def get_time_slicer(ts, f, time_interval):
"""get time slicer from the time_interval
Following options are available
1. time_interval with [ts_begin, ts_end]
2. only one timestamp is selected and the found
right one would be beyond the ts range -> argnearest instead searchsorted
3. only one is timestamp
"""
# select first timestamp right of begin (not left if nearer as above)
#print(f'start time {h.ts_to_dt(ts[0])}')
it_b = 0 if ts.shape[0] == 1 else np.searchsorted(
ts, h.dt_to_ts(time_interval[0]), side='right')
if len(time_interval) == 2:
it_e = h.argnearest(ts, h.dt_to_ts(time_interval[1]))
if it_b == ts.shape[0]: it_b = it_b - 1
valid_step = 3 * np.median(np.diff(ts))
if ts[it_e] < h.dt_to_ts(
time_interval[0]) - valid_step or ts[it_b] < h.dt_to_ts(
time_interval[0]):
# second condition is to ensure that no timestamp before
# the selected interval is choosen
# (problem with limrad after change of sampling frequency)
str = 'found last profile of file {}\n at ts[it_e] {} too far ({}s) from {}\n'.format(
f, h.ts_to_dt(ts[it_e]), valid_step, time_interval[0]) \
+ 'or begin too early {} < {}\n returning None'.format(h.ts_to_dt(ts[it_b]), time_interval[0])
logger.warning(str)
return None
it_e = it_e + 1 if not it_e == ts.shape[0] - 1 else None
slicer = [slice(it_b, it_e)]
elif it_b == ts.shape[0]:
# only one timestamp is selected
# and the found right one would be beyond the ts range
it_b = h.argnearest(ts, h.dt_to_ts(time_interval[0]))
slicer = [slice(it_b, it_b + 1)]
else:
slicer = [slice(it_b, it_b + 1)]
return slicer
def random_choice(xr_ds, rg_int, N=4, iclass=4, var='voodoo_classification'):
nts, nrg = xr_ds.ZSpec.ts.size, xr_ds.ZSpec.rg.size
icnt = 0
indices = np.zeros((N, 2), dtype=np.int)
nnearest = h.argnearest(xr_ds.ZSpec.rg.values, rg_int)
while icnt < N:
while True:
idxts = int(np.random.randint(0, high=nts, size=1))
idxrg = int(np.random.randint(0, high=nnearest, size=1))
if ~xr_ds.mask[idxts, idxrg] and xr_ds[var].values[
idxts, idxrg] == iclass:
indices[icnt, :] = [idxts, idxrg]
icnt += 1
break
return indices
def get_1st_cloud_base_idx(cb_first_ts, range_list):
"""Extract the indices of the first cloud base.
Args:
cb_first_ts (list) : list or np.array of fist cloud base occurrence, fill_value=nan
range_list (list) : range bins
Return:
idx_1st_cloud_base (np.array) : sliced container
"""
idx_1st_cloud_base = []
for i_cb_ts in cb_first_ts:
if np.isnan(i_cb_ts):
idx_1st_cloud_base.append(np.nan)
else:
idx_1st_cloud_base.append(h.argnearest(range_list, i_cb_ts))
return np.array(idx_1st_cloud_base)
def t_r(f, time_interval, *further_intervals):
"""function that converts the trace netCDF to the data container
"""
logger.debug("filename at reader {}".format(f))
with netCDF4.Dataset(f, 'r') as ncD:
times = ncD.variables[paraminfo['time_variable']][:].astype(
np.float64)
timeconverter, _ = h.get_converter_array(
paraminfo['time_conversion'], ncD=ncD)
ts = timeconverter(times)
#print('timestamps ', ts[:5])
# setup slice to load base on time_interval
it_b = h.argnearest(ts, h.dt_to_ts(time_interval[0]))
if len(time_interval) == 2:
it_e = h.argnearest(ts, h.dt_to_ts(time_interval[1]))
if ts[it_e] < h.dt_to_ts(
time_interval[0]) - 3 * np.median(np.diff(ts)):
logger.warning(
'last profile of file {}\n at {} too far from {}'.
