diff --git a/PV_scenarios_2050/Scenarios_hourly.ipynb b/PV_scenarios_2050/Scenarios_hourly.ipynb
index 3b06b09..5d7be0d 100644
--- a/PV_scenarios_2050/Scenarios_hourly.ipynb
+++ b/PV_scenarios_2050/Scenarios_hourly.ipynb
@@ -1,1182 +1,1591 @@
 {
  "cells": [
   {
    "cell_type": "code",
    "execution_count": 1,
    "id": "extended-parallel",
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
     "import xarray as xr \n",
     "\n",
     "%matplotlib notebook\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from demand_mapping import get_yearly_sum\n",
     "\n",
     "import os\n",
     "import time"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "id": "sixth-protection",
    "metadata": {},
    "outputs": [],
    "source": [
     "SCENARIO_FP = '/work/hyenergy/output/solar_potential/scenarios_2050'"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "herbal-dealer",
+   "id": "hispanic-sampling",
    "metadata": {},
    "outputs": [],
    "source": [
-    "YEAR = 2019"
+    "YEAR = 2020"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "senior-bubble",
+   "execution_count": 4,
+   "id": "toxic-secret",
    "metadata": {},
    "outputs": [],
    "source": [
     "PV_FP = '/work/hyenergy/output/solar_potential/hourly_potential/'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "distributed-beach",
+   "execution_count": 5,
+   "id": "worth-bankruptcy",
    "metadata": {},
    "outputs": [],
    "source": [
     "hourly_FP_EW = '/scratch/walch/workdir_solar_EW_scenarios'\n",
     "hourly_FP    = '/scratch/walch/workdir_solar_hourly'"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "roman-exhibit",
    "metadata": {},
    "source": [
     "# Load data"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "mathematical-today",
    "metadata": {},
    "source": [
     "Load base scenario and EW roofs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 6,
    "id": "passing-initial",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
       "/home/walch/miniconda3/envs/py3_all/lib/python3.7/site-packages/numpy/lib/arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
       "  mask |= (ar1 == a)\n"
      ]
     }
    ],
    "source": [
     "roofs_EW = pd.read_csv(os.path.join(PV_FP, '%d_annual_roofs_flat_EW.csv' %(YEAR)), index_col = 0)\n",
     "roofs    = pd.read_csv(os.path.join(PV_FP, '%d_annual_roofs.csv'    %(YEAR)), index_col = 0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 7,
    "id": "nuclear-coverage",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
        "    }\n",
        "\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>available_area</th>\n",
        "      <th>available_area_std</th>\n",
        "      <th>BFS_NUMMER</th>\n",
        "      <th>suitable</th>\n",
        "      <th>Gt_kWh_m2</th>\n",
        "      <th>EPV_kWh</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DF_UID</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>2.115733</td>\n",
        "      <td>2.315795</td>\n",
        "      <td>2823.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1014.36084</td>\n",
-       "      <td>298.69815</td>\n",
+       "      <td>1031.4386</td>\n",
+       "      <td>304.00772</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td>12.098284</td>\n",
        "      <td>3.899759</td>\n",
        "      <td>2823.0</td>\n",
        "      <td>1</td>\n",
-       "      <td>809.07477</td>\n",
-       "      <td>1384.71190</td>\n",
+       "      <td>824.8346</td>\n",
+       "      <td>1413.21520</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td>15.384748</td>\n",
        "      <td>10.230719</td>\n",
        "      <td>2887.0</td>\n",
        "      <td>1</td>\n",
-       "      <td>903.29320</td>\n",
-       "      <td>1938.98750</td>\n",
+       "      <td>923.2536</td>\n",
+       "      <td>1983.89230</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td>391.328091</td>\n",
        "      <td>47.003696</td>\n",
        "      <td>2886.0</td>\n",
        "      <td>1</td>\n",
-       "      <td>1124.52440</td>\n",
-       "      <td>62210.24200</td>\n",
+       "      <td>1143.2656</td>\n",
+       "      <td>63308.27700</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td>335.269524</td>\n",
        "      <td>49.648887</td>\n",
        "      <td>2886.0</td>\n",
        "      <td>1</td>\n",
-       "      <td>1071.32620</td>\n",
-       "      <td>50888.32000</td>\n",
+       "      <td>1089.5498</td>\n",
+       "      <td>51804.19500</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "        available_area  available_area_std  BFS_NUMMER  suitable   Gt_kWh_m2  \\\n",
-       "DF_UID                                                                         \n",
-       "7             2.115733            2.315795      2823.0         0  1014.36084   \n",
-       "33           12.098284            3.899759      2823.0         1   809.07477   \n",
-       "49           15.384748           10.230719      2887.0         1   903.29320   \n",
-       "50          391.328091           47.003696      2886.0         1  1124.52440   \n",
-       "51          335.269524           49.648887      2886.0         1  1071.32620   \n",
+       "        available_area  available_area_std  BFS_NUMMER  suitable  Gt_kWh_m2  \\\n",
+       "DF_UID                                                                        \n",
+       "7             2.115733            2.315795      2823.0         0  1031.4386   \n",
+       "33           12.098284            3.899759      2823.0         1   824.8346   \n",
+       "49           15.384748           10.230719      2887.0         1   923.2536   \n",
+       "50          391.328091           47.003696      2886.0         1  1143.2656   \n",
+       "51          335.269524           49.648887      2886.0         1  1089.5498   \n",
        "\n",
        "            EPV_kWh  \n",
        "DF_UID               \n",
-       "7         298.69815  \n",
-       "33       1384.71190  \n",
-       "49       1938.98750  \n",
-       "50      62210.24200  \n",
-       "51      50888.32000  "
+       "7         304.00772  \n",
+       "33       1413.21520  \n",
+       "49       1983.89230  \n",
+       "50      63308.27700  \n",
+       "51      51804.19500  "
       ]
      },
-     "execution_count": 28,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "roofs_EW.head()"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "romance-collapse",
    "metadata": {},
    "source": [
     "Create annual dataset of full EW scenario"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "equivalent-penetration",
    "metadata": {},
    "outputs": [],
    "source": [
     "annual_EW = roofs.copy()\n",
     "annual_EW.loc[ roofs_EW.index ] = roofs_EW.copy()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "greater-trade",
-   "metadata": {},
-   "source": [
-    "Load hourly data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "assumed-galaxy",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PV_hourly_EW   = xr.open_mfdataset(os.path.join( hourly_FP_EW, 'tmp_%d' %YEAR, 'pv_potential_*.nc'), \n",
-    "                                   chunks = {'DF_UID' : 10000})\n",
-    "PV_hourly_base = xr.open_mfdataset(os.path.join( hourly_FP, 'tmp_%d' %YEAR, 'pv_potential_*.nc'),\n",
-    "                                   chunks = {'DF_UID' : 10000})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fabulous-hybrid",
+   "id": "acquired-winter",
    "metadata": {},
    "source": [
     "Load scenario file"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "sitting-quarter",
+   "execution_count": 9,
+   "id": "broadband-benjamin",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
       "/home/walch/miniconda3/envs/py3_all/lib/python3.7/site-packages/numpy/lib/arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
       "  mask |= (ar1 == a)\n"
      ]
     }
    ],
    "source": [
     "scenarios = pd.read_csv(os.path.join( SCENARIO_FP, 'MMH_cumsum_for_scenarios.csv' ), index_col = 0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "funky-deviation",
+   "execution_count": 10,
+   "id": "transparent-silicon",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
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        "<style scoped>\n",
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        "    }\n",
        "\n",
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        "        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>available_area</th>\n",
        "      <th>BFS_NUMMER</th>\n",
        "      <th>suitable</th>\n",
        "      <th>Gt_kWh_m2</th>\n",
        "      <th>EPV_kWh</th>\n",
        "      <th>EPV_cumsum_TWh</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DF_UID</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>6867695</th>\n",
        "      <td>78533.987001</td>\n",
        "      <td>5922.0</td>\n",
        "      <td>1</td>\n",
        "      <td>1076.4060</td>\n",
        "      <td>11923870.0</td>\n",
        "      <td>0.011924</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6843226</th>\n",
        "      <td>48406.995178</td>\n",
        "      <td>5919.0</td>\n",
        "      <td>1</td>\n",
        "      <td>1172.5608</td>\n",
        "      <td>7974506.5</td>\n",
        "      <td>0.019898</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6862160</th>\n",
        "      <td>44153.687130</td>\n",
        "      <td>5938.0</td>\n",
        "      <td>1</td>\n",
        "      <td>1120.8943</td>\n",
        "      <td>6962978.5</td>\n",
        "      <td>0.026861</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8712412</th>\n",
        "      <td>46188.641819</td>\n",
        "      <td>2534.0</td>\n",
        "      <td>1</td>\n",
        "      <td>1043.6494</td>\n",
        "      <td>6785268.5</td>\n",
        "      <td>0.033647</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6305482</th>\n",
        "      <td>39787.496676</td>\n",
        "      <td>2053.0</td>\n",
        "      <td>1</td>\n",
        "      <td>1173.7075</td>\n",
        "      <td>6531450.5</td>\n",
        "      <td>0.040178</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "         available_area  BFS_NUMMER  suitable  Gt_kWh_m2     EPV_kWh  \\\n",
        "DF_UID                                                                 \n",
        "6867695    78533.987001      5922.0         1  1076.4060  11923870.0   \n",
        "6843226    48406.995178      5919.0         1  1172.5608   7974506.5   \n",
        "6862160    44153.687130      5938.0         1  1120.8943   6962978.5   \n",
        "8712412    46188.641819      2534.0         1  1043.6494   6785268.5   \n",
        "6305482    39787.496676      2053.0         1  1173.7075   6531450.5   \n",
        "\n",
        "         EPV_cumsum_TWh  \n",
        "DF_UID                   \n",
        "6867695        0.011924  \n",
        "6843226        0.019898  \n",
        "6862160        0.026861  \n",
        "8712412        0.033647  \n",
        "6305482        0.040178  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "scenarios.head()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "greater-trade",
+   "metadata": {},
+   "source": [
+    "Load hourly data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "assumed-galaxy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PV_hourly_EW   = xr.open_mfdataset(os.path.join( hourly_FP_EW, 'tmp_%d' %YEAR, 'pv_potential_*.nc'), \n",
+    "                                   chunks = {'DF_UID' : 10000})\n",
+    "PV_hourly_base = xr.open_mfdataset(os.path.join( hourly_FP, 'tmp_%d' %YEAR, 'pv_potential_*.nc'),\n",
+    "                                   chunks = {'DF_UID' : 10000})"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "coral-arbitration",
    "metadata": {},
    "source": [
     "# Loop over communes (scenario: all roofs)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
    "id": "cutting-filter",
    "metadata": {},
    "outputs": [],
    "source": [
     "# SCENARIO DEFINITION\n",
     "MIN_AREA = 8\n",
     "# no restriction for roof tilt and roof aspect"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "id": "incorporate-shore",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processed commune 50 of 2309 in 141.11s\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-32-1d6d1af0bece>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;31m# Baseline scenario\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mDF_UIDs\u001b[0m          \u001b[0;34m=\u001b[0m \u001b[0mroofs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroofs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBFS_NUMMER\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mcommune\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mroofs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mavailable_area\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mMIN_AREA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mcommune_pot_base\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPV_hourly_base\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDF_UID\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDF_UIDs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpv_potential\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'DF_UID'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;31m# Baseline scenario (flat roofs only)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mto_series\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2688\u001b[0m         \"\"\"\n\u001b[1;32m   2689\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2690\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2691\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2692\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_masked_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mvalues\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    632\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m         \u001b[0;34m\"\"\"The array's data as a numpy.ndarray\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 634\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    635\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36mvalues\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    552\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m         \u001b[0;34m\"\"\"The variable's data as a numpy.ndarray\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 554\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_as_array_or_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    555\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36m_as_array_or_item\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    285\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"M\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m     \"\"\"\n\u001b[0;32m---> 83\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/dask/array/core.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[1;32m   1481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1482\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1483\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1484\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1485\u001b[0m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    281\u001b[0m         \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m         \"\"\"\n\u001b[0;32m--> 283\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraverse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    284\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    563\u001b[0m         \u001b[0mpostcomputes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_postcompute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 565\u001b[0;31m     \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    566\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrepack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpostcomputes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/dask/threaded.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(dsk, result, cache, num_workers, pool, **kwargs)\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0mget_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_thread_get_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0mpack_exception\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpack_exception\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m         \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m     )\n\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/dask/local.py\u001b[0m in \u001b[0;36mget_async\u001b[0;34m(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)\u001b[0m\n\u001b[1;32m    474\u001b[0m             \u001b[0;31m# Main loop, wait on tasks to finish, insert new ones\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    475\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"waiting\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ready\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"running\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 476\u001b[0;31m                 \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_info\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfailed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mqueue_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    477\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mfailed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    478\u001b[0m                     \u001b[0mexc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/dask/local.py\u001b[0m in \u001b[0;36mqueue_get\u001b[0;34m(q)\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mqueue_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/queue.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    168\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m                 \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_qsize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    171\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"'timeout' must be a non-negative number\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    294\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
    "source": [
     "commune_pot_EW = {}\n",
     "\n",
     "tt = time.time()\n",
     "for i, commune in enumerate(roofs.BFS_NUMMER.unique()):\n",
     "    \n",
     "    # Baseline scenario\n",
     "    DF_UIDs          = roofs[(roofs.BFS_NUMMER == commune) & (roofs.available_area >= MIN_AREA)].index\n",
     "    commune_pot_base = PV_hourly_base.sel(DF_UID = DF_UIDs).pv_potential.sum(dim = 'DF_UID').to_series()\n",
     "    \n",
     "    # Baseline scenario (flat roofs only)\n",
     "    DF_UIDs_flat          = roofs_EW[roofs_EW.index.isin( DF_UIDs )].index\n",
     "    commune_pot_flat_base = PV_hourly_base.sel(DF_UID = DF_UIDs_flat).pv_potential.sum(dim = 'DF_UID').to_series()\n",
     "    \n",
     "    # EW scenario (flat roofs only)\n",
     "    DF_UIDs_flat_EW     = roofs_EW[(roofs_EW.BFS_NUMMER == commune) & (roofs_EW.available_area >= MIN_AREA)].index\n",
     "    commune_pot_flat_EW = PV_hourly_EW.sel(DF_UID = DF_UIDs_flat_EW).pv_potential.sum(dim = 'DF_UID').to_series()\n",
     "   \n",
     "    commune_pot_EW[commune] = commune_pot_base - commune_pot_flat_base + commune_pot_flat_EW\n",
     "    \n",
     "    if ((i+1) % 50) == 0:\n",
     "        print('Processed commune %d of %d in %.2fs' %((i+1), len( roofs.BFS_NUMMER.unique()), time.time()-tt))\n",
     "        tt = time.time()\n",
     "        \n",
     "# Merge all commune potentials\n",
     "commune_pot_EW = pd.DataFrame(commune_pot_EW) "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "guided-compilation",
+   "id": "chubby-landscape",
    "metadata": {},
    "source": [
     "## Verify coherence with annual data"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "id": "proud-scope",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "41.447183715959234"
       ]
      },
      "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "annual_EW[annual_EW.available_area >= 8].dropna(subset = ['BFS_NUMMER']).EPV_kWh.sum() / 1e9"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "id": "refined-installation",
    "metadata": {},
    "outputs": [
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        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray ()&gt;\n",
        "array(41.44718838)</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-14d2f9ba-0094-437e-a453-7e1089e43300' class='xr-array-in' type='checkbox' checked><label for='section-14d2f9ba-0094-437e-a453-7e1089e43300' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>41.45</span></div><div class='xr-array-data'><pre>array(41.44718838)</pre></div></div></li><li class='xr-section-item'><input id='section-f3ac9488-aab7-4539-a216-c6c7f329067f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f3ac9488-aab7-4539-a216-c6c7f329067f' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-834b77ac-8f1f-4b90-974e-9f717c9cf018' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-834b77ac-8f1f-4b90-974e-9f717c9cf018' 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>"
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        "<xarray.DataArray ()>\n",
        "array(41.44718838)"
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      },
      "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "get_yearly_sum(commune_pot_EW.sum(axis = 1).to_xarray(), week_weighting = False) / 1e12"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "spoken-agriculture",
    "metadata": {},
    "source": [
     "# Save output data"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "id": "specified-haven",
    "metadata": {},
    "outputs": [],
    "source": [
     "outfile = ( os.path.join( SCENARIO_FP, '%d_PV_communes_all.csv' %YEAR) )\n",
     "(commune_pot_EW/1e6).to_csv( outfile )"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "accessible-strain",
    "metadata": {},
    "source": [
     "# Scenarios:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 36,
    "id": "engaging-tracy",
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
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        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>available_area</th>\n",
        "      <th>BFS_NUMMER</th>\n",
        "      <th>suitable</th>\n",
        "      <th>Gt_kWh_m2</th>\n",
        "      <th>EPV_kWh</th>\n",
        "      <th>EPV_cumsum_TWh</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DF_UID</th>\n",
        "      <th></th>\n",
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       "text/plain": [
        "         available_area  BFS_NUMMER  suitable  Gt_kWh_m2     EPV_kWh  \\\n",
        "DF_UID                                                                 \n",
        "6867695    78533.987001      5922.0         1  1076.4060  11923870.0   \n",
        "6843226    48406.995178      5919.0         1  1172.5608   7974506.5   \n",
        "6862160    44153.687130      5938.0         1  1120.8943   6962978.5   \n",
        "8712412    46188.641819      2534.0         1  1043.6494   6785268.5   \n",
        "6305482    39787.496676      2053.0         1  1173.7075   6531450.5   \n",
        "\n",
        "         EPV_cumsum_TWh  \n",
        "DF_UID                   \n",
        "6867695        0.011924  \n",
        "6843226        0.019898  \n",
        "6862160        0.026861  \n",
        "8712412        0.033647  \n",
        "6305482        0.040178  "
       ]
      },
      "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "scenarios.head()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 38,
    "id": "suffering-oliver",
    "metadata": {},
    "outputs": [],
    "source": [
     "def aggregate_scenario_to_communes(roofs_base, roofs_flat_EW, PV_hourly_base, PV_hourly_EW, DF_UID_scenario,\n",
     "                                   MIN_AREA = 8):\n",
     "    # Select all roofs in DF_UID_scenario for the EW option for flat roofs and all roofs < 10° considered flat \n",
     "    # All roofs in DF_UID_scenario are considered suitable except those with an available area < MIN_AREA\n",
     "    # IMPORTANT: the roofs_base and roofs_flat_EW must have the DF_UID as index,\n",
     "    # and the BFS_NUMMER as commune ID and the available_area as attributes\n",
     "    \n",
     "    commune_pot_EW = {}\n",
     "    suitable_DF_UIDs_commune = lambda roofs, communeID: roofs[ (roofs.BFS_NUMMER == communeID) \n",
     "                                                             & (roofs.available_area >= MIN_AREA)\n",
     "                                                             & (roofs.index.isin( DF_UID_scenario )) ].index\n",
     "    commune_IDs = roofs_base.BFS_NUMMER.unique()\n",
     "\n",
     "    tt = time.time()\n",
     "    for i, commune in enumerate(commune_IDs):\n",
     "\n",
     "        # Baseline scenario\n",
     "        DF_UIDs          = suitable_DF_UIDs_commune( roofs_base, commune ) \n",
     "        commune_pot_base = PV_hourly_base.sel(DF_UID = DF_UIDs).pv_potential.sum(dim = 'DF_UID').to_series()\n",
     "\n",
     "        # Baseline scenario (flat roofs only)\n",
     "        DF_UIDs_flat          = roofs_flat_EW[ roofs_flat_EW.index.isin( DF_UIDs ) ].index\n",
     "        commune_pot_flat_base = PV_hourly_base.sel(DF_UID = DF_UIDs_flat).pv_potential.sum(dim = 'DF_UID').to_series()\n",
     "\n",
     "        # EW scenario (flat roofs only)\n",
     "        DF_UIDs_flat_EW     = suitable_DF_UIDs_commune( roofs_flat_EW, commune ) \n",
     "        commune_pot_flat_EW = PV_hourly_EW.sel( DF_UID = DF_UIDs_flat_EW).pv_potential.sum(dim = 'DF_UID').to_series()\n",
     "\n",
     "        commune_pot_EW[commune] = commune_pot_base - commune_pot_flat_base + commune_pot_flat_EW\n",
     "\n",
     "        if ((i+1) % 20) == 0:\n",
     "            print('Processed commune %d of %d in %.2fs' %((i+1), len( commune_IDs ), time.time()-tt))\n",
     "            tt = time.time()\n",
     "\n",
     "    # Merge all commune potentials\n",
     "    return pd.DataFrame(commune_pot_EW) / 1e6 "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "id": "exterior-perception",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "for target_PV in [15, 25, 35]:\n",
+    "    tt = time.time()\n",
+    "    scenario_UIDs = scenarios[scenarios.EPV_cumsum_TWh <= target_PV].index.values\n",
+    "    \n",
+    "    commune_PV_scenario = aggregate_scenario_to_communes( roofs, roofs_EW, \n",
+    "                                                          PV_hourly_base, PV_hourly_EW, scenario_UIDs )\n",
+    "    \n",
+    "    outfile = ( os.path.join( SCENARIO_FP, '%d_PV_communes_%d_TWh.csv' %(YEAR, target_PV) ) )\n",
+    "    commune_PV_scenario.to_csv( outfile )\n",
+    "    print('Computed and saved %s in %.2fs' %(outfile, time.time()-tt))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mature-feeding",
    "metadata": {},
+   "source": [
+    "# CHECK AND POSTPROCESS RESULTS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "toxic-devil",
+   "metadata": {},
+   "source": [
+    "## CORRECT ALL TIMESTAMPS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "municipal-collapse",
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Processed commune 20 of 2309 in 55.33s\n",
-      "Processed commune 40 of 2309 in 50.15s\n",
-      "Processed commune 60 of 2309 in 46.93s\n",
-      "Processed commune 80 of 2309 in 37.45s\n",
-      "Processed commune 100 of 2309 in 67.89s\n"
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2010-01-01 08:00:00  0.401085  0.056092  0.202465  0.140103  0.075368\n",
+      "2010-01-01 09:00:00  1.164141  0.164486  0.576082  0.451822  0.242240\n",
+      "2010-01-01 10:00:00  1.203159  0.166736  0.596952  0.491640  0.262022\n",
+      "2010-01-01 11:00:00  0.813273  0.108335  0.399956  0.333870  0.166874\n",
+      "2010-01-01 12:00:00  0.740450  0.101305  0.377458  0.364116  0.146045\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2010_PV_communes_all.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2010-01-01 08:00:00  0.139721  0.004495  0.044514  0.029658  0.022811\n",
+      "2010-01-01 09:00:00  0.401282  0.012763  0.126100  0.093042  0.070956\n",
