├── .flake8 ├── .github └── workflows │ └── tests.yml ├── .gitignore ├── .pre-commit-config.yaml ├── LICENSE ├── MANIFEST.in ├── README.md ├── examples ├── sample_figure.png └── sample_figure_layout.png ├── figrid ├── __init__.py └── example_figures │ └── __init__.py ├── figrid_example_notebook.ipynb ├── setup.cfg ├── setup.py └── tests ├── __init__.py └── test_figrid.py /.flake8: -------------------------------------------------------------------------------- 1 | [flake8] 2 | # Match black's default line length 3 | max-line-length = 88 4 | # See https://github.com/PyCQA/pycodestyle/issues/373 5 | extend-ignore = E203 6 | exclude = .git,__pycache__,build,dist 7 | per-file-ignores = 8 | # Allow unused imports in __init__.py 9 | __init__.py: F401 -------------------------------------------------------------------------------- /.github/workflows/tests.yml: -------------------------------------------------------------------------------- 1 | name: Tests 2 | 3 | on: 4 | push: 5 | branches: [ main ] 6 | pull_request: 7 | branches: [ main ] 8 | 9 | jobs: 10 | lint: 11 | runs-on: ubuntu-latest 12 | steps: 13 | - uses: actions/checkout@v3 14 | - name: Set up Python 15 | uses: actions/setup-python@v4 16 | with: 17 | python-version: "3.13" 18 | - name: Install dependencies 19 | run: | 20 | python -m pip install --upgrade pip 21 | pip install -e ".[dev]" 22 | - name: Check formatting with black 23 | run: | 24 | black --check . 25 | - name: Lint with flake8 26 | run: | 27 | flake8 . 28 | 29 | test: 30 | needs: lint 31 | runs-on: ubuntu-latest 32 | strategy: 33 | matrix: 34 | python-version: ["3.8", "3.9", "3.10", "3.11", "3.12", "3.13"] 35 | steps: 36 | - uses: actions/checkout@v3 37 | - name: Set up Python ${{ matrix.python-version }} 38 | uses: actions/setup-python@v4 39 | with: 40 | python-version: ${{ matrix.python-version }} 41 | - name: Install dependencies 42 | run: | 43 | python -m pip install --upgrade pip 44 | pip install -e ".[dev]" 45 | - name: Run tests with coverage 46 | run: | 47 | pytest --cov=figrid --cov-report=term-missing -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Python 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | *.so 6 | .Python 7 | build/ 8 | develop-eggs/ 9 | dist/ 10 | downloads/ 11 | eggs/ 12 | .eggs/ 13 | lib/ 14 | lib64/ 15 | parts/ 16 | sdist/ 17 | var/ 18 | wheels/ 19 | *.egg-info/ 20 | .installed.cfg 21 | *.egg 22 | 23 | # Testing 24 | .coverage 25 | .coverage.* 26 | .pytest_cache/ 27 | htmlcov/ 28 | coverage.xml 29 | 30 | # IDE 31 | .idea/ 32 | .vscode/ 33 | *.swp 34 | .DS_Store 35 | 36 | # Byte-compiled / optimized / DLL files 37 | __pycache__/ 38 | *.py[cod] 39 | *$py.class 40 | 41 | # visual studio code 42 | .vscode 43 | 44 | # dist 45 | /dist 46 | 47 | # notebook checkpoints 48 | *checkpoint* -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | repos: 2 | - repo: https://github.com/psf/black 3 | rev: 23.12.1 4 | hooks: 5 | - id: black 6 | language_version: python3 7 | - repo: https://github.com/pycqa/flake8 8 | rev: 6.1.0 9 | hooks: 10 | - id: flake8 -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Doug Ollerenshaw 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | recursive-include figrid *.py 2 | include README.md LICENSE -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # figrid 2 | 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: 3 | 4 | Example Layout 5 | 6 | ## try it out in colab 7 | Open In Colab 8 | 9 | ## how it works 10 | 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. 