{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import bpy\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import tempfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_img(size=[480,640]):\n",
    "    scene = bpy.context.scene\n",
    "    scene.render.resolution_x = size[1]\n",
    "    scene.render.resolution_y = size[0]\n",
    "    scene.render.resolution_percentage = 100\n",
    "    tmpdir=tempfile.TemporaryDirectory()\n",
    "    scene.render.filepath=tmpdir.name+\"/hoge\"\n",
    "    bpy.ops.render.render(write_still=True)\n",
    "    img=Image.open(tmpdir.name+\"/hoge.png\")\n",
    "    tmpdir.cleanup()\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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p00OMXEVHOT5KU+p0QZAqpifrmjJlStFerZ+e7Pnz53X+/HkCNqJKGblwFap5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a32da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c=bpy.data.objects['Cube']\n",
    "\n",
    "xnum=4\n",
    "ynum=4\n",
    "for i,x in enumerate(np.linspace(-5,5,xnum*ynum)):\n",
    "    c.location.x=x\n",
    "    plt.subplot(ynum,xnum,i+1)\n",
    "    plt.imshow(get_img())\n",
    "    plt.xticks([]);plt.yticks([])\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}