Memory Error Python Numpy
Contents |
here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn numpy asarray memory error more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags
How To Solve Memory Error In Python
Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 6.2 million programmers, just like python memory error increase memory you, helping each other. Join them; it only takes a minute: Sign up Python/Numpy MemoryError up vote 10 down vote favorite 7 Basically, I am getting a memory error in python when trying to perform an algebraic operation numpy memory error zeros on a numpy matrix. The variable u, is a large matrix of double (in the failing case its a 288x288x156 matrix of doubles. I only get this error in this huge case, but I am able to do this on other large matrices, just not this big). Here is the Python error: Traceback (most recent call last): File "S:\3D_Simulation_Data\Patient SPM Segmentation\20 pc t perim erosion flattop\SwSim.py", line 121, in __init__ self.mainSimLoop() File "S:\3D_Simulation_Data\Patient SPM Segmentation\20 pc
Memory Error Pandas
t perim erosion flattop\SwSim.py", line 309, in mainSimLoop u = solver.solve_cg(u,b,tensors,param,fdHold,resid) # Solve the left hand si de of the equation Au=b with conjugate gradient method to approximate u File "S:\3D_Simulation_Data\Patient SPM Segmentation\20 pc t perim erosion flattop\conjugate_getb.py", line 47, in solv e_cg u = u + alpha*p MemoryError u = u + alpha*p is the line of code that fails. alpha is just a double, while u and r are the large matrices described above (both of the same size). I don't know that much about memory errors especially in Python. Any insight/tips into solving this would be very appreciated! Thanks python memory numpy scipy share|improve this question edited Mar 24 '15 at 23:45 ali_m 28.3k660113 asked Nov 30 '10 at 21:03 tylerthemiler 85331435 add a comment| 3 Answers 3 active oldest votes up vote 23 down vote accepted Rewrite to p *= alpha u += p and this will use much less memory. Whereas p = p*alpha allocates a whole new matrix for the result of p*alpha and then discards the old p; p*= alpha does the same thing in place. In general, with big matrices, try to use op= assignment. share|improve this answer answered Nov 30 '10 at 22:22 luispedro 4,29932246 This is very helpful, I didn't know this. –tylerthemiler Nov 30 '10 at 22:36 add a comment| up vo
here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more memoryerror python about Stack Overflow the company Business Learn more about hiring developers or posting ads python sparse with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow
Python Clear Memory
is a community of 6.2 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up Numpy memory error up vote 0 down vote favorite I'm running into http://stackoverflow.com/questions/4318615/python-numpy-memoryerror a memory error issue with numpy. The following line of code seems to be the issue: self.D_r = numpy.diag(1/numpy.sqrt(self.r)) Where self.r is a relatively small numpy array. The interesting thing is I monitored the memory usage and the process took up at most 3% of the RAM on the machine. So I'm thinking there's something that is killing the script before all the RAM is taken up because there http://stackoverflow.com/questions/36162662/numpy-memory-error is an expectation that the process will do so. If anybody has any ideas I would be very grateful. Edit 1: Here's the traceback: Traceback (most recent call last): File "/path_to_file/my_script.py", line 82, in
here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business http://stackoverflow.com/questions/37213750/memoryerror-when-creating-a-very-large-numpy-array Learn more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation http://gis.stackexchange.com/questions/191237/memory-error-in-python-when-reclassifying-array Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 6.2 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up MemoryError when creating a very large numpy array up vote 4 down vote favorite I'm trying to create a very large numpy array memory error of zeros and then copy values from another array into the large array of zeros. I am using Pycharm and I keep getting: MemoryError even when I try and only create the array. Here is how I've tried to create the array of zeros: import numpy as np last_array = np.zeros((211148,211148)) I've tried increasing the memory heap in Pycharm from 750m to 1024m as per this question: http://superuser.com/questions/919204/how-can-i-increase-the-memory-heap-in-pycharm, but that doesn't seem to help. Let memory error in me know if you'd like any further clarification. Thanks! python arrays numpy share|improve this question asked May 13 at 15:20 Andrew Earl 716 10 You have created an array that is over 100GB in size, assuming the size of int is 4 bytes –smac89 May 13 at 15:22 Oh lord, I had no idea. That is terrifying. It is a very sparse array though. Is there any way to create an empty array with values in certain positions, such as: last_array[211148][9] but everywhere else would be empty? –Andrew Earl May 13 at 15:28 This may be helpful: stackoverflow.com/questions/1857780/… –Keozon May 13 at 15:35 3 Or the scipy.sparse module... –schwobaseggl May 13 at 15:37 It depends on what you are trying to do, but in this case it will be really impossible to create an array that big unless you have the memory for it. In graph problems, we sacrifice speed by making use of an adjacency list. –smac89 May 13 at 15:37 | show 1 more comment 1 Answer 1 active oldest votes up vote 5 down vote accepted Look into using the sparse array capabilities within scipy: scipy.sparse documentation. There are a set of examples and tutorials on the scipy.sparse library here: Scipy lecture notes: Sparse Matrices in SciPy This may help you sol
Badges sign up log in tour help Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Geographic Information Systems Questions Tags Users Badges Unanswered Ask Question _ Geographic Information Systems Stack Exchange is a question and answer site for cartographers, geographers and GIS professionals. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Memory error in python when reclassifying array up vote 3 down vote favorite I have a .tif that I read into an array (call it tifArray), and I would like to classify the array based on set of conditions: Where 1200 <= tifArray <= 4000, outputArray = 1 Where tifArray < 1200, outputArray = 2 Where tifArrary > 4000, outputArray = 3 I've tried both creating a new array (preferred) and replacing the values in place (see below) but I regardless I get a MemoryError. I've tried on machines that have 10 and 8 GB of free RAM. I've also tried using the np.where function and just plain indexing (below). I have no clue why I'm getting a memory error, and I don't think I should be running into that problem. Some information about tifArray: tifArray.shape = (55500, 55500) tifArray.dtype = uint16 And here is one method I tried: threshold_low = 1200 threshold_high = 4000 tifArray[(tifArray >= threshold_low) & (tifArray <= threshold_high)] = 1 tifArray[(tifArray < threshold_low)] = 2 tifArray[(tifArray > threshold_high)] = 3 The error: File "./classify_segmented_fromAmplitude.py", line 90, in process_tile tifArray[(tifArray >= threshold_low) & (tifArray <= threshold_high)] = 1 MemoryError When I comment out the first condition: tifArray[(tifArray >= threshold_low) & (tifArray <= threshold_high)] = 1 and just run the following two line