diff --git a/src/era5data.py b/src/era5data.py
index 883255604834891505e493a5f422efcdefac0260..a1c002e889a212f5011db9888469af550931c4d8 100644
--- a/src/era5data.py
+++ b/src/era5data.py
@@ -12,7 +12,7 @@ regions = {'EU': '75/-20/30/40'}
years = ['2017', '2018']
months = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']
-directory = join(data_path, 'input_data/resource_data/', str(spatial_resolution))
+directory = join(data_path, 'input/resource_data/', str(spatial_resolution))
if not os.path.exists(directory):
os.makedirs(directory)
@@ -22,9 +22,9 @@ for region, coords in regions.items():
for month in months:
c.retrieve(
'reanalysis-era5-single-levels',
- {'variable':['100m_u_component_of_wind','100m_v_component_of_wind',
- '2m_temperature', 'surface_solar_radiation_downwards', 'forecast_surface_roughness'],
- 'product_type':'reanalysis',
+ {'variable': ['100m_u_component_of_wind','100m_v_component_of_wind',
+ '2m_temperature', 'surface_solar_radiation_downwards', 'forecast_surface_roughness'],
+ 'product_type': 'reanalysis',
'area': str(coords),
'grid': str(spatial_resolution)+'/'+str(spatial_resolution),
'year': year,
diff --git a/src/helpers.py b/src/helpers.py
index 6980055126c275d9d7cdf4237b8fb74d0e085da3..4dcd401e7168cebfaa54196a9777c0ffff756cf6 100644
--- a/src/helpers.py
+++ b/src/helpers.py
@@ -143,18 +143,23 @@ def get_deployment_vector(regions, technologies, deployments):
def return_region_divisions(region_list, data_path):
- onshore_shapes = get_onshore_shapes(region_list, data_path)
- region_subdivisions = None
+ arcgis_fn = f"{data_path}input/shapefiles/Longitude_Graticules_and_World_Countries_Boundaries-shp/" \
+ f"99bfd9e7-bb42-4728-87b5-07f8c8ac631c2020328-1-1vef4ev.lu5nk.shp"
+ shapes = read_file(arcgis_fn)
+ shapes["CNTRY_NAME"] = shapes["CNTRY_NAME"].apply(convert_old_country_names)
+ shapes["iso2"] = Series(convert_country_codes(shapes["CNTRY_NAME"].values, "name", "alpha_2"))
+ shapes = shapes[notnull(shapes["iso2"])]
+ shapes = shapes.set_index("iso2")['geometry']
regions = []
for region in region_list:
if region == 'EU':
region_subdivisions = ['AT', 'BE', 'DE', 'DK', 'ES',
- 'FR', 'UK', 'IE', 'IT', 'LU',
+ 'FR', 'GB', 'IE', 'IT', 'LU',
'NL', 'NO', 'PT', 'SE', 'CH', 'CZ',
- 'EE', 'LV', 'RO', 'BG', 'EL', 'HR', 'RS',
- 'FI', 'EL', 'HR', 'HU', 'LT',
+ 'EE', 'LV', 'RO', 'BG', 'HR', 'RS',
+ 'FI', 'GR', 'HR', 'HU', 'LT',
'PL', 'SI', 'SK']
elif region == 'NA':
region_subdivisions = ['DZ', 'EG', 'MA', 'LY', 'TN']
@@ -163,8 +168,11 @@ def return_region_divisions(region_list, data_path):
'PS', 'QA', 'SA', 'SY', 'YE']
elif region == 'CWE':
region_subdivisions = ['FR', 'BE', 'LU', 'NL', 'DE']
- elif region in onshore_shapes.index:
+ elif region in shapes.index:
region_subdivisions = [region]
+ else:
+ custom_log(f"{region} not in shapes list!")
+ continue
regions.extend(region_subdivisions)
@@ -239,7 +247,7 @@ def get_onshore_shapes(regions, data_path):
"""
- arcgis_fn = f"{data_path}input_data/shapefiles/Longitude_Graticules_and_World_Countries_Boundaries-shp/" \
+ arcgis_fn = f"{data_path}input/shapefiles/Longitude_Graticules_and_World_Countries_Boundaries-shp/" \
f"99bfd9e7-bb42-4728-87b5-07f8c8ac631c2020328-1-1vef4ev.lu5nk.shp"
shapes = read_file(arcgis_fn)
shapes["CNTRY_NAME"] = shapes["CNTRY_NAME"].apply(convert_old_country_names)
@@ -249,7 +257,7 @@ def get_onshore_shapes(regions, data_path):
if regions is not None:
missing_codes = set(regions) - set(shapes.index)
- assert not missing_codes, f"Error: Shapes are not available for the " \
+ assert not missing_codes, f"Shapes are not available for the " \
f"following codes: {sorted(list(missing_codes))}"
shapes = shapes[regions]
@@ -264,7 +272,7 @@ def get_offshore_shapes(regions, data_path):
# Remove landlocked countries for which there is no offshore shapes
iso_codes = remove_landlocked_countries(regions)
- eez_fn = f"{data_path}input_data/shapefiles/eez/World_EEZ_v8_2014.shp"
+ eez_fn = f"{data_path}input/shapefiles/eez/World_EEZ_v8_2014.shp"
eez_shapes = read_file(eez_fn)
eez_shapes = eez_shapes[notnull(eez_shapes['ISO_3digit'])]
@@ -334,7 +342,7 @@ def return_coordinates_from_shapefiles(resource_dataset, shapefiles_region):
def retrieve_load_data_partitions(data_path, date_slice, alpha, delta, regions, norm_type):
- load_data_fn = join(data_path, 'input_data/load_data', 'load_2009_2018.csv')
+ load_data_fn = join(data_path, 'input/load_data', 'load_2009_2018.csv')
load_data = read_csv(load_data_fn, index_col=0)
load_data.index = date_range('2009-01-01T00:00', '2018-12-31T23:00', freq='H')
load_data_sliced = load_data.loc[date_slice[0]:date_slice[1]]
@@ -383,7 +391,7 @@ def filter_onshore_offshore_locations(coordinates_in_region, data_path, spatial_
"""
land_fn = 'ERA5_surface_characteristics_20181231_' + str(spatial_resolution) + '.nc'
- land_path = join(data_path, 'input_data/land_data', land_fn)
+ land_path = join(data_path, 'input/land_data', land_fn)
dataset = xr.open_dataset(land_path)
dataset = dataset.sortby([dataset.longitude, dataset.latitude])
@@ -457,7 +465,7 @@ def init_folder(parameters, c, suffix=None):
Relative path of the folder.
