NanoParticleTools tools is a python module that facilitates monte carlo simulation of Upconverting Nanoparticles (UCNP) using RNMC.
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.
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 developor
pip install .An example of local execution can be seen below.
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)In this example, the target maggma.stores.MemoryStore used to collect output is volatile and will be lost if the Store is reinitialized or the python kernel is restarted. Therefore, one may opt to use a MongoDB server to save calculation output to ensure data persistence. To integrate a MongoDB, use a MongoStore instead of a MemoryStore.
from maggma.stores.mongolike import MongoStore
docs_store = MongoStore(<mongo credentials or URI here>)
data_store = MongoStore(<mongo credentials or URI here>)
Refer to the maggma Stores documentation for more information.
After running simulations, you may wish to average the outputs of trajectories obtained from the same recipe (using different dopant and simulation seeds). We have included a maggma builder in NanoParticleTools to easily group documents and perform the averaging. More information on builders can be found in the maggma Builder documentation
An example of instantiating a builder is as follows:
from maggma.stores.mongolike import MongoStore
source_store = MongoStore(collection_name = "docs_npmc", <mongo credentials here>)
target_store = MongoStore(collection_name = "avg_npmc", <mongo credentials here>)
builder = UCNPBuilder(source_store, target_store, docs_filter={'data.simulation_time': {'$gte': 0.01}}, chunk_size=4)
Here, the source_store is a maggma Store which contains the trajectory documents produced from the SimulationReplayer analysis of NPMC runs. target_store is the Store in which you would like your averaged documents to be populated to. Optional arguments include a docs_filter, which is a pymongo query to target specific documents. chunk_size may also be specified and is dependent on the memory and speed of the machine executing the builder.
To execute the builder locally, use the builder.run() function. The builder may also be run in parallel or distributed mode, see the maggma "Running a Builder Pipeline" documentation
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.
pytest --cov-report term-missing --cov=src tests/
Further guidance on contributing via Pull Requests will be added in the near future.