NanoParticleTools tools is a python module that facilitates monte carlo simulation of Upconverting Nanoparticles (UCNP) using RNMC.

Using NanoParticleTools

NanoParticleTools provides functionality to generate inputs for running Monte Carlo Simulations on nanoparticles and analyzing outputs. Monte Carlo simulation uses NMPC within the RNMC package. While NanoParticleTools provides wrapper functions to run the C++ based simulator, RNMC must be installed to perform simulations.

Installation

To install NanoParticleTools to a python environment, clone the repository and use one of the following commands from within the NanoParticleTools directory

python setup.py develop

or

pip install .

NixOS

A NixOS environment is also provided for an alternative setup method. This environment includes access to a compiled RNMC executable. To access the Nix development shell

nix develop

Note: To use the NixOS environment, you must have root access on the system you are running on (i.e. This is usually not the case on supercomputers).

Running Simulations

from NanoParticleTools.flows.flows import get_npmc_flow
from NanoParticleTools.inputs.nanoparticle import SphericalConstraint

constraints = [SphericalConstraint(20)]
dopant_specifications = [(0, 0.1, 'Yb', 'Y'),
                         (0, 0.02, 'Er', 'Y')]

npmc_args = {'npmc_command': <NPMC_command>,
             'num_sims':2,
             'base_seed': 1000,
             'thread_count': 8,
             'simulation_length': 1000,
             }
spectral_kinetics_args = {'excitation_power': 1e12,
                          'excitation_wavelength':980}

flow = get_npmc_flow(constraints = constraints,
                     dopant_specifications = dopant_specifications,
                     doping_seed = 0,
                     spectral_kinetics_args = spectral_kinetics_args,
                     npmc_args = npmc_args,
                     output_dir = './scratch')

from jobflow import run_locally
from maggma.stores import MemoryStore
from jobflow import JobStore

# Store the output data locally in a MemoryStore
docs_store = MemoryStore()
data_store = MemoryStore()
store = JobStore(docs_store, additional_stores={'trajectories': data_store})

responses = run_locally(flow, store=store, ensure_success=True)

Contributing

If you wish to make changes to NanoParticle tools, it may be wise to install the package in development mode. After cloning the package, use the following command.

python -m pip install -e .

Modifications should now be reflected when you run any functions in NanoParticleTools.

Further guidance on contributing via Pull Requests will be added in the near future.