+      "2010-01-01 10:00:00  0.412992  0.012868  0.129441  0.100378  0.076367\n",
+      "2010-01-01 11:00:00  0.276653  0.008163  0.086204  0.067542  0.048346\n",
+      "2010-01-01 12:00:00  0.251024  0.007273  0.085982  0.073015  0.042226\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2010_PV_communes_15_TWh.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2010-01-01 08:00:00  0.219235  0.015912  0.094669  0.064531  0.037062\n",
+      "2010-01-01 09:00:00  0.632191  0.045770  0.265181  0.205052  0.117075\n",
+      "2010-01-01 10:00:00  0.650347  0.045894  0.272667  0.221941  0.125833\n",
+      "2010-01-01 11:00:00  0.435883  0.029551  0.181943  0.149883  0.079845\n",
+      "2010-01-01 12:00:00  0.394644  0.027337  0.176000  0.162264  0.069758\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2010_PV_communes_25_TWh.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2010-01-01 08:00:00  0.325245  0.040503  0.159367  0.103943  0.057621\n",
+      "2010-01-01 09:00:00  0.939520  0.117666  0.449473  0.331565  0.183338\n",
+      "2010-01-01 10:00:00  0.967615  0.117847  0.462805  0.359263  0.197160\n",
+      "2010-01-01 11:00:00  0.651655  0.076074  0.309021  0.243037  0.125143\n",
+      "2010-01-01 12:00:00  0.592996  0.071173  0.293311  0.263924  0.109422\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2010_PV_communes_35_TWh.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2016-01-01 08:00:00  1.758777  0.231467  0.900034  0.932055  0.403170\n",
+      "2016-01-01 09:00:00  3.832418  0.521839  1.896139  1.218903  0.603852\n",
+      "2016-01-01 10:00:00  4.478135  0.636215  2.429833  1.297116  0.601701\n",
+      "2016-01-01 11:00:00  5.912643  0.813390  3.180205  1.603402  0.922647\n",
+      "2016-01-01 12:00:00  6.161373  0.832591  3.041107  1.077894  0.818499\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2016_PV_communes_all.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2016-01-01 08:00:00  0.522388  0.017544  0.185117  0.162322  0.104965\n",
+      "2016-01-01 09:00:00  1.256816  0.034911  0.408935  0.240545  0.168133\n",
+      "2016-01-01 10:00:00  1.540767  0.042586  0.515152  0.266938  0.175489\n",
+      "2016-01-01 11:00:00  2.048467  0.053153  0.677064  0.336474  0.263489\n",
+      "2016-01-01 12:00:00  2.143245  0.054894  0.629980  0.230919  0.227330\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2016_PV_communes_15_TWh.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2016-01-01 08:00:00  0.894772  0.071482  0.454805  0.382917  0.187671\n",
+      "2016-01-01 09:00:00  2.048104  0.153237  0.917248  0.540140  0.292212\n",
+      "2016-01-01 10:00:00  2.441250  0.185171  1.149586  0.590217  0.293561\n",
+      "2016-01-01 11:00:00  3.246967  0.238535  1.517805  0.742061  0.452038\n",
+      "2016-01-01 12:00:00  3.409071  0.245463  1.441528  0.512996  0.399065\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2016_PV_communes_25_TWh.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2016-01-01 08:00:00  1.424100  0.179998  0.751585  0.690364  0.318581\n",
+      "2016-01-01 09:00:00  3.158933  0.399646  1.566436  0.913073  0.472970\n",
+      "2016-01-01 10:00:00  3.716360  0.484625  2.001229  0.968715  0.463363\n",
+      "2016-01-01 11:00:00  4.949354  0.624556  2.646840  1.205334  0.722323\n",
+      "2016-01-01 12:00:00  5.168808  0.639755  2.529564  0.817251  0.643744\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2016_PV_communes_35_TWh.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2017-01-01 08:00:00  1.941675  0.244167  0.945904  1.068332  0.459955\n",
+      "2017-01-01 09:00:00  4.234482  0.580368  2.173382  2.049036  0.997748\n",
+      "2017-01-01 10:00:00  6.083260  0.831546  3.106133  2.815748  1.423663\n",
+      "2017-01-01 11:00:00  7.183801  0.961171  3.669805  3.304552  1.682798\n",
+      "2017-01-01 12:00:00  7.293878  0.965039  3.743707  3.318602  1.715047\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2017_PV_communes_all.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2017-01-01 08:00:00  0.571949  0.018838  0.192838  0.181782  0.118060\n",
+      "2017-01-01 09:00:00  1.371867  0.038077  0.471620  0.391441  0.269552\n",
+      "2017-01-01 10:00:00  2.081174  0.053435  0.702161  0.590688  0.403140\n",
+      "2017-01-01 11:00:00  2.496082  0.061117  0.823975  0.707763  0.476034\n",
+      "2017-01-01 12:00:00  2.533651  0.058868  0.821198  0.712047  0.479191\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2017_PV_communes_15_TWh.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2017-01-01 08:00:00  0.985623  0.076213  0.481377  0.433417  0.213187\n",
+      "2017-01-01 09:00:00  2.252107  0.172403  1.064905  0.885186  0.482897\n",
+      "2017-01-01 10:00:00  3.333039  0.245204  1.532742  1.296301  0.707022\n",
+      "2017-01-01 11:00:00  3.970631  0.283417  1.805473  1.539809  0.840101\n",
+      "2017-01-01 12:00:00  4.031252  0.286061  1.833543  1.548253  0.858520\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2017_PV_communes_25_TWh.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2017-01-01 08:00:00  1.572168  0.190809  0.793717  0.790996  0.365624\n",
+      "2017-01-01 09:00:00  3.495904  0.448881  1.812123  1.543845  0.797774\n",
+      "2017-01-01 10:00:00  5.099389  0.645156  2.605747  2.182861  1.147001\n",
+      "2017-01-01 11:00:00  6.056603  0.745851  3.088106  2.566463  1.361562\n",
+      "2017-01-01 12:00:00  6.150407  0.747512  3.153779  2.572082  1.393616\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2017_PV_communes_35_TWh.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2018-01-01 08:00:00  1.636478  0.170674  0.665772  0.735138  0.376623\n",
+      "2018-01-01 09:00:00  3.418323  0.373668  1.284704  1.436628  0.658652\n",
+      "2018-01-01 10:00:00  5.031509  0.674925  2.429542  2.414174  1.082087\n",
+      "2018-01-01 11:00:00  6.074809  0.846555  3.242469  2.933829  1.461862\n",
+      "2018-01-01 12:00:00  5.463978  0.708292  2.755114  2.507379  1.318279\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2018_PV_communes_all.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2018-01-01 08:00:00  0.494437  0.011853  0.146090  0.132909  0.100108\n",
+      "2018-01-01 09:00:00  1.117743  0.024066  0.308783  0.279054  0.183846\n",
+      "2018-01-01 10:00:00  1.728149  0.043920  0.566388  0.501539  0.310157\n",
+      "2018-01-01 11:00:00  2.114725  0.054757  0.728137  0.624372  0.416258\n",
+      "2018-01-01 12:00:00  1.889376  0.044718  0.609174  0.532694  0.371369\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2018_PV_communes_15_TWh.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2018-01-01 08:00:00  0.837434  0.050304  0.335945  0.307675  0.176317\n",
+      "2018-01-01 09:00:00  1.818892  0.106052  0.643629  0.623224  0.318628\n",
+      "2018-01-01 10:00:00  2.750627  0.195733  1.204365  1.098594  0.534082\n",
+      "2018-01-01 11:00:00  3.350614  0.247352  1.584405  1.359962  0.726564\n",
+      "2018-01-01 12:00:00  2.998912  0.205657  1.335375  1.160761  0.654885\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2018_PV_communes_25_TWh.csv\n"
      ]
     },
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-39-9445bb18ee8c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     commune_PV_scenario = aggregate_scenario_to_communes( roofs, roofs_EW, \n\u001b[0;32m----> 6\u001b[0;31m                                                           PV_hourly_base, PV_hourly_EW, scenario_UIDs )\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0moutfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mSCENARIO_FP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'MMH_PV_communes_%d_TWh.csv'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0mtarget_PV\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-38-69d9c06ffba4>\u001b[0m in \u001b[0;36maggregate_scenario_to_communes\u001b[0;34m(roofs_base, roofs_flat_EW, PV_hourly_base, PV_hourly_EW, DF_UID_scenario, MIN_AREA)\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0;31m# EW scenario (flat roofs only)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mDF_UIDs_flat_EW\u001b[0m     \u001b[0;34m=\u001b[0m \u001b[0msuitable_DF_UIDs_commune\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mroofs_flat_EW\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcommune\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m         \u001b[0mcommune_pot_flat_EW\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPV_hourly_EW\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msel\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mDF_UID\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDF_UIDs_flat_EW\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpv_potential\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'DF_UID'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0mcommune_pot_EW\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcommune\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcommune_pot_base\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mcommune_pot_flat_base\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcommune_pot_flat_EW\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mto_series\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2688\u001b[0m         \"\"\"\n\u001b[1;32m   2689\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2690\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2691\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2692\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_masked_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mvalues\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    632\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m         \u001b[0;34m\"\"\"The array's data as a numpy.ndarray\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 634\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    635\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36mvalues\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    552\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m         \u001b[0;34m\"\"\"The variable's data as a numpy.ndarray\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 554\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_as_array_or_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    555\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36m_as_array_or_item\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    285\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"M\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m     \"\"\"\n\u001b[0;32m---> 83\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/dask/array/core.