def read_heat_flux_xyz(filename="heat_flux.out",
step_range=slice(None),
is_out_step=True):
if not os.path.exists(filename):
error("file %s does not exist!" % filename)
with open(filename, "r") as f:
md = f.read()
if md.find("Heat Flux") == -1:
error("file %s does not contain information about heat flux")
hf = np.array([
np.fromstring(line.strip().split("\n")[0], dtype="double", sep=" ")
for line in md.split("Heat Flux (au)")[1:]
])
# for line in md.split("HF_ve (au)")[1:]])
# for line in md.split("HF_electric (au)")[1:]])
# for line in md.split("HF_dispersion (au)")[1:]])
# for line in md.split("HF_repulsive (au)")[1:]])
# for line in md.split("HF_virial (au)")[1:]])
if is_out_step:
step = np.array([
int(line.strip().split("\n")[0])
for line in md.split("MD step:")[1:]
])
return hf[step_range] * au_energy * au_velocity, step[step_range]
else:
return hf[step_range] * au_energy * au_velocity
def read_xyz_cv_file(filename, step_range=slice(None)):
"the unit of velocity is Angstrom/ps, coordinates: Angstrom"
x = file(filename, 'r')
try:
num_atoms = int(x.readline())
except:
print "the format of input file %s does not obey the xyz format"
return 0
parameters = {}
parameters.setdefault("num_atom", num_atoms)
xall = x.read()
all_steps = xall.split('MD iter:')[1:]
try:
sliced_steps = all_steps[step_range]
except IndexError:
error("Invalid range in reading %s!" % filename)
num_steps = len(sliced_steps)
atom_velocities = np.zeros((num_steps, num_atoms, 3))
atom_coordinates = np.zeros_like(atom_velocities)
step_indices = np.zeros(num_steps, dtype="intc")
for index, one_step in enumerate(sliced_steps):
one_step_list = one_step.split("\n")[:num_atoms + 1]
step_indices[index] = int(one_step_list[0])
coordinates = [map(float, v.split()[1:4]) for v in one_step_list[1:]]
atom_coordinates[index] = np.array(coordinates)
velocities = [map(float, v.split()[-3:]) for v in one_step_list[1:]]
atom_velocities[index] = np.array(velocities)
atom_types = [id.strip().split()[0] for id in one_step_list[1:]]
parameters.setdefault('num_step', len(all_steps))
parameters.setdefault("velocity", atom_coordinates)
parameters.setdefault('coordinate', atom_coordinates)
parameters.setdefault('atom_type', atom_types)
parameters.setdefault('step_indices', step_indices)
return parameters
def check_settting(self):
if self.num_steps<self.sample_length:
error("number of total time steps %d is lower than sampling length %d!"
% (self.num_steps, self.sample_length))
if self.corr_length >= self.sample_length:
error("correlation length %d is larger than sampling length %d!"
%(self.corr_length, self.sample_length))
elif self.sample_length-self.corr_length<5:
warning("correlation of the last few time steps may be incorrect!")
def read_heat_flux_xyz(filename="heat_flux.out", step_range = slice(None)):
if not os.path.exists(filename):
error("file %s does not exist!"%filename)
with open(filename, "r") as f:
md=f.read()
if md.find("Heat Flux") == -1:
error("file %s does not contain information about heat flux")
hf = np.array([np.fromstring(line.strip().split("\n")[0],dtype="double", sep=" ")
for line in md.split("Heat Flux (au)")[1:]])
return hf[step_range] * au_energy * au_velocity
def read_heat_flux_lammps(filename="heatflux.dat", step_range = slice(None), is_out_step=True):
if not os.path.exists(filename):
error("file %s does not exist!"%filename)
with open(filename, "r") as f:
md=f.readlines()
if md[0].find("Jx") == -1 or md[0].find("Jy") == -1 or md[0].find("Jz")==-1:
error("file %s does not contain information about heat flux")
index = [md[0].split().index(l) for l in ("Jx","Jy","Jz")]
data = np.array([num.split() for num in md[1:]], dtype="double")
hf = data[:,index]
if is_out_step:
step = data[:,0]
return hf[step_range], step[step_range]
else:
return hf[step_range]
def __init__(self,
heat_flux,
step_indices=None,
temperature=300,
volume = None,
# atom_types=None,
is_ai=False, # is average input
sample_l=500, # sample length for correlation
corr_l=400, # correlation length
smooth_tune=1.0,
time_step=1.0, # time step in md calculation(fs)
is_diff = False,
is_sum=True,# is average in the 3 dimensions
is_wac=False,
is_sm = False,
is_normalize=False,
dirt=None,
out_file_name="dos.csv"):
self.kappa_omega={}
self.num_atom = None
self.temperature = temperature
if volume is not None:
self.volume = volume
else:
error("The volume is not set!")
