def read_many_files_individually(filenames, usr_variables):
"""
Read multiple Variables from many NetCDF files manually - i.e. not with MFDataset as this doesn't always work,
in particular for NetCDF4 files.
:param filenames: A list of NetCDF filenames to read, or a string with wildcards.
:param usr_variables: A list of variable (dataset) names to read from the files. The names must appear exactly as
in in the NetCDF file. Variable names may be fully qualified NetCDF4 Hierarchical group variables in the form
``<group1>/<group2....>/<variable_name>``, e.g. ``AVHRR/Ch4CentralWavenumber``.
:return: A dictionary of lists of variable instances constructed from all of the input files with the fully
qualified variable name as the key
"""
from cis.utils import add_element_to_list_in_dict
usr_variables = listify(usr_variables)
var_data = {}
for filename in filenames:
var_dict = read(filename, usr_variables)
for var in list(var_dict.keys()):
add_element_to_list_in_dict(var_data, var, var_dict[var])
return var_data
def read_many_files_individually(filenames, usr_variables):
"""
Read multiple Variables from many NetCDF files manually - i.e. not with MFDataset as this doesn't always work,
in particular for NetCDF4 files.
:param filenames: A list of NetCDF filenames to read, or a string with wildcards.
:param usr_variables: A list of variable (dataset) names to read from the files. The names must appear exactly as
in in the NetCDF file. Variable names may be fully qualified NetCDF4 Hierarchical group variables in the form
``<group1>/<group2....>/<variable_name>``, e.g. ``AVHRR/Ch4CentralWavenumber``.
:return: A dictionary of lists of variable instances constructed from all of the input files with the fully
qualified variable name as the key
"""
from cis.utils import add_element_to_list_in_dict
usr_variables = listify(usr_variables)
var_data = {}
for filename in filenames:
var_dict = read(filename, usr_variables)
for var in list(var_dict.keys()):
add_element_to_list_in_dict(var_data, var, var_dict[var])
return var_data
def _create_one_dimensional_coord_list(self, filenames, index_offset=1):
"""
Create a set of coordinates appropriate for a ond-dimensional (column integrated) variable
:param filenames:
:param int index_offset: For 5km products this will choose the coordinates which represent the start (0),
middle (1) and end (2) of the 15 shots making up each column retrieval.
:return:
"""
from pyhdf.error import HDF4Error
from cis.data_io import hdf_sd
import datetime as dt
from cis.time_util import convert_sec_since_to_std_time, cis_standard_time_unit
variables = ['Latitude', 'Longitude', "Profile_Time"]
logging.info("Listing coordinates: " + str(variables))
# 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])
# latitude
lat_data = hdf.read_data(sdata['Latitude'], self._get_calipso_data)[:, index_offset]
lat_metadata = hdf.read_metadata(sdata['Latitude'], "SD")
lat_coord = Coord(lat_data, lat_metadata, 'Y')
# longitude
lon = sdata['Longitude']
lon_data = hdf.read_data(lon, self._get_calipso_data)[:, index_offset]
lon_metadata = hdf.read_metadata(lon, "SD")
lon_coord = Coord(lon_data, lon_metadata, 'X')
# profile time, x
time = sdata['Profile_Time']
time_data = hdf.read_data(time, self._get_calipso_data)[:, index_offset]
time_data = convert_sec_since_to_std_time(time_data, dt.datetime(1993, 1, 1, 0, 0, 0))
time_coord = Coord(time_data, Metadata(name='Profile_Time', standard_name='time', shape=time_data.shape,
units=cis_standard_time_unit), "T")
# create the object containing all coordinates
coords = CoordList()
coords.append(lat_coord)
coords.append(lon_coord)
coords.append(time_coord)
return coords
def load_multiple_aeronet(filenames, variables=None):
from cis.utils import add_element_to_list_in_dict, concatenate
