def main():
"""
"""
file_base = '/store/msrad/radar/pyrad_products/rad4alp_hydro_PHA/'
time_dir_list = ['2017-06-29']
trt_cell_id = '2017062913000174'
datatype_list = ['dBZc', 'ZDRc', 'RhoHVc', 'KDPc', 'TEMP', 'hydro']
dataset_list = [
'reflectivity', 'ZDRc', 'RhoHVc', 'KDPc', 'temperature', 'hydroclass'
]
print("====== Plot time-hist started: %s" %
datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S"))
atexit.register(_print_end_msg, "====== Plot time-hist finished: ")
for time_dir in time_dir_list:
for i, datatype in enumerate(datatype_list):
dataset = dataset_list[i]
file_path = file_base + time_dir + '/' + dataset + '_trt_traj/HISTOGRAM/'
flist = glob.glob(file_path + '*_' + trt_cell_id +
'_histogram_*_' + datatype + '.csv')
if not flist:
warn('No histogram files found in ' + file_path +
' for TRT cell ' + trt_cell_id)
continue
tbin_edges, bin_edges, data_ma = read_histogram_ts(flist, datatype)
basepath_out = os.path.dirname(flist[0])
fname = (basepath_out + '/' + trt_cell_id + '_trt_HISTOGRAM_' +
datatype + '.png')
field_name = get_fieldname_pyart(datatype)
field_dict = get_metadata(field_name)
titl = 'TRT cell ' + trt_cell_id + '\n' + get_field_name(
field_dict, field_name)
_plot_time_range(tbin_edges,
bin_edges,
data_ma,
'frequency_of_occurrence', [fname],
titl=titl,
ylabel=get_colobar_label(field_dict, field_name),
vmin=0.,
vmax=np.max(data_ma),
figsize=[10, 8],
dpi=72)
print("----- plot to '%s'" % fname)
def main():
"""
"""
# parse the arguments
parser = argparse.ArgumentParser(
description='Entry to Pyrad processing framework')
# positional arguments
parser.add_argument(
'proc_cfgfile', type=str, help='name of main configuration file')
parser.add_argument(
'starttime', type=str,
help=('starting time of the data to be processed. ' +
'Format ''YYYYMMDDhhmmss'''))
parser.add_argument(
'endtime', type=str,
help='end time of the data to be processed. Format ''YYYYMMDDhhmmss''')
# keyword arguments
parser.add_argument(
'--cfgpath', type=str,
default=os.path.expanduser('~')+'/pyrad/config/processing/',
help='configuration file path')
parser.add_argument(
'--storepath', type=str,
default='/store/msrad/radar/pyrad_products/rad4alp_birds_PHA/',
help='Base data storing path')
parser.add_argument(
'--hres', type=int, default=200, help='Height resolution [m]')
args = parser.parse_args()
print("====== PYRAD data processing started: %s" %
datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S"))
atexit.register(_print_end_msg,
"====== PYRAD data processing finished: ")
print('config path: '+args.cfgpath)
print('config file: '+args.proc_cfgfile)
print('start time: '+args.starttime)
print('end time: '+args.endtime)
proc_starttime = datetime.datetime.strptime(
args.starttime, '%Y%m%d%H%M%S')
proc_endtime = datetime.datetime.strptime(
args.endtime, '%Y%m%d%H%M%S')
cfgfile_proc = args.cfgpath+args.proc_cfgfile
pyrad_main(cfgfile_proc, starttime=proc_starttime, endtime=proc_endtime)
# Plot time-height
file_base = args.storepath
hres = args.hres
datatype_list = [
'dBZc', 'eta_h', 'bird_density', 'WIND_SPEED', 'WIND_DIRECTION',
'wind_vel_h_u', 'wind_vel_h_v', 'wind_vel_v']
startdate = proc_starttime.replace(hour=0, minute=0, second=0, microsecond=0)
enddate = proc_endtime.replace(hour=0, minute=0, second=0, microsecond=0)
ndays = int((enddate-startdate).days)+1
for datatype in datatype_list:
flist = []
for i in range(ndays):
time_dir = (
proc_starttime+datetime.timedelta(days=i)).strftime('%Y-%m-%d')
filepath = (
file_base+time_dir+'/VAD/PROFILE_WIND/' +
'*_wind_profile_VAD_WIND_hres'+str(hres)+'.csv')
labels = [
'u_wind', 'std_u_wind', 'np_u_wind',
'v_wind', 'std_v_wind', 'np_v_wind',
'w_wind', 'std_w_wind', 'np_w_wind',
'mag_h_wind', 'dir_h_wind']
label_nr = 0
if datatype == 'dBZc':
filepath = (
file_base+time_dir+'/velFilter/PROFILE_dBZc/' +
'*_rhi_profile_*_dBZc_hres'+str(hres)+'.csv')
labels = [
'50.0-percentile', '25.0-percentile', '75.0-percentile']
# dBZ mean data
# filepath = (
# file_base+time_dir+'/velFilter/PROFILE_dBZc_mean/' +
# '*_rhi_profile_*_dBZc_hres'+str(hres)+'.csv')
# labels = [
# 'Mean', 'Min', 'Max']
# dBZ linear mean data
# filepath = (
# file_base+time_dir+'/velFilter/PROFILE_dBZc_linear_mean/' +
# '*_rhi_profile_*_dBZc_hres'+str(hres)+'.csv')
# labels = [
# 'Mean', 'Min', 'Max']
# dBZ before filtering with fitted velocity
# filepath = (
# file_base+time_dir+'/echoFilter/PROFILE_dBZc/' +
# '*_rhi_profile_*_dBZc_hres'+str(hres)+'.csv')
# labels = [
# '50.0-percentile', '25.0-percentile', '75.0-percentile']
#
# dBZ before filtering with fitted velocity. Linear mean
# filepath = (
# file_base+time_dir+'/echoFilter/PROFILE_dBZc_linear_mean/' +
# '*_rhi_profile_*_dBZc_hres'+str(hres)+'.csv')
# labels = [
# 'Mean', 'Min', 'Max']
elif datatype == 'eta_h':
filepath = (
file_base+time_dir+'/vol_refl/PROFILE/' +
'*_rhi_profile_*_eta_h_hres'+str(hres)+'.csv')
labels = [
'50.0-percentile', '25.0-percentile', '75.0-percentile']
# mean data
# filepath = (
# file_base+time_dir+'/vol_refl/PROFILE_mean/' +
# '*_rhi_profile_*_eta_h_hres'+str(hres)+'.csv')
# labels = [
# 'Mean', 'Min', 'Max']
elif datatype == 'bird_density':
filepath = (
file_base+time_dir+'/bird_density/PROFILE/' +
'*_rhi_profile_*_bird_density_hres'+str(hres)+'.csv')
labels = [
'50.0-percentile', '25.0-percentile', '75.0-percentile']
# mean data
# filepath = (
# file_base+time_dir+'/bird_density/PROFILE_mean/' +
# '*_rhi_profile_*_bird_density_hres'+str(hres)+'.csv')
# labels = [
# 'Mean', 'Min', 'Max']
elif datatype == 'WIND_SPEED':
label_nr = 9
elif datatype == 'WIND_DIRECTION':
label_nr = 10
elif datatype == 'wind_vel_h_u':
label_nr = 0
elif datatype == 'wind_vel_h_v':
label_nr = 3
