def write_stereo_dct(spc_str, allstereo=False):
""" read the species file in a .csv format and write a new one
that has stero information
"""
# Read the headers
headers = [
header for header in read_csv_headers(spc_str) if header != 'name'
]
if 'inchi' not in headers:
headers.append('inchi')
headers_noich = [
header for header in headers if header not in ('inchi', 'inchikey')
]
new_headers = ['inchi', 'inchikey'] + headers_noich
# Read in the initial CSV information (deal with mult stereo)
init_dct = csv_dct(spc_str, values=headers, key='name')
# Build a new dict
new_dct = {}
names_in_order = list(init_dct.keys())
randomized_names = list(init_dct.keys())
nproc_avail = max(len(os.sched_getaffinity(0)) - 1, 1)
num_spc = len(randomized_names)
spc_per_proc = math.floor(num_spc / nproc_avail)
queue = multiprocessing.Queue()
procs = []
random.shuffle(randomized_names)
for proc_n in range(nproc_avail):
spc_start = proc_n * spc_per_proc
if proc_n == nproc_avail - 1:
spc_end = num_spc
else:
spc_end = (proc_n + 1) * spc_per_proc
names = randomized_names[spc_start:spc_end]
proc = multiprocessing.Process(target=_add_stereo_to_dct,
args=(
queue,
names,
init_dct,
headers_noich,
allstereo,
))
procs.append(proc)
proc.start()
for _ in procs:
new_dct.update(queue.get())
for proc in procs:
proc.join()
return new_dct, new_headers, names_in_order
def _set_headers(spc_str):
"""
"""
headers = [header for header in read_csv_headers(spc_str)
if header != 'name']
if 'inchi' not in headers:
headers.append('inchi')
headers_noich = [header for header in headers
if header not in ('inchi', 'inchikey')]
new_headers = ['inchi', 'inchikey' ] + headers_noich
return new_headers
def write_basis_csv(spc_str, outname='species_hof_basis.csv', path='.'):
"""read the species file in a .csv format and write a new one
that has hof basis species added to it
"""
headers = [header for header in read_csv_headers(spc_str)
if header != 'name']
if 'inchi' not in headers:
headers.append('inchi')
headers_noich = [header for header in headers
if header not in ('inchi', 'inchikey')]
new_headers = ['inchi' ] + headers_noich
# Read in the initial CSV information (deal with mult stereo)
init_dct = csv_dct(spc_str, values=headers, key='name')
# Build a new dict
names = list(init_dct.keys())
spc_queue = []
for name in names:
spc_queue.append([name, ''])
new_dct = {}
new_names = []
# Get the hof basis molecules
ref_schemes = ['cbh0', 'cbh1', 'cbh2']
for ref_scheme in ref_schemes:
_, uniref_dct = basis.prepare_refs(
ref_scheme, init_dct, spc_queue)
for newn in list(uniref_dct.keys()):
tempn_smiles = automol.inchi.smiles(uniref_dct[newn]['inchi'])
tempn = ref_scheme + '_' + tempn_smiles
if tempn not in new_names:
new_names.append(tempn)
new_dct[tempn] = uniref_dct[newn]
new_dct[tempn]['smiles'] = tempn_smiles
new_dct.update(init_dct)
init_dct = new_dct
# Writer string
spc_str = ','.join(['name'] + new_headers) + '\n'
for name in init_dct:
spc_str += '{},'.format(name)
for idx, header in enumerate(new_headers):
val = init_dct[name][header]
if isinstance(val, str):
val = "'{}'".format(val)
spc_str += str(val)
if idx+1 < len(new_headers):
spc_str += ','
spc_str += '\n'
# Write the file
basis_file = os.path.join(path, outname)
with open(basis_file, 'w') as file_obj:
file_obj.write(spc_str)
def write_basis_csv(spc_str, outname='species_hof_basis.csv', path='.', parallel=False):
"""read the species file in a .csv format and write a new one
that has hof basis species added to it
"""
headers = [header for header in read_csv_headers(spc_str)
if header != 'name']
if 'inchi' not in headers:
headers.append('inchi')
headers_noich = [header for header in headers
if header not in ('inchi', 'inchikey')]
new_headers = ['inchi', 'inchikey' ] + headers_noich
# Read in the initial CSV information (deal with mult stereo)
init_dct = csv_dct(spc_str, values=headers, key='name')
# Build a new dict
names = list(init_dct.keys())
spc_queue = []
for name in names:
spc_queue.append([name, ''])
new_dct = {}
# Get the hof basis molecules
ref_schemes = ['cbh0', 'cbh1', 'cbh2']
ref_dct = {}
spc_str = ','.join(['name'] + new_headers) + '\n'
for ref_scheme in ref_schemes:
formula_dct = {}
_, uniref_dct = basis.prepare_refs(
ref_scheme, init_dct, spc_queue, repeats=True, parallel=parallel)
for name in uniref_dct:
spc_str += ref_scheme + '_'
smiles = automol.inchi.smiles(uniref_dct[name]['inchi'])
uniref_dct[name]['smiles'] = smiles
formula = automol.inchi.formula_string(uniref_dct[name]['inchi'])
if formula in formula_dct:
formula_dct[formula] += 1
formula = formula + '({})'.format(formula_dct[formula])
else:
formula_dct[formula] = 0
spc_str += formula + ','
for idx, header in enumerate(new_headers):
val = uniref_dct[name][header]
if isinstance(val, str):
val = "'{}'".format(val)
spc_str += str(val)
if idx+1 < len(new_headers):
spc_str += ','
spc_str += '\n'
basis_file = os.path.join(path, outname + '_basis')
with open(basis_file, 'w') as file_obj:
file_obj.write(spc_str)
new_names = []
for ref_scheme in ref_schemes:
_, uniref_dct = basis.prepare_refs(
ref_scheme, init_dct, spc_queue, repeats=False, parallel=parallel)
for newn in list(uniref_dct.keys()):
tempn_smiles = automol.inchi.smiles(uniref_dct[newn]['inchi'])
tempn = ref_scheme + '_' + tempn_smiles
if tempn not in new_names:
new_names.append(tempn)
new_dct[tempn] = uniref_dct[newn]
new_dct[tempn]['smiles'] = tempn_smiles
ref_dct.update(new_dct)
new_dct.update(init_dct)
init_dct = new_dct
# Writer string
spc_str = ','.join(['name'] + new_headers) + '\n'
for name in init_dct:
formula_dct = {}
if 'cbh' in name:
namelabel = name.split('_')[0]
if not namelabel in formula_dct:
formula_dct[namelabel] = {}
frmdct = formula_dct[namelabel]
spc_str += namelabel + '_'
formula = automol.inchi.formula_string(init_dct[name]['inchi'])
if formula in frmdct:
frmdct[formula] += 1
formula = formula + '({})'.format(frmdct[formula])
else:
frmdct[formula] = 0
spc_str += formula + ','
else:
spc_str += '{},'.format(name)
for idx, header in enumerate(new_headers):
val = init_dct[name][header]
if isinstance(val, str):
val = "'{}'".format(val)
spc_str += str(val)
if idx+1 < len(new_headers):
spc_str += ','
spc_str += '\n'
# Write the file
basis_file = os.path.join(path, outname)
with open(basis_file, 'w') as file_obj:
file_obj.write(spc_str)
def write_stereo_csv(spc_str, outname='species_stereo.csv', path='.',
allstereo=False):
""" read the species file in a .csv format and write a new one
that has stero information
"""
# Read the headers
headers = [header for header in read_csv_headers(spc_str)
if header != 'name']
if 'inchi' not in headers:
headers.append('inchi')
headers_noich = [header for header in headers
if header not in ('inchi', 'inchikey')]
new_headers = ['inchi', 'inchikey'] + headers_noich
# Read in the initial CSV information (deal with mult stereo)
init_dct = csv_dct(spc_str, values=headers, key='name')
# Build a new dict
new_dct = {}
names_in_order = list(init_dct.keys())
randomized_names = list(init_dct.keys())
nproc_avail = max(len(os.sched_getaffinity(0)) - 1, 1)
num_spc = len(randomized_names)
spc_per_proc = math.floor(num_spc / nproc_avail)
queue = multiprocessing.Queue()
procs = []
random.shuffle(randomized_names)
for proc_n in range(nproc_avail):
spc_start = proc_n*spc_per_proc
if proc_n == nproc_avail - 1:
spc_end = num_spc
else:
spc_end = (proc_n+1)*spc_per_proc
names = randomized_names[spc_start:spc_end]
proc = multiprocessing.Process(
target=_add_stereo_to_dct,
args=(queue, names, init_dct, headers_noich, allstereo,))
procs.append(proc)
proc.start()
for _ in procs:
new_dct.update(queue.get())
for proc in procs:
proc.join()
# Writer string
spc_str = ','.join(['name'] + new_headers) + '\n'
for name in names_in_order:
if name in new_dct:
spc_str += '{},'.format(name)
for idx, header in enumerate(new_headers):
val = new_dct[name][header]
if isinstance(val, str):
val = "'{}'".format(val)
spc_str += str(val)
if idx+1 < len(new_headers):
spc_str += ','
spc_str += '\n'
# Write the file
stereo_file = os.path.join(path, outname)
