generic_distribution#

generic_distribution(network, func, seeds='pore.seed')[source]#

Accepts an object from the Scipy.stats submodule and returns values from the distribution for the given seeds

Parameters:

  • network (OpenPNM Network object) – The network object to which this model is associated (i.e. attached). This controls the length of the calculated array(s), and also provides access to other necessary properties.

  • seeds (str (dict key)) – Name of the dictionary key on the target object pointing to the array containing values of eed

  • func (object) – An object from the scipy.stats library. Can be a ‘frozen’ object, where all the parameters were specified upon creation, or a handle to an unintialized object. In the latter case the parameters for the distribution should be provided as keyword arguments.

Returns:

values – An ndarray of either Np or Nt length, with values taken from the supplied distribution.

Return type:

ndarray

Examples

The following code illustrates the process of obtaining a ‘frozen’ Scipy stats object and adding it as a model:

import numpy
import scipy.stats
import openpnm as op
import matplotlib.pyplot as plt

pn = op.network.Cubic(shape=[3, 3, 3])
pn.add_model(propname='pore.seed',
             model=op.models.geometry.pore_seed.random)

# Now retrieve the stats distribution and add to ``geo`` as a model
stats_obj = scipy.stats.weibull_min(c=2, scale=.0001, loc=0)
pn.add_model(propname='pore.size',
             model=op.models.geometry.pore_size.generic_distribution,
             seeds='pore.seed',
             func=stats_obj)

plt.hist(stats_obj.ppf(q=numpy.random.rand(1000)), bins=50)

plt.show()
../../_images/openpnm-models-geometry-pore_size-generic_distribution-1.svg
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