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Make objective function plot optional #3231

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Make objective function plot optional #3231

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107 changes: 64 additions & 43 deletions process/io/plot_solutions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,7 @@ def plot_mfile_solutions(
normalising_tag: Optional[str] = None,
rmse: bool = False,
normalisation_type: Optional[str] = "init",
plot_obj_func: bool = True,
) -> Tuple[mpl.figure.Figure, pd.DataFrame]:
"""Plot multiple solutions, optionally normalised by a given solution.

Expand All @@ -68,6 +69,8 @@ def plot_mfile_solutions(
:type rmse: bool, optional
:param normalisation_type: opt param normalisation to use: one of ["init", "range", None], defaults to "init"
:type normalisation_type: str, optional
:param plot_obj_func: show the objective function plot, defaults to True
:type: plot_obj_func: bool, optional
:return: figure and dataframe of solutions
:rtype: Tuple[mpl.figure.Figure, pd.DataFrame]
"""
Expand Down Expand Up @@ -122,6 +125,7 @@ def plot_mfile_solutions(
rmse_df=rmse_df,
normalising_tag=normalising_tag,
normalisation_type=normalisation_type,
plot_obj_func=plot_obj_func,
)

# Return fig for optional further customisation
Expand Down Expand Up @@ -314,6 +318,7 @@ def _plot_solutions(
plot_title: str,
normalising_tag: Union[str, None],
rmse_df: pd.DataFrame,
plot_obj_func: bool,
) -> mpl.figure.Figure:
"""Plot multiple solutions, optionally normalised by a given solution.

Expand All @@ -329,6 +334,8 @@ def _plot_solutions(
:type normalising_tag: Union[str, None]
:param rmse_df: RMS errors relative to reference solution
:type rmse_df: pd.DataFrame
:param plot_obj_func: show the objective function plot if True
:type: plot_obj_func: bool
:return: figure containing varying numbers of axes
:rtype: mpl.figure.Figure
"""
Expand All @@ -344,7 +351,7 @@ def _plot_solutions(
# Acquire objective function name(s), then check only one type is being plotted
objf_list = norm_objf_df[NORM_OBJF_NAME].unique()

if len(objf_list) != 1:
if plot_obj_func and len(objf_list) != 1:
raise ValueError("Can't plot different objective functions on the same plot")

objf_name = objf_list[0]
Expand Down Expand Up @@ -400,29 +407,42 @@ def _plot_solutions(
) + external_legend_height
figsize = [7, final_subfig_height_opt_params]

# Add obj func/RMSE subfig
base_subfig_height_obj_func = 0.75
if rmse_df is not None:
# Add fig height for obj func/RMSE subfig if required
if plot_obj_func and rmse_df is not None:
nrows_obj_func_rmse_subplot = 2
final_subfig_height_obj_func = (
nrows_obj_func_rmse_subplot * base_subfig_height_obj_func
)
else:
elif plot_obj_func or rmse_df is not None:
nrows_obj_func_rmse_subplot = 1
final_subfig_height_obj_func = base_subfig_height_obj_func
else:
nrows_obj_func_rmse_subplot = 0

base_subfig_height_obj_func = 0.75
final_subfig_height_obj_func = (
nrows_obj_func_rmse_subplot * base_subfig_height_obj_func
)
figsize[1] += final_subfig_height_obj_func

fig = plt.figure(layout="constrained", figsize=figsize)
fig.suptitle(plot_title)
# 2 subfig rows: 1 for opt params, 1 for objective function and optional RMSE
# Use actual calculated height to define height ratios
subfigs = fig.subfigures(
nrows=2,
ncols=1,
height_ratios=[final_subfig_height_opt_params, final_subfig_height_obj_func],
)
axs_opt_params = subfigs[0].subplots(nrows=nrows_opt_param_subplot)

# Plot opt params plot only: one figure
# Opt params and objective function/RMSE plots: fig with 2 subfigures

if nrows_obj_func_rmse_subplot == 0:
# Just plotting opt params fig
opt_params_subfig = fig
else:
# Plotting opt params and obj func/rmse subfigs
# Use actual calculated height to define height ratios
subfigs = fig.subfigures(
nrows=2,
ncols=1,
height_ratios=[
final_subfig_height_opt_params,
final_subfig_height_obj_func,
],
)
opt_params_subfig = subfigs[0]
obj_func_subfig = subfigs[1]
axs_opt_params = opt_params_subfig.subplots(nrows=nrows_opt_param_subplot)

# Adapt x axis label for normalisation type
if normalisation_type == "init":
Expand Down Expand Up @@ -468,8 +488,8 @@ def _plot_solutions(
handles, labels = axs_opt_params[1].get_legend_handles_labels()
axs_opt_params[0].legend(handles=handles, labels=labels, ncols=3, loc="center")

subfigs[0].supxlabel(x_axis_label)
subfigs[0].supylabel("Optimisation parameter")
opt_params_subfig.supxlabel(x_axis_label)
opt_params_subfig.supylabel("Optimisation parameter")
else:
sns.stripplot(
data=opt_params_values_with_names_df_melt,
Expand All @@ -485,30 +505,31 @@ def _plot_solutions(
axs_opt_params.legend()
axs_opt_params.grid()

# Plot objf change separately
axs_opt_params = subfigs[1].subplots(nrows=nrows_obj_func_rmse_subplot)
if nrows_obj_func_rmse_subplot > 1:
ax_obj_func = axs_opt_params[0]
ax_rmse = axs_opt_params[1]
else:
ax_obj_func = axs_opt_params

# Melt for seaborn stripplot
norm_objf_values_df_melt = norm_objf_values_df.melt(id_vars=TAG)
sns.stripplot(
data=norm_objf_values_df_melt,
x="value",
y="variable",
hue=TAG,
jitter=True,
ax=ax_obj_func,
formatter=lambda label: objf_name,
)
if plot_obj_func:
# Plot objf change separately
axs_opt_params = obj_func_subfig.subplots(nrows=nrows_obj_func_rmse_subplot)
if nrows_obj_func_rmse_subplot > 1:
ax_obj_func = axs_opt_params[0]
ax_rmse = axs_opt_params[1]
else:
ax_obj_func = axs_opt_params

# Melt for seaborn stripplot
norm_objf_values_df_melt = norm_objf_values_df.melt(id_vars=TAG)
sns.stripplot(
data=norm_objf_values_df_melt,
x="value",
y="variable",
hue=TAG,
jitter=True,
ax=ax_obj_func,
formatter=lambda label: objf_name,
)

ax_obj_func.get_legend().remove()
# Objective function values are not normalised (no initial value): be explicit
ax_obj_func.set_xlabel("Objective function value")
ax_obj_func.set_ylabel("")
ax_obj_func.get_legend().remove()
# Objective function values are not normalised (no initial value): be explicit
ax_obj_func.set_xlabel("Objective function value")
ax_obj_func.set_ylabel("")

if rmse_df is not None:
# Ensure solution legend colours are the same: tags match between opt
Expand Down
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