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 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()
