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