From 44ed4ea5736a5b303b76518cc2cacce398504a31 Mon Sep 17 00:00:00 2001 From: nwlandry Date: Mon, 18 Jul 2022 15:51:32 -0400 Subject: [PATCH 01/72] add distributions file --- netrw/analysis/distributions.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 netrw/analysis/distributions.py diff --git a/netrw/analysis/distributions.py b/netrw/analysis/distributions.py new file mode 100644 index 0000000..e69de29 From 81f8dcd5c7940178fc25c57798d3b235a2e1c1cb Mon Sep 17 00:00:00 2001 From: nwlandry Date: Mon, 18 Jul 2022 16:14:27 -0400 Subject: [PATCH 02/72] added init files --- netrw/__init__.py | 1 + netrw/analysis/__init__.py | 1 + netrw/analysis/distributions.py | 15 +++++++++++++++ 3 files changed, 17 insertions(+) create mode 100644 netrw/analysis/__init__.py diff --git a/netrw/__init__.py b/netrw/__init__.py index 83f2d6c..b6cfce4 100644 --- a/netrw/__init__.py +++ b/netrw/__init__.py @@ -7,4 +7,5 @@ A 2022 NetSI Collabathon Product. """ +from .analysis import * from .rewire import * diff --git a/netrw/analysis/__init__.py b/netrw/analysis/__init__.py new file mode 100644 index 0000000..9703d54 --- /dev/null +++ b/netrw/analysis/__init__.py @@ -0,0 +1 @@ +from .distributions import * \ No newline at end of file diff --git a/netrw/analysis/distributions.py b/netrw/analysis/distributions.py index e69de29..1c27760 100644 --- a/netrw/analysis/distributions.py +++ b/netrw/analysis/distributions.py @@ -0,0 +1,15 @@ +from copy import deepcopy +import numpy as np + +def get_property_distribution(G, rewiring_method, property, burn_in=100, num_samples=1000): + G_copy = deepcopy(G) + + property_list = np.zeros() + for i in range(num_samples): + for j in range(burn_in): + rewiring_method(G_copy, copy=False) + if j > burn_in: + property_list[i] = property(G_copy) + + + return property_list \ No newline at end of file From cfa2a3f7a15656f5978c3b308d928c570be12d90 Mon Sep 17 00:00:00 2001 From: "alice.schwarze" <4884425-aliceschwarze@users.noreply.gitlab.com> Date: Mon, 18 Jul 2022 16:41:35 -0400 Subject: [PATCH 03/72] added distance_trajectory.py --- netrw/analysis/distance_trajectory.py | 30 ++++++++++ netrw/analysis/playground.ipynb | 80 +++++++++++++++++++++++++++ 2 files changed, 110 insertions(+) create mode 100644 netrw/analysis/distance_trajectory.py create mode 100644 netrw/analysis/playground.ipynb diff --git a/netrw/analysis/distance_trajectory.py b/netrw/analysis/distance_trajectory.py new file mode 100644 index 0000000..0449efb --- /dev/null +++ b/netrw/analysis/distance_trajectory.py @@ -0,0 +1,30 @@ +from matplotlib import pyplot +import networkx +import netrd + + + +def distanceTrajectory(G, distance=..., rewire=..., null_model=None, + num_rewire=100, num_networks=100, distance_kwargs={}, rewire_kwargs={}, + null_model_kwargs={}, **kwargs): + + G0 = copy.deepcopy(G) + + if hasattr(num_rewire, "__iter__"): + rewire_steps = num_rewire + else: + rewire_steps = range(num_rewire) + + data = np.zeros(len(rewire_steps)) + + for i, r in enumerate(rewire_steps): + rewire(G0, **rewire_kwargs) + data[i] = distance(G0, G, **distance_kwargs) + + return data + +def plotDistanceTrajectory(G, **kwargs): + + data = distanceTrajectory(G, kwargs**) + + \ No newline at end of file diff --git a/netrw/analysis/playground.ipynb b/netrw/analysis/playground.ipynb new file mode 100644 index 0000000..7d1e805 --- /dev/null +++ b/netrw/analysis/playground.ipynb @@ -0,0 +1,80 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "id": "ab4e64eb-1e76-48f7-b3ac-0f94447e66d5", + "metadata": {}, + "outputs": [], + "source": [ + "import netrd\n", + "import networkx as nx" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ed16a0f3-5a43-40f5-8164-a558befef926", + "metadata": {}, + "outputs": [], + "source": [ + "from netrd.distance import Hamming" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c984fa12-1e95-4e62-9ca1-39ac8c9feb69", + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'Graph' object has no attribute 'results'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[1;32mIn [7]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m G1 \u001b[38;5;241m=\u001b[39m nx\u001b[38;5;241m.\u001b[39mgnp_random_graph(n\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m, p\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m)\n\u001b[0;32m 2\u001b[0m G2 \u001b[38;5;241m=\u001b[39m nx\u001b[38;5;241m.\u001b[39mGraph(G1)\n\u001b[1;32m----> 3\u001b[0m \u001b[43mHamming\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdist\u001b[49m\u001b[43m(\u001b[49m\u001b[43mG1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mG2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mG2\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\anaconda3\\envs\\netrwenv\\lib\\site-packages\\netrd\\utilities\\graph.py:136\u001b[0m, in \u001b[0;36munweighted..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 130\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[0;32m 131\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 132\u001b[0m args \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m 133\u001b[0m ensure_unweighted(arg) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(arg\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m, nx\u001b[38;5;241m.\u001b[39mGraph) \u001b[38;5;28;01melse\u001b[39;00m arg\n\u001b[0;32m 134\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m arg \u001b[38;5;129;01min\u001b[39;00m args\n\u001b[0;32m 135\u001b[0m ]\n\u001b[1;32m--> 136\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32m~\\anaconda3\\envs\\netrwenv\\lib\\site-packages\\netrd\\distance\\hamming.py:88\u001b[0m, in \u001b[0;36mHamming.dist\u001b[1;34m(self, G1, G2)\u001b[0m\n\u001b[0;32m 83\u001b[0m mask \u001b[38;5;241m=\u001b[39m (np\u001b[38;5;241m.\u001b[39mappend(mask[\u001b[38;5;241m0\u001b[39m], new_mask[\u001b[38;5;241m0\u001b[39m]), np\u001b[38;5;241m.\u001b[39mappend(mask[\u001b[38;5;241m1\u001b[39m], new_mask[\u001b[38;5;241m1\u001b[39m]))\n\u001b[0;32m 85\u001b[0m dist \u001b[38;5;241m=\u001b[39m scipy\u001b[38;5;241m.\u001b[39mspatial\u001b[38;5;241m.\u001b[39mdistance\u001b[38;5;241m.\u001b[39mhamming(\n\u001b[0;32m 86\u001b[0m adj1[mask]\u001b[38;5;241m.\u001b[39mflatten(), adj2[mask]\u001b[38;5;241m.\u001b[39mflatten()\n\u001b[0;32m 87\u001b[0m )\n\u001b[1;32m---> 88\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresults\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdist\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m dist\n\u001b[0;32m 89\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124madjacency_matrices\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m adj1, adj2\n\u001b[0;32m 90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dist\n", + "\u001b[1;31mAttributeError\u001b[0m: 'Graph' object has no attribute 'results'" + ] + } + ], + "source": [ + "G1 = nx.gnp_random_graph(n=10, p=0.2)\n", + "G2 = nx.Graph(G1)\n", + "Hamming.dist(G1, G2, G2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe15a91b-841d-4e25-b34c-664e8908ed2d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From be70ba2543de27a0a36d62e2c1f4be48f9bbdd66 Mon Sep 17 00:00:00 2001 From: nwlandry Date: Mon, 18 Jul 2022 16:51:36 -0400 Subject: [PATCH 04/72] updated distribution --- netrw/analysis/distributions.py | 12 +++++------- netrw/rewire/__init__.py | 1 + 2 files changed, 6 insertions(+), 7 deletions(-) diff --git a/netrw/analysis/distributions.py b/netrw/analysis/distributions.py index 1c27760..ecba90a 100644 --- a/netrw/analysis/distributions.py +++ b/netrw/analysis/distributions.py @@ -2,14 +2,12 @@ import numpy as np def get_property_distribution(G, rewiring_method, property, burn_in=100, num_samples=1000): - G_copy = deepcopy(G) - property_list = np.zeros() + property_list = np.zeros(num_samples) for i in range(num_samples): for j in range(burn_in): - rewiring_method(G_copy, copy=False) - if j > burn_in: - property_list[i] = property(G_copy) - - + rewiring_method(G) + if j >= burn_in - 1: + property_list[i] = property(G) + return property_list \ No newline at end of file diff --git a/netrw/rewire/__init__.py b/netrw/rewire/__init__.py index 420f987..468c985 100644 --- a/netrw/rewire/__init__.py +++ b/netrw/rewire/__init__.py @@ -1,3 +1,4 @@ from .base import BaseRewirer +from .karrer import KarrerRewirer __all__ = [] From c22f370a3ff78a3a15ff8f7ea20882d910db7828 Mon Sep 17 00:00:00 2001 From: "alice.schwarze" <4884425-aliceschwarze@users.noreply.gitlab.com> Date: Tue, 19 Jul 2022 10:08:14 -0400 Subject: [PATCH 05/72] added playground --- netrw/analysis/playground.ipynb | 39 +++++++++++++-------------------- 1 file changed, 15 insertions(+), 24 deletions(-) diff --git a/netrw/analysis/playground.ipynb b/netrw/analysis/playground.ipynb index 7d1e805..02c110e 100644 --- a/netrw/analysis/playground.ipynb +++ b/netrw/analysis/playground.ipynb @@ -2,49 +2,40 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "id": "ab4e64eb-1e76-48f7-b3ac-0f94447e66d5", "metadata": {}, "outputs": [], "source": [ "import netrd\n", - "import networkx as nx" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ed16a0f3-5a43-40f5-8164-a558befef926", - "metadata": {}, - "outputs": [], - "source": [ + "import networkx as nx\n", "from netrd.distance import Hamming" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "id": "c984fa12-1e95-4e62-9ca1-39ac8c9feb69", "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "'Graph' object has no attribute 'results'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Input \u001b[1;32mIn [7]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m G1 \u001b[38;5;241m=\u001b[39m nx\u001b[38;5;241m.\u001b[39mgnp_random_graph(n\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m, p\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m)\n\u001b[0;32m 2\u001b[0m G2 \u001b[38;5;241m=\u001b[39m nx\u001b[38;5;241m.\u001b[39mGraph(G1)\n\u001b[1;32m----> 3\u001b[0m \u001b[43mHamming\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdist\u001b[49m\u001b[43m(\u001b[49m\u001b[43mG1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mG2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mG2\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\anaconda3\\envs\\netrwenv\\lib\\site-packages\\netrd\\utilities\\graph.py:136\u001b[0m, in \u001b[0;36munweighted..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 130\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[0;32m 131\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 132\u001b[0m args \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m 133\u001b[0m ensure_unweighted(arg) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(arg\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m, nx\u001b[38;5;241m.\u001b[39mGraph) \u001b[38;5;28;01melse\u001b[39;00m arg\n\u001b[0;32m 134\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m arg \u001b[38;5;129;01min\u001b[39;00m args\n\u001b[0;32m 135\u001b[0m ]\n\u001b[1;32m--> 136\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32m~\\anaconda3\\envs\\netrwenv\\lib\\site-packages\\netrd\\distance\\hamming.py:88\u001b[0m, in \u001b[0;36mHamming.dist\u001b[1;34m(self, G1, G2)\u001b[0m\n\u001b[0;32m 83\u001b[0m mask \u001b[38;5;241m=\u001b[39m (np\u001b[38;5;241m.\u001b[39mappend(mask[\u001b[38;5;241m0\u001b[39m], new_mask[\u001b[38;5;241m0\u001b[39m]), np\u001b[38;5;241m.\u001b[39mappend(mask[\u001b[38;5;241m1\u001b[39m], new_mask[\u001b[38;5;241m1\u001b[39m]))\n\u001b[0;32m 85\u001b[0m dist \u001b[38;5;241m=\u001b[39m scipy\u001b[38;5;241m.\u001b[39mspatial\u001b[38;5;241m.\u001b[39mdistance\u001b[38;5;241m.\u001b[39mhamming(\n\u001b[0;32m 86\u001b[0m adj1[mask]\u001b[38;5;241m.\u001b[39mflatten(), adj2[mask]\u001b[38;5;241m.\u001b[39mflatten()\n\u001b[0;32m 87\u001b[0m )\n\u001b[1;32m---> 88\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresults\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdist\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m dist\n\u001b[0;32m 89\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124madjacency_matrices\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m adj1, adj2\n\u001b[0;32m 90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dist\n", - "\u001b[1;31mAttributeError\u001b[0m: 'Graph' object has no attribute 'results'" - ] + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "G1 = nx.gnp_random_graph(n=10, p=0.2)\n", "G2 = nx.Graph(G1)\n", - "Hamming.dist(G1, G2, G2)" + "\n", + "distance = Hamming()\n", + "\n", + "distance(G1, G2)" ] }, { From b6cd41c69bec7e69e7a1caf99f552c212a0b3b62 Mon Sep 17 00:00:00 2001 From: Alice Schwarze Date: Tue, 19 Jul 2022 11:14:24 -0400 Subject: [PATCH 06/72] added networkx edge swap method --- netrw/analysis/distance_trajectory.py | 61 +++++++++++++++++++++++---- netrw/analysis/playground.ipynb | 49 +++++++++++++++++++-- netrw/rewire/networkXEdgeSwap.py | 28 ++++++++++++ 3 files changed, 125 insertions(+), 13 deletions(-) create mode 100644 netrw/rewire/networkXEdgeSwap.py diff --git a/netrw/analysis/distance_trajectory.py b/netrw/analysis/distance_trajectory.py index 0449efb..945d36e 100644 --- a/netrw/analysis/distance_trajectory.py +++ b/netrw/analysis/distance_trajectory.py @@ -2,29 +2,72 @@ import networkx import netrd - - -def distanceTrajectory(G, distance=..., rewire=..., null_model=None, - num_rewire=100, num_networks=100, distance_kwargs={}, rewire_kwargs={}, +def distanceTrajectory(G, distance=netrd.distance.Hamming, rewire=netrw.rewire.KarrerRewirer, null_model=None, + num_rewire=100, num_runs=100, distance_kwargs={}, rewire_kwargs={}, null_model_kwargs={}, **kwargs): + ''' + Get some data on graph distances as a function of number of rewiring steps. + + Parameters + ---------- + G : networkx Graph or DiGraph + + distance : netrd graph distance class + + rewire : netrw rewire class + + null_model : ??? + + num_rewire : integer or list + number of rewiring steps to be tracked or ordered list of rewiring + steps to be tracked + + num_runs : integer + number of trajectories to be generated for evaluating the standard + deviation for a set of rewiring trajectories + + distance_kwargs : dictionary + a dictionary of keyword arguments for an instantiation of the netrd + distance class + + rewire_kwargs : dictionary + a dictionary of keyword arguments for an instantiation of + the netrw rewire class + + null_model_kwargs : dictionary + a dictionary of keyword arguments for the null model (?) + + ''' G0 = copy.deepcopy(G) + # check whether input for num rewire in a number of rewiring steps (int) + # or a list of steps if hasattr(num_rewire, "__iter__"): rewire_steps = num_rewire else: rewire_steps = range(num_rewire) - data = np.zeros(len(rewire_steps)) + # initialize data array + data = np.zeros(len(rewire_steps)) + + # define a rewire function + step_rewire = rewire().step + rewire_function = lambda g : step_rewire(g, copy_graph=False, **rewire_kwargs) - for i, r in enumerate(rewire_steps): - rewire(G0, **rewire_kwargs) - data[i] = distance(G0, G, **distance_kwargs) + # define a distance function + distfun = distance() # get a class instantiation + distance_function = lambda g1, g2: distfun(g1, g2, **distance_kwargs) + + for i in range(max(rewire_steps)): + rewire_function(G0) + data[i+1] = distance(G0, G) return data + def plotDistanceTrajectory(G, **kwargs): - data = distanceTrajectory(G, kwargs**) + data = distanceTrajectory(G, **kwargs) \ No newline at end of file diff --git a/netrw/analysis/playground.ipynb b/netrw/analysis/playground.ipynb index 02c110e..fb13335 100644 --- a/netrw/analysis/playground.ipynb +++ b/netrw/analysis/playground.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "id": "ab4e64eb-1e76-48f7-b3ac-0f94447e66d5", "metadata": {}, "outputs": [], @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "id": "c984fa12-1e95-4e62-9ca1-39ac8c9feb69", "metadata": {}, "outputs": [ @@ -24,7 +24,7 @@ "0.0" ] }, - "execution_count": 12, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -40,10 +40,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "fe15a91b-841d-4e25-b34c-664e8908ed2d", "metadata": {}, "outputs": [], + "source": [ + "distance_class = netrd.distance.Hamming" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "94337e3b-5de7-4b07-a319-1f123d0f651a", + "metadata": {}, + "outputs": [], + "source": [ + "distance = distance_class()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "888c9111-e239-41ba-be24-20cf3b6e4d55", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "distance(G1, G2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9f58d9c-7ec4-4d34-82ba-0fe35ac9509b", + "metadata": {}, + "outputs": [], "source": [] } ], diff --git a/netrw/rewire/networkXEdgeSwap.py b/netrw/rewire/networkXEdgeSwap.py new file mode 100644 index 0000000..ec4fcc6 --- /dev/null +++ b/netrw/rewire/networkXEdgeSwap.py @@ -0,0 +1,28 @@ +from . import BaseRewirer +import copy +import itertools as it +import random +import networkx as nx + + +class NetworkXEdgeSwap(BaseRewirer): + """Perturb one edge of node `i` in the way described by Karrer et al. (2008). + Choose an edge incident on `i` at random; delete it and replace it with + a new edge that is not already present in the network (and is not + necessarily incident on `i`). + + Karrer, Brian, Elizaveta Levina, and + M. E. J. Newman. 2008. “Robustness of Community Structure in + Networks.” Physical Review E 77 + (4). https://doi.org/10.1103/PhysRevE.77.046119. + + """ + + def rewire(self, G, copy_graph=True): + + if copy_graph: + G = copy.deepcopy(G) + + nx.double_edge_swap(G, nswap=1) + + return G From 0b72c12842c964e5fd279f124fd6f47acf89221c Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Tue, 19 Jul 2022 11:37:54 -0400 Subject: [PATCH 07/72] Create properties_overtime.py method for plotting network properties over time as the rewiring process occurs --- properties_overtime.py | 65 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) create mode 100644 properties_overtime.py diff --git a/properties_overtime.py b/properties_overtime.py new file mode 100644 index 0000000..2614c37 --- /dev/null +++ b/properties_overtime.py @@ -0,0 +1,65 @@ +from copy import deepcopy +import numpy as np +import networkx as nx +import matplotlib.pyplot as plt + + +def properties_overtime(init_graph, rewire_method, property1, tmax, numit): + ''' + Look at network properties as rewiring method changes the network. + Input: + init_graph = original graph that will be rewired + rewire_method = method of rewiring that you want to implement, outputs a graph + property1 = property of interest, (ex. nx.average_clustering, nx.average_shortest_path_length, etc.) + that outputs a single value for a given network + tmax = amount of time steps (rewirings) + numit = number of iterations of rewiring the original graph using the method to see variation in results + + Output: + property_dict = dictionary of property values for each iteration for each step of the rewiring process + fig = plot of mean and standard deviation of property of interest at each step of rewiring process + ''' + + G0 = deepcopy(init_graph) + property_dict = {} + + for i in range(numit): + property_list = [property1(G0)] # calculate property of initial network + for j in range(tmax): + rewire_method(G0, nswap=1) #rewire + property_list.append(property1(G0)) #calculate property of the rewired network + property_dict[i] = property_list + + + alllist = [] # list of all properties + for k in range(tmax): + alllist.append([]) + for l in range(numit): + alllist[k].append(property_dict[l][k]) + # find mean and standard deviation over different iterations of rewiring process + meanlist = [] + sdlist = [] + for k in range(tmax): + meanlist.append(np.mean(alllist[k])) + sdlist.append(np.std(alllist[k])) + + # find upper and lower bound of standard deviation interval around the mean + upperbd = [] + lowerbd = [] + for a in range(len(meanlist)): + upperbd.append(meanlist[a]+sdlist[a]) + lowerbd.append(meanlist[a]-sdlist[a]) + + # plot mean and standard deviation for chosen property for the given time steps of rewiring + fig, (ax0) = plt.subplots(nrows=1) + ax0.plot(range(tmax), meanlist, marker='o', color = 'blue') + ax0.plot(range(tmax), upperbd, color = 'blue') + ax0.plot(range(tmax), lowerbd, color = 'blue' ) + ax0.fill_between(range(tmax), upperbd,lowerbd, color='cornflowerblue',alpha=.5) + ax0.set_xlabel('time step', fontsize=15) + ax0.set_ylabel('Mean property value', fontsize=15) + + fig.show() + + return property_dict, fig + From ed74d15ac9eb5229041e49a0a88940aae0c0166d Mon Sep 17 00:00:00 2001 From: piazza-b <109612575+piazza-b@users.noreply.github.com> Date: Tue, 19 Jul 2022 11:46:17 -0400 Subject: [PATCH 08/72] Added rewiring and viz methods --- netrw/rewire/robust_rewiring.py | 61 ++++++++++++++++++++++++++++ netrw/visualization/visualization.py | 29 +++++++++++++ 2 files changed, 90 insertions(+) create mode 100644 netrw/rewire/robust_rewiring.py create mode 100644 netrw/visualization/visualization.py diff --git a/netrw/rewire/robust_rewiring.py b/netrw/rewire/robust_rewiring.py new file mode 100644 index 0000000..ec7cc02 --- /dev/null +++ b/netrw/rewire/robust_rewiring.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In[16]: + + +import networkx as nx +import numpy as np +from operator import itemgetter +import random + + +# In[17]: + + +def robust_rewire(G): + A = nx.adjacency_matrix(G) + degree_list = G.degree + + neighbors = [] + for i in range(len(degree_list)): + sorted_degrees = sorted(list(degree_list(np.nonzero(A[i,:])[1])),key=itemgetter(1)) + if len(sorted_degrees) > 1: + if sorted_degrees[-2][1] > 1 and sorted_degrees[-1][1] > 1: + neighbors.append(i) + + index_i = neighbors[random.randint(0,len(neighbors)-1)] + sorted_degrees_i = sorted(list(degree_list(np.nonzero(A[index_i,:])[1])),key=itemgetter(1)) + + min_degree = sorted_degrees_i[0][1] + max_degree = sorted_degrees_i[-1][1] + + j = [] + k = [] + + for item in sorted_degrees_i: + if item[1] == min_degree: + j.append(item[0]) + if item[1] == max_degree: + k.append(item[0]) + + index_j = j[random.randint(0,len(j)-1)] + index_k = k[random.randint(0,len(k)-1)] + + m = sorted(list(degree_list(np.nonzero(A[index_j,:])[1])),key=itemgetter(1)) + n = sorted(list(degree_list(np.nonzero(A[index_k,:])[1])),key=itemgetter(1)) + + index_m = m[random.randint(0,len(m)-1)][0] + index_n = n[random.randint(0,len(n)-1)][0] + + if len(np.unique([index_i,index_j,index_k,index_m,index_n])) == 5: + G.remove_edge(index_j,index_m) + G.remove_edge(index_k,index_n) + G.add_edge(index_k,index_j) + G.add_edge(index_m,index_n) + print("HI") + + G_out = G + + return G_out + diff --git a/netrw/visualization/visualization.py b/netrw/visualization/visualization.py new file mode 100644 index 0000000..0abf991 --- /dev/null +++ b/netrw/visualization/visualization.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In[322]: + + +import networkx as nx +import numpy as np +import matplotlib.pyplot as plt + + +# In[326]: + + +def visualize_rewiring(G1,G2,pos): + A1 = nx.adjacency_matrix(G1) + A2 = nx.adjacency_matrix(G2) + A_dif = abs(A2 - A1) + G3 = nx.Graph(A_dif) + nx.draw(G3,pos,edge_color='r',node_color='b',node_size=0,width=8) + nx.draw(G2,pos,edge_color='b',node_color='b',node_size=80,width=5) + + +# In[327]: + + +def visualize_graph(G,pos): + nx.draw(G,pos,edge_color='b',node_color='b',node_size=80,width=5) + From 4526f1b1d099af83fb20212be73d756a9eb9ba7c Mon Sep 17 00:00:00 2001 From: nwlandry Date: Tue, 19 Jul 2022 12:01:38 -0400 Subject: [PATCH 09/72] updates --- netrw/analysis/distributions.py | 14 +++++++++++++- netrw/rewire/__init__.py | 1 + netrw/rewire/algebraic_connectivity.py | 2 +- netrw/rewire/networkXEdgeSwap.py | 2 +- 4 files changed, 16 insertions(+), 3 deletions(-) diff --git a/netrw/analysis/distributions.py b/netrw/analysis/distributions.py index ecba90a..7c7c6e9 100644 --- a/netrw/analysis/distributions.py +++ b/netrw/analysis/distributions.py @@ -2,7 +2,7 @@ import numpy as np def get_property_distribution(G, rewiring_method, property, burn_in=100, num_samples=1000): - + G = deepcopy(G) property_list = np.zeros(num_samples) for i in range(num_samples): for j in range(burn_in): @@ -10,4 +10,16 @@ def get_property_distribution(G, rewiring_method, property, burn_in=100, num_sam if j >= burn_in - 1: property_list[i] = property(G) + return property_list + +def get_property_distribution_choosing_chaos(G, rewiring_method, property, burn_in=100, num_samples=1000): + G = deepcopy(G) + rw = rewiring_method() + property_list = np.zeros(num_samples) + for i in range(num_samples): + for j in range(burn_in): + G = rw.rewire(G, copy_graph=False) + if j >= burn_in - 1: + property_list[i] = property(G) + return property_list \ No newline at end of file diff --git a/netrw/rewire/__init__.py b/netrw/rewire/__init__.py index 54f6639..ef77015 100644 --- a/netrw/rewire/__init__.py +++ b/netrw/rewire/__init__.py @@ -1,5 +1,6 @@ from .base import BaseRewirer from .karrer import KarrerRewirer from .algebraic_connectivity import AlgebraicConnectivity +from .networkXEdgeSwap import NetworkXEdgeSwap __all__ = [] diff --git a/netrw/rewire/algebraic_connectivity.py b/netrw/rewire/algebraic_connectivity.py index 9c56bc9..19f2904 100644 --- a/netrw/rewire/algebraic_connectivity.py +++ b/netrw/rewire/algebraic_connectivity.py @@ -22,7 +22,7 @@ class AlgebraicConnectivity(BaseRewirer): Applied Mathematics and computation 219.10 (2013): 5465-5479. """ - def maximize_algebraic_connectivity( + def rewire( self, G, phi=1, copy_network=False, directed=False ): """ diff --git a/netrw/rewire/networkXEdgeSwap.py b/netrw/rewire/networkXEdgeSwap.py index ec4fcc6..2dda155 100644 --- a/netrw/rewire/networkXEdgeSwap.py +++ b/netrw/rewire/networkXEdgeSwap.py @@ -24,5 +24,5 @@ def rewire(self, G, copy_graph=True): G = copy.deepcopy(G) nx.double_edge_swap(G, nswap=1) - + return G From 8293131c9011b0d3ba216fa31c30c83bf35700b9 Mon Sep 17 00:00:00 2001 From: nwlandry Date: Tue, 19 Jul 2022 12:04:37 -0400 Subject: [PATCH 10/72] change to copy_graph --- netrw/rewire/algebraic_connectivity.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/netrw/rewire/algebraic_connectivity.py b/netrw/rewire/algebraic_connectivity.py index 19f2904..149b16c 100644 --- a/netrw/rewire/algebraic_connectivity.py +++ b/netrw/rewire/algebraic_connectivity.py @@ -23,7 +23,7 @@ class AlgebraicConnectivity(BaseRewirer): """ def rewire( - self, G, phi=1, copy_network=False, directed=False + self, G, phi=1, copy_graph=False, directed=False ): """ Rewire phi edges to maximize algebraic connectivity. @@ -31,13 +31,13 @@ def rewire( Parameters: G (networkx) phi (int) - number of edge rewires - copy_network (bool) - return a copy of the network + copy_graph (bool) - return a copy of the network directed (bool) - compute for directed network on undirected copy Return: G (networkx) """ - if copy_network: + if copy_graph: G = copy.deepcopy(G) if not nx.is_connected(G): From afacd325d0c8bc1a68208f1a91ab2124f92fbbbf Mon Sep 17 00:00:00 2001 From: Alice Schwarze Date: Tue, 19 Jul 2022 12:07:02 -0400 Subject: [PATCH 11/72] added a doc string to distance_trajectory.py --- netrw/analysis/distance_trajectory.py | 25 ++++++++++-- netrw/analysis/playground.ipynb | 58 +++++++++++++++++++++++---- 2 files changed, 72 insertions(+), 11 deletions(-) diff --git a/netrw/analysis/distance_trajectory.py b/netrw/analysis/distance_trajectory.py index 945d36e..9e45212 100644 --- a/netrw/analysis/distance_trajectory.py +++ b/netrw/analysis/distance_trajectory.py @@ -3,7 +3,7 @@ import netrd def distanceTrajectory(G, distance=netrd.distance.Hamming, rewire=netrw.rewire.KarrerRewirer, null_model=None, - num_rewire=100, num_runs=100, distance_kwargs={}, rewire_kwargs={}, + num_steps=100, num_runs=100, distance_kwargs={}, rewire_kwargs={}, null_model_kwargs={}, **kwargs): ''' Get some data on graph distances as a function of number of rewiring steps. @@ -18,7 +18,7 @@ def distanceTrajectory(G, distance=netrd.distance.Hamming, rewire=netrw.rewire.K null_model : ??? - num_rewire : integer or list + num_steps : integer or list number of rewiring steps to be tracked or ordered list of rewiring steps to be tracked @@ -66,8 +66,25 @@ def distanceTrajectory(G, distance=netrd.distance.Hamming, rewire=netrw.rewire.K return data -def plotDistanceTrajectory(G, **kwargs): +def plotDistanceTrajectory(G, num_rewire=100, **kwargs): + + # check whether input for num rewire in a number of rewiring steps (int) + # or a list of steps + if hasattr(num_rewire, "__iter__"): + rewire_steps = num_rewire + else: + rewire_steps = range(num_rewire) + + data = distanceTrajectory(G, num_rewire=num_rewire, **kwargs) + + + mean = np.mean(data, axis=1) + std = np.std(data, axis=1) + + plt.fill_between(rewire_steps, mean-std, mean+std) + plt.plot(rewire_steps, mean) + + - data = distanceTrajectory(G, **kwargs) \ No newline at end of file diff --git a/netrw/analysis/playground.ipynb b/netrw/analysis/playground.ipynb index fb13335..3b94787 100644 --- a/netrw/analysis/playground.ipynb +++ b/netrw/analysis/playground.ipynb @@ -8,13 +8,15 @@ "outputs": [], "source": [ "import netrd\n", + "import numpy as np\n", "import networkx as nx\n", + "from matplotlib import pyplot as plt\n", "from netrd.distance import Hamming" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "c984fa12-1e95-4e62-9ca1-39ac8c9feb69", "metadata": {}, "outputs": [ @@ -24,7 +26,7 @@ "0.0" ] }, - "execution_count": 5, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -40,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "fe15a91b-841d-4e25-b34c-664e8908ed2d", "metadata": {}, "outputs": [], @@ -50,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "id": "94337e3b-5de7-4b07-a319-1f123d0f651a", "metadata": {}, "outputs": [], @@ -60,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "id": "888c9111-e239-41ba-be24-20cf3b6e4d55", "metadata": {}, "outputs": [ @@ -70,7 +72,7 @@ "0.0" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -81,9 +83,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "f9f58d9c-7ec4-4d34-82ba-0fe35ac9509b", "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "operands 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\u001b[49m\u001b[43mmean\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\anaconda3\\envs\\netrwenv\\lib\\site-packages\\matplotlib\\pyplot.py:2555\u001b[0m, in \u001b[0;36mfill_between\u001b[1;34m(x, y1, y2, where, interpolate, step, data, **kwargs)\u001b[0m\n\u001b[0;32m 2551\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mfill_between)\n\u001b[0;32m 2552\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfill_between\u001b[39m(\n\u001b[0;32m 2553\u001b[0m x, y1, y2\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, interpolate\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, step\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m 2554\u001b[0m data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m-> 2555\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m gca()\u001b[38;5;241m.