woche12 > master: code py - vorberechnungen gemäß modell

code/python/src/models/random_walk/landscape.py

@@ -27,16 +27,23 @@ __all__ = [
 
 class Landscape():
     _fct: np.ndarray;
+    _labels: list[str];
     _metric: EnumLandscapeMetric;
     _radius: float;
+    _one_based: bool;
 
     def __init__(
         self,
         values: DataTypeLandscapeValues,
+        labels: List[str],
         metric: EnumLandscapeMetric = EnumLandscapeMetric.maximum,
+        one_based: bool = False,
     ):
         self._fct = convert_to_nparray(values);
+        assert len(labels) == self.dim, 'A label is required for each axis/dimension!';
+        self._labels = labels;
         self._metric = metric;
+        self._one_based = one_based;
         return;
 
     @property
@@ -47,14 +54,27 @@ class Landscape():
     def dim(self) -> int:
         return len(self._fct.shape);
 
+    @property
+    def coords_middle(self) -> tuple:
+        return tuple(math.floor(s/2) for s in self.shape);
+
     def fitness(self, *x: int) -> float:
         return self._fct[x];
 
+    def label(self, *x: int) -> str:
+        if self._one_based:
+            x = tuple(xx + 1 for xx in x);
+        expr = ','.join([ f'{name}{xx}' for name, xx in zip(self._labels, x)]);
+        if self.dim > 1:
+            expr = f'({expr})';
+        return expr;
+
     def neighbourhood(self, *x: int, r: float, strict: bool = False) -> List[tuple]:
+        r = int(r);
         sides = [
-            [ xx - j for j in range(1,r+1) if xx - j in range(s) ]
+            [ xx - j for j in range(1, r+1) if xx - j in range(s) ]
             + ([ xx ] if xx in range(s) else [])
-            + [ xx + j for j in range(1,r+1) if xx + j in range(s) ]
+            + [ xx + j for j in range(1, r+1) if xx + j in range(s) ]
             for xx, s in zip(x, self.shape)
         ];
         match self._metric: