def getPDFfromSetup(pdfoption,lonlatPframe,xMeshP,yMeshP):
row,col = np.shape(xMeshP)
if pdfoption.lower()=='kde' or pdfoption.lower()=='kernel':
ZZpdfPframe = SK.serialK(int(nSamples),lonlatPframe,row,col,xMeshP,yMeshP,.0,0)
elif pdfoption=='normal' or pdfoption=='gaussian' or pdfoption=='gauss':
nSamples,dims = np.shape(lonlatPframe)
if dims!=2:
print 'Error in lonlatPframe, dimensions are not correct'
print 'Check inputs to getPDFfromSetup in casualtyEstimate.py'
exit()
mean,covar = meancov.meancovmatrix(lonlatPframe,nSamples)
ZZpdfPframe = meancov.normalbivariate(mean,covar,xMeshP,yMeshP,col,row) #fortran version
return ZZpdfPframe
def getPDFfromSetup(pdfoption, lonlatPframe, xMeshP, yMeshP):
row, col = np.shape(xMeshP)
if pdfoption.lower() == 'kde' or pdfoption.lower() == 'kernel':
ZZpdfPframe = SK.serialK(int(nSamples), lonlatPframe, row, col, xMeshP,
yMeshP, .0, 0)
elif pdfoption == 'normal' or pdfoption == 'gaussian' or pdfoption == 'gauss':
nSamples, dims = np.shape(lonlatPframe)
if dims != 2:
print 'Error in lonlatPframe, dimensions are not correct'
print 'Check inputs to getPDFfromSetup in casualtyEstimate.py'
exit()
mean, covar = meancov.meancovmatrix(lonlatPframe, nSamples)
ZZpdfPframe = meancov.normalbivariate(mean, covar, xMeshP, yMeshP, col,
row) #fortran version
return ZZpdfPframe
def getPDFfromSetup(nSamples,pdfoption,lonlatPframe,lonOrMesh,latOrMesh,U,xMeshP,yMeshP):
row,col = np.shape(xMeshP)
#print xMeshP
if pdfoption.lower()=='kde' or pdfoption.lower()=='kernel':
ZZpdfPframe = SK.serialK(int(nSamples),lonlatPframe,row,col,xMeshP,yMeshP,.0,0)
elif pdfoption=='normal' or pdfoption=='gaussian' or pdfoption=='gauss':
ZZpdfPframe = meancov.normalbivariate(mean,covar,xMeshP,yMeshP,col,row) #fortran version
#ZZpdf on P frame
#xlen,ylen = np.shape(xMeshP)
return (xMeshP,yMeshP,ZZpdfPframe,lonOrMesh,latOrMesh)
def calculatePDF(refLonLat, xMesh, yMesh, pdfoption):
'''
plt.figure()
plt.plot(refLonLat[:,0],refLonLat[:,1],'x')
plt.show()
exit()
'''
xyz, x, y, z = pdf.getxyz(refLonLat)
xmean = np.mean(x)
ymean = np.mean(y)
zmean = np.mean(z)
mag = (xmean**2 + ymean**2 + zmean**2) #**.5
lonlat0 = pdf.getlonlat(xmean / mag, ymean / mag, zmean / mag)
lon0 = lonlat0[0, 0]
lat0 = lonlat0[0, 1]
beta = 0 # initial guess
nSamples, colvals = np.shape(refLonLat)
row, col = np.shape(xMesh)
beta = pdf.optimizeHeadingAngle(refLonLat, nSamples, lon0, lat0, beta)
R1 = pdf.Rotz(lon0 * np.pi / 180.) # generating rotation matrices
R2 = pdf.Roty(-lat0 * np.pi / 180.)
