saimn/SlowCompoundModel.ipynb

## SlowCompoundModel.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numpy 1.16.0rc2\n",
      "Astropy 3.2.dev23675\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%astropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from astropy.modeling import models, fitting\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 0.004322528839111328\n",
      "10 0.007405757904052734\n",
      "15 0.05642366409301758\n",
      "20 1.1133241653442383\n",
      "25 36.887035846710205\n",
      "30 1324.6839685440063\n"
     ]
    }
   ],
   "source": [
    "ind = list(range(5, 32, 5))\n",
    "times = []\n",
    "\n",
    "for n in ind:\n",
    "    x0 = np.random.rand(n) * 100\n",
    "    y0 = np.random.rand(n) * 100\n",
    "    xstd = np.random.rand(n) * 5\n",
    "    ystd = np.random.rand(n) * 5\n",
    "\n",
    "    model = None\n",
    "    for x_mean, y_mean, x_stddev, y_stddev in zip(x0, y0, xstd, ystd):\n",
    "        gauss = models.Gaussian2D(x_mean=x_mean, y_mean=y_mean, x_stddev=x_stddev, y_stddev=y_stddev)\n",
    "        if model is None:\n",
    "            model = gauss\n",
    "        else:\n",
    "            model += gauss\n",
    "\n",
    "    out = np.zeros((100, 100))\n",
    "    \n",
    "    t0 = time()\n",
    "    out = model.render(out)\n",
    "    times.append(time() - t0)\n",
    "    print(n, times[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "times1 = times.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(ind, times1)\n",
    "plt.ylim((1e-4, 1e5))\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 0.007493257522583008\n",
      "12 0.011241674423217773\n",
      "14 0.03549075126647949\n",
      "16 0.08400416374206543\n",
      "18 0.2851390838623047\n",
      "20 1.1023526191711426\n",
      "22 4.309922695159912\n",
      "24 17.368453979492188\n",
      "26 73.37567853927612\n",
      "28 292.9972379207611\n"
     ]
    }
   ],
   "source": [
    "ind = list(range(10, 30, 2))\n",
    "times = []\n",
    "\n",
    "for n in ind:\n",
    "    x0 = np.random.rand(n) * 100\n",
    "    y0 = np.random.rand(n) * 100\n",
    "    xstd = np.random.rand(n) * 5\n",
    "    ystd = np.random.rand(n) * 5\n",
    "\n",
    "    model = None\n",
    "    for x_mean, y_mean, x_stddev, y_stddev in zip(x0, y0, xstd, ystd):\n",
