def Prism(dx, dy, dz, depth, pinc, pdec, npts2D, xylim, rx_h, View_elev,
View_azim):
#p = definePrism(dx, dy, dz, depth,pinc=pinc, pdec=pdec, susc = 1., Einc=90., Edec=0., Bigrf=1e-6)
p = definePrism()
p.dx, p.dy, p.dz, p.z0 = dx, dy, dz, -depth
p.pinc, p.pdec = pinc, pdec
srvy = MAG.survey()
srvy.rx_h, srvy.npts2D, srvy.xylim = rx_h, npts2D, xylim
# Create problem
prob = MAG.problem()
prob.prism = p
prob.survey = srvy
return plotObj3D(p, rx_h, View_elev, View_azim, npts2D, xylim,
profile="X"), prob
def animate(ii):
removePlt()
#ii=1
if ii<18:
dec = 90
inc = 0. + ii*5.
elif ii < 36:
dec = 270.
inc = 90. - (ii-18)*5.
elif ii < 54:
dec = 270.
inc = 0.+ (ii-36)*5.
else:
dec = 90
inc = 90. - (ii-54)*5.
ax1.axis('equal')
block_xyz = np.asarray([[-.2, -.2, .2, .2, 0],
[-.25, -.25, -.25, -.25, 0.5],
[-.2, .2, .2, -.2, 0]])*10.
block_xyz[1][:] -=20.
# rot = Utils.mkvc(Utils.dipazm_2_xyz(pinc, pdec))
# xyz = Utils.rotatePointsFromNormals(block_xyz.T, np.r_[0., 1., 0.], rot,
# np.r_[p.xc, p.yc, p.zc])
R = Utils.rotationMatrix(inc, dec)
xyz = R.dot(block_xyz).T
xyz[:,2] -= depth + dz/2.
#print xyz
# Face 1
ax1.add_collection3d(Poly3DCollection([zip(xyz[:4, 0],
xyz[:4, 1],
xyz[:4, 2])], facecolors='b'))
ax1.add_collection3d(Poly3DCollection([zip(xyz[[1, 2, 4], 0],
xyz[[1, 2, 4], 1],
xyz[[1, 2, 4], 2])], facecolors='b'))
ax1.add_collection3d(Poly3DCollection([zip(xyz[[0, 1, 4], 0],
xyz[[0, 1, 4], 1],
xyz[[0, 1, 4], 2])], facecolors='b'))
ax1.add_collection3d(Poly3DCollection([zip(xyz[[2, 3, 4], 0],
xyz[[2, 3, 4], 1],
xyz[[2, 3, 4], 2])], facecolors='b'))
ax1.add_collection3d(Poly3DCollection([zip(xyz[[0, 3, 4], 0],
xyz[[0, 3, 4], 1],
xyz[[0, 3, 4], 2])], facecolors='b'))
ax1.w_yaxis.set_ticklabels('')
ax1.w_yaxis.set_label_text('')
ax1.w_zaxis.set_ticklabels('')
ax1.w_zaxis.set_label_text('')
