def plot_surface(spectra, dimension1, dimension2,
fig_num=1, show_plot=True, **kwargs):
""" Plot the two dimensions from spectra as a 2D histogram
Args:
spectra (:class:`echidna.core.spectra`): The spectra to plot.
dimension1 (string): The name of the dimension you want to plot.
dimension2 (string): The name of the dimension you want to plot.
Returns:
matplotlib.pyplot.figure: Plot of the surface of the two dimensions.
"""
fig = plt.figure(num=fig_num)
axis = fig.add_subplot(111)
index1 = spectra.get_config().get_index(dimension1)
index2 = spectra.get_config().get_index(dimension2)
if index1 < index2:
x = _produce_axis(spectra, dimension2)
y = _produce_axis(spectra, dimension1)
else:
x = _produce_axis(spectra, dimension1)
y = _produce_axis(spectra, dimension2)
data = spectra.surface(dimension1, dimension2)
par1 = spectra.get_config().get_par(dimension1)
par2 = spectra.get_config().get_par(dimension2)
if index1 < index2:
axis.set_xlabel("%s (%s)" % (dimension2, par2.get_unit()))
axis.set_ylabel("%s (%s)" % (dimension1, par1.get_unit()))
else:
axis.set_xlabel("%s (%s)" % (dimension1, par1.get_unit()))
axis.set_ylabel("%s (%s)" % (dimension2, par2.get_unit()))
# `plot_surface` expects `x` and `y` data to be 2D
X, Y = numpy.meshgrid(x, y)
# Set sensible levels, pick the desired colormap and define normalization
if kwargs.get("color_scheme") is None:
color_scheme = "hot_r" # default
else:
color_scheme = kwargs.get("color_scheme")
color_map = plt.get_cmap(color_scheme)
linear = numpy.linspace(numpy.sqrt(data.min()),
numpy.sqrt(data.max()), num=100)
locator = FixedLocator(linear**2)
levels = locator.tick_values(data.min(), data.max())
norm = BoundaryNorm(levels, ncolors=color_map.N)
# Plot color map
color_map = axis.pcolormesh(X, Y, data, cmap=color_map, norm=norm)
color_bar = fig.colorbar(color_map)
color_bar.set_label("Counts per bin")
if show_plot:
plt.show()
return fig
def chi_squared_map(syst_analyser, fig_num=1, **kwargs):
""" Plot chi squared surface for systematic vs. signal counts
Args:
syst_analyser (:class:`echidna.limit.limit_setting.SystAnalyser`): Systematic
analyser object, created during limit setting. Can be used
during limit setting setting or can load an instance from
hdf5
fig_num (int): Fig number. When creating multiple plots in the
same script, ensures matplotlib doesn't overwrite them.
.. note::
Keyword arguments include:
* contours (*bool*): if True produces a contour plot of chi
squared surface. Default (*False*).
* preferred_values (*bool*): if False "preferred values" curve
is not overlayed on colour map. Default (*True*)
* minima (*bool*): if False "minima" are not overlayed on
colour map. Default (*True*)
* save_as (*string*): supply file name to save image
Default is to produce a colour map, with "preferred values" curve
and "minima" overlayed.
"""
# Set kwargs defaults
if kwargs.get("preferred_values") is None:
kwargs["preferred_values"] = True
if kwargs.get("minima") is None:
kwargs["minima"] = True
# Set x and y axes
x = syst_analyser.get_actual_counts()
y = syst_analyser.get_syst_values()
# Set chi squared map values
data = numpy.average(syst_analyser.get_chi_squareds(), axis=1)
data = numpy.transpose(data) # transpose it so that axes are correct
# Set preferred value values
y_2 = numpy.average(syst_analyser.get_preferred_values(), axis=1)
# Set minima values
x_3 = syst_analyser.get_minima()[0]
y_3 = syst_analyser.get_minima()[1]
# Create meshgrid
X, Y = numpy.meshgrid(x, y)
# Set sensible levels, pick the desired colormap and define normalization
color_map = plt.get_cmap('hot_r')
linear = numpy.linspace(numpy.sqrt(data.min()), numpy.sqrt(data.max()),
num=100)
locator = FixedLocator(linear**2)
levels = locator.tick_values(data.min(), data.max())
norm = BoundaryNorm(levels, ncolors=color_map.N)
if kwargs.get("contours"):
fig = plt.figure(fig_num, figsize=(16, 10)) # Fig. 2
fig.text(0.1, 0.9, syst_analyser._name, **BOLD_FONT)
ax = Axes3D(fig)
ax.view_init(elev=17.0, azim=-136.0) # set intial viewing position
# Plot surface
surf = ax.plot_surface(X, Y, data, rstride=1, cstride=1,
cmap=color_map, norm=norm, linewidth=0,
antialiased=False)
ax.zaxis.set_minor_locator(locator)
ax.ticklabel_format(style="scientific", scilimits=(3, 4))
# Set axis labels
ax.set_xlabel("\nSignal counts", **BOLD_FONT)
ax.set_ylabel("\nValue of systematic", **BOLD_FONT)
for label in (ax.get_xticklabels() +
ax.get_yticklabels() +
ax.get_zticklabels()):
label.set_fontsize(MAIN_FONT.get("size")) # tick label size
ax.dist = 11 # Ensures tick labels are not cut off
ax.margins(0.05, 0.05, 0.05) # Adjusts tick margins
# Draw colorbar
color_bar = fig.colorbar(surf, ax=ax, orientation="vertical",
fraction=0.2, shrink=0.5, aspect=10)
# kwargs here control axes that the colorbar is drawn in
color_bar.set_label(r"$\chi^2$", size=MAIN_FONT.get("size"))
color_bar.ax.tick_params(labelsize=MAIN_FONT.get("size"))
plt.show()
if kwargs.get("save_as") is not None:
fig.savefig(kwargs.get("save_as") + "_contour.png", dpi=300)
else:
fig = plt.figure(fig_num, figsize=(12, 10)) # Fig. 2
fig.text(0.1, 0.95, syst_analyser._name, **BOLD_FONT)
ax = fig.add_subplot(1, 1, 1)
# Set labels
ax.set_xlabel("Signal counts", **BOLD_FONT)
ax.set_ylabel("Value of systematic", **BOLD_FONT)
# Plot color map
color_map = ax.pcolormesh(X, Y, data, cmap=color_map, norm=norm)
color_bar = fig.colorbar(color_map)
color_bar.set_label("$\chi^2$", size=MAIN_FONT.get("size"))
color_bar.ax.tick_params(labelsize=MAIN_FONT.get("size")) # tick label size
# Set axes limits
ax.set_xlim([X.min(), X.max()])
ax.set_ylim([Y.min(), Y.max()])
if kwargs.get("preferred_values"):
ax.plot(x, y_2, "bo-", label="Preferred values")
if kwargs.get("minima"):
ax.plot(x_3, y_3, "ko", label="Minima")
# Set axes tick label size
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(MAIN_FONT.get("size"))
ax.legend(loc="upper left")
if kwargs.get("save_as") is not None:
fig.savefig(kwargs.get("save_as") + "_color_map.png", dpi=300)
return fig
def push_pull(syst_analyser, fig=1, **kwargs):
""" Plot penalty value - poisson likelihood chi squared.
When does minimising chi squared, which wants to "pull" the away
from the data/prior value dominate and when does the penalty term,
which wants to "pull" towards the data/prior, constraining the fit
dominate?
Args:
syst_analyser (:class:`echidna.limit.limit_setting.SystAnalyser`): Systematic
analyser object, created during limit setting. Can be used
during limit setting setting or can load an instance from
hdf5
fig_num (int): Fig number. When creating multiple plots in the
same script, ensures matplotlib doesn't overwrite them.
