Better logging and added BHI160 sensor

spatz/logger.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
 
-from typing import Any, Tuple
+from typing import Any, Tuple, List
 from numpy.typing import ArrayLike
 
 from abc import abstractmethod
@@ -56,7 +56,7 @@ class Logger(Advanceable):
     def _on_reset(self):
         self.__df = pd.DataFrame.from_dict({'time': [self.get_time()]}).astype(np.float64)
 
-    def write(self, attrib: str, value: Any, domain: str = 'all'):
+    def write(self, attrib: str | List[str], value: Any | List[Any] | List[ArrayLike], domain: str = 'all'):
         """Writes a value to the logger.
 
         Args:
@@ -64,12 +64,16 @@ class Logger(Advanceable):
             value (Any): The value to log.
             domain (str, optional): The domain the value belongs to. Defaults to 'any'.
         """
-        name = f'{domain}/{attrib}'
+        if not isinstance(attrib, str):
+            for attr, val in zip(attrib, value):
+                self.write(attr, val, domain=domain)
+        else:
+            name = f'{domain}/{attrib}'
 
-        if name not in self.__df.columns:
-            self.__df[name] = pd.Series([pd.NA] * len(self.__df))
+            if name not in self.__df.columns:
+                self.__df[name] = pd.Series([pd.NA] * len(self.__df))
 
-        self.__df.at[self.__idx, name] = value
+            self.__df.at[self.__idx, name] = value
 
     def get_dataframe(self) -> pd.DataFrame:
         return self.__df

spatz/models/kalman.py

@@ -6,6 +6,9 @@ from numpy.typing import ArrayLike
 
 def inv(val):
     if np.isscalar(val):
+        if val == 0:
+            return 0
+
         return 1 / val
     
     if len(val) == 1:
@@ -76,6 +79,8 @@ class KalmanFilter:
         H = self.__H
         R = self.__R
 
+        n = len(x)
+
         if hasattr(self.__H, '__call__'):
             H = self.__H(dt)
 
@@ -86,9 +91,9 @@ class KalmanFilter:
         K = err @ H.T @ inv(H @ err @ H.T + R)
 
         # Compute the corrected state.
-        x = x + K @ (z - H @ x)
+        x = x + (K @ (z - H @ x)).T
 
         # Compute the error after correction.
-        err = (np.identity('TODO') - K @ H) @ err
+        err = (np.identity(n) - K @ H) @ err
 
-        return x, err
+        return np.squeeze(np.asarray(x)), err

spatz/sensors/imu/bhi160.py

@@ -1,4 +1,4 @@
-from typing import List
+from typing import List, AnyStr
 
 from numpy.typing import ArrayLike
 from spatz.dataset import ArrayLike, Dataset
@@ -8,13 +8,27 @@ from spatz.transforms import Transform
 
 
 class BHI160Gyro(Gyroscope):
-    def __init__(self, dataset: Dataset, logger: Logger, offset: float = 0, transforms: List[Transform] = ...):
+    def __init__(self, dataset: Dataset, logger: Logger, offset: float = 0, transforms: List[Transform] = []):
         super().__init__(dataset, logger, offset, transforms)
 
+    def _get_name(self) -> AnyStr:
+        return 'BHI160'
+
     def _get_data(self) -> ArrayLike:
         rots = self._dataset.fetch_values(['roll_l', 'pitch_l', 'yaw_l'])
 
-        
+        return rots
 
     def _sensor_specific_effects(self, x: ArrayLike) -> ArrayLike:
         return x
+    
+
+class BHI160Acc(Accelerometer):
+    def __init__(self, dataset: Dataset, logger: Logger, offset: float = 0, transforms: List[Transform] = []):
+        super().__init__(dataset, logger, offset, transforms)
+
+    def _get_name(self) -> AnyStr:
+        return 'BHI160'
+    
+    def _sensor_specific_effects(self, x: ArrayLike) -> ArrayLike:
+        return x

spatz/sensors/sensor.py

@@ -47,8 +47,10 @@ class Sensor:
         out = self._get_data()
         out = self._sensor_specific_effects(out)
 
+        t = self._dataset.get_time()
+
         for transform in self._transforms:
-            out = transform(out)
+            out = transform(t, out)
 
         # Log the outputs of the sensor.
         if np.isscalar(out):

spatz/simulation.py

@@ -35,7 +35,6 @@ class UniformTimeSteps:
         return self.__dt + noise
 
 
-
 class Simulation:
     def __init__(self, time_steps=UniformTimeSteps(0.01)):
         self.__dataset = None
@@ -123,4 +122,3 @@ class Simulation:
 
         return self.__sensors[-1]
 
-    

spatz/transforms/failures.py

@@ -26,4 +26,9 @@ class Downtime(Transform):
         if self.__state == 1:
             return x
         
-        return np.zeros_like(x)
+        if np.isscalar(x):
+            x = 0
+        else:
+            x = np.zeros_like(x)
+        
+        return x