numpy.greater(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_equal, less, less_equal, equal, not_equal Examples >>> np.greater([4,2],[2,2]) array([ True, False], dtype=bool)

numpy.greater_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, less_equal, equal, not_equal Examples >>> np.greater_equal([4, 2, 1], [2, 2, 2]) array([ True, True,

numpy.gradient(f, *varargs, **kwargs) [source] Return the gradient of an N-dimensional array. The gradient is computed using second order accurate central differences in the interior and either first differences or second order accurate one-sides (forward or backwards) differences at the boundaries. The returned gradient hence has the same shape as the input array. Parameters: f : array_like An N-dimensional array containing samples of a scalar function. varargs : scalar or list of scala

numpy.geterrobj() Return the current object that defines floating-point error handling. The error object contains all information that defines the error handling behavior in Numpy. geterrobj is used internally by the other functions that get and set error handling behavior (geterr, seterr, geterrcall, seterrcall). Returns: errobj : list The error object, a list containing three elements: [internal numpy buffer size, error mask, error callback function]. The error mask is a single integer

numpy.get_printoptions() [source] Return the current print options. Returns: print_opts : dict Dictionary of current print options with keys precision : int threshold : int edgeitems : int linewidth : int suppress : bool nanstr : str infstr : str formatter : dict of callables For a full description of these options, see set_printoptions. See also set_printoptions, set_string_function

numpy.geterrcall() [source] Return the current callback function used on floating-point errors. When the error handling for a floating-point error (one of ?divide?, ?over?, ?under?, or ?invalid?) is set to ?call? or ?log?, the function that is called or the log instance that is written to is returned by geterrcall. This function or log instance has been set with seterrcall. Returns: errobj : callable, log instance or None The current error handler. If no handler was set through seterrcall

numpy.getbuffer(obj[, offset[, size]]) Create a buffer object from the given object referencing a slice of length size starting at offset. Default is the entire buffer. A read-write buffer is attempted followed by a read-only buffer. Parameters: obj : object offset : int, optional size : int, optional Returns: buffer_obj : buffer Examples >>> buf = np.getbuffer(np.ones(5), 1, 3) >>> len(buf) 3 >>> buf[0] '\x00' >>> buf <read-write buffer for 0x8af1

numpy.getbufsize() [source] Return the size of the buffer used in ufuncs. Returns: getbufsize : int Size of ufunc buffer in bytes.

numpy.geterr() [source] Get the current way of handling floating-point errors. Returns: res : dict A dictionary with keys ?divide?, ?over?, ?under?, and ?invalid?, whose values are from the strings ?ignore?, ?print?, ?log?, ?warn?, ?raise?, and ?call?. The keys represent possible floating-point exceptions, and the values define how these exceptions are handled. See also geterrcall, seterr, seterrcall Notes For complete documentation of the types of floating-point exceptions and treatm

class numpy.generic [source] Base class for numpy scalar types. Class from which most (all?) numpy scalar types are derived. For consistency, exposes the same API as ndarray, despite many consequent attributes being either ?get-only,? or completely irrelevant. This is the class from which it is strongly suggested users should derive custom scalar types. Attributes T transpose base base object data pointer to start of data dtype get array data-descriptor flags integer value of flags flat a 1