def sensitivity_mag(x, y, z, xs, ys, zs, sinc, sdec, inc, dec):
'''
Calculate the sensitivity matrix
input
return
'''
N = x.size # number of data
M = xs.size # number of parameters
#pM = p[:M] # magnetic moment of eqsources
#inc,dec = p[M],p[M+1] # magnetization direction of eqsources
A = np.empty((N, M)) # sensitivity matrix
F0x, F0y, F0z = ang2vec(1., sinc, sdec) # main field
mx, my, mz = ang2vec(
1., inc, dec) # magnetization direction in Cartesian coordinates
for i in range(N):
phi_xx = kernelxx(x[i], y[i], z[i], xs, ys, zs)
phi_yy = kernelyy(x[i], y[i], z[i], xs, ys, zs)
phi_xy = kernelxy(x[i], y[i], z[i], xs, ys, zs)
phi_xz = kernelxz(x[i], y[i], z[i], xs, ys, zs)
phi_yz = kernelyz(x[i], y[i], z[i], xs, ys, zs)
phi_zz = -phi_xx - phi_yy
gi = (F0x*mx - F0z*mz)*phi_xx + (F0y*my - F0z*mz)*phi_yy + (F0x*my + F0y*mx)*phi_xy +\
(F0x*mz + F0z*mx)*phi_xz + (F0y*mz + F0z*my)*phi_yz
A[i, :] = gi
A *= CM * T2NT
return A
def test_ignore_none_and_missing_properties():
'gravmag.prism ignores None and prisms without the required property'
inc, dec = 50, -30
model = [None,
Prism(-6000, -2000, 2000, 4000, 0, 3000,
{'density': 1000,
'magnetization': utils.ang2vec(10, inc, dec)}),
Prism(2000, 6000, 2000, 4000, 0, 1000,
{'magnetization': utils.ang2vec(15, inc, dec)}),
None,
Prism(-6000, -2000, -4000, -2000, 500, 2000,
{'density': -1000})]
area = [-10000, 10000, -5000, 5000]
x, y, z = gridder.regular(area, (101, 51), z=-1)
for mod in [prism, _prism_numpy]:
# Test gravity functions
funcs = ['potential', 'gx', 'gy', 'gz',
'gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
for f in funcs:
combined = getattr(mod, f)(x, y, z, model)
separate = getattr(mod, f)(x, y, z, [model[1], model[4]])
precision = 10
assert_almost(separate, combined, precision, 'Field = %s' % (f))
# Test magnetic functions
funcs = ['tf', 'bx', 'by', 'bz']
for f in funcs:
mag_only = [model[1], model[2]]
if f == 'tf':
combined = getattr(mod, f)(x, y, z, model, inc, dec)
separate = getattr(mod, f)(x, y, z, mag_only, inc, dec)
else:
combined = getattr(mod, f)(x, y, z, model)
separate = getattr(mod, f)(x, y, z, mag_only)
precision = 10
assert_almost(separate, combined, precision, 'Field = %s' % (f))
def test_pel_polereduce():
"PELTotalField can reduce data to the pole"
# Use remanent magnetization
sinc, sdec = -70, 30
model = [Prism(-100, 100, -500, 500, 0, 100,
{'magnetization': utils.ang2vec(5, sinc, sdec)})]
inc, dec = -60, -15
shape = (40, 40)
area = [-2000, 2000, -2000, 2000]
x, y, z = gridder.regular(area, shape, z=-100)
data = prism.tf(x, y, z, model, inc, dec)
true = prism.tf(x, y, z, model, -90, 0, pmag=utils.ang2vec(5, -90, 0))
layer = PointGrid(area, 100, shape)
windows = (20, 20)
degree = 3
pel = PELTotalField(x, y, z, data, inc, dec, layer, windows, degree,
sinc, sdec)
eql = pel + 1e-25*PELSmoothness(layer, windows, degree)
eql.fit()
assert_array_almost_equal(eql[0].predicted(), data, decimal=1)
layer.addprop('magnetization',
utils.ang2vec(eql.estimate_, inc=-90, dec=0))
calc = sphere.tf(x, y, z, layer, inc=-90, dec=0)
assert_allclose(calc, true, atol=10, rtol=0.05)
def test_eqlayer_polereduce():
"EQLTotalField can reduce data to the pole"
# Use remanent magnetization
sinc, sdec = -70, 30
model = [Prism(-100, 100, -500, 500, 0, 100,
{'magnetization': utils.ang2vec(5, sinc, sdec)})]
inc, dec = -60, -15
shape = (50, 50)
area = [-2000, 2000, -2000, 2000]
x, y, z = gridder.regular(area, shape, z=-100)
data = prism.tf(x, y, z, model, inc, dec)
true = prism.tf(x, y, z, model, -90, 0, pmag=utils.ang2vec(5, -90, 0))
layer = PointGrid(area, 200, shape)
eql = (EQLTotalField(x, y, z, data, inc, dec, layer, sinc, sdec)
+ 1e-24*Damping(layer.size))
eql.fit()
assert_allclose(eql[0].predicted(), data, rtol=0.01)
layer.addprop('magnetization',
utils.ang2vec(eql.estimate_, inc=-90, dec=0))
calc = sphere.tf(x, y, z, layer, inc=-90, dec=0)
assert_allclose(calc, true, atol=10, rtol=0.05)
def setup():
global model, xp, yp, zp, inc, dec
inc, dec = -30, 50
reg_field = np.array(utils.dircos(inc, dec))
model = [
Sphere(500, 0, 1000, 1000,
{'density': -1., 'magnetization': utils.ang2vec(-2, inc, dec)}),
Sphere(-1000, 0, 700, 700,
{'density': 2., 'magnetization': utils.ang2vec(5, 25, -10)})]
xp, yp, zp = gridder.regular([-2000, 2000, -2000, 2000], (50, 50), z=-1)
def setup():
global model, xp, yp, zp, inc, dec
inc, dec = -30, 50
reg_field = np.array(utils.dircos(inc, dec))
model = [
Sphere(500, 0, 1000, 1000,
{'density': -1., 'magnetization': utils.ang2vec(-2, inc, dec)}),
Sphere(-1000, 0, 700, 700,
{'density': 2., 'magnetization': utils.ang2vec(5, 25, -10)})]
xp, yp, zp = gridder.regular([-2000, 2000, -2000, 2000], (50, 50), z=-1)
def setup():
global model, xp, yp, zp, inc, dec
inc, dec = -30, 50
reg_field = np.array(utils.dircos(inc, dec))
model = [
Prism(100, 300, -100, 100, 0, 400,
{'density':-1., 'magnetization':utils.ang2vec(-2, inc, dec)}),
Prism(-300, -100, -100, 100, 0, 200,
{'density':2., 'magnetization':utils.ang2vec(5, 25, -10)})]
tmp = np.linspace(-500, 500, 50)
xp, yp = [i.ravel() for i in np.meshgrid(tmp, tmp)]
zp = -1*np.ones_like(xp)
def test_upcontinue():
"gravmag.transform upward continuation matches analytical solution"
model = [
Prism(-1000, 1000, -500, 500, 0, 1000, {
'density': 1000,
'magnetization': utils.ang2vec(5, 20, -30)
})
]
shape = (100, 100)
inc, dec = -10, 15
x, y, z = gridder.regular([-5000, 5000, -5000, 5000], shape, z=-500)
dz = 10
fields = 'potential gx gy gz gxx gxy gxz gyy gyz gzz'.split()
accuracy = [0.002, 0.2, 0.2, 0.3, 2, 2, 4, 4, 4, 6]
for f, atol in zip(fields, accuracy):
func = getattr(prism, f)
data = func(x, y, z, model)
analytical = func(x, y, z + dz, model)
up = transform.upcontinue(x, y, data, shape, dz)
diff = np.abs(up - analytical)
check = diff <= atol
assert np.all(check), \
'Failed for {} (mismatch {:.2f}%)'.format(
f, 100*(check.size - check.sum())/check.size)
data = prism.tf(x, y, z, model, inc, dec)
analytical = prism.tf(x, y, z + dz, model, inc, dec)
up = transform.upcontinue(x, y, data, shape, dz)
diff = np.abs(up - analytical)
check = diff <= 15
assert np.all(check), \
'Failed for tf (mismatch {:.2f}%)'.format(
100*(check.size - check.sum())/check.size)
def test_totalfield_prism():
'''
This test compare the results obtained by both function that calculates the total field anomaly due to a rectangular prism. The model has the same dimensions, magnetization intensity and also the same pair for inclination and declination. We use the function from Fatiando a Terra in order to compare with our function.
