Mailman 3 September 2010 - NumPy-Discussion

how to store variable length string in array and get single char by it's position?
by kee chen 14 Sep '10

14 Sep '10

Dear All, Suppose I have a list group some kind like DNA sequence: 1 ATGCATGCAATTGGCC 2 ATGCATGCAATTGGCCATCD 3 CATGCAATTGGCCCCCCCCC ...... 100000 CATGCAAATTGGCCCCCCCCC the string length of each item is not sure and may get change/update later, then how can I store above in a numpy array (include the ID) and easy to get the single value? for example 1. ATGCATGCAATTGGCC I want get the first T then I use something like array[1][1], means A[T]G...... and if I want to update the 3rd postion I can use array[1][2] = T to set the AT[G]C... to AT[T]C...? Thanks for any hints. Rgs, KC

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Cython distutils
by Robert Bradshaw 14 Sep '10

14 Sep '10

I've pushed an implementation of http://wiki.cython.org/enhancements/distutils_preprocessing . This allows one do write, e.g. module_list = cythonize("*.pyx") in your setup.py, and it handle the .pyx dependencies (including transitive dependence of libraries) and does all the .pyx -> .c translation. (In particular, this does away with the need for a customized build_ext extension, which is especially nice for projects their own. There are also mechanisms for specifying more build information in the source file itself--see http://hg.cython.org/cython-devel/file/tip/tests/build/inline_distutils.src… for an example. This will also be useful for non-distutils setups (e.g. pyximport and inline code). Try it out and let me know what you think in terms of features and API. - Robert

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Difference in astype() for scalars and arrays?
by Lorenz Hüdepohl 14 Sep '10

14 Sep '10

Hi All, I'm fiddling with some endianness / binary representation issue and stumbled upon this puzzling behavior of astype(): It does not seem to work as expected - for me at least ;) - when given a scalar: >>> import numpy as np >>> np.array(42).astype(">i4").dtype dtype('>i4') >>> np.array(42, dtype=">i4").astype('>i4').dtype dtype('int32') >>> >>> np.array([42]).astype(">i4").dtype dtype('>i4') >>> np.array([42], dtype=">i4").astype('>i4').dtype dtype('>i4') Shouldn't I always get dtype('>i4')? Regards, Lorenz PS: >>> np.version.version '1.4.0.dev7417'

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Hey datarray guys, did you see tablib in The Changelog?
by Joshua Holbrook 14 Sep '10

14 Sep '10

Seeing this made me think it might be of interest to some of us who want to work with tabular data: http://thechangelog.com/post/1112527716/tablib-pythonic-tabular-data-library It's definitely working in 2 dimensions instead of datarray's n dimensions, but it looks nice! Plus, import/export. Nice. Thoughts? Reactions? Discuss. --Josh

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Can't install numpy
by Felix Stödter 12 Sep '10

12 Sep '10

Hi numpy-team, unfortunately I can't install numpy. When I try to excecute the exe-file I get a message like "Pyhton 2.6 required". Pyhton is installed within another programme (Marc/Mentat). Can't he find it because of that? I know that it is possible to install numpy with this Python in Marc/Mentat. But how? Do you know what I can do about it? Thank you in advance. Kind regards, Felix Stoedter ___________________________________________________________ WEB.DE DSL SOMMER-SPECIAL: Surf & Phone Flat 16.000 für nur 19,99 €/mtl.!* http://produkte.web.de/go/DSL-Doppel-Flatrate/2

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scan array to extract min-max values (with if condition)
by Massimo Di Stefano 12 Sep '10

12 Sep '10

Hello All, i need to extract data from an array, that are inside a rectangle area defined as : N, S, E, W = 234560.94503118, 234482.56929822, 921336.53116178, 921185.3779625 the data are in a csv (comma delimited text file, with 3 columns X,Y,Z) #X,Y,Z 3020081.5500,769999.3100,0.0300 3020086.2000,769991.6500,0.4600 3020099.6600,769996.2700,0.9000 ... ... i read it using " numpy.loadtxt " data : http://www.geofemengineering.it/data/csv.txt 5,3 mb (158735 rows) to extract data that are inside the boundy-box area (N, S, E, W) i'm using a loop inside a function like : import numpy as np def getMinMaxBB(data, N, S, E, W): mydata = data * 0.3048006096012 for i in range(len(mydata)): if mydata[i,0] < E or mydata[i,0] > W or mydata[i,1] < N or mydata[i,1] > S : if i == 0: newdata = np.array((mydata[i,0],mydata[i,1],mydata[i,2]), float) else : newdata = np.vstack((newdata,(mydata[i,0], mydata[i,1], mydata[i,2]))) results = {} results['Max_Z'] = newdata.max(0)[2] results['Min_Z'] = newdata.min(0)[2] results['Num_P'] = len(newdata) return results N, S, E, W = 234560.94503118, 234482.56929822, 921336.53116178, 921185.3779625 data = '/Users/sasha/csv.txt' mydata = np.loadtxt(data, comments='#', delimiter=',') out = getMinMaxBB(mydata, N, S, E, W) print out This method works, but maybe is not soo fast, have you any hints on how to improve code with better performance ? thanks!!! Massimo.

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lexsort() of datetime objects doesn't work
by Ernest Adrogué 11 Sep '10

11 Sep '10

Hi, I was trying to use lexsort with an array of datetime.date objects, but it doesn't seem to work. In [86]: date = np.array([datetime.date(2000, 9, 17), datetime.date(2000, 10, 1), datetime.date(2000, 10, 22), datetime.date(2000, 11, 1)]) In [90]: date Out[90]: array([2000-09-17, 2000-10-01, 2000-10-22, 2000-11-01], dtype=object) In [91]: np.lexsort((date,)) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /home/ernest/src/footy/dataset.py in <module>() ----> 1 2 3 4 5 TypeError: merge sort not available for item 0 The normal argsort works, however this is in a method that uses lexsort and I can't change it without breaking things. Ernest

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ANNOUNCE: mahotas 0.5
by Luis Pedro Coelho 11 Sep '10

11 Sep '10

Hello everyone, My numpy based image processing toolbox has just had a new release: 0.5 New features are: Distance transform bwperim() freeimage interface [borrowed and improved from scikits.image] zernike moment computation There were some fixes to the namespace (in particular, functions that were previously available as mahotas.bbox.bbox are now just mahotas.bbox), which is good, but might break code. Sorry, but the new system is just so much better. Many small fixes and performance improvements all around. You can get it from pypi: http://pypi.python.org/pypi/mahotas (including through easy_install mahotas or pip install mahotas). Bleeding edge is at: http://github.com/luispedro/mahotas API Documentation at http://packages.python.org/mahotas/ (very complete, but rather ugly; will fix uglyness). Mailing-List (for Python-based computer vision related subjects in general including this package): http://groups.google.com/group/pythonvision -- Luis Pedro Coelho | Carnegie Mellon University | http://luispedro.org

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dot() performance depends on data?
by Hagen Fürstenau 11 Sep '10

11 Sep '10

Hi, I'm multiplying two 1000x1000 arrays with numpy.dot() and seeing significant performance differences depending on the data. It seems to take much longer on matrices with many zeros than on random ones. I don't know much about optimized MM implementations, but is this normal behavior for some reason? Cheers, Hagen

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weighted random integers
by josef.pktd＠gmail.com 10 Sep '10

10 Sep '10

I saw some questions on the web how to create non-uniform random integers in python. I don't know what the best way is but here is another way that looks reasonably fast >>> rvs = np.dot(np.random.multinomial(1, [0.1, 0.2, 0.5, 0.2], size=1000000),np.arange(4)) >>> np.bincount(rvs)/1000000. array([ 0.099741, 0.199943, 0.499317, 0.200999]) Josef

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