Clean up

lineage/states/StateDistributionGaPhs.py

@@ -2,12 +2,12 @@
 
 import numpy as np
 
-from .stateCommon import basic_censor, StateDistributionClass
+from .stateCommon import basic_censor
 from .StateDistributionGamma import StateDistribution as GammaSD
 from ..CellVar import Time, CellVar
 
 
-class StateDistribution(StateDistributionClass):
+class StateDistribution:
     """For G1 and G2 separated as observations."""
 
     def __init__(

lineage/states/StateDistributionGamma.py

@@ -4,11 +4,11 @@
 import scipy.stats as sp
 from typing import Union, Literal
 
-from .stateCommon import gamma_estimator, basic_censor, bern_estimator, StateDistributionClass
+from .stateCommon import gamma_estimator, basic_censor, bern_estimator
 from ..CellVar import Time, CellVar
 
 
-class StateDistribution(StateDistributionClass):
+class StateDistribution:
     """
     StateDistribution for cells with gamma distributed times.
     """

lineage/states/stateCommon.py

@@ -6,7 +6,6 @@
 from numba import njit
 import numpy.typing as npt
 from scipy.optimize import minimize, Bounds, LinearConstraint
-from ..CellVar import CellVar
 from ctypes import CFUNCTYPE, c_double
 from numba.extending import get_cython_function_address
 
@@ -16,26 +15,6 @@
 warnings.filterwarnings("ignore", message="Values in x were outside bounds")
 
 
-class StateDistributionClass:
-    def rvs(self, size: int, rng=None):
-        raise NotImplementedError("dist not implemented.")
-
-    def dist(self, other) -> float:
-        raise NotImplementedError("dist not implemented.")
-
-    def dof(self) -> int:
-        raise NotImplementedError("dof not implemented.")
-
-    def logpdf(self, x: np.ndarray) -> np.ndarray:
-        raise NotImplementedError("logpdf not implemented.")
-
-    def estimator(self, x: np.ndarray, gammas: np.ndarray):
-        raise NotImplementedError("estimator not implemented.")
-
-    def censor_lineage(self, censor_condition: int, full_lineage: list[CellVar], desired_experiment_time=2e12) -> list[CellVar]:
-        raise NotImplementedError("censor_lineage not implemented.")
-
-
 def basic_censor(cells: list):
     """
     Censors a cell if the cell's parent is censored.

lineage/tHMM.py

@@ -84,7 +84,7 @@ def predict(self) -> list:
         return Viterbi(self)
 
     def get_BIC(
-        self, LL: float, num_cells: int, atonce: bool=False, mcf10a: bool=False
+        self, LL: float, num_cells: int, atonce: bool = False, mcf10a: bool = False
     ) -> Tuple[float, float]:
         """
         Gets the BIC values. Akaike Information Criterion, used for model selection and deals with the trade off
@@ -117,7 +117,9 @@ def get_BIC(
 
         return BIC_value, degrees_of_freedom
 
-    def log_score(self, X_state_tree_sequence: list, pi=None, T=None, E=None) -> list[float]:
+    def log_score(
+        self, X_state_tree_sequence: list, pi=None, T=None, E=None
+    ) -> list[float]:
         """
         This function returns the log-likelihood of a possible state assignment
         given the estimated model parameters.