fixed some bugs, added dockerfile for running on windows. output files must be extracted manually.

code/simulator.py

@@ -171,6 +171,7 @@ class BooleanNetwork:
         if type(state) is str:
             for i in range(self.size):
                 self.nodes[i] = bool(int(state[i]))
+            self.time_step = 0
             return self
 
         if isinstance(state, (list, tuple)):
@@ -178,19 +179,23 @@ class BooleanNetwork:
                 f"SetState error: given state list contains elements of type different from bool. All elements must be bools. got {state}"
             )
             self.nodes = list(state).copy()
+            self.time_step = 0
             return self
 
         raise Exception(
             "SetState error: end of function reached. given state is not of type string nor list[bool]."
         )
 
-    def Update(self, n: int = 1, /, verbose=False) -> None:
+    def Update(
+        self, n: int = 1, /, verbose: bool = False, writeToFile: bool = False
+    ) -> None:
         assert type(n) is int and n >= 0, (
             f"Update error: amount of updates must be an integer and positive. got {n=}"
         )
         assert self.__has_update_functions, "Update error: no update functions defined"
         assert self.__has_update_scheme, "Update error: no update scheme defined"
         assert type(verbose) is bool, "Update error: verbose must be a bool"
+        assert type(writeToFile) is bool, "Update error: writeToFile must be a bool"
 
         selected_update: Callable
 
@@ -208,11 +213,29 @@ class BooleanNetwork:
             case _:
                 raise Exception("Update error: update scheme selection went wrong")
 
-        for _ in range(n):
-            selected_update()
-            self.time_step += 1
-            if verbose:
-                print(self)
+        match (verbose, writeToFile):
+            case (False, False):
+                for _ in range(n):
+                    selected_update()
+                    self.time_step += 1
+            case (False, True):
+                with open("output.txt", "w") as f:
+                    for _ in range(n):
+                        selected_update()
+                        self.time_step += 1
+                        f.writelines([self.state, "\n"])
+            case (True, False):
+                for _ in range(n):
+                    selected_update()
+                    self.time_step += 1
+                    print(self)
+            case (True, True):
+                with open("output.txt", "w") as f:
+                    for _ in range(n):
+                        selected_update()
+                        self.time_step += 1
+                        f.writelines([self.state, "\n"])
+                        print(self)
 
     def __str__(self) -> str:
         return f"{self.time_step:>5} | {''.join(str(int(node)) for node in self.nodes)}"
@@ -222,13 +245,18 @@ class BooleanNetwork:
         return "".join(str(int(node)) for node in self.nodes)
 
     def GetStableProbabilityDistribution(self) -> np.ndarray:
-        matrix: np.ndarray = self.__get_transition_matrix()
-        eigenvalues, eigenvectors = scipy.linalg.eig(matrix.T)
-        stationary_vector = np.real(eigenvectors[:, np.isclose(eigenvalues, 1)])
-        stationary_distribution = stationary_vector / np.sum(stationary_vector)
-        return stationary_distribution.flatten()
+        matrix: np.ndarray = self.GetTransitionMatrix()
 
-    def __get_transition_matrix(self) -> np.ndarray:
+        eigenvalues, eigenvectors = scipy.linalg.eig(matrix.T)
+
+        idx = np.argmin(np.abs(eigenvalues - 1.0))
+        pi = eigenvectors[:, idx].real
+
+        pi = pi / pi.sum()
+
+        return pi
+
+    def GetTransitionMatrix(self) -> np.ndarray:
         dimension = 2**self.size
         matrix: np.ndarray = np.zeros((dimension, dimension))