import numpy as np
import matplotlib.pyplot as plt



rdir='./data/'


clabels=[r'$K_{perm}$',r'$K_{diss}$',r'$K_{average}$',r'$K_{1/3}$']
names=['Kperm','Kdiss','Kaverage','Kpower']
cases=[r'$Lognormal \ \sigma^{2}_{\log(k)} = 0.1$',r'$Lognormal \ \sigma^{2}_{\log(k)} = 7$', r'$Binary p = 0.2; k+/k- = 10^4$']

scales=np.array([4,8,16,32,64])
lcs=[16,16,8]
est=3
ranges=[(-0.5,0.5),(-5,5),(-4,4)]
for i in range(3):


	for scale in range(len(scales)):
		if est==0:
			keff=np.log(np.load(rdir+str(i)+'/kperm/'+str(scales[scale])+'.npy'))
		if est==1:
			keff=np.log(np.load(rdir+str(i)+'/KpostProcess/Kd'+str(scales[scale])+'.npy'))

		if est==2:
			keff=np.log(np.load(rdir+str(i)+'/KpostProcess/Kv'+str(scales[scale])+'.npy'))
		if est==3:
			keff=np.log(np.load(rdir+str(i)+'/KpostProcess/Kpo'+str(scales[scale])+'.npy'))


		plt.hist(keff.reshape(-1),label=r'$\lambda = $'+' ' +str(scales[scale]),density=True,histtype='step',range=ranges[i])
	#plt.semilogx(scales/512.0,kpost[:,1],label=clabels[1],marker='s')
	#plt.semilogx(scales/512.0,kpost[:,2],label=clabels[2],marker='^')
	#plt.semilogx(scales/512.0,kpost[:,3],label=clabels[3],marker='o')
	#plt.vlines(lcs[i]/512.0,kpost[:,0].min(),kpost[:,0].max(),label=r'$lc = $'+str(lcs[i]))
	plt.xlabel(r'$\log(K_{eff})$')
	plt.ylabel(r'$P(K_{eff})$')
	plt.legend()
	plt.grid()
	plt.title(cases[i]+'  '+str(names[est])) 
	plt.tight_layout()
	plt.savefig(rdir+str(i)+'/Kpost_dist_scales_'+names[est]+'.png')
	plt.close()