format(f, h.ts_to_dt(ts[it_e]), time_interval[0]))
return None
it_e = it_e + 1 if not it_e == ts.shape[0] - 1 else None
slicer = [slice(it_b, it_e)]
else:
slicer = [slice(it_b, it_b + 1)]
print(slicer)
range_interval = further_intervals[0]
ranges = ncD.variables[paraminfo['range_variable']]
logger.debug('loader range conversion {}'.format(
paraminfo['range_conversion']))
rangeconverter, _ = h.get_converter_array(
paraminfo['range_conversion'], altitude=paraminfo['altitude'])
ir_b = h.argnearest(rangeconverter(ranges[:]), range_interval[0])
if len(range_interval) == 2:
if not range_interval[1] == 'max':
ir_e = h.argnearest(rangeconverter(ranges[:]),
range_interval[1])
ir_e = ir_e + 1 if not ir_e == ranges.shape[0] - 1 else None
else:
ir_e = None
slicer.append(slice(ir_b, ir_e))
else:
slicer.append(slice(ir_b, ir_b + 1))
varconverter, maskconverter = h.get_converter_array(
paraminfo['var_conversion'])
its = np.arange(ts.shape[0])[tuple(slicer)[0]]
irs = np.arange(ranges.shape[0])[tuple(slicer)[1]]
var = np.empty((its.shape[0], irs.shape[0]))
mask = np.empty((its.shape[0], irs.shape[0]))
mask[:] = False
var = ncD.variables[paraminfo['variable_name']][
tuple(slicer)[0], tuple(slicer)[1], :]
data = {}
data['dimlabel'] = ['time', 'range', 'cat']
data["filename"] = f
data["paraminfo"] = paraminfo
data['ts'] = ts[tuple(slicer)[0]]
data['system'] = paraminfo['system']
data['name'] = paraminfo['paramkey']
data['colormap'] = paraminfo['colormap']
if 'meta' in paraminfo:
data['meta'] = NcReader.get_meta_from_nc(
ncD, paraminfo['meta'], paraminfo['variable_name'])
variable = ncD.variables[paraminfo['variable_name']]
var_definition = ast.literal_eval(
variable.getncattr(paraminfo['identifier_var_def']))
if var_definition[1] == "forrest":
var_definition[1] = "forest"
data['var_definition'] = var_definition
data['rg'] = rangeconverter(ranges[tuple(slicer)[1]])
data['rg_unit'] = NcReader.get_var_attr_from_nc(
"identifier_rg_unit", paraminfo, ranges)
logger.debug('shapes {} {} {}'.format(ts.shape, ranges.shape,
var.shape))
data['var_unit'] = NcReader.get_var_attr_from_nc(
"identifier_var_unit", paraminfo, var)
data['var_lims'] = [float(e) for e in \
NcReader.get_var_attr_from_nc("identifier_var_lims",
paraminfo, var)]
data['var'] = varconverter(var)
data['mask'] = maskconverter(mask)
return data
def pt_ret(f, time_interval, *further_intervals):
"""function that converts the peakTree netCDF to the data container
"""
logger.debug("filename at reader {}".format(f))
with netCDF4.Dataset(f, 'r') as ncD:
times = ncD.variables[paraminfo['time_variable']][:].astype(np.float64)
if 'time_millisec_variable' in paraminfo.keys() and \
paraminfo['time_millisec_variable'] in ncD.variables:
subsec = ncD.variables[paraminfo['time_millisec_variable']][:]/1.0e3
times += subsec
if 'time_microsec_variable' in paraminfo.keys() and \
paraminfo['time_microsec_variable'] in ncD.variables:
subsec = ncD.variables[paraminfo['time_microsec_variable']][:]/1.0e6
times += subsec
timeconverter, _ = h.get_converter_array(
paraminfo['time_conversion'], ncD=ncD)
ts = timeconverter(times)
#print('timestamps ', ts[:5])
# setup slice to load base on time_interval
it_b = h.argnearest(ts, h.dt_to_ts(time_interval[0]))
if len(time_interval) == 2:
it_e = h.argnearest(ts, h.dt_to_ts(time_interval[1]))
if ts[it_e] < h.dt_to_ts(time_interval[0])-3*np.median(np.diff(ts)):
logger.warning(
'last profile of file {}\n at {} too far from {}'.format(
f, h.ts_to_dt(ts[it_e]), time_interval[0]))
return None
it_e = it_e+1 if not it_e == ts.shape[0]-1 else None
slicer = [slice(it_b, it_e)]
else:
slicer = [slice(it_b, it_b+1)]
print(slicer)
if paraminfo['ncreader'] == 'peakTree':
range_tg = True
range_interval = further_intervals[0]
ranges = ncD.variables[paraminfo['range_variable']]
logger.debug('loader range conversion {}'.format(paraminfo['range_conversion']))
rangeconverter, _ = h.get_converter_array(
paraminfo['range_conversion'],
altitude=paraminfo['altitude'])
ir_b = h.argnearest(rangeconverter(ranges[:]), range_interval[0])
if len(range_interval) == 2:
if not range_interval[1] == 'max':
ir_e = h.argnearest(rangeconverter(ranges[:]), range_interval[1])
ir_e = ir_e+1 if not ir_e == ranges.shape[0]-1 else None
else:
ir_e = None
slicer.append(slice(ir_b, ir_e))
else:
slicer.append(slice(ir_b, ir_b+1))
varconverter, maskconverter = h.get_converter_array(
paraminfo['var_conversion'])
its = np.arange(ts.shape[0])[tuple(slicer)[0]]
irs = np.arange(ranges.shape[0])[tuple(slicer)[1]]
var = np.empty((its.shape[0], irs.shape[0]), dtype=object)
