Mailman 3 June 2008 - NumPy-Discussion

Re: [Numpy-discussion] Pointers to arrays in C
by Jose Martin June 3, 2008

June 3, 2008

Thanks for the fast responses! > data. Storing bare pointers in the C side and not holding a > reference to the object providing the data in the C side is really > error prone. It's true, I don't do it because I have to process a large number of arrays, and each has thousands of elements; so I tried to avoid making copies because it'd make it slow. > another option is that you could increase the reference count to > that array in your C code (Py_INCREF). Then it would not be > destroyed on the Python side. However, you'd have to make sure you > Py_DECREF it when you don't need it anymore. Thanks for the suggestion, I didn't know that was possible; I'm new to python. -Jose _______________________________________________ Join Excite! - http://www.excite.com The most personalized portal on the Web!

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Pointers to arrays in C
by Jose Martin June 3, 2008

June 3, 2008

Hi, I read a file with array data (one per line), and send the arrays to a module in C. In the C module, I need to store pointers to the arrays, so I don't have to make a copy for each array it receives from python. I found that if I reuse the same variable name to create the array, the pointer's values in C program will be lost next time the variable is reassigned. For example: - for line in file: - myarray=create an int array from line - my_c_module.function(myarray) 'myarray' is reused to create a new array for each line, however I see that the data in myarray in previous loops (which is stored in C program) is destroyed. To avoid this, I create a python list that appends each 'myarray' instances, and thus are not overwritten. Is there a better approach to avoid this? Thanks in advance! _______________________________________________ Join Excite! - http://www.excite.com The most personalized portal on the Web!

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object-arrays and isnan
by ahalda＠cs.mcgill.ca June 3, 2008

June 3, 2008

Hi I'm trying to make a python class to be used in object arrays (specifically, an mpfr type for multiprecision). Numpy lets me create member functions like 'cos' which get called elemenwise when I call cos(a) on an object array a. However, this doesn't work for some functions, like isnan. Looking in the numpy code, I see in numpy/core/code_generators/generate_umath.py that the type description for isnan does not include objects (type 'M'). Every math ufunc takes objects in the type description except the following ones: isnan isfinite isinf signbit modf Is there a reason for this? I want to be able to use my mpfr type with scipy functions, but for example I can't use optimize.fmin_bfgs because it calls isnan. Other scipy functions work great though,. If there is a reason, how can I get around this? In case I was not clear, here is a demonstration: from scipy import cos, isnan, array class myNumber: def __init__(self, n): self.n = n def cos(self): return cos(self.n) def isnan(self): return isnan(self.n) a = array([myNumber(3)]) print "cos: %s" % str( cos(a) ) print "isnan %s" % str( isnan(a) ) which prints: cos: [-0.9899924966] Traceback (most recent call last): File "./test.py", line 18, in <module> print "isnan %s" % str( isnan(a) ) TypeError: function not supported for these types, and can't coerce safely to supported types Thanks, Allan

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numpy 1.1.0 build can not find lahey (lf95) fortran compiler (short version)
by Lewis E. Randerson June 3, 2008

June 3, 2008

Hi, When trying to build numpy 1.1.0 with python 2.5.2 on an RHEL3 32-bit system, using command: python setup.py config_fc --fcompiler=lahey install &> install_LEW.log I am seeing these warnings: warning: build_ext: f77_compiler=lahey is not available. and warning: build_ext: extension 'numpy.linalg.lapack_lite' has Fortran \ libraries but no Fortran linker found, using default linker Then lapack_lite.so is linked incorrectly with gcc instead of lf95. File distutils/fcompiler/lahey.py is being read but the information inside is not being used. --Lew ---------------------------------------------------------------------- Lewis E. Randerson DOE Princeton University Plasma Physics Laboratory, Princeton University, James Forrestal Campus 100 Stellarator Road, Princeton, NJ 08543 Work: 609/243-3134, Fax: 609/243-3086, PPPL Web: http://www.pppl.gov

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PyTables, array and recarray
by Hanno Klemm June 2, 2008

June 2, 2008

Hi, I try to save the contents of a numpy recarray with PyTables into a file. That works well, however, if I then try to retrieve the data, I get back an array with matching dtypes rather than a recarray. What is the best way to get a recarray back, or if that's not possible what's the most efficient way to convert an array to a recarray? This is what I am doing at the moment: Python 2.5 (r25:51908, Oct 4 2006, 17:28:51) [GCC 3.2.3 20030502 (Red Hat Linux 3.2.3-52)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import numpy as N >>> import tables as t >>> num = 2 >>> a = N.recarray(num, formats='i4,f8,f8',names='id,x,y') >>> a['id'] = [3,4] >>> a['x'] = [3.4,4.5] >>> a['y'] = [4.6,4.5] >>> a recarray([(3, 3.3999999999999999, 4.5999999999999996), (4, 4.5, 4.5)], dtype=[('id', '<i4'), ('x', '<f8'), ('y', '<f8')]) >>> f = t.openFile('test.h5', 'w') >>> f.createTable('/', 'test', a) /test (Table(2L,)) '' description := { "id": Int32Col(shape=(), dflt=0, pos=0), "x": Float64Col(shape=(), dflt=0.0, pos=1), "y": Float64Col(shape=(), dflt=0.0, pos=2)} byteorder := 'little' chunkshape := (409,) >>> f.close() >>> f = t.openFile('test.h5', 'r') >>> b = f.root.test[:] >>> b array([(3, 3.3999999999999999, 4.5999999999999996), (4, 4.5, 4.5)], dtype=[('id', '<i4'), ('x', '<f8'), ('y', '<f8')]) >>> type(b) <type 'numpy.ndarray'> >>> type(a) <class 'numpy.core.records.recarray'> >>> Best regards, Hanno -- Hanno Klemm klemm(a)phys.ethz.ch

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numpy import problem
by Bala subramanian June 2, 2008

June 2, 2008

Dear friends, I installed numpy in a 32-bit machines running with RHEL3. The installation was successful. I tested the installtion by importint numpy inside python interpreter. By once i shutdown the system and restart, and try the same, it says ImportError: No module named numpy. What could be the problem i dnt get. Kindly someone write me on the same. Thanks, Bala

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array += masked_array gives a normal array, not masked array
by Tony Yu June 2, 2008

June 2, 2008

Ok, so you guys shot down my last attempt at finding a bug :). Here's another attempt. array + masked_array outputs a masked array array += masked_array outputs an array. I'm actually not sure if this is a bug (works the same for both the old and new masked arrays), but I thought this was unexpected. *However*, if the right side were an old masked array, the masked values would not be added to the output array. With the new masked arrays, the data from the masked array is added regardless of whether the value is masked (example, below). -Tony Example: ======= In [1]: import numpy In [2]: normal = numpy.array([1, 1]) In [3]: masked = numpy.ma.array([1, 1], mask=[True, False]) In [4]: new_masked = normal + masked In [5]: new_masked Out[5]: masked_array(data = [-- 2], mask = [ True False], fill_value=999999) In [6]: normal += masked In [7]: normal Out[7]: array([2, 2]) # If used old masked arrays in the above, the final output would be: array([1, 2])

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Strange behavior in setting masked array values in Numpy 1.1.0
by Tony Yu June 1, 2008

June 1, 2008

Great job getting numpy 1.1.0 out and thanks for including the old API of masked arrays. I've been playing around with some software using numpy 1.0.4 and took a crack at upgrading it to numpy 1.1.0, but I ran into some strange behavior when assigning to slices of a masked array. I made the simplest example I could think of to show this weird behavior. Basically, reordering the masked array and assigning back to itself *on the same line* seems to work for part of the array, but other parts are left unchanged. In the example below, half of the array is assigned "properly" and the other half isn't. This problem is eliminated if the assignment is done with a copy of the array. Alternatively, this problem is eliminated if I using numpy.oldnumeric.ma.masked_array instead of the new masked array implementation. Is this just a problem on my setup? Thanks in advance for your help. -Tony Yu Example: ======== In [1]: import numpy In [2]: masked = numpy.ma.masked_array([[1, 2, 3, 4, 5]], mask=False) In [3]: masked[:] = numpy.fliplr(masked.copy()) In [4]: print masked [[5 4 3 2 1]] In [5]: masked[:] = numpy.fliplr(masked) In [6]: print masked [[1 2 3 2 1]] Specs: ====== Numpy 1.1.0 Python 2.5.1 OS X Leopard 10.5.3

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ANN: PyQwt-5.1.0 released
by Gerard Vermeulen June 1, 2008

June 1, 2008

What is PyQwt ( http://pyqwt.sourceforge.net ) ? - it is a set of Python bindings for the Qwt C++ class library which extends the Qt framework with widgets for scientific and engineering applications. It provides a 2-dimensional plotting widget and various widgets to display and control bounded or unbounded floating point values. - it requires and extends PyQt, a set of Python bindings for Qt. - it supports the use of PyQt, Qt, Qwt, and optionally NumPy or SciPy in a GUI Python application or in an interactive Python session. - it runs on POSIX, Mac OS X and Windows platforms (practically any platform supported by Qt and Python). - it plots fast: fairly good hardware allows a rate of 100,000 points/second. (PyQwt with Qt-3 is faster than with Qt-4). - it is licensed under the GPL with an exception to allow dynamic linking with non-free releases of Qt and PyQt. The most important new features of PyQwt-5.1.0 are: - support for Qwt-5.1.0. - support for PyQt-4.4.2 and SIP-4.7.6. - support for Qt-4.4. - the CartesianDemo.py, MaskedDataDemo.py, PickerDemo.py examples. The most important bug fixes in PyQwt-5.1.0 are: - fixed QwtPicker::stateMachine() to allow for subclassing of QwtPicker in Python. PyQwt-5.1.0 supports: 1. Python-2.5, or -2.4. 2. PyQt-3.17. 3. PyQt-4.4, PyQt-4.3, or PyQt-4.2. 3 SIP-4.7, or SIP-4.6. 4. Qt-3.3, or Qt-3.2. 5. Qt-4.4, Qt-4.3, or Qt-4.2. 6. Qwt-5.1, or Qwt-5.0. 7. Recent versions of NumPy, numarray, and/or Numeric. Enjoy -- Gerard Vermeulen

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What does "Ignoring attempt to set 'name' (from ... " mean ?
by David Cournapeau June 1, 2008

June 1, 2008

Hi, I encounter this message when I am building a subtree of scipy (for example scipy/sparsetools). What does it mean exactly ? Is the setup.py doing something wrong ? cheers, David

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