11 | 12 | ## what it's good for 13 | 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. 14 | 15 | ## a sample workflow 16 | 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. 17 | 2) Define a figure canvas of the desired size. 18 | 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). 19 | 4) Call your plotting functions with the axes as inputs. 20 | 5) Add some axis labels using `figrid.add_labels()` that you can refer to from your figure legend. 21 | 22 | ## installation: 23 | 24 | pip install figrid 25 | 26 | For development installation with testing dependencies: 27 | 28 | pip install -e ".[dev]" 29 | 30 | ## syntax 31 | `figrid.place_axes_on_grid` takes the following inputs: 32 | * fig - the figure handle on which the axis will be placed 33 | * 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. 34 | * 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. 35 | * dim - a two-element tuple defining the number of rows/columns of the axis. Default = [1, 1], giving a single axis. 36 | * hspace = a float defining the horizontal space between subplots (if dim is specified) 37 | * vspace = a float defining the vertical space between subplots (if dim is specified) 38 | 39 | ## sample use: 40 | 41 | some imports: 42 | 43 | # import the package as fg 44 | import figrid as fg 45 | 46 | # import example figure code 47 | import example_figures 48 | 49 | # import maptlotlib 50 | import matplotlib.pyplot as plt 51 | 52 | define a function to lay out the axes on a figure 53 | 54 | # define function to set up figure and axes 55 | def make_fig_ax(): 56 | fig = plt.figure(figsize=(11,8.5)) 57 | ax = { 58 | 'panel_A': fg.place_axes_on_grid(fig, xspan=[0.05, 0.3], yspan=[0.05, 0.45]), 59 | 'panel_B': fg.place_axes_on_grid(fig, xspan=[0.4, 1], yspan=[0.05, 0.45], dim=[3, 1], hspace=0.4), 60 | 'panel_C': fg.place_axes_on_grid(fig, xspan=[0.05, 0.4], yspan=[0.57, 1]), 61 | 'panel_D': fg.place_axes_on_grid(fig, xspan=[0.5, 1], yspan=[0.57, 1]) 62 | } 63 | 64 | return fig, ax 65 | 66 | make the figure 67 | 68 | # call function to make figure and axes 69 | fig, ax = make_fig_ax() 70 | 71 | # call individual plotting functions, with axes as inputs 72 | example_figures.heatmap(ax['panel_A']) 73 | example_figures.sinusoids(ax['panel_B']) 74 | example_figures.violins(ax['panel_C']) 75 | example_figures.scatterplot(ax['panel_D']) 76 | 77 | add some labels 78 | 79 | labels = [ 80 | {'label_text':'A', 'xpos':0, 'ypos':0.05, 'fontsize':20, 'weight': 'bold', 'ha': 'right', 'va': 'bottom'}, 81 | {'label_text':'B', 'xpos':0.37, 'ypos':0.05, 'fontsize':20, 'weight': 'bold', 'ha': 'right', 'va': 'bottom'}, 82 | {'label_text':'C', 'xpos':0, 'ypos':0.55, 'fontsize':20, 'weight': 'bold', 'ha': 'right', 'va': 'bottom'}, 83 | {'label_text':'D', 'xpos':0.45, 'ypos':0.55, 'fontsize':20, 'weight': 'bold', 'ha': 'right', 'va': 'bottom'}, 84 | ] 85 | fg.add_labels(fig, labels) 86 | 87 | Then we have this: 88 | 89 | Example Figure 90 | 91 | ## development and testing 92 | 93 | [![Tests](https://github.com/dougollerenshaw/figrid/actions/workflows/tests.yml/badge.svg)](https://github.com/dougollerenshaw/figrid/actions/workflows/tests.yml) 94 | 95 | For development, install with testing dependencies: 96 | 97 | pip install -e ".[dev]" 98 | 99 | To set up pre-commit hooks for automatic code formatting: 100 | 101 | pre-commit install 102 | 103 | This will automatically run black (code formatter) and flake8 (linter) on your commits. 