Metadata-Version: 2.1
Name: PyPartMC
Version: 1.2.0
Summary: Python interface to PartMC
Author: PyPartMC team (see https://github.com/open-atmos/PyPartMC/graphs/contributors)
Author-email: nriemer@illinois.edu
License: GPL-3.0
Project-URL: Tracker, https://github.com/open-atmos/PyPartMC/issues
Project-URL: Documentation, https://open-atmos.github.io/PyPartMC
Project-URL: Source, https://github.com/open-atmos/PyPartMC/
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Provides-Extra: tests
Requires-Dist: pytest ; extra == 'tests'
Requires-Dist: pytest-order ; extra == 'tests'
Requires-Dist: fastcore !=1.5.8 ; extra == 'tests'
Requires-Dist: ghapi ; extra == 'tests'

![logo](https://raw.githubusercontent.com/wiki/open-atmos/PyPartMC/logo.svg)

# PyPartMC

PyPartMC is a Python interface to [PartMC](https://lagrange.mechse.illinois.edu/partmc/), 
  a particle-resolved Monte-Carlo code for atmospheric aerosol simulation.
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)](https://doi.org/10.1016/j.softx.2023.101613) (please cite if PyPartMC is used in your research).
For a list of talks and other relevant resources, please see [project Wiki](https://github.com/open-atmos/PyPartMC/wiki/).

[![US Funding](https://img.shields.io/static/v1?label=US%20DOE%20Funding%20by&color=267c32&message=ASR&logoWidth=25&logo=image/png;base64,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)](https://asr.science.energy.gov/) [![PL Funding](https://img.shields.io/static/v1?label=PL%20Funding%20by&color=d21132&message=NCN&logoWidth=25&logo=image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAANCAYAAACpUE5eAAAABmJLR0QA/wD/AP+gvaeTAAAAKUlEQVQ4jWP8////fwYqAiZqGjZqIHUAy4dJS6lqIOMdEZvRZDPcDQQAb3cIaY1Sbi4AAAAASUVORK5CYII=)](https://www.ncn.gov.pl/?language=en)
   
[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0.html)
[![Copyright](https://img.shields.io/static/v1?label=Copyright&color=249fe2&message=UIUC&)](https://atmos.illinois.edu/)
[![Github Actions Build Status](https://github.com/open-atmos/PyPartMC/workflows/tests+pypi/badge.svg?branch=main)](https://github.com/open-atmos/PyPartMC/actions)
[![API docs](https://img.shields.io/badge/API_docs-pdoc3-blue.svg)](https://open-atmos.github.io/PyPartMC/)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7662635.svg)](https://doi.org/10.5281/zenodo.7662635)
[![PyPI version](https://badge.fury.io/py/PyPartMC.svg)](https://pypi.org/p/PyPartMC)

