├── LICENSE.md
├── Neuroscience and AI.ipynb
├── README.md
├── SpectraVis-Demo.gif
├── app
├── css
│ └── spectra.css
└── js
│ └── main.js
├── bower.json
├── gulpfile.js
├── matlab
├── ES_NetworkData.m
├── createEdges.m
└── createNodes.m
├── package.json
└── public
├── css
├── main.css
└── main.min.css
├── fonts
├── glyphicons-halflings-regular.eot
├── glyphicons-halflings-regular.svg
├── glyphicons-halflings-regular.ttf
├── glyphicons-halflings-regular.woff
└── glyphicons-halflings-regular.woff2
├── index.html
└── js
├── main.js
├── main.min.js
├── vendor.js
└── vendor.min.js
/LICENSE.md:
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--------------------------------------------------------------------------------
/Neuroscience and AI.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# Neuroscience and Machine Learning\n",
8 | "\n",
9 | "## Demo - An interactive network visualization tool for exploring functional brain connectivity \n",
10 | "\n",
11 | "\n",
12 | "\n",
13 | "- History of Neuroscience in AI\n",
14 | "- Contemporary Neuroscience in AI\n",
15 | "- Future of Neuroscience in AI\n",
16 | "\n",
17 | "## The History of Neuroscience in AI\n",
18 | "\n",
19 | "\n",
20 | "\n",
21 | "- At the dawn of the computer age, AI research and neuroscience were completely intertwined\n",
22 | "- Early pioneers straddled both fields\n",
23 | "- And the collaborations between disciplines where highly productive\n",
24 | "- Let's look at some examples\n",
25 | "\n",
26 | "### Examples\n",
27 | "\n",
28 | "#### Alan Turing \n",
29 | "\n",
30 | "\n",
31 | "\n",
32 | "###### \"We are not interested in the fact that the brain has the consistency of cold porridge\" (Turing 1952)\n",
33 | "\n",
34 | "- Said the key to understanding the brain is not in mapping its anatomy or measuring its density, but in characterising its behaviour mathematically. \n",
35 | "- His view is still shared by many. \n",
36 | "- Mathematical equations are expressive and unambiguous — and arguably the only language suited to describing so complex an object\n",
37 | "\n",
38 | "\n",
39 | "\n",
40 | "- Hearing aids and prosthetic limbs exploit algorithms that mimic the equations of the brain and nervous system\n",
41 | "- Mathematical characterisations of the brain inform the field of AI and the design of novel computer components\n",
42 | "\n",
43 | "\n",
44 | "\n",
45 | "- Wrote a short computer program that accepted a single number, performed a series of unspecified calculations on it, and returned a second number. \n",
46 | "- He argued that the brain accept tens-of-thousands of inputs from sensory receptors around the body, but the calculations these inputs undergo are far more complicated than anything written by a single programmer. \n",
47 | "- He underscored his argument with a wager: that it would take an investigator at least a thousand years to guess the full set of calculations his Manchester program employed. \n",
48 | "- Guessing the full set of calculations taking place in the brain, he noted, would appear prohibitively time-consuming\n",
49 | "- With modern supercomputers and neuroimaging techniques, this reduces the complexity orders of magnitude\n",
50 | "\n",
51 | "#### McCulloch and Pitts\n",
52 | "\n",
53 | "\n",
54 | "\n",
55 | "- Both tried to understand how the brain could produce highly complex patterns by using many basic cells that are connected together\n",
56 | "\n",
57 | "#### Hopfield\n",
58 | "\n",
59 | "\n",
60 | "\n",
61 | "- Combined McCulloch Pitts Neurons into a Hopfield Network\n",
62 | "\n",
63 | "#### Hinton \n",
64 | "\n",
65 | "\n",
66 | "\n",
67 | "- Invented backpropagation\n",
68 | "- The motivation for backpropagation is to train a multi-layered neural network such that it can learn the appropriate internal representations to allow it to learn any arbitrary mapping of input to output \n",
69 | "- Big proponent of Dropout, Non-linearities, Layered Networks during AI winter\n",
70 | "\n",
71 | "#### Leading up until now\n",
72 | "\n",
73 | "\n",
74 | "\n",
75 | "- More recently, the interaction between neuroscience an AI has become much less common\n",
76 | "- Both subjects have grown enormously in complexity and disciplinary boundaries have solidified.\n",
77 | "- This is a problem, the human brain is the only existing proof that such an intelligence is even possible. \n",
78 | "- Neuroscience provides a rich source of inspiration for new types of algorithms and architectures, independent of and complementary to the mathematical and logic-based methods and ideas that have largely dominated traditional approaches to AI.\n",
79 | "- neuroscience can also provide validation of AI techniques that already exist.\n",
80 | "\n",
81 | "\n",
82 | "\n",
83 | "- Biological plausibility is a guide, not a strict requirement.\n",
84 | "- We need a systems neuroscience-level understanding of the brain, namely the algorithms, architectures, functions, and representations it utilizes.\n",
85 | "- By focusing on the computational and algorithmic levels, we can gain transferrable insights into general mechanisms of brain function, while leaving room to accommodate the distinctive opportunities and challenges that arise when building intelligent machines in silico.\n",
86 | "\n",
87 | "\n",
88 | "## Contemporary Neuroscience in AI (4 Key Areas Affected)\n",
89 | "\n",
90 | "### Attention\n",
91 | "\n",
92 | "\n",
93 | "\n",
94 | "- The brain does not learn by implementing a single, global optimization principle within a uniform and undifferentiated neural network. \n",
95 | "- Rather, biological brains are modular, with distinct but interacting subsystems underpinning key functions such as memory, language, and cognitive control\n",
96 | "\n",
97 | "\n",
98 | "\n",
99 | "- Up until quite lately, most CNN models worked directly on entire images or video frames, with equal priority given to all image pixels at the earliest stage of processing. \n",
100 | "- The primate visual system works differently. Rather than processing all input in parallel, visual attention shifts strategically among locations and objects, centering processing resources and representational coordinates on a series of regions in turn\n",
101 | "\n",
102 | "### Episodic Memory\n",
103 | "\n",
104 | "\n",
105 | "\n",
106 | "- A canonical theme in neuroscience is that that intelligent behavior relies on multiple memory systems\n",
107 | "- Reinforcement based mechanisms (incremental) and instance based mechanisms (one shot)\n",
108 | "- Episodic memory is associated with circuits in the medial temporal lobe\n",
109 | "\n",
110 | "\n",
111 | "\n",
112 | "\n",
113 | "\n",
114 | "- DeepMind's Deep Q Network was inspired by it, using ‘experience replay', where the network stores a subset of the training data in an instance-based way, and then ‘‘replays’’ it offline, \n",
115 | "- It learns anew from successes or failures that occurred in the past.\n",
116 | "- Experience replay is critical to maximizing data efficiency, destabilizing effects of learning from consecutive correlated ex- periences, and allows the network to learn a viable value function even in complex, highly structured sequential environments such as video games.\n",
117 | "\n",
118 | "### Working Memory\n",
119 | "\n",
120 | "\n",
121 | "\n",
122 | "- Human intelligence can maintain and manipulate information within an active store known as working memory\n",
123 | "- This is thought to be instantiated within the preforntal cortex and interconnected areas\n",
124 | "- Classic theories suggest this depends on interactions between a central controller and speerate domain specific memory buffers\n",
125 | "\n",
126 | "\n",
127 | "\n",
128 | "- LSTM networks were on step towards implementing this\n",
129 | "- In LSTMs the functions of sequence control and memory storage are closely intertwined\n",
130 | "- But this contrasts with classic models of human working memory, which, separate these two.\n",
131 | "\n",
132 | "\n",
133 | "\n",
134 | "- A possible improvement, the differential neural computer (DNC) involves a neural network controller that attends to and reads/writes from an external memory matrix\n",
135 | "- This externalization allows the network controller to learn from scratch (i.e., via end-to-end optimization) to perform a wide range of complex memory and reasoning tasks that currently elude LSTMs, such as finding the shortest path through a graph-like structure, such as a subway map, or manipulating blocks in a variant of the Tower of Hanoi task\n",
136 | "\n",
137 | "### Continual Learning\n",
138 | "\n",
139 | "\n",
140 | "\n",
141 | "- Intelligent agents must be able to learn and remember many different tasks that are encountered over multiple timescales.\n",
142 | "- Continual Learning is an ability to master new tasks without forgetting how to perform prior tasks \n",
143 | "\n",
144 | "\n",
145 | "\n",
146 | "- While animals appear relatively adept at continual learning, neural networks suffer from the problem of catastrophic forgetting \n",
147 | "- This occurs as the network parameters shift toward the optimal state for performing the second of two successive tasks, overwriting the configuration that allowed them to perform the first. \n",
148 | "\n",
149 | "\n",
150 | "\n",
151 | "- Advanced neuroimaging techniques (e.g., two-photon imaging) now allow dynamic in vivo visualization of the structure and function of dendritic spines during learning, at the spatial scale of single synapses\n",
152 | "- This approach can be used to study neocortical plasticity during continual learning\n",
153 | "\n",
154 | "\n",
155 | "\n",
156 | "- Elastic Weight Consolidtion is one newer step in this direction\n",
157 | "- acts by slowing down learning in a subset of network weights identified as important to previous tasks, thereby anchoring these parameters to previously found solutions. \n",
158 | "- This allows multiple tasks to be learned without an increase in network capacity, with weights shared efficiently between tasks with related structure. \n",
159 | "\n",
160 | "## The Future (5 Areas of AI Neuroscience will Improve)\n",
161 | "\n",
162 | "\n",
163 | "\n",
164 | "- The advent of new tools for brain imaging and genetic bioengineering have begun to offer a detailed characterization of the computations occurring in neural circuits\n",
165 | "- This will revolutionize our understanding of mammalian brain function\n",
166 | "\n",
167 | "## Understanding of Physical reality \n",
168 | "\n",
169 | "\n",
170 | "\n",
171 | "- Human Infants have knowledge of core concepts relating to the physical world, such as space, number, and objectness, which allow them to construct compositional mental models that can guide inference and prediction \n",
172 | "\n",
173 | "\n",
174 | "\n",
175 | "- Novel neural network architectures have been developed that interpret and reason about scenes in a humanlike way, by decomposing them into individual objects and their relations\n",
176 | "- Deep RL has been used to capture the processes by which children gain commonsense understanding of the world through interactive experiments\n",
177 | "\n",
178 | "## Efficient learning\n",
179 | "\n",
180 | "\n",
181 | "\n",
182 | "- Human cognition is distinguished by its ability to rapidly learn about new concepts from only a handful of examples\n",
183 | "\n",
184 | "## Transfer Learning\n",
185 | "\n",
186 | "\n",
187 | "\n",
188 | "- Humans excel at generalizing or transferring generalized knowledge gained in one context to novel, previously unseen domains\n",
189 | "- one recent report made the very interesting claim that neural codes thought to be important in the representation of map-like spaces might be critical for abstract reasoning in more general domains (Constantinescu et al., 2016). \n",
190 | "\n",
191 | "\n",
192 | "\n",
193 | "- In the mammalian cortex, cells encode the geometry of map-like space with a periodic ‘‘grid’’ code, with receptive fields that tile the local space in a hexagonal pattern\n",
194 | "- Grid codes may be an excellent candidate for organizing conceptual knowledge, because they allow state spaces to be decomposed efficiently, in a way that could support discovery of subgoals and hierarchical planning \n",
195 | "- Using functional neuroimaging, the researchers provided evidence for the existence of such codes while humans performed an abstract categorization task, supporting the view that periodic encoding is a generalized hallmark of human knowledge organization (Constantinescu et al., 2016). \n",
196 | "\n",
197 | "## Imagination and Planning\n",
198 | "\n",
199 | "\n",
200 | "\n",
201 | "- Despite their strong performance on goal-directed tasks, deep RL systems such as DQN operate mostly in a reactive way, learning the mapping from perceptual inputs to actions that maximize future value. \n",
202 | "- Two major drawbacks: it is relatively data inefficient, requiring large amounts of experience to derive accurate estimates\n",
203 | "- and it is inflexible, being insensitive to changes in the value of outcomes\n",
204 | "\n",
205 | "https://arxiv.org/abs/1707.06203\n",
206 | "\n",
207 | "\n",
208 | "\n",
209 | "\n",
210 | "- By contrast, humans can more flexibly select actions based on forecasts of long-term future outcomes through simulation-based planning, which uses predictions generated from an internal model of the environment learned through experience\n",
211 | "- An emerging picture from neuroscience research suggests that the hippo- campus supports planning by instantiating an internal model of the environment, with goal-contingent valuation of simulated outcomes occurring in areas downstream of the hippocampus (Redish, 2016)\n",
212 | "- AI research will benefit from a close reading of the related literature on how humans imagine possible scenarios, envision the future, and carry out simulation- based planning, functions that depend on a common neural substrate in the hippocampus\n",
213 | "- Research into human imagination emphasizes its constructive nature, with humans able to construct fictitious mental scenarios by recombining familiar elements in novel ways, necessitating compositional/disentangled representations of the form present in certain generative models\n",
214 | "\n",
215 | "## Virtual Brain Analytics\n",
216 | "\n",
217 | "\n",
218 | "\n",
219 | "- Due to their complexity, the products of AI research often remain ‘‘black boxes’’; \n",
220 | "- we understand only poorly the nature of the computations that occur, or representations that are formed, during learning of complex tasks.\n",
221 | "\n",
222 | "\n",
223 | "\n",
224 | "- But by applying tools from neuroscience to AI systems, synthetic equiv- alents of single-cell recording, neuroimaging, and lesion techniques, we can gain insights into the key drivers of successful learning in AI research and increase the interpretability of these systems. i.e ‘‘virtual brain analytics.'\n",
225 | "- visualizing brain states through dimensionality reduction is commonplace in neuroscience, can be applied more in AI research\n",
226 | "- Important as complexity of neural architecture increases"
227 | ]
228 | },
229 | {
230 | "cell_type": "code",
231 | "execution_count": null,
232 | "metadata": {},
233 | "outputs": [],
234 | "source": []
235 | }
236 | ],
237 | "metadata": {
238 | "kernelspec": {
239 | "display_name": "Python 3",
240 | "language": "python",
241 | "name": "python3"
242 | },
243 | "language_info": {
244 | "codemirror_mode": {
245 | "name": "ipython",
246 | "version": 3
247 | },
248 | "file_extension": ".py",
249 | "mimetype": "text/x-python",
250 | "name": "python",
251 | "nbconvert_exporter": "python",
252 | "pygments_lexer": "ipython3",
253 | "version": "3.6.3"
254 | }
255 | },
256 | "nbformat": 4,
257 | "nbformat_minor": 2
258 | }
259 |
--------------------------------------------------------------------------------
/README.md:
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1 | SpectraVis
2 | ==========
3 |
4 | This is the code from [this](https://youtu.be/e_BOJS1BLj8) video on Youtube by Siraj Raval.
