1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
|
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()
|