## TL;DR (try in a Jupyter notebook)

[![Python 3](https://img.shields.io/static/v1?label=Python&logo=Python&color=3776AB&message=3)](https://www.python.org/)
[![Linux OK](https://img.shields.io/static/v1?label=Linux&logo=Linux&color=yellow&message=%E2%9C%93)](https://en.wikipedia.org/wiki/Linux)
[![macOS OK](https://img.shields.io/static/v1?label=macOS&logo=Apple&color=silver&message=%E2%9C%93)](https://en.wikipedia.org/wiki/macOS)
[![Windows OK](https://img.shields.io/static/v1?label=Windows&logo=Windows&color=white&message=%E2%9C%93)](https://en.wikipedia.org/wiki/Windows)
[![Jupyter](https://img.shields.io/static/v1?label=Jupyter&logo=Jupyter&color=f37626&message=%E2%9C%93)](https://jupyter.org/)

```python
! 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).

- Urban plume scenario demo (as in [PartMC](https://github.com/compdyn/partmc/tree/master/scenarios/1_urban_plume)):    
[![View notebook](https://img.shields.io/static/v1?label=render%20on&logo=github&color=87ce3e&message=GitHub)](https://github.com/open-atmos/PyPartMC/blob/main/examples/particle_simulation.ipynb) 
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-atmos/PyPartMC/blob/main/examples/particle_simulation.ipynb) 
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/open-atmos/PyPartMC.git/main?urlpath=lab/tree/examples/particle_simulation.ipynb)
[![ARM JupyterHub](https://img.shields.io/static/v1?label=launch%20in&logo=jupyter&color=lightblue&message=ARM+JupyterHub)](https://jupyterhub.arm.gov/hub/user-redirect/git-pull?repo=https%3A//github.com/open-atmos/PyPartMC&branch=main&urlPath=)   
- Dry-Wet Particle Size Equilibration with PartMC and PySDM:   
[![View notebook](https://img.shields.io/static/v1?label=render%20on&logo=github&color=87ce3e&message=GitHub)](https://github.com/open-atmos/PyPartMC/blob/main/examples/lognorm_ex.ipynb) 
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-atmos/PyPartMC/blob/main/examples/lognorm_ex.ipynb)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/open-atmos/PyPartMC.git/main?urlpath=lab/tree/examples/lognorm_ex.ipynb)
[![ARM JupyterHub](https://img.shields.io/static/v1?label=launch%20in&logo=jupyter&color=lightblue&message=ARM+JupyterHub)](https://jupyterhub.arm.gov/hub/user-redirect/git-pull?repo=https%3A//github.com/open-atmos/PyPartMC&branch=main&urlPath=)
[![Voila](https://img.shields.io/static/v1?label=Voil%C3%A0&logo=jupyter&color=teal&message=web+app)](https://mybinder.org/v2/gh/open-atmos/PyPartMC/main?urlpath=voila%2Frender%2Fexamples%2Flognorm_ex.ipynb)
- Simulation output processing example (loading from netCDF files using PyPartMC):    
[![View notebook](https://img.shields.io/static/v1?label=render%20on&logo=github&color=87ce3e&message=GitHub)](https://github.com/open-atmos/PyPartMC/blob/main/examples/process_simulation_output.ipynb)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-atmos/PyPartMC/blob/main/examples/process_simulation_output.ipynb)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/open-atmos/PyPartMC.git/main?urlpath=lab/tree/examples/process_simulation_output.ipynb)
[![ARM JupyterHub](https://img.shields.io/static/v1?label=launch%20in&logo=jupyter&color=lightblue&message=ARM+JupyterHub)](https://jupyterhub.arm.gov/hub/user-redirect/git-pull?repo=https%3A//github.com/open-atmos/PyPartMC&branch=main&urlPath=)
- Optical properties calculation using external Python package ([PyMieScatt](https://pymiescatt.readthedocs.io/en/latest/)):       
[![View notebook](https://img.shields.io/static/v1?label=render%20on&logo=github&color=87ce3e&message=GitHub)](https://github.com/open-atmos/PyPartMC/blob/main/examples/mie_optical.ipynb)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-atmos/PyPartMC/blob/main/examples/mie_optical.ipynb)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/open-atmos/PyPartMC.git/main?urlpath=lab/tree/examples/mie_optical.ipynb)
[![ARM JupyterHub](https://img.shields.io/static/v1?label=launch%20in&logo=jupyter&color=lightblue&message=ARM+JupyterHub)](https://jupyterhub.arm.gov/hub/user-redirect/git-pull?repo=https%3A//github.com/open-atmos/PyPartMC&branch=main&urlPath=)
- Cloud parcel example featuring supersaturation-evolution-coupled CCN activation and drop growth:    
[![View notebook](https://img.shields.io/static/v1?label=render%20on&logo=github&color=87ce3e&message=GitHub)](https://github.com/open-atmos/PyPartMC/blob/main/examples/cloud_parcel.ipynb)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-atmos/PyPartMC/blob/main/examples/cloud_parcel.ipynb)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/open-atmos/PyPartMC.git/main?urlpath=lab/tree/examples/cloud_parcel.ipynb)
[![ARM JupyterHub](https://img.shields.io/static/v1?label=launch%20in&logo=jupyter&color=lightblue&message=ARM+JupyterHub)](https://jupyterhub.arm.gov/hub/user-redirect/git-pull?repo=https%3A//github.com/open-atmos/PyPartMC&branch=main&urlPath=)

## Features

- works on Linux, macOS and Windows (compatibility assured with [CI builds](https://github.com/open-atmos/PyPartMC/blob/main/.github/workflows/tests.yml))
- hassle-free installation using `pip` (prior PartMC installation **not needed**)
- works out of the box on [mybinder.org](https://mybinder.org/), [Google Colab](colab.research.google.com/) and alike
- ships with [a set of examples](https://github.com/open-atmos/PyPartMC/tree/main/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](https://open-atmos.github.io/PyPartMC/)
- support for [de]serialization of selected wrapped structures using JSON 
- based on [unmodified PartMC code](https://github.com/open-atmos/PyPartMC/tree/main/gitmodules)
- does not use or require shell or any pre-installed libraries
- aiming at 100% [unit test coverage](https://github.com/open-atmos/PyPartMC/tree/main/tests)

## 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](https://github.com/open-atmos/PyPartMC/tree/main/readme_fortran).

#### Python

