def get_vlayer(offset_km=-5, layer='LAB'):
z = np.arange(0, 300, 0.5)
lats = np.arange(33, 43)
lons = np.arange(-115, -102)
t_LAB = 5
vs, bls, bis = define_models.load_all_models(z, lats, lons, t_LAB,
'_highQ')
# offset_km = -5
vlayer = np.zeros_like(vs[:, :, 0])
for ila in range(vs.shape[0]):
for ilo in range(vs.shape[1]):
if offset_km <= 0:
if layer == 'Moho': # above top of Moho
BL = bis[ila, ilo, 0]
if layer == 'LAB': # above top of LAB
BL = bis[ila, ilo, 2]
else:
if layer == 'Moho': # below bottom of Moho
BL = bis[ila, ilo, 1]
if layer == 'LAB': # below bottom of LAB
BL = bis[ila, ilo, 3]
offset = int(offset_km // np.diff(z[:2]))
dind = BL + offset
vlayer[ila, ilo] = vs[ila, ilo, dind]
return vlayer
def load_stuff():
z = np.arange(0, 300, 0.5)
lats = np.arange(33, 43)
lons = np.arange(-115, -102)
t_LAB = 5
vs, bls, bis = define_models.load_all_models(z, lats, lons, t_LAB,
'_highQ')
half_tlab_i = int(t_LAB / np.diff(z[:2]) / 2)
v_above_LAB = np.zeros_like(vs[:, :, 0])
v_below_LAB = np.zeros_like(v_above_LAB)
v_above_Moho = np.zeros_like(v_above_LAB)
v_below_Moho = np.zeros_like(v_above_LAB)
av_grad_SCLM = np.zeros_like(v_above_LAB)
max_grad_SCLM = np.zeros_like(v_above_LAB)
min_grad_SCLM = np.zeros_like(v_above_LAB)
for ila in range(vs.shape[0]):
for ilo in range(vs.shape[1]):
v_above_Moho[ila, ilo] = vs[ila, ilo, bis[ila, ilo, 0]]
v_below_Moho[ila, ilo] = vs[ila, ilo, bis[ila, ilo, 1]]
v_above_LAB[ila, ilo] = vs[ila, ilo, bis[ila, ilo, 2]]
v_below_LAB[ila, ilo] = vs[ila, ilo, bis[ila, ilo, 3]]
av_grad_SCLM[ila, ilo] = (
(v_above_LAB[ila, ilo] - v_below_Moho[ila, ilo]) /
(z[bis[ila, ilo, 2]] - z[bis[ila, ilo, 1]]))
max_grad_SCLM[ila, ilo] = (max(
np.diff(vs[ila, ilo, bis[ila, ilo, 1]:bis[ila, ilo, 2] + 1]) /
np.diff(z[:2])))
min_grad_SCLM[ila, ilo] = (min(
np.diff(vs[ila, ilo, bis[ila, ilo, 1]:bis[ila, ilo, 2] + 1]) /
np.diff(z[:2])))
return (z, lats, lons, vs, bls, bis, v_above_LAB, v_below_LAB,
v_above_Moho, v_below_Moho, av_grad_SCLM, max_grad_SCLM,
min_grad_SCLM)
def load_models(zmax=350):
z = np.arange(0, min((zmax, 350)), 0.5)
lats = np.arange(34, 41) #(33, 41)
lons = np.arange(-114, -106) #(-117, -102)
t_LAB = 5
vs, bls, bis = define_models.load_all_models(z, lats, lons, t_LAB,
'_smoothed')
return vs, bls, bis, z, lats, lons
def plot_v_model_comparison_on_map():
z = np.arange(0, 300, 0.5)
lats = np.arange(33, 43)
lons = np.arange(-115, -105)
t_LAB = 5.
