pygwtf.models.taylort2ecc
=========================

.. py:module:: pygwtf.models.taylort2ecc


Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/pygwtf/models/taylort2ecc/common/index
   /autoapi/pygwtf/models/taylort2ecc/model/index


Classes
-------

.. autoapisummary::

   pygwtf.models.taylort2ecc.TaylorT2Ecc


Package Contents
----------------

.. py:class:: TaylorT2Ecc(backend: str | pygwtf.backend.Backend | None = None)

   Bases: :py:obj:`pygwtf.models.base.AnalyticModel`


   AnalyticModel is the base class for all analytic waveform models in pygwtf.

   The model class is responsible for
    - Defining the input parameters of the model
    - Computing any derived parameters (such as the time to coalescence) from the input parameters
    - Providing access to the numba-compiled functions required for the waveform generation kernel

   See the docstrings for each required method for more details.


   .. py:property:: parameters
      :type: list[str]


      Returns a list of the input parameters required for this model.
      The order of the parameters in this list must match the order of the parameters expected by the numba-compiled functions.



   .. py:property:: derived_parameters
      :type: list[str]


      Returns a list of any derived parameters that are computed from the input parameters.
      The order of the parameters in this list must match the order of the derived parameters expected by the numba-compiled functions.
      These parameters will be appended to the input parameters in the parameter array passed to the numba-compiled functions.



   .. py:method:: compute_derived_parameters(parameters: numpy.ndarray) -> None

      Computes any derived parameters from the input parameters.
      The input parameter array will have shape (n_sources, n_parameters + n_derived_parameters) and the derived parameters
      should be computed in place and stored in the appropriate columns of this array (i.e. the columns after the input parameters).
      The default implementation of this method does nothing, as not all models will have derived parameters.



   .. py:property:: amplitude_function
      :type: Callable


      The numba-compiled function that computes the amplitude of the waveform at a given time, frequency and frequency derivative.
      This function should have the signature `amplitude(t, f, fdot, parameters)` where `parameters` is a 1D array containing the input parameters
        for a single source (in the order specified by the `parameters` method).



   .. py:property:: phi_f_fdot_function
      :type: Callable


      The numba-compiled function that computes the phase, frequency and frequency derivative of the waveform at a given time.
      This function should have the signature `phi_f_fdot(t, parameters)` where `parameters` is a 1D array containing the input parameters
        for a single source (in the order specified by the `parameters` method).



   .. py:property:: get_TT_polarisations_function
      :type: Callable


      The numba-compiled function that computes the plus and cross polarisations of the waveform at a given time, frequency and frequency derivative.
      This function is only required for non-TDI waveform generation.
      This function should have the signature `hplus_hcross(hlm, parameters)` where `parameters` is a 1D array containing the input parameters
        for a single source (in the order specified by the `parameters` method) and `hlm` is the multipole.
      The default implementation of this method returns None for flexibility, as not all models will require this function.



   .. py:method:: get_time_bounds(parameters: numpy.ndarray, frequency_band: tuple[float, float]) -> tuple[numpy.ndarray, numpy.ndarray]

      Computes the start and end times of the waveform for each source in the input parameter array.
      This is used to determine the time segments over which to compute the waveform for each source.