with open(magoutfile1, 'w') as f:
f.write('! model 2 magtotal-field magnetic anomaly (nT) with 5% noise\n')
f.write('{}\n'.format(len(field_mag1)))
for i in range(len(field_mag1)):
f.write('{} {} {} {}\n'.format(yp[i],xp[i],zp[i],np.array(field_mag1[i]).ravel()[0]))
#画图
plt.figure(figsize=(16, 16))
plt.subplot(2, 2, 1)
plt.axis('scaled')
plt.title('model 2 gravity anomlay (mGal)')
levels = giplt.contourf(yp , xp , field_gra, nshape, 15)
cb = plt.colorbar(orientation='horizontal')
giplt.contour(yp, xp, field_gra, nshape,
levels, clabel=False, linewidth=0.1)
plt.subplot(2, 2, 2)
plt.axis('scaled')
plt.title('model 2 magtotal-field magnetic anomaly (nT)')
levels = giplt.contourf(yp , xp , field_mag, nshape, 15)
cb = plt.colorbar(orientation='horizontal')
giplt.contour(yp, xp, field_mag, nshape,
levels, clabel=False, linewidth=0.1)
plt.subplot(2, 2, 3)
plt.axis('scaled')
plt.title('model 2 gravity anomlay (mGal) with 5% noise')
levels = giplt.contourf(yp , xp , field_gra1, nshape, 15)
cb = plt.colorbar(orientation='horizontal')
giplt.contour(yp, xp, field_gra1, nshape,
levels, clabel=False, linewidth=0.1)
print('hello freqinv!')
shape = (156, 156)
gu, gulist, xp, yp, zp = gen_grav(shape)
titles = [
'potential', 'gx', 'gy', 'gz', 'gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz'
]
plt.figure(figsize=(8, 8))
plt.axis('scaled')
plt.title(titles[0])
levels = giplt.contourf(yp * 0.001, xp * 0.001, gu, shape, 15)
cb = plt.colorbar()
giplt.contour(yp * 0.001,
xp * 0.001,
gu,
shape,
levels,
clabel=False,
linewidth=0.1)
plt.show()
plt.figure(figsize=(8, 8))
plt.axis('scaled')
plt.title('gz by freq')
levels = giplt.contourf(yp * 0.001, xp * 0.001, gulist, shape, 15)
cb = plt.colorbar()
giplt.contour(yp * 0.001,
xp * 0.001,
gulist,
shape,
levels,
field0=np.mat(kk)*np.transpose(np.mat(density.ravel()))
field = giutils.contaminate(np.array(field0).ravel(), 0.05, percent = True)
#零均值
print(field.mean())
#field = field + 300. # field.mean()
#画图
plt.figure(figsize=(16, 8))
plt.subplot(1, 2, 1)
plt.title('gravity anomlay')
plt.axis('scaled')
levels = giplt.contourf(yp * 0.001, xp * 0.001, field0, nshape, 15)
cb = plt.colorbar(orientation='horizontal')
giplt.contour(yp * 0.001, xp * 0.001, field0, nshape,
levels, clabel=False, linewidth=0.1)
plt.subplot(1, 2, 2)
plt.title('gravity anomlay with 5% noise')
plt.axis('scaled')
levels = giplt.contourf(yp * 0.001, xp * 0.001, field, nshape, 15)
cb = plt.colorbar(orientation='horizontal')
giplt.contour(yp * 0.001, xp * 0.001, field, nshape,
levels, clabel=False, linewidth=0.1)
plt.show()
print('starting inversion')
smop = SmoothOperator()
nz = shape[0]
ny = shape[1]
nx = shape[2]
prism.gx(xp, yp, zp, model),
prism.gy(xp, yp, zp, model),
prism.gz(xp, yp, zp, model),
prism.gxx(xp, yp, zp, model),
prism.gxy(xp, yp, zp, model),
prism.gxz(xp, yp, zp, model),
prism.gyy(xp, yp, zp, model),
prism.gyz(xp, yp, zp, model),
prism.gzz(xp, yp, zp, model)
]
titles = [
'potential', 'gx', 'gy', 'gz', 'gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz'
]
plt.figure(figsize=(8, 9))
plt.subplots_adjust(left=0.03, right=0.95, bottom=0.05, top=0.92, hspace=0.3)
plt.suptitle("Potential fields produced by a 3 prism model")
for i, field in enumerate(fields):
plt.subplot(4, 3, i + 3)
plt.axis('scaled')
plt.title(titles[i])
levels = giplt.contourf(yp * 0.001, xp * 0.001, field, shape, 15)
cb = plt.colorbar()
giplt.contour(yp * 0.001,
xp * 0.001,
field,
shape,
levels,
clabel=False,
linewidth=0.1)
plt.show()
plt.figure()
plt.title("Original gravity anomaly")
plt.axis('scaled')
giplt.contourf(xp, yp, gz, shape, 15)
plt.colorbar(shrink=0.7)
giplt.m2km()
plt.figure(figsize=(14, 10))
plt.subplots_adjust(top=0.95, left=0.05, right=0.95)
plt.subplot(2, 3, 1)
plt.title("x deriv (contour) + true (color map)")
plt.axis('scaled')
levels = giplt.contourf(yp, xp, gxz_true, shape, 12)
plt.colorbar(shrink=0.7)
giplt.contour(yp, xp, gxz, shape, 12, color='k')
giplt.m2km()
plt.subplot(2, 3, 2)
plt.title("y deriv (contour) + true (color map)")
plt.axis('scaled')
