Utils

Matrix Utilities

SimPEG.utils.mat_utils.diagEst(matFun, n, k=None, approach='Probing')

Estimate the diagonal of a matrix, A. Note that the matrix may be a function which returns A times a vector.

Three different approaches have been implemented:

1. Probing: cyclic permutations of vectors with 1’s and 0’s (default)

2. Ones: random +/- 1 entries

3. Random: random vectors

Parameters

• matFun (callable) – takes a (numpy.ndarray) and multiplies it by a matrix to estimate the diagonal

• n (int) – size of the vector that should be used to compute matFun(v)

• k (int) – number of vectors to be used to estimate the diagonal

• approach (str) – approach to be used for getting vectors

Return type

numpy.ndarray

Returns

est_diag(A)

Based on Saad http://www-users.cs.umn.edu/~saad/PDF/umsi-2005-082.pdf, and https://www.cita.utoronto.ca/~niels/diagonal.pdf

SimPEG.utils.mat_utils.uniqueRows(M)

SimPEG.utils.mat_utils.cartesian2spherical(m)

Convert from cartesian to spherical

SimPEG.utils.mat_utils.spherical2cartesian(m)

Convert from spherical to cartesian

SimPEG.utils.mat_utils.dip_azimuth2cartesian(dip, azm_N)

Function converting degree angles for dip and azimuth from north to a 3-components in cartesian coordinates.

INPUT dip : Value or vector of dip from horizontal in DEGREE azm_N : Value or vector of azimuth from north in DEGREE

OUTPUT M : [n-by-3] Array of xyz components of a unit vector in cartesian

Created on Dec, 20th 2015

@author: dominiquef

SimPEG.utils.mat_utils.coterminal(theta)

Compute coterminal angle so that [-pi < theta < pi]

Solver Utilities

SimPEG.utils.solver_utils.SolverWrapD(fun, factorize=True, checkAccuracy=True, accuracyTol=1e-06, name=None)

Wraps a direct Solver.

import scipy.sparse as sp
Solver   = solver_utils.SolverWrapD(sp.linalg.spsolve, factorize=False)
SolverLU = solver_utils.SolverWrapD(sp.linalg.splu, factorize=True)

SimPEG.utils.solver_utils.SolverWrapI(fun, checkAccuracy=True, accuracyTol=1e-05, name=None)

Wraps an iterative Solver.

import scipy.sparse as sp
SolverCG = solver_utils.SolverWrapI(sp.linalg.cg)

class SimPEG.utils.solver_utils.Solver(A, **kwargs)

clean()

class SimPEG.utils.solver_utils.SolverLU(A, **kwargs)

clean()

class SimPEG.utils.solver_utils.SolverCG(A, **kwargs)

clean()

class SimPEG.utils.solver_utils.SolverBiCG(A, **kwargs)

clean()

class SimPEG.utils.solver_utils.SolverDiag(A, **kwargs)

docstring for SolverDiag

clean()

Curv Utilities

Mesh Utilities

Model Builder Utilities

SimPEG.utils.model_builder.addBlock(gridCC, modelCC, p0, p1, blockProp)

Add a block to an exsisting cell centered model, modelCC

Parameters

• gridCC (numpy.ndarray) – mesh.gridCC is the cell centered grid

• modelCC (numpy.ndarray) – cell centered model

• p0 (numpy.ndarray) – bottom, southwest corner of block

• p1 (numpy.ndarray) – top, northeast corner of block

BlockProp float blockProp

property to assign to the model

Return numpy.ndarray, modelBlock

model with block

SimPEG.utils.model_builder.getIndicesBlock(p0, p1, ccMesh)

Creates a vector containing the block indices in the cell centers mesh. Returns a tuple

The block is defined by the points

p0, describe the position of the left upper front corner, and

p1, describe the position of the right bottom back corner.

ccMesh represents the cell-centered mesh

The points p0 and p1 must live in the the same dimensional space as the mesh.

