def read_prologue(self):
"""Read the prologue metadata."""
with open(self.filename, "rb") as fp_:
fp_.seek(self.mda['total_header_length'])
data = np.fromfile(fp_, dtype=hrit_prologue, count=1)
self.prologue.update(recarray2dict(data))
try:
impf = np.fromfile(fp_, dtype=impf_configuration, count=1)[0]
except IndexError:
logger.info('No IMPF configuration field found in prologue.')
else:
self.prologue.update(recarray2dict(impf))
def read_prologue(self):
"""Read the prologue metadata."""
with open(self.filename, "rb") as fp_:
fp_.seek(self.mda['total_header_length'])
data = np.fromfile(fp_, dtype=hrit_prologue, count=1)
self.prologue.update(recarray2dict(data))
try:
impf = np.fromfile(fp_, dtype=impf_configuration, count=1)[0]
except IndexError:
logger.info('No IMPF configuration field found in prologue.')
else:
self.prologue.update(recarray2dict(impf))
def read_epilogue(self):
"""Read the epilogue metadata."""
with open(self.filename, "rb") as fp_:
fp_.seek(self.mda['total_header_length'])
data = np.fromfile(fp_, dtype=hrit_epilogue, count=1)
self.epilogue.update(recarray2dict(data))
def read_prologue(self):
"""Read the prologue metadata."""
with utils.generic_open(self.filename, mode="rb") as fp_:
fp_.seek(self.mda['total_header_length'])
data = np.frombuffer(fp_.read(hrit_prologue.itemsize),
dtype=hrit_prologue,
count=1)
self.prologue.update(recarray2dict(data))
try:
impf = np.frombuffer(fp_.read(impf_configuration.itemsize),
dtype=impf_configuration,
count=1)[0]
except ValueError:
logger.info('No IMPF configuration field found in prologue.')
else:
self.prologue.update(recarray2dict(impf))
def read_epilogue(self):
"""Read the epilogue metadata."""
with open(self.filename, "rb") as fp_:
fp_.seek(self.mda['total_header_length'])
data = np.fromfile(fp_, dtype=hrit_epilogue, count=1)
self.epilogue.update(recarray2dict(data))
def read_epilogue(self):
"""Read the epilogue metadata."""
with utils.generic_open(self.filename, mode="rb") as fp_:
fp_.seek(self.mda['total_header_length'])
data = np.frombuffer(fp_.read(hrit_epilogue.itemsize),
dtype=hrit_epilogue,
count=1)
self.epilogue.update(recarray2dict(data))
def _read_trailer(self):
hdr_size = native_header.itemsize
data_size = (self._get_data_dtype().itemsize *
self.mda['number_of_lines'])
with open(self.filename) as fp:
fp.seek(hdr_size + data_size)
data = np.fromfile(fp, dtype=native_trailer, count=1)
self.trailer.update(recarray2dict(data))
def _read_trailer(self):
hdr_size = native_header.itemsize
data_size = (self._get_data_dtype().itemsize *
self.mda['number_of_lines'])
with open(self.filename) as fp:
fp.seek(hdr_size + data_size)
data = np.fromfile(fp, dtype=native_trailer, count=1)
self.trailer.update(recarray2dict(data))
def test_mpef_product_header(self):
"""Test function for TestRecarray2Dict and mpef product header."""
