def TopHTSection(lines, rstep, tstep):
fig, ax = plt.subplots(1, 2)
ax[0], img = HThist(lines['AvgRho'],
lines['AvgTheta'],
rstep=rstep,
tstep=tstep,
ax=ax[0])
h = img[0]
xedges = img[1]
yedges = img[2]
[i, j] = np.unravel_index(h.argmax(), h.shape)
xe = [xedges[i], xedges[i + 1]]
ye = [yedges[j], yedges[j + 1]]
print(h.max(), "clustered lines at", xe, "degrees", ye, "rho (m)")
# masklat= (dikeset['latitude'] > lat1) & (dikeset['latitude'] < lat2)
# masklong=(dikeset['longitude'] > lon1) & (dikeset['longitude'] < lon2)
# masklatlong= (masklat==1) & (masklong==1)
maskTheta = (lines['AvgTheta'] > xe[0]) & (lines['AvgTheta'] < xe[1])
maskRho = (lines['AvgRho'] > ye[0]) & (lines['AvgRho'] < ye[1])
mask = (maskTheta == True) & (maskRho == True)
print(np.sum(mask), "should equal", h.max())
print(np.sum(mask) == h.max())
toplines = lines[mask]
plotlines(lines, 'k', ax[1], alpha=0.1)
plotlines(toplines, 'r', ax[1])
print(toplines['Size'].sum(), "dike segements")
return toplines
def checkoutCluster(dikeset, label):
fig, ax = plt.subplots()
mask = (dikeset['Labels'] == label)
lines = dikeset[mask]
plotlines(lines, 'r', ax)
xc, yc = HT_center(dikeset)
x, y = endpoints2(lines)
plotlines(lines, 'r', ax, linewidth=3)
#pltRec(lines, xc, yc, ax)
#pltLine(lines, xc, yc, ax)
return fig, ax
def rotateHT(dikeset, deg):
xci, yci = HT_center(dikeset)
p = 2
fig, ax = plt.subplots(1, 4)
fig2, ax2 = plt.subplots(2, 3)
plotlines(dikeset, 'k', ax[0])
theta, rho, xc, yc = AKH_HT(dikeset)
theta0 = theta
rho0 = rho
ax[0].plot(xc,
yc,
"*",
color='grey',
markeredgecolor="black",
markersize=20)
ax[1].set_title('not Rotated (black)')
ax[1].scatter(theta, rho, c='grey')
mrho = max(rho)
minrho = min(rho)
colors = [
'red', 'green', 'blue', 'yellow', 'orange', 'purple', 'magenta',
'lime', 'tab:cyan', 'turquoise'
]
for i in [deg, 2 * deg]:
dfr = rotateData2(dikeset, i)
theta, rho, xc, yc = AKH_HT(dfr)
dist = np.sqrt((theta0 - theta)**2 + (rho0 - rho)**2)
print("jittering mean:", np.mean(dist), "max:", max(dist))
ax[p].scatter(theta0, rho0, c='grey', alpha=0.5)
ax[p].scatter(theta, rho, c=dist, cmap="plasma")
ax[p].set_title("Rotated " + str(i) + colors[p])
print(colors[p])
plotlines(dfr, colors[p], ax[0])
ax2[0, p - 1].scatter(theta0, dist)
ax2[0, p - 1].set_xlabel("theta")
ax2[0, p - 1].set_ylabel("jitter")
ax2[1, p - 1].scatter((rho0), dist)
ax2[1, p - 1].set_xlabel("rho")
ax2[1, p - 1].set_xlim([-90, 90])
ax2[1, p - 1].set_ylabel("jitter")
p = p + 1
print(" ")
for i in range(p - 1):
ax[i + 1].set_ylim(minrho, mrho)
ax[i + 1].set_xlim([-90, 90])
def plotlabel(df, label):
fig, ax = plt.subplots()
mask = df['Labels'] == label
xc, yc = HT_center(df)
lines = df[mask]
plotlines(lines, "r", ax)
x, y = endpoints2(lines)
w, l = fit_Rec(lines, xc, yc)
r = squaresError(lines, xc, yc)
avgtheta = np.average(lines['theta'])
x1, x2, y1, y2 = clustered_lines(x, y, avgtheta)
ax.plot([x1, x2], [y1, y2], 'g')
ax.set_title("Label" + str(label))
ax.text(.89, .89, "W:" + str(w), transform=ax.transAxes)
ax.text(.84, .84, "L:" + str(l), transform=ax.transAxes)
ax.text(.80, .80, "errors:" + str(r), transform=ax.transAxes)
"""
import numpy as np
from plotmod import plotlines
import pandas as pd
from htMOD import AKH_HT as HT
import matplotlib.pyplot as plt
from sklearn.cluster import DBSCAN
import matplotlib.lines as mlines
from sklearn.preprocessing import scale
true=pd.read_csv('/home/akh/myprojects/Linking-and-Clustering-Dikes/test_rand1.csv')
theta, r, xc, yc= HT(true, xc=0, yc=0)
fig,ax=plt.subplots(2)
plotlines(true,'r', ax[0], linewidth=3)
ax[1].scatter(theta,r,c='red', edgecolor="black", s=100)
ax[1].set_ylabel('Rho (m)')
ax[1].set_xlabel('Theta (deg)')
true=pd.read_csv('/home/akh/myprojects/Linking-and-Clustering-Dikes/test_rand2.csv')
theta, r, xc, yc= HT(true, xc=0, yc=0)
plotlines(true,'b', ax[0], linewidth=3)
ax[1].scatter(theta,r, c="blue", edgecolor="black", s=100)
ax[1].set_ylabel('Rho (m)')
ax[1].set_xlabel('Theta (deg)')
def moveHTcenter(dikeset, r):
xci, yci = HT_center(dikeset)
p = 2
fig, ax = plt.subplots(1, 10)
fig2, ax2 = plt.subplots(2, 9)
plotlines(dikeset, 'k', ax[0])
theta, rho, xc, yc = AKH_HT(dikeset)
theta0 = theta
rho0 = rho
ax[0].plot(xc,
yc,
"*",
color='grey',
markeredgecolor="black",
markersize=20)
ax[1].set_title('grey')
ax[1].scatter(theta, rho, c='grey')
mrho = max(rho)
minrho = min(rho)
colors = [
'red', 'green', 'blue', 'yellow', 'orange', 'purple', 'magenta',
'lime', 'tab:cyan', 'turquoise'
]
for i in [-r, 0, r]:
for j in [-r, 0, r]:
if i == 0 and j == 0:
continue
print(colors[p], i, j, np.sqrt(i**2 + j**2))
theta, rho, xc, yc = AKH_HT(dikeset, xc=xci + i, yc=yci + j)
if max(rho) > mrho:
mrho = max(rho)
if min(rho) < minrho:
minrho = min(rho)
dist = np.sqrt((theta0 - theta)**2 + (rho0 - rho)**2)
print("jittering mean:", np.mean(dist), "max:", max(dist))
ax[p].scatter(theta0, rho0, c='grey', alpha=0.5)
ax[p].scatter(theta,
rho,
c=dist / np.sqrt(i**2 + j**2),
cmap="plasma")
ax[p].set_title(colors[p])
ax2[0, p - 1].scatter(theta, dist)
ax2[0, p - 1].set_xlabel("theta")
ax2[0, p - 1].set_ylabel("jitter (m)")
ax2[1, p - 1].scatter((rho), dist - np.sqrt(i**2 + j**2))
ax2[1, p - 1].set_xlabel("rho")
ax2[1, p - 1].set_ylabel("jitter (m)")
ax[0].plot(xc,
yc,
"*",
color=colors[p],
markeredgecolor="black",
markersize=20)
p = p + 1
print(" ")
for i in range(p - 1):
ax[i + 1].set_ylim(minrho, mrho)
points.sort_values(by='FormationID')
npoints = len(points)
myProj = Proj("+proj=utm +zone=11, +ellps=WGS84 +datum=WGS84 +units=m")
#myProj = Proj("+proj=utm +zone=11, +ellps=WGS84 +datum=NAD83 +units=m")
#lon1, lat1 = myProj(df['Xstart'].values, df['Ystart'].values, inverse = True)
#lon2, lat2 = myProj(df['Xend'].values, df['Yend'].values, inverse = True)
linelocSegments = np.empty([npoints, 4])
fig1, ax1 = plt.subplots(2)
#ax[0].scatter(points['longitude'], points['latitude'], c='r')
#plotlines(df, 'k', ax[0], myProj=myProj)
UTMx, UTMy = myProj(points['Long.'], points['Lat.'])
