def test_fileread(self, tmpdir):
contentfile = os.path.join(str(tmpdir), 'content.txt')
with open(contentfile, 'w') as f:
f.write(self.content)
with uber_open_rmode(contentfile) as f:
content = f.read()
assert content.decode('UTF-8') == self.content
def test_fileread(self, tmpdir):
contentfile = os.path.join(str(tmpdir), 'content.txt')
with open(contentfile, 'w') as f:
f.write(self.content)
with uber_open_rmode(contentfile) as f:
content = f.read()
assert content.decode('UTF-8') == self.content
def test_objectread(self, tmpdir):
contentfile = os.path.join(tmpdir, 'content.txt')
with open(contentfile, 'w') as f:
f.write(self.content)
with open(contentfile, 'rb') as openf:
with uber_open_rmode(openf) as f:
content = f.read()
assert content.decode('UTF-8') == self.content
def read(self, log_info, append=True):
"""Parses a LAMMPS screen output/log file for thermodynamic data."""
#Remove existing data if append is False
if append is False:
self.__simulations = []
#Handle file names, strings and open file-like objects equivalently
with uber_open_rmode(log_info) as log_info:
headers = []
footers = []
i = 0
#for all lines in file/output
for line in log_info:
#skip blank lines
if len(line.split()) == 0:
continue
#This is listed before both run and minimize simulations
if 'Memory usage per processor =' in line:
headers.append(i + 1)
#This follows both run and minimize simulations
elif 'Loop time of' in line:
footers.append(i - 1)
i += 1
#Add last line to footers for incomplete logs
footers.append(i)
#Reset file pointer
log_info.seek(0)
#for all lines in file/output
for header, footer in zip(headers, footers):
#Initialize simulation data dictionary
sim = {}
#Read thermo data and reset file pointer
sim['thermo'] = pd.read_csv(log_info,
header=header,
nrows=footer - header,
sep='\s+',
engine='python',
skip_blank_lines=True)
log_info.seek(0)
#Append simulation results
self.__simulations.append(sim)
def load(poscar):
"""
Reads a poscar-style coordination file for a system.
Returns an atomman.System, and a list of elements if the file gives them.
"""
#Read in all lines of the file
with uber_open_rmode(poscar) as f:
lines = f.read().split('\n')
#Interpret box information
box_scale = float(lines[1])
avect = np.array(lines[2].split(), dtype='float64') * box_scale
bvect = np.array(lines[3].split(), dtype='float64') * box_scale
cvect = np.array(lines[4].split(), dtype='float64') * box_scale
box = am.Box(avect=avect, bvect=bvect, cvect=cvect)
#Read in elements, number of types, and style info
try:
typenums = np.array(lines[5].split(), dtype='int32')
elements = [None for n in xrange(len(typenums))]
style = lines[6]
start_i = 7
except:
elements = lines[5].split()
typenums = np.array(lines[6].split(), dtype='int32')
style = lines[7]
start_i = 8
#Build atype list
atype = np.array([], dtype='int32')
for i in xrange(len(typenums)):
atype = np.hstack((atype, np.full(typenums[i], i + 1, dtype='int32')))
#Check which coordinate style to use
if style[0] in 'cCkK':
scale = False
else:
scale = True
#Read in positions
natoms = np.sum(typenums)
pos = np.empty((natoms, 3), dtype='float64')
count = 0
for i in xrange(start_i, len(lines)):
terms = lines[i].split()
if len(terms) > 0:
pos[count, :] = np.array(terms, dtype='float64')
count += 1
atoms = am.Atoms(natoms=natoms, prop={'atype': atype, 'pos': pos})
system = am.System(atoms=atoms, box=box, scale=scale)
return system, elements
def load(poscar):
"""
Reads a poscar-style coordination file for a system.
Returns an atomman.System, and a list of elements if the file gives them.
