435 lines
20 KiB
Python
435 lines
20 KiB
Python
import json
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import threading
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import openpyxl
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import pandas
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import re
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import csv
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from common import clibs
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def initialization(path, w2t, insert_logdb):
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_, data_files = clibs.traversal_files(path, w2t)
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count, config_file = 0, None
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for data_file in data_files:
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filename = data_file.split("/")[-1]
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if re.match(".*\\.cfg", filename):
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config_file = filename
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count += 1
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elif filename == "T_电机电流.xlsx":
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count += 1
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else:
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if not re.match("j[1-7].*\\.data", filename):
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msg = f"不合规 {data_file}\n"
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msg += "所有数据文件必须以 j[1-7]_ 开头，以 .data 结尾，比如j1_abcdef.data，请检查整改后重新运行\n"
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w2t(msg, "red", "FilenameIllegal")
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if count != 2:
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msg = "需要有一个机型配置文件\"*.cfg\"，以及一个数据处理文件\"T_电机电流.xlsx\"表格，请检查整改后重新运行\n"
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w2t(msg, "red", "FilenameIllegal")
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insert_logdb("INFO", "current", f"current: 获取必要文件：{data_files}")
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return data_files, config_file
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def current_max(data_files, rcs, trq, w2t, insert_logdb):
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insert_logdb("INFO", "current", "MAX: 正在处理最大电流值逻辑...")
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current = {1: [], 2: [], 3: [], 4: [], 5: [], 6: []}
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for data_file in data_files:
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if data_file.endswith(".data"):
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df = pandas.read_csv(data_file, sep="\t")
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else:
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continue
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insert_logdb("INFO", "current", f"MAX: 正在处理 {data_file}")
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cols = len(df.columns)
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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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insert_logdb("INFO", "current", f"MAX: 最大列数为 {cols}，{axis} 轴的额定电流为 {rca}")
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col = df.columns.values[trq-1] # 获取 "device_servo_trq_feedback"
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c_max = df[col].abs().max()
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scale = 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}\n")
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insert_logdb("INFO", "current", f"MAX: 获取到的列名为 {col}，最大电流为 {_}")
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with open(data_file, "a+") as f_data:
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csv_writer = csv.writer(f_data, delimiter="\t")
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csv_writer.writerow([""] * (cols-1) + [_])
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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：")
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for value in cur:
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w2t(f"{value:.4f} ")
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w2t("\n")
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w2t("\n【MAX】数据处理完毕......")
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insert_logdb("INFO", "current", f"MAX: 获取最大电流值结束 current_max = {current}")
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return current
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def current_avg(data_files, rcs, trqh, w2t, insert_logdb):
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insert_logdb("INFO", "current", "AVG: 正在处理平均电流值逻辑...")
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current = {1: [], 2: [], 3: [], 4: [], 5: [], 6: []}
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for data_file in data_files:
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if data_file.endswith(".data"):
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df = pandas.read_csv(data_file, sep="\t")
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else:
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continue
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insert_logdb("INFO", "current", f"AVG: 正在处理 {data_file}")
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cols = len(df.columns)
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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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insert_logdb("INFO", "current", f"AVG: 最大列数为 {cols}，{axis} 轴的额定电流为 {rca}")
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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 = 1000
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_ = (abs(c_avg)+c_std*3)/scale*rca
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current[axis].append(_)
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w2t(f"{data_file}: {_:.4f}\n")
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insert_logdb("INFO", "current", f"AVG: 获取到的列名为 {col}，平均电流为 {_}")
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with open(data_file, "a+") as f_data:
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csv_writer = csv.writer(f_data, delimiter="\t")
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csv_writer.writerow([""] * (cols-1) + [_])
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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：")
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for value in cur:
