import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap, LinearSegmentedColormap def plotK(kk, pdir, logn): y = np.arange(kk.shape[0]) x = np.arange(kk.shape[1]) newcolors = np.zeros((2, 4)) alto = np.array([0.0, 0.0, 0.0, 1]) bajo = np.array( [191 / 256.0, 191 / 256.0, 191 / 256.0, 1] ) # [108.0/256, 122.0/256, 137.0/256, 1]) alto = np.array([204.0 / 254, 0.0, 0.0, 1]) bajo = np.array([0.0, 0.0, 153.0 / 254, 1]) # [108.0/256, 122.0/256, 137.0/256, 1]) newcolors[0, :] = bajo newcolors[1, :] = alto newcmp = ListedColormap(newcolors) if logn == True: kk = np.log(kk) vmin, vmax = -2 * np.var(kk) + np.mean(kk), 2 * np.var(kk) + np.mean(kk) # print(vmax) colormap = "viridis" plt.pcolormesh(x, y, kk, cmap=colormap) # ,vmin=vmin,vmax=vmax) else: # colormap='binary' plt.pcolormesh(x, y, kk, cmap=newcmp) cbar = plt.colorbar() cbar.set_label("k") # plt.title('Guassian N(0,1)') plt.savefig(pdir + "k.png") plt.close() """ if logn==True: plt.hist(kk.reshape(-1),range=(2*vmin,2*vmax),histtype='step',bins=250,density=True) plt.xlabel('k') plt.ylabel('p(k)') plt.savefig(pdir+'histo.png') """ return def plotK_imshow(kk, pdir, logn): kk = np.rot90(kk) y = np.arange(kk.shape[0]) x = np.arange(kk.shape[1]) newcolors = np.zeros((2, 4)) alto = np.array([0.0, 0.0, 0.0, 1]) bajo = np.array( [191 / 256.0, 191 / 256.0, 191 / 256.0, 1] ) # [108.0/256, 122.0/256, 137.0/256, 1]) alto = np.array([204.0 / 254, 0.0, 0.0, 1]) bajo = np.array([0.0, 0.0, 153.0 / 254, 1]) # [108.0/256, 122.0/256, 137.0/256, 1]) newcolors[0, :] = bajo newcolors[1, :] = alto newcmp = ListedColormap(newcolors) if logn == True: kk = np.log(kk) vmin, vmax = -3 * np.var(kk) + np.mean(kk), 3 * np.var(kk) + np.mean(kk) # print(vmax) colormap = "viridis" plt.imshow(kk, vmin=vmin, vmax=vmax) # ,cmap='binary' else: # colormap='binary' plt.imshow(kk, cmap="binary") # ,cmap='binary' plt.colorbar() # cbar.set_label('k') # plt.title('Guassian N(0,1)') plt.tight_layout() plt.savefig(pdir + "k.png") plt.close() """ if logn==True: plt.hist(kk.reshape(-1),range=(2*vmin,2*vmax),histtype='step',bins=250,density=True) plt.xlabel('k') plt.ylabel('p(k)') plt.savefig(pdir+'histo.png') """ return def plot_hist(k, pdir, logn): plt.figure(1) if logn == True: k = np.log(k) vmin, vmax = -4 * np.var(k) + np.mean(k), 4 * np.var(k) + np.mean(k) plt.hist(k.reshape(-1), range=(vmin, vmax)) else: plt.hist(k.reshape(-1)) plt.xlabel("k") plt.ylabel("Counts") plt.savefig(pdir + "-histo.png") plt.close() return rdir = "./perco_lc8/" for i in range(11): k = np.load(rdir + str(i) + "/k.npy")[:, :, 0] log = "False" plotK_imshow(k, rdir + str(i) + "Map", log) # plot_hist(k,rdir+'Res/'+resname,log)