def test_handles_not_too_many_nans_within_shapes(single_shape, make_spatiotemporal_data):
spatiotemporal_data = make_spatiotemporal_data([0, np.nan, 2, 3])
area_weighted_time_series(
shapes=single_shape,
spatiotemporal=spatiotemporal_data,
gridcell_overlap_threshold=0.75
)
def test_fails_with_diverging_crs(single_shape, any_spatiotemporal_data, crs1,
crs2):
single_shape = single_shape.to_crs(crs1)
any_spatiotemporal_data.attrs["crs"] = crs2
with pytest.raises(AssertionError):
area_weighted_time_series(shapes=single_shape,
spatiotemporal=any_spatiotemporal_data)
def test_fails_with_too_much_nodata_within_shapes(single_shape, spatiotemporal_data_with_nodata_within_single_shape):
with pytest.raises(AssertionError):
area_weighted_time_series(
shapes=single_shape,
spatiotemporal=spatiotemporal_data_with_nodata_within_single_shape,
gridcell_overlap_threshold=DEFAULT_THRESHOLD
)
def test_fails_with_wrong_dimension_names(single_shape, any_spatiotemporal_data, correct_name, wrong_name):
any_spatiotemporal_data = any_spatiotemporal_data.rename({correct_name: wrong_name})
with pytest.raises(AssertionError):
area_weighted_time_series(
shapes=single_shape,
spatiotemporal=any_spatiotemporal_data,
gridcell_overlap_threshold=DEFAULT_THRESHOLD
)
def test_allows_all_crs(single_shape, any_spatiotemporal_data, crs):
single_shape = single_shape.set_crs(crs, allow_override=True)
any_spatiotemporal_data.attrs["crs"] = crs
area_weighted_time_series(
shapes=single_shape,
spatiotemporal=any_spatiotemporal_data,
gridcell_overlap_threshold=DEFAULT_THRESHOLD
)
def test_fails_without_crs_in_spatiotemporal(single_shape, any_spatiotemporal_data):
del any_spatiotemporal_data.attrs["crs"]
with pytest.raises(AssertionError):
area_weighted_time_series(
shapes=single_shape,
spatiotemporal=any_spatiotemporal_data,
gridcell_overlap_threshold=DEFAULT_THRESHOLD
)
def test_fails_with_too_many_nans_within_shapes(single_shape, make_spatiotemporal_data):
spatiotemporal_data = make_spatiotemporal_data([0, np.nan, 2, 3])
with pytest.raises(AssertionError):
area_weighted_time_series(
shapes=single_shape,
spatiotemporal=spatiotemporal_data,
gridcell_overlap_threshold=DEFAULT_THRESHOLD
)
def test_fails_with_wrong_dimension_names(single_shape,
any_spatiotemporal_data,
correct_name, wrong_name):
any_spatiotemporal_data = any_spatiotemporal_data.rename(
{correct_name: wrong_name})
with pytest.raises(AssertionError):
area_weighted_time_series(shapes=single_shape,
spatiotemporal=any_spatiotemporal_data)
def test_fails_with_diverging_crs(single_shape, any_spatiotemporal_data, crs1, crs2):
single_shape = single_shape.to_crs(crs1)
any_spatiotemporal_data.attrs["crs"] = crs2
with pytest.raises(AssertionError):
area_weighted_time_series(
shapes=single_shape,
spatiotemporal=any_spatiotemporal_data,
gridcell_overlap_threshold=DEFAULT_THRESHOLD
)
def test_handles_non_rectangular_data(single_shape, non_rectangular_spatiotemporal_data):
weighted_ts = area_weighted_time_series(
shapes=single_shape,
spatiotemporal=non_rectangular_spatiotemporal_data,
gridcell_overlap_threshold=DEFAULT_THRESHOLD
)
assert (weighted_ts.iloc[:, 0].values == 8).all()
def test_returns_normal_with_nodata_outside_shape(single_shape, spatiotemporal_data_with_nodata_outside_single_shape):
weighted_ts = area_weighted_time_series(
shapes=single_shape,
spatiotemporal=spatiotemporal_data_with_nodata_outside_single_shape,
gridcell_overlap_threshold=DEFAULT_THRESHOLD
)
assert (weighted_ts.iloc[:, 0].values == 8).all()
def test_partial_match(single_shape_small, make_spatiotemporal_data):
spatiotemporal_data = make_spatiotemporal_data([1, 1, 2, 2])
weighted_ts = area_weighted_time_series(shapes=single_shape_small,
spatiotemporal=spatiotemporal_data)
expected_mean = 1 * (8 / 12) + 2 * (4 / 12)
assert pytest.approx(expected_mean,
rel=0.001) == weighted_ts.iloc[:, 0].values.mean()
def test_equal_values(single_shape, make_spatiotemporal_data):
spatiotemporal_data = make_spatiotemporal_data([8, 8, 8, 8])
weighted_ts = area_weighted_time_series(
shapes=single_shape,
spatiotemporal=spatiotemporal_data,
gridcell_overlap_threshold=DEFAULT_THRESHOLD
)