SimPEG.utils.model_builder.defineBlock(ccMesh, p0, p1, vals=None)

Build a block with the conductivity specified by condVal. Returns an array. vals[0] conductivity of the block vals[1] conductivity of the ground

SimPEG.utils.model_builder.defineElipse(ccMesh, center=None, anisotropy=None, slope=10.0, theta=0.0)

SimPEG.utils.model_builder.getIndicesSphere(center, radius, ccMesh)

Creates a vector containing the sphere indices in the cell centers mesh. Returns a tuple

The sphere is defined by the points

p0, describe the position of the center of the cell

r, describe the radius of the sphere.

ccMesh represents the cell-centered mesh

The points p0 must live in the the same dimensional space as the mesh.

SimPEG.utils.model_builder.defineTwoLayers(ccMesh, depth, vals=None)

Define a two layered model. Depth of the first layer must be specified. CondVals vector with the conductivity values of the layers. Eg:

Convention to number the layers:

<----------------------------|------------------------------------>
0                          depth                                 zf
     1st layer                       2nd layer

SimPEG.utils.model_builder.scalarConductivity(ccMesh, pFunction)

Define the distribution conductivity in the mesh according to the analytical expression given in pFunction

SimPEG.utils.model_builder.layeredModel(ccMesh, layerTops, layerValues)

Define a layered model from layerTops (z-positive up)

Parameters

• ccMesh (numpy.ndarray) – cell-centered mesh

• layerTops (numpy.ndarray) – z-locations of the tops of each layer

• layerValue (numpy.ndarray) – values of the property to assign for each layer (starting at the top)

Return type

numpy.ndarray

Returns

M, layered model on the mesh

SimPEG.utils.model_builder.randomModel(shape, seed=None, anisotropy=None, its=100, bounds=None)

Create a random model by convolving a kernel with a uniformly distributed model.

Parameters

• shape (tuple) – shape of the model.

• seed (int) – pick which model to produce, prints the seed if you don’t choose.

• anisotropy (numpy.ndarray) – this is the (3 x n) blurring kernel that is used.

• its (int) – number of smoothing iterations

• bounds (list) – bounds on the model, len(list) == 2

Return type

numpy.ndarray

Returns

M, the model

(Source code, png, hires.png, pdf)

SimPEG.utils.model_builder.PolygonInd(mesh, pts)

Finde a volxel indices included in mpolygon (2D) or polyhedra (3D) uniformly distributed model.

Parameters

• shape (tuple) – shape of the model.

• seed (int) – pick which model to produce, prints the seed if you don’t choose.

• anisotropy (numpy.ndarray) – this is the (3 x n) blurring kernel that is used.

• its (int) – number of smoothing iterations

• bounds (list) – bounds on the model, len(list) == 2

Return type

numpy.ndarray

Returns

M, the model

(Source code, png, hires.png, pdf)

Interpolation Utilities

Counter Utilities

class MyClass(object):
    def __init__(self, url):
        self.counter = Counter()

    @count
    def MyMethod(self):
        pass

    @timeIt
    def MySecondMethod(self):
        pass

c = MyClass('blah')
for i in range(100): c.MyMethod()
for i in range(300): c.MySecondMethod()
c.counter.summary()

Counters:
  MyClass.MyMethod                        :      100

Times:                                        mean      sum
  MyClass.MySecondMethod                  : 1.70e-06, 5.10e-04,  300x

The API

class SimPEG.utils.counter_utils.Counter

Counter allows anything that calls it to record iterations and timings in a simple way.

Also has plotting functions that allow quick recalls of data.

If you want to use this, import count or timeIt and use them as decorators on class methods.

class MyClass(object):
    def __init__(self, url):
        self.counter = Counter()

    @count
    def MyMethod(self):
        pass

    @timeIt
    def MySecondMethod(self):
        pass

c = MyClass('blah')
for i in range(100): c.MyMethod()
for i in range(300): c.MySecondMethod()
c.counter.summary()

count(prop)

Increases the count of the property.

countTic(prop)

Times a property call, this is the init call.

countToc(prop)

Times a property call, this is the end call.

summary()

Provides a text summary of the current counters and timers.

SimPEG.utils.counter_utils.count(f)

SimPEG.utils.counter_utils.timeIt(f)