.. note::
Keyword arguments include:
* save_as (*string*): supply file name to save image
"""
# Set x and y axes
x = syst_analyser.get_actual_counts()
y = syst_analyser.get_syst_values()
# Set chi squared map values
data = numpy.average(syst_analyser.get_chi_squareds(), axis=1)
data = numpy.transpose(data) # transpose it so that axes are correct
# Create meshgrid
X, Y = numpy.meshgrid(x, y)
# Define an array penalty values
penalty_values = syst_analyser._penalty_values[1, 0:len(y)]
penalty_array = numpy.zeros(data.shape) # zeroed array the same size as data
for y, penalty_value in enumerate(penalty_values):
for x in range(len(penalty_array[y])):
penalty_array[y][x] = penalty_value
# Define the push pull array penalty term - chi_squared
# --> push_pull > 0 when penalty_value > chi_squared
# --> push_pull < 1 when penalty_value < chi_squared
push_pull = (2.*penalty_array) - data
# Set sensible levels, pick the desired colormap and define normalization
color_map = plt.get_cmap('coolwarm')
if push_pull.min() < 0.:
negatives = numpy.linspace(push_pull.min(), 0.,
num=50, endpoint=False)
else:
negatives = numpy.zeros((50))
if push_pull.max() > 0.:
positives = numpy.linspace(0., push_pull.max(), num=51)
else:
positives = numpy.zeros((51))
# Add the pull part to the push part
full_scale = numpy.append(negatives, positives)
locator = FixedLocator(full_scale)
levels = locator.tick_values(push_pull.min(), push_pull.max())
norm = BoundaryNorm(levels, ncolors=color_map.N)
fig = plt.figure(fig, figsize=(12, 10)) # Fig. 4
fig.text(0.1, 0.95, syst_analyser._name, **BOLD_FONT)
ax = fig.add_subplot(1, 1, 1)
# Set labels
ax.set_xlabel("Signal counts", **BOLD_FONT)
ax.set_ylabel("Value of systematic", **BOLD_FONT)
# Plot color map
color_map = ax.pcolormesh(X, Y, push_pull, cmap=color_map, norm=norm)
color_bar = fig.colorbar(color_map)
color_bar.set_label("$s-\chi^{2}_{\lambda,p}$", size=MAIN_FONT.get("size"))
color_bar.ax.tick_params(labelsize=MAIN_FONT.get("size")) # tick label size
# Set axes limits
ax.set_xlim([X.min(), X.max()])
ax.set_ylim([Y.min(), Y.max()])
# Set axes tick label size
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(MAIN_FONT.get("size"))
ax.legend(loc="upper left")
if kwargs.get("save_as") is not None:
fig.savefig(kwargs.get("save_as") + "_push_pull.png", dpi=300)
return fig
def turn_on(syst_analyser, signal_config, fig=1, **kwargs):
""" Plot deviation from chi-squared with no floated systematics.
When does the effect of floating the systematic "turn on"?
Args:
syst_analyser (:class:`echidna.limit.limit_setting.SystAnalyser`): Systematic
analyser object, created during limit setting. Can be used
during limit setting setting or can load an instance from
hdf5
signal_config (:class:`echidna.limit.limit_config.LimitConfig`): Signal
config class, where chi squareds have been stored.
fig_num (int): Fig number. When creating multiple plots in the
same script, ensures matplotlib doesn't overwrite them.
.. note::
Keyword arguments include:
* save_as (*string*): supply file name to save image
"""
# Set x and y axes
x = syst_analyser.get_actual_counts()
y = syst_analyser.get_syst_values()
# Set chi squared map values
data = numpy.average(syst_analyser.get_chi_squareds(), axis=1)
data = numpy.transpose(data) # transpose it so that axes are correct
# Create meshgrid
X, Y = numpy.meshgrid(x, y)
# Define an array of \chi_0 values - chi squared without floating systematics
chi_squareds = signal_config.get_chi_squareds()[0]
data_np = numpy.zeros(data.shape) # zeroed array the same size as data
for y in range(len(data_np)):
for x, chi_squared in enumerate(chi_squareds):
data_np[y][x] = chi_squared
#if numpy.any((numpy.average(data_np, axis=0) != chi_squareds)):
