# Creation of SimulationData objects

The data of simulations to be validated need to be represented by objects of the SimulationData type. The SimulationData objects are consisting of information about the simulation and the system. This information is collected in objects of different classes, namely

The SimulationData objects can either be constructed directly from arrays and numbers, or (partially) automatically via parsers.

## Create SimulationData objects from python data

Example usage, system of 900 water molecules in GROMACS units simulated in NVT:

import numpy as np
import physical_validation

simulation_data = physical_validation.data.SimulationData()

num_molecules = 900
simulation_data.system = physical_validation.data.SystemData(
# Each water molecule has three atoms
natoms=num_molecules * 3,
# Each molecule has three constraints
nconstraints=num_molecules * 3,
# In this simulation, translational center of mass motion was removed
ndof_reduction_tra=3,
# Rotational center of mass motion was not removed
ndof_reduction_rot=0,
# Repeat weight of one oxygen and two hydrogen atoms 900 times
mass=np.tile([15.9994, 1.008, 1.008], num_molecules),
# Denotes the first atom of each molecules: [0, 3, 6, ...]
molecule_idx=np.linspace(0, num_molecules * 3, num_molecules, endpoint=False, dtype=int),
# Each molecule has three constraints
nconstraints_per_molecule=3 * np.ones(num_molecules),
)

# Set GROMACS units
simulation_data.units = physical_validation.data.UnitData.units("GROMACS")

# Simulation was performed under NVT conditions
simulation_data.ensemble = physical_validation.data.EnsembleData(
ensemble='NVT',
natoms=num_molecules * 3,
volume=3.01125 ** 3,
temperature=298.15,
)

# This snippet is assuming that kin_ene, pot_ene and tot_ene are lists
# or numpy arrays filled with the time series of kinetic, potential and total energy
# of a simulation run. These might be obtained, e.g., from the python
# API of a simulation code, or from other python-based analysis tools.
simulation_data.observables = physical_validation.data.ObservableData(
kinetic_energy=kin_ene,
potential_energy=pot_ene,
total_energy=tot_ene,
)

# We are further assuming that positions and velocities are arrays
# of shape (number of frames) x (number of atoms) x 3, where the last
# number stands for the 3 spatial dimensions. Again, these arrays would
# most likely have been obtained from a python interface of the simulation
# package or from other python-based analysis tools
simulation_data.trajectory = physical_validation.data.TrajectoryData(
position=positions,
velocity=velocities,
)


## Package-specific instructions

### GROMACS

GROMACS does not offer a well-established Python interface to read out energies or trajectories. physical_validation therefore offers a parser, which will return a fully populated SimulationData object by reading in GROMACS input and output files.

The GromacsParser takes the GROMACS input files mdp (run options) and top (topology file) to read the details about the system, the ensemble and the time step. The observable trajectory is extracted from an edr (binary energy trajectory), while the position and velocity trajectory can be read either from a trr (binary trajectory) or a gro (ASCII trajectory) file. The constructor optionally takes the path to a gromacs binary as well as the path to the topology library as inputs. The first is necessary to extract information from binary files (using gmx energy and gmx dump), while the second becomes necessary if the top file contains #include statements which usually rely on GROMACS environment variables. The parser is able to find GROMACS installations which are in the path (e.g. after sourcing the GMXRC file) and the corresponding topology library automatically.

Example usage:

import physical_validation

parser = physical_validation.data.GromacsParser()

res = parser.get_simulation_data(
mdp='mdout.mdp',
top='system.top',
gro='system.gro',
edr='system.edr'
)


Note

Always double-check the results received from the automatic parser. Since this is not an official GROMACS tool, it is very likely that some special cases or changes in recent versions might not be interpreted correctly.

### LAMMPS

To analyze simulations performed with LAMMPS, we strongly suggest using its Python interface Pizza.py to create a SimulationData object as explained in Create SimulationData objects from python data. Note that UnitData offers access to a UnitData object representing the LAMMPS real units by using .data.UnitData.units("LAMMPS real").

As an alternative, physical_validation ships with a LAMMPS parser, which tries to read part of the system information, the observable and position / velocity trajectories from LAMMPS output files.

Example usage:

import physical_validation

parser = physical_validation.data.LammpsParser()

res = parser.get_simulation_data(
# The LAMMPS parser cannot infer the ensemble from the LAMMPS files,
# so we pass an EnsembleData object with the information matching the simulation
ensemble=physical_validation.data.EnsembleData(
ensemble="NVT",
natoms=900,
volume=20**3,
temperature=300
),
in_file=dir_1 + '/water.in',
log_file=dir_1 + '/log.lammps',
data_file=dir_1 + '/water.lmp',
dump_file=dir_1 + '/dump.atom'
)


Warning

The LAMMPS parser is in an early development stage. It is part of the physical_validation package in the hope that it is helpful to someone, but it is very likely to go wrong in a number of cases. Please check any object data create by the LAMMPS parser carefully.

## Flatfile parser

For MD packages not supported by the package-specific parsers, it is possible to create the SimulationData objects via the FlatfileParser. This parser fills the SimulationData.trajectory object via 3-dimensional ASCII files containing the position and velocity trajectories, and the SimulationData.observables via 1-dimensional ASCII files containing the trajectories for the observables of interest. As the details on the units, the simulated system and the sampled ensemble can not easily be read from such files, this information has to be provided by the user by passing objects of the respective data structures. See FlatfileParser.get_simulation_data for more details on the SimulationData creation via the flat file parser, and Data contained in SimulationData objects for details on which test requires which information.

