Convert MATLAB Code to Python using numpy

Question

I am trying to convert a MATLAB line of code to Python using Numpy.

octave:20> fs=10
fs =  10
octave:21> [-fs:1/fs:fs]
ans =

 Columns 1 through 7:

  -10.00000   -9.90000   -9.80000   -9.70000   -9.60000   -9.50000   -9.40000

 Columns 8 through 14:

   -9.30000   -9.20000   -9.10000   -9.00000   -8.90000   -8.80000   -8.70000

 Columns 15 through 21:

   -8.60000   -8.50000   -8.40000   -8.30000   -8.20000   -8.10000   -8.00000

 Columns 22 through 28:

   -7.90000   -7.80000   -7.70000   -7.60000   -7.50000   -7.40000   -7.30000

 Columns 29 through 35:

   -7.20000   -7.10000   -7.00000   -6.90000   -6.80000   -6.70000   -6.60000

 Columns 36 through 42:

   -6.50000   -6.40000   -6.30000   -6.20000   -6.10000   -6.00000   -5.90000

 Columns 43 through 49:

   -5.80000   -5.70000   -5.60000   -5.50000   -5.40000   -5.30000   -5.20000

 Columns 50 through 56:

   -5.10000   -5.00000   -4.90000   -4.80000   -4.70000   -4.60000   -4.50000

 Columns 57 through 63:

   -4.40000   -4.30000   -4.20000   -4.10000   -4.00000   -3.90000   -3.80000

 Columns 64 through 70:

   -3.70000   -3.60000   -3.50000   -3.40000   -3.30000   -3.20000   -3.10000

 Columns 71 through 77:

   -3.00000   -2.90000   -2.80000   -2.70000   -2.60000   -2.50000   -2.40000

 Columns 78 through 84:

   -2.30000   -2.20000   -2.10000   -2.00000   -1.90000   -1.80000   -1.70000

 Columns 85 through 91:

   -1.60000   -1.50000   -1.40000   -1.30000   -1.20000   -1.10000   -1.00000

 Columns 92 through 98:

   -0.90000   -0.80000   -0.70000   -0.60000   -0.50000   -0.40000   -0.30000

 Columns 99 through 105:

   -0.20000   -0.10000    0.00000    0.10000    0.20000    0.30000    0.40000

 Columns 106 through 112:

    0.50000    0.60000    0.70000    0.80000    0.90000    1.00000    1.10000

 Columns 113 through 119:

    1.20000    1.30000    1.40000    1.50000    1.60000    1.70000    1.80000

 Columns 120 through 126:

    1.90000    2.00000    2.10000    2.20000    2.30000    2.40000    2.50000

 Columns 127 through 133:

    2.60000    2.70000    2.80000    2.90000    3.00000    3.10000    3.20000

 Columns 134 through 140:

    3.30000    3.40000    3.50000    3.60000    3.70000    3.80000    3.90000

 Columns 141 through 147:

    4.00000    4.10000    4.20000    4.30000    4.40000    4.50000    4.60000

 Columns 148 through 154:

    4.70000    4.80000    4.90000    5.00000    5.10000    5.20000    5.30000

 Columns 155 through 161:

    5.40000    5.50000    5.60000    5.70000    5.80000    5.90000    6.00000

 Columns 162 through 168:

    6.10000    6.20000    6.30000    6.40000    6.50000    6.60000    6.70000

 Columns 169 through 175:

    6.80000    6.90000    7.00000    7.10000    7.20000    7.30000    7.40000

 Columns 176 through 182:

    7.50000    7.60000    7.70000    7.80000    7.90000    8.00000    8.10000

 Columns 183 through 189:

    8.20000    8.30000    8.40000    8.50000    8.60000    8.70000    8.80000

 Columns 190 through 196:

    8.90000    9.00000    9.10000    9.20000    9.30000    9.40000    9.50000

 Columns 197 through 201:

    9.60000    9.70000    9.80000    9.90000   10.00000

So In Python:

>>> np.arange(-fs, (fs)+(1./fs), 1./fs)
array([ -1.00000000e+01,  -9.90000000e+00,  -9.80000000e+00,
        -9.70000000e+00,  -9.60000000e+00,  -9.50000000e+00,
        -9.40000000e+00,  -9.30000000e+00,  -9.20000000e+00,
        -9.10000000e+00,  -9.00000000e+00,  -8.90000000e+00,
        -8.80000000e+00,  -8.70000000e+00,  -8.60000000e+00,
        -8.50000000e+00,  -8.40000000e+00,  -8.30000000e+00,
        -8.20000000e+00,  -8.10000000e+00,  -8.00000000e+00,
        -7.90000000e+00,  -7.80000000e+00,  -7.70000000e+00,
        -7.60000000e+00,  -7.50000000e+00,  -7.40000000e+00,
        -7.30000000e+00,  -7.20000000e+00,  -7.10000000e+00,
        -7.00000000e+00,  -6.90000000e+00,  -6.80000000e+00,
        -6.70000000e+00,  -6.60000000e+00,  -6.50000000e+00,
        -6.40000000e+00,  -6.30000000e+00,  -6.20000000e+00,
        -6.10000000e+00,  -6.00000000e+00,  -5.90000000e+00,
        -5.80000000e+00,  -5.70000000e+00,  -5.60000000e+00,
        -5.50000000e+00,  -5.40000000e+00,  -5.30000000e+00,
        -5.20000000e+00,  -5.10000000e+00,  -5.00000000e+00,
        -4.90000000e+00,  -4.80000000e+00,  -4.70000000e+00,
        -4.60000000e+00,  -4.50000000e+00,  -4.40000000e+00,
        -4.30000000e+00,  -4.20000000e+00,  -4.10000000e+00,
        -4.00000000e+00,  -3.90000000e+00,  -3.80000000e+00,
        -3.70000000e+00,  -3.60000000e+00,  -3.50000000e+00,
        -3.40000000e+00,  -3.30000000e+00,  -3.20000000e+00,
        -3.10000000e+00,  -3.00000000e+00,  -2.90000000e+00,
        -2.80000000e+00,  -2.70000000e+00,  -2.60000000e+00,
        -2.50000000e+00,  -2.40000000e+00,  -2.30000000e+00,
        -2.20000000e+00,  -2.10000000e+00,  -2.00000000e+00,
        -1.90000000e+00,  -1.80000000e+00,  -1.70000000e+00,
        -1.60000000e+00,  -1.50000000e+00,  -1.40000000e+00,
        -1.30000000e+00,  -1.20000000e+00,  -1.10000000e+00,
        -1.00000000e+00,  -9.00000000e-01,  -8.00000000e-01,
        -7.00000000e-01,  -6.00000000e-01,  -5.00000000e-01,
        -4.00000000e-01,  -3.00000000e-01,  -2.00000000e-01,
        -1.00000000e-01,  -3.55271368e-14,   1.00000000e-01,
         2.00000000e-01,   3.00000000e-01,   4.00000000e-01,
         5.00000000e-01,   6.00000000e-01,   7.00000000e-01,
         8.00000000e-01,   9.00000000e-01,   1.00000000e+00,
         1.10000000e+00,   1.20000000e+00,   1.30000000e+00,
         1.40000000e+00,   1.50000000e+00,   1.60000000e+00,
         1.70000000e+00,   1.80000000e+00,   1.90000000e+00,
         2.00000000e+00,   2.10000000e+00,   2.20000000e+00,
         2.30000000e+00,   2.40000000e+00,   2.50000000e+00,
         2.60000000e+00,   2.70000000e+00,   2.80000000e+00,
         2.90000000e+00,   3.00000000e+00,   3.10000000e+00,
         3.20000000e+00,   3.30000000e+00,   3.40000000e+00,
         3.50000000e+00,   3.60000000e+00,   3.70000000e+00,
         3.80000000e+00,   3.90000000e+00,   4.00000000e+00,
         4.10000000e+00,   4.20000000e+00,   4.30000000e+00,
         4.40000000e+00,   4.50000000e+00,   4.60000000e+00,
         4.70000000e+00,   4.80000000e+00,   4.90000000e+00,
         5.00000000e+00,   5.10000000e+00,   5.20000000e+00,
         5.30000000e+00,   5.40000000e+00,   5.50000000e+00,
         5.60000000e+00,   5.70000000e+00,   5.80000000e+00,
         5.90000000e+00,   6.00000000e+00,   6.10000000e+00,
         6.20000000e+00,   6.30000000e+00,   6.40000000e+00,
         6.50000000e+00,   6.60000000e+00,   6.70000000e+00,
         6.80000000e+00,   6.90000000e+00,   7.00000000e+00,
         7.10000000e+00,   7.20000000e+00,   7.30000000e+00,
         7.40000000e+00,   7.50000000e+00,   7.60000000e+00,
         7.70000000e+00,   7.80000000e+00,   7.90000000e+00,
         8.00000000e+00,   8.10000000e+00,   8.20000000e+00,
         8.30000000e+00,   8.40000000e+00,   8.50000000e+00,
         8.60000000e+00,   8.70000000e+00,   8.80000000e+00,
         8.90000000e+00,   9.00000000e+00,   9.10000000e+00,
         9.20000000e+00,   9.30000000e+00,   9.40000000e+00,
         9.50000000e+00,   9.60000000e+00,   9.70000000e+00,
         9.80000000e+00,   9.90000000e+00,   1.00000000e+01])
>>>

The values seems correct to that of Matlab's output but

octave:22> sincNum = sin(pi*[-fs:1/fs:fs]); 
octave:23> sincNum 
sincNum =

 Columns 1 through 7:

   0.00000   0.30902   0.58779   0.80902   0.95106   1.00000   0.95106

 Columns 8 through 14:

   0.80902   0.58779   0.30902  -0.00000  -0.30902  -0.58779  -0.80902

 Columns 15 through 21:

  -0.95106  -1.00000  -0.95106  -0.80902  -0.58779  -0.30902   0.00000

 Columns 22 through 28:

   0.30902   0.58779   0.80902   0.95106   1.00000   0.95106   0.80902

 Columns 29 through 35:

   0.58779   0.30902  -0.00000  -0.30902  -0.58779  -0.80902  -0.95106

 Columns 36 through 42:

  -1.00000  -0.95106  -0.80902  -0.58779  -0.30902   0.00000   0.30902

 Columns 43 through 49:

   0.58779   0.80902   0.95106   1.00000   0.95106   0.80902   0.58779

 Columns 50 through 56:

   0.30902  -0.00000  -0.30902  -0.58779  -0.80902  -0.95106  -1.00000

 Columns 57 through 63:

  -0.95106  -0.80902  -0.58779  -0.30902   0.00000   0.30902   0.58779

 Columns 64 through 70:

   0.80902   0.95106   1.00000   0.95106   0.80902   0.58779   0.30902

 Columns 71 through 77:

  -0.00000  -0.30902  -0.58779  -0.80902  -0.95106  -1.00000  -0.95106

 Columns 78 through 84:

  -0.80902  -0.58779  -0.30902   0.00000   0.30902   0.58779   0.80902

 Columns 85 through 91:

   0.95106   1.00000   0.95106   0.80902   0.58779   0.30902  -0.00000

 Columns 92 through 98:

  -0.30902  -0.58779  -0.80902  -0.95106  -1.00000  -0.95106  -0.80902

 Columns 99 through 105:

  -0.58779  -0.30902   0.00000   0.30902   0.58779   0.80902   0.95106

 Columns 106 through 112:

   1.00000   0.95106   0.80902   0.58779   0.30902   0.00000  -0.30902

 Columns 113 through 119:

  -0.58779  -0.80902  -0.95106  -1.00000  -0.95106  -0.80902  -0.58779

 Columns 120 through 126:

  -0.30902  -0.00000   0.30902   0.58779   0.80902   0.95106   1.00000

 Columns 127 through 133:

   0.95106   0.80902   0.58779   0.30902   0.00000  -0.30902  -0.58779

 Columns 134 through 140:

  -0.80902  -0.95106  -1.00000  -0.95106  -0.80902  -0.58779  -0.30902

 Columns 141 through 147:

  -0.00000   0.30902   0.58779   0.80902   0.95106   1.00000   0.95106

 Columns 148 through 154:

   0.80902   0.58779   0.30902   0.00000  -0.30902  -0.58779  -0.80902

 Columns 155 through 161:

  -0.95106  -1.00000  -0.95106  -0.80902  -0.58779  -0.30902  -0.00000

 Columns 162 through 168:

   0.30902   0.58779   0.80902   0.95106   1.00000   0.95106   0.80902

 Columns 169 through 175:

   0.58779   0.30902   0.00000  -0.30902  -0.58779  -0.80902  -0.95106

 Columns 176 through 182:

  -1.00000  -0.95106  -0.80902  -0.58779  -0.30902  -0.00000   0.30902

 Columns 183 through 189:

   0.58779   0.80902   0.95106   1.00000   0.95106   0.80902   0.58779

 Columns 190 through 196:

   0.30902   0.00000  -0.30902  -0.58779  -0.80902  -0.95106  -1.00000

 Columns 197 through 201:

  -0.95106  -0.80902  -0.58779  -0.30902  -0.00000

octave:24>

Are not equal to that of Python's

>>> sincNum = np.sin(np.dot(np.pi, np.array(np.arange(-fs, (fs)+(1./fs), 1./fs))))
>>> sincNum
array([  1.22464680e-15,   3.09016994e-01,   5.87785252e-01,
         8.09016994e-01,   9.51056516e-01,   1.00000000e+00,
         9.51056516e-01,   8.09016994e-01,   5.87785252e-01,
         3.09016994e-01,   9.55595892e-15,  -3.09016994e-01,
        -5.87785252e-01,  -8.09016994e-01,  -9.51056516e-01,
        -1.00000000e+00,  -9.51056516e-01,  -8.09016994e-01,
        -5.87785252e-01,  -3.09016994e-01,  -2.03365646e-14,
         3.09016994e-01,   5.87785252e-01,   8.09016994e-01,
         9.51056516e-01,   1.00000000e+00,   9.51056516e-01,
         8.09016994e-01,   5.87785252e-01,   3.09016994e-01,
         3.11171703e-14,  -3.09016994e-01,  -5.87785252e-01,
        -8.09016994e-01,  -9.51056516e-01,  -1.00000000e+00,
        -9.51056516e-01,  -8.09016994e-01,  -5.87785252e-01,
        -3.09016994e-01,  -4.54504897e-14,   3.09016994e-01,
         5.87785252e-01,   8.09016994e-01,   9.51056516e-01,
         1.00000000e+00,   9.51056516e-01,   8.09016994e-01,
         5.87785252e-01,   3.09016994e-01,   5.44547386e-14,
        -3.09016994e-01,  -5.87785252e-01,  -8.09016994e-01,
        -9.51056516e-01,  -1.00000000e+00,  -9.51056516e-01,
        -8.09016994e-01,  -5.87785252e-01,  -3.09016994e-01,
        -6.70117012e-14,   3.09016994e-01,   5.87785252e-01,
         8.09016994e-01,   9.51056516e-01,   1.00000000e+00,
         9.51056516e-01,   8.09016994e-01,   5.87785252e-01,
         3.09016994e-01,   7.77923069e-14,  -3.09016994e-01,
        -5.87785252e-01,  -8.09016994e-01,  -9.51056516e-01,
        -1.00000000e+00,  -9.51056516e-01,  -8.09016994e-01,
        -5.87785252e-01,  -3.09016994e-01,  -8.94610910e-14,
         3.09016994e-01,   5.87785252e-01,   8.09016994e-01,
         9.51056516e-01,   1.00000000e+00,   9.51056516e-01,
         8.09016994e-01,   5.87785252e-01,   3.09016994e-01,
         1.00241697e-13,  -3.09016994e-01,  -5.87785252e-01,
        -8.09016994e-01,  -9.51056516e-01,  -1.00000000e+00,
        -9.51056516e-01,  -8.09016994e-01,  -5.87785252e-01,
        -3.09016994e-01,  -1.11611792e-13,   3.09016994e-01,
         5.87785252e-01,   8.09016994e-01,   9.51056516e-01,
         1.00000000e+00,   9.51056516e-01,   8.09016994e-01,
         5.87785252e-01,   3.09016994e-01,   1.22691087e-13,
        -3.09016994e-01,  -5.87785252e-01,  -8.09016994e-01,
        -9.51056516e-01,  -1.00000000e+00,  -9.51056516e-01,
        -8.09016994e-01,  -5.87785252e-01,  -3.09016994e-01,
        -1.34359871e-13,   3.09016994e-01,   5.87785252e-01,
         8.09016994e-01,   9.51056516e-01,   1.00000000e+00,
         9.51056516e-01,   8.09016994e-01,   5.87785252e-01,
         3.09016994e-01,   1.46028655e-13,  -3.09016994e-01,
        -5.87785252e-01,  -8.09016994e-01,  -9.51056516e-01,
        -1.00000000e+00,  -9.51056516e-01,  -8.09016994e-01,
        -5.87785252e-01,  -3.09016994e-01,  -1.56809261e-13,
         3.09016994e-01,   5.87785252e-01,   8.09016994e-01,
         9.51056516e-01,   1.00000000e+00,   9.51056516e-01,
         8.09016994e-01,   5.87785252e-01,   3.09016994e-01,
         1.67589866e-13,  -3.09016994e-01,  -5.87785252e-01,
        -8.09016994e-01,  -9.51056516e-01,  -1.00000000e+00,
        -9.51056516e-01,  -8.09016994e-01,  -5.87785252e-01,
        -3.09016994e-01,  -1.78370472e-13,   3.09016994e-01,
         5.87785252e-01,   8.09016994e-01,   9.51056516e-01,
         1.00000000e+00,   9.51056516e-01,   8.09016994e-01,
         5.87785252e-01,   3.09016994e-01,   1.89151078e-13,
        -3.09016994e-01,  -5.87785252e-01,  -8.09016994e-01,
        -9.51056516e-01,  -1.00000000e+00,  -9.51056516e-01,
        -8.09016994e-01,  -5.87785252e-01,  -3.09016994e-01,
        -2.03484397e-13,   3.09016994e-01,   5.87785252e-01,
         8.09016994e-01,   9.51056516e-01,   1.00000000e+00,
         9.51056516e-01,   8.09016994e-01,   5.87785252e-01,
         3.09016994e-01,   2.14265003e-13,  -3.09016994e-01,
        -5.87785252e-01,  -8.09016994e-01,  -9.51056516e-01,
        -1.00000000e+00,  -9.51056516e-01,  -8.09016994e-01,
        -5.87785252e-01,  -3.09016994e-01,  -2.25045609e-13])
>>>

Am I doing something wrong or missed out something?

You can simplify the python np.sin(np.pi * np.arange(...)) — hpaulj
– hpaulj, Commented Mar 20, 2015 at 8:01

Esdes · Accepted Answer · 2015-03-20 06:47:12Z

2

Actually, it is the same in both cases. Set precision options in numpy to see:

np.set_printoptions(precision=6, suppress=True)

answered Mar 20, 2015 at 6:47

Esdes

1,0229 silver badges13 bronze badges

Sign up to request clarification or add additional context in comments.

Collectives™ on Stack Overflow

Convert MATLAB Code to Python using numpy

1 Answer 1

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Your Answer

Sign up or log in

Post as a guest

Related