R3 = pdf.Rotx(beta * np.pi / 180.)
transformDetails = [R1, R2, R3, lon0, lat0, beta]
Uint = np.dot(R2, R1)
U = np.dot(R3, Uint)
lonlatPframe = pdf.originalFrame2Pframe(refLonLat, U)
lonlatMesh = np.zeros((np.size(xMesh), 2))
lonlatMesh[:, 0] = np.reshape(xMesh, np.size(xMesh))
lonlatMesh[:, 1] = np.reshape(yMesh, np.size(yMesh))
lonlatPMesh = pdf.originalFrame2Pframe(lonlatMesh, U)
ylen, xlen = np.shape(xMesh)
lonMeshP = np.reshape(lonlatPMesh[:, 0], [ylen, xlen])
latMeshP = np.reshape(lonlatPMesh[:, 1], [ylen, xlen])
if pdfoption.lower() == 'kde' or pdfoption.lower() == 'kernel':
ZZpdfPframe = SK.serialK(int(nSamples), lonlatPframe, row, col,
lonMeshP, latMeshP, .0, 0)
elif pdfoption == 'normal' or pdfoption == 'gaussian' or pdfoption == 'gauss':
ZZpdfPframe = meancov.normalbivariate(mean, covar, lonMeshP, latMeshP,
col, row) #fortran version
'''
plt.figure()
plt.contour(lonMeshP,latMeshP,ZZpdfPframe,30)
plt.plot(lonlatPframe[:,0],lonlatPframe[:,1],'x')
plt.figure()
plt.plot(lonlatPMesh[:,0],lonlatPMesh[:,1],'x')
exit()
'''
pqrVec, pVec, qVec, rVec = pdf.getxyz(
lonlatPMesh) # mesh locations in P frame
pdfN = pdf.transformPDF(U, pqrVec, np.pi / 180 * lonlatMesh[:, 0],
np.pi / 180 * lonlatMesh[:, 1],
np.reshape(ZZpdfPframe, xlen * ylen))
ZZpdfN = np.reshape(pdfN, [ylen, xlen])
lonMesh = np.reshape(lonlatMesh[:, 0], [ylen, xlen])
latMesh = np.reshape(lonlatMesh[:, 1], [ylen, xlen])
'''
plt.figure()
plt.contour(lonMeshP,latMeshP,ZZpdfPframe,30)
plt.plot(refLonLat[:,0],refLonLat[:,1],'x')
plt.show()
plt.figure()
plt.contour(lonMesh,latMesh,ZZpdfN,30)
plt.plot(refLonLat[:,0],refLonLat[:,1],'x')
plt.show()
'''
return (lonMesh, latMesh, ZZpdfN)
def getPDF(lonlat,nSamples,delta,nsigma,pdfoption):
# lon0, lat0 : main vehicle lat and lon used as initial conditions for debris propagation
# beta : main vehicle heading angle (counterclockwise starting East) [deg]
xyz,x,y,z = getxyz(lonlat)
xmean = np.mean(x)
ymean = np.mean(y)
zmean = np.mean(z)
mag = (xmean**2+ymean**2+zmean**2)**.5
lonlat0 = getlonlat(xmean/mag,ymean/mag,zmean/mag)
lon0 = lonlat0[0,0]
lat0 = lonlat0[0,1]
beta = 0 # initial guess
beta = optimizeHeadingAngle(lonlat,nSamples,lon0,lat0,beta)
R1 = Rotz(lon0*np.pi/180.) # generating rotation matrices
R2 = Roty(-lat0*np.pi/180.)
R3 = Rotx(beta*np.pi/180.)