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[1;32m   1481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1482\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1483\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1484\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1485\u001b[0m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    281\u001b[0m         \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m         \"\"\"\n\u001b[0;32m--> 283\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraverse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    284\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    563\u001b[0m         \u001b[0mpostcomputes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_postcompute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 565\u001b[0;31m     \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    566\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrepack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpostcomputes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/dask/threaded.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(dsk, result, cache, num_workers, pool, **kwargs)\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0mget_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_thread_get_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0mpack_exception\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpack_exception\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m         \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m     )\n\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/dask/local.py\u001b[0m in \u001b[0;36mget_async\u001b[0;34m(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)\u001b[0m\n\u001b[1;32m    474\u001b[0m             \u001b[0;31m# Main loop, wait on tasks to finish, insert new ones\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    475\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"waiting\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ready\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"running\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 476\u001b[0;31m                 \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_info\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfailed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mqueue_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    477\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mfailed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    478\u001b[0m                     \u001b[0mexc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/site-packages/dask/local.py\u001b[0m in \u001b[0;36mqueue_get\u001b[0;34m(q)\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mqueue_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/queue.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    168\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m                 \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_qsize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    171\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"'timeout' must be a non-negative number\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/py3_all/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    294\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2018-01-01 08:00:00  1.323934  0.130142  0.550955  0.545291  0.297267\n",
+      "2018-01-01 09:00:00  2.806052  0.280159  1.056126  1.070717  0.517034\n",
+      "2018-01-01 10:00:00  4.191119  0.516053  2.021608  1.850436  0.859753\n",
+      "2018-01-01 11:00:00  5.090479  0.649787  2.705386  2.260693  1.172253\n",
+      "2018-01-01 12:00:00  4.560279  0.537019  2.283488  1.918774  1.056977\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2018_PV_communes_35_TWh.csv\n",
+      "                  time    2823.0    2887.0    2886.0    2869.0\n",
+      "0  2019-01-01 08:00:00  0.728006  0.097477  0.337424  0.196788\n",
+      "1  2019-01-01 09:00:00  1.118406  0.118843  0.396026  0.340598\n",
+      "2  2019-01-01 10:00:00  0.619173  0.069453  0.254603  0.239012\n",
+      "3  2019-01-01 11:00:00  0.289316  0.047697  0.187637  0.135651\n",
+      "4  2019-01-01 12:00:00  0.289533  0.037838  0.142366  0.130284\n",
+      "Skipping /work/hyenergy/output/solar_potential/scenarios_2050/2019_PV_communes_all.csv\n",
+      "                  time    2823.0    2887.0    2886.0    2869.0\n",
+      "0  2019-01-01 08:00:00  0.242089  0.007435  0.073478  0.040055\n",
+      "1  2019-01-01 09:00:00  0.372501  0.009003  0.087081  0.068663\n",
+      "2  2019-01-01 10:00:00  0.207364  0.005351  0.051829  0.048368\n",
+      "3  2019-01-01 11:00:00  0.098617  0.003629  0.037851  0.027327\n",
+      "4  2019-01-01 12:00:00  0.098673  0.002675  0.032456  0.026205\n",
+      "Skipping /work/hyenergy/output/solar_potential/scenarios_2050/2019_PV_communes_15_TWh.csv\n",
+      "                  time    2823.0    2887.0    2886.0    2869.0\n",
+      "0  2019-01-01 08:00:00  0.389016  0.027663  0.159259  0.090217\n",
+      "1  2019-01-01 09:00:00  0.595579  0.032684  0.182408  0.152863\n",
+      "2  2019-01-01 10:00:00  0.330270  0.019116  0.112700  0.106600\n",
+      "3  2019-01-01 11:00:00  0.155142  0.013159  0.082783  0.060791\n",
+      "4  2019-01-01 12:00:00  0.155045  0.010171  0.066442  0.058232\n",
+      "Skipping /work/hyenergy/output/solar_potential/scenarios_2050/2019_PV_communes_25_TWh.csv\n",
+      "                  time    2823.0    2887.0    2886.0    2869.0\n",
+      "0  2019-01-01 08:00:00  0.590709  0.071720  0.268689  0.146768\n",
+      "1  2019-01-01 09:00:00  0.905409  0.084761  0.308746  0.249402\n",
+      "2  2019-01-01 10:00:00  0.501988  0.048849  0.195601  0.174158\n",
+      "3  2019-01-01 11:00:00  0.231917  0.033519  0.143885  0.098727\n",
+      "4  2019-01-01 12:00:00  0.231985  0.026569  0.110635  0.094625\n",
+      "Skipping /work/hyenergy/output/solar_potential/scenarios_2050/2019_PV_communes_35_TWh.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2020-01-01 08:00:00  1.923827  0.242412  0.940110  1.081695  0.461322\n",
+      "2020-01-01 09:00:00  4.270090  0.582917  2.193787  2.072125  1.012027\n",
+      "2020-01-01 10:00:00  6.147459  0.836935  3.126982  2.825039  1.438667\n",
+      "2020-01-01 11:00:00  7.212720  0.966796  3.675542  3.298008  1.700230\n",
+      "2020-01-01 12:00:00  7.291962  0.966838  3.724036  3.310389  1.711270\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2020_PV_communes_all.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2020-01-01 08:00:00  0.567446  0.018721  0.191653  0.183186  0.119120\n",
+      "2020-01-01 09:00:00  1.383686  0.038070  0.476539  0.395575  0.274837\n",
+      "2020-01-01 10:00:00  2.102304  0.053816  0.706785  0.592959  0.408903\n",
+      "2020-01-01 11:00:00  2.502070  0.061571  0.826641  0.706335  0.481310\n",
+      "2020-01-01 12:00:00  2.531916  0.059002  0.820421  0.709902  0.478507\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2020_PV_communes_15_TWh.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2020-01-01 08:00:00  0.976792  0.075713  0.479050  0.437290  0.214435\n",
+      "2020-01-01 09:00:00  2.271336  0.173349  1.076670  0.894962  0.491113\n",
+      "2020-01-01 10:00:00  3.369663  0.247036  1.543744  1.301484  0.715893\n",
+      "2020-01-01 11:00:00  3.983614  0.285058  1.809932  1.538093  0.848829\n",
+      "2020-01-01 12:00:00  4.028150  0.286507  1.827640  1.544997  0.856986\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2020_PV_communes_25_TWh.csv\n",
+      "                       2823.0    2887.0    2886.0    2869.0    2863.0\n",
+      "time                                                                 \n",
+      "2020-01-01 08:00:00  1.557884  0.189660  0.789426  0.800430  0.367319\n",
+      "2020-01-01 09:00:00  3.527282  0.451196  1.831393  1.562149  0.810494\n",
+      "2020-01-01 10:00:00  5.156467  0.649932  2.624826  2.191260  1.160540\n",
+      "2020-01-01 11:00:00  6.079169  0.750071  3.093598  2.561829  1.375358\n",
+      "2020-01-01 12:00:00  6.149002  0.749003  3.138964  2.565229  1.390407\n",
+      "Saved /work/hyenergy/output/solar_potential/scenarios_2050/2020_PV_communes_35_TWh.csv\n"