mask = np.empty((its.shape[0], irs.shape[0]), dtype=bool)
mask[:] = True
param_list = [
ncD.variables['parent'][tuple(slicer)[0],tuple(slicer)[1],:], #0
ncD.variables['Z'][tuple(slicer)[0],tuple(slicer)[1],:], #1
ncD.variables['v'][tuple(slicer)[0],tuple(slicer)[1],:], #2
ncD.variables['width'][tuple(slicer)[0],tuple(slicer)[1],:], #3
ncD.variables['skew'][tuple(slicer)[0],tuple(slicer)[1],:], #4
ncD.variables['threshold'][tuple(slicer)[0],tuple(slicer)[1],:], #5
ncD.variables['prominence'][tuple(slicer)[0],tuple(slicer)[1],:], #6
ncD.variables['bound_l'][tuple(slicer)[0],tuple(slicer)[1],:], #7
ncD.variables['bound_r'][tuple(slicer)[0],tuple(slicer)[1],:] #8
]
if 'LDR' in ncD.variables.keys():
ldr_avail = True
param_list.append(ncD.variables['LDR'][tuple(slicer)[0],tuple(slicer)[1],:]) #9
param_list.append(ncD.variables['ldrmax'][tuple(slicer)[0],tuple(slicer)[1],:]) #10
else:
ldr_avail = False
data = np.stack(tuple(param_list), axis=3)
print(data.shape)
if fastbuilder:
var, mask = peakTree_fastbuilder.array_to_tree_c(data.astype(float), ldr_avail)
else:
var, mask = array_to_tree_py(data, ldr_avail)
data = {}
data['dimlabel'] = ['time', 'range', 'dict']
data["filename"] = f
data["paraminfo"] = paraminfo
data['ts'] = ts[tuple(slicer)[0]]
data['system'] = paraminfo['system']
data['name'] = paraminfo['paramkey']
data['colormap'] = paraminfo['colormap']
data['rg'] = rangeconverter(ranges[tuple(slicer)[1]])
data['rg_unit'] = NcReader.get_var_attr_from_nc("identifier_rg_unit",
paraminfo, ranges)
logger.debug('shapes {} {} {}'.format(ts.shape, ranges.shape, var.shape))
logger.debug('shapes {} {}'.format(ts.shape, var.shape))
data['var_unit'] = NcReader.get_var_attr_from_nc("identifier_var_unit",
paraminfo, var)
data['var_lims'] = [float(e) for e in \
NcReader.get_var_attr_from_nc("identifier_var_lims",
paraminfo, var)]
data['var'] = varconverter(var)
data['mask'] = maskconverter(mask)
return data
def retfunc(f, time_interval, range_interval):
"""function that converts the netCDF to the larda-data-format
"""
logger.debug("filename at reader {}".format(f))
with netCDF4.Dataset(f, 'r') as ncD:
ranges = ncD.variables[paraminfo['range_variable']]
times = ncD.variables[paraminfo['time_variable']][:].astype(
np.float64)
locator_mask = ncD.variables[paraminfo['mask_var']][:].astype(
np.int)
if 'time_millisec_variable' in paraminfo.keys() and \
paraminfo['time_millisec_variable'] in ncD.variables:
subsec = ncD.variables[
paraminfo['time_millisec_variable']][:] / 1.0e3
times += subsec
if 'time_microsec_variable' in paraminfo.keys() and \
paraminfo['time_microsec_variable'] in ncD.variables:
subsec = ncD.variables[
paraminfo['time_microsec_variable']][:] / 1.0e6
times += subsec
if 'base_time_variable' in paraminfo.keys() and \
paraminfo['base_time_variable'] in ncD.variables:
basetime = ncD.variables[
paraminfo['base_time_variable']][:].astype(np.float64)
times += basetime
timeconverter, _ = h.get_converter_array(
paraminfo['time_conversion'], ncD=ncD)
ts = timeconverter(times)
it_b = np.searchsorted(ts,
h.dt_to_ts(time_interval[0]),
side='right')
if len(time_interval) == 2:
it_e = h.argnearest(ts, h.dt_to_ts(time_interval[1]))
if it_b == ts.shape[0]: it_b = it_b - 1
if ts[it_e] < h.dt_to_ts(time_interval[0]) - 3 * np.median(np.diff(ts)) \
or ts[it_b] < h.dt_to_ts(time_interval[0]):
# second condition is to ensure that no timestamp before
# the selected interval is chosen
# (problem with limrad after change of sampling frequency)
logger.warning(
'last profile of file {}\n at {} too far from {}'.
format(f, h.ts_to_dt(ts[it_e]), time_interval[0]))
return None
it_e = it_e + 1 if not it_e == ts.shape[0] - 1 else None
slicer = [slice(it_b, it_e)]
elif it_b == ts.shape[0]:
# only one timestamp is selected
# and the found right one would be beyond the ts range
it_b = h.argnearest(ts, h.dt_to_ts(time_interval[0]))
slicer = [slice(it_b, it_b + 1)]
else:
slicer = [slice(it_b, it_b + 1)]
rangeconverter, _ = h.get_converter_array(
paraminfo['range_conversion'])
varconverter, _ = h.get_converter_array(
paraminfo['var_conversion'])
ir_b = h.argnearest(rangeconverter(ranges[:]), range_interval[0])
if len(range_interval) == 2:
if not range_interval[1] == 'max':
ir_e = h.argnearest(rangeconverter(ranges[:]),
range_interval[1])
ir_e = ir_e + 1 if not ir_e == ranges.shape[0] - 1 else None
else:
ir_e = None
slicer.append(slice(ir_b, ir_e))
else:
slicer.append(slice(ir_b, ir_b + 1))
range_out = rangeconverter(ranges[tuple(slicer)[1]])
cal = getattr(ncD, paraminfo['cal_const'])
var = ncD.variables[paraminfo['variable_name']][:].astype(
np.float64)
