relief = mesher.PrismRelief(0, gridder.spacing(area, shape), nodes)
relief.addprop('density', (2670 for i in xrange(relief.size)))
gridarea = (-80, 80, -220, 220)
gridshape = (100, 100)
xp, yp, zp = gridder.regular(gridarea, gridshape, z=-200)
gz = prism.gz(xp, yp, zp, relief)
mpl.figure(figsize=(10, 7))
mpl.subplot(1, 2, 1)
mpl.title("Synthetic topography")
mpl.axis('scaled')
mpl.pcolor(x, y, height, shape)
cb = mpl.colorbar()
cb.set_label("meters")
mpl.square(gridarea, label='Computation grid')
mpl.legend()
mpl.subplot(1, 2, 2)
mpl.title("Topographic effect")
mpl.axis('scaled')
mpl.pcolor(xp, yp, gz, gridshape)
cb = mpl.colorbar()
cb.set_label("mGal")
mpl.show()
myv.figure()
myv.prisms(relief, prop='density')
axes = myv.axes(myv.outline())
myv.wall_bottom(axes.axes.bounds, opacity=0.2)
myv.wall_north(axes.axes.bounds)
myv.show()
fig = plt.figure()
ax = plt.gca()
pEXP.plot_xy(mesh, label=label_prop, ax=ax) #, ldg=)
pEXP.plot_ridges_harmonic(dfI_f, dfII_f, dfIII_f, ax=ax, label=True)
df_fit = dEXP.fit_ridges(df_f, rmvOutliers=True) # fit ridges on filtered data
# pEXP.plot_ridges_sources(df_fit, ax=ax, z_max_source=-max_elevation*1.2,
# ridge_type=[0,1,2],ridge_nb=None)
pEXP.plot_ridges_sources(df_fit,
ax=ax,
z_max_source=-6000,
ridge_type=[0, 1, 2],
ridge_nb=None)
square([x1, x2, -z1, -z2])
plt.annotate(dens, [(x1 + x2) / 2, -(z1 + z2) / 2])
#%%
# ridges analysis
z0 = -2000
points, fit, SI, EXTnb = dEXP.scalFUN(dfI_f, EXTnb=[1], z0=z0)
pEXP.plot_scalFUN(points, fit, z0=z0)
# z0 = -2000
# points, fit, SI, EXTnb = dEXP.scalFUN(dfI_f,EXTnb=[3],z0=z0)
# pEXP.plot_scalFUN(points, fit, z0=z0)
#%%
# ridges analysis
#%%
plt.figure()
ax = plt.gca()
i = 0
pEXP.plot_xy(MESH[i], label=LABEL[i], ax=ax) #, ldg=)
dfI_f, dfII_f, dfIII_f = DF_F[i]
pEXP.plot_ridges_harmonic(dfI_f, dfII_f, dfIII_f, ax=ax, label=False)
pEXP.plot_ridges_sources(DF_FIT[i],
ax=ax,
z_max_source=-max_elevation * 1.2,
ridge_type=[0, 1, 2],
ridge_nb=None)
x1, x2, z1, z2 = XXZZ[i]
square([x1, x2, z1, z2])
plt.annotate(CTm[i], [(x1 + x2) / 2, (z1 + z2) / 2])
plt.figure()
ax = plt.gca()
i = 1
pEXP.plot_xy(MESH[i], label=LABEL[i], ax=ax) #, ldg=)
dfI_f, dfII_f, dfIII_f = DF_F[i]
pEXP.plot_ridges_harmonic(dfI_f, dfII_f, dfIII_f, ax=ax, label=False)
pEXP.plot_ridges_sources(DF_FIT[i],
ax=ax,
z_max_source=-max_elevation * 1.2,
ridge_type=[0, 1, 2],
ridge_nb=None)
x1, x2, z1, z2 = XXZZ[i]
ax.set_ylabel('x (km)')
