![](\"images/ipysig_diagram1.png\")
"
73 | ]
74 | },
75 | {
76 | "cell_type": "markdown",
77 | "metadata": {},
78 | "source": [
79 | "### Overview of IPySigma Application System\n",
80 | "\n",
81 | "#### Jupyter Notebook Frontend/ Jupyter Notebook Server\n",
82 | "\n",
83 | "To boot up the app using a running notebook server, first the IPySig controller class is imported and instantiated within the user’s notebook session. This is the core Python class of the application and is implemented as a Singleton. An IPySig object does a few key things on instantiation:\n",
84 | "If run for the first time, IPySig injects the API found in the ipysig.sigma_addon_methods into the nx.Graph class. This adds custom functionality to any existing or newly created graphs in the current session for exporting node and edge data in JSON format suitable for SigmaJS. \n",
85 | "Next, using a system call it fetches and stores the url and token (if any) of the running notebook server. \n",
86 | "Using the url and token, an express server process is spun up and stored in a class variable. This is run through several sets of protected method calls.\n",
87 | "\n",
88 | "Once the express server is running, a “session” can be created by calling the connect() method passing in the graph object and a key name. The graph object reference gets stored in IPySig under this key name. The key name is also emitted via a socket.io client to a listener on the express server which stores the reference in node.js and sends a callback response that tells IPySig to spin up a webbrowser tab. When the browser tab gets opened an event is emitted to fetch the key name to the graph reference, thus completing the connection between the new browser tab and the running graph object in the IPython kernel.\n",
89 | "\n",
90 | "A session has now been created and the user can use the frontend to display the graph object using the Load Graph button and giving the graph a Title. The title will serve as a means of saving a graph in BrowserDB via a LokiJS adaptor, allowing persistence of multiple graph objects in a single browser session. This functionality has not been implemented yet as of version 0.1.\n",
91 | "\n",
92 | "#### Jupyterlab Services/Express/SigmaJS \n",
93 | "\n",
94 | "Once a browser tab session is bound, pressing Load Graph and submitting a Title will emit to the ‘main-room’ socket.io listener on the express server. The jupyterlab services API connects to the running kernel and calls IPySig.export_graph_instance() with the correct graph_name as a promise. \n",
95 | "\n",
96 | "Once the promise is fulfilled, the JSON graph data is emitted back up to the browser via the correct socket.id. The graph data is then rendered in the browser main_room listener via a call to make_graph which returns the sigma graph.\n",
97 | "\n",
98 | "In future releases, each new graph will be saved to BrowserDB via a Loki.js adaptor. The user will be able to select from a list of past graphs rendered in that session for easy comparison. The option will also be given for the contents of the session to be exported to JSON.\n"
99 | ]
100 | },
101 | {
102 | "cell_type": "markdown",
103 | "metadata": {},
104 | "source": [
105 | "### The Future"
106 | ]
107 | },
108 | {
109 | "cell_type": "markdown",
110 | "metadata": {},
111 | "source": [
112 | "There are a number of avenues that could be explored using the protocol and platform defined above. Over the next few months, Daina and John will define an applied usecase to be presented at JupyterCon along with a presentation on IPySigma's development and logic. Improvements will also be made to the code to address a number of issues currently [documented on GitHub](https://github.com/bsnacks000/IPySigma-Demo/issues). Additionally, documentation will be automated using [Sphinx](http://www.sphinx-doc.org/en/stable/) to improve the seamlessness of updates and allow us to create more professional web presence. We have also begun creating a [website](https://dbouquin.github.io/IPySigma-Demo/) that will act as a splash page for the tool."
113 | ]
114 | }
115 | ],
116 | "metadata": {
117 | "anaconda-cloud": {},
118 | "kernelspec": {
119 | "display_name": "Python [Root]",
120 | "language": "python",
121 | "name": "Python [Root]"
122 | },
123 | "language_info": {
124 | "codemirror_mode": {
125 | "name": "ipython",
126 | "version": 2
127 | },
128 | "file_extension": ".py",
129 | "mimetype": "text/x-python",
130 | "name": "python",
131 | "nbconvert_exporter": "python",
132 | "pygments_lexer": "ipython2",
133 | "version": "2.7.9"
134 | }
135 | },
136 | "nbformat": 4,
137 | "nbformat_minor": 0
138 | }
139 |
--------------------------------------------------------------------------------
/IPySig_demo_data/IPySig_demo_data.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "## Data pull from ADS\n",
8 | "Focusing in on 50 most highly cited astronomy papers from the last 5 years"
9 | ]
10 | },
11 | {
12 | "cell_type": "code",
13 | "execution_count": 7,
14 | "metadata": {},
15 | "outputs": [],
16 | "source": [
17 | "import networkx as nx\n",
18 | "import os\n",
19 | "import ads as ads \n",
20 | "import matplotlib.pyplot as plt\n",
21 | "import pandas as pd\n",
22 | "from networkx.algorithms import bipartite as bi"
23 | ]
24 | },
25 | {
26 | "cell_type": "code",
27 | "execution_count": 8,
28 | "metadata": {
29 | "collapsed": true
30 | },
31 | "outputs": [],
32 | "source": [
33 | "os.environ[\"ADS_DEV_KEY\"] = \"kNUoTurJ5TXV9hsw9KQN1k8wH4U0D7Oy0CJoOvyw\""
34 | ]
35 | },
36 | {
37 | "cell_type": "code",
38 | "execution_count": 9,
39 | "metadata": {
40 | "collapsed": true
41 | },
42 | "outputs": [],
43 | "source": [
44 | "ads.config.token = 'ADS_DEV_KEY' "
45 | ]
46 | },
47 | {
48 | "cell_type": "code",
49 | "execution_count": 10,
50 | "metadata": {},
51 | "outputs": [],
52 | "source": [
53 | "#Search for papers \n",
54 | "# 50 most cited papers recent papers\n",
55 | "papers1 = list(ads.SearchQuery(q=\"*\",\n",
56 | " sort=\"citation_count desc\",\n",
57 | " year=\"2012, 2013, 2014, 2015, 2016, 2017\",\n",
58 | " rows=50 ))"
59 | ]
60 | },
61 | {
62 | "cell_type": "code",
63 | "execution_count": 11,
64 | "metadata": {},
65 | "outputs": [],
66 | "source": [
67 | "# find author names\n",
68 | "a = []\n",
69 | "for i in papers1:\n",
70 | " authors1 = i.author\n",
71 | " a.append(authors1)\n",
72 | "author_names = a"
73 | ]
74 | },
75 | {
76 | "cell_type": "code",
77 | "execution_count": 12,
78 | "metadata": {},
79 | "outputs": [
80 | {
81 | "name": "stderr",
82 | "output_type": "stream",
83 | "text": [
84 | "/home/bsnacks/Py_projects/ipysigma_demo/venv/lib/python2.7/site-packages/ads/utils.py:31: UserWarning: You are lazy loading attributes via 'pub', and so are making multiple calls to the API. This will impact your overall rate limits.\n",
85 | " UserWarning,\n"
86 | ]
87 | }
88 | ],
89 | "source": [
90 | "# find the journals\n",
91 | "j = []\n",
92 | "for i in papers1:\n",
93 | " journals1 = i.pub\n",
94 | " j.append(journals1)\n",
95 | "journals = j"
96 | ]
97 | },
98 | {
99 | "cell_type": "code",
100 | "execution_count": 13,
101 | "metadata": {},
102 | "outputs": [],
103 | "source": [
104 | "# find year of publication\n",
105 | "y = []\n",
106 | "for i in papers1:\n",
107 | " year1 = i.year\n",
108 | " y.append(year1)\n",
109 | "year = y"
110 | ]
111 | },
112 | {
113 | "cell_type": "code",
114 | "execution_count": 14,
115 | "metadata": {},
116 | "outputs": [],
117 | "source": [
118 | "# find titles\n",
119 | "t = []\n",
120 | "for i in papers1:\n",
121 | " title1 = i.title\n",
122 | " t.append(title1)\n",
123 | "title = t"
124 | ]
125 | },
126 | {
127 | "cell_type": "code",
128 | "execution_count": 15,
129 | "metadata": {},
130 | "outputs": [
131 | {
132 | "name": "stderr",
133 | "output_type": "stream",
134 | "text": [
135 | "/home/bsnacks/Py_projects/ipysigma_demo/venv/lib/python2.7/site-packages/ads/utils.py:31: UserWarning: You are lazy loading attributes via 'keyword', and so are making multiple calls to the API. This will impact your overall rate limits.\n",
136 | " UserWarning,\n"
137 | ]
138 | }
139 | ],
140 | "source": [
141 | "# find keywords\n",
142 | "k = []\n",
143 | "for i in papers1:\n",
144 | " keyword1 = i.keyword\n",
145 | " k.append(keyword1)\n",
146 | "keyword = k"
147 | ]
148 | },
149 | {
150 | "cell_type": "code",
151 | "execution_count": 16,
152 | "metadata": {},
153 | "outputs": [],
154 | "source": [
155 | "# create an initial df\n",
156 | "df = pd.DataFrame({'Author_Names' : author_names,\n",
157 | " 'Journal':journals,\n",
158 | " 'Year':year,\n",
159 | " 'Title':title,\n",
160 | " 'Keyword':keyword\n",
161 | " })"
162 | ]
163 | },
164 | {
165 | "cell_type": "code",
166 | "execution_count": 17,
167 | "metadata": {},
168 | "outputs": [
169 | {
170 | "name": "stdout",
171 | "output_type": "stream",
172 | "text": [
173 | "(34752, 5)\n"
174 | ]
175 | }
176 | ],
177 | "source": [
178 | "# Expand the df with melt\n",
179 | "s1 = df.apply(lambda x: pd.Series(x['Author_Names']),axis=1).stack().reset_index(level=1, drop=True)\n",
180 | "s1.name = 'Author_Name'\n",
181 | "s2 = df.apply(lambda x: pd.Series(x['Title']),axis=1).stack().reset_index(level=1, drop=True)\n",
182 | "s2.name = 'Title'\n",
183 | "s3 = df.apply(lambda x: pd.Series(x['Keyword']),axis=1).stack().reset_index(level=1, drop=True)\n",
184 | "s3.name = 'Keyword'\n",
185 | "df_m = df.drop(['Author_Names','Title', 'Keyword'], axis=1).join(s1)\n",
186 | "df_m = df_m.join(s2)\n",
187 | "df_m = df_m.join(s3)\n",
188 | "\n",
189 | "print df_m.shapeG.nodes(data=True)"
190 | ]
191 | },
192 | {
193 | "cell_type": "code",
194 | "execution_count": 18,
195 | "metadata": {},
196 | "outputs": [],
197 | "source": [
198 | "df_m.to_csv('top_50.csv', sep=',', encoding='utf-8')"
199 | ]
200 | },
201 | {
202 | "cell_type": "code",
203 | "execution_count": 19,
204 | "metadata": {},
205 | "outputs": [],
206 | "source": [
207 | "G=nx.from_pandas_dataframe(df_m, 'Journal', 'Author_Name', ['Title', 'Year', 'Keyword'])"
208 | ]
209 | },
210 | {
211 | "cell_type": "code",
212 | "execution_count": 20,
213 | "metadata": {},
214 | "outputs": [
215 | {
216 | "ename": "KeyboardInterrupt",
217 | "evalue": "",
218 | "output_type": "error",
219 | "traceback": [
220 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
221 | "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
222 | "\u001b[0;32mIPySigma: ProtoType
21 |
22 |
23 |
30 |
31 |
38 |
39 |
40 |
41 |
42 |
43 |
44 |
49 |
50 |
45 |
46 |
47 |
48 |
51 |
66 |
67 |
68 |
69 |
70 |
71 |
72 |
73 |
74 |
75 |
76 |
77 |
78 |
79 |
80 |
81 |
82 |
83 |
84 |
85 |
86 |
--------------------------------------------------------------------------------
/app/browser/sigma/sigma.layout.forceAtlas2.min.js:
--------------------------------------------------------------------------------
1 | (function(undefined){"use strict";function Supervisor(sigInst,options){var _this=this,workerFn=sigInst.getForceAtlas2Worker&&sigInst.getForceAtlas2Worker();if(options=options||{},_root.URL=_root.URL||_root.webkitURL,this.sigInst=sigInst,this.graph=this.sigInst.graph,this.ppn=10,this.ppe=3,this.config={},this.shouldUseWorker=options.worker!==!1&&webWorkers,this.workerUrl=options.workerUrl,this.started=!1,this.running=!1,this.shouldUseWorker){if(this.workerUrl)this.worker=new Worker(this.workerUrl);else{var blob=this.makeBlob(workerFn);this.worker=new Worker(URL.createObjectURL(blob))}this.worker.postMessage=this.worker.webkitPostMessage||this.worker.postMessage}else eval(workerFn);this.msgName=this.worker?"message":"newCoords",this.listener=function(t){_this.nodesByteArray=new Float32Array(t.data.nodes),_this.running&&(_this.applyLayoutChanges(),_this.sendByteArrayToWorker(),_this.sigInst.refresh())},(this.worker||document).addEventListener(this.msgName,this.listener),this.graphToByteArrays(),sigInst.bind("kill",function(){sigInst.killForceAtlas2()})}if("undefined"==typeof sigma)throw"sigma is not declared";var _root=this,webWorkers="Worker"in _root;Supervisor.prototype.makeBlob=function(t){var e;try{e=new Blob([t],{type:"application/javascript"})}catch(s){_root.BlobBuilder=_root.BlobBuilder||_root.WebKitBlobBuilder||_root.MozBlobBuilder,e=new BlobBuilder,e.append(t),e=e.getBlob()}return e},Supervisor.prototype.graphToByteArrays=function(){var t,e,s,r=this.graph.nodes(),i=this.graph.edges(),o=r.length*this.ppn,n=i.length*this.ppe,a={};for(this.nodesByteArray=new Float32Array(o),this.edgesByteArray=new Float32Array(n),t=e=0,s=r.length;t
52 |
53 |
64 |
65 |
24 | 
30 |
31 |
25 |
29 | IPySigma: a network visualization frontend for Jupyter notebooks
26 |Create beautiful networks
27 |An experiment from John DeBlase and Daina Bouquin
28 |
34 | 
35 |
45 |
36 |
44 | About IPySigma
37 |Network analytics using tools like NetworkX and Jupyter often leave programmers with difficult to examine hairballs rather than useful visualizations. Meanwhile, more flexible tools like SigmaJS have high learning curves for people new to Javascript. This extensible framework can help you create beautiful SigmaJS graphs, without ditching your Jupyter notebook.
IPySigma makes use of the following: Jupyter, Loki, SigmaJS, NetworkX.
Check out the code on GitHub
55 | 
60 |
61 |
56 |
59 | How It Works
57 |[instert description of all components with links]
58 |