labelposition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "x,y = np.meshgrid(np.linspace(0.1,0.9,9),np.linspace(0.1,0.9,9))\n",
    "\n",
    "#xlabel position\n",
    "fig,ax = plt.subplots(1,3,figsize=(8,3)) \n",
    "ax = ax.ravel()  \n",
    "fig.patch.set_facecolor('white')\n",
    "loc=['left','center','right']\n",
    "\n",
    "for a,i in zip(ax,range(3)):\n",
    "    a.patch.set_facecolor('k')\n",
    "    a.grid(color='gray', linestyle='-', linewidth=4)\n",
    "    a.tick_params(labelbottom=False, labelleft=False)\n",
    "    a.tick_params(color='white')\n",
    "    a.plot(x,y,'o',c='white', ms=3)\n",
    "    a.set_xlabel('xlabel loc='+str(loc[i])+'', loc=loc[i])\n",
    "    a.xaxis.set_major_locator(plt.MaxNLocator(10))\n",
    "    a.yaxis.set_major_locator(plt.MaxNLocator(10))\n",
    "    a.set_box_aspect(1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"kirameki_xlabel.png\",dpi=100, pad_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAC1CAYAAABSxIXZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAAAh30lEQVR4nO3de3BU5cEG8Ofs+YDGKAimFjIBFgcGiQZMFlACNkltCkxBi6NUpCQTJ7IdLuNUGCDT6RAYLaZ2xLReAEFo+COjoNSGIhY0JAZMTORiqS2DMJtL06nGAkJYSjg53x+UnSTsZrN73nN78/xmGGbPep48e/K683Kuiq7rOoiIiIgk4rG7ABEREZFonOAQERGRdDjBISIiIulwgkNERETS4QSHiIiIpMMJDhEREUnn/8wIVRQl7PIRI0Z0e/2vf/0rrnxRObJ3Epnl5k533HEH2tra4voZfRVuzLt5m/X3TiKzrO7E8W5OFjtZn2X0O96UCU4kfr+/2+vi4mJbc0RmObGTyCw3d/J6vXHlG+XmbWZllhM7icyyulM84/3dd99FTU0NFEXBjBkzMG/ePFO6uTmLnazPMvodz0NURET92JIlS7Bp0yakpaXh3nvvxebNm7F06VK7axEZZukeHCIicpaqqiqcPHkydNgpPz8faWlpNrciMo57cIiI+rHx48ejqakp9Lq5uRkTJ060sRGRGIoZz6JKTk6+6djZDSkpKfB6vQgEAmhpaYn7Z4jKkb2TyCy3dqqoqEBDQ0PcP6MvIo15t24zdhKbZWWnWMd7VlYW6uvrMXXqVABAfX09pk2bhltuuQUA8Kc//emmddw03kVmsZP1WUa+4y09RJWSkoK8vDyoqgpN01BWVhbXBxeVI3snkVmydzKD7NtM5k4is5zYqav169cbWt+Mbk7MYifrs4zmmHKIKjExMexyr9cLVVXh8Xjg8XjivrpFVI7snURmyd7JqHBjXvZtJnMnkVlO7NRVVlYW7r77bly8eBEXL17EhAkTkJWVFfoTjlvGu8gsdrI+y2iOKROc9vb2sMsDgQA0TYOmaejs7EQgEIgrX1SO7J1EZsneyahwY172bSZzJ5FZTuzU1dtvv42pU6di165dePvtt3H//fdj9+7dva7jlvEuMoudrM8ymmPKOTiRbvRXXFzc7XhaYWFhXPmicmTvJDLLzZ18Pp/p5+CEG/Nu3mb9vZPILKs