# block_xyz[1][:] +=20.
# # rot = Utils.mkvc(Utils.dipazm_2_xyz(pinc, pdec))
#
# # xyz = Utils.rotatePointsFromNormals(block_xyz.T, np.r_[0., 1., 0.], rot,
# # np.r_[p.xc, p.yc, p.zc])
#
# R = Utils.rotationMatrix(rinc, rdec)
#
# xyz = R.dot(block_xyz).T
# xyz[:,2] -= depth + dz/2.
#
# #print xyz
# # Face 1
# ax1.add_collection3d(Poly3DCollection([zip(xyz[:4, 0],
# xyz[:4, 1],
# xyz[:4, 2])], facecolors='y'))
#
# ax1.add_collection3d(Poly3DCollection([zip(xyz[[1, 2, 4], 0],
# xyz[[1, 2, 4], 1],
# xyz[[1, 2, 4], 2])], facecolors='y'))
#
# ax1.add_collection3d(Poly3DCollection([zip(xyz[[0, 1, 4], 0],
# xyz[[0, 1, 4], 1],
# xyz[[0, 1, 4], 2])], facecolors='y'))
#
# ax1.add_collection3d(Poly3DCollection([zip(xyz[[2, 3, 4], 0],
# xyz[[2, 3, 4], 1],
# xyz[[2, 3, 4], 2])], facecolors='y'))
#
# ax1.add_collection3d(Poly3DCollection([zip(xyz[[0, 3, 4], 0],
# xyz[[0, 3, 4], 1],
# xyz[[0, 3, 4], 2])], facecolors='y'))
MAG.plotObj3D(p, rx_h, View_elev, View_azim, npts2D, xylim, profile="X", fig= fig, axs = ax1, plotSurvey=False)
# Create problem
prob = PFlocal.problem()
prob.prism = p
prob.survey = srvy
prob.Bdec, prob.Binc, prob.Bigrf = dec, inc, Bigrf
prob.Q, prob.rinc, prob.rdec = Q, rinc, rdec
prob.uType, prob.mType = comp, 'total'
prob.susc = susc
# Compute fields from prism
b_ind, b_rem = prob.fields()
if irt == 'total':
out = b_ind + b_rem
elif irt == 'induced':
out = b_ind
else:
out = b_rem
#out = plogMagSurvey2D(prob, susc, Einc, Edec, Bigrf, x1, y1, x2, y2, comp, irt, Q, rinc, rdec, fig=fig, axs1=ax2, axs2=ax3)
#dat = axs1.contourf(X,Y, np.reshape(out, (X.shape)).T
global im1
im1 = ax1.contourf(X,Y,np.reshape(out, (X.shape)).T,20,zdir='z',offset=rx_h+5., clim=clim, vmin=clim[0],vmax=clim[1], cmap = 'RdBu_r')
ax5 = fig.add_axes([pos.x0 , pos.y0+0.25, pos.height*0.02, pos.height*0.4])
cb = plt.colorbar(im1,cax=ax5, orientation="vertical", ax = ax1, ticks=np.linspace(im1.vmin,im1.vmax, 4), format="${%.0f}$")
cb.set_label("$B^{TMI}\;(nT)$",size=12)
cb.ax.yaxis.set_ticks_position('left')
cb.ax.yaxis.set_label_position('left')
global im5
im5 = ax1.text(0,0,-60,'$B_0, I: ' + str(inc) + '^\circ, D: ' + str(dec) + '^\circ$', horizontalalignment='center')
H0 = (Bigrf,inc,dec)
actv = np.ones(mesh.nC)==1
# Create active map to go from reduce space to full
actvMap = Maps.InjectActiveCells(mesh, actv, -100)
nC = len(actv)
# Create a MAGsurvey
rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)]
rxLoc = PF.BaseMag.RxObs(rxLoc)
srcField = PF.BaseMag.SrcField([rxLoc],param = H0)
survey = PF.BaseMag.LinearSurvey(srcField)
# We can now create a susceptibility model and generate data
# Lets start with a simple block in half-space
# model = np.zeros((mesh.nCx,mesh.nCy,mesh.nCz))
# model[(midx-2):(midx+2),(midy-2):(midy+2),-6:-2] = 0.02
# model = mkvc(model)
# model = model[actv]
# Create active map to go from reduce set to full
actvMap = Maps.InjectActiveCells(mesh, actv, -100)
# Creat reduced identity map
idenMap = Maps.IdentityMap(nP = nC)
# Create the forward model operator
probinv = PF.Magnetics.MagneticIntegral(mesh, mapping = idenMap, actInd = actv)
# Pair the survey and problem
survey.pair(probinv)
# Compute linear forward operator and compute some data
# d = probinv.fields(model)
# Plot the model
# m_true = actvMap * model
# m_true[m_true==-100] = np.nan
#plt.figure()
#ax = plt.subplot(212)
#mesh.plotSlice(m_true, ax = ax, normal = 'Y', ind=midy, grid=True, clim = (0., model.max()/3.), pcolorOpts={'cmap':'viridis'})
#plt.title('A simple block model.')