104 | 105 | To run the tests: 106 | 107 | pytest 108 | 109 | To run tests with coverage reporting: 110 | 111 | pytest --cov=figrid --cov-report=term-missing 112 | 113 | To manually format code: 114 | 115 | black . 116 | 117 | To manually check code style: 118 | 119 | flake8 . 120 | 121 | Tests and code quality checks are automatically run on push and pull request to the main branch using GitHub Actions. 122 | 123 | -------------------------------------------------------------------------------- /examples/sample_figure.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dougollerenshaw/figrid/d781c7ff14feb18ae0ca0c322ac03aead9aab266/examples/sample_figure.png -------------------------------------------------------------------------------- /examples/sample_figure_layout.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dougollerenshaw/figrid/d781c7ff14feb18ae0ca0c322ac03aead9aab266/examples/sample_figure_layout.png -------------------------------------------------------------------------------- /figrid/__init__.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import matplotlib.pyplot as plt 3 | import matplotlib.gridspec as gridspec 4 | 5 | __version__ = "0.1.7" 6 | 7 | 8 | def place_axes_on_grid( 9 | fig, 10 | dim=[1, 1], 11 | xspan=[0, 1], 12 | yspan=[0, 1], 13 | wspace=None, 14 | hspace=None, 15 | sharex=False, 16 | sharey=False, 17 | frameon=True, 18 | ): 19 | """ 20 | Takes a figure with a gridspec defined and places an array of sub-axes on a portion 21 | of the gridspec. 22 | 23 | Takes as arguments: 24 | fig: figure handle - required 25 | dim: number of rows and columns in the subaxes - defaults to 1x1 26 | xspan: fraction of figure that the subaxes subtends in the x-direction 27 | (0 = left edge, 1 = right edge) 28 | yspan: fraction of figure that the subaxes subtends in the y-direction 29 | (0 = top edge, 1 = bottom edge) 30 | wspace and hspace: white space between subaxes in vertical and horizontal 31 | directions, respectively 32 | returns: 33 | subaxes handles 34 | """ 35 | 36 | outer_grid = gridspec.GridSpec(100, 100) 37 | inner_grid = gridspec.GridSpecFromSubplotSpec( 38 | dim[0], 39 | dim[1], 40 | subplot_spec=outer_grid[ 41 | int(100 * yspan[0]) : int(100 * yspan[1]), 42 | int(100 * xspan[0]) : int(100 * xspan[1]), 43 | ], 44 | wspace=wspace, 45 | hspace=hspace, 46 | ) 47 | 48 | # NOTE: A cleaner way to do this is with list comprehension: 49 | # inner_ax = [[0 for ii in range(dim[1])] for ii in range(dim[0])] 50 | inner_ax = dim[0] * [ 51 | dim[1] * [fig] 52 | ] # fill with figure objects to prevent later errors 53 | inner_ax = np.array(inner_ax) 54 | idx = 0 55 | for row in range(dim[0]): 56 | for col in range(dim[1]): 57 | if row > 0 and sharex: 58 | share_x_with = inner_ax[0][col] 59 | else: 60 | share_x_with = None 61 | 62 | if col > 0 and sharey: 63 | share_y_with = inner_ax[row][0] 64 | else: 65 | share_y_with = None 66 | 67 | inner_ax[row][col] = plt.Subplot( 68 | fig, 69 | inner_grid[idx], 70 | sharex=share_x_with, 71 | sharey=share_y_with, 72 | frameon=frameon, 73 | ) 74 | 75 | if row == dim[0] - 1 and sharex: 76 | # For shared x-axes, only show tick labels on the bottom subplot 77 | inner_ax[row][col].xaxis.set_ticks_position("bottom") 78 | elif row < dim[0] and sharex: 79 | # Hide tick labels (but keep ticks) on all but the bottom subplot 80 | plt.setp(inner_ax[row][col].get_xticklabels(), visible=False) 81 | 82 | if col == 0 and sharey: 83 | # For shared y-axes, only show tick labels on the leftmost subplot 84 | inner_ax[row][col].yaxis.set_ticks_position("left") 85 | elif col > 0 and sharey: 86 | # Hide tick labels (but keep ticks) on all but the leftmost subplot 87 | plt.setp(inner_ax[row][col].get_yticklabels(), visible=False) 88 | 89 | fig.add_subplot(inner_ax[row, col]) 90 | idx += 1 91 | 92 | inner_ax = np.array(inner_ax).squeeze().tolist() # remove redundant dimension 93 | return inner_ax 94 | 95 | 96 | def add_label(fig, label_text, xpos, ypos, **kwargs): 97 | """ 98 | add a single label to a figure canvas using the place_axes_on_grid infrastructure 99 | inputs: 100 | fig: figure handle 101 | label_text : text of label, 102 | xpos, ypos: floats from 0 to 1 defining where on the canvas the label should be 103 | kwargs: additional keyword arguments for matplotlib text() 104 | """ 105 | label_axis = place_axes_on_grid( 106 | fig, 107 | xspan=[xpos, xpos + 0.01], 108 | yspan=[ypos, ypos + 0.01], 109 | ) 110 | label_axis.text(0, 0, label_text, **kwargs) 111 | label_axis.axis("off") 112 | 113 | 114 | def add_labels(fig, labels): 115 | """ 116 | Add multiple labels to a figure canvas using the place_axes_on_grid infrastructure. 117 | 118 | inputs: 119 | fig: figure handle 120 | labels: a list of dictionaries with the following key/value pairs: 121 | * label_text (required): text of label 122 | * xpos (required): float from 0 to 1 defining horizontal position 123 | * ypos (required): float from 0 to 1 defining vertical position 124 | * any additional keyword arguments that can be passed to the matplotlib 125 | text function (e.g., fontsize, weight, etc) 126 | """ 127 | for label in labels: 128 | add_label(fig, **label) 129 | 130 | 131 | def scalebar( 132 | axis, 133 | x_pos, 134 | y_pos, 135 | x_length=None, 136 | y_length=None, 137 | x_text=None, 138 | y_text=None, 139 | x_buffer=0.25, 140 | y_buffer=0.25, 141 | scalebar_color="black", 142 | text_color="black", 143 | fontsize=10, 144 | linewidth=3, 145 | ): 146 | """ 147 | add a scalebar 148 | input params: 149 | axis: axis on which to add scalebar 150 | x_pos: x position, in pixels 151 | y_pos: y position, in pixels 152 | """ 153 | if x_length is not None: 154 | axis.plot( 155 | [x_pos, x_pos + x_length], 156 | [y_pos, y_pos], 157 | color=scalebar_color, 158 | linewidth=linewidth, 159 | ) 160 | axis.text( 161 | x_pos + x_length / 2, 162 | y_pos - y_buffer, 163 | x_text, 164 | color=text_color, 165 | fontsize=fontsize, 166 | ha="center", 167 | va="top", 168 | ) 169 | 170 | if y_length is not None: 171 | axis.plot( 172 | [x_pos, x_pos], 173 | [y_pos, y_pos + y_length], 174 | color=scalebar_color, 175 | linewidth=linewidth, 176 | ) 177 | 178 | axis.text( 179 | x_pos - x_buffer, 180 | y_pos + y_length / 2, 181 | y_text, 182 | color=text_color, 183 | fontsize=fontsize, 184 | ha="right", 185 | va="center", 186 | ) 187 | -------------------------------------------------------------------------------- /figrid/example_figures/__init__.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import numpy as np 3 | import seaborn as sns 4 | 5 | import figrid as fg 6 | 7 | 8 | def heatmap(axis): 9 | """ 10 | plot a random pixel image 11 | input: a single axis handle 12 | """ 13 | axis.imshow(np.random.randn(100, 100), cmap="gray") 14 | axis.set_title("An image") 15 | axis.axis("off") 16 | 17 | # add a scalebar 18 | fg.scalebar( 19 | axis=axis, 20 | x_pos=10, 21 | y_pos=85, 22 | x_length=30, 23 | x_text="60 um", 24 | scalebar_color="white", 25 | text_color="white", 26 | fontsize=12, 27 | y_buffer=-2, 28 | ) 29 | 