def plot_mean_orog_precip(inputs, outputs):
cubes = iris.load(str(inputs[0]))
orog_precip_mean = cubes.extract_strict('orog_precipitation_flux_mean')
nonorog_precip_mean = cubes.extract_strict('non_orog_precipitation_flux_mean')
ocean_precip_mean = cubes.extract_strict('ocean_precipitation_flux_mean')
extent = util.get_extent_from_cube(orog_precip_mean)
for i, (name, precip) in enumerate([('orog', orog_precip_mean.data),
('non orog', nonorog_precip_mean.data),
('ocean/water', ocean_precip_mean.data)]):
plt.figure(figsize=(10, 7.5))
ax = plt.axes(projection=ccrs.PlateCarree())
configure_ax_asia(ax)
im = ax.imshow(precip, origin='lower', extent=extent, norm=mpl.colors.LogNorm(),
vmin=1e-2, vmax=1e2)
plt.colorbar(im, orientation='horizontal', label=f'{name} (mm day$^{{-1}}$)', pad=0.1)
plt.savefig(outputs[i])
plt.figure(figsize=(10, 7.5))
ax = plt.axes(projection=ccrs.PlateCarree())
configure_ax_asia(ax)
im = ax.imshow(100 * orog_precip_mean.data / (orog_precip_mean.data + nonorog_precip_mean.data),
origin='lower', extent=extent, vmin=0, vmax=100)
plt.colorbar(im, orientation='horizontal', label='% orog', pad=0.1)
plt.savefig(outputs[3])
def plot_dem(inputs, outputs):
hydrosheds_dir = PATHS['hydrosheds_dir']
bounds, tx, dem, mask = load_hydrosheds_dem(hydrosheds_dir, 'as')
hb_gdf = load_hydrobasins_geodataframe(hydrosheds_dir, 'as', [1])
raster = build_raster_from_geometries(hb_gdf.geometry, dem.shape, tx)
extent = (bounds.left, bounds.right, bounds.bottom, bounds.top)
ma_dem = np.ma.masked_array(dem, (mask == -1) | (raster == 0))
plt.figure(figsize=(5, 3.6))
ax = plt.axes(projection=ccrs.PlateCarree())
cmap = plt.cm.get_cmap('terrain')
# Fills masked values.
cmap.set_bad(color='k', alpha=0.1)
im = ax.imshow(ma_dem[::-1, :], extent=extent, origin='lower', cmap=cmap)
configure_ax_asia(ax, tight_layout=False)
rect = Rectangle((97.5, 18),
125 - 97.5,
41 - 18,
linewidth=1.5,
edgecolor='k',
facecolor='none')
ax.add_patch(rect)
plt.colorbar(im, orientation='horizontal', label='elevation (m)', pad=0.1)
plt.subplots_adjust(left=0.11, right=0.94, top=0.95, bottom=0.04)
plt.savefig(outputs[0])
def plot_dc_anom_wind_region(inputs, outputs, region, num_per_day):
(lon, lat, extent, lst_offset, orog_region, precip_region, u_region,
v_region) = get_region_data(inputs, region)
dc_u = dc(u_region, num_per_day)
dc_v = dc(v_region, num_per_day)
u_mean = u_region.data.mean(axis=0)
v_mean = v_region.data.mean(axis=0)
anom_dc_u = dc(u_region - u_mean, num_per_day)
anom_dc_v = dc(v_region - v_mean, num_per_day)
for h, output in enumerate(outputs):
print(h)
fig, axes = plt.subplots(1,
