3wB1m1tmT2JeR7FHUtH7cxVX3VVblF7oWFjd4F+ZlKnymGsVrO5QzHAfEOdcpQqqYjgcWEq0id6ekOZJqmXqjrc2aN8CUq6NlEY3oAjvl6vsynfhuxuyFrChacm3FKgtupC4+Vj0N3vB6qeG0mfODkoO3OzjX2kIGyj0maQh4NX7sB7w168BaWfHYA4nUeWRWrDLOvP6BhpciKnUZraUXUr0N3iEjpJO83cG51hdSxfQo8BxwJfC/wCfNrLoln9pI8YUwbcnEpErv1/v9aWrphVTvvEyhU3crPqYPiHOu9YXMxfQd4J3AMcAE4HZJvzOzRzONrEVVu4YBRAPS6rkgJkssI8rMgAq135lPmTSOKVfft0aVVfeI8AbvkOmae3u6mX/G+6uOzTmXj5AqpvOA8yRtAJwAnEk08V51K320iVqqb+pt8E2WGMolh7qnqiiefzdgzeVC8qrE10Nwbvip2Egt6RuS7gbuBsYDXwK2zziullXrILJqG3wLQkssXVJdK5XNmL1wrYblFSvLN3iHLhnqK4A5NzyFVDHdBXzNzJ7OOpjhYMqkccFdOIvVWvoIUe9UFbU0UoeOkPaurM4NTyFVTFdLOlTSPvGm283shozjam1lrsXdXWKDN6yT2qupltJHuVXXkurtMlpLI3WlhOc9lZwb3kKqmKYDpxKtJf0gcEq8rSOljT0o6OvtYcaRu3LGITs1bAqLkOTQiAtx2rQbIirBlBpV3bted8nj+Qhp54a/kCqmg4Hxha6tki4D5gHTsgysVZW6axZrjwpuxKjqvhJ39l0Sq8wa1mW0eAF58XpBqbiRvdAwXWoN4d6ebq9Wcq4NhCQIgF7g+fj5xtmEMjyEVsXUuzpWcjBc8mIN1a35UI1CzGnrRCQb2SuNw3ixwWM/nHP5CEkQ04F5kn5LdKO8Dx1WeigeOd3dpTUaqRtd117ctdVgdZJoxqpr5RqsQxqmGz1duHMuHyGN1FdJug3Yneg69Xkz+3vWgbWKtJHT3SPEJut1By/tWa20i3AhOTSj6mbjnu7UEeAb93R7w7RzHaRkgpD09qJNi+OfoySNMrN7swurdaRdrFesMtYbuQ7zvlR6VHA9M7s2a6W3UlRigFylaUN8TWnn2ku5EsQ3yrxnQEe0QtZysa536ux650Wq12CJxudyvGHaufZTspurme1X5tExV4JaVm2rd+rsZq30Vkoticgbpp1rPyUThKR3lttR0kaS3tb4kFpLLRfrequIild6a/aYgrRzrsQbpp1rP+WqmI6Q9DXgZmAu8CywLvBmojUhtiFar7qtJccHhLYnNKKKqN5usvUoHhNRiTdMO9eeSiYIM/uUpE2AI4EPAlsBQ8BDwA/M7A/NCXH4mTJp3FpjBaq9iOayfGlCIUGdPmsBV9z1t7XeX39kF8tfXelrOzjXxsp2czWzF4Afxo+OVEuDcy2ljnq/MyvnTN4ZgKvuXsRKM7okjtlz9Ortzrn2JatzFtBW0t/fb3PmzGnoMdNGFUO2YxLy+E7nXGeSNNfM+tPeqzhZX6fLY0xC3uMgnHMOPEGUNGveABPPvbXkzN5Z9tqppWutc841WrmR1IeX29HMrm18OK2huA2gWNa9dhrRyO2cc/Uq10h9SPxzC2Bv4Nb49X7AbUDbJohyE9LVMp1EtT2S6m3kds65RijXzfUEAEm/AHY0s6fi11sBFzQnvHxUs+ZDJbX2SMpzHIRzzkFYG8S2heQQexp4S0bxtIRGtgHUO+2Gc87lJSRB3CZptqSPSDoeuBH4bcZx5aqRcyF5jyTn3HAVsh7EJyT9K9FCQQAXmdl12YaVr0a2AVQz7Ubeo6edcy4pdMnRO4HXiKb5/lN24bSORrUBhPZIaqXR0845BwFVTJI+RJQUjgQ+BNwt6cisA2sXoTOzeluFc67VhJQgvgjsbmbPAEjaHPg1cE2WgeWt2dU93lbhnGs1IQliRCE5xP5Bm4/AbmR1T+ix8l5FzjnnioVc6G9O9GL6CFEvppuyDStfjazuCT1W3qvIOedcsYoJwsymAD8AdgF2JerF9PmQg0s6QNJCSY9Impry/g6S/ijpFUmfrWbfLDWyuif0WHmvIuecc8VCezHdAaygil5MkrqIRly/D1gM3CPpejN7MPGx54FTgMk17JuZRlb3VHMsHz3tnGslWfZi2gN4xMweM7NXgZ8ChyU/YGbPmNk9RMmnqn