def test_harvest_restrict():
def fill(i, case):
# Returns density of 10 for center prism and prism given by 'case'
cdir = {'above': 4, 'below': 22, 'north': 14, 'south': 12, 'east': 16,
'west': 10}
if i == 13:
return 10
for key in cdir:
if case == key and i == cdir.get(key):
return 10
return 0
# The test cases as string list
cases = ['above', 'below', 'north', 'south', 'east', 'west']
# Create reference model
bounds = (0, 3, 0, 3, 0, 3)
shape = (3, 3, 3)
shapegz = (10, 10)
for testcase in cases:
mref = PrismMesh(bounds, shape)
mesh = mref.copy()
mref.addprop('density', [fill(i, testcase) for i in xrange(mref.size)])
# Calculate reference gravity field
xp, yp, zp = gridder.regular(bounds[:4], shapegz, z=-1)
gzref = prism.gz(xp, yp, zp, mref)
# Initiate harvest
hgref = [harvester.Gz(xp, yp, zp, gzref)]
loc = [[1.5, 1.5, 1.5, {'density': 10}]]
seeds = harvester.sow(loc, mesh)
# est0 should be incorrect and thus fail wilst est1 should yield the
# same geometry as mref
est0, pred0 = harvester.harvest(hgref, seeds, mesh, compactness=0.1,
threshold=0.001, restrict=[testcase])
est1, pred1 = harvester.harvest(hgref, seeds, mesh, compactness=0.1,
threshold=0.001)
res0 = mesh.copy()
res0.addprop('density', est0['density'])
res1 = mesh.copy()
res1.addprop('density', est1['density'])
l0 = []
l1 = []
for i, p in enumerate(res0):
l0.append(p.props['density'] == mref[i].props['density'])
for i, p in enumerate(res1):
l1.append(p.props['density'] == mref[i].props['density'])
assert not np.all(l0)
assert np.all(l1)
mpl.m2km()
mpl.show()
# Inversion setup
# Create a mesh
mesh = PrismMesh(bounds, (30, 30, 30))
# Wrap the data so that harvester can use it
data = [harvester.Gxx(xp, yp, zp, gxx),
harvester.Gxy(xp, yp, zp, gxy),
harvester.Gxz(xp, yp, zp, gxz),
harvester.Gyy(xp, yp, zp, gyy),
harvester.Gyz(xp, yp, zp, gyz),
harvester.Gzz(xp, yp, zp, gzz)]
# Make the seeds
seeds = harvester.sow([
[500, 400, 210, {'density':1000}],
[500, 550, 510, {'density':1000}]], mesh)
# Run the inversioin
estimate, predicted = harvester.harvest(data, seeds, mesh,
compactness=0.5, threshold=0.001)
# Put the estimated density values in the mesh
mesh.addprop('density', estimate['density'])
# Plot the adjustment
mpl.figure()
mpl.suptitle("True: color | Inversion: contour")
for i in xrange(len(tensor)):
mpl.subplot(2, 3, i + 1)
mpl.title(titles[i])
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, tensor[i], shape, 12)
data = [harvester.Gz(xp, yp, zp, gz)]
# Plot the data and pick the location of the seeds
mpl.figure()
mpl.suptitle("Pick the seeds (polygon is the true source)")
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, gz, shape, 12)
mpl.colorbar()
mpl.polygon(model[0], xy2ne=True)
mpl.xlabel('Horizontal coordinate y (km)')
mpl.ylabel('Horizontal coordinate x (km)')
seedx, seedy = mpl.pick_points(area, mpl.gca(), xy2ne=True).T
# Set the right density and depth
locations = [[x, y, 1500, {'density': 1000}] for x, y in zip(seedx, seedy)]
mpl.show()
# Make the seed and set the compactness regularizing parameter mu
seeds = harvester.sow(locations, mesh)
# Run the inversion
estimate, predicted = harvester.harvest(data, seeds, mesh,
compactness=0.05, threshold=0.0005)
# Put the estimated density values in the mesh
mesh.addprop('density', estimate['density'])
# Plot the adjustment and the result
mpl.figure()
mpl.title("True: color | Predicted: contour")
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, gz, shape, 12)
mpl.colorbar()
mpl.contour(yp, xp, predicted[0], shape, levels, color='k')
mpl.xlabel('Horizontal coordinate y (km)')
mpl.ylabel('Horizontal coordinate x (km)')
mpl.m2km()
GravMag: Iterate through a 3D gravity inversion by planting anomalous densities
"""
from fatiando import gridder
from fatiando.gravmag import prism, harvester
from fatiando.mesher import Prism, PrismMesh
from fatiando.vis import myv
model = [Prism(200, 800, 400, 600, 200, 400, {'density': 1000})]
shape = (20, 20)
bounds = [0, 1000, 0, 1000, 0, 1000]
area = bounds[0:4]
x, y, z = gridder.regular(area, shape, z=-1)
gz = prism.gz(x, y, z, model)
mesh = PrismMesh(bounds, (10, 10, 10))
data = [harvester.Gz(x, y, z, gz)]
seeds = harvester.sow([[500, 500, 250, {'density': 1000}]], mesh)
fig = myv.figure(size=(700, 700))
plot = myv.prisms(model, style='wireframe', linewidth=4)
plot.actor.mapper.scalar_visibility = False
myv.prisms(seeds, 'density')
myv.outline(bounds)
myv.wall_bottom(bounds)
myv.wall_east(bounds)
for update in harvester.iharvest(data, seeds, mesh, compactness=0.5,
threshold=0.001):
best, neighborhood = update[2:4]
if best is not None:
myv.prisms([mesh[best.i]])
plot = myv.prisms(
[mesh[n] for neighbors in neighborhood for n in neighbors],
# Set up the inversion:
# Create a prism mesh
mesh = PrismMesh(bounds, (15, 50, 50))
# Wrap the data so that harvester can use it
data = [harvester.Gyy(x, y, z, gyy),
harvester.Gyz(x, y, z, gyz),
harvester.Gzz(x, y, z, gzz)]
# and the seeds
seeds = harvester.sow(
[( 800, 3250, 600, {'density':1200}),
(1200, 3250, 600, {'density':1200}),
(1700, 3250, 600, {'density':1200}),
(2100, 3250, 600, {'density':1200}),
(2500, 3250, 600, {'density':1200}),
(2900, 3250, 600, {'density':1200}),
(3300, 3250, 600, {'density':1200}),
(3700, 3250, 600, {'density':1200}),
(4200, 3250, 600, {'density':1200}),
(3300, 2050, 600, {'density':1200}),
(3600, 2050, 600, {'density':1200}),
(4000, 2050, 600, {'density':1200}),
(4300, 2050, 600, {'density':1200})],
mesh)
# Run the inversion and collect the results
estimate, predicted = harvester.harvest(data, seeds, mesh,
compactness=1., threshold=0.0001)
# Insert the estimated density values into the mesh
mesh.addprop('density', estimate['density'])
# and get only the prisms corresponding to our estimate
density_model = vremove(0, 'density', mesh)
# Generate some synthetic total field anomaly data
bounds = [0, 10000, 0, 10000, 0, 5000]
props = {'density': 500}
props2 = {'density': 1000}
model = [
mesher.Prism(4000, 6000, 4000, 6000, 500, 2500, props),
mesher.Prism(2000, 2500, 2000, 2500, 500, 1000, props2),
mesher.Prism(7500, 8000, 5500, 6500, 500, 1000, props2),
mesher.Prism(1500, 2000, 4000, 5000, 500, 1000, props2)
]
area = bounds[:4]
shape = (50, 50)
x, y, z = gridder.regular(area, shape, z=-1)
