Mailman 3 August 2012 - SciPy-User

argsort TypeError
by Chris Weisiger Aug. 9, 2012

Aug. 9, 2012

I'm trying to find the indices in an array that have the lowest values, so that I can plot the corresponding values in another array. numpy.argsort would seem to be the proper approach here, but when I try it I get this error: Traceback (most recent call last): File "../scripts/findWorstFitPixel.py", line 48, in <module> indices = np.argsort(result) File "/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/numpy/core/fromnumeric.py", line 680, in argsort return argsort(axis, kind, order) TypeError: an integer is required The weird thing is that when I try to replicate the circumstances in the REPL (same argument shape/dtype passed to argsort), it succeeds. So there must be something subtle going on here. My input is a length-270336 1D array of float32s, each of which was generated by scipy.stats.pearsonr and almost all of which have values between .95 and 1. Any ideas what I can investigate to get this working? Unfortunately my script relies on reading a bunch of files, so I can't very well upload a working sample, but the script itself is here: http://pastebin.com/Rxcbyb8p Thanks for any assistance! -Chris

2 1

watershed
by dlibyp＠gmail.com Aug. 9, 2012

Aug. 9, 2012

Hi all, My question is a digest code question, so that I post on scipy-user, please tell me if I should no more do that. I would like to see source code for watershed algorithm. Code in scipy.ndimage.measurements for watershed is a wrapper for method watershed_ift in _nd_image. Module is imported in measurements.py, but I cannot find it in parent repository. Sorry for naive question: does _nd_image refers to C implementation of watershed_ift() method? Thanks. Rig

2 1

Sample queueing analysis
by Mohan Radhakrishnan Aug. 9, 2012

Aug. 9, 2012

Hi, I am trying to learn to draw graphs for performance models. As part of this process I have a sample problem from a technical paper "The Operational Analysis of Queueing Network Models" There are 3 componens and their visit rations and service times. V1S1 = (20)(.05) = 1.00 seconds (Total CPU time is the bottleneck ) V2S2 = (11)(.08) = .88 seconds (Total Disk time) V3S3 = (8)(.04)= .32 seconds (Total Drum time) These products sum to the minimal response time of 2.2 seconds M1 = Z/VISI = 20/1 = 20 terminals Now I want to draw the graph by sending these values as inputs to a queueing model. So if I plot terminals vs seconds the asymptote of the response time curve should intersect The y-axis at 20 which is M1 give above. Is this type of work done using Sci-Py to solve a queueing model ? Can I read some references ? Thanks, Mohan DISCLAIMER: ==========================================================================================================================================================The information contained in this e-mail message may be privileged and/or confidential and protected from disclosure under applicable law. It is intended only for the individual to whom or entity to which it is addressed as shown at the beginning of the message. If the reader of this message is not the intended recipient, or if the employee or agent responsible for delivering the message is not an employee or agent of the intended recipient, you are hereby notified that any review, dissemination,distribution, use, or copying of this message is strictly prohibited. If you have received this message in error, please notify us immediately by return e-mail and permanently delete this message and your reply to the extent it includes this message. Any views or opinions presented in this message or attachments are those of the author and do not necessarily represent those of the Company. All e-mails and attachments sent and received are subject to monitoring, reading, and archival by the Company.==========================================================================================================================================================

2 2

Re: [SciPy-User] kmeans2 question/issue
by James Abel Aug. 9, 2012

Aug. 9, 2012

Thanks Gael. BTW, I modified my code to loop until it gets the same clustering twice in a row. This yields more consistent results. I don't know if this is a general solution but it worked for my simple test case. Code below. James import sys import scipy import warnings from scipy.cluster.vq import * print sys.version vals = scipy.array((0.0,0.1,0.5,0.6,1.0,1.1)) print vals white_vals = whiten(vals) print white_vals.shape, white_vals # Check for same clustering def clustering_test(a,b): # have to create copies, then sort so we don't modify the original ea = a.copy() eb = b.copy() ea.sort() eb.sort() r = (ea == eb).all() print a,b,ea,eb,r return r # try it until we get the same clustering twice in a row found = False prior_idx = None while not found: with warnings.catch_warnings(): warnings.simplefilter("ignore") # suppress the warning message (happens if it doesn't find the right number of clusters) res, idx = kmeans2(white_vals, 3) # changing iter doesn't seem to matter #print res, idx if prior_idx is not None: eq = clustering_test(idx, prior_idx) #print eq.all() if eq: found = True prior_idx = idx print "result", res, idx

2 1

(no subject)
by Alia Aug. 9, 2012

Aug. 9, 2012

http://izou33.com/01/site.php?perfect233.jpg

1 0

qHull zero division error, scipy 0.10.1, linux
by David Baddeley Aug. 7, 2012

Aug. 7, 2012

I get a zero division error following error when trying to use the qHull bindings in scipy.spatial, e.g. In [8]: from scipy.spatial import qhull In [9]: qhull.Delaunay(randn(100, 2)) --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) /home/david/<ipython console> in <module>() /usr/local/lib/python2.6/dist-packages/scipy-0.10.1-py2.6-linux-x86_64.egg/scipy/spatial/qhull.so in scipy.spatial.qhull.Delaunay.__init__ (scipy/spatial/qhull.c:4109)() /usr/local/lib/python2.6/dist-packages/scipy-0.10.1-py2.6-linux-x86_64.egg/scipy/spatial/qhull.so in scipy.spatial.qhull._construct_delaunay (scipy/spatial/qhull.c:1344)() ZeroDivisionError: float division All the tests involving the qhull module also fail with the same error if I run scipy.test(). full system details below: NumPy version 1.6.2 NumPy is installed in /usr/local/lib/python2.6/dist-packages/numpy-1.6.2-py2.6-linux-x86_64.egg/numpy SciPy version 0.10.1 SciPy is installed in /usr/local/lib/python2.6/dist-packages/scipy-0.10.1-py2.6-linux-x86_64.egg/scipy Python version 2.6.5 (r265:79063, Apr 16 2010, 13:57:41) [GCC 4.4.3] nose version 1.1.2 Ubuntu 10.04 LTS x64 both numpy and scipy were installed using easy_install. before I download the source and start messing with qhull.pyx / the qhull sources themselves. I thought I'd ask if there were any known bugs / clues. many thanks, David

3 3

help speeding up a Runge-Kuta algorithm (cython, f2py, ...)
by Ryan Krauss Aug. 7, 2012

Aug. 7, 2012

I need help speeding up some code I wrote to perform a Runge-Kuta integration. I need to do the integration as part of a real-time control algorithm, so it needs to be fairly fast. scipy.integrate.odeint does too much error checking to be fast enough. My pure Python version was just a little too slow, so I tried coding it up in Cython. I have only used Cython once before, so I don't know if I did it correctly (the .pyx file is attached). The code runs just fine, but there is almost no speed up. I think the core issue is that my dxdt_runge_kuta function gets called about 4000 times per second, so most of my overhead is in the function calls (I think). I am running my real-time control algorithm at 500 Hz and I need at least 2 Runge-Kuta integration steps per real-time steps for numeric stability. And the Runge-Kuta algorithm needs to evaluate the derivative 4 times per times step. So, 500 Hz * 2 * 4 = 4000 calls per second. I also tried coding this up in fortran and using f2py, but I am getting a type mismatch error I don't understand. I have a function that declares its return values as double precision: double precision function dzdt(x,voltage) and I declare the variable I want to store the returned value in to also be double precision: double precision F,z,vel,accel,zdot1,zdot2,zdot3,zdot4 zdot1 = dzdt(x_prev,volts) but some how it is not happy. My C skills are pretty weak (the longer I use Python, the more C I forget, and I didn't know that much to start with). I started looking into Boost as well as using f2py on C code, but I got stuck. Can anyone either make my Cython or Fortran approaches work or point me in a different direction? Thanks, Ryan

5 15

Matrix-free version of connected_components
by Per Nielsen Aug. 7, 2012

Aug. 7, 2012

Hi all, I work with large sparse matrices which consists of several disconnected components and I only need one of these. So far I have succesfully been using scipy.sparse.csgraph.connected_components to dig out the component I needed. This algorithm does however require the entire sparse matrix as input, which sometimes is too large to fit in memory. For this reason I wondered whether there existed a matrix-free version of connected_components, or another algorithm achieving the same, where I would only need to provide a function calculating the sparse matrix-vector product. Any help, hints, or information would be greatly appreciated :) Cheers, Per

4 10

Brentq return values
by The Helmbolds Aug. 6, 2012

Aug. 6, 2012

When the brentq routine is called with `full_output` set to `True` it returns a tuple. The first element of that tuple is the root, i.e., a simple real number. So far, so good. However, the second element of that tuple is: <scipy.optimize.zeros.RootResults object at 0x03B4F450> I don't know how to read or "decode" such a thing. I'd welcome any instruction on how to do that. I also observe that perhaps the return value could be more transparent, i.e., easy to interpret.

2 1

Saving result of sparse.linalg.splu and spilu to file\disk
by Dave Smith Aug. 6, 2012

Aug. 6, 2012

Hi guys/girls, I'm trying to save the result of a (sparse.linalg.spilu) LU decomposition of A such that Ax = b to disk. I only need the result once to apply to many sets of b (solutions). The spilu takes ~ 4hours to compute. Unfortunately, the object returned is a 'factored_lu' type and I don't know how to save this to disk. It won't pickel or mashal: Below is some test code that builds a small ILU 'factored_lu' object called EquSpaInvish ____________________________________________________________________________ from scipy.sparse import lil_matrix from scipy.sparse.linalg import spsolve,spilu from numpy.linalg import solve, norm, inv from numpy.random import rand size = 250 A = lil_matrix((size, size)) A[0, :size] = rand(size) A.setdiag(rand(size)) A = A.tocsc() b = rand(size) EquSpaInvish = spilu(A) iluelap = tm.time() - ilustart x = EquSpaInvish.solve(b) _____________________________________________________________________________ I want to save\load EquSpaInvish to\from disk so i don't have to calculate it each time. Any ideas......? Kind Regards Dave :)

2 1