def plot(self, axis=None, **kwargs):
"""Plot centroids scatter points over earth.
Parameters
----------
axis : matplotlib.axes._subplots.AxesSubplot, optional
axis to use
kwargs : optional
arguments for scatter matplotlib function
Returns
-------
axis : matplotlib.axes._subplots.AxesSubplot
"""
if self.meta and not self.coord.size:
self.set_meta_to_lat_lon()
pad = np.abs(u_coord.get_resolution(self.lat, self.lon)).min()
proj_data, _ = u_plot.get_transformation(self.crs)
proj_plot = proj_data
if isinstance(proj_data, ccrs.PlateCarree):
# use different projections for plot and data to shift the central lon in the plot
xmin, ymin, xmax, ymax = u_coord.latlon_bounds(self.lat, self.lon, buffer=pad)
proj_plot = ccrs.PlateCarree(central_longitude=0.5 * (xmin + xmax))
else:
xmin, ymin, xmax, ymax = (self.lon.min() - pad, self.lat.min() - pad,
self.lon.max() + pad, self.lat.max() + pad)
if not axis:
_, axis = u_plot.make_map(proj=proj_plot)
axis.set_extent((xmin, xmax, ymin, ymax), crs=proj_data)
u_plot.add_shapes(axis)
axis.scatter(self.lon, self.lat, transform=proj_data, **kwargs)
return axis
def plot_percen(irma_tc, exp, if_exp, axs):
""" Plot irma damage in %. """
# south
extent = [
exp.longitude.min() - BUFFER_DEG,
exp.longitude.max() + BUFFER_DEG,
exp.latitude.min() - BUFFER_DEG,
exp.latitude.max() + BUFFER_DEG
]
axs.set_extent((extent))
u_plot.add_shapes(axs)
imp_irma = Impact()
imp_irma.calc(exp, if_exp, irma_tc)
imp_irma.eai_exp[exp.value > 0] = \
imp_irma.eai_exp[exp.value > 0]/exp.value[exp.value > 0]*100
imp_irma.eai_exp[exp.value == 0] = 0.
sel_exp = imp_irma.eai_exp > 0
im = axs.hexbin(exp.longitude[sel_exp],
exp.latitude[sel_exp],
C=imp_irma.eai_exp[sel_exp],
reduce_C_function=np.average,
transform=ccrs.PlateCarree(),
gridsize=2000,
cmap='YlOrRd',
vmin=0,
vmax=50)
axs.set_title('')
axs.grid(False)
scale_bar(axs, (0.90, 0.90), 10)
return im
def plot_left(exp, data_irma, tc_irma, ax, scale_pos, cntour_loc, label_loc):
""" Plot exposed value, irma track and irma wind field. """
extent = u_plot._get_borders(exp.coord)
extent = ([extent[0] - BUFFER_DEG, extent[1] + BUFFER_DEG, extent[2] -\
BUFFER_DEG, extent[3] + BUFFER_DEG])
ax.set_extent((extent))
u_plot.add_shapes(ax)
sel_pos = np.argwhere(exp.value > 0)[:, 0]
ax.hexbin(exp.coord[sel_pos, 1],
exp.coord[sel_pos, 0],
C=exp.value[sel_pos],
reduce_C_function=np.average,
transform=ccrs.PlateCarree(),
gridsize=2000,
norm=LogNorm(vmin=MIN_VAL, vmax=MAX_VAL),
cmap='YlOrRd',
vmin=1.0e2,
vmax=MAX_VAL)
ax.set_title('')
ax.grid(False)
scale_bar(ax, scale_pos, 10)
track = data_irma.data[0]
ax.plot(track.lon.values,
track.lat.values,
linestyle='solid',
transform=ccrs.PlateCarree(),
lw=2,
color='k')
leg_lines = [
Line2D([0], [0], color='k', lw=2),
Line2D([0], [0], color='grey', lw=1, ls=':')
]
leg_names = ['Irma track', 'wind field (kn)']
if 'bbox' in label_loc:
ax.legend(leg_lines,
leg_names,
bbox_to_anchor=label_loc['bbox'],
loc=label_loc['loc'])
else:
ax.legend(leg_lines, leg_names, loc=label_loc['loc'])
tc_irma.intensity *= MS2KN
grid_x, grid_y = np.mgrid[tc_irma.centroids.coord[:, 1].min() : \
tc_irma.centroids.coord[:, 1].max() : complex(0, 2000), \
tc_irma.centroids.coord[:, 0].min() : \
tc_irma.centroids.coord[:, 0].max() : complex(0, 2000)]
grid_im = griddata(
(tc_irma.centroids.coord[:, 1], tc_irma.centroids.coord[:, 0]),
np.array(tc_irma.intensity[0].todense()).squeeze(), (grid_x, grid_y))
cs = ax.contour(grid_x, grid_y, grid_im, linewidths=1.0, linestyles=':', \
levels=[60, 80, 100, 120], colors=['grey', 'grey', 'grey', 'grey', 'grey'])
ax.clabel(cs,
inline=1,
fontsize=10,
manual=cntour_loc,
rotation=-20,
fmt='%1.0f')
tc_irma.intensity /= MS2KN
def plot_raster(self, res=None, raster_res=None, save_tiff=None,
raster_f=lambda x: np.log10((np.fmax(x+1, 1))),
label='value (log10)', scheduler=None, axis=None, **kwargs):
""" Generate raster from points geometry and plot it using log10 scale:
np.log10((np.fmax(raster+1, 1))).
