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 = '$\gamma_i$'
# %%
