--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/metfrag.py	Wed Feb 05 12:30:06 2020 -0500
@@ -0,0 +1,520 @@
+from __future__ import absolute_import, print_function
+
+import ConfigParser
+import argparse
+import csv
+import glob
+import multiprocessing
+import os
+import re
+import shutil
+import sys
+import tempfile
+from collections import defaultdict
+
+import six
+
+print(sys.version)
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--input_pth')
+parser.add_argument('--result_pth', default='metfrag_result.csv')
+
+parser.add_argument('--temp_dir')
+parser.add_argument('--polarity', default='pos')
+parser.add_argument('--minMSMSpeaks', default=1)
+
+parser.add_argument('--MetFragDatabaseType', default='PubChem')
+parser.add_argument('--LocalDatabasePath', default='')
+parser.add_argument('--LocalMetChemDatabaseServerIp', default='')
+
+parser.add_argument('--DatabaseSearchRelativeMassDeviation', default=5)
+parser.add_argument('--FragmentPeakMatchRelativeMassDeviation', default=10)
+parser.add_argument('--FragmentPeakMatchAbsoluteMassDeviation', default=0.001)
+parser.add_argument('--NumberThreads', default=1)
+parser.add_argument('--UnconnectedCompoundFilter', action='store_true')
+parser.add_argument('--IsotopeFilter', action='store_true')
+
+parser.add_argument('--FilterMinimumElements', default='')
+parser.add_argument('--FilterMaximumElements', default='')
+parser.add_argument('--FilterSmartsInclusionList', default='')
+parser.add_argument('--FilterSmartsExclusionList', default='')
+parser.add_argument('--FilterIncludedElements', default='')
+parser.add_argument('--FilterExcludedElements', default='')
+parser.add_argument('--FilterIncludedExclusiveElements', default='')
+
+parser.add_argument('--score_thrshld', default=0)
+parser.add_argument('--pctexplpeak_thrshld', default=0)
+parser.add_argument('--schema')
+parser.add_argument('--cores_top_level', default=1)
+parser.add_argument('--chunks', default=1)
+parser.add_argument('--meta_select_col', default='name')
+parser.add_argument('--skip_invalid_adducts', action='store_true')
+
+parser.add_argument('--ScoreSuspectLists', default='')
+parser.add_argument('--MetFragScoreTypes',
+                    default="FragmenterScore,OfflineMetFusionScore")
+parser.add_argument('--MetFragScoreWeights', default="1.0,1.0")
+
+args = parser.parse_args()
+print(args)
+
+config = ConfigParser.ConfigParser()
+config.read(
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.ini'))
+
+if os.stat(args.input_pth).st_size == 0:
+    print('Input file empty')
+    exit()
+
+# Create temporary working directory
+if args.temp_dir:
+    wd = args.temp_dir
+else:
+    wd = tempfile.mkdtemp()
+
+if os.path.exists(wd):
+    shutil.rmtree(wd)
+    os.makedirs(wd)
+else:
+    os.makedirs(wd)
+
+######################################################################
+# Setup regular expressions for MSP parsing dictionary
+######################################################################
+regex_msp = {}
+regex_msp['name'] = [r'^Name(?:=|:)(.*)$']
+regex_msp['polarity'] = [r'^ion.*mode(?:=|:)(.*)$',
+                         r'^ionization.*mode(?:=|:)(.*)$',
+                         r'^polarity(?:=|:)(.*)$']
+regex_msp['precursor_mz'] = [r'^precursor.*m/z(?:=|:)\s*(\d*[.,]?\d*)$',
+                             r'^precursor.*mz(?:=|:)\s*(\d*[.,]?\d*)$']
+regex_msp['precursor_type'] = [r'^precursor.*type(?:=|:)(.*)$',
+                               r'^adduct(?:=|:)(.*)$',
+                               r'^ADDUCTIONNAME(?:=|:)(.*)$']
+regex_msp['num_peaks'] = [r'^Num.*Peaks(?:=|:)\s*(\d*)$']
+regex_msp['msp'] = [r'^Name(?:=|:)(.*)$']  # Flag for standard MSP format
+
+regex_massbank = {}
+regex_massbank['name'] = [r'^RECORD_TITLE:(.*)$']
+regex_massbank['polarity'] = [r'^AC\$MASS_SPECTROMETRY:\s+ION_MODE\s+(.*)$']
+regex_massbank['precursor_mz'] = [
+    r'^MS\$FOCUSED_ION:\s+PRECURSOR_M/Z\s+(\d*[.,]?\d*)$']
+regex_massbank['precursor_type'] = [
+    r'^MS\$FOCUSED_ION:\s+PRECURSOR_TYPE\s+(.*)$']
+regex_massbank['num_peaks'] = [r'^PK\$NUM_PEAK:\s+(\d*)']
+regex_massbank['cols'] = [r'^PK\$PEAK:\s+(.*)']
+regex_massbank['massbank'] = [
+    r'^RECORD_TITLE:(.*)$']  # Flag for massbank format
+
+if args.schema == 'msp':
+    meta_regex = regex_msp
+elif args.schema == 'massbank':
+    meta_regex = regex_massbank
+elif args.schema == 'auto':
+    # If auto we just check for all the available paramter names and then
+    # determine if Massbank or MSP based on the name parameter
+    meta_regex = {}
+    meta_regex.update(regex_massbank)
+    meta_regex['name'].extend(regex_msp['name'])
+    meta_regex['polarity'].extend(regex_msp['polarity'])
+    meta_regex['precursor_mz'].extend(regex_msp['precursor_mz'])
+    meta_regex['precursor_type'].extend(regex_msp['precursor_type'])
+    meta_regex['num_peaks'].extend(regex_msp['num_peaks'])
+    meta_regex['msp'] = regex_msp['msp']
+else:
+    sys.exit("No schema selected")
+
+adduct_types = {
+    '[M+H]+': 1.007276,
+    '[M+NH4]+': 18.034374,
+    '[M+Na]+': 22.989218,
+    '[M+K]+': 38.963158,
+    '[M+CH3OH+H]+': 33.033489,
+    '[M+ACN+H]+': 42.033823,
+    '[M+ACN+Na]+': 64.015765,
+    '[M+2ACN+H]+': 83.06037,
+    '[M-H]-': -1.007276,
+    '[M+Cl]-': 34.969402,
+    '[M+HCOO]-': 44.99819,
+    '[M-H+HCOOH]-': 44.99819,
+    # same as above but different style of writing adduct
+    '[M+CH3COO]-': 59.01385,
+    '[M-H+CH3COOH]-': 59.01385
+    # same as above but different style of writing adduct
+}
+inv_adduct_types = {int(round(v, 0)): k for k, v in adduct_types.iteritems()}
+
+
+# function to extract the meta data using the regular expressions
+def parse_meta(meta_regex, meta_info=None):
+    if meta_info is None:
+        meta_info = {}
+    for k, regexes in six.iteritems(meta_regex):
+        for reg in regexes:
+            m = re.search(reg, line, re.IGNORECASE)
+            if m:
+                meta_info[k] = '-'.join(m.groups()).strip()
+    return meta_info
+
+
+######################################################################
+# Setup parameter dictionary
+######################################################################
+def init_paramd(args):
+    paramd = defaultdict()
+
+    paramd["MetFragDatabaseType"] = args.MetFragDatabaseType
+
+    if args.MetFragDatabaseType == "LocalCSV":
+        paramd["LocalDatabasePath"] = args.LocalDatabasePath
+    elif args.MetFragDatabaseType == "MetChem":
+        paramd["LocalMetChemDatabase"] = \
+            config.get('MetChem', 'LocalMetChemDatabase')
+        paramd["LocalMetChemDatabasePortNumber"] = \
+            config.get('MetChem', 'LocalMetChemDatabasePortNumber')
+        paramd["LocalMetChemDatabaseServerIp"] =  \
+            args.LocalMetChemDatabaseServerIp
+        paramd["LocalMetChemDatabaseUser"] = \
+            config.get('MetChem', 'LocalMetChemDatabaseUser')
+        paramd["LocalMetChemDatabasePassword"] = \
+            config.get('MetChem', 'LocalMetChemDatabasePassword')
+
+    paramd["FragmentPeakMatchAbsoluteMassDeviation"] =  \
+        args.FragmentPeakMatchAbsoluteMassDeviation
+    paramd["FragmentPeakMatchRelativeMassDeviation"] =  \
+        args.FragmentPeakMatchRelativeMassDeviation
+    paramd["DatabaseSearchRelativeMassDeviation"] = \
+        args.DatabaseSearchRelativeMassDeviation
+    paramd["SampleName"] = ''
+    paramd["ResultsPath"] = os.path.join(wd)
+
+    if args.polarity == "pos":
+        paramd["IsPositiveIonMode"] = True
+        paramd["PrecursorIonModeDefault"] = "1"
+        paramd["PrecursorIonMode"] = "1"
+        paramd["nm_mass_diff_default"] = 1.007276
+    else:
+        paramd["IsPositiveIonMode"] = False
+        paramd["PrecursorIonModeDefault"] = "-1"
+        paramd["PrecursorIonMode"] = "-1"
+        paramd["nm_mass_diff_default"] = -1.007276
+
+    paramd["MetFragCandidateWriter"] = "CSV"
+    paramd["NumberThreads"] = args.NumberThreads
+
+    if args.ScoreSuspectLists:
+        paramd["ScoreSuspectLists"] = args.ScoreSuspectLists
+
+    paramd["MetFragScoreTypes"] = args.MetFragScoreTypes
+    paramd["MetFragScoreWeights"] = args.MetFragScoreWeights
+
+    dct_filter = defaultdict()
+    filterh = []
+
+    if args.UnconnectedCompoundFilter:
+        filterh.append('UnconnectedCompoundFilter')
+
+    if args.IsotopeFilter:
+        filterh.append('IsotopeFilter')
+
+    if args.FilterMinimumElements:
+        filterh.append('MinimumElementsFilter')
+        dct_filter['FilterMinimumElements'] = args.FilterMinimumElements
+
+    if args.FilterMaximumElements:
+        filterh.append('MaximumElementsFilter')
+        dct_filter['FilterMaximumElements'] = args.FilterMaximumElements
+
+    if args.FilterSmartsInclusionList:
+        filterh.append('SmartsSubstructureInclusionFilter')
+        dct_filter[
+            'FilterSmartsInclusionList'] = args.FilterSmartsInclusionList
+
+    if args.FilterSmartsExclusionList:
+        filterh.append('SmartsSubstructureExclusionFilter')
+        dct_filter[
+            'FilterSmartsExclusionList'] = args.FilterSmartsExclusionList
+
+    # My understanding is that both 'ElementInclusionExclusiveFilter'
+    # and 'ElementExclusionFilter' use 'FilterIncludedElements'
+    if args.FilterIncludedExclusiveElements:
+        filterh.append('ElementInclusionExclusiveFilter')
+        dct_filter[
+            'FilterIncludedElements'] = args.FilterIncludedExclusiveElements
+
+    if args.FilterIncludedElements:
+        filterh.append('ElementInclusionFilter')
+        dct_filter['FilterIncludedElements'] = args.FilterIncludedElements
+
+    if args.FilterExcludedElements:
+        filterh.append('ElementExclusionFilter')
+        dct_filter['FilterExcludedElements'] = args.FilterExcludedElements
+
+    if filterh:
+        fcmds = ','.join(filterh) + ' '
+        for k, v in six.iteritems(dct_filter):
+            fcmds += "{0}={1} ".format(str(k), str(v))
+
+        paramd["MetFragPreProcessingCandidateFilter"] = fcmds
+
+    return paramd
+
+
+######################################################################
+# Function to run metfrag when all metainfo and peaks have been parsed
+######################################################################
+def run_metfrag(meta_info, peaklist, args, wd, spectrac, adduct_types):
+    # Get sample details (if possible to extract) e.g. if created as part of
+    # the msPurity pipeline) choose between getting additional details to add
+    # as columns as either all meta data from msp, just details from the
+    # record name (i.e. when using msPurity and we have the columns coded into
+    # the name) or just the spectra index (spectrac)].
+    # Returns the parameters used and the command line call
+
+    paramd = init_paramd(args)
+    if args.meta_select_col == 'name':
+        # have additional column of just the name
+        paramd['additional_details'] = {'name': meta_info['name']}
+    elif args.meta_select_col == 'name_split':
+        # have additional columns split by "|" and
+        # then on ":" e.g. MZ:100.2 | RT:20 | xcms_grp_id:1
+        paramd['additional_details'] = {
+            sm.split(":")[0].strip(): sm.split(":")[1].strip() for sm in
+            meta_info['name'].split("|")}
+    elif args.meta_select_col == 'all':
+        # have additional columns based on all the meta information
+        # extracted from the MSP
+        paramd['additional_details'] = meta_info
+    else:
+        # Just have and index of the spectra in the MSP file
+        paramd['additional_details'] = {'spectra_idx': spectrac}
+
+    paramd["SampleName"] = "{}_metfrag_result".format(spectrac)
+
+    # =============== Output peaks to txt file  ==============================
+    paramd["PeakListPath"] = os.path.join(wd,
+                                          "{}_tmpspec.txt".format(spectrac))
+
+    # write spec file
+    with open(paramd["PeakListPath"], 'w') as outfile:
+        for p in peaklist:
+            outfile.write(p[0] + "\t" + p[1] + "\n")
+
+    # =============== Update param based on MSP metadata ======================
+    # Replace param details with details from MSP if required
+    if 'precursor_type' in meta_info and \
+            meta_info['precursor_type'] in adduct_types:
+        adduct = meta_info['precursor_type']
+        nm = float(meta_info['precursor_mz']) - adduct_types[
+            meta_info['precursor_type']]
+        paramd["PrecursorIonMode"] = \
+            int(round(adduct_types[meta_info['precursor_type']], 0))
+    elif not args.skip_invalid_adducts:
+        adduct = inv_adduct_types[int(paramd['PrecursorIonModeDefault'])]
+        paramd["PrecursorIonMode"] = paramd['PrecursorIonModeDefault']
+        nm = float(meta_info['precursor_mz']) - paramd['nm_mass_diff_default']
+    else:
+        print('Skipping {}'.format(paramd["SampleName"]))
+        return '', ''
+
+    paramd['additional_details']['adduct'] = adduct
+    paramd["NeutralPrecursorMass"] = nm
+
+    # ============== Create CLI cmd for metfrag ===============================
+    cmd = "metfrag"
+    for k, v in six.iteritems(paramd):
+        if k not in ['PrecursorIonModeDefault', 'nm_mass_diff_default',
+                     'additional_details']:
+            cmd += " {}={}".format(str(k), str(v))
+
+    # ============== Run metfrag ==============================================
+    # print(cmd)
+    # Filter before process with a minimum number of MS/MS peaks
+    if plinesread >= float(args.minMSMSpeaks):
+
+        if int(args.cores_top_level) == 1:
+            os.system(cmd)
+
+    return paramd, cmd
+
+
+def work(cmds):
+    return [os.system(cmd) for cmd in cmds]
+
+
+######################################################################
+# Parse MSP file and run metfrag CLI
+######################################################################
+# keep list of commands if performing in CLI in parallel
+cmds = []
+# keep a dictionary of all params
+paramds = {}
+# keep count of spectra (for uid)
+spectrac = 0
+# this dictionary will store the meta data results form the MSp file
+meta_info = {}
+
+with open(args.input_pth, "r") as infile:
+    # number of lines for the peaks
+    pnumlines = 0
+    # number of lines read for the peaks
+    plinesread = 0
+    for line in infile:
+        line = line.strip()
+
+        if pnumlines == 0:
+            # ============== Extract metadata from MSP ========================
+            meta_info = parse_meta(meta_regex, meta_info)
+
+            if ('massbank' in meta_info and 'cols' in meta_info) or (
+                    'msp' in meta_info and 'num_peaks' in meta_info):
+                pnumlines = int(meta_info['num_peaks'])
+                plinesread = 0
+                peaklist = []
+
+        elif plinesread < pnumlines:
+            # ============== Extract peaks from MSP ==========================
+            # .split() will split on any empty space (i.e. tab and space)
+            line = tuple(line.split())
+            # Keep only m/z and intensity, not relative intensity
+            save_line = tuple(line[0].split() + line[1].split())
+            plinesread += 1
+            peaklist.append(save_line)
+
+        elif plinesread and plinesread == pnumlines:
+            # ======= Get sample name and additional details for output =======
+            spectrac += 1
+            paramd, cmd = run_metfrag(meta_info, peaklist, args, wd, spectrac,
+                                      adduct_types)
+
+            if paramd:
+                paramds[paramd["SampleName"]] = paramd
+                cmds.append(cmd)
+
+            meta_info = {}
+            pnumlines = 0
+            plinesread = 0
+
+    # end of file. Check if there is a MSP spectra to run metfrag on still
+    if plinesread and plinesread == pnumlines:
+
+        paramd, cmd = run_metfrag(meta_info, peaklist, args, wd, spectrac + 1,
+                                  adduct_types)
+
+        if paramd:
+            paramds[paramd["SampleName"]] = paramd
+            cmds.append(cmd)
+
+# Perform multiprocessing on command line call level
+if int(args.cores_top_level) > 1:
+    cmds_chunks = [cmds[x:x + int(args.chunks)] for x in
+                   list(range(0, len(cmds), int(args.chunks)))]
+    pool = multiprocessing.Pool(processes=int(args.cores_top_level))
+    pool.map(work, cmds_chunks)
+    pool.close()
+    pool.join()
+
+######################################################################
+# Concatenate and filter the output
+######################################################################
+# outputs might have different headers. Need to get a list of all the
+# headers before we start merging the files
+# outfiles = [os.path.join(wd, f) for f in glob.glob(os.path.join(wd,
+# "*_metfrag_result.csv"))]
+outfiles = glob.glob(os.path.join(wd, "*_metfrag_result.csv"))
+
+if len(outfiles) == 0:
+    print('No results')
+    sys.exit()
+
+headers = []
+c = 0
+for fn in outfiles:
+    with open(fn, 'r') as infile:
+        reader = csv.reader(infile)
+        if sys.version_info >= (3, 0):
+            headers.extend(next(reader))
+        else:
+            headers.extend(reader.next())
+        # check if file has any data rows
+        for i, row in enumerate(reader):
+            c += 1
+            if i == 1:
+                break
+
+# if no data rows (e.g. matches) then do not save an
+# output and leave the program
+if c == 0:
+    print('No results')
+    sys.exit()
+
+additional_detail_headers = ['sample_name']
+for k, paramd in six.iteritems(paramds):
+    additional_detail_headers = list(set(
+        additional_detail_headers + list(paramd['additional_details'].keys())))
+
+# add inchikey if not already present (missing in metchem output)
+if 'InChIKey' not in headers:
+    headers.append('InChIKey')
+
+headers = additional_detail_headers + sorted(list(set(headers)))
+
+# Sort files nicely
+outfiles.sort(
+    key=lambda s: int(re.match(r'^.*/(\d+)_metfrag_result.csv', s).group(1)))
+
+print(outfiles)
+
+# merge outputs
+with open(args.result_pth, 'a') as merged_outfile:
+    dwriter = csv.DictWriter(merged_outfile, fieldnames=headers,
+                             delimiter='\t', quotechar='"')
+    dwriter.writeheader()
+
+    for fn in outfiles:
+
+        with open(fn) as infile:
+            reader = csv.DictReader(infile, delimiter=',', quotechar='"')
+            for line in reader:
+                bewrite = True
+                for key, value in line.items():
+                    # Filter when no MS/MS peak matched
+                    if key == "ExplPeaks":
+                        if float(args.pctexplpeak_thrshld) > 0 and \
+                                "NA" in value:
+                            bewrite = False
+                    # Filter with a score threshold
+                    elif key == "Score":
+                        if float(value) <= float(args.score_thrshld):
+                            bewrite = False
+                    elif key == "NoExplPeaks":
+                        nbfindpeak = float(value)
+                    elif key == "NumberPeaksUsed":
+                        totpeaks = float(value)
+                # Filter with a relative number of peak matched
+                try:
+                    pctexplpeak = nbfindpeak / totpeaks * 100
+                except ZeroDivisionError:
+                    bewrite = False
+                else:
+                    if pctexplpeak < float(args.pctexplpeak_thrshld):
+                        bewrite = False
+
+                # Write the line if it pass all filters
+                if bewrite:
+                    bfn = os.path.basename(fn)
+                    bfn = bfn.replace(".csv", "")
+                    line['sample_name'] = paramds[bfn]['SampleName']
+                    ad = paramds[bfn]['additional_details']
+
+                    if args.MetFragDatabaseType == "MetChem":
+                        # for some reason the metchem database option does
+                        # not report the full inchikey (at least in the Bham
+                        # setup. This ensures we always get the fully inchikey
+                        line['InChIKey'] = '{}-{}-{}'.format(line['InChIKey1'],
+                                                             line['InChIKey2'],
+                                                             line['InChIKey3'])
+
+                    line.update(ad)
+                    dwriter.writerow(line)