```Python
import numpy as np

import PyPartMC as ppmc
from PyPartMC import si

aero_data = ppmc.AeroData((
    #      [density, ions in solution, molecular weight, kappa]
    {"OC": [1000 *si.kg/si.m**3, 0, 1e-3 *si.kg/si.mol, 0.001]},
    {"BC": [1800 *si.kg/si.m**3, 0, 1e-3 *si.kg/si.mol, 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](https://github.com/JuliaPy/PyCall.jl))
```Julia
using Pkg
Pkg.add("PyCall")

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

aero_data = ppmc.AeroData((
  #       (density, ions in solution, molecular weight, kappa)
  Dict("OC"=>(1000 * si.kg/si.m^3, 0, 1e-3 * si.kg/si.mol, 0.001)),
  Dict("BC"=>(1800 * si.kg/si.m^3, 0, 1e-3 * si.kg/si.mol, 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,
    )
  ),
  Dict( 
    "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](https://www.mathworks.com/help/matlab/python-language.html))

notes (see the [PyPartMC Matlab CI workflow](https://github.com/open-atmos/PyPartMC/blob/main/.github/workflows/readme_listings.yml) 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](https://www.mathworks.com/support/requirements/supported-compilers-linux.html));
- Matlab needs to [use the same Python interpretter/venv](https://www.mathworks.com/support/requirements/python-compatibility.html) as the pip invocation used to install PyPartMC;
- a single-line `pybind11_builtins.py` file with just `pybind11_type=type` inside needs to be placed within Matlab's `PYTHONPATH` to sort out a [Matlab-pybind11 incompatibility](https://github.com/pybind/pybind11/issues/3945). 

````Matlab
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}))), ...
  py.dict(pyargs("BC", py.tuple({1800 * si.kg/si.m^3, 0, 1e-3 * si.kg/si.mol, 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 ...
    )) ...
  )), ...
  py.dict(pyargs( ... 
    "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](https://github.com/atmos-cloud-sim-uj/PySDM/tree/main/tests/smoke_tests/box/partmc).

## Implementation outline

- PyPartMC is written in C++, Fortran and uses [pybind11](https://pybind11.readthedocs.io/en/stable/) and [CMake](https://cmake.org/).
- JSON support is handled with [nlohmann::json](https://github.com/nlohmann/json) and [pybind11_json](https://github.com/pybind/pybind11_json)
- PartMC and selected parts of SUNDIALS are statically linked (and compiled in during `pip install` or `python -m build`) 
- C (SUNDIALS, netCDF), C++ (pybind11, ...) and Fortran (PartMC, CAMP, netCDF-fortran) dependencies are linked through [git submodules](https://github.com/open-atmos/PyPartMC/blob/main/.gitmodules)
- MOSAIC dependency is optionally linked through setting the environmental variable `MOSAIC_HOME`
- a [drop-in replacement of the PartMC spec file routines](https://github.com/open-atmos/PyPartMC/blob/main/src/spec_file_pypartmc.F90) is used for i/o from/to JSON 

