snickerdoodle-codes / cli-resolve

Python CLI app to keep track of habits and make progress on New Year's resolutions

Resolve CLI

Resolve is an open-source CLI app for people who make New Year's resolutions¹ and intend to keep them. I decided to develop this tool after six years of using spreadsheets to track habits that I wanted to adopt.

You're welcome to fork and clone this repo to your heart's content. If you've found value in my work, please consider buying me a coffee.

Getting Started

1. Install pipenv for package management if you don't already have it (instructions)
2. Clone this repo to your local machine and navigate into the cli-resolve directory
3. Install all dependencies: pipenv install
4. Run the program: pipenv run python cli_resolve

Features

Core Features

Add and Log Resolutions

Keep track of your progress on existing resolutions and add new resolutions.

Graph Resolutions

Generate heatmaps from your data across multiple years and resolutions. Heatmaps are temporarily stored as PDFs in data/exports and saved over the next time you graph.

You can overlay event labels on your graph, for example, to mark days when you were traveling (see how it's stored).

You can also generate minimaps of individual resolutions.

Export Data

Export your data (stored locally as JSON) as a CSV file. Exported CSVs reside in data/exports).

Standalone Scripts

Convert Legacy Data for Graphing

If you used to keep track of your resolutions in a spreadsheet (or you want to keep using a spreadsheet) but now want to import and visualize them in Resolve, you can use convert_legacy_resolutions.py (location).

Instructions

1. Save legacy spreadsheet as a CSV file under data/legacy (here's my actual data from 2022). In order for the conversion script to work, your data must meet the following assumptions:
   • Must have a date field under the column header of "Date" or "date"
   • Date field must be in MM/DD/YYYY format
   • Must contain columns with headers that refer to resolutions (such as "exercise")
   • Each row must correspond to a unique date, as well as contain data pertaining to whether and how a resolution was met on that date
2. Run python convert_legacy_resolutions.py {filename}.csv from the scripts folder. The output is a cleaned CSV saved to data/cleaned which can then be used for graphing.
3. Run the CLI tool and export graph from file.

Backpopulate App with Legacy Data

You may want to import some or all of your legacy data into the app if you are ditching spreadsheets altogether or wish to generate graphs spanning multiple years.

Instructions

1. Follow the steps above to Convert Legacy Data for Graphing. This will generate a clean and standardized CSV in the data/cleaned folder, which can then be used to backpopulate the application data stored locally in data/resolutions.json.
2. Run python backpopulate_data.py {filename}.csv in the scripts folder and follow the instructions.

Imaginary FAQs

Is it really that hard to use a spreadsheet?

No, but sometimes you just gotta let a gal overengineer.

Also, you can schedule a cron job to run this program or host it in the cloud to automatically run once a day.

Shouldn't this really be a web/desktop/mobile app?

It would be useful to have features X, Y, and Z.

Thing A, B, and C could do with a bit of refactoring. / Here be bugs.

Probably, possibly, and most definitely!

I do have it somewhere on my backlog to turn this into a more accessible GUI app (with additional features), but I'm just one person and my backlog never shrinks. You're welcome to contribute, though!

How do I get in touch with you?

Feel free to shoot me an email if you have any questions or are interested in collaborating.

Footnotes

1. Or, indeed, at any time of the year. ↩