jmbejara / blank_project

Project Title HERE

About this project

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Quick Start

To quickest way to run code in this repo is to use the following steps. First, you must have the conda
package manager installed (e.g., via Anaconda). However, I recommend using mamba, via [miniforge] (https://github.com/conda-forge/miniforge) as it is faster and more lightweight than conda. Second, you must have TexLive (or another LaTeX distribution) installed on your computer and available in your path. You can do this by downloading and installing it from here (windows and mac installers). Having done these things, open a terminal and navigate to the root directory of the project and create a conda environment using the following command:

conda create -n blank python=3.12
conda activate blank

and then install the dependencies with pip

pip install -r requirements.txt

Finally, you can then run

doit

And that's it!

If you would also like to run the R code included in this project, you can either install R and the required packages manually, or you can use the included environment.yml file. To do this, run

mamba env create -f environment.yml

I'm using mamba here because conda is too slow. Activate the environment. Then, make sure to uncomment out the RMarkdown task from the dodo.py file. Then, run doit as before.

Other commands

You can run the unit test, including doctests, with the following command:

pytest --doctest-modules

You can build the documentation with:

rm ./src/.pytest_cache/README.md 
jupyter-book build -W ./

Use del instead of rm on Windows

General Directory Structure

• The assets folder is used for things like hand-drawn figures or other pictures that were not generated from code. These things cannot be easily recreated if they are deleted.

• The output folder, on the other hand, contains tables and figures that are generated from code. The entire folder should be able to be deleted, because the code can be run again, which would again generate all of the contents.

• I'm using the doit Python module as a task runner. It works like make and the associated Makefiles. To rerun the code, install doit (https://pydoit.org/) and execute the command doit from the src directory. Note that doit is very flexible and can be used to run code commands from the command prompt, thus making it suitable for projects that use scripts written in multiple different programming languages.

• I'm using the .env file as a container for absolute paths that are private to each collaborator in the project. You can also use it for private credentials, if needed. It should not be tracked in Git.

Data and Output Storage

I'll often use a separate folder for storing data. I usually write code that will pull the data and save it to a directory in the data folder called "pulled" to let the reader know that anything in the "pulled" folder could hypothetically be deleted and recreated by rerunning the PyDoit command (the pulls are in the dodo.py file).

I'll usually store manually created data in the "assets" folder if the data is small enough. Because of the risk of manually data getting changed or lost, I prefer to keep it under version control if I can.

Output is stored in the "output" directory. This includes tables, charts, and rendered notebooks. When the output is small enough, I'll keep this under version control. I like this because I can keep track of how tables change as my analysis progresses, for example.

Of course, the data directory and output directory can be kept elsewhere on the machine. To make this easy, I always include the ability to customize these locations by defining the path to these directories in environment variables, which I intend to be defined in the .env file, though they can also simply be defined on the command line or elsewhere. The config.py is reponsible for loading these environment variables and doing some like preprocessing on them. The config.py file is the entry point for all other scripts to these definitions. That is, all code that references these variables and others are loading by importing config.

Dependencies and Virtual Environments

Working with pip requirements

conda allows for a lot of flexibility, but can often be slow. pip, however, is fast for what it does. You can install the requirements for this project using the requirements.txt file specified here. Do this with the following command:

pip install -r requirements.txt

The requirements file can be created like this:

pip list --format=freeze

Working with conda environments

The dependencies used in this environment (along with many other environments commonly used in data science) are stored in the conda environment called blank which is saved in the file called environment.yml. To create the environment from the file (as a prerequisite to loading the environment), use the following command:

conda env create -f environment.yml

Now, to load the environment, use

conda activate blank

Note that an environment file can be created with the following command:

conda env export > environment.yml

However, it's often preferable to create an environment file manually, as was done with the file in this project.

Also, these dependencies are also saved in requirements.txt for those that would rather use pip. Also, GitHub actions work better with pip, so it's nice to also have the dependencies listed here. This file is created with the following command:

pip freeze > requirements.txt

Other helpful conda commands

• Create conda environment from file: conda env create -f environment.yml
• Activate environment for this project: conda activate blank
• Remove conda environment: conda remove --name blank --all
• Create blank conda environment: conda create --name myenv --no-default-packages
• Create blank conda environment with different version of Python: conda create --name myenv --no-default-packages python Note that the addition of "python" will install the most up-to-date version of Python. Without this, it may use the system version of Python, which will likely have some packages installed already.

mamba and conda performance issues

Since conda has so many performance issues, it's recommended to use mamba instead. I recommend installing the miniforge distribution. See here: https://github.com/conda-forge/miniforge