diff --git a/README.md b/README.md index 474fc7a..7e850af 100644 --- a/README.md +++ b/README.md @@ -4,11 +4,11 @@ [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14183390.svg)](https://doi.org/10.5281/zenodo.14183390) -The PEtab extension for model selection, including additional file formats and library. +The PEtab extension for model selection, including additional file formats and a package. ## Install -The Python 3 library provides both the Python 3 and command-line (CLI) +The Python 3 package provides both the Python 3 and command-line (CLI) interfaces, and can be installed from PyPI, with `pip3 install petab-select`. ## Documentation diff --git a/doc/analysis.rst b/doc/analysis.rst index 888320c..60e8964 100644 --- a/doc/analysis.rst +++ b/doc/analysis.rst @@ -2,7 +2,7 @@ Analysis ======== After using PEtab Select to perform model selection, you may want to operate on all "good" calibrated models. -The PEtab Select Python library provides some methods to help with this. Please request any missing methods. +The PEtab Select Python package provides some methods to help with this. Please request any missing methods. See the Python API docs for the :class:`petab_select.Models` class, which provides some methods. In particular, :attr:`petab_select.Models.df` can be used to get a quick overview over all models, as a pandas dataframe. @@ -13,7 +13,7 @@ or compute "weights" (e.g. Akaike weights). Model hashes ^^^^^^^^^^^^ -Model hashes are special objects in the library, that are generated from model-specific information that is unique within a single PEtab Select problem. +Model hashes are special objects in the package, that are generated from model-specific information that is unique within a single PEtab Select problem. This means you can reconstruct the model given some model hash. For example, with this model hash `M1-000`, you can reconstruct the :class:`petab_select.ModelHash` from a string, then reconstruct the :class:`petab_select.Model`. diff --git a/doc/index.rst b/doc/index.rst index 13a2626..8de4567 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -9,7 +9,7 @@ Welcome to PEtab Select's documentation! PEtab Select brings `model selection `_ to `PEtab `_. PEtab Select comprises file -formats, a Python library and a command line interface. +formats, a Python package and a command line interface. Model selection is the process of choosing the best model from a set of candidate models. PEtab Select provides a standardized and compact way to @@ -40,7 +40,7 @@ PEtab Select is well-integrated with: (`example `__) Other model calibration tools can easily be integrated using the provided -Python library or command line interface. +Python package or command line interface. Installation ------------ diff --git a/doc/problem_definition.rst b/doc/problem_definition.rst index 1220c96..90f5444 100644 --- a/doc/problem_definition.rst +++ b/doc/problem_definition.rst @@ -9,7 +9,7 @@ Model selection problems for PEtab Select are defined by the following files: The different file formats are described below. The YAML file formats come with a YAML-formatted JSON schema, such that these files can be -easily worked with independently of the PEtab Select library. +easily worked with independently of the PEtab Select package. 1. Selection problem -------------------- @@ -155,7 +155,7 @@ Brief format description - ``model_subspace_id``: Same as in the model space files. - ``model_subspace_indices``: The indices that locate this model in its model subspace. - ``criteria``: The value of the criterion by which model selection was performed, at least. Optionally, other criterion values too. -- ``model_hash``: The model hash, generated by the PEtab Select library. The format is ``[MODEL_SUBSPACE_ID]-[MODEL_SUBSPACE_INDICES_HASH]``. If all parameters are in the model are defined like ``0;estimate``, then the hash is a string of ``1`` and ``0``, for parameters that are estimated or not, respectively. +- ``model_hash``: The model hash, generated by the PEtab Select package. The format is ``[MODEL_SUBSPACE_ID]-[MODEL_SUBSPACE_INDICES_HASH]``. If all parameters are in the model are defined like ``0;estimate``, then the hash is a string of ``1`` and ``0``, for parameters that are estimated or not, respectively. - ``model_subspace_petab_yaml``: Same as in model space files. - ``estimated_parameters``: Parameter estimates, including all estimated parameters that are not in the model selection problem; i.e., parameters that are set to be estimated in the model subspace PEtab problem but don't appear in the column header of the model space file. - ``iteration``: The iteration of model selection in which this model appeared.