Source code for pyart.aux_io.noxp_iphex_nc

Routines for reading IPHEx NOXP 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

# Only providing updated names for the most common fields.
# Many more fields in the files, and these are read in but not renamed.
    # uncorrected reflectivity, horizontal
    'ZH': 'reflectivity',
    # uncorrected differential reflectivity
    'ZDR': 'differential_reflectivity',
    'RHOHV': 'cross_correlation_ratio',
    'KDP': 'differential_phase',
    'KDPM': 'specific_differential_phase',
    'VR': 'velocity',
    'SVR': 'spectrum_width',

[docs]def read_noxp_iphex_nc(filename, field_names=None, additional_metadata=None, file_field_names=False, exclude_fields=None, **kwargs): """ Read a NOXP IPHEX netCDF file. Parameters ---------- filename : str Name of the netCDF file to read. field_names : dict, optional Dictionary mapping netCDF 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 netCDF 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. Returns ------- radar : Radar Radar object containing data from netCDF 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 # Getting import error on this function, skipping for now. # Only issues a warning anyway. # _test_arguments(kwargs) # create metadata retrieval object if field_names is None: field_names = NOXP_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(ncvars['Latitude']) longitude['data'] = np.array(ncvars['Longitude']) altitude['data'] = np.array(ncvars['Altitude']) # metadata metadata = filemetadata('metadata') metadata['source'] = 'NOAA NSSL' metadata['original_container'] = 'noxp_iphex_nc' metadata['system'] = ncobj.Radar # 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['AZ'][:]) 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') scan_type = getattr(ncobj, 'Scan type').lower() if scan_type in ['ppi', 'rhi']: sweep_mode['data'] = np.array(nsweeps * ['manual_' + scan_type]) else: # Guessing that if not RHI or PPI, then a pointing scan sweep_mode['data'] = np.array(nsweeps * ['pointing']) # fixed_angle fixed_angle = filemetadata('fixed_angle') if scan_type == 'rhi': sweep_el = ncvars['AZ'][0] else: sweep_el = ncvars['EL'][0] fixed_angle['data'] = np.array([sweep_el], dtype='float32') # elevation elevation = filemetadata('elevation') elevation['data'] = np.array(ncvars['EL']) # range _range = filemetadata('range') rng = 1000.0 * ncvars['Range'][:].T _range['data'] = rng[0] _range['meters_to_center_of_first_gate'] = np.mean(rng[0][0:2]) # Gate spacing mostly but not completely constant _range['meters_between_gates'] = np.median(np.diff(_range['data'])) _range['spacing_is_constant'] = 0 # azimuth azimuth = filemetadata('azimuth') azimuth['data'] = np.array(ncvars['AZ']) # time # Basing everything around 6/3/2014 because better than 1/0/0000 # for datetime module. _time = filemetadata('time') frac_since_basetime = ncvars['time'][0] - 735762.0 start_time = \ datetime.datetime(2014, 6, 3) + datetime.timedelta(frac_since_basetime) _time['units'] = make_time_unit_str(start_time) _time['data'] = 3600.0 * 24.0 * \ (ncvars['time'][:]-ncvars['time'][0]).astype('float32') # fields # nearly all variables w/ dimensions of 'Gate', 'Time' are fields keys = [k for k, v in ncvars.items() if v.dimensions == ('Gate', 'Time') and k not in ['Range', 'Distance', 'Height']] 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] = _ncvar_to_dict(ncvars[key]) # instrument_parameters instrument_parameters = {} prt = filemetadata('prt') prt['data'] = 1.0 / float(ncobj.PRF[0:5]) instrument_parameters['prt'] = prt frequency = filemetadata('frequency') frequency['frequency'] = float(ncobj.Frequency[0:4]) * 10e9 instrument_parameters['frequency'] = frequency hits = filemetadata('n_samples') hits['data'] = ncobj.Hits instrument_parameters['hits'] = hits # Go get the radar object! 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[:].T # Data originally stored as (gate, azimuth) 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