transformDetails = [R1,R2,R3,lon0,lat0,beta]
Uint = np.dot(R2,R1)
U = np.dot(R3,Uint)
lonlatPframe = originalFrame2Pframe(lonlat,U)
# error check for this case not yet implemented
#mean,covar = meancov.meancovmatrix(lonlatPframe,nSamples)
#D,Ulocal = np.linalg.eig(covar)
#lonlatAframe = np.dot(lonlatPframe,Ulocal)
# ZZ = meancov.normalbivariate(mean,covar,XX,YY,xlen,ylen) #fortran version
if pdfoption.lower()=='kde' or pdfoption.lower()=='kernel':
xMeshP,yMeshP,xlen,ylen,areaInt = statsPython.areaOfInterestKDE(lonlatPframe,delta) # A frame could also be used
elif pdfoption=='normal' or pdfoption=='gaussian' or pdfoption=='gauss':
mean,covar = meancov.meancovmatrix(lonlatPframe,nSamples)
xMeshP,yMeshP,xlen,ylen = statsPython.areaOfInterest(mean,covar,delta,nsigma)
lonlatPframeMesh = np.zeros((np.size(xMeshP),2))
lonlatPframeMesh[:,0] = np.reshape(xMeshP,np.size(xMeshP))
lonlatPframeMesh[:,1] = np.reshape(yMeshP,np.size(yMeshP))
lonlatOrFrameMesh = Pframe2originalFrame(lonlatPframeMesh,U)
lonMesh,latMesh = getLonLatMesh(lonlatOrFrameMesh[:,0],lonlatOrFrameMesh[:,1],delta)
lonVec = np.reshape(lonMesh,np.size(lonMesh))
latVec = np.reshape(latMesh,np.size(latMesh))
lonlatVec = np.zeros((len(lonVec),2))
lonlatVec[:,0] = lonVec
lonlatVec[:,1] = latVec
lonlatVecP = originalFrame2Pframe(lonlatVec,U)
ylen,xlen = np.shape(lonMesh)
lonMeshP = np.reshape(lonlatVecP[:,0],[ylen,xlen])
latMeshP = np.reshape(lonlatVecP[:,1],[ylen,xlen])
pqrVec ,pVec,qVec,rVec= getxyz(lonlatVecP) # mesh locations in P frame
if pdfoption.lower()=='kde' or pdfoption.lower()=='kernel':
#ZZpdf = meancov.kde(lonlatPframe,lonMeshP,latMeshP,[nSamples, xlen,ylen])
print 'Starting quad KDE'
ZZpdf = SK.serialK(nSamples,lonlatPframe,ylen,xlen,lonMeshP,latMeshP,.1,1)
print 'Done with quad KDE'
elif pdfoption=='normal' or pdfoption=='gaussian' or pdfoption=='gauss':
ZZpdf = meancov.normalbivariate(mean,covar,lonMeshP,latMeshP,xlen,ylen) #fortran version
pdfN = transformPDF(U,pqrVec,np.pi/180*lonlatVec[:,0],np.pi/180*lonlatVec[:,1],np.reshape(ZZpdf,xlen*ylen))
ZZpdfN = np.reshape(pdfN,[ylen,xlen])
#plt.figure()
#plt.contourf(lonMesh,latMesh,np.reshape(pdfN,[ylen,xlen]))
#plt.show()
return (lonMesh,latMesh,ZZpdfN,lonMeshP,latMeshP,xlen,ylen,transformDetails,areaInt) #
def getPDF2(lonlat,nSamples,delta,nsigma,pdfoption):
# lon0, lat0 : main vehicle lat and lon used as initial conditions for debris propagation
# beta : main vehicle heading angle (counterclockwise starting East) [deg]
#import time
xyz,x,y,z = getxyz(lonlat)
xmean = np.mean(x)
ymean = np.mean(y)
zmean = np.mean(z)
mag = (xmean**2+ymean**2+zmean**2)
lonlat0 = getlonlat(xmean/mag,ymean/mag,zmean/mag)
lon0 = lonlat0[0,0]
lat0 = lonlat0[0,1]
beta = 0 # initial guess
beta = optimizeHeadingAngle(lonlat,nSamples,lon0,lat0,beta)
R1 = Rotz(lon0*np.pi/180.) # generating rotation matrices
R2 = Roty(-lat0*np.pi/180.)
R3 = Rotx(beta*np.pi/180.)