2,
subplot_kw={'projection': ccrs.PlateCarree()})
fig.set_size_inches(10, 4.5)
lst = (h + lst_offset) % 24
fig.suptitle(f'LST: {lst:0.1f}')
for ax in axes.flatten():
configure_ax_asia(ax, extent=extent)
ax.contour(lon,
lat,
orog_region.data,
[500, 1000, 2000, 3000, 4000, 5000, 6000],
cmap='terrain')
im = axes[0].quiver(lon[::3], lat[::3], dc_u[h][::3, ::3],
dc_v[h][::3, ::3])
im = axes[1].quiver(lon[::3], lat[::3], anom_dc_u[h][::3, ::3],
anom_dc_v[h][::3, ::3])
plt.savefig(output)
plt.close('all')
def plot_precip_wind_region(inputs, outputs, region, month):
(lon, lat, extent, lst_offset, orog_region, precip_region, u_region,
v_region) = get_region_data(inputs, region)
region_constraint = get_region_constraint(region)
orog_mask_cubes_region = iris.load(str(inputs['orog_mask']),
region_constraint)
dotprod = orog_mask_cubes_region.extract_strict('surf_wind x del orog')
expanded_mask = orog_mask_cubes_region.extract_strict(
'expanded surf_wind x del orog > thresh')
precip_vmax = precip_region.data.max()
dotprod_vmin = dotprod.data.min()
dotprod_vmax = dotprod.data.max()
# dotprod_absmax = np.max([np.abs(dotprod_vmin), np.abs(dotprod_vmax)])
dotprod_absmax = 0.05
for i, output in enumerate(outputs):
print(i)
h = i % 24
dom = i // 24 + 1
fig, axes = plt.subplots(2,
2,
subplot_kw={'projection': ccrs.PlateCarree()})
fig.set_size_inches(10, 8.5)
lst = (h + lst_offset) % 24
fig.suptitle(f'{month} {dom} LST: {lst:0.1f}')
for ax in axes.flatten():
configure_ax_asia(ax, extent=extent)
ax.contour(lon,
lat,
orog_region.data,
[500, 1000, 2000, 3000, 4000, 5000, 6000],
cmap='terrain')
im = axes[0, 0].imshow(precip_region.data[i],
origin='lower',
extent=extent,
vmax=precip_vmax,
norm=mpl.colors.LogNorm())
im = axes[0, 1].quiver(lon[::3], lat[::3], u_region.data[i][::3, ::3],
v_region.data[i][::3, ::3])
im = axes[1, 0].imshow(dotprod.data[i],
origin='lower',
extent=extent,
vmin=-dotprod_absmax,
vmax=dotprod_absmax,
cmap='bwr')
im = axes[1, 1].imshow(expanded_mask.data[i],
origin='lower',
extent=extent)
# plt.colorbar(im, orientation='horizontal', label='precip (??)', pad=0.1)
plt.savefig(output)
plt.close('all')
def plot_hydrobasins_files(inputs, outputs, hb_name):
hb_size = gpd.read_file(str(inputs['shp']))
fig = plt.figure(figsize=(10, 8))
ax = plt.subplot(projection=ccrs.PlateCarree())
ax.set_title(f'scale:{hb_name}, #basins:{len(hb_size)}')
hb_size.plot(ax=ax)
hb_size.geometry.boundary.plot(ax=ax,
color=None,
edgecolor='k',
linewidth=0.5)
configure_ax_asia(ax)
plt.savefig(outputs[0])
plt.close('all')
def plot_gridpoint_mean_precip_asia(inputs, outputs):
