def main():
"""
"""
# parse the arguments
parser = argparse.ArgumentParser(
description='Entry to Pyrad processing framework')
# keyword arguments
parser.add_argument('--database',
type=str,
default='/store/msrad/radar/pyrad_products/',
help='base path to the radar data')
parser.add_argument(
'--datadirs',
type=str,
default=(
'mals_sha_windmills_point_psr_filtered_WM1_20200304-20200311,'
'mals_sha_windmills_point_psr_filtered_WM1_20200312-20200315,'
'mals_sha_windmills_point_psr_filtered_WM1_20200316-20200320,'
'mals_sha_windmills_point_psr_filtered_WM1_20200321-20200325'),
help='directories containing data')
parser.add_argument(
'--datatypes',
type=str,
default='dBuZ,dBuZv,rcs_h,rcs_v,uPhiDPu,RhoHVu,ZDRu,Vu,Wu',
help='Data types. Coma separated')
parser.add_argument(
'--orientations',
type=str,
default=
'0,10,20,30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180,190,200,210,220,230,240,250,260,270,280,290,300,310,320,330,340,350',
help='Orientation respect to radar')
parser.add_argument('--span', type=float, default=10., help='Span')
parser.add_argument('--vel_limit',
type=float,
default=0.,
help='Velocity limit')
args = parser.parse_args()
print("====== PYRAD windmill data processing started: %s" %
datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S"))
atexit.register(_print_end_msg,
"====== PYRAD windmill data processing finished: ")
datadirs = args.datadirs.split(',')
datatypes = args.datatypes.split(',')
orientations = np.asarray(args.orientations.split(','), dtype=float)
speeds = [
'speed_GT' + str(args.vel_limit), 'speed_LE' + str(args.vel_limit)
]
scan_type = 'ppi'
for ori in orientations:
for speed in speeds:
for datatype in datatypes:
first_read = False
for datadir in datadirs:
# Read data time series files
flist = glob.glob(args.database + datadir + '/' +
datatype + '_TS/TS/' + datatype +
'_span' + str(args.span) + '_ori' +
str(ori) + '_' + speed + '_hist.csv')
if not flist:
continue
hist_aux, bin_edges_aux = read_histogram(flist[0])
if not first_read:
hist = hist_aux
bin_edges = bin_edges_aux
first_read = True
continue
hist += hist_aux
if not first_read:
warn('No files for orientation ' + str(ori) + ' and ' +
speed)
continue
# Histogram plots
field_name = get_fieldname_pyart(datatype)
field_dict = get_metadata(field_name)
fname = (args.database + datatype + '_span' + str(args.span) +
'_ori' + str(ori) + '_' + speed + '_hist.png')
titl = (datatype + ' span ' + str(args.span) + ' ori ' +
str(ori) + ' ' + speed)
bin_centers = bin_edges[:-1] + (
(bin_edges[1] - bin_edges[0]) / 2.)
fname = plot_histogram2(bin_centers,
hist, [fname],
labelx=get_colobar_label(
field_dict, field_name),
titl=titl)
print('Plotted ' + ' '.join(fname))
fname = (args.database + datatype + '_span' + str(args.span) +
'_ori' + str(ori) + '_' + speed + '_hist.csv')
fname = write_histogram(bin_edges, hist, fname)
print('Written ' + fname)
def main():
"""
"""
# parse the arguments
parser = argparse.ArgumentParser(
description='Entry to Pyrad processing framework')
# keyword arguments
parser.add_argument('--database',
type=str,
default='/store/msrad/radar/pyrad_products/',
help='base path to the radar data')
parser.add_argument(
'--datadirs',
type=str,
default=(
'mals_sha_windmills_point_psr_filtered_WM1_20200304-20200311,'
'mals_sha_windmills_point_psr_filtered_WM1_20200312-20200315,'
'mals_sha_windmills_point_psr_filtered_WM1_20200316-20200320,'
'mals_sha_windmills_point_psr_filtered_WM1_20200321-20200325'),
help='directories containing data')
parser.add_argument(
'--datatypes',
type=str,
default='dBuZ,dBuZv,rcs_h,rcs_v,ZDRu,RhoHVu,uPhiDPu,Vu,Wu',
help='Data types. Coma separated')
args = parser.parse_args()
print("====== PYRAD windmill data processing started: %s" %
datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S"))
atexit.register(_print_end_msg,
"====== PYRAD windmill data processing finished: ")
datadirs = args.datadirs.split(',')
datatypes = args.datatypes.split(',')
# Read periods of processing
for datatype in datatypes:
first_read = False
for datadir in datadirs:
# Read data time series files
flist = glob.glob(args.database + datadir + '/' + datatype +
'_TS/TS/ts_POINT_MEASUREMENT_hist_' + datatype +
'.csv')
if not flist:
continue
hist_aux, bin_edges_aux = read_histogram(flist[0])
if not first_read:
hist = hist_aux
bin_edges = bin_edges_aux
first_read = True
continue
hist += hist_aux
basepath = os.path.dirname(flist[0]) + '/'
# Histogram plots
field_name = get_fieldname_pyart(datatype)
field_dict = get_metadata(field_name)
fname = args.database + 'ts_POINT_MEASUREMENT_hist_' + datatype + '.png'
bin_centers = bin_edges[:-1] + ((bin_edges[1] - bin_edges[0]) / 2.)
