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[WIP] Helper function to deal with timepoint-specific parameters #128

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merged 50 commits into from
Oct 13, 2019

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@dweindl dweindl commented Aug 20, 2019

Closes #125

@elbaraim : your turn to make it pass ;)

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Please fix flake8 issues

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dweindl commented Sep 7, 2019

ping

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dweindl commented Sep 26, 2019

ping

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dweindl commented Oct 4, 2019

@elbaraim : ping

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elbaraim commented Oct 4, 2019

@elbaraim : ping

will take care of this now

elba.raimundez and others added 16 commits October 4, 2019 12:36
here just making the example work now, deletion of experimentId then in #111
* try new travis login

* re-fix and update version

* tmp do on test branch

* undo tmp changes. new password seems to work. updated to 0.0.0a15 meanwhile, so next version must be 0.0.0a16
* adding main file for visualization

* adding file for grouping the data which should be plotted

* adding file with plotting routine for distinct cases

* added file comments, tidy up the code

* fix pep8

* update version

* some beauty corrections

* now: function which should be used, to plot data

* style corrections

* new example file

* minor edits

* add test example for visualization with specification file routine

* add typehints

* minor corrections

* flake8 corrections

* update folder structure

* minor changes & __init__

* move visualization folder to petab subdir

* create viz notebook; tidy up code; fix imports and flake8

* add seaborn as requirement

* fix flake8, correct link to benchmarking repo
* adding local files for testing visualization

* adding the possibility to plot without visualization file or simulation file

* update documentation and tests

* add the possibility to group plots via simple lists, if no visualization specification file is around

* extend and tidy up the examples accordingly

* regroup code into new files, try to have each function smaller and better understandable

* update gitignore for macOS

* updated tests to capture warnings and exceptions

* Delete the check for identical noise before plotting points of a dataset - I can think of many applications, in which I want to plot one dataset in which points with different noise values appear. We should allow this to the user

* add the check for NANs in a dataset to avoid plotting empty datasets and cluttering the legend
* added an initial parameter sampling functionality

* add tests for startpoint sampling

* added documentation for the additional columns intialization and objectivePriorType and Parameters
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codecov-io commented Oct 4, 2019

Codecov Report

Merging #128 into develop will increase coverage by 3.01%.
The diff coverage is 90.32%.

Impacted file tree graph

@@             Coverage Diff             @@
##           develop     #128      +/-   ##
===========================================
+ Coverage    68.44%   71.45%   +3.01%     
===========================================
  Files           11       11              
  Lines         1090     1121      +31     
  Branches       256      262       +6     
===========================================
+ Hits           746      801      +55     
+ Misses         307      272      -35     
- Partials        37       48      +11
Impacted Files Coverage Δ
petab/core.py 79.94% <90.32%> (+1.02%) ⬆️
petab/sbml.py 65.83% <0%> (+4.16%) ⬆️
petab/lint.py 53.2% <0%> (+10.83%) ⬆️

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please add a little more info on what the code does, then I will have another look :)

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just some more clarifications :)

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just some clarifications :)

df = petab_problem.measurement_df.loc[
(petab_problem.measurement_df['observableId'] ==
df_unique_values.loc[nrow, "observableId"])
& (petab_problem.measurement_df['preequilibrationConditionId'] <=
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now observableId is checked with ==, but why are the others still checked with <=?

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@elbaraim elbaraim merged commit 9483070 into develop Oct 13, 2019
@elbaraim elbaraim deleted the feature_125_flatten_timepoint_specific_overrides branch October 13, 2019 09:28
dweindl pushed a commit that referenced this pull request Oct 22, 2019
…ameters flatten_timepoint_specific_output_overrides (#128) (Closes #125)

Co-authored-by: elba.raimundez <elba.raimundez@helmholtz-muenchen.de>
This was referenced Dec 4, 2019
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6 participants