numpy.ma.argsort(a, axis=None, kind='quicksort', order=None, fill_value=None) [source] Return an ndarray of indices that sort the array along the specified axis. Masked values are filled beforehand to fill_value. Parameters: axis : int, optional Axis along which to sort. The default is -1 (last axis). If None, the flattened array is used. fill_value : var, optional Value used to fill the array before sorting. The default is the fill_value attribute of the input array. kind : {?quicksor

numpy.union1d(ar1, ar2) [source] Find the union of two arrays. Return the unique, sorted array of values that are in either of the two input arrays. Parameters: ar1, ar2 : array_like Input arrays. They are flattened if they are not already 1D. Returns: union1d : ndarray Unique, sorted union of the input arrays. See also numpy.lib.arraysetops Module with a number of other functions for performing set operations on arrays. Examples >>> np.union1d([-1, 0, 1], [-2, 0, 2]) a

record.view() 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.seterrcall(func) [source] Set the floating-point error callback function or log object. There are two ways to capture floating-point error messages. The first is to set the error-handler to ?call?, using seterr. Then, set the function to call using this function. The second is to set the error-handler to ?log?, using seterr. Floating-point errors then trigger a call to the ?write? method of the provided object. Parameters: func : callable f(err, flag) or object with write method Fun

Polynomials in NumPy can be created, manipulated, and even fitted using the Using the Convenience Classes of the numpy.polynomial package, introduced in NumPy 1.4. Prior to NumPy 1.4, numpy.poly1d was the class of choice and it is still available in order to maintain backward compatibility. However, the newer Polynomial package is more complete than numpy.poly1d and its convenience classes are better behaved in the numpy environment. Therefore Polynomial is recommended for new coding. Transit

static ndarray.__new__(S, ...) ? a new object with type S, a subtype of T

Laguerre.mapparms() [source] Return the mapping parameters. The returned values define a linear map off + scl*x that is applied to the input arguments before the series is evaluated. The map depends on the domain and window; if the current domain is equal to the window the resulting map is the identity. If the coefficients of the series instance are to be used by themselves outside this class, then the linear function must be substituted for the x in the standard representation of the base

Hermite.degree() [source] The degree of the series. New in version 1.5.0. Returns: degree : int Degree of the series, one less than the number of coefficients.

numpy.testing.assert_warns(warning_class, *args, **kwargs) [source] Fail unless the given callable throws the specified warning. A warning of class warning_class should be thrown by the callable when invoked with arguments args and keyword arguments kwargs. If a different type of warning is thrown, it will not be caught, and the test case will be deemed to have suffered an error. If called with all arguments other than the warning class omitted, may be used as a context manager: with asser

numpy.ma.power(a, b, third=None) [source] Returns element-wise base array raised to power from second array. This is the masked array version of numpy.power. For details see numpy.power. See also numpy.power Notes The out argument to numpy.power is not supported, third has to be None.