def read_icnctraj_sub(set_name, time_pt, clams, time0, timef):
basedir = '/work/bb1018/b380873/traj_output/' + set_name + '/'
if clams == True:
start_str = 'cirrus_tst'
end_str = '_trim_extract_clams.nc'
rho = []
icnc = []
t = []
rhi = []
for file_id in np.arange(1, 27):
filename = start_str + general_prefix(
time_pt,
8) + '_p' + traj_prefix(file_id) + str(file_id) + end_str
traj = xr.open_dataset(basedir + filename)
vals = traj['RHO'].sel(time=slice(time0, timef)).values.flatten()
rho.extend(vals)
vals = (traj['ICN_hom'] + traj['ICN_het'] + traj['ICN_pre']).sel(
time=slice(time0, timef)).values.flatten()
icnc.extend(vals)
vals = traj['T'].sel(time=slice(time0, timef)).values.flatten()
t.extend(vals)
vals = traj['RHI'].sel(time=slice(time0, timef)).values.flatten()
rhi.extend(vals)
else:
start_str = 'traj_tst'
end_str = '_trim_extract_dt.nc'
rho = []
icnc = []
t = []
rhi = []
for file_id in np.arange(1, 27):
filename = start_str + general_prefix(
time_pt,
8) + '_p' + traj_prefix(file_id) + str(file_id) + end_str
traj = xr.open_dataset(basedir + filename)
vals = traj['rho'].sel(time=slice(time0, timef)).values.flatten()
rho.extend(vals)
vals = traj['qni'].sel(time=slice(time0, timef)).values.flatten()
icnc.extend(vals)
vals = traj['t'].sel(time=slice(time0, timef)).values.flatten()
t.extend(vals)
p = traj['p'].sel(time=slice(time0, timef)).values.flatten()
qv = traj['qv'].sel(time=slice(time0, timef)).values.flatten()
vals = calc_RHi(vals, p, qv)
rhi.extend(vals)
rho = np.array(rho)
icnc = np.array(icnc)
t = np.array(t)
rhi = np.array(rhi)
# Filter out instances where the ICNC value is not 0 or nan
icnc, t, rhi = filter_icnc_temp(icnc, rho, t, rhi)
print(icnc.shape, t.shape, rhi.shape)
print(np.nanmin(np.log10(icnc)), np.nanmean(np.log10(icnc)),
np.nanmax(np.log10(icnc)))
print(np.nanmin(rhi), np.nanmean(rhi), np.nanmax(rhi))
return t, rhi, icnc
def read_qseditraj(set_name, time_pt, clams):
basedir = '/work/bb1018/b380873/traj_output/' + set_name + '/'
if clams == True:
start_str = 'cirrus_tst'
end_str = '_trim_extract_clams.nc'
qsedi = []
qsedo = []
t = []
rhi = []
for file_id in np.arange(1, 27):
filename = start_str + general_prefix(
time_pt,
8) + '_p' + traj_prefix(file_id) + str(file_id) + end_str
traj = xr.open_dataset(basedir + filename)
vals = 10**6 * traj['qsedi'].values.flatten()
qsedi.extend(vals)
vals = 10**7 * traj['qsedo'].values.flatten()
qsedo.extend(vals)
vals = traj['T'].values.flatten()
t.extend(vals)
vals = traj['RHI'].values.flatten()
rhi.extend(vals)
else:
start_str = 'traj_tst'
end_str = '_trim_extract.nc'
qsedi = []
qsedo = []
t = []
rhi = []
for file_id in np.arange(1, 27):
filename = start_str + general_prefix(
time_pt,
8) + '_p' + traj_prefix(file_id) + str(file_id) + end_str
traj = xr.open_dataset(basedir + filename)
vals = 10**6 * traj['qsedi'].values.flatten()
qsedi.extend(vals)
vals = 10**6 * traj['qsedo'].values.flatten()
qsedo.extend(vals)
vals = traj['t'].values.flatten()
t.extend(vals)
p = traj['p'].values.flatten()
qv = traj['qv'].values.flatten()
vals = calc_RHi(vals, p, qv)
rhi.extend(vals)
qsedi = np.array(iwc)
t = np.array(t)
rhi = np.array(rhi)
# Filter out instances where the IWC value is not 0 or nan
iwc, t, rhi = filter_iwc_temp(iwc, t, rhi)
print(qsedi.shape, t.shape, rhi.shape)
print(np.nanmin(np.log10(qsedi)), np.nanmean(np.log10(qsedi)),
np.nanmax(np.log10(qsedi)))
print(np.nanmin(rhi), np.nanmean(rhi), np.nanmax(rhi))
return t, rhi, qsedi
def read_icnctraj_all(set_name, time_pt):
basedir = '/work/bb1018/b380873/traj_output/' + set_name + '/'
start_str = 'traj_tst'
end_str = '.nc'
rho = []
icnc = []
t = []
rhi = []
for file_id in np.arange(1, 6):
filename = start_str + general_prefix(
time_pt, 8) + '_p' + traj_prefix(file_id) + str(file_id) + end_str
traj = xr.open_dataset(basedir + filename)
