[Numpy-discussion] ndarray sub-classing and append function
Hi, I am sub-classing numpy.ndarry for vector array representation. The append function is like this: def append(self, other): self = numpy.append(self, [other], axis=0) Example: vary = VectorArray([v1, v2]) #vary = numpy.append(vary, [v1], axis=0) vary.append(v1) The commented syntax (numpy syntax) is working but "vary.append(v1)" is not working. Any help? Cheers Prashant___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy/numexpr performance (particle simulation)
Hi, I am doing a little test using numpy and numexpr to do a particle simulation. I never used either of them much and this is the first time I have to go deeper. Here is the code: import numpy as np import numexpr as nexpr class Particle( object ): def __init__( self, id ): self.position = [0.0, 0.0, 0.0] self.color = [0.5, 0.5, 0.5] self.direction = [0.0, 0.0, 0.0] self..id = id self.lifeSpan = 0 self.type = 0 class Emitter( object ): def __init__( self ): self.particles = np.empty([], dtype=Particle()) self.currentParticleId = 0 self.numParticles = 1000 self.emissionRate = 10 self.position = [0.0, 0.0, 0.0] self.rotation = [0.0, 0.0, 0.0] # Add a single particle def addParticle( self ): """ Add a single particle in the emitter. """ if self.currentParticleId < self.numParticles: self.particles = np.append( self.particles, Particle( self..currentParticleId ) ) self.currentParticleId+=1 ### Problem 1: self.particles = np.empty([], dtype=Particle()) In "Emitter" class, how do I initialize a numpy array of "Particle" type? Problem 2: If problem 1 can be solved, is it possible to use numexpr to alter the position of each particle's position in emitter.particles array. For example, multiply x,y and z of each particle by using three random values. In other words self.particles[0].position*= random(1.0) self.particles[1].position*= random(2.0) self.particles[2].position*= random(3.0) If problem 1 cannot be solved then may be I could use something like this using simple list: a = [Particle(0), Particle(1)] How do I modify the position of each particle in array"a" using numexpr? I would be using 1-5 million particles where each particle's attribute postion, rotation and color will be modified using some complex equations. The above code is just the begining hence any tips for performance would be greatly apperciated. Regards Prashant Yahoo! recommends that you upgrade to the new and safer Internet Explorer 8. http://downloads.yahoo.com/in/internetexplorer/___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] image processing using numpy-scipy?
Well, I came up with a slightly different approach. Get the first row on the image. It would be something like this: [1,1,1,0,0,0,1,1,0,0,1] 1 = white, 0 = black. Run the floodfill on [0],[6] and [10] pixel if floodfill area is smaller then given area then paint that area black if floodfill area is larger then given area then copy that area to another image of same dimension and paint this area also black here. This would save tons of per pixel iteration. Next row would surely be having less number of white pixels to do the iteration. Now I need an efficient floodfill algorithm for above logic and I hope this will work. Prashant From: Zachary Pincus To: Discussion of Numerical Python Sent: Friday, 27 February, 2009 11:41:43 PM Subject: Re: [Numpy-discussion] image processing using numpy-scipy? Hello, >> This a little wiered problem. I am having a black and white image. >> (black >> background) >> Entire image is filled with noisy white patterns of different size >> and >> shape. I need to fill the >> white patches if there area is more then given one. Logically this >> could >> possible to use a quickfill algorithm >> for every pixel and if the filled area generated by that pixel is >> more then >> given area(in pixel) then paint that >> patch (filled area) with black. >> >> I have read the docs of PIL but there is no function for this. Can >> I use >> numpy-scipy for the matter? > > Sure, there are a couple of options. First, look at scipy.ndimage if > there is anything there you can use (i.e. maybe binary dilation is > sufficient). > > Otherwise, I've got some connected component code at: > > http://mentat.za.net/source/connected_components.tar.bz2 > > (The repository is at http://mentat.za.net/hg/ccomp if you prefer) I think that scipy.ndimage.label also provides connected-component labeling, with arbitrary connectivity. But this problem could also be solvable via dilation / erosion, if the area constraint is loose -- e.g. if you want to quash all blobs smaller than, say, 5x5 pixels, you could just erode for 5 iterations, and then dilate the eroded image back for '-1' iterations (which causes the ndimage algorithm to iterate until no pixels change), using a mask of the original image (so that no pixels outside of the original blobs are turned on). This basically means that any object that survives the original erosion will be dilated back to its initial size. (Similar tricks can also be used to find / kill objects touching the edges -- use a non-zero constant out-of-bounds value and dilate an all-zeros array for -1 iterations, using the original array as the mask. Then only objects touching the edge will get filled in...) Alternately, if you need a very stringent area threshold -- e.g. remove all objects with five or fewer total pixels, regardless of their configuration -- then the connected-components approach is required. Below is a stab at it, though note that there's a slow step that I can't right now figure out how to avoid, short of coding just that in C or with cython or something... Zach import numpy import scipy.ndimage as ndimage _4_connected = numpy.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=bool) def kill_small(binary_image, min_size, structure=_4_connected): label_image, num_objects = ndimage.label(binary_image, structure) # Label 0 is the background... object_labels = numpy.arange(1, num_objects+1) # Get the area of each labeled object by summing up the pixel values in the # binary image. (Divide the binary image by its max val to ensure that the image # consists of 1s and 0s, so that the sum equals the area in pixels.) areas = numpy.array(nd.sum(binary_image / binary_image.max(), label_image, object_labels)) big_objects = object_labels[areas >= min_size] # This part will be pretty slow! But I can't think right now how to speed it up. # (If there are more big objects than small objects, it would be faster to # reverse this process and xor out the small objects from the binary image, # rather than the below, which or's up a new image of just the large objects.) big_object_image = numpy.zeros(binary_image.shape, dtype=bool) for bo in big_objects: big_object_image |= label_image == bo return big_object_image ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy..org/mailman/listinfo/numpy-discussion Add more friends to your messenger and enjoy! Go to http://messenger.yahoo.com/invite/___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] image processing using numpy-scipy?
Hi, This a little wiered problem. I am having a black and white image. (black background) Entire image is filled with noisy white patterns of different size and shape. I need to fill the white patches if there area is more then given one. Logically this could possible to use a quickfill algorithm for every pixel and if the filled area generated by that pixel is more then given area(in pixel) then paint that patch (filled area) with black. I have read the docs of PIL but there is no function for this. Can I use numpy-scipy for the matter? The image size is 1K. Any help would be greatly apperciated. Regards Prashant Connect with friends all over the world. Get Yahoo! India Messenger at http://in.messenger.yahoo.com/?wm=n/___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] replace array values efficiently
Hi,
I am new to numpy and getting my hands on slowly.
How do you replace integers from strings in an integer array. (1D)
For example:
array = {1,1,1,2,3,3,4}
replace 1 with "apple"
replace 2 with "cheery"
replace 3 with "mango"
replace 4 with "banana"
I know the general solution, but I am looking for an efficient way, supported
by numpy/scipy to do this kind of conversion as fast as possible.
Thanks
Prashant
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Re: [Numpy-discussion] array not appending
ST = np.empty((), dtype=np.float32) ST = np.append(ST, 10.0) This works, is it proper way to do so? One more prob ST.size returns 2. Why? I have added only one element. Prashant From: Gael Varoquaux To: Discussion of Numerical Python Sent: Thursday, 18 December, 2008 4:20:49 PM Subject: Re: [Numpy-discussion] array not appending On Thu, Dec 18, 2008 at 04:19:20PM +0530, Prashant Saxena wrote: >How do I solve this? If you want appending in place you have to use a python list. If you don't need modification in place, np.append returns an array with the appended number. Gaël ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy..org/mailman/listinfo/numpy-discussion Add more friends to your messenger and enjoy! Go to http://messenger.yahoo.com/invite/___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] array not appending
How do I solve this? Thanks Prashant From: Gael Varoquaux To: Discussion of Numerical Python Sent: Thursday, 18 December, 2008 4:03:34 PM Subject: Re: [Numpy-discussion] array not appending On Thu, Dec 18, 2008 at 03:52:23PM +0530, Prashant Saxena wrote: >In [43]: ST = np.empty([], dtype=np.float32) >In [44]: np.append(ST, 10.0) >Out[44]: array([ 3.8603e-38, 1.e+01]) >In [45]: np.append(ST, 10.0) >Out[45]: array([ 3.8603e-38, 1.e+01]) >In [46]: print ST >3.8602707e-038 >What's wrong here? Nothing. If you look at the documentation, np.append does not modify in place the array.: ''' Returns --- out : ndarray A copy of `arr` with `values` appended to `axis`. Note that `append` does not occur in-place: a new array is allocated and filled. ''' Modification in place is not possible with the numpy model of an array. Gaël ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion Add more friends to your messenger and enjoy! Go to http://messenger.yahoo.com/invite/___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] array not appending
Hi, This is copied from ipython console. In [42]: import numpy as np In [43]: ST = np.empty([], dtype=np.float32) In [44]: np..append(ST, 10.0) Out[44]: array([ 3.8603e-38, 1.e+01]) In [45]: np.append(ST, 10.0) Out[45]: array([ 3.8603e-38, 1.e+01]) In [46]: print ST 3.8602707e-038 What's wrong here? win XP 32 numpy 1.2.1 python 2.5.2 Prashant Bollywood news, movie reviews, film trailers and more! Go to http://in.movies.yahoo.com/___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy fileIO
I have just started doing my tests using numpy and hdpy( http://h5py.alfven..org/ ). Hope either of these would give me what I need. Prashant - Original Message From: Francesc Alted <[EMAIL PROTECTED]> To: Discussion of Numerical Python Sent: Thursday, 16 October, 2008 10:03:22 PM Subject: Re: [Numpy-discussion] numpy fileIO A Thursday 16 October 2008, Prashant Saxena escrigué: > 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. Your structure seems like can be expressed through a record array [1]. If that is the case, just try the included tofile()/fromfile() utilities included in numpy for saving/retrieving them. If you need more functionality for recarray I/O, you can have a look at pytables [2], which allows you to access directly the recarrays on disk row by row (even if compressed) or use complex expressions to quickly select interesting parts, among other niceties. If your data structures can't be fit in recarray structures directly, you may want to try to decompose them in smaller constructs and relate them in file through pointers (indices to other constructs). [1] http://www.scipy.org/RecordArrays [2] http://www.pytables.org Hope that helps, -- Francesc Alted ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion Add more friends to your messenger and enjoy! Go to http://messenger.yahoo.com/invite/___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy fileIO
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 <[EMAIL PROTECTED]> To: Discussion of Numerical Python 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: [EMAIL PROTECTED] 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 Add more friends to your messenger and enjoy! Go to http://messenger.yahoo.com/invite/ Unlimited freedom, unlimited storage. Get it now, on http://help.yahoo.com/l/in/yahoo/mail/yahoomail/tools/tools-08.html/___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[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 Add more friends to your messenger and enjoy! Go to http://messenger.yahoo.com/invite/___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] first post, simple questions
Hi, numpy rocks!!! import numpy linsp = numpy.linspace red = linsp(0, 255, 50) green = linsp(125, 150, 50) blue = linsp(175, 255, 50) array's elements are float. How do I convert them into integer? I need to build a new array from red, green, blue. like this: [[ red[0], green[0], blue[0] ], [ red[1], green[1], blue[1] ], [ red[2], green[3], blue[3] ], [ red[49], green[49], blue[49] ], ] how do I create such an array? Are there any issues regarding compilation of simple python scripts using numpy functions and cython? Thanks ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
