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/pytrnsys_process/plotting/plotters.py

import typing as _tp
from abc import abstractmethod
from dataclasses import dataclass

import matplotlib.pyplot as _plt
import numpy as _np
import pandas as _pd

import pytrnsys_process.constants as const
import pytrnsys_process.headers as h
from pytrnsys_process import settings as sett

# TODO: provide A4 and half A4 plots to test sizes in latex # pylint: disable=fixme
# TODO: provide height as input for plot?  # pylint: disable=fixme
# TODO: deal with legends (curve names, fonts, colors, linestyles) # pylint: disable=fixme
# TODO: clean up old stuff by refactoring # pylint: disable=fixme
# TODO: make issue for docstrings of plotting # pylint: disable=fixme
# TODO: Add colormap support # pylint: disable=fixme


# TODO find a better place for this to live in # pylint : disable=fixme
plot_settings = sett.settings.plot


class ChartBase(h.HeaderValidationMixin):
    cmap: str | None = None

    def plot(
        self,
        df: _pd.DataFrame,
        columns: list[str],
        **kwargs,
    ) -> tuple[_plt.Figure, _plt.Axes]:
        fig, ax = self._do_plot(df, columns, **kwargs)
        return fig, ax

    # TODO: Test validation # pylint: disable=fixme
    def plot_with_column_validation(
        self,
        df: _pd.DataFrame,
        columns: list[str],
        headers: h.Headers,
        **kwargs,
    ) -> tuple[_plt.Figure, _plt.Axes]:
        """Base plot method with header validation.

        Parameters
        __________
            df:
                DataFrame containing the data to plot

            columns:
                List of column names to plot

            headers:
                Headers instance for validation

            **kwargs:
                Additional plotting arguments


        Raises
        ______
            ValueError: If any columns are missing from the headers index
        """
        # TODO: Might live somewhere else in the future # pylint: disable=fixme
        is_valid, missing = self.validate_headers(headers, columns)
        if not is_valid:
            missing_details = []
            for col in missing:
                missing_details.append(col)
            raise ValueError(
                "The following columns are not available in the headers index:\n"
                + "\n".join(missing_details)
            )

        return self._do_plot(df, columns, **kwargs)

    @abstractmethod
    def _do_plot(
        self,
        df: _pd.DataFrame,
        columns: list[str],
        use_legend: bool = True,
        size: tuple[float, float] = const.PlotSizes.A4.value,
        **kwargs: _tp.Any,
    ) -> tuple[_plt.Figure, _plt.Axes]:
        """Implement actual plotting logic in subclasses"""

    def check_for_cmap(self, kwargs, plot_kwargs):
        if "cmap" not in kwargs and "colormap" not in kwargs:
            plot_kwargs["cmap"] = self.cmap


class StackedBarChart(ChartBase):
    cmap = "inferno_r"

    def _do_plot(
        self,
        df: _pd.DataFrame,
        columns: list[str],
        use_legend: bool = True,
        size: tuple[float, float] = const.PlotSizes.A4.value,
        **kwargs: _tp.Any,
    ) -> tuple[_plt.Figure, _plt.Axes]:
        fig, ax = _plt.subplots(
            figsize=size,
            layout="constrained",
        )
        plot_kwargs = {
            "stacked": True,
            "legend": use_legend,
            "ax": ax,
            **kwargs,
        }
        self.check_for_cmap(kwargs, plot_kwargs)
        ax = df[columns].plot.bar(**plot_kwargs)
        ax.set_xticklabels(
            _pd.to_datetime(df.index).strftime(plot_settings.date_format)
        )

        return fig, ax


class BarChart(ChartBase):

    def _do_plot(
        self,
        df: _pd.DataFrame,
        columns: list[str],
        use_legend: bool = True,
        size: tuple[float, float] = const.PlotSizes.A4.value,
        **kwargs: _tp.Any,
    ) -> tuple[_plt.Figure, _plt.Axes]:
        # TODO: deal with cmap  # pylint: disable=fixme
        fig, ax = _plt.subplots(
            figsize=size,
            layout="constrained",
        )
        x = _np.arange(len(df.index))
        width = 0.8 / len(columns)

        for i, col in enumerate(columns):
            ax.bar(x + i * width, df[col], width, label=col)

        if use_legend:
            ax.legend()

        ax.set_xticks(x + width * (len(columns) - 1) / 2)
        ax.set_xticklabels(
            _pd.to_datetime(df.index).strftime(plot_settings.date_format)
        )
        ax.tick_params(axis="x", labelrotation=90)
        return fig, ax


class LinePlot(ChartBase):
    cmap: str | None = None

    def _do_plot(
        self,
        df: _pd.DataFrame,
        columns: list[str],
        use_legend: bool = True,
        size: tuple[float, float] = const.PlotSizes.A4.value,
        **kwargs: _tp.Any,
    ) -> tuple[_plt.Figure, _plt.Axes]:
        fig, ax = _plt.subplots(
            figsize=size,
            layout="constrained",
        )
        plot_kwargs = {
            "legend": use_legend,
            "ax": ax,
            **kwargs,
        }
        self.check_for_cmap(kwargs, plot_kwargs)

        df[columns].plot.line(**plot_kwargs)
        return fig, ax


@dataclass
class Histogram(ChartBase):
    bins: int = 50

    def _do_plot(
        self,
        df: _pd.DataFrame,
        columns: list[str],
        use_legend: bool = True,
        size: tuple[float, float] = const.PlotSizes.A4.value,
        **kwargs: _tp.Any,
    ) -> tuple[_plt.Figure, _plt.Axes]:
        fig, ax = _plt.subplots(
            figsize=size,
            layout="constrained",
        )
        plot_kwargs = {
            "legend": use_legend,
            "ax": ax,
            "bins": self.bins,
            **kwargs,
        }
        self.check_for_cmap(kwargs, plot_kwargs)
        df[columns].plot.hist(**plot_kwargs)
        return fig, ax


@dataclass
class ScatterPlot(ChartBase):
    """Handles comparative scatter plots with dual grouping by color and markers."""

    # pylint: disable=too-many-arguments,too-many-locals
    def _do_plot(
        self,
        df: _pd.DataFrame,
        columns: list[str],
        use_legend: bool = True,
        size: tuple[float, float] = const.PlotSizes.A4.value,
        group_by_color: str | None = None,
        group_by_marker: str | None = None,
        **kwargs: _tp.Any,
    ) -> tuple[_plt.Figure, _plt.Axes]:
        self._validate_inputs(columns)
        x_column, y_column = columns

        if not group_by_color and not group_by_marker:
            fig, ax = _plt.subplots(
                figsize=size,
                layout="constrained",
            )
            df.plot.scatter(x=x_column, y=y_column, ax=ax, **kwargs)
            return fig, ax
        # See: https://stackoverflow.com/questions/4700614/
        # how-to-put-the-legend-outside-the-plot
        # This is required to place the legend in a dedicated subplot
        fig, (ax, lax) = _plt.subplots(
            layout="constrained",
            figsize=size,
            ncols=2,
            gridspec_kw={"width_ratios": [4, 1]},
        )
        df_grouped, group_values = self._prepare_grouping(
            df, group_by_color, group_by_marker
        )
        # TODO: deal with cmap  # pylint: disable=fixme
        color_map, marker_map = self._create_style_mappings(*group_values)

        self._plot_groups(
            df_grouped,
            x_column,
            y_column,
            color_map,
            marker_map,
            ax,
        )

        if use_legend:
            self._create_legends(
                lax, color_map, marker_map, group_by_color, group_by_marker
            )

        return fig, ax

    def _validate_inputs(
        self,
        columns: list[str],
    ) -> None:
        if len(columns) != 2:
            raise ValueError(
                "ScatterComparePlotter requires exactly 2 columns (x and y)"
            )

    def _prepare_grouping(
        self,
        df: _pd.DataFrame,
        color: str | None,
        marker: str | None,
    ) -> tuple[
        _pd.core.groupby.generic.DataFrameGroupBy, tuple[list[str], list[str]]
    ]:
        group_by = []
        if color:
            group_by.append(color)
        if marker:
            group_by.append(marker)

        df_grouped = df.groupby(group_by)

        color_values = sorted(df[color].unique()) if color else []
        marker_values = sorted(df[marker].unique()) if marker else []

        return df_grouped, (color_values, marker_values)

    def _create_style_mappings(
        self, color_values: list[str], marker_values: list[str]
    ) -> tuple[dict[str, _tp.Any], dict[str, str]]:
        if color_values:
            cmap = _plt.get_cmap(plot_settings.color_map, len(color_values))
            color_map = {val: cmap(i) for i, val in enumerate(color_values)}
        else:
            color_map = {}
        if marker_values:
            marker_map = dict(zip(marker_values, plot_settings.markers))
        else:
            marker_map = {}

        return color_map, marker_map

    # pylint: disable=too-many-arguments
    def _plot_groups(
        self,
        df_grouped: _pd.core.groupby.generic.DataFrameGroupBy,
        x_column: str,
        y_column: str,
        color_map: dict[str, _tp.Any],
        marker_map: dict[str, str],
        ax: _plt.Axes,
    ) -> None:
        ax.set_xlabel(x_column, fontsize=plot_settings.label_font_size)
        ax.set_ylabel(y_column, fontsize=plot_settings.label_font_size)
        for val, group in df_grouped:
            sorted_group = group.sort_values(x_column)
            x = sorted_group[x_column]
            y = sorted_group[y_column]
            plot_args = {"color": "black"}
            scatter_args = {"marker": "None", "color": "black", "alpha": 0.5}
            if color_map:
                plot_args["color"] = color_map[val[0]]
            if marker_map:
                scatter_args["marker"] = marker_map[val[-1]]
            ax.plot(x, y, **plot_args)  # type: ignore
            ax.scatter(x, y, **scatter_args)  # type: ignore

    def _create_legends(
        self,
        lax: _plt.Axes,
        color_map: dict[str, _tp.Any],
        marker_map: dict[str, str],
        color_legend_title: str | None,
        marker_legend_title: str | None,
    ) -> None:
        lax.axis("off")

        if color_map:
            self._create_color_legend(
                lax, color_map, color_legend_title, bool(marker_map)
            )
        if marker_map:
            self._create_marker_legend(
                lax, marker_map, marker_legend_title, bool(color_map)
            )

    def _create_color_legend(
        self,
        lax: _plt.Axes,
        color_map: dict[str, _tp.Any],
        color_legend_title: str | None,
        has_markers: bool,
    ) -> None:
        color_handles = [
            _plt.Line2D([], [], color=color, linestyle="-", label=label)
            for label, color in color_map.items()
        ]

        legend = lax.legend(
            handles=color_handles,
            title=color_legend_title,
            bbox_to_anchor=(0, 0, 1, 1),
            loc="upper left",
            alignment="left",
            fontsize=plot_settings.legend_font_size,
            borderaxespad=0,
        )

        if has_markers:
            lax.add_artist(legend)

    def _create_marker_legend(
        self,
        lax: _plt.Axes,
        marker_map: dict[str, str],
        marker_legend_title: str | None,
        has_colors: bool,
    ) -> None:
        marker_position = 0.7 if has_colors else 1
        marker_handles = [
            _plt.Line2D(
                [],
                [],
                color="black",
                marker=marker,
                linestyle="None",
                label=label,
            )
            for label, marker in marker_map.items()
            if label is not None
        ]

        lax.legend(
            handles=marker_handles,
            title=marker_legend_title,
            bbox_to_anchor=(0, 0, 1, marker_position),
            loc="upper left",
            alignment="left",
            fontsize=plot_settings.legend_font_size,
            borderaxespad=0,
        )

