def test_dec10216(self):
res = dec10216(np.array([255, 255, 255, 255, 255], dtype=np.uint8))
exp = (np.ones((4, )) * 1023).astype(np.uint16)
self.assertTrue(np.all(res == exp))
res = dec10216(np.array([1, 1, 1, 1, 1], dtype=np.uint8))
exp = np.array([4, 16, 64, 257], dtype=np.uint16)
self.assertTrue(np.all(res == exp))
def test_dec10216(self):
res = dec10216(np.array([255, 255, 255, 255, 255], dtype=np.uint8))
exp = (np.ones((4, )) * 1023).astype(np.uint16)
self.assertTrue(np.all(res == exp))
res = dec10216(np.array([1, 1, 1, 1, 1], dtype=np.uint8))
exp = np.array([4, 16, 64, 257], dtype=np.uint16)
self.assertTrue(np.all(res == exp))
def get_dataset(self, dsid, info,
xslice=slice(None), yslice=slice(None)):
channel = dsid.name
channel_list = self._channel_list
if channel not in channel_list:
raise KeyError('Channel % s not available in the file' % channel)
elif channel not in ['HRV']:
shape = (self.mda['number_of_lines'], self.mda['number_of_columns'])
# Check if there is only 1 channel in the list as a change
# is needed in the arrray assignment ie channl id is not present
if len(channel_list) == 1:
raw = self.dask_array['visir']['line_data']
else:
i = channel_list.index(channel)
raw = self.dask_array['visir']['line_data'][:, i, :]
data = dec10216(raw.flatten())
data = da.flipud(da.fliplr((data.reshape(shape))))
else:
shape = (self.mda['hrv_number_of_lines'], self.mda['hrv_number_of_columns'])
raw2 = self.dask_array['hrv']['line_data'][:, 2, :]
raw1 = self.dask_array['hrv']['line_data'][:, 1, :]
raw0 = self.dask_array['hrv']['line_data'][:, 0, :]
shape_layer = (self.mda['number_of_lines'], self.mda['hrv_number_of_columns'])
data2 = dec10216(raw2.flatten())
data2 = da.flipud(da.fliplr((data2.reshape(shape_layer))))
data1 = dec10216(raw1.flatten())
data1 = da.flipud(da.fliplr((data1.reshape(shape_layer))))
data0 = dec10216(raw0.flatten())
data0 = da.flipud(da.fliplr((data0.reshape(shape_layer))))
data = np.zeros(shape)
idx = range(0, shape[0], 3)
data[idx, :] = data2
idx = range(1, shape[0], 3)
data[idx, :] = data1
idx = range(2, shape[0], 3)
data[idx, :] = data0
xarr = xr.DataArray(data, dims=['y', 'x']).where(data != 0).astype(np.float32)
if xarr is None:
dataset = None
else:
dataset = self.calibrate(xarr, dsid)
dataset.attrs['units'] = info['units']
dataset.attrs['wavelength'] = info['wavelength']
dataset.attrs['standard_name'] = info['standard_name']
dataset.attrs['platform_name'] = self.platform_name
dataset.attrs['sensor'] = 'seviri'
return dataset
def get_dataset(self,
key,
info,
out=None,
xslice=slice(None),
yslice=slice(None)):
if key.name not in self.channel_order_list:
raise KeyError('Channel % s not available in the file' % key.name)
elif key.name not in ['HRV']:
ch_idn = self.channel_order_list.index(key.name)
data = dec10216(
self.memmap['visir']['line_data'][:, ch_idn, :])[::-1, ::-1]
data = np.ma.masked_array(data, mask=(data == 0))
res = Dataset(data, dtype=np.float32)
else:
data2 = dec10216(self.memmap["hrv"]["line_data"][:,
2, :])[::-1, ::-1]
data1 = dec10216(self.memmap["hrv"]["line_data"][:,
1, :])[::-1, ::-1]
data0 = dec10216(self.memmap["hrv"]["line_data"][:,
0, :])[::-1, ::-1]
# Make empty array:
shape = data0.shape[0] * 3, data0.shape[1]
data = np.zeros(shape)
idx = range(0, shape[0], 3)
data[idx, :] = data2
idx = range(1, shape[0], 3)
data[idx, :] = data1
idx = range(2, shape[0], 3)
data[idx, :] = data0
data = np.ma.masked_array(data, mask=(data == 0))
res = Dataset(data, dtype=np.float32)
if res is not None:
out = res
else:
return None
self.calibrate(out, key)
out.info['units'] = info['units']
out.info['wavelength'] = info['wavelength']
out.info['standard_name'] = info['standard_name']
out.info['platform_name'] = self.platform_name
out.info['sensor'] = 'seviri'
return out
def read_band(self, key, info):
"""Read the data."""
shape = int(np.ceil(self.mda['data_field_length'] / 8.))
