def makechi(filename):
key = int(filename.split('/')[-1].split('_')[-1].split('.')[0])
print "chi_condensed_profile", key
condensedfile = Dataset('cdf/condensed_profile_%08d.nc' % key)
envfile = Dataset('cdf/condensed_env_profile_%08d.nc' % key)
shellfile = Dataset('cdf/condensed_shell_profile_%08d.nc' % key)
statfile = Dataset(mc.get_stat())
t = numpy.atleast_1d(condensedfile.variables['ids'][:])
cloud_duration = len(t)
n = len(t)
z = envfile.variables['z'][:]
p = statfile.variables['PRES'][0,:]*100.
area_condensed = numpy.atleast_2d(condensedfile.variables['AREA'][:])
thetal_condensed = numpy.atleast_2d(condensedfile.variables['THETAL'][:])
qt_condensed = numpy.atleast_2d(condensedfile.variables['QT'][:])
ql_condensed = numpy.atleast_2d(condensedfile.variables['QN'][:])
T_condensed = numpy.atleast_2d(condensedfile.variables['TABS'][:])
thetal_env = numpy.atleast_2d(envfile.variables['THETAL'][:])
qt_env = numpy.atleast_2d(envfile.variables['QT'][:])
thetal_shell = numpy.atleast_2d(shellfile.variables['THETAL'][:])
qt_shell = numpy.atleast_2d(shellfile.variables['QT'][:])
mask = ~(area_condensed > 0.)
thetal_condensed_mask = numpy.ma.array(thetal_condensed, mask=mask)
qt_condensed_mask = numpy.ma.array(qt_condensed, mask=mask)
ql_condensed_mask = numpy.ma.array(ql_condensed, mask=mask)
T_condensed_mask = numpy.ma.array(T_condensed, mask=mask)
thetal_env_mask = numpy.ma.array(thetal_env, mask=mask)
qt_env_mask = numpy.ma.array(qt_env, mask=mask)
thetal_shell_mask = numpy.ma.array(thetal_shell, mask=mask)
qt_shell_mask = numpy.ma.array(qt_shell, mask=mask)
chi = SAM.find_chi_ql(ql_condensed,
thetal_condensed, thetal_env,
qt_condensed, qt_env,
T_condensed, p)
chi_mean = SAM.find_chi_ql(ql_condensed_mask.mean(0),
thetal_condensed_mask.mean(0),
thetal_env_mask.mean(0),
qt_condensed_mask.mean(0),
qt_env_mask.mean(0),
T_condensed_mask.mean(0),
p)
chi_mean_condensed = SAM.find_chi_ql(ql_condensed_mask.mean(0),
thetal_condensed_mask.mean(0),
thetal_env,
qt_condensed_mask.mean(0), qt_env,
T_condensed_mask.mean(0), p)
chi_mean_env = SAM.find_chi_ql(ql_condensed,
thetal_condensed, thetal_env_mask.mean(0),
qt_condensed, qt_env_mask.mean(0),
T_condensed, p)
# chi[isnan(chi)] = 0.
savefile = Dataset('cdf/condensed_chi_profile_%08d.nc' % key, 'w', format='NETCDF3_64BIT')
savefile.createDimension('id', cloud_duration)
savefile.createDimension('z', len(z))
var_t = savefile.createVariable('id', 'd', ('id',))
var_z = savefile.createVariable('z', 'd', ('z',))
var_chi = savefile.createVariable('chi', 'd', ('id', 'z'))
var_chi_mean = savefile.createVariable('chi_mean', 'd', ('id', 'z'))
var_chi_mean_env = savefile.createVariable('chi_mean_env', 'd', ('id', 'z'))
var_chi_mean_condensed = savefile.createVariable('chi_mean_condensed', 'd', ('id', 'z'))
var_t[:] = t
var_z[:] = z
var_chi[:] = chi
var_chi_mean[:] = chi_mean*(~mask)
var_chi_mean_condensed[:] = chi_mean_condensed*(~mask)
var_chi_mean_env[:] = chi_mean_env*(~mask)
condensedfile.close()
envfile.close()
shellfile.close()
statfile.close()
savefile.close()
def main():
sample_types = ('CONDENSED', 'EDGE', 'SHELL', 'ENV',)
stats_dict = {}
for l in range(mc.nt):
print l
cluster_dict = {}
nc_files = {}
nc_files['CONDENSED'] = Dataset('../time_profiles/cdf/condensed_profile_%08d.nc' % l)
nc_files['PLUME'] = Dataset('../time_profiles/cdf/plume_profile_%08d.nc' % l)
area = nc_files['CONDENSED'].variables['AREA'][:]
mask = (area > 0.)
