def __init__(self, compulsory_elements='', elements=''):
if isinstance(elements, str):
self.element_filter=formula_to_element_vector(parse_formula(elements));
elif isinstance(elements, dict):
self.element_filter=formula_to_element_vector(elements);
elif isinstance(elements, list):
self.element_filter=list_to_element_vector(elements);
elif isinstance(elements, set):
self.element_filter=set_to_element_vector(elements);
else:
raise TypeError('Wrong type argument for ElementCompositionFilter initialization! str, dict, set and list supported only!');
if isinstance(compulsory_elements, str):
self.compulsory_element_filter=formula_to_element_vector(parse_formula(compulsory_elements));
elif isinstance(compulsory_elements, dict):
self.compulsory_element_filter=formula_to_element_vector(compulsory_elements);
elif isinstance(compulsory_elements, list):
self.compulsory_element_filter=list_to_element_vector(compulsory_elements);
elif isinstance(compulsory_elements, set):
self.compulsory_element_filter=set_to_element_vector(compulsory_elements);
else:
raise TypeError('Wrong type argument for ElementCompositionFilter initialization! str, dict, set and list supported only!');
self.element_filter=np.bitwise_or(self.element_filter,self.compulsory_element_filter);
self.required_fields=['ElementVector'];
def _pipe_from_textfile(self, finp):
while True:
s = finp.readline()
if s == '':
return
s = s.rstrip('\n').lstrip()
if '##' in s:
s = s[:s.index('##')]
if '=' in s:
s = s.split('=', 1)
if s[0].lower().startswith('formula'):
s = s[1].split(',')
self.formulas.append(
encode_formula_to_array(parse_formula(s[0])))
self.scores.append(float(s[1]))
elif s[0].lower().startswith('unknown_score'):
self.unknown_score = float(s[1])
elif s.lower().startswith('dict_form'):
self.vector_form = False
elif s.lower().startswith('end'):
if not self.vector_form:
self.required_fields = ['Formula']
for i in range(len(self.formulas)):
self.formulas[i] = decode_formula_from_array(
self.formulas[i])
self.process_molecular_candidate_record = self.__process_molecular_candidate_record_formula
return
def _pipe_from_textfile(self, finp):
while True:
s = finp.readline()
if s == '':
return
s = s.rstrip('\n').lstrip()
if '##' in s:
s = s[:s.index('##')]
if '=' in s:
s = s.split('=', 1)
if s[0].lower().startswith('formula'):
self.filter.append(
encode_formula_to_array(parse_formula(s[1])))
elif s.lower().startswith('dict_form'):
self.vector_form = False
elif s.lower().startswith('end'):
if not self.vector_form:
self.required_fields = ['Formula']
for i in range(len(self.filter)):
self.filter[i] = decode_formula_from_array(
self.filter[i])
self.rejected = self.__rejected_formula
return
def __init__(self, formulas=None, use_vector_form=True):
self.filter = []
self.vector_form = use_vector_form
#self.supported_adducts=set();
if self.vector_form:
self.required_fields = ['FormulaVector']
else:
self.required_fields = ['Formula']
if formulas is None:
return
if isinstance(formulas, str):
formulas = formulas.split(',')
for formula in formulas:
self.filter.append(
encode_formula_to_array(parse_formula(formula)))
elif isinstance(formulas, list):
for formula in formulas:
if isinstance(formula, dict):
self.filter.append(encode_formula_to_array(formula))
elif isinstance(formula, np.ndarray):
self.filter.append(formula)
elif isinstance(formula, str):
fs = formula.split(',')
for f in fs:
self.filter.append(
encode_formula_to_array(parse_formula(f)))
else:
raise TypeError(
'Wrong type argument for FormulasFilter initialization! str, dict, list of (dict or str) supported only!'
)
elif isinstance(formulas, dict):
self.filter.append(encode_formula_to_array(formulas))
elif isinstance(formulas, np.ndarray):
self.filter.append(formulas)
else:
raise TypeError(
'Wrong type argument for FormulasFilter initialization! str, dict, list of (dict or str) supported only!'
)
if not self.vector_form:
for i in range(len(self.filter)):
self.filter[i] = decode_formula_from_array(self.filter[i])
def process_file(fname, correct_key, best_results, worst_results,
correct_formula):
min_correct = -1
max_correct = -1
correct_elements = ElementCompositionFilter(correct_formula,
correct_formula)
correct_formula = FormulasFilter(correct_formula)
with open(fname, 'rb') as finp:
results = list(csv.reader(finp))
del results[0]
for i in reversed(range(1, len(results))):
if results[i][9] == results[i - 1][9]:
del results[i]
elif correct_elements.rejected({
'ElementVector':
formula_to_element_vector(parse_formula(results[i][6]))
}):
#print('removed wrong elements %s %s'%(results[i][6], correct_formula))
del results[i]
elif correct_formula.rejected({
'FormulaVector':
encode_formula_to_array(parse_formula(results[i][6]))
}):
#print('removed wrong formulas %s %s'%(results[i][6], correct_formula))
del results[i]
for i in range(len(results)):
if correct_key == results[i][9]:
if min_correct == -1:
min_correct = i
max_correct = i
if min_correct > -1:
for i in range(len(best_results)):
if min_correct <= i:
best_results[i] += 1
if max_correct <= i:
worst_results[i] += 1
def _get_next_raw_record(self):
if self.currentfile=='':
self.currentfile=os.path.join(self.database_path, self.db_name, self.subf,
str(self.mzcurrent//1000),
str(self.mzcurrent%1000//100),
str(self.mzcurrent%100//10),
'%s.st2'%str(self.mzcurrent%10));
while (not os.path.isfile(self.currentfile)) and (self.mzcurrent<=self.mzmax_int):
self.mzcurrent+=1;
self.currentfile=os.path.join(self.database_path, self.db_name, self.subf,
str(self.mzcurrent//1000),
str(self.mzcurrent%1000//100),
str(self.mzcurrent%100//10),
'%s.st2'%str(self.mzcurrent%10));
if self.mzcurrent<=self.mzmax_int:
self.datafile=open(self.currentfile,'r');
self.record_index=-1;
else:
raise StopIteration();
s=self.datafile.readline();
self.record_index+=1;
while s=='':
self.datafile.close();
self.mzcurrent+=1;
self.currentfile=os.path.join(self.database_path, self.db_name, self.subf,
str(self.mzcurrent//1000),
str(self.mzcurrent%1000//100),
str(self.mzcurrent%100//10),
'%s.st2'%str(self.mzcurrent%10));
while (not os.path.isfile(self.currentfile)) and (self.mzcurrent<=self.mzmax_int):
self.mzcurrent+=1;
self.currentfile=os.path.join(self.database_path, self.db_name, self.subf,
str(self.mzcurrent//1000),
str(self.mzcurrent%1000//100),
str(self.mzcurrent%100//10),
'%s.st2'%str(self.mzcurrent%10));
if self.mzcurrent<=self.mzmax_int:
self.datafile=open(self.currentfile,'r');
self.record_index=-1;
else:
raise StopIteration();
s=self.datafile.readline();
self.record_index+=1;
s=s.rstrip('\n').split('\t');
record=MolecularRecord();
record['MZ']=float(s[0]);
if self.charged:
record['Mass']=float(s[1]);
record['Charge']=float(s[2]);
else:
record['Mass']=record['MZ'];
record['Charge']=0;
if 'ShortInChI' in self.required_fields:
record['ShortInChI']=parse_inchi(s[2+self.offs])[0];
if 'InChI' in self.required_fields:
record['InChI']=s[2+self.offs];
if 'SMILES' in self.required_fields:
record['SMILES']=s[3+self.offs];
if 'IDs' in self.required_fields:
record['IDs']=s[4+self.offs];
if 'FPT' in self.required_fields:
record['FPT']=decode_from_base64(s[5+self.offs]);
# Mask FPT here !
if 'Frag' in self.required_fields:
record['Frag']=s[6+self.offs];
if self.charged:
record['FragCharge']=s[9];
if 'InChIKeyValues' in self.required_fields:
record['InChIKeyValues']=inchikeyvalues_from_inchi(s[2+self.offs]);
if 'InChIKey' in self.required_fields:
record['InChIKey']=inchikey_from_inchi(s[2+self.offs]);
if ('Formula' in self.required_fields) or ('ElementVector' in self.required_fields) or ('FormulaVector' in self.required_fields):
fla=parse_formula(s[1+self.offs].split('/')[0]);
if 'Formula' in self.required_fields:
record['Formula']=fla;
if 'ElementVector' in self.required_fields:
record['ElementVector']=formula_to_element_vector(fla);
if 'FormulaVector' in self.required_fields:
record['FormulaVector']=encode_formula_to_array(fla);
return record;
elif isinstance(obj, np.float32):
return float(obj)
elif isinstance(obj, np.uint16):
return int(obj)
elif isinstance(obj, np.uint8):
return int(obj)
return json.JSONEncoder.default(self, obj)
def spectra_to_json(fname, spectral_list):
dirname, filename = os.path.split(fname)
if not os.path.exists(dirname):
os.makedirs(dirname)
with open(fname, 'w') as fout:
json.dump(spectral_list,
fout,
cls=MS_Data_Encoder,
sort_keys=True,
indent=4,
separators=(',', ': '))
if __name__ == '__main__':
spectra_to_json(os.path.join(chemdistiller_path, 'testlogs/t1.jsn'),
[parse_formula('C2H5OH'), 25])
s1=''.join(element_vector_to_list(self.compulsory_element_filter));
s2=''.join(element_vector_to_list(self.element_filter));
return 'ElementCompositionFilter(compulsory="%s", allowed="%s")'%(s1,s2);
registered_filters[ElementCompositionFilter.__name__]=ElementCompositionFilter;
#full_periodic_table_filter=np.full((12,),255,dtype=np.uint8);
CHNOPS_filter=ElementCompositionFilter('','CHNOPS');
CHNOPS_halogens_filter=ElementCompositionFilter('','CHNOPSFClBrIAt');
if __name__=='__main__':
test=ElementCompositionFilter('Cl','CHON');
print(test.rejected({'ElementVector':formula_to_element_vector(parse_formula('C2H5'))}));
print(test.rejected({'ElementVector':formula_to_element_vector(parse_formula('C2H5Cl'))}));
print(test.rejected({'ElementVector':formula_to_element_vector(parse_formula('C2H5S'))}));
print(test.rejected({'ElementVector':formula_to_element_vector(parse_formula('C2H5SCl'))}));
test=ElementCompositionFilter('Cl','CHONCl');
print(test.rejected({'ElementVector':formula_to_element_vector(parse_formula('C2H5'))}));
print(test.rejected({'ElementVector':formula_to_element_vector(parse_formula('C2H5Cl'))}));
print(test.rejected({'ElementVector':formula_to_element_vector(parse_formula('C2H5S'))}));
print(test.rejected({'ElementVector':formula_to_element_vector(parse_formula('C2H5SCl'))}));
test=ElementCompositionFilter('','CHONCl');
print(test)
def setup_scorer(self, formulas, scores, unknown_score=0.0):
self.formulas = []
self.scores = []
self.unknown_score = unknown_score
if isinstance(formulas, str):
formulas = formulas.split(',')
for formula in formulas:
self.formulas.append(
encode_formula_to_array(parse_formula(formula)))
elif isinstance(formulas, list):
for formula in formulas:
if isinstance(formula, dict):
self.formulas.append(encode_formula_to_array(formula))
elif isinstance(formula, np.ndarray):
self.formulas.append(formula)
elif isinstance(formula, str):
fs = formula.split(',')
for f in fs:
self.formulas.append(
encode_formula_to_array(parse_formula(f)))
else:
raise TypeError(
'Wrong type argument (formulas) for FormulaVectors initialization! str, formula, list of (formula, formulavector or str) supported only!'
)
elif isinstance(formulas, dict):
self.formulas.append(encode_formula_to_array(formulas))
elif isinstance(formulas, np.ndarray):
self.formulas.append(formulas)
else:
raise TypeError(
'Wrong type argument (formulas) for FormulaVectors initialization! str, formula, list of (formula, formulavector or str) supported only!'
)
if isinstance(scores, str):
scores = scores.split(',')
for score in scores:
self.scores.append(float(score))
elif isinstance(scores, float):
self.scores.append(scores)
elif isinstance(scores, int):
self.scores.append(float(scores))
elif isinstance(scores, list):
for score in scores:
if isinstance(score, float):
self.scores.append(score)
elif isinstance(score, dict):
self.scores.append(score)
elif isinstance(score, int):
self.scores.append(float(score))
elif isinstance(score, str):
score = score.split(',')
for s in score:
self.scores.append(float(s))
else:
raise TypeError(
'Wrong type argument (scores) for FormulaScorer initialization! str, float, int, list of (float, int or str) or dictionary supported only!'
)
else:
raise TypeError(
'Wrong type argument (scores) for FormulaScorer initialization! str, float, int, list of (float, int or str) supported only!'
)
if len(self.scores) != len(self.formulas):
raise TypeError(
'Number of formulas and number of scores supplied do not match!'
)
if not self.vector_form:
for i in range(len(self.formulas)):
self.formulas[i] = decode_formula_from_array(self.formulas[i])
for key in self.scores[index].keys():
record['Scores'][key] = self.scores[index][key]
break
if not found:
record['Scores']['Formula'] = self.unknown_score
registered_scorers[FormulaScorer.__name__] = FormulaScorer
if __name__ == '__main__':
test = FormulaScorer(use_vector_form=False)
test.setup_scorer('C2H5OH,CH4,PO4', '0.3,0.1,0.5', 1.0)
record = {
'Formula': parse_formula('CH4'),
'Scores': {}
}
test.process_molecular_candidate_record(None, record)
print(record)
record = {
'Formula': parse_formula('PO4'),
'Scores': {}
}
test.process_molecular_candidate_record(None, record)
print(record)
record = {
'Formula': parse_formula('C2H5OH'),
'Scores': {}
accepted = False
break
if accepted:
result = False
break
return result
registered_filters[FormulasFilter.__name__] = FormulasFilter
if __name__ == '__main__':
test = FormulasFilter('C2H5OH', use_vector_form=False)
print(test.filter)
test = FormulasFilter('C2H5OH,CH4,PO4', use_vector_form=False)
print(test.filter)
test = FormulasFilter(parse_formula('C2H5OH'), use_vector_form=False)
print(test.filter)
test = FormulasFilter(['C2H5OH,PO4', parse_formula('CH4')],
use_vector_form=False)
print(test.filter)
print(test.rejected({'Formula': parse_formula('CH4')}))
#False
print(test.rejected({'Formula': parse_formula('PO4')}))
#False
print(test.rejected({'Formula': parse_formula('H4C')}))
#False
print(test.rejected({'Formula': parse_formula('CH4N')}))
#True
print(test.rejected({'Formula': parse_formula('CH3')}))
#True
print(test.rejected({'Formula': parse_formula('C2H4')}))
record['Scores']['Elements'] = score
registered_scorers[ElementScorer.__name__] = ElementScorer
if __name__ == '__main__':
test = ElementScorer()
test.setup_scorer({
'C': 0.8,
'O': 0.9,
'Si': 0.1