assert (weighted_ts.iloc[:, 0].values == 8).all()
def test_four_shapes(four_shapes, make_spatiotemporal_data):
spatiotemporal_data = make_spatiotemporal_data([0, 1, 2, 3])
weighted_ts = area_weighted_time_series(shapes=four_shapes,
spatiotemporal=spatiotemporal_data)
assert (weighted_ts.iloc[:, 0].values == 0).all()
assert (weighted_ts.iloc[:, 1].values == 1).all()
assert (weighted_ts.iloc[:, 2].values == 2).all()
assert (weighted_ts.iloc[:, 3].values == 3).all()
def test_four_shapes(four_shapes, make_spatiotemporal_data):
spatiotemporal_data = make_spatiotemporal_data([0, 1, 2, 3])
weighted_ts = area_weighted_time_series(
shapes=four_shapes,
spatiotemporal=spatiotemporal_data,
gridcell_overlap_threshold=DEFAULT_THRESHOLD
)
assert (weighted_ts.iloc[:, 0].values == 0).all()
assert (weighted_ts.iloc[:, 1].values == 1).all()
assert (weighted_ts.iloc[:, 2].values == 2).all()
assert (weighted_ts.iloc[:, 3].values == 3).all()
def capacityfactors(path_to_eez,
path_to_shared_coast,
path_to_timeseries,
threshold,
path_to_result,
year=None):
"""Generate offshore capacityfactor time series for each location."""
eez = gpd.read_file(path_to_eez).set_index("id").to_crs(EPSG3035).geometry
shared_coast = pd.read_csv(path_to_shared_coast, index_col=0)
shared_coast.index = shared_coast.index.map(lambda x: x.replace(".", "-"))
ts = xr.open_dataset(path_to_timeseries)
if year:
ts = ts.sel(time=str(year))
ts = convert_old_style_capacity_factor_time_series(ts)
capacityfactors_per_eez = area_weighted_time_series(shapes=eez,
spatiotemporal=ts)
capacityfactors = _allocate_to_onshore_locations(capacityfactors_per_eez,
shared_coast)
capacityfactors.where(capacityfactors >= threshold,
0).to_csv(path_to_result)
def capacityfactors(path_to_locations,
path_to_timeseries,
path_to_timeseries_with_coordinates,
cf_threshold,
path_to_result,
gridcell_overlap_threshold,
first_year=None,
final_year=None):
"""Generate capacityfactor time series for each location."""
locations = gpd.read_file(path_to_locations).set_index("id").to_crs(
EPSG3035).geometry
locations.index = locations.index.map(lambda x: x.replace(".", "-"))
ts = xr.open_dataset(path_to_timeseries)
ts = ts.sel(time=slice(first_year, final_year))
# xarray will silently miss the fact that data doesn't exist with slice
if first_year is not None and first_year not in ts.time.to_index(
).year.astype(str).unique():
raise ValueError(
f"Cannot access capacity factor data for timeseries {path_to_timeseries} "
f"with a start year of {first_year}.")
if final_year is not None and final_year not in ts.time.to_index(
).year.astype(str).unique():
raise ValueError(
f"Cannot access capacity factor data for timeseries {path_to_timeseries} "
f"with an end year of {final_year}.")
if ("lat" not in ts.dims) or (
"lon" not in ts.dims): # rooftop pv comes without coordinates
ts_with_latlon = xr.open_dataset(path_to_timeseries_with_coordinates)
ts["lat"] = ts_with_latlon["lat"]
ts["lon"] = ts_with_latlon["lon"]
ts = convert_old_style_capacity_factor_time_series(ts)
capacityfactors = area_weighted_time_series(
shapes=locations,
spatiotemporal=ts,
gridcell_overlap_threshold=gridcell_overlap_threshold)
capacityfactors.where(capacityfactors >= cf_threshold,
0).to_csv(path_to_result)
def capacityfactors(path_to_eez,
path_to_shared_coast,
path_to_timeseries,
cf_threshold,
path_to_result,
gridcell_overlap_threshold,
first_year=None,
final_year=None):
"""Generate offshore capacityfactor time series for each location."""
eez = gpd.read_file(path_to_eez).set_index("mrgid").to_crs(
EPSG3035).geometry
shared_coast = pd.read_csv(path_to_shared_coast, index_col=0)
shared_coast.index = shared_coast.index.map(lambda x: x.replace(".", "-"))
shared_coast.columns = shared_coast.columns.astype(int)
ts = xr.open_dataset(path_to_timeseries)
ts = ts.sel(time=slice(first_year, final_year))
# xarray will silently miss the fact that data doesn't exist with slice
if first_year is not None and first_year not in ts.time.to_index(
).year.astype(str).unique():
raise ValueError(
f"Cannot access capacity factor data for timeseries {path_to_timeseries} "
f"with a start year of {first_year}.")
