def resample(self, target_area_def):
"""Resamples image to area definition using nearest neighbour
approach in projection coordinates.
Parameters
----------
target_area_def : object
Target area definition as AreaDefinition object
Returns
-------
image_container : object
ImageContainerQuick object of resampled area
"""
resampled_image = grid.get_resampled_image(target_area_def,
self.geo_def,
self.image_data,
fill_value=self.fill_value,
nprocs=self.nprocs,
segments=self.segments)
return ImageContainerQuick(resampled_image, target_area_def,
fill_value=self.fill_value,
nprocs=self.nprocs, segments=self.segments)
def resample(self, target_area_def):
"""Resamples image to area definition using nearest neighbour
approach in projection coordinates.
Parameters
----------
target_area_def : object
Target area definition as AreaDefinition object
Returns
-------
image_container : object
ImageContainerQuick object of resampled area
"""
resampled_image = grid.get_resampled_image(target_area_def,
self.geo_def,
self.image_data,
fill_value=self.fill_value,
nprocs=self.nprocs,
segments=self.segments)
return ImageContainerQuick(resampled_image,
target_area_def,
fill_value=self.fill_value,
nprocs=self.nprocs,
segments=self.segments)
def convert_files():
"""
Converts all files in a directory of .nc files into 500x500 .npy files of California brightness temperatures,
and deletes the originals. If the conversion fails, the file name is written to a log.
"""
convert_log = open("../../GOES_Files/convert_log.txt", "w")
fail_log = open("../../GOES_Files/fail_log.txt", "w")
for file in os.listdir('../../GOES_Files/nc_files/'):
try:
data = xr.open_dataset('../../GOES_Files/nc_files/' + file)
dat = data.metpy.parse_cf('Rad')
geos = dat.metpy.cartopy_crs
rad = dat.data
fk1 = float(data.metpy.parse_cf('planck_fk1'))
fk2 = float(data.metpy.parse_cf('planck_fk2'))
bc1 = float(data.metpy.parse_cf('planck_bc1'))
bc2 = float(data.metpy.parse_cf('planck_bc2'))
bt = (fk2 / (xr.ufuncs.log(fk1 / rad + 1)) - bc1) / bc2
bt = np.asarray(bt)
goes_params = geos.proj4_params
source_area = geometry.AreaDefinition.from_cf(data)
target_area = geometry.AreaDefinition.from_extent(
'CA', pc_params, shape, target_extents)
result = grid.get_resampled_image(target_area, source_area, bt)
np.save('../../GOES_Files/npy_files/' + file[:-3], result)
data.close()
os.remove('../../GOES_Files/nc_files/' + file)
convert_log.write('\n' + file)
except:
fail_log.write('\n' + file)
convert_log.close()
fail_log.close()
def test_resampled_image_mp(self):
data = np.fromfunction(lambda y, x: y*x*10**-6, (3712, 3712))
target_def = self.area_def
source_def = self.msg_area
res = grid.get_resampled_image(target_def, source_def, data, nprocs=2, segments=1)
cross_sum = res.sum()
expected = 399936.39392500359
self.assertAlmostEqual(cross_sum, expected, msg='Resampling of image mp failed')
def test_resampled_image_mp(self):
data = np.fromfunction(lambda y, x: y * x * 10 ** -6, (3712, 3712))
target_def = self.area_def
source_def = self.msg_area
res = grid.get_resampled_image(
target_def, source_def, data, nprocs=2, segments=1)
cross_sum = res.sum()
expected = 399936.39392500359
self.assertAlmostEqual(
cross_sum, expected, msg='Resampling of image mp failed')
def test_resampled_image_masked(self):
# Generate test image with masked elements
data = np.ma.ones(self.msg_area.shape)
data.mask = np.zeros(data.shape)
data.mask[253:400, 1970:2211] = 1
# Resample image using multiple segments
target_def = self.area_def
source_def = self.msg_area
res = grid.get_resampled_image(
target_def, source_def, data, segments=4, fill_value=None)
# Make sure the mask has been preserved
self.assertGreater(res.mask.sum(), 0,
msg='Resampling did not preserve the mask')
def test_resampled_image_masked(self):
# Generate test image with masked elements
data = np.ma.ones(self.msg_area.shape)
data.mask = np.zeros(data.shape)
data.mask[253:400, 1970:2211] = 1
# Resample image using multiple segments
target_def = self.area_def
source_def = self.msg_area
res = grid.get_resampled_image(
target_def, source_def, data, segments=4, fill_value=None)
# Make sure the mask has been preserved
self.assertGreater(res.mask.sum(), 0,
msg='Resampling did not preserve the mask')