Testing PUDL#

We use Tox to coordinate our software testing and to manage other build and sanity checking tools. Under the hood, it invokes a variety of other collections of command-line tools in predefined combinations that are described in tox.ini. These include software tests defined using pytest, code linters like flake8, documentation generators like Sphinx, and sanity checks defined as git pre-commit hooks. Each of these tools, or sometimes collections of related tools, can be selected at the command line. They can also be run independently without using Tox, but for the sake of simplicitly and standardization, we try to mostly just run them using the predefined settings we have configured in Tox.

The simplest way to test PUDL – which is also how the code is tested automatically by our continuous integration setup – is to just run Tox alone with no arguments. This will typically take 25 minutes to run.

$ tox

Note

If you aren’t familiar with pytest and Tox already, you may want to go peruse their introductory documentation.

Software Tests#

Our pytest based software tests are all stored under the test/ directory in the main repository. They are organized into 3 broad categories, each with its own subdirectory:

  • Software Unit Tests (test/unit/) can be run in seconds and don’t require any external data. They test the basic functionality of various functions and classes, often using minimal inline data structures that are specified in the test modules themselves.

  • Software Integration Tests (test/integration/) test larger collections of functionality including the interactions between different parts of the overall software system and in some cases interactions with external systems requiring network connectivity. The main thing our integration tests do is run the full PUDL data processing pipeline for the most recent year of data. This takes around 15 minutes.

  • Data Validations (test/validate/) sanity check the PUDL outputs generated by the data processing pipeline. This helps us catch issues with the input data as well as more subtle bugs that don’t prevent the code from executing but do have unintended or unexpected impacts on the output data. The data validation requires a fully populated PUDL database and is quite different from the other tests.

Running tests with Tox#

Tox installs the PUDL package in a fresh Python environment, ensuring that the tests only have access to packages which would be installed on a new user’s computer. Tox’s overall behavior is configured with the tox.ini file in the main repository directory. There are several different “test environments” defined to test different aspects of the software or to perform other actions like building the documentation. We’ll go through some of the most common ones below.

Continuous Integration Tests#

Our default tox test environment is ci – that includes all of the tests that will be run in continuous integration using a GitHub Action. You should run these tests before pushing code to the repository or making a pull request. Because it’s the default test environment, it will be run if you call Tox without any arguments:

$ tox

This is equivalent to:

$ tox -e ci

If the PUDL package’s dependencies have been changed (in setup.py) or you recently ran the tests while on another branch of the repository with other dependencies, you may need to tell Tox to recreate the software environment it uses with the -r flag. This behavior is turned on by default for the ci, full, and validate tests since they take a long time to run and the extra time required to recreate the software environment is short by comparison.

Note

You will need to register for an EIA API key to run the integration tests which are included as part of the ci tests. We use data from the EIA API to fill in missing monthly fuel costs in the marginal cost of electricity calculations. Once you have the API key, you’ll need to store it in an environment variable named API_KEY_EIA within the shell where you are running the tests. You may want to add it to your .bashrc or .zshrc so that it’s automatically available to PUDL in the future. There are many tutorials on how to manage environment variables online. Here’s one tutorial from Digital Ocean.

In addition to running the unit and integration tests, the CI test environment lints the code and documentation input files and uses Sphinx to build the documentation. It also generates a test coverage report. Running the full set of CI tests takes 20-25 minutes and requires a fair amount of data. If you don’t already have that data downloaded, it will be downloaded automatically and put in your local datastore

Note

Locally the tests will run using whatever version of Python is part of your pudl-dev conda environment, but we have our CI set up to test on both Python 3.8 and 3.9 in parallel.

Software Unit and Integration Tests#

To run the unit or integration tests on their own, you use the -e flag to choose those test environments explicitly:

$ tox -e unit

or:

$ tox -e integration

Full ETL Tests#

As mentioned above, the CI tests process a single year of data. If you would like to more exhaustively test the ETL process without affecting your existing FERC 1 and PUDL databases, you can use the full test environment which may take close to an hour to run:

$ tox -e full

This will process all years of data for the EIA and FERC datasets and all years of EPA CEMS data for a single state (Idaho). The ETL parameters for this test are defined in test/settings/full-integration-tests.yml

Running Other Commands with Tox#

You can run any of the individual test environments that tox -av lists on their own:

$ tox -av

default environments:
ci               -> Run all continuous integration (CI) checks & generate test coverage.

additional environments:
flake8           -> Run the full suite of flake8 linters on the PUDL codebase.
pre_commit       -> Run git pre-commit hooks not covered by the other linters.
bandit           -> Check the PUDL codebase for common insecure code patterns.
linters          -> Run the pre-commit, flake8, and bandit linters.
doc8             -> Check the documentation input files for syntactical correctness.
docs             -> Remove old docs output and rebuild HTML from scratch with Sphinx
unit             -> Run all the software unit tests.
ferc1_solo       -> Test whether FERC 1 can be loaded into the PUDL database alone.
integration      -> Run all software integration tests and process a full year of data.
validate         -> Run all data validation tests. This requires a complete PUDL DB.
ferc1_schema     -> Verify FERC Form 1 DB schema are compatible for all years.
full_integration -> Run ETL and integration tests for all years and data sources.
full             -> Run all CI checks, but for all years of data.
build            -> Prepare Python source and binary packages for release.
testrelease      -> Do a dry run of Python package release using the PyPI test server.
release          -> Release the PUDL package to the production PyPI server.

