Migrated .plot() from using xarray

CHANGELOG.md

@@ -16,6 +16,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 **Fixed**
 
 **Dependencies**
+- Changed `Timeseries.plot()` implementation to no longer rely on xarray under the hood while keeping same functionality. [#2932](https://github.com/unit8co/darts/pull/2932) by [Jakub Chłapek](https://github.com/jakubchlapek)
 
 ### For developers of the library:
 

darts/timeseries.py

@@ -4318,8 +4318,6 @@ def plot(
     ) -> matplotlib.axes.Axes:
         """Plot the series.
 
-        This is a wrapper method around :func:`xarray.DataArray.plot()`.
-
         Parameters
         ----------
         new_plot
@@ -4340,7 +4338,7 @@ def plot(
         default_formatting
             Whether to use the darts default scheme.
         title
-            Optionally, a custom plot title. If `None`, will use the name of the underlying `xarray.DataArray`.
+            Optionally, a custom plot title. If `None`, will use an empty string.
         label
             Can either be a string or list of strings. If a string and the series only has a single component, it is
             used as the label for that component. If a string and the series has multiple components, it is used as
@@ -4452,19 +4450,18 @@ def plot(
             if ax is None:
                 ax = plt.gca()
 
-        # TODO: migrate from xarray plotting to something else
-        data_array = self.data_array(copy=False)
-        for i, c in enumerate(data_array.component[:n_components_to_plot]):
-            comp_name = str(c.values)
-            comp = data_array.sel(component=c)
+        for i, comp_name in enumerate(self.components[:n_components_to_plot]):
+            comp_ts = self[comp_name]
 
-            if comp.sample.size > 1:
+            if self.is_stochastic:
                 if central_quantile == "mean":
-                    central_series = comp.mean(dim=DIMS[2])
+                    central_ts = comp_ts.mean()
                 else:
-                    central_series = comp.quantile(q=central_quantile, dim=DIMS[2])
+                    central_ts = comp_ts.quantile(q=central_quantile)
             else:
-                central_series = comp.mean(dim=DIMS[2])
+                central_ts = comp_ts.mean()
+
+            central_series = central_ts.to_series()  # shape: (time,)
 
             if custom_labels:
                 label_to_use = label[i]
@@ -4479,10 +4476,17 @@ def plot(
             kwargs["c"] = color[i] if custom_colors else color
 
             kwargs_central = deepcopy(kwargs)
-            if not self.is_deterministic:
+            if self.is_stochastic:
                 kwargs_central["alpha"] = 1
             if central_series.shape[0] > 1:
-                p = central_series.plot(*args, ax=ax, **kwargs_central)
+                p = central_series.plot(
+                    *args,
+                    ax=ax,
+                    **kwargs_central,
+                )
+                color_used = (
+                    p.get_lines()[-1].get_color() if default_formatting else None
+                )
             # empty TimeSeries
             elif central_series.shape[0] == 0:
                 p = ax.plot(
@@ -4491,6 +4495,7 @@ def plot(
                     *args,
                     **kwargs_central,
                 )
+                color_used = p[0].get_color() if default_formatting else None
             else:
                 p = ax.plot(
                     [self.start_time()],
@@ -4499,17 +4504,17 @@ def plot(
                     *args,
                     **kwargs_central,
                 )
+                color_used = p[0].get_color() if default_formatting else None
             ax.set_xlabel(self.time_dim)
-            color_used = p[0].get_color() if default_formatting else None
 
             # Optionally show confidence intervals
             if (
-                comp.sample.size > 1
+                self.is_stochastic
                 and low_quantile is not None
                 and high_quantile is not None
             ):
-                low_series = comp.quantile(q=low_quantile, dim=DIMS[2])
-                high_series = comp.quantile(q=high_quantile, dim=DIMS[2])
+                low_series = comp_ts.quantile(q=low_quantile).to_series()
+                high_series = comp_ts.quantile(q=high_quantile).to_series()
                 if low_series.shape[0] > 1:
                     ax.fill_between(
                         self.time_index,
@@ -4528,7 +4533,7 @@ def plot(
                     )
 
         ax.legend()
-        ax.set_title(title if title is not None else data_array.name)
+        ax.set_title(title if title is not None else "")
         return ax
 
     def with_columns_renamed(