simpeg.maps.Projection#
- class simpeg.maps.Projection(nP, index, **kwargs)[source]#
- Bases: - IdentityMap- Projection mapping. - Projectionmapping can be used to project and/or rearange model parameters. For a set of model parameter \(\mathbf{m}\), the mapping \(\mathbf{u}(\mathbf{m})\) can be defined by a linear projection matrix \(\mathbf{P}\) acting on the model, i.e.:\[\mathbf{u}(\mathbf{m}) = \mathbf{Pm}\]- The number of model parameters the mapping acts on is defined by nP. Projection and/or rearrangement of the parameters is defined by index. Thus the dimensions of the mapping is (nInd, nP). - Parameters:
- nPint
- Number of model parameters the mapping acts on 
- indexnumpy.ndarrayofint
- Indexes defining the projection from the model space 
 
- nP
 - Attributes - Determine whether or not this mapping is a linear operation. - The mesh used for the mapping - Number of parameters the mapping acts on. - Dimensions of the mapping. - Methods - deriv(m[, v])- Derivative of the mapping with respect to the input parameters. - dot(map1)- Multiply two mappings to create a - simpeg.maps.ComboMap.- inverse(D)- The transform inverse is not implemented. - test([m, num, random_seed])- Derivative test for the mapping. - Examples - Here we define a mapping that rearranges and projects 2 model parameters to a vector space spanning 4 parameters. - >>> from simpeg.maps import Projection >>> import numpy as np - >>> nP = 2 >>> index = np.array([1, 0, 1, 0], dtype=int) >>> mapping = Projection(nP, index) - >>> m = np.array([6, 8]) >>> mapping * m array([8, 6, 8, 6]) 
Galleries and Tutorials using simpeg.maps.Projection#
 
Petrophysically guided inversion: Joint linear example with nonlinear relationships
 
Cross-gradient Joint Inversion of Gravity and Magnetic Anomaly Data
 
Joint PGI of Gravity + Magnetic on an Octree mesh using full petrophysical information
 
Joint PGI of Gravity + Magnetic on an Octree mesh without petrophysical information
 
     
 
 
 
