mirror of
https://github.com/wassname/simpeg.git
synced 2026-06-27 20:23:01 +08:00
Moved parameters to separate file. Documentation updates.
This commit is contained in:
rowanc1
@@ -1,6 +1,6 @@
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Simulation and Parameter Estimation in Geoscience - A python package for simulation and gradient based parameter estimation in the context of geophysical applications.
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Simulation and Parameter Estimation in Geophysics - A python package for simulation and gradient based parameter estimation in the context of geophysical applications.
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The vision is to create a package for finite volume simulation with applications to geophysical imaging and subsurface flow. To enable the understanding of the many different components, this package has the following features:
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@@ -10,4 +10,20 @@ The vision is to create a package for finite volume simulation with applications
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* supports 1D, 2D and 3D problems
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* designed for large-scale inversions
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Documentation:
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[http://simpeg.readthedocs.org/en/latest/](http://simpeg.readthedocs.org/en/latest/)
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Code:
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[https://github.com/simpeg/simpeg](https://github.com/simpeg/simpeg)
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Tests:
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[https://travis-ci.org/simpeg/simpeg](https://travis-ci.org/simpeg/simpeg)
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Build Status:
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[](https://travis-ci.org/simpeg/simpeg)
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Bugs & Issues:
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[https://github.com/simpeg/simpeg/issues](https://github.com/simpeg/simpeg/issues)
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Code Snippets & Tutorials:
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[http://www.row1.ca/simpeg](http://www.row1.ca/simpeg)
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+1
-2
@@ -1,5 +1,4 @@
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import Utils
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import numpy as np
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import Utils, numpy as np
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class BaseData(object):
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+1
-1
@@ -1,4 +1,4 @@
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from SimPEG import Utils, np, sp
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import Utils, Parameters, numpy as np, scipy.sparse as sp
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class BaseModel(object):
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+2
-83
@@ -1,11 +1,11 @@
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from SimPEG import Utils, np, sp
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import Utils, Parameters, numpy as np, scipy.sparse as sp
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class BaseObjFunction(object):
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"""BaseObjFunction(data, reg, **kwargs)"""
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__metaclass__ = Utils.Save.Savable
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beta = Utils.ParameterProperty('beta', default=1, doc='Regularization trade-off parameter')
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beta = Parameters.ParameterProperty('beta', default=1, doc='Regularization trade-off parameter')
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debug = False #: Print debugging information
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counter = None #: Set this to a SimPEG.Utils.Counter() if you want to count things
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@@ -213,84 +213,3 @@ class BaseObjFunction(object):
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dmisfit = self.data.prob.Jt_approx(m, self.data.Wd * self.data.Wd * self.data.prob.J_approx(m, v, u=u), u=u)
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return dmisfit
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class BetaSchedule(Utils.Parameter):
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"""BetaSchedule"""
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beta0 = 'guess' #: The initial Beta (regularization parameter)
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beta0_ratio = 0.1 #: When beta0 is set to 'guess', estimateBeta0 is used with this ratio
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coolingFactor = 2.
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coolingRate = 3
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beta = None #: Beta parameter
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def __init__(self, *args, **kwargs):
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Utils.Parameter.__init__(self, *args, **kwargs)
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Utils.setKwargs(self, **kwargs)
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def initialize(self):
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self.beta = self.beta0
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@Utils.requires('parent')
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def nextIter(self):
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if self.beta is 'guess':
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if self.debug: print 'BetaSchedule is estimating Beta0.'
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self.beta = self.estimateBeta0()
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opt = self.parent.parent.opt
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if opt.iter > 0 and opt.iter % self.coolingRate == 0:
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if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % opt.iter
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self.beta /= self.coolingFactor
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return self.beta
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@Utils.requires('parent')
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def estimateBeta0(self):
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"""estimateBeta0(u=None)
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The initial beta is calculated by comparing the estimated
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eigenvalues of JtJ and WtW.
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To estimate the eigenvector of **A**, we will use one iteration
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of the *Power Method*:
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.. math::
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\mathbf{x_1 = A x_0}
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Given this (very course) approximation of the eigenvector,
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we can use the *Rayleigh quotient* to approximate the largest eigenvalue.
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.. math::
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\lambda_0 = \\frac{\mathbf{x^\\top A x}}{\mathbf{x^\\top x}}
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We will approximate the largest eigenvalue for both JtJ and WtW, and
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use some ratio of the quotient to estimate beta0.
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.. math::
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\\beta_0 = \gamma \\frac{\mathbf{x^\\top J^\\top J x}}{\mathbf{x^\\top W^\\top W x}}
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:param numpy.array u: fields
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:param float ratio: desired ratio of the eigenvalues, default is 0.1
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:rtype: float
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:return: beta0
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"""
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objFunc = self.parent
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data = objFunc.data
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m = objFunc.m_current
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u = objFunc.u_current
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if u is None:
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u = data.prob.field(m)
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x0 = np.random.rand(*m.shape)
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t = x0.dot(objFunc.dataObj2Deriv(m,x0,u=u))
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b = x0.dot(objFunc.reg.modelObj2Deriv()*x0)
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return self.beta0_ratio*(t/b)
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@@ -1,4 +1,5 @@
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from SimPEG import Solver, Utils, sp, np
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import Utils, numpy as np, scipy.sparse as sp
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from Solver import Solver
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norm = np.linalg.norm
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@@ -0,0 +1,164 @@
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import Utils, numpy as np
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class Parameter(object):
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"""Parameter"""
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debug = False #: Print debugging information
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current = None #: This hold
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currentIter = 0
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def __init__(self, **kwargs):
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Utils.setKwargs(self, **kwargs)
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@property
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def parent(self):
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"""This is the parent of the Parameter instance."""
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return getattr(self,'_parent',None)
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@parent.setter
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def parent(self, p):
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startupName = '_startup_paramProperty_'+self._propertyName
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if getattr(self,'_parent',None) is not None:
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delattr(self._parent,startupName)
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print 'Warning: Parameter %s has switched to a new parent.' % self._propertyName
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if self.debug: print '%s function has been deleted' % startupName
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self._parent = p
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prop = self
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def _startup_paramProperty(self, *args):
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if prop.debug: print 'initializing %s' % prop._propertyName
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prop.initialize()
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Utils.hook(self._parent, _startup_paramProperty, name=startupName, overwrite=True)
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@property
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def inv(self): return self.parent.inv
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@property
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def objFunc(self): return self.parent.objFunc
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@property
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def opt(self): return self.parent.opt
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@property
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def reg(self): return self.parent.reg
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@property
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def data(self): return self.parent.data
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@property
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def prob(self): return self.parent.prob
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@property
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def model(self): return self.parent.model
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@property
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def mesh(self): return self.parent.mesh
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def initialize(self):
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pass
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def get(self):
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if (self.current is None or
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not self.opt.iter == self.currentIter):
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self.current = self.nextIter()
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self.currentIter = self.opt.iter
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return self.current
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def nextIter(self):
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raise NotImplementedError('Getting the Parameter is not yet implemented.')
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def ParameterProperty(name, default=None, doc=""):
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def getter(self):
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out = getattr(self,'_'+name,default)
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if isinstance(out, Parameter):
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out = out.get()
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return out
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def setter(self, value):
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if isinstance(value, Parameter):
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value._propertyName = name
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value.parent = self
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setattr(self, '_'+name, value)
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return property(fget=getter, fset=setter, doc=doc)
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class BetaEstimate(Parameter):
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"""BetaEstimate"""
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beta0 = 'guess' #: The initial Beta (regularization parameter)
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beta0_ratio = 0.1 #: When beta0 is set to 'guess', estimateBeta0 is used with this ratio
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beta = None #: Beta parameter
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def __init__(self, **kwargs):
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Parameter.__init__(self, **kwargs)
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def initialize(self):
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self.beta = self.beta0
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@Utils.requires('parent')
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def nextIter(self):
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if self.beta is 'guess':
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if self.debug: print 'BetaSchedule is estimating Beta0.'
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self.beta = self.estimateBeta0()
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return self.beta
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@Utils.requires('parent')
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def estimateBeta0(self):
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"""estimateBeta0(u=None)
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The initial beta is calculated by comparing the estimated
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eigenvalues of JtJ and WtW.
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To estimate the eigenvector of **A**, we will use one iteration
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of the *Power Method*:
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.. math::
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\mathbf{x_1 = A x_0}
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Given this (very course) approximation of the eigenvector,
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we can use the *Rayleigh quotient* to approximate the largest eigenvalue.
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.. math::
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\lambda_0 = \\frac{\mathbf{x^\\top A x}}{\mathbf{x^\\top x}}
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We will approximate the largest eigenvalue for both JtJ and WtW, and
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use some ratio of the quotient to estimate beta0.
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.. math::
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\\beta_0 = \gamma \\frac{\mathbf{x^\\top J^\\top J x}}{\mathbf{x^\\top W^\\top W x}}
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:rtype: float
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:return: beta0
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"""
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objFunc = self.parent
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data = objFunc.data
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m = objFunc.m_current
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u = objFunc.u_current
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if u is None:
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u = data.prob.field(m)
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x0 = np.random.rand(*m.shape)
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t = x0.dot(objFunc.dataObj2Deriv(m,x0,u=u))
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b = x0.dot(objFunc.reg.modelObj2Deriv()*x0)
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return self.beta0_ratio*(t/b)
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class BetaSchedule(BetaEstimate):
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"""BetaSchedule"""
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coolingFactor = 2.
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coolingRate = 3
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@Utils.requires('parent')
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def nextIter(self):
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if self.beta is 'guess':
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if self.debug: print 'BetaSchedule is estimating Beta0.'
