def test_make_filename():
path = Path("reanalysis-era5-single-levels-monthly-means"
"/2m_temperature/1979_2019/01_12.nc")
name = ERA5MonthlyMeanPreprocessor.create_filename(path, "kenya")
expected_name = "1979_2019_01_12_2m_temperature_kenya.nc"
assert name == expected_name, f"{name} generated, expected {expected_name}"
def test_make_filename():
path = Path('reanalysis-era5-single-levels-monthly-means'
'/2m_temperature/1979_2019/01_12.nc')
name = ERA5MonthlyMeanPreprocessor.create_filename(path, 'kenya')
expected_name = '1979_2019_01_12_2m_temperature_kenya.nc'
assert name == expected_name, f'{name} generated, expected {expected_name}'
def preprocess_era5():
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 = ERA5MonthlyMeanPreprocessor(data_path)
processor.preprocess(subset_str="kenya", regrid=regrid_path)
def test_init(self, tmp_path):
ERA5MonthlyMeanPreprocessor(tmp_path)
assert (tmp_path / 'interim/reanalysis-era5-single-levels-'
'monthly-means_preprocessed').exists()
assert (tmp_path / 'interim/reanalysis-era5-single-levels-'
'monthly-means_interim').exists()
def preprocess_era5(subset_str: str = "kenya"):
data_path = get_data_path()
# regrid_path = data_path / f"interim/reanalysis-era5-land_preprocessed/data_{subset_str}.nc"
# assert regrid_path.exists(), f"{regrid_path} not available"
regrid_path = None
processor = ERA5MonthlyMeanPreprocessor(data_path)
processor.preprocess(subset_str=subset_str, regrid=regrid_path)
def preprocess_era5():
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 = ERA5MonthlyMeanPreprocessor(data_path)
processor.preprocess(subset_str='kenya', regrid=regrid_path)
def test_preprocess(self, tmp_path):
(tmp_path / "raw/reanalysis-era5-single-levels-monthly-means/"
"2m_temperature/1979_2019").mkdir(parents=True)
data_path = (tmp_path /
"raw/reanalysis-era5-single-levels-monthly-means/"
"2m_temperature/1979_2019/01_12.nc")
dataset = self._make_era5_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 = ERA5MonthlyMeanPreprocessor(tmp_path)
processor.preprocess(subset_str="kenya",
regrid=regrid_path,
parallel=False)
expected_out_path = (tmp_path /
"interim/reanalysis-era5-single-levels-monthly-"
"means_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 out_data.t2m.values.shape[1:] == (20, 20)
assert (not processor.interim.exists()
), f"Interim era5 folder should have been deleted"
def test_get_filenames(tmp_path):
(tmp_path / 'raw/reanalysis-era5-single-levels-monthly-means/'
'2m_temperature/1979_2019').mkdir(parents=True)
test_file = tmp_path / 'raw/reanalysis-era5-single-levels-' \
'monthly-means/2m_temperature/1979_2019.01_12.nc'
test_file.touch()
processor = ERA5MonthlyMeanPreprocessor(tmp_path)
files = processor.get_filepaths()
assert files[0] == test_file, f'Expected {test_file} to be retrieved'
def test_make_filename(tmp_path, granularity):
if granularity == "monthly":
basename = "reanalysis-era5-single-levels-monthly-means"
processor = ERA5MonthlyMeanPreprocessor(tmp_path)
elif granularity == "hourly":
basename = "reanalysis-era5-single-levels"
processor = ERA5HourlyPreprocessor(tmp_path)
path = Path(basename + "/2m_temperature/1979_2019/01_12.nc")
name = processor.create_filename(path, "kenya")
expected_name = "1979_2019_01_12_2m_temperature_kenya.nc"
assert name == expected_name, f"{name} generated, expected {expected_name}"
def test_get_filenames(tmp_path, granularity):
if granularity == "monthly":
basename = "reanalysis-era5-single-levels-monthly-means"
processor = ERA5MonthlyMeanPreprocessor(tmp_path)
elif granularity == "hourly":
basename = "reanalysis-era5-single-levels"
processor = ERA5HourlyPreprocessor(tmp_path)
(tmp_path / f"raw/{basename}/" "2m_temperature/1979_2019").mkdir(parents=True)
test_file = tmp_path / f"raw/{basename}/" "/2m_temperature/1979_2019.01_12.nc"
test_file.touch()
files = processor.get_filepaths()
assert files[0] == test_file, f"Expected {test_file} to be retrieved"
data_dir = Path('/soge-home/projects/crop_yield/ml_drought/data/')
base_out_dir = Path('/soge-home/projects/crop_yield/hackathon/')
final_out_dir = Path('/soge-home/projects/crop_yield/hackathon/v_wind')
v_wind_dir = Path(
'/soge-home/data/analysis/era5/0.28125x0.28125/hourly/v_component_of_wind/nc'
)
# make directories
out_dir = base_out_dir / 'africa' / 'v_component_of_wind_hourly'
if not out_dir.exists():
out_dir.mkdir(exist_ok=True, parents=True)
if not final_out_dir.exists():
final_out_dir.mkdir(exist_ok=True, parents=True)
processor = ERA5MonthlyMeanPreprocessor(data_dir)
# # SUBSET AFRICA from hourly files
# nc_files = [f for f in v_wind_dir.glob('*.nc')]
# for netcdf_filepath in nc_files:
# ds = xr.open_dataset(netcdf_filepath).rename(
# {'longitude': 'lon', 'latitude': 'lat'}
# )
# ds = processor.chop_roi(ds, subset_str='africa', inverse_lat=True)
# ds.to_netcdf(out_dir / file.name)
# print(f'Done for {file.name}')
# JOIN ALL FILES AND MAKE DAILY
ds = xr.open_mfdataset(str(out_dir / '*.nc'), chunks={'time': 1})
ds = processor.resample_time(ds, resample_length='D', upsampling=False)
