PyPartMC

PyPartMC is a Python interface to PartMC, a particle-resolved Monte-Carlo code for atmospheric aerosol simulation. Development of PyPartMC has been intended to remove limitations to the use of Fortran-implemented PartMC. PyPartMC facilitates the dissemination of computational research results by streamlining independent execution of PartMC simulations (also during peer-review processes). Additionally, the ability to easily package examples, simple simulations, and results in a web-based notebook allows PyPartMC to support the efforts of many members of the scientific community, including researchers, instructors, and students, with nominal software and hardware requirements.

Documentation of PyPartMC is hosted at https://un5mvxtq4u190mnmhk2zcphc7zg0m.julianrbryant.com/PyPartMC. PyPartMC is implemented in C++ and it also constitutes a C++ API to the PartMC Fortran internals. The Python API can facilitate using PartMC from other environments - see, e.g., Julia and Matlab examples below.

For an outline of the project, rationale, architecture, and features, refer to: D'Aquino et al., 2024 (SoftwareX) (please cite if PyPartMC is used in your research). For a list of talks and other relevant resources, please see project Wiki. If interested in contributing to PyPartMC, please have a look a the notes for developers.

Installation

Using the command-line pip tool (also applies to conda environments)

pip install PyPartMC

Note that, depending on the environment (OS, hardware, Python version), the pip-install invocation may either trigger a download of a pre-compiled binary, or trigger compilation of PyPartMC. In the latter case, a Fortran compiler and some development tools includiong CMake, m4 and perl are required (while all non-Python dependencies are included in the PyPartMC source archive). In both cases, all Python dependencies will be resolved by pip.

In a Jupyter notebook cell (also on Colab or jupyter-hub instances)

! pip install PyPartMC
import PyPartMC

Jupyter notebooks with examples

Note: clicking the badges below redirects to cloud-computing platforms. The mybinder.org links allow anonymous execution, Google Colab requires logging in with a Google account, ARM JupyerHub requires logging in with an ARM account (and directing Jupyter to a particular notebook within the examples folder).

The example notebooks feature additional dependencies that can be installed with:

pip install PyPartMC[examples]

• Urban plume scenario demo (as in PartMC):
  
• Chamber simulation example from Barrel Study (Tian el al., 2017):
  
• Dry-Wet Particle Size Equilibration with PartMC and PySDM:
  
• Simulation output processing example (loading from netCDF files using PyPartMC):
  
• Optical properties calculation using external Python package (PyMieScatt):
  
• Cloud parcel example featuring supersaturation-evolution-coupled CCN activation and drop growth:
  
• Immersion freezing example:
  
• Coagulation model intercomparison for additive (Golovin) kernel with: PyPartMC, PySDM, Droplets.jl and dustpy:
  
• Particle simulation with multiphase chemistry handled using CAMP (without coagulation):
  

Features

• works on Linux, macOS and Windows (compatibility assured with CI builds)
• hassle-free installation using pip (prior PartMC installation not needed)
• works out of the box on mybinder.org, Google Colab and alike
• ships with a set of examples maintained in a form of Jupyter notebooks
• Pythonic API (but retaining PartMC jargon) incl. Python GC deallocation of Fortran objects
• specification of parameters using native Python datatypes (lists, dicts) in place of PartMC spec files
• code snippets in README depicting how to use PyPartMC from Julia and Matlab (also executed on CI)
• auto-generated API docs on the web
• support for [de]serialization of selected wrapped structures using JSON
• based on unmodified PartMC code
• does not use or require shell or any pre-installed libraries
• aiming at 100% unit test coverage

Usage examples

The listings below depict how the identical task of randomly sampling particles from an aerosol size distribution in PartMC can be done in different programming languages.

For a Fortran equivalent of the Python, Julia and Matlab programs below, see the readme_fortran folder.