vs, bls, bis = define_models.load_all_models(z, lats, lons, t_LAB)
lit_models = ('Y14','P14','P15','C15','S15','SR16','F18')
vs_lit = np.zeros((vs.shape[0], vs.shape[1], vs.shape[2], len(lit_models)))
cp_outline = pd.read_csv('data/earth_models/CP_outline.csv').values
cp_outline = np.vstack((cp_outline, cp_outline[0, :]))
f = plt.figure(figsize=(8, 12))
a = f.add_axes([0.1, 0.1, 0.7, 0.8], aspect=1.2)
a.plot(cp_outline[:, 1], cp_outline[:, 0], '-', color='#BDBDBD')
a.set(xlim=[lons[0]-0.5, lons[-1]+0.5], ylim=[lats[0]-0.5, lats[-1]+0.5],
xlabel='Longitude (W)', ylabel='Latitude (N)')
a_leg = f.add_axes([0.85, 0.3, 0.1, 0.4])
i = 0
cols = []
for mod in lit_models:
vs_lit[:, :, :, i] = constraints.interpolate_lit_model(mod, z, lats, lons)
p = a_leg.plot([0, 1], [i] * 2)
cols += [p[0].get_color()]
a_leg.text(1.5, i, mod, va='center')
i += 1
a_leg.set_axis_off()
a_leg.set_xlim([0, 3])
vlim = (3.5, 4.7)
zlim = (0, 150)
vz = vs[:, :, (zlim[0] <= z) & (z < zlim[1])]
vs_lit_z = vs_lit[:, :, (zlim[0] <= z) & (z < zlim[1]), :]
for ila in range(len(lats)):
for ilo in range(len(lons)):
pz = np.linspace(lats[ila] + 0.5, lats[ila] - 0.5, vz.shape[2])
i = 0
for mod in lit_models:
a.plot(_scale_vel_profile(vs_lit_z[ila, ilo, :, i], vlim, lons[ilo]),
pz, linewidth=2, color=cols[i])
i += 1
a.plot(_scale_vel_profile(vz[ila, ilo, :], vlim, lons[ilo]),
pz, 'k-', linewidth=2)
a.plot(lons[ilo] + np.array([-0.5, -0.5, 0.5, 0.5, -0.5]),
lats[ila] + np.array([-0.5, 0.5, 0.5, -0.5, -0.5]),
color='#F7F7F7', linewidth=2)
def plot_BLs_dVs_map(t_LAB=5.):
f = plt.figure(figsize=(14, 6))
ax_bl1 = f.add_axes([0.1, 0.2, 0.4, 0.7])
ax_bl2 = f.add_axes([0.55, 0.2, 0.4, 0.7])
z = np.arange(0, 300, 5)
lats = np.arange(33, 43)
lons = np.arange(-115, -105)
vs, bls, bis = define_models.load_all_models(z, lats, lons, t_LAB)
cp_outline = pd.read_csv('data/earth_models/CP_outline.csv').values
cp_outline = np.vstack((cp_outline, cp_outline[0, :]))
im = ax_bl1.imshow(bls[:, :, 2] * 100, cmap=plt.cm.RdBu_r, aspect=1.4,
vmin=0, vmax=np.quantile(bls[:, :, 2], 0.95) * 100)
ax_bl1.set(xticks=range(len(lons)), xticklabels = abs(lons),
yticks=range(len(lats)), yticklabels = lats,
xlabel='Longitude (W)', ylabel='Latitude (N)')
c1 = plt.colorbar(im, cax=f.add_axes([0.15, 0.1, 0.3, 0.02]),
orientation='horizontal')
c1.ax.set_xlabel('Moho dVs (%)')
ax_bl1.set_title('Moho dVs')
ax_bl1.set_ylim((0, len(lats) - 1))
ax_bl1.plot(cp_outline[:, 1] - lons[0],
cp_outline[:, 0] - lats[0], 'k:')