30 | 31 | def sinusoids(axis): 32 | """ 33 | plot 3 sinusoids plus a scalebar 34 | input: a 3 row by 1 column array of axis handles 35 | """ 36 | t = np.arange(0, 10, 0.01) 37 | for row in range(3): 38 | f = 0.8 * (row + 1) 39 | axis[row].plot(t, np.sin(2 * np.pi * f * t), color="black", linewidth=2) 40 | axis[row].axis("off") 41 | axis[row].set_xlim(-0.5, 10.5) 42 | axis[row].set_ylim(-1.35, 1.05) 43 | axis[row].set_title("frequency = {:0.1f} Hz".format(f)) 44 | 45 | # add a scalebar 46 | fg.scalebar( 47 | axis=axis[2], 48 | x_pos=-0.25, 49 | y_pos=-1.25, 50 | x_length=1, 51 | y_length=1, 52 | x_text="1 s", 53 | y_text="1 u", 54 | ) 55 | 56 | 57 | def violins(axis): 58 | """ 59 | violinplot example from: 60 | https://seaborn.pydata.org/examples/simple_violinplots.html 61 | """ 62 | # Create a random dataset across several variables 63 | rs = np.random.default_rng(0) 64 | n, p = 40, 8 65 | d = rs.normal(0, 2, (n, p)) 66 | d += np.log(np.arange(1, p + 1)) * -5 + 10 67 | 68 | # Show each distribution with both violins and points 69 | sns.violinplot(data=d, palette="light:g", inner="points", orient="h", ax=axis) 70 | sns.despine() 71 | 72 | 73 | def scatterplot(axis): 74 | """ 75 | scatterplot example from: 76 | https://seaborn.pydata.org/examples/layered_bivariate_plot.html 77 | """ 78 | # Simulate data from a bivariate Gaussian 79 | n = 10000 80 | mean = [0, 0] 81 | cov = [(2, 0.4), (0.4, 0.2)] 82 | rng = np.random.RandomState(0) 83 | x, y = rng.multivariate_normal(mean, cov, n).T 84 | 85 | # Draw a combo histogram and scatterplot with density contours 86 | sns.scatterplot(x=x, y=y, s=5, color=".15", ax=axis) 87 | sns.kdeplot(x=x, y=y, levels=5, color="w", linewidths=1, ax=axis) 88 | -------------------------------------------------------------------------------- /figrid_example_notebook.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "figrid_example_notebook.ipynb", 7 | "provenance": [], 8 | "collapsed_sections": [], 9 | "authorship_tag": "ABX9TyO01ULmqHO0gKMYxabl74rd", 10 | "include_colab_link": true 11 | }, 12 | "kernelspec": { 13 | "name": "python3", 14 | "display_name": "Python 3" 15 | }, 16 | "language_info": { 17 | "name": "python" 18 | } 19 | }, 20 | "cells": [ 21 | { 22 | "cell_type": "markdown", 23 | "metadata": { 24 | "id": "view-in-github", 25 | "colab_type": "text" 26 | }, 27 | "source": [ 28 | "\"Open" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": { 34 | "id": "iU5XTqLcBktY" 35 | }, 36 | "source": [ 37 | "# A simple example use case\n", 38 | "This example shows how to place four figures on a figure canvas using `figrid.place_axes_on_grid`. \n", 39 | "It uses plots that are generated in the `example_figures` module of the package. \n", 40 | "Plots are arranged in desired locations, then figure labels are added." 41 | ] 42 | }, 43 | { 44 | "cell_type": "markdown", 45 | "metadata": { 46 | "id": "yEPnvyXhBngr" 47 | }, 48 | "source": [ 49 | "## Install figrid\n", 50 | "First install figrid in the current environment using pip" 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "metadata": { 56 | "id": "OVpb05qhBM7N" 57 | }, 58 | "source": [ 59 | "!pip install figrid" 60 | ], 61 | "execution_count": null, 62 | "outputs": [] 63 | }, 64 | { 65 | "cell_type": "markdown", 66 | "metadata": { 67 | "id": "3qHXBOH8Bz9S" 68 | }, 69 | "source": [ 70 | "## Imports\n", 71 | "Now import figrid, example_figure definitions, and matplotlib" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "metadata": { 77 | "id": "k0GEpkUvBO0Q" 