# TODO: Saturated colour scale.
cmap, norm, bounds, cbar_kwargs = load_cmap_data(
'cmap_data/li2018_fig2_cb1.pkl')
ppt_cubes = []
for dataset, path in inputs.items():
ppt_cube = iris.load_cube(str(path), 'precip_flux_mean')
assert ppt_cube.units == 'mm hr-1'
ppt_cubes.append(ppt_cube)
extent = get_extent_from_cube(ppt_cube)
fig, axes = plt.subplots(2,
2,
figsize=(10, 7),
subplot_kw=dict(projection=ccrs.PlateCarree()))
for ax, cube, dataset in zip(axes.flatten(), ppt_cubes, inputs.keys()):
ax.set_title(STANDARD_NAMES[dataset])
# Convert from mm hr-1 to mm day-1
im = ax.imshow(cube.data * 24, extent=extent, norm=norm, cmap=cmap)
configure_ax_asia(ax, tight_layout=False)
xticks = range(60, 160, 40)
ax.set_xticks(xticks)
ax.set_xticklabels([f'${t}\\degree$ E' for t in xticks])
for ax in axes[:, 1]:
ax.get_yaxis().set_ticklabels([])
for ax in axes[0, :].flatten():
ax.get_xaxis().set_ticklabels([])
for i, ax in enumerate(axes.flatten()):
c = string.ascii_lowercase[i]
ax.text(0.01, 1.06, f'({c})', size=12, transform=ax.transAxes)
cax = fig.add_axes([0.12, 0.10, 0.74, 0.02])
cbar_kwargs['extend'] = 'max'
cbar_kwargs['ticks'] = [0] + cbar_kwargs['ticks']
plt.colorbar(im,
cax=cax,
orientation='horizontal',
label='precipitation (mm day$^{-1}$)',
**cbar_kwargs)
plt.subplots_adjust(left=0.06,
right=0.95,
top=0.95,
bottom=0.2,
wspace=0.08)
# plt.subplots_adjust(left=0.06, right=0.94, top=0.98, bottom=0.17, wspace=0.1)
plt.savefig(outputs[0])
def plot_mean_precip(inputs, outputs, dataset, hb_name):
weighted_basin_mean_precip_filename = inputs['weighted']
df_mean_precip = pd.read_hdf(weighted_basin_mean_precip_filename)
mean_max_min_precip = pickle.loads(
inputs['mean_precip_max_min'].read_bytes())
max_mean_precip = mean_max_min_precip['max_mean_precip']
# min_mean_precip = mean_max_min_precip['min_mean_precip']
raster_hb_name = hb_name
raster_cube = iris.load_cube(str(inputs['raster_cubes']),
f'hydrobasins_raster_{raster_hb_name}')
raster = raster_cube.data
logger.debug(f'Plot maps - {hb_name}: {dataset}')
mean_precip_map = np.zeros_like(raster, dtype=float)
for i in range(1, raster.max() + 1):
mean_precip_map[raster == i] = df_mean_precip.values[i - 1]
extent = get_extent_from_cube(raster_cube)
cmap, norm, bounds, cbar_kwargs = load_cmap_data(
'cmap_data/li2018_fig2_cb1.pkl')
plt.figure(figsize=(10, 8))
ax = plt.subplot(projection=ccrs.PlateCarree())
plt.title(f'{dataset} mean_precip')
grey_fill = np.zeros(
(mean_precip_map.shape[0], mean_precip_map.shape[1], 3), dtype=int)
grey_fill[raster_cube.data == 0] = (200, 200, 200)
ax.imshow(grey_fill, extent=extent)
masked_mean_precip_map = np.ma.masked_array(mean_precip_map,
raster_cube.data == 0)
im = ax.imshow(
masked_mean_precip_map * 24,
cmap=cmap,
norm=norm,
# vmin=1e-3, vmax=max_mean_precip,
origin='lower',
extent=extent)
plt.colorbar(im,
