Source code for pyart.aux_io.d3r_gcpex_nc

Routines for reading GCPEX D3R files.


import datetime

import netCDF4
import numpy as np

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

    # 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 [295.0] 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"][:] / 1000.0 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] * 1000.0 _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 key not 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 = { 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