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))
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():
"""
"""
# 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))