def prepare_ego_demand(egofile):
ego_demand = geometries.Geometry(name='ego demand')
ego_demand.load_csv(cfg.get('paths', 'static_sources'),
cfg.get('open_ego', 'ego_input_file'))
ego_demand.create_geo_df(wkt_column='st_astext')
# Add column with name of the federal state (Bayern, Berlin,...)
federal_states = geometries.Geometry('federal states')
federal_states.load(cfg.get('paths', 'geometry'),
cfg.get('geometry', 'federalstates_polygon'))
# Add column with federal_states
ego_demand.gdf = geometries.spatial_join_with_buffer(
ego_demand, federal_states)
# Overwrite Geometry object with its DataFrame, because it is not
# needed anymore.
ego_demand = pd.DataFrame(ego_demand.gdf)
ego_demand['geometry'] = ego_demand['geometry'].astype(str)
# Write out file (hdf-format).
ego_demand.to_hdf(egofile, 'demand')
return ego_demand
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.Geometry(name="Offshore wind patch", df=df)
goffsh.create_geo_df()
# Add column with region names of the model_region
new_col = 'federal_states'
if new_col in goffsh.gdf:
del goffsh.gdf[new_col]
federal_states = geo.Geometry(new_col)
federal_states.load(cfg.get('paths', 'geometry'),
cfg.get('geometry', 'federalstates_polygon'))
goffsh.gdf = geo.spatial_join_with_buffer(goffsh, federal_states)
# Add column with coastdat id
new_col = 'coastdat2'
if new_col in goffsh.gdf:
del goffsh.gdf[new_col]
coastdat = geo.Geometry(new_col)
coastdat.load(cfg.get('paths', 'geometry'),
cfg.get('coastdat', 'coastdatgrid_polygon'))
goffsh.gdf = geo.spatial_join_with_buffer(goffsh, coastdat)
offsh_df = goffsh.get_df()
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))
logging.warning(
"Offshore wind is patched. {0} MW were replaced by {1} MW".format(
old_cap, new_cap))
return patched_df
def de21_regions(suffix='vg'):
name = os.path.join(
cfg.get('paths', 'geo_de21'),
cfg.get('geometry', 'de21_polygon').format(suffix=suffix))
regions = geo.Geometry(name='de21_region')
regions.load(fullname=name)
return regions
def convert_shp2csv(infile, outfile):
logging.info("Converting {0} to {1}.".format(infile, outfile))
geo = geometries.Geometry()
df = geo.load(fullname=infile).get_df()
df.loc[df.KLASSENNAM == 'FL_Vattenfall', 'KLASSENNAM'] = 'FL_Vattenfall_1'
df.loc[df.KLASSENNAM == 'FL_Vattenfall_2', 'STIFT'] = 229
df.to_csv(outfile)
def spatial_preparation_power_plants(pp):
"""Add spatial names to DataFrame. Three columns will be added to the
power plant table:
federal_states: The federal state of Germany
model_region: The name of the model region defined by the user.
coastdat: The id of the nearest coastdat weather data set.
Parameters
----------
pp : reegis_tools.Geometry
An object containing Germany's power plants.
Returns
-------
reegis_tools.Geometry
"""
if pp.gdf is None:
logging.info("Create GeoDataFrame from lat/lon.")
pp.create_geo_df()
logging.info("Remove invalid geometries")
pp.remove_invalid_geometries()
# Add column with name of the federal state (Bayern, Berlin,...)
federal_states = geo.Geometry('federal states')
federal_states.load(cfg.get('paths', 'geometry'),
cfg.get('geometry', 'federalstates_polygon'))
pp.gdf = geo.spatial_join_with_buffer(pp, federal_states)
# Add country code to federal state if country code is not 'DE'.
if 'country_code' in pp.gdf.columns:
country_codes = list(pp.gdf.country_code.unique())
country_codes.remove('DE')
for c_code in country_codes:
pp.gdf.loc[pp.gdf.country_code == c_code,
'federal_states'] = (c_code)
# Add column with coastdat id
coastdat = geo.Geometry('coastdat2')
coastdat.load(cfg.get('paths', 'geometry'),
cfg.get('coastdat', 'coastdatgrid_polygon'))
pp.gdf = geo.spatial_join_with_buffer(pp, coastdat)
return pp
def federal_state_average_weather(year, parameter):
federal_states = geometries.Geometry(name='federal_states')
federal_states.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,
outfile=filename)
return pd.read_csv(filename, index_col=[0], parse_dates=True)
def opsd_power_plants(overwrite=False, csv=False):
"""
Parameters
----------
csv
overwrite
Returns
-------
"""
strcols = {
'conventional': [
'name_bnetza', 'block_bnetza', 'name_uba', 'company', 'street',
'postcode', 'city', 'state', 'country_code', 'fuel', 'technology',
'chp', 'commissioned_original', 'status', 'type', 'eic_code_plant',
'eic_code_block', 'efficiency_source', 'energy_source_level_1',
'energy_source_level_2', 'energy_source_level_3', 'eeg',
'network_node', 'voltage', 'network_operator', 'merge_comment',
'geometry', 'federal_states'
],
'renewable': [
'commissioning_date', 'decommissioning_date',
'energy_source_level_1', 'energy_source_level_2',
'energy_source_level_3', 'technology', 'voltage_level', 'comment',
'geometry', 'federal_states'
]
}
if csv:
opsd_file_name = os.path.join(
cfg.get('paths', 'opsd'),
cfg.get('opsd', 'opsd_prepared_csv_pattern'))
hdf = None
else:
opsd_file_name = os.path.join(cfg.get('paths', 'opsd'),
cfg.get('opsd', 'opsd_prepared'))
if os.path.isfile(opsd_file_name) and not overwrite:
hdf = None
else:
if os.path.isfile(opsd_file_name):
os.remove(opsd_file_name)
hdf = pd.HDFStore(opsd_file_name, mode='a')
# If the power plant file does not exist, download and prepare it.
for category in ['conventional', 'renewable']:
# Define file and path pattern for power plant file.
cleaned_file_name = os.path.join(
cfg.get('paths', 'opsd'),
cfg.get('opsd', 'cleaned_csv_file_pattern').format(cat=category))
if csv:
exist = os.path.isfile(opsd_file_name) and not overwrite
else:
exist = hdf is None
if not exist:
logging.info("Preparing {0} opsd power plants".format(category))
df = load_opsd_file(category, overwrite, prepared=True)
pp = geo.Geometry('{0} power plants'.format(category), df=df)
pp = spatial_preparation_power_plants(pp)
if csv:
pp.get_df().to_csv(opsd_file_name)
else:
df = pp.get_df()
df[strcols[category]] = df[strcols[category]].astype(str)
hdf[category] = df
logging.info(
"Opsd power plants stored to {0}".format(opsd_file_name))
if os.path.isfile(cleaned_file_name):
os.remove(cleaned_file_name)
if hdf is not None:
hdf.close()
return opsd_file_name
def spatial_average_weather(year, geo, parameter, outpath=None, outfile=None):
"""
Calculate the average temperature for all regions (de21, states...).
Parameters
----------
year : int
Select the year you want to calculate the average temperature for.
geo : geometries.Geometry object
Polygons to calculate the average parameter for.
outpath : str
Place to store the outputfile.
outfile : str
Set your own name for the outputfile.
parameter : str
Name of the item (temperature, wind speed,... of the weather data set.
Returns
-------
str : Full file name of the created file.
"""
logging.info("Getting average {0} for {1} in {2} from coastdat2.".format(
parameter, geo.name, year))
col_name = geo.name.replace(' ', '_')
# Create a Geometry object for the coastdat centroids.
coastdat_geo = geometries.Geometry(name='coastdat')
coastdat_geo.load(cfg.get('paths', 'geometry'),
cfg.get('coastdat', 'coastdatgrid_polygon'))
coastdat_geo.gdf['geometry'] = coastdat_geo.gdf.centroid
# Join the tables to create a list of coastdat id's for each region.
coastdat_geo.gdf = geometries.spatial_join_with_buffer(
coastdat_geo, geo, limit=0)
# Fix regions with no matches (this my happen if a region ist to small).
fix = {}
for reg in set(geo.gdf.index) - set(coastdat_geo.gdf[col_name].unique()):
reg_point = geo.gdf.representative_point().loc[reg]
coastdat_poly = geometries.Geometry(name='coastdat_poly')
coastdat_poly.load(cfg.get('paths', 'geometry'),
cfg.get('coastdat', 'coastdatgrid_polygon'))
fix[reg] = coastdat_poly.gdf.loc[coastdat_poly.gdf.intersects(
reg_point)].index[0]
# Open the weather file
weatherfile = os.path.join(
cfg.get('paths', 'coastdat'),
cfg.get('coastdat', 'file_pattern').format(year=year))
if not os.path.isfile(weatherfile):
get_coastdat_data(year, weatherfile)
weather = pd.HDFStore(weatherfile, mode='r')
# Calculate the average temperature for each region with more than one id.
avg_value = pd.DataFrame()
for region in geo.gdf.index:
cd_ids = coastdat_geo.gdf[coastdat_geo.gdf[col_name] == region].index
number_of_sets = len(cd_ids)
tmp = pd.DataFrame()
logging.debug((region, len(cd_ids)))
for cid in cd_ids:
try:
cid = int(cid)
except ValueError:
pass
if isinstance(cid, int):
key = 'A' + str(cid)
else:
key = cid
tmp[cid] = weather[key][parameter]
if len(cd_ids) < 1:
key = 'A' + str(fix[region])
avg_value[region] = weather[key][parameter]
else:
avg_value[region] = tmp.sum(1).div(number_of_sets)
weather.close()
# Create the name an write to file
regions = sorted(geo.gdf.index)
if outfile is None:
out_name = '{0}_{1}'.format(regions[0], regions[-1])
outfile = os.path.join(
outpath,
'average_{parameter}_{type}_{year}.csv'.format(
year=year, type=out_name, parameter=parameter))
avg_value.to_csv(outfile)
logging.info("Average temperature saved to {0}".format(outfile))
return outfile
