[SciPy-User] RAM problem during code execution - Numpy arrays

Fri Aug 23 09:53:35 EDT 2013

Hi experts. I need your help with a RAM porblem during execution of my script.
I wrote the next code. I use SAGE. In 1-2 hours of execution time the RAM of my laptop (8gb) is filled and the sistem crash:

fromscipy.stats importuniform importnumpy asnp cant_de_cadenas =[700,800,900]cantidad_de_cadenas=np.array([])forkkkkk incant_de_cadenas:cantidad_de_cadenas=np.append(cantidad_de_cadenas,kkkkk)cantidad_de_cadenas=np.transpose(cantidad_de_cadenas)b=10h=b Longitud=1numero_experimentos=150densidad_de_cadenas =cantidad_de_cadenas/(b**2)prob_perc=np.array([])tiempos=np.array([])S_int=np.array([])S_medio=np.array([])desviacion_standard=np.array([])desviacion_standard_nuevo=np.array([])anisotropia_macroscopica_porcentual=np.array([])componente_y=np.array([])componente_x=np.array([])importtime forN incant_de_cadenas:empieza=time.clock()PERCOLACION=np.array([])size_medio_intuitivo =np.array([])size_medio_nuevo =np.array([])std_dev_size_medio_intuitivo =np.array([])std_dev_size_medio_nuevo =np.array([])comp_y =np.array([])comp_x =np.array([])foru inxrange(numero_experimentos):perco
 =Falsearray_x1=uniform.rvs(loc=-b/2,scale=b,size=N)array_y1=uniform.rvs(loc=-h/2,scale=h,size=N)array_angle=uniform.rvs(loc=-0.5*(np.pi),scale=np.pi,size=N)array_pendiente_x=1./np.tan(array_angle)random=uniform.rvs(loc=-1,scale=2,size=N)lambda_sign=np.zeros([N])fort inxrange(N):ifrandom[t]<0:lambda_sign[t]=-1else:lambda_sign[t]=1array_lambdas=(lambda_sign*Longitud)/np.sqrt(1+array_pendiente_x**2)array_x2=array_x1 +array_lambdas*array_pendiente_x array_y2=array_y1 +array_lambdas*1array_x1 =np.append(array_x1,[-b/2,b/2,-b/2,-b/2])array_y1 =np.append(array_y1,[-h/2,-h/2,-h/2,h/2])array_x2 =np.append(array_x2,[-b/2,b/2,b/2,b/2])array_y2 =np.append(array_y2,[h/2,h/2,-h/2,h/2])M =np.zeros([N+4,N+4])forj inxrange(N+4):ifj>0:x_A1B1 =array_x2[j]-array_x1[j]y_A1B1 =array_y2[j]-array_y1[j]x_A1A2 =array_x1[0:j]-array_x1[j]y_A1A2 =array_y1[0:j]-array_y1[j]x_A2A1 =-1*x_A1A2 y_A2A1 =-1*y_A1A2 x_A2B2 =array_x2[0:j]-array_x1[0:j]y_A2B2 =array_y2[0:j]-array_y1[0:j]x_A1B2
 =array_x2[0:j]-array_x1[j]y_A1B2 =array_y2[0:j]-array_y1[j]x_A2B1 =array_x2[j]-array_x1[0:j]y_A2B1 =array_y2[j]-array_y1[0:j]p1 =x_A1B1*y_A1A2 -y_A1B1*x_A1A2 p2 =x_A1B1*y_A1B2 -y_A1B1*x_A1B2 p3 =x_A2B2*y_A2B1 -y_A2B2*x_A2B1 p4 =x_A2B2*y_A2A1 -y_A2B2*x_A2A1 condicion_1=p1*p2 condicion_2=p3*p4 fork inxrange (j):ifcondicion_1[k]<=0andcondicion_2[k]<=0:M[j,k]=1delcondicion_1 delcondicion_2 ifj+1<N+4:x_A1B1 =array_x2[j]-array_x1[j]y_A1B1 =array_y2[j]-array_y1[j]x_A1A2 =array_x1[j+1:]-array_x1[j]y_A1A2 =array_y1[j+1:]-array_y1[j]x_A2A1 =-1*x_A1A2 y_A2A1 =-1*y_A1A2 x_A2B2 =array_x2[j+1:]-array_x1[j+1:]y_A2B2 =array_y2[j+1:]-array_y1[j+1:]x_A1B2 =array_x2[j+1:]-array_x1[j]y_A1B2 =array_y2[j+1:]-array_y1[j]x_A2B1 =array_x2[j]-array_x1[j+1:]y_A2B1 =array_y2[j]-array_y1[j+1:]p1 =x_A1B1*y_A1A2 -y_A1B1*x_A1A2 p2 =x_A1B1*y_A1B2 -y_A1B1*x_A1B2 p3 =x_A2B2*y_A2B1 -y_A2B2*x_A2B1 p4 =x_A2B2*y_A2A1 -y_A2B2*x_A2A1 condicion_1=p1*p2 condicion_2=p3*p4 fork inxrange