      ]
     }
    ],
    "source": [
-    "for target_PV in [15, 25, 35]:\n",
-    "    tt = time.time()\n",
-    "    scenario_UIDs = scenarios[scenarios.EPV_cumsum_TWh <= target_PV].index.values\n",
+    "for year in [2010, 2016, 2017, 2018, 2019, 2020]:\n",
     "    \n",
-    "    commune_PV_scenario = aggregate_scenario_to_communes( roofs, roofs_EW, \n",
-    "                                                          PV_hourly_base, PV_hourly_EW, scenario_UIDs )\n",
-    "    \n",
-    "    outfile = ( os.path.join( SCENARIO_FP, '%d_PV_communes_%d_TWh.csv' %(YEAR, target_PV) ) )\n",
-    "    commune_PV_scenario.to_csv( outfile )\n",
-    "    print('Computed and saved %s in %.2fs' %(outfile, time.time()-tt))"
+    "    # Load the original hourly commune aggregate of year to get the correct timestamp\n",
+    "    time_EW = pd.read_csv(os.path.join(PV_FP, '%d_commune_hourly_pv_EW.csv' %year), usecols = [0])\n",
+    "    time_EW['time' ] = pd.to_datetime(time_EW.time)\n",
+    "    time_EW['day'  ] = time_EW['time'].dt.day\n",
+    "    time_EW['hour' ] = time_EW['time'].dt.hour\n",
+    "    time_EW['month'] = time_EW['time'].dt.month\n",
+    "\n",
+    "    hourly_timestamp = time_EW.set_index(['month','day','hour'])\n",
+    "\n",
+    "    for scenario in ['all', '15_TWh', '25_TWh', '35_TWh']:\n",
+    "        file = os.path.join(SCENARIO_FP, '%d_PV_communes_%s.csv' %(year,scenario))\n",
+    "        \n",
+    "        # load commune data\n",
+    "        communes = pd.read_csv( file )\n",
+    "        \n",
+    "        if 'time' in communes.columns:\n",
+    "            print(communes.iloc[:,:5].head())\n",
+    "            print('Skipping %s' %file)\n",
+    "            continue\n",
+    "            \n",
+    "        communes['timestamp'] = pd.to_datetime( communes['timestamp'] )\n",
+    "        \n",
+    "        # Extract hour and month to concatenate real timestamp\n",
+    "        communes['hour' ] = communes['timestamp'].dt.hour\n",
+    "        communes['month'] = communes['timestamp'].dt.month\n",
+    "        \n",
+    "        communes = communes.drop(columns = 'timestamp').set_index(['month','day','hour'])\n",
+    "        \n",
+    "        # Set the correct timestamp\n",
+    "        commune_corrected = pd.concat([communes, hourly_timestamp], \n",
+    "                                      axis = 1).dropna(subset = ['time']).set_index('time')\n",
+    "        \n",
+    "        commune_corrected.to_csv(file)\n",
+    "        print(commune_corrected.iloc[:,:5].head())\n",
+    "        print('Saved %s' %file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "right-begin",
+   "metadata": {},
+   "source": [
+    "## CHECK ALL FILES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "loving-therapy",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/walch/miniconda3/envs/py3_all/lib/python3.7/site-packages/numpy/lib/arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  mask |= (ar1 == a)\n"
+     ]
+    }
+   ],
+   "source": [
+    "scenarios = pd.read_csv(os.path.join( SCENARIO_FP, 'MMH_cumsum_for_scenarios.csv' ), index_col = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "solid-faculty",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sum of annual totals   (2020, all): 43.3503\n",
+      "Sum of hourly communes (2020, all): 43.3503\n",
+      "\n",
+      "Sum of annual totals   (2020, 15_TWh): 15.6342\n",
+      "Sum of hourly communes (2020, 15_TWh): 15.6342\n",
+      "\n",
+      "Sum of annual totals   (2020, 25_TWh): 26.0191\n",
+      "Sum of hourly communes (2020, 25_TWh): 26.0191\n",
+      "\n",
+      "Sum of annual totals   (2020, 35_TWh): 36.3744\n",
+      "Sum of hourly communes (2020, 35_TWh): 36.3744\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/walch/miniconda3/envs/py3_all/lib/python3.7/site-packages/numpy/lib/arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  mask |= (ar1 == a)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sum of annual totals   (2019, all): 42.4116\n",
+      "Sum of hourly communes (2019, all): 42.4116\n",
+      "\n",
+      "Sum of annual totals   (2019, 15_TWh): 15.2966\n",
+      "Sum of hourly communes (2019, 15_TWh): 15.2966\n",
+      "\n",
+      "Sum of annual totals   (2019, 25_TWh): 25.4562\n",
+      "Sum of hourly communes (2019, 25_TWh): 25.4562\n",
+      "\n",
+      "Sum of annual totals   (2019, 35_TWh): 35.5901\n",
+      "Sum of hourly communes (2019, 35_TWh): 35.5901\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/walch/miniconda3/envs/py3_all/lib/python3.7/site-packages/numpy/lib/arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  mask |= (ar1 == a)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sum of annual totals   (2018, all): 42.8350\n",
+      "Sum of hourly communes (2018, all): 42.8350\n",
+      "\n",
+      "Sum of annual totals   (2018, 15_TWh): 15.4201\n",
+      "Sum of hourly communes (2018, 15_TWh): 15.4201\n",
+      "\n",
+      "Sum of annual totals   (2018, 25_TWh): 25.6637\n",
+      "Sum of hourly communes (2018, 25_TWh): 25.6637\n",
+      "\n",
+      "Sum of annual totals   (2018, 35_TWh): 35.9041\n",
+      "Sum of hourly communes (2018, 35_TWh): 35.9041\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/walch/miniconda3/envs/py3_all/lib/python3.7/site-packages/numpy/lib/arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  mask |= (ar1 == a)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sum of annual totals   (2017, all): 43.2124\n",
+      "Sum of hourly communes (2017, all): 43.2124\n",
+      "\n",
+      "Sum of annual totals   (2017, 15_TWh): 15.6041\n",
+      "Sum of hourly communes (2017, 15_TWh): 15.6041\n",
+      "\n",
+      "Sum of annual totals   (2017, 25_TWh): 25.9413\n",
+      "Sum of hourly communes (2017, 25_TWh): 25.9413\n",
+      "\n",
+      "Sum of annual totals   (2017, 35_TWh): 36.2524\n",
+      "Sum of hourly communes (2017, 35_TWh): 36.2524\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/walch/miniconda3/envs/py3_all/lib/python3.7/site-packages/numpy/lib/arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  mask |= (ar1 == a)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sum of annual totals   (2016, all): 39.5194\n",
+      "Sum of hourly communes (2016, all): 39.5194\n",
+      "\n",
+      "Sum of annual totals   (2016, 15_TWh): 14.2193\n",
+      "Sum of hourly communes (2016, 15_TWh): 14.2193\n",
+      "\n",
+      "Sum of annual totals   (2016, 25_TWh): 23.6716\n",
+      "Sum of hourly communes (2016, 25_TWh): 23.6716\n",
+      "\n",
+      "Sum of annual totals   (2016, 35_TWh): 33.1307\n",
+      "Sum of hourly communes (2016, 35_TWh): 33.1307\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/walch/miniconda3/envs/py3_all/lib/python3.7/site-packages/numpy/lib/arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  mask |= (ar1 == a)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sum of annual totals   (2010, all): 38.0644\n",
+      "Sum of hourly communes (2010, all): 38.0644\n",
+      "\n",
+      "Sum of annual totals   (2010, 15_TWh): 13.7124\n",
+      "Sum of hourly communes (2010, 15_TWh): 13.7124\n",
+      "\n",
+      "Sum of annual totals   (2010, 25_TWh): 22.8092\n",
+      "Sum of hourly communes (2010, 25_TWh): 22.8092\n",
+      "\n",
+      "Sum of annual totals   (2010, 35_TWh): 31.9057\n",
+      "Sum of hourly communes (2010, 35_TWh): 31.9057\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for year in [2020, 2019, 2018, 2017, 2016, 2010]:\n",
+    "    roofs_EW = pd.read_csv(os.path.join(PV_FP, '%d_annual_roofs_flat_EW.csv' %(year)), index_col = 0)\n",
+    "    roofs    = pd.read_csv(os.path.join(PV_FP, '%d_annual_roofs.csv'    %(year)), index_col = 0)\n",
+    "\n",
+    "    annual_EW = roofs.copy()\n",
+    "    annual_EW.loc[ roofs_EW.index ] = roofs_EW.copy()\n",
+    "\n",
+    "    for scenario in ['all', '15_TWh', '25_TWh', '35_TWh']:\n",
+    "        communes = pd.read_csv( os.path.join(SCENARIO_FP, '%d_PV_communes_%s.csv' %(year,scenario)), \n",
+    "                                index_col = [0] )\n",
+    "\n",
+    "        if scenario == 'all':\n",
+    "            UUID = annual_EW[annual_EW.available_area >= 8].index.values\n",
+    "        else:\n",
+    "            scenario_TWh = int(scenario.split('_')[0])\n",
+    "            UUID = scenarios[scenarios.EPV_cumsum_TWh <= scenario_TWh].index.values\n",
+    "\n",
+    "        annual_sum = ( annual_EW[annual_EW.available_area >= 8].loc[UUID]\n",
+    "                      .dropna(subset = ['BFS_NUMMER']).EPV_kWh.sum() / 1e9 )\n",
+    "        commune_sum = communes.sum().sum() / 1e6\n",
+    "        print('Sum of annual totals   (%d, %s): %.4f' %(year, scenario, annual_sum))\n",
+    "        print('Sum of hourly communes (%d, %s): %.4f\\n' %(year, scenario, commune_sum))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "southern-fellow",
+   "id": "continued-fitting",
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
     "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.7.7"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
 }
diff --git a/PV_scenarios_2050/Scenarios_hourly.py b/PV_scenarios_2050/Scenarios_hourly.py
index d1e3e5f..52a3281 100644
--- a/PV_scenarios_2050/Scenarios_hourly.py
+++ b/PV_scenarios_2050/Scenarios_hourly.py
@@ -1,145 +1,137 @@
 #!/usr/bin/env python
 # coding: utf-8
 
