"""
Routines for reading GCPEX D3R files.
"""
import datetime
import numpy as np
import netCDF4
from ..config import FileMetadata
from ..io.common import make_time_unit_str, _test_arguments
from ..core.radar import Radar
D3R_FIELD_NAMES = {
# corrected reflectivity, horizontal
'Reflectivity': 'reflectivity',
# corrected reflectivity, vertical
'DBZV': 'reflectivity',
# differential reflectivity
'DifferentialReflectivity': 'differential_reflectivity',
'CrossPolCorrelation': 'cross_correlation_ratio',
'ClutterPowerH': 'clutter_power_h',
'ClutterPowerV': 'clutter_power_v',
'DifferentialPhase': 'differential_phase',
'KDP': 'specific_differential_phase',
'NormalizedCoherentPower': 'normalized_coherent_power',
'Signal+Clutter_toNoise_H': 'signal_to_noise_ratio',
'Velocity': 'velocity',
'SpectralWidth': 'spectrum_width',
'SignalPower_H': 'signal_power_h',
}
[docs]def read_d3r_gcpex_nc(filename, field_names=None, additional_metadata=None,
file_field_names=False, exclude_fields=None,
include_fields=None, read_altitude_from_nc=False, **kwargs):
"""
Read a D3R GCPEX netCDF file.
Parameters
----------
filename : str
Name of the ODIM_H5 file to read.
field_names : dict, optional
Dictionary mapping ODIM_H5 field names to radar field names. If a
data type found in the file does not appear in this dictionary or has
a value of None it will not be placed in the radar.fields dictionary.
A value of None, the default, will use the mapping defined in the
Py-ART configuration file.
additional_metadata : dict of dicts, optional
Dictionary of dictionaries to retrieve metadata from during this read.
This metadata is not used during any successive file reads unless
explicitly included. A value of None, the default, will not
introduct any addition metadata and the file specific or default
metadata as specified by the Py-ART configuration file will be used.
file_field_names : bool, optional
True to use the MDV data type names for the field names. If this
case the field_names parameter is ignored. The field dictionary will
likely only have a 'data' key, unless the fields are defined in
`additional_metadata`.
exclude_fields : list or None, optional
List of fields to exclude from the radar object. This is applied
after the `file_field_names` and `field_names` parameters. Set
to None to include all fields specified by include_fields.
include_fields : list or None, optional
List of fields to include from the radar object. This is applied
after the `file_field_names` and `field_names` parameters. Set
to None to include all fields not specified by exclude_fields.
read_altitude_from_nc : bool, optional
True if you want the altitude value to be read from the provider netCDF file.
False will default to the value np.array([295.], dtype='float64')
Returns
-------
radar : Radar
Radar object containing data from ODIM_H5 file.
"""
# TODO before moving to pyart.io
# * unit test
# * add default field mapping, etc to default config
# * auto-detect file type with pyart.io.read function
# * instrument parameters
# * add additional checks for HOW attributes
# * support for other objects (SCAN, XSEC)
# test for non empty kwargs
_test_arguments(kwargs)
# create metadata retrieval object
if field_names is None:
field_names = D3R_FIELD_NAMES
filemetadata = FileMetadata('cfradial', field_names, additional_metadata,
file_field_names, exclude_fields,
include_fields)
# read the data
ncobj = netCDF4.Dataset(filename)
ncvars = ncobj.variables
# One sweep per file
nsweeps = 1
# latitude, longitude and altitude
latitude = filemetadata('latitude')
longitude = filemetadata('longitude')
altitude = filemetadata('altitude')
latitude['data'] = np.array([ncobj.Latitude], dtype='float64')
longitude['data'] = np.array([ncobj.Longitude], dtype='float64')
altitude_list = [ncobj.Altitude] if read_altitude_from_nc else [295.]
