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fdeheeger/nvd3_checksave.ipynb

Created July 12, 2013 10:07
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ipynb save check with nvd3
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nvd3_checksave.ipynb
{
"metadata": {
"name": "nvd3_checksave"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as p\n",
"import matplotlib.pyplot as plt\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = p.util.testing.makeDataFrame()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from nvd3 import pieChart, scatterChart\n",
"from IPython.display import HTML\n",
"chart2 = scatterChart(name='scatterChart', height=600, width=800)\n",
"\n",
"kwargs1 = {'shape': 'circle'}\n",
"extra_serie = {\"tooltip\": {\"y_start\": \"\", \"y_end\": \" call\"}}\n",
"\n",
"chart2.add_serie(name='scatter',\n",
" y=df.A.values,\n",
" x=df.B.values,\n",
" #extra=extra_serie,\n",
" **kwargs1)\n",
"\n",
"chart2.buildhtml()\n",
"HTML(chart2.htmlcontent)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
"<!DOCTYPE html>\n",
"<html lang=\"en\">\n",
"<head>\n",
"<link media=\"all\" href=\"http://nvd3.org/src/nv.d3.css\" type=\"text/css\" rel=\"stylesheet\" />\n",
"<script src=\"http://nvd3.org/lib/d3.v2.js\" type=\"text/javascript\"></script>\n",
"<script src=\"http://nvd3.org/nv.d3.js\" type=\"text/javascript\"></script>\n",
"\n",
"</head>\n",
"<body>\n",
"<div id=\"scatterChart\"><svg style=\"width:800px;height:600px;\"></svg></div>\n",
"\n",
"\n",
"<script type=\"text/javascript\">\n",
" nv.addGraph(function() {\n",
" var chart = nv.models.scatterChart()\n",
" .showDistX(true)\n",
" .showDistY(true)\n",
" .color(d3.scale.category10().range());\n",
" chart.color(d3.scale.category10().range());\n",
" chart.xAxis\n",
" .tickFormat(d3.format(',.02f'));\n",
" chart.yAxis\n",
" .tickFormat(d3.format(',.02f'));\n",
" chart.showLegend(true)\n",
" d3.select('#scatterChart svg')\n",
" .datum(data_scatterChart)\n",
" .transition().duration(500)\n",
" .attr('width', 800)\n",
".attr('height', 600)\n",
" .call(chart);\n",
"\n",
" return chart;\n",
"});\n",
"data_scatterChart=[{\"values\": [{\"y\": -2.2728777854392845, \"x\": -0.12686184477794624, \"shape\": \"circle\", \"size\": 1}, {\"y\": -0.57355162626958656, \"x\": -0.92719377994537588, \"shape\": \"circle\", \"size\": 1}, {\"y\": 0.041958202206298985, \"x\": -1.8830988346801503, \"shape\": \"circle\", \"size\": 1}, {\"y\": -0.68799167343004641, \"x\": -0.34845037533146167, \"shape\": \"circle\", \"size\": 1}, {\"y\": 0.55009364949194017, \"x\": -1.1841262026135744, \"shape\": \"circle\", \"size\": 1}, {\"y\": -0.67532004716053706, \"x\": 1.2296248034116888, \"shape\": \"circle\", \"size\": 1}, {\"y\": -0.42428447212236953, \"x\": 2.0745726931620134, \"shape\": \"circle\", \"size\": 1}, {\"y\": -1.3595034618007316, \"x\": -0.26841066479127751, \"shape\": \"circle\", \"size\": 1}, {\"y\": 1.2856859250911306, \"x\": -0.25765274311514463, \"shape\": \"circle\", \"size\": 1}, {\"y\": 0.49640066109868186, \"x\": 0.10494158351325901, \"shape\": \"circle\", \"size\": 1}, {\"y\": -1.6148864166510908, \"x\": 0.34068672997574861, \"shape\": \"circle\", \"size\": 1}, {\"y\": 0.79932097848315486, \"x\": 1.8529013425663035, \"shape\": \"circle\", \"size\": 1}, {\"y\": 0.044512218352573268, \"x\": 1.5051742648173327, \"shape\": \"circle\", \"size\": 1}, {\"y\": 0.50437119716853507, \"x\": 0.40942127654545435, \"shape\": \"circle\", \"size\": 1}, {\"y\": -0.10117158575509672, \"x\": -0.20843954370971471, \"shape\": \"circle\", \"size\": 1}, {\"y\": 0.58192315235530279, \"x\": -0.7139694124433783, \"shape\": \"circle\", \"size\": 1}, {\"y\": -0.46303860417084508, \"x\": -0.57890285876324166, \"shape\": \"circle\", \"size\": 1}, {\"y\": -0.92633202624719202, \"x\": 1.4358844294195365, \"shape\": \"circle\", \"size\": 1}, {\"y\": -0.83347127971600576, \"x\": 1.3161380209649256, \"shape\": \"circle\", \"size\": 1}, {\"y\": -0.54353595715735736, \"x\": -2.4582277601919293, \"shape\": \"circle\", \"size\": 1}, {\"y\": -0.71234404318114641, \"x\": 1.3804725464219385, \"shape\": \"circle\", \"size\": 1}, {\"y\": -1.189729988844596, \"x\": -0.20720726661290134, \"shape\": \"circle\", \"size\": 1}, {\"y\": 1.4724659943896505, \"x\": -0.37247869390007549, \"shape\": \"circle\", \"size\": 1}, {\"y\": -0.989011884010358, \"x\": -0.7876691265197423, \"shape\": \"circle\", \"size\": 1}, {\"y\": 1.3531947847574464, \"x\": -0.036472138720760869, \"shape\": \"circle\", \"size\": 1}, {\"y\": 0.51918361015590098, \"x\": 0.71489781041419109, \"shape\": \"circle\", \"size\": 1}, {\"y\": 0.11332375980191822, \"x\": -0.82917147073962827, \"shape\": \"circle\", \"size\": 1}, {\"y\": -0.78651500674412156, \"x\": 1.1438725058308818, \"shape\": \"circle\", \"size\": 1}, {\"y\": 0.60789493568161213, \"x\": -0.60226755159127277, \"shape\": \"circle\", \"size\": 1}, {\"y\": -1.137152903708021, \"x\": 0.53902606917407625, \"shape\": \"circle\", \"size\": 1}], \"key\": \"scatter\", \"yAxis\": \"1\"}];\n",
"</script>\n",
"</body>\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<IPython.core.display.HTML at 0x4ad4710>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"check if that cell is saved or not ?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.A.hist()\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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PkBNWlTcRUVBYVd5S9mBSckrZfzKnORIyAnKeISesKm8ioqCwqryl7MGk5JSy/2ROcyRk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\n",
"text": [
"<matplotlib.figure.Figure at 0x4c03610>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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nvd3_checksave_.ipynb
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