def _read_comment(self, frame):
"""Internal xyz method to get header metadata from comment line of given `frame`.
We assume metadata format is a space-separated sequence of
comma separated entries such as:
columns:id,x,y,z step:10
columns=id,x,y,z step=10
"""
# Go to line and skip Npart info
self.trajectory.seek(self._index_header[frame])
npart = int(self.trajectory.readline())
data = self.trajectory.readline()
# Fill metadata dictionary
meta = {}
meta['npart'] = npart
for e in data.split():
s = re.search(r'(\S+)\W*[=:]\W*(\S+)', e)
if s is not None:
tag, data = s.group(1), s.group(2)
# Remove dangling commas
data = data.strip(',')
# If there are commas, this is a list, else a scalar.
# We convert the string to appropriate types
if ',' in data:
meta[tag] = [tipify(_) for _ in data.split(',')]
else:
meta[tag] = tipify(data)
return meta
def tabulate(potential,
parameters,
cutoff='c',
rc=2.5,
npoints=10000,
rmin=0.5,
fmt='lammps',
metadata='',
fileout=None,
precision=14):
"""Tabulate a potential."""
from atooms.core.utils import tipify
from atooms.interaction.potential import PairPotential
from atooms.interaction.cutoff import CutOff
if isinstance(parameters, dict):
param_dict = parameters
else:
param_dict = {}
for param in parameters.split(','):
key, value = param.split('=')
param_dict[key] = tipify(value)
potential = PairPotential(potential, param_dict, (1, 1))
if cutoff is not None:
potential.cutoff = CutOff(cutoff, rc)
rsq, u0, u1, u2 = potential.tabulate(npoints, rmin=rmin, what='uwh')
r = rsq**0.5
if fmt == 'lammps':
u1 *= r
txt = """
POTENTIAL
N {}
""".format(len(rsq))
i = 1
for x, y, z in zip(r, u0, u1):
txt += '{} {} {} {}\n'.format(i, x, y, z)
i += 1
elif fmt == 'uwh':
txt = '# {} columns: r, u, w, h\n'.format(metadata)
for x, y, z, w in zip(r, u0, u1, u2):
txt += '{:.14g} {:.14g} {:.14g} {:.14g}\n'.format(x, y, z, w)
else:
u1 *= r
txt = '# {} columns: r, u, f\n'.format(metadata)
for x, y, z in zip(r, u0, u1):
txt += '{} {} {}\n'.format(x, y, z)
if fileout is None:
return txt
else:
with open(fileout, 'w') as fh:
fh.write(txt)
def _read_comment(self, frame):
"""
Internal xyz method to get header metadata from comment line of
given `frame`.
"""
# Go to line and skip Npart info
self.trajectory.seek(self._index_header[frame])
npart = int(self.trajectory.readline())
data = self.trajectory.readline()
meta = {}
# We first gather all keys
keys = []
for entry in data.split("=")[:-1]:
keys.append(entry.split()[-1])
# Now we extract the values for each key using a dynamic regexp
import re
regexp = ''
for key in keys:
regexp += '{}=(.*)'.format(key)
match = re.match(regexp, data)
for i, key in enumerate(keys):
meta[key] = match.groups()[i].strip()
# The Properties key is special so we deal with it first
entries = meta['Properties'].split(':')
assert len(entries) % 3 == 0, len(entries)
properties = []
for i in range(0, len(entries), 3):
key, fmt, ndims = entries[i:i + 3]
properties.append((key, fmt, ndims))
meta['Properties'] = properties
# Go through keys, listify and tipify them
for key in meta:
if key != 'Properties':
if meta[key].startswith('"'):
meta[key] = meta[key].strip('"')
meta[key] = [tipify(_) for _ in meta[key].split()]
else:
meta[key] = tipify(meta[key])
return meta
def _read_comment(self, frame):
"""
Internal xyz method to get header metadata from comment line of
given `frame`.
We assume metadata fmt is a space-separated sequence of comma
separated entries such as:
columns:id,x,y,z step:10
columns=id,x,y,z step=10
"""
# Go to line and skip Npart info
self.trajectory.seek(self._index_header[frame])
npart = int(self.trajectory.readline())
data = self.trajectory.readline()
meta = {}
meta['npart'] = npart
# Read metadata line
if not ('=' in data or ':' in data):
# The comment line contains a list of unspecified fields
# We add them to the dict using a sequential integer key
for i, value in enumerate(data.split()):
meta[i] = tipify(value)
else:
# The comment line contains self descriptive fields
# Remove spaces around : or = and replace = by :
data = re.sub(r'\s*[=:]\s*', ':', data)
# Tolerate spaces between entries of vectors
data = re.sub(r'\s*,\s*', ',', data)
# Fill metadata dictionary
for e in data.split():
s = re.search(r'(\S+):(\S+)', e)
if s is not None:
tag, data = s.group(1), s.group(2)
# Remove dangling commas
data = data.strip(',')
# If there are commas, this is a list, else a scalar.
# We convert the string to appropriate types
if ',' in data:
meta[tag] = [tipify(_) for _ in data.split(',')]
else:
meta[tag] = tipify(data)
# Apply an alias dict to tags, e.g. to add step if Cfg was found instead
for alias, tag in self.alias.items():
try:
meta[tag] = meta[alias]
except KeyError:
pass
# Fix dimensions based on side of cell.
# Fallback to ndim metadata or 3.
try:
if 'ndim' not in meta:
meta['ndim'] = len(meta['cell'])
except TypeError:
meta['ndim'] = 1 # it is a scalar
except KeyError:
meta['ndim'] = 3 # default
# Make sure columns is a list
if 'columns' in meta:
if not isinstance(meta['columns'], list):
meta['columns'] = [meta['columns']]
return meta