extents.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -----------------------------------------------------------------------------\n",
    "# Matplotlib cheat sheet\n",
    "# Released under the BSD License\n",
    "# -----------------------------------------------------------------------------\n",
    "import pathlib\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.style.use([\n",
    "    'styles/base.mplstyle',\n",
    "])\n",
    "mpl.rc('figure.constrained_layout', wspace=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\frank\\AppData\\Local\\Temp\\ipykernel_11332\\421424129.py:38: UserWarning: The figure layout has changed to tight\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\frank\\AppData\\Local\\Temp\\ipykernel_11332\\421424129.py:50: UserWarning: The figure layout has changed to tight\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = np.arange(5*5).reshape(5,5)\n",
    "\n",
    "(fig, axs) = plt.subplots(figsize=(8, 5), nrows=2, ncols=2)\n",
    "\n",
    "ax = axs[0, 0]\n",
    "ax.imshow(Z, extent=[0,10,0,5], interpolation=\"nearest\", origin=\"upper\")\n",
    "ax.set_xlim(-1, 11), ax.set_xticks([])\n",
    "ax.set_ylim(-1, 6), ax.set_yticks([0,5])\n",
    "ax.text(1, 4.5, \"(0,0)\", ha=\"center\", va=\"center\", color=\"white\", size=\"large\")\n",
    "ax.text(9, 0.5, \"(4,4)\", ha=\"center\", va=\"center\", color=\"black\", size=\"large\")\n",
    "ax.text(5.0, 5.5, 'origin=\"upper\"',\n",
    "        ha=\"center\", va=\"center\", color=\"black\", size=\"large\")\n",
    "ax.text(5.0, -0.5, \"extent=[0,10,0,5]\",\n",
    "        ha=\"center\", va=\"center\", color=\"black\", size=\"large\")\n",
    "\n",
    "ax = axs[1, 0]\n",
    "ax.imshow(Z, extent=[0,10,0,5], interpolation=\"nearest\", origin=\"lower\")\n",
    "ax.set_xlim(-1, 11), ax.set_xticks([0,10])\n",
    "ax.set_ylim(-1, 6), ax.set_yticks([0,5])\n",
    "ax.text(1, 0.5, \"(0,0)\", ha=\"center\", va=\"center\", color=\"white\", size=\"large\")\n",
    "ax.text(9, 4.5, \"(4,4)\", ha=\"center\", va=\"center\", color=\"black\", size=\"large\")\n",
    "\n",
    "ax.text(5.0, 5.5, 'origin=\"lower\"',\n",
    "        ha=\"center\", va=\"center\", color=\"black\", size=\"large\")\n",
    "ax.text(5.0, -0.5, \"extent=[0,10,0,5]\",\n",
    "        ha=\"center\", va=\"center\", color=\"black\", size=\"large\")\n",
    "\n",
    "ax = axs[1, 1]\n",
    "ax.imshow(Z, extent=[10,0,0,5], interpolation=\"nearest\", origin=\"lower\")\n",
    "ax.set_xlim(-1, 11), ax.set_xticks([0,10])\n",
    "ax.set_ylim(-1, 6), ax.set_yticks([])\n",
    "ax.text(9, 0.5, \"(0,0)\", ha=\"center\", va=\"center\", color=\"white\", size=\"large\")\n",
    "ax.text(1, 4.5, \"(4,4)\", ha=\"center\", va=\"center\", color=\"black\", size=\"large\")\n",
    "ax.text(5.0, 5.5, 'origin=\"lower\"',\n",
    "        ha=\"center\", va=\"center\", color=\"black\", size=\"large\")\n",
    "ax.text(5.0, -0.5, \"extent=[10,0,0,5]\",\n",
    "        ha=\"center\", va=\"center\", color=\"black\", size=\"large\")\n",
    "plt.tight_layout()\n",
    "\n",
    "ax = axs[0, 1]\n",
    "ax.imshow(Z, extent=[10,0,0,5], interpolation=\"nearest\", origin=\"upper\")\n",
    "ax.set_xlim(-1, 11), ax.set_xticks([])\n",
    "ax.set_ylim(-1, 6), ax.set_yticks([])\n",
    "ax.text(9, 4.5, \"(0,0)\", ha=\"center\", va=\"center\", color=\"white\", size=\"large\")\n",
    "ax.text(1, 0.5, \"(4,4)\", ha=\"center\", va=\"center\", color=\"black\", size=\"large\")\n",
    "ax.text(5.0, 5.5, 'origin=\"upper\"',\n",
    "        ha=\"center\", va=\"center\", color=\"black\", size=\"large\")\n",
    "ax.text(5.0, -0.5, \"extent=[10,0,0,5]\",\n",
    "        ha=\"center\", va=\"center\", color=\"black\", size=\"large\")\n",
    "plt.tight_layout()\n",
    "\n",
    "# plt.savefig(\"../figures/extents.pdf\", dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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