def test_time_unit_conversion(self): """Test that the output cube contains the desired units and datatypes for the time, forecast_reference_time and forecast_period coordinate, where a unit conversion has been required for the time and forecast reference time coordinates.""" self.cube.coord("time").convert_units("hours since 1970-01-01 00:00") frt_coord = DimCoord( 1519099200, standard_name="forecast_reference_time", units="seconds since 1970-01-01 00:00:00", ) frt_coord.convert_units("hours since 1970-01-01 00:00") self.cube.add_aux_coord(frt_coord) self.cube.coord("forecast_reference_time").convert_units( "hours since 1970-01-01 00:00") result = self.plugin.process(self.cube, self.timestep) self.assertEqual(result.coord("forecast_period").points, 600) # 2017-11-10 04:10:00 self.assertEqual(result.coord("time").points, 1519099800) self.assertEqual( result.coord("forecast_reference_time").points, 1519099200) self.assertEqual(result.coord("forecast_period").units, "seconds") self.assertEqual( result.coord("time").units, "seconds since 1970-01-01 00:00:00") self.assertEqual( result.coord("forecast_reference_time").units, "seconds since 1970-01-01 00:00:00", ) self.assertEqual(result.coord("forecast_period").dtype, np.int32) self.assertEqual(result.coord("time").dtype, np.int64) self.assertEqual( result.coord("forecast_reference_time").dtype, np.int64)
def create_data_object(self, filenames, variable, index_offset=1): from cis.data_io.hdf_vd import get_data from cis.data_io.hdf_vd import VDS from pyhdf.error import HDF4Error from cis.data_io import hdf_sd from iris.coords import DimCoord, AuxCoord from iris.cube import Cube from cis.data_io.gridded_data import GriddedData from cis.time_util import cis_standard_time_unit logging.debug("Creating data object for variable " + variable) variables = ['Latitude', 'Longitude', "Profile_Time", "Pressure"] logging.info("Listing coordinates: " + str(variables)) variables.append(variable) # reading data from files sdata = {} for filename in filenames: try: sds_dict = hdf_sd.read(filename, variables) except HDF4Error as e: raise IOError(str(e)) for var in list(sds_dict.keys()): utils.add_element_to_list_in_dict(sdata, var, sds_dict[var]) alt_name = "altitude" logging.info("Additional coordinates: '" + alt_name + "'") # work out size of data arrays # the coordinate variables will be reshaped to match that. # NOTE: This assumes that all Caliop_L1 files have the same altitudes. # If this is not the case, then the following line will need to be changed # to concatenate the data from all the files and not just arbitrarily pick # the altitudes from the first file. alt_data = get_data(VDS(filenames[0], "Lidar_Data_Altitudes"), True) alt_coord = DimCoord(alt_data, standard_name='altitude', units='km') alt_coord.convert_units('m') lat_data = hdf.read_data(sdata['Latitude'], self._get_calipso_data)[:, index_offset] lat_coord = AuxCoord(lat_data, standard_name='latitude') pres_data = hdf.read_data(sdata['Pressure'], self._get_calipso_data) pres_coord = AuxCoord(pres_data, standard_name='air_pressure', units='hPa') # longitude lon = sdata['Longitude'] lon_data = hdf.read_data(lon, self._get_calipso_data)[:, index_offset] lon_coord = AuxCoord(lon_data, standard_name='longitude') # profile time, x time = sdata['Profile_Time'] time_data = hdf.read_data(time, self._get_calipso_data)[:, index_offset] time_coord = DimCoord(time_data, long_name='Profile_Time', standard_name='time', units="seconds since 1993-01-01 00:00:00") time_coord.convert_units(cis_standard_time_unit) # retrieve data + its metadata var = sdata[variable] metadata = hdf.read_metadata(var, "SD") if variable in MIXED_RESOLUTION_VARIABLES: logging.warning("Using Level 2 resolution profile for mixed resolution variable {}. See CALIPSO " "documentation for more details".format(variable)) data = hdf.read_data(var, self._get_mixed_resolution_calipso_data) else: data = hdf.read_data(var, self._get_calipso_data) cube = Cube(data, long_name=metadata.long_name, units=self.clean_units(metadata.units), dim_coords_and_dims=[(alt_coord, 1), (time_coord, 0)], aux_coords_and_dims=[(lat_coord, (0,)), (lon_coord, (0,)), (pres_coord, (0, 1))]) gd = GriddedData.make_from_cube(cube) return gd
