Using_scikits_bootstrap.pynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of using scikits.bootstrap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import all the modules needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scikits.bootstrap as bootstrap\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create an array"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [
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     "name": "stdout",
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     "text": [
      "[[  0.   1.   2.]\n",
      " [ nan   4.   5.]\n",
      " [  6.   7.   8.]\n",
      " [ nan  10.  11.]\n",
      " [ 12.  13.  14.]\n",
      " [ nan  16.  17.]\n",
      " [ 18.  19.  20.]\n",
      " [ nan  22.  23.]\n",
      " [ 24.  25.  26.]\n",
      " [ nan  28.  29.]\n",
      " [ 30.  31.  32.]\n",
      " [ nan  34.  35.]]\n"
     ]
    }
   ],
   "source": [
    "ma = np.arange(36.).reshape(12,3)\n",
    "idx = np.array([False, False, False, True, False, False] * 6).reshape(12,3)\n",
    "ma[idx] = np.nan\n",
    "print ma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1.    4.5   7.   10.5  13.   16.5  19.   22.5  25.   28.5  31.   34.5]\n"
     ]
    }
   ],
   "source": [
    "medians = np.nanmedian(ma, axis=1)\n",
    "print medians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  3. ,   8.5,   9.5],\n",
       "       [ 27. ,  26.5,  27.5]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CIzero = bootstrap.ci(data=ma, statfunction=lambda x: np.nanmedian(x,axis = 0), n_samples=1000, method='pi')\n",
    "CIzero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   4.,   6.,  10.,  12.,  16.,  18.,  22.,  24.,  28.,  30.,\n",
       "         34.],\n",
       "       [  2.,  nan,   8.,  nan,  14.,  nan,  20.,  nan,  26.,  nan,  32.,\n",
       "         nan]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CIT = bootstrap.ci(data=ma.T, statfunction=lambda x: np.nanmedian(x,axis = 0), n_samples=1000, method='pi')\n",
    "CIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1. ,   1. ,   1. ,   1. ,   1. ,   1. ,   1. ,   1. ,   1. ,\n",
       "          1. ,   1. ,   1. ],\n",
       "       [ 34.5,  34.5,  34.5,  34.5,  34.5,  34.5,  34.5,  34.5,  34.5,\n",
       "         34.5,  34.5,  34.5]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CIone = bootstrap.ci(data=ma, statfunction=lambda x: np.nanmedian(x,axis = 1), n_samples=1000, method='pi')\n",
    "CIone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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