# raise AssertionError("Incorrect chi squareds (no floating) array.")
# Make an array of the offsets
offsets = data - data_np
# Set sensible levels, pick the desired colormap and define normalization
color_map = plt.get_cmap('coolwarm')
positives = numpy.linspace(numpy.log10(offsets.max())*-1.,
numpy.log10(offsets.max()), num=50)
# linear array in log space
if offsets.min() < 0.:
negatives = numpy.linspace(offsets.min(), 0.0, num=51)
else:
negatives = numpy.zeros((51))
# Add the positive part to the negative part
full_scale = numpy.append(negatives, numpy.power(10, positives))
locator = FixedLocator(full_scale)
levels = locator.tick_values(offsets.min(), offsets.max())
norm = BoundaryNorm(levels, ncolors=color_map.N)
fig = plt.figure(fig, figsize=(12, 10)) # Fig. 4
fig.text(0.1, 0.95, syst_analyser._name, **BOLD_FONT)
ax = fig.add_subplot(1, 1, 1)
# Set labels
ax.set_xlabel("Signal counts", **BOLD_FONT)
ax.set_ylabel("Value of systematic", **BOLD_FONT)
# Plot color map
color_map = ax.pcolormesh(X, Y, offsets, cmap=color_map, norm=norm)
color_bar = fig.colorbar(color_map)
color_bar.set_label("$\chi^2 - \chi_0^2$", size=MAIN_FONT.get("size"))
color_bar.ax.tick_params(labelsize=MAIN_FONT.get("size")) # tick label size
# Set axes limits
ax.set_xlim([X.min(), X.max()])
ax.set_ylim([Y.min(), Y.max()])
# Set axes tick label size
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(MAIN_FONT.get("size"))
ax.legend(loc="upper left")
if kwargs.get("save_as") is not None:
fig.savefig(kwargs.get("save_as") + "_turn_on.png", dpi=300)
return fig
def push_pull(syst_analyser, fig=1, **kwargs):
""" Plot penalty value - poisson likelihood chi squared.
When does minimising chi squared, which wants to "pull" the away
from the data/prior value dominate and when does the penalty term,
which wants to "pull" towards the data/prior, constraining the fit
dominate?
Args:
syst_analyser (:class:`echidna.limit.limit_setting.SystAnalyser`): Systematic
analyser object, created during limit setting. Can be used
during limit setting setting or can load an instance from
hdf5
fig_num (int): Fig number. When creating multiple plots in the
same script, ensures matplotlib doesn't overwrite them.
.. note::
Keyword arguments include:
* save_as (*string*): supply file name to save image
"""
# Set x and y axes
x = syst_analyser.get_actual_counts()
y = syst_analyser.get_syst_values()
# Set chi squared map values
data = numpy.average(syst_analyser.get_chi_squareds(), axis=1)
data = numpy.transpose(data) # transpose it so that axes are correct
# Create meshgrid
X, Y = numpy.meshgrid(x, y)
# Define an array penalty values
penalty_values = syst_analyser._penalty_values[1, 0:len(y)]
penalty_array = numpy.zeros(
data.shape) # zeroed array the same size as data
for y, penalty_value in enumerate(penalty_values):
for x in range(len(penalty_array[y])):
penalty_array[y][x] = penalty_value
# Define the push pull array penalty term - chi_squared
# --> push_pull > 0 when penalty_value > chi_squared
# --> push_pull < 1 when penalty_value < chi_squared
push_pull = (2. * penalty_array) - data
# Set sensible levels, pick the desired colormap and define normalization
color_map = plt.get_cmap('coolwarm')
if push_pull.min() < 0.:
negatives = numpy.linspace(push_pull.min(), 0., num=50, endpoint=False)
else:
negatives = numpy.zeros((50))
if push_pull.max() > 0.:
positives = numpy.linspace(0., push_pull.max(), num=51)
else:
positives = numpy.zeros((51))
# Add the pull part to the push part
full_scale = numpy.append(negatives, positives)
locator = FixedLocator(full_scale)
levels = locator.tick_values(push_pull.min(), push_pull.max())
norm = BoundaryNorm(levels, ncolors=color_map.N)
fig = plt.figure(fig, figsize=(12, 10)) # Fig. 4
fig.text(0.1, 0.95, syst_analyser._name, **BOLD_FONT)
ax = fig.add_subplot(1, 1, 1)
# Set labels
ax.set_xlabel("Signal counts", **BOLD_FONT)
ax.set_ylabel("Value of systematic", **BOLD_FONT)
# Plot color map
color_map = ax.pcolormesh(X, Y, push_pull, cmap=color_map, norm=norm)
color_bar = fig.colorbar(color_map)
color_bar.set_label("$s-\chi^{2}_{\lambda,p}$", size=MAIN_FONT.get("size"))
color_bar.ax.tick_params(
labelsize=MAIN_FONT.get("size")) # tick label size
# Set axes limits
ax.set_xlim([X.min(), X.max()])
ax.set_ylim([Y.min(), Y.max()])
# Set axes tick label size
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(MAIN_FONT.get("size"))
ax.legend(loc="upper left")
if kwargs.get("save_as") is not None:
fig.savefig(kwargs.get("save_as") + "_push_pull.png", dpi=300)
return fig
def turn_on(syst_analyser, signal_config, fig=1, **kwargs):
""" Plot deviation from chi-squared with no floated systematics.
