class numpy.vectorize(pyfunc, otypes='', doc=None, excluded=None, cache=False) [source] Generalized function class. Define a vectorized function which takes a nested sequence of objects or numpy arrays as inputs and returns a numpy array as output. The vectorized function evaluates pyfunc over successive tuples of the input arrays like the python map function, except it uses the broadcasting rules of numpy. The data type of the output of vectorized is determined by calling the function with

numpy.random.standard_t(df, size=None) Draw samples from a standard Student?s t distribution with df degrees of freedom. A special case of the hyperbolic distribution. As df gets large, the result resembles that of the standard normal distribution (standard_normal). Parameters: df : int Degrees of freedom, should be > 0. 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

MaskedArray.__imul__(other) [source] Multiply self by other in-place.

numpy.polynomial.hermite.hermx = array([ 0. , 0.5])

chararray.copy(order='C') Return a copy of the array. Parameters: order : {?C?, ?F?, ?A?, ?K?}, optional Controls the memory layout of the copy. ?C? means C-order, ?F? means F-order, ?A? means ?F? if a is Fortran contiguous, ?C? otherwise. ?K? means match the layout of a as closely as possible. (Note that this function and :func:numpy.copy are very similar, but have different default values for their order= arguments.) See also numpy.copy, numpy.copyto Examples >>> x = np.arr

There is a general need for looping over not only functions on scalars but also over functions on vectors (or arrays). This concept is realized in Numpy by generalizing the universal functions (ufuncs). In regular ufuncs, the elementary function is limited to element-by-element operations, whereas the generalized version (gufuncs) supports ?sub-array? by ?sub-array? operations. The Perl vector library PDL provides a similar functionality and its terms are re-used in the following. Each general

record.resize() 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

numpy.nanmin(a, axis=None, out=None, keepdims=False) [source] Return minimum of an array or minimum along an axis, ignoring any NaNs. When all-NaN slices are encountered a RuntimeWarning is raised and Nan is returned for that slice. Parameters: a : array_like Array containing numbers whose minimum is desired. If a is not an array, a conversion is attempted. axis : int, optional Axis along which the minimum is computed. The default is to compute the minimum of the flattened array. out :

numpy.fft.hfft(a, n=None, axis=-1, norm=None) [source] Compute the FFT of a signal which has Hermitian symmetry (real spectrum). Parameters: a : array_like The input array. n : int, optional Length of the transformed axis of the output. For n output points, n//2+1 input points are necessary. If the input is longer than this, it is cropped. If it is shorter than this, it is padded with zeros. If n is not given, it is determined from the length of the input along the axis specified by axi

class numpy.poly1d(c_or_r, r=0, variable=None) [source] A one-dimensional polynomial class. A convenience class, used to encapsulate ?natural? operations on polynomials so that said operations may take on their customary form in code (see Examples). Parameters: c_or_r : array_like The polynomial?s coefficients, in decreasing powers, or if the value of the second parameter is True, the polynomial?s roots (values where the polynomial evaluates to 0). For example, poly1d([1, 2, 3]) returns a