def plot_synth():
# Load synthetic data
data = numpy.loadtxt('data.txt', unpack=True)
x, y, z = data[0:3]
tensor = data[3:]
with open('model.pickle') as f:
model = pickle.load(f)
for comp in tensor:
shape = (26, 26)
pyplot.figure(figsize=(5,4))
pyplot.axis('scaled')
levels = vis.contourf(y*0.001, x*0.001, comp, shape, 8)
vis.contour(y*0.001, x*0.001, comp, shape, levels)
pyplot.xlabel('Horizontal coordinate y (km)')
pyplot.ylabel('Horizontal coordinate x (km)')
pyplot.show()
extent = [0, 5000, 0, 5000, 0, 1000]
vis.mayavi_figure()
vis.prisms3D(model, extract('density', model))
vis.add_axes3d(vis.add_outline3d(extent), ranges=[i*0.001 for i in extent],
fmt='%.1f', nlabels=6)
vis.wall_bottom(extent)
vis.wall_north(extent)
vis.mlab.show()
def plot_result():
data = numpy.loadtxt("data.txt", unpack=True)
x, y, z, topo = data[0:4]
tensor = data[-3:]
# Load the inversion results
with open(sys.argv[1]) as f:
results = pickle.load(f)
predicted = results["predicted"]
shape = (60, 100)
for true, pred in zip(tensor, predicted):
pyplot.figure()
pyplot.axis("scaled")
levels = vis.contourf(y * 0.001, x * 0.001, true, shape, 12, interpolate=True)
pyplot.colorbar()
vis.contour(y * 0.001, x * 0.001, pred, shape, levels, color="k", interpolate=True)
pyplot.xlabel("Horizontal coordinate y (km)")
pyplot.ylabel("Horizontal coordinate x (km)")
pyplot.show()
extent = [x.min(), x.max(), y.min(), y.max(), -topo.max(), -400]
# density_model = vfilter(900, 1200, 'density', results['mesh'])
seeds = [results["mesh"][s] for s in results["seeds"]]
vis.mayavi_figure()
vis.prisms3D(seeds, extract("density", seeds), vmin=0)
# vis.prisms3D(density_model, extract('density', density_model), vmin=0)
vis.prisms3D(results["mesh"], results["mesh"].props["density"], vmin=0)
vis.add_axes3d(vis.add_outline3d(extent), ranges=[i * 0.001 for i in extent], fmt="%.1f", nlabels=6)
vis.wall_bottom(extent)
vis.wall_north(extent)
vis.mlab.show()
def plot_synth():
# Load synthetic data
data = numpy.loadtxt('data.txt', unpack=True)
x, y, z = data[0:3]
tensor = data[-3:]
with open('model.pickle') as f:
model = pickle.load(f)
for comp in tensor:
shape = (51, 51)
pyplot.figure(figsize=(5, 4))
pyplot.axis('scaled')
levels = vis.contourf(y * 0.001, x * 0.001, comp, shape, 8)
vis.contour(y * 0.001, x * 0.001, comp, shape, levels)
pyplot.xlabel('Horizontal coordinate y (km)')
pyplot.ylabel('Horizontal coordinate x (km)')
pyplot.show()
extent = [0, 5000, 0, 5000, 0, 1500]
vis.mayavi_figure()
vis.prisms3D(model, extract('density', model))
vis.add_axes3d(vis.add_outline3d(extent),
ranges=[i * 0.001 for i in extent],
fmt='%.1f',
nlabels=6)
vis.wall_bottom(extent)
vis.wall_north(extent)
vis.mlab.show()
def plot_result():
# Load synthetic data
data = numpy.loadtxt('data.txt', unpack=True)
x, y, z = data[0:3]
tensor = data[3:]
with open('model.pickle') as f:
model = pickle.load(f)
# Load the inversion results
with open(sys.argv[1]) as f:
results = pickle.load(f)
predicted = results['predicted']
shape = (26, 26)
names = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
i = 0
for true, pred in zip(tensor, predicted):
pyplot.figure(figsize=(3.33, 4))
pyplot.axis('scaled')
levels = vis.contourf(y * 0.001, x * 0.001, true, shape, 8)
