Python Memory Error Numpy
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Numpy Asarray Memory Error
Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is numpy memory error zeros 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 creating huge matrix how to solve memory error in python up vote 2 down vote favorite 3 I am using numpy and trying to create a huge matrix. While doing this, I receive a memory error Because the matrix is not important, I will just show the way how to easily reproduce the
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error. a = 10000000000 data = np.array([float('nan')] * a) not surprisingly, this throws me MemoryError There are two things I would like to tell: I really need to create and to use a big matrix I think I have enough RAM to handle this matrix (I have 24 Gb or RAM) Is there an easy way to handle big matrices in numpy? Just to be on the safe side, I previously read these posts (which sounds similar): Python Numpy Very Large Matrices Python/Numpy MemoryError Processing a very very
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big data set in python - memory error P.S. apparently I have some problems with multiplication and division of numbers, which made me think that I have enough memory. So I think it is time for me to go to sleep, review math and may be to buy some memory. May be during this time some genius might come up with idea how to actually create this matrix using only 24 Gb of Ram. Why I need this big matrix I am not going to do any manipulations with this matrix. All I need to do with it is to save it into pytables. python memory numpy share|improve this question edited Sep 30 '13 at 1:18 asked Sep 30 '13 at 0:53 Salvador Dali 50.5k41232311 2 How do you expect to fit 10 billion floats in 24 GB? If a float were 2.4 bytes, and 100% of your RAM were devoted to holding this array - sure ;-) –Tim Peters Sep 30 '13 at 0:56 What do you need to do with this matrix? That might give an insight to a workaround. –Rohit Sep 30 '13 at 1:14 cant you save it piece by piece? n work on partitions of your data? –usethedeathstar Sep 30 '13 at 7:20 Also, The way you create it first creates a python list of that size. Now the float is always the same object, but the list itself will have the same size as the resulting array (pointer is 8 bytes and double is 8 bytes). So use np.
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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; gc.collect python it only takes a minute: Sign up Processing a very very big data set in python - memory error up vote 6 down vote favorite 2 I'm trying to process data obtained from a csv file using csv module in http://stackoverflow.com/questions/19085012/numpy-memory-error-creating-huge-matrix python. there are about 50 columns & 401125 rows in this. I used the following code chunk to put that data into a list csv_file_object = csv.reader(open(r'some_path\Train.csv','rb')) header = csv_file_object.next() data = [] for row in csv_file_object: data.append(row) I can get length of this list using len(data) & it returns 401125. I can even get each individual record by calling list indices. But when I try to get the size of the list by calling np.size(data) (I imported numpy http://stackoverflow.com/questions/14551451/processing-a-very-very-big-data-set-in-python-memory-error as np) I get the following stack trace. MemoryError Traceback (most recent call last) in () ----> 1 np.size(data) C:\Python27\lib\site-packages\numpy\core\fromnumeric.pyc in size(a, axis) 2198 return a.size 2199 except AttributeError: -> 2200 return asarray(a).size 2201 else: 2202 try: C:\Python27\lib\site-packages\numpy\core\numeric.pyc in asarray(a, dtype, order) 233 234 """ --> 235 return array(a, dtype, copy=False, order=order) 236 237 def asanyarray(a, dtype=None, order=None): MemoryError: I can't even divide that list into a multiple parts using list indices or convert this list into a numpy array. It give this same memory error. how can I deal with this kind of big data sample. Is there any other way to process large data sets like this one. I'm using ipython notebook in windows 7 professional. python numpy python-2.7 data-analysis share|improve this question asked Jan 27 '13 at 19:45 maheshakya 74331527 Can you process the file line by line as suggested in a related answer? –gauden Jan 27 '13 at 19:49 Do you need to have all rows in memory at once? Are you just doing one-off processing on each row? An alternative would be to make a collections.NamedTuple and convert each row into one of these tuples. They use up a really minimal amount of memory. –hughdbrown Jan 27 '13 at 19:50 more rows I have in memory at once, better. I'll try NamedTuple Thank you –maheshakya Jan 27 '13 at 19:53 Or, if you d