7xTreJ02ahAMHDuDOO+8EAHz99df44Q9/iBMnTkRcxy3jXWQWO1mfZfQ73vLLxFtaWoScJyEqR2SWEzuJzJK9kxlk32YydxKZ5cRON3R2doYmN8D1u8J2dnbGleXUzynz79GJnURmGcnhfXCIiPqxWbNmYebMmViwYAEA4K233sLs2bNtbkVkHCc4RET92Isvvhh6VIOu61i8eHFcj2ogchpOcIiI+rHVq1ejpKQEjz766E3LiNyMdzImIurHDhw4cNOy999/34YmRGJxDw4RUT/0+uuv47XXXsPZs2e7PZrh4sWLmD59uo3NiMTgBIeIqB968sknMXv2bBQVFeGFF14ILb/tttswbNgwG5sRicEJDhFRPzRkyBAMGTIE5eXldlchMgXPwSEi6udmzJjR7W8iGXCCQ0TUz12+fBlA5MfsELkRJzhEREQkHU5wiIiISDqc4BAREZF0OMEhIurndF23uwKRcIpuwshOTk6G3+8P+17XR58bedKoqBzZO4nMcmuniooKNDQ0xP0z+iLSmHfrNmMnsVlWdopnvB86dAjZ2dmhv6Nx03gXmcVO1mcZ+Y639D44KSkpyMvLg6qq0DQNZWVlcX1wUTmydxKZJXsnM8i+zWTuJDLLiZ16ujGp6cvkxopuTsxiJ+uzjOaYcogqMTEx7HKv1wtVVeHxeODxeOD1euPKF5UjeyeRWbJ3MircmJd9m8ncSWSWEzt1lZubi/Pnz4denzt3DjNnzux1HbeMd5FZ7GR9ltEcU/bgRLqXQiAQgKZp0HUdnZ2dCAQCceWLypG9k8gs2TsZFW7My77NZO4kMsuJnbpqa2vD7bffHno9dOhQfPXVV72u45bxLjKLnazPMppjyjk4iqKEXV5cXNzteFphYWFc+aJyZO8kMsvNnXw+n+nn4IQb827eZv29k8gsqzvFOt59Ph/27NmDUaNGAQAaGxsxb948HD16NOI6bhnvIrPYyfoso9/xlj+LqqWlRcgxY1E5IrOc2ElkluydzCD7NpO5k8gsJ3a64fnnn8eMGTOQlZUFAKiursaWLVts7+bELHayPstIDh+2SUTUj82aNQtHjx5FbW0tAGDjxo1ISkqyuRWRcZzgEBH1c0eOHEF1dXXo9Zw5c2xsQyQGb/RHRNSPrVmzBqWlpUhNTUVqaipKS0tRVFRkdy0iw7gHh4ioH9u3bx+OHz8Oj+f6v3fz8/ORnp6ODRs22NyMyBjuwSEi6ue63gfnwoUL9hUhEoh7cIiI+rGioiKkp6cjJycHuq6jurqae29ICpzgEBH1YwsWLEB2djbq6+uh6zpKSkowfPhwu2sRGcYJDhFRP9TzRn4pKSkAgNbWVrS2tiIjI8OOWkTCcIJD1IWu62hpacHIkSPtrkJkqhUrVkR8T1EUfPTRRxa2IRKPExyiLhRFwU9+8hN89tlndlchMlVlZaXdFYhMxauoiHp44IEHUF9fb3cNIiIygHtwiHqorKzEpk2b4PV6kZiYCF3XoSgKPv/8c7urERFRH5nyNPHk5GT4/f6w73V9MqiRB3GJypG9k8gst3aqqKiI6enKjY2NYZePHj064jqRxrxbtxk7ic2yslOs4z0ebhrvIrPYyfosI9/xlu7BSUlJQV5eHlRVhaZpKCsri+uDi8qRvZPILNk7dTV69GjU1NTg9OnTKCgowNdff41Lly7Z2s+J20zmTiKznNgJuPkqqp5ivYrKqZ9T5t+jEzuJzDKaY8oEJzExMexyr9cLVVXh8Xig6zq8Xm9cH1pUjuydRGbJ3qmrdevWoaGhAadOnUJBQQE6Ojrws5/9DIcPH464TrgxL/s2k7mTyCwndgKMXUXllvEuMoudrM8ymmPKBKe9vT3s8kAgAE3ToOs6Ojs7EQgE4soXlSN7J5FZsnfqas+ePTh27FjoX7DJycm4ePFir+uEG/OybzOZO4nMcmInwNhVVG4Z7yKz2Mn6LKM5ppyDoyhK2OXFxcXdjqcVFhbGlS8qR/ZOIrPc3Mnn88V0TsLUqVPx6aefIiMjA0ePHkV7ezumTZvW60nG4ca8m7dZf+8kMsvqTrGO98uXL+Oll15CU1MTtmzZgtOnT+PUqVOYM2dOxHXcMt