KISS SciPy-based CCN activation model
Project description
☁️ KISS SciPy-based CCN activation model
📌 overview
ccnact is a simple, yet complete, adiabatic/hydrostatic air-parcel framework employing
moving-sectional/particle-resolved aerosol-cloud microphysics, featuring:
- 🧮 integration using SciPy's LSODA interface
- 📝 ODE system based on Arabas & Shima 2017 (extended to polydisperse aerosol size spectrum)
- 🏁 wet radii equilibration for input dry-size distribution using SciPy's elementwise scalar optimisation
- 🌪️ capable of resolving aerosol activation, deactivation, drop growth, evaporation and ripening
- ⚙️ single-function interface allowing to modify every single constants, and returning a tuple of:
- concentration of activated droplets (at STP)
- maximal supersaturation
- 🧩 effective interfacing options for Matlab, Julia, etc
- 📈 mulit-modal lognormal (using SciPy's stats routines) spectrum specification (with concentration interpretted as at STP)
- ⚖️ implemeted using Pint dimensional analysis (physical units consistency checks) enabled for tests only
- 🚀 subsecond execution times for common parameter settings
- 🔗 KISS design: SciPy, NumPy & Pint are the only dependencies; single ~500 LOC file (physics + setup + tests)
The last four points motivated the development of this package - the project originated from a search for a simple, lightweight (in dependencies) and fast CCN activation air-parcel model with concise code, automated testing and no hardcoded constants.
💻 notes for users
To install the package, try: pip install ccnact
Using from Python:
from ccnact import parcel
help(parcel)
n_act, s_max = parcel(...)
Interfacing from Matlab (using the built-in Python bridge):
ccnact = py.importlib.import_module('ccnact');
ccnact.parcel(pyargs(...
'MAC', 1,...
'n_bins', int32(100),...
'p', 101300,...
'T', 300,...
'RH', .99,...
'dt', 1,...
'nt', int32(100),...
'w', 2,...
'sigma', 0.072,...
'kappa', py.tuple({1}),...
'meanr', py.tuple({3e-8}),...
'gstdv', py.tuple({1.5}),...
'n_tot', py.tuple({1e9})...
))
⚙ notes for developers
To execute the tests after checking out from git: pip install -e .[dev]; pytest ccnact.py
To set-up pre-commit: pip install pre-commit; pre-commit install
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file ccnact-0.0.5.tar.gz.
File metadata
- Download URL: ccnact-0.0.5.tar.gz
- Upload date:
- Size: 22.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/6.1.0 CPython/3.13.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
4910271b669428c5ce70e21274f8c0a16c8d0dfe64bcb8fc7ceaaa4908db1495
|
|
| MD5 |
37bb35768f00e02d80cc6aaaba9efe0c
|
|
| BLAKE2b-256 |
ca55d966f8b5a6a83ad29a5ee63e5a7eb421b5a155352311be02901d03f702fc
|
File details
Details for the file ccnact-0.0.5-py3-none-any.whl.
File metadata
- Download URL: ccnact-0.0.5-py3-none-any.whl
- Upload date:
- Size: 20.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/6.1.0 CPython/3.13.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
4e8731717831adfba9044810bd54cf40b0946506c94e5fb1b030f4e8c67a7526
|
|
| MD5 |
d408f11d6882f4a56a57185566190e9a
|
|
| BLAKE2b-256 |
84372dbb01adb0d450c68d79662a00d16aedcf0394c9a727b535eb27960cec6e
|