'''
incf = 30.
decf = 30.
incs = 60.
decs = 45.
magnetization = 3.
# Modelo para o Fatiando
xp, yp, zp = gridder.regular((-1000, 1000, -1000, 1000), (200, 200),
z=-345.)
model = [
mesher.Prism(
-200., 200., -250., 180., 120., 1000.,
{'magnetization': utils.ang2vec(magnetization, incs, decs)})
]
tf = prism.tf(xp, yp, zp, model, incf, decf)
tf = tf.reshape(200, 200)
# Modelo para minha funcao
x, y = numpy.meshgrid(numpy.linspace(-1000., 1000., 200),
numpy.linspace(-1000., 1000., 200))
z = -345. * numpy.ones((200, 200))
mymodel = [-200., 200., -250., 180., 120., 1000., magnetization]
mytf = prism_tf(y, x, z, mymodel, incf, decf, incs, decs)
assert_almost_equal(tf, mytf, decimal=5)
def sensitivity_bx(x, y, z, xs, ys, zs, inc, dec):
'''
Calculate the sensitivity matrix
input
return
'''
N = x.size # number of data
M = xs.size # number of parameters
A = np.empty((N, M)) # sensitivity matrix
mx, my, mz = ang2vec(
1., inc, dec) # magnetization direction in Cartesian coordinates
for i in range(N):
phi_xx = kernelxx(x[i], y[i], z[i], xs, ys, zs)
phi_xy = kernelxy(x[i], y[i], z[i], xs, ys, zs)
phi_xz = kernelxz(x[i], y[i], z[i], xs, ys, zs)
gi = phi_xx * mx + phi_xy * my + phi_xz * mz
A[i, :] = gi
A *= CM * T2NT
return A
def by_layer(x, y, z, xs, ys, zs, p, inc, dec):
'''
Calculate the by produced by a layer
input
x,y,z : float - Cartesian coordinates (in m) of the i-th observation point
xs,ys,zs : numpy arrays - Cartesian coordinates of equivalent sources
p: numpy array - Vector composed by magnetic moment and magnetization direction of the
equivalent sources
return
by : numpy array - bz of the equivalent layer
'''
N = x.size
by = np.empty(N, dtype=float)
mx, my, mz = ang2vec(1., inc, dec)
for i in range(N):
phi_xy = kernelxy(x[i], y[i], z[i], xs, ys, zs)
phi_yy = kernelyy(x[i], y[i], z[i], xs, ys, zs)
phi_yz = kernelyz(x[i], y[i], z[i], xs, ys, zs)
gi = phi_xy * mx + phi_yy * my + phi_yz * mz
by[i] = np.dot(p.T, gi)
by *= CM * T2NT
return by
def test_pole_reduce():
"gravmag.transform pole reduction matches analytical solution"
# Use remanent magnetization
sinc, sdec = -70, 30
model = [Prism(-100, 100, -500, 500, 0, 100,
{'density': 1000,
'magnetization': utils.ang2vec(5, sinc, sdec)})]
# Use low latitudes to make sure that there are no problems with FFT
# instability.
inc, dec = -60, -15
shape = (50, 50)
x, y, z = gridder.regular([-2000, 2000, -2000, 2000], shape, z=-100)
data = prism.tf(x, y, z, model, inc, dec)
pole = transform.reduce_to_pole(x, y, data, shape, inc, dec, sinc, sdec)
pole_true = prism.tf(x, y, z, model, -90, 0, pmag=utils.ang2vec(5, -90, 0))
npt.assert_allclose(pole, pole_true, atol=10, rtol=0.01)
def test_upcontinue():
"gravmag.transform upward continuation matches analytical solution"
model = [Prism(-1000, 1000, -500, 500, 0, 1000,
{'density': 1000,
'magnetization': utils.ang2vec(5, 20, -30)})]
shape = (100, 100)
inc, dec = -10, 15
x, y, z = gridder.regular([-5000, 5000, -5000, 5000], shape, z=-500)
dz = 10
fields = 'potential gx gy gz gxx gxy gxz gyy gyz gzz'.split()
accuracy = [0.002, 0.2, 0.2, 0.3, 2, 2, 4, 4, 4, 6]
for f, atol in zip(fields, accuracy):
func = getattr(prism, f)
data = func(x, y, z, model)
analytical = func(x, y, z + dz, model)
up = transform.upcontinue(x, y, data, shape, dz)
diff = np.abs(up - analytical)
check = diff <= atol
assert np.all(check), \
'Failed for {} (mismatch {:.2f}%)'.format(
f, 100*(check.size - check.sum())/check.size)
data = prism.tf(x, y, z, model, inc, dec)
analytical = prism.tf(x, y, z + dz, model, inc, dec)
up = transform.upcontinue(x, y, data, shape, dz)
diff = np.abs(up - analytical)
check = diff <= 15
assert np.all(check), \
'Failed for tf (mismatch {:.2f}%)'.format(
100*(check.size - check.sum())/check.size)
def sensitivity_dir(x, y, z, xs, ys, zs, sinc, sdec, p, inc, dec):
'''
Calculate the sensitivity matrix
input
return
'''
N = x.size # number of data
M = xs.size # number of parameters
F0x, F0y, F0z = ang2vec(1., sinc, sdec) # main field
mx, my, mz = ang2vec(
1., inc, dec) # magnetization direction in Cartesian coordinates
dmx_I, dmy_I, dmz_I = derivative_inclination(1., inc, dec)
dmx_D, dmy_D, dmz_D = derivative_declination(1., inc, dec)
dgi_I = np.empty(N, dtype=float)
for i in range(N):
phi_xx = kernelxx(x[i], y[i], z[i], xs, ys, zs)
phi_yy = kernelyy(x[i], y[i], z[i], xs, ys, zs)
phi_xy = kernelxy(x[i], y[i], z[i], xs, ys, zs)
phi_xz = kernelxz(x[i], y[i], z[i], xs, ys, zs)
phi_yz = kernelyz(x[i], y[i], z[i], xs, ys, zs)
phi_zz = -phi_xx - phi_yy
gi = (F0x*dmx_I - F0z*dmz_I)*phi_xx + (F0y*dmy_I - F0z*dmz_I)*phi_yy + (F0x*dmy_I + F0y*dmx_I)*phi_xy +\
(F0x*dmz_I + F0z*dmx_I)*phi_xz + (F0y*dmz_I + F0z*dmy_I)*phi_yz
dgi_I[i] = np.dot(p.T, gi)
dgi_D = np.empty(N, dtype=float)
for i in range(N):
phi_xx = kernelxx(x[i], y[i], z[i], xs, ys, zs)
phi_yy = kernelyy(x[i], y[i], z[i], xs, ys, zs)
phi_xy = kernelxy(x[i], y[i], z[i], xs, ys, zs)
phi_xz = kernelxz(x[i], y[i], z[i], xs, ys, zs)
phi_yz = kernelyz(x[i], y[i], z[i], xs, ys, zs)
phi_zz = -phi_xx - phi_yy
gi = (F0x*dmx_D - F0z*dmz_D)*phi_xx + (F0y*dmy_D - F0z*dmz_D)*phi_yy + (F0x*dmy_D + F0y*dmx_D)*phi_xy +\
(F0x*dmz_D + F0z*dmx_D)*phi_xz + (F0y*dmz_D + F0z*dmy_D)*phi_yz
dgi_D[i] = np.dot(p.T, gi)
A = np.column_stack([dgi_I, dgi_D])
A *= CM * T2NT
return A
def setup():
global model, xp, yp, zp, inc, dec, prismmodel
inc, dec = -30, 50
props1 = {'density': 2., 'magnetization': utils.ang2vec(-20, 25, -10)}
props2 = {'density': -3., 'magnetization': utils.ang2vec(10, inc, dec)}
model = [
PolygonalPrism([[100, -100], [100, 100], [-100, 100], [-100, -100]],
100, 300, props1),
PolygonalPrism([[400, -100], [600, -100], [600, 100], [400, 100]], 100,
300, props2)
]
prismmodel = [
Prism(-100, 100, -100, 100, 100, 300, props1),
Prism(400, 600, -100, 100, 100, 300, props2)
]
tmp = np.linspace(-500, 1000, 50)
xp, yp = [i.ravel() for i in np.meshgrid(tmp, tmp)]
zp = -1 * np.ones_like(xp)
def test_pole_reduce():
"gravmag.transform pole reduction matches analytical solution"
# Use remanent magnetization
sinc, sdec = -70, 30
model = [
Prism(-100, 100, -500, 500, 0, 100, {
'density': 1000,
'magnetization': utils.ang2vec(5, sinc, sdec)
})
]