#plt.xlabel('x'); plt.ylabel('z')
#plt.gca().set_aspect('equal', adjustable='box')
# We can now generate data
data = out + np.random.randn(len(out)) # We add some random Gaussian noise (1nT)
wd = np.ones(len(data))*1. # Assign flat uncertainties
# Create distance weights from our linera forward operator
wr = np.sum(probinv.G**2.,axis=0)**0.5
wr = ( wr/np.max(wr) )
#survey.makeSyntheticData(data, std=0.01)
survey.dobs= data
survey.std = wd
survey.mtrue = model
# Create a regularization
reg = Regularization.Sparse(mesh, indActive=actv, mapping=idenMap)
reg.cell_weights = wr
dmis = DataMisfit.l2_DataMisfit(survey)
dmis.Wd = 1/wd
# Add directives to the inversion
opt = Optimization.ProjectedGNCG(maxIter=100 ,lower=0.,upper=1., maxIterLS = 20, maxIterCG= 10, tolCG = 1e-3)
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 (run last cell to see the histogram before and after IRLS)
IRLS = Directives.Update_IRLS( norms=([2,2,2,2]), eps=(2e-3,2e-3), f_min_change = 1e-3, minGNiter=3,beta_tol=1e-2)
update_Jacobi = Directives.Update_lin_PreCond()
inv = Inversion.BaseInversion(invProb, directiveList=[IRLS,betaest,update_Jacobi])
m0 = np.ones(nC)*1e-4
mrec = inv.run(m0)
# Here is the recovered susceptibility model
ypanel = midx
zpanel = -4
m_l2 = actvMap * reg.l2model
m_l2[m_l2==-100] = np.nan
m_lp = actvMap * mrec
m_lp[m_lp==-100] = np.nan
# m_true = actvMap * model
# m_true[m_true==-100] = np.nan
#Plot L2 model
#global im2
xx, zz = mesh.gridCC[:,0].reshape(mesh.vnC, order="F"), mesh.gridCC[:,2].reshape(mesh.vnC, order="F")
yy = mesh.gridCC[:,1].reshape(mesh.vnC, order="F")
temp = m_lp.reshape(mesh.vnC, order='F')
ptemp = temp[:,:,indz].T
#ptemp = ma.array(ptemp ,mask=np.isnan(ptemp))
global im2
im2 = ax2.contourf(xx[:,:,indz].T,yy[:,:,indz].T,ptemp,20, vmin = vmin, vmax= vmax, clim=[vmin,vmax])
ax2.plot(([mesh.vectorCCx[0],mesh.vectorCCx[-1]]),([mesh.vectorCCy[indy],mesh.vectorCCy[indy]]),color='w')
ax2.set_aspect('equal')
ax2.xaxis.set_visible(False)
ax2.set_xlim(-60,60)
ax2.set_ylim(-60,60)
ax2.set_title('Induced Model')
ax2.set_ylabel('Northing (m)',size=14)
ptemp = temp[:,indy,:].T
global im3
im3 = ax3.contourf(xx[:,indy,:].T,zz[:,indy,:].T,ptemp,20, vmin = vmin, vmax= vmax, clim=[vmin,vmax])
ax3.set_aspect('equal')
ax3.set_xlim(-60,60)
ax3.set_ylim(-60,0)
ax3.set_title('EW Section')
ax3.set_xlabel('Easting (m)',size=14)
ax3.set_ylabel('Elevation (m)',size=14)
ax4 = fig.add_axes([pos.x0 + 0.75, pos.y0+0.25, pos.height*0.02, pos.height*0.4])
cb = plt.colorbar(im3,cax=ax4, orientation="vertical", ax = ax1, ticks=np.linspace(im3.vmin,im3.vmax, 4), format="${%.3f}$")
cb.set_label("Susceptibility (SI)",size=12)
susc = 0.25
vmin, vmax = 0., 0.015
ax1.axis('equal')
ax1.set_title('Forward Simulation')
# Define the problem interactively
p = MAG.definePrism()
p.dx, p.dy, p.dz, p.z0 = dx, dy, dz, -depth
p.pinc, p.pdec = pinc, pdec
srvy = PFlocal.survey()
srvy.rx_h, srvy.npts2D, srvy.xylim = rx_h, npts2D, xylim
# Create problem
prob = PFlocal.problem()
prob.prism = p
prob.survey = srvy
X, Y = np.meshgrid(prob.survey.xr, prob.survey.yr)
Z = np.ones(X.shape)*rx_h
x, y = MAG.linefun(x1, x2, y1, y2, prob.survey.npts2D)
xyz_line = np.c_[x, y, np.ones_like(x)*prob.survey.rx_h]
# Create a mesh
dx = 5.
