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

numpy.fft.ifft2(a, s=None, axes=(-2, -1), norm=None) [source] Compute the 2-dimensional inverse discrete Fourier Transform. This function computes the inverse of the 2-dimensional discrete Fourier Transform over any number of axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). In other words, ifft2(fft2(a)) == a to within numerical accuracy. By default, the inverse transform is computed over the last two axes of the input array. The input, analogously to ifft, shoul

numpy.cumsum(a, axis=None, dtype=None, out=None) [source] Return the cumulative sum of the elements along a given axis. Parameters: a : array_like Input array. axis : int, optional Axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array. dtype : dtype, optional Type of the returned array and of the accumulator in which the elements are summed. If dtype is not specified, it defaults to the dtype of a, unless a has an integer

numpy.mask_indices(n, mask_func, k=0) [source] Return the indices to access (n, n) arrays, given a masking function. Assume mask_func is a function that, for a square array a of size (n, n) with a possible offset argument k, when called as mask_func(a, k) returns a new array with zeros in certain locations (functions like triu or tril do precisely this). Then this function returns the indices where the non-zero values would be located. Parameters: n : int The returned indices will be vali

MaskedArray.__delitem__ x.__delitem__(y) <==> del x[y]

Truth value testing all(a[, axis, out, keepdims]) Test whether all array elements along a given axis evaluate to True. any(a[, axis, out, keepdims]) Test whether any array element along a given axis evaluates to True. Array contents isfinite(x[, out]) Test element-wise for finiteness (not infinity or not Not a Number). isinf(x[, out]) Test element-wise for positive or negative infinity. isnan(x[, out]) Test element-wise for NaN and return result as a boolean array. isneginf(x[, y]) Te

numpy.random.f(dfnum, dfden, size=None) Draw samples from an F distribution. Samples are drawn from an F distribution with specified parameters, dfnum (degrees of freedom in numerator) and dfden (degrees of freedom in denominator), where both parameters should be greater than zero. The random variate of the F distribution (also known as the Fisher distribution) is a continuous probability distribution that arises in ANOVA tests, and is the ratio of two chi-square variates. Parameters: dfnu

numpy.pmt(rate, nper, pv, fv=0, when='end') [source] Compute the payment against loan principal plus interest. Given: a present value, pv (e.g., an amount borrowed) a future value, fv (e.g., 0) an interest rate compounded once per period, of which there are nper total and (optional) specification of whether payment is made at the beginning (when = {?begin?, 1}) or the end (when = {?end?, 0}) of each period Return: the (fixed) periodic payment. Parameters: rate : array_like Rate of inte

MaskedArray.__itruediv__(other) [source] True divide self by other in-place.

record.argmin() 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