if final_year is not None and final_year not in ts.time.to_index(
).year.astype(str).unique():
raise ValueError(
f"Cannot access capacity factor data for timeseries {path_to_timeseries} "
f"with an end year of {final_year}.")
ts = convert_old_style_capacity_factor_time_series(ts)
capacityfactors_per_eez = area_weighted_time_series(
shapes=eez,
spatiotemporal=ts,
gridcell_overlap_threshold=gridcell_overlap_threshold)
capacityfactors = _allocate_to_onshore_locations(capacityfactors_per_eez,
shared_coast)
capacityfactors.where(capacityfactors >= cf_threshold,
0).to_csv(path_to_result)
def capacityfactors(path_to_locations,
path_to_timeseries,
path_to_timeseries_with_coordinates,
threshold,
path_to_result,
year=None):
"""Generate capacityfactor time series for each location."""
locations = gpd.read_file(path_to_locations).set_index("id").to_crs(
EPSG3035).geometry
locations.index = locations.index.map(lambda x: x.replace(".", "-"))
ts = xr.open_dataset(path_to_timeseries)
if year:
ts = ts.sel(time=str(year))
if ("lat" not in ts.dims) or (
"lon" not in ts.dims): # rooftop pv comes without coordinates
ts_with_latlon = xr.open_dataset(path_to_timeseries_with_coordinates)
ts["lat"] = ts_with_latlon["lat"]
ts["lon"] = ts_with_latlon["lon"]
ts = convert_old_style_capacity_factor_time_series(ts)
capacityfactors = area_weighted_time_series(shapes=locations,
spatiotemporal=ts)
capacityfactors.where(capacityfactors >= threshold,
0).to_csv(path_to_result)
def test_handles_reverted_coordinates_gracefully(single_shape,
reverted_spatiotemporal_data):
area_weighted_time_series(
shapes=single_shape,
spatiotemporal=reverted_spatiotemporal_data,
)
def test_handles_not_too_much_nodata_within_shapes(single_shape, spatiotemporal_data_with_nodata_within_single_shape):
area_weighted_time_series(
shapes=single_shape,
spatiotemporal=spatiotemporal_data_with_nodata_within_single_shape,
gridcell_overlap_threshold=0.75
)
def test_returns_normal_with_nodata_outside_shape(
single_shape, spatiotemporal_data_with_nodata_outside_single_shape):
weighted_ts = area_weighted_time_series(
shapes=single_shape,
spatiotemporal=spatiotemporal_data_with_nodata_outside_single_shape)
assert (weighted_ts.iloc[:, 0].values == 8).all()
def test_returns_nan_with_nodata_within_shapes(
single_shape, spatiotemporal_data_with_nodata_within_single_shape):
weighted_ts = area_weighted_time_series(
shapes=single_shape,
spatiotemporal=spatiotemporal_data_with_nodata_within_single_shape)
assert pd.isna(weighted_ts).all().all()
def test_not_equal_values(single_shape, make_spatiotemporal_data):
spatiotemporal_data = make_spatiotemporal_data([8, 8, 8, 0])
weighted_ts = area_weighted_time_series(shapes=single_shape,
spatiotemporal=spatiotemporal_data)
expected_mean = (8 + 8 + 8 + 0) / 4
assert (weighted_ts.iloc[:, 0].values == expected_mean).all()
def test_handles_non_rectangular_data(single_shape,
non_rectangular_spatiotemporal_data):
weighted_ts = area_weighted_time_series(
shapes=single_shape,
spatiotemporal=non_rectangular_spatiotemporal_data)
assert (weighted_ts.iloc[:, 0].values == 8).all()
def test_equal_values(single_shape, make_spatiotemporal_data):
spatiotemporal_data = make_spatiotemporal_data([8, 8, 8, 8])
weighted_ts = area_weighted_time_series(shapes=single_shape,
spatiotemporal=spatiotemporal_data)
assert (weighted_ts.iloc[:, 0].values == 8).all()
def test_handles_reverted_coordinates_gracefully(single_shape, reverted_spatiotemporal_data):
area_weighted_time_series(
shapes=single_shape,
spatiotemporal=reverted_spatiotemporal_data,
gridcell_overlap_threshold=DEFAULT_THRESHOLD
)
def test_allows_all_crs(single_shape, any_spatiotemporal_data, crs):
single_shape = single_shape.set_crs(crs, allow_override=True)
any_spatiotemporal_data.attrs["crs"] = crs
area_weighted_time_series(shapes=single_shape,
spatiotemporal=any_spatiotemporal_data)
def test_fails_without_crs_in_shapes(single_shape, any_spatiotemporal_data):
single_shape.crs = None
with pytest.raises(AssertionError):
area_weighted_time_series(shapes=single_shape,
spatiotemporal=any_spatiotemporal_data)
def test_fails_without_crs_in_spatiotemporal(single_shape,
any_spatiotemporal_data):
del any_spatiotemporal_data.attrs["crs"]
with pytest.raises(AssertionError):
area_weighted_time_series(shapes=single_shape,
spatiotemporal=any_spatiotemporal_data)