adata = {}
for filename in filenames:
logging.debug("reading file: " + filename)
# reading in all variables into a dictionary:
# a_dict, key: variable name, value: list of masked arrays
a_dict = load_aeronet(filename, variables)
for var in list(a_dict.keys()):
add_element_to_list_in_dict(adata, var, a_dict[var])
for var in list(adata.keys()):
adata[var] = concatenate(adata[var])
return adata
def read(filenames, variables):
sdata = {}
vdata = {}
for filename in filenames:
logging.debug("reading file: " + filename)
# reading in all variables into a 2 dictionaries:
# sdata, key: variable name, value: list of sds
# vdata, key: variable name, value: list of vds
sds_dict, vds_dict = _read_hdf4(filename, variables)
for var in list(sds_dict.keys()):
utils.add_element_to_list_in_dict(sdata, var, sds_dict[var])
for var in list(vds_dict.keys()):
utils.add_element_to_list_in_dict(vdata, var, vds_dict[var])
return sdata, vdata
def read(filenames, variables):
sdata = {}
vdata = {}
for filename in filenames:
logging.debug("reading file: " + filename)
# reading in all variables into a 2 dictionaries:
# sdata, key: variable name, value: list of sds
# vdata, key: variable name, value: list of vds
sds_dict, vds_dict = _read_hdf4(filename, variables)
for var in list(sds_dict.keys()):
utils.add_element_to_list_in_dict(sdata, var, sds_dict[var])
for var in list(vds_dict.keys()):
utils.add_element_to_list_in_dict(vdata, var, vds_dict[var])
return sdata, vdata
def load_multiple_hysplit(fnames, variables=None):
from cis.utils import add_element_to_list_in_dict, concatenate
hdata = {}
for filename in fnames:
logging.debug("reading file: " + filename)
# read in all trajectories
# h_dict, key: trajectory starting lat/lon/altm value: dict containing trajectory data
h_dict = load_hysplit(filename, variables)
for traj in list(h_dict.keys()):
if (traj in hdata):
for var in list(h_dict[traj].keys()):
# TODO error appending masked array! add these manually
add_element_to_list_in_dict(hdata[traj], var, h_dict[traj][var])
for var in list(hdata[traj].keys()):
hdata[traj][var] = concatenate(hdata[traj][var])
else:
hdata[traj] = h_dict[traj]
return hdata
def load_hysplit(fname, variables=None):
import numpy as np
from numpy import ma
from datetime import datetime, timedelta
from cis.time_util import cis_standard_time_unit
from cis.utils import add_element_to_list_in_dict, concatenate
std_day = cis_standard_time_unit.num2date(0)
fmetadata = get_file_metadata(fname)
try:
rawd = np.genfromtxt(fname,
skip_header=fmetadata['data_start'],
dtype=np.float64,
usemask=True)
except (StopIteration, IndexError) as e:
raise IOError(e)
data_dict = {}
# Get data for one trajectory at a time
for t in range(1, fmetadata['n_trajectories']+1):
tdata_dict = {}
trajectory_data = rawd[rawd[:,hysplit_default_var.index('TRAJECTORY_NO')] == t]
# Convert time from each row to standard time
for trajectory in trajectory_data:
day = datetime((int(trajectory[2]) + 2000), # TODO Dan: is it okay to assume this?
int(trajectory[3]),
int(trajectory[4]))
sday = float((day - std_day).days)
td = timedelta(hours=int(trajectory[5]), minutes=int(trajectory[6]))
fractional_day = td.total_seconds()/(24.0*60.0*60.0)
dt = sday + fractional_day
add_element_to_list_in_dict(tdata_dict, 'DATETIMES', [dt])
# Clean up data
tdata_dict['DATETIMES'] = ma.array(concatenate(tdata_dict['DATETIMES'])) # TODO mask is only one value
# Add other default data
tdata_dict['LAT'] = trajectory_data[:,hysplit_default_var.index('LAT')]
tdata_dict['LON'] = trajectory_data[:,hysplit_default_var.index('LON')]
tdata_dict['ALT'] = trajectory_data[:,hysplit_default_var.index('ALT')]
tdata_dict['PRESSURE'] = trajectory_data[:, hysplit_default_var.index('PRESSURE')]
