for slicedir in enumerate('x'):
if slicedir[1] == 'y':
print(slicedir[1])
x_axis = slicedir[1] # direction of p1p2 profil
SI = 1.5
p1 = [0, (easting[0])]
p2 = [20e3, (easting[0])]
else:
p1 = [(northing[0]), 0]
p2 = [(northing[0]), 20e3]
x_axis = slicedir[1] # direction of p1p2 profil
SI = 2
pEXP.plot_line(xp, yp, gravity, p1, p2, interp=True, Xaxis=x_axis)
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,
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)
# # interp = True
pEXP.plot_line(xp,
UF = U_int_scipyf
# UF = dEXP.smooth2d(XFs, YFs, UF, sigma=2)
# plt.savefig('smooth2d' + str(file) + '.png', dpi=450)
#%%
plt.figure()
plt.scatter(XFs, YFs, c=UF, cmap='viridis')
plt.colorbar()
plt.axis('square')
plt.show()
offset = 255 #260
p1_s = np.array([p1f_r[0] - min(xp_r) + offset, p2f_r[0] - min(xp_r) + offset])
p2_s = np.array([p1f_r[1] - min(yp_r), p2f_r[1] - min(yp_r)])
ax, plt = pEXP.plot_field(XFs, YFs, UF, shape, Vminmax=[0, 0.02])
ax.plot(coords_liner_s[2:5, 0], coords_liner_s[2:5, 1], 'k')
ax.plot(p1_s, p2_s, 'r')
x_axis = 'x'
# xx, yy, distance, profile, ax, plt = pEXP.plot_line(Xs, Ys, U ,p1_s,p2_s,
# interp=True, smooth=True,
# xaxis = x_axis)
p1_s = np.array([p1f_r[0] - min(xp_r) + offset, p1f_r[1] - min(yp_r)])
p2_s = np.array([
p2f_r[0] - min(xp_r) + offset, p2f_r[1] - min(yp_r) +
abs(p1f_r[1] - min(yp_r) - (yA_r[0] + yA_r[1]) / 2) -
abs(p2f_r[1] - min(yp_r) - (yA_r[0] + yA_r[1]) / 2)
])
# p1_s = np.array([p1[0] -min(xp_r)+260,0])
# p2_s = np.array([p2[0] -min(xp_r)+260,800])
extrapolate=True)
Xs, Ys = xint_scipy, yint_scipy
U = U_int_scipy
#%%
# ax, plt = pEXP.plot_field(xp,yp,U, shape,
# Vminmax=[0,0.35])
# ax.plot(coords_liner[2:5,0],coords_liner[2:5,1],'k')
# p1_s = np.array([p1[0] -min(xp_r),p2[0] -min(xp_r) ])
# p2_s = np.array([p1[1] -min(yp_r),p2[1] -min(yp_r) ])
offset = 255 #255 #260
p1_s = np.array([p1[0] - min(xp_r) + offset, p2[0] - min(xp_r) + offset])
p2_s = np.array([p1[1] - min(yp_r), p2[1] - min(yp_r)])
ax, plt = pEXP.plot_field(Xs, Ys, U, shape, Vminmax=[0, 0.35])
ax.plot(coords_liner_s[2:5, 0], coords_liner_s[2:5, 1], 'k')
ax.plot(p1_s, p2_s, 'r')
x_axis = 'x'
# xx, yy, distance, profile, ax, plt = pEXP.plot_line(Xs, Ys, U ,p1_s,p2_s,
# interp=True, smooth=True,
# xaxis = x_axis)
p1_s = np.array([p1[0] - min(xp_r) + offset, p1[1] - min(yp_r)])
p2_s = np.array([
p2[0] - min(xp_r) + offset,
p2[1] - min(yp_r) + abs(p1[1] - min(yp_r) - (yA_r[0] + yA_r[1]) / 2) -
abs(p2[1] - min(yp_r) - (yA_r[0] + yA_r[1]) / 2)
])
# p1_s = np.array([p1[0] -min(xp_r)+260,0])
# p2_s = np.array([p2[0] -min(xp_r)+260,800])
extrapolate=False) InterpData = np.array([xint_scipy, yint_scipy, U_int_scipy]).T where_are_NaNs = np.isnan(InterpData) InterpData[where_are_NaNs] = 0.0074 xint_scipy, yint_scipy, U_int_scipy = InterpData.T plt.scatter(xint_scipy, yint_scipy) plt.scatter(xp, yp) #%% Solution 2 # Extrapolate = True # U_int_scipy = gridder.interp_at(xp,yp,U, xint_scipy, yint_scipy, algorithm='cubic', extrapolate=True) # %% # len(U) pEXP.plot_field(xint_scipy, yint_scipy, U_int_scipy, shape) # pEXP.plot_field(xp_int,yp_int,U_int, shape) # xx, yy, distance, profile = pEXP.plot_line(xp_int,yp_int,U_int,p1,p2, # interp=True, smooth=False, Xaxis=x_axis) # %% plt.figure() plt.subplot(1, 2, 1) plt.scatter(xint_scipy, yint_scipy, c=U_int_scipy, label='U_int') plt.grid() plt.legend() p12x = [p1[0], p2[0]] p12y = [p1[1], p2[1]] plt.plot(p12x, p12y, c='red')