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kapadia/Ruse IPython Notebook

Last active December 21, 2015 20:09
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WebGL plotting in IPython Notebook
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Ruse IPython Notebook
{
"metadata": {
"name": "Ruse Integration"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": "import json\nimport numpy as np\nfrom astropy.io import fits\nfrom IPython.display import HTML\nfrom IPython.display import Javascript\nfrom IPython.display import display",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": "glmatrix_src = \"http://astrojs.s3.amazonaws.com/ruse/examples/lib/gl-matrix.js\"\nruse_src = \"http://astrojs.s3.amazonaws.com/ruse/dist/ruse.js\"",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Require gl-matrix\nJavascript('require([\"%s\"], function(glmatrix) {window.mat4 = glmatrix.mat4; window.vec3 = glmatrix.vec3; console.log(glmatrix);})' % glmatrix_src)",
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": "require([\"http://astrojs.s3.amazonaws.com/ruse/examples/lib/gl-matrix.js\"], function(glmatrix) {window.mat4 = glmatrix.mat4; window.vec3 = glmatrix.vec3; console.log(glmatrix);})",
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": "<IPython.core.display.Javascript at 0x103757c90>"
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": "rusejs = HTML('<script src=\"%s\"></script>' % (ruse_src)); rusejs",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<script src=\"http://astrojs.s3.amazonaws.com/ruse/dist/ruse.js\"></script>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": "<IPython.core.display.HTML at 0x103757e90>"
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": "table = fits.getdata(\"/Users/akapadia/astrojs/fitsjs/test/data/spec-0406-51869-0012.fits\")\nwavelength = table['wavelength']\nbest_fit = table['best_fit']\nflux = table['flux']",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": "div = HTML(\"\"\"<div id='ruse' style='height: 500px; width: 800px'></div>\"\"\"); div",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<div id='ruse' style='height: 500px; width: 800px'></div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": "<IPython.core.display.HTML at 0x103757d10>"
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": "arr = []\nfor i in xrange(len(wavelength)):\n obj = {}\n obj[\"wavelength\"] = wavelength[i]\n obj[\"best_fit\"] = best_fit[i]\n obj[\"flux\"] = flux[i]\n arr.append(obj)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": "create_plot = Javascript('''var el = document.querySelector(\"#ruse\"); var ruse = new astro.Ruse(el, 800, 500); ruse.plot(%s)''' % (json.dumps(arr)))",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": "display(create_plot)",
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": "var el = document.querySelector(\"#ruse\"); var ruse = new astro.Ruse(el, 800, 500); ruse.plot([{\"wavelength\": 3797.5193897099998, \"best_fit\": 4.3956017000000003, \"flux\": 3.3827862739562988}, {\"wavelength\": 3798.39390155, \"best_fit\": 4.3818421000000001, \"flux\": 3.3826639652252197}, {\"wavelength\": 3799.2686147600002, \"best_fit\": 4.4043330999999997, \"flux\": 3.3825416564941406}, {\"wavelength\": 3800.14352942, \"best_fit\": 4.3530955000000002, \"flux\": 3.3824195861816406}, {\"wavelength\": 3801.0186455500002, \"best_fit\": 4.3348168999999999, \"flux\": 3.3822975158691406}, {\"wavelength\": 3801.89396321, \"best_fit\": 4.3606863000000002, \"flux\": 3.3821759223937988}, {\"wavelength\": 3802.76948244, \"best_fit\": 4.3819455999999999, \"flux\": 3.382054328918457}, {\"wavelength\": 3803.6452032900002, \"best_fit\": 4.3510689999999999, \"flux\": 3.3819332122802734}, {\"wavelength\": 3804.5211257999999, \"best_fit\": 4.2861213999999999, \"flux\": 3.3818123340606689}, {\"wavelength\": 3805.3972500300001, \"best_fit\": 4.3255315000000003, \"flux\": 3.3816914558410645}, {\"wavelength\": 3806.2735760099999, \"best_fit\": 4.3109473999999999, \"flux\": 3.3815710544586182}, {\"wavelength\": 3807.1501038000001, \"best_fit\": 4.2897768000000003, \"flux\": 3.381450891494751}, {\"wavelength\": 3808.0268334399998, \"best_fit\": 4.2854175999999997, \"flux\": 3.3813307285308838}, {\"wavelength\": 3808.9037649799998, \"best_fit\": 4.2885274999999998, \"flux\": 3.3812108039855957}, {\"wavelength\": 3809.7808984600001, \"best_fit\": 4.3463554000000002, \"flux\": 