Parameters:
res (float, optional): resolution of current data in units of latitude
and longitude, approximated if not provided.
raster_res (float, optional): desired resolution of the raster
save_tiff (str, optional): file name to save the raster in tiff
format, if provided
raster_f (lambda function): transformation to use to data. Default:
log10 adding 1.
label (str): colorbar label
scheduler (str): used for dask map_partitions. “threads”,
“synchronous” or “processes”
axis (matplotlib.axes._subplots.AxesSubplot, optional): axis to use
kwargs (optional): arguments for imshow matplotlib function
Returns:
matplotlib.figure.Figure, cartopy.mpl.geoaxes.GeoAxesSubplot
"""
if self.meta and self.meta['height']*self.meta['width'] == len(self):
raster = self.value.values.reshape((self.meta['height'],
self.meta['width']))
# check raster starts by upper left corner
if self.latitude.values[0] < self.latitude.values[-1]:
raster = np.flip(raster, axis=0)
if self.longitude.values[0] > self.longitude.values[-1]:
LOGGER.error('Points are not ordered according to meta raster.')
raise ValueError
else:
raster, meta = co.points_to_raster(self, ['value'], res, raster_res,
scheduler)
raster = raster.reshape((meta['height'], meta['width']))
# save tiff
if save_tiff is not None:
ras_tiff = rasterio.open(save_tiff, 'w', driver='GTiff', \
height=meta['height'], width=meta['width'], count=1, \
dtype=np.float32, crs=self.crs, transform=meta['transform'])
ras_tiff.write(raster.astype(np.float32), 1)
ras_tiff.close()
# make plot
crs_epsg, _ = u_plot.get_transformation(self.crs)
xmin, ymin, xmax, ymax = self.longitude.min(), self.latitude.min(), \
self.longitude.max(), self.latitude.max()
if not axis:
_, axis = u_plot.make_map(proj=crs_epsg)
cbar_ax = make_axes_locatable(axis).append_axes('right', size="6.5%", \
pad=0.1, axes_class=plt.Axes)
axis.set_extent([max(xmin, crs_epsg.x_limits[0]), \
min(xmax, crs_epsg.x_limits[1]), max(ymin, crs_epsg.y_limits[0]), \
min(ymax, crs_epsg.y_limits[1])], crs_epsg)
u_plot.add_shapes(axis)
imag = axis.imshow(raster_f(raster), **kwargs, origin='upper',
extent=[xmin, xmax, ymin, ymax], transform=crs_epsg)
plt.colorbar(imag, cax=cbar_ax, label=label)
plt.draw()
return axis
def plot_raster(self, res=None, raster_res=None, save_tiff=None,
raster_f=lambda x: np.log10((np.fmax(x+1, 1))),
label='value (log10)', **kwargs):
""" Generate raster from points geometry and plot it using log10 scale:
np.log10((np.fmax(raster+1, 1))).
Parameters:
res (float, optional): resolution of current data in units of latitude
and longitude, approximated if not provided.
raster_res (float, optional): desired resolution of the raster
save_tiff (str, optional): file name to save the raster in tiff
format, if provided
raster_f (lambda function): transformation to use to data. Default:
log10 adding 1.
label (str): colorbar label
kwargs (optional): arguments for imshow matplotlib function
Returns:
matplotlib.figure.Figure, cartopy.mpl.geoaxes.GeoAxesSubplot
"""
if not 'geometry' in self.columns:
self.set_geometry_points()
crs_epsg, crs_unit = self._get_transformation()
if not res:
res= min(get_resolution(self.latitude.values, self.longitude.values))
if not raster_res:
raster_res = res
LOGGER.info('Raster from resolution %s%s to %s%s.', res, crs_unit,
raster_res, crs_unit)
exp_poly = self[['value']].set_geometry(self.buffer(res/2).envelope)
# construct raster
xmin, ymin, xmax, ymax = self.total_bounds
rows, cols, ras_trans = points_to_raster((xmin, ymin, xmax, ymax), raster_res)
raster = rasterize([(x, val) for (x, val) in zip(exp_poly.geometry, exp_poly.value)],
out_shape=(rows, cols), transform=ras_trans, fill=0,
all_touched=True, dtype=rasterio.float32, )
# save tiff
if save_tiff is not None:
ras_tiff = rasterio.open(save_tiff, 'w', driver='GTiff', \
height=raster.shape[0], width=raster.shape[1], count=1, \
dtype=np.float32, crs=self.crs, transform=ras_trans)
ras_tiff.write(raster.astype(np.float32), 1)
ras_tiff.close()
# make plot
fig, axis = u_plot.make_map(proj=crs_epsg)
cbar_ax = fig.add_axes([0.99, 0.238, 0.03, 0.525])
fig.subplots_adjust(hspace=0, wspace=0)
axis[0, 0].set_extent([max(xmin, crs_epsg.x_limits[0]),
min(xmax, crs_epsg.x_limits[1]),
max(ymin, crs_epsg.y_limits[0]),
min(ymax, crs_epsg.y_limits[1])], crs_epsg)
u_plot.add_shapes(axis[0, 0])
imag = axis[0, 0].imshow(raster_f(raster), **kwargs, origin='upper',
extent=[xmin, xmax, ymin, ymax], transform=crs_epsg)
plt.colorbar(imag, cax=cbar_ax, label=label)
plt.draw()
posn = axis[0, 0].get_position()
cbar_ax.set_position([posn.x0 + posn.width + 0.01, posn.y0, 0.04, posn.height])
return fig, axis
def plot(self, axis=None, **kwargs):
"""Plot centroids scatter points over earth.
Parameters:
axis (matplotlib.axes._subplots.AxesSubplot, optional): axis to use
kwargs (optional): arguments for scatter matplotlib function
Returns:
matplotlib.axes._subplots.AxesSubplot
"""
if not axis:
_, axis = u_plot.make_map()
u_plot.add_shapes(axis)
if self.meta and not self.coord.size:
self.set_meta_to_lat_lon()
axis.scatter(self.lon, self.lat, **kwargs)
return axis
def plot(self, **kwargs):
""" Plot centroids scatter points over earth.
Parameters:
kwargs (optional): arguments for scatter matplotlib function
Returns:
matplotlib.figure.Figure, matplotlib.axes._subplots.AxesSubplot
"""
if 's' not in kwargs:
kwargs['s'] = 1
fig, axis = u_plot.make_map()
axis = axis[0][0]
u_plot.add_shapes(axis)
if self.meta and not self.coord.size:
self.set_meta_to_lat_lon()
axis.scatter(self.lon, self.lat, **kwargs)
return fig, axis
def plot(self, **kwargs):
""" Plot centroids points over earth.