var = var[locator_mask]
vel = ncD.variables[paraminfo['vel_variable']][:].astype(
np.float64)
# print('var dict ',ch1var.__dict__)
# print('shapes ', ts.shape, ch1range.shape, ch1var.shape)
# print("time indices ", it_b, it_e)
data = {}
data['dimlabel'] = ['time', 'range', 'vel']
data["filename"] = f
data["paraminfo"] = paraminfo
data['ts'] = ts[tuple(slicer)[0]]
data['rg'] = range_out
data['system'] = paraminfo['system']
data['name'] = paraminfo['paramkey']
data['colormap'] = paraminfo['colormap']
# also experimental: vis_varconverter
if 'plot_varconverter' in paraminfo and paraminfo[
'plot_varconverter'] != 'none':
data['plot_varconverter'] = paraminfo['plot_varconverter']
else:
data['plot_varconverter'] = ''
data['rg_unit'] = get_var_attr_from_nc("identifier_rg_unit",
paraminfo, ranges)
#data['var_unit'] = get_var_attr_from_nc("identifier_var_unit",
# paraminfo, var)
data['var_unit'] = 'dBZ m-1 s'
data['var_lims'] = [float(e) for e in \
get_var_attr_from_nc("identifier_var_lims",
paraminfo, var)]
data['vel'] = vel
if "identifier_fill_value" in paraminfo.keys(
) and not "fill_value" in paraminfo.keys():
fill_value = var.getncattr(paraminfo['identifier_fill_value'])
data['mask'] = (var[tuple(slicer)].data == fill_value)
elif "fill_value" in paraminfo.keys():
fill_value = paraminfo["fill_value"]
data['mask'] = np.isclose(var[tuple(slicer)], fill_value)
elif "mask_var" in paraminfo.keys():
# combine locator mask and mask of infinite values
mask = locator_mask.mask[tuple(slicer)]
data["mask"] = np.logical_or(
~np.isfinite(var[tuple(slicer)].data),
np.repeat(mask[:, :, np.newaxis], len(data['vel']),
axis=2))
else:
data['mask'] = ~np.isfinite(var[tuple(slicer)].data)
if isinstance(times, np.ma.MaskedArray):
var = varconverter(var[tuple(slicer)].data)
else:
var = varconverter(var[tuple(slicer)])
var2 = h.z2lin(var) * h.z2lin(float(
cal[:-3])) * (range_out**2)[np.newaxis, :, np.newaxis]
data['var'] = var2
return data
def retfunc(f, time_interval, *further_intervals):
"""function that converts the netCDF to the larda-data-format
"""
logger.debug("filename at reader {}".format(f))
with netCDF4.Dataset(f, 'r') as ncD:
if 'auto_mask_scale' in paraminfo and paraminfo[
'auto_mask_scale'] == False:
ncD.set_auto_mask(False)
varconv_args = {}
times = ncD.variables[paraminfo['time_variable']][:].astype(
np.float64)
if 'time_millisec_variable' in paraminfo.keys() and \
paraminfo['time_millisec_variable'] in ncD.variables:
subsec = ncD.variables[
paraminfo['time_millisec_variable']][:] / 1.0e3
times += subsec
if 'time_microsec_variable' in paraminfo.keys() and \
paraminfo['time_microsec_variable'] in ncD.variables:
subsec = ncD.variables[
paraminfo['time_microsec_variable']][:] / 1.0e6
times += subsec
if 'base_time_variable' in paraminfo.keys() and \
paraminfo['base_time_variable'] in ncD.variables:
basetime = ncD.variables[
paraminfo['base_time_variable']][:].astype(np.float64)
times += basetime
timeconverter, _ = h.get_converter_array(
paraminfo['time_conversion'], ncD=ncD)
if isinstance(times, np.ma.MaskedArray):
ts = timeconverter(times.data)
else:
ts = timeconverter(times)
# get the time slicer from time_interval
slicer = get_time_slicer(ts, f, time_interval)
if slicer is None and paraminfo['ncreader'] != 'pollynet_profile':
logger.critical(f'No time slice found!\nfile :: {f}\n')
return None
if paraminfo['ncreader'] == "pollynet_profile":
slicer = [slice(None)]
if paraminfo['ncreader'] in [
'timeheight', 'spec', 'mira_noise', 'pollynet_profile'
]:
range_tg = True
try:
range_interval = further_intervals[0]
except IndexError as e:
logger.error('No range interval was given.')
ranges = ncD.variables[paraminfo['range_variable']]
logger.debug('loader range conversion {}'.format(
paraminfo['range_conversion']))
rangeconverter, _ = h.get_converter_array(
paraminfo['range_conversion'],
altitude=paraminfo['altitude'])
ir_b = h.argnearest(rangeconverter(ranges[:]),
range_interval[0])
if len(range_interval) == 2:
if not range_interval[1] == 'max':
ir_e = h.argnearest(rangeconverter(ranges[:]),
range_interval[1])
ir_e = ir_e + 1 if not ir_e == ranges.shape[
0] - 1 else None
else:
ir_e = None
slicer.append(slice(ir_b, ir_e))
else:
slicer.append(slice(ir_b, ir_b + 1))
if paraminfo['ncreader'] == 'spec':
if 'compute_velbins' in paraminfo:
if paraminfo['compute_velbins'] == "mrrpro":
wl = 1.238 * 10**(-2) # wavelength (fixed) - 24 GHz
varconv_args.update({"wl": wl})
vel_tg = True
slicer.append(slice(None))
varconverter, maskconverter = h.get_converter_array(
paraminfo['var_conversion'], **varconv_args)
if 'vel_conversion' in paraminfo:
velconverter, _ = h.get_converter_array(
paraminfo['vel_conversion'])
var = ncD.variables[paraminfo['variable_name']]
# print('var dict ',ncD.variables[paraminfo['variable_name']].__dict__)
# print("time indices ", it_b, it_e)
data = {}
if paraminfo['ncreader'] == 'timeheight':
data['dimlabel'] = ['time', 'range']
elif paraminfo['ncreader'] == 'time':
data['dimlabel'] = ['time']
elif paraminfo['ncreader'] == 'spec':
data['dimlabel'] = ['time', 'range', 'vel']
elif paraminfo['ncreader'] == 'mira_noise':
data['dimlabel'] = ['time', 'range']
elif paraminfo['ncreader'] == "pollynet_profile":
data['dimlabel'] = ['time', 'range']
data["filename"] = f
data["paraminfo"] = paraminfo
data['ts'] = ts[tuple(slicer)[0]]
data['system'] = paraminfo['system']
data['name'] = paraminfo['paramkey']
data['colormap'] = paraminfo['colormap']
# experimental: put history into data container
if 'identifier_history' in paraminfo and paraminfo[
'identifier_history'] != 'none':
data['file_history'] = [
ncD.getncattr(paraminfo['identifier_history'])
]
# also experimental: vis_varconverter
if 'plot_varconverter' in paraminfo and paraminfo[
'plot_varconverter'] != 'none':
data['plot_varconverter'] = paraminfo['plot_varconverter']
else:
data['plot_varconverter'] = ''
if paraminfo['ncreader'] in [
'timeheight', 'spec', 'mira_noise', 'pollynet_profile'
]:
if isinstance(times, np.ma.MaskedArray):
data['rg'] = rangeconverter(ranges[tuple(slicer)[1]].data)
else:
data['rg'] = rangeconverter(ranges[tuple(slicer)[1]])
data['rg_unit'] = get_var_attr_from_nc("identifier_rg_unit",
paraminfo, ranges)
logger.debug('shapes {} {} {}'.format(ts.shape, ranges.shape,
var.shape))
if paraminfo['ncreader'] == 'spec':
if 'vel_ext_variable' in paraminfo:
# this special field is needed to load limrad spectra
vel_ext = ncD.variables[paraminfo['vel_ext_variable'][0]][
int(paraminfo['vel_ext_variable'][1])]
vel_res = 2 * vel_ext / float(var[:].shape[2])
data['vel'] = np.linspace(-vel_ext + (0.5 * vel_res),
+vel_ext - (0.5 * vel_res),
var[:].shape[2])
elif 'compute_velbins' in paraminfo:
if paraminfo['compute_velbins'] == 'mrrpro':
# this is used to read in MRR-PRO spectra
fs = 500000 # sampling rate of MRR-Pro (fixed)
vel_ext = fs / 4 / ncD.dimensions['range'].size * wl
vel_res = vel_ext / float(var[:].shape[2])
data['vel'] = np.linspace(0 - (0.5 * vel_res),
-vel_ext + (0.5 * vel_res),
var[:].shape[2])
else:
data['vel'] = ncD.variables[paraminfo['vel_variable']][:]
if 'vel_conversion' in paraminfo:
data['vel'] = velconverter(data['vel'])
logger.debug('shapes {} {}'.format(ts.shape, var.shape))
data['var_unit'] = get_var_attr_from_nc("identifier_var_unit",
paraminfo, var)
data['var_lims'] = [float(e) for e in \
get_var_attr_from_nc("identifier_var_lims",
paraminfo, var)]
# by default assume dimensions of (time, range, ...)
# or define a custom order in the param toml file
if 'dimorder' in paraminfo:
slicer = [slicer[i] for i in paraminfo['dimorder']]
if paraminfo['ncreader'] == "pollynet_profile":
del slicer[0]
# read in the variable definition dictionary
#
if "identifier_var_def" in paraminfo.keys(
) and not "var_def" in paraminfo.keys():
data['var_definition'] = h.guess_str_to_dict(
var.getncattr(paraminfo['identifier_var_def']))
elif "var_def" in paraminfo.keys():
data['var_definition'] = paraminfo['var_def']
if paraminfo['ncreader'] == 'mira_noise':
r_c = ncD.variables[paraminfo['radar_const']][:]
snr_c = ncD.variables[paraminfo['SNR_corr']][:]
npw = ncD.variables[paraminfo['noise_pow']][:]
calibrated_noise = r_c[slicer[0], np.newaxis] * var[tuple(slicer)].data * snr_c[tuple(slicer)].data / \
npw[slicer[0], np.newaxis] * (data['rg'][np.newaxis, :] / 5000.) ** 2
data['var'] = calibrated_noise
else:
data['var'] = varconverter(var[:])[tuple(slicer)]
#if paraminfo['compute_velbins'] == "mrrpro":
# data['var'] = data['var'] * wl** 4 / (np.pi** 5) / 0.93 * 10**6
if "identifier_fill_value" in paraminfo.keys(
) and not "fill_value" in paraminfo.keys():
fill_value = var.getncattr(paraminfo['identifier_fill_value'])
mask = (data['var'] == fill_value)
elif "fill_value" in paraminfo.keys():
fill_value = paraminfo['fill_value']
mask = np.isclose(data['var'], fill_value)
else:
mask = ~np.isfinite(data['var'])
data['mask'] = mask
if paraminfo['ncreader'] == "pollynet_profile":
data['var'] = data['var'][np.newaxis, :]
data['mask'] = data['mask'][np.newaxis, :]
return data
def retfunc(f, time_interval, *further_intervals):
"""function that converts the netCDF to the larda-data-format
"""
logger.debug("filename at reader {}".format(f))
