def test_Image_set_landcover_vars_set_asset(tol=1E-6):
"""Test setting the land cover image and type directly on the object"""
d_obj = disalexi.Image(test_img)
d_obj.landcover = ee.Image(asset_ws + 'landcover'),
d_obj.lc_type = 'NLCD'
d_obj._set_landcover_vars()
assert utils.image_value(ee.Image(d_obj.aleafv))['aleafv'] == 0.83
def test_Image_set_landcover_vars_init_asset(tol=1E-6):
"""Test setting the land cover image and type as the object is initialized"""
d_obj = disalexi.Image(test_img,
lc_type='NLCD',
landcover=ee.Image(asset_ws + 'landcover'))
d_obj._set_landcover_vars()
assert utils.image_value(ee.Image(d_obj.aleafv))['aleafv'] == 0.83
def test_Image_set_landcover_vars_invalid_lc_type():
"""Test that setting an invalid lc_type value raises a KeyError exception"""
d_obj = disalexi.Image(test_img,
landcover=ee.Image('USGS/NLCD/NLCD2011'),
lc_type='DEADBEEF')
# print(d_obj._set_landcover_vars())
with pytest.raises(KeyError) as e_info:
d_obj._set_landcover_vars()
def test_Image_set_elevation_vars(xy, elevation, pressure, tol=1E-6):
""""""
d_obj = disalexi.Image(test_img)
d_obj.elevation = ee.Image.constant(elevation)
d_obj._set_elevation_vars()
assert abs(
utils.image_value(ee.Image(d_obj.pressure), xy=xy)['pressure'] -
pressure) <= tol
def test_Image_set_weather_var_assets(tol=0.01):
d_obj = disalexi.Image(test_img)
d_obj.windspeed_coll = ee.ImageCollection([
ee.Image([ee.Image(asset_ws + 'u'), ee.Image(asset_ws + 'u').multiply(0)]) \
.setMulti({'system:time_start': img_date_start})])
d_obj._set_weather_vars()
assert abs(
utils.image_value(ee.Image(d_obj.windspeed))['windspeed'] -
7.02662301063538) <= tol
def test_Image_set_solar_vars_defaults(xy, interp, rs1, rs24, tol=1E-4):
"""Test that the default MERRA2 Rs values are returned"""
d_obj = disalexi.Image(test_img)
d_obj._set_solar_vars(interpolate_flag=interp)
assert abs(
utils.image_value(ee.Image(d_obj.rs1), xy, scale=0.1)['rs'] -
rs1) <= tol
assert abs(
utils.image_value(ee.Image(d_obj.rs24), xy, scale=0.1)['rs'] -
rs24) <= tol
def test_Image_set_time_vars_defaults(xy, t_rise, t_end, tol=1E-8):
"""Test setting the land cover image and type directly on the object
High NDVI test point values
CGM - Should probably switch this to a constant image test
"""
d_obj = disalexi.Image(test_img)
d_obj._set_time_vars()
assert abs(
utils.image_value(ee.Image(d_obj.t_rise), xy)['t_rise'] -
t_rise) <= tol
assert abs(utils.image_value(ee.Image(d_obj.t_end), xy)['t_end'] -
t_end) <= tol
def test_Image_set_solar_vars_assets_no_interp(xy, rs1, rs24, tol=1E-4):
"""Test that the default MERRA2 Rs values are returned"""
d_obj = disalexi.Image(test_img)
d_obj.rs_hourly_coll = ee.ImageCollection([
ee.Image(asset_ws + 'Insol1').setMulti(
{'system:time_start': img_hour_start})
])
d_obj.rs_daily_coll = ee.ImageCollection([
ee.Image(asset_ws + 'Insol24') \
.setMulti({'system:time_start': img_date_start})])
d_obj._set_solar_vars(interpolate_flag=False)
assert abs(utils.image_value(ee.Image(d_obj.rs1), xy)['rs'] - rs1) <= tol
assert abs(utils.image_value(ee.Image(d_obj.rs24), xy)['rs'] - rs24) <= tol
def test_Image_set_landcover_vars_default(tol=1E-6):
"""Test default land cover image and type
It might make more sense to just test that the value at the test pixel
is 82 (for NLCD) for 10 (for GLC30)"""
d_obj = disalexi.Image(test_img)
d_obj._set_landcover_vars()
assert utils.image_value(ee.Image(d_obj.aleafv))['aleafv'] == 0.83
assert utils.image_value(ee.Image(d_obj.aleafn))['aleafn'] == 0.35
assert utils.image_value(ee.Image(d_obj.aleafl))['aleafl'] == 0.95
assert utils.image_value(ee.Image(d_obj.adeadv))['adeadv'] == 0.49
assert utils.image_value(ee.Image(d_obj.adeadn))['adeadn'] == 0.13
assert utils.image_value(ee.Image(d_obj.adeadl))['adeadl'] == 0.95
assert utils.image_value(ee.Image(d_obj.leaf_width))['xl'] == 0.05
assert utils.image_value(ee.Image(d_obj.clump))['omega'] == 0.83
def test_Image_compute_ta_test_asset(xy, iterations, expected, tol=0.01):
"""Test coarse scale air temperature at a single point using the test assets"""
d_obj = disalexi.Image(test_img,
elevation=ee.Image.constant(350.0),
iterations=iterations,
lc_type='NLCD',
landcover=ee.Image(asset_ws + 'landcover'))
# Overwrite the default ancillary images with the test assets
d_obj.windspeed_coll = ee.ImageCollection([
ee.Image([
ee.Image(asset_ws + 'u'),
ee.Image(asset_ws + 'u').multiply(0)]) \
.setMulti({'system:time_start': img_date_start})])
d_obj.rs_hourly_coll = ee.ImageCollection([
ee.Image(asset_ws + 'Insol1').setMulti(
{'system:time_start': img_hour_start.subtract(3600000)}),
ee.Image(asset_ws + 'Insol1').setMulti(
{'system:time_start': img_hour_start}),
ee.Image(asset_ws + 'Insol1').setMulti(
{'system:time_start': img_hour_start.add(3600000)})
])
d_obj.rs_daily_coll = ee.ImageCollection([
ee.Image(asset_ws + 'Insol24') \
.setMulti({'system:time_start': img_date_start})])
d_obj.et_coll = ee.ImageCollection([
ee.Image(asset_ws + 'alexiET') \
.setMulti({'system:time_start': img_date_start})])
d_obj.et_transform = [0.04, 0, -96.442, 0, -0.04, 41.297]
d_obj._set_solar_vars(interpolate_flag=False)
d_obj._set_weather_vars()
# Get the spatial reference and geoTransform of the assets
# asset_crs = ee.Image(asset_ws + 'albedo').projection().crs().getInfo()
# asset_transform = ee.Image(asset_ws + 'albedo') \
# .projection().getInfo()['transform']
# Compute ALEXI scale air temperature
ta_coarse_img = d_obj.compute_ta_test()
# .reproject(crs=asset_crs, crsTransform=asset_transform)
# .reproject(crs='EPSG:4326', crsTransform=d_obj.et_transform)
# Extract image values at a point using reduceRegion (with point geom)
output = utils.image_value(ta_coarse_img.select(['t_air']), tile_scale=4)
logging.debug(' Target values: {}'.format(expected['t_air']))
logging.debug(' Output values: {}'.format(output['t_air']))
assert abs(output['t_air'] - expected['t_air']) <= tol
def test_Image_set_alexi_et_vars_assets(xy, et, tol=1E-6):
"""
Don't use scale parameter in image_value since ALEXI ET assets are already
resampled to the Landsat grid.
Add separate check that band name is set correctly?