    "        gauss = models.Gaussian2D(x_mean=x_mean, y_mean=y_mean, x_stddev=x_stddev, y_stddev=y_stddev)\n",
    "        if model is None:\n",
    "            model = gauss\n",
    "        else:\n",
    "            model += gauss\n",
    "\n",
    "    out = np.zeros((100, 100))\n",
    "    \n",
    "    t0 = time()\n",
    "    out = model.render(out)\n",
    "    times.append(time() - t0)\n",
    "    print(n, times[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind = list(range(5, 32, 5)) + list(range(10, 30, 2))\n",
    "plt.scatter(ind, times1 + times)\n",
    "plt.ylim((1e-4, 1e5))\n",
    "plt.yscale('log')"
   ]
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"Numpy 1.16.0rc2\n",
	"Astropy 3.2.dev23675\n"
	]
	}
	],
	"source": [
	"%matplotlib inline\n",
	"%astropy"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [],
	"source": [
	"import matplotlib.pyplot as plt\n",
	"from astropy.modeling import models, fitting\n",
	"from time import time"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 35,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"5 0.004322528839111328\n",
	"10 0.007405757904052734\n",
	"15 0.05642366409301758\n",
	"20 1.1133241653442383\n",
	"25 36.887035846710205\n",
	"30 1324.6839685440063\n"
	]
	}
	],
	"source": [
	"ind = list(range(5, 32, 5))\n",
	"times = []\n",
	"\n",
	"for n in ind:\n",
	" x0 = np.random.rand(n) * 100\n",
	" y0 = np.random.rand(n) * 100\n",
	" xstd = np.random.rand(n) * 5\n",
	" ystd = np.random.rand(n) * 5\n",
	"\n",
	" model = None\n",
	" for x_mean, y_mean, x_stddev, y_stddev in zip(x0, y0, xstd, ystd):\n",
	" gauss = models.Gaussian2D(x_mean=x_mean, y_mean=y_mean, x_stddev=x_stddev, y_stddev=y_stddev)\n",
	" if model is None:\n",
	" model = gauss\n",
	" else:\n",
	" model += gauss\n",
	"\n",
	" out = np.zeros((100, 100))\n",
	" \n",
	" t0 = time()\n",
	" out = model.render(out)\n",
	" times.append(time() - t0)\n",
	" print(n, times[-1])"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 43,
	"metadata": {},
	"outputs": [],
	"source": [
	"times1 = times.copy()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 44,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": "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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"plt.scatter(ind, times1)\n",