5 |
6 | An interactive network visualization tool for exploring [functional brain connectivity](http://www.scholarpedia.org/article/Brain_connectivity) using [d3.js](http://d3js.org/). See [this](http://ericdeno.com/research/SpectraVis/) for an example of SpectraVis in action.
7 |
8 | 
9 |
10 | SpectraVis allows you to:
11 | + examine how network dynamics change over time and frequency
12 | + compare local (statistical dependencies between a single pair of nodes) and global (statistical dependencies between all nodes) dynamics.
13 | + compare different types of functional connectivity measures (correlation, coherence).
14 | + compare between different subjects.
15 | + examine only within- or between-brain area connections
16 | + switch between multiple network views for better understanding of the network structure
17 |
18 | ## Installation
19 | To install SpectraVis, download the latest release:
20 | + [https://github.com/edeno/SpectraVis/releases](https://github.com/edeno/SpectraVis/releases)
21 |
22 | Or use Node.js and its package manager (npm):
23 |
24 | 1. Open a terminal (Mac) or a Windows Command Prompt (`Start > All Programs > Accessories > Windows Command Prompt `)
25 | 2. Download or clone the repository: `git clone https://github.com/edeno/SpectraVis.git`
26 | 3. Install Node.js using one of the [installers](https://nodejs.org/).
27 | 4. Enter in the terminal or command prompt: `npm install spectravis`
28 |
29 | This will install the relevant development dependencies. Running `gulp` in the terminal will automatically launch a webserver on [http://localhost:8000/](http://localhost:8000/) where you can view the visualization.
30 |
31 | ## Usage
32 | `spectravis.init(params)` starts the visualization in `index.html`.
33 |
34 | See the [wiki](https://github.com/edeno/SpectraVis/wiki) for more information on how to view the visualization on your local machine, the expected structure of the data, and converting data from Matlab to JSON.
35 |
36 | ## Modifying and Contributing
37 | Fork, then clone the repo:
38 | ````
39 | git clone git@github.com/your-username/SpectraVis.git
40 | ````
41 | Use `npm install` to get the development dependencies. Place your Data in `app/DATA/`.
42 |
43 | Push to your fork and submit a pull request to the `develop` branch.
44 |
45 | ## License
46 | [GPL-v2](http://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html)
47 |
48 |
49 | ## Credits
50 |
51 | The credits for this code go to [Neurophysvis](https://github.com/NeurophysVis/SpectraVis). I've merely created a wrapper to get people started.
52 |
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/SpectraVis-Demo.gif:
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https://raw.githubusercontent.com/llSourcell/machine_learning_and_neuroscience/80319df7d4621964b3ce5c282cc8350b51961dd9/SpectraVis-Demo.gif
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/app/css/spectra.css:
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1 | .axis path, .axis line {
2 | fill: none;
3 | stroke: #000;
4 | shape-rendering: crispEdges;
5 | }
6 | .axis text {
7 | font: 11px sans-serif;
8 | color: #777;
9 | }
10 | .hideAxisLines path {
11 | display: none;
12 | }
13 | .edge {
14 | stroke-opacity: .6;
15 | }
16 | .node {
17 | stroke: #FFF;
18 | stroke-width 0.5px;
19 | }
20 | text.nodeLabel {
21 | font-family: sans-serif;
22 | font-size: 11px;
23 | text-anchor: middle;
24 | line-height: 100%;
25 | dominant-baseline: central;
26 | cursor: pointer;
27 | }
28 | button.dropdown-toggle {
29 | width: 100%;
30 | }
31 | output {
32 | padding-top: 0px;
33 | text-align: center;
34 | }
35 | .bottom-buffer {
36 | margin-bottom: 10px;
37 | }
38 | #export-link-help > button {
39 | border-style: none;
40 | }
41 | #sidebar {
42 | background: rgba(221, 221, 221, 0.4);
43 | padding: 15px 30px;
44 | border-radius: 25px;
45 | }
46 | button#PlayButtonPanel {
47 | width: 50%;
48 | }
49 | .dropdown li {
50 | cursor: pointer;
51 | }
52 | .dropdown button {
53 | overflow: hidden;
54 | }
55 | g#spect2Slice > g#x.axis, g#spect2Slice > g.zeroLine {
56 | display: none;
57 | }
58 | .load {
59 | width: 100%;
60 | height: 100%;
61 | position: relative;
62 | }
63 | #loading {
64 | position: absolute;
65 | left: 0;
66 | top: 0;
67 | right: 0;
68 | bottom: 0;
69 | margin: auto;
70 | z-index: 200;
71 | }
72 | #overlay {
73 | position: fixed;
74 | top: 0;
75 | left: 0;
76 | width: 100%;
77 | height: 100%;
78 | z-index: 200;
79 | background: rgba(0, 0, 0, 0.5);
80 | }
81 | #helpToolTip {
82 | padding: 4px 20px;
83 | font-size: 14px;
84 | background: #f5f5f5;
85 | max-width: 44em;
86 | max-height: 44em;
87 | vertical-align: middle;
88 | visibility: visible;
89 | outline: medium none;
90 | position: fixed;
91 | z-index: 200;
92 | top: 50%;
93 | left: 50%;
94 | transform: translate(-50%, -75%);
95 | border: 1px solid #e3e3e3;
96 | border-radius: 4px;
97 | }
98 | #permalink {
99 | position: absolute;
100 | z-index: 200;
101 | background: #EEE;
102 | border: 1px solid #888;
103 | width: 520px;
104 | margin: 25px 10px;
105 | font-size: 8pt;
106 | display: none;
107 | padding: 10px;
108 | }
109 | #permalink > label {
110 | margin-top: 5px;
111 | }
112 | .copy-link {
113 | width: 100%;
114 | padding: 7px 9px;
115 | overflow: hidden;
116 | resize: none;
117 | }
118 |
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/app/js/main.js:
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1 | (function() {
2 | spectraVis = {};
3 | params = {};
4 | var networkWidth;
5 | var networkHeight;
6 | var svgNetworkMap;
7 | var subjectObject;
8 | var curSubject;
9 | var edgeStatID;
10 | var NUM_COLORS = 11;
11 | var NODE_RADIUS = 10;
12 | var EDGE_WIDTH = 2;
13 | var stopAnimation = true;
14 | var curCh1;
15 | var curCh2;
16 | var curFreqInd;
17 | var curTimeInd;
18 | var mouseFlag = true;
19 | var edgeFilter;
20 | var networkView;
21 | var svgCh1;
22 | var svgEdgeStat;
23 | var svgCh2;
24 | var svgSpectraLegend;
25 | var svgEdgeStatLegend;
26 | var edgeStatLegendTitle;
27 | var svgAnatomicalLegend;
28 | var svgTimeSlice;
29 | var panelWidth;
30 | var edgeFilterDropdown;
31 | var networkSpinner;
32 | var spect1Spinner;
33 | var spect2Spinner;
34 | var edgeSpinner;
35 | var panelWidth;
36 | var panelHeight;
37 | var legendWidth;
38 | var timeSliceWidth;
39 | var timeSliceHeight;
40 | var edgeInfo;
41 | var subjectData;
42 | var visInfo;
43 | var margin;
44 | var edgeStatName;
45 | var channel;
46 | var edgeData;
47 | var time;
48 | var freq;
49 |
50 | colorbrewer.PiYG[NUM_COLORS].reverse();
51 | colorbrewer.RdBu[NUM_COLORS].reverse();
52 |
53 | spectraVis.init = function(params) {
54 | margin = {
55 | top: 30,
56 | right: 30,
57 | bottom: 30,
58 | left: 30,
59 | };
60 | panelWidth = document.getElementById('SpectraCh1Panel').offsetWidth - margin.left - margin.right;
61 | panelHeight = document.getElementById('SpectraCh1Panel').offsetWidth * (4 / 5) - margin.top - margin.bottom;
62 | legendWidth = document.getElementById('legendKey').offsetWidth - 5 - 5 - 30; // -30 comes from css padding. Kind of hacky.
63 | var colorbarLegendHeight = 60;
64 | var anatomicalLegendHeight = 100 - margin.top - margin.bottom;
65 | timeSliceWidth = panelWidth;
66 | timeSliceHeight = 180 - margin.top - margin.bottom;
67 | var spinnerOpts = {
68 | zIndex: 100,
69 | };
70 | freq = +params.freq;
71 | time = +params.time;
72 | edgeFilter = params.edgeFilter || 'All';
73 | networkView = params.networkView || 'Anatomical';
74 | curCh1 = params.curCh1;
75 | curCh2 = params.curCh2;
76 | curSubject = params.curSubject;
77 | edgeStatID = params.edgeStatID;
78 |
79 | // Heatmap Panels
80 | svgCh1 = d3.select('#SpectraCh1Panel')
81 | .append('svg')
82 | .attr('width', panelWidth + margin.left + margin.right)
83 | .attr('height', panelHeight + margin.top + margin.bottom)
84 | .append('g')
85 | .attr('transform', 'translate(' + margin.left + ',' + margin.top + ')');
86 | svgEdgeStat = d3.select('#EdgeStatPanel')
87 | .append('svg')
88 | .attr('width', panelWidth + margin.left + margin.right)
89 | .attr('height', panelHeight + margin.top + margin.bottom)
90 | .append('g')
91 | .attr('transform', 'translate(' + margin.left + ',' + margin.top + ')');
92 | svgCh2 = d3.select('#SpectraCh2Panel')
93 | .append('svg')
94 | .attr('width', panelWidth + margin.left + margin.right)
95 | .attr('height', panelHeight + margin.top + margin.bottom)
96 | .append('g')
97 | .attr('transform', 'translate(' + margin.left + ',' + margin.top + ')');
98 |
99 | // Legend SVG
100 | svgSpectraLegend = d3.selectAll('#legendKey').select('#spectraLegend')
101 | .append('svg')
102 | .attr('width', legendWidth + 5 + 5)
103 | .attr('height', 50)
104 | .append('g')
105 | .attr('transform', 'translate(' + 5 + ',' + 25 + ')');
106 | svgSpectraLegend.append('text')
107 | .attr('transform', 'translate(-5, -5)')
108 | .attr('font-size', 12)
109 | .attr('font-weight', 700)
110 | .text('Difference in Power');
111 | svgEdgeStatLegend = d3.selectAll('#legendKey').select('#edgeStatLegend')
112 | .append('svg')
113 | .attr('width', legendWidth + 5 + 5)
114 | .attr('height', 50)
115 | .append('g')
116 | .attr('transform', 'translate(' + 5 + ',' + 25 + ')');
117 | edgeStatLegendTitle = svgEdgeStatLegend.append('text')
118 | .attr('transform', 'translate(-5, -5)')
119 | .attr('font-size', 12)
120 | .attr('font-weight', 700)
121 | .text('Edge Statistic');
122 | svgAnatomicalLegend = d3.selectAll('#legendKey').select('#anatomicalLegend')
123 | .append('svg')
124 | .attr('width', legendWidth + 5 + 5)
125 | .append('g')
126 | .attr('transform', 'translate(' + 5 + ',' + 25 + ')');
127 | svgAnatomicalLegend.append('text')
128 | .attr('transform', 'translate(-5, -10)')
129 | .attr('font-size', 12)
130 | .attr('font-weight', 700)
131 | .text('Brain Areas');
132 |
133 | // Time Slice SVG
134 | svgTimeSlice = d3.select('#timeSlice')
135 | .append('svg')
136 | .attr('width', timeSliceWidth + 40 + 40)
137 | .attr('height', timeSliceHeight + 40 + 40)
138 | .append('g')
139 | .attr('transform', 'translate(' + 40 + ',' + 40 + ')');
140 |
141 | // Set up help overlay
142 | var overlay = d3.select('#overlay');
143 | var helpButton = d3.select('button#help-button');
144 | overlay.selectAll('.close')
145 | .on('click', function() {
146 | overlay.style('display', 'none');
147 | });
148 |
149 | helpButton
150 | .on('click', function() {
151 | overlay
152 | .style('display', 'block');
153 | });
154 |
155 | // Set up permalink button
156 | var permalink = d3.select('#permalink');
157 | var linkButton = d3.select('button#link');
158 | linkButton
159 | .on('click', function() {
160 | permalink
161 | .style('display', 'block');
162 | var linkString = window.location.origin + window.location.pathname + '?' +
163 | 'curSubject=' + curSubject +
164 | '&edgeStatID=' + edgeStatID +
165 | '&edgeFilter=' + edgeFilter +
166 | '&networkView=' + networkView +
167 | '&time=' + visInfo.tax[curTimeInd] +
168 | '&freq=' + visInfo.fax[curFreqInd] +
169 | '&curCh1=' + curCh1 +
170 | '&curCh2=' + curCh2;
171 | permalink.selectAll('textarea').html(linkString);
172 | permalink.selectAll('.bookmark').attr('href', linkString);
173 | });
174 |
175 | permalink.selectAll('.close')
176 | .on('click', function() {
177 | permalink.style('display', 'none');
178 | });
179 |
180 | // Set up export svg button
181 | var exportButton = d3.select('button#export');
182 | exportButton
183 | .on('click', function() {
184 | var networkSVG = d3.select('#NetworkPanel').select('svg').node();
185 | var networkSaveName = 'Network' + '_' +
186 | curSubject + '_' +
187 | edgeStatID + '_' +
188 | networkView + '_' +
189 | visInfo.tax[curTimeInd] + visInfo.tunits + '_' +
190 | visInfo.fax[curFreqInd] + visInfo.funits;
191 |
192 | d3_save_svg.save(networkSVG, {filename: networkSaveName});
193 |
194 | var ch1SaveName = 'Spectra' + '_' +
195 | curSubject + '_' +
196 | 'Ch' + curCh1;
197 |
198 | var ch1SVG = d3.select('#SpectraCh1Panel').select('svg').node();
199 | d3_save_svg.save(ch1SVG, {filename: ch1SaveName});
200 |
201 | var ch2SaveName = 'Spectra' + '_' +
202 | curSubject + '_' +
203 | 'Ch' + curCh2;
204 |
205 | var ch2SVG = d3.select('#SpectraCh2Panel').select('svg').node();
206 | d3_save_svg.save(ch2SVG, {filename: ch2SaveName});
207 |
208 | var edgeSaveName = edgeStatID + '_' +
209 | curSubject + '_' +
210 | 'Ch' + curCh1 + '_' +
211 | 'Ch' + curCh2;
212 |
213 | var edgeSVG = d3.select('#EdgeStatPanel').select('svg').node();
214 | d3_save_svg.save(edgeSVG, {filename: edgeSaveName});
215 |
216 | d3.selectAll('circle.node')[0]
217 | .forEach(function(n) {n.setAttribute('style', '');
218 | });
219 | });
220 |
221 | // Set up edge area dropdown menus
222 | edgeFilterDropdown = d3.select('#EdgeFilterDropdown');
223 | edgeFilterDropdown.selectAll('button')
224 | .text(edgeFilter)
225 | .append('span')
226 | .attr('class', 'caret');
227 |
228 | // Spinners
229 | networkSpinner = new Spinner(spinnerOpts);
230 | spect1Spinner = new Spinner(spinnerOpts);
231 | spect2Spinner = new Spinner(spinnerOpts);
232 | edgeSpinner = new Spinner(spinnerOpts);
233 |
234 | // Load subject data
235 | queue()
236 | .defer(d3.json, 'DATA/subjects.json')
237 | .defer(d3.json, 'DATA/visInfo.json')
238 | .defer(d3.json, 'DATA/edgeTypes.json')
239 | .await(createMenu);
240 | };
241 |
242 | // Functions
243 | function createMenu(error, subjects, vI, eI) {
244 | visInfo = vI;
245 | edgeInfo = eI;
246 |
247 | if (visInfo.tax.indexOf(time) !== -1) {
248 | curTimeInd = visInfo.tax.indexOf(time);
249 | } else {
250 | curTimeInd = 0;
251 | }
252 |
253 | if (visInfo.fax.indexOf(freq) !== -1) {
254 | curFreqInd = visInfo.fax.indexOf(freq);
255 | } else {
256 | curFreqInd = 0;
257 | }
258 |
259 | // Populate dropdown menu with subjects
260 | subjectData = subjects;
261 | var subjectDropdown = d3.select('#SubjectDropdown');
262 | var subjectMenu = subjectDropdown.selectAll('.dropdown-menu').selectAll('li').data(subjectData);
263 | subjectMenu.enter()
264 | .append('li')
265 | .attr('id', function(d) {
266 | return d.subjectID;
267 | })
268 | .attr('role', 'presentation')
269 | .append('a')
270 | .attr('role', 'menuitem')
271 | .attr('tabindex', -1)
272 | .text(function(d) {
273 | return d.subjectID;
274 | });
275 |
276 | // Default to the first subject
277 | curSubject = curSubject || subjectData[0].subjectID;
278 | subjectDropdown.selectAll('button')
279 | .text(curSubject)
280 | .append('span')
281 | .attr('class', 'caret');
282 |
283 | // Create dropdown for edge types
284 | var edgeDropdown = d3.select('#EdgeStatTypeDropdown');
285 | var edgeOptions = edgeDropdown.select('ul').selectAll('li').data(edgeInfo);
286 | edgeOptions.enter()
287 | .append('li')
288 | .attr('id', function(d) {
289 | return d.edgeStatID;
290 | })
291 | .append('a')
292 | .attr('role', 'menuitem')
293 | .attr('tabindex', -1)
294 | .text(function(d) {
295 | return d.edgeStatName;
296 | });
297 |
298 | edgeOptions.exit()
299 | .remove();
300 |
301 | // Default to the first subject
302 | edgeStatID = edgeStatID || edgeInfo[0].edgeStatID;
303 | edgeStatName = edgeInfo.filter(function(e) {return e.edgeStatID === edgeStatID;})[0].edgeStatName;
304 | edgeDropdown.selectAll('button')
305 | .text(edgeStatName)
306 | .append('span')
307 | .attr('class', 'caret');
308 |
309 | // Set brain area legend height
310 | d3.selectAll('#legendKey').selectAll('#anatomicalLegend').selectAll('svg')
311 | .attr('height', 14 + visInfo.brainRegions.length * 15.14);
312 |
313 | // Load channel data
314 | loadChannelData();
315 | }
316 |
317 | // Load channel file and set the network svg to be the right aspect ratio for the brain
318 | function loadChannelData() {
319 |
320 | var channelFile = 'channels_' + curSubject + '.json';
321 |
322 | subjectObject = subjectData.filter(function(d) {
323 | return d.subjectID === curSubject;
324 | })[0];
325 |
326 | var aspectRatio = subjectObject.brainXpixels / subjectObject.brainYpixels;
327 | networkWidth = document.getElementById('NetworkPanel').offsetWidth - margin.left - margin.right;
328 | networkHeight = document.getElementById('NetworkPanel').offsetWidth * (1 / aspectRatio) - margin.top - margin.bottom;
329 |
330 | svgNetworkMap = d3.select('#NetworkPanel').selectAll('svg').data([subjectObject], function(d) {
331 | return d.subjectID;
332 | });
333 |
334 | svgNetworkMap.exit().remove();
335 | svgNetworkMap = svgNetworkMap.enter()
336 | .append('svg')
337 | .attr('width', networkWidth)
338 | .attr('height', networkHeight)
339 | .append('g');
340 | networkSpinner.spin(document.getElementById('NetworkPanel'));
341 | svgNetworkMap.style('display', 'none');
342 | d3.json('DATA/' + channelFile, function(isError, channelData) {
343 |
344 | channel = channelData;
345 |
346 | // Default to first two channels if no channels are not already specified
347 | curCh1 = curCh1 || channel[0].channelID;
348 | curCh2 = curCh2 || channel[1].channelID;
349 |
350 | loadEdges();
351 | });
352 | }
353 |
354 | function loadEdges() {
355 | // Load the edge file for the current subject
356 | var edgeFile = 'edges_' + curSubject + '_' + edgeStatID + '.json';
357 | d3.json('DATA/' + edgeFile, function(isError, eD) {
358 | edgeData = eD;
359 | loadSpectra();
360 | });
361 | }
362 |
363 | function loadSpectra() {
364 |
365 | // Start loading spinners
