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