1	import typing as _tp	2✔
2	from abc import abstractmethod	2✔
3	from dataclasses import dataclass	2✔
4
5	import matplotlib.pyplot as _plt	2✔
6	import numpy as _np	2✔
7	import pandas as _pd	2✔
8
9	import pytrnsys_process.constants as const	2✔
10	import pytrnsys_process.headers as h	2✔
11	from pytrnsys_process import settings as sett	2✔
12
13	# TODO: provide A4 and half A4 plots to test sizes in latex # pylint: disable=fixme
14	# TODO: provide height as input for plot? # pylint: disable=fixme
15	# TODO: deal with legends (curve names, fonts, colors, linestyles) # pylint: disable=fixme
16	# TODO: clean up old stuff by refactoring # pylint: disable=fixme
17	# TODO: make issue for docstrings of plotting # pylint: disable=fixme
18	# TODO: Add colormap support # pylint: disable=fixme
19
20
21	# TODO find a better place for this to live in # pylint : disable=fixme
22	plot_settings = sett.settings.plot	2✔
23
24
25	class ChartBase(h.HeaderValidationMixin):	2✔
26	cmap: str \| None = None	2✔
27
28	def plot(	2✔
29	self,
30	df: _pd.DataFrame,
31	columns: list[str],
32	**kwargs,
33	) -> tuple[_plt.Figure, _plt.Axes]:
34	fig, ax = self._do_plot(df, columns, **kwargs)	2✔
35	return fig, ax	2✔
36
37	# TODO: Test validation # pylint: disable=fixme
38	def plot_with_column_validation(	2✔
39	self,
40	df: _pd.DataFrame,
41	columns: list[str],
42	headers: h.Headers,
43	**kwargs,
44	) -> tuple[_plt.Figure, _plt.Axes]:
45	"""Base plot method with header validation.
46
47	Parameters
48	__________
49	df:
50	DataFrame containing the data to plot
51
52	columns:
53	List of column names to plot
54
55	headers:
56	Headers instance for validation
57
58	**kwargs:
59	Additional plotting arguments
60
61
62	Raises
63	______
64	ValueError: If any columns are missing from the headers index
65	"""
66	# TODO: Might live somewhere else in the future # pylint: disable=fixme
67	is_valid, missing = self.validate_headers(headers, columns)	2✔
68	if not is_valid:	2✔
69	missing_details = []	2✔
70	for col in missing:	2✔
71	missing_details.append(col)	2✔
72	raise ValueError(	2✔
73	"The following columns are not available in the headers index:\n"
74	+ "\n".join(missing_details)
75	)
76
77	return self._do_plot(df, columns, **kwargs)	2✔
78
79	@abstractmethod	2✔
80	def _do_plot(	2✔
81	self,
82	df: _pd.DataFrame,
83	columns: list[str],
84	use_legend: bool = True,
85	size: tuple[float, float] = const.PlotSizes.A4.value,
86	**kwargs: _tp.Any,
87	) -> tuple[_plt.Figure, _plt.Axes]:
88	"""Implement actual plotting logic in subclasses"""
89
90	def check_for_cmap(self, kwargs, plot_kwargs):	2✔
91	if "cmap" not in kwargs and "colormap" not in kwargs:	2✔
92	plot_kwargs["cmap"] = self.cmap	2✔
93
94
95	class StackedBarChart(ChartBase):	2✔
96	cmap = "inferno_r"	2✔
97
98	def _do_plot(	2✔
99	self,
100	df: _pd.DataFrame,
101	columns: list[str],
102	use_legend: bool = True,
103	size: tuple[float, float] = const.PlotSizes.A4.value,
104	**kwargs: _tp.Any,
105	) -> tuple[_plt.Figure, _plt.Axes]:
106	fig, ax = _plt.subplots(	2✔
107	figsize=size,
108	layout="constrained",
109	)
110	plot_kwargs = {	2✔
111	"stacked": True,
112	"legend": use_legend,
113	"ax": ax,
114	**kwargs,
115	}
116	self.check_for_cmap(kwargs, plot_kwargs)	2✔
117	ax = df[columns].plot.bar(**plot_kwargs)	2✔
118	ax.set_xticklabels(	2✔
119	_pd.to_datetime(df.index).strftime(plot_settings.date_format)
120	)
121
122	return fig, ax	2✔
123
124
125	class BarChart(ChartBase):	2✔
126
127	def _do_plot(	2✔
128	self,
129	df: _pd.DataFrame,
130	columns: list[str],
131	use_legend: bool = True,
132	size: tuple[float, float] = const.PlotSizes.A4.value,
133	**kwargs: _tp.Any,
134	) -> tuple[_plt.Figure, _plt.Axes]:
135	# TODO: deal with cmap # pylint: disable=fixme
136	fig, ax = _plt.subplots(	2✔
