def plot_errors_in_one_figure(season_to_diff, fig_path="", **kwargs):
fig = plt.figure()
basemap = kwargs["basemap"]
x, y = kwargs["x"], kwargs["y"]
vmin, vmax = None, None
for k, field in season_to_diff.items():
pl, ph = field.min(), field.max()
if vmin is None:
vmin = pl
vmax = ph
else:
vmin = min(vmin, pl)
vmax = max(vmax, ph)
print("min,max = ({0}, {1})".format(vmin, vmax))
ncolors = 25
if vmin * vmax <= 0:
cmap = cm.get_cmap("RdBu_r", ncolors)
else:
if vmin >= 0:
cmap = cm.get_cmap("Reds", ncolors)
else:
cmap = cm.get_cmap("Blues_r", ncolors)
bn, bounds, vmin, vmax = infovar.get_boundary_norm(vmin,
vmax,
ncolors,
exclude_zero=True,
difference=True)
gs = GridSpec(len(season_to_diff), 2, width_ratios=[1, 0.05])
row = 0
img = None
for the_seasson, the_diff in season_to_diff.items():
ax = fig.add_subplot(gs[row, 0])
#img = basemap.pcolormesh(x, y, the_diff, ax = ax, norm = bn, cmap = cmap, vmin = vmin, vmax = vmax)
img = basemap.contourf(x,
y,
the_diff,
ax=ax,
norm=bn,
cmap=cmap,
extend="both",
levels=bounds)
ax.set_title(the_seasson)
basemap.drawcoastlines()
row += 1
cax = fig.add_subplot(gs[:, 1])
plt.colorbar(img,
ticks=bounds,
orientation="vertical",
cax=cax,
extend="both")
fig.savefig(fig_path, bbox_inches="tight")
def plot_errors_in_one_figure(season_to_diff, fig_path="", **kwargs):
fig = plt.figure()
basemap = kwargs["basemap"]
x, y = kwargs["x"], kwargs["y"]
vmin, vmax = None, None
for k, field in season_to_diff.items():
pl, ph = field.min(), field.max()
if vmin is None:
vmin = pl
vmax = ph
else:
vmin = min(vmin, pl)
vmax = max(vmax, ph)
print("min,max = ({0}, {1})".format(vmin, vmax))
ncolors = 25
if vmin * vmax <= 0:
cmap = cm.get_cmap("RdBu_r", ncolors)
else:
if vmin >= 0:
cmap = cm.get_cmap("Reds", ncolors)
else:
cmap = cm.get_cmap("Blues_r", ncolors)
bn, bounds, vmin, vmax = infovar.get_boundary_norm(vmin, vmax, ncolors, exclude_zero=True, difference=True)
gs = GridSpec(len(season_to_diff), 2, width_ratios=[1, 0.05])
row = 0
img = None
for the_seasson, the_diff in season_to_diff.items():
ax = fig.add_subplot(gs[row, 0])
#img = basemap.pcolormesh(x, y, the_diff, ax = ax, norm = bn, cmap = cmap, vmin = vmin, vmax = vmax)
img = basemap.contourf(x, y, the_diff, ax = ax, norm = bn, cmap = cmap, extend = "both", levels = bounds)
ax.set_title(the_seasson)
basemap.drawcoastlines()
row += 1
cax = fig.add_subplot(gs[:, 1])
plt.colorbar(img,ticks = bounds, orientation = "vertical", cax = cax, extend = "both")
fig.savefig(fig_path, bbox_inches = "tight")
def compare(paths=None, path_to_control_data=None, control_label="",
labels=None, varnames=None, levels=None, months_of_interest=None,
start_year=None, end_year=None):
"""
Comparing 2D fields
:param paths: paths to the simulation results
:param varnames:
:param labels: Display name for each simulation (number of labels should
be equal to the number of paths)
:param path_to_control_data: the path with which the comparison done i.e. a in the following
formula
delta = (x - a)/a * 100%
generates one image file per variable (in the folder images_for_lake-river_paper):
compare_varname_<control_label>_<label1>_..._<labeln>_startyear_endyear.png
"""
# get coordinate data (assumes that all the variables and runs have the same coordinates)
lons2d, lats2d, basemap = analysis.get_basemap_from_hdf(file_path=path_to_control_data)
x, y = basemap(lons2d, lats2d)
lake_fraction = analysis.get_array_from_file(path=path_to_control_data, var_name="lake_fraction")
if lake_fraction is None:
lake_fraction = np.zeros(lons2d.shape)
ncolors = 10
# +1 to include white
diff_cmap = cm.get_cmap("RdBu_r", ncolors + 1)
for var_name, level in zip(varnames, levels):
sfmt = infovar.get_colorbar_formatter(var_name)
control_means = analysis.get_mean_2d_fields_for_months(path=path_to_control_data, var_name=var_name,
months=months_of_interest,
