Financial Modelling in Python

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Extending Python from C++ 19

An example of the use of the epsilon function is again provided by a ppf.math unit

test:

class bisect tests(unittest.TestCase):

def test1(self):

tol = 5*ppf.math.epsilon()

left, right, num

its = \

ppf.math.bisect(lambda x: x*x + 2.0*x - 1.0

, -3, -2

, lambda x, y: math.fabs(x-y) < tol, 100)

Further examples of the use of these special functions can be found in the next chapter.

3.3 NUMPY ARRAYS

Despite the efforts of the preceding section regarding reflection of C++ Boost.MultiArray

types into Python, in practice, when working in Python, the authors have found the facilities

of NumPy arrays to be far more convenient (NumPy was mentioned briefly in section 1.2).

Specifically, their notational conveniences and the large body of functionality provided by the

NumPy library motivates their use in Python beyond the argument of C++ interoperability.

Indeed, when working in C++, a library dedicated to scientific manipulation of arrays such as

Blitz++

2

wins the authors’ favour for such work over ‘lower-level’ container types like native

C arrays or Boost.MultiArray types. But now to the crux of the matter. If we haven’t made this

point earlier then we’ll make it for the first time now. One of the great strengths of Python is

the ability to drop into C or C++ code ‘when performance counts’. That is, the ability to factor ...