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rowanc1
130 lines
3.3 KiB
Python
130 lines
3.3 KiB
Python
import Utils, Parameters, numpy as np, scipy.sparse as sp
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class BaseModel(object):
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"""
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SimPEG Model
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"""
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__metaclass__ = Utils.Save.Savable
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counter = None #: A SimPEG.Utils.Counter object
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mesh = None #: A SimPEG Mesh
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def __init__(self, mesh):
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self.mesh = mesh
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def transform(self, m):
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"""
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:param numpy.array m: model
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:rtype: numpy.array
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:return: transformed model
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The *transform* changes the model into the physical property.
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"""
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return m
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def transformInverse(self, D):
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"""
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:param numpy.array D: physical property
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:rtype: numpy.array
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:return: model
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The *transformInverse* changes the physical property into the model.
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.. note:: The *transformInverse* may not be easy to create in general.
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"""
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raise NotImplementedError('The transformInverse is not implemented.')
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def transformDeriv(self, m):
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"""
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:param numpy.array m: model
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:rtype: scipy.csr_matrix
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:return: derivative of transformed model
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The *transform* changes the model into the physical property.
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The *transformDeriv* provides the derivative of the *transform*.
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"""
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return sp.identity(m.size)
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@property
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def nP(self):
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"""Number of parameters in the model."""
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return self.mesh.nC
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def example(self, modelType=None):
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return np.random.rand(self.mesh.nC)
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class LogModel(BaseModel):
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"""SimPEG LogModel"""
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def __init__(self, mesh, **kwargs):
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BaseModel.__init__(self, mesh, **kwargs)
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def transform(self, m):
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"""
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:param numpy.array m: model
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:rtype: numpy.array
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:return: transformed model
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The *transform* changes the model into the physical property.
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A common example of this is to invert for electrical conductivity
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in log space. In this case, your model will be log(sigma) and to
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get back to sigma, you can take the exponential:
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.. math::
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m = \log{\sigma}
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\exp{m} = \exp{\log{\sigma}} = \sigma
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"""
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return np.exp(Utils.mkvc(m))
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def transformInverse(self, D):
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"""
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:param numpy.array D: physical property
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:rtype: numpy.array
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:return: model
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The *transformInverse* changes the physical property into the model.
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.. math::
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m = \log{\sigma}
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"""
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return np.log(Utils.mkvc(D))
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def transformDeriv(self, m):
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"""
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:param numpy.array m: model
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:rtype: scipy.csr_matrix
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:return: derivative of transformed model
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The *transform* changes the model into the physical property.
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The *transformDeriv* provides the derivative of the *transform*.
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If the model *transform* is:
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.. math::
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m = \log{\sigma}
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\exp{m} = \exp{\log{\sigma}} = \sigma
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Then the derivative is:
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.. math::
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\\frac{\partial \exp{m}}{\partial m} = \\text{sdiag}(\exp{m})
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"""
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return Utils.sdiag(np.exp(Utils.mkvc(m)))
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