Sebastian Höpfl

I get the same bad behaviour by zooming in with xlim. When I select only 5 samples for visualization it works fine and the mean is inside the posterior predictive...

If there is yet no other fix, would it be possible to write this into the dependencies in pypi? Here, it should be specified that numpy has to be

Hi Frank, thank you very much for the tipps. I tried newt and it works really nice - also immediately with my SBML L3.1 file. I agree solving the problems...

Dear @piotr-gawron thanks for the additional information. At the moment the use of the web interface was quite fast and efficient for me, however for other projects I would probably...

Okay, so at least for Emcee this will work for me. Thats great, thanks.

If anyone else stumbles across this issue, there is a workaround with the `get_data_to_plot` function: This function allows to plot saved chains via arviz and also to combine and compare...

Hi Jan, my idea was to take the Highest Posterior Density (HPD), which could be calculated by sorting the posterior values and throwing away the lowest x-percent of these values....

Adding to this, here would be a function that calculates HPD parameters based on the Pypesto sampling result: `def calculate_hpd(pypesto_result, ci_level: float, stepsize: int = 1) -> pd.DataFrame:` ` """Calculate...

The problem seems to be that my ensemble_predicion (from ensemble.predict) also only saves condition_0 etc. not the condition ids which makes a mapping afterwards impossible

Ordering according to the conditions file was also my first guess. However, I realized this issue because it is for my example not the ordering of the PEtab conditions file....