🙈 first iteration of bugfix for #144 (#145)

Author: jonasvdd

plotly_resampler/figure_resampler/figure_resampler_interface.py

@@ -625,7 +625,7 @@ def _parse_get_trace_props(
 
         """
         hf_x: np.ndarray = (
-            trace["x"]
+            (np.asarray(trace["x"]) if trace["x"] is not None else None)
             if hasattr(trace, "x") and hf_x is None
             else hf_x.values
             if isinstance(hf_x, pd.Series)
@@ -641,7 +641,7 @@ def _parse_get_trace_props(
             if isinstance(hf_y, (pd.Series, pd.Index))
             else hf_y
         )
-        hf_y : np.ndarray = np.asarray(hf_y)
+        hf_y: np.ndarray = np.asarray(hf_y)
 
         hf_text = (
             hf_text
@@ -696,9 +696,7 @@ def _parse_get_trace_props(
                     hf_hovertext = hf_hovertext[not_nan_mask]
 
             # Try to parse the hf_x data if it is of object type or
-            if len(hf_x) and (
-                hf_x.dtype.type is np.str_ or hf_x.dtype == "object"
-            ):  
+            if len(hf_x) and (hf_x.dtype.type is np.str_ or hf_x.dtype == "object"):
                 try:
                     # Try to parse to numeric
                     hf_x = pd.to_numeric(hf_x, errors="raise")
@@ -901,7 +899,7 @@ def add_trace(
             As this is a costly operation, it is recommended to set this parameter to
             False if you are sure that your data does not contain NaNs (or when the
             downsampler can handle NaNs, e.g., EveryNthPoint). This should considerably
-            speed up the graph construction time. 
+            speed up the graph construction time.
         **trace_kwargs: dict
             Additional trace related keyword arguments.
             e.g.: row=.., col=..., secondary_y=...
@@ -973,7 +971,9 @@ def add_trace(
 
         # construct the hf_data_container
         # TODO in future version -> maybe regex on kwargs which start with `hf_`
-        dc = self._parse_get_trace_props(trace, hf_x, hf_y, hf_text, hf_hovertext, check_nans)
+        dc = self._parse_get_trace_props(
+            trace, hf_x, hf_y, hf_text, hf_hovertext, check_nans
+        )
 
         # These traces will determine the autoscale its RANGE!
         #   -> so also store when `limit_to_view` is set.
@@ -1069,7 +1069,7 @@ def add_traces(
         limit_to_views : None | List[bool] | bool, optional
             List of limit_to_view booleans for the added traces. If set to True the
             trace's datapoints will be cut to the corresponding front-end view, even if
-            the total number of samples is lower than ``max_n_samples``. 
+            the total number of samples is lower than ``max_n_samples``.
             If a single boolean is passed, all to be added traces will use this value,
             by default False.\n
             Remark that setting this parameter to True ensures that low frequency traces

tests/test_figure_resampler.py

@@ -621,6 +621,14 @@ def test_proper_copy_of_wrapped_fig(float_series):
     assert len(plotly_resampler_fig.data[0].y) == 500
 
 
+def test_low_dim_input():
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=[1, 2, 3], y=[1, 2, 3], name='a'))
+
+    fig = FigureResampler(go.Figure())
+    fig.add_trace(go.Scatter(x=[1, 2, 3], y=[1, 2, 3], name='a'))
+    fig.add_trace(go.Scatter(), hf_x=[1, 2, 3], hf_y=[1, 2, 3])
+
 def test_2d_input_y():
     # Create some dummy dataframe with a nan
     df = pd.DataFrame(