To Prep Debian Environment

First, clone repo and change into cloned repo directory.

Then run:

sudo apt-get install python3 python-is-python3
python -m pip install virtualenv
python -m virtualenv venv
source venv/bin/activate
python -m pip install -r requirements.txt

To Activate the Virtual Python Environment

(keeps package management clean if more imports are needed later)

source venv/bin/activate

To Run the test cases

(typically the descriptions in the puzzles for the day with simple data and answers)

pytest -o log_cli=true -o log_cli_level="INFO" -v

OR, if there are problems in the code, add debug statements with

logger.debug(f"INSERT YOUR TEXT HERE WITH {var} DUMPING")

and upgrade your output level to DEBUG from INFO:

pytest -o log_cli=true -o log_cli_level="DEBUG" -v

Recommendation for Jupyter Notebook

When utilizing the pandas and numpy packages, it was often useful to have a dynamic environment to play around with data outside of the Test Driven Development construct

I excluded the jupyter notebooks from git commits, but generally setup ones per Day where it was useful.

From inside a day's folder, in a dedicated terminal

mkdir -p ./nb
cd ./nb
jupyter notebook

Then inside the notebook in first two cells, copied and ran:

Cell 1:

import os
import sys
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
    sys.path.append(module_path)

Cell 2:

import solve
import importlib
importlib.reload(solve)
from solve import *