def test_tilt_analytical_derivatives():
"gravmag.transform tilt returns same values given analytical derivatives"
model = [Prism(-100, 100, -100, 100, 0, 100, {'density': 1000})]
shape = (400, 400)
x, y, z = gridder.regular([-10000, 10000, -10000, 10000], shape, z=-100)
data = utils.mgal2si(prism.gz(x, y, z, model))
dx = utils.eotvos2si(prism.gxz(x, y, z, model))
dy = utils.eotvos2si(prism.gyz(x, y, z, model))
dz = utils.eotvos2si(prism.gzz(x, y, z, model))
tilt_analytical = transform.tilt(x, y, data, shape, dx, dy, dz)
tilt_numerical = transform.tilt(x, y, data, shape)
npt.assert_allclose(tilt_numerical, tilt_analytical, rtol=0.10)
def test_tilt_analytical_derivatives():
"gravmag.transform tilt returns same values given analytical derivatives"
model = [Prism(-100, 100, -100, 100, 0, 100, {'density': 1000})]
shape = (400, 400)
x, y, z = gridder.regular([-10000, 10000, -10000, 10000], shape, z=-100)
data = utils.mgal2si(prism.gz(x, y, z, model))
dx = utils.eotvos2si(prism.gxz(x, y, z, model))
dy = utils.eotvos2si(prism.gyz(x, y, z, model))
dz = utils.eotvos2si(prism.gzz(x, y, z, model))
tilt_analytical = transform.tilt(x, y, data, shape, dx, dy, dz)
tilt_numerical = transform.tilt(x, y, data, shape)
npt.assert_allclose(tilt_numerical, tilt_analytical, rtol=0.10)
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())
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')
tmp = ax.tricontourf(y / 1000, x / 1000, data_at_pole, 30, cmap='RdBu_r')
plt.colorbar(tmp, pad=0.1, aspect=30, orientation='horizontal').set_label('nT')
ax.set_xlabel('y (km)')
ax.set_ylabel('x (km)')
ax.set_xlim(area[2] / 1000, area[3] / 1000)
ax.set_ylim(area[0] / 1000, area[1] / 1000)
ax = plt.subplot(1, 2, 2)
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')
tmp = ax.tricontourf(y/1000, x/1000, data_at_pole, 30, cmap='RdBu_r')
plt.colorbar(tmp, pad=0.1, aspect=30, orientation='horizontal').set_label('nT')
ax.set_xlabel('y (km)')
ax.set_ylabel('x (km)')
ax.set_xlim(area[2]/1000, area[3]/1000)
ax.set_ylim(area[0]/1000, area[1]/1000)
ax = plt.subplot(1, 2, 2)