Python

import numpy as np

import PyPartMC as ppmc
from PyPartMC import si

aero_data = ppmc.AeroData((
    #      [density, ions in solution, molecular weight, kappa, abifm_m, abifm_c]
    {"OC": [1000 *si.kg/si.m**3, 0, 1e-3 *si.kg/si.mol, 0.001, 0, 0]},
    {"BC": [1800 *si.kg/si.m**3, 0, 1e-3 *si.kg/si.mol, 0, 0 , 0]},
))

aero_dist = ppmc.AeroDist(
    aero_data,
    [{
        "cooking": {
            "mass_frac": [{"OC": [1]}],
            "diam_type": "geometric",
            "mode_type": "log_normal",
            "num_conc": 3200 / si.cm**3,
            "geom_mean_diam": 8.64 * si.nm,
            "log10_geom_std_dev": 0.28,
        },
        "diesel": {
            "mass_frac": [{"OC": [0.3]}, {"BC": [0.7]}],
            "diam_type": "geometric",
            "mode_type": "log_normal",
            "num_conc": 2900 / si.cm**3,
            "geom_mean_diam": 50 * si.nm,
            "log10_geom_std_dev": 0.24,
        }
    }],
)

n_part = 100
aero_state = ppmc.AeroState(aero_data, n_part, "nummass_source")
aero_state.dist_sample(aero_dist)
print(np.dot(aero_state.masses(), aero_state.num_concs), "# kg/m3")

Julia (using PyCall.jl)

using Pkg
Pkg.add("PyCall")

using PyCall
ppmc = pyimport("PyPartMC")
si = ppmc["si"]

aero_data = ppmc.AeroData((
  #       (density, ions in solution, molecular weight, kappa, abifm_m, abifm_c)
  Dict("OC"=>(1000 * si.kg/si.m^3, 0, 1e-3 * si.kg/si.mol, 0.001, 0, 0)),
  Dict("BC"=>(1800 * si.kg/si.m^3, 0, 1e-3 * si.kg/si.mol, 0, 0, 0))
))

aero_dist = ppmc.AeroDist(aero_data, (
  Dict( 
    "cooking" => Dict(
      "mass_frac" => (Dict("OC" => (1,)),),
      "diam_type" => "geometric",
      "mode_type" => "log_normal",
      "num_conc" => 3200 / si.cm^3,
      "geom_mean_diam" => 8.64 * si.nm,
      "log10_geom_std_dev" => .28,
    ),
    "diesel" => Dict(
      "mass_frac" => (Dict("OC" => (.3,)), Dict("BC" => (.7,))),
      "diam_type" => "geometric",
      "mode_type" => "log_normal",
      "num_conc" => 2900 / si.cm^3,
      "geom_mean_diam" => 50 * si.nm,
      "log10_geom_std_dev" => .24,
    )
  ),
))

n_part = 100
aero_state = ppmc.AeroState(aero_data, n_part, "nummass_source")
aero_state.dist_sample(aero_dist)
print(aero_state.masses()'aero_state.num_concs, "# kg/m3")

Matlab (using Matlab's built-in Python interface)

notes (see the PyPartMC Matlab CI workflow for an example on how to achieve it on Ubuntu 20):

• Matlab ships with convenience copies of C, C++ and Fortran runtime libraries which are dlopened() by default; one way to make PyPartMC OK with it is to [pip-]install by compiling from source using the very same version of GCC that Matlab borrowed these libraries from (e.g., GCC 9 for Matlab R2022a, etc);
• Matlab needs to use the same Python interpretter/venv as the pip invocation used to install PyPartMC;

ppmc = py.importlib.import_module('PyPartMC');
si = py.importlib.import_module('PyPartMC').si;

aero_data = ppmc.AeroData(py.tuple({ ...
  py.dict(pyargs("OC", py.tuple({1000 * si.kg/si.m^3, 0, 1e-3 * si.kg/si.mol, 0.001, 0, 0}))), ...
  py.dict(pyargs("BC", py.tuple({1800 * si.kg/si.m^3, 0, 1e-3 * si.kg/si.mol, 0, 0, 0}))) ...
}));

aero_dist = ppmc.AeroDist(aero_data, py.tuple({ ...
  py.dict(pyargs( ...
    "cooking", py.dict(pyargs( ...
      "mass_frac", py.tuple({py.dict(pyargs("OC", py.tuple({1})))}), ...
      "diam_type", "geometric", ...
      "mode_type", "log_normal", ...
      "num_conc", 3200 / si.cm^3, ...
      "geom_mean_diam", 8.64 * si.nm, ...
      "log10_geom_std_dev", .28 ...
    )), ...
    "diesel", py.dict(pyargs( ...
      "mass_frac", py.tuple({ ...
        py.dict(pyargs("OC", py.tuple({.3}))), ...
        py.dict(pyargs("BC", py.tuple({.7}))), ...
      }), ...
      "diam_type", "geometric", ...
      "mode_type", "log_normal", ...
      "num_conc", 2900 / si.cm^3, ...
      "geom_mean_diam", 50 * si.nm, ...
      "log10_geom_std_dev", .24 ...
    )) ...
  )) ...
}));

n_part = 100;
aero_state = ppmc.AeroState(aero_data, n_part, "nummass_source");
aero_state.dist_sample(aero_dist);
masses = cell(aero_state.masses());
num_concs = cell(aero_state.num_concs);
fprintf('%g # kg/m3\n', dot([masses{:}], [num_concs{:}]))

usage in other projects

PyPartMC is used within the test workflow of the PySDM project.

Other packages with relevant feature scope

• aerosolGDEFoam: OpenFOAM CFD-coupled aerosol dynamics including nucleation, coagulation, and surface growth
• AIOMFAC and AIOMFAC-web: Fortran-implemented aerosol thermodynamic model for calculation of activity coefficients in organic-inorganic mixtures – from simple binary solutions to complex multicomponent systems
• DustPy: Python package for modelling dust evolution in protoplanetary disks (differences: focus on astrophysical applications vs. atmospheric aerosol)
• multilayerpy: kinetic multi-layer model for aerosol particles and films
• PyBox: aerosol simulation model featuring gas and particle chamistry (differences: PyBox focuses on chemical mechanisms; PyPartMC is an interface to PartMC which focuses on physics - e.g., collisions of aerosol particles - while chemical processes are handled with external software, e.g., CAMP or MOSAIC)
• PyCHAM: CHemistry with Aerosol Microphysics in Python Box Model for modelling of indoor environments, including aerosol chambers
• PySDM: particle-based Monte-Carlo aerosol-cloud simulation package (differences: PySDM focuses on growth and breakup processes relevant to cloud droplets; PyPartMC focuses on processes relevant to air pollutants and their chemical and physical transformations)
• SSH-aerosol: C++/Fortran package for simulating evolution of primary and secondary atmospheric aerosols

FAQ

• Q: How to install PyPartMC with MOSAIC enabled?
  A: Installation can be done using pip, however, pip needs to be instructed not to use binary packages available at pypi.org but rather to compile from source (pip will download the source from pip.org), and the path to compiled MOSAIC library needs to be provided at compile-time; the following command should convey it:

MOSAIC_HOME=<<PATH_TO_MOSAIC_LIB>> pip install --force-reinstall --no-binary=PyPartMC PyPartMC

• Q: Why pip install PyPartMC triggers compilation on my brand new Apple machine, while it quickly downloads and installs binary packages when executed on older Macs, Windows or Linux?
  A: We are providing binary wheels on PyPI for Apple-silicon (arm64) machines for selected macOS version made available by Github. In case the macOS version you are using is newer, compilation from source is triggered.

• Q: Why some of the constructors expect data to be passed as lists of single-entry dictionaries instead of multi-element dictionaries?
  A: This is intentional and related with PartMC relying on the order of elements within spec-file input; while Python dictionaries preserve ordering (insertion order), JSON format does not, and we intend to make these data structures safe to be [de]serialized using JSON.

