[Numpy-discussion] Trying to read 500M txt file using numpy.genfromtxt within ipython shell

[Chao YUE]

Dear all,

I received a file from others which contains ~30 million lines and in size of ~500M.
I try read it with numpy.genfromtxt in ipython interactive mode. Then ipython crashed.
The data contains lat,lon,var1,year, the year ranges from 1001 to 2006. Finally I want to write the
data to netcdf for separate years and feed them into the model. I guess I need a better way to do this?
anyone would be any idea is highly appreciated.


lon,lat,year,area_burned
-180.0,65.0,1001,0
-180.0,65.0,1002,0
-180.0,65.0,1003,0
-180.0,65.0,1004,0
-180.0,65.0,1005,0
-180.0,65.0,1006,0
-180.0,65.0,1007,0

thanks and cheers,

Chao

--

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Chao YUE
Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL)
UMR 1572 CEA-CNRS-UVSQ
Batiment 712 - Pe 119
91191 GIF Sur YVETTE Cedex

Tel: (33) 01 69 08 29 02; Fax:01.69.08.77.16

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[David Froger]

Hi,

I think writing a Python script that convert your txt file to one netcdf file,
reading the txt file one line at a time, and then use the netcdf file normally
would be a good solution!

Best,
David

Excerpts from Chao YUE's message of mar. mars 20 13:33:56 +0100 2012:

> Dear all,
>
> I received a file from others which contains ~30 million lines and in size
> of ~500M.
> I try read it with numpy.genfromtxt in ipython interactive mode. Then
> ipython crashed.
> The data contains lat,lon,var1,year, the year ranges from 1001 to 2006.
> Finally I want to write the
> data to netcdf for separate years and feed them into the model. I guess I
> need a better way to do this?
> anyone would be any idea is highly appreciated.
>
>
> lon,lat,year,area_burned
> -180.0,65.0,1001,0
> -180.0,65.0,1002,0
> -180.0,65.0,1003,0
> -180.0,65.0,1004,0
> -180.0,65.0,1005,0
> -180.0,65.0,1006,0
> -180.0,65.0,1007,0
>
> thanks and cheers,
>
> Chao
>

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[Chao YUE]

I would be in agree. thanks!
I use gawk to separate the file into many files by year, then it would be easier to handle.
anyway, it's not a good practice to produce such huge line txt files....

Chao

2012/3/20 David Froger <david....@gmail.com>

[Derek Homeier]

On 20 Mar 2012, at 14:40, Chao YUE wrote:

> I would be in agree. thanks!
> I use gawk to separate the file into many files by year, then it would be easier to handle.
> anyway, it's not a good practice to produce such huge line txt files....

Indeed it's not, but it's also not good practice to load the entire content
of text files as python lists into memory, as unfortunately all the numpy
readers are still doing. But this has been discussed on this list and
improvements are under way.
For your problem at hand the textreader Warren Weckesser recently
made known - can't find the post right now, but you can find it at

https://github.com/WarrenWeckesser/textreader

might be helpful. It is still under construction, but for a plain csv file such
as yours it should be working already. And since the text parsing is
implemented in C, it should also give you a huge speedup for your 1/2 GB!

For additional profiling, similar to what David suggested, it would certainly
be a good idea to read in smaller chunks of the file and write it directly to
the netCDF file. Note that you can already read single lines at a time with the
likes of

from StringIO import StringIO
f = open('file.txt'. 'r')
np.genfromtxt(StringIO(f.next()), delimiter=',')

but I don't think it would work this way with textreader, and iterating such a small
loop over lines in Python would beat the point of using a fast reader.
As your actual data would be < 1GB in numpy, memory usage with textreader
should also not be critical yet.

Cheers,
Derek