v0.1.9.2(2024/07/13)

1. [APIs: do_current.py]
   - 删除多余的时序矫正语句——item['value'].reverse()，使输出的曲线为平滑的自然顺序
2. [current: current.py]
   - max功能计算逻辑矫正，应该是取绝对值的最大值
   - 整体梳理了trq/trqh的传递路径，现已修正完毕

aio/README.md

@@ -473,5 +473,9 @@ v0.1.9.2(2024/07/13)
    - 调整单轴测试时间为35s，适配大负载机型，调整堵转电流持续时间15s，适当减少测试时间
    - 将act信号置为False的动作放在初始化，增加程序健壮性
    - 修改所有输出文件的命名，在扩展名之前加入时间戳
-3. [current: current.py]: 在find_point函数种，当无法找到正确点位时，继续执行，而不是直接终止执行
+   - 删除多余的时序矫正语句——item['value'].reverse()，使输出的曲线为平滑的自然顺序
+3. [current: current.py]
+   - 在find_point函数种，当无法找到正确点位时，继续执行，而不是直接终止执行
+   - max功能计算逻辑矫正，应该是取绝对值的最大值
+   - 整体梳理了trq/trqh的传递路径，现已修正完毕
 

aio/code/automatic_test/do_current.py

@@ -120,10 +120,8 @@ def data_proc_regular(path, filename, channel, scenario_time):
                 for item in data:
                     item['value'].reverse()
                     if item.get('channel', None) == channel and item.get('name', None) == 'hw_joint_vel_feedback':
-                        item['value'].reverse()
                         _d2d_vel['hw_joint_vel_feedback'].extend(item['value'])
                     elif item.get('channel', None) == channel and item.get('name', None) == 'device_servo_trq_feedback':
-                        item['value'].reverse()
                         _d2d_trq['device_servo_trq_feedback'].extend(item['value'])
 
             df1 = pandas.DataFrame.from_dict(_d2d_vel)

aio/code/data_process/current.py

@@ -79,7 +79,7 @@ def initialization(path, sub, w2t):
     return data_files
 
 
-def current_max(data_files, rcs, trqh, w2t):
+def current_max(data_files, rcs, trq, w2t):
     current = {1: [], 2: [], 3: [], 4: [], 5: [], 6: [], 7: []}
     for data_file in data_files:
         if data_file.endswith('.data'):
@@ -93,8 +93,8 @@ def current_max(data_files, rcs, trqh, w2t):
         axis = int(data_file.split('\\')[-1].split('_')[0].removeprefix('j'))
         rca = rcs[axis-1]
 
-        col = df.columns.values[trqh-1]
+        col = df.columns.values[trq-1]
-        c_max = df[col].max()
+        c_max = df[col].abs().max()
 
         scale = 1 if data_file.endswith('.csv') else 1000
         _ = abs(c_max/scale*rca)
@@ -118,7 +118,7 @@ def current_max(data_files, rcs, trqh, w2t):
     return current
 
 
-def current_avg(data_files, rcs, trqh, w2t):
+def current_avg(data_files, rcs, trq, w2t):
     current = {1: [], 2: [], 3: [], 4: [], 5: [], 6: [], 7: []}
     for data_file in data_files:
         if data_file.endswith('.data'):
@@ -132,7 +132,7 @@ def current_avg(data_files, rcs, trqh, w2t):
         axis = int(data_file.split('\\')[-1].split('_')[0].removeprefix('j'))
         rca = rcs[axis-1]
 
-        col = df.columns.values[trqh - 1]
+        col = df.columns.values[trq-1]
         c_std = df[col].std()
         c_avg = df[col].mean()
 
@@ -223,8 +223,7 @@ def find_point(data_file, pos, flag, df, _row_s, _row_e, w2t, exitcode, threshol
             else:
                 return _row_s, _row_e
         else:
-            # w2t(f"[{pos}] {data_file}数据有误，需要检查，无法找到第{exitcode}个有效点...", 0, exitcode, 'red')
+            w2t(f"[{pos}] {data_file}数据有误，需要检查，无法找到第{exitcode}个有效点...", 0, exitcode, 'red')
-            w2t(f"[{pos}] {data_file}数据有误，需要检查，无法找到第{exitcode}个有效点...", 0, 0, 'red')
     elif flag == 'gt':
         while _row_e > end_point:
             speed_avg = df.iloc[_row_s:_row_e, 0].abs().mean()
@@ -235,8 +234,7 @@ def find_point(data_file, pos, flag, df, _row_s, _row_e, w2t, exitcode, threshol
             else:
                 return _row_s, _row_e
         else:
-            # w2t(f"[{pos}] {data_file}数据有误，需要检查，无法找到有效起始点或结束点...", 0, exitcode, 'red')
+            w2t(f"[{pos}] {data_file}数据有误，需要检查，无法找到有效起始点或结束点...", 0, exitcode, 'red')
-            w2t(f"[{pos}] {data_file}数据有误，需要检查，无法找到有效起始点或结束点...", 0, 0, 'red')
 
 
 def p_single(wb, single, vel, trq, rpms, w2t):
@@ -395,9 +393,9 @@ def main(path, sub, dur, vel, trq, trqh, w2t):
     data_files = initialization(path, sub, w2t)
     rpms, rcs = get_configs(path + '\\configs.xlsx', w2t)
     if sub == 'max':
-        current_max(data_files, rcs, trqh, w2t)
+        current_max(data_files, rcs, trq, w2t)
     elif sub == 'avg':
-        current_avg(data_files, rcs, trqh, w2t)
+        current_avg(data_files, rcs, trq, w2t)
     elif sub == 'cycle':
         current_cycle(dur, data_files, rcs, vel, trq, trqh, rpms, w2t)
     else: