def add_additional_values(table_collection):
"""
Parameters
----------
table_collection
Returns
-------
"""
transf = table_collection["power plants"]
for values in ["variable_costs", "downtime_factor"]:
if cfg.get("creator", "use_{0}".format(values)) is True:
add_values = getattr(data.get_ewi_data(), values)
if cfg.has_option("creator", "downtime_bioenergy"):
add_values.loc["bioenergy",
"value"] = cfg.get("creator",
"downtime_bioenergy")
transf = transf.merge(
add_values,
right_index=True,
how="left",
left_on="fuel",
)
transf.drop(["unit", "source"], axis=1, inplace=True)
transf.rename({"value": values}, axis=1, inplace=True)
else:
transf[values] = 0
table_collection["power plants"] = transf
return table_collection
def fetch_data_coordinates_by_id(coastdat_id):
"""
Returns the coordinates of the weather data set.
Parameters
----------
coastdat_id : int or str
ID of the coastdat weather data set
Returns
-------
namedtuple : Fields are latitude and longitude
Examples
--------
>>> location=fetch_data_coordinates_by_id(1132101)
>>> round(location.latitude, 3)
53.692
>>> round(location.longitude, 3)
11.351
"""
coord = namedtuple("weather_location", "latitude, longitude")
coastdat_polygons = geometries.load(
cfg.get("paths", "geometry"),
cfg.get("coastdat", "coastdatgrid_polygon"),
)
c = coastdat_polygons.loc[int(coastdat_id)].geometry.centroid
return coord(latitude=c.y, longitude=c.x)
def pv_yield_by_orientation():
global START
START = datetime.datetime.now()
reduced = get_coastdat_onshore_polygons()
sandia_modules = pvlib.pvsystem.retrieve_sam('SandiaMod')
sapm_inverters = pvlib.pvsystem.retrieve_sam('cecinverter')
module = sandia_modules['LG_LG290N1C_G3__2013_']
inverter = sapm_inverters['ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_']
system = {'module': module, 'inverter': inverter}
system['installed_capacity'] = (system['module']['Impo'] *
system['module']['Vmpo'])
orientation_sets = []
n = 2
for n in range(18):
ts = n * 10
te = (n + 1) * 10
if te == 90:
te = 91
orientation_sets.append(
sorted(
set((x / 2, y / 2) for x in range(ts, te)
for y in range(0, 721))))
year = 2014
key = 1129089
weather_file_name = os.path.join(
cfg.get('paths', 'coastdat'),
cfg.get('coastdat', 'file_pattern').format(year=year))
if not os.path.isfile(weather_file_name):
coastdat.get_coastdat_data(year, weather_file_name)
weather = pd.read_hdf(weather_file_name, mode='r', key='/A' + str(key))
path = os.path.join(cfg.get('paths', 'analysis'),
'pv_yield_by_orientation_c', '{0}')
point = reduced.centroid[key]
# pv_orientation(key, point, weather, system, orientation, path)
coastdat_fields = []
for orientation in orientation_sets:
d = "tilt_{0}".format(str(orientation[0][0]).replace('.', ''))
coastdat_fields.append({
'key': key,
'geom': point,
'weather': weather,
'system': system,
'orientation': orientation,
'path': path.format(d),
})
p = multiprocessing.Pool(6)
p.map(_pv_orientation, coastdat_fields)
p.close()
p.join()
def get_entsoe_renewable_data(file=None, version=None):
"""
Load the default file for re time series or a specific file.
Returns
-------
Examples
--------
>>> my_re=get_entsoe_renewable_data()
>>> int(my_re['DE_solar_generation_actual'].sum())
188160676
"""
if version is None:
version = cfg.get("entsoe", "timeseries_version")
path_pattern = os.path.join(cfg.get("paths", "entsoe"), "{0}")
if file is None:
fn = path_pattern.format(
cfg.get("entsoe", "renewables_file_csv").format(version=version))
else:
fn = file.format(version=version)
if not os.path.isfile(fn):
if file is None:
renewables = split_timeseries_file(version=version).renewables
renewables.to_csv(fn)
re = pd.read_csv(
fn,
index_col=[0],
parse_dates=True,
date_parser=lambda x: datetime.datetime.strptime(
x.split("+")[0], "%Y-%m-%d %H:%M:%S"),
)
return re
def get_kba_table():
"""
Get the "kfz" table for all vehicles and the "pkw" table for more
statistics about passenger cars.
