MaskedArray.__array_priority__ = 15

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

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

Laguerre.__call__(arg) [source]

numpy.core.defchararray.title(a) [source] Return element-wise title cased version of string or unicode. Title case words start with uppercase characters, all remaining cased characters are lowercase. Calls str.title element-wise. For 8-bit strings, this method is locale-dependent. Parameters: a : array_like, {str, unicode} Input array. Returns: out : ndarray Output array of str or unicode, depending on input type See also str.title Examples >>> c=np.array(['a1b c','1b ca'

numpy.random.binomial(n, p, size=None) Draw samples from a binomial distribution. Samples are drawn from a binomial distribution with specified parameters, n trials and p probability of success where n an integer >= 0 and p is in the interval [0,1]. (n may be input as a float, but it is truncated to an integer in use) Parameters: n : float (but truncated to an integer) parameter, >= 0. p : float parameter, >= 0 and <=1. size : int or tuple of ints, optional Output shape. I

numpy.polynomial.chebyshev.chebint(c, m=1, k=[], lbnd=0, scl=1, axis=0) [source] Integrate a Chebyshev series. Returns the Chebyshev series coefficients c integrated m times from lbnd along axis. At each iteration the resulting series is multiplied by scl and an integration constant, k, is added. The scaling factor is for use in a linear change of variable. (?Buyer beware?: note that, depending on what one is doing, one may want scl to be the reciprocal of what one might expect; for more in

Numpy binary files (NPY, NPZ) load(file[, mmap_mode, allow_pickle, ...]) Load arrays or pickled objects from .npy, .npz or pickled files. save(file, arr[, allow_pickle, fix_imports]) Save an array to a binary file in NumPy .npy format. savez(file, *args, **kwds) Save several arrays into a single file in uncompressed .npz format. savez_compressed(file, *args, **kwds) Save several arrays into a single file in compressed .npz format. The format of these binary file types is documented in h

numpy.choose(a, choices, out=None, mode='raise') [source] Construct an array from an index array and a set of arrays to choose from. First of all, if confused or uncertain, definitely look at the Examples - in its full generality, this function is less simple than it might seem from the following code description (below ndi = numpy.lib.index_tricks): np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)]). But this omits some subtleties. Here is a fully general summary: Given