Current state of the simulation

setup.py

@@ -8,7 +8,7 @@ with open('README.md', 'r') as f:
 setup(
     name='spatz',
     version='0.0.10',
-    packages=find_packages(exclude=["_tests"]),
+    packages=['spatz'],
     long_description=longdescription,
     long_description_content_type='text/markdown',
 )

spatz/sensors/imu/bhi160.py

@@ -1,10 +1,12 @@
+import numpy as np
+
 from typing import List, AnyStr
 
 from numpy.typing import ArrayLike
 from spatz.dataset import ArrayLike, Dataset
 from spatz.logger import ArrayLike, Logger
 from spatz.sensors import IMU, Accelerometer, Gyroscope
-from spatz.transforms import Transform
+from spatz.transforms import Transform, GaussianNoise
 
 
 class BHI160Gyro(Gyroscope):
@@ -27,8 +29,10 @@ class BHI160Acc(Accelerometer):
     def __init__(self, dataset: Dataset, logger: Logger, offset: float = 0, transforms: List[Transform] = []):
         super().__init__(dataset, logger, offset, transforms)
 
+        self.__noise = GaussianNoise(0, 0.05)
+
     def _get_name(self) -> AnyStr:
         return 'BHI160'
     
     def _sensor_specific_effects(self, x: ArrayLike) -> ArrayLike:
-        return x
+        return self.__noise(0, x)

spatz/transforms/noise.py

@@ -13,15 +13,25 @@ class GaussianNoise(Transform):
         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."
-
+    def __call__(self, _: float, x: ArrayLike) -> ArrayLike:
         if np.isscalar(x):
             noise = np.random.normal(0, 1)
             x += self.__sigma * noise + self.__mu
         else:
+            dim = len(x)
+
+            if np.isscalar(self.__sigma):
+                sigma = np.identity(dim) * self.__sigma
+            else:
+                sigma = self.__sigma
+                
+            if np.isscalar(self.__mu):
+                mu = np.ones(dim)
+            else:
+                mu = self.__mu
+
             noise = np.random.normal(0, 1, np.shape(x))
-            x += self.__sigma @ noise + self.__mu
+            x += sigma @ noise + mu
 
         return x
 

tests.ipynb

@@ -631,7 +631,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x24cf15adfa0>]"
+       "[<matplotlib.lines.Line2D at 0x28b3b18fc20>]"
       ]
      },
      "execution_count": 13,
@@ -819,7 +819,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.767031884916352"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(np.array(acc_total) / g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -839,7 +859,7 @@
        "Name: altitude, Length: 1721, dtype: float64"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -850,7 +870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -859,7 +879,7 @@
        "19335.3982925117"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -870,7 +890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -890,7 +910,7 @@
        "Name: Time, Length: 1721, dtype: float64"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -901,7 +921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [