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.log2(x[, out]) = Base-2 logarithm of x.

numpy.square(x[, out]) = Return the element-wise square of the input.

numpy.exp(x[, out]) = Calculate the exponential of all elements in the input array.

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

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

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

numpy.sqrt(x[, out]) = Return the positive square-root of an array, element-wise.

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