def export_s5(region_str="kenya"):
granularity = "monthly"
pressure_level = False
exporter = S5Exporter(
data_folder=get_data_path(),
granularity=granularity,
pressure_level=pressure_level,
)
min_year = 1993
max_leadtime = None
pressure_levels = None # [200, 500, 925]
n_parallel_requests = 1
for variable in variables:
print(f"\n\nWORKING ON: {variable}\n\n")
exporter.export(
variable=variable,
min_year=min_year,
max_year=max_year,
min_month=min_month,
max_month=max_month,
max_leadtime=max_leadtime,
pressure_levels=pressure_levels,
n_parallel_requests=n_parallel_requests,
region_str=region_str,
break_up=False,
)
def export_s5():
# 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')
granularity = 'hourly'
pressure_level = False
exporter = S5Exporter(
data_folder=data_path,
granularity=granularity,
pressure_level=pressure_level,
)
variable = 'total_precipitation'
min_year = 1993
max_year = 2014
min_month = 1
max_month = 12
max_leadtime = None
pressure_levels = [200, 500, 925]
n_parallel_requests = 20
exporter.export(
variable=variable,
min_year=min_year,
max_year=max_year,
min_month=min_month,
max_month=max_month,
max_leadtime=max_leadtime,
pressure_levels=pressure_levels,
n_parallel_requests=n_parallel_requests,
)
def test_leadtimes_produces_correct_keys_values_monthly(self, tmp_path):
granularity = "monthly"
pressure_level = False
s5 = S5Exporter(
data_folder=tmp_path, granularity=granularity, pressure_level=pressure_level
)
# 5 months because monthly data
max_leadtime = 5
selection_request = s5.get_s5_leadtimes(max_leadtime)
# assert returning correct keys
expected_keys = ["leadtime_month"]
assert all(
np.isin(expected_keys, [k for k in selection_request.keys()])
), f"Expecting keys: {expected_keys}. \
Got: {[k for k in selection_request.keys()]}"
# assert that returning a list of strings
assert isinstance(
selection_request["leadtime_month"][0], str
), f"Expected a list of str, got: {type(selection_request['leadtime_month'][0])}"
# assert that returning the correct max forecast month
leadtimes_int = [int(lt) for lt in selection_request["leadtime_month"]]
assert (
max(leadtimes_int) == 5
), f"Expected max leadtime to be 5months\
def export_s5():
granularity = "hourly"
pressure_level = False
exporter = S5Exporter(
data_folder=get_data_path(),
granularity=granularity,
pressure_level=pressure_level,
)
variable = "total_precipitation"
min_year = 1993
max_year = 2014
min_month = 1
max_month = 12
max_leadtime = None
pressure_levels = [200, 500, 925]
n_parallel_requests = 20
exporter.export(
variable=variable,
min_year=min_year,
max_year=max_year,
min_month=min_month,
max_month=max_month,
max_leadtime=max_leadtime,
pressure_levels=pressure_levels,
n_parallel_requests=n_parallel_requests,
)
def test_product_type_single_levels_monthly(self, tmp_path):
granularity = "monthly"
pressure_level = False
s5 = S5Exporter(
data_folder=tmp_path, granularity=granularity, pressure_level=pressure_level
)
# monthly_mean is the product_type
assert (
s5.get_product_type() == "monthly_mean"
), f"\
Expecting `product_type` for `seasonal-original-single-levels` \
dataset to be 'monthly_mean'. Returned: {s5.get_product_type(None)}"
# hindcast_climate_mean is a valid product type
assert (
s5.get_product_type("hindcast_climate_mean") == "hindcast_climate_mean"
), f"\
Expecting `product_type` for `seasonal-original-single-levels`\
dataset to be 'hindcast_climate_mean' Got: {s5.get_product_type('hindcast_climate_mean')}"
# assert erroneous product_type
with pytest.raises(AssertionError) as e:
s5.get_product_type("dsgdfgdfh")
e.match(r"Invalid `product_type`*")
def test_export_functionality(self, cdsapi_mock, tmp_path):
cdsapi_mock.return_value = Mock()
granularity = "monthly"
pressure_level = False
s5 = S5Exporter(
data_folder=tmp_path, granularity=granularity, pressure_level=pressure_level
)
variable = "total_precipitation"
min_year = 2017
max_year = 2017
min_month = 1
max_month = 1
max_leadtime = 1
n_parallel_requests = 1
show_api_request = True
s5.export(
variable=variable,
min_year=min_year,
max_year=max_year,
min_month=min_month,
max_month=max_month,