area[~mask] = 0.
cluster_dict['AREA'] = area[mask]
if mask.sum() == 0:
nc_files['CONDENSED'].close()
stats_dict[l] = {}
continue
mask_top = mask.copy()
mask_top[:, 1:-1] = mask[:, 1:-1] & ~mask[:, 2:] & mask[:, :-2]
mask_bottom = mask.copy()
mask_bottom[:, 1:-1] = mask[:, 1:-1] & mask[:, 2:] & ~mask[:, :-2]
nc_files['EDGE'] = Dataset('../time_profiles/cdf/condensed_edge_profile_%08d.nc' % l)
nc_files['SHELL'] = Dataset('../time_profiles/cdf/condensed_shell_profile_%08d.nc' % l)
nc_files['ENV'] = Dataset('../time_profiles/cdf/condensed_env_profile_%08d.nc' % l)
entrain_file = Dataset('../time_profiles/cdf/condensed_entrain_profile_%08d.nc' % l)
surface_file = Dataset('../time_profiles/cdf/surface_profile_%08d.nc' % l)
condensed_shell_file = Dataset('../time_profiles/cdf/condensed_shell_profile_%08d.nc' % l)
chi_file = Dataset('../time_profiles/cdf/condensed_chi_profile_%08d.nc' % l)
stat_file = Dataset(mc.get_stat())
z = nc_files['CONDENSED'].variables['z'][:]
z = np.resize(z, mask.shape)
cluster_dict['z'] = z[mask]
# Calculate and store cloud thickness for each sample
# Use maked arrays to preserve axes; if z_min == z_max, thickness = dz
masked_z = np.ma.masked_where(area==0., z)
depth = np.ones_like(z)*(masked_z.max(axis=1) -
masked_z.min(axis=1))[:, np.newaxis] + mc.dz
cluster_dict['depth'] = depth[mask]
# Calculate and store condensed shell relative humidity
r = condensed_shell_file.variables['QV'][:]
p = condensed_shell_file.variables['PRES'][:]
T = condensed_shell_file.variables['TABS'][:]
relh = thermo.e(r, p)/thermo.e_star(T)
cluster_dict['RELH_COND_SHELL'] = relh[mask]
z = z*mask
zmax = np.ones_like(mask)*(z.max(1))[:, np.newaxis]
z[~mask] = 1e8
zmin = np.ones_like(mask)*(z.min(1))[:, np.newaxis]
cluster_dict['z_scaled'] = ((z - zmin.min())/(zmax-zmin.min()))[mask]
rho = nc_files['CONDENSED'].variables['RHO'][:]
cluster_dict['RHO'] = rho[mask]
mf = rho*area*nc_files['CONDENSED'].variables['W'][:]
cluster_dict['MF'] = mf[mask]
for var in ('W', 'QT', 'THETAV', 'THETAL', 'QN'):
for type in sample_types:
temp = nc_files[type].variables[var][:]
cluster_dict[var + '_' + type] = temp[mask]
if var != 'W':
temp = stat_file.variables[var][:]
if var == 'QT':
temp = temp/1000.
temp2 = nc_files['CONDENSED'].variables[var][:] - temp[l, :]
cluster_dict[var + '_' + type + '-MEAN'] = temp2[mask]
cluster_dict[var + '_CONDENSED-ENV'] = cluster_dict[var + '_CONDENSED'] - cluster_dict[var + '_ENV']
cluster_dict[var + '_CONDENSED-SHELL'] = cluster_dict[var + '_CONDENSED'] - cluster_dict[var + '_SHELL']
tv = stat_file.variables['THETAV'][l, :]
tv[1:-1] = (tv[2:]-tv[:-2])/mc.dz/2.
