Re: [Numpy-discussion] use index array of len n to select columns of n x m array
Keith Goodman wrote: Here's one way: a.flat[i + a.shape[1] * np.arange(a.shape[0])] array([0, 3, 5, 6, 9]) I'm afraid I made my example a little too simple. In retrospect, what I really want is to be able to use a 2D index array i, like this: a = np.array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15], [16, 17, 18, 19]]) i = np.array([[2, 1], [3, 1], [1, 1], [0, 0], [3, 1]]) foo(a, i) array([[ 2, 1], [ 7, 5], [ 9, 9], [12, 12], [19, 17]]) I think the flat iterator indexing suggestion is about the only thing that'll work. Here's the function I've pretty much settled on: def rowtake(a, i): For each row in a, return values according to column indices in the corresponding row in i. Returned shape == i.shape assert a.ndim == 2 assert i.ndim = 2 if i.ndim == 1: return a.flat[i + a.shape[1] * np.arange(a.shape[0])] else: # i.ndim == 2 return a.flat[i + a.shape[1] * np.vstack(np.arange(a.shape[0]))] This is about half as fast as my Cython function, but the Cython function is limited to fixed dtypes and ndim: @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) def rowtake_cy(np.ndarray[np.int32_t, ndim=2] a, np.ndarray[np.int32_t, ndim=2] i): For each row in a, return values according to column indices in the corresponding row in i. Returned shape == i.shape cdef Py_ssize_t nrows, ncols, rowi, coli cdef np.ndarray[np.int32_t, ndim=2] out nrows = i.shape[0] ncols = i.shape[1] # num cols to take from a for each row assert a.shape[0] == nrows assert i.max() a.shape[1] out = np.empty((nrows, ncols), dtype=np.int32) for rowi in range(nrows): for coli in range(ncols): out[rowi, coli] = a[rowi, i[rowi, coli]] return out Cheers, Martin ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] use index array of len n to select columns of n x m array
On Fri, Aug 6, 2010 at 6:01 AM, Martin Spacek nu...@mspacek.mm.st wrote: Keith Goodman wrote: Here's one way: a.flat[i + a.shape[1] * np.arange(a.shape[0])] array([0, 3, 5, 6, 9]) I'm afraid I made my example a little too simple. In retrospect, what I really want is to be able to use a 2D index array i, like this: a = np.array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15], [16, 17, 18, 19]]) i = np.array([[2, 1], [3, 1], [1, 1], [0, 0], [3, 1]]) foo(a, i) array([[ 2, 1], [ 7, 5], [ 9, 9], [12, 12], [19, 17]]) I think the flat iterator indexing suggestion is about the only thing that'll work. Here's the function I've pretty much settled on: def rowtake(a, i): For each row in a, return values according to column indices in the corresponding row in i. Returned shape == i.shape assert a.ndim == 2 assert i.ndim = 2 if i.ndim == 1: return a.flat[i + a.shape[1] * np.arange(a.shape[0])] else: # i.ndim == 2 return a.flat[i + a.shape[1] * np.vstack(np.arange(a.shape[0]))] I still find broadcasting easier to read, even if it might be a bit slower a[np.arange(5)[:,None], i] array([[ 2, 1], [ 7, 5], [ 9, 9], [12, 12], [19, 17]]) Josef This is about half as fast as my Cython function, but the Cython function is limited to fixed dtypes and ndim: @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) def rowtake_cy(np.ndarray[np.int32_t, ndim=2] a, np.ndarray[np.int32_t, ndim=2] i): For each row in a, return values according to column indices in the corresponding row in i. Returned shape == i.shape cdef Py_ssize_t nrows, ncols, rowi, coli cdef np.ndarray[np.int32_t, ndim=2] out nrows = i.shape[0] ncols = i.shape[1] # num cols to take from a for each row assert a.shape[0] == nrows assert i.max() a.shape[1] out = np.empty((nrows, ncols), dtype=np.int32) for rowi in range(nrows): for coli in range(ncols): out[rowi, coli] = a[rowi, i[rowi, coli]] return out Cheers, Martin ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] use index array of len n to select columns of n x m array
On Fri, Aug 6, 2010 at 3:01 AM, Martin Spacek nu...@mspacek.mm.st wrote: Keith Goodman wrote: Here's one way: a.flat[i + a.shape[1] * np.arange(a.shape[0])] array([0, 3, 5, 6, 9]) I'm afraid I made my example a little too simple. In retrospect, what I really want is to be able to use a 2D index array i, like this: a = np.array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15], [16, 17, 18, 19]]) i = np.array([[2, 1], [3, 1], [1, 1], [0, 0], [3, 1]]) foo(a, i) array([[ 2, 1], [ 7, 5], [ 9, 9], [12, 12], [19, 17]]) I think the flat iterator indexing suggestion is about the only thing that'll work. Here's the function I've pretty much settled on: def rowtake(a, i): For each row in a, return values according to column indices in the corresponding row in i. Returned shape == i.shape assert a.ndim == 2 assert i.ndim = 2 if i.ndim == 1: return a.flat[i + a.shape[1] * np.arange(a.shape[0])] else: # i.ndim == 2 return a.flat[i + a.shape[1] * np.vstack(np.arange(a.shape[0]))] This is about half as fast as my Cython function, but the Cython function is limited to fixed dtypes and ndim: You can speed it up by getting rid of two copies: idx = np.arange(a.shape[0]) idx *= a.shape[1] idx += i ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] {OT} Mailing trends
