# exppy - module that automates numerical experiments
This module is intended for performing numerical experiments with continuous parameters. Currently, parameters of the model can be randomized with uniform or log-uniform distributions in the given range. Simple random, latin hypercube or generalized subset designs can be used (supported by `pyDOE2` package).
Example:
```python
from exppy import LHSDesign, Experiment
from math import sin, cos
import pylab
class MyDesign(LHSDesign):
spec = (('x', (0, 6.28, 'uniform', 10)),
('y', (0, 6.28, 'uniform', 10)))
samples = 50
class MyModel:
# model can be any class, but it is required to have 'solve' method
# which takes sample `d` as argument and returns dict of results `res`
def solve(self, d):
x, y = d.x, d.y
res = {'F': sin(x)*cos(y), 'G': x*y}
return res
# Evaluate experiments and dump everything to 'test' directory:
ex = Experiment(MyDesign(), MyModel(), dirname='test')
ex.run()
# Plot 'F'
x = ex.design.x
y = ex.design.y
F = ex.result.F
pylab.tricontour(x, y, F, levels=14, linewidths=0.5, colors='k')
cntr = pylab.tricontourf(x, y, F, levels=14, cmap="RdBu_r")
pylab.colorbar(cntr)
pylab.plot(x, y, 'ko', ms=3)
pylab.title('$\sin(x)\cos(y)$\n (%d LHS samples)' % ex.design.samples)
```
The resulting figure looks like this:
For more hints look into the `test_exppy.py` file. Docs are in plans...
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"description": "# exppy - module that automates numerical experiments\nThis module is intended for performing numerical experiments with continuous parameters. Currently, parameters of the model can be randomized with uniform or log-uniform distributions in the given range. Simple random, latin hypercube or generalized subset designs can be used (supported by `pyDOE2` package).\n\nExample:\n\n```python\nfrom exppy import LHSDesign, Experiment\nfrom math import sin, cos\nimport pylab\n\n\nclass MyDesign(LHSDesign):\n spec = (('x', (0, 6.28, 'uniform', 10)),\n ('y', (0, 6.28, 'uniform', 10)))\n samples = 50\n\n\nclass MyModel:\n # model can be any class, but it is required to have 'solve' method\n # which takes sample `d` as argument and returns dict of results `res`\n def solve(self, d):\n x, y = d.x, d.y\n res = {'F': sin(x)*cos(y), 'G': x*y}\n return res\n\n# Evaluate experiments and dump everything to 'test' directory:\nex = Experiment(MyDesign(), MyModel(), dirname='test')\nex.run() \n\n# Plot 'F'\nx = ex.design.x\ny = ex.design.y\nF = ex.result.F\npylab.tricontour(x, y, F, levels=14, linewidths=0.5, colors='k')\ncntr = pylab.tricontourf(x, y, F, levels=14, cmap=\"RdBu_r\")\npylab.colorbar(cntr)\npylab.plot(x, y, 'ko', ms=3)\npylab.title('$\\sin(x)\\cos(y)$\\n (%d LHS samples)' % ex.design.samples)\n```\n\nThe resulting figure looks like this:\n\n\n\nFor more hints look into the `test_exppy.py` file. Docs are in plans...\n",
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