tv = tv*ones_like(temp)
cluster_dict['dTHETAV_dz_MEAN'] = tv[mask]
for var in ('DWDZ', 'DPDZ', 'THETAV_LAPSE'):
temp = nc_files['CONDENSED'].variables[var][:]
cluster_dict[var + '_CONDENSED'] = temp[mask]
chi = chi_file.variables['chi'][:]
# chi_shell = chi_file.variables['chi_shell'][:]
# chi_se = chi_file.variables['chi_se'][:]
cluster_dict['CHI'] = chi[mask]
# cluster_dict['CHI_SHELL'] = chi_shell[mask]
surface = surface_file.variables['CONDENSED_SURFACE'][:]
cluster_dict['SURFACE'] = surface[mask]
lsmf = stat_file.variables['MFTETCLD'][l, :]
lsrhoa = stat_file.variables['RHO'][l, :]*stat_file.variables['VTETCLD'][l,:]
E = entrain_file.variables['ETETCLD'][:]
D = entrain_file.variables['DTETCLD'][:]
massflux = entrain_file.variables['MFTETCLD'][:]
volume = entrain_file.variables['VTETCLD'][:]
epsilon = E/massflux
delta = D/massflux
wepsilon = E/rho/volume
wdelta = D/rho/volume
cluster_dict['E'] = E[mask]
cluster_dict['D'] = D[mask]
cluster_dict['EPSILON'] = epsilon[mask]
cluster_dict['DELTA'] = delta[mask]
cluster_dict['EPSILON_LS'] = (E/lsmf)[mask]
cluster_dict['DELTA_LS'] = (D/lsmf)[mask]
cluster_dict['WEPSILON'] = wepsilon[mask]
cluster_dict['WDELTA'] = wdelta[mask]
cluster_dict['WEPSILON_LS'] = (E/lsrhoa)[mask]
cluster_dict['WDELTA_LS'] = (D/lsrhoa)[mask]
for var in (('MF', mf), ('AREA', area)):
temp = var[1]
temp_result = (temp[:, 2:] - temp[:, :-2])/mc.dz/2.
temp_top = (temp[:, 2:] - temp[:, 1:-1])/mc.dz
temp_bottom = (temp[:, 1:-1] - temp[:, :-2])/mc.dz
temp_result[mask_top] = temp_top[mask_top]
temp_result[mask_bottom] = temp_bottom[mask_bottom]
cluster_dict['d_' + var[0] + '_dz'] = temp_result[mask]
cluster_dict['TIME'] = ones_like(z[mask])*l*mc.dt
for item in cluster_dict:
if item in stats_dict:
stats_dict[item].append(cluster_dict[item])
else:
stats_dict[item] = [cluster_dict[item]]
for type in sample_types:
nc_files[type].close()
entrain_file.close()
chi_file.close()
for item in stats_dict:
stats_dict[item] = np.hstack(stats_dict[item])
pickle.dump(stats_dict, open('pkl/condensed_time_stats.pkl', 'wb'))
#!/usr/bin/env python
#Runtime (690, 130, 128, 128): 3 hours 40 minutes
from pylab import *
import numpy
import cPickle
from netCDF4 import Dataset
import os
from ent_analysis.lib.thermo import SAM
import ent_analysis.lib.model_param as mc
# Load mean cloud field stat
stat_file = Dataset(mc.get_stat())
data = {'z': stat_file.variables['z'][:].astype(double),
'RHO' : stat_file.variables['RHO'][0,:].astype(double),
'PRES' : stat_file.variables['PRES'][0,:].astype(double)*100.}
stat_file.close()
def create_savefile(t, data, vars, profile_name):
ids = data['ids'][:]
z = data['z'][:]
savefile = Dataset('cdf/%s_profile_%08d.nc' % (profile_name, t), 'w')
# Create savefile
savefile.createDimension('ids', len(ids))
savefile.createDimension('z', len(z))
tsavevar = savefile.createVariable('ids', 'd', ('ids',))
tsavevar[:] = ids[:]
zsavevar = savefile.createVariable('z', 'd', ('z',))
zsavevar[:] = z[:]
def main():
sample_types = ('CORE', 'ENV', 'PLUME')
stats_dict = {}
for l in range(mc.nt):
print l
cluster_dict = {}
nc_files = {}
nc_files['CORE'] = Dataset('../time_profiles/cdf/core_profile_%08d.nc' % l)
nc_files['PLUME'] = Dataset('../time_profiles/cdf/plume_profile_%08d.nc' % l)
area = nc_files['CORE'].variables['AREA'][:]
mask = (area > 0.)