label=f'precip. (mm day$^{{-1}}$)',
**cbar_kwargs,
spacing='uniform',
orientation='horizontal')
configure_ax_asia(ax, extent)
mean_precip_filename = outputs[0]
plt.savefig(mean_precip_filename)
plt.close()
def _configure_hb_name_dataset_map_grid(axes, hb_names, datasets):
for ax in axes.flatten():
configure_ax_asia(ax, tight_layout=False)
for ax, hb_name in zip(axes[0], hb_names):
if hb_name == 'med':
hb_name = 'medium'
ax.set_title(f'{hb_name} basins')
for ax in axes[:, 2].flatten():
ax.get_yaxis().tick_right()
for ax in axes[:, :2].flatten():
ax.get_yaxis().set_ticks([])
for ax in axes[:len(axes) - 1, :].flatten():
ax.get_xaxis().set_ticks([])
for i, ax in enumerate(axes.flatten()):
c = string.ascii_lowercase[i]
ax.text(0.01, 1.04, f'({c})', size=12, transform=ax.transAxes)
def plot_compare_mean_orog_precip(inputs, outputs, models, months):
inputs1 = [path for (key, path) in inputs.items() if key[0] == models[0]]
inputs2 = [path for (key, path) in inputs.items() if key[0] == models[1]]
orog_precip_cubes1 = iris.load([str(p) for p in inputs1]).concatenate()
orog_precip_cubes2 = iris.load([str(p) for p in inputs2]).concatenate()
extent1, nonorog_precip_mean1, ocean_precip_mean1, orog_precip_mean1 = _extract_precip_fields(orog_precip_cubes1)
extent2, nonorog_precip_mean2, ocean_precip_mean2, orog_precip_mean2 = _extract_precip_fields(orog_precip_cubes2)
assert extent1 == extent2
extent = extent1
for i, (name, precip1, precip2) in enumerate([('orog', orog_precip_mean1, orog_precip_mean2),
('non orog', nonorog_precip_mean1, nonorog_precip_mean2),
('ocean/water', ocean_precip_mean1, ocean_precip_mean2)]):
plt.figure(figsize=(10, 7.5))
ax = plt.axes(projection=ccrs.PlateCarree())
configure_ax_asia(ax)
im = ax.imshow(precip2 / precip1 * 100, origin='lower', extent=extent,
vmin=0, vmax=200, cmap='bwr_r')
plt.colorbar(im, orientation='horizontal', label=f'% change {models[1]}/{models[0]} (%)', pad=0.1)
plt.savefig(outputs[i])
def plot_cmorph_min_max(inputs, outputs):
min_max_text = inputs['min_max'].read_text()
min_data = min_max_text.split('\n')[0].split('=')
max_data = min_max_text.split('\n')[1].split('=')
assert min_data[0] == 'min' and max_data[0] == 'max'
min_start_year = int(min_data[1])
max_start_year = int(max_data[1])
print(min_start_year)
print(max_start_year)
cmap, norm, bounds, cbar_kwargs = load_cmap_data(
'cmap_data/li2018_fig2_cb1.pkl')
fig, axes = plt.subplots(1, 3)
keys = ['full', min_start_year, max_start_year]
title_min_max = {min_start_year: '(min)', max_start_year: '(max)'}
ppt_cubes = []
for ax, key in zip(axes, keys):
path = inputs[key]
ppt_cube = iris.load_cube(str(path), 'precip_flux_mean')
assert ppt_cube.units == 'mm hr-1'
ppt_cubes.append(ppt_cube)
extent = get_extent_from_cube(ppt_cube)
fig, axes = plt.subplots(1,
3,
figsize=(10, 3.5),
subplot_kw=dict(projection=ccrs.PlateCarree()))
