# Taylor moment expansion (TME) in Python
Please see the documentation of the package in https://tme.readthedocs.io.
# Install
Install via `pip install tme` or `python setup.py install` (Please note that if you would like to use JaX, please
install `jax` by yourself beforehand).
# Examples
```python
import tme.base_jax as tme
import jax.numpy as jnp
from jax import jit, vmap
# Define SDE coefficients.
alp = 1.
def drift(x):
return jnp.array([x[1],
x[0] * (alp - x[0] ** 2) - x[1]])
def dispersion(x):
return jnp.array([0, x[0]])
# Jit the 3-order TME mean and cov approximation functions
@jit
def tme_m_cov(x, dt):
return tme.mean_and_cov(x, dt, drift, dispersion, order=3)
# Compute E[X(t) | X(0)=x0]
x0 = jnp.array([0., -1])
ts = jnp.array([0.25, 0.5, 1.])
m_t, cov_t = vmap(tme_m_cov, in_axes=[None, 0])(x0, ts)
```
Inside folder `examples`, there are a few Jupyter notebooks showing how to use the TME method (in SymPy and JaX).
# License
The GNU General Public License v3 or later
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