NumPy-Discussion December 2008

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101 participants
98 discussions

[F2PY] Fortran call fails in IDLE / PyScripter
by Christophe Chappet 02 Dec '08

02 Dec '08

Hi all, I compile the followinq code using "f2py -c --fcompiler=gnu95 --compiler=mingw32" -m hello subroutine AfficheMessage(szText) character szText*100 write (*,*) szText return end Using python console : >>>import hello >>>hello.affichemessage(" Hello") works fine ! I do the same in the program window of IDLE and : - no message is displayed. - the shell restart (or IDLE crah if launched with -n) Same problem with PyScripter IDE. (crash). Any suggestion ? Regards, Christophe

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bug in ma.masked_all()?
by Eric Firing 02 Dec '08

02 Dec '08

Pierre, ma.masked_all does not seem to work with fancy dtypes and more then one dimension: In [1]:import numpy as np In [2]:dt = np.dtype({'names': ['a', 'b'], 'formats': ['f', 'f']}) In [3]:x = np.ma.masked_all((2,), dtype=dt) In [4]:x Out[4]: masked_array(data = [(--, --) (--, --)], mask = [(True, True) (True, True)], fill_value=(1.0000000200408773e+20, 1.0000000200408773e+20)) In [5]:x = np.ma.masked_all((2,2), dtype=dt) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /home/efiring/<ipython console> in <module>() /usr/local/lib/python2.5/site-packages/numpy/ma/extras.pyc in masked_all(shape, dtype) 78 """ 79 a = masked_array(np.empty(shape, dtype), ---> 80 mask=np.ones(shape, bool)) 81 return a 82 /usr/local/lib/python2.5/site-packages/numpy/ma/core.pyc in __new__(cls, data, mask, dtype, copy, subok, ndmin, fill_value, keep_mask, hard_mask, flag, shrink, **options) 1304 except TypeError: 1305 mask = np.array([tuple([m]*len(mdtype)) for m in mask], -> 1306 dtype=mdtype) 1307 # Make sure the mask and the data have the same shape 1308 if mask.shape != _data.shape: TypeError: expected a readable buffer object ----------------- Eric

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np.loadtxt : yet a new implementation...
by Pierre GM 02 Dec '08

02 Dec '08

All, Please find attached to this message another implementation of np.loadtxt, which focuses on missing values. It's basically a combination of John Hunter's et al mlab.csv2rec, Ryan May's patches and pieces of code I'd been working on over the last few weeks. Besides some helper classes (StringConverter to convert a string into something else, NameValidator to check names..._), you'll find 3 functions: * `genloadtxt` is the base function that makes all the work. It outputs 2 arrays, one for the data (missing values being substituted by the appropriate default) and one for the mask. It would go in np.lib.io * `loadtxt` would replace the current np.loadtxt. It outputs a ndarray, where missing data being filled. It would also go in np.lib.io * `mloadtxt` would go into np.ma.io (to be created) and renamed `loadtxt`. Right now, I needed a different name to avoid conflicts. It combines the outputs of `genloadtxt` into a single masked array. You'll also several series of tests, that you can use as examples. Please give it a try and send me some feedback (bugs, wishes, suggestions). I'd like it to make the 1.3.0 release (I need some of the functionalities to improve the corresponding function in scikits.timeseries, currently fubar...) P.

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fromiter typo?
by Neal Becker 01 Dec '08

01 Dec '08

Says it takes a default dtype arg, but doesn't act like it's an optional arg: fromiter (iterator or generator, dtype=None) Construct an array from an iterator or a generator. Only handles 1-dimensional cases. By default the data-type is determined from the objects returned from the iterator. ---> 20 z = fromiter (y) TypeError: function takes at least 2 arguments (1 given)

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Fwd: np.loadtxt : yet a new implementation...
by Pierre GM 01 Dec '08

01 Dec '08

(Sorry about that, I pressed "Reply" instead of "Reply all". Not my day for emails...) > On Dec 1, 2008, at 1:54 PM, John Hunter wrote: >> >> It looks like I am doing something wrong -- trying to parse a CSV >> file >> with dates formatted like '2008-10-14', with:: >> >> import datetime, sys >> import dateutil.parser >> StringConverter.upgrade_mapper(dateutil.parser.parse, >> default=datetime.date(1900,1,1)) >> r = loadtxt(sys.argv[1], delimiter=',', names=True) > > John, > The problem you have is that the default dtype is 'float' (for > backwards compatibility w/ the original np.loadtxt). What you want > is to automatically change the dtype according to the content of > your file: you should use dtype=None > > r = loadtxt(sys.argv[1], delimiter=',', names=True, dtype=None) > > As you'll want a recarray, we could make a np.records.loadtxt > function where dtype=None would be the default...

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Re: [Numpy-discussion] np.loadtxt : yet a new implementation...
by Pierre GM 01 Dec '08

01 Dec '08

And now for the tests:

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memmap & dtype issue
by Wim Bakker 01 Dec '08

01 Dec '08

For a long time now, numpy's memmap has me puzzled by its behavior. When I use memmap straightforward on a file it seems to work fine, but whenever I try to do a memmap using a dtype it seems to gobble up the whole file into memory. This, of course, makes the use of memmap futile. I would expect that the result of such an operation would give me a true memmap and that the data would be converted to dtype on the fly. I've seen this behavior in version version 1.04, 1.1.1 and still in 1.2.1. I'm working on Windows haven't tried it on Linux. Am I doing something wrong? Are my expectations wrong? Or is this an issue somewhere deeper in numpy? I looked at the memmap.py and it seems to me that most of the work is delegated to numpy.ndarray.__new__. Something wrong there maybe? Can somebody help please? Thanks! Regards, Wim Bakker

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optimising single value functions for array calculations
by Timmie 01 Dec '08

01 Dec '08

Hello, I am developing a module which bases its calculations on another specialised module. My module uses numpy arrays a lot. The problem is that the other module I am building upon, does not work with (whole) arrays but with single values. Therefore, I am currently forces to loop over the array: ### a = numpy.arange(100) b = numpy.arange(100,200) for i in range(0,a.size): a[i] = myfunc(a[i])* b[i] ### The results come out well. But the problem is that this way of calculation is very ineffiecent and takes time. May anyone give me a hint on how I can improve my code without having to modify the package I am building upon. I do not want to change it a lot because I would always have to run behind the chnages in the other package. To summarise: How to I make a calculation function array-aware? Thanks in advance, Timmie

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NumPy-Discussion December 2008