Example usage, system of 900 water molecules in GROMACS units simulated in NVT (note that this example leaves some fields in SystemData empty, as well as the trajectory of some observables and the position and velocities):

import physical_validation as pv

parser = pv.data.FlatfileParser()

system = pv.data.SystemData(
natoms=900*3,
nconstraints=900*3,
ndof_reduction_tra=3,
ndof_reduction_rot=0
)

# We need to specify the units in which the simulation was performed,
# specifically the value of k_B in the used energy units, the conversion
# factor of the simulation units to the physical validation units
# (*_conversion keywords), and a string representation of the simulation
# units (*_str keywords, used for output only).
# See documentation below about UnitData object for more details.
units = pv.data.UnitData(
kb=8.314462435405199e-3,
energy_str='kJ/mol',
energy_conversion=1.0,
length_str='nm',
length_conversion=1.0,
volume_str='nm^3',
volume_conversion=1.0,
temperature_str='K',
temperature_conversion=1.0,
pressure_str='bar',
pressure_conversion=1.0,
time_str='ps',
time_conversion=1.0
)

ensemble = pv.data.EnsembleData(
ensemble='NVT',
natoms=900*3,
volume=3.01125**3,
temperature=298.15
)

res = parser.get_simulation_data(
units=units, ensemble=ensemble, system=system,
kinetic_ene_file='kinetic.dat',
potential_ene_file='potential.dat',
total_ene_file='total.dat'
)


### Use MDAnalysis to create mass vector

Using MDAnalysis, creating a mass vector which can be fed to SystemData.mass is straightforward. See the following snippet for an example using a GROMACS topology:

import MDAnalysis as mda
import numpy as np

u = mda.Universe('system.gro')
mass=np.array([u.atoms[i].mass for i in range(len(u.atoms))])


### Use MDAnalysis to define molecule groups for equipartition testing

physical_validation.kinetic_energy.equipartition() allows to specify molecule groups which can be tested for equipartition. The segments used in MDAnalysis can easily be used to define molecule groups as input to the equipartition check:

import MDAnalysis as mda
import numpy as np

u = mda.Universe('system.tpr', 'system.gro')
molec_groups = []
for i in range(len(u.segments)):
seg = u.segments[i]
molec_groups.append(np.array([seg.atoms[j].index for j in range(len(seg.atoms))]))


### Use MDAnalysis to read position and velocity trajectory

MDAnalysis also makes it easy to create TrajectoryData objects which require position and velocity trajectories as inputs. Given a Universe object which contains a trajectory, we can simply use a list comprehension to create a full trajectory in memory:

import MDAnalysis as mda
import numpy as np
import physical_validation

u = mda.Universe('system.tpr', 'system.trr')
trajectory = physical_validation.data.TrajectoryData(
position=[frame.positions for frame in u.trajectory],
velocity=[frame.velocities for frame in u.trajectory])


We can also use the atom selector to only feed part of the trajectory to the physical_validation tests. This is useful if we want to analyze the equipartition of parts of the system only (e.g. the solute) which can massively speed up the validation check. Note that we have to adapt the SystemData object accordingly to inform physical_validation that we are only analyzing part of the system.

import MDAnalysis as mda
import numpy as np
import physical_validation

u = mda.Universe('system.tpr', 'system.trr')
protein = u.select_atoms('protein')
trajectory = physical_validation.data.TrajectoryData(
position=[protein.positions for _ in u.trajectory],
velocity=[protein.velocities for _ in u.trajectory])


Note

MDAnalysis uses Å (ångström) as a length unit. Don’t forget to choose the UnitData accordingly!

# Data contained in SimulationData objects

## Units: SimulationData.units of type UnitData

Attributes:

The information about units consists of different parts:

• The value of $$k_B$$ in the used energy units,

• the conversion factor to physical_validation units (kJ/mol, nm, nm^3, K, bar, ps, the same as the GROMACS default units), and

• the name of the units (energy_str, length_str, volume_str, temperature_str, pressure_str, time_str).

The names are only used for output (console printing and plotting), and are optional. The conversion factors and kB are, on the other hand, used in computations and need to be given. To avoid silent errors, these keywords to not have defaults and must be specified.

Needed by

## Ensemble: SimulationData.ensemble of type EnsembleData

Attributes:

The ensemble is a string indicating the thermodynamical ensemble a simulation was performed in, and is any of :code:’NVE’, :code:’NVT’, :code:’NPT’, :code:’muVT’.

Depending on the ensemble, EnsembleData then holds additional information defining the ensemble, such as the number of particles N, the chemical potential mu, the volume V, the pressure P, the constant energy E or the temperature T. While any of these additional information are technically optional, most of them are needed by certain tests, such that not fully defining the ensemble results in warnings. The notable exception to this rule is the constant energy E for NVE, which is not needed by any test and can hence be omitted without raising a warning.

Needed by

## System: SimulationData.system of type SystemData

Attributes:

Todo

Currently, there is some redundancy in the attributes listed above. The SystemData.bonds and SystemData.constrained_bonds are reserved for future use - included already in the information about the system, but not yet used by any tests included in the currently published package. In a future version, the SystemData should be streamlined to make the object initialization easier.

Needed by

Attributes:

Needed by

Attributes:

Needed by

## Time step: SimulationData.dt of type float

The timestep used during the simulation run, a single float value.

Needed by