bounds = [-5000, 5000, -5000, 5000, 0, 5000]
area = bounds[:4]
#axis = plt.figure().gca()
#plt.axis('scaled')
verts = [[-2000, -3000], [-2000, 2000], [3000, 3000], [2000, -3000]]
model = [
geometry.PolygonalPrism(
#giplt.draw_polygon(area, axis, xy2ne=True),
verts,
# Use only induced magnetization
#0, 2000, {'magnetization': 2})]
0,
2000,
{'magnetization': giutils.ang2vec(2, inc, dec)})
]
# Calculate the effect
shape = (100, 100)
xp, yp, zp = gridder.regular(area, shape, z=-500)
tf = polyprism.tf(xp, yp, zp, model, inc, dec)
# and plot it
plt.figure()
plt.axis('scaled')
plt.title("Total field anomalyproduced by prism model (nT)")
giplt.contourf(yp, xp, tf, shape, 20)
plt.colorbar()
for p in model:
giplt.polygon(p, '.-k', xy2ne=True)
giplt.set_area(area)
giplt.m2km()
plt.show()
'ymax': ymax,
'dx': dx,
'dy': dy,
'nx': nx,
'ny': ny
}
bgascgrd = xyz2Grid(x, y, bgas_contf)
grid2srf(bgascgrd)
args = dict(shape=shape, levels=20, cmap=plt.cm.RdBu_r)
fig, axes = plt.subplots(2, 2, figsize=(10, 10))
plt.sca(axes[0, 0])
plt.title("Derivative of BGA in X")
plt.axis('scaled')
giplt.contourf(x, y, bgas_dx, **args)
plt.colorbar(pad=0).set_label('mGal/m')
giplt.m2km()
plt.sca(axes[0, 1])
plt.title("Derivative of BGA in Y")
plt.axis('scaled')
giplt.contourf(x, y, bgas_dy, **args)
plt.colorbar(pad=0).set_label('mGal/m')
giplt.m2km()
plt.sca(axes[1, 0])
plt.title("Derivative of BGA in Z")
plt.axis('scaled')
giplt.contourf(x, y, bgas_dz, **args)
plt.colorbar(pad=0).set_label('mGal/m')
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 = (100, 100)
z0 = -500
x, y, z = gridder.regular(area, shape, z=z0)
tf = giutils.contaminate(prism.tf(x, y, z, model, inc, dec), 1, seed=0)
# Reduce to the pole using FFT. Since there is only induced magnetization, the
# magnetization direction (sinc and sdec) is the same as the geomagnetic field
pole = pftrans.reduce_to_pole(x, y, tf, shape, inc, dec, sinc=inc, sdec=dec)
# Calculate the true value at the pole for comparison
true = prism.tf(x, y, z, model, 90, 0, pmag=giutils.ang2vec(10, 90, 0))
fig, axes = plt.subplots(1, 3, figsize=(14, 4))
for ax in axes:
ax.set_aspect('equal')
plt.sca(axes[0])
plt.title("Original total field anomaly")
giplt.contourf(y, x, tf, shape, 30, cmap=plt.cm.RdBu_r)
plt.colorbar(pad=0).set_label('nT')
giplt.m2km()
plt.sca(axes[1])
plt.title("True value at pole")
giplt.contourf(y, x, true, shape, 30, cmap=plt.cm.RdBu_r)
plt.colorbar(pad=0).set_label('nT')
giplt.m2km()
plt.sca(axes[2])
plt.title("Reduced to the pole")
giplt.contourf(y, x, pole, shape, 30, cmap=plt.cm.RdBu_r)
plt.colorbar(pad=0).set_label('nT')
giplt.m2km()
plt.tight_layout()
plt.show()
for i in range(len(field_gra1)):
f.write('{} {} {} {}\n'.format(yp[i],xp[i],zp[i],np.array(field_gra1[i]).ravel()[0]))
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)
area = [-5000, 5000, -5000, 5000]
model = [Prism(-3000, 3000, -1000, 1000, 0, 1000, {'density': 1000})]
shape = (100, 100)
xp, yp, zp = gridder.regular(area, shape, z=-500)
