def test_peak_fitting(self):
# first, a simple case
params, residual = peaks.findAllPeaks(
self.x,
self.one_gauss,
)
np.testing.assert_allclose(params,
self.one_gauss_params,
atol=self.std / 2)
params, residual = peaks.findAllPeaks(
self.x, self.one_gauss, peak_find_method=PEAK_FINDING_DERIVATIVE)
np.testing.assert_allclose(params,
self.one_gauss_params,
atol=self.std / 2)
params, residual = peaks.findAllPeaks(self.x, self.two_gauss)
np.testing.assert_allclose(params,
self.two_gauss_params,
atol=self.std / 2)
params, residual = peaks.findAllPeaks(
self.x, self.two_gauss, peak_find_method=PEAK_FINDING_DERIVATIVE)
np.testing.assert_allclose(params,
self.two_gauss_params,
atol=self.std / 2)
params, residual = peaks.findAllPeaks(self.x,
self.noisy_two_gauss,
rt_peak=None,
filter=True,
max_peaks=30,
bigauss_fit=True,
snr=1)
np.testing.assert_allclose(params[1::4],
self.two_gauss_params[1::3],
atol=self.std / 2)
def test_segmenty_negatives(self):
# Regression where a mostly positive dataset with negatives led to -inf values in the data array
# due to np.max(segment_y) being 0 since all data was negative
with open(os.path.join(self.data_dir, "peak_data.pickle"),
"rb") as peak_file:
data = pickle.load(peak_file, encoding="latin1")
x, y = data["invalid_operands"]
params, res = peaks.findAllPeaks(
x,
y,
max_peaks=-1,
bigauss_fit=True,
peak_find_method=PEAK_FINDING_DERIVATIVE,
)
means = params[1::4]
desired = np.array([
0.13515435795212014,
0.33,
1.474992882679938,
1.799090776628427,
2.1804381077669395,
2.6350000000000002,
3.227084689771589,
3.617021549048893,
4.903333333333333,
5.296162908137783,
5.8366172292356175,
])
np.testing.assert_allclose(means, desired=desired, atol=0.1)
def test_experimental(self):
# Experimental data
x, y = self.peak_data["offset_fit"]
params, residual = peaks.findAllPeaks(x,
y,
bigauss_fit=True,
filter=True)
np.testing.assert_allclose(
params,
np.array([
13219262.587656807,
46.821340819991505,
0.06523272363478014,
0.18374422913588656,
3347200.309180678,
47.497,
0.6166821402545103,
0.3817876338966981,
1880722.1582992678,
48.14756707645761,
0.17537885391522443,
0.4846763157315077,
1473766.5256005626,
49.52607160264086,
0.020199999999999108,
0.22250905781532157,
]),
# The rtol is 1e-7, so we really don't care about the atol
atol=20,
)
def test_max_peaks_with_rt_peak_regression(self):
with open(os.path.join(self.data_dir, 'peak_data.pickle'), 'rb') as peak_file:
data = pickle.load(peak_file, encoding='latin1') if six.PY3 else pickle.load(peak_file)
x, y = data['max_peaks_rt-peak-regression']
params, residual = peaks.findAllPeaks(x, y, max_peaks=1, rt_peak=360, fit_mode=PEAK_FIT_MODE_FAST)
np.testing.assert_allclose(params[1], desired=365.78, atol=0.1)
def test_peak_fitting(self):
# first, a simple case
params, residual = peaks.findAllPeaks(self.x, self.one_gauss,)
np.testing.assert_allclose(params, self.one_gauss_params, atol=self.std/2)
params, residual = peaks.findAllPeaks(self.x, self.two_gauss)
# print(self.two_gauss, ',',peaks.gauss_ndim(self.x, params))
np.testing.assert_allclose(params, self.two_gauss_params, atol=self.std/2)
failures = 0
for i in range(10):
self.noisy_two_gauss = self.two_gauss + np.random.normal(0, 0.05, size=len(self.two_gauss))
params, residual = peaks.findAllPeaks(self.x, self.noisy_two_gauss, rt_peak=-1, filter=True, max_peaks=30, bigauss_fit=True)
# we compare just the means here because the amplitude and variance can change too much due to the noise for reliable testing, but manual
# inspect reveals the fits to be accurate
try:
np.testing.assert_allclose(params[1::4], self.two_gauss_params[1::3], atol=self.std/2)
except AssertionError:
failures += 1
self.assertLess(failures, 3)
def test_max_peaks(self):
# Regression where relative-max is reporting 2 peaks when max_peaks is set to 1. This occurred
# because we enforced max_peaks for each peak width when using the relative-max setting. Thus,
# the max peak for each peak width was combined to the final peak report. The update was to
# pick the peak_width with the lowest BIC.
with open(os.path.join(self.data_dir, 'peak_data.pickle'), 'rb') as peak_file:
data = pickle.load(peak_file, encoding='latin1') if six.PY3 else pickle.load(peak_file)
x, y = data['max_peaks_relative-max']
params, residual = peaks.findAllPeaks(x, y, max_peaks=1, peak_find_method=PEAK_FINDING_REL_MAX, fit_mode=PEAK_FIT_MODE_FAST)
self.assertEqual(len(params), 3)
def test_max_peaks_with_rt_peak_regression(self):
with open(os.path.join(self.data_dir, "peak_data.pickle"),
"rb") as peak_file:
data = pickle.load(peak_file, encoding="latin1")
x, y = data["max_peaks_rt-peak-regression"]
params, residual = peaks.findAllPeaks(x,
y,
max_peaks=1,
rt_peak=360,
fit_mode=PEAK_FIT_MODE_FAST)
np.testing.assert_allclose(params[1], desired=365.78, atol=0.1)
def test_fit_baseline_derivative(self):
with open(os.path.join(self.data_dir, "peak_data.pickle"),
"rb") as peak_file:
data = pickle.load(peak_file, encoding="latin1")
x, y = data["max_peaks_rt-peak-regression"]
params, residual = peaks.findAllPeaks(
x,
y,
max_peaks=1,
fit_baseline=True,
rt_peak=328,
peak_find_method="derivative",
fit_mode=PEAK_FIT_MODE_FAST,
)
np.testing.assert_allclose(params[:3],
desired=np.array([1320.60, 330.15, 4.22]),
atol=10)
def test_baseline_correction_derivative(self):
with open(os.path.join(self.data_dir, 'peak_data.pickle'), 'rb') as peak_file:
data = pickle.load(peak_file, encoding='latin1') if six.PY3 else pickle.load(peak_file)
x, y = data['max_peaks_rt-peak-regression']
params, residual = peaks.findAllPeaks(
x,
y,
max_peaks=1,
baseline_correction=True,
rt_peak=328,
peak_find_method='derivative',
fit_mode=PEAK_FIT_MODE_FAST,
)
np.testing.assert_allclose(
params[:3],
desired=np.array([1320.60, 330.15, 4.22]),
atol=10
)
def test_returns_with_bad_data(self):
params, residual = peaks.findAllPeaks(np.array([1, 2, 3, 4, 5, 6]),
np.array([0, 0, 0, 0, 0, 0]))
self.assertEqual(len(params), 0)
self.assertEqual(residual, np.inf)