Source code for pyart.aux_io.d3r_gcpex_nc

Routines for reading GCPEX D3R files.


import datetime

import numpy as np
import netCDF4

from ..config import FileMetadata
from import make_time_unit_str, _test_arguments
from ..core.radar import Radar

    # 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 # * unit test # * add default field mapping, etc to default config # * auto-detect file type with 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