137	figsize=size,
138	layout="constrained",
139	)
140	x = _np.arange(len(df.index))	2✔
141	width = 0.8 / len(columns)	2✔
142
143	for i, col in enumerate(columns):	2✔
144	ax.bar(x + i * width, df[col], width, label=col)	2✔
145
146	if use_legend:	2✔
147	ax.legend()	2✔
148
149	ax.set_xticks(x + width * (len(columns) - 1) / 2)	2✔
150	ax.set_xticklabels(	2✔
151	_pd.to_datetime(df.index).strftime(plot_settings.date_format)
152	)
153	ax.tick_params(axis="x", labelrotation=90)	2✔
154	return fig, ax	2✔
155
156
157	class LinePlot(ChartBase):	2✔
158	cmap: str \| None = None	2✔
159
160	def _do_plot(	2✔
161	self,
162	df: _pd.DataFrame,
163	columns: list[str],
164	use_legend: bool = True,
165	size: tuple[float, float] = const.PlotSizes.A4.value,
166	**kwargs: _tp.Any,
167	) -> tuple[_plt.Figure, _plt.Axes]:
168	fig, ax = _plt.subplots(	2✔
169	figsize=size,
170	layout="constrained",
171	)
172	plot_kwargs = {	2✔
173	"legend": use_legend,
174	"ax": ax,
175	**kwargs,
176	}
177	self.check_for_cmap(kwargs, plot_kwargs)	2✔
178
179	df[columns].plot.line(**plot_kwargs)	2✔
180	return fig, ax	2✔
181
182
183	@dataclass	2✔
184	class Histogram(ChartBase):	2✔
185	bins: int = 50	2✔
186
187	def _do_plot(	2✔
188	self,
189	df: _pd.DataFrame,
190	columns: list[str],
191	use_legend: bool = True,
192	size: tuple[float, float] = const.PlotSizes.A4.value,
193	**kwargs: _tp.Any,
194	) -> tuple[_plt.Figure, _plt.Axes]:
195	fig, ax = _plt.subplots(	2✔
196	figsize=size,
197	layout="constrained",
198	)
199	plot_kwargs = {	2✔
200	"legend": use_legend,
201	"ax": ax,
202	"bins": self.bins,
203	**kwargs,
204	}
205	self.check_for_cmap(kwargs, plot_kwargs)	2✔
206	df[columns].plot.hist(**plot_kwargs)	2✔
207	return fig, ax	2✔
208
209
210	@dataclass	2✔
211	class ScatterPlot(ChartBase):	2✔
212	"""Handles comparative scatter plots with dual grouping by color and markers."""
213
214	# pylint: disable=too-many-arguments,too-many-locals
215	def _do_plot(	2✔
216	self,
217	df: _pd.DataFrame,
218	columns: list[str],
219	use_legend: bool = True,
220	size: tuple[float, float] = const.PlotSizes.A4.value,
221	group_by_color: str \| None = None,
222	group_by_marker: str \| None = None,
223	**kwargs: _tp.Any,
224	) -> tuple[_plt.Figure, _plt.Axes]:
225	self._validate_inputs(columns)	2✔
226	x_column, y_column = columns	2✔
227
228	if not group_by_color and not group_by_marker:	2✔
229	fig, ax = _plt.subplots(	2✔
230	figsize=size,
231	layout="constrained",
232	)
233	df.plot.scatter(x=x_column, y=y_column, ax=ax, **kwargs)	2✔
234	return fig, ax	2✔
235	# See: https://stackoverflow.com/questions/4700614/
236	# how-to-put-the-legend-outside-the-plot
237	# This is required to place the legend in a dedicated subplot
238	fig, (ax, lax) = _plt.subplots(	2✔
239	layout="constrained",
240	figsize=size,
241	ncols=2,
242	gridspec_kw={"width_ratios": [4, 1]},
243	)
244	df_grouped, group_values = self._prepare_grouping(	2✔
245	df, group_by_color, group_by_marker
246	)
247	# TODO: deal with cmap # pylint: disable=fixme
248	color_map, marker_map = self._create_style_mappings(*group_values)	2✔
249
250	self._plot_groups(	2✔
251	df_grouped,
252	x_column,
253	y_column,
254	color_map,
255	marker_map,
256	ax,
257	)
258
259	if use_legend:	2✔
260	self._create_legends(	2✔
261	lax, color_map, marker_map, group_by_color, group_by_marker
262	)
263
264	return fig, ax	2✔
265
266	def _validate_inputs(	2✔
267	self,
268	columns: list[str],
269	) -> None:
270	if len(columns) != 2:	2✔
UNCOV 271	raise ValueError(	×
272	"ScatterComparePlotter requires exactly 2 columns (x and y)"
273	)
274
275	def _prepare_grouping(	2✔
276	self,
277	df: _pd.DataFrame,
278	color: str \| None,
279	marker: str \| None,
280	) -> tuple[
281	_pd.core.groupby.generic.DataFrameGroupBy, tuple[list[str], list[str]]
282	]:
283	group_by = []	2✔
284	if color:	2✔
285	group_by.append(color)	2✔
286	if marker:	2✔
287	group_by.append(marker)	2✔
288
289	df_grouped = df.groupby(group_by)	2✔
290
291	color_values = sorted(df[color].unique()) if color else []	2✔
292	marker_values = sorted(df[marker].unique()) if marker else []	2✔
293
294	return df_grouped, (color_values, marker_values)	2✔
295
296	def _create_style_mappings(	2✔
297	self, color_values: list[str], marker_values: list[str]
298	) -> tuple[dict[str, _tp.Any], dict[str, str]]:
299	if color_values:	2✔
300	cmap = _plt.get_cmap(plot_settings.color_map, len(color_values))	2✔
301	color_map = {val: cmap(i) for i, val in enumerate(color_values)}	2✔
302	else:
UNCOV 303	color_map = {}	×
304	if marker_values:	2✔
305	marker_map = dict(zip(marker_values, plot_settings.markers))	2✔
306	else:
UNCOV 307	marker_map = {}	×
308
309	return color_map, marker_map	2✔
310
311	# pylint: disable=too-many-arguments
312	def _plot_groups(	2✔
313	self,
314	df_grouped: _pd.core.groupby.generic.DataFrameGroupBy,
315	x_column: str,
316	y_column: str,
317	color_map: dict[str, _tp.Any],
318	marker_map: dict[str, str],
319	ax: _plt.Axes,
320	) -> None:
321	ax.set_xlabel(x_column, fontsize=plot_settings.label_font_size)	2✔
322	ax.set_ylabel(y_column, fontsize=plot_settings.label_font_size)	2✔
323	for val, group in df_grouped:	2✔
324	sorted_group = group.sort_values(x_column)	2✔
325	x = sorted_group[x_column]	2✔
326	y = sorted_group[y_column]	2✔
327	plot_args = {"color": "black"}	2✔
328	scatter_args = {"marker": "None", "color": "black", "alpha": 0.5}	2✔
329	if color_map:	2✔
330	plot_args["color"] = color_map[val[0]]	2✔
331	if marker_map:	2✔
332	scatter_args["marker"] = marker_map[val[-1]]	2✔
333	ax.plot(x, y, **plot_args) # type: ignore	2✔
334	ax.scatter(x, y, **scatter_args) # type: ignore	2✔
335
336	def _create_legends(	2✔
337	self,
338	lax: _plt.Axes,
339	color_map: dict[str, _tp.Any],
340	marker_map: dict[str, str],
341	color_legend_title: str \| None,
342	marker_legend_title: str \| None,
343	) -> None:
344	lax.axis("off")	2✔
345
346	if color_map:	2✔
347	self._create_color_legend(	2✔
348	lax, color_map, color_legend_title, bool(marker_map)
349	)
350	if marker_map:	2✔
351	self._create_marker_legend(	2✔
352	lax, marker_map, marker_legend_title, bool(color_map)
353	)
354
355	def _create_color_legend(	2✔
356	self,
357	lax: _plt.Axes,
358	color_map: dict[str, _tp.Any],
359	color_legend_title: str \| None,
360	has_markers: bool,
361	) -> None:
362	color_handles = [	2✔
363	_plt.Line2D([], [], color=color, linestyle="-", label=label)
364	for label, color in color_map.items()
365	]
366
367	legend = lax.legend(	2✔
368	handles=color_handles,
369	title=color_legend_title,
370	bbox_to_anchor=(0, 0, 1, 1),
371	loc="upper left",
372	alignment="left",
373	fontsize=plot_settings.legend_font_size,
374	borderaxespad=0,
375	)
376
377	if has_markers:	2✔
378	lax.add_artist(legend)	2✔
379
380	def _create_marker_legend(	2✔
381	self,
382	lax: _plt.Axes,
383	marker_map: dict[str, str],
384	marker_legend_title: str \| None,
385	has_colors: bool,
386	) -> None:
387	marker_position = 0.7 if has_colors else 1	2✔
388	marker_handles = [	2✔
389	_plt.Line2D(
390	[],
391	[],
392	color="black",
393	marker=marker,
394	linestyle="None",
395	label=label,
396	)
397	for label, marker in marker_map.items()
398	if label is not None
399	]
400
401	lax.legend(	2✔
402	handles=marker_handles,
403	title=marker_legend_title,
404	bbox_to_anchor=(0, 0, 1, marker_position),
405	loc="upper left",
406	alignment="left",
407	fontsize=plot_settings.legend_font_size,
408	borderaxespad=0,
409	)

SPF-OST / pytrnsys_process / 13560850870

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