Amended import of gaussian function from scipy

diff --git a/rsl/test/test_steps.py b/rsl/test/test_steps.py
index 44cdad258e933cafc7b70d3827273824f91cbc20..c111529bcea82a253c9f75bc319508d73a2f8fa1 100644 (file)
--- a/rsl/test/test_steps.py
+++ b/rsl/test/test_steps.py
@@ -1,12 +1,12 @@
 import unittest
 import numpy as np
 import matplotlib.pyplot as plt
-import scipy.signal
+from scipy.signal.windows import gaussian
 from numpy.testing import assert_array_equal
 from ..steps import (
     DivideStep, MultiplyStep, SubtractStep, AddStep, PowerStep,
     ZapStep, TruncateStep, NormalizeStep, InterpolateStep,
-    SmoothStep, PeakPickStep)
+    SmoothStep, PeakPickStep, BaselineSubtractionStep)
 
 
 class TestArithmeticSteps(unittest.TestCase):
@@ -76,7 +76,7 @@ class TestArithmeticSteps(unittest.TestCase):
 class TestProcessingSteps(unittest.TestCase):
     def test_zap_linear(self):
         xlist = [x for x in range(11)]
-        ilist = scipy.signal.gaussian(11, 2, True)
+        ilist = gaussian(11, 2, True)
         spectrum = np.array([xlist, ilist], dtype='float')
         f = ZapStep(2, 8, kind='linear')
         result = f(spectrum)
@@ -86,7 +86,7 @@ class TestProcessingSteps(unittest.TestCase):
 
     def test_zap_cubic(self):
         xlist = [x for x in range(11)]
-        ilist = scipy.signal.gaussian(11, 2, True)
+        ilist = gaussian(11, 2, True)
         spectrum = np.array([xlist, ilist], dtype='float')
         f = ZapStep(2, 8, kind='cubic')
         result = f(spectrum)
@@ -95,7 +95,7 @@ class TestProcessingSteps(unittest.TestCase):
 
     def test_truncate(self):
         xlist = [x for x in range(100)]
-        ilist = scipy.signal.gaussian(100, 10, True)
+        ilist = gaussian(100, 10, True)
         spectrum = np.array([xlist, ilist], dtype='float')
         f = TruncateStep(20, 80)
         result = f(spectrum)
@@ -104,7 +104,7 @@ class TestProcessingSteps(unittest.TestCase):
 
     def test_normalize(self):
         xlist = [x for x in range(100)]
-        ilist = np.multiply(scipy.signal.gaussian(100, 10, True), 10.0)
+        ilist = np.multiply(gaussian(100, 10, True), 10.0)
         spectrum = np.array([xlist, ilist], dtype='float')
         f = NormalizeStep()
         result = f(spectrum)