def main(filename):
time_step = mc.time_picker(filename)
# Load all the data needed to calculation core, clouds, updrafts, etc
# at the current time_step.
nc_file = Dataset(filename)
tabs_field = nc_file.variables["TABS"][0, :].astype(double)
qv_field = nc_file.variables["QV"][0, :].astype(double) / 1000.0
qn_field = nc_file.variables["QN"][0, :].astype(double) / 1000.0
p_field = nc_file.variables["p"][:].astype(double) * 100.0
cloud_field = qn_field > 0.0
thetav_field = SAM.theta_v(p_field[:, numpy.newaxis, numpy.newaxis], tabs_field, qv_field, qn_field, 0.0)
buoy_field = thetav_field > (thetav_field.mean(2).mean(1))[:, numpy.newaxis, numpy.newaxis]
u_field = nc_file.variables["U"][0, :].astype(double)
u_field[:, :-1, :] += u_field[:, 1:, :]
u_field[:, -1, :] += u_field[:, 0, :]
u_field = u_field / 2.0
v_field = nc_file.variables["V"][0, :].astype(double)
v_field[:, :, :-1] += v_field[:, :, 1:]
v_field[:, :, -1] += v_field[:, :, 0]
v_field = v_field / 2.0
# print "Load w"
w_field = nc_file.variables["W"][0, :].astype(double)
w_field[:-1, :, :] += w_field[1:, :, :]
w_field[:-1, :, :] = w_field[:-1, :, :] / 2.0
up_field = w_field > 0.0
core_field = up_field & buoy_field & cloud_field
# print "Load plume"
tr_field = nc_file.variables["TR01"][0, :].astype(double)
x = nc_file.variables["x"][:].astype(double)
y = nc_file.variables["y"][:].astype(double)
z = nc_file.variables["z"][:].astype(double)
nc_file.close()
tr_mean = tr_field.reshape((len(z), len(y) * len(x))).mean(1)
tr_stdev = numpy.sqrt(tr_field.reshape((len(z), len(y) * len(x))).var(1))
tr_min = 0.05 * numpy.cumsum(tr_stdev) / (numpy.arange(len(tr_stdev)) + 1)
# plume_field = (tr_field > numpy.max(numpy.array([tr_mean + tr_stdev, tr_min]), 0)[:, numpy.newaxis, numpy.newaxis]) & up_field
plume_field = tr_field > numpy.max(numpy.array([tr_mean + tr_stdev, tr_min]), 0)[:, numpy.newaxis, numpy.newaxis]
save_file = Dataset("%s/tracking/cloudtracker_input_%08g.nc" % (mc.data_directory, time_step), "w")
save_file.createDimension("x", len(x))
save_file.createDimension("y", len(y))
save_file.createDimension("z", len(z))
xvar = save_file.createVariable("x", "f", ("x",))
yvar = save_file.createVariable("y", "f", ("y",))
zvar = save_file.createVariable("z", "f", ("z",))
corevar = save_file.createVariable("core", "i", ("z", "y", "x"))
condvar = save_file.createVariable("condensed", "i", ("z", "y", "x"))
plumevar = save_file.createVariable("plume", "i", ("z", "y", "x"))
uvar = save_file.createVariable("u", "f", ("z", "y", "x"))
vvar = save_file.createVariable("v", "f", ("z", "y", "x"))
wvar = save_file.createVariable("w", "f", ("z", "y", "x"))
xvar[:] = x[:]
yvar[:] = y[:]
zvar[:] = z[:]
corevar[:] = core_field[:]
condvar[:] = cloud_field[:]
plumevar[:] = plume_field[:]
uvar[:] = u_field[:]
vvar[:] = v_field[:]
wvar[:] = w_field[:]
save_file.close()
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 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()