def process_peaks(self, mph=0.0, mpd=0, startx=0.0, endx=0.0):
if mph > 0:
self.mph = mph
if mpd > 0:
self.mpd = mpd
x = self.mass
y = self.spectrum
p = Peaks()
ref = startx + (abs(endx - startx) / 2)
delta = 1.0
mph, mpd, mask = p.prepare_detect(ref, delta, x, y, startx, endx)
# Detect peak on rising edge
edge = 'rising'
# Detect peak greater than threshold
threshold = 0.0
# Don't use default plot
ind = p.detect_peaks(y[mask], self.mph, self.mpd, threshold, edge)
# print("mass =", x[mask][ind])
# print("inten=", y[mask][ind])
self.mph = mph
self.mpd = mpd
self.mask = mask
self.ind = ind
def process_peaks(self, mph=0.0, mpd=0, startx=0.0, endx=0.0):
if mph > 0:
self.mph = mph
if mpd > 0:
self.mpd = mpd
x = self.mass
y = self.spectrum
p = Peaks()
ref = startx + (abs(endx - startx) / 2)
delta = 1.0
mph, mpd, mask = p.prepare_detect(ref, delta, x, y, startx, endx)
# Detect peak on rising edge
edge = 'rising'
# Detect peak greater than threshold
threshold = 0.0
# Don't use default plot
ind = p.detect_peaks(y[mask], self.mph, self.mpd, threshold, edge)
# print("mass =", x[mask][ind])
# print("inten=", y[mask][ind])
self.mph = mph
self.mpd = mpd
self.mask = mask
self.ind = ind
def automatic_fill(self):
log.debug("event from %s", self.sender())
if len(self.mass_list) == 0:
return
# if self.ana.pip.signal is None:
# return
x = self.ana.pip.mass
y = self.ana.pip.spectrum
self.acc_event()
p = Peaks()
dict_peak = p.masstab_peaks(x, y, self.mass_list, self.acc)
text = str(self.short_name).ljust(21)
for mass in self.mass_list:
text = text + "{:.3f}".format(float(dict_peak[mass])).ljust(8)
self.ui.plainTextEdit_Viewer.appendPlainText(text)
def automatic_fill(self):
log.debug("event from %s", self.sender())
if len(self.mass_list) == 0:
return
# if self.ana.pip.signal is None:
# return
x = self.ana.pip.mass
y = self.ana.pip.spectrum
self.acc_event()
p = Peaks()
dict_m, dict_i = p.masstab_peaks(x, y, self.mass_list, self.acc)
text = str(self.short_name).ljust(24)
for mass in self.mass_list:
text = text + \
"{:.4f}".format(float(dict_m[mass])).ljust(9) + \
"{:.3f}".format(float(dict_i[mass])).ljust(9)
self.ui.plainTextEdit_Viewer.appendPlainText(text)
def process_peaks(self, xin, yin, mph=0.0, mpd=0, x1=0.0, x2=0.0):
"""
Within a range of mass [x1, x2], get indices of maximum intensity (ind)
with a peak height and a peak distance provided as input params (mph, mpd)
suggested values are returned into mph_o, mpd_o, if needed
==> caution ! indices are from x[mask], not full x array
"""
x = np.asarray(xin)
y = np.asarray(yin)
p = Peaks()
mph_o, mpd_o, mask, ind = p.get_peaks(x, y, x1, x2, mph, mpd)
xx = x[mask]
yy = y[mask]
return mph_o, mpd_o, xx, yy, xx[ind], yy[ind]
pip = Pipeline(filename)
# Put your own settings here: start signal, end signal and Hanning
start = pip.start
start = 10000
end = pip.end
end = 1010000
hann = False
pip.process_signal(start, end, hann, False, False, False)
pip.process_spectrum(factor=1000.0, ref_mass=300.0939, cyclo_freq=255.692e3,
mag_freq=0.001e3)
x = np.asarray(pip.mass)
y = np.asarray(pip.spectrum)
# Peak search
p = Peaks()
dict_m, dict_i = p.masstab_peaks(x, y, mass_list, acc)
short_name = os.path.basename(filename)
text = text + "\n" + str(short_name).ljust(24)
for mass in mass_list:
text = text + \
"{:.4f}".format(float(dict_m[mass])).ljust(9) + \
"{:.3f}".format(float(dict_i[mass])).ljust(9)
# Write result into file: this is the same masstab.txt file as within sofa
with open(out_filename, mode='w', encoding='utf_8') as file:
file.write(text)
# debug
print(text)
