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# numpy/numpy

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 """ Module of functions that are like ufuncs in acting on arrays and optionally storing results in an output array. """ from __future__ import division, absolute_import, print_function __all__ = ['fix', 'isneginf', 'isposinf'] import numpy.core.numeric as nx def fix(x, y=None): """ Round to nearest integer towards zero. Round an array of floats element-wise to nearest integer towards zero. The rounded values are returned as floats. Parameters ---------- x : array_like An array of floats to be rounded y : ndarray, optional Output array Returns ------- out : ndarray of floats The array of rounded numbers See Also -------- trunc, floor, ceil around : Round to given number of decimals Examples -------- >>> np.fix(3.14) 3.0 >>> np.fix(3) 3.0 >>> np.fix([2.1, 2.9, -2.1, -2.9]) array([ 2., 2., -2., -2.]) """ x = nx.asanyarray(x) y1 = nx.floor(x) y2 = nx.ceil(x) if y is None: y = nx.asanyarray(y1) y[...] = nx.where(x >= 0, y1, y2) return y def isposinf(x, y=None): """ Test element-wise for positive infinity, return result as bool array. Parameters ---------- x : array_like The input array. y : array_like, optional A boolean array with the same shape as `x` to store the result. Returns ------- y : ndarray A boolean array with the same dimensions as the input. If second argument is not supplied then a boolean array is returned with values True where the corresponding element of the input is positive infinity and values False where the element of the input is not positive infinity. If a second argument is supplied the result is stored there. If the type of that array is a numeric type the result is represented as zeros and ones, if the type is boolean then as False and True. The return value `y` is then a reference to that array. See Also -------- isinf, isneginf, isfinite, isnan Notes ----- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Errors result if the second argument is also supplied when `x` is a scalar input, or if first and second arguments have different shapes. Examples -------- >>> np.isposinf(np.PINF) array(True, dtype=bool) >>> np.isposinf(np.inf) array(True, dtype=bool) >>> np.isposinf(np.NINF) array(False, dtype=bool) >>> np.isposinf([-np.inf, 0., np.inf]) array([False, False, True], dtype=bool) >>> x = np.array([-np.inf, 0., np.inf]) >>> y = np.array([2, 2, 2]) >>> np.isposinf(x, y) array([0, 0, 1]) >>> y array([0, 0, 1]) """ if y is None: x = nx.asarray(x) y = nx.empty(x.shape, dtype=nx.bool_) nx.logical_and(nx.isinf(x), ~nx.signbit(x), y) return y def isneginf(x, y=None): """ Test element-wise for negative infinity, return result as bool array. Parameters ---------- x : array_like The input array. y : array_like, optional A boolean array with the same shape and type as `x` to store the result. Returns ------- y : ndarray A boolean array with the same dimensions as the input. If second argument is not supplied then a numpy boolean array is returned with values True where the corresponding element of the input is negative infinity and values False where the element of the input is not negative infinity. If a second argument is supplied the result is stored there. If the type of that array is a numeric type the result is represented as zeros and ones, if the type is boolean then as False and True. The return value `y` is then a reference to that array. See Also -------- isinf, isposinf, isnan, isfinite Notes ----- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Errors result if the second argument is also supplied when x is a scalar input, or if first and second arguments have different shapes. Examples -------- >>> np.isneginf(np.NINF) array(True, dtype=bool) >>> np.isneginf(np.inf) array(False, dtype=bool) >>> np.isneginf(np.PINF) array(False, dtype=bool) >>> np.isneginf([-np.inf, 0., np.inf]) array([ True, False, False], dtype=bool) >>> x = np.array([-np.inf, 0., np.inf]) >>> y = np.array([2, 2, 2]) >>> np.isneginf(x, y) array([1, 0, 0]) >>> y array([1, 0, 0]) """ if y is None: x = nx.asarray(x) y = nx.empty(x.shape, dtype=nx.bool_) nx.logical_and(nx.isinf(x), nx.signbit(x), y) return y
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