# Use low latitudes to make sure that there are no problems with FFT
# instability.
inc, dec = -60, -15
shape = (50, 50)
x, y, z = gridder.regular([-2000, 2000, -2000, 2000], shape, z=-100)
data = prism.tf(x, y, z, model, inc, dec)
pole = transform.reduce_to_pole(x, y, data, shape, inc, dec, sinc, sdec)
pole_true = prism.tf(x, y, z, model, -90, 0, pmag=utils.ang2vec(5, -90, 0))
npt.assert_allclose(pole, pole_true, atol=10, rtol=0.01)
def setup():
global model, xp, yp, zp, inc, dec, prismmodel
inc, dec = -30, 50
props1 = {'density':2., 'magnetization':utils.ang2vec(-20, 25, -10)}
props2 = {'density':-3., 'magnetization':utils.ang2vec(10, inc, dec)}
model = [PolygonalPrism([
[100, -100],
[100, 100],
[-100, 100],
[-100, -100]], 100, 300, props1),
PolygonalPrism([
[400, -100],
[600, -100],
[600, 100],
[400, 100]], 100, 300, props2)]
prismmodel = [Prism(-100, 100, -100, 100, 100, 300, props1),
Prism(400, 600, -100, 100, 100, 300, props2)]
tmp = np.linspace(-500, 1000, 50)
xp, yp = [i.ravel() for i in np.meshgrid(tmp, tmp)]
zp = -1*np.ones_like(xp)
def anomag_model(coords,radii=1.5e3,inc=50, dec=-30):
model =[]
for coord, radius in zip(coords, radii):
model_tmp = mesher.Sphere(x=coord[0], y=coord[1], z=coord[2], radius=radius,
props={'magnetization': utils.ang2vec(1, inc=50, dec=-30)})
model.append(model_tmp)
return model
def sample(Lx,Ly,Lz,P,m=None,inc=None,dec=None):
'''
Define the interpretation model as a 1D array of rectangular prisms
along the x-axis.
input
Lx: float - side length of all prisms along x (m).
Ly: float - side length of all prisms along x (m).
Lz: float - side length of all prisms along x (m).
P: int - number of prisms
m: list - magnetization intensity (A/m) of each prism.
inc: list - magnetization inclination (graus) of each prism.
dec: list - magnetization declination (graus) of each prism.
output
model: list of geometrical elements mesher class of the
Fatiando a Terra package - interpretation model.
'''
sizex = Lx
sizey = Ly
sizez = Lz
L = P*sizex
a = -0.5*L
model = []
if ((m is not None) & (inc is not None) & (dec is not None)):
intensity = np.array(m)
inclinacao = np.array(inc)
declinacao = np.array(dec)
mag = []
for i in range(P):
mag.append(ang2vec(intensity[i],inclinacao[i],declinacao[i]))
for i in range(P):
model.append(mesher.Prism(a+i*sizex, a+(i+1)*sizex, \
-0.5*sizey, 0.5*sizey, \
-0.5*sizez, 0.5*sizez, \
{'magnetization': mag[i]}))
else:
for i in range(P):
model.append(mesher.Prism(a+i*sizex, a+(i+1)*sizex, \
-0.5*sizey, 0.5*sizey, \
-0.5*sizez, 0.5*sizez))
return model
def test_regional2():
'''
In this test, we set a values for the field magnetization intensity. Then we compare the results obtained by the regional calculation function and the function ang2vec from Fatiando a Terra. In thes test we use a float values for the three parameters.
'''
intensity = 22458.25
inclination = 33.67
declination = 60.60
regional_fatiando = utils.ang2vec(intensity, inclination, declination)
regional_myresult = regional(intensity, inclination, declination)
assert_almost_equal(regional_fatiando, regional_myresult, decimal=6)
def test_tilt_sane_values():
"gravmag.transform tilt returns sane values, between -90 and 90 degrees"
inc, dec = 90, 0
mag = utils.ang2vec(200, inc, dec)
model = [Prism(-500, 500, -500, 500, 0, 2000, {'magnetization': mag})]
shape = (300, 300)
x, y, z = gridder.regular([-10000, 10000, -10000, 10000], shape, z=-100)
data = prism.tf(x, y, z, model, inc, dec)
tilt = np.degrees(transform.tilt(x, y, data, shape))
assert tilt.max() < 90, \
"Maximum tilt greater than 90: {}".format(tilt.max())
assert tilt.min > -90, \
"Minimum tilt less than -90: {}".format(tilt.min())
def test_tilt_sane_values():
"gravmag.transform tilt returns sane values, between -90 and 90 degrees"
inc, dec = 90, 0
mag = utils.ang2vec(200, inc, dec)
model = [Prism(-500, 500, -500, 500, 0, 2000, {'magnetization': mag})]
shape = (300, 300)
x, y, z = gridder.regular([-10000, 10000, -10000, 10000], shape, z=-100)
data = prism.tf(x, y, z, model, inc, dec)
tilt = np.degrees(transform.tilt(x, y, data, shape))
assert tilt.max() < 90, \
"Maximum tilt greater than 90: {}".format(tilt.max())
assert tilt.min > -90, \
"Minimum tilt less than -90: {}".format(tilt.min())
def tfa_layer(x, y, z, xs, ys, zs, sinc, sdec, p, inc, dec):
'''
Calculate the Total Field Anomaly produced by a layer
input
x,y,z : float - Cartesian coordinates (in m) of the i-th observation point
xs,ys,zs : numpy arrays - Cartesian coordinates of equivalent sources
sinc,sdec: float - Main field direction
p: numpy array - Vector composed by magnetic moment and magnetization direction of the
equivalent sources
return
tfa : numpy array - total field anomaly of the equivalent layer
'''
N = x.size
M = xs.size
tfa = np.empty(N, dtype=float)
F0x, F0y, F0z = ang2vec(1., sinc, sdec)
mx, my, mz = ang2vec(1., inc, dec)
for i in range(N):
phi_xx = kernelxx(x[i], y[i], z[i], xs, ys, zs)
phi_yy = kernelyy(x[i], y[i], z[i], xs, ys, zs)
phi_xy = kernelxy(x[i], y[i], z[i], xs, ys, zs)
phi_xz = kernelxz(x[i], y[i], z[i], xs, ys, zs)
phi_yz = kernelyz(x[i], y[i], z[i], xs, ys, zs)
phi_zz = -phi_xx - phi_yy
gi = (F0x*mx - F0z*mz)*phi_xx + (F0y*my - F0z*mz)*phi_yy + (F0x*my + F0y*mx)*phi_xy +\
(F0x*mz + F0z*mx)*phi_xz + (F0y*mz + F0z*my)*phi_yz
tfa[i] = np.dot(p.T, gi)
tfa *= CM * T2NT
return tfa
def test_force_physical_property():
'gravmag.prism gives correct results when passed a property value as arg'
inc, dec = 10, 0
model = [Prism(-6000, -2000, 2000, 4000, 0, 3000,
{'density': 1000,
'magnetization': utils.ang2vec(10, inc, dec)}),
Prism(2000, 6000, 2000, 4000, 0, 1000,
{'density': -1000,
'magnetization': utils.ang2vec(15, inc, dec)})]
density = -500
mag = utils.ang2vec(-5, -30, 15)
reference = [
Prism(-6000, -2000, 2000, 4000, 0, 3000,
{'density': density, 'magnetization': mag}),
Prism(2000, 6000, 2000, 4000, 0, 1000,
{'density': density, 'magnetization': mag})]
area = [-10000, 10000, -5000, 5000]
x, y, z = gridder.regular(area, (51, 101), z=-1)
for mod in [prism, _prism_numpy]:
# Test gravity functions
funcs = ['potential', 'gx', 'gy', 'gz',
'gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz']
for f in funcs:
forced = getattr(mod, f)(x, y, z, model, dens=density)
ref = getattr(mod, f)(x, y, z, reference)
precision = 10
assert_almost(forced, ref, precision, 'Field = %s' % (f))
# Test magnetic functions
funcs = ['tf', 'bx', 'by', 'bz']
for f in funcs:
if f == 'tf':
forced = getattr(mod, f)(x, y, z, model, inc, dec, pmag=mag)
ref = getattr(mod, f)(x, y, z, reference, inc, dec)
else:
forced = getattr(mod, f)(x, y, z, model, pmag=mag)
ref = getattr(mod, f)(x, y, z, reference)
precision = 10
assert_almost(forced, ref, precision, 'Field = %s' % (f))
def test_coordinate_transformation():
'Test for calculation transformation coordinate'
### Directions in Spherical coordinates
inc, dec = -60., 30.