Parameters:
kwargs (optional): arguments for scatter matplotlib function
Returns:
matplotlib.figure.Figure, matplotlib.axes._subplots.AxesSubplot
"""
if 's' not in kwargs:
kwargs['s'] = 1
fig, axis = u_plot.make_map()
axis = axis[0][0]
u_plot.add_shapes(axis)
axis.set_title(self.tag.join_file_names())
axis.scatter(self.lon, self.lat, **kwargs)
return fig, axis
def plot_right(irma_tc, exp, ax, scale_pos, plot_line=False):
""" Plot irma damage in USD. """
if_exp = ImpactFuncSet()
if_em = IFTropCyclone()
if_em.set_emanuel_usa()
if_exp.append(if_em)
imp_irma = Impact()
imp_irma.calc(exp, if_exp, irma_tc)
extent = [
exp.longitude.min() - BUFFER_DEG,
exp.longitude.max() + BUFFER_DEG,
exp.latitude.min() - BUFFER_DEG,
exp.latitude.max() + BUFFER_DEG
]
ax.set_extent((extent))
u_plot.add_shapes(ax)
sel_pos = np.argwhere(imp_irma.eai_exp > 0)[:, 0]
hex_bin = ax.hexbin(imp_irma.coord_exp[sel_pos, 1],
imp_irma.coord_exp[sel_pos, 0],
C=imp_irma.eai_exp[sel_pos],
reduce_C_function=np.average,
transform=ccrs.PlateCarree(),
gridsize=2000,
norm=LogNorm(vmin=MIN_VAL, vmax=MAX_VAL),
cmap='YlOrRd',
vmin=MIN_VAL,
vmax=MAX_VAL)
ax.set_title('')
ax.grid(False)
add_cntry_names(ax, extent)
scale_bar(ax, scale_pos, 10)
if plot_line:
x1, y1 = [-64.57, -64.82], [18.28, 18.47]
ax.plot(x1, y1, linewidth=1.0, color='grey', linestyle='--')
return hex_bin
def plot_raster(self,
res=None,
raster_res=None,
save_tiff=None,
raster_f=lambda x: np.log10((np.fmax(x + 1, 1))),
label='value (log10)',
scheduler=None,
axis=None,
figsize=(9, 13),
**kwargs):
"""Generate raster from points geometry and plot it using log10 scale:
np.log10((np.fmax(raster+1, 1))).
Parameters:
res (float, optional): resolution of current data in units of latitude
and longitude, approximated if not provided.
raster_res (float, optional): desired resolution of the raster
save_tiff (str, optional): file name to save the raster in tiff
format, if provided
raster_f (lambda function): transformation to use to data. Default:
log10 adding 1.
label (str): colorbar label
scheduler (str): used for dask map_partitions. “threads”,
“synchronous” or “processes”
axis (matplotlib.axes._subplots.AxesSubplot, optional): axis to use
figsize (tuple, optional): figure size for plt.subplots
kwargs (optional): arguments for imshow matplotlib function
Returns:
matplotlib.figure.Figure, cartopy.mpl.geoaxes.GeoAxesSubplot
"""
if self.meta and self.meta.get('height', 0) * self.meta.get(
'height', 0) == len(self.gdf):
raster = self.gdf.value.values.reshape(
(self.meta['height'], self.meta['width']))
# check raster starts by upper left corner
if self.gdf.latitude.values[0] < self.gdf.latitude.values[-1]:
raster = np.flip(raster, axis=0)
if self.gdf.longitude.values[0] > self.gdf.longitude.values[-1]:
LOGGER.error(
'Points are not ordered according to meta raster.')
raise ValueError
else:
raster, meta = u_coord.points_to_raster(self.gdf, ['value'], res,
raster_res, scheduler)
raster = raster.reshape((meta['height'], meta['width']))
# save tiff
if save_tiff is not None:
with rasterio.open(save_tiff,
'w',
driver='GTiff',
height=meta['height'],
width=meta['width'],
count=1,
dtype=np.float32,
crs=self.crs,
transform=meta['transform']) as ras_tiff:
ras_tiff.write(raster.astype(np.float32), 1)
# make plot
proj_data, _ = u_plot.get_transformation(self.crs)
proj_plot = proj_data
if isinstance(proj_data, ccrs.PlateCarree):
# use different projections for plot and data to shift the central lon in the plot
xmin, ymin, xmax, ymax = u_coord.latlon_bounds(
self.gdf.latitude.values, self.gdf.longitude.values)
proj_plot = ccrs.PlateCarree(central_longitude=0.5 * (xmin + xmax))
else:
xmin, ymin, xmax, ymax = (self.gdf.longitude.min(),
self.gdf.latitude.min(),
self.gdf.longitude.max(),
self.gdf.latitude.max())
if not axis:
_, axis = u_plot.make_map(proj=proj_plot, figsize=figsize)
cbar_ax = make_axes_locatable(axis).append_axes('right',
