def test_refet_daily_output(daily_params):
"""Test daily RefET calculation at a single point and time"""
# If I don't copy, the pop changes the test values in daily_data()
inputs = daily_params.copy()
surface = inputs.pop('surface')
expected = inputs.pop('expected')
# print('ETr: {}'.format(expected))
# ETr/ETo values only have 4 significant figures
# Small number of days don't match if difference is set < 0.008
diff = 0.05 if expected >= 10.0 else 0.008
# Cast all numeric inputs to ee.Number type except tmin (for now)
adj_inputs = {}
for k, v in inputs.items():
if k == 'rso_type':
adj_inputs[k] = v
elif k in ['tmin', 'tmax', 'ea', 'rs', 'uz']:
adj_inputs[k] = ee.Image.constant(v)
else:
adj_inputs[k] = ee.Number(v)
if surface.lower() == 'etr':
refet = Daily(**adj_inputs).etr
elif surface.lower() == 'eto':
refet = Daily(**adj_inputs).eto
output = refet\
.reduceRegion(ee.Reducer.first(), geometry=constant_geom, scale=1)\
.getInfo()
assert float(output[surface.lower()]) == pytest.approx(expected, abs=diff)
def test_refet_daily_nldas_etr():
"""Generate a fake NLDAS image from the test values
Convert the test Rs from MJ m-2 d-1 to W m-2, then allocate half to each image
"""
band_names = [
'temperature', 'specific_humidity', 'shortwave_radiation', 'wind_u',
'wind_v'
]
wind_u = d_args['uz'] / (2**0.5)
nldas_coll = ee.ImageCollection.fromImages([
ee.Image.constant([d_args['tmin'], d_args['q_asce'],
0.0, wind_u, wind_u]) \
.double().rename(band_names) \
.set({'system:time_start': ee.Date('2015-07-01T00:00:00', 'UTC').millis()}),
ee.Image.constant([d_args['tmax'], d_args['q_asce'],
d_args['rs'] / 0.0036, wind_u, wind_u]) \
.double().rename(band_names) \
.set({'system:time_start': ee.Date('2015-07-01T12:00:00', 'UTC').millis()})
])
refet = Daily.nldas(nldas_coll,
elev=ee.Number(s_args['elev']),
lat=ee.Number(s_args['lat']),
zw=ee.Number(s_args['zw']),
method='asce')
output = refet.etr\
.reduceRegion(ee.Reducer.first(), geometry=constant_geom, scale=1)\
.getInfo()
assert float(output['etr']) == pytest.approx(d_args['etr_asce'])
def test_refet_daily_etsz(surface, expected):
refet = Daily(tmax=ee.Image.constant(d_args['tmax']),
tmin=ee.Image.constant(d_args['tmin']),
ea=ee.Image.constant(d_args['ea']),
rs=ee.Image.constant(d_args['rs']),
uz=ee.Image.constant(d_args['uz']),
zw=ee.Number(s_args['zw']),
elev=ee.Number(s_args['elev']),
lat=ee.Number(s_args['lat']),
doy=ee.Number(d_args['doy']),
method='refet')
output = refet.etsz(surface).rename(['etsz']).reduceRegion(
reducer=ee.Reducer.first(),
geometry=ee.Geometry.Rectangle([0, 0, 10, 10], 'EPSG:32613', False),
scale=1).getInfo()
assert float(output['etsz']) == pytest.approx(expected)
def test_refet_daily_rso_type_exception():
with pytest.raises(ValueError):
refet = Daily(tmax=ee.Image.constant(d_args['tmax']),
tmin=ee.Image.constant(d_args['tmin']),
ea=ee.Image.constant(d_args['ea']),
rs=ee.Image.constant(d_args['rs']),