3.3810913562774658}, {\"wavelength\": 3810.6582339400002, \"best_fit\": 4.3277812000000004, \"flux\": 3.3809719085693359}, {\"wavelength\": 3811.5357714500001, \"best_fit\": 4.3195338000000003, \"flux\": 3.3808526992797852}, {\"wavelength\": 3812.4135110399998, \"best_fit\": 4.3119921999999997, \"flux\": 3.3807334899902344}, {\"wavelength\": 3813.29145277, \"best_fit\": 4.3081697999999999, \"flux\": 3.3806149959564209}, {\"wavelength\": 3814.1695966699999, \"best_fit\": 4.3217911999999998, \"flux\": 3.3804965019226074}, {\"wavelength\": 3815.04794279, \"best_fit\": 4.3113441000000003, \"flux\": 3.3803780078887939}, {\"wavelength\": 3815.92649119, \"best_fit\": 4.2874675, \"flux\": 3.3802599906921387}, {\"wavelength\": 3816.8052419000001, \"best_fit\": 4.2746057999999998, \"flux\": 1.8146873712539673}, {\"wavelength\": 3817.68419498, \"best_fit\": 4.3041983000000004, \"flux\": 7.188107967376709}, {\"wavelength\": 3818.56335046, \"best_fit\": 4.3280044000000002, \"flux\": 6.2640271186828613}, {\"wavelength\": 3819.4427083999999, \"best_fit\": 4.3081526999999999, \"flux\": 2.7198390960693359}, {\"wavelength\": 3820.3222688400001, \"best_fit\": 4.3230633999999997, \"flux\": 1.4559042453765869}, {\"wavelength\": 3821.20203184, \"best_fit\": 4.3126454000000001, \"flux\": 4.9450802803039551}, {\"wavelength\": 3822.0819974299998, \"best_fit\": 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{\"wavelength\": 3857.4472867200002, \"best_fit\": 4.3183188000000001, \"flux\": 1.4153999090194702}, {\"wavelength\": 3858.3355990499999, \"best_fit\": 4.3339653, \"flux\": 2.3258967399597168}, {\"wavelength\": 3859.2241159499999, \"best_fit\": 4.3425145000000001, \"flux\": 1.2052249908447266}, {\"wavelength\": 3860.1128374499999, \"best_fit\": 4.3255109999999997, \"flux\": 2.5028395652770996}, {\"wavelength\": 3861.00176362, \"best_fit\": 4.3276095000000003, \"flux\": 3.3914785385131836}, {\"wavelength\": 3861.8908944899999, \"best_fit\": 4.3531345999999997, \"flux\": 2.1814630031585693}, {\"wavelength\": 3862.78023011, \"best_fit\": 4.3069018999999997, \"flux\": 3.3734920024871826}, {\"wavelength\": 3863.6697705400002, \"best_fit\": 4.3332614999999999, \"flux\": 4.2948932647705078}, {\"wavelength\": 3864.55951581, \"best_fit\": 4.3564005000000003, \"flux\": 5.2769646644592285}, {\"wavelength\": 3865.4494659799998, \"best_fit\": 4.3312697, \"flux\": 3.586646556854248}, {\"wavelength\": 3866.33962109, \"best_fit\": 4.3161386999999998, \"flux\": 1.0780308246612549}, {\"wavelength\": 3867.2299811900002, \"best_fit\": 4.3466711, \"flux\": 4.3589348793029785}, {\"wavelength\": 3868.1205463299998, \"best_fit\": 4.3257370000000002, \"flux\": 8.1344480514526367}, {\"wavelength\": 3869.0113165500002, \"best_fit\": 4.3258466999999996, \"flux\": 3.6966757774353027}, {\"wavelength\": 3869.9022918999999, \"best_fit\": 4.2882294999999999, \"flux\": 6.4694108963012695}, {\"wavelength\": 3870.7934724299998, \"best_fit\": 4.3107848000000004, \"flux\": 7.0537991523742676}, {\"wavelength\": 3871.6848581899999, \"best_fit\": 4.3439417000000002, \"flux\": 7.9132876396179199}, {\"wavelength\": 3872.5764492200001, \"best_fit\": 4.2991953000000001, \"flux\": 2.4082140922546387}, {\"wavelength\": 3873.46824556, \"best_fit\": 4.3106837000000002, \"flux\": 5.6676249504089355}, {\"wavelength\": 3874.3602472799998, \"best_fit\": 4.3768573000000002, \"flux\": 6.2145133018493652}, {\"wavelength\": 3875.2524544100002, \"best_fit\": 4.3218594000000001, \"flux\": 5.1395540237426758}, {\"wavelength\": 3876.144867, \"best_fit\": 4.2833737999999997, \"flux\": 5.5475821495056152}, {\"wavelength\": 3877.0374851000001, \"best_fit\": 4.2992262999999999, \"flux\": 4.8418984413146973}, {\"wavelength\": 3877.9303087600001, \"best_fit\": 4.3076071999999996, \"flux\": 4.6501064300537109}, {\"wavelength\": 3878.8233380199999, \"best_fit\": 4.3501643999999997, \"flux\": 4.163731575012207}, {\"wavelength\": 3879.7165729399999, \"best_fit\": 4.3424211000000001, \"flux\": 4.9458975791931152}, {\"wavelength\": 3880.6100135500001, \"best_fit\": 4.3181257000000004, \"flux\": 7.2984981536865234}, {\"wavelength\": 3881.5036599099999, \"best_fit\": 4.3203082000000004, \"flux\": 5.3062639236450195}, {\"wavelength\": 3882.3975120599998, \"best_fit\": 4.3133526, \"flux\": 2.7542116641998291}, {\"wavelength\": 3883.2915700499998, \"best_fit\": 4.2739716000000003, \"flux\": 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