Mailman 3 December 2008 - NumPy-Discussion

Importance of order when summing values in an array
by Hanni Ali 10 Dec '08

10 Dec '08

Hi All, I have encountered a puzzling issue and I am not certain if this is a mistake of my own doing or not. Would someone kindly just look over this issue to make sure I'm not doing something very silly. So, why would the sum of an array have a different value depending on the order I select the indices of the array? >>> vector[[39, 46, 49, 50, 6, 9, 12, 14, 15, 17, 21]].sum() 8933281.8757099733 >>> vector[[6, 9, 12, 14, 15, 17, 21, 39, 46, 49, 50]].sum() 8933281.8757099714 >>> sum(vector[[39, 46, 49, 50, 6, 9, 12, 14, 15, 17, 21]]) 8933281.8757099733 >>> sum(vector[[6, 9, 12, 14, 15, 17, 21, 39, 46, 49, 50]]) 8933281.8757099714 Any thoughts? Cheers, Hanni

7 17

Find index of repeated numbers in array
by Ross Williamson 10 Dec '08

10 Dec '08

Hi Everyone I think I'm missing something really obvious but what I would like to do is extract the indexes from an array where a number matches - For example data = [0,1,2,960,5,6,960,7] I would like to know, for example the indices which match 960 - i.e. it would return 3 and 6 I could do this with a loop but I was wondering if there was a built in numpy function to do this? BTW if anyone is interested I'm converting some idl code to numpy and trying to mmic the IDL function where Cheers Ross

4 4

Python2.4 support
by Pierre GM 10 Dec '08

10 Dec '08

All, * What versions of Python should be supported by what version of numpy ? Are we to expect users to rely on Python2.5 for the upcoming 1.3.x ? Could we have some kind of timeline on the trac site or elsewhere (and if such a timeline exists already, can I get the link?) ? * Talking about 1.3.x, what's the timeline? Are we still shooting for a release in 2008 or could we wait till mid Jan. 2009 ? Thx a lot in advance

11 18

access ndarray in C++
by Thomas Hrabe 10 Dec '08

10 Dec '08

Hi all! I am currently developing a python module in C/C++ which is supposed to access nd arrays as defined by the following command in python a = numpy.array([1,1,1]) I want to access the array the following way and use the nd array data for further processing in C. mymod.doSthg(a) The example code on http://numpy.sourceforge.net/numdoc/HTML/numdoc.htm#pgfId-36721 (!PyArg_ParseTuple(args, "O!",&PyArray_Type, &array)) does not work for nd arrays. I always get TypeError: argument 1 must be array, not numpy.ndarray I assume the error is the constant provided as the third parameter, saying that the imput is of PyArray_Type and no nd array. So here are my questions: 1. is there any good tutorial / example code for acessing nd arrays in C? 2. what is the difference between both (arrays and nd arrays? - I am new to python and heard there were different approaches to arrays and that nd arrays work better for multi dimensional applications. Is this right?) 3. which one of both will be used in the future? Thank you in advance for your help, Thomas

15 31

Superclassing numpy.matrix: got an unexpected keyword argument 'dtype'
by Robert.Conde＠sungard.com 10 Dec '08

10 Dec '08

Hello, I'm using numpy-1.1.1 for Python 2.3. I'm trying to create a class that acts just like the numpy.matrix class with my own added methods and attributes. I want to pass my class a list of custom "instrument" objects and do some math based on these objects to set the matrix. To this end I've done the following: from numpy import matrix class rcMatrix(matrix): def __init__(self,instruments): """Do some calculations and set the values of the matrix.""" self[0,0] = 100 # Just an example self[0,1] = 100 # The real init method self[1,0] = 200 # Does some math based on the input objects self[1,1] = 300 # def __new__(cls,instruments): """When creating a new instance begin by creating an NxN matrix of zeroes.""" len_ = len(instruments) return matrix.__new__(cls,[[0.0]*len_]*len_) It works great and I can, for example, multiply two of my custom matrices seamlessly. I can also get the transpose. However, when I try to get the inverse I get an error: > rcm = rcMatrix(['instrument1','instrument2']) > print rcm [[ 100. 100.] [ 200. 300.]] > print rcm.T [[ 100. 200.] [ 100. 300.]] > print [5,10] * rcm [[ 2500. 3500.]] > print rcm.I Traceback (most recent call last): File "[Standard]/deleteme", line 29, in ? File "C:\Python23\Lib\site-packages\numpy\core\defmatrix.py", line 492, in getI return asmatrix(func(self)) File "C:\Python23\Lib\site-packages\numpy\core\defmatrix.py", line 52, in asmatrix return matrix(data, dtype=dtype, copy=False) TypeError: __init__() got an unexpected keyword argument 'dtype' I've had to overwrite the getI function in order for things to work out: def getI(self): return matrix(self.tolist()).I I = property(getI, None, doc="inverse") Is this the correct way to achieve my goals? Please let me know if anything is unclear. Thanks, Robert Conde

2 1

Little vectorization help...
by Andrea Gavana 10 Dec '08

10 Dec '08

Hi All, I am tryin to "vectorize" 3 nested for loops but I am not having much success. Here is the code I use: import numpy import numpy.ma as masked grid = numpy.zeros((nx, ny), dtype=numpy.float32) xOut = numpy.zeros((nx, ny), dtype=numpy.float32) yOut = numpy.zeros((nx, ny), dtype=numpy.float32) z = GetCentroids() # Some vector z values prop = GetValue() # Some other vector values NaN = numpy.NaN for I in xrange(1, nx+1): for J in xrange(1, ny+1): theSum = [] for K in xrange(1, nz+1): cellPos = I-1 + nx*(J-1) + nx*ny*(K-1) centroid = z[cellPos] if low <= centroid <= high and actnum[cellPos] > 0: theSum.append(prop[cellPos]) if theSum: grid[I-1, J-1] = sum(theSum)/len(theSum) else: grid[I-1, J-1] = NaN xOut[I-1, J-1], yOut[I-1, J-1] = x[cellPos], y[cellPos] grid = masked.masked_where(numpy.isnan(grid), grid) Some explanation: 1) "z" is a vector of nx*ny*nz components, where nx = 100, ny = 73, nz = 23, which represents 3D hexahedron cell centroids; 2) "prop" is a vector like z, with the same shape, with some floating point values in it; 3) "actnum" is a vector of integers (0 or 1) with the same shape as z, and indicates if a cell should be considered in the loop or not; 4) low and high are 2 floating point values with low < high: if the cell centroid fall between low and high and the cell is active (as stated in "actnum"), then I take the value of "prop" in that cell and I append it to the "theSum" list; 5) At the end of the K loop, I just take an arithmetic mean of the values in "theSum" list. I think I may be able to figure out how to vectorize the part regarding the "grid" variable, but I have no idea on what to do for the xOut and yOut variables, and I need them because I use them in a later call to matplotlib.contourf. If you could drop some hint on how to proceed, ot would be very appreciated. Thank you for your suggestions. Andrea. "Imagination Is The Only Weapon In The War Against Reality." http://xoomer.alice.it/infinity77/

1 0

Little vectorization help...
by Andrea Gavana 10 Dec '08

10 Dec '08

Hi All, I am tryin to "vectorize" 3 nested for loops but I am not having much success. Here is the code I use: import numpy import numpy.ma as masked grid = numpy.zeros((nx, ny), dtype=numpy.float32) xOut = numpy.zeros((nx, ny), dtype=numpy.float32) yOut = numpy.zeros((nx, ny), dtype=numpy.float32) z = GetCentroids() # Some vector z values prop = GetValue() # Some other vector values NaN = numpy.NaN for I in xrange(1, nx+1): for J in xrange(1, ny+1): theSum = [] for K in xrange(1, nz+1): cellPos = I-1 + nx*(J-1) + nx*ny*(K-1) centroid = z[cellPos] if low <= centroid <= high and actnum[cellPos] > 0: theSum.append(prop[cellPos]) if theSum: grid[I-1, J-1] = sum(theSum)/len(theSum) else: grid[I-1, J-1] = NaN xOut[I-1, J-1], yOut[I-1, J-1] = x[cellPos], y[cellPos] grid = masked.masked_where(numpy.isnan(grid), grid) Some explanation: 1) "z" is a vector of nx*ny*nz components, where nx = 100, ny = 73, nz = 23, which represents 3D hexahedron cell centroids; 2) "prop" is a vector like z, with the same shape, with some floating point values in it; 3) "actnum" is a vector of integers (0 or 1) with the same shape as z, and indicates if a cell should be considered in the loop or not; 4) low and high are 2 floating point values with low < high: if the cell centroid fall between low and high and the cell is active (as stated in "actnum"), then I take the value of "prop" in that cell and I append it to the "theSum" list; 5) At the end of the K loop, I just take an arithmetic mean of the values in "theSum" list. I think I may be able to figure out how to vectorize the part regarding the "grid" variable, but I have no idea on what to do for the xOut and yOut variables, and I need them because I use them in a later call to matplotlib.contourf. If you could drop some hint on how to proceed, ot would be very appreciated. Thank you for your suggestions. Andrea. "Imagination Is The Only Weapon In The War Against Reality." http://xoomer.alice.it/infinity77/

1 0