np.nan also misbehaves.
On Friday, 30 September 2022 at 16:09:38 UTC+1 AB wrote:
> Since posting earlier, I've tried float("nan"), float("inf") and np.inf
> and they all behave exactly the same. I'll see if I can cut my
> application's code down to something I can share.
>
> I did manage to "stop" the graph while the glitch was displayed (and by
> "stop" I mean that the application was running but I stopped making further
> calls to setData). Before zooming the graph I used the context-menu to
> export data to CSV and I could clearly see that there was a period exported
> as "inf" where the line was being displayed.
>
> On Friday, 30 September 2022 at 16:02:50 UTC+1 [email protected] wrote:
>
>> Hmm, that certainly should not be happening, not sure quite why things
>> would be going like that. If you can, I would try slowing down the update
>> rate of the plot, to easily catch one of those instances and post some code
>> here if you can for us to try and replicate.
>>
>> Another suggestion would be to set the invalid points to numpy.nan
>> instead of float("inf") and see if that gets better behavior; I think that
>> was the value we were originally looking for.
>>
>> On Fri, Sep 30, 2022 at 7:27 AM AB <[email protected]> wrote:
>>
>>> I am trying to display some data in a line graph - there are 1024
>>> points, updating ~40 times/second. Some of the points are invalid (set to
>>> float("inf") ) and are supposed to cause a break in the line if I
>>> understand the default behaviour of the "connect" parameter correctly.
>>> However, I am periodically seeing that there is a straight line joining the
>>> valid points either side of the invalid period.
>>>
>>> I can stop updating the graph and catch this happening - as soon as I
>>> use the mouse zoom functionality to change the scale of the graph in this
>>> state the erroneous line vanishes, and the graph is drawn with breaks in
>>> the line as I expect that it should.
>>>
>>> Can anyone suggest why the erroneous lines are appearing, and how I can
>>> ensure they don't appear?
>>>
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