Metadata-Version: 2.2
Name: figrid
Version: 0.1.7
Summary: Formats multipanel figures
Home-page: https://github.com/dougollerenshaw/figrid
Author: Doug Ollerenshaw
Author-email: d.ollerenshaw@gmail.com
License: MIT
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: seaborn
Provides-Extra: dev
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: pytest-cov>=4.0.0; extra == "dev"
Requires-Dist: black>=23.0.0; extra == "dev"
Requires-Dist: flake8>=6.0.0; extra == "dev"
Requires-Dist: pre-commit>=3.0.0; extra == "dev"
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: home-page
Dynamic: license
Dynamic: provides-extra
Dynamic: requires-dist
Dynamic: summary

# figrid
A wrapper for the matplotlib gridspec function.  Designed to make it easy to place axes on a pre-defined grid on a figure canvas. For example, maybe you want to lay out axes like this:

<img src="examples/sample_figure_layout.png?raw=true " alt="Example Layout" style="zoom:40%;" />

## try it out in colab
<a href="https://colab.research.google.com/github/dougollerenshaw/figrid/blob/colab_example/figrid_example_notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

## how it works
The fundamental function to use is `place_axes_on_grid`. This will generate an evenly spaced 100x100 grid on the desired figure canvas. You can then specify how much of the figure canvas a given axis (or set of axes) will span.  

## what it's good for
Maybe it's just me, but I've always found matplotlib's `gridspec` function to be confusing. And simple NxM grids with the `subplots` function can be too limiting. This makes it easy to place any number of axes at arbitrary locations on a figure. It's handy for making figures for publication.

## a sample workflow
1) Make some functions to generate the various subplots you want to display on a figure. Those functions should take an axis handle as an input.
2) Define a figure canvas of the desired size.
3) Define your axes, specifying their locations using `figrid.place_axes_on_grid()` (a dictionary is a useful data structure for storing your axis handles).
4) Call your plotting functions with the axes as inputs.
5) Add some axis labels using `figrid.add_labels()` that you can refer to from your figure legend.

## installation:

    pip install figrid

For development installation with testing dependencies:

    pip install -e ".[dev]"

## syntax
`figrid.place_axes_on_grid` takes the following inputs:
* fig - the figure handle on which the axis will be placed
* xspan - a two-element list or tuple defining the left and right edges of the axis, respectively. Numbers should be floats ranging from 0 to 1 and will be rounded to 2 decimal places.
* yspan - a two-element list or tuple defining the top and bottom edges of the axis, respectively. Numbers should be floats ranging from 0 to 1 and will be rounded to 2 decimal places.
* dim - a two-element tuple defining the number of rows/columns of the axis. Default = [1, 1], giving a single axis.
* hspace = a float defining the horizontal space between subplots (if dim is specified)
* vspace = a float defining the vertical space between subplots (if dim is specified)

## sample use:

some imports:

    # import the package as fg
    import figrid as fg

    # import example figure code
    import example_figures

    # import maptlotlib
    import matplotlib.pyplot as plt

define a function to lay out the axes on a figure

    # define function to set up figure and axes
    def make_fig_ax():
        fig = plt.figure(figsize=(11,8.5))
        ax = {
            'panel_A': fg.place_axes_on_grid(fig, xspan=[0.05, 0.3], yspan=[0.05, 0.45]),
            'panel_B': fg.place_axes_on_grid(fig, xspan=[0.4, 1], yspan=[0.05, 0.45], dim=[3, 1], hspace=0.4),
            'panel_C': fg.place_axes_on_grid(fig, xspan=[0.05, 0.4], yspan=[0.57, 1]),
            'panel_D': fg.place_axes_on_grid(fig, xspan=[0.5, 1], yspan=[0.57, 1])
        }
        
        return fig, ax

make the figure

    # call function to make figure and axes
    fig, ax = make_fig_ax()

    # call individual plotting functions, with axes as inputs
    example_figures.heatmap(ax['panel_A'])
    example_figures.sinusoids(ax['panel_B'])
    example_figures.violins(ax['panel_C'])
    example_figures.scatterplot(ax['panel_D'])

add some labels

    labels = [
        {'label_text':'A', 'xpos':0,    'ypos':0.05, 'fontsize':20, 'weight': 'bold', 'ha': 'right', 'va': 'bottom'},
        {'label_text':'B', 'xpos':0.37, 'ypos':0.05, 'fontsize':20, 'weight': 'bold', 'ha': 'right', 'va': 'bottom'},
        {'label_text':'C', 'xpos':0,    'ypos':0.55, 'fontsize':20, 'weight': 'bold', 'ha': 'right', 'va': 'bottom'},
        {'label_text':'D', 'xpos':0.45, 'ypos':0.55, 'fontsize':20, 'weight': 'bold', 'ha': 'right', 'va': 'bottom'},
    ]
    fg.add_labels(fig, labels)

Then we have this:

<img src="examples/sample_figure.png?raw=true " alt="Example Figure" style="zoom:100%;" />

## development and testing

[![Tests](https://github.com/dougollerenshaw/figrid/actions/workflows/tests.yml/badge.svg)](https://github.com/dougollerenshaw/figrid/actions/workflows/tests.yml)

For development, install with testing dependencies:

    pip install -e ".[dev]"

To set up pre-commit hooks for automatic code formatting:

    pre-commit install

This will automatically run black (code formatter) and flake8 (linter) on your commits.

To run the tests:

    pytest

To run tests with coverage reporting:

    pytest --cov=figrid --cov-report=term-missing

To manually format code:

    black .

To manually check code style:

    flake8 .

Tests and code quality checks are automatically run on push and pull request to the main branch using GitHub Actions.