When does the effect of floating the systematic "turn on"?
Args:
syst_analyser (:class:`echidna.limit.limit_setting.SystAnalyser`): Systematic
analyser object, created during limit setting. Can be used
during limit setting setting or can load an instance from
hdf5
signal_config (:class:`echidna.limit.limit_config.LimitConfig`): Signal
config class, where chi squareds have been stored.
fig_num (int): Fig number. When creating multiple plots in the
same script, ensures matplotlib doesn't overwrite them.
.. note::
Keyword arguments include:
* save_as (*string*): supply file name to save image
"""
# Set x and y axes
x = syst_analyser.get_actual_counts()
y = syst_analyser.get_syst_values()
# Set chi squared map values
data = numpy.average(syst_analyser.get_chi_squareds(), axis=1)
data = numpy.transpose(data) # transpose it so that axes are correct
# Create meshgrid
X, Y = numpy.meshgrid(x, y)
# Define an array of \chi_0 values - chi squared without floating systematics
chi_squareds = signal_config.get_chi_squareds()[0]
data_np = numpy.zeros(data.shape) # zeroed array the same size as data
for y in range(len(data_np)):
for x, chi_squared in enumerate(chi_squareds):
data_np[y][x] = chi_squared
#if numpy.any((numpy.average(data_np, axis=0) != chi_squareds)):
# raise AssertionError("Incorrect chi squareds (no floating) array.")
# Make an array of the offsets
offsets = data - data_np
# Set sensible levels, pick the desired colormap and define normalization
color_map = plt.get_cmap('coolwarm')
positives = numpy.linspace(numpy.log10(offsets.max()) * -1.,
numpy.log10(offsets.max()),
num=50)
# linear array in log space
if offsets.min() < 0.:
negatives = numpy.linspace(offsets.min(), 0.0, num=51)
else:
negatives = numpy.zeros((51))
# Add the positive part to the negative part
full_scale = numpy.append(negatives, numpy.power(10, positives))
locator = FixedLocator(full_scale)
levels = locator.tick_values(offsets.min(), offsets.max())
norm = BoundaryNorm(levels, ncolors=color_map.N)
fig = plt.figure(fig, figsize=(12, 10)) # Fig. 4
fig.text(0.1, 0.95, syst_analyser._name, **BOLD_FONT)
ax = fig.add_subplot(1, 1, 1)
# Set labels
ax.set_xlabel("Signal counts", **BOLD_FONT)
ax.set_ylabel("Value of systematic", **BOLD_FONT)
# Plot color map
color_map = ax.pcolormesh(X, Y, offsets, cmap=color_map, norm=norm)
color_bar = fig.colorbar(color_map)
color_bar.set_label("$\chi^2 - \chi_0^2$", size=MAIN_FONT.get("size"))
color_bar.ax.tick_params(
labelsize=MAIN_FONT.get("size")) # tick label size
# Set axes limits
ax.set_xlim([X.min(), X.max()])
ax.set_ylim([Y.min(), Y.max()])
# Set axes tick label size
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(MAIN_FONT.get("size"))
ax.legend(loc="upper left")
if kwargs.get("save_as") is not None:
fig.savefig(kwargs.get("save_as") + "_turn_on.png", dpi=300)
return fig
def chi_squared_map(syst_analyser, fig_num=1, **kwargs):
""" Plot chi squared surface for systematic vs. signal counts
Args:
syst_analyser (:class:`echidna.limit.limit_setting.SystAnalyser`): Systematic
analyser object, created during limit setting. Can be used
during limit setting setting or can load an instance from
hdf5
fig_num (int): Fig number. When creating multiple plots in the
same script, ensures matplotlib doesn't overwrite them.
.. note::
Keyword arguments include:
* contours (*bool*): if True produces a contour plot of chi
squared surface. Default (*False*).
* preferred_values (*bool*): if False "preferred values" curve
is not overlayed on colour map. Default (*True*)
* minima (*bool*): if False "minima" are not overlayed on
colour map. Default (*True*)
* save_as (*string*): supply file name to save image
Default is to produce a colour map, with "preferred values" curve
and "minima" overlayed.
"""
# Set kwargs defaults
if kwargs.get("preferred_values") is None:
kwargs["preferred_values"] = True
if kwargs.get("minima") is None:
kwargs["minima"] = True
# Set x and y axes
x = syst_analyser.get_actual_counts()
y = syst_analyser.get_syst_values()
# Set chi squared map values
data = numpy.average(syst_analyser.get_chi_squareds(), axis=1)
data = numpy.transpose(data) # transpose it so that axes are correct
# Set preferred value values
y_2 = numpy.average(syst_analyser.get_preferred_values(), axis=1)
# Set minima values
x_3 = syst_analyser.get_minima()[0]
y_3 = syst_analyser.get_minima()[1]
# Create meshgrid
X, Y = numpy.meshgrid(x, y)
# Set sensible levels, pick the desired colormap and define normalization
color_map = plt.get_cmap('hot_r')
linear = numpy.linspace(numpy.sqrt(data.min()),
numpy.sqrt(data.max()),
num=100)
locator = FixedLocator(linear**2)
levels = locator.tick_values(data.min(), data.max())
norm = BoundaryNorm(levels, ncolors=color_map.N)
if kwargs.get("contours"):
fig = plt.figure(fig_num, figsize=(16, 10)) # Fig. 2
fig.text(0.1, 0.9, syst_analyser._name, **BOLD_FONT)
ax = Axes3D(fig)
ax.view_init(elev=17.0, azim=-136.0) # set intial viewing position
# Plot surface
surf = ax.plot_surface(X,
Y,
data,
rstride=1,
cstride=1,
cmap=color_map,
norm=norm,
linewidth=0,
antialiased=False)
ax.zaxis.set_minor_locator(locator)
ax.ticklabel_format(style="scientific", scilimits=(3, 4))
# Set axis labels
ax.set_xlabel("\nSignal counts", **BOLD_FONT)
ax.set_ylabel("\nValue of systematic", **BOLD_FONT)
for label in (ax.get_xticklabels() + ax.get_yticklabels() +
ax.get_zticklabels()):
label.set_fontsize(MAIN_FONT.get("size")) # tick label size
ax.dist = 11 # Ensures tick labels are not cut off
ax.margins(0.05, 0.05, 0.05) # Adjusts tick margins
# Draw colorbar
color_bar = fig.colorbar(surf,
ax=ax,
orientation="vertical",
fraction=0.2,
shrink=0.5,
aspect=10)
# kwargs here control axes that the colorbar is drawn in
color_bar.set_label(r"$\chi^2$", size=MAIN_FONT.get("size"))
color_bar.ax.tick_params(labelsize=MAIN_FONT.get("size"))
plt.show()
if kwargs.get("save_as") is not None:
fig.savefig(kwargs.get("save_as") + "_contour.png", dpi=300)
else:
fig = plt.figure(fig_num, figsize=(12, 10)) # Fig. 2
fig.text(0.1, 0.95, syst_analyser._name, **BOLD_FONT)
ax = fig.add_subplot(1, 1, 1)
# Set labels
ax.set_xlabel("Signal counts", **BOLD_FONT)
ax.set_ylabel("Value of systematic", **BOLD_FONT)
# Plot color map
color_map = ax.pcolormesh(X, Y, data, cmap=color_map, norm=norm)
color_bar = fig.colorbar(color_map)
color_bar.set_label("$\chi^2$", size=MAIN_FONT.get("size"))
color_bar.ax.tick_params(
labelsize=MAIN_FONT.get("size")) # tick label size
# Set axes limits
ax.set_xlim([X.min(), X.max()])
ax.set_ylim([Y.min(), Y.max()])
if kwargs.get("preferred_values"):
ax.plot(x, y_2, "bo-", label="Preferred values")
if kwargs.get("minima"):
ax.plot(x_3, y_3, "ko", label="Minima")
# Set axes tick label size
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(MAIN_FONT.get("size"))
ax.legend(loc="upper left")
if kwargs.get("save_as") is not None:
fig.savefig(kwargs.get("save_as") + "_color_map.png", dpi=300)
return fig
