def setup():
global model, x, y, z, inc, dec, struct_ind, field, xderiv, yderiv, \
zderiv, base, pos
inc, dec = -30, 50
pos = np.array([1000, 1000, 200])
model = Sphere(pos[0], pos[1], pos[2], 1,
{'magnetization': utils.ang2vec(10000, inc, dec)})
struct_ind = 3
shape = (128, 128)
x, y, z = gridder.regular((0, 3000, 0, 3000), shape, z=-1)
base = 10
field = utils.nt2si(sphere.tf(x, y, z, [model], inc, dec)) + base
xderiv = fourier.derivx(x, y, field, shape)
yderiv = fourier.derivy(x, y, field, shape)
zderiv = fourier.derivz(x, y, field, shape)
"""
from fatiando import mesher, gridder, utils
from fatiando.gravmag import prism, transform
from fatiando.vis import mpl
model = [mesher.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 = utils.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 = transform.tga(x, y, utils.nt2si(tf), shape)
mpl.figure()
mpl.subplot(1, 2, 1)
mpl.title("Original total field anomaly")
mpl.axis('scaled')
mpl.contourf(y, x, tf, shape, 30, cmap=mpl.cm.RdBu_r)
mpl.colorbar(orientation='horizontal').set_label('nT')
mpl.m2km()
mpl.subplot(1, 2, 2)
mpl.title("Total Gradient Amplitude")
mpl.axis('scaled')
mpl.contourf(y, x, total_grad_amp, shape, 30, cmap=mpl.cm.RdBu_r)
mpl.colorbar(orientation='horizontal').set_label('nT/m')
mpl.m2km()
mpl.show()
# The regional field
inc, dec = -45, 0
# Make a model
bounds = [-5000, 5000, -5000, 5000, 0, 5000]
model = [
Prism(-1500, -500, -500, 500, 1000, 2000, {'magnetization': 2})]
# Generate some data from the model
shape = (200, 200)
area = bounds[0:4]
xp, yp, zp = gridder.regular(area, shape, z=-1)
# Add a constant baselevel
baselevel = 10
# Convert from nanoTesla to Tesla because euler and derivatives require things
# in SI
tf = (utils.nt2si(prism.tf(xp, yp, zp, model, inc, dec)) + baselevel)
# Calculate the derivatives using FFT
xderiv = fourier.derivx(xp, yp, tf, shape)
yderiv = fourier.derivy(xp, yp, tf, shape)
zderiv = fourier.derivz(xp, yp, tf, shape)
mpl.figure()
titles = ['Total field', 'x derivative', 'y derivative', 'z derivative']
for i, f in enumerate([tf, xderiv, yderiv, zderiv]):
mpl.subplot(2, 2, i + 1)
mpl.title(titles[i])
mpl.axis('scaled')
mpl.contourf(yp, xp, f, shape, 50)
mpl.colorbar()
mpl.m2km()
mpl.show()
"""
from fatiando import mesher, gridder, utils
from fatiando.gravmag import prism, fourier
from fatiando.vis import mpl
model = [mesher.Prism(-100,100,-100,100,0,2000,{'magnetization':10})]
area = (-5000, 5000, -5000, 5000)
shape = (100, 100)
z0 = -500
xp, yp, zp = gridder.regular(area, shape, z=z0)
inc, dec = -30, 0
tf = utils.contaminate(prism.tf(xp, yp, zp, model, inc, dec), 0.001,
percent=True)
# Need to convert gz to SI units so that the result is also in SI
ansig = fourier.ansig(xp, yp, utils.nt2si(tf), shape)
mpl.figure()
mpl.subplot(1, 2, 1)
mpl.title("Original total field anomaly")
mpl.axis('scaled')
mpl.contourf(yp, xp, tf, shape, 30)
mpl.colorbar(orientation='horizontal')
mpl.m2km()
mpl.subplot(1, 2, 2)
mpl.title("Analytic signal")
mpl.axis('scaled')
mpl.contourf(yp, xp, ansig, shape, 30)
mpl.colorbar(orientation='horizontal')
mpl.m2km()
mpl.show()
# The regional field
inc, dec = -45, 0
# Make a model
bounds = [-5000, 5000, -5000, 5000, 0, 5000]
model = [
mesher.Prism(-1500, -500, -500, 500, 1000, 2000, {'magnetization':2})]
