pprob.append(prob)
plist.append(cmean)
shearlist.append(((shearbins[id + 1]) - c) / 2)
xtick = shearbins[0:-1]
xtickwidth = (shearbins[1::] - shearbins[0:-1])
fig = plt.figure(figsize=(25, 45), dpi=70)
ax1 = fig.add_subplot(331)
xy = np.vstack([sh, pp])
z = gaussian_kde(xy)(xy)
test = z / (z.max() - z.min())
r = u_stat.pcor(sh, pp, qq)
mappable = ax1.scatter(sh, pp, c=test, edgecolor='', cmap='viridis_r',
s=20) # viridis_r
ax1.set_ylabel('Max. rainfall (mm h$^{-1}$)')
ax1.set_xlabel('u925hPa')
ax1.set_title('P-corr. u925hPa/rain | q removed: ' +
str(np.round(r[0], decimals=2)),
fontsize=cc)
ax1.tick_params(direction='in')
cbar = fig.colorbar(mappable)
cbar.set_label('Density')
print('Partial correlation umin_mid:', u_stat.pcor(umin, pp, qq))
####################################################################################
ax2 = fig.add_subplot(332)
pprob.append(prob)
plist.append(cmean)
shearlist.append(((shearbins[id + 1]) - c) / 2)
xtick = shearbins[0:-1]
xtickwidth = (shearbins[1::] - shearbins[0:-1])
fig = plt.figure(figsize=(10, 6), dpi=200)
ax1 = fig.add_subplot(221)
xy = np.vstack([sh, tt])
z = gaussian_kde(xy)(xy)
test = z / (z.max() - z.min())
r = u_stat.pcor(sh, tt, qq)
mappable = ax1.scatter(sh, tt, c=test, edgecolor='', cmap='viridis_r',
s=20) # viridis_r
ax1.set_ylabel('Min. T ($^{\circ}$C)')
ax1.set_xlabel('Max. zonal wind shear (m s$^{-1}$)')
ax1.set_title('P-corr. shear/T | q removed: ' +
str(np.round(r[0], decimals=2)),
fontsize=cc)
ax1.tick_params(direction='in')
cbar = fig.colorbar(mappable)
cbar.set_label('Density')
####################################################################################
ax2 = fig.add_subplot(222)
pprob.append(prob)
plist.append(cmean)
shearlist.append(((shearbins[id + 1]) - c) / 2)
xtick = shearbins[0:-1]
xtickwidth = (shearbins[1::] - shearbins[0:-1])
fig = plt.figure(figsize=(25, 45), dpi=70)
ax1 = fig.add_subplot(221)
xy = np.vstack([sh, pp])
z = gaussian_kde(xy)(xy)
test = z / (z.max() - z.min())
r = u_stat.pcor(sh, pp, qq)
mappable = ax1.scatter(sh, pp, c=test, edgecolor='', cmap='viridis_r',
s=20) # viridis_r
ax1.set_ylabel('Max. rainfall (mm h$^{-1}$)')
ax1.set_xlabel('u925hPa')
ax1.set_title('P-corr. u925hPa/rain | q removed: ' +
str(np.round(r[0], decimals=2)),
fontsize=cc)
ax1.tick_params(direction='in')
cbar = fig.colorbar(mappable)
cbar.set_label('Density')
print('Partial correlation umin_mid:', u_stat.pcor(umin, pp, qq))
####################################################################################
ax2 = fig.add_subplot(222)
plist.append(cmean)
shearlist.append(((shearbins[id+1])-c)/2)
xtick = shearbins[0:-1]
xtickwidth= (shearbins[1::]-shearbins[0:-1])
fig = plt.figure(figsize=(25, 45), dpi=70)
ax1 = fig.add_subplot(221)
xy = np.vstack([sh, pp])
z = gaussian_kde(xy)(xy)
test = z / (z.max() - z.min())
r = u_stat.pcor(sh,pp, qq)
r=stats.pearsonr(sh,pp)
mappable = ax1.scatter(sh, pp, c=test, edgecolor='', cmap='viridis_r', s=20) # viridis_r
ax1.set_ylabel('Max. rainfall (mm h$^{-1}$)')
ax1.set_xlabel('u925hPa')
ax1.set_title('P-corr. u925hPa/rain | q removed: '+str(np.round(r[0], decimals=2)), fontsize=cc)
ax1.tick_params(direction='in')
cbar = fig.colorbar(mappable)
cbar.set_label('Density')
print('Partial correlation umin_mid:', u_stat.pcor(umin,pp, qq) )
####################################################################################
ax2 = fig.add_subplot(222)
xy = np.vstack([pp,qq])
plist.append(cmean)
shearlist.append(((shearbins[id+1])-c)/2)
xtick = shearbins[0:-1]
xtickwidth= (shearbins[1::]-shearbins[0:-1])
fig = plt.figure(figsize=(10, 6), dpi=200)
ax1 = fig.add_subplot(221)
xy = np.vstack([sh, tt])
z = gaussian_kde(xy)(xy)
test = z / (z.max() - z.min())
r = u_stat.pcor(sh,tt, qq)
mappable = ax1.scatter(sh, tt, c=test, edgecolor='', cmap='viridis_r', s=20) # viridis_r
ax1.set_ylabel('Min. T ($^{\circ}$C)')
ax1.set_xlabel('Max. zonal wind shear (m s$^{-1}$)')
ax1.set_title('P-corr. shear/T | q removed: '+str(np.round(r[0], decimals=2)), fontsize=cc)
ax1.tick_params(direction='in')
cbar = fig.colorbar(mappable)
cbar.set_label('Density')
####################################################################################
ax2 = fig.add_subplot(222)
xy = np.vstack([qq,tt])
z = gaussian_kde(xy)(xy)
test = z / (z.max() - z.min())