1 files changed, 0 insertions, 169 deletions

diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/readCSV.py b/Metrics/Metrics-Calculation/metrics_plot/src/readCSV.py
deleted file mode 100644
index e0402519..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/src/readCSV.py
+++ /dev/null
@@ -1,169 +0,0 @@
-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:]
-            # all other contants are integer
-            else:
-                contents[arr[0]] = list(map(int, arr[1:]))
-    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()
-