[Numpy-discussion] numpy fileIO

Prashant Saxena animator333@yahoo....
Thu Oct 16 10:25:46 CDT 2008


I am seeing all the OSS for this purpose but I would stick to use pure python and the scene graph I am developing for the application. I already did some test using pyOpenGL/python/wx.GLcanvas and a large data set of roughly 4000+ objects consisting nearly 1 million polygons using display lists and results were more then satisfactory. Although it was done quickly and preformence can be improved further by code optimizations. numpy can play a crucial role
in my application and I have to make sure before pinning down any decisions regarding tools and techniques I would be using.   

Prashant



----- Original Message ----
From: Nadav Horesh <nadavh@visionsense.com>
To: Discussion of Numerical Python <numpy-discussion@scipy.org>
Sent: Thursday, 16 October, 2008 8:13:38 PM
Subject: Re: [Numpy-discussion] numpy fileIO

Did you consider VTK?
I've used it a *little*: Probably it contains all the structures you need, along with c++ routines for I/O, manipulation and
(OpenGL) display, and a python interface.

  Nadav.

-----הודעה מקורית-----
מאת: numpy-discussion-bounces@scipy.org בשם Prashant Saxena
נשלח: ה 16-אוקטובר-08 13:12
אל: numpy-discussion@scipy.org
נושא: [Numpy-discussion] numpy fileIO

Hi,

I have never used numpy in my python applications until now. I am writing a python/openGL based tool to manipulate 3d geometrical data(meshes, curve, points etc.) I would be using numpy to store these objects(vertices, edges, faces, index etc.) at run time. One of my major concern is numpy's file IO capabilities. Geometrical objects would be stored in a structures, for example a logical structure to store a mesh goes like this:

struct(
name(string)
vertex(numpy.float16)[x, y, z]
normal(numpy.float16)[x, y, z]
edges(numpy.int)
st(2d numpy.float16)[u, v]
index(numpy.int)
)

There would be different structures for curve, patch, points and rest of the primitives. I am sure numpy users must have encounered similar scenerio where
you need to dump this data to a file and read it back. In my case, a general assumption of nearly 50-150 megs of data would be considered as normal size.
Before I go deep into coding It would be great if numpy user can share their expreience for the task.

I am also open for unconventional or off the route methods, provided they can do the job.(C/c++, 3rd party modules etc.)
Here is the summery of IO operations I would be working on:

1. Write different structures to a file.
2. Read data back from file.
3. if structure can be tagged(int or string) then read a particular structure using tag, from file.

Hope to here from numpy users soon :-)

Regards

Prashant



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