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@@ -113,10 +113,11 @@ class Batchrun:
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return posdata
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map_id = get_map_id(posdata_ego)
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+ # map_id = 0
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posdata_obs = trans_pos(posdata_obs, map_id)
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posdata_ego = trans_pos(posdata_ego, map_id)
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- posdata_obs['simtime'] = posdata_obs['simtime'].round(1)
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- posdata_ego['simtime'] = posdata_ego['simtime'].round(1)
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+ posdata_obs['simtime'] = np.round(posdata_obs['simtime'], decimals=1)
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+ posdata_ego['simtime'] = np.round(posdata_ego['simtime'], decimals=1)
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# 对障碍物数据进行处理
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def filter_rows(group):
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@@ -134,10 +135,9 @@ class Batchrun:
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# 2. 创建新的DataFrame，并与原始DataFrame对齐
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new_df = pd.DataFrame(new_simtime, columns=['simtime'])
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-
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+ new_df['simtime'] = np.round(new_df['simtime'], decimals=1)
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# 3. 将原始DataFrame与新simtime进行合并
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new_df = pd.merge(new_df, df, on='simtime', how='left')
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-
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# 4. 对列进行插值（线性插值）
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new_df = new_df.interpolate(method='linear')
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@@ -464,10 +464,10 @@ class Batchrun:
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if __name__ == "__main__":
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- # rootPath = "/media/hancheng/Simulation5/anqing/11_5" # 跟车
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+ # rootPath = "/media/hancheng/Simulation5/pujin/pujin_outdoor/11_12" # 跟车
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+ # vehicle_type = "0"
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rootPath = sys.argv[1]
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vehicle_type = sys.argv[2]
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- # vehicle_type = "0"
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# 生成场景
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a = Batchrun(rootPath, "pos_pji.csv")
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a.batchRun(rootPath, vehicle_type) # 0为占位参数
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