376 lines
14 KiB
Python
376 lines
14 KiB
Python
from openpyxl import load_workbook
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from os import scandir
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from os.path import exists
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from sys import argv
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from pandas import read_csv, concat, set_option
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from re import match
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from threading import Thread
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from time import sleep
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from csv import reader
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class GetThreadResult(Thread):
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def __init__(self, func, args=()):
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super(GetThreadResult, self).__init__()
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self.func = func
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self.args = args
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self.result = 0
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def run(self):
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sleep(1)
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self.result = self.func(*self.args)
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def get_result(self):
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Thread.join(self) # 等待线程执行完毕
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try:
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return self.result
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except Exception as Err:
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return None
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def w2t_local(msg, wait, w2t):
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while True:
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global stop
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if stop == 0 and wait != 0:
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sleep(1)
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w2t(msg, wait, 0, 'orange')
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else:
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break
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def traversal_files(path, w2t):
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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 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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w2t(msg, 0, 8, 'red')
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else:
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dirs = []
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files = []
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for item in scandir(path):
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if item.is_dir():
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dirs.append(item.path)
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elif item.is_file():
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files.append(item.path)
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return dirs, files
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def initialization(path, sub, w2t):
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_, data_files = traversal_files(path, w2t)
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count = 0
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for data_file in data_files:
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filename = data_file.split('\\')[-1]
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if sub != 'cycle':
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if not (match('j[1-7].*\\.data', filename) or match('j[1-7].*\\.csv', filename)):
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msg = f"所有文件必须以 jx_ 开头，以 .data/csv 结尾（x取值1-7），请检查后重新运行。"
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w2t(msg, 0, 6, 'red')
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else:
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if filename.endswith('.xlsx'):
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count += 1
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elif not (match('j[1-7].*\\.data', filename) or match('j[1-7].*\\.csv', filename)):
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msg = f"所有文件必须以 jx_ 开头，以 .data/csv 结尾（x取值1-7），请检查后重新运行。"
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w2t(msg, 0, 7, 'red')
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if sub == 'cycle' and count != 1:
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w2t("未找到电机电流数据处理excel表格，确认后重新运行！", 0, 5, 'red')
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return data_files
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def current_max(data_files, rcs, trqh, w2t):
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current = {1: [], 2: [], 3: [], 4: [], 5: [], 6: [], 7: []}
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for data_file in data_files:
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if data_file.endswith('.data'):
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df = read_csv(data_file, sep='\t')
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elif data_file.endswith('.csv'):
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df = read_csv(data_file, sep=',', encoding='gbk', header=8)
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axis = int(data_file.split('\\')[-1].split('_')[0].removeprefix('j'))
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rca = rcs[axis-1]
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col = df.columns.values[trqh-1]
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c_max = df[col].max()
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scale = 1 if data_file.endswith('.csv') else 1000
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_ = abs(c_max/scale*rca)
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current[axis].append(_)
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w2t(f"{data_file}: {_:.4f}")
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for axis, cur in current.items():
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if not cur:
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continue
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else:
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w2t(f"{axis}轴数据：", 1, 0, 'purple')
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for value in cur:
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w2t(f"{value:.4f} ", 1, 0, 'purple')
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w2t('')
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w2t("\n【MAX】数据处理完毕......")
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return current
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def current_avg(data_files, rcs, trqh, w2t):
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current = {1: [], 2: [], 3: [], 4: [], 5: [], 6: [], 7: []}
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for data_file in data_files:
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if data_file.endswith('.data'):
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df = read_csv(data_file, sep='\t')
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elif data_file.endswith('.csv'):
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df = read_csv(data_file, sep=',', encoding='gbk', header=8)
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axis = int(data_file.split('\\')[-1].split('_')[0].removeprefix('j'))
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rca = rcs[axis-1]
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col = df.columns.values[trqh - 1]
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c_std = df[col].std()
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c_avg = df[col].mean()
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scale = 1 if data_file.endswith('.csv') else 1000
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_ = (abs(c_avg)+c_std)/scale*rca
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current[axis].append(_)
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w2t(f"{data_file}: {_:.4f}")
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for axis, cur in current.items():
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if not cur:
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continue
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else:
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w2t(f"{axis}轴数据：", 1, 0, 'purple')
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for value in cur:
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w2t(f"{value:.4f} ", 1, 0, 'purple')
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w2t('')
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w2t("\n【AVG】数据处理完毕......")