cb = pyplot.colorbar(orientation='horizontal', shrink=0.95)
cb.set_ticks([l for j, l in enumerate(levels) if j % 2 != 0])
vis.contour(y * 0.001, x * 0.001, pred, shape, levels, color='k')
pyplot.xlabel('Horizontal coordinate y (km)')
pyplot.ylabel('Horizontal coordinate x (km)')
pyplot.savefig('.'.join([names[i], 'pdf']))
i += 1
pyplot.show()
extent = [0, 5000, 0, 5000, 0, 1000]
density_model = vfilter(-2000, -1, 'density', results['mesh'])
density_model.extend(vfilter(1, 2000, 'density', results['mesh']))
seeds = [results['mesh'][s] for s in results['seeds']]
vis.mayavi_figure()
vis.prisms3D(model, extract('density', model), style='wireframe')
vis.prisms3D(seeds, extract('density', seeds))
vis.prisms3D(density_model, extract('density', density_model))
vis.add_axes3d(vis.add_outline3d(extent),
ranges=[i * 0.001 for i in extent],
fmt='%.1f',
nlabels=3)
vis.wall_bottom(extent)
vis.wall_north(extent)
vis.mlab.show()
def plot_result():
# Load synthetic data
data = numpy.loadtxt('data.txt', unpack=True)
x, y, z = data[0:3]
tensor = data[3:]
with open('model.pickle') as f:
model = pickle.load(f)
# Load the inversion results
with open(sys.argv[1]) as f:
results = pickle.load(f)
predicted = results['predicted']
shape = (26, 26)
names = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
i = 0
for true, pred in zip(tensor, predicted):
pyplot.figure(figsize=(3.33,4))
pyplot.axis('scaled')
levels = vis.contourf(y*0.001, x*0.001, true, shape, 8)
cb = pyplot.colorbar(orientation='horizontal', shrink=0.95)
cb.set_ticks([l for j, l in enumerate(levels) if j%2 != 0])
vis.contour(y*0.001, x*0.001, pred, shape, levels, color='k')
pyplot.xlabel('Horizontal coordinate y (km)')
pyplot.ylabel('Horizontal coordinate x (km)')
pyplot.savefig('.'.join([names[i], 'pdf']))
i += 1
pyplot.show()
extent = [0, 5000, 0, 5000, 0, 1000]
density_model = vfilter(-2000, -1, 'density', results['mesh'])
density_model.extend(vfilter(1, 2000, 'density', results['mesh']))
seeds = [results['mesh'][s] for s in results['seeds']]
vis.mayavi_figure()
vis.prisms3D(model, extract('density', model), style='wireframe')
vis.prisms3D(seeds, extract('density', seeds))
vis.prisms3D(density_model, extract('density', density_model))
vis.add_axes3d(vis.add_outline3d(extent), ranges=[i*0.001 for i in extent],
fmt='%.1f', nlabels=3)
vis.wall_bottom(extent)
vis.wall_north(extent)
vis.mlab.show()
def plot_result():
# Load synthetic data
data = numpy.loadtxt('data.txt', unpack=True)
x, y, z = data[0:3]
tensor = data[-3:]
with open('model.pickle') as f:
model = pickle.load(f)
# Load the inversion results
with open(sys.argv[1]) as f:
results = pickle.load(f)
predicted = results['predicted']
shape = (51, 51)
for true, pred in zip(tensor, predicted):
pyplot.figure(figsize=(5, 4))
pyplot.axis('scaled')
levels = vis.contourf(y * 0.001, x * 0.001, true, shape, 8)
pyplot.colorbar()
vis.contour(y * 0.001, x * 0.001, pred, shape, levels, color='k')
pyplot.xlabel('Horizontal coordinate y (km)')
pyplot.ylabel('Horizontal coordinate x (km)')
pyplot.show()
extent = [0, 5000, 0, 5000, 0, 1500]
density_model = vfilter(1100, 1200, 'density', results['mesh'])
seeds = [results['mesh'][s] for s in results['seeds']]
vis.mayavi_figure()
vis.prisms3D(model, extract('density', model), style='wireframe')