})
record = {
'ElementVector': formula_to_element_vector(parse_formula('CH4')),
'Scores': {}
}
test.process_molecular_candidate_record(None, record)
print(record)
record = {
'ElementVector': formula_to_element_vector(parse_formula('CH4Si')),
'Scores': {}
}
test.process_molecular_candidate_record(None, record)
print(record)
record = {
'ElementVector': formula_to_element_vector(parse_formula('CH4CO')),
'Scores': {}
def hdf5_import_from_st2raw(self,
inpath,
fptmask=np.ones((11416, ), np.uint8)):
if not os.path.isfile(os.path.join(inpath, 'dbinfo.dat')):
raise IOError('Database info file not found: %s' %
os.path.join(inpath, 'dbinfo.dat'))
self.HDF5container.attrs['HDF5ContainerType'] = np.string_(
'DistilledChemicalDatabase')
self.HDF5container.attrs['HDF5ContainerVersion'] = np.string_('1.0')
finp = open(os.path.join(inpath, 'dbinfo.dat'), 'r')
#fout=open(os.path.join(self.folderpath,'dbinfo.dat'),'w');
for s in finp:
s = s.rstrip('\n').lstrip().split('=', 1)
if s[0].upper() == 'DBFORMAT':
s[1] = '3'
if s[0] != '':
#fout.write('%s=%s\n'%(s[0],s[1]));
self.HDF5container.attrs[s[0]] = np.string_(s[1])
#fout.close();
finp.close()
fptlist = []
for i in range(11416):
if fptmask[i] == 1:
fptlist.append(i)
fptlen = len(fptlist)
fptsubmask = np.packbits(np.ones((fptlen, ), np.uint8))
fptmasklen = len(fptsubmask)
fptindexes = np.array(fptlist, dtype=np.uint32)
packedmask = np.packbits(fptmask)
packedmasklen = len(packedmask)
#hdf5_ascii_string = h5py.special_dtype(vlen=bytes);
fptgroup = self.HDF5container.create_group('FingerPrints')
#Original mask, packed
fptoriginalmask = fptgroup.create_dataset("FPTOriginalMask",
(packedmasklen, ),
maxshape=(packedmasklen, ),
dtype=np.uint8)
fptoriginalmask[:] = packedmask[:]
#List of indeces of original FPT bits (11416)
fptmask = fptgroup.create_dataset("FPTMask", (fptlen, ),
maxshape=(fptlen, ),
compression="gzip",
compression_opts=4,
dtype=np.uint32)
fptmask[:] = fptindexes[:]
#Mask for working bits (packed)
fptsubmask = fptgroup.create_dataset("FPTsubmask", (1, fptmasklen),
chunks=(100, fptmasklen),
maxshape=(None, fptmasklen),
compression="gzip",
compression_opts=4,
dtype=np.uint8)
#FPT info: 0 - original bit count=11416, 1 - length of new fpt after masking, 2 - length of the packed fpt, 3 - No of padding bits
fptinfo = fptgroup.create_dataset("FPTInfo", (4, ),
maxshape=(4, ),
dtype=np.uint32)
fptinfo[0] = 11416
fptinfo[1] = fptlen
fptinfo[2] = fptmasklen
fptinfo[3] = fptmasklen * 8 - fptlen
print('Listing input files')
subpaths = ['/Negative', '/Positive', '/Neutral']
#subpaths=['/Positive'];
for subpath in subpaths:
print(subpath)
fptgroup = self.HDF5container.create_group(subpath +
'/FingerPrints')
fraggroup = self.HDF5container.create_group(subpath +
'/FragPrints')
chemgroup = self.HDF5container.create_group(subpath + '/ChemInfo')
chargegroup = self.HDF5container[subpath]
#New FPT Array, packed and trimmed to fptmask
fptdataset = fptgroup.create_dataset("FPTArray", (1, fptmasklen),
chunks=(100, fptmasklen),
maxshape=(None, fptmasklen),
compression="gzip",
compression_opts=4,
dtype=np.uint8)
if subpath != '/Neutral':
masschargedataset = chargegroup.create_dataset(
"MZMassCharge", (1, 3),
chunks=(10000, 3),
maxshape=(None, 3),
compression="gzip",
compression_opts=4,
dtype=np.float32)
else:
mzdataset = chargegroup.create_dataset("MZ", (1, ),
chunks=(10000, ),
maxshape=(None, ),
compression="gzip",
compression_opts=4,
dtype=np.float32)
inchikey_dataset = chemgroup.create_dataset("InChiKeyValues",
(1, 15),
chunks=(10000, 15),
maxshape=(None, 15),
compression="gzip",
compression_opts=4,
dtype=np.uint8)
elementsvector_dataset = chemgroup.create_dataset(
"ElementsVector", (1, 12),
chunks=(10000, 12),
maxshape=(None, 12),
compression="gzip",
compression_opts=4,
dtype=np.uint8)
formulavector_dataset = chemgroup.create_dataset(
"FormulaVector", (1, 96),
chunks=(10000, 96),
maxshape=(None, 96),
compression="gzip",
compression_opts=4,
dtype=np.uint16)
fragprintindex_dataset = fraggroup.create_dataset(
"FragPrintIndex", (1, 2),
chunks=(10000, 2),
maxshape=(None, 2),
compression="gzip",
compression_opts=4,
dtype=np.int64)
fragprintvalues_dataset = fraggroup.create_dataset(
"FragPrintValues", (1, ),
chunks=(10000, ),
maxshape=(None, ),
compression="gzip",
compression_opts=4,
dtype=np.float32)
smiles_dataset = chemgroup.create_dataset("SMILES", (1, 2),
chunks=(10000, 2),
maxshape=(None, 2),
compression="gzip",
compression_opts=4,
dtype=np.int64)
inchi_dataset = chemgroup.create_dataset("InChi", (1, 4, 2),
chunks=(10000, 4, 2),
maxshape=(None, 4, 2),
compression="gzip",
compression_opts=4,
dtype=np.int64)
ids_dataset = chemgroup.create_dataset("IDs", (1, 2),
chunks=(10000, 2),
maxshape=(None, 2),
compression="gzip",
compression_opts=4,
dtype=np.int64)
ascii_dataset = chemgroup.create_dataset("ASCII", (1, ),
chunks=(10000, ),
maxshape=(None, ),
compression="gzip",
compression_opts=4,
dtype=np.uint8)
recordindex = -1
fileslist = []
for i in range(0, 2000):
if os.path.exists(inpath + subpath + '/%s' % i):
print(inpath + subpath + '/%s' % i)
for j in range(0, 10):
if os.path.exists(inpath + subpath + '/%s/%s' %
(i, j)):
for k in range(0, 10):
if os.path.exists(inpath + subpath +
'/%s/%s/%s' % (i, j, k)):
for l in range(0, 10):
if os.path.isfile(inpath + subpath +
'/%s/%s/%s/%s.st2' %
(i, j, k, l)):
fileslist.append(
inpath + subpath +
'/%s/%s/%s/%s.st2' %
(i, j, k, l))
print('Total number of input files: %s' % len(fileslist))
for filename in fileslist:
fpath, fname = os.path.split(filename)
subpath = fpath.replace(inpath, '')
if 'Neutral' in subpath:
charged = False
offs = 0
else:
charged = True
offs = 2
print('Importing: .../%s/%s' % (subpath, fname))
dblist = []
with open(filename, 'r') as finp:
for s in finp:
try:
s = s.replace('\n', '').replace('\r',
'').split('\t')
mz = float(s[0])
if charged:
mass = float(s[1])
charge = float(s[2])
else:
mass = mz
charge = 0.0
if mass >= 12.0:
#shortinchi=s[1+offs];
inchi = s[2 + offs]
smiles = s[3 + offs]
ids = s[4 + offs]
fpt = s[5 + offs]
fpt = decode_from_base64(fpt)
fpt = np.unpackbits(fpt)
frag = s[6 + offs]
if charged:
fragcharge = s[9]
else:
fragcharge = ''
recordindex += 1
if recordindex % 1000 == 0:
print('Total: %s' % (recordindex + 1))
dblist.append([
recordindex, mz, charged, mass, charge,
inchi, fpt, frag, fragcharge, smiles, ids
])
except:
print('Error! Skipping!')
if len(dblist) > 0:
#expand datasets here
fptdataset.resize((recordindex + 1, fptmasklen))
if charged:
masschargedataset.resize((recordindex + 1, 3))
else:
mzdataset.resize((recordindex + 1, ))
inchikey_dataset.resize((recordindex + 1, 15))
elementsvector_dataset.resize((recordindex + 1, 12))
formulavector_dataset.resize((recordindex + 1, 96))
fragprintindex_dataset.resize((recordindex + 1, 2))
smiles_dataset.resize((recordindex + 1, 2))
ids_dataset.resize((recordindex + 1, 2))
inchi_dataset.resize((recordindex + 1, 4, 2))
for db in dblist:
currentindex = db[0]
fptdataset[currentindex, :] = np.packbits(
db[6][fptindexes])[:]
#print(inchi)
inchi = parse_inchi(db[5])
#print(inchi)
inchikeyvalues = inchikeyvalues_from_inchi(db[5])
sformula = inchi[0].split('/', 1)[0]
#print(sformula);
formula = parse_formula(sformula)
elementsvector = formula_to_element_vector(formula)
encodedformula = encode_formula_to_array(formula)
charge = db[4]
charged = db[2]
if charged:
#print(db[7],db[8])
frags = parse_string_fragment_charges(
charge, db[7], db[8])
#print(frags)
else:
frags = parse_string_fragments(db[7])
if charged:
masschargedataset[currentindex, 0] = db[1]
masschargedataset[currentindex, 1] = db[3]
masschargedataset[currentindex, 2] = charge
else:
mzdataset[currentindex] = db[1]
inchikey_dataset[currentindex, :] = inchikeyvalues[:]
elementsvector_dataset[
currentindex, :] = elementsvector[:]
formulavector_dataset[
currentindex, :] = encodedformula[:]
fragcount = len(frags)
frags = np.array(frags, dtype=np.float32)
fragprintindex_dataset[currentindex,
0] = self.fragprintpos
fragprintindex_dataset[
currentindex, 1] = self.fragprintpos + fragcount
fragprintvalues_dataset.resize(
(self.fragprintpos + fragcount, ))
fragprintvalues_dataset[self.fragprintpos:self.
fragprintpos +
fragcount] = frags[:]
self.fragprintpos += fragcount
smiles = bytearray(db[9].encode('ascii'))
smileslen = len(smiles)
smiles = np.array(smiles, dtype=np.uint8)
ids = bytearray(db[10].encode('ascii'))
idslen = len(ids)
ids = np.array(ids, dtype=np.uint8)
sinchi = inchi[0].split('/', 1)
if len(sinchi) > 1:
sinchi = sinchi[1]
else:
sinchi = ''
inchi0 = bytearray(sformula.encode('ascii'))
inchi1 = bytearray(sinchi.encode('ascii'))
inchi2 = bytearray(inchi[2].encode('ascii'))
inchi3 = bytearray(inchi[1].encode('ascii'))
inchi0len = len(inchi0)
inchi1len = len(inchi1)
inchi2len = len(inchi2)
inchi3len = len(inchi3)
inchi0 = np.array(inchi0, dtype=np.uint8)
inchi1 = np.array(inchi1, dtype=np.uint8)
inchi2 = np.array(inchi2, dtype=np.uint8)
inchi3 = np.array(inchi3, dtype=np.uint8)
ascii_dataset.resize(
(self.asciipos + smileslen + idslen + inchi0len +
inchi1len + inchi2len + inchi3len, ))
smiles_dataset[currentindex, 0] = self.asciipos
smiles_dataset[currentindex,
1] = self.asciipos + smileslen
ascii_dataset[self.asciipos:self.asciipos +
smileslen] = smiles[:]
self.asciipos += smileslen
ids_dataset[currentindex, 0] = self.asciipos
ids_dataset[currentindex, 1] = self.asciipos + idslen
ascii_dataset[self.asciipos:self.asciipos +
idslen] = ids[:]
self.asciipos += idslen
inchi_dataset[currentindex, 0, 0] = self.asciipos
inchi_dataset[currentindex, 0,
1] = self.asciipos + inchi0len
ascii_dataset[self.asciipos:self.asciipos +
inchi0len] = inchi0[:]
self.asciipos += inchi0len
inchi_dataset[currentindex, 1, 0] = self.asciipos
inchi_dataset[currentindex, 1,
1] = self.asciipos + inchi1len
ascii_dataset[self.asciipos:self.asciipos +
inchi1len] = inchi1[:]
self.asciipos += inchi1len
inchi_dataset[currentindex, 2, 0] = self.asciipos
inchi_dataset[currentindex, 2,
1] = self.asciipos + inchi2len
ascii_dataset[self.asciipos:self.asciipos +
inchi2len] = inchi2[:]
self.asciipos += inchi2len
inchi_dataset[currentindex, 3, 0] = self.asciipos
inchi_dataset[currentindex, 3,
1] = self.asciipos + inchi3len
ascii_dataset[self.asciipos:self.asciipos +
inchi3len] = inchi3[:]
self.asciipos += inchi3len
print('Import Finished!')
def _pipe_from_textfile(self, finp):
while True:
s = finp.readline()
if s == '':
return
s = s.rstrip('\n').lstrip()
if '##' in s:
s = s[:s.index('##')]
if '=' in s:
s = s.split('=', 1)
if s[0].lower().startswith('totalscore'):
self['TotalScore'] = float(s[1])
elif s[0].lower().startswith('adduct'):
self['Adduct'] = s[1]
elif s[0].lower().startswith('isotopeextramass'):
self['IsotopeExtraMass'] = float(s[1])
elif s[0].lower().startswith('isotope'):
self['Isotope'] = int(s[1])
elif s[0].lower().startswith('mz'):
self['MZ'] = float(s[1])
elif s[0].lower().startswith('mass'):
self['Mass'] = float(s[1])
elif s[0].lower().startswith('charge'):
self['Charge'] = int(s[1])
elif s[0].lower().startswith('dbformat'):
self['DBFormat'] = int(s[1])
elif s[0].lower().startswith('dbindex'):
self['DBIndex'] = int(s[1])
elif s[0].lower().startswith('rindex'):
self['RIndex'] = int(s[1])
elif s[0].lower().startswith('dbname'):
self['DBName'] = s[1]
elif s[0].lower().startswith('rfile'):
self['RFile'] = s[1]
elif s[0].lower().startswith('smiles'):
self['SMILES'] = s[1]
elif s[0].lower().startswith('ids'):
self['IDs'] = s[1]
elif s[0].lower().startswith('annotation'):
self['Annotation'] = s[1]
elif s[0].lower().startswith('shortinchi'):
self['ShortInChI'] = s[1]
elif s[0].lower().startswith('inchikeyvalues'):
self['InChIKeyValues'] = string_to_numpy_byte_array(s[1])
elif s[0].lower().startswith('inchikey'):
self['InChiKey'] = s[1]
elif s[0].lower().startswith('inchi'):
self['InChI'] = s[1]
elif s[0].lower().startswith('formulavector'):
self['FormulaVector'] = string_to_numpy_uint16_array(s[1])
elif s[0].lower().startswith('elementvector'):
self['ElementVector'] = string_to_numpy_byte_array(s[1])
elif s[0].lower().startswith('frag'):
self['Frag'] = string_to_float_list(s[1])
elif s[0].lower().startswith('formula'):
self['Formula'] = parse_formula(s[1])
elif s[0].lower().startswith('fpt'):
self['FPT'] = decode_from_base64(s[1])
elif s[0].lower().startswith('scores'):
if not ('Scores' in self):
self['Scores'] = {}
score = s[1].split(':', 1)
self['Scores'][score[0]] = float(score[1])
elif s.lower().startswith('end'):
return