size="6.5%",
pad=0.1,
axes_class=plt.Axes)
axis.set_extent((xmin, xmax, ymin, ymax), crs=proj_data)
u_plot.add_shapes(axis)
imag = axis.imshow(raster_f(raster),
**kwargs,
origin='upper',
extent=(xmin, xmax, ymin, ymax),
transform=proj_data)
plt.colorbar(imag, cax=cbar_ax, label=label)
plt.draw()
return axis
def _plot_imp_map(
self,
run_datetime,
title,
cbar_label,
polygon_file=None,
polygon_file_crs="epsg:4326",
proj=ccrs.PlateCarree(),
figsize=(9, 13),
adapt_fontsize=True,
):
# select hazard with run_datetime
# pylint: disable=protected-access
if run_datetime is None:
run_datetime = self.run_datetime[0]
haz_ind = np.argwhere(np.isin(self.run_datetime, run_datetime))[0][0]
# tryout new plot with right projection
extend = "neither"
value = self._impact[haz_ind].eai_exp
# value[np.invert(mask)] = np.nan
coord = self._impact[haz_ind].coord_exp
# Generate array of values used in each subplot
array_sub = value
shapes = True
if not polygon_file:
shapes = False
var_name = cbar_label
geo_coord = coord
num_im, list_arr = u_plot._get_collection_arrays(array_sub)
list_tit = to_list(num_im, title, "title")
list_name = to_list(num_im, var_name, "var_name")
list_coord = to_list(num_im, geo_coord, "geo_coord")
kwargs = dict()
kwargs["cmap"] = CMAP_IMPACT
kwargs["s"] = 5
kwargs["marker"] = ","
kwargs["norm"] = BoundaryNorm(
np.append(
np.append([0], [10**x for x in np.arange(0, 2.9, 2.9 / 9)]),
[10**x for x in np.arange(3, 7, 4 / 90)],
),
CMAP_IMPACT.N,
clip=True,
)
# Generate each subplot
fig, axis_sub, _fontsize = u_plot.make_map(
num_im, proj=proj, figsize=figsize, adapt_fontsize=adapt_fontsize)
if not isinstance(axis_sub, np.ndarray):
axis_sub = np.array([[axis_sub]])
fig.set_size_inches(9, 8)
for array_im, axis, tit, name, coord in zip(list_arr,
axis_sub.flatten(),
list_tit, list_name,
list_coord):
if coord.shape[0] != array_im.size:
raise ValueError("Size mismatch in input array: %s != %s." %
(coord.shape[0], array_im.size))
# Binned image with coastlines
extent = u_plot._get_borders(coord)
axis.set_extent((extent), ccrs.PlateCarree())
hex_bin = axis.scatter(coord[:, 1],
coord[:, 0],
c=array_im,
transform=ccrs.PlateCarree(),
**kwargs)
if shapes:
# add warning regions
shp = shapereader.Reader(polygon_file)
transformer = pyproj.Transformer.from_crs(
polygon_file_crs,
self._impact[haz_ind].crs,
always_xy=True)
for geometry, _ in zip(shp.geometries(), shp.records()):
geom2 = shapely.ops.transform(transformer.transform,
geometry)
axis.add_geometries(
[geom2],
crs=ccrs.PlateCarree(),
facecolor="none",
edgecolor="gray",
)
else: # add country boundaries
u_plot.add_shapes(axis)
# Create colorbar in this axis
cbax = make_axes_locatable(axis).append_axes("bottom",
size="6.5%",
pad=0.3,
axes_class=plt.Axes)
cbar = plt.colorbar(hex_bin,
cax=cbax,
orientation="horizontal",
extend=extend)
cbar.set_label(name)
cbar.formatter.set_scientific(False)
cbar.set_ticks([0, 1000, 10000, 100000, 1000000])
cbar.set_ticklabels(
["0", "1 000", "10 000", "100 000", "1 000 000"])
title_position = {
"model_text": [0.02, 0.85],
"explain_text": [0.02, 0.81],
"event_day": [0.98, 0.85],
"run_start": [0.98, 0.81],
}
left_right = {
"model_text": "left",
"explain_text": "left",
"event_day": "right",
"run_start": "right",
}
color = {
"model_text": "k",
"explain_text": "k",
"event_day": "r",
"run_start": "k",
}
for t_i in tit:
plt.figtext(
title_position[t_i][0],
title_position[t_i][1],
tit[t_i],
fontsize="xx-large",
color=color[t_i],
ha=left_right[t_i],
)
fig.tight_layout()
fig.subplots_adjust(top=0.8)
return fig, axis_sub
def plot_raster(self,
res=None,
raster_res=None,
save_tiff=None,
raster_f=lambda x: np.log10((np.fmax(x + 1, 1))),
label='value (log10)',
scheduler=None,
axis=None,
figsize=(9, 13),
fill=True,
adapt_fontsize=True,
**kwargs):
"""Generate raster from points geometry and plot it using log10 scale:
np.log10((np.fmax(raster+1, 1))).