vis.prisms3D(seeds, extract('density', seeds), vmin=0)
vis.prisms3D(density_model, extract('density', density_model), vmin=0)
vis.add_axes3d(vis.add_outline3d(extent),
ranges=[i * 0.001 for i in extent],
fmt='%.1f',
nlabels=6)
vis.wall_bottom(extent)
vis.wall_north(extent)
vis.mlab.show()
def plot_result():
# Load synthetic data
data = numpy.loadtxt('data.txt', unpack=True)
x, y, z = data[0:3]
tensor = data[-3:]
with open('model.pickle') as f:
model = pickle.load(f)
# Load the inversion results
with open(sys.argv[1]) as f:
results = pickle.load(f)
predicted = results['predicted']
shape = (51, 51)
for true, pred in zip(tensor, predicted):
pyplot.figure(figsize=(5,4))
pyplot.axis('scaled')
levels = vis.contourf(y*0.001, x*0.001, true, shape, 8)
pyplot.colorbar()
vis.contour(y*0.001, x*0.001, pred, shape, levels, color='k')
pyplot.xlabel('Horizontal coordinate y (km)')
pyplot.ylabel('Horizontal coordinate x (km)')
pyplot.show()
extent = [0, 5000, 0, 5000, 0, 1500]
density_model = vfilter(1100, 1200, 'density', results['mesh'])
seeds = [results['mesh'][s] for s in results['seeds']]
vis.mayavi_figure()
vis.prisms3D(model, extract('density', model), style='wireframe')
vis.prisms3D(seeds, extract('density', seeds), vmin=0)
vis.prisms3D(density_model, extract('density', density_model), vmin=0)
vis.add_axes3d(vis.add_outline3d(extent), ranges=[i*0.001 for i in extent],
fmt='%.1f', nlabels=6)
vis.wall_bottom(extent)
vis.wall_north(extent)
vis.mlab.show()
def plot_result():
data = numpy.loadtxt('data.txt', unpack=True)
x, y, z, topo = data[0:4]
tensor = data[-3:]
# Load the inversion results
with open(sys.argv[1]) as f:
results = pickle.load(f)
predicted = results['predicted']
shape = (60, 100)
for true, pred in zip(tensor, predicted):
pyplot.figure()
pyplot.axis('scaled')
levels = vis.contourf(y * 0.001,
x * 0.001,
true,
shape,
12,
interpolate=True)
pyplot.colorbar()
vis.contour(y * 0.001,
x * 0.001,
pred,
shape,
levels,
color='k',
interpolate=True)
pyplot.xlabel('Horizontal coordinate y (km)')
pyplot.ylabel('Horizontal coordinate x (km)')
pyplot.show()
extent = [x.min(), x.max(), y.min(), y.max(), -topo.max(), -400]
#density_model = vfilter(900, 1200, 'density', results['mesh'])
seeds = [results['mesh'][s] for s in results['seeds']]
vis.mayavi_figure()
vis.prisms3D(seeds, extract('density', seeds), vmin=0)
#vis.prisms3D(density_model, extract('density', density_model), vmin=0)
vis.prisms3D(results['mesh'], results['mesh'].props['density'], vmin=0)
vis.add_axes3d(vis.add_outline3d(extent),
ranges=[i * 0.001 for i in extent],
fmt='%.1f',
nlabels=6)
vis.wall_bottom(extent)
vis.wall_north(extent)
vis.mlab.show()
harvester.PrismGyzModule(x, y, z, gyz, norm=1),
harvester.PrismGzzModule(x, y, z, gzz, norm=1)
]
extent = [x.min(), x.max(), y.min(), y.max(), -topo.max(), -400]
mesh = PrismMesh3D(extent, params.shape)
mesh.carvetopo(x, y, topo)
seeds = harvester.sow(mesh, params.seeds)
log.info("Making the jury")
regul = harvester.ConcentrationRegularizer(seeds, mesh, params.mu,
params.power)
jury = harvester.standard_jury(regul, thresh=params.thresh)
log.info("Running the inversion")
results, goals = harvester.harvest(seeds, mesh, datamods, jury)
for prop in results['estimate']:
