Merge afb0887 into 794137e

reegis/dictionary.ini

@@ -287,3 +287,52 @@ Thüringen = TH
 14 = SN
 15 = ST
 16 = TH
+
+
+[KFZ]
+Krafträder insgesamt = motorcycle, total
+Krafträder davon zwei-rädrige Kfz = ignore
+Krafträder davon dreirädrige Kfz = ignore
+Krafträder davon leichte vierrädrige Kfz = ignore
+Krafträder darunter weibliche Halter = ignore
+Personenkraftwagen  insgesamt = passenger car
+Personenkraftwagen  Hubraum bis  1.399 cm³ = ignore
+Personenkraftwagen  1.400 bis 1.999 cm³ = ignore
+Personenkraftwagen  2.000 und mehr cm³ = ignore
+Personenkraftwagen  unbekannt = ignore
+Personenkraftwagen  und zwar  mit offenem Aufbau = ignore
+Personenkraftwagen  und zwar  mit Allradantrieb = ignore
+Personenkraftwagen  und zwar Wohnmobile = ignore
+Personenkraftwagen  und zwar Krankenwagen, Notarzteinsatzfz. = ignore
+Personenkraftwagen  und zwar gewerbliche Halter = ignore
+Personenkraftwagen  und zwar weibliche Halter = ignore
+Personenkraftwagen  PKW-Dichte je 1.000  Einwohner = ignore
+Kraftomnibusse Unnamed 21_level_1 = bus
+Lastkraftwagen  insgesamt = ignore
+Lastkraftwagen  mit zulässiger Gesamtmasse bis 2.800 kg = lorry, =< 7500
+Lastkraftwagen  2.801 bis 3.500 kg = lorry, =< 7500
+Lastkraftwagen  3.501 bis 5.000 kg = lorry, =< 7500
+Lastkraftwagen  5.001 bis 7.500 kg = lorry, =< 7500
+Lastkraftwagen  7.501 bis 12.000 kg = lorry, > 7500
+Lastkraftwagen  12.001 bis 20.000 kg = lorry, > 7500
+Lastkraftwagen  20.001 und mehr kg = lorry, > 7500
+Lastkraftwagen  unbekannt  = other
+Zugmaschinen insgesamt = traction engine
+Zugmaschinen darunter  Sattelzugmaschinen = ignore
+Zugmaschinen darunter land-/forstwirtschaftliche Zugmaschinen = traction engine, agriculture and forestry
+Zugmaschinen land-/forstwirtschaftliche Zugmaschinen beinhalten leichte Zug-maschinen = traction engine, agriculture and forestry
+Sonstige Kfz Unnamed 35_level_1 = other
+Nutzfahrzeuge insgesamt  Unnamed 36_level_1 = ignore
+Kraftfahrzeuge insgesamt = ignore
+Kraftfahrzeuge Kfz-Dichte je 1.000  Einwohner = ignore
+Kraftfahrzeuganhänger Unnamed 39_level_1 = ignore
+
+
+[PKW]
+Benzin = petrol
+Diesel = diesel
+Gas (einschl. bivalent) = natural gas (incl. bivalent)
+Hybrid  insgesamt = hybrid
+darunter Hybrid mit  Plug-in = ignore
+Elektro = electricity
+sonstige = other

reegis/geometries.py

@@ -214,6 +214,11 @@ def spatial_join_with_buffer(
     if jcol == "index":
         jcol = "index_right"
 
+    if isinstance(geo1, gpd.GeoSeries):
+        crs = geo1.crs
+        geo1 = gpd.GeoDataFrame(geo1, columns=["geometry"])
+        geo1.crs = crs
+
     logging.debug("Doing spatial join...")
 
     # Spatial (left) join with the "within" operation.

reegis/mobility.py

@@ -0,0 +1,195 @@
+# -*- coding: utf-8 -*-
+
+"""Calculte the mobility demand.
+
+SPDX-FileCopyrightText: 2016-2019 Uwe Krien <krien@uni-bremen.de>
+
+SPDX-License-Identifier: MIT
+"""
+__copyright__ = "Uwe Krien <krien@uni-bremen.de>"
+__license__ = "MIT"
+
+
+import os
+import pandas as pd
+from collections import namedtuple
+
+from reegis import geometries, config as cfg, tools, energy_balance
+
+
+def format_kba_table(filename, table):
+    """
+
+    Parameters
+    ----------
+    filename
+    table
+
+    Returns
+    -------
+
+    """
+
+    # Read table
+    df = pd.read_excel(filename, table, skiprows=7, header=[0, 1])
+
+    # Drop empty column
+    df = df.drop([("Unnamed: 0_level_0", "Unnamed: 0_level_1")], axis=1)
+
+    idx1 = df.columns[0]
+    idx2 = df.columns[1]
+    idx3 = df.columns[2]
+
+    # Remove lines with subtotal
+    df.loc[(df[idx1] == "SONSTIGE"), idx2] = "SONSTIGE"
+    df.loc[(df[idx1] == "SONSTIGE"), idx3] = "00000 SONSTIGE"
+    df = df.drop(df.loc[df[idx3].isnull()].index)
+    df[df.columns[[0, 1, 2]]] = df[df.columns[[0, 1, 2]]].fillna(
+        method="ffill"
+    )
+
+    # Add column with name of subregion and remove name from index
+    df[df.columns[2]] = df[df.columns[2]].str[:5]
+
+    # set MultiIndex
+    df.set_index(list(df.columns[[0, 1, 2]]), inplace=True)
+    df.index = df.index.set_names(["state", "region", "subregion"])
+
+    # Remove format-strings from column names
+    level1 = (
+        df.columns.get_level_values(1)
+        .str.replace("\n", " ")
+        .str.replace("- ", "")
+        .str.replace(":", "")
+    )
+    level0 = (
+        df.columns.get_level_values(0)
+        .str.replace("\n", " ")
+        .str.replace("- ", "")
+        .str.replace(":", "")
+    )
+    df.columns = pd.MultiIndex.from_arrays([level0, level1])
+
+    return df
+
+
+def get_kba_table():
+    """
+
+    Returns
+    -------
+
+    """
+    kba_table = namedtuple("kba_table", "kfz pkw")
+    kba_filename = os.path.join(
+        cfg.get("paths", "general"), cfg.get("mobility", "table_kba")
+    )
+
+    # Download table if it does not exit
+    if not os.path.isfile(kba_filename):
+        tools.download_file(kba_filename, cfg.get("mobility", "url_kba"))
+
+    return kba_table(
+        kfz=format_kba_table(kba_filename, "Kfz_u_Kfz_Anh"),
+        pkw=format_kba_table(kba_filename, "Pkw"),
+    )
+
+
+def create_grouped_table_kfz():
+    """Group the kfz-table by main groups."""
+    df = get_kba_table().kfz
+    df.index = df.index.droplevel([0, 1])
+    df.columns = [" ".join(col).strip() for col in df.columns]
+    kfz_dict = cfg.get_dict("KFZ")
+    for col in df.columns:
+        df[col] = pd.to_numeric(df[col].replace("-", ""))
+    df = df.groupby(by=kfz_dict, axis=1).sum()
+    df["traction engine, general"] = (
+        df["traction engine"] - df["traction engine, agriculture and forestry"]
+    )
+    df.drop("traction engine", axis=1, inplace=True)
+    df.drop("ignore", axis=1, inplace=True)
+    return df
+
+
+def create_grouped_table_pkw():
+    """
+    Extract fuel groups of passenger cars
+
+    Examples
+    --------
+    >>> pkw = create_grouped_table_pkw()
+    >>> pkw['petrol'].sum()
+    31031021.0
+    >>> pkw['diesel'].sum()
+    15153364.0
+    """
+    df = get_kba_table().pkw
+    df.index = df.index.droplevel([0, 1])
+    df = df["Nach Kraftstoffarten"]
+    df = df.groupby(by=cfg.get_dict("PKW"), axis=1).sum()
+    df.drop("ignore", axis=1, inplace=True)
+    return df
+
+
+def get_admin_by_region(region):
+    """Allocate admin keys to the given regions."""
+    fn = os.path.join(cfg.get("paths", "geometry"), "vg1000_geodata.geojson")
+    vg = geometries.load(fullname=fn)
+    vg.set_index("RS", inplace=True)
+
+    reg2vg = geometries.spatial_join_with_buffer(
+        vg.representative_point(), region, "fs", limit=0
+    )
+
+    return pd.DataFrame(reg2vg.drop("geometry", axis=1))
+
+
+def get_grouped_kfz_by_region(region):
+    """Get the main vehicle groups by region.
+
+    Examples
+    --------
+    >>> fs = geometries.get_federal_states_polygon()
+    >>> total = get_grouped_kfz_by_region(fs).sum()
+    >>> int(total["passenger car"])
+    47095784
+    >>> int(total["lorry, > 7500"])
+    295826
+    """
+    df = create_grouped_table_kfz()
+    reg2vg = get_admin_by_region(region)
+    df2reg = df.merge(reg2vg, left_index=True, right_index=True, how="left")
+    df2reg["fs"] = df2reg["fs"].fillna("unknown")
+    return df2reg.groupby("fs").sum()
+
+
+def get_traffic_fuel_energy(year):
+    """
+
+    Parameters
+    ----------
+    year
+
+    Returns
+    -------
+
+    Examples
+    --------
+    >>> fuel_energy = get_traffic_fuel_energy(2017)
+    >>> int(fuel_energy["Ottokraftstoffe"])
+    719580
+    >>> fuel_share = fuel_energy.div(fuel_energy.sum()) * 100
+    >>> round(fuel_share["Dieselkraftstoffe"], 1)
+    62.7
+    """
+    fuel_energy = energy_balance.get_de_balance(year).loc["Straßenverkehr"]
+    fuel_energy = fuel_energy[fuel_energy != 0]
+    fuel_energy.drop(
+        ["primär (gesamt)", "sekundär (gesamt)", "Row", "gesamt"], inplace=True
+    )
+    return fuel_energy
+
+
+if __name__ == "__main__":
+    pass

reegis/reegis.ini

@@ -143,6 +143,10 @@ heat_profile_region_var = heat_profile_{map}_{year}_weather_{weather_year}.csv
 heat_profile_state = heat_profile_state_{year}.csv
 heat_profile_state_var = heat_profile_state_{year}_weather_{weather_year}.csv
 
+[mobility]
+url_kba = https://www.kba.de/SharedDocs/Publikationen/DE/Statistik/Fahrzeuge/FZ/2019/fz1_2019_xlsx.xlsx;jsessionid=2913FE1A8D3294C88D4F4FC29A516B51.live21302?__blob=publicationFile&v=10
+table_kba = kfz_pkw_table.xlsx
+
 [entsoe]
 timeseries_version = latest
 timeseries_data = http://data.open-power-system-data.org/time_series/{version}/time_series_60min_singleindex.csv

setup.py

@@ -12,7 +12,7 @@
         "demandlib",
         "tables",
         "shapely",
-        "pvlib",
+        "pvlib < 0.7",
         "geopandas",
         "requests",
         "numpy",