def kdp_ukmo(radar,
phidpfield='uPhiDP',
rhohvfield='RhoHV',
FILTER_BINS_1=11,
FILTER_BINS_2=9,
METEO_THRESH=0.7,
RAIN_THRESH=0.85,
SMOOTH_BINS_1=5,
SMOOTH_BINS_2=3):
print "In kdp_ukmo"
phidp = copy.deepcopy(radar.fields[phidpfield]['data'])
rhohv = copy.deepcopy(radar.fields[rhohvfield]['data'])
rays = copy.deepcopy(radar.nrays)
bins = copy.deepcopy(radar.ngates)
binlength = radar.range['data'][1] - radar.range['data'][0]
#print 'Beginning KDP estimation'
# flags = np.where(rad2.fields['classification']['data']==1,0,1)
flags = np.zeros((rays, bins))
# generate non-meteo mask:
#print 'generating non-meteo mask ...'
(meteoMask) = kdpfun.generate_meteo_mask(rays, bins, flags, rhohv,
METEO_THRESH)
radar.add_field_like('uPhiDP', 'meteoMask', meteoMask)
# remove phi_dp wrap-around:
#print 'unwrapping phidp ...'
(phidp_unwrap) = kdpfun.unwrap_phidp(rays, bins, meteoMask, phidp)
# remove non-meteo data / filter phi_dp:
#print 'cleaning / filtering phi_dp ...'
(phidp_meteo) = kdpfun.clean_phidp(rays, bins, phidp_unwrap, meteoMask,
FILTER_BINS_1)
# generate non-rain mask:
#print 'generating non-rain mask ...'
(rainMask) = kdpfun.generate_rain_mask(rays, bins, rhohv, RAIN_THRESH)
# remove non-rain data / filter phi_dp:
#print 'removing non-rain components from phidp ...'
(phidp_rain) = kdpfun.clean_phidp(rays, bins, phidp_meteo, rainMask,
FILTER_BINS_2)
# smooth phi_dp (twice):
#print 'smoothing phi_dp ...'
(phidp_smooth) = kdpfun.smooth_data(rays, bins, phidp_rain, SMOOTH_BINS_1)
(phidp_smooth) = kdpfun.smooth_data(rays, bins, phidp_smooth,
SMOOTH_BINS_2)
radar.add_field_like('uPhiDP', 'sPhiDP', phidp_smooth)
# calculate kdpfun:
#print 'calculating kdpfun ...'
(kdp) = kdpfun.calc_kdp_v3(rays, bins, binlength, phidp_smooth, rainMask)
radar.add_field_like('KDP', 'KDP_UKMO',
np.ma.masked_array(data=kdp, mask=phidp.mask))
print 'KDP_UKMO added to radar object'
def altitude_parameter_averaging_qvp_original(radar, elevation, field, azimuth_exclude, verbose=False):
mask_field = field
expected_ele = elevation
try:
sweep = np.where(radar.elevation['data'] == expected_ele)[0][0] / (radar.nrays / radar.nsweeps)
except:
print('Elevation not in sweep', radar.time['units'])
zero_array = np.zeros((radar.fields[mask_field]['data'].data.shape[1],))
zero_array[:] = np.nan
timeofsweep = num2date(radar.time['data'][0],
radar.time['units'],
radar.time['calendar'])
return zero_array, zero_array, zero_array, zero_array, timeofsweep
else:
if radar.elevation['data'][radar.sweep_start_ray_index['data'][sweep]] == expected_ele:
#try:
My_pyart_functions.field_fill_to_nan(radar, mask_field)
#except:
# print('No fill value for:', mask_field)
try:
sweep_ind = (radar.sweep_start_ray_index['data'][sweep], radar.sweep_end_ray_index['data'][sweep])
sweep_data = radar.fields[mask_field]['data'][sweep_ind[0]:sweep_ind[1]]
timeofsweep = num2date(np.nanmean(radar.time['data'][sweep_ind[0]:sweep_ind[1]]),
radar.time['units'],
radar.time['calendar'])
if mask_field in ['dBuZ', 'dBZ', 'dBuZv', 'dBZv']:
sweep_data = np.power(10, sweep_data / 10.0)
# unfold uPhiDP values
# remove phi_dp wrap-around:
#print 'unwrapping phidp ...'
if mask_field in ['uPhiDP']:
rays = sweep_data.shape[0]
bins = sweep_data.shape[1]
# METEO_THRESH=0.7 OK for meteo QVP should be lower for Biodar
METEO_THRESH=0.7
# flags = np.where(rad2.fields['classification']['data']==1,0,1)
flags = np.zeros((rays, bins))
# generate non-meteo mask:
#print 'generating non-meteo mask ...'
rhohv = radar.fields['RhoHV']['data'][sweep_ind[0]:sweep_ind[1]]
(meteoMask) = kdpfun.generate_meteo_mask(rays, bins, flags, rhohv, METEO_THRESH)
sweep_data = kdpfun.unwrap_phidp(rays, bins, meteoMask, sweep_data)
summed = np.zeros(sweep_data.shape)
counted = np.zeros(sweep_data.shape)
mask = np.ma.masked_invalid(sweep_data).mask
inv_mask = np.where(mask, 0, 1)
summed = np.nansum([summed, sweep_data], axis=0)
counted = np.nansum([counted, inv_mask], axis=0)
summed = np.where(counted == 0, np.nan, summed)
counted = np.where(counted == 0, np.nan, counted)
mean_values = np.nanmean((summed / counted), axis=0)
std_values = np.nanstd((summed / counted), axis=0)
if mask_field in ['dBuZ', 'dBZ', 'dBuZv', 'dBZv']:#mask_field == 'dBuZ' or mask_field == 'dBZ':
mean_values = 10 * np.log10(mean_values)
std_values = 10 * np.log10(std_values)
observation_count = np.nansum(counted, axis=0)
if expected_ele == 90.0:
altitudes = radar.range['data']
else:
altitudes = radar.fields['scan_altitude']['data'][radar.sweep_start_ray_index['data'][sweep], :]
# clean up the nearest beans influenced by sidelobs
ranges=radar.range['data']
mean_values[ranges<400] = np.nan
return altitudes, observation_count, mean_values, std_values, timeofsweep
except:
print(radar.time, ' failed for unknown reason')
zero_array = np.zeros((radar.fields[mask_field]['data'].data.shape[1],))
zero_array[:] = np.nan
timeofsweep = num2date(radar.time['data'][0],
radar.time['units'],
radar.time['calendar'])
return zero_array, zero_array, zero_array, zero_array, timeofsweep
else:
print(radar.elevation['data'][radar.sweep_start_ray_index['data'][sweep]])
zero_array = np.zeros((radar.fields[mask_field]['data'].data.shape[1],))
zero_array[:] = np.nan
timeofsweep = num2date(radar.time['data'][0],
radar.time['units'],
radar.time['calendar'])
return zero_array, zero_array, zero_array, zero_array, timeofsweep
def altitude_parameter_meteo_averaging_qvp(radar, elevation, field, azimuth_exclude, METEO_THRESH=0.7):
mask_field = field
expected_ele = elevation
try:
sweep = np.where(radar.elevation['data'] == expected_ele)[0][0] / (radar.nrays / radar.nsweeps)
except:
print('Elevation not in sweep', radar.time['units'])
zero_array = np.zeros((radar.fields[mask_field]['data'].data.shape[1],))
zero_array[:] = np.nan
timeofsweep = num2date(radar.time['data'][0],
radar.time['units'],
radar.time['calendar'])
return zero_array, zero_array, zero_array, zero_array, timeofsweep
else:
if radar.elevation['data'][radar.sweep_start_ray_index['data'][sweep]] == expected_ele:
#try:
My_pyart_functions.field_fill_to_nan(radar, mask_field)
#except:
# print('No fill value for:', mask_field)
try:
sweep_ind = (radar.sweep_start_ray_index['data'][sweep], radar.sweep_end_ray_index['data'][sweep])
this_data = radar.fields[mask_field]['data'][sweep_ind[0]:sweep_ind[1]]
sweep_data = np.fill_like(this_data,np.nan)
timeofsweep = num2date(np.nanmean(radar.time['data'][sweep_ind[0]:sweep_ind[1]]),
radar.time['units'],
radar.time['calendar'])
rays = sweep_data.shape[0]
bins = sweep_data.shape[1]
flags = np.zeros((rays, bins))
rhohv = radar.fields['RhoHV']['data'][sweep_ind[0]:sweep_ind[1]]
(meteoMask) = kdpfun.generate_meteo_mask(rays, bins, flags, rhohv, METEO_THRESH)
snrH = radar.fields['SNR']['data'][sweep_ind[0]:sweep_ind[1]]
snrV = radar.fields['SNRv']['data'][sweep_ind[0]:sweep_ind[1]]
snrMask = np.fill_like(meteoMask,0)
snrMask[(snrH > 8) & (snrV > 8)] - 1
sweep_data[(meteoMask == 1) & (snrMask == 1)] = this_data[(meteoMask == 1) & (snrMask == 1)]
if mask_field in ['dBuZ', 'dBZ', 'dBuZv', 'dBZv']:
sweep_data[(sweep_data < -10) | (sweep_data > 60)] = np.nan
sweep_data = np.power(10, sweep_data / 10.0)
# unfold uPhiDP values
# remove phi_dp wrap-around:
#print 'unwrapping phidp ...'
elif mask_field in ['uPhiDP']:
sweep_data = kdpfun.unwrap_phidp(rays, bins, meteoMask, sweep_data)
elif mask_field in ['ZDR']:
sweep_data[(sweep_data < -1.5) | (sweep_data > 5)] = np.nan
summed = np.zeros(sweep_data.shape)
counted = np.zeros(sweep_data.shape)
mask = np.ma.masked_invalid(sweep_data).mask
inv_mask = np.where(mask, 0, 1)
summed = np.nansum([summed, sweep_data], axis=0)
counted = np.nansum([counted, inv_mask], axis=0)
summed = np.where(counted == 0, np.nan, summed)
counted = np.where(counted == 0, np.nan, counted)
mean_values = np.nanmean((summed / counted), axis=0)
std_values = np.nanstd((summed / counted), axis=0)
if mask_field == 'dBuZ' or mask_field == 'dBZ':
mean_values = 10 * np.log10(mean_values)
std_values = 10 * np.log10(std_values)
observation_count = np.nansum(counted, axis=0)
if expected_ele == 90.0:
altitudes = radar.range['data']
else:
altitudes = radar.fields['scan_altitude']['data'][radar.sweep_start_ray_index['data'][sweep], :]
return altitudes, observation_count, mean_values, std_values, timeofsweep
except:
print(radar.time, ' failed for unknown reason')
zero_array = np.zeros((radar.fields[mask_field]['data'].data.shape[1],))
zero_array[:] = np.nan
timeofsweep = num2date(radar.time['data'][0],
radar.time['units'],
radar.time['calendar'])
return zero_array, zero_array, zero_array, zero_array, timeofsweep
else:
print(radar.elevation['data'][radar.sweep_start_ray_index['data'][sweep]])
zero_array = np.zeros((radar.fields[mask_field]['data'].data.shape[1],))
zero_array[:] = np.nan
timeofsweep = num2date(radar.time['data'][0],
radar.time['units'],
radar.time['calendar'])
return zero_array, zero_array, zero_array, zero_array, timeofsweep
def altitude_parameter_averaging_qvp(radar, elevation, field, azimuth_exclude, verbose=False):
if verbose:
print "in altitude_parameter_averaging_qvp"
#print "azimuth_exclude"
#print azimuth_exclude
#azimuth_exclude = [45,185]
mask_field = field
expected_ele = elevation
try:
sweep = np.where(radar.elevation['data'] == expected_ele)[0][0] / (radar.nrays / radar.nsweeps)
except:
print('Elevation not in sweep', radar.time['units'])
zero_array = np.zeros((radar.fields[mask_field]['data'].data.shape[1],))
zero_array[:] = np.nan
timeofsweep = num2date(radar.time['data'][0],
radar.time['units'],
radar.time['calendar'])
return zero_array, zero_array, zero_array, zero_array, timeofsweep
else:
if radar.elevation['data'][radar.sweep_start_ray_index['data'][sweep]] == expected_ele:
try:
My_pyart_functions.field_fill_to_nan(radar, mask_field)
except:print('No fill value for:', mask_field)
#try:
sweep_ind = (radar.sweep_start_ray_index['data'][sweep], radar.sweep_end_ray_index['data'][sweep])
#print "sweep_ind.shape = {}".format(sweep_ind.shape)
#print sweep_ind
azimuth_data = radar.azimuth['data'][sweep_ind[0]:sweep_ind[1]]
#print " azimuth_data np.where(azimuth_data>=45&azimuth_data<=185)"
#print azimuth_data
#print np.where((azimuth_data==azimuth_exclude))
sweep_data = radar.fields[mask_field]['data'][sweep_ind[0]:sweep_ind[1]]
#sweep_data[np.where((azimuth_data>=45)&(azimuth_data<=185))[0]] = np.nan
#print "sweep_data.shape = {}".format(sweep_data.shape)
timeofsweep = num2date(np.nanmean(radar.time['data'][sweep_ind[0]:sweep_ind[1]]),
radar.time['units'],
radar.time['calendar'])
if mask_field in ['dBuZ', 'dBZ', 'dBuZv', 'dBZv']:
sweep_data = np.power(10, sweep_data / 10.0)
# unfold uPhiDP values
# remove phi_dp wrap-around:
#print 'unwrapping phidp ...'
