"""
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 ..io 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)