with netCDF4.Dataset(f, 'r') as ncD:
times = ncD.variables[paraminfo['time_variable']][:].astype(
np.float64)
if 'time_millisec_variable' in paraminfo.keys() and \
paraminfo['time_millisec_variable'] in ncD.variables:
subsec = ncD.variables[
paraminfo['time_millisec_variable']][:] / 1.0e3
times += subsec
if 'time_microsec_variable' in paraminfo.keys() and \
paraminfo['time_microsec_variable'] in ncD.variables:
subsec = ncD.variables[
paraminfo['time_microsec_variable']][:] / 1.0e6
times += subsec
timeconverter, _ = h.get_converter_array(
paraminfo['time_conversion'], ncD=ncD)
if isinstance(times, np.ma.MaskedArray):
ts = timeconverter(times.data)
else:
ts = timeconverter(times)
# load the whole time-range from the file
slicer = [slice(None)]
if paraminfo['ncreader'] == 'scan_timeheight':
range_tg = True
range_interval = further_intervals[0]
ranges = ncD.variables[paraminfo['range_variable']]
logger.debug('loader range conversion {}'.format(
paraminfo['range_conversion']))
rangeconverter, _ = h.get_converter_array(
paraminfo['range_conversion'],
altitude=paraminfo['altitude'])
ir_b = h.argnearest(rangeconverter(ranges[:]),
range_interval[0])
if len(range_interval) == 2:
if not range_interval[1] == 'max':
ir_e = h.argnearest(rangeconverter(ranges[:]),
range_interval[1])
ir_e = ir_e + 1 if not ir_e == ranges.shape[
0] - 1 else None
else:
ir_e = None
slicer.append(slice(ir_b, ir_e))
else:
slicer.append(slice(ir_b, ir_b + 1))
varconverter, maskconverter = h.get_converter_array(
paraminfo['var_conversion'],
mira_azi_zero=paraminfo['mira_azi_zero'])
var = ncD.variables[paraminfo['variable_name']]
# print('var dict ',ncD.variables[paraminfo['variable_name']].__dict__)
# print("time indices ", it_b, it_e)
data = {}
if paraminfo['ncreader'] == 'scan_timeheight':
data['dimlabel'] = ['time', 'range']
elif paraminfo['ncreader'] == 'scan_time':
data['dimlabel'] = ['time']
# elif paraminfo['ncreader'] == 'spec':
# data['dimlabel'] = ['time', 'range', 'vel']
data["filename"] = f
data["paraminfo"] = paraminfo
data['ts'] = ts[tuple(slicer)[0]]
data['system'] = paraminfo['system']
data['name'] = paraminfo['paramkey']
data['colormap'] = paraminfo['colormap']
# also experimental: vis_varconverter
if 'plot_varconverter' in paraminfo and paraminfo[
'plot_varconverter'] != 'none':
data['plot_varconverter'] = paraminfo['plot_varconverter']
else:
data['plot_varconverter'] = ''
if paraminfo['ncreader'] == 'scan_timeheight':
if isinstance(times, np.ma.MaskedArray):
data['rg'] = rangeconverter(ranges[tuple(slicer)[1]].data)
else:
data['rg'] = rangeconverter(ranges[tuple(slicer)[1]])
data['rg_unit'] = get_var_attr_from_nc("identifier_rg_unit",
paraminfo, ranges)
logger.debug('shapes {} {} {}'.format(ts.shape, ranges.shape,
var.shape))
logger.debug('shapes {} {}'.format(ts.shape, var.shape))
data['var_unit'] = get_var_attr_from_nc("identifier_var_unit",
paraminfo, var)
data['var_lims'] = [float(e) for e in \
get_var_attr_from_nc("identifier_var_lims",
paraminfo, var)]
# by default assume dimensions of (time, range, ...)
# or define a custom order in the param toml file
if 'dimorder' in paraminfo:
slicer = [slicer[i] for i in paraminfo['dimorder']]
if "identifier_fill_value" in paraminfo.keys(
) and not "fill_value" in paraminfo.keys():
fill_value = var.getncattr(paraminfo['identifier_fill_value'])
mask = (var[tuple(slicer)].data == fill_value)
elif "fill_value" in paraminfo.keys():
fill_value = paraminfo['fill_value']
mask = np.isclose(var[tuple(slicer)].data, fill_value)
else:
mask = ~np.isfinite(var[tuple(slicer)].data)
data['var'] = varconverter(var[tuple(slicer)].data)
data['mask'] = maskconverter(mask)
return data
def retfunc(f, time_interval, range_interval):
"""function that converts the netCDF to the larda-data-format
"""
logger.debug("filename at reader {}".format(f))
with netCDF4.Dataset(f, 'r') as ncD:
times = ncD.variables[paraminfo['time_variable']][:].astype(
np.float64)
if 'time_millisec_variable' in paraminfo.keys() and \
paraminfo['time_millisec_variable'] in ncD.variables:
subsec = ncD.variables[
paraminfo['time_millisec_variable']][:] / 1.0e3
times += subsec
if 'time_microsec_variable' in paraminfo.keys() and \
paraminfo['time_microsec_variable'] in ncD.variables:
subsec = ncD.variables[
paraminfo['time_microsec_variable']][:] / 1.0e6
times += subsec
timeconverter, _ = h.get_converter_array(
paraminfo['time_conversion'], ncD=ncD)
ts = timeconverter(times)
no_chirps = ncD.dimensions['Chirp'].size
ranges_per_chirp = [
ncD.variables['C{}Range'.format(i + 1)]
for i in range(no_chirps)
]
ch1range = ranges_per_chirp[0]