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self.beta = self.estimateBeta0()
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if self.opt.iter > 0 and self.opt.iter % self.coolingRate == 0:
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if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % self.opt.iter
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self.beta /= self.coolingFactor
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return self.beta
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+1
-3
@@ -1,6 +1,4 @@
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import Utils, Data
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import scipy.sparse as sp
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import numpy as np
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import Utils, Data, numpy as np, scipy.sparse as sp
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class BaseProblem(object):
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@@ -1,4 +1,4 @@
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from SimPEG import Utils, Model, np, sp
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import Utils, Model, Parameters, numpy as np, scipy.sparse as sp
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class BaseRegularization(object):
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"""
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@@ -23,7 +23,7 @@ class BaseRegularization(object):
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assert isinstance(model, self.modelPair), "Incorrect model for this regularization"
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self.model = model
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mref = Utils.ParameterProperty('mref', default=None, doc='Reference model.')
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mref = Parameters.ParameterProperty('mref', default=None, doc='Reference model.')
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@property
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def parent(self):
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@@ -267,83 +267,6 @@ def timeIt(f):
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return wrapper
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class Parameter(object):
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"""Parameter"""
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debug = False #: Print debugging information
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current = None #: This hold
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currentIter = 0
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def __init__(self, *args, **kwargs):
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pass
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@property
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def parent(self):
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"""This is the parent of the Parameter instance."""
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return getattr(self,'_parent',None)
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@parent.setter
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def parent(self, p):
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startupName = '_startup_paramProperty_'+self._propertyName
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if getattr(self,'_parent',None) is not None:
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delattr(self._parent,startupName)
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print 'Warning: Parameter %s has switched to a new parent.' % self._propertyName
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if self.debug: print '%s function has been deleted' % startupName
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self._parent = p
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prop = self
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def _startup_paramProperty(self, *args):
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if prop.debug: print 'initializing %s' % prop._propertyName
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prop.initialize()
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hook(self._parent, _startup_paramProperty, name=startupName, overwrite=True)
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@property
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def inv(self): return self.parent.inv
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@property
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def objFunc(self): return self.parent.objFunc
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@property
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def opt(self): return self.parent.opt
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@property
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def reg(self): return self.parent.reg
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@property
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def data(self): return self.parent.data
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@property
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def prob(self): return self.parent.prob
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@property
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def model(self): return self.parent.model
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@property
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def mesh(self): return self.parent.mesh
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def initialize(self):
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pass
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def get(self):
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if (self.current is None or
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not self.opt.iter == self.currentIter):
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self.current = self.nextIter()
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self.currentIter = self.opt.iter
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return self.current
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def nextIter(self):
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raise NotImplementedError('Getting the Parameter is not yet implemented.')
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def ParameterProperty(name, default=None, doc=""):
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def getter(self):
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out = getattr(self,'_'+name,default)
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if isinstance(out, Parameter):
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out = out.get()
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return out
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def setter(self, value):
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if isinstance(value, Parameter):
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value._propertyName = name
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value.parent = self
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setattr(self, '_'+name, value)
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return property(fget=getter, fset=setter, doc=doc)
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if __name__ == '__main__':
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class MyClass(object):
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def __init__(self, url):
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@@ -10,6 +10,7 @@ import Regularization
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import ObjFunction
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import Optimization
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import Inversion
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import Parameters
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import Examples
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import Tests
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@@ -1,6 +1,15 @@
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.. _api_Inverse:
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Regularization
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**************
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.. automodule:: SimPEG.Regularization
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:show-inheritance:
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:members:
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:undoc-members:
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Objective Function
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******************
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@@ -27,10 +36,3 @@ Inversion
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:undoc-members:
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Regularization
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**************
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.. automodule:: SimPEG.Regularization
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:show-inheritance:
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:members:
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:undoc-members:
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@@ -0,0 +1,11 @@
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.. _api_Parameters:
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Parameters
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**********
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.. automodule:: SimPEG.Parameters
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:show-inheritance:
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:members:
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:undoc-members:
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:inherited-members:
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+7
-7
@@ -3,7 +3,7 @@
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:alt: SimPEG
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:align: center
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SimPEG (Simulation and Parameter Estimation in Geoscience) is a python
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SimPEG (Simulation and Parameter Estimation in Geophysics) is a python
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package for simulation and gradient based parameter estimation in the
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context of geoscience applications.
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@@ -16,11 +16,10 @@ these goals, this package has the following features:
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* provides a framework for geophysical and hydrogeologic problems
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* supports 1D, 2D and 3D problems
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|
||||
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.. raw:: html
|
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<iframe src="http://row1.ca/labs/simpegvis" width="100%" height="500px" style="border:none;background:#eee;margin:50px 0;"></iframe>
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.. image:: simpeg-framework.png
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:width: 400 px
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:alt: Framework
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:align: center
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Meshing & Operators
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||||
===================
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@@ -49,7 +48,8 @@ Inversion
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.. toctree::
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:maxdepth: 2
|
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|
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api_Optimize
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api_Inverse
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api_Parameters
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|
||||
Testing SimPEG
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||||
==============
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