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### Jacobi Theta Function θ4

##### Synopsis

    #include <boost/math/special_functions/jacobi_theta.hpp>

    namespace boost { namespace math {
        template <class T, class U>
        calculated-result-type jacobi_theta4(T x, U q);

        template <class T, class U, class Policy>
        calculated-result-type jacobi_theta4(T x, U q, const Policy&);

        template <class T, class U>
        calculated-result-type jacobi_theta4tau(T x, U tau);

        template <class T, class U, class Policy>
        calculated-result-type jacobi_theta4tau(T x, U tau, const Policy&);

        template <class T, class U>
        calculated-result-type jacobi_theta4m1(T x, U q);

        template <class T, class U, class Policy>
        calculated-result-type jacobi_theta4m1(T x, U q, const Policy&);

        template <class T, class U>
        calculated-result-type jacobi_theta4m1tau(T x, U tau);

        template <class T, class U, class Policy>
        calculated-result-type jacobi_theta4m1tau(T x, U tau, const Policy&);
    }} // namespaces

##### Description

The functions calculate the value of fourth Jacobi Theta function, parameterized either in terms of the nome q:

Or in terms of an imaginary τ:

The nome q is restricted to the domain (0, 1), returning the result of domain_error otherwise. The following graph shows the theta function at various values of q:

The final Policy argument is optional and can be used to control the behaviour of the function: how it handles errors, what level of precision to use etc. Refer to the policy documentation for more details.

A second quartet of functions (functions containing `m1`) compute one less than the value of the fourth theta function. These versions of the functions provide increased accuracy when the result is close to unity.

##### Accuracy

The following ULPs plot is representative, fixing q=0.5 and varying x from 0 to 2π:

The envelope represents the function's condition number. Note that relative accuracy degenerates periodically near θ4=1.

Fixing x=5 and varying q, the ULPs plot looks like:

Accuracy tends to degenerate near q=1 (small τ).

##### Implementation

The q parameterization is implemented using the τ parameterization, where τ=-log(q)/π.

If τ is greater than or equal to 1, the summation above is used as-is. However if τ < 1, the following identity DLMF 20.7.33 is used, defining τ'=-1/τ:

This transformation of variables ensures that the function will always converge in a small number of iterations.