Bitcoin network example, with igraph
This commit is contained in:
Dimitri Lozeve
parent
26b7ce87a5
commit
033d9d76e3
3 changed files with 402 additions and 7 deletions
3
Pipfile
3
Pipfile
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@ -13,7 +13,8 @@ matplotlib = "*"
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jupyter = "*"
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networkx = "*"
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jupyterlab = "*"
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altair = "*"
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python-igraph = {git = "https://github.com/igraph/python-igraph.git", ref="8864b46849b031a3013764d03e167222963c0f5d"}
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[dev-packages]
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29
Pipfile.lock
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29
Pipfile.lock
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@ -1,7 +1,7 @@
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{
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"_meta": {
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"hash": {
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"sha256": "433fe4f585fb489189f98fca98bf4cb140597c32e8a662b212bc3af15471c865"
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"sha256": "3a67e54d8190ab442ef984eb4e0e4a28809e7131b1f41e6d865996b8c5926dff"
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},
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"host-environment-markers": {
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"implementation_name": "cpython",
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@ -9,9 +9,9 @@
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"os_name": "posix",
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"platform_machine": "x86_64",
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"platform_python_implementation": "CPython",
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"platform_release": "4.15.3-2-ARCH",
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"platform_release": "4.15.6-1-ARCH",
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"platform_system": "Linux",
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"platform_version": "#1 SMP PREEMPT Thu Feb 15 00:13:49 UTC 2018",
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"platform_version": "#1 SMP PREEMPT Sun Feb 25 12:53:23 UTC 2018",
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"python_full_version": "3.6.4",
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"python_version": "3.6",
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"sys_platform": "linux"
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@ -27,6 +27,12 @@
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]
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"default": {
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"sha256:c1303f77f1ba4d632f2958c83c0f457b2b969860b1ac9adfb872aefa1780baa7"
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"version": "==1.2.1"
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},
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"bleach": {
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"sha256:cf567e7ed30ea5e05b31231d88ae170af1c5544758b9d7bebbc20590b7c30b1e",
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@ -144,10 +150,10 @@
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},
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"jupyterlab": {
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"sha256:842914beca4d8a6fe62c45157b7f8fa44aaa570efdba7b19dab79605a51612e5"
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"sha256:543f2f9a7f393d545ecf5a95a2290c97b88d78974a44b299b2bd387dc08b738e",
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"sha256:96e01d709eda1e77eb4181b3109c8e0cf7ad7a69d0dfa2c42c94b5bcd65d220f"
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],
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"version": "==0.31.8"
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"version": "==0.31.10"
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"jupyterlab-launcher": {
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"hashes": [
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@ -308,6 +314,7 @@
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"sha256:e8c43b5eee76b2083a9badde89fd1bbce6c8942d1045146e100b7b5e014f4f1a",
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"sha256:e64193f0047ad603b71f202332ab5527c5e52aa7c8b609704fc28c0dc20c4365"
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],
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"markers": "os_name != 'nt'",
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"version": "==0.5.2"
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},
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"pygments": {
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@ -336,6 +343,10 @@
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],
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"version": "==2.6.1"
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},
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"python-igraph": {
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"git": "https://github.com/igraph/python-igraph.git",
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"ref": "8864b46849b031a3013764d03e167222963c0f5d"
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},
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"pytz": {
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"hashes": [
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"sha256:ed6509d9af298b7995d69a440e2822288f2eca1681b8cce37673dbb10091e5fe",
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@ -436,6 +447,12 @@
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],
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"version": "==4.3.2"
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},
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"vega": {
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"hashes": [
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"sha256:b531938b7623c3804fc06a008bcf7559d06e04b485067840dd7181699d126922"
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],
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"version": "==0.4.4"
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},
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"wcwidth": {
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"hashes": [
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"sha256:f4ebe71925af7b40a864553f761ed559b43544f8f71746c2d756c7fe788ade7c",
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377
bitcoin.ipynb
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377
bitcoin.ipynb
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@ -0,0 +1,377 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline"
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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": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"import igraph as ig"
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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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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"source": [
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"# Load the graph"
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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": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"G = ig.read(\"data/bitcoin/bitcoinotc.graphml\")"
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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": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"G.to_undirected(combine_edges=\"first\")"
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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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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"source": [
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"# Clique distribution"
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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": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"G.clique_number()"
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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": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"cl = G.cliques()"
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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": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"plt.hist(list(map(len,cl)), bins=10);"
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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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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"source": [
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"# Rescale the timestamps"
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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": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"edges = pd.DataFrame([e.attributes().values() for e in G.es])\n",
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"edges.columns = G.es.attribute_names()\n",
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"edges.drop([\"Edge Label\", \"id\"], axis=1, inplace=True)\n",
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"edges.describe()"
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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": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"timerange = edges.time.max() - edges.time.min()\n",
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"timemin = edges.time.min()\n",
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"G.es[\"time\"] = (G.es[\"time\"] - timemin) / timerange"
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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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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"source": [
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"# Temporal subgraphs"
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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": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"G.subgraph_edges(G.es(lambda e: e[\"time\"] < 0.1)).summary()"
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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": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"def temporal_subgraph(graph, tmin=None, tmax=None, delete_vertices=True):\n",
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" if tmin==None:\n",
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" tmin = min(graph.es[\"time\"])\n",
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" if tmax==None:\n",
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" tmax = max(graph.es[\"time\"])\n",
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" return G.subgraph_edges(G.es(lambda e: (e[\"time\"] > tmin) & (e[\"time\"] < tmax)), delete_vertices=delete_vertices)"
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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": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"temporal_subgraph(G, tmin=0.1, tmax=0.3).summary()"
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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": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"ti = 0\n",
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"dt = 0.01\n",
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"subg = temporal_subgraph(G, tmin=ti, tmax=ti+dt)\n",
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"layout = subg.layout(\"kk\")\n",
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"ig.plot(subg, layout=layout)"
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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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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"source": [
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"# Export multilayer graph"
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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": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"dt = 0.05\n",
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"layout = []\n",
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"subg = []\n",
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"for i in range(2):\n",
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" subg.append(temporal_subgraph(G, tmin=i*dt, tmax=i*dt+dt))\n",
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" layout.append(subg[i].layout(\"kk\").coords)"
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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": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"vs_df = pd.DataFrame()\n",
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"es_df = pd.DataFrame()\n",
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"for i in range(2):\n",
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" subg_vs = pd.DataFrame(layout[i])\n",
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" subg_vs.columns = [\"x\",\"y\"]\n",
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" subg_vs[\"id\"] = np.arange(len(layout[i]))\n",
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" subg_vs[\"layer\"] = i+1\n",
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" # subg_vs.to_csv(\"vs1.csv\", index=False)\n",
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" vs_df = pd.concat([vs_df,subg_vs])\n",
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" subg_es = pd.DataFrame([[e.source,e.target] for e in subg[i].es])\n",
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" subg_es.columns = [\"u\",\"v\"]\n",
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" # subg_es.to_csv(\"es1.csv\", index=False)\n",
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" es_df = pd.concat([es_df,subg_es])\n",
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" \n",
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"vs_df.to_csv(\"vs.csv\", index=False)\n",
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"es_df.to_csv(\"es.csv\", index=False)"
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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": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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},
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"name": "bitcoin.ipynb"
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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