# TODO any other default variables to add?
# If variables set, fetch only set variables
if variables is not None:
for key in variables:
try:
tdata_dict[key] = trajectory_data[:,fmetadata['labels'].index(key)]
except ValueError:
raise InvalidVariableError(key + "does not exist in " + fname)
# Else, return all variables in file
else:
for label in fmetadata['custom_labels']:
try:
tdata_dict[label] = trajectory_data[:,fmetadata['labels'].index(label)]
except ValueError:
raise InvalidVariableError(key + " does not exist in " + fname)
# TODO trajectory keys are tuples of lat/long/alt
tkey = fmetadata['trajectories'][t]
data_dict[tkey] = tdata_dict
return data_dict
def _create_coord_list(self, filenames, index_offset=0):
import logging
from cis.data_io import hdf as hdf
from cis.data_io.Coord import Coord, CoordList
from cis.data_io.ungridded_data import Metadata
import cis.utils as utils
from cis.data_io.hdf_vd import VDS
from pyhdf.error import HDF4Error
from cis.data_io import hdf_sd
import datetime as dt
from cis.time_util import convert_sec_since_to_std_time, cis_standard_time_unit
variables = ['Latitude', 'Longitude', "Profile_Time", "Pressure"]
logging.info("Listing coordinates: " + str(variables))
# 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_data *= 1000.0 # Convert to m
len_x = alt_data.shape[0]
lat_data = hdf.read_data(sdata['Latitude'], self._get_calipso_data)
len_y = lat_data.shape[0]
new_shape = (len_x, len_y)
# altitude
alt_data = utils.expand_1d_to_2d_array(alt_data, len_y, axis=0)
alt_metadata = Metadata(name=alt_name, standard_name=alt_name, shape=new_shape)
alt_coord = Coord(alt_data, alt_metadata)
# pressure
if self.include_pressure:
pres_data = hdf.read_data(sdata['Pressure'], self._get_calipso_data)
pres_metadata = hdf.read_metadata(sdata['Pressure'], "SD")
# Fix badly formatted units which aren't CF compliant and will break if they are aggregated
if str(pres_metadata.units) == "hPA":
pres_metadata.units = "hPa"
pres_metadata.shape = new_shape
pres_coord = Coord(pres_data, pres_metadata, 'P')
# latitude
lat_data = utils.expand_1d_to_2d_array(lat_data[:, index_offset], len_x, axis=1)
lat_metadata = hdf.read_metadata(sdata['Latitude'], "SD")
lat_metadata.shape = new_shape
lat_coord = Coord(lat_data, lat_metadata, 'Y')
# longitude
lon = sdata['Longitude']
lon_data = hdf.read_data(lon, self._get_calipso_data)
lon_data = utils.expand_1d_to_2d_array(lon_data[:, index_offset], len_x, axis=1)
lon_metadata = hdf.read_metadata(lon, "SD")
lon_metadata.shape = new_shape
lon_coord = Coord(lon_data, lon_metadata, 'X')
# profile time, x
time = sdata['Profile_Time']
time_data = hdf.read_data(time, self._get_calipso_data)
time_data = convert_sec_since_to_std_time(time_data, dt.datetime(1993, 1, 1, 0, 0, 0))
time_data = utils.expand_1d_to_2d_array(time_data[:, index_offset], len_x, axis=1)
time_coord = Coord(time_data, Metadata(name='Profile_Time', standard_name='time', shape=time_data.shape,
units=cis_standard_time_unit), "T")
# create the object containing all coordinates
coords = CoordList()
coords.append(lat_coord)
coords.append(lon_coord)
coords.append(time_coord)
coords.append(alt_coord)
if self.include_pressure and (pres_data.shape == alt_data.shape):
# For MODIS L1 this may is not be true, so skips the air pressure reading. If required for MODIS L1 then
# some kind of interpolation of the air pressure would be required, as it is on a different (smaller) grid
# than for the Lidar_Data_Altitudes.
coords.append(pres_coord)
return coords
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
