Mailman 3 April 2012 - NumPy-Discussion

why does eigvalsh return a complex array?
by Christoph Groth 07 Apr '12

07 Apr '12

I noticed that numpy.linalg.eigvalsh returns a complex array, even though mathematically the resulting eigenvalues are guaranteed to be real. Looking at the source code, the underlying zheevd routine of LAPACK indeed returns an array of real numbers which is than converted to complex in the numpy wrapper. Does numpy policy require the type of the result to be the same as the type of input? Copying an array twice to arrive at the original result seems pointless to me.

2 1

speed of append_fields() in numpy.lib.recfunctions vs matplotlib.mlab
by cgraves 07 Apr '12

07 Apr '12

It seems that the speed of append_fields() in numpy.lib.recfunctions is much slower than rec_append_fields() in matplotlib.mlab. See the following code: import numpy as np import matplotlib.mlab as mlab import numpy.lib.recfunctions as nprf import time # Set up recarray nr_pts = 1E6 dt = np.dtype([('x', float), ('y', float)]) data = np.zeros(nr_pts, dtype=dt) data = data.view(np.recarray) data.x = np.linspace(0,5,nr_pts) data.y = np.linspace(5,10,nr_pts) z = np.linspace(20,15,nr_pts) # Test mlab last_time_clock = time.clock() data_mlab = mlab.rec_append_fields(data, ['z'], [z]) time_taken = time.clock() - last_time_clock print 'mlab took %i milliseconds.' % (time_taken*1000) # Test nprf last_time_clock = time.clock() data_nprf = nprf.append_fields(data, ['z'], [z], usemask=False, asrecarray=True) time_taken = time.clock() - last_time_clock print 'nprf took %i milliseconds.' % (time_taken*1000) On this computer, the output is (+/- 10 ms): mlab took 49 milliseconds. nprf took 440 milliseconds. Does anyone know why the numpy.lib.recfunctions version is so much slower? I thought these were a port from matplotlib.mlab. Changing to usemask=True has an effect on the time of the nprf way, making it vary 330-480 ms (still much slower). I'm using numpy 1.5.1. Best, Chris -- View this message in context: http://old.nabble.com/speed-of-append_fields%28%29-in-numpy.lib.recfunction… Sent from the Numpy-discussion mailing list archive at Nabble.com.

2 1

(no subject)
by Jean-Baptiste Rudant 06 Apr '12

06 Apr '12

<a href="http://alumnos.digicap.cl/images/rmngl.html"> http://alumnos.digicap.cl/images/rmngl.html</a>

4 3

Can't access NumPy documentation elements
by Whitcomb, Mr. Tim 06 Apr '12

06 Apr '12

Good afternoon - we're having some issues here accessing the online documentation for the latest NumPy version: 1. Search for "numpy asarray" on Google 2. Top result is "numpy.asarray - NumPy v1.7.dev-72185d3 Manual (DRAFT)" (or just go directly to link) 3. Click link - arrive at http://docs.scipy.org/doc/numpy/reference/generated/numpy.asarray.html 4. Page has a title, links to previous/next topics, but no content. If I manually go to the NumPy docs page and follow the links down to "array manipulation routines", I see that there are a bunch of entries in the table that I remember as being links, but are just plain text currently. Did something go wrong with a build? Thanks, Tim [w]