"""
d_obj = disalexi.Image(test_img)
d_obj.et_coll = ee.ImageCollection([
ee.Image(asset_ws + 'alexiET') \
.setMulti({'system:time_start': img_date_start})])
d_obj.et_transform = [0.04, 0, -96.442, 0, -0.04, 41.297]
d_obj._set_alexi_et_vars()
assert abs(
utils.image_value(ee.Image(d_obj.alexi_et), xy=xy)['alexi_et'] -
et) <= tol
def test_Image_compute_ta_asset(xy, iterations, expected, tol=0.01):
"""Test fine scale air temperature at a single point using the test assets"""
d_obj = disalexi.Image(test_img,
elevation=ee.Image.constant(350.0),
iterations=iterations,
lc_type='NLCD',
landcover=ee.Image(asset_ws + 'landcover'))
# Overwrite the default ancillary images with the test assets
d_obj.windspeed_coll = ee.ImageCollection([
ee.Image([
ee.Image(asset_ws + 'u'),
ee.Image(asset_ws + 'u').multiply(0)]) \
.setMulti({'system:time_start': img_date_start})])
d_obj.rs_hourly_coll = ee.ImageCollection([
ee.Image(asset_ws + 'Insol1').setMulti(
{'system:time_start': img_hour_start.subtract(3600000)}),
ee.Image(asset_ws + 'Insol1').setMulti(
{'system:time_start': img_hour_start}),
ee.Image(asset_ws + 'Insol1').setMulti(
{'system:time_start': img_hour_start.add(3600000)})
])
d_obj.rs_daily_coll = ee.ImageCollection([
ee.Image(asset_ws + 'Insol24') \
.setMulti({'system:time_start': img_date_start})])
d_obj.et_coll = ee.ImageCollection([
ee.Image(asset_ws + 'alexiET') \
.setMulti({'system:time_start': img_date_start})])
d_obj.et_transform = [0.04, 0, -96.442, 0, -0.04, 41.297]
# Get the spatial reference and geoTransform of the assets
asset_crs = ee.Image(asset_ws + 'albedo').projection().crs().getInfo()
asset_transform = ee.Image(asset_ws + 'albedo') \
.projection().getInfo()['transform']
# Compute Tair
ta_img = d_obj.compute_ta() \
.reproject(crs=asset_crs, crsTransform=asset_transform)
# Extract image values at a point using reduceRegion (with point geom)
output = list(utils.image_value(ta_img, xy=xy).values())[0]
# output = utils.image_value(ta_img)['t_air']
logging.debug(' Target values: {}'.format(expected))
logging.debug(' Output values: {}'.format(output))
assert abs(output - expected) <= tol
def test_Image_set_weather_vars_defaults(tol=0.01):
d_obj = disalexi.Image(test_img)
d_obj._set_weather_vars()
assert abs(
utils.image_value(ee.Image(d_obj.windspeed))['windspeed'] -
4.12) <= tol
def test_Image_set_alexi_et_vars_defaults(xy, et, tol=1E-6):
d_obj = disalexi.Image(test_img)
d_obj._set_alexi_et_vars()
assert abs(
utils.image_value(ee.Image(d_obj.alexi_et), xy=xy, scale=0.1)
['alexi_et'] - et) <= tol
def test_Image_init_missing_lc_type():
"""Test that only setting a land cover image raises a ValueError exception"""
with pytest.raises(ValueError) as e_info:
d_obj = disalexi.Image(test_img,
landcover=ee.Image('USGS/NLCD/NLCD2011'))
def test_Image_init_missing_landcover():
"""Test that only setting a land cover type raises a ValueError exception"""
with pytest.raises(ValueError) as e_info:
d_obj = disalexi.Image(test_img, lc_type='NLCD')
def test_Image_init_dates():
d_obj = disalexi.Image(test_img)
assert d_obj.date.format('yyyy-MM-dd').getInfo() == img_date_str
assert d_obj.doy.getInfo() == img_doy
assert int(d_obj.hour.getInfo()) == img_hour
assert float(d_obj.time.getInfo()) == img_time
