numpy.ma.mask_or()

numpy.ma.mask_or(m1, m2, copy=False, shrink=True) [source] Combine two masks with the logical_or operator. The result may be a view on m1 or m2 if the other is nomask (i.e. False). Parameters: m1, m2 : array_like Input masks. copy : bool, optional If copy is False and one of the inputs is nomask, return a view of the other input mask. Defaults to False. shrink : bool, optional Whether to shrink the output to nomask if all its values are False. Defaults to True. Returns: mask : outp

Chebyshev.roots()

Chebyshev.roots() [source] Return the roots of the series polynomial. Compute the roots for the series. Note that the accuracy of the roots decrease the further outside the domain they lie. Returns: roots : ndarray Array containing the roots of the series.

MaskedArray.dumps()

MaskedArray.dumps() Returns the pickle of the array as a string. pickle.loads or numpy.loads will convert the string back to an array. Parameters: None

ndarray.__le__

ndarray.__le__ x.__le__(y) <==> x<=y

masked_array.mask

masked_array.mask Mask

ndarray.itemsize

ndarray.itemsize Length of one array element in bytes. Examples >>> x = np.array([1,2,3], dtype=np.float64) >>> x.itemsize 8 >>> x = np.array([1,2,3], dtype=np.complex128) >>> x.itemsize 16

MaskedArray.__radd__()

MaskedArray.__radd__(other) [source] Add other to self, and return a new masked array.

broadcast.size

broadcast.size Total size of broadcasted result. Examples >>> x = np.array([1, 2, 3]) >>> y = np.array([[4], [5], [6]]) >>> b = np.broadcast(x, y) >>> b.size 9

matrix.conjugate()

matrix.conjugate() Return the complex conjugate, element-wise. Refer to numpy.conjugate for full documentation. See also numpy.conjugate equivalent function

numpy.fft.rfft()

numpy.fft.rfft(a, n=None, axis=-1, norm=None) [source] Compute the one-dimensional discrete Fourier Transform for real input. This function computes the one-dimensional n-point discrete Fourier Transform (DFT) of a real-valued array by means of an efficient algorithm called the Fast Fourier Transform (FFT). Parameters: a : array_like Input array n : int, optional Number of points along transformation axis in the input to use. If n is smaller than the length of the input, the input is cr