<commit_before>"""
PF: Gravity: Inversion Linear
=============================

Create a synthetic block model and invert
with a compact norm

"""
import numpy as np
import matplotlib.pyplot as plt

from SimPEG import Mesh
from SimPEG import Utils
from SimPEG import Maps
from SimPEG import Regularization
from SimPEG import DataMisfit
from SimPEG import Optimization
from SimPEG import InvProblem
from SimPEG import Directives
from SimPEG import Inversion
from SimPEG import PF


def run(plotIt=True):

    # Create a mesh
    dx = 5.

    hxind = [(dx, 5, -1.3), (dx, 15), (dx, 5, 1.3)]
    hyind = [(dx, 5, -1.3), (dx, 15), (dx, 5, 1.3)]
    hzind = [(dx, 5, -1.3), (dx, 7), (3.5, 1), (2, 5)]

    mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC')

    # Get index of the center
    midx = int(mesh.nCx/2)
    midy = int(mesh.nCy/2)

    # Lets create a simple Gaussian topo and set the active cells
    [xx, yy] = np.meshgrid(mesh.vectorNx, mesh.vectorNy)
    zz = -np.exp((xx**2 + yy**2) / 75**2) + mesh.vectorNz[-1]

    # We would usually load a topofile
    topo = np.c_[Utils.mkvc(xx), Utils.mkvc(yy), Utils.mkvc(zz)]

    # Go from topo to actv cells
    actv = Utils.surface2ind_topo(mesh, topo, 'N')
    actv = np.asarray([inds for inds, elem in enumerate(actv, 1) if elem],
                      dtype=int) - 1

    # Create active map to go from reduce space to full
    actvMap = Maps.InjectActiveCells(mesh, actv, -100)
    nC = len(actv)

    # Create and array of observation points
    xr = np.linspace(-30., 30., 20)
    yr = np.linspace(-30., 30., 20)
    X, Y = np.meshgrid(xr, yr)

    # Move the observation points 5m above the topo
    Z = -np.exp((X**2 + Y**2) / 75**2) + mesh.vectorNz[-1] + 0.1

    # Create a MAGsurvey
    rxLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)]
    rxLoc = PF.BaseGrav.RxObs(rxLoc)
    srcField = PF.BaseGrav.SrcField([rxLoc])
    survey = PF.BaseGrav.LinearSurvey(srcField)

    # We can now create a susceptibility model and generate data
    # Here a simple block in half-space
    model = np.zeros((mesh.nCx, mesh.nCy, mesh.nCz))
    model[(midx-5):(midx-1), (midy-2):(midy+2), -10:-6] = 0.5
    model[(midx+1):(midx+5), (midy-2):(midy+2), -10:-6] = -0.5
    model = Utils.mkvc(model)
    model = model[actv]

    # Create active map to go from reduce set to full
    actvMap = Maps.InjectActiveCells(mesh, actv, -100)

    # Create reduced identity map
    idenMap = Maps.IdentityMap(nP=nC)

    # Create the forward model operator
    prob = PF.Gravity.GravityIntegral(mesh, rhoMap=idenMap, actInd=actv)

    # Pair the survey and problem
    survey.pair(prob)

    # Compute linear forward operator and compute some data
    d = prob.fields(model)

    # Add noise and uncertainties
    # We add some random Gaussian noise (1nT)
    data = d + np.random.randn(len(d))*1e-3
    wd = np.ones(len(data))*1e-3  # Assign flat uncertainties

    survey.dobs = data
    survey.std = wd
    survey.mtrue = model

    # Create sensitivity weights from our linear forward operator
    rxLoc = survey.srcField.rxList[0].locs
    wr = np.sum(prob.G**2., axis=0)**0.5
    wr = (wr/np.max(wr))

    # Create a regularization
    reg = Regularization.Sparse(mesh, indActive=actv, mapping=idenMap)
    reg.cell_weights = wr
    reg.norms = [0, 0, 0, 0]

    # Data misfit function
    dmis = DataMisfit.l2_DataMisfit(survey)
    dmis.W = Utils.sdiag(1/wd)

    # Add directives to the inversion
    opt = Optimization.ProjectedGNCG(maxIter=100, lower=-1., upper=1.,
                                     maxIterLS=20, maxIterCG=20,
                                     tolCG=1e-4)
    invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
    betaest = Directives.BetaEstimate_ByEig()

    # Here is where the norms are applied
    # Use pick a treshold parameter empirically based on the distribution of
    # model parameters
    IRLS = Directives.Update_IRLS(
        f_min_change=1e-2, minGNiter=1, maxIRLSiter=20
    )

    update_Jacobi = Directives.UpdatePreconditioner()
    inv = Inversion.BaseInversion(invProb, directiveList=[IRLS,
                                                          betaest,
                                                          update_Jacobi])

    # Run the inversion<commit_msg>Small update to example to test smoothModDiff<commit_after>"""
PF: Gravity: Inversion Linear
=============================

