DemoΒΆ

Output of Oct2Py demo script, showing most of the features of the library. Note that the two plot commands will generate an interactive plot in the actual demo. To run interactively:

>>> #########################
>>> # 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...
../_images/plot.png
>>> 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...
../_images/surf.png
>>> 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]], nout=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!
>>> #########################