 ((N+4)-j-1):ifcondicion_1[k]<=0andcondicion_2[k]<=0:M[j,k+j+1]=1delcondicion_1 delcondicion_2 M[N,N+2]=0M[N,N+3]=0M[N+1,N+2]=0M[N+1,N+3]=0M[N+2,N]=0M[N+2,N+1]=0M[N+3,N]=0M[N+3,N+1]=0CD=np.array([])POPOPO=[]forg inxrange(N):lala=0r=Falsewhilelala<=len(POPOPO)-1:esta=g inPOPOPO[lala]ifesta isTrue:lala=len(POPOPO)r=Trueelse:lala=lala+1ifr isFalse:L=np.array([g])fors inxrange(N):ifM[g,s]!=0:L=np.append(L,s)x=0whilex<=N:forl inxrange(N):z=l inL               d=L[x]ifz isFalseandM[d,l]!=0:L=np.append(L,l)ifx+1<len(L):x+=1else:x=N+1.q=len (L)CD=np.append(CD,q)POPOPO.append(L)M_horizontal=M.copy()M_horizontal[:,N+2]=np.zeros(N+4)M_horizontal[:,N+3]=np.zeros(N+4)M_horizontal[N+2]=np.zeros(N+4)M_horizontal[N+3]=np.zeros(N+4)L=np.array([N])fors inxrange(N+4):ifM_horizontal[N,s]!=0:L=np.append(L,s)x=0whilex<=N+4:forl inxrange(N+4):z=l inL                d=L[x]ifz isFalseandM_horizontal[d,l]!=0:L=np.append(L,l)ifx+1<len(L):x+=1else:x=(N+4)+1.LV1_in_L =N inL 
 LV2_in_L=(N+1)inL ifLV1_in_L isTrueandLV2_in_L isTrue:perc_horiz=Trueelse:perc_horiz=FalseM_vertical=M.copy()M_vertical[:,N]=np.zeros(N+4)M_vertical[:,N+1]=np.zeros(N+4)M_vertical[N]=np.zeros(N+4)M_vertical[N+1]=np.zeros(N+4)L=np.array([N+2])fors inxrange(N+4):ifM_vertical[N+2,s]!=0:L=np.append(L,s)x=0whilex<=N+4:forl inxrange(N+4):z=l inL                d=L[x]ifz isFalseandM_vertical[d,l]!=0:L=np.append(L,l)ifx+1<len(L):x+=1else:x=(N+4)+1.LH1_in_L =(N+2)inL   LH2_in_L=(N+3)inL ifLH1_in_L isTrueandLH2_in_L isTrue:perc_ver =Trueelse:perc_ver =Falseifperc_ver isTrueorperc_horiz isTrue:PERCOLACION=np.append(PERCOLACION,1)perco=TrueD =np.array([])W =np.array([])forc inxrange (int(min(CD)),int(max(CD)+1),1):D=np.append(D,c)frec =sum (CD ==c)W =np.append(W,frec)ifperco isTrue:posicion=np.argmax(D)D=np.delete(D,posicion)W=np.delete(W,posicion)iflen(D)==0andlen(W)==0:S_medio_intuitivo_exp_u=0S_medio_nuevo_exp_u =0std_dev_exp_u =0std_dev_nuevo_exp_u
 =0else:S_medio_intuitivo_exp_u =np.average (D,weights=W)peso_nuevo=D*W S_medio_nuevo_exp_u =np.average (D,weights=peso_nuevo)tipos=sum(W)X=W*((D-S_medio_intuitivo_exp_u)**2)S=sum(X)std_dev_exp_u =np.sqrt(S/(tipos-1.))tipos_nuevo=sum(peso_nuevo)X_nuevo=peso_nuevo*((D-S_medio_nuevo_exp_u)**2)S_nuevo=sum(X_nuevo)std_dev_nuevo_exp_u =np.sqrt(S_nuevo/(tipos_nuevo-1.))componente_longitudinal=Longitud*np.abs(np.cos(array_angle))comp_y=np.append(comp_y,sum(componente_longitudinal)/N)componente_transversal=Longitud*np.abs(np.sin(array_angle))comp_x=np.append(comp_x,sum(componente_transversal)/N)std_dev_size_medio_intuitivo=np.append(std_dev_size_medio_intuitivo,std_dev_exp_u)std_dev_size_medio_nuevo=np.append(std_dev_size_medio_nuevo,std_dev_nuevo_exp_u)size_medio_intuitivo=np.append(size_medio_intuitivo,S_medio_intuitivo_exp_u)size_medio_nuevo=np.append(size_medio_nuevo,S_medio_nuevo_exp_u)percolation_probability=sum(PERCOLACION)/numero_experimentos
 prob_perc=np.append(prob_perc,percolation_probability)S_int =np.append (S_int,sum(size_medio_intuitivo)/numero_experimentos)S_medio=np.append (S_medio,sum(size_medio_nuevo)/numero_experimentos)desviacion_standard =np.append (desviacion_standard,sum(std_dev_size_medio_intuitivo)/numero_experimentos)desviacion_standard_nuevo=np.append (desviacion_standard_nuevo,sum(std_dev_size_medio_nuevo)/numero_experimentos)tiempos=np.append(tiempos,time.clock()-empieza)componente_y=np.append(componente_y,sum(comp_y)/numero_experimentos)componente_x=np.append(componente_x,sum(comp_x)/numero_experimentos)anisotropia_macroscopica_porcentual=100*(1-(componente_y/componente_x))

I tryed with gc and gc.collect() and 'del'command for deleting arrays after his use and nothing work! 

What am I doing wrong? Why the memory becomes full while running 
(starts with 10% of RAM used and in 1-2hour is totally full used)?

Please help me, I'm totally stuck!
Thanks a lot!
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