Pattern-recognition

The command-line interface (CLI) that provides a set of commands to interact with the server and recognize the pattern.

• Getting started
• Usage
  • Service
• Development
  • Docker
• Production

Getting started

Blank.

Usage

Service

Get the version of the package — pattern_recognition --version:

$ pattern_recognition --version
pattern_recognition, version 0.1.0

Get all possible package's commands — pattern_recognition --help:

$ pattern_recognition --help
Usage: pattern_recognition [OPTIONS] COMMAND [ARGS]...
  Command line interface for PyPi version checking.
Options:
  --version  Show the version and exit.
  --help     Show this message and exit.
...

Development

Requirements

• Docker — https://www.docker.com. Install it with the following reference.

Docker

Clone the project and move to project folder:

$ git clone https://github.com/Alladin9393/Pattern-recognition && cd Pattern-recognition

If you already worked with the project, you can clean it's container and images with the following command:

$ docker rm pattern-recognition -f || true && docker rmi pattern-recognition -f || true

Run the Docker container with the project source code in the background mode:

$ docker build -t pattern-recognition . -f Dockerfile.development
$ docker run -d --network host -v $PWD:/pattern-recognition --name pattern-recognition pattern-recognition

Enter the container bash:

$ docker exec -it pattern-recognition bash

And now being in the container, you can develop the project. For instance, run tests and linters:

$ coverage run -m pytest -vv tests
$ coverage report -m && coverage xml
$ flake8 cli && flake8 tests/
$ bash <(curl -s https://linters.io/isort-diff) cli tests

When you have developed new functionality, check it with the following command. This command creates the Python package from source code instead of installing it from the PyPi.

$ pip3 uninstall -y pattern-recognition && rm -rf dist/ pattern_recognition_cli.egg-info && \
      python3 setup.py sdist && pip3 install dist/*.tar.gz

So after this command, you are free to execute the command line interface as if you installed in through pip3 install:

$ pattern_recognition --version

With the commands above you could test your features as if user will use it on own.

$ docker rm $(docker ps -a -q) -f
$ docker rmi $(docker images -q) -f

Production

To build the package and upload it to PypI to be accessible through pip, use the following commands. Twine requires the username and password of the account package is going to be uploaded to.

$ python3 setup.py sdist
$ twine upload dist/*