# Generate some data from the model
shape = (200, 200)
area = bounds[0:4]
xp, yp, zp = gridder.regular(area, shape, z=-1)
# Add a constant baselevel
baselevel = 10
# Convert from nanoTesla to Tesla because euler and derivatives require things
# in SI
tf = (utils.nt2si(gravmag.prism.tf(xp, yp, zp, model, inc, dec)) + baselevel)
# Calculate the derivatives using FFT
xderiv = gravmag.fourier.derivx(xp, yp, tf, shape)
yderiv = gravmag.fourier.derivy(xp, yp, tf, shape)
zderiv = gravmag.fourier.derivz(xp, yp, tf, shape)
mpl.figure()
titles = ['Total field', 'x derivative', 'y derivative', 'z derivative']
for i, f in enumerate([tf, xderiv, yderiv, zderiv]):
mpl.subplot(2, 2, i + 1)
mpl.title(titles[i])
mpl.axis('scaled')
mpl.contourf(yp, xp, f, shape, 50)
mpl.colorbar()
mpl.m2km()
mpl.show()
# The regional field
inc, dec = -45, 0
# Make a model
bounds = [-5000, 5000, -5000, 5000, 0, 5000]
model = [
mesher.Prism(-1500, -500, -1500, -500, 1000, 2000, {'magnetization':2}),
mesher.Prism(500, 1500, 500, 2000, 1000, 2000, {'magnetization':2})]
# Generate some data from the model
shape = (100, 100)
area = bounds[0:4]
xp, yp, zp = gridder.regular(area, shape, z=-1)
# Add a constant baselevel
baselevel = 10
# Convert from nanoTesla to Tesla because euler and derivatives require things
# in SI
tf = (utils.nt2si(gravmag.prism.tf(xp, yp, zp, model, inc, dec))
+ baselevel)
# Calculate the derivatives using FFT
xderiv = gravmag.fourier.derivx(xp, yp, tf, shape)
yderiv = gravmag.fourier.derivy(xp, yp, tf, shape)
zderiv = gravmag.fourier.derivz(xp, yp, tf, shape)
mpl.figure()
titles = ['Total field', 'x derivative', 'y derivative', 'z derivative']
for i, f in enumerate([tf, xderiv, yderiv, zderiv]):
mpl.subplot(2, 2, i + 1)
mpl.title(titles[i])
mpl.axis('scaled')
mpl.contourf(yp, xp, f, shape, 50)
mpl.colorbar()
mpl.m2km()
log.info(logger.header())
# Make a model
bounds = [-5000, 5000, -5000, 5000, 0, 5000]
model = [
mesher.Prism(-1500, -500, -1500, -500, 1000, 2000, {'magnetization':2}),
mesher.Prism(500, 1500, 500, 2000, 1000, 2000, {'magnetization':2})]
# Generate some data from the model
shape = (100, 100)
area = bounds[0:4]
xp, yp, zp = gridder.regular(area, shape, z=-1)
# Add a constant baselevel
baselevel = 10
# Convert from nanoTesla to Tesla because euler and derivatives require things
# in SI
tf = (utils.nt2si(gravmag.prism.tf(xp, yp, zp, model, inc=-45, dec=0))
+ baselevel)
# Calculate the derivatives using FFT
xderiv = gravmag.fourier.derivx(xp, yp, tf, shape)
yderiv = gravmag.fourier.derivy(xp, yp, tf, shape)
zderiv = gravmag.fourier.derivz(xp, yp, tf, shape)
mpl.figure()
titles = ['Total field', 'x derivative', 'y derivative', 'z derivative']
for i, f in enumerate([tf, xderiv, yderiv, zderiv]):
mpl.subplot(2, 2, i + 1)
mpl.title(titles[i])
mpl.axis('scaled')
mpl.contourf(yp, xp, f, shape, 50)
mpl.colorbar()
mpl.m2km()
"""
from fatiando import mesher, gridder, utils, gravmag
from fatiando.vis import mpl
prisms = [mesher.Prism(-100, 100, -100, 100, 0, 2000, {'magnetization': 10})]
area = (-5000, 5000, -5000, 5000)
shape = (100, 100)
z0 = -500
xp, yp, zp = gridder.regular(area, shape, z=z0)
inc, dec = -30, 0
tf = utils.contaminate(gravmag.prism.tf(xp, yp, zp, prisms, inc, dec),
0.001,
