thesamovar/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\nfrom matplotlib import transforms",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def panel_specs(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)-set('.')\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 axis specs\n    gs = gridspec.GridSpec(ncols=width, nrows=height, figure=fig)\n    specs = {}\n    for letter, (left, right, top, bottom) in panel_grid.items():\n        specs[letter] = gs[top:bottom, left:right]\n    return specs, gs\n\ndef panels(layout, fig=None):\n    # default arguments\n    if fig is None:\n        fig = plt.gcf()\n    specs, gs = panel_specs(layout, fig=fig)\n    axes = {}\n    for letter, spec in specs.items():\n        axes[letter] = fig.add_subplot(spec)\n    return axes, gs\n\ndef label_panel(ax, letter, *,\n                offset_left=0.8, offset_up=0.2, prefix='', postfix='.', **font_kwds):\n    kwds = dict(fontsize=18)\n    kwds.update(font_kwds)\n    # this mad looking bit of code says that we should put the code offset a certain distance in\n    # inches (using the fig.dpi_scale_trans transformation) from the top left of the frame\n    # (which is (0, 1) in ax.transAxes transformation space)\n    fig = ax.figure\n    trans = ax.transAxes + transforms.ScaledTranslation(-offset_left, offset_up, fig.dpi_scale_trans)\n    ax.text(0, 1, prefix+letter+postfix, transform=trans, **kwds)\n\ndef label_panels(axes, letters=None, **kwds):\n    if letters is None:\n        letters = axes.keys()\n    for letter in letters:\n        ax = axes[letter]\n        label_panel(ax, letter, **kwds)\n    \nlayout = '''\n AAB\n AA.\n .CC\n '''\nfig = plt.figure(figsize=(10, 7))\naxes, spec = panels(layout, fig=fig)\nspec.set_width_ratios([1, 3, 1])\nlabel_panels(axes, letters='ABC')\nplt.tight_layout()",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x504 with 3 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "layout = '''\n AAAB\n CDEB\n '''\nfig = plt.figure(figsize=(10, 5))\naxes, spec = panels(layout, fig=fig)\nlabel_panels(axes)\nplt.tight_layout()",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x360 with 5 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def tight_xticklabels(ax=None):\n    if ax is None:\n        ax = plt.gca()\n    ticklabels = ax.get_xticklabels()\n    ticklabels[0].set_ha('left')\n    ticklabels[0].set_text(' '+ticklabels[0].get_text())\n    ticklabels[-1].set_ha('right')\n    ticklabels[-1].set_text(ticklabels[-1].get_text()+' ')\n    ax.set_xticklabels(ticklabels)\n    \ndef tight_yticklabels(ax=None):\n    if ax is None:\n        ax = plt.gca()\n    ticklabels = ax.get_yticklabels()\n    ticklabels[0].set_va('bottom')\n    ticklabels[-1].set_va('top')",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "layout = '''\n    AAAB\n    AAAC\n    AAAD\n    '''\nN = 5\nfig = plt.figure(figsize=(8, 6))\nspecs, gs = panel_specs(layout, fig=fig)\naxes = {}\nfor letter in 'BCD':\n    axes[letter] = ax = fig.add_subplot(specs[letter])\n    label_panel(ax, letter)\nsubgs = specs['A'].subgridspec(N, N, wspace=0, hspace=0)\ntriaxes = {}\ntighten = []\nfor i in range(N):\n    for j in range(i+1):\n        triaxes[i, j] = ax = fig.add_subplot(subgs[i, j])\n        if i==N-1:\n            ax.set_xlabel(chr(ord('α')+j))\n            tighten.append((tight_xticklabels, ax))\n        else:\n            ax.set_xticks([])\n        if j==0:\n            ax.set_ylabel(chr(ord('α')+i))\n            tighten.append((tight_yticklabels, ax))\n        else:\n            ax.set_yticks([])\nlabel_panel(triaxes[0, 0], 'A')\nplt.tight_layout()\nfor f, ax in tighten:\n    f(ax)\nplt.tight_layout()",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x432 with 18 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "_draft": {
      "nbviewer_url": "https://gist.github.com/52dbbb3a58a73c590d54c34f5f719bac"
    },
    "gist": {
      "id": "52dbbb3a58a73c590d54c34f5f719bac",
      "data": {
        "description": "Automatic scientific axes layout for matplotlib.ipynb",
        "public": true
      }
    },
    "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"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}