"""
#Read in all lines of the file
with uber_open_rmode(poscar) as f:
lines = f.read().split('\n')
#Interpret box information
box_scale = float(lines[1])
avect = np.array(lines[2].split(), dtype='float64') * box_scale
bvect = np.array(lines[3].split(), dtype='float64') * box_scale
cvect = np.array(lines[4].split(), dtype='float64') * box_scale
box = am.Box(avect=avect, bvect=bvect, cvect=cvect)
#Read in elements, number of types, and style info
try:
typenums = np.array(lines[5].split(), dtype='int32')
elements = [None for n in xrange(len(typenums))]
style = lines[6]
start_i = 7
except:
elements = lines[5].split()
typenums = np.array(lines[6].split(), dtype='int32')
style = lines[7]
start_i = 8
#Build atype list
atype = np.array([], dtype='int32')
for i in xrange(len(typenums)):
atype = np.hstack((atype, np.full(typenums[i], i+1, dtype='int32')))
#Check which coordinate style to use
if style[0] in 'cCkK':
scale = False
else:
scale = True
#Read in positions
natoms = np.sum(typenums)
pos = np.empty((natoms, 3), dtype='float64')
count = 0
for i in xrange(start_i, len(lines)):
terms = lines[i].split()
if len(terms) > 0:
pos[count,:] = np.array(terms, dtype='float64')
count += 1
atoms = am.Atoms(natoms=natoms, prop={'atype':atype, 'pos':pos})
system = am.System(atoms=atoms, box=box, scale=scale)
return system, elements
def load(self, model, pot_dir=None):
"""
loads potential-LAMMPS data model info.
Arguments:
model -- a string or file-like obect of a json/xml data model containing a potential-LAMMPS branch.
pot_dir -- (optional) the directory location of any artifacts associated with the potential.
"""
# Load model and find potential-LAMMPS
if isinstance(model, DM):
self.__dm = model.find('potential-LAMMPS')
else:
with uber_open_rmode(model) as f:
self.__dm = DM(f).find('potential-LAMMPS')
for atom in self.__dm.iteraslist('atom'):
#Check if element is listed
try:
test = atom['element']
#If no element is listed, symbol and mass must be
except:
try:
test = atom['symbol']
test = atom['mass']
atom['element'] = atom['symbol']
except:
raise KeyError("Error reading Potential's atomic info: mass and symbol are needed if element is not given!")
#Check if symbol is listed. If not, make symbol = element
try:
test = atom['symbol']
except:
atom['symbol'] = atom['element']
#Check if mass is listed. If not, set to standard value of element
try:
mass_check = atom['mass']
except:
atom['mass'] = atomic_mass(atom['element'])
assert isinstance(atom['mass'], float), 'Mass needs to be a number!'
if pot_dir is not None:
self.pot_dir = pot_dir
else:
self.pot_dir = ''
def load(data, pbc=(True, True, True), atom_style='atomic', units='metal'):
"""
Read a LAMMPS-style atom data file and return a System.
Argument:
data = file name, file-like object or string to read data from.
Keyword Arguments:
pbc -- list or tuple of three boolean values indicating which System directions are periodic. Default is (True, True, True).
atom_style -- LAMMPS atom_style option associated with the data file. Default is 'atomic'.
units -- LAMMPS units option associated with the data file. Default is 'metal'.
When the file is read in, the units of all property values are automatically converted to atomman's set working units.