uz=ee.Image.constant(d_args['uz']),
zw=ee.Number(s_args['zw']),
elev=ee.Number(s_args['elev']),
lat=ee.Number(s_args['lat']),
doy=ee.Number(d_args['doy']),
rso_type='nonsense',
method='refet')
def test_refet_daily_cfsv2_etr():
"""Generate a fake CFSv2 image from the test values
Convert the test Rs from MJ m-2 d-1 to W m-2
"""
band_names = [
'Maximum_temperature_height_above_ground_6_Hour_Interval',
'Minimum_temperature_height_above_ground_6_Hour_Interval',
'Specific_humidity_height_above_ground',
'Downward_Short-Wave_Radiation_Flux_surface_6_Hour_Average',
'u-component_of_wind_height_above_ground',
'v-component_of_wind_height_above_ground',
]
wind_u = d_args['uz'] / (2**0.5)
cfsv2_coll = ee.ImageCollection.fromImages([
ee.Image.constant([d_args['tmax'] + 273.15, d_args['tmin'] + 273.15,
d_args['q_asce'], d_args['rs'] / 0.0864,
wind_u, wind_u]) \
.double().rename(band_names) \
.set({'system:time_start': ee.Date('2015-07-01T00:00:00', 'UTC').millis()}),
ee.Image.constant([d_args['tmax'] + 273.15, d_args['tmin'] + 273.15,
d_args['q_asce'], d_args['rs'] / 0.0864,
wind_u, wind_u]) \
.double().rename(band_names) \
.set({'system:time_start': ee.Date('2015-07-01T06:00:00', 'UTC').millis()}),
ee.Image.constant([d_args['tmax'] + 273.15, d_args['tmin'] + 273.15,
d_args['q_asce'], d_args['rs'] / 0.0864,
wind_u, wind_u]) \
.double().rename(band_names) \
.set({'system:time_start': ee.Date('2015-07-01T12:00:00', 'UTC').millis()}),
ee.Image.constant([d_args['tmax'] + 273.15, d_args['tmin'] + 273.15,
d_args['q_asce'], d_args['rs'] / 0.0864,
wind_u, wind_u]) \
.double().rename(band_names) \
.set({'system:time_start': ee.Date('2015-07-01T18:00:00', 'UTC').millis()}),
])
refet = Daily.cfsv2(cfsv2_coll,
elev=ee.Number(s_args['elev']),
lat=ee.Number(s_args['lat']),
zw=ee.Number(s_args['zw']),
method='asce')
output = refet.etr\
.reduceRegion(ee.Reducer.first(), geometry=constant_geom, scale=1)\
.getInfo()
assert float(output['etr']) == pytest.approx(d_args['etr_asce'])
def test_refet_daily_etw():
refet = Daily(tmax=ee.Image.constant(d_args['tmax']),
tmin=ee.Image.constant(d_args['tmin']),
ea=ee.Image.constant(d_args['ea']),
rs=ee.Image.constant(d_args['rs']),
uz=ee.Image.constant(d_args['uz']),
zw=ee.Number(s_args['zw']),
elev=ee.Number(s_args['elev']),
lat=ee.Number(s_args['lat']),
doy=ee.Number(d_args['doy']),
method='refet')
output = refet.etw\
.reduceRegion(ee.Reducer.first(), geometry=constant_geom, scale=1)\
.getInfo()
assert float(output['etw']) == pytest.approx(d_args['etw_refet'])
def test_refet_daily_nldas_eto():
"""Compare values to previous calculations"""
test_point = ee.Geometry.Point(-120.113, 36.336)
nldas_coll = ee.ImageCollection('NASA/NLDAS/FORA0125_H002')\
.filterDate('2017-07-01T06:00:00', '2017-07-02T06:00:00')
output = Daily.nldas(nldas_coll, method='asce').eto \
.reduceRegion(ee.Reducer.first(), geometry=test_point, scale=1)\
.getInfo()['eto']
expected = ee.Image('projects/eddi-noaa/nldas/daily/20170701')\
