text-alignments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tick Alignments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 425x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----------------------------------------------------------------------------\n",
    "# Matplotlib cheat book\n",
    "# Released under the BSD License\n",
    "# -----------------------------------------------------------------------------\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "dpi = 100\n",
    "fig = plt.figure(figsize=(4.25, 1.5), dpi=dpi)\n",
    "ax = fig.add_axes([0,0,1,1], frameon=False,\n",
    "                  xlim=(0,4.25), ylim=(0,1.5), xticks=[], yticks=[])\n",
    "\n",
    "fontsize = 48\n",
    "renderer = fig.canvas.get_renderer()\n",
    "horizontalalignment = \"left\"\n",
    "verticalalignment = \"center\"\n",
    "position = (0.25, 1.5/2)\n",
    "color = \"0.25\"\n",
    "\n",
    "# Compute vertical and horizontal alignment offsets\n",
    "text = ax.text(0, 0, \"Matplotlib\", fontsize=fontsize)\n",
    "yoffset = {}\n",
    "for alignment in [\"top\", \"center\", \"baseline\", \"bottom\"]:\n",
    "    text.set_verticalalignment(alignment)\n",
    "    y = text.get_window_extent(renderer).y0/dpi\n",
    "    yoffset[alignment] = y\n",
    "\n",
    "xoffset = {}\n",
    "for alignment in [\"left\", \"center\", \"right\"]:\n",
    "    text.set_horizontalalignment(alignment)\n",
    "    x = text.get_window_extent(renderer).x0/dpi\n",
    "    xoffset[alignment] = x\n",
    "\n",
    "# Actual positioning of the text\n",
    "text.set_horizontalalignment(horizontalalignment)\n",
    "text.set_verticalalignment(verticalalignment)\n",
    "text.set_position(position)\n",
    "\n",
    "\n",
    "for name,y in yoffset.items():\n",
    "    y = position[1] - y + yoffset[verticalalignment]\n",
    "    plt.plot([0.1, 3.75], [y, y], linewidth=0.5, color=color)\n",
    "    plt.text(3.75, y, \" \"+name, color=color,\n",
    "             ha=\"left\", va=\"center\", size=\"x-small\")\n",
    "\n",
    "for name,x in xoffset.items():\n",
    "    x = position[0] - x + xoffset[horizontalalignment]\n",
    "    plt.plot([x,x], [0.25, 1.25], linewidth=0.5, color=color)\n",
    "    plt.text(x, 0.24, name, color = color,\n",
    "             ha=\"center\", va=\"top\", size=\"x-small\")\n",
    "\n",
    "P = []\n",
    "for x in xoffset.values():\n",
    "    x = position[0] - x + xoffset[horizontalalignment]\n",
    "    for y in yoffset.values():\n",
    "        y = position[1] - y + yoffset[verticalalignment]\n",
    "        P.append((x,y))\n",
    "P = np.array(P)\n",
    "\n",
    "ax.scatter(P[:,0], P[:,1], s=10, zorder=10,\n",
    "           facecolor=\"white\", edgecolor=color, linewidth=0.75)\n",
    "\n",
    "epsilon = 0.05\n",
    "plt.text(P[3,0]+epsilon, P[3,1]-epsilon, \"(0,0)\",\n",
    "         color=color, ha=\"left\", va=\"top\", size=\"x-small\")\n",
    "plt.text(P[8,0]-epsilon, P[8,1]+epsilon, \"(1,1)\",\n",
    "         color=color, ha=\"right\", va=\"bottom\", size=\"x-small\")\n",
    "\n",
    "#plt.savefig(\"../figures/text-alignments.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}