altitude['data'] = np.array(altitude_list, dtype='float64')
# metadata
metadata = filemetadata('metadata')
metadata['source'] = "Colorado State EE - chandrasekar"
metadata['original_container'] = 'D3R_gcpex_nc'
metadata['nc_conventions'] = ncobj.NetCDFRevision
metadata['version'] = ncobj.NetCDFRevision
metadata['source'] = "Chandra"
metadata['system'] = ncobj.RadarName
metadata['software'] = ncobj.NetCDFRevision
metadata['sw_version'] = ncobj.NetCDFRevision
# sweep_start_ray_index, sweep_end_ray_index
sweep_start_ray_index = filemetadata('sweep_start_ray_index')
sweep_end_ray_index = filemetadata('sweep_end_ray_index')
rays_per_sweep = np.shape(ncvars['Azimuth'][:])
ssri = np.cumsum(np.append([0], rays_per_sweep[:-1])).astype('int32')
seri = np.cumsum(rays_per_sweep).astype('int32') - 1
sweep_start_ray_index['data'] = ssri
sweep_end_ray_index['data'] = seri
# sweep_number
sweep_number = filemetadata('sweep_number')
sweep_number['data'] = np.arange(nsweeps, dtype='int32')
# sweep_mode
sweep_mode = filemetadata('sweep_mode')
sweep_mode['data'] = np.array(nsweeps * ['azimuth_surveillance'])
# scan_type
if ncobj.ScanType == 2:
scan_type = 'ppi'
else:
scan_type = 'rhi'
# fixed_angle
fixed_angle = filemetadata('fixed_angle')
if ncobj.ScanType == 2:
sweep_el = ncvars['Elevation'][0]
else:
sweep_el = ncvars['Azimuth'][0]
fixed_angle['data'] = np.array([sweep_el], dtype='float32')
# elevation
elevation = filemetadata('elevation')
elevation['data'] = ncvars['Elevation']
# range
_range = filemetadata('range')
# check that the gate spacing is constant between sweeps
rstart = ncvars['StartRange'][:]
if any(rstart != rstart[0]):
raise ValueError('range start changes between sweeps')
rscale = ncvars['GateWidth'][:]/1000.
if any(rscale != rscale[0]):
raise ValueError('range scale changes between sweeps')
nbins = ncobj.NumGates
_range['data'] = (np.arange(nbins, dtype='float32') * rscale[0] +
rstart[0] * 1000.)
_range['meters_to_center_of_first_gate'] = rstart[0]
_range['meters_between_gates'] = float(rscale[0])
# azimuth
azimuth = filemetadata('azimuth')
azimuth['data'] = ncvars['Azimuth'][:]
# time
_time = filemetadata('time')
start_time = datetime.datetime.utcfromtimestamp(ncobj.Time)
_time['units'] = make_time_unit_str(start_time)
_time['data'] = (ncvars['Time']-ncobj.Time).astype('float32')
# fields
# all variables with dimensions of 'Radial', 'Gate' are fields
keys = [k for k, v in ncvars.items()
if v.dimensions == ('Radial', 'Gate')]
fields = {}
for key in keys:
field_name = filemetadata.get_field_name(key)
if field_name is None:
if exclude_fields is not None and key in exclude_fields:
continue
if include_fields is not None:
if not key in include_fields:
continue
field_name = key
fields[field_name] = _ncvar_to_dict(ncvars[key])
# instrument_parameters
instrument_parameters = None
return Radar(
_time, _range, fields, metadata, scan_type,
latitude, longitude, altitude,
sweep_number, sweep_mode, fixed_angle, sweep_start_ray_index,
sweep_end_ray_index,
azimuth, elevation,
instrument_parameters=instrument_parameters)
def _ncvar_to_dict(ncvar):
""" Convert a NetCDF Dataset variable to a dictionary. """
# copy all attribute except for scaling parameters
d = dict((k, getattr(ncvar, k)) for k in ncvar.ncattrs()
if k not in ['scale_factor', 'add_offset'])
d['data'] = ncvar[:]
if np.isscalar(d['data']):
# netCDF4 1.1.0+ returns a scalar for 0-dim array, we always want
# 1-dim+ arrays with a valid shape.
d['data'] = np.array(d['data'])
d['data'].shape = (1, )
return d