Multivariate Pattern Analysis in Python

# mvpa2.clfs.gpr.array

`mvpa2.clfs.gpr.``array`(object, dtype=None, copy=True, order=None, subok=False, ndmin=0)

  Create an array.

  Parameters:

  object : array_like

  > An array, any object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence.

  dtype : data-type, optional

  > The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence. This argument can only be used to ‘upcast’ the array. For downcasting, use the .astype(t) method.

  copy : bool, optional

  > If true (default), then the object is copied. Otherwise, a copy will only be made if __array__ returns a copy, if obj is a nested sequence, or if a copy is needed to satisfy any of the other requirements (`dtype`, `order`, etc.).

  order : {‘C’, ‘F’, ‘A’}, optional

  > Specify the order of the array. If order is ‘C’ (default), then the array will be in C-contiguous order (last-index varies the fastest). If order is ‘F’, then the returned array will be in Fortran-contiguous order (first-index varies the fastest). If order is ‘A’, then the returned array may be in any order (either C-, Fortran-contiguous, or even discontiguous).

  subok : bool, optional

  > If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default).

  ndmin : int, optional

  > Specifies the minimum number of dimensions that the resulting array should have. Ones will be pre-pended to the shape as needed to meet this requirement.

  Returns:

  out : ndarray

  > An array object satisfying the specified requirements.

  See also

  `empty`, `empty_like`, `zeros`, `zeros_like`, `ones`, `ones_like`, `fill`

  Examples

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

  Upcasting:

      >>> np.array([1, 2, 3.0])
      array([ 1.,  2.,  3.])

  More than one dimension:

      >>> np.array([[1, 2], [3, 4]])
      array([[1, 2],
             [3, 4]])

  Minimum dimensions 2:

      >>> np.array([1, 2, 3], ndmin=2)
      array([[1, 2, 3]])

  Type provided:

      >>> np.array([1, 2, 3], dtype=complex)
      array([ 1.+0.j,  2.+0.j,  3.+0.j])

  Data-type consisting of more than one element:

      >>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])
      >>> x['a']
      array([1, 3])

  Creating an array from sub-classes:

      >>> np.array(np.mat('1 2; 3 4'))
      array([[1, 2],
             [3, 4]])

      >>> np.array(np.mat('1 2; 3 4'), subok=True)
      matrix([[1, 2],
              [3, 4]])