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import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
import glob
import random
import constants
#
# read csvfile returns outdegree, node activity, mpc
# as matrix with the first row of values and second row of count
#
def readcsvfile(filename):
contents = {}
with open(filename) as f:
for i, line in enumerate(f):
arr = line.split(',')
# if there is no element in the line, continue
if len(line) < 0: continue
# else check for contents
# if it is MPC then use float
if arr[0] == constants.MPC_VALUE:
contents[constants.MPC_VALUE] = list(map(float, arr[1:]))
# meta models are string
elif(arr[0] == constants.METAMODEL):
contents[constants.METAMODEL] = arr[1:]
# NA and OD are integers
else:
try:
contents[arr[0]] = list(map(int, arr[1:]))
except:
print(arr[0], ' is not integer')
f.close()
return contents
def checkAndReshape(arr):
if len(arr.shape) < 2:
arr = np.reshape(arr, (arr.shape[0],1))
return arr
def readTrajectory(filename):
state_codes = []
with open(filename) as f:
for i, line in enumerate(f):
if(line == ''): continue
state_codes.append(int(line))
return state_codes
#
# take a matrix as input
# return the sample array
#
def getsample(dataMatrix):
data = []
value = dataMatrix[0, :]
count = dataMatrix[1, :]
for i, v in enumerate(value):
for x in range(0, int(count[i])):
data.append(v)
return data
def reproduceSample(values, counts):
arr = np.array([values, counts])
return getsample(arr)
#
# take an array of filenames as input
# return the samples of outdegree, na, mpc
#
def getmetrics(filename):
contents = readcsvfile(filename)
outdegree_sample = reproduceSample(contents[constants.OUT_DEGREE_VALUE], contents[constants.OUT_DEGREE_COUNT])
na_sample = reproduceSample(contents[constants.NA_VALUE], contents[constants.NA_COUNT])
mpc_sample = reproduceSample(contents[constants.MPC_VALUE], contents[constants.MPC_COUNT])
return contents,outdegree_sample, na_sample, mpc_sample
#
# read number of files in the given path RANDOMLY
#
def readmultiplefiles(dirName, maxNumberOfFiles, shouldShuffle = True):
list_of_files = glob.glob(dirName + '*.csv') # create the list of file
if shouldShuffle:
random.shuffle(list_of_files)
#if the number of files is out of bound then just give the whole list
file_names = list_of_files[:maxNumberOfFiles]
# print(file_names)
return file_names
def plotlines(x, y, ax):
l1, = ax.plot(x, y)
def testgetsamplesfromfiles():
files = readmultiplefiles('../statistics/viatraOutput/', 2)
for file in files:
getmetrics(file)
def probability(data):
sum = np.sum(data)
probabilityList = []
for d in data:
p = d/sum
probabilityList.append(p)
a = np.array(probabilityList)
return a
def cumulativeProbability(p):
cdf = np.cumsum(p)
return cdf
def plot():
fig, ax = plt.subplots()
fig, ax1 = plt.subplots()
fig, ax2 = plt.subplots()
fig, ax3 = plt.subplots()
fig, ax4 = plt.subplots()
fig, ax5 = plt.subplots()
list_of_files = readmultiplefiles('../statistics/iatraOutput/')
for file_name in list_of_files:
contents = readcsvfile(file_name)
outdegree = [contents[constants.OUT_DEGREE_VALUE], contents[constants.OUT_DEGREE_COUNT]]
na = [contents[constants.NA_VALUE], contents[constants.NA_COUNT]]
mpc = [contents[constants.MPC_VALUE], contents[constants.MPC_COUNT]]
outV = outdegree[0, :]
outC = outdegree[1, :]
outP = probability(outC)
outCumP = cumulativeProbability(outP)
plotlines(outV, outP, ax)
naV = na[0, :]
naC = na[1, :]
naP = probability(naC)
naCumP = cumulativeProbability(naP)
plotlines(naV, naP, ax1)
mpcV = mpc[0, :]
mpcC = mpc[1, :]
mpcP = probability(mpcC)
mpcCumP = cumulativeProbability(mpcP)
plotlines(mpcV, mpcP, ax2)
plotlines(outV, outCumP, ax3)
plotlines(naV, naCumP, ax4)
plotlines(mpcV, mpcCumP, ax5)
ax.set_xlabel('ourdegree')
ax.set_ylabel('pdf')
ax.grid()
ax1.set_xlabel('node activity')
ax1.set_ylabel('pdf')
ax1.grid()
ax2.set_xlabel('multiplex participation coefficient')
ax2.set_ylabel('pdf')
ax2.grid()
ax3.set_xlabel('ourdegree')
ax3.set_ylabel('cdf')
ax3.grid()
ax4.set_xlabel('node activity')
ax4.set_ylabel('cdf')
ax4.grid()
ax5.set_xlabel('multiplex participation coefficient')
ax5.set_ylabel('cdf')
ax5.grid()
plt.show()
# plot()
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