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 http://stackoverflow.com/questions/32789331/getting-numpy-memory-error-on-relatively-small-matrix Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack http://codereview.stackexchange.com/questions/41316/python-numpy-optimize-module-to-handle-big-file 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 Getting numpy Memory Error on (relatively) small memory error matrix up vote 0 down vote favorite I am trying to create a numpy array of certain dimensions however I am getting a Memory Error. no_of_frames = 1404 no_of_cells = 136192 original_vals = np.zeros((no_of_frames, no_of_cells), dtype=np.float32) The error I am getting is: difference_array = np.zeros((no_of_frames, no_of_cells), dtype=np.float32) MemoryError According to my calculations 1404 x 136192 * 4 is ~729 MB. Which seems pretty reasonable. The machine I python memory error am running this code on has 8 GB of RAM. So why am I getting this error? I would greatly appreciate any help with this. Thanks! python numpy memory share|improve this question asked Sep 25 '15 at 19:28 user3057470 31 add a comment| 2 Answers 2 active oldest votes up vote 1 down vote accepted if you are working in 32bit python you are limited to 32bit address space (~2GB) if you have other stuff going on it you maybe are exceeding this limit in addition numpy requires contiguous memory space in order to create its lists ... this means it has to find an uninterupted 768MB block of ram (which is kind of hard) share|improve this answer answered Sep 25 '15 at 19:43 Joran Beasley 54.8k34979 Thank you I guess I will have to shift to 64-bit Python. –user3057470 Sep 25 '15 at 22:48 add a comment| up vote 1 down vote I just tried the code you gave on my 4GB machine with no problem: Python 3.4.3 (v3.4.3:9b73f1c3e601, Feb 24 2015, 22:43:06) [MSC v.1600 32 bit (Intel)] on win32 Type "copyright", "credits" or "license()" for more information. >>> no_of_frames = 1404 >>> no_of_cells = 1361
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 Code Review Questions Tags Users Badges Unanswered Ask Question _ Code Review Stack Exchange is a question and answer site for peer programmer code reviews. 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 Python, numpy, optimize module to handle big file up vote 7 down vote favorite 2 With the help of some people here and there, I wrote the module below which works very well if the file targets.csv is not too big. As soon as this file exceeds 100 MB, a MemoryError is returned. The problem is that I used numpy to make the computations faster (and they became really fast compared to the previous versions of this module), and I think numpy requires to load the entire file into memory. However, there should be a way to handle the MemoryError that happens there: targets = np.array([(float(X), float(Y), float(Z)) for X, Y, Z in csv.reader(open('targets.csv'))]) Any ideas? Let me know if you need more details (files, etc.) import csv import numpy as np import scipy.spatial import cv2 import random """loading files""" points = np.array([(int(R), int(G), int(B), float(X), float(Y), float(Z)) for R, G, B, X, Y, Z in csv.reader(open('colorlist.csv'))]) # load X,Y,Z coordinates of 'points' in a np.array print "colorlist loaded" targets = np.array([(float(X), float(Y), float(Z)) for X, Y, Z in csv.reader(open('targets.csv'))]) # load the XYZ target values in a np.array print "targets loaded" img = cv2.imread("MAP.tif", -1) height, width = img.shape total = height * width # load dimensions of tif image print "MAP loaded" ppm = file("walladrien.ppm", 'w') ppm.write("P3" + "\n" + str(height) + " " + str(width) +"\n" + "255" + "\n") # write PPM file header """doing geometry""" tri = scipy.spatial.Delaunay(points[:,[3,4,5]], furthest_site=False) # True makes an almost BW picture # Delaunay triangulation indices = tri.simplices # indices of vertices vertices = points[indices] # the vertices for each tetrahedron tet = tri.find_simplex(targets) U = tri.transform[tet,:3] V = targets - tri.transform[tet,3] b