78 | }, 79 | "source": [ 80 | "# import the package as fg\n", 81 | "import figrid as fg\n", 82 | "\n", 83 | "# import code for example figures\n", 84 | "import figrid.example_figures as example_figures\n", 85 | "\n", 86 | "# import maptlotlib\n", 87 | "import matplotlib.pyplot as plt" 88 | ], 89 | "execution_count": null, 90 | "outputs": [] 91 | }, 92 | { 93 | "cell_type": "markdown", 94 | "metadata": { 95 | "id": "_kPNwzuKByKU" 96 | }, 97 | "source": [ 98 | "## Define a function to make a figure and place axes\n", 99 | "Use `figrid.place_axes_on_grid` to define four axes on an 11 x 8.5 inch figure canvas" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "metadata": { 105 | "id": "wxBkoalxBTwU" 106 | }, 107 | "source": [ 108 | "def make_fig_ax():\n", 109 | " fig = plt.figure(figsize=(11,8.5))\n", 110 | " ax = {\n", 111 | " 'panel_A': fg.place_axes_on_grid(fig, xspan=[0.05, 0.3], yspan=[0.05, 0.45]),\n", 112 | " 'panel_B': fg.place_axes_on_grid(fig, xspan=[0.4, 1], yspan=[0.05, 0.45], dim=[3, 1], hspace=0.4),\n", 113 | " 'panel_C': fg.place_axes_on_grid(fig, xspan=[0.05, 0.4], yspan=[0.57, 1]),\n", 114 | " 'panel_D': fg.place_axes_on_grid(fig, xspan=[0.5, 1], yspan=[0.57, 1])\n", 115 | " }\n", 116 | " \n", 117 | " return fig, ax" 118 | ], 119 | "execution_count": null, 120 | "outputs": [] 121 | }, 122 | { 123 | "cell_type": "markdown", 124 | "metadata": { 125 | "id": "kyV1sguvB_lX" 126 | }, 127 | "source": [ 128 | "## Make the figure\n", 129 | "Add the plots to the axes, then add some labels" 130 | ] 131 | }, 132 | { 133 | "cell_type": "code", 134 | "metadata": { 135 | "colab": { 136 | "base_uri": "https://localhost:8080/", 137 | "height": 503 138 | }, 139 | "id": "CjixSobGBa_I", 140 | "outputId": "a77e739d-5bca-4eb4-e463-ee1abe9326fa" 141 | }, 142 | "source": [ 143 | "fig, ax = make_fig_ax()\n", 144 | "\n", 145 | "example_figures.heatmap(ax['panel_A'])\n", 146 | "example_figures.sinusoids(ax['panel_B'])\n", 147 | "example_figures.violins(ax['panel_C'])\n", 148 | "example_figures.scatterplot(ax['panel_D'])\n", 149 | "\n", 150 | "# add labels\n", 151 | "labels = [\n", 152 | " {'label_text':'A', 'xpos':0, 'ypos':0.05, 'fontsize':20, 'weight': 'bold', 'ha': 'right', 'va': 'bottom'},\n", 153 | " {'label_text':'B', 'xpos':0.37, 'ypos':0.05, 'fontsize':20, 'weight': 'bold', 'ha': 'right', 'va': 'bottom'},\n", 154 | " {'label_text':'C', 'xpos':0, 'ypos':0.55, 'fontsize':20, 'weight': 'bold', 'ha': 'right', 'va': 'bottom'},\n", 155 | " {'label_text':'D', 'xpos':0.45, 'ypos':0.55, 'fontsize':20, 'weight': 'bold', 'ha': 'right', 'va': 'bottom'},\n", 156 | "]\n", 157 | "fg.add_labels(fig, labels)" 158 | ], 159 | "execution_count": null, 160 | "outputs": [ 161 | { 162 | "output_type": "display_data", 163 | "data": { 164 | "image/png": "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\n", 165 | "text/plain": [ 166 | "
" 167 | ] 168 | }, 169 | "metadata": { 170 | "tags": [], 171 | "needs_background": "light" 172 | } 173 | } 174 | ] 175 | } 176 | ] 177 | } -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | long_description = file: README.md 3 | long_description_content_type = text/markdown -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | 3 | """ 4 | figrid provides a set of convenience functions to make it easier to 5 | place axes on a grid using matplotlib 6 | """ 7 | setup( 8 | name="figrid", 9 | version="0.1.7", 10 | packages=["figrid"], 11 | include_package_data=True, 12 | description="Formats multipanel figures", 13 | url="https://github.com/dougollerenshaw/figrid", 14 | author="Doug Ollerenshaw", 15 | author_email="d.ollerenshaw@gmail.com", 16 | license="MIT", 17 | install_requires=["matplotlib", "numpy", "seaborn"], 18 | extras_require={ 19 | "dev": [ 20 | "pytest>=7.0.0", 21 | "pytest-cov>=4.0.0", 22 | "black>=23.0.0", 23 | "flake8>=6.0.0", 24 | "pre-commit>=3.0.0", 25 | ], 26 | }, 27 | classifiers=[ 28 | "Development Status :: 3 - Alpha", 29 | "Intended Audience :: Science/Research", 30 | "License :: OSI Approved :: MIT License", 31 | "Natural Language :: English", 32 | "Programming Language :: Python :: 3.8", 33 | "Programming Language :: Python :: 3.9", 34 | "Programming Language :: Python :: 3.10", 35 | "Programming Language :: Python :: 3.11", 36 | "Programming Language :: Python :: 3.12", 37 | "Programming Language :: Python :: 3.13", 38 | ], 39 | ) 40 | -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dougollerenshaw/figrid/d781c7ff14feb18ae0ca0c322ac03aead9aab266/tests/__init__.py -------------------------------------------------------------------------------- /tests/test_figrid.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | import matplotlib.pyplot as plt 3 | from figrid import place_axes_on_grid, add_label, add_labels, scalebar 4 | from figrid.example_figures import heatmap, sinusoids, violins, scatterplot 5 | 6 | 7 | @pytest.fixture 8 | def figure(): 9 | """Create a new figure for each test.""" 10 | fig = plt.figure(figsize=(10, 10)) 11 | yield fig 12 | plt.close(fig) 13 | 14 | 15 | def test_example_layout(figure): 16 | """Test the complete layout example from README.""" 17 | ax = { 18 | "panel_A": place_axes_on_grid(figure, xspan=[0.05, 0.3], yspan=[0.05, 0.45]), 19 | "panel_B": place_axes_on_grid( 20 | figure, xspan=[0.4, 1], yspan=[0.05, 0.45], dim=[3, 1], hspace=0.4 21 | ), 22 | "panel_C": place_axes_on_grid(figure, xspan=[0.05, 0.4], yspan=[0.57, 1]), 23 | "panel_D": place_axes_on_grid(figure, xspan=[0.5, 1], yspan=[0.57, 1]), 24 | } 25 | 26 | # Verify all panels exist 27 | assert all(key in ax for key in ["panel_A", "panel_B", "panel_C", "panel_D"]) 28 | 29 | # Verify panel_B is a 3x1 grid 30 | assert len(ax["panel_B"]) == 3 31 | 32 | # Add example labels 33 | labels = [ 34 | { 35 | "label_text": "A", 36 | "xpos": 0, 37 | "ypos": 0.05, 38 | "fontsize": 20, 39 | "weight": "bold", 40 | "ha": "right", 41 | "va": "bottom", 42 | }, 43 | { 44 | "label_text": "B", 45 | "xpos": 0.37, 46 | "ypos": 0.05, 47 | "fontsize": 20, 48 | "weight": "bold", 49 | "ha": "right", 50 | "va": "bottom", 51 | }, 52 | { 53 | "label_text": "C", 54 | "xpos": 0, 55 | "ypos": 0.55, 56 | "fontsize": 20, 57 | "weight": "bold", 58 | "ha": "right", 59 | "va": "bottom", 60 | }, 61 | { 62 | "label_text": "D", 63 | "xpos": 0.45, 64 | "ypos": 0.55, 65 | "fontsize": 20, 66 | "weight": "bold", 67 | "ha": "right", 68 | "va": "bottom", 69 | }, 70 | ] 71 | add_labels(figure, labels) 72 | 73 | 74 | def test_single_axis(figure): 75 | """Test creating a single axis.""" 76 | ax = place_axes_on_grid(figure) 77 | assert isinstance(ax, plt.Axes) 78 | 79 | 80 | def test_multi_axis_grid(figure): 81 | """Test creating a multi-axis grid.""" 82 | axes = place_axes_on_grid(figure, dim=[2, 2]) 83 | assert len(axes) == 2 84 | assert len(axes[0]) == 2 85 | 86 | 87 | def test_shared_axes(figure): 88 | """Test axes with shared x and y axes.""" 89 | axes = place_axes_on_grid(figure, dim=[2, 1], sharex=True) 90 | 91 | # Axes should exist and be properly configured 92 | assert len(axes) == 2 93 | 94 | # Bottom axis should show ticks, top axis should share x scale 95 | assert axes[0].get_shared_x_axes().joined(axes[0], axes[1]) 96 | assert axes[1].xaxis.get_ticks_position() == "bottom" 97 | 98 | # Check that top axis has hidden tick labels 99 | assert not any(label.get_visible() for label in axes[0].get_xticklabels()) 100 | assert all(label.get_visible() for label in axes[1].get_xticklabels()) 101 | 102 | 103 | def test_axis_positioning(figure): 104 | """Test custom axis positioning.""" 