[modify]
v0.0.5(2024/05/23) 1. 完善了函数注释 2. 调整了阈值和步长 3. 删除了just_open函数，以及对应的win32com库（Thank GOD!终于可以不用这个库了） 4. 重写了获取开始点位的代码，直接使用speed来判断，而不用角度，所以find_row_start_dp以及copy_data_to_excel_file函数也被一并删除
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@ -2,25 +2,15 @@
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import os
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import sys
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import openpyxl
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from win32com.client import DispatchEx
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import time
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from threading import Thread
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import pythoncom
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import pandas
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def just_open(filename):
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pythoncom.CoInitialize()
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xlapp = DispatchEx("Excel.Application")
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xlapp.Visible = False
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xlbook = xlapp.Workbooks.Open(filename)
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xlapp.DisplayAlerts = 0
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xlbook.SaveAs(filename)
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xlbook.Close()
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xlapp.Quit()
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def traversal_files(path):
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# 功能：以列表的形式分别返回指定路径下的文件和文件夹，不包含子目录
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# 参数：路径
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# 返回值：路径下的文件夹列表 路径下的文件列表
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if not os.path.exists(path):
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msg = f'数据文件夹{path}不存在，请确认后重试......'
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warn_pause_exit(msg, 1, 11)
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@ -37,55 +27,57 @@ def traversal_files(path):
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def get_threshold_step(excel_file, AXIS):
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# 功能：负载和速度100%，且是j2的时候，做特殊处理
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# 参数：新生成的excel，轴号
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# 返回值：速度差阈值，处理步长
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conditions = sorted(excel_file.split('\\')[-2].split('_'))
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# 只有负载和速度是100%时，才会启用更敏感的step
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flg = 1 if conditions[0][-3:] == '100' and conditions[2][-3:] == '100' else 0
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if flg == 1 and AXIS == 'j2':
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threshold = 50
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step = 20
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threshold = 30
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step = 5
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else:
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threshold = 50
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step = 100
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threshold = 10
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step = 5
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return threshold, step
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def find_row_start(excel_file, ws_data, conditions, AV, RR, AXIS):
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# 功能：查找数据文件中有效数据的行号，也即最后一个速度下降的点位
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# 参数：如上
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# 返回值：速度下降点位，最后的数据点位
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ratio = float(conditions[1].removeprefix('speed'))/100
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speed_max = AV * ratio * RR / 6
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row_max = ws_data.max_row
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row_start = row_max - 1000
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row_max = row_start = ws_data.max_row
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threshold, step = get_threshold_step(excel_file, AXIS)
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while row_start > 0:
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while row_start > step+1:
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speed = ws_data[f"A{row_start}"].value
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if speed is None or int(speed) < 1:
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row_start -= step
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row_start -= 50
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continue
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row_end = row_start - step
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if row_end < 2:
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msg = f"可能是{excel_file.replace('xlsx', 'data')}, 这个文件数据采集有问题，也有可能是程序步长设定问题......" \
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f"建议重新采集，或者先删除该文件夹，重新运行程序，先手动处理"
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warn_pause_exit(msg, 1, 10)
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_a = ws_data[f"A{row_start}"].value
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_b = ws_data[f"A{row_end}"].value
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if abs(_a-speed_max) < threshold and abs(_b-speed_max) < threshold and abs(_a-_b) < threshold:
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row_start -= (step + 200)
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_ = []
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for i in range(row_start, row_start-step+1, -1):
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_.append(ws_data[f"A{i}"].value)
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speed_avg = abs(sum(_))/len(_)
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if abs(speed_avg-speed_max) < threshold:
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row_start = row_start - 10
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break
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else:
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row_start -= step
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else:
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os.remove(excel_file)
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msg = f"可能是{excel_file.replace('xlsx', 'data')},这个文件数据采集有问题，比如采集的时机不对，请检查......"
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msg = f"可能是{excel_file.replace('xlsx', 'data')},这个文件数据采集有问题，比如采集的时机不对，也有可能是程序步长设定问题，请检查......"
