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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('../')\n",
"from pyvis.network import Network"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Basic Example"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"500px\"\n",
" height=\"500px\"\n",
" src=\"example.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7fbe1d273d30>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g = Network(notebook=True)\n",
"g.add_nodes(range(5))\n",
"g.add_edges([\n",
" (0, 2),\n",
" (0, 3),\n",
" (0, 4),\n",
" (1, 1),\n",
" (1, 3),\n",
" (1, 2)\n",
"])\n",
"g.show(\"example.html\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dot File example"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"500px\"\n",
" height=\"500px\"\n",
" src=\"dot.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7fbe3d2ead00>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h = Network(notebook=True)\n",
"h.from_DOT(\"test.dot\")\n",
"\n",
"# All properties have to be enclosed by double quotes and \n",
"# there and there must be no comma at the end of a list.\n",
"# See https://visjs.github.io/vis-network/docs/network/ for all options\n",
"h.set_options(\"\"\"\n",
"var options = {\n",
" \"physics\": {\n",
" \"enabled\": true,\n",
" \"barnesHut\": {\n",
" \"theta\": 0.5,\n",
" \"gravitationalConstant\": -2000,\n",
" \"centralGravity\": 0.3,\n",
" \"springLength\": 200,\n",
" \"springConstant\": 0.04,\n",
" \"damping\": 0.09,\n",
" \"avoidOverlap\": 0\n",
" },\n",
" \"maxVelocity\": 50,\n",
" \"minVelocity\": 0.1,\n",
" \"solver\": \"barnesHut\",\n",
" \"stabilization\": {\n",
" \"enabled\": true,\n",
" \"iterations\": 1000,\n",
" \"updateInterval\": 100,\n",
" \"onlyDynamicEdges\": false,\n",
" \"fit\": true\n",
" }\n",
" }\n",
"}\n",
"\"\"\")\n",
"h.show(\"dot.html\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Basic NetworkX example"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import networkx as nx"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"500px\"\n",
" height=\"500px\"\n",
" src=\"example.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f3ef910af40>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nxg = nx.random_tree(20)\n",
"g.from_nx(nxg)\n",
"g.show(\"example.html\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Disabling Physics interaction"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"500px\"\n",
" height=\"500px\"\n",
" src=\"example.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" \n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f882040c6d0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.toggle_physics(False)\n",
"g.show(\"example.html\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Explicit coordinates to layout nodes"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"500px\"\n",
" height=\"500px\"\n",
" src=\"example.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" \n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f87dc679b50>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g = Network(notebook=True)\n",
"g.add_nodes([1,2,3],\n",
" value=[10, 100, 400],\n",
" title=[\"I am node 1\", \"node 2 here\", \"and im node 3\"],\n",
" x=[21.4, 21.4, 21.4], y=[100.2, 223.54, 32.1],\n",
" label=[\"NODE 1\", \"NODE 2\", \"NODE 3\"],\n",
" color=[\"#00ff1e\", \"#162347\", \"#dd4b39\"])\n",
"g.show(\"example.html\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Full Game of Thrones example"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"100%\"\n",
" height=\"750px\"\n",
" src=\"example.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" \n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f87da76e430>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"got_net = Network(notebook=True, height=\"750px\", width=\"100%\", bgcolor=\"#222222\", font_color=\"white\")\n",
"\n",
"# set the physics layout of the network\n",
"got_net.barnes_hut()\n",
"got_data = pd.read_csv(\"https://www.macalester.edu/~abeverid/data/stormofswords.csv\")\n",
"\n",
"sources = got_data['Source']\n",
"targets = got_data['Target']\n",
"weights = got_data['Weight']\n",
"\n",
"edge_data = zip(sources, targets, weights)\n",
"\n",
"for e in edge_data:\n",
" src = e[0]\n",
" dst = e[1]\n",
" w = e[2]\n",
"\n",
" got_net.add_node(src, src, title=src)\n",
" got_net.add_node(dst, dst, title=dst)\n",
" got_net.add_edge(src, dst, value=w)\n",
"\n",
"neighbor_map = got_net.get_adj_list()\n",
"\n",
"# add neighbor data to node hover data\n",
"for node in got_net.nodes:\n",
" node[\"title\"] += \" Neighbors:<br>\" + \"<br>\".join(neighbor_map[node[\"id\"]])\n",
" node[\"value\"] = len(neighbor_map[node[\"id\"]]) # this value attrribute for the node affects node size\n",
"\n",
"got_net.show(\"example.html\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Experimenting with options UI\n",
"Scroll down underneath the graph to play around with the physics settings to acheive optimal layout and behavior. You can use the generate options button to display the JSON representation of the configuration."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"100%\"\n",
" height=\"750px\"\n",
" src=\"example.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" \n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f87da76ee50>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"got_net.show_buttons(filter_=\"physics\")\n",
"got_net.show(\"example.html\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"got_net.set_options('''var options = {\n",
" \"physics\": {\n",
" \"barnesHut\": {\n",
" \"gravitationalConstant\": -80000,\n",
" \"springLength\": 250,\n",
" \"springConstant\": 0.001\n",
" },\n",
" \"maxVelocity\": 34,\n",
" \"minVelocity\": 0.75\n",
" }\n",
"}''')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import networkx as nx"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"100%\"\n",
" height=\"750px\"\n",
" src=\"nx.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" \n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f88443e75b0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nx_graph = nx.cycle_graph(10)\n",
"nx_graph.nodes[1]['title'] = 'Number 1'\n",
"nx_graph.nodes[1]['group'] = 1\n",
"nx_graph.nodes[3]['title'] = 'I belong to a different group!'\n",
"nx_graph.nodes[3]['group'] = 10\n",
"nx_graph.add_node(20, size=20, title='couple', group=2)\n",
"nx_graph.add_node(21, size=15, title='couple', group=2)\n",
"nx_graph.add_edge(20, 21, weight=5)\n",
"nx_graph.add_node(25, size=25, label='lonely', title='lonely node', group=3)\n",
"\n",
"nt = Network(notebook=True, height=\"750px\", width=\"100%\")\n",
"\n",
"nt.from_nx(nx_graph)\n",
"nt.show(\"nx.html\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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