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Doc: misc (#54)
* Link docs * docstring * raise * format * Remove PEtab process * Apply suggestions from code review Co-authored-by: Yannik Schälte <[email protected]> Co-authored-by: Yannik Schälte <[email protected]>
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README.md

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@@ -34,16 +34,18 @@ Development versions of the PEtab library can be installed using
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When setting up a new parameter estimation problem, the most useful tools will
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be:
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- The **PEtab validator**, which is now automatically installed using Python
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entrypoints to be available as a shell command from anywhere called
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- The [PEtab validator](https://petab.readthedocs.io/projects/libpetab-python/en/latest/example/example_petablint.html),
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which is automatically installed using Python
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entrypoints to be available as a shell command from anywhere, called
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`petablint`
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- `petab.create_parameter_df` to create the parameter table, once you
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have set up the model, condition table, observable table and measurement
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table
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- [`petab.create_parameter_df`](https://petab.readthedocs.io/projects/libpetab-python/en/latest/build/_autosummary/petab.parameters.html#petab.parameters.create_parameter_df)
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to create the parameter table, once you have set up the model,
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condition table, observable table and measurement table
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- `petab.create_combine_archive` to create a
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[COMBINE Archive](https://combinearchive.org/index/) from PEtab files
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- [`petab.create_combine_archive`](https://petab.readthedocs.io/projects/libpetab-python/en/latest/build/_autosummary/petab.core.html#petab.core.create_combine_archive)
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to create a [COMBINE Archive](https://combinearchive.org/index/) from PEtab
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files
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## Examples
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doc/conf.py

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# -- Project information -----------------------------------------------------
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project = 'PEtab'
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project = 'libpetab-python'
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copyright = '2018, the PEtab developers'
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author = 'PEtab developers'
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doc/development.rst

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doc/index.rst

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:caption: About
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Changelog <CHANGELOG.md>
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Development <development>
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how_to_cite
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license
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petab/calculate.py

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nllh = log(2*sigma*m) + abs((log(s)-log(m))/sigma)
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elif noise_distribution == LAPLACE and scale == LOG10:
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nllh = log(2*sigma*m*log(10)) + abs((log10(s)-log10(m))/sigma)
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else:
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raise NotImplementedError(
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"Unsupported combination of noise_distribution and scale "
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f"specified: {noise_distribution}, {scale}.")
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llh = - nllh
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return llh

petab/core.py

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def create_combine_archive(
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yaml_file: str, filename: str,
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yaml_file: str,
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filename: str,
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family_name: Optional[str] = None,
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given_name: Optional[str] = None,
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email: Optional[str] = None,
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Arguments:
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yaml_file: Path to PEtab YAML file
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filename: Destination file name
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family_name: Family name of archive creator
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given_name: Given name of archive creator
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email: E-mail address of archive creator

petab/lint.py

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def assert_measured_observables_defined(
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measurement_df: pd.DataFrame,
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observable_df: pd.DataFrame) -> None:
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"""Check if all observables in the measurement table have been defined in the
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observable table
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"""Check if all observables in the measurement table have been defined in
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the observable table
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Arguments:
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measurement_df: PEtab measurement table

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