mr_REF_nc.close()
wind_REF_nc.close()
bouy_U05_nc.close()
mr_U05_nc.close()
wind_U05_nc.close()
C_REF_ts = np.nanmean(C_REF, axis = (1, 2))
C_U05_ts = np.nanmean(C_U05, axis = (1, 2))
### Do plotting ###
print 'Plotting'
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
if (len(times_REF) == len(C_REF_ts)) and (len(times_U05) == len(C_U05_ts)):
ax.plot(times_REF+240., C_REF_ts, color = 'k', marker = 'o', markerfacecolor = 'white', label = 'REF')
ax.plot([240., 1200.], [10., 10.], color = 'grey', ls = '--', label = 'REF:spd')
ax.plot([240., 1200.], [5., 5.], color = 'blue', ls = '--', label = 'U05:spd')
ax.plot(times_U05+240., C_U05_ts, color = 'k', marker = 'o', markerfacecolor = 'k', label = 'U05')
else:
ax.plot(np.linspace(240., 1200., len(C_REF_ts)), C_REF_ts, color = 'k', marker = 'o', markerfacecolor = 'white', label = 'REF')
ax.plot(np.linspace(240., 1200., len(C_U05_ts)), C_U05_ts, color = 'k', marker = 'o', markerfacecolor = 'k', label = 'U05')
ax.plot([240., 1200.], [10., 10.], color = 'grey', ls = '--', label = 'REF:spd')
ax.plot([240., 1200.], [5., 5.], color = 'blue', ls = '--', label = 'U05:spd')
plt.legend(loc = 0)
plt.savefig('../cold_pool_stuff.png')
send_email(message = 'Cold pools is complete.', subject = 'cold_pools.py -> complete', attachments = ['../cold_pool_stuff.png'], isAttach = True)
input_nc.close()
print '[' + dt.now().strftime("%H:%M:%S") + '] Closing the output_nc'
output_nc.close()
print '[' + dt.now().strftime("%H:%M:%S") + '] Complete.'
# Create the land-sea mask interpolated field
<<<<<<< HEAD
lsm_nc = Dataset('/work/n02/n02/xb899100/island_masks/lsm50_1600m.nc', 'r')
=======
lsm_nc = Dataset('/work/n02/n02/xb899100/island_masks/lsm50.nc', 'r')
>>>>>>> 62279a4ff074ba2a906de6d583e07e7c7ce0c696
lsm_var_interp = bilinear_interpolation(X, Y, lsm_nc.variables['lsm'][0,:,:,:], swath_x.flatten(), swath_y.flatten(), kind = 2).reshape((int(swath_width/res + 1), len(R)))
lsm_nc.close()
p = Pool(processes = len(hours))
p.map(createSwathNC, hours)
p.close()
p.join()
<<<<<<< HEAD
send_email(message = 'Completed w swath for ' + my_path.split('/')[-2] + ' experiment.', subject = 'regrid_w_along_flow.py', attachments = [''], isAttach = False)
=======
#in_plane_winds(u, v, orientation = 90.)
