utils.contaminate(prism.gxy(xp, yp, zp, model), noise),
utils.contaminate(prism.gxz(xp, yp, zp, model), noise),
utils.contaminate(prism.gyy(xp, yp, zp, model), noise),
utils.contaminate(prism.gyz(xp, yp, zp, model), noise),
utils.contaminate(prism.gzz(xp, yp, zp, model), noise)]
# Plot the data
titles = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
mpl.figure()
for i, title in enumerate(titles):
mpl.subplot(3, 2, i + 1)
mpl.title(title)
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, data[i], shape, 10)
mpl.contour(yp, xp, data[i], shape, levels)
mpl.m2km()
mpl.show()
# Get the eigenvectors from the tensor data
eigenvals, eigenvecs = tensor.eigen(data)
# Use the first eigenvector to estimate the center of mass
cm = tensor.center_of_mass(xp, yp, zp, eigenvecs[0])
# Plot the prism and the estimated center of mass
myv.figure()
myv.points([cm], size=200.)
myv.prisms(model, prop='density', opacity=0.5)
axes = myv.axes(
myv.outline(extent=[-5000, 5000, -5000, 5000, 0, 5000]))
myv.wall_bottom(axes.axes.bounds, opacity=0.2)
myv.wall_north(axes.axes.bounds)
myv.show()
utils.contaminate(prism.gxy(xp, yp, zp, model), noise),
utils.contaminate(prism.gxz(xp, yp, zp, model), noise),
utils.contaminate(prism.gyy(xp, yp, zp, model), noise),
utils.contaminate(prism.gyz(xp, yp, zp, model), noise),
utils.contaminate(prism.gzz(xp, yp, zp, model), noise)
]
# Plot the data
titles = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
mpl.figure()
for i, title in enumerate(titles):
mpl.subplot(3, 2, i + 1)
mpl.title(title)
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, data[i], shape, 10)
mpl.contour(yp, xp, data[i], shape, levels)
mpl.m2km()
mpl.show()
# Get the eigenvectors from the tensor data
eigenvals, eigenvecs = tensor.eigen(data)
# Use the first eigenvector to estimate the center of mass
cm = tensor.center_of_mass(xp, yp, zp, eigenvecs[0])
# Plot the prism and the estimated center of mass
myv.figure()
myv.points([cm], size=200.)
myv.prisms(model, prop='density', opacity=0.5)
axes = myv.axes(myv.outline(extent=[-5000, 5000, -5000, 5000, 0, 5000]))
myv.wall_bottom(axes.axes.bounds, opacity=0.2)
myv.wall_north(axes.axes.bounds)
myv.show()
mpl.subplot(3, 2, i + 1)
mpl.title(title)
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, data[i], shape, 10)
mpl.contour(yp, xp, data[i], shape, levels)
mpl.m2km()
mpl.show()
# Pick the centers of the expanding windows
# The number of final solutions will be the number of points picked
mpl.figure()
mpl.suptitle('Pick the centers of the expanding windows')
mpl.axis('scaled')
mpl.contourf(yp, xp, data[-1], shape, 50)
mpl.colorbar()
centers = mpl.pick_points(area, mpl.gca(), xy2ne=True)
# Use the first eigenvector to estimate the center of mass for each expanding
# window group
cms = [tensor.center_of_mass(xp, yp, zp, eigenvecs[0], windows=100, wcenter=c)
for c in centers]
# Plot the prism and the estimated center of mass
# It won't work well because we're using only a single window
myv.figure()
myv.points(cms, size=200.)
myv.prisms(model, prop='density', opacity=0.5)
axes = myv.axes(myv.outline(extent=[-5000, 5000, -5000, 5000, 0, 5000]))
myv.wall_bottom(axes.axes.bounds, opacity=0.2)
myv.wall_north(axes.axes.bounds)
myv.show()
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, data[i], shape, 10)
mpl.contour(yp, xp, data[i], shape, levels)
mpl.m2km()
mpl.show()
# Pick the centers of the expanding windows
# The number of final solutions will be the number of points picked
mpl.figure()
mpl.suptitle('Pick the centers of the expanding windows')
mpl.axis('scaled')
mpl.contourf(yp, xp, data[-1], shape, 50)
mpl.colorbar()
centers = mpl.pick_points(area, mpl.gca(), xy2ne=True)
# Use the first eigenvector to estimate the center of mass for each expanding
# window group
cms = [
tensor.center_of_mass(xp, yp, zp, eigenvecs[0], windows=100, wcenter=c)
for c in centers
]
# Plot the prism and the estimated center of mass
# It won't work well because we're using only a single window
myv.figure()
myv.points(cms, size=200.)
myv.prisms(model, prop='density', opacity=0.5)
axes = myv.axes(myv.outline(extent=[-5000, 5000, -5000, 5000, 0, 5000]))
myv.wall_bottom(axes.axes.bounds, opacity=0.2)
myv.wall_north(axes.axes.bounds)
myv.show()