\u001b[39mfill_between(\n\u001b[0;32m 2556\u001b[0m x, y1, y2\u001b[38;5;241m=\u001b[39my2, where\u001b[38;5;241m=\u001b[39mwhere, interpolate\u001b[38;5;241m=\u001b[39minterpolate, step\u001b[38;5;241m=\u001b[39mstep,\n\u001b[0;32m 2557\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m: data} \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m {}), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32m~\\anaconda3\\envs\\netrwenv\\lib\\site-packages\\matplotlib\\__init__.py:1412\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1409\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m 1410\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 1411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1412\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(ax, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(sanitize_sequence, args), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1414\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1415\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[0;32m 1416\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", + "File \u001b[1;32m~\\anaconda3\\envs\\netrwenv\\lib\\site-packages\\matplotlib\\axes\\_axes.py:5245\u001b[0m, in \u001b[0;36mAxes.fill_between\u001b[1;34m(self, x, y1, y2, where, interpolate, step, **kwargs)\u001b[0m\n\u001b[0;32m 5243\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfill_between\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, y1, y2\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, interpolate\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 5244\u001b[0m step\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m-> 5245\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fill_between_x_or_y(\n\u001b[0;32m 5246\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m, x, y1, y2,\n\u001b[0;32m 5247\u001b[0m where\u001b[38;5;241m=\u001b[39mwhere, interpolate\u001b[38;5;241m=\u001b[39minterpolate, step\u001b[38;5;241m=\u001b[39mstep, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32m~\\anaconda3\\envs\\netrwenv\\lib\\site-packages\\matplotlib\\axes\\_axes.py:5166\u001b[0m, in \u001b[0;36mAxes._fill_between_x_or_y\u001b[1;34m(self, ind_dir, ind, dep1, dep2, where, interpolate, step, **kwargs)\u001b[0m\n\u001b[0;32m 5163\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m where\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m!=\u001b[39m ind\u001b[38;5;241m.\u001b[39msize:\n\u001b[0;32m 5164\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhere size (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mwhere\u001b[38;5;241m.\u001b[39msize\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) does not match \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 5165\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mind_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m size (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mind\u001b[38;5;241m.\u001b[39msize\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 5166\u001b[0m where \u001b[38;5;241m=\u001b[39m where \u001b[38;5;241m&\u001b[39m \u001b[38;5;241m~\u001b[39m\u001b[43mfunctools\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 5167\u001b[0m \u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlogical_or\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdep1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdep2\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 5169\u001b[0m ind, dep1, dep2 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mbroadcast_arrays(\n\u001b[0;32m 5170\u001b[0m np\u001b[38;5;241m.\u001b[39matleast_1d(ind), dep1, dep2, subok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 5172\u001b[0m polys \u001b[38;5;241m=\u001b[39m []\n", + "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (20,) (100,) " + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "rewire_steps = range(100)\n", + "arr = np.random.random(size=(100,10))\n", + "mean = np.mean(arr, axis=1)\n", + "std = np.std(arr, axis=1)\n", + "plt.fill_between(rewire_steps, mean-std, mean+std)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d757ebdb-c825-4a09-b13c-8ef80d7882ee", + "metadata": {}, "outputs": [], "source": [] } From c8da982ffcf08aa65faf3544a74795a9eb84a20f Mon Sep 17 00:00:00 2001 From: piazza-b <109612575+piazza-b@users.noreply.github.com> Date: Tue, 19 Jul 2022 12:09:06 -0400 Subject: [PATCH 12/72] Formatting --- netrw/rewire/networkXEdgeSwap.py | 4 +- netrw/rewire/robust_rewiring.py | 114 +++++++++++++++++-------------- 2 files changed, 65 insertions(+), 53 deletions(-) diff --git a/netrw/rewire/networkXEdgeSwap.py b/netrw/rewire/networkXEdgeSwap.py index ec4fcc6..382c729 100644 --- a/netrw/rewire/networkXEdgeSwap.py +++ b/netrw/rewire/networkXEdgeSwap.py @@ -19,10 +19,10 @@ class NetworkXEdgeSwap(BaseRewirer): """ def rewire(self, G, copy_graph=True): - + if copy_graph: G = copy.deepcopy(G) - + nx.double_edge_swap(G, nswap=1) return G diff --git a/netrw/rewire/robust_rewiring.py b/netrw/rewire/robust_rewiring.py index ec7cc02..bee8cdb 100644 --- a/netrw/rewire/robust_rewiring.py +++ b/netrw/rewire/robust_rewiring.py @@ -1,61 +1,73 @@ #!/usr/bin/env python # coding: utf-8 -# In[16]: - - +from .base import BaseRewirer import networkx as nx import numpy as np +import copy from operator import itemgetter import random -# In[17]: - - -def robust_rewire(G): - A = nx.adjacency_matrix(G) - degree_list = G.degree - - neighbors = [] - for i in range(len(degree_list)): - sorted_degrees = sorted(list(degree_list(np.nonzero(A[i,:])[1])),key=itemgetter(1)) - if len(sorted_degrees) > 1: - if sorted_degrees[-2][1] > 1 and sorted_degrees[-1][1] > 1: - neighbors.append(i) - - index_i = neighbors[random.randint(0,len(neighbors)-1)] - sorted_degrees_i = sorted(list(degree_list(np.nonzero(A[index_i,:])[1])),key=itemgetter(1)) - - min_degree = sorted_degrees_i[0][1] - max_degree = sorted_degrees_i[-1][1] - - j = [] - k = [] - - for item in sorted_degrees_i: - if item[1] == min_degree: - j.append(item[0]) - if item[1] == max_degree: - k.append(item[0]) - - index_j = j[random.randint(0,len(j)-1)] - index_k = k[random.randint(0,len(k)-1)] - - m = sorted(list(degree_list(np.nonzero(A[index_j,:])[1])),key=itemgetter(1)) - n = sorted(list(degree_list(np.nonzero(A[index_k,:])[1])),key=itemgetter(1)) - - index_m = m[random.randint(0,len(m)-1)][0] - index_n = n[random.randint(0,len(n)-1)][0] - - if len(np.unique([index_i,index_j,index_k,index_m,index_n])) == 5: - G.remove_edge(index_j,index_m) - G.remove_edge(index_k,index_n) - G.add_edge(index_k,index_j) - G.add_edge(index_m,index_n) - print("HI") - - G_out = G - - return G_out +class RobustRewiring(BaseRewirer): + """ + Increases network robustness by building triangles around high degree nodes following algorithm described in: + Louzada, V. H. P., Daolio, F., Herrmann, H. J., & Tomassini, M. (2013). Smart rewiring for network robustness. Journal of Complex Networks, 1(2), 150–159. https://doi.org/10.1093/comnet/cnt010 + """ + + def robust_rewire(self, G, copy_graph=False, num_steps=100): + + if copy_graph: + G = copy.deepcopy(G) + + for t in range(num_steps): + A = nx.adjacency_matrix(G) + degree_list = G.degree + + neighbors = [] + for i in range(len(degree_list)): + sorted_degrees = sorted( + list(degree_list(np.nonzero(A[i, :])[1])), key=itemgetter(1) + ) + if len(sorted_degrees) > 1: + if sorted_degrees[-2][1] > 1 and sorted_degrees[-1][1] > 1: + neighbors.append(i) + + index_i = neighbors[random.randint(0, len(neighbors) - 1)] + sorted_degrees_i = sorted( + list(degree_list(np.nonzero(A[index_i, :])[1])), key=itemgetter(1) + ) + + min_degree = sorted_degrees_i[0][1] + max_degree = sorted_degrees_i[-1][1] + + j = [] + k = [] + + for item in sorted_degrees_i: + if item[1] == min_degree: + j.append(item[0]) + if item[1] == max_degree: + k.append(item[0]) + + index_j = j[random.randint(0, len(j) - 1)] + index_k = k[random.randint(0, len(k) - 1)] + + m = sorted( + list(degree_list(np.nonzero(A[index_j, :])[1])), key=itemgetter(1) + ) + n = sorted( + list(degree_list(np.nonzero(A[index_k, :])[1])), key=itemgetter(1) + ) + + index_m = m[random.randint(0, len(m) - 1)][0] + index_n = n[random.randint(0, len(n) - 1)][0] + + if len(np.unique([index_i, index_j, index_k, index_m, index_n])) == 5: + G.remove_edge(index_j, index_m) + G.remove_edge(index_k, index_n) + G.add_edge(index_k, index_j) + G.add_edge(index_m, index_n) + print("HI") + return G From e78ebabbdd3cc0f6383f9174da0a9ad132505037 Mon Sep 17 00:00:00 2001 From: piazza-b <109612575+piazza-b@users.noreply.github.com> Date: Tue, 19 Jul 2022 12:10:10 -0400 Subject: [PATCH 13/72] Formatting --- netrw/rewire/robust_rewiring.py | 1 - 1 file changed, 1 deletion(-) diff --git a/netrw/rewire/robust_rewiring.py b/netrw/rewire/robust_rewiring.py index bee8cdb..c8fba0f 100644 --- a/netrw/rewire/robust_rewiring.py +++ b/netrw/rewire/robust_rewiring.py @@ -68,6 +68,5 @@ def robust_rewire(self, G, copy_graph=False, num_steps=100): G.remove_edge(index_k, index_n) G.add_edge(index_k, index_j) G.add_edge(index_m, index_n) - print("HI") return G From 70a86710d0cf0ad288dd0e8d352cd0dcf43295f2 Mon Sep 17 00:00:00 2001 From: piazza-b <109612575+piazza-b@users.noreply.github.com> Date: Tue, 19 Jul 2022 14:06:04 -0400 Subject: [PATCH 14/72] Update robust_rewiring.py --- netrw/rewire/robust_rewiring.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/netrw/rewire/robust_rewiring.py b/netrw/rewire/robust_rewiring.py index c8fba0f..8c5dcb3 100644 --- a/netrw/rewire/robust_rewiring.py +++ b/netrw/rewire/robust_rewiring.py @@ -15,11 +15,14 @@ class RobustRewiring(BaseRewirer): Louzada, V. H. P., Daolio, F., Herrmann, H. J., & Tomassini, M. (2013). Smart rewiring for network robustness. Journal of Complex Networks, 1(2), 150–159. https://doi.org/10.1093/comnet/cnt010 """ - def robust_rewire(self, G, copy_graph=False, num_steps=100): + def robust_rewire(self, G, copy_graph=False, num_steps=100, step_rewire=False): if copy_graph: G = copy.deepcopy(G) + if step_rewire: + num_steps = 1 + for t in range(num_steps): A = nx.adjacency_matrix(G) degree_list = G.degree From 7843000559c0ea0278a9a36641aaefa38f010ea1 Mon Sep 17 00:00:00 2001 From: piazza-b <109612575+piazza-b@users.noreply.github.com> Date: Tue, 19 Jul 2022 14:25:21 -0400 Subject: [PATCH 15/72] Update robust_rewiring.py --- netrw/rewire/robust_rewiring.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/netrw/rewire/robust_rewiring.py b/netrw/rewire/robust_rewiring.py index 8c5dcb3..89c9738 100644 --- a/netrw/rewire/robust_rewiring.py +++ b/netrw/rewire/robust_rewiring.py @@ -15,15 +15,15 @@ class RobustRewiring(BaseRewirer): Louzada, V. H. P., Daolio, F., Herrmann, H. J., & Tomassini, M. (2013). Smart rewiring for network robustness. Journal of Complex Networks, 1(2), 150–159. https://doi.org/10.1093/comnet/cnt010 """ - def robust_rewire(self, G, copy_graph=False, num_steps=100, step_rewire=False): + def robust_rewire(self, G, copy_graph=False, timesteps=1000, step_rewire=False): if copy_graph: G = copy.deepcopy(G) if step_rewire: - num_steps = 1 + timesteps = 1 - for t in range(num_steps): + for t in range(timesteps): A = nx.adjacency_matrix(G) degree_list = G.degree From 6744074259a417fa190ce5369ba3b72a84f433a1 Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Tue, 19 Jul 2022 14:42:57 -0400 Subject: [PATCH 16/72] Create properties_overtime.py network properties during rewiring, some edits i think? --- netrw/analysis/properties_overtime.py | 65 +++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) create mode 100644 netrw/analysis/properties_overtime.py diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py new file mode 100644 index 0000000..2614c37 --- /dev/null +++ b/netrw/analysis/properties_overtime.py @@ -0,0 +1,65 @@ +from copy import deepcopy +import numpy as np +import networkx as nx +import matplotlib.pyplot as plt + + +def properties_overtime(init_graph, rewire_method, property1, tmax, numit): + ''' + Look at network properties as rewiring method changes the network. + Input: + init_graph = original graph that will be rewired + rewire_method = method of rewiring that you want to implement, outputs a graph + property1 = property of interest, (ex. nx.average_clustering, nx.average_shortest_path_length, etc.) + that outputs a single value for a given network + tmax = amount of time steps (rewirings) + numit = number of iterations of rewiring the original graph using the method to see variation in results + + Output: + property_dict = dictionary of property values for each iteration for each step of the rewiring process + fig = plot of mean and standard deviation of property of interest at each step of rewiring process + ''' + + G0 = deepcopy(init_graph) + property_dict = {} + + for i in range(numit): + property_list = [property1(G0)] # calculate property of initial network + for j in range(tmax): + rewire_method(G0, nswap=1) #rewire + property_list.append(property1(G0)) #calculate property of the rewired network + property_dict[i] = property_list + + + alllist = [] # list of all properties + for k in range(tmax): + alllist.append([]) + for l in range(numit): + alllist[k].append(property_dict[l][k]) + # find mean and standard deviation over different iterations of rewiring process + meanlist = [] + sdlist = [] + for k in range(tmax): + meanlist.append(np.mean(alllist[k])) + sdlist.append(np.std(alllist[k])) + + # find upper and lower bound of standard deviation interval around the mean + upperbd = [] + lowerbd = [] + for a in range(len(meanlist)): + upperbd.append(meanlist[a]+sdlist[a]) + lowerbd.append(meanlist[a]-sdlist[a]) + + # plot mean and standard deviation for chosen property for the given time steps of rewiring + fig, (ax0) = plt.subplots(nrows=1) + ax0.plot(range(tmax), meanlist, marker='o', color = 'blue') + ax0.plot(range(tmax), upperbd, color = 'blue') + ax0.plot(range(tmax), lowerbd, color = 'blue' ) + ax0.fill_between(range(tmax), upperbd,lowerbd, color='cornflowerblue',alpha=.5) + ax0.set_xlabel('time step', fontsize=15) + ax0.set_ylabel('Mean property value', fontsize=15) + + fig.show() + + return property_dict, fig + From d9b08541a6f5af350fd9b48f7a6f3dc3d3bc4198 Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Tue, 19 Jul 2022 15:08:59 -0400 Subject: [PATCH 17/72] trying to make changes --- .../analysis/properties_overtime.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) rename properties_overtime.py => netrw/analysis/properties_overtime.py (90%) diff --git a/properties_overtime.py b/netrw/analysis/properties_overtime.py similarity index 90% rename from properties_overtime.py rename to netrw/analysis/properties_overtime.py index 2614c37..fdab591 100644 --- a/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -2,6 +2,8 @@ import numpy as np import networkx as nx import matplotlib.pyplot as plt +import netrw +from netrw.rewire import KarrerRewirer, AlgebraicConnectivity, NetworkXEdgeSwap def properties_overtime(init_graph, rewire_method, property1, tmax, numit): @@ -9,7 +11,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): Look at network properties as rewiring method changes the network. Input: init_graph = original graph that will be rewired - rewire_method = method of rewiring that you want to implement, outputs a graph + rewire_method = netrw method of rewiring that you want to implement that outputs a graph property1 = property of interest, (ex. nx.average_clustering, nx.average_shortest_path_length, etc.) that outputs a single value for a given network tmax = amount of time steps (rewirings) @@ -18,7 +20,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): Output: property_dict = dictionary of property values for each iteration for each step of the rewiring process fig = plot of mean and standard deviation of property of interest at each step of rewiring process - ''' + ''' G0 = deepcopy(init_graph) property_dict = {} @@ -26,7 +28,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): for i in range(numit): property_list = [property1(G0)] # calculate property of initial network for j in range(tmax): - rewire_method(G0, nswap=1) #rewire + rewire_method.rewire(G0) #rewire property_list.append(property1(G0)) #calculate property of the rewired network property_dict[i] = property_list @@ -63,3 +65,4 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): return property_dict, fig + From 58e789f9def7b3a9f18cf7df9de5f39e5d0e8076 Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Tue, 19 Jul 2022 15:13:17 -0400 Subject: [PATCH 18/72] Delete properties_overtime.py --- properties_overtime.py | 65 ------------------------------------------ 1 file changed, 65 deletions(-) delete mode 100644 properties_overtime.py diff --git a/properties_overtime.py b/properties_overtime.py deleted file mode 100644 index 2614c37..0000000 --- a/properties_overtime.py +++ /dev/null @@ -1,65 +0,0 @@ -from copy import deepcopy -import numpy as np -import networkx as nx -import matplotlib.pyplot as plt - - -def properties_overtime(init_graph, rewire_method, property1, tmax, numit): - ''' - Look at network properties as rewiring method changes the network. - Input: - init_graph = original graph that will be rewired - rewire_method = method of rewiring that you want to implement, outputs a graph - property1 = property of interest, (ex. nx.average_clustering, nx.average_shortest_path_length, etc.) - that outputs a single value for a given network - tmax = amount of time steps (rewirings) - numit = number of iterations of rewiring the original graph using the method to see variation in results - - Output: - property_dict = dictionary of property values for each iteration for each step of the rewiring process - fig = plot of mean and standard deviation of property of interest at each step of rewiring process - ''' - - G0 = deepcopy(init_graph) - property_dict = {} - - for i in range(numit): - property_list = [property1(G0)] # calculate property of initial network - for j in range(tmax): - rewire_method(G0, nswap=1) #rewire - property_list.append(property1(G0)) #calculate property of the rewired network - property_dict[i] = property_list - - - alllist = [] # list of all properties - for k in range(tmax): - alllist.append([]) - for l in range(numit): - alllist[k].append(property_dict[l][k]) - # find mean and standard deviation over different iterations of rewiring process - meanlist = [] - sdlist = [] - for k in range(tmax): - meanlist.append(np.mean(alllist[k])) - sdlist.append(np.std(alllist[k])) - - # find upper and lower bound of standard deviation interval around the mean - upperbd = [] - lowerbd = [] - for a in range(len(meanlist)): - upperbd.append(meanlist[a]+sdlist[a]) - lowerbd.append(meanlist[a]-sdlist[a]) - - # plot mean and standard deviation for chosen property for the given time steps of rewiring - fig, (ax0) = plt.subplots(nrows=1) - ax0.plot(range(tmax), meanlist, marker='o', color = 'blue') - ax0.plot(range(tmax), upperbd, color = 'blue') - ax0.plot(range(tmax), lowerbd, color = 'blue' ) - ax0.fill_between(range(tmax), upperbd,lowerbd, color='cornflowerblue',alpha=.5) - ax0.set_xlabel('time step', fontsize=15) - ax0.set_ylabel('Mean property value', fontsize=15) - - fig.show() - - return property_dict, fig - From 70fee069de0bdc66eb0f24e7a5cc6cc3c871577a Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Tue, 19 Jul 2022 15:21:14 -0400 Subject: [PATCH 19/72] fixed small merge problems i think --- netrw/analysis/properties_overtime.py | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index 6fbbf49..48e1c49 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -2,11 +2,8 @@ import numpy as np import networkx as nx import matplotlib.pyplot as plt -<<<<<<< HEAD import netrw from netrw.rewire import KarrerRewirer, AlgebraicConnectivity, NetworkXEdgeSwap -======= ->>>>>>> 6744074259a417fa190ce5369ba3b72a84f433a1 def properties_overtime(init_graph, rewire_method, property1, tmax, numit): @@ -14,11 +11,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): Look at network properties as rewiring method changes the network. Input: init_graph = original graph that will be rewired -<<<<<<< HEAD rewire_method = netrw method of rewiring that you want to implement that outputs a graph -======= - rewire_method = method of rewiring that you want to implement, outputs a graph ->>>>>>> 6744074259a417fa190ce5369ba3b72a84f433a1 property1 = property of interest, (ex. nx.average_clustering, nx.average_shortest_path_length, etc.) that outputs a single value for a given network tmax = amount of time steps (rewirings) @@ -27,11 +20,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): Output: property_dict = dictionary of property values for each iteration for each step of the rewiring process fig = plot of mean and standard deviation of property of interest at each step of rewiring process -<<<<<<< HEAD ''' -======= - ''' ->>>>>>> 6744074259a417fa190ce5369ba3b72a84f433a1 G0 = deepcopy(init_graph) property_dict = {} @@ -39,11 +28,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): for i in range(numit): property_list = [property1(G0)] # calculate property of initial network for j in range(tmax): -<<<<<<< HEAD rewire_method.rewire(G0) #rewire -======= - rewire_method(G0, nswap=1) #rewire ->>>>>>> 6744074259a417fa190ce5369ba3b72a84f433a1 property_list.append(property1(G0)) #calculate property of the rewired network property_dict[i] = property_list @@ -80,7 +65,3 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): return property_dict, fig -<<<<<<< HEAD - -======= ->>>>>>> 6744074259a417fa190ce5369ba3b72a84f433a1 From 2c7af91e39e65b689302709dc5ea4f5cac89738b Mon Sep 17 00:00:00 2001 From: nwlandry Date: Tue, 19 Jul 2022 15:23:08 -0400 Subject: [PATCH 20/72] updated distributions --- netrw/analysis/distributions.py | 25 +++++++------------------ 1 file changed, 7 insertions(+), 18 deletions(-) diff --git a/netrw/analysis/distributions.py b/netrw/analysis/distributions.py index 7c7c6e9..c7b0518 100644 --- a/netrw/analysis/distributions.py +++ b/netrw/analysis/distributions.py @@ -1,25 +1,14 @@ from copy import deepcopy import numpy as np -def get_property_distribution(G, rewiring_method, property, burn_in=100, num_samples=1000): - G = deepcopy(G) - property_list = np.zeros(num_samples) - for i in range(num_samples): - for j in range(burn_in): - rewiring_method(G) - if j >= burn_in - 1: - property_list[i] = property(G) - - return property_list - -def get_property_distribution_choosing_chaos(G, rewiring_method, property, burn_in=100, num_samples=1000): +def get_property_distribution(G, rewiring_method, property, skip=10, num_samples=1000): G = deepcopy(G) rw = rewiring_method() - property_list = np.zeros(num_samples) + properties = np.zeros(num_samples) for i in range(num_samples): - for j in range(burn_in): + for j in range(skip): G = rw.rewire(G, copy_graph=False) - if j >= burn_in - 1: - property_list[i] = property(G) - - return property_list \ No newline at end of file + if j >= skip - 1: + properties[i] = property(G) + print(i) + return properties \ No newline at end of file From 95f10c7922012c3f5ad10826c2e03ce09308f199 Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Tue, 19 Jul 2022 15:28:43 -0400 Subject: [PATCH 21/72] making it so each iteration copies original graph to rewire --- netrw/analysis/properties_overtime.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index 48e1c49..d787f8e 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -22,10 +22,11 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): fig = plot of mean and standard deviation of property of interest at each step of rewiring process ''' - G0 = deepcopy(init_graph) + property_dict = {} for i in range(numit): + G0 = deepcopy(init_graph) property_list = [property1(G0)] # calculate property of initial network for j in range(tmax): rewire_method.rewire(G0) #rewire From 43b127a5763bed31738261d8657a32470866515c Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Tue, 19 Jul 2022 15:35:12 -0400 Subject: [PATCH 22/72] network properties over time --- netrw/analysis/properties_overtime.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index d787f8e..56677c9 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -29,7 +29,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): G0 = deepcopy(init_graph) property_list = [property1(G0)] # calculate property of initial network for j in range(tmax): - rewire_method.rewire(G0) #rewire + G0 = rewire_method.rewire(G0, copy_graph=False) #rewire property_list.append(property1(G0)) #calculate property of the rewired network property_dict[i] = property_list From bee8d279d85658511e15d00ec66ae99ff288a27a Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Tue, 19 Jul 2022 15:41:47 -0400 Subject: [PATCH 23/72] small change --- netrw/analysis/properties_overtime.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index 56677c9..11be654 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -24,12 +24,12 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): property_dict = {} - + rw = rewire_method() for i in range(numit): G0 = deepcopy(init_graph) property_list = [property1(G0)] # calculate property of initial network for j in range(tmax): - G0 = rewire_method.rewire(G0, copy_graph=False) #rewire + G0 = rw.rewire(G0, copy_graph=False) #rewire property_list.append(property1(G0)) #calculate property of the rewired network property_dict[i] = property_list From 18dfd9295fafbcb3c409c7d56d08e8c725c22e60 Mon Sep 17 00:00:00 2001 From: Alice Schwarze Date: Tue, 19 Jul 2022 16:01:49 -0400 Subject: [PATCH 24/72] playing on the playground --- netrw/analysis/distance_trajectory.py | 107 ++++++++++++++---- netrw/analysis/playground.ipynb | 156 -------------------------- netrw/rewire/networkXEdgeSwap.py | 9 ++ 3 files changed, 93 insertions(+), 179 deletions(-) delete mode 100644 netrw/analysis/playground.ipynb diff --git a/netrw/analysis/distance_trajectory.py b/netrw/analysis/distance_trajectory.py index 9e45212..85ae7d8 100644 --- a/netrw/analysis/distance_trajectory.py +++ b/netrw/analysis/distance_trajectory.py @@ -1,10 +1,14 @@ -from matplotlib import pyplot -import networkx +from matplotlib import pyplot as plt +import networkx as nx +import numpy as np +import warnings, copy import netrd +from ..rewire import NetworkXEdgeSwap -def distanceTrajectory(G, distance=netrd.distance.Hamming, rewire=netrw.rewire.KarrerRewirer, null_model=None, - num_steps=100, num_runs=100, distance_kwargs={}, rewire_kwargs={}, - null_model_kwargs={}, **kwargs): +def distanceTrajectory(G, distance=netrd.distance.Hamming, + rewire=NetworkXEdgeSwap, null_model=None, + num_steps=100, num_runs=100, distance_kwargs={}, + rewire_kwargs={}, null_model_kwargs={}, **kwargs): ''' Get some data on graph distances as a function of number of rewiring steps. @@ -43,48 +47,105 @@ def distanceTrajectory(G, distance=netrd.distance.Hamming, rewire=netrw.rewire.K # check whether input for num rewire in a number of rewiring steps (int) # or a list of steps - if hasattr(num_rewire, "__iter__"): + if hasattr(num_steps, "__iter__"): rewire_steps = num_rewire else: - rewire_steps = range(num_rewire) + rewire_steps = range(num_steps) # initialize data array - data = np.zeros(len(rewire_steps)) + data = np.zeros((len(rewire_steps),num_runs)) # define a rewire function - step_rewire = rewire().step + step_rewire = rewire().step_rewire rewire_function = lambda g : step_rewire(g, copy_graph=False, **rewire_kwargs) # define a distance function distfun = distance() # get a class instantiation distance_function = lambda g1, g2: distfun(g1, g2, **distance_kwargs) - for i in range(max(rewire_steps)): - rewire_function(G0) - data[i+1] = distance(G0, G) + for j in range(num_runs): + for i in range(max(rewire_steps)): + rewire_function(G0) + data[i+1,j] = distance_function(G0, G) return data -def plotDistanceTrajectory(G, num_rewire=100, **kwargs): +def plotDistanceTrajectory(G, distance=netrd.distance.Hamming, num_steps=100, + show=['mean', 'median', 'std-env'], labels=None, add_legend=True, fig=None, + ax=None, linecolors=None, envcolor='cyan', + xlabel='Number of rewiring steps', ylabel=None, **kwargs): - # check whether input for num rewire in a number of rewiring steps (int) + # check whether input for num steps in a number of rewiring steps (int) # or a list of steps - if hasattr(num_rewire, "__iter__"): - rewire_steps = num_rewire + if hasattr(num_steps, "__iter__"): + rewire_steps = num_steps else: - rewire_steps = range(num_rewire) + rewire_steps = range(num_steps) - data = distanceTrajectory(G, num_rewire=num_rewire, **kwargs) - - - mean = np.mean(data, axis=1) + # set ylabel + if ylabel is None: + ylabel = distance.__name__ + r' distance to $G_0$' + + # set line labels + if labels is None: + labels = show + elif hasattr(labels, "__iter__"): + if len(labels) != len(show): + raise ValueError('List for keyword argument `show` and list for' + +'`keyword argument` must have the same length.') + else: + raise ValueError( + 'Keyword argument `labels` must be None or list.') + + # set line colors + if linecolors is None: + tabcolors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple'] + linecolors = [tabcolors[i % len(tabcolors)] for i in range(len(show))] + elif hasattr(linecolors, "__iter__"): + if len(linecolors) != len(show): + raise ValueError('List for keyword argument `show` and list for' + +'`keyword argument` must have the same length.') + else: + raise ValueError( + 'Keyword argument `labels` must be None or list.') + + # get data + data = distanceTrajectory(G, distance=distance, num_steps=num_steps, **kwargs) + + # get data for lines for plot + line_data = [] + for s in show: + if s=='mean': + line_data += [np.mean(data, axis=1)] + elif s=='std': + line_data += [np.std(data, axis=1)] + elif s=='median': + line_data += [np.median(data, axis=1)] + elif s=='std-env': + mean = np.mean(data, axis=1) + std = np.std(data, axis=1) + env_data = [std-mean, std+mean] + else: + warnings.warn("Unknown summary statistic", s, "will be ignored.") + std = np.std(data, axis=1) - plt.fill_between(rewire_steps, mean-std, mean+std) - plt.plot(rewire_steps, mean) + if fig is None: + fig = plt.gcf() + if ax is None: + ax = plt.subplot(111) + if 'std-env' in show: + ax.fill_between(rewire_steps, env_data[0], env_data[1], color=envcolor) + + for i in range(len(line_data)): + ax.plot(rewire_steps, line_data[i], color=linecolors[i], label=labels[i]) + if add_legend: + plt.legend() + plt.xlabel(xlabel) + plt.ylabel(ylabel) \ No newline at end of file diff --git a/netrw/analysis/playground.ipynb b/netrw/analysis/playground.ipynb deleted file mode 100644 index 3b94787..0000000 --- a/netrw/analysis/playground.ipynb +++ /dev/null @@ -1,156 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "ab4e64eb-1e76-48f7-b3ac-0f94447e66d5", - "metadata": {}, - "outputs": [], - "source": [ - "import netrd\n", - "import numpy as np\n", - "import networkx as nx\n", - "from matplotlib import pyplot as plt\n", - "from netrd.distance import Hamming" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c984fa12-1e95-4e62-9ca1-39ac8c9feb69", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G1 = nx.gnp_random_graph(n=10, p=0.2)\n", - "G2 = nx.Graph(G1)\n", - "\n", - "distance = Hamming()\n", - "\n", - "distance(G1, G2)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fe15a91b-841d-4e25-b34c-664e8908ed2d", - "metadata": {}, - "outputs": [], - "source": [ - "distance_class = netrd.distance.Hamming" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "94337e3b-5de7-4b07-a319-1f123d0f651a", - "metadata": {}, - "outputs": [], - "source": [ - "distance = distance_class()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "888c9111-e239-41ba-be24-20cf3b6e4d55", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "distance(G1, G2)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f9f58d9c-7ec4-4d34-82ba-0fe35ac9509b", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "operands could not be broadcast together with shapes (20,) (100,) ", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Input \u001b[1;32mIn [6]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m mean \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmean(arr, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 4\u001b[0m std \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mstd(arr, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m----> 5\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfill_between\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrewire_steps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmean\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmean\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mstd\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\anaconda3\\envs\\netrwenv\\lib\\site-packages\\matplotlib\\pyplot.py:2555\u001b[0m, in \u001b[0;36mfill_between\u001b[1;34m(x, y1, y2, where, interpolate, step, data, **kwargs)\u001b[0m\n\u001b[0;32m 2551\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mfill_between)\n\u001b[0;32m 2552\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfill_between\u001b[39m(\n\u001b[0;32m 2553\u001b[0m x, y1, y2\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, interpolate\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, step\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m 2554\u001b[0m data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m-> 2555\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m gca()\u001b[38;5;241m.\u001b[39mfill_between(\n\u001b[0;32m 2556\u001b[0m x, y1, y2\u001b[38;5;241m=\u001b[39my2, where\u001b[38;5;241m=\u001b[39mwhere, interpolate\u001b[38;5;241m=\u001b[39minterpolate, step\u001b[38;5;241m=\u001b[39mstep,\n\u001b[0;32m 2557\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m: data} \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m {}), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32m~\\anaconda3\\envs\\netrwenv\\lib\\site-packages\\matplotlib\\__init__.py:1412\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1409\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m 1410\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 1411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1412\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(ax, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(sanitize_sequence, args), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1414\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1415\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[0;32m 1416\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", - "File \u001b[1;32m~\\anaconda3\\envs\\netrwenv\\lib\\site-packages\\matplotlib\\axes\\_axes.py:5245\u001b[0m, in \u001b[0;36mAxes.fill_between\u001b[1;34m(self, x, y1, y2, where, interpolate, step, **kwargs)\u001b[0m\n\u001b[0;32m 5243\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfill_between\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, y1, y2\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, interpolate\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 5244\u001b[0m step\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m-> 5245\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fill_between_x_or_y(\n\u001b[0;32m 5246\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m, x, y1, y2,\n\u001b[0;32m 5247\u001b[0m where\u001b[38;5;241m=\u001b[39mwhere, interpolate\u001b[38;5;241m=\u001b[39minterpolate, step\u001b[38;5;241m=\u001b[39mstep, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32m~\\anaconda3\\envs\\netrwenv\\lib\\site-packages\\matplotlib\\axes\\_axes.py:5166\u001b[0m, in \u001b[0;36mAxes._fill_between_x_or_y\u001b[1;34m(self, ind_dir, ind, dep1, dep2, where, interpolate, step, **kwargs)\u001b[0m\n\u001b[0;32m 5163\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m where\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m!=\u001b[39m ind\u001b[38;5;241m.\u001b[39msize:\n\u001b[0;32m 5164\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhere size (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mwhere\u001b[38;5;241m.\u001b[39msize\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) does not match \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 5165\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mind_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m size (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mind\u001b[38;5;241m.\u001b[39msize\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 5166\u001b[0m where \u001b[38;5;241m=\u001b[39m where \u001b[38;5;241m&\u001b[39m \u001b[38;5;241m~\u001b[39m\u001b[43mfunctools\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 5167\u001b[0m \u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlogical_or\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdep1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdep2\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 5169\u001b[0m ind, dep1, dep2 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mbroadcast_arrays(\n\u001b[0;32m 5170\u001b[0m np\u001b[38;5;241m.\u001b[39matleast_1d(ind), dep1, dep2, subok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 5172\u001b[0m polys \u001b[38;5;241m=\u001b[39m []\n", - "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (20,) (100,) " - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "rewire_steps = range(100)\n", - "arr = np.random.random(size=(100,10))\n", - "mean = np.mean(arr, axis=1)\n", - "std = np.std(arr, axis=1)\n", - "plt.fill_between(rewire_steps, mean-std, mean+std)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d757ebdb-c825-4a09-b13c-8ef80d7882ee", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/netrw/rewire/networkXEdgeSwap.py b/netrw/rewire/networkXEdgeSwap.py index d5c25c9..02cb41b 100644 --- a/netrw/rewire/networkXEdgeSwap.py +++ b/netrw/rewire/networkXEdgeSwap.py @@ -26,3 +26,12 @@ def rewire(self, G, copy_graph=True): nx.double_edge_swap(G, nswap=1) return G + + def step_rewire(self, G, copy_graph=True): + + if copy_graph: + G = copy.deepcopy(G) + + nx.double_edge_swap(G, nswap=1) + + return G \ No newline at end of file From 326cb721182c5d62ebbde8f27c9e777604a48ece Mon Sep 17 00:00:00 2001 From: Alice Schwarze Date: Tue, 19 Jul 2022 16:02:19 -0400 Subject: [PATCH 25/72] Create playground.ipynb --- playground.ipynb | 271 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 271 insertions(+) create mode 100644 playground.ipynb diff --git a/playground.ipynb b/playground.ipynb new file mode 100644 index 0000000..30f7628 --- /dev/null +++ b/playground.ipynb @@ -0,0 +1,271 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "ab4e64eb-1e76-48f7-b3ac-0f94447e66d5", + "metadata": {}, + "outputs": [], + "source": [ + "import netrd\n", + "import numpy as np\n", + "import networkx as nx\n", + "from matplotlib import pyplot as plt\n", + "from netrd.distance import Hamming" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c984fa12-1e95-4e62-9ca1-39ac8c9feb69", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G1 = nx.gnp_random_graph(n=10, p=0.2)\n", + "G2 = nx.Graph(G1)\n", + "\n", + "distance = Hamming()\n", + "\n", + "distance(G1, G2)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fe15a91b-841d-4e25-b34c-664e8908ed2d", + "metadata": {}, + "outputs": [], + "source": [ + "distance_class = netrd.distance.Hamming" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "94337e3b-5de7-4b07-a319-1f123d0f651a", + "metadata": {}, + "outputs": [], + "source": [ + "distance = distance_class()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "888c9111-e239-41ba-be24-20cf3b6e4d55", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "distance(G1, G2)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f9f58d9c-7ec4-4d34-82ba-0fe35ac9509b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "rewire_steps = range(100)\n", + "arr = np.random.random(size=(100,10))\n", + "mean = np.mean(arr, axis=1)\n", + "std = np.std(arr, axis=1)\n", + "plt.fill_between(rewire_steps, mean-std, mean+std, color='cyan')\n", + "plt.plot(rewire_steps, mean)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d757ebdb-c825-4a09-b13c-8ef80d7882ee", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "863c164d-9b10-49e9-aed6-b7f920f7448d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hamming'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "distance_class.__name__" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "76db3b1d-7ff7-4baa-944a-f2efb20324bc", + "metadata": {}, + "outputs": [], + "source": [ + "import netrw\n", + "from netrw.rewire import KarrerRewirer, AlgebraicConnectivity, NetworkXEdgeSwap\n", + "from netrw.analysis.distance_trajectory import *\n", + "import networkx as nx" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "06fef0d8-239b-4df2-8d50-abadfffa1398", + "metadata": {}, + "outputs": [], + "source": [ + "G = nx.path_graph(40)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "021b4a0b-3ac4-4636-a59a-df1ac17ded9d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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0N5mko4CT4uivzex3VYxFJaZZBWXebmbrJO0H3CXpKTO7r9yNm9liYDFAW1tb7+1WRIUcnXiycc65RNk1G0mXADcQagb7AT+S9KkqxtIOHJAanwqsK7dMqu+2jcCthGY5gA1JU1t83VjFmEtSIUdnxpONc84lKmlGuwA43sy+bGZfBk4A/nsVY3kYmClphqQm4Fxgaa8yS4GPxrvSTgC2mNl6SXtJGg0gaS/g3fTcor2Unr7dzgduq2LMJWWKOW9Gc865lEqa0UR4OmcieVJnVZhZXtLFwJ1AFlhiZqslXRjnXwMsIzzaYA2wDZgfF58E3CoJwj7daGb/EectAm6WdAHwAnBOtWLuS7bYRacnG+ec266SZPND4DeSbo3jZwNLqhmMmS0jJJT0tGtS7w34ZInlngOO6mOdm4HTqhnnQBqKneQyzXtyk845V9cq+Z3NdyTdC5xIqNHMN7NHaxXYUJYtdtHZsPdgh+Gcc3Wj7GQj6Rtm9nngkRLTXEpjsctrNs45l1LJDQLvKjHtzGoFMpw0WhedWU82zjmXKOex0J8ALgIOlLQqNWs08ECtAhvKGq2bnCcb55zbrpxmtBuBO4B/JvQrlnjdzF6uSVRDXDM5r9k451xKOY8Y2AJsAc6rfTjDQ5N1k2vwZOOcc4l66htteDCjmS46G/zxAs45l/BkU2WFfDdZGR2ebJxzbjtPNlXW1bkNwGs2zjmXUklHnJL0YUlfjuPTJM0ZaLmRpqtzKwAdjX7NxjnnEpXUbP4VeBs9Nwq8TniypkvJdXYA0NnQOsiROOdc/aikb7TjzewYSY8CmNkrsXdml9KdC81oHY3ejOacc4lKajbdkrLEh5VJmggUaxLVENbdFWo2W5u8ZuOcc4lKks0VhIeS7Sfp68D9hB96upR8ZycAnU1es3HOuUQlvT7fIGkFobt+AWeb2ZM1i2yIynfFGwS8ZuOcc9tVcjfadcAfzewqM/se8EdJVX2ezXCQzyU1G082zjmXqKQZbZaZvZqMmNkrwNHVDEbSXElPS1ojaWGJ+ZJ0RZy/StIxcfoBku6R9KSk1ZIuSS1zmaSXJK2Mw1nVjLm3Qne4ZtPR7M1ozjmXqORutIykcTHJIGl8hcv3K958cBXhUQbtwMOSlprZE6liZwIz43A8cHV8zQOfMbNHJI0GVki6K7Xsv5jZ5dWKtT+FrlCz6Wjea09szjnnhoRKksW3gQcl/TSOnwP8UxVjmQOsiY94RtJNwDwgnWzmAdfHx0M/JGmspMlmth5YD2Bmr0t6EpjSa9k9otiV1Gy8Gc055xJlN6OZ2fXAB4ANcfhAnFYtU4AXU+PtcVpFZSRNJzTv/SY1+eLY7LZE0rhSG5e0QNJyScs3bdq0i7sA1p3UbEbt8jqcc264qeQGgWZgNjAGGA/8ZdJ1TZWoxDSrpIykvYFbgEvN7LU4+WrgIELs6wk1tJ1XYrbYzNrMrG3ixIkVhp5aT3LNpsVrNs45l6ikGe02wnNtVgC5GsTSDhyQGp8KrCu3jKRGQqK5wcx+nhQwsw3Je0k/AG6vbtg7KubDofFk45xzPSpJNlPNbG7NIoGHgZmSZgAvAecCH+pVZimhSewmwo0BW8xsvSQB/wY8aWbfSS+QuqYD8H7g8RruAyTNaC1+g4BzziUqSTb/JelIM3usFoGYWV7SxcCdQBZYYmarJV0Y518DLAPOAtYA24D5cfG3Ax8BHpO0Mk77ezNbBnxT0mxCc9ta4OO1iH+7Qo5Oa4SsP73BOecSlSSbE4G/lvQHQjOaADOzWdUKJiaHZb2mXZN6b8AnSyx3P6Wv52BmH6lWfOVQvpMcjXtyk845V/cqSTZn1iyKYUT5TnLeGbZzzu2gkr7Rno+3Dc8E0j+Pf77qUQ1hmULOazbOOddL2clG0seASwh3gK0ETgAeBE6tSWRDVKbYRaf8KZ3OOZdWyVXsS4DjgOfN7J2EH07u+q8fh6lMIUcu481ozjmXVkmy6TSzTgg/8DSzp4CDaxPW0JUtdtLp12ycc24Hldwg0C5pLPAL4C5Jr7Dzjy5HvGyxy2s2zjnXSyU3CLw/vr1M0j3APsAdNYlqCGsodpFr2Geww3DOubpSSd9o30jem9l/mtlS4H/WJKohrKHYRUfGbxBwzrm0Sq7ZvKvENP/tTS+N1kUu68nGOefSBmxGk/QJ4CLgQEmrUrNGAw/UKrChqslynmycc66Xcq7Z3Ei4NvPPQPpRza+b2cs1iWoIa6SbXNZvEHDOubQBk42ZbSE8WuC82ocz9DVbF53ZloELOufcCFLJDQLnSBod339R0s8lHVO70IamJrrpbPBk45xzaZXcIPAlM3td0onAGcB1hKdguqiQ76ZRBU82zjnXSyXJphBf/xy42sxuA/ziREquc1t4bfQbBJxzLq2SZPOSpMXAB4FlkporXH7Y6+oIycZrNs45t6NKksU5hLvS3m1mrwLjgM9WMxhJcyU9LWmNpIUl5kvSFXH+qvQ1o76WlTRe0l2Sno2v46oZc1ouF5NNoycb55xLGzDZSLo/vt1IuE7ziKTXgGeBW6oViKQscBXhh6KHAedJOqxXsTMJz9OZCSwgXjMaYNmFwN1mNhO4mx1v366q7s4OwJONc871NmCyMbMT4+toMxuTGkab2ZgqxjIHWGNmz5lZF3ATMK9XmXnA9RY8BIyVNHmAZecRkiTx9ewqxryD7q6QbLY1tdZqE845NyTV0zWXKcCLqfH2OK2cMv0tO8nM1gPE1/2qGPMO8rmQbDo82Tjn3A7K6a7mf/Q338y+U6VYVGr1ZZYpZ9n+Ny4tIDTNMW3atEoW3W78/gfywyMvY+XMY3dpeeecG67K6a5mdHw9mPCkzqVx/L3AfVWMpR04IDU+lZ2fl9NXmaZ+lt0gabKZrY9NbhtLbdzMFgOLAdra2ipKVIkJk6bS+Rd/S/uuLOycc8NYOddsvmpmXwX2BY4xs8+Y2WeAYwlf6tXyMDBT0gxJTcC59CS2xFLgo/GutBOALbFprL9llwLnx/fnA7dVMeadtFJfbZPOOVcPKnlS5zSgKzXeBUyvViBmlpd0MXAnkAWWmNlqSRfG+dcAy4CzgDXANmB+f8vGVS8CbpZ0AfAC4RbumvFk45xzO6sk2fw78FtJtxKuh7yfnru8qsLMlhESSnraNan3Bnyy3GXj9M3AadWMsz+tlL6A5JxzI1klj4X+uqQ7gJPipPlm9mhtwhq6PNk459zOKqnZYGaPAI/UKJZhYdRgB+Ccc3XILy9UWSsV3nPtnHMjgCebKvNk45xzOyu7Ga2PH3duAVaY2cqqRTTEtQLFwQ7COefqTCU1mzbgQnq6h1kAnAL8QNLnqh/a0NRKz4N/nHPOBZXcIDCB8KPONwAkfQX4GXAysAL4ZvXDG3pG4cnGOed6q6Rm0/tHnd3Am82sA8hVNaohrBXID3YQzjlXZyqp2dwIPCQp6e7lvcCPJe0FPFH1yIaoBsLvbPwmAeec61HJjzr/UdIy4ETC9+mFZrY8zv5vtQhuqGrEq3rOOZdW6Y86VxCuz7h+NOPJxjnn0iq59bkZ+AtC55vblzOzr1U/rKGtabADcM65OlNJzeY24u9q8BP3fjUPdgDOOVdnKkk2U81sbs0iGUZaBjsA55yrM5Xc+vxfko6sWSTDiCcb55zbUSU1mxOB+ZKeIzSjifCImVk1iWwI856fnXNuR5Ukm7n4T0jK4snGOed2NGAzmqT749vVwGPA43FYHV93m6Txku6S9Gx8HddHubmSnpa0RtLC1PRvSXpK0ipJt0oaG6dPl9QhaWUcrim13mrba09sxDnnhpABk42ZnRhfR5vZmNQw2szGVCmOhcDdZjYTuDuO70BSFrgKOBM4DDhP0mFx9l3AEbFJ7xngC6lFf29ms+NwYZXi7ZcnG+ec21G9PM9mHnBdfH8dcHaJMnOANWb2nJl1ATfF5TCzX5pZ0iXZQ8DU2obbv70Hc+POOVeHyk42ktpiE9UjsbnqMUmrqhTHJDNbDxBf9ytRZgrwYmq8PU7r7W+AO1LjMyQ9Kuk/JZ3UVwCSFkhaLmn5pk2bKt+DlNG7tbRzzg0/ldwgcAPwd4TrNhU/H0zSr4A3lZj1D+WuosS0HW5WkPQPhE6Xb4iT1gPTzGyzpGOBX0g63Mxe22lFZouBxQBtbW27dROEJxvnnNtRJclmk5kt3dUNmdnpfc2TtEHSZDNbL2kysLFEsXbggNT4VGBdah3nA+8BTjMzi9vMEXs7MLMVkn4PvBVYTg2NIlQZ/YmdzjkXVJJsviLpWsIF/O3d1ZjZz6sQx1LgfGBRfL2tRJmHgZmSZgAvAecCH4JwlxrweeAdZrYtWUDSROBlMytIOhCYCTxXhXj71Uo4sF0DFXTOuRGikmQzHziE0IN+ctJuQDWSzSLgZkkXAC8A5wBI2h+41szOMrO8pIuBO4EssMTMVsflv0fokuwuSQAPxTvPTga+JilPeIDmhWb2chXi7deoGKBzzrmgkmRzlJnVpLsaM9sMnFZi+jrgrNT4MmBZiXJv6WO9twC3VC/S8rTiycY559IqufX5odTvWlw/Wqmfe8qdc64eVNo32vmS/oD3jdavVkrfOueccyNVpX2juTK0DnYAzjlXZ8pONmb2fC0DGU68I07nnNtRJY+FbiP8APPNcTlvRutDK941tnPOpe2xHgRGEk82zjm3oz3Wg8BI0opnY+ecS6uXHgSGlVGEDtqcc84F9dKDwLDSSuiuwDnnXFAXPQgMN614zcY559K8B4EayODd1TjnXJr3IFAjjXhTmnPOJbwHgRppBjoHOwjnnKsTFfUgIGkc4ZkwLalZ3rNACc2DHYBzztWRSnoQ+BhwCeEJmSuBE4AHgVNrEtkQ58nGOed6VHKDwCXAccDzZvZO4GhgU02iGgZaBi7inHMjRiXJptPMOgEkNZvZU8DB1QhC0nhJd0l6Nr6O66PcXElPS1ojaWFq+mWSXpK0Mg5npeZ9IZZ/WtIZ1Yi3HJ5snHOuRyXJpl3SWOAXhMcv3wasq1IcC4G7zWwmoYeChb0LSMoCVwFnAocB5/W6FftfzGx2HJbFZQ4DzgUOJ9zg8K9xPTW3157YiHPODRGV3CDw/vj2Mkn3APsAd1QpjnnAKfH9dcC9wOd7lZkDrDGz5wAk3RSXe2KA9d5kZjngD5LWxPU8WKW4++TPtHHOuR4DJhtJV9J3J8anA5+uQhyTzGw9gJmtl7RfiTJTgBdT4+3A8anxiyV9FFgOfMbMXonLPNRrmSmlApC0AFgAMG3atF3dj+28ZuOccz3KaUZbDqyIw/tS75OhLJJ+JenxEsO8cldRYlqSBK8GDgJmA+uBb5exzI4TzRabWZuZtU2cOLHMkPrmycY553oMWLMxs+uS95IuTY9XwsxO72uepA2SJsdazWRgY4li7cABqfGpxGtGZrYhta4fALcPtEytjd4TG3HOuSGikhsEoHbPBFsKnB/fnw/cVqLMw8BMSTMkNREu/C8FiAkq8X7g8dR6z5XULGkG4Qepv61B/DvxZOOccz0q6a6mlhYBN0u6AHgBOAdA0v7AtWZ2lpnlJV0M3Eno53KJma2Oy39T0mxCMlwLfBzAzFZLuplwE0Ee+KSZ7ZEuy/beExtxzrkhQmb9V1YkvU5PjWYUsC2ZReiIc0ztwhscbW1ttnz58t1axyLgi3hnnM65oWUMsGUXl5W0wszaSs0r55qNtwjtglbCwfVk45xzlV+zcWUahT/TxjnnEp5saqQVP7jOOZfw78Ma8WTjnHM9/PuwRlop/YtS55wbiTzZ1Ij3jeaccz082dTIqMEOwDnn6ognmxpppXbdLTjn3FDjyaZGWoHiYAfhnHN1ol66q6l73d3dtLe309nZWVb5PPAzRk7tpgisaWnhsqlTeaWxcbDDcc7VGU82ZWpvb2f06NFMnz4daeD7zPJAFyMn2WDG+M2buay9nUtmzBjsaJxzdcab0crU2dnJhAkTyko0EA7siEk0ABINEybwljJrfs65kcWTTQXKTTQwQn9jI/kHyjlXkn831IgYoQnHOedK8GRTQ55snHMu8GRTQ55snHMuqIu70SSNB34CTCc8afOvzOyVEuXmAt8l9N5/rZktitN/Ahwci40FXjWz2ZKmA08CT8d5D5nZhbsb71f/z2qeWPfagOW2Uv5vbWbsP4b57z283zLr1q7l03PnMvvEE3nsoYeYedRRvHf+fBZ/5Su8snEj/3jDDRx4+OF861OfYs1jj1HI51lw2WW8Y9481q1dy1c+8hE6tm4F4O++9z2O+rM/Y8W997L4sssYu+++/P7xxznk2GP5xx/9qKLrU845N5C6SDbAQuBuM1skaWEc/3y6gKQscBXwLqAdeFjSUjN7wsw+mCr3bXZ80NzvzWx2rXdgT2lfs4ZFP/0pf794Mecfdxx33ngj195/P/ctXcoP/+mfmHHYYbSdeipfXrKE1199lb+eM4c5p5/O+P3243t33UVzSwsvPPssXzzvPK6PTyN9+tFH+cnq1Uzcf38+9va387sHHmD2iScO8p4654aTekk284BT4vvrgHvplWyAOcAaM3sOQNJNcbknkgIKp+N/BZxay2C/MkANJPE4UO0bgfefMYO3HHkkAAcefjjHnXYakjjoyCNZt3YtG9vbuW/pUn50+eUA5Do7+eMLLzBx//355sUX88zKlWSyWV545pnt6zx8zhwmTZ0KwFtnz2bd2rWebJxzVVUvyWaSma0HMLP1kvYrUWYK8GJqvB04vleZk4ANZvZsatoMSY8CrwFfNLNfVzHuftWiIaqxubln/ZkMTXE8k8lQyOfJZrN845ZbmH7wwTsst/iyyxg/aRI3/u53FItFTmxp2T6vKbXOTDZLIZ+vQeTOuZFsj90gIOlXkh4vMcwrdxUlpvX+3eR5wI9T4+uBaWZ2NPA/gBsljekjvgWSlktavmnTpjJD6t9gPBb6hDPO4OYrr8QsHJqnH30UgDe2bGHfyZPJZDIs+/d/p1AoDEJ0zrmRao8lGzM73cyOKDHcBmyQNBkgvm4ssYp24IDU+FRgXTIiqQH4AOFGg2SbOTPbHN+vAH4PvLWP+BabWZuZtU2cOHH3djYajFv9LvjSl8h3d3PerFl88IgjuOZLXwLgLy+6iP973XXMP+EEXnjmGVr32msQonPOjVRKzoAHNQjpW8Dm1A0C483sc73KNADPAKcBLwEPAx8ys9Vx/lzgC2b2jtQyE4GXzawg6UDg18CRZvZyf/G0tbXZ8njxPPHkk09y6KGHVrRfa4BXK1pi6PvTk09yZoXHyTlXP8aw4x1WlZC0wszaSs2rl9/ZLALeJelZwt1myS3N+0taBmBmeeBi4E7C7cw3J4kmOpcdm9AATgZWSfodoRPmCwdKNNVULwfXOecGW13cIBCbuk4rMX0dcFZqfBmwrI91/HWJabcAt1Qt0AoNxjUb55yrR37yXUN+cJ1zLvDvwxrymo1zzgWebGoog/eP5pxz4MmmpjzROOdc4Mmmhhrjaz0e5I+fcgpPxNu7LznrLF5/9dXBDcg5N6zVxd1ow9U4YBTwBuG+9TeAPD2PjC63R+ha++6ykjf4Oedc1Xiy2RV3LIQ/PjZgMQEtcdg3TkuSTDIk41vfdCQvnLkIxWkZQs0oXb49PmLgqF14xEBnRwdfmz+fPzzxBNMPPZRcR8f2ON83fTrXL1/O2H335bNnn82GF18k19nJuZdcwgcWLADg5L335txLLuH+22+nubWVy2+7jQmTJlXjaDrnRgBPNnuYCHep9b5TbRQwHugmJJlSd7KNAV5cs4brf/pTpi1ezFnHHcddN97ID++/n3vjIwYOPOww5px6KpfFRwx8dM4c3nb66dz6/e/TOmoUP1m1imdWreLDxxxTMr4vLVnCPuPH09nRwfnHHcepf/EXjJ0wgY6tWznihBO46Otf54rPfY5f/OAHXPDFL1bvwDjnhjVPNrvizEU1WW2pJNR7/owZMzgpPmLg2MMP54zTTuMYibFHHsn1a9eyqr2dh5Yu5SeXX44Bhc5OGl54gafuu48Fn/40bwamzJrFIbNmMQpoiutWHH5yxRXce+utAGx48UVeevZZJkyYQGNTEye95z0AHHLssfz2rrtqcQicc8OUJ5shpjn9OIBMZvt4JpMhHx8xcOstt3Bwr0cMNAJjJCbE8SbCY1FnxfdHAI/dey9P/OpXrHzwQUaNGsU7TjmFN3V2ciDQ2NjIFIkcsHc2C/k8jYSaWD3cACGgmZCQO6if62HOucCTzTBzxhlncOWVV3LllVciiUcffZSjjz6ak08+mRtuuIF3vvOdPP7446xatWqH5bLA1i1bGDduHKNGjeKpp57iNw89RDOwD+HLfP9Ydn/Cs7ePInypd8YhD3QBn47rayEksuQ6VAHIEZLBNuB5Qjfc6+NyDXG5JHklt45bHLpjmYnAfoRmxX0IzY9T47RRhIcePUV4vvjrcVu5WHYiMCHG+yrhIUfJwxaSJs6GOHTG5d+I6+iMsWVj2eR9JsbWwc7PvHDOBZ5shpkvfelLXHrppcyaNQszY/r06dx+++184hOfYP78+cyaNYvZs2czZ86cnZadO3cu11xzDbNmzeLggw/mhBNOGHB7GcIX/Kg4vhn47i7EnSd8WXcQvriTmyeMnubFvYHRu7DuajFCfEli7QZeISS3FwjJc0183Rzn5wmJ7g1CQk1qX0nC6ms7yZAk6HqvqWWAvei507IQ3yfjyYlI+rF8SdNtkqDLSdRN9PxYOt3sbHFa+ngln6E9oTkOittN/u5ZoJXw/5GcmGQJd6ruRzhR+hPhhOvluGxyTLYQPkMtqfWmj2v6R+Pp/ez9uUpuMsrH5dMndaXKNlIbdfGIgXpTrUcMjER+nPrWTahNbSUkn630fAHkU0M34Yuqk1CjepnwTI2XCMltC6HGla6RtRJqm+Pja1LrKxAS38txe7k4QEjcY+KyhdT6WgmJozHGmdTsMvScnb4e96WD0Bx7DHAIMCnGsE+ctykOL8fY/xS300r4Ek2SEXE7G2L5N1LHrZlQm55G+JJuinE0E2qp+8Z4NxK+tDfSk9hycX0bYwwNcbvN9NSgLW7v5XhskxOKXCzTkoq3KQ6jUsdvv3gMpgBvijElf4eeRu9d8wbh7w49d7YW6Gkd2EbP5ylJREntvDnG2kr4e+wTx5Pyb6TWkYvlW4DJwFt2Md7+HjHgNRvn9pBGQjNedR7N50aCvYGDByxVmZKPKt4D6uHarnPOuWHOk00FvMmxf358nHN98WRTppaWFjZv3uxfqH0wMzZv3kxLS8tgh+Kcq0N1cc1G0njgJ4TrbGuBvzKzV0qUWwK8B9hoZkeUs7ykLwAXEK6ffdrM7tyVGKdOnUp7ezubNm3alcVHhJaWFqZOnTrYYTjn6lBd3I0m6ZvAy2a2SNJCYJyZfb5EuZMJN1Fc3yvZlFxe0mHAj4E5hBtafgW81cwKvdedVupuNOecc/3r7260emlGmwdcF99fB5xdqpCZ3Ue4Q7Hc5ecBN5lZzsz+QPgZxM4/MHHOOVdT9ZJsJpnZeoD4ul+Vlp9C+M1doj1O24mkBZKWS1ruTWXOOVdde+yajaRfEX7z1Ns/1HKzJaaVbDc0s8XAYgjNaDWMyTnnRpw9lmzM7PS+5knaIGmyma2XNJnwg99K9LV8O3BAqtxUYN1AK1uxYsWfJD1fYQxp+xJ+LD2SjMR9hpG5377PI0el+/3mvmbUxd1owFLgfGBRfL2tSssvBW6U9B3CDQIzgd8OtDIz260feUta3tdFsuFqJO4zjMz99n0eOaq53/VyzWYR8C5JzwLviuNI2l/S9mcWS/ox8CBwsKR2SRf0t7yZrQZuBp4A/gP45EB3ojnnnKu+uqjZmNlm4LQS09cBZ6XGz6tk+Tjv68DXqxOpc865XVEvNZvhZvFgBzAIRuI+w8jcb9/nkaNq+10XP+p0zjk3vHnNxjnnXM15snHOOVdznmyqSNJcSU9LWhP7aBt2JB0g6R5JT0paLemSOH28pLskPRtfxw12rLUgKSvpUUm3x/Fhvd+Sxkr6maSn4t/8bcN9nwEk/W38fD8u6ceSWobjfktaImmjpMdT0/rcT0lfiN9vT0s6o5JtebKpEklZ4CrgTOAw4LzYEehwkwc+Y2aHAicAn4z7uRC428xmAnfH8eHoEuDJ1Phw3+/vAv9hZocARxH2fVjvs6QpwKeBttjhbxY4l+G53/8bmNtrWsn9jP/n5wKHx2X+NX7vlcWTTfXMAdaY2XNm1gXcROgIdFgxs/Vm9kh8/zrhy2cKZXamOpRJmgr8OXBtavKw3W9JY4CTgX8DMLMuM3uVYbzPKQ1Aq6QGYBSh55Fht999dG5ck46NPdlUT9mdfg4XkqYDRwO/Yfc7Ux0K/hfwOaCYmjac9/tAYBPww9h0eK2kvRje+4yZvQRcDrwArAe2mNkvGeb7nbLbHRuX4smmesru9HM4kLQ3cAtwqZm9Ntjx1Jqk5KF9KwY7lj2oATgGuNrMjga2MjyajvoVr1HMA2YQurnaS9KHBzequrBb33GebKpnlzr9HIokNRISzQ1m9vM4eUPsBJVd7Ey13r0deJ+ktYQm0lMl/Yjhvd/tQLuZ/SaO/4yQfIbzPgOcDvzBzDaZWTfwc+DPGP77nehrP3frO86TTfU8DMyUNENSE+FC2tJBjqnqJInQhv+kmX0nNSvpDBV2rTPVumZmXzCzqWY2nfC3/X9m9mGG8X6b2R+BFyUdHCedRuhncNjuc/QCcIKkUfHzfhrh2uRw3+9EX/u5FDhXUrOkGZTZsXHCexCoIklnEdr1s8CS2C/bsCLpRODXwGP0XLv4e8J1m5uBaYR/1nPMrNRTVYc8SacAnzWz90iawDDeb0mzCTdENAHPAfMJJ6nDdp8BJH0V+CDh7stHgY8BezPM9jt2bnwK4VECG4CvAL+gj/2U9A/A3xCOy6VmdkfZ2/Jk45xzrta8Gc0551zNebJxzjlXc55snHPO1ZwnG+ecczXnycY551zNebJxQ5Ikk/Tt1PhnJV1WpXX/b0l/WY11DbCdc2JPyvfUcBv/tSvzqrDdsZIuqtX63dDjycYNVTngA5L2HexA0irpBRe4ALjIzN5ZpfXtxMz+rK91lppXRWMBTzZuO082bqjKE56P/re9Z/SumUh6I76eIuk/Jd0s6RlJiyT9N0m/lfSYpINSqzld0q9juffE5bOSviXpYUmrJH08td57JN1I+LFr73jOi+t/XNI34rQvAycC10j6Vq/yO6yvn+3+q6T3xfe3SloS318g6X+W2PcdYuw17171PLfmhvjLeSSdFafdL+kKxef49Ir38HgMV8b4ZgKLgIPitG/Fcn+X2oevxmnT4/qvi9N/JmlUnLdI0hNx+uV9fRDc0NAw2AE4txuuAlZJ+mYFyxwFHEroVv054Fozm6PwELhPAZfGctOBdwAHAfdIegvwUUIPwMdJagYekPTLWH4OcETsen07SfsD3wCOBV4BfinpbDP7mqRTCT0RLC8R5/b1SVrQx3bvA04idCMyBZgclz2R0H9bn+ssMe9ownNK1gEPAG+XtBz4PnByjOPHJZYDuBD4rpndoNBVU5bQYecRZjY7Hod3E7o3mUPo0HGppJMJv1A/GLjAzB6ICfOi+Pp+4BAzM0lj+9i2GyK8ZuOGrNjb9PWEB12V6+H4TJ4c8HsgSRaPERJM4mYzK5rZs4SkdAjwbuCjklYSuueZQPgCBfhtH1/ixwH3xk4d88ANhGfEDCS9vr62+2vgJIWHWj1BTweKbwNKXY/pK8ZkXruZFYGVhGNxCPBcapm+ks2DwN9L+jzwZjPrKFHm3XF4FHgkrjs5di+a2QPx/Y8IyfI1oBO4VtIHgG19bNsNEV6zcUPd/yJ8ef0wNS1PPJGKzUFNqXm51PtiarzIjv8PvftxMsIZ+afM7M70DIW+0rb2EV+pbtnLkV5fye3GbY8jPDXxPmA88FfAG/HBdv2ts7f0cSkQjkVZsZvZjZJ+Q3iw3J2SPkZI0DuECvyzmX2/V/zTKXGszSwvaQ6hE8xzgYuBU8uJx9Unr9m4IS12EHgz4WJ7Yi2h2QrCc0kad2HV50jKxOs4BwJPA3cCn1B4xAKS3qrwMLH+/AZ4h6R944X584D/rDCW/rb7IKHp7z5CTeez8bUangIOjAkBQseUO5F0IKEGdAWhSW8W8Dowutc+/I3Cc5CQNEVS8lCuaZLeFt+fB9wfy+1jZssI+ze7SvvkBonXbNxw8G3CmW/iB8Btkn5LeIZ6f2f0fXmakBQmAReaWaekawnNS4/EGtMmBng0sJmtl/QF4B7C2f0yM6u0a/r+tvtr4N1mtkbS84TaTVWSjZl1KNy+/B+S/kTf3cl/EPiwpG7gj8DXzOxlSQ9Iehy4w8z+TtKhwIPx3oM3gA8TalFPAudL+j7wLHA1sA/hb9hCOG473Qjihhbv9dk51ydJe5vZGzHJXQU8a2b/UsX1TwduN7MjqrVOV5+8Gc0515//Hm9MWE2obXy//+LOleY1G+ecczXnNRvnnHM158nGOedczXmycc45V3OebJxzztWcJxvnnHM19/8BBDtCjh5YW+EAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plotDistanceTrajectory(G)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8d20c33c-1cfa-48e9-89d8-999b60f4552a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1 % 9" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fdb39935-073a-47dd-85fb-bb543ea77c75", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "10 % 9" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45368b18-c092-4b1a-9918-1fd128c5d7f0", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 76acd2adab8811f95b3b17d7091c720034019055 Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Tue, 19 Jul 2022 16:03:06 -0400 Subject: [PATCH 26/72] network properties over time --- netrw/analysis/properties_overtime.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index 11be654..4f8b293 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -25,6 +25,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): property_dict = {} rw = rewire_method() + for i in range(numit): G0 = deepcopy(init_graph) property_list = [property1(G0)] # calculate property of initial network @@ -34,11 +35,12 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): property_dict[i] = property_list - alllist = [] # list of all properties + alllist = [] # list of all properties for all iterations at each time step for k in range(tmax): alllist.append([]) for l in range(numit): alllist[k].append(property_dict[l][k]) + # find mean and standard deviation over different iterations of rewiring process meanlist = [] sdlist = [] From cce4ed34a525313e4a4518b9e58cc3f91cdac69b Mon Sep 17 00:00:00 2001 From: Alice Schwarze Date: Tue, 19 Jul 2022 16:15:22 -0400 Subject: [PATCH 27/72] distance_trajecotry thing works now --- netrw/analysis/distance_trajectory.