def plt_spatial_seasonal_mean(variable, variable_id, add_colorbar=None, title=None):
fig, axsm = plt.subplots(
2, 2, figsize=[10, 7], subplot_kw={"projection": ccrs.PlateCarree()}
)
fig.suptitle(title, fontsize=16, fontweight="bold")
axs = axsm.flatten()
for ax, i in zip(axs, variable.season):
im = variable.sel(season=i,).plot.contourf(
ax=ax,
transform=ccrs.PlateCarree(),
cmap=cm.devon_r,
robust=True,
vmin=plt_dict[variable_id][plt_dict["header"].index("vmin")],
vmax=plt_dict[variable_id][plt_dict["header"].index("vmax")],
levels=plt_dict[variable_id][plt_dict["header"].index("levels")],
extend="max",
add_colorbar=add_colorbar,
)
ax.coastlines()
gl = ax.gridlines()
ax.add_feature(cy.feature.BORDERS)
gl.top_labels = False
ax.set_title("season: {}".format(i.values))
plt.tight_layout()
fig.subplots_adjust(top=0.88)
return (
fig,
axs,
im,
)
def plt_twodhist_season(x,
y,
starty,
endy,
bins=None,
cmap=None,
range=None,
norm=None):
""" Plots a two-dimensional histogram of variable x and y.
Parameters:
-----------
x : variable on x-axis
y : variable on y-axis
starty : string of analysis begin
endy : string of analysis end
bisn : None or int or [int, int] or array-like or [array, array]
cmap : Colormap or str
range : array-like shape(2, 2), optional,
norm : Normalize, optional
"""
f, axs = plt.subplots(2, 2, figsize=[10, 10], sharex=True, sharey=True)
for sea, ax in zip(
x.groupby('time.season').sum('time').season, axs.flatten()):
_pp = np.asarray(x.sel(time=(x['time.season'] == sea), ))
_cl = np.asarray(y.sel(time=(y['time.season'] == sea), ))
_p = _pp[~np.isnan(_pp)]
_c = _cl[~np.isnan(_pp)]
_p = _p[~np.isnan(_c)]
_c = _c[~np.isnan(_c)]
counts, xedges, yedges, im = ax.hist2d(
_p, #p.flatten(), # use .flatten to pass a (N,) shape array as requested by hist2d
_c, #l.flatten(),
#bins=(20,50),
bins=bins,
density=False,
# density = True, # If False, the default, returns the number of samples in each bin. If True, returns the probability density function at the bin, bin_count / sample_count / bin_area.
cmap=cmap,
range=range,
cmin=0.5,
norm=norm)
ax.set_title(sea.values)
ax.set_xlabel('Precipitation')
ax.set_ylabel('Mass Fraction of Cloud Liquid + Ice Water')
f.subplots_adjust(top=.85, right=0.8)
f.suptitle('2D Histogram ' + starty + '-' + endy, fontweight='bold')
#f.suptitle("Precipitation vs. Cloud Mass")
cbar_ax = f.add_axes([1.01, 0.15, 0.025, 0.7])
cbar = f.colorbar(im, cax=cbar_ax)
cbar.ax.set_ylabel('Counts')
ax.ticklabel_format(style='sci', axis='both', scilimits=(1, 0))
plt.tight_layout()
def plt_scatter_iwp_sf_seasonal(
ds, linreg, iteration, step, title=None, xlim=None, ylim=None
):
fig, axsm = plt.subplots(
2,
2,
figsize=[10, 7],
sharex=True,
sharey=True,
)
fig.suptitle(title, fontsize=16, fontweight="bold")
axs = axsm.flatten()
for ax, i in zip(axs, ds.season):
ax.grid()
for _lat, c in zip(iteration, cm.romaO(range(0, 256, int(256 / 4)))):