2z1MjqnmqOVZhBduzUG5l47q3MmjdQ9fc551yjZNmLqQ9YlHi9GNgzMK7gfSWdBJwEMGbMmMDDl9fIgXJpx9pvh82ZMXshn5o5f/WxAR8H4ZxrKVn2YlLKttDl64L3NbOLgIsgWlEu8PgVNbK6J3msUr2a1u0eUbIx2xOEcy4PIQniZkmzgavi10cR1otpMTA68XprYElgXPXs29JK9WoqNb24j4NwzuUlZC6mKZKOACYS3dmHzsV0D7C9pLHAAHA0cGxgXPXs29KqveD7OAjnXF6CejGZ2c+Bn1dzYDN7TdIngNlAF3CJmT0g6eT4/Qsl/QswB9gIWCXpNKK1J5am7VvN97eqjXu6GRwqbpNP5+MgnHN5qpgg4qVHv0q0spzih5nZRpX2NbObKKqOMrMLE8//TlR9FLRvO1Ba60qKWlauc865RgopQXwNOMTMHso6mE4wuLxy6aFLqnrlOueca7SQ3khPe3JonJA2hZXWsM5YzjlXs5IliLhqCWCOpJnALOCVwvtmdm22obWntPUhinWF1kM551yGylUxHZJ4vhx4f+K1AZ4gapAcOJc2BQfAMXuOTt3unHPNVDJBmNkJzQykkyQHzp0+awFX3b2IlWZ0SRyz52jOmbxzzhE65xzIStR3S/qcmX1N0ndJGcVsZqdkHVy1+vv7bc6cOXmH4Zxzw4akuWbWn/ZeuSqmQsN0x1xxm72KnHPOtbJyVUw3xD8va144+WnkKnLOOdcOyvViuoEyk+uZ2aGZRJSTciu/eYJwznWiclVMX29aFC2gkavIOedcOyhXxXR74bmkHmCMmVW/KPMw0chV5Jxzrh2ErCh3CDAfuDl+PV7S9RnH1XSNXEXOOefaQchUG2cSLQE6CGBm84FtswooL5Mn9DH98J3p6+1BRJPlTT98Z29/cM51rJDJ+l4zsxfVAdM/NHIVOeecG+5CEsSfJR0LdEnaHjgFuDPbsJxzzuUtpIrpk8BORBP1XQksBU7NMijnnHP5C0kQx5jZF81s9/jxReCsrANzzjmXr5AqpiMl/dPMfgIg6QJg3WzDcs45l7eQBHE4cL2kVcCBwPNm9vFsw3LOOZe3clNtvDHx8kSiBYPuAM6W9EYzez7j2JxzzuWoXAliLtFcTEr8PDh+GPCmzKNzzjmXm3JTbYxtZiDOOedaS7kqpv3N7NbE2tRr8DWpnXOuvZWrYno3cCtrrk1d4GtSO+dcmytXxXRG/HOttaklHZFlUM455/IXMlAuzbcaGoVzzrmWU2uCaP+Z+5xzrsPVmiBKLkXqnHOuPZTrxbSA9EQgYMvMInLOOdcSyvVi+kDTonDOOddyyvVierKZgTjnnGsttbZBOOeca3OeIJxzzqXyBOGccy5VxfUgSvRmehGYA5xjZv/IIjDnnHP5Clkw6JfASqL1qAGOjn8uBX5E+lxNzjnnhrmQBDHRzCYmXi+QdIeZTZR0XFaB5WHWvAFmzF7IksEhRvX2MGXSOCZP6Ms7LOecy0VIG8QGkvYsvJC0B7BB/PK1TKLKwax5A0y7dgEDg0MYMDA4xLRrFzBr3kDeoTnnXC5CEsSJwMWSHpf0BHAxcKKk9YHpWQbXTDNmL2Roxco1tg2tWMmM2Qtzisg55/JVsYrJzO4Bdpa0MSAzG0y8/bOsAmu2JYNDVW13zrl2V7EEIWljSd8EfgP8WtI34mRRkaQDJC2U9IikqSnvS9J34vfvl/T2xHtPSFogab6kOdWcVC1G9fZUtd0559pdSBXTJcAy4EPxYylwaaWdJHUBFwAHAjsCx0jasehjBwLbx4+TgO8Xvb+fmY03s/6AOOsyZdI4erq71tjW093FlEnjsv5q55xrSSG9mLYzs+QKcmdJmh+w3x7AI2b2GICknwKHAQ8mPnMYcLmZGXCXpF5JW5nZU2HhN06ht5L3YnLOuUhIghiS9E4z+wOApIlASMV8H7Ao8XoxsGfAZ/qAp4gG590iyYAfmNlFaV8i6SSi0gdjxowJCKu0yRP6PCE451wsJEGcDFyeaHd4ATg+YL+0VeeKR2SX+8xEM1siaQvgV5IeNrPfrfXhKHFcBNDf3+8LGTnnXINUbIMws/vMbFdgF2AXM5sA7B9w7MXA6MTrrYEloZ8xs8LPZ4DriKqsnHPONUnwZH1mttTMlsYvPx2wyz3A9pLGShpJNEXH9UWfuR7497g3017Ai2b2lKT1JW0IEI+3eD/w59BYnXPO1S+kiilNWtXQGszsNUmfAGYDXcAlZvaApJPj9y8EbgIOAh4BlgMnxLtvCVwnqRDjlWZ2c42xOuecq0GtCSKort/MbiJKAsltFyaeG/DxlP0eA3atMTbnnHMNUDJBSFpGeiIQ4KPHnHOuzZVbk3rDZgbinHOutfiKcs4551J5gnDOOZfKE4RzzrlUQQlC0jaS3hs/7ymMUXDOOde+Qqb7/k/gGuAH8aatgVkZxuScc64FhJQgPg5MJJrmGzP7K7BFlkE555zLX0iCeMXMXi28kLQOgQPlnHPODV8hCeJ2SV8AeiS9D7gauCHbsJxzzuUtJEFMBZ4FFgAfI5o64/Qsg3LOOZe/kLmYCqu+/TDrYJxzzrWOkBLEocBfJP1Y0sFxG4Rzzrk2F7Jg0AnAm4naHo4FHpV0cdaBOeecy1dQacDMVkj6JVHvpR6iaqcTswysWWbNG2DG7IUsGRxiVG8PUyaN83WpnXOOsIFyB0j6EdGiPkcCFwNbZRxXU8yaN8C0axcwMDiEAQODQ0y7dgGz5g3kHZpzzuUupA3iI0Qjp99iZseb2U1m9lqmUTXJjNkLGVqxco1tQytWMmP2wpwics651lGxisnMjm5GIHlYMjhU1XbnnOskJUsQkv4Q/1wmaWnisUzS0uaFmJ1RvekL45Xa7pxznaRkgjCzd8Y/NzSzjRKPDc1so+aFmJ0pk8bR0921xrae7i6mTBqXU0TOOdc6QhqpfxyybTiaPKGP6YfvTF9vDwL6enuYfvjO3ovJOecI6+a6U/JFPFBut2zCab7JE/o8ITjnXIpybRDTJC0Ddkm2PwBPA//XtAidc87lolwbxHQz2xCYUdT+sKmZTWtijM4553IQ0s11mqRNgO2BdRPbf5dlYM455/JVMUFIOhE4lWip0fnAXsAfgf0zjcw551yuQkZSnwrsDjxpZvsBE4jWh3DOOdfGQhLEP83snwCS3mBmDwM+UMA559pcSDfXxZJ6ieZj+pWkF4AlWQblnHMufyGN1P8aPz1T0m+BjYGbM43KOedc7kIaqd+YeLkg/mnZhOOcc65VhLRB3EvUKP0X4K/x88cl3SupbUZUO+ecW1NIgrgZOMjMNjOzTYEDgZ8B/w/4XpbBOeecy09Igug3s9mFF2Z2C7CPmd0FvCGzyJxzzuUqpBfT85I+D/w0fn0U8IKkLmBVZpE555zLVUgJ4liiUdSz4sfoeFsX8KGsAnPOOZevkG6uzwGflLSBmb1U9PYj2YTlnHMubyELBu0t6UHgwfj1rpK8cdo559pcSBXTt4BJwD8AzOw+YJ8sg3LOOZe/kASBmS0q2rQyZD9JB0haKOkRSVNT3pek78Tv3y/p7aH7Ouecy1ZIglgkaW/AJI2U9FngoUo7xb2cLiAaN7EjcIykHYs+diDROhPbAycB369iX+eccxkKSRAnAx8H+oDFwPj4dSV7AI+Y2WNm9ipRN9nDij5zGHC5Re4CeiVtFbivc865DIX2YvpwDcfuA5JVU4uBPQM+0xe4LwCSTiIqfTBmzJgawnTOOZemZIKQ9KUy+5mZfbnCsZW2X+BnQvYtBHIRcBFAf39/1ZMIzpo3wIzZC1kyOMSo3h6mTBrH5Al91R7GOefaTrkSxMsp29YHPgpsClRKEIuJBtUVbM3a60iU+szIgH3rNmveANOuXcDQiqjNfWBwiGnXRhPWepJwznW6km0QZvaNwoPoDr0HOIGoPeBNAce+B9he0lhJI4GjgeuLPnM98O9xb6a9gBfN7KnAfes2Y/bC1cmhYGjFSmbMXtjor3LOuWGnbBtEvBbEp4naIC4D3m5mL4Qc2Mxek/QJYDbRtByXmNkDkk6O378QuAk4iGhE9nKiBFRy3xrOr6wlg0NVbXfOuU5Srg1iBnA4Uelh55RpNioys5uIkkBy24WJ50aJHlFp+zbaqN4eBlKSwajeniy/1jnnhoVy3Vw/A4wCTgeWSFoaP5ZJWtqc8LI1ZdI4erq71tjW093FlEnjcorIOedaR8kShJkFjbIezgoN0d6LyTnn1hayHkRbmzyhzxOCc86laPtSgnPOudp4gnDOOZfKE4RzzrlUniCcc86l8gThnHMulaKxau1B0rPAkzXuvhnwXAPDGW78/P38/fw70zZmtnnaG22VIOohaY6Z9ecdR178/P38/fw79/xL8Som55xzqTxBOOecS+UJ4nUX5R1Azvz8O5ufv1uLt0E455xL5SUI55xzqTxBOOecS9XxCULSAZIWSnpE0tS848mapNGSfivpIUkPSDo13v5GSb+S9Nf45yZ5x5olSV2S5kn6Rfy6Y85fUq+kayQ9HP8dvKPDzv9T8d/+nyVdJWndTjr/anR0gpDUBVwAHAjsCBwjacd8o8rca8BnzOytwF7Ax+Nzngr8xsy2B34Tv25npwIPJV530vmfB9xsZjsAuxL9Hjri/CX1AacA/Wb2NqIljY+mQ86/Wh2dIIA9gEfM7DEzexX4KXBYzjFlysyeMrN74+fLiC4OfUTnfVn8scuAybkE2ASStgYOBi5ObO6I85e0EbAP8L8AZvaqmQ3SIecfWwfokbQOsB6whM46/2CdniD6gEWJ14vjbR1B0rbABOBuYEszewqiJAJskWNoWfs28DlgVWJbp5z/m4BngUvjKraLJa1Ph5y/mQ0AXwf+BjwFvGhmt9Ah51+tTk8QStnWEf1+JW0A/Bw4zczaYo3xEJI+ADxjZnPzjiUn6wBvB75vZhOAl+mg6pS4beEwYCwwClhf0nH5RtW6Oj1BLAZGJ15vTVTcbGuSuomSw0/M7Np489OStorf3wp4Jq/4MjYROFTSE0RVivtLuoLOOf/FwGIzuzt+fQ1RwuiU838v8LiZPWtmK4Brgb3pnPOvSqcniHuA7SWNlTSSqLHq+pxjypQkEdU/P2Rm30y8dT1wfPz8eOD/mh1bM5jZNDPb2sy2Jfr3vtXMjqNzzv/vwCJJ4+JN7wEepEPOn6hqaS9J68X/F95D1A7XKedflY4fSS3pIKI66S7gEjP7n3wjypakdwK/Bxbweh38F4jaIX4GjCH6T/RBM3s+lyCbRNK+wGfN7AOSNqVDzl/SeKIG+pHAY8AJRDeLnXL+ZwFHEfXomwecCGxAh5x/NTo+QTjnnEvX6VVMzjnnSvAE4ZxzLpUnCOecc6k8QTjnnEvlCcI551wqTxCuY0h6qej1RySdn1c8iTh+JOnIDI7bEufnhi9PEM7VKZ4V2Lm24wnCOUDSNpJ+I+n++OeYePsad/eFUoikfeN1Na4EFkhaX9KNku6L1xk4KuU7/lPSPfFnfi5pvcTb+0i6U9JjRd83Jd7n/niAV2H7LElz43UNTkpsP0HSXyTdTjStiHM18wThOkmPpPmFB3B24r3zgcvNbBfgJ8B3Ao63B/BFM9sROABYYma7xusM3Jzy+WvNbHczK6zB8NHEe1sB7wQ+AJwLIOn9wPbx94wHdpO0T/z5/zCz3YB+4BRJm8ZzCJ1FlBjeR7TGiXM18wThOsmQmY0vPIAvJd57B3Bl/PzHRBfrSv5kZo/HzxcA75X0VUnvMrMXUz7/Nkm/l7QA+DCwU+K9WWa2ysweBLaMt70/fswD7gV2IEoYECWF+4C7iCac3B7YE7gtnojuVWBmwDk4V9I6eQfgXIsqzEHzGvGNVDy528jEZ15e/WGzv0jaDTgImC7pFjNLllAAfgRMNrP7JH0E2Dfx3iuJ50r8nG5mP0geJJ5D6r3AO8xsuaTbgHWL4naubl6CcC5yJ9HsrhDd3f8hfv4EsFv8/DCgO21nSaOA5WZ2BdGCNG9P+diGwFPxdOsfDohpNvAf8dodSOqTtAWwMfBCnBx2IFo6FqIJF/eNq5u6gQ8GfIdzJXkJwrnIKcAlkqYQrbh2Qrz9h8D/SfoT0VrFL5fYf2dghqRVwArgv1I+899EF/EniaqkNiwXkJndIumtwB+jwgsvAccRtW+cLOl+YCFRNRNm9pSkM4E/Eq2Wdi/RLMXO1cRnc3XOOZfKq5icc86l8gThnHMulScI55xzqTxBOOecS+UJwjnnXCpPEM4551J5gnDOOZfq/wMGDGyXvqSk+gAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3336,27 +3730,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-11T11:02:13.812271Z",
-     "start_time": "2020-10-11T11:02:13.581935Z"
+     "end_time": "2020-10-16T04:42:58.284011Z",
+     "start_time": "2020-10-16T04:42:58.123318Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f2a6a4950d0>"
+       "<matplotlib.collections.PathCollection at 0x7f4d26bdffd0>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3470,10 +3864,128 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
+   "execution_count": 29,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T04:42:59.170079Z",
+     "start_time": "2020-10-16T04:42:58.285409Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# this is hard because we need to take the mean over t_ahead\n",
+    "# then group by t_source\n",
+    "d = ds_preds.mean('t_ahead').groupby('t_source').mean()\n",
+    "# And even then it's clearer with smoothing\n",
+    "d.plot.scatter('t_source', 'nll')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('NLL over time (lower is better)')\n",
+    "1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T04:43:00.044504Z",
+     "start_time": "2020-10-16T04:42:59.171439Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Lets zoom in and see how fast the solution expires\n",
+    "d = ds_preds.mean('t_ahead').groupby('t_source').mean().isel(t_source=slice(0, 24))\n",
+    "d.plot.scatter('t_source', 'nll')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('NLL over time (lower is better)')\n",
+    "1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-16T04:43:00.831224Z",
+     "start_time": "2020-10-16T04:43:00.045832Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f4d26a54320>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A scatter plot is easy with xarray\n",
+    "ds_preds.plot.scatter('y_true', 'y_pred', s=.01)"
+   ]
   },
   {
    "cell_type": "code",
@@ -3503,7 +4015,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/notebooks/c07_Recurrent_Neural_Networks/03_mike-seq2seq_timeseries.py b/notebooks/c07_Recurrent_Neural_Networks/03_mike-seq2seq_timeseries.py
index b27fbe1..46a57f2 100644
--- a/notebooks/c07_Recurrent_Neural_Networks/03_mike-seq2seq_timeseries.py
+++ b/notebooks/c07_Recurrent_Neural_Networks/03_mike-seq2seq_timeseries.py
@@ -79,6 +79,8 @@ def get_smartmeter_df(indir=Path('../../data/processed/smartmeter')):
     # Load csv files
     csv_files = sorted((indir/'halfhourly_dataset').glob('*.csv'))[:1]
     