names = ['ImageLocation', 'GsicsCalMode', 'GsicsCalValidity',
'Padding', 'OffsetToData', 'Padding2']
mpef_header = np.dtype([(name, mpef_product_header.fields[name][0])
for name in names])
mph_struct = np.array([('OPE', True, False, 'XX', 1000, '12345678')], dtype=mpef_header)
test_mph = {'ImageLocation': "OPE",
'GsicsCalMode': True,
'GsicsCalValidity': False,
'Padding': 'XX',
'OffsetToData': 1000,
'Padding2': '12345678'
}
self.assertEqual(recarray2dict(mph_struct), test_mph)
def test_fun(self):
# datatype definition
pat_dt = np.dtype([('TrueRepeatCycleStart', time_cds_expanded),
('PlanForwardScanEnd', time_cds_expanded),
('PlannedRepeatCycleEnd', time_cds_expanded)])
# planned acquisition time, add extra dimensions
# these should be removed by recarray2dict
pat = np.array([[[((21916, 41409544, 305, 262),
(21916, 42160340, 659, 856),
(21916, 42309417, 918, 443))]]],
dtype=pat_dt)
expected = {
'TrueRepeatCycleStart': datetime(2018, 1, 2, 11, 30, 9, 544305),
'PlanForwardScanEnd': datetime(2018, 1, 2, 11, 42, 40, 340660),
'PlannedRepeatCycleEnd': datetime(2018, 1, 2, 11, 45, 9, 417918)
}
self.assertEqual(recarray2dict(pat), expected)
def test_fun(self):
# datatype definition
pat_dt = np.dtype([
('TrueRepeatCycleStart', time_cds_expanded),
('PlanForwardScanEnd', time_cds_expanded),
('PlannedRepeatCycleEnd', time_cds_expanded)
])
# planned acquisition time, add extra dimensions
# these should be removed by recarray2dict
pat = np.array([[[(
(21916, 41409544, 305, 262),
(21916, 42160340, 659, 856),
(21916, 42309417, 918, 443))]]], dtype=pat_dt)
expected = {
'TrueRepeatCycleStart': datetime(2018, 1, 2, 11, 30, 9, 544305),
'PlanForwardScanEnd': datetime(2018, 1, 2, 11, 42, 40, 340660),
'PlannedRepeatCycleEnd': datetime(2018, 1, 2, 11, 45, 9, 417918)
}
self.assertEqual(recarray2dict(pat), expected)
def _read_header(self):
"""Read the header info."""
data = np.fromfile(self.filename,
dtype=native_header, count=1)
self.header.update(recarray2dict(data))
data15hd = self.header['15_DATA_HEADER']
sec15hd = self.header['15_SECONDARY_PRODUCT_HEADER']
# Set the list of available channels:
self.mda['available_channels'] = get_available_channels(self.header)
self.mda['channel_list'] = [i for i in CHANNEL_NAMES.values()
if self.mda['available_channels'][i]]
self.platform_id = data15hd[
'SatelliteStatus']['SatelliteDefinition']['SatelliteId']
self.mda['platform_name'] = "Meteosat-" + SATNUM[self.platform_id]
equator_radius = data15hd['GeometricProcessing'][
'EarthModel']['EquatorialRadius'] * 1000.
north_polar_radius = data15hd[
'GeometricProcessing']['EarthModel']['NorthPolarRadius'] * 1000.
south_polar_radius = data15hd[
'GeometricProcessing']['EarthModel']['SouthPolarRadius'] * 1000.
polar_radius = (north_polar_radius + south_polar_radius) * 0.5
ssp_lon = data15hd['ImageDescription'][
'ProjectionDescription']['LongitudeOfSSP']
self.mda['projection_parameters'] = {'a': equator_radius,
'b': polar_radius,
'h': 35785831.00,
'ssp_longitude': ssp_lon}
north = int(sec15hd['NorthLineSelectedRectangle']['Value'])
east = int(sec15hd['EastColumnSelectedRectangle']['Value'])
south = int(sec15hd['SouthLineSelectedRectangle']['Value'])
west = int(sec15hd['WestColumnSelectedRectangle']['Value'])
ncolumns = west - east + 1
nrows = north - south + 1
# check if the file has less rows or columns than
# the maximum, if so it is an area of interest file
if (nrows < VISIR_NUM_LINES) or (ncolumns < VISIR_NUM_COLUMNS):
self.mda['is_full_disk'] = False
# If the number of columns in the file is not divisible by 4,
# UMARF will add extra columns to the file
modulo = ncolumns % 4
padding = 0
if modulo > 0:
padding = 4 - modulo
cols_visir = ncolumns + padding
# Check the VISIR calculated column dimension against
# the header information
cols_visir_hdr = int(sec15hd['NumberColumnsVISIR']['Value'])
if cols_visir_hdr != cols_visir:
logger.warning(
"Number of VISIR columns from the header is incorrect!")
