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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
        return plot_kwargs

    def get_cmap(self, kwargs) -> str | None:
        if "cmap" not in kwargs and "colormap" not in kwargs:
            return self.cmap

        if "cmap" in kwargs:
            return kwargs["cmap"]

        if "colormap" in kwargs:
            return kwargs["colormap"]

        raise ValueError


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):
    cmap = 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]:
        # TODO: deal with colors  # pylint: disable=fixme
        fig, ax = _plt.subplots(
            figsize=size,
            layout="constrained",
        )
        x = _np.arange(len(df.index))
        width = 0.8 / len(columns)

        cmap = self.get_cmap(kwargs)
        if cmap:
            cm = _plt.cm.get_cmap(cmap)
            colors = cm(_np.linspace(0, 1, len(columns)))
        else:
            colors = [None] * len(columns)

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

        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."""

    cmap = "Paired"  # This is ignored when no categorical groupings are used.

    # 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
        )
        cmap = self.get_cmap(kwargs)
        color_map, marker_map = self._create_style_mappings(
            *group_values, cmap=cmap
        )

        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],
        cmap: str | None,
    ) -> tuple[dict[str, _tp.Any], dict[str, str]]:
        if color_values:
            cm = _plt.get_cmap(cmap, len(color_values))
            color_map = {val: cm(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	return plot_kwargs	2✔
94
95	def get_cmap(self, kwargs) -> str \| None:	2✔
96	if "cmap" not in kwargs and "colormap" not in kwargs:	2✔
97	return self.cmap	2✔
98
99	if "cmap" in kwargs:	2✔
100	return kwargs["cmap"]	2✔
101
102	if "colormap" in kwargs:	2✔
103	return kwargs["colormap"]	2✔
104
UNCOV 105	raise ValueError	×
106
107
108	class StackedBarChart(ChartBase):	2✔
109	cmap = "inferno_r"	2✔
110
111	def _do_plot(	2✔
112	self,
113	df: _pd.DataFrame,
114	columns: list[str],
115	use_legend: bool = True,
116	size: tuple[float, float] = const.PlotSizes.A4.value,
117	**kwargs: _tp.Any,
118	) -> tuple[_plt.Figure, _plt.Axes]:
119	fig, ax = _plt.subplots(	2✔
120	figsize=size,
121	layout="constrained",
122	)
123	plot_kwargs = {	2✔
124	"stacked": True,
125	"legend": use_legend,
126	"ax": ax,
127	**kwargs,
128	}
129	self.check_for_cmap(kwargs, plot_kwargs)	2✔
130	ax = df[columns].plot.bar(**plot_kwargs)	2✔
131	ax.set_xticklabels(	2✔
132	_pd.to_datetime(df.index).strftime(plot_settings.date_format)
133	)
134
135	return fig, ax	2✔
136
137
138	class BarChart(ChartBase):	2✔
139	cmap = None	2✔
140
141	def _do_plot(	2✔
142	self,
143	df: _pd.DataFrame,
144	columns: list[str],
145	use_legend: bool = True,
146	size: tuple[float, float] = const.PlotSizes.A4.value,
147	**kwargs: _tp.Any,
148	) -> tuple[_plt.Figure, _plt.Axes]:
149	# TODO: deal with colors # pylint: disable=fixme
150	fig, ax = _plt.subplots(	2✔
151	figsize=size,
152	layout="constrained",
153	)
154	x = _np.arange(len(df.index))	2✔
155	width = 0.8 / len(columns)	2✔
156
157	cmap = self.get_cmap(kwargs)	2✔
158	if cmap:	2✔
159	cm = _plt.cm.get_cmap(cmap)	2✔
160	colors = cm(_np.linspace(0, 1, len(columns)))	2✔
161	else:
162	colors = [None] * len(columns)	2✔
163
164	for i, col in enumerate(columns):	2✔
165	ax.bar(x + i * width, df[col], width, label=col, color=colors[i])	2✔
166
167	if use_legend:	2✔
168	ax.legend()	2✔
169
170	ax.set_xticks(x + width * (len(columns) - 1) / 2)	2✔
171	ax.set_xticklabels(	2✔
172	_pd.to_datetime(df.index).strftime(plot_settings.date_format)
173	)
174	ax.tick_params(axis="x", labelrotation=90)	2✔
175	return fig, ax	2✔
176
177
178	class LinePlot(ChartBase):	2✔
179	cmap: str \| None = None	2✔
180
181	def _do_plot(	2✔
182	self,
183	df: _pd.DataFrame,
184	columns: list[str],
185	use_legend: bool = True,
186	size: tuple[float, float] = const.PlotSizes.A4.value,
187	**kwargs: _tp.Any,
188	) -> tuple[_plt.Figure, _plt.Axes]:
189	fig, ax = _plt.subplots(	2✔
190	figsize=size,
191	layout="constrained",
192	)
193	plot_kwargs = {	2✔
194	"legend": use_legend,
195	"ax": ax,
196	**kwargs,
197	}
198	self.check_for_cmap(kwargs, plot_kwargs)	2✔
199
200	df[columns].plot.line(**plot_kwargs)	2✔
201	return fig, ax	2✔
202
203
204	@dataclass	2✔
205	class Histogram(ChartBase):	2✔
206	bins: int = 50	2✔
207
208	def _do_plot(	2✔
209	self,
210	df: _pd.DataFrame,
211	columns: list[str],
212	use_legend: bool = True,
213	size: tuple[float, float] = const.PlotSizes.A4.value,
214	**kwargs: _tp.Any,
215	) -> tuple[_plt.Figure, _plt.Axes]:
216	fig, ax = _plt.subplots(	2✔
217	figsize=size,
218	layout="constrained",
219	)
220	plot_kwargs = {	2✔
221	"legend": use_legend,
222	"ax": ax,
223	"bins": self.bins,
224	**kwargs,
225	}
226	self.check_for_cmap(kwargs, plot_kwargs)	2✔
227	df[columns].plot.hist(**plot_kwargs)	2✔
228	return fig, ax	2✔
229
230
231	@dataclass	2✔
232	class ScatterPlot(ChartBase):	2✔
233	"""Handles comparative scatter plots with dual grouping by color and markers."""
234
235	cmap = "Paired" # This is ignored when no categorical groupings are used.	2✔
236
237	# pylint: disable=too-many-arguments,too-many-locals
238	def _do_plot(	2✔
239	self,
240	df: _pd.DataFrame,
241	columns: list[str],
242	use_legend: bool = True,
243	size: tuple[float, float] = const.PlotSizes.A4.value,
244	group_by_color: str \| None = None,
245	group_by_marker: str \| None = None,
246	**kwargs: _tp.Any,
247	) -> tuple[_plt.Figure, _plt.Axes]:
248	self._validate_inputs(columns)	2✔
249	x_column, y_column = columns	2✔
250
251	if not group_by_color and not group_by_marker:	2✔
252	fig, ax = _plt.subplots(	2✔
253	figsize=size,
254	layout="constrained",
255	)
256	df.plot.scatter(x=x_column, y=y_column, ax=ax, **kwargs)	2✔
257	return fig, ax	2✔
258	# See: https://stackoverflow.com/questions/4700614/
259	# how-to-put-the-legend-outside-the-plot
260	# This is required to place the legend in a dedicated subplot
261	fig, (ax, lax) = _plt.subplots(	2✔
262	layout="constrained",
263	figsize=size,
264	ncols=2,
265	gridspec_kw={"width_ratios": [4, 1]},
266	)
267	df_grouped, group_values = self._prepare_grouping(	2✔
268	df, group_by_color, group_by_marker
269	)
270	cmap = self.get_cmap(kwargs)	2✔
271	color_map, marker_map = self._create_style_mappings(	2✔
272	*group_values, cmap=cmap
273	)
274
275	self._plot_groups(	2✔
276	df_grouped,
277	x_column,
278	y_column,
279	color_map,
280	marker_map,
281	ax,
282	)
283
284	if use_legend:	2✔
285	self._create_legends(	2✔
286	lax, color_map, marker_map, group_by_color, group_by_marker
287	)
288
289	return fig, ax	2✔
290
291	def _validate_inputs(	2✔
292	self,
293	columns: list[str],
294	) -> None:
295	if len(columns) != 2:	2✔
UNCOV 296	raise ValueError(	×
297	"ScatterComparePlotter requires exactly 2 columns (x and y)"
298	)
299
300	def _prepare_grouping(	2✔
301	self,
302	df: _pd.DataFrame,
303	color: str \| None,
304	marker: str \| None,
305	) -> tuple[
306	_pd.core.groupby.generic.DataFrameGroupBy, tuple[list[str], list[str]]
307	]:
308	group_by = []	2✔
309	if color:	2✔
310	group_by.append(color)	2✔
311	if marker:	2✔
312	group_by.append(marker)	2✔
313
314	df_grouped = df.groupby(group_by)	2✔
315
316	color_values = sorted(df[color].unique()) if color else []	2✔
317	marker_values = sorted(df[marker].unique()) if marker else []	2✔
318
319	return df_grouped, (color_values, marker_values)	2✔
320
321	def _create_style_mappings(	2✔
322	self,
323	color_values: list[str],
324	marker_values: list[str],
325	cmap: str \| None,
326	) -> tuple[dict[str, _tp.Any], dict[str, str]]:
327	if color_values:	2✔
328	cm = _plt.get_cmap(cmap, len(color_values))	2✔
329	color_map = {val: cm(i) for i, val in enumerate(color_values)}	2✔
330	else:
UNCOV 331	color_map = {}	×
332	if marker_values:	2✔
333	marker_map = dict(zip(marker_values, plot_settings.markers))	2✔
334	else:
UNCOV 335	marker_map = {}	×
336
337	return color_map, marker_map	2✔
338
339	# pylint: disable=too-many-arguments
340	def _plot_groups(	2✔
341	self,
342	df_grouped: _pd.core.groupby.generic.DataFrameGroupBy,
343	x_column: str,
344	y_column: str,
345	color_map: dict[str, _tp.Any],
346	marker_map: dict[str, str],
347	ax: _plt.Axes,
348	) -> None:
349	ax.set_xlabel(x_column, fontsize=plot_settings.label_font_size)	2✔
350	ax.set_ylabel(y_column, fontsize=plot_settings.label_font_size)	2✔
351	for val, group in df_grouped:	2✔
352	sorted_group = group.sort_values(x_column)	2✔
353	x = sorted_group[x_column]	2✔
354	y = sorted_group[y_column]	2✔
355	plot_args = {"color": "black"}	2✔
356	scatter_args = {"marker": "None", "color": "black", "alpha": 0.5}	2✔
357	if color_map:	2✔
358	plot_args["color"] = color_map[val[0]]	2✔
359	if marker_map:	2✔
360	scatter_args["marker"] = marker_map[val[-1]]	2✔
361	ax.plot(x, y, **plot_args) # type: ignore	2✔
362	ax.scatter(x, y, **scatter_args) # type: ignore	2✔
363
364	def _create_legends(	2✔
365	self,
366	lax: _plt.Axes,
367	color_map: dict[str, _tp.Any],
368	marker_map: dict[str, str],
369	color_legend_title: str \| None,
370	marker_legend_title: str \| None,
371	) -> None:
372	lax.axis("off")	2✔
373
374	if color_map:	2✔
375	self._create_color_legend(	2✔
376	lax, color_map, color_legend_title, bool(marker_map)
377	)
378	if marker_map:	2✔
379	self._create_marker_legend(	2✔
380	lax, marker_map, marker_legend_title, bool(color_map)
381	)
382
383	def _create_color_legend(	2✔
384	self,
385	lax: _plt.Axes,
386	color_map: dict[str, _tp.Any],
387	color_legend_title: str \| None,
388	has_markers: bool,
389	) -> None:
390	color_handles = [	2✔
391	_plt.Line2D([], [], color=color, linestyle="-", label=label)
392	for label, color in color_map.items()
393	]
394
395	legend = lax.legend(	2✔
396	handles=color_handles,
397	title=color_legend_title,
398	bbox_to_anchor=(0, 0, 1, 1),
399	loc="upper left",
400	alignment="left",
401	fontsize=plot_settings.legend_font_size,
402	borderaxespad=0,
403	)
404
405	if has_markers:	2✔
406	lax.add_artist(legend)	2✔
407
408	def _create_marker_legend(	2✔
409	self,
410	lax: _plt.Axes,
411	marker_map: dict[str, str],
412	marker_legend_title: str \| None,
413	has_colors: bool,
414	) -> None:
415	marker_position = 0.7 if has_colors else 1	2✔
416	marker_handles = [	2✔
417	_plt.Line2D(
418	[],
419	[],
420	color="black",
421	marker=marker,
422	linestyle="None",
423	label=label,
424	)
425	for label, marker in marker_map.items()
426	if label is not None
427	]
428
429	lax.legend(	2✔
430	handles=marker_handles,
431	title=marker_legend_title,
432	bbox_to_anchor=(0, 0, 1, marker_position),
433	loc="upper left",
434	alignment="left",
435	fontsize=plot_settings.legend_font_size,
436	borderaxespad=0,
437	)

SPF-OST / pytrnsys_process / 13563079957

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