start_year=start_year, end_year=end_year,
level=level)
control_mean = np.mean(control_means, axis=0)
fig = plt.figure()
assert isinstance(fig, Figure)
gs = gridspec.GridSpec(2, len(paths) + 1, wspace=0.5)
# plot the control
ax = fig.add_subplot(gs[0, 0])
assert isinstance(ax, Axes)
ax.set_title("{0}".format(control_label))
ax.set_ylabel("Mean: $X_{0}$")
to_plot = infovar.get_to_plot(var_name, control_mean,
lake_fraction=lake_fraction, mask_oceans=True, lons=lons2d, lats=lats2d)
# determine colorabr extent and spacing
field_cmap, field_norm = infovar.get_colormap_and_norm_for(var_name, to_plot, ncolors=ncolors)
basemap.pcolormesh(x, y, to_plot, cmap=field_cmap, norm=field_norm)
cb = basemap.colorbar(format=sfmt)
assert isinstance(cb, Colorbar)
# cb.ax.set_ylabel(infovar.get_units(var_name))
units = infovar.get_units(var_name)
info = "Variable:" \
"\n{0}" \
"\nPeriod: {1}-{2}" \
"\nMonths: {3}" \
"\nUnits: {4}"
info = info.format(infovar.get_long_name(var_name), start_year, end_year,
",".join([datetime(2001, m, 1).strftime("%b") for m in months_of_interest]), units)
ax.annotate(info, xy=(0.1, 0.3), xycoords="figure fraction")
sel_axes = [ax]
for the_path, the_label, column in zip(paths, labels, list(range(1, len(paths) + 1))):
means_for_years = analysis.get_mean_2d_fields_for_months(path=the_path, var_name=var_name,
months=months_of_interest,
start_year=start_year, end_year=end_year)
the_mean = np.mean(means_for_years, axis=0)
# plot the mean value
ax = fig.add_subplot(gs[0, column])
sel_axes.append(ax)
ax.set_title("{0}".format(the_label))
to_plot = infovar.get_to_plot(var_name, the_mean, lake_fraction=lake_fraction,
mask_oceans=True, lons=lons2d, lats=lats2d)
basemap.pcolormesh(x, y, to_plot, cmap=field_cmap, norm=field_norm)
ax.set_ylabel("Mean: $X_{0}$".format(column))
cb = basemap.colorbar(format=sfmt)
# cb.ax.set_ylabel(infovar.get_units(var_name))
# plot the difference
ax = fig.add_subplot(gs[1, column])
sel_axes.append(ax)
ax.set_ylabel("$X_{0} - X_0$".format(column))
# #Mask only if the previous plot (means) is masked
thediff = the_mean - control_mean
if hasattr(to_plot, "mask"):
to_plot = np.ma.masked_where(to_plot.mask, thediff)
else:
to_plot = thediff
if var_name == "PR": # convert to mm/day
to_plot = infovar.get_to_plot(var_name, to_plot, mask_oceans=False)
vmin = np.ma.min(to_plot)
vmax = np.ma.max(to_plot)
d = max(abs(vmin), abs(vmax))
vmin = -d
vmax = d
field_norm, bounds, vmn_nice, vmx_nice = infovar.get_boundary_norm(vmin, vmax, diff_cmap.N,
exclude_zero=False)
basemap.pcolormesh(x, y, to_plot, cmap=diff_cmap, norm=field_norm, vmin=vmn_nice, vmax=vmx_nice)
cb = basemap.colorbar(format=sfmt)
t, pval = ttest_ind(means_for_years, control_means, axis=0)
sig = pval < 0.1
basemap.contourf(x, y, sig.astype(int), nlevels=2, hatches=["+", None], colors="none")
# cb.ax.set_ylabel(infovar.get_units(var_name))
# plot coastlines
for the_ax in sel_axes:
basemap.drawcoastlines(ax=the_ax, linewidth=common_plot_params.COASTLINE_WIDTH)
# depends on the compared simulations and the months of interest
fig_file_name = "compare_{0}_{1}_{2}_months-{3}.jpeg".format(var_name, control_label,
"_".join(labels),
"-".join([str(m) for m in months_of_interest]))
figpath = os.path.join(images_folder, fig_file_name)
fig.savefig(figpath, dpi=cpp.FIG_SAVE_DPI, bbox_inches="tight")
plt.close(fig)
def _plot_row(axes, data, sim_label, var_name, increments=False,
domain_props=None, season_list=None, significance=None):
# data is a dict of season -> field
# the field is a control mean in the case of the control mean
# and the difference between the modified simulation and the control mean in the case of the modified simulation
exclude_0_from_diff_colorbar = False
assert isinstance(domain_props, DomainProperties)
print("plotting row for {0}; increments = ({1})".format(var_name, increments))
