numpy.binary_repr(num, width=None) [source] Return the binary representation of the input number as a string. For negative numbers, if width is not given, a minus sign is added to the front. If width is given, the two?s complement of the number is returned, with respect to that width. In a two?s-complement system negative numbers are represented by the two?s complement of the absolute value. This is the most common method of representing signed integers on computers [R16]. A N-bit two?s-com

numpy.result_type(*arrays_and_dtypes) Returns the type that results from applying the NumPy type promotion rules to the arguments. Type promotion in NumPy works similarly to the rules in languages like C++, with some slight differences. When both scalars and arrays are used, the array?s type takes precedence and the actual value of the scalar is taken into account. For example, calculating 3*a, where a is an array of 32-bit floats, intuitively should result in a 32-bit float output. If the

MaskedArray.ctypes An object to simplify the interaction of the array with the ctypes module. This attribute creates an object that makes it easier to use arrays when calling shared libraries with the ctypes module. The returned object has, among others, data, shape, and strides attributes (see Notes below) which themselves return ctypes objects that can be used as arguments to a shared library. Parameters: None Returns: c : Python object Possessing attributes data, shape, strides, etc.

numpy.random.exponential(scale=1.0, size=None) Draw samples from an exponential distribution. Its probability density function is for x > 0 and 0 elsewhere. is the scale parameter, which is the inverse of the rate parameter . The rate parameter is an alternative, widely used parameterization of the exponential distribution [R218]. The exponential distribution is a continuous analogue of the geometric distribution. It describes many common situations, such as the size of raindrops mea

ndarray.__abs__() <==> abs(x)

Standard FFTs fft(a[, n, axis, norm]) Compute the one-dimensional discrete Fourier Transform. ifft(a[, n, axis, norm]) Compute the one-dimensional inverse discrete Fourier Transform. fft2(a[, s, axes, norm]) Compute the 2-dimensional discrete Fourier Transform ifft2(a[, s, axes, norm]) Compute the 2-dimensional inverse discrete Fourier Transform. fftn(a[, s, axes, norm]) Compute the N-dimensional discrete Fourier Transform. ifftn(a[, s, axes, norm]) Compute the N-dimensional inverse dis

record.imag imaginary part of scalar

numpy.core.defchararray.add(x1, x2) [source] Return element-wise string concatenation for two arrays of str or unicode. Arrays x1 and x2 must have the same shape. Parameters: x1 : array_like of str or unicode Input array. x2 : array_like of str or unicode Input array. Returns: add : ndarray Output array of string_ or unicode_, depending on input types of the same shape as x1 and x2.

MaskedArray.__truediv__(other) [source] Divide other into self, and return a new masked array.

numpy.fft.ifft(a, n=None, axis=-1, norm=None) [source] Compute the one-dimensional inverse discrete Fourier Transform. This function computes the inverse of the one-dimensional n-point discrete Fourier transform computed by fft. In other words, ifft(fft(a)) == a to within numerical accuracy. For a general description of the algorithm and definitions, see numpy.fft. The input should be ordered in the same way as is returned by fft, i.e., a[0] should contain the zero frequency term, a[1:n//