Nexus User Scripts¶
Users interact with Nexus by writing a Python script that often resembles an input file. A Nexus user script typically consists of six main sections, as described below:
- Nexus imports:
functions unique to Nexus are drawn into the user environment.
- Nexus settings:
specify machine information and configure the runtime behavior of Nexus.
- Physical system specification:
create a data description of the physical system. Generate a crystal structure or import one from an external data file. Physical system details can be shared among many simulations.
- Workflow specification:
describe the simulations to be performed. Link simulations together by their data dependencies to form workflows.
- Workflow execution:
pass control to Nexus for active workflow management. Simulation input files are generated, jobs are submitted and monitored, output files are collected and preprocessed for later analysis.
- Data analysis:
control returns to the user to extract preprocessed simulation output data for further analysis.
Each of the six input sections is the subject of lengthier discussion: Nexus imports, Nexus settings: global state and user-specific information, Physical system specification, Workflow specification, Workflow execution, and Data analysis. These sections are also illustrated in the abbreviated example script below. For more complete examples and further discussion, please refer to the user walkthroughs in Complete Examples.
Each script begins with imports from the main Nexus module. Items imported include the interface to provide settings to Nexus, helper functions to make objects representing atomic structures or simulations of particular types (e.g. QMCPACK or VASP), and the interface to provide simulation workflows to Nexus for active management.
The import of all Nexus components is accomplished with the brief
from nexus import *”. Each component can also be imported
separately by name, as in the example below.
from nexus import settings # Nexus settings function from nexus import generate_physical_system # for creating atomic structures from nexus import generate_pwscf # for creating PWSCF sim. objects from nexus import Job # for creating job objects from nexus import run_project # for active workflow management
This has the advantage of avoided unwanted namespace collisions with user defined variables. The major Nexus components available for import are listed in Table 1.
Alter runtime behavior. Provide machine information.
Create atomic structure including electronic information.
Create atomic structure without electronic information.
Create generic simulation object.
Create PWSCF simulation object.
Create VASP simulation object.
Create GAMESS simulation object.
Create QMCPACK simulation object.
Create SQD simulation object.
Create generic input file object.
Create generic input file object representing multiple files.
Create PWSCF input file object.
Create VASP input file object.
Create GAMESS input file object.
Create QMCPACK input file object.
Create SQD input file object.
Provide job information for simulation run.
Initiate active workflow management.
Generic container object. Store inputs for later use.
Nexus settings: global state and user-specific information¶
Following imports, the next section of a Nexus script is dedicated to
providing information regarding the local machine, the location of
various files, and the desired runtime behavior. This information is
communicated to Nexus through the
settings function. To make
settings available in your project script, use the following import
from nexus import settings
In most cases, it is sufficient to supply only four pieces of
information through the
settings function: whether to run all jobs
or just create the input files, how often to check jobs for completion,
the location of pseudopotential files, and a description of the local
settings( generate_only = True, # only write input files, do not run sleep = 3, # check on jobs every 3 seconds pseudo_dir = './pseudopotentials', # path to PP file collection machine = 'ws8' # local machine is an 8 core workstation )
A few additional parameters are available in
settings to control
where runs are performed, where output data is gathered, and whether to
print job status information. More detailed information about machines
can be provided, such as allocation account numbers, filesystem
structure, and where executables are located.
settings( status_only = True, # only show job status, do not write or run generate_only = True, # only write input files, do not run sleep = 3, # check on jobs every 3 seconds pseudo_dir = './pseudopotentials', # path to PP file collection runs = '', # base path for runs is local directory results = '/home/jtk/results/', # light output data copied elsewhere machine = 'titan', # Titan supercomputer account = 'ABC123', # user account number )
Physical system specification¶
After providing settings information, the user often defines the atomic structure to be studied (whether generated or read in). The same structure can be used to form input to various simulations (e.g. DFT and QMC) performed on the same system. The examples below illustrate the main options for structure input.