5FZrGT9VlGv+Mtv4qqpaVFyGEEUTkis5zYSWSW7J1umD9/Pvx+P86fP4833ngDb775Ztz/g8q+zWTuJDLLiZ1uKCgogM/nw5EjRwBcP+/h8ccf73WCY0U3J2axk/VZRnIsn+AQOd3KlStx4MABDB48GKdOncL69euRm5trdy0iU5w5cwZvvfUWysvLAQAJCQkwYcc+keU4wSHqYfXq1SgpKek2qbmxjEg2AwcORDAYDB12OnPmDAYNGmRzKyLjeCdjoh4OHDhw07L333/fhiZE5lu3bh1mzZqF5uZmLFy4EA899BB+85vf2F2LyDDuwSH6n9dffx2vvfYazp49i4kTJ4aWX7x4EdOnT7exGZF5cnNzkZGRgdraWui6jtLSUiQlJdldi8gwTnCI/ufJJ5/E7NmzUVRUhBdeeCG0/LbbbsOwYcNsbEZkrqqqKtTU1EBRFHR0dGDevHl2VyIyjBMcov8ZMmQIhgwZgvLycmiahn//+9+4du0aLl26hEuXLmHUqFF2VyQSbsmSJfjyyy+xYMECAMDmzZtx8OBBvPrqqzY3IzKGExyiHl555RUUFxfje9/7Hjye66ep8WGbJKuqqiqcPHkydJJxfn4+0tLSbG5FZBwnOEQ9vPzyyzh16hTuuOMOu6sQmW78+PFoamoKPUy2ubm52zloRG7FCQ5RDyNHjsSQIUPsrkFkqrlz50JRFFy4cAETJkzA1KlToSgK6urqkJmZaXc9IsM4wSHq4a677kJ2djZ+/OMfd7sfyLPPPmtjKyKxVq5caXcFIlNxgkPUw6hRozBq1ChcvXoVV69etbsOkSmysrLsrkBkKk5wiHpYu3YtgOtPTE5MTLS5DZG5amtrsXz5cvz973/H1atXoWkaEhMT8e2339pdjcgQ3smYqIdPPvkEqampmDBhAgDgxIkTWLJkic2tiMyxbNkylJeXY9y4cQgGg9i6dSuWLVtmdy0iwxTdhKeqJScnw+/3h32v66PPjTxpVFSO7J1EZrm1U0VFBRoaGvqcef/992P37t14+OGHcezYMQDAvffei5MnT0ZcJ9KYd+s2YyexWVZ2inW8T548GQ0NDZg4cWLoVgiZmZmhp4uH46bxLjKLnazPMvIdb+khqpSUFOTl5UFVVWiahrKysrg+uKgc2TuJzJK9U08jR47s9lpVVVv7OXGbydxJZJYTO3V1yy234OrVq7jvvvuwatUqjBgxAu3t7bZ2c2IWO1mfZTTHlENUkc5b8Hq9UFUVHo8HHo8HXq83rnxRObJ3Epkle6euRo4ciSNHjkBRFFy9ehW//e1vQ4erIgk35mXfZjJ3EpnlxE5d7dy5E5qm4ZVXXkFiYiKam5vxzjvv9LqOW8a7yCx2sj7LaI4pe3Aizf4DgQA0TYOu6+js7EQgEIgrX1SO7J1EZsneqatNmzbhmWeewT//+U+kpKTgRz/6UdTb1ocb87JvM5k7icxyYqeubtzgLyEhIXSCfTRuGe8is9jJ+iyjOaacg3Pjlt89FRcXdzueVlhYGFe+qBzZO4nMcnMnn88X0zkJ8Qg35t28zfp7J5FZVnfq63hPS0uL+F0NoNdHk7hlvIvMYifrs4x+x1t+mXhLS4uQ8yRE5YjMcmInkVmyd7ohPz8fpaWluP322wEA586dw4oVK/Dmm2/a2s+J20zmTiKznNhp7969Atp058TPKTKLnazPMpLD++AQ9fD555+HJjcAMHTo0NDVVESyuHFo6osvvkBqamq39w4dOhR6n8iteB8coh46Oztx7ty50Ov//Oc/uHbtmo2NiMwzf/58lJSUQNd1BINBLF++HEVFRXbXIjKMe3CIelixYgUyMzPx2GOPQVEUvP322/jlL39pdy0iU9TV1WH16tXIzMzExYsXsXDhQhw+fNjuWkSGcYJD1ENeXh58Ph8qKyuh6zrefffdm3bhE8liwIABSEhIQDAYxJUrVzBmzBh4PNy5T+7HCQ5RGPfccw/uueceu2sQmW7KlCl45JFHUF9fj2+++QZ+vx+7d+/G7t277a5GZAin6URhzJgxo9vfRLLatm0b1q9fjwEDBmD48OF477338Mgjj9hdi8gwTnCIwrh8+TKAyDetJJLF5