numpy.ma.dstack(tup) = Stack arrays in sequence depth wise (along third axis). Takes a sequence of arrays and stack them along the third axis to make a single array. Rebuilds arrays divided by dsplit. This is a simple way to stack 2D arrays (images) into a single 3D array for processing. Parameters: tup : sequence of arrays Arrays to stack. All of them must have the same shape along all but the third axis. Returns: stacked : ndarray The array formed by stacking the given arrays.

record.base base object

numpy.nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False) [source] Compute the variance along the specified axis, while ignoring NaNs. Returns the variance of the array elements, a measure of the spread of a distribution. The variance is computed for the flattened array by default, otherwise over the specified axis. For all-NaN slices or slices with zero degrees of freedom, NaN is returned and a RuntimeWarning is raised. New in version 1.8.0. Parameters: a : array_like Ar

chararray.flags Information about the memory layout of the array. Notes The flags object can be accessed dictionary-like (as in a.flags['WRITEABLE']), or by using lowercased attribute names (as in a.flags.writeable). Short flag names are only supported in dictionary access. Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by the user, via direct assignment to the attribute or dictionary entry, or by calling ndarray.setflags. The array flags cannot be set arbitrarily: UPDAT

numpy.einsum(subscripts, *operands, out=None, dtype=None, order='K', casting='safe') Evaluates the Einstein summation convention on the operands. Using the Einstein summation convention, many common multi-dimensional array operations can be represented in a simple fashion. This function provides a way to compute such summations. The best way to understand this function is to try the examples below, which show how many common NumPy functions can be implemented as calls to einsum. Parameters:

ndarray.cumsum(axis=None, dtype=None, out=None) Return the cumulative sum of the elements along the given axis. Refer to numpy.cumsum for full documentation. See also numpy.cumsum equivalent function

numpy.obj2sctype(rep, default=None) [source] Return the scalar dtype or NumPy equivalent of Python type of an object. Parameters: rep : any The object of which the type is returned. default : any, optional If given, this is returned for objects whose types can not be determined. If not given, None is returned for those objects. Returns: dtype : dtype or Python type The data type of rep. See also sctype2char, issctype, issubsctype, issubdtype, maximum_sctype Examples >>>

numpy.ma.outer(a, b) [source] Compute the outer product of two vectors. Given two vectors, a = [a0, a1, ..., aM] and b = [b0, b1, ..., bN], the outer product [R50] is: [[a0*b0 a0*b1 ... a0*bN ] [a1*b0 . [ ... . [aM*b0 aM*bN ]] Parameters: a : (M,) array_like First input vector. Input is flattened if not already 1-dimensional. b : (N,) array_like Second input vector. Input is flattened if not already 1-dimensional. out : (M, N) ndarray, optional A location w

recarray.nbytes Total bytes consumed by the elements of the array. Notes Does not include memory consumed by non-element attributes of the array object. Examples >>> x = np.zeros((3,5,2), dtype=np.complex128) >>> x.nbytes 480 >>> np.prod(x.shape) * x.itemsize 480

numpy.fft.fft(a, n=None, axis=-1, norm=None) [source] Compute the one-dimensional discrete Fourier Transform. This function computes the one-dimensional n-point discrete Fourier Transform (DFT) with the efficient Fast Fourier Transform (FFT) algorithm [CT]. Parameters: a : array_like Input array, can be complex. n : int, optional Length of the transformed axis of the output. If n is smaller than the length of the input, the input is cropped. If it is larger, the input is padded with zer