Simple biggus use case

biggus.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import biggus\n",
      "import numpy as np\n",
      "\n",
      "measured = biggus.NumpyArrayAdapter(np.ones((2, 3, 4)) * 2.5)\n",
      "predicted = biggus.NumpyArrayAdapter(np.ones((2, 3, 4)))\n",
      "\n",
      "error = predicted - measured\n",
      "mean_error = biggus.mean(error, axis=0)\n",
      "std_error = biggus.std(error, axis=0)\n",
      "biggus.ndarrays([mean_error, std_error])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[array([[-1.5, -1.5, -1.5, -1.5],\n",
        "        [-1.5, -1.5, -1.5, -1.5],\n",
        "        [-1.5, -1.5, -1.5, -1.5]]), array([[ 0.,  0.,  0.,  0.],\n",
        "        [ 0.,  0.,  0.,  0.],\n",
        "        [ 0.,  0.,  0.,  0.]])]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_error._streams_handler_class"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "biggus._MeanStreamsHandler"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_error.sources"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(<_Elementwise shape=(2, 3, 4) dtype=dtype('float64')>,)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_error._axis"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 12
    }
   ],
   "metadata": {}
  }
 ]
}