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/netrw/analysis/distance_trajectory.py b/netrw/analysis/distance_trajectory.py index 85ae7d8..7e78a98 100644 --- a/netrw/analysis/distance_trajectory.py +++ b/netrw/analysis/distance_trajectory.py @@ -42,8 +42,6 @@ def distanceTrajectory(G, distance=netrd.distance.Hamming, a dictionary of keyword arguments for the null model (?) ''' - - G0 = copy.deepcopy(G) # check whether input for num rewire in a number of rewiring steps (int) # or a list of steps @@ -64,6 +62,7 @@ def distanceTrajectory(G, distance=netrd.distance.Hamming, distance_function = lambda g1, g2: distfun(g1, g2, **distance_kwargs) for j in range(num_runs): + G0 = copy.deepcopy(G) for i in range(max(rewire_steps)): rewire_function(G0) data[i+1,j] = distance_function(G0, G) @@ -100,7 +99,9 @@ def plotDistanceTrajectory(G, distance=netrd.distance.Hamming, num_steps=100, # set line colors if linecolors is None: - tabcolors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple'] + tabcolors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', + 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', + 'tab:cyan'] linecolors = [tabcolors[i % len(tabcolors)] for i in range(len(show))] elif hasattr(linecolors, "__iter__"): if len(linecolors) != len(show): @@ -125,7 +126,7 @@ def plotDistanceTrajectory(G, distance=netrd.distance.Hamming, num_steps=100, elif s=='std-env': mean = np.mean(data, axis=1) std = np.std(data, axis=1) - env_data = [std-mean, std+mean] + env_data = [mean-std, mean+std] else: warnings.warn("Unknown summary statistic", s, "will be ignored.") From 812bd582f498d27b3fca6020adcdb962f1ba24f1 Mon Sep 17 00:00:00 2001 From: Alice Schwarze Date: Tue, 19 Jul 2022 16:15:31 -0400 Subject: [PATCH 28/72] Update playground.ipynb --- playground.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/playground.ipynb b/playground.ipynb index 30f7628..aff2c3f 100644 --- a/playground.ipynb +++ b/playground.ipynb @@ -181,7 +181,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] From 5f363d451ef7bad419b4d92dde6c5d4c58648043 Mon Sep 17 00:00:00 2001 From: nwlandry Date: Tue, 19 Jul 2022 16:22:13 -0400 Subject: [PATCH 29/72] updates --- netrw/__init__.py | 1 + netrw/rewire/networkXEdgeSwap.py | 4 +- netrw/rewire/robust_rewiring.py | 2 +- requirements.txt | 3 +- test.ipynb | 65 ++++++++++++++------------------ 5 files changed, 34 insertions(+), 41 deletions(-) diff --git a/netrw/__init__.py b/netrw/__init__.py index b6cfce4..61aedb3 100644 --- a/netrw/__init__.py +++ b/netrw/__init__.py @@ -9,3 +9,4 @@ from .analysis import * from .rewire import * +from .visualization import * \ No newline at end of file diff --git a/netrw/rewire/networkXEdgeSwap.py b/netrw/rewire/networkXEdgeSwap.py index 02cb41b..0ba51e1 100644 --- a/netrw/rewire/networkXEdgeSwap.py +++ b/netrw/rewire/networkXEdgeSwap.py @@ -18,12 +18,12 @@ class NetworkXEdgeSwap(BaseRewirer): """ - def rewire(self, G, copy_graph=True): + def full_rewire(self, G, timesteps=100, copy_graph=True): if copy_graph: G = copy.deepcopy(G) - nx.double_edge_swap(G, nswap=1) + nx.double_edge_swap(G, nswap=timesteps) return G diff --git a/netrw/rewire/robust_rewiring.py b/netrw/rewire/robust_rewiring.py index 89c9738..2aa6bf4 100644 --- a/netrw/rewire/robust_rewiring.py +++ b/netrw/rewire/robust_rewiring.py @@ -15,7 +15,7 @@ class RobustRewiring(BaseRewirer): Louzada, V. H. P., Daolio, F., Herrmann, H. J., & Tomassini, M. (2013). Smart rewiring for network robustness. Journal of Complex Networks, 1(2), 150–159. https://doi.org/10.1093/comnet/cnt010 """ - def robust_rewire(self, G, copy_graph=False, timesteps=1000, step_rewire=False): + def full_rewire(self, G, copy_graph=False, timesteps=1000, step_rewire=False): if copy_graph: G = copy.deepcopy(G) diff --git a/requirements.txt b/requirements.txt index 17ecdf0..6070d0c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ networkx>=2.0.0 numpy>=1.10.0 scipy>=1.0.0 - +netrd>=0.2 +matplotlib>=3.3 \ No newline at end of file diff --git a/test.ipynb b/test.ipynb index 50f8beb..8c55c66 100644 --- a/test.ipynb +++ b/test.ipynb @@ -7,54 +7,45 @@ "outputs": [], "source": [ "import netrw\n", - "from netrw.rewire import KarrerRewirer, AlgebraicConnectivity, NetworkXEdgeSwap\n", + "from netrw.analysis import dist_confusion_thang\n", + "from netrw.rewire import KarrerRewirer, AlgebraicConnectivity, NetworkXEdgeSwap, GlobalRewiring, RobustRewiring\n", + "from netrd.distance import Hamming, LaplacianSpectral, NonBacktrackingSpectral, ResistancePerturbation\n", "import networkx as nx" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "G = nx.fast_gnp_random_graph(100, 0.1)\n", + "rewiring_methods = [AlgebraicConnectivity, NetworkXEdgeSwap, GlobalRewiring]\n", + "distance_measures = [Hamming, LaplacianSpectral, NonBacktrackingSpectral, ResistancePerturbation]\n", + "dist_confusion_thang(G, rewiring_methods, distance_measures, timesteps=100, ensemble_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - " compilation 16:4: FutureWarning: laplacian_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0.\n", - " import inspect\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n" + "ename": "AttributeError", + "evalue": "module 'netrw' has no attribute 'get_property_distribution_choosing_chaos'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\nicho\\Documents\\GitHub\\netrw\\test.ipynb Cell 2\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m G \u001b[39m=\u001b[39m nx\u001b[39m.\u001b[39mfast_gnp_random_graph(\u001b[39m100\u001b[39m, \u001b[39m0.1\u001b[39m)\n\u001b[1;32m----> 3\u001b[0m p \u001b[39m=\u001b[39m netrw\u001b[39m.\u001b[39;49mget_property_distribution_choosing_chaos(G, AlgebraicConnectivity, nx\u001b[39m.\u001b[39massortativity\u001b[39m.\u001b[39mdegree_pearson_correlation_coefficient, burn_in\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m, num_samples\u001b[39m=\u001b[39m\u001b[39m20\u001b[39m)\n", + "\u001b[1;31mAttributeError\u001b[0m: module 'netrw' has no attribute 'get_property_distribution_choosing_chaos'" ] } ], "source": [ "G = nx.fast_gnp_random_graph(100, 0.1)\n", "\n", - "p = netrw.get_property_distribution_choosing_chaos(G, AlgebraicConnectivity, nx.assortativity.degree_pearson_correlation_coefficient, burn_in=10, num_samples=20)" + "p = netrw.get_property_distribution(G, AlgebraicConnectivity, nx.assortativity.degree_pearson_correlation_coefficient, burn_in=10, num_samples=20)" ] }, { @@ -159,7 +150,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.10.5 64-bit", + "display_name": "Python 3.10.4 ('netrw')", "language": "python", "name": "python3" }, @@ -173,12 +164,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.10.4" }, "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "88880b30d22c3e82d444bc5a40c679bbf2837a658927a3eed0b1fab0c002b813" + "hash": "03db511f66ea892806a1991ef134c9aefc3c132affad238c8c8346a021ec52c3" } } }, From 93178c5490ff858ce6f8bc6a40d0d85cc0180621 Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Tue, 19 Jul 2022 16:24:04 -0400 Subject: [PATCH 30/72] change plot label --- netrw/analysis/properties_overtime.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index 4f8b293..a1d9b89 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -61,8 +61,8 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): ax0.plot(range(tmax), upperbd, color = 'blue') ax0.plot(range(tmax), lowerbd, color = 'blue' ) ax0.fill_between(range(tmax), upperbd,lowerbd, color='cornflowerblue',alpha=.5) - ax0.set_xlabel('time step', fontsize=15) - ax0.set_ylabel('Mean property value', fontsize=15) + ax0.set_xlabel('number of rewiring steps', fontsize=15) + ax0.set_ylabel('Mean '+ property1.__name__, fontsize=15) fig.show() From 3e5bd52f44461d6a2ab93f7246734f34431aeeff Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Tue, 19 Jul 2022 16:50:55 -0400 Subject: [PATCH 31/72] change axes --- netrw/analysis/properties_overtime.py | 1 + 1 file changed, 1 insertion(+) diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index a1d9b89..075c322 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -60,6 +60,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): ax0.plot(range(tmax), meanlist, marker='o', color = 'blue') ax0.plot(range(tmax), upperbd, color = 'blue') ax0.plot(range(tmax), lowerbd, color = 'blue' ) + ax0.set_ylim(bottom=0, upper=None) ax0.fill_between(range(tmax), upperbd,lowerbd, color='cornflowerblue',alpha=.5) ax0.set_xlabel('number of rewiring steps', fontsize=15) ax0.set_ylabel('Mean '+ property1.__name__, fontsize=15) From ca9864636777c8f46eeba4f810819ff9df50bfac Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Tue, 19 Jul 2022 16:51:57 -0400 Subject: [PATCH 32/72] axes changes --- netrw/analysis/properties_overtime.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index 075c322..5eb2aa1 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -60,7 +60,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): ax0.plot(range(tmax), meanlist, marker='o', color = 'blue') ax0.plot(range(tmax), upperbd, color = 'blue') ax0.plot(range(tmax), lowerbd, color = 'blue' ) - ax0.set_ylim(bottom=0, upper=None) + ax0.set_ylim(0,None) ax0.fill_between(range(tmax), upperbd,lowerbd, color='cornflowerblue',alpha=.5) ax0.set_xlabel('number of rewiring steps', fontsize=15) ax0.set_ylabel('Mean '+ property1.__name__, fontsize=15) From d0c1577edc7ea395e81ba20b995a339e121eac4b Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Tue, 19 Jul 2022 16:52:48 -0400 Subject: [PATCH 33/72] undo change --- netrw/analysis/properties_overtime.py | 1 - 1 file changed, 1 deletion(-) diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index 5eb2aa1..a1d9b89 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -60,7 +60,6 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): ax0.plot(range(tmax), meanlist, marker='o', color = 'blue') ax0.plot(range(tmax), upperbd, color = 'blue') ax0.plot(range(tmax), lowerbd, color = 'blue' ) - ax0.set_ylim(0,None) ax0.fill_between(range(tmax), upperbd,lowerbd, color='cornflowerblue',alpha=.5) ax0.set_xlabel('number of rewiring steps', fontsize=15) ax0.set_ylabel('Mean '+ property1.__name__, fontsize=15) From 983ba29115b1d1aaa4ed844f448483e1f0d426fe Mon Sep 17 00:00:00 2001 From: nwlandry Date: Tue, 19 Jul 2022 17:10:26 -0400 Subject: [PATCH 34/72] updates --- netrw/rewire/__init__.py | 1 + netrw/rewire/global_rewiring.py | 4 ++-- test.ipynb | 37 +++++++++++++++++++++++++++------ 3 files changed, 34 insertions(+), 8 deletions(-) diff --git a/netrw/rewire/__init__.py b/netrw/rewire/__init__.py index 13b243a..67d62ad 100644 --- a/netrw/rewire/__init__.py +++ b/netrw/rewire/__init__.py @@ -3,5 +3,6 @@ from .algebraic_connectivity import AlgebraicConnectivity from .networkXEdgeSwap import NetworkXEdgeSwap from .global_rewiring import GlobalRewiring +from .robust_rewiring import RobustRewiring __all__ = [] diff --git a/netrw/rewire/global_rewiring.py b/netrw/rewire/global_rewiring.py index bbec43f..6727d3b 100644 --- a/netrw/rewire/global_rewiring.py +++ b/netrw/rewire/global_rewiring.py @@ -15,7 +15,7 @@ def full_rewire( """ Run a full rewire of the global edge rewiring. """ - return step_rewire(G, p, timesteps, tries, copy_graph, verbose) + return self.step_rewire(G, p, timesteps, tries, copy_graph, verbose) def step_rewire(self, G, p, timesteps=1, tries=100, copy_graph=True, verbose=False): """ @@ -101,7 +101,7 @@ def step_rewire(self, G, p, timesteps=1, tries=100, copy_graph=True, verbose=Fal G.add_edge(new_edge[0], new_edge[1]) if verbose: - return G, prev_edges, new_edges + return G#, prev_edges, new_edges else: return G diff --git a/test.ipynb b/test.ipynb index 8c55c66..81118e2 100644 --- a/test.ipynb +++ b/test.ipynb @@ -2,27 +2,52 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import netrw\n", "from netrw.analysis import dist_confusion_thang\n", - "from netrw.rewire import KarrerRewirer, AlgebraicConnectivity, NetworkXEdgeSwap, GlobalRewiring, RobustRewiring\n", + "from netrw.rewire import NetworkXEdgeSwap, GlobalRewiring, RobustRewiring\n", "from netrd.distance import Hamming, LaplacianSpectral, NonBacktrackingSpectral, ResistancePerturbation\n", "import networkx as nx" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\nicho\\miniconda3\\envs\\netrw\\lib\\site-packages\\netrd\\distance\\laplacian_spectral_method.py:256: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n", + " If increasing the limit yields no improvement it is advised to analyze \n", + " the integrand in order to determine the difficulties. If the position of a \n", + " local difficulty can be determined (singularity, discontinuity) one will \n", + " probably gain from splitting up the interval and calling the integrator \n", + " on the subranges. Perhaps a special-purpose integrator should be used.\n", + " return quad(integrand, a, b)[0]\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[0. , 0. , 0. , 0. ],\n", + " [0.122944 , 0.1225733 , 0.12312975, 0.11393781]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "G = nx.fast_gnp_random_graph(100, 0.1)\n", - "rewiring_methods = [AlgebraicConnectivity, NetworkXEdgeSwap, GlobalRewiring]\n", + "rewiring_methods = [RobustRewiring, NetworkXEdgeSwap]\n", "distance_measures = [Hamming, LaplacianSpectral, NonBacktrackingSpectral, ResistancePerturbation]\n", - "dist_confusion_thang(G, rewiring_methods, distance_measures, timesteps=100, ensemble_size=10)\n" + "dist_confusion_thang(G, rewiring_methods, distance_measures, timesteps=10, ensemble_size=10)\n" ] }, { From 8525a4723536eafb48c94389dd62718d030c1e39 Mon Sep 17 00:00:00 2001 From: Alice Schwarze Date: Tue, 19 Jul 2022 17:12:06 -0400 Subject: [PATCH 35/72] Added docstring to plotDistanceTrajectory --- netrw/analysis/distance_trajectory.py | 41 ++++++++++++++++++++++++++ playground.ipynb | 42 --------------------------- 2 files changed, 41 insertions(+), 42 deletions(-) diff --git a/netrw/analysis/distance_trajectory.py b/netrw/analysis/distance_trajectory.py index 7e78a98..8c35b82 100644 --- a/netrw/analysis/distance_trajectory.py +++ b/netrw/analysis/distance_trajectory.py @@ -74,6 +74,47 @@ def plotDistanceTrajectory(G, distance=netrd.distance.Hamming, num_steps=100, show=['mean', 'median', 'std-env'], labels=None, add_legend=True, fig=None, ax=None, linecolors=None, envcolor='cyan', xlabel='Number of rewiring steps', ylabel=None, **kwargs): + ''' + Make a nice plot of how a graph distance changes as a function of number of + rewiring steps. + + Parameters + ---------- + G : a networkx Graph or DiGraph + + distance : netrd distance class (default: Hamming distance) + Distance to be tracked over time + + num_steps : integer + Number of rewiring steps + + show : list of strings + A list of strings indicating + + labels : list of strings + A list of labels for the shown in plot legend. + + add_legend : bool + If True, add a legend to the output plot. + + fig : matplotlib figure (default=None) + Figure into which the results should be drawn. + + ax : matplotlib axes (default=None) + Axes into which results should be drawn. + + linecolors : list (default=None) + List of color strings for lines plotted. + + envcolor : string + Color of the shaded region + + xlabel : string (default='Number of rewiring steps') + Label to go on the x axis + + ylabel : string (default=Name of distance class) + Label to go on the y axis + ''' # check whether input for num steps in a number of rewiring steps (int) # or a list of steps diff --git a/playground.ipynb b/playground.ipynb index aff2c3f..ced4997 100644 --- a/playground.ipynb +++ b/playground.ipynb @@ -196,48 +196,6 @@ "plotDistanceTrajectory(G)" ] }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8d20c33c-1cfa-48e9-89d8-999b60f4552a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "1 % 9" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "fdb39935-073a-47dd-85fb-bb543ea77c75", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "10 % 9" - ] - }, { "cell_type": "code", "execution_count": null, From 82076bf9a14d591390421a311abb120320c9ba1b Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Wed, 20 Jul 2022 10:16:12 -0400 Subject: [PATCH 36/72] documentation and step rewire addition --- netrw/analysis/properties_overtime.py | 45 +++++++++++++++++---------- 1 file changed, 29 insertions(+), 16 deletions(-) diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index a1d9b89..f320c9b 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -7,22 +7,34 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): - ''' - Look at network properties as rewiring method changes the network. - Input: - init_graph = original graph that will be rewired - rewire_method = netrw method of rewiring that you want to implement that outputs a graph - property1 = property of interest, (ex. nx.average_clustering, nx.average_shortest_path_length, etc.) - that outputs a single value for a given network - tmax = amount of time steps (rewirings) - numit = number of iterations of rewiring the original graph using the method to see variation in results - - Output: - property_dict = dictionary of property values for each iteration for each step of the rewiring process - fig = plot of mean and standard deviation of property of interest at each step of rewiring process - ''' - + """ + Analyze the property values of a network as a function of rewire steps. + Looks at how a network property changes as a rewiring process occurs. + Parameters + ---------- + init_graph : NetworkX graph + Initial graph upon which rewiring will occur. + rewire_method : netrw rewire class object + Algorithm for rewiring a network with step_rewire option + property1 : NetworkX function + Network description property that outputs a single value for a given network. Should work with any function that + summarizes a NetworkX graph object into a single value. For example, nx.average_clustering, nx.average_shortest_path_length, etc. + tmax : int + Number of rewiring steps to perform for each iteration. + numit : int + Number of rewiring iterations to perform on the initial graph. The given rewiring process will be performed numit + times on the initial graph to look at the distribution of outcomes for this rewiring process on the initial graph. + Returns + ------- + property_dict: dictionary + Dictionary of output where keys are the iteration number and the values are a list of the network property calculated + at each step of the rewiring process. + fig: matplotlib figure + Plot of the mean value of the given network property at each step of the rewiring process, where shading within + the upper and lower bounds represent the standard deviation of the property value around the mean. + """ + property_dict = {} rw = rewire_method() @@ -30,7 +42,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): G0 = deepcopy(init_graph) property_list = [property1(G0)] # calculate property of initial network for j in range(tmax): - G0 = rw.rewire(G0, copy_graph=False) #rewire + G0 = rw.step_rewire(G0, copy_graph=False) #rewire property_list.append(property1(G0)) #calculate property of the rewired network property_dict[i] = property_list @@ -68,3 +80,4 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): return property_dict, fig + From 85270fbe067f43f4be109533142bf82af21b174f Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Wed, 20 Jul 2022 10:22:57 -0400 Subject: [PATCH 37/72] added documentation --- netrw/analysis/properties_overtime.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index f320c9b..196b643 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -28,7 +28,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): Returns ------- property_dict: dictionary - Dictionary of output where keys are the iteration number and the values are a list of the network property calculated + Dictionary of output where the keys are the iteration number and the values are a list of the network property calculated at each step of the rewiring process. fig: matplotlib figure Plot of the mean value of the given network property at each step of the rewiring process, where shading within @@ -47,7 +47,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): property_dict[i] = property_list - alllist = [] # list of all properties for all iterations at each time step + alllist = [] # list of all properties for all iterations at each of the time steps for k in range(tmax): alllist.append([]) for l in range(numit): From bc26b0296f705ed215cf353de1296d240e03938f Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Wed, 20 Jul 2022 11:01:29 -0400 Subject: [PATCH 38/72] removed plotting --- netrw/analysis/properties_heatmap.py | 0 netrw/analysis/properties_overtime.py | 15 +--- test.ipynb | 104 ++++++++++++++++++++++++++ 3 files changed, 105 insertions(+), 14 deletions(-) create mode 100644 netrw/analysis/properties_heatmap.py create mode 100644 test.ipynb diff --git a/netrw/analysis/properties_heatmap.py b/netrw/analysis/properties_heatmap.py new file mode 100644 index 0000000..e69de29 diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index 196b643..d6abd01 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -67,17 +67,4 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): upperbd.append(meanlist[a]+sdlist[a]) lowerbd.append(meanlist[a]-sdlist[a]) - # plot mean and standard deviation for chosen property for the given time steps of rewiring - fig, (ax0) = plt.subplots(nrows=1) - ax0.plot(range(tmax), meanlist, marker='o', color = 'blue') - ax0.plot(range(tmax), upperbd, color = 'blue') - ax0.plot(range(tmax), lowerbd, color = 'blue' ) - ax0.fill_between(range(tmax), upperbd,lowerbd, color='cornflowerblue',alpha=.5) - ax0.set_xlabel('number of rewiring steps', fontsize=15) - ax0.set_ylabel('Mean '+ property1.__name__, fontsize=15) - - fig.show() - - return property_dict, fig - - + return property_dict \ No newline at end of file diff --git a/test.ipynb b/test.ipynb new file mode 100644 index 0000000..cdd148e --- /dev/null +++ b/test.ipynb @@ -0,0 +1,104 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import netrw\n", + "import numpy as np\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "from netrw.rewire import KarrerRewirer, AlgebraicConnectivity, NetworkXEdgeSwap\n", + "from netrw.analysis import properties_overtime\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "G = nx.erdos_renyi_graph(100,.15)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "G0 = NetworkXEdgeSwap().rewire(G)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "rewire() got an unexpected keyword argument 'nswap'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/clara/netrw/test.ipynb Cell 4'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m property_dict, fig \u001b[39m=\u001b[39m properties_overtime\u001b[39m.\u001b[39;49mproperties_overtime(init_graph\u001b[39m=\u001b[39;49mG, rewire_method\u001b[39m=\u001b[39;49m NetworkXEdgeSwap(), property1\u001b[39m=\u001b[39;49m nx\u001b[39m.\u001b[39;49maverage_clustering, tmax\u001b[39m=\u001b[39;49m\u001b[39m100\u001b[39;49m, numit\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m)\n", + "File \u001b[0;32m~/netrw/netrw/analysis/properties_overtime.py:29\u001b[0m, in \u001b[0;36mproperties_overtime\u001b[0;34m(init_graph, rewire_method, property1, tmax, numit)\u001b[0m\n\u001b[1;32m 27\u001b[0m property_list \u001b[39m=\u001b[39m [property1(G0)] \u001b[39m# calculate property of initial network\u001b[39;00m\n\u001b[1;32m 28\u001b[0m \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(tmax):\n\u001b[0;32m---> 29\u001b[0m rewire_method(G0, nswap\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m) \u001b[39m#rewire \u001b[39;00m\n\u001b[1;32m 30\u001b[0m property_list\u001b[39m.\u001b[39mappend(property1(G0)) \u001b[39m#calculate property of the rewired network\u001b[39;00m\n\u001b[1;32m 31\u001b[0m property_dict[i] \u001b[39m=\u001b[39m property_list\n", + "File \u001b[0;32m~/netrw/netrw/rewire/base.py:13\u001b[0m, in \u001b[0;36mBaseRewirer.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m---> 13\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrewire(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", + "\u001b[0;31mTypeError\u001b[0m: rewire() got an unexpected keyword argument 'nswap'" + ] + } + ], + "source": [ + "property_dict, fig = properties_overtime.properties_overtime(init_graph=G, rewire_method= NetworkXEdgeSwap(), property1= nx.average_clustering, tmax=100, numit=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "module" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(properties_overtime)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "abdefde89911577035689be2b705da6a7f51bdbfe4520de839bf860af06394b4" + }, + "kernelspec": { + "display_name": "Python 3.9.7 ('base')", + "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": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From f0752fc2cf5e1af6f03238ab73c093573bfae06f Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Wed, 20 Jul 2022 11:04:58 -0400 Subject: [PATCH 39/72] updated docs --- netrw/analysis/properties_overtime.