dt = datetime.datetime.strptime(date_string, "%Y%m%d%H") fpath = os.path.join(fpath, fname) print fpath f = netCDF4.Dataset(fpath, "r") lons = f.variables["lon_0"][:] lats = f.variables["lat_0"][::-1] # Read in reverse direction levs = f.variables["lv_ISBL0"][:] trtempin = f.variables["TMP_P0_L109_GLL0"][record_num, 1, ::-1, :].squeeze() trpresin = f.variables["PRES_P0_L109_GLL0"][record_num, 1, ::-1, :].squeeze() # slp = f.variables['PRMSL_P0_L101_GLL0'][record_num,::-1,:].squeeze()/10**2 f.close trth = wm.temp_to_theta(trtempin, trpresin) cbar_min_trth = 280 cbar_max_trth = 360 cint_trth = 2 cflevs_trth = np.arange(cbar_min_trth, cbar_max_trth, cint_trth) cflevs_trth_ticks = np.arange(cbar_min_trth, cbar_max_trth, 4 * cint_trth) base_cntr_slp = 1012 cint_slp = 2 cbar_min_slp = base_cntr_slp - 20 * cint_slp cbar_max_slp = base_cntr_slp + 20 * cint_slp cbar_max_slp = cbar_max_slp + (cint_slp / 2) cflevs_slp = np.arange(cbar_min_slp, cbar_max_slp, cint_slp) cflevs_slp_ticks = np.arange(cbar_min_slp, cbar_max_slp, 4 * cint_slp)
nx = np.size(lons)
ny = np.size(lats)
#if plot_winds == 'true':
uv_plot = np.sqrt(u_plot**2 + v_plot**2)
# Convert temperature to potential temperature
if plot_theta == 'true' :
pres = np.zeros_like(temperature).astype('f')
for kk in range(0,len(levs)):
pres[kk,:,:] = levs[kk]
theta = wm.temp_to_theta(temperature, pres)
# Convert wind to knots
u_plot = u_plot * 1.94384
v_plot = v_plot * 1.94384
if cross_orient == 'east-west':
latind = np.ravel(lats==lat_range[0])
loninds = np.ravel((lons<=lon_range[1])&(lons>=lon_range[0]))
x_cross = lons[loninds]
var_cross = var2fill[:,latind,loninds].squeeze()
var_cross_cntr = var2cntr[:,latind,loninds].squeeze()
u_cross = u_plot[:,latind,loninds].squeeze()
v_cross = v_plot[:,latind,loninds].squeeze()
uv_cross = uv_plot[:,latind,loninds].squeeze()
f.close
nx = np.size(lons)
ny = np.size(lats)
#if plot_winds == 'true':
uv_plot = np.sqrt(u_plot**2 + v_plot**2)
# Convert temperature to potential temperature
if plot_theta == 'true':
pres = np.zeros_like(temperature).astype('f')
for kk in range(0, len(levs)):
pres[kk, :, :] = levs[kk]
theta = wm.temp_to_theta(temperature, pres)
# Convert wind to knots
u_plot = u_plot * 1.94384
v_plot = v_plot * 1.94384
if cross_orient == 'east-west':
latind = np.ravel(lats == lat_range[0])
loninds = np.ravel((lons <= lon_range[1]) & (lons >= lon_range[0]))
x_cross = lons[loninds]
var_cross = var2fill[:, latind, loninds].squeeze()
var_cross_cntr = var2cntr[:, latind, loninds].squeeze()
u_cross = u_plot[:, latind, loninds].squeeze()
v_cross = v_plot[:, latind, loninds].squeeze()
uv_cross = uv_plot[:, latind, loninds].squeeze()
if plot_theta == 'true':
# Open the netcdf file and read select variables dt = datetime.datetime.strptime(date_string, '%Y%m%d%H') fpath = os.path.join(fpath, fname) print fpath f = netCDF4.Dataset(fpath, 'r') lons = f.variables['lon_0'][:] lats = f.variables['lat_0'][::-1] # Read in reverse direction levs = f.variables['lv_ISBL0'][:] trtempin = f.variables['TMP_P0_L109_GLL0'][record_num, 1, ::-1, :].squeeze() trpresin = f.variables['PRES_P0_L109_GLL0'][record_num, 1, ::-1, :].squeeze() #slp = f.variables['PRMSL_P0_L101_GLL0'][record_num,::-1,:].squeeze()/10**2 f.close trth = wm.temp_to_theta(trtempin, trpresin) cbar_min_trth = 280 cbar_max_trth = 360 cint_trth = 2 cflevs_trth = np.arange(cbar_min_trth, cbar_max_trth, cint_trth) cflevs_trth_ticks = np.arange(cbar_min_trth, cbar_max_trth, 4 * cint_trth) base_cntr_slp = 1012 cint_slp = 2 cbar_min_slp = base_cntr_slp - 20 * cint_slp cbar_max_slp = base_cntr_slp + 20 * cint_slp cbar_max_slp = cbar_max_slp + (cint_slp / 2) cflevs_slp = np.arange(cbar_min_slp, cbar_max_slp, cint_slp) cflevs_slp_ticks = np.arange(cbar_min_slp, cbar_max_slp, 4 * cint_slp)