On Thu, Aug 5, 2010 at 2:55 PM, josef.p...@gmail.com wrote: On Thu, Aug 5, 2010 at 3:43 PM, Gökhan Sever gokhanse...@gmail.com wrote: Hello, There is a nice e-mailing trend tool for Gmail users at http://code.google.com/p/mail-trends/ It is a command line tool producing an html output showing your e-mailing statistics. In my inbox, the following threads are highly ranked in the top threads section. [Numpy-discussion] Announcing toydist, improving distribution and packaging situation [SciPy-Dev] scipy.stats [Numpy-discussion] curious about how people would feel about moving to github Just out of curiosity, are there any mailing trends (top threads, top posters, etc...) provided for the Python related mailing archives? Share your comments please. I only know the top poster statistics for googlegroups http://groups.google.ca/group/scipy-user/about?hl=en but numpy-discussion and scipy-dev are not on google groups Is scipy-user a google group or just mirrored? As a side note or idea I was playing with trying to search for each function/module in the archive as a way to rank/prioritize what documentation may need improvement. For example searching http://groups.google.ca/group/scipy-user/about?hl=en http://groups.google.ca/group/scipy-user/about?hl=en*38* results for * randint** *6,950 results for optimize** Of course there are many reasons an it just might be popular in example code and not really the question in the post. Vincent Josef -- Gökhan ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion *Vincent Davis 720-301-3003 * vinc...@vincentdavis.net my blog http://vincentdavis.net | LinkedInhttp://www.linkedin.com/in/vincentdavis ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy histogram normed=True (bug / confusing behavior)
Hi, I found what looks like a bug in histogram, when the option normed=True is used together with non-uniform bins. Consider this example: import numpy as np data = np.array([1, 2, 3, 4]) bins = np.array([.5, 1.5, 4.5]) bin_widths = np.diff(bins) (counts, dummy) = np.histogram(data, bins) (densities, dummy) = np.histogram(data, bins, normed=True) What this gives is: bin_widths array([ 1., 3.]) counts array([1, 3]) densities array([ 0.1, 0.3]) The documentation claims that histogram with normed=True gives a density, which integrates to 1. In this example, it is true that (densities * bin_widths).sum() is 1. However, clearly the data are equally spaced, so their density should be uniform and equal to 0.25. Note that (0.25 * bin_widths).sum() is also 1. I believe np.histogram(data, bins, normed=True) effectively does : np.histogram(data, bins, normed=False) / (bins[-1] - bins[0]). However, it _should_ do np.histogram(data, bins, normed=False) / bins_widths to get a true density over the data coordinate as a result. It's easy to fix by hand, but I think the documentation is at least misleading?! sorry if this has been discussed before; I did not find it anyway (numpy 1.3) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] {OT} Mailing trends
On Fri, Aug 6, 2010 at 11:13 AM, Vincent Davis vinc...@vincentdavis.net wrote: On Thu, Aug 5, 2010 at 2:55 PM, josef.p...@gmail.com wrote: On Thu, Aug 5, 2010 at 3:43 PM, Gökhan Sever gokhanse...