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)
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)
def find_peaks(self):
width = self.peak_minimum_sample_distance
indices = peakutils.indexes(self.filtered_data, min_dist=width)
final_idxs = []
fits = []
for idx in indices:
w = width
if idx - width < 0:
w = idx
if idx + w > len(self.t):
w = len(self.t) - idx
ti = np.array(self.t)[idx - w:idx + w]
datai = np.array(self.filtered_data)[idx - w:idx + w]
try:
params = peakutils.gaussian_fit(ti, datai, center_only=False)
y = [peakutils.gaussian(x, *params) for x in ti]
# ax.plot(ti, y)
ssr = np.sum(np.power(np.subtract(y, datai), 2.0))
sst = np.sum(np.power(np.subtract(y, datai), 2.0))
r2 = 1 - (ssr / sst)
fits.append(r2)
if params[2] < self.gaussian_cutoff:
final_idxs.append(idx)
except RuntimeError:
pass
return final_idxs, fits
def find_peaks_simplified(data, t, width=5, gaussian_cutoff=10.0):
indices = peakutils.indexes(data, min_dist=width)
final_idxs = []
fits = []
for idx in indices:
w = width
if idx - width < 0:
w = idx
if idx + w > len(t):
w = len(t) - idx
ti = np.array(t)[idx - w:idx + w]
datai = np.array(data)[idx - w:idx + w]
try:
params = peakutils.gaussian_fit(ti, datai, center_only=False)
y = [peakutils.gaussian(x, *params) for x in ti]
# ax.plot(ti, y)
ssr = np.sum(np.power(np.subtract(y, datai), 2.0))
sst = np.sum(np.power(np.subtract(y, datai), 2.0))
r2 = 1 - (ssr / sst)
fits.append(r2)
if params[2] < gaussian_cutoff:
final_idxs.append(idx)
except RuntimeError:
pass
return final_idxs, fits
def find_peaks(filename, ax, fs):
data_dir = r"C:\Users\kevin\Desktop\Active Projects\Video Magnification Videos\\"
t, data = np.transpose(np.load(data_dir + filename))
ax.plot(t, data)
width = 5
order = 3
cutoff = 1.0
data = butter_lowpass_filter(data, cutoff, fs, order)
indices = peakutils.indexes(data, min_dist=width)
peak_ts = np.take(t, indices)
peak_ds = np.take(data, indices)
ax.plot(t, data)
ax.scatter(peak_ts, peak_ds, c='r', alpha=0.5)
final_idxs = []
post_gaussian = []
for idx in indices:
w = width
if idx - width < 0:
w = idx
if idx + w > len(t):
w = len(t) - idx
ti = t[idx - w:idx + w]
datai = data[idx - w:idx + w]
# ax.plot(ti, datai)
try:
params = peakutils.gaussian_fit(ti, datai, center_only=False)
y = [peakutils.gaussian(x, *params) for x in ti]
# ax.plot(ti, y)
ssr = np.sum(np.power(np.subtract(y, datai), 2.0))
sst = np.sum(np.power(np.subtract(y, datai), 2.0))
r2 = 1 - (ssr / sst)
post_gaussian.append(idx)
if params[2] < 10.0:
final_idxs.append(idx)
print('idx={0}, r2={1}, params={2}'.format(idx, r2, params))
except RuntimeError:
pass
peak_ts = np.take(t, post_gaussian)
peak_ds = np.take(data, post_gaussian)
ax.scatter(peak_ts, peak_ds, c='g', alpha=0.75)
peak_ts = np.take(t, final_idxs)
peak_ds = np.take(data, final_idxs)
ax.scatter(peak_ts, peak_ds, c='b', alpha=1.0)
for i, txt in enumerate(final_idxs):
ax.annotate(str(txt), (peak_ts[i], peak_ds[i]))
def area_gauss_fit_pk(self, x, y):
dataset = pk.gaussian_fit(x,y,center_only=False)
return dataset