Chapter 4 - Mass distribution

C4_mass_dis.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(context='paper',
        style='ticks',
        font_scale=1,
        rc={'figure.figsize': (8, 5),
            'figure.dpi': 100,  # need fixed
            'xtick.direction': 'in',
            'ytick.direction': 'in',
            'axes.linewidth': '1.3',
            'axes.labelsize': 12,
            'xtick.labelsize': 12,
            'ytick.labelsize': 12,
            'savefig.dpi': 300,
            'legend.fontsize': 10,
            # 'text.usetex':True,
             })

# 链接:https://pan.baidu.com/s/1D-Djfo0ve81lRJ3jUpwhKg  密码:wo9r
para_train = pd.DataFrame(np.load('./GWaveform/data_T2_fs8192_1.81.8_keys_train.npy'),columns=['train'])
para_train_m1 = para_train.train.map(lambda x: x.split('|')).map(lambda x: float(x[0]) ).values
para_train_m2 = para_train.train.map(lambda x: x.split('|')).map(lambda x: float(x[1]) ).values
para_test = pd.DataFrame(np.load('./GWaveform/data_T2_fs8192_1.81.8_keys_test.npy'),columns=['test'])
para_test_m1 = para_test.test.map(lambda x: x.split('|')).map(lambda x: float(x[0]) ).values
para_test_m2 = para_test.test.map(lambda x: x.split('|')).map(lambda x: float(x[1]) ).values

plt.figure(figsize=(8,5), dpi=100)
plt.scatter(para_train_m1, para_train_m2, s=4, marker='.', label = 'Training data')
plt.scatter(para_test_m1, para_test_m2, s=4,marker=',', label = 'Testing data')
plt.plot([0,80], [0,80], 'k--')
plt.xlabel('$m_1\,\,[M_\odot]$')
plt.ylabel('$m_2 \,\,[M_\odot]$')
plt.legend()
# plt.savefig('C4_imags/C4_distrimass.png', dpi=300, bbox_inches='tight')