numpy.testing.assert_array_equal(x, y, err_msg='', verbose=True) [source] Raises an AssertionError if two array_like objects are not equal. Given two array_like objects, check that the shape is equal and all elements of these objects are equal. An exception is raised at shape mismatch or conflicting values. In contrast to the standard usage in numpy, NaNs are compared like numbers, no assertion is raised if both objects have NaNs in the same positions. The usual caution for verifying equali

record.conjugate() 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

dtype.shape Shape tuple of the sub-array if this data type describes a sub-array, and () otherwise.

MaskedArray.ndim Number of array dimensions. Examples >>> x = np.array([1, 2, 3]) >>> x.ndim 1 >>> y = np.zeros((2, 3, 4)) >>> y.ndim 3

numpy.less_equal(x1, x2[, out]) = Return the truth value of (x1 =< x2) element-wise. Parameters: x1, x2 : array_like Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape (which may be the shape of one or the other). Returns: out : bool or ndarray of bool Array of bools, or a single bool if x1 and x2 are scalars. See also greater, less, greater_equal, equal, not_equal Examples >>> np.less_equal([4, 2, 1], [2, 2, 2]) array([False, True,

numpy.random.f(dfnum, dfden, size=None) Draw samples from an F distribution. Samples are drawn from an F distribution with specified parameters, dfnum (degrees of freedom in numerator) and dfden (degrees of freedom in denominator), where both parameters should be greater than zero. The random variate of the F distribution (also known as the Fisher distribution) is a continuous probability distribution that arises in ANOVA tests, and is the ratio of two chi-square variates. Parameters: dfnu

numpy.core.defchararray.array(obj, itemsize=None, copy=True, unicode=None, order=None) [source] Create a chararray. Note This class is provided for numarray backward-compatibility. New code (not concerned with numarray compatibility) should use arrays of type string_ or unicode_ and use the free functions in numpy.char for fast vectorized string operations instead. Versus a regular Numpy array of type str or unicode, this class adds the following functionality: values automatically have

numpy.ctypeslib.as_array(obj, shape=None) [source] Create a numpy array from a ctypes array or a ctypes POINTER. The numpy array shares the memory with the ctypes object. The size parameter must be given if converting from a ctypes POINTER. The size parameter is ignored if converting from a ctypes array numpy.ctypeslib.as_ctypes(obj) [source] Create and return a ctypes object from a numpy array. Actually anything that exposes the __array_interface__ is accepted. numpy.ctypeslib.ct

MaskedArray.__or__ x.__or__(y) <==> x|y

MaskedArray.__copy__([order]) Return a copy of the array. Parameters: order : {?C?, ?F?, ?A?}, optional If order is ?C? (False) then the result is contiguous (default). If order is ?Fortran? (True) then the result has fortran order. If order is ?Any? (None) then the result has fortran order only if the array already is in fortran order.