.select(['eto_asce'])\
.reduceRegion(ee.Reducer.first(), geometry=test_point, scale=1)\
.getInfo()['eto_asce']
assert output == pytest.approx(expected, rel=0.001)
def test_refet_daily_gridmet_etr():
"""Generate a fake GRIDMET image from the test values"""
gridmet_img = ee.Image.constant([
d_args['tmax'] + 273.15, d_args['tmin'] + 273.15,
d_args['q_asce'], d_args['rs'] / 0.0864, d_args['uz']])\
.rename(['tmmx', 'tmmn', 'sph', 'srad', 'vs'])\
.set('system:time_start', ee.Date('2015-07-01').millis())
refet = Daily.gridmet(ee.Image(gridmet_img),
elev=ee.Number(s_args['elev']),
lat=ee.Number(s_args['lat']),
zw=ee.Number(s_args['zw']),
method='asce')
output = refet.etr\
.reduceRegion(ee.Reducer.first(), geometry=constant_geom, scale=1)\
.getInfo()
assert float(output['etr']) == pytest.approx(d_args['etr_asce'])
def test_refet_daily_pet_hargreaves():
refet = Daily(tmax=ee.Image.constant(d_args['tmax']),
tmin=ee.Image.constant(d_args['tmin']),
ea=ee.Image.constant(0),
rs=ee.Image.constant(0),
uz=ee.Image.constant(0),
zw=ee.Number(s_args['zw']),
elev=ee.Number(0),
lat=ee.Number(s_args['lat']),
doy=ee.Number(d_args['doy']),
method='asce')
output = refet.pet_hargreaves\
.reduceRegion(ee.Reducer.first(), geometry=constant_geom, scale=1)\
.getInfo()
assert float(output['pet_hargreaves']) == pytest.approx(
d_args['pet_hargreaves'])
def test_refet_daily_maca_etr():
"""Generate a fake MACA image from the test values"""
maca_img = ee.Image.constant([
d_args['tmax'] + 273.15, d_args['tmin'] + 273.15,
d_args['q_asce'], d_args['rs'] / 0.0864,
d_args['uz'] / (2 ** 0.5), d_args['uz'] / (2 ** 0.5)])\
.rename(['tasmax', 'tasmin', 'huss', 'rsds', 'uas', 'vas'])\
.set('system:time_start', ee.Date('2015-07-01').millis())
refet = Daily.maca(ee.Image(maca_img),
elev=ee.Number(s_args['elev']),
lat=ee.Number(s_args['lat']),
zw=ee.Number(s_args['zw']),
method='asce')
output = refet.etr\
.reduceRegion(ee.Reducer.first(), geometry=constant_geom, scale=1)\
.getInfo()
assert float(output['etr']) == pytest.approx(d_args['etr_asce'])
def test_refet_daily_rtma_etr():
"""Generate a fake RTMA image from the test values"""
band_names = ['TMP', 'SPFH', 'WIND']
rtma_coll = ee.ImageCollection.fromImages([
ee.Image.constant([d_args['tmin'], d_args['q_asce'], d_args['uz']]) \
.double().rename(band_names) \
.set({'system:time_start': ee.Date('2015-07-01T00:00:00', 'UTC').millis()}),
ee.Image.constant([d_args['tmax'], d_args['q_asce'], d_args['uz']]) \
.double().rename(band_names) \
.set({'system:time_start': ee.Date('2015-07-01T12:00:00', 'UTC').millis()})
])
refet = Daily.rtma(rtma_coll,
rs=ee.Image.constant(d_args['rs']),
elev=ee.Number(s_args['elev']),
lat=ee.Number(s_args['lat']),
zw=ee.Number(s_args['zw']),
method='asce')
output = refet.etr\
.reduceRegion(ee.Reducer.first(), geometry=constant_geom, scale=1)\
.getInfo()
assert float(output['etr']) == pytest.approx(d_args['etr_asce'])