### Generating a vector with ang2vec
F = ang2vec(1., inc, dec)
### Generating a vector using a function within auxiliary function
F_aux = fc.sph2cart(1., inc, dec)
result = np.allclose(F, F_aux)
assert_almost_equal(result, True)
def test_eql_mag_jacobian():
"EQLTotalField produces the right Jacobian matrix for single source"
inc, dec = -30, 20
model = PointGrid([-10, 10, -10, 10], 500, (2, 2))[0]
model.addprop('magnetization', utils.ang2vec(1, inc, dec))
n = 1000
x, y, z = gridder.scatter([-10, 10, -10, 10], n, z=-100, seed=42)
data = sphere.tf(x, y, z, [model], inc, dec)
eql = EQLTotalField(x, y, z, data, inc, dec, [model])
A = eql.jacobian(None)
assert A.shape == (n, 1)
assert_allclose(A[:, 0], data, rtol=0.01)
def syn_data(inc, dec,syn_bounds,area,depth=-300,magnetization = 5,cel=200):
# Build the synthetic Anomaly
mag = utils.ang2vec(magnetization, inc, dec)
model = []
for i in range(len(syn_bounds)):
model.append(Prism(syn_bounds[i][0], syn_bounds[i][1], syn_bounds[i][2], syn_bounds[i][3], syn_bounds[i][4], syn_bounds[i][5], {'magnetization': mag}))
numcols, numrows = int((area[1] - area[0])/cel), int((area[3] - area[2])/cel)
x, y, z = gridder.regular(area[:4], (numcols, numrows), z=depth)
#Calculate the Total Magnetic Field in nT
mag = prism.tf(x, y, z, model, inc, dec)
return x,y,mag,model
def test_cython_agains_numpy():
"gravmag.prism numpy and cython implementations give same result"
inc, dec = -30, 50
model = [
Prism(100, 300, -100, 100, 0, 400,
{'density': -1000,
'magnetization': utils.ang2vec(-2, inc, dec)}),
Prism(-300, -100, -100, 100, 0, 200,
{'density': 2000, 'magnetization': utils.ang2vec(5, 25, -10)})]
tmp = np.linspace(-500, 500, 101)
xp, yp = [i.ravel() for i in np.meshgrid(tmp, tmp)]
zp = -1 * np.ones_like(xp)
kernels = ['xx', 'xy', 'xz', 'yy', 'yz', 'zz']
for comp in kernels:
for p in model:
py = getattr(_prism_numpy, 'kernel' + comp)(xp, yp, zp, p)
cy = getattr(prism, 'kernel' + comp)(xp, yp, zp, p)
assert_almost(py, cy, 10,
'Kernel = %s, max field %.15g max diff %.15g'
% (comp, np.abs(cy).max(), np.abs(py - cy).max()))
funcs = ['potential', 'gx', 'gy', 'gz',
'gxx', 'gxy', 'gxz', 'gyy', 'gyz', 'gzz',
'bx', 'by', 'bz', 'tf']
for f in funcs:
if f == 'tf':
py = getattr(_prism_numpy, f)(xp, yp, zp, model, inc, dec)
cy = getattr(prism, f)(xp, yp, zp, model, inc, dec)
else:
py = getattr(_prism_numpy, f)(xp, yp, zp, model)
cy = getattr(prism, f)(xp, yp, zp, model)
if f in ['bx', 'by', 'bz', 'tf']:
precision = 8
else:
precision = 10
assert_almost(py, cy, precision,
'Field = %s, max field %.15g max diff %.15g'
% (f, np.abs(cy).max(), np.abs(py - cy).max()))
def dipolesrand(N, seed,n,deg_dec,deg_inc,std,mag,raio,Lx,Ly,Lz):
'''
Generates a set of spheres randomly distribuited within the model
input
N: int - number of prisms
s: int - seed value
n: int - number of magnetized spheres
deg_dec: float - mean value of the declination (degrees)
deg_inc: float - mean value of inclination (degrees)
std: float - standard deviation of the declination and inclination (degrees)
mag: float - magnetization intensity of each sphere (A/m)
raio: float - radius of each sphere (m)
Lx,Ly,Lz: int - prisms dimensions (m)
return
modelrand: list of geometrical objects using the library Fatiando a Terra.
'''
np.random.seed(seed=seed)
sizex = Lx
sizey = Ly
sizez = Lz
L = N*sizex
R = raio
Coordx = np.random.uniform(-0.5*L+R,+0.5*L-R,n)
Coordy = np.random.uniform(-0.5*sizey+R,+0.5*sizey-R,n)
Coordz = np.random.uniform(-0.5*sizez+R,+0.5*sizez-R,n)
Dec_rand = np.random.normal(deg_dec, std,n)
Inc_rand = np.random.normal(deg_inc, std,n)
magrand = []
for i in range(n):
magrand.append(ang2vec(mag,Inc_rand[i],Dec_rand[i]))
modelrand = []
for i in range(n):
modelrand.append(mesher.Sphere(Coordx[i], Coordy[i], Coordz[i], R , {'magnetization': magrand[i]}))
return modelrand,Coordx,Coordy,Coordz
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, 1200, 200])
model = Sphere(pos[0], pos[1], pos[2], 1, {"magnetization": utils.ang2vec(10000, inc, dec)})
struct_ind = 3
shape = (200, 200)
x, y, z = gridder.regular((0, 3000, 0, 3000), shape, z=-100)