if mask_field in ['uPhiDP']:
rays = sweep_data.shape[0]
bins = sweep_data.shape[1]
METEO_THRESH=0.7
# flags = np.where(rad2.fields['classification']['data']==1,0,1)
flags = np.zeros((rays, bins))
# generate non-meteo mask:
#print 'generating non-meteo mask ...'
rhohv = radar.fields['RhoHV']['data'][sweep_ind[0]:sweep_ind[1]]
(meteoMask) = kdpfun.generate_meteo_mask(rays, bins, flags, rhohv, METEO_THRESH)
sweep_data = kdpfun.unwrap_phidp(rays, bins, meteoMask, sweep_data)
summed = np.zeros(sweep_data.shape)
counted = np.zeros(sweep_data.shape)
#rays = sweep_data.shape[0]
#bins = sweep_data.shape[1]
#azimuth_mask = np.ones((rays, bins))
#azimuth_mask[0:azimuth_exclude[0],:] = 0
#azimuth_mask[azimuth_exclude[1]:360,:] = 0
#print radar
#sweep_data[np.where(azimuth_data[sweep_ind[0]:sweep_ind[1]] in azimuth_exclude)[0]] = np.nan
# print "EXCLUDE azimuthes"
# print "azimuth_data"
# print azimuth_data.astype(int)
# print len(azimuth_data)
# print "np.isin(azimuth_data,azimuth_exclude)"
# print np.isin(azimuth_data.astype(int),azimuth_exclude)
# #print "azimuth_exclude"
# #print azimuth_exclude
# print "np.where(np.isin(azimuth_data.astype(int),azimuth_exclude)==True)"
# print np.where(np.isin(azimuth_data.astype(int),azimuth_exclude)==True)
# print len(np.where(np.isin(azimuth_data.astype(int),azimuth_exclude)==True))
# print "sweep_data"
# print sweep_data
#print "np.where(sweep_data != np.nan)"
#print np.where(sweep_data != np.nan)
#print "len(np.where(sweep_data != np.nan)[0])"
#print len(np.where(sweep_data != np.nan)[0])
#print "sweep_data[np.where(sweep_data != np.nan)]"
#print sweep_data[np.where(sweep_data != np.nan)]
# print "sweep_data[0,0]"
#print sweep_data[0,0]
#print "np.isnan(sweep_data[0,0])"
#print np.isnan(sweep_data[0,0])
#print "sweep_data[0,0]==float('nan')"
#print sweep_data[0,0]==float('NaN')
#print "np.isnan(sweep_data)"
#print np.isnan(sweep_data)
#print "sweep_data[np.isfinite(sweep_data)]"
#print sweep_data[np.isfinite(sweep_data)]
#print len(sweep_data[np.isfinite(sweep_data)])
#print "sweep_data[0,0] = np.nan"
#sweep_data[0,0] = np.nan
#print "np.isnan(sweep_data[0,0])"
#print np.isnan(sweep_data[0,0])
#print "sweep_data[0,0]==float('nan')"
#print sweep_data[0,0]==float('nan')
#print sweep_data[0,0]== np.nan
#print "np.where(np.isnan(sweep_data))"
#print np.where(sweep_data != NaN)
#print "len(np.where(sweep_data != float('nan'))[0])"
#print len(np.where(sweep_data != float('nan'))[0])
#print "sweep_data[np.where(sweep_data != float('nan'))]"
#print sweep_data[np.where(sweep_data != float('nan'))]
#print "sweep_data[np.where(np.isin(azimuth_data.astype(int),azimuth_exclude)==True)]"
in_excluded = sweep_data[np.where(np.isin(azimuth_data.astype(int),azimuth_exclude)==True)[0],:]
#print in_excluded[np.isfinite(in_excluded)]
#print len(in_excluded[np.isfinite(in_excluded)])
#print "in_excluded.shape"
#print in_excluded.shape
#print "np.tile(in_excluded,(sweep_data.shape[1],1))"
