def generated_spectrum_vector(self,
peptide=None,
attenuation_ratio=0.0,
tolerance=0.5,
bin_size=1):
peaks_to_vectorize = self.peaks
max_mass = 1500
if peptide != None:
charge_set = range(1, self.charge + 1)
theoretical_peaks = ming_psm_library.create_theoretical_peak_map(
self.peptide,
["b", "y", "b-H2O", "b-NH3", "y-H2O", "y-NH3", "a"],
charge_set=charge_set)
annotated_peaks, unannotated_peaks = ming_psm_library.extract_annotated_peaks(
theoretical_peaks, self.peaks, tolerance)
new_peaks = annotated_peaks
if attenuation_ratio > 0:
for unannotated_peak in unannotated_peaks:
unannotated_peak[1] *= attenuation_ratio
new_peaks.append(unannotated_peak)
peaks_to_vectorize = sorted(new_peaks, key=lambda peak: peak[0])
#Doing
peak_vector = ming_numerical_utilities.vectorize_peaks(
self.peaks, max_mass, bin_size)
return peak_vector
def attentuate_unannotated_peaks(self, attenuation_ratio=0.0, tolerance=0.5):
charge_set = range(1, self.charge + 1)
theoretical_peaks = ming_psm_library.create_theoretical_peak_map(self.peptide, ["b", "y", "b-H2O", "b-NH3", "y-H2O", "y-NH3", "a"], charge_set=charge_set)
annotated_peaks, unannotated_peaks = ming_psm_library.extract_annotated_peaks(theoretical_peaks, self.peaks, tolerance)
new_peaks = annotated_peaks
if attenuation_ratio > 0:
for unannotated_peak in unannotated_peaks:
unannotated_peak[1] *= attenuation_ratio
new_peaks.append(unannotated_peak)
new_peaks = sorted(new_peaks, key=lambda peak: peak[0])
self.peaks = new_peaks
def attenuate_unannotated_peaks(peaks, max_charge, tolerance, peptide, attenuation_ratio=0):
charge_set = range(1, max_charge)
theoretical_peaks = ming_psm_library.create_theoretical_peak_map(peptide, ["b", "y", "b-H2O", "b-NH3", "y-H2O", "y-NH3", "a"], charge_set=charge_set)
annotated_peaks, unannotated_peaks = ming_psm_library.extract_annotated_peaks(theoretical_peaks, peaks, tolerance)
new_peaks = annotated_peaks
if attenuation_ratio > 0:
for unannotated_peak in unannotated_peaks:
unannotated_peak[1] *= attenuation_ratio
new_peaks.append(unannotated_peak)
new_peaks = sorted(new_peaks, key=lambda peak: peak[0])
return new_peaks
def attentuate_unannotated_peaks(self, attenuation_ratio=0.0, tolerance=0.5):
charge_set = range(1, self.charge + 1)
theoretical_peaks = ming_psm_library.create_theoretical_peak_map(self.peptide, ["b", "y", "b-H2O", "b-NH3", "y-H2O", "y-NH3", "a"], charge_set=charge_set)
annotated_peaks, unannotated_peaks = ming_psm_library.extract_annotated_peaks(theoretical_peaks, self.peaks, tolerance)
new_peaks = annotated_peaks
if attenuation_ratio > 0:
for unannotated_peak in unannotated_peaks:
unannotated_peak[1] *= attenuation_ratio
new_peaks.append(unannotated_peak)
new_peaks = sorted(new_peaks, key=lambda peak: peak[0])
self.peaks = new_peaks
def attenuate_unannotated_peaks(peaks, max_charge, tolerance, peptide, attenuation_ratio=0):
charge_set = range(1, max_charge)
theoretical_peaks = ming_psm_library.create_theoretical_peak_map(peptide, ["b", "y", "b-H2O", "b-NH3", "y-H2O", "y-NH3", "a"], charge_set=charge_set)
annotated_peaks, unannotated_peaks = ming_psm_library.extract_annotated_peaks(theoretical_peaks, peaks, tolerance)
new_peaks = annotated_peaks
if attenuation_ratio > 0:
for unannotated_peak in unannotated_peaks:
unannotated_peak[1] *= attenuation_ratio
new_peaks.append(unannotated_peak)
new_peaks = sorted(new_peaks, key=lambda peak: peak[0])
return new_peaks
def generated_spectrum_vector(self, peptide=None, attenuation_ratio=0.0, tolerance=0.5, bin_size=1):
peaks_to_vectorize = self.peaks
max_mass = 1500
if peptide != None:
charge_set = range(1, self.charge + 1)
theoretical_peaks = ming_psm_library.create_theoretical_peak_map(self.peptide, ["b", "y", "b-H2O", "b-NH3", "y-H2O", "y-NH3", "a"], charge_set=charge_set)
annotated_peaks, unannotated_peaks = ming_psm_library.extract_annotated_peaks(theoretical_peaks, self.peaks, tolerance)
new_peaks = annotated_peaks
if attenuation_ratio > 0:
for unannotated_peak in unannotated_peaks:
unannotated_peak[1] *= attenuation_ratio
new_peaks.append(unannotated_peak)
peaks_to_vectorize = sorted(new_peaks, key=lambda peak: peak[0])
#Doing
peak_vector = ming_numerical_utilities.vectorize_peaks(self.peaks, max_mass, bin_size)
return peak_vector
