def param_extr(filename, high_th, low_th, offset):
signal = tp.trace_extr(filename)
time = pu.time_vector(filename)
"""
Max Height
"""
mask_height = ~pu.disc_edges_full(
signal, high_th, low_th
) + pu.disc_peak_full(
signal, high_th, low_th, offset
) # exclude edge traces which contain partial pulses that do not report max height of integer traces
if np.sum(mask_height) == 0:
mask_height = np.ones(len(signal)).astype('bool') #
# plt.plot(time,mask_height*np.max(signal))
height = np.max(signal[mask_height])
"""
RMS
"""
rms = np.sqrt(np.mean(np.square(signal[mask_height])))
"""
Background
"""
bg = tp.find_bg(signal) * 1e3
# height = np.max(signal)
"""
Area within discriminator
"""
mask_area = pu.disc_peak_full(signal, high_th, low_th, offset)
area_win_abs = np.sum(np.abs(signal[mask_area]))
area_win = np.sum(signal[mask_area])
# plt.plot(time,pu.disc_peak_full(signal,high_th,low_th,0)*np.max(signal))
# plt.plot(time,pu.disc_peak_full(signal,high_th,low_th,offset)*np.max(signal))
"""
Simple area above threshold
"""
# area = np.sum(signal[signal>high_th])
"""
Number of edges detected
"""
numedges = np.sum(
mask_area[1:] >
mask_area[:-1]) # if next element > current element, True and count
return np.array(
(filename, numedges, area_win, area_win_abs, height, rms, bg),
dtype=[('filename', 'U256'), ('numedges', 'int8'),
('area_win', 'float64'), ('area_win_abs', 'float64'),
('height', 'float64'), ('rms', 'float64'),
('bg_mv', 'float64')])
def time_offset(time_v, signal, high_th, low_th, offset):
"""
shift a trace so that the steepest point is at time 0
steepest points must exist within discriminator window
"""
# start by finding the max of the derivative
dt = np.diff(time_v)[0]
# derivative of the signal, smoothed
d_signal = savgol_filter(np.diff(signal), 301, 3)
# ax = plt.gca()
# ax2 = ax.twinx()
# ax2.plot(d_signal/np.max(d_signal), color='green')
# use peaks only if they are legitimate edges identified by the set-reset switch
mask = pu.disc_peak_full(signal, high_th, low_th, offset)
# print np.where(mask==True)
# print peakutils.indexes(d_signal, .5, 3000)
# find peaks in the derivative to find the steepest point
idx = np.array([i for i in peakutils.indexes(d_signal, .5, 3000) if mask[i]==True])
# print idx
if len(idx) == 0:
return [np.nan for _ in time_v]
idx_s = np.flipud(idx[d_signal[idx].argsort()])[0]
try:
time_p = peakutils.interpolate(time_v[:-1], d_signal, [idx_s])[0]
except (RuntimeError, ValueError) as e:
time_p = time_v[idx_s]
n_shift = int(time_p / dt)
return pa.shift(signal, - n_shift+int(len(signal)/2))
def fit_shift(time_s, signal, fit_model, high_th, low_th, offset):
"""
fit the trace with a sample pulse and shift it to match the staritngtime
"""
dt = np.diff(time_s)[0]
d_signal = savgol_filter(np.diff(signal), 301, 3)
# ax = plt.gca()
# ax2 = ax.twinx()
# ax2.plot(d_signal/np.max(d_signal), color='green')
# use peaks only if they are legitimate edges identified by the set-reset switch
mask = pu.disc_peak_full(signal, high_th, low_th, offset)
# find peaks in the derivative to find the steepest point
idx = np.array([i for i in peakutils.indexes(d_signal, .5, 3000) if mask[i]==True])
# print(time_s[idx])
# print time_v[left_edges], time_v[idx]
if len(idx) == 0:
return [np.nan for _ in time_s]
idx_s = np.flipud(idx[d_signal[idx].argsort()])
# print(time_s[idx_s[0]])
p = Parameters()
p.add('x_offset', time_s[np.argmax(signal)])
if len(idx_s) > 0:
p.add('x_offset', time_s[idx_s[0]])
p.add('amplitude', 1, vary=1)
result = fit_model.fit(signal,
x=time_s,
params=p,
# weights=1 / 0.001
)
n_shift = int(result.best_values['x_offset'] / dt)
# print(result.fit_report())
# result.plot_fit()
# print n_shift*dt
return pa.shift(signal, -n_shift+int(len(signal)/2))
def fit_two_cw(time, signal, two_pulse_fit, pulse_params, height_th, sigma0):
# signal = signal - np.median(signal[signal<height_th])
(sum_a, sum_mu, sum_b, sum_tau, diff_a, diff_b, diff_tau) = pulse_params
mask = pu.disc_peak_full(signal, height_th, 0, 0)
# signal = signal - trcp.find_bg(signal[~mask])
# mask = pu.disc_peak_full(signal,height_th,0,0)
# plt.plot(time,mask*np.max(signal),linestyle='--')
left_edges = find_crossing(mask, 0.5, 'left')
right_edges = find_crossing(mask, 0.5, 'right')
one_x_offset_init_min = time[left_edges[0]]
# one_x_offset_init_max = time[right_edges[0]]
one_x_offset_init_max = time[
left_edges[0] + np.argmax(signal[left_edges[0]:right_edges[0]])]
if len(left_edges) == 1:
two_x_offset_init_min = one_x_offset_init_min
two_x_offset_init_max = time[right_edges[0]]
one_x_offset_init = one_x_offset_init_min
two_x_offset_init = two_x_offset_init_max - 0.5e-6
else:
two_x_offset_init_min = time[left_edges[1]]
two_x_offset_init_max = time[
left_edges[1] + np.argmax(signal[left_edges[1]:right_edges[1]])]
one_x_offset_init = (one_x_offset_init_min + one_x_offset_init_max) / 2
two_x_offset_init = (two_x_offset_init_min + two_x_offset_init_max) / 2
one_amplitude_init = sum_mu / 2
two_amplitude_init = sum_mu / 2
"""Compulsory LMFIT for both overlapping and non-overlapping cases"""
p = Parameters()
p.add('one_x_offset',
one_x_offset_init,
min=one_x_offset_init_min,
max=one_x_offset_init_max,
vary=True)
p.add('two_x_offset',
two_x_offset_init,
min=two_x_offset_init_min,
max=two_x_offset_init_max,
vary=True)
p.add(
'sum_amplitudes',
(one_amplitude_init + two_amplitude_init) *
1.01, #not sure why, but some assymetry is required... sum_mu can't be avg of sum_a and sum_b
min=sum_a,
max=sum_b,
vary=True)
p.add('diff_amplitudes',
one_amplitude_init - two_amplitude_init,
min=diff_a,
max=diff_b,
vary=True)
p.add(
'one_amplitude',
one_amplitude_init,
expr='(sum_amplitudes + diff_amplitudes)/2',
vary=True
) #warning: max >= 2 causes n=2 & noise to be fitted on a tau~0 2ph trace.
p.add(
'two_amplitude',
two_amplitude_init,
expr='(sum_amplitudes - diff_amplitudes)/2',
vary=True
) #warning: max >= 2 causes n=2 & noise to be fitted on a tau~0 2ph trace.
result = two_pulse_fit.fit(np.array(signal),
x=np.array(time),
params=p,
weights=1 / sigma0,
method='powell')
return result