Returns
-------
namedtuple
Examples
--------
>>> table = get_kba_table()
>>> kfz = table.kfz
>>> print(type(kfz))
<class 'pandas.core.frame.DataFrame'>
"""
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 deflex_profile_from_entsoe(year,
share,
annual_demand=None,
overwrite=False):
load_file = os.path.join(cfg.get('paths', 'entsoe'),
cfg.get('entsoe', 'load_file'))
if not os.path.isfile(load_file) or overwrite:
reegis.entsoe.split_timeseries_file(overwrite)
# Fetch de load profile from entsoe
de_load_profile = reegis.entsoe.get_entsoe_load(year).DE_load_
load_profile = pd.DataFrame(index=de_load_profile.index)
regions = pd.read_csv(os.path.join(cfg.get('paths', 'geo_deflex'),
cfg.get('geometry',
'deflex_polygon')).format(
map=cfg.get('init', 'map'),
type='polygon',
suffix='reegis'),
index_col=[0])
for region in regions.index:
region = 'DE{:02}'.format(region)
if region not in share:
share[region] = 0
load_profile[region] = de_load_profile.multiply(float(share[region]))
if annual_demand is not None:
load_profile = load_profile.div(
load_profile.sum().sum()).multiply(annual_demand)
return load_profile
def split_timeseries_file(filename=None, overwrite=False, version=None):
"""Split table into load and renewables."""
entsoe_ts = namedtuple("entsoe", ["load", "renewables"])
logging.info("Splitting time series.")
if version is None:
version = cfg.get("entsoe", "timeseries_version")
path_pattern = os.path.join(cfg.get("paths", "entsoe"), "{0}")
if filename is None:
filename = path_pattern.format(
cfg.get("entsoe", "de_file").format(version=version))
if not os.path.isfile(filename) or overwrite:
prepare_de_file(filename, overwrite, version)
de_ts = pd.read_csv(
filename.format(version=version),
index_col="utc_timestamp",
parse_dates=True,
date_parser=lambda col: pd.to_datetime(col, utc=True),
)
de_ts.index = de_ts.index.tz_convert("Europe/Berlin")
de_ts.index.rename("cet_timestamp", inplace=True)
de_ts["DE_load_"] = de_ts["DE_load_actual_entsoe_transparency"]
if "DE_load_actual_entsoe_power_statistics" in de_ts:
berlin = pytz.timezone("Europe/Berlin")
end_date = berlin.localize(datetime.datetime(2015, 1, 1, 0, 0, 0))
de_ts.loc[de_ts.index < end_date, "DE_load_"] = de_ts.loc[
de_ts.index < end_date, "DE_load_actual_entsoe_power_statistics"]
load = pd.DataFrame(de_ts[pd.notnull(de_ts["DE_load_"])]["DE_load_"],
columns=["DE_load_"])
re_columns = [
"DE_solar_capacity",
"DE_solar_generation_actual",
"DE_solar_profile",
"DE_wind_capacity",
"DE_wind_generation_actual",
"DE_wind_profile",
"DE_wind_offshore_capacity",
"DE_wind_offshore_generation_actual",
"DE_wind_offshore_profile",
"DE_wind_onshore_capacity",
"DE_wind_onshore_generation_actual",
"DE_wind_onshore_profile",
]
re_subset = [
"DE_solar_capacity",
"DE_solar_generation_actual",
"DE_solar_profile",
"DE_wind_capacity",
"DE_wind_generation_actual",
"DE_wind_profile",
]
renewables = de_ts.dropna(subset=re_subset, how="any")[re_columns]
return entsoe_ts(load=load, renewables=renewables)
def scenario_elec_demand(year, table, weather_year=None):
if weather_year is None:
demand_year = year
else:
demand_year = weather_year
annual_demand = cfg.get('electricity_demand', 'annual_demand')
demand_method = cfg.get('electricity_demand', 'demand_method')
if annual_demand == 'bmwi':
annual_demand = reegis.bmwi.get_annual_electricity_demand_bmwi(year)
msg = ("Unit of BMWI electricity demand is 'TWh'. "
"Will multiply it with {0} to get 'MWh'")
converter = 1e+6
annual_demand = annual_demand * 1e+6
logging.warning(msg.format(converter))
df = deflex.demand.get_deflex_profile(demand_year,
demand_method,
annual_demand=annual_demand)
df = pd.concat([df], axis=1, keys=['electrical_load']).swaplevel(0, 1, 1)
df = df.reset_index(drop=True)
if not calendar.isleap(year) and len(df) > 8760:
df = df.iloc[:8760]
return pd.concat([table, df], axis=1).sort_index(1)
def scenario_transmission(table_collection):
vs = table_collection['volatile_source']
