SimKit ia a framework for simulating mathematical models that decouples the models from the simulation implementation. It takes care of boilerplate routines such as loading data from various sources into a key store that can be used from any calculation, determining the correct order of calculations, stepping through dynamic simulations and generating output reports and visualizations, so that you can focus on developing models and don't have to worry about how to add new models or how to integrate changes.
SimKit releases are on PyPI and on
GitHub. You can use
pip or distutils to install SimKit.
$ pip install simkit
Extract the archive to use disutils
$ python setup.py install
SimKit documentation is
online. It's also included in the distribution and can be built using
Sphinx by running the Makefile
found in the docs folder of the SimKit package. Once built documentation
will be found in the _build folder under the tree corresponding to the type
of documentation built. EG: HTML documentation is in docs/_build/html.
SimKit source code is online. Fork it and report issues, make suggestions or create pull requests. Discuss the roadmap or download presentations on the wiki
The change log for all releases is on GitHub.
Define data, outputs, formulas, calculations, simulations and model:
#! python
from simkit.core.data_sources import DataSource, DataParameter
from simkit.core.outputs import Output, OutputParameter
from simkit.core.formulas import Formula, FormulaParameter
from simkit.core.calculations import Calc, CalcParameter
from simkit.core.simulations import Simulation, SimParameter
from simkit.core.models import Model, ModelParameter
from simkit.contrib.readers import ArgumentReader
from simkit.core import UREG
import numpy as np
import os
DATA = {'PythagoreanData': {'adjacent_side': 3.0, 'opposite_side': 4.0}}
class PythagoreanData(DataSource):
adjacent_side = DataParameter(units='cm', uncertainty=1.0)
opposite_side = DataParameter(units='cm', uncertainty=1.0)
def __prepare_data__(self):
for k, v in self.parameters.iteritems():
self.uncertainty[k] = {k: v['uncertainty'] * UREG.percent}
class Meta:
data_cache_enabled = False
data_reader = ArgumentReader
class PythagoreanOutput(Output):
hypotenuse = OutputParameter(units='cm')
def f_pythagorean(a, b):
a, b = np.atleast_1d(a), np.atleast_1d(b)
return np.sqrt(a * a + b * b).reshape(1, -1)
class PythagoreanFormula(Formula):
f_pythagorean = FormulaParameter(
units=[('=A', ), ('=A', '=A')],
isconstant=[]
)
class Meta:
module = __name__
class PythagoreanCalc(Calc):
pythagorean_thm = CalcParameter(
formula='f_pythagorean',
args={'data': {'a': 'adjacent_side', 'b': 'opposite_side'}},
returns=['hypotenuse']
)
class PythagoreanSim(Simulation):
settings = SimParameter(
ID='Pythagorean Theorem',
commands=['start', 'load', 'run'],
sim_length=[0, 'hour'],
write_fields={
'data': ['adjacent_side', 'opposite_side'],
'outputs': ['hypotenuse']
}
)
class PythagoreanModel(Model):
data = ModelParameter(sources=[PythagoreanData])
outputs = ModelParameter(sources=[PythagoreanOutput])
formulas = ModelParameter(sources=[PythagoreanFormula])
calculations = ModelParameter(sources=[PythagoreanCalc])
simulations = ModelParameter(sources=[PythagoreanSim])
class Meta:
modelpath = os.path.dirname(__file__)
if __name__ == '__main__':
m = PythagoreanModel()
m.command('run', data=DATA)
out_reg = m.registries['outputs']
fmt = {
'output': out_reg['hypotenuse'],
'uncertainty': out_reg.uncertainty['hypotenuse']['hypotenuse']
}
print 'hypotenuse = %(output)s +/- %(uncertainty)s' % fmt
This is the MCVE of a SimKit model.