 # In[1]:
 
 
 import pandas as pd
 import numpy as np
 import xarray as xr 
 
-from demand_mapping import get_yearly_sum
-
 import os
 import time
 import sys
 
 
 ### INPUTS
 YEAR = int(sys.argv[1])
 
 ### FILE PATHS
 SCENARIO_FP = '/work/hyenergy/output/solar_potential/scenarios_2050'
 PV_FP = '/work/hyenergy/output/solar_potential/hourly_potential/'
 
 hourly_FP_EW = '/scratch/walch/workdir_solar_EW_scenarios'
 hourly_FP    = '/scratch/walch/workdir_solar_hourly'
 
 
 # # Load data
 
 # Load base scenario and EW roofs
 roofs_EW = pd.read_csv(os.path.join(PV_FP, '%d_annual_roofs_flat_EW.csv' %(YEAR)), index_col = 0)
 roofs    = pd.read_csv(os.path.join(PV_FP, '%d_annual_roofs.csv'    %(YEAR)), index_col = 0)
 
 # Create annual dataset of full EW scenario
 annual_EW = roofs.copy()
 annual_EW.loc[ roofs_EW.index ] = roofs_EW.copy()
 
 
 # Load hourly data
 PV_hourly_EW   = xr.open_mfdataset(os.path.join( hourly_FP_EW, 'tmp_%d' %YEAR, 'pv_potential_*.nc'), 
                                    chunks = {'DF_UID' : 10000})
 PV_hourly_base = xr.open_mfdataset(os.path.join( hourly_FP, 'tmp_%d' %YEAR, 'pv_potential_*.nc'),
                                    chunks = {'DF_UID' : 10000})
 
 
 # Load scenario file
 scenarios = pd.read_csv(os.path.join( SCENARIO_FP, 'MMH_cumsum_for_scenarios.csv' ), index_col = 0)
 
 # # Loop over communes (scenario: all roofs)
 
 # SCENARIO DEFINITION
 MIN_AREA = 8
 # no restriction for roof tilt and roof aspect
 
 commune_pot_EW = {}
 
 tt = time.time()
 for i, commune in enumerate(roofs.BFS_NUMMER.unique()):
     
     # Baseline scenario
     DF_UIDs          = roofs[(roofs.BFS_NUMMER == commune) & (roofs.available_area >= MIN_AREA)].index
     commune_pot_base = PV_hourly_base.sel(DF_UID = DF_UIDs).pv_potential.sum(dim = 'DF_UID').to_series()
     
     # Baseline scenario (flat roofs only)
     DF_UIDs_flat          = roofs_EW[roofs_EW.index.isin( DF_UIDs )].index
     commune_pot_flat_base = PV_hourly_base.sel(DF_UID = DF_UIDs_flat).pv_potential.sum(dim = 'DF_UID').to_series()
     
     # EW scenario (flat roofs only)
     DF_UIDs_flat_EW     = roofs_EW[(roofs_EW.BFS_NUMMER == commune) & (roofs_EW.available_area >= MIN_AREA)].index
     commune_pot_flat_EW = PV_hourly_EW.sel(DF_UID = DF_UIDs_flat_EW).pv_potential.sum(dim = 'DF_UID').to_series()
    
     commune_pot_EW[commune] = commune_pot_base - commune_pot_flat_base + commune_pot_flat_EW
     
     if ((i+1) % 50) == 0:
         print('Processed commune %d of %d in %.2fs' %((i+1), len( roofs.BFS_NUMMER.unique()), time.time()-tt))
         tt = time.time()
         
 # Merge all commune potentials
 commune_pot_EW = pd.DataFrame(commune_pot_EW) 
 
-
-# ## Verify coherence with annual data
-
-print(annual_EW[annual_EW.available_area >= 8].dropna(subset = ['BFS_NUMMER']).EPV_kWh.sum() / 1e9)
-print(get_yearly_sum(commune_pot_EW.sum(axis = 1).to_xarray(), week_weighting = False) / 1e12)
-
 # # Save output data
 
 outfile = ( os.path.join( SCENARIO_FP, '%d_PV_communes_all.csv' %YEAR) )
 (commune_pot_EW/1e6).to_csv( outfile )
 
 
 # # Scenarios:
 
 def aggregate_scenario_to_communes(roofs_base, roofs_flat_EW, PV_hourly_base, PV_hourly_EW, DF_UID_scenario,
                                    MIN_AREA = 8):
     # Select all roofs in DF_UID_scenario for the EW option for flat roofs and all roofs < 10° considered flat 
     # All roofs in DF_UID_scenario are considered suitable except those with an available area < MIN_AREA
     # IMPORTANT: the roofs_base and roofs_flat_EW must have the DF_UID as index,
     # and the BFS_NUMMER as commune ID and the available_area as attributes
     
     commune_pot_EW = {}
     suitable_DF_UIDs_commune = lambda roofs, communeID: roofs[ (roofs.BFS_NUMMER == communeID) 
                                                              & (roofs.available_area >= MIN_AREA)
                                                              & (roofs.index.isin( DF_UID_scenario )) ].index
     commune_IDs = roofs_base.BFS_NUMMER.unique()
 
     tt = time.time()
     for i, commune in enumerate(commune_IDs):
 
         # Baseline scenario
         DF_UIDs          = suitable_DF_UIDs_commune( roofs_base, commune ) 
         commune_pot_base = PV_hourly_base.sel(DF_UID = DF_UIDs).pv_potential.sum(dim = 'DF_UID').to_series()
 
         # Baseline scenario (flat roofs only)
         DF_UIDs_flat          = roofs_flat_EW[ roofs_flat_EW.index.isin( DF_UIDs ) ].index
         commune_pot_flat_base = PV_hourly_base.sel(DF_UID = DF_UIDs_flat).pv_potential.sum(dim = 'DF_UID').to_series()
 
         # EW scenario (flat roofs only)
         DF_UIDs_flat_EW     = suitable_DF_UIDs_commune( roofs_flat_EW, commune ) 
         commune_pot_flat_EW = PV_hourly_EW.sel( DF_UID = DF_UIDs_flat_EW).pv_potential.sum(dim = 'DF_UID').to_series()
 
         commune_pot_EW[commune] = commune_pot_base - commune_pot_flat_base + commune_pot_flat_EW
 
         if ((i+1) % 20) == 0:
             print('Processed commune %d of %d in %.2fs' %((i+1), len( commune_IDs ), time.time()-tt))
             tt = time.time()
 
     # Merge all commune potentials
     return pd.DataFrame(commune_pot_EW) / 1e6 
 
 for target_PV in [15, 25, 35]:
     tt = time.time()
     scenario_UIDs = scenarios[scenarios.EPV_cumsum_TWh <= target_PV].index.values
     
     commune_PV_scenario = aggregate_scenario_to_communes( roofs, roofs_EW, 
                                                           PV_hourly_base, PV_hourly_EW, scenario_UIDs )
     
     outfile = ( os.path.join( SCENARIO_FP, '%d_PV_communes_%d_TWh.csv' %(YEAR, target_PV) ) )
     commune_PV_scenario.to_csv( outfile )
     print('Computed and saved %s in %.2fs' %(outfile, time.time()-tt))
 
 
 
diff --git a/PV_scenarios_2050/Scenarios_hourly.py b/PV_scenarios_2050/Scenarios_hourly_lesopbpc58.py
similarity index 90%
copy from PV_scenarios_2050/Scenarios_hourly.py
copy to PV_scenarios_2050/Scenarios_hourly_lesopbpc58.py
index d1e3e5f..6980b87 100644
--- a/PV_scenarios_2050/Scenarios_hourly.py
+++ b/PV_scenarios_2050/Scenarios_hourly_lesopbpc58.py
@@ -1,145 +1,137 @@
 #!/usr/bin/env python
 # coding: utf-8
 