self.time_step=(step_indices[1]-step_indices[0]) * time_step * fs2ps # [ps]
self.is_wac=is_wac
self.is_diff = is_diff
self.is_normalize=is_normalize
self.is_ai=is_ai
self.is_summation=is_sum
self.num_dim = 3 if is_sum==False else 1
self.is_sm = is_sm
self.smooth_tune = smooth_tune
self.dos_file_name = out_file_name
self.num_steps = len(step_indices)
self.sample_length = sample_l
self.corr_length=corr_l
self.dirt = dirt
self.kappa=None
num_corr=self.num_steps // self.sample_length
self.eflux = heat_flux[:num_corr*self.sample_length].reshape(num_corr, self.sample_length, 3)
self.conversion_factor = 1 / self.volume / kb / self.temperature ** 2 * unit_conversion
self.check_settting()
self.num_corr=self.num_steps // self.sample_length
self.print_information()
def __init__(self,
md_cvfe, #c: coordinate, v: velocity, f: force and e: energy
temperature=300,
volume = None,
# atom_types=None,
is_ai=False, # is average input
sample_l=500, # sample length for correlation
corr_l=400, # correlation length
smooth_tune=1.0,
time_step=1.0, # time step in md calculation(fs)
is_diff = False,
is_sum=True,# is average in the 3 dimensions
is_wac=False,
is_sm = False,
is_normalize=False,
dirt=None, #direction tensor
out_file_name="dos.csv"):
self.kappa_omega={}
self._md_cvfe = md_cvfe
self.temperature = temperature
self.box_bounds = self._md_cvfe.lattice_bounds
if self.box_bounds is not None:
self.volume = np.prod(self.box_bounds[:,1] - self.box_bounds[:,0])
elif volume is not None:
self.volume = volume
else:
error("The volume is not set!")
self.time_step=(md_cvfe.step_indices[1]-md_cvfe.step_indices[0]) * time_step * fs2ps # [ps]
self.is_wac=is_wac
self.is_diff = is_diff
self.is_normalize=is_normalize
self.is_ai=is_ai
self.is_summation=is_sum
self.num_dim = 3 if is_sum==False else 1
self.is_sm = is_sm
self.smooth_tune = smooth_tune
self.dos_file_name = out_file_name
self.dirt = dirt
self.kappa=None
self.num_atom = md_cvfe.num_atom
self.num_steps = md_cvfe.num_steps
self.sample_length = sample_l
self.corr_length=corr_l
self.conversion_factor = 1 / self.volume / kb / self.temperature ** 2 * unit_conversion
self.check_settting()
self.correlation_init()
self.print_information()
def set_settings(self):
if self._conf.has_key("input_filename"):
self.input_filename = self._conf["input_filename"]
if self._conf.has_key("file_format"):
self.file_format = self._conf["file_format"][0]
if self._conf.has_key("atom_type"):
#self.atom_type=self._conf["atom_type"]
try:
typ = self._conf["atom_type"]
typ = typ.replace(" ", "")
if typ != "":
if typ.find("*") != -1:
assert typ.count("*") == typ.count(",") + 1
specie = typ.split(",")
atom_type = [[s.split("*")[0]] * int(s.split("*")[1])
for s in specie]
atom_type = sum(atom_type, []) # flattening
else:
if typ.find(",") != -1:
atom_type = typ.split(",")
else:
atom_type = [typ]
assert "" not in atom_type
self.atom_type = np.array(atom_type)
except:
error("Wrong format for atom_type!")
if self._conf.has_key("out_filename"):
self.out_filename = self._conf["out_filename"]
if self._conf.has_key("step_range"):
ran = self._conf["step_range"]
if ran == "":
self.step_range = slice(None, None, None)
elif ran.count(":") > 0:
exec "s=np.s_[%s]" % ran
self.step_range = s
else:
error("Wrong format for step_range")
if self._conf.has_key("is_average_input"):
self.is_average_input = string2bool(self._conf["is_average_input"])
if self._conf.has_key("is_normalize"):
self.is_normalize = string2bool(self._conf["is_normalize"])
if self._conf.has_key("is_summation"):
self.is_summation = string2bool(self._conf["is_summation"])
if self._conf.has_key("is_smoothing"):
self.is_smoothing = string2bool(self._conf["is_smoothing"])
if self._conf.has_key("is_average_output"):
self.is_average_output = string2bool(
self._conf["is_average_output"])
if self._conf.has_key("is_write_ac"):
self.is_write_ac = string2bool(self._conf["is_write_ac"])
if self._conf.has_key("is_convert_velocity"):
self.is_convert_velocity = string2bool(
self._conf["is_convert_velocity"])
if self._conf.has_key("is_plot"):
self.is_plot = string2bool(self._conf["is_plot"])
if self._conf.has_key("is_save"):
self.is_save = string2bool(self._conf["is_save"])
if self._conf.has_key("smooth_tune"):
self.smooth_tune = float(self._conf["smooth_tune"])
if self.smooth_tune == 0.0:
error("tune factor for Gaussian smoothing cannot be 0!")
