require std dev input for whitegaussnoise (#276)

* require std dev input for `whitegaussnoise`

* fix bugs in tests

* change stdev to std

deerlab/whitegaussnoise.py

@@ -1,19 +1,19 @@
 import numpy as np
 
-def whitegaussnoise(t,level=1,rescale=False,seed=None):
+def whitegaussnoise(t, std, rescale=False, seed=None):
     r"""
     Generates a vector of white Gaussian (normal) noise
     
     Parameters
     ----------
     t : array_like
         Time axis.
-    level : float scalar
+    std : float scalar
         Noise level, i.e. standard deviation of underlying Gaussian distribution.
     rescale : boolean, optional
-        If true, rescales the noise vector such that its standard deviation is exactly equal
-        to ``level``. If false (default), the standard deviation of the noise vector can deviate
-        slightly from ``level``, particularly for short vectors.
+        If ``True``, rescales the noise vector such that its standard deviation is exactly equal
+        to ``std``. If ``False`` (default), the standard deviation of the noise vector can deviate
+        slightly from ``std``, particularly for short vectors.
     seed : integer scalar, optional
         If ``None`` (default), do not seed the random number generator. If an integer scalar is
         given (e.g. ``seed=137``), seed the random number generator with this number.
@@ -36,12 +36,12 @@ def whitegaussnoise(t,level=1,rescale=False,seed=None):
     if seed is not None:
         np.random.seed(seed)
 
-    # Draw from standard normal distribution
+    # Draw from normal distribution
     N = len(np.atleast_1d(t))
-    noise = np.random.normal(0,level,N)
+    noise = np.random.normal(0, std, N)
 
     # Rescale to sample std if wanted
     if rescale:
-        noise = level/np.std(noise)*noise
+        noise *= std/np.std(noise)
 
     return noise

test/test_model_class.py

@@ -515,7 +515,7 @@ def test_bootCIs_parametric():
     "Check the bootstrapped confidence intervals of the fitted parameters"
     model = _getmodel('parametric')
 
-    noisydata = mock_data + whitegaussnoise(0.01,seed=1)
+    noisydata = mock_data + whitegaussnoise(x,0.01,seed=1)
     fitResult = fit(model,noisydata,bootstrap=3)
 
     assert_attributes_cis(fitResult,['mean1','mean2','width1','width2','amp1','amp2'])
@@ -526,7 +526,7 @@ def test_bootCIs_semiparametric():
     "Check the bootstrapped confidence intervals of the fitted parameters"
     model = _getmodel('semiparametric')
 
-    noisydata = mock_data + whitegaussnoise(0.01,seed=1)
+    noisydata = mock_data + whitegaussnoise(x,0.01,seed=1)
     fitResult = fit(model,noisydata,bootstrap=3)
 
     assert_attributes_cis(fitResult,['mean1','mean2','width1','width2','amp1','amp2'])
@@ -537,7 +537,7 @@ def test_bootCIs_nonparametric():
     "Check the bootstrapped confidence intervals of the fitted parameters"
     model = _getmodel('semiparametric')
 
-    noisydata = mock_data + whitegaussnoise(0.01,seed=1)
+    noisydata = mock_data + whitegaussnoise(x,0.01,seed=1)
     fitResult = fit(model,noisydata,bootstrap=3)
 
     assert_attributes_cis(fitResult,['amp1','amp2'])

test/test_whitegaussnoise.py

@@ -1,5 +1,6 @@
 from deerlab.utils.utils import assert_docstring
 import numpy as np
+import pytest
 from deerlab import whitegaussnoise
 from deerlab.utils import assert_docstring
 
@@ -56,8 +57,24 @@ def test_noseed():
     assert not np.array_equal(noise1,noise2)
 # ======================================================================
 
+
 def test_docstring():
 # ======================================================================
     "Check that the docstring includes all variables and keywords."
     assert_docstring(whitegaussnoise)
 # ======================================================================
+
+
+def test_requiredstd():
+# ======================================================================
+    "Check that the standard deviation is a required argument"
+
+    N = 10
+    t = np.linspace(0,3,N)
+
+    with pytest.raises(TypeError):
+        noise = whitegaussnoise(t)
+
+# ======================================================================
+
+