numpy.ma.round(a, decimals=0, out=None) [source] Return a copy of a, rounded to ?decimals? places. When ?decimals? is negative, it specifies the number of positions to the left of the decimal point. The real and imaginary parts of complex numbers are rounded separately. Nothing is done if the array is not of float type and ?decimals? is greater than or equal to 0. Parameters: decimals : int Number of decimals to round to. May be negative. out : array_like Existing array to use for outpu

MaskedArray.__reduce__() [source] Return a 3-tuple for pickling a MaskedArray.

numpy.core.defchararray.upper(a) [source] Return an array with the elements converted to uppercase. Calls str.upper element-wise. For 8-bit strings, this method is locale-dependent. Parameters: a : array_like, {str, unicode} Input array. Returns: out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See also str.upper Examples >>> c = np.array(['a1b c', '1bca', 'bca1']); c array(['a1b c', '1bca', 'bca1'], dtype='|S5') >>> np.ch

HermiteE.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.issubdtype(arg1, arg2) [source] Returns True if first argument is a typecode lower/equal in type hierarchy. Parameters: arg1, arg2 : dtype_like dtype or string representing a typecode. Returns: out : bool See also issubsctype, issubclass_ numpy.core.numerictypes Overview of numpy type hierarchy. Examples >>> np.issubdtype('S1', str) True >>> np.issubdtype(np.float64, np.float32) False

numpy.add(x1, x2[, out]) = Add arguments element-wise. Parameters: x1, x2 : array_like The arrays to be added. If x1.shape != x2.shape, they must be broadcastable to a common shape (which may be the shape of one or the other). Returns: add : ndarray or scalar The sum of x1 and x2, element-wise. Returns a scalar if both x1 and x2 are scalars. Notes Equivalent to x1 + x2 in terms of array broadcasting. Examples >>> np.add(1.0, 4.0) 5.0 >>> x1 = np.arange(9.0).reshap

numpy.seterr(all=None, divide=None, over=None, under=None, invalid=None) [source] Set how floating-point errors are handled. Note that operations on integer scalar types (such as int16) are handled like floating point, and are affected by these settings. Parameters: all : {?ignore?, ?warn?, ?raise?, ?call?, ?print?, ?log?}, optional Set treatment for all types of floating-point errors at once: ignore: Take no action when the exception occurs. warn: Print a RuntimeWarning (via the Python w

numpy.indices(dimensions, dtype=) [source] Return an array representing the indices of a grid. Compute an array where the subarrays contain index values 0,1,... varying only along the corresponding axis. Parameters: dimensions : sequence of ints The shape of the grid. dtype : dtype, optional Data type of the result. Returns: grid : ndarray The array of grid indices, grid.shape = (len(dimensions),) + tuple(dimensions). See also mgrid, meshgrid Notes The output shape is obtained b

numpy.array_equal(a1, a2) [source] True if two arrays have the same shape and elements, False otherwise. Parameters: a1, a2 : array_like Input arrays. Returns: b : bool Returns True if the arrays are equal. See also allclose Returns True if two arrays are element-wise equal within a tolerance. array_equiv Returns True if input arrays are shape consistent and all elements equal. Examples >>> np.array_equal([1, 2], [1, 2]) True >>> np.array_equal(np.array([1,

numpy.core.records.fromfile(fd, dtype=None, shape=None, offset=0, formats=None, names=None, titles=None, aligned=False, byteorder=None) [source] Create an array from binary file data If file is a string then that file is opened, else it is assumed to be a file object. >>> from tempfile import TemporaryFile >>> a = np.empty(10,dtype='f8,i4,a5') >>> a[5] = (0.5,10,'abcde') >>> >>> fd=TemporaryFile() >>> a = a.newbyteorder('<') >>&