levels = giplt.contourf(yp, xp, gyz_true, shape, 12)
plt.colorbar(shrink=0.7)
giplt.contour(yp, xp, gyz, shape, 12, color='k')
giplt.m2km()
plt.subplot(2, 3, 3)
plt.title("z deriv (contour) + true (color map)")
plt.axis('scaled')
levels = giplt.contourf(yp, xp, gzz_true, shape, 8)
plt.colorbar(shrink=0.7)
giplt.contour(yp, xp, gzz, shape, levels, color='k')
giplt.m2km()
meshfile = r"d:\msh.txt" #StringIO()
densfile = r"d:\den.txt" #StringIO()
mesh = PrismMesh((0, 10, 0, 20, 0, 5), (5, 2, 2))
mesh.addprop('density', 1000.0 * np.random.rand(20))
mesh.dump(meshfile, densfile, 'density')
#print(meshfile.getvalue().strip())
#print(densfile.getvalue().strip())
model = []
for i, layer in enumerate(mesh.layers()):
for j, p in enumerate(layer):
#print(i,j, p)
x1 = mesh.get_layer(i)[j].x1
x2 = mesh.get_layer(i)[j].x2
y1 = mesh.get_layer(i)[j].y1
y2 = mesh.get_layer(i)[j].y2
z1 = mesh.get_layer(i)[j].z1
z2 = mesh.get_layer(i)[j].z2
den = mesh.get_layer(i)[j].props
model.append(geometry.Prism(x1, x2, y1, y2, z1, z2, den))
#model.append(geometry.Prism(x1, x2, y1, y2, z1, z2, {'density': 1000}))
xp, yp, zp = gridder.regular((-5, 15, -5, 25), (20, 20), z=-1)
field = prism.gz(xp, yp, zp, model)
plt.figure(figsize=(8, 9))
plt.axis('scaled')
plt.title('forward gravity anomaly')
levels = giplt.contourf(yp, xp, field, (20, 20), 15)
cb = plt.colorbar()
giplt.contour(yp, xp, field, (20, 20), levels, clabel=False, linewidth=0.1)
plt.show()
shape = (nobsyx[0], nobsyx[1])
# plot field
fig = plt.figure(figsize=(16, 8))
# original field
axes = fig.subplots(2, 2)
# plt.axis('scaled')
ca = axes[0][0]
plt.sca(ca)
levels = giplt.contourf(small_model.yp * 0.001, small_model.xp * 0.001,
field0, shape, 15)
cb = plt.colorbar()
giplt.contour(small_model.yp * 0.001,
small_model.xp * 0.001,
field0,
shape,
levels,
clabel=False,
linewidth=0.1)
ca.set_title('gravity anomlay')
# field from frequency
ca = axes[0][1]
plt.sca(ca)
# levels = giplt.contourf(small_model.yp * 0.001, small_model.xp * 0.001,
# small_model.obs_field, shape, 15)
levels = giplt.contourf(small_model.yp * 0.001, small_model.xp * 0.001,
small_model.obs_field, shape, 15)
cb = plt.colorbar()
giplt.contour(small_model.yp * 0.001,
small_model.xp * 0.001,
"""
# 3rd imports
import matplotlib.pyplot as plt
# local imports
from geoist import gridder
from geoist.inversion import geometry
from geoist.pfm import prism, giutils
from geoist.vis import giplt
model = [geometry.Prism(-1000, 1000, -1000, 1000, 0, 2000, {'density': 1000})]
shape = (100, 100)
xp, yp, zp = gridder.regular((-5000, 5000, -5000, 5000), shape, z=-200)
components = [prism.gxx, prism.gxy, prism.gxz,
prism.gyy, prism.gyz, prism.gzz]
print("Calculate the tensor components and contaminate with 5 Eotvos noise")
ftg = [giutils.contaminate(comp(xp, yp, zp, model), 5.0) for comp in components]
print("Plotting...")
plt.figure(figsize=(14, 6))
plt.suptitle("Contaminated FTG data")
names = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
for i, data in enumerate(ftg):
plt.subplot(2, 3, i + 1)
plt.title(names[i])
plt.axis('scaled')
levels = giplt.contourf(xp * 0.001, yp * 0.001, data, (100, 100), 12)
plt.colorbar()
giplt.contour(xp * 0.001, yp * 0.001, data, shape, levels, clabel=False)
plt.show()
solver = DipoleMagDir(x, y, z, tf, inc, dec, centers).fit()
# Print the estimated and true dipole monents, inclinations and declinations
print('Estimated magnetization (intensity, inclination, declination)')
for e in solver.estimate_:
print(e)
# Plot the fit and the normalized histogram of the residuals
plt.figure(figsize=(14, 5))
plt.subplot(1, 2, 1)
plt.title("Total Field Anomaly (nT)", fontsize=14)
plt.axis('scaled')
nlevels = giplt.contour(y,
x,
tf, (50, 50),
15,
interp=True,
color='r',
label='Observed',
linewidth=2.0)
giplt.contour(y,
x,
solver.predicted(), (50, 50),
nlevels,
interp=True,
color='b',
label='Predicted',
style='dashed',
linewidth=2.0)
plt.legend(loc='upper left', shadow=True, prop={'size': 13})
plt.xlabel('East y (m)', fontsize=14)
plt.ylabel('North x (m)', fontsize=14)