This package is meant to house common tools used in experiments design and analysis. There is little "novel" functionality here, but it is hopefully packaged in a way that is convenient and useful for users.

I began this package for two reasons. First, I was dissatisfied with the existing packages available for experimentation in python. They seemed like collections of random tools rather than a cohesive set of utilities that work together in harmony to a united purpose. Second, I used this as an opportunity to refresh my understanding of various statistical tools and methods.

James Montgomery - Initial Work - jamesmontgomery.us

This project is licensed under the MIT License - see the LICENSE.md file for details

Looking for a name for this package I tried looking back into the history of experimentation. I was tempted to name the package after King Nebuchadnezzar in reference to the "legumes and water" anecdote from the book of Daniel. This is often considered one of the earliest controlled "trials".

However, some of the first modern controlled trials were conducted by Dr. James Lind. There are many scatter references to trials throughout history, but Lind represented the start of the modern era of controlled trials and their integration into the scientific method. Hence I named the package after Lind. If you have a chance, I recommend taking an afternoon and reading about the work Lind did to fight the disease Scurvy.

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

For a local installation, first git clone this repository. Then follow these instructions:

pip install .

To install from pypi:

pip install lind

To install the package with test dependencies add [tests] to the install command:

pip install lind[tests]

To install with test dependencies and R backends:

pip install -U "lind[tests, r_backends]"

Some functionality requires pre-computed designs available as static files. To install with static file support:

pip install -U "lind[tests, r_backends, static_designs]"

Many of the best experiment design packages are written in R due to the language's popularity in academia. However, R is not always a convenient language to work with (especially for industry practitioners). If you install lind with the r_backends extra requirement, you will get access to additional functionality drawing from popular R experimental design package. The default installation relies only on python native code.

Warning: We have chosen respected and reputable R packages to use as our backend where R code is used. However, code quality and accuracy of backend R code is not tested in this package. Please see the documentation for those packages to learn more about them. R package name are documented in the appropriate module docstrings.

TODO

Testing is an important part of creating maintainable, production grade code. Below are instructions for running unit and style tests as well as installing the necessary testing packages. Tests have intentionally been separated from the installable pypi package for a variety of reasons.

Make sure you have the required testing packages:

pip install -r requirements_test.txt

To install the project with test dependencies see the install section.

We use the pytest framework for unit testing. Test preset args are defined in pytest.ini.

pytest

We aspire to no lower than 80% code coverage for unit tests.

Having neat and legible code is important. Having documentation is also important. We use pylint as our style guide framework. Many of our naming conventions follow directly from the literary sources they come from. This makes it easier to read the mathematical equations and see how they translate into the code. This sometimes forces us to break pep8 conventions for naming. Linting presets are defined in pylintrc.

pylint lind

We aspire to no lower than an 8.0 / 10.0 style score when linting.

Here are some basic guidelines for contributing.

This repository doesn't use a complicated branching strategy. Simply create a feature branch off of master. When the feature is ready to be integrated with master, submit a pull request. A pull request will re quire at least one peer review and approval from the repository owner.

Please stick to pep8 standards when for your code. Use numpy style docstrings.

Please use pytest as your testing suite. You code should have >= 80% coverage.

Updating the documentation is simple. First, let auto-docs check for updates to the package structure.

cd docs
make html

A big thanks to Mack Sweeney, Tom Caputo, and Matt Van Adlesberg, each of which has put up with my many questions about experimental design and analysis. A special thanks to Mack Sweeney who continues to challenge me to become a better software engineer.

1. Install R in docker containers

Many of the best packages for experimental design are written in R. The link below is a comprehensive survey of useful DOE (Design of Experiments) packages in R: LINK.