elif datatype == 'wind_vel_v':
label_nr = 6
flist_aux = glob.glob(filepath)
if not flist_aux:
warn('No profile files found in '+filepath)
continue
flist.extend(flist_aux)
if not flist:
warn('No profile files found')
continue
flist.sort()
field_name = get_fieldname_pyart(datatype)
field_dict = get_metadata(field_name)
titl = 'bird retrieval '+args.starttime+'\n'+get_field_name(
field_dict, field_name)
tbin_edges, hbin_edges, np_ma, data_ma, t_start = read_profile_ts(
flist, labels, hres=hres, label_nr=label_nr)
basepath_out = os.path.dirname(flist[0])
fname = (
basepath_out+'/'+args.starttime+'_TIME_HEIGHT_' +
datatype+'_hres'+str(hres)+'.png')
vmin = vmax = None
_plot_time_range(
tbin_edges, hbin_edges/1000., data_ma, field_name, [fname],
titl=titl, figsize=[10, 8], vmin=vmin, vmax=vmax, dpi=72)
print("----- plot to '%s'" % fname)
# Plot number of points
field_dict = get_metadata('number_of_samples')
titl = 'bird retrieval '+args.starttime+'\n'+get_field_name(
field_dict, 'number_of_samples')
fname = (
basepath_out+'/'+args.starttime+'_TIME_HEIGHT_' +
datatype+'nsamples_hres'+str(hres)+'.png')
vmin = vmax = None
_plot_time_range(
tbin_edges, hbin_edges/1000., np_ma, 'number_of_samples', [fname],
titl=titl, figsize=[10, 8], vmin=vmin, vmax=vmax, dpi=72)
print("----- plot to '%s'" % fname)
def main():
"""
"""
# parse the arguments
parser = argparse.ArgumentParser(
description='Entry to Pyrad processing framework')
# positional arguments
parser.add_argument('proc_cfgfile',
type=str,
help='name of main configuration file')
parser.add_argument('days',
nargs='+',
type=str,
help='Dates to process. Format YYYY-MM-DD')
# keyword arguments
parser.add_argument('--trtbase',
type=str,
default='/store/msrad/radar/trt/',
help='name of folder containing the TRT cell data')
parser.add_argument(
'--radarbase',
type=str,
default='/store/msrad/radar/pyrad_products/rad4alp_hydro_PHA/',
help='name of folder containing the radar data')
parser.add_argument('--cfgpath',
type=str,
default=os.path.expanduser('~') +
'/pyrad/config/processing/',
help='configuration file path')
parser.add_argument(
'--datatypes',
type=str,
default='hydro,KDPc,dBZc,RhoHVc,TEMP,ZDRc',
help='Name of the polarimetric moments to process. Coma separated')
parser.add_argument(
'--datasets',
type=str,
default='hydroclass,KDPc,reflectivity,RhoHVc,temperature,ZDRc',
help='Name of the directory containing the datasets')
parser.add_argument('--hres',
type=float,
default=250.,
help='Height resolution')
args = parser.parse_args()
print("====== PYRAD TRT data processing started: %s" %
datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S"))
atexit.register(_print_end_msg,
"====== PYRAD TRT data processing finished: ")
print('config path: ' + args.cfgpath)
print('config file: ' + args.proc_cfgfile)
print('trt path: ' + args.trtbase)
print('radar data path: ' + args.radarbase)
cfgfile_proc = args.cfgpath + args.proc_cfgfile
trajtype = 'trt'
time_dir_list = args.days
datatype_list = args.datatypes.split(',')
dataset_list = args.datasets.split(',')
if np.size(datatype_list) != np.size(dataset_list):
warn(
str(np.size(datatype_list)) + ' datatypes but ' +
str(np.size(dataset_list)) +
' dataset directories. Their number must be equal')
return
# Find all TRT files in directory
trt_list = []
for time_dir in time_dir_list:
trt_list.extend(
glob.glob(args.trtbase + time_dir + '/TRTC_cell_plots/All/*.trt'))
trt_list.extend(
glob.glob(args.trtbase + time_dir + '/TRTC_cell_plots/Some/*.trt'))
# Pyrad data processing
trt_cell_id_list = []
trt_file_list = []
for fname in trt_list:
print('processing TRT cell file ' + fname)
try:
infostr = os.path.basename(fname).split('.')[0]
pyrad_main(cfgfile_proc,
trajfile=fname,
infostr=infostr,
trajtype=trajtype)
trt_cell_id_list.append(infostr)
trt_file_list.append(fname)
except ValueError:
print(ValueError)
# plot time series and get altitude of graupel column
if 'hydro' in datatype_list:
cell_ID_list = np.asarray([], dtype=int)
time_list = np.asarray([], dtype=datetime.datetime)
lon_list = np.asarray([], dtype=float)
lat_list = np.asarray([], dtype=float)
area_list = np.asarray([], dtype=float)
rank_list = np.asarray([], dtype=float)
rm_hmin_list = np.ma.asarray([], dtype=float)
rm_hmax_list = np.ma.asarray([], dtype=float)
for i, trt_cell_id in enumerate(trt_cell_id_list):
print('\n\nPost-processing cell: ' + trt_cell_id)
dt_str = trt_cell_id[0:12]
dt_cell = datetime.datetime.strptime(dt_str, "%Y%m%d%H%M")
time_dir = dt_cell.strftime("%Y-%m-%d")
for j, datatype in enumerate(datatype_list):
dataset = dataset_list[j]
file_base2 = args.radarbase + time_dir + '/' + dataset + '_trt_traj/'
field_name = get_fieldname_pyart(datatype)
field_dict = get_metadata(field_name)
titl = 'TRT cell ' + trt_cell_id + '\n' + get_field_name(
field_dict, field_name)
# plot time-height
flist = glob.glob(file_base2 + 'PROFILE/*_' + trt_cell_id +
'_rhi_profile_*_' + datatype + '_hres' +
str(int(args.hres)) + '.csv')
if not flist:
warn('No profile files found in ' + file_base2 +
'PROFILE/ for TRT cell ' + trt_cell_id +
' with resolution ' + str(args.hres))
else:
labels = [
'50.0-percentile', '25.0-percentile', '75.0-percentile'
]
if datatype == 'RhoHVc':
labels = [
'80.0-percentile', '65.0-percentile', '95.0-percentile'
]
elif datatype == 'hydro':
labels = [
'Mode', '2nd most common', '3rd most common',
'% points mode', '% points 2nd most common',
'% points 3rd most common'
]
elif datatype == 'entropy' or 'prop' in datatype:
labels = ['Mean', 'Min', 'Max']
tbin_edges, hbin_edges, _, data_ma, start_time = (
read_profile_ts(flist, labels, hres=args.hres))
basepath_out = os.path.dirname(flist[0])
fname = (basepath_out + '/' + trt_cell_id +