percent=True)
# Need to convert gz to SI units so that the result is also in SI
ansig = gravmag.fourier.ansig(xp, yp, utils.nt2si(tf), shape)
mpl.figure()
mpl.subplot(1, 2, 1)
mpl.title("Original total field anomaly")
mpl.axis('scaled')
mpl.contourf(yp, xp, tf, shape, 30)
mpl.colorbar(orientation='horizontal')
mpl.m2km()
mpl.subplot(1, 2, 2)
mpl.title("Analytic signal")
mpl.axis('scaled')
mpl.contourf(yp, xp, ansig, shape, 30)
mpl.colorbar(orientation='horizontal')
mpl.m2km()
mpl.show()
GravMag: Calculate the analytic signal of a total field anomaly using FFT
"""
from fatiando import mesher, gridder, utils, gravmag
from fatiando.vis import mpl
prisms = [mesher.Prism(-100,100,-100,100,0,2000,{'magnetization':10})]
area = (-5000, 5000, -5000, 5000)
shape = (100, 100)
z0 = -500
xp, yp, zp = gridder.regular(area, shape, z=z0)
inc, dec = -30, 0
tf = utils.contaminate(gravmag.prism.tf(xp, yp, zp, prisms, inc, dec), 0.001,
percent=True)
# Need to convert gz to SI units so that the result is also in SI
ansig = gravmag.fourier.ansig(xp, yp, utils.nt2si(tf), shape)
mpl.figure()
mpl.subplot(1, 2, 1)
mpl.title("Original total field anomaly")
mpl.axis('scaled')
mpl.contourf(yp, xp, tf, shape, 30)
mpl.colorbar(orientation='horizontal')
mpl.m2km()
mpl.subplot(1, 2, 2)
mpl.title("Analytic signal")
mpl.axis('scaled')
mpl.contourf(yp, xp, ansig, shape, 30)
mpl.colorbar(orientation='horizontal')
mpl.m2km()
mpl.show()
from fatiando.vis import mpl, myv
# The regional field
inc, dec = 30, -15
# Make a model
bounds = [-5000, 5000, -5000, 5000, 0, 5000]
model = [mesher.Prism(-500, 500, -500, 500, 1000, 2000, {'magnetization': 2})]
# Generate some data from the model
shape = (100, 100)
area = bounds[0:4]
xp, yp, zp = gridder.regular(area, shape, z=-1)
# Add a constant baselevel
baselevel = 10
# Convert from nanoTesla to Tesla because euler and derivatives require things
# in SI
tf = (utils.contaminate(utils.nt2si(
gravmag.prism.tf(xp, yp, zp, model, inc, dec)),
0.005,
percent=True) + baselevel)
# Calculate the derivatives using FFT
xderiv = gravmag.fourier.derivx(xp, yp, tf, shape)
yderiv = gravmag.fourier.derivy(xp, yp, tf, shape)
zderiv = gravmag.fourier.derivz(xp, yp, tf, shape)
mpl.figure()
titles = ['Total field', 'x derivative', 'y derivative', 'z derivative']
for i, f in enumerate([tf, xderiv, yderiv, zderiv]):
mpl.subplot(2, 2, i + 1)
mpl.title(titles[i])
mpl.axis('scaled')
mpl.contourf(yp, xp, f, shape, 50)
mpl.colorbar()
GravMag: Calculate the analytic signal of a total field anomaly using FFT
"""
from fatiando import mesher, gridder, utils
from fatiando.gravmag import prism, transform
from fatiando.vis import mpl
model = [mesher.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 = utils.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 = transform.tga(x, y, utils.nt2si(tf), shape)
mpl.figure()
mpl.subplot(1, 2, 1)
mpl.title("Original total field anomaly")
mpl.axis('scaled')
mpl.contourf(y, x, tf, shape, 30, cmap=mpl.cm.RdBu_r)
mpl.colorbar(orientation='horizontal').set_label('nT')
mpl.m2km()
mpl.subplot(1, 2, 2)
mpl.title("Total Gradient Amplitude")
mpl.axis('scaled')
mpl.contourf(y, x, total_grad_amp, shape, 30, cmap=mpl.cm.RdBu_r)
mpl.colorbar(orientation='horizontal').set_label('nT/m')
mpl.m2km()
mpl.show()