105 | ax = place_axes_on_grid(figure, dim=[1, 1], xspan=[0.2, 0.8], yspan=[0.2, 0.8]) 106 | bbox = ax.get_position() 107 | # Allow for some margin adjustment while ensuring the axis is roughly where expected 108 | assert 0.2 < bbox.x0 < 0.3 # Axis starts in the first third 109 | assert 0.7 < bbox.x1 < 0.8 # Axis ends in the last third 110 | assert 0.2 < bbox.y0 < 0.3 # Similar bounds for y-axis 111 | 112 | 113 | def test_single_label(figure): 114 | """Test adding a single label.""" 115 | label_text = "Test Label" 116 | add_label(figure, label_text, 0.5, 0.5, fontsize=12) 117 | all_texts = [] 118 | for ax in figure.axes: 119 | all_texts.extend(ax.texts) 120 | assert any(text.get_text() == label_text for text in all_texts) 121 | 122 | 123 | def test_multiple_labels(figure): 124 | """Test adding multiple labels.""" 125 | labels = [ 126 | {"label_text": "A", "xpos": 0.1, "ypos": 0.1, "fontsize": 12}, 127 | {"label_text": "B", "xpos": 0.9, "ypos": 0.1, "fontsize": 12}, 128 | ] 129 | add_labels(figure, labels) 130 | all_texts = [] 131 | for ax in figure.axes: 132 | all_texts.extend(ax.texts) 133 | assert any(text.get_text() == "A" for text in all_texts) 134 | assert any(text.get_text() == "B" for text in all_texts) 135 | 136 | 137 | def test_scalebar_creation(figure): 138 | """Test adding a scalebar to an axis.""" 139 | ax = place_axes_on_grid(figure) 140 | scalebar( 141 | ax, 142 | x_pos=0.5, 143 | y_pos=0.5, 144 | x_length=1.0, 145 | y_length=1.0, 146 | x_text="1 unit", 147 | y_text="1 unit", 148 | ) 149 | assert len(ax.lines) > 0 # Has at least one line 150 | assert len(ax.texts) > 0 # Has at least one text element 151 | 152 | 153 | def test_example_heatmap(figure): 154 | """Test the heatmap example figure.""" 155 | ax = place_axes_on_grid(figure) 156 | heatmap(ax) 157 | # Check that image was added 158 | assert len(ax.images) == 1 159 | # Check that scalebar was added 160 | assert len(ax.lines) > 0 161 | assert len(ax.texts) > 0 162 | 163 | 164 | def test_example_sinusoids(figure): 165 | """Test the sinusoids example figure.""" 166 | axes = place_axes_on_grid(figure, dim=[3, 1]) 167 | sinusoids(axes) 168 | # Check that each subplot has the sinusoid 169 | for ax in axes: 170 | assert len(ax.lines) > 0 # At least one line exists 171 | # Check that scalebar was added to bottom plot 172 | assert len(axes[2].texts) > 0 173 | 174 | 175 | def test_example_violins(figure): 176 | """Test the violin plot example figure.""" 177 | ax = place_axes_on_grid(figure) 178 | violins(ax) 179 | # Check that violin plot elements exist 180 | assert len(ax.collections) > 0 # Violin shapes and points 181 | 182 | 183 | def test_example_scatterplot(figure): 184 | """Test the scatter plot example figure.""" 185 | ax = place_axes_on_grid(figure) 186 | scatterplot(ax) 187 | # Check that plot elements exist 188 | assert len(ax.collections) > 0 # Scatter points and contours 189 | --------------------------------------------------------------------------------