#print np.tile(np.isin(azimuth_data.astype(int),azimuth_exclude).reshape(len(azimuth_data),1),(1,sweep_data.shape[1]))
#print np.tile(np.isin(azimuth_data.astype(int),azimuth_exclude).reshape(len(azimuth_data),1),(1,sweep_data.shape[1])).shape
#print "sweep_data.shape[1]"
#print sweep_data.shape[1]
azimuth_mask = np.tile(np.isin(azimuth_data.astype(int),azimuth_exclude).reshape(len(azimuth_data),1),(1,sweep_data.shape[1]))
#print "sweep_data[np.where(np.isin(azimuth_data.astype(int),azimuth_exclude)==False)]"
#out_excluded = sweep_data[np.where(np.isin(azimuth_data.astype(int),azimuth_exclude)==False)[0],:]
#print out_excluded[np.isfinite(out_excluded)]
#print len(out_excluded[np.isfinite(out_excluded)])
sweep_data = np.ma.masked_where(azimuth_mask,sweep_data)
#print "np.where(azimuth_data[sweep_ind[0]:sweep_ind[1]] in azimuth_exclude)[0] = {}".format(np.where(azimuth_data[sweep_ind[0]:sweep_ind[1]] in azimuth_exclude))
#print "np.where(azimuth_data[sweep_ind[0]:sweep_ind[1]] in azimuth_exclude)[0] len = {}".format(len(np.where(azimuth_data[sweep_ind[0]:sweep_ind[1]] in azimuth_exclude)[0]))
#print "inv_mask = {}".format(inv_mask)
mask = np.ma.masked_invalid(sweep_data).mask
inv_mask = np.where(mask, 0, 1)
#print "np.where(inv_mask==1) = {}".format(len(np.where(inv_mask==1)[1]))
#print "np.where(inv_mask==0) = {}".format(len(np.where(inv_mask==0)[1]))
summed = np.nansum([summed, sweep_data], axis=0)
#print "summed"
#print summed
counted = np.nansum([counted, inv_mask], axis=0)
#print "counted"
#print counted
summed = np.where(counted == 0, np.nan, summed)
counted = np.where(counted == 0, np.nan, counted)
mean_values = np.nanmean((summed / counted), axis=0)
std_values = np.nanstd((summed / counted), axis=0)
if mask_field in ['dBuZ', 'dBZ', 'dBuZv', 'dBZv']:#mask_field == 'dBuZ' or mask_field == 'dBZ':
mean_values = 10 * np.log10(mean_values)
std_values = 10 * np.log10(std_values)
observation_count = np.nansum(counted, axis=0)
if expected_ele == 90.0:
altitudes = radar.range['data']
else:
altitudes = radar.fields['scan_altitude']['data'][radar.sweep_start_ray_index['data'][sweep], :]
# clean up the nearest beans influenced by sidelobs
ranges=radar.range['data']
mean_values[ranges<400] = np.nan
return altitudes, observation_count, mean_values, std_values, timeofsweep
# except:
# print(radar.time, ' failed for unknown reason')
# zero_array = np.zeros((radar.fields[mask_field]['data'].data.shape[1],))
# zero_array[:] = np.nan
# timeofsweep = num2date(radar.time['data'][0],
# radar.time['units'],
# radar.time['calendar'])
# return zero_array, zero_array, zero_array, zero_array, timeofsweep
else:
print(radar.elevation['data'][radar.sweep_start_ray_index['data'][sweep]])
zero_array = np.zeros((radar.fields[mask_field]['data'].data.shape[1],))
zero_array[:] = np.nan
timeofsweep = num2date(radar.time['data'][0],
radar.time['units'],
radar.time['calendar'])