That’s perfect.

I’s working now.

Thank you.

Ricardo

It is the length of the form that seems to be the make the difference. I suggest using a tool such as wireshark to look at the bytes IE is sending and look for the extra CRLF. This bug is infuriating. In my case my client upgraded to IE 8 and this was no longer an issue. Supporting old versions of IE is a royal pain, especially when they get simple things like this wrong.

Dan

I’m sending you my hacked xlwt. It has a save_large_file method that is more memory efficient when saving.

I have a script that convert a “txt” file in a “xls” file.

The “txt” contains result of queries in a database.

In one case the “txt” file contains more than 600 000 lines. When processing this file, there is lack of memory error.

I tested your script with some changes to simulate the same situation I have in described process.

My computer has 4 GB of memory.

thank you

Ricardo

Depending on what xlwt you are running the save command can still be very expensive. Is there some reason you need to write you 700,000 lines of 200 columns each? How much ram does your machine have?

import guppy
import xlwt
import time

h = guppy.hpy()
print h.heap()

def write():
workbook = xlwt.Workbook()

row = 0
sheet_index = 0

ws = workbook.add_sheet(“foo” + str(sheet_index))

for i in xrange(700000):
for col in xrange(200):
ws.write(row, col, i+col)

row += 1
if row%1000 == 0:
ws.flush_row_data()
if row > 65000:
print h.heap()
sheet_index += 1
ws = workbook.add_sheet(“foo” + str(sheet_index))
row = 0

print h.heap()

workbook.save(“temp2.xls”)
print h.heap()

start = time.time()
write()
print h.heap()
diff = time.time() – start

print “xl gen time: “, diff

—-
result of execution:
Partition of a set of 81396 objects. Total size = 5839948 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 38697 48 2456796 42 2456796 42 str
1 19880 24 826292 14 3283088 56 tuple
2 194 0 424984 7 3708072 63 dict of module
3 4784 6 325312 6 4033384 69 types.CodeType
4 4626 6 277560 5 4310944 74 function
5 595 1 269924 5 4580868 78 dict (no owner)
6 489 1 224172 4 4805040 82 dict of type
7 489 1 220712 4 5025752 86 type
8 3074 4 175976 3 5201728 89 list
9 291 0 157092 3 5358820 92 dict of class

Partition of a set of 146670 objects. Total size = 8235244 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 38713 26 2457336 30 2457336 30 str
1 621 0 1854492 23 4311828 52 dict (no owner)
2 19882 14 826444 10 5138272 62 tuple
3 66906 46 802872 10 5941144 72 int
4 194 0 424984 5 6366128 77 dict of module
5 4784 3 325312 4 6691440 81 types.CodeType
6 4626 3 277560 3 6969000 85 function
7 489 0 224172 3 7193172 87 dict of type
8 489 0 220712 3 7413884 90 type
9 3080 2 176256 2 7590140 92 list

Partition of a set of 211852 objects. Total size = 10613392 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 629 0 3434620 32 3434620 32 dict (no owner)
1 38726 18 2457748 23 5892368 56 str
2 131650 62 1579800 15 7472168 70 int
3 19882 9 826444 8 8298612 78 tuple
4 194 0 424984 4 8723596 82 dict of module
5 4784 2 325312 3 9048908 85 types.CodeType
6 4626 2 277560 3 9326468 88 function
7 489 0 224172 2 9550640 90 dict of type
8 489 0 220712 2 9771352 92 type
9 3083 1 176364 2 9947716 94 list

Partition of a set of 277034 objects. Total size = 12991532 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 637 0 5014748 39 5014748 39 dict (no owner)
1 38739 14 2458160 19 7472908 58 str
2 196394 71 2356728 18 9829636 76 int
3 19882 7 826444 6 10656080 82 tuple
4 194 0 424984 3 11081064 85 dict of module
5 4784 2 325312 3 11406376 88 types.CodeType
6 4626 2 277560 2 11683936 90 function
7 489 0 224172 2 11908108 92 dict of type
8 489 0 220712 2 12128820 93 type
9 3086 1 176472 1 12305292 95 list

Partition of a set of 342216 objects. Total size = 15369712 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 645 0 6594876 43 6594876 43 dict (no owner)
1 261138 76 3133656 20 9728532 63 int
2 38752 11 2458572 16 12187104 79 str
3 19882 6 826444 5 13013548 85 tuple
4 194 0 424984 3 13438532 87 dict of module
5 4784 1 325312 2 13763844 90 types.CodeType
6 4626 1 277560 2 14041404 91 function
7 489 0 224172 1 14265576 93 dict of type
8 489 0 220712 1 14486288 94 type
9 3089 1 176612 1 14662900 95 list

Partition of a set of 407398 objects. Total size = 17747852 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 653 0 8175004 46 8175004 46 dict (no owner)
1 325882 80 3910584 22 12085588 68 int
2 38765 10 2458984 14 14544572 82 str
3 19882 5 826444 5 15371016 87 tuple
4 194 0 424984 2 15796000 89 dict of module
5 4784 1 325312 2 16121312 91 types.CodeType
6 4626 1 277560 2 16398872 92 function
7 489 0 224172 1 16623044 94 dict of type
8 489 0 220712 1 16843756 95 type
9 3092 1 176720 1 17020476 96 list

Partition of a set of 472578 objects. Total size = 20126300 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 661 0 9755516 48 9755516 48 dict (no owner)
1 390626 83 4687512 23 14443028 72 int
2 38777 8 2459356 12 16902384 84 str
3 19882 4 826444 4 17728828 88 tuple
4 194 0 424984 2 18153812 90 dict of module
5 4784 1 325312 2 18479124 92 types.CodeType
6 4626 1 277560 1 18756684 93 function
7 489 0 224172 1 18980856 94 dict of type
8 489 0 220712 1 19201568 95 type
9 3095 1 176828 1 19378396 96 list

Partition of a set of 537762 objects. Total size = 22504532 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 669 0 11335644 50 11335644 50 dict (no owner)
1 455370 85 5464440 24 16800084 75 int
2 38791 7 2459816 11 19259900 86 str
3 19882 4 826444 4 20086344 89 tuple
4 194 0 424984 2 20511328 91 dict of module
5 4784 1 325312 1 20836640 93 types.CodeType
6 4626 1 277560 1 21114200 94 function
7 489 0 224172 1 21338372 95 dict of type
8 489 0 220712 1 21559084 96 type
9 3098 1 176936 1 21736020 97 list

Partition of a set of 602944 objects. Total size = 24882760 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 677 0 12915772 52 12915772 52 dict (no owner)
1 520114 86 6241368 25 19157140 77 int
2 38804 6 2460244 10 21617384 87 str
3 19882 3 826444 3 22443828 90 tuple
4 194 0 424984 2 22868812 92 dict of module
5 4784 1 325312 1 23194124 93 types.CodeType
6 4626 1 277560 1 23471684 94 function
7 489 0 224172 1 23695856 95 dict of type
8 489 0 220712 1 23916568 96 type
9 3101 1 177108 1 24093676 97 list

Partition of a set of 668126 objects. Total size = 27260916 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 685 0 14495900 53 14495900 53 dict (no owner)
1 584858 88 7018296 26 21514196 79 int
2 38817 6 2460672 9 23974868 88 str
3 19882 3 826444 3 24801312 91 tuple
4 194 0 424984 2 25226296 93 dict of module
5 4784 1 325312 1 25551608 94 types.CodeType
6 4626 1 277560 1 25829168 95 function
7 489 0 224172 1 26053340 96 dict of type
8 489 0 220712 1 26274052 96 type
9 3104 0 177216 1 26451268 97 list

Partition of a set of 733308 objects. Total size = 29639080 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 693 0 16076028 54 16076028 54 dict (no owner)
1 649602 89 7795224 26 23871252 81 int
2 38830 5 2461100 8 26332352 89 str
3 19882 3 826444 3 27158796 92 tuple
4 194 0 424984 1 27583780 93 dict of module
5 4784 1 325312 1 27909092 94 types.CodeType
6 4626 1 277560 1 28186652 95 function
7 489 0 224172 1 28410824 96 dict of type
8 489 0 220712 1 28631536 97 type
9 3107 0 177324 1 28808860 97 list

Partition of a set of 1181057 objects. Total size = 49244344 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 1690 0 23895608 49 23895608 49 dict (no owner)
1 699335 59 8392020 17 32287628 66 int
2 200000 17 8000000 16 40287628 82 xlwt.Cell.NumberCell
3 200122 17 3201952 7 43489580 88 float
4 38843 3 2461528 5 45951108 93 str
5 19882 2 826444 2 46777552 95 tuple
6 194 0 424984 1 47202536 96 dict of module
7 4784 0 325312 1 47527848 97 types.CodeType
8 4626 0 277560 1 47805408 97 function
9 489 0 224172 0 48029580 98 dict of type

Traceback (most recent call last):
File “C:\Documents and Settings\C068446\Desktop\Nova pasta\xlwt_flush.py”, line 35, in
write()
File “C:\Documents and Settings\C068446\Desktop\Nova pasta\xlwt_flush.py”, line 31, in write
workbook.save(“temp2.xls”)
File “C:\Python26\lib\site-packages\xlwt\Workbook.py”, line 634, in save
doc.save(filename, self.get_biff_data())
File “C:\Python26\lib\site-packages\xlwt\Workbook.py”, line 620, in get_biff_data
sheets += data
MemoryError
—–

can help me?

Thanks for trying out Math Battle.

From your description I am guessing that you’ve got the thrusters running in reverse. The thruster control is on the right side of the screen. It starts out in “neutral” and you can drag it up to thrust forward and down past “neutral” to go backwards.

Aim at the blue box by dragging on the screen. Then drag the thrusters up a little to start going forward. Once the asteroid that has the crystal is in view it should be easy to destroy with the lasers. Once the crystal is exposed a tractor beam function brings it in closer. You might have to aim towards it a little to collect it. It is acutally possible to outrun objects that you’re pulling with the tractor beam. Maybe I should address that.

I hope these instructions are helpful. You can also check out the tutorial video that is available in the game or here:

http://www.youtube.com/watch?v=Dpvit2vtc5c