import numpy as np
import matplotlib.pyplot as plt



rdir='./data/'


clabels=[r'$K_{perm}$',r'$K_{diss}$',r'$K_{average}$',r'$K_{1/3}$']
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([4,8,16,32,64,128,256,512])
lcs=[16,16,8]

for i in range(3):

	kpost=np.zeros((len(scales),4))


	for scale in range(len(scales)):
		kpost[scale,0]=np.exp(np.nanmean(np.log(np.load(rdir+str(i)+'/kperm/'+str(scales[scale])+'.npy'))))
		kpost[scale,1]=np.exp(np.nanmean(np.log(np.load(rdir+str(i)+'/KpostProcess/Kd'+str(scales[scale])+'.npy'))))
		kpost[scale,2]=np.exp(np.nanmean(np.log(np.load(rdir+str(i)+'/KpostProcess/Kv'+str(scales[scale])+'.npy'))))
		kpost[scale,3]=np.exp(np.nanmean(np.log(np.load(rdir+str(i)+'/KpostProcess/Kpo'+str(scales[scale])+'.npy'))))
	plt.semilogx(scales/512.0,kpost[:,0],label=clabels[0],marker='x')
	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 = $'+str(lcs[i]))
	plt.xlabel(r'$\lambda / L$')
	plt.ylabel(r'$<K_{eff}>_G$')
	plt.legend()
	plt.grid()
	plt.title(cases[i]) 
	plt.tight_layout()
	plt.savefig(rdir+str(i)+'/Kpost_mean.png')
	plt.close()