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index 2eede36..d93fe81 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -30,9 +30,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): property_dict: dictionary Dictionary of output where the keys are the iteration number and the values are a list of the network property calculated at each step of the rewiring process. - fig: matplotlib figure - Plot of the mean value of the given network property at each step of the rewiring process, where shading within - the upper and lower bounds represent the standard deviation of the property value around the mean. + """ property_dict = {} From 3965885931a87370bc586a0b65a7ecf757658ab2 Mon Sep 17 00:00:00 2001 From: hartle <32047935+hartle@users.noreply.github.com> Date: Wed, 20 Jul 2022 09:07:44 -0600 Subject: [PATCH 40/72] Add notebook measuring various network properties over time --- evaluate_rewiring.ipynb | 279 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 279 insertions(+) create mode 100644 evaluate_rewiring.ipynb diff --git a/evaluate_rewiring.ipynb b/evaluate_rewiring.ipynb new file mode 100644 index 0000000..d2bd2d3 --- /dev/null +++ b/evaluate_rewiring.ipynb @@ -0,0 +1,279 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "f12ade0a-2052-4762-9337-fe8ef0ddb1a9", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import networkx as nx\n", + "import netrw\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def run_rewiring(G0,T,method, **kwargs):\n", + " G = [G0]\n", + " for t in range(1,T):\n", + " G.append(method.step_rewire(G[-1], **kwargs))\n", + " return G\n", + "\n", + "def basic_metrics(G):\n", + " # G: a single networkx object\n", + " n = G.number_of_nodes()\n", + " m = G.number_of_edges()\n", + " l = average_shortest_path_length(G)\n", + " NC = nx.number_connected_components(G)\n", + " rho = nx.assortativity.degree_assortativity_coefficient(G)\n", + " k = np.array(list(dict(nx.degree(G)).values()))\n", + " k2 = np.sum(k**2)/n\n", + " kmin = np.min(k)\n", + " kmax = np.max(k)\n", + " c = average_local_clustering(G)\n", + " return n,m,l,NC,rho,k2,kmin,kmax,c\n", + "\n", + "def gather_metrics_over_time(G):\n", + " # G: a list of networkx objects\n", + " T = len(G)\n", + " n_ = np.zeros(T,dtype=int)\n", + " m_ = np.zeros(T,dtype=int)\n", + " l_ = np.zeros(T)\n", + " NC_ = np.zeros(T,dtype=int)\n", + " rho_ = np.zeros(T)\n", + " k2_ = np.zeros(T)\n", + " kmin_ = np.zeros(T)\n", + " kmax_ = np.zeros(T)\n", + " c_ = np.zeros(T)\n", + " for t,g in enumerate(G):\n", + " n_[t],m_[t],l_[t],NC_[t],rho_[t],k2_[t],kmin_[t],kmax_[t],c_[t] = basic_metrics(g)\n", + " return n_,m_,l_,NC_,rho_,k2_,kmin_,kmax_,c_\n", + "\n", + "def average_local_clustering(G):\n", + " k = np.array(list(dict(nx.degree(G)).values()))\n", + " n_kg1 = np.sum(k>1)\n", + " clu = nx.clustering(G)\n", + " tot = np.sum(list(nx.clustering(G).values()))\n", + " barc = tot/n_kg1\n", + " return barc\n", + "\n", + "def average_shortest_path_length(G):\n", + " C = list(nx.connected_components(G))\n", + " Nv = list(map(len,C))\n", + " Npairs = np.sum([N*(N-1)/2 for N in Nv])\n", + " total = np.sum([np.sum(list(v[1].values())) for v in nx.all_pairs_shortest_path_length(G)])\n", + " barl = total/Npairs\n", + " return barl" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1125353d-a199-4f68-8085-8f9c145d02ab", + "metadata": {}, + "outputs": [], + "source": [ + "n = 100\n", + "m = 2\n", + "G0 = nx.barabasi_albert_graph(n,m)\n", + "\n", + "T = 1000\n", + "\n", + "p = 0.8\n", + "method = netrw.rewire.DegreeAssortativeRewirer()\n", + "assortative = True\n", + "G = run_rewiring(G0,T,method, p=p,assortative=assortative)\n", + "\n", + "n_,m_,l_,NC_,rho_,k2_,kmin_,kmax_,c_ = gather_metrics_over_time(G)\n", + "\n", + "names = ['Number of nodes',\n", + " 'Number of edges',\n", + " 'Average shortest path length',\n", + " 'Number of components',\n", + " 'Degree correlation coefficient',\n", + " 'Second moment of degree distribution',\n", + " 'Minimum degree',\n", + " 'Maximum degree',\n", + " 'Average local clustering coefficient']\n", + "\n", + "props = [n_,m_,l_,NC_,rho_,k2_,kmin_,kmax_,c_]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f33473e4-3b7b-47cd-8bba-ad6cde2cc431", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for j,(prop,name) in enumerate(zip(props,names)):\n", + " fi,ax = plt.subplots(1,figsize=(5,2),dpi=200)\n", + " plt.plot(range(T),prop,lw=1,color='purple')\n", + " plt.title(name)\n", + " plt.xlabel('$t$')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34b7062c-5241-48fc-afc4-661f09cbbe92", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a70d64e-4e87-43d8-b241-f44b47cead7c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58196456-2dfb-4a7f-a906-718907f2c944", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fee84955-d947-4add-865f-e087c5801edd", + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 9ebadd458663171833b4948da7ab6bcd5f5e0d29 Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Wed, 20 Jul 2022 11:26:43 -0400 Subject: [PATCH 41/72] separate plot function for properties over time --- .../plot_property_values_over_time.py | 53 +++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 netrw/analysis/plot_property_values_over_time.py diff --git a/netrw/analysis/plot_property_values_over_time.py b/netrw/analysis/plot_property_values_over_time.py new file mode 100644 index 0000000..63d528c --- /dev/null +++ b/netrw/analysis/plot_property_values_over_time.py @@ -0,0 +1,53 @@ +import numpy as np +import networkx as nx +import matplotlib.pyplot as plt +import netrw + +def plot_property_values_over_time(propvals, ylabel ): + """ + Plot how a network property changes over time during a rewiring process. + + Parameters + ---------- + propvals : NetworkX graph + Initial graph upon which rewiring will occur. + + Returns + ------- + property_dict: dictionary + Dictionary of output where the keys are the iteration number and the values are a list of the network property calculated + at each step of the rewiring process. + + """ + alllist = [] # list of all properties for all iterations at each of the time steps + for k in range(len(propvals[0])): + alllist.append([]) + for l in range(len(list(propvals.keys()))): + alllist[k].append(propvals[l][k]) + + # find mean and standard deviation over different iterations of rewiring process + meanlist = [] + sdlist = [] + for k in range(len(propvals[0])): + meanlist.append(np.mean(alllist[k])) + sdlist.append(np.std(alllist[k])) + + # find upper and lower bound of standard deviation interval around the mean + upperbd = [] + lowerbd = [] + for a in range(len(meanlist)): + upperbd.append(meanlist[a]+sdlist[a]) + lowerbd.append(meanlist[a]-sdlist[a]) + + fig, (ax0) = plt.subplots(nrows=1) + ax0.plot(range(len(propvals[0])), meanlist, color = 'blue') + ax0.plot(range(len(propvals[0])), upperbd, color = 'blue') + ax0.plot(range(len(propvals[0])), lowerbd, color = 'blue') + ax0.fill_between(range(len(propvals[0])),upperbd, lowerbd, color = 'cornflowerblue') + + ax0.set_xlabel('number of rewiring steps') + ax0.set_ylabel(ylabel) + + fig.show() + + return fig \ No newline at end of file From f114fc4d66c193b80f456422448f2a74c4a97a87 Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 11:28:45 -0400 Subject: [PATCH 42/72] updates --- netrw/analysis/confusion.py | 42 +++++++++++++++++++++++++++++++++ netrw/analysis/distributions.py | 27 +++++++++++++++++++-- 2 files changed, 67 insertions(+), 2 deletions(-) create mode 100644 netrw/analysis/confusion.py diff --git a/netrw/analysis/confusion.py b/netrw/analysis/confusion.py new file mode 100644 index 0000000..33f94a4 --- /dev/null +++ b/netrw/analysis/confusion.py @@ -0,0 +1,42 @@ +import numpy as np + +def rewiring_distance_confusion_matrix(G, rewiring_methods, distance_measures, timesteps=100, ensemble_size=10): + """Plotting distances from start graph for different rewiring schemes and distance metrics + + Parameters + ---------- + G : NetworkX Graph + The starting graph + rewiring_methods : list of Rewiring classes in the rewiring module. + methods for rewiring graphs. each class specified must have a + `full_rewire` method and that method must have `timesteps` as + a keyword argument. + distance_measures : netrd Distance + metric for measuring the distance between the before and after graphs + timesteps : int, default: 100 + the number of iterations + ensemble_size : int, default: 10 + the number of rewiring trajectories to run. + + Returns + ------- + numpy matrix + a matrix where rows are rewiring methods, columns are distance metrics + and entries are average distances. + + Notes + ----- + Currently this method does not support keyword args for the rewiring methods + and distance metrics. + """ + n = len(rewiring_methods) + m = len(distance_measures) + C = np.zeros([n, m]) + for i in range(n): + rw = rewiring_methods[i]() + for j in range(m): + dist = distance_measures[i]() + for k in range(ensemble_size): + rG = rw.full_rewire(G, timesteps=timesteps) + C[i, j] += dist(rG, G)/ensemble_size + return C \ No newline at end of file diff --git a/netrw/analysis/distributions.py b/netrw/analysis/distributions.py index c7b0518..68c3a27 100644 --- a/netrw/analysis/distributions.py +++ b/netrw/analysis/distributions.py @@ -1,13 +1,36 @@ from copy import deepcopy import numpy as np -def get_property_distribution(G, rewiring_method, property, skip=10, num_samples=1000): +def get_property_distribution(G, rewiring_method, property, skip=10, num_samples=1000, **kwargs): + """_summary_ + + More details + + Parameters + ---------- + G : NetworkX graph + The initial graph + rewiring_method : Rewire method + The class that will rewire the graph step-by-step. Must have the method `step_rewire`. + property : function + a function that accepts a NetworkX Graph object as an input. This computes a property of interest. + skip : int, default:100 + How often to store the property of interest. + num_samples : int, default: 1000 + The number of samples to form the empirical distribution. + **kwargs : optional keyword args for the rewiring method + + Returns + ------- + numpy array + an array of properties from each point outputted in the rewiring process. + """ G = deepcopy(G) rw = rewiring_method() properties = np.zeros(num_samples) for i in range(num_samples): for j in range(skip): - G = rw.rewire(G, copy_graph=False) + G = rw.step_rewire(G, copy_graph=False, **kwargs) if j >= skip - 1: properties[i] = property(G) print(i) From 8b94dd6437de061f621d67ee01c307b612da607c Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Wed, 20 Jul 2022 11:30:30 -0400 Subject: [PATCH 43/72] plot --- netrw/analysis/plot_property_values_over_time.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/netrw/analysis/plot_property_values_over_time.py b/netrw/analysis/plot_property_values_over_time.py index 63d528c..72735f7 100644 --- a/netrw/analysis/plot_property_values_over_time.py +++ b/netrw/analysis/plot_property_values_over_time.py @@ -3,14 +3,19 @@ import matplotlib.pyplot as plt import netrw -def plot_property_values_over_time(propvals, ylabel ): +def plot_property_values_over_time(propvals, ylabel = '' ): """ Plot how a network property changes over time during a rewiring process. Parameters ---------- - propvals : NetworkX graph - Initial graph upon which rewiring will occur. + propvals : Dictionary + Dictionary of output from properties_overtime. + The keys are the iteration number and the values are a list of the network property calculated + at each step of the rewiring process. + ylabel: string, optional + Label for y axis of graph for mean and standard deviation of network property + Default is no label. Returns ------- From 203ac9b3a10acd1a4655f05284c4061ae4bd7657 Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Wed, 20 Jul 2022 11:34:59 -0400 Subject: [PATCH 44/72] plots for properties_overtime --- netrw/analysis/plot_property_values_over_time.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/netrw/analysis/plot_property_values_over_time.py b/netrw/analysis/plot_property_values_over_time.py index 72735f7..46d5edc 100644 --- a/netrw/analysis/plot_property_values_over_time.py +++ b/netrw/analysis/plot_property_values_over_time.py @@ -19,9 +19,8 @@ def plot_property_values_over_time(propvals, ylabel = '' ): Returns ------- - property_dict: dictionary - Dictionary of output where the keys are the iteration number and the values are a list of the network property calculated - at each step of the rewiring process. + fig: matplotlib figure + Figure of mean and standard deviation for a network property throughout the rewiring process. """ alllist = [] # list of all properties for all iterations at each of the time steps From bc4d14e31bb584414003103ee4f1e2a238de9418 Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 11:35:57 -0400 Subject: [PATCH 45/72] style: format with black --- netrw/analysis/__init__.py | 2 +- netrw/analysis/confusion.py | 11 +- netrw/analysis/distance_trajectory.py | 137 ++++++++++++++----------- netrw/analysis/distributions.py | 7 +- netrw/analysis/properties_overtime.py | 37 +++---- netrw/rewire/algebraic_connectivity.py | 4 +- netrw/rewire/networkXEdgeSwap.py | 8 +- netrw/visualization/visualization.py | 11 +- setup.py | 21 ++-- 9 files changed, 131 insertions(+), 107 deletions(-) diff --git a/netrw/analysis/__init__.py b/netrw/analysis/__init__.py index 9703d54..8d3176c 100644 --- a/netrw/analysis/__init__.py +++ b/netrw/analysis/__init__.py @@ -1 +1 @@ -from .distributions import * \ No newline at end of file +from .distributions import * diff --git a/netrw/analysis/confusion.py b/netrw/analysis/confusion.py index 33f94a4..dbf6543 100644 --- a/netrw/analysis/confusion.py +++ b/netrw/analysis/confusion.py @@ -1,6 +1,9 @@ import numpy as np -def rewiring_distance_confusion_matrix(G, rewiring_methods, distance_measures, timesteps=100, ensemble_size=10): + +def rewiring_distance_confusion_matrix( + G, rewiring_methods, distance_measures, timesteps=100, ensemble_size=10 +): """Plotting distances from start graph for different rewiring schemes and distance metrics Parameters @@ -14,7 +17,7 @@ def rewiring_distance_confusion_matrix(G, rewiring_methods, distance_measures, t distance_measures : netrd Distance metric for measuring the distance between the before and after graphs timesteps : int, default: 100 - the number of iterations + the number of iterations ensemble_size : int, default: 10 the number of rewiring trajectories to run. @@ -38,5 +41,5 @@ def rewiring_distance_confusion_matrix(G, rewiring_methods, distance_measures, t dist = distance_measures[i]() for k in range(ensemble_size): rG = rw.full_rewire(G, timesteps=timesteps) - C[i, j] += dist(rG, G)/ensemble_size - return C \ No newline at end of file + C[i, j] += dist(rG, G) / ensemble_size + return C diff --git a/netrw/analysis/distance_trajectory.py b/netrw/analysis/distance_trajectory.py index 85ae7d8..535367d 100644 --- a/netrw/analysis/distance_trajectory.py +++ b/netrw/analysis/distance_trajectory.py @@ -5,77 +5,97 @@ import netrd from ..rewire import NetworkXEdgeSwap -def distanceTrajectory(G, distance=netrd.distance.Hamming, - rewire=NetworkXEdgeSwap, null_model=None, - num_steps=100, num_runs=100, distance_kwargs={}, - rewire_kwargs={}, null_model_kwargs={}, **kwargs): - ''' + +def distanceTrajectory( + G, + distance=netrd.distance.Hamming, + rewire=NetworkXEdgeSwap, + null_model=None, + num_steps=100, + num_runs=100, + distance_kwargs={}, + rewire_kwargs={}, + null_model_kwargs={}, + **kwargs +): + """ Get some data on graph distances as a function of number of rewiring steps. - + Parameters ---------- G : networkx Graph or DiGraph - + distance : netrd graph distance class - + rewire : netrw rewire class - + null_model : ??? - + num_steps : integer or list number of rewiring steps to be tracked or ordered list of rewiring steps to be tracked - + num_runs : integer - number of trajectories to be generated for evaluating the standard + number of trajectories to be generated for evaluating the standard deviation for a set of rewiring trajectories - + distance_kwargs : dictionary a dictionary of keyword arguments for an instantiation of the netrd distance class - + rewire_kwargs : dictionary a dictionary of keyword arguments for an instantiation of the netrw rewire class - + null_model_kwargs : dictionary a dictionary of keyword arguments for the null model (?) - - ''' + + """ G0 = copy.deepcopy(G) - + # check whether input for num rewire in a number of rewiring steps (int) # or a list of steps if hasattr(num_steps, "__iter__"): rewire_steps = num_rewire else: rewire_steps = range(num_steps) - + # initialize data array - data = np.zeros((len(rewire_steps),num_runs)) - + data = np.zeros((len(rewire_steps), num_runs)) + # define a rewire function step_rewire = rewire().step_rewire - rewire_function = lambda g : step_rewire(g, copy_graph=False, **rewire_kwargs) - + rewire_function = lambda g: step_rewire(g, copy_graph=False, **rewire_kwargs) + # define a distance function - distfun = distance() # get a class instantiation + distfun = distance() # get a class instantiation distance_function = lambda g1, g2: distfun(g1, g2, **distance_kwargs) - + for j in range(num_runs): for i in range(max(rewire_steps)): rewire_function(G0) - data[i+1,j] = distance_function(G0, G) + data[i + 1, j] = distance_function(G0, G) return data -def plotDistanceTrajectory(G, distance=netrd.distance.Hamming, num_steps=100, - show=['mean', 'median', 'std-env'], labels=None, add_legend=True, fig=None, - ax=None, linecolors=None, envcolor='cyan', - xlabel='Number of rewiring steps', ylabel=None, **kwargs): - +def plotDistanceTrajectory( + G, + distance=netrd.distance.Hamming, + num_steps=100, + show=["mean", "median", "std-env"], + labels=None, + add_legend=True, + fig=None, + ax=None, + linecolors=None, + envcolor="cyan", + xlabel="Number of rewiring steps", + ylabel=None, + **kwargs +): + # check whether input for num steps in a number of rewiring steps (int) # or a list of steps if hasattr(num_steps, "__iter__"): @@ -85,67 +105,66 @@ def plotDistanceTrajectory(G, distance=netrd.distance.Hamming, num_steps=100, # set ylabel if ylabel is None: - ylabel = distance.__name__ + r' distance to $G_0$' + ylabel = distance.__name__ + r" distance to $G_0$" # set line labels if labels is None: labels = show elif hasattr(labels, "__iter__"): if len(labels) != len(show): - raise ValueError('List for keyword argument `show` and list for' - +'`keyword argument` must have the same length.') + raise ValueError( + "List for keyword argument `show` and list for" + + "`keyword argument` must have the same length." + ) else: - raise ValueError( - 'Keyword argument `labels` must be None or list.') - + raise ValueError("Keyword argument `labels` must be None or list.") + # set line colors if linecolors is None: - tabcolors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple'] + tabcolors = ["tab:blue", "tab:orange", "tab:green", "tab:red", "tab:purple"] linecolors = [tabcolors[i % len(tabcolors)] for i in range(len(show))] elif hasattr(linecolors, "__iter__"): if len(linecolors) != len(show): - raise ValueError('List for keyword argument `show` and list for' - +'`keyword argument` must have the same length.') + raise ValueError( + "List for keyword argument `show` and list for" + + "`keyword argument` must have the same length." + ) else: - raise ValueError( - 'Keyword argument `labels` must be None or list.') - + raise ValueError("Keyword argument `labels` must be None or list.") + # get data data = distanceTrajectory(G, distance=distance, num_steps=num_steps, **kwargs) - + # get data for lines for plot line_data = [] for s in show: - if s=='mean': + if s == "mean": line_data += [np.mean(data, axis=1)] - elif s=='std': + elif s == "std": line_data += [np.std(data, axis=1)] - elif s=='median': + elif s == "median": line_data += [np.median(data, axis=1)] - elif s=='std-env': + elif s == "std-env": mean = np.mean(data, axis=1) std = np.std(data, axis=1) - env_data = [std-mean, std+mean] + env_data = [std - mean, std + mean] else: warnings.warn("Unknown summary statistic", s, "will be ignored.") - + std = np.std(data, axis=1) - + if fig is None: fig = plt.gcf() if ax is None: ax = plt.subplot(111) - if 'std-env' in show: + if "std-env" in show: ax.fill_between(rewire_steps, env_data[0], env_data[1], color=envcolor) - + for i in range(len(line_data)): - ax.plot(rewire_steps, line_data[i], color=linecolors[i], label=labels[i]) - + ax.plot(rewire_steps, line_data[i], color=linecolors[i], label=labels[i]) + if add_legend: plt.legend() - + plt.xlabel(xlabel) plt.ylabel(ylabel) - - - \ No newline at end of file diff --git a/netrw/analysis/distributions.py b/netrw/analysis/distributions.py index 68c3a27..97e455b 100644 --- a/netrw/analysis/distributions.py +++ b/netrw/analysis/distributions.py @@ -1,7 +1,10 @@ from copy import deepcopy import numpy as np -def get_property_distribution(G, rewiring_method, property, skip=10, num_samples=1000, **kwargs): + +def get_property_distribution( + G, rewiring_method, property, skip=10, num_samples=1000, **kwargs +): """_summary_ More details @@ -34,4 +37,4 @@ def get_property_distribution(G, rewiring_method, property, skip=10, num_samples if j >= skip - 1: properties[i] = property(G) print(i) - return properties \ No newline at end of file + return properties diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index d93fe81..d2acbcd 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -8,9 +8,9 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): """ - Analyze the property values of a network as a function of rewire steps. + Analyze the property values of a network as a function of rewire steps. Looks at how a network property changes as a rewiring process occurs. - + Parameters ---------- init_graph : NetworkX graph @@ -18,12 +18,12 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): rewire_method : netrw rewire class object Algorithm for rewiring a network with step_rewire option property1 : NetworkX function - Network description property that outputs a single value for a given network. Should work with any function that - summarizes a NetworkX graph object into a single value. For example, nx.average_clustering, nx.average_shortest_path_length, etc. + Network description property that outputs a single value for a given network. Should work with any function that + summarizes a NetworkX graph object into a single value. For example, nx.average_clustering, nx.average_shortest_path_length, etc. tmax : int - Number of rewiring steps to perform for each iteration. + Number of rewiring steps to perform for each iteration. numit : int - Number of rewiring iterations to perform on the initial graph. The given rewiring process will be performed numit + Number of rewiring iterations to perform on the initial graph. The given rewiring process will be performed numit times on the initial graph to look at the distribution of outcomes for this rewiring process on the initial graph. Returns ------- @@ -32,26 +32,27 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): at each step of the rewiring process. """ - + property_dict = {} rw = rewire_method() for i in range(numit): G0 = deepcopy(init_graph) - property_list = [property1(G0)] # calculate property of initial network + property_list = [property1(G0)] # calculate property of initial network for j in range(tmax): - G0 = rw.step_rewire(G0, copy_graph=False) #rewire - property_list.append(property1(G0)) #calculate property of the rewired network + G0 = rw.step_rewire(G0, copy_graph=False) # rewire + property_list.append( + property1(G0) + ) # calculate property of the rewired network property_dict[i] = property_list - - - alllist = [] # list of all properties for all iterations at each of the time steps + + alllist = [] # list of all properties for all iterations at each of the time steps for k in range(tmax): alllist.append([]) for l in range(numit): alllist[k].append(property_dict[l][k]) - - # find mean and standard deviation over different iterations of rewiring process + + # find mean and standard deviation over different iterations of rewiring process meanlist = [] sdlist = [] for k in range(tmax): @@ -62,7 +63,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): upperbd = [] lowerbd = [] for a in range(len(meanlist)): - upperbd.append(meanlist[a]+sdlist[a]) - lowerbd.append(meanlist[a]-sdlist[a]) + upperbd.append(meanlist[a] + sdlist[a]) + lowerbd.append(meanlist[a] - sdlist[a]) - return property_dict \ No newline at end of file + return property_dict diff --git a/netrw/rewire/algebraic_connectivity.py b/netrw/rewire/algebraic_connectivity.py index 149b16c..45acede 100644 --- a/netrw/rewire/algebraic_connectivity.py +++ b/netrw/rewire/algebraic_connectivity.py @@ -22,9 +22,7 @@ class AlgebraicConnectivity(BaseRewirer): Applied Mathematics and computation 219.10 (2013): 5465-5479. """ - def rewire( - self, G, phi=1, copy_graph=False, directed=False - ): + def rewire(self, G, phi=1, copy_graph=False, directed=False): """ Rewire phi edges to maximize algebraic connectivity. diff --git a/netrw/rewire/networkXEdgeSwap.py b/netrw/rewire/networkXEdgeSwap.py index 02cb41b..2fd42b1 100644 --- a/netrw/rewire/networkXEdgeSwap.py +++ b/netrw/rewire/networkXEdgeSwap.py @@ -24,14 +24,14 @@ def rewire(self, G, copy_graph=True): G = copy.deepcopy(G) nx.double_edge_swap(G, nswap=1) - + return G def step_rewire(self, G, copy_graph=True): - + if copy_graph: G = copy.deepcopy(G) - + nx.double_edge_swap(G, nswap=1) - return G \ No newline at end of file + return G diff --git a/netrw/visualization/visualization.py b/netrw/visualization/visualization.py index 0abf991..d48c25e 100644 --- a/netrw/visualization/visualization.py +++ b/netrw/visualization/visualization.py @@ -12,18 +12,17 @@ # In[326]: -def visualize_rewiring(G1,G2,pos): +def visualize_rewiring(G1, G2, pos): A1 = nx.adjacency_matrix(G1) A2 = nx.adjacency_matrix(G2) A_dif = abs(A2 - A1) G3 = nx.Graph(A_dif) - nx.draw(G3,pos,edge_color='r',node_color='b',node_size=0,width=8) - nx.draw(G2,pos,edge_color='b',node_color='b',node_size=80,width=5) + nx.draw(G3, pos, edge_color="r", node_color="b", node_size=0, width=8) + nx.draw(G2, pos, edge_color="b", node_color="b", node_size=80, width=5) # In[327]: -def visualize_graph(G,pos): - nx.draw(G,pos,edge_color='b',node_color='b',node_size=80,width=5) - +def visualize_graph(G, pos): + nx.draw(G, pos, edge_color="b", node_color="b", node_size=80, width=5) diff --git a/setup.py b/setup.py index 058a530..fd99d53 100644 --- a/setup.py +++ b/setup.py @@ -1,15 +1,16 @@ import setuptools setuptools.setup( - name='netrw', - version='0.0.1', - author='NetSI 2022 Collabathon Team', - author_email='b.klein@northeastern.edu', - description='Repository of network rewiring methods', - url='https://github.com/netsiphd/netrw', + name="netrw", + version="0.0.1", + author="NetSI 2022 Collabathon Team", + author_email="b.klein@northeastern.edu", + description="Repository of network rewiring methods", + url="https://github.com/netsiphd/netrw", packages=setuptools.find_packages(), - classifiers=['Programming Language :: Python :: 3', - 'License :: OSI Approved :: MIT License', - 'Operating System :: OS Independent'] + classifiers=[ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", + ], ) - From b19f61fd90afae1f39a3bfe4861f02865f214453 Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Wed, 20 Jul 2022 11:38:25 -0400 Subject: [PATCH 46/72] plot for properties_overtime --- netrw/analysis/plot_property_values_over_time.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/netrw/analysis/plot_property_values_over_time.py b/netrw/analysis/plot_property_values_over_time.py index 46d5edc..a96a314 100644 --- a/netrw/analysis/plot_property_values_over_time.py +++ b/netrw/analysis/plot_property_values_over_time.py @@ -5,7 +5,7 @@ def plot_property_values_over_time(propvals, ylabel = '' ): """ - Plot how a network property changes over time during a rewiring process. + Plot mean and standard deviation of how a network property changes over time during multiple iterations of a rewiring process. Parameters ---------- From 1a0d8dc1f56dd0413c7b9e1ced16e3bb354bf749 Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Wed, 20 Jul 2022 11:57:33 -0400 Subject: [PATCH 47/72] small updates --- netrw/analysis/plot_property_values_over_time.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/netrw/analysis/plot_property_values_over_time.py b/netrw/analysis/plot_property_values_over_time.py index a96a314..98330c9 100644 --- a/netrw/analysis/plot_property_values_over_time.py +++ b/netrw/analysis/plot_property_values_over_time.py @@ -44,10 +44,10 @@ def plot_property_values_over_time(propvals, ylabel = '' ): lowerbd.append(meanlist[a]-sdlist[a]) fig, (ax0) = plt.subplots(nrows=1) - ax0.plot(range(len(propvals[0])), meanlist, color = 'blue') + ax0.plot(range(len(propvals[0])), meanlist, color = 'blue', linewidth = 2) ax0.plot(range(len(propvals[0])), upperbd, color = 'blue') ax0.plot(range(len(propvals[0])), lowerbd, color = 'blue') - ax0.fill_between(range(len(propvals[0])),upperbd, lowerbd, color = 'cornflowerblue') + ax0.fill_between(range(len(propvals[0])),upperbd, lowerbd, color = 'cornflowerblue', alpha =0.5) ax0.set_xlabel('number of rewiring steps') ax0.set_ylabel(ylabel) From 0f377b9bca7ae5d4c64b1003197c53fe814442db Mon Sep 17 00:00:00 2001 From: hartle <32047935+hartle@users.noreply.github.com> Date: Wed, 20 Jul 2022 10:01:32 -0600 Subject: [PATCH 48/72] Added analysis code --- netrw/analysis/rewiring_analysis.py | 169 ++++++++++++++++++++++++++++ 1 file changed, 169 insertions(+) create mode 100644 netrw/analysis/rewiring_analysis.py diff --git a/netrw/analysis/rewiring_analysis.py b/netrw/analysis/rewiring_analysis.py new file mode 100644 index 0000000..33686c4 --- /dev/null +++ b/netrw/analysis/rewiring_analysis.py @@ -0,0 +1,169 @@ +from copy import deepcopy +import numpy as np +import networkx as nx +import matplotlib.pyplot as plt +import netrw +from netrw.rewire import KarrerRewirer, AlgebraicConnectivity, NetworkXEdgeSwap + + +def various_properties_overtime(init_graph, rewire_method, property_functions, function_names, tmax, numit): + """ + Analyze the property values of a network as a function of rewire steps. + Looks at how a network property changes as a rewiring process occurs. + + Parameters + ---------- + init_graph : NetworkX graph + Initial graph upon which rewiring will occur. + rewire_method : netrw rewire class object + Algorithm for rewiring a network with step_rewire option + property_functions : list of functions with input being NetworkX graphs + function_names : list of strings describing the functions + Network description property that outputs a single value for a given network. Should work with any function that + summarizes a NetworkX graph object into a single value. For example, nx.average_clustering, nx.average_shortest_path_length, etc. + tmax : int + Number of rewiring steps to perform for each iteration. + numit : int + Number of rewiring iterations to perform on the initial graph. The given rewiring process will be performed numit + times on the initial graph to look at the distribution of outcomes for this rewiring process on the initial graph. + Returns + ------- + property_dict: dictionary + Dictionary of output where the keys are the iteration number and the values are a list of the network property calculated + at each step of the rewiring process. + """ + + all_properties = {} + + rw = rewire_method() + + for name in function_names: + + all_properties[name] = np.zeros((numit,tmax)) + + # loop over rewiring instances + for i in range(numit): + + G0 = deepcopy(init_graph) + + # calculate properties of initial network + for name,func in zip(property_functions,function_names): + + all_properties[name][i,0] = func(G0) + + + # loop over timesteps + for j in range(1,tmax): + + G0 = rw.step_rewire(G0, copy_graph=False) #rewire + + # calculate properties of the rewired network + + for name,func in zip(property_functions,function_names): + + all_properties[name][i,j] = func(G0) + + return all_properties + + +def calculate_statistics(all_properties): + """ + Find the mean, standard deviation, mean-std, and mean+std of the data from rewirings + + Inputs: + all_properties: dict of 2D np.arrays of shape numit x tmax with keys that are property names + + Outputs: + all_means: dict of mean values of each property at each timestep (keys are property names, values are 1D np.arrays of length tmax) + all_stds: likewise for standard deviations + all_lowers: likewise for mean-std + all_uppers: likewise for mean+std + + """ + + # find mean and standard deviation over different iterations of rewiring process + all_means = {} + all_stds = {} + all_lowers = {} + all_uppers = {} + + for name,data in all_properties.items(): + + all_means[name] = np.mean(data,axis=0) + all_stds[name] = np.std(data,axis=0) + all_lowers[name] = all_means[name]-all_stds[name] + all_uppers[name] = all_means[name]+all_stds[name] + + return