data = [
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)
]
# Calculate the 3 invariants
invariants = tensor.invariants(data)
data = data + invariants
# and plot it
plt.figure()
plt.axis('scaled')
plt.suptitle("Tensor and invariants (Eotvos)")
titles = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz', 'I1', 'I2', 'I']
for i in range(len(data)):
plt.subplot(3, 3, i + 1)
plt.title(titles[i])
levels = 20
if i == 8:
levels = np.linspace(0, 1, levels)
giplt.contourf(yp, xp, data[i], shape, levels, cmap=plt.cm.RdBu_r)
plt.colorbar()
giplt.m2km()
plt.show()
# -*- coding: utf-8 -*-
"""
Created on Sun Dec 2 11:36:59 2018
使用giplt画等值线图
@author: chens
"""
from geoist import gridder
import matplotlib.pyplot as plt
from geoist.vis import giplt
area = [-20, 20, -50, 50]
x, y = gridder.scatter(area, n=100)
data = x**2 + y**2
plt.figure()
plt.axis('scaled')
giplt.contourf(y, x, data, shape=(50, 50), levels=30, interp=True)
plt.colorbar(orientation='horizontal')
plt.plot(y, x, '.k')
plt.xlabel('y (East-West)')
plt.ylabel('x (North-South)')
plt.show()
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()
x, y, z = gridder.regular(area, shape, z=z0)
gz = giutils.contaminate(prism.gz(x, y, z, model), 0.5, seed=0)
height = 1000 # How much higher to go
gzcontf = pftrans.upcontinue(x, y, gz, shape, height)
# Compute the true value at the new height for comparison
gztrue = prism.gz(x, y, z - height, model)
args = dict(shape=shape, levels=20, cmap=plt.cm.RdBu_r)
fig, axes = plt.subplots(1, 3, figsize=(12, 3.5))
axes = axes.ravel()
plt.sca(axes[0])
plt.title("Original")
plt.axis('scaled')
giplt.contourf(x, y, gz, **args)
plt.colorbar(pad=0).set_label('mGal')
giplt.m2km()
plt.sca(axes[1])
plt.title('True higher')
plt.axis('scaled')
giplt.contourf(y, x, gztrue, **args)
plt.colorbar(pad=0).set_label('mGal')
giplt.m2km()
plt.sca(axes[2])
plt.title("Continued (Fourier)")
plt.axis('scaled')
giplt.contourf(y, x, gzcontf, **args)
plt.colorbar(pad=0).set_label('mGal')
giplt.m2km()
plt.tight_layout()
# -*- coding: utf-8 -*- """ Created on Sun Dec 2 11:36:59 2018 使用giplt画投影地图 @author: chens """ # 3rd packages import matplotlib.pyplot as plt from mpl_toolkits.basemap import Basemap # local packages from geoist.vis import giplt from geoist import gridder area = [-20, 20, -50, 50] x, y = gridder.scatter(area, n=100) data = x**2 + y**2 plt.figure() bm = giplt.basemap(area, projection='robin') giplt.draw_countries(bm) giplt.draw_coastlines(bm) giplt.contourf(y, x, data, shape=(50, 50), levels=30, interp=True, basemap=bm) plt.colorbar(orientation='horizontal') plt.show()
"""
# 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()
x, y, elev = Grid2Xyz(grid1)
x, y, fga = Grid2Xyz(grid2)
x, y, bgas = Grid2Xyz(grid3)
x, y, bgar = Grid2Xyz(grid4)
shape = (grid3.getGeoDict().nx, grid3.getGeoDict().ny)
height = 1000 # How much higher to go
bgas_contf = pftrans.upcontinue(x, y, bgas, shape, height)
args = dict(shape=shape, levels=20, cmap=plt.cm.RdBu_r)
fig, axes = plt.subplots(1, 2, figsize=(10, 5))
axes = axes.ravel()
plt.sca(axes[0])
plt.title("Original")
plt.axis('scaled')
giplt.contourf(x, y, bgas, **args)
plt.colorbar(pad=0).set_label('mGal')
giplt.m2km()
plt.sca(axes[1])
plt.title('Upward continuation 1000m')
plt.axis('scaled')
giplt.contourf(x, y, bgas_contf, **args)
plt.colorbar(pad=0).set_label('mGal')
giplt.m2km()
fig.tight_layout()
fig, axes = plt.subplots(2, 2, figsize=(10, 10))
plt.sca(axes[0, 0])
plt.title("Elevation")
plt.axis('scaled')
giplt.contourf(x, y, elev, **args)
tesseroid.gxx(lons, lats, heights, model),
tesseroid.gxy(lons, lats, heights, model),
tesseroid.gxz(lons, lats, heights, model),
tesseroid.gyy(lons, lats, heights, model),
tesseroid.gyz(lons, lats, heights, model),
tesseroid.gzz(lons, lats, heights, model)
]
print("Time it took: %s" % (time.time() - start))
titles = [
'potential', 'gx', 'gy', 'gz', 'gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz'
]
bm = giplt.basemap(area, 'merc')
plt.figure()
plt.title(titles[0])
giplt.contourf(lons, lats, fields[0], shape, 40, basemap=bm)
giplt.draw_coastlines(bm)
plt.colorbar()
plt.figure()
for i, field in enumerate(fields[1:4]):
plt.subplot(1, 3, i + 1)
plt.title(titles[i + 1])
giplt.contourf(lons, lats, field, shape, 40, basemap=bm)
giplt.draw_coastlines(bm)
plt.colorbar()
plt.figure()
for i, field in enumerate(fields[4:]):
plt.subplot(2, 3, i + 1)
plt.title(titles[i + 4])
giplt.contourf(lons, lats, field, shape, 40, basemap=bm)
giplt.draw_coastlines(bm)
xp, yp, zp = gridder.regular(area, shape, z=z0)
gz = giutils.contaminate(prism.gz(xp, yp, zp, model), 0.001)
# Need to convert gz to SI units so that the result can be converted to Eotvos
gxz = giutils.si2eotvos(pftrans.derivx(xp, yp, giutils.mgal2si(gz), shape))
gyz = giutils.si2eotvos(pftrans.derivy(xp, yp, giutils.mgal2si(gz), shape))
gzz = giutils.si2eotvos(pftrans.derivz(xp, yp, giutils.mgal2si(gz), shape))
gxz_true = prism.gxz(xp, yp, zp, model)
gyz_true = prism.gyz(xp, yp, zp, model)
gzz_true = prism.gzz(xp, yp, zp, model)
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')
# gridToVTK(str(work_dir/"results"),xs,ys,zs,cellData={"recover":arr.reshape(nz,ny,nx),
# "orig":model_density})
#
field0 = prism.gz(small_model.xp, small_model.yp, small_model.zp,
small_model.mesh)
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)
kk=np.array(kernel)
kk=np.transpose(kernel) #kernel matrix for inversion, 500 cells * 400 points
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()
depths = [0, 1000, 2000, 3000, 4000]
model = []
for i in range(1, len(depths)):
#axes = plt.figure().gca()
#plt.axis('scaled')
#for p in model:
# giplt.polygon(p, '.-k', xy2ne=True)
verts = [[-5000, -3000], [-5000, 4000], [5000, 3000], [2000, -5000]]
model.append(
geometry.PolygonalPrism(
#giplt.draw_polygon(area, axes, xy2ne=True),
verts,
depths[i - 1],
depths[i],
{'density': 500}))
# Calculate the effect
shape = (100, 100)
xp, yp, zp = gridder.regular(area, shape, z=-1)
