IdealG¶

class dcprogs.likelihood.IdealG(*args)¶

Ideal Likelihood.

This object can be instantiated one of several way:

• With a matrix and an integer

  >>> idealg = IdealG(array([...]), 2)
  

• With a QMatrix

  >>> matrix = QMatrix(array([...]), 2)
  >>> idealg = IdealG(matrix)
  

__weakref__¶

list of weak references to the object (if defined)

af(self, t) → DCProgs::t_rmatrix¶

AF transitions with respect to time

Implements the ideal likelihood:

$$e^{t\mathcal{Q}_{FF}}\mathcal{Q}_{FA}.$$

Parameters:	t – A scalar or something to a numpy array. In the latter case, the return is an array of matrices.

fa(self, t) → DCProgs::t_rmatrix¶

FA transitions with respect to time

Implements the ideal likelihood:

$$e^{t\mathcal{Q}_{AA}}\mathcal{Q}_{AF}.$$

Parameters:	t – A scalar or something to a numpy array. In the latter case, the return is an array of matrices.

final_occupancies¶

Equilibrium occupancies for final states.

Computes the right eigenvector of $\mathcal{G}_{FA}\mathcal{G}_{AF}$, where $\mathcal{G}_{FA}$ is the laplacian for $s=0$ of the likelihood.

initial_occupancies¶

Equilibrium occupancies for initial states.

Computes the left eigenvector of $\mathcal{G}_{AF}\mathcal{G}_{FA}$, where $\mathcal{G}_{AF}$ is the laplacian for $s=0$ of the likelihood.

laplace_af(self, s) → DCProgs::t_rmatrix¶

AF transitions with respect to scale

Implements the laplace transform of the likelihood:

$$(sI - \mathcal{Q}_{AA})^{-1}\mathcal{Q}_{AF}.$$

Parameters:	s – A scalar or something to a numpy array. In the latter case, the return is an array of matrices.

laplace_fa(self, s) → DCProgs::t_rmatrix¶

FA transitions with respect to scale

Implements the laplace transform of the likelihood:

$$(sI - \mathcal{Q}_{FF})^{-1}\mathcal{Q}_{FA}.$$

Parameters:	s – A scalar or something to a numpy array. In the latter case, the return is an array of matrices.

nopen¶

Number of open-states.

nshut¶

Number of shut-states.