only the first 4 types are implemented in SciPy. **Type I** There are several definitions of the DST-I; we use the following for ``norm="backward"``. DST-I assumes the input is odd around :math:`n=-1` and :math:`n=N`. .. math:: y_k = 2 \sum_{n=0}^{N-1} x_n \sin\left(\frac{\pi(k+1)(n+1)}{N+1}\right) Note that the DST-I is only supported for input size > 1. The (unnormalized) DST-I is its own inverse, up to a factor :math:`2(N+1)`. The orthonormalized DST-I is exactly its own inverse. ``orthogonalize`` has no effect here, as the DST-I matrix is already orthogonal up to a scale factor of ``2N``. **Type II** There are several definitions of the DST-II; we use the following for ``norm="backward"``. DST-II assumes the input is odd around :math:`n=-1/2` and :math:`n=N-1/2`; the output is odd around :math:`k=-1` and even around :math:`k=N-1` .. math:: y_k = 2 \sum_{n=0}^{N-1} x_n \sin\left(\frac{\pi(k+1)(2n+1)}{2N}\right) If ``orthogonalize=True``, ``y[0]`` is divided :math:`\sqrt{2}` which, when combined with ``norm="ortho"``, makes the corresponding matrix of coefficients orthonormal (``O @ O.T = np.eye(N)``). **Type III** There are several definitions of the DST-III, we use the following (for ``norm="backward"``). DST-III assumes the input is odd around :math:`n=-1` and even around :math:`n=N-1` .. math:: y_k = (-1)^k x_{N-1} + 2 \sum_{n=0}^{N-2} x_n \sin\left( \frac{\pi(2k+1)(n+1)}{2N}\right) If ``orthogonalize=True``, ``x[0]`` is multiplied by :math:`\sqrt{2}` which, when combined with ``norm="ortho"``, makes the corresponding matrix of coefficients orthonormal (``O @ O.T = np.eye(N)``). The (unnormalized) DST-III is the inverse of the (unnormalized) DST-II, up to a factor :math:`2N`. The orthonormalized DST-III is exactly the inverse of the orthonormalized DST-II. **Type IV** There are several definitions of the DST-IV, we use the following (for ``norm="backward"``). DST-IV assumes the input is odd around :math:`n=-0.5` and even around :math:`n=N-0.5` .. math:: y_k = 2 \sum_{n=0}^{N-1} x_n \sin\left(\frac{\pi(2k+1)(2n+1)}{4N}\right) ``orthogonalize`` has no effect here, as the DST-IV matrix is already orthogonal up to a scale factor of ``2N``. The (unnormalized) DST-IV is its own inverse, up to a factor :math:`2N`. The orthonormalized DST-IV is exactly its own inverse. References ---------- .. [1] Wikipedia, "Discrete sine transform", https://en.wikipedia.org/wiki/Discrete_sine_transform r