我正在编写一个代码，该代码应该绘制围绕 x、y 和 z 轴的旋转。角度将根据位置数据计算得出，位置数据针对 4 个标记给出，每个标记都有 x、y 和 z 坐标。使用运动捕捉系统随时间测量位置，然后将其提取到 csv 文件中。 我正在尝试让它与大多数聊天 GPT 一起使用，但我已经有了基本的了解。 将数据与已经计算和绘制的数据集进行比较，使用不同的系统进行测量，该系统给出角速度作为输出。 目前的问题基本上是，3 个角度中的 2 个似乎相当精确，而第 3 个角度大约有 30° 偏差。
我如何提供我的数据？目前不确定在哪里/如何上传。谢谢！
这是到目前为止我的代码： 工作速度绘图：
import pandas as pd
import matplotlib.pyplot as plt
#from mpl_toolkits.mplot3d import Axes3D
import numpy as np
# load data
data = pd.read_csv('path_to_file', delimiter=';')
#%%
# extract time data
zeit = pd.to_numeric(data['time'].str.replace(',', '.'), errors='coerce')
#%%
# extract each velocity
winkelgeschwindigkeit_x = pd.to_numeric(data['wx (rad/s)'].str.replace(',', '.'), errors='coerce')
winkelgeschwindigkeit_y = pd.to_numeric(data['wy (rad/s)'].str.replace(',', '.'), errors='coerce')
winkelgeschwindigkeit_z = pd.to_numeric(data['wz (rad/s)'].str.replace(',', '.'), errors='coerce')
#%%
# compute orientation
orientierung_x = np.zeros_like(zeit)
orientierung_y = np.zeros_like(zeit)
orientierung_z = np.zeros_like(zeit)
#%%
for i in range(1, len(zeit)):
dt = zeit.iloc[i] - zeit.iloc[i-1]
orientierung_x[i] = orientierung_x[i-1] + winkelgeschwindigkeit_x.iloc[i] * dt
orientierung_y[i] = orientierung_y[i-1] + winkelgeschwindigkeit_y.iloc[i] * dt
orientierung_z[i] = orientierung_z[i-1] + winkelgeschwindigkeit_z.iloc[i] * dt
orientierung_x = orientierung_x * (180/3.1415)
orientierung_y = orientierung_y * (180/3.1415)
orientierung_z = orientierung_z * (180/3.1415)
#%%
# Initialise the subplot function using number of rows and columns
figure, axis = plt.subplots(2, 2)
# For Sine Function
axis[0, 0].plot(zeit, orientierung_x)
axis[0, 0].set_title("x over t")
# For Cosine Function
axis[0, 1].plot(zeit, orientierung_y)
axis[0, 1].set_title("y over t")
# For Tangent Function
axis[1, 0].plot(zeit, orientierung_z)
axis[1, 0].set_title("z over t")
# For Tanh Function
axis[1, 1].plot()
axis[1, 1].set_title("spare square")
# Combine all the operations and display
plt.show()
# Plot der Bewegung in 3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot(orientierung_x, orientierung_y, orientierung_z)
ax.set_xlabel('X-Achse')
ax.set_ylabel('Y-Achse')
ax.set_zlabel('Z-Achse')
ax.set_title('Bewegung des Handys in 3D')
plt.show()
这是我在位置计算方面的最佳尝试：
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# read data
dfraw = pd.read_csv('path_to_file, skiprows = [0,1,2,3,4,6,7], delimiter=',')
df = dfraw
df.rename(columns = {'Time':'time',
'LASI':'x1',
'Unnamed: 2':'y1',
'Unnamed: 3':'z1',
'RASI':'x2',
'Unnamed: 5':'y2',
'Unnamed: 6':'z2',
'LPSI':'x3',
'Unnamed: 8':'y3',
'Unnamed: 9':'z3',
'RPSI':'x4',
'Unnamed: 11':'y4',
'Unnamed: 12':'z4'},
inplace=True)
#%%
# naming data
zeit = df[["time"]].to_numpy()
Marker1 = df[["x1", "y1", "z1"]].to_numpy()
Marker2 = df[["x2", "y2", "z2"]].to_numpy()
Marker3 = df[["x3", "y3", "z3"]].to_numpy()
Marker4 = df[["x4", "y4", "y4"]].to_numpy()
import numpy as np
import matplotlib.pyplot as plt
# function to compute vectors
def compute_normal_vectors(marker_coordinates):
normal_vectors = []
for i in range(len(marker_coordinates[0])):
# Extracting coordinates for each marker at the current time point
marker1 = marker_coordinates[0][i]
marker2 = marker_coordinates[1][i]
marker3 = marker_coordinates[2][i]
marker4 = marker_coordinates[3][i]
# Computing vectors between the markers
vector1 = np.array(marker4) - np.array(marker1)
vector2 = np.array(marker3) - np.array(marker2)
# Computing the cross product to get the normal vector
normal_vector = np.cross(vector1, vector2)
# Normalizing the normal vector
normal_vector /= np.linalg.norm(normal_vector)
normal_vectors.append(normal_vector)
return normal_vectors
# function to compute normal angles
def compute_normal_angles(normal_vectors):
angles = []
for normal_vector in normal_vectors:
# Calculate angles with x, y, and z axes
z_angle = np.degrees(np.arccos(np.dot(normal_vector, [0, 0, 1])))
y_angle = np.degrees(np.arccos(np.dot(normal_vector, [0, 1, 0])))
x_angle = np.degrees(np.arccos(np.dot(normal_vector, [1, 0, 0])))
angles.append((x_angle, y_angle, z_angle))
return angles
# function to plot
def plot_angle_over_time(angle, time_points, title):
time_points = np.array(time_points)
angle = np.array(angle)
# Subtracting angle at time point 0 from all other time points
angle_at_t0 = angle[0]
angle = angle - angle_at_t0
plt.figure(figsize=(10, 6))
plt.plot(time_points, angle)
plt.title(title)
plt.xlabel('Zeitpunkt')
plt.ylabel('Winkel (in Grad)')
plt.xticks(time_points[::2])
plt.grid(True)
plt.show()
# adding coordinates
marker_coordinates = [Marker1, Marker2, Marker3, Marker4]
# time steps
time_points = zeit
# computing normal vectors
normal_vectors = compute_normal_vectors(marker_coordinates)
# Computing angles
angles = compute_normal_angles(normal_vectors)
# Plotting angles over time
for i, axis_name in enumerate(['X-Achse', 'Y-Achse', 'Z-Achse']):
plot_angle_over_time([angle[i] for angle in angles], time_points, f'Winkel zur {axis_name} über der Zeit')
不确定您到底要什么，但如果您只是想知道应该在哪里输入数据，那就从一开始就可以。 在
dfraw = pd.read_csv('path_to_file, skiprows = [0,1,2,3,4,6,7], delimiter=',')pd.read_csv()另请注意，目前您的代码在
path_to_filedelimiter