Demo¶

>>> #########################
>>> # Oct2Py demo
>>> #########################
>>> import numpy as np
>>> from oct2py import Oct2Py
>>> oc = Oct2Py()
>>> # basic commands
>>> print(oc.abs(-1))
1
>>> print(oc.upper('xyz'))
XYZ
>>> # plotting
>>> oc.plot([1,2,3],'-o', linewidth=2)
Press Enter to continue...

>>> oc.close()
>>> xx = np.arange(-2*np.pi, 2*np.pi, 0.2)
>>> oc.surf(np.subtract.outer(np.sin(xx), np.cos(xx)))
Press Enter to continue...

>>> oc.close()
>>> # getting help
>>> help(oc.svd)

Help on function svd in module oct2py.session:

svd(*args, **kwargs)
    `svd' is a function from the file c:\Program Files\Octave-3.6.2\lib\octave\3.6.2\oct\i686-pc-mingw32\svd.oct

     -- Loadable Function: S = svd (A)
     -- Loadable Function: [U, S, V] = svd (A)
     -- Loadable Function: [U, S, V] = svd (A, ECON)
         Compute the singular value decomposition of A

              A = U*S*V'

         The function `svd' normally returns only the vector of singular
         values.  When called with three return values, it computes U, S,
         and V.  For example,

              svd (hilb (3))

         returns

              ans =

               1.4083189
               0.1223271
               0.0026873

         and

              [u, s, v] = svd (hilb (3))

         returns

              u =

               -0.82704   0.54745   0.12766
               -0.45986  -0.52829  -0.71375
               -0.32330  -0.64901   0.68867

              s =

               1.40832  0.00000  0.00000
               0.00000  0.12233  0.00000
               0.00000  0.00000  0.00269

              v =

               -0.82704   0.54745   0.12766
               -0.45986  -0.52829  -0.71375
               -0.32330  -0.64901   0.68867

         If given a second argument, `svd' returns an economy-sized
         decomposition, eliminating the unnecessary rows or columns of U or
         V.

         See also: svd_driver, svds, eig



    Additional help for built-in functions and operators is
    available in the on-line version of the manual.  Use the command
    `doc <topic>' to search the manual index.

    Help and information about Octave is also available on the WWW
    at http://www.octave.org and via the help@octave.org
    mailing list.

>>> # single vs. multiple return values
>>> print(oc.svd(np.array([[1,2], [1,3]])))
[[ 3.86432845]
 [ 0.25877718]]
>>> U, S, V = oc.svd([[1,2], [1,3]])
>>> print(U, S, V)
[[-0.57604844 -0.81741556]
 [-0.81741556  0.57604844]] [[ 3.86432845  0.        ]
 [ 0.          0.25877718]] [[-0.36059668 -0.93272184]
 [-0.93272184  0.36059668]]
>>> # low level constructs
>>> oc.eval("y=ones(3,3)")
>>> print(oc.pull("y"))
[[ 1.  1.  1.]
 [ 1.  1.  1.]
 [ 1.  1.  1.]]
>>> oc.eval("x=zeros(3,3)", verbose=True)
>>> t = oc.eval('rand(1, 2)', verbose=True)
>>> y = np.zeros((3,3))
>>> oc.push('y', y)
>>> print(oc.pull('y'))
[[ 0.  0.  0.]
 [ 0.  0.  0.]
 [ 0.  0.  0.]]
>>> from oct2py import Struct
>>> y = Struct()
>>> y.b = 'spam'
>>> y.c.d = 'eggs'
>>> print(y.c['d'])
eggs
>>> print(y)
{'c': {'d': 'eggs'}, 'b': 'spam'}
>>> #########################
>>> # Demo Complete!
>>> #########################