adjustments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adjustments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -----------------------------------------------------------------------------\n",
    "# Matplotlib cheat book\n",
    "# Released under the BSD License\n",
    "# -----------------------------------------------------------------------------\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "from matplotlib.collections import PatchCollection\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PUNO2E6SeURH"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 918x758.348 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(3 * 4.25, 3 * 4.25 * 95/115))\n",
    "ax = fig.add_axes([0,0,1,1], frameon=False, aspect=1,\n",
    "                  xlim=(0-5,100+10), ylim=(-10,80+5), xticks=[], yticks=[])\n",
    "\n",
    "\n",
    "box = mpatches.FancyBboxPatch(\n",
    "    (0,0), 100, 83,  mpatches.BoxStyle(\"Round\", pad=0, rounding_size=2),\n",
    "    linewidth=1., facecolor=\"0.9\", edgecolor=\"black\")\n",
    "ax.add_artist(box)\n",
    "\n",
    "box = mpatches.FancyBboxPatch(\n",
    "    (0,0), 100, 75, mpatches.BoxStyle(\"Round\", pad=0, rounding_size=0),\n",
    "    linewidth=1., facecolor=\"white\", edgecolor=\"black\")\n",
    "ax.add_artist(box)\n",
    "\n",
    "\n",
    "box = mpatches.Rectangle(\n",
    "    (5,5), 45, 30, zorder=10,\n",
    "    linewidth=1.0, facecolor=\"white\", edgecolor=\"black\")\n",
    "ax.add_artist(box)\n",
    "\n",
    "box = mpatches.Rectangle(\n",
    "    (5,40), 45, 30, zorder=10,\n",
    "    linewidth=1.0, facecolor=\"white\", edgecolor=\"black\")\n",
    "ax.add_artist(box)\n",
    "\n",
    "box = mpatches.Rectangle(\n",
    "    (55,5), 40, 65, zorder=10,\n",
    "    linewidth=1.0, facecolor=\"white\", edgecolor=\"black\")\n",
    "ax.add_artist(box)\n",
    "\n",
    "# Window button\n",
    "X, Y = [5,10,15], [79,79,79]\n",
    "plt.scatter(X, Y, s=75,  zorder=10,\n",
    "            edgecolor=\"black\", facecolor=\"white\", linewidth=1)\n",
    "\n",
    "\n",
    "# Window size extension\n",
    "X, Y = [0, 0], [0, -8]\n",
    "plt.plot(X, Y, color=\"black\", linestyle=\":\", linewidth=1, clip_on=False)\n",
    "\n",
    "X, Y = [100, 100], [0, -8]\n",
    "plt.plot(X, Y, color=\"black\", linestyle=\":\", linewidth=1, clip_on=False)\n",
    "\n",
    "X, Y = [100, 108], [0, 0]\n",
    "plt.plot(X, Y, color=\"black\", linestyle=\":\", linewidth=1, clip_on=False)\n",
    "\n",
    "X, Y = [100, 108], [75, 75]\n",
    "plt.plot(X, Y, color=\"black\", linestyle=\":\", linewidth=1, clip_on=False)\n",
    "\n",
    "\n",
    "def ext_arrow(p0,p1,p2,p3):\n",
    "    p0, p1 = np.asarray(p0), np.asarray(p1)\n",
    "    p2, p3 = np.asarray(p2), np.asarray(p3)\n",
    "    ax.arrow(*p0, *(p1-p0), zorder=20, linewidth=0,\n",
    "             length_includes_head=True, width=.4, \n",
    "             head_width=2, head_length=2, color=\"black\")\n",
    "    ax.arrow(*p3, *(p2-p3), zorder=20, linewidth=0,\n",
    "             length_includes_head=True, width=.4,\n",
    "             head_width=2, head_length=2, color=\"black\")\n",
    "    plt.plot([p1[0],p2[0]], [p1[1],p2[1]], linewidth=.9, color=\"black\")\n",
    "\n",
    "def int_arrow(p0,p1):\n",
    "    p0, p1 = np.asarray(p0), np.asarray(p1)\n",
    "    ax.arrow(*((p0+p1)/2), *((p1-p0)/2), zorder=20, linewidth=0,\n",
    "             length_includes_head=True, width=.4, \n",
    "             head_width=2, head_length=2, color=\"black\")\n",
    "    ax.arrow(*((p0+p1)/2), *(-(p1-p0)/2), zorder=20, linewidth=0,\n",
    "             length_includes_head=True, width=.4, \n",
    "             head_width=2, head_length=2, color=\"black\")\n",
    "\n",
    "    \n",
    "\n",
    "x = 0\n",
    "y = 10\n",
    "ext_arrow( (x-4,y), (x,y), (x+5,y), (x+9,y) )\n",
    "ax.text(x+9.5, y, \"left\", ha=\"left\", va=\"center\", size=\"large\", zorder=20)\n",
    "\n",
    "x += 50\n",
    "ext_arrow( (x-4,y), (x,y), (x+5,y), (x+9,y) )\n",
    "ax.text(x-4.5, y, \"wspace\", ha=\"right\", va=\"center\", size=\"large\", zorder=20)\n",
    "\n",
    "x += 45\n",
    "ext_arrow( (x-4,y), (x,y), (x+5,y), (x+9,y) )\n",
    "ax.text(x-4.5, y, \"right\", ha=\"right\", va=\"center\", size=\"large\", zorder=20)\n",
    "\n",
    "y = 0\n",
    "x = 25\n",
    "ext_arrow( (x,y-4), (x,y), (x,y+5), (x,y+9) )\n",
    "ax.text(x, y+9.5, \"bottom\", ha=\"center\", va=\"bottom\", size=\"large\", zorder=20)\n",
    "\n",
    "y += 35\n",
    "ext_arrow( (x,y-4), (x,y), (x,y+5), (x,y+9) )\n",
    "ax.text(x, y-4.5, \"hspace\", ha=\"center\", va=\"top\", size=\"large\", zorder=20)\n",
    "\n",
    "y += 35\n",
    "ext_arrow( (x,y-4), (x,y), (x,y+5), (x,y+9) )\n",
    "ax.text(x, y-4.5, \"top\", ha=\"center\", va=\"top\", size=\"large\", zorder=20)\n",
    "\n",
    "int_arrow((0,-5), (100,-5))\n",
    "ax.text(50, -5, \"figure width\", backgroundcolor=\"white\", zorder=30,\n",
    "        ha=\"center\", va=\"center\", size=\"large\")\n",
    "\n",
    "int_arrow((105,0), (105,75))\n",
    "ax.text(105, 75/2, \"figure height\", backgroundcolor=\"white\", zorder=30,\n",
    "        rotation = \"vertical\", ha=\"center\", va=\"center\", size=\"large\")\n",
    "\n",
    "int_arrow((55,62.5), (95,62.5))\n",
    "ax.text(75, 62.5, \"axes width\", backgroundcolor=\"white\", zorder=30,\n",
    "        ha=\"center\", va=\"center\", size=\"large\")\n",
    "\n",
    "int_arrow((62.5,5), (62.5,70))\n",
    "ax.text(62.5, 35, \"axes height\", backgroundcolor=\"white\", zorder=30,\n",
    "        rotation = \"vertical\", ha=\"center\", va=\"center\", size=\"large\")\n",
    "\n",
    "\n",
    "#plt.savefig(\"../figures/adjustments.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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