im2 = ax_bl2.imshow(bls[:, :, 3] * 100, cmap=plt.cm.RdBu, aspect=1.4,
vmin=np.quantile(bls[:, :, 3], 0.05) * 100, vmax=0)
ax_bl2.set(xticks=range(len(lons)), xticklabels = abs(lons),
yticks=range(len(lats)), yticklabels = lats,
xlabel='Longitude (W)', ylabel='Latitude (N)')
c2 = plt.colorbar(im2, cax=f.add_axes([0.6, 0.1, 0.3, 0.02]),
orientation='horizontal')
c2.ax.set_xlabel('LAB dVs (%)')
ax_bl2.set_title('LAB dVs')
ax_bl2.set_ylim((0, len(lats) - 1))
ax_bl2.plot(cp_outline[:, 1] - lons[0],
cp_outline[:, 0] - lats[0], 'k:')
plt.savefig(
'/media/sf_VM_Shared/rftests/map_BLdVs.png'
)
def plot_results_map(depth, t_LAB=5., vmi=0, vma=0, ifsave=False):
f = plt.figure(figsize=(6, 6))
ax_map = f.add_axes([0.1, 0.2, 0.8, 0.7])
z = np.arange(0, 300, 5)
lats = np.arange(33, 43)
lons = np.arange(-115, -105)
vs, bls, bis = define_models.load_all_models(z, lats, lons, t_LAB)
cp_outline = pd.read_csv('data/earth_models/CP_outline.csv').values
cp_outline = np.vstack((cp_outline, cp_outline[0, :]))
idep = np.argmin(abs(z - depth))
if vmi == 0:
if z[idep] < 40:
vmi = 3.
vma = 4.
else:
vmi = 3.75
vma = 4.5
im = ax_map.imshow(vs[:, :, idep], cmap=plt.cm.RdBu, aspect=1.4,
vmin=vmi, vmax=vma)
ax_map.set(xticks=range(len(lons)), xticklabels = abs(lons),
yticks=range(len(lats)), yticklabels = lats,
xlabel='Longitude (W)', ylabel='Latitude (N)')
c = plt.colorbar(im, cax=f.add_axes([0.15, 0.1, 0.7, 0.02]),
orientation='horizontal')
c.ax.set_xlabel('Vsv (km/s)')
ax_map.set_title('Velocity slice at {} km'.format(z[idep]))
ax_map.set_ylim((0, len(lats) - 1))
ax_map.plot(cp_outline[:, 1] - lons[0],
cp_outline[:, 0] - lats[0], 'k:')
if ifsave:
plt.savefig(
'/media/sf_VM_Shared/rftests/map_{}kmLAB_{}kmDepth.png'.format(
t_LAB, z[idep]
)
)
return ax_map
def plot_all_v_models_on_map():
z = np.arange(0, 350, 0.5)
lats = np.arange(34, 41)
lons = np.arange(-114, -106)
t_LAB = 5
vs_older, bls, bis = define_models.load_all_models(z, lats, lons, 5, '_highQ')
vs_old, bls, bis = define_models.load_all_models(z, lats, lons, 5, '_damped')
vs, bls, bis = define_models.load_all_models(z, lats, lons, t_LAB, '_smoothed')
#vs = constraints.interpolate_lit_model('P15', z, lats, lons)
cp_outline = pd.read_csv('data/earth_models/CP_outline.csv').values
cp_outline = np.vstack((cp_outline, cp_outline[0, :]))
f = plt.figure(figsize=(10, 12))
a = f.add_axes([0.1, 0.1, 0.8, 0.8], aspect=1.2)
a.plot(cp_outline[:, 1], cp_outline[:, 0], 'k-')
a.set(xlim=[lons[0]-0.5, lons[-1]+0.5], ylim=[lats[0]-0.5, lats[-1]+0.5],