'_trt_TIME_HEIGHT_' + datatype + '_hres' +
str(args.hres) + '.png')
vmin = vmax = None
if datatype == 'RhoHVc':
vmin = 0.95
vmax = 1.00
xlabel = ('time (s from ' +
start_time.strftime("%Y-%m-%d %H:%M:%S") + ')')
_plot_time_range(tbin_edges,
hbin_edges,
data_ma,
field_name, [fname],
titl=titl,
xlabel=xlabel,
ylabel='height (m MSL)',
figsize=[10, 8],
vmin=vmin,
vmax=vmax,
dpi=72)
print("----- plot to '%s'" % fname)
# Get min and max altitude of graupel/hail area
if datatype == 'hydro':
(traj_ID, yyyymmddHHMM, lon, lat, _, _, _, area, _, _, _,
RANKr, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _,
_) = read_trt_traj_data(trt_file_list[i])
hmin, hmax = get_graupel_column(tbin_edges, hbin_edges,
data_ma, start_time,
yyyymmddHHMM)
cell_ID_list = np.append(cell_ID_list, traj_ID)
time_list = np.append(time_list, yyyymmddHHMM)
lon_list = np.append(lon_list, lon)
lat_list = np.append(lat_list, lat)
area_list = np.append(area_list, area)
rank_list = np.append(rank_list, RANKr)
rm_hmin_list = np.ma.append(rm_hmin_list, hmin)
rm_hmax_list = np.ma.append(rm_hmax_list, hmax)
# plot time-hist
flist = glob.glob(file_base2 + 'HISTOGRAM/*_' + trt_cell_id +
'_histogram_*_' + datatype + '.csv')
if not flist:
warn('No histogram files found in ' + file_base2 +
'HISTOGRAM/ for TRT cell ' + trt_cell_id)
else:
tbin_edges, bin_edges, data_ma, start_time = read_histogram_ts(
flist, datatype)
basepath_out = os.path.dirname(flist[0])
fname = (basepath_out + '/' + trt_cell_id + '_trt_HISTOGRAM_' +
datatype + '.png')
data_ma[data_ma == 0.] = np.ma.masked
xlabel = ('time (s from ' +
start_time.strftime("%Y-%m-%d %H:%M:%S") + ')')
_plot_time_range(tbin_edges,
bin_edges,
data_ma,
'frequency_of_occurrence', [fname],
titl=titl,
xlabel=xlabel,
ylabel=get_colobar_label(
field_dict, field_name),
vmin=0.,
vmax=np.max(data_ma),
figsize=[10, 8],
dpi=72)
print("----- plot to '%s'" % fname)
# plot quantiles
flist = glob.glob(file_base2 + 'QUANTILES/*_' + trt_cell_id +
'_quantiles_*_' + datatype + '.csv')
if not flist:
warn('No quantiles files found in ' + file_base2 +
'QUANTILES/ for TRT cell ' + trt_cell_id)
continue
tbin_edges, qbin_edges, data_ma, start_time = read_quantiles_ts(
flist, step=5., qmin=0., qmax=100.)
basepath_out = os.path.dirname(flist[0])
fname = (basepath_out + '/' + trt_cell_id + '_trt_QUANTILES_' +
datatype + '.png')
vmin = vmax = None
if datatype == 'RhoHVc':
vmin = 0.95
vmax = 1.00
xlabel = ('time (s from ' +
start_time.strftime("%Y-%m-%d %H:%M:%S") + ')')
_plot_time_range(tbin_edges,
qbin_edges,
data_ma,
field_name, [fname],
titl=titl,
xlabel=xlabel,
ylabel='Quantile',
vmin=vmin,
vmax=vmax,
figsize=[10, 8],
dpi=72)
print("----- plot to '%s'" % fname)
if 'hydro' in datatype_list:
fname = args.trtbase + 'cell_rimmed_particles_column.csv'
write_trt_cell_lightning(cell_ID_list, time_list, lon_list, lat_list,
area_list, rank_list, rm_hmin_list,
rm_hmax_list, fname)
print("----- written to '%s'" % fname)
def main():
"""
"""
file_base = '/store/msrad/radar/pyrad_products/rad4alp_hydro_PHA/'
time_dir_list = ['2017-06-29']
trt_cell_id = '2017062913000174'
hres = 250
datatype_list = ['dBZc', 'ZDRc', 'RhoHVc', 'KDPc', 'TEMP', 'hydro']
dataset_list = [
'reflectivity', 'ZDRc', 'RhoHVc', 'KDPc', 'temperature', 'hydroclass'
]
print("====== Plot time-height started: %s" %
datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S"))
atexit.register(_print_end_msg, "====== Plot time-height finished: ")
for time_dir in time_dir_list:
for i, datatype in enumerate(datatype_list):
labels = ['50.0-percentile', '25.0-percentile', '75.0-percentile']
if datatype == 'RhoHVc':
labels = [
'80.0-percentile', '65.0-percentile', '95.0-percentile'
]
elif datatype == 'hydro':
labels = [
'Mode', '2nd most common', '3rd most common',
'% points mode', '% points 2nd most common',
'% points 3rd most common'
]
dataset = dataset_list[i]
file_path = file_base + time_dir + '/' + dataset + '_trt_traj/PROFILE/'
flist = glob.glob(file_path + '*_' + trt_cell_id +
'_rhi_profile_*_' + datatype + '_hres' +
str(hres) + '.csv')
if not flist:
warn('No profile files found in ' + file_path +
' for TRT cell ' + trt_cell_id + ' with resolution ' +
str(hres))
continue
tbin_edges, hbin_edges, data_ma = read_profile_ts(flist,
labels,
hres=hres)
basepath_out = os.path.dirname(flist[0])
fname = (basepath_out + '/' + trt_cell_id + '_trt_TIME_HEIGHT_' +
datatype + '_hres' + str(hres) + '.png')
field_name = get_fieldname_pyart(datatype)
field_dict = get_metadata(field_name)
titl = 'TRT cell ' + trt_cell_id + '\n' + get_field_name(
field_dict, field_name)
vmin = vmax = None
if datatype == 'RhoHVc':
vmin = 0.95
vmax = 1.00
_plot_time_range(tbin_edges,
hbin_edges,
data_ma,
field_name, [fname],
titl=titl,
figsize=[10, 8],
vmin=vmin,
vmax=vmax,
dpi=72)
print("----- plot to '%s'" % fname)
def main():
"""
"""
# basepath = '/data/pyrad_products/rad4alp_hydro_PHA/'
basepath = '/store/msrad/radar/pyrad_products/rad4alp_hydro_PHA/'
day_vec = [
datetime.datetime(2017, 6, 29),
datetime.datetime(2017, 6, 30),
datetime.datetime(2017, 7, 10),
datetime.datetime(2017, 7, 14),
datetime.datetime(2017, 7, 18),
datetime.datetime(2017, 7, 19),
datetime.datetime(2017, 7, 30),
datetime.datetime(2017, 8, 1)
]
# day_vec = [
# datetime.datetime(2017, 7, 14)]
basename = 'Santis_data_entropy_CGpn'
filt_type = 'keep_all'
nsources_min = 10
if 'entropy' in basename:
pol_vals_labels = [
'hydro', 'entropy', 'propAG', 'propCR', 'propIH', 'propLR',
'propMH', 'propRN', 'propRP', 'propVI', 'propWS'
]
datatype_vec = [
'hydro', 'entropy', 'propAG', 'propCR', 'propIH', 'propLR',
'propMH', 'propRN', 'propRP', 'propVI', 'propWS'
]
step_list = [None, 0.1, 1., 1., 1., 1., 1., 1., 1., 1., 1.]