Read structure from a file¶
dia16 = generate_physical_system( structure = './dia16.POSCAR', # load a POSCAR file C = 4 # pseudo-carbon (4 electrons) )
Generate structure directly¶
dia16 = generate_physical_system( lattice = 'cubic', # cubic lattice cell = 'primitive', # primitive cell centering = 'F', # face-centered constants = 3.57, # a = 3.57 units = 'A', # Angstrom units atoms = 'C', # monoatomic C crystal basis = [[0,0,0], # basis vectors [.25,.25,.25]], # in lattice units tiling = (2,2,2), # tile from 2 to 16 atom cell C = 4 # pseudo-carbon (4 electrons) )
Provide cell, elements, and positions explicitly:¶
dia16 = generate_physical_system( units = 'A', # Angstrom units axes = [[1.785,1.785,0. ], # cell axes [0. ,1.785,1.785], [1.785,0. ,1.785]], elem = ['C','C'], # atom labels pos = [[0. ,0. ,0. ], # atomic positions [0.8925,0.8925,0.8925]], tiling = (2,2,2), # tile from 2 to 16 atom cell kgrid = (4,4,4), # 4 by 4 by 4 k-point grid kshift = (0,0,0), # centered at gamma C = 4 # pseudo-carbon (4 electrons) )
In each of these cases, the text “
C = 4” refers to the number of
electrons in the valence for a particular element. Here a
pseudopotential is being used for carbon and so it effectively has four
valence electrons. One line like this should be included for each
element in the structure.
The next section in a Nexus user script is the specification of simulation workflows. This stage can be logically decomposed into two sub-stages: (1) specifying inputs to each simulation individually, and (2) specifying the data dependencies between simulations.
Generating simulation objects¶
Simulation objects are created through calls to “
functions, where “
xxxxxx” represents the name of a particular
simulation code, such as
generate function shares certain inputs, such as the path where the
simulation will be performed, computational resources required by the
simulation job, an identifier to differentiate between simulations (must
be unique only for simulations occurring in the same directory), and the
atomic/electronic structure to simulate:
relax = generate_pwscf( identifier = 'relax', # identifier for the run path = 'diamond/relax', # perform run at this location job = Job(cores=16,app='pw.x'), # run on 16 cores using pw.x executable system = dia16, # 16 atom diamond cell made earlier pseudos = ['C.BFD.upf'], # pseudopotential file files = , # any other files to be copied in ... # PWSCF-specific inputs follow )
The simulation objects created in this way are just data. They represent requests for particular simulations to be carried out at a later time. No simulation runs are actually performed during the creation of these objects. A basic example of generation input for each of the four major codes currently supported by Nexus is given below.
Quantum Espresso (PWSCF) generation:¶
scf = generate_pwscf( identifier = 'scf', path = 'diamond/scf', job = scf_job, system = dia16, pseudos = ['C.BFD.upf'], input_type = 'generic', calculation = 'scf', input_dft = 'lda', ecutwfc = 75, conv_thr = 1e-7, kgrid = (2,2,2), kshift = (0,0,0), )
conv_thr will be familiar to the casual user of PWSCF. Any input
keyword that normally appears as part of a namelist in PWSCF input can
be directly supplied here. The
generate_pwscf function, like most of
the others, actually takes an arbitrary number of keyword arguments.
These are later screened against known inputs to PWSCF to avoid errors.
kshift inputs inform the
KPOINTS card in the
PWSCF input file, overriding any similar information provided in
relax = generate_vasp( identifier = 'relax', path = 'diamond/relax', job = relax_job, system = dia16, pseudos = ['C.POTCAR'], input_type = 'generic', istart = 0, icharg = 2, encut = 450, nsw = 5, ibrion = 2, isif = 2, kcenter = 'monkhorst', kgrid = (2,2,2), kshift = (0,0,0), )
generate_vasp accepts an arbitrary
number of keyword arguments and any VASP input file keyword is accepted
(the VASP keywords provided here are
kshift keywords are used to form the
KPOINTS input file.
Pseudopotentials provided through the
pseudos keyword will fused
into a single
POTCAR file following the order of the atoms created
uhf = generate_gamess( identifier = 'uhf', path = 'water/uhf', job = Job(cores=16,app='gamess.x'), system = h2o, pseudos = [H.BFD.gms,O.BFD.gms], symmetry = 'Cnv 2', scftyp = 'uhf', runtyp = 'energy', ispher = 1, exetyp = 'run', maxit = 200, memory = 150000000, guess = 'hcore', )
generate_gamess function also accepts arbitrary GAMESS keywords
guess here). The pseudopotential files
O.BFD.gms include the gaussian basis sets as well
as the pseudopotential channels (the two parts are just concatenated
into the same file, commented lines are properly ignored). Nexus drives
the GAMESS executable (
gamess.x here) directly without the
rungms script as is often done. To do this, the
ericfmt keyword must be provided in
settings specifying the path
qmc = generate_qmcpack( identifier = 'vmc', path = 'diamond/vmc', job = Job(cores=16,threads=4,app='qmcpack'), input_type = 'basic', system = dia16, pseudos = ['C.BFD.xml'], jastrows = , calculations = [ vmc( walkers = 1, warmupsteps = 20, blocks = 200, steps = 10, substeps = 2, timestep = .4 ) ], dependencies = (conv,'orbitals') )
Unlike the other
generate_qmcpack takes only
selected inputs. The reason for this is that QMCPACK’s input file is
highly structured (nested XML) and cannot be directly mapped to
keyword-value pairs. The full set of allowed keywords is beyond the
scope of this section. Please refer to the user walkthroughs provided in
Complete Examples for further examples.
