Spikes in reversed ecdf

šŸ“Š Plotly Python
,
1

Hello,
Iā€™m using reversed ECDF for data analysis and the resulting diagrams contain a sort of strange spikes.
Looks like a bug.

image
image1125Ɨ563 57.2 KB

Any idea or suggestion?
Thanks a lot.

2

Hi @enric.pastor welcome to the forums.

This could be anything. Maybe you could add some more information concerning the chart? How does the data look like, how did you create the chart?

The first thing you could check is the dtype of the x and y axes, specially the x axis

3

It is hard to add the data as its is generated through a complex process.
The strange thing is that the spikes only appear when the reversed distribution is requested.

non-Rev-SpuriousCaution_Time_Comparison_Histogram-CDF
non-Rev-SpuriousCaution_Time_Comparison_Histogram-CDF1000Ɨ500 20.7 KB

SpuriousCaution_Time_Comparison_Histogram-CDF
SpuriousCaution_Time_Comparison_Histogram-CDF1000Ɨ500 21.2 KB

The code per se is fairly simple:

fig = px.ecdf(df3, group_labels, ecdfmode="reversed", marginal="box")
fig.update_layout(margin_b= 0, margin_l= 0, margin_r= 0, width= 1000)
fig.update_xaxes(title_text='Time(s)', range=[0, 80])
fig.update_yaxes(title_text='CDF')
fig.update_layout(title_text='Spurious Caution Alert')
fig.update_layout(legend=dict(
    yanchor="top",
    y=0.99,
    xanchor="right",
    x=0.99
))
4

Did you check the dtype for the columns of df3?

5

Sorry. I forgot about that.
Time to CPA and the durations are int64.
The predictions are float64.

I have extracted the dataframe as is not large in this test case.

group_labels = [ā€˜Time to CPAā€™, ā€˜Spurious Caution durationā€™, ā€˜First Caution durationā€™, ā€œPredicted Time to LoWCā€, ā€œFirst Predicted Time to LoWCā€]

[[ 13. 4. 4. 38.52282079 38.52282079]
[ 13. 4. 4. 38.52282079 38.52282079]
[ 21. 2. 2. 36.40388629 36.40388629]
[ 21. 2. 2. 36.40388629 36.40388629]
[ 2. 6. 6. 43.17394253 43.17394253]
[ 2. 6. 6. 43.17394253 43.17394253]
[-19. 49. 5. 23.15611685 39.88214533]
[-19. 49. 5. 23.15611685 39.88214533]
[ 28. 44. 4. 44.64795822 38.85908162]
[ 28. 44. 4. 44.64795822 38.85908162]
[ -3. 3. 3. 25.68102444 25.68102444]
[ -3. 3. 3. 25.68102444 25.68102444]
[ 21. 5. 5. 34.04343412 34.04343412]
[ 21. 5. 5. 34.04343412 34.04343412]
[ 12. 18. 1. 36.11286997 28.71333269]
[ 12. 18. 1. 36.11286997 28.71333269]]