lons2d, lats2d, basemap = domain_props.get_lon_lat_and_basemap()
x, y = domain_props.x, domain_props.y
vmin = None
vmax = None
# determine vmin and vmax for the row
for season, field in data.items():
# field = _get_to_plot(var_name, field, lake_fraction=domain_props.lake_fraction, lons=lons2d, lats = lats2d)
min_current, max_current = np.percentile(field[~field.mask], 1), np.percentile(field[~field.mask], 99)
if vmin is None or min_current < vmin:
vmin = min_current
if vmax is None or max_current > vmax:
vmax = max_current
ncolors = 13 if exclude_0_from_diff_colorbar else 10
bounds = None
if increments:
# +1 to include white
if vmin * vmax >= 0:
if vmin >= 0:
field_cmap = cm.get_cmap("YlOrBr_r", ncolors)
else:
field_cmap = cm.get_cmap("YlGnBu_r", ncolors)
field_norm, bounds, bounds_min, bounds_max = infovar.get_boundary_norm(vmin, vmax, ncolors,
exclude_zero=exclude_0_from_diff_colorbar,
varname=var_name,
difference=increments)
else:
field_cmap = cm.get_cmap("RdBu_r", ncolors)
d = max(abs(vmin), abs(vmax))
field_norm, bounds, bounds_min, bounds_max = infovar.get_boundary_norm(-d, d, ncolors,
exclude_zero=exclude_0_from_diff_colorbar,
varname=var_name,
difference=increments)
else:
# determine colorabr extent and spacing
field_cmap, field_norm = infovar.get_colormap_and_norm_for(var_name, vmin=vmin, vmax=vmax, ncolors=ncolors)
print("vmin = {0}; vmax = {1}".format(vmin, vmax))
col = 0
# axes[0].set_ylabel(sim_label)
im = None
for season in season_list:
field = data[season]
ax = axes[col]
if not increments:
# since the increments go below
ax.set_title(season)
else:
mean_val = float("{0:.1e}".format(field.mean()))
sf = ScalarFormatter(useMathText=True)
sf.set_powerlimits((-2, 3))
# ax.set_title(r"$\Delta_{\rm mean} = " + sf.format_data(mean_val) + " $")
basemap.drawmapboundary(ax=ax, fill_color="gray")
im = basemap.pcolormesh(x, y, field, norm=field_norm, cmap=field_cmap, ax=ax)
basemap.drawcoastlines(ax=ax, linewidth=cpp.COASTLINE_WIDTH)
if significance is not None:
cs = basemap.contourf(x, y, significance[season], levels=[0, 0.5, 1],
colors="none",
hatches=[None, ".."],
ax=ax)
# basemap.contour(x, y, significance[season], levels = [0.5, ], ax = ax,
# linewidths = 0.5, colors="k")
if col == 0 and False:
# create a legend for the contour set
artists, labels = cs.legend_elements()
ax.legend([artists[-1], ], ["Significant changes \n with (p = 0.1)", ],
handleheight=0.5, fontsize="x-small")
col += 1
# plot the common colorbar
if isinstance(field_norm, LogNorm):
cb = plt.colorbar(im, cax=axes[-1])
else:
cb = plt.colorbar(im, cax=axes[-1], extend="both", ticks=bounds)
cb.ax.set_title(infovar.get_units(var_name))
def compare(paths=None,
path_to_control_data=None,
control_label="",
labels=None,
varnames=None,
levels=None,
months_of_interest=None,
start_year=None,
end_year=None):
"""
Comparing 2D fields
:param paths: paths to the simulation results
:param varnames:
:param labels: Display name for each simulation (number of labels should
be equal to the number of paths)
:param path_to_control_data: the path with which the comparison done i.e. a in the following
formula
delta = (x - a)/a * 100%
generates one image file per variable (in the folder images_for_lake-river_paper):
compare_varname_<control_label>_<label1>_..._<labeln>_startyear_endyear.png
"""
# get coordinate data (assumes that all the variables and runs have the same coordinates)
lons2d, lats2d, basemap = analysis.get_basemap_from_hdf(
file_path=path_to_control_data)
x, y = basemap(lons2d, lats2d)
lake_fraction = analysis.get_array_from_file(path=path_to_control_data,
var_name="lake_fraction")
if lake_fraction is None:
lake_fraction = np.zeros(lons2d.shape)
ncolors = 10
# +1 to include white
diff_cmap = cm.get_cmap("RdBu_r", ncolors + 1)
for var_name, level in zip(varnames, levels):