+#     import pdb; pdb.set_trace() # you can use debugging in jupyter to interact with variables inside a function
+    
     # concatendate them
     df = pd.concat([pd.read_csv(f, parse_dates=[1], na_values=['Null']) for f in csv_files])
 
@@ -91,7 +93,7 @@ def get_smartmeter_df(indir=Path('../../data/processed/smartmeter')):
            'pressure', 'apparentTemperature', 'windSpeed', 
            'humidity']
     df_weather = df_weather[use_cols].set_index('time')
-    df_weather = df_weather.resample(freq).ffill()  # Resample to match energy data    
+    df_weather = df_weather.resample(freq).first().ffill()  # Resample to match energy data    
 
     # Join weather and energy data
     df = pd.concat([df, df_weather], 1).dropna()    
@@ -101,7 +103,10 @@ def get_smartmeter_df(indir=Path('../../data/processed/smartmeter')):
     holidays = set(df_hols['Bank holidays'].dt.round('D'))  
 
     time = df.index.to_series()
-    df['holiday'] = time.apply(lambda dt:dt.floor('D') in holidays).astype(int)
+    def is_holiday(dt):
+        return dt.floor('D') in holidays
+    df['holiday'] = time.apply(is_holiday).astype(int)
+
 
     # Add time features    
     df["month"] = time.dt.month
@@ -128,12 +133,28 @@ def get_smartmeter_df(indir=Path('../../data/processed/smartmeter')):
 # +
 df = get_smartmeter_df()
 
-df = df.resample(freq).mean().dropna() # Where empty we will backfill, this will respect causality, and mostly maintain the mean
+df = df.resample(freq).first().dropna() # Where empty we will backfill, this will respect causality, and mostly maintain the mean
 
 df = df.tail(int(max_rows)) # Just use last X rows
 df
 # -
 
+df.describe()
+
+#  <div class="alert alert-success">
+#   <h2>Exercise: Debug</h2>
+#
+#   Sometimes the best way to understand something is to interact with it. But if the code is inside a function it's difficult. Use the python debugger to play with the dataloading code.
+#     
+#    - insert the line `import pdb; pdb.set_trace()` in the function above
+#    - run the function definition
+#    - run the function again
+#    - you should be in a debugger, try pressing `?` then enter
+#    - try printing a variable
+#    - `q` to exit
+#
+#   </div>
+
 # Normalise
 from sklearn.preprocessing import StandardScaler
 input_columns = df.columns[1:]
@@ -151,7 +172,7 @@ def get_smartmeter_df(indir=Path('../../data/processed/smartmeter')):
 
 
 # +
-# split data
+# split data, with the test in the future
 n_split = -int(len(df)*0.2)
 df_train = df_norm[:n_split]
 df_test = df_norm[n_split:]
@@ -163,6 +184,8 @@ def get_smartmeter_df(indir=Path('../../data/processed/smartmeter')):
 plt.legend()
 # -
 
+
+
 # ## Pytorch Dataset
 #
 # A sequence to sequence model needs a sequence of inputs and a sequence of outputs. 
@@ -192,6 +215,7 @@ class SmartMeterSeq2SeqDataSet(torch.utils.data.Dataset):
     """
     
     def __init__(self, df, window_past=40, window_future=10, label_names=['energy(kWh/hh)']):
+        # Use numpy instead of pandas, for speed
         self.x = df.drop(columns=label_names).copy().values
         self.y = df[label_names].copy().values
         self.t = df[label_names].index.copy()
@@ -201,8 +225,7 @@ def __init__(self, df, window_past=40, window_future=10, label_names=['energy(kW
         self.label_names = label_names
 
     def get_components(self, i):
-        """Get  rows."""
-        # Get past and future rows
+        """Get past and future rows."""
         x = self.x[i : i + (self.window_past + self.window_future)].copy()
         y = self.y[i : i + (self.window_past + self.window_future)].copy()
         
@@ -221,12 +244,20 @@ def get_components(self, i):
             x_future[:, :8]=0
         return x_past, y_past, x_future, y_future
 
+
+    def __getitem__(self, i):
+        """This is how python implements square brackets"""
+        if i<0:
+            # Handle negative integers
+            i = len(self)+i
+        data = self.get_components(i)
+        # From dataframe to torch
+        return [d.astype(np.float32) for d in data]
+    
     
     def get_rows(self, i):
         """
-        A helper to put index and columns back on.
-        
-        We take them off originally for training speed
+        Output pandas dataframes for display purposes.
         """
         x_cols = list(self.columns)[1:] + ['tstp', 'is_past']
         x_past, y_past, x_future, y_future = self.get_components(i)
@@ -237,15 +268,6 @@ def get_rows(self, i):
         y_past = pd.DataFrame(y_past, columns=self.label_names, index=t_past)
         y_future = pd.DataFrame(y_future, columns=self.label_names, index=t_future)
         return x_past, y_past, x_future, y_future
-
-
-    def __getitem__(self, i):
-        if i<0:
-            # Handle negative integers
-            i = len(self)+i
-        data = self.get_components(i)
-        # From dataframe to torch
-        return [d.astype(np.float32) for d in data]
         
     def __len__(self):
         return len(self.x) - (self.window_past + self.window_future)
@@ -258,7 +280,9 @@ def __repr__(self):
 print(ds_train)
 print(ds_test)
 
