bottleneck_mean_error.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import bottleneck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 300.*np.ones((800**2,), dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300.0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "302.6018981933594"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottleneck.nanmean(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = []\n",
    "\n",
    "for n in range(10, 1000, 50):\n",
    "    a = 1.6*np.ones((n**2,), dtype=np.float32)\n",
    "    \n",
    "    arr.append((n, np.abs(np.mean(a) - bottleneck.nanmean(a))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'error')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr = np.array(arr)\n",
    "plt.scatter(arr[:,0], arr[:,1])\n",
    "plt.grid(True)\n",
    "plt.xlabel('N')\n",
    "plt.ylabel('error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}