sfmt = infovar.get_colorbar_formatter(var_name)
control_means = analysis.get_mean_2d_fields_for_months(
path=path_to_control_data,
var_name=var_name,
months=months_of_interest,
start_year=start_year,
end_year=end_year,
level=level)
control_mean = np.mean(control_means, axis=0)
fig = plt.figure()
assert isinstance(fig, Figure)
gs = gridspec.GridSpec(2, len(paths) + 1, wspace=0.5)
# plot the control
ax = fig.add_subplot(gs[0, 0])
assert isinstance(ax, Axes)
ax.set_title("{0}".format(control_label))
ax.set_ylabel("Mean: $X_{0}$")
to_plot = infovar.get_to_plot(var_name,
control_mean,
lake_fraction=lake_fraction,
mask_oceans=True,
lons=lons2d,
lats=lats2d)
# determine colorabr extent and spacing
field_cmap, field_norm = infovar.get_colormap_and_norm_for(
var_name, to_plot, ncolors=ncolors)
basemap.pcolormesh(x, y, to_plot, cmap=field_cmap, norm=field_norm)
cb = basemap.colorbar(format=sfmt)
assert isinstance(cb, Colorbar)
# cb.ax.set_ylabel(infovar.get_units(var_name))
units = infovar.get_units(var_name)
info = "Variable:" \
"\n{0}" \
"\nPeriod: {1}-{2}" \
"\nMonths: {3}" \
"\nUnits: {4}"
info = info.format(
infovar.get_long_name(var_name), start_year, end_year, ",".join([
datetime(2001, m, 1).strftime("%b") for m in months_of_interest
]), units)
ax.annotate(info, xy=(0.1, 0.3), xycoords="figure fraction")
sel_axes = [ax]
for the_path, the_label, column in zip(paths, labels,
list(range(1,
len(paths) + 1))):
means_for_years = analysis.get_mean_2d_fields_for_months(
path=the_path,
var_name=var_name,
months=months_of_interest,
start_year=start_year,
end_year=end_year)
the_mean = np.mean(means_for_years, axis=0)
# plot the mean value
ax = fig.add_subplot(gs[0, column])
sel_axes.append(ax)
ax.set_title("{0}".format(the_label))
to_plot = infovar.get_to_plot(var_name,
the_mean,
lake_fraction=lake_fraction,
mask_oceans=True,
lons=lons2d,
lats=lats2d)
basemap.pcolormesh(x, y, to_plot, cmap=field_cmap, norm=field_norm)
ax.set_ylabel("Mean: $X_{0}$".format(column))
cb = basemap.colorbar(format=sfmt)
# cb.ax.set_ylabel(infovar.get_units(var_name))
# plot the difference
ax = fig.add_subplot(gs[1, column])
sel_axes.append(ax)
ax.set_ylabel("$X_{0} - X_0$".format(column))
# #Mask only if the previous plot (means) is masked
thediff = the_mean - control_mean
if hasattr(to_plot, "mask"):
to_plot = np.ma.masked_where(to_plot.mask, thediff)
else:
to_plot = thediff
if var_name == "PR": # convert to mm/day
to_plot = infovar.get_to_plot(var_name,
to_plot,
mask_oceans=False)
vmin = np.ma.min(to_plot)
vmax = np.ma.max(to_plot)
d = max(abs(vmin), abs(vmax))
vmin = -d
vmax = d
field_norm, bounds, vmn_nice, vmx_nice = infovar.get_boundary_norm(
vmin, vmax, diff_cmap.N, exclude_zero=False)
basemap.pcolormesh(x,
y,
to_plot,
cmap=diff_cmap,
norm=field_norm,
vmin=vmn_nice,
vmax=vmx_nice)
cb = basemap.colorbar(format=sfmt)
t, pval = ttest_ind(means_for_years, control_means, axis=0)
sig = pval < 0.1
basemap.contourf(x,
y,
sig.astype(int),
nlevels=2,
hatches=["+", None],
colors="none")
# cb.ax.set_ylabel(infovar.get_units(var_name))
# plot coastlines
for the_ax in sel_axes:
basemap.drawcoastlines(
ax=the_ax, linewidth=common_plot_params.COASTLINE_WIDTH)
# depends on the compared simulations and the months of interest
fig_file_name = "compare_{0}_{1}_{2}_months-{3}.jpeg".format(
var_name, control_label, "_".join(labels),
"-".join([str(m) for m in months_of_interest]))
figpath = os.path.join(images_folder, fig_file_name)
fig.savefig(figpath, dpi=cpp.FIG_SAVE_DPI, bbox_inches="tight")
plt.close(fig)
def _plot_row(axes,
data,
sim_label,
var_name,
increments=False,
domain_props=None,
season_list=None,
significance=None):
# data is a dict of season -> field
# the field is a control mean in the case of the control mean
# and the difference between the modified simulation and the control mean in the case of the modified simulation
exclude_0_from_diff_colorbar = False
assert isinstance(domain_props, DomainProperties)
print("plotting row for {0}; increments = ({1})".format(
var_name, increments))
lons2d, lats2d, basemap = domain_props.get_lon_lat_and_basemap()
x, y = domain_props.x, domain_props.y
vmin = None
vmax = None
# determine vmin and vmax for the row
for season, field in data.items():
# field = _get_to_plot(var_name, field, lake_fraction=domain_props.lake_fraction, lons=lons2d, lats = lats2d)
min_current, max_current = np.percentile(field[~field.mask],
1), np.percentile(
field[~field.mask], 99)
if vmin is None or min_current < vmin:
vmin = min_current
if vmax is None or max_current > vmax:
vmax = max_current
ncolors = 13 if exclude_0_from_diff_colorbar else 10
bounds = None
if increments:
# +1 to include white
if vmin * vmax >= 0:
if vmin >= 0:
field_cmap = cm.get_cmap("YlOrBr_r", ncolors)
else:
field_cmap = cm.get_cmap("YlGnBu_r", ncolors)
field_norm, bounds, bounds_min, bounds_max = infovar.get_boundary_norm(
vmin,
vmax,
ncolors,
exclude_zero=exclude_0_from_diff_colorbar,
varname=var_name,
difference=increments)
else:
field_cmap = cm.get_cmap("RdBu_r", ncolors)
d = max(abs(vmin), abs(vmax))
field_norm, bounds, bounds_min, bounds_max = infovar.get_boundary_norm(
-d,
d,
ncolors,
exclude_zero=exclude_0_from_diff_colorbar,
varname=var_name,
difference=increments)
else:
# determine colorabr extent and spacing
field_cmap, field_norm = infovar.get_colormap_and_norm_for(
var_name, vmin=vmin, vmax=vmax, ncolors=ncolors)
print("vmin = {0}; vmax = {1}".format(vmin, vmax))
col = 0
# axes[0].set_ylabel(sim_label)
im = None
for season in season_list:
field = data[season]
ax = axes[col]
if not increments:
# since the increments go below
ax.set_title(season)
else:
mean_val = float("{0:.1e}".format(field.mean()))
sf = ScalarFormatter(useMathText=True)
sf.set_powerlimits((-2, 3))
# ax.set_title(r"$\Delta_{\rm mean} = " + sf.format_data(mean_val) + " $")
basemap.drawmapboundary(ax=ax, fill_color="gray")
im = basemap.pcolormesh(x,
y,
field,
norm=field_norm,
cmap=field_cmap,
ax=ax)
basemap.drawcoastlines(ax=ax, linewidth=cpp.COASTLINE_WIDTH)
if significance is not None:
cs = basemap.contourf(x,
y,
significance[season],
levels=[0, 0.5, 1],
colors="none",
hatches=[None, ".."],
ax=ax)
# basemap.contour(x, y, significance[season], levels = [0.5, ], ax = ax,
# linewidths = 0.5, colors="k")
if col == 0 and False:
# create a legend for the contour set
artists, labels = cs.legend_elements()
ax.legend([
artists[-1],
], [
"Significant changes \n with (p = 0.1)",
],
handleheight=0.5,
fontsize="x-small")
col += 1
# plot the common colorbar
if isinstance(field_norm, LogNorm):
cb = plt.colorbar(im, cax=axes[-1])
else:
cb = plt.colorbar(im, cax=axes[-1], extend="both", ticks=bounds)
cb.ax.set_title(infovar.get_units(var_name))
def validate_precip(model_file="",
simlabel="",
obs_manager=None,
season_to_months=None,
start_year=None,
end_year=None,
season_to_plot_indices=None,
station_ids_list=None):
"""
:param model_file:
:param obs_manager: should implement the method
getMeanFieldForMonthsInterpolatedTo(self, months = None, lonsTarget = None, latsTarget = None)
anusplin data is in mm/day
model data is in m/s
"""
model_var_name = "PR"
if obs_manager is None:
print(
"Skipping validation of {}, since the obs manager is None.".format(
model_var_name))
return
if station_ids_list is not None:
# Get the list of stations to indicate on the bias map
stations = cehq_station.read_station_data(
start_date=None, end_date=None, selected_ids=station_ids_list)
else:
stations = []
model_level = 0
reasonable_error_mm_per_day = 1
assert isinstance(obs_manager, AnuSplinManager)
fig = plt.figure()
assert isinstance(fig, Figure)
fig.suptitle("({0}) - ({1})".format(simlabel, "Obs."))
