def test_find_peaks_window_size(self):
"""
Verify that window_size is passed correctly to private function and
affects the result.
"""
sigmas = [2.0, 2.0]
num_points = 1000
test_data, act_locs = _gen_gaussians_even(sigmas, num_points)
widths = np.arange(0.1, max(sigmas), 0.2)
noise_amp = 0.05
np.random.seed(18181911)
test_data += (np.random.rand(num_points) - 0.5) * (2 * noise_amp)
# Possibly contrived negative region to throw off peak finding
# when window_size is too large
test_data[250:320] -= 1
found_locs = find_peaks_cwt(test_data,
widths,
gap_thresh=2,
min_snr=3,
min_length=None,
window_size=None)
with pytest.raises(AssertionError):
assert found_locs.size == act_locs.size
found_locs = find_peaks_cwt(test_data,
widths,
gap_thresh=2,
min_snr=3,
min_length=None,
window_size=20)
assert found_locs.size == act_locs.size
def test_find_peaks_withnoise(self):
"""
Verify that peak locations are (approximately) found
for a series of gaussians with added noise.
"""
sigmas = [5.0, 3.0, 10.0, 20.0, 10.0, 50.0]
num_points = 500
test_data, act_locs = _gen_gaussians_even(sigmas, num_points)
widths = np.arange(0.1, max(sigmas))
noise_amp = 0.07
np.random.seed(18181911)
test_data += (np.random.rand(num_points) - 0.5) * (2 * noise_amp)
found_locs = find_peaks_cwt(test_data,
widths,
min_length=15,
gap_thresh=1,
min_snr=noise_amp / 5)
np.testing.assert_equal(
len(found_locs), len(act_locs),
'Different number' + 'of peaks found than expected')
diffs = np.abs(found_locs - act_locs)
max_diffs = np.array(sigmas) / 5
np.testing.assert_array_less(
diffs, max_diffs,
'Maximum location differed' + 'by more than %s' % (max_diffs))
def test_find_peaks_with_one_width(self):
"""
Verify that the `width` argument
in `find_peaks_cwt` can be a float
"""
xs = np.arange(0, np.pi, 0.05)
test_data = np.sin(xs)
widths = 1
found_locs = find_peaks_cwt(test_data, widths)
np.testing.assert_equal(found_locs, 32)
def test_find_peaks_nopeak(self):
"""
Verify that no peak is found in
data that's just noise.
"""
noise_amp = 1.0
num_points = 100
np.random.seed(181819141)
test_data = (np.random.rand(num_points) - 0.5)*(2*noise_amp)
widths = np.arange(10, 50)
found_locs = find_peaks_cwt(test_data, widths, min_snr=5, noise_perc=30)
np.testing.assert_equal(len(found_locs), 0)
def test_find_peaks_exact(self):
"""
Generate a series of gaussians and attempt to find the peak locations.
"""
sigmas = [5.0, 3.0, 10.0, 20.0, 10.0, 50.0]
num_points = 500
test_data, act_locs = _gen_gaussians_even(sigmas, num_points)
widths = np.arange(0.1, max(sigmas))
found_locs = find_peaks_cwt(test_data, widths, gap_thresh=2, min_snr=0,
min_length=None)
np.testing.assert_array_equal(found_locs, act_locs,
"Found maximum locations did not equal those expected")
def test_find_peaks_nopeak(self):
"""
Verify that no peak is found in
data that's just noise.
"""
noise_amp = 1.0
num_points = 100
np.random.seed(181819141)
test_data = (np.random.rand(num_points) - 0.5)*(2*noise_amp)
widths = np.arange(10, 50)
found_locs = find_peaks_cwt(test_data, widths, min_snr=5, noise_perc=30)
np.testing.assert_equal(len(found_locs), 0)
def test_find_peaks_exact(self):
"""
Generate a series of gaussians and attempt to find the peak locations.
"""
sigmas = [5.0, 3.0, 10.0, 20.0, 10.0, 50.0]
num_points = 500
test_data, act_locs = _gen_gaussians_even(sigmas, num_points)
widths = np.arange(0.1, max(sigmas))
found_locs = find_peaks_cwt(test_data, widths, gap_thresh=2, min_snr=0,
min_length=None)
np.testing.assert_array_equal(found_locs, act_locs,
"Found maximum locations did not equal those expected")
def test_find_peaks_withnoise(self):
"""
Verify that peak locations are (approximately) found
for a series of gaussians with added noise.
"""
sigmas = [5.0, 3.0, 10.0, 20.0, 10.0, 50.0]
num_points = 500
test_data, act_locs = _gen_gaussians_even(sigmas, num_points)
widths = np.arange(0.1, max(sigmas))
noise_amp = 0.07
np.random.seed(18181911)
test_data += (np.random.rand(num_points) - 0.5)*(2*noise_amp)
found_locs = find_peaks_cwt(test_data, widths, min_length=15,
gap_thresh=1, min_snr=noise_amp / 5)
np.testing.assert_equal(len(found_locs), len(act_locs), 'Different number' +
'of peaks found than expected')
diffs = np.abs(found_locs - act_locs)
max_diffs = np.array(sigmas) / 5
np.testing.assert_array_less(diffs, max_diffs, 'Maximum location differed' +
'by more than %s' % (max_diffs))
def find_peaks_2(s, eta=None, arc=None):
"""
"""
logger.info('using calibration line location algorithm 2')
s = s[:-20] # Crop out the back portion
s -= np.amin(s)
coeffs = np.polyfit(np.arange(len(s)), s, 10)
s_fit = np.polyval(coeffs, np.arange(len(s)))
sp = s - s_fit
if eta is not None or arc is not None:
sp[0:20] = 0. # Set the first few pixels to zero since we don't want false lines
sp -= np.amin(sp)
sp -= np.median(sp)
#print(np.median(sp), 1.1*np.median(sp))
if eta is not None or arc is not None:
#peaks_i = argrelextrema(sp, np.greater, order=2)
#print(peaks_i)
peaks_i = find_peaks_cwt(sp, [4,5,6,7,8])#, min_length=20)#, min_snr=3)
#print('PEAKS', peaks_i)
else:
peaks_i = argrelextrema(sp, np.greater)
peaks_y = sp[peaks_i]
#for p,z in zip(peaks_i[0], peaks_y):
# print(p, z, z/peaks_y.mean())
# big_peaks = peaks_i[0][np.where(peaks_y > 1.7 * peaks_y.mean())]
if eta is not None:
#big_peaks = peaks_i[np.where(peaks_y > 1.1 * sp.mean())]
#big_peaks = peaks_i[np.where(peaks_y > 1.1 * np.median(sp))]
big_peaks = peaks_i[np.where(peaks_y >= 0.15)] # XXX We could change this to use percentiles to estimate the cutoff
#print(big_peaks)
elif arc is not None:
s = s - np.median(s)
peaks_y = s[peaks_i]
big_peaks = peaks_i[np.where(peaks_y >= 0.005)] # XXX We could change this to use percentiles to estimate the cutoff
else:
big_peaks = peaks_i[0][np.where(peaks_y > 1.4 * peaks_y.mean())]
### TESTING
'''
import pylab as pl
pl.figure(figsize=(15, 5))
pl.cla()
if eta is not None or arc is not None: offsetPlot = 0
else: offsetPlot = 20
pl.plot(s + offsetPlot, 'r-')
pl.plot(sp, 'k-')
pl.plot(s_fit + offsetPlot, 'm-', alpha=0.5)
for peak in peaks_i:
if peak == peaks_i[0]: pl.axvline(peak, color='r', ls=':', lw=1.5, label='peaks')
else: pl.axvline(peak, color='r', ls=':', lw=1.5)
#pl.scatter(big_peaks, sp[big_peaks], color='C0')
for peak in big_peaks:
if peak == big_peaks[0]: pl.axvline(peak, color='b', ls='--', lw=0.9, label='big peaks')
else: pl.axvline(peak, color='b', ls='--', lw=0.9)
pl.legend()
pl.show()
#sys.exit()
'''
### TESTING
return(big_peaks)
def test_find_peaks_with_non_default_wavelets(self):
x = gaussian(200, 2)
widths = np.array([1, 2, 3, 4])
a = find_peaks_cwt(x, widths, wavelet=gaussian)
np.testing.assert_equal(np.array([100]), a)