for ax, cube, key in zip(axes.flatten(), ppt_cubes, keys):
if key == 'full':
name = '1998-2018'
else:
name = f'{key}-{key + 3}'
ax.set_title(name)
# Convert from mm hr-1 to mm day-1
im = ax.imshow(cube.data * 24, extent=extent, norm=norm, cmap=cmap)
configure_ax_asia(ax, tight_layout=False)
xticks = range(60, 160, 40)
ax.set_xticks(xticks)
ax.set_xticklabels([f'${t}\\degree$ E' for t in xticks])
for ax in axes[1:]:
ax.get_yaxis().set_ticklabels([])
for i, ax in enumerate(axes.flatten()):
c = string.ascii_lowercase[i]
ax.text(0.01, 1.06, f'({c})', size=12, transform=ax.transAxes)
cax = fig.add_axes([0.12, 0.20, 0.74, 0.02])
plt.colorbar(im,
cax=cax,
orientation='horizontal',
label='precipitation (mm day$^{-1}$)',
**cbar_kwargs)
plt.subplots_adjust(left=0.06,
right=0.94,
top=0.99,
bottom=0.2,
wspace=0.1)
plt.savefig(outputs[0])
def plot_fig3(inputs, outputs):
m1_cubes = iris.load([
str(inputs[f'al508_diag_{month:02}']) for month in [6, 7, 8]
]).concatenate()
m2_cubes = iris.load(str(inputs['al508_direct']))
m1_orog_precip = m1_cubes.extract_strict('orog_precipitation_flux')
m1_nonorog_precip = m1_cubes.extract_strict('non_orog_precipitation_flux')
m1_ocean_precip = m1_cubes.extract_strict('ocean_precipitation_flux')
assert m1_orog_precip.units == 'kg m-2 s-1'
assert m1_nonorog_precip.units == 'kg m-2 s-1'
assert m1_ocean_precip.units == 'kg m-2 s-1'
m1_extent = util.get_extent_from_cube(m1_orog_precip)
m1_orog_precip_mean = m1_orog_precip.data.mean(
axis=0) * 3600 * 24 # kg m-2 s-1 -> mm day-1
m1_nonorog_precip_mean = m1_nonorog_precip.data.mean(
axis=0) * 3600 * 24 # kg m-2 s-1 -> mm day-1
m1_ocean_precip_mean = m1_nonorog_precip.data.mean(
axis=0) * 3600 * 24 # kg m-2 s-1 -> mm day-1
m2_orog_precip_mean = m2_cubes.extract_strict(
'orog_precipitation_flux_mean')
m2_nonorog_precip_mean = m2_cubes.extract_strict(
'non_orog_precipitation_flux_mean')
m2_ocean_precip_mean = m2_cubes.extract_strict(
'ocean_precipitation_flux_mean')
m2_extent = util.get_extent_from_cube(m2_orog_precip_mean)
fig, axes = plt.subplots(1,
2,
figsize=(10, 4),
subplot_kw={'projection': ccrs.PlateCarree()})
for ax, (nonorog, orog, ocean), extent in zip(
axes.flatten(),
[(m1_nonorog_precip_mean, m1_orog_precip_mean, m1_ocean_precip_mean),
(m2_nonorog_precip_mean.data, m2_orog_precip_mean.data,
m2_ocean_precip_mean.data)], [m1_extent, m2_extent]):
configure_ax_asia(ax, tight_layout=False)
im = ax.imshow(100 * orog / (orog + nonorog),
origin='lower',
extent=extent,
vmin=0,
vmax=100)
axes[1].get_yaxis().set_ticks([])
cax = fig.add_axes([0.1, 0.12, 0.8, 0.03])
for i, ax in enumerate(axes.flatten()):
c = string.ascii_lowercase[i]
ax.text(-0.07, 0.95, f'({c})', size=12, transform=ax.transAxes)
axes[0].set_title('M1')
axes[1].set_title('M2')
plt.colorbar(im, cax=cax, orientation='horizontal', label='% orog.')
