numpy.repeat(a, repeats, axis=None) [source] Repeat elements of an array. Parameters: a : array_like Input array. repeats : int or array of ints The number of repetitions for each element. repeats is broadcasted to fit the shape of the given axis. axis : int, optional The axis along which to repeat values. By default, use the flattened input array, and return a flat output array. Returns: repeated_array : ndarray Output array which has the same shape as a, except along the given a

matrix.getH() [source] Returns the (complex) conjugate transpose of self. Equivalent to np.transpose(self) if self is real-valued. Parameters: None Returns: ret : matrix object complex conjugate transpose of self Examples >>> x = np.matrix(np.arange(12).reshape((3,4))) >>> z = x - 1j*x; z matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j], [ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j], [ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]]) >>> z.get

ndarray.flatten(order='C') Return a copy of the array collapsed into one dimension. Parameters: order : {?C?, ?F?, ?A?, ?K?}, optional ?C? means to flatten in row-major (C-style) order. ?F? means to flatten in column-major (Fortran- style) order. ?A? means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ?K? means to flatten a in the order the elements occur in memory. The default is ?C?. Returns: y : ndarray A copy of the input array,

ndarray.repeat(repeats, axis=None) Repeat elements of an array. Refer to numpy.repeat for full documentation. See also numpy.repeat equivalent function

ndarray.setfield(val, dtype, offset=0) Put a value into a specified place in a field defined by a data-type. Place val into a?s field defined by dtype and beginning offset bytes into the field. Parameters: val : object Value to be placed in field. dtype : dtype object Data-type of the field in which to place val. offset : int, optional The number of bytes into the field at which to place val. Returns: None See also getfield Examples >>> x = np.eye(3) >>> x.getf

HermiteE.cutdeg(deg) [source] Truncate series to the given degree. Reduce the degree of the series to deg by discarding the high order terms. If deg is greater than the current degree a copy of the current series is returned. This can be useful in least squares where the coefficients of the high degree terms may be very small. New in version 1.5.0. Parameters: deg : non-negative int The series is reduced to degree deg by discarding the high order terms. The value of deg must be a non-ne

record.reshape() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

numpy.shares_memory(a, b, max_work=None) Determine if two arrays share memory Parameters: a, b : ndarray Input arrays max_work : int, optional Effort to spend on solving the overlap problem (maximum number of candidate solutions to consider). The following special values are recognized: max_work=MAY_SHARE_EXACT (default) The problem is solved exactly. In this case, the function returns True only if there is an element shared between the arrays. max_work=MAY_SHARE_BOUNDS Only the memo

numpy.core.defchararray.swapcase(a) [source] Return element-wise a copy of the string with uppercase characters converted to lowercase and vice versa. Calls str.swapcase element-wise. For 8-bit strings, this method is locale-dependent. Parameters: a : array_like, {str, unicode} Input array. Returns: out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See also str.swapcase Examples >>> c=np.array(['a1B c','1b Ca','b Ca1','cA1b'],'S5'); c a

numpy.atleast_2d(*arys) [source] View inputs as arrays with at least two dimensions. Parameters: arys1, arys2, ... : array_like One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have two or more dimensions are preserved. Returns: res, res2, ... : ndarray An array, or tuple of arrays, each with a.ndim >= 2. Copies are avoided where possible, and views with two or more dimensions are returned. See also atleast_1d, atleast_3d Examples