def polaranisotropy(data, pwdist, lags, tol, nsectors):
angle = 360.0 / nsectors
atol = angle / 2.0
sectors = [atol + i * angle for i in range(nsectors)]
fig, ax = subplots()
cnorm = colors.Normalize(vmin=0, vmax=1)
scalarmap = cm.ScalarMappable(norm=cnorm, cmap=cm.jet)
for sector in sectors:
for lag in lags:
anisodata = (data, pwdist, lag, tol, sector, atol)
indices = variograms.anilagindices(*anisodata)
sv = variograms.semivariance(data, indices)
fc = scalarmap.to_rgba(sv)
center, r, width = (0, 0), lag, lags[0] * 2
theta1 = utilities.degree_to_bearing(sector + atol)
theta2 = utilities.degree_to_bearing(sector - atol)
wedge = mpatches.Wedge(center, r, theta1, theta2, width, color=fc)
ax.add_patch(wedge)
ax.set_xlim(-lags[-1], lags[-1])
ax.set_ylim(-lags[-1], lags[-1])
ax.set_aspect('equal')
def anisotropiclags(data, pwdist, lag, tol, angle, atol):
'''
SPatial ANIsotropy PLOT
'''
index = variograms.lagindices(pwdist, lag, tol)
anindex = variograms.anilagindices(data, pwdist, lag, tol, angle, atol)
fig, ax = subplots()
# plot the lagged distances
for pair in index:
head, tail = data[pair]
hx, hy, hz = head
tx, ty, tz = tail
x = [hx, tx]
y = [hy, ty]
ax.plot(x, y, 'k-', lw=2, alpha=0.25)
# plot the lagged distances within
# the anisotropy angle and tolerance
for pair in anindex:
head, tail = data[pair]
hx, hy, hz = head
tx, ty, tz = tail
x = [hx, tx]
y = [hy, ty]
ax.plot(x, y, 'r-', lw=1)
ax.set_xlabel('X')
ax.set_ylabel('Y')
def anisotropiclags(data, pwdist, lag, tol, angle, atol):
'''
SPatial ANIsotropy PLOT
'''
index = variograms.lagindices(pwdist, lag, tol)
anindex = variograms.anilagindices(data, pwdist, lag, tol, angle, atol)
fig, ax = subplots()
# plot the lagged distances
for pair in index:
head, tail = data[pair]
hx, hy, hz = head
tx, ty, tz = tail
x = [hx, tx]
y = [hy, ty]
ax.plot(x, y, 'k-', lw=2, alpha=0.25)
# a sanity check
# mx, my = np.mean( x ), np.mean( y )
# br = utilities.bearing( ( hx, hy ), ( tx, ty ) )
# ax.text( mx, my, '{0:3.2f}'.format( br ) )
# plot the lagged distances within
# the anisotropy angle and tolerance
for pair in anindex:
head, tail = data[pair]
hx, hy, hz = head
tx, ty, tz = tail
x = [hx, tx]
y = [hy, ty]
ax.plot(x, y, 'r-', lw=1)
ax.set_xlabel('X')
ax.set_ylabel('Y')
def polaranisotropy(data, pwdist, lags, tol, nsectors):
angle = 360.0 / nsectors
atol = angle / 2.0
sectors = [atol + i * angle for i in range(nsectors)]
fig, ax = subplots()
cnorm = colors.Normalize(vmin=0, vmax=1)
scalarmap = cm.ScalarMappable(norm=cnorm, cmap=cm.jet)
for sector in sectors:
for lag in lags:
anisodata = (data, pwdist, lag, tol, sector, atol)
indices = variograms.anilagindices(*anisodata)
sv = variograms.semivariance(data, indices)
fc = scalarmap.to_rgba(sv)
center, r, width = (0, 0), lag, lags[0] * 2
theta1 = utilities.degree_to_bearing(sector + atol)
theta2 = utilities.degree_to_bearing(sector - atol)
wedge = mpatches.Wedge(center, r, theta1, theta2, width, color=fc)
ax.add_patch(wedge)
ax.set_xlim(-lags[-1], lags[-1])
ax.set_ylim(-lags[-1], lags[-1])
ax.set_aspect('equal')
def anisotropiclags(data, pwdist, lag, tol, angle, atol):
'''
SPatial ANIsotropy PLOT
'''
index = variograms.lagindices(pwdist, lag, tol)
anindex = variograms.anilagindices(data, pwdist, lag, tol, angle, atol)
fig, ax = subplots()
# plot the lagged distances
for pair in index:
head, tail = data[pair]
hx, hy, hz = head
tx, ty, tz = tail
x = [hx, tx]
y = [hy, ty]
ax.plot(x, y, 'k-', lw=2, alpha=0.25)
# plot the lagged distances within
# the anisotropy angle and tolerance
for pair in anindex:
head, tail = data[pair]
hx, hy, hz = head
tx, ty, tz = tail
x = [hx, tx]
y = [hy, ty]
ax.plot(x, y, 'r-', lw=1)
ax.set_xlabel('X')
ax.set_ylabel('Y')