Automatic scientific axes layout for matplotlib.ipynb

Automatic scientific axes layout for matplotlib.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec",
      "execution_count": 21,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from collections import namedtuple\ndef make_panelled_axes(layout, fig=None):\n    # default arguments\n    if fig is None:\n        fig = plt.gcf()\n    # format and sanity check grid\n    lines = layout.split('\\n')\n    lines = [line.strip() for line in lines if line.strip()]\n    linewidths = set(len(line) for line in lines)\n    if len(linewidths)>1:\n        raise ValueError('Invalid layout (all lines must have same width)')\n    width = linewidths.pop()\n    height = len(lines)\n    panel_letters = set(c for line in lines for c in line)\n    # find bounding boxes for each panel\n    panel_grid = {}\n    for letter in panel_letters:\n        left = min(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        right = 1+max(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        top = min(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        bottom = 1+max(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        panel_grid[letter] = (left, right, top, bottom)\n        # check that this layout is consistent, i.e. all squares are filled\n        valid = all(lines[y][x]==letter for x in range(left, right) for y in range(top, bottom))\n        if not valid:\n            raise ValueError('Invalid layout (not all square)')\n    # build axes\n    axes = {}\n    spec = gridspec.GridSpec(ncols=width, nrows=height, figure=fig)\n    for letter, (left, right, top, bottom) in panel_grid.items():\n        axes[letter] = fig.add_subplot(spec[top:bottom, left:right])\n    return axes, spec\n\ndef label_panels(axes, letters=None, *, prefix='', postfix='.', spaces=6, pad=10, fontsize=18):\n    if letters is None:\n        letters = axes.keys()\n    for letter in letters:\n        ax = axes[letter]\n        ax.set_title(prefix+letter+postfix+' '*spaces, loc='left', pad=pad,\n                     fontdict={'horizontalalignment': 'right',\n                               'fontsize': fontsize})\n    \nlayout = '''\n AAB\n AAx\n CDD\n '''\nfig = plt.figure(figsize=(10, 7))\naxes, spec = make_panelled_axes(layout, fig=fig)\nspec.set_width_ratios([1, 3, 1])\nlabel_panels(axes, letters='ABCD')\nplt.tight_layout()",
      "execution_count": 56,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 720x504 with 5 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "layout = '''\n AAAB\n CDEB\n '''\nfig = plt.figure(figsize=(10, 5))\naxes, spec = make_panelled_axes(layout, fig=fig)\nlabel_panels(axes)\nplt.tight_layout()",
      "execution_count": 57,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 720x360 with 5 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "conda-env-brian-py",
      "display_name": "Python [conda env:brian]",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.2",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Automatic scientific axes layout for matplotlib.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}