# Then plot model slices in the x, y, z directions through the middle of the
# model. Also show the outline of the true model for comparison.
scale = 0.1*np.abs([image.min(), image.max()]).max()
x = mesh.get_xs()/1000
y = mesh.get_ys()/1000
z = mesh.get_zs()/1000
x1, x2, y1, y2, z1, z2 = np.array(model[0].get_bounds())/1000
ax = plt.subplot(2, 2, 2)
ax.set_title('Model slice at z={} km'.format(z[len(z)//2]))
ax.set_aspect('equal')
ax.pcolormesh(y, x, image[mesh.shape[0]//2, :, :], cmap="cubehelix",
vmin=-scale, vmax=scale)
square([y1, y2, x1, x2])
ax.set_ylim(x.min(), x.max())
ax.set_xlim(y.min(), y.max())
ax.set_xlabel('y (km)')
ax.set_ylabel('x (km)')
ax = plt.subplot(2, 2, 3)
ax.set_title('Model slice at y={} km'.format(y[len(y)//2]))
ax.set_aspect('equal')
ax.pcolormesh(x, z, image[:, :, mesh.shape[1]//2], cmap="cubehelix",
vmin=-scale, vmax=scale)
square([x1, x2, z1, z2])
ax.set_ylim(z.max(), z.min())
ax.set_xlim(x.min(), x.max())
ax.set_xlabel('x (km)')
ax.set_ylabel('z (km)')
p1 = [(x1 + x2) / 2, min(yp)]
p2 = [(x1 + x2) / 2, max(yp)]
x_axis = slicedir[1] # direction of p1p2 profil
SI = 1.5
else:
p1 = [min(xp), (y1 + y2) / 2]
p2 = [max(xp), (y1 + y2) / 2]
x_axis = slicedir[1] # direction of p1p2 profil
SI = 2
#%%
# Plot field data over a 2d line crossing the anomalies
# pEXP.plot_line(xp, yp, U,p1,p2, interp=False, Xaxis='x')
pEXP.plot_line(xp, yp, U, p1, p2, interp=True, Xaxis=x_axis)
square([x1, x2, y1, y2])
pEXP.plot_field(xp, yp, U, shape)
square([x1, x2, y1, y2])
# Gaussian function to derivate
#%% ------------------------------- Plot the derivatives
# http://campar.in.tum.de/Chair/HaukeHeibelGaussianDerivatives
xderiv = transform.derivx(xp, yp, U, shape, order=qorder)
yderiv = transform.derivy(xp, yp, U, shape, order=qorder)
zderiv = transform.derivz(xp, yp, U, shape, order=qorder)
# # plt.plot(xderiv)
# # plt.plot(yderiv)
fig = plt.figure()
ax = plt.gca()
pEXP.plot_xy(mesh, label=label_prop, ax=ax) #, ldg=)
pEXP.plot_ridges_harmonic(dfI_f, dfII_f, dfIII_f, ax=ax, label=True)
df_fit = dEXP.fit_ridges(df_f, rmvOutliers=True) # fit ridges on filtered data
# pEXP.plot_ridges_sources(df_fit, ax=ax, z_max_source=-max_elevation*1.2,
# ridge_type=[0,1,2],ridge_nb=None)
pEXP.plot_ridges_sources(df_fit,
ax=ax,
z_max_source=-6000,
ridge_type=[0, 1, 2],
ridge_nb=None)
square([x1, x2, -z1, -z2])
plt.annotate(dens, [(x1 + x2) / 2, -(z1 + z2) / 2])
#%%
# ridges analysis: scaling function to determine the SI index
z0 = -(z1 + z2) / 2 + 100 # choose an estimate of the depth of the anomaly
# z0 = -100 # choose an estimate of the depth of the anomaly
df_height = D_f[3:6]
ncol = 0
for r_type in range(len(df_height)): # loop over ridges type I, II, III
ncol = ncol + df_f[r_type].shape[1] - 1
fig, axs = plt.subplots(1, ncol, figsize=(15, 6), facecolor='w', edgecolor='k')
# fig.subplots_adjust(hspace = .5, wspace=.001)
#plt.savefig(pathFig+ strname + '_ExempleFig_prof' + str(za) + str(zb) + '.png')
# ------------------------------- Plot the data
plt.figure()
plt.axis('scaled')
mpl.contourf(yp, xp, gz, shape, 30)
plt.colorbar()
plt.xlabel('East (km)')
plt.ylabel('North (km)')
mpl.m2km()
plt.show()
#plt.title(strname +'ztop' + str(za) +'_zbot'+ str(zb) + '_data', fontsize=20)
#plt.savefig(pathFig+strname + '_ExempleFig_z' + str(za) + str(zb) + '_data' + '.png')
#x1, x2, y1, y2, z1, z2 = np.array(model[0].get_bounds())
square([y1, y2, x1, x2])
# %% ------------------------------- derivatives
xderiv = transform.derivx(xp, yp, gz, shape, order=1)
yderiv = transform.derivy(xp, yp, gz, shape)
zderiv = transform.derivz(xp, yp, gz, shape, order=1)
plt.subplots_adjust()
levels = 30
cbargs = dict(orientation='horizontal')
ax = plt.subplot(1, 3, 1)
plt.axis('scaled')
plt.title('x derivative')
mpl.contourf(yp, xp, xderiv, shape, levels, cmap=plt.cm.RdBu_r)
"""
GravMag: Calculate the gravity gradient tensor invariants
"""
import numpy
from fatiando import mesher, gridder
from fatiando.gravmag import polyprism, tensor
from fatiando.vis import mpl
print "Draw the polygons one by one"
area = [-10000, 10000, -10000, 10000]
dataarea = [-5000, 5000, -5000, 5000]
model = []
for depth in [5000, 5000]:
fig = mpl.figure()
mpl.axis('scaled')
mpl.square(dataarea)
for p in model:
mpl.polygon(p, '.-k', xy2ne=True)
mpl.set_area(area)
model.append(
mesher.PolygonalPrism(
mpl.draw_polygon(area, fig.gca(), xy2ne=True),
0, depth, {'density':500}))
# Calculate the effect
shape = (100, 100)
xp, yp, zp = gridder.regular(dataarea, 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),
"""
Gridding: Cut a section from a grid
"""
from fatiando import gridder, utils
from fatiando.vis import mpl
# Generate some synthetic data on a regular grid
x, y = gridder.regular((-10, 10, -10, 10), (100,100))
# Using a 2D Gaussian
z = utils.gaussian2d(x, y, 1, 1)
subarea = [-2, 2, -3, 3]
subx, suby, subscalar = gridder.cut(x, y, [z], subarea)
mpl.figure(figsize=(12, 5))
mpl.subplot(1, 2, 1)
mpl.title("Whole grid")
mpl.axis('scaled')
mpl.pcolor(x, y, z, (100,100))
mpl.square(subarea, 'k', linewidth=2, label='Cut this region')
mpl.legend(loc='lower left')
mpl.subplot(1, 2, 2)
mpl.title("Cut grid")
mpl.axis('scaled')
mpl.pcolor(subx, suby, subscalar[0], (40,60), interp=True)
mpl.show()
relief = mesher.PrismRelief(0, gridder.spacing(area,shape), nodes)
relief.addprop('density', (2670 for i in xrange(relief.size)))
gridarea = (-80, 80, -220, 220)
gridshape = (100, 100)
xp, yp, zp = gridder.regular(gridarea, gridshape, z=-200)
gz = gravmag.prism.gz(xp, yp, zp, relief)
mpl.figure(figsize=(10,7))
mpl.subplot(1, 2, 1)
mpl.title("Synthetic topography")
mpl.axis('scaled')
mpl.pcolor(x, y, height, shape)
cb = mpl.colorbar()
cb.set_label("meters")
mpl.square(gridarea, label='Computation grid')
mpl.legend()
mpl.subplot(1, 2, 2)
mpl.title("Topographic effect")
mpl.axis('scaled')
mpl.pcolor(xp, yp, gz, gridshape)
cb = mpl.colorbar()
cb.set_label("mGal")
mpl.show()
myv.figure()
myv.prisms(relief, prop='density')
axes = myv.axes(myv.outline())
myv.wall_bottom(axes.axes.bounds, opacity=0.2)
myv.wall_north(axes.axes.bounds)
myv.show()
import numpy
from fatiando import logger, mesher, gridder, gravmag
from fatiando.vis import mpl
log = logger.get()
log.info(logger.header())
log.info(__doc__)
log.info("Draw the polygons one by one")
area = [-10000, 10000, -10000, 10000]
dataarea = [-5000, 5000, -5000, 5000]
prisms = []
for depth in [5000, 5000]:
fig = mpl.figure()
mpl.axis('scaled')
mpl.square(dataarea)
for p in prisms:
mpl.polygon(p, '.-k', xy2ne=True)
mpl.set_area(area)
prisms.append(
mesher.PolygonalPrism(mpl.draw_polygon(area, fig.gca(), xy2ne=True), 0,
depth, {'density': 500}))
# Calculate the effect
shape = (100, 100)
xp, yp, zp = gridder.regular(dataarea, shape, z=-500)
tensor = [
gravmag.polyprism.gxx(xp, yp, zp, prisms),
gravmag.polyprism.gxy(xp, yp, zp, prisms),
gravmag.polyprism.gxz(xp, yp, zp, prisms),
gravmag.polyprism.gyy(xp, yp, zp, prisms),
gravmag.polyprism.gyz(xp, yp, zp, prisms),
plt, cmap = pEXP.plot_xy(mesh_dexp,
label=label_dexp,
markerMax=True,
qratio=str(qratio),
Vminmax=[0, 0.075],
p1p2=np.array([p1_s, p2_s]),
ax=ax,
Xaxis=x_axis) #, ldg=)
# plt, cmap = pEXP.plot_xy(mesh_dexp, label=label_dexp,
# markerMax=True,qratio=str(qratio)
# ax=ax, Xaxis=x_axis) #, ldg=)
cbar = plt.colorbar(cmap, shrink=0.25, pad=0.04)
cbar.set_label('ratio voltage (V)')
if x_axis == 'y':
square([xA_r_new[0], xA_r_new[1], -z1, -z2])
else:
square([yA_r[0], yA_r[1], -z1, -z2])
plt.xlim([200, 600])
plt.savefig('ratios_' + str(file) + '.png', dpi=450)
# %% ridges identification
dEXP.ridges_minmax_plot(Xs,
Ys,
mesh,
p1_s,
p2_s,
label=label_prop,
interp=interp,
x_resolution=interp_size,
ax = plt.gca()
plt, cmap = pEXP.plot_xy(mesh_dexp,
scaled=0,
label=label_dexp,
markerMax=True,
qratio=str(qratio),
p1p2=np.array([p1_s, p2_s]),
ax=ax,
Xaxis=x_axis) #, ldg=)
# plt, cmap = pEXP.plot_xy(mesh_dexp, label=label_dexp,
# markerMax=True,qratio=str(qratio)
# ax=ax, Xaxis=x_axis) #, ldg=)
plt.colorbar(cmap)
if x_axis == 'y':
square([xA_r[0], xA_r[1], -z1, -z2])
# plt.annotate('\Omega',[x1, -(z1+z2)/2])
else:
square([yA_r[0], yA_r[1], -z1, -z2])
# plt.annotate('\Omega',[(y1 + y2)/2, -(z1+z2)/2])
# p1p2=np.array([p1_r,p2_r])
# p_xaxis = []
# for i in p1p2[0]:
# if(i in p1p2[1]):
# p_xaxis.append(i)
#%% ------------------------------- ridges identification
# import Find_peaks_Du_et_al_2006 as fpeak
# from Find_peaks_Du_et_al_2006 import _boolrelextrema, _identify_ridge_lines, _filter_ridge_lines
scale = 0.1 * np.abs([image.min(), image.max()]).max()
x = mesh.get_xs() / 1000
y = mesh.get_ys() / 1000
z = mesh.get_zs() / 1000
x1, x2, y1, y2, z1, z2 = np.array(model[0].get_bounds()) / 1000
ax = plt.subplot(2, 2, 2)
ax.set_title('Model slice at z={} km'.format(z[len(z) // 2]))
ax.set_aspect('equal')
ax.pcolormesh(y,
x,
image[mesh.shape[0] // 2, :, :],
cmap="cubehelix",
vmin=-scale,
vmax=scale)
square([y1, y2, x1, x2])
ax.set_ylim(x.min(), x.max())
ax.set_xlim(y.min(), y.max())
ax.set_xlabel('y (km)')
ax.set_ylabel('x (km)')
ax = plt.subplot(2, 2, 3)
ax.set_title('Model slice at y={} km'.format(y[len(y) // 2]))
ax.set_aspect('equal')
ax.pcolormesh(x,
z,
image[:, :, mesh.shape[1] // 2],
cmap="cubehelix",
vmin=-scale,
vmax=scale)
square([x1, x2, z1, z2])
"""
Gridding: Cut a section from a grid
"""
from fatiando import gridder, utils
from fatiando.vis import mpl
# Generate some synthetic data on a regular grid
x, y = gridder.regular((-10, 10, -10, 10), (100, 100))
# Using a 2D Gaussian
z = utils.gaussian2d(x, y, 1, 1)
subarea = [-2, 2, -3, 3]
subx, suby, subscalar = gridder.cut(x, y, [z], subarea)
mpl.figure(figsize=(12, 5))
mpl.subplot(1, 2, 1)
mpl.title("Whole grid")
mpl.axis('scaled')
mpl.pcolor(x, y, z, (100, 100))
mpl.square(subarea, 'k', linewidth=2, label='Cut this region')
mpl.legend(loc='lower left')
mpl.subplot(1, 2, 2)
mpl.title("Cut grid")
mpl.axis('scaled')
mpl.pcolor(subx, suby, subscalar[0], (40, 60), interp=True)
mpl.show()