area[~mask] = 0.
cluster_dict['AREA'] = area[mask]
if mask.sum() == 0:
nc_files['CORE'].close()
nc_files['PLUME'].close()
continue
nc_files['EDGE'] = Dataset('../time_profiles/cdf/core_edge_profile_%08d.nc' % l)
nc_files['SHELL'] = Dataset('../time_profiles/cdf/core_shell_profile_%08d.nc' % l)
nc_files['ENV'] = Dataset('../time_profiles/cdf/core_env_profile_%08d.nc' % l)
entrain_file = Dataset('../time_profiles/cdf/core_entrain_profile_%08d.nc' % l)
surface_file = Dataset('../time_profiles/cdf/surface_profile_%08d.nc' % l)
condensed_shell_file = Dataset('../time_profiles/cdf/condensed_shell_profile_%08d.nc' % l)
chi_file = Dataset('../time_profiles/cdf/core_chi_profile_%08d.nc' % l)
stat_file = Dataset(mc.get_stat())
z = nc_files['CORE'].variables['z'][:]
z = np.resize(z, mask.shape)
cluster_dict['z'] = z[mask]
# Calculate and store cloud thickness for each sample
# Use maked arrays to preserve axes; if z_min == z_max, thickness = dz
masked_z = np.ma.masked_where(area==0., z)
depth = np.ones_like(z)*(masked_z.max(axis=1) -
masked_z.min(axis=1))[:, np.newaxis] + mc.dz
cluster_dict['depth'] = depth[mask]
# Calculate and store condensed shell relative humidity
r = condensed_shell_file.variables['QV'][:]
p = condensed_shell_file.variables['PRES'][:]
T = condensed_shell_file.variables['TABS'][:]
relh = thermo.e(r, p)/thermo.e_star(T)
cluster_dict['RELH_COND_SHELL'] = relh[mask]
stat_core = stat_file.variables['COR'][l, :]
if (stat_core > 0.).any():
k_cb = np.nonzero(stat_core > 0.)[0].min()
else:
k_cb = np.nonzero(area)[1].min()
z_cb = ones_like(z)*z[0, k_cb]
cluster_dict['z_cb'] = z_cb[mask]
z = z*mask
zmax = np.ones_like(mask)*(z.max(1))[:, np.newaxis]
z[~mask] = 1e10
zmin = np.ones_like(mask)*(z.min(1))[:, np.newaxis]
cluster_dict['z_scaled'] = ((z - zmin.min())/(zmax-zmin.min()))[mask]
rho = nc_files['CORE'].variables['RHO'][:]
cluster_dict['RHO'] = rho[mask]
mf = rho*area*nc_files['CORE'].variables['W'][:]
cluster_dict['MF'] = mf[mask]
for var in ('W', 'QT', 'THETAV', 'THETAL', 'QN'):
for type in sample_types:
temp = nc_files[type].variables[var][:]
cluster_dict[var + '_' + type] = temp[mask]
if var != 'W':
temp = stat_file.variables[var][:]
if var == 'QT':
temp = temp/1000.
temp2 = nc_files['CORE'].variables[var][:] - temp[l, :]
cluster_dict[var + '_' + type + '-MEAN'] = temp2[mask]
cluster_dict[var + '_CORE-ENV'] = cluster_dict[var + '_CORE'] - cluster_dict[var + '_ENV']
# cluster_dict[var + '_CORE-SHELL'] = cluster_dict[var + '_CORE'] - cluster_dict[var + '_SHELL']
qsat = SAM.qsatw(nc_files['CORE'].variables['TABS'][:],
nc_files['CORE'].variables['PRES'][:])
cluster_dict['QSAT_CORE'] = qsat[mask]
qsat_cb = qsat[:, k_cb]
qsat_cb = ones_like(qsat)*qsat_cb[:, np.newaxis]
cluster_dict['QSAT_CB'] = qsat_cb[mask]
tv = stat_file.variables['THETAV'][l, :]
tv[1:-1] = (tv[2:]-tv[:-2])/mc.dz/2.