 # In[1]:
 
 
 import pandas as pd
 import numpy as np
 import xarray as xr 
 
-from demand_mapping import get_yearly_sum
-
 import os
 import time
 import sys
 
 
 ### INPUTS
 YEAR = int(sys.argv[1])
 
 ### FILE PATHS
-SCENARIO_FP = '/work/hyenergy/output/solar_potential/scenarios_2050'
-PV_FP = '/work/hyenergy/output/solar_potential/hourly_potential/'
+SCENARIO_FP = '/raid/alina/solar/scenarios_EW_2050'
+PV_FP = '/raid/alina/solar/hourly_potential_results'
 
-hourly_FP_EW = '/scratch/walch/workdir_solar_EW_scenarios'
-hourly_FP    = '/scratch/walch/workdir_solar_hourly'
+hourly_FP_EW = '/raid/alina/solar/hourly_flat_ew'
+hourly_FP    = '/raid/alina/solar/hourly_base'
 
 
 # # Load data
 
 # Load base scenario and EW roofs
 roofs_EW = pd.read_csv(os.path.join(PV_FP, '%d_annual_roofs_flat_EW.csv' %(YEAR)), index_col = 0)
 roofs    = pd.read_csv(os.path.join(PV_FP, '%d_annual_roofs.csv'    %(YEAR)), index_col = 0)
 
 # Create annual dataset of full EW scenario
 annual_EW = roofs.copy()
 annual_EW.loc[ roofs_EW.index ] = roofs_EW.copy()
 
 
 # Load hourly data
 PV_hourly_EW   = xr.open_mfdataset(os.path.join( hourly_FP_EW, 'tmp_%d' %YEAR, 'pv_potential_*.nc'), 
                                    chunks = {'DF_UID' : 10000})
 PV_hourly_base = xr.open_mfdataset(os.path.join( hourly_FP, 'tmp_%d' %YEAR, 'pv_potential_*.nc'),
                                    chunks = {'DF_UID' : 10000})
 
 
 # Load scenario file
 scenarios = pd.read_csv(os.path.join( SCENARIO_FP, 'MMH_cumsum_for_scenarios.csv' ), index_col = 0)
 
 # # Loop over communes (scenario: all roofs)
 
 # SCENARIO DEFINITION
 MIN_AREA = 8
 # no restriction for roof tilt and roof aspect
 
 commune_pot_EW = {}
 
 tt = time.time()
 for i, commune in enumerate(roofs.BFS_NUMMER.unique()):
     
     # Baseline scenario
     DF_UIDs          = roofs[(roofs.BFS_NUMMER == commune) & (roofs.available_area >= MIN_AREA)].index
     commune_pot_base = PV_hourly_base.sel(DF_UID = DF_UIDs).pv_potential.sum(dim = 'DF_UID').to_series()
     
     # Baseline scenario (flat roofs only)
     DF_UIDs_flat          = roofs_EW[roofs_EW.index.isin( DF_UIDs )].index
     commune_pot_flat_base = PV_hourly_base.sel(DF_UID = DF_UIDs_flat).pv_potential.sum(dim = 'DF_UID').to_series()
     
     # EW scenario (flat roofs only)
     DF_UIDs_flat_EW     = roofs_EW[(roofs_EW.BFS_NUMMER == commune) & (roofs_EW.available_area >= MIN_AREA)].index
     commune_pot_flat_EW = PV_hourly_EW.sel(DF_UID = DF_UIDs_flat_EW).pv_potential.sum(dim = 'DF_UID').to_series()
    
     commune_pot_EW[commune] = commune_pot_base - commune_pot_flat_base + commune_pot_flat_EW
     
     if ((i+1) % 50) == 0:
         print('Processed commune %d of %d in %.2fs' %((i+1), len( roofs.BFS_NUMMER.unique()), time.time()-tt))
         tt = time.time()
         
 # Merge all commune potentials
 commune_pot_EW = pd.DataFrame(commune_pot_EW) 
 
-
-# ## Verify coherence with annual data
-
-print(annual_EW[annual_EW.available_area >= 8].dropna(subset = ['BFS_NUMMER']).EPV_kWh.sum() / 1e9)
-print(get_yearly_sum(commune_pot_EW.sum(axis = 1).to_xarray(), week_weighting = False) / 1e12)
-
 # # Save output data
 
 outfile = ( os.path.join( SCENARIO_FP, '%d_PV_communes_all.csv' %YEAR) )
 (commune_pot_EW/1e6).to_csv( outfile )
 
 
 # # Scenarios:
 
 def aggregate_scenario_to_communes(roofs_base, roofs_flat_EW, PV_hourly_base, PV_hourly_EW, DF_UID_scenario,
                                    MIN_AREA = 8):
     # Select all roofs in DF_UID_scenario for the EW option for flat roofs and all roofs < 10° considered flat 
     # All roofs in DF_UID_scenario are considered suitable except those with an available area < MIN_AREA
     # IMPORTANT: the roofs_base and roofs_flat_EW must have the DF_UID as index,
     # and the BFS_NUMMER as commune ID and the available_area as attributes
     
     commune_pot_EW = {}
     suitable_DF_UIDs_commune = lambda roofs, communeID: roofs[ (roofs.BFS_NUMMER == communeID) 
                                                              & (roofs.available_area >= MIN_AREA)
                                                              & (roofs.index.isin( DF_UID_scenario )) ].index
     commune_IDs = roofs_base.BFS_NUMMER.unique()
 
     tt = time.time()
     for i, commune in enumerate(commune_IDs):
 
         # Baseline scenario
         DF_UIDs          = suitable_DF_UIDs_commune( roofs_base, commune ) 
         commune_pot_base = PV_hourly_base.sel(DF_UID = DF_UIDs).pv_potential.sum(dim = 'DF_UID').to_series()
 
         # Baseline scenario (flat roofs only)
         DF_UIDs_flat          = roofs_flat_EW[ roofs_flat_EW.index.isin( DF_UIDs ) ].index
         commune_pot_flat_base = PV_hourly_base.sel(DF_UID = DF_UIDs_flat).pv_potential.sum(dim = 'DF_UID').to_series()
 
         # EW scenario (flat roofs only)
         DF_UIDs_flat_EW     = suitable_DF_UIDs_commune( roofs_flat_EW, commune ) 
         commune_pot_flat_EW = PV_hourly_EW.sel( DF_UID = DF_UIDs_flat_EW).pv_potential.sum(dim = 'DF_UID').to_series()
 
         commune_pot_EW[commune] = commune_pot_base - commune_pot_flat_base + commune_pot_flat_EW
 
         if ((i+1) % 20) == 0:
             print('Processed commune %d of %d in %.2fs' %((i+1), len( commune_IDs ), time.time()-tt))
             tt = time.time()
 
     # Merge all commune potentials
     return pd.DataFrame(commune_pot_EW) / 1e6 
 
 for target_PV in [15, 25, 35]:
     tt = time.time()
     scenario_UIDs = scenarios[scenarios.EPV_cumsum_TWh <= target_PV].index.values
     
     commune_PV_scenario = aggregate_scenario_to_communes( roofs, roofs_EW, 
                                                           PV_hourly_base, PV_hourly_EW, scenario_UIDs )
     
     outfile = ( os.path.join( SCENARIO_FP, '%d_PV_communes_%d_TWh.csv' %(YEAR, target_PV) ) )
     commune_PV_scenario.to_csv( outfile )
     print('Computed and saved %s in %.2fs' %(outfile, time.time()-tt))
 
 
 
diff --git a/PV_scenarios_2050/re_paste_PV_data.sh b/PV_scenarios_2050/re_paste_PV_data.sh
new file mode 100644
index 0000000..33012b6
--- /dev/null
+++ b/PV_scenarios_2050/re_paste_PV_data.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+
+echo STARTING AT $(date)
+echo
+
+TARGET_PATH=walch@fidis:/scratch/walch
+SOURCE_PATH=/raid/alina/solar
+
+TARGET_EW=workdir_solar_EW_scenarios
+SOURCE_EW=hourly_flat_ew
+
+TARGET_BASE=workdir_solar_hourly
+SOURCE_EW=hourly_base
+
+for YEAR in 2010 2016 2017 2018
+do
+    scp $SOURCE_PATH\/$SOURCE_EW\/tmp_$YEAR\/pv_potential_*.nc $TARGET_PATH\/$TARGET_EW\/tmp_$YEAR\/.
+    scp $SOURCE_PATH\/$SOURCE_BASE\/tmp_$YEAR\/pv_potential_*.nc $TARGET_PATH\/$TARGET_BASE\/tmp_$YEAR\/.
+done
\ No newline at end of file