pEXP.plot_line(xp, yp, U, p1, p2, interp=interp, Xaxis=x_axis)
#%%
# Upward continuation of the field data
mesh, label_prop = dEXP.upwc(xp,
yp,
zp,
U,
shape,
zmin=0,
zmax=max_elevation,
nlayers=nlay,
qorder=qorder)
plt, cmap = pEXP.plot_xy(mesh, label=label_prop, Xaxis=x_axis)
plt.colorbar(cmap)
#%%
# ridges identification
# dEXP.ridges_minmax_plot(xp, yp, mesh, p1, p2,
# label=label_prop,
# fix_peak_nb=2,
# method_peak='find_peaks')
# or find_peaks or peakdet or spline_roots
dfI, dfII, dfIII = dEXP.ridges_minmax(xp,
yp,
mesh,
p1,
p2,
#%%
# save data loop
MESH.append(mesh)
LABEL.append(label_prop)
DF_F.append(df_f)
DF_FIT.append(df_fit)
XXZZ.append(xxzz)
CTm.append(CT)
#%%
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
MESHratio.append(mesh_ratio)
LABELratio.append(label_ratio)
XXZZ.append(xxzz)
CTm.append(CT)
#%%
# Plot the results
scl = 0
i = 0
fig = plt.figure()
ax = plt.gca()
plt, cmap = pEXP.plot_xy(MESHratio[i],
scaled=scl,
label=LABELratio[i],
markerMax=True,
qratio=str(qratio),
p1p2=np.array([p1, p2]),
ax=ax,
Xaxis=x_axis)
cbar = plt.colorbar(cmap, shrink=0.25, pad=0.04)
cbar.set_label('ratio voltage (V)')
x1, x2, z1, z2 = XXZZ[i]
square([x1, x2, -z1, -z2])
plt.annotate(CTm[i], [(x1 + x2) / 2, -(z1 + z2) / 2])
i = 1
fig = plt.figure()
ax = plt.gca()
plt, cmap = pEXP.plot_xy(MESHratio[i],
scaled=scl,
pEXP.plot_field(xp, yp, gravity, shape_grid)
#%%
# Upward continuation of the field data with discretisation in altitude controlled by the number of layers (nlay) and the maximum elevation desired (max_elevation)
mesh, label_prop = dEXP.upwc(xp,
yp,
zp,
gravity,
shape_grid,
zmin=0,
zmax=max_elevation,
nlayers=nlay,
qorder=qorder)
plt, cmap = pEXP.plot_xy(mesh,
label=label_prop,
Xaxis=x_axis,
p1p2=np.array([p1, p2]))
plt.colorbar(cmap)
#%%
xderiv = transform.derivx(xp, yp, gravity, shape_grid, order=qorder)
yderiv = transform.derivy(xp, yp, gravity, shape_grid, order=qorder)
zderiv = transform.derivz(xp, yp, gravity, shape_grid, order=qorder)
# # plt.plot(xderiv)
# # plt.plot(yderiv)
# # interp = True
pEXP.plot_line(xp,
yp,
# ------------------------------- Model
xp, yp, zp, U, shape, p1, p2, coord= magfwd.load_mag_synthetic()
max_elevation=2*max(coord[:,2])
scaled, SI, zp, qorder, nlay, minAlt_ridge, maxAlt_ridge = para.set_par(shape=shape,max_elevation=max_elevation)
interp = True
#%% ------------------------------- Plot the data
pEXP.plot_line(xp, yp, U,p1,p2, interp=interp)
#%% ------- upward continuation of the field data
mesh, label_prop = dEXP.upwc(xp, yp, zp, U, shape,
zmin=0, zmax=max_elevation, nlayers=nlay,
qorder=qorder)
plt, cmap = pEXP.plot_xy(mesh, label=label_prop)
plt.colorbar(cmap)
# %% ridges identification
dEXP.ridges_minmax_plot(xp, yp, mesh, p1, p2,
label=label_prop,
method_peak='find_peaks')
# or find_peaks or peakdet or spline_roots
dfI,dfII, dfIII = dEXP.ridges_minmax(xp, yp, mesh, p1, p2,
label=label_prop,
method_peak='find_peaks')
#%% ------- upward continuation of the field data
p = [p1_s, p2_s]
mesh, label_prop = dEXP.upwc(Xs,
Ys,
zp,
U,
shape,
zmin=0,
zmax=max_elevation,
nlayers=nlay,
qorder=qorder)
plt, cmap = pEXP.plot_xy(mesh,
label=label_prop,
Xaxis=x_axis,
Vminmax=[0, 0.35],
p1p2=p)
cbar = plt.colorbar(cmap, shrink=0.25, pad=0.04)
cbar.set_label('upwc voltage (V)')
plt.tight_layout()
plt.savefig('upwc voltage' + str(file) + '.png', dpi=450)
#%% DEXP ratio
# x_axis = 'x'
qratio = [1, 0]
mesh_dexp, label_dexp = dEXP.dEXP_ratio(Xs,
Ys,
zp,
U,
shape,
smooth=smooth,
Xaxis=x_axis)
#%% ------- upward continuation of the field data
mesh, label_prop = dEXP.upwc(Xs,
Ys,
zp,
U,
shape,
zmin=0,
zmax=max_elevation,
nlayers=nlay,
qorder=qorder)
plt, cmap = pEXP.plot_xy(mesh, label=label_prop, Xaxis=x_axis)
plt.colorbar(cmap)
#%% DEXP ratio
# x_axis = 'x'
qratio = [1, 0]
mesh_dexp, label_dexp = dEXP.dEXP_ratio(Xs,
Ys,
zp,
U,
shape,
zmin=0,
zmax=max_elevation,
nlayers=nlay,
qorders=qratio)
fig = plt.figure()
interp=interp,
Xaxis=x_axis)
#%%
# Upward continuation of the field data with discretisation in altitude controlled by the number of layers (nlay) and the maximum elevation desired (max_elevation)
mesh, label_prop = dEXP.upwc(xp,
yp,
zp,
U,
shape,
zmin=0,
zmax=max_elevation,
nlayers=nlay,
qorder=qorder)
plt, cmap = pEXP.plot_xy(mesh, label=label_prop)
plt.colorbar(cmap)
p = np.array([p1, p2])
plt, cmap = pEXP.plot_xy(mesh, label=label_prop, Xaxis=x_axis,
p1p2=p) #, ldg=)
plt.colorbar(cmap)
# plt, cmap = pEXP.plot_xy(mesh, label=label_prop, Xaxis='y', p1p2=p) #, ldg=)
# plt.colorbar(cmap)
# plt, cmap = pEXP.slice_mesh(xp, yp, mesh, label_prop, p1, p2, interp=True, Xaxis='x')
# plt.colorbar(cmap)
#%%
# Ridges identification: plot all extremas obtained via find_peaks function (numpy) for a given altitude
