analistica/ex-6/plots/emd-round.py
Giù Marcer 5634f2f418 ex-6: generated plots
All the usefull plots were generated and the codes were suitably modified
for this purpose.
2020-07-05 11:35:55 +02:00

47 lines
1.4 KiB
Python

import numpy as np
import matplotlib.pyplot as plt
def plot(table, title='', log=False):
plt.figure(figsize=(5, 2))
plt.rcParams['font.size'] = 8
plt.suptitle(title)
plt.subplot(111)
if log:
plt.xscale('log')
plt.title('EMD' + ' '*10, loc='right')
plt.plot(table[0], table[1], color='#92182b')
plt.tick_params(axis='y', labelcolor='#92182b')
plt.ylabel('average', color='#92182b')
plt.ticklabel_format(style='sci', axis='y',
scilimits=(0, 0), useMathText=True)
twin = plt.twinx()
twin.plot(table[0], table[2], color='gray')
twin.tick_params(axis='y', labelcolor='gray')
twin.set_ylabel('standard deviation', color='gray')
twin.ticklabel_format(style='sci', axis='y',
scilimits=(0, 0), useMathText=True)
# plt.subplot(212)
# if log:
# plt.xscale('log')
# plt.title('skewness', loc='right')
# plt.xlabel('RL rounds')
# plt.plot(table[0], table[3], color='xkcd:gray')
plt.tight_layout()
table = np.loadtxt('ex-6/plots/emd-round-noise.txt')
# plot(table[:27].T, title='noiseless', log=True)
plot(table[27:47].T, title=r'noise at $\sigma_N = 0.005$')
plt.savefig('notes/images/6-rounds-noise-0.005.pdf')
plot(table[47:67].T, title=r'noise at $\sigma_N = 0.01$')
plt.savefig('notes/images/6-rounds-noise-0.01.pdf')
plot(table[67:].T, title=r'noise at $\sigma_N = 0.05$')
plt.savefig('notes/images/6-rounds-noise-0.05.pdf')