all_means,all_stds,all_lowers,all_uppers + + +def average_local_clustering(G): + """ + Calculates average local clustering of networkx graph G + + Note: divides by the number of nodes with degree at least 2, + since local clustering is undefined for nodes with degree less than 2. + + """ + # get degrees and find how many nodes have degree at least 2 + k = np.array(list(dict(nx.degree(G)).values())) + n_kg1 = np.sum(k>1) + + # find and sum up local clustering coefficient of all nodes + clu = nx.clustering(G) + tot = np.sum(list(nx.clustering(G).values())) + + # calculate average + barc = tot/n_kg1 + return barc + +def average_shortest_path_length(G): + """ + Calculates average shortest path length of networkx graph G + + Note: divides by total number of shortest paths, namely the sum over components + of component-size-choose-2, so as to still get a meaningful result when + the graph is not connected. + """ + # find the sizes of connected components + C = list(nx.connected_components(G)) + Nv = list(map(len,C)) + + # calculate the total number of shortest paths + Npairs = np.sum([N*(N-1)/2 for N in Nv]) + + # sum up all shortest path lengths + total = np.sum([np.sum(list(v[1].values())) for v in nx.all_pairs_shortest_path_length(G)]) + + # calculate average + barl = total/Npairs + return barl + + +property_functions = [lambda G: G.number_of_nodes(), + lambda G: G.number_of_edges(), + lambda G: average_shortest_path_length(G), + lambda G: nx.number_connected_components(G), + lambda G: nx.assortativity.degree_assortativity_coefficient(G), + lambda G: np.sum(np.array(list(dict(nx.degree(G)).values()))**2)/n, + lambda G: np.min(np.array(list(dict(nx.degree(G)).values()))), + lambda G: np.max(np.array(list(dict(nx.degree(G)).values()))), + lambda G: average_local_clustering(G)] + +function_names = ['Number of nodes', + 'Number of edges', + 'Average shortest path length', + 'Number of components', + 'Degree correlation coefficient', + 'Second moment of degree distribution', + 'Minimum degree', + 'Maximum degree', + 'Average local clustering coefficient'] + + +# test run +init_graph = nx.fast_gnp_random_graph(100,0.03) +rewire_method = KarrerRewirer +tmax = 100 +numit = 10 +all_properties = various_properties_overtime(init_graph, rewire_method, property_functions, function_names, tmax, numit) From e2b3e96c970c71bc0b57d5556acee6d80c573c3f Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 12:09:22 -0400 Subject: [PATCH 49/72] updates --- netrw/analysis/distance_trajectory.py | 13 ++----------- 1 file changed, 2 insertions(+), 11 deletions(-) diff --git a/netrw/analysis/distance_trajectory.py b/netrw/analysis/distance_trajectory.py index 535367d..89f9842 100644 --- a/netrw/analysis/distance_trajectory.py +++ b/netrw/analysis/distance_trajectory.py @@ -10,13 +10,10 @@ def distanceTrajectory( G, distance=netrd.distance.Hamming, rewire=NetworkXEdgeSwap, - null_model=None, num_steps=100, num_runs=100, distance_kwargs={}, rewire_kwargs={}, - null_model_kwargs={}, - **kwargs ): """ Get some data on graph distances as a function of number of rewiring steps. @@ -29,8 +26,6 @@ def distanceTrajectory( rewire : netrw rewire class - null_model : ??? - num_steps : integer or list number of rewiring steps to be tracked or ordered list of rewiring steps to be tracked @@ -46,18 +41,14 @@ def distanceTrajectory( rewire_kwargs : dictionary a dictionary of keyword arguments for an instantiation of the netrw rewire class - - null_model_kwargs : dictionary - a dictionary of keyword arguments for the null model (?) - """ G0 = copy.deepcopy(G) # check whether input for num rewire in a number of rewiring steps (int) # or a list of steps - if hasattr(num_steps, "__iter__"): - rewire_steps = num_rewire + if isinstance(num_steps, list): + rewire_steps = num_steps else: rewire_steps = range(num_steps) From 68dde6740e45fb09c7526bfe5f8afdfc5a82ffaa Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 12:09:50 -0400 Subject: [PATCH 50/72] style: format with black --- .../plot_property_values_over_time.py | 33 ++++--- netrw/analysis/rewiring_analysis.py | 96 ++++++++++--------- 2 files changed, 71 insertions(+), 58 deletions(-) diff --git a/netrw/analysis/plot_property_values_over_time.py b/netrw/analysis/plot_property_values_over_time.py index 98330c9..3fc6b33 100644 --- a/netrw/analysis/plot_property_values_over_time.py +++ b/netrw/analysis/plot_property_values_over_time.py @@ -3,10 +3,11 @@ import matplotlib.pyplot as plt import netrw -def plot_property_values_over_time(propvals, ylabel = '' ): + +def plot_property_values_over_time(propvals, ylabel=""): """ Plot mean and standard deviation of how a network property changes over time during multiple iterations of a rewiring process. - + Parameters ---------- propvals : Dictionary @@ -16,20 +17,20 @@ def plot_property_values_over_time(propvals, ylabel = '' ): ylabel: string, optional Label for y axis of graph for mean and standard deviation of network property Default is no label. - + Returns ------- fig: matplotlib figure - Figure of mean and standard deviation for a network property throughout the rewiring process. + Figure of mean and standard deviation for a network property throughout the rewiring process. """ - alllist = [] # list of all properties for all iterations at each of the time steps + alllist = [] # list of all properties for all iterations at each of the time steps for k in range(len(propvals[0])): alllist.append([]) for l in range(len(list(propvals.keys()))): alllist[k].append(propvals[l][k]) - - # find mean and standard deviation over different iterations of rewiring process + + # find mean and standard deviation over different iterations of rewiring process meanlist = [] sdlist = [] for k in range(len(propvals[0])): @@ -40,18 +41,20 @@ def plot_property_values_over_time(propvals, ylabel = '' ): upperbd = [] lowerbd = [] for a in range(len(meanlist)): - upperbd.append(meanlist[a]+sdlist[a]) - lowerbd.append(meanlist[a]-sdlist[a]) + upperbd.append(meanlist[a] + sdlist[a]) + lowerbd.append(meanlist[a] - sdlist[a]) fig, (ax0) = plt.subplots(nrows=1) - ax0.plot(range(len(propvals[0])), meanlist, color = 'blue', linewidth = 2) - ax0.plot(range(len(propvals[0])), upperbd, color = 'blue') - ax0.plot(range(len(propvals[0])), lowerbd, color = 'blue') - ax0.fill_between(range(len(propvals[0])),upperbd, lowerbd, color = 'cornflowerblue', alpha =0.5) + ax0.plot(range(len(propvals[0])), meanlist, color="blue", linewidth=2) + ax0.plot(range(len(propvals[0])), upperbd, color="blue") + ax0.plot(range(len(propvals[0])), lowerbd, color="blue") + ax0.fill_between( + range(len(propvals[0])), upperbd, lowerbd, color="cornflowerblue", alpha=0.5 + ) - ax0.set_xlabel('number of rewiring steps') + ax0.set_xlabel("number of rewiring steps") ax0.set_ylabel(ylabel) fig.show() - return fig \ No newline at end of file + return fig diff --git a/netrw/analysis/rewiring_analysis.py b/netrw/analysis/rewiring_analysis.py index 33686c4..648a36d 100644 --- a/netrw/analysis/rewiring_analysis.py +++ b/netrw/analysis/rewiring_analysis.py @@ -6,7 +6,9 @@ from netrw.rewire import KarrerRewirer, AlgebraicConnectivity, NetworkXEdgeSwap -def various_properties_overtime(init_graph, rewire_method, property_functions, function_names, tmax, numit): +def various_properties_overtime( + init_graph, rewire_method, property_functions, function_names, tmax, numit +): """ Analyze the property values of a network as a function of rewire steps. Looks at how a network property changes as a rewiring process occurs. @@ -39,29 +41,28 @@ def various_properties_overtime(init_graph, rewire_method, property_functions, f for name in function_names: - all_properties[name] = np.zeros((numit,tmax)) + all_properties[name] = np.zeros((numit, tmax)) # loop over rewiring instances for i in range(numit): G0 = deepcopy(init_graph) - # calculate properties of initial network - for name,func in zip(property_functions,function_names): - - all_properties[name][i,0] = func(G0) + # calculate properties of initial network + for name, func in zip(property_functions, function_names): + all_properties[name][i, 0] = func(G0) # loop over timesteps - for j in range(1,tmax): + for j in range(1, tmax): - G0 = rw.step_rewire(G0, copy_graph=False) #rewire + G0 = rw.step_rewire(G0, copy_graph=False) # rewire # calculate properties of the rewired network - for name,func in zip(property_functions,function_names): + for name, func in zip(property_functions, function_names): - all_properties[name][i,j] = func(G0) + all_properties[name][i, j] = func(G0) return all_properties @@ -87,14 +88,14 @@ def calculate_statistics(all_properties): all_lowers = {} all_uppers = {} - for name,data in all_properties.items(): + for name, data in all_properties.items(): - all_means[name] = np.mean(data,axis=0) - all_stds[name] = np.std(data,axis=0) - all_lowers[name] = all_means[name]-all_stds[name] - all_uppers[name] = all_means[name]+all_stds[name] + all_means[name] = np.mean(data, axis=0) + all_stds[name] = np.std(data, axis=0) + all_lowers[name] = all_means[name] - all_stds[name] + all_uppers[name] = all_means[name] + all_stds[name] - return all_means,all_stds,all_lowers,all_uppers + return all_means, all_stds, all_lowers, all_uppers def average_local_clustering(G): @@ -107,16 +108,17 @@ def average_local_clustering(G): """ # get degrees and find how many nodes have degree at least 2 k = np.array(list(dict(nx.degree(G)).values())) - n_kg1 = np.sum(k>1) + n_kg1 = np.sum(k > 1) # find and sum up local clustering coefficient of all nodes clu = nx.clustering(G) tot = np.sum(list(nx.clustering(G).values())) # calculate average - barc = tot/n_kg1 + barc = tot / n_kg1 return barc + def average_shortest_path_length(G): """ Calculates average shortest path length of networkx graph G @@ -127,43 +129,51 @@ def average_shortest_path_length(G): """ # find the sizes of connected components C = list(nx.connected_components(G)) - Nv = list(map(len,C)) + Nv = list(map(len, C)) # calculate the total number of shortest paths - Npairs = np.sum([N*(N-1)/2 for N in Nv]) + Npairs = np.sum([N * (N - 1) / 2 for N in Nv]) # sum up all shortest path lengths - total = np.sum([np.sum(list(v[1].values())) for v in nx.all_pairs_shortest_path_length(G)]) + total = np.sum( + [np.sum(list(v[1].values())) for v in nx.all_pairs_shortest_path_length(G)] + ) # calculate average - barl = total/Npairs + barl = total / Npairs return barl -property_functions = [lambda G: G.number_of_nodes(), - lambda G: G.number_of_edges(), - lambda G: average_shortest_path_length(G), - lambda G: nx.number_connected_components(G), - lambda G: nx.assortativity.degree_assortativity_coefficient(G), - lambda G: np.sum(np.array(list(dict(nx.degree(G)).values()))**2)/n, - lambda G: np.min(np.array(list(dict(nx.degree(G)).values()))), - lambda G: np.max(np.array(list(dict(nx.degree(G)).values()))), - lambda G: average_local_clustering(G)] - -function_names = ['Number of nodes', - 'Number of edges', - 'Average shortest path length', - 'Number of components', - 'Degree correlation coefficient', - 'Second moment of degree distribution', - 'Minimum degree', - 'Maximum degree', - 'Average local clustering coefficient'] +property_functions = [ + lambda G: G.number_of_nodes(), + lambda G: G.number_of_edges(), + lambda G: average_shortest_path_length(G), + lambda G: nx.number_connected_components(G), + lambda G: nx.assortativity.degree_assortativity_coefficient(G), + lambda G: np.sum(np.array(list(dict(nx.degree(G)).values())) ** 2) / n, + lambda G: np.min(np.array(list(dict(nx.degree(G)).values()))), + lambda G: np.max(np.array(list(dict(nx.degree(G)).values()))), + lambda G: average_local_clustering(G), +] + +function_names = [ + "Number of nodes", + "Number of edges", + "Average shortest path length", + "Number of components", + "Degree correlation coefficient", + "Second moment of degree distribution", + "Minimum degree", + "Maximum degree", + "Average local clustering coefficient", +] # test run -init_graph = nx.fast_gnp_random_graph(100,0.03) +init_graph = nx.fast_gnp_random_graph(100, 0.03) rewire_method = KarrerRewirer tmax = 100 numit = 10 -all_properties = various_properties_overtime(init_graph, rewire_method, property_functions, function_names, tmax, numit) +all_properties = various_properties_overtime( + init_graph, rewire_method, property_functions, function_names, tmax, numit +) From a6ab4bf45387a9f1037779deb983867912ce86c8 Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 13:59:07 -0400 Subject: [PATCH 51/72] style: format with black --- netrw/rewire/__init__.py | 2 - .../visualize_example_full_rewire.py | 131 +++++++++++------- tests/test_karrer.py | 2 +- 3 files changed, 83 insertions(+), 52 deletions(-) diff --git a/netrw/rewire/__init__.py b/netrw/rewire/__init__.py index 853375a..5f5294a 100644 --- a/netrw/rewire/__init__.py +++ b/netrw/rewire/__init__.py @@ -5,6 +5,4 @@ from .global_rewiring import GlobalRewiring from .local_edge_rewire import LocalEdgeRewiring -# from .algebraic_connectivity import AlgebraicConnectivity - __all__ = [] diff --git a/netrw/visualization/visualize_example_full_rewire.py b/netrw/visualization/visualize_example_full_rewire.py index 5e58b4e..371c247 100644 --- a/netrw/visualization/visualize_example_full_rewire.py +++ b/netrw/visualization/visualize_example_full_rewire.py @@ -1,16 +1,20 @@ import matplotlib.pyplot as plt -plt.rcParams['figure.facecolor'] = 'white' -plt.rcParams['axes.facecolor'] = 'white' -plt.rcParams['savefig.facecolor'] = 'white' -plt.rc('axes', axisbelow=True) - -def visualize_example_full_rewire(RewiringTechnique=LocalEdgeRewiring, - rewiring_technique_label='Local edge rewire', - list_of_graphs=[], - timesteps=100, - save_fig=False, - save_fig_folder='', - save_fig_filename=''): + +plt.rcParams["figure.facecolor"] = "white" +plt.rcParams["axes.facecolor"] = "white" +plt.rcParams["savefig.facecolor"] = "white" +plt.rc("axes", axisbelow=True) + + +def visualize_example_full_rewire( + RewiringTechnique=LocalEdgeRewiring, + rewiring_technique_label="Local edge rewire", + list_of_graphs=[], + timesteps=100, + save_fig=False, + save_fig_folder="", + save_fig_filename="", +): """ This is a useful function for visualizing outputs from repeated runs of `step_rewire`. Users can use this to get a sense of what is happening @@ -45,30 +49,43 @@ def visualize_example_full_rewire(RewiringTechnique=LocalEdgeRewiring, """ if list_of_graphs == []: - list_of_graphs = [nx.karate_club_graph(), - nx.ring_of_cliques(4, 16), - nx.random_geometric_graph(50, 0.2), - nx.erdos_renyi_graph(50, 0.05), - nx.erdos_renyi_graph(50, 0.30), - nx.barabasi_albert_graph(50, 2), - ] + list_of_graphs = [ + nx.karate_club_graph(), + nx.ring_of_cliques(4, 16), + nx.random_geometric_graph(50, 0.2), + nx.erdos_renyi_graph(50, 0.05), + nx.erdos_renyi_graph(50, 0.30), + nx.barabasi_albert_graph(50, 2), + ] # example params for node sizes, edge widths, etc. - ns = 100; lw = 2; ew = 2.5; n_ec = '.3' + ns = 100 + lw = 2 + ew = 2.5 + n_ec = ".3" # fig width and height - base_width = 5; base_height = 5 - - fig, ax = plt.subplots(len(list_of_graphs),2, - figsize=(base_width*2, - base_height*len(list_of_graphs)), - dpi=100) - - ax[(0,0)].text(1.1, 1.2, "Method: "+rewiring_technique_label, - ha='center', va='center', transform=ax[(0,0)].transAxes, - fontsize='xx-large') - - for ix,G0 in enumerate(list_of_graphs): + base_width = 5 + base_height = 5 + + fig, ax = plt.subplots( + len(list_of_graphs), + 2, + figsize=(base_width * 2, base_height * len(list_of_graphs)), + dpi=100, + ) + + ax[(0, 0)].text( + 1.1, + 1.2, + "Method: " + rewiring_technique_label, + ha="center", + va="center", + transform=ax[(0, 0)].transAxes, + fontsize="xx-large", + ) + + for ix, G0 in enumerate(list_of_graphs): pos = nx.kamada_kawai_layout(G0) G = G0.copy() @@ -77,31 +94,47 @@ def visualize_example_full_rewire(RewiringTechnique=LocalEdgeRewiring, G = RewiringTechnique().step_rewire(G) # draw original network - nx.draw_networkx_nodes(G0, pos, ax=ax[(ix,0)], node_size=ns, - node_color='w', edgecolors=n_ec, linewidths=lw) - nx.draw_networkx_edges(G0, pos, ax=ax[(ix,0)], edge_color='.5', - width=ew, alpha=0.35) + nx.draw_networkx_nodes( + G0, + pos, + ax=ax[(ix, 0)], + node_size=ns, + node_color="w", + edgecolors=n_ec, + linewidths=lw, + ) + nx.draw_networkx_edges( + G0, pos, ax=ax[(ix, 0)], edge_color=".5", width=ew, alpha=0.35 + ) # draw rewired network - nx.draw_networkx_nodes(G, pos, ax=ax[(ix,1)], node_size=ns, - node_color='w', edgecolors=n_ec, linewidths=lw) - nx.draw_networkx_edges(G, pos, ax=ax[(ix,1)], edge_color='.5', - width=ew, alpha=0.35) - - ax[(ix,0)].set_title('Original network') - ax[(ix,1)].set_title('Rewired network (n=%i timesteps)'%timesteps) + nx.draw_networkx_nodes( + G, + pos, + ax=ax[(ix, 1)], + node_size=ns, + node_color="w", + edgecolors=n_ec, + linewidths=lw, + ) + nx.draw_networkx_edges( + G, pos, ax=ax[(ix, 1)], edge_color=".5", width=ew, alpha=0.35 + ) + + ax[(ix, 0)].set_title("Original network") + ax[(ix, 1)].set_title("Rewired network (n=%i timesteps)" % timesteps) if save_fig: - if save_fig_filename=='': - save_fig_filename = rewiring_technique_label.lower().replace(' ','_') + if save_fig_filename == "": + save_fig_filename = rewiring_technique_label.lower().replace(" ", "_") - if save_fig_filename[-4:] != '.png' and save_fig_filename[-4:] != '.pdf': - save_fig_filename = save_fig_filename+'.png' + if save_fig_filename[-4:] != ".png" and save_fig_filename[-4:] != ".pdf": + save_fig_filename = save_fig_filename + ".png" fn = save_fig_folder + save_fig_filename print(fn) - plt.savefig(fn, dpi=300, bbox_inches='tight') + plt.savefig(fn, dpi=300, bbox_inches="tight") plt.close() - + else: plt.show() diff --git a/tests/test_karrer.py b/tests/test_karrer.py index 77ce7a9..c914eb3 100644 --- a/tests/test_karrer.py +++ b/tests/test_karrer.py @@ -19,4 +19,4 @@ def test_same_return_type(): avg_degree /= iterations - assert np.linalg.norm(original_degree - avg_degree) < 1 \ No newline at end of file + assert np.linalg.norm(original_degree - avg_degree) < 1 From 41f4976dcf3f51ad0fb65510c4a838846cae9340 Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 13:59:24 -0400 Subject: [PATCH 52/72] added netrd as a requirement --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 17ecdf0..12f9652 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ networkx>=2.0.0 numpy>=1.10.0 scipy>=1.0.0 - +netrd>=0.2 From 16a4cedb4a90b7ba80f79ab0c7c096d8af7ad149 Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Wed, 20 Jul 2022 14:11:01 -0400 Subject: [PATCH 53/72] update output format --- netrw/analysis/properties_overtime.py | 35 ++++++--------------------- 1 file changed, 8 insertions(+), 27 deletions(-) diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index d2acbcd..e20f449 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -32,38 +32,19 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): at each step of the rewiring process. """ - property_dict = {} + property_dict[property1.__name__] = np.zeros((numit, tmax)) rw = rewire_method() for i in range(numit): G0 = deepcopy(init_graph) - property_list = [property1(G0)] # calculate property of initial network - for j in range(tmax): + propertyval = property1(G0) # calculate property of initial network + property_dict[property1.__name__][i,0] = propertyval + for j in range(1,tmax): G0 = rw.step_rewire(G0, copy_graph=False) # rewire - property_list.append( - property1(G0) - ) # calculate property of the rewired network - property_dict[i] = property_list - - alllist = [] # list of all properties for all iterations at each of the time steps - for k in range(tmax): - alllist.append([]) - for l in range(numit): - alllist[k].append(property_dict[l][k]) - - # find mean and standard deviation over different iterations of rewiring process - meanlist = [] - sdlist = [] - for k in range(tmax): - meanlist.append(np.mean(alllist[k])) - sdlist.append(np.std(alllist[k])) - - # find upper and lower bound of standard deviation interval around the mean - upperbd = [] - lowerbd = [] - for a in range(len(meanlist)): - upperbd.append(meanlist[a] + sdlist[a]) - lowerbd.append(meanlist[a] - sdlist[a]) + propertyval = property1(G0) # calculate property of the rewired network + property_dict[property1.__name__][i,j] = propertyval + + return property_dict From bcb7b4f3502541ae0214a2df7d5e5f54d10c4349 Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Wed, 20 Jul 2022 14:14:18 -0400 Subject: [PATCH 54/72] update output format --- netrw/analysis/properties_overtime.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index e20f449..ce16989 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -28,8 +28,9 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): Returns ------- property_dict: dictionary - Dictionary of output where the keys are the iteration number and the values are a list of the network property calculated - at each step of the rewiring process. + Dictionary of output where the keys are the property name and the values are a 2D numpy arry of the network property + calculated at each step and iteration of the rewiring process. Rows are single iteration over a rewiring process. + Columns show different iterations of the rewiring process from the initial graph. """ property_dict = {} @@ -45,6 +46,4 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): propertyval = property1(G0) # calculate property of the rewired network property_dict[property1.__name__][i,j] = propertyval - - return property_dict From c637a97f43c760e6a05f9c326925d53cb840ed5a Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Wed, 20 Jul 2022 14:28:42 -0400 Subject: [PATCH 55/72] updated input type --- .../plot_property_values_over_time.py | 33 +++++++++++-------- 1 file changed, 19 insertions(+), 14 deletions(-) diff --git a/netrw/analysis/plot_property_values_over_time.py b/netrw/analysis/plot_property_values_over_time.py index 3fc6b33..8141c53 100644 --- a/netrw/analysis/plot_property_values_over_time.py +++ b/netrw/analysis/plot_property_values_over_time.py @@ -10,10 +10,11 @@ def plot_property_values_over_time(propvals, ylabel=""): Parameters ---------- - propvals : Dictionary - Dictionary of output from properties_overtime. - The keys are the iteration number and the values are a list of the network property calculated - at each step of the rewiring process. + propvals :2d nump array + 2d numpy array of output values from dictionary of properties_overtime. + The network property is calculated at each step and iteration of the rewiring process. Rows are single iteration over a rewiring process. + Columns show different iterations of the rewiring process from the initial graph. + ylabel: string, optional Label for y axis of graph for mean and standard deviation of network property Default is no label. @@ -24,16 +25,22 @@ def plot_property_values_over_time(propvals, ylabel=""): Figure of mean and standard deviation for a network property throughout the rewiring process. """ + alllist = [] # list of all properties for all iterations at each of the time steps - for k in range(len(propvals[0])): + + + valarray = propvals + num_rows, num_cols = valarray.shape + + for k in range(num_cols): alllist.append([]) - for l in range(len(list(propvals.keys()))): - alllist[k].append(propvals[l][k]) + for l in range(num_rows): + alllist[k].append(valarray[l][k]) # find mean and standard deviation over different iterations of rewiring process meanlist = [] sdlist = [] - for k in range(len(propvals[0])): + for k in range(num_rows): meanlist.append(np.mean(alllist[k])) sdlist.append(np.std(alllist[k])) @@ -45,12 +52,10 @@ def plot_property_values_over_time(propvals, ylabel=""): lowerbd.append(meanlist[a] - sdlist[a]) fig, (ax0) = plt.subplots(nrows=1) - ax0.plot(range(len(propvals[0])), meanlist, color="blue", linewidth=2) - ax0.plot(range(len(propvals[0])), upperbd, color="blue") - ax0.plot(range(len(propvals[0])), lowerbd, color="blue") - ax0.fill_between( - range(len(propvals[0])), upperbd, lowerbd, color="cornflowerblue", alpha=0.5 - ) + ax0.plot(range(num_cols), meanlist, color="blue", linewidth=2) + ax0.plot(range(num_cols), upperbd, color="blue") + ax0.plot(range(num_cols), lowerbd, color="blue") + ax0.fill_between(range(num_cols), upperbd, lowerbd, color="cornflowerblue", alpha=0.5) ax0.set_xlabel("number of rewiring steps") ax0.set_ylabel(ylabel) From 8121fd71704c4abd433a0aa7dba0f17122e564b4 Mon Sep 17 00:00:00 2001 From: clarabay <98752502+clarabay@users.noreply.github.com> Date: Wed, 20 Jul 2022 14:35:03 -0400 Subject: [PATCH 56/72] fixing errors --- netrw/analysis/plot_property_values_over_time.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/netrw/analysis/plot_property_values_over_time.py b/netrw/analysis/plot_property_values_over_time.py index 8141c53..072c67f 100644 --- a/netrw/analysis/plot_property_values_over_time.py +++ b/netrw/analysis/plot_property_values_over_time.py @@ -28,7 +28,6 @@ def plot_property_values_over_time(propvals, ylabel=""): alllist = [] # list of all properties for all iterations at each of the time steps - valarray = propvals num_rows, num_cols = valarray.shape @@ -40,7 +39,7 @@ def plot_property_values_over_time(propvals, ylabel=""): # find mean and standard deviation over different iterations of rewiring process meanlist = [] sdlist = [] - for k in range(num_rows): + for k in range(num_cols): meanlist.append(np.mean(alllist[k])) sdlist.append(np.std(alllist[k])) From 584120806184d244568ca3b0a3a302fe30458fcf Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 14:53:26 -0400 Subject: [PATCH 57/72] fix format --- .../plot_property_values_over_time.py | 12 +++--- netrw/analysis/properties_overtime.py | 12 +++--- netrw/rewire/robust_rewiring.py | 40 ++++++++++++++----- 3 files changed, 42 insertions(+), 22 deletions(-) diff --git a/netrw/analysis/plot_property_values_over_time.py b/netrw/analysis/plot_property_values_over_time.py index 072c67f..943ebe7 100644 --- a/netrw/analysis/plot_property_values_over_time.py +++ b/netrw/analysis/plot_property_values_over_time.py @@ -14,7 +14,7 @@ def plot_property_values_over_time(propvals, ylabel=""): 2d numpy array of output values from dictionary of properties_overtime. The network property is calculated at each step and iteration of the rewiring process. Rows are single iteration over a rewiring process. Columns show different iterations of the rewiring process from the initial graph. - + ylabel: string, optional Label for y axis of graph for mean and standard deviation of network property Default is no label. @@ -25,12 +25,12 @@ def plot_property_values_over_time(propvals, ylabel=""): Figure of mean and standard deviation for a network property throughout the rewiring process. """ - + alllist = [] # list of all properties for all iterations at each of the time steps - + valarray = propvals num_rows, num_cols = valarray.shape - + for k in range(num_cols): alllist.append([]) for l in range(num_rows): @@ -54,7 +54,9 @@ def plot_property_values_over_time(propvals, ylabel=""): ax0.plot(range(num_cols), meanlist, color="blue", linewidth=2) ax0.plot(range(num_cols), upperbd, color="blue") ax0.plot(range(num_cols), lowerbd, color="blue") - ax0.fill_between(range(num_cols), upperbd, lowerbd, color="cornflowerblue", alpha=0.5) + ax0.fill_between( + range(num_cols), upperbd, lowerbd, color="cornflowerblue", alpha=0.5 + ) ax0.set_xlabel("number of rewiring steps") ax0.set_ylabel(ylabel) diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index ce16989..c3ec55d 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -28,7 +28,7 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): Returns ------- property_dict: dictionary - Dictionary of output where the keys are the property name and the values are a 2D numpy arry of the network property + Dictionary of output where the keys are the property name and the values are a 2D numpy arry of the network property calculated at each step and iteration of the rewiring process. Rows are single iteration over a rewiring process. Columns show different iterations of the rewiring process from the initial graph. @@ -40,10 +40,10 @@ def properties_overtime(init_graph, rewire_method, property1, tmax, numit): for i in range(numit): G0 = deepcopy(init_graph) propertyval = property1(G0) # calculate property of initial network - property_dict[property1.__name__][i,0] = propertyval - for j in range(1,tmax): + property_dict[property1.__name__][i, 0] = propertyval + for j in range(1, tmax): G0 = rw.step_rewire(G0, copy_graph=False) # rewire - propertyval = property1(G0) # calculate property of the rewired network - property_dict[property1.__name__][i,j] = propertyval - + propertyval = property1(G0) # calculate property of the rewired network + property_dict[property1.