74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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CSV = '/home/lgv/cmvr/cmvr-es/data/ik_psi_sweep.csv'
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df = pd.read_csv(CSV)
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# ---- 曲线图：q1~q7 (+ 可选 psi) vs index ----
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joint_cols_expect = ['q1','q2','q3','q4','q5','q6','q7']
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joint_cols = [c for c in joint_cols_expect if c in df.columns]
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assert len(joint_cols) == 7, f"CSV 缺少关节列，期望 {joint_cols_expect}，实际 {list(df.columns)}"
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x = np.arange(len(df))
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plt.figure(figsize=(12, 5))
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for c in joint_cols:
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plt.plot(x, df[c].to_numpy(), label=c, linewidth=1.2)
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if 'psi' in df.columns:
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plt.plot(x, df['psi'].to_numpy(), '--', label='psi', linewidth=1.2)
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plt.xlabel('index')
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plt.ylabel('angle (rad)')
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plt.title('q1..q7 (and psi) vs index')
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plt.grid(True, alpha=0.35)
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plt.legend(ncol=4, fontsize=9)
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plt.tight_layout()
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plt.show()
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# ---- 柱状图：最近限位距离（选“最危险帧”） ----
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limits = np.array([
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[-0.26, 1.57],
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[-0.78, 1.57],
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[-np.pi, np.pi],
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[ 0.00, 2.05],
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[-3.00, 3.00],
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[-2.00, 2.00],
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[-0.57, 1.57],
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])
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Q = df[joint_cols].to_numpy() # [N,7]
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lo = limits[:, 0][None, :] # [1,7]
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hi = limits[:, 1][None, :]
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dist_low = Q - lo # 到下限的距离
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dist_high = hi - Q # 到上限的距离
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nearest = np.minimum(dist_low, dist_high) # 最近限位（可为负，负值=超限）
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# 找“最危险”的 index（全关节最小裕度最小）
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min_margin_per_row = nearest.min(axis=1) # 每帧的最小关节裕度
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worst_idx = int(np.argmin(min_margin_per_row))
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vals = nearest[worst_idx, :]
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psi_text = f", psi={df['psi'].iloc[worst_idx]:.4f}" if 'psi' in df.columns else ""
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plt.figure(figsize=(9, 4.5))
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plt.bar(joint_cols, vals)
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plt.axhline(0.0, linewidth=1, color='k')
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plt.ylabel('Nearest distance to limit (rad)')
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plt.title(f'Per-joint margin at worst frame (index={worst_idx}{psi_text})')
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plt.grid(True, axis='y', alpha=0.35)
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plt.tight_layout()
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plt.show()
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# ---- 可选：整段最小裕度曲线（帮助定位危险段）----
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plt.figure(figsize=(12, 3.2))
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plt.plot(min_margin_per_row, linewidth=1.2)
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plt.axhline(0.0, linewidth=1, color='k')
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plt.xlabel('index')
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plt.ylabel('min margin (rad)')
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plt.title('Minimum per-frame joint margin over sweep (rad)')
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plt.grid(True, alpha=0.35)
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plt.tight_layout()
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plt.show()
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