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library-> py_lib cpp_lib

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py_fun-> py_independent py_abort_recording py_fun_ctor py_fun_property py_fun_new_dynamic py_fun_jacobian py_fun_hessian py_fun_forward py_fun_reverse py_fun_optimize

py_independent-> fun_dynamic_xam.py

fun_dynamic_xam.py

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import numpy
import cppad_py
def fun_dynamic_xam() :
     ok  = True
     nx  = 2
     nd  = 2
     #
     # value of independent variables during recording
     x = numpy.empty(nx, dtype=float)
     x[0] = 1.0
     x[1] = 2.0
     #
     # value of independent dynamic parameters during recording
     dynamic = numpy.empty(nd, dtype=float)
     dynamic[0] = 3.0
     dynamic[1] = 4.0
     #
     # start recording
     (ax, adynamic) = cppad_py.independent(x, dynamic)
     #
     # create another dynamic paramerer
     adyn  = adynamic[0] + adynamic[1]
     #
     # create another variable
     avar  = ax[0] + ax[1] + adyn
     #
     # create f(x) = x[0] + x[1] + dynamic[0] + dynamic[1]
     ay    = numpy.empty(1, dtype=cppad_py.a_double)
     ay[0] = avar
     f     = cppad_py.d_fun(ax, ay)
     #
     # check some properties of f
     ok = ok and f.size_domain() == nx
     ok = ok and f.size_order()  == 0
     #
     # zero order forward mode using same values as during the recording
     y  = cppad_py.vec_double(1)
     y  = f.forward(0, x)
     ok = ok and y[0] == (x[0] + x[1] + dynamic[0] + dynamic[1])
     #
     #  zero order forward mode using different value for dynamic parameters
     dynamic[0] = dynamic[0] + 1.0
     dynamic[1] = dynamic[1] + 1.0
     f.new_dynamic(dynamic)
     y   = f.forward(0, x)
     ok  = ok and y[0] == (x[0] + x[1] + dynamic[0] + dynamic[1])
     #
     return ok