Data Samples Printer

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Documentation: https://ernestum.github.io/data-samples-printer

Source Code: https://github.com/ernestum/data-samples-printer

PyPI: https://pypi.org/project/data-samples-printer/

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Don't be just mean and standard, print histograms as unicode instead!

This project is inspired by sparklines but works for any kind of data samples that can be represented as a histogram.

Did you ever enjoy being graded by a single number? No? Then why do you do it to your data?

Installation

pip install data-samples-printer

Basic Usage

import data_samples_printer as dsp
import numpy as np

s1 = np.random.normal(size=100)
s2 = np.random.normal(size=100, scale=0.2)

# Plain printing
dsp.print(s1)
> ▁   ▃ ▁▃▁  ▃▃▅▇▅█▄▄▄▇▃▃█▄▅▇▄▃▃▁█▅█ ▁▃▃▁▃▁        ▁

# Printing multiple samples aligns their range
dsp.print(s1, s2)
> ▁   ▃ ▁▃▁  ▃▃▅▇▅█▄▄▄▇▃▃█▄▅▇▄▃▃▁█▅█ ▁▃▃▁▃▁        ▁
>                   ▁▂▃█▄▅▃▂▁

# Printing with labels
dsp.print(normal=s1, squeezed=s2)
> ▁   ▃ ▁▃▁  ▃▃▅▇▅█▄▄▄▇▃▃█▄▅▇▄▃▃▁█▅█ ▁▃▃▁▃▁        ▁ normal
>                    ▁▂▃█▄▅▃▂▁                       squeezed

# Pretty printing
dsp.pprint(s1, s2)
> ▂ ▂▁▁ ▁▂▄▃▁▁▂▂▂▄▃▂█▃▃▂▃▂▂▂▁▃▂▄▃▂ ▁▂▁ ▁▁▂       ▁ ▁ 0.00 ±1.00
>                 ▁▂▃▆█▅▆▄▁                          0.04 ±0.20

dsp.mprint(normal=s1, squeezed=s2)
> dist | mean | std | name
> -----|------|-----|-----
> `▕▁ ▂▁▁▁▁▁▃▁ ▂▆▂▁▅▅▅█▂▅▃▅█▃▇▆▂▂▂▂▂▂▅▃▁ ▁     ▁   ▂ ▁▏` | -0.04 | ±0.85 | normal
> `▕                ▁ ▁▄▃█▆█▆▂▂                       ▏` | 0.01 | ±0.19 | squeezed
> `▕-1.93                                         2.41▏` |

renders as:

dist	mean	std	name
▕▁ ▂▁▁▁▁▁▃▁ ▂▆▂▁▅▅▅█▂▅▃▅█▃▇▆▂▂▂▂▂▂▅▃▁ ▁ ▁ ▂ ▁▏	-0.04	±0.85	normal
▕ ▁ ▁▄▃█▆█▆▂▂ ▏	0.01	±0.19	squeezed
▕-1.93 2.41▏

Development

• Clone this repository
• Requirements:
  • Poetry
  • Python 3.7+
• Create a virtual environment and install the dependencies

poetry install

• Activate the virtual environment

poetry shell

Testing

pytest

Documentation

The documentation is automatically generated from the content of the docs directory and from the docstrings of the public signatures of the source code. The documentation is updated and published as a Github project page automatically as part each release.

Releasing

To make a new release:

poetry version <major|minor|patch>  # Update the version number
poetry run kacl-cli release <new release number> --modify --auto-link  # Update the changelog
git add CHANGELOG.md pyproject.toml
git commit -m "Release <new release number>"
git tag <new release number>
git push origin <new release number>

Then create a new release on GitHub with the output of:

poetry run kacl-cli get <new release number>

GitHub releases and publish it. When a release is published, it'll trigger release workflow which creates PyPI release and deploys updated documentation.

Then run

poetry run mkdocs gh-deploy --force

to update the documentation.

Pre-commit

Pre-commit hooks run all the auto-formatters (e.g. black, isort), linters (e.g. mypy, flake8), and other quality checks to make sure the changeset is in good shape before a commit/push happens.

You can install the hooks with (runs for each commit):

pre-commit install

Or if you want them to run only for each push:

pre-commit install -t pre-push

Or if you want e.g. want to run all checks manually for all files:

pre-commit run --all-files

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This project was generated using the wolt-python-package-cookiecutter template.