pyMS package

Subpackages

• pyMS.pyisocalc package
  • Subpackages
    • pyMS.pyisocalc.canopy package
      • Submodules
      • pyMS.pyisocalc.canopy.sum_formula module
      • pyMS.pyisocalc.canopy.sum_formula_actions module
      • Module contents
  • Submodules
  • pyMS.pyisocalc.periodic_table module
  • pyMS.pyisocalc.pyisocalc module
  • pyMS.pyisocalc.tools module
  • Module contents

Submodules

pyMS.MSdataset module

class pyMS.MSdataset.MSdataset

add_spectrum(profile_mzs=[], profile_intensities=[], centroids_mz=[], centroid_intensity=[], index=[])

data_summary()

get_spectrum(index)

pyMS.centroid_detection module

pyMS.centroid_detection.gradient(mzs, intensities, **opt_args)

pyMS.centroid_detection.pick_max_(mzs, intensities, mzs_list, intensities_list, indices_list, weighted_bins)

pyMS.mass_spectrum module

class pyMS.mass_spectrum.MSn_spectrum(ms_level='')

Bases: pyMS.mass_spectrum.MassSpectrum

a data container for fragmentation spectrum

add_transition(transitions)

class pyMS.mass_spectrum.MassSpectrum(profile_spec=[], centroid_spec=[])

a data container for a single mass spectrum includes methods for signal processing

add_centroids(mz_list, intensity_list)

add_spectrum(mzs, intensities)

get_spectrum(source='profile')

normalise_spectrum(method='tic', method_args={})

smooth_spectrum(method='sg_smooth', method_args={})

pyMS.mass_spectrum.mass_spectrum

alias of MassSpectrum

pyMS.normalisation module

pyMS.normalisation.apply_normalisation(counts, type_str='')

helper function to apply a normalisation function (with some input testing etc) :param counts: numpy array of values to normalise :param type_str: normalisation type to apply (name) :return: numpy array of normalised counts

pyMS.normalisation.check_zeros(counts)

helper function to check if vector is all zero :param counts: :return: bool

pyMS.normalisation.mad(counts)

normalisation function, divides each intensity by the median-absolute-deviation of all intensities :param counts: numpy array :return:counts normalised: numpy array

pyMS.normalisation.none(counts)

does nothing, just returns input. is a dummy for programmatic case where a function must be supplied :param counts: numpy array :return: counts:

pyMS.normalisation.rms(counts)

normalisation function, divides each intensity by the root-mean-square of all intensities :param counts: numpy array :return:counts normalised: numpy array

pyMS.normalisation.shift_and_scale(counts, scale=1.0, shift=0.0)

applys the generic scaling a shifting operation :param counts: numpy array :param scale: float :param shift: float :return: numpy array of scaled and shifted values

pyMS.normalisation.sqrt(counts)

normalisation function, returns the square root of intensities :param counts: numpy array :return:counts normalised: numpy array

pyMS.normalisation.tic(counts)

normalisation function, divides each intensity by the sum of all intensities (each spectrum sums to 1) :param counts: numpy array :return:counts normalised: numpy array

pyMS.smoothing module

pyMS.smoothing.apodization(mzs, intensities, w_size=10)

apodization with slepian window :param mzs: numpy array numpy array of mz values :param counts: numpy array numpy array of values to smooth :param w_size: int window size :return: mzs: numpy array :return: counts: numpy array

pyMS.smoothing.apply_smoothing(mzs, counts, type_str='', method_args={})

helper function to apply a smoothing function (with some input testing etc) :param counts: numpy array of values to smooth :param type_str: smooting type to apply (name) :return: tuple (mzs: numpy array, counts: numpy array)

pyMS.smoothing.fast_change(mzs, intensities, diff_thresh=0.01)

remove high frequency noise from the data :param mzs: numpy array numpy array of mz values :param counts: numpy array numpy array of values to smooth :param diff_thresh: float numeric change to remove :return: mzs: numpy array :return: counts: numpy array

pyMS.smoothing.median(mzs, intensities, w_size=3)

apply median filter :param mzs: numpy array numpy array of mz values :param counts: numpy array numpy array of values to smooth :param w_size: int window size :return: mzs: numpy array :return: counts: numpy array

pyMS.smoothing.nosmooth(mzs, intensities)

does nothing, just returns input. is a dummy for programmatic case where a function must be supplied :param counts: numpy array :return: mzs: numpy array :return: counts: numpy array

pyMS.smoothing.rebin(mzs, intensities, delta_mz=0.1)

rebin spectrum :param mzs: numpy array numpy array of mz values :param counts: numpy array numpy array of values to smooth :param delta_mz: float, new mz bin width (constant across mz axis) :return: mzs: numpy array :return: counts: numpy array

pyMS.smoothing.sg_smooth(mzs, intensities, n_smooth=1, w_size=5)

sav gol :param mzs: numpy array numpy array of mz values :param counts: numpy array numpy array of values to smooth :param n_smooth: int number of times to apply smoothing :param w_size: int window size :return: mzs: numpy array :return: counts: numpy array

Module contents