max_leadtime=max_leadtime,
show_api_request=show_api_request,
n_parallel_requests=n_parallel_requests,
)
(
tmp_path / "raw/seasonal-monthly-single-levels\
/total_precipitation/2017/M01.grib"
).as_posix().replace(' ', '')
cdsapi_mock.assert_called()
def test_expected_filepath(self, cdsapi_mock, tmp_path):
cdsapi_mock.return_value = Mock()
granularity = "monthly"
pressure_level = False
s5 = S5Exporter(data_folder=tmp_path,
granularity=granularity,
pressure_level=pressure_level)
expected_filepath = (tmp_path / "raw/seasonal-monthly-single-levels/\
total_precipitation/2017_2018/Y2017_2018_M01_12.grib").as_posix()
expected_filepath = expected_filepath.replace(" ", "")
variable = "total_precipitation"
max_leadtime = 5
min_year = 2017
max_year = 2018
min_month = 1
max_month = 12
processed_selection_request = s5.create_selection_request(
variable=variable,
max_leadtime=max_leadtime,
min_year=min_year,
max_year=max_year,
min_month=min_month,
max_month=max_month,
)
filepath = s5.make_filename(
dataset=s5.dataset, selection_request=processed_selection_request)
assert (expected_filepath == filepath.as_posix()), f"\
def test_None_product_type_for_original_single_levels(self, tmp_path):
granularity = "hourly"
pressure_level = False
s5 = S5Exporter(data_folder=tmp_path,
granularity=granularity,
pressure_level=pressure_level)
assert (s5.get_product_type() is
None), f"Expected the product_type for\
def test_initialisation_times_produces_correct_keys(self, tmp_path):
s5 = S5Exporter(pressure_level=True, data_folder=tmp_path)
selection_request = s5.get_s5_initialisation_times(
"hourly", min_year=2017, max_year=2018, min_month=1, max_month=1
)
expected_keys = ["year", "month", "day"]
assert all(
np.isin(expected_keys, [k for k in selection_request.keys()])
), f"Expecting keys: {expected_keys}. \
def test_dataset_reference_creation(self, tmp_path):
granularity = "monthly"
pressure_level = False
s5 = S5Exporter(
data_folder=tmp_path, granularity=granularity, pressure_level=pressure_level
)
expected_dataset_reference = dataset_reference["seasonal-monthly-single-levels"]
assert (
s5.dataset_reference == expected_dataset_reference
), f"for dataset seasonal-monthly-single-levels we are not \
def test_create_selection_request(self, tmp_path):
granularity = "monthly"
pressure_level = False
s5 = S5Exporter(
data_folder=tmp_path, granularity=granularity, pressure_level=pressure_level
)
variable = "total_precipitation"
max_leadtime = 5
min_year = 2017
max_year = 2018
min_month = 1
max_month = 12
processed_selection_request = s5.create_selection_request(
variable=variable,
max_leadtime=max_leadtime,
min_year=min_year,
max_year=max_year,
min_month=min_month,
max_month=max_month,
)
# CHECK default arguments
assert (
processed_selection_request["originating_centre"] == "ecmwf"
), "\
Expected originating_centre to be: {'ecmwf'}. Got:\
{processed_selection_request['originating_centre']}"
assert (
processed_selection_request["system"] == "5"
), f"\
Expected 'system' to be '5'. Got:\
{processed_selection_request['system']}"
# CHECK time arguments
assert processed_selection_request["year"] == [
"2017",
"2018",
], f"\
Expected 'year' to be ['2017','2018']. Got:\
{processed_selection_request['year']}"
exp_months = [
"{:02d}".format(month) for month in range(min_month, max_month + 1)
]
assert (
processed_selection_request["month"] == exp_months
), f"\
def test_dataset_created_properly(self, tmp_path):
expected_datasets = [
"seasonal-monthly-single-levels",
"seasonal-monthly-pressure-levels",
"seasonal-original-single-levels",
"seasonal-original-pressure-levels",
]
pressure_levels = [False, True, False, True]
granularities = ["monthly", "monthly", "hourly", "hourly"]
for ix, expected in enumerate(expected_datasets):
s5 = S5Exporter(
data_folder=tmp_path,
granularity=granularities[ix],
pressure_level=pressure_levels[ix],
)
assert (s5.dataset == expected), f"\
def test_failure_on_invalid_granularity(self, tmp_path):
s5 = S5Exporter(pressure_level=True, data_folder=tmp_path)
with pytest.raises(AssertionError) as e:
s5.get_s5_initialisation_times("daily")
e.match(r"Invalid granularity*")