tv = tv*ones_like(temp)
cluster_dict['dTHETAV_dz_MEAN'] = tv[mask]
chi = chi_file.variables['chi_theta'][:]
cluster_dict['CHI'] = chi[mask]
surface = surface_file.variables['CORE_SURFACE'][:]
cluster_dict['SURFACE'] = surface[mask]
lsmf = stat_file.variables['MFTETCOR'][l, :]
lsrhoa = stat_file.variables['RHO'][l, :]*stat_file.variables['VTETCOR'][l,:]
qc = stat_file.variables['QTCOR'][l, :]/1000.
qe = stat_file.variables['QTCEN'][l, :]/1000.
tc = stat_file.variables['TLCOR'][l, :]
te = stat_file.variables['TLCEN'][l, :]
wc = stat_file.variables['WCOR'][l, :]
we = stat_file.variables['WCEN'][l, :]
dwdt = entrain_file.variables['DWDT'][:]
E = entrain_file.variables['ETETCOR'][:]
D = entrain_file.variables['DTETCOR'][:]
Eq = entrain_file.variables['EQTETCOR'][:]
Dq = entrain_file.variables['DQTETCOR'][:]
Et = entrain_file.variables['ETTETCOR'][:]
Dt = entrain_file.variables['DTTETCOR'][:]
Ew = entrain_file.variables['EWTETCOR'][:]
Dw = entrain_file.variables['DWTETCOR'][:]
massflux = entrain_file.variables['MFTETCOR'][:]
volume = entrain_file.variables['VTETCOR'][:]
cluster_dict['DWDT'] = dwdt[mask]
cluster_dict['E'] = E[mask]
cluster_dict['D'] = D[mask]
cluster_dict['EQ'] = Eq[mask]
cluster_dict['DQ'] = Dq[mask]
cluster_dict['ET'] = Et[mask]
cluster_dict['DT'] = Dt[mask]
cluster_dict['EW'] = Ew[mask]
cluster_dict['DW'] = Dw[mask]
Aq = ((Eq/E) - qe)/(qc - qe)
Bq = (qc - (Dq/D))/(qc - qe)
At = ((Et/E) - te)/(tc - te)
Bt = (tc - (Dt/D))/(tc - te)
Aw = ((Ew/E) - we)/(wc - we)
Bw = (wc -(Dw/D))/(wc - we)
cluster_dict['AQ'] = Aq[mask]
cluster_dict['BQ'] = Bq[mask]
cluster_dict['AT'] = At[mask]
cluster_dict['BT'] = Bt[mask]
cluster_dict['AW'] = Aw[mask]
cluster_dict['BW'] = Bw[mask]
Eq_T = ((qc*(E-D) - (Eq-Dq))/(qc-qe))
Dq_T = ((qe*(E-D) - (Eq-Dq))/(qc-qe))
Et_T = ((tc*(E-D) - (Et-Dt))/(tc-te))
Dt_T = ((te*(E-D) - (Et-Dt))/(tc-te))
Ew_T = ((wc*(E-D) - (Ew-Dw))/(wc-we))
Dw_T = ((we*(E-D) - (Ew-Dw))/(wc-we))
cluster_dict['EQ_T'] = Eq_T[mask]
cluster_dict['DQ_T'] = Dq_T[mask]
cluster_dict['ET_T'] = Et_T[mask]
cluster_dict['DT_T'] = Dt_T[mask]
cluster_dict['EW_T'] = Ew_T[mask]
cluster_dict['DW_T'] = Dw_T[mask]
cluster_dict['EPSILON'] = (E/massflux)[mask]
cluster_dict['Q_EPSILON'] = (Eq/massflux)[mask]
cluster_dict['T_EPSILON'] = (Et/massflux)[mask]
cluster_dict['W_EPSILON'] = (Ew/massflux)[mask]
cluster_dict['Q_EPSILON_T'] = (Eq_T/massflux)[mask]
cluster_dict['T_EPSILON_T'] = (Et_T/massflux)[mask]
cluster_dict['W_EPSILON_T'] = (Ew_T/massflux)[mask]
cluster_dict['DELTA'] = (D/massflux)[mask]
cluster_dict['Q_DELTA'] = (Dq/massflux)[mask]
cluster_dict['T_DELTA'] = (Dt/massflux)[mask]
cluster_dict['W_DELTA'] = (Dw/massflux)[mask]
cluster_dict['Q_DELTA_T'] = (Dq_T/massflux)[mask]
cluster_dict['T_DELTA_T'] = (Dt_T/massflux)[mask]
cluster_dict['W_DELTA_T'] = (Dw_T/massflux)[mask]
for var in ('DWDZ', 'DPDZ', 'THETAV_LAPSE'):
temp = nc_files['CORE'].variables[var][:]
cluster_dict[var + '_CORE'] = temp[mask]
ww_reyn = nc_files['CORE'].variables['WWREYN'][:]
wq_reyn = nc_files['CORE'].variables['WQREYN'][:]
ww_reyn = ww_reyn*rho*area
wq_reyn = wq_reyn*rho*area
ww_reyn[~mask] = 0.