mesh.addprop(prop, results['estimate'][prop])
with open("predicted.txt", 'w') as f:
pred = [x, y, z]
pred.extend([dm.predicted for dm in datamods])
numpy.savetxt(f, numpy.transpose(pred), fmt="%.4f")
with open(params.output, 'w') as f:
output = {
'mesh': mesh,
'goals': goals,
'seeds': [s for s in extract('index', seeds)]
}
pickle.dump(output, f)
log = logger.get()
logger.tofile('%s.log' % (sys.argv[1]))
log.info(logger.header())
# Get the parameters form the input file
params = __import__(sys.argv[1])
data = numpy.loadtxt('data.txt', unpack=True)
x, y, z, gxx, gxy, gxz, gyy, gyz, gzz = data
datamods = [harvester.PrismGyyModule(x, y, z, gyy, norm=1),
harvester.PrismGyzModule(x, y, z, gyz, norm=1),
harvester.PrismGzzModule(x, y, z, gzz, norm=1)]
extent = [0, 5000, 0, 5000, 0, 1500]
mesh = PrismMesh3D(extent, params.shape)
seeds = harvester.sow(mesh, params.seeds)
regul = harvester.ConcentrationRegularizer(seeds, mesh, params.mu, params.power)
jury = harvester.standard_jury(regul, thresh=params.thresh)
results, goals = harvester.harvest(seeds, mesh, datamods, jury)
for prop in results['estimate']:
mesh.addprop(prop, results['estimate'][prop])
with open(params.output, 'w') as f:
output = {'mesh':mesh, 'goals':goals,
'predicted':[dm.predicted for dm in datamods],
'seeds':[s for s in extract('index', seeds)]}
pickle.dump(output, f)
def setview(s):
s.scene.camera.position = [-2263.7301544182874, 488.95715870190594, 318.19750997028473]
s.scene.camera.focal_point = [490.77486582744939, 489.04595996051546, 562.78867616870843]
s.scene.camera.view_angle = 30.0
s.scene.camera.view_up = [0.088448540522960639, -0.0026066904949591562, -0.99607733677863675]
s.scene.camera.clipping_range = [1662.1503239591332, 4168.9269870750722]
s.scene.camera.compute_view_plane_normal()
s.scene.render()
extent = [0, 1000, 0, 1000, 0, 1000]
density_model = vfilter(1, 2000, 'density', results['mesh'])
seeds = [results['mesh'][s] for s in results['seeds']]
fmt = 'png'
f = vis.mayavi_figure(size=(900,900))
p = vis.prisms3D(model, extract('density', model), style='wireframe')
p.actor.mapper.scalar_visibility = False
p.actor.property.color = (0,0,0)
p.actor.property.line_width = 5
p = vis.prisms3D(seeds, extract('density', seeds), vmin=0, vmax=2000,
cmap='gist_yarg')
vis.add_outline3d(extent)
#a = vis.add_axes3d(vis.add_outline3d(extent), ranges=[i*0.001 for i in extent],
#fmt='%.1f', nlabels=3)
#a.axes.x_label, a.axes.y_label, a.axes.z_label = '', '', ''
#a.axes.font_factor = 2
vis.wall_north(extent)
vis.wall_bottom(extent)
setview(f)
vis.mlab.savefig('.'.join([name + '-seeds', fmt]))
from fatiando import vis
from fatiando.mesher.volume import extract
modelfile = __import__(sys.argv[1].split(".")[0])
model = modelfile.model
def setview(s):
s.scene.camera.position = [-2263.7301544182874, 488.95715870190594, 318.19750997028473]
s.scene.camera.focal_point = [490.77486582744939, 489.04595996051546, 562.78867616870843]
s.scene.camera.view_angle = 30.0
s.scene.camera.view_up = [0.088448540522960639, -0.0026066904949591562, -0.99607733677863675]
s.scene.camera.clipping_range = [1662.1503239591332, 4168.9269870750722]
s.scene.camera.compute_view_plane_normal()
s.scene.render()
extent = [0, 1000, 0, 1000, 0, 1000]
fmt = "pdf"
f = vis.mayavi_figure(size=(900, 900))
p = vis.prisms3D(model, extract("density", model), style="wireframe")
p.actor.actor.visibility = False
a = vis.add_axes3d(vis.add_outline3d(extent), ranges=[i * 0.001 for i in extent], fmt="%.1f", nlabels=3)
# a.axes.x_label, a.axes.y_label, a.axes.z_label = '', '', ''
a.axes.font_factor = 2
setview(f)
vis.mlab.savefig(".".join(["axis", fmt]))
# vis.mlab.show()
-2263.7301544182874, 488.95715870190594, 318.19750997028473
]
s.scene.camera.focal_point = [
490.77486582744939, 489.04595996051546, 562.78867616870843
]
s.scene.camera.view_angle = 30.0
s.scene.camera.view_up = [
0.088448540522960639, -0.0026066904949591562, -0.99607733677863675
]
s.scene.camera.clipping_range = [1662.1503239591332, 4168.9269870750722]
s.scene.camera.compute_view_plane_normal()
s.scene.render()
extent = [0, 1000, 0, 1000, 0, 1000]
fmt = 'pdf'
f = vis.mayavi_figure(size=(900, 900))
p = vis.prisms3D(model, extract('density', model), style='wireframe')
p.actor.actor.visibility = False
a = vis.add_axes3d(vis.add_outline3d(extent),
ranges=[i * 0.001 for i in extent],
fmt='%.1f',
nlabels=3)
#a.axes.x_label, a.axes.y_label, a.axes.z_label = '', '', ''
a.axes.font_factor = 2
setview(f)
vis.mlab.savefig('.'.join(["axis", fmt]))
#vis.mlab.show()
s.scene.camera.view_angle = 30.0
s.scene.camera.view_up = [
0.088448540522960639, -0.0026066904949591562, -0.99607733677863675
]
s.scene.camera.clipping_range = [1662.1503239591332, 4168.9269870750722]
s.scene.camera.compute_view_plane_normal()
s.scene.render()
extent = [0, 1000, 0, 1000, 0, 1000]
density_model = vfilter(1, 2000, 'density', results['mesh'])
seeds = [results['mesh'][s] for s in results['seeds']]
fmt = 'png'
f = vis.mayavi_figure(size=(900, 900))
p = vis.prisms3D(model, extract('density', model), style='wireframe')
p.actor.mapper.scalar_visibility = False
p.actor.property.color = (0, 0, 0)
p.actor.property.line_width = 5
p = vis.prisms3D(seeds,
extract('density', seeds),
vmin=0,
vmax=2000,
cmap='gist_yarg')
vis.add_outline3d(extent)
#a = vis.add_axes3d(vis.add_outline3d(extent), ranges=[i*0.001 for i in extent],
#fmt='%.1f', nlabels=3)
#a.axes.x_label, a.axes.y_label, a.axes.z_label = '', '', ''
#a.axes.font_factor = 2
vis.wall_north(extent)
vis.wall_bottom(extent)
with open(sys.argv[1]) as f:
model = pickle.load(f)
def setview(s):
s.scene.camera.position = [-2263.7301544182874, 488.95715870190594, 318.19750997028473]
s.scene.camera.focal_point = [490.77486582744939, 489.04595996051546, 562.78867616870843]
s.scene.camera.view_angle = 30.0
s.scene.camera.view_up = [0.088448540522960639, -0.0026066904949591562, -0.99607733677863675]
s.scene.camera.clipping_range = [1662.1503239591332, 4168.9269870750722]
s.scene.camera.compute_view_plane_normal()
s.scene.render()
extent = [0, 1000, 0, 1000, 0, 1000]
fmt = 'png'
f = vis.mayavi_figure(size=(900,900))
p = vis.prisms3D(model, extract('density', model), vmin=0, vmax=2000,
cmap='gist_yarg')
a = vis.add_axes3d(vis.add_outline3d(extent), ranges=[i*0.001 for i in extent],
fmt='%.1f', nlabels=3)
a.axes.x_label, a.axes.y_label, a.axes.z_label = '', '', ''
a.axes.font_factor = 2
vis.wall_north(extent)
vis.wall_bottom(extent)
setview(f)
vis.mlab.savefig('.'.join(['model', fmt]))
vis.mlab.show()