MmTb1q2aNEiG5oQiRX1EFVDQwOef/55NDY24tq1a9B1HYqi9HoTKCIiIiI7RZ3gLFy4EC+++CLS0tJ44hkRERG5QtQJzne/+108/PDDVnQhIiIiEiLqBGfdunUoLCzEQw89hEGDBoWWP/roo6YWI7KTCY9oIyIiC0Wd4Gzfvh3/+Mc/0NHRETpEpSgKJzgktY0bN3b7m4iI3CXqBOfEiRP461//akUXIsfIzs7u9jcREbmLokfZF//000/jF7/4RUx3ck1OTobf7w/7XtdHnxt50qioHNk7icxya6eKigo0NDT0OTM3Nxe7du3q9jTxJ554Ah988EHEdSKNebduM3YSm2Vlp1jHezzcNN5FZrGT9VlGvuOj7sGpqanBH/7wB4wZMwaDBg0ydJl4SkoK8vLyoKoqNE1DWVlZXB9cVI7snURmyd6pq7a2tpueJv7VV1/Z2s+J20zmTiKznNjJDE79nDL/Hp3YSWSW0Zyo133v378fp0+fxl/+8hdUVFRg7969qKio6HWdxMTEsMu9Xi9UVYXH44HH44HX6+1zUTNyZO8kMkv2Tl15PB40NTWFXjc2NkJRlF7XCTfmZd9mMncSmeXETka5ZbyLzGIn67OM5kTdgzN69GicOHECH3/8MQDgwQcfxKRJk3pdJ9LdXwOBADRNg67r6OzsRCAQiKms6BzZO4nMkr1TV88//zxmzJiBrKwsAEB1dTW2bNnS6zrhxrzs20zmTiKznNjJKLeMd5FZ7GR9ltGcqOfglJaW4o033ghdNbVnzx4sXrwYy5cvjxwa4V+7xcXF3Y6nFRYWxlRWdI7snURmubmTz+eL+ZyEtrY21NbWAgAeeOABJCUl9frfhxvzbt5m/b2TyCyrO8Uz3mPllvEuMoudrM8y+h0fdQ/Otm3bUFdXF9oluXr1akybNq3XCU5vWlpahBwzFpUjMsuJnURmyd6pqyNHjqC6ujr0es6cOXHlyL7NZO4kMsuJnczg1M8p8+/RiZ1EZhnJiXoOjq7rUFU19FpVVd4EjaS2Zs0alJaWIjU1FampqSgtLUVRUZHdtYiIKAZR9+AUFBTg/vvvx7x58wAAf/zjH/HUU0+ZXozILvv27cPx48dDN7bMz89Heno6NmzYYHMzIiLqq6gTnGeffRbZ2dmoqamBruvYvn070tPTrehGZJvz589j2LBhAIALFy7Y3IaIiGIVdYKzaNEi7Ny5ExkZGTctI5JRUVER0tPTkZOTA13XUV1dzb03REQuE3WC87e//a3ba03T8Nlnn5lWiMhuCxYsQHZ2Nurr66HrOkpKSjB8+HC7axERUQwiTnA2bNiAX//61wgGgxg8eDCA6yccDxw4EE8//bRlBYmscvTo0W6vU1JSAACtra1obW3ttheTiIicLeIEp6ioKPSHu+epP1ixYkXE9xRFwUcffWRhGyIiMiLqIapPP/30pmUPPfQQPvzwQ1MKEdmlsrLS7gpERCRIxAnOlStX0N7ejra2Npw7dy5075tvv/0Wra2tlhUkIiIiilXECc7mzZvx8ssv33TuweDBg7F06VJLyhERERHFI+IE55lnnsEzzzyD3//+93E/loGIiIjIDlHPwfH7/fjd734Xei5PdnY2/H4/BgwYYHo5Iiv1vIqqJ15FRUTkHlGfJl5YWIiOjg7k5+cDAHbu3AlVVbF169aI6yQnJ8Pv94d9r+uTQY08iEtUjuydRGa5tVNFRUWfnq6ck5MT8b1oV1FFGvNu3WbsJDbLyk59He9GuGm8i8xiJ+uzjHzHR92DU19fjxMnToRe/+AHP8CkSZPiLpqXlwdVVaFpGsrKyuL64KJyZO8kMkv2ToD4q6hk32YydxKZ5cROZnDq55T59+jETiKzjOZEfZq4qqo4c+ZM6PXZs2e7PV08nMTExLDLvV4vVFWFx+OBx+OB1+vtc1EzcmTvJDJL9k5dXb58Gc899xwWL14MADh9+jT27t3b6zrhxrzs20zmTiKznNjJKLeMd5FZ7GR9ltGcqHtwXnzxReTk5OCuu+6CrutobGzE9u3be12nvb097PJAIABN06DrOjo7OxEIBGIqKzpH9k4is2Tv1FVBQQF8Ph+OHDkC4Pq/Ih5//HHMmTMn4jrhxrzs20zmTiKznNjJKLeMd5FZ7GR9ltGcqOfgAMB///tfnDp1Crqu4+6778agQYN6D1WUsMuLi4u7HU8rLCyMqazoHNk7icxycyefzxfTOQmTJ09GQ0MD0tPTcezYMQDApEmTuh2q7SncmHfzNuvvnURmWd0p1vEeD7eMd5FZ7GR9ltHv+Kh7cK5cuYLXXnsNNTU1UBQFDz74IH7+85/jO9/5TlyFW1pahBwzFpUjMsuJnURmyd7phoEDByIYDIa+xM+cORN1Uh+J7NtM5k4is5zYyQxO/Zwy/x6d2ElklpGcqBOcvLw83HbbbaF74ZSXl2PRokXYtWtXXD+QyOnWrVuHWbNmobm5GQsXLsThw4exY8cOu2sREVEMok5wTp061W3XfE5OTtxXURG5QW5uLjIyMlBbWwtd11FaWoqkpCS7axERUQyiXkWVnp6O2tra0Ou6ujpMnz7d1FJEdquqqsKHH36IyspKfPzxx3bXISKiGEXcg5OWlgZFUdDR0YGysjKMGjUKiqKgsbERqampVnYkstSSJUvw5ZdfYsGCBQCuP5ft4MGDePXVV21uRkREfRVxghPtvh9EsqqqqsLJkydDJxnn5+cjLS3N5lZERBSLiBOc0aNHW9mDyDHGjx+Ppqam0P8Dzc3NmDhxos2tiIgoFlHPwQGAGTNmdPubSEZz587Fww8/jG+++QYTJkxAdnY2cnJyMGHCBHz99dd21yMiohhEvYoKuH7reiDyHYqJZLBy5Uq7KxARkSB9muAQ9QdZWVl2VyAiIkH6dIiKqD+pra3FlClTcOutt2LgwIFQVRWDBw+2uxYREcWAExyiHpYtW4by8nKMGzcOwWAQW7duxbJly+yuRUREMejTIao+PI+TSCpjx46FpmlQVRUFBQXIzMy0uxIREcWgTxOcjRs3dvubSGa33HILrl69ivvuuw+rVq3CiBEjeII9EZHLKLoJu2eSk5Ph9/vDvtf10edGnjQqKkf2TiKz3NqpoqICDQ0Nfc5sbGzEnXfeiY6ODmzcuBEXLlzAkiVLMHbs2IjrRBrzbt1m7CQ2y8pOsY73eLhpvIvMYifrs4x8x0fdg5Obm4tdu3bh9ttvBwCcO3cOTzzxBD744IO4iubl5UFVVWiahrKysrg+uKgc2TuJzJK9U1c3bvCXkJCAtWvXxp0j+zaTuZPILCd2MoNTP6fMv0cndhKZZTQn6knGbW1tockNAAwdOhRfffVVr+skJiaGXe71eqGqKjweDzweD7xeb5+LmpEjeyeRWbJ3Aq4/f23ixIkR//Qm3JiXfZvJ3ElklhM7GeWW8S4yi52szzKaE3UPjsfjQVNTE0aNGgXg+u77G8/oiSTS+QqBQACapkHXdXR2diIQCMRUVnSO7J1EZsneCTD2/LVwY172bSZzJ5FZTuxklFvGu8gsdrI+y2hO1HNw9u/fj8WLF4duglZdXY0tW7Zg5syZkUMjTICKi4u7HU8rLCyMqazoHNk7icxycyefzxfTOQlffPEFUlNTuy07dOgQsrOzI64Tbsy7eZv1904is6zuFOt4j4dbxrvILHayPsvod3zUPTizZs3C0aNHUVtbC+D6lVRJSUlxlQWAlpYWIceMReWIzHJiJ5FZsne6Yf78+Vi0aBFWrVqFK1euYNWqVWhoaMAnn3xiaz8nbjOZO4nMcmInMzj1c8r8e3RiJ5FZRnL6dKO/I0eO4NChQzh06FBookMkq7q6OjQ3NyMzMxNTpkxBcnIyDh8+bHctIiKKQdQJzpo1a1BaWorU1FSkpqaitLQURUVFVnQjssWAAQOQkJCAYDCIK1euYMyYMfB4eNNvIiI3ifqtvW/fPhw4cABPPfUUnnrqKezfvx9//vOfrehGZIspU6YgISEB9fX1qKmpQXl5OR577DG7axERUQz6dCfj8+fPY9iwYQCACxcumFqIyG7btm3D5MmTAQDDhw/He++9h507d9rcioiIYhF1glNUVIT09HTk5ORA13VUV1djw4YNVnQjssWNyU1XixYtsqEJERHFK+oEZ8GCBcjOzkZ9fT10XUdJSQmGDx9uRTciIiKiuESc4Bw9erTb65SUFABAa2srWltbkZGRYW4zIiIiojhFnOCsWLEi4kqKouCjjz4ypRARERGRUREnOJWVlVb2ICIiIhKGN/cgIiIi6XCCQ0RERNLhBIeIiIikE/Fp4j2vouqpt6uokpOT4ff7w77X9cmgRh7EJSpH9k4is9zaqaKiwvSnK0ca827dZuwkNsvKThzv5mWxk/VZRr7jLb2KKiUlBXl5eVBVFZqmoaysLK4PLipH9k4is2TvZAbZt5nMnURmObGTGZz6OWX+PTqxk8gsozkRD1FVVlZG/BNtcpOYmBh2udfrhaqq8Hg88Hg88Hq9fS5qRo7snURmyd7JqHBjXvZtJnMnkVlO7GSUW8a7yCx2sj7LaE7Uc3AuX76M5557DosXLwYAnD59Gnv37u11nfb29rDLA4EANE2Dpmno7OxEIBCIqazoHNk7icySvZNR4ca87NtM5k4is5zYySi3jHeRWexkfZbRnIjn4Nzw05/+FD6fD2VlZTh58iSCwSCmTZuG48ePRw5VlLDLi4uLux1PKywsjKms6BzZO4nMcnMnn89n+jkJ4ca8m7dZf+8kMsvqThzv5mSxk/VZRr/joz6L6syZM3jrrbdQXl4OAEhISECUOVGvWlpahBwzFpUjMsuJnURmyd7JDLJvM5k7icxyYiczOPVzyvx7dGInkVlGcqIeoho4cCCCwWBoxn7mzBkMGjQorh9GREREZIWoe3DWrVuHWbNmobm5GQsXLsThw4exY8cOC6oRERERxSfqBCc3NxcZGRmora2FrusoLS1FUlKSFd2IiIiI4hJ1ggMAVVVVqKmpgaIo6OjowLx588zuRURERBS3qOfgLFmyBJs2bUJaWhruvfdebN68GUuXLrWiGxEREVFcou7BqaqqwsmTJ0MnGefn5yMtLc30YkRERETxiroHZ/z48Whqagq9bm5uxsSJE00tRURERGRExD04c+fOhaIouHDhAiZMmICpU6dCURTU1dUhMzPTyo5EREREMYk4wVm5cqWVPYiIiIiEiTjBycrKsrIHERERkTBRz8Gpra3FlClTcOutt2LgwIFQVRWDBw+2ohsRERFRXKJOcJYtW4by8nKMGzcOwWAQW7duxbJly6zoRkRERBSXPt3ob+zYsdA0DaqqoqCggCcZExERkaMpepRHg3//+9/HwYMHUVhYiOHDh2PEiBHYsWMHTpw4EXGd5ORk+P3+sO91ffS5kSeNisqRvZPILLd2qqioQENDQ9w/oy8ijXm3bjN2EptlZSeOd/Oy2Mn6LCPf8VH34OzcuROapuGVV17Bxo0b0dzcjHfeeSfuonl5eVBVFZqmoaysLK4PLipH9k4is2TvZAbZt5nMnURmObGTGZz6OWX+PTqxk8gsozlRz8EZPXo0EhISMHjwYKxduxYvvfQSxo4d2+s6iYmJYZd7vV6oqgqPxwOPxwOv19vnombkyN5JZJbsnYwKN+Zl32YydxKZ5cRORrllvIvMYifrs4zmRNyDk5aWFno8Qziff/55xPfa29vDLg8EAtA0Dbquo7OzE4FAoO9NTciRvZPILNk7GRVuzMu+zWTuJDLLiZ2Mcst4F5nFTtZnGc2JeA5OY2NjryuOHj06cmiEiVFxcXG342mFhYUxVBWfI3snkVlu7uTz+Uw/JyHcmHfzNuvvnURmWd2J492cLHayPsvod3zEPTg3JjBffPEFUlNTu7136NChXic4vWlpaRFyzFhUjsgsJ3YSmSV7JzPIvs1k7iQyy4mdzODUzynz79GJnURmGcmJeg7O/PnzUVJSAl3XEQwGsXz5chQVFcX1w4iIiIisEHWCU1dXh+bmZmRmZmLKlClITk7G4cOHrehGREREFJeoE5wBAwYgISEBwWAQV65cwZgxY+DxRF2NiIiIyDZRb/Q3adIkPPLII/jVr36Fb775Bn6/HwMGDMDu3bsjrpOUlGTrJbtEXQUCAbS1tZn6MzjmySk43qm/iTTmo05wGhoaMHny5G7Ldu7ciUWLFoltSERERCRI1AkOERERkdvwZBoiIiKSDic4REREJB1OcIiIiEg6nOAQERGRdDjBISIiIulwgkNERETS4QSHiIiIpMMJDhEREUnn/wGh0manRpaQkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ylabel position\n",
