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plot.py
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plot.py
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import numpy as np
import matplotlib.pyplot as plt
z = np.load('data_new4.npz')
data = z['data']
rho = z['rho']
betatype = z['betatype']
method = z['method']
snr = z['snr']
color = ['r', 'g', 'b']
x = snr
fig0, axes0 = plt.subplots(len(betatype), len(rho), figsize=(12, 9), dpi=100, sharex='all', sharey='all')
for i, row in enumerate(axes0):
for j, col in enumerate(row):
for k in range(len(method)):
axes0[i, j].plot(np.log10(x), data[k, j, i, :, 0], color=color[k], label=method[k])
axes0[i, j].grid()
if i == 0:
axes0[i, j].set_title('Correlation = '+str(rho[j]))
if j == 0:
axes0[i, j].set_ylabel('Betatype '+str(betatype[i]))
if (i == 0)*(j == len(method)-1):
axes0[i, j].legend()
fig0.text(0.5, 0.05, 'Signal-to-noise ratio ', ha='center')
fig0.text(0.05, 0.5, 'Relative risk (to null model)', va='center', rotation='vertical')
fig1, axes1 = plt.subplots(len(betatype), len(rho), figsize=(12, 9), dpi=100, sharex='all', sharey='all')
for i, row in enumerate(axes1):
for j, col in enumerate(row):
for k in range(len(method)):
axes1[i, j].plot(np.log10(x), data[k, j, i, :, 1], color=color[k], label=method[k])
axes1[i, j].grid()
if i == 0:
axes1[i, j].set_title('Correlation = '+str(rho[j]))
if j == 0:
axes1[i, j].set_ylabel('Betatype '+str(betatype[i]))
if (i == 0)*(j == len(rho)-1):
axes1[i, j].legend()
fig1.text(0.5, 0.05, 'Signal-to-noise ratio ', ha='center')
fig1.text(0.05, 0.5, 'Relative risk (to null model)', va='center', rotation='vertical')
fig2, axes2 = plt.subplots(len(betatype), len(rho), figsize=(12, 9), dpi=100, sharex='all', sharey='all')
for i, row in enumerate(axes2):
for j, col in enumerate(row):
for k in range(len(method)):
axes2[i, j].plot(np.log10(x), data[k, j, i, :, 2], color=color[k], label=method[k])
axes2[i, j].grid()
if i == 0:
axes2[i, j].set_title('Correlation = '+str(rho[j]))
if j == 0:
axes2[i, j].set_ylabel('Betatype '+str(betatype[i]))
if (i == 0)*(j == len(method)-1):
axes2[i, j].legend()
fig2.text(0.5, 0.05, 'Signal-to-noise ratio ', ha='center')
fig2.text(0.05, 0.5, 'Relative risk (to null model)', va='center', rotation='vertical')
fig3, axes3 = plt.subplots(len(betatype), len(rho), figsize=(12, 9), dpi=100, sharex='all', sharey='all')
for i, row in enumerate(axes1):
for j, col in enumerate(row):
for k in range(len(method)):
axes3[i, j].plot(np.log10(x), data[k, j, i, :, 3], color=color[k], label=method[k])
axes3[i, j].grid()
if i == 0:
axes3[i, j].set_title('Correlation = '+str(rho[j]))
if j == 0:
axes3[i, j].set_ylabel('Betatype '+str(betatype[i]))
if (i == 0)*(j == len(method)-1):
axes3[i, j].legend()
fig3.text(0.5, 0.05, 'Signal-to-noise ratio ', ha='center')
fig3.text(0.05, 0.5, 'Relative risk (to null model)', va='center', rotation='vertical')
plt.show()