Updated sensors

spatz/transforms/__init__.py

@@ -1,3 +1,3 @@
 from spatz.transforms.transform import Transform
-from spatz.transforms.noise import GaussianNoise, ProportionalGaussian
+from spatz.transforms.noise import GaussianNoise, ProportionalGaussian, DriftingBias
 from spatz.transforms.failures import Downtime

spatz/transforms/noise.py

@@ -37,6 +37,35 @@ class GaussianNoise(Transform):
         return x
 
 
+class DriftingBias(Transform):
+    def __init__(self, init: ArrayLike, covariance: ArrayLike, Tc: float) -> None:
+        """First order Gauss-Markov (GM) model used to model drift.
+
+        Args:
+            init (ArrayLike): The initial bias.
+            covariance (ArrayLike): Covariance matrix of the process.
+            Tc (float): Correlation time of the process.
+        """
+        super().__init__()
+
+        self.__t = 0
+        self.__beta = 1 / Tc
+        self.__covariance = covariance
+        self.__Tc = Tc
+        self.__x_old = np.copy(init)
+
+    def __call__(self, t: float, x: ArrayLike) -> ArrayLike:
+        dt = t - self.__t
+        self.__t = t
+
+        w = np.random.normal(np.zeros_like(x), self.__covariance*(1-np.exp(-2 * dt / self.__Tc)))
+        drift = (1 - self.__beta * dt) * self.__x_old + w 
+        
+        self.__x_old = drift
+
+        return x + drift
+
+
 class ProportionalGaussian(Transform):
     def __init__(self, mu, sigma) -> None:
         super().__init__()