>>>>>>> 62279a4ff074ba2a906de6d583e07e7c7ce0c696
spinup_data['theta_new'] = np.concatenate(
(spinup_data['theta_new'], np.array([1200.0])), axis=0)
spinup_data['theta_new_z'] = np.concatenate(
(spinup_data['theta_new_z'], np.array([40000.0])), axis=0)
# RDP to a few heights
heights, spinup_data['theta_new'] = RDP(spinup_data['theta_new_z'],
spinup_data['theta_new'], 0.1)
print[int(h) for h in heights]
print[round(t, 2) for t in spinup_data['theta_new']]
print len(heights)
my_message = 'heights:'
for h in heights:
my_message = my_message + str(int(h)) + ','
# remove the last comma and add a new line
my_message = my_message[:-1] + '\n'
for t in spinup_data['theta_new']:
my_message = my_message + str(round(t, 2)) + ','
# remove the last comma and add a new line
my_message = my_message[:-1] + '\n'
my_message = my_message + str(len(heights))
send_email(message=my_message,
subject='Stronger inversion',
attachments=[
'../original_spinup_skewT.png', '../strong_inversion_skewT.png'
])
Y - np.max(Y),
lsm_data[0, 0, :, :],
levels=[1e-08],
colors=my_island_contour)
ax.set_xlabel('x (km)')
ax.set_ylabel('y (km)')
# Plot the original domain
ax.plot([0, 0, np.max(X), np.max(X), 0],
[0, np.max(Y), np.max(Y), 0, 0], 'k--')
ax.set_title(r'$\theta_e$ at ' + str(round(z[0], 2)) + ' m and T+' +
"{0:04d}".format(int(times[it])) + ' mins')
plt.savefig('../theta_e/theta_e_T' + "{0:04d}".format(int(times[it])) +
'.png',
dpi=100)
plt.close('all')
p = Pool()
p.map(my_plot, range(1, len(times)))
plt.show()
p.close()
my_command = 'convert -delay 30 -loop 0 ../theta_e/*.png ../theta_e/theta_e_anim.gif'
os.system(my_command)
send_email(message='Finished creating gif.',
subject='cold_pools_thetaE.py',
attachments=['../theta_e/theta_e_anim.gif'],
isAttach=True)
# read in the netCDFs
bouy_nc = Dataset('../bouy_' + hour + '.nc', 'r')
fluxes_nc = Dataset('../fluxes_' + hour + '.nc', 'r')
wind_nc = Dataset('../wind_' + hour + '.nc', 'r')
# keys
temp_key = u'STASH_m01s16i004' # bouy.nc
pres_key = u'STASH_m01s00i408' # fluxes.nc
q_key = u'STASH_m01s00i010' # bouy.nc
u_key = u'STASH_m01s00i002' # wind.nc
v_key = u'STASH_m01s00i003' # wind.nc
z = bouy_nc.variables[u'thlev_zsea_theta'][:] * 1.
times = bouy_nc.variables[u'min10_0'][:] * 1.
for I in xrange(len(times)):
if not ((hour == '00') and (I == 0)):
print 'Hour = ' + hour + ', time = ' + str(int(times[I]))
get_soundings(I)
bouy_nc.close()
fluxes_nc.close()
wind_nc.close()
send_email(message="Finished plotting downwind soundings!",
subject="downwind_soundings.py",
attachments=[
'../Soundings/Radius_10/AlongWind_sounding_T0720_R10.png',
'../Soundings/Radius_20/AlongWind_sounding_T0720_R10.png'
],
isAttach=True)
print 'l_spinup : ' + str(l_spinup)
print 'l_short : ' + str(l_short)
print 'create_netCDF: ' + str(create_netCDF)
print '\nRegridding winds...'
if create_WIND:
winds(path, l_spinup, l_short, l_diagnostics)
print '\nComputing boundary layer and cloud characteristics...'
zi_lcl(path, ID, l_spinup, l_short, create_netCDF, l_diagnostics)
if l_spinup:
UG = -10.0#float(raw_input('Input zonal geostrophic wind:'))
create_IC(path, ID, UG, l_diagnostics)
send_email(message = 'Finished Processing ' + ID, subject='Process 1', attachments=['../zi_lcl_cc_lwp_'+ID+'.png'], isAttach = True)
=======
path = '/nerc/n02/n02/xb899100/CloudTrail/H150E250/' # raw_input('Input path:')
ID = 'H150E250' # raw_input('Input experiment ID:')
l_spinup = False # bool(int(raw_input('Is this a spinup experiment? (yes = 1, no = 0):')))
l_short = False
#if not l_spinup:
# l_short = bool(int(raw_input('Is this a short experiment? (yes = 1, no = 0):')))
#else:
# l_short = False
create_WIND = False # bool(int(raw_input('Do you want to regrid the winds? (yes = 1, no = 0):')))
create_netCDF = True # bool(int(raw_input('Do you want to create zi_lcl netCDF? (yes = 1, no = 0):')))
l_diagnostics = False
print 'path : ' + path
### Define plume as areas that are 2 standard deviations warmer than the
# horizontal mean at that height at that time ###
x = np.arange(0., 116000., 100.)
y = np.arange(0., 31900., 100.)
X, Y = np.meshgrid(x, y)
island_area = 50. # square kilometres
island_radius = np.sqrt(island_area / np.pi) * 1000. # metres
x_c = 100000. + 2. * island_radius
y_c = 4. * island_radius
R = np.sqrt((X - x_c)**2. + (Y - y_c)**2.)
#print 'Working on hour == 00 for experiment: ' + experiment
send_email(message='Working on experiment: ' + experiment,
subject='Plume Lengths Update',
attachments=[''],
isAttach=False)
my_length[experiment] = plume_length(
bouy_nc.variables[theta_key][:, iz, :, :])
my_area[experiment] = plume_area(bouy_nc.variables[theta_key][:, iz, :, :])
bouy_nc.close()
for hour in hours:
#print 'Working on hour == ' + hour + ' for experiment: ' + experiment
#send_email(message = 'Working on hour == ' + hour + ' for experiment: ' + experiment, subject = 'Plume Lengths Update', attachments = [''], isAttach = False)
bouy_nc = Dataset(paths[experiment] + '/bouy_' + hour + '.nc', 'r')
my_length[experiment] = np.concatenate(
(my_length[experiment],
plume_length(bouy_nc.variables[theta_key][:, iz, :, :])),
axis=0)
my_area[experiment] = np.concatenate(
>>>>>>> 62279a4ff074ba2a906de6d583e07e7c7ce0c696
f.close()
## qcld in updraft ##
f = open(resultpath+'/qcld_ud_udthres_'+str(ud_thres)+'_'+str(pickup_ud[0])+'.pkl','wb')
pickle.dump(qcld_ud_stat,f)
f.close()
## qt in updraft ##
f = open(resultpath+'/qt_ud_udthres_'+str(ud_thres)+'_'+str(pickup_ud[0])+'.pkl','wb')
<<<<<<< HEAD
pickle.dump(mt_ud_stat,f)
=======
pickle.dump(qt_ud_stat,f)
>>>>>>> 62279a4ff074ba2a906de6d583e07e7c7ce0c696
f.close()
## qt perturbation in updraft ##
f = open(resultpath+'/qt_p_ud_udthres_'+str(ud_thres)+'_'+str(pickup_ud[0])+'.pkl','wb')
<<<<<<< HEAD
pickle.dump(mt_p_ud_stat,f)
f.close()
send_email('Gathering cloud statistics complete.\n\nRegards,\ncld_composite.py', 'cld_composite.py --> complete!', attachments = [], isAttach = False)
=======
pickle.dump(qt_p_ud_stat,f)
f.close()
>>>>>>> 62279a4ff074ba2a906de6d583e07e7c7ce0c696
ax.plot(R, zi_cs / 1000., 'k', lw=2)
ax.set_xlabel('Distance Downwind (km)')
ax.set_ylabel('Height (km)')
ax.set_ylim([0, 5])
ax.set_title('Latent Heat Flux (W m$^{-2}$)')
# Resolved TKE
ax = fig.add_subplot(3, 1, 3)
TKE = ax.contourf(R,
z_theta / 1000.,
tke_cs,
cmap='viridis',
levels=np.linspace(0., 5., 11),
extend='max')
fig.colorbar(TKE, ax=ax, label='TKE (m$^{2}$ s$^{-2}$)')
ax.plot(R, zi_cs / 1000., 'k', lw=2)
ax.set_xlabel('Distance Downwind (km)')
ax.set_ylabel('Height (km)')
ax.set_ylim([0, 5])
ax.set_title('Turbulence Kinetic Energy (m$^{2}$ s$^{-2}$)')
plt.suptitle('Time-mean values for the period T+550 to T+900 mins',
fontsize=20)
fig.subplots_adjust(hspace=0.35)
plt.savefig('../turbulence_analysis.png', dpi=100)
plt.close('all')
send_email('turbulence_analysis finished', 'turbulent_fluxes.py',
['../turbulence_analysis.png'])
