"""
Routines for reading IPHEx NOXP files.
"""
import datetime
import netCDF4
import numpy as np
from ..config import FileMetadata
from ..core.radar import Radar
from ..io.common 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.
NOXP_FIELD_NAMES = {
# 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 pyart.io
# * unit test
# * add default field mapping, etc to default config
# * auto-detect file type with pyart.io.read 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