Home

Awesome

Travis CI Code Coverage Documentation Status

<img width="200" alt="logo" src="docs/source/logo.png"/>

This package is a plugin for the pytest unit testing framework that enables testing of workflows written in Workflow Description Language.

Dependencies

Other python dependencies are installed when you install the library.

Installation

Install from PyPI

$ pip install pytest-wdl

Install from source

You can to clone the repository and install:

$ make install

Or use pip to install from github:

$ pip install git+https://github.com/elilillyco/pytest-wdl.git

Install optional dependencies

Some optional features of pytest-wdl have additional dependencies that are loaded on-demand.

The plugins that have extra dependencies are:

To install a plugin's dependencies:

$ pip install pytest-wdl[<plugin>]

To do this locally, you can clone the repo and run:

$ pip install -e .[<data_type>]

To install pytest-wdl and all extras dependencies:

$ pip install pytest-wdl[all]

Usage

The pytest-wdl plugin provides a set of fixtures for use with pytest. Here is a quick example that tests the following workflow.

# variant_caller.wdl
version 1.0

import "variant_caller.wdl"

struct Index {
  File fasta
  String organism
}

workflow call_variants {
  input {
    File bam
    File bai
    Index index
  }
  
  call variant_caller.variant_caller {
    input:
      bam=bam,
      bai=bai,
      index=index
  }

  output {
    File vcf = variant_caller.vcf
  }
}

Inputs and expected outputs are defined in a test_data.json file in the same directory as your test script:

{
  "bam": {
    "url": "http://example.com/my.bam"
  },
  "bai": {
    "url": "http://example.com/my.bam.bai"
  },
  "index_fa": {
    "name": "chr22.fasta"
  },
  "vcf": {
    "url": "http://example.com/expected.vcf.gz",
    "type": "vcf",
    "allowed_diff_lines": 2
  }
}

You can write the test code in Python, or - in most cases - you can define the test in a JSON or YAML file instead. The following Python and JSON code define exactly the same test. This test will cause the workflow to be run with the specified inputs, and the outputs will be compared to the specified expected outputs.

# test_variant_caller.py
def test_variant_caller(workflow_data, workflow_runner):
    inputs = workflow_data.get_dict("bam", "bai")
    inputs["index"] = {
        "fasta": workflow_data["index_fa"],
        "organism": "human"
    }
    expected = workflow_data.get_dict("vcf")
    workflow_runner(
        "variant_caller.wdl",
        inputs,
        expected
    )
# test_variant_caller.json
{
  "tests": [
    {
      "name": "test_variant_caller",
      "wdl": "variant_caller.wdl",
      "inputs": {
        "bam": "bam",
        "bai": "bai",
        "index": {
          "fasta": "index_fa",
          "organism": "human"
        }
      },
      "outputs": {
        "vcf": "vcf"
      }
    }
  ]
}

For details, read the docs.

Configuration

You can get started with pytest-wdl with no configuration required. However, some advanced features can be configured via environment variables, fixture functions, and/or a config file. To get started, copy one of the following example config files to $HOME/.pytest_wdl_config.json and modify as necessary:

See the manual for more details on configuring pytest-wdl.

Contributing

If you would like to contribute code to pytest-wdl, please fork the repository and submit your changes via pull request.

To get started developing pytest-wdl, first install all the development requirements:

$ git clone https://github.com/EliLillyCo/pytest-wdl.git
$ make install_development_requirements

To run the full build and unit tests, run:

$ make

Support

pytest-wdl is not an official product of Eli Lilly or DNAnexus. Please do not contact these companies (or any employees thereof) for support. To report a bug or feature request, please open an issue in the issue tracker.