Merge pull request #127 from predict-idlab/fix_max_samples

✨  fix + test for #124

Author: jvdd

plotly_resampler/figure_resampler/figure_resampler_interface.py

@@ -794,6 +794,23 @@ def _construct_hf_data_dict(
             "hovertext": dc.hovertext,
         }
 
+    @staticmethod
+    def _add_trace_to_add_traces_kwargs(kwargs: dict) -> dict:
+        """Convert the `add_trace` kwargs to the `add_traces` kwargs."""
+        # The keywords that need to be converted to a list
+        convert_keywords = ["row", "col", "secondary_y"]
+
+        updated_kwargs = {}  # The updated kwargs (from `add_trace` to `add_traces`)
+        for keyword in convert_keywords:
+            value = kwargs.pop(keyword, None)
+            if value is not None:
+                updated_kwargs[f"{keyword}s"] = [value]
+            else:
+                updated_kwargs[f"{keyword}s"] = None
+    
+        return {**kwargs, **updated_kwargs}
+
+
     def add_trace(
         self,
         trace: Union[BaseTraceType, dict],
@@ -955,17 +972,22 @@ def add_trace(
                 # Hence, you first downsample the trace.
                 trace = self._check_update_trace_data(trace)
                 assert trace is not None
-                return super(self._figure_class, self).add_trace(trace, **trace_kwargs)
+                return super(AbstractFigureAggregator, self).add_traces(
+                    [trace], **self._add_trace_to_add_traces_kwargs(trace_kwargs)
+                )
             else:
                 self._print(f"[i] NOT resampling {trace['name']} - len={n_samples}")
                 # TODO: can be made more generic
                 trace.x = dc.x
                 trace.y = dc.y
                 trace.text = dc.text
                 trace.hovertext = dc.hovertext
-                return super(self._figure_class, self).add_trace(trace, **trace_kwargs)
-
-        return super(self._figure_class, self).add_trace(trace, **trace_kwargs)
+                return super(AbstractFigureAggregator, self).add_traces(
+                    [trace], **self._add_trace_to_add_traces_kwargs(trace_kwargs)
+                )
+        return super(AbstractFigureAggregator, self).add_traces(
+            [trace], **self._add_trace_to_add_traces_kwargs(trace_kwargs)
+        )
 
     def add_traces(
         self,
@@ -1152,8 +1174,8 @@ def replace(self, figure: go.Figure, convert_existing_traces: bool = True):
         )
 
     def construct_update_data(
-        self, 
-        relayout_data: dict
+        self,
+        relayout_data: dict,
     ) -> Union[List[dict], dash.no_update]:
         """Construct the to-be-updated front-end data, based on the layout change.
 

tests/test_figure_resampler.py

@@ -102,6 +102,18 @@ def test_add_trace_not_resampling(float_series):
     )
 
 
+def test_max_n_samples(float_series):
+    s = float_series[:5000]
+
+    fig = FigureResampler()
+    fig.add_trace(
+        go.Scattergl(name="test"), hf_x=s.index, hf_y=s, max_n_samples=len(s) + 1
+    )
+    # make sure that there is not hf_data
+    assert len(fig.hf_data) == 0
+    assert len(fig.data[0]["x"]) == len(s)
+
+
 def test_add_scatter_trace_no_data():
     fig = FigureResampler(default_n_shown_samples=1000)
 
@@ -1045,7 +1057,7 @@ def test_fr_update_layout_axes_range(driver):
         y=np.arange(nb_datapoints)
     )
 
-    def check_data(fr: FigureResampler, min_v=0, max_v=nb_datapoints-1):
+    def check_data(fr: FigureResampler, min_v=0, max_v=nb_datapoints - 1):
         # closure for n_shown and nb_datapoints
         assert len(fr.data[0]["y"]) == min(n_shown, nb_datapoints)
         assert len(fr.data[0]["x"]) == min(n_shown, nb_datapoints)
@@ -1121,7 +1133,7 @@ def check_data(fr: FigureResampler, min_v=0, max_v=nb_datapoints-1):
     finally:
         proc.terminate()
         f_pr.stop_server()
-    
+
 
 def test_fr_update_layout_axes_range_no_update(driver):
     nb_datapoints = 2_000
@@ -1133,7 +1145,7 @@ def test_fr_update_layout_axes_range_no_update(driver):
         y=np.arange(nb_datapoints)
     )
 
-    def check_data(fr: FigureResampler, min_v=0, max_v=nb_datapoints-1):
+    def check_data(fr: FigureResampler, min_v=0, max_v=nb_datapoints - 1):
         # closure for n_shown and nb_datapoints
         assert len(fr.data[0]["y"]) == min(n_shown, nb_datapoints)
         assert len(fr.data[0]["x"]) == min(n_shown, nb_datapoints)