def test_mzml(self):
mzml = mzML(
os.path.join(validation_file_path, 'MultiTest'),
verbose=False
)
self.assertEqual( # check that the correct function keys were pulled
mzml.functions.keys(),
{1, 3, 4},
)
@mzml.foreachchrom
def testperchrom(chromatogram):
attr = branch_attributes(chromatogram)
return attr['id']
self.assertEqual( # test chromatogram decorator
testperchrom(),
[u'TIC', u'SRM SIC Q1=200 Q3=100 function=2 offset=0']
)
@mzml.foreachscan
def testperspec(spectrum):
p = branch_cvparams(spectrum)
return p["MS:1000016"].value
self.assertEqual( # test spectrum decorator
testperspec(),
[0.0171000008, 0.135733336, 0.254333347, 0.372983336, 0.491699994, 0.0510833338, 0.169750005,
0.288383335, 0.407000005, 0.525833309, 0.0847499967, 0.20341666, 0.322033346, 0.440683335]
)
self.assertEqual( # test intensity summing
sum(mzml.sum_scans()[1]),
162804754.0
)
self.assertEqual( # test scan indexing
sum((mzml[2])[1]),
6742121
)
self.assertEqual( # test time indexing
sum((mzml[0.01])[1]),
56270834
)
def bin_spectra(filename, start=None, end=None, save=True, dec=3, function=None):
"""
Sums spectra from raw file and outputs to excel file
:param filename: raw or mzML filename
:param start: start scan (None will default to 1)
:param end: end scan (None will default to the last scan)
:param save: whether to save into an excel document (if a string is provided, that filename will be used)
:param dec: decimal places to track when binning the spectrum
:param function: mzml function number to sum (usually this is 1)
:return: paired x, summed y lists
"""
st = ScriptTime()
st.printstart()
mzml = mzML(filename) # create mzML object
if function is None:
function = mzml.associate_to_function()
if start is None:
start = mzml.functions[function]['sr'][0] + 1
if end is None:
end = mzml.functions[function]['sr'][1] + 1
x, y = mzml.sum_scans(
start=start,
end=end,
function=function,
dec=dec,
)
if save is not False:
if type(save) == str: # if a filename was provided for the Excel file
xlfile = XLSX(save, create=True)
else: # otherwise use the mzML filename
xlfile = XLSX(
f'{filename}.xlsx',
create=True
)
xlfile.writespectrum( # write the spectrum to file
x,
y,
'summed spectra (scans %d-%d)' % (start, end)
)
xlfile.save()
st.printend()
return x, y # return if specified
def bin_spectra(filename,
start=None,
end=None,
save=True,
dec=3,
function=None):
"""
Sums spectra from raw file and outputs to excel file
:param filename: raw or mzML filename
:param start: start scan (None will default to 1)
:param end: end scan (None will default to the last scan)
:param save: whether to save into an excel document (if a string is provided, that filename will be used)
:param dec: decimal places to track when binning the spectrum
:param function: mzml function number to sum (usually this is 1)
:return: paired x, summed y lists
"""
st = ScriptTime()
st.printstart()
mzml = mzML(filename) # create mzML object
if function is None:
function = mzml.associate_to_function()
if start is None:
start = mzml.functions[function]['sr'][0] + 1
if end is None:
end = mzml.functions[function]['sr'][1] + 1
x, y = mzml.sum_scans(
start=start,
end=end,
function=function,
dec=dec,
)
if save is not False:
if type(save) == str: # if a filename was provided for the Excel file
xlfile = XLSX(save, create=True)
else: # otherwise use the mzML filename
xlfile = XLSX(f'{filename}.xlsx', create=True)
xlfile.writespectrum( # write the spectrum to file
x, y, 'summed spectra (scans %d-%d)' % (start, end))
xlfile.save()
st.printend()
return x, y # return if specified
def test_mzml(self):
mzml = mzML(os.path.join(validation_file_path, 'MultiTest'),
verbose=False)
self.assertEqual( # check that the correct function keys were pulled
mzml.functions.keys(),
{1, 3, 4},
)
@mzml.foreachchrom
def testperchrom(chromatogram):
attr = branch_attributes(chromatogram)
return attr['id']
self.assertEqual( # test chromatogram decorator
testperchrom(),
[u'TIC', u'SRM SIC Q1=200 Q3=100 function=2 offset=0'])
@mzml.foreachscan
def testperspec(spectrum):
p = branch_cvparams(spectrum)
return p["MS:1000016"].value
self.assertEqual( # test spectrum decorator
testperspec(), [
0.0171000008, 0.135733336, 0.254333347, 0.372983336,
0.491699994, 0.0510833338, 0.169750005, 0.288383335,
0.407000005, 0.525833309, 0.0847499967, 0.20341666,
0.322033346, 0.440683335
])
self.assertEqual( # test intensity summing
sum(mzml.sum_scans()[1]), 162804754.0)
self.assertEqual( # test scan indexing
sum((mzml[2])[1]), 6742121)
self.assertEqual( # test time indexing
sum((mzml[0.01])[1]), 56270834)
sys.stdout.flush()
return ipsetting[typ]
except KeyError:
raise KeyError(
'\nThe specified figure setting "%s" is not defined.\nPlease check your spelling'
% setting)
if __name__ == '__main__':
os.chdir(curdir) # change to current working directory
keywords = presets(setting) # pull preset kwargs
if spectrum.lower().endswith('.mzml.gz') or spectrum.lower().endswith(
'.raw'): # if supplied with a mass spec file
mzml = mzML(spectrum)
exp = mzml.sum_scans()
keywords.update({'outname': mzml.filename.split('.')[0]
}) # set default output filename
else: # otherwise assume that it is an excel file
xlfile = XLSX(spectrum, verbose=True) # load excel file
if sheetname is None: # otherwise use the first sheet
sheetname = xlfile.wb.sheetnames[0]
exp = xlfile.pullspectrum(sheetname, skiplines=skiplines)[
0] # load spectrum from first sheet in workbook
keywords.update({ # set default output filename
'outname': f'{xlfile.bookname[:-5]} ({sheetname})',
})
keywords.update(override) # apply any user overrides
def mia(filename, show=True, specific_components=None, write=True, save=False):
"""
MS/MS fragmentation interpreter assistant
:param filename: mass spectrum file to parse
:param show: whether to show the annotated spectrum
:param specific_components: dictionary of the molecular formula of specific components in the mixture
:param write: whether to write the output to console
:param save: whether to save the results to an excel file
"""
if specific_components is None:
specific_components = []
mzml = mzML(filename) # load the mzML
x, y = mzml.sum_scans() # sum all the scans
# if not all peaks are being detected, decrease the last value handed to indexes
inds = indexes(x, y, 0.01, 7)
diffs = []
for i in inds: # for each index
difline = []
for j in inds: # append the difference
difline.append(x[i] - x[j])
diffs.append(difline)
loss = com_loss(*specific_components)
guesses = []
for ind, peak in enumerate(diffs):
for ind2, otherpeak in enumerate(diffs[ind]):
val = int(round(otherpeak))
if val > 0 and val in loss:
guesses.append([x[inds[ind]], x[inds[ind2]], val, loss[val]])
# print the results to console if specified
if write is True:
string = '\t'
for ind in inds:
string += f'{x[ind]:.1f}\t'
sys.stdout.write(string + '\n')
for ind, row in enumerate(diffs):
string = f'{round(x[inds[ind]], 1):.1f}\t'
for col in diffs[ind]:
string += f'{round(col, 1):.1f}\t'
sys.stdout.write(string + '\n')
sys.stdout.write(
'\nPossible fragment assignments (from common losses):\n')
for a, b, val, change in guesses:
sys.stdout.write(f'{a} -> {b}: {val} {change}\n')
annotations = {}
top = max(y)
for i in inds:
annotations[str(x[i])] = [x[i], float(y[i]) / float(top) * 100.]
if show is True:
from pythoms.tome import plot_mass_spectrum
plot_mass_spectrum([x, y], annotations=annotations, output='show')
if save is True:
from pythoms.xlsx import XLSX
xlfile = XLSX(filename, create=True)
xlfile.writespectrum(
x,
y,
'MSMS',
# norm=False, # don't normalized data
# chart=False, # don't save basic chart to sheet
)
cs = xlfile.wb.get_sheet_by_name('MSMS')
cs.cell(row=1, column=6).value = 'differences'
for ind, val in enumerate(
inds): # write column headers using cell references
cs[xlfile.inds_to_cellname(
0, 6 + ind)] = f'={xlfile.inds_to_cellname(val, 0)}' # across
cs[xlfile.inds_to_cellname(
1 + ind, 5)] = f'={xlfile.inds_to_cellname(val, 0)}' # down
for ind, val in enumerate(
inds): # write differences based on cell references
for ind2, val2 in enumerate(inds):
cs[xlfile.inds_to_cellname(1 + ind, 6 + ind2)] = f'={xlfile.inds_to_cellname(val, 0)}' \
f'-{xlfile.inds_to_cellname(val2, 0)}' # value
cs[xlfile.inds_to_cellname(
1 + ind, 6 + ind2)].number_format = '0' # number format
# write guesses
cs[xlfile.inds_to_cellname(3 + ind, 5)] = 'from'
cs[xlfile.inds_to_cellname(3 + ind, 6)] = 'to'
cs[xlfile.inds_to_cellname(3 + ind, 7)] = 'difference'
cs[xlfile.inds_to_cellname(3 + ind, 8)] = 'guess'
for i, val in enumerate(guesses):
cs[xlfile.inds_to_cellname(4 + ind + i, 5)] = val[0]
cs[xlfile.inds_to_cellname(4 + ind + i, 6)] = val[1]
cs[xlfile.inds_to_cellname(4 + ind + i, 7)] = val[2]
cs[xlfile.inds_to_cellname(4 + ind + i, 8)] = val[3]
xlfile.save()
# time spacing between the traces
deltat = 10
# override settings here
override = {
# 'fs':16, # font size
# 'lw':1.5, # line width of traces
# 'size':[7.87,4.87], # image size [width,length] in inches
# 'xrange':[500,700], # wavelength bounds (in nm)
# 'yrange':[0,3], # absorbance bounds(in a.u.)
# 'legloc':0, # legend location (see ttp://matplotlib.org/api/legend_api.html for more location codes)
}
if __name__ == '__main__':
mzml = mzML(filename, ftt=True) # initiate mzml object
fn = mzml.associate_to_function(
'UV') # determine which function contains UV-Vis data
uvspecs = mzml.retrieve_scans(start, end, fn) # pull uv spectra
wavelengths = list(uvspecs[0][0]) # wavelength list
uvspecs = [y
for x, y in uvspecs] # set uvspecs list to be only the y values
timepoints = mzml.functions[fn][
'timepoints'] # pull time points of the UV function
l, r = locate_in_list(timepoints, start, 'greater'), locate_in_list(
timepoints, end, 'lesser') # locate indicies of timepoints
timepoints = timepoints[l:r + 1] # trim time list accordingly
times = arange(start, end,
deltat) # evenly spaced times between start and end
specin = []
sys.stdout.flush()
return ipsetting[typ]
except KeyError:
raise KeyError(
'\nThe specified figure setting "%s" is not defined.\nPlease check your spelling'
% setting)
if __name__ == '__main__':
os.chdir(curdir) # change to current working directory
keywords = presets(setting) # pull preset kwargs
if spectrum.lower().endswith('.mzml.gz') or spectrum.lower().endswith(
'.raw'): # if supplied with a mass spec file
mzml = mzML(spectrum, verbose=False)
exp = mzml.sum_scans()
keywords.update({'outname': mzml.filename.split('.')[0]
}) # set default output filename
else: # otherwise assume that it is an excel file
xlfile = XLSX(spectrum, verbose=True) # load excel file
if sheetname is None: # otherwise use the first sheet
sheetname = xlfile.wb.sheetnames[0]
exp = xlfile.pullspectrum(sheetname, skiplines=skiplines)[
0] # load spectrum from first sheet in workbook
keywords.update({ # set default output filename
'outname': f'{xlfile.bookname[:-5]} ({sheetname})',
})
keywords.update(override) # apply any user overrides
# time spacing between the traces
deltat = 10
# override settings here
override = {
# 'fs':16, # font size
# 'lw':1.5, # line width of traces
# 'size':[7.87,4.87], # image size [width,length] in inches
# 'xrange':[500,700], # wavelength bounds (in nm)
# 'yrange':[0,3], # absorbance bounds(in a.u.)
# 'legloc':0, # legend location (see ttp://matplotlib.org/api/legend_api.html for more location codes)
}
if __name__ == '__main__':
mzml = mzML(filename, ftt=True) # initiate mzml object
fn = mzml.associate_to_function('UV') # determine which function contains UV-Vis data
uvspecs = mzml.retrieve_scans(start, end, fn) # pull uv spectra
wavelengths = list(uvspecs[0][0]) # wavelength list
uvspecs = [y for x, y in uvspecs] # set uvspecs list to be only the y values
timepoints = mzml.functions[fn]['timepoints'] # pull time points of the UV function
l, r = locate_in_list(timepoints, start, 'greater'), locate_in_list(timepoints, end,
'lesser') # locate indicies of timepoints
timepoints = timepoints[l:r + 1] # trim time list accordingly
times = arange(start, end, deltat) # evenly spaced times between start and end
specin = []
for time in times:
ind = locate_in_list(timepoints, time) # find the closest time to that
specin.append(uvspecs[ind]) # append that spectrum to the input list
def pyrsir(
filename,
xlsx,
n,
plot=True, # plot the data for a quick look
verbose=True, # chatty
bounds_confidence=0.99, #
combine_spectra=True, # whether or not to output a summed spectrum
return_data=False, #
):
"""
A method for generating reconstructed single ion monitoring traces.
:param filename: path to mzML or raw file to process
:param xlsx: path to excel file with correctly formatted columns
:param n: number of scans to sum together (for binning algorithm)
:param plot: whether to plot and show the data for a quick look
:param verbose: chatty mode
:param bounds_confidence: confidence interval for automatically generated bounds (only applicable if molecular
formulas are provided).
:param combine_spectra: whether to output a summed spectrum
:param return_data: whether to return data (if the data from the function is required by another function)
:return:
"""
def check_integer(val, name):
"""
This function checks that the supplied values are integers greater than 1
A integer value that is non-negative is required for the summing function.
Please check your input value.
"""
if type(val) != list and type(
val) != tuple: # if only one value given for n
val = [val]
for num in val:
if type(num) != int:
sys.exit('\nThe %s value (%s) is not an integer.\n%s' %
(name, str(num), check_integer.__doc__))
if num < 1:
sys.exit('\nThe %s value (%s) is less than 1.\n%s' %
(name, str(num), check_integer.__doc__))
return val
def plots():
"""
Function for generating a set of plots for rapid visual assessment of the supplied n-level
Outputs all MS species with the same sum level onto the same plot
requirements: pylab as pl
"""
pl.clf() # clears and closes old figure (if still open)
pl.close()
nplots = len(n) + 1
# raw data
pl.subplot(nplots, 1, 1) # top plot
for mode in mskeys:
modekey = 'raw' + mode
if modekey in rtime.keys():
pl.plot(rtime[modekey],
tic[modekey],
linewidth=0.75,
label='TIC') # plot tic
for key in sp: # plot each species
if sp[key]['affin'] is mode:
pl.plot(rtime[modekey],
sp[key]['raw'],
linewidth=0.75,
label=key)
pl.title('Raw Data')
pl.ylabel('Intensity')
pl.tick_params(axis='x', labelbottom='off')
# summed data
loc = 2
for num in n:
pl.subplot(nplots, 1, loc)
sumkey = str(num) + 'sum'
for mode in mskeys:
modekey = str(num) + 'sum' + mode
if modekey in rtime.keys():
pl.plot(rtime[modekey],
tic[modekey],
linewidth=0.75,
label='TIC') # plot tic
for key in sp:
if sp[key]['affin'] is mode: # if a MS species
pl.plot(rtime[modekey],
sp[key][sumkey],
linewidth=0.75,
label=key)
pl.title('Summed Data (n=%i)' % (num))
pl.ylabel('Intensity')
pl.tick_params(axis='x', labelbottom='off')
loc += 1
pl.tick_params(axis='x', labelbottom='on')
pl.show()
def output():
"""
Writes the retrieved and calculated values to the excel workbook using the XLSX object
"""
if newpeaks is True: # looks for and deletes any sheets where the data will be changed
if verbose is True:
sys.stdout.write('Clearing duplicate XLSX sheets.')
delete = []
for key in newsp: # generate strings to look for in excel file
delete.append('Raw Data (' + sp[key]['affin'] + ')')
for num in n:
delete.append(str(num) + ' Sum (' + sp[key]['affin'] + ')')
delete.append(
str(num) + ' Normalized (' + sp[key]['affin'] + ')')
delete.append('Isotope Patterns')
xlfile.removesheets(delete) # remove those sheets
if verbose is True:
sys.stdout.write(' DONE.\n')
if verbose is True:
sys.stdout.write('Writing to "%s"' % xlfile.bookname)
sys.stdout.flush()
for mode in mskeys: # write raw data to sheets
modekey = 'raw' + mode
if modekey in rtime.keys():
sheetname = 'Raw Data (' + mode + ')'
xlfile.writersim(sp, rtime[modekey], 'raw', sheetname, mode,
tic[modekey])
for num in n: # write summed and normalized data to sheets
sumkey = str(num) + 'sum'
normkey = str(num) + 'norm'
for mode in mskeys:
modekey = 'raw' + mode
if modekey in rtime.keys():
if max(n) > 1: # if data were summed
sheetname = str(num) + ' Sum (' + mode + ')'
xlfile.writersim(
sp, rtime[sumkey + mode], sumkey, sheetname, mode,
tic[sumkey + mode]) # write summed data
sheetname = str(num) + ' Normalized (' + mode + ')'
xlfile.writersim(sp, rtime[sumkey + mode], normkey,
sheetname, mode) # write normalized data
for key, val in sorted(sp.items()): # write isotope patterns
if sp[key]['affin'] in mskeys:
xlfile.writemultispectrum(
sp[key]['spectrum'][0], # x values
sp[key]['spectrum'][1], # y values
key, # name of the spectrum
xunit='m/z', # x unit
yunit='Intensity (counts)', # y unit
sheetname='Isotope Patterns', # sheet name
chart=True, # output excel chart
)
if rd is None:
for key, val in sorted(chroms.items()): # write chromatograms
xlfile.writemultispectrum(chroms[key]['x'], chroms[key]['y'],
chroms[key]['xunit'],
chroms[key]['yunit'],
'Function Chromatograms', key)
uvstuff = False
for key in sp: # check for UV-Vis spectra
if sp[key]['affin'] is 'UV':
uvstuff = True
break
if uvstuff is True:
for ind, val in enumerate(
tic['rawUV']): # normalize the UV intensities
tic['rawUV'][ind] = val / 1000000.
xlfile.writersim(sp, rtime['rawUV'], 'raw', 'UV-Vis', 'UV',
tic['rawUV']) # write UV-Vis data to sheet
if sum_spectra is not None: # write all summed spectra
for fn in sum_spectra:
specname = '%s %s' % (mzml.functions[fn]['mode'],
mzml.functions[fn]['level'])
if 'target' in mzml.functions[fn]:
specname += ' %.3f' % mzml.functions[fn]['target']
specname += ' (%.3f-%.3f)' % (mzml.functions[fn]['window'][0],
mzml.functions[fn]['window'][1])
xlfile.writemultispectrum(
sum_spectra[fn][0], # x values
sum_spectra[fn][1], # y values
specname, # name of the spectrum
xunit='m/z', # x unit
yunit='Intensity (counts)', # y unit
sheetname='Summed Spectra', # sheet name
chart=True, # output excel chart
)
if verbose is True:
sys.stdout.write(' DONE\n')
def prepformula(dct):
"""looks for formulas in a dictionary and prepares them for pullspeciesdata"""
for species in dct:
if 'affin' not in dct[species]: # set affinity if not specified
fn = dct[species]['function']
if mzml.functions[fn]['type'] == 'MS':
dct[species]['affin'] = mzml.functions[fn]['mode']
if mzml.functions[fn]['type'] == 'UV':
dct[species]['affin'] = 'UV'
if 'formula' in dct[species] and dct[species][
'formula'] is not None:
try:
dct[species][
'mol'].res = res # sets resolution in Molecule object
except NameError:
res = int(mzml.auto_resolution())
dct[species]['mol'].res = res
# dct[species]['mol'].sigma = dct[species]['mol'].sigmafwhm()[1] # recalculates sigma with new resolution
dct[species]['bounds'] = dct[species][
'mol'].bounds # caclulates bounds
return dct
# ----------------------------------------------------------
# -------------------PROGRAM BEGINS-------------------------
# ----------------------------------------------------------
if verbose is True:
stime = ScriptTime()
stime.printstart()
n = check_integer(
n,
'number of scans to sum') # checks integer input and converts to list
if type(xlsx) != dict:
if verbose is True:
sys.stdout.write('Loading processing parameters from excel file')
sys.stdout.flush()
xlfile = XLSX(xlsx, verbose=verbose)
sp = xlfile.pullrsimparams()
else: # if parameters were provided in place of an excel file
sp = xlsx
mskeys = ['+', '-']
for key in sp:
if 'formula' in sp[key] and sp[key][
'formula'] is not None: # if formula is specified
sp[key]['mol'] = IPMolecule(
sp[key]['formula']) # create Molecule object
sp[key]['bounds'] = sp[key]['mol'].calculate_bounds(
bounds_confidence
) # generate bounds from molecule object with this confidence interval
if verbose is True:
sys.stdout.write(' DONE\n')
rtime = {} # empty dictionaries for time and tic
tic = {}
rd = False
for mode in mskeys: # look for existing positive and negative mode raw data
try:
modedata, modetime, modetic = xlfile.pullrsim('Raw Data (' + mode +
')')
except KeyError:
continue
except UnboundLocalError: # catch for if pyrsir was not handed an excel file
continue
if verbose is True:
sys.stdout.write(
'Existing (%s) mode raw data were found, grabbing those values.'
% mode)
sys.stdout.flush()
rd = True # bool that rd is present
modekey = 'raw' + mode
sp.update(modedata) # update sp dictionary with raw data
for key in modedata: # check for affinities
if 'affin' not in sp[key]:
sp[key]['affin'] = mode
rtime[modekey] = list(modetime) # update time list
tic[modekey] = list(modetic) # update tic list
if verbose is True:
sys.stdout.write(' DONE\n')
# sp = prepformula(sp)
newpeaks = False
if rd is True:
newsp = {}
sum_spectra = None
for key in sp: # checks whether there is a MS species that does not have raw data
if 'raw' not in sp[key]:
newsp[key] = sp[key] # create references in the namespace
if len(newsp) is not 0:
newpeaks = True
if verbose is True:
sys.stdout.write(
'Some peaks are not in the raw data, extracting these from raw file.\n'
)
ips = xlfile.pullmultispectrum(
'Isotope Patterns'
) # pull predefined isotope patterns and add them to species
for species in ips: # set spectrum list
sp[species]['spectrum'] = [
ips[species]['x'], ips[species]['y']
]
mzml = mzML(filename) # load mzML class
sp = prepformula(sp) # prep formula etc for summing
newsp = prepformula(newsp) # prep formula species for summing
for species in newsp:
if 'spectrum' not in newsp[species]:
newsp[species]['spectrum'] = Spectrum(
3, newsp[species]['bounds'][0],
newsp[species]['bounds'][1])
newsp = mzml.pull_species_data(newsp) # pull data
else:
if verbose is True:
sys.stdout.write(
'No new peaks were specified. Proceeding directly to summing and normalization.\n'
)
if rd is False: # if no raw data is present, process mzML file
mzml = mzML(filename, verbose=verbose) # load mzML class
sp = prepformula(sp)
sp, sum_spectra = mzml.pull_species_data(
sp, combine_spectra) # pull relevant data from mzML
chroms = mzml.pull_chromatograms() # pull chromatograms from mzML
rtime = {}
tic = {}
for key in sp: # compare predicted isotope patterns to the real spectrum and save standard error of the regression
func = sp[key]['function']
if mzml.functions[func]['type'] == 'MS': # determine mode key
if combine_spectra is True:
sp[key]['spectrum'] = sum_spectra[
sp[key]['function']].trim(
xbounds=sp[key]
['bounds']) # extract the spectrum object
mode = 'raw' + mzml.functions[func]['mode']
if mzml.functions[func]['type'] == 'UV':
mode = 'rawUV'
if mode not in rtime: # if rtime and tic have not been pulled from that function
rtime[mode] = mzml.functions[func]['timepoints']
tic[mode] = mzml.functions[func]['tic']
# if 'formula' in sp[key] and sp[key]['formula'] is not None:
# sp[key]['match'] = sp[key]['mol'].compare(sp[key]['spectrum'])
if combine_spectra is True:
for fn in sum_spectra:
sum_spectra[fn] = sum_spectra[fn].trim(
) # convert Spectrum objects into x,y lists
# if max(n) > 1: # run combine functions if n > 1
for num in n: # for each n to sum
if verbose is True:
sys.stdout.write('\r%d Summing species traces.' % num)
sumkey = str(num) + 'sum'
for key in sp: # bin each species
if sp[key]['affin'] in mskeys or mzml.functions[sp[key][
'function']]['type'] == 'MS': # if species is MS related
sp[key][sumkey] = bindata(num, sp[key]['raw'])
for mode in mskeys:
sumkey = str(num) + 'sum' + mode
modekey = 'raw' + mode
if modekey in rtime.keys(): # if there is data for that mode
rtime[sumkey] = bindata(num, rtime[modekey], num)
tic[sumkey] = bindata(num, tic[modekey])
if verbose is True:
sys.stdout.write(' DONE\n')
sys.stdout.flush()
# else:
# for key in sp: # create key for normalization
# sp[key]['1sum'] = sp[key]['raw']
for num in n: # normalize each peak's chromatogram
if verbose is True:
sys.stdout.write('\r%d Normalizing species traces.' % num)
sys.stdout.flush()
sumkey = str(num) + 'sum'
normkey = str(num) + 'norm'
for mode in mskeys:
modekey = 'raw' + mode
if modekey in rtime.keys(): # if there is data for that mode
for key in sp: # for each species
if sp[key]['affin'] in mskeys or mzml.functions[
sp[key]['function']][
'type'] == 'MS': # if species has affinity
sp[key][normkey] = []
for ind, val in enumerate(sp[key][sumkey]):
# sp[key][normkey].append(val/(mzml.function[func]['tic'][ind]+0.01)) #+0.01 to avoid div/0 errors
sp[key][normkey].append(
val / (tic[sumkey + sp[key]['affin']][ind] +
0.01)) # +0.01 to avoid div/0 errors
if verbose is True:
sys.stdout.write(' DONE\n')
if return_data is True: # if data is to be used by another function, return the calculated data
return mzml, sp, rtime, tic, chroms
# import pickle #pickle objects (for troubleshooting)
# pickle.dump(rtime,open("rtime.p","wb"))
# pickle.dump(tic,open("tic.p","wb"))
# pickle.dump(chroms,open("chroms.p","wb"))
# pickle.dump(sp,open("sp.p","wb"))
output() # write data to excel file
if verbose is True:
sys.stdout.write('\rUpdating paramters')
sys.stdout.flush()
xlfile.updatersimparams(sp) # update summing parameters
if verbose is True:
sys.stdout.write(' DONE\n')
if verbose is True:
sys.stdout.write('\rSaving "%s" (this may take some time)' %
xlfile.bookname)
sys.stdout.flush()
xlfile.save()
if verbose is True:
sys.stdout.write(' DONE\n')
if verbose is True:
if verbose is True:
sys.stdout.write('Plotting traces')
if plot is True:
plots() # plots for quick review
if verbose is True:
sys.stdout.write(' DONE\n')
if verbose is True:
stime.printelapsed()
def pyrsir(
filename,
xlsx,
n,
plot=True, # plot the data for a quick look
verbose=True, # chatty
bounds_confidence=0.99, #
combine_spectra=True, # whether or not to output a summed spectrum
return_data=False, #
):
"""
A method for generating reconstructed single ion monitoring traces.
:param filename: path to mzML or raw file to process
:param xlsx: path to excel file with correctly formatted columns
:param n: number of scans to sum together (for binning algorithm)
:param plot: whether to plot and show the data for a quick look
:param verbose: chatty mode
:param bounds_confidence: confidence interval for automatically generated bounds (only applicable if molecular
formulas are provided).
:param combine_spectra: whether to output a summed spectrum
:param return_data: whether to return data (if the data from the function is required by another function)
:return:
"""
def check_integer(val, name):
"""
This function checks that the supplied values are integers greater than 1
A integer value that is non-negative is required for the summing function.
Please check your input value.
"""
if type(val) != list and type(val) != tuple: # if only one value given for n
val = [val]
for num in val:
if type(num) != int:
sys.exit('\nThe %s value (%s) is not an integer.\n%s' % (name, str(num), check_integer.__doc__))
if num < 1:
sys.exit('\nThe %s value (%s) is less than 1.\n%s' % (name, str(num), check_integer.__doc__))
return val
def plots():
"""
Function for generating a set of plots for rapid visual assessment of the supplied n-level
Outputs all MS species with the same sum level onto the same plot
requirements: pylab as pl
"""
pl.clf() # clears and closes old figure (if still open)
pl.close()
nplots = len(n) + 1
# raw data
pl.subplot(nplots, 1, 1) # top plot
for mode in mskeys:
modekey = 'raw' + mode
if modekey in rtime.keys():
pl.plot(rtime[modekey], tic[modekey], linewidth=0.75, label='TIC') # plot tic
for key in sp: # plot each species
if sp[key]['affin'] is mode:
pl.plot(rtime[modekey], sp[key]['raw'], linewidth=0.75, label=key)
pl.title('Raw Data')
pl.ylabel('Intensity')
pl.tick_params(axis='x', labelbottom='off')
# summed data
loc = 2
for num in n:
pl.subplot(nplots, 1, loc)
sumkey = str(num) + 'sum'
for mode in mskeys:
modekey = str(num) + 'sum' + mode
if modekey in rtime.keys():
pl.plot(rtime[modekey], tic[modekey], linewidth=0.75, label='TIC') # plot tic
for key in sp:
if sp[key]['affin'] is mode: # if a MS species
pl.plot(rtime[modekey], sp[key][sumkey], linewidth=0.75, label=key)
pl.title('Summed Data (n=%i)' % (num))
pl.ylabel('Intensity')
pl.tick_params(axis='x', labelbottom='off')
loc += 1
pl.tick_params(axis='x', labelbottom='on')
pl.show()
def output():
"""
Writes the retrieved and calculated values to the excel workbook using the XLSX object
"""
if newpeaks is True: # looks for and deletes any sheets where the data will be changed
if verbose is True:
sys.stdout.write('Clearing duplicate XLSX sheets.')
delete = []
for key in newsp: # generate strings to look for in excel file
delete.append('Raw Data (' + sp[key]['affin'] + ')')
for num in n:
delete.append(str(num) + ' Sum (' + sp[key]['affin'] + ')')
delete.append(str(num) + ' Normalized (' + sp[key]['affin'] + ')')
delete.append('Isotope Patterns')
xlfile.removesheets(delete) # remove those sheets
if verbose is True:
sys.stdout.write(' DONE.\n')
if verbose is True:
sys.stdout.write('Writing to "%s"' % xlfile.bookname)
sys.stdout.flush()
for mode in mskeys: # write raw data to sheets
modekey = 'raw' + mode
if modekey in rtime.keys():
sheetname = 'Raw Data (' + mode + ')'
xlfile.writersim(sp, rtime[modekey], 'raw', sheetname, mode, tic[modekey])
for num in n: # write summed and normalized data to sheets
sumkey = str(num) + 'sum'
normkey = str(num) + 'norm'
for mode in mskeys:
modekey = 'raw' + mode
if modekey in rtime.keys():
if max(n) > 1: # if data were summed
sheetname = str(num) + ' Sum (' + mode + ')'
xlfile.writersim(sp, rtime[sumkey + mode], sumkey, sheetname, mode,
tic[sumkey + mode]) # write summed data
sheetname = str(num) + ' Normalized (' + mode + ')'
xlfile.writersim(sp, rtime[sumkey + mode], normkey, sheetname, mode) # write normalized data
for key, val in sorted(sp.items()): # write isotope patterns
if sp[key]['affin'] in mskeys:
xlfile.writemultispectrum(
sp[key]['spectrum'][0], # x values
sp[key]['spectrum'][1], # y values
key, # name of the spectrum
xunit='m/z', # x unit
yunit='Intensity (counts)', # y unit
sheetname='Isotope Patterns', # sheet name
chart=True, # output excel chart
)
if rd is None:
for key, val in sorted(chroms.items()): # write chromatograms
xlfile.writemultispectrum(chroms[key]['x'], chroms[key]['y'], chroms[key]['xunit'],
chroms[key]['yunit'], 'Function Chromatograms', key)
uvstuff = False
for key in sp: # check for UV-Vis spectra
if sp[key]['affin'] is 'UV':
uvstuff = True
break
if uvstuff is True:
for ind, val in enumerate(tic['rawUV']): # normalize the UV intensities
tic['rawUV'][ind] = val / 1000000.
xlfile.writersim(sp, rtime['rawUV'], 'raw', 'UV-Vis', 'UV', tic['rawUV']) # write UV-Vis data to sheet
if sum_spectra is not None: # write all summed spectra
for fn in sum_spectra:
specname = '%s %s' % (mzml.functions[fn]['mode'], mzml.functions[fn]['level'])
if 'target' in mzml.functions[fn]:
specname += ' %.3f' % mzml.functions[fn]['target']
specname += ' (%.3f-%.3f)' % (mzml.functions[fn]['window'][0], mzml.functions[fn]['window'][1])
xlfile.writemultispectrum(
sum_spectra[fn][0], # x values
sum_spectra[fn][1], # y values
specname, # name of the spectrum
xunit='m/z', # x unit
yunit='Intensity (counts)', # y unit
sheetname='Summed Spectra', # sheet name
chart=True, # output excel chart
)
if verbose is True:
sys.stdout.write(' DONE\n')
def prepformula(dct):
"""looks for formulas in a dictionary and prepares them for pullspeciesdata"""
for species in dct:
if 'affin' not in dct[species]: # set affinity if not specified
fn = dct[species]['function']
if mzml.functions[fn]['type'] == 'MS':
dct[species]['affin'] = mzml.functions[fn]['mode']
if mzml.functions[fn]['type'] == 'UV':
dct[species]['affin'] = 'UV'
if 'formula' in dct[species]and dct[species]['formula'] is not None:
try:
dct[species]['mol'].res = res # sets resolution in Molecule object
except NameError:
res = int(mzml.auto_resolution())
dct[species]['mol'].res = res
# dct[species]['mol'].sigma = dct[species]['mol'].sigmafwhm()[1] # recalculates sigma with new resolution
dct[species]['bounds'] = dct[species]['mol'].bounds # caclulates bounds
return dct
# ----------------------------------------------------------
# -------------------PROGRAM BEGINS-------------------------
# ----------------------------------------------------------
if verbose is True:
stime = ScriptTime()
stime.printstart()
n = check_integer(n, 'number of scans to sum') # checks integer input and converts to list
if type(xlsx) != dict:
if verbose is True:
sys.stdout.write('Loading processing parameters from excel file')
sys.stdout.flush()
xlfile = XLSX(xlsx, verbose=verbose)
sp = xlfile.pullrsimparams()
else: # if parameters were provided in place of an excel file
sp = xlsx
mskeys = ['+', '-']
for key in sp:
if 'formula' in sp[key] and sp[key]['formula'] is not None: # if formula is specified
sp[key]['mol'] = IPMolecule(sp[key]['formula']) # create Molecule object
sp[key]['bounds'] = sp[key]['mol'].calculate_bounds(
bounds_confidence
) # generate bounds from molecule object with this confidence interval
if verbose is True:
sys.stdout.write(' DONE\n')
rtime = {} # empty dictionaries for time and tic
tic = {}
rd = False
for mode in mskeys: # look for existing positive and negative mode raw data
try:
modedata, modetime, modetic = xlfile.pullrsim('Raw Data (' + mode + ')')
except KeyError:
continue
except UnboundLocalError: # catch for if pyrsir was not handed an excel file
continue
if verbose is True:
sys.stdout.write('Existing (%s) mode raw data were found, grabbing those values.' % mode)
sys.stdout.flush()
rd = True # bool that rd is present
modekey = 'raw' + mode
sp.update(modedata) # update sp dictionary with raw data
for key in modedata: # check for affinities
if 'affin' not in sp[key]:
sp[key]['affin'] = mode
rtime[modekey] = list(modetime) # update time list
tic[modekey] = list(modetic) # update tic list
if verbose is True:
sys.stdout.write(' DONE\n')
# sp = prepformula(sp)
newpeaks = False
if rd is True:
newsp = {}
sum_spectra = None
for key in sp: # checks whether there is a MS species that does not have raw data
if 'raw' not in sp[key]:
newsp[key] = sp[key] # create references in the namespace
if len(newsp) is not 0:
newpeaks = True
if verbose is True:
sys.stdout.write('Some peaks are not in the raw data, extracting these from raw file.\n')
ips = xlfile.pullmultispectrum(
'Isotope Patterns') # pull predefined isotope patterns and add them to species
for species in ips: # set spectrum list
sp[species]['spectrum'] = [ips[species]['x'], ips[species]['y']]
mzml = mzML(filename) # load mzML class
sp = prepformula(sp) # prep formula etc for summing
newsp = prepformula(newsp) # prep formula species for summing
for species in newsp:
if 'spectrum' not in newsp[species]:
newsp[species]['spectrum'] = Spectrum(3, newsp[species]['bounds'][0], newsp[species]['bounds'][1])
newsp = mzml.pull_species_data(newsp) # pull data
else:
if verbose is True:
sys.stdout.write('No new peaks were specified. Proceeding directly to summing and normalization.\n')
if rd is False: # if no raw data is present, process mzML file
mzml = mzML(filename, verbose=verbose) # load mzML class
sp = prepformula(sp)
sp, sum_spectra = mzml.pull_species_data(sp, combine_spectra) # pull relevant data from mzML
chroms = mzml.pull_chromatograms() # pull chromatograms from mzML
rtime = {}
tic = {}
for key in sp: # compare predicted isotope patterns to the real spectrum and save standard error of the regression
func = sp[key]['function']
if mzml.functions[func]['type'] == 'MS': # determine mode key
if combine_spectra is True:
sp[key]['spectrum'] = sum_spectra[sp[key]['function']].trim(
xbounds=sp[key]['bounds']) # extract the spectrum object
mode = 'raw' + mzml.functions[func]['mode']
if mzml.functions[func]['type'] == 'UV':
mode = 'rawUV'
if mode not in rtime: # if rtime and tic have not been pulled from that function
rtime[mode] = mzml.functions[func]['timepoints']
tic[mode] = mzml.functions[func]['tic']
# if 'formula' in sp[key] and sp[key]['formula'] is not None:
# sp[key]['match'] = sp[key]['mol'].compare(sp[key]['spectrum'])
if combine_spectra is True:
for fn in sum_spectra:
sum_spectra[fn] = sum_spectra[fn].trim() # convert Spectrum objects into x,y lists
# if max(n) > 1: # run combine functions if n > 1
for num in n: # for each n to sum
if verbose is True:
sys.stdout.write('\r%d Summing species traces.' % num)
sumkey = str(num) + 'sum'
for key in sp: # bin each species
if sp[key]['affin'] in mskeys or mzml.functions[sp[key]['function']][
'type'] == 'MS': # if species is MS related
sp[key][sumkey] = bindata(num, sp[key]['raw'])
for mode in mskeys:
sumkey = str(num) + 'sum' + mode
modekey = 'raw' + mode
if modekey in rtime.keys(): # if there is data for that mode
rtime[sumkey] = bindata(num, rtime[modekey], num)
tic[sumkey] = bindata(num, tic[modekey])
if verbose is True:
sys.stdout.write(' DONE\n')
sys.stdout.flush()
# else:
# for key in sp: # create key for normalization
# sp[key]['1sum'] = sp[key]['raw']
for num in n: # normalize each peak's chromatogram
if verbose is True:
sys.stdout.write('\r%d Normalizing species traces.' % num)
sys.stdout.flush()
sumkey = str(num) + 'sum'
normkey = str(num) + 'norm'
for mode in mskeys:
modekey = 'raw' + mode
if modekey in rtime.keys(): # if there is data for that mode
for key in sp: # for each species
if sp[key]['affin'] in mskeys or mzml.functions[sp[key]['function']][
'type'] == 'MS': # if species has affinity
sp[key][normkey] = []
for ind, val in enumerate(sp[key][sumkey]):
# sp[key][normkey].append(val/(mzml.function[func]['tic'][ind]+0.01)) #+0.01 to avoid div/0 errors
sp[key][normkey].append(
val / (tic[sumkey + sp[key]['affin']][ind] + 0.01)) # +0.01 to avoid div/0 errors
if verbose is True:
sys.stdout.write(' DONE\n')
if return_data is True: # if data is to be used by another function, return the calculated data
return mzml, sp, rtime, tic, chroms
# import pickle #pickle objects (for troubleshooting)
# pickle.dump(rtime,open("rtime.p","wb"))
# pickle.dump(tic,open("tic.p","wb"))
# pickle.dump(chroms,open("chroms.p","wb"))
# pickle.dump(sp,open("sp.p","wb"))
output() # write data to excel file
if verbose is True:
sys.stdout.write('\rUpdating paramters')
sys.stdout.flush()
xlfile.updatersimparams(sp) # update summing parameters
if verbose is True:
sys.stdout.write(' DONE\n')
if verbose is True:
sys.stdout.write('\rSaving "%s" (this may take some time)' % xlfile.bookname)
sys.stdout.flush()
xlfile.save()
if verbose is True:
sys.stdout.write(' DONE\n')
if verbose is True:
if verbose is True:
sys.stdout.write('Plotting traces')
if plot is True:
plots() # plots for quick review
if verbose is True:
sys.stdout.write(' DONE\n')
if verbose is True:
stime.printelapsed()
plt.savefig('../{OUTFILE}.png'.format(OUTFILE=outputFile + str(minFilter)), bbox_inches='tight')
##################################
###############################################################
# MAIN
###############################################################
# _mzML processing variables
filename = 'HZ-140516_HOTKEYMSMS 1376 II.raw' # raw or mzml file name
fillzeros = True # fills spectrum with zeros
decpl = 1 # number of decimal places to track
mzrange = None # mzrange to track
sr = 'all' # scan range to track
mzml = mzML(filename, verbose=True)
# EDESI Plot Production variable
minFilter = 20 # minFilter intensity value
threshold = 1156 # threshold of peak height for Breakdown tracing
plotBreakdown = True # Construct Plot with Breakdown?
plotZoom = True # Construct Plot with Zoom in region of interest? (Autozoom)
msmsfns = []
for func in mzml.functions: # identify MSMS functions in the provided file
if mzml.functions[func]['type'] == 'MS' and mzml.functions[func]['level'] > 1:
msmsfns.append(func)
if len(msmsfns) > 1: # if there is more than one msms function, ask the user which one to process
sys.stdout.write(
'More than one MS/MS function is contained in this mzML file. Please indicate which one you wish to process:\nFunction\ttarget\n')
for func in msmsfns:
}
try:
sys.stdout.write('Using figure preset "%s"\n' % (setting))
sys.stdout.flush()
return ipsetting[typ]
except KeyError:
raise KeyError('\nThe specified figure setting "%s" is not defined.\nPlease check your spelling' % setting)
if __name__ == '__main__':
os.chdir(curdir) # change to current working directory
keywords = presets(setting) # pull preset kwargs
if spectrum.lower().endswith('.mzml.gz') or spectrum.lower().endswith('.raw'): # if supplied with a mass spec file
mzml = mzML(spectrum, verbose=False)
exp = mzml.sum_scans()
keywords.update({'outname': mzml.filename.split('.')[0]}) # set default output filename
else: # otherwise assume that it is an excel file
xlfile = XLSX(spectrum, verbose=True) # load excel file
if sheetname is None: # otherwise use the first sheet
sheetname = xlfile.wb.sheetnames[0]
exp = xlfile.pullspectrum(sheetname, skiplines=skiplines)[0] # load spectrum from first sheet in workbook
keywords.update({ # set default output filename
'outname': f'{xlfile.bookname[:-5]} ({sheetname})',
})
keywords.update(override) # apply any user overrides
plot_mass_spectrum(exp, simdict, **keywords)
import gc