leaspy.models.shared_speed_logistic
===================================

.. py:module:: leaspy.models.shared_speed_logistic


Classes
-------

.. autoapisummary::

   leaspy.models.shared_speed_logistic.SharedSpeedLogisticModel


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

.. py:class:: SharedSpeedLogisticModel(name, **kwargs)

   Bases: :py:obj:`leaspy.models.logistic.LogisticInitializationMixin`, :py:obj:`leaspy.models.time_reparametrized.TimeReparametrizedModel`


   
   Logistic model for multiple variables of interest, imposing same average
   evolution pace for all variables (logistic curves are only time-shifted).


   :Parameters:

       **name** : :obj:`str`
           The name of the model.

       **\*\*kwargs**
           Hyperparameters of the model.














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   .. py:method:: metric(*, g_deltas_exp)
      :staticmethod:


      
      Compute the metric term for the logistic model.

      This scaling term modulates the curvature of the logistic trajectory.

      :Parameters:

          **g_deltas_exp** : :class:`torch.Tensor`
              Product of slope and exp(-deltas).



      :Returns:

          :class:`torch.Tensor`
              Metric value, computed as:
                    .. math::
              
                         \frac{(g \cdot e^{-\delta} + 1)^2}{g \cdot e^{-\delta}}











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   .. py:method:: deltas_exp(*, deltas_padded)
      :staticmethod:


      
      Compute the exponential of the negative deltas.

      Parameters:
          deltas_padded (:class:`torch.Tensor`): Padded deltas.

      Returns:
          :class:`torch.Tensor`: Exponential of the negative deltas.















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   .. py:method:: g_deltas_exp(*, g, deltas_exp)
      :staticmethod:



   .. py:method:: pad_deltas(*, deltas)
      :staticmethod:


      
      Prepend deltas with a zero as delta_1 is set to zero in the equations.
      .
              Parameters:
                  deltas (:class:`torch.Tensor`): Deltas tensor.

              Returns:
                  :class:`torch.Tensor`: Padded deltas tensor.















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   .. py:method:: denom(*, g_deltas_exp)
      :staticmethod:


      
      Compute the denominator for the :math:`\gamma_{t_0}` calculation.
















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   .. py:method:: gamma_t0(*, denom)
      :staticmethod:


      
      Compute the :math:`\gamma_{t_0}` value, which is the inverse of the denominator.


      :Parameters:

          **denom** : :class:`torch.Tensor`
              Denominator term.



      :Returns:

          :class:`torch.Tensor`
              :math:`\gamma_{t_0}` value.











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   .. py:method:: g_metric(*, gamma_t0)
      :staticmethod:


      
      Compute the g_metric value, which is the square of gamma_t0.


      :Parameters:

          **gamma_t0** : :class:`torch.Tensor`
              :math:`\gamma_{t_0}` value.



      :Returns:

          :class:`torch.Tensor`
              :math:`g_{metric}` value, computed as:
                 .. math::
              
                      g\_metric = \frac{1}{(\gamma_{t0} \cdot (1 - \gamma_{t0}))^2}











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   .. py:method:: collin_to_d_gamma_t0(*, deltas_exp, denom)
      :staticmethod:


      
      Compute the collinear term to d_gamma_t0.


      :Parameters:

          **deltas_exp** : :class:`torch.Tensor`
              Exponential of the negative deltas.

          **denom** : :class:`torch.Tensor`
              Denominator term.



      :Returns:

          :class:`torch.Tensor`
              Collinear term to d_gamma_t0











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   .. py:method:: model_with_sources(*, rt, space_shifts, metric, deltas_padded, log_g)
      :classmethod:


      
      Returns a model with sources.
















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   .. py:method:: model_no_sources(*, rt, metric, deltas_padded, log_g)
      :classmethod:


      
      Returns a model without sources.
















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   .. py:method:: get_variables_specs()

      
      Get the specifications of the variables used in the model.
















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