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POPython memory problem
 

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  1. POPython memory problem
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    <p>i started programming on python but i have a memory problem (sorry for my bad english). I made a while loop on my algorithm, but on every cicle, the program consummes a lot of memory. I have 3Gb of RAM an AMD 64 x2 processor, and Windows 7 64 bits.</p> <p>For every cicle, it consummes about 800 Mb of RAM, it's too much i think. Part of my code is here</p> <pre><code>from sympy import Symbol, diff, flatten import numpy as np from numpy import linalg from math import log, sqrt, cos, pi import matplotlib.pyplot as plt L = 7 #numero de variables X = [Symbol('x%d' % i) for i in range(L*(L+1))] #Las variables simbolicas XX = [X[i] for i in xrange(L)] LAM = [] # Parametros Pr = 10 Eps = 0 Ome = 5 LL = 0.5 b = 2 Gam = 0.2*2*(pi**2) ran1 = xrange(L) ran2 = xrange(L*L) ran3 = xrange(0,L*(L-1)+1,L) ran4 = xrange(L,2*L,1) dt = 0.01 TMAX = 60 def f(x,R,Tau): return [Pr*((1 + Eps*cos(Ome*Tau))*x[2] - LL*x[0] - (1 -LL*x[5])) , \ Pr*((1 + Eps*cos(Ome*Tau))*x[3] - LL*x[1] - (1 - LL)*x[6]),\ R*x[0] - x[2] - x[0]*x[4],R*x[1] - x[3] - x[1]*x[4],(x[0]*x[2] + x[1]*x[3])/2 - b*x[4],\ (1/Gam)*(x[0] - x[5]),(1/Gam)*(x[1] - x[6])] def Jacobian(f,x): #num son los numeros en el que se evalua la matriz jacobiana, x las variables y f la funcion return [[diff(f[i],x[n]) for i in ran1] for n in ran1] def Y(x): return[[x[i+j] for j in ran3] for i in ran4] #Ahora la multiplicacion de Y traspuesto por Jacobian traspuesto def JY(r,Tau): J = flatten((np.dot(np.array(Jacobian(f(XX,r,Tau),XX)),np.array(Y(X)))).T) return [J[i] for i in ran2] def Func(x,r,Tau): #Expandemos las funciones en un solo arreglo FFF = [] map(lambda g: FFF.append(g),f(XX,r,Tau)) map(lambda g: FFF.append(g),JY(r,Tau)) return map(lambda f: f.evalf(subs={X[j]:x[j] for j in xrange(L*(L+1))}),FFF) def RKutta(xi,r): i = 1 while i &lt;= int(TMAX/dt): Tau = 0 YY = xi k1 = np.array(Func(YY,r,Tau))*dt k2 = (np.array(Func(YY + k1/2,r,Tau/2)))*dt k3 = (np.array(Func(YY + k2/2,r,Tau/2)))*dt k4 = (np.array(Func(YY + k3,r,Tau)))*dt xi = YY + (k1/6) + (k2/3) + (k3/3) + (k4/6) Tau = Tau + dt i = i + 1 return [xi[j] for j in xrange(len(xi))] def lyap(xxi): u = [i for i in flatten(np.random.rand(1,L))] PhiT = (np.array([[float(xxi[i+j]) for j in ran3] for i in ran4])).T PU = np.dot(PhiT,u) summ = 0 jj = 0 while jj &lt; len(PU): summ += (float(PU[jj]))**2 jj = jj + 1 lam = log(sqrt(summ))/TMAX return lam R = 46.5 Rmax = 48.5 Rstep = 0.5 while R &lt;= Rmax: xi = [5,5,5,5,5,5,5] #Condiciones Iniciales for i in ran2: xi.append(None) for i in ran4: for j in ran3: if (i+j+1)%(L+1) == 0: xi[i+j] = 1 else: xi[i+j] = 0 #Ahora el Runge Kutta para integrar todo el sistema #Y.append([r for r in xx]) # savetxt('butterfly.txt', Y, fmt="%12.6G") #print Y XI = RKutta(xi,R) lamb = lyap(XI) LAM.append([R,lamb]) print [R,lamb] R = R + Rstep #print LAM #x = [LAM[i][0] for i in xrange(len(LAM))] #y = [LAM[i][1] for i in xrange(len(LAM))] np.savetxt('lyap3.txt', LAM, fmt="%12.6G") #plt.axis([10,30,-3,3]); #plt.scatter(x,y) #plt.show() </code></pre> <p>I don't know where the problem could be. Maybe at the Runge Kutta steps or an architecture problem. The memory don't seem to be cleaned at every step and i'm not storing anything, just a pair of numbers at the end of the code. I hope i expressed myself well.</p> <h3>#</h3> <p>OK, i edited this and posted the whole code, i hope someone can help :) . I changed a lot of things, but i still have the memory problem. Each cicle uses about 600 Mb of RAM.</p> <h2>#</h2> <p>Thanks in advance</p>
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    1. COYour code isn't syntactically correct Python because the indentation is messed up. What's supposed to be inside that while loop? Also, what are the values of L, R, LL?
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      1. USLaurence Gonsalves
    2. COYou indent only be 1 space per nesting? You are an evil, evil person.
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    3. COL is just the number of variables, in this case, 7. I had another with 3 variables but it still consummes a lot of memory.
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      1. USDavid Winchester
 

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