Skip real-time plot if higher-frequency data is missing

- Skip generating the real-time view plot when no high-frequency
  reporting data is available.
- Log the issue using `NoDataAvailableError` and clear the figure to
  avoid rendering an empty plot.
- Ensure `rolling_view_real_time` and output dict are safely set to
  empty DataFrames in such cases.

Signed-off-by: cyiallou - Costas <[email protected]>

Author: cyiallou

src/frequenz/lib/notebooks/solar/maintenance/solar_maintenance_app.py

@@ -409,8 +409,12 @@ async def run_workflow(user_config_changes: dict[str, Any]) -> SolarAnalysisData
         rolling_view_average = RollingPreparer(rolling_view_average_config).prepare(
             data[[long_term_view_col_to_plot]]
         )
-        rolling_view_real_time = RollingPreparer(rolling_view_real_time_config).prepare(
-            data_higher_fs[real_time_view_col_to_plot] / normalisation_factor
+        rolling_view_real_time = (
+            pd.DataFrame()
+            if data_higher_fs.empty
+            else RollingPreparer(rolling_view_real_time_config).prepare(
+                data_higher_fs[real_time_view_col_to_plot] / normalisation_factor
+            )
         )
         daily_production_view = DailyPreparer(daily_plot_config).prepare(data)
         statistical_view = ProfilePreparer(statistical_plot_config).prepare(data)
@@ -493,54 +497,67 @@ async def run_workflow(user_config_changes: dict[str, Any]) -> SolarAnalysisData
             fig=figures_and_axes["fig_real_time"]["figure"],
             ax=figures_and_axes["fig_real_time"]["axes"][0],
         )
-        if plot_settings["show_annotation"]:
-            if len(real_time_view_col_to_plot) == 1:
-                for col in real_time_view_col_to_plot:
-                    recent_y = rolling_view_real_time[str(col)].iloc[-2]
-                    _annotate_last_point(
-                        figures_and_axes["fig_real_time"]["axes"][0], recent_y
-                    )
-                    patch = Patch(
-                        color=plot_settings["patch_colour"], label=patch_label
-                    )
-        figures_and_axes["fig_real_time"]["axes"][0].set_ylabel(real_time_view_ylabel)
-
-        if plot_settings["legend_update_on"] == "figure":
-            _legend_kwargs_copy = plot_settings["legend_kwargs"].copy()
-            # divide legend labels into groups of 2 if needed
-            _legend_kwargs_copy["ncol"] = max(
-                _legend_kwargs_copy["ncol"],
-                (
-                    len(
-                        figures_and_axes["fig_real_time"]["axes"][
-                            0
-                        ].get_legend_handles_labels()[1]
-                    )
-                    + 1
-                )
-                // 2,
-            )
+        if data_higher_fs.empty:
+            reason = NoDataAvailableError("No data available for real-time view.")
+            print(f"{reason} Skipping this plot.")
+            print(f"{type(reason).__name__}: {reason}. Skipping this plot.")
+            # the plotter automatically hides the axis when data is empty
+            # but we need to deal with the figure itself
+            # we can safely clear the figure like this because it only plots rolling_view_real_time
+            figures_and_axes["fig_real_time"]["figure"].clf()
         else:
-            _legend_kwargs_copy = plot_settings["legend_kwargs"]
-        plot_manager.update_legend(
-            fig_id="fig_real_time",
-            axs=[figures_and_axes["fig_real_time"]["axes"][0]],
-            on=plot_settings["legend_update_on"],
-            modifications={
-                "additional_items": (
-                    [(patch, patch_label)] if plot_settings["show_annotation"] else None
-                ),
-                "replace_label": {
-                    str(col): (
-                        tm.translate("component_{value}", value=col)
-                        if config.split_real_time_view_per_inverter
-                        else production_legend_label
+            if plot_settings["show_annotation"]:
+                if len(real_time_view_col_to_plot) == 1:
+                    for col in real_time_view_col_to_plot:
+                        recent_y = rolling_view_real_time[str(col)].iloc[-2]
+                        _annotate_last_point(
+                            figures_and_axes["fig_real_time"]["axes"][0], recent_y
+                        )
+                        patch = Patch(
+                            color=plot_settings["patch_colour"], label=patch_label
+                        )
+            figures_and_axes["fig_real_time"]["axes"][0].set_ylabel(
+                real_time_view_ylabel
+            )
+
+            if plot_settings["legend_update_on"] == "figure":
+                _legend_kwargs_copy = plot_settings["legend_kwargs"].copy()
+                # divide legend labels into groups of 2 if needed
+                _legend_kwargs_copy["ncol"] = max(
+                    _legend_kwargs_copy["ncol"],
+                    (
+                        len(
+                            figures_and_axes["fig_real_time"]["axes"][
+                                0
+                            ].get_legend_handles_labels()[1]
+                        )
+                        + 1
                     )
-                    for col in real_time_view_col_to_plot
+                    // 2,
+                )
+            else:
+                _legend_kwargs_copy = plot_settings["legend_kwargs"]
+            plot_manager.update_legend(
+                fig_id="fig_real_time",
+                axs=[figures_and_axes["fig_real_time"]["axes"][0]],
+                on=plot_settings["legend_update_on"],
+                modifications={
+                    "additional_items": (
+                        [(patch, patch_label)]
+                        if plot_settings["show_annotation"]
+                        else None
+                    ),
+                    "replace_label": {
+                        str(col): (
+                            tm.translate("component_{value}", value=col)
+                            if config.split_real_time_view_per_inverter
+                            else production_legend_label
+                        )
+                        for col in real_time_view_col_to_plot
+                    },
                 },
-            },
-            **_legend_kwargs_copy,
-        )
+                **_legend_kwargs_copy,
+            )
         # ------------------- #
 
         # --- plot the statistical production profile --- #
@@ -810,7 +827,9 @@ async def run_workflow(user_config_changes: dict[str, Any]) -> SolarAnalysisData
         for fig in figures_and_axes.keys():
             plot_manager.adjust_axes_spacing(fig_id=fig, pixels=100.0)
 
-        output.real_time_view[mid] = rolling_view_real_time
+        output.real_time_view[mid] = (
+            pd.DataFrame() if data_higher_fs.empty else rolling_view_real_time
+        )
         output.rolling_view_short_term[mid] = rolling_view_short_term
         output.rolling_view_long_term[mid] = rolling_view_long_term
         output.rolling_view_average[mid] = rolling_view_average