if self.mda['number_of_bits_per_pixel'] == 16:
dtype = '>u2'
shape //= 2
elif self.mda['number_of_bits_per_pixel'] in [8, 10]:
dtype = np.uint8
shape = (shape, )
data = np.memmap(self.filename, mode='r',
offset=self.mda['total_header_length'],
dtype=dtype,
shape=shape)
data = da.from_array(data, chunks=shape[0])
if self.mda['number_of_bits_per_pixel'] == 10:
data = dec10216(data)
data = data.reshape((self.mda['number_of_lines'],
self.mda['number_of_columns']))
return data
def read_band(self, key, info):
"""Read the data."""
shape = int(np.ceil(self.mda['data_field_length'] / 8.))
if self.mda['number_of_bits_per_pixel'] == 16:
dtype = '>u2'
shape /= 2
elif self.mda['number_of_bits_per_pixel'] in [8, 10]:
dtype = np.uint8
shape = (shape, )
data = np.memmap(self.filename, mode='r',
offset=self.mda['total_header_length'],
dtype=dtype,
shape=shape)
data = da.from_array(data, chunks=shape[0])
if self.mda['number_of_bits_per_pixel'] == 10:
data = dec10216(data)
data = data.reshape((self.mda['number_of_lines'],
self.mda['number_of_columns']))
return data
def get_dataset(self, dsid, info, xslice=slice(None), yslice=slice(None)):
channel = dsid.name
channel_list = self._channel_list
if channel not in channel_list:
raise KeyError('Channel % s not available in the file' % channel)
elif channel not in ['HRV']:
shape = (self.mda['number_of_lines'],
self.mda['number_of_columns'])
# Check if there is only 1 channel in the list as a change
# is needed in the arrray assignment ie channl id is not present
if len(channel_list) == 1:
raw = self.dask_array['visir']['line_data']
else:
i = channel_list.index(channel)
raw = self.dask_array['visir']['line_data'][:, i, :]
data = dec10216(raw.flatten())
data = da.flipud(da.fliplr((data.reshape(shape))))
else:
# Upper HRV stacked directly on top of Lower HRV
# (in actuality, these two are offset to eachother)
shape = (self.mda['hrv_number_of_lines'],
self.mda['hrv_number_of_columns'])
raw2 = self.dask_array['hrv']['line_data'][:, 2, :]
raw1 = self.dask_array['hrv']['line_data'][:, 1, :]
raw0 = self.dask_array['hrv']['line_data'][:, 0, :]
shape_layer = (self.mda['number_of_lines'],
self.mda['hrv_number_of_columns'])
data2 = dec10216(raw2.flatten())
data2 = da.flipud(da.fliplr((data2.reshape(shape_layer))))
data1 = dec10216(raw1.flatten())
data1 = da.flipud(da.fliplr((data1.reshape(shape_layer))))
data0 = dec10216(raw0.flatten())
data0 = da.flipud(da.fliplr((data0.reshape(shape_layer))))
data = np.zeros(shape)
idx = range(0, shape[0], 3)
data[idx, :] = data2
idx = range(1, shape[0], 3)
data[idx, :] = data1
idx = range(2, shape[0], 3)
data[idx, :] = data0
# Full disk area (ReferenceGridHRV), with HRV-UPPER and
# HRV-Lower embedded inside.
# see section 3.1.5 of MSG Level 1.5 Image Data Format Description
ref_grid = self.header['15_DATA_HEADER']['ImageDescription'][
'ReferenceGridHRV']
ysize = ref_grid['NumberOfLines']
xsize = ref_grid['NumberOfColumns']
fulldisk = np.zeros((ysize, xsize))
hrv_coverage = self.trailer['15TRAILER']['ImageProductionStats'][
'ActualL15CoverageHRV']
un = ysize - hrv_coverage['UpperNorthLineActual']
uw = xsize - hrv_coverage['UpperWestColumnActual']
us = ysize - hrv_coverage['UpperSouthLineActual']
ue = xsize - hrv_coverage['UpperEastColumnActual']
fulldisk[un:us + 1, uw:ue + 1] = data[:us + 1 - un, :]
ln = ysize - hrv_coverage['LowerNorthLineActual']
lw = xsize - hrv_coverage['LowerWestColumnActual']
ls = ysize - hrv_coverage['LowerSouthLineActual']
le = xsize - hrv_coverage['LowerEastColumnActual']
fulldisk[ln:ls + 1, lw:le + 1] = data[ln:, :]
data = fulldisk
xarr = xr.DataArray(data,
dims=['y',
'x']).where(data != 0).astype(np.float32)
if xarr is None:
dataset = None
else:
dataset = self.calibrate(xarr, dsid)
dataset.attrs['units'] = info['units']
dataset.attrs['wavelength'] = info['wavelength']
dataset.attrs['standard_name'] = info['standard_name']
dataset.attrs['platform_name'] = self.platform_name
dataset.attrs['sensor'] = 'seviri'
return dataset