2 1

MemoryError : with scipy.spatial.distance
by Abhishek Pratap 05 Apr '12

05 Apr '12

Hey Guys I am new to both python and more so to numpy. I am trying to cluster close to a 900K points using DBSCAN algo. My input is a list of ~900k tuples each having two points (x,y) coordinates. I am converting them to numpy array and passing them to pdist method of scipy.spatial.distance for calculating distance between each point. Here is some size info on my numpy array shape of input array : (828575, 2) Size : 6872000 bytes I think the error has something to do with the default double dtype of numpy array of pdist function. I would appreciate if you could help me debug this. I am sure I overlooking some naive thing here See the traceback below. MemoryError Traceback (most recent call last) /house/homedirs/a/apratap/Dropbox/dev/ipython/<ipython-input-83-ee29361b7276> in <module>() 36 37 print cleaned_senseBam ---> 38 cluster_pet_points_per_chromosome(sense_bamFile) /house/homedirs/a/apratap/Dropbox/dev/ipython/<ipython-input-83-ee29361b7276> in cluster_pet_points_per_chromosome(bamFile) 30 print 'Size of list points is %d' % sys.getsizeof(points) 31 print 'Size of numpy array is %d' % sys.getsizeof(points_array) ---> 32 cluster_points_DBSCAN(points_array) 33 #print points_array 34 /house/homedirs/a/apratap/Dropbox/dev/ipython/<ipython-input-72-77005d7cd900> in cluster_points_DBSCAN(data_numpy_array) 9 def cluster_points_DBSCAN(data_numpy_array): 10 #eucledian distance calculation ---> 11 D = distance.pdist(data_numpy_array) 12 S = distance.squareform(D) 13 H = 1 - S/np.max(S) /house/homedirs/a/apratap/playground/software/epd-7.2-2-rh5-x86_64/lib/python2.7/site-packages/scipy/spatial/distance.pyc in pdist(X, metric, p, w, V, VI) 1155 1156 m, n = s -> 1157 dm = np.zeros((m * (m - 1) / 2,), dtype=np.double) 1158 1159 wmink_names = ['wminkowski', 'wmi', 'wm', 'wpnorm']

3 6

don't understand nditer
by Neal Becker 05 Apr '12

05 Apr '12

Along the lines of my question about apply getitem to each element... If I try to use nditer, I seem to run into trouble: for d in np.nditer (y, ['refs_ok'],['readwrite']): ....: y[...].w = 2 ....: --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) /home/nbecker/hn-8psk/<ipython-input-73-713fc95a44d2> in <module>() 1 for d in np.nditer (y, ['refs_ok'],['readwrite']): ----> 2 y[...].w = 2 3 AttributeError: 'numpy.ndarray' object has no attribute 'w' y is a 2D array of 'noop' class instances: class noop: pass y Out[75]: array([[<__main__.noop instance at 0x4241098>, <__main__.noop instance at 0x4241098>, <__main__.noop instance at 0x4241098>, <__main__.noop instance at 0x4241098>, <__main__.noop instance at 0x4241098>], [<__main__.noop instance at 0x4241098>, <__main__.noop instance at 0x4241098>, <__main__.noop instance at 0x4241098>, <__main__.noop instance at 0x4241098>, <__main__.noop instance at 0x4241098>]], dtype=object) Any idea how to do this setattr per element using nditer?

1 0

apply 'getitem to each element of obj array?
by Neal Becker 05 Apr '12

05 Apr '12

I have an array of object. How can I apply attribute access to each element? I want to do, for example, np.all (u.some_attribute == 0) for all elements in u?

4 6

small bug in ndarray.flatten()?
by Chao YUE 05 Apr '12

05 Apr '12

Dear all, Is there a small bug in following? In [2]: b Out[2]: array([[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17], [18, 19, 20, 21, 22, 23]]) In [3]: b.flatten(order='C') --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /mnt/f/<ipython console> in <module>() TypeError: flatten() takes no keyword arguments order='F' gave tha same. cheers, chao -- *********************************************************************************** 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 ************************************************************************************

3 7

(no subject)
by Jean-Baptiste Rudant 05 Apr '12

05 Apr '12

<a href="http://donnamaui.com/images/uploads/_thumbs/fjgvkd.html"> http://donnamaui.com/images/uploads/_thumbs/fjgvkd.html</a>

1 0

numpy.sum(..., keepdims=False)
by Frédéric Bastien 04 Apr '12

04 Apr '12

Hi, Someone told me that on this page, there was a new parameter to numpy.sum: keepdims=False http://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html Currently the doc don't build correctly this page. Can someone fix this? He gived the link to the google cache that showed it, but the google cache was just replaced by a newer version. I would like to add this parameter to Theano. So my question is, will the interface change or is it stable? The new parameter will make the output have the same number of dimensions as the inputs. The shape will be one on the summed dimensions gived by the axis parameter. thanks Frédéric

3 2