• Q: How to check the version of PartMC that PyPartMC was compiled against?
  A: Version numbers of compile-time dependencies of PyPartMC, including PartMC, can be accessed as follows:

import PyPartMC
PyPartMC.__versions_of_build_time_dependencies__['PartMC']

• Q: Why m4 and perl are required at compile time?
  A: PyPartMC includes parts of netCDF and HDF5 codebases which depend on m4 and perl, respectively, for generating source files before compilation.

Troubleshooting

Common installation issues

error: [Errno 2] No such file or directory: 'cmake'

Try rerunning after installing CMake, e.g., using apt-get install cmake (Ubuntu/Debian), brew install cmake (homebrew on macOS) or using MSYS2 on Windows.

No CMAKE_Fortran_COMPILER could be found.

Try installing a Fortran compiler (e.g., brew reinstall gcc with Homebrew on macOS or using MSYS2 on Windows).

Could not find NC_M4 using the following names: m4, m4.exe

Try installing m4 (e.g., using MSYS2 on Windows).

Acknowledgement and citations

We would greatly appreciate citation of the PartMC model description paper (Riemer et al., 2009) and the PyPartMC description paper (D’Aquino et al., 2024) if PyPartMC was used in your study. The citations are:

• Riemer, N., M. West, R. A. Zaveri, R. C. Easter: Simulating the evolution of soot mixing-state with a particle-resolved aerosol model
  J. Geophys. Res., 114, D09202, 2009, DOI: 10.1029/2008JD011073
• D’Aquino, Z., S. Arabas, J. H. Curtis, A. Vaishnav, N. Riemer, M. West: PyPartMC: A pythonic interfact to a particle-resolved, Monte Carlo aerosol simulation framework
  SoftwareX, 25, 101613, 2024, DOI: 10.1016/j.softx.2023.101613

The following paragraph provides a more substantial description of PartMC (text released into the public domain and can be freely copied by anyone for any purpose):

PartMC is a stochastic, particle-resolved aerosol box model. It tracks the composition of many computational particles (10⁴ to 10⁶) within a well-mixed air volume, each represented by a composition vector that evolves based on physical and chemical processes. The physical processes—including Brownian coagulation, new particle formation, emissions, dilution, and deposition—are simulated using a stochastic Monte Carlo approach via a Poisson process while chemical processes are simulated deterministically for each computational particle. The weighted flow algorithm (DeVille, Riemer, and West, 2011, 2019) enhances efficiency and reduces ensemble variance. Detailed numerical methods are described in Riemer et al. (2009), DeVille et al. (2011, 2019), and Curtis et al. (2016). PartMC is open-source under the GNU GPL v2 and available at github.com/compdyn/partmc.

References:

• Curtis, J. H., M. D. Michelotti, N. Riemer, M. T. Heath, M. West: Accelerated simulation of stochastic particle removal processes in particle-resolved aerosol models, J. Computational Phys., 322, 21-32, 2016, DOI: 10.1016/j.jcp.2016.06.029
• DeVille, L., N. Riemer, M. West, Convergence of a generalized weighted flow algorithm for stochastic particle coagulation, J. Computational Dynamics, 6, 69-94, 2019, DOI: 10.3934/jcd.2019003
• DeVille, R. E. L., N. Riemer, M. West, The Weighted Flow Algorithm (WFA) for stochastic particle coagulation, J. Computational Phys., 230, 8427-8451, 2011, DOI: 10.1016/j.jcp.2011.07.027
• Riemer, N., M. West, R. A. Zaveri, R. C. Easter, Simulating the evolution of soot mixing-state with a particle-resolved aerosol model, J. Geophys. Res., 114, D09202, 2009., DOI: 10.1029/2008JD011073

Credits

PyPartMC:

authors: PyPartMC developers
funding: US Department of Energy Atmospheric System Research programme, Polish National Science Centre
copyright: University of Illinois at Urbana-Champaign
licence: GPL v3

PartMC:

authors: Nicole Riemer, Matthew West, Jeff Curtis et al.
licence: GPL v2 or later