transformDetails = [R1,R2,R3,lon0,lat0,beta]
Uint = np.dot(R2,R1)
U = np.dot(R3,Uint)
lonlatPframe = originalFrame2Pframe(lonlat,U)
# error check for this case not yet implemented
#mean,covar = meancov.meancovmatrix(lonlatPframe,nSamples)
#D,Ulocal = np.linalg.eig(covar)
#lonlatAframe = np.dot(lonlatPframe,Ulocal)
# ZZ = meancov.normalbivariate(mean,covar,XX,YY,xlen,ylen) #fortran version
if pdfoption.lower()=='kde' or pdfoption.lower()=='kernel':
xMeshP,yMeshP,xlen,ylen= statsPython.areaOfInterestKDE(lonlatPframe,delta) # A frame could also be used
elif pdfoption=='normal' or pdfoption=='gaussian' or pdfoption=='gauss':
mean,covar = meancov.meancovmatrix(lonlatPframe,nSamples)
xMeshP,yMeshP,xlen,ylen = statsPython.areaOfInterest(mean,covar,delta,nsigma)
lonlatPframeMesh = np.zeros((np.size(xMeshP),2))
lonlatPframeMesh[:,0] = np.reshape(xMeshP,np.size(xMeshP))
lonlatPframeMesh[:,1] = np.reshape(yMeshP,np.size(yMeshP))
row,col = np.shape(xMeshP)
#print xMeshP
if pdfoption.lower()=='kde' or pdfoption.lower()=='kernel':
ZZpdfPframe = SK.serialK(int(nSamples),lonlatPframe,row,col,xMeshP,yMeshP,.0,0)
elif pdfoption=='normal' or pdfoption=='gaussian' or pdfoption=='gauss':
ZZpdfPframe = meancov.normalbivariate(mean,covar,xMeshP,yMeshP,col,row) #fortran version
#ZZpdf on P frame
lonlatOrFrameMesh = Pframe2originalFrame(lonlatPframeMesh,U)
lonOrMesh = np.reshape(lonlatOrFrameMesh[:,0],[ylen,xlen])
latOrMesh = np.reshape(lonlatOrFrameMesh[:,1],[ylen,xlen])
'''
print 'maxABS',np.max(np.abs(ZZpdfPframe-ZZpdfPframe0))
plt.figure()
plt.contourf(xMeshP,yMeshP,ZZpdfPframe)
plt.figure()
plt.contourf(xMeshP,yMeshP,ZZpdfPframe0)
plt.figure()
plt.contourf(xMeshP,yMeshP,ZZpdfPframe0-ZZpdfPframe)
plt.figure()
plt.plot(lonlatPframe[:,0],lonlatPframe[:,1],'x')
plt.figure()
plt.contourf(lonOrMesh,latOrMesh,ZZpdfPframe)
plt.plot(lonlat[:,0],lonlat[:,1],'x')
plt.show()
'''
return (xMeshP,yMeshP,ZZpdfPframe,lonOrMesh,latOrMesh,xlen,ylen,transformDetails) #
def getPDF(lonlat,pdfoption='kde',delta=None,nMesh=50):
#lonlat is a nx2 matrix with lon lat locations
# nsigma is only used for getting PDF area when using normal distribution
# pdf option 'kde' or 'normal' dist
# delta is spacing used for mesh in PDF
# if delta not specified,then it will use nMesh which is the number of points used in one dim...total mesh will be nMesh x nMesh
#this routine rotates the SPHERICAL planet to get a better 'flat planet' approximation by using local lat and lons and then applies the KDE or normal PDF routines
xyz,x,y,z = getxyz(lonlat)
xmean = np.mean(x)
ymean = np.mean(y)
zmean = np.mean(z)
mag = (xmean**2+ymean**2+zmean**2)**.5
lonlat0 = getlonlat(xmean/mag,ymean/mag,zmean/mag)
lon0 = lonlat0[0,0]
lat0 = lonlat0[0,1]
beta = 0 # initial guess
nSamples,dims = np.shape(lonlat)
if dims!=2:
print 'Error in lonlat, dimensions are not correct'
print 'Check inputs to getPDF in casualtyEstimate.py'
exit()
beta = optimizeHeadingAngle(lonlat,nSamples,lon0,lat0,beta)
R1 = Rotz(lon0*np.pi/180.) # generating rotation matrices
R2 = Roty(-lat0*np.pi/180.)
R3 = Rotx(beta*np.pi/180.)
transformDetails = [R1,R2,R3,lon0,lat0,beta]
Uint = np.dot(R2,R1)
U = np.dot(R3,Uint)
lonlatPframe = originalFrame2Pframe(lonlat,U)
if pdfoption.lower()=='kde' or pdfoption.lower()=='kernel':
xMeshP,yMeshP,xlen,ylen,areaInt = statsPython.areaOfInterestKDE(lonlatPframe,delta=delta,nMesh=nMesh) # A frame could also be used
elif pdfoption=='normal' or pdfoption=='gaussian' or pdfoption=='gauss':
#mean,covar = meancov.meancovmatrix(lonlatPframe,nSamples)
#xMeshP,yMeshP,xlen,ylen = statsPython.areaOfInterest(mean,covar,delta,nsigma)
xMeshP,yMeshP,xlen,ylen,areaInt = statsPython.areaOfInterestKDE(lonlatPframe,delta=delta,nMesh=nMesh) # A frame could also be used
lonlatPframeMesh = np.zeros((np.size(xMeshP),2))
lonlatPframeMesh[:,0] = np.reshape(xMeshP,np.size(xMeshP))
lonlatPframeMesh[:,1] = np.reshape(yMeshP,np.size(yMeshP))
lonlatOrFrameMesh = Pframe2originalFrame(lonlatPframeMesh,U)
lonMesh,latMesh = getLonLatMesh(lonlatOrFrameMesh[:,0],lonlatOrFrameMesh[:,1],delta)
lonVec = np.reshape(lonMesh,np.size(lonMesh))
latVec = np.reshape(latMesh,np.size(latMesh))
lonlatVec = np.zeros((len(lonVec),2))
lonlatVec[:,0] = lonVec
lonlatVec[:,1] = latVec
lonlatVecP = originalFrame2Pframe(lonlatVec,U)
ylen,xlen = np.shape(lonMesh)
lonMeshP = np.reshape(lonlatVecP[:,0],[ylen,xlen])
latMeshP = np.reshape(lonlatVecP[:,1],[ylen,xlen])
pqrVec ,pVec,qVec,rVec= getxyz(lonlatVecP) # mesh locations in P frame
if pdfoption.lower()=='kde' or pdfoption.lower()=='kernel':
#ZZpdf = meancov.kde(lonlatPframe,lonMeshP,latMeshP,[nSamples, xlen,ylen])
print 'Starting quad KDE'
ZZpdf = SK.serialK(nSamples,lonlatPframe,ylen,xlen,lonMeshP,latMeshP,.1,1)
print 'Done with quad KDE'
elif pdfoption=='normal' or pdfoption=='gaussian' or pdfoption=='gauss':
ZZpdf = meancov.normalbivariate(mean,covar,lonMeshP,latMeshP,xlen,ylen) #fortran version
pdfN = transformPDF(U,pqrVec,np.pi/180*lonlatVec[:,0],np.pi/180*lonlatVec[:,1],np.reshape(ZZpdf,xlen*ylen))
ZZpdfN = np.reshape(pdfN,[ylen,xlen])
PframeVals = [lonPMesh,latPMesh,ZZpdf]
OrFrameVals = [lonMesh,latMesh,ZZpdfN]
return (PframeVals,OrFrameVals,transformDetails,areaInt)
mean = [10, -5]
cov = [[1, -.6], [-.6, 1]]
x, y = np.random.multivariate_normal(mean, cov, 5000).T
xnew = np.array(np.matrix(x).T)
ynew = np.array(np.matrix(y).T)
xvec = np.linspace(0, 20, 600)
yvec = np.linspace(-15, 5, 600)
dx = xvec[2] - xvec[1]
dy = yvec[2] - yvec[1]
xy = np.concatenate((xnew, ynew), 1)
#print xy
#print np.std(ynew)
X, Y = np.meshgrid(xvec, yvec)
fhatFAST = SK.serialK(5000, xy, 600, 600, X, Y, 2, 1)
print fhatFAST
print np.sum(fhatFAST * dx * dy)
plt.figure(1)
plt.plot(x, y, 'x')
plt.axis('equal')
plt.figure(2)
plt.contour(X, Y, fhatFAST, 50)
plt.show()
def calculatePDF(refLonLat,xMesh,yMesh,pdfoption):
'''
plt.figure()
plt.plot(refLonLat[:,0],refLonLat[:,1],'x')
plt.show()
exit()
'''
xyz,x,y,z = pdf.getxyz(refLonLat)
xmean = np.mean(x)
ymean = np.mean(y)
zmean = np.mean(z)
mag = (xmean**2+ymean**2+zmean**2)#**.5
lonlat0 = pdf.getlonlat(xmean/mag,ymean/mag,zmean/mag)
lon0 = lonlat0[0,0]
lat0 = lonlat0[0,1]
beta = 0 # initial guess
nSamples,colvals = np.shape(refLonLat)
row,col = np.shape(xMesh)
beta = pdf.optimizeHeadingAngle(refLonLat,nSamples,lon0,lat0,beta)
R1 = pdf.Rotz(lon0*np.pi/180.) # generating rotation matrices
R2 = pdf.Roty(-lat0*np.pi/180.)
R3 = pdf.Rotx(beta*np.pi/180.)
transformDetails = [R1,R2,R3,lon0,lat0,beta]
Uint = np.dot(R2,R1)
U = np.dot(R3,Uint)
lonlatPframe = pdf.originalFrame2Pframe(refLonLat,U)
lonlatMesh = np.zeros((np.size(xMesh),2))
lonlatMesh[:,0] = np.reshape(xMesh,np.size(xMesh))
lonlatMesh[:,1] = np.reshape(yMesh,np.size(yMesh))
lonlatPMesh = pdf.originalFrame2Pframe(lonlatMesh,U)
ylen,xlen = np.shape(xMesh)
lonMeshP = np.reshape(lonlatPMesh[:,0],[ylen,xlen])
latMeshP = np.reshape(lonlatPMesh[:,1],[ylen,xlen])
if pdfoption.lower()=='kde' or pdfoption.lower()=='kernel':
ZZpdfPframe = SK.serialK(int(nSamples),lonlatPframe,row,col,lonMeshP,latMeshP,.0,0)
elif pdfoption=='normal' or pdfoption=='gaussian' or pdfoption=='gauss':
ZZpdfPframe = meancov.normalbivariate(mean,covar,lonMeshP,latMeshP,col,row) #fortran version
'''
plt.figure()
plt.contour(lonMeshP,latMeshP,ZZpdfPframe,30)
plt.plot(lonlatPframe[:,0],lonlatPframe[:,1],'x')
plt.figure()
plt.plot(lonlatPMesh[:,0],lonlatPMesh[:,1],'x')
exit()
'''
pqrVec ,pVec,qVec,rVec= pdf.getxyz(lonlatPMesh) # mesh locations in P frame
pdfN = pdf.transformPDF(U,pqrVec,np.pi/180*lonlatMesh[:,0],np.pi/180*lonlatMesh[:,1],np.reshape(ZZpdfPframe,xlen*ylen))
ZZpdfN = np.reshape(pdfN,[ylen,xlen])
lonMesh = np.reshape(lonlatMesh[:,0],[ylen,xlen])
latMesh = np.reshape(lonlatMesh[:,1],[ylen,xlen])
'''
plt.figure()
plt.contour(lonMeshP,latMeshP,ZZpdfPframe,30)
plt.plot(refLonLat[:,0],refLonLat[:,1],'x')
plt.show()
plt.figure()
plt.contour(lonMesh,latMesh,ZZpdfN,30)
plt.plot(refLonLat[:,0],refLonLat[:,1],'x')
plt.show()
'''
return (lonMesh,latMesh,ZZpdfN)
def getPDF(lonlat, pdfoption='kde', delta=None, nMesh=50):
#lonlat is a nx2 matrix with lon lat locations
# nsigma is only used for getting PDF area when using normal distribution
# pdf option 'kde' or 'normal' dist
# delta is spacing used for mesh in PDF
# if delta not specified,then it will use nMesh which is the number of points used in one dim...total mesh will be nMesh x nMesh
#this routine rotates the SPHERICAL planet to get a better 'flat planet' approximation by using local lat and lons and then applies the KDE or normal PDF routines
xyz, x, y, z = getxyz(lonlat)
xmean = np.mean(x)
ymean = np.mean(y)
zmean = np.mean(z)
mag = (xmean**2 + ymean**2 + zmean**2)**.5
lonlat0 = getlonlat(xmean / mag, ymean / mag, zmean / mag)
lon0 = lonlat0[0, 0]
lat0 = lonlat0[0, 1]
beta = 0 # initial guess
nSamples, dims = np.shape(lonlat)
if dims != 2:
print 'Error in lonlat, dimensions are not correct'
print 'Check inputs to getPDF in casualtyEstimate.py'
exit()
beta = optimizeHeadingAngle(lonlat, nSamples, lon0, lat0, beta)
R1 = Rotz(lon0 * np.pi / 180.) # generating rotation matrices
R2 = Roty(-lat0 * np.pi / 180.)