hxind = [(dx,5,-1.3), (dx, 10), (dx,5,1.3)]
hyind = [(dx,5,-1.3), (dx, 10), (dx,5,1.3)]
hzind = [(dx,5,-1.3),(dx, 10)]
def animate(ii):
removePlt()
#ii=1
#inc = 45
#dec = 90
if ii < 18:
dec = 90
inc = 0. + ii * 5.
elif ii < 36:
dec = 270.
inc = 90. - (ii - 18) * 5.
elif ii < 54:
dec = 270.
inc = 0. + (ii - 36) * 5.
else:
dec = 90
inc = 90. - (ii - 54) * 5.
ax1.axis('equal')
block_xyz = np.asarray([[-.2, -.2, .2, .2, 0], [
-.25, -.25, -.25, -.25, 0.5
], [-.2, .2, .2, -.2, 0]]) * 10.
block_xyz[1][:] -= 20.
# rot = Utils.mkvc(Utils.dipazm_2_xyz(pinc, pdec))
# xyz = Utils.rotatePointsFromNormals(block_xyz.T, np.r_[0., 1., 0.], rot,
# np.r_[p.xc, p.yc, p.zc])
R = Utils.rotationMatrix(inc, dec)
xyz = R.dot(block_xyz).T
xyz[:, 2] -= depth + dz / 2.
#print xyz
# Face 1
ax1.add_collection3d(
Poly3DCollection([zip(xyz[:4, 0], xyz[:4, 1], xyz[:4, 2])],
facecolors='b'))
ax1.add_collection3d(
Poly3DCollection(
[zip(xyz[[1, 2, 4], 0], xyz[[1, 2, 4], 1], xyz[[1, 2, 4], 2])],
facecolors='b'))
ax1.add_collection3d(
Poly3DCollection(
[zip(xyz[[0, 1, 4], 0], xyz[[0, 1, 4], 1], xyz[[0, 1, 4], 2])],
facecolors='b'))
ax1.add_collection3d(
Poly3DCollection(
[zip(xyz[[2, 3, 4], 0], xyz[[2, 3, 4], 1], xyz[[2, 3, 4], 2])],
facecolors='b'))
ax1.add_collection3d(
Poly3DCollection(
[zip(xyz[[0, 3, 4], 0], xyz[[0, 3, 4], 1], xyz[[0, 3, 4], 2])],
facecolors='b'))
block_xyz[1][:] += 20.
# rot = Utils.mkvc(Utils.dipazm_2_xyz(pinc, pdec))
# xyz = Utils.rotatePointsFromNormals(block_xyz.T, np.r_[0., 1., 0.], rot,
# np.r_[p.xc, p.yc, p.zc])
R = Utils.rotationMatrix(rinc, rdec)
xyz = R.dot(block_xyz).T
xyz[:, 2] -= depth + dz / 2.
#print xyz
# Face 1
ax1.add_collection3d(
Poly3DCollection([zip(xyz[:4, 0], xyz[:4, 1], xyz[:4, 2])],
facecolors='y'))
ax1.add_collection3d(
Poly3DCollection(
[zip(xyz[[1, 2, 4], 0], xyz[[1, 2, 4], 1], xyz[[1, 2, 4], 2])],
facecolors='y'))
ax1.add_collection3d(
Poly3DCollection(
[zip(xyz[[0, 1, 4], 0], xyz[[0, 1, 4], 1], xyz[[0, 1, 4], 2])],
facecolors='y'))
ax1.add_collection3d(
Poly3DCollection(
[zip(xyz[[2, 3, 4], 0], xyz[[2, 3, 4], 1], xyz[[2, 3, 4], 2])],
facecolors='y'))
ax1.add_collection3d(
Poly3DCollection(
[zip(xyz[[0, 3, 4], 0], xyz[[0, 3, 4], 1], xyz[[0, 3, 4], 2])],
facecolors='y'))
MAG.plotObj3D(p,
rx_h,
View_elev,
View_azim,
npts2D,
xylim,
profile="X",
fig=fig,
axs=ax1,
plotSurvey=False)
ax1.w_yaxis.set_ticklabels('')
ax1.w_yaxis.set_label_text('')
ax1.w_zaxis.set_ticklabels('')
ax1.w_zaxis.set_label_text('')
# Create problem
prob = PFlocal.problem()
prob.prism = p
prob.survey = srvy
prob.Bdec, prob.Binc, prob.Bigrf = dec, inc, Bigrf
prob.Q, prob.rinc, prob.rdec = Q, rinc, rdec
prob.uType, prob.mType = 'tf', 'total'
prob.susc = susc
# Compute fields from prism
b_ind, b_rem = prob.fields()
out = b_ind + b_rem
#out = plogMagSurvey2D(prob, susc, Einc, Edec, Bigrf, x1, y1, x2, y2, comp, irt, Q, rinc, rdec, fig=fig, axs1=ax2, axs2=ax3)
#dat = axs1.contourf(X,Y, np.reshape(out, (X.shape)).T
global im1
im1 = ax1.contourf(X,
Y,
np.reshape(out, (X.shape)).T,
20,
zdir='z',
offset=rx_h + 5.,
clim=clim,
vmin=clim[0],
vmax=clim[1],
cmap='RdBu_r')
ax5 = fig.add_axes(
[pos.x0, pos.y0 + 0.25, pos.height * 0.02, pos.height * 0.4])
cb = plt.colorbar(im1,
cax=ax5,
orientation="vertical",
ax=ax1,
ticks=np.linspace(im1.vmin, im1.vmax, 4),
format="${%.0f}$")
cb.set_label("$B^{TMI}\;(nT)$", size=12)
cb.ax.yaxis.set_ticks_position('left')
cb.ax.yaxis.set_label_position('left')
global im5
im5 = ax1.text(0,
0,
-60,
'$B_0, I: ' + str(inc) + '^\circ, D: ' + str(dec) +
'^\circ$',
horizontalalignment='center')
H0 = (Bigrf, inc, dec)
actv = np.ones(mesh.nC) == 1
# Create active map to go from reduce space to full
actvMap = Maps.InjectActiveCells(mesh, actv, -100)
nC = len(actv)
# Create a MAGsurvey
rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)]
rxLoc = PF.BaseMag.RxObs(rxLoc)
srcField = PF.BaseMag.SrcField([rxLoc], param=H0)
survey = PF.BaseMag.LinearSurvey(srcField)
# Create active map to go from reduce set to full
actvMap = Maps.InjectActiveCells(mesh, actv, -100)
# Creat reduced identity map
idenMap = Maps.IdentityMap(nP=3 * nC)
# Create the forward model operator
#probinv = PF.Magnetics.MagneticIntegral(mesh, mapping = idenMap, actInd = actv)
probinv = PF.Magnetics.MagneticVector(mesh, mapping=idenMap, actInd=actv)
# Pair the survey and problem
survey.pair(probinv)
# We can now generate data
data = out + np.random.randn(
