Permute the dimensions of an array.

>>> x = np.arange(4).reshape((2,2))
>>> x
array([[0, 1],
       [2, 3]])

>>> np.transpose(x)
array([[0, 2],
       [1, 3]])

>>> x = np.ones((1, 2, 3))
>>> np.transpose(x, (1, 0, 2)).shape
(2, 1, 3)

Returns the outer product of two vectors.

Given two vectors, [a0, a1, ..., aM] and [b0, b1, ..., bN], the outer product becomes:

[[a0*b0  a0*b1 ... a0*bN ]
 [a1*b0    .
 [ ...          .
 [aM*b0            aM*bN ]]

>>> x = np.array(['a', 'b', 'c'], dtype=object)

>>> np.outer(x, [1, 2, 3])
array([[a, aa, aaa],
       [b, bb, bbb],
       [c, cc, ccc]], dtype=object)

Return a new array of given shape and type, filled with ones.

Please refer to the documentation for zeros.

>>> np.ones(5)
array([ 1.,  1.,  1.,  1.,  1.])

>>> np.ones((5,), dtype=np.int)
array([1, 1, 1, 1, 1])

>>> np.ones((2, 1))
array([[ 1.],
       [ 1.]])

>>> s = (2,2)
>>> np.ones(s)
array([[ 1.,  1.],
       [ 1.,  1.]])

Compute the 2-dimensional discrete Fourier Transform

This function computes the *n*-dimensional discrete Fourier Transform
over any axes in an *M*-dimensional array by means of the
Fast Fourier Transform (FFT).  By default, the transform is computed over
the last two axes of the input array, i.e., a 2-dimensional FFT.

Parameters
----------
a : array_like
    Input array, can be complex
s : sequence of ints, optional
    Shape (length of each transformed axis) of the output
    (`s[0]` refers to axis 0, `s[1]` to axis 1, etc.).
    This corresponds to `n` for `fft(x, n)`.
    Along each axis, if the given shape is smaller than that of the input,
    the input is cropped.  If it is larger, the input is padded with zeros.
    if `s` is not given, the shape of the input (along the axes specified
    by `axes`) is used.
axes : sequence of ints, optional
    Axes over which to compute the FFT.  If not given, the last 2
    axes are used.  A repeated index in `axes` means the transform over
    that axis is performed multiple times.  A one-element sequence means
    that a one-dimensional FFT is performed.

Returns
-------
out : complex ndarray
    The truncated or zero-padded input, transformed along the axes
    indicated by `axes`, or the last two axes if `axes` is not given.

Raises
------
ValueError
    if `s` and `axes` have different length, or
    `axes` not given and `len(s) != 2`
IndexError
    if an element of `axes` is larger than than the number of axes of `a`.

See Also
--------
numpy.fft : Overall view of discrete Fourier transforms, with definitions
     and conventions used.
ifft2 : The inverse two-dimensional FFT
fft : The one-dimensional FFT
fftn : The *n*-dimensional FFT
fftshift : shifts zero-frequency terms to centre of array.
    For two-dimensional input, swaps first and third quadrants, and second
    and fourth quadrants.

Notes
-----

`fft2` is just `fftn` with a different default for `axes`.

The output, analogously to `fft`, contains the term for zero frequency in
the low-order corner of the transformed axes, the positive frequency terms
in the first half of these axes, the term for the Nyquist frequency in the
middle of the axes and the negative frequency terms in the second half of
the axes, in order of decreasingly negative frequency.

See `fftn` for details and a plotting example, and `numpy.fft` for
definitions and conventions used.


Examples
--------
>>> from numpy import mgrid
>>> from numpy.fft import fft2
>>> a = mgrid[:5, :5][0]
>>> fft2(a)
array([[  0.+0.j,   0.+0.j,   0.+0.j,   0.+0.j,   0.+0.j],
       [  5.+0.j,   0.+0.j,   0.+0.j,   0.+0.j,   0.+0.j],
       [ 10.+0.j,   0.+0.j,   0.+0.j,   0.+0.j,   0.+0.j],
       [ 15.+0.j,   0.+0.j,   0.+0.j,   0.+0.j,   0.+0.j],
       [ 20.+0.j,   0.+0.j,   0.+0.j,   0.+0.j,   0.+0.j]])

Shift zero-frequency component to center of spectrum.

This function swaps half-spaces for all axes listed (defaults to all). If len(x) is even then the Nyquist component is y[0].

Circularly alias the numeric value x into the range [lower,upper).

Valid for cyclic quantities like orientations or hues.

A range function that accepts float increments.

Otherwise, works just as the inbuilt range() function. If inclusive is False, as in the default, the range is exclusive (not including the end value), as in the inbuilt range(). If inclusive is true, the range may include the end value.

'All theoretic restrictions apply, but in practice this is more useful than in theory.'

From: http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/66472

Factory function for constructing a Plot object whose type is not yet known.

Typically, a TemplatePlot will be constructed through this call, because it selects the appropriate type automatically, rather than calling one of the Plot subclasses automatically. See TemplatePlot.__init__ for a description of the arguments.

Create a new PlotGroup and add it to the plotgroups list.

Convenience function to make it simpler to use the name of the PlotGroup as the key in the plotgroups list.

template_plot_type: Whether the plots are bitmap images or curves ('curve').

Simple plotting for any matrix as a 3D surface with axes.

Currently requires the gnuplot-py package to be installed, plus the external gnuplot program; likely to be removed once Matplotlib supports 3D plots better.

Unlikely to work on non-UNIX systems.

Should return when it completes, but for some reason the Topographica prompt is not available until this command finishes.

Plot a coordinate mapping for a sheet.

Given a CoordinateMapperFn (as for a CFProjection) and a sheet of the projection, plot a grid showing where the sheet's units are mapped.