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warn_pause_exit(msg, 1, 9)
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return row_max, row_start
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def find_result_sheet_name(conditions, count):
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# 该函数比较简单，功能是获取结果文件准确的sheet页名称
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# 功能：获取结果文件准确的sheet页名称
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# 参数：臂展和速度的列表
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# 返回值：结果文件对应的sheet name
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# 33%臂展_33%速度_正1
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reach = conditions[0].removeprefix('reach')
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speed = conditions[1].removeprefix('speed')
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@ -95,12 +87,17 @@ def find_result_sheet_name(conditions, count):
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def copy_data_to_result(ws_data, ws_result, row_max, row_start):
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# 功能：将数据文件中有效数据拷贝至结果文件对应的 sheet
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# 参数：如上
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# 返回值：-
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# 结果文件数据清零
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for row in ws_result.iter_rows(min_row=2, min_col=1, max_row=6000 - row_start + 2, max_col=2):
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for row in ws_result.iter_rows(min_row=2, min_col=1, max_row=2000, max_col=2):
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for cell in row:
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cell.value = None
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# 将合适的数据复制到结果文件
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row_max = row_start + 399 if row_max-row_start > 400 else row_max
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data = []
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for row in ws_data.iter_rows(min_row=row_start, min_col=1, max_row=row_max, max_col=2):
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for cell in row:
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@ -112,74 +109,16 @@ def copy_data_to_result(ws_data, ws_result, row_max, row_start):
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i = i + 1
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def copy_data_to_excel_file(wb_data, ws_result, row_max, row_start, excel_file, RC, RR):
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try:
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del wb_data['dp']
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wb_data.create_sheet('dp')
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ws_dp = wb_data['dp']
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except Exception as Err:
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wb_data.create_sheet('dp')
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ws_dp = wb_data['dp']
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data = []
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for row in ws_result.iter_rows(min_row=1, min_col=1, max_row=row_max-row_start+2, max_col=5):
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for cell in row:
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data.append(cell.value)
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i = 0
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for row in ws_dp.iter_rows(min_row=1, min_col=1, max_row=row_max-row_start+2, max_col=5):
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for cell in row:
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cell.value = data[i]
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i = i + 1
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ws_dp.cell(row=5, column=7).value = RC
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ws_dp.cell(row=6, column=7).value = RR
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wb_data.save(excel_file)
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wb_data.close()
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just_open(excel_file) # 为了能读取到公式计算的数值，必须要用 win32com 打开关闭一次
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wb_data = openpyxl.load_workbook(excel_file, data_only=True)
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ws_dp = wb_data['dp']
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return wb_data, ws_dp
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def find_row_start_dp(data_file, ws_dp, row_max, row_start, conditions, AV):
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ratio = float(conditions[1].removeprefix('speed'))/100
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av_max = AV * ratio
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row_max_dp = row_max - row_start + 1 + 1 # title row
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row_start_dp = row_max_dp - 5
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while row_start_dp > 6:
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# 处理异常数据：当从数据文件中拷贝的有效数据超过5000时，会触发下面代码块
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angular = ws_dp.cell(row=row_start_dp, column=4).value
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if angular is None or str(angular) == '0':
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row_start_dp -= 50
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continue
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_a = float(ws_dp.cell(row=row_start_dp, column=4).value)
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_b = float(ws_dp.cell(row=row_start_dp - 1, column=4).value)
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_c = float(ws_dp.cell(row=row_start_dp - 2, column=4).value)
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_d = float(ws_dp.cell(row=row_start_dp - 3, column=4).value)
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_e = float(ws_dp.cell(row=row_start_dp - 4, column=4).value)
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avg = (_a + _b + _c + _d + _e) / 5
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if abs(avg - av_max) < 1:
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row_start_dp = row_start_dp + 10 - 5 # +10 是因为结果文件 C2 的值是 10，-5是做了保守处理，相当于再往前移动 5 个点位
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break
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else:
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row_start_dp -= 5 # 保守一点，每次移动 5 个点位，如果想要加快程序运行，可适当调整更大一些，建议不超过 15
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else:
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msg = "数据有误，未找到平衡的点，请确认！"
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warn_pause_exit(msg, 1, 1)
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return row_start_dp
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def single_file_process(data_file, wb_result, count, AV, RR, RC, AXIS):
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# 功能：完成单个数据文件的处理
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# 参数：如上
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# 返回值：-
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excel_file = data_file.replace('.data', '.xlsx')
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sheet_name = data_file.split('\\')[-1].removesuffix('.data')
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df = pandas.read_csv(data_file, sep='\t')
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df.to_excel(excel_file, sheet_name=sheet_name, index=False)
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conditions = sorted(data_file.split('\\')[-2].split('_')[1:])
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# print(f"conditions = {conditions}")
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result_sheet_name = find_result_sheet_name(conditions, count)
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ws_result = wb_result[result_sheet_name]
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@ -188,16 +127,17 @@ def single_file_process(data_file, wb_result, count, AV, RR, RC, AXIS):
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row_max, row_start = find_row_start(excel_file, ws_data, conditions, AV, RR, AXIS)
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copy_data_to_result(ws_data, ws_result, row_max, row_start)
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wb_data, ws_dp = copy_data_to_excel_file(wb_data, ws_result, row_max, row_start, excel_file, RC, RR)
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row_start_dp = find_row_start_dp(data_file, ws_dp, row_max, row_start, conditions, AV)
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ws_result["G2"] = int(row_start_dp)
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ws_result["C2"] = int(2)