lon, lat, basemap = analysis.get_basemap_from_hdf(file_path=model_file)
# do calculations and only after that do the plotting
season_to_field = {}
# calculate global min and max for plotting
vmin = None
vmax = None
for season, months in season_to_months.items():
model_field = analysis.get_seasonal_climatology(
start_year=start_year,
end_year=end_year,
months=months,
level=model_level,
var_name=model_var_name,
hdf_path=model_file)
# convert m/s to mm/day for comparison with anusplin data
model_field *= 1000.0 * 60 * 60 * 24
obs_field = obs_manager.getMeanFieldForMonthsInterpolatedTo(
months=months,
lonstarget=lon,
latstarget=lat,
start_year=start_year,
end_year=end_year)
# calculate the difference between the modelled and observed fields
the_diff = model_field - obs_field
current_min = np.min(the_diff)
current_max = np.max(the_diff)
if vmin is not None:
vmin = current_min if current_min < vmin else vmin
vmax = current_max if current_max > vmax else vmax
else:
vmin = current_min
vmax = current_max
season_to_field[season] = the_diff
ncolors = 12
gs = gridspec.GridSpec(2, 3, width_ratios=[1, 1, 0.05])
cmap = cm.get_cmap("RdBu_r", ncolors)
x, y = basemap(lon, lat)
im = None
d = min(abs(vmin), abs(vmax))
vmin = -d
vmax = d
bn, bounds, _, _ = infovar.get_boundary_norm(vmin,
vmax,
ncolors,
exclude_zero=False)
print("bounds: ", bounds)
cs = None
for season, field in season_to_field.items():
row, col = season_to_plot_indices[season]
ax = fig.add_subplot(gs[row, col])
ax.set_title(season)
basemap.drawmapboundary(fill_color="gray", ax=ax)
im = basemap.pcolormesh(x,
y,
season_to_field[season],
vmin=vmin,
vmax=vmax,
cmap=cmap,
norm=bn)
basemap.drawcoastlines(ax=ax, linewidth=cpp.COASTLINE_WIDTH)
for the_station in stations:
assert isinstance(the_station, Station)
xst, yst = basemap(the_station.longitude, the_station.latitude)
# ax.annotate(the_station.id, (xst, yst), font_properties=FontProperties(size=6),
# bbox=dict(facecolor="w"), va="top", ha="right")
basemap.scatter(xst, yst, c="g", ax=ax)
# small_error = (np.abs(season_to_field[season]) < reasonable_error_mm_per_day).astype(int)
# nlevs = 1
# ax.contour(x, y, small_error, nlevs, colors = "black", linestyle = "-")
# cs = ax.contourf(x, y, small_error, nlevs, colors="none", hatches=["/", None], extend="lower", linewidth=2)
# artists, labels = cs.legend_elements()
# plt.legend(artists, labels, handleheight=2)
cax = fig.add_subplot(gs[:, 2])
cax.set_title("mm/day\n")
plt.colorbar(im, cax=cax, extend="both")
seasons_str = "_".join(
sorted([str(s) for s in list(season_to_field.keys())]))
atm_val_folder = os.path.join(images_folder, "validate_atm")
if not os.path.isdir(atm_val_folder):
os.mkdir(atm_val_folder)
out_filename = "{3}/validate_2d_{0}_{1}_{2}.png".format(
model_var_name, simlabel, seasons_str, atm_val_folder)
fig.savefig(os.path.join(images_folder, out_filename), bbox_inches="tight")
def validate_temperature(model_file="",
simlabel="",
obs_manager=None,
season_to_months=None,
start_year=None,
end_year=None,
season_to_plot_indices=None,
model_var_name="TT_max"):
"""
:param model_file:
:param obs_manager: should implement the method
getMeanFieldForMonthsInterpolatedTo(self, months = None, lonsTarget = None, latsTarget = None)
anusplin data is in degrees Celsium
model data is in deg C
"""
if obs_manager is None:
print(
"Skipping validation of {}, since the obs manager is None.".format(
model_var_name))
return
model_level = 0
reasonable_error_deg = 2
assert isinstance(obs_manager, AnuSplinManager)
fig = plt.figure()
assert isinstance(fig, Figure)
fig.suptitle("({0}) - ({1})".format(simlabel, obs_manager.name))
lon, lat, basemap = analysis.get_basemap_from_hdf(file_path=model_file)
# do calculations and only after that do the plotting
season_to_field = {}
# calculate global min and max for plotting
vmin = None
vmax = None
for season, months in season_to_months.items():
model_field = analysis.get_seasonal_climatology(
start_year=start_year,
end_year=end_year,
months=months,
level=model_level,
var_name=model_var_name,
hdf_path=model_file)
obs_field = obs_manager.getMeanFieldForMonthsInterpolatedTo(
months=months,
lonstarget=lon,
latstarget=lat,
start_year=start_year,
end_year=end_year)
# calculate the difference between the modelled and observed fields
the_diff = model_field - obs_field
current_min = np.min(the_diff)
current_max = np.max(the_diff)
if vmin is not None:
vmin = current_min if current_min < vmin else vmin