366 | spect1Spinner.spin(document.getElementById('SpectraCh1Panel'));
367 | spect2Spinner.spin(document.getElementById('SpectraCh2Panel'));
368 | edgeSpinner.spin(document.getElementById('EdgeStatPanel'));
369 | svgCh1.style('display', 'none');
370 | svgCh2.style('display', 'none');
371 | svgEdgeStat.style('display', 'none');
372 | svgTimeSlice.style('display', 'none');
373 |
374 | var spectCh1File = 'spectrogram_' + curSubject + '_' + curCh1 + '.json';
375 | var spectCh2File = 'spectrogram_' + curSubject + '_' + curCh2 + '.json';
376 |
377 | // Load the rest of the files in parallel
378 | queue()
379 | .defer(d3.json, 'DATA/' + spectCh1File)
380 | .defer(d3.json, 'DATA/' + spectCh2File)
381 | .await(display);
382 | }
383 |
384 | // Draw
385 | function display(isError, spect1, spect2) {
386 |
387 | var timeScale;
388 | var timeScaleLinear;
389 | var freqScale;
390 | var powerScale;
391 | var tAx;
392 | var fAx;
393 | var powerScale;
394 | var networkXScale;
395 | var networkYScale;
396 | var force;
397 | var timeSlider;
398 | var freqSlider;
399 | var timeSliderText;
400 | var freqSliderText;
401 | var subjectDropdown;
402 | var edgeStatScale;
403 | var edgeStatTypeDropdown;
404 | var brainRegionColor;
405 | var timeSliderStep;
406 | var timeMaxStepInd;
407 | var networkXExtent;
408 | var networkYExtent;
409 | var edgeStat;
410 | var powerLineFun;
411 | var edgeStatLineFun;
412 | var timeSlicePowerScale;
413 | var timeSliceNetworkStatScale;
414 | var spect1Line;
415 | var spect2Line;
416 | var edgeStatLine;
417 | var powerScale;
418 | var edgeStatScale;
419 | var isFreq;
420 | var isWeightedNetwork;
421 | var corrScale;
422 | var curEdgeInfo;
423 | var scaledChannel;
424 |
425 | tAx = visInfo.tax; // Time Axis
426 | fAx = visInfo.fax; // Frequency Axis
427 | // Get the edge statistic corresponding to the selected channels
428 | edgeStat = edgeData.filter(function(e) {
429 | return (e.source === curCh1 && e.target === curCh2) ||
430 | (e.source === curCh2 && e.target === curCh1);
431 | })[0];
432 |
433 | // Get the edge statastic name and units
434 | curEdgeInfo = edgeInfo
435 | .filter(function(e) {
436 | return e.edgeStatID === edgeStatID;
437 | })[0];
438 |
439 | isFreq = curEdgeInfo.isFreq;
440 | isWeightedNetwork = curEdgeInfo.isWeightedNetwork;
441 |
442 | // Set up scales and slider values
443 | setupScales();
444 | setupSliders();
445 |
446 | // Initialize charts
447 | var powerChart = heatmap()
448 | .height(panelHeight)
449 | .width(panelWidth)
450 | .yScale(freqScale)
451 | .xScale(timeScale)
452 | .xLabel('Time (' + visInfo.tunits + ')')
453 | .yLabel('Frequency (' + visInfo.funits + ')')
454 | .colorScale(powerScale)
455 | .rectMouseOver(rectMouseOver)
456 | .rectMouseClick(rectMouseClick);
457 |
458 | var cohChart = heatmap()
459 | .height(panelHeight)
460 | .width(panelWidth)
461 | .yScale(freqScale)
462 | .xScale(timeScale)
463 | .xLabel('Time (' + visInfo.tunits + ')')
464 | .yLabel('Frequency (' + visInfo.funits + ')')
465 | .colorScale(edgeStatScale)
466 | .rectMouseOver(rectMouseOver)
467 | .rectMouseClick(rectMouseClick);
468 |
469 | var corrChart = timeseries()
470 | .height(panelHeight)
471 | .width(panelWidth)
472 | .yScale(corrScale)
473 | .xScale(timeScale)
474 | .xLabel('Time (' + visInfo.tunits + ')')
475 | .yLabel(edgeStatName)
476 | .rectMouseOver(rectMouseOver)
477 | .rectMouseClick(rectMouseClick);
478 |
479 | var cohTimeSlice = timeseries()
480 | .height(timeSliceHeight)
481 | .width(timeSliceWidth)
482 | .yScale(timeSliceEdgeStatScale)
483 | .xScale(timeScale)
484 | .yAxisOrientation('left')
485 | .xLabel('Time (' + visInfo.tunits + ')')
486 | .yLabel(edgeStatName);
487 |
488 | var powerTimeSlice = timeseries()
489 | .height(timeSliceHeight)
490 | .width(timeSliceWidth)
491 | .yScale(timeSlicePowerScale)
492 | .xScale(timeScale)
493 | .yAxisOrientation('right')
494 | .xLabel('Time (' + visInfo.tunits + ')')
495 | .yLabel('Power Difference')
496 | .lineColor('green');
497 |
498 | // Draw charts
499 | drawNetwork();
500 | svgNetworkMap.style('display', '');
501 | networkSpinner.stop();
502 |
503 | svgCh1
504 | .datum(spect1.data)
505 | .call(powerChart);
506 | svgCh1.style('display', '');
507 | spect1Spinner.stop();
508 |
509 | svgCh2
510 | .datum(spect2.data)
511 | .call(powerChart);
512 | spect2Spinner.stop();
513 | svgCh2.style('display', '');
514 |
515 | if (isFreq) {
516 | svgEdgeStat
517 | .html('');
518 | svgEdgeStat
519 | .datum(edgeStat.data)
520 | .call(cohChart);
521 | drawTimeSlice();
522 | } else {
523 | // Remove coherence and time slice charts
524 | svgEdgeStat
525 | .html('');
526 | svgTimeSlice
527 | .html('');
528 | svgEdgeStat
529 | .datum(edgeStat.data)
530 | .call(corrChart);
531 | }
532 |
533 | svgEdgeStat.style('display', '');
534 | edgeSpinner.stop();
535 | svgTimeSlice.style('display', '');
536 |
537 | // Draw legends and titles
538 | drawTitles();
539 | drawLegends();
540 |
541 | // Handle buttons
542 | subjectLoad();
543 | edgeStatTypeLoad();
544 | edgeFilterLoad();
545 | networkViewLoad();
546 | playButtonStart();
547 | resetButton();
548 |
549 | function setupSliders() {
550 |
551 | timeSlider = d3.select('#timeSlider');
552 | timeSliderText = d3.select('#timeSlider-value');
553 | freqSlider = d3.select('#freqSlider');
554 | freqSliderText = d3.select('#freqSlider-value');
555 |
556 | timeSliderStep = d3.round(tAx[1] - tAx[0], 4);
557 | timeMaxStepInd = tAx.length - 1;
558 |
559 | timeSlider.property('min', d3.min(tAx));
560 | timeSlider.property('max', d3.max(tAx));
561 | timeSlider.property('step', timeSliderStep);
562 | timeSlider.property('value', tAx[curTimeInd]);
563 | timeSlider.on('input', updateTimeSlider);
564 | timeSliderText.text(tAx[curTimeInd] + ' ms');
565 |
566 | freqSlider.property('min', d3.min(fAx));
567 | freqSlider.property('max', d3.max(fAx));
568 | freqSlider.property('step', fAx[1] - fAx[0]);
569 | freqSlider.property('value', fAx[curFreqInd]);
570 | freqSlider.on('input', updateFreqSlider);
571 | freqSliderText.text(fAx[curFreqInd] + ' Hz');
572 | }
573 |
574 | function setupScales() {
575 | var powerColors = colorbrewer.PiYG[NUM_COLORS];
576 | var edgeStatColors = colorbrewer.RdBu[NUM_COLORS];
577 | var powerMin;
578 | var powerMax;
579 | var powerExtent;
580 | var edgeStatMin;
581 | var edgeStatMax;
582 | var edgeStatExtent;
583 |
584 | brainRegionColor = d3.scale.ordinal()
585 | .domain(visInfo.brainRegions)
586 | .range(colorbrewer.Pastel1[7]);
587 |
588 | powerMin = d3.min(
589 | [d3.min(spect1.data, function(d) {
590 | return d3.min(d, function(e) {
591 | return e;
592 | });
593 | }),
594 |
595 | d3.min(spect2.data, function(d) {
596 | return d3.min(d, function(e) {
597 | return e;
598 | });
599 | }),
600 | ]
601 |
602 | );
603 |
604 | powerMax = d3.max(
605 | [d3.max(spect1.data, function(d) {
606 | return d3.max(d, function(e) {
607 | return e;
608 | });
609 | }),
610 |
611 | d3.max(spect2.data, function(d) {
612 | return d3.max(d, function(e) {
613 | return e;
614 | });
615 | }),
616 | ]
617 | );
618 |
619 | powerExtent = symmetricExtent(powerMin, powerMax);
620 |
621 | networkXExtent = subjectObject.brainXLim;
622 | networkYExtent = subjectObject.brainYLim;
623 |
624 | edgeStatMin = d3.min(edgeData, function(d) {
625 | return d3.min(d.data, function(e) {
626 | return d3.min(e, function(f) {
627 | return f;
628 | });
629 | });
630 | });
631 |
632 | edgeStatMax = d3.max(edgeData, function(d) {
633 | return d3.max(d.data, function(e) {
634 | return d3.max(e, function(f) {
635 | return f;
636 | });
637 | });
638 | });
639 |
640 | edgeStatExtent = symmetricExtent(edgeStatMin, edgeStatMax);
641 |
642 | powerScale = d3.scale.linear()
643 | .domain(powerExtent)
644 | .range(powerColors);
645 | if (isWeightedNetwork) {
646 | edgeStatScale = d3.scale.linear()
647 | .domain(edgeStatExtent)
648 | .range(edgeStatColors);
649 | } else {
650 | edgeStatColors = [0, (NUM_COLORS - 1) / 2, NUM_COLORS - 1].map(function(n) { return edgeStatColors[n];});
651 |
652 | edgeStatScale = d3.scale.ordinal()
653 | .domain([-1, 0, 1])
654 | .range(edgeStatColors);
655 | }
656 |
657 | timeScale = d3.scale.ordinal()
658 | .domain(tAx)
659 | .rangeBands([0, panelWidth]);
660 |
661 | timeScaleLinear = d3.scale.linear()
662 | .domain(d3.extent(tAx))
663 | .range([0, panelWidth]);
664 |
665 | freqScale = d3.scale.ordinal()
666 | .domain(fAx)
667 | .rangeBands([panelHeight, 0]);
668 |
669 | timeSlicePowerScale = d3.scale.linear()
670 | .domain(powerExtent)
671 | .range(linspace(timeSliceHeight, 0, NUM_COLORS));
672 | timeSliceEdgeStatScale = d3.scale.linear()
673 | .domain(edgeStatExtent)
674 | .range(linspace(timeSliceHeight, 0, NUM_COLORS));
675 |
676 | networkXScale = d3.scale.linear()
677 | .domain(networkXExtent)
678 | .range([0, networkWidth]);
679 | networkYScale = d3.scale.linear()
680 | .domain(networkYExtent)
681 | .range([networkHeight, 0]);
682 |
683 | corrScale = d3.scale.linear()
684 | .domain(edgeStatExtent)
685 | .range(linspace(0, panelHeight, NUM_COLORS));
686 |
687 | function symmetricExtent(min, max) {
688 | if (Math.abs(min) >= Math.abs(max)) {
689 | max = Math.abs(min);
690 | } else {
691 | min = -1 * max;
692 | }
693 |
694 | return linspace(min, max, 11);
695 | }
696 |
697 | // from https://github.com/sloisel/numeric
698 | function linspace(a, b, n) {
699 | if (typeof n === 'undefined') {
700 | n = Math.max(Math.round(b - a) + 1, 1);
701 | };
702 |
703 | if (n < 2) {
704 | return n === 1 ? [a] : [];
705 | }
706 |
707 | var i;
708 | var ret = Array(n);
709 | n--;
710 | for (i = n; i >= 0; i--) {
711 | ret[i] = (i * b + (n - i) * a) / n;
712 | }
713 |
714 | return ret;
715 | }
716 | }
717 |
718 | function drawNetwork() {
719 | var nodesGroup;
720 | var edgesGroup;
721 | var nodeG;
722 | var strokeStyle;
723 | var nodeClickNames = [];
724 | var brainImage;
725 | var edge;
726 | var edgeLine;
727 | var brainImageG;
728 | var nodeCircle;
729 | var nodeText;
730 |
731 | if (networkView === 'Anatomical') {
732 | scaledChannel = channel.map(function(n) {
733 | var obj = copyObject(n);
734 | obj.x = networkXScale(n.x);
735 | obj.y = networkYScale(n.y);
736 | obj.fixed = true;
737 | return obj;
738 | });
739 | } else {
740 | scaledChannel = channel.map(function(n, i) {
741 | var obj = copyObject(n);
742 | if (typeof scaledChannel === 'undefined') {
743 | obj.x = networkXScale(n.x);
744 | obj.y = networkYScale(n.y);
745 | } else {
746 | obj.x = scaledChannel[i].x;
747 | obj.y = scaledChannel[i].y;
748 | obj.px = scaledChannel[i].px;
749 | obj.py = scaledChannel[i].py;
750 | }
751 |
752 | obj.fixed = false;
753 | return obj;
754 | });
755 | }
756 |
757 | // Replace source name by source object
758 | edge = edgeData.map(function(e) {
759 | var obj = copyObject(e);
760 | obj.source = scaledChannel.filter(function(n) {
761 | return n.channelID === e.source;
762 | })[0];
763 |
764 | obj.target = scaledChannel.filter(function(n) {
765 | return n.channelID === e.target;
766 | })[0];
767 |
768 | obj.data = obj.data[curTimeInd][curFreqInd];
769 | return obj;
770 | });
771 |
772 | edge = edge.filter(edgeFiltering);
773 |
774 | force = d3.layout.force()
775 | .nodes(scaledChannel)
776 | .links(edge)
777 | .charge(-375)
778 | .linkDistance(networkHeight / 3)
779 | .size([networkWidth, networkHeight])
780 | .start();
781 |
782 | brainImageG = svgNetworkMap.selectAll('g#BRAIN_IMAGE').data([{}]);
783 | brainImageG.enter()
784 | .append('g')
785 | .attr('id', 'BRAIN_IMAGE');
786 | edgesGroup = svgNetworkMap.selectAll('g#EDGES').data([{}]);
787 | edgesGroup.enter()
788 | .append('g')
789 | .attr('id', 'EDGES');
790 | nodesGroup = svgNetworkMap.selectAll('g#NODES').data([{}]);
791 | nodesGroup.enter()
792 | .append('g')
793 | .attr('id', 'NODES');
794 |
795 | edgeLine = edgesGroup.selectAll('.edge').data(edge, function(e) {
796 | return curSubject + '_' + e.source.channelID + '_' + e.target.channelID;
797 | });
798 |
799 | edgeLine.enter()
800 | .append('line')
801 | .attr('class', 'edge')
802 | .style('stroke-width', EDGE_WIDTH)
803 | .attr('x1', function(d) {
804 | return xPos(d.source);
805 | })
806 | .attr('y1', function(d) {
807 | return yPos(d.source);
808 | })
809 | .attr('x2', function(d) {
810 | return xPos(d.target);
811 | })
812 | .attr('y2', function(d) {
813 | return yPos(d.target);
814 | });
815 |
816 | edgeLine.exit()
817 | .remove();
818 | edgeLine
819 | .style('stroke', function(d) {
820 | return edgeStatScale(d.data);
821 | })
822 | .on('mouseover', edgeMouseOver)
823 | .on('mouseout', edgeMouseOut)
824 | .on('click', edgeMouseClick);
825 |
826 | nodeG = nodesGroup.selectAll('g.gnode').data(scaledChannel, function(d) {
827 | return curSubject + '_' + d.channelID;
828 | });
829 |
830 | nodeG.enter()
831 | .append('g')
832 | .attr('class', 'gnode')
833 | .attr('transform', function(d) {
834 | return 'translate(' + [xPos(d), yPos(d)] + ')';
835 | })
836 | .on('click', nodeMouseClick);
837 | nodeG.exit().remove();
838 |
839 | nodeCircle = nodeG.selectAll('circle.node').data(function(d) {
840 | return [d];
841 | });
842 |
843 | nodeCircle.enter()
844 | .append('circle')
845 | .attr('class', 'node')
846 | .attr('r', NODE_RADIUS)
847 | .attr('fill', '#ddd')
848 | .attr('opacity', 1);
849 | nodeCircle
850 | .attr('fill', function(d) {
851 | return brainRegionColor(d.region);
852 | })
853 | .style('stroke', 'white');
854 |
855 | nodeCircle.filter(function(d) {return d.channelID == curCh1 || d.channelID == curCh2;})
856 | .style('stroke', 'black');
857 |
858 | nodeText = nodeG.selectAll('text.nodeLabel').data(function(d) {
859 | return [d];
860 | });
861 |
862 | nodeText.enter()
863 | .append('text')
864 | .attr('class', 'nodeLabel')
865 | .text(function(d) {
866 | return d.channelID;
867 | });
868 |
869 | // For every iteration of force simulation 'tick'
870 | force.on('tick', function() {
871 |
872 | // Translate the groups
873 | nodeG.attr('transform', function(d) {
874 | return 'translate(' + [xPos(d), yPos(d)] + ')';
875 | });
876 |
877 | edgeLine.attr('x1', function(d) {
878 | return xPos(d.source);
879 | })
880 | .attr('y1', function(d) {
881 | return yPos(d.source);
882 | })
883 | .attr('x2', function(d) {
884 | return xPos(d.target);
885 | })
886 | .attr('y2', function(d) {
887 | return yPos(d.target);
888 | });
889 |
890 | if (networkView !== 'Topological') {
891 | force.stop();
892 | }
893 |
894 | });
895 |
896 | brainImage = brainImageG.selectAll('image').data([subjectObject], function(d) {
897 | return d.brainFilename;
898 | });
899 |
900 | if (networkView === 'Anatomical') {
901 | brainImage.enter()
902 | .append('image')
903 | .attr('width', networkWidth)
904 | .attr('height', networkHeight);
905 |
906 | // replace link by data URI
907 | getImageBase64('DATA/brainImages/' + subjectObject.brainFilename, function(err, d) {
908 | brainImage
909 | .attr('xlink:href', 'data:image/png;base64,' + d);
910 | });
911 | }
912 |
913 | brainImage.exit()
914 | .remove();
915 | if (networkView === 'Topological') {
916 | brainImage.remove();
917 | };
918 |
919 | function xPos(d) {
920 | return Math.max(NODE_RADIUS, Math.min(networkWidth - NODE_RADIUS, d.x));
921 | }
922 |
923 | function yPos(d) {
924 | return Math.max(NODE_RADIUS, Math.min(networkHeight - NODE_RADIUS, d.y));
925 | }
926 |
927 | function edgeMouseOver(e) {
928 |
929 | var curEdge = d3.select(this);
930 | strokeStyle = curEdge.style('stroke');
931 | curEdge
932 | .style('stroke-width', 2 * EDGE_WIDTH)
933 | .style('stroke', function() {
934 | if (e.data < 0) {
935 | return edgeStatScale(d3.min(edgeStatScale.domain()));
936 | } else {
937 | return edgeStatScale(d3.max(edgeStatScale.domain()));
938 | }
939 | });
940 |
941 | var curNodes = d3.selectAll('circle.node')
942 | .filter(function(n) {
943 | return (n.channelID === e.source.channelID) || (n.channelID === e.target.channelID);
944 | })
945 | .attr('r', NODE_RADIUS * 1.2);
946 | }
947 |
948 | function edgeMouseOut(e) {
949 | var curEdge = d3.select(this);
950 | if (typeof strokeStyle !== 'undefined') {
951 | curEdge
952 | .style('stroke-width', EDGE_WIDTH)
953 | .style('stroke', strokeStyle);
954 | d3.selectAll('circle.node')
955 | .filter(function(n) {
956 | return (n.channelID === e.source.channelID) || (n.channelID === e.target.channelID);
957 | })
958 | .attr('r', NODE_RADIUS);
959 | }
960 | }
961 |
962 | function edgeMouseClick(e) {
963 | var re = /\d+/;
964 | curCh1 = re.exec(e.source.channelID)[0];
965 | curCh2 = re.exec(e.target.channelID)[0];
966 | mouseFlag = true;
967 | svgNetworkMap.select('text#HOLD').remove();
968 | loadSpectra();
969 | edgeMouseOut.call(this, e);
970 | }
971 |
972 | function nodeMouseClick(e) {
973 | var curNode = d3.select(this);
974 | var nodeInd = nodeClickNames.indexOf(+e.channelID);
975 |
976 | if (nodeInd > -1) {
977 | // If clicked on node is in the array, remove
978 | curNode.selectAll('circle')
979 | .attr('r', NODE_RADIUS);
980 | nodeClickNames.splice(nodeInd, 1);
981 | } else {
982 | // Else add to array
983 | curNode.selectAll('circle')
984 | .attr('r', 1.2 * NODE_RADIUS);
985 | nodeClickNames.push(+e.channelID);
986 | }
987 |
988 | if (nodeClickNames.length === 2) {
989 | var re = /\d+/;
990 | curCh1 = re.exec(nodeClickNames[0])[0];
991 | curCh2 = re.exec(nodeClickNames[1])[0];
992 | mouseFlag = true;
993 | svgNetworkMap.select('text#HOLD').remove();
994 | d3.selectAll('circle.node')
995 | .filter(function(n) {
996 | return (n.channelID === nodeClickNames[0].toString()) || (n.channelID === nodeClickNames[1].toString());
997 | })
998 | .attr('fill', '#ddd')
999 | .attr('r', NODE_RADIUS);
1000 | nodeClickNames = [];
1001 | loadSpectra();
1002 | }
1003 | }
1004 |
1005 | function edgeFiltering(e) {
1006 | var showEdge = true;
1007 |
1008 | // If edge type is binary, don't display edges corresponding to no edge
1009 | if (!isWeightedNetwork) {
1010 | showEdge = (e.data !== 0);
1011 | }
1012 |
1013 | // Now filter by edge connection type (within brain area, etc)
1014 | switch (edgeFilter) {
1015 | case 'Within':
1016 | showEdge = (e.source.region === e.target.region) && showEdge;
1017 | break;
1018 | case 'Between':
1019 | showEdge = (e.source.region !== e.target.region) && showEdge;
1020 | break;
1021 | }
1022 |
1023 | return showEdge;
1024 | }
1025 | };
1026 |
1027 | function heatmap() {
1028 | var colorScale = d3.scale.linear();
1029 | var xScale = d3.scale.ordinal();
1030 | var yScale = d3.scale.ordinal();
1031 | var rectMouseOver = function() {};
1032 |
1033 | var rectMouseClick = function() {};
1034 |
1035 | var height = 500;
1036 | var width = 500;
1037 | var xLabel = '';
1038 | var yLabel = '';
1039 |
1040 | function chart(selection) {
1041 |
1042 | selection.each(function(data) {
1043 | var heatmapG;
1044 | var heatmapRect;
1045 | var xAxis;
1046 | var yAxis;
1047 | var zeroG;
1048 | var xAxisG;
1049 | var yAxisG;
1050 | var zeroLine;
1051 | var curPlot = d3.select(this);
1052 |
1053 | xScale.rangeBands([0, width]);
1054 | yScale.rangeBands([height, 0]);
1055 |
1056 | heatmapG = curPlot.selectAll('g.heatmapX').data(data);
1057 | heatmapG.enter()
1058 | .append('g')
1059 | .attr('transform', function(d, i) {
1060 | return 'translate(' + xScale(xScale.domain()[i]) + ', 0)';
1061 | })
1062 | .attr('class', 'heatmapX');
1063 | heatmapG.exit()
1064 | .remove();
1065 | heatmapRect = heatmapG.selectAll('rect').data(function(d) {
1066 | return d;
1067 | });
1068 |
1069 | heatmapRect.enter()
1070 | .append('rect')
1071 | .attr('x', 0)
1072 | .attr('y', function(d, i) {
1073 | return yScale(yScale.domain()[i]);
1074 | })
1075 | .attr('height', yScale.rangeBand())
1076 | .attr('width', xScale.rangeBand())
1077 | .style('fill', 'white');
1078 | heatmapRect
1079 | .style('fill', function(d) {
1080 | return colorScale(d);
1081 | })
1082 | .style('stroke', function(d) {
1083 | return colorScale(d);
1084 | })
1085 | .on('mouseover', rectMouseOver)
1086 | .on('click', rectMouseClick);
1087 | heatmapRect.exit()
1088 | .remove();
1089 |
1090 | xAxis = d3.svg.axis()
1091 | .scale(xScale)
1092 | .orient('bottom')
1093 | .ticks(3)
1094 | .tickValues([d3.min(xScale.domain()), 0, d3.max(xScale.domain())])
1095 | .tickSize(0, 0, 0);
1096 | yAxis = d3.svg.axis()
1097 | .scale(yScale)
1098 | .orient('left')
1099 | .tickValues(['10', '20', '40', '60', '90', '150', '200'])
1100 | .tickSize(0, 0, 0);
1101 |
1102 | xAxisG = curPlot.selectAll('g.axis#x').data([{}]);
1103 | xAxisG.enter()
1104 | .append('g')
1105 | .attr('class', 'axis')
1106 | .attr('id', 'x')
1107 | .attr('transform', 'translate(0,' + height + ')')
1108 | .append('text')
1109 | .attr('x', xScale(0))
1110 | .attr('y', 0)
1111 | .attr('text-anchor', 'middle')
1112 | .attr('dy', 2 + 'em')
1113 | .text(xLabel);
1114 | xAxisG.call(xAxis);
1115 |
1116 | yAxisG = curPlot.selectAll('g.axis#y').data([{}]);
1117 | yAxisG.enter()
1118 | .append('g')
1119 | .attr('class', 'axis')
1120 | .attr('id', 'y')
1121 | .append('text')
1122 | .attr('x', -height / 2)
1123 | .attr('dy', -2 + 'em')
1124 | .attr('transform', 'rotate(-90)')
1125 | .attr('text-anchor', 'middle')
1126 | .text(yLabel);
1127 | yAxisG.call(yAxis);
1128 |
1129 | zeroG = curPlot.selectAll('g.zeroLine').data([
1130 | [
1131 | [0, height],
1132 | ],
1133 | ]);
1134 | zeroG.enter()
1135 | .append('g')
1136 | .attr('class', 'zeroLine');
1137 | zeroLine = zeroG.selectAll('path').data(function(d) {
1138 | return d;
1139 | });
1140 |
1141 | zeroLine.enter()
1142 | .append('path');
1143 | zeroLine
1144 | .attr('d', d3.svg.line()
1145 | .x(xScale(0))
1146 | .y(function(d) {
1147 | return d;
1148 | })
1149 | .interpolate('linear'))
1150 | .attr('stroke', 'black')
1151 | .attr('stroke-width', 2)
1152 | .attr('fill', 'none')
1153 | .style('opacity', 0.7);
1154 | });
1155 | }
1156 |
1157 | chart.colorScale = function(scale) {
1158 | if (!arguments.length) return colorScale;
1159 | colorScale = scale;
1160 | return chart;
1161 | };
1162 |
1163 | chart.xScale = function(scale) {
1164 | if (!arguments.length) return xScale;
1165 | xScale = scale;
1166 | return chart;
1167 | };
1168 |
1169 | chart.yScale = function(scale) {
1170 | if (!arguments.length) return yScale;
1171 | yScale = scale;
1172 | return chart;
1173 | };
1174 |
1175 | chart.rectMouseOver = function(fun) {
1176 | if (!arguments.length) return rectMouseOver;
1177 | rectMouseOver = fun;
1178 | return chart;
1179 | };
1180 |
1181 | chart.rectMouseClick = function(fun) {
1182 | if (!arguments.length) return rectMouseClick;
1183 | rectMouseClick = fun;
1184 | return chart;
1185 | };
1186 |
1187 | chart.width = function(value) {
1188 | if (!arguments.length) return width;
1189 | width = value;
1190 | return chart;
1191 | };
1192 |
1193 | chart.height = function(value) {
1194 | if (!arguments.length) return height;
1195 | height = value;
1196 | return chart;
1197 | };
1198 |
1199 | chart.yLabel = function(value) {
1200 | if (!arguments.length) return yLabel;
1201 | yLabel = value;
1202 | return chart;
1203 | };
1204 |
1205 | chart.xLabel = function(value) {
1206 | if (!arguments.length) return xLabel;
1207 | xLabel = value;
1208 | return chart;
1209 | };
1210 |
1211 | return chart;
1212 | }
1213 |
1214 | function drawTitles() {
1215 |
1216 | spectTitle(svgCh1, curCh1);
1217 | spectTitle(svgCh2, curCh2);
1218 | edgeTitle();
1219 | edgeStatLegendTitle.text(edgeStatName);
1220 |
1221 | function edgeTitle() {
1222 | var titleEdge = svgEdgeStat.selectAll('g.title').data([edgeStat], function(d) {
1223 | return edgeStatName + '-' + d.source + '-' + d.target;
1224 | });
1225 |
1226 | titleEdge.exit()
1227 | .remove();
1228 | titleEdge.enter()
1229 | .append('g')
1230 | .attr('transform', 'translate(' + timeScaleLinear(0) + ', -10)')
1231 | .attr('class', 'title');
1232 |
1233 | var titleLabel = titleEdge.selectAll('text.infoLabel').data(function(d) {
1234 | return [d];
1235 | });
1236 |
1237 | titleLabel.enter()
1238 | .append('text')
1239 | .attr('class', 'infoLabel')
1240 | .attr('text-anchor', 'middle');
1241 | titleLabel
1242 | .text(edgeStatName + ':');
1243 |
1244 | var boundBox = titleLabel.node().getBBox();
1245 |
1246 | var titleLine = titleEdge.selectAll('line.nodeLabel').data([{}]);
1247 |
1248 | titleLine.enter()
1249 | .append('line')
1250 | .attr('class', 'edge')
1251 | .style('stroke-width', EDGE_WIDTH)
1252 | .attr('stroke', 'black');
1253 | titleLine
1254 | .attr('x1', boundBox.x + boundBox.width + NODE_RADIUS)
1255 | .attr('y1', -NODE_RADIUS / 2)
1256 | .attr('x2', boundBox.x + boundBox.width + NODE_RADIUS + 30)
1257 | .attr('y2', -NODE_RADIUS / 2);
1258 |
1259 | var titleCircleG = titleEdge.selectAll('g').data(
1260 | [
1261 | channel.filter(function(d) {
1262 | return d.channelID === curCh1;
1263 | }),
1264 |
1265 | channel.filter(function(d) {
1266 | return d.channelID === curCh2;
1267 | }),
1268 | ]
1269 | );
1270 |
1271 | titleCircleG.enter()
1272 | .append('g');
1273 |
1274 | titleCircleG
1275 | .attr('transform', function(d, i) {
1276 | return 'translate(' + (boundBox.x + boundBox.width + NODE_RADIUS + i * 30) + ', ' + (-NODE_RADIUS / 2) + ')';
1277 | });
1278 |
1279 | var titleCircle = titleCircleG.selectAll('circle.node').data(function(d, i) {
1280 | return [
1281 | [d, i],
1282 | ];
1283 | });
1284 |
1285 | titleCircle.enter()
1286 | .append('circle')
1287 | .attr('class', 'node')
1288 | .attr('r', NODE_RADIUS)
1289 | .attr('fill', '#ddd')
1290 | .attr('opacity', 1);
1291 |
1292 | titleCircle
1293 | .attr('fill', function(d) {
1294 | return brainRegionColor(d[0][0].region);
1295 | });
1296 |
1297 | var titleText = titleCircleG.selectAll('text.nodeLabel').data(function(d, i) {
1298 | return [
1299 | [d, i],
1300 | ];
1301 | });
1302 |
1303 | titleText.enter()
1304 | .append('text')
1305 | .attr('class', 'nodeLabel');
1306 |
1307 | titleText
1308 | .text(function(d) {
1309 | return d[0][0].channelID;
1310 | });
1311 |
1312 | }
1313 |
1314 | function spectTitle(svgCh, channelID) {
1315 | var titleCh = svgCh.selectAll('g.title')
1316 | .data(channel.filter(function(d) {
1317 | return d.channelID === channelID;
1318 | }), function(d) {
1319 |
1320 | return d.SubjectID + '_' + d.channelID;
1321 | });
1322 |
1323 | titleCh.exit().remove();
1324 | titleCh.enter()
1325 | .append('g')
1326 | .attr('transform', 'translate(' + timeScaleLinear(0) + ', -10)')
1327 | .attr('class', 'title');
1328 |
1329 | titleLabel = titleCh.selectAll('text.infoLabel').data(function(d) {
1330 | return [d];
1331 | });
1332 |
1333 | titleLabel.enter()
1334 | .append('text')
1335 | .attr('class', 'infoLabel')
1336 | .attr('text-anchor', 'middle')
1337 | .text('Spectra: Ch');
1338 |
1339 | var boundBox = titleLabel.node().getBBox();
1340 |
1341 | var titleCircle = titleCh.selectAll('circle.node').data(function(d) {
1342 | return [d];
1343 | });
1344 |
1345 | titleCircle.enter()
1346 | .append('circle')
1347 | .attr('class', 'node')
1348 | .attr('r', NODE_RADIUS)
1349 | .attr('transform', 'translate(' + (-boundBox.x + NODE_RADIUS + 5) + ', ' + (-NODE_RADIUS / 2) + ')')
1350 | .attr('fill', '#ddd')
1351 | .attr('opacity', 1);
1352 |
1353 | titleCircle
1354 | .attr('fill', function(d) {
1355 | return brainRegionColor(d.region);
1356 | });
1357 |
1358 | var titleText = titleCh.selectAll('text.nodeLabel').data(function(d) {
1359 | return [d];
1360 | });
1361 |
1362 | titleText.enter()
1363 | .append('text')
1364 | .attr('class', 'nodeLabel')
1365 | .attr('transform', 'translate(' + (-boundBox.x + NODE_RADIUS + 5) + ', ' + (-NODE_RADIUS / 2) + ')');
1366 | titleText
1367 | .text(function(d) {
1368 | return d.channelID;
1369 | });
1370 |
1371 | }
1372 | }
1373 |
1374 | function drawLegends() {
1375 | var powerG;
1376 | var powerLegendRect;
1377 | var legendScale;
1378 | var powerAxisG;
1379 | var powerAxis;
1380 | var edgeStatG;
1381 | var edgeStatLegendRect;
1382 | var edgeStatAxisG;
1383 | var edgeStatAxis;
1384 | var anatomicalG;
1385 | var anatomicalCircle;
1386 | var anatomicalText;
1387 | var formatter = d3.format('.1f');
1388 |
1389 | var rectWidth = legendWidth / (NUM_COLORS + 1);
1390 | var rectHeight = rectWidth * 0.5;
1391 |
1392 | // Power Legend
1393 | powerG = svgSpectraLegend.selectAll('g#powerLegend').data([{}]);
1394 | powerG.enter()
1395 | .append('g')
1396 | .attr('id', 'powerLegend');
1397 |
1398 | powerLegend = d3.legend.color()
1399 | .shape('rect')
1400 | .shapeWidth(rectWidth)
1401 | .shapeHeight(rectHeight)
1402 | .labelOffset(5)
1403 | .cells(NUM_COLORS)
1404 | .orient('horizontal')
1405 | .scale(powerScale);
1406 | powerG.call(powerLegend);
1407 |
1408 | // Edge Statistic Legend
1409 | edgeStatG = svgEdgeStatLegend.selectAll('g#edgeStatLegend').data([{}]);
1410 | edgeStatG.enter()
1411 | .append('g')
1412 | .attr('id', 'edgeStatLegend');
1413 |
1414 | edgeLegend = d3.legend.color()
1415 | .shape('rect')
1416 | .shapeWidth(rectWidth)
1417 | .shapeHeight(rectHeight)
1418 | .labelOffset(5)
1419 | .cells(NUM_COLORS)
1420 | .orient('horizontal')
1421 | .scale(edgeStatScale);
1422 | edgeStatG.call(edgeLegend);
1423 |
1424 | // Anatomical legend
1425 | anatomicalLegendG = svgAnatomicalLegend.selectAll('g#anatomicalLegend').data([{}]);
1426 | anatomicalLegendG.enter()
1427 | .append('g')
1428 | .attr('id', 'anatomicalLegend');
1429 |
1430 | anatomicalLegend = d3.legend.color()
1431 | .shape('circle')
1432 | .shapeRadius(NODE_RADIUS / 2)
1433 | .labelOffset(5)
1434 | .orient('vertical')
1435 | .scale(brainRegionColor);
1436 | anatomicalLegendG.call(anatomicalLegend);
1437 | }
1438 |
1439 | function drawTimeSlice() {
1440 |
1441 | var cohSlice = svgTimeSlice.selectAll('g#cohSlice')
1442 | .data([edgeStat.data.map(function(d) {
1443 | return d[curFreqInd];
1444 | }),
1445 | ]);
1446 |
1447 | cohSlice.enter()
1448 | .append('g')
1449 | .attr('id', 'cohSlice');
1450 | cohSlice
1451 | .call(cohTimeSlice);
1452 |
1453 | var spect1Slice = svgTimeSlice.selectAll('g#spect1Slice')
1454 | .data([spect1.data.map(function(d) {
1455 | return d[curFreqInd];
1456 | }),
1457 | ]);
1458 |
1459 | spect1Slice.enter()
1460 | .append('g')
1461 | .attr('id', 'spect1Slice');
1462 | spect1Slice
1463 | .call(powerTimeSlice);
1464 |
1465 | var spect2Slice = svgTimeSlice.selectAll('g#spect2Slice')
1466 | .data([spect2.data.map(function(d) {
1467 | return d[curFreqInd];
1468 | }),
1469 | ]);
1470 |
1471 | spect2Slice.enter()
1472 | .append('g')
1473 | .attr('id', 'spect2Slice');
1474 | spect2Slice
1475 | .call(powerTimeSlice);
1476 |
1477 | var timeTitle = svgTimeSlice.selectAll('text.title').data([fAx[curFreqInd]]);
1478 | timeTitle.enter()
1479 | .append('text')
1480 | .attr('text-anchor', 'middle')
1481 | .attr('class', 'title')
1482 | .attr('x', timeSliceWidth / 2)
1483 | .attr('y', 0)
1484 | .attr('dy', -1 + 'em');
1485 | timeTitle
1486 | .text(function(d) {
1487 | return 'Time Slice @ Frequency ' + d + ' ' + visInfo.funits;
1488 | });
1489 | }
1490 |
1491 | function timeseries() {
1492 |
1493 | var xScale = d3.scale.ordinal();
1494 | var yScale = d3.scale.linear();
1495 | var height = 600;
1496 | var width = 600;
1497 | var rectMouseOver = function() {};
1498 |
1499 | var rectMouseClick = function() {};
1500 |
1501 | var xLabel = '';
1502 | var yLabel = '';
1503 | var yAxisOrientation = 'left';
1504 | var lineColor = 'blue';
1505 |
1506 | function chart(selection) {
1507 |
1508 | selection.each(function(data) {
1509 | var curPlot = d3.select(this);
1510 |
1511 | xScale.rangeBands([0, width]);
1512 |
1513 | var orient = (yAxisOrientation === 'right') ? 1 : -1;
1514 |
1515 | var lineFun = d3.svg.line()
1516 | .x(function(d, i) {
1517 | return xScale(xScale.domain()[i]);
1518 | })
1519 | .y(function(d) {
1520 | return yScale(d);
1521 | })
1522 | .interpolate('linear');
1523 |
1524 | var line = curPlot.selectAll('path.timeseries').data([data]);
1525 | line.enter()
1526 | .append('path')
1527 | .attr('class', 'timeseries')
1528 | .attr('fill', 'none')
1529 | .attr('stroke', lineColor);
1530 | line
1531 | .transition()
1532 | .duration(5)
1533 | .ease('linear')
1534 | .attr('d', lineFun);
1535 |
1536 | var yAxis = d3.svg.axis()
1537 | .scale(yScale)
1538 | .orient(yAxisOrientation)
1539 | .ticks(3)
1540 | .tickValues([d3.min(yScale.domain()), 0, d3.max(yScale.domain())])
1541 | .tickSize(0, 0, 0);
1542 | var yAxisG = curPlot.selectAll('g.axis#y').data([{}]);
1543 |
1544 | yAxisG.enter()
1545 | .append('g')
1546 | .attr('class', 'axis')
1547 | .attr('id', 'y')
1548 | .attr('transform', 'translate(' + (yAxisOrientation === 'right' ? width : 0) + ',0)')
1549 | .append('text')
1550 | .attr('x', orient * height / 2)
1551 | .attr('dy', -2.5 + 'em')
1552 | .attr('transform', 'rotate(' + (orient * 90) + ')')
1553 | .attr('text-anchor', 'middle')
1554 | .text(yLabel);
1555 | yAxisG.call(yAxis);
1556 |
1557 | var xAxis = d3.svg.axis()
1558 | .scale(xScale)
1559 | .orient('bottom')
1560 | .ticks(3)
1561 | .tickValues([d3.min(xScale.domain()), 0, d3.max(xScale.domain())])
1562 | .tickSize(0, 0, 0);
1563 | var xAxisG = curPlot.selectAll('g.axis#x').data([{}]);
1564 | xAxisG.enter()
1565 | .append('g')
1566 | .attr('class', 'axis')
1567 | .attr('transform', 'translate(0,' + height + ')')
1568 | .attr('id', 'x')
1569 | .append('text')
1570 | .attr('x', xScale(0))
1571 | .attr('y', 0)
1572 | .attr('text-anchor', 'middle')
1573 | .attr('dy', 3 + 'em')
1574 | .text(xLabel);
1575 | xAxisG.call(xAxis);
1576 |
1577 | var zeroG = curPlot.selectAll('g.zeroLine').data([
1578 | [
1579 | [0, height],
1580 | ],
1581 | ]);
1582 | zeroG.enter()
1583 | .append('g')
1584 | .attr('class', 'zeroLine');
1585 | var zeroLine = zeroG.selectAll('path').data(function(d) {
1586 | return d;
1587 | });
1588 |
1589 | zeroLine.enter()
1590 | .append('path');
1591 | zeroLine
1592 | .attr('d', d3.svg.line()
1593 | .x(xScale(0))
1594 | .y(function(d) {
1595 | return d;
1596 | })
1597 | .interpolate('linear'))
1598 | .attr('stroke', 'black')
1599 | .attr('stroke-width', 2)
1600 | .attr('fill', 'none')
1601 | .style('opacity', 0.7);
1602 |
1603 | var heatmapG = curPlot.selectAll('g.heatmapX').data(data);
1604 | heatmapG.enter()
1605 | .append('g')
1606 | .attr('transform', function(d, i) {
1607 | return 'translate(' + xScale(xScale.domain()[i]) + ', 0)';
1608 | })
1609 | .attr('class', 'heatmapX');
1610 | heatmapG.exit()
1611 | .remove();
1612 | var heatmapRect = heatmapG.selectAll('rect').data(function(d) {
1613 | return d;
1614 | });
1615 |
1616 | heatmapRect.enter()
1617 | .append('rect')
1618 | .attr('opacity', 1e-6)
1619 | .attr('height', height)
1620 | .attr('width', xScale.rangeBand())
1621 | .style('fill', 'white');
1622 | heatmapRect
1623 | .on('mouseover', rectMouseOver)
1624 | .on('click', rectMouseClick);
1625 | heatmapRect.exit()
1626 | .remove();
1627 | });
1628 |
1629 | }
1630 |
1631 | chart.xScale = function(scale) {
1632 | if (!arguments.length) return xScale;
1633 | xScale = scale;
1634 | return chart;
1635 | };
1636 |
1637 | chart.yScale = function(scale) {
1638 | if (!arguments.length) return yScale;
1639 | yScale = scale;
1640 | return chart;
1641 | };
1642 |
1643 | chart.rectMouseOver = function(fun) {
1644 | if (!arguments.length) return rectMouseOver;
1645 | rectMouseOver = fun;
1646 | return chart;
1647 | };
1648 |
1649 | chart.rectMouseClick = function(fun) {
1650 | if (!arguments.length) return rectMouseClick;
1651 | rectMouseClick = fun;
1652 | return chart;
1653 | };
1654 |
1655 | chart.width = function(value) {
1656 | if (!arguments.length) return width;
1657 | width = value;
1658 | return chart;
1659 | };
1660 |
1661 | chart.height = function(value) {
1662 | if (!arguments.length) return height;
1663 | height = value;
1664 | return chart;
1665 | };
1666 |
1667 | chart.yLabel = function(value) {
1668 | if (!arguments.length) return yLabel;
1669 | yLabel = value;
1670 | return chart;
1671 | };
1672 |
1673 | chart.xLabel = function(value) {
1674 | if (!arguments.length) return xLabel;
1675 | xLabel = value;
1676 | return chart;
1677 | };
1678 |
1679 | chart.yAxisOrientation = function(value) {
1680 | if (!arguments.length) return yAxisOrientation;
1681 | yAxisOrientation = value;
1682 | return chart;
1683 | };
1684 |
1685 | chart.lineColor = function(value) {
1686 | if (!arguments.length) return lineColor;
1687 | lineColor = value;
1688 | return chart;
1689 | };
1690 |
1691 | return chart;
1692 |
1693 | }
1694 |
1695 | function subjectLoad() {
1696 | subjectDropdown = d3.select('#SubjectDropdown');
1697 | subjectDropdown.selectAll('li')
1698 | .on('click', function() {
1699 |
1700 | subjectDropdown.selectAll('button')
1701 | .text(this.id)
1702 | .append('span')
1703 | .attr('class', 'caret');
1704 | curSubject = this.id;
1705 | curCh1 = '';
1706 | curCh2 = '';
1707 | loadChannelData();
1708 | });
1709 | }
1710 |
1711 | function edgeStatTypeLoad() {
1712 | edgeStatTypeDropdown = d3.select('#EdgeStatTypeDropdown');
1713 | edgeStatTypeDropdown.selectAll('li')
1714 | .on('click', function() {
1715 | edgeStatTypeDropdown.selectAll('button')
1716 | .text(d3.select(this).select('a').html())
1717 | .append('span')
1718 | .attr('class', 'caret');
1719 | edgeStatID = this.id;
1720 |
1721 | isFreq = edgeInfo
1722 | .filter(function(e) {
1723 | return e.edgeStatID === edgeStatID;
1724 | })[0]
1725 | .isFreq;
1726 | curFreqInd = isFreq ? curFreqInd : 0;
1727 | freqSlider.property('value', fAx[curFreqInd]);
1728 | freqSliderText.text(fAx[curFreqInd] + ' Hz');
1729 |
1730 | loadEdges();
1731 | });
1732 | }
1733 |
1734 | function edgeFilterLoad() {
1735 | edgeFilterDropdown = d3.select('#EdgeFilterDropdown');
1736 | edgeFilterDropdown.selectAll('li')
1737 | .on('click', function() {
1738 | edgeFilterDropdown.selectAll('button')
1739 | .text(this.id)
1740 | .append('span')
1741 | .attr('class', 'caret');
1742 | edgeFilter = this.id;
1743 | drawNetwork();
1744 | });
1745 | }
1746 |
1747 | function networkViewLoad() {
1748 | var networkViewRadio = d3.select('#NetworkViewPanel');
1749 | networkViewRadio.selectAll('input')
1750 | .on('click', function() {
1751 | var radioValue = this.value;
1752 | networkViewRadio.selectAll('input')
1753 | .property('checked', false);
1754 | d3.select(this).property('checked', true);
1755 | networkView = radioValue;
1756 | drawNetwork();
1757 | });
1758 | }
1759 |
1760 | function playButtonStart() {
1761 | var playButton = d3.select('#playButton');
1762 | playButton.on('click', function() {
1763 |
1764 | d3.select('#playButton').text('Stop');
1765 | stopAnimation = !stopAnimation;
1766 | var intervalID = setInterval(function() {
1767 | if (curTimeInd < timeMaxStepInd && stopAnimation === false) {
1768 | curTimeInd++;
1769 | updateTimeSlider.call({
1770 | value: tAx[curTimeInd],
1771 | });
1772 | } else {
1773 | d3.select('#playButton').text('Start');
1774 | stopAnimation = true;
1775 | clearInterval(intervalID);
1776 | }
1777 | }, 100);
1778 | });
1779 | }
1780 |
1781 | function resetButton() {
1782 | var resetButton = d3.select('#resetButton');
1783 | resetButton.on('click', function() {
1784 | curTimeInd = 0;
1785 | stopAnimation = true;
1786 | updateTimeSlider.call({
1787 | value: tAx[curTimeInd],
1788 | });
1789 | });
1790 | }
1791 |
1792 | function updateTimeSlider() {
1793 | curTimeInd = tAx.indexOf(+this.value);
1794 | timeSlider.property('value', tAx[curTimeInd]);
1795 | timeSliderText.text(tAx[curTimeInd] + ' ms');
1796 | if (!this.noUpdate) drawNetwork();
1797 | }
1798 |
1799 | function updateFreqSlider() {
1800 | curFreqInd = isFreq ? fAx.indexOf(+this.value) : 0;
1801 | freqSlider.property('value', fAx[curFreqInd]);
1802 | freqSliderText.text(fAx[curFreqInd] + ' Hz');
1803 |
1804 | if (!this.noUpdate) drawNetwork();
1805 |
1806 | if (isFreq & !this.noUpdate) drawTimeSlice();
1807 | }
1808 |
1809 | function rectMouseOver(d, freqInd, timeInd) {
1810 | // Mouse click can freeze visualization in place
1811 | if (mouseFlag) {
1812 | curFreqInd = isFreq ? freqInd : 0;
1813 | curTimeInd = timeInd;
1814 |
1815 | updateTimeSlider.call({
1816 | value: tAx[curTimeInd],
1817 | noUpdate: true,
1818 | });
1819 | updateFreqSlider.call({
1820 | value: fAx[curFreqInd],
1821 | });
1822 | };
1823 | }
1824 |
1825 | function rectMouseClick() {
1826 | mouseFlag = !mouseFlag;
1827 | if (!mouseFlag) {
1828 | svgNetworkMap.append('text')
1829 | .attr('x', networkWidth)
1830 | .attr('y', networkHeight)
1831 | .attr('text-anchor', 'end')
1832 | .attr('id', 'HOLD')
1833 | .text('HOLD');
1834 | force.stop();
1835 | } else {
1836 | svgNetworkMap.select('text#HOLD').remove();
1837 | force.start();
1838 | }
1839 | }
1840 |
1841 | function converterEngine(input) { // fn BLOB => Binary => Base64 ?
1842 | var uInt8Array = new Uint8Array(input);
1843 | var i = uInt8Array.length;
1844 | var biStr = []; //new Array(i);
1845 | while (i--) {
1846 | biStr[i] = String.fromCharCode(uInt8Array[i]);
1847 | }
1848 |
1849 | var base64 = window.btoa(biStr.join(''));
1850 | return base64;
1851 | };
1852 |
1853 | function getImageBase64(url, callback) {
1854 | var xhr = new XMLHttpRequest(url);
1855 | var img64;
1856 | xhr.open('GET', url, true); // url is the url of a PNG/JPG image.
