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minor fix

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gitea 2024-07-18 11:04:31 +08:00
parent 5ab03d23f2
commit dd0873637f
5 changed files with 38 additions and 30 deletions
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7
aio/README.md
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@ -8,6 +8,7 @@
4. wavelogger 波形处理，几乎不花费时间
5. 制动自动化测试
6. 电机电流自动化测试
7. 耐久工程曲线指标采集（仅适用于六轴）
---
@ -154,6 +155,12 @@ pyinstaller --noconfirm --onedir --windowed --add-data "C:/Users/Administrator/A
基本同第五点
#### 7) 耐久工程曲线指标采集
可实现固定周期指定曲线指标的采集，用于长时间观察指标的变化情况，输入文件：
- configs.xlsx，执行之前需要手动修改好configs.xlsx中的参数，以及间隔时间
- target.zip，需要确认工程点位和动作无问题后，保存导出
#### 其他
customtkinter的tabview组件不支持修改字体大小，可以参考 [Changing Font of a Tabview](https://github.com/TomSchimansky/CustomTkinter/issues/2296) 进行手动修改源码实现：
a. 运行 `pip show customtkinter`，获取到库的路径

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aio/assets/configs.xlsx
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aio/assets/templates/durable/durable_data_current.xlsx
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aio/assets/templates/durable/durable_data_velocity.xlsx
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61
aio/code/durable_action/factory_test.py
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@ -3,12 +3,13 @@ from os.path import exists, dirname
from os import scandir
from paramiko import SSHClient, AutoAddPolicy
from json import loads
from time import sleep, time
from time import sleep, time, strftime, localtime
import pandas as pd
from openpyxl import load_workbook
from math import sqrt
tab_name = 'Durable Action'
count = 0
durable_data_current_xlsx = f'{dirname(__file__)}/../../assets/templates/durable/durable_data_current.xlsx'
durable_data_velocity_xlsx = f'{dirname(__file__)}/../../assets/templates/durable/durable_data_velocity.xlsx'
display_pdo_params = [
@ -170,26 +171,32 @@ def run_rl(path, config_file, hr, md, w2t):
# 6. 关闭诊断曲线，停止程序运行，下电并且换成手动模式
_response = execution('diagnosis.open', hr, w2t, open=False, display_open=False)
_response = execution('diagnosis.set_params', hr, w2t, display_pdo_params=[])
_response = execution('rl_task.stop', hr, w2t, tasks=['current'])
sleep(1) # 保证所有数据均已返回
# 7. 保留数据并处理输出
get_durable_data(path, config_file, data_all, scenario_time, hr, w2t)
_wb = load_workbook(config_file, read_only=True)
_ws = _wb['Target']
wait_time = float(_ws.cell(row=2, column=10).value)
rcs = []
for i in range(6):
rcs.append(float(_ws.cell(row=6, column=i + 2).value))
get_durable_data(path, data_all, scenario_time, wait_time, rcs, hr, w2t)
# 8. 继续运行
_response = execution('rl_task.run', hr, w2t, tasks=['current'])
while True:
# 每3分钟，更新一次数据，打开曲线，获取周期内电流，关闭曲线
sleep(180)
sleep(wait_time)
_response = execution('diagnosis.open', hr, w2t, open=True, display_open=True)
_response = execution('diagnosis.set_params', hr, w2t, display_pdo_params=display_pdo_params)
sleep(scenario_time + 10)
sleep(scenario_time+5)
_response = execution('diagnosis.open', hr, w2t, open=False, display_open=False)
_response = execution('diagnosis.set_params', hr, w2t, display_pdo_params=[])
sleep(2)
# 7. 保留数据并处理输出
get_durable_data(path, config_file, data_all, scenario_time, hr, w2t)
get_durable_data(path, data_all, scenario_time, wait_time, rcs, hr, w2t)
def get_durable_data(path, config_file, data, scenario_time, hr, w2t):
def get_durable_data(path, data, scenario_time, wait_time, rcs, hr, w2t):
_data_list = []
for _msg in hr.c_msg:
if 'diagnosis.result' in _msg:
@ -207,12 +214,6 @@ def get_durable_data(path, config_file, data, scenario_time, hr, w2t):
# for _ in _data_list:
# f_obj.write(f"{_}\n")
_wb = load_workbook(config_file, read_only=True)
_ws = _wb['Target']
rcs = []
for i in range(6):
rcs.append(float(_ws.cell(row=6, column=i + 2).value))
_d2d_trq = {
'device_servo_trq_feedback_0': [], 'device_servo_trq_feedback_1': [], 'device_servo_trq_feedback_2': [],
'device_servo_trq_feedback_3': [], 'device_servo_trq_feedback_4': [], 'device_servo_trq_feedback_5': [],
@ -231,35 +232,35 @@ def get_durable_data(path, config_file, data, scenario_time, hr, w2t):
_d2d_vel[f'hw_joint_vel_feedback_{i}'].extend(item['value'])
if len(_d2d_trq['device_servo_trq_feedback_0']) / 1000 > scenario_time + 1:
_df = pd.DataFrame(_d2d_trq)
# ========= for trq =========
_df_1 = pd.DataFrame(_d2d_trq)
for i in range(6):
_ = sqrt(100*_df[f'device_servo_trq_feedback_{i}'].apply(lambda x: (rcs[i]*x/10000)**2).sum()/len(_df[f'device_servo_trq_feedback_{i}']))
_ = sqrt(100*_df_1[f'device_servo_trq_feedback_{i}'].apply(lambda x: (rcs[i]*x/10000)**2).sum()/len(_df[f'device_servo_trq_feedback_{i}']))
del data[0][f"axis{i + 1}"][0]
data[0][f"axis{i + 1}"].append(_)
_df = pd.DataFrame(data[0])
while True:
if not hr.durable_lock:
hr.durable_lock = 1
_df.to_excel(durable_data_current_xlsx, index=False)
hr.durable_lock = 0
break
else:
sleep(1)
_df = pd.DataFrame(_d2d_vel)
_df_1 = pd.DataFrame(data[0])
# ========= for vel =========
_df_2 = pd.DataFrame(_d2d_vel)
for i in range(6):
_ = sqrt(100*_df[f'hw_joint_vel_feedback_{i}'].apply(lambda x: (rcs[i]*x/10000)**2).sum()/len(_df[f'hw_joint_vel_feedback_{i}']))
_ = sqrt(100*_df_2[f'hw_joint_vel_feedback_{i}'].apply(lambda x: (rcs[i]*x/10000)**2).sum()/len(_df[f'hw_joint_vel_feedback_{i}']))
del data[1][f"axis{i + 1}"][0]
data[1][f"axis{i + 1}"].append(_)
_df = pd.DataFrame(data[1])
_df_2 = pd.DataFrame(data[1])
while True:
if not hr.durable_lock:
hr.durable_lock = 1
_df.to_excel(durable_data_velocity_xlsx, index=False)
_df_1.to_excel(durable_data_current_xlsx, index=False)
_df_2.to_excel(durable_data_velocity_xlsx, index=False)
hr.durable_lock = 0
break
else:
sleep(1)
global count
count += 1
this_time = strftime("%Y-%m-%d %H:%M:%S", localtime(time()))
next_time = strftime("%Y-%m-%d %H:%M:%S", localtime(time()+wait_time+10+scenario_time)).split()[-1]
w2t(f"[{this_time}] 当前次数：{count:09d} | 预计下次数据更新时间：{next_time}", 0, 0, '#008B8B', tab_name)
break
else:
with open(f'{path}\\device_servo_trq_feedback_0.txt', 'w', encoding='utf-8') as f_obj: