Pyteomics documentation v4.1.3dev1

pyteomics.ms1

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Source code for pyteomics.ms1

"""
ms1 - read and write MS/MS data in MS1 format
=============================================

Summary
-------

`MS1 <http://dx.doi.org/10.1002/rcm.1603>`_ is a simple
human-readable format for MS1 data. It allows storing MS1 peak lists and
exprimental parameters.

This module provides minimalistic infrastructure for access to data stored in
MS1 files.
Two main classes are :py:class:`MS1`, which provides an iterative, text-mode parser,
and :py:class:`IndexedMS1`, which is a binary-mode parser that supports random access using scan IDs
and retention times.
The function :py:func:`read` helps dispatch between the two classes.
Also, common parameters can be read from MS1 file header with
:py:func:`read_header` function.

Functions
---------

  :py:func:`read` - iterate through spectra in MS1 file. Data from a
  single spectrum are converted to a human-readable dict.

  :py:func:`chain` - read multiple files at once.

  :py:func:`chain.from_iterable` - read multiple files at once, using an
  iterable of files.

  :py:func:`read_header` - get a dict with common parameters for all spectra
  from the beginning of MS1 file.

-------------------------------------------------------------------------------
"""

#   Copyright 2012 Anton Goloborodko, Lev Levitsky
#
#   Licensed under the Apache License, Version 2.0 (the "License");
#   you may not use this file except in compliance with the License.
#   You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
#   Unless required by applicable law or agreed to in writing, software
#   distributed under the License is distributed on an "AS IS" BASIS,
#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#   See the License for the specific language governing permissions and
#   limitations under the License.

from . import auxiliary as aux
try:
    import numpy as np
except ImportError:
    np = None


[docs]class MS1Base(object): """Abstract class representing an MS1 file. Subclasses implement different approaches to parsing.""" _array_keys = ['m/z array', 'intensity array']
[docs] def __init__(self, source=None, use_header=False, convert_arrays=True, dtype=None): if convert_arrays and np is None: raise aux.PyteomicsError('numpy is required for array conversion') self._convert_arrays = convert_arrays self._dtype_dict = dtype if isinstance(dtype, dict) else {k: dtype for k in self._array_keys} self._use_header = use_header if use_header: self._header = self._read_header() else: self._header = None self._source_name = getattr(source, 'name', str(source))
@property def header(self): return self._header def _read_header_lines(self, lines): header = {} for line in lines: if line[0] != 'H': break tokens = line.split('\t', 2) if len(tokens) < 3: tokens = line.split(None, 2) key = tokens[1] val = tokens[2].strip() header[key] = val return header def _make_scan(self, params, masses, intensities): if 'RTime' in params: params['RTime'] = float(params['RTime']) out = {'params': params} if self._convert_arrays: data = {'m/z array': masses, 'intensity array': intensities} for key, values in data.items(): out[key] = np.array(values, dtype=self._dtype_dict.get(key)) else: out['m/z array'] = masses out['intensity array'] = intensities return out def _handle_S(self, line, sline, params): sline = line.strip().split(None, 3) params['scan'] = tuple(sline[1:3]) if len(sline) == 4: # in MS2 the S line contains the precursor m/z as a 4th column params['precursor m/z'] = float(sline[3]) def _handle_I(self, line, sline, params): params[sline[1]] = sline[2] def _handle_Z(self, line, sline, params): params.setdefault('charge', []).append(float(sline[1])) params.setdefault('neutral mass', []).append(float(sline[2])) def _handle_D(self, line, sline, params): params.setdefault('analyzer', []).append(sline[1:]) def _read_spectrum_lines(self, lines): reading_spectrum = False params = {} masses = [] intensities = [] if self._use_header: params.update(self.header) for line in lines: sline = line.strip().split(None, 2) if not sline: continue if not reading_spectrum: if sline[0] == 'S': reading_spectrum = True self._handle_S(line, sline, params) # otherwise we are not interested; do nothing, just move along else: if not sline: pass elif sline[0] == 'S': return self._make_scan(params, masses, intensities) else: if sline[0] == 'I': # spectrum-specific parameters! self._handle_I(line, sline, params) elif sline[0] == 'Z': # MS2-specific charge state guess self._handle_Z(line, sline, params) elif sline[0] == 'D': # MS2-specific analyzer annotation self._handle_D(line, sline, params) else: # this must be a peak list try: masses.append(float(sline[0])) # this may cause intensities.append(float(sline[1])) # exceptions...\ except ValueError: raise aux.PyteomicsError( 'Error when parsing %s. Line: %s' % ( self._source_name, line)) except IndexError: pass return self._make_scan(params, masses, intensities)
[docs]class MS1(aux.FileReader, MS1Base): """ A class representing an MS1 file. Supports the `with` syntax and direct iteration for sequential parsing. :py:class:`MS1` object behaves as an iterator, **yielding** spectra one by one. Each 'spectrum' is a :py:class:`dict` with three keys: 'm/z array', 'intensity array', and 'params'. 'm/z array' and 'intensity array' store :py:class:`numpy.ndarray`'s of floats, and 'params' stores a :py:class:`dict` of parameters. Attributes ---------- header : dict The file header. """
[docs] def __init__(self, source=None, use_header=False, convert_arrays=True, dtype=None, encoding=None): aux.FileReader.__init__(self, source, 'r', self._read, False, (), {}, encoding) MS1Base.__init__(self, source, use_header, convert_arrays, dtype) self.encoding = encoding
@aux._keepstate_method def _read_header(self): return self._read_header_lines(self._source) def _read_spectrum(self, firstline): return self._read_spectrum_lines(self._source, firstline) def _read(self): reading_spectrum = False params = {} masses = [] intensities = [] if self._use_header: params.update(self.header) for line in self._source: sline = line.strip().split(None, 2) if not sline: continue if not reading_spectrum: if sline[0] == 'S': reading_spectrum = True self._handle_S(line, sline, params) # otherwise we are not interested; do nothing, just move along else: if not sline: pass elif sline[0] == 'S': yield self._make_scan(params, masses, intensities) params = dict(self.header) if self._use_header else {} masses = [] intensities = [] self._handle_S(line, sline, params) else: if sline[0] == 'I': # spectrum-specific parameters! self._handle_I(line, sline, params) elif sline[0] == 'Z': # MS2-specific charge state guess self._handle_Z(line, sline, params) elif sline[0] == 'D': # MS2-specific analyzer annotation self._handle_D(line, sline, params) else: # this must be a peak list try: masses.append(float(sline[0])) # this may cause intensities.append(float(sline[1])) # exceptions... except ValueError: raise aux.PyteomicsError( 'Error when parsing %s. Line: %s' % (self._source_name, line)) except IndexError: pass yield self._make_scan(params, masses, intensities)
[docs]class IndexedMS1(aux.TaskMappingMixin, aux.TimeOrderedIndexedReaderMixin, aux.IndexedTextReader, MS1Base): """ A class representing an MS1 file. Supports the `with` syntax and direct iteration for sequential parsing. Specific spectra can be accessed by title using the indexing syntax in constant time. If created using a file object, it needs to be opened in binary mode. When iterated, :py:class:`IndexedMS1` object yields spectra one by one. Each 'spectrum' is a :py:class:`dict` with four keys: 'm/z array', 'intensity array', 'charge array' and 'params'. 'm/z array' and 'intensity array' store :py:class:`numpy.ndarray`'s of floats, 'charge array' is a masked array (:py:class:`numpy.ma.MaskedArray`) of ints, and 'params' stores a :py:class:`dict` of parameters (keys and values are :py:class:`str`, keys corresponding to MS1). .. warning :: Labels for scan objects are constructed as the first number in the S line, as follows: for a line ``S 0 1`` the label is `'0'`. If these labels are not unique for the scans in the file, the indexed parser will not work correctly. Consider using :py:class:`MS1` instead. Attributes ---------- header : dict The file header. time : RTLocator A property used for accessing spectra by retention time. """ delimiter = '\nS' label = r'^[\n]?S\s+(\S+)'
[docs] def __init__(self, source=None, use_header=False, convert_arrays=True, dtype=None, encoding='utf-8', block_size=1000000, _skip_index=False): aux.TimeOrderedIndexedReaderMixin.__init__(self, source, self._read, False, (), {}, encoding, block_size, _skip_index=_skip_index) MS1Base.__init__(self, source, use_header, convert_arrays, dtype)
def __reduce_ex__(self, protocol): return (self.__class__, (self._source_init, False, self._convert_arrays, self._read_charges, self._dtype_dict, self.encoding, self.block_size, True), self.__getstate__()) def __getstate__(self): state = super(IndexedMS1, self).__getstate__() state['use_header'] = self._use_header state['header'] = self._header return state def __setstate__(self, state): super(IndexedMS1, self).__setstate__(state) self._use_header = state['use_header'] self._header = state['header'] @aux._keepstate_method def _read_header(self): try: first = next(v for v in self._offset_index.values())[0] except StopIteration: # the index is empty, no spectra in file first = -1 header_lines = self.read(first).decode(self.encoding).split('\n') return self._read_header_lines(header_lines) def _item_from_offsets(self, offsets): start, end = offsets lines = self._read_lines_from_offsets(start, end) return self._read_spectrum_lines(lines) def _read(self, **kwargs): for _, offsets in self._offset_index.items(): spectrum = self._item_from_offsets(offsets) yield spectrum def get_spectrum(self, key): return self.get_by_id(key) def _get_time(self, spectrum): try: return spectrum['params']['RTime'] except KeyError: raise aux.PyteomicsError('RT information not found.')
[docs]def read_header(source, *args, **kwargs): """ Read the specified MS1 file, get the parameters specified in the header as a :py:class:`dict`. Parameters ---------- source : str or file File name or file object representing an file in MS1 format. Returns ------- header : dict """ kwargs['use_header'] = True return read(source, *args, **kwargs).header
[docs]def read(*args, **kwargs): """Read an MS1 file and return entries iteratively. Read the specified MS1 file, **yield** spectra one by one. Each 'spectrum' is a :py:class:`dict` with three keys: 'm/z array', 'intensity array', and 'params'. 'm/z array' and 'intensity array' store :py:class:`numpy.ndarray`'s of floats, and 'params' stores a :py:class:`dict` of parameters. Parameters ---------- source : str or file or None, optional A file object (or file name) with data in MS1 format. Default is :py:const:`None`, which means read standard input. use_header : bool, optional Add the info from file header to each dict. Spectrum-specific parameters override those from the header in case of conflict. Default is :py:const:`False`. convert_arrays : bool, optional If :py:const:`False`, m/z and intensities will be returned as regular lists. If :py:const:`True` (default), they will be converted to regular :py:class:`numpy.ndarray`'s. Conversion requires :py:mod:`numpy`. dtype : type or str or dict, optional dtype argument to :py:mod:`numpy` array constructor, one for all arrays or one for each key. Keys should be 'm/z array' and/or 'intensity array'. encoding : str, optional File encoding. use_index : bool, optional Determines which parsing method to use. If :py:const:`True`, an instance of :py:class:`IndexedMS1` is created. This facilitates random access by scan titles. If an open file is passed as `source`, it needs to be open in binary mode. If :py:const:`False` (default), an instance of :py:class:`MS1` is created. It reads `source` in text mode and is suitable for iterative parsing. .. warning :: Labels for scan objects are constructed as the first number in the S line, as follows: for a line ``S 0 1`` the label is `'0'`. If these labels are not unique for the scans in the file, the indexed parser will not work correctly. block_size : int, optinal Size of the chunk (in bytes) used to parse the file when creating the byte offset index. (Accepted only for :py:class:`IndexedMS1`.) Returns ------- out : :py:class:`MS1Base` An instance of :py:class:`MS1` or :py:class:`IndexedMS1`, depending on `use_index` and `source`. """ if args: source = args[0] else: source = kwargs.get('source') use_index = kwargs.pop('use_index', None) use_index = aux._check_use_index(source, use_index, False) tp = IndexedMS1 if use_index else MS1 return tp(*args, **kwargs)
chain = aux._make_chain(read, 'read')

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