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import os
import unittest
import warnings
import itertools
import numpy
from numpy.testing import assert_array_almost_equal
import scipy.signal
import numpy as np
import pandas as pd
import peakutils
def load(name):
p = os.path.join(os.path.dirname(__file__), name)
return numpy.loadtxt(p)
class LPGPeaks(unittest.TestCase):
"""Tests with experimental data from long period gratings"""
def test_peaks(self):
y = load('noise')[:, 1]
filtered = scipy.signal.savgol_filter(y, 21, 1)
n_peaks = 8
idx = peakutils.indexes(filtered, thres=0.08, min_dist=50)
for p in range(idx.size, 1):
self.assertGreater(idx[p], 0)
self.assertLess(idx[p], idx.size - 1)
self.assertGreater(idx[p], idx[p - 1])
self.assertEqual(idx.size, n_peaks)
class FBGPeaks(unittest.TestCase):
"""Tests with experimental data from fiber Bragg gratings"""
def test_peaks(self):
data = load('baseline')
x, y = data[:, 0], data[:, 1]
n_peaks = 2
prepared = y - peakutils.baseline(y, 3)
idx = peakutils.indexes(prepared, thres=0.03, min_dist=5)
for p in range(idx.size, 1):
self.assertGreater(idx[p], 0)
self.assertLess(idx[p], idx.size - 1)
self.assertGreater(idx[p], idx[p - 1])
self.assertEqual(idx.size, n_peaks)
assert_array_almost_equal(x[idx], numpy.array([1527.3, 1529.77]))
class SimulatedData(unittest.TestCase):
"""Tests with simulated data"""
def setUp(self):
self.near = numpy.array([0, 1, 0, 2, 0, 3, 0, 2, 0, 1, 0])
def aux_test_peaks(self, dtype):
"""(3 peaks + baseline + noise)"""
x = numpy.linspace(0, 100, 1000)
centers = (20, 40, 70)
y = (1000 * (peakutils.gaussian(x, 1, centers[0], 3) +
peakutils.gaussian(x, 2, centers[1], 5) +
peakutils.gaussian(x, 3, centers[2], 1) +
numpy.random.random(x.size) * 0.2)).astype(dtype)
filtered = scipy.signal.savgol_filter(y, 51, 3).astype(dtype)
idx = peakutils.indexes(filtered, thres=0.3, min_dist=100)
peaks = peakutils.interpolate(x, y, idx, width=30)
self.assertEqual(idx.size, len(centers))
self.assertEqual(peaks.size, len(centers))
# interpolation should work!
for i in range(peaks.size):
self.assertAlmostEqual(peaks[i], centers[i], delta=0.5)
def test_peaks(self):
self.aux_test_peaks('float64')
self.aux_test_peaks('float32')
self.aux_test_peaks('int32')
self.assertRaises(ValueError, self.aux_test_peaks, 'uint32')
def test_near_peaks1(self):
out = peakutils.indexes(self.near, thres=0, min_dist=2)
expected = numpy.array([1, 5, 9])
assert_array_almost_equal(out, expected)
def test_near_peaks2(self):
out = peakutils.indexes(self.near, thres=0, min_dist=1)
expected = numpy.array([1, 3, 5, 7, 9])
assert_array_almost_equal(out, expected)
def test_list_peaks(self):
out = peakutils.indexes([1, 2, 1, 3, 5, 7, 4, 1], thres=0, min_dist=1)
expected = numpy.array([1, 5])
assert_array_almost_equal(out, expected)
def test_pandas_series(self):
x = ["a", "b", "c", "d", "e"]
y = [ 0, 2, 0, 3, 0 ]
data = pd.Series(data=y, index=x)
out = peakutils.indexes(data, thres=0, min_dist=1)
expected = numpy.array([1, 3])
assert_array_almost_equal(out, expected)
def test_absolute_threshold(self):
x = [0, 5, 0, 8, 0, 15, 0]
out1 = peakutils.indexes(x, thres=3, thres_abs=True)
assert_array_almost_equal(out1, [1, 3, 5])
out2 = peakutils.indexes(x, thres=5, thres_abs=True)
assert_array_almost_equal(out2, [3, 5])
out3 = peakutils.indexes(x, thres=7, thres_abs=True)
assert_array_almost_equal(out3, [3, 5])
out4 = peakutils.indexes(x, thres=14, thres_abs=True)
assert_array_almost_equal(out4, [5])
out5 = peakutils.indexes(x, thres=15, thres_abs=True)
assert_array_almost_equal(out5, [])
out6 = peakutils.indexes(x, thres=16, thres_abs=True)
assert_array_almost_equal(out6, [])
class Baseline(unittest.TestCase):
"""Tests the conditioning of the lsqreg in the implementation"""
def test_conditioning(self):
data = data = load('exp')
y = data[:, 1]
mult = 1e-6
while mult < 100001:
ny = y * mult
base = peakutils.baseline(ny, 9) / mult
self.assertTrue(0.8 < base.max() < 1.0)
self.assertTrue(-0.1 <= base.min() < 0.1)
mult *= 10
def test_negative(self):
data = np.array([-1, -2, -3, -4, -3, -2, -1] * 10)
base = peakutils.baseline(data)
self.assertEqual(data.shape, base.shape)
class Prepare(unittest.TestCase):
"""Tests the prepare module"""
def test_scale(self):
orig = numpy.array([-2, -1, 0.5, 1, 3])
x1, range_old = peakutils.scale(orig, (-10, 8))
x2, range_new = peakutils.scale(x1, range_old)
assert_array_almost_equal(orig, x2)
self.assertTupleEqual(range_new, (-10, 8))
def test_scale_degenerate(self):
orig = numpy.array([-3, -3, -3])
x1, range_old = peakutils.scale(orig, (5, 7))
x2, range_new = peakutils.scale(x1, range_old)
assert_array_almost_equal(x1, [6, 6, 6])
assert_array_almost_equal(x2, orig)
class Centroid(unittest.TestCase):
"""Tests the centroid implementations."""
def test_centroid(self):
y = np.ones(10)
x = np.arange(10)
self.assertEqual(peakutils.centroid(x, y), 4.5)
def test_centroid2(self):
y = np.ones(3)
x = np.array([0., 1., 9.])
c, v = peakutils.centroid2(y, x)
self.assertEqual(c, 4.5)
class GaussianFit(unittest.TestCase):
""" Tests the Gaussian fit implementation """
def test_gaussian_fit(self):
params = np.array([0.5, 6, 2])
x = np.arange(10)
y = peakutils.gaussian(x, *params)
self.assertAlmostEqual(peakutils.gaussian_fit(x, y), params[1])
res = peakutils.gaussian_fit(x, y, center_only=False)
np.testing.assert_allclose(res, params)
class Plateau(unittest.TestCase):
"""Issue #4"""
def test_plateau1(self):
y = np.zeros(20)
y[1:6] = 1.0
y[8:9] = 2.0
y[11:19] = 3.0
idx = peakutils.indexes(y)
np.testing.assert_array_equal(idx, [3, 8, 14])
def test_plateau2(self):
y = np.zeros(20)
y[0:6] = 1.0
y[8:9] = 2.0
y[11:20] = 3.0
idx = peakutils.indexes(y)
np.testing.assert_array_equal(idx, [8])
# note: there are no peaks in the first and last series as the data
# to the left of 0 and right of 19 is unknown
def test_flat(self):
ra = (0.2, 0.4, 0.6, 0.8, 0.95)
rb = (1, 2, 3, 4, 5, 6)
N = 20
# all equal
for t, m in itertools.product(ra, rb):
y = np.ones(N)
peakutils.indexes(y, thres=t, min_dist=m)
# a single peak
for t, m in itertools.product(ra, rb):
for z in range(m + 1, N - m):
y = np.ones(N)
y[z] = 1e3
p = peakutils.indexes(y, thres=t, min_dist=m)
self.assertEqual(p, np.array([z]))
class Float64(unittest.TestCase):
"""Issue #11 (false alarm)"""
def setUp(self):
self.col = [
u'2161', u'183', u'167', u'270', u'164', u'475', u'327', u'279', u'0',
u'183', u'360', u'81', u'81', u'81', u'81', u'45', u'81', u'0', u'81', u'81'
]
def test_int_high_thres(self):
y = np.atleast_1d(self.col).astype('int')
peaks = peakutils.indexes(y, thres=0.3)
np.testing.assert_array_almost_equal(peaks, [])
def test_float64_high_thres(self):
y = np.atleast_1d(self.col).astype('float64')
peaks = peakutils.indexes(y, thres=0.3)
np.testing.assert_array_almost_equal(peaks, [])
def test_int_low_thres(self):
y = np.atleast_1d(self.col).astype('int')
peaks = peakutils.indexes(y, thres=0.01)
np.testing.assert_array_almost_equal(peaks, [3, 5, 10, 16])
def test_float64_low_thres(self):
y = np.atleast_1d(self.col).astype('float64')
peaks = peakutils.indexes(y, thres=0.01)
np.testing.assert_array_almost_equal(peaks, [3, 5, 10, 16])
class InterpolateExceptions(unittest.TestCase):
""" Issue #14: convert fitting errors to warnings """
def test_interpolate_bounds(self):
x = np.arange(5)
y = np.array([0, 0, 1, 0, 0])
with warnings.catch_warnings(record=True) as record:
for w in range(1, 10):
peakutils.interpolate(x, y, [2], width=w)
self.assertGreater(len(record), 0)
class HighEnvelope(unittest.TestCase):
def test_up_envelope(self):
data = np.array([0, 2, 0, 0, 4, 0, 0, 0, 7, 0, 0, 0, 0, 9, 0, 0, 0, 11, 0, 0, 0, 0, 0, 9, 0,
0, 0, 0, 7, 0, 0, 4, 0, 0, 3, 0, 0, 0, 1])
env = peakutils.envelope(data, 5)
tol = 1.05
for a, b in zip(data, env):
self.assertLess(a, b * tol)
if __name__ == '__main__':
numpy.random.seed(1997)
unittest.main()
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