source: experiments/frams/multi-threading/test-mt-plots.py @ 884

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
import numpy as np
import os
import sys

def wykres(threadsy,capacitiesy,wyniki,iteracje,atrybut):
        kolory=['red','green','blue']
        alfy=[0.1,0.1,0.8]
        fig = plt.figure()
        ax = fig.add_subplot(111, projection='3d')
        ax.view_init(30,-150)
        for i,iteracja in enumerate(iteracje):
                X, Y = np.meshgrid(threadsy, capacitiesy) #X,Y are arrays of same size, 2D, with separated indexes
                Z=np.empty(X.shape)
                Z.fill(np.nan) #instead of nan, one could set -10 to clearly see on the plot which values are missing
                for wynik in wyniki:
                        threads,capacity,slownik=wynik
                        if slownik['iter']==iteracja:
                                print(threads,capacity,slownik[atrybut] if atrybut in slownik else np.nan)
                                threadsindex=threadsy.index(threads)
                                capacitiesindex=capacitiesy.index(capacity)
                                Z[capacitiesindex,threadsindex]=slownik[atrybut] if atrybut in slownik else np.nan
                print(Z)
                ktore=len(kolory)-len(iteracje)+i
                ax.plot_surface(X, Y, Z, rstride=1, cstride=1, color=kolory[ktore], linewidth=1, alpha=alfy[ktore],edgecolor=(0,0,0,alfy[ktore]))
        ax.set_xlabel('threads')
        ax.set_ylabel('capacity')
        ax.set_zlabel(atrybut)
        ax.set_xticks(threadsy)
        ax.set_yticks(capacitiesy)
        ax.set_zlim((0,ax.get_zlim()[1])) #scale from (forced) zero to auto
        plt.savefig("wykres_%d_%s.png" % (iteracja,atrybut))
        #plt.show() #if you wanted to rotate the chart interactively 
        plt.close()

        



####################################################### main #######################################################

threadsy=set() #used to store values of 'threads' that are in use. This is helpful because later a single multi-dimensional numpy array is used to keep all results
capacitiesy=set() #used to store values of 'capacity' that are in use
wyniki=[] #list of all results

os.chdir(sys.argv[1]) #files with plots will also be created in that directory
for plik in os.listdir('.'):
        if plik.endswith(".out") and plik.count('_')==2:
                podzielone=plik.replace('.','_').split('_')
                threads=int(podzielone[1])
                capacity=int(podzielone[2])
                threadsy.add(threads)
                capacitiesy.add(capacity)
                plik = open("test_%d_%d.out" % (threads,capacity), "r") #should be the same name as the original file
                slowniki = []
                for linia in plik:
                        linia=linia.strip()
                        if linia.startswith("Script.Message: iter="):
                                slownik={}
                                for pole in linia.split(' '):
                                        if '=' in pole:
                                                pole=pole.strip(',')
                                                pole=pole.split('=')
                                                slownik[pole[0]]=float(pole[1])
                                print(slownik)
                                slowniki.append(slownik)
                                wyniki.append((threads,capacity,slownik))
                plik.close()

threadsy=sorted(list(threadsy))
capacitiesy=sorted(list(capacitiesy))
print(threadsy)
print(capacitiesy)

wykres(threadsy,capacitiesy,wyniki,[3.0,6.0,9.0],'simsteps')
wykres(threadsy,capacitiesy,wyniki,[3.0,6.0,9.0],'evals')
wykres(threadsy,capacitiesy,wyniki,[3.0,6.0,9.0],'migrations')
wykres(threadsy,capacitiesy,wyniki,[4.0,9.0],'fit_avg')
wykres(threadsy,capacitiesy,wyniki,[4.0,9.0],'fit_max')

#draws simple 2D plots of progress in consecutive iter's:
#x=[]
#y1=[]
#y2=[]
#for s in slowniki:
#       x.append(s['iter'])
#       y1.append(s['avg_fit'])
#       y2.append(s['max_fit'])
#plt.plot(x,y1)
#plt.plot(x,y2)
#plt.savefig("avg_%d_%d.png" % (threads,capacity))
#plt.close()