ranges = np.hstack([rg[:] for rg in ranges_per_chirp])
# get the time slicer from time_interval
slicer = get_time_slicer(ts, f, time_interval)
if slicer == None:
return None
rangeconverter, _ = h.get_converter_array(
paraminfo['range_conversion'])
varconverter, _ = h.get_converter_array(
paraminfo['var_conversion'])
ir_b = h.argnearest(rangeconverter(ranges[:]), range_interval[0])
if len(range_interval) == 2:
if not range_interval[1] == 'max':
ir_e = h.argnearest(rangeconverter(ranges[:]),
range_interval[1])
ir_e = ir_e + 1 if not ir_e == ranges.shape[0] - 1 else None
else:
ir_e = None
slicer.append(slice(ir_b, ir_e))
else:
slicer.append(slice(ir_b, ir_b + 1))
vars_per_chirp = [
ncD.variables['C{}{}'.format(i + 1,
paraminfo['variable_name'])]
for i in range(no_chirps)
]
ch1var = vars_per_chirp[0]
# print('var dict ',ch1var.__dict__)
# print('shapes ', ts.shape, ch1range.shape, ch1var.shape)
# print("time indices ", it_b, it_e)
data = {}
data['dimlabel'] = ['time', 'range', 'vel']
data["filename"] = f
data["paraminfo"] = paraminfo
data['ts'] = ts[tuple(slicer)[0]]
data['rg'] = rangeconverter(ranges[tuple(slicer)[1]])
data['system'] = paraminfo['system']
data['name'] = paraminfo['paramkey']
data['colormap'] = paraminfo['colormap']
# also experimental: vis_varconverter
if 'plot_varconverter' in paraminfo and paraminfo[
'plot_varconverter'] != 'none':
data['plot_varconverter'] = paraminfo['plot_varconverter']
else:
data['plot_varconverter'] = ''
data['rg_unit'] = get_var_attr_from_nc("identifier_rg_unit",
paraminfo, ch1range)
data['var_unit'] = get_var_attr_from_nc("identifier_var_unit",
paraminfo, ch1var)
data['var_lims'] = [float(e) for e in \
get_var_attr_from_nc("identifier_var_lims",
paraminfo, ch1var)]
if 'vel_ext_variable' in paraminfo:
# define the function
get_vel_ext = lambda i: ncD.variables[paraminfo[
'vel_ext_variable'][0]][:][i]
# apply it to every chirp
vel_ext_per_chirp = [get_vel_ext(i) for i in range(no_chirps)]
vel_dim_per_chirp = [v.shape[2] for v in vars_per_chirp]
calc_vel_res = lambda v_e, v_dim: 2.0 * v_e / float(v_dim)
vel_res_per_chirp = [calc_vel_res(v_e, v_dim) for v_e, v_dim \
in zip(vel_ext_per_chirp, vel_dim_per_chirp)]
# for some very obscure reason lambda is not able to unpack 3 values
def calc_vel(vel_ext, vel_res, v_dim):
return np.linspace(-vel_ext + (0.5 * vel_res),
+vel_ext - (0.5 * vel_res), v_dim)
vel_per_chirp = [calc_vel(v_e, v_res, v_dim) for v_e, v_res, v_dim \
in zip(vel_ext_per_chirp, vel_res_per_chirp, vel_dim_per_chirp)]
else:
raise NotImplemented(
"other means of getting the var dimension are not implemented yet"
)
data['vel'] = vel_per_chirp[0]
# interpolate the variables here
if 'var_conversion' in paraminfo and paraminfo[
'var_conversion'] == 'keepNyquist':
# the interpolation is only done for the number of spectral lines, not the velocity itself
quot = [
i / vel_dim_per_chirp[0] for i in vel_dim_per_chirp[1:]
]
vars_interp = [vars_per_chirp[0]]
ich = 1
for var, vel in zip(vars_per_chirp[1:], vel_per_chirp[1:]):
data['vel_ch{}'.format(ich + 1)] = vel_per_chirp[ich]
new_vel = np.linspace(vel[0], vel[-1],
vel_dim_per_chirp[0])
vars_interp.append(
interp_only_3rd_dim(var[:] * quot[ich - 1],
vel,
new_vel,
kind='nearest'))
ich += 1
else:
vars_interp = [vars_per_chirp[0]] + \
[interp_only_3rd_dim(var, vel, vel_per_chirp[0]) \
for var, vel in zip(vars_per_chirp[1:], vel_per_chirp[1:])]
var = np.hstack([v[:] for v in vars_interp])
logger.debug('interpolated spectra from\n{}\n{} to\n{}'.format(
[v[:].shape for v in vars_per_chirp],
['{:5.3f}'.format(vel[0])
for vel in vel_per_chirp], [v[:].shape for v in vars_interp]))
logger.info('var.shape interpolated spectra {}'.format(var.shape))
if "identifier_fill_value" in paraminfo.keys(
) and not "fill_value" in paraminfo.keys():
fill_value = var.getncattr(paraminfo['identifier_fill_value'])
data['mask'] = (var[tuple(slicer)].data == fill_value)
elif "fill_value" in paraminfo.keys():
fill_value = paraminfo["fill_value"]
data['mask'] = np.isclose(var[tuple(slicer)], fill_value)
else:
data['mask'] = ~np.isfinite(var[tuple(slicer)].data)
if isinstance(times, np.ma.MaskedArray):
data['var'] = varconverter(var[tuple(slicer)].data)
else:
data['var'] = varconverter(var[tuple(slicer)])
return data
def retfunc(f, time_interval, range_interval):
"""function that converts the netCDF to the larda-data-format
"""
logger.debug("filename at reader {}".format(f))
with netCDF4.Dataset(f, 'r') as ncD:
no_chirps = ncD.dimensions['Chirp'].size
ranges_per_chirp = [
ncD.variables['C{}Range'.format(i + 1)]
for i in range(no_chirps)
