SPATZ migration + proper directory structure

spatz/transforms/__init__.py

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+from failures import *
+from noise import *
+from transform import *

spatz/transforms/failures.py

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+import numpy as np
+
+from numpy.typing import ArrayLike
+from typing import Any, Tuple
+
+from spatz.transforms import Transform
+
+
+class Downtime(Transform):
+    def __init__(self, mu_duration: float, sigma_duration: float) -> None:
+        super().__init__()
+
+        self.__mu = mu_duration
+        self.__sigma = sigma_duration
+        self.__state = 1
+        self.__until = abs(np.random.normal(mu_duration, sigma_duration))
+
+    def get_state(self) -> Tuple[int, float]:
+        return self.__state, self.__until
+
+    def __call__(self, t: float, x: ArrayLike) -> Any:
+        if t >= self.__until:
+            self.__state = 1 - self.__state
+            self.__until = t + abs(np.random.normal(self.__mu, self.__sigma))
+
+        if self.__state == 1:
+            return x
+        
+        return np.zeros_like(x)

spatz/transforms/noise.py

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+import numpy as np
+
+from numpy.typing import ArrayLike
+from typing import Any, Tuple
+
+from spatz.transforms import Transform
+
+
+class GaussianNoise(Transform):
+    def __init__(self, mu: ArrayLike, sigma: ArrayLike) -> None:
+        super().__init__()
+
+        self.__mu = mu
+        self.__sigma = sigma
+
+    def __call__(self, t: float, x: ArrayLike) -> ArrayLike:
+        assert np.shape(self.__mu) == np.shape(x), "Mu and x have to match in shape."
+
+        if np.isscalar(x):
+            noise = np.random.normal(0, 1)
+            x += self.__sigma * noise + self.__mu
+        else:
+            noise = np.random.normal(0, 1, np.shape(x))
+            x += self.__sigma @ noise + self.__mu
+
+        return x
+
+
+class PinkNoise(Transform):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def __call__(self, t: float, x: ArrayLike) -> Any:
+        pass

spatz/transforms/transform.py

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+import numpy as np
+
+from numpy.typing import ArrayLike
+from typing import Any, Tuple
+
+
+class Transform:
+    def apply(self, t: float, x: np.array):
+        y = self(t, x)
+        assert x.shape == y.shape, "Transform has to maintain the array's shape."
+
+        return y