from collections import OrderedDict, defaultdict
from typing import List
import numpy as np
from scm.plams.core.basejob import MultiJob
from scm.plams.core.results import Results
from scm.plams.interfaces.adfsuite.amsanalysis import AMSAnalysisJob, AMSAnalysisResults
from scm.plams.tools.units import Units
__all__ = ["AMSRDFJob", "AMSMSDJob", "AMSMSDResults", "AMSVACFJob", "AMSVACFResults"]
class AMSConvenientAnalysisJob(AMSAnalysisJob):
def __init__(self, previous_job, atom_indices=None, **kwargs): # needs to be finished
"""
previous_job: AMSJob
An AMSJob with an MD trajectory. Note that the trajectory should have been equilibrated before it starts.
All other settings can be set as for AMS
"""
AMSAnalysisJob.__init__(self, **kwargs)
self.previous_job = previous_job
self.atom_indices = atom_indices
def _get_max_dt_frames(self, max_correlation_time_fs):
if max_correlation_time_fs is None:
return None
historylength = self.previous_job.results.readrkf("History", "nEntries")
max_dt_frames = int(max_correlation_time_fs / self.previous_job.results.get_time_step())
max_dt_frames = min(max_dt_frames, historylength // 2)
return max_dt_frames
def _parent_prerun(self, section):
# use previously run previous_job
assert self.previous_job.status != "created", "You can only pass in a finished AMSJob"
self.settings.input.TrajectoryInfo.Trajectory.KFFileName = self.previous_job.results.rkfpath()
if self.atom_indices and self._parent_write_atoms:
self.settings.input[section].Atoms.Atom = self.atom_indices
class AMSMSDResults(AMSAnalysisResults):
"""Results class for AMSMSDJob"""
def get_msd(self):
"""
returns time [fs], msd [ang^2]
"""
msd_xy = self.get_xy()
time = np.array(msd_xy.x[0]) # fs
y = np.array(msd_xy.y) * Units.convert(1.0, "bohr", "angstrom") ** 2
return time, y
def get_linear_fit(self, start_time_fit_fs=None, end_time_fit_fs=None):
"""
Fits the MSD between start_time_fit_fs and end_time_fit_fs
Returns a 3-tuple LinRegress.result, fit_x (fs), fit_y (ang^2)
result.slope is given in ang^2/fs
"""
from scipy.stats import linregress
time, y = self.get_msd()
end_time_fit_fs = end_time_fit_fs or max(time)
start_time_fit_fs = start_time_fit_fs or self.job.start_time_fit_fs
if start_time_fit_fs >= end_time_fit_fs:
start_time_fit_fs = end_time_fit_fs / 2
y = y[time >= start_time_fit_fs]
time = time[time >= start_time_fit_fs]
y = y[time <= end_time_fit_fs]
time = time[time <= end_time_fit_fs]
result = linregress(time, y)
fit_x = time
fit_y = result.slope * fit_x + result.intercept
return result, fit_x, fit_y
def get_diffusion_coefficient(self, start_time_fit_fs=None, end_time_fit_fs=None):
"""
Returns D in m^2/s
"""
result, _, _ = self.get_linear_fit(start_time_fit_fs=start_time_fit_fs, end_time_fit_fs=end_time_fit_fs)
D = result.slope * 1e-20 / (6 * 1e-15) # convert from ang^2/fs to m^2/s, divide by 6 because 3-dimensional (2d)
return D
[docs]class AMSMSDJob(AMSConvenientAnalysisJob):
"""A convenient class wrapping around the trajectory analysis MSD tool"""
_result_type = AMSMSDResults
_parent_write_atoms = True
[docs] def __init__(
self,
previous_job, # needs to be finished
max_correlation_time_fs: float = 10000,
start_time_fit_fs: float = 2000,
atom_indices: List[int] = None,
**kwargs,
):
"""
previous_job: AMSJob
An AMSJob with an MD trajectory. Note that the trajectory should have been equilibrated before it starts.
max_correlation_time_fs: float
Maximum correlation time in femtoseconds
start_time_fit_fs : float
Smallest correlation time for the linear fit
atom_indices : List[int]
If None, use all atoms. Otherwise use the specified atom indices (starting with 1)
kwargs: dict
Other options to AMSAnalysisJob
"""
AMSConvenientAnalysisJob.__init__(self, previous_job=previous_job, atom_indices=atom_indices, **kwargs)
self.max_correlation_time_fs = max_correlation_time_fs
self.start_time_fit_fs = start_time_fit_fs
[docs] def prerun(self): # noqa F811
"""
Constructs the final settings
"""
self._parent_prerun("MeanSquareDisplacement") # trajectory and atom_indices handled
max_dt_frames = self._get_max_dt_frames(self.max_correlation_time_fs)
self.settings.input.Task = "MeanSquareDisplacement"
self.settings.input.MeanSquareDisplacement.Property = "DiffusionCoefficient"
self.settings.input.MeanSquareDisplacement.StartTimeSlope = self.start_time_fit_fs
self.settings.input.MeanSquareDisplacement.MaxFrame = max_dt_frames
[docs] def postrun(self):
"""
Creates msd.txt, fit_msd.txt, and D.txt
"""
time, msd = self.results.get_msd()
with open(self.path + "/msd.txt", "w") as f:
f.write("#Time(fs) MSD(ang^2)")
for x, y in zip(time, msd):
f.write(f"{x} {y}\n")
_, fit_x, fit_y = self.results.get_linear_fit()
with open(self.path + "/fit_msd.txt", "w") as f:
f.write("#Time(fs), LinearFitToMSD(ang^2)")
for x, y in zip(fit_x, fit_y):
f.write(f"{x} {y}\n")
D = self.results.get_diffusion_coefficient()
with open(self.path + "/D.txt", "w") as f:
f.write(f"{D}\n")
class AMSVACFResults(AMSAnalysisResults):
"""Results class for AMSVACFJob"""
def get_vacf(self):
xy = self.get_xy()
time = np.array(xy.x[0]) # fs
y = np.array(xy.y)
return time, y
def get_power_spectrum(self, max_freq=None):
max_freq = max_freq or self.job.max_freq or 5000
xy = self.get_xy("Spectrum")
freq = np.array(xy.x[0])
y = np.array(xy.y)
y = y[freq < max_freq]
freq = freq[freq < max_freq]
return freq, y
def get_acf(self):
return self.get_vacf()
def get_spectrum(self, max_freq=None):
return self.get_power_spectrum(max_freq=max_freq)
[docs]class AMSVACFJob(AMSConvenientAnalysisJob):
"""A class for calculating the velocity autocorrelation function and its power spectrum"""
_result_type = AMSVACFResults
_parent_write_atoms = True
[docs] def __init__(
self,
previous_job, # needs to be finished
max_correlation_time_fs=5000, # fs
max_freq=5000, # cm^-1
atom_indices=None,
**kwargs,
):
"""
previous_job: AMSJob
An AMSJob with an MD trajectory. Note that the trajectory should have been equilibrated before it starts.
max_correlation_time_fs: float
Maximum correlation time in femtoseconds
max_freq: float
The maximum frequency for the power spectrum in cm^-1
atom_indices: List[int]
Atom indices (starting with 1). If None, use all atoms.
"""
AMSConvenientAnalysisJob.__init__(self, previous_job=previous_job, atom_indices=atom_indices, **kwargs)
self.max_correlation_time_fs = max_correlation_time_fs
self.max_freq = max_freq
[docs] def prerun(self): # noqa F811
"""
Creates final settings
"""
self._parent_prerun("AutoCorrelation") # trajectory and atom_indices handled
max_dt_frames = self._get_max_dt_frames(self.max_correlation_time_fs)
self.settings.input.Task = "AutoCorrelation"
self.settings.input.AutoCorrelation.Property = "Velocities"
self.settings.input.AutoCorrelation.MaxFrame = max_dt_frames
[docs] def postrun(self):
"""
Creates vacf.txt and power_spectrum.txt
"""
try:
time, vacf = self.results.get_vacf()
with open(self.path + "/vacf.txt", "w") as f:
f.write("#Time(fs) VACF")
for x, y in zip(time, vacf):
f.write(f"{x} {y}\n")
freq, intens = self.results.get_power_spectrum()
with open(self.path + "/power_spectrum.txt", "w") as f:
f.write("#Frequency(cm^-1) Intensity(arb.units)")
for x, y in zip(freq, intens):
f.write(f"{x} {y}\n")
except:
pass
class AMSDipoleDerivativeACFResults(AMSAnalysisResults):
"""Results class for AMSVACFJob"""
def get_acf(self):
xy = self.get_xy()
time = np.array(xy.x[0]) # fs
y = np.array(xy.y)
return time, y
def get_ir_spectrum(self, max_freq=None):
max_freq = max_freq or self.job.max_freq or 5000
xy = self.get_xy("Spectrum")
freq = np.array(xy.x[0])
y = np.array(xy.y)
y = y[freq < max_freq]
freq = freq[freq < max_freq]
return freq, y
def get_spectrum(self, max_freq=None):
return self.get_ir_spectrum(max_freq=max_freq)
class AMSDipoleDerivativeACFJob(AMSConvenientAnalysisJob):
"""A class for calculating the velocity autocorrelation function and its power spectrum"""
_result_type = AMSDipoleDerivativeACFResults
_parent_write_atoms = True
def __init__(
self,
previous_job, # needs to be finished
max_correlation_time_fs=5000, # fs
max_freq=5000, # cm^-1
atom_indices=None,
**kwargs,
):
"""
previous_job: AMSJob
An AMSJob with an MD trajectory. Note that the trajectory should have been equilibrated before it starts.
max_correlation_time_fs: float
Maximum correlation time in femtoseconds
max_freq: float
The maximum frequency for the power spectrum in cm^-1
atom_indices: List[int]
Atom indices (starting with 1). If None, use all atoms.
"""
AMSConvenientAnalysisJob.__init__(self, previous_job=previous_job, atom_indices=atom_indices, **kwargs)
self.max_correlation_time_fs = max_correlation_time_fs
self.max_freq = max_freq
def prerun(self): # noqa F811
"""
Creates final settings
"""
self._parent_prerun("AutoCorrelation") # trajectory and atom_indices handled
max_dt_frames = self._get_max_dt_frames(self.max_correlation_time_fs)
self.settings.input.Task = "AutoCorrelation"
self.settings.input.AutoCorrelation.Property = "DipoleDerivativeFromCharges"
self.settings.input.AutoCorrelation.MaxFrame = max_dt_frames
def postrun(self):
"""
Creates dipolederivativeacf.txt and ir_spectrum.txt
"""
try:
time, acf = self.results.get_acf()
with open(self.path + "/dipolederivativeacf.txt", "w") as f:
f.write("#Time(fs) DipoleDerivativeACF")
for x, y in zip(time, acf):
f.write(f"{x} {y}\n")
freq, intens = self.results.get_ir_spectrum()
with open(self.path + "/ir_spectrum.txt", "w") as f:
f.write("#Frequency(cm^-1) Intensity(arb.units)")
for x, y in zip(freq, intens):
f.write(f"{x} {y}\n")
except:
pass
class AMSRDFResults(AMSAnalysisResults):
"""Results class for AMSRDFJob"""
def get_rdf(self):
"""
Returns a 2-tuple r, rdf.
r: numpy array of float (angstrom)
rdf: numpy array of float
"""
xy = self.get_xy()
r = np.array(xy.x[0])
y = np.array(xy.y)
return r, y
[docs]class AMSRDFJob(AMSConvenientAnalysisJob):
_result_type = AMSRDFResults
_parent_write_atoms = False
[docs] def __init__(self, previous_job, atom_indices=None, atom_indices_to=None, rmin=0.5, rmax=6.0, rstep=0.1, **kwargs):
"""
previous_job: AMSJob
AMSJob with finished MD trajectory.
atom_indices: List[int]
Atom indices (starting with 1). If None, calculate RDF *from* all atoms.
atom_indices_to: List[int]
Atom indices (starting with 1). If None, calculate RDF *to* all atoms.
rmin: float
Minimum distance (angstrom)
rmax: float
Maximum distance (angstrom)
rstep: float
Bin width for the histogram (angstrom)
"""
AMSConvenientAnalysisJob.__init__(self, previous_job=previous_job, atom_indices=atom_indices, **kwargs)
self.atom_indices_to = atom_indices_to
self.rmin = rmin
self.rmax = rmax
self.rstep = rstep
[docs] def prerun(self): # noqa F811
"""
Creates the final settings. Do not call or override this method.
"""
self._parent_prerun("RadialDistribution")
self.settings.input.Task = "RadialDistribution"
main_mol = self.previous_job.results.get_main_molecule()
if not self.atom_indices:
self.atom_indices = list(range(1, len(main_mol) + 1))
if not self.atom_indices_to:
self.atom_indices_to = list(range(1, len(main_mol) + 1))
self.settings.input.RadialDistribution.AtomsFrom.Atom = self.atom_indices
self.settings.input.RadialDistribution.AtomsTo.Atom = self.atom_indices_to
self.settings.input.RadialDistribution.Range = f"{self.rmin} {self.rmax} {self.rstep}"
[docs] def postrun(self):
"""
Creates rdf.txt. Do not call or override this method.
"""
try:
r, gr = self.results.get_rdf()
with open(self.path + "/rdf.txt", "w") as f:
f.write("#r(angstrom) g(r)")
for x, y in zip(r, gr):
f.write(f"{x} {y}\n")
except:
pass
class AMSConvenientAnalysisPerRegionResults(Results):
def _getter(self, analysis_job_type, method, kwargs):
assert (
self.job.analysis_job_type is analysis_job_type
), f"{method}() can only be called for {analysis_job_type}, tried for type {self.job.analysis_job_type}"
ret = {}
for name, job in self.job.children.items():
ret[name] = getattr(job.results, method)(**kwargs)
return ret
def get_diffusion_coefficient(self, **kwargs):
return self._getter(AMSMSDJob, "get_diffusion_coefficient", kwargs)
def get_msd(self, **kwargs):
return self._getter(AMSMSDJob, "get_msd", kwargs)
def get_linear_fit(self, **kwargs):
return self._getter(AMSMSDJob, "get_linear_fit", kwargs)
def get_vacf(self, **kwargs):
return self._getter(AMSVACFJob, "get_vacf", kwargs)
def get_power_spectrum(self, **kwargs):
return self._getter(AMSVACFJob, "get_power_spectrum", kwargs)
class AMSConvenientAnalysisPerRegionJob(MultiJob):
_result_type = AMSConvenientAnalysisPerRegionResults
def __init__(self, previous_job, analysis_job_type, name=None, regions=None, per_element=False, **kwargs):
MultiJob.__init__(self, children=OrderedDict(), name=name or "analysis_per_region")
self.previous_job = previous_job
self.analysis_job_type = analysis_job_type
self.analysis_job_kwargs = kwargs
self.regions_dict = regions
self.per_element = per_element
@staticmethod
def get_regions_dict(molecule, per_element: bool = False):
regions_dict = defaultdict(lambda: [])
for i, at in enumerate(molecule, 1):
regions = set([at.properties.region]) if isinstance(at.properties.region, str) else at.properties.region
if len(regions) == 0:
region_name = "NoRegion" if not per_element else f"NoRegion_{at.symbol}"
regions_dict[region_name].append(i)
for region in regions:
region_name = region if not per_element else f"{region}_{at.symbol}"
regions_dict[region_name].append(i)
regions_dict["All"].append(i)
if per_element:
regions_dict[f"All_{at.symbol}"].append(i)
return regions_dict
def prerun(self): # noqa F811
regions_dict = self.regions_dict or self.get_regions_dict(
self.previous_job.results.get_main_molecule(), per_element=self.per_element
)
for region in regions_dict:
self.children[region] = self.analysis_job_type(
previous_job=self.previous_job,
name=region,
atom_indices=regions_dict[region],
**self.analysis_job_kwargs,
)
@staticmethod
def get_mean_std_per_region(list_of_jobs, function_name, **kwargs):
"""
list_of_jobs: List[AMSConvenientAnalysisPerRegionJob]
List of jobs over which to average
function_name: str
e.g. 'get_msd', 'get_power_spectrum'
"""
if isinstance(list_of_jobs, dict):
list_of_jobs = [x for x in list_of_jobs.values()]
all_x = defaultdict(lambda: [])
all_y = defaultdict(lambda: [])
for vacfjob in list_of_jobs:
# let's calculate mean and std for each region
results_dict = getattr(vacfjob.results, function_name)(**kwargs)
for region_name, ret_value in results_dict.items():
if np.isscalar(ret_value):
all_x[region_name].append(np.atleast_1d(ret_value))
elif len(ret_value) == 2:
all_x[region_name].append(np.atleast_1d(ret_value[0]))
all_y[region_name].append(np.atleast_1d(ret_value[1]))
mean_x = {}
std_x = {}
mean_y = {}
std_y = {}
for region_name, values in all_x.items():
mean_x[region_name] = np.mean(values, axis=0)
std_x[region_name] = np.std(values, axis=0)
if len(all_y) > 0:
mean_y[region_name] = np.mean(all_y[region_name], axis=0)
std_y[region_name] = np.std(all_y[region_name], axis=0)
if len(mean_y) > 0:
return mean_x, std_x, mean_y, std_y
else:
return mean_x, std_x
