| { |
| "metadata": { |
| "name": "", |
| "signature": "sha256:f9a4dac23e4eb9ae2d6a54b24b420bc7ab1c50118b6f10b8e1bf6d90ae86657b" |
| }, |
| "nbformat": 3, |
| "nbformat_minor": 0, |
| "worksheets": [ |
| { |
| "cells": [ |
| { |
| "cell_type": "code", |
| "collapsed": false, |
| "input": [ |
| "%matplotlib inline\n", |
| "import pandas as pd\n", |
| "import trappy" |
| ], |
| "language": "python", |
| "metadata": {}, |
| "outputs": [], |
| "prompt_number": 1 |
| }, |
| { |
| "cell_type": "code", |
| "collapsed": false, |
| "input": [ |
| "dfr = pd.DataFrame({\"foo\": [1, 2, 3], \"bar\": [2, 3, 1], \"baz\":[3, 2, 1]})" |
| ], |
| "language": "python", |
| "metadata": {}, |
| "outputs": [], |
| "prompt_number": 2 |
| }, |
| { |
| "cell_type": "heading", |
| "level": 1, |
| "metadata": {}, |
| "source": [ |
| "Simple bar plot of one column" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "collapsed": false, |
| "input": [ |
| "trappy.BarPlot(dfr, column=\"foo\").view()" |
| ], |
| "language": "python", |
| "metadata": {}, |
| "outputs": [ |
| { |
| "metadata": {}, |
| "output_type": "display_data", |
| "png": 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nESiFfQdQTfAGAACAhARvgER0HoFS2HcA1QRvAAAASEjwBkhE5xEohX0HUE3w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\ngg==\n", |
| "text": [ |
| "<matplotlib.figure.Figure at 0x7f4ccb7c04d0>" |
| ] |
| } |
| ], |
| "prompt_number": 3 |
| }, |
| { |
| "cell_type": "heading", |
| "level": 1, |
| "metadata": {}, |
| "source": [ |
| "Multiple columns" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "collapsed": false, |
| "input": [ |
| "trappy.BarPlot(dfr, column=[\"foo\", \"bar\"], title=\"Multiple columns\").view()" |
| ], |
| "language": "python", |
| "metadata": {}, |
| "outputs": [ |
| { |
| "metadata": {}, |
| "output_type": "display_data", |
| "png": 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qumB4JXSAqIJ9B9SFjjdVcJ6llTQN3iNHjsz48eOTJEOGDMnYsWOzcuXKfscs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\nxN/RqV2dqQAAAABJRU5ErkJggg==\n", |
| "text": [ |
| "<matplotlib.figure.Figure at 0x7f4cb691dc90>" |
| ] |
| } |
| ], |
| "prompt_number": 4 |
| }, |
| { |
| "cell_type": "heading", |
| "level": 1, |
| "metadata": {}, |
| "source": [ |
| "Stacked plot" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "collapsed": false, |
| "input": [ |
| "trappy.BarPlot(dfr, column=[\"foo\", \"bar\", \"baz\"], stacked=True).view()" |
| ], |
| "language": "python", |
| "metadata": {}, |
| "outputs": [ |
| { |
| "metadata": {}, |
| "output_type": "display_data", |
| "png": 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k0DEEcmHeARQTqgEAACCRUA2QQMcQyIV5B1BMqAYAAIBEQjVAAh1DIBfmHUAxoRoAAAAS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\nAwcOLHspQMn+H7aWafSb1eZBAAAAAElFTkSuQmCC\n", |
| "text": [ |
| "<matplotlib.figure.Figure at 0x7f4cb6c77290>" |
| ] |
| } |
| ], |
| "prompt_number": 5 |
| }, |
| { |
| "cell_type": "heading", |
| "level": 1, |
| "metadata": {}, |
| "source": [ |
| "Signals" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Signals only work with trappy traces. Let's first get a trace." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "collapsed": false, |
| "input": [ |
| "trace = trappy.BareTrace()\n", |
| "trace.add_parsed_event(\"my_event\", dfr)" |
| ], |
| "language": "python", |
| "metadata": {}, |
| "outputs": [], |
| "prompt_number": 6 |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "If you have a trappy trace, you can use the signal syntax instead of columns and templates" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "collapsed": false, |
| "input": [ |
| "trappy.BarPlot(trace, signals=[\"my_event:baz\", \"my_event:foo\"]).view()" |
| ], |
| "language": "python", |
| "metadata": {}, |
| "outputs": [ |
| { |
| "metadata": {}, |
| "output_type": "display_data", |
| "png": 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|
| "text": [ |
| "<matplotlib.figure.Figure at 0x7f4cb6da0350>" |
| ] |
| } |
| ], |
| "prompt_number": 7 |
| } |
| ], |
| "metadata": {} |
| } |
| ] |
| } |