Skip to main content

BSIC Plotting Library

Project description


Logo

mpl-bsic

Create matplotlib plots in BSIC Style!

Explore the docs »

Report Bug - Request Feature

Contributors Issues License

About the Project

Plot Example

This package allows you to style matplotlib plots using BSIC Style (fonts, colors, logos, etc.) to later use them in BSIC articles. It also provides utility function to handle the formatting of the axis, check the size of the figures, and more!

Read the sections below for an overview of how to install and use the package. For further information, be sure to read the docs!

Table Of Contents

Installation

The package supports all python versions starting from 3.9.0

To install, run

pip install mpl-bsic

Then you can import the functions from the module, for example

from mpl_bsic import apply_bsic_style

Docs and TLDR

Read the docs on this link. All the functions are explained extensively and you can find example code/plots.

WARNING: Be sure to read the docs for apply_bsic_style, and in particular how to make sure the style gets applied. You always have to make sure you call plt.show() (if in a script) even if you only plan to export the plot, since otherwise matplotlib won't run the animations which are required to apply the style to the title. And also read carefully the part about the figsize to use, especially when exporting to use in a Word file.

A brief overview of the functions of the module:

  • apply_bsic_style: applies the BSIC styles to a plot (font families, font sizes).
  • apply_bsic_logo: applies the BSIC logo to the plot. You can specify the size, location and logo type.
  • check_figsize: checks the figsize of your plot, to make sure it will be rendered correctly in MS Word. To learn more about this, look at the documentation
  • format_timeseries_axis: formats the x axis of a timeseries plot. You can specify the time unit (yearly, monthly, daily), the frequency (e.g. a tick every 3M), and the format (e.g. MM/YYYY or MMM YYYY)
  • preprocess_dataframe: preprocesses a dataframe, by setting the index to the date (and converting to datetime) and transforming all the columns to lowercase for easier use in the project
  • plot_trade: WIP

If the matplotlib fonts do not work

Check the full guide on the documentation. Anyway, you need to install Garamond and Gill Sans MT on your system and clear your matplotlib cache.

Contributing

If you have any ideas, features you would like to have implemented, or you find out any bugs within the function, be sure to open an issue and I will work on it as soon as possible. Or you can also fork the repo yourself and make a pull request to the project!

Roadmap

  1. plot trade (as bloomberg with last price and stuff)
  2. plot tables (instead of having to style them using Excel)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mpl_bsic-1.2.2.tar.gz (5.1 MB view details)

Uploaded Source

Built Distribution

mpl_bsic-1.2.2-py3-none-any.whl (5.1 MB view details)

Uploaded Python 3

File details

Details for the file mpl_bsic-1.2.2.tar.gz.

File metadata

  • Download URL: mpl_bsic-1.2.2.tar.gz
  • Upload date:
  • Size: 5.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.12.0

File hashes

Hashes for mpl_bsic-1.2.2.tar.gz
Algorithm Hash digest
SHA256 3f262268319f9f9dc276a6031b9d1d442fba5f24a2130dc407926739ff698a7f
MD5 e01ce650b5ee3efcd20617277bfca785
BLAKE2b-256 dfe4869c6cc3e373c857ee9992b950843cb3ea5b2e5712aa96424eeafbe11c4f

See more details on using hashes here.

Provenance

File details

Details for the file mpl_bsic-1.2.2-py3-none-any.whl.

File metadata

  • Download URL: mpl_bsic-1.2.2-py3-none-any.whl
  • Upload date:
  • Size: 5.1 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.12.0

File hashes

Hashes for mpl_bsic-1.2.2-py3-none-any.whl
Algorithm Hash digest
SHA256 84c97885bc3a7f5bbe42aee888b6a7dfbb3ea4f360d84a784912020156d0d259
MD5 db4513c266614322218b002eb4c05b46
BLAKE2b-256 f16cc5fd98e62ffdcbe8bd499f7d7c7feaa8fe5b7465cbd26d085838ecc506e2

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page