while a <= nexp:
ax = plt.subplot(gs[a-1])
ax.contourf(xxv, yyv, np.ones_like(xxv)*np.nan) #contour valid dates and a forecast hour ranges
if a == 1:
yy,xx = np.meshgrid(models_dates_array[a-1,:], fcsthrs)
C0 = ax.contourf(xx, yy, model_stat_array[a-1,:,:], cmap=plt.cm.winter_r, extend='both')
C = ax.contour(xx, yy, model_stat_array[a-1,:,:], levels=C0.levels, colors='k', linewidths=1.0)
ax.clabel(C,C0.levels, fmt='%1.2f', inline=True, fontsize=12.5)
ax.set_title(str(model_fcst[a-1]), loc='left')
elif a == 2:
yy,xx = np.meshgrid(models_dates_array[a-1,:], fcsthrs)
print(np.min(model_stat_array[a-1,:,:]-model_stat_array[0,:,:]))
print(np.max(model_stat_array[a-1,:,:]-model_stat_array[0,:,:]))
print(np.nanmin(model_stat_array[a-1,:,:]-model_stat_array[0,:,:]))
print(np.nanmax(model_stat_array[a-1,:,:]-model_stat_array[0,:,:]))
c1levels = pd.get_clevels(model_stat_array[a-1,:,:]-model_stat_array[0,:,:])
C1 = ax.contourf(xx, yy, model_stat_array[a-1,:,:]-model_stat_array[0,:,:], levels=c1levels, cmap=plt.cm.coolwarm, locator=matplotlib.ticker.MaxNLocator(symmetric='True'), extend='both')
C = ax.contour(xx, yy, model_stat_array[a-1,:,:]-model_stat_array[0,:,:], levels=C1.levels, colors='k', linewidths=1.0)
ax.clabel(C,C1.levels, fmt='%1.2f', inline=True, fontsize=12.5)
ax.set_title(str(model_fcst[a-1])+'-'+str(model_fcst[0]), loc='left')
elif a > 2:
ax.contourf(xx, yy, model_stat_array[a-1,:,:]-model_stat_array[0,:,:], levels=C1.levels, cmap=plt.cm.coolwarm, extend='both')
C = ax.contour(xx, yy, model_stat_array[a-1,:,:]-model_stat_array[0,:,:], levels=C1.levels, colors='k', linewidths=1.0)
ax.clabel(C,C1.levels, fmt='%1.2f', inline=True, fontsize=12.5)
ax.set_title(str(model_fcst[a-1])+'-'+str(model_fcst[0]), loc='left')
ax.grid(True)
ax.set_ylabel('Verification Date')
if len(dates) <= 31:
ax.yaxis.set_major_locator(md.DayLocator(interval=7))
ax.yaxis.set_major_formatter(md.DateFormatter('%d%b\n%Y'))
ax.yaxis.set_minor_locator(md.DayLocator())
float)
model_now_stat_now_means_array = np.ma.masked_array(
model_now_stat_now_means_array,
mask=model_now_stat_now_means_array == np.nan)
#create image directory if does not exist
if not os.path.exists(
os.path.join(plotting_out_dir, "imgs", cycle + "Z")):
os.makedirs(os.path.join(plotting_out_dir, "imgs", cycle + "Z"))
#make plot
ax = plt.subplot(gs[m - 1])
yy, xx = np.meshgrid(fcst_var_levels_num, leads)
if m == 1:
logger.debug("Plotting " + stat_now + " leads - pressure for " +
model_now)
if stat_now == 'bias':
c0levels = pd.get_clevels(model_now_stat_now_means_array)
C0 = ax.contourf(
xx,
yy,
model_now_stat_now_means_array,
levels=c0levels,
cmap=cmap_bias,
locator=matplotlib.ticker.MaxNLocator(symmetric=True),
extend='both')
else:
C0 = ax.contourf(xx,
yy,
model_now_stat_now_means_array,
cmap=cmap,
extend='both')
model_1_stat_now_dates_array = model_now_stat_now_means_array
gs = gridspec.GridSpec(2,2)
gs.update(wspace=0.3, hspace=0.25)
elif nexp > 4 and nexp <= 6:
fig = plt.figure(figsize=(19,12))
gs = gridspec.GridSpec(2,3)
gs.update(wspace=0.3, hspace=0.25)
elif nexp > 6:
fig = plt.figure(figsize=(21,17))
gs = gridspec.GridSpec(3,3)
gs.update(wspace=0.35, hspace=0.25)
while a <= nexp:
ax = plt.subplot(gs[a-1])
#ax.contourf(xxv, yyv, np.ones_like(xxv)*np.nan)
if a == 1:
if stat_now_name == 'bias':
c0levels = pd.get_clevels(models_tp_stat_array[a-1,:,:])
C0 = ax.contourf(xx, yy, models_tp_stat_array[a-1,:,:], levels=c0levels, cmap=plt.cm.RdYlGn_r, locator=matplotlib.ticker.MaxNLocator(symmetric=True), extend='both')
else:
C0 = ax.contourf(xx, yy, models_tp_stat_array[a-1,:,:], cmap=plt.cm.BuPu_r, extend='both')
C = ax.contour(xx, yy, models_tp_stat_array[a-1,:,:], levels=C0.levels, colors='k', linewidths=1.0)
ax.clabel(C,C0.levels, fmt='%1.2f', inline=True, fontsize=12.5)
ax.set_title(str(model_fcst[a-1]), loc='left')
elif a == 2:
if stat_now_name == 'bias':
C1 = ax.contourf(xx, yy, models_tp_stat_array[a-1,:,:], levels=C0.levels, cmap=plt.cm.RdYlGn_r, extend='both')
C = ax.contour(xx, yy, models_tp_stat_array[a-1,:,:], levels=C0.levels, colors='k', linewidths=1.0)
ax.clabel(C,C0.levels, fmt='%1.2f', inline=True, fontsize=12.5)
ax.set_title(str(model_fcst[a-1]), loc='left')
else:
c1levels = pf.get_clevels( models_tp_stat_array[a-1,:,:]-models_tp_stat_array[0,:,:])
C1 = ax.contourf(xx, yy, models_tp_stat_array[a-1,:,:]-models_tp_stat_array[0,:,:], levels=c1levels, cmap=plt.cm.coolwarm, locator=matplotlib.ticker.MaxNLocator(symmetric=True), extend='both')
