src.Refactoring
===============

.. py:module:: src.Refactoring


Attributes
----------

.. autoapisummary::

   src.Refactoring.start_time
   src.Refactoring.m_max
   src.Refactoring.t_span
   src.Refactoring.nu
   src.Refactoring.v_e
   src.Refactoring.q
   src.Refactoring.sol
   src.Refactoring.v_grid
   src.Refactoring.f_vt


Functions
---------

.. autoapisummary::

   src.Refactoring.reconstruct_fv


Module Contents
---------------

.. py:data:: start_time

.. py:data:: m_max
   :value: 200


.. py:data:: t_span
   :value: None


.. py:data:: nu
   :value: 2


.. py:data:: v_e

.. py:data:: q

.. py:data:: sol

.. py:function:: reconstruct_fv(f_m_t, v_grid)

   Reconstructs the time-evolving function f(v, t) on a velocity grid, given its
   decomposition in terms of Hermite functions. The reconstruction is performed
   by computing the Hermite functions for all modes and velocities, normalizing
   them, and summing over the modes to obtain the desired function.

   :param f_m_t: Coefficients f_m(t) of the Hermite decomposition at each time step.
                 The shape is (T, M), where T is the number of time steps, and M
                 is the number of modes.
   :type f_m_t: ndarray or jnp.ndarray
   :param v_grid: The velocity grid over which the function is reconstructed.
                  The length of v_grid corresponds to the number of velocity points.
   :type v_grid: ndarray or jnp.ndarray
   :return: The reconstructed function f(v, t) on the velocity grid. The shape of
            the output is (T, V), where V is the number of velocity points.
   :rtype: jnp.ndarray


.. py:data:: v_grid
   :value: None


.. py:data:: f_vt