vals = traj['rho'].values.flatten()
rho.extend(vals)
vals = traj['qni'].values.flatten()
icnc.extend(vals)
vals = traj['t'].values.flatten()
t.extend(vals)
p = traj['p'].values.flatten()
qv = traj['qv'].values.flatten()
vals = calc_RHi(vals, p, qv)
rhi.extend(vals)
rho = np.array(rho)
icnc = np.array(icnc)
t = np.array(t)
rhi = np.array(rhi)
# Filter out instances where the ICNC value is not 0 or nan
icnc, t, rhi = filter_icnc_temp(icnc, rho, t, rhi)
print(icnc.shape, t.shape, rhi.shape)
print(np.nanmin(np.log10(icnc)), np.nanmean(np.log10(icnc)),
np.nanmax(np.log10(icnc)))
print(np.nanmin(rhi), np.nanmean(rhi), np.nanmax(rhi))
return t, rhi, icnc
def read_iwctraj_all(set_name, time_pt, outflow):
basedir = '/work/bb1018/b380873/traj_output/' + set_name + '/'
start_str = 'traj_tst'
end_str = '.nc'
iwc = []
t = []
rhi = []
for file_id in np.arange(1, 6):
filename = start_str + general_prefix(
time_pt, 8) + '_p' + traj_prefix(file_id) + str(file_id) + end_str
traj = xr.open_dataset(basedir + filename)
vals = 10**6 * traj['qi'].values.flatten()
iwc.extend(vals)
vals = traj['t'].values.flatten()
t.extend(vals)
p = traj['p'].values.flatten()
qv = traj['qv'].values.flatten()
vals = calc_RHi(vals, p, qv)
rhi.extend(vals)
iwc = np.array(iwc)
t = np.array(t)
rhi = np.array(rhi)
# Filter out instances where the IWC value is not 0 or nan
if outflow == True:
iwc, t, rhi = filter_iwc_temp_outflow(iwc, t, rhi)
else:
iwc, t, rhi = filter_iwc_temp_insitu(iwc, t, rhi)
print(iwc.shape, t.shape, rhi.shape)
print(np.nanmin(np.log10(iwc)), np.nanmean(np.log10(iwc)),
np.nanmax(np.log10(iwc)))
print(np.nanmin(rhi), np.nanmean(rhi), np.nanmax(rhi))
return t, rhi, iwc
def time_to_datetime(set_name, time_pt, clams):
basedir = '/work/bb1018/b380873/traj_output/' + set_name + '/'
start_str = 'traj_tst'
end_str = '_trim_extract.nc'
base_time = datetime(2017, 8, 6, 0, 0)
traj = xr.open_dataset(basedir + start_str + general_prefix(time_pt, 8) +
'_p001' + end_str)
traj_times = traj['rtime']
real_times = np.array(
[base_time + timedelta(seconds=int(t.values)) for t in traj_times])
for file_id in np.arange(1, 31):
print(file_id)
traj = xr.open_dataset(basedir + start_str +
general_prefix(time_pt, 8) + '_p' +
traj_prefix(file_id) + str(file_id) + end_str)
traj['time'] = real_times
traj.to_netcdf(basedir + start_str + general_prefix(time_pt, 8) +
'_p' + traj_prefix(file_id) + str(file_id) +
'_trim_extract_dt.nc')
# We have <file_num> files and 3826 frequencies. I don't know where the latter number comes from.
PSD_ff_mean = np.zeros((file_num, 3826))
PSD_Pxx_mean = np.zeros((file_num, 3826))
if PSDcalc == True:
PSD_ff = [[] for i in np.arange(file_num)]
PSD_Pxx = [[] for i in np.arange(file_num)]
for j in np.arange(1, file_num + 1):
print(j)
# Read in the temperature from the trajectory file above.
# Its dimension will be [time steps, trajectory id].
#traj_file = basedir + 'traj_tst00000450_p' + file_prefix(j) + str(j) + '_trim.nc'
#traj_T_series = xr.open_dataset(traj_file).t.values
#traj_Tfluc_series = traj_T_series.T - np.nanmean(traj_T_series,axis=1)
#traj_w_series = xr.open_dataset(traj_file).w_v.values
traj_file = basedir + 'cirrus_tst00000450_p' + traj_prefix(j) + str(
j) + '_trim_clams.nc'
#traj_T_series = xr.open_dataset(traj_file)['T'].values
traj_w_series = xr.open_dataset(traj_file)['w_v'].values
# Store the PSDs for multiple temperature time series.
freqs = int(traj_T_series.shape[0] / 2 + 1)
traj_id = traj_T_series.shape[1]
#all_PSD = np.zeros((2,traj_id,freqs))
# Iterate over the trajectories and save their PSDs.
for i in np.arange(traj_id):
# Generate an instance of Class traj_psd with temp = input temperature time series.
#traj_obj = traj_psd(temp=traj_T_series[:7651,i])
# Generate an instance of Class_traj_psd with temp = input vertical velocity series.
