On 27/12/2007, devnew@gmail.com wrote:

in my code i am trying to normalise a matrix as below

mymatrix=matrix(..# items are of double type..can be negative values....) numrows,numcols=mymatrix.shape

for i in range(numrows): temp=mymatrix[i].max() for j in range(numcols): mymatrix[i,j]=abs(mymatrix[i,j]/temp)

# i am using abs() to make neg vals positive

this way the code takes too much time to run for a case of numrows=25, and numcols=8100 etc.. being a beginner in numpy and python ,i would like to know if this can be done more efficiently..can anyone advise?

Yes. A major advantage - really the reason for existence - of numpy is that you can do operations to whole matrices at once in a single instruction, without loops. This is convenient from a coding point of view, and it also allows numpy to greatly accelerate calculations. Writing code the way you have above takes no advantage of numpy's machinery, and it would probably run faster using python lists than numpy arrays. I strongly recommend you read some numpy documentation; try starting with the tutorial: http://www.scipy.org/Tentative_NumPy_Tutorial For example, to extract an array containing the maxima of each row of mymatrix, you can use the amax() function: temp = numpy.amax(mymatrix, axis=1) Multiplying a whole matrix by a constant can be done simply as well: 7*mymatrix A final comment is that numpy is designed for computations with multidimensional data. Its basic data type is the array. It has a specialized data type called "matrix" which provides slightly more convenient matrix multiplication (in the sense of linear algebra). I recommend you do not use numpy's matrix class until you are more accustomed to the software. Read the documentation. Start with the tutorial. Good luck. Anne

Anne had it right -- much of the point of numpy is to use nd-arrays as the powerful objects they are - not just containers. Below is a version of your code for comparison. Note to numpy devs: I like the array methods a lot -- is there any particular reason there is no ndarray.abs(), or has it just not been added? -Chris #!/usr/bin/env python """ Simple exmaple of normalizing an array """ import numpy as N from numpy import random mymatrix=random.uniform(-100, 100,(3,4)) print "before:", mymatrix mymatrix2 = mymatrix.copy() numrows,numcols=mymatrix.shape for i in range(numrows): temp=mymatrix[i].max() for j in range(numcols): mymatrix[i,j]=abs(mymatrix[i,j]/temp) print "old way:", mymatrix ## "vectorized" way: # the "reshape" is a bit awkward, but it makes the 1-d result the right shape to "broadcast" to the original array row_max = mymatrix2.max(axis=1).reshape((-1, 1)) print row_max mymatrix2 = N.absolute((mymatrix2 / row_max)) print "vectorized:", mymatrix2 if (mymatrix == mymatrix2).all(): print "They are the same" -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker@noaa.gov