-
+ds_train[0]
+len(ds_train)
+ds_train[0][2]
 
 # +
 # We can get rows
@@ -268,6 +292,7 @@ def __repr__(self):
 y_past['energy(kWh/hh)'].plot(label='past')
 y_future['energy(kWh/hh)'].plot(ax=plt.gca(), label='future')
 plt.legend()
+plt.ylabel('energy(kWh/hh)')
 
 # Notice we've added on two new columns tsp (time since present) and is_past
 x_past.tail()
@@ -281,6 +306,7 @@ def __repr__(self):
 ds_train[0][0].shape
 
 
+
 #  <div class="alert alert-success">
 #   <h2>Exercise: Dataset</h2>
 #
@@ -289,7 +315,15 @@ def __repr__(self):
 #   - get the 2nd to last element of the dataset
 #   - get the shape of each of the 4 returned elements of ds_train[0]
 #   - get the type of each of the 4 returned elements of ds_train[0]
+#   <details>
+#   <summary>
+#     <b>→ Hints</b>
+#   </summary>
+#       
+#   - `type(x)`
+#   - `x.shape`
 #
+#   </details>
 #   <br/>
 #   <br/>
 #   <details>
@@ -298,15 +332,27 @@ def __repr__(self):
 #   </summary>
 #
 #   ```python
-#   ds_train[-2]
+#   # x_past, y_past, x_future, y_future
+#   print(ds_train[-2])
 #   print([x.shape for x in ds_train[0]])
 #   print([type(x) for x in ds_train[0]])
 #   ```
+#   or 
+#
+#   ```python
+#   print(ds_train[-2])
+#   for x in ds_train[0]:
+#       print(x.shape)
+#       print(type(x))
+#   ```
 #
+#       
 #   </details>
 #
 #   </div>
 
+
+
 # ## LSTM: A minute of Theory
 #
 # This is a hand on course, not theory so we will look at a high level view of one type of RNN, the LSTM. But lets look at the theory for a moment, to get some broad idea of how they work
@@ -329,7 +375,7 @@ def __repr__(self):
 #
 # To understand more see these visualisations:
 #
-# - [distill.pub memorization in rnns](memorization-in-rnns)
+# - [distill.pub memorization in rnns](https://distill.pub/2019/memorization-in-rnns/)
 # - [Chris Olah Understanding LSTMs](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)
 #
 # And see these chapters:
@@ -340,50 +386,6 @@ def __repr__(self):
 
 # ## Model
 
-# +
-
-class Seq2SeqNet(nn.Module):
-    def __init__(self, input_size, output_size, hidden_size=32, lstm_layers=2, lstm_dropout=0, _min_std = 0.05):
-        super().__init__()
-        self._min_std = _min_std
-
-        self.encoder = nn.LSTM(
-            input_size=input_size + output_size,
-            hidden_size=hidden_size,
-            batch_first=True,
-            num_layers=lstm_layers,
-            dropout=lstm_dropout,
-        )
-        self.mean = nn.Linear(hidden_size, output_size)
-        self.std = nn.Linear(hidden_size, output_size)
-
-    def forward(self, past_x, past_y, future_x, future_y=None):
-        past = torch.cat([past_x, past_y], -1)
-        
-        # Placeholder
-        B, S, _ = future_x.shape
-        future_y_fake = torch.zeros((B, S, 1)).to(device)
-
-        future = torch.cat([future_x, future_y_fake], -1)
-        x = torch.cat([past, future], 1).detach()
-        
-        outputs, _ = self.encoder(x)        
-        
-        # We only want the future
-        outputs = outputs[:, -S:]
-        
-        # outputs: [B, T, num_direction * H]
-        mean = self.mean(outputs)      
-        
-        log_sigma = self.std(outputs)
-        log_sigma = torch.clamp(log_sigma, np.log(self._min_std), -np.log(self._min_std))
-
-        sigma = torch.exp(log_sigma)
-        y_dist = torch.distributions.Normal(mean, sigma)
-        return y_dist
-
-
-
 # +
 
 class Seq2SeqNet(nn.Module):
@@ -444,6 +446,8 @@ def forward(self, context_x, context_y, target_x, target_y=None):
 model
 # -
 
+
+
 # Init the optimiser
 optimizer = optim.Adam(model.parameters(), lr=1e-3)
 