# This should be done automatic e.g. if representative point outside the
# landmass polygon.
offshore_regions = (cfg.get_dict_list('offshore_regions_set')[cfg.get(
'init', 'map')])
elec_trans = deflex.transmission.get_electrical_transmission_deflex()
# Set transmission capacity of offshore power lines to installed capacity
# Multiply the installed capacity with 1.1 to get a buffer of 10%.
for offreg in offshore_regions:
elec_trans.loc[elec_trans.index.str.contains(offreg),
'capacity'] = (vs[offreg].sum().sum() * 1.1)
elec_trans = (pd.concat([elec_trans], axis=1,
keys=['electrical']).sort_index(1))
general_efficiency = cfg.get('transmission', 'general_efficiency')
if general_efficiency is not None:
elec_trans['electrical', 'efficiency'] = general_efficiency
else:
msg = ("The calculation of the efficiency by distance is not yet "
"implemented")
raise NotImplementedError(msg)
if cfg.get('init', 'map') == 'de22':
elec_trans.loc['DE22-DE01', ('electrical', 'efficiency')] = 0.9999
elec_trans.loc['DE22-DE01', ('electrical', 'capacity')] = 9999999
return elec_trans
def read_original_timeseries_file(overwrite=False):
"""Read timeseries file if it exists. Otherwise download it from opsd.
"""
orig_csv_file = os.path.join(cfg.get('paths', 'entsoe'),
cfg.get('entsoe', 'original_file'))
readme = os.path.join(cfg.get('paths', 'entsoe'),
cfg.get('entsoe', 'readme_file'))
json = os.path.join(cfg.get('paths', 'entsoe'),
cfg.get('entsoe', 'json_file'))
if not os.path.isfile(orig_csv_file) or overwrite:
req = requests.get(cfg.get('entsoe', 'timeseries_data'))
if not overwrite:
logging.warning("File not found. Try to download it from server.")
else:
logging.warning("Will download file from server and overwrite"
"existing ones")
logging.warning("Check URL if download does not work.")
with open(orig_csv_file, 'wb') as fout:
fout.write(req.content)
logging.warning("Downloaded from {0} and copied to '{1}'.".format(
cfg.get('entsoe', 'timeseries_data'), orig_csv_file))
req = requests.get(cfg.get('entsoe', 'timeseries_readme'))
with open(readme, 'wb') as fout:
fout.write(req.content)
req = requests.get(cfg.get('entsoe', 'timeseries_json'))
with open(json, 'wb') as fout:
fout.write(req.content)
orig = pd.read_csv(orig_csv_file, index_col=[0], parse_dates=True)
orig = orig.tz_localize('UTC').tz_convert('Europe/Berlin')
return orig
def get_ew_by_deflex_subregions(year):
"""Get a GeoDataFrame with the inhabitants of each region.
Parameters
----------
year : int
Returns
-------
geopandas.geoDataFrame
"""
deflex_sub = reegis.geometries.load(
cfg.get('paths', 'geo_deflex'),
cfg.get('geometry', 'overlap_federal_states_deflex_polygon').format(
map=cfg.get('init', 'map')))
deflex_sub['state'] = deflex_sub.index.to_series().str[2:]
deflex_sub['region'] = deflex_sub.index.to_series().str[:2]
deflex_sub['ew'] = reegis.inhabitants.get_ew_by_region(
year, deflex_sub, name='deflex_subregions')
deflex_sub = deflex_sub.replace({'state': cfg.get_dict('STATE_KEYS')})
deflex_sub['region'] = deflex_sub.region.astype(str).apply(
'DE{:0>2}'.format)
no_inhabitants = deflex_sub[deflex_sub.ew == 0]
deflex_sub = deflex_sub[deflex_sub.ew != 0]
logging.info("States with no inhabitants have been removed: {0}".format(
no_inhabitants.index))
return deflex_sub
def test_opsd2reegis():
path = os.path.join(os.path.dirname(__file__), 'data')
cfg.tmp_set('paths', 'opsd', path)
cfg.tmp_set('paths', 'powerplants', path)
fn_opsd = opsd.opsd_power_plants()
fn_reegis = powerplants.pp_opsd2reegis()
os.remove(fn_opsd)
filename = str(fn_reegis.split(os.sep)[-1])
geo_path = cfg.get('paths', 'geometry')
geo_file = cfg.get('geometry', 'federalstates_polygon')
gdf = geo.load(path=geo_path, filename=geo_file)
powerplants.add_regions_to_powerplants(
gdf, 'fed_states', filename=filename, path=path, dump=True)
geo_path = cfg.get('paths', 'geometry')
geo_file = cfg.get('coastdat', 'coastdatgrid_polygon')
gdf = geo.load(path=geo_path, filename=geo_file)
pp = powerplants.add_regions_to_powerplants(
gdf, 'coastdat2', filename=filename, path=path, dump=False)
os.remove(fn_reegis)
eq_(int(pp.groupby('fed_states').sum().loc['BE', 'capacity']), 2427)
year = 2000
pp = powerplants.get_reegis_powerplants(year, pp=pp)
eq_(int(pp.groupby('fed_states').sum().loc['BE', 'capacity_2000']), 2391)
eq_(coastdat.windzone_region_fraction(
pp, name='fed_states', year=year).round(2).loc['NI', 3], 0.24)
def get_entsoe_load(year):
"""
Parameters
----------
year
Returns
-------
Examples
--------
>>> entsoe=get_entsoe_load(2015)
>>> int(entsoe.sum())
477923089
"""
filename = os.path.join(
cfg.get("paths", "entsoe"), cfg.get("entsoe", "load_file")
)
if not os.path.isfile(filename):
load = split_timeseries_file().load
load.to_hdf(filename, "entsoe")
# Read entsoe time series for the given year
f = datetime.datetime(year, 1, 1, 0)
t = datetime.datetime(year, 12, 31, 23)
f = f.astimezone(pytz.timezone("Europe/Berlin"))
t = t.astimezone(pytz.timezone("Europe/Berlin"))
logging.info("Read entsoe load series from {0} to {1}".format(f, t))
df = pd.DataFrame(pd.read_hdf(filename, "entsoe"))
return df.loc[f:t]
def get_ego_demand(filename=None, sectors=False, overwrite=False):
"""
Parameters
----------
filename : str
sectors : bool
overwrite : bool
Returns
-------
pandas.DataFrame
"""
if filename is None:
path = cfg.get("paths", "demand")
filename = os.path.join(path, cfg.get("open_ego", "ego_file"))
if sectors is True:
filename = filename.replace(".", "_sectors.")
if os.path.isfile(filename) and not overwrite:
return pd.DataFrame(pd.read_hdf(filename, "demand"))
else:
load = get_ego_data(osf=True, sectors=sectors)
load.to_hdf(filename, "demand")
return load
def guess_coordinates_by_spatial_names_opsd(df, fs_column, cap_col,
total_cap, stat):
# *** Use municipal_code and federal_state to define coordinates ***
if fs_column in df:
if 'municipality_code' in df:
if df.municipality_code.dtype == str:
df.loc[df.municipality_code == 'AWZ', fs_column] = 'AWZ_NS'
if 'postcode' in df:
df.loc[df.postcode == '000XX', fs_column] = 'AWZ'
states = df.loc[df.lon.isnull()].groupby(
fs_column).sum()[cap_col]
logging.debug("Fraction of undefined capacity by federal state " +
"(percentage):")
for (state, capacity) in states.iteritems():
logging.debug("{0}: {1:.4f}".format(
state, capacity / total_cap * 100))
stat.loc[state, 'undefined_capacity'] = capacity
# A simple table with the centroid of each federal state.
f2c = pd.read_csv(
os.path.join(cfg.get('paths', 'geometry'),
cfg.get('geometry', 'federalstates_centroid')),
index_col='name')