    "fig,ax = plt.subplots(1,3,figsize=(8,3)) \n",
    "ax = ax.ravel()  \n",
    "fig.patch.set_facecolor('white')\n",
    "loc=['bottom', 'center', 'top']\n",
    "\n",
    "for a,i in zip(ax,range(3)):\n",
    "    a.patch.set_facecolor('k')\n",
    "    a.grid(color='gray', linestyle='-', linewidth=4)\n",
    "    a.tick_params(labelbottom=False, labelleft=False)\n",
    "    a.tick_params(color='white')\n",
    "    a.plot(x,y,'o',c='white', ms=3)\n",
    "    a.set_ylabel('xlabel loc='+str(loc[i])+'', loc=loc[i])\n",
    "    a.xaxis.set_major_locator(plt.MaxNLocator(10))\n",
    "    a.yaxis.set_major_locator(plt.MaxNLocator(10))\n",
    "    a.set_box_aspect(1)\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.savefig(\"kirameki_ylabel.png\",dpi=100, pad_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "x,y = np.meshgrid(np.linspace(0.1,0.9,9),np.linspace(0.1,0.9,9))\n",
    "\n",
    "#xlabel position\n",
    "fig,ax = plt.subplots(1,3,figsize=(8,3)) \n",
    "ax = ax.ravel()  \n",
    "fig.patch.set_facecolor('white')\n",
    "pad=[10,20,30]\n",
    "\n",
    "for a,i in zip(ax,range(3)):\n",
    "    a.patch.set_facecolor('k')\n",
    "    a.grid(color='gray', linestyle='-', linewidth=4)\n",
    "    a.tick_params(labelbottom=False, labelleft=False)\n",
    "    a.tick_params(color='white')\n",
    "    a.plot(x,y,'o',c='white', ms=3)\n",
    "    a.set_xlabel('labelpad='+str(pad[i])+'',labelpad=pad[i])\n",
    "    a.xaxis.set_major_locator(plt.MaxNLocator(10))\n",
    "    a.yaxis.set_major_locator(plt.MaxNLocator(10))\n",
    "    a.set_box_aspect(1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"kirameki_xlabelpad.png\",dpi=100, pad_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3.0\n"
     ]
    }
   ],
   "source": [
    "#version\n",
    "import matplotlib\n",
    "print(matplotlib.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.19.1\n"
     ]
    }
   ],
   "source": [
    "print(np.__version__)"
   ]
  },
  {
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}