base = 10
field = sphere.tf(x, y, z, [model], inc, dec) + base
# Use finite difference derivatives so that these tests don't depend on the
# performance of the FFT derivatives.
xderiv = (sphere.tf(x + 1, y, z, [model], inc, dec) - sphere.tf(x - 1, y, z, [model], inc, dec)) / 2
yderiv = (sphere.tf(x, y + 1, z, [model], inc, dec) - sphere.tf(x, y - 1, z, [model], inc, dec)) / 2
zderiv = (sphere.tf(x, y, z + 1, [model], inc, dec) - sphere.tf(x, y, z - 1, [model], inc, dec)) / 2
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)
def test_triaxial_ellipsoid_force_prop():
"Test the triaxial_ellipsoid code with an imposed physical property"
# forced physical property
pmag = utils.ang2vec(5, 43, -8)
# magnetic field produced by the ellipsoids
# with the forced physical property
bx = triaxial_ellipsoid.bx(x, y, z, model,
F, inc, dec, demag=False, pmag=pmag)
by = triaxial_ellipsoid.by(x, y, z, model,
F, inc, dec, demag=False, pmag=pmag)
bz = triaxial_ellipsoid.bz(x, y, z, model,
F, inc, dec, demag=False, pmag=pmag)
tf = triaxial_ellipsoid.tf(x, y, z, model,
F, inc, dec, demag=False, pmag=pmag)
# constant factor
f = 3.71768
# magnetic field produced by the ellipsoids
# with the forced physical property multiplied by the constant factor
bx2 = triaxial_ellipsoid.bx(x, y, z, model,
F, inc, dec, demag=False, pmag=f*pmag)
by2 = triaxial_ellipsoid.by(x, y, z, model,
F, inc, dec, demag=False, pmag=f*pmag)
bz2 = triaxial_ellipsoid.bz(x, y, z, model,
F, inc, dec, demag=False, pmag=f*pmag)
tf2 = triaxial_ellipsoid.tf(x, y, z, model,
F, inc, dec, demag=False, pmag=f*pmag)
# the fields must be proportional
assert_almost_equal(bx2, f*bx, decimal=12)
assert_almost_equal(by2, f*by, decimal=12)
assert_almost_equal(bz2, f*bz, decimal=12)
assert_almost_equal(tf2, f*tf, decimal=12)
# pmag not None requires demag not True
raises(AssertionError, triaxial_ellipsoid.bx, x, y, z, model,
F, inc, dec, demag=True, pmag=pmag)
raises(AssertionError, triaxial_ellipsoid.by, x, y, z, model,
F, inc, dec, demag=True, pmag=pmag)
raises(AssertionError, triaxial_ellipsoid.bz, x, y, z, model,
F, inc, dec, demag=True, pmag=pmag)
raises(AssertionError, triaxial_ellipsoid.tf, x, y, z, model,
F, inc, dec, demag=True, pmag=pmag)
def setup():
global model, x, y, z, inc, dec, struct_ind, field, dx, dy, dz, base, pos
inc, dec = -30, 50
pos = np.array([1000, 1200, 200])
model = Sphere(pos[0], pos[1], pos[2], 1,
{'magnetization': utils.ang2vec(10000, inc, dec)})
struct_ind = 3
shape = (200, 200)
x, y, z = gridder.regular((0, 3000, 0, 3000), shape, z=-100)
base = 10
field = sphere.tf(x, y, z, [model], inc, dec) + base
# Use finite difference derivatives so that these tests don't depend on the
# performance of the FFT derivatives.
dx = (sphere.tf(x + 1, y, z, [model], inc, dec) -
sphere.tf(x - 1, y, z, [model], inc, dec)) / 2
dy = (sphere.tf(x, y + 1, z, [model], inc, dec) -
sphere.tf(x, y - 1, z, [model], inc, dec)) / 2
dz = (sphere.tf(x, y, z + 1, [model], inc, dec) -
sphere.tf(x, y, z - 1, [model], inc, dec)) / 2
def test_triaxial_ellipsoid_ignore_none():
"Triaxial ellipsoid ignores model elements that are None"
# forced physical property
pmag = utils.ang2vec(7, -52, 13)
# copy of the original model
model_none = deepcopy(model)
# force an element of the copy to be None
model_none[1] = None
# magnetic field produced by the original model
# without the removed element
bx = triaxial_ellipsoid.bx(x, y, z, [model[0], model[2]],
F, inc, dec, demag=False, pmag=pmag)
by = triaxial_ellipsoid.by(x, y, z, [model[0], model[2]],
F, inc, dec, demag=False, pmag=pmag)
bz = triaxial_ellipsoid.bz(x, y, z, [model[0], model[2]],
F, inc, dec, demag=False, pmag=pmag)
tf = triaxial_ellipsoid.tf(x, y, z, [model[0], model[2]],
F, inc, dec, demag=False, pmag=pmag)
# magnetic field produced by the copy
bx2 = triaxial_ellipsoid.bx(x, y, z, model_none,
F, inc, dec, demag=False, pmag=pmag)
by2 = triaxial_ellipsoid.by(x, y, z, model_none,
F, inc, dec, demag=False, pmag=pmag)
bz2 = triaxial_ellipsoid.bz(x, y, z, model_none,
F, inc, dec, demag=False, pmag=pmag)
tf2 = triaxial_ellipsoid.tf(x, y, z, model_none,
F, inc, dec, demag=False, pmag=pmag)
assert_almost_equal(bx2, bx, decimal=15)
assert_almost_equal(by2, by, decimal=15)
assert_almost_equal(bz2, bz, decimal=15)
assert_almost_equal(tf2, tf, decimal=15)
def euler_deconv(inc, dec,syn_bounds,area,depth=-300,magnetization = 0.5,si=1.0,size = (1000, 1000),windows=(10, 10),proc_data='None'):
# IN PROGRESSING #####
mag = utils.ang2vec(magnetization, inc, dec)
model = []
for i in range(len(syn_bounds)):
model.append(Prism(syn_bounds[i][0], syn_bounds[i][1], syn_bounds[i][2], syn_bounds[i][3], syn_bounds[i][4], syn_bounds[i][5], {'magnetization': mag}))
#model = [Prism(syn_bounds[0]-1000, syn_bounds[1]-1000, syn_bounds[2]-1000, syn_bounds[3]-1000, syn_bounds[4], syn_bounds[5], {'magnetization': mag}),Prism(syn_bounds[0]+1000, syn_bounds[1]+1000, syn_bounds[2]+1000, syn_bounds[3]+1000, syn_bounds[4], syn_bounds[5], {'magnetization': mag})]
cel = 200
numcols, numrows = int((area[1] - area[0])/cel), int((area[3] - area[2])/cel)
x, y, z = gridder.regular(area[:4], (numcols, numrows), z=depth)
mag = prism.tf(x, y, z, model, inc, dec)
#derivatives
deriv_x = transform.derivx(x, y, mag, (numcols, numrows))
deriv_y = transform.derivy(x, y, mag, (numcols, numrows))
deriv_z = transform.derivz(x, y, mag, (numcols, numrows))
#label = 'Total Magnetic Intensity (nT)'
solver = euler.EulerDeconvMW(x, y, z, mag, deriv_x, deriv_y, deriv_z,
structural_index=si, windows=windows,
size=size)
solver_expand = euler.EulerDeconvEW(x, y, z, mag, deriv_x, deriv_y, deriv_z,
structural_index=si, center = (-1250,0),sizes=np.linspace(300, 7000, 20))
solver.fit()
solver_expand.fit()
#print('Kept Euler solutions after the moving window scheme:')
#print(solver_expand.estimate_)
return solver,solver_expand
"""
Meshing: Making a grid of 3D point sources
"""
from fatiando import mesher, utils, gravmag, gridder
from fatiando.vis import mpl
grid = mesher.PointGrid([0, 1000, 0, 2000], 500, (50, 50))
# Add some density to the grid
grid.addprop('density', 1000000000*utils.gaussian2d(grid.x, grid.y, 100, 500,
x0=500, y0=1000, angle=-60))
# and some magnetization
inc, dec = -45, 0
grid.addprop('magnetization', [d/100.*utils.ang2vec(1, inc, dec)
for d in grid.props['density']])
# plot the layer
mpl.figure()
mpl.subplot(2, 1, 1)
mpl.axis('scaled')
mpl.title('Density (mass)')
mpl.pcolor(grid.y, grid.x, grid.props['density'], grid.shape)
mpl.colorbar()
mpl.subplot(2, 1, 2)
mpl.axis('scaled')
mpl.title('Magnetization intensity (dipole moment)')
mpl.pcolor(grid.y, grid.x, utils.vecnorm(grid.props['magnetization']),
grid.shape)
mpl.colorbar()
mpl.show()
# Now do some calculations with the grid
shape = (100, 100)
in the module than the one we are showing here. They can be more efficient but
usually require more configuration.
