Created
July 28, 2014 12:17
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prov convenience methods
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{ | |
"metadata": { | |
"name": "", | |
"signature": "sha256:9ee457fa9b765923b85be3b978bf4fb269508f935638da909bb2d8cdcf3a953a" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"%pylab inline" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"Populating the interactive namespace from numpy and matplotlib\n" | |
] | |
} | |
], | |
"prompt_number": 1 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"import prov" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 2 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"doc = prov.read(\"./example_35.xml\")" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 3 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"doc.plot()" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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gcrlwOp24XC5sNhtutxuXy4XD4aBbt25s2bIFg8FAfn6+51SxrKyM5cuX07Nn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\nEARBuIr8f8tO4UZYqNQmAAAAAElFTkSuQmCC\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x10e276550>" | |
] | |
} | |
], | |
"prompt_number": 4 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 4 | |
} | |
], | |
"metadata": {} | |
} | |
] | |
} |
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