locs2 = locs[colours == 1] ### make an mtPP format object pp1 = PPP(locs1) pp2 = PPP(locs2) # pps = np.array([pp1]) pps = np.array([pp1,pp2]) mtppR = mtPPP(pps) mtppR.plot() #### est g function N = 10000 rho = 5 steps = np.linspace(0.0, 0.8, num=50)
plt.legend(bbox_to_anchor=(1, 0.8)) plt.show() ### make an mtPP format object pp1 = PPP(locs1) pp2 = PPP(locs2) pp3 = PPP(locs3) pps = np.array([pp1,pp2,pp3]) mtpp = mtPPP(pps) mtpp.plot() ### initialize other MCMC parameters size=1000 nInsDelMov = lam//10 n=5 K = mtpp.K
### thinp = 5/12 hickoryPP = PPP(hickory) hickoryPP.thin(thinp) maplePP = PPP(maple) maplePP.thin(thinp) mtpp = mtPPP(np.array([hickoryPP,maplePP])) mtpp.plot() ### mcmc K = mtpp.K lam_est = mtpp.nObs*(K+1)/K size=10000 nInsDelMov = lam_est//10
return(np.exp(-(0.25-np.sqrt((x[:,0]-0.5)**2+(x[:,1]-0.5)**2))**2/0.005))
pointpo = PPP.randomNonHomog(lam,fct)
pointpo.plot()
###
pointpo.loc=np.loadtxt("maple.csv", delimiter=",")
# pointpo.loc=np.loadtxt("hickory.csv", delimiter=",")
thinp=1/2
pointpo.thin(thinp)
mtpp = mtPPP(np.array([pointpo]))
mtpp.plot()
### initialize other MCMC parameters
K = mtpp.K
lam_est = mtpp.nObs*(K+1)/K
size=10000
nInsDelMov = lam_est//10