numpy.core.defchararray.rstrip(a, chars=None) [source] For each element in a, return a copy with the trailing characters removed. Calls str.rstrip element-wise. Parameters: a : array-like of str or unicode chars : str or unicode, optional The chars argument is a string specifying the set of characters to be removed. If omitted or None, the chars argument defaults to removing whitespace. The chars argument is not a suffix; rather, all combinations of its values are stripped. Returns: ou

numpy.ma.zeros(shape, dtype=float, order='C') = Return a new array of given shape and type, filled with zeros. Parameters: shape : int or sequence of ints Shape of the new array, e.g., (2, 3) or 2. dtype : data-type, optional The desired data-type for the array, e.g., numpy.int8. Default is numpy.float64. order : {?C?, ?F?}, optional Whether to store multidimensional data in C- or Fortran-contiguous (row- or column-wise) order in memory. Returns: out : ndarray Array of zeros with

numpy.mgrid = nd_grid instance which returns a dense multi-dimensional ?meshgrid?. An instance of numpy.lib.index_tricks.nd_grid which returns an dense (or fleshed out) mesh-grid when indexed, so that each returned argument has the same shape. The dimensions and number of the output arrays are equal to the number of indexing dimensions. If the step length is not a complex number, then the stop is not inclusive. However, if the step length is a complex number (e.g. 5j), then the integer par

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.logaddexp(x1, x2[, out]) = Logarithm of the sum of exponentiations of the inputs. Calculates log(exp(x1) + exp(x2)). This function is useful in statistics where the calculated probabilities of events may be so small as to exceed the range of normal floating point numbers. In such cases the logarithm of the calculated probability is stored. This function allows adding probabilities stored in such a fashion. Parameters: x1, x2 : array_like Input values. Returns: result : ndarray

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

numpy.ma.cumsum(self, axis=None, dtype=None, out=None) = Return the cumulative sum of the elements along the given axis. The cumulative sum is calculated over the flattened array by default, otherwise over the specified axis. Masked values are set to 0 internally during the computation. However, their position is saved, and the result will be masked at the same locations. Parameters: axis : {None, -1, int}, optional Axis along which the sum is computed. The default (axis = None) is to co

matrix.newbyteorder(new_order='S') Return the array with the same data viewed with a different byte order. Equivalent to: arr.view(arr.dtype.newbytorder(new_order)) Changes are also made in all fields and sub-arrays of the array data type. Parameters: new_order : string, optional Byte order to force; a value from the byte order specifications below. new_order codes can be any of: ?S? - swap dtype from current to opposite endian {?<?, ?L?} - little endian {?>?, ?B?} - big endian {?=