import matplotlib.pyplot as plt, numpy as np, scipy, multiprocessing as mp, os, \ re, networkx as nx from tqdm import tqdm from Utils import IO, stats, misc, plotting as plotz from functools import partial """ - work from a folder directory """ # standard stuff #root = '/run/media/casper/test/1550482875.0001953/' root = '/home/casper/projects/information_impact/Data/1548025318.5751357' data = IO.DataLoader(root) # extracts data folders settings = {key: IO.Settings(root) for key in data} # load corresponding settings centralities = { r'$c_i^{deg}$' : partial(nx.degree, weight = 'weight'), \ r'$c_i^{betw}$': partial(nx.betweenness_centrality, weight = 'weight'),\ r'$c_i^{ic}$' : partial(nx.information_centrality, weight = 'weight'),\ r'$c_i^{ev}$' : partial(nx.eigenvector_centrality, weight = 'weight'),\ } figDir = '../thesis/figures/' information_impact = '$\mu_i$' causal_impact = '$\gamma_i$' # %% # begin the uggliness
step = step,\ burninSamples = burninSamples,\ pulseSizes = pulseSizes,\ updateType = updateType,\ nNodes = graph.number_of_nodes(),\ nTrials = nTrials,\ # this is added graph = nx.readwrite.json_graph.node_link_data(graph),\ mapping = model.mapping,\ rmapping = model.rmapping,\ model = type(model).__name__,\ directory = targetDirectory,\ nudgeType = nudgeType,\ ) settingsObject = IO.Settings(settings) settingsObject.save(targetDirectory) IO.savePickle(f'{targetDirectory}/mags.pickle', tmp) for t, mag in zip(matchedTemps, magRange): print(f'{datetime.datetime.now().isoformat()} Setting {t}') model.t = t # update beta tempDir = f'{targetDirectory}/{mag}' if not os.path.exists(tempDir): print('making directory') os.mkdir(tempDir) for trial in range(nTrials): from multiprocessing import cpu_count # st = [random.choice(model.agentStates, size = model.nNodes) for i in range(nSamples)] print(f'{datetime.datetime.now().isoformat()} Getting snapshots')
from functools import partial """ - work from a folder directory """ # standard stuff #root = '/run/media/casper/test/1550482875.0001953/' root = 'Data/cveltere/2019-05-09T16:10:34.645885' root = '/run/media/casper/fc7e7a2a-73e9-41fe-9020-f721489b1900/cveltere' root = 'Data/2019-05-13T13:34:02.290439' root = 'Data/1548025318.5751357' root = 'Data/new3' #root = '/run/media/casper/4fdab2ee-95ad-4fc5-8027-8d079de9d4f8/Data/1548025318' data = IO.DataLoader(root) # extracts data folders settings = {key : IO.Settings(root) for key in data} # load corresponding settings centralities = { r'$c_i^{deg}$' : partial(nx.degree, weight = 'weight'), \ r'$c_i^{betw}$': partial(nx.betweenness_centrality, weight = 'weight'),\ r'$c_i^{ic}$' : partial(nx.information_centrality, weight = 'weight'),\ r'$c_i^{ev}$' : partial(nx.eigenvector_centrality, weight = 'weight'),\ } copying build/lib.linux-x86_64-3.7/Toolbox/infcy.cpython-37m-x86_64-linux-gnu.so -> Toolbox #centralities = {key : partial(value, weight = 'weight') for key, value in nx.__dict__.items() if '_centrality' in key} figDir = '../thesis/figures/' information_impact = '$\mu_i$' causal_impact = '$\gamma_i$' # %%