gz = utils.contaminate(prism.gz(x, y, z, model), 0.1)
mesh = mesher.PrismMesh(bounds, (20, 40, 40))
seeds = harvester.sow([[5000, 5000, 1000, props]], mesh)
# Run the inversion without using weights
data = [harvester.Gz(x, y, z, gz)]
estimate, predicted = harvester.harvest(data,
seeds,
mesh,
compactness=1.5,
threshold=0.001)
mesh.addprop('density', estimate['density'])
bodies = mesher.vremove(0, 'density', mesh)
mpl.figure()
mpl.axis('scaled')
mpl.title('No weights: Observed (color) vs Predicted (black)')
levels = mpl.contourf(y, x, gz, shape, 17)
mpl.colorbar()
# Set up the inversion:
# Create a prism mesh
mesh = PrismMesh(bounds, (15, 50, 50))
# Wrap the data so that harvester can use it
data = [harvester.Gyy(x, y, z, gyy),
harvester.Gyz(x, y, z, gyz),
harvester.Gzz(x, y, z, gzz)]
# and the seeds
seeds = harvester.sow(
[(800, 3250, 600, {'density': 1200}),
(1200, 3250, 600, {'density': 1200}),
(1700, 3250, 600, {'density': 1200}),
(2100, 3250, 600, {'density': 1200}),
(2500, 3250, 600, {'density': 1200}),
(2900, 3250, 600, {'density': 1200}),
(3300, 3250, 600, {'density': 1200}),
(3700, 3250, 600, {'density': 1200}),
(4200, 3250, 600, {'density': 1200}),
(3300, 2050, 600, {'density': 1200}),
(3600, 2050, 600, {'density': 1200}),
(4000, 2050, 600, {'density': 1200}),
(4300, 2050, 600, {'density': 1200})],
mesh)
# Run the inversion and collect the results
estimate, predicted = harvester.harvest(data, seeds, mesh,
compactness=1., threshold=0.0001)
# Insert the estimated density values into the mesh
mesh.addprop('density', estimate['density'])
# and get only the prisms corresponding to our estimate
density_model = vremove(0, 'density', mesh)
data = [harvester.Gz(xp, yp, zp, gz)]
# Plot the data and pick the location of the seeds
mpl.figure()
mpl.suptitle("Pick the seeds (polygon is the true source)")
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, gz, shape, 12)
mpl.colorbar()
mpl.polygon(model[0], xy2ne=True)
mpl.xlabel('Horizontal coordinate y (km)')
mpl.ylabel('Horizontal coordinate x (km)')
seedx, seedy = mpl.pick_points(area, mpl.gca(), xy2ne=True).T
# Set the right density and depth
locations = [[x, y, 1500, {'density': 1000}] for x, y in zip(seedx, seedy)]
mpl.show()
# Make the seed and set the compactness regularizing parameter mu
seeds = harvester.sow(locations, mesh)
# Run the inversion
estimate, predicted = harvester.harvest(data, seeds, mesh,
compactness=0.05, threshold=0.0005)
# Put the estimated density values in the mesh
mesh.addprop('density', estimate['density'])
# Plot the adjustment and the result
mpl.figure()
mpl.title("True: color | Predicted: contour")
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, gz, shape, 12)
mpl.colorbar()
mpl.contour(yp, xp, predicted[0], shape, levels, color='k')
mpl.xlabel('Horizontal coordinate y (km)')
mpl.ylabel('Horizontal coordinate x (km)')
mpl.m2km()
gzz = utils.contaminate(prism.gzz(x, y, z, model), 1)
# Wrap the data so that harvester can use it
data = [harvester.Gxy(x, y, z, gxy),
harvester.Gzz(x, y, z, gzz)]
# Create a prism mesh
mesh = PrismMesh(bounds, (20, 50, 50))