"""
- output_data_path = join(parameters['data_path'], 'output_data')
+ output_data_path = join(parameters['data_path'], 'output')
no_locs = str(sum(parameters['deployments']))
no_part = str(len(parameters['regions']))
diff --git a/src/tools.py b/src/tools.py
index de30e09d3981c7c48ef9d2159b3911c356f8f2ae..071e4054a5832954d742bf9d883ce6cf53aa42d3 100644
--- a/src/tools.py
+++ b/src/tools.py
@@ -36,7 +36,7 @@ def read_database(data_path, spatial_resolution):
dataset: xarray.Dataset
"""
- file_path = join(data_path, 'input_data/resource_data', str(spatial_resolution))
+ file_path = join(data_path, 'input/resource_data', str(spatial_resolution))
# Read through all files, extract the first 2 characters (giving the
# macro-region) and append in a list that will keep the unique elements.
files = [f for f in listdir(file_path) if isfile(join(file_path, f))]
@@ -104,7 +104,7 @@ def filter_locations_by_layer(regions, start_coordinates, model_params, tech_par
threshold_distance = tech_params['protected_areas_distance_threshold']
coords_to_remove = []
- areas_fn = join(data_path, 'input_data/land_data', 'WDPA_Feb2019-shapefile-points.shp')
+ areas_fn = join(data_path, 'input/land_data', 'WDPA_Feb2019-shapefile-points.shp')
dataset = read_file(areas_fn)
lons = []
@@ -191,7 +191,7 @@ def filter_locations_by_layer(regions, start_coordinates, model_params, tech_par
elif which == 'orography':
orography_fn = 'ERA5_orography_characteristics_20181231_' + str(model_params['spatial_resolution']) + '.nc'
- orography_path = join(data_path, 'input_data/land_data', orography_fn)
+ orography_path = join(data_path, 'input/land_data', orography_fn)
dataset = xr.open_dataset(orography_path).astype(float32)
dataset = dataset.sortby([dataset.longitude, dataset.latitude])
dataset = dataset.assign_coords(longitude=(((dataset.longitude
@@ -217,7 +217,7 @@ def filter_locations_by_layer(regions, start_coordinates, model_params, tech_par
elif which in ['forestry', 'water_mask', 'bathymetry']:
surface_fn = 'ERA5_surface_characteristics_20181231_' + str(model_params['spatial_resolution']) + '.nc'
- surface_path = join(data_path, 'input_data/land_data', surface_fn)
+ surface_path = join(data_path, 'input/land_data', surface_fn)
dataset = xr.open_dataset(surface_path).astype(float32)
dataset = dataset.sortby([dataset.longitude, dataset.latitude])
dataset = dataset.assign_coords(longitude=(((dataset.longitude
@@ -263,7 +263,7 @@ def filter_locations_by_layer(regions, start_coordinates, model_params, tech_par
elif which == 'population_density':
population_fn = 'gpw_v4_population_density_adjusted_rev11_0.5.nc'
- population_path = join(data_path, 'input_data/population_density', population_fn)
+ population_path = join(data_path, 'input/population_density', population_fn)
dataset = xr.open_dataset(population_path)
varname = [item for item in dataset.data_vars][0]
@@ -435,9 +435,9 @@ def return_output(input_dict, data_path, smooth_wind_power_curve=True):
output_dict = deepcopy(input_dict)
tech_dict = read_inputs('../config_techs.yml')
- wind_data_path = join(data_path, 'input_data/transfer_functions', 'data_wind_turbines.csv')
+ wind_data_path = join(data_path, 'input/transfer_functions', 'data_wind_turbines.csv')
data_converter_wind = read_csv(wind_data_path, sep=';', index_col=0)
- solar_data_path = join(data_path, 'input_data/transfer_functions', 'data_solar_modules.csv')
+ solar_data_path = join(data_path, 'input/transfer_functions', 'data_solar_modules.csv')
data_converter_solar = read_csv(solar_data_path, sep=';', index_col=0)
for region, tech in key_list:
@@ -753,7 +753,7 @@ def retrieve_site_data(model_parameters, deployment_dict, coordinates_dict, outp
# Capacity credit sites.
- load_data_fn = join(model_parameters['data_path'], 'input_data/load_data', 'load_2009_2018.csv')
+ load_data_fn = join(model_parameters['data_path'], 'input/load_data', 'load_2009_2018.csv')
load_data = read_csv(load_data_fn, index_col=0)
load_data.index = to_datetime(load_data.index)
load_data = load_data[(load_data.index > time_slice[0]) & (load_data.index < time_slice[1])]