# Use the centroid of each federal state if the federal state is given.
# This is not very precise and should not be used for a high fraction
# of plants.
f2c = f2c.applymap(wkt_loads).centroid
for l in df.loc[(df.lon.isnull() & df[fs_column].notnull())].index:
if df.loc[l, fs_column] in f2c.index:
df.loc[l, 'lon'] = f2c[df.loc[l, fs_column]].x
df.loc[l, 'lat'] = f2c[df.loc[l, fs_column]].y
return df
def patch_offshore_wind(orig_df, columns):
df = pd.DataFrame(columns=columns)
offsh = pd.read_csv(os.path.join(
cfg.get('paths', 'static_sources'),
cfg.get('static_sources', 'patch_offshore_wind')),
header=[0, 1],
index_col=[0])
offsh = offsh.loc[offsh['reegis', 'com_year'].notnull(), 'reegis']
for column in offsh.columns:
df[column] = offsh[column]
df['decom_year'] = 2050
df['decom_month'] = 12
df['energy_source_level_1'] = 'Renewable energy'
df['energy_source_level_2'] = 'Wind'
df['energy_source_level_3'] = 'Offshore'
goffsh = geo.create_geo_df(df)
offsh_df = pd.DataFrame(goffsh)
new_cap = offsh_df['capacity'].sum()
old_cap = orig_df.loc[orig_df['technology'] == 'Offshore',
'capacity'].sum()
# Remove Offshore technology from power plant table
orig_df = orig_df.loc[orig_df['technology'] != 'Offshore']
patched_df = pd.DataFrame(
pd.concat([orig_df, offsh_df], ignore_index=True, sort=True))
logging.warning(
"Offshore wind is patched. {0} MW were replaced by {1} MW".format(
old_cap, new_cap))
return patched_df
def get_heat_profiles_by_state(year=None, weather_year=None):
if weather_year is None:
heat_demand_state_file = os.path.join(
cfg.get('paths', 'demand'),
cfg.get('demand', 'heat_profile_state').format(year=year))
else:
heat_demand_state_file = os.path.join(
cfg.get('paths', 'demand'),
cfg.get('demand', 'heat_profile_state_var').format(
year=year, weather_year=weather_year))
# Load demand heat profiles by state
if os.path.isfile(heat_demand_state_file):
logging.info("Demand profiles by state exist. Reading file.")
demand_state = pd.read_csv(heat_demand_state_file,
index_col=[0],
parse_dates=True,
header=[0, 1, 2])
demand_state = demand_state.tz_localize('UTC').tz_convert(
'Europe/Berlin')
else:
demand_state = reegis.heat_demand.get_heat_profiles_by_state(
year, to_csv=True, weather_year=weather_year)
return demand_state
def get_pv_wind_areas_by_nuts3(create_geojson=False):
"""
Parameters
----------
year : int
Year of interest
region_pick : list
Selected regions in NUTS-3 format
Returns: pd.DataFrame
Dataframe containing yearly heat CTS heat consumption by NUTS-3 region
-------
"""
path = os.path.join(cfg.get("paths", "GLAES"), 'nuts3_geojson')
if create_geojson:
save_nuts3_to_geojson(path)
fn = os.path.join(cfg.get("paths", "GLAES"), 'suitable_area_wind_pv.csv')
if not os.path.isfile(fn):
suitable_area = calc_wind_pv_areas(path)
suitable_area.to_csv(fn)
else:
suitable_area = pd.read_csv(fn)
suitable_area.set_index('nuts3', drop=True, inplace=True)
return suitable_area
def prepare_ego_demand(rmap=None, overwrite=False):
if rmap is None:
rmap = cfg.get('init', 'map')
egofile_deflex = os.path.join(cfg.get('paths', 'demand'),
cfg.get('demand',
'ego_file_deflex')).format(map=rmap)
if os.path.isfile(egofile_deflex) and not overwrite:
ego_demand_deflex = pd.read_hdf(egofile_deflex, 'demand')
else:
ego_demand_df = reegis.openego.get_ego_demand(overwrite=overwrite)
# Create GeoDataFrame from ego demand file.
ego_demand = reegis.geometries.create_geo_df(ego_demand_df)
# Load region polygons
deflex_regions = deflex.geometries.deflex_regions()
# Add column with region id
ego_demand = reegis.geometries.spatial_join_with_buffer(ego_demand,
deflex_regions,
name=rmap +
'_region')
# Overwrite Geometry object with its DataFrame, because it is not
# needed anymore.
ego_demand_deflex = pd.DataFrame(ego_demand)
# Delete the geometry column, because spatial grouping will be done
# only with the region column.
del ego_demand_deflex['geometry']
# Write out file (hdf-format).
ego_demand_deflex.to_hdf(egofile_deflex, 'demand')
return ego_demand_deflex.groupby('{0}_region'.format(rmap)).sum()
def federal_state_average_weather(year, parameter):
"""
Example for spatial_average_weather() with federal states polygons.
Parameters
----------
year
parameter
Returns
-------
"""
federal_states = geometries.load(
cfg.get('paths', 'geometry'),
cfg.get('geometry', 'federalstates_polygon'))
filename = os.path.join(
cfg.get('paths', 'coastdat'),
'average_{0}_BB_TH_{1}.csv'.format(parameter, year))
if not os.path.isfile(filename):
spatial_average_weather(year,
federal_states,
parameter,
'federal_states',
outfile=filename)
return pd.read_csv(filename, index_col=[0], parse_dates=True)
def get_entsoe_load(year, version=None):
"""
Parameters
----------
year
version
Returns
-------
Examples
--------
>>> entsoe=get_entsoe_load(2015)
>>> float(round(entsoe.sum()/1e6, 1))
479.5
"""
if version is None:
version = cfg.get("entsoe", "timeseries_version")
filename = os.path.join(cfg.get("paths", "entsoe"),
cfg.get("entsoe", "load_file"))
if not os.path.isfile(filename):
load = split_timeseries_file(version=version).load
load.to_hdf(filename.format(version=version), "entsoe")
# Read entsoe time series for the given year
f = datetime.datetime(year, 1, 1, 0)
t = datetime.datetime(year, 12, 31, 23)
f = f.astimezone(pytz.timezone("Europe/Berlin"))
t = t.astimezone(pytz.timezone("Europe/Berlin"))
logging.info("Read entsoe load series from {0} to {1}".format(f, t))
df = pd.DataFrame(pd.read_hdf(filename.format(version=version), "entsoe"))
return df.loc[f:t]
def aggregate_by_region(year, state):
# Create the path for the output files.
feedin_state_path = cfg.get('paths_pattern',
'state_feedin').format(year=year)
os.makedirs(feedin_state_path, exist_ok=True)
# Create pattern for the name of the resulting files.
feedin_berlin_outfile_name = os.path.join(
feedin_state_path,
cfg.get('feedin', 'feedin_state_pattern').format(year=year,
type='{type}',
state=state))
# Filter the capacity of the powerplants for the given year.
pp = get_grouped_power_plants(year)
# Loop over weather depending feed-in categories.
# WIND and PV
for cat in ['Wind', 'Solar']:
outfile_name = feedin_berlin_outfile_name.format(type=cat.lower())
if not os.path.isfile(outfile_name):
aggregate_by_region_coastdat_feedin(pp, [state], year, cat,
outfile_name)
# HYDRO
outfile_name = feedin_berlin_outfile_name.format(type='hydro')
if not os.path.isfile(outfile_name):
aggregate_by_region_hydro(pp, [state], year, outfile_name)
# GEOTHERMAL
outfile_name = feedin_berlin_outfile_name.format(type='geothermal')
if not os.path.isfile(outfile_name):
aggregate_by_region_geothermal([state], year, outfile_name)
def get_ego_demand(overwrite=False):
egofile = os.path.join(cfg.get('paths', 'demand'),
cfg.get('open_ego', 'ego_file'))
if os.path.isfile(egofile) and not overwrite:
return pd.read_hdf(egofile, 'demand')
else:
return prepare_ego_demand(egofile)
def get_feedin_by_state(year, feedin_type, state):
"""
Parameters
----------
year
feedin_type
state : str
Official abbreviation of state in Germany e.g. 'BE', 'SH', 'TH'...