__name__][i, j] = propertyval + return property_dict diff --git a/netrw/rewire/robust_rewiring.py b/netrw/rewire/robust_rewiring.py index 89c9738..a4a0445 100644 --- a/netrw/rewire/robust_rewiring.py +++ b/netrw/rewire/robust_rewiring.py @@ -1,27 +1,35 @@ -#!/usr/bin/env python -# coding: utf-8 - -from .base import BaseRewirer import networkx as nx import numpy as np -import copy from operator import itemgetter import random +import copy +from .base import BaseRewirer +import warnings -class RobustRewiring(BaseRewirer): +class RobustRewirer(BaseRewirer): """ Increases network robustness by building triangles around high degree nodes following algorithm described in: Louzada, V. H. P., Daolio, F., Herrmann, H. J., & Tomassini, M. (2013). Smart rewiring for network robustness. Journal of Complex Networks, 1(2), 150–159. https://doi.org/10.1093/comnet/cnt010 + + * full_rewire rewires the graph N times """ - def robust_rewire(self, G, copy_graph=False, timesteps=1000, step_rewire=False): + def step_rewire( + self, G, copy_graph=False, timesteps=1, directed=False, verbose=False + ): if copy_graph: G = copy.deepcopy(G) - - if step_rewire: - timesteps = 1 + if nx.is_directed(G) and directed is True: + warnings.warn( + "This algorithm is designed for undirected graphs. The graph input is directed and will be formatted to an undirected graph.", + SyntaxWarning, + ) + G = nx.to_undirected(G) + if verbose: + removed_edges = {} + added_edges = {} for t in range(timesteps): A = nx.adjacency_matrix(G) @@ -35,7 +43,6 @@ def robust_rewire(self, G, copy_graph=False, timesteps=1000, step_rewire=False): if len(sorted_degrees) > 1: if sorted_degrees[-2][1] > 1 and sorted_degrees[-1][1] > 1: neighbors.append(i) - index_i = neighbors[random.randint(0, len(neighbors) - 1)] sorted_degrees_i = sorted( list(degree_list(np.nonzero(A[index_i, :])[1])), key=itemgetter(1) @@ -72,4 +79,15 @@ def robust_rewire(self, G, copy_graph=False, timesteps=1000, step_rewire=False): G.add_edge(index_k, index_j) G.add_edge(index_m, index_n) + if verbose: + return G, removed_edges, added_edges + else: + return G + + def full_rewire( + self, G, copy_graph=False, timesteps=-1, directed=False, verbose=False + ): + if timesteps == -1: + timesteps = int(len(G.nodes())) + G = self.step_rewire(G, copy_graph, timesteps, directed, verbose) return G From 36655f8c0cea2cb7bef6c511fc9b3d66a12c412d Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 14:54:22 -0400 Subject: [PATCH 58/72] change kwarg default --- netrw/rewire/robust_rewiring.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/netrw/rewire/robust_rewiring.py b/netrw/rewire/robust_rewiring.py index a4a0445..fe7a6e9 100644 --- a/netrw/rewire/robust_rewiring.py +++ b/netrw/rewire/robust_rewiring.py @@ -85,7 +85,7 @@ def step_rewire( return G def full_rewire( - self, G, copy_graph=False, timesteps=-1, directed=False, verbose=False + self, G, copy_graph=True, timesteps=-1, directed=False, verbose=False ): if timesteps == -1: timesteps = int(len(G.nodes())) From b7e67b45ac00f08e66d20a15b2db1a170a84f63a Mon Sep 17 00:00:00 2001 From: Brennan Klein Date: Wed, 20 Jul 2022 14:54:42 -0400 Subject: [PATCH 59/72] Update robust_rewiring.py --- netrw/rewire/robust_rewiring.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/netrw/rewire/robust_rewiring.py b/netrw/rewire/robust_rewiring.py index fe7a6e9..a4a0445 100644 --- a/netrw/rewire/robust_rewiring.py +++ b/netrw/rewire/robust_rewiring.py @@ -85,7 +85,7 @@ def step_rewire( return G def full_rewire( - self, G, copy_graph=True, timesteps=-1, directed=False, verbose=False + self, G, copy_graph=False, timesteps=-1, directed=False, verbose=False ): if timesteps == -1: timesteps = int(len(G.nodes())) From b922fd9ceefb4381639d6b25c5ac694f441fa87c Mon Sep 17 00:00:00 2001 From: Brennan Klein Date: Wed, 20 Jul 2022 14:54:59 -0400 Subject: [PATCH 60/72] Update robust_rewiring.py --- netrw/rewire/robust_rewiring.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/netrw/rewire/robust_rewiring.py b/netrw/rewire/robust_rewiring.py index a4a0445..fe7a6e9 100644 --- a/netrw/rewire/robust_rewiring.py +++ b/netrw/rewire/robust_rewiring.py @@ -85,7 +85,7 @@ def step_rewire( return G def full_rewire( - self, G, copy_graph=False, timesteps=-1, directed=False, verbose=False + self, G, copy_graph=True, timesteps=-1, directed=False, verbose=False ): if timesteps == -1: timesteps = int(len(G.nodes())) From be358c30625f320e38617003940f3d2af9c77a84 Mon Sep 17 00:00:00 2001 From: Brennan Klein Date: Wed, 20 Jul 2022 15:11:24 -0400 Subject: [PATCH 61/72] Update local_edge_rewire.py --- netrw/rewire/local_edge_rewire.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/netrw/rewire/local_edge_rewire.py b/netrw/rewire/local_edge_rewire.py index b0dcdbf..8afbf1a 100644 --- a/netrw/rewire/local_edge_rewire.py +++ b/netrw/rewire/local_edge_rewire.py @@ -121,6 +121,8 @@ def full_rewire(self, G, timesteps=-1, copy_graph=True, verbose=False): G, remov_t, added_t = self.step_rewire(G, copy_graph, verbose) removed_edges[t] = remov_t[0] added_edges[t] = added_t[0] + else: + G = self.step_rewire(G, copy_graph, verbose) if not verbose: return G From 917cf272361bfdbeeb1f177df315aaf2ab1e469d Mon Sep 17 00:00:00 2001 From: Brennan Klein Date: Wed, 20 Jul 2022 15:12:54 -0400 Subject: [PATCH 62/72] Update visualization.py --- netrw/visualization/visualization.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/netrw/visualization/visualization.py b/netrw/visualization/visualization.py index d48c25e..196378b 100644 --- a/netrw/visualization/visualization.py +++ b/netrw/visualization/visualization.py @@ -1,17 +1,8 @@ -#!/usr/bin/env python -# coding: utf-8 - -# In[322]: - - import networkx as nx import numpy as np import matplotlib.pyplot as plt -# In[326]: - - def visualize_rewiring(G1, G2, pos): A1 = nx.adjacency_matrix(G1) A2 = nx.adjacency_matrix(G2) @@ -21,8 +12,5 @@ def visualize_rewiring(G1, G2, pos): nx.draw(G2, pos, edge_color="b", node_color="b", node_size=80, width=5) -# In[327]: - - def visualize_graph(G, pos): nx.draw(G, pos, edge_color="b", node_color="b", node_size=80, width=5) From 31417de937b591c4b880090fdb24c246f3e41028 Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 15:13:21 -0400 Subject: [PATCH 63/72] update NetworkXEdgeSwap --- netrw/rewire/networkXEdgeSwap.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/netrw/rewire/networkXEdgeSwap.py b/netrw/rewire/networkXEdgeSwap.py index 2fd42b1..fbc3caf 100644 --- a/netrw/rewire/networkXEdgeSwap.py +++ b/netrw/rewire/networkXEdgeSwap.py @@ -18,12 +18,12 @@ class NetworkXEdgeSwap(BaseRewirer): """ - def rewire(self, G, copy_graph=True): + def full_rewire(self, G, timesteps=1000, copy_graph=True): if copy_graph: G = copy.deepcopy(G) - nx.double_edge_swap(G, nswap=1) + nx.double_edge_swap(G, nswap=timesteps) return G From 6beb32ed8d2df4168cf4ddad105c65db2b6b7c1c Mon Sep 17 00:00:00 2001 From: Brennan Klein Date: Wed, 20 Jul 2022 15:15:04 -0400 Subject: [PATCH 64/72] Update requirements.txt --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 12f9652..5993c77 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ networkx>=2.0.0 numpy>=1.10.0 scipy>=1.0.0 +matplotlib>=3.3.2 netrd>=0.2 From afb3af389060a59f5877128cc83c5bc49c386ca1 Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 15:15:37 -0400 Subject: [PATCH 65/72] deleted test files --- evaluate_rewiring.ipynb | 279 ---------------------------------------- playground.ipynb | 271 -------------------------------------- 2 files changed, 550 deletions(-) delete mode 100644 evaluate_rewiring.ipynb delete mode 100644 playground.ipynb diff --git a/evaluate_rewiring.ipynb b/evaluate_rewiring.ipynb deleted file mode 100644 index d2bd2d3..0000000 --- a/evaluate_rewiring.ipynb +++ /dev/null @@ -1,279 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "f12ade0a-2052-4762-9337-fe8ef0ddb1a9", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import networkx as nx\n", - "import netrw\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def run_rewiring(G0,T,method, **kwargs):\n", - " G = [G0]\n", - " for t in range(1,T):\n", - " G.append(method.step_rewire(G[-1], **kwargs))\n", - " return G\n", - "\n", - "def basic_metrics(G):\n", - " # G: a single networkx object\n", - " n = G.number_of_nodes()\n", - " m = G.number_of_edges()\n", - " l = average_shortest_path_length(G)\n", - " NC = nx.number_connected_components(G)\n", - " rho = nx.assortativity.degree_assortativity_coefficient(G)\n", - " k = np.array(list(dict(nx.degree(G)).values()))\n", - " k2 = np.sum(k**2)/n\n", - " kmin = np.min(k)\n", - " kmax = np.max(k)\n", - " c = average_local_clustering(G)\n", - " return n,m,l,NC,rho,k2,kmin,kmax,c\n", - "\n", - "def gather_metrics_over_time(G):\n", - " # G: a list of networkx objects\n", - " T = len(G)\n", - " n_ = np.zeros(T,dtype=int)\n", - " m_ = np.zeros(T,dtype=int)\n", - " l_ = np.zeros(T)\n", - " NC_ = np.zeros(T,dtype=int)\n", - " rho_ = np.zeros(T)\n", - " k2_ = np.zeros(T)\n", - " kmin_ = np.zeros(T)\n", - " kmax_ = np.zeros(T)\n", - " c_ = np.zeros(T)\n", - " for t,g in enumerate(G):\n", - " n_[t],m_[t],l_[t],NC_[t],rho_[t],k2_[t],kmin_[t],kmax_[t],c_[t] = basic_metrics(g)\n", - " return n_,m_,l_,NC_,rho_,k2_,kmin_,kmax_,c_\n", - "\n", - "def average_local_clustering(G):\n", - " k = np.array(list(dict(nx.degree(G)).values()))\n", - " n_kg1 = np.sum(k>1)\n", - " clu = nx.clustering(G)\n", - " tot = np.sum(list(nx.clustering(G).values()))\n", - " barc = tot/n_kg1\n", - " return barc\n", - "\n", - "def average_shortest_path_length(G):\n", - " C = list(nx.connected_components(G))\n", - " Nv = list(map(len,C))\n", - " Npairs = np.sum([N*(N-1)/2 for N in Nv])\n", - " total = np.sum([np.sum(list(v[1].values())) for v in nx.all_pairs_shortest_path_length(G)])\n", - " barl = total/Npairs\n", - " return barl" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1125353d-a199-4f68-8085-8f9c145d02ab", - "metadata": {}, - "outputs": [], - "source": [ - "n = 100\n", - "m = 2\n", - "G0 = nx.barabasi_albert_graph(n,m)\n", - "\n", - "T = 1000\n", - "\n", - "p = 0.8\n", - "method = netrw.rewire.DegreeAssortativeRewirer()\n", - "assortative = True\n", - "G = run_rewiring(G0,T,method, p=p,assortative=assortative)\n", - "\n", - "n_,m_,l_,NC_,rho_,k2_,kmin_,kmax_,c_ = gather_metrics_over_time(G)\n", - "\n", - "names = ['Number of nodes',\n", - " 'Number of edges',\n", - " 'Average shortest path length',\n", - " 'Number of components',\n", - " 'Degree correlation coefficient',\n", - " 'Second moment of degree distribution',\n", - " 'Minimum degree',\n", - " 'Maximum degree',\n", - " 'Average local clustering coefficient']\n", - "\n", - "props = [n_,m_,l_,NC_,rho_,k2_,kmin_,kmax_,c_]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f33473e4-3b7b-47cd-8bba-ad6cde2cc431", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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Xun0ZERcpHbhLaf2u8RzCt2qss6oLlK7CdhQRt2l8vXttkw4BhqhcB38QEed0SxgRP1C68t/Pm5Wa8EkpKDq7V+JIzcqPzm/XUmoC/Qin50q+rPjoAxGxpEeW79XEeq08OCLu65OmrN8HRsTdfdL/u9IJFUnaNzcxL71U6cqpJP0yejR3lKS8Pn0+v11R0t59pt/LCzTWMda9Sq0suk3370qtN4Zh9WK8SfO1UTquX/2chLPzOtNRRCzU2LKUejQPXxrk/dHbio8+GRFXdUsfEScoNXls6XrLQ+EnEfGzHt+frHS/upSOgbZokCcmgcAMExYR90TE3kpniH+mdCa3k1WV7rU506lnrm69ar24GD8uOjyouoPTi/Fnl1/YXkmpyWXLFxvk103Zc9f3+x1sRGrbfmHx0R598v9bRPyhT5r/K8bntn9pezWNP2j+Vp8yPijpu32mORlnR8RNPaZ/v1Jzwpbj++R3UTG+SZc05f98VJ/8pBTstg4411M6qdDLj3sdwEZqf39P8dGk5sn2Chq7n+7BBvm1gvgzWlko3XvWS/vJlU561r0pUK5/38zz2MsJSvfWSOkK9s490g7b7sV4z3UwO6ZBmnLb2PTkUtdto1KdaN3De73SFdSuIuJqpSv6g7hN6YpwV7bXU2qGJ0nXRsSv+mWatxut+xzXULqiWxr2fzWIcvvz03ySoJdjNf7k1ESVB+v7ecDea4dsoic/m2iyrhxdjD+9x/HG0mBLpSu6UtpPHd0jbcv/FOO7N0jfc/uft7V/Kj6a2yBPTAKdf2DSIuI0Sac5dRG/m9JOf7s8tJ+p/kdJ59jeOSLabyouD552tt31rHihvMKxUdt32yjdlyCls5e/b5BfN9sW4006iWile2oe365XQo0P4ropOwnodAXgaRo72XKn0r1m/Uy4M4YG/twgTXng8pcB0q7e/qXtDZQ6CWj5Tb+JR8TNthdq7Czgdhq72tRJk3m6XWMdr0xqnpSWaSuv+yV9tv9FPUnjr761rxfthlH3RiafNd6m+Kjv+hcRD9g+V2NXirZTOnk0UrkOrl181GSb02QdLLeNb3SXrubblI806bRtbFnQINCV0lXT3Rukazk/unQ6Uijnyw23+dL4jn02UrqK2inPl9jepkF+ZZ3ut770Uk6r77KPiLttX6TJX4U+Wek+oVWV9lWX2v6mUu/Jf4iISXW6MaB+Jxgno++6EhGX5Q511lI6PthGYyeqljblcccl+SprP+X2cV3b60fEdT3ST+vt/0xEYIahyRuN/81DqxfDZyg1eXyrUs8+Urqh9NNK96CVyucv7anxZ8mbaD8zVvaIdnW3ZoINlQdbVzb8zaJivF9zsjsa5FfuXDs9A6cs4zUND7aubpBmoprMU7lMeqaPiAeLoKTTtquc//t6Xa1rs0hjgdkwllPjeWpL22mZluvEqkpdpQ+q3xnjYdS9UVqjbZqjWP+GpayD9zY8kOq5DuZeZVcrPnrDBMrVXgfK/+Oahnl07YmviyZN6tZvGx9G/S7zfPkQ8htEufy7Njlrc7UmGZhFxK2236TUG+QspR5VP5yH+/JJirOUOoQYZeAkjbYp5SD/aetZomv3SjjNDXzcERE32r5fYyel5yj12trNdN/+zzg0ZcTIRMTDEbEgIt6ltOMp7415q+2V234y2TMx7V3clwcz/e5b6GfVYvyerqnGK9Ot1jVV0iSI6qcsY9Mun5vOy0QMOk+T/Q8msoza0w57OU12noZxdrLfCbhh1L1RWrXt/SjWv2EZxTo4ijowFduKfveWSaOZt8nmOZkT1tW2wZEeC7G9UtO0srn1ykotWT4q6bz8IOZdhzHNLuVostwnaiL/6VSt+6MwHfdpGDGumGFKRMTFtt+rdEZPSmdzdtD4+xbKjcnLI+JHk5xs2VSy/eBuUHdrbIfftP1+mW4oz0fqoww+Z3dNNV7NexGGrZz/QeZrqpfTIMp14vyI2LZrymVX+0mVVdTsIKXGch3FOtg+r4+NiCZnuZvmWXNbUZbjxIh4xZDybG2rt4mIP/VKPGRVt8ERcaGkvW2vrtSb6675dXuNXel4htKzG/eJiCb3l04ns9VsXR72ul/rIsayuE9DH1wxw1Q6pe39em3vywdEb67JK/PbKHekMFFl84yNu6Ya7wnF+C2TmHZTZRk3atDDoDS5+ymmm3L+V27QG2HLVC+nQZR1eNMOPdDNBHdofFOa6br+SePr4Ox8320/PdfBiLhd4zuIGMa2sfw/NuyaarwNhjDddsPe5o8qz6Ymsp8Y+jY4Iu6MiJ9GxMER8Uyl5mzzNNa8dzlJX+nQamW6m8h/2mndL7cnTY4Lat1XNXB9sv14jTVjlKbfPg19zMSdPOq5v+19e29U5c3SLxjC9M4vpjlb6QGUE1X2Stevl7uWZxXjf5zEtJu6QGM9DK4m6SkNfrPT6IoztSLiWqUHnbf0XU75wLnsiXEqltMgztfYerK6prZ3wUGNpElMvlfy/OKjJst1BaVnKLZMyXLNdbA8mGqyfjVJc24xPqxtY8v2DU/idHucw2SUnTlsZbtpkNjLsPcjgzi/GO+7XPP9g+29Sg5dDtSOVrpvu7U9maPO25Pp3LSt7/bP9uYau7+s9Wy8duVVpCYnT57aP8lI/rey7Fvkx1z0Ux533NCn4w9MQwRmmErbtL1vv5H3J8X4c2w32Rh2lZ8nVvbG9M5uaRsou1Pex/ZjuqaUZHs7pR71WkbeK1Tu5bK8sfv1vdLng9fXjrRQU6/8n+c1SL+fxraD10m6dNgFmox8v0ZZ9w6qVZYGyhMvw75BvPwP9msQSPyjxg64yu7Vp8KZxXjPdTDbr0Gactv49n7bnwZ+o7GrBuurT2+LtjdSahI3VPnxEmXvse8eQrblf/XafAVhqpTbnxc3OJB+jdLjHKZE/r/LnmXX6ZBslOvxZDXp+GZeMf6nLo8sKJ/fuU2vzGxvr+6PZymN4n+7WGP35i+vZtuTNxXjS2tvlDMagRkmxPZ7bD93gPQraPzDoG/U+LOLrWd/ndn6iaRv57byTfKf1eV5JZ8rxvexvU/TMrf5rsbOsq0n6WO9yqLxz0w7IyKm6oD/G8X4v+Szh928V812OEuTI4vxV9h+YbeE+WCzfFD4kQ17spxqhxXj/2R7XtMf2l63f6qhKXsgHHazt69r7Grwdhr/0NVx8sOk/6P46HtDuCdrEOU6+BrbXQMa269Rs+dmHamxhypvqNQMrdFzE2zPsT2uY6TcW2T58PX/yNutbv5Tj+5caVjK+v2uAfcrner38ZIuz+OzlfYjjQ6Uba86yWeA/VxjvVfO1vh5a5/WWpI+PolplXk1arad98PlLQSdelAc5Xo8WbvmdaajvL97d/HR/3RJWl6B7npiJP9fhzcs29D/t7w/+lrx0UfzIzk6sv0SpQest3x1GOXA1CIww0TtqPTssj/YPjA/KLQj21sr3V9W7nAP6/KMmwM1dsPr0ySd22tHbfuJtv9V6QzYs9q/j4hfaPwDFL9t+6O2H3Vjtu3lbO9h+4R8cFfmc6ekTxUfHWL7k+0HM7bXUXq4bau51YOSPtit/CNwtKSFeXy2pJ/nM36PcPIuSf+m8b13LfUi4gyNv5fxONuvbk9ne1tJv9RY19hXS/rv0ZdwcBFxlsY/WPQo25/tdv+S7ZVsv8z2CRp/8D1q5fNwXtC+Dk1GRPxV44PuL9l+R/s9d7Y3k3Sqxp5zdaekTw6rHA2dqtQ1uZROMJ1o+/ntifJJom+qwTqYA8vyaumbJP3Y9had0ud1vPUsyCs19qiS0sc1dtVse0n/a3tc1+K2Z9s+QtLeGs6DkDv5tsauiK4g6ae2D+4WIOXgaV/bp2v8CTBJUkQ8JOkASQ/lj54n6WzbXZti2n6a7X9XasUx4ZNVedofLT7aP6+r7fuJTZWCuA00nG3wZ22fY3u/LicolZftURoLzO5U52cCluvx3kMo2zAtkTTf9qOuHNneUdJpGut05TKNP0lS+r7GTvTsbPsz7ScvcrPanyjty5vU/VFt/76gsWB/LUm/dIdn8+WA9QfFRz+OiEEfCo9pgF4ZMVmtB0n/t+1FShunW5R2+GsqBVdPbvvNCeqwQ5WkiLjI9r5KG5jZ+ben2b5a6QGntyg9p2VtSU9XsxvX91fqCGBHpbO+H5f0Adu/Vjogt9IOcnuNNX/qdDb6P5XObrfOSH1Y0gG2z1B6UPBGkvbQ+KYp74+IyTzYeiARcb/t/ZSCjtlKBxnn2v6dUrOIxyg1SWrdHH2wpM9PVfmmyJuUDjg2U+qN81jblynde7JE0pZK93+0lvE9kvbNnSxMV/+sdED1fKVyv0/piugCSX9V6pp8DaV5fqrGbv4e9TOLSguUDmw3lrSupEtsn6q0zrauRC6IiB90+X0/71NaR3dQ2nd9SekEya+UTuZsptQLXesA60FJb8nNt6ZMRITttyg1n1xb0uOUTpCcr9RKYHmlbVFru/hupYOvfvnOzwf0H8kfvUSpudxFki5SOtBeRWlbtq2kx/bJ78J8Uqt1dfElkq7M27NrlbaFe+Z8blfaTrSu8PR7cHRjEfGQ7b2VDqq3Vdq+f0bp6sDvlOrUEqX9yZOU7p1tXQE7vkuev7B9gKQjlP7vnZS2g5cp3bdzm1Kwuq5SU7ahNXeMiKPylYtX5o/eJ2le/l/vUNom76ZUhxcoBRCTbVJupX3TsyU9ZPtSpYfbt+ZzQ6UAowwQ39ela/vjJb09jx/g1Cz/jxrfVf0R+WTJVHu/0hWsb9k+VGkdW6L0bNTyHvJ7Jb0xItrva5ckRcSVtr8q6f/ljw6WtK/ts5WaJG6mdKJ3ltK+9Hr1b0Y4ku1fRNxm+7VKJxxbx0R/tP17pWU8S2ney9Yxl0l6yyDTwTQSEQwMAw9KD4z+m9IGp+lwr9JBxQoN8n+6pPMGyPsKpa6Ru+W3slKTgAcb5HWfpNW65LOCUlDZL5/bJc3rM4/zivTzG/wnc4v0i/qk3VOpuWi38i1R2ik1zrNhvTi0yO/QBunPLNLv3iD9I/PQJ906SjvUfsv6Mkk79MlrfpG+5zLN6RcV6ecOcZkur9Qc+J6G68QSSV8a0v/eaLlKerHSgU23MvWt533KsarSSZt+836dpBcNc1lNoKxPUwqau5XxYUmfHqRe57R7KwVOTbeNv5e0Uo/8Pqh0Iq3Xf/lMpW1+67PDu+Q1b6LLWmkbfUSfspTDvZI+2CfPPZRaEDT9ry6StP4Qlv0spUfD9FsuG6jB9kV91j+lfVLTebxT0lv7lP9bffLYvS194/pb/GZu8btFPdKNyzv/Fw/3KNv17eXrku9jJP20z3z+WOnERN9llPMcaPvXb7m2pd1JvbcnreE0SWv3yWtRkX5ug/+q0fwzDGfgihkmJCK+LunruZnibkobjS2UrkytoXQG7y6lG1cvUGqq8sPofCNup/z/pNRb2PMlvVzp7NX6ShvJxUpt4xcq9er1c0m/jbwF6ZLffZLeZvtzkt4o6TlKO4bHKR3AXp/LeZqkH0TqSKNTPg9KOjCfbXtzzmcjpV4Qb81lOlnS1yPdx1FFRJxue0ulpqGvkLSpUtPla5QCliMiXZ2cW6uMoxQRNyp1IPMCSfsonUleV+lM+01KZ81PlPTtiHigWz7TSeRmUra/qFSHn6t09WCO0nzdqdRs7UKlm75PjohO95CMsown236GUkc7z1baHqyqzlegJ5L/3Ur3bX1BqSOA3ZW2CysrnZm+SKn50VERMcqHp/cVERc4dWB0gFIw9SSlq+nXKV3RPTIifjOBfI+1/SOlev0CpSuIayv9z/coBW0XSzpHqQ4s7JZXzu/fbf9EaZk9V+n/vFfp4O14SV+LiFs8/l652wctdz95G32A7cOUrk7sqfSfraW07bpD6WTgn5S2YT+L1MS8V55n5Oaer1C6IriT0nZgdaV5vFHSJUqdoZwSEecPaV6WSHqd7WOUAtqdldbTvyt1MPRdSUdHxBI3u1Ww3/QOtP0VpeW3k9IVpI2V9ksP5un+WamZ7bci4qZueWVvVApaXqd0RXGOxnfBXk1EHGr7FKVWBLso1dcHlIKWE5RORt3eIJ/7be8laV+l+8y2Uzp2uUmpjs1XOmaJpstolNu/iPhd3qe/XumYaBulK70PKB1n/UrpftpTJzst1OUex7IAAACy/R2NNbnbNyK+X7M8ALAsIjADAABd5Y44rtHYfWtPjDr3GAHAMo1eGQEAQC+f0lhQtoCgDABGg8AMAIAZyParcnfuT+zy/Zx879K7i48/OyWFA4AZiKaMAADMQE4PK/9mfrtQqeOYvyt1UrKJUjfc5eM/vhMR/boNBwBMEL0yAgCAJ+Whk4ckfVnSe6auOAAw83DFDACAGcj2LEnPk/RCpe7C11HqGn220sOJFyk98+6oiLi0TikBYOYgMAMAAACAyuj8AwAAAAAqIzADAAAAgMoIzAAAAACgMgIzAAAAAKiMwAwAAAAAKiMwAwAAAIDKeMB0A7ZXkvTU/PZmpYdtAgAAAJh5lpe0dh6/MCIWDyNTArNmnippQe1CAAAAAJhWdpB03jAyoikjAAAAAFTGFbNmbm6NnHvuuVpvvfVqlgUAAABAJddff7123HHH1tube6UdxEgDM9vbSXqhpF0kbS3p8ZIekHSdpN9I+kZEnNMwr00k/Yuk50l6gtLVvmslnSbpKxHx56HPwJhH7ilbb731tOGGG45wUgAAAACWEkPre2JkgZntsyTt2uGrWZI2z8N+tr8laf+IWNIjr7dJ+mL+bamVz/623x0RRwyl8AAAAAAwhUZ5xWyD/HqdpOMknSPpKqVeTHaW9N6c5g25HK/tlIntfSQdmd/eIem/JJ0uabGkbSV9QNITJX3Z9s0R8cNRzAwAAAAAjIojYjQZ2z+RdIyk4yPiUZf4bM+R9GtJT8of7drerNH2bElXKDWBvFvSzhFxUVua1SX9SqnnxBskPTEi7hnyvGwo6WpJuvrqq2nKCAAAAMxQ11xzjTbaaKPW240i4pph5DuyXhkjYq+IOLZTUJa/v0XpqlnLqzoke5FSUCZJh7cHZTmfOyW9J79dV9K8CRcaAAAAACqo3V3+mcX4Zh2+36EYP6VPPvfn8U4BHgAAAABMW7UDs7Izj4c7fP+4YvzGbplExIOSbs1vn2mbxwAAAAAAWGrUDsx2K8Yv6fB9ea/YGt0ysW1Jq+e3s5Q6AwEAAACApUK1K0u2l5N0SPHRsR2SXVyM7ybpD12y21bSqsX7jdU50OtWln69eazbNC8AAAAAGFTNJn8HSWo9MvuEiDivQ5qTlR5IvaKk99g+Jnca8ogc4H267XerDViWqwdMDwAAAABDU6Upo+3dJH0mv71J0gGd0uWuJ1sPjd5A0q9tv8z26rYfY3snpeDthZLKB1SvPJqSAwAAAMDwTfkVM9tbSTohT3uxpL0jomvHHpLeL2kTSS9VeubZiR3S/E3S/0p6X35/14DF2qjP9+tKWjBgngAAAADQyJReMbO9iaRTJa0p6SFJ+0bEWb1+ExFLJL1M0puU7jEre2+8XdIXJW0nycXntw1Sroi4pteg9OBqAAAAABiJKbtiZnt9Sb+QtL6kkPTmiDihyW8jIiTNlzTf9qqS1lFqunhd6wHWtp9W/OQvQyw6AAAAAIzUlARmtudIOk3SpvmjAyPimInkFRF3S7q7Lf9ZGutI5G/tHYQAAAAAwHQ28qaMtteQ9HNJT8kfHRIRXx7yZF6sseecdep2HwAAAACmrZEGZrZnS/qp0j1gkvTpiDhsyNNYQdLH89sHJH19mPkDAAAAwKiNLDDLzQtPkPSs/NHhEfHhCeQzJwd43aZxlKTW/WWHRcTfJlJeAAAAAKhllPeYfU/S8/P46ZK+YXvrHumXRMTCDp/vLunrtr+j1HnIVZJmS9pW0ts11kTyVEmfHEK5AQAAAGBKjTIwe2UxvqekC/qkv1LS3C7fPVbSO/LQyXxJB+Su9QEAAABgqTLlD5iegHOUHjK9p6QtlLrKf1jSdZLOkDQ/In5Xr3gAAAAAMDkjC8wiwv1TNcrnRkn/mQcAAAAAWOaMvLt8AAAAAEBvBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFDZSAMz29vZ/pDtU2xfbXux7bttL7Q93/YuA+T1BNufsf0H27fbfsD2rbZ/Y/sjttce5bwAAAAAwKisMKqMbZ8ladcOX82StHke9rP9LUn7R8SSHnm9VtLXJc1u+2pNSTvn4V22946I04dRfgAAAACYKiMLzCRtkF+vk3ScpHMkXSVpeaVA6r05zRtyOV7bKRPbO0s6Jv/uYUlHS/pRzndjSftJeqmktSSdZHvriFg0kjkCAAAAgBEYZVPGSyS9RtLGEfHuiDg+IhZExO8i4vOStpG0MKfdt0ezxg8pBWWSdGBEvDkifpTzOj4i/lHS5/L3q0h6z2hmBwAAAABGY2SBWUTsFRHHRsRDXb6/RemqWcurumT1rPz694j4Spc0nyjGnzlYSQEAAACgrtq9Mp5ZjG/WJc2s/HpFt0wi4g5Jt+S3K02+WAAAAAAwdWoHZrOK8Ye7pGk1d9ykWya2V5c0py09AAAAACwVagdmuxXjl3RJc2R+Xcv227uk+UiH9AAAAACwVBhlr4w92V5O0iHFR8d2Sfo/knaR9DpJX7b9DEknSbpeqVfG10t6RU57WEScOoGybNgnybqD5gkAAAAATVULzCQdJGnHPH5CRJzXKVHuPOT1tk9SCuT2z0PpDEmfmUhQll09wd8BAAAAwKRVacpoezdJn8lvb5J0QJ/0Wyg95+ypXZLsLOmNttcbWiEBAAAAYIpMeWBmeytJJyhdrVssae+IuLFH+l0k/VbSyyRdq/RA6nWVOg7ZSNI7JN2n1NTxXNtbTqBYG/UZdphAngAAAADQyJQ2ZbS9iaRTJa0p6SFJ+0bEWT3SryTpe5IeK+kGSTtFxA1FkmskfcX2WZLOk7ShpGM0YCAVEdf0Kfcg2QEAAADAQKbsipnt9SX9QtL6kkLSmyPihD4/e6GkDfL4F9uCskdExJ8lfTu/3d7204dQZAAAAACYElMSmNmeI+k0SZvmjw6MiGMa/LRslvjHPmn/UIxvMUDxAAAAAKCqkQdmtteQ9HNJT8kfHRIRX2748weL8X7NLlfs8jsAAAAAmNZGGpjZni3pp5K2yx99OiIOGyCLK4rxXfqkLR9WfUXXVAAAAAAwzYwsMLM9S6n3xWfljw6PiA8PmM0vJd2bxw+w3bG7fNsv0thDpq+VdP6A0wEAAACAakbZK+P3JD0/j58u6Ru2t+6RfklELCw/iIjbbX9G0ickrSbpN7a/qHS/2m2S1lHqRv+tGgsyD4mIh4c3GwAAAAAwWqMMzF5ZjO8p6YI+6a+UNLfD55+S9DhJ75K0qqQP5qHdA5I+FBHf7vAdAAAAAExbU/ocs4mIiJB0kO1vS9pf0rMlPUHSbEl3S7pc0lmSjmy/4gYAAAAAS4ORBWYRMdSnMkfEHzS+S3wAAAAAWCZM2QOmAQAAAACdEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUr1C4ABve17b+mu2+4u3YxAAAAgCm16rqr6m3nva12MUaCwGwpdPcNd+uua++qXQwAAAAAQ0JgthRadd1VaxcBAAAAmHLL8nEwgdlSaFm9fAsAAADMVHT+AQAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFDZSAMz29vZ/pDtU2xfbXux7bttL7Q93/YufX4/13YMOCwa5TwBAAAAwLCtMKqMbZ8ladcOX82StHke9rP9LUn7R8SSIU360iHlAwAAAABTYmSBmaQN8ut1ko6TdI6kqyQtL2lnSe/Nad6Qy/HaDnlcK+mpDab1weL3R0+8yAAAAAAw9UYZmF0i6UOSjo+Ih9q++12+UvZrSU+StK/tIyLinDJRRDwg6aJeE7G9vKTd89u7JJ04+aIDAAAAwNQZ2T1mEbFXRBzbIShrfX+L0lWzlldNcFLPlbR+Hv9hRNw7wXwAAAAAoIravTKeWYxvNsE83liM04wRAAAAwFKndmA2qxh/eNAf215N0svz2yslnT2EMgEAAADAlKodmO1WjF8ygd+/StLsPH5MRMTkiwQAAAAAU2uUnX/0ZHs5SYcUHx07gWzKZozHTKIsG/ZJsu5E8wYAAACAfqoFZpIOkrRjHj8hIs4b5Me2N9bYFbffRMTlkyjL1ZP4LQAAAABMSpWmjLZ3k/SZ/PYmSQdMIJvXS3Ien/DVMgAAAACobcqvmNneStIJedqLJe0dETdOIKs35NfFkn4wyWJt1Of7dSUtmOQ0AAAAAKCjKQ3MbG8i6VRJa0p6SNK+EXHWBPLZUdIW+e1JEXH7ZMoVEdf0md5ksgcAAACAnqasKaPt9SX9Qulh0CHpzRFxwgSzG0qnHwAAAAAwHUxJYGZ7jqTTJG2aPzowIiYUUNleUdJr8tubJP1s8iUEAAAAgHpGHpjZXkPSzyU9JX90SER8eRJZvkTSnDz+3Yh4cDLlAwAAAIDaRhqY2Z4t6aeStssffToiDptktmUzxqMnmRcAAAAAVDeywMz2LKXeF5+VPzo8Ij48yTwfp3TFTJIujIjzJ5MfAAAAAEwHo+yV8XuSnp/HT5f0Ddtb90i/JCIW9slzH0mz8jhXywAAAAAsE0YZmL2yGN9T0gV90l8paW6fNK1mjA9J+s7EigUAAAAA08uUdZc/WbY3l/QP+e1pEXFDzfIAAAAAwLCM7IpZRAz1qcwRcZkknvQMAAAAYJmz1FwxAwAAAIBlFYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUNkKtQuwlFi+NXL99dfXLAcAAACAitrigeW7pRuUI2JYeS2zbG8vaUHtcgAAAACYVnaIiPOGkRFNGQEAAACgMq6YNWB7JUlPzW9vlvRQxeKsq7GrdztIuqFiWbB0oM5gUNQZDIo6g0FRZzCo6VRnlpe0dh6/MCIWDyNT7jFrIP/ZQ7lEOVm2y7c3RMQ1tcqCpQN1BoOizmBQ1BkMijqDQU3DOnPlsDOkKSMAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlPGAaAAAAACrjihkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmC1FbG9s+z9tX2z7Htu32j7X9vtsz65dPkye7e1sf8j2Kbavtr3Y9t22F9qeb3uXAfN7oe3/tX1Nzuua/P6FA+Qx2/b7c127NZfn4lwXNx58LjEVbP+H7SiG3Rv8hvoyw9ieY/sDtn9t+4a83K+z/Xvbn7W9c4M8qDczhO1Ztt9i+2e2ry/2UZfaPsr2Tg3zoc4sxWw/3vZetj+Rj1duKfY18yeQ37SpD7a3sv1V25fbvs/2zbbPtv3PtlcYdN4GFhEMS8Eg6SWSbpcUXYZLJG1au5wMk1rGZ/VYvuVwjKRZffKypCP75HOkJPfJZ7Nct7rlcbukF9f+7xgetdyeLumBtmW1O/WFoW15vVrSLX2W+4nUG4a8nDaSdEGDfdTnui1z6syyMfRZfvMHyGda1QdJb5F0f498fitprZH+t7UXLkODhZQOsu7JleIuSR+StLOkPSV9ragwF0tatXZ5GSa8nC/Py/FaSV+Q9E+SdpC0k6SDJF1TLOvv9snr00XaP0raJ+e1T37f+u5TPfJYNdepVtqv5Tq3c66Dd+XP75H0tNr/H8Mjy205SefmZXNjsfx2p74wFMvrjZIeKurJoZKeK2k7SS+WdKCkUyUdR71hkLSCxgdlf5K0X94/PU/SxyXdXXz/furMsjsU/31IukrSz4v38wfIZ9rUB0kvKLaJN+Rt4I6SXijp+CL/syQtN7L/tvbCZWiwkKQzcmV4QNLOHb5/f1FhPlq7vAwTXs4/kbS3pOW7fD9H0qXFst6lS7onauxqyQJJK7d9Pzt/3qpTm3XJ59BeO9m80WtN5/Ta/x/DI8vl3Ro7UfNvxTLcnfrCkJfFlho7K3y2pDV6pO14dZ56M7MGpROFreX0m077KUnPkLQkp7lV0grUmWVzUArE95K0Tn4/t1gm8xvmMW3qg9KJh8tymjs6TUvSl4vpvHFk/23thcvQZwGlMwetivDVLmmWk/SXYmO4Yu1yM4ysPuxV1IfDu6QpNx47dUmzU5Hmix2+X1HSbfn7v6jL2SFJXy3yeUbt/2emD0pNjVpnB3dv22HtTn1hyMvhF3kZ3CxpzgTzoN7MoEGpeWJrGby0R7r/LdJtTZ2ZGYMmFphNm/qg1Ky79f0hXfKYrXSMHZIuHNV/Secf09/Li/FvdkoQEQ8r3XckSWsqHZBh2XRmMb5Z+5e2Lell+e0lEfG7Tpnkzy/Nb1+ef1faXdJj8/jRuY51Mr8Yf2W3QmPKfEWpWcfREXFmv8TUl5nH9haSnpPffikibplAHtSbmWdWMf63Hun+Woyv1BqhzqA0DevDy7ukLctyr6Rj89utbW/eZVqTQmA2/bV64btH0h96pDurGH/26IqDysqdY6cN0CaSNsjjZ3X4vtT6fkOls12lXTqk6+Q8pbopUe+qsr230hXVW5WaNzdBfZl5Xl2MH9casb2m7c1tr9UgD+rNzLOwGN+0R7rWCcNQahrWQp1BabrVh1Y+l0bEDQ3K0i2fSSMwm/62zK+XR8SDPdJd0uE3WPbsVoxf0uH7Lft8ry7ft9eZRvnkOtk6Q0q9q8T2YyUdnt8eHBE3N/wp9WXmaXVnfoeki22/zvaflAL6hZJusf032x+zvWqXPKg3M8/3JN2Zxw+2vXx7AtvbKvUgLUnfj4g7i6+pMyhNm/qQt3MbDqEsQ0FgNo3ZfoxShw9S6pGvq4i4TWNnAzYaZblQh+3lJB1SfHRsh2Tlsu9ZZyRd3eV35ft7IuL2hvmsbXulnikxKv8haV2lm/K/McDvqC8zz1Py6yJJX5T0bUlPa0uzidL9ib+1vX6HPKg3M0w+2TNP0n2SniVpge032t7J9nNtf0zpasIsSedLek9bFtQZlKZTfdhQqdv+yZZlKAjMprfVivG7G6RvBWbdznJi6XaQUtetknRCRJzXIc0gdeaeYry9zrTyGaTedcoHI2b72ZL2l/SgpLdHvku5IerLzPO4/LqFpHcoPd/n7ZIeL+kxSh1OnZLTbC3puHxSqES9mYEi4gRJ2yud/NlG0tFKz3U6TSmQv1cpIHt2h+Zg1BmUplN9GFZZhoLAbHp7TDG+pEH6xfl15RGUBRXZ3k3SZ/LbmyQd0CXpIHVmcTHeXmda+QxS7zrlgxGyPUvpmS2W9PmIuHDALKgvM88q+XUlpWf2vCgijoyImyNicT7hs5fGgrNn6tE3y1NvZiDbK0p6raSXauwKQ2kdSfuqcwdk1BmUplN9GFZZhoLAbHq7vxif1TXVmNal2ftGUBZUYnsrSScoPWdjsaS9I+LGLskHqTPlpfz2OtPKZ5B61ykfjNaHlNq5X6X0XJlBUV9mnnKZH9epN7Tcs1nZgcy+PfKg3swAtldReszCv0paS6n59JZKy2UNSc+X9CulK64/tv2utiyoMyhNp/owrLIMBYHZ9HZXMd7kkmnrTGiTS7pYCtjeRNKpSo9BeEjSvhHRq+ehQerMKsV4e51p5TNIveuUD0Ykd3v+wfz2wIi4p1f6LqgvM0+5zE/pligi/izp2vx2hx55UG9mho9L2jWPvyUiDo6ISyJiSUTcGRGnSdpD0hlKV9M+Z7u8d5E6g9J0qg/DKstQEJhNYxFxv6TWM2Y27JXW9poaqzBX90qLpUO+6f4XktZX6nr4zbmNfy/ljas964zG37jaXmda+aySe/xrks/NEbG4Z0oM00FKZ/f+Jmm27X3aB6V7hFr2LL5rbSuoLzNPueya3uj++LbPqTczSH521Jvy24URcXSndLnnu4/kt8sVv5GoMxhvOtWHYZVlKAjMpr+L8+sTba/QI90WHX6DpZTtOUo3VLeeF3NgRBzT4yctfynGt+ia6tHft9eZRvnkOtl6bg31bmq1mlRsqtSVdafhn4r0Hyk+Xzt/Rn2Zef5cjD+qy/M2re/bH9VCvZlZ1tFYpzH/1ydt+bzVcplSZ1CaNvUhIu7WWJA1mbIMBYHZ9Per/LqKpGf0SFc+3+rXoysORs32GpJ+rrFurQ+JiC83/PkVkq7L47v1SqixZinXKnWdXfpVMd4rn+01dqWWerf0ob7MPGcX45t1TZW0Tgxd2/Y59WZmKQPzXieIJWnFLr+jzqA03epDK58n2163Rz4jP9YmMJv+TizG39QpQe7K+I357e1KbbyxFLI9W9JPJW2XP/p0RBzW9Pe5q/Qf5bdb2N6pU7r8eevMz486dLF+ptIDaCVpv9yUpZN5xXi/ZpYYooiYFxHuNWh8hyB7FN8tynlQX2aekyQ9kMfbe1t8RO4Jdq389pzyO+rNjHOrxh4uvXOf1jvlgesVrRHqDErTsD6c2CVtWZbZkvbOb/8SEQu7TGtyIoJhmg9KZzhDaWe6c4fv35+/D0mH1i4vw4SX8yylK2WtZfmFCebzpFxXQtICSSu3fb9y/rxVpzbvks8nirK8v8P3OxfTObP2/8fQcRkeWizD3akvDHlZfKVYVvt0+H41pSZrrTQ7UG9m9iDpu8Vy+liXNGsqNZVtpXs+dWZmDJLmFstkfsPfTJv6oHSl9/Kc5g5Jm3VI8+ViOvNG9l/WXpgMDRaStK3SgxtDqfeYD0raSakHpCOLinKppNVql5dhwsv5+GJZ/lLSU5U6b+g2PKlHXv9e5PVHSa9RupT/mvy+9d2/9chjtVynWmmPzHVup1wH78qf3ytpm9r/H0PHZXhosfx2p74w5GW1tqQriwOeL+Zl9Qyls8UXF8vxK9QbBqWrFvcUy+kkpXtYt1U68D2oqFMh6RfUmWV3kPTsvK1oDe8rlsev2r6b1yOfaVMfJL1YqffrkHSDpHdK2lHSCyT9sMj/HEnLj+y/rb1wGRouqPRAxzuKitE+XCrpibXLyTCpZdxt2XYbFvXIazlJ3+jz+/+RtFyfMj1R0sIeedwhaa/a/x1D1+V3aLGsdqe+MBTLaktJl/VZ5t+QtCL1hiEvp+dKurnBvumXktakziy7g6T5DerBI0OPfKZVfZD0VqXnxXbL5/eS5ozyv3UuCJYCtp8g6V2SXqLUpecSpUuvx0n6UkTcW7F4mCTbg66MV0bE3D55vljS25SeQzRH6fELCyQdGRFdn2HUlscqkt4h6dVKG79ZSj0YnSzp8Ii4csByY4rYPlTSx/LbPSLizD7pqS8zSF5WB0h6laTNlZ7hc5PSTe1HRsQZDfOh3swQtteS9BZJL5K0laTHKnXycYPSMv+upJOiz8EldWbpZnu+pP2apo90z3Ov/KZNfbC9taR/kfQcpccV3aPUiuA7kv4n0mMhRobADAAAAAAqo1dGAAAAAKiMwAwAAAAAKiMwAwAAAIDKCMwAAAAAoDICMwAAAACojMAMAAAAACojMAMAAACAygjMAAAAAKAyAjMAAAAAqIzADAAAAAAqIzADAAAAgMoIzAAAAACgMgIzAAAAAKiMwAwAAAAAKiMwAwAAAIDKCMwAAAAAoDICMwAAAACojMAMAAAAACojMAMAoAHbj7G9xHbYPqR2eQAAyxYCMwAAmtlO0op5fEHNggAAlj0EZgAANLNjfg1Jf6hZEADAsofADACAZnbIr5dHxO01CwIAWPY4ImqXAQCAacv2zZLm9En2g4jYZyrKAwBYNnHFDACALmyvr/5BmSRdMOqyAACWbSvULgAAANPYbZKeKunJkn6YP3uXpNPb0l0zlYUCACx7CMwAAOgiIu6TdJHtbYqPT46IyysVCQCwjKIpIwAA/W2TX++S9NeK5QAALKMIzAAA6G+b/HpB0GsWAGAECMwAAOjv6fn1/JqFAAAsuwjMAADowfYGGuuZ8fyKRQEALMMIzAAA6G2bYvxPtQoBAFi2EZgBANDbNvn1IUkXViwHAGAZRmAGAEBvrfvLLo2I+6uWBACwzCIwAwCgtyfn1z9XLQUAYJlGYAYAQG+r59cVqpYCALBMYycDAEBvf5M0V9Jett8p6XeSWk0ar4yIu2oVDACw7DDPyQQAoDvbe0k6SZI7fP2MiPjjFBcJALAMIjADAKAP2y+Q9F5J20t6rFKQ9oCkVSNiScWiAQCWEQRmAAAAAFAZnX8AAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGUEZgAAAABQGYEZAAAAAFRGYAYAAAAAlRGYAQAAAEBlBGYAAAAAUBmBGQAAAABURmAGAAAAAJURmAEAAABAZQRmAAAAAFAZgRkAAAAAVEZgBgAAAACVEZgBAAAAQGX/HyPlG8AfBt7pAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for j,(prop,name) in enumerate(zip(props,names)):\n", - " fi,ax = plt.subplots(1,figsize=(5,2),dpi=200)\n", - " plt.plot(range(T),prop,lw=1,color='purple')\n", - " plt.title(name)\n", - " plt.xlabel('$t$')\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "34b7062c-5241-48fc-afc4-661f09cbbe92", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6a70d64e-4e87-43d8-b241-f44b47cead7c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58196456-2dfb-4a7f-a906-718907f2c944", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fee84955-d947-4add-865f-e087c5801edd", - "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": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/playground.ipynb b/playground.ipynb deleted file mode 100644 index 30f7628..0000000 --- a/playground.ipynb +++ /dev/null @@ -1,271 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "ab4e64eb-1e76-48f7-b3ac-0f94447e66d5", - "metadata": {}, - "outputs": [], - "source": [ - "import netrd\n", - "import numpy as np\n", - "import networkx as nx\n", - "from matplotlib import pyplot as plt\n", - "from netrd.distance import Hamming" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c984fa12-1e95-4e62-9ca1-39ac8c9feb69", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G1 = nx.gnp_random_graph(n=10, p=0.2)\n", - "G2 = nx.Graph(G1)\n", - "\n", - "distance = Hamming()\n", - "\n", - "distance(G1, G2)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fe15a91b-841d-4e25-b34c-664e8908ed2d", - "metadata": {}, - "outputs": [], - "source": [ - "distance_class = netrd.distance.Hamming" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "94337e3b-5de7-4b07-a319-1f123d0f651a", - "metadata": {}, - "outputs": [], - "source": [ - "distance = distance_class()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "888c9111-e239-41ba-be24-20cf3b6e4d55", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "distance(G1, G2)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "f9f58d9c-7ec4-4d34-82ba-0fe35ac9509b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "rewire_steps = range(100)\n", - "arr = np.random.random(size=(100,10))\n", - "mean = np.mean(arr, axis=1)\n", - "std = np.std(arr, axis=1)\n", - "plt.fill_between(rewire_steps, mean-std, mean+std, color='cyan')\n", - "plt.plot(rewire_steps, mean)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d757ebdb-c825-4a09-b13c-8ef80d7882ee", - "metadata": {}, - "outputs": [], - "source": [ - "import warnings" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "863c164d-9b10-49e9-aed6-b7f920f7448d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Hamming'" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "distance_class.__name__" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "76db3b1d-7ff7-4baa-944a-f2efb20324bc", - "metadata": {}, - "outputs": [], - "source": [ - "import netrw\n", - "from netrw.rewire import KarrerRewirer, AlgebraicConnectivity, NetworkXEdgeSwap\n", - "from netrw.analysis.distance_trajectory import *\n", - "import networkx as nx" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "06fef0d8-239b-4df2-8d50-abadfffa1398", - "metadata": {}, - "outputs": [], - "source": [ - "G = nx.path_graph(40)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "021b4a0b-3ac4-4636-a59a-df1ac17ded9d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
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"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": "3.10.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 6d36075c592625abf0de3eda4e6581bf5831c1d3 Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 15:17:32 -0400 Subject: [PATCH 66/72] update __init__ --- netrw/rewire/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/netrw/rewire/__init__.py b/netrw/rewire/__init__.py index 5f5294a..338906c 100644 --- a/netrw/rewire/__init__.py +++ b/netrw/rewire/__init__.py @@ -4,5 +4,6 @@ from .networkXEdgeSwap import NetworkXEdgeSwap from .global_rewiring import GlobalRewiring from .local_edge_rewire import LocalEdgeRewiring +from .robust_rewiring import RobustRewirer __all__ = [] From 24c97389f5328aa4ad56934a20aab4707d620037 Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 15:18:27 -0400 Subject: [PATCH 67/72] added imports --- netrw/rewire/__init__.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/netrw/rewire/__init__.py b/netrw/rewire/__init__.py index 338906c..0df8ed9 100644 --- a/netrw/rewire/__init__.py +++ b/netrw/rewire/__init__.py @@ -1,9 +1,9 @@ -from .base import BaseRewirer from .karrer import KarrerRewirer -from .algebraic_connectivity import AlgebraicConnectivity -from .networkXEdgeSwap import NetworkXEdgeSwap from .global_rewiring import GlobalRewiring from .local_edge_rewire import LocalEdgeRewiring +from .algebraic_connectivity import AlgebraicConnectivity +from .randomized_weights import RandomizedWeightCM_swap +from .randomized_weights import RandomizedWeightCM_redistribution from .robust_rewiring import RobustRewirer __all__ = [] From 1305796ef89bfa07e6f46c4c48f4e177d9dcdaa0 Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 15:19:27 -0400 Subject: [PATCH 68/72] removed unnecessary file --- test.ipynb | 104 ----------------------------------------------------- 1 file changed, 104 deletions(-) delete mode 100644 test.ipynb diff --git a/test.ipynb b/test.ipynb deleted file mode 100644 index cdd148e..0000000 --- a/test.ipynb +++ /dev/null @@ -1,104 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "import netrw\n", - "import numpy as np\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt\n", - "from netrw.rewire import KarrerRewirer, AlgebraicConnectivity, NetworkXEdgeSwap\n", - "from netrw.analysis import properties_overtime\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "G = nx.erdos_renyi_graph(100,.15)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "G0 = NetworkXEdgeSwap().rewire(G)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "rewire() got an unexpected keyword argument 'nswap'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/clara/netrw/test.ipynb Cell 4'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m property_dict, fig \u001b[39m=\u001b[39m properties_overtime\u001b[39m.\u001b[39;49mproperties_overtime(init_graph\u001b[39m=\u001b[39;49mG, rewire_method\u001b[39m=\u001b[39;49m NetworkXEdgeSwap(), property1\u001b[39m=\u001b[39;49m nx\u001b[39m.\u001b[39;49maverage_clustering, tmax\u001b[39m=\u001b[39;49m\u001b[39m100\u001b[39;49m, numit\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m)\n", - "File \u001b[0;32m~/netrw/netrw/analysis/properties_overtime.py:29\u001b[0m, in \u001b[0;36mproperties_overtime\u001b[0;34m(init_graph, rewire_method, property1, tmax, numit)\u001b[0m\n\u001b[1;32m 27\u001b[0m property_list \u001b[39m=\u001b[39m [property1(G0)] \u001b[39m# calculate property of initial network\u001b[39;00m\n\u001b[1;32m 28\u001b[0m \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(tmax):\n\u001b[0;32m---> 29\u001b[0m rewire_method(G0, nswap\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m) \u001b[39m#rewire \u001b[39;00m\n\u001b[1;32m 30\u001b[0m property_list\u001b[39m.\u001b[39mappend(property1(G0)) \u001b[39m#calculate property of the rewired network\u001b[39;00m\n\u001b[1;32m 31\u001b[0m property_dict[i] \u001b[39m=\u001b[39m property_list\n", - "File \u001b[0;32m~/netrw/netrw/rewire/base.py:13\u001b[0m, in \u001b[0;36mBaseRewirer.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m---> 13\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrewire(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", - "\u001b[0;31mTypeError\u001b[0m: rewire() got an unexpected keyword argument 'nswap'" - ] - } - ], - "source": [ - "property_dict, fig = properties_overtime.properties_overtime(init_graph=G, rewire_method= NetworkXEdgeSwap(), property1= nx.average_clustering, tmax=100, numit=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "module" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(properties_overtime)" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "abdefde89911577035689be2b705da6a7f51bdbfe4520de839bf860af06394b4" - }, - "kernelspec": { - "display_name": "Python 3.9.7 ('base')", - "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": "3.9.7" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} From b7b637ecd566b83c94da7b0ff1105b42e6edd4b0 Mon Sep 17 00:00:00 2001 From: hartle <32047935+hartle@users.noreply.github.com> Date: Wed, 20 Jul 2022 13:25:43 -0600 Subject: [PATCH 69/72] bug fixes --- netrw/analysis/rewiring_analysis.py | 39 ++++++++++++++++++----------- 1 file changed, 25 insertions(+), 14 deletions(-) diff --git a/netrw/analysis/rewiring_analysis.py b/netrw/analysis/rewiring_analysis.py index 648a36d..181e5e2 100644 --- a/netrw/analysis/rewiring_analysis.py +++ b/netrw/analysis/rewiring_analysis.py @@ -43,26 +43,26 @@ def various_properties_overtime( all_properties[name] = np.zeros((numit, tmax)) - # loop over rewiring instances - for i in range(numit): + # loop over rewiring instances + for i in range(numit): - G0 = deepcopy(init_graph) + G0 = deepcopy(init_graph) - # calculate properties of initial network - for name, func in zip(property_functions, function_names): + # calculate properties of initial network + for func, name in zip(property_functions, function_names): - all_properties[name][i, 0] = func(G0) + all_properties[name][i, 0] = func(G0) - # loop over timesteps - for j in range(1, tmax): + # loop over timesteps + for j in range(1, tmax): - G0 = rw.step_rewire(G0, copy_graph=False) # rewire + G0 = rw.step_rewire(G0, copy_graph=False) # rewire - # calculate properties of the rewired network + # calculate properties of the rewired network - for name, func in zip(property_functions, function_names): + for name, func in zip(function_names, property_functions): - all_properties[name][i, j] = func(G0) + all_properties[name][i, j] = func(G0) return all_properties @@ -150,7 +150,8 @@ def average_shortest_path_length(G): lambda G: average_shortest_path_length(G), lambda G: nx.number_connected_components(G), lambda G: nx.assortativity.degree_assortativity_coefficient(G), - lambda G: np.sum(np.array(list(dict(nx.degree(G)).values())) ** 2) / n, + lambda G: np.sum(np.array(list(dict(nx.degree(G)).values())) ** 2) + / G.number_of_nodes(), lambda G: np.min(np.array(list(dict(nx.degree(G)).values()))), lambda G: np.max(np.array(list(dict(nx.degree(G)).values()))), lambda G: average_local_clustering(G), @@ -171,9 +172,19 @@ def average_shortest_path_length(G): # test run init_graph = nx.fast_gnp_random_graph(100, 0.03) -rewire_method = KarrerRewirer +rewire_method = NetworkXEdgeSwap tmax = 100 numit = 10 all_properties = various_properties_overtime( init_graph, rewire_method, property_functions, function_names, tmax, numit ) + + +# test plot +for name in function_names: + fi, ax = plt.subplots(1, figsize=(5, 2), dpi=200) + plt.plot(range(tmax), np.mean(all_properties[name], axis=0)) + plt.title(name) + plt.xlabel("$t$") + plt.tight_layout() + plt.savefig("figures/" + name) From 4ab7ea5728c6c0e99fa3483135d994c51a779e04 Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 15:46:43 -0400 Subject: [PATCH 70/72] update --- netrw/rewire/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/netrw/rewire/__init__.py b/netrw/rewire/__init__.py index 8903638..6aa1891 100644 --- a/netrw/rewire/__init__.py +++ b/netrw/rewire/__init__.py @@ -2,6 +2,7 @@ from .karrer import KarrerRewirer from .global_rewiring import GlobalRewiring from .local_edge_rewire import LocalEdgeRewiring +from .networkXEdgeSwap import NetworkXEdgeSwap from .assortative import DegreeAssortativeRewirer from .algebraic_connectivity import AlgebraicConnectivity from .randomized_weights import RandomizedWeightCM_swap From 0d821c9107c6aa8d305c29ff1723853ec61f111d Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 15:52:20 -0400 Subject: [PATCH 71/72] style: format with black and isort --- netrw/analysis/__init__.py | 6 +- netrw/analysis/distance_trajectory.py | 9 +- netrw/analysis/distributions.py | 1 + .../plot_property_values_over_time.py | 5 +- netrw/analysis/properties_overtime.py | 8 +- netrw/analysis/rewiring_analysis.py | 8 +- netrw/rewire/__init__.py | 12 +- netrw/rewire/algebraic_connectivity.py | 8 +- netrw/rewire/assortative.py | 22 ++- netrw/rewire/global_rewiring.py | 3 +- netrw/rewire/karrer.py | 4 +- netrw/rewire/local_edge_rewire.py | 4 +- netrw/rewire/networkXEdgeSwap.py | 4 +- netrw/rewire/randomized_weights.py | 4 +- netrw/rewire/robust_rewiring.py | 10 +- netrw/rewire/spatial_small_worlds.py | 161 ++++++++++++++---- netrw/visualization/visualization.py | 2 +- tests/test_karrer.py | 1 + 18 files changed, 201 insertions(+), 71 deletions(-) diff --git a/netrw/analysis/__init__.py b/netrw/analysis/__init__.py index 8d3176c..77bad4f 100644 --- a/netrw/analysis/__init__.py +++ b/netrw/analysis/__init__.py @@ -1 +1,5 @@ -from .distributions import * +from .confusion import * +from .distance_trajectory import * +from .plot_property_values_over_time import * +from .properties_heatmap import * +from .properties_overtime import * diff --git a/netrw/analysis/distance_trajectory.py b/netrw/analysis/distance_trajectory.py index 89f9842..a946045 100644 --- a/netrw/analysis/distance_trajectory.py +++ b/netrw/analysis/distance_trajectory.py @@ -1,8 +1,11 @@ -from matplotlib import pyplot as plt +import copy +import warnings + +import netrd import networkx as nx import numpy as np -import warnings, copy -import netrd +from matplotlib import pyplot as plt + from ..rewire import NetworkXEdgeSwap diff --git a/netrw/analysis/distributions.py b/netrw/analysis/distributions.py index 97e455b..ffba35f 100644 --- a/netrw/analysis/distributions.py +++ b/netrw/analysis/distributions.py @@ -1,4 +1,5 @@ from copy import deepcopy + import numpy as np diff --git a/netrw/analysis/plot_property_values_over_time.py b/netrw/analysis/plot_property_values_over_time.py index 943ebe7..ba0a6f5 100644 --- a/netrw/analysis/plot_property_values_over_time.py +++ b/netrw/analysis/plot_property_values_over_time.py @@ -1,6 +1,7 @@ -import numpy as np -import networkx as nx import matplotlib.pyplot as plt +import networkx as nx +import numpy as np + import netrw diff --git a/netrw/analysis/properties_overtime.py b/netrw/analysis/properties_overtime.py index c3ec55d..013a59c 100644 --- a/netrw/analysis/properties_overtime.py +++ b/netrw/analysis/properties_overtime.py @@ -1,9 +1,11 @@ from copy import deepcopy -import numpy as np -import networkx as nx + import matplotlib.pyplot as plt +import networkx as nx +import numpy as np + import netrw -from netrw.rewire import KarrerRewirer, AlgebraicConnectivity, NetworkXEdgeSwap +from netrw.rewire import AlgebraicConnectivity, KarrerRewirer, NetworkXEdgeSwap def properties_overtime(init_graph, rewire_method, property1, tmax, numit): diff --git a/netrw/analysis/rewiring_analysis.py b/netrw/analysis/rewiring_analysis.py index 181e5e2..abcc2dd 100644 --- a/netrw/analysis/rewiring_analysis.py +++ b/netrw/analysis/rewiring_analysis.py @@ -1,9 +1,11 @@ from copy import deepcopy -import numpy as np -import networkx as nx + import matplotlib.pyplot as plt +import networkx as nx +import numpy as np + import netrw -from netrw.rewire import KarrerRewirer, AlgebraicConnectivity, NetworkXEdgeSwap +from netrw.rewire import AlgebraicConnectivity, KarrerRewirer, NetworkXEdgeSwap def various_properties_overtime( diff --git a/netrw/rewire/__init__.py b/netrw/rewire/__init__.py index 6aa1891..6728242 100644 --- a/netrw/rewire/__init__.py +++ b/netrw/rewire/__init__.py @@ -1,12 +1,14 @@ +from .algebraic_connectivity import AlgebraicConnectivity +from .assortative import DegreeAssortativeRewirer from .base import BaseRewirer -from .karrer import KarrerRewirer from .global_rewiring import GlobalRewiring +from .karrer import KarrerRewirer from .local_edge_rewire import LocalEdgeRewiring from .networkXEdgeSwap import NetworkXEdgeSwap -from .assortative import DegreeAssortativeRewirer -from .algebraic_connectivity import AlgebraicConnectivity -from .randomized_weights import RandomizedWeightCM_swap -from .randomized_weights import RandomizedWeightCM_redistribution +from .randomized_weights import ( + RandomizedWeightCM_redistribution, + RandomizedWeightCM_swap, +) from .robust_rewiring import RobustRewirer from .spatial_small_worlds import SpatialSmallWorld diff --git a/netrw/rewire/algebraic_connectivity.py b/netrw/rewire/algebraic_connectivity.py index e741a4e..081431f 100644 --- a/netrw/rewire/algebraic_connectivity.py +++ b/netrw/rewire/algebraic_connectivity.py @@ -1,9 +1,11 @@ -from .base import BaseRewirer +import copy +import warnings + import networkx as nx import numpy as np -import copy from scipy import linalg as la -import warnings + +from .base import BaseRewirer class AlgebraicConnectivity(BaseRewirer): diff --git a/netrw/rewire/assortative.py b/netrw/rewire/assortative.py index ffede69..722b96b 100644 --- a/netrw/rewire/assortative.py +++ b/netrw/rewire/assortative.py @@ -1,16 +1,18 @@ -from . import BaseRewirer import copy import random + import networkx as nx import numpy as np +from . import BaseRewirer + class DegreeAssortativeRewirer(BaseRewirer): """ - Do degree-preserving rewiring that increases/decreases assortativity - as described in CHANGING CORRELATIONS IN NETWORKS: ASSORTATIVITY AND DISSORTATIVITY, - R. Xulvi-Brunet and I.M. Sokolov + Do degree-preserving rewiring that increases/decreases assortativity + as described in CHANGING CORRELATIONS IN NETWORKS: ASSORTATIVITY AND DISSORTATIVITY, + R. Xulvi-Brunet and I.M. Sokolov """ @@ -70,7 +72,9 @@ def step_rewire(self, G, p=0.5, assortative=True, copy_graph=True, verbose=False else: return G - def full_rewire(self, G, timesteps=1000, p=0.5, assortative=True, copy_graph=True, verbose=False): + def full_rewire( + self, G, timesteps=1000, p=0.5, assortative=True, copy_graph=True, verbose=False + ): """ Runs step_rewire for a number of steps (default 1000 for no reason) """ @@ -78,11 +82,15 @@ def full_rewire(self, G, timesteps=1000, p=0.5, assortative=True, copy_graph=Tru removed_edges = {} added_edges = {} for t in range(timesteps): - G,removed,added = self.step_rewire(G, p=p, assortative=assortative, copy_graph=copy_graph,verbose=True) + G, removed, added = self.step_rewire( + G, p=p, assortative=assortative, copy_graph=copy_graph, verbose=True + ) removed_edges[t] = removed added_edges[t] = added else: for t in range(timesteps): - G = self.step_rewire(G, p=p, assortative=assortative, copy_graph=copy_graph) + G = self.step_rewire( + G, p=p, assortative=assortative, copy_graph=copy_graph + ) return G diff --git a/netrw/rewire/global_rewiring.py b/netrw/rewire/global_rewiring.py index ca43daa..85a4911 100644 --- a/netrw/rewire/global_rewiring.py +++ b/netrw/rewire/global_rewiring.py @@ -1,8 +1,9 @@ -from .base import BaseRewirer import copy import random import warnings +from .base import BaseRewirer + class GlobalRewiring(BaseRewirer): """ diff --git a/netrw/rewire/karrer.py b/netrw/rewire/karrer.py index 1def5a5..bd23e2e 100644 --- a/netrw/rewire/karrer.py +++ b/netrw/rewire/karrer.py @@ -1,8 +1,10 @@ -from . import BaseRewirer import copy + import networkx as nx import numpy as np +from . import BaseRewirer + class KarrerRewirer(BaseRewirer): """Perturb the graph in the way described by Karrer et al. (2008). diff --git a/netrw/rewire/local_edge_rewire.py b/netrw/rewire/local_edge_rewire.py index 8afbf1a..edf07c2 100644 --- a/netrw/rewire/local_edge_rewire.py +++ b/netrw/rewire/local_edge_rewire.py @@ -1,9 +1,11 @@ -from . import BaseRewirer import copy import random + import networkx as nx import numpy as np +from . import BaseRewirer + class LocalEdgeRewiring(BaseRewirer): """Perturb one edge of node `i` in the way described by Klein & McCabe diff --git a/netrw/rewire/networkXEdgeSwap.py b/netrw/rewire/networkXEdgeSwap.py index fbc3caf..c9818a9 100644 --- a/netrw/rewire/networkXEdgeSwap.py +++ b/netrw/rewire/networkXEdgeSwap.py @@ -1,9 +1,11 @@ -from . import BaseRewirer import copy import itertools as it import random + import networkx as nx +from . import BaseRewirer + class NetworkXEdgeSwap(BaseRewirer): """Perturb one edge of node `i` in the way described by Karrer et al. (2008). diff --git a/netrw/rewire/randomized_weights.py b/netrw/rewire/randomized_weights.py index cf61695..1945da5 100644 --- a/netrw/rewire/randomized_weights.py +++ b/netrw/rewire/randomized_weights.py @@ -1,10 +1,12 @@ -from .base import BaseRewirer import copy import itertools as it import random + import networkx as nx import numpy as np +from .base import BaseRewirer + class RandomizedWeightCM_swap(BaseRewirer): """ diff --git a/netrw/rewire/robust_rewiring.py b/netrw/rewire/robust_rewiring.py index fe7a6e9..079482f 100644 --- a/netrw/rewire/robust_rewiring.py +++ b/netrw/rewire/robust_rewiring.py @@ -1,10 +1,12 @@ +import copy +import random +import warnings +from operator import itemgetter + import networkx as nx import numpy as np -from operator import itemgetter -import random -import copy + from .base import BaseRewirer -import warnings class RobustRewirer(BaseRewirer): diff --git a/netrw/rewire/spatial_small_worlds.py b/netrw/rewire/spatial_small_worlds.py index e0354f2..0602748 100644 --- a/netrw/rewire/spatial_small_worlds.py +++ b/netrw/rewire/spatial_small_worlds.py @@ -1,7 +1,9 @@ +import copy +import random + import networkx as nx import numpy as np -import random -import copy + from .base import BaseRewirer @@ -22,7 +24,20 @@ class SpatialSmallWorld(BaseRewirer): """ - def step_rewire(self,G,p,dim,alpha,copy_graph=False,is_periodic=True,does_remove=True,manhattan_dist=True,timesteps=1,directed=False,verbose=False): + def step_rewire( + self, + G, + p, + dim, + alpha, + copy_graph=False, + is_periodic=True, + does_remove=True, + manhattan_dist=True, + timesteps=1, + directed=False, + verbose=False, + ): if copy_graph: G = copy.deepcopy(G) if nx.is_directed(G) and directed is True: @@ -44,75 +59,153 @@ def step_rewire(self,G,p,dim,alpha,copy_graph=False,is_periodic=True,does_remove if dimsize == 3: non_edge_list = list(nx.Graph(nx.non_edges(G)).edges()) if not is_periodic: - edge_p = [((edge_pair[0][0]-edge_pair[1][0])**2+(edge_pair[0][1]-edge_pair[1][1])**2+(edge_pair[0][2]-edge_pair[1][2])**2)**(1/2) for edge_pair in non_edge_list] + edge_p = [ + ( + (edge_pair[0][0] - edge_pair[1][0]) ** 2 + + (edge_pair[0][1] - edge_pair[1][1]) ** 2 + + (edge_pair[0][2] - edge_pair[1][2]) ** 2 + ) + ** (1 / 2) + for edge_pair in non_edge_list + ] unique_lengths = np.unique(edge_p) else: - edge_p = [(abs(edge_pair[0][0]-edge_pair[1][0]),abs(edge_pair[0][1]-edge_pair[1][1]),abs(edge_pair[0][2]-edge_pair[1][2])) for edge_pair in non_edge_list] - edge_p = [((dim[2] - dists[0]) if dists[0] > (dim[2])/2 else dists[0] , (dim[1] - dists[1]) if dists[1] > (dim[1])/2 else dists[1] , (dim[0] - dists[2]) if dists[2] > (dim[0])/2 else dists[2]) for dists in edge_p] + edge_p = [ + ( + abs(edge_pair[0][0] - edge_pair[1][0]), + abs(edge_pair[0][1] - edge_pair[1][1]), + abs(edge_pair[0][2] - edge_pair[1][2]), + ) + for edge_pair in non_edge_list + ] + edge_p = [ + ( + (dim[2] - dists[0]) + if dists[0] > (dim[2]) / 2 + else dists[0], + (dim[1] - dists[1]) + if dists[1] > (dim[1]) / 2 + else dists[1], + (dim[0] - dists[2]) + if dists[2] > (dim[0]) / 2 + else dists[2], + ) + for dists in edge_p + ] if manhattan_dist: edge_p = [(dists[0] + dists[1] + dists[2]) for dists in edge_p] else: - edge_p = [(dists[0]**2 + dists[1]**2 + dists[2]**2)**(1/2) for dists in edge_p] + edge_p = [ + (dists[0] ** 2 + dists[1] ** 2 + dists[2] ** 2) ** (1 / 2) + for dists in edge_p + ] unique_lengths = np.unique(edge_p) randomVal = random.choices( - unique_lengths, weights=(1 / np.power(unique_lengths,(alpha))), k=1) + unique_lengths, weights=(1 / np.power(unique_lengths, (alpha))), k=1 + ) indices = list(np.where(np.array(edge_p) == randomVal)[0]) - randomList = random.choices( - [non_edge_list[i] for i in indices], k=1) + randomList = random.choices([non_edge_list[i] for i in indices], k=1) edge_list = list(G.edges()) - rand_edge = edge_list[random.randint(0,len(edge_list)-1)] + rand_edge = edge_list[random.randint(0, len(edge_list) - 1)] if does_remove: - G.remove_edge(rand_edge[0],rand_edge[1]) - G.add_edge(randomList[0][0],randomList[0][1]) + G.remove_edge(rand_edge[0], rand_edge[1]) + G.add_edge(randomList[0][0], randomList[0][1]) elif dimsize == 2: non_edge_list = list(nx.Graph(nx.non_edges(G)).edges()) if not is_periodic: - edge_p = [((edge_pair[0][0]-edge_pair[1][0])**2+(edge_pair[0][1]-edge_pair[1][1])**2)**(1/2) for edge_pair in non_edge_list] + edge_p = [ + ( + (edge_pair[0][0] - edge_pair[1][0]) ** 2 + + (edge_pair[0][1] - edge_pair[1][1]) ** 2 + ) + ** (1 / 2) + for edge_pair in non_edge_list + ] unique_lengths = np.unique(edge_p) else: - edge_p = [(abs(edge_pair[0][0]-edge_pair[1][0]),abs(edge_pair[0][1]-edge_pair[1][1])) for edge_pair in non_edge_list] - edge_p = [(dim[1] - dists[0] if dists[0] > (dim[1])/2 else dists[0] , dim[0] - dists[1] if dists[1] > (dim[0])/2 else dists[1]) for dists in edge_p] + edge_p = [ + ( + abs(edge_pair[0][0] - edge_pair[1][0]), + abs(edge_pair[0][1] - edge_pair[1][1]), + ) + for edge_pair in non_edge_list + ] + edge_p = [ + ( + dim[1] - dists[0] if dists[0] > (dim[1]) / 2 else dists[0], + dim[0] - dists[1] if dists[1] > (dim[0]) / 2 else dists[1], + ) + for dists in edge_p + ] if manhattan_dist: edge_p = [(dists[0] + dists[1]) for dists in edge_p] else: - edge_p = [(dists[0]**2 + dists[1]**2)**(1/2) for dists in edge_p] + edge_p = [ + (dists[0] ** 2 + dists[1] ** 2) ** (1 / 2) + for dists in edge_p + ] unique_lengths = np.unique(edge_p) randomVal = random.choices( - unique_lengths, weights=(1 / np.power(unique_lengths,(alpha))), k=1) + unique_lengths, weights=(1 / np.power(unique_lengths, (alpha))), k=1 + ) indices = list(np.where(np.array(edge_p) == randomVal)[0]) - randomList = random.choices( - [non_edge_list[i] for i in indices], k=1) + randomList = random.choices([non_edge_list[i] for i in indices], k=1) edge_list = list(G.edges()) - rand_edge = edge_list[random.randint(0,len(edge_list)-1)] + rand_edge = edge_list[random.randint(0, len(edge_list) - 1)] if does_remove: - G.remove_edge(rand_edge[0],rand_edge[1]) - G.add_edge(randomList[0][0],randomList[0][1]) + G.remove_edge(rand_edge[0], rand_edge[1]) + G.add_edge(randomList[0][0], randomList[0][1]) if verbose: if does_remove: - removed_edges[t] = [rand_edge[0],rand_edge[1]] - added_edges[t] = [randomList[0][0],randomList[0][1]] + removed_edges[t] = [rand_edge[0], rand_edge[1]] + added_edges[t] = [randomList[0][0], randomList[0][1]] if verbose: return G, removed_edges, added_edges else: return G - def full_rewire(self,G,p,dim,alpha,copy_graph=False,is_periodic=True,does_remove=True,manhattan_dist=True,timesteps=-1,directed=False,verbose=False): + def full_rewire( + self, + G, + p, + dim, + alpha, + copy_graph=False, + is_periodic=True, + does_remove=True, + manhattan_dist=True, + timesteps=-1, + directed=False, + verbose=False, + ): if timesteps == -1: - timesteps = int(p*len(G.nodes())) - G = self.step_rewire(G,p,dim,alpha,copy_graph,is_periodic,does_remove,manhattan_dist,timesteps,directed,verbose) + timesteps = int(p * len(G.nodes())) + G = self.step_rewire( + G, + p, + dim, + alpha, + copy_graph, + is_periodic, + does_remove, + manhattan_dist, + timesteps, + directed, + verbose, + ) return G - def initialize_graph(self,dim): + def initialize_graph(self, dim): if len(dim) == 3: dimsize = 3 elif len(dim) == 2: dimsize = 2 else: raise ValueError("Lattice Dimension is not 2-3") - G = nx.grid_graph(dim=dim,periodic=False) + G = nx.grid_graph(dim=dim, periodic=False) return G - def plot(self,G,dim): + def plot(self, G, dim): if len(dim) == 3: dimsize = 3 elif len(dim) == 2: @@ -120,7 +213,7 @@ def plot(self,G,dim): else: raise ValueError("Lattice Dimension is not 2-3") if dimsize == 3: - pos = {(x,y,z):(x+5*z/7,y+5*z/7) for x,y,z in G.nodes()} + pos = {(x, y, z): (x + 5 * z / 7, y + 5 * z / 7) for x, y, z in G.nodes()} elif dimsize == 2: - pos = {(x,y):(x,y) for x,y in G.nodes()} - nx.draw(G,pos) + pos = {(x, y): (x, y) for x, y in G.nodes()} + nx.draw(G, pos) diff --git a/netrw/visualization/visualization.py b/netrw/visualization/visualization.py index 196378b..48c5996 100644 --- a/netrw/visualization/visualization.py +++ b/netrw/visualization/visualization.py @@ -1,6 +1,6 @@ +import matplotlib.pyplot as plt import networkx as nx import numpy as np -import matplotlib.pyplot as plt def visualize_rewiring(G1, G2, pos): diff --git a/tests/test_karrer.py b/tests/test_karrer.py index c914eb3..9648163 100644 --- a/tests/test_karrer.py +++ b/tests/test_karrer.py @@ -1,5 +1,6 @@ import networkx as nx import numpy as np + from netrw.rewire import KarrerRewirer From 8f4c1ad672a7b103bb163eb6dd4755daf4b7e66d Mon Sep 17 00:00:00 2001 From: nwlandry Date: Wed, 20 Jul 2022 15:53:13 -0400 Subject: [PATCH 72/72] delete distributions file. (Redundant) --- netrw/analysis/distributions.py | 41 --------------------------------- 1 file changed, 41 deletions(-) delete mode 100644 netrw/analysis/distributions.py diff --git a/netrw/analysis/distributions.py b/netrw/analysis/distributions.py deleted file mode 100644 index ffba35f..0000000 --- a/netrw/analysis/distributions.py +++ /dev/null @@ -1,41 +0,0 @@ -from copy import deepcopy - -import numpy as np - - -def get_property_distribution( - G, rewiring_method, property, skip=10, num_samples=1000, **kwargs -): - """_summary_ - - More details - - Parameters - ---------- - G : NetworkX graph - The initial graph - rewiring_method : Rewire method - The class that will rewire the graph step-by-step. Must have the method `step_rewire`. - property : function - a function that accepts a NetworkX Graph object as an input. This computes a property of interest. - skip : int, default:100 - How often to store the property of interest. - num_samples : int, default: 1000 - The number of samples to form the empirical distribution. - **kwargs : optional keyword args for the rewiring method - - Returns - ------- - numpy array - an array of properties from each point outputted in the rewiring process. - """ - G = deepcopy(G) - rw = rewiring_method() - properties = np.zeros(num_samples) - for i in range(num_samples): - for j in range(skip): - G = rw.step_rewire(G, copy_graph=False, **kwargs) - if j >= skip - 1: - properties[i] = property(G) - print(i) - return properties