Note that not all of them literally run tests. For instance, to lint and build the documentation you can run:

$ tox -e docs

To run all of the code and documentation linters, but not run any of the other tests:

$ tox -e linters

Each of the test environments defined in tox.ini is just a collection of dependencies and commands. To see what they consist of, you can open the file in your text editor. Each section starts with [testenv:xxxxxx] and the section called commands is a list of shell commands that that test environment will run.

Selecting Input Data for Integration Tests#

The software integration tests need a year’s worth of input data to process. By default they will look in your local PUDL datastore to find it. If the data they need isn’t available locally, they will download it from Zenodo and put it in the local datastore.

However, if you’re editing code that affects how the datastore works, you probably don’t want to risk contaminating your working datastore. You can use a disposable temporary datastore instead by having Tox pass the --tmp-data flag in to pytest like this:

$ tox -e integration -- --tmp-data

The floating -- isn’t a typo, it tells Tox that you’re done giving it command line arguments, and that any additional arguments it gets should be passed through to pytest. We’ve configured pytest (through the test/conftest.py configuration file) to be on the lookout for the --tmp-data flag and act accordingly.

See also

Data Validation#

Given the processed outputs of the PUDL ETL pipeline, we have a collection of tests that can be run to verify that the outputs look correct. We run all available data validations before each data release is archived on Zenodo. It is useful to run the data validation tests prior to making a pull request that makes changes to the ETL process or output functions to ensure that the outputs have not been unintentionally affected.

These data validation tests are organized into datasource specific modules under test/validate. Running the full data validation can take as much as an hour, depending on your computer. These tests require a fully populated PUDL database which contains all available FERC and EIA data, as specified by the src/pudl/package_data/settings/etl_full.yml input file. They are run against the “live” SQLite database in your pudl workspace at sqlite/pudl.sqlite. To run the full data validation against an existing database:

$ tox -e validate

The data validation cases that pertain to the contents of the data tables are currently stored as part of the pudl.validate module.

The expected number of records in each output table is stored in the validation test modules under test/validate as pytest parameterizations.

Data Validation Notebooks#

We have a collection of Jupyter Notebooks that run the same functions as the data validation. The notebooks also produce some visualizations of the data to make it easier to understand what’s wrong when validation fails. These notebooks are stored in test/notebooks

Like the data validations, the notebooks will only run successfully when there’s a full PUDL SQLite database available in your PUDL workspace.

Running pytest Directly#

Running tests directly with pytest gives you the ability to run only tests from a particular test module or even a single individual test case. It’s also faster because there’s no testing environment to set up. Instead, it just uses your Python environment which should be the pudl-dev conda environment discussed in Development Setup. This is convenient if you’re debugging something specific or developing new test cases, but it’s not as robust as using Tox.

Running specific tests#

To run the software unit tests with pytest directly (the same set of tests that would be run by tox -e unit):

$ pytest test/unit

To run only the unit tests for the Excel spreadsheet extraction module:

$ pytest test/unit/extract/excel_test.py

To run only the unit tests defined by a single test class within that module:

$ pytest test/unit/extract/excel_test.py::TestGenericExtractor

Custom PUDL pytest flags#

We have defined several custom flags to control pytest’s behavior when running the PUDL tests. They are mostly intended for use internally to specify the behavior we want in the high level Tox test environments.

You can always check to see what custom flags exist by running pytest --help and looking at the custom options section:

custom options:
--live-dbs            Use existing PUDL/FERC1 DBs instead of creating temporary ones.
--tmp-data            Download fresh input data for use with this test run only.
--etl-settings=ETL_SETTINGS
                      Path to a non-standard ETL settings file to use.
--gcs-cache-path=GCS_CACHE_PATH
                      If set, use this GCS path as a datastore cache layer.
--sandbox             Use raw inputs from the Zenodo sandbox server.

The main flexibility that these custom options provide is in selecting where the raw input data comes from and what data the tests should be run against. Being able to specify the tests to run and the data to run them against independently simplifies the test suite and keeps the data and tests very clearly separated.

The --live-dbs option lets you use your existing FERC 1 and PUDL databases instead of building a new database at all. This can be useful if you want to test code that only operates on an existing database, and has nothing to do with the construction of that database. For example, the output routines:

$ pytest --live-dbs test/integration/output_test.py

We also use this option to run the data validations.

Assuming you do want to run the ETL and build new databases as part of the test you’re running, the contents of that database are determined by an ETL settings file. By default, the settings file that’s used is test/settings/integration-test.yml But it’s also possible to use a different input file, generating a different database, and then run some tests against that database.

For example, we test that FERC 1 data can be loaded into a PUDL database all by itself by running the ETL tests with a settings file that includes only A couple of FERC 1 tables for a single year. This is the ferc1_solo Tox test environment:

$ pytest --etl-settings=test/settings/ferc1-solo-test.yml test/integration/etl_test.py

Similarly, we use the test/settings/full-integration-test.yml settings file to specify an exhaustive collection of input data, and then we run a test that checks that the database schemas extracted from all historical FERC 1 databases are compatible with each other. This is the ferc1_schema test:

$ pytest --etl-settings test/settings/full-integration-test.yml test/integration/etl_test.py::test_ferc1_schema

The raw input data that all the tests use is ultimately coming from our archives on Zenodo. However, you can optionally tell the tests to look in a different places for more rapidly accessible caches of that data and to force the download of a fresh copy (especially useful when you are testing the datastore functionality specifically). By default, the tests will use the datastore that’s part of your local PUDL workspace.

For example, to run the ETL portion of the integration tests and download fresh input data to a temporary datastore that’s later deleted automatically:

$ pytest --tmp-data test/integration/etl_test.py