Created
August 12, 2014 09:52
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Effects of NaN-elements on bottleneck sum.
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{ | |
"metadata": { | |
"name": "", | |
"signature": "sha256:4ccdc8580536b8596c3f61f406528a085be45e6ed17a3775b5fda9d7b310f6cd" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"import numpy as np\n", | |
"import bottleneck as bn" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 1 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"arr = np.random.rand(3000, 3200)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 2 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"%timeit bn.nansum(arr, axis=1)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"10 loops, best of 3: 24 ms per loop\n" | |
] | |
} | |
], | |
"prompt_number": 3 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"miss_10 = arr.copy()\n", | |
"miss_10[miss_10 < 0.1] = np.nan" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 4 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"%timeit bn.nansum(miss_10, axis=1)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"10 loops, best of 3: 32.7 ms per loop\n" | |
] | |
} | |
], | |
"prompt_number": 5 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"miss_30 = arr.copy()\n", | |
"miss_30[miss_30 < 0.1] = np.nan" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 6 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"%timeit bn.nansum(miss_30, axis=1)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"10 loops, best of 3: 32.8 ms per loop\n" | |
] | |
} | |
], | |
"prompt_number": 7 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"miss_50 = arr.copy()\n", | |
"miss_50[miss_50 < 0.1] = np.nan" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 8 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"%timeit bn.nansum(miss_50, axis=1)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"10 loops, best of 3: 32.7 ms per loop\n" | |
] | |
} | |
], | |
"prompt_number": 9 | |
} | |
], | |
"metadata": {} | |
} | |
] | |
} |
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