# plot scatter
ax.scatter(
ds["iwp_{}_{}".format(_lat, _lat + step)].sel(season=i),
ds["sf_{}_{}".format(_lat, _lat + step)].sel(season=i),
label="{}, {}".format(_lat, _lat + step),
color=c,
alpha=0.5,
)
# plot regression line
y = (
np.linspace(0, 350)
* linreg["slope_{}_{}".format(_lat, _lat + step)].sel(season=i).values
+ linreg["intercept_{}_{}".format(_lat, _lat + step)]
.sel(season=i)
.values
)
ax.plot(np.linspace(0, 350), y, color=c, linewidth="2")
ax.set_ylabel("Snowfall (mm$\,$day$^{-1}$)", fontweight="bold")
ax.set_xlabel("Ice Water Path (g$\,$m$^{-2}$)", fontweight="bold")
ax.set_title(
"season: {}; lat: ({}, {})".format(
i.values, iteration[0], iteration[-1] + step
)
)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
axs[1].legend(
loc="upper left",
bbox_to_anchor=(1, 1),
fontsize="small",
fancybox=True,
)
plt.tight_layout()
def plt_bar_area_mean(
ax,
var_model,
var_obs,
loc,
bar_width=None,
hatch=None,
alpha=None,
label=None,
ylabel=None,
):
for k, c, pos in zip(
var_model.model.values,
cm.romaO(range(0, 256, int(256 / len(var_model.model.values)))),
range(len(var_model.model.values)),
):
ax.bar(
pos + loc * bar_width,
var_model.sel(model=k).values,
color=c,
width=bar_width,
edgecolor="black",
hatch=hatch,
alpha=alpha,
)
ax.bar(
len(var_model.model.values) + loc * bar_width,
var_obs.values,
color="k",
width=bar_width,
edgecolor="white",
hatch=hatch,
alpha=alpha,
label=label,
)
ax.set_xticks(range(len(np.append((var_model.model.values), "ERA5").tolist())))
ax.set_xticklabels(
np.append((var_model.model.values), "ERA5").tolist(), fontsize=12, rotation=90
)
ax.set_ylabel(ylabel, fontweight="bold")
ax.legend(
loc="upper left",
bbox_to_anchor=(1, 1),
title="MEAN",
fontsize="small",
fancybox=True,
)
plt.tight_layout()
def plt_diff_seasonal(
true_var, estimated_var, cbar_label, vmin=None, vmax=None, levels=None, title=None
):
fig, axsm = plt.subplots(
2, 2, figsize=[10, 7], subplot_kw={"projection": ccrs.PlateCarree()}
)
fig.suptitle(title, fontsize=16, fontweight="bold")
axs = axsm.flatten()
for ax, i in zip(axs, true_var.season):
im = (
(true_var - estimated_var)
.sel(season=i)
.plot.contourf(
ax=ax,
transform=ccrs.PlateCarree(),
cmap=cm.bam,
robust=True,
add_colorbar=False,
extend="both",
vmin=vmin,
vmax=vmax,
levels=levels,
)
)
# Plot cosmetics
ax.coastlines()
gl = ax.gridlines()
ax.add_feature(cy.feature.BORDERS)
gl.top_labels = False
ax.set_title("season: {}".format(i.values))
# colorbar
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([1, 0.15, 0.025, 0.7])
cb = fig.colorbar(im, cax=cbar_ax, orientation="vertical", fraction=0.046, pad=0.04)
# set cbar label
cb.set_label(label=cbar_label, weight="bold")
plt.tight_layout()
fig.subplots_adjust(top=1)
return axs
def plt_mean_std_var_skew_kurt(var):
"""Plots the mean, standard deviation, variance, skewness and kurtosis for a given variable.