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ws_result["G2"] = int(10+4)
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wb_data.save(excel_file)
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wb_data.close()
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def now_doing_msg(docs, flag):
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# 功能：输出正在处理的文件或目录
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# 参数：文件或目录，start 或 done 标识
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# 返回值：-
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now = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
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file_type = 'file' if os.path.isfile(docs) else 'dir'
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if flag == 'start' and file_type == 'dir':
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@ -211,6 +151,9 @@ def now_doing_msg(docs, flag):
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def data_process(result_file, raw_data_dirs, AV, RR, RC, AXIS):
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# 功能：完成一个结果文件的数据处理
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# 参数：结果文件，数据目录，以及预读取的参数
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# 返回值：-
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prefix = result_file.split('\\')[-1].split('_')[0]
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wb_result = openpyxl.load_workbook(result_file) # 打开和关闭结果文件夹十分耗时间
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for raw_data_dir in raw_data_dirs:
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@ -220,12 +163,11 @@ def data_process(result_file, raw_data_dirs, AV, RR, RC, AXIS):
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# 数据文件串行处理模式---------------------------------
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# count = 1
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# for data_file in data_files:
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# now_doing_msg(data_file, 'start')
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# single_file_process(data_file, wb_result, count, AV, RR, RC, AXIS)
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# count += 1
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# now_doing_msg(data_file, 'done')
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# now_doing_msg(data_file, 'start')
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# single_file_process(data_file, wb_result, count, AV, RR, RC, AXIS)
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# count += 1
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# now_doing_msg(data_file, 'done')
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# ---------------------------------------------------
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# 数据文件并行处理模式---------------------------------
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threads = [Thread(target=single_file_process, args=(data_files[0], wb_result, 1, AV, RR, RC, AXIS)),
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Thread(target=single_file_process, args=(data_files[1], wb_result, 2, AV, RR, RC, AXIS)),
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@ -242,6 +184,9 @@ def data_process(result_file, raw_data_dirs, AV, RR, RC, AXIS):
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def warn_pause_exit(msg, pause_num, exit_num):
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# 功能：打印告警信息，并推出程序
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# 参数：告警信息，暂停的次数，退出的值
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# 返回值：-
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print(msg + '\n')
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for i in range(pause_num):
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_ = input("Press ENTER to continue......\n")
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@ -249,6 +194,9 @@ def warn_pause_exit(msg, pause_num, exit_num):
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def check_files(raw_data_dirs, result_files):
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# 功能：检查数据文件以及结果文件的合规性
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# 参数：数据文件夹，结果文件
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# 返回值：-
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if len(result_files) != 3:
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msg = "结果文件数目错误，结果文件有且只有三个，请确认！"
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for result_file in result_files:
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@ -299,6 +247,9 @@ def check_files(raw_data_dirs, result_files):
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def delete_excel_files(raw_data_dirs):
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# 功能：删除数据文件夹里的 .xlsx 文件
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# 参数：数据文件夹
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# 返回值：-
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for raw_data_dir in raw_data_dirs:
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_, raw_data_files = traversal_files(raw_data_dir)
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for raw_data_file in raw_data_files:
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@ -307,6 +258,9 @@ def delete_excel_files(raw_data_dirs):
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def initialization():
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# 功能：初始化，记录开始时间，读取预定义参数
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# 参数：-
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# 返回值：结果文件，数据文件夹，以及预定义参数
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time_start = time.time() # 记录开始时间
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try:
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# read init configurations from config file
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@ -331,6 +285,9 @@ def initialization():
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def execution(args):
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# 功能：执行处理所有数据文件
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# 参数：initialization函数的返回值
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# 返回值：-
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raw_data_dirs, result_files, time_start, AV, RR, RC, AXIS = args
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prefix = []
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for raw_data_dir in raw_data_dirs:
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Binary file not shown.
@ -6,8 +6,8 @@ VSVersionInfo(
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ffi=FixedFileInfo(
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# filevers and prodvers should be always a tuple with four items: (1, 2, 3, 4)
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# Set not needed items to zero 0.
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filevers=(0, 0, 4, 0),
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prodvers=(0, 0, 4, 0),
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filevers=(0, 0, 5, 0),
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prodvers=(0, 0, 5, 0),
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# Contains a bitmask that specifies the valid bits 'flags'r
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mask=0x3f,
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# Contains a bitmask that specifies the Boolean attributes of the file.
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@ -31,12 +31,12 @@ VSVersionInfo(
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'040904b0',
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[StringStruct('CompanyName', 'Rokae - https://www.rokae.com/'),
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StringStruct('FileDescription', 'All in one automatic operating tool'),
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StringStruct('FileVersion', '0.0.4 (2024-05-20)'),
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StringStruct('FileVersion', '0.0.5 (2024-05-20)'),
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StringStruct('InternalName', 'AIO.exe'),
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StringStruct('LegalCopyright', '© 2024-2024 Manford Fan'),
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StringStruct('OriginalFilename', 'AIO.exe'),
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StringStruct('ProductName', 'AIO'),
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StringStruct('ProductVersion', '0.0.4 (2024-05-20)')])
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StringStruct('ProductVersion', '0.0.5 (2024-05-20)')])
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]),
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VarFileInfo([VarStruct('Translation', [1033, 1200])])
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]
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