if self._conf.has_key("correlation_length"):
self.correlation_length = int(self._conf["correlation_length"])
if self._conf.has_key("time_step"):
self.time_step = float(self._conf["time_step"])
if self._conf.has_key("sample_length"):
self.sample_length = int(self._conf["sample_length"])
if self._conf.has_key("direction_tensor"):
d = self._conf['direction_tensor']
tran = maketrans("xyz", "012")
if d is not None:
try:
self.direction_tensor = tuple(map(int, d.translate(tran)))
except ValueError:
error(
"The direction can only be set as characters 'x', 'y' and 'z'"
)
if len(self.direction_tensor) == 1:
self.direction_tensor = (self.direction_tensor[0],
self.direction_tensor[0])
elif len(self.direction_tensor) != 2:
error(
"The direction is a second-order tensor, please recheck the settings!"
)
else:
self.direction_tensor = d
def set_settings(self):
if self._conf.has_key("input_filename"):
self.input_filename=self._conf["input_filename"]
if self._conf.has_key("file_format"):
self.file_format=self._conf["file_format"][0]
if self._conf.has_key("atom_type"):
#self.atom_type=self._conf["atom_type"]
try:
typ=self._conf["atom_type"]
typ=typ.replace(" ","")
if typ!="":
if typ.find("*")!=-1:
assert typ.count("*")==typ.count(",")+1
specie=typ.split(",")
atom_type=[[s.split("*")[0]]*int(s.split("*")[1]) for s in specie]
atom_type=sum(atom_type,[])# flattening
else:
if typ.find(",")!=-1:
atom_type=typ.split(",")
else:
atom_type=[typ]
assert "" not in atom_type
self.atom_type=np.array(atom_type)
except:
error("Wrong format for atom_type!")
if self._conf.has_key("out_filename"):
self.out_filename=self._conf["out_filename"]
if self._conf.has_key("step_range"):
ran=self._conf["step_range"]
if ran=="":
self.step_range=slice(None, None, None)
elif ran.count(":")>0:
exec "s=np.s_[%s]"%ran
self.step_range=s
else:
error("Wrong format for step_range")
if self._conf.has_key("is_average_input"):
self.is_average_input=string2bool(self._conf["is_average_input"])
if self._conf.has_key("is_normalize"):
self.is_normalize=string2bool(self._conf["is_normalize"])
if self._conf.has_key("is_summation"):
self.is_summation=string2bool(self._conf["is_summation"])
if self._conf.has_key("is_smoothing"):
self.is_smoothing=string2bool(self._conf["is_smoothing"])
if self._conf.has_key("is_average_output"):
self.is_average_output=string2bool(self._conf["is_average_output"])
if self._conf.has_key("is_write_ac"):
self.is_write_ac=string2bool(self._conf["is_write_ac"])
if self._conf.has_key("is_convert_velocity"):
self.is_convert_velocity=string2bool(self._conf["is_convert_velocity"])
if self._conf.has_key("is_plot"):
self.is_plot=string2bool(self._conf["is_plot"])
if self._conf.has_key("is_save"):
self.is_save=string2bool(self._conf["is_save"])
if self._conf.has_key("smooth_tune"):
self.smooth_tune=float(self._conf["smooth_tune"])
if self.smooth_tune==0.0:
error("tune factor for Gaussian smoothing cannot be 0!")
if self._conf.has_key("correlation_length"):
self.correlation_length=int(self._conf["correlation_length"])
if self._conf.has_key("time_step"):
self.time_step=float(self._conf["time_step"])
if self._conf.has_key("sample_length"):
self.sample_length=int(self._conf["sample_length"])
if self._conf.has_key("direction_tensor"):
d=self._conf['direction_tensor']
tran=maketrans("xyz", "012")
if d is not None:
try:
self.direction_tensor=tuple(map(int, d.translate(tran)))
except ValueError:
error("The direction can only be set as characters 'x', 'y' and 'z'")
if len(self.direction_tensor) == 1:
self.direction_tensor = (self.direction_tensor[0],self.direction_tensor[0])
elif len(self.direction_tensor) != 2:
error("The direction is a second-order tensor, please recheck the settings!")
else:
self.direction_tensor = d