"""
from __future__ import division, print_function
import matplotlib.pyplot as plt
import numpy as np
from fatiando.gravmag import prism, sphere
from fatiando.gravmag.eqlayer import EQLTotalField
from fatiando.inversion import Damping
from fatiando import gridder, utils, mesher
# First thing to do is make some synthetic data to test the method. We'll use a
# single prism with only induced magnetization to keep it simple
inc, dec = -5, 23
props = {'magnetization': utils.ang2vec(5, inc, dec)}
model = [mesher.Prism(-2000, 2000, -200, 200, 100, 4000, props)]
# The synthetic data will be generated on a regular grid
area = [-8000, 8000, -5000, 5000]
shape = (40, 40)
x, y, z = gridder.regular(area, shape, z=-150)
# Generate some noisy data from our model
data = utils.contaminate(prism.tf(x, y, z, model, inc, dec), 5, seed=0)
# Now for the equivalent layer. We must setup a layer of dipoles where we'll
# estimate a magnetization intensity distribution that fits our synthetic data.
# Notice that we only estimate the intensity. We must provide the magnetization
# direction of the layer through the sinc and sdec parameters.
layer = mesher.PointGrid(area, 700, shape)
eql = (EQLTotalField(x, y, z, data, inc, dec, layer, sinc=inc, sdec=dec)
def test_fails_if_shape_mismatch():
'gravmag.prism fails if given computation points with different shapes'
inc, dec = 10, 0
model = [Prism(-6000, -2000, 2000, 4000, 0, 3000,
{'density': 1000,
'magnetization': utils.ang2vec(10, inc, dec)})]
area = [-5000, 5000, -10000, 10000]
x, y, z = gridder.regular(area, (101, 51), z=-1)
raises(ValueError, prism.potential, x[:-2], y, z, model)
raises(ValueError, prism.potential, x, y[:-2], z, model)
raises(ValueError, prism.potential, x, y, z[:-2], model)
raises(ValueError, prism.potential, x[:-5], y, z[:-2], model)
raises(ValueError, prism.gx, x[:-2], y, z, model)
raises(ValueError, prism.gx, x, y[:-2], z, model)
raises(ValueError, prism.gx, x, y, z[:-2], model)
raises(ValueError, prism.gx, x[:-5], y, z[:-2], model)
raises(ValueError, prism.gy, x[:-2], y, z, model)
raises(ValueError, prism.gy, x, y[:-2], z, model)
raises(ValueError, prism.gy, x, y, z[:-2], model)
raises(ValueError, prism.gy, x[:-5], y, z[:-2], model)
raises(ValueError, prism.gz, x[:-2], y, z, model)
raises(ValueError, prism.gz, x, y[:-2], z, model)
raises(ValueError, prism.gz, x, y, z[:-2], model)
raises(ValueError, prism.gz, x[:-5], y, z[:-2], model)
raises(ValueError, prism.gxx, x[:-2], y, z, model)
raises(ValueError, prism.gxx, x, y[:-2], z, model)
raises(ValueError, prism.gxx, x, y, z[:-2], model)
raises(ValueError, prism.gxx, x[:-5], y, z[:-2], model)
raises(ValueError, prism.gxy, x[:-2], y, z, model)
raises(ValueError, prism.gxy, x, y[:-2], z, model)
raises(ValueError, prism.gxy, x, y, z[:-2], model)
raises(ValueError, prism.gxy, x[:-5], y, z[:-2], model)
raises(ValueError, prism.gxz, x[:-2], y, z, model)
raises(ValueError, prism.gxz, x, y[:-2], z, model)
raises(ValueError, prism.gxz, x, y, z[:-2], model)
raises(ValueError, prism.gxz, x[:-5], y, z[:-2], model)
raises(ValueError, prism.gyy, x[:-2], y, z, model)
raises(ValueError, prism.gyy, x, y[:-2], z, model)
raises(ValueError, prism.gyy, x, y, z[:-2], model)
raises(ValueError, prism.gyy, x[:-5], y, z[:-2], model)
raises(ValueError, prism.gyz, x[:-2], y, z, model)
raises(ValueError, prism.gyz, x, y[:-2], z, model)
raises(ValueError, prism.gyz, x, y, z[:-2], model)
raises(ValueError, prism.gyz, x[:-5], y, z[:-2], model)
raises(ValueError, prism.gzz, x[:-2], y, z, model)
raises(ValueError, prism.gzz, x, y[:-2], z, model)
raises(ValueError, prism.gzz, x, y, z[:-2], model)
raises(ValueError, prism.gzz, x[:-5], y, z[:-2], model)
raises(ValueError, prism.bx, x[:-2], y, z, model)
raises(ValueError, prism.bx, x, y[:-2], z, model)
raises(ValueError, prism.bx, x, y, z[:-2], model)
raises(ValueError, prism.bx, x[:-5], y, z[:-2], model)
raises(ValueError, prism.by, x[:-2], y, z, model)
raises(ValueError, prism.by, x, y[:-2], z, model)
raises(ValueError, prism.by, x, y, z[:-2], model)
raises(ValueError, prism.by, x[:-5], y, z[:-2], model)
raises(ValueError, prism.bz, x[:-2], y, z, model)
raises(ValueError, prism.bz, x, y[:-2], z, model)
raises(ValueError, prism.bz, x, y, z[:-2], model)
raises(ValueError, prism.bz, x[:-5], y, z[:-2], model)
raises(ValueError, prism.tf, x[:-2], y, z, model, inc, dec)
raises(ValueError, prism.tf, x, y[:-2], z, model, inc, dec)
raises(ValueError, prism.tf, x, y, z[:-2], model, inc, dec)
raises(ValueError, prism.tf, x[:-5], y, z[:-2], model, inc, dec)
raises(ValueError, prism.kernelxx, x[:-2], y, z, model[0])
raises(ValueError, prism.kernelxx, x, y[:-2], z, model[0])
raises(ValueError, prism.kernelxx, x, y, z[:-2], model[0])
raises(ValueError, prism.kernelxx, x[:-5], y, z[:-2], model[0])
raises(ValueError, prism.kernelxy, x[:-2], y, z, model[0])
raises(ValueError, prism.kernelxy, x, y[:-2], z, model[0])
raises(ValueError, prism.kernelxy, x, y, z[:-2], model[0])
raises(ValueError, prism.kernelxy, x[:-5], y, z[:-2], model[0])
raises(ValueError, prism.kernelxz, x[:-2], y, z, model[0])
raises(ValueError, prism.kernelxz, x, y[:-2], z, model[0])
raises(ValueError, prism.kernelxz, x, y, z[:-2], model[0])
raises(ValueError, prism.kernelxz, x[:-5], y, z[:-2], model[0])
raises(ValueError, prism.kernelyy, x[:-2], y, z, model[0])
raises(ValueError, prism.kernelyy, x, y[:-2], z, model[0])
raises(ValueError, prism.kernelyy, x, y, z[:-2], model[0])
raises(ValueError, prism.kernelyy, x[:-5], y, z[:-2], model[0])
raises(ValueError, prism.kernelyz, x[:-2], y, z, model[0])
raises(ValueError, prism.kernelyz, x, y[:-2], z, model[0])
raises(ValueError, prism.kernelyz, x, y, z[:-2], model[0])
raises(ValueError, prism.kernelyz, x[:-5], y, z[:-2], model[0])
raises(ValueError, prism.kernelzz, x[:-2], y, z, model[0])
raises(ValueError, prism.kernelzz, x, y[:-2], z, model[0])
raises(ValueError, prism.kernelzz, x, y, z[:-2], model[0])
raises(ValueError, prism.kernelzz, x[:-5], y, z[:-2], model[0])
"""
GravMag: 3D forward modeling of total-field magnetic anomaly using rectangular
prisms (model with induced and remanent magnetization)
"""
from fatiando import mesher, gridder, utils
from fatiando.gravmag import prism
from fatiando.vis import mpl, myv
# The regional field
inc, dec = 30, -15
bounds = [-5000, 5000, -5000, 5000, 0, 5000]
model = [
mesher.Prism(-4000, -3000, -4000, -3000, 0, 2000,
{'magnetization': utils.ang2vec(1, inc, dec)}),
mesher.Prism(-1000, 1000, -1000, 1000, 0, 2000,
{'magnetization': utils.ang2vec(1, inc, dec)}),
# This prism will have magnetization in a different direction
mesher.Prism(2000, 4000, 3000, 4000, 0, 2000,
{'magnetization': utils.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
mpl.figure()
mpl.title("Total-field anomaly (nT)")
mpl.axis('scaled')
mpl.contourf(yp, xp, tf, shape, 15)
mpl.colorbar()
"""
GravMag: 3D forward modeling of total-field magnetic anomaly using spheres
"""
from fatiando import mesher, gridder, utils
from fatiando.gravmag import sphere
from fatiando.vis import mpl
# Set the inclination and declination of the regional field
inc, dec = -30, 45
# Create a sphere model
model = [
# One with induced magnetization
mesher.Sphere(0, 2000, 600, 500, {'magnetization':5}),
# and one with remanent
mesher.Sphere(0, -2000, 600, 500,
{'magnetization':utils.ang2vec(10, 70, -50)})] # induced + remanet
# Create a regular grid at 100m height
shape = (100, 100)
area = (-5000, 5000, -5000, 5000)
xp, yp, zp = gridder.regular(area, shape, z=-100)
# Calculate the anomaly for a given regional field
tf = sphere.tf(xp, yp, zp, model, inc, dec)
# Plot
mpl.figure()
mpl.title("Total-field anomaly (nT)")
mpl.axis('scaled')
mpl.contourf(yp, xp, tf, shape, 15)
mpl.colorbar()
mpl.xlabel('East y (km)')
mpl.ylabel('North x (km)')
mpl.m2km()
approach used in all industry software. This is implemented in
:class:`fatiando.gravmag.euler.EulerDeconvMW`.