vmax = current_max if current_max > vmax else vmax
else:
vmin = current_min
vmax = current_max
season_to_field[season] = the_diff
ncolors = 10
gs = gridspec.GridSpec(2, 3, width_ratios=[1, 1, 0.05])
cmap = cm.get_cmap("RdBu_r", ncolors)
x, y = basemap(lon, lat)
im = None
d = min(abs(vmin), abs(vmax))
vmin = -d
vmax = d
bn, bounds, _, _ = infovar.get_boundary_norm(vmin, vmax, ncolors)
print("bounds: ", bounds)
cs = None
for season, field in season_to_field.items():
row, col = season_to_plot_indices[season]
ax = fig.add_subplot(gs[row, col])
ax.set_title(season)
im = basemap.pcolormesh(x,
y,
season_to_field[season],
vmin=vmin,
vmax=vmax,
cmap=cmap,
norm=bn)
basemap.drawcoastlines(ax=ax, linewidth=cpp.COASTLINE_WIDTH)
small_error = (np.abs(season_to_field[season]) <
reasonable_error_deg).astype(int)
nlevs = 1
# ax.contour(x, y, small_error, nlevs, colors = "black", linestyle = "-")
cs = ax.contourf(x,
y,
small_error,
nlevs,
colors="none",
hatches=["/", None],
extend="lower",
linewidth=2)
# artists, labels = cs.legend_elements()
# plt.legend(artists, labels, handleheight=2)
cax = fig.add_subplot(gs[:, 2])
units_str = r"${\rm ^\circ}$"
var_str = r"$T_{\max}$" if model_var_name.endswith(
"_max") else r"$T_{\min}$"
cax.set_title("{0}, {1}".format(var_str, units_str))
plt.colorbar(im, cax=cax, extend="both")
seasons_str = "_".join(
sorted([str(s) for s in list(season_to_field.keys())]))
atm_val_folder = os.path.join(images_folder, "validate_atm")
if not os.path.isdir(atm_val_folder):
os.mkdir(atm_val_folder)
out_filename = "{3}/validate_2d_{0}_{1}_{2}.png".format(
model_var_name, simlabel, seasons_str, atm_val_folder)
fig.savefig(os.path.join(images_folder, out_filename),
dpi=cpp.FIG_SAVE_DPI,
bbox_inches="tight")
def validate_precip(model_file="", simlabel="", obs_manager=None, season_to_months=None,
start_year=None, end_year=None, season_to_plot_indices=None, station_ids_list=None):
"""
:param model_file:
:param obs_manager: should implement the method
getMeanFieldForMonthsInterpolatedTo(self, months = None, lonsTarget = None, latsTarget = None)
anusplin data is in mm/day
model data is in m/s
"""
model_var_name = "PR"
if obs_manager is None:
print("Skipping validation of {}, since the obs manager is None.".format(model_var_name))
return
if station_ids_list is not None:
# Get the list of stations to indicate on the bias map
stations = cehq_station.read_station_data(
start_date=None, end_date=None, selected_ids=station_ids_list
)
else:
stations = []
model_level = 0
reasonable_error_mm_per_day = 1
assert isinstance(obs_manager, AnuSplinManager)
fig = plt.figure()
assert isinstance(fig, Figure)
fig.suptitle("({0}) - ({1})".format(simlabel, "Obs."))
lon, lat, basemap = analysis.get_basemap_from_hdf(file_path=model_file)
# do calculations and only after that do the plotting
season_to_field = {}
# calculate global min and max for plotting
vmin = None
vmax = None
for season, months in season_to_months.items():
model_field = analysis.get_seasonal_climatology(start_year=start_year, end_year=end_year,
months=months,
level=model_level,
var_name=model_var_name, hdf_path=model_file)
# convert m/s to mm/day for comparison with anusplin data
model_field *= 1000.0 * 60 * 60 * 24
obs_field = obs_manager.getMeanFieldForMonthsInterpolatedTo(months=months, lonstarget=lon, latstarget=lat,
start_year=start_year, end_year=end_year)
# calculate the difference between the modelled and observed fields
the_diff = model_field - obs_field
current_min = np.min(the_diff)
current_max = np.max(the_diff)
if vmin is not None:
vmin = current_min if current_min < vmin else vmin
vmax = current_max if current_max > vmax else vmax
else:
vmin = current_min
vmax = current_max
season_to_field[season] = the_diff
ncolors = 12
gs = gridspec.GridSpec(2, 3, width_ratios=[1, 1, 0.05])
cmap = cm.get_cmap("RdBu_r", ncolors)
x, y = basemap(lon, lat)
im = None
d = min(abs(vmin), abs(vmax))
vmin = -d
vmax = d
bn, bounds, _, _ = infovar.get_boundary_norm(vmin, vmax, ncolors, exclude_zero=False)
print("bounds: ", bounds)
cs = None
for season, field in season_to_field.items():
row, col = season_to_plot_indices[season]
ax = fig.add_subplot(gs[row, col])
ax.set_title(season)
basemap.drawmapboundary(fill_color="gray", ax=ax)
im = basemap.pcolormesh(x, y, season_to_field[season], vmin=vmin, vmax=vmax, cmap=cmap, norm=bn)
basemap.drawcoastlines(ax=ax, linewidth=cpp.COASTLINE_WIDTH)
for the_station in stations:
assert isinstance(the_station, Station)
xst, yst = basemap(the_station.longitude, the_station.latitude)
# ax.annotate(the_station.id, (xst, yst), font_properties=FontProperties(size=6),
# bbox=dict(facecolor="w"), va="top", ha="right")
basemap.scatter(xst, yst, c="g", ax=ax)
# small_error = (np.abs(season_to_field[season]) < reasonable_error_mm_per_day).astype(int)
# nlevs = 1
# ax.contour(x, y, small_error, nlevs, colors = "black", linestyle = "-")
# cs = ax.contourf(x, y, small_error, nlevs, colors="none", hatches=["/", None], extend="lower", linewidth=2)
# artists, labels = cs.legend_elements()
# plt.legend(artists, labels, handleheight=2)
cax = fig.add_subplot(gs[:, 2])
cax.set_title("mm/day\n")
plt.colorbar(im, cax=cax, extend="both")
seasons_str = "_".join(sorted([str(s) for s in list(season_to_field.keys())]))
atm_val_folder = os.path.join(images_folder, "validate_atm")
if not os.path.isdir(atm_val_folder):
os.mkdir(atm_val_folder)
out_filename = "{3}/validate_2d_{0}_{1}_{2}.png".format(model_var_name, simlabel, seasons_str, atm_val_folder)
fig.savefig(os.path.join(images_folder, out_filename), bbox_inches="tight")
def validate_temperature(
model_file="", simlabel="", obs_manager=None, season_to_months=None,
start_year=None, end_year=None, season_to_plot_indices=None, model_var_name="TT_max"):
"""
:param model_file:
:param obs_manager: should implement the method
getMeanFieldForMonthsInterpolatedTo(self, months = None, lonsTarget = None, latsTarget = None)
anusplin data is in degrees Celsium
model data is in deg C
"""
if obs_manager is None:
print("Skipping validation of {}, since the obs manager is None.".format(model_var_name))
return
model_level = 0
reasonable_error_deg = 2
assert isinstance(obs_manager, AnuSplinManager)
fig = plt.figure()
assert isinstance(fig, Figure)
fig.suptitle("({0}) - ({1})".format(simlabel, obs_manager.name))
lon, lat, basemap = analysis.get_basemap_from_hdf(file_path=model_file)
# do calculations and only after that do the plotting
season_to_field = {}
# calculate global min and max for plotting
vmin = None
vmax = None
for season, months in season_to_months.items():
model_field = analysis.get_seasonal_climatology(start_year=start_year, end_year=end_year,
months=months,
level=model_level,
var_name=model_var_name, hdf_path=model_file)
obs_field = obs_manager.getMeanFieldForMonthsInterpolatedTo(months=months, lonstarget=lon, latstarget=lat,
start_year=start_year, end_year=end_year)
# calculate the difference between the modelled and observed fields
the_diff = model_field - obs_field
current_min = np.min(the_diff)
current_max = np.max(the_diff)
if vmin is not None:
vmin = current_min if current_min < vmin else vmin
vmax = current_max if current_max > vmax else vmax
else:
vmin = current_min
vmax = current_max
season_to_field[season] = the_diff
ncolors = 10
gs = gridspec.GridSpec(2, 3, width_ratios=[1, 1, 0.05])
cmap = cm.get_cmap("RdBu_r", ncolors)
x, y = basemap(lon, lat)
im = None
d = min(abs(vmin), abs(vmax))
vmin = -d
vmax = d
bn, bounds, _, _ = infovar.get_boundary_norm(vmin, vmax, ncolors)
print("bounds: ", bounds)
cs = None
for season, field in season_to_field.items():
row, col = season_to_plot_indices[season]
ax = fig.add_subplot(gs[row, col])
ax.set_title(season)
im = basemap.pcolormesh(x, y, season_to_field[season], vmin=vmin, vmax=vmax, cmap=cmap, norm=bn)
basemap.drawcoastlines(ax=ax, linewidth=cpp.COASTLINE_WIDTH)
small_error = (np.abs(season_to_field[season]) < reasonable_error_deg).astype(int)
nlevs = 1
# ax.contour(x, y, small_error, nlevs, colors = "black", linestyle = "-")
cs = ax.contourf(x, y, small_error, nlevs, colors="none", hatches=["/", None], extend="lower", linewidth=2)
# artists, labels = cs.legend_elements()
# plt.legend(artists, labels, handleheight=2)
cax = fig.add_subplot(gs[:, 2])
units_str = r"${\rm ^\circ}$"
var_str = r"$T_{\max}$" if model_var_name.endswith("_max") else r"$T_{\min}$"
cax.set_title("{0}, {1}".format(var_str, units_str))
plt.colorbar(im, cax=cax, extend="both")
seasons_str = "_".join(sorted([str(s) for s in list(season_to_field.keys())]))
atm_val_folder = os.path.join(images_folder, "validate_atm")
if not os.path.isdir(atm_val_folder):
os.mkdir(atm_val_folder)
out_filename = "{3}/validate_2d_{0}_{1}_{2}.png".format(model_var_name, simlabel, seasons_str, atm_val_folder)
fig.savefig(os.path.join(images_folder, out_filename), dpi=cpp.FIG_SAVE_DPI, bbox_inches="tight")
