def setUp(self):
self.mol = Molecule('L2PdAr+I')
self.ipmol = IPMolecule(
'L2PdAr+I',
ipmethod='multiplicative',
dropmethod='threshold',
threshold=0.01,
)
def test_ipmolecule_methods(self):
for ipmethod in VALID_IPMETHODS:
for dropmethod in VALID_DROPMETHODS:
mol = IPMolecule(
'Pd2C10H5',
ipmethod=ipmethod,
dropmethod=dropmethod,
)
test = mol.gaussian_isotope_pattern # test gaussian isotope pattern generation
def test_ipmolecule(self):
self.assertEqual(
self.ipmol.estimated_exact_mass,
1109.1303706381723,
)
self.assertEqual(
self.ipmol.barip,
[[
1105.130443, 1106.133823749481, 1107.1290292337153,
1108.1305157201678, 1109.1303706381723, 1110.1328590930914,
1111.1301978511672, 1112.1325950611867, 1113.1318575059308,
1114.134086933976, 1115.1370272665604, 1116.140052, 1117.143407
],
[
2.287794397621507, 1.5228133756325326, 25.476059354316945,
66.8193866193291, 100.0, 52.65050639843156, 74.88108058795096,
42.5730473226288, 39.36707265932168, 20.17253048748261,
5.990476280101723, 1.1848920932846654, 0.16082254122736006
]])
self.ipmol - 'PPh3' # test subtraction
self.ipmol + 'PPh3' # test addition
mol2 = IPMolecule('N(Et)2(CH2(13C)H2(2H))2')
self.ipmol + mol2 # test class addition
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()
from pythoms.molecule import IPMolecule
kwargs = {
'dropmethod': 'threshold'
}
Ar = 'Ar+'
X = 'I'
arunit = 'C6H4'
cap = 'Ph'
n = 6
print(Ar)
print('aromatic units')
print('n\tmass')
for i in range(1, n):
print('%d\t%.2f' % (i, IPMolecule(Ar + (arunit * i) + X, **kwargs).estimated_exact_mass))
print('\nPd units')
print('n\tmass')
for i in range(1, n):
print('%d\t%.2f' % (i, IPMolecule('L2Pd' + Ar + (arunit * i) + X, **kwargs).estimated_exact_mass))
print('\ncapped')
print('n\tmass')
for i in range(0, n + 3):
print('%d\t%.2f' % (i, IPMolecule(cap + Ar + (arunit * i), **kwargs).estimated_exact_mass))