	"plt.ylim((1e-4, 1e5))\n",
	"plt.yscale('log')"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 45,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"10 0.007493257522583008\n",
	"12 0.011241674423217773\n",
	"14 0.03549075126647949\n",
	"16 0.08400416374206543\n",
	"18 0.2851390838623047\n",
	"20 1.1023526191711426\n",
	"22 4.309922695159912\n",
	"24 17.368453979492188\n",
	"26 73.37567853927612\n",
	"28 292.9972379207611\n"
	]
	}
	],
	"source": [
	"ind = list(range(10, 30, 2))\n",
	"times = []\n",
	"\n",
	"for n in ind:\n",
	" x0 = np.random.rand(n) * 100\n",
	" y0 = np.random.rand(n) * 100\n",
	" xstd = np.random.rand(n) * 5\n",
	" ystd = np.random.rand(n) * 5\n",
	"\n",
	" model = None\n",
	" for x_mean, y_mean, x_stddev, y_stddev in zip(x0, y0, xstd, ystd):\n",
	" gauss = models.Gaussian2D(x_mean=x_mean, y_mean=y_mean, x_stddev=x_stddev, y_stddev=y_stddev)\n",
	" if model is None:\n",
	" model = gauss\n",
	" else:\n",
	" model += gauss\n",
	"\n",
	" out = np.zeros((100, 100))\n",
	" \n",
	" t0 = time()\n",
	" out = model.render(out)\n",
	" times.append(time() - t0)\n",
	" print(n, times[-1])"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 14,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD+CAYAAAAzmNK6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAFmFJREFUeJzt3XGMHOd93vHv06vOJpTETFLXsE9kRZsCHcJqo4qQUigIBCOuqMY0WdWwRcRAXBNiFJRBigKCqUZA48KF1KoNUMOqBbYWGCEJWUJRWbpiQAdQBKaB4pISnVIyy4olnPDOqilHpVsVRG3Jv/5xe/LpxDvu3dzsze1+P4Cgm/dmZ97xWvvcvO+7v0lVIUkaPX9ppTsgSVoZBoAkjSgDQJJGlAEgSSPKAJCkEWUASNKIMgAkaUQZAJI0ogYWAEluT/JHSR5NcvugzitJurJGAZDksSQXk7wwp31rkrNJziXZ22su4DXgncBkk/NKkppLk1IQSX6O6Q/1x6vqQ722MeC/Ax9h+oP+BLAT+G9V9YMk7wF+s6p+sWnnJUlL95ebvLiqjie5fk7zLcC5qjoPkOQgsL2qvtH7/f8C3jHfMZPsBnYDXHvttTd/8IMfbNJFSRo5zz333Heq6t1X269RAMxjArgwa3sSuDXJXcAdwFrgi/O9uKr2JXkZ2Pbe97735pMnT7bQRUkaXkn+rJ/9BjYJXFVPVtUvV9Unq+qZq+z7lara/a53vWtAvZOk0dNGAEwB62ZtX9dr61uSbUn2ffe7313WjkmSfqiNADgB3JBkQ5Jx4G7gSAvnkSQ10HQZ6AHgWWBTkskku6rqdWAPcAw4AxyqqhcXc1yHgCSpfU1XAe2cp/0ocHSpx02yDdi2cePGpR5CknQVnSwF4R2AJLWvkwHgJLAkta+TAeAdgCS1r5MB4B2AJLWvkwHgHYAkta+TASBJal8nA8AhIElqXycDwCEgSWpfJwNAktQ+A0CSRlQnA8A5AElqXycDwDkASWpfJwNAktQ+A0CSRtRAAyDJtUlOJvnoIM8rSXq7pg+EeSzJxSQvzGnfmuRsknNJ9s761WeBQ03OKUlaHk3vAPYDW2c3JBkDHgHuBDYDO5NsTvIR4BvAxYbnlCQtg6ZPBDue5Po5zbcA56rqPECSg8B24EeAa5kOhctJjlbVD5qcX5