Source code for pyart.aux_io.arm_vpt

Routines for reading ARM vertically-pointing radar ingest (e.g., a1) files.
These files are characterized by being NetCDF files that do not fully conform
to the CF/Radial convention. Nonetheless this module borrows heavily from the
existing CF/Radial module.


import netCDF4
import numpy as np

from import cfradial
from ..config import FileMetadata
from ..core.radar import Radar

[docs]def read_kazr(filename, field_names=None, additional_metadata=None, file_field_names=False, exclude_fields=None, include_fields=None): """ Read K-band ARM Zenith Radar (KAZR) NetCDF ingest data. Parameters ---------- filename : str Name of NetCDF file to read data from. field_names : dict, optional Dictionary mapping field names in the file names to radar field names. Unlike other read functions, fields not in this dictionary or having a value of None are still included in the radar.fields dictionary, to exclude them use the `exclude_fields` parameter. Fields which are mapped by this dictionary will be renamed from key to value. additional_metadata : dict of dicts, optional This parameter is not used, it is included for uniformity. file_field_names : bool, optional True to force the use of the field names from the file in which case the `field_names` parameter is ignored. False will use to `field_names` parameter to rename fields. 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. Returns ------- radar : Radar Radar object. """ # create metadata retrieval object filemetadata = FileMetadata( 'cfradial', field_names, additional_metadata, file_field_names, exclude_fields, include_fields) # read the data ncobj = netCDF4.Dataset(filename) ncvars = ncobj.variables # 4.1 Global attribute -> move to metadata dictionary metadata = dict([(k, getattr(ncobj, k)) for k in ncobj.ncattrs()]) metadata['n_gates_vary'] = 'false' # 4.2 Dimensions (do nothing) # 4.3 Global variable -> move to metadata dictionary if 'volume_number' in ncvars: metadata['volume_number'] = int(ncvars['volume_number'][:]) else: metadata['volume_number'] = 0 global_vars = {'platform_type': 'fixed', 'instrument_type': 'radar', 'primary_axis': 'axis_z'} # ignore time_* global variables, these are calculated from the time # variable when the file is written. for var, default_value in global_vars.items(): if var in ncvars: metadata[var] = str(netCDF4.chartostring(ncvars[var][:])) else: metadata[var] = default_value # 4.4 coordinate variables -> create attribute dictionaries time = cfradial._ncvar_to_dict(ncvars['time']) _range = cfradial._ncvar_to_dict(ncvars['range']) # 4.5 Ray dimension variables # 4.6 Location variables -> create attribute dictionaries # the only difference in this section to cfradial.read_cfradial is the # minor variable name differences: # latitude -> lat # longitude -> lon # altitdue -> alt latitude = cfradial._ncvar_to_dict(ncvars['lat']) longitude = cfradial._ncvar_to_dict(ncvars['lon']) altitude = cfradial._ncvar_to_dict(ncvars['alt']) # 4.7 Sweep variables -> create atrribute dictionaries # this is the section that needed the most work since the initial NetCDF # file did not contain any sweep information sweep_number = filemetadata('sweep_number') sweep_number['data'] = np.array([0], dtype=np.int32) sweep_mode = filemetadata('sweep_mode') sweep_mode['data'] = np.array(['vertical_pointing'], dtype=np.str) fixed_angle = filemetadata('fixed_angle') fixed_angle['data'] = np.array([90.0], dtype=np.float32) sweep_start_ray_index = filemetadata('sweep_start_ray_index') sweep_start_ray_index['data'] = np.array([0], dtype=np.int32) sweep_end_ray_index = filemetadata('sweep_end_ray_index') sweep_end_ray_index['data'] = np.array( [ncvars['time'].size - 1], dtype=np.int32) # first sweep mode determines scan_type # this module is specific to vertically-pointing data scan_type = 'vpt' # 4.8 Sensor pointing variables -> create attribute dictionaries # this section also required some changes since the initial NetCDF did not # contain any sensor pointing variables azimuth = filemetadata('azimuth') azimuth['data'] = 0.0 * np.ones(ncvars['time'].size, dtype=np.float32) elevation = filemetadata('elevation') elevation['data'] = 90.0 * np.ones(ncvars['time'].size, dtype=np.float32) # 4.9 Moving platform geo-reference variables # 4.10 Moments field data variables -> field attribute dictionary # all variables with dimensions of 'time', 'range' are fields keys = [k for k, v in ncvars.items() if v.dimensions == ('time', 'range')] 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 and not key in include_fields: continue field_name = key fields[field_name] = cfradial._ncvar_to_dict(ncvars[key]) # 4.5 instrument_parameters sub-convention -> instrument_parameters dict # this section needed multiple changes and/or additions since the # instrument parameters were primarily located in the global attributes # this section is likely still incomplete omega = float(ncobj.radar_operating_frequency.split()[0]) frequency = filemetadata('frequency') frequency['data'] = np.array([omega / 1e9], dtype=np.float32) prt_mode = filemetadata('prt_mode') prt_mode['data'] = np.array(['fixed'], dtype=np.str) prf = float(ncobj.pulse_repetition_frequency.split()[0]) prt = filemetadata('prt') prt['data'] = (1.0 / prf) * np.ones(ncvars['time'].size, dtype=np.float32) v_nq = float(ncobj.nyquist_velocity.split()[0]) nyquist_velocity = filemetadata('nyquist_velocity') nyquist_velocity['data'] = v_nq * np.ones(ncvars['time'].size, dtype=np.float32), samples = int(ncobj.num_spectral_averages) n_samples = filemetadata('n_samples') n_samples['data'] = samples * np.ones(ncvars['time'].size, dtype=np.int32) # 4.6 radar_parameters sub-convention -> instrument_parameters dict # this section needed multiple changes and/or additions since the # radar instrument parameters were primarily located in the global # attributes # this section is likely still incomplete instrument_parameters = { 'frequency': frequency, 'prt_mode': prt_mode, 'prt': prt, 'nyquist_velocity': nyquist_velocity, 'n_samples': n_samples, } # 4.7 lidar_parameters sub-convention -> skip # 4.8 radar_calibration sub-convention -> skip # close NetCDF object ncobj.close() 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)