From e3d5c51b6fb6937c35cfa435b1016e6be11be6de Mon Sep 17 00:00:00 2001 From: Pat Thoyts Date: Mon, 5 Jan 2026 06:53:18 +0000 Subject: [PATCH] Amended import of gaussian function from scipy --- rsl/test/test_steps.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/rsl/test/test_steps.py b/rsl/test/test_steps.py index 44cdad2..c111529 100644 --- 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) -- 2.51.2