bowl = np.ma.average(bowl, axis=0)
# == Plot ==
# Create variable bundles
varPac = {'name': 'Pacific', 'var_std': stdvar_p, 'bowl': bowl[2, :]}
varAtl = {'name': 'Atlantic', 'var_std': stdvar_a, 'bowl': bowl[1, :]}
varInd = {'name': 'Indian', 'var_std': stdvar_i, 'bowl': bowl[3, :]}
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(17, 5))
cmap = cmaps.viridis
#levels = np.arange(0,0.1201,0.01) # Salinity
levels = np.arange(0, 40, 2) # Depth
cnplot = zonal_2D(plt, 'var_std', axes[0, 0], axes[1, 0], 'left', lat,
density, varAtl, domrho, cmap, levels)
cnplot = zonal_2D(plt, 'var_std', axes[0, 1], axes[1, 1], 'mid', lat,
density, varPac, domrho, cmap, levels)
cnplot = zonal_2D(plt, 'var_std', axes[0, 2], axes[1, 2], 'right', lat,
density, varInd, domrho, cmap, levels)
plt.subplots_adjust(hspace=.0001, wspace=0.05, left=0.04, right=0.86)
cb = plt.colorbar(cnplot[0],
ax=axes.ravel().tolist(),
ticks=levels[::2],
fraction=0.015,
shrink=2.0,
pad=0.05)
cb.set_label('Standard deviation (%s)' % (unit, ), fontweight='bold')
'bowl1': bowl1_a, 'bowl2': bowl2_a, 'labBowl': labBowl}
varInd = {'name': 'Indian', 'var_change': var_change_i, 'var_mean': None,
'bowl1': bowl1_i, 'bowl2': bowl2_i, 'labBowl': labBowl}
# ------------------------------------
# Plot
# ------------------------------------
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(17,5))
if name == 'Durack & Wijffels':
levels = np.linspace(minmax[0], minmax[1], minmax[2])
cmap = custom_div_cmap()
cnplot = zonal_2D(plt, 'total', axes[0,0], axes[1,0], 'left', lat, density, varAtl, domrho, cmap, levels, clevsm, clevsm_bold)
cnplot = zonal_2D(plt, 'total', axes[0,1], axes[1,1], 'mid', lat, density, varPac, domrho, cmap, levels, clevsm, clevsm_bold)
cnplot = zonal_2D(plt, 'total', axes[0,2], axes[1,2], 'right', lat, density, varInd, domrho, cmap, levels, clevsm, clevsm_bold)
else:
levels = np.linspace(minmax[0], minmax[1], minmax[2])
cmap = custom_div_cmap() # plt.get_cmap('bwr')
cnplot = zonal_2D(plt, 'total_mme', axes[0, 0], axes[1, 0], 'left', lat, density, varAtl, domrho, cmap, levels, clevsm, clevsm_bold)
cnplot = zonal_2D(plt, 'total_mme', axes[0, 1], axes[1, 1], 'mid', lat, density, varPac, domrho, cmap, levels, clevsm, clevsm_bold)
cnplot = zonal_2D(plt, 'total_mme', axes[0, 2], axes[1, 2], 'right', lat, density, varInd, domrho, cmap, levels, clevsm, clevsm_bold)
varInd = {'name': 'Indian', 'var_change': var_change_i, 'var_mean': var_mean_i, 'var_error': var_change_er_i,
'var_1950': var_1950[:,:,3], 'var_2000': var_2000[:,:,3],
'dvar_dy': spiciness_mean_i, 'spiciness_change': spiciness_change[:,:,3], 'bowl': bowl_i,
'dvar_dy_1950': spiciness_1950[:,:,3], 'dvar_dy_2000': spiciness_2000[:,:,3]}
# ==== dS/dy ====
fig1, axes = plt.subplots(nrows=2, ncols=3, figsize=(17,5))
levels = np.linspace(-0.1,0.1,16)
cmap = plt.get_cmap('bwr')
cnplot1 = zonal_2D(plt, 'dvar_dy', axes[0,0], axes[1,0], 'left', lat, density, varAtl,
domrho, cmap, levels)
cnplot1 = zonal_2D(plt, 'dvar_dy', axes[0,1], axes[1,1], 'mid', lat, density, varPac,
domrho, cmap, levels)
cnplot1 = zonal_2D(plt, 'dvar_dy', axes[0,2], axes[1,2], 'right', lat, density, varInd,
domrho, cmap, levels)
plt.subplots_adjust(hspace=.0001, wspace=0.05, left=0.04, right=0.86)
cb = plt.colorbar(cnplot1, ax=axes.ravel().tolist(), ticks=levels[::3])
plt.suptitle('dS/dy (%s)' %(name,),
fontweight='bold', fontsize=14, verticalalignment='top')
# Date
now = datetime.datetime.now()
date = now.strftime("%Y-%m-%d")
if figure == 'median':
# -- Median
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(17, 5))
minmax = [min, finalyear - 20 + 0.1, deltat]
unit = 'ToE'
cmap = 'jet_r'
levels = np.arange(minmax[0], minmax[1], minmax[2])
cnplot = zonal_2D(plt, 'ToE', axes[0, 0], axes[1, 0], 'left', lat, density,
varAtlmedian, domrho, cmap, levels)
cnplot[0].cmap.set_over('0.8')
cnplot[1].cmap.set_over('0.8')
# cb = fig.colorbar(cnplot[0],ax=axes.ravel().tolist(), ticks = levels, fraction=0.015, shrink=2.0, pad=0.05)
cb = fig.colorbar(cnplot[1],
ax=axes.ravel().tolist(),
ticks=levels,
fraction=0.015,
shrink=2.0,
pad=0.05)
cb.set_label('%s' % (unit, ), fontweight='bold')
cnplot = zonal_2D(plt, 'ToE', axes[0, 1], axes[1, 1], 'mid', lat, density,
varPacmedian, domrho, cmap, levels)
cnplot[0].cmap.set_over('0.8')
# plt.subplots_adjust(hspace=.0001, wspace=0.05, left=0.04, right=0.86)
#
# cb = plt.colorbar(cnplot1, ax=axes.ravel().tolist(), ticks=levels[::3])
# cb.set_label('%s (%s)' % (legVar, unit), fontweight='bold')
#
# plt.suptitle('Total %s changes (%s)' %(legVar, name),
# fontweight='bold', fontsize=14, verticalalignment='top')
#
#
# ==== Isopycnal migration term ====
fig2, axes = plt.subplots(nrows=2, ncols=3, figsize=(17, 5))
cmap = custom_div_cmap()
levels = np.linspace(minmax[0], minmax[1], minmax[2])
cnplot2 = zonal_2D(plt, 'isopyc_mig', axes[0, 0], axes[1, 0], 'left', lat,
density, varAtl, domrho, cmap, levels)
cnplot2 = zonal_2D(plt, 'isopyc_mig', axes[0, 1], axes[1, 1], 'mid', lat,
density, varPac, domrho, cmap, levels)
cnplot2 = zonal_2D(plt, 'isopyc_mig', axes[0, 2], axes[1, 2], 'right', lat,
density, varInd, domrho), cmap, levels
plt.subplots_adjust(hspace=.0001, wspace=0.05, left=0.04, right=0.86)
cb = plt.colorbar(cnplot2, ax=axes.ravel().tolist(), ticks=levels[::3])
cb.set_label('%s (%s)' % (legVar, unit), fontweight='bold')
plt.suptitle('%s changes due to isopycnal migration (%s)' % (legVar, name),
fontweight='bold',
fontsize=14,
'var_2000': var_2000[:, :, 3],
'dvar_dy': spiciness_mean_i,
'spiciness_change': spiciness_change[:, :, 3],
'bowl': bowl_i,
'dvar_dy_1950': spiciness_1950[:, :, 3],
'dvar_dy_2000': spiciness_2000[:, :, 3]
}
# ==== dS/dy ====
fig1, axes = plt.subplots(nrows=2, ncols=3, figsize=(17, 5))
levels = np.linspace(-0.1, 0.1, 16)
cmap = plt.get_cmap('bwr')
cnplot1 = zonal_2D(plt, 'dvar_dy', axes[0, 0], axes[1, 0], 'left', lat,
density, varAtl, domrho, cmap, levels)
cnplot1 = zonal_2D(plt, 'dvar_dy', axes[0, 1], axes[1, 1], 'mid', lat, density,
varPac, domrho, cmap, levels)
cnplot1 = zonal_2D(plt, 'dvar_dy', axes[0, 2], axes[1, 2], 'right', lat,
density, varInd, domrho, cmap, levels)
plt.subplots_adjust(hspace=.0001, wspace=0.05, left=0.04, right=0.86)
cb = plt.colorbar(cnplot1, ax=axes.ravel().tolist(), ticks=levels[::3])
plt.suptitle('dS/dy (%s)' % (name, ),
fontweight='bold',
fontsize=14,
verticalalignment='top')
# -- Create figure and axes instances
fig12, axes12 = plt.subplots(nrows=2, ncols=3, figsize=(17, 5))
# -- Color map
cmap = custom_div_cmap()
# -- Unit
unit = varname['unit']
# -- Levels
minmax = varname['minmax_zonal_rcp85']
minmax = [-2, 2, 16]
levels = np.linspace(minmax[0], minmax[1], minmax[2])
# -- Contourf
# Atlantic
cnplot12 = zonal_2D(plt, 'total_mme', axes12[0, 0], axes12[1, 0], 'left', lat,
lev, varAtl, domrho, cmap, levels)
# -- Add colorbar
cb12 = fig12.colorbar(cnplot12[1],
ax=axes12.ravel().tolist(),
ticks=levels[::3],
fraction=0.015,
shrink=2.0,
pad=0.05)
cb12.set_label('%s' % (unit, ), fontweight='bold')
# Pacific
cnplot12 = zonal_2D(plt, 'total_mme', axes12[0, 1], axes12[1, 1], 'mid', lat,
lev, varPac, domrho, cmap, levels)
# Indian