"""Towerpy: an open-source toolbox for processing polarimetric radar data."""
import warnings
import numpy as np
from ..datavis import rad_display
warnings.filterwarnings("ignore", category=RuntimeWarning)
[docs]
class SNR_Classif:
r"""
A class to compute the Signal-to-Noise Ratio on radar data.
Attributes
----------
elev_angle : float
Elevation angle at which the scan was taken, in deg.
file_name : str
Name of the file containing radar data.
scandatetime : datetime
Date and time of scan.
site_name : str
Name of the radar site.
min_snr : float
Reference noise value.
snr_class : dict
Results of the SNR method.
vars : dict
Radar variables with noise removed.
"""
def __init__(self, radobj=None):
[docs]
self.elev_angle = getattr(radobj, 'elev_angle',
None) if radobj else None
[docs]
self.file_name = getattr(radobj, 'file_name',
None) if radobj else None
[docs]
self.scandatetime = getattr(radobj, 'scandatetime',
None) if radobj else None
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self.site_name = getattr(radobj, 'site_name',
None) if radobj else None
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def signalnoiseratio(self, rad_georef, rad_params, rad_vars, min_snr=0,
rad_cst=None, snr_linu=False, data2correct=None,
classid=None, plot_method=False):
"""
Compute the SNR and discard data using a reference noise value.
Parameters
----------
rad_georef : dict
Georeferenced data containing descriptors of the azimuth, gates
and beam height, amongst others.
rad_params : dict
Radar technical details.
rad_vars : dict
Radar variables used to compute the SNR.
min_snr : float64, optional
Reference noise value. The default is 0.
data2correct : dict, optional
Variables into which noise is removed. The default is None.
plot_method : Bool, optional
Plot the SNR classification method. The default is False.
"""
self.echoesID = {'pcpn': 0,
'noise': 3}
if classid is not None:
self.echoesID.update(classid)
if rad_cst:
rc = rad_cst
else:
rc = rad_params['radar constant [dB]']
rh, _ = np.meshgrid(rad_georef['range [m]']/1000,
rad_georef['azim [rad]'])
snrc = rad_vars['ZH [dBZ]'] - 20*np.log10(rh) + rc
idx = np.nonzero(snrc >= min_snr)
snrclass = np.full(snrc.shape, np.nan)
snrclass[idx] = 1
snr = {'snrclass': snrclass, 'snr [dB]': snrc}
if snr_linu is True:
snrlu = 10 ** (0.1*snrc)
snr['snr [linear]'] = snrlu
if data2correct is not None:
rdatsnr = data2correct.copy()
for key in rdatsnr:
rdatsnr[key] = rdatsnr[key]*snrclass
self.vars = rdatsnr
snr['snrclass'][np.isnan(snr['snrclass'])] = self.echoesID['noise']
snr['snrclass'][snr['snrclass'] == 1] = self.echoesID['pcpn']
if plot_method:
rad_display.plot_snr(rad_georef, rad_params, snr, min_snr)
self.min_snr = min_snr
self.snr_class = snr
@staticmethod
[docs]
def static_signalnoiseratio(rad_georef, rad_params, rad_vars,
min_snr=0, rad_cst=None, snr_linu=False,
data2correct=None, plot_method=False):
"""
Compute the SNR (in dB) and discard data using a reference noise value.
Parameters
----------
rad_georef : dict
Georeferenced data containing descriptors of the azimuth, gates
and beam height, amongst others.
rad_params : dict
Radar technical details.
rad_vars : dict
Radar variables used to compute the SNR..
min_snr : float64, optional
Reference noise value. The default is 0.
data2correct : dict, optional
Variables into which noise is removed. The default is None.
plot_method : Bool, optional
Plot the SNR classification method. The default is False.
Returns
-------
Object containing the signal/noise classification.
"""
if rad_cst:
rc = rad_cst
else:
rc = rad_params['radar constant [dB]']
rh, _ = np.meshgrid(rad_georef['range [m]']/1000,
rad_georef['azim [rad]'])
snrc = rad_vars['ZH [dBZ]'] - 20*np.log10(rh) + rc
idx = np.nonzero(snrc >= min_snr)
snrclass = np.full(snrc.shape, np.nan)
snrclass[idx] = 1
snr = {'snrclass': snrclass, 'snr [dB]': snrc}
if snr_linu is True:
snrlu = 10 ** (0.1*snrc)
snr['snr [linear]'] = snrlu
if data2correct is not None:
rdatsnr = data2correct.copy()
for key in rdatsnr:
rdatsnr[key] = rdatsnr[key]*snrclass
return snr, rdatsnr
return snr
# =============================================================================
# %% xarray implementation
# =============================================================================
[docs]
def signal2noiseratio(Z, rng_km, rc, scale="db"):
"""
Compute signal-to-noise ratio (SNR).
Parameters
----------
Z : array_like or xarray.DataArray
Reflectivity factor [dBZ].
rng_km : array_like or xarray.DataArray
Range [km].
rc : float
Radar constant [dB].
scale : {"db", "lin", "both"}, optional
Output format:
- "db" : return SNR in dB (default)
- "lin" : return SNR in linear scale
- "both" : return dict with both
Returns
-------
snr : ndarray, DataArray, or dict
Depending on `scale`:
- "db" → snr_db
- "lin" → snr_lin
- "both" → {"snr_db": snr_db, "snr_lin": snr_lin}
"""
if scale == "db":
return Z - 20.0 * np.log10(rng_km) + rc
elif scale == "lin":
snr_db = Z - 20.0 * np.log10(rng_km) + rc
return 10.0 ** (snr_db / 10.0)
elif scale == "both":
snr_db = Z - 20.0 * np.log10(rng_km) + rc
snr_lin = 10.0 ** (snr_db / 10.0)
return {"snr_db": snr_db, "snr_lin": snr_lin}
else: raise ValueError(f"Unknown scale '{scale}',"
" expected 'db', 'lin', or 'both'.")
