【python】6DOF analyse tool2

1. result show
   1.1 note: 图1为3D X Y Z combination位置，图4，5，6分别表示yaw,pitch，roll角随着时间的变化的波动。
   

#这个代码假设数据存储在名为`data.csv`的文件中，并使用Python内置的csv模块来读取。
#然后，它提取出了时间、位置和姿态角度信息，并使用Matplotlib绘制了四个子图，分别是位置散点图和yaw、pitch、roll角度的曲线图。
#最后，它使用pandas库计算了位置的均值、yaw角度的方差和pitch、roll角度之间的相关系数，并输出了这些结果。

import csv
import numpy as np
import matplotlib
from pylab import *
import cmath
import math
import matplotlib.pyplot as plt

path = '/home/sun/code/jitter/jitter_analyse_r3.txt'
compare_path = '/home/sun/code/jitter/jitter_analyse_fit_spots.txt'

data_list = []
with open(path,'r') as f:
    for line in f:
        data_list.append(line.strip().split(' '))

data = np.array(data_list,dtype=np.float64)
# print(data)

# 提取出时间、位置和姿态角度信息
time = [row[0] for row in data]
positions = [[float(row[1]), float(row[2]), float(row[3])] for row in data]
angles = [[float(row[4]), float(row[5]), float(row[6])] for row in data]

# print(positions)
# load comapre data_list
compare_data_list = []
with open(compare_path,'r') as fc:
    for line in fc:
        compare_data_list.append(line.strip().split(' '))

compare_data = np.array(compare_data_list,dtype=np.float64)
# print(data)

# 提取出时间、位置和姿态角度信息
compare_time = [row[0] for row in compare_data]
compare_positions = [[float(row[1]), float(row[2]), float(row[3])] for row in compare_data]
compare_angles = [[float(row[4]), float(row[5]), float(row[6])] for row in compare_data]


# 绘制图形
fig, axs = plt.subplots(3, 2)

# 绘制位置散点图
axs[0, 0].scatter([p[0] for p in positions], [p[1] for p in positions], c='b')
axs[0, 0].scatter([compare_p[0] for compare_p in compare_positions], [compare_p[1] for compare_p in compare_positions], c='g')
axs[0, 0].set_xlabel('x')
axs[0, 0].set_ylabel('y')

# 绘制位置散点图
axs[0, 1].scatter([p[0] for p in positions], [p[2] for p in positions], c='b')
axs[0, 1].scatter([compare_p[0] for compare_p in compare_positions], [compare_p[2] for compare_p in compare_positions], c='g')
axs[0, 1].set_xlabel('x')
axs[0, 1].set_ylabel('z')

# 绘制位置散点图
axs[1, 0].scatter([p[1] for p in positions], [p[2] for p in positions], c='b')
axs[1, 0].scatter([compare_p[1] for compare_p in compare_positions], [compare_p[2] for compare_p in compare_positions], c='g')
axs[1, 0].set_xlabel('y')
axs[1, 0].set_ylabel('z')

# 绘制x曲线图
axs[1, 1].plot(time, [p[0] for p in positions], c='b')
axs[1, 1].plot(compare_time, [compare_p[0] for compare_p in compare_positions], c='g')
axs[1, 1].set_xlabel('time')
axs[1, 1].set_ylabel('x')

# 绘制y曲线图
axs[2, 0].plot(time, [p[1] for p in positions], c='b')
axs[2, 0].plot(compare_time, [compare_p[1] for compare_p in compare_positions], c='g')
axs[2, 0].set_xlabel('time')
axs[2, 0].set_ylabel('y')

# 绘制z曲线图
axs[2, 1].plot(time, [p[2] for p in positions], c='b')
axs[2, 1].plot(compare_time, [compare_p[2] for compare_p in compare_positions], c='g')
axs[2, 1].set_xlabel('time')
axs[2, 1].set_ylabel('z')
# # 绘制yaw角度曲线图
# axs[0, 1].plot(time, [a[0] for a in angles])
# axs[0, 1].set_xlabel('time')
# axs[0, 1].set_ylabel('yaw')

# # 绘制pitch角度曲线图
# axs[1, 0].plot(time, [a[1] for a in angles])
# axs[1, 0].set_xlabel('time')
# axs[1, 0].set_ylabel('pitch')

# # 绘制roll角度曲线图
# axs[1, 1].plot(time, [a[2] for a in angles])
# axs[1, 1].set_xlabel('time')
# axs[1, 1].set_ylabel('roll')

plt.show()

# 分析图形
mean_position = pd.DataFrame(positions).mean()
var_yaw = pd.DataFrame(angles)[0].var()
corr_pitch_roll = pd.DataFrame(angles)[1:].corr().iloc[0, 1]

print('Mean position:', mean_position)
print('Variance of yaw angle:', var_yaw)
print('Correlation between pitch and roll angles:', corr_pitch_roll)

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