Skip to main content

Framework for building and solving ODE-based models, an extension of xarray-simlab

Project description

xarray-simlab-ode

PyPI Documentation Status

The xso framework for building and solving models based on ordinary differential equations (ODEs), an extension of xarray-simlab.

Xarray-simlab provides a generic framework for building computational models in a modular fashion and an xarray extension for setting and running simulations using xarray's Dataset structure.

Xarray-simlab-ode (XSO) extends the framework with a set of variables, processes and a solver backend, suited towards ODE-based models. It is designed for flexible, interactive and reproducible modeling workflows.

Installation

$ pip install xso

In a nutshell

A highly simplified model based on ordinary differential equations is shown below.

  1. Create new model components by writing compact Python classes:
import xso

@xso.component
class Variable:
    var = xso.variable(description='basic state variable', attrs={'units':'µM'})

@xso.component
class LinearGrowth:
    var_ext = xso.variable(foreign=True, flux='growth', description='external state variable')
    rate = xso.parameter(description='linear growth rate', attrs={'units':'$d^{-1}$'})

    @xso.flux
    def growth(self, var_ext, rate):
        return var_ext * rate
  1. Create a new model just by providing a dictionary of model components:
model = xso.create({'Var':Variable,'Growth':LinearGrowth}, time_unit='d')
  1. Create an input xarray.Dataset, run the model and get an output xarray.Dataset:
import numpy as np

input_ds = xso.setup(solver='solve_ivp',
                     model=model,
                     time=np.arange(1,10,.1),
                     input_vars={
                         'Var':{'value_label':'X', 'value_init':1},
                         'Growth':{'var_ext':'X', 'rate':1.},
                     })

with model:
    output_ds = input_ds.xsimlab.run()

4.Perform model setup, pre-processing, run, post-processing and visualization in a functional style, using method chaining:

with model:
    batchout_ds = (input_ds
     .xsimlab.update_vars(
         input_vars={'Growth': {'rate': ('batch', [0.9, 1.0, 1.1, 1.2])}}
     )
     .xsimlab.run(parallel=True, batch_dim='batch')
     .swap_dims({'batch':'Growth__rate'})
     .Var__var_value.plot.line(x='time')
     )

plot

Documentation

Documentation is hosted on ReadTheDocs: https://xarray-simlab-ode.readthedocs.io

Contributing

The package is in the early stages of development, and contributions are very welcome. See GitHub Issues for specific issues, or raise your own. Code contributions can be made via Pull Requests on GitHub. Check out the contributing guidelines for more specific information.

License

xarray-simlab-ode was created by Benjamin Post. It is licensed under the terms of the BSD 3-Clause license.

Credits

Xarray-simlab-ode is an extension of xarray-simlab, created by Benoît Bovy.

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

xso-0.1.0.tar.gz (20.4 kB view details)

Uploaded Source

Built Distribution

xso-0.1.0-py3-none-any.whl (22.4 kB view details)

Uploaded Python 3

File details

Details for the file xso-0.1.0.tar.gz.

File metadata

  • Download URL: xso-0.1.0.tar.gz
  • Upload date:
  • Size: 20.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.5.1 CPython/3.11.0 Linux/5.15.0-1041-azure

File hashes

Hashes for xso-0.1.0.tar.gz
Algorithm Hash digest
SHA256 bedc16b228790ae1427968ad18bb49319a02aa46146bfb8e26dc238d15a5639a
MD5 890f779c23f538c3f4e4b87b5a021956
BLAKE2b-256 a3f3ef93c6afd503aeff4e6b520a7273af3f311c4da3049ecd6cfd2e5e9e26f6

See more details on using hashes here.

File details

Details for the file xso-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: xso-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 22.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.5.1 CPython/3.11.0 Linux/5.15.0-1041-azure

File hashes

Hashes for xso-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 d61e8fee1cae07e165bdf95664e640f3c6de18df748277d995a36bd3951a519d
MD5 65ecbefcfba751979b35070fc48d0664
BLAKE2b-256 4ba6e697bf620d5f3c2f0ccc0edf347663be3e36d8f0c7d5400bc2af49bd184f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page