def test_find_peaks_empty(self):
x = []
hp, sp = processing.find_peaks(x)
assert hp.shape == (0,)
assert sp.shape == (0,)
def test_find_peaks(self):
x = [0, 2, 1, 0, -10, -15, -15, -15, 9, 8, 0, 0, 1, 2, 10]
hp, sp = processing.find_peaks(x)
assert np.array_equal(hp, [1, 8])
assert np.array_equal(sp, [6, 10])
def test_find_peaks_empty(self):
x = []
hp, sp = processing.find_peaks(x)
assert hp.shape == (0,)
assert sp.shape == (0,)
def expand_ecg(datas, type=1, gain=1.1, bate=0.9) -> list:
datas_expand = datas.copy()
if type == 0: # R-
r_peak_index = detext_xqrs(datas)
i_r = r_peak_index + 10
if i_r > 179:
i_r = 179
for i in range(r_peak_index - 10, i_r, 1):
# for i in range(len(datas)):
datas_expand[i] *= bate
return datas_expand
if type == 1: # R+
# r_peak_index = detext_xqrs(datas)
# for i in range(r_peak_index - 5, r_peak_index + 5, 1):
for i in range(len(datas)):
datas_expand[i] *= gain
return datas_expand
elif type == 2: # R scale
expand_width_percent = 0.15
expand_width_step = int(math.floor(1.0 / expand_width_percent))
for i in range(75 + 20, 75 - 20, -expand_width_step):
sub_datas = datas_expand[i - 1:i + 1]
# Max
# s = max(sub_datas)
# Mean
s = 0.
for sub_d in sub_datas:
s += sub_d
s = s / len(sub_datas)
datas_expand.insert(i, s)
iC = int((20 + 25.0) / expand_width_step / 2.0) - 1
# datas_expand.extend([0 for j in range(iC)])
# r_peak_index = detext_xqrs(datas_expand)
r_peak_index = 75 + iC
# if r_peak_index < iC:
# r_peak_index = iC
datas_expand = datas_expand[r_peak_index - 75:r_peak_index + 105]
return datas_expand
elif type == 3:
datas_expand = expand_ecg(datas_expand, 1)
datas_expand = expand_ecg(datas_expand, 2)
return datas_expand
elif type == 4:
datas_expand = expand_ecg(datas_expand, 0)
datas_expand = expand_ecg(datas_expand, 2)
return datas_expand
elif type == 5: # --
return expand_ecg(datas_expand, 0, bate=0.82)
elif type == 6: # ++
return expand_ecg(datas_expand, 1, gain=1.18)
elif type == 7: # -+All Peak Up/Down
peaks, _ = wfp.find_peaks(datas_expand)
peaks = peaks.tolist()
n_peak = []
last_peak_value = 0
for k in range(len(peaks) - 2):
if abs(peaks[k] - last_peak_value) > 10:
n_peak.append(peaks[k])
last_peak_value = peaks[k]
for p in n_peak:
p_l = p - 5
if p_l < 0:
p_l = 0
p_r = p + 5
if p_r > 179:
p_r = 179
for i in range(p_l, p_r):
datas_expand[i] *= gain
# if datas_expand[i] > 0:
# datas_expand[i] *= gain
# else:
# datas_expand[i] *= bate
return datas_expand
elif type == 8: # ++--All Peak Random 不好
peaks, _ = wfp.find_peaks(datas_expand)
peaks = peaks.tolist()
n_peak = []
last_peak_value = 0
for k in range(len(peaks) - 2):
if abs(peaks[k] - last_peak_value) > 10:
n_peak.append(peaks[k])
last_peak_value = peaks[k]
for p in n_peak:
p_l = p - 5
if p_l < 0:
p_l = 0
p_r = p + 5
if p_r > 179:
p_r = 179
r_i = np.random.randint(2)
for i in range(p_l, p_r):
if r_i == 0:
datas_expand[i] *= gain
else:
datas_expand[i] *= bate
return datas_expand
elif type == 9: # ++--All Peak Up +
peaks, _ = wfp.find_peaks(datas_expand)
peaks = peaks.tolist()
n_peak = []
last_peak_value = 0
for k in range(len(peaks) - 2):
if abs(peaks[k] - last_peak_value) > 10:
n_peak.append(peaks[k])
last_peak_value = peaks[k]
for p in n_peak:
p_l = p - 5
if p_l < 0:
p_l = 0
p_r = p + 5
if p_r > 179:
p_r = 179
for i in range(p_l, p_r):
datas_expand[i] *= 1.15
return datas_expand
else:
return datas
def test_find_peaks(self):
x = [0, 2, 1, 0, -10, -15, -15, -15, 9, 8, 0, 0, 1, 2, 10]
hp, sp = processing.find_peaks(x)
assert np.array_equal(hp, [1, 8])
assert np.array_equal(sp, [6, 10])