Update plot_tsi

* Update default values of `x_offset` and `y_offset` and update type
hints.
* Rename argument `fname` to `save`.
* Add arguments `figsize` and `dpi`.
* Update docstrings.
* Update figure setup.
* Fix x-axis labels caused by latest changes to `get_tsi`.
* Use `save_fig` function.
* Return matplotlib `Figure` and `Axis` instances.

src/cellrank/estimators/terminal_states/_gpcca.py

@@ -588,39 +588,35 @@ def get_tsi_score(self) -> float:
 
         return self.tsi["Identified terminal states"].sum() / optimal_score
 
+    @d.dedent
     def plot_tsi(
         self,
         tsi_df: pd.DataFrame,
-        fname: Optional[Path] = None,
-        x_offset: Optional[Tuple[float, float]] = None,
-        y_offset: Optional[Tuple[float, float]] = None,
+        x_offset: Tuple[float, float] = (0.2, 0.2),
+        y_offset: Tuple[float, float] = (0.1, 0.1),
+        figsize: Tuple[float, float] = (6, 4),
+        dpi: Optional[int] = None,
+        save: Optional[Union[str, Path]] = None,
         **kwargs: Any,
-    ):
+    ) -> Tuple[plt.Figure, Axes]:
         """Plot terminal state identificiation (TSI).
 
         Parameters
         ----------
         tsi_df
-            Pre-computed TSI DataFrame.
-        fname
-            File name under which the plot is saved. The plot is not saved if the argument is not specified.
+            Pre-computed TSI DataFrame with :meth:`get_tsi_score`.
         x_offset
-            Offset of x-axis. Defaults to `[0.2, 0.2]` if not specified.
+            Offset of x-axis.
         y_offset
-            Offset of y-axis. Defaults to `[0.1, 0.1]` if not specified.
+            Offset of y-axis.
+        %(plotting)s
         kwargs
             Keyword arguments for :meth:`~seaborn.lineplot`.
 
         Returns
         -------
-        Returns TSI as a Pandas DataFrame and adds the class attribute :attr:`tsi`.
+        Plot TSI of the kernel and an optimal identification strategy.
         """
-        if x_offset is None:
-            x_offset = [0.2, 0.2]
-
-        if y_offset is None:
-            y_offset = [0.1, 0.1]
-
         optimal_identification = tsi_df[["Number of macrostates", "Optimal identification"]]
         optimal_identification = optimal_identification.rename(
             columns={"Optimal identification": "Identified terminal states"}
@@ -634,7 +630,7 @@ def plot_tsi(
 
         df = pd.concat([df, optimal_identification])
 
-        fig, ax = plt.subplots(figsize=(6, 4))
+        fig, ax = plt.subplots(figsize=figsize, dpi=dpi, tight_layout=True)
         sns.lineplot(
             data=df,
             x="Number of macrostates",
@@ -647,7 +643,8 @@ def plot_tsi(
         )
 
         ax.set_xticks(df["Number of macrostates"].unique().astype(int))
-        for label_id, label in enumerate(ax.xaxis.get_ticklabels()):
+        # Plot is generated from large to small values on the x-axis
+        for label_id, label in enumerate(ax.xaxis.get_ticklabels()[::-1]):
             if ((label_id + 1) % 5 != 0) and label_id != 0:
                 label.set_visible(False)
         ax.set_yticks(df["Identified terminal states"].unique())
@@ -666,17 +663,11 @@ def plot_tsi(
         handles = handles[: (n_methods + 1)]
         labels = labels[: (n_methods + 1)]
         fig.legend(handles=handles, labels=labels, loc="lower center", ncol=(n_methods + 1), bbox_to_anchor=(0.5, -0.1))
-        plt.tight_layout()
-        plt.show()
-
-        if fname is not None:
-            format = fname.suffix[1:]
-            fig.savefig(
-                fname=fname,
-                format=format,
-                transparent=True,
-                bbox_inches="tight",
-            )
+
+        if save is not None:
+            save_fig(fig=fig, path=save)
+
+        return fig, ax
 
     @d.dedent
     def fit(