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)
# Wrap the data so that harvester can use it
data = [gm.harvester.Gxx(xp, yp, zp, gxx),
gm.harvester.Gxy(xp, yp, zp, gxy),
gm.harvester.Gxz(xp, yp, zp, gxz),
gm.harvester.Gyy(xp, yp, zp, gyy),
gm.harvester.Gyz(xp, yp, zp, gyz),
gm.harvester.Gzz(xp, yp, zp, gzz)]
# Make the seeds
seeds = gm.harvester.sow([
[500, 400, 210, {'density':1000}],
[500, 550, 510, {'density':1000}]], mesh)
# Run the inversioin
estimate, predicted = gm.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)
mpl.colorbar()
mpl.contour(yp, xp, predicted[i], shape, levels, color='k')
mpl.xlabel('y (km)')
mpl.ylabel('x (km)')
mpl.m2km()
mpl.figure()
gm.harvester.Gzz(xp, yp, zp, gzz)
]
# Make the seeds
seeds = gm.harvester.sow([[500, 400, 210, {
'density': 1000
}], [500, 550, 510, {
'density': 1000
}]], mesh)
# Run the inversioin
estimate, predicted = gm.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)
mpl.colorbar()
mpl.contour(yp, xp, predicted[i], shape, levels, color='k')
mpl.xlabel('y (km)')
mpl.ylabel('x (km)')
mpl.m2km()
mpl.figure()
--------------------
The mesh classes in Fatiando are more efficient ways of representing regular
meshes than simples lists of :class:`~fatiando.mesher.Prism` objects. This is
how you can create a :class:`~fatiando.mesher.PrismMesh` and assign it a
density for each prism.
"""
from __future__ import print_function
from fatiando.mesher import PrismMesh
from fatiando.vis import myv
mesh = PrismMesh(bounds=(0, 100, 0, 200, 0, 150), shape=(5, 6, 7))
# We'll give each prism a density value corresponding to it's index on the
# mesh. Notice that meshes take lists/arrays as their property values
mesh.addprop('density', list(range(mesh.size)))
# You can iterate over meshes like lists of elements
for p in mesh:
print(p.props['density'], end=' ')
scene = myv.figure(size=(600, 600))
# Because you can iterate over a mesh, you can pass it anywhere a list of
# prisms is accepted
plot = myv.prisms(mesh, prop='density')
# The code below enables and configures the color bar. This will be automated
# on a function in the future (write to the mailing list if you'd like to help
# out!)
plot.module_manager.scalar_lut_manager.show_scalar_bar = True
plot.module_manager.scalar_lut_manager.lut_mode = 'Greens'
plot.module_manager.scalar_lut_manager.reverse_lut = True
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()
mpl.show()
# Plot the result
myv.figure()
myv.polyprisms(model, "density", opacity=0.6, linewidth=5)
myv.prisms(vremove(0, "density", mesh), "density")
mpl.ylabel('Horizontal coordinate x (km)')
mpl.m2km()
mpl.show()
# Inversion setup
# Create a mesh
mesh = PrismMesh(bounds, (25, 25, 25))
# Wrap the data so that harvester can use it
data = [gm.harvester.TotalField(xp, yp, zp, tf, inc, dec)]
# Make the seed
seeds = gm.harvester.sow([[500, 500, 450, {'magnetization':mag}]], mesh)
# Run the inversioin
estimate, predicted = gm.harvester.harvest(data, seeds, mesh,
compactness=1, threshold=0.0001)
#Put the estimated magnetization vector in the mesh
mesh.addprop('magnetization', estimate['magnetization'])
# Plot the adjustment
mpl.figure()
mpl.title("True: color | Inversion: contour")
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, tf, 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()
residuals = tf - predicted[0]
mpl.figure()
mpl.title('Residuals: mean=%g stddev=%g' % (residuals.mean(), residuals.std()))
mpl.hist(residuals, bins=10)
def test_prism_mesh_copy():
p1 = PrismMesh((0, 1, 0, 2, 0, 3), (1, 2, 2))
p1.addprop('density', 3200 + np.zeros(p1.size))
p2 = p1.copy()
assert p1 is not p2
assert np.array_equal(p1.props['density'], p2.props['density'])
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'])