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return current
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def current_cycle(dur, data_files, rcs, vel, trq, trqh, rpm, w2t):
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result = None
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hold = []
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single = []
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for data_file in data_files:
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filename = data_file.split('\\')[-1]
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if data_file.endswith('.xlsx'):
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result = data_file
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elif match('j[1-7]_hold_.*\\.data', filename) or match('j[1-7]_hold_.*\\.csv', filename):
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hold.append(data_file)
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else:
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single.append(data_file)
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w2t(f"正在打开文件 {result}，需要 10s 左右", 1, 0, 'orange')
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global stop
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stop = 0
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t_excel = GetThreadResult(load_workbook, args=(result, ))
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t_wait = Thread(target=w2t_local, args=('.', 1, w2t))
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t_excel.start()
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t_wait.start()
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t_excel.join()
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wb = t_excel.get_result()
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stop = 1
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sleep(1.1)
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w2t('')
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if hold != []:
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avg = current_avg(hold, rcs, trqh, w2t)
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for axis, cur_value in avg.items():
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try:
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shtname = f"J{axis}"
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wb[shtname]["J4"].value = float(cur_value)
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except:
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pass
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if dur == 0:
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p_single(wb, single, vel, trq, rpm, w2t)
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else:
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p_scenario(wb, single, vel, trq, rpm, dur, w2t)
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w2t(f"正在保存文件 {result}，需要 10s 左右", 1, 0, 'orange')
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stop = 0
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t_excel = Thread(target=wb.save, args=(result, ))
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t_wait = Thread(target=w2t_local, args=('.', 1, w2t))
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t_excel.start()
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t_wait.start()
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t_excel.join()
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stop = 1
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sleep(1.1)
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w2t('\n')
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w2t("----------------------------------------------------------")
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w2t("全部处理完毕")
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def find_point(data_file, pos, flag, df, _row_s, _row_e, w2t, exitcode, threshold, step, end_point):
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if flag == 'lt':
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while _row_e > end_point:
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speed_avg = df.iloc[_row_s:_row_e, 0].abs().mean()
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if speed_avg < threshold:
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_row_e -= step
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_row_s -= step
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continue
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else:
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return _row_s, _row_e
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else:
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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')
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elif flag == 'gt':
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while _row_e > end_point:
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speed_avg = df.iloc[_row_s:_row_e, 0].abs().mean()
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if speed_avg > threshold:
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_row_e -= step
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_row_s -= step
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continue
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else:
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return _row_s, _row_e
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else:
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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')
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def p_single(wb, single, vel, trq, rpm, w2t):
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# 1. 先找到第一个速度为零的点，数据从后往前找，一开始就是零的情况不予考虑
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# 2. 记录第一个点的位置，继续向前查找第二个速度为零的点，同理，一开始为零的点不予考虑
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# 3. 记录第二个点的位置，并将其中的数据拷贝至对应位置
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for data_file in single:
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rpm = 1 if rpm == 0 else rpm
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scale = 1000 if data_file.endswith('.csv') else 1
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axis = int(data_file.split('\\')[-1].split('_')[0].removeprefix('j'))
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shtname = f"J{axis}"
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ws = wb[shtname]
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addition = 1
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set_option("display.precision", 2)
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if data_file.endswith('.data'):
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df = read_csv(data_file, sep='\t')
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rr = float(wb['统计'].cell(row=2, column=axis+1).value)
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addition = 180 / 3.1415926 * 60 / 360 * rr
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elif data_file.endswith('.csv'):
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df = read_csv(data_file, sep=',', encoding='gbk', header=8)
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csv_reader = reader(open(data_file))
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i = 0
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cycle = 0.001
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for row in csv_reader:
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i += 1
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if i == 3:
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cycle = float(row[0].split(':')[1].split('ms')[0]) / 1000
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break
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ws["H11"] = cycle
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col_names = list(df.columns)
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df_1 = df[col_names[vel-1]].multiply(rpm*addition)
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df_2 = df[col_names[trq-1]].multiply(scale)
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df = concat([df_1, df_2], axis=1)
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_step = 5 if data_file.endswith('.csv') else 50
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_end_point = 30 if data_file.endswith('.csv') else 200
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_adjust = 0 if data_file.endswith('.csv') else 150
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_row_e = df.index[-1]
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_row_s = _row_e - _end_point
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speed_avg = df.iloc[_row_s:_row_e, 0].abs().mean()
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if speed_avg < 2:
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# 过滤尾部为零无效数据
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_row_s, _row_e = find_point(data_file, 'a1', 'lt', df, _row_s, _row_e, w2t, 1, threshold=5, step=_step, end_point=_end_point)
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# 找到第一个起始点 row_end，继续找到有数据的部分，后面有一段有效数据区
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row_end = _row_e - _adjust
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_row_e -= _end_point
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_row_s -= _end_point
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_row_s, _row_e = find_point(data_file, 'a2', 'gt', df, _row_s, _row_e, w2t, 3, threshold=5, step=_step, end_point=_end_point)
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# 速度已经快要降为零了，继续寻找下一个速度上升点
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_row_e -= _end_point
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_row_s -= _end_point
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_row_s, _row_e = find_point(data_file, 'a3', 'lt', df, _row_s, _row_e, w2t, 3, threshold=5, step=_step, end_point=_end_point)
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elif speed_avg > 2:
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# 过滤尾部非零无效数据
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_row_s, _row_e = find_point(data_file, 'b1', 'gt', df, _row_s, _row_e, w2t, 2, threshold=5, step=_step, end_point=_end_point)
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# 找到第一个起始点 row_end，继续找到有数据的部分，后面有一段零数据区
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row_end = _row_e - _adjust
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_row_e -= _end_point
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_row_s -= _end_point
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_row_s, _row_e = find_point(data_file, 'b2', 'lt', df, _row_s, _row_e, w2t, 4, threshold=5, step=_step, end_point=_end_point)
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# 目前已经有一点的速度值了，继续往前搜寻下一个速度为零的点
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_row_e -= _end_point
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_row_s -= _end_point
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_row_s, _row_e = find_point(data_file, 'b3', 'gt', df, _row_s, _row_e, w2t, 4, threshold=5, step=_step, end_point=_end_point)
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row_start = _row_s + _adjust
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data = []
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for row in range(row_start, row_end):
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data.append(df.iloc[row, 0])
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data.append(df.iloc[row, 1])
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i = 0
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for row in ws.iter_rows(min_row=2, min_col=2, max_row=70000, max_col=3):
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for cell in row:
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try:
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_ = f"{data[i]:.2f}"
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cell.value = float(_)
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i += 1
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except:
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cell.value = None
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def p_scenario(wb, single, vel, trq, rpm, dur, w2t):
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for data_file in single:
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cycle = 0.001
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rpm = 1 if rpm == 0 else rpm
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scale = 1000 if data_file.endswith('.csv') else 1
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axis = int(data_file.split('\\')[-1].split('_')[0].removeprefix('j'))
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shtname = f"J{axis}"
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ws = wb[shtname]
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addition = 1
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set_option("display.precision", 2)
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if data_file.endswith('.data'):
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df = read_csv(data_file, sep='\t')
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rr = float(wb['统计'].cell(row=2, column=axis+1).value)
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addition = 180 / 3.1415926 * 60 / 360 * rr
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elif data_file.endswith('.csv'):
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df = read_csv(data_file, sep=',', encoding='gbk', header=8)
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csv_reader = reader(open(data_file))
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i = 0
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for row in csv_reader:
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i += 1
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if i == 3:
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cycle = float(row[0].split(':')[1].split('ms')[0]) / 1000
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break
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ws["H11"] = cycle
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col_names = list(df.columns)
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df_1 = df[col_names[vel-1]].multiply(rpm*addition)
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df_2 = df[col_names[trq-1]].multiply(scale)
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df = concat([df_1, df_2], axis=1)
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row_start = 300
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row_end = row_start + int(dur/cycle)
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if row_end > df.index[-1]:
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w2t(f"位置超限：{data_file} 共有 {df.index[-1]} 条数据，无法取到第 {row_end} 条数据，需要确认场景周期时间...", 0, 9, 'red')
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data = []
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for row in range(row_start, row_end):
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data.append(df.iloc[row, 0])
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data.append(df.iloc[row, 1])
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i = 0
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for row in ws.iter_rows(min_row=2, min_col=2, max_row=70000, max_col=3):
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for cell in row:
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try:
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_ = f"{data[i]:.2f}"
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cell.value = float(_)
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i += 1
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except:
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cell.value = None
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# =======================================
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def main(path, sub, rcs, vel, trq, trqh, dur, rpm, w2t):
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data_files = initialization(path, sub, w2t)
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if sub == 'max':
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current_max(data_files, rcs, trqh, w2t)
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elif sub == 'avg':
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current_avg(data_files, rcs, trqh, w2t)
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elif sub == 'cycle':
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current_cycle(dur, data_files, rcs, vel, trq, trqh, rpm, w2t)
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else:
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pass
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if __name__ == '__main__':
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stop = 0
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main(*argv[1:])
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