def _profiles_contour(profiles, contours=_CONTOURS, npoints=200):
import pylab
any_magnetic = False
for p in profiles.values():
# Find limits of all profiles
z = np.hstack([line[0] for line in p])
zp = np.linspace(np.min(z), np.max(z), npoints)
if len(p[0]) == 3:
rho_color = next_color()
# Interpolate rho on common z
rho = np.vstack([np.interp(zp, L[0], L[1]) for L in p])
# Plot the quantiles
plot_quantiles(zp, rho, contours, rho_color)
# Plot the best
pylab.plot(zp, rho[0], '-', color=dhsv(rho_color, dv=-0.2))
else:
any_magnetic = True
rho_color = next_color()
rhoM_color = next_color()
# Interpolate rho, rhoM on common z
rho = np.vstack([np.interp(zp, L[0], L[1]) for L in p])
rhoM = np.vstack([np.interp(zp, L[0], L[3]) for L in p])
# Plot the quantiles
plot_quantiles(zp, rho, contours, rho_color)
plot_quantiles(zp, rhoM, contours, rhoM_color)
# Plot the best
pylab.plot(zp, rho[0], '-', color=dhsv(rho_color, dv=-0.2))
pylab.plot(zp, rhoM[0], '-', color=dhsv(rhoM_color, dv=-0.2))
_profiles_labels(any_magnetic)
def _profiles_overplot(profiles):
import pylab
alpha = 0.1
any_magnetic = False
for p in profiles.values():
if len(p[0]) == 3:
rho_color = next_color()
for z, rho, _ in p[1:]:
pylab.plot(z, rho, '-', color=rho_color, alpha=alpha)
# Plot best
z, rho, _ = p[0]
pylab.plot(z, rho, '-', color=dhsv(rho_color, dv=-0.2))
else:
any_magnetic = True
rho_color = next_color()
rhoM_color = next_color()
for z, rho, _, rhoM, _ in p[1:]:
pylab.plot(z, rho, '-', color=rho_color, alpha=alpha)
pylab.plot(z, rhoM, '-', color=rhoM_color, alpha=alpha)
# Plot best
z, rho, _, rhoM, _ = p[0]
pylab.plot(z, rho, '-', color=dhsv(rho_color, dv=-0.2))
pylab.plot(z, rhoM, '-', color=dhsv(rhoM_color, dv=-0.2))
_profiles_labels(any_magnetic)
def _profiles_overplot(profiles):
for model, group in profiles.items():
name = model.name
absorbing = any((L[2] != 1e-4).any() for L in group)
magnetic = (len(group[0]) > 3)
# Note: Use 3 colours per dataset for consistency
_draw_overplot(group, 1, name + ' rho')
if absorbing:
_draw_overplot(group, 2, name + ' irho')
else:
next_color()
if magnetic:
_draw_overplot(group, 3, name + ' rhoM')
else:
next_color()
_profile_labels()
def _residuals_contour(Q, residuals, contours=CONTOURS):
import matplotlib.pyplot as plt
shift = 0
for m, r in residuals.items():
color = next_color()
plot_quantiles(Q[m], shift + r.T, contours, color)
plt.plot(Q[m],
shift + r[:, 0],
'.',
label=m.name,
markersize=1,
color=dark(color))
# Use 3 colours from cycle so reflectivity matches rho for each dataset
next_color()
next_color()
shift += 5
_residuals_labels()
def _draw_contours(group, index, label, zp, contours):
import matplotlib.pyplot as plt
color = next_color()
# Interpolate on common z
fp = np.vstack([np.interp(zp, L[0], L[index]) for L in group])
# Plot the quantiles
plot_quantiles(zp, fp, contours, color)
# Plot the best
plt.plot(zp, fp[0], '-', label=label, color=dark(color))
def _draw_overplot(group, index, label):
import matplotlib.pyplot as plt
alpha = 0.1
color = next_color()
for L in group[1:]:
plt.plot(L[0], L[index], '-', color=color, alpha=alpha)
# Plot best
L = group[0]
plt.plot(L[0], L[index], '-', label=label, color=dark(color))
def _residuals_contour(Q, residuals, contours=_CONTOURS):
import pylab
shift = 0
for m, r in residuals.items():
color = next_color()
plot_quantiles(Q[m], shift+r.T, contours, color)
pylab.plot(Q[m], shift+r[:, 0], '.', markersize=1,
color=dhsv(color, dv=-0.2)) # best
shift += 5
_residuals_labels()
def _profiles_contour(profiles, contours=CONTOURS, npoints=200):
for model, group in profiles.items():
name = model.name
absorbing = any((L[2] > 1e-4).any() for L in group)
magnetic = (len(group[0]) > 3)
# Find limits of all profiles
z = np.hstack([line[0] for line in group])
zp = np.linspace(np.min(z), np.max(z), npoints)
# Note: Use 3 colours per dataset for consistency
_draw_contours(group, 1, name + ' rho', zp, contours)
if absorbing:
_draw_contours(group, 2, name + ' irho', zp, contours)
else:
next_color()
if magnetic:
_draw_contours(group, 3, name + ' rhoM', zp, contours)
else:
next_color()
_profile_labels()
def _residuals_overplot(Q, residuals):
import pylab
alpha = 0.4
shift = 0
for m, r in residuals.items():
color = next_color()
pylab.plot(Q[m], shift+r[:, 1:], '.', markersize=1,
color=color, alpha=alpha)
pylab.plot(Q[m], shift+r[:, 0], '.', markersize=1,
color=dhsv(color, dv=-0.2)) # best
shift += 5
_residuals_labels()
def _residuals_overplot(Q, residuals):
import matplotlib.pyplot as plt
alpha = 0.4
shift = 0
for m, r in residuals.items():
color = next_color()
plt.plot(Q[m],
shift + r[:, 1:],
'.',
markersize=1,
color=color,
alpha=alpha)
plt.plot(Q[m],
shift + r[:, 0],
'.',
label=m.name,
markersize=1,
color=dark(color))
# Use 3 colours from cycle so reflectivity matches rho for each dataset
next_color()
next_color()
shift += 5
_residuals_labels()