"""
units_dict = style.unit(units)
readtime = False
count = 0
xy = 0.0
xz = 0.0
yz = 0.0
system = None
with uber_open_rmode(data) as fp:
#loop over all lines in fp
for line in fp:
terms = line.split()
if len(terms)>0:
#read atomic information if time to do so
if readtime == True:
a_id = int(terms[0]) - 1
prop_vals[a_id] = terms[1:]
count += 1
#save values to system once all atoms read in
if count == natoms:
readtime = False
count = 0
start = 0
#iterate over all atom_style properties
for name, v in props.iteritems():
if name != 'a_id':
size, dim, dtype = v
value = np.asarray(prop_vals[:, start:start+size], dtype=dtype)
start += size
#set units according to LAMMPS units style
unit = units_dict.get(dim, None)
system.atoms_prop(key=name, value=uc.set_in_units(value, unit))
#read number of atoms
elif len(terms) == 2 and terms[1] == 'atoms':
natoms = int(terms[0])
#read number of atom types
elif len(terms) == 3 and terms[1] == 'atom' and terms[2] == 'types':
natypes = int(terms[0])
#read boundary info
elif len(terms) == 4 and terms[2] == 'xlo' and terms[3] == 'xhi':
xlo = uc.set_in_units(float(terms[0]), units_dict['length'])
xhi = uc.set_in_units(float(terms[1]), units_dict['length'])
elif len(terms) == 4 and terms[2] == 'ylo' and terms[3] == 'yhi':
ylo = uc.set_in_units(float(terms[0]), units_dict['length'])
yhi = uc.set_in_units(float(terms[1]), units_dict['length'])
elif len(terms) == 4 and terms[2] == 'zlo' and terms[3] == 'zhi':
zlo = uc.set_in_units(float(terms[0]), units_dict['length'])
zhi = uc.set_in_units(float(terms[1]), units_dict['length'])
elif len(terms) == 6 and terms[3] == 'xy' and terms[4] == 'xz' and terms[5] == 'yz':
xy = uc.set_in_units(float(terms[0]), units_dict['length'])
xz = uc.set_in_units(float(terms[1]), units_dict['length'])
yz = uc.set_in_units(float(terms[2]), units_dict['length'])
#Flag when reached data and setup for reading
elif len(terms) == 1 and terms[0] in ('Atoms', 'Velocities'):
#create system if not already
if system is None:
box = am.Box(xlo=xlo, xhi=xhi,
ylo=ylo, yhi=yhi,
zlo=zlo, zhi=zhi,
xy=xy, xz=xz, yz=yz)
system = am.System(box=box, atoms=am.Atoms(natoms=natoms), pbc = pbc)
if terms[0] == 'Atoms':
props = style.atom(atom_style)
else:
props = style.velocity(atom_style)
nvals = 0
for name, v in props.iteritems():
nvals += v[0]
prop_vals = np.empty((natoms, nvals-1), dtype=float)
readtime = True
assert system.natypes == natypes, 'Number of atom types does not match!'
return system
def read(self, log_info, append=True):
"""Parses a LAMMPS screen output/log file for thermodynamic data."""
#Rset properties and values if append is False
if append is False:
self.__simulations = []
self.__lammps_version = None
self.__lammps_date = None
#Strings found before run and mimize simulations
sim_trigger = ['Memory usage per processor =','Per MPI rank memory allocation (min/avg/max) =']
#Handle file names, strings and open file-like objects equivalently
with uber_open_rmode(log_info) as log_info:
headers = []
footers = []
i = 0
#for all lines in file/output
for line in log_info:
#skip blank lines
if len(line.split()) == 0:
continue
#Save the LAMMPS version information
if line[:8] == 'LAMMPS (' and self.lammps_version is None:
month = {'Jan': 1, 'Feb': 2, 'Mar': 3, 'Apr': 4,
'May': 5, 'Jun': 6, 'Jul': 7, 'Aug': 8,
'Sep': 9, 'Oct': 10,'Nov': 11,'Dec': 12}
self.__lammps_version = line.strip()[8:-1]
d = self.lammps_version.split('-')[0].split()
self.__lammps_date = datetime.date(int(d[2]), month[d[1]], int(d[0]))
#Check for strings listed before run and minimize simulations
if any([trigger in line for trigger in sim_trigger]):
headers.append(i+1)
#This follows both run and minimize simulations
elif 'Loop time of' in line:
footers.append(i-1)
i += 1
#Add last line to footers for incomplete logs
footers.append(i)
#Reset file pointer
log_info.seek(0)
#for all lines in file/output
for header, footer in zip(headers, footers):
#Initialize simulation data dictionary
sim = {}
#Read thermo data and reset file pointer
sim['thermo'] = pd.read_csv(log_info, header=header, nrows=footer-header, sep='\s+', engine='python', skip_blank_lines=True)
log_info.seek(0)
#Append simulation results
self.__simulations.append(sim)
def test_stringread(self):
with uber_open_rmode(self.content) as f:
content = f.read()
assert content.decode('UTF-8') == self.content
def parse(inscript: Union[str, io.IOBase],
singularkeys: Optional[List[str]] = None,
allsingular: bool = False) -> dict:
"""
Parses an input file and returns a dictionary of parameter terms.
These are the parsing rules:
- The first word in a line is taken as the key name of the parameter.
- All other words are joined together into a single string value for the
parameter.
- Words that start with # indicate comments with that word and all words
to the right of it in the same line being ignored.