logger.warning("Header: %d", cols_visir_hdr)
logger.warning("Calculated: = %d", cols_visir)
# HRV Channel - check if the area is reduced in east west
# direction as this affects the number of columns in the file
cols_hrv_hdr = int(sec15hd['NumberColumnsHRV']['Value'])
if ncolumns < VISIR_NUM_COLUMNS:
cols_hrv = cols_hrv_hdr
else:
cols_hrv = int(cols_hrv_hdr / 2)
# self.mda represents the 16bit dimensions not 10bit
self.mda['number_of_lines'] = int(sec15hd['NumberLinesVISIR']['Value'])
self.mda['number_of_columns'] = cols_visir
self.mda['hrv_number_of_lines'] = int(sec15hd["NumberLinesHRV"]['Value'])
self.mda['hrv_number_of_columns'] = cols_hrv
def _read_amv_header(self):
"""Read AMV product header."""
hdr = np.fromfile(self.filename, SegProdHeaders.amvFinal_prd_hdr, 1)
hdr = hdr.newbyteorder('>')
return recarray2dict(hdr)
def _read_mpef_header(self):
"""Read MPEF header."""
hdr = np.fromfile(self.filename, mpef_product_header, 1)
return recarray2dict(hdr)
def _read_header(self):
"""Read the header info"""
data = np.fromfile(self.filename, dtype=native_header, count=1)
self.header.update(recarray2dict(data))
data15hd = self.header['15_DATA_HEADER']
sec15hd = self.header['15_SECONDARY_PRODUCT_HEADER']
# Set the list of available channels:
self.available_channels = get_available_channels(self.header)
self._channel_list = [
i for i in CHANNEL_NAMES.values() if self.available_channels[i]
]
self.platform_id = data15hd['SatelliteStatus']['SatelliteDefinition'][
'SatelliteId']
self.platform_name = "Meteosat-" + SATNUM[self.platform_id]
equator_radius = data15hd['GeometricProcessing']['EarthModel'][
'EquatorialRadius'] * 1000.
north_polar_radius = data15hd['GeometricProcessing']['EarthModel'][
'NorthPolarRadius'] * 1000.
south_polar_radius = data15hd['GeometricProcessing']['EarthModel'][
'SouthPolarRadius'] * 1000.
polar_radius = (north_polar_radius + south_polar_radius) * 0.5
ssp_lon = data15hd['ImageDescription']['ProjectionDescription'][
'LongitudeOfSSP']
self.mda['projection_parameters'] = {
'a': equator_radius,
'b': polar_radius,
'h': 35785831.00,
'ssp_longitude': ssp_lon
}
west = int(sec15hd['WestColumnSelectedRectangle']['Value'])
east = int(sec15hd['EastColumnSelectedRectangle']['Value'])
ncols_hrv_hdr = int(sec15hd['NumberColumnsHRV']['Value'])
# We suspect the UMARF will pad out any ROI colums that
# arent divisible by 4 so here we work out how many pixels have
# been added to the column.
x = ((west - east + 1) * (10.0 / 8) % 1)
y = int((1 - x) * 4)
if y < 4:
# column has been padded with y pixels
cols_visir = int((west - east + 1 + y) * 1.25)
else:
# no padding has occurred
cols_visir = int((west - east + 1) * 1.25)
# HRV Channel - check if an ROI
if (west - east) < 3711:
cols_hrv = int(np.ceil(ncols_hrv_hdr * 10.0 / 8)) # 6960
else:
cols_hrv = int(np.ceil(5568 * 10.0 / 8)) # 6960
# self.mda should represent the 16bit dimensions not 10bit
self.mda['number_of_lines'] = int(sec15hd['NumberLinesVISIR']['Value'])
self.mda['number_of_columns'] = int(cols_visir / 1.25)
self.mda['hrv_number_of_lines'] = int(
sec15hd["NumberLinesHRV"]['Value'])
self.mda['hrv_number_of_columns'] = int(cols_hrv / 1.25)
# Check the calculated row,column dimensions against the header information:
ncols = self.mda['number_of_columns']
ncols_hdr = int(sec15hd['NumberLinesVISIR']['Value'])
if ncols != ncols_hdr:
logger.warning(
"Number of VISIR columns from header and derived from data are not consistent!"
