zderiv,
p1,
p2,
title='zderiv',
savefig=False,
interp=interp,
Xaxis=x_axis)
#%%
# Ridges identification: plot all extremas obtained via find_peaks function (numpy) for a given altitude
dEXP.ridges_minmax_plot(xp,
yp,
mesh,
p1,
p2,
label=label_prop,
method_peak='find_peaks',
showfig=True,
interp=True,
smooth=True,
Xaxis=x_axis)
#%%
# Ridges identification at all levels: plot extremas obtained via find_peaks function (numpy) for all 3 types of extremas familly RI, RII and RIII
D = dEXP.ridges_minmax(xp,
yp,
mesh,
p1,
p2,
label=label_prop,
method_peak='find_peaks',
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')
#%% ------------------------------- plot ridges over continuated section
fig = plt.figure()
ax = plt.gca()
pEXP.plot_xy(mesh, label=label_prop, ax=ax) #, ldg=)
pEXP.plot_ridges_harmonic(dfI,dfII,dfIII,ax=ax)
# plt, cmap = pEXP.plot_xy(mesh, label=label_prop, Xaxis='y', p1p2=np.array([p1, p2])) #, 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
dEXP.ridges_minmax_plot(xp,
yp,
mesh,
p1,
p2,
label=label_prop,
method_peak='find_peaks',
showfig=True,
Xaxis=x_axis,
qorder=qorder)
#%%
# Ridges identification at all levels: plot extremas obtained via find_peaks function (numpy) for all 3 types of extremas familly RI, RII and RIII
D = dEXP.ridges_minmax(xp,
yp,
mesh,
p1,
p2,
label=label_prop,
method_peak='find_peaks',
fix_peak_nb=3,
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,
smooth=smooth,
fix_peak_nb=2,
method_peak='find_peaks',
showfig=True,
Xaxis=x_axis)
#%%
# or find_peaks or peakdet or spline_roots
# dfI,dfII, dfIII = dEXP.ridges_minmax(Xs, Ys, mesh, p1_s, p2_s,interp=interp,x_resolution= interp_size,
# label=label_prop,fix_peak_nb=2,
# smooth=smooth, # true = low pass, otherwise specify the filter to apply
# method_peak='find_peaks',
# showfig=True,
# Xaxis=x_axis)
# pEXP.plot_xy(mesh, label=label_prop, Xaxis='x') #, ldg=)
pEXP.plot_xy(mesh, label=label_prop, Xaxis=x_axis, p1p2=p) #, ldg=)
# p1p2 = np.array([1,1,1,1])
# plt, cmap = pEXP.slice_mesh(xp, yp, mesh, label_prop, p1, p2, interp=True, Xaxis='y')
# plt.colorbar(cmap)
# %% ridges identification
dEXP.ridges_minmax_plot(xp,
yp,
mesh,
p1,
p2,
label=label_prop,
fix_peak_nb=nb_of_peak,
interp=interp,
smooth=smooth,
method_peak='find_peaks',
showfig=True,
Xaxis=x_axis)
# or find_peaks or peakdet or spline_roots
# dfI,dfII, dfIII = dEXP.ridges_minmax(xp, yp, mesh, p1, p2,interp=interp,
# label=label_prop,fix_peak_nb=nb_of_peak,
# smooth=smooth,
# method_peak='find_peaks',
# showfig=True,
# Xaxis=x_axis)
D = dEXP.ridges_minmax(xp,