Structuring your Project¶
Now that your have a working version of python on your computer, you can start doing research.
One of the key elements of a project is for it to be reproducible by others. Having this in mind when you’re structuring your project will allow others to look at your code, understand it well enough to be able to recreate your results.
This is a short guid on 2 ways to structure your code, without having to do much creating of documets, etc.
Table of Contents
- Cookiecutter and Folder structure
- Data Science - Cookiecutter
- Personal version - Cookiecutter
- Links and Resources
Cookiecutter and Folder structure¶
Cookiecutter is a command-line utility that creates projects from cookiecutters (project templates), e.g. Python package projects, LaTeX documents, etc.
Cookiecutter has been widely used for many projects, and each team and organization can create their own template. For more information, visit the cookiecutter documentation.
As the famous say goes:
Don’t reinvent the wheel!
You can always create your own folder and file structures, and organize your documents the old-fashioned way. The problem with this is that it may vary from project to project, and it will be more difficult to be consistent and effective throught your projects.
For this reason, I rely on
cookiecutter templates to create the
file and folder structure of a project.
There are many different
cookiecutter templates out there, but
after trying to find the best one that suits my needs in research and
programming, I found one that works great! And after some modifications,
I came up with a version of this template.
These two templates are shown in Data Science - Cookiecutter and Personal version - Cookiecutter.
Data Science - Cookiecutter¶
Cookiecutter Data Science is best described as
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
This folder structure allows everyone looking at your code to understand
it right away. It also provides many different functions (as part of a
Makefile) that simplify the workflow of your project.
In a nutshell, this cookiecutter includes:
- A Makefile file with **useful functions.
- Documentation to make your project easily accessible and readable
- And more!
In order to use this template, you follow the documentation in Cookiecutter Data Science.
Personal version - Cookiecutter¶
If you need more than the normal Data Science Cookiecutter template, you can use my version. Some of the differences are:
- It includes and easy-to-use
environment.ymlfile that makes it easy to install dependencies.
- Extra functions in the
- Choice of what kind of documenation to use. One has the option choose from traditional Read The Docs style or the Astropy Sphinx Theme.
You can check how these two styles look like:
Next, you can create your own Project based on this cookiecutter version
Createing your own Project using Cookiecutter¶
The first thing to do is to install cookiecutter
$ pip install cookiecutter
$ conda config --add channels conda-forge $ conda install cookiecutter
To start a new project¶
To start a new project, type the following:
$ cookiecutter https://github.com/vcalderon2009/cookiecutter-data-science
If you want the default project scheme from DrivenData (see above), run:
Depending on what kind of folder structure you want, you might want to choose from the different types.
After running this command, you will be prompted some questions regarding the parameters for the project.
The resulting directory structure¶
The directory structure of your new project looks like this:
├── LICENSE ├── Makefile <- Makefile with commands like `make data` or `make train` ├── README.md <- The top-level README for developers using this project. ├── data │ ├── external <- Data from third party sources. │ ├── interim <- Intermediate data that has been transformed. │ ├── processed <- The final, canonical data sets for modeling. │ └── raw <- The original, immutable data dump. │ ├── docs <- A default Sphinx project; see sphinx-doc.org for details │ ├── models <- Trained and serialized models, model predictions, or model summaries │ ├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering), │ the creator's initials, and a short `-` delimited description, e.g. │ `1.0-jqp-initial-data-exploration`. │ ├── references <- Data dictionaries, manuals, and all other explanatory materials. │ ├── reports <- Generated analysis as HTML, PDF, LaTeX, etc. │ └── figures <- Generated graphics and figures to be used in reporting │ ├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g. │ generated with `pip freeze > requirements.txt` │ ├── environment.yml <- The Anaconda environment requirements file for reproducing the analysis environment. │ This file is used by Anaconda to create the project environment. │ ├── src <- Source code for use in this project. │ ├── __init__.py <- Makes src a Python module │ │ │ ├── data <- Scripts to download or generate data │ │ │ │ │ └── make_dataset.py │ │ │ ├── features <- Scripts to turn raw data into features for modeling │ │ └── build_features.py │ │ │ ├── models <- Scripts to train models and then use trained models to make │ │ │ predictions │ │ ├── predict_model.py │ │ └── train_model.py │ │ │ └── visualization <- Scripts to create exploratory and results oriented visualizations │ └── visualize.py │ └── tox.ini <- tox file with settings for running tox; see tox.testrun.org
Editing your environment¶
Now that you have a working proect from cookiecutter, you can start by editing the environment of your project.
If you downloaded my version of cookiecutter, you should be able to edit
environment.yml file. This file states which packages
need to be installed by Anaconda and
pip in order to run the
scripts of the package.
environment.yml file looks like the following:
name: name_of_environment channels: - defaults dependencies: - python>=3.6 - ipython - anaconda - astropy - h5py - numpy - pandas - scipy - seaborn - pip - pip: - GitPython - progressbar2
You can edit the
environment.yml file to include/exclude packages
needed by your project.
After having edited the list of packages needed by your project, you can execute the command
$ make environment
to create the environment.
If you have done this step before, and you want to update the environment, you need to run
$ make update_environment
Adding your Project repository to Github¶
If you follow the instructions from above, you should have
- Downloaded the repository
- Created your own project with the desired file and folder structure
- Created your working environment for you project
The next step is to add it to Github and make it accessible.
To do this, your should do the following:
- Create a Github repository with the same name as the repository.
git add remote origin email@example.com:<username>/<project_name>.git. In here you need to replace
project_namewith your details.
git push origin master- This will push your project to Github.
To check that you did this correctly, type
git remote -v
and you should get something that looks like this:
origin https://github.com/<username>/<project_name>.git (fetch) origin https://github.com/<username>/<project_name>.git (push)
project_name pertain to your repository on
Now all of the files are online on Github, and should be ready to integrate them with Read The Docs.
Documentation for your new project¶
Now that you have both a working local and online copy of your code, the next step is to create the documentation for the project.
For this, you can easily use Read The Docs (RTD).
You need to do the following:
- Create an account on “Read the Docs”
- Go to your
- From there, you should import the repository manually (it’s easier).
Import a Projectand follow the instructions.
- You should add the project with the same name as the Github Repo if
possible. Otherwise, you might need to change the links to the badges
README.mdfiles in the project, among others.
- Make sure that the repository was correctly built by looking at the
Buildsand see that it compiled correctly. If not, it should tell you if there was an error and what the error was.
- Now you go and change the documentation depending on the project’s needs.
Continuous Integration for your Project¶
Continuous integration deals with testing your code for possible errors, and making sure that everything is working as expected. Depending on your project’s needs.
This template includes a
.travis.yml, which the files used by
Travis CI. Travis CI is a Continuous integration
platform for testing your code, and checking the functionality of your
More to come!
Links and Resources¶
For more information on, you can take a look at Code Structure for links and resources on how to structure your code and more.