def test_GLDAS_Noah_v21_025Ds_img_reading_landpoints():
landgrid = GLDAS025LandGrid()
parameter = ['SoilMoi10_40cm_inst', 'SoilMoi0_10cm_inst', 'SoilTMP0_10cm_inst',
'AvgSurfT_inst', 'SWE_inst']
img = GLDAS_Noah_v21_025Ds(data_path=os.path.join(os.path.dirname(__file__),
'test-data',
'GLDAS_NOAH_image_data'),
parameter=parameter,
subgrid=landgrid,
array_1D=True)
image = img.read(
datetime(2015, 1, 1, 0))
assert sorted(image.data.keys()) == sorted(parameter)
# gpi for testing on the land grid: 527549, lat: 1.625, lon: -52.625
assert image.timestamp == datetime(2015, 1, 1, 0)
assert round(image.data['SoilMoi0_10cm_inst'][50000],3) == 26.181
assert round(image.data['SoilMoi10_40cm_inst'][50000],3) == 84.558
assert round(image.data['SoilTMP0_10cm_inst'][50000],3) == 301.276
assert round(image.data['AvgSurfT_inst'][50000],3) == 294.863
assert round(image.data['SWE_inst'][50000],3) == 0
assert (image.lon.size, image.lat.size) == (landgrid.activearrlat.size,
landgrid.activearrlon.size)
assert sorted(list(image.metadata.keys())) == sorted(parameter)
assert image.metadata['AvgSurfT_inst']['units'] == u'K'
assert image.metadata['AvgSurfT_inst']['long_name'] == u'Average Surface Skin temperature'
def test_GLDAS_Noah_v21_025Ds_timestamps_for_daterange():
parameter = [
"SoilMoi10_40cm_inst",
"SoilMoi0_10cm_inst",
"SoilTMP0_10cm_inst",
"AvgSurfT_inst",
"SWE_inst",
]
img = GLDAS_Noah_v21_025Ds(
data_path=os.path.join(
os.path.dirname(__file__), "test-data", "GLDAS_NOAH_image_data"
),
parameter=parameter,
subgrid=None,
array_1D=True,
)
tstamps = img.tstamps_for_daterange(
datetime(2000, 1, 1), datetime(2000, 1, 1)
)
assert len(tstamps) == 8
assert tstamps == [
datetime(2000, 1, 1, 0),
datetime(2000, 1, 1, 3),
datetime(2000, 1, 1, 6),
datetime(2000, 1, 1, 9),
datetime(2000, 1, 1, 12),
datetime(2000, 1, 1, 15),
datetime(2000, 1, 1, 18),
datetime(2000, 1, 1, 21),
]
def test_GLDAS_Noah_v21_025Ds_img_reading():
parameter = ['SoilMoi10_40cm_inst', 'SoilMoi0_10cm_inst', 'SoilTMP0_10cm_inst',
'AvgSurfT_inst', 'SWE_inst']
img = GLDAS_Noah_v21_025Ds(data_path=os.path.join(os.path.dirname(__file__),
'test-data',
'GLDAS_NOAH_image_data'),
parameter=parameter,
subgrid=None,
array_1D=True)
image = img.read(
datetime(2015, 1, 1, 0))
assert sorted(image.data.keys()) == sorted(parameter)
assert image.timestamp == datetime(2015, 1, 1, 0)
assert round(image.data['SoilMoi0_10cm_inst'][998529],3) == 38.804
assert round(image.data['SoilMoi10_40cm_inst'][998529],3) == 131.699
assert round(image.data['SoilTMP0_10cm_inst'][998529],3) == 254.506
assert round(image.data['AvgSurfT_inst'][998529],3) == 235.553
assert round(image.data['SWE_inst'][998529],3) == 108.24
assert image.lon.shape == (360 * 180 * (1 / 0.25)**2,)
assert image.lon.shape == image.lat.shape
assert sorted(list(image.metadata.keys())) == sorted(parameter)
assert image.metadata['AvgSurfT_inst']['units'] == u'K'
assert image.metadata['AvgSurfT_inst']['long_name'] == u'Average Surface Skin temperature'
def reshuffle(input_root, outputpath,
startdate, enddate,
parameters, land_points=True,
imgbuffer=50):
"""
Reshuffle method applied to GLDAS data.
Parameters
----------
input_root: string
input path where gldas data was downloaded
outputpath : string
Output path.
startdate : datetime
Start date.
enddate : datetime
End date.
parameters: list
parameters to read and convert
imgbuffer: int, optional
How many images to read at once before writing time series.
"""
if land_points:
landgrid = GLDAS025LandGrid()
else:
landgrid = None
if get_filetype(input_root) == 'grib':
input_dataset = GLDAS_Noah_v1_025Ds(input_root, parameters,
array_1D=True)
if land_points:
warnings.warn('Land Grid is fit to GLDAS 2.x netCDF data')
else:
input_dataset = GLDAS_Noah_v21_025Ds(input_root, parameters, landgrid,
array_1D=True)
if not os.path.exists(outputpath):
os.makedirs(outputpath)
global_attr = {'product': 'GLDAS'}
# get time series attributes from first day of data.
data = input_dataset.read(startdate)
ts_attributes = data.metadata
if landgrid:
grid = landgrid
else:
grid = BasicGrid(data.lon, data.lat)
reshuffler = Img2Ts(input_dataset=input_dataset, outputpath=outputpath,
startdate=startdate, enddate=enddate, input_grid=grid,
imgbuffer=imgbuffer, cellsize_lat=5.0,
cellsize_lon=5.0, global_attr=global_attr, zlib=True,
unlim_chunksize=1000, ts_attributes=ts_attributes)
reshuffler.calc()