else:
pol_vals_labels = [
'hydro [-]', 'KDPc [deg/Km]', 'dBZc [dBZ]', 'RhoHVc [-]',
'TEMP [deg C]', 'ZDRc [dB]'
]
datatype_vec = ['hydro', 'KDPc', 'dBZc', 'RhoHVc', 'TEMP', 'ZDRc']
step_list = [None, 0.05, 0.5, 0.001, 1., 0.1]
for label in pol_vals_labels:
if 'hydro' in label:
hydro_label = label
break
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: ")
# read all the data to analyze
flashnr, time_data, time_in_flash, lat, lon, alt, dBm, pol_vals_dict = (
read_data(basepath,
day_vec,
basename=basename,
pol_vals_labels=pol_vals_labels))
# flashnr, time_data, time_in_flash, lat, lon, alt, dBm, pol_vals_dict = (
# read_data_two_sources(
# basepath, day_vec, basename1='Santis_data_entropy',
# basename2='Santis_data_entropy_CGt', basename_out='Santis_data_entropy_no_CG',
# keep_common=False,
# pol_vals_labels=pol_vals_labels))
# Get indices of data to keep
if filt_type == 'keep_all':
ind, data_ID, subtitl = get_indices_all_data(flashnr,
nsources_min=nsources_min)
elif filt_type == 'keep_solid':
ind, data_ID, subtitl = get_indices_solid_phase(
flashnr, pol_vals_dict[hydro_label], nsources_min=nsources_min)
elif filt_type == 'keep_liquid':
ind, data_ID, subtitl = get_indices_liquid_phase(
flashnr, pol_vals_dict[hydro_label], nsources_min=nsources_min)
elif filt_type == 'keep_liquid_origin':
ind, data_ID, subtitl = get_indices_liquid_phase_origin(
flashnr, pol_vals_dict[hydro_label], nsources_min=nsources_min)
else:
warn('Unknown filter type ' + filt_type)
return
flashnr_filt = flashnr[ind]
time_data_filt = time_data[ind]
time_in_flash_filt = time_in_flash[ind]
lat_filt = lat[ind]
lon_filt = lon[ind]
alt_filt = alt[ind]
dBm_filt = dBm[ind]
pol_vals_dict_filt = deepcopy(pol_vals_dict)
for key in pol_vals_dict.keys():
pol_vals_dict_filt[key] = pol_vals_dict[key][ind]
# # write the filtered data in a file
# vals_list = []
# for label in pol_vals_labels:
# vals_list.append(pol_vals_dict_filt[label])
#
# fname = basepath+basename'_'+data_ID+'.csv'
# write_ts_lightning(
# flashnr_filt, time_data_filt, time_in_flash_filt, lat_filt, lon_filt,
# alt_filt, dBm_filt, vals_list, fname, pol_vals_labels)
# print('written to '+fname)
# get flashes origin of filtered data
flashnr_first, ind_first = np.unique(flashnr_filt, return_index=True)
time_data_first = time_data_filt[ind_first]
time_in_flash_first = time_in_flash_filt[ind_first]
lat_first = lat_filt[ind_first]
lon_first = lon_filt[ind_first]
alt_first = alt_filt[ind_first]
dBm_first = dBm_filt[ind_first]
pol_vals_dict_first = deepcopy(pol_vals_dict_filt)
for key in pol_vals_dict_filt.keys():
pol_vals_dict_first[key] = pol_vals_dict_filt[key][ind_first]
# get duration and area of flash
duration_filt = np.ma.masked_all(flashnr_first.size)
area_filt = np.ma.masked_all(flashnr_first.size)
chy_filt, chx_filt, _ = wgs84_to_swissCH1903(lon_filt,
lat_filt,
alt_filt,
no_altitude_transform=True)
for i, flash_ID in enumerate(flashnr_first):
time_data_flash = time_data_filt[flashnr_filt == flash_ID]
duration_filt[i] = (
1e3 * (time_data_flash[-1] - time_data_flash[0]).total_seconds())
chy_flash = chy_filt[flashnr_filt == flash_ID]
chx_flash = chx_filt[flashnr_filt == flash_ID]
points_flash = shapely.geometry.MultiPoint(
list(zip(chy_flash, chx_flash)))
area_filt[i] = points_flash.minimum_rotated_rectangle.area * 1e-6
print('N flashes: ' + str(flashnr_first.size))
print('N sources: ' + str(flashnr_filt.size))
# Analyse the data
# create histograms of hydrometeor proportions
if 'propAG' in pol_vals_dict_filt:
bins_centers = np.arange(0, 10, 1)
bins_edges = np.arange(-0.5, 10.5, 1)
# Create histogram of number of differnt hydrometeors types in each
# radar range gate. All sources
nhydros_hist = hist_nhydros_gate(pol_vals_dict_filt, percent_min=10.)
fname = (basepath + data_ID +
'_allsources_ts_trajlightning_nhydro.png')
plot_histogram2(bins_centers,
nhydros_hist, [fname],
labelx='Number of hydrometeors in radar range gate',
labely='occurrence',
titl='Trajectory Histogram All Sources' + subtitl)
print("----- plot to '%s'" % fname)
# store histogram
fname = (basepath + data_ID +
'_allsources_ts_trajlightning_nhydro.csv')
write_histogram(bins_edges, nhydros_hist, fname)
print('Written ' + fname)
# Create histogram of number of different hydrometeors types in each
# radar range gate. First source
nhydros_hist = hist_nhydros_gate(pol_vals_dict_first, percent_min=10.)
fname = (basepath + data_ID +
'_firstsource_ts_trajlightning_nhydro.png')
plot_histogram2(bins_centers,
nhydros_hist, [fname],
labelx='Number of hydrometeors in radar range gate',
labely='occurrence',
titl='Trajectory Histogram First Sources' + subtitl)
print("----- plot to '%s'" % fname)
# store histogram
fname = (basepath + data_ID +
'_firstsource_ts_trajlightning_nhydro.csv')
write_histogram(bins_edges, nhydros_hist, fname)
print('Written ' + fname)
return
# Create histograms of dominant hydrometeors all sources
hydro_hist2 = hist_dominant_hydrometeors(pol_vals_dict_filt,
percent_min=10.)