def create_data_object(self, filenames, variable, index_offset=1): from cis.data_io.hdf_vd import get_data from cis.data_io.hdf_vd import VDS from pyhdf.error import HDF4Error from cis.data_io import hdf_sd from iris.coords import DimCoord, AuxCoord from iris.cube import Cube, CubeList from cis.data_io.gridded_data import GriddedData from cis.time_util import cis_standard_time_unit from datetime import datetime from iris.util import new_axis import numpy as np logging.debug("Creating data object for variable " + variable) variables = ["Pressure_Mean"] logging.info("Listing coordinates: " + str(variables)) variables.append(variable) # reading data from files sdata = {} for filename in filenames: try: sds_dict = hdf_sd.read(filename, variables) except HDF4Error as e: raise IOError(str(e)) for var in list(sds_dict.keys()): utils.add_element_to_list_in_dict(sdata, var, sds_dict[var]) # work out size of data arrays # the coordinate variables will be reshaped to match that. # NOTE: This assumes that all Caliop_L1 files have the same altitudes. # If this is not the case, then the following line will need to be changed # to concatenate the data from all the files and not just arbitrarily pick # the altitudes from the first file. alt_data = self._get_calipso_data(hdf_sd.HDF_SDS(filenames[0], 'Altitude_Midpoint'))[0, :] alt_coord = DimCoord(alt_data, standard_name='altitude', units='km') alt_coord.convert_units('m') lat_data = self._get_calipso_data(hdf_sd.HDF_SDS(filenames[0], 'Latitude_Midpoint'))[0, :] lat_coord = DimCoord(lat_data, standard_name='latitude', units='degrees_north') lon_data = self._get_calipso_data(hdf_sd.HDF_SDS(filenames[0], 'Longitude_Midpoint'))[0, :] lon_coord = DimCoord(lon_data, standard_name='longitude', units='degrees_east') cubes = CubeList() for f in filenames: t = get_data(VDS(f, "Nominal_Year_Month"), True)[0] time_data = cis_standard_time_unit.date2num(datetime(int(t[0:4]), int(t[4:6]), 15)) time_coord = AuxCoord(time_data, long_name='Profile_Time', standard_name='time', units=cis_standard_time_unit) # retrieve data + its metadata var = sdata[variable] metadata = hdf.read_metadata(var, "SD") data = self._get_calipso_data(hdf_sd.HDF_SDS(f, variable)) pres_data = self._get_calipso_data(hdf_sd.HDF_SDS(f, 'Pressure_Mean')) pres_coord = AuxCoord(pres_data, standard_name='air_pressure', units='hPa') if data.ndim == 2: # pres_coord = new_axis() cube = Cube(data, long_name=metadata.long_name or variable, units=self.clean_units(metadata.units), dim_coords_and_dims=[(lat_coord, 0), (lon_coord, 1)], aux_coords_and_dims=[(time_coord, ())]) # Promote the time scalar coord to a length one dimension new_cube = new_axis(cube, 'time') cubes.append(new_cube) elif data.ndim == 3: # pres_coord = new_axis() cube = Cube(data, long_name=metadata.long_name or variable, units=self.clean_units(metadata.units), dim_coords_and_dims=[(lat_coord, 0), (lon_coord, 1), (alt_coord, 2)], aux_coords_and_dims=[(time_coord, ())]) # Promote the time scalar coord to a length one dimension new_cube = new_axis(cube, 'time') # Then add the (extended) pressure coord so that it is explicitly a function of time new_cube.add_aux_coord(pres_coord[np.newaxis, ...], (0, 1, 2, 3)) cubes.append(new_cube) else: raise ValueError("Unexpected number of dimensions for CALIOP data: {}".format(data.ndim)) # Concatenate the cubes from each file into a single GriddedData object gd = GriddedData.make_from_cube(cubes.concatenate_cube()) return gd
def create_data_object(self, filenames, variable, index_offset=1): from cis.data_io.hdf_vd import get_data from cis.data_io.hdf_vd import VDS from pyhdf.error import HDF4Error from cis.data_io import hdf_sd from iris.coords import DimCoord, AuxCoord from iris.cube import Cube from cis.data_io.gridded_data import GriddedData from cis.time_util import cis_standard_time_unit logging.debug("Creating data object for variable " + variable) variables = ['Latitude', 'Longitude', "Profile_Time", "Pressure"] logging.info("Listing coordinates: " + str(variables)) variables.append(variable) # reading data from files sdata = {} for filename in filenames: try: sds_dict = hdf_sd.read(filename, variables) except HDF4Error as e: raise IOError(str(e)) for var in list(sds_dict.keys()): utils.add_element_to_list_in_dict(sdata, var, sds_dict[var]) alt_name = "altitude" logging.info("Additional coordinates: '" + alt_name + "'") # work out size of data arrays # the coordinate variables will be reshaped to match that. # NOTE: This assumes that all Caliop_L1 files have the same altitudes. # If this is not the case, then the following line will need to be changed # to concatenate the data from all the files and not just arbitrarily pick # the altitudes from the first file. alt_data = get_data(VDS(filenames[0], "Lidar_Data_Altitudes"), True) alt_coord = DimCoord(alt_data, standard_name='altitude', units='km') alt_coord.convert_units('m') lat_data = hdf.read_data(sdata['Latitude'], self._get_calipso_data)[:, index_offset] lat_coord = AuxCoord(lat_data, standard_name='latitude') pres_data = hdf.read_data(sdata['Pressure'], self._get_calipso_data) pres_coord = AuxCoord(pres_data, standard_name='air_pressure', units='hPa') # longitude lon = sdata['Longitude'] lon_data = hdf.read_data(lon, self._get_calipso_data)[:, index_offset] lon_coord = AuxCoord(lon_data, standard_name='longitude') # profile time, x time = sdata['Profile_Time'] time_data = hdf.read_data(time, self._get_calipso_data)[:, index_offset] time_coord = DimCoord(time_data, long_name='Profile_Time', standard_name='time', units="seconds since 1993-01-01 00:00:00") time_coord.convert_units(cis_standard_time_unit) # retrieve data + its metadata var = sdata[variable] metadata = hdf.read_metadata(var, "SD") if variable in MIXED_RESOLUTION_VARIABLES: logging.warning( "Using Level 2 resolution profile for mixed resolution variable {}. See CALIPSO " "documentation for more details".format(variable)) data = hdf.read_data(var, self._get_mixed_resolution_calipso_data) else: data = hdf.read_data(var, self._get_calipso_data) cube = Cube(data, long_name=metadata.long_name, units=self.clean_units(metadata.units), dim_coords_and_dims=[(alt_coord, 1), (time_coord, 0)], aux_coords_and_dims=[(lat_coord, (0, )), (lon_coord, (0, )), (pres_coord, (0, 1))]) gd = GriddedData.make_from_cube(cube) return gd
def create_data_object(self, filenames, variable, index_offset=1): from cis.data_io.hdf_vd import get_data from cis.data_io.hdf_vd import VDS from pyhdf.error import HDF4Error from cis.data_io import hdf_sd from iris.coords import DimCoord, AuxCoord from iris.cube import Cube, CubeList from cis.data_io.gridded_data import GriddedData from cis.time_util import cis_standard_time_unit from datetime import datetime from iris.util import new_axis import numpy as np logging.debug("Creating data object for variable " + variable) variables = ["Pressure_Mean"] logging.info("Listing coordinates: " + str(variables)) variables.append(variable) # reading data from files sdata = {} for filename in filenames: try: sds_dict = hdf_sd.read(filename, variables) except HDF4Error as e: raise IOError(str(e)) for var in list(sds_dict.keys()): utils.add_element_to_list_in_dict(sdata, var, sds_dict[var]) # work out size of data arrays # the coordinate variables will be reshaped to match that. # NOTE: This assumes that all Caliop_L1 files have the same altitudes. # If this is not the case, then the following line will need to be changed # to concatenate the data from all the files and not just arbitrarily pick # the altitudes from the first file. alt_data = self._get_calipso_data( hdf_sd.HDF_SDS(filenames[0], 'Altitude_Midpoint'))[0, :] alt_coord = DimCoord(alt_data, standard_name='altitude', units='km') alt_coord.convert_units('m') lat_data = self._get_calipso_data( hdf_sd.HDF_SDS(filenames[0], 'Latitude_Midpoint'))[0, :] lat_coord = DimCoord(lat_data, standard_name='latitude', units='degrees_north') lon_data = self._get_calipso_data( hdf_sd.HDF_SDS(filenames[0], 'Longitude_Midpoint'))[0, :] lon_coord = DimCoord(lon_data, standard_name='longitude', units='degrees_east') cubes = CubeList() for f in filenames: t = get_data(VDS(f, "Nominal_Year_Month"), True)[0] time_data = cis_standard_time_unit.date2num( datetime(int(t[0:4]), int(t[4:6]), 15)) time_coord = AuxCoord(time_data, long_name='Profile_Time', standard_name='time', units=cis_standard_time_unit) # retrieve data + its metadata var = sdata[variable] metadata = hdf.read_metadata(var, "SD") data = self._get_calipso_data(hdf_sd.HDF_SDS(f, variable)) pres_data = self._get_calipso_data( hdf_sd.HDF_SDS(f, 'Pressure_Mean')) pres_coord = AuxCoord(pres_data, standard_name='air_pressure', units='hPa') if data.ndim == 2: # pres_coord = new_axis() cube = Cube(data, long_name=metadata.long_name or variable, units=self.clean_units(metadata.units), dim_coords_and_dims=[(lat_coord, 0), (lon_coord, 1)], aux_coords_and_dims=[(time_coord, ())]) # Promote the time scalar coord to a length one dimension new_cube = new_axis(cube, 'time') cubes.append(new_cube) elif data.ndim == 3: # pres_coord = new_axis() cube = Cube(data, long_name=metadata.long_name or variable, units=self.clean_units(metadata.units), dim_coords_and_dims=[(lat_coord, 0), (lon_coord, 1), (alt_coord, 2)], aux_coords_and_dims=[(time_coord, ())]) # Promote the time scalar coord to a length one dimension new_cube = new_axis(cube, 'time') # Then add the (extended) pressure coord so that it is explicitly a function of time new_cube.add_aux_coord(pres_coord[np.newaxis, ...], (0, 1, 2, 3)) cubes.append(new_cube) else: raise ValueError( "Unexpected number of dimensions for CALIOP data: {}". format(data.ndim)) # Concatenate the cubes from each file into a single GriddedData object gd = GriddedData.make_from_cube(cubes.concatenate_cube()) return gd