rename and add slope

src/progpy/loading/__init__.py

@@ -2,5 +2,5 @@
 # National Aeronautics and Space Administration.  All Rights Reserved.
 
 from progpy.loading.moving_average import MovingAverage
-from progpy.loading.gaussian_wrapper import GaussianNoiseLoadWrapper
+from progpy.loading.gaussian_wrapper import GaussianNoiseLoadWrapper, GaussianNoiseWrapper
 from progpy.loading.piecewise import Piecewise

src/progpy/loading/gaussian_wrapper.py

@@ -5,7 +5,7 @@
 import numpy as np
 
 
-class GaussianNoiseLoadWrapper():
+class GaussianNoiseWrapper():
     """
     .. versionadded:: 1.5.0
     
@@ -22,6 +22,8 @@ class GaussianNoiseLoadWrapper():
     -------------------
     seed: {int, SeedSequence, BitGenerator, Generator}, optional
         The seed for random number generator. This can be set to make results repeatable.
+    std_slope: float, optional
+        The increase in standard deviation per second of time. Std is the standard deviation at t=0.    
 
     Example
     -------
@@ -30,23 +32,31 @@ class GaussianNoiseLoadWrapper():
     >>> future_load = GaussianNoiseLoadWrapper(future_load, STANDARD_DEV)
     >>> m.simulate_to_threshold(future_load)
     """
-    def __init__(self, fcn: Callable, std: float, seed: int = None):
+    def __init__(self, fcn: Callable, std: float, seed: int = None, std_slope: float = 0):
         self.fcn = fcn
         self.std = std
+        self.std_slope = std_slope
         self.gen = np.random.default_rng(seed)
 
     def __call__(self, t: float, x=None):
         """
         Return the load with noise added
 
-        Args:
-            t (float): Time (s)
-            x (StateContainer, optional): Current state. Defaults to None.
+        Arguments
+        ------------
+            t: float
+                Time (s)
+            x: StateContainer, optional
+                Current state. Defaults to None.
 
         Returns:
             InputContainer: The load with noise added
         """
         input = self.fcn(t, x)
+        std = self.std + self.std_slope*t
         for key, value in input.items():
-            input[key] = self.gen.normal(value, self.std)
+            input[key] = self.gen.normal(value, std)
         return input
+
+# Old name kept for backwards compatability
+GaussianNoiseLoadWrapper = GaussianNoiseWrapper

tests/test_loading.py

@@ -1,8 +1,9 @@
 # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration.  All Rights Reserved.
 
+import numpy as np
 import unittest
 
-from progpy.loading import Piecewise, GaussianNoiseLoadWrapper
+from progpy.loading import Piecewise, GaussianNoiseWrapper
 
 
 class Testloading(unittest.TestCase):
@@ -30,21 +31,45 @@ def loading(t, x=None):
             return {'a': 10}
 
         # Default: two values should be different (because of randomness)
-        loading_with_noise = GaussianNoiseLoadWrapper(loading, 10)
+        loading_with_noise = GaussianNoiseWrapper(loading, 10)
         load1 = loading_with_noise(10)
 
-        loading_with_noise = GaussianNoiseLoadWrapper(loading, 10)
+        loading_with_noise = GaussianNoiseWrapper(loading, 10)
         load2 = loading_with_noise(10)
 
         self.assertNotEqual(load1['a'], load2['a'])
 
         # Setting seed, two values should be the same now
-        loading_with_noise = GaussianNoiseLoadWrapper(loading, 10, seed=550)
+        loading_with_noise = GaussianNoiseWrapper(loading, 10, seed=550)
         load1 = loading_with_noise(10)
 
-        loading_with_noise = GaussianNoiseLoadWrapper(loading, 10, seed=550)
+        loading_with_noise = GaussianNoiseWrapper(loading, 10, seed=550)
         load2 = loading_with_noise(10)
         self.assertEqual(load1['a'], load2['a'])
+
+    def test_wedge_gaussian_load(self):
+        def loading(t, x=None):
+            return {'a': 10}
+
+        loading_with_noise = GaussianNoiseWrapper(loading, 0, std_slope=10)
+
+        # Standard deviation at t=0 should be 0.
+        self.assertEqual(
+            loading_with_noise(0),
+            loading_with_noise(0))
+
+        # At any greater time, the standard deviation is not 0
+        self.assertNotEqual(
+            loading_with_noise(10),
+            loading_with_noise(10))
+
+        # Check that standard deviation increases with time. 
+        values = [loading_with_noise(10)['a'] for _ in range(20)]
+        std = np.std(values)
+
+        values = [loading_with_noise(1e4)['a'] for _ in range(20)]
+        std2 = np.std(values)
+        self.assertGreater(std2, std)
 
 # This allows the module to be executed directly    
 def main():