Source code for pyart.aux_io.noxp_iphex_nc

Routines for reading IPHEx NOXP files.


import datetime

import netCDF4
import numpy as np

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

# 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, include_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 key not 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 = { 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