numpy.trapz(y, x=None, dx=1.0, axis=-1)

numpy.minimum(x1, x2[, out]) = Element-wise minimum of array elements. Compare two arrays and returns a new array containing

numpy.mod(x1, x2[, out]) = Return element-wise remainder of division. Computes the remainder complementary to the

numpy.frexp(x[, out1, out2]) = Decompose the elements of x into mantissa and twos exponent. Returns (mantissa

numpy.tanh(x[, out]) = Compute hyperbolic tangent element-wise. Equivalent to np.sinh(x)/np.cosh(x) or -1j

numpy.logaddexp2(x1, x2[, out]) = Logarithm of the sum of exponentiations of the inputs in base-2. Calculates log2(2**x1

numpy.cosh(x[, out]) = Hyperbolic cosine, element-wise. Equivalent to 1/2 * (np.exp(x) + np.exp(-x)) and np

numpy.maximum(x1, x2[, out]) = Element-wise maximum of array elements. Compare two arrays and returns a new array containing

numpy.nan_to_num(x)

numpy.cos(x[, out]) = Cosine element-wise.