)
logger.warning("Number of columns read from header = %d",
ncols_hdr)
logger.warning("Number of columns calculated from data = %d",
ncols)
ncols_hrv = self.mda['hrv_number_of_columns']
if ncols_hrv != ncols_hrv_hdr:
logger.warning(
"Number of HRV columns from header and derived from data are not consistent!"
)
logger.warning("Number of columns read from header = %d",
ncols_hrv_hdr)
logger.warning("Number of columns calculated from data = %d",
ncols_hrv)
def _read_header(self):
"""Read the header info"""
data = np.fromfile(self.filename,
dtype=native_header, count=1)
self.header.update(recarray2dict(data))
data15hd = self.header['15_DATA_HEADER']
sec15hd = self.header['15_SECONDARY_PRODUCT_HEADER']
# Set the list of available channels:
self.available_channels = get_available_channels(self.header)
self._channel_list = [i for i in CHANNEL_NAMES.values()
if self.available_channels[i]]
self.platform_id = data15hd[
'SatelliteStatus']['SatelliteDefinition']['SatelliteId']
self.platform_name = "Meteosat-" + SATNUM[self.platform_id]
equator_radius = data15hd['GeometricProcessing'][
'EarthModel']['EquatorialRadius'] * 1000.
north_polar_radius = data15hd[
'GeometricProcessing']['EarthModel']['NorthPolarRadius'] * 1000.
south_polar_radius = data15hd[
'GeometricProcessing']['EarthModel']['SouthPolarRadius'] * 1000.
polar_radius = (north_polar_radius + south_polar_radius) * 0.5
ssp_lon = data15hd['ImageDescription'][
'ProjectionDescription']['LongitudeOfSSP']
self.mda['projection_parameters'] = {'a': equator_radius,
'b': polar_radius,
'h': 35785831.00,
'ssp_longitude': ssp_lon}
west = int(sec15hd['WestColumnSelectedRectangle']['Value'])
east = int(sec15hd['EastColumnSelectedRectangle']['Value'])
ncols_hrv_hdr = int(sec15hd['NumberColumnsHRV']['Value'])
# We suspect the UMARF will pad out any ROI colums that
# arent divisible by 4 so here we work out how many pixels have
# been added to the column.
x = ((west - east + 1) * (10.0 / 8) % 1)
y = int((1 - x) * 4)
if y < 4:
# column has been padded with y pixels
cols_visir = int((west - east + 1 + y) * 1.25)
else:
# no padding has occurred
cols_visir = int((west - east + 1) * 1.25)
# HRV Channel - check if an ROI
if (west - east) < 3711:
cols_hrv = int(np.ceil(ncols_hrv_hdr * 10.0 / 8)) # 6960
else:
cols_hrv = int(np.ceil(5568 * 10.0 / 8)) # 6960
# self.mda should represent the 16bit dimensions not 10bit
self.mda['number_of_lines'] = int(sec15hd['NumberLinesVISIR']['Value'])
self.mda['number_of_columns'] = int(cols_visir / 1.25)
self.mda['hrv_number_of_lines'] = int(sec15hd["NumberLinesHRV"]['Value'])
self.mda['hrv_number_of_columns'] = int(cols_hrv / 1.25)
# Check the calculated row,column dimensions against the header information:
ncols = self.mda['number_of_columns']
ncols_hdr = int(sec15hd['NumberLinesVISIR']['Value'])
if ncols != ncols_hdr:
logger.warning(
"Number of VISIR columns from header and derived from data are not consistent!")
logger.warning("Number of columns read from header = %d", ncols_hdr)
logger.warning("Number of columns calculated from data = %d", ncols)
ncols_hrv = self.mda['hrv_number_of_columns']
if ncols_hrv != ncols_hrv_hdr:
logger.warning(
"Number of HRV columns from header and derived from data are not consistent!")
logger.warning("Number of columns read from header = %d", ncols_hrv_hdr)
logger.warning("Number of columns calculated from data = %d", ncols_hrv)
