def get_dataset(self, dsid, info,
xslice=slice(None), yslice=slice(None)):
if dsid.name not in self.mda['channel_list']:
raise KeyError('Channel % s not available in the file' % dsid.name)
elif dsid.name 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(self.mda['channel_list']) == 1:
raw = self.dask_array['visir']['line_data']
else:
i = self.mda['channel_list'].index(dsid.name)
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.mda['platform_name']
dataset.attrs['sensor'] = 'seviri'
dataset.attrs['orbital_parameters'] = {
'projection_longitude': self.mda['projection_parameters']['ssp_longitude'],
'projection_latitude': 0.,
'projection_altitude': self.mda['projection_parameters']['h']}
return dataset
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, dsid, info,
xslice=slice(None), yslice=slice(None)):
channel = dsid.name
shape = (self.mda['number_of_lines'], self.mda['number_of_columns'])
raw = self.dask_array['DataRecord']['LineRecord']['{}'.format(channel)]
tmp1 = raw[:, 0, :]
if (channel == 'afm'):
not_processed = np.where(tmp1 == 0)
not_processed1 = np.where(tmp1 == 3)
else:
not_processed = np.where(tmp1 == 0)
not_processed1 = np.where(tmp1 > 100)
data = np.array(tmp1.astype(np.float32))
data[not_processed] = np.nan
data[not_processed1] = np.nan
data = da.flipud(da.fliplr((data.reshape(shape))))
xarr = xr.DataArray(data, dims=['y', 'x'])
if xarr is None:
dataset = None
else:
dataset = xarr
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, dsid, info,
xslice=slice(None), yslice=slice(None)):
channel = dsid.name
shape = (self.mda['number_of_lines'], self.mda['number_of_columns'])
raw = self.dask_array['DataRecord']['sst']
data = raw[:, 0, :]
shifted = np.left_shift(data, 5).astype(np.uint16)
tmp = np.right_shift(shifted, 5)
not_processed = np.where(tmp == 8)
data = np.array(tmp.astype(np.float32))
# data[not_processed] = np.nan
data = da.flipud(da.fliplr((data.reshape(shape))))
xarr = xr.DataArray(data, dims=['y', 'x'])
if xarr is None:
dataset = None
else:
dataset = xarr
dataset *= 0.1
dataset += 170.
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, dsid, info,
xslice=slice(None), yslice=slice(None)):
channel = dsid.name
shape = (self.mda['number_of_lines'], self.mda['number_of_columns'])
raw = self.dask_array['DataRecord']['LineRecord']['{}'.format(channel)]
data = raw[:, 0, :]
data = da.flipud(da.fliplr((data.reshape(shape))))
xarr = xr.DataArray(data, dims=['y', 'x']).where(data != 0)
if xarr is None:
dataset = None
else:
dataset = xarr
# Create new dataset where 255 values are ignored and interpreted
# as nan's
# dataset=dataset.where(dataset!=255,other=float('NaN'),drop=False)
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, dsid, info,
xslice=slice(None), yslice=slice(None)):
channel = dsid.name
shape = (self.mda['number_of_lines'], self.mda['number_of_columns'])
raw = self.dask_array['DataRecord']['LineRecord']['{}'.format(channel)].astype(np.uint16)
raw1 = self.dask_array['DataRecord']['LineRecord'][:]
data = raw[:, 0, :]
data = da.flipud(da.fliplr((data.reshape(shape))))
# xarr = xr.DataArray(data, dims=['y', 'x']).where(data != 0)
xarr = xr.DataArray(data, dims=['y', 'x'])
if xarr is None:
dataset = None
else:
dataset = xarr
if channel == 'CTT' or channel == 'CTP':
dataset = dataset.where(dataset != 64537.)
if channel == 'EFF' or channel == 'SCE':
dataset = dataset.where(dataset != 0.)
if channel == 'CTT':
dataset += 170
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, dsid, info,
xslice=slice(None), yslice=slice(None)):
channel = dsid.name
shape = (self.mda['number_of_lines'], self.mda['number_of_columns'])
#print (self.dask_array)
print (self.dask_array['DataRecord']['Data']['CloudStats'])
#raw = self.dask_array['DataRecord']['Data]']['CloudStats'][0]['{}'.format(channel)].astype(np.uint16)
raw = self.dask_array['DataRecord']['{}'.format(channel)]
print (raw)
shape = (232,232)
print ('shape',shape)
datax = np.empty(shape, dtype=np.float32)
datax.fill(np.nan)
# MPEF rows and columns start with 1, not 0
rows = self.dask_array['DataRecord']['SegmentRow']-1
cols = self.dask_array['DataRecord']['SegmentCol']-1
datax[rows, cols] = raw[:]
data = da.flipud(da.fliplr(datax))
#data = da.flipud(da.fliplr((data.reshape(shape))))
# xarr = xr.DataArray(data, dims=['y', 'x']).where(data != 0)
xarr = xr.DataArray(data, dims=['y', 'x'])
if xarr is None:
dataset = None
else:
dataset = xarr
if channel == 'CTT' or channel == 'CTP':
dataset = dataset.where(dataset != 64537.)
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, dsid, info,
xslice=slice(None), yslice=slice(None)):
channel = dsid.name
shape = (self.mda['number_of_lines'], self.mda['number_of_columns'])
raw = self.dask_array['DataRecord']['{}'.format(channel)]
# MPEF rows and columns start with 1, not 0
# rows = self.dask_array['DataRecord']['SegmentRow']-1
# cols = self.dask_array['DataRecord']['SegmentCol']-1
# datax[rows, cols] = raw[:]
data = raw[:,:]
data = da.flipud(da.fliplr(data))
#data = da.flipud(da.fliplr((data.reshape(shape))))
# xarr = xr.DataArray(data, dims=['y', 'x']).where(data != 0)
xarr = xr.DataArray(data, dims=['y', 'x'])
if xarr is None:
dataset = None
else:
dataset = xarr
if channel == 'CTT' or channel == 'CTP':
dataset = dataset.where(dataset != 64537.)
if channel == 'EFF' or channel == 'SCE':
dataset = dataset.where(dataset != 0.)
if channel == 'CTT':
dataset += 170
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, dsid, info,
xslice=slice(None), yslice=slice(None)):
channel = dsid.name
shape = (self.mda['number_of_lines'], self.mda['number_of_columns'])
raw = self.dask_array['DataRecord']['LineRecord']['{}'.format(channel)]
data = raw[:, 0, :]
data = da.flipud(da.fliplr((data.reshape(shape))))
xarr = xr.DataArray(data, dims=['y', 'x']).where(data != 32767)
if xarr is None:
dataset = None
else:
dataset = xarr
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, 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