wq_reyn[~mask] = 0.
qt_core = nc_files['CORE'].variables['QT'][:]
qt_core[~mask] = 0.
mask_top = mask.copy()
mask_top[:, 1:-1] = mask[:, 1:-1] & ~mask[:, 2:] & mask[:, :-2]
mask_bottom = mask.copy()
mask_bottom[:, 1:-1] = mask[:, 1:-1] & mask[:, 2:] & ~mask[:, :-2]
for var in (('MF', mf),
('AREA', area),
('WW', ww_reyn),
('WQ', wq_reyn),
('Q', qt_core),):
temp = var[1]
temp_result = (temp[:, 2:] - temp[:, :-2])/mc.dz/2.
temp_top = (temp[:, 2:] - temp[:, 1:-1])/mc.dz
temp_bottom = (temp[:, 1:-1] - temp[:, :-2])/mc.dz
temp_result[mask_top] = temp_top[mask_top]
temp_result[mask_bottom] = temp_bottom[mask_bottom]
cluster_dict['D' + var[0] + 'DZ_CORE'] = temp_result[mask]
cluster_dict['TIME'] = ones_like(z[mask])*l*mc.dt
ids = nc_files['CORE'].variables['ids'][:]
cluster_dict['ID'] = (ones_like(z)*ids[:, np.newaxis])[mask]
for item in cluster_dict:
if item in stats_dict:
stats_dict[item].append(cluster_dict[item])
else:
stats_dict[item] = [cluster_dict[item]]
for type in sample_types:
nc_files[type].close()
entrain_file.close()
chi_file.close()
for item in stats_dict:
stats_dict[item] = np.hstack(stats_dict[item])
pickle.dump(stats_dict, open('pkl/core_time_stats.pkl', 'wb'))
def main(filename):
vars = {
'ETETCLD': var_calcs.etetcld,
'DTETCLD': var_calcs.dtetcld,
'EQTETCLD': var_calcs.eqtetcld,
'DQTETCLD': var_calcs.dqtetcld,
'ETTETCLD': var_calcs.ettetcld,
'DTTETCLD': var_calcs.dttetcld,
'EWTETCLD': var_calcs.ewtetcld,
'DWTETCLD': var_calcs.dwtetcld,
'VTETCLD': var_calcs.vtetcld,
'MFTETCLD': var_calcs.mftetcld,
}
# Automatically load time step from output file name
time = mc.time_picker(filename)
# Load CDF Files
nc_file = Dataset(filename)
stat_file = Dataset(mc.get_stat())
data = {'z': stat_file.variables['z'][:].astype(double),
'RHO' : stat_file.variables['RHO'][time,:].astype(double),
'PRES' : stat_file.variables['PRES'][time,:].astype(double)*100.}
stat_file.close()
# For each cloud, iterate over all times
cloud_filename = '../cloudtracker/pkl/cloud_data_%08d.pkl' % time
# Load the cloud data at that timestep
clouds = cPickle.load(open(cloud_filename, 'rb'))
ids = clouds.keys()
ids.sort()
data['ids'] = numpy.array(ids)
for name in ('ETETCLD', 'DTETCLD',
'EQTETCLD', 'DQTETCLD',
'ETTETCLD', 'DTTETCLD',
'EWTETCLD', 'DWTETCLD',
'VTETCLD', 'MFTETCLD'):
data[name] = nc_file.variables[name][0, :].astype(numpy.double)
# For each cloud, create a savefile for each profile
savefiles = {}
profiles = {}
for item in ('condensed_entrain',):
savefile, variables = create_savefile(time, data, vars, item)
savefiles[item] = savefile
profiles[item] = variables
for n, id in enumerate(ids):
print "time: ", time, " id: ", id
# Select the current cloud id
cloud = clouds[id]
cloud['condensed_entrain'] = numpy.hstack([cloud['condensed'], cloud['condensed_shell']])
make_profiles(profiles, cloud, vars, data, n)
for savefile in savefiles.values():