plt.subplots_adjust(left=0.1,
top=0.96,
right=0.98,
bottom=0.2,
hspace=0.07,
wspace=0.09)
plt.savefig(outputs[0])
def plot_fig4(inputs, outputs):
al508_cubes = iris.load(str(inputs['al508_direct']))
ak543_cubes = iris.load(str(inputs['ak543_direct']))
al508_orog_precip_mean = al508_cubes.extract_strict(
'orog_precipitation_flux_mean')
al508_nonorog_precip_mean = al508_cubes.extract_strict(
'non_orog_precipitation_flux_mean')
al508_ocean_precip_mean = al508_cubes.extract_strict(
'ocean_precipitation_flux_mean')
al508_extent = util.get_extent_from_cube(al508_orog_precip_mean)
ak543_orog_precip_mean = ak543_cubes.extract_strict(
'orog_precipitation_flux_mean')
ak543_nonorog_precip_mean = ak543_cubes.extract_strict(
'non_orog_precipitation_flux_mean')
ak543_ocean_precip_mean = ak543_cubes.extract_strict(
'ocean_precipitation_flux_mean')
ak543_extent = util.get_extent_from_cube(ak543_orog_precip_mean)
al508_mean = al508_orog_precip_mean.data + al508_nonorog_precip_mean.data + al508_ocean_precip_mean.data
ak543_mean = ak543_orog_precip_mean.data + ak543_nonorog_precip_mean.data + ak543_ocean_precip_mean.data
al508_opf = 100 * al508_orog_precip_mean.data / (
al508_orog_precip_mean.data + al508_nonorog_precip_mean.data)
ak543_opf = 100 * ak543_orog_precip_mean.data / (
ak543_orog_precip_mean.data + ak543_nonorog_precip_mean.data)
fig, axes = plt.subplots(2,
2,
figsize=(10, 6.4),
subplot_kw={'projection': ccrs.PlateCarree()})
extent = al508_extent
for ax in axes.flatten():
configure_ax_asia(ax, tight_layout=False)
im0 = axes[0, 0].imshow(al508_mean,
origin='lower',
extent=extent,
norm=mpl.colors.LogNorm(),
vmin=1,
vmax=1e2)
im1 = axes[1, 0].imshow(ak543_mean,
origin='lower',
extent=extent,
norm=mpl.colors.LogNorm(),
vmin=1,
vmax=1e2)
im3 = axes[0, 1].imshow(al508_opf,
origin='lower',
extent=extent,
vmin=0,
vmax=100)
im4 = axes[1, 1].imshow(ak543_opf,
origin='lower',
extent=extent,
vmin=0,
vmax=100)
for ax in axes.flatten()[:2]:
ax.get_xaxis().set_ticks([])
for ax in axes[:, 1].flatten():
ax.get_yaxis().set_ticks([])
for i, ax in enumerate(axes.flatten()):
c = string.ascii_lowercase[i]
ax.text(-0.07, 0.95, f'({c})', size=12, transform=ax.transAxes)
axes[0, 0].set_title('total precip.')
axes[0, 1].set_title('% orog.')
axes[0, 0].set_ylabel('N1280')
axes[1, 0].set_ylabel('N1280-EC')
cax1 = fig.add_axes([0.1, 0.1, 0.4, 0.03])
cax2 = fig.add_axes([0.56, 0.1, 0.4, 0.03])
plt.colorbar(im1,
cax=cax1,
orientation='horizontal',
label='precip. (mm day$^{-1}$)')
plt.colorbar(im3, cax=cax2, orientation='horizontal', label='% orog.')
plt.subplots_adjust(left=0.1,
top=0.96,
right=0.98,
bottom=0.2,
hspace=0.07,
wspace=0.09)
plt.savefig(outputs[0])
fig, axes = plt.subplots(1,
2,
figsize=(10, 4),
subplot_kw={'projection': ccrs.PlateCarree()})
for ax in axes.flatten():
configure_ax_asia(ax, tight_layout=False)
for i, ax in enumerate(axes.flatten()):
c = string.ascii_lowercase[i]
ax.text(-0.07, 0.95, f'({c})', size=12, transform=ax.transAxes)
levels = [-20, -10, -5, -1, 1, 5, 10, 20]
colour_levels = [0, 0.2, 0.4, 0.5, 0.6, 0.8, 0.99]
norm1 = mpl.colors.BoundaryNorm(levels, ncolors=256)
colours = [(i / (len(colour_levels) - 1), mpl.cm.bwr_r(v))
for i, v in enumerate(colour_levels)]
cmap1 = mpl.colors.LinearSegmentedColormap.from_list('frac_list', colours)
im2 = axes[0].imshow(ak543_mean - al508_mean,
origin='lower',
extent=extent,
cmap=cmap1,
norm=norm1)
levels = [-100, -50, -10, -1, 1, 10, 50, 100]
colour_levels = [0, 0.2, 0.4, 0.5, 0.6, 0.8, 0.99]
norm2 = mpl.colors.BoundaryNorm(levels, ncolors=256)
colours = [(i / (len(colour_levels) - 1), mpl.cm.bwr_r(v))
for i, v in enumerate(colour_levels)]
cmap2 = mpl.colors.LinearSegmentedColormap.from_list('frac_list', colours)
im5 = axes[1].imshow(ak543_opf - al508_opf,
origin='lower',
extent=extent,
vmin=-100,
vmax=100,
cmap=cmap2,
norm=norm2)
axes[1].get_yaxis().set_ticks([])
axes[0].set_title('$\Delta$ total precip.')
