New function to plot trajectory of a model with custom timepoints. #739
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@@ Coverage Diff @@
## develop #739 +/- ##
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+ Coverage 46.43% 89.65% +43.21%
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Files 99 99
Lines 6671 6720 +49
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+ Hits 3098 6025 +2927
+ Misses 3573 695 -2878
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| timepoints = np.linspace(start=0, | ||
| stop=end_time, | ||
| num=n_timepoints) |
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Might be nice to have this handled by AmiciObjective.set_custom_timepoints so it can be used in other contexts
| stop=end_time, | ||
| num=n_timepoints) | ||
| obj = problem.objective.set_custom_timepoints( | ||
| timepoints_global=timepoints) |
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Typehint says timepoints can be a Sequence[np.ndarray], which would currently be specified with the timepoints argument of set_custom_timepoints instead. AmiciObjective.set_custom_timepoints could be changed to have only one argument timepoints that changes behaviour with isinstance.
| # add timepoints as needed | ||
| if timepoints is None: | ||
| end_time = max(problem.objective.edatas[0].getTimepoints()) | ||
| timepoints = np.linspace(start=0, |
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also use min(getTimepoints)
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no that would be a problem again, as in that scenario having only one measured timepoints at the end would give you no trajectory again 🤔 I would say in case you do not want to start at 0, just supply the timepoints.
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Ah, how are steady-state measurements handled?
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would you want a trajectory of a steady state? 🤔
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No but if there is an inf timepoint then this might ruin the np.linspace timepoints
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| def _time_trajectory_model_with_states( |
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Could move this back and put plotStateTrajectories inside an if state_indices: block. The extra computation related to states should be insignificant
AMICI uses petab_problem.get_simulation_conditions_from_measurement_df()in |
ahh but in that case I would need to supply the petab_problem as well... will think about it a bit more and probably make an issue out of it and an extra PR. Better imo. |
… into added_more_options_to_model_fit
Thankful for any ideas on how to get the condition id's nicely. Otherwise, anything I forgot?