savefile.close()
nc_file.close()
def makechi(filename):
key = int(filename.split("/")[-1].split("_")[-1].split(".")[0])
print "chi_core_profile", key
corefile = Dataset("cdf/core_profile_%08d.nc" % key)
envfile = Dataset("cdf/core_env_profile_%08d.nc" % key)
shellfile = Dataset("cdf/core_shell_profile_%08d.nc" % key)
statfile = Dataset(mc.get_stat())
t = numpy.atleast_1d(corefile.variables["ids"][:])
cloud_duration = len(t)
n = len(t)
z = envfile.variables["z"][:]
p = statfile.variables["PRES"][0, :] * 100.0
thetav_mean = statfile.variables["THETAV"][int(key), :]
area_core = numpy.atleast_2d(corefile.variables["AREA"][:])
thetal_core = numpy.atleast_2d(corefile.variables["THETAL"][:])
thetav_core = numpy.atleast_2d(corefile.variables["THETAV"][:])
qt_core = numpy.atleast_2d(corefile.variables["QT"][:])
ql_core = numpy.atleast_2d(corefile.variables["QN"][:])
T_core = numpy.atleast_2d(corefile.variables["TABS"][:])
p = numpy.atleast_2d(corefile.variables["PRES"][:])
thetal_env = numpy.atleast_2d(envfile.variables["THETAL"][:])
qt_env = numpy.atleast_2d(envfile.variables["QT"][:])
thetal_shell = numpy.atleast_2d(shellfile.variables["THETAL"][:])
qt_shell = numpy.atleast_2d(shellfile.variables["QT"][:])
mask = ~(area_core > 0.0)
thetal_core_mask = numpy.ma.array(thetal_core, mask=mask)
thetav_core_mask = numpy.ma.array(thetav_core, mask=mask)
qt_core_mask = numpy.ma.array(qt_core, mask=mask)
ql_core_mask = numpy.ma.array(ql_core, mask=mask)
T_core_mask = numpy.ma.array(T_core, mask=mask)
thetal_env_mask = numpy.ma.array(thetal_env, mask=mask)
qt_env_mask = numpy.ma.array(qt_env, mask=mask)
thetal_shell_mask = numpy.ma.array(thetal_shell, mask=mask)
qt_shell_mask = numpy.ma.array(qt_shell, mask=mask)
chi_theta = SAM.find_chi_theta(thetal_core, thetal_env, thetav_core, thetav_mean, qt_core, qt_env, T_core, p)
chi_theta_mean = SAM.find_chi_theta(
thetal_core_mask.mean(0),
thetal_env_mask.mean(0),
thetav_core_mask.mean(0),
thetav_mean,
qt_core_mask.mean(0),
qt_env_mask.mean(0),
T_core_mask.mean(0),
p,
)
chi_theta_mean_core = SAM.find_chi_theta(
thetal_core_mask.mean(0),
thetal_env,
thetav_core_mask.mean(0),
thetav_mean,
qt_core_mask.mean(0),
qt_env,
T_core_mask.mean(0),
p,
)
chi_theta_mean_env = SAM.find_chi_theta(
thetal_core, thetal_env_mask.mean(0), thetav_core, thetav_mean, qt_core, qt_env_mask.mean(0), T_core, p
)
chi_ql = SAM.find_chi_ql(ql_core, thetal_core, thetal_env, qt_core, qt_env, T_core, p)
chi_ql_mean = SAM.find_chi_ql(
ql_core_mask.mean(0),
thetal_core_mask.mean(0),
thetal_env_mask.mean(0),
qt_core_mask.mean(0),
qt_env_mask.mean(0),
T_core_mask.mean(0),
p,
)
chi_ql_mean_core = SAM.find_chi_ql(
ql_core_mask.mean(0), thetal_core_mask.mean(0), thetal_env, qt_core_mask.mean(0), qt_env, T_core_mask.mean(0), p
)
chi_ql_mean_env = SAM.find_chi_ql(
ql_core, thetal_core, thetal_env_mask.mean(0), qt_core, qt_env_mask.mean(0), T_core, p
)