fname_hist = basepath + data_ID + '_allsources_ts_trajlightning_hydro_dominant.png'
fname_hist = _plot_time_range(bins_edges,
bins_edges,
hydro_hist2,
None, [fname_hist],
titl='Trajectory Histogram All Sources' +
subtitl,
xlabel='Dominant hydrometeor',
ylabel='2nd most dominant hydrometeor',
vmin=0,
clabel='Occurrence',
figsize=[10, 8],
dpi=72)
print('Plotted ' + ' '.join(fname_hist))
# Create histogram of dominant hydrometeors first sources
hydro_hist2 = hist_dominant_hydrometeors(pol_vals_dict_first,
percent_min=10.)
fname_hist = basepath + data_ID + '_firstsource_ts_trajlightning_hydro_dominant.png'
fname_hist = _plot_time_range(
bins_edges,
bins_edges,
hydro_hist2,
None, [fname_hist],
titl='Trajectory Histogram First Sources' + subtitl,
xlabel='Dominant hydrometeor',
ylabel='2nd most dominant hydrometeor',
vmin=0,
clabel='Occurrence',
figsize=[10, 8],
dpi=72)
print('Plotted ' + ' '.join(fname_hist))
# create histogram of percentage of dominant hydrometeor all sources
hydro_hist = hist_hydrometeor_mixtures(pol_vals_dict_filt)
fname = (basepath + data_ID +
'_allsources_ts_trajlightning_hydro_prop.png')
plot_histogram2(bins_centers,
hydro_hist, [fname],
labelx='radar echo classification (-)',
labely='percentage',
titl='Trajectory Histogram All Sources' + subtitl)
print("----- plot to '%s'" % fname)
# store histogram
fname = (basepath + data_ID +
'_allsources_ts_trajlightning_hydro_prop.csv')
write_histogram(bins_edges, hydro_hist, fname)
print('Written ' + fname)
# create histogram of percentage of dominant hydrometeor first sources
hydro_hist = hist_hydrometeor_mixtures(pol_vals_dict_first)
fname = (basepath + data_ID +
'_firstsource_ts_trajlightning_hydro_prop.png')
plot_histogram2(bins_centers,
hydro_hist, [fname],
labelx='radar echo classification (-)',
labely='percentage',
titl='Trajectory Histogram First Sources' + subtitl)
print("----- plot to '%s'" % fname)
# store histogram
fname = (basepath + data_ID +
'_firstsource_ts_trajlightning_hydro_prop.csv')
write_histogram(bins_edges, hydro_hist, fname)
print('Written ' + fname)
for i, key in enumerate(pol_vals_labels):
step = step_list[i]
datatype = datatype_vec[i]
field_name = get_fieldname_pyart(datatype)
field_dict = get_metadata(field_name)
labelx = get_colobar_label(field_dict, field_name)
vals = pol_vals_dict_filt[key]
bins, values = compute_histogram(vals, field_name, step=step)
print(datatype + ' min: ' + str(vals.min()))
print(datatype + ' max: ' + str(vals.max()))
# Plot all sources histogram
fname_first_source = (basepath + data_ID +
'_allsources_ts_trajlightning_' + datatype +
'.png')
plot_histogram(bins,
values, [fname_first_source],
labelx=labelx,
titl='Trajectory Histogram All Sources' + subtitl)
print("----- plot to '%s'" % fname_first_source)
# store histogram
fname_first_source = (basepath + data_ID +
'_allsources_ts_trajlightning_' + datatype +
'.csv')
hist_values, _ = np.histogram(values, bins=bins)
write_histogram(bins, hist_values, fname_first_source)
print('Written ' + fname_first_source)
# First sources
vals = pol_vals_dict_first[key]
bins, values = compute_histogram(vals, field_name, step=step)
# Plot first source histogram
fname_first_source = (basepath + data_ID +
'_firstsource_ts_trajlightning_' + datatype +
'.png')
plot_histogram(bins,
values, [fname_first_source],
labelx=labelx,
titl='Trajectory Histogram First Source' + subtitl)
print("----- plot to '%s'" % fname_first_source)
# store histogram
fname_first_source = (basepath + data_ID +
'_firstsource_ts_trajlightning_' + datatype +
'.csv')
hist_values_first, _ = np.histogram(values, bins=bins)
write_histogram(bins, hist_values_first, fname_first_source)
print('Written ' + fname_first_source)
# Get histograms of sources altitude and power
# define histogram bin edges
bin_edges_alt = np.arange(-50., 14150., 100.)
bin_edges_dBm = np.arange(-17., 47., 1.)
bin_edges_time = np.arange(0, 25, 1)
bin_edges_area = np.arange(0., 2100., 100.)
bin_edges_duration = np.arange(0., 1100., 100.)
# Plot histogram of LMA flash area
_, area_filt_values = compute_histogram(area_filt,
None,
bin_edges=bin_edges_area)
fname_hist = basepath + data_ID + '_Santis_hist_area.png'
fname_hist = plot_histogram(bin_edges_area,
area_filt_values, [fname_hist],
labelx='Area [km2]',
titl='Flash area' + subtitl)
print('Plotted ' + ' '.join(fname_hist))
fname_hist = basepath + data_ID + '_Santis_hist_area.csv'
hist_area, _ = np.histogram(area_filt_values, bins=bin_edges_area)
fname_hist = write_histogram(bin_edges_area, hist_area, fname_hist)
print('Written ' + fname_hist)
# Plot histogram of LMA flash duration
_, duration_filt_values = compute_histogram(duration_filt,
None,
bin_edges=bin_edges_duration)
fname_hist = basepath + data_ID + '_Santis_hist_duration.png'
fname_hist = plot_histogram(bin_edges_duration,
duration_filt_values, [fname_hist],
labelx='Duration [ms]',
titl='Flash duration' + subtitl)
print('Plotted ' + ' '.join(fname_hist))
fname_hist = basepath + data_ID + '_Santis_hist_duration.csv'
hist_duration, _ = np.histogram(duration_filt_values,
bins=bin_edges_duration)
fname_hist = write_histogram(bin_edges_duration, hist_duration, fname_hist)
print('Written ' + fname_hist)
# Plot histogram time of occurrence
time_hour_first = occurrence_time(time_data_first)
fname_hist = basepath + data_ID + '_Santis_hist_time.png'
fname_hist = plot_histogram(bin_edges_time,
time_hour_first, [fname_hist],
labelx='Hour [UTC]',
titl='Flash occurrence time' + subtitl)
print('Plotted ' + ' '.join(fname_hist))
fname_hist = basepath + data_ID + '_Santis_hist_time.csv'
hist_time, _ = np.histogram(time_hour_first, bins=bin_edges_time)
fname_hist = write_histogram(bin_edges_time, hist_time, fname_hist)
print('Written ' + fname_hist)
# Plot histogram altitude all sources
_, alt_filt_values = compute_histogram(alt_filt,
None,
bin_edges=bin_edges_alt)
fname_hist = basepath + data_ID + '_Santis_hist_alt.png'
fname_hist = plot_histogram(bin_edges_alt,
alt_filt_values, [fname_hist],
labelx='Altitude [m MSL]',
titl='Flash sources altitude' + subtitl)
print('Plotted ' + ' '.join(fname_hist))
fname_hist = basepath + data_ID + '_Santis_hist_alt.csv'
hist_alt, _ = np.histogram(alt_filt_values, bins=bin_edges_alt)
fname_hist = write_histogram(bin_edges_alt, hist_alt, fname_hist)
print('Written ' + fname_hist)
# Plot histogram altitude first sources
_, alt_first_values = compute_histogram(alt_first,
None,
bin_edges=bin_edges_alt)
fname_hist = basepath + data_ID + '_Santis_hist_alt_first_source.png'
fname_hist = plot_histogram(bin_edges_alt,
alt_first_values, [fname_hist],
labelx='Altitude [m MSL]',
titl='Flash first source altitude' + subtitl)
print('Plotted ' + ' '.join(fname_hist))
fname_hist = basepath + data_ID + '_Santis_hist_alt_first_source.csv'
hist_alt_fist, _ = np.histogram(alt_first_values, bins=bin_edges_alt)
fname_hist = write_histogram(bin_edges_alt, hist_alt_fist, fname_hist)
print('Written ' + fname_hist)
# Plot histogram power all sources
_, dBm_filt_values = compute_histogram(dBm_filt,
None,
bin_edges=bin_edges_dBm)
fname_hist = basepath + data_ID + '_Santis_hist_dBm.png'
fname_hist = plot_histogram(bin_edges_dBm,
dBm_filt_values, [fname_hist],
labelx='Power [dBm]',
titl='Flash sources power' + subtitl)
print('Plotted ' + ' '.join(fname_hist))
fname_hist = basepath + data_ID + '_Santis_hist_dBm.csv'
hist_dBm, _ = np.histogram(dBm_filt_values, bins=bin_edges_dBm)
fname_hist = write_histogram(bin_edges_dBm, hist_dBm, fname_hist)
print('Written ' + fname_hist)
# Plot histogram power first sources
_, dBm_first_values = compute_histogram(dBm_first,
None,
bin_edges=bin_edges_dBm)
fname_hist = basepath + data_ID + '_Santis_hist_dBm_first_source.png'
fname_hist = plot_histogram(bin_edges_dBm,
dBm_first_values, [fname_hist],
labelx='Power [dBm]',
titl='Flash first source power' + subtitl)
print('Plotted ' + ' '.join(fname_hist))
fname_hist = basepath + data_ID + '_Santis_hist_dBm_first_source.csv'
hist_dBm_first, _ = np.histogram(dBm_first_values, bins=bin_edges_dBm)
fname_hist = write_histogram(bin_edges_dBm, hist_dBm_first, fname_hist)
print('Written ' + fname_hist)
# Plot 2D histogram all sources
H, _, _ = np.histogram2d(alt_filt_values,
dBm_filt_values,
bins=[bin_edges_alt, bin_edges_dBm])
# set 0 values to blank
H = np.ma.asarray(H)
H[H == 0] = np.ma.masked
fname_hist = basepath + data_ID + '_Santis_2Dhist_alt_dBm.png'
fname_hist = _plot_time_range(bin_edges_alt,
bin_edges_dBm,
H,
None, [fname_hist],
titl='LMA sources Altitude-Power histogram' +
subtitl,
xlabel='Altitude [m MSL]',
ylabel='Power [dBm]',
clabel='Occurrence',
vmin=0,
vmax=None,
figsize=[10, 8],
dpi=72)
print('Plotted ' + ' '.join(fname_hist))
# Plot 2D histogram first sources
H, _, _ = np.histogram2d(alt_first_values,
dBm_first_values,
bins=[bin_edges_alt, bin_edges_dBm])
# set 0 values to blank
H = np.ma.asarray(H)
H[H == 0] = np.ma.masked
fname_hist = basepath + data_ID + '_Santis_2Dhist_alt_dBm_first_source.png'
fname_hist = _plot_time_range(
bin_edges_alt,
bin_edges_dBm,
H,
None, [fname_hist],
titl='LMA first sources Altitude-Power histogram' + subtitl,
xlabel='Altitude [m MSL]',
ylabel='Power [dBm]',
clabel='Occurrence',
vmin=0,
vmax=None,
figsize=[10, 8],
dpi=72)
print('Plotted ' + ' '.join(fname_hist))
# plot position all sources
figfname = basepath + data_ID + '_Santis_LMA_sources_pos_max_height_on_top.png'
figfname = plot_pos(lat_filt,
lon_filt,
alt_filt, [figfname],
sort_altitude='Highest_on_top',
cb_label='Source height [m MSL]',
titl='Flash sources position. Highest on top' +
subtitl)
print('Plotted ' + ' '.join(figfname))
figfname = basepath + data_ID + '_Santis_LMA_sources_pos_min_height_on_top.png'
figfname = plot_pos(lat_filt,
lon_filt,
alt_filt, [figfname],
sort_altitude='Lowest_on_top',
cb_label='Source height [m MSL]',
titl='Flash sources position. Lowest on top' + subtitl)
print('Plotted ' + ' '.join(figfname))
# plot position first source
figfname = (basepath + data_ID +
'_Santis_LMA_first_source_pos_max_height_on_top.png')
figfname = plot_pos(lat_first,
lon_first,
alt_first, [figfname],
sort_altitude='Highest_on_top',
cb_label='Source height [m MSL]',
titl='First flash source position. Highest on top' +
subtitl)
print('Plotted ' + ' '.join(figfname))
figfname = (basepath + data_ID +
'_Santis_LMA_first_source_pos_min_height_on_top.png')
figfname = plot_pos(lat_first,
lon_first,
alt_first, [figfname],
sort_altitude='Lowest_on_top',
cb_label='Source height [m MSL]',
titl='First flash source position. Lowest on top' +
subtitl)
print('Plotted ' + ' '.join(figfname))
def main():
"""
"""
# parse the arguments
parser = argparse.ArgumentParser(
description='Entry to Pyrad processing framework')
# positional arguments
parser.add_argument(
'days', nargs='+', type=str,
help='Dates to process. Format YYYY-MM-DD')
# keyword arguments
parser.add_argument(
'--nsources_min', type=int, default=10,
help='Minimum number of sources to consider the LMA flash valid')
parser.add_argument(
'--trtbase', type=str,
default='/store/msrad/radar/trt/',
help='name of folder containing the TRT cell data')
parser.add_argument(
'--flashpath', type=str,
default='/store/msrad/lightning/LMA/Santis/',
help='name of the folder containing the Santis LMA data')
args = parser.parse_args()
print("====== LMA data TRT processing started: %s" %
datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S"))
atexit.register(_print_end_msg,
"====== LMA data TRT processing finished: ")
print('trt path: '+args.trtbase)
time_dir_list = args.days
# Get bins altitude
alt_min = 0.
alt_max = 14000.
step = 100.
bin_edges = np.linspace(
alt_min-step/2., alt_max+step/2, num=int((alt_max-alt_min)/step)+2)
trt_list = []
for time_dir in time_dir_list:
trt_list.extend(glob.glob(
args.trtbase+time_dir+'/TRTC_cell_plots/All/*.trt'))
trt_list.extend(glob.glob(
args.trtbase+time_dir+'/TRTC_cell_plots/Some/*.trt'))
cell_ID_list = np.ma.asarray([], dtype=int)
time_list = np.ma.asarray([], dtype=datetime.datetime)
lon_list = np.ma.asarray([], dtype=float)
lat_list = np.ma.asarray([], dtype=float)
flash_density_list = np.ma.asarray([], dtype=float)
sources_density_list = np.ma.asarray([], dtype=float)
rank_flash_density_list = np.ma.asarray([], dtype=float)
area_list = np.ma.asarray([], dtype=float)
nflash_list = np.ma.asarray([], dtype=int)
nsources_list = np.ma.asarray([], dtype=int)
for trt_fname in trt_list:
print('processing TRT cell file '+trt_fname)
trtpath = os.path.dirname(trt_fname)+'/'
# reading TRT cell file
(traj_ID, yyyymmddHHMM, lon_trt, lat_trt, _, _, _, area, _, _, _,
RANKr, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, cell_contour) = (
read_trt_traj_data(trt_fname))
# reading lightning file
infostr = os.path.basename(trt_fname).split('.')[0]
dt_str = infostr[0:12]
dt = datetime.datetime.strptime(dt_str, '%Y%m%d%H%M')
flashnr, time_data, _, lat, lon, alt, _ = read_lightning(
args.flashpath+dt.strftime("%y%m%d")+'.txt')
if flashnr is None:
continue
# Filter data with less than nsources_min sources
unique_flashnr = np.unique(flashnr, return_index=False)
ind = []
for flash in unique_flashnr:
ind_flash = np.where(flashnr == flash)[0]
if ind_flash.size < args.nsources_min:
continue
ind.extend(ind_flash)
if np.size(ind) == 0:
continue
flashnr = flashnr[ind]
time_data = time_data[ind]
lat = lat[ind]
lon = lon[ind]
alt = alt[ind]
# Get first sources data
_, unique_ind = np.unique(flashnr, return_index=True)
lat_first = lat[unique_ind]
lon_first = lon[unique_ind]
alt_first = alt[unique_ind]
# dBm_first = dBm[unique_ind]
time_first = time_data[unique_ind]
# Find cell period
cell_dt_s = np.empty(yyyymmddHHMM.size, dtype=float)
for i, cell_time in enumerate(yyyymmddHHMM):
cell_dt_s[i] = (cell_time-yyyymmddHHMM[0]).total_seconds()
t_res = np.mean(cell_dt_s[1:]-cell_dt_s[:-1])
# analyze first sources
print('\n\n--- Processing first sources ----')
nflashes = np.zeros(yyyymmddHHMM.size, dtype=int)
for i, cell_time in enumerate(yyyymmddHHMM):
cell_time_str = cell_time.strftime("%Y%m%d%H%M%S")
# Find flashes within time step of cell
tstart_cell_step = cell_time-datetime.timedelta(seconds=t_res)
inds = np.where(np.logical_and(
time_first > tstart_cell_step, time_first <= cell_time))[0]
if inds.size == 0:
warn('No flashes within time step')
fname_hist = (
trtpath+cell_time_str+'_'+infostr +
'_hist_alt_first_source.csv')
fname_hist = write_histogram(
bin_edges, np.zeros(bin_edges.size-1, dtype=int),
fname_hist, step=step)
print('----- save to '+fname_hist)
continue
lat_cell = lat_first[inds]
lon_cell = lon_first[inds]
alt_cell = alt_first[inds]
# Find flashes within cell contour
inds, is_roi = belongs_roi_indices(
lat_cell, lon_cell, cell_contour[i])
if is_roi == 'None':
warn('No flashes within cell contour')
fname_hist = (
trtpath+cell_time_str+'_'+infostr +
'_hist_alt_first_source.csv')
fname_hist = write_histogram(
bin_edges, np.zeros(bin_edges.size-1, dtype=int),
fname_hist, step=step)
print('----- save to '+fname_hist)
continue
elif is_roi == 'All':
inds = inds[0]
lat_cell = lat_cell[inds]
lon_cell = lon_cell[inds]
alt_cell = alt_cell[inds]
# compute number of flashes
nflashes[i] = lat_cell.size
# Plot altitude histogram
fname_hist = (
trtpath+cell_time_str+'_'+infostr +
'_hist_alt_first_source.png')
titl = (
cell_time_str+' '+infostr +
' TRT cell. Flash first source altitude')
fname_hist = plot_histogram(
bin_edges, alt_cell, [fname_hist], labelx='Altitude [m MSL]',
titl=titl)
print('Plotted '+' '.join(fname_hist))
fname_hist = (
trtpath+cell_time_str+'_'+infostr +
'_hist_alt_first_source.csv')
hist, bin_edges = np.histogram(alt_cell, bins=bin_edges)
fname_hist = write_histogram(
bin_edges, hist, fname_hist, step=step)
print('----- save to '+fname_hist)
# plot position first source
figfname = (
trtpath+cell_time_str+'_'+infostr +
'_first_source_pos_max_height_on_top.png')
titl = (
cell_time_str+' '+infostr +
' first flash source position. Highest on top')
figfname = plot_pos(
lat_cell, lon_cell, alt_cell, [figfname],
sort_altitude='Highest_on_top',
cb_label='Source height [m MSL]', titl=titl)
print('Plotted '+' '.join(figfname))
# plot time series lightning density
figfname = (
trtpath+cell_time_str+'_'+infostr+'_dens_first_sources.png')
titl = infostr+' First sources density'
figfname = plot_timeseries(
yyyymmddHHMM, [nflashes/area], [figfname], labelx='Time [UTC]',
labely='Flash dens [Flashes/Km2]', labels=['Flash density'],
title=titl, period=0, timeformat=None, colors=None,
linestyles=None, markers=None, ymin=None, ymax=None, dpi=72)
print('Plotted '+' '.join(figfname))
# plot time series lightning
figfname = (
trtpath+cell_time_str+'_'+infostr+'_N_first_sources.png')
titl = infostr+' Number of first sources in cell'
figfname = plot_timeseries(
yyyymmddHHMM, [nflashes], [figfname], labelx='Time [UTC]',
labely='N flashes', labels=['N flashes'], title=titl,
period=0, timeformat=None, colors=None, linestyles=None,
markers=None, ymin=None, ymax=None, dpi=72)
print('Plotted '+' '.join(figfname))
# plot time-hist_height
flist = glob.glob(
trtpath+'*_'+infostr+'_hist_alt_first_source.csv')
if not flist:
warn('No histogram files found in '+trtpath +
' for TRT cell '+infostr)
else:
tbin_edges, bin_edges, data_ma, _ = read_histogram_ts(
flist, 'flash_altitude')
vmax = np.max(data_ma)
if vmax == 0.:
warn('Unable to plot histogram. No valid data')
else:
data_ma[data_ma == 0.] = np.ma.masked
fname_hist = (
trtpath+infostr+'_trt_HISTOGRAM_alt_first_source.png')
titl = ('TRT cell '+infostr+'\n' +
'Altitude of first flash source')
_plot_time_range(
tbin_edges, bin_edges, data_ma, 'frequency_of_occurrence',
[fname_hist], titl=titl,
ylabel='Altitude [m MSL]',
vmin=0., vmax=vmax, figsize=[10, 8], dpi=72)
print("----- plot to '%s'" % fname_hist)
# Append flash data
cell_ID_list = np.ma.append(cell_ID_list, traj_ID)
time_list = np.ma.append(time_list, yyyymmddHHMM)
lon_list = np.ma.append(lon_list, lon_trt)
lat_list = np.ma.append(lat_list, lat_trt)
flash_density_list = np.ma.append(flash_density_list, nflashes/area)
rank_flash_density_list = np.ma.append(
rank_flash_density_list, RANKr)
area_list = np.ma.append(area_list, area)
nflash_list = np.ma.append(nflash_list, nflashes)
# analyze all flashes
print('\n\n--- Processing all sources ----')
nflashes = np.zeros(yyyymmddHHMM.size, dtype=int)
for i, cell_time in enumerate(yyyymmddHHMM):
cell_time_str = cell_time.strftime("%Y%m%d%H%M%S")
# Find flashes within time step of cell
tstart_cell_step = cell_time-datetime.timedelta(seconds=t_res)
inds = np.where(np.logical_and(
time_data > tstart_cell_step, time_data <= cell_time))[0]
if inds.size == 0:
warn('No flashes within time step')
fname_hist = (
trtpath+cell_time_str+'_'+infostr +
'_hist_alt_all_sources.csv')
fname_hist = write_histogram(
bin_edges, np.zeros(bin_edges.size-1, dtype=int),
fname_hist, step=step)
print('----- save to '+fname_hist)
continue
lat_cell = lat[inds]
lon_cell = lon[inds]
alt_cell = alt[inds]
# Find flashes within cell contour
inds, is_roi = belongs_roi_indices(
lat_cell, lon_cell, cell_contour[i])
if is_roi == 'None':
warn('No flashes within cell contour')
fname_hist = (
trtpath+cell_time_str+'_'+infostr +
'_hist_alt_all_sources.csv')
fname_hist = write_histogram(
bin_edges, np.zeros(bin_edges.size-1, dtype=int),
fname_hist, step=step)
print('----- save to '+fname_hist)
continue
elif is_roi == 'All':
inds = inds[0]
lat_cell = lat_cell[inds]
lon_cell = lon_cell[inds]
alt_cell = alt_cell[inds]
# compute number of flashes
nflashes[i] = lat_cell.size
# Plot altitude histogram
fname_hist = (
trtpath+cell_time_str+'_'+infostr +
'_hist_alt_all_sources.png')
titl = (
cell_time_str+' '+infostr +
' TRT cell. Flash all sources altitude')
fname_hist = plot_histogram(
bin_edges, alt_cell, [fname_hist], labelx='Altitude [m MSL]',
titl=titl)
print('Plotted '+' '.join(fname_hist))
fname_hist = (
trtpath+cell_time_str+'_'+infostr +
'_hist_alt_all_sources.csv')
hist, bin_edges = np.histogram(alt_cell, bins=bin_edges)
fname_hist = write_histogram(
bin_edges, hist, fname_hist, step=step)
print('----- save to '+fname_hist)
# plot position first source
figfname = (
trtpath+cell_time_str+'_'+infostr +
'_all_sources_pos_max_height_on_top.png')
titl = (
cell_time_str+' '+infostr +
' all flash sources position. Highest on top')
figfname = plot_pos(
lat_cell, lon_cell, alt_cell, [figfname],
sort_altitude='Highest_on_top',
cb_label='Source height [m MSL]', titl=titl)
print('Plotted '+' '.join(figfname))
# plot time series lightning
figfname = (
trtpath+cell_time_str+'_'+infostr+'_N_all_sources.png')
titl = infostr+' Number of sources in cell'
figfname = plot_timeseries(
yyyymmddHHMM, [nflashes], [figfname], labelx='Time [UTC]',
labely='N flashes', labels=['N flashes'], title=titl,
period=0, timeformat=None, colors=None, linestyles=None,
markers=None, ymin=None, ymax=None, dpi=72)
print('Plotted '+' '.join(figfname))
# plot time series lightning density
figfname = (
trtpath+cell_time_str+'_'+infostr+'_dens_all_sources.png')
titl = infostr+' Sources density'
figfname = plot_timeseries(
yyyymmddHHMM, [nflashes/area], [figfname], labelx='Time [UTC]',
labely='Source dens [Sources/Km2]', labels=['Source density'],
title=titl, period=0, timeformat=None, colors=None,
linestyles=None, markers=None, ymin=None, ymax=None, dpi=72)
print('Plotted '+' '.join(figfname))
# plot time-hist_height
flist = glob.glob(
trtpath+'*_'+infostr+'_hist_alt_all_sources.csv')
if not flist:
warn('No histogram files found in '+trtpath +
' for TRT cell '+infostr)
else:
tbin_edges, bin_edges, data_ma, _ = read_histogram_ts(
flist, 'flash_altitude')
vmax = np.max(data_ma)
if vmax == 0.:
warn('Unable to plot histogram. No valid data')
else:
data_ma[data_ma == 0.] = np.ma.masked
fname_hist = (
trtpath+'/'+infostr+'_trt_HISTOGRAM_alt_all_source.png')
titl = (
'TRT cell '+infostr+'\n'+'Altitude of all flash sources')
_plot_time_range(
tbin_edges, bin_edges, data_ma, 'frequency_of_occurrence',
[fname_hist], titl=titl,
ylabel='Altitude [m MSL]',
vmin=0., vmax=np.max(data_ma), figsize=[10, 8], dpi=72)
print("----- plot to '%s'" % fname_hist)
# Append sources data
sources_density_list = np.ma.append(sources_density_list, nflashes/area)
nsources_list = np.ma.append(nsources_list, nflashes)
fname = args.trtbase+'cell_LMA_flashes.csv'
write_trt_cell_lightning(
cell_ID_list, time_list, lon_list, lat_list, area_list,
rank_flash_density_list, nflash_list, flash_density_list, fname)
fname = args.trtbase+'cell_LMA_sources.csv'
write_trt_cell_lightning(
cell_ID_list, time_list, lon_list, lat_list, area_list,
rank_flash_density_list, nsources_list, sources_density_list, fname)