## Implementation architecture

```mermaid
flowchart TD
    subgraph J ["Julia"]
        julia_user_code["Julia user code"] --> PyCall.jl
    end
    subgraph M ["Matlab"]
        matlab_user_code["Matlab user code"] --> matlab_python["Matlab built-in\nPython interface"]
    end
    subgraph P ["Python"]
        python_user_code -.-> NumPy
        python_user_code["Python user code"] ---> PyPartMC["pubind11-generated\nPyPartMC module"]
        matlab_python --> PyPartMC
        PyCall.jl --> PyPartMC
    end
    subgraph Cpp ["C++"]
        cpp_user_code["C++ user code"] ----> ppmc_cpp
        PyPartMC --> ppmc_cpp["PyPartMC-C++"]
        ppmc_cpp --> pybind11_json
        pybind11_json ---> nlohmann::JSON
        spec_file_pypartmc_cpp --> nlohmann::JSON
    end
    subgraph C ["C"]
        spec_file_pypartmc_c --> spec_file_pypartmc_cpp["SpecFile-C++"]
        ppmc_cpp --> ppmc_c["PyPartMC-C"]
        netCDF-C
        SUNDIALS
        camp_c["CAMP C code"]
    end
    subgraph Fortran ["Fortran"]
        PartMC -....-> MOSAIC
        ppmc_c --> ppmc_f["PyPartMC-F"]
        ppmc_f ---> PartMC
        PartMC --> netCDF-F
        netCDF-F --> netCDF-C
        PartMC --> SUNDIALS
        PartMC ---> camp_f
        camp_f["CAMP"] --> camp_c
        PartMC ----> spec_file_pypartmc_f[SpecFile-F]
        spec_file_pypartmc_f --> spec_file_pypartmc_c["SpecFile-C"]
    end

    style PartMC fill:#7ae7ff,stroke-width:2px,color:#2B2B2B
```

## 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:
```bash
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:
```Python
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](https://www.msys2.org/docs/cmake/) 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](https://packages.msys2.org/package/mingw-w64-x86_64-gcc-fortran?repo=mingw64) on Windows).

```
Could not find NC_M4 using the following names: m4, m4.exe
```
Try installing `m4` (e.g., using [MSYS2](https://packages.msys2.org/package/m4?repo=msys&variant=x86_64) on Windows).

## Notes for developers
#### How to debug
```sh
git clone --recursive git+https://github.com/open-atmos/PyPartMC.git
cd PyPartMC
DEBUG=1 VERBOSE=1 pip --verbose install -e .
gdb python 
(gdb) run -m pytest -s -vv -We -p no:unraisableexception tests
```
#### Pre-commit hooks
PyPartMC codebase benefits from Pylint, Black and isort code analysis (which are all part of the CI workflows where we also use pre-commit hooks. The pre-commit hooks can be run locally, and then the resultant changes need to be staged before committing. To set up the hooks locally, install pre-commit via `pip install pre-commit` and set up the git hooks via `pre-commit install` (this needs to be done every time you clone the project). To run all pre-commit hooks, run `pre-commit run --all-files`. The `.pre-commit-config.yaml` file can be modified in case new hooks are to be added or existing ones need to be altered.

## Credits

#### PyPartMC:

authors: [PyPartMC developers](https://github.com/open-atmos/PyPartMC/graphs/contributors)   
funding: [US Department of Energy Atmospheric System Research programme](https://asr.science.energy.gov/), [Polish National Science Centre](https://ncn.gov.pl/en)   
copyright: [University of Illinois at Urbana-Champaign](https://atmos.illinois.edu/)   
licence: [GPL v3](https://www.gnu.org/licenses/gpl-3.0.en.html)

#### PartMC:
authors: [Nicole Riemer](https://www.atmos.uiuc.edu/~nriemer/), [Matthew West](https://lagrange.mechse.illinois.edu/mwest/), [Jeff Curtis](https://publish.illinois.edu/jcurtis2/) et al.   
licence: [GPL v2](https://www.gnu.org/licenses/old-licenses/gpl-2.0.html) or later

