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# numpy matrix operations

# sr 12/01/2013

import numpy

ary1 = numpy.array([1,2,3,4,5]) # must be same type

ary2 = numpy.zeros((3,4)) # 3x4 matrix consisiting of 0s

ary3 = numpy.ones((3,4)) # 3x4 matrix consisiting of 1s

ary4 = numpy.identity(3) # 3x3 identity matrix

ary5 = ary1.copy() # make a copy of ary1

item1 = ary3[0, 0] # item in row1, column1

ary2.shape # tuple of dimensions. Here: (3,4)

ary2.size # number of elements. Here: 12

ary2_t = ary2.transpose() # transposes matrix

ary2.ravel() # makes an array linear (1-dimensional)

# by concatenating rows

ary2.reshape(2,6) # reshapes array (must have same dimensions)

ary3[0:2, 0:3] # submatrix of first 2 rows and first 3 columns

ary3 = ary3[[2,0,1]] # re-arrange rows

# element-wise operations

ary1 + ary1

ary1 * ary1

numpy.dot(ary1, ary1) # matrix/vector (dot) product

numpy.sum(ary1) # sums up all elements in the array

numpy.mean(ary1) # average of all elements in the array