K0dI0CYB4TwIVZ25PArVW1ByDJp4HvzPfhn2Q3sBtg/fr1LXRPkgTtBMCCqmr/VX6/L8nLwLbx8fGbB9MrSRo9bawCmgLWzdq+rtcmSeqQNgLgBHBDkg1JxoG7gSMtnEeS1EDTZaAHgGeBTUkmk+yqqteBPcAx4AxwqKpeXMxxLQUhSe1rugpo5zztR4GjSz1ukm3Ato0bNy71EJKkq+hkKQjvACSpfZ0MAMtBS1L7OhkA3gFIUvs6GQDeAUhS+zoZAN4BSFL7OhkAkqT2dTIAHAKSpPZ1MgAcApKk9nUyACRJ7TMAJGlEdTIAnAOQpPZ1MgCcA5Ck9g38gTCSpCs7fGqKh4+d5VuXLvO+tWu4745N7LhporXzGQCS1AGHT01x/5Onufz9NwCYunSZ+588DdBaCAxsCCjJTyV5NMkTSX5lUOeVpNXg4WNn3/zwn3H5+2/w8LGzrZ2z6QNhHktyMckLc9q3Jjmb5FySvQBVdaaq7gU+AdzW5LySNGy+denyotqXQ9M7gP3A1tkNScaAR4A7gc3AziSbe7/7GPAUDR4WI0nD6H1r1yyqfTk0CoCqOg68Oqf5FuBcVZ2vqu8BB4Htvf2PVNWdwC82Oa8kDZv77tjEmmvG3tK25pox7rtjU2vnbGMSeAK4MGt7Erg1ye3AXcA7WOAOIMluYDfA+vXrW+ieJHXPzETvUK4CqqpngGf62G9fkpeBbePj4ze33S9J6oodN020+oE/VxurgKaAdbO2r+u1SZI6pI0AOAHckGRDknHgbuBIC+eRJDXQdBnoAeBZYFOSySS7qup1YA9wDDgDHKqqFxdzXEtBSFL7Gs0BVNXOedqP0mCpZ5JtwLaNGzcu9RCSpKuwGJwkjahOBoDloCWpfZ0MAO8AJKl9nQwA7wAkqX2dDADvACR10eFTU9z20NNs2PsUtz30NIdPre6vOPk8AEnqw0rU629bJ+8AHAKS1DUrUa+/bZ0MAIeAJHXNStTrb1snA0CSumYl6vW3zQCQpD6sRL3+tjkJLEl9WIl6/W3rZABYC0hSFw26Xn/bOjkE5CSwJLWvkwEgSWrfwIaAkuwAfgH4MeDLVfXVQZ1bkvR2TR8I81iSi0lemNO+NcnZJOeS7AWoqsNVdQ9wL/DJJueVJDXXdAhoP7B1dkOSMeAR4E5gM7AzyeZZuzzQ+70kaQU1CoCqOg68Oqf5FuBcVZ2vqu8BB4HtmfbPgd+vquebnFeS1FwbcwATwIVZ25PArcCvAj8PvCvJxqp69EovTrIb2A2wfv36FronaVgdPjU1VOv02zawSeCq+gLwhT7225fkZWDb+Pj4ze33TNIwGMZqnW1rYxnoFLBu1vZ1vTZJas0wVutsWxsBcAK4IcmGJOPA3cCRxRzAL4JJWqxhrNbZtqbLQA8AzwKbkkwm2VVVrwN7gGPAGeBQVb24yOP6PABJizKM1Trb1mgOoKp2ztN+FDja5NiStBj33bHpLXMAsPqrdbatk6UgHAKStFg7bprgwbtuZGLtGgJMrF3Dg3fd6ATwAqwGKqmzFrusc9iqdbbNOwBJnTSzrHPq0mWKHy7rPHzKRYXLpZMB4CSwJJd1tq+TAeAdgCSXdbavkwEgSS7rbF8nA8AhIEnD+BD2rulkADgEJMllne3r5DJQSQKXdbbNAJA0EJZq7h4DQFLrLNXcTZ2cA3ASWBourunvpk4GgJPA0nBxTX83dTIAJA0X1/R3kwEgqXWu6e+mgU0CJ3k/8OvAu6rq44M6r6SVNzPR6yqgbmkUAEkeAz4KXKyqD81q3wr8a2AM+HdV9VBVnQd2JXmiyTklrU6u6e+epkNA+4GtsxuSjAGPAHcCm4GdSTY3PI8kaZk1CoCqOg68Oqf5FuBcVZ2vqu8BB4Ht/R4zye4kJ5OcfOWVV5p0T5K0gDYmgSeAC7O2J4GJJD+Z5FHgpiT3z/fiqtoHfA54fnx8vIXuSZJggJPAVfUXwL2DOp8kaWFt3AFMAetmbV/Xa+ubXwSTpPa1EQAngBuSbEgyDtwNHFnMASwFIUntaxQASQ4AzwKbkkwm2VVVrwN7gGPAGeBQVb3YvKuSpOWUqlrpPsxry5YtdfLkyZXuhjQSLNc8PJI8V1VbrrZfJ8tBJ9kGbNu4ceNKd0UaCZZrHk2drAXkJLA0WJZrHk2dDABJg2W55tHUyQBwFZA0WJZrHk2dDACHgKTBslzzaOrkJLCkwbJc82jqZAC4CkgaPMs1jx6HgCRpRHUyACRJ7TMAJGlEdTIAXAYqSe3rZAA4ByBJ7etkAEiS2tfJZaCS3u6Bw6c58LULvFHFWMLOW9fx+R03rnS3tIoNLACSXAv8G+B7wDNV9TuDOre02j1w+DS//Sd//ub2G1VvbhsCWqqmD4R5LMnFJC/Mad+a5GySc0n29prvAp6oqnuAjzU5rzRqDnztwqLapX40nQPYD2yd3ZBkDHgEuBPYDOxMspnpZwPP/L/1rXVnJS3ojXke3DRfu9SPRgFQVceBV+c03wKcq6rzVfU94CCwHZhkOgQWPG+S3UlOJjn5yiuvNOmeNDTGkkW1S/1oYxXQBD/8Sx+mP/gngCeBv5fkS8BX5ntxVe0DPgc8Pz4+3kL3pNVn563rFtUu9WNgk8BV9X+Bv9/nvl8BvrJly5Z72u2VtDrMTPS6CkjLqY0AmAJm/1lyXa+tb1YDld7u8ztu9ANfy6qNIaATwA1JNiQZB+4GjrRwHklSA02XgR4AngU2JZlMsquqXgf2AMeAM8ChqnpxMce1FIQkta/REFBV7Zyn/ShwdKnHdQhIktpnLSBJGlGdDACHgCSpfZ0MAElS+zpZDdQ5AK02h09N8fCxs3zr0mXet3YN992xyQesq/M6eQfgEJBWk8Onprj/ydNMXbpMAVOXLnP/k6c5fGpRX3+RBq6TASCtJg8fO8vl77+1vuHl77/Bw8fOrlCPpP50MgB8JrBWk29duryodqkrOhkADgFpNXnf2jWLape6opMBIK0m992xiTXXjL2lbc01Y9x3x6YV6pHUn06uApJWk5nVPq4C0mrTyQBwGahWmx03TfiBr1Wnk0NAzgFIUvs6GQCSpPYZAJI0ogYWAEnen+TLSZ4Y1DklSfPrKwCSPJbkYpIX5rRvTXI2ybkkexc6RlWdr6pdTTorSVo+/a4C2g98EXh8piHJGPAI8BFgEjiR5AgwBjw45/WfqaqLjXsrLZHF2qS36ysAqup4kuvnNN8CnKuq8wBJDgLbq+pB4KNL7VCS3cBugPXr1y/1MNKbZoq1zdTrmSnWBhgCGmlN5gAmgAuztid7bVeU5CeTPArclOT++farqn3A54Dnx8fHG3RPmmaxNunKBjYJXFV/UVX3VtUHencJC+3r9wC0bCzWJl1Zk28CTwHrZm1f12trzG8C62oWM6b/vrVrmLrCh73F2jTqmtwBnABuSLIhyThwN3BkebolzW+xD2CxWJt0Zf0uAz0APAtsSjKZZFdVvQ7sAY4BZ4BDVfXicnTKISAtZLFj+jtumuDBu25kYu0aAkysXcODd93oBLBGXr+rgHbO034UOLqsPcIhIC1sKWP6FmuT3q6TpSC8A9BCfACLtDw6GQA+EnL0HD41xW0PPc2GvU9x20NPL/hAdcf0peXRyQDwDmC0LHZS1zF9aXmkqla6D28zaw7gnpdeemmlu6OW3fbQ01dcpjmxdg1/vPfDK9AjaXVL8lxVbbnaft4BaMX5RS1pZXQyADRanNSVVkYnA8BJ4NHipK60MjoZAA4BjRYndaWV0aQWkLRs/KKWNHidvAOQJLWvkwHgHIAkta+TAeAcgCS1r5MBIElqn5PAuiofqC4Np4EGQJIdwC8APwZ8uaq+Osjza/F8oLo0vPoeAkryWJKLSV6Y0741ydkk55LsXegYVXW4qu4B7gU+ubQua5B8oLo0vBZzB7Af+CLw+ExDkjHgEeAjwCRwIskRYAyY++D3z1TVxd7PD/Rep46zTo80vPoOgKo6nuT6Oc23AOeq6jxAkoPA9qp6EPjo3GMkCfAQ8PtV9fyVzpNkN7AbYP369f12Ty3xgerS8Gq6CmgCuDBre7LXNp9fBX4e+HiSe6+0Q1Xtq6otVbXl3e9+d8PujYYHDp/mA/cf5fq9T/GB+4/ywOHTy3Zs6/RIw2ugk8BV9QXgC1fbz2cC9++Bw6f57T/58ze336h6c/vzO25sfPyZiV5XAUnDp2kATAHrZm1f12vTgBz42oV525cjAMA6PdKwajoEdAK4IcmGJOPA3cCR5t1Sv96Y54lu87VL0ozFLAM9ADwLbEoymWRXVb0O7AGOAWeAQ1X1YtNOWQqif2PJotolacZiVgHtnKf9KHB02XpEszmAUfvW6s5b171lDmB2uyQtpJO1gJZ6BzDzrdWpS5cpfvit1cOnhnda4vM7buRTP7P+zb/4xxI+9TPrl238X9LwSnVwrHjWHcA9L730Ut+vu+2hp6+4Zn1i7Rr+eO+Hl7GHktRdSZ6rqi1X22+o7gD81qok9a+TAbDUB8LM9+1Uv7UqSW/XyQBY6h2A31qVpP4N1fMA/NaqJPWvkwHQZBmo31qVpP4M1RCQJKl/nQwASVL7DABJGlGdDIClLgOVJPWvkwHgHIAkta+TASBJap8BIEkjamABkOSnkjya5IkkvzKo80