axes[1].set_title('$\Delta$ % orog.')
cax3 = fig.add_axes([0.1, 0.12, 0.4, 0.03])
cax4 = fig.add_axes([0.56, 0.12, 0.4, 0.03])
plt.suptitle('N1280-EC $\minus$ N1280')
plt.colorbar(im2,
cax=cax3,
orientation='horizontal',
label='$\Delta$ precip. (mm day$^{-1}$)',
extend='both')
plt.colorbar(im5,
cax=cax4,
orientation='horizontal',
label='$\Delta$ % orog.')
plt.subplots_adjust(left=0.1,
top=0.96,
right=0.98,
bottom=0.2,
hspace=0.07,
wspace=0.09)
plt.savefig(outputs[1])
def plot_phase_mag(inputs, outputs, dataset, hb_name, mode):
weighted_basin_phase_mag_filename = inputs['weighted']
df_phase_mag = pd.read_hdf(weighted_basin_phase_mag_filename)
raster_hb_name = hb_name
raster_cube = iris.load_cube(str(inputs['raster_cubes']),
f'hydrobasins_raster_{raster_hb_name}')
raster = raster_cube.data
phase_filename, alpha_phase_filename, mag_filename = outputs
print(f'Plot maps - {hb_name}_{mode}: {dataset}')
cmap, norm, bounds, cbar_kwargs = load_cmap_data(
'cmap_data/li2018_fig3_cb.pkl')
phase_map, mag_map = gen_map_from_basin_values(df_phase_mag, raster)
phase_map = iris.cube.Cube(phase_map,
long_name='phase_map',
units='hr',
dim_coords_and_dims=[
(raster_cube.coord('latitude'), 0),
(raster_cube.coord('longitude'), 1)
])
mag_map = iris.cube.Cube(mag_map,
long_name='magnitude_map',
units='-',
dim_coords_and_dims=[
(raster_cube.coord('latitude'), 0),
(raster_cube.coord('longitude'), 1)
])
extent = get_extent_from_cube(phase_map)
plt.figure(figsize=(10, 8))
ax = plt.subplot(projection=ccrs.PlateCarree())
ax.set_title(f'{dataset} {mode} phase')
masked_phase_map = np.ma.masked_array(phase_map.data,
raster_cube.data == 0)
masked_mag_map = np.ma.masked_array(mag_map.data, raster_cube.data == 0)
im = ax.imshow(masked_phase_map,
cmap=cmap,
norm=norm,
origin='lower',
extent=extent,
vmin=0,
vmax=24)
plt.colorbar(im, orientation='horizontal')
configure_ax_asia(ax, extent)
# plt.tight_layout()
plt.savefig(phase_filename)
plt.close()
fig = plt.figure(figsize=(10, 8))
ax = plt.subplot(projection=ccrs.PlateCarree())
ax.set_title(f'{dataset} {mode} phase (alpha)')
_plot_phase_alpha(ax, masked_phase_map, masked_mag_map, cmap, norm, extent)
configure_ax_asia(ax, extent)
cax, _ = cbar.make_axes_gridspec(ax, orientation='horizontal')
v = np.linspace(0, 1, 24)
d = cmap(v)[None, :, :4] * np.ones((3, 24, 4))
d[1, :, 3] = 0.66
d[0, :, 3] = 0.33
cax.imshow(d, origin='lower', extent=(0, 24, 0, 2), aspect='auto')
cax.set_yticks([])
cax.set_xticks(np.linspace(0, 24, 9))
# plt.tight_layout()
plt.savefig(alpha_phase_filename)
plt.close()
plt.figure(figsize=(10, 8))
ax = plt.subplot(projection=ccrs.PlateCarree())
ax.set_title(f'{dataset} {mode} strength')
im = ax.imshow(masked_mag_map,
origin='lower',
extent=extent,
vmin=1e-2,
norm=LogNorm())
plt.colorbar(im, orientation='horizontal')
configure_ax_asia(ax, extent)
# plt.tight_layout()
plt.savefig(mag_filename)
plt.close()
def plot_obs_mean_precip_diff(inputs, outputs, dataset, hb_name):
weighted_basin_mean_precip_filename = inputs['dataset_weighted']
obs_weighted_basin_mean_precip_filename = inputs['obs_weighted']
df_mean_precip = pd.read_hdf(weighted_basin_mean_precip_filename)
df_obs_mean_precip = pd.read_hdf(obs_weighted_basin_mean_precip_filename)
# There can be NaNs in the datasets, as in e.g. APHRODITE at some fine-scale basins.
# Use version of RMSE and MAE that mask these out.
obs_rmse = rmse_mask_out_nan(
df_mean_precip.basin_weighted_mean_precip_mm_per_hr.values.astype(
float),
df_obs_mean_precip.basin_weighted_mean_precip_mm_per_hr.values.astype(
float))
obs_mae = mae_mask_out_nan(
df_mean_precip.basin_weighted_mean_precip_mm_per_hr.values.astype(
float),
df_obs_mean_precip.basin_weighted_mean_precip_mm_per_hr.values.astype(
float))
raster_hb_name = hb_name
raster_cube = iris.load_cube(str(inputs['raster_cubes']),
f'hydrobasins_raster_{raster_hb_name}')
raster = raster_cube.data
mean_precip_map = np.zeros_like(raster, dtype=float)
for i in range(1, raster.max() + 1):
mean_precip_map[raster == i] = df_mean_precip.values[i - 1]
obs_mean_precip_map = np.zeros_like(raster, dtype=float)
for i in range(1, raster.max() + 1):
obs_mean_precip_map[raster == i] = df_obs_mean_precip.values[i - 1]
extent = get_extent_from_cube(raster_cube)
plt.figure(figsize=(10, 8))
ax = plt.subplot(projection=ccrs.PlateCarree())
plt.title(
f'{dataset} mean_precip. RMSE: {obs_rmse:.3f} mm hr$^{{-1}}$; MAE: {obs_mae:.3f} mm hr$^{{-1}}$'
)
grey_fill = np.zeros(
(mean_precip_map.shape[0], mean_precip_map.shape[1], 3), dtype=int)
grey_fill[raster_cube.data == 0] = (200, 200, 200)
ax.imshow(grey_fill, extent=extent)
masked_mean_precip_map = np.ma.masked_array(
mean_precip_map - obs_mean_precip_map, raster_cube.data == 0)
im = ax.imshow(
masked_mean_precip_map,
cmap='bwr',
norm=MidPointNorm(0, -1, 3),
# vmin=-absmax, vmax=absmax,
origin='lower',
extent=extent)
plt.colorbar(im,
label=f'precip. (mm hr$^{{-1}}$)',
orientation='horizontal')
configure_ax_asia(ax, extent)
mean_precip_filename = outputs[0]
plt.savefig(mean_precip_filename)
plt.close()
