04_Demo_CTS_Industry_disaggregation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This Notebook shows how the disaggregator works for the sectors CTS and industry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The disaggregator enables the user to generate DataFrames with:\n",
    "- spatial data, by using functions from the `spatial` module\n",
    "- temporal data, by using functions from the `temporal` module"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. General Settings and functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# at first, the user has to import the respective packages and modules from the disaggregator\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from disaggregator import config, data, spatial, temporal, plot\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Base year\n",
    "- By setting the `base year` in the `config.yaml`, you define the year on which the spatial and temporal functions operate\n",
    "- some functions are not affected, e. g. ``generate_specific_consumption_per_branch()`` because it makes use of particular publications from these years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Region Codes\n",
    "Since different statistics and publications use different notations for region codes, we compiled a dictionary that can be used to transform the different codes.\n",
    "- The function ``config.dict_region_code()`` returns a dictionary with conversions of different kinds of region denotation, if ``raw = False``\n",
    "- the dictionary can convert between the following designations ``ags_lk``, ``id_ags``, ``id_ags_lk``, ``id_nuts3``, ``id_t_nuts3_lk``, ``name``, ``natcode_nuts3``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "     id_t_nuts3_lk  id_nuts3 natcode_nuts3                 name  id_ags_lk  \\\n",
       "idx                                                                          \n",
       "0                5  27600005         DEF05         Dithmarschen          5   \n",
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       "\n",
       "     ags_lk   id_ags  ags_bl                  bl  \n",
       "idx                                               \n",
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "config.dict_region_code(raw=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Spatial Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spatial functions perform the task to calculate energy demand per district and branch. Therefore, a specific energy consumption in `MWh/employee` is calculated per branch and district using information from the database. The functions that perform this task are called `generate_specific_consumption_per_branch()` and `generate_specific_consumption_per_branch_and_district()`. Their ouput is also saved in the folder `data_in`.\n",
    "Subsequently, the specific consumption is multiplied with the number of employees in each branch and district. This is done by the function `disagg_CTS_industry()`. This function also calls the aforementioned functions, if the folder `data_in` does not contain the respective output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `spatial.disagg_CTS_industry()` requires at least two arguments:\n",
    "\n",
    "1. ``source`` is the source of energy and must be one of ``['power', 'gas']``.  \n",
    "2. ``sector`` is the sector whose energy consumption is to be disaggregated and must be one of ``['CTS', 'industry']``.\n",
    "\n",
    "\n",
    "Additionally, you can use NUTS-3 codes instead of the AGS ('Amtlicher Gemeindeschlüssel'), through setting ``use_nuts3code=True``.\n",
    "\n",
    "Also you can choose to disaggregate solely the \n",
    "\n",
    "    - electricity consumption obtained from the grid (when 'source' = 'power')\n",
    "    - gas consumption without consumption for self-generation of electricity (when 'source' = 'gas')\n",
    "by setting ``no_self_gen=True``.\n",
    "\n",
    "The function returns a DataFrame with absolute gas or electricity consumption per branch and district for the sectors CTS or industry in **MWh** for the ``base year`` (see ``config.yaml``)\n",
    "It uses files \"../data_in/regional/specific_`source`_consumption.csv\", with ``source`` being in [``power``, ``gas``], if file does not exist, the function ``generate_specific_consumption_per_branch_and_district()`` is called\n",
    "\n",
    "Let's start with ``power`` for both ``CTS`` and ``industry``:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Electricity Consumption in [MWh/a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ec_CTS = spatial.disagg_CTS_industry(sector='CTS', source='power', use_nuts3code=True)\n",
    "ec_CTS.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40775.665514</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>64028.621347</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>799.468351</td>\n",
       "      <td>0.0</td>\n",
       "      <td>884.541758</td>\n",
       "      <td>5589.280455</td>\n",
       "      <td>252.606841</td>\n",
       "      <td>277.299339</td>\n",
       "      <td>1739.770532</td>\n",
       "      <td>1303.873588</td>\n",
       "      <td>310.712952</td>\n",
       "      <td>...</td>\n",
       "      <td>1004.861678</td>\n",
       "      <td>170.607931</td>\n",
       "      <td>1111.383146</td>\n",
       "      <td>560.19884</td>\n",
       "      <td>2036.655707</td>\n",
       "      <td>4170.059958</td>\n",
       "      <td>116.866387</td>\n",
       "      <td>2114.613266</td>\n",
       "      <td>295.400193</td>\n",
       "      <td>3738.518695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6160.882210</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>436.426769</td>\n",
       "      <td>1702.012462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 401 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    DEF01       DEF02  DEF03       DEF04         DEF05       DEF06  \\\n",
       "WZ                                                                   \n",
       "5     0.0    0.000000    0.0    0.000000      0.000000    0.000000   \n",
       "6     0.0    0.000000    0.0    0.000000  64028.621347    0.000000   \n",
       "7     0.0    0.000000    0.0    0.000000      0.000000    0.000000   \n",
       "8     0.0  799.468351    0.0  884.541758   5589.280455  252.606841   \n",
       "9     0.0    0.000000    0.0    0.000000      0.000000    0.000000   \n",
       "\n",
       "         DEF07        DEF08        DEF09       DEF0A  ...        DEG0D  \\\n",
       "WZ                                                    ...                \n",
       "5     0.000000     0.000000     0.000000    0.000000  ...     0.000000   \n",
       "6     0.000000     0.000000     0.000000    0.000000  ...     0.000000   \n",
       "7     0.000000     0.000000     0.000000    0.000000  ...     0.000000   \n",
       "8   277.299339  1739.770532  1303.873588  310.712952  ...  1004.861678   \n",
       "9     0.000000     0.000000     0.000000    0.000000  ...     0.000000   \n",
       "\n",
       "         DEG0E         DEG0F      DEG0G        DEG0H        DEG0I       DEG0J  \\\n",
       "WZ                                                                              \n",
       "5     0.000000  40775.665514    0.00000     0.000000     0.000000    0.000000   \n",
       "6     0.000000      0.000000    0.00000     0.000000     0.000000    0.000000   \n",
       "7     0.000000      0.000000    0.00000     0.000000     0.000000    0.000000   \n",
       "8   170.607931   1111.383146  560.19884  2036.655707  4170.059958  116.866387   \n",
       "9     0.000000      0.000000    0.00000     0.000000  6160.882210    0.000000   \n",
       "\n",
       "          DEG0K       DEG0L        DEG0M  \n",
       "WZ                                        \n",
       "5      0.000000    0.000000     0.000000  \n",
       "6      0.000000    0.000000     0.000000  \n",
       "7      0.000000    0.000000     0.000000  \n",
       "8   2114.613266  295.400193  3738.518695  \n",
       "9      0.000000  436.426769  1702.012462  \n",
       "\n",
       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec_industry = spatial.disagg_CTS_industry(sector='industry', source='power', use_nuts3code=True)\n",
    "ec_industry.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot your result using functions from the plot module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 604.8x475.2 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ec_combined = pd.concat([ec_CTS.sum()/1000, ec_industry.sum()/1000], keys=['CTS', 'Industry'], axis=1, join='inner')\n",
    "fig, ax = plot.choropleth_map(ec_combined, relative=False, unit='GWh', axtitle='Electricity consumption by', \n",
    "                              colorbar_each_subplot=True,  add_percentages=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's continue with ``gas`` for both ``CTS`` and ``industry``:\n",
    "\n",
    "### 2.2 Gas Consumption in [MWh/a]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>DEF01</th>\n",
       "      <th>DEF02</th>\n",
       "      <th>DEF03</th>\n",
       "      <th>DEF04</th>\n",
       "      <th>DEF05</th>\n",
       "      <th>DEF06</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>87.573336</td>\n",
       "      <td>390.711808</td>\n",
       "      <td>606.276944</td>\n",
       "      <td>1151.926194</td>\n",
       "      <td>9902.523420</td>\n",
       "      <td>4404.234567</td>\n",
       "      <td>8009.592073</td>\n",
       "      <td>5894.359179</td>\n",
       "      <td>13371.774823</td>\n",
       "      <td>4634.650417</td>\n",
       "      <td>...</td>\n",
       "      <td>7558.252570</td>\n",
       "      <td>4448.109715</td>\n",
       "      <td>3122.790783</td>\n",
       "      <td>5806.785842</td>\n",
       "      <td>1219.290299</td>\n",
       "      <td>4708.750933</td>\n",
       "      <td>8764.070048</td>\n",
       "      <td>9249.091603</td>\n",
       "      <td>6320.034734</td>\n",
       "      <td>4991.680173</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.069842</td>\n",
       "      <td>0.076301</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026209</td>\n",
       "      <td>1.362178</td>\n",
       "      <td>0.011306</td>\n",
       "      <td>0.118691</td>\n",
       "      <td>0.095143</td>\n",
       "      <td>0.610412</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027975</td>\n",
       "      <td>0.407139</td>\n",
       "      <td>0.386747</td>\n",
       "      <td>0.150352</td>\n",
       "      <td>1.773025</td>\n",
       "      <td>0.322162</td>\n",
       "      <td>0.034633</td>\n",
       "      <td>0.746059</td>\n",
       "      <td>0.280162</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.161582</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.390674</td>\n",
       "      <td>0.015608</td>\n",
       "      <td>0.689007</td>\n",
       "      <td>0.156270</td>\n",
       "      <td>0.053217</td>\n",
       "      <td>0.106548</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005475</td>\n",
       "      <td>0.014026</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.065905</td>\n",
       "      <td>0.022015</td>\n",
       "      <td>0.049722</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.289317</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.370139</td>\n",
       "      <td>3.574893</td>\n",
       "      <td>5.965509</td>\n",
       "      <td>10.721606</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.845956</td>\n",
       "      <td>2.935934</td>\n",
       "      <td>7.521533</td>\n",
       "      <td>5.013432</td>\n",
       "      <td>41.180715</td>\n",
       "      <td>23.932307</td>\n",
       "      <td>2.677587</td>\n",
       "      <td>6.576253</td>\n",
       "      <td>16.060556</td>\n",
       "      <td>14.851708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>51.732842</td>\n",
       "      <td>6.320802</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.559983</td>\n",
       "      <td>8.940401</td>\n",
       "      <td>77.997911</td>\n",
       "      <td>33.812290</td>\n",
       "      <td>9.645752</td>\n",
       "      <td>46.143840</td>\n",
       "      <td>14.301104</td>\n",
       "      <td>...</td>\n",
       "      <td>14.303205</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.161718</td>\n",
       "      <td>23.793834</td>\n",
       "      <td>11.384100</td>\n",
       "      <td>10.644610</td>\n",
       "      <td>5.906989</td>\n",
       "      <td>10.633276</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.727394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 401 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        DEF01       DEF02       DEF03        DEF04        DEF05        DEF06  \\\n",
       "WZ                                                                             \n",
       "1   87.573336  390.711808  606.276944  1151.926194  9902.523420  4404.234567   \n",
       "2    0.000000    0.069842    0.076301     0.000000     0.026209     1.362178   \n",
       "3    0.000000    0.000000    0.161582     0.000000     0.390674     0.015608   \n",
       "36   0.000000   17.289317    0.000000     0.000000     0.000000     8.370139   \n",
       "37  51.732842    6.320802    0.000000    32.559983     8.940401    77.997911   \n",
       "\n",
       "          DEF07        DEF08         DEF09        DEF0A  ...        DEG0D  \\\n",
       "WZ                                                       ...                \n",
       "1   8009.592073  5894.359179  13371.774823  4634.650417  ...  7558.252570   \n",
       "2      0.011306     0.118691      0.095143     0.610412  ...     0.027975   \n",
       "3      0.689007     0.156270      0.053217     0.106548  ...     0.000000   \n",
       "36     3.574893     5.965509     10.721606     0.000000  ...     8.845956   \n",
       "37    33.812290     9.645752     46.143840    14.301104  ...    14.303205   \n",
       "\n",
       "          DEG0E        DEG0F        DEG0G        DEG0H        DEG0I  \\\n",
       "WZ                                                                    \n",
       "1   4448.109715  3122.790783  5806.785842  1219.290299  4708.750933   \n",
       "2      0.407139     0.386747     0.150352     1.773025     0.322162   \n",
       "3      0.005475     0.014026     0.000000     0.000000     0.065905   \n",
       "36     2.935934     7.521533     5.013432    41.180715    23.932307   \n",
       "37     0.000000    12.161718    23.793834    11.384100    10.644610   \n",
       "\n",
       "          DEG0J        DEG0K        DEG0L        DEG0M  \n",
       "WZ                                                      \n",
       "1   8764.070048  9249.091603  6320.034734  4991.680173  \n",
       "2      0.034633     0.746059     0.280162     0.000000  \n",
       "3      0.022015     0.049722     0.000000     0.000000  \n",
       "36     2.677587     6.576253    16.060556    14.851708  \n",
       "37     5.906989    10.633276     0.000000    18.727394  \n",
       "\n",
       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc_CTS = spatial.disagg_CTS_industry(sector='CTS', source='gas', use_nuts3code=True)\n",
    "gc_CTS.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    DEF01       DEF02  DEF03       DEF04          DEF05       DEF06  \\\n",
       "WZ                                                                    \n",
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       "\n",
       "         DEF07       DEF08        DEF09       DEF0A  ...       DEG0D  \\\n",
       "WZ                                                   ...               \n",
       "5     0.000000     0.00000     0.000000    0.000000  ...    0.000000   \n",
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       "\n",
       "        DEG0E       DEG0F       DEG0G        DEG0H        DEG0I     DEG0J  \\\n",
       "WZ                                                                          \n",
       "5    0.000000  1168.55946    0.000000     0.000000     0.000000   0.00000   \n",
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       "\n",
       "         DEG0K       DEG0L        DEG0M  \n",
       "WZ                                       \n",
       "5     0.000000    0.000000     0.000000  \n",
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       "\n",
       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc_industry = spatial.disagg_CTS_industry(sector='industry', source='gas', use_nuts3code=True)\n",
    "gc_industry.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 604.8x475.2 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gc_combined = pd.concat([gc_CTS.sum()/1000, gc_industry.sum()/1000], keys=['CTS', 'Industry'], axis=1, join='inner')\n",
    "fig, ax = plot.choropleth_map(gc_combined, relative=False, unit='GWh', axtitle='Gas consumption by', \n",
    "                              colorbar_each_subplot=True,  add_percentages=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now save this figure e.g. as PDF file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from disaggregator.config import data_out\n",
    "fig.savefig(data_out('disagg_gas.pdf'), bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Functions that work in the background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function ``generate_specific_consumption_per_branch_and_district()``\n",
    "\n",
    "The function `generate_specific_consumption_per_branch_and_district()` returns specific power and gas consumption per branch and district and requires at least two arguments:\n",
    "\n",
    "1. ``iterations_power`` is the number of iterations for specific power consumption and must be a positiv integer (8 is recommended).  \n",
    "2. ``iterations_gas`` is the number of iterations for specific gas consumption and must be a positiv integer (8 is recommended).\n",
    "\n",
    "\n",
    "Additionally, you can choose to generate solely the specific electricity consumption obtained from the grid and the specific gas consumption without consumption for self-generation of electricity by setting ``no_self_gen=True``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec_ec_district, spec_gc_district = data.generate_specific_consumption_per_branch_and_district(8,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>16068</th>\n",
       "      <th>16069</th>\n",
       "      <th>16070</th>\n",
       "      <th>16071</th>\n",
       "      <th>16072</th>\n",
       "      <th>16073</th>\n",
       "      <th>16074</th>\n",
       "      <th>16075</th>\n",
       "      <th>16076</th>\n",
       "      <th>16077</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>...</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "      <td>21.431806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>...</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "      <td>6.047879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>...</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "      <td>47.561125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>167.180743</td>\n",
       "      <td>238.494784</td>\n",
       "      <td>138.232790</td>\n",
       "      <td>144.713079</td>\n",
       "      <td>1001.644188</td>\n",
       "      <td>155.656725</td>\n",
       "      <td>400.074771</td>\n",
       "      <td>252.001533</td>\n",
       "      <td>235.649094</td>\n",
       "      <td>400.074771</td>\n",
       "      <td>...</td>\n",
       "      <td>400.074771</td>\n",
       "      <td>299.714927</td>\n",
       "      <td>400.074771</td>\n",
       "      <td>400.074771</td>\n",
       "      <td>400.074771</td>\n",
       "      <td>898.696747</td>\n",
       "      <td>225.113494</td>\n",
       "      <td>458.380276</td>\n",
       "      <td>223.771238</td>\n",
       "      <td>400.074771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>76.647782</td>\n",
       "      <td>109.343313</td>\n",
       "      <td>63.375940</td>\n",
       "      <td>66.346975</td>\n",
       "      <td>459.226368</td>\n",
       "      <td>71.364336</td>\n",
       "      <td>183.423302</td>\n",
       "      <td>115.535786</td>\n",
       "      <td>108.038642</td>\n",
       "      <td>183.423302</td>\n",
       "      <td>...</td>\n",
       "      <td>183.423302</td>\n",
       "      <td>137.411068</td>\n",
       "      <td>183.423302</td>\n",
       "      <td>183.423302</td>\n",
       "      <td>183.423302</td>\n",
       "      <td>412.027792</td>\n",
       "      <td>103.208358</td>\n",
       "      <td>210.154776</td>\n",
       "      <td>102.592971</td>\n",
       "      <td>183.423302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 401 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        1001        1002        1003        1004         1051        1053   \\\n",
       "1   21.431806   21.431806   21.431806   21.431806    21.431806   21.431806   \n",
       "2    6.047879    6.047879    6.047879    6.047879     6.047879    6.047879   \n",
       "3   47.561125   47.561125   47.561125   47.561125    47.561125   47.561125   \n",
       "5  167.180743  238.494784  138.232790  144.713079  1001.644188  155.656725   \n",
       "6   76.647782  109.343313   63.375940   66.346975   459.226368   71.364336   \n",
       "\n",
       "        1054        1055        1056        1057   ...       16068  \\\n",
       "1   21.431806   21.431806   21.431806   21.431806  ...   21.431806   \n",
       "2    6.047879    6.047879    6.047879    6.047879  ...    6.047879   \n",
       "3   47.561125   47.561125   47.561125   47.561125  ...   47.561125   \n",
       "5  400.074771  252.001533  235.649094  400.074771  ...  400.074771   \n",
       "6  183.423302  115.535786  108.038642  183.423302  ...  183.423302   \n",
       "\n",
       "        16069       16070       16071       16072       16073       16074  \\\n",
       "1   21.431806   21.431806   21.431806   21.431806   21.431806   21.431806   \n",
       "2    6.047879    6.047879    6.047879    6.047879    6.047879    6.047879   \n",
       "3   47.561125   47.561125   47.561125   47.561125   47.561125   47.561125   \n",
       "5  299.714927  400.074771  400.074771  400.074771  898.696747  225.113494   \n",
       "6  137.411068  183.423302  183.423302  183.423302  412.027792  103.208358   \n",
       "\n",
       "        16075       16076       16077  \n",
       "1   21.431806   21.431806   21.431806  \n",
       "2    6.047879    6.047879    6.047879  \n",
       "3   47.561125   47.561125   47.561125  \n",
       "5  458.380276  223.771238  400.074771  \n",
       "6  210.154776  102.592971  183.423302  \n",
       "\n",
       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec_ec_district.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function ``generate_specific_consumption_per_branch()``\n",
    "This function is the basis for ``generate_specific_consumption_per_branch_and_district()`` and doesn't take any arguments. It returns multiple dataframes which you can see in the demonstation below.\n",
    "\n",
    "The values are based on the publication [Umweltökonomische Gesamtrechnung, 2017](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Umwelt/Publikationen/Umweltnutzung-Wirtschaft/umweltnutzung-und-wirtschaft-energie-pdf-5850014.html) for 2015, which is also available from the DemandRegio database (table_id = 71, with some industry branches (WZ) being grouped together and with ET = 12 for gas and ET = 18 for electricity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "[df_spez_ec, df_spez_gc, df_abs_consumption, df_emp, \n",
    " df_f_sv_no_self_gen, df_f_gv_no_self_gen] = data.generate_specific_consumption_per_branch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>spez. SV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.431806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.047879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.561125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>362.567391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>194.389774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     spez. SV\n",
       "1   21.431806\n",
       "2    6.047879\n",
       "3   47.561125\n",
       "5  362.567391\n",
       "6  194.389774"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## specific electricity consumption for every branch\n",
    "df_spez_ec.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>value</th>\n",
       "      <th>WZ</th>\n",
       "      <th>ET</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>idx</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.912033e+07</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.965345e+06</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.305735e+05</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6.722733e+06</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.524831e+07</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            value  WZ  ET\n",
       "idx                      \n",
       "5    4.912033e+07  20  18\n",
       "13   2.965345e+06  21  12\n",
       "16   4.305735e+05  21  18\n",
       "26   6.722733e+06  22  12\n",
       "29   1.524831e+07  22  18"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## absolute consumption as in \"Umweltökonomischer Gesamtrechnung\" for the year 2015, in Database dimension='spatial', table_id=71,\n",
    "df_abs_consumption.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ags</th>\n",
       "      <th>1001</th>\n",
       "      <th>1002</th>\n",
       "      <th>1003</th>\n",
       "      <th>1004</th>\n",
       "      <th>1051</th>\n",
       "      <th>1053</th>\n",
       "      <th>1054</th>\n",
       "      <th>1055</th>\n",
       "      <th>1056</th>\n",
       "      <th>1057</th>\n",
       "      <th>...</th>\n",
       "      <th>16068</th>\n",
       "      <th>16069</th>\n",
       "      <th>16070</th>\n",
       "      <th>16071</th>\n",
       "      <th>16072</th>\n",
       "      <th>16073</th>\n",
       "      <th>16074</th>\n",
       "      <th>16075</th>\n",
       "      <th>16076</th>\n",
       "      <th>16077</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WZ</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1</th>\n",
       "      <td>13.0</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>171.0</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>653.795456</td>\n",
       "      <td>1189.00000</td>\n",
       "      <td>875.0</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>688.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1122.000000</td>\n",
       "      <td>660.308590</td>\n",
       "      <td>463.568957</td>\n",
       "      <td>862.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>699.00000</td>\n",
       "      <td>1301.000000</td>\n",
       "      <td>1373.0</td>\n",
       "      <td>938.190264</td>\n",
       "      <td>741.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.238066</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.091415</td>\n",
       "      <td>160.673202</td>\n",
       "      <td>1.33352</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.222433</td>\n",
       "      <td>72.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.299752</td>\n",
       "      <td>48.023294</td>\n",
       "      <td>45.618046</td>\n",
       "      <td>17.734525</td>\n",
       "      <td>209.133881</td>\n",
       "      <td>38.00000</td>\n",
       "      <td>4.085042</td>\n",
       "      <td>88.0</td>\n",
       "      <td>33.045953</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.747843</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>2.197390</td>\n",
       "      <td>97.00000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.492031</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.770763</td>\n",
       "      <td>1.974607</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.27825</td>\n",
       "      <td>3.099355</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>101.920112</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>139.427145</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 401 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ags  1001       1002       1003   1004         1051        1053        1054   \\\n",
       "WZ                                                                             \n",
       "1     13.0  58.000000  90.000000  171.0  1470.000000  653.795456  1189.00000   \n",
       "2      0.0   8.238066   9.000000    0.0     3.091415  160.673202     1.33352   \n",
       "3      0.0   0.000000  22.747843    0.0    55.000000    2.197390    97.00000   \n",
       "5      0.0   0.000000   0.000000    0.0     0.000000    0.000000     0.00000   \n",
       "6      0.0   0.000000   0.000000    0.0   139.427145    0.000000     0.00000   \n",
       "\n",
       "ags  1055         1056   1057   ...        16068       16069       16070  \\\n",
       "WZ                              ...                                        \n",
       "1    875.0  1985.000000  688.0  ...  1122.000000  660.308590  463.568957   \n",
       "2     14.0    11.222433   72.0  ...     3.299752   48.023294   45.618046   \n",
       "3     22.0     7.492031   15.0  ...     0.000000    0.770763    1.974607   \n",
       "5      0.0     0.000000    0.0  ...     0.000000    0.000000  101.920112   \n",
       "6      0.0     0.000000    0.0  ...     0.000000    0.000000    0.000000   \n",
       "\n",
       "ags       16071       16072      16073        16074   16075       16076  16077  \n",
       "WZ                                                                              \n",
       "1    862.000000  181.000000  699.00000  1301.000000  1373.0  938.190264  741.0  \n",
       "2     17.734525  209.133881   38.00000     4.085042    88.0   33.045953    0.0  \n",
       "3      0.000000    0.000000    9.27825     3.099355     7.0    0.000000    0.0  \n",
       "5      0.000000    0.000000    0.00000     0.000000     0.0    0.000000    0.0  \n",
       "6      0.000000    0.000000    0.00000     0.000000     0.0    0.000000    0.0  \n",
       "\n",
       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## employees by branch and district\n",
    "df_emp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1.000000\n",
       "2    1.000000\n",
       "3    1.000000\n",
       "5    0.853051\n",
       "6    0.803576\n",
       "Name: Strom Netzbezug, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataframes contain factors for calculating specific electricity consumption solely obtained from the grid\n",
    "# and specific gas consumption without consumption for self-generation of electricity (this is needed when no_self_gen = True)\n",
    "df_f_sv_no_self_gen.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1.000000\n",
       "2    1.000000\n",
       "3    1.000000\n",
       "5    0.160950\n",
       "6    0.955144\n",
       "Name: f_GV_WZ_no_self_gen, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f_gv_no_self_gen.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Temporal Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The temporal functions perform the task to calculate energy demand time series per district and branch. Therefore, the output from the `spatial` functions is needed. \n",
    "\n",
    "Furthermore, normalized load time series are generated for every branch and district. The CTS load time series for power and gas are standard load profiles, the industry time series represent typical working shifts in the respective branch, with a low load level for non-working hours and a high load level for working hours. 9 different load profiles have been defined for industry representing 1-, 2- and 3-shift operation on 5, 6 and 7 days per week. Important functions, that fulfill the tasks in the background are `shift_load_profile_generator()`, `CTS_power_slp_generator()` as well as `t_allo()`, `h_value()`, `gas_slp_weekday_params` and `disagg_daily_gas_slp`.\n",
    "\n",
    "The results of these functions are brought together in 3 functions:\n",
    "- `disagg_temporal_power_CTS()` for the results on CTS for power\n",
    "- `disagg_temporal_gas_CTS()` for the results on CTS for gas\n",
    "- `disagg_temporal_industry()` for the results on industry for gas and power\n",
    "\n",
    "Each of these 3 function has a boolean argument `detailed` which is set on `False` by default. If `detailed` is put on `True` the function will return a multi-index-DataFrame() which contains a time series per branch AND district. If set on `False` it will only contain aggregated time series per district."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 CTS electricity consumption [MWh/15min]\n",
    "#### 3.1.1 Disaggregate using **standard load profiles**: Function ``disagg_temporal_power_CTS()``\n",
    "- Disagreggate spatial data of CTS' **power** demand temporally in **MWh**\n",
    "- returns DataFrame with districts as columns and timesteps as rows \n",
    "- takes two optional arguments:\n",
    "    - if ``detailed = True``, returns DataFrame with multiindex in columns with ``level=0`` for districts and ``level=1`` for branches, timesteps as rows\n",
    "    - if ``use_nuts3code=True``, uses NUTS-3 Codes instead of AGS\n",
    "- Uses temporal and spatial functions `disagg_CTS('power')` , `CTS_power_slp_generator` and information from `config.py`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disaggregator.temporal: INFO     Working on state: SH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NI.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: RP.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BY.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SL.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: MV.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SN.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: ST.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: TH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n"
     ]
    }
   ],
   "source": [
    "df_CTS = temporal.disagg_temporal_power_CTS(detailed=False, use_nuts3code=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2015-01-01 00:00:00</th>\n",
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       "      <td>2.365671</td>\n",
       "      <td>1.948543</td>\n",
       "      <td>1.907598</td>\n",
       "      <td>2.791247</td>\n",
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       "      <td>2.411546</td>\n",
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       "      <th>2015-01-01 00:45:00</th>\n",
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       "2015-01-01 00:15:00  3.272524  8.954310  6.343744  2.895361  3.784762   \n",
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       "2015-01-01 00:45:00  3.109915  8.458016  6.000229  2.743821  3.586475   \n",
       "2015-01-01 01:00:00  3.053411  8.291141  5.875231  2.690501  3.515937   \n",
       "\n",
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       "2015-01-01 00:15:00  3.837316  5.734542  5.401852  6.876733  2.259250  ...   \n",
       "2015-01-01 00:30:00  3.743681  5.513620  5.203116  6.684633  2.182117  ...   \n",
       "2015-01-01 00:45:00  3.662532  5.309586  5.017806  6.517762  2.113354  ...   \n",
       "2015-01-01 01:00:00  3.602490  5.136775  4.859782  6.390216  2.057655  ...   \n",
       "\n",
       "                        DEG0D     DEG0E     DEG0F     DEG0G     DEG0H  \\\n",
       "2015-01-01 00:00:00  1.834572  1.247310  2.339211  1.945665  1.869235   \n",
       "2015-01-01 00:15:00  1.873499  1.275523  2.423719  1.999271  1.934031   \n",
       "2015-01-01 00:30:00  1.829904  1.242645  2.365671  1.948543  1.907598   \n",
       "2015-01-01 00:45:00  1.794044  1.214799  2.316215  1.905113  1.886801   \n",
       "2015-01-01 01:00:00  1.768597  1.194038  2.280938  1.872580  1.875832   \n",
       "\n",
       "                        DEG0I     DEG0J     DEG0K     DEG0L     DEG0M  \n",
       "2015-01-01 00:00:00  2.747839  2.046323  2.031765  2.400117  2.177006  \n",
       "2015-01-01 00:15:00  2.857229  2.091217  2.073656  2.464980  2.240333  \n",
       "2015-01-01 00:30:00  2.791247  2.037906  2.015934  2.411546  2.193227  \n",
       "2015-01-01 00:45:00  2.735422  1.993336  1.967264  2.367216  2.154155  \n",
       "2015-01-01 01:00:00  2.695362  1.960433  1.930250  2.336541  2.127334  \n",
       "\n",
       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_CTS.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export your data using\n",
    "df_CTS.to_csv(data_out('CTS_Power_2015_MWh.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 864x216 with 2 Axes>,\n",
       " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000027F6B848BA8>]],\n",
       "       dtype=object))"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# or plot your data using\n",
    "plot.heatmap_timeseries(df_CTS['DEF01'].resample('H').sum(), unit = 'MWh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### or use detailed version of the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disaggregator.temporal: INFO     Working on state: SH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NI.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: RP.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BY.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SL.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: MV.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SN.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: ST.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: TH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n"
     ]
    }
   ],
   "source": [
    "df_CTS_detail = temporal.disagg_temporal_power_CTS(detailed=True, use_nuts3code=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>WZ</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>81</th>\n",
       "      <th>3</th>\n",
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       "      <th>37</th>\n",
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       "      <th>39</th>\n",
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       "      <th>91</th>\n",
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       "      <th>56</th>\n",
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       "      <th>92</th>\n",
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       "      <th>88</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>94</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01 00:00:00</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.011680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279572</td>\n",
       "      <td>0.003571</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>7.841506e-07</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.038908</td>\n",
       "      <td>0.221993</td>\n",
       "      <td>0.084544</td>\n",
       "      <td>0.011768</td>\n",
       "      <td>0.027650</td>\n",
       "      <td>0.141953</td>\n",
       "      <td>0.004224</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>2015-01-01 00:15:00</th>\n",
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       "      <td>0.011033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.285756</td>\n",
       "      <td>0.003650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.351541</td>\n",
       "      <td>8.014951e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005062</td>\n",
       "      <td>0.042482</td>\n",
       "      <td>0.242383</td>\n",
       "      <td>0.092309</td>\n",
       "      <td>0.012849</td>\n",
       "      <td>0.030189</td>\n",
       "      <td>0.169684</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>2015-01-01 00:30:00</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.010662</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.287383</td>\n",
       "      <td>0.003671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.353543</td>\n",
       "      <td>8.060594e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004885</td>\n",
       "      <td>0.039600</td>\n",
       "      <td>0.225939</td>\n",
       "      <td>0.086047</td>\n",
       "      <td>0.011977</td>\n",
       "      <td>0.028141</td>\n",
       "      <td>0.157273</td>\n",
       "      <td>0.004680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089323</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-01 00:45:00</th>\n",
       "      <td>0.004478</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.289661</td>\n",
       "      <td>0.003700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.356345</td>\n",
       "      <td>8.124494e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004717</td>\n",
       "      <td>0.036660</td>\n",
       "      <td>0.209166</td>\n",
       "      <td>0.079659</td>\n",
       "      <td>0.011088</td>\n",
       "      <td>0.026052</td>\n",
       "      <td>0.145443</td>\n",
       "      <td>0.004328</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.081774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 01:00:00</th>\n",
       "      <td>0.004388</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010177</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.293241</td>\n",
       "      <td>0.003746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.360750</td>\n",
       "      <td>8.224909e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004565</td>\n",
       "      <td>0.033836</td>\n",
       "      <td>0.193051</td>\n",
       "      <td>0.073522</td>\n",
       "      <td>0.010234</td>\n",
       "      <td>0.024045</td>\n",
       "      <td>0.134002</td>\n",
       "      <td>0.003988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.075891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "WZ                         1    2         81   3    36        37        38  \\\n",
       "2015-01-01 00:00:00  0.005035  0.0  0.011680  0.0  0.0  0.279572  0.003571   \n",
       "2015-01-01 00:15:00  0.004757  0.0  0.011033  0.0  0.0  0.285756  0.003650   \n",
       "2015-01-01 00:30:00  0.004597  0.0  0.010662  0.0  0.0  0.287383  0.003671   \n",
       "2015-01-01 00:45:00  0.004478  0.0  0.010387  0.0  0.0  0.289661  0.003700   \n",
       "2015-01-01 01:00:00  0.004388  0.0  0.010177  0.0  0.0  0.293241  0.003746   \n",
       "\n",
       "WZ                    39        49            50  ...        91        55  \\\n",
       "2015-01-01 00:00:00  0.0  0.343933  7.841506e-07  ...  0.004717  0.038908   \n",
       "2015-01-01 00:15:00  0.0  0.351541  8.014951e-07  ...  0.005062  0.042482   \n",
       "2015-01-01 00:30:00  0.0  0.353543  8.060594e-07  ...  0.004885  0.039600   \n",
       "2015-01-01 00:45:00  0.0  0.356345  8.124494e-07  ...  0.004717  0.036660   \n",
       "2015-01-01 01:00:00  0.0  0.360750  8.224909e-07  ...  0.004565  0.033836   \n",
       "\n",
       "WZ                         56        87        92        93        88  \\\n",
       "2015-01-01 00:00:00  0.221993  0.084544  0.011768  0.027650  0.141953   \n",
       "2015-01-01 00:15:00  0.242383  0.092309  0.012849  0.030189  0.169684   \n",
       "2015-01-01 00:30:00  0.225939  0.086047  0.011977  0.028141  0.157273   \n",
       "2015-01-01 00:45:00  0.209166  0.079659  0.011088  0.026052  0.145443   \n",
       "2015-01-01 01:00:00  0.193051  0.073522  0.010234  0.024045  0.134002   \n",
       "\n",
       "WZ                         97   98        94  \n",
       "2015-01-01 00:00:00  0.004224  0.0  0.066772  \n",
       "2015-01-01 00:15:00  0.005050  0.0  0.098344  \n",
       "2015-01-01 00:30:00  0.004680  0.0  0.089323  \n",
       "2015-01-01 00:45:00  0.004328  0.0  0.081774  \n",
       "2015-01-01 01:00:00  0.003988  0.0  0.075891  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter your results using multiindex\n",
    "df_lk_DEF01 = df_CTS_detail.loc[:, 'DEF01']\n",
    "df_lk_DEF01.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2015-01-01 00:15:00</th>\n",
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       "      <td>0.034564</td>\n",
       "      <td>0.009870</td>\n",
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       "      <th>2015-01-01 00:45:00</th>\n",
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       "LK                      DEF01     DEF02     DEF03     DEF04     DEF05  \\\n",
       "2015-01-01 00:00:00  0.011680  0.048792  0.056588  0.006384  0.007781   \n",
       "2015-01-01 00:15:00  0.011033  0.046093  0.053458  0.006031  0.007351   \n",
       "2015-01-01 00:30:00  0.010662  0.044541  0.051657  0.005828  0.007103   \n",
       "2015-01-01 00:45:00  0.010387  0.043393  0.050327  0.005678  0.006920   \n",
       "2015-01-01 01:00:00  0.010177  0.042516  0.049309  0.005563  0.006781   \n",
       "\n",
       "LK                      DEF06     DEF07     DEF08     DEF09     DEF0A  ...  \\\n",
       "2015-01-01 00:00:00  0.012692  0.016063  0.018931  0.037863  0.010812  ...   \n",
       "2015-01-01 00:15:00  0.011990  0.015174  0.017884  0.035768  0.010213  ...   \n",
       "2015-01-01 00:30:00  0.011586  0.014663  0.017282  0.034564  0.009870  ...   \n",
       "2015-01-01 00:45:00  0.011288  0.014286  0.016837  0.033673  0.009615  ...   \n",
       "2015-01-01 01:00:00  0.011060  0.013997  0.016496  0.032993  0.009421  ...   \n",
       "\n",
       "LK                      DEG0D     DEG0E     DEG0F     DEG0G     DEG0H  \\\n",
       "2015-01-01 00:00:00  0.004207  0.007543  0.007303  0.010061  0.002015   \n",
       "2015-01-01 00:15:00  0.003974  0.007126  0.006899  0.009505  0.001904   \n",
       "2015-01-01 00:30:00  0.003840  0.006886  0.006667  0.009185  0.001840   \n",
       "2015-01-01 00:45:00  0.003741  0.006709  0.006495  0.008948  0.001792   \n",
       "2015-01-01 01:00:00  0.003666  0.006573  0.006364  0.008767  0.001756   \n",
       "\n",
       "LK                      DEG0I     DEG0J     DEG0K     DEG0L     DEG0M  \n",
       "2015-01-01 00:00:00  0.009326  0.007311  0.006664  0.006658  0.006163  \n",
       "2015-01-01 00:15:00  0.008810  0.006906  0.006295  0.006289  0.005822  \n",
       "2015-01-01 00:30:00  0.008513  0.006674  0.006083  0.006077  0.005626  \n",
       "2015-01-01 00:45:00  0.008294  0.006502  0.005926  0.005921  0.005481  \n",
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       "\n",
       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use DataFrame.reorder_levels to filter for CTS branches\n",
    "df_wz_81 = df_CTS_detail.reorder_levels([1,0], axis=1).loc[:,81]\n",
    "df_wz_81.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.2 Disaggregation of electricity consumption using **branch load profiles**: Function ``disagg_temporal_power_CTS_blp()``\n",
    "- works similar as disaggregation with standard load profiles (SLP), however uses industry specific branch load profiles (BLP), which have been created using measured data\n",
    "- covers 39 industries from CTS sector and 12 from industrial sector\n",
    "- industries where no BLP is available will be disaggregated using SLP\n",
    "- the function can be called using the same arguments as ``disagg_temporal_power_CTS()`` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disaggregator.temporal: INFO     Working on state: SH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NI.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: RP.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BY.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SL.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: MV.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SN.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: ST.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: TH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Start downloading BLP from Database now. This may take a while depending on the connection. ca. 250MB per industry will be downloaded and stored in your local folder ../data_in/cache.\n",
      "disaggregator.temporal: INFO     Working on WZ37\n",
      "disaggregator.temporal: INFO     Working on WZ38\n",
      "disaggregator.temporal: INFO     Working on WZ41\n",
      "disaggregator.temporal: INFO     Working on WZ42\n",
      "disaggregator.temporal: INFO     Working on WZ43\n",
      "disaggregator.temporal: INFO     Working on WZ46\n",
      "disaggregator.temporal: INFO     Working on WZ47\n",
      "disaggregator.temporal: INFO     Working on WZ52\n",
      "disaggregator.temporal: INFO     Working on WZ55\n",
      "disaggregator.temporal: INFO     Working on WZ58\n",
      "disaggregator.temporal: INFO     Working on WZ59\n",
      "disaggregator.temporal: INFO     Working on WZ62\n",
      "disaggregator.temporal: INFO     Working on WZ63\n",
      "disaggregator.temporal: INFO     Working on WZ64\n",
      "disaggregator.temporal: INFO     Working on WZ65\n",
      "disaggregator.temporal: INFO     Working on WZ66\n",
      "disaggregator.temporal: INFO     Working on WZ68\n",
      "disaggregator.temporal: INFO     Working on WZ69\n",
      "disaggregator.temporal: INFO     Working on WZ70\n",
      "disaggregator.temporal: INFO     Working on WZ71\n",
      "disaggregator.temporal: INFO     Working on WZ72\n",
      "disaggregator.temporal: INFO     Working on WZ73\n",
      "disaggregator.temporal: INFO     Working on WZ74\n",
      "disaggregator.temporal: INFO     Working on WZ75\n",
      "disaggregator.temporal: INFO     Working on WZ77\n",
      "disaggregator.temporal: INFO     Working on WZ78\n",
      "disaggregator.temporal: INFO     Working on WZ82\n",
      "disaggregator.temporal: INFO     Working on WZ84\n",
      "disaggregator.temporal: INFO     Working on WZ85\n",
      "disaggregator.temporal: INFO     Working on WZ86\n",
      "disaggregator.temporal: INFO     Working on WZ87\n",
      "disaggregator.temporal: INFO     Working on WZ88\n",
      "disaggregator.temporal: INFO     Working on WZ90\n",
      "disaggregator.temporal: INFO     Working on WZ91\n",
      "disaggregator.temporal: INFO     Working on WZ93\n",
      "disaggregator.temporal: INFO     Working on WZ94\n",
      "disaggregator.temporal: INFO     Working on WZ95\n",
      "disaggregator.temporal: INFO     Working on WZ96\n",
      "disaggregator.temporal: INFO     Working on WZ99\n"
     ]
    },
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       "      <th>DEG0D</th>\n",
       "      <th>DEG0E</th>\n",
       "      <th>DEG0F</th>\n",
       "      <th>DEG0G</th>\n",
       "      <th>DEG0H</th>\n",
       "      <th>DEG0I</th>\n",
       "      <th>DEG0J</th>\n",
       "      <th>DEG0K</th>\n",
       "      <th>DEG0L</th>\n",
       "      <th>DEG0M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <td>3.800213</td>\n",
       "      <td>10.482711</td>\n",
       "      <td>7.073365</td>\n",
       "      <td>2.998896</td>\n",
       "      <td>4.064003</td>\n",
       "      <td>4.247980</td>\n",
       "      <td>6.401625</td>\n",
       "      <td>5.864854</td>\n",
       "      <td>7.306001</td>\n",
       "      <td>2.476614</td>\n",
       "      <td>...</td>\n",
       "      <td>2.043913</td>\n",
       "      <td>1.364739</td>\n",
       "      <td>2.814136</td>\n",
       "      <td>2.224591</td>\n",
       "      <td>2.073852</td>\n",
       "      <td>3.097030</td>\n",
       "      <td>2.209300</td>\n",
       "      <td>2.310941</td>\n",
       "      <td>2.655907</td>\n",
       "      <td>2.443931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:15:00</th>\n",
       "      <td>3.831911</td>\n",
       "      <td>10.563830</td>\n",
       "      <td>7.135415</td>\n",
       "      <td>3.027080</td>\n",
       "      <td>4.080126</td>\n",
       "      <td>4.271770</td>\n",
       "      <td>6.462183</td>\n",
       "      <td>5.920724</td>\n",
       "      <td>7.339348</td>\n",
       "      <td>2.486122</td>\n",
       "      <td>...</td>\n",
       "      <td>2.036444</td>\n",
       "      <td>1.361525</td>\n",
       "      <td>2.828614</td>\n",
       "      <td>2.224506</td>\n",
       "      <td>2.099233</td>\n",
       "      <td>3.114340</td>\n",
       "      <td>2.202448</td>\n",
       "      <td>2.299270</td>\n",
       "      <td>2.663873</td>\n",
       "      <td>2.451080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:30:00</th>\n",
       "      <td>3.802691</td>\n",
       "      <td>10.502479</td>\n",
       "      <td>7.075830</td>\n",
       "      <td>3.002880</td>\n",
       "      <td>4.035507</td>\n",
       "      <td>4.233381</td>\n",
       "      <td>6.358255</td>\n",
       "      <td>5.833662</td>\n",
       "      <td>7.262339</td>\n",
       "      <td>2.453226</td>\n",
       "      <td>...</td>\n",
       "      <td>2.012524</td>\n",
       "      <td>1.345941</td>\n",
       "      <td>2.805092</td>\n",
       "      <td>2.196900</td>\n",
       "      <td>2.091344</td>\n",
       "      <td>3.091345</td>\n",
       "      <td>2.170955</td>\n",
       "      <td>2.269202</td>\n",
       "      <td>2.641631</td>\n",
       "      <td>2.429442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:45:00</th>\n",
       "      <td>3.773852</td>\n",
       "      <td>10.444069</td>\n",
       "      <td>7.018136</td>\n",
       "      <td>2.980183</td>\n",
       "      <td>3.997429</td>\n",
       "      <td>4.198337</td>\n",
       "      <td>6.258193</td>\n",
       "      <td>5.749872</td>\n",
       "      <td>7.193444</td>\n",
       "      <td>2.422938</td>\n",
       "      <td>...</td>\n",
       "      <td>1.992848</td>\n",
       "      <td>1.333039</td>\n",
       "      <td>2.783816</td>\n",
       "      <td>2.172548</td>\n",
       "      <td>2.086419</td>\n",
       "      <td>3.072330</td>\n",
       "      <td>2.144049</td>\n",
       "      <td>2.244283</td>\n",
       "      <td>2.623960</td>\n",
       "      <td>2.411229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 01:00:00</th>\n",
       "      <td>3.749071</td>\n",
       "      <td>10.396706</td>\n",
       "      <td>6.967936</td>\n",
       "      <td>2.961747</td>\n",
       "      <td>3.968435</td>\n",
       "      <td>4.169310</td>\n",
       "      <td>6.167122</td>\n",
       "      <td>5.673649</td>\n",
       "      <td>7.138117</td>\n",
       "      <td>2.396130</td>\n",
       "      <td>...</td>\n",
       "      <td>1.977709</td>\n",
       "      <td>1.323205</td>\n",
       "      <td>2.766815</td>\n",
       "      <td>2.152261</td>\n",
       "      <td>2.087257</td>\n",
       "      <td>3.059965</td>\n",
       "      <td>2.122587</td>\n",
       "      <td>2.224864</td>\n",
       "      <td>2.613096</td>\n",
       "      <td>2.397899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 401 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        DEF01      DEF02     DEF03     DEF04     DEF05  \\\n",
       "2017-01-01 00:00:00  3.800213  10.482711  7.073365  2.998896  4.064003   \n",
       "2017-01-01 00:15:00  3.831911  10.563830  7.135415  3.027080  4.080126   \n",
       "2017-01-01 00:30:00  3.802691  10.502479  7.075830  3.002880  4.035507   \n",
       "2017-01-01 00:45:00  3.773852  10.444069  7.018136  2.980183  3.997429   \n",
       "2017-01-01 01:00:00  3.749071  10.396706  6.967936  2.961747  3.968435   \n",
       "\n",
       "                        DEF06     DEF07     DEF08     DEF09     DEF0A  ...  \\\n",
       "2017-01-01 00:00:00  4.247980  6.401625  5.864854  7.306001  2.476614  ...   \n",
       "2017-01-01 00:15:00  4.271770  6.462183  5.920724  7.339348  2.486122  ...   \n",
       "2017-01-01 00:30:00  4.233381  6.358255  5.833662  7.262339  2.453226  ...   \n",
       "2017-01-01 00:45:00  4.198337  6.258193  5.749872  7.193444  2.422938  ...   \n",
       "2017-01-01 01:00:00  4.169310  6.167122  5.673649  7.138117  2.396130  ...   \n",
       "\n",
       "                        DEG0D     DEG0E     DEG0F     DEG0G     DEG0H  \\\n",
       "2017-01-01 00:00:00  2.043913  1.364739  2.814136  2.224591  2.073852   \n",
       "2017-01-01 00:15:00  2.036444  1.361525  2.828614  2.224506  2.099233   \n",
       "2017-01-01 00:30:00  2.012524  1.345941  2.805092  2.196900  2.091344   \n",
       "2017-01-01 00:45:00  1.992848  1.333039  2.783816  2.172548  2.086419   \n",
       "2017-01-01 01:00:00  1.977709  1.323205  2.766815  2.152261  2.087257   \n",
       "\n",
       "                        DEG0I     DEG0J     DEG0K     DEG0L     DEG0M  \n",
       "2017-01-01 00:00:00  3.097030  2.209300  2.310941  2.655907  2.443931  \n",
       "2017-01-01 00:15:00  3.114340  2.202448  2.299270  2.663873  2.451080  \n",
       "2017-01-01 00:30:00  3.091345  2.170955  2.269202  2.641631  2.429442  \n",
       "2017-01-01 00:45:00  3.072330  2.144049  2.244283  2.623960  2.411229  \n",
       "2017-01-01 01:00:00  3.059965  2.122587  2.224864  2.613096  2.397899  \n",
       "\n",
       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_CTS_blp = temporal.disagg_temporal_power_CTS_blp(year=2017, use_nuts3code=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>DEF01</th>\n",
       "      <th>DEF02</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <td>3.800213</td>\n",
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       "      <td>5.864854</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-01 00:15:00</th>\n",
       "      <td>3.831911</td>\n",
       "      <td>10.563830</td>\n",
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       "      <td>5.920724</td>\n",
       "      <td>7.339348</td>\n",
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       "      <td>...</td>\n",
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       "      <td>2.224506</td>\n",
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       "      <td>2.299270</td>\n",
       "      <td>2.663873</td>\n",
       "      <td>2.451080</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-01 00:30:00</th>\n",
       "      <td>3.802691</td>\n",
       "      <td>10.502479</td>\n",
       "      <td>7.075830</td>\n",
       "      <td>3.002880</td>\n",
       "      <td>4.035507</td>\n",
       "      <td>4.233381</td>\n",
       "      <td>6.358255</td>\n",
       "      <td>5.833662</td>\n",
       "      <td>7.262339</td>\n",
       "      <td>2.453226</td>\n",
       "      <td>...</td>\n",
       "      <td>2.012524</td>\n",
       "      <td>1.345941</td>\n",
       "      <td>2.805092</td>\n",
       "      <td>2.196900</td>\n",
       "      <td>2.091344</td>\n",
       "      <td>3.091345</td>\n",
       "      <td>2.170955</td>\n",
       "      <td>2.269202</td>\n",
       "      <td>2.641631</td>\n",
       "      <td>2.429442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:45:00</th>\n",
       "      <td>3.773852</td>\n",
       "      <td>10.444069</td>\n",
       "      <td>7.018136</td>\n",
       "      <td>2.980183</td>\n",
       "      <td>3.997429</td>\n",
       "      <td>4.198337</td>\n",
       "      <td>6.258193</td>\n",
       "      <td>5.749872</td>\n",
       "      <td>7.193444</td>\n",
       "      <td>2.422938</td>\n",
       "      <td>...</td>\n",
       "      <td>1.992848</td>\n",
       "      <td>1.333039</td>\n",
       "      <td>2.783816</td>\n",
       "      <td>2.172548</td>\n",
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       "      <td>2.244283</td>\n",
       "      <td>2.623960</td>\n",
       "      <td>2.411229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 01:00:00</th>\n",
       "      <td>3.749071</td>\n",
       "      <td>10.396706</td>\n",
       "      <td>6.967936</td>\n",
       "      <td>2.961747</td>\n",
       "      <td>3.968435</td>\n",
       "      <td>4.169310</td>\n",
       "      <td>6.167122</td>\n",
       "      <td>5.673649</td>\n",
       "      <td>7.138117</td>\n",
       "      <td>2.396130</td>\n",
       "      <td>...</td>\n",
       "      <td>1.977709</td>\n",
       "      <td>1.323205</td>\n",
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       "      <td>2.122587</td>\n",
       "      <td>2.224864</td>\n",
       "      <td>2.613096</td>\n",
       "      <td>2.397899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 401 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        DEF01      DEF02     DEF03     DEF04     DEF05  \\\n",
       "2017-01-01 00:00:00  3.800213  10.482711  7.073365  2.998896  4.064003   \n",
       "2017-01-01 00:15:00  3.831911  10.563830  7.135415  3.027080  4.080126   \n",
       "2017-01-01 00:30:00  3.802691  10.502479  7.075830  3.002880  4.035507   \n",
       "2017-01-01 00:45:00  3.773852  10.444069  7.018136  2.980183  3.997429   \n",
       "2017-01-01 01:00:00  3.749071  10.396706  6.967936  2.961747  3.968435   \n",
       "\n",
       "                        DEF06     DEF07     DEF08     DEF09     DEF0A  ...  \\\n",
       "2017-01-01 00:00:00  4.247980  6.401625  5.864854  7.306001  2.476614  ...   \n",
       "2017-01-01 00:15:00  4.271770  6.462183  5.920724  7.339348  2.486122  ...   \n",
       "2017-01-01 00:30:00  4.233381  6.358255  5.833662  7.262339  2.453226  ...   \n",
       "2017-01-01 00:45:00  4.198337  6.258193  5.749872  7.193444  2.422938  ...   \n",
       "2017-01-01 01:00:00  4.169310  6.167122  5.673649  7.138117  2.396130  ...   \n",
       "\n",
       "                        DEG0D     DEG0E     DEG0F     DEG0G     DEG0H  \\\n",
       "2017-01-01 00:00:00  2.043913  1.364739  2.814136  2.224591  2.073852   \n",
       "2017-01-01 00:15:00  2.036444  1.361525  2.828614  2.224506  2.099233   \n",
       "2017-01-01 00:30:00  2.012524  1.345941  2.805092  2.196900  2.091344   \n",
       "2017-01-01 00:45:00  1.992848  1.333039  2.783816  2.172548  2.086419   \n",
       "2017-01-01 01:00:00  1.977709  1.323205  2.766815  2.152261  2.087257   \n",
       "\n",
       "                        DEG0I     DEG0J     DEG0K     DEG0L     DEG0M  \n",
       "2017-01-01 00:00:00  3.097030  2.209300  2.310941  2.655907  2.443931  \n",
       "2017-01-01 00:15:00  3.114340  2.202448  2.299270  2.663873  2.451080  \n",
       "2017-01-01 00:30:00  3.091345  2.170955  2.269202  2.641631  2.429442  \n",
       "2017-01-01 00:45:00  3.072330  2.144049  2.244283  2.623960  2.411229  \n",
       "2017-01-01 01:00:00  3.059965  2.122587  2.224864  2.613096  2.397899  \n",
       "\n",
       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_CTS_blp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 864x216 with 2 Axes>,\n",
       " array([[<AxesSubplot:title={'center':'DEF01'}, xlabel='Tag des Jahres', ylabel='Stunde'>]],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.heatmap_timeseries(df_CTS_blp['DEF01'].resample('H').sum(), unit = 'MWh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disaggregator.temporal: INFO     Working on state: SH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NI.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: RP.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BY.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SL.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: MV.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SN.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: ST.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: TH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Start downloading BLP from Database now. This may take a while depending on the connection. ca. 250MB per industry will be downloaded and stored in your local folder ../data_in/cache.\n",
      "disaggregator.temporal: INFO     Working on WZ37\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ37&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ38\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ38&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ41\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ41&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ42\n",
      "disaggregator.temporal: INFO     Working on WZ43\n",
      "disaggregator.temporal: INFO     Working on WZ46\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ46&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ47\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ47&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ52\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ52&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ55\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ55&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ58\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ64&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ59\n",
      "disaggregator.temporal: INFO     Working on WZ62\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ62&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ63\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ63&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ64\n",
      "disaggregator.temporal: INFO     Working on WZ65\n",
      "disaggregator.temporal: INFO     Working on WZ66\n",
      "disaggregator.temporal: INFO     Working on WZ68\n",
      "disaggregator.temporal: INFO     Working on WZ69\n",
      "disaggregator.temporal: INFO     Working on WZ70\n",
      "disaggregator.temporal: INFO     Working on WZ71\n",
      "disaggregator.temporal: INFO     Working on WZ72\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ72&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ73\n",
      "disaggregator.temporal: INFO     Working on WZ74\n",
      "disaggregator.temporal: INFO     Working on WZ75\n",
      "disaggregator.temporal: INFO     Working on WZ77\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ77&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ78\n",
      "disaggregator.temporal: INFO     Working on WZ82\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ82&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ84\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ84&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ85\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ85&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ86\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ86&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ87\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ87&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ88\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ88&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ90\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ90&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ91\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ91&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ93\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ93&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ94\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ94&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ95\n",
      "disaggregator.temporal: INFO     Working on WZ96\n",
      "disaggregator.temporal: INFO     Working on WZ99\n"
     ]
    }
   ],
   "source": [
    "# function can also be called using detaild=True to provide an output per nuts3-region and industry via multicolumn\n",
    "df_CTS_blp_detailed = temporal.disagg_temporal_power_CTS_blp(year=2016, detailed=True, use_nuts3code=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 CTS gas consumption [MWh/h]\n",
    "#### Function disagg_temporal_gas_CTS()\n",
    "- Disagreggate spatial data of CTS' **gas** demand temporally in **MWh**\n",
    "- returns DataFrame with districts as columns and timesteps as rows \n",
    "- takes two optional arguments:\n",
    "    - if ``detailed = True``, returns DataFrame with multiindex in columns with ``level=0`` for districts and ``level=1`` for branches, timesteps as rows\n",
    "    - if ``use_nuts3code=True``, uses NUTS-3 Codes instead of AGS\n",
    "- Uses temporal and spatial functions like `disagg_daily_gas_slp(state)` and information from `config.py` and `./data_in/Gas Load Profiles/`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run function\n",
    "df_CTS_gas = temporal.disagg_temporal_gas_CTS(detailed=False, use_nuts3code=False)\n",
    "df_CTS_gas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export your data using\n",
    "df_CTS_gas.to_csv(data_out('CTS_Power_2015_MWh.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 864x216 with 2 Axes>,\n",
       " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002208BB8E2E8>]],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# or plot your data using\n",
    "plot.heatmap_timeseries(df_CTS_gas['1001'], unit = 'MWh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22080203240>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot your data\n",
    "df_CTS_gas[[\"1001\", \"1002\"]].plot(figsize=(12,6), ylim = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Industry electricity consumption [MWh/15min]\n",
    "#### 3.3.1 Disaggregation of electricity consumption using **shift load profiles**: Function ``disagg_temporal_industry()``\n",
    "- Disagreggate spatial data of industry **power** and **gas** demand per quarter-hour for all districts in **MWh**\n",
    "- returns DataFrame with districts as columns and timesteps as rows\n",
    "- requires at least two arguments:\n",
    "    - ``source`` which is the source of energy and must be one of ``['power','gas']``\n",
    "    - ``low`` is the low load level for non-working hours and must be a float between ``[0,1]`` (default is 0.35, meaning that the lower level is 35% of the higher level)\n",
    "- takes three optional arguments:\n",
    "    - if ``no_self_gen = True`` then only electricity consumption obtained from the grid (for 'source' = 'power') and only gas consumption without gas consumption for self-generation of electricity (for 'source' = 'gas') will be disaggregated\n",
    "    - if ``detailed = True``, returns DataFrame with multiindex in columns with ``level=0`` for districts and ``level=1`` for branches, timesteps as rows\n",
    "    - if ``use_nuts3code=True``, uses NUTS-3 Codes instead of AGS\n",
    "- Uses temporal and spatial functions like `shift_load_profile_generator()`, `disagg_CTS_industry()` and information from `config.py`\n",
    "\n",
    "\n",
    "Let's start with industrial power consumption and compare consumption with and without self generation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disaggregator.temporal: INFO     Working on state: SH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NI.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: RP.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BY.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SL.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: MV.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SN.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: ST.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: TH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n"
     ]
    }
   ],
   "source": [
    "df_ec_industry_no_self_gen = temporal.disagg_temporal_industry(source='power', no_self_gen=True,\n",
    "                                                               detailed=False, use_nuts3code=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>1001</th>\n",
       "      <th>1002</th>\n",
       "      <th>1003</th>\n",
       "      <th>1004</th>\n",
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       "      <th>1053</th>\n",
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       "      <th>16075</th>\n",
       "      <th>16076</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01 00:00:00</th>\n",
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       "      <td>16.71226</td>\n",
       "      <td>2.195035</td>\n",
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       "      <td>1.87823</td>\n",
       "      <td>5.863123</td>\n",
       "      <td>1.160922</td>\n",
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       "      <td>1.87823</td>\n",
       "      <td>5.863123</td>\n",
       "      <td>1.160922</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-01 00:30:00</th>\n",
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       "2015-01-01 00:45:00  4.067236  3.640382  6.047988  2.895766  6.730523   \n",
       "2015-01-01 01:00:00  4.067236  3.640382  6.047988  2.895766  6.730523   \n",
       "\n",
       "                         16073    16074     16075     16076     16077  \n",
       "2015-01-01 00:00:00  12.689618  3.44743  8.795279  3.256991  3.279316  \n",
       "2015-01-01 00:15:00  12.689618  3.44743  8.795279  3.256991  3.279316  \n",
       "2015-01-01 00:30:00  12.689618  3.44743  8.795279  3.256991  3.279316  \n",
       "2015-01-01 00:45:00  12.689618  3.44743  8.795279  3.256991  3.279316  \n",
       "2015-01-01 01:00:00  12.689618  3.44743  8.795279  3.256991  3.279316  \n",
       "\n",
       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ec_industry_no_self_gen.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disaggregator.temporal: INFO     Working on state: SH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NI.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: RP.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BY.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SL.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: MV.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SN.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: ST.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: TH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n"
     ]
    }
   ],
   "source": [
    "df_ec_industry = temporal.disagg_temporal_industry(source='power', no_self_gen=False, detailed=False, \n",
    "                                                   use_nuts3code=False, low=0.35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "\n",
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       "[5 rows x 401 columns]"
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     "execution_count": 5,
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   ],
   "source": [
    "df_ec_industry.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th>2015-01-01 01:00:00</th>\n",
       "      <td>5444.975448</td>\n",
       "      <td>4248.745249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        industry  industry without self-generation\n",
       "2015-01-01 00:00:00  5444.975448                       4248.745249\n",
       "2015-01-01 00:15:00  5444.975448                       4248.745249\n",
       "2015-01-01 00:30:00  5444.975448                       4248.745249\n",
       "2015-01-01 00:45:00  5444.975448                       4248.745249\n",
       "2015-01-01 01:00:00  5444.975448                       4248.745249"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec_industry_combined = pd.concat([df_ec_industry.sum(axis=1), df_ec_industry_no_self_gen.sum(axis=1)], \n",
    "                                 keys=['industry', 'industry without self-generation'], \n",
    "                                 axis=1, join='inner')\n",
    "ec_industry_combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZblnVu4wgOJxNWcdsvYIgSLmCYBSGBwkumZ8JYydCLyGo232QYLl/jiB7Nmdh6dXbCE5cthBk6K6bZTPXEl5Sd/dd07z3FoLL3Y/U/W7Azyd5/00ENzA/bmazKY2ouZzgcvujBDdG3jbTPwz3AecR3BS6iWD7+/E0fzaTfVr9Z9xHEIytDssRpizXmsE2+AmCUSbWE6y/XxrXxAcIkgpbzewPJ/iI4wn2kzsJ1oO/dPcHw/XkdILa/HUEpRW1m7xnxd3vIdgnXUWQnd5BcA/IvvAtnwo/+7vh/vo24MQZNj/bv51u/bgUODJcXhNdlfoIcCfBaEu/IthXNeVhQTK/asNLiUgKmNmfENyJf3Kz+9JKzOwFBAHuilnW77YcM/sdwbB532t2XyRdwvr6rcAR7v5gs/sjMhPKSIuITCEssfhL4HMKoqdmZq8kKAG4qdl9kXQws5eE5SfdBPX5v2L/jX4iiadAWkRkEmHd51aCETU+2eTuJJqZ3Qz8O3CRTjhkFs5l/0NMjgDOd10qlxRRaYeIiIiISAOUkRYRERERaUBh+rckw8KFC33JkqmepSEiIiIiMnf33HPPRncfnu59qQmklyxZwte/Pn7UKxERERGRaK1cuXJGT9FNTWlHoZCamF9EREREWkBqAulKpTL9m0RERERE5klqAul8Pt/sLoiIiIiIjElNIF2talhSEREREUmO1ATSZtbsLoiIiIiIjElNIC0iIiIikiSpCaT1BEYRERERSZLUBNIq7RARERGRJElNIK2bDUVEREQkSVITSGv4OxERERFJktQE0nogi4iIiIgkSWoCaT0iXERERESSJDXRaalUanYXZIZuXr2DR7fP3/c11JXntCN6yemGVJFIPLRlHz9+eNe8fV4hB6cd3stgV2oOSVMqVZwbHtjGtr3zd2/P4QvbOXF597x9Xtzu27CXux7dPW+f1543znxqLz3t2Sgj3VOq8p3fbmN3af5GPDt6UQfPXNI1b5+XFKnZaxWLxWZ3QepUqs6m3eUnTf/V+j187Jb1896fXaUqJx/SE2mbfR152gupuWgjMmtb9pQpVQ480JaqzntueJRNu+e3nO72tbt51wsWRdpmeyFHX0d8gdHecpXte5+8nL593zau/OWW2D53IgZcfPpSDu5vi7TdhV0F8rn4khSbdpepVA9cB3eOVnn3dx5ld2l+Bxn4zYa9vPmEoUjb7CzmWBBjcL6nVGXHvievg1/6xWb+97fbY/vciRRzxsfPXsbCrmjnd6i7EFuizN3ZuLvMXEZYtrSMz/yMZzzDv/71rze7GxK65ObH+cHqHRO+dkh/G//20uUUYtz51jjw7u88yi8f3xN52ysG2vjPlx8SebsiSXD7ml2898Z1E75mwCfPWcZThzrmpS/f+e12PvWTJyJv14BPn7ucwxfGMx9v+vrDPLx1dMLXXnhoD+9+4eJYPne8PaUqb7r24VhOfk49bAHvimk+vvPbbfzzjyb+3ot54z9edjBLFsxPEu3Kuzdz+c83R95u3uCLf3goQ93R5y0rVef8qx5k2wQncwAvP7KfCyM+MZjMlj0V3vj1h2M5+Tnv6AHedHw883Hl3Zv5wl2bJnzt4Y+dc5e7HzddG8pIS0M27CqxvK/Iq44eeNJrJy7rntdM7vtPXcKtj+yiEuFJ4c2/28EDm/ZF1p5I0mzYFVxRuvD4IbrbD9xeD+5r4+kjnfPWl7NX9rKwK8+WSQKCRqzfUebLd29m0+4Khy+MrNkDbNhV5tlLu3jhUw68GlbMGc87pCfWTG69nvY8n3jxcn7+WLSlEFf/cgsbdz35ymNUauvgXz1vJDjrqXPEwnaW9UWbXZ/Ka44Z5JCBNnaORhcIrt68j2/cu40te8qxBNKjFWfb3grPX9HDccsOLKnoKOQ4eR7XwaHuAv/ykuXc80S0Sa3/vmsTG3fFVyq6YVeZrmKON5/45ED97R+bWRupCaRVI50spYqzqKfIWU/ta3ZXWNCe5/QjeiNtc83WUe59Ym+kbYokSSm8nH7aEb2xlj/MhJlx0sHRlmb9btM+vnz35ieVrkSpVHGOWNieiP3g4gVFzloQbT9u/t0O9sW8/HIGZ61s/vLL54znr1gQaZt3rN3FN+7dNratRa3W7jMWdSRiHTy4vy3y0qKv/3pr7NtwV1tuwuX39hm2kZoCUGWkk6VcDS69ZVUxb7Ht/ESSoBwenLK6HdfmK67t2N0pVT2zyw/C/WCMQUy5BZYf7N/Wopb1bRhqx+L42i9VnbY5Zu1TE0iXy/FdXpLZK1V9Xmqgm6WYM6rOk26CEcmKWoCZ1ftpi+H+Ka5AsBwe3LO8Hyzk4k0olCo+9j1lUW3eRmPOSGd5HSzm4j2ZK1WcwhxPRFKzC9WTDZOlXGmRTIICacmo2sEpq4FM3Ntwrd2s7wfjyqYCLZHRB2Wk56KYt1iPw+Xq3E/mUhNIV6vzOwyOTG00gpUvyQoxZ7NEmi24qhTUJ2dRLdMe1zZcazfz2cCYg5gsL7+x40hMy7CW6c76sTj2jPQcI+HUBNJZ3dmnVatkpFUnLVlVzvpl9Zi34VKLZKTjDmKyvvwAyjENyV5uhZO5mO9XiuKqSGoC6bSMd90qsp5JqAUYKu2QrCpX514bmGRj23BsNdK1bGAszSdCIRfvZfVSNfvZVIjvZK4lyoty8R6Ho0goZHgXIHGK4nJIkqm0Q7JuNOMZ6ULcGelaNjDDQUwhb2M3VcahXGmNhIzKixoXe2lHBAmF1IRCKu1Ilpa5SUSl+ZJRWR96LGdG3uLLSJdaoD61bR6CmCyvg3Hf8FpbB9syvgyVkY6ISjuSo1J1qp7tA4gy0pJ1pYxnAyHIqMadkc5yIFhbfnEdf8uZv7IZ/K+MdOPizkiPtlKNdC6Xmq5mXivUZbXpZkPJuKxnpCHeUSfKLZCR3n+vSDztt0pGOr51MPg/y4F0W8w3G7ZURrpSiem2V5m12gEkyxtvIeYblUSarVTN9jYM8Y6DXG6RGmmIsTQh63X6Md+03gojxxRy8Y5lXq7OfRtOTSBdKBSa3QUJjbbAJU0NfydZV6p4pmsrId4n87XCGL7FeRh1IsvHkZwZhVz8pR2ZXgfnY/i7VslIl0qlZndBQq1wSVM10pJ1WR/CEmrjIMfTdms8VS74P85AMOvrYJxDCLZCmWWw/OK7T66lHhFeLBab3QUJtcINDmMHEGWkJaNaIYiJs0a61EolbjEuwywnZCBcB2O/2TCW5hNhPh6sFHtG2sxWmtkv6v5tN7O3j3tPu5l9xcxWmdlPzWxF3WvvCaffb2Zn1E0/M5y2yszePV0/lJFOjtoNDlk+C477YQ4izZb1G70g3vrK2tPqsrwMx4KYGAPBLC8/iLc0oRVqpOO84dXdI3lK87SFx+5+P/AsADPLA48C14572xuALe5+uJmdD3wMeLWZHQmcDxwFLAW+Z2ZPDf/m08BpwFrgDjO7zt3vnawfykgnRytkpON+mINIswU3ejW7F/GajyAmy/vB+aiRzvLyg3iHb2uFGulC/clcxGFgxcGZ+zY8293oqcDv3P3hcdPPBS4Lf74GONWCJ6icC1zl7vvc/UFgFXBC+G+Vu69291HgqvC9k1JGOjlaYhD4mA8gIs2W9Ru9ILjkHdsDWSotsB/Mx3tlrlzNdjYV4n2gyNgIWhlehsUYy4uiGgt+toH0+cCVE0w/CFgD4O5lYBuwsH56aG04bbLpBzCzC83sTjO7c/v27XR2dtLd3U1PTw8dHR309/dTKBQYGhoCYNGiRQAsXrz4gN+HhoYoFAr09/fT0dFBT08P3d3ddHZ20tfXR7FYZHBwEDNjZGTkgDZq/w8PD5PP5xkYGKCtrY3e3l66urro6uqit7eXtrY2BgYGyOfzDA8PT9jGyMgIZsbg4CDFYpG+vr5UzlO5LiOdlXka/z21hUVnTi4z85TF70nz1Pg8lavQ3dmRqXka/z0V84YVirHMUzm8+amzLbvrXndHBwCF9s5Y5qlUcfoWdGdy3au1UcgZ+WJ7LPNUJUc+Z3R3dWVu3Rv7nrqCdbCze0Hk8zSwMGhrsK93wnmaKZvpnZBm1gasA45y9/XjXrsHOMPd14a//44g6/wh4FZ3vyKcfilwPUEAf4a7vzGc/lrgBHf/i8k+/+ijj/avfe1rM54xic/P1+3mXd95lH86exlHL+5sdndisXNfhVd8aTVvOXGIVxw10OzuiETu/CtXc9LB3bz9eYua3ZXYvPe7j7JlT4VPn3tw5G1f86st/OcdG/mf1x5GV0ZrZH7x2G7e+b+P8vGzDuKZS7oibdvdOeMLq/jjZw3yumcvjLTtJPnzbzzCwq4CHz5taeRt/+ftG/jmfdv45usOj7ztpPjB73ZwyS2Pc+krD2F5X1ukbW/eXeb8qx7kbb83zDlP63/S6ytXrrzL3Y+brp3ZbP1nAT8bH0SH1gLLAcysAPQBm+unh5YRBOOTTZ/UbM4OJF6tVCOtmw0lq1phxIQ4hx7bXyMdS/OJUFs/RmNYhq3wVD4IlqFGPWlcIQz94jgWR/VwudnsAl5DXVmHmb3VzN4a/nodcEH486uAmzxIdV8HnB+O6nEocARwO3AHcISZHRpmus8P3zspPdkwOfbfKdzkjsQozgOISBK0xPB3+fkYeiy7yzDOJ7y2Qn0vBPOnUU8aV4jxfqWotuEZPS7QzLoIRth4c93kpwE/Dn++FPiima0iyESfD+Du95jZ1cC9QBm4yN0rYZtvBW4A8sDn3f2eqfqQy2X4tD9lyi1wp3A+Z+Rs/xBXIlnTCjcbxjmOdDDiRPD0uqwau9kwhqHHxhIyGT6OQDB/e+NYgLTGqCdxDsEY1cAJMwqk3X03wc2D9VYA7whf3wucN8nfXgxcPMH06wnqpWckrqfayOyVWiWTEONBWKSZqu6Uq9nOpkL82cDML78Yn/C6f8SEyJtOlGIetu+LMSOd8XUwzhG05jUjPRF3P2dOnzxLluGz/rRpmUxCPr6HOYg0Uys8WhjirU9tiYx+jOPpl1vlOBJrjXQLbMMx3q8U1X5Q9RIya+WIxl5MOmWkJavGnk6a8SCmkLdYyhKgNW70Ks5DRroVrmzG93TNFroqEkdGOqIbXlMTSKu0Izla4YleENZXKiMtGdQqQUxbzE+Vy/o+sDYiSSwPw2iRjHQh5qdrZj2hFe/JXPWAz2hUagJp3WyYHK2SkY7ziVQizdQql9VrQUwciRiVdsxNyxxHYjyZCzLSsTSdGPtveI1vCMaWKe3Q8HfJMdoqB+FcPGfBIs022iJBzNjwbXGMOtECGelYs4GtcmUz7ox0xpfffNzw2jKlHXogS3KUK5CzYIi4LItzByjSTK2SkY4zm9UKN3oV4swGtsjJXDHGGulWGEd6/1WR6NsutdrNhuVyudldkFCpBcauBNVIS3btr5FuckdiVntyd1zZrKyfiOTMyFs8y69lrmzGnJHO+rF4Pm54nes62PDwd/OtWCw2uwupdu09W/nZut2RtPXwln2ZPwuG4Cz1txv38r4b19FZMN763BF6OzIeeUhiPbBxL1f8YjNRHJN3jQalclkPYmr7qb+/+fHI91mrNu1lxUB7pG0mUTFv3PLgTh7cMhppu1v3lMfaz7Jg+Dt4343rIm/78R0lDulvi7zdJKmtH9++f1tkMUzNhp2lAz6jUakJpEulUrO7kGrfuHcrO/ZVWLRg7ickPe15jl/WHUGvku3kFT18b9UOHt02ytrtJU4/opfjWmC+JZlufWQXtz6yi8MXRhO8PWNRB08ZzHYgeORIJ08f7mD7vujvsRnpKfK8Q3oibzdpXnR4L/dt2Mum3dFfFX7Wkk4O6s12kuxZS7u4Y+2uWJbfsr42Tlye7WNSR8F4wYoe1u0oRb4McznjxOXd9HfOLUGWmkBaGem5qbhz4sHdvPMFi5vdldR4xVEDvOKoAe7fsJe/+OYajeAhTVVxJ2fwmXMPbnZXUuMpg+186iXLm92NVHvb7400uwup9szFnfzrS7XNNsrMeO8fLGl2N6aUmhppZaTnptwCtVRxifPOf5GZqrTAI71FRNImNYG0MtJzU9ZBuGG1cTorykhLE+lkWEQkeVITSCsjPTfVqpPxezpiE+cjSkVmquJ2fnSaAAAgAElEQVTahkVEkiY1gXShkJpy7kQqVz3z4z7HpbbclJGWZqpUsz92u4hI2qQmOtWTDeem4joIN2r/Qx3m7zO3761QjeGxxhPJmWlYvxSoVJ18alIfIiKtITWBtJ5sODflqlNQHN2Q2uX0+Rq1499v28C1926dl8+qefmR/fzZScPz+pkyO6qRFhFJntQE0tWqhkxolLtTVUa6YftH7YgukN6yp8y/3bqBvePS3O5w56O7eeGhPRy9qDOyz5vKr9bv4dp7t7Jm2ygW4SpSzBlvPnGYJRGMXS7hVaUovyAREZmz1ATSpgNIw2pP1lQg3ZhCPvoa6fs37OWHD+3kkP42OsZdKnj+ih7ecfIiOovzcx3/tCN6cYf1O6O7obdUcVZvGeW5B3ezZEFfZO22MpV2iIgkT2oCaWlcLQAs6CDckP2jdkTXZm0EkPecsrjpT5frLOYiH/B+0+4yr7nqQY10EiHdMCwikjypCa18nm68yqJaSYIuCzemViMdZUa6FF4mKGZ0PLPafNXmU+ZOD2QREUme1ATSKu1onEo75sbMKOSiHUd6LJDO6HdSmy8F0tEpayx4EZHESU0grZsNG1fLpKq+snH5nEWbka5mOyPdFs7XqEo7IlNxlXaIiCRNakIrDX/XuPJYjbQOwo0q5CzScaSzXtqRzxk5U0Y6SirtEBFJntQE0nogS+MqYQCY0ZhtXgSBtEo7ZqOQMwXSEaqotENEJHFSE0jrEeGN21/aoaNwowq5aMeRHs14aQcE86ZRO6KjUTtERJInNYF0qRTdGLetppYU1GXhxkWdkS5XwMj2VYJizigrIx2Zih6qJCKSOKkJpItFPR2tUWXdbDhnhYiDwlLVKeYt06PRKCMdrUrVNRa8iEjCpKZeQhnpxlXm82bDaoWFv/wsbTvWxP9ZoXLXMBuPuQgvdMT2GYWcUY4wJixVPNNlHRAG0spIRyYY/m5+1pnuR39I7+pvz8tnAXguz5YjX8e+gZXz9plxyu3bxtDdnyG/b9u8febewaex5cgLICMn5wse/F961t4yb5/n+XY2Hf1GSguWz9tnxqmwez0Lf/kf5Eq75+0zdy8+nm1HvHLePi8pUhNIKyPduPLYzYbR7WALO9fRufFXT5resenXDNx/FaMLDsZz8zDSisOCNd8nV9rJ7sUnRdr0aN+KsQN7Ierh7yrVTN9oCEFphwLp6ERd2mGVfXSv+zFWOTBJYdVRRu74B6rFLirt8/N498LuDXRs+g2bnvnmSNutFjrYtfRkiGlf1L75ftq2P/Sk6Qse+R7dj/6Q0d6DY/nc8awyGuwHK6OM9h4Sadt7ho6m3LM00jZrrLSL7sduxaoHDiaQK+1k5M5/pNyxkGpbdyyfPV5x1+O0bX+QLU/7o0jbrbQtYPeS58Z2gtOx4W6Kux5/0vS+VdfSsfFXlBYcFMvnjpcr7WHBmu9jlX1UOhZG2vbuRc+h0jkUaZs1uX1b6X7sdqDxY1VqAmllpBtX9ehLOxbd8TG6H/vJhK/tXPZC1j3/H+ctMzJy+0fpX/U1+lZ/K9J2S10jPPiyICtXyEU7lFuttCPLVNoRrUrVI92Ge9b8gCU/ed/En9XWyyNnXEa5e3F0HziFzsfvYNnNb2PJT94bedtrTv0sexY9J/J2AQ665e0U9myc8LWNR1/I5qPfFMvnPkm1wvLvXcjCX/9X5E3vXHoy6075ROTtAvSvupbhn39qwtdKXYt4+OwvU23rjeWzx+t5+Lss+fF7Y1kHHzznGkoRn+AAUC2z/PtvwarlCV9ef/y75y1DbOW9HPyd1zLys+jXla2HvYwnTvzbyNsFGLz3MgZ/c8Wc2phRIG1m/cDngGcQhO1/6u631r3eDlwOPAfYBLza3R8KX3sP8AagArzN3W8Ip58JfArIA59z949O1QdlpBtXjmHUjlxpJ3uGjubxk/7uSa+VFhw8r5cXnzj+XWx5+h+BRzdE4uA9/03PmpvHfo/8gSwtUNrRljdGlZGOTNTjSOdKOwF45LTPPSnzXOkYotrWE9lnTWfP4uNZfe63yJV2RNZm+7bVLP3hu8bmMw650Z1se8pL2Xzkaw+Y7rki5Z75yQQGHcmz5kX/QXHn2kibXXzrB8iVdkXaZr3ad/Pgi78a3H1dp9y1CC90xvbZ4+085HRWDx9Lrhzd/Hat/xmL7rgktnUwV9mLVctsfvrr2HbYSw54zfMd83YiDOCFDh4584sUdj85Oz4XB938dvIxb8Pl9n7WnDbBSegHz5hRGzPNSH8K+I67v8rM2oCuca+/Adji7oeb2fnAx4BXm9mRwPnAUcBS4Htm9tTwbz4NnAasBe4ws+vc/d7JOlAuT3zGJdOrjSNdiDC4tWqJcscgpd4VkbXZMLPI69oqnQux6ujY78U4aqRV2iGzEPUjwq0SrN+jfU+h2rYguoYbVOlcSKUzukvCtSydVeO7mmnVEuXOoWTsB3OFyPtRbVtAbjS6k5vxrFKimmuj1Lcits+YjUrXMBWGI2uvtHs9wJPKp6JSa7fctSgR66AXOqJfBwtdsW/DXuicU7+nvVBoZr3AC4BLAdx91N23jnvbucBl4c/XAKdaMBzBucBV7r7P3R8EVgEnhP9Wuftqdx8FrgrfOyk92bBxcWSkrTKK57J7lcBzbeSqJfD9yy6OUTuyrKCbDSMVjNoR7ckwkNnt2HNtwP4ThshVK5hXMrv8IFiGsS0/gnp8z2d7+QEHJGWiVGs308swH/M6GEEsM5OKu6cAG4AvmNnPzexzZja++v8gYA2Au5eBbcDC+umhteG0yaYfwMwuNLM7zezOTZs20dnZSXd3Nz09PXR0dNDf30+hUGBoKChCX7RoEQCLFy8+4PehoSEKhQL9/f10dHTQ09NDd3c3nZ2d9PX1USwWGRwcxMwYGRk5oI3a/8PDw+TzeQYGBmhra6O3t5euri66urro7e2lra2NgYEB8vk8w8PDE7YxMjKCmTE4OEixWKSvr29e5qmnN6gxWzS8MLJ5KlgVz7c1bZ7i/p48H+wAi3no6+ujrZinahbZPFU8R0dbIdPrXjFveK6QqXlq5vdUcWhvK0Q2TwXCUqhCe+bWPTNjcGQJAP09XbHMU86DjHeurSOz616urZM8lfjmqVrCwtGWsrTu1b6ngeGgjYW9PfHMk4UjcnV0Z27dq31PuWIHeSqxzVOuWiLfPvE+YqbMfeqMkZkdB9wGPM/df2pmnwK2u/v76t5zD3CGu68Nf/8dQdb5Q8Ct7n5FOP1S4HqCAP4Md39jOP21wAnu/heT9eOYY47xr371qzOeMdnvRw/t5EM3PcZnX3YwTxlsj6TNQ//nJexe9BzWP/cDkbSXNAO/uYLhn3+KVef9gGqxhw98bx2P7yjx2ZdHc8PIX1+/FgM+fvaySNpLog/f9BgPbx3lc6+I4SabFnTOZas498h+3nR8NHevL7z7MwzeezkPvOa2SNpLmvyeTRx27ZmsP+6dbHvqeZG3nxvdzuHXnMoTz34HW5/2msjbT4LFP/k7OjbczUPnfiOW9hfd9iG6Hv/p2E3dWdO25bes+N8/Yt3z/4Gdy38/+va3rWbFt1/NuuddzM5DTo+8/SQ46KaLyJX3sub0S+Np/wd/SX7fFh458/InvbZy5cq73P246dqYSUZ6LbDW3X8a/n4N8OwJ3rMcwMwKQB+wuX56aBmwborpk5ou4JfJjZV2RFlfWR0dy9pm0f7LwsHl70Iu2hEoWuFmQ40jHa1K1SlEXCOd6W04X7usHld9au2yeraXYS7O+tTK6Ni+NovGH0eiVms368sw3hrpue8Hpw2k3f1xYI2Z1UbKPxW418zeamZvDaddB1wQ/vwq4CYPIt/rgPPNrN3MDgWOAG4H7gCOMLNDw5sXzw/fKzGoePQPZLFKKdsbb1hzVqtBC0btiK593Wwos+Hu0Y8jXc34NhzWPcZVX5n1GnMI5i2u+l4I18FM1/ceeByJWmvUSBdjrtMvzXkbnumoHX8BfCkMelcDrwc+DPw4fP1S4ItmtoogE30+gLvfY2ZXA/cCZeAi92CMsjAIv4Fg+LvPu/s9U3Ugy49SjlstAMxFehDO9k0i1XGZhGJ+f2Y/Cq1ws6HGkY5ObTFGfsNwhrdhZaTnLrjZUBnpRsV+s2FtHcz4Mow1I10ZpVqY20N/ZhRIu/svgAPqRMxsBfCO8PW9wIRFaO5+MXDxBNOvJ6iXnhGVdjRu/yPCo2sz89msWiahsg8IngoZaSCt0g6ZhTjKs3IZD2KwHG75+DLSrRDE5OchI53x5QfzcFUkyydz+eLYcTgOwVWRuS2/hp9s6O7nzOmTZymXizAKbDHlasSlHdUy5tWMX9I8MJtVyMUQSGe8tKNNpR2RiXwbJvtXlSAcOkuX1RsWZAPL4FWw6I/BWV8H46+Rrp3MZXsZxp6Rnofh7xKhUonuqXWtphbL5CMqj2mVs2DYv6Mq5I1ylDXSLVTaoatJcze2Deuq0qx4ro2cbvRqWHWsxje+ZZjl5be/vEgZ6UZ5bh5qpOO+2TApCoWGk+ctb/8DWaJpr1XOgiHmjHQLBNKA6qQjUNFDlRoSZ2lCq2SkIcaMasYz0lgex+ahvCi7yzC4qhR3nX6LZKRLpfgWZNZFfRDefxYczZjUSTSWSahlpC3imw1boLSjVoag8o65q90wHPWTDasZzmRBvDfL5VogIx37qBMZz0hjFmsg2FIZ6ZiubAYZ6bnFMqkJpIvF7J5xxS3qg3Ct8D/LmYTxl+QKeaPqUI1gY3b3lintAAXSUYhlLHhlpOektTLScQXS+zK9/CDe0oSxY3GGt2PPt2E4eDzlvVGsg6kJpJWRblzUB+FWqA0cG4O2rrQDoslK12qts56RLo4tsyZ3JANqY8FHPo50hjNZUMtIx5dNrX1GVo2/Mhe1XCvU6SsjPSexn8xFsA6mJpBWRrpxFXdyFt1Y3K2YiakFMFE8lKVWM6yMtMxUbb1TjfTsxPlAkdbYD8b/QJEsLz+IeR1skSEYIaZ1MKIRyFITSCsj3bhKNdraytaoDTzwZsNiuKVEceNcLbBsmUBaNxvOWTmuseAznMkCqObjq5EeC6RbaD8YtczXSBNvnX5rPF0zvhteo8rop2YoDGWkG1eperS1la2UiRmXkf7SzzfTUZjbwtxTDgPpFintuObXWxjoyHPC8m6OWtTZ5F6l09gNwxE+4bVVMtK5yt5Y2q4d2KsZDgTjf8x6C2Sk46zTr4zilodcPpb2kyDOjHRUo56kJpAul8vN7kJqld0jf5ADtFYm5pD+NtoLxnW/2RpJ+x0F4+D+7C4/gGV9Rbrbctz4wHYqDr9+Yi//dPayZncrlcqxjNoxmvmMtOfbsNEdsbTdEgmFODPSXsWq5UwfRyDujHQrnAwrIx2ZfD67Z1xxq1QhF2ltZQuUdoxlYoK7oo9Z0sU3X3d4M7uUOisG2rn2jw8D4GO3PM6vHt/T5B6lV220mCgf8GpZf0Q4qk+dqzgz0q1woxzE+4jrXCX75Vm1E9VcNfplGFVGOjU10nqyYeMqVY+4tjJY+aotkImJ66lorWakp8DG3eWxEgWZnXgeEd4KB+H2WB/P7BjkUpOPmrXa+LpxZKRboTQGwnUwtlE7WuCqUi5cB2PJSNeuKrXIqB25KFMxLabiHnFtZfYz0lgOt3xs2axWs6inSNVh426VaDVibNSOqLZj99aokY51HOnwRCTCfWvSxJuRzn5pDNTq9PVAm0ZVY62RjiaWSU106jE91aYVlCMetaNldoAx3vHfakZ6gqzdEzsVSDci8lE7vILhLZDNinEM35Y4EYmvRroVSmMg7nGkW+BmzVhrpKOJZVITSEc1BnIrqlSdfKS1lS2QkaZ2EFZGOgqLeoId1fqdOjFpRNQPZGmZICbOp8q1QmmMaqTnLN6xzLOfkY531I5oYpnUFHet3zHKp299otndSKXfbtxH+xyHbKsXVV1R0sWZSWg1w93Brubb923j/g3xDEeWZet2BOthVKUdLXVVKc5sYMtkpJM79FjSxTpqRytcFZmXjHSLBNLb91a4aXU8wxi1gmcv7YmsrbGzuIwH0tUYs1mtpqOQ4zkHdfHbjXt5ZJuWaSOW9hYZ7olml906V5XCbdg98lrmVqhPTcPQY0kXd51+tQWWH8RzMhfVw+VSE0g/daSLq//osGZ3Q6gfRzrjZ8LKSEfqkjMOanYXJNRSGWkcvAIW7eGuJepTU/AwjKSLOyNdDUdWyar9J3Px3fA61xHIUlMjreHvkiN4mlIu08M+Qbz1lSLN1DoZ6RgPwi2RkQ7H8FVGumHV+Rg5JsPiveG1xUbt0ANZkiO4wSHbWQSoZaQVSEv2tE5GOsaMagtkpLEcnisoIz0HQUY6xkeEZ375JX8IxtSkFPWI8LlZ8sN30bP2lmga8yrVtt5o2kowz3fQ/dhtHHHlSVQLXTxyxhco9R7S7G5Ji+p55Hss/sn7MY/u6lzWLwtX8x0AHPb1MyNv27zC7kXHRd5u0lTzHQzcezkDv7ki4paDkWg8/I6yygsdmFc44sqTIm/bvMK+/mw/cbf2UKDhn32C4Z9/KurWD/iMRqUmkC4Ws33WFbf2zfcx2ruCncteEEl7+waeGkk7SbbpmW9mz2O3Utizgb7V36K481EF0tI07VtXkauOsumo10fSnhc62TPy7EjaSqqdy05h454Nsd3rsGvp82JpN0nWn/A3tG99IJa2q8UFmQ8Etx/64mD982os7e88+NRY2k0KL3Tw+Invpbjz0VjaL3cOUekcmlMbqQmkSyXd9DUX5mX2LDySTcf8ebO7khp7Ro5lz8ixtG++j77V39KNh9JUVi3juYK24VmodvSz+eg3NbsbqbbzkNPYechpze5GapW7F7PpmW9pdjdSbfth5za7C1NKTY20MtJzUzsIy+zVlptVVV4kTVQt4xGPPCEiInOTmkBaGem5sWr0wz+1irGbHZSRlibSybCISPKkJpBWRnqOdBBumAJpSQJzbcMiIkmTmr2yMtJzo4Nw4/YH0vNY2hHTAP6TyvowXhlg1XLmx24XEUmb1OyVC4XUdDWRdFm4cWPjdM5TRnrh3f/Owns+Py+fVbPpqD9l0zF/Nq+fKbOkGmkRkcRJzV5ZTzacA/dg7FkdhBsTw82G+b1bGLnzH7Dy3gOmm1fpeuxWdi49mb3Dz4zs86bSseGXDN7zBTo23xc8sTIini+y4di/pNyjR4NHQSfDIiLJk5q9sp5sOAdhAKiDcGP2j9oRXUa6Y9OvWfDI99jXeyheOPCBBDuXn8LjJ70fL3ZH9nlTsdIuFt/2QYq7Ho+uzWqJ9q2r2LX0ZLYrkI6EeUXbsIhIwsxor2xmDwE7gApQdvfjxr3eDlwOPAfYBLza3R8KX3sP8Ibwb9/m7jeE088EPgXkgc+5+0en6kO1Gs9g5q3AXIH0XOx/RGl0gXTtcaePnfz3jDb5gQRe7Oax5/9DpG3m92zisGvP1CPWI6QaaRGR5JnNXvn33X3jJK+9Adji7oeb2fnAx4BXm9mRwPnAUcBS4HtmVnsk3qeB04C1wB1mdp273zvZh5vZLLoq9UwZ6bnJ5XHLRZqRtso+ADzXFlmbSeL5YL5qJwwSAdVIi4gkTlQFkecCl4U/XwOcakHkey5wlbvvc/cHgVXACeG/Ve6+2t1HgavC90oMxmp7dRBumOeKkdZI14LyWsCZNQqko6caaRGR5JlpIO3Ad83sLjO7cILXDwLWALh7GdgGLKyfHlobTpts+uQdcJ9hV+VJlJGeM88V4slIZzWQDsthcgqkI6MhLEVEkmemgfTz3P3ZwFnARWb2gnGvT1R34Q1MP7BRswvN7E4zu3Pr1q10dnbS3d1NT08PHR0d9Pf3UygUGBoaAmDRokUALF68+IDfh4aGKBQK9Pf309HRQU9PD93d3XR2dtLX10exWGRwcBAzY2Rk5IA2av8PDw+Tz+cZGBigra2N3t5eurq66Orqore3l7a2NgYGBsjn8wwPD0/YxsjICGbG4OAgxWKRvr6+eZmnocE+AHoHFmZmnub7eyLfRjFPZPPUFm55+fbubK57C4eo5op0dxSzM09N/p7y5uQK7Zmapyx+T5onzZPmKRvzNFM220yvmX0A2OnuH6+bdgPwAXe/1cwKwOPAMPBuAHe/pP594Z99wN3PCKe/p/59Ezn66KP9a1/72qz6KoHijjUc+s1X8NhzP8iOQ89udndS6SnXnsXOpSfzxIl/G0l7g/d8gaG7P8NvX/2TzD4M5bCvnsL2p7yUDc95R7O7kgnLb3g91bYeHv39f212V0REMm/lypV3jR9cYyLTZqTNrNvMFtR+Bk4Hfm1mbzWzt4Zvuw64IPz5VcBNHkTo1wHnm1m7mR0KHAHcDtwBHGFmh5pZG8ENiddN1Q8Nf9c43Ww4d0GNdPSjdmR5FAbPtWnUjgiZbjYUEUmcmeyVFwHXhqNmFIAvu/t3zOzfgB+H77kU+KKZrQI2EwTGuPs9ZnY1cC9QBi5y9wpAGITfQDD83efd/Z6pOqEHssxB7Sa5DAdtcYv8ZsPKKNV8O2R4NBrPt+lmwwipRlpEJHmm3Su7+2rgmAleWgG8I3zPXuC8Sf7+YuDiCaZfD1w/447qEeENGxshQtmshsVxs+HYo8czynMKpKNk1ZJOhkVEEqbhvbK7nxNlR6ZTKkUXxLQalXbMXeSlHdVSZkfsqPG8SjsipeHvREQSJ6pxpGNXLGY7excnPdlw7qKvkd6H59sjay+JVNoRLY0jLSKSPKkJpJWRngPVSM9ZHA9kyXxphwLpSOlmQxGR5ElNIK2MdOPGSjt0EG5Y1DXSucpo5jPS1VwbOZV2RMa8rJNhEZGESU0grYx041QjHYFcAavoZsPZUEY6YirtEBFJnNQE0spIN0410nPnuSJEfLNhNeMZaQXS0VKNtIhI8qQmkC6Xo6tPbTmqkZ4zzxXHTkiiENxsmPFROzT8XaRUIy0ikjypCaT1ZMPGqUZ67jxXwCpRBtItcrOhaqSj4Y55RSfDIiIJk5pAulqtNrsLqaUa6bnzfNTjSGv4O5mF4IGw2oZFRBImNYG0ZfhRynEbK0nQQbhh0Y8jPYrnM56RVmlHZHQyLCKSTKkJpN292V1ILx2E5yzycaQrpdbISKu0IxIqzxIRSabUBNLSOGWzImARjyNd3Yfnsn2zYTXfTq5aAldZ1pzphmERkURKTSCt0o7GWTWsr1Q2q2GeL0Q7/F2lRDXzpR3B/EV5AtKqNISliEgypSaQVmlH41QjPXeeK4bZ1WjWw2D4u+yXdgCqk46AriqJiCRTagLpXC41XU0eHYTnbGyounD0hDmpVjCvtMTwdxCcNMjcqEZaRCSZUhOdVioRBDAtKniQQw4sNV934kRZplBrI/MZ6bAGXKUdEVCNtIhIIqUmsioUdABplJ6INne1bH4UI3fUMrSZf7JheKKgjPTcqUZaRCSZUrNXLpWU1WqUVcuZLyOIW235Ddz3ZaqFrjm1lSvvCtvMeiAdLLP+B66l3DnE7iUnsG9gZZN7lU6qkRYRSabU7JWLRQWCDfOyLgnPUalnGW45Fv76c5G055ajtOCgSNpKqlL3QXiuwMD9XwZg97rnsPZFn21yr9JJgbSISDKlZq+sjHTjgox0vtndSLXdS5/Lqj/8v+jGRLZc5muk9w2uZNV5t4BXWHT7JXSuv6vZXUov1UiLiCRSavbKykg3LgikU/NVJ1bWA9841OrAR3sPofeh/8XKe/FCR5N7lT5jNdK610FEJFFSc7OhMtKNs2pJB2BpqlLPcgCKO9c2uSfpZJVwpBedEIuIJEpqAmllpOegqhppaa7RBcsAKO58tMk9SSeN2iEikkyp2Sv7zifov/+qZncjldq2P6QDsDRVKQykex+8nuKux5rcm/Rp37oq+EHbsYhIoqRmr1zY9Tgjd/1Ts7uRWjuXntzsLkgLq7b1MrrgYBasuYkFa25qdndSqZpvp9yxsNndEBGROqkJpEcHV7LqlZc1uxupVS32NLsL0uIeOvsqcuXdze5Ganm+XTdqiogkTGoCacsXqbb3NbsbItKofJFqXtuwiIhkR2puNnT3ZndBRERERGRMagJpM2t2F0RERERExqQmkBYRERERSZLUBNIq7RARERGRJJlxIG1meTP7uZl9a4LXBs3sRjN7IPx/IJxuZvYvZrbKzH5pZs+u+5sLwvc/YGYXTNvRXGpifhERERFpAbOJTv8S+M0kr70b+L67HwF8P/wd4CzgiPDfhcC/QxB4A+8HTgROAN5fC74nU6lUZtFVEREREZF4zSiQNrNlwIuBz03ylnOB2iDPlwEvq5t+uQduA/rNbAlwBnCju2929y3AjcCZU/Uhn8/PpKsiIiIiIvNiphnpTwLvBKqTvL7I3R8DCP8fCacfBKype9/acNpk0w9gZhea2Z1mduemTZvo7Oyku7ubnp4eOjo66O/vp1AoMDQ0FHRi0SIAFi9efMDvQ0NDFAoF+vv76ejooKenh+7ubjo7O+nr66NYLDI4OIiZMTIyckAbtf+Hh4fJ5/MMDAzQ1tZGb28vXV1ddHV10dvbS1tbGwMDA+TzeYaHhydsY2RkBDNjcHCQYrFIX1+f5knzpHnSPGmeNE+aJ82T5ilh8zRTNt1NfGZ2DnC2u/+5mZ0C/H/ufs6492x19/6637e4+4CZfRu4xN1/FE7/PkFA/gdAu7t/JJz+PmC3u0/6DPBjjz3Wv/KVr8x4xkREREREGrFy5cq73P246d43k4z084CXmtlDwFXAH5jZFePesz4s2SD8/4lw+lpged37lgHrppg+qVKpNIOuioiIiIjMj2kDaXd/j7svc/cVwPnATe7+x2Z2iZm9PHzbdUBt5I0LgG/UTX9dOHrHScC2sPTjBuB0MxsIbzI8PZw2qWKxONt5ExERERGJTWEOf3s0QaAM8FHgajN7A6vugYIAABn8SURBVPAIcF44/XrgbGAVsBt4PYC7bzazDwN3hO/7kLtvnurDlJEWERERkSSZVSDt7jcDN4e/Ft391nD6JuDUCd7vwEWTtPV54PMz/WxlpEVEREQkSRp+yom7nxFlR6ajjLSIiIiIJElqHhdYKMylCkVEREREJFqpCaT1ZEMRERERSZLUBNJ6sqGIiIiIJElqAulqdbKHKoqIiIiIzL/UBNJm1uwuiIiIiIiMSU0gLSIiIiKSJKkJpIMhqUVEREREkiE1gbRKO0REREQkSVITSOtmQxERERFJktQE0hr+TkRERESSJDWBtB7IIiIiIiJJkppAWo8IFxEREZEkSU0gXSqVmt0FEREREZExqQmki8Vis7sgIiIiIjImNYG0MtIiIiIikiSpCaSVkRYRERGRJElNIK2MtIiIiIgkSWoCaWWkRURERCRJUhNIl8vlZndBRERERGRMagJpPdlQRERERJIkNYF0tVptdhdERERERMakJpA2s2Z3QURERERkTGoCaXdvdhdERERERMakJpAWEREREUmS1ATSKu0QERERkSRJTSCt0g4RERERSZLUBNK5XGq6KiIiIiItIDXRaaVSaXYXRERERETGpCaQLhQKze6CiIiIiMiYaQNpM+sws9vN7G4zu8fMPjjBewbN7EYzeyD8fyCcbmb2L2a2ysx+aWbPrvubC8L3P2BmF0zXj1KpNNt5ExERERGJzUwy0vuAP3D3Y4BnAWea2Unj3vNu4PvufgTw/fB3gLOAI8J/FwL/DkHgDbwfOBE4AXh/LfieTLFYnNEMiYiIiIjMh2kDaQ/sDH8thv/GD6FxLnBZ+PNlwMvqpl8etnEb0G9mS4AzgBvdfbO7bwFuBM6cqh/KSIuIiIhIksyoRtrM8mb2C+AJggD4p+PessjdHwMI/x8Jpx8ErKl739pw2mTTJ6WMtIiIiIgkyYwCaXevuPuzgGXACWb2jBm2P9FTVHyK6Qf+sdmFZnanmd25ceNGOjs76e7upqenh46ODvr7+ykUCgwNDQGwaNEiABYvXnzA70NDQxQKBfr7++no6KCnp4fu7m46Ozvp6+ujWCwyODiImTEyMnJAG7X/h4eHyefzDAwM0NbWRm9vL11dXXR1ddHb20tbWxsDAwPk83mGh4cnbGNkZAQzY3BwkGKxSF9fn+ZJ86R50jxpnjRPmifNk+YpYfM0UzbbB52Y2fuBXe7+8bpp9wOnuPtjYenGze6+0sz+I/z5yvr31f65+5vD6Qe8byLHHnusf+UrX5lVX0VEREREZmvlypV3uftx071vJqN2DJtZf/hzJ/Ai4D4zu8TMXh6+7TqgNvLGBcA36qa/Lhy94yRgW1j6cQNwupkNhDcZnh5Om1S5XJ6uqyIiIiIi82YmgzMvAS4zszxB4H21u3/LzN5CECgDfBS42szeADwCnBdOvx44G1gF7AZeD+Dum83sw8Ad4fs+5O6bp+rEbNLsIiIiIiJxmzaQdvdfAsdO8FLR3W8N37MJOHWCv3Xgokna/Tzw+Zl2VE82FBEREZEkafjJhu5+RpQdmU4ul5qHMIqIiIhIC0hNdDrbmyJFREREROKUmkDabKIR80REREREmiM1gbSIiIiISJKkJpBWaYeIiIiIJElqAmndbCgiIiIiSZKa6FTD34mIiIhIkqQmkNYDWUREREQkSVITSOsR4SIiIiKSJKkJpIvFYrO7ICIiIiIyJjWBdKlUanYXRERERETGpCaQVkZaRERERJIkNYG0MtIiIiIikiSpCaSVkRYRERGRJElNIK2MtIiIiIgkSWoC6UKh0OwuiIiIiIiMSU0grScbioiIiEiSpCaQ1pMNRURERCRJUhNIV6vVZndBRERERGRMagJpM2t2F0RERERExqQmkBYRERERSZLUBNLu3uwuiIiIiIiMSU0grdIOEREREUmS1ATSutlQRERERJIkNYG0hr8TERERkSRJTSCtB7KIiIiISJKkJpDWI8JFREREJElSE0iXSqVmd0FEREREZExqAulisdjsLoiIiIiIjElNIK2MtIiIiIgkSWoCaWWkRURERCRJpg2kzWy5mf3AzH5jZveY2V9O8J52M/uKma0ys5+a2Yq6194TTr/fzM6om35mOG2Vmb17un4oIy0iIiIiSTKTjHQZ+Gt3fzpwEnCRmR057j1vALa4++HAJ4CPAYTvOx84CjgT+IyZ5c0sD3waOAs4EnjNBG0eQBlpEREREUmSaQNpd3/M3X8W/rwD+A1w0Li3nQtcFv58DXCqBc/0Phe4yt33ufuDwCrghPDfKndf7e6jwFXheydVLpdnPlciIiIiIjGbVY10WLJxLPDTcS8dBKwBcPcysA1YWD89tDacNtn08Z93oZndaWZ3btu2jc7OTrq7u+np6aGjo4P+/n4KhQJDQ0MALFq0CIDFixcf8PvQ0BCFQoH+/n46Ojro6emhu7ubzs5O+vr6KBaLDA4OYmaMjIwc0Ebt/+HhYfL5PAMDA7S1tdHb20tXVxddXV309vbS1tbGwMAA+Xye4eHhCdsYGRnBzBgcHKRYLNLX16d50jxpnjRPmifNk+ZJ86R5Stg8zZS5+8zeaNYD3AJc7O5fH/faPcAZ7r42/P13BFnnDwG3uvsV4fRLgesJAvgz3P2N4fTXAie4+19M9vnPfOYz/ZprrpnxjImIiIiINGLlypV3uftx071vRhlpMysCXwO+ND6IDq0FlofvLQB9wOb66aFlwLoppk/Vh5l0VURERERkXsxk1A4DLgV+4+7/XDf9rWb21vDX64ALwp9fBdzkQar7OuD8cFSPQ4EjgNuBO4AjzOxQM2sjuCHxuqn6MdPMuYiIiIjIfCjM4D3PA14L/MrMfhFO+xvgacCPw98vBb5oZqsIMtHnA7j7PWZ2NXAvwegfF7l7BYJAHLgByAOfd/d7opklEREREZH4TRtIu/uPgCfVVZjZnwPvCN+zFzhvkr+/GLh4gunXE9RLz4hKO0REREQkSWaSkZ6Qu58TZUdm8Hnz+XEiIiIiIlNKzSPCc7nUdFVEREREWkBqotNKpdLsLoiIiIiIjElNIF0oNFyFIiIiIiISudQE0qVSqdldEBEREREZk5pAulgsNrsLIiIiIiJjUhNIKyMtIiIiIkmSmkBaGWkRERERSZLUBNLKSIuIiIhIkqQmkFZGWkRERESSJDWBdLlcbnYXRERERETGpCaQzufzze6CiIiIiMiY1ATSerKhiIiIiCRJagLpXC41XRURERGRFpCa6NTdm90FEREREZExqQmkzazZXRARERERGZOaQFpEREREJElSE0irtENEREREkiQ1gbRuNhQRERGRJElNdKrh70REREQkSVITSOuBLCIiIiKSJKkJpPWIcBERERFJktQE0sVisdldEBEREREZk5pAulQqNbsLIiIiIiJjUhNIKyMtIiIiIkmSmkBaGWkRERERSZLUBNLKSIuIiIhIkqQmkFZGWkRERESSJDWBdKFQaHYXRERERETGpCaQ1pMNRURERCRJpg2kzezzZvaEmf16ktfbzewrZrbKzH5qZivqXntPOP1+MzujbvqZ4bRVZvbumXRUTzYUERERkf+/vbuP0aws7zj+/e28wL6wy667CxaoYFlARLQp5Y++Wl+R2EBbjGBjxWqJtmtbjVHaNDEhpaGx0baKtRgQqhQ0oHa1tGjUBGpRWemirPiyCq1bqNsGqtAK7s5c/eOcnS4wz8w4A3POYb+f5GTmuZ97zlz3lfPMcz333OecPlnIjPSVwBlzPP8a4P6qOh54J/CnAElOBs4Fntn+/HuSjCUZAy4FXgKcDJzX9p3T9PT0AkKVJEmSlse8hXRV3QTcN0eXs4Cr2u+vA56fJG37tVX1cFXdBewCTm+3XVX17ar6IXBt23dOzS4lSZKkfng81kgfBXwHoKr2Ad8DnnJge2t32zaq/TGSXJBke5Lt9913HytXrmT16tWsWbOGQw89lMMPP5zx8XE2btwIwBFHHAHAkUce+YjHGzduZHx8nMMPP5xDDz2UNWvWsHr1alauXMm6deuYmJhgw4YNJGHz5s2P2Mf+r5s2bWJsbIz169czOTnJ2rVrWbVqFatWrWLt2rVMTk6yfv16xsbG2LRp06z72Lx5M0nYsGEDExMTrFu3zjE5JsfkmByTY3JMjskx9WxMC5Wqmr9Ts+75E1V1yizP7QReXFW728ffopl1vgi4pao+2LZfDtxAU7y/uKpe27a/Eji9qt4wVwynnnpqXXfddQsemCRJkrQYJ5544peq6rT5+j0eM9K7gWMAkowD62iWgsy0t44G7pmjfU4u7ZAkSVKfLKqQTrI1ydb24TbgVe335wCfqWaaextwbntVj+OALcAXgVuBLUmOSzJJc0Litvl+pycbSpIkqU/mvctJkmuA5wIbk+wG3gacBHyu7XI58IEku2hmos8FqKqdST4MfBXYB/xOVU21+9wK3AiMAVdU1c754vDyd5IkSeqTBa2RfswPJZ8AfrW96sayeNaznlXXX3/9cv06SZIkHaQWukZ6UffdrqqXLubnlsJbhEuSJKlPBnOL8L1793YdgiRJkjRjMIX0xMRE1yFIkiRJMwZTSDsjLUmSpD4ZTCHtjLQkSZL6ZDCFtDPSkiRJ6pPBFNLOSEuSJKlPBlNI79u3r+sQJEmSpBmDKaS9s6EkSZL6ZDCF9PT0dNchSJIkSTMGU0gn6ToESZIkacZgCumq6joESZIkacZgCmlJkiSpTwZTSLu0Q5IkSX0ymELapR2SJEnqk8EU0itWDCZUSZIkHQQGU51OTU11HYIkSZI0YzCF9Pj4eNchSJIkSTMGU0jv3bu36xAkSZKkGYMppCcmJroOQZIkSZoxmELaGWlJkiT1yWAKaWekJUmS1CeDKaSdkZYkSVKfDKaQdkZakiRJfTKYQnrfvn1dhyBJkiTNGEwhPTY21nUIkiRJ0ozBFNLe2VCSJEl9MphCesWKwYQqSZKkg8BgqtOq6joESZIkacZgCukkXYcgSZIkzRhMIS1JkiT1SaeFdJIzknw9ya4kF87V16UdkiRJ6pPOCukkY8ClwEuAk4Hzkpw8qr8nG0qSJKlPxjv83acDu6rq2wBJrgXOAr46W+ddu3Zx/vnnL190kiRJ0hy6nOY9CvjOAY93t20zklyQZHuS7VNTU4yNjc1sK1asYHx8nCQztw+fnJyc9evExARJGB8fZ8WKFY/Yz/62/X1G7WNycnLmd+3/3Y/nPhyTY3JMjskxOSbH5JgcUz/GtFBdzkjPFuUjFkJX1WXAZQCnnHJKXX755csRlyRJkg5iJ5544oL6dTkjvRs45oDHRwP3jOq8/1OIJEmS1AddFtK3AluSHJdkEjgX2Daq8969e5ctMEmSJGk+nS3tqKp9SbYCNwJjwBVVtXNUf2ekJUmS1CddrpGmqm4AblhIX2ekJUmS1CeDuTizM9KSJEnqk8EU0s5IS5IkqU8GU0iPj3e6CkWSJEl6hMEU0lNTU12HIEmSJM0YTCE9NjbWdQiSJEnSjMEU0tPT012HIEmSJM0YTCH9o9z3XJIkSXqiDaaQliRJkvpkMIV0VXUdgiRJkjQjQylQk/wAGHkLcS3IOuB7XQcxYOZv6czh0pi/pTOHS2P+ls4cLs1y5e/Eqjpsvk5Dujjzg1V1WtdBDFmSy6rqgq7jGCrzt3TmcGnM39KZw6Uxf0tnDpdmufKXZPtC+g1maQfw310H8CTw8a4DGDjzt3TmcGnM39KZw6Uxf0tnDpemV/kb0tKO7c5IS5Ik6Ym20LpzSDPSl3UdgCRJkg4KC6o7BzMjLUmSJPXJkGak9SNIckaSryfZleTCtu3qtu2OJFckmeg6zj4bkcPLk9ye5MtJrkuypus4+2q2/B3w3LuSPNhVbEMx4hi8MsldSXa023O6jrOvRuQvSS5O8o0kdyb53a7j7KsR+bv5gGPvniQf6zrOPhuRw+cnua3N4T8lOb7rOPtqRP6e1+bvjiRXJen2whlV1csNOAP4OrALuLBt29o+LmBj1zH2dQPGgG8BTwcmgduBk4EzgbTbNcDru461r9scOVx7QJ937D823RaWv/a504AP0FyJp/NY+7rNcQxeCZzTdXx93+bI36uBvwFWtP02dx1rH7e5XsMH9Lke+I2uY+3rNscx+A3gGW2f3wau7DrWPm5z5O87wAltn4uA13QZZy9npJOMAZcCL6FJ2nlJTgY+B7wA+NcOwxuC04FdVfXtqvohcC1wVlXdUC3gi8DRnUbZb6Ny+H1oZrWAlTQf6vRYs+avfW2/HXhLp9ENw6w57DimIRmVv9cDF1XVNEBV7ekwxj6b8/hLchjwPMAZ6dFG5bCAtW2fdcA9HcXXd7Pl79eAh6vqG22fT7VtnellIc3oIuZfqurubkMbhKNoPrHtt7ttA6Bd0vFK4B+XOa4hGZnDJO8H/gM4CXjX8oc2CKPytxXYVlX3dhLVsMz1Or64XV70ziSHLH9ogzAqfz8BvDzJ9iT/kGRLJ9H135zvI8CvAJ/eP7mgWY3K4WuBG5LspnkvvqSD2IZgtvwdCUwk2X81jXOAY5Y7sAP1tZCe7wWsuWWWtgNnTt8D3FRVNy9TPEM0ModV9Wrgx4A7gZcvZ1ADMlv+DgFehh8+FmrUMfgHNB/ifhrYALx1OYMakFH5OwR4qJrLWr0PuGJZoxqO+d5HzqNZIqjRRuXwjcCZVXU08H6aZYJ6rNnyNw2cC7wzyReBB4B9yxrVo/S1kJ7vBay57eaRn9COpv3XUZK3AZuAN3UQ15CMzCFAVU0BH6Ljfyn12Gz5uxs4HtiV5G5gVZJdyx/aYMx6DFbVve0KrYdp3oRP7yS6/hv1Gt5Ns7YX4KPAqcsc11DM9T7yFJrj7u87iGtIZsvhHuDZVfWFtu1DwM8sd2ADMepv4C1V9fNVdTpwE/DNTqJr9bWQnrOI0bxuBbYkOS7JJM2nt21JXgu8GDhv//pAjTQqh8fDzBrpXwa+1mGMfTZb/j5WVUdW1bFVdSzwv1Xl2eqjjToGnwozx+DZwB0dxthns+aPZk3v89o+v0hz4pcea1T+oPnP0ieq6qHOohuGUTlcl+SEts8Laf67qcca9TdwM0C7rO2twHs7jJFuLxky2kzygH+nSd4rug1pOKpqX5KtwI00Z71eUVU7k9xOc6LmLc17MB+pqos6DLW3ZsshzR+7m5Ospfmvye00Jy7pUUYdgx2HNShzvI4/k2QTzTG4A3hdl3H21Rz5uwS4OskbgQdp1qvqUeZ5DZ+L63rnNSKHtyf5LeD6JNPA/cBvdhlnX83xGn57kpfSTAb/VVV9pss4e3tDliRnAn/O/yfv4vZ6n2+hWWy+B7ihqvwjKEmSpGXX20JakiRJ6rO+rpGWJEmSes1CWpIkSVoEC2lJkiRpEXpXSCd5sOsYJEmSpPn0rpCWJEmShqCXhXSSNUk+neS2JF9JclbbfmySO5O8L8nOJJ9MsrLreCVJknTw6d3l79qlHYcDq6rq+0k2Ap8HtgBPA3YBp1XVjiQfBrZV1Qe7i1iSJEkHo77e2TDAnyT5BWAaOAo4on3urqra0X7/JeDY5Q9PkiRJB7u+FtK/DmwCfqqq9ia5Gzi0fe7hA/pNAS7tkCRJ0rLr5RppYB2wpy2if4lmSYckSZLUG72akU4yTjPjfDXw8STbgR3A1zoNTJIkSXqUXp1smOTZwPuq6vSuY5EkSZLm0pulHUleB1wD/FHXsUiSJEnz6dWMtCRJkjQUvZmRliRJkoak00I6yTFJPtverXBnkt9r2zck+VSSb7Zf17ftJyW5JcnDSd78qH3d3d4FcUd7kqIkSZL0hOl0aUeSpwJPrarbkhxGc4OVs4Hzgfuq6pIkFwLrq+qtSTbTXArvbOD+qvqzA/Z1N80dD/9rucchSZKkg0+nM9JVdW9V3dZ+/wBwJ81dDM8Crmq7XUVTOFNVe6rqVmBvB+FKkiRJM3qzRjrJscBPAl8Ajqiqe6EptoHNC9hFAZ9M8qUkFzxRcUqSJEnQkxuyJFkDXA/8flV9P8lidvOzVXVPu/zjU0m+VlU3Pa6BSpIkSa3OZ6STTNAU0VdX1Ufa5u+266f3r6PeM99+quqe9use4KOAN3WRJEnSE6brq3YEuBy4s6reccBT24BXtd+/Cvi7efazuj1ZkSSrgRcBdzz+EUuSJEmNrq/a8XPAzcBXgOm2+Q9p1kl/GPhx4N+Al1XVfUmOBLYDa9v+DwInAxtpZqGhWa7yt1V18XKNQ5IkSQcf72woSZIkLULna6QlSZKkIbKQliRJkhbBQlqSJElaBAtpSZIkaREspCVJkqRFsJCWpIFJMpVkR5KdSW5P8qYkc/49T3JsklcsV4ySdDCwkJak4flBVT2nqp4JvBA4E3jbPD9zLGAhLUmPI68jLUkDk+TBqlpzwOOnA7fS3JzqacAHgNXt01ur6p+TfB54BnAXcBXwl8AlwHOBQ4BLq+qvl20QkvQkYCEtSQPz6EK6bbsfOAl4AJiuqoeSbAGuqarTkjwXeHNVvbTtfwGwuar+OMkhwOdo7iJ717IORpIGbLzrACRJj4u0XyeAdyd5DjAFnDCi/4uAU5Oc0z5eB2yhmbGWJC2AhbQkDVy7tGMK2EOzVvq7wLNpzoN5aNSPAW+oqhuXJUhJehLyZENJGrAkm4D3Au+uZq3eOuDeqpoGXgmMtV0fAA474EdvBF6fZKLdzwlJViNJWjBnpCVpeFYm2UGzjGMfzcmF72ifew9wfZKXAZ8F/qdt/zKwL8ntwJXAX9BcyeO2JAH+Ezh7uQYgSU8GnmwoSZIkLYJLOyRJkqRFsJCWJEmSFsFCWpIkSVoEC2lJkiRpESykJUmSpEWwkJYkSZIWwUJakiRJWoT/AwT24BzFLFbQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = [12,6])\n",
    "ec_industry_combined['industry'].head(800).plot(ax=ax)\n",
    "ec_industry_combined['industry without self-generation'].head(800).plot(ax=ax)\n",
    "plot.set_ax_format(ax=ax, ylabel='[MWh]', xlabel='Date',\n",
    "                   axtitle='Power Consumption of Industry in Germany with and without self generation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.2 Disaggregation of electricity consumption using **branch load profiles**: Function ``disagg_temporal_power_industry_blp()``\n",
    "- works similar as disaggregation with shift load profiles, however uses industry specific branch load profiles (BLP), which have been created using measured data\n",
    "- can only be applied to the electricity consumtion, so ``source = 'power'`` has to be set \n",
    "- covers 39 industries from CTS sector and 12 from industrial sector\n",
    "- industries where no BLP is available will be disaggregated using SLP\n",
    "- the function can be called using the same arguments as ``disagg_temporal_power_CTS()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disaggregator.temporal: INFO     Working on state: SH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NI.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: RP.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BY.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SL.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: MV.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SN.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: ST.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: TH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Start downloading BLP from Database now. This may take a while depending on the connection. ca. 250MB per industry will be downloaded and stored in your local folder ../data_in/cache.\n",
      "disaggregator.temporal: INFO     Working on WZ10\n",
      "disaggregator.temporal: INFO     Working on WZ11\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ11&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ12\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ12&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ17\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ17&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ21\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ21&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ22\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ22&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ24\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ24&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ25\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ25&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ26\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ26&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ28\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ28&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ29\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ29&type=eq.Mean\n",
      "disaggregator.temporal: INFO     Working on WZ32\n",
      "disaggregator.config: INFO     Querying from:\n",
      "http://opendata.ffe.de:4000/demandregio_regional_load_profiles?&year=eq.2016&wz=eq.WZ32&type=eq.Mean\n"
     ]
    }
   ],
   "source": [
    "df_industry_blp_detailed = temporal.disagg_temporal_industry_blp(year=2016, detailed=True, use_nuts3code=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "[5 rows x 11629 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_industry_blp_detailed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Industry gas consumption\n",
    "- uses the same function ``disagg_temporal_industry()`` but with argument ``source='gas'``\n",
    "- works similar as temporal disaggregation for electricity\n",
    "- uses **shift load profiles**, branch load profiles are not available for gas consumption\n",
    "- example shows disaggregation with argument ``low=0.2``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disaggregator.temporal: INFO     Working on state: SH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NI.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: RP.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BY.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SL.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: MV.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SN.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: ST.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: TH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n"
     ]
    }
   ],
   "source": [
    "df_gas_industry_no_self_gen = temporal.disagg_temporal_industry(source='gas', no_self_gen=True, \n",
    "                                                                detailed=False, use_nuts3code=False, low=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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       "\n",
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       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gas_industry_no_self_gen.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disaggregator.temporal: INFO     Working on state: SH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NI.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: NW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: HE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: RP.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BY.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SL.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: BB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: MV.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: SN.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: ST.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n",
      "disaggregator.temporal: INFO     Working on state: TH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles to WZs\n"
     ]
    }
   ],
   "source": [
    "df_gas_industry = temporal.disagg_temporal_industry(source='gas', detailed=False, use_nuts3code=False, low=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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       "\n",
       "                        1053      1054      1055       1056      1057   ...  \\\n",
       "2015-01-01 00:00:00  1.581741  1.387479  3.062978  18.531616  1.160091  ...   \n",
       "2015-01-01 00:15:00  1.581741  1.387479  3.062978  18.531616  1.160091  ...   \n",
       "2015-01-01 00:30:00  1.581741  1.387479  3.062978  18.531616  1.160091  ...   \n",
       "2015-01-01 00:45:00  1.581741  1.387479  3.062978  18.531616  1.160091  ...   \n",
       "2015-01-01 01:00:00  1.581741  1.387479  3.062978  18.531616  1.160091  ...   \n",
       "\n",
       "                        16068     16069     16070     16071      16072  \\\n",
       "2015-01-01 00:00:00  2.835136  3.126634  7.018164  1.808697  16.640491   \n",
       "2015-01-01 00:15:00  2.835136  3.126634  7.018164  1.808697  16.640491   \n",
       "2015-01-01 00:30:00  2.835136  3.126634  7.018164  1.808697  16.640491   \n",
       "2015-01-01 00:45:00  2.835136  3.126634  7.018164  1.808697  16.640491   \n",
       "2015-01-01 01:00:00  2.835136  3.126634  7.018164  1.808697  16.640491   \n",
       "\n",
       "                         16073     16074    16075     16076     16077  \n",
       "2015-01-01 00:00:00  11.634421  5.344466  6.47785  4.175993  2.125346  \n",
       "2015-01-01 00:15:00  11.634421  5.344466  6.47785  4.175993  2.125346  \n",
       "2015-01-01 00:30:00  11.634421  5.344466  6.47785  4.175993  2.125346  \n",
       "2015-01-01 00:45:00  11.634421  5.344466  6.47785  4.175993  2.125346  \n",
       "2015-01-01 01:00:00  11.634421  5.344466  6.47785  4.175993  2.125346  \n",
       "\n",
       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gas_industry.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>industry</th>\n",
       "      <th>industry without self-generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01 00:00:00</th>\n",
       "      <td>7313.367798</td>\n",
       "      <td>6220.145895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 00:15:00</th>\n",
       "      <td>7313.367798</td>\n",
       "      <td>6220.145895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 00:30:00</th>\n",
       "      <td>7313.367798</td>\n",
       "      <td>6220.145895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 00:45:00</th>\n",
       "      <td>7313.367798</td>\n",
       "      <td>6220.145895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 01:00:00</th>\n",
       "      <td>7313.367798</td>\n",
       "      <td>6220.145895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        industry  industry without self-generation\n",
       "2015-01-01 00:00:00  7313.367798                       6220.145895\n",
       "2015-01-01 00:15:00  7313.367798                       6220.145895\n",
       "2015-01-01 00:30:00  7313.367798                       6220.145895\n",
       "2015-01-01 00:45:00  7313.367798                       6220.145895\n",
       "2015-01-01 01:00:00  7313.367798                       6220.145895"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc_industry_combined = pd.concat([df_gas_industry.sum(axis=1), df_gas_industry_no_self_gen.sum(axis=1)], \n",
    "                                 keys=['industry', 'industry without self-generation'], \n",
    "                                 axis=1, join='inner')\n",
    "gc_industry_combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = [12,6])\n",
    "ec_industry_combined['industry'].head(800).plot(ax=ax)\n",
    "ec_industry_combined['industry without self-generation'].head(800).plot(ax=ax)\n",
    "plot.set_ax_format(ax=ax, ylabel='[MWh]', xlabel='Date',\n",
    "                   axtitle='Gas Consumption of Industry in Germany with and without self generation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export your data using\n",
    "# df_gas_industry.to_csv(data_out('Industry_Gas_2015_MWh.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 864x216 with 2 Axes>,\n",
       " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001954B4FDFD0>]],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.heatmap_timeseries(df_gas_industry[1001].resample('H').sum(), unit = 'MWh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1954b191780>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_gas_industry.loc[:,[1001, 1002]].head(672).plot(figsize=(12,6))"
   ]
  }
 ],
 "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}