@gmail.com wrote: Hello, There is a nice e-mailing trend tool for Gmail users at http://code.google.com/p/mail-trends/ It is a command line tool producing an html output showing your e-mailing statistics. In my inbox, the following threads are highly ranked in the top threads section. [Numpy-discussion] Announcing toydist, improving distribution and packaging situation [SciPy-Dev] scipy.stats [Numpy-discussion] curious about how people would feel about moving to github Just out of curiosity, are there any mailing trends (top threads, top posters, etc...) provided for the Python related mailing archives? Share your comments please. I only know the top poster statistics for googlegroups http://groups.google.ca/group/scipy-user/about?hl=en but numpy-discussion and scipy-dev are not on google groups Is scipy-user a google group or just mirrored? just mirrored, original is on scipy.org As a side note or idea I was playing with trying to search for each function/module in the archive as a way to rank/prioritize what documentation may need improvement. For example searching http://groups.google.ca/group/scipy-user/about?hl=en 38 results for randint* 6,950 results for optimize* Of course there are many reasons an it just might be popular in example code and not really the question in the post. I didn't know adding stars works: 12,600 results for *stats* 120 results for *stats* bug I would guess that the most popular functions are also the ones that are the best documented (or they are popular because they have the least obvious API). (for example I didn't find much on ttest, too obvious ? except for a possible enhancement/addition http://groups.google.ca/group/scipy-user/browse_thread/thread/bc3c36f8908a20af/a85a5d6b7d457436?hl=enlnk=gstq=t_test*#a85a5d6b7d457436 ) Josef Vincent Josef -- Gökhan ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion Vincent Davis 720-301-3003 vinc...@vincentdavis.net my blog | LinkedIn ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy histogram normed=True (bug / confusing behavior)
On Fri, Aug 6, 2010 at 11:46 AM, Nils Becker n.bec...@amolf.nl wrote: Hi, I found what looks like a bug in histogram, when the option normed=True is used together with non-uniform bins. Consider this example: import numpy as np data = np.array([1, 2, 3, 4]) bins = np.array([.5, 1.5, 4.5]) bin_widths = np.diff(bins) (counts, dummy) = np.histogram(data, bins) (densities, dummy) = np.histogram(data, bins, normed=True) What this gives is: bin_widths array([ 1., 3.]) counts array([1, 3]) densities array([ 0.1, 0.3]) The documentation claims that histogram with normed=True gives a density, which integrates to 1. In this example, it is true that (densities * bin_widths).sum() is 1. However, clearly the data are equally spaced, so their density should be uniform and equal to 0.25. Note that (0.25 * bin_widths).sum() is also 1. I believe np.histogram(data, bins, normed=True) effectively does : np.histogram(data, bins, normed=False) / (bins[-1] - bins[0]). However, it _should_ do np.histogram(data, bins, normed=False) / bins_widths to get a true density over the data coordinate as a result. It's easy to fix by hand, but I think the documentation is at least misleading?! sorry if this has been discussed before; I did not find it anyway (numpy 1.3) Either I also don't understand histogram or this is a bug. data = np.arange(1,10) bins = np.array([.5, 1.5, 4.5, 7.5, 8.5, 9.5]) np.histogram(data, bins, normed=True) (array([ 0.04761905, 0.14285714, 0.14285714, 0.04761905, 0.04761905]), array([ 0.5, 1.5, 4.5, 7.5, 8.5, 9.5])) np.histogram(data, bins) (array([1, 3, 3, 1, 1]), array([ 0.5, 1.5, 4.5, 7.5, 8.5, 9.5])) np.diff(bins) array([ 1., 3., 3., 1., 1.]) I don't see what the normed=True numbers are in this case. np.array([ 1., 3., 3., 1., 1.])/7 array([ 0.14285714, 0.42857143, 0.42857143, 0.14285714, 0.14285714]) Josef ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Installing numpy with MKL
2010/8/5, David Warde-Farley d...@cs.toronto.edu: I've been having a similar problem compiling NumPy with MKL on a cluster with a site-wide license. Dag's site.cfg fails to config if I use 'iomp5' in it, since (at least with this version, 11.1) libiomp5 is located in /scinet/gpc/intel/Compiler/11.1/072/lib/intel64/ whereas the actual proper MKL /scinet/gpc/intel/Compiler/11.1/072/mkl/lib/em64t/ I've tried putting both in my library_dirs separated by a colon as is suggested by the docs, but python setup.py config fails to find MKL in this case. Has anyone else run into this issue? I've made a patch to solve this some time ago: http://projects.scipy.org/numpy/ticket/993 but it did not make into the repo yet. -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Broken links on new.scipy
Hi, @ http://new.scipy.org/download.html numpy and scipy links for Fedora is broken. Could you update the links with these? https://admin.fedoraproject.org/pkgdb/acls/name/numpy https://admin.fedoraproject.org/pkgdb/acls/name/numpy https://admin.fedoraproject.org/pkgdb/acls/name/scipy Thanks. -- Gökhan ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] {OT} Mailing trends
On Fri, Aug 6, 2010 at 10:16 AM, josef.p...@gmail.com wrote: On Fri, Aug 6, 2010 at 11:13 AM, Vincent Davis vinc...@vincentdavis.net wrote: On Thu, Aug 5, 2010 at 2:55 PM, josef.p...@gmail.com wrote: On Thu, Aug 5, 2010 at 3:43 PM, Gökhan Sever gokhanse...@gmail.com wrote: Hello, There is a nice e-mailing trend tool for Gmail users at http://code.google.com/p/mail-trends/ It is a command line tool producing an html output showing your e-mailing statistics. In my inbox, the following threads are highly ranked in the top threads section. [Numpy-discussion] Announcing toydist, improving distribution and packaging situation [SciPy-Dev] scipy.stats [Numpy-discussion] curious about how people would feel about moving to github Just out of curiosity, are there any mailing trends (top threads, top posters, etc...) provided for the Python related mailing archives? Share your comments please. I only know the top poster statistics for googlegroups http://groups.google.ca/group/scipy-user/about?hl=en but numpy-discussion and scipy-dev are not on google groups Is scipy-user a google group or just mirrored? just mirrored, original is on scipy.org As a side note or idea I was playing with trying to search for each function/module in the archive as a way to rank/prioritize what documentation may need improvement. For example searching http://groups.google.ca/group/scipy-user/about?hl=en 38 results for randint* 6,950 results for optimize* Of course there are many reasons an it just might be popular in example code and not really the question in the post. I didn't know adding stars works: 12,600 results for *stats* 120 results for *stats* bug I would guess that the most popular functions are also the ones that are the best documented (or they are popular because they have the least obvious API). This is cool. http://groups.google.ca/advanced_search?q= So the google group is just a mirror of the mailman list? I was thinking this might be valid way to prioritize the documentation (if one wanted to do such a thing) 1; the documentation is poor, or there is no example (based on a quick look) 2; prioritize based on hits in the mail list. Vincent (for example I didn't find much on ttest, too obvious ? except for a possible enhancement/addition http://groups.google.ca/group/scipy-user/browse_thread/thread/bc3c36f8908a20af/a85a5d6b7d457436?hl=enlnk=gstq=t_test*#a85a5d6b7d457436 ) Josef Vincent Josef -- Gökhan ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion Vincent Davis 720-301-3003 vinc...@vincentdavis.net my blog | LinkedIn ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion *Vincent Davis 720-301-3003 * vinc...@vincentdavis.net my blog http://vincentdavis.net | LinkedInhttp://www.linkedin.com/in/vincentdavis ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy histogram normed=True (bug / confusing behavior)
Hi again, first a correction: I posted I believe np.histogram(data, bins, normed=True) effectively does : np.histogram(data, bins, normed=False) / (bins[-1] - bins[0]). However, it _should_ do np.histogram(data, bins, normed=False) / bins_widths but there is a normalization missing; it should read I believe np.histogram(data, bins, normed=True) effectively does np.histogram(data, bins, normed=False) / (bins[-1] - bins[0]) / data.sum() However, it _should_ do np.histogram(data, bins, normed=False) / bins_widths / data.sum() Bruce Southey replied: As I recall, there as issues with this aspect. Please search the discussion regarding histogram especially David Huard's reply in this thread: http://thread.gmane.org/gmane.comp.python.numeric.general/22445 I think this discussion pertains to a switch in calling conventions which happened at the time. The last reply of D. Huard (to me) seems to say that they did not fix anything in the _old_ semantics, but that the new semantics is expected to work properly. I tried with an infinite bin: counts, dmy = np.histogram([1,2,3,4], [0.5,1.5,np.inf]) counts array([1,3]) ncounts, dmy = np.histogram([1,2,3,4], [0.5,1.5,np.inf], normed=1) ncounts array([0.,0.]) this also does not make a lot of sense to me. A better result would be array([0.25, 0.]), since 25% of the points fall in the first bin; 75% fall in the second but are spread out over an infinite interval, giving 0. This is what my second proposal would give. I cannot find anything wrong with it so far... Cheers, Nils ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] use index array of len n to select columns of n x m array
On 2010-08-06 13:11, Martin Spacek wrote: Josef, I'd forgotten you could use None to increase the dimensionality of an array. Neat. And, somehow, it's almost twice as fast as the Cython version!: timeit a[np.arange(a.shape[0])[:, None], i] 10 loops, best of 3: 5.76 us per loop I just realized why the Cython version was slower - the two assertion lines. commenting those out: @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) # might be necessary to release the GIL? def rowtake_cy(np.ndarray[np.int32_t, ndim=2] a, np.ndarray[np.int32_t, ndim=2] i): For each row in a, return values according to column indices in the corresponding row in i. Returned shape == i.shape cdef Py_ssize_t nrows, ncols, rowi, coli cdef np.ndarray[np.int32_t, ndim=2] out nrows = i.shape[0] ncols = i.shape[1] # num cols to take for each row #assert a.shape[0] == nrows #assert i.max() a.shape[1] out = np.empty((nrows, ncols), dtype=np.int32) for rowi in range(nrows): for coli in range(ncols): out[rowi, coli] = a[rowi, i[rowi, coli]] return out gives me: timeit rowtake_cy(a, i) 100 loops, best of 3: 1.44 us per loop which is 4X faster than the a[np.arange(a.shape[0])[:, None], i] broadcasting method. Martin ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy histogram normed=True (bug / confusing behavior)
On Fri, Aug 6, 2010 at 4:53 PM, Nils Becker n.bec...@amolf.nl wrote: Hi again, first a correction: I posted I believe np.histogram(data, bins, normed=True) effectively does : np.histogram(data, bins, normed=False) / (bins[-1] - bins[0]). However, it _should_ do np.histogram(data, bins, normed=False) / bins_widths but there is a normalization missing; it should read I believe np.histogram(data, bins, normed=True) effectively does np.histogram(data, bins, normed=False) / (bins[-1] - bins[0]) / data.sum() However, it _should_ do np.histogram(data, bins, normed=False) / bins_widths / data.sum() Bruce Southey replied: As I recall, there as issues with this aspect. Please search the discussion regarding histogram especially David Huard's reply in this thread: http://thread.gmane.org/gmane.comp.python.numeric.general/22445 I think this discussion pertains to a switch in calling conventions which happened at the time. The last reply of D. Huard (to me) seems to say that they did not fix anything in the _old_ semantics, but that the new semantics is expected to work properly. I tried with an infinite bin: counts, dmy = np.histogram([1,2,3,4], [0.5,1.5,np.inf]) counts array([1,3]) ncounts, dmy = np.histogram([1,2,3,4], [0.5,1.5,np.inf], normed=1) ncounts array([0.,0.]) this also does not make a lot of sense to me. A better result would be array([0.25, 0.]), since 25% of the points fall in the first bin; 75% fall in the second but are spread out over an infinite interval, giving 0. This is what my second proposal would give. I cannot find anything wrong with it so far... I didn't find any different information about the meaning of normed=True on the mailing list nor in the trac history 169 170 if normed: 171 db = array(np.diff(bins), float) 172 return n/(n*db).sum(), bins this does not look like the correct piecewise density with unequal binsizes. Thanks Nils for pointing this out, I tried only equal binsizes for a histogram distribution. Josef Cheers, Nils ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] dtype.type for structured arrays
On Jul 24, 2010, at 2:42 PM, Thomas Robitaille wrote:
Hi,
If I create a structured array with vector columns:
array = np.array(zip([[1,2],[1,2],[1,3]]),dtype=[('a',float,2)])
then examine the type of the column, I get:
array.dtype[0]
dtype(('float64',(2,)))
Then, if I try and view the numerical type, I see:
array.dtype[0].type
type 'numpy.void'
I have to basically do
array.dtype[0].subdtype[0]
dtype('float64')
to get what I need. I seem to remember that this used not to be the case, and
that even for vector columns, one could access array.dtype[0].type to get the
numerical type. Is this a bug, or deliberate?
This looks the same as I remember it. The dtype is a structured array with
the filed name 'a' having an element which is a vector of floats. As a
result, the first dtype[0] extracts the vector of floats dtype. This must be
type void because it is a vector of floats. To get to the underlying type,
you have to do what you did. I don't see how it would have worked another way
in the past.
-Travis
Thanks,
Thomas
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Enthought, Inc.
oliph...@enthought.com
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