xlabel='Longitude (W)', ylabel='Latitude (N)')
vlim = (3.2, 4.8)
zlim = (0, 350)
vz = vs[:, :, (zlim[0] <= z) & (z < zlim[1])]
#iiz = vz.shape[2] #np.argmax(z > 120) #
vsold = vs_old[:, :, (zlim[0] <= z) & (z < zlim[1])]
vsolder = vs_older[:, :, (zlim[0] <= z) & (z < zlim[1])]
# maxv = (np.quantile(np.quantile(vz, 0.75, 0), 0.75, 0) - vlim[0]) / np.diff(vlim) - 0.5
# minv = (np.quantile(np.quantile(vz, 0.25, 0), 0.25, 0) - vlim[0]) / np.diff(vlim) - 0.5
lit_models = ('Y14', 'P14','P15','C15','S15','SR16','F18')
vs_lit = np.zeros((vs.shape[0], vs.shape[1], vs.shape[2], len(lit_models)))
i = 0
for mod in lit_models:
vs_lit[:, :, :, i] = constraints.interpolate_lit_model(mod, z, lats, lons)
i += 1
i = 0
for mod in lit_models:
ds = constraints.load_literature_vel_model(mod)
z_a = ds.depth.values
if zlim[0] < z_a[0]:
iz = np.argmax(z_a[0] < z)
vs_lit[:, :, :iz, i] = np.median(vs_lit[:, :, :iz, :], 3)
if z_a[-1] < zlim[1]:
iz = np.argmax(z_a[-1] < z)
vs_lit[:, :, iz:, i] = np.median(vs_lit[:, :, iz:, :], 3)
i += 1
vs_lit_z = vs_lit[:, :, (zlim[0] <= z) & (z < zlim[1]), :]
maxv = (np.max(vs_lit_z, 3) - vlim[0]) / np.diff(vlim) - 0.5
minv = (np.min(vs_lit_z, 3) - vlim[0]) / np.diff(vlim) - 0.5
maxv[maxv < -0.5] = -0.5
minv[minv < -0.5] = -0.5
for ila in range(len(lats)):
for ilo in range(len(lons)):
pz = np.linspace(lats[ila] + 0.5, lats[ila] - 0.5, vz.shape[2])
# a.fill(np.append(maxv + lons[ilo], minv[::-1] + lons[ilo]),
# np.append(pz, pz[::-1]), color='#8B8B8B')
a.fill(np.append(maxv[ila, ilo, :] + lons[ilo],
minv[ila, ilo, ::-1] + lons[ilo]),
np.append(pz, pz[::-1]), color='#8B8B8B')
v = (vsolder[ila, ilo, :] - vlim[0]) / np.diff(vlim) - 0.5
v[v < -0.5] = -0.5
v += lons[ilo]
a.plot(v, pz, 'c-', linewidth=1.5)
v = (vsold[ila, ilo, :] - vlim[0]) / np.diff(vlim) - 0.5
v[v < -0.5] = -0.5
v += lons[ilo]
a.plot(v, pz, 'b-', linewidth=1)
v = (vz[ila, ilo, :] - vlim[0]) / np.diff(vlim) - 0.5
ilab = np.argmax(z > bls[ila, ilo, 1] + 5)
# im = np.argmax(np.diff(v[ilab:-200]) <= 0) + ilab#np.argmin(v[ilab:]) + ilab
v[v < -0.5] = -0.5
v += lons[ilo]
a.plot([lons[ilo]] * len(pz), pz, 'k-', linewidth=0.5)
a.plot(lons[ilo] + [-0.5, 0.5], [lats[ila]] * 2, 'k-', linewidth=0.5)
a.plot(v, pz, 'r-', linewidth=1)
# a.plot(v[:iiz], pz[:iiz], 'r-', linewidth=1)
# if im > ilab:
# a.plot(v, pz, 'b-', linewidth=1)
a.plot(lons[ilo] + np.array([-0.5, -0.5, 0.5, 0.5, -0.5]),
lats[ila] + np.array([-0.5, 0.5, 0.5, -0.5, -0.5]),
color='#F7F7F7', linewidth=2)