- Any lines with less than two non-comment terms are ignored. In other
words, blank lines and lines with keys but not values are skipped over.
- Multiple values can be assigned to the same term by repeating the key
name on a different line.
- The keyword arguments can be used to issue an error if multiple values
are trying to be assigned to terms that should only have a single
values.
Parameters
----------
inscript : string or file-like-object
The file, path to file, or contents of the input script to parse.
singularkeys : list of str, optional
List of term keys that should not have multiple values.
allsingular : bool, optional
Indicates if all term keys should be singular (Default is False).
Returns
-------
params : dict
Dictionary of parsed input key-value pairs
Raises
------
ValueError
If both singularkeys and allsingular are given, or if multiple values
found for a singular key.
"""
# Argument check
if singularkeys is None:
singularkeys = []
singularkeys = aslist(singularkeys)
if allsingular and len(singularkeys) > 0:
raise ValueError(
'allsingular and singularkeys options cannot both be given')
params = {}
# Open inscript
with uber_open_rmode(inscript) as infile:
# Iterate over all lines in infile
for line in infile:
try:
line = line.decode('utf-8')
except:
pass
terms = line.split()
# Remove comments
i = 0
index = len(line)
while i < len(terms):
if len(terms[i]) > 0 and terms[i][0] == '#':
index = line.index(terms[i])
break
i += 1
terms = terms[:i]
line = line[:index]
# Skip empty, comment, and valueless lines
if len(terms) > 1:
# Split into key and value
key = terms[0]
value = line.replace(key, '', 1).strip()
# First time key is called save as is
if key not in params:
params[key] = value
# Append value to key if not singular
elif not allsingular and key not in singularkeys:
# Append value if parameter is already a list
if isinstance(params[key], list):
params[key].append(value)
# Convert parameter to list if needed and then append value
else:
params[key] = [params[key]]
params[key].append(value)
# Issue error for trying to append to a singular value
else:
raise ValueError(
'multiple values found for singular input parameter ' +
key)
return params
def load(data, prop_info=None):
"""
Read a LAMMPS-style dump file and return a System.
Argument:
data = file name, file-like object or string to read data from.
Keyword Argument:
prop_info -- DataModelDict for relating the per-atom properties to/from the dump file and the System. Will create a default json instance <data>.json if prop_info is not given and <data>.json doesn't already exist.
"""
#read in prop_info if supplied
if prop_info is not None:
if isinstance(prop_info, (str, unicode)) and os.path.isfile(prop_info):
with open(prop_info) as f:
prop_info = f.read()
prop_info = DataModelDict(prop_info)
#check for default prop_info file
else:
try:
with open(data+'.json') as fj:
prop_info = DataModelDict(fj)
except:
prop_info = None
box_unit = None
#read box_unit if specified in prop_info
if prop_info is not None:
prop_info = prop_info.find('LAMMPS-dump-atoms_prop-relate')
box_unit = prop_info['box_prop'].get('unit', None)
with uber_open_rmode(data) as f:
pbc = None
box = None
natoms = None
system = None
readnatoms = False
readatoms = False
readtimestep = False
acount = 0
bcount = 3
#loop over all lines in file
for line in f:
terms = line.split()
if len(terms) > 0:
#read atomic values if time to do so
if readatoms:
#sort values by a_id and save to prop_vals
a_id = long(terms[id_index]) - 1
prop_vals[a_id] = terms
acount += 1
#save values to sys once all atoms read in
if acount == natoms:
readatoms = False
#cycle over the defined atoms_prop in prop_info
for prop, p_keys in prop_info['atoms_prop'].iteritems():
#set default keys
dtype = p_keys.get('dtype', None)
shape = p_keys.get('shape', None)
shape = (natoms,) + np.empty(shape).shape
value = np.empty(shape)
#cycle over the defined LAMMPS-attributes in prop_info
for attr, a_keys in prop_info['LAMMPS-attribute'].iteritems():
