86 lines
22 KiB
Plaintext
86 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Sandbox"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## How to plot a directed Graph:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f449722f630>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import networkx as nx\n",
|
|
"import numpy as np\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"fig_1, ax_1 = plt.subplots()\n",
|
|
"\n",
|
|
"G = nx.DiGraph()\n",
|
|
"G.add_nodes_from(range(1,10))\n",
|
|
"G.add_edges_from([(i,1) for i in range(2,10)])\n",
|
|
"\n",
|
|
"nx.draw(G)\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
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