# and the seeds
seeds = harvester.sow(
[(901, 701, 750, {'density': 1500}),
(901, 1201, 750, {'density': 1500}),
(901, 1701, 750, {'density': 1500}),
(901, 2201, 750, {'density': 1500}),
(901, 2701, 750, {'density': 1500}),
(901, 3201, 750, {'density': 1500}),
(901, 3701, 750, {'density': 1500}),
(3701, 1201, 501, {'density': 1000}),
(3201, 1201, 501, {'density': 1000}),
(3701, 1701, 501, {'density': 1000}),
(3201, 1701, 501, {'density': 1000}),
(2951, 3951, 301, {'density': 800}),
(2951, 3951, 701, {'density': 800})],
mesh)
# Run the inversion and collect the results
estimate, predicted = harvester.harvest(data, seeds, mesh,
compactness=1, threshold=0.0001)
# Insert the estimated density values into the mesh
mesh.addprop('density', estimate['density'])
# and get only the prisms corresponding to our estimate
density_model = vremove(0, 'density', mesh)
from fatiando.vis import mpl, myv
# Generate some synthetic total field anomaly data
bounds = [0, 10000, 0, 10000, 0, 5000]
props = {'density':500}
props2 = {'density':1000}
model = [mesher.Prism(4000, 6000, 4000, 6000, 500, 2500, props),
mesher.Prism(2000, 2500, 2000, 2500, 500, 1000, props2),
mesher.Prism(7500, 8000, 5500, 6500, 500, 1000, props2),
mesher.Prism(1500, 2000, 4000, 5000, 500, 1000, props2)]
area = bounds[:4]
shape = (50, 50)
x, y, z = gridder.regular(area, shape, z=-1)
gz = utils.contaminate(prism.gz(x, y, z, model), 0.1)
mesh = mesher.PrismMesh(bounds, (20, 40, 40))
seeds = harvester.sow([[5000, 5000, 1000, props]], mesh)
# Run the inversion without using weights
data = [harvester.Gz(x, y, z, gz)]
estimate, predicted = harvester.harvest(data, seeds, mesh,
compactness=1.5, threshold=0.001)
mesh.addprop('density', estimate['density'])
bodies = mesher.vremove(0, 'density', mesh)
mpl.figure()
mpl.axis('scaled')
mpl.title('No weights: Observed (color) vs Predicted (black)')
levels = mpl.contourf(y, x, gz, shape, 17)
mpl.colorbar()
mpl.contour(y, x, predicted[0], shape, levels, color='k')
mpl.m2km()
mpl.xlabel('East (km)')
cases = ['above', 'below', 'north', 'south', 'east', 'west']
# Create reference model
bounds = (0, 3, 0, 3, 0, 3)
shape = (3, 3, 3)
shapegz = (10, 10)
for testcase in cases:
mref = PrismMesh(bounds, shape)
mesh = mref.copy()
mref.addprop('density', [fill(i, testcase) for i in xrange(mref.size)])
# Calculate reference gravity field
xp, yp, zp = gridder.regular(bounds[:4], shapegz, z=-1)
gzref = prism.gz(xp, yp, zp, mref)
# Initiate harvest
hgref = [harvester.Gz(xp, yp, zp, gzref)]
loc = [[1.5, 1.5, 1.5, {'density': 10}]]
seeds = harvester.sow(loc, mesh)
# est0 should be incorrect and thus fail wilst est1 should yield the
# same geometry as mref
est0, pred0 = harvester.harvest(hgref, seeds, mesh, compactness=0.1,
threshold=0.001, restrict=[testcase])
est1, pred1 = harvester.harvest(hgref, seeds, mesh, compactness=0.1,
threshold=0.001)
res0 = mesh.copy()
res0.addprop('density', est0['density'])
res1 = mesh.copy()
res1.addprop('density', est1['density'])
l0 = []
l1 = []
for i, p in enumerate(res0):
l0.append(p.props['density'] == mref[i].props['density'])
for i, p in enumerate(res1):