#cycle over list of relations for each LAMMPS-attribute
for relation in a_keys.iteraslist('relation'):
#if atoms prop and relation prop match
if relation['prop'] == prop:
#get unit and scale info
unit = relation.get('unit', None)
if unit == 'scaled':
unit = None
scale = True
else:
scale = False
#find index of attribute in name_list
a_index = name_list.index(attr)
#check if relation has index listed
try:
index = relation['index']
if isinstance(index, list):
index = (Ellipsis,) + tuple(index)
else:
index = (Ellipsis,) + (index,)
value[index] = prop_vals[:, a_index]
#scalar if no index
except:
value[:] = prop_vals[:, a_index]
#test if values are ints if dtype not specified
if dtype is None and np.allclose(np.asarray(value, dtype=int), value):
value = np.asarray(value, dtype=int)
else:
value = np.asarray(value, dtype=dtype)
#save prop values to system
system.atoms_prop(key=prop, value=uc.set_in_units(value, unit), scale=scale)
#read number of atoms if time to do so
elif readnatoms:
natoms = int(terms[0])
readnatoms = False
elif readtimestep:
timestep = int(terms[0])
readtimestep = False
#read x boundary condition values if time to do so
elif bcount == 0:
xlo = uc.set_in_units(float(terms[0]), box_unit)
xhi = uc.set_in_units(float(terms[1]), box_unit)
if len(terms) == 3:
xy = uc.set_in_units(float(terms[2]), box_unit)
bcount += 1
#read y boundary condition values if time to do so
elif bcount == 1:
ylo = uc.set_in_units(float(terms[0]), box_unit)
yhi = uc.set_in_units(float(terms[1]), box_unit)
if len(terms) == 3:
xz = uc.set_in_units(float(terms[2]), box_unit)
bcount += 1
#read z boundary condition values if time to do so
elif bcount == 2:
zlo = uc.set_in_units(float(terms[0]), box_unit)
zhi = uc.set_in_units(float(terms[1]), box_unit)
if len(terms) == 3:
yz = uc.set_in_units(float(terms[2]), box_unit)
xlo = xlo - min((0.0, xy, xz, xy + xz))
xhi = xhi - max((0.0, xy, xz, xy + xz))
ylo = ylo - min((0.0, yz))
yhi = yhi - max((0.0, yz))
box = am.Box(xlo=xlo, xhi=xhi, ylo=ylo, yhi=yhi, zlo=zlo, zhi=zhi, xy=xy, xz=xz, yz=yz)
else:
box = am.Box(xlo=xlo, xhi=xhi, ylo=ylo, yhi=yhi, zlo=zlo, zhi=zhi)
bcount += 1
#if not time to read value, check the ITEM: header information
else:
#only consider ITEM: lines
if terms[0] == 'ITEM:':
#ITEM: TIMESTEP indicates it is time to read the timestep
if terms[1] == 'TIMESTEP':
readtimestep = True
#ITEM: NUMBER indicates it is time to read natoms
elif terms[1] == 'NUMBER':
readnatoms = True
#ITEM: BOX gives pbc and indicates it is time to read box parameters
elif terms[1] == 'BOX':
pbc = [True, True, True]
for i in xrange(3):
if terms[i + len(terms) - 3] != 'pp':
pbc[i] = False
bcount = 0
#ITEM: ATOMS gives list of per-Atom property names and indicates it is time to read atomic values
elif terms[1] == 'ATOMS':
assert box is not None, 'Box information not found'
assert natoms is not None, 'Number of atoms not found'
#read list of property names
name_list = terms[2:]
id_index = name_list.index('id')
#create empty array for reading property values
prop_vals = np.empty((natoms, len(name_list)))
#create and save default prop_info Data Model if needed
if prop_info is None:
prop_info = __prop_info_default_load(name_list)
if isinstance(data, (str, unicode)) and len(data) < 80:
with open(data+'.json', 'w') as fj:
prop_info.json(fp=fj, indent=4)
prop_info = prop_info.find('LAMMPS-dump-atoms_prop-relate')
#create system and flag that it is time to read data
system = am.System(atoms=am.Atoms(natoms=natoms), box=box, pbc=pbc)
system.prop['timestep'] = timestep
readatoms = True
if system is None:
raise ValueError('Failed to properly load dump file '+str(data)[:50])
return system
def test_stringread(self):
with uber_open_rmode(self.content) as f:
content = f.read()
assert content.decode('UTF-8') == self.content
def load(data, prop_info=None):
"""
Read a LAMMPS-style dump file and return a System.
Argument:
data = file name, file-like object or string to read data from.
Keyword Argument:
prop_info -- DataModelDict for relating the per-atom properties to/from the dump file and the System. Will create a default json instance <data>.json if prop_info is not given and <data>.json doesn't already exist.
"""
#read in prop_info if supplied
if prop_info is not None:
if isinstance(prop_info, (str, unicode)) and os.path.isfile(prop_info):
with open(prop_info) as f:
prop_info = f.read()
prop_info = DataModelDict(prop_info)
#check for default prop_info file
else:
try:
with open(data + '.json') as fj:
prop_info = DataModelDict(fj)
except:
prop_info = None
box_unit = None
#read box_unit if specified in prop_info
if prop_info is not None:
prop_info = prop_info.find('LAMMPS-dump-atoms_prop-relate')
box_unit = prop_info['box_prop'].get('unit', None)
with uber_open_rmode(data) as f:
pbc = None
box = None
natoms = None
system = None
readnatoms = False
readatoms = False
readtimestep = False
acount = 0
bcount = 3
#loop over all lines in file
for line in f:
terms = line.split()
if len(terms) > 0:
#read atomic values if time to do so
if readatoms:
#sort values by a_id and save to prop_vals
a_id = long(terms[id_index]) - 1
prop_vals[a_id] = terms
acount += 1
#save values to sys once all atoms read in
if acount == natoms:
readatoms = False
#cycle over the defined atoms_prop in prop_info
for prop, p_keys in prop_info['atoms_prop'].iteritems(
):
#set default keys
dtype = p_keys.get('dtype', None)
shape = p_keys.get('shape', None)
shape = (natoms, ) + np.empty(shape).shape
value = np.empty(shape)
#cycle over the defined LAMMPS-attributes in prop_info
for attr, a_keys in prop_info[
'LAMMPS-attribute'].iteritems():
#cycle over list of relations for each LAMMPS-attribute
for relation in a_keys.iteraslist('relation'):
#if atoms prop and relation prop match
if relation['prop'] == prop:
#get unit and scale info
unit = relation.get('unit', None)
if unit == 'scaled':
unit = None
scale = True
else:
scale = False
#find index of attribute in name_list
a_index = name_list.index(attr)
#check if relation has index listed
try:
index = relation['index']
if isinstance(index, list):
index = (
Ellipsis, ) + tuple(index)
else:
index = (Ellipsis, ) + (
index, )
value[index] = prop_vals[:,
a_index]
#scalar if no index
except:
value[:] = prop_vals[:, a_index]
#test if values are ints if dtype not specified
if dtype is None and np.allclose(
np.asarray(value, dtype=int), value):
value = np.asarray(value, dtype=int)
else:
value = np.asarray(value, dtype=dtype)
#save prop values to system
system.atoms_prop(key=prop,
value=uc.set_in_units(
value, unit),
scale=scale)
#read number of atoms if time to do so
elif readnatoms:
natoms = int(terms[0])
readnatoms = False
elif readtimestep:
timestep = int(terms[0])
readtimestep = False
#read x boundary condition values if time to do so
elif bcount == 0:
xlo = uc.set_in_units(float(terms[0]), box_unit)
xhi = uc.set_in_units(float(terms[1]), box_unit)
if len(terms) == 3:
xy = uc.set_in_units(float(terms[2]), box_unit)
bcount += 1
#read y boundary condition values if time to do so
elif bcount == 1:
ylo = uc.set_in_units(float(terms[0]), box_unit)
yhi = uc.set_in_units(float(terms[1]), box_unit)
if len(terms) == 3:
xz = uc.set_in_units(float(terms[2]), box_unit)
bcount += 1
#read z boundary condition values if time to do so
elif bcount == 2:
zlo = uc.set_in_units(float(terms[0]), box_unit)
zhi = uc.set_in_units(float(terms[1]), box_unit)
if len(terms) == 3:
yz = uc.set_in_units(float(terms[2]), box_unit)
xlo = xlo - min((0.0, xy, xz, xy + xz))
xhi = xhi - max((0.0, xy, xz, xy + xz))
ylo = ylo - min((0.0, yz))
yhi = yhi - max((0.0, yz))
box = am.Box(xlo=xlo,
xhi=xhi,
ylo=ylo,
yhi=yhi,
zlo=zlo,
zhi=zhi,
xy=xy,
xz=xz,
yz=yz)
else:
box = am.Box(xlo=xlo,
xhi=xhi,
ylo=ylo,
yhi=yhi,
zlo=zlo,
zhi=zhi)
bcount += 1
#if not time to read value, check the ITEM: header information
else:
#only consider ITEM: lines
if terms[0] == 'ITEM:':
#ITEM: TIMESTEP indicates it is time to read the timestep
if terms[1] == 'TIMESTEP':
readtimestep = True
#ITEM: NUMBER indicates it is time to read natoms
elif terms[1] == 'NUMBER':
readnatoms = True
#ITEM: BOX gives pbc and indicates it is time to read box parameters
elif terms[1] == 'BOX':
pbc = [True, True, True]
for i in xrange(3):
if terms[i + len(terms) - 3] != 'pp':
pbc[i] = False
bcount = 0
#ITEM: ATOMS gives list of per-Atom property names and indicates it is time to read atomic values
elif terms[1] == 'ATOMS':
assert box is not None, 'Box information not found'
assert natoms is not None, 'Number of atoms not found'
#read list of property names
name_list = terms[2:]
id_index = name_list.index('id')
#create empty array for reading property values
prop_vals = np.empty((natoms, len(name_list)))
#create and save default prop_info Data Model if needed
if prop_info is None:
prop_info = __prop_info_default_load(name_list)
if isinstance(
data,
(str, unicode)) and len(data) < 80:
with open(data + '.json', 'w') as fj:
prop_info.json(fp=fj, indent=4)
prop_info = prop_info.find(
'LAMMPS-dump-atoms_prop-relate')
#create system and flag that it is time to read data
system = am.System(atoms=am.Atoms(natoms=natoms),
box=box,
pbc=pbc)
system.prop['timestep'] = timestep
readatoms = True
if system is None:
raise ValueError('Failed to properly load dump file ' + str(data)[:50])
return system
def parse(inscript, singularkeys=[], allsingular=False):
"""
Parses an input file and returns a dictionary of parameter terms.
These are the parsing rules:
- The first word in a line is taken as the key name of the parameter.
- All other words are joined together into a single string value for the
parameter.
- Words that start with # indicate comments with that word and all words
to the right of it in the same line being ignored.
- Any lines with less than two non-comment terms are ignored. In other
words, blank lines and lines with keys but not values are skipped over.
- Multiple values can be assigned to the same term by repeating the key
name on a different line.
- The keyword arguments can be used to issue an error if multiple values
are trying to be assigned to terms that should only have a single
values.
Parameters
----------
inscript : string or file-like-object
The file, path to file, or contents of the input script to parse.
singularkeys : list of str, optional
List of term keys that should not have multiple values.
allsingular : bool, optional
Indicates if all term keys should be singular (Default is False).
Returns
-------
params : dict
Dictionary of parsed input key-value pairs
Raises
------
ValueError
If both singularkeys and allsingular are given, or if multiple values
found for a singular key.
"""
# Argument check
singularkeys = aslist(singularkeys)
if allsingular and len(singularkeys) > 0:
raise ValueError('allsingular and singularkeys options cannot both be given')
params = {}
# Open inscript
with uber_open_rmode(inscript) as infile:
# Iterate over all lines in infile
for line in infile:
try:
line = line.decode('utf-8')
except:
pass
terms = line.split()
# Remove comments
i = 0
index = len(line)
while i < len(terms):
if len(terms[i]) > 0 and terms[i][0] == '#':
index = line.index(terms[i])
break
i += 1
terms = terms[:i]
line = line[:index]
# Skip empty, comment, and valueless lines
if len(terms) > 1:
# Split into key and value
key = terms[0]
value = line.replace(key, '', 1).strip()
# First time key is called save as is
if key not in params:
params[key] = value
# Append value to key if not singular
elif not allsingular and key not in singularkeys:
# Append value if parameter is already a list
if isinstance(params[key], list):
params[key].append(value)
# Convert parameter to list if needed and then append value
else:
params[key] = [params[key]]
params[key].append(value)
# Issue error for trying to append to a singular value
else:
raise ValueError('multiple values found for singular input parameter ' + key)
return params
