def test_fls_algorithm_no_cth_use_clusters(self):
self.input.pop('cth')
self.input['single'] = False
flsalgo = DayFogLowStratusAlgorithm(**self.input)
ret, mask = flsalgo.run()
self.assertEqual(flsalgo.ir108.shape, (141, 298))
self.assertEqual(ret.shape, (141, 298))
self.assertEqual(flsalgo.shape, (141, 298))
self.assertEqual(np.ma.is_mask(flsalgo.mask), True)
def test_fls_algorithm_other(self):
flsalgo = DayFogLowStratusAlgorithm(**self.input2)
ret, mask = flsalgo.run()
self.assertEqual(flsalgo.ir108.shape, (141, 298))
self.assertEqual(ret.shape, (141, 298))
self.assertEqual(flsalgo.shape, (141, 298))
self.assertEqual(np.ma.is_mask(flsalgo.mask), True)
cthdiff = flsalgo.cluster_cth - self.input['elev'].squeeze()
self.assertLessEqual(np.nanmax(cthdiff), 1000)
def test_fls_algorithm_other(self):
flsalgo = DayFogLowStratusAlgorithm(**self.input2)
ret, mask = flsalgo.run()
self.assertEqual(flsalgo.ir108.shape, (141, 298))
self.assertEqual(ret.shape, (141, 298))
self.assertEqual(flsalgo.shape, (141, 298))
self.assertEqual(np.ma.is_mask(flsalgo.mask), True)
cthdiff = flsalgo.cluster_cth - self.input['elev'].squeeze()
self.assertLessEqual(np.nanmax(cthdiff), 1000)
# Plot result with added station data
result_img = flsalgo.plot_result(save=True, resize=1)
add_synop.add_to_image(result_img,
area_def,
self.input2['time'],
stationfile2,
bgimg=self.input2['ir108'])
def setUp(self):
# Define artificial test data with low standard deviation
flsalgo = DayFogLowStratusAlgorithm()
self.low_sd_ir = 1.0 * np.random.randn(10, 10) + 260
self.low_sd_mask = np.random.randint(2, size=(10, 10))
self.low_sd_ir_masked = np.ma.masked_where(self.low_sd_mask,
self.low_sd_ir)
self.low_sd_cluster = np.ma.masked_invalid(np.ones((10, 10)))
self.low_sd_clusterma = flsalgo.get_cloud_cluster(self.low_sd_mask)
# Define artificial test data with high standard deviation
self.high_sd_ir = 30.0 * np.random.randn(10, 10) + 260
self.high_sd_mask = np.random.randint(2, size=(10, 10))
self.high_sd_ir_masked = np.ma.masked_where(self.high_sd_mask,
self.high_sd_ir)
self.high_sd_cluster = np.ma.masked_invalid(np.ones((10, 10)))
self.high_sd_clusterma = flsalgo.get_cloud_cluster(self.high_sd_mask)
# Define artificial test data with oversized cloud cluster
self.large_ir = 1.0 * np.random.randn(150, 150) + 260
self.large_mask = np.zeros((150, 150))
self.large_mask[0, 0] = 1
self.large_masked = np.ma.masked_where(self.large_mask, self.large_ir)
self.large_clusterma = flsalgo.get_cloud_cluster(self.large_mask)
def setUp(self):
# Load test data
inputs = np.dsplit(testdata, 14)
self.ir108 = inputs[0]
self.ir039 = inputs[1]
self.vis008 = inputs[2]
self.nir016 = inputs[3]
self.vis006 = inputs[4]
self.ir087 = inputs[5]
self.ir120 = inputs[6]
self.elev = inputs[7]
self.cot = inputs[8]
self.reff = inputs[9]
self.lwp = inputs[10]
self.lat = inputs[11]
self.lon = inputs[12]
self.cth = inputs[13]
self.time = datetime(2013, 11, 12, 8, 30, 00)
self.input = {
'vis006': self.vis006,
'vis008': self.vis008,
'ir108': self.ir108,
'nir016': self.nir016,
'ir039': self.ir039,
'ir120': self.ir120,
'ir087': self.ir087,
'lat': self.lat,
'lon': self.lon,
'time': self.time,
'elev': self.elev,
'cot': self.cot,
'reff': self.reff,
'lwp': self.lwp,
'cth': self.cth,
'plot': True,
'save': True,
'dir': '/tmp/FLS',
'resize': '5'
}
# Create cloud top heights and clusters
from fogpy.algorithms import DayFogLowStratusAlgorithm as FLS
# Calculate cloud and snow masks
cloudfilter = CloudFilter(self.ir108, **self.input)
ret, self.cloudmask = cloudfilter.apply()
snowfilter = SnowFilter(cloudfilter.result, **self.input)
ret, snowmask = snowfilter.apply()
# Get clusters
fls = FLS(**self.input)
self.clusters = FLS.get_cloud_cluster(self.cloudmask, False)
self.clusters.mask[self.clusters != 120] = True
self.input = {
'ir108': self.ir108,
'lwp': self.lwp,
'reff': self.reff,
'elev': self.elev,
'clusters': self.clusters,
'cth': self.cth
}
# Define small artificial test data
dim = (2, 2, 1)
test_ir = np.empty(dim)
test_ir.fill(260)
lwp_choice = np.array([0.01, 0.1, 1, 10, 100, 1000])
test_lwp_choice = np.random.choice(lwp_choice, dim)
test_lwp_static = np.empty(dim)
test_lwp_static.fill(94)
test_reff = np.empty(dim)
test_reff.fill(10e-6)
test_cmask = np.empty(dim)
test_cmask.fill(0)
test_clusters = np.empty(dim)
test_clusters.fill(1)
test_clusters = np.ma.masked_array(test_clusters, test_cmask)
test_elev = np.empty(dim)
test_elev.fill(100)
test_cth = np.empty(dim)
test_cth.fill(200)
# Testdata for ground cloud fog
self.test_lwp1 = {
'ir108': test_ir,
'lwp': test_lwp_static,
'reff': test_reff,
'elev': test_elev,
'clusters': test_clusters,
'cth': test_cth
}
# Init with big droplet radius
# Increase in droplet radius prevend declaration as ground fog
test_reff = np.empty(dim)
test_reff.fill(20e-5)
self.test_lwp2 = {
'ir108': test_ir,
'lwp': test_lwp_static,
'reff': test_reff,
'elev': test_elev,
'clusters': test_clusters,
'cth': test_cth
}
# Randomly choose liquid water paths
test_reff = np.empty(dim)
test_reff.fill(10e-6) # Reset radius
self.test_lwp3 = {
'ir108': test_ir,
'lwp': test_lwp_choice,
'reff': test_reff,
'elev': test_elev,
'clusters': test_clusters,
'cth': test_cth
}