MaskedArray.__idiv__(other) [source] Divide self by other in-place.

class numpy.lib.Arrayterator(var, buf_size=None) [source] Buffered iterator for big arrays. Arrayterator creates a buffered iterator for reading big arrays in small contiguous blocks. The class is useful for objects stored in the file system. It allows iteration over the object without reading everything in memory; instead, small blocks are read and iterated over. Arrayterator can be used with any object that supports multidimensional slices. This includes NumPy arrays, but also variables f

RandomState.standard_cauchy(size=None) Draw samples from a standard Cauchy distribution with mode = 0. Also known as the Lorentz distribution. Parameters: size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned. Returns: samples : ndarray or scalar The drawn samples. Notes The probability density function for the full Cauchy distribution is and the Stand

numpy.fft.fft2(a, s=None, axes=(-2, -1), norm=None) [source] Compute the 2-dimensional discrete Fourier Transform This function computes the n-dimensional discrete Fourier Transform over any axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). By default, the transform is computed over the last two axes of the input array, i.e., a 2-dimensional FFT. Parameters: a : array_like Input array, can be complex s : sequence of ints, optional Shape (length of each transfo

ndarray.__itruediv__ x.__itruediv__(y) <==> x/y

numpy.polynomial.chebyshev.chebval2d(x, y, c) [source] Evaluate a 2-D Chebyshev series at points (x, y). This function returns the values: The parameters x and y are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars and they must have the same shape after conversion. In either case, either x and y or their elements must support multiplication and addition both with themselves and with the elements of c. If c is a 1-D array a one is implicitly

numpy.core.defchararray.greater(x1, x2) [source] Return (x1 > x2) element-wise. Unlike numpy.greater, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters: x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns: out : ndarray or bool Output array of bools, or a single bool if x1 and x2 are scalars. See also equal, not_equal, greater_e

classmethod HermiteE.fit(x, y, deg, domain=None, rcond=None, full=False, w=None, window=None) [source] Least squares fit to data. Return a series instance that is the least squares fit to the data y sampled at x. The domain of the returned instance can be specified and this will often result in a superior fit with less chance of ill conditioning. Parameters: x : array_like, shape (M,) x-coordinates of the M sample points (x[i], y[i]). y : array_like, shape (M,) or (M, K) y-coordinates o

numpy.dsplit(ary, indices_or_sections) [source] Split array into multiple sub-arrays along the 3rd axis (depth). Please refer to the split documentation. dsplit is equivalent to split with axis=2, the array is always split along the third axis provided the array dimension is greater than or equal to 3. See also split Split an array into multiple sub-arrays of equal size. Examples >>> x = np.arange(16.0).reshape(2, 2, 4) >>> x array([[[ 0., 1., 2., 3.],

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