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w2t(f"{value:.4f} ")
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w2t("\n")
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w2t("\n【AVG】数据处理完毕......\n")
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insert_logdb("INFO", "current", f"AVG: 获取平均电流值结束 current_avg = {current}")
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return current
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def current_cycle(data_files, vel, trq, trqh, sensor, rrs, rcs, params, w2t, insert_logdb):
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result, hold, single, scenario, dur_time = None, [], [], [], 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 filename == "T_电机电流.xlsx":
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result = data_file
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elif re.match("j[1-7]_hold_.*\\.data", filename):
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hold.append(data_file)
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elif re.match("j[1-7]_s_.*\\.data", filename):
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scenario.append(data_file)
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dur_time = float(filename.split("_")[3])
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elif re.match("j[1-7]_.*\\.data", filename):
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single.append(data_file)
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clibs.stop = True
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w2t(f"正在打开文件 {result}，需要 10s 左右......\n")
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t_excel = clibs.GetThreadResult(openpyxl.load_workbook, args=(result, ))
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t_excel.daemon = True
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t_excel.start()
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t_progress = threading.Thread(target=clibs.tl_prg, args=("Processing......", ))
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t_progress.daemon = True
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t_progress.start()
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wb = t_excel.get_result()
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ws = wb["统计"]
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for idx in range(len(params)):
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row = idx + 2
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for col in range(2, 8):
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ws.cell(row=row, column=col).value = params[idx][col-2]
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if hold:
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avg = current_avg(hold, rcs, trqh, w2t, insert_logdb)
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for axis, cur_value in avg.items():
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sht_name = f"J{axis}"
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wb[sht_name]["O4"].value = float(cur_value[0])
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if dur_time == 0:
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p_single(wb, single, vel, trq, sensor, rrs, w2t, insert_logdb)
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else:
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p_scenario(wb, scenario, vel, trq, sensor, rrs, dur_time, w2t)
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clibs.stop = True
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w2t(f"正在保存文件 {result}，需要 10s 左右......\n")
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t_excel = threading.Thread(target=wb.save, args=(result, ))
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t_excel.daemon = True
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t_excel.start()
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t_excel.join()
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clibs.stop = False
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t_progress.join()
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w2t("----------------------------------------------------------\n")
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w2t("全部处理完毕")
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def find_point(data_file, df, flag, row_s, row_e, threshold, step, end_point, skip_scale, axis, seq, w2t, insert_logdb):
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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].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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# one more time，如果连续两次 200 个点的平均值都大于 threshold，说明已经到了临界点了（其实也不一定，只不过相对遇到一次就判定临界点更安全一点点）
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# 从实际数据看，这开逻辑很小概率能触发到
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speed_avg = df.iloc[row_s-end_point*skip_scale:row_e-end_point*skip_scale].abs().mean()
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if speed_avg < threshold:
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insert_logdb("WARNING", "current", f"【lt】{axis} 轴第 {seq} 次查找数据可能有异常，row_s = {row_s}, row_e = {row_e}！")
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return row_s, row_e
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else:
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w2t(f"{data_file} 数据有误，需要检查，无法找到第 {seq} 个有效点...", "red", "AnchorNotFound")
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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].abs().mean()
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# if axis == 1 and seq == 1:
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# insert_logdb("DEBUG", "current", f"【gt】{axis} 轴，speed_avg = {speed_avg}，row_s = {row_s}, row_e = {row_e}！")
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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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# one more time，如果连续两次 200 个点的平均值都小于 threshold，说明已经到了临界点了（其实也不一定，只不过相对遇到一次就判定临界点更安全一点点）
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# 从实际数据看，这开逻辑很小概率能触发到
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speed_avg = df.iloc[row_s-end_point*skip_scale:row_e-end_point*skip_scale].abs().mean()
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if speed_avg > threshold:
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insert_logdb("WARNING", "current", f"【gt】{axis} 轴第 {seq} 次查找数据可能有异常，row_s = {row_s}, row_e = {row_e}！")
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return row_s, row_e
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else:
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w2t(f"{data_file} 数据有误，需要检查，无法找到第 {seq} 个有效点...", "red", "AnