  The article is devoted to the implementation of strong numerical methods with
convergence orders $0.5,$ $1.0,$ $1.5,$ $2.0,$ $2.5,$ and $3.0$ for Ito
stochastic differential equations with multidimensional non-commutative noise
based on the unified Taylor--Ito and Taylor-Stratonovich expansions and
multiple Fourier-Legendre series. Algorithms for the implementation of these
methods are constructed and a package of programs in the Python programming
language is presented. An important part of this software package, concerning
the mean-square approximation of iterated Ito and Stratonovich stochastic
integrals of multiplicities 1 to 6 with respect to components of the
multidimensional Wiener process is based on the method of generalized multiple
Fourier series. More precisely, we used the multiple Fourier-Legendre series
converging in the sense of norm in Hilbert space $L_2([t, T]^k)$
$(k=1,\ldots,6)$ for the mean-square approximation of iterated Ito and
Stratonovich stochastic integrals.
