numpy.vsplit(ary, indices_or_sections) [source] Split an array into multiple sub-arrays vertically (row-wise). Please refer to the split documentation. vsplit is equivalent to split with axis=0 (default), the array is always split along the first axis regardless of the array dimension. See also split Split an array into multiple sub-arrays of equal size. Examples >>> x = np.arange(16.0).reshape(4, 4) >>> x array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7

numpy.ma.soften_mask(self) = Force the mask to soft. Whether the mask of a masked array is hard or soft is determined by its hardmask property. soften_mask sets hardmask to False. See also hardmask

numpy.core.defchararray.split(a, sep=None, maxsplit=None) [source] For each element in a, return a list of the words in the string, using sep as the delimiter string. Calls str.rsplit element-wise. Parameters: a : array_like of str or unicode sep : str or unicode, optional If sep is not specified or None, any whitespace string is a separator. maxsplit : int, optional If maxsplit is given, at most maxsplit splits are done. Returns: out : ndarray Array of list objects See also str.

record.real real part of scalar

numpy.sin(x[, out]) = Trigonometric sine, element-wise. Parameters: x : array_like Angle, in radians ( rad equals 360 degrees). Returns: y : array_like The sine of each element of x. See also arcsin, sinh, cos Notes The sine is one of the fundamental functions of trigonometry (the mathematical study of triangles). Consider a circle of radius 1 centered on the origin. A ray comes in from the axis, makes an angle at the origin (measured counter-clockwise from that axis), and depar

MaskedArray.tofile(fid, sep='', format='%s') [source] Save a masked array to a file in binary format. Warning This function is not implemented yet. Raises: NotImplementedError When tofile is called.

numpy.stack(arrays, axis=0) [source] Join a sequence of arrays along a new axis. The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension. New in version 1.10.0. Parameters: arrays : sequence of array_like Each array must have the same shape. axis : int, optional The axis in the result array along which the input arrays are stacked. Returns: stacked : nd

numpy.conj(x[, out]) = Return the complex conjugate, element-wise. The complex conjugate of a complex number is obtained by changing the sign of its imaginary part. Parameters: x : array_like Input value. Returns: y : ndarray The complex conjugate of x, with same dtype as y. Examples >>> np.conjugate(1+2j) (1-2j) >>> x = np.eye(2) + 1j * np.eye(2) >>> np.conjugate(x) array([[ 1.-1.j, 0.-0.j], [ 0.-0.j, 1.-1.j]])

chararray.isdecimal() [source] For each element in self, return True if there are only decimal characters in the element. See also char.isdecimal

numpy.full(shape, fill_value, dtype=None, order='C') [source] Return a new array of given shape and type, filled with fill_value. Parameters: shape : int or sequence of ints Shape of the new array, e.g., (2, 3) or 2. fill_value : scalar Fill value. dtype : data-type, optional The desired data-type for the array, e.g., np.int8. Default is float, but will change to np.array(fill_value).dtype in a future release. order : {?C?, ?F?}, optional Whether to store multidimensional data in C-