Different behavior with hf_x with type int64 vs float64 #63
Description
Hello !
This is a really neat project ! Great job !
I have experienced a weird behavior. I work in a jupyter notebook inside vscode. The data has a shape of (6261089,).
This snippet work perfectly fine.
fig = FigureResampler(go.Figure())
fig.add_trace(go.Scattergl(name='Trace', showlegend=True), hf_x=x.astype(np.int64), hf_y=raw_data)
fig.show_dash(mode='inline')But when I changed the parameter hf_x=x.astype(np.float64)
fig = FigureResampler(go.Figure())
fig.add_trace(go.Scattergl(name='Trace', showlegend=True), hf_x=x.astype(np.float64), hf_y=raw_data)
fig.show_dash(mode='inline')I get the following error :
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/data_inspection.ipynb Cell 9' in <cell line: 13>()
10 print(raw_data.shape, times.shape)
12 fig = FigureResampler(go.Figure())
---> 13 fig.add_trace(go.Scattergl(name='Trace', showlegend=True), hf_x=ranged_arr.astype(np.float64), hf_y=raw_data)
14 fig.show_dash(mode='inline')
File ~/.venv/lib/python3.9/site-packages/plotly_resampler/figure_resampler/figure_resampler_interface.py:743, in AbstractFigureAggregator.add_trace(self, trace, max_n_samples, downsampler, limit_to_view, hf_x, hf_y, hf_hovertext, **trace_kwargs)
732 trace = {
733 k: trace[k]
734 for k in set(trace.keys()).difference(
735 {"text", "hovertext", "x", "y"}
736 )
737 }
739 # NOTE:
740 # If all the raw data needs to be sent to the javascript, and the trace
741 # is high-frequency, this would take significant time!
742 # Hence, you first downsample the trace.
--> 743 trace = self._check_update_trace_data(trace)
744 assert trace is not None
745 super(self._figure_class, self).add_trace(trace=trace, **trace_kwargs)
File ~/.venv/lib/python3.9/site-packages/plotly_resampler/figure_resampler/figure_resampler_interface.py:240, in AbstractFigureAggregator._check_update_trace_data(self, trace, start, end)
238 # Downsample the data and store it in the trace-fields
239 downsampler: AbstractSeriesAggregator = hf_trace_data["downsampler"]
--> 240 s_res: pd.Series = downsampler.aggregate(
241 hf_series, hf_trace_data["max_n_samples"]
242 )
243 trace["x"] = s_res.index
244 trace["y"] = s_res.values
File ~/.venv/lib/python3.9/site-packages/plotly_resampler/aggregation/aggregation_interface.py:142, in AbstractSeriesAggregator.aggregate(self, s, n_out)
138 s = s.astype("uint8")
140 if len(s) > n_out:
141 # More samples that n_out -> perform data aggregation
--> 142 s = self._aggregate(s, n_out=n_out)
144 # When data aggregation is performed -> we do not "insert" gaps but replace
145 # The end of gap periods (i.e. the first non-gap sample) with None to
146 # induce such gaps
147 if self.interleave_gaps:
File ~/.venv/lib/python3.9/site-packages/plotly_resampler/aggregation/aggregators.py:240, in EfficientLTTB._aggregate(self, s, n_out)
238 def _aggregate(self, s: pd.Series, n_out: int) -> pd.Series:
239 if s.shape[0] > n_out * 1_000:
--> 240 s = self.minmax._aggregate(s, n_out * 50)
241 return self.lttb._aggregate(s, n_out)
File ~/.venv/lib/python3.9/site-packages/plotly_resampler/aggregation/aggregators.py:134, in MinMaxOverlapAggregator._aggregate(self, s, n_out)
127 offset = np.arange(
128 0, stop=s.shape[0] - block_size - argmax_offset, step=block_size
129 )
131 # Calculate the argmin & argmax on the reshaped view of `s` &
132 # add the corresponding offset
133 argmin = (
--> 134 s[: block_size * offset.shape[0]]
135 .values.reshape(-1, block_size)
136 .argmin(axis=1)
137 + offset
138 )
139 argmax = (
140 s[argmax_offset : block_size * offset.shape[0] + argmax_offset]
141 .values.reshape(-1, block_size)
(...)
144 + argmax_offset
145 )
146 # Sort the argmin & argmax (where we append the first and last index item)
147 # and then slice the original series on these indexes.
ValueError: cannot reshape array of size 6260945 into shape (251)Thank you !