def style_figure(fig: matplotlib.figure.Figure, title: str, bottom: float = 0.2) -> None:
"""Stylize the supplied matplotlib Figure instance."""
fig.tight_layout()
if bottom is not None:
fig.subplots_adjust(bottom=bottom)
fig.suptitle(title)
fig.legend(ncol=10, handlelength=0.75, handletextpad=0.25, columnspacing=0.5, loc='lower left')
def multi_nyquist_title(fig: matplotlib.figure.Figure, params: paramsTup,
settings) -> matplotlib.figure.Figure:
# plt.figure(fig) # make sure fig is focus
(filename_strip, solv, elec, ref_elec, work_elec, voltage) = params
elec = "TBFTFB"
fig.suptitle(
f"Nyquist plots of {work_elec} in {elec} ({solv}) vs {ref_elec} reference",
y=0.98)
# xmin, xmax = plt.xlim() # pylint: disable=W0612
# expt_vars = f"WE = {work_elec} \nRE = {ref_elec} \nElectrolyte = {elec} \nSolvent = {solv}"
# fig.text(xmax * 1.1, 0, expt_vars, fontdict=settings, withdash=False)
return fig
def add_metric_description_title(df_plot: pd.DataFrame,
fig: mpl.figure.Figure,
y: float = 1.0):
"""Adds a suptitle to the figure, describing the metric used."""
assert df_plot.Metric.nunique() == 1, "More than one metric in DataFrame."
binning_scheme = utils.assert_and_get_constant(df_plot.binning_scheme)
num_bins = utils.assert_and_get_constant(df_plot.num_bins)
norm = utils.assert_and_get_constant(df_plot.norm)
title = (f"ECE variant: {binning_scheme} binning, "
f"{num_bins:.0f} bins, "
f"{norm} norm")
display_names = {
"adaptive": "equal-mass",
"even": "equal-width",
"l1": "L1",
"l2": "L2",
}
for old, new in display_names.items():
title = title.replace(old, new)
fig.suptitle(title, y=y, verticalalignment="bottom")
def plot_avg_decay_data(t_sol: Union[np.ndarray, List[np.array]],
list_sim_data: List[np.array],
list_exp_data: List[np.array] = None,
state_labels: List[str] = None,
concentration: Conc = None,
atol: float = A_TOL,
colors: Union[str, Tuple[ColorMap, ColorMap]] = 'rk',
fig: mpl.figure.Figure = None,
title: str = '') -> None:
''' Plot the list of simulated and experimental data (optional) against time in t_sol.
If concentration is given, the legend will show the concentrations.
colors is a string with two chars. The first is the sim color,
the second the exp data color.
'''
num_plots = len(list_sim_data)
num_rows = 3
num_cols = int(np.ceil(num_plots / 3))
# optional lists default to list of None
list_exp_data = list_exp_data or [None] * num_plots
state_labels = state_labels or [''] * num_plots
list_t_sim = t_sol if len(
t_sol) == num_plots else [t_sol] * num_plots # type: List[np.array]
if concentration:
conc_str = '_' + str(concentration.S_conc) + 'S_' + str(
concentration.A_conc) + 'A'
# state_labels = [label+conc_str for label in state_labels]
else:
conc_str = ''
sim_color = colors[0]
exp_color = colors[1]
exp_size = 2 # marker size
exp_marker = '.'
if fig is None:
fig = plt.figure()
fig.suptitle(title + '. Time in ms.')
list_axes = fig.get_axes() # type: List
if not list_axes:
for num in range(num_plots):
fig.add_subplot(num_rows, num_cols, num + 1)
list_axes = fig.get_axes()
for sim_data, t_sim, exp_data, state_label, axes\
in zip(list_sim_data, list_t_sim, list_exp_data, state_labels, list_axes):
if state_label:
axes.set_title(
state_label.replace('_', ' '), {
'horizontalalignment': 'center',
'verticalalignment': 'center',
'fontweight': 'bold',
'fontsize': 10
})
if sim_data is None or np.isnan(sim_data).any() or not np.any(
sim_data > 0):
continue
# no exp data: either a GS or simply no exp data available
if exp_data is 0 or exp_data is None:
# nonposy='clip': clip non positive values to a very small positive number
axes.semilogy(t_sim * 1000,
sim_data,
color=sim_color,
label=state_label + conc_str)
axes.axis('tight')
axes.set_xlim(left=t_sim[0] * 1000.0)
# add some white space above and below
margin_factor = np.array([0.7, 1.3])
axes.set_ylim(*np.array(axes.get_ylim()) * margin_factor)
if axes.set_ylim()[0] < atol:
axes.set_ylim(bottom=atol) # don't show noise below atol
# detect when the simulation goes above and below atol
above = sim_data > atol
change_indices = np.where(np.roll(above, 1) != above)[0]
# make sure change_indices[-1] happens when the population is going BELOW atol
if change_indices.size > 1 and sim_data[
change_indices[-1]] < atol: # pragma: no cover
# last time it changes
max_index = change_indices[-1]
# show simData until it falls below atol
axes.set_xlim(right=t_sim[max_index] * 1000)
min_y = min(*axes.get_ylim())
max_y = max(*axes.get_ylim())
axes.set_ylim(bottom=min_y, top=max_y)
else: # exp data available
sim_handle, = axes.semilogy(t_sim * 1000,
sim_data,
color=sim_color,
label=state_label + conc_str,
zorder=10)
# convert exp_data time to ms
exp_handle, = axes.semilogy(exp_data[:, 0] * 1000,
exp_data[:, 1] * np.max(sim_data),
color=exp_color,
marker=exp_marker,
linewidth=0,
markersize=exp_size,
zorder=1)
axes.axis('tight')
axes.set_ylim(top=axes.get_ylim()[1] *
1.2) # add some white space on top
tmin = min(exp_data[-1, 0], t_sim[0])
axes.set_xlim(left=tmin * 1000.0, right=exp_data[-1, 0] *
1000) # don't show beyond expData
if conc_str:
list_axes[0].legend(loc="best", fontsize='small')
curr_handles, curr_labels = list_axes[0].get_legend_handles_labels()
new_labels = [
label.replace(state_labels[0] + '_', '').replace('_', ', ')
for label in curr_labels
]
list_axes[0].legend(curr_handles,
new_labels,
markerscale=5,
loc="best",
fontsize='small')
fig.subplots_adjust(top=0.918,
bottom=0.041,
left=0.034,
right=0.99,
hspace=0.275,
wspace=0.12)