R3 = Rotx(beta * np.pi / 180.)
transformDetails = [R1, R2, R3, lon0, lat0, beta]
Uint = np.dot(R2, R1)
U = np.dot(R3, Uint)
lonlatPframe = originalFrame2Pframe(lonlat, U)
if pdfoption.lower() == 'kde' or pdfoption.lower() == 'kernel':
xMeshP, yMeshP, xlen, ylen, areaInt = statsPython.areaOfInterestKDE(
lonlatPframe, delta=delta,
nMesh=nMesh) # A frame could also be used
elif pdfoption == 'normal' or pdfoption == 'gaussian' or pdfoption == 'gauss':
#mean,covar = meancov.meancovmatrix(lonlatPframe,nSamples)
#xMeshP,yMeshP,xlen,ylen = statsPython.areaOfInterest(mean,covar,delta,nsigma)
xMeshP, yMeshP, xlen, ylen, areaInt = statsPython.areaOfInterestKDE(
lonlatPframe, delta=delta,
nMesh=nMesh) # A frame could also be used
lonlatPframeMesh = np.zeros((np.size(xMeshP), 2))
lonlatPframeMesh[:, 0] = np.reshape(xMeshP, np.size(xMeshP))
lonlatPframeMesh[:, 1] = np.reshape(yMeshP, np.size(yMeshP))
lonlatOrFrameMesh = Pframe2originalFrame(lonlatPframeMesh, U)
lonMesh, latMesh = getLonLatMesh(lonlatOrFrameMesh[:, 0],
lonlatOrFrameMesh[:, 1], delta)
lonVec = np.reshape(lonMesh, np.size(lonMesh))
latVec = np.reshape(latMesh, np.size(latMesh))
lonlatVec = np.zeros((len(lonVec), 2))
lonlatVec[:, 0] = lonVec
lonlatVec[:, 1] = latVec
lonlatVecP = originalFrame2Pframe(lonlatVec, U)
ylen, xlen = np.shape(lonMesh)
lonMeshP = np.reshape(lonlatVecP[:, 0], [ylen, xlen])
latMeshP = np.reshape(lonlatVecP[:, 1], [ylen, xlen])
pqrVec, pVec, qVec, rVec = getxyz(lonlatVecP) # mesh locations in P frame
if pdfoption.lower() == 'kde' or pdfoption.lower() == 'kernel':
#ZZpdf = meancov.kde(lonlatPframe,lonMeshP,latMeshP,[nSamples, xlen,ylen])
print 'Starting quad KDE'
ZZpdf = SK.serialK(nSamples, lonlatPframe, ylen, xlen, lonMeshP,
latMeshP, .1, 1)
print 'Done with quad KDE'
elif pdfoption == 'normal' or pdfoption == 'gaussian' or pdfoption == 'gauss':
ZZpdf = meancov.normalbivariate(mean, covar, lonMeshP, latMeshP, xlen,
ylen) #fortran version
pdfN = transformPDF(U, pqrVec, np.pi / 180 * lonlatVec[:, 0],
np.pi / 180 * lonlatVec[:, 1],
np.reshape(ZZpdf, xlen * ylen))
ZZpdfN = np.reshape(pdfN, [ylen, xlen])
PframeVals = [lonPMesh, latPMesh, ZZpdf]
OrFrameVals = [lonMesh, latMesh, ZZpdfN]
return (PframeVals, OrFrameVals, transformDetails, areaInt)