daisypy-vis 0.3.0

Visualization module for Daisy log files (dlf)

daisypy-vis

Visualisation library and tools for Daisy model output

See doc for examples.

Installation

The visualization tools are available from pypi

pip install daisypy-vis

Development

Checkout repository

git clone [e-mail]:daisy-model/daisypy-vis.git
cd daisypy-vis

Install package as editable

pip install -e ".[all]"

pylint

Use pyproject.toml for package-wide settings, e.g. ignore-trailing-whitespace.

pylint daisypy/vis
pylint doc

Tests

See pyproject.tomlfor configuration.

To install test dependencies, use either the [all] or the [test] target, e.g.

pip install ".[test]"

To run tests

pytest

Use pytest-mpl to compare images. Generate baselines images by running

pytest --mpl-generate-path=test-data/baseline

and inspect the output...

Note that image comparison tests can fail between different version of matplotlib and freetype. You can force test against images generated with a specific version of matplotlib with

pytest --mpl-baseline-path=test-data/baseline/matplotlib-<matplotlib-version-number>

If no baseline images are available for a specific version, you can generate with

pytest --mpl-generate-path=test-data/baseline/matplotlib-<matplotlib-version-number>

and compare manually.

Baseline for animations are generated by running the tests, this will produce a new set of images in test-data/tmp. Inspect and move to test-data/baseline when happy. In general, text should be removed from to avoid tests failing due to slight variations in font rendering. See for example render_and_compare_animation in daisy_vis/animate/test/test_animate_depth_timeseries.py

Coverage

See pyproject.tomlfor configuration. To generate coverage report

coverage run

To inspect coverage report

coverage report

Tests should as a minimum have 100% code coverage. When applicable, fixed tests should cover boundary conditions and randomized tests should sample the full parameter space.