https://github.com/tidepool-org/data-analytics
Home for code we use to analyze data for the Tidepool Big Data Donation project and similar.
https://github.com/tidepool-org/data-analytics
Last synced: 4 months ago
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Home for code we use to analyze data for the Tidepool Big Data Donation project and similar.
- Host: GitHub
- URL: https://github.com/tidepool-org/data-analytics
- Owner: tidepool-org
- License: bsd-2-clause
- Created: 2017-10-20T20:04:46.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-03-29T20:04:37.000Z (about 3 years ago)
- Last Synced: 2024-05-01T11:40:27.566Z (12 months ago)
- Language: Python
- Size: 42 MB
- Stars: 9
- Watchers: 17
- Forks: 14
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# data-analytics
Welcome to the Tidepool Data Analytics Repository. This is the home
for the code we use to download, clean, and analyze data for the Tidepool
Big Data Donation project.
## About our use of Python & R
We use the [Anaconda](https://www.anaconda.com/) distribution of Python & R.
You are welcome to install the full Anaconda installer, but will only need
Miniconda to get started.
## Getting started
### Project Setup
1. Install [Miniconda](https://conda.io/miniconda.html) for your platform.
1. In a terminal, navigate to the data-analytics directory where the environment.yml
is located.
1. Run `conda env create`. This will download all of the package dependencies
and install them in a virtual environment named tidepool-analytics. PLEASE NOTE: this
may take close to 30 minutes to complete.
## To list the Virtual Environments
Run `conda env list`
## To use the Virtual Environment
In Bash run `source activate tidepool-analytics`, or in the Anaconda Prompt
run `conda activate tidepool-analytics` to start the environment.
Run `deactivate` to stop the environment.
## Testing
This project uses the testing framework named pyTest. https://docs.pytest.org/en/latest/
After following the project setup instructions, including creating and activating the
virtual environment, you can simply run your tests within Bash
``` bash
# Run tests via
pytest
```
## Running Tests with Test Coverage
This project uses pytest-cov (https://pytest-cov.readthedocs.io/en/latest/) to run test and produce code
test coverage.
To execute a basic test coverage report, run the following from within the virtual environment created during project setup
. This will give the output directly in the Terminal.
``` bash
# Run tests via
pytest --cov
```
To execute a detailed test coverage report, run the following command from within the virtual environment created during
the project setup.
This will create an htmlcov directory containing an index.html page with coverage details.
``` bash
# Run tests via
pytest --cov --cov-report html
```