# chi[isnan(chi)] = 0.
savefile = Dataset("cdf/core_chi_profile_%08d.nc" % key, "w", format="NETCDF3_64BIT")
savefile.createDimension("id", cloud_duration)
savefile.createDimension("z", len(z))
var_t = savefile.createVariable("id", "d", ("id",))
var_z = savefile.createVariable("z", "d", ("z",))
var_chi_theta = savefile.createVariable("chi_theta", "d", ("id", "z"))
var_chi_theta_mean = savefile.createVariable("chi_theta_mean", "d", ("id", "z"))
var_chi_theta_mean_env = savefile.createVariable("chi_theta_mean_env", "d", ("id", "z"))
var_chi_theta_mean_core = savefile.createVariable("chi_theta_mean_core", "d", ("id", "z"))
var_chi_ql = savefile.createVariable("chi_ql", "d", ("id", "z"))
var_chi_ql_mean = savefile.createVariable("chi_ql_mean", "d", ("id", "z"))
var_chi_ql_mean_env = savefile.createVariable("chi_ql_mean_env", "d", ("id", "z"))
var_chi_ql_mean_core = savefile.createVariable("chi_ql_mean_core", "d", ("id", "z"))
var_t[:] = t
var_z[:] = z
var_chi_theta[:] = chi_theta
var_chi_theta_mean[:] = chi_theta_mean * (~mask)
var_chi_theta_mean_core[:] = chi_theta_mean_core * (~mask)
var_chi_theta_mean_env[:] = chi_theta_mean_env * (~mask)
var_chi_ql[:] = chi_ql
var_chi_ql_mean[:] = chi_ql_mean * (~mask)
var_chi_ql_mean_core[:] = chi_ql_mean_core * (~mask)
var_chi_ql_mean_env[:] = chi_ql_mean_env * (~mask)
corefile.close()
envfile.close()
shellfile.close()
statfile.close()
savefile.close()
def main(filename):
vars = {
'AREA': var_calcs.area,
'TABS': var_calcs.tabs,
'QN': var_calcs.qn,
'QV': var_calcs.qv,
'QT': var_calcs.qt,
'U': var_calcs.u,
'V': var_calcs.v,
'W': var_calcs.w,
'THETAV': var_calcs.thetav,
'THETAV_LAPSE': var_calcs.thetav_lapse,
'THETAL': var_calcs.thetal,
'MSE': var_calcs.mse,
'RHO': var_calcs.rho,
'PRES': var_calcs.press,
'WQREYN': var_calcs.wqreyn,
'WWREYN': var_calcs.wwreyn,
'DWDZ': var_calcs.dw_dz,
'DPDZ': var_calcs.dp_dz,
'TR01': var_calcs.tr01,
}
# Automatically load time step from output file name
time = mc.time_picker(filename)
# Load CDF Files
nc_file = Dataset(filename)
stat_file = Dataset(mc.get_stat())
data = {'z': nc_file.variables['z'][:].astype(double),
'p': nc_file.variables['p'][:].astype(double),
'RHO' : stat_file.variables['RHO'][time,:].astype(double),
}
stat_file.close()
# For each cloud, iterate over all times
cloud_filename = '../cloudtracker/pkl/cloud_data_%08d.pkl' % time
# Load the cloud data at that timestep
clouds = cPickle.load(open(cloud_filename, 'rb'))
ids = clouds.keys()
ids.sort()
data['ids'] = numpy.array(ids)
for name in ('QV', 'QN', 'TABS', 'PP', 'U', 'V', 'W', 'TR01'):
data[name] = nc_file.variables[name][0, :].astype(numpy.double)
# For each cloud, create a savefile for each profile
savefiles = {}
profiles = {}
for item in ('core', 'condensed', 'condensed_shell',
'condensed_edge', 'condensed_env',
'core_shell',
'core_edge', 'core_env',
'plume'):
savefile, variables = create_savefile(time, data, vars, item)
savefiles[item] = savefile
profiles[item] = variables
for n, id in enumerate(ids):
print "time: ", time, " id: ", id
# Select the current cloud id
cloud = clouds[id]
make_profiles(profiles, cloud, vars, data, n)
for savefile in savefiles.values():
savefile.close()
nc_file.close()