"""
from __future__ import print_function
from fatiando.gravmag import sphere, transform, euler
from fatiando import gridder, utils, mesher
import matplotlib.pyplot as plt
# Make some synthetic magnetic data to test our Euler deconvolution.
# The regional field
inc, dec = -45, 0
# Make a model of two spheres magnetized by induction only
model = [
mesher.Sphere(x=-1000, y=-1000, z=1500, radius=1000, props={"magnetization": utils.ang2vec(2, inc, dec)}),
mesher.Sphere(x=1000, y=1500, z=1000, radius=1000, props={"magnetization": utils.ang2vec(1, inc, dec)}),
]
print("Centers of the model spheres:")
print(model[0].center)
print(model[1].center)
# Generate some magnetic data from the model
shape = (100, 100)
area = [-5000, 5000, -5000, 5000]
x, y, z = gridder.regular(area, shape, z=-150)
data = sphere.tf(x, y, z, model, inc, dec)
# We also need the derivatives of our data
xderiv = transform.derivx(x, y, data, shape)
GravMag: Use the DipoleMagDir class to estimate the magnetization direction
of dipoles with known centers
"""
import numpy
from fatiando import mesher, gridder
from fatiando.utils import ang2vec, vec2ang, contaminate
from fatiando.gravmag import sphere
from fatiando.vis import mpl
from fatiando.gravmag.magdir import DipoleMagDir
from fatiando.constants import CM
# Make noise-corrupted synthetic data
inc, dec = -10.0, -15.0 # inclination and declination of the Geomagnetic Field
model = [mesher.Sphere(3000, 3000, 1000, 1000,
{'magnetization': ang2vec(6.0, -20.0, -10.0)}),
mesher.Sphere(7000, 7000, 1000, 1000,
{'magnetization': ang2vec(10.0, 3.0, -67.0)})]
area = (0, 10000, 0, 10000)
x, y, z = gridder.scatter(area, 1000, z=-150, seed=0)
tf = contaminate(sphere.tf(x, y, z, model, inc, dec), 5.0, seed=0)
# Give the centers of the dipoles
centers = [[3000, 3000, 1000], [7000, 7000, 1000]]
# Estimate the magnetization vectors
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_:
of dipoles with known centers
"""
import numpy
from fatiando import mesher, gridder
from fatiando.utils import ang2vec, vec2ang, contaminate
from fatiando.gravmag import sphere
from fatiando.vis import mpl
from fatiando.gravmag.magdir import DipoleMagDir
from fatiando.constants import CM
# Make noise-corrupted synthetic data
inc, dec = -10.0, -15.0 # inclination and declination of the Geomagnetic Field
model = [mesher.Sphere(3000, 3000, 1000, 1000,
{'magnetization': ang2vec(6.0, -20.0, -10.0)}),
mesher.Sphere(7000, 7000, 1000, 1000,
{'magnetization': ang2vec(10.0, 3.0, -67.0)})]
area = (0, 10000, 0, 10000)
x, y, z = gridder.scatter(area, 1000, z=-150, seed=0)
tf = contaminate(sphere.tf(x, y, z, model, inc, dec), 5.0)
# Give the centers of the dipoles
centers = [[3000, 3000, 1000], [7000, 7000, 1000]]
p_true = numpy.hstack((ang2vec(CM * (4. * numpy.pi / 3.) * 6.0 * 1000 ** 3, -20.0, -10.0),
ang2vec(CM * (4. * numpy.pi / 3.) * 10.0 * 1000 ** 3, 3.0, -67.0)))
estimate_true = [[CM * (4. * numpy.pi / 3.) * 6.0 * 1000 ** 3, -20.0, -10.0],
[CM * (4. * numpy.pi / 3.) * 10.0 * 1000 ** 3, 3.0, -67.0]]
# Estimate the magnetization vectors
mag_c = 1.5
inc_c = 45.
dec_c = 45.
#vertical prism
x1_cr, x2_cr = 4000, 6000
y1_cr, y2_cr = 14000, 16000
z1_cr, z2_cr = 200, 5000
#Magnetization intensity
mag_cr = 1.5
inc_cr = 60.
dec_cr = 60.
#From Fatiando a Terra
# Compute the cosine directions of the main geomagetic field (F)
F = utils.ang2vec(1, inc_o, dec_o)
#Generate a model From Fatiando a Terra
model_mag = [
mesher.Prism(x1_cr, x2_cr, y1_cr, y2_cr, z1_cr, z2_cr,
{'magnetization': utils.ang2vec(mag_cr, inc_cr, dec_cr)}),
mesher.Prism(x1_c, x2_c, y1_c, y2_c, z1_c, z2_c,
{'magnetization': utils.ang2vec(mag_c, inc_c, dec_c)}),
mesher.Prism(x3_c, x4_c, y3_c, y4_c, z3_c, z4_c,
{'magnetization': utils.ang2vec(mag_c, inc_c, dec_c)}),
mesher.Prism(x3_c, x4_c, y5_c, y6_c, z3_c, z4_c,
{'magnetization': utils.ang2vec(mag_c, inc_c, dec_c)}),
mesher.Prism(x1_L, x2_L, y1_L, y2_L, z1_L, z2_L,
{'magnetization': utils.ang2vec(mag_L, inc_s, dec_s)}),
mesher.Prism(x3_L, x4_L, y3_L, y4_L, z3_L, z4_L,
{'magnetization': utils.ang2vec(mag_L, inc_s, dec_s)}),
data is already reduced to the pole. You can use
:func:`fatiando.gravmag.transform.reduce_to_pole` to reduce your data.
The zero contour of the tilt is said to outline the body so we've plotted it as
a dashed line on the tilt map.
"""
from __future__ import division, print_function
import matplotlib.pyplot as plt
from fatiando.gravmag import prism, transform
from fatiando.mesher import Prism
from fatiando import gridder, utils
# Create some synthetic magnetic data. We'll assume the data is already reduced
# to the pole.
inc, dec = 90, 0
mag = utils.ang2vec(1, inc, dec)
model = [Prism(-1500, 1500, -500, 500, 0, 2000, {'magnetization': mag})]
area = (-7e3, 7e3, -7e3, 7e3)
shape = (100, 100)
x, y, z = gridder.regular(area, shape, z=-300)
data_at_pole = prism.tf(x, y, z, model, inc, dec)
# Calculate the tilt
tilt = transform.tilt(x, y, data_at_pole, shape)
# Make some plots
plt.figure(figsize=(8, 6))
ax = plt.subplot(1, 2, 1)
ax.set_title('Original data at the pole')
ax.set_aspect('equal')
"""
from fatiando import mesher, gridder, utils
from fatiando.gravmag import prism
from fatiando.vis import mpl, myv
# The regional field
inc, dec = 30, -15
bounds = [-5000, 5000, -5000, 5000, 0, 5000]
model = [
mesher.Prism(-4000, -3000, -4000, -3000, 0, 2000,
{'magnetization': 2}), # a scalar magnetization means only induced
mesher.Prism(-1000, 1000, -1000, 1000, 0, 2000,
{'magnetization': 1}),
# This prism will have magnetization in a different direction
mesher.Prism(2000, 4000, 3000, 4000, 0, 2000,
{'magnetization': utils.ang2vec(3, -10, 45)})] # induced + remanent
# 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
mpl.figure()
mpl.title("Total-field anomaly (nT)")
mpl.axis('scaled')
mpl.contourf(yp, xp, tf, shape, 15)
mpl.colorbar()
mpl.xlabel('East y (km)')
mpl.ylabel('North x (km)')
mpl.m2km()
the geomagnetic field direction. This example shows how to use it in this case.