with open(stereo_file, 'w') as file_obj:
file_obj.write(spc_str)
def write_stereo_csv(spc_str, outname='species_stereo.csv', path='.',
allstereo=False):
""" read the species file in a .csv format and write a new one
that has stero information
"""
# Read the headers
headers = [header for header in read_csv_headers(spc_str)
if header != 'name']
if 'inchi' not in headers:
headers.append('inchi')
headers_noich = [header for header in headers
if header not in ('inchi', 'inchikey')]
new_headers = ['inchi', 'inchikey'] + headers_noich
# Read in the initial CSV information (deal with mult stereo)
init_dct = csv_dct(spc_str, values=headers, key='name')
# Build a new dict
new_dct = {}
for name in init_dct:
# Get the inchi key
ich = init_dct[name]['inchi']
# Generate ichs with stereo and hashes
ichs_wstereo = _generate_stereo(ich, allstereo=allstereo)
# Add name and inchi info to string
for idx, ich_wstereo in enumerate(ichs_wstereo):
# Augment name if needed
if idx == 0:
sname = name
else:
sname = name + '-{}'.format(str(idx+1))
# Initialize
new_dct[sname] = {}
# Generate hash key from InChI
hashkey = automol.inchi.inchi_key(ich_wstereo)
# Add vals to dct
new_dct[sname].update({'inchi': ich_wstereo, 'inchikey': hashkey})
for header in headers_noich:
new_dct[sname][header] = init_dct[name][header]
# Writer string
spc_str = ','.join(['name'] + new_headers) + '\n'
for name in new_dct:
spc_str += '{},'.format(name)
for idx, header in enumerate(new_headers):
val = new_dct[name][header]
if isinstance(val, str):
val = "'{}'".format(val)
spc_str += str(val)
if idx+1 < len(new_headers):
spc_str += ','
spc_str += '\n'
# Write the file
stereo_file = os.path.join(path, outname)
with open(stereo_file, 'w') as file_obj:
file_obj.write(spc_str)
def write_basis_csv(spc_str,
outname='species_hof_basis.csv',
path='.',
parallel=False):
""" Read the species file in a .csv format and write a new one
that has hof basis species added to it.
"""
headers = [
header for header in read_csv_headers(spc_str) if header != 'name'
]
if 'inchi' not in headers:
headers.append('inchi')
headers_noich = [
header for header in headers if header not in ('inchi', 'inchikey')
]
new_headers = ['inchi', 'inchikey'] + headers_noich
csv_str = ','.join(['name'] + new_headers) + '\n'
# Read in the initial CSV information (deal with mult stereo)
init_dct = csv_dct(spc_str, values=headers, key='name')
# Build a new dict
names = list(init_dct.keys())
spc_queue = []
for name in names:
spc_queue.append([name, ''])
# Find all references
ref_schemes = ['cbh0', 'cbh1', 'cbh2']
for ref_scheme in ref_schemes:
formula_dct = {}
_, uniref_dct = thermfit.prepare_refs(ref_scheme,
init_dct,
spc_queue,
repeats=True,
parallel=parallel)
for name in uniref_dct:
spc_str, formula_dct = _species_row_string(uniref_dct, formula_dct,
name, new_headers)
csv_str += ref_scheme + '_' + spc_str
basis_file = os.path.join(path, outname + '_basis')
with open(basis_file, 'w') as file_obj:
file_obj.write(spc_str)
# Find only the unique references
new_names = []
for ref_scheme in ref_schemes:
_, uniref_dct = thermfit.prepare_refs(ref_scheme,
init_dct,
spc_queue,
repeats=False,
parallel=parallel)
new_names, init_dct, uniref_dct = _add_unique_references_to_dct(
new_names, init_dct, uniref_dct, ref_scheme)
spc_str = ','.join(['name'] + new_headers) + '\n'
formula_dct = {}
for name in init_dct:
if 'cbh' in name:
namelabel = name.split('_')[0]
if namelabel not in formula_dct:
formula_dct[namelabel] = {}
frmdct = formula_dct[namelabel]
spc_str += namelabel + '_'
formula = _get_formula_from_dct(init_dct, name)
formula, frmdct = _assign_unique_name(frmdct, formula)
spc_str += formula + ','
else:
spc_str += '{},'.format(name)
for idx, header in enumerate(new_headers):
val = init_dct[name][header]
if isinstance(val, str):
val = "'{}'".format(val)
spc_str += str(val)
if idx + 1 < len(new_headers):
spc_str += ','
spc_str += '\n'
# Write the file
basis_file = os.path.join(path, outname)
with open(basis_file, 'w') as file_obj:
file_obj.write(spc_str)