memote.suite.cli.runner

Provide commands for running the test suite on a metabolic model.

Module Contents

Functions

cli() Metabolic model testing command line tool.
run(model, collect, filename, location, ignore_git, pytest_args, exclusive, skip, solver, solver_timeout, experimental, custom_tests, deployment, skip_unchanged) Run the test suite on a single model and collect results.
new(directory, replay) Create a suitable model repository structure from a template.
_model_from_stream(stream, filename)
_test_history(model, sbml_ver, solver, solver_timeout, manager, commit, pytest_args, skip, exclusive, experimental)
history(model, message, rewrite, solver, solver_timeout, location, pytest_args, deployment, commits, skip, exclusive, experimental=None) Re-compute test results for the git branch history.
_setup_gh_repo(github_repository, github_username, note)
_setup_travis_ci(gh_repo_name, auth_token, repo_access_token)
online(note, github_repository, github_username) Upload the repository to GitHub and enable testing on Travis CI.
memote.suite.cli.runner.LOGGER[source]
memote.suite.cli.runner.cli()[source]

Metabolic model testing command line tool.

In its basic invocation memote runs a test suite on a metabolic model. Through various subcommands it can further generate three types of pretty HTML reports (snapshot, diff and history), generate a model repository structure for starting a new project, and recreate the test result history.

memote.suite.cli.runner.run(model, collect, filename, location, ignore_git, pytest_args, exclusive, skip, solver, solver_timeout, experimental, custom_tests, deployment, skip_unchanged)[source]

Run the test suite on a single model and collect results.

MODEL: Path to model file. Can also be supplied via the environment variable MEMOTE_MODEL or configured in ‘setup.cfg’ or ‘memote.ini’.

memote.suite.cli.runner.new(directory, replay)[source]

Create a suitable model repository structure from a template.

By using a cookiecutter template, memote will ask you a couple of questions and set up a new directory structure that will make your life easier. The new directory will be placed in the current directory or respect the given –directory option.

memote.suite.cli.runner._model_from_stream(stream, filename)[source]
memote.suite.cli.runner._test_history(model, sbml_ver, solver, solver_timeout, manager, commit, pytest_args, skip, exclusive, experimental)[source]
memote.suite.cli.runner.history(model, message, rewrite, solver, solver_timeout, location, pytest_args, deployment, commits, skip, exclusive, experimental=None)[source]

Re-compute test results for the git branch history.

MODEL is the path to the model file.

MESSAGE is a commit message in case results were modified or added.

[COMMIT] … It is possible to list out individual commits that should be re-computed or supply a range <oldest commit>..<newest commit>, for example,

memote history model.xml “chore: re-compute history” 6b84d05..cd49c85

There are two distinct modes:

 1. Completely re-compute test results for each commit in the git history.

This should only be necessary when memote is first used with existing model repositories.
  1. By giving memote specific commit hashes, it will re-compute test results for those only. This can also be achieved by supplying a commit range.
memote.suite.cli.runner._setup_gh_repo(github_repository, github_username, note)[source]
memote.suite.cli.runner._setup_travis_ci(gh_repo_name, auth_token, repo_access_token)[source]
memote.suite.cli.runner.online(note, github_repository, github_username)[source]

Upload the repository to GitHub and enable testing on Travis CI.