<reponame>anamaria-uofg/mass-spec-utils
# spectrum.py
# classes / methods for handling spectra

import math

class Spectrum(object):
    def __init__(self,precursor_mz,peaks):
        self.peaks = peaks
        self.peaks.sort(key = lambda x: x[0])
        self.precursor_mz = precursor_mz
        self._sqrt_normalise()
        self.n_peaks = len(self.peaks)
    
    def __str__(self):
        return str(self.precursor_mz)

    def __repr__(self):
        return self.__str__()

    def __lt__(self,other):
        return(self.precursor_mz < other.precursor_mz)


    def _sqrt_normalise(self):
        temp = []
        total = 0.0
        for mz,intensity in self.peaks:
            temp.append((mz,math.sqrt(intensity)))
            total += intensity
        norm_facc = math.sqrt(total)
        self.normalised_peaks = []
        for mz,intensity in temp:
            self.normalised_peaks.append((mz,intensity/norm_facc))


class SpectralRecord(Spectrum):
    def __init__(self,precursor_mz,peaks,metadata,original_file,spectrum_id):
        super().__init__(precursor_mz,peaks)
        self.spectrum_id = spectrum_id
        self.metadata = metadata
        self.original_file = original_file        

    def __str__(self):
        try:
            if 'names' in self.metadata:
                names = self.metadata['names'][0]
            else:
                names = ""
            return ", ".join([self.spectrum_id,names,str(self.precursor_mz),self.original_file])
        except:
            return self.spectrum_id


# spectrum.py
# classes / methods for handling spectra

import math

class Spectrum(object):
    def __init__(self,precursor_mz,peaks):
        self.peaks = peaks
        self.peaks.sort(key = lambda x: x[0])
        self.precursor_mz = precursor_mz
        self._sqrt_normalise()
        self.n_peaks = len(self.peaks)
    
    def __str__(self):
        return str(self.precursor_mz)

    def __repr__(self):
        return self.__str__()

    def __lt__(self,other):
        return(self.precursor_mz < other.precursor_mz)


    def _sqrt_normalise(self):
        temp = []
        total = 0.0
        for mz,intensity in self.peaks:
            temp.append((mz,math.sqrt(intensity)))
            total += intensity
        norm_facc = math.sqrt(total)
        self.normalised_peaks = []
        for mz,intensity in temp:
            self.normalised_peaks.append((mz,intensity/norm_facc))


class SpectralRecord(Spectrum):
    def __init__(self,precursor_mz,peaks,metadata,original_file,spectrum_id):
        super().__init__(precursor_mz,peaks)
        self.spectrum_id = spectrum_id
        self.metadata = metadata
        self.original_file = original_file        

    def __str__(self):
        try:
            if 'names' in self.metadata:
                names = self.metadata['names'][0]
            else:
                names = ""
            return ", ".join([self.spectrum_id,names,str(self.precursor_mz),self.original_file])
        except:
            return self.spectrum_id
# spectrum.py # classes / methods for handling spectra