def test_bz():
"gravmag.sphere.bz python vs cython implementation"
py = _sphere_numpy.bz(xp, yp, zp, model)
cy = sphere.bz(xp, yp, zp, model)
diff = np.abs(py - cy)
assert np.all(diff <= lower_precision), \
'max diff: %g python: %g cython %g' \
% (max(diff), py[diff == max(diff)][0], cy[diff == max(diff)][0])
def test_bz():
"gravmag.sphere.bz python vs cython implementation"
py = _sphere_numpy.bz(xp, yp, zp, model)
cy = sphere.bz(xp, yp, zp, model)
diff = np.abs(py - cy)
assert np.all(diff <= lower_precision), \
'max diff: %g python: %g cython %g' \
% (max(diff), py[diff == max(diff)][0], cy[diff == max(diff)][0])
out = out.T
np.savetxt('data\predict_' + str(lambida) + '.dat',
out,
delimiter=' ',
fmt='%1.3f')
out = None
# Compute the transformed matrix
Tx = np.empty((N, M), dtype=float)
Ty = np.empty((N, M), dtype=float)
Tz = np.empty((N, M), dtype=float)
for i, c in enumerate(layer):
Tx[:, i] = sphere.bx(xi, yi, zi, [c], pmag=F)
Ty[:, i] = sphere.by(xi, yi, zi, [c], pmag=F)
Tz[:, i] = sphere.bz(xi, yi, zi, [c], pmag=F)
# compute the components
Bx_eq = np.dot(Tx, p)
By_eq = np.dot(Ty, p)
Bz_eq = np.dot(Tz, p)
Tx = None
Ty = None
Tz = None
p = None
#Amplitude of the magnetic anomaly vector
B_eq = np.sqrt(Bx_eq * Bx_eq + By_eq * By_eq + Bz_eq * Bz_eq)
Bx_eq = None
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)],
]
# Make maps of all fields calculated
fig = plt.figure(figsize=(7, 6))
plt.rcParams['font.size'] = 10
X, Y = x.reshape(shape) / 1000, y.reshape(shape) / 1000
for i, tmp in enumerate(fields):
ax = plt.subplot(2, 2, i + 1)
field, data = tmp
scale = np.abs([data.min(), data.max()]).max()
ax.set_title(field)
plot = ax.pcolormesh(Y,
X,
data.reshape(shape),
cmap='RdBu_r',
def magnetic_data(x, y, z, model, alpha, eff_area = None, grains = None):
'''
Calculates the magnetic indution on the plane alpha
located around the sample.
input
x, y, z: numpy arrays - Cartesian coordinates (in m) of
the points on which the magnetic field is calculated.
model: list - geometrical elements of the Fatiando a Terra
class mesher - interpretation model.
grains: None or list - if not None, is a list of geometrical elements
of the Fatiando a Terra class mesher - randomly magnetized grains.
alpha: int - index of the plane on which the data are calculated.
eff_area: None or tuple of floats - effective area of the simulated sensor.
If None, calculates the field at the points x, y, z.
If not None, eff_area = (lx, ly), where lx and ly
are the side lengths (in microns) of the effective area of the sensor
along the x and y axes.
ns: None or tuple of ints - number of points on which the field is
averaged within the effective area of the sensor.
Is None if eff_area is none.
If effe_area is not None, ns = (nsx,nsy), where nsx and nsy are
the number of points on which the filed is averaged within the
the effective area of the sensor along the x and y axes.
output
B: numpy array - magnetic data
'''
assert (alpha == 0) or (alpha == 1) or (alpha == 2) or (alpha == 3), \
'alpha must be equal to 0, 1, 2 or 3'
if eff_area is not None:
ns = 7
xg, yg, zg = point2grid(x, y, eff_area, ns, z)
if (alpha == 0) or (alpha == 2):
B = np.mean(np.reshape(prism.bz(xg, yg, zg, model), (x.size, ns*ns)), axis=1)
if grains is not None:
B += np.mean(np.reshape(sphere.bz(xg, yg, zg, grains), (x.size, ns*ns)), axis=1)
if (alpha == 1) or (alpha == 3):
B = np.mean(np.reshape(prism.by(xg, yg, zg, model), (x.size, ns*ns)), axis=1)
if grains is not None:
B += np.mean(np.reshape(sphere.by(xg, yg, zg, grains), (x.size, ns*ns)), axis=1)
else:
if (alpha == 0) or (alpha == 2):
B = prism.bz(x, y, z, model)
if grains is not None:
B += sphere.bz(x, y, z, grains)
if (alpha == 1) or (alpha == 3):
B = prism.by(x, y, z, model)
if grains is not None:
B += sphere.by(x, y, z, grains)
return B
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)],
]
# Make maps of all fields calculated
fig = plt.figure(figsize=(7, 6))
plt.rcParams['font.size'] = 10
X, Y = x.reshape(shape)/1000, y.reshape(shape)/1000
for i, tmp in enumerate(fields):
ax = plt.subplot(2, 2, i + 1)
field, data = tmp
scale = np.abs([data.min(), data.max()]).max()
ax.set_title(field)
plot = ax.pcolormesh(Y, X, data.reshape(shape), cmap='RdBu_r',
vmin=-scale, vmax=scale)
plt.colorbar(plot, ax=ax, aspect=30, pad=0)
ax.set_xlabel('y (km)')