len(out)) # We add some random Gaussian noise (1nT)
wd = np.ones(len(data)) * 1. # Assign flat uncertainties
# Create distance weights from our linera forward operator
wr = np.sum(probinv.G**2., axis=0)**0.5
wr = (wr / np.max(wr))
#survey.makeSyntheticData(data, std=0.01)
survey.dobs = data
survey.std = wd
survey.mtrue = model
# Create a regularization
reg = Regularization.Sparse(mesh,
indActive=actv,
mapping=idenMap,
nModels=3)
reg.cell_weights = wr
reg.mref = np.zeros(3 * nC)
dmis = DataMisfit.l2_DataMisfit(survey)
dmis.Wd = 1 / wd
# Add directives to the inversion
opt = Optimization.ProjectedGNCG(maxIter=100,
lower=-1,
upper=1.,
maxIterLS=20,
maxIterCG=10,
tolCG=1e-3)
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 (run last cell to see the histogram before and after IRLS)
IRLS = Directives.Update_IRLS(norms=([2, 2, 2, 2]),
eps=(1e-4, 1e-4),
f_min_change=1e-2,
minGNiter=3,
beta_tol=1e-2)
update_Jacobi = Directives.Update_lin_PreCond()
inv = Inversion.BaseInversion(invProb,
directiveList=[update_Jacobi, IRLS, betaest])
mrec = inv.run(np.ones(3 * len(actv)) * 1e-4)
# Here is the recovered susceptibility model
ypanel = midx
zpanel = -4
m_lpx = actvMap * mrec[0:nC]
m_lpy = actvMap * mrec[nC:2 * nC]
m_lpz = actvMap * -mrec[2 * nC:]
m_lpx[m_lpx == -100] = np.nan
m_lpy[m_lpy == -100] = np.nan
m_lpz[m_lpz == -100] = np.nan
amp = np.sqrt(m_lpx**2. + m_lpy**2. + m_lpz**2.)
m_lpx = (m_lpx / amp).reshape(mesh.vnC, order='F')
m_lpy = (m_lpy / amp).reshape(mesh.vnC, order='F')
m_lpz = (m_lpz / amp).reshape(mesh.vnC, order='F')
amp = amp.reshape(mesh.vnC, order='F')
sub = 2
# m_true = actvMap * model
# m_true[m_true==-100] = np.nan
#Plot L2 model
# global im2
xx, zz = mesh.gridCC[:, 0].reshape(
mesh.vnC, order="F"), mesh.gridCC[:, 2].reshape(mesh.vnC, order="F")
yy = mesh.gridCC[:, 1].reshape(mesh.vnC, order="F")
#ptemp = ma.array(ptemp ,mask=np.isnan(ptemp))
global im2
im2 = ax2.contourf(xx[:, :, zpanel].T,
yy[:, :, zpanel].T,
amp[:, :, zpanel].T,
40,
vmin=vmin,
vmax=vmax,
clim=[vmin, vmax])
global im4
im4 = ax2.quiver(mkvc(xx[::sub, ::sub, zpanel].T),
mkvc(yy[::sub, ::sub, zpanel].T),
mkvc(m_lpx[::sub, ::sub, zpanel].T),
mkvc(m_lpy[::sub, ::sub, zpanel].T),
pivot='mid',
units="xy",
scale=0.2,
linewidths=(1, ),
edgecolors=('k'),
headaxislength=0.1,
headwidth=10,
headlength=30)
ax2.set_aspect('equal')
ax2.xaxis.set_visible(False)
ax2.set_xlim(-60, 60)
ax2.set_ylim(-60, 60)
ax2.set_title('Effective Susceptibility')
ax2.set_ylabel('Northing (m)', size=14)
global im3
im3 = ax3.contourf(xx[:, ypanel, :].T,
zz[:, ypanel, :].T,
amp[:, ypanel, :].T,
40,
vmin=vmin,
vmax=vmax,
clim=[vmin, vmax])
global im6
im6 = ax3.quiver(mkvc(xx[::sub, ypanel, ::sub].T),
mkvc(zz[::sub, ypanel, ::sub].T),
mkvc(m_lpx[::sub, ypanel, ::sub].T),
mkvc(m_lpz[::sub, ypanel, ::sub].T),
pivot='mid',
units="xy",
scale=0.2,
linewidths=(1, ),
edgecolors=('k'),
headaxislength=0.1,
headwidth=10,
headlength=30)
ax3.set_aspect('equal')
ax3.set_xlim(-60, 60)
ax3.set_ylim(-60, 0)
ax3.set_title('EW Section')
ax3.set_xlabel('Easting (m)', size=14)
ax3.set_ylabel('Elevation (m)', size=14)
ax4 = fig.add_axes(
[pos.x0 + 0.75, pos.y0 + 0.25, pos.height * 0.02, pos.height * 0.4])
cb = plt.colorbar(im3,
cax=ax4,
orientation="vertical",
ax=ax1,
ticks=np.linspace(im3.vmin, im3.vmax, 4),
format="${%.3f}$")
cb.set_label("$\kappa_{e}$ (SI)", size=12)
def plotProfile(p, data, Binc, Bdec, Bigrf, susc, Q, rinc, rdec):
if data is 'MonSt':
filename = "data/StudentData2015_Monday.csv"
elif data is 'WedSt':
filename = "data/StudentData2015_Wednesday.csv"
elif data is 'WedTA':
filename = "data/TAData2015_Wednesday.csv"
dat = pd.DataFrame(pd.read_csv(filename, header=0))
tf = dat["Corrected Total Field Data (nT)"].values
std = dat["Standard Deviation (nT)"].values
loc = dat["Location (m)"].values
teams = dat["Team"].values
tfa = tf - Bigrf
nx, ny = 100, 1
shape = (nx, ny)
xLoc = np.linspace(xlim[0], xlim[1], nx)
zLoc = np.ones(np.shape(xLoc)) * rx_h
yLoc = np.zeros(np.shape(xLoc))
#xpl, ypl, zpl = fatiandoGridMesh.regular(surveyArea,shape, z=z)
rxLoc = np.c_[Utils.mkvc(xLoc), Utils.mkvc(yLoc), Utils.mkvc(zLoc)]
prob1D = MAG.problem()
srvy1D = MAG.survey()
srvy1D._rxLoc = rxLoc
prob1D.prism = p
prob1D.survey = srvy1D
prob1D.Bdec, prob1D.Binc, prob1D.Bigrf = Bdec, Binc, Bigrf
prob1D.Q, prob1D.rinc, prob1D.rdec = Q, rinc, rdec
prob1D.uType, prob1D.mType = 'tf', 'total'
prob1D.susc = susc
# Compute fields from prism
magi, magr = prob1D.fields()
#out_linei, out_liner = getField(p, xyz_line, comp, 'total')
#out_linei = getField(p, xyz_line, comp,'induced')
#out_liner = getField(p, xyz_line, comp,'remanent')
# distance = np.sqrt((x-x1)**2.+(y-y1)**2.)
f = plt.figure(figsize=(10, 5))
gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
ax1.plot(p.x0, p.z0, 'ko')
ax1.text(p.x0 + 0.5, p.z0, 'Rebar', color='k')
ax1.text(xlim[0] + 1., -1.2, 'Magnetometer height (1.9 m)', color='b')
ax1.plot(xlim, np.r_[-rx_h, -rx_h], 'b--')
# magi,magr = getField(p, rxLoc, 'bz', 'total')
ax1.plot(xlim, np.r_[0., 0.], 'k--')
ax1.set_xlim(xlim)
ax1.set_ylim(-2.5, 2.5)
ax0.scatter(loc, tfa, c=teams)
ax0.errorbar(loc, tfa, yerr=std, linestyle="None", color="k")
ax0.set_xlim(xlim)
ax0.grid(which="both")
ax0.plot(xLoc, magi, 'b', label='induced')
ax0.plot(xLoc, magr, 'r', label='remnant')
ax0.plot(xLoc, magi + magr, 'k', label='total')
ax0.legend(loc=2)
# ax[1].plot(loc-8, magnT[::-1], )
ax1.set_xlabel("Northing (m)")
ax1.set_ylabel("Depth (m)")
ax0.set_ylabel("Total field anomaly (nT)")
ax0.grid(True)
ax0.set_xlabel("Northing (m)")
ax1.grid(True)
ax1.set_xlabel("Northing (m)")
ax1.invert_yaxis()
plt.tight_layout()
plt.show()
return True
def plogMagSurvey2D(prob2D,
susc,
Einc,
Edec,
Bigrf,
x1,
y1,
x2,
y2,
comp,
irt,
Q,
rinc,
rdec,
fig=None,
axs1=None,
axs2=None):
import matplotlib.gridspec as gridspec
# The MAG problem created is stored in result[1]
# prob2D = Box.result[1]
if fig is None:
fig = plt.figure(figsize=(18 * 1.5, 3.4 * 1.5))
plt.rcParams.update({'font.size': 14})
gs1 = gridspec.GridSpec(2, 7)
gs1.update(left=0.05, right=0.48, wspace=0.05)
if axs1 is None:
axs1 = plt.subplot(gs1[:2, :3])
if axs2 is None:
axs2 = plt.subplot(gs1[0, 4:])
axs1.axis("equal")
prob2D.Bdec, prob2D.Binc, prob2D.Bigrf = Edec, Einc, Bigrf
prob2D.Q, prob2D.rinc, prob2D.rdec = Q, rinc, rdec
prob2D.uType, prob2D.mType = comp, 'total'
prob2D.susc = susc
# Compute fields from prism
b_ind, b_rem = prob2D.fields()
if irt == 'total':
out = b_ind + b_rem
elif irt == 'induced':
out = b_ind
else:
out = b_rem
X, Y = np.meshgrid(prob2D.survey.xr, prob2D.survey.yr)
dat = axs1.contourf(X, Y, np.reshape(out, (X.shape)).T, 25)
cb = plt.colorbar(dat, ax=axs1, ticks=np.linspace(out.min(), out.max(), 5))
cb.set_label("nT")
axs1.plot(X, Y, '.k')
# Compute fields on the line by creating a similar mag problem
x, y = linefun(x1, x2, y1, y2, prob2D.survey.npts2D)
xyz_line = np.c_[x, y, np.ones_like(x) * prob2D.survey.rx_h]
# Create problem
prob1D = MAG.problem()
srvy1D = MAG.survey()
srvy1D._rxLoc = xyz_line
prob1D.prism = prob2D.prism
prob1D.survey = srvy1D
prob1D.Bdec, prob1D.Binc, prob1D.Bigrf = Edec, Einc, Bigrf
prob1D.Q, prob1D.rinc, prob1D.rdec = Q, rinc, rdec
prob1D.uType, prob1D.mType = comp, 'total'
prob1D.susc = susc
# Compute fields from prism
out_linei, out_liner = prob1D.fields()
#out_linei, out_liner = getField(p, xyz_line, comp, 'total')
#out_linei = getField(p, xyz_line, comp,'induced')
#out_liner = getField(p, xyz_line, comp,'remanent')
out_linet = out_linei + out_liner
distance = np.sqrt((x - x1)**2. + (y - y1)**2.)
axs1.plot(x, y, 'w.', ms=3)
axs1.text(x[0], y[0], 'A', fontsize=16, color='w')
axs1.text(x[-1],
y[-1],
'B',
fontsize=16,
color='w',
horizontalalignment='right')
axs1.set_xlabel('Easting (X; m)')
axs1.set_ylabel('Northing (Y; m)')
axs1.set_xlim(X.min(), X.max())
axs1.set_ylim(Y.min(), Y.max())
axs1.set_title(irt + ' ' + comp)
axs2.plot(distance, out_linei, 'b.-')
axs2.plot(distance, out_liner, 'r.-')
axs2.plot(distance, out_linet, 'k.-')
axs2.set_xlim(distance.min(), distance.max())
axs2.set_xlabel("Distance (m)")
axs2.set_ylabel("Magnetic field (nT)")
axs2.text(distance.min(), out_linei.max() * 0.8, 'A', fontsize=16)
axs2.text(distance.max() * 0.97, out_linei.max() * 0.8, 'B', fontsize=16)
axs2.legend(("induced", "remanent", "total"), bbox_to_anchor=(0.5, -0.3))
axs2.grid(True)
plt.show()
return True