Use ``sinc=inc`` and ``sdec=dec`` if there is only induced magnetization.
"""
from __future__ import division, print_function
import matplotlib.pyplot as plt
from fatiando.gravmag import prism, transform
from fatiando.mesher import Prism
from fatiando import gridder, utils
# Create some synthetic magnetic data with a total magnetization that is
# different from the geomagnetic field (so there is remanent magnetization or
# some demagnetizing effect)
inc, dec = -60, 23 # Geomagnetic field direction
sinc, sdec = -30, -20 # Source magnetization direction
mag = utils.ang2vec(1, sinc, sdec)
model = [Prism(-1500, 1500, -500, 500, 0, 2000, {'magnetization': mag})]
area = (-7e3, 7e3, -7e3, 7e3)
shape = (100, 100)
x, y, z = gridder.regular(area, shape, z=-300)
data = prism.tf(x, y, z, model, inc, dec)
# Reduce to the pole
data_at_pole = transform.reduce_to_pole(x, y, data, shape, inc, dec, sinc,
sdec)
# Make some plots
plt.figure(figsize=(8, 6))
ax = plt.subplot(1, 2, 1)
ax.set_title('Original data')
"""
GravMag: 3D forward modeling of total-field magnetic anomaly using spheres
"""
from fatiando import mesher, gridder, utils
from fatiando.gravmag import sphere
from fatiando.vis import mpl
# Set the inclination and declination of the regional field
inc, dec = -30, 45
# Create a sphere model
model = [
# One with induced magnetization
mesher.Sphere(0, 2000, 600, 500,
{'magnetization': utils.ang2vec(5, inc, dec)}),
# and one with remanent
mesher.Sphere(0, -2000, 600, 500,
{'magnetization': utils.ang2vec(10, 70, -50)})]
# Create a regular grid at 100m height
shape = (100, 100)
area = (-5000, 5000, -5000, 5000)
xp, yp, zp = gridder.regular(area, shape, z=-100)
# Calculate the anomaly for a given regional field
tf = sphere.tf(xp, yp, zp, model, inc, dec)
# Plot
mpl.figure()
mpl.title("Total-field anomaly (nT)")
mpl.axis('scaled')
mpl.contourf(yp, xp, tf, shape, 15)
mpl.colorbar()
mpl.xlabel('East y (km)')
mpl.ylabel('North x (km)')
"""
from __future__ import division, print_function
from fatiando import mesher, gridder, utils
from fatiando.gravmag import sphere
import matplotlib.pyplot as plt
import numpy as np
# Create a model using geometric objects from fatiando.mesher
# Each model element has a dictionary with its physical properties.
# The spheres have different total magnetization vectors (total = induced +
# remanent + any other effects). Notice that the magnetization has to be a
# vector. Function utils.ang2vec converts intensity, inclination, and
# declination into a 3 component vector for easier handling.
model = [
mesher.Sphere(x=10e3, y=10e3, z=2e3, radius=1.5e3,
props={'magnetization': utils.ang2vec(1, inc=50, dec=-30)}),
mesher.Sphere(x=20e3, y=20e3, z=2e3, radius=1.5e3,
props={'magnetization': utils.ang2vec(1, inc=-70, dec=30)})]
# Set the inclination and declination of the geomagnetic field.
inc, dec = -10, 13
# Create a regular grid at a constant height
shape = (300, 300)
area = [0, 30e3, 0, 30e3]
x, y, z = gridder.regular(area, shape, z=-10)
fields = [
['Total field Anomaly (nt)', sphere.tf(x, y, z, model, inc, dec)],
['Bx (nT)', sphere.bx(x, y, z, model)],
['By (nT)', sphere.by(x, y, z, model)],
['Bz (nT)', sphere.bz(x, y, z, model)],
:class:`fatiando.gravmag.euler.EulerDeconvEW`.
"""
from __future__ import print_function
from fatiando.gravmag import sphere, transform, euler
from fatiando import gridder, utils, mesher
import matplotlib.pyplot as plt
import numpy as np
# Make some synthetic magnetic data to test our Euler deconvolution.
# The regional field
inc, dec = -45, 0
# Make a model of two spheres magnetized by induction only
model = [
mesher.Sphere(x=-1000, y=-1000, z=1500, radius=1000,
props={'magnetization': utils.ang2vec(2, inc, dec)}),
mesher.Sphere(x=1000, y=1500, z=1000, radius=1000,
props={'magnetization': utils.ang2vec(1, inc, dec)}),
]
# Generate some magnetic data from the model
shape = (100, 100)
area = [-5000, 5000, -5000, 5000]
x, y, z = gridder.regular(area, shape, z=-150)
data = sphere.tf(x, y, z, model, inc, dec)
# We also need the derivatives of our data
xderiv = transform.derivx(x, y, data, shape)
yderiv = transform.derivy(x, y, data, shape)
zderiv = transform.derivz(x, y, data, shape)
# Now we can run our Euler deconv solver using expanding windows. We'll run 2
"""
GravMag: Reduction to the pole of a total field anomaly using FFT
"""
from fatiando import mesher, gridder, utils
from fatiando.gravmag import prism, transform
from fatiando.vis import mpl
# Direction of the Geomagnetic field
inc, dec = -60, 0
# Make a model with only induced magnetization
model = [
mesher.Prism(-100, 100, -100, 100, 0, 2000,
{'magnetization': utils.ang2vec(10, inc, dec)})
]
area = (-5000, 5000, -5000, 5000)
shape = (100, 100)
z0 = -500
x, y, z = gridder.regular(area, shape, z=z0)
tf = utils.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 = transform.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=utils.ang2vec(10, 90, 0))
fig, axes = mpl.subplots(1, 3, figsize=(14, 4))
for ax in axes:
ax.set_aspect('equal')
mpl.sca(axes[0])
mpl.title("Original total field anomaly")
mpl.contourf(y, x, tf, shape, 30, cmap=mpl.cm.RdBu_r)
"""
GravMag: 3D magnetic inversion by planting anomalous densities using
``harvester``
"""
from fatiando import logger, gridder, utils
from fatiando import gravmag as gm
from fatiando.mesher import Prism, PrismMesh, vremove
from fatiando.vis import mpl, myv
log = logger.get()
log.info(logger.header())
# The regional field
inc, dec = -10, 25
# Model with induced + remanent magnetization
mag = utils.ang2vec(10, 20, -15)
# Create a synthetic model
model = [Prism(250, 750, 250, 750, 200, 700, {'magnetization':mag})]
# and generate synthetic data from it
shape = (25, 25)
bounds = [0, 1000, 0, 1000, 0, 1000]
area = bounds[0:4]
xp, yp, zp = gridder.regular(area, shape, z=-1)
noise = 5
tf = utils.contaminate(gm.prism.tf(xp, yp, zp, model, inc, dec), noise)
# plot the data
mpl.figure()
mpl.title("Synthetic total field anomaly (nT)")
mpl.axis('scaled')
levels = mpl.contourf(yp, xp, tf, shape, 12)
mpl.colorbar()
"""
GravMag: Reduction to the pole of a total field anomaly using FFT
"""
from fatiando import mesher, gridder, utils
from fatiando.gravmag import prism, transform
from fatiando.vis import mpl
# Direction of the Geomagnetic field
inc, dec = -60, 0
# Make a model with only induced magnetization
model = [mesher.Prism(-100, 100, -100, 100, 0, 2000,
{'magnetization': utils.ang2vec(10, inc, dec)})]
area = (-5000, 5000, -5000, 5000)
shape = (100, 100)
z0 = -500
x, y, z = gridder.regular(area, shape, z=z0)
tf = utils.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 = transform.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=utils.ang2vec(10, 90, 0))
fig, axes = mpl.subplots(1, 3, figsize=(14, 4))
for ax in axes:
ax.set_aspect('equal')
mpl.sca(axes[0])
mpl.title("Original total field anomaly")
mpl.contourf(y, x, tf, shape, 30, cmap=mpl.cm.RdBu_r)
mpl.colorbar(pad=0).set_label('nT')