def test_alternative_region(self, tmp_path):
# make the dataset
(tmp_path / "raw/gleam/monthly").mkdir(parents=True)
data_path = tmp_path / "raw/gleam/monthly/testy_test.nc"
dataset = self._make_gleam_dataset(size=(100, 100))
dataset.to_netcdf(path=data_path)
ethiopia = get_ethiopia()
# regrid the datasets
regrid_dataset, _, _ = _make_dataset(
size=(20, 20),
latmin=ethiopia.latmin,
latmax=ethiopia.latmax,
lonmin=ethiopia.lonmin,
lonmax=ethiopia.lonmax,
)
regrid_path = tmp_path / "regridder.nc"
regrid_dataset.to_netcdf(regrid_path)
# build the Preprocessor object and subset with a different subset_str
processor = GLEAMPreprocessor(tmp_path)
processor.preprocess(subset_str="ethiopia", regrid=regrid_path)
expected_out_path = tmp_path / "interim/gleam_preprocessed/data_ethiopia.nc"
assert (
expected_out_path.exists()
), f"Expected processed file to be saved to {expected_out_path}"
def process_gleam():
data_path = get_data_path()
regrid_path = data_path / "interim/VCI_preprocessed/data_kenya.nc"
assert regrid_path.exists(), f"{regrid_path} not available"
processor = GLEAMPreprocessor(data_path)
processor.preprocess(
subset_str="kenya", regrid=regrid_path, resample_time="M", upsampling=False
)
def test_get_filenames(tmp_path):
(tmp_path / 'raw/gleam/monthly').mkdir(parents=True)
test_file = tmp_path / 'raw/gleam/monthly/testy_test.nc'
test_file.touch()
processor = GLEAMPreprocessor(tmp_path)
files = processor.get_filepaths()
assert files[0] == test_file, f'Expected {test_file} to be retrieved'
def test_preprocess(self, tmp_path):
(tmp_path / "raw/gleam/monthly").mkdir(parents=True)
data_path = tmp_path / "raw/gleam/monthly/testy_test.nc"
dataset = self._make_gleam_dataset(size=(100, 100))
dataset.to_netcdf(path=data_path)
kenya = get_kenya()
regrid_dataset, _, _ = _make_dataset(
size=(20, 20),
latmin=kenya.latmin,
latmax=kenya.latmax,
lonmin=kenya.lonmin,
lonmax=kenya.lonmax,
)
regrid_path = tmp_path / "regridder.nc"
regrid_dataset.to_netcdf(regrid_path)
processor = GLEAMPreprocessor(tmp_path)
processor.preprocess(subset_str="kenya", regrid=regrid_path)
expected_out_path = tmp_path / "interim/gleam_preprocessed/data_kenya.nc"
assert (
expected_out_path.exists()
), f"Expected processed file to be saved to {expected_out_path}"
# check the subsetting happened correctly
out_data = xr.open_dataset(expected_out_path)
expected_dims = ["lat", "lon", "time"]
assert len(list(out_data.dims)) == len(expected_dims)
for dim in expected_dims:
assert dim in list(
out_data.dims
), f"Expected {dim} to be in the processed dataset dims"
lons = out_data.lon.values
assert (lons.min() >= kenya.lonmin) and (
lons.max() <= kenya.lonmax
), "Longitudes not correctly subset"
lats = out_data.lat.values
assert (lats.min() >= kenya.latmin) and (
lats.max() <= kenya.latmax
), "Latitudes not correctly subset"
assert set(out_data.data_vars) == {"E"}, f"Got unexpected variables!"
assert (
not processor.interim.exists()
), f"Interim gleam folder should have been deleted"
def process_gleam():
# if the working directory is alread ml_drought don't need ../data
if Path('.').absolute().as_posix().split('/')[-1] == 'ml_drought':
data_path = Path('data')
else:
data_path = Path('../data')
regrid_path = data_path / 'interim/VCI_preprocessed/data_kenya.nc'
assert regrid_path.exists(), f'{regrid_path} not available'
processor = GLEAMPreprocessor(data_path)
processor.preprocess(subset_str='kenya',
regrid=regrid_path,
resample_time='M',
upsampling=False)
def test_swapaxes(self):
dataset = self._make_gleam_dataset(size=(20, 30))
out = GLEAMPreprocessor._swap_dims_and_filter(dataset)
assert out.E.values.shape[1:] == (30, 20), f"Array axes not properly swapped!"