Parameters
----------
res : float, optional
resolution of current data in units of latitude
and longitude, approximated if not provided.
raster_res : float, optional
desired resolution of the raster
save_tiff : str, optional
file name to save the raster in tiff
format, if provided
raster_f : lambda function
transformation to use to data. Default:
log10 adding 1.
label : str
colorbar label
scheduler : str
used for dask map_partitions. “threads”,
“synchronous” or “processes”
axis : matplotlib.axes._subplots.AxesSubplot, optional
axis to use
figsize : tuple, optional
figure size for plt.subplots
fill : bool, optional
If false, the areas with no data will be plotted
in white. If True, the areas with missing values are filled as 0s.
The default is True.
adapt_fontsize : bool, optional
If set to true, the size of the fonts will be adapted to the size of the figure.
Otherwise the default matplotlib font size is used. Default is True.
kwargs : optional
arguments for imshow matplotlib function
Returns
-------
matplotlib.figure.Figure, cartopy.mpl.geoaxes.GeoAxesSubplot
"""
if self.meta and self.meta.get('height', 0) * self.meta.get(
'height', 0) == len(self.gdf):
raster = self.gdf.value.values.reshape(
(self.meta['height'], self.meta['width']))
# check raster starts by upper left corner
if self.gdf.latitude.values[0] < self.gdf.latitude.values[-1]:
raster = np.flip(raster, axis=0)
if self.gdf.longitude.values[0] > self.gdf.longitude.values[-1]:
raise ValueError(
'Points are not ordered according to meta raster.')
else:
raster, meta = u_coord.points_to_raster(self.gdf, ['value'], res,
raster_res, scheduler)
raster = raster.reshape((meta['height'], meta['width']))
# save tiff
if save_tiff is not None:
with rasterio.open(save_tiff,
'w',
driver='GTiff',
height=meta['height'],
width=meta['width'],
count=1,
dtype=np.float32,
crs=self.crs,
transform=meta['transform']) as ras_tiff:
ras_tiff.write(raster.astype(np.float32), 1)
# make plot
proj_data, _ = u_plot.get_transformation(self.crs)
proj_plot = proj_data
if isinstance(proj_data, ccrs.PlateCarree):
# use different projections for plot and data to shift the central lon in the plot
xmin, ymin, xmax, ymax = u_coord.latlon_bounds(
self.gdf.latitude.values, self.gdf.longitude.values)
proj_plot = ccrs.PlateCarree(central_longitude=0.5 * (xmin + xmax))
else:
xmin, ymin, xmax, ymax = (self.gdf.longitude.min(),
self.gdf.latitude.min(),
self.gdf.longitude.max(),
self.gdf.latitude.max())
if not axis:
_, axis, fontsize = u_plot.make_map(proj=proj_plot,
figsize=figsize,
adapt_fontsize=adapt_fontsize)
else:
fontsize = None
cbar_ax = make_axes_locatable(axis).append_axes('right',
size="6.5%",
pad=0.1,
axes_class=plt.Axes)
axis.set_extent((xmin, xmax, ymin, ymax), crs=proj_data)
u_plot.add_shapes(axis)
if not fill:
raster = np.where(raster == 0, np.nan, raster)
raster_f = lambda x: np.log10((np.maximum(x + 1, 1)))
if 'cmap' not in kwargs:
kwargs['cmap'] = CMAP_RASTER
imag = axis.imshow(raster_f(raster),
**kwargs,
origin='upper',
extent=(xmin, xmax, ymin, ymax),
transform=proj_data)
cbar = plt.colorbar(imag, cax=cbar_ax, label=label)
plt.colorbar(imag, cax=cbar_ax, label=label)
plt.tight_layout()
plt.draw()
if fontsize:
cbar.ax.tick_params(labelsize=fontsize)
cbar.ax.yaxis.get_offset_text().set_fontsize(fontsize)
for item in [axis.title, cbar.ax.xaxis.label, cbar.ax.yaxis.label]:
item.set_fontsize(fontsize)
return axis