]
ch1range = ranges_per_chirp[0]
ranges = np.hstack([rg[:] for rg in ranges_per_chirp])
times = ncD.variables[paraminfo['time_variable']][:].astype(
np.float64)
if 'time_millisec_variable' in paraminfo.keys() and \
paraminfo['time_millisec_variable'] in ncD.variables:
subsec = ncD.variables[
paraminfo['time_millisec_variable']][:] / 1.0e3
times += subsec
if 'time_microsec_variable' in paraminfo.keys() and \
paraminfo['time_microsec_variable'] in ncD.variables:
subsec = ncD.variables[
paraminfo['time_microsec_variable']][:] / 1.0e6
times += subsec
timeconverter, _ = h.get_converter_array(
paraminfo['time_conversion'], ncD=ncD)
ts = timeconverter(times)
# get the time slicer from time_interval
slicer = get_time_slicer(ts, f, time_interval)
if slicer == None:
return None
rangeconverter, _ = h.get_converter_array(
paraminfo['range_conversion'])
varconverter, _ = h.get_converter_array(
paraminfo['var_conversion'])
ir_b = h.argnearest(rangeconverter(ranges[:]), range_interval[0])
if len(range_interval) == 2:
if not range_interval[1] == 'max':
ir_e = h.argnearest(rangeconverter(ranges[:]),
range_interval[1])
ir_e = ir_e + 1 if not ir_e == ranges.shape[0] - 1 else None
else:
ir_e = None
slicer.append(slice(ir_b, ir_e))
else:
slicer.append(slice(ir_b, ir_b + 1))
no_chirps = ncD.dimensions['Chirp'].size
var_per_chirp = [
ncD.variables['C{}'.format(i + 1) + paraminfo['variable_name']]
for i in range(no_chirps)
]
ch1var = var_per_chirp[0]
# ch1var = ncD.variables['C1'+paraminfo['variable_name']]
# ch2var = ncD.variables['C2'+paraminfo['variable_name']]
# ch3var = ncD.variables['C3'+paraminfo['variable_name']]
# print('var dict ',ch1var.__dict__)
# print('shapes ', ts.shape, ch1range.shape, ch1var.shape)
# print("time indices ", it_b, it_e)
data = {}
data['dimlabel'] = ['time', 'range']
data["filename"] = f
data["paraminfo"] = paraminfo
data['ts'] = ts[tuple(slicer)[0]]
data['rg'] = rangeconverter(ranges[tuple(slicer)[1]])
data['system'] = paraminfo['system']
data['name'] = paraminfo['paramkey']
data['colormap'] = paraminfo['colormap']
# also experimental: vis_varconverter
if 'plot_varconverter' in paraminfo and paraminfo[
'plot_varconverter'] != 'none':
data['plot_varconverter'] = paraminfo['plot_varconverter']
else:
data['plot_varconverter'] = ''
data['rg_unit'] = get_var_attr_from_nc("identifier_rg_unit",
paraminfo, ch1range)
data['var_unit'] = get_var_attr_from_nc("identifier_var_unit",
paraminfo, ch1var)
data['var_lims'] = [float(e) for e in \
get_var_attr_from_nc("identifier_var_lims",
paraminfo, ch1var)]
var = np.hstack([v[:] for v in var_per_chirp])
# var = np.hstack([ch1var[:], ch2var[:], ch3var[:]])
if "identifier_fill_value" in paraminfo.keys(
) and not "fill_value" in paraminfo.keys():
fill_value = var.getncattr(paraminfo['identifier_fill_value'])
data['mask'] = (var[tuple(slicer)].data == fill_value)
elif "fill_value" in paraminfo.keys():
fill_value = paraminfo["fill_value"]
data['mask'] = np.isclose(var[tuple(slicer)], fill_value)
else:
data['mask'] = ~np.isfinite(var[tuple(slicer)].data)
data['var'] = varconverter(var[tuple(slicer)].data)
return data
cloudnet_dn = classification_tot['ts_class_time'].reshape((n_tot_ts_class,))
cloudnet_dt = np.array([datenum2datetime(ts) for ts in cloudnet_dn])
cloudnet_rg = classification_tot['h_class'].reshape((n_tot_rg_class,))
for case in case_list[cases:cases+1]:
# if case['notes'] == 'ex': continue # exclude this case and check the next one
begin_dt, end_dt = case['begin_dt'], case['end_dt']
# create directory for plots
h.change_dir(f'{PLOTS_PATH}case_study_{begin_dt:%Y%m%d%H%M%S}-{end_dt:%Y%m%d%H%M%S}/')
logging.basicConfig(filename=f'case_study_{begin_dt:%Y%m%d%H%M%S}-{end_dt:%Y%m%d%H%M%S}.log', level=logging.INFO)
# find indices for slicing
rg0val, rgNval = case['plot_range']
rg0idx, rgNidx = h.argnearest(cloudnet_rg, rg0val), h.argnearest(cloudnet_rg, rgNval)
rgN = rgNidx - rg0idx
rg_case = cloudnet_rg[rg0idx:rgNidx]
ts0val, tsNval = datetime2datenum(begin_dt), datetime2datenum(end_dt)
ts0idx, tsNidx = h.argnearest(cloudnet_dn, ts0val), h.argnearest(cloudnet_dn, tsNval)
tsN = tsNidx - ts0idx
dt_case = cloudnet_dt[ts0idx:tsNidx]
assert tsN * rgN > 0, ValueError('Error occurred! Number of time steps or range bins invalid, check rgN and tsN!')
ts_smth = '_smoothed' if span_smoo_MWRlwp > 0 else ''
logger.info(f'******************BEGIN CASE STUDY******************\n')
logger.info(f'Slicing time from {begin_dt:%Y-%m-%d %H:%M:%S} (UTC) to {end_dt:%Y-%m-%d %H:%M:%S} (UTC)')
logger.info(f'Slicing range from {rg0val:.2f} (km) to {rgNval:.2f} (km)\n')