fname = plot_histogram2(bin_centers,
hist, [fname],
labelx=get_colobar_label(
field_dict, field_name),
titl=datatype)
print('Plotted ' + ' '.join(fname))
fname = args.database + 'ts_POINT_MEASUREMENT_hist_' + datatype + '.csv'
fname = write_histogram(bin_edges, hist, fname)
print('Written ' + fname)
def main():
"""
"""
# basepath = '/data/pyrad_products/rad4alp_hydro_PHA/'
basepath = '/store/msrad/radar/pyrad_products/rad4alp_hydro_PHA/data_analysis_min10sources/'
print("====== Lightning post-processing started: %s" %
datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S"))
atexit.register(_print_end_msg,
"====== Lightning post-processing finished: ")
# prefix = ['All', 'no_CG', 'CGt']
# dir = ['all_data/', 'no_CG/', 'CGt/']
prefix = ['CGt', 'CGn', 'CGp']
dir = ['CGt/', 'CGn/', 'CGp/']
sources = ['allsources', 'firstsource']
titles = ['Value at VHF source location', 'Value at flash origin location']
datatypes = [
'dBZc', 'entropy', 'hydro', 'hydro_prop', 'KDPc', 'nhydro', 'RhoHVc',
'TEMP', 'ZDRc'
]
labels = [
'horizontal reflectivity [dBZ]', 'entropy [-]',
'radar echo classification [-]', 'proportion of hydrometeors [%]',
'specific differential phase [deg/km]',
'Number of hydrometeor in radar gate',
'copolar correlation coefficient [-]', 'temperature [deg Celsius]',
'differential reflectivity [dB]'
]
# sources = ['', '_first_source']
# titles = ['Value at VHF source location', 'Value at flash origin location']
# datatypes = ['alt', 'dBm']
# labels = [
# 'VHF source altitude [m MSL]', 'VHF source power [dBm]']
for source, titl in zip(sources, titles):
for datatype, labelx in zip(datatypes, labels):
hist1, bin_edges1 = read_histogram(basepath + dir[0] + prefix[0] +
'_' + source +
'_ts_trajlightning_' +
datatype + '.csv')
hist2, bin_edges2 = read_histogram(basepath + dir[1] + prefix[1] +
'_' + source +
'_ts_trajlightning_' +
datatype + '.csv')
hist3, bin_edges3 = read_histogram(basepath + dir[2] + prefix[2] +
'_' + source +
'_ts_trajlightning_' +
datatype + '.csv')
# hist1, bin_edges1 = read_histogram(
# basepath+dir[0]+prefix[0]+'_Santis_hist_'+datatype+source+'.csv')
# hist2, bin_edges2 = read_histogram(
# basepath+dir[1]+prefix[1]+'_Santis_hist_'+datatype+source+'.csv')
# hist3, bin_edges3 = read_histogram(
# basepath+dir[2]+prefix[2]+'_Santis_hist_'+datatype+source+'.csv')
if (not np.array_equal(bin_edges1, bin_edges2)
or not np.array_equal(bin_edges1, bin_edges3)):
warn('Bin edges should be identical to group histograms')
continue
if hist1 is None or hist2 is None or hist3 is None:
warn('Dataset not available')
continue
invert_xaxis = False
if datatype == 'TEMP':
invert_xaxis = True
bin_res = bin_edges1[1] - bin_edges1[0]
bin_centers = bin_edges1[1:] - bin_res / 2.
fname = basepath + 'group_Santis_CG_hist_' + source + '_' + datatype + '.png'
fig, ax = plot_histogram2(bin_centers,
hist1, [fname],
labelx=labelx,
titl=titl,
alpha=0.25,
save_fig=False,
color='b',
invert_xaxis=invert_xaxis)
fig, ax = plot_histogram2(bin_centers,
hist2, [fname],
labelx=labelx,
titl=titl,
ax=ax,
fig=fig,
save_fig=False,
color='g',
alpha=0.25,
invert_xaxis=invert_xaxis)
fname_list = plot_histogram2(bin_centers,
hist3, [fname],
labelx=labelx,
titl=titl,
ax=ax,
fig=fig,
save_fig=True,
color='r',
alpha=0.25,
invert_xaxis=invert_xaxis)
# Total number of values
n1 = np.ma.sum(hist1)
n2 = np.ma.sum(hist2)
n3 = np.ma.sum(hist3)
print(n1)
print(n2)
print(n3)
# Mode
print(bin_centers[np.ma.argmax(hist1)])
print(bin_centers[np.ma.argmax(hist2)])
print(bin_centers[np.ma.argmax(hist3)])
# Median
freq1 = np.ma.cumsum(hist1) / n1
freq2 = np.ma.cumsum(hist2) / n2
freq3 = np.ma.cumsum(hist3) / n3
ind1 = np.where(freq1 >= 0.5)[0][0]
ind2 = np.where(freq2 >= 0.5)[0][0]
ind3 = np.where(freq3 >= 0.5)[0][0]
print(bin_centers[ind1])
print(bin_centers[ind2])
print(bin_centers[ind3])
if datatype == 'hydro':
print(hist1 / n1 * 100.)
print(hist2 / n2 * 100.)
print(hist3 / n3 * 100.)
print('plotted ' + ''.join(fname_list))