numpy.arctan(x[, out]) = Trigonometric inverse tangent, element-wise. The inverse of tan, so that if y = tan(x) then x = arctan(y). Parameters: x : array_like Input values. arctan is applied to each element of x. Returns: out : ndarray Out has the same shape as x. Its real part is in [-pi/2, pi/2] (arctan(+/-inf) returns +/-pi/2). It is a scalar if x is a scalar. See also arctan2 The ?four quadrant? arctan of the angle formed by (x, y) and the positive x-axis. angle Argument

numpy.ma.swapaxes(self, *args, **params) a.swapaxes(axis1, axis2) = Return a view of the array with axis1 and axis2 interchanged. Refer to numpy.swapaxes for full documentation. See also numpy.swapaxes equivalent function

numpy.testing.decorators.setastest(tf=True) [source] Signals to nose that this function is or is not a test. Parameters: tf : bool If True, specifies that the decorated callable is a test. If False, specifies that the decorated callable is not a test. Default is True. Notes This decorator can?t use the nose namespace, because it can be called from a non-test module. See also istest and nottest in nose.tools. Examples setastest can be used in the following way: from numpy.testing.decorat

generic.tofile() 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

Buffer objects getbuffer(obj [,offset[, size]]) Create a buffer object from the given object referencing a slice of length size starting at offset. newbuffer(size) Return a new uninitialized buffer object. Performance tuning alterdot() Change dot, vdot, and inner to use accelerated BLAS functions. restoredot() Restore dot, vdot, and innerproduct to the default non-BLAS implementations. setbufsize(size) Set the size of the buffer used in ufuncs. getbufsize() Return the size of the b

numpy.random.normal(loc=0.0, scale=1.0, size=None) Draw random samples from a normal (Gaussian) distribution. The probability density function of the normal distribution, first derived by De Moivre and 200 years later by both Gauss and Laplace independently [R250], is often called the bell curve because of its characteristic shape (see the example below). The normal distributions occurs often in nature. For example, it describes the commonly occurring distribution of samples influenced by a

MaskedArray.T

generic.ptp() 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

generic.max() 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.polynomial.laguerre.lagdomain = array([0, 1])