def test_directories_created(tmp_path):
v = GLEAMPreprocessor(tmp_path)
assert (tmp_path / v.preprocessed_folder / 'gleam_preprocessed').exists(), \
'Should have created a directory tmp_path/interim/chirps_preprocessed'
assert (tmp_path / v.preprocessed_folder / 'gleam_interim').exists(), \
'Should have created a directory tmp_path/interim/chirps_interim'
def test_make_filename():
test_file = 'testy_test.nc'
expected_output = 'testy_test_kenya.nc'
filename = GLEAMPreprocessor.create_filename(test_file, 'kenya')
assert filename == expected_output, \
f'Expected output to be {expected_output}, got {filename}'
def process_gleam():
# if the working directory is alread ml_drought don't need ../data
if Path(".").absolute().as_posix().split("/")[-1] == "ml_drought":
data_path = Path("data")
else:
data_path = Path("../data")
regrid_path = (
data_path /
"interim/reanalysis-era5-single-levels-monthly-means_preprocessed/data_kenya.nc"
)
assert regrid_path.exists(), f"{regrid_path} not available"
processor = GLEAMPreprocessor(data_path)
processor.preprocess(subset_str=subset_str,
regrid=regrid_path,
resample_time="M",
upsampling=False)
def test_make_filename():
test_file = "testy_test.nc"
expected_output = "testy_test_kenya.nc"
filename = GLEAMPreprocessor.create_filename(test_file, "kenya")
assert (
filename == expected_output
), f"Expected output to be {expected_output}, got {filename}"
def test_preprocess(self, tmp_path):
(tmp_path / 'raw/gleam/monthly').mkdir(parents=True)
data_path = tmp_path / 'raw/gleam/monthly/testy_test.nc'
dataset = self._make_gleam_dataset(size=(100, 100))
dataset.to_netcdf(path=data_path)
kenya = get_kenya()
regrid_dataset, _, _ = _make_dataset(size=(20, 20),
latmin=kenya.latmin,
latmax=kenya.latmax,
lonmin=kenya.lonmin,
lonmax=kenya.lonmax)
regrid_path = tmp_path / 'regridder.nc'
regrid_dataset.to_netcdf(regrid_path)
processor = GLEAMPreprocessor(tmp_path)
processor.preprocess(subset_str='kenya', regrid=regrid_path)
expected_out_path = tmp_path / 'interim/gleam_preprocessed/data_kenya.nc'
assert expected_out_path.exists(), \
f'Expected processed file to be saved to {expected_out_path}'
# check the subsetting happened correctly
out_data = xr.open_dataset(expected_out_path)
expected_dims = ['lat', 'lon', 'time']
assert len(list(out_data.dims)) == len(expected_dims)
for dim in expected_dims:
assert dim in list(out_data.dims), \
f'Expected {dim} to be in the processed dataset dims'
lons = out_data.lon.values
assert (lons.min() >= kenya.lonmin) and (lons.max() <= kenya.lonmax), \
'Longitudes not correctly subset'
lats = out_data.lat.values
assert (lats.min() >= kenya.latmin) and (lats.max() <= kenya.latmax), \
'Latitudes not correctly subset'
assert set(out_data.data_vars) == {'E'}, f'Got unexpected variables!'
assert not processor.interim.exists(), \
f'Interim gleam folder should have been deleted'
def preprocess_data(data_path):
# preprocess VHI
print('** Preprocessing VHI **')
processor = VHIPreprocessor(data_path)
processor.preprocess(
subset_str='kenya', regrid=regrid_path,
n_parallel_processes=1, resample_time='M',
upsampling=False
)
regrid_path = data_path / 'interim' / 'vhi_preprocessed' / 'vhi_kenya.nc'
# preprocess CHIRPS Rainfall
print('** Preprocessing CHIRPS Precipitation **')
processor = CHIRPSPreprocesser(data_path)
processor.preprocess(
subset_str='kenya', regrid=regrid_path,
n_parallel_processes=1
)
# preprocess GLEAM evaporation
print('** Preprocessing GLEAM Evaporation **')
processor = GLEAMPreprocessor(data_path)
processor.preprocess(
subset_str='kenya', regrid=regrid_path,
resample_time='M', upsampling=False
)
# preprocess SRTM Topography
print('** Preprocessing SRTM Topography **')
processor = SRTMPreprocessor(data_path)
processor.preprocess(
subset_str='kenya', regrid=regrid_path
)
# preprocess ESA CCI Landcover
print('** Preprocessing ESA CCI Landcover **')
processor = ESACCIPreprocessor(data_path)
processor.preprocess(
subset_str='kenya', regrid=regrid_path,
resample_time='M', upsampling=False
)
def preprocess_data(data_path):
# preprocess VHI
print("** Preprocessing VHI **")
processor = VHIPreprocessor(data_path)
processor.preprocess(
subset_str="kenya",
regrid=regrid_path,
n_parallel_processes=1,
resample_time="M",
upsampling=False,
)
regrid_path = data_path / "interim" / "vhi_preprocessed" / "vhi_kenya.nc"
# preprocess CHIRPS Rainfall
print("** Preprocessing CHIRPS Precipitation **")
processor = CHIRPSPreprocessor(data_path)
processor.preprocess(subset_str="kenya",
regrid=regrid_path,
n_parallel_processes=1)
# preprocess GLEAM evaporation
print("** Preprocessing GLEAM Evaporation **")
processor = GLEAMPreprocessor(data_path)
processor.preprocess(subset_str="kenya",
regrid=regrid_path,
resample_time="M",
upsampling=False)
# preprocess SRTM Topography
print("** Preprocessing SRTM Topography **")
processor = SRTMPreprocessor(data_path)
processor.preprocess(subset_str="kenya", regrid=regrid_path)
# preprocess ESA CCI Landcover
print("** Preprocessing ESA CCI Landcover **")
processor = ESACCIPreprocessor(data_path)
processor.preprocess(subset_str="kenya",
regrid=regrid_path,
resample_time="M",
upsampling=False)