Returns
-------
"""
feedin_state_file_name = os.path.join(
cfg.get('paths_pattern', 'state_feedin'),
cfg.get('feedin', 'feedin_state_pattern')).format(year=year,
type=feedin_type,
state=state)
# Add any federal state to get its normalised feed-in.
if feedin_type in ['solar', 'wind']:
if not os.path.isfile(feedin_state_file_name):
aggregate_by_region(year, state)
return pd.read_csv(feedin_state_file_name,
index_col=[0],
header=[0, 1, 2])
elif feedin_type in ['hydro', 'geothermal']:
if not os.path.isfile(feedin_state_file_name):
aggregate_by_region(year, state)
return pd.read_csv(feedin_state_file_name, index_col=[0], header=[0])
else:
return None
def fetch_id_by_coordinates(latitude, longitude):
"""
Get nearest weather data set to a given location.
Parameters
----------
latitude : float
longitude : float
Returns
-------
int : coastdat id
Examples
--------
>>> fetch_id_by_coordinates(53.655119, 11.181475)
1132101
"""
coastdat_polygons = geometries.load(
cfg.get("paths", "geometry"),
cfg.get("coastdat", "coastdatgrid_polygon"),
)
location = Point(longitude, latitude)
cid = coastdat_polygons[coastdat_polygons.contains(location)].index
if len(cid) == 0:
msg = "No id found for latitude {0} and longitude {1}."
logging.warning(msg.format(latitude, longitude))
return None
elif len(cid) == 1:
return cid[0]
def get_time_series_for_one_location(latitude, longitude, year, set_name=None):
coastdat_id = fetch_id_by_coordinates(latitude, longitude)
# set_name = 'M_LG290G3__I_ABB_MICRO_025_US208'
df = pd.DataFrame()
if set_name is not None:
hd_file = pd.HDFStore(os.path.join(
cfg.get('paths', 'feedin'), 'coastdat', str(year), 'solar',
cfg.get('feedin', 'file_pattern').format(year=year,
type='solar',
set_name=set_name)),
mode='r')
df = hd_file['/A{0}'.format(coastdat_id)]
hd_file.close()
else:
path = os.path.join(cfg.get('paths', 'feedin'), 'coastdat', str(year),
'solar')
for file in os.listdir(path):
hd_file = pd.HDFStore(os.path.join(path, file), mode='r')
tmp = hd_file['/A{0}'.format(coastdat_id)]
hd_file.close()
df = pd.concat([df, tmp], axis=1)
opt = int(round(feedin.get_optimal_pv_angle(latitude)))
df.columns = df.columns.str.replace('opt', str(opt))
return df
def fetch_coastdat_weather(year, coastdat_id):
"""
Fetch weather one coastdat weather data set.
Parameters
----------
year : int
Year of the weather data set
coastdat_id : numeric
ID of the coastdat data set.
Returns
-------
pd.DataFrame : Weather data set.
Examples
--------
>>> coastdat_id=fetch_id_by_coordinates(53.655119, 11.181475)
>>> fetch_coastdat_weather(2014, coastdat_id)['v_wind'].mean().round(2)
4.39
"""
weather_file_name = os.path.join(
cfg.get("paths", "coastdat"),
cfg.get("coastdat", "file_pattern").format(year=year),
)
if not os.path.isfile(weather_file_name):
download_coastdat_data(filename=weather_file_name, year=year)
key = "/A{0}".format(int(coastdat_id))
return pd.DataFrame(pd.read_hdf(weather_file_name, key))
def guess_coordinates_by_postcode_opsd(df):
# *** Use postcode ***
if 'postcode' in df:
df_pstc = df.loc[(df.lon.isnull() & df.postcode.notnull())]
if len(df_pstc) > 0:
pstc = pd.read_csv(
os.path.join(cfg.get('paths', 'geometry'),
cfg.get('geometry', 'postcode_polygon')),
index_col='zip_code')
for idx, val in df_pstc.iterrows():
try:
# If the postcode is not number the integer conversion will
# raise a ValueError. Some postcode look like this '123XX'.
# It would be possible to add the mayor regions to the postcode
# map in order to search for the first two/three digits.
postcode = int(val.postcode)
if postcode in pstc.index:
df.loc[df.id == val.id, 'lon'] = wkt_loads(
pstc.loc[postcode].values[0]).centroid.x
df.loc[df.id == val.id, 'lat'] = wkt_loads(
pstc.loc[postcode].values[0]).centroid.y
# Replace the last number with a zero and try again.
elif round(postcode / 10) * 10 in pstc.index:
postcode = round(postcode / 10) * 10
df.loc[df.id == val.id, 'lon'] = wkt_loads(
pstc.loc[postcode].values[0]).centroid.x
df.loc[df.id == val.id, 'lat'] = wkt_loads(
pstc.loc[postcode].values[0]).centroid.y
else:
logging.debug("Cannot find postcode {0}.".format(postcode))
except ValueError:
logging.debug("Cannot find postcode {0}.".format(val.postcode))
return df
def pp_reegis2deflex(regions, name, filename_in=None, filename_out=None):
"""
Add federal states and deflex regions to powerplant table from reegis. As
the process takes a while the result is stored for further usage.
Returns
-------
str : The full path where the result file is stored.
"""
if filename_out is None:
filename_out = os.path.join(
cfg.get("paths", "powerplants"),
cfg.get("powerplants", "deflex_pp"),
).format(map=name)
# Add deflex regions to powerplants
pp = powerplants.add_regions_to_powerplants(regions,
name,
dump=False,
filename=filename_in)
# Add federal states to powerplants
federal_states = reegis_geometries.get_federal_states_polygon()
pp = powerplants.add_regions_to_powerplants(federal_states,
"federal_states",
pp=pp,
dump=False)
# store the results for further usage of deflex
pp.to_hdf(filename_out, "pp")
return filename_out
def share_houses_flats(key=None):
"""
Parameters
----------
key str
Valid keys are: 'total_area', 'avg_area', 'share_area', 'total_number',
'share_number'.
Returns
-------
dict or pd.DataFrame
"""
size = pd.Series([1, 25, 50, 70, 90, 110, 130, 150, 170, 190, 210])
infile = os.path.join(cfg.get('paths', 'data_de21'),
cfg.get('general_sources', 'zensus_flats'))
whg = pd.read_csv(infile,
delimiter=';',
index_col=[0],
header=[0, 1],
skiprows=5)
whg = whg.loc[whg['Insgesamt', 'Insgesamt'].notnull()]
new_index = []
states = cfg.get_dict('STATES')
for i in whg.index:
new_index.append(states[i[3:-13]])
whg.index = new_index
flat = {
'total_area': pd.DataFrame(),
'total_number': pd.DataFrame(),
}
for f in whg.columns.get_level_values(0).unique():
df = pd.DataFrame(whg[f].values * size.values,
columns=whg[f].columns,
index=whg.index)
flat['total_area'][f] = df.sum(1) - df['Insgesamt']
flat['total_number'][f] = df['Insgesamt']
flat['total_area']['1 + 2 Wohnungen'] = (flat['total_area']['1 Wohnung'] +
flat['total_area']['2 Wohnungen'])
flat['total_number']['1 + 2 Wohnungen'] = (
flat['total_number']['1 Wohnung'] +
flat['total_number']['2 Wohnungen'])
flat['avg_area'] = flat['total_area'].div(flat['total_number'])
flat['share_area'] = (flat['total_area'].transpose().div(
flat['total_area']['Insgesamt'])).transpose().round(3)
flat['share_number'] = (flat['total_number'].transpose().div(
flat['total_number']['Insgesamt'])).transpose().round(3)
if key is None:
return flat
elif key in flat:
return flat[key].sort_index()
else:
logging.warning(
"'{0}' is an invalid key for function 'share_houses_flats'".format(
key))
return None
