updated ctmc docs to reflect newest functions

copa-uniandes · Dec 20, 2024 · e60a176 · e60a176
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diff --git a/dist/jmarkov-0.1.1-py3-none-any.whl b/dist/jmarkov-0.1.1-py3-none-any.whl
diff --git a/dist/jmarkov-0.1.1.tar.gz b/dist/jmarkov-0.1.1.tar.gz
diff --git a/docs/source/usage.rst b/docs/source/usage.rst
@@ -26,6 +26,14 @@ Continuous-time Markov chains
 
    .. automethod:: jmarkov.ctmc.ctmc.transient_probabilities
 
+   .. automethod:: jmarkov.ctmc.ctmc.first_passage_time
+
+   .. automethod:: jmarkov.ctmc.ctmc.occupation_time
+
+   .. automethod:: jmarkov.ctmc.ctmc.is_irreducible
+
+   .. automethod:: jmarkov.ctmc.ctmc.is_ergodic
+
 Discrete-time Markov chains 
 -----------------------------
 

diff --git a/jmarkov/ctmc.py b/jmarkov/ctmc.py
@@ -127,6 +127,15 @@ def first_passage_time(self, target:str):
         return t
 
     def occupation_time(self,T,Epsilon=0.00001):
+        """
+        Computes the expected occupation time in each state until time T.
+
+        This method computes the expected time the chain spends in each state,
+        from time 0 until time T. To this end it uses the embedded matrix and 
+        the uniformization method. 
+
+        Returns the expected time in each state from 0 to T   
+        """
         n=self.n_states
         m=self.generator.copy()
         def P_from_R(n,m):            

diff --git a/pyproject.toml b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "jmarkov"
-version = "0.1.0"
+version = "0.1.1"
 authors = [
   { name="Juan F Perez", email="juanfernando.perez@gmail.com" },
 ]