@@ -465,8 +469,7 @@ def forward(self, context_x, context_y, target_x, target_y=None):
 #   <details>
 #   <summary><b>→ Hints</b></summary>
 #
-#   * One
-#   * Two
+#   * `x_past = torch.rand((batch_size, window_past, input_size)).to(device)`
 #
 #   </details>
 #
@@ -484,13 +487,29 @@ def forward(self, context_x, context_y, target_x, target_y=None):
 #     future_y = torch.rand((batch_size, window_future, output_size)).to(device)
 #     output = model(past_x, past_y, future_x, future_y)  
 #     print(output)
+#     
+#     # We can also use torchsummaryX to summarise the model size
+#     from deep_ml_curriculum.torchsummaryX import summary
+#     summary(model, past_x, past_y, future_x, future_y)
 #   ```
 #
 #   </details>
 #
 #   </div>
 
+# +
+
+past_x = torch.rand((batch_size, window_past, input_size)).to(device)
+future_x = torch.rand((batch_size, window_future, input_size)).to(device)
+past_y = torch.rand((batch_size, window_past, output_size)).to(device)
+future_y = torch.rand((batch_size, window_future, output_size)).to(device)
+output = model(past_x, past_y, future_x, future_y)  
+print(output)
 
+from deep_ml_curriculum.torchsummaryX import summary
+summary(model, past_x, past_y, future_x, future_y )
+1
+# -
 
 # ## Concept: Likelihood
 #
@@ -527,7 +546,7 @@ def forward(self, context_x, context_y, target_x, target_y=None):
 #  <div class="alert alert-success">
 #   <h2>Exercise Likelihood</h2>
 #
-#   If you have a normal distribution with mean 1 and std 3. What is the liklihood of 2?
+#   If you have a normal distribution with mean 1 and std 3. What is the likelihood of 2?
 #     
 #     
 #       
@@ -584,11 +603,16 @@ def train_epoch(ds, model, bs=128):
     )
 
     for batch in tqdm(load_train, leave=False, desc='train'):
-        # make it a pytorch gpu variable
+        # Send data to gpu
         x_past, y_past, x_future, y_future = [d.to(device) for d in batch]
 
+        # Discard previous gradients
         optimizer.zero_grad()
+        
+        # Run model
         y_dist = model(x_past, y_past, x_future, y_future)
+        
+        # Get loss, it's Negative Log Likelihood
         loss = -y_dist.log_prob(y_future).mean()
 
         # Backprop
@@ -610,9 +634,12 @@ def test_epoch(ds, model, bs=512):
                                                        pin_memory=False,
                                                        num_workers=num_workers)
     for batch in tqdm(load_test, leave=False, desc='test'):
+        # Send data to gpu
         x_past, y_past, x_future, y_future = [d.to(device) for d in batch]
         with torch.no_grad():
+            # Run model
             y_dist = model(x_past, y_past, x_future, y_future)
+            # Get loss, it's Negative Log Likelihood
             loss = -y_dist.log_prob(y_future).mean()
 
         test_loss.append(loss.item())
@@ -661,7 +688,7 @@ def training_loop(ds_train, ds_test, model, epochs=1, bs=128):
 #
 # But we also care about how far we were predicting into the future, so we have 3 dimensions: source time, target time, time ahead.
 #
-# It's hard to use pandas for data with more than 2 dimensions, so we will use xarray. Xarray has an interface similar to pandas but can have N dimensions.
+# It's hard to use pandas for data with virtual dimensions so we will use xarray. Xarray has an interface similar to pandas but can have N dimensions. It also allow coordinates which are virtual dimensions.
 
 # +
 import xarray as xr 
@@ -874,6 +901,26 @@ def plot_prediction(ds_preds, i):
 #
 #   </div>
 
+# +
+
+# this is hard because we need to take the mean over t_ahead
+# then group by t_source
+d = ds_preds.mean('t_ahead').groupby('t_source').mean()
+# And even then it's clearer with smoothing
+d.plot.scatter('t_source', 'nll')
+plt.xticks(rotation=45)
+plt.title('NLL over time (lower is better)')
+1
+# -
+
+# Lets zoom in and see how fast the solution expires
+d = ds_preds.mean('t_ahead').groupby('t_source').mean().isel(t_source=slice(0, 24))
+d.plot.scatter('t_source', 'nll')
+plt.xticks(rotation=45)
+plt.title('NLL over time (lower is better)')
+1
 
+# A scatter plot is easy with xarray
+ds_preds.plot.scatter('y_true', 'y_pred', s=.01)