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"pygame.error: Out of memory" when loading level with a large area [duplicate]

Is there a limit to memory for python? I've been using a python script to calculate the average values from a file which is a minimum of 150mb big.
Depending on the size of the file I sometimes encounter a MemoryError.
Can more memory be assigned to the python so I don't encounter the error?
EDIT: Code now below
NOTE: The file sizes can vary greatly (up to 20GB) the minimum size of the a file is 150mb
file_A1_B1 = open("A1_B1_100000.txt", "r")
file_A2_B2 = open("A2_B2_100000.txt", "r")
file_A1_B2 = open("A1_B2_100000.txt", "r")
file_A2_B1 = open("A2_B1_100000.txt", "r")
file_write = open ("average_generations.txt", "w")
mutation_average = open("mutation_average", "w")
files = [file_A2_B2,file_A2_B2,file_A1_B2,file_A2_B1]
for u in files:
line = u.readlines()
list_of_lines = []
for i in line:
values = i.split('\t')
list_of_lines.append(values)
count = 0
for j in list_of_lines:
count +=1
for k in range(0,count):
list_of_lines[k].remove('\n')
length = len(list_of_lines[0])
print_counter = 4
for o in range(0,length):
total = 0
for p in range(0,count):
number = float(list_of_lines[p][o])
total = total + number
average = total/count
print average
if print_counter == 4:
file_write.write(str(average)+'\n')
print_counter = 0
print_counter +=1
file_write.write('\n')

(This is my third answer because I misunderstood what your code was doing in my original, and then made a small but crucial mistake in my second—hopefully three's a charm.
Edits: Since this seems to be a popular answer, I've made a few modifications to improve its implementation over the years—most not too major. This is so if folks use it as template, it will provide an even better basis.
As others have pointed out, your MemoryError problem is most likely because you're attempting to read the entire contents of huge files into memory and then, on top of that, effectively doubling the amount of memory needed by creating a list of lists of the string values from each line.
Python's memory limits are determined by how much physical ram and virtual memory disk space your computer and operating system have available. Even if you don't use it all up and your program "works", using it may be impractical because it takes too long.
Anyway, the most obvious way to avoid that is to process each file a single line at a time, which means you have to do the processing incrementally.
To accomplish this, a list of running totals for each of the fields is kept. When that is finished, the average value of each field can be calculated by dividing the corresponding total value by the count of total lines read. Once that is done, these averages can be printed out and some written to one of the output files. I've also made a conscious effort to use very descriptive variable names to try to make it understandable.
try:
from itertools import izip_longest
except ImportError: # Python 3
from itertools import zip_longest as izip_longest
GROUP_SIZE = 4
input_file_names = ["A1_B1_100000.txt", "A2_B2_100000.txt", "A1_B2_100000.txt",
"A2_B1_100000.txt"]
file_write = open("average_generations.txt", 'w')
mutation_average = open("mutation_average", 'w') # left in, but nothing written
for file_name in input_file_names:
with open(file_name, 'r') as input_file:
print('processing file: {}'.format(file_name))
totals = []
for count, fields in enumerate((line.split('\t') for line in input_file), 1):
totals = [sum(values) for values in
izip_longest(totals, map(float, fields), fillvalue=0)]
averages = [total/count for total in totals]
for print_counter, average in enumerate(averages):
print(' {:9.4f}'.format(average))
if print_counter % GROUP_SIZE == 0:
file_write.write(str(average)+'\n')
file_write.write('\n')
file_write.close()
mutation_average.close()

You're reading the entire file into memory (line = u.readlines()) which will fail of course if the file is too large (and you say that some are up to 20 GB), so that's your problem right there.
Better iterate over each line:
for current_line in u:
do_something_with(current_line)
is the recommended approach.
Later in your script, you're doing some very strange things like first counting all the items in a list, then constructing a for loop over the range of that count. Why not iterate over the list directly? What is the purpose of your script? I have the impression that this could be done much easier.
This is one of the advantages of high-level languages like Python (as opposed to C where you do have to do these housekeeping tasks yourself): Allow Python to handle iteration for you, and only collect in memory what you actually need to have in memory at any given time.
Also, as it seems that you're processing TSV files (tabulator-separated values), you should take a look at the csv module which will handle all the splitting, removing of \ns etc. for you.

Python can use all memory available to its environment. My simple "memory test" crashes on ActiveState Python 2.6 after using about
1959167 [MiB]
On jython 2.5 it crashes earlier:
239000 [MiB]
probably I can configure Jython to use more memory (it uses limits from JVM)
Test app:
import sys
sl = []
i = 0
# some magic 1024 - overhead of string object
fill_size = 1024
if sys.version.startswith('2.7'):
fill_size = 1003
if sys.version.startswith('3'):
fill_size = 497
print(fill_size)
MiB = 0
while True:
s = str(i).zfill(fill_size)
sl.append(s)
if i == 0:
try:
sys.stderr.write('size of one string %d\n' % (sys.getsizeof(s)))
except AttributeError:
pass
i += 1
if i % 1024 == 0:
MiB += 1
if MiB % 25 == 0:
sys.stderr.write('%d [MiB]\n' % (MiB))
In your app you read whole file at once. For such big files you should read the line by line.

No, there's no Python-specific limit on the memory usage of a Python application. I regularly work with Python applications that may use several gigabytes of memory. Most likely, your script actually uses more memory than available on the machine you're running on.
In that case, the solution is to rewrite the script to be more memory efficient, or to add more physical memory if the script is already optimized to minimize memory usage.
Edit:
Your script reads the entire contents of your files into memory at once (line = u.readlines()). Since you're processing files up to 20 GB in size, you're going to get memory errors with that approach unless you have huge amounts of memory in your machine.
A better approach would be to read the files one line at a time:
for u in files:
for line in u: # This will iterate over each line in the file
# Read values from the line, do necessary calculations

Not only are you reading the whole of each file into memory, but also you laboriously replicate the information in a table called list_of_lines.
You have a secondary problem: your choices of variable names severely obfuscate what you are doing.
Here is your script rewritten with the readlines() caper removed and with meaningful names:
file_A1_B1 = open("A1_B1_100000.txt", "r")
file_A2_B2 = open("A2_B2_100000.txt", "r")
file_A1_B2 = open("A1_B2_100000.txt", "r")
file_A2_B1 = open("A2_B1_100000.txt", "r")
file_write = open ("average_generations.txt", "w")
mutation_average = open("mutation_average", "w") # not used
files = [file_A2_B2,file_A2_B2,file_A1_B2,file_A2_B1]
for afile in files:
table = []
for aline in afile:
values = aline.split('\t')
values.remove('\n') # why?
table.append(values)
row_count = len(table)
row0length = len(table[0])
print_counter = 4
for column_index in range(row0length):
column_total = 0
for row_index in range(row_count):
number = float(table[row_index][column_index])
column_total = column_total + number
column_average = column_total/row_count
print column_average
if print_counter == 4:
file_write.write(str(column_average)+'\n')
print_counter = 0
print_counter +=1
file_write.write('\n')
It rapidly becomes apparent that (1) you are calculating column averages (2) the obfuscation led some others to think you were calculating row averages.
As you are calculating column averages, no output is required until the end of each file, and the amount of extra memory actually required is proportional to the number of columns.
Here is a revised version of the outer loop code:
for afile in files:
for row_count, aline in enumerate(afile, start=1):
values = aline.split('\t')
values.remove('\n') # why?
fvalues = map(float, values)
if row_count == 1:
row0length = len(fvalues)
column_index_range = range(row0length)
column_totals = fvalues
else:
assert len(fvalues) == row0length
for column_index in column_index_range:
column_totals[column_index] += fvalues[column_index]
print_counter = 4
for column_index in column_index_range:
column_average = column_totals[column_index] / row_count
print column_average
if print_counter == 4:
file_write.write(str(column_average)+'\n')
print_counter = 0
print_counter +=1

search a 2GB WAV file for dropouts using wave module

`What is the best way to analyze a 2GB WAV file (1khz Tone) for audio dropouts using wave module? I tried the script below
import wave
file1 = wave.open("testdropout.wav", "r")
file2 = open("silence.log", "w")
for i in xrange(file1.getnframes()):
frame = file1.readframes(i)
zero = True
for j in xrange(len(frame)):
# check if amplitude is greater than 0
# the ord() function converts the hex values to integers
if ord(frame[j]) > 0:
zero = False
break
if zero:
print >> file2, 'dropout at second %s' % (file1.tell()/file1.getframerate())
file1.close()
file2.close()

I haven't used the wave module before, but file1.readframes(i) looks like it's reading 1 frame when you're at the first frame, 2 frames when you're at the second frame, 10 frames when you're in the tenth frame, and a 2Gb CD quality file might have a million frames - by the time you're at frame 100,000 reading 100,000 frames ... getting slower each time through the loop as well?
And from my comment, in Python 2 range() generates an in-memory array of the full size first, and xrange() doesn't, but not using range at all helps even more.
And push the looping down into the lower layers with any() to make the code shorter, and possibly faster:
import wave
file1 = wave.open("testdropout.wav", "r")
file2 = open("silence.log", "w")
chunksize = file1.getframerate()
chunk = file1.readframes(chunksize)
while chunk:
if not any(ord(sample) for sample in chunk):
print >> file2, 'dropout at second %s' % (file1.tell()/chunksize)
chunk = file1.readframes(chunksize)
file1.close()
file2.close()
This should read the file in 1-second chunks.

I think a simple solution to this would be to consider that the frame rate on audio files is pretty high. A sample file on my computer happens to have a framerate of 8,000. That means for every second of audio, I have 8,000 samples. If you have missing audio, I'm sure it will exist across multiple frames within a second, so you can essentially reduce your comparisons as drastically as your standards would allow. If I were you, I would try iterating over every 1,000th sample instead of every single sample in the audio file. That basically means it will examine every 1/8th of a second of audio to see if it's dead. Not as precise, but hopefully it will get the job done.
import wave
file1 = wave.open("testdropout.wav", "r")
file2 = open("silence.log", "w")
for i in range(file1.getnframes()):
frame = file1.readframes(i)
zero = True
for j in range(0, len(frame), 1000):
# check if amplitude is greater than 0
# the ord() function converts the hex values to integers
if ord(frame[j]) > 0:
zero = False
break
if zero:
print >> file2, 'dropout at second %s' % (file1.tell()/file1.getframerate())
file1.close()
file2.close()

At the moment, you're reading the entire file into memory, which is not ideal. If you look at the methods available for a "Wave_read" object, one of them is setpos(pos), which sets the position of the file pointer to pos. If you update this position, you should be able to only keep the frame you want in memory at any given time, preventing errors. Below is a rough outline:
import wave
file1 = wave.open("testdropout.wav", "r")
file2 = open("silence.log", "w")
def scan_frame(frame):
for j in range(len(frame)):
# check if amplitude is less than 0
# It makes more sense here to check for the desired case (low amplitude)
# rather than breaking at higher amplitudes
if ord(frame[j]) <= 0:
return True
for i in range(file1.getnframes()):
frame = file1.readframes(1) # only read the frame at the current file position
zero = scan_frame(frame)
if zero:
print >> file2, 'dropout at second %s' % (file1.tell()/file1.getframerate())
pos = file1.tell() # States current file position
file1.setpos(pos + len(frame)) # or pos + 1, or whatever a single unit in a wave
# file is, I'm not entirely sure
file1.close()
file2.close()
Hope this can help!

Why is loading this file taking so much memory?

Trying to load a file into python. It's a very big file (1.5Gb), but I have the available memory and I just want to do this once (hence the use of python, I just need to sort the file one time so python was an easy choice).
My issue is that loading this file is resulting in way to much memory usage. When I've loaded about 10% of the lines into memory, Python is already using 700Mb, which is clearly too much. At around 50% the script hangs, using 3.03 Gb of real memory (and slowly rising).
I know this isn't the most efficient method of sorting a file (memory-wise) but I just want it to work so I can move on to more important problems :D So, what is wrong with the following python code that's causing the massive memory usage:
print 'Loading file into memory'
input_file = open(input_file_name, 'r')
input_file.readline() # Toss out the header
lines = []
totalLines = 31164015.0
currentLine = 0.0
printEvery100000 = 0
for line in input_file:
currentLine += 1.0
lined = line.split('\t')
printEvery100000 += 1
if printEvery100000 == 100000:
print str(currentLine / totalLines)
printEvery100000 = 0;
lines.append( (lined[timestamp_pos].strip(), lined[personID_pos].strip(), lined[x_pos].strip(), lined[y_pos].strip()) )
input_file.close()
print 'Done loading file into memory'
EDIT: In case anyone is unsure, the general consensus seems to be that each variable allocated eats up more and more memory. I "fixed" it in this case by 1) calling readLines(), which still loads all the data, but only has one 'string' variable overhead for each line. This loads the entire file using about 1.7Gb. Then, when I call lines.sort(), I pass a function to key that splits on tabs and returns the right column value, converted to an int. This is slow computationally, and memory-intensive overall, but it works. Learned a ton about variable allocation overhad today :D

Here is a rough estimate of the memory needed, based on the constants derived from your example. At a minimum you have to figure the Python internal object overhead for each split line, plus the overhead for each string.
It estimates 9.1 GB to store the file in memory, assuming the following constants, which are off by a bit, since you're only using part of each line:
1.5 GB file size
31,164,015 total lines
each line split into a list with 4 pieces
Code:
import sys
def sizeof(lst):
return sys.getsizeof(lst) + sum(sys.getsizeof(v) for v in lst)
GIG = 1024**3
file_size = 1.5 * GIG
lines = 31164015
num_cols = 4
avg_line_len = int(file_size / float(lines))
val = 'a' * (avg_line_len / num_cols)
lst = [val] * num_cols
line_size = sizeof(lst)
print 'avg line size: %d bytes' % line_size
print 'approx. memory needed: %.1f GB' % ((line_size * lines) / float(GIG))
Returns:
avg line size: 312 bytes
approx. memory needed: 9.1 GB

I don't know about the analysis of the memory usage, but you might try this to get it to work without running out of memory. You'll sort into a new file which is accessed using a memory mapping (I've been led to believe this will work efficiently [in terms of memory]). Mmap has some OS specific workings, I tested this on Linux (very small scale).
This is the basic code, to make it run with a decent time efficiency you'd probably want to do a binary search on the sorted file to find where to insert the line otherwise it will probably take a long time.
You can find a file-seeking binary search algorithm in this question.
Hopefully a memory efficient way of sorting a massive file by line:
import os
from mmap import mmap
input_file = open('unsorted.txt', 'r')
output_file = open('sorted.txt', 'w+')
# need to provide something in order to be able to mmap the file
# so we'll just copy the first line over
output_file.write(input_file.readline())
output_file.flush()
mm = mmap(output_file.fileno(), os.stat(output_file.name).st_size)
cur_size = mm.size()
for line in input_file:
mm.seek(0)
tup = line.split("\t")
while True:
cur_loc = mm.tell()
o_line = mm.readline()
o_tup = o_line.split("\t")
if o_line == '' or tup[0] < o_tup[0]: # EOF or we found our spot
mm.resize(cur_size + len(line))
mm[cur_loc+len(line):] = mm[cur_loc:cur_size]
mm[cur_loc:cur_loc+len(line)] = line
cur_size += len(line)
break

Most efficient way to concatenate and rearrange files

I am reading from several files, each file is divided into 2 pieces, first a header section of a few thousand lines followed by a body of a few thousand. My problem is I need to concatenate these files into one file where all the headers are on the top followed by the body.
Currently I am using two loops: one to pull out all the headers and write them, and the second to write the body of each file (I also include a tmp_count variable to limit the number of lines to be loading into memory before dumping to file).
This is pretty slow - about 6min for 13gb file. Can anyone tell me how to optimize this or if there is a faster way to do this in python ?
Thanks!
Here is my code:
def cat_files_sam(final_file_name,work_directory_master,file_count):
final_file = open(final_file_name,"w")
if len(file_count) > 1:
file_count=sort_output_files(file_count)
# only for # headers
for bowtie_file in file_count:
#print bowtie_file
tmp_list = []
tmp_count = 0
for line in open(os.path.join(work_directory_master,bowtie_file)):
if line.startswith("#"):
if tmp_count == 1000000:
final_file.writelines(tmp_list)
tmp_list = []
tmp_count = 0
tmp_list.append(line)
tmp_count += 1
else:
final_file.writelines(tmp_list)
break
for bowtie_file in file_count:
#print bowtie_file
tmp_list = []
tmp_count = 0
for line in open(os.path.join(work_directory_master,bowtie_file)):
if line.startswith("#"):
continue
if tmp_count == 1000000:
final_file.writelines(tmp_list)
tmp_list = []
tmp_count = 0
tmp_list.append(line)
tmp_count += 1
final_file.writelines(tmp_list)
final_file.close()

How fast would you expect it to be to move 13Gb of data around? This problem is I/O bound and not a problem with Python. To make it faster, do less I/O. Which means that you are either (a) stuck with the speed you've got or (b) should retool later elements of your toolchain to handle the files in-place rather than requiring one giant 13 Gb file.

You can save the time it takes the 2nd time to skip the headers, as long as you have a reasonable amount of spare disk space: as well as the final file, also open (for 'w+') a temporary file temp_file, and do:
import shutil
hdr_list = []
bod_list = []
dispatch = {True: (hdr_list, final_file),
False: (bod_list, temp_file)}
for bowtie_file in file_count:
with open(os.path.join(work_directory_master,bowtie_file)) as f:
for line in f:
L, fou = dispatch[line[0]=='#']
L.append(f)
if len(L) == 1000000:
fou.writelines(L)
del L[:]
# write final parts, if any
for L, fou in dispatch.items():
if L: fou.writelines(L)
temp_file.seek(0)
shutil.copyfileobj(temp_file, final_file)
This should enhance your program's performance. Fine-tuning that now-hard-coded 1000000, or even completely doing away with the lists and writing each line directly to the appropriate file (final or temporary), are other options you should benchmark (but if you have unbounded amounts of memory, then I expect that they won't matter much -- however, intuitions about performance are often misleading, so it's best to try and measure!-).

There are two gross inefficiencies in the code you meant to write (which is not the code presented):
You are building up huge lists of header lines in the first major for block instead of just writing them out.
You are skipping the headers of the files again in the second major for block line by line when you've already determined where the headers end in (1). See file.seek and file.tell

How to get progress of file move in python?

I've got a little script for sorting out my dowloaded files and it works great, but I'd like to print out the progress of a file move, for when it's doing the big ones, right now I do something like:
print "moving..."
os.renames(pathTofile, newName)
print "done"
But I'd like to be able to see something like a progress bar ( [..... ] style) or a percentage printed to stdout.
I don't need/want a gui of any sort, just the simplest / least-work ( :) ) way to get the operation progress).
Thanks!

You won't be able to get that kind of information using os.renames. Your best bet is to replace that with a home grown file copy operation but call stat on the file beforehand in order to get the complete size so you can track how far through you are.
Something like this:
source_size = os.stat(SOURCE_FILENAME).st_size
copied = 0
source = open(SOURCE_FILENAME, 'rb')
target = open(TARGET_FILENAME, 'wb')
while True:
chunk = source.read(32768)
if not chunk:
break
target.write(chunk)
copied += len(chunk)
print '\r%02d%%' % (copied * 100 / source_size),
source.close()
target.close()
Note however that this will more than likely be markedly slower than using os.rename.

There isn't any way to get a progress bar because the "rename" call that moves the file is a single OS call.
It's worth noting that the "rename" call only takes time if the source and destination are on different physical volumes. If they're on the same volume, then the rename will take almost no time. If you know that you're copying data between volumes, you may wish to use functions from the shutil module such as copyfileobj. There is no callback for progress monitoring, however you can implement your own source or destination file-like object to track progress.

This example method expands on the answer by Benno by estimating the time remaining and removing the progress line when the copy is complete.
def copy_large_file(src, dst):
'''
Copy a large file showing progress.
'''
print('copying "{}" --> "{}"'.format(src, dst))
# Start the timer and get the size.
start = time.time()
size = os.stat(src).st_size
print('{} bytes'.format(size))
# Adjust the chunk size to the input size.
divisor = 10000 # .1%
chunk_size = size / divisor
while chunk_size == 0 and divisor > 0:
divisor /= 10
chunk_size = size / divisor
print('chunk size is {}'.format(chunk_size))
# Copy.
try:
with open(src, 'rb') as ifp:
with open(dst, 'wb') as ofp:
copied = 0 # bytes
chunk = ifp.read(chunk_size)
while chunk:
# Write and calculate how much has been written so far.
ofp.write(chunk)
copied += len(chunk)
per = 100. * float(copied) / float(size)
# Calculate the estimated time remaining.
elapsed = time.time() - start # elapsed so far
avg_time_per_byte = elapsed / float(copied)
remaining = size - copied
est = remaining * avg_time_per_byte
est1 = size * avg_time_per_byte
eststr = 'rem={:>.1f}s, tot={:>.1f}s'.format(est, est1)
# Write out the status.
sys.stdout.write('\r{:>6.1f}% {} {} --> {} '.format(per, eststr, src, dst))
sys.stdout.flush()
# Read in the next chunk.
chunk = ifp.read(chunk_size)
except IOError as obj:
print('\nERROR: {}'.format(obj))
sys.exit(1)
sys.stdout.write('\r\033[K') # clear to EOL
elapsed = time.time() - start
print('copied "{}" --> "{}" in {:>.1f}s"'.format(src, dst, elapsed))
You can see a fully functioning version in the gist entry here: https://gist.github.com/jlinoff/0f7b290dc4e1f58ad803.