1857 | xhr.responseType = 'arraybuffer';
1858 | xhr.callback = callback;
1859 | xhr.onload = function() {
1860 | img64 = converterEngine(this.response); // convert BLOB to base64
1861 | this.callback(null, img64); // callback : err, data
1862 | };
1863 |
1864 | xhr.onerror = function() {
1865 | callback('B64 ERROR', null);
1866 | };
1867 |
1868 | xhr.send();
1869 | };
1870 |
1871 | function copyObject(obj) {
1872 | var newObj = {};
1873 | for (var key in obj) {
1874 | // Copy all the fields
1875 | newObj[key] = obj[key];
1876 | }
1877 |
1878 | return newObj;
1879 | }
1880 | }
1881 | })();
1882 |
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/bower.json:
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1 | {
2 | "name": "SpectraVis",
3 | "version": "0.1.0",
4 | "description": "An interactive visualization tool for spectra, coherences, corrrelations, and associated networks",
5 | "keywords": [
6 | "neuroscience",
7 | "spectra",
8 | "coherence",
9 | "correlation",
10 | "networks"
11 | ],
12 | "authors": [
13 | "Eric Denovellis"
14 | ],
15 | "license": "GPL-2.0",
16 | "ignore": [
17 | "**/.*",
18 | "node_modules",
19 | "bower_components",
20 | "test",
21 | "tests"
22 | ],
23 | "dependencies": {
24 | "bootstrap": "~3.3.6",
25 | "d3": "~3.5.10",
26 | "queue-async": "~1.0.7",
27 | "colorbrewer": "~1.0.0",
28 | "spin.js": "~2.3.2",
29 | "d3-legend": "~1.6.0"
30 | }
31 | }
32 |
--------------------------------------------------------------------------------
/gulpfile.js:
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1 | var gulp = require('gulp');
2 | var gutil = require('gulp-util');
3 | var imagemin = require('gulp-imagemin');
4 | var pngquant = require('imagemin-pngquant');
5 | var streamify = require('gulp-streamify');
6 | var autoprefixer = require('gulp-autoprefixer');
7 | var cssmin = require('gulp-cssmin');
8 | var concat = require('gulp-concat');
9 | var plumber = require('gulp-plumber');
10 | var uglify = require('gulp-uglify');
11 | var jsonminify = require('gulp-jsonminify');
12 | var connect = require('gulp-connect');
13 | var watch = require('gulp-watch');
14 | var rename = require('gulp-rename');
15 | var zip = require('gulp-zip');
16 |
17 | /*
18 | |--------------------------------------------------------------------------
19 | | Combine all JS libraries into a single file for fewer HTTP requests.
20 | |--------------------------------------------------------------------------
21 | */
22 | gulp.task('createVendorJS', function() {
23 | return gulp.src([
24 | 'app/components/jquery/dist/jquery.js',
25 | 'app/components/bootstrap/js/dropdown.js',
26 | 'app/components/d3/d3.js',
27 | 'app/components/queue-async/queue.js',
28 | 'app/components/colorbrewer/colorbrewer.js',
29 | 'app/components/spin.js/spin.js',
30 | 'app/components/d3-legend/d3-legend.js',
31 | 'node_modules/d3-save-svg/build/d3-save-svg.js',
32 | ]).pipe(concat('vendor.js'))
33 | .pipe(gulp.dest('public/js'))
34 | .pipe(connect.reload());
35 | });
36 |
37 | gulp.task('createVendorJS-build', function() {
38 | return gulp.src([
39 | 'app/components/jquery/dist/jquery.js',
40 | 'app/components/bootstrap/js/dropdown.js',
41 | 'app/components/d3/d3.js',
42 | 'app/components/queue-async/queue.js',
43 | 'app/components/colorbrewer/colorbrewer.js',
44 | 'app/components/spin.js/spin.js',
45 | 'app/components/d3-legend/d3-legend.js',
46 | 'node_modules/d3-save-svg/build/d3-save-svg.js',
47 | ]).pipe(concat('vendor.js'))
48 | .pipe(uglify())
49 | .pipe(rename({
50 | suffix: '.min',
51 | }))
52 | .pipe(gulp.dest('public/js'))
53 | .pipe(connect.reload());
54 | });
55 |
56 | gulp.task('createMainJS', function() {
57 | return gulp.src('app/js/*.js')
58 | .pipe(concat('main.js'))
59 | .pipe(gulp.dest('public/js'))
60 | .pipe(connect.reload());
61 | });
62 |
63 | gulp.task('createMainJS-build', function() {
64 | return gulp.src('app/js/*.js')
65 | .pipe(concat('main.js'))
66 | .pipe(uglify())
67 | .pipe(rename({
68 | suffix: '.min',
69 | }))
70 | .pipe(gulp.dest('public/js'));
71 | });
72 |
73 | gulp.task('copyCSS', function() {
74 | return gulp.src([
75 | 'app/components/bootstrap/dist/css/bootstrap.css',
76 | 'app/css/*.css',
77 | ]).pipe(cssmin())
78 | .pipe(concat('main.css'))
79 | .pipe(gulp.dest('public/css'))
80 | .pipe(connect.reload());
81 | });
82 |
83 | gulp.task('minifyCSS', function() {
84 | return gulp.src([
85 | 'app/components/bootstrap/dist/css/bootstrap.css',
86 | 'app/css/*.css',
87 | ]).pipe(cssmin())
88 | .pipe(concat('main.css'))
89 | .pipe(rename({
90 | suffix: '.min',
91 | }))
92 | .pipe(gulp.dest('public/css'))
93 | .pipe(connect.reload());
94 | });
95 |
96 | gulp.task('compressImages', function() {
97 | return gulp.src('app/DATA/brainImages/*')
98 | .pipe(imagemin({
99 | progressive: true,
100 | svgoPlugins: [{removeViewBox: false}],
101 | use: [pngquant()],
102 | }))
103 | .pipe(gulp.dest('public/DATA/brainImages'));
104 | });
105 |
106 | gulp.task('minifyJSON', function() {
107 | return gulp.src('app/DATA/*.json')
108 | .pipe(jsonminify())
109 | .pipe(gulp.dest('public/DATA'));
110 | });
111 |
112 | gulp.task('webserver', function() {
113 | connect.server({
114 | port: 8000,
115 | livereload: true,
116 | root: ['public', 'app'],
117 | });
118 | });
119 |
120 | gulp.task('watch', function() {
121 | gulp.watch('app/css/spectra.css', ['copyCSS']);
122 | gulp.watch('app/js/main.js', ['createMainJS']);
123 | gulp.watch('app/js/vendor.js', ['createVendorJS']);
124 | });
125 |
126 | gulp.task('copyDATA', function() {
127 | return gulp
128 | .src('app/DATA/*.json')
129 | .pipe(gulp.dest('public/DATA'));
130 | });
131 |
132 | gulp.task('zip', function() {
133 | return gulp
134 | .src(['public/*', 'public/js/*.js', 'public/css/*.css', 'public/fonts/*', '!public/DATA/*.json', './LICENSE.md', './README.md', './SpectraVis-Demo.gif'], {base: '.'})
135 | .pipe(zip('spectraVis.zip'))
136 | .pipe(gulp.dest('./'));
137 | });
138 |
139 | gulp.task('default', ['copyDATA', 'createMainJS', 'createVendorJS', 'copyCSS', 'webserver', 'watch']);
140 | gulp.task('build', ['createMainJS', 'createMainJS', 'createVendorJS', 'copyCSS', 'createMainJS-build', 'createVendorJS-build', 'minifyCSS', 'compressImages', 'minifyJSON']);
141 |
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/matlab/ES_NetworkData.m:
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https://raw.githubusercontent.com/llSourcell/machine_learning_and_neuroscience/80319df7d4621964b3ce5c282cc8350b51961dd9/matlab/ES_NetworkData.m
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/matlab/createEdges.m:
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1 | %% Creates edges for every node
2 | subjects = {'SIM03_B0.00T0.63','SIM03_B1.00T1.00'};
3 |
4 | edges_ind = nchoosek(1:9, 2);
5 |
6 | for s = 1:length(subjects),
7 |
8 | for n = 1:length(edges_ind),
9 | edges(n).SubjectID = subjects{s};
10 | edges(n).source = ['Channel ', num2str(edges_ind(n, 1))];
11 | edges(n).target = ['Channel ', num2str(edges_ind(n, 2))];
12 | end
13 |
14 | opt.FileName = ['edges_',subjects{s}, '_C2s_coh','.json'];
15 | savejson('', edges, opt)
16 | end
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/matlab/createNodes.m:
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1 | subjects = {'SIM03_B0.00T0.63','SIM03_B1.00T1.00'};
2 |
3 | x = [0.6427876097, 0.984807753, 0.8660254038, 0.3420201433, -0.3420201433, -0.8660254038, -0.984807753, -0.6427876097, -2.449293598e-16];
4 | y = [0.7660444431, 0.1736481777, -0.5, -0.9396926208, -0.9396926208, -0.5, 0.1736481777, 0.7660444431, 1];
5 |
6 | for s = 1:length(subjects),
7 | for n = 1:length(x),
8 | nodes(n).SubjectID = subjects{s};
9 | nodes(n).name = ['Channel ', num2str(n)];
10 | nodes(n).x = x(n);
11 | nodes(n).y = y(n);
12 | nodes(n).fixed = 'true';
13 | end
14 |
15 | opt.FileName = ['channels_',subjects{s},'.json'];
16 | savejson('', nodes, opt)
17 | end
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/package.json:
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1 | {
2 | "name": "spectravis",
3 | "version": "1.1.0",
4 | "description": "An interactive web-based neuroscience app for analyzing task-related functional networks over time and frequency.",
5 | "main": "index.js",
6 | "scripts": {
7 | "postinstall": "bower cache clean && bower install && gulp",
8 | "prepublish": "gulp build && gulp zip"
9 | },
10 | "repository": {
11 | "type": "git",
12 | "url": "git+https://github.com/edeno/SpectraVis.git"
13 | },
14 | "keywords": [
15 | "neuroscience",
16 | "spectra",
17 | "coherence",
18 | "correlation",
19 | "networks",
20 | "functional connectivity"
21 | ],
22 | "author": {
23 | "name": "Eric Denovellis",
24 | "url": "http://ericdeno.com"
25 | },
26 | "contributors": [
27 | {
28 | "name": "Emily Stephen",
29 | "url": "http://emilystephen.com/"
30 | }
31 | ],
32 | "dependencies": {
33 | "d3-save-svg": "0.0.2"
34 | },
35 | "devDependencies": {
36 | "bower": "^1.4.1",
37 | "gulp": "^3.9.0",
38 | "gulp-autoprefixer": "^2.3.1",
39 | "gulp-concat": "^2.6.0",
40 | "gulp-connect": "^2.2.0",
41 | "gulp-cssmin": "^0.1.7",
42 | "gulp-imagemin": "^2.3.0",
43 | "gulp-jsonminify": "^1.0.0",
44 | "gulp-plumber": "^1.0.1",
45 | "gulp-rename": "^1.2.2",
46 | "gulp-streamify": "0.0.5",
47 | "gulp-uglify": "^1.2.0",
48 | "gulp-util": "^3.0.6",
49 | "gulp-watch": "^4.3.5",
50 | "gulp-zip": "^3.0.2",
51 | "imagemin-pngquant": "^4.1.2"
52 | },
53 | "license": "GPL-2.0",
54 | "bugs": {
55 | "url": "https://github.com/edeno/SpectraVis/issues"
56 | },
57 | "homepage": "https://github.com/edeno/SpectraVis#readme"
58 | }
59 |
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/public/fonts/glyphicons-halflings-regular.eot:
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/public/fonts/glyphicons-halflings-regular.svg:
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1 |
2 |
3 |
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/public/fonts/glyphicons-halflings-regular.ttf:
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/public/fonts/glyphicons-halflings-regular.woff:
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/public/fonts/glyphicons-halflings-regular.woff2:
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/public/index.html:
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1 |
2 |
3 |
4 |
134 | WARNING: This visualization may take a long time to load due to some large files. Please be patient on the first load.
135 |
136 | Click on any two nodes or the edge between them to load the spectra and coherences/correlations between those two nodes.
137 |
138 | Mouse over the spectra or cohereograms/correlograms to see the network at that time and/or frequency
139 |
140 | Click on the spectra or cohereograms/correlograms to freeze the network at a particular time and frequency value
141 |
142 |
143 | Edge Filter allows you to look at only edges between areas, edges within areas, or all edges
144 |
145 | Network View allows you to toggle between viewing the nodes in their anatomical location or in a layout designed to give you a sense of the structure of the network (topological)