99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
import numpy as np
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from matplotlib import pyplot as plt
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import scipy as sp
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from scipy.stats import pearsonr
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from matplotlib import markers
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blue = '#0984e3'
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red = '#d63031'
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def error(f,x,y):
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return sp.sum((f(x)-y)**2)
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def p1(x, coopr, e, postfix, show=True):
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fig = plt.figure(figsize=(4.5, 3))
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ax = fig.gca()
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ax.plot(x, coopr, color=blue, linewidth=2, label="$f_c$")
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ax.set_ylim(0, 1)
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ax.set_yticks(sp.linspace(0, 1, 5))
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ax2 = ax.twinx()
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ax2.plot(x, e, color=red, linewidth=2, label=r"$E_{i,D}$")
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# ax2.set_ylim(0, 1440)
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# ax2.set_yticks(sp.linspace(0, 1440, 5))
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ax2.tick_params(labelsize=18)
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ax.tick_params(labelsize=18)
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ax.set_xlim(1, 15)
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ax.set_xlabel("Rounds", size=22)
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ax.set_ylabel(r"$f_c$", family='sans-serif', color=blue, size=22)
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ax.tick_params(axis='y', labelcolor=blue)
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ax2.set_ylabel(r"$E_{i,D}$", family='sans-serif', color=red, size=22)
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ax2.tick_params(axis='y', labelcolor=red)
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plt.tight_layout()
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if show:
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plt.show()
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else:
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plt.savefig("graph/eid_co_plot_%s.eps" % postfix)
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def p2(e, coopr, postfix, show=True):
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# p2散点图
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fig = plt.figure(figsize=(4, 3))
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ax = fig.gca()
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fp1,residuals,rank,sv,rcond = sp.polyfit(e, coopr, 1, full=True)
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print("残差：",residuals)
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print('Model parameter:',fp1)
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print("Other parameters: rank=%s, sv=%s, rcond=%s"%(str(rank), str(sv), str(rcond)))
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f1 = sp.poly1d(fp1)
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print("error= %f" % error(f1, e, coopr))
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fx = sp.linspace(np.min(e), np.max(e), 2)
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plt.plot(fx,f1(fx),linewidth=2,color=red, ls='--', zorder=0)
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plt.scatter(e, coopr, color='white', edgecolors=blue, linewidths=2, zorder=101)
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ax.set_xlabel(r'$E_{i,D}$', family='sans-serif', size=20)
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ax.set_ylabel(r'$f_{c}$', family='sans-serif', size=20)
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# ax.set_xlim(0, 1440)
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# ax.set_xticks(sp.linspace(0, 1440, 5))
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ax.tick_params(labelsize=14)
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ax.set_ylim(0.5, 1)
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ax.set_yticks(sp.linspace(0.5, 1, 5))
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plt.tight_layout()
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if show:
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plt.show()
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else:
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plt.savefig("graph/eid_co_sca_%s.eps" % postfix)
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# 皮尔逊相关系数
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print("pearson: %f, p-value: %f" % pearsonr(e, coopr))
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if __name__ == '__main__':
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mode = 'CLASSIC'
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mode = 'SURVIVE'
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show = False
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show = True
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coopr = np.loadtxt("outputs/CR_%s.csv"%mode, delimiter=',')
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x = np.arange(1, 16)
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e = np.loadtxt("outputs/EID_%s.csv"%mode, delimiter=',')
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p1(x,coopr, e, mode, show)
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# p2(e, coopr, mode, show)
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"""
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SURVIVE
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残差： [ 0.10769174]
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Model parameter: [ 1.33390242e-04 7.29761895e-01]
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Other parameters: rank=2, sv=[ 1.37254846 0.34075025], rcond=3.33066907388e-15
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error= 0.107692
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pearson: 0.709599, p-value: 0.003045
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CLASSIC
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残差： [ 0.00992721]
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Model parameter: [ 3.14661037e-05 7.35284315e-01]
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Other parameters: rank=2, sv=[ 1.41231835 0.07319068], rcond=3.33066907388e-15
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error= 0.009927
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pearson: 0.309326, p-value: 0.261915
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""" |