Create a synthetic block model and invert
with a compact norm

"""
import numpy as np
import matplotlib.pyplot as plt

from SimPEG import Mesh
from SimPEG import Utils
from SimPEG import Maps
from SimPEG import Regularization
from SimPEG import DataMisfit
from SimPEG import Optimization
from SimPEG import InvProblem
from SimPEG import Directives
from SimPEG import Inversion
from SimPEG import PF


def run(plotIt=True):

    # Create a mesh
    dx = 5.

    hxind = [(dx, 5, -1.3), (dx, 15), (dx, 5, 1.3)]
    hyind = [(dx, 5, -1.3), (dx, 15), (dx, 5, 1.3)]
    hzind = [(dx, 5, -1.3), (dx, 7), (3.5, 1), (2, 5)]

    mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC')

    # Get index of the center
    midx = int(mesh.nCx/2)
    midy = int(mesh.nCy/2)
    midz = int(-7)
    # Lets create a simple Gaussian topo and set the active cells
    [xx, yy] = np.meshgrid(mesh.vectorNx, mesh.vectorNy)
    zz = -np.exp((xx**2 + yy**2) / 75**2) + mesh.vectorNz[-1]

    # We would usually load a topofile
    topo = np.c_[Utils.mkvc(xx), Utils.mkvc(yy), Utils.mkvc(zz)]

    # Go from topo to actv cells
    actv = Utils.surface2ind_topo(mesh, topo, 'N')
    actv = np.asarray([inds for inds, elem in enumerate(actv, 1) if elem],
                      dtype=int) - 1

    # Create active map to go from reduce space to full
    actvMap = Maps.InjectActiveCells(mesh, actv, -100)
    nC = len(actv)

    # Create and array of observation points
    xr = np.linspace(-30., 30., 20)
    yr = np.linspace(-30., 30., 20)
    X, Y = np.meshgrid(xr, yr)

    # Move the observation points 5m above the topo
    Z = -np.exp((X**2 + Y**2) / 75**2) + mesh.vectorNz[-1] + 0.1

    # Create a MAGsurvey
    rxLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)]
    rxLoc = PF.BaseGrav.RxObs(rxLoc)
    srcField = PF.BaseGrav.SrcField([rxLoc])
    survey = PF.BaseGrav.LinearSurvey(srcField)

    # We can now create a susceptibility model and generate data
    # Here a simple block in half-space
    model = np.zeros((mesh.nCx, mesh.nCy, mesh.nCz))
    model[(midx-5):(midx-1), (midy-2):(midy+2), (midz-2):(midz+2)] = 0.5
    model[(midx+1):(midx+5), (midy-2):(midy+2), (midz-2):(midz+2)] = -0.5
    model = Utils.mkvc(model)
    model = model[actv]

    # Create active map to go from reduce set to full
    actvMap = Maps.InjectActiveCells(mesh, actv, -100)

    # Create reduced identity map
    idenMap = Maps.IdentityMap(nP=nC)

    # Create the forward model operator
    prob = PF.Gravity.GravityIntegral(mesh, rhoMap=idenMap, actInd=actv)

    # Pair the survey and problem
    survey.pair(prob)

    # Compute linear forward operator and compute some data
    d = prob.fields(model)

    # Add noise and uncertainties
    # We add some random Gaussian noise (1nT)
    data = d + np.random.randn(len(d))*1e-3
    wd = np.ones(len(data))*1e-3  # Assign flat uncertainties

    survey.dobs = data
    survey.std = wd
    survey.mtrue = model

    # Create sensitivity weights from our linear forward operator
    rxLoc = survey.srcField.rxList[0].locs
    wr = np.sum(prob.G**2., axis=0)**0.5
    wr = (wr/np.max(wr))

    # Create a regularization
    reg = Regularization.Sparse(
        mesh, indActive=actv, mapping=idenMap, mrefInSmooth=True
    )

    reg.cell_weights = wr
    reg.norms = [0, 0, 0, 0]

    # Data misfit function
    dmis = DataMisfit.l2_DataMisfit(survey)
    dmis.W = Utils.sdiag(1/wd)

    # Add directives to the inversion
    opt = Optimization.ProjectedGNCG(maxIter=100, lower=-1., upper=1.,
                                     maxIterLS=20, maxIterCG=20,
                                     tolCG=1e-4)
    invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
    betaest = Directives.BetaEstimate_ByEig()

    # Here is where the norms are applied
    # Use pick a treshold parameter empirically based on the distribution of
    # model parameters
    IRLS = Directives.Update_IRLS(
        f_min_change=1e-2, minGNiter=1, maxIRLSiter=20
    )

    update_Jacobi = Directives.UpdatePreconditioner()
    inv = Inversion.BaseInversion(invProb, directiveList=[IRLS,
                                                          betaest,
                                                          update_Jacobi])

    # Run the inversion
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