gz = polyprism.gz(xp, yp, zp, model)
# and plot it
plt.figure()
plt.axis('scaled')
plt.title("gz produced by prism model (mGal)")
giplt.contourf(yp, xp, gz, shape, 20)
plt.colorbar()
for p in model:
giplt.polygon(p, '.-k', xy2ne=True)
giplt.set_area(area)
plt.show()
# The regional field
inc, dec = 30, -15
bounds = [-5000, 5000, -5000, 5000, 0, 5000]
model = [
geometry.Prism(-4000, -3000, -4000, -3000, 0, 2000,
{'magnetization': giutils.ang2vec(1, inc, dec)}),
geometry.Prism(-1000, 1000, -1000, 1000, 0, 2000,
{'magnetization': giutils.ang2vec(1, inc, dec)}),
# This prism will have magnetization in a different direction
geometry.Prism(2000, 4000, 3000, 4000, 0, 2000,
{'magnetization': giutils.ang2vec(3, -10, 45)})
]
# Create a regular grid at 100m height
shape = (200, 200)
area = bounds[:4]
xp, yp, zp = gridder.regular(area, shape, z=-500)
# Calculate the anomaly for a given regional field
tf = prism.tf(xp, yp, zp, model, inc, dec)
# Plot
plt.figure()
plt.title("Total-field anomaly (nT)")
plt.axis('scaled')
giplt.contourf(yp, xp, tf, shape, 15)
plt.colorbar()
plt.xlabel('East y (km)')
plt.ylabel('North x (km)')
giplt.m2km()
plt.show()
0,
1000,
{'density': 500})
]
# Calculate the effect
shape = (100, 100)
xp, yp, zp = gridder.regular(area, shape, z=-500)
data = [
polyprism.gxx(xp, yp, zp, model),
polyprism.gxy(xp, yp, zp, model),
polyprism.gxz(xp, yp, zp, model),
polyprism.gyy(xp, yp, zp, model),
polyprism.gyz(xp, yp, zp, model),
polyprism.gzz(xp, yp, zp, model)
]
# and plot it
titles = ['gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
plt.figure()
plt.axis('scaled')
plt.suptitle("Gravity tensor produced by prism model (Eotvos)")
for i in range(len(data)):
plt.subplot(3, 2, i + 1)
plt.title(titles[i])
giplt.contourf(yp, xp, data[i], shape, 20)
plt.colorbar()
for p in model:
giplt.polygon(p, '.-k', xy2ne=True)
giplt.set_area(area)
giplt.m2km()
plt.show()
## 3. wave2num
if __name__ == '__main__':
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()
from geoist.inversion import geometry
from geoist.pfm import prism, pftrans, giutils
from geoist.vis import giplt
model = [geometry.Prism(-100, 100, -100, 100, 0, 2000, {'magnetization': 10})]
area = (-5000, 5000, -5000, 5000)
shape = (100, 100)
z0 = -500
x, y, z = gridder.regular(area, shape, z=z0)
inc, dec = -30, 0
tf = giutils.contaminate(prism.tf(x, y, z, model, inc, dec), 0.001,
percent=True)
# Need to convert gz to SI units so that the result is also in SI
total_grad_amp = pftrans.tga(x, y, giutils.nt2si(tf), shape)
plt.figure()
plt.subplot(1, 2, 1)
plt.title("Original total field anomaly")
plt.axis('scaled')
giplt.contourf(y, x, tf, shape, 30, cmap=plt.cm.RdBu_r)
plt.colorbar(orientation='horizontal').set_label('nT')
giplt.m2km()
plt.subplot(1, 2, 2)
plt.title("Total Gradient Amplitude")
plt.axis('scaled')
giplt.contourf(y, x, total_grad_amp, shape, 30, cmap=plt.cm.RdBu_r)
plt.colorbar(orientation='horizontal').set_label('nT/m')
giplt.m2km()
plt.show()