ax1[0].scatter(UTMx, UTMy, c='r')
plotlines(dfSegments, 'k', ax1[0])
x1 = dfSegments['Xstart'].values
x2 = dfSegments['Xend'].values
y1 = dfSegments['Ystart'].values
y2 = dfSegments['Yend'].values
b = np.linspace(0, 50, 10)
for i in range(npoints):
#print(i)
dist = abs((x2 - x1) * (y1 - UTMy[i]) - (x1 - UTMx[i]) *
(y2 - y1)) / np.sqrt((x2 - x1)**2 + (y2 - y1)**2)
loc = np.argmin(dist)
linelocSegments[i, 0] = loc
linelocSegments[i, 1] = np.min(dist)
m2 = np.append(m2, linearm * np.ones(20))
df = pd.DataFrame({
'Xstart': Xstart,
'Xend': Xend,
'Ystart': Ystart,
'Yend': Yend,
'Slope': m2
})
df.to_csv('/home/akh/myprojects/Linking-and-Clustering-Dikes/synthetic.csv')
#df=rotateData2(df, 50)
fig, ax = plt.subplots(2, 2)
plotlines(df, 'k', ax[0][0])
theta1, rho1, xc, yc = AKH_HT(df.astype(float), xc=0, yc=0)
ax[1][0].scatter(theta1, rho1, edgecolor='black')
ax[0][0].plot(0, 0, "*", mec='black', markersize=20)
ax[1][1].set_xlabel('Theta (deg)')
ax[1][0].set_xlabel('Theta (deg)')
ax[0][1].set_label('Perfect Radial Swarm')
ax[0][0].set_label('With Noise')
ax[1][0].set_ylabel('Rho (m)')
#dfFrag1=fragmentDikes(df, distortion=1)
d = 20000
# plotlines(dfFrag1, 'r', ax[0][1], linewidth=2)
# plotlines(df, 'k', ax[0][1], alpha=0.1)
Xend = np.append(Xend1, dikelength[1] / np.sqrt(1 + m**2))
m2 = np.append(m, m)
Yend = m2 * Xend
labels = np.arange(0, ndikes * 2)
df = pd.DataFrame({
"label": labels,
'Xstart': Xstart,
'Xend': Xend,
'Ystart': Ystart,
'Yend': Yend,
'Slope': m2
})
npoints = 70
points = np.random.rand(npoints, 2) * dikelength[0] / 2
lineloc = np.empty([npoints])
fig, ax = plt.subplots()
ax.scatter(points[:, 0], points[:, 1], c='r')
plotlines(df, 'k', ax)
x1 = df['Xstart'].values
x2 = df['Xend'].values
y1 = df['Ystart'].values
y2 = df['Yend'].values
for i in range(npoints):
dist = abs((x2 - x1) * (y1 - points[i, 1]) - (x1 - points[i, 0]) *
(y2 - y1)) / np.sqrt((x2 - x1)**2 + (y2 - y1)**2)
loc = np.argmin(dist)
print(labels[loc])