def read_image(mda, filename=None, mask=True, calibrate=1):
mda = copy.copy(mda)
mda.calibrate = _Calibrator(mda)
mda.calibration_unit = 'counts'
mda.is_calibrated = False
del mda.calibrated
mda.product_name = (mda.time_start.strftime("%Y%m%d_%H%M%S") + '_' +
mda.satellite_name + '_' + mda.product_type + '_' +
mda.polarisation)
logger.info('Product name: %s' % mda.product_name)
if not filename:
filename = mda.image_filename
params, data = read_geotiff(filename)
area_def = tiff2areadef(params['projection'], params['geotransform'],
data.shape)
mda.proj4_params = area_def.proj4_string.replace('+', '')
mda.area_extent = area_def.area_extent
mda.tiff_params = params
if calibrate:
data, mda.calibration_unit = mda.calibrate(data, calibrate)
mda.is_calibrated = True
logger.info('calibrated: %s %s [%.2f -> %.2f -> %.2f] %s' %
(str(data.shape), data.dtype, data.min(), data.mean(),
data.max(), mda.calibration_unit))
if mask:
mask = (data == mda.no_data_value)
data = np.ma.array(data, mask=mask, copy=False)
return mda, data
def load_channel(self, filename):
"""Load one sentinel channel file"""
from geotiepoints.basic_interpolator import BasicSatelliteInterpolator
params, data = read_geotiff(filename)
tie_lons = params['tiepoints']['lons']
tie_lats = params['tiepoints']['lats']
tie_cols = params['tiepoints']['cols']
tie_rows = params['tiepoints']['rows']
interpolator = BasicSatelliteInterpolator(tie_cols, tie_rows, tie_lats,
tie_lons)
lats, lons = interpolator.interpolate()
return lons, lats, data
def load_channel(self, filename):
"""Load one sentinel channel file"""
from geotiepoints.basic_interpolator import BasicSatelliteInterpolator
params, data = read_geotiff(filename)
tie_lons = params['tiepoints']['lons']
tie_lats = params['tiepoints']['lats']
tie_cols = params['tiepoints']['cols']
tie_rows = params['tiepoints']['rows']
interpolator = BasicSatelliteInterpolator(tie_cols,
tie_rows,
tie_lats,
tie_lons)
lats, lons = interpolator.interpolate()
return lons, lats, data
def read_image(mda, filename=None, mask=True, calibrate=1):
mda = copy.copy(mda)
mda.calibrate = _Calibrator(mda)
mda.calibration_unit = 'counts'
mda.is_calibrated = False
del mda.calibrated
mda.product_name = (mda.time_start.strftime("%Y%m%d_%H%M%S") + '_' +
mda.satellite_name + '_' + mda.sensor_type + '_' +
mda.beam_mode + '_' + mda.polarisation)
logger.info('Product name: %s'% mda.product_name)
if not filename:
filename = mda.image_filename
params, data = read_geotiff(filename)
area_def = tiff2areadef(params['projection'],
params['geotransform'],
data.shape)
mda.proj4_params = area_def.proj4_string.replace('+', '')
mda.area_extent = area_def.area_extent
mda.tiff_params = params
mda.no_data_value = no_data_value
if calibrate:
data, mda.calibration_unit = mda.calibrate(data, calibrate)
mda.is_calibrated = True
logger.info('calibrated: %s %s [%.2f -> %.2f -> %.2f] %s'%
(str(data.shape), data.dtype, data.min(),
data.mean(), data.max(), mda.calibration_unit))
if mask:
mask = (data == no_data_value)
data = np.ma.array(data, mask=mask, copy=False)
return mda, data
b=6356759.0,
h=35785831.0,
rf=295.49)
x_ll, y_ll = prj(3, 47)
x_ur, y_ur = prj(19, 55)
ger_extent = (x_ll, y_ll, x_ur, y_ur)
germ_areadef = utils.load_area(
os.path.join(os.getenv("PPP_CONFIG_DIR"), "areas.yaml"), 'germ')
germ_extent = germ_areadef.area_extent_ll
x_ll, y_ll = prj(*germ_extent[0:2])
x_ur, y_ur = prj(*germ_extent[2:])
ger_extent = (x_ll, y_ll, x_ur, y_ur)
# Import geoftiff with DEM information
tiff = "/media/nas/satablage/Thomas/Grassdata/srtm_germany_dsm.tif"
params, dem = gtiff.read_geotiff(tiff)
tiffarea = gtiff.tiff2areadef(params['projection'], params['geotransform'],
dem.shape)
elevation = image.ImageContainerQuick(dem, tiffarea)
# Directory for cloud physical properties
cpp_dir = '/media/nas/satablage/Thomas/Nebel/CMSAF_microphysics'
# Define time series for analysis
#start = "201408270700"
#end = "201408270715"
start = "201311120830"
end = "201311120845"
#start = "201407110800"
#end = "201407110815"