qSrqyvAEjyWJKLSV6Y0741ydkk55LsXegYVXWmqu4FPgHctvQuS5KWQ793APuBrbMbkowBjwB3ApuBnUk2J7kxyX+a889f7b3mY8BTLPMDZCRJi9dXKYiqOp7k+jnNtwDnquo8QJKDwPaqehD46DzHOQIcSfIU8LtL7bQkqbkmtYAmgAuztieBW+fbOcntwF3AO1jgDiDJbmA3wPr16xt0T5K0kIEVg6uqZ4Bn+thvX5KXgW3j4+M3t90vSRpVTVYBTQGznzx+Xa9NkrQKNAmAE8ANSTYkGQfuBo4sT7ckSW3rdxnoAeBZYFOSySS7qup1YA9wDDgDHKqqF5ejU5aCkKT29bsKaOc87UdpYUlnkwfCSJL608lSEN4BSFL7OhkAloOWpPZ1MgC8A5Ck9nUyALwDkKT2dTIAvAOQpPZ1MgAkSe3rZAA4BCRJ7etkADgEJEnt62QASJLaZwBI0ogyACRpRHUyAJwElqT2dTIAnASWpPZ1MgAkSe0baAAkuTbJySRXfGi8JGlw+n0gzGNJLiZ5YU771iRnk5xLsrePQ30WOLSUjkqSlle/D4XfD3wReHymIckY8AjwEWASOJHkCDAGPDjn9Z8B/gbwDeCdzbosSVoO/T4R7HiS6+c03wKcq6rzAEkOAtur6kHgbUM8SW4HrgU2A5eTHK2qHyy965KkJvq9A7iSCeDCrO1J4Nb5dq6qXwdI8mngO/N9+CfZDezubb6W5OwS+/dXgO8s8bWrldc8Grzm4df0ev9aPzs1CYAlqar9V/n9PmBf0/MkOVlVW5oeZzXxmkeD1zz8BnW9TVYBTQHrZm1f12uTJK0CTQLgBHBDkg1JxoG7gSPL0y1JUtv6XQZ6AHgW2JRkMsmuqnod2AMcA84Ah6rqxfa6umiNh5FWIa95NHjNw28g15uqGsR5JEkdYykISRpRBoAkjaihDIAk30xyOsnXk5xc6f604UrlOZL8RJI/SPJS798/vpJ9XG7zXPNvJJnqvddfT/J3VrKPyynJuiR/mOQbSV5M8mu99qF9nxe45mF+n9+Z5L8k+dPeNX+u174hydd6pXb+fW+xzfKeexjnAJJ8E9hSVUP7xZEkPwe8BjxeVR/qtf0L4NWqeqhXm+nHq+qzK9nP5TTPNf8G8FpV/cuV7FsbkrwXeG9VPZ/kR4HngB3ApxnS93mBa/4Ew/s+B7i2ql5Lcg3wn4FfA/4R8GRVHUzyKPCnVfWl5Tz3UN4BjIKqOg68Oqd5O/BbvZ9/i+n/cIbGPNc8tKrq5ap6vvfz/2F6td0EQ/w+L3DNQ6umvdbbvKb3TwEfBp7otbfyPg9rABTw1STP9UpLjIr3VNXLvZ//J/CelezMAO1J8l97Q0RDMxwyW68W103A1xiR93nONcMQv89JxpJ8HbgI/AHwP4BLveX2MF1qZ9mDcFgD4Ger6m8CdwL/oDd0MFJqemxv+Mb33u5LwAeAnwZeBv7VynZn+SX5EeD3gH9YVf979u+G9X2+wjUP9ftcVW9U1U8zXVHhFuCDgzjvUAZAVU31/n0R+A9M/w86Cr7dG0OdGUu9uML9aV1Vfbv3H88PgH/LkL3XvTHh3wN+p6qe7DUP9ft8pWse9vd5RlVdAv4Q+FvA2iQz9dpaKbUzdAHQe+rYj878DPxt4IWFXzU0jgC/1Pv5l4D/uIJ9GYiZD8Kev8sQvde9ycEvA2eq6jdn/Wpo3+f5rnnI3+d3J1nb+3kN089YOcN0EHy8t1sr7/PQrQJK8n6m/+qH6Wqnv1tV/2wFu9SKXnmO25kuG/tt4J8Ah5l+4tp64M+AT1TV0EyaznPNtzM9LFDAN4FfnjU+vqol+Vngj4DTwEz59H/M9Jj4UL7PC1zzTob3ff7rTE/yjjH9R/mhqvqnvc+yg8BPAKeAT1XV/1vWcw9bAEiS+jN0Q0CSpP4YAJI0ogwASRpRBoAkjSgDQJJGlAEgSSPKAJCkEfX/AUeAeQiRY62pAAAAAElFTkSuQmCC\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"ind = list(range(5, 32, 5)) + list(range(10, 30, 2))\n",
	"plt.scatter(ind, times1 + times)\n",
	"plt.ylim((1e-4, 1e5))\n",
	"plt.yscale('log')"
	]
	},
	{
	"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": "3.7.0"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}