analytics-mesh
Interfaces and facades that facilitate a common approach to analytics tasks
Getting Started
Please install the requirements:
pip install -r requirements
If you are going to be making modifications to the ipynb notebooks, then be sure to install the pre-commit hook (see below):
pre-commit install
Tests
Tests are currently split into unit and integration tests. As this package integrates with storage systems, the integration tests are typically running against things like Google Cloud Platform.
Tests may be run in the root folder of the repo with:
coverage run -m unittest discover tests && coverage report
If you want to run just the unit tests (and ignore coverage) then,
python -m unittest discover tests/unit
Similarly, for the integration tests.
Pre-Commit Hooks and Notebook Workflow
In this repo we are using the python precommit package (included in requirements.txt file). In order to leverage it in your development workflow, you need to run the following commands (assuming you have already installed your requirements).
pre-commit install
We follow the convention that a version controlled ipynb file is converted to a markdown (md) file to form an ipynb-md pair that are both version controlled. This allows us to code review the markdown files whilst keeping the original ipynb file output intact for easy perusal on the repository manager (Gitlab in our case).
Packaging the Code
The contents of the mesh package are packaged in the build pipeline and submitted to pypi when merged to the master branch. The contents of the demos folder and tests is omitted from the package. See the .gitlab-ci.yml file for the pypi instructions we use, as well as the tests and linting that are performed.
The consequences of this is that you will convert ipynb notebooks to md.
Contributions
Contributions are most welcome. Please submit patches or new features for code review by any of the main contributors:
Please be sure to run the tests prior to submission.
1 maintainer
Metadata
1 maintainer
Release files for analytics-mesh 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| analytics_mesh-1.0.0-py3-none-any.whl | Python 3 | none | any | Details for analytics_mesh-1.0.0-py3-none-any.whl |
Release files / analytics_mesh-1.0.0-py3-none-any.whl
| Download URL | analytics_mesh-1.0.0-py3-none-any.whl |
|---|---|
| Size | 35.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
d24400518c14f57c6b71fdf0f040c694d5967c1dc693e09015586070fd833bca
|
|
BLAKE2b-256 checksum How to use checksums |
3b84f26fc7117725f77aabdccfa19823dc7c596956f72a9ef5bad1abacefcab8
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.10.15
|
Security for analytics-mesh 1.0.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
