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 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 hscattergram(data, pwdist, lag, tol):
'''
Input: (data) NumPy array with three columns, the first two
columns should be the x and y coordinates, and
third should be the measurements of the variable
of interest
(lag) the lagged distance of interest
(tol) the allowable tolerance about (lag)
(pwdist) a square pairwise distance matrix
Output: h-scattergram figure showing the distribution of
measurements taken at a certain lag and tolerance
'''
# calculate the pairwise distances
indices = variograms.lagindices(pwdist, lag, tol)
# collect the head and tail measurements
head = data[indices[:, 0], 2]
tail = data[indices[:, 1], 2]
# create a scatterplot with equal axes
fig, ax = subplots()
ax.scatter(head, tail, marker="o", facecolor="none", edgecolor="k", alpha=0.5)
ax.set_aspect("equal")
# set the labels and the title
ax.set_ylabel("$z(u+h)$")
ax.set_xlabel("$z(u)$")
ax.set_title("Lags Between " + str(lag - tol) + " and " + str(lag + tol))
# grab the limits of the axes
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
# calculate the covariance and annotate
cv = variograms.covariance(data, indices)
ax.text(xmin * 1.25, ymin * 1.050, 'Covariance = {:3.2f}'.format(cv))
# calculate the semivariance and annotate
sv = variograms.semivariance(data, indices)
ax.text(xmin * 1.25, ymin * 1.025, 'Semivariance = {:3.2f}'.format(sv))
show()
def hscattergram(data, pwdist, lag, tol):
'''
Input: (data) NumPy array with three columns, the first two
columns should be the x and y coordinates, and
third should be the measurements of the variable
of interest
(lag) the lagged distance of interest
(tol) the allowable tolerance about (lag)
(pwdist) a square pairwise distance matrix
Output: h-scattergram figure showing the distribution of
measurements taken at a certain lag and tolerance
'''
# calculate the pairwise distances
indices = variograms.lagindices(pwdist, lag, tol)
# collect the head and tail measurements
head = data[indices[:, 0], 2]
tail = data[indices[:, 1], 2]
# create a scatterplot with equal axes
fig, ax = subplots()
ax.scatter(head,
tail,
marker="o",
facecolor="none",
edgecolor="k",
alpha=0.5)
ax.set_aspect("equal")
# set the labels and the title
ax.set_ylabel("$z(u+h)$")
ax.set_xlabel("$z(u)$")
ax.set_title("Lags Between " + str(lag - tol) + " and " + str(lag + tol))
# grab the limits of the axes
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
# calculate the covariance and annotate
cv = variograms.covariance(data, indices)
ax.text(xmin * 1.25, ymin * 1.050, 'Covariance = {:3.2f}'.format(cv))
# calculate the semivariance and annotate
sv = variograms.semivariance(data, indices)
ax.text(xmin * 1.25, ymin * 1.025, 'Semivariance = {:3.2f}'.format(sv))
show()
