Merge branch 'RMA-Distributions' of...

Merge branch 'RMA-Distributions' of http://lorca.act.uji.es/gitlab/martini/malleability_benchmark into RMA-Distributions

Analysis/iterTimes.py

-import sys
-import glob
-import numpy as numpy
-import pandas as pd
-
-#-----------------------------------------------
-def read_file(f, dataA, dataB, itA, itB):
-  compute_tam = 0
-  comm_tam = 0
-  sdr = 0
-  adr = 0
-  dist = 0
-  css = 0
-  cst = 0
-  time = 0
-  recording = False
-  it_line = 0
-  aux_itA = 0
-  aux_itB = 0
-  iters = 0
-  np = 0
-  np_par = 0
-  ns = 0
-  array = []
-  columnas = ['Titer','Ttype','Top']
-
-  #print(f)
-  for line in f: 
-    lineS = line.split()
-    if len(lineS) > 1:
-
-        if recording and lineS[0].split(':')[0] in columnas: #Record data
-          aux_itA = 0
-          lineS.pop(0)
-
-          if it_line==0:
-            for observation in lineS:
-              dataA.append([None]*15)
-              dataA[itA+aux_itA][0] = sdr
-              dataA[itA+aux_itA][1] = adr
-              dataA[itA+aux_itA][2] = np
-              dataA[itA+aux_itA][3] = np_par
-              dataA[itA+aux_itA][4] = ns
-              dataA[itA+aux_itA][5] = dist
-              dataA[itA+aux_itA][6] = compute_tam
-              dataA[itA+aux_itA][7] = comm_tam
-              dataA[itA+aux_itA][8] = cst
-              dataA[itA+aux_itA][9] = css
-              dataA[itA+aux_itA][10] = time
-              dataA[itA+aux_itA][11] = iters
-              dataA[itA+aux_itA][12] = float(observation)
-              array.append(float(observation))
-              aux_itA+=1
-          elif it_line==1:
-            deleted = 0
-            for observation in lineS:
-              dataA[itA+aux_itA][13] = float(observation)
-              if float(observation) == 0:
-                  array.pop(aux_itA - deleted)
-                  deleted+=1
-              aux_itA+=1
-          else:
-            for observation in lineS:
-              dataA[itA+aux_itA][14] = float(observation)
-              aux_itA+=1
-
-          it_line += 1
-          if(it_line % 3 == 0): # Comprobar si se ha terminado de mirar esta ejecucion
-            recording = False
-            it_line = 0
-            itA = itA + aux_itA
-            if ns != 0: # Solo obtener datos de grupos con hijos
-                dataB.append([None]*14)
-                dataB[itB][0] = sdr
-                dataB[itB][1] = adr
-                dataB[itB][2] = np
-                dataB[itB][3] = np_par
-                dataB[itB][4] = ns
-                dataB[itB][5] = dist
-                dataB[itB][6] = compute_tam
-                dataB[itB][7] = comm_tam
-                dataB[itB][8] = cst
-                dataB[itB][9] = css
-                dataB[itB][10] = time
-                dataB[itB][11] = iters
-                dataB[itB][12] = tuple(array)
-                dataB[itB][13] = numpy.sum(array)
-                itB+=1
-                array = []
-
-        if lineS[0] == "Config:":
-          compute_tam = int(lineS[1].split('=')[1].split(',')[0])
-          comm_tam = int(lineS[2].split('=')[1].split(',')[0])
-          sdr = int(lineS[3].split('=')[1].split(',')[0])
-          adr = int(lineS[4].split('=')[1].split(',')[0]) 
-          css = int(lineS[6].split('=')[1].split(',')[0])
-          cst = int(lineS[7].split('=')[1].split(',')[0])
-          time = float(lineS[8].split('=')[1])
-        elif lineS[0] == "Config":
-          recording = True
-          iters = int(lineS[2].split('=')[1].split(',')[0])
-          dist = int(lineS[4].split('=')[1].split(',')[0])
-          np = int(lineS[5].split('=')[1].split(',')[0])
-          np_par = int(lineS[6].split('=')[1].split(',')[0])
-          ns = int(float(lineS[7].split('=')[1]))
-
-  return itA,itB
-#-----------------------------------------------
-#Config: matrix=1000, sdr=1000000000, adr=0, aib=0 time=2.000000
-#Config Group: iters=100, factor=1.000000, phy=2, procs=2, parents=0, sons=4
-#Ttype: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 
-
-if len(sys.argv) < 2:
-    print("The files name is missing\nUsage: python3 iterTimes.py resultsName directory csvOutName")
-    exit(1)
-
-if len(sys.argv) >= 3:
-    BaseDir = sys.argv[2]
-    print("Searching in directory: "+ BaseDir)
-else: #FIXME
-    BaseDir = sys.argv[2]
-
-if len(sys.argv) >= 4:
-  print("Csv name will be: " + sys.argv[3] + ".csv and "+ sys.argv[3] + "_Total.csv")
-  name = sys.argv[3]
-else:
-  name = "data"
-
-insideDir = "Run"
-lista = glob.glob("./" + BaseDir + insideDir + "*/" + sys.argv[1]+ "*ID*.o*")
-print("Number of files found: "+ str(len(lista)));
-
-itA = itB = 0
-dataA = []
-dataB = []  #0   #1        #2    #3       #4     #5      #6             #7          #8     #9   #10    #11       #12   #13   #14
-columnsA = ["N", "%Async", "NP", "N_par", "NS", "Dist", "Compute_tam", "Comm_tam", "Cst", "Css","Time", "Iters", "Ti", "Tt", "To"] #15
-columnsB = ["N", "%Async", "NP", "N_par", "NS", "Dist", "Compute_tam", "Comm_tam", "Cst", "Css","Time", "Iters", "Ti", "Sum"] #14
-
-for elem in lista:
-  f = open(elem, "r")
-  itA,itB = read_file(f, dataA, dataB, itA, itB)
-  f.close()
-
-#print(data)
-dfA = pd.DataFrame(dataA, columns=columnsA)
-dfB = pd.DataFrame(dataB, columns=columnsB)
-
-dfA['N'] += dfA['%Async']
-dfA['%Async'] = (dfA['%Async'] / dfA['N']) * 100
-dfA.to_csv(name + '.csv')
-
-dfB['N'] += dfB['%Async']
-dfB['%Async'] = (dfB['%Async'] / dfB['N']) * 100
-dfB.to_csv(name + '_Total.csv')