Mailman 3 August 2013 - SciPy-User

How to display a RGB image (MFISH)?
by jp 03 Apr '23

03 Apr '23

Hi, I have written a script to try to combine five images into a three channels RGB images<http://dip4fish.blogspot.com/2011/05/trying-to-combine-mfish-images-into-rg…> . The resulting RGB image looks like a grey scale image instead of a color one. I have three np.array Rnorm, Gnorm, Bnorm which are copied into a rgb array: *rgb = np.zeros((shape[0],shape[1],3),dtype=float) mxr=np.max(R) mxg=np.max(G) mxb=np.max(B) Rnorm=np.uint8((255*(R/mxr)))Gnorm=np.uint8((255*(R/mxg)))Bnorm=np.uint8((255*(R/mxb)))#copy each RGB component in an RGB array rgb[:,:,0]=Rnorm rgb[:,:,1]=Gnorm rgb[:,:,2]=Bnorm* *pylab.subplot(224, aspect='equal',frameon=False, xticks=[], yticks=[]) pylab.imshow(rgb) pylab.show()* Any advice? Thank you Jean-Patrick

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Invitation to connect on LinkedIn
by Jelle Feringa via LinkedIn 28 Jul '22

28 Jul '22

LinkedIn ------------ Jelle Feringa requested to add you as a connection on LinkedIn: ------------------------------------------ Jose, I'd like to add you to my professional network on LinkedIn. - Jelle Accept invitation from Jelle Feringa http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/Q6WKH0LACopGJkAw_6fSqajo6R7VM… View invitation from Jelle Feringa http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/Q6WKH0LACopGJkAw_6fSqajo6R7VM… ------------------------------------------ DID YOU KNOW your LinkedIn profile helps you control your public image when people search for you? Setting your profile as public means your LinkedIn profile will come up when people enter your name in leading search engines. Take control of your image! http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/ewp/inv-22/ -- (c) 2011, LinkedIn Corporation

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Help required regding Eigenvectors
by Sudheer Phani 14 Mar '22

14 Mar '22

Hello All, I have been encountering some strange problems with the calculation of eigen vectors using python. The problem is with the sign of the eigen vectors calculated. On Very few occasions the eigen vectors calculated using python have an opposite sign than the actual eigenvectors, though the magnitude is correct. Can any one of you plz help me in this regard and suggest me some solution if you have encoutered similar problems . Any help in this regd is appreciated Thanks in Advace regds Sudheer

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Allan Variance
by Daniele Nicolodi 04 Feb '14

04 Feb '14

Hello, does anyone know or have written and is willing to share some code to compute the Allan variance, modified Allan variance and friends, ideally both from frequency than from phase data? I'm writing my own code, it is not that difficult, but I would prefer the time required for testing and validation doing something more fun. If there is no available code implementing Allan variance, would it be interesting to include in in SciPy? Thank you so much. Best, Daniele

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Re: [SciPy-User] SciPy-User Digest, Vol 120, Issue 12
by Michal Romaniuk 24 Jan '14

24 Jan '14

Hi, > Hi, > > On Mon, Aug 19, 2013 at 7:44 AM, Michal Romaniuk > <michal.romaniuk06(a)imperial.ac.uk> wrote: >> Hi, >> >> I'm saving a large batch of data using savemat and although I get no >> errors, the files produced are not readable for either matlab or scipy. >> Is there a limit on file size? > > Ah - yes there is - the individual matrices in the mat file cannot be > larger than 4GB. Is it possible you hit this limit? > > Sorry, I only realized this when Richard Llewellyn pointed this out a > couple of weeks ago on the list: > > http://scipy-user.10969.n7.nabble.com/SciPy-User-scipy-io-loadmat-throws-Ty… > > The current scipy code has an error message for matrices that are too large. > > Cheers, > > Matthew Well, I managed to work around the problem to some extent by setting do_compression=True. Now Matlab can read those files (so they must be valid to some extent) but SciPy can't (even though they were written with SciPy). I get this error: PATH/lib/python2.6/site-packages/scipy/io/matlab/mio.pyc in loadmat(file_name, mdict, appendmat, **kwargs) 173 variable_names = kwargs.pop('variable_names', None) 174 MR = mat_reader_factory(file_name, appendmat, **kwargs) --> 175 matfile_dict = MR.get_variables(variable_names) 176 if mdict is not None: 177 mdict.update(matfile_dict) PATH/lib/python2.6/site-packages/scipy/io/matlab/mio5.pyc in get_variables(self, variable_names) 290 continue 291 try: --> 292 res = self.read_var_array(hdr, process) 293 except MatReadError, err: 294 warnings.warn( PATH/lib/python2.6/site-packages/scipy/io/matlab/mio5.pyc in read_var_array(self, header, process) 253 `process`. 254 ''' --> 255 return self._matrix_reader.array_from_header(header, process) 256 257 def get_variables(self, variable_names=None): PATH/lib/python2.6/site-packages/scipy/io/matlab/mio5_utils.so in scipy.io.matlab.mio5_utils.VarReader5.array_from_header (scipy/io/matlab/mio5_utils.c:5401)() PATH/lib/python2.6/site-packages/scipy/io/matlab/mio5_utils.so in scipy.io.matlab.mio5_utils.VarReader5.array_from_header (scipy/io/matlab/mio5_utils.c:4849)() PATH/lib/python2.6/site-packages/scipy/io/matlab/mio5_utils.so in scipy.io.matlab.mio5_utils.VarReader5.read_real_complex (scipy/io/matlab/mio5_utils.c:5602)() ValueError: total size of new array must be unchanged The size of the main array is about 9 GB before compression, but the compressed files are less than 500 MB and closer to 400 MB. There are some other arrays in the file too but they are much smaller. Any ideas on how I could get SciPy to read this data back? Right now I can only think of storing the data in single precision format... Thanks, Michal

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Akima interpolation?
by Andreas Hilboll 23 Sep '13

23 Sep '13

I'm looking for 1d Akima interpolation, and couldn't find it in Scipy. 1.) Did I miss something? 2.) Is there interest in having Akima interpolation in Scipy? 3.) Does anyone know of a good, fast interpolation I should investigate for inclusion in Scipy? Cheers, Andreas. -- Andreas Hilboll PhD Student Institute of Environmental Physics University of Bremen U3145 Otto-Hahn-Allee 1 D-28359 Bremen Germany +49(0)421 218 62133 (phone) +49(0)421 218 98 62133 (fax) http://www.iup.uni-bremen.de/~hilboll

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May I joint this list?
by Yuxiang Wang 19 Sep '13

19 Sep '13

Thank you so much! -- Yuxiang "Shawn" Wang Gerling Research Lab University of Virginia yw5aj(a)virginia.edu +1 (434) 284-0836

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how to get the indexes of non zero rows of sparse matric efficiently when using scipy?
by teccy 18 Sep '13

18 Sep '13

for example, a 500x1000 sparse matrix with 300 non zero rows and 5000 non zero items, what i want is the index for the 300 rows, not the index for 5000 items, how to do this efficiently, seems scipy haven't provicde such kind of API ...

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Cannot subscribe to mailing lists
by Yuxiang Wang 18 Sep '13

18 Sep '13

Hi all, I tried to subscribe to mailing list on page: http://www.scipy.org/scipylib/mailing-lists.html However, I think the links are broken. Would anyone have any idea about where to report it? Thanks! -Shawn

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RAM problem during code execution - Numpy arrays
by Josè Luis Mietta 18 Sep '13

18 Sep '13

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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