def filter_peaks(self, minscore=0.4):
newpeaks = []
indexes = []
for i, p in enumerate(self.pks.peaks):
if p.dscore > minscore:
newpeaks.append([p.mass, p.height, p.dscore])
indexes.append(i)
newpeaks = np.array(newpeaks)
indexes = np.array(indexes)
if len(newpeaks) > 0:
self.pks = peakstructure.Peaks()
self.pks.add_peaks(newpeaks,
massbins=self.config.massbins,
scores_included=True)
self.pks.default_params(cmap=self.config.peakcmap)
self.data.exgrid = self.data.exgrid[indexes]
self.normalize_exgrid()
ud.peaks_error_FWHM(self.pks, self.data.massdat)
self.peaks_error_replicates(self.pks, self.data.spectra,
self.config)
self.update_history()
self.export_params()
else:
print("No Peaks Found with Scores Above", minscore)
self.pks = peakstructure.Peaks()
def pick_peaks(self):
self.config.write_hdf5()
self.sum_masses()
scores_included = False
try:
if len(self.data.peaks[0]) == 3:
scores_included = True
except:
pass
self.pks = peakstructure.Peaks()
self.pks.add_peaks(self.data.peaks,
massbins=self.config.massbins,
scores_included=scores_included)
self.pks.default_params(cmap=self.config.peakcmap)
ud.peaks_error_FWHM(self.pks, self.data.massdat)
self.peaks_error_replicates(self.pks, self.data.spectra, self.config)
for i, p in enumerate(self.pks.peaks):
p.extracts = self.data.exgrid[i]
self.update_history()
self.export_params()
def initialize(self):
"""
Initialize Config, DataContainer, and Peaks
:return: None
"""
self.config = unidecstructure.UniDecConfig()
self.clear_history()
self.config.initialize_system_paths()
self.reset_config()
self.data = unidecstructure.DataContainer()
self.pks = peakstructure.Peaks()
def pick_peaks(self):
self.config.write_hdf5()
self.sum_masses()
self.pks = peakstructure.Peaks()
self.pks.add_peaks(self.data.peaks, massbins=self.config.massbins)
self.pks.default_params(cmap=self.config.peakcmap)
self.peaks_error_FWHM(self.pks, self.data.massdat)
self.peaks_error_replicates(self.pks, self.data.spectra, self.config)
for i, p in enumerate(self.pks.peaks):
p.extracts = self.data.exgrid[i]
self.update_history()
self.export_params()
def sub_div(self, e=0):
try:
masses = []
for p in self.eng.pks.peaks:
if p.ignore == 0:
masses.append(p.mass)
defaultsub = np.amin(masses)
except:
defaultsub = 44088
defaultdiv = self.eng.config.molig
dlg = miscwindows.DoubleInputDialog(self.view)
dlg.initialize_interface("Subtract and Divide", "Subtract:",
str(defaultsub), "Divide:", str(defaultdiv))
dlg.ShowModal()
try:
sub = float(dlg.value)
div = float(dlg.value2)
except:
print("Error with Subtract and Divide Inputs:", dlg.value,
dlg.value2)
return 0
try:
sd_results = []
message = "Average Masses:\n\n"
outstring = ""
for s in self.eng.data.spectra:
pks = peakstructure.Peaks()
peaks = ud.peakdetect(s.massdat, self.eng.config)
pks.add_peaks(peaks, massbins=self.eng.config.massbins)
sd_result = ud.subtract_and_divide(pks, sub, div)
sd_results.append(sd_result)
outstring += str(sd_result) + "\n"
avg = np.mean(sd_results)
message += outstring
message += "\nOverall Average: " + str(avg)
outstring += "\n" + str(avg)
self.copy_to_clipboard(outstring)
self.warn(message, caption="Subtract and Divide Results")
except:
sd_result = ud.subtract_and_divide(self.eng.pks, sub, div)
outstring = str(sd_result)
message = "Average Mass: " + outstring
self.copy_to_clipboard(outstring)
self.warn(message, caption="Subtract and Divide Results")
def open(self, path):
self.data = MetaDataSet(self)
self.pks = peakstructure.Peaks()
if path is None:
path = self.config.hdf_file
else:
self.config.hdf_file = path
self.config.filename = os.path.split(path)[1]
self.config.outfname = os.path.splitext(self.config.filename)[0]
self.config.outfname = os.path.join("UniDec_Figures_and_Files", self.config.outfname)
dir = os.path.dirname(path)
os.chdir(dir)
dirnew = os.path.split(self.config.outfname)[0]
if not os.path.isdir(dirnew):
os.mkdir(dirnew)
self.config.default_file_names()
self.config.read_hdf5(path)
self.data.import_hdf5(path)
self.update_history()
def run_autocorr(self, e):
"""
Performs autocorrelation using ud.autocorr from unidectools.
Plots the results and adds peaks to listctrl.
:param e: Unused event
:return: None
"""
corr, peaks = ud.autocorr(self.massdat, self.config)
self.plot1.plotrefreshtop(corr[:, 0], corr[:, 1], "Autocorrelation", "Mass Difference", "", "", self.config)
pks2 = peakstructure.Peaks()
pks2.add_peaks(peaks, self.config.massbins)
pks2.default_params()
if pks2.plen > 0:
for p in pks2.peaks:
if p.ignore == 0:
self.plot1.plotadddot(p.mass, p.height, p.color, p.marker)
self.plot1.repaint()
print(peaks)
self.listpanel.list.populate(pks2)
def pick_scanpeaks(self):
self.config.write_hdf5()
self.out = metaunidec_call(self.config, "-scanpeaks")
self.data.import_hdf5()
self.sum_masses()
combined_peaks = self.combine_scanpeaks()
self.pks = peakstructure.Peaks()
self.pks.add_peaks(combined_peaks,
massbins=self.config.massbins * 0.1,
scores_included=True)
self.pks.default_params(cmap=self.config.peakcmap)
self.scanpeaks_extracts()
ud.peaks_error_FWHM(self.pks, self.data.massdat)
self.peaks_error_replicates(self.pks, self.data.spectra, self.config)
self.update_history()
self.export_params()
pass
def clear(self):
self.data = MetaDataSet(self)
self.pks = peakstructure.Peaks()
sub = float(sub)
except:
sub = None
self.sub = sub
div = self.ctldiv.GetValue()
try:
div = float(div)
except:
div = None
self.div = div
self.subdiv()
self.make_plot()
def change_type(self, e=None):
self.replot()
if __name__ == "__main__":
path = "C:\Python\\UniDec3\\unidec_bin\\Example Data\\POPC_Nanodiscs_unidecfiles\\POPC_Nanodiscs_mass.txt"
data = np.loadtxt(path)
pks = peakstructure.Peaks()
peaks = ud.peakdetect(data, threshold=0.1)
pks.add_peaks(peaks)
app = wx.App()
panel = SubDivFrame(None, data, pks)
panel.replot()
app.MainLoop()