Composing workflows from simulation objects¶
Simulation workflows are created by specifying the data dependencies between simulation runs. An example workflow is shown in Fig. 1. In this case, a single relaxation calculation performed with PWSCF is providing a relaxed structure to each of the subsequent simulations. PWSCF is used to create a converged charge density (SCF) and then orbitals at specific k-points (NSCF). These orbitals are used by each of the two QMCPACK runs; the first optimization run provides a Jastrow factor to the final DMC run.
Below is an example of how this workflow can be created with Nexus. Most
keywords to the
generate functions have been omitted for brevity.
conv step listed below is implicit in Fig. 1.
relax = generate_pwscf( ... ) scf = generate_pwscf( dependencies = (relax,'structure'), ... ) nscf = generate_pwscf( dependencies = [(relax,'structure' ), (scf ,'charge_density')], ... ) conv = generate_pw2qmcpack( dependencies = (nscf ,'orbitals' ), ... ) opt = generate_qmcpack( dependencies = [(relax,'structure'), (conv ,'orbitals' )], ... ) dmc = generate_qmcpack( dependencies = [(relax,'structure'), (conv ,'orbitals' ), (opt ,'jastrow' )], ... )
As suggested at the beginning of this section, workflow composition logically breaks into two parts: simulation generation and workflow dependency specification. This type of breakup can also be performed explicitly within a Nexus user script, if desired:
# simulation generation relax = generate_pwscf(...) scf = generate_pwscf(...) nscf = generate_pwscf(...) conv = generate_pw2qmcpack(...) opt = generate_qmcpack(...) dmc = generate_qmcpack(...) # workflow dependency specification scf.depends(relax,'structure') nscf.depends((relax,'structure' ), (scf ,'charge_density')) conv.depends(nscf ,'orbitals' ) opt.depends((relax,'structure'), (conv ,'orbitals' )) dmc.depends((relax,'structure'), (conv ,'orbitals' ), (opt ,'jastrow' ))
More complicated workflows or scans over parameters of interest can be created with for loops and if-else logic constructs. This is fairly straightforward to accomplish because any keyword input can given a Python variable instead of a constant, as is mostly the case in the brief examples above.
Simulation jobs are actually executed when the corresponding simulation
objects are passed to the
run_project function. Within the
run_project function, most of the workflow management operations
unique to Nexus are actually performed. The details of the management
process is not the purpose of this section. This process is discussed in
context in the Complete Examples walkthroughs.
run_project function can be invoked in a couple of ways. The
most straightforward is simply to provide all simulation objects
directly as arguments to this function:
When complex workflows are being created (e.g. when the
function appear in
for loops and
if statements), it is generally
more convenient to accumulate a list of simulation objects and then pass
the list to
run_project as follows:
sims =  relax = generate_pwscf(...) sims.append(relax) scf = generate_pwscf(...) sims.append(scf) nscf = generate_pwscf(...) sims.append(nscf) conv = generate_pw2qmcpack(...) sims.append(conv) opt = generate_qmcpack(...) sims.append(opt) dmc = generate_qmcpack(...) sims.append(dmc) run_project(sims)
run_project function returns, all simulation runs should be
Following the call to
run_project, the user can perform data
analysis tasks, if desired, as the analyzer object associated with each
simulation contains a collection of post-processed output data rendered
in numeric form (ints, floats, numpy arrays) and stored in a structured
format. An interactive example for QMCPACK data analysis is shown below.
Note that all analysis objects are interactively browsable in a similar
>>> qa=dmc.load_analyzer_image() >>> qa.qmc 0 VmcAnalyzer 1 DmcAnalyzer 2 DmcAnalyzer >>> qa.qmc dmc DmcDatAnalyzer info QAinformation scalars ScalarsDatAnalyzer scalars_hdf ScalarsHDFAnalyzer >>> qa.qmc.scalars_hdf Coulomb obj ElecElec obj Kinetic obj LocalEnergy obj LocalEnergy_sq obj LocalPotential obj data QAHDFdata >>> print qa.qmc.scalars_hdf.LocalEnergy error = 0.0201256357883 kappa = 12.5422841447 mean = -75.0484800012 sample_variance = 0.00645881103012 variance = 0.850521272106