Parameters:
-----------
var : xarray variable to be plotted
"""
f, axsm = sp_map(3, 2, figsize=[18, 8])
axs = axsm.flatten()
stat = ['Mean', 'Std', 'Variance', 'Skewness', 'Kurtosis']
# Mean
var.mean('time', keep_attrs=True).plot(ax=axs[0],
transform=ccrs.PlateCarree(),
robust=True)
# STD
var.std('time', keep_attrs=True).plot(ax=axs[1],
transform=ccrs.PlateCarree(),
robust=True)
# Var
var.var('time', keep_attrs=True).plot(ax=axs[2],
transform=ccrs.PlateCarree(),
robust=True)
# skewness
var.reduce(stats.skew, dim=('time'),
keep_attrs=True).plot(ax=axs[3],
transform=ccrs.PlateCarree(),
robust=True)
# kurtosis
var.reduce(stats.kurtosis, dim=('time'),
keep_attrs=True).plot(ax=axs[4],
transform=ccrs.PlateCarree(),
robust=True)
axs[5].axis('off')
for ax, i in zip(axs, range(len(stat))):
ax.coastlines()
ax.set_title(stat[i])
plt.tight_layout()
def plt_stat_season(variable, stat, starty, endy):
"""Plots the seasonal statistics of the chosen statistic
Parameters:
-----------
variable : xarray variable to be plotted
stat : defines the statistics to plot,
must be: 'mean', 'st', 'var', 'skew', 'kur'
starty : string of analysis begin
endy : string of analysis end
"""
f, axsm = sp_map(2, 2, figsize=[18, 8])
axs = axsm.flatten()
for sea, ax in zip(
variable.groupby('time.season').sum('time').season, axs.flatten()):
if stat == 'mean':
_title = 'Mean'
im = variable.sel(time=(variable['time.season'] == sea), ).mean(
'time', keep_attrs=True).plot(ax=ax,
transform=ccrs.PlateCarree(),
robust=True,
add_colorbar=False)
if stat == 'std':
_title = 'Standard deviation'
im = variable.sel(time=(variable['time.season'] == sea), ).std(
'time', keep_attrs=True).plot(ax=ax,
transform=ccrs.PlateCarree(),
robust=True,
add_colorbar=False)
if stat == 'var':
_title = 'Variance'
im = variable.sel(time=(variable['time.season'] == sea), ).var(
'time', keep_attrs=True).plot(ax=ax,
transform=ccrs.PlateCarree(),
robust=True,
add_colorbar=False)
if stat == 'skew':
_title = 'Skewness'
im = variable.sel(time=(variable['time.season'] == sea), ).reduce(
stats.skew, dim=('time'),
keep_attrs=True).plot(ax=ax,
transform=ccrs.PlateCarree(),
robust=True,
add_colorbar=False)
if stat == 'kur':
_title = 'Kurtosis'
im = variable.sel(time=(variable['time.season'] == sea), ).reduce(
stats.kurtosis, dim=('time'),
keep_attrs=True).plot(ax=ax,
transform=ccrs.PlateCarree(),
robust=True,
add_colorbar=False)
ax.coastlines()
f.subplots_adjust(right=0.8, top=0.9)
f.suptitle('%s %s - %s' % (_title, starty, endy), fontweight='bold')
cbar_ax = f.add_axes([0.85, 0.15, 0.025, 0.7])
f.colorbar(im, cax=cbar_ax, extend='both',
format='%.0e').ax.set_ylabel(variable.attrs['long_name'] +
' [' + variable.attrs['units'] +
']')
plt.tight_layout()
ax.set_title('Mean of 1990');
# There is obious higher values of CLW over China, Chile, Labrador See and around Svalbard in 1990. Lower values of CLW show in the tropics. High CLW values in China related to higher Aerosol content? High CLW because of the Andes?
# In 1991 Mt. Pinatubo errupted. I wonder how the CLW looks for 1991 and 1992.
# +
fig, axs = fct.sp_map(1,2, figsize=[18,8], sharex=True, sharey=True)
for yy, ax in zip(np.arange(1991,1993), axs.flatten()):
im = fn['clw'].clw.sel(time = slice(str(yy)+'-01', str(yy)+'-12')).sum('lev',keep_attrs = True).mean(['time'], keep_attrs = True).plot.pcolormesh(ax = ax,
transform = ccrs.PlateCarree(),
)
ax.coastlines()
ax.set_title('Mean of %s' %yy)
plt.tight_layout()
# -
# Besides the different colorbar dimension one can not see a difference to 1990. The same areas have still high CLW. But I'm looking at model simulations, so that a model represents exactly a certain year is not very likely. One could say that the CLW is higher in 1992 (comparing the colorbar).
#
# Let's have a look on the decade.
f, ax = fct.sp_map(1, figsize = [8,5])
fn['clw'].clw.sel(time = slice('1990-01', '1999-12')).sum('lev',keep_attrs = True).mean(['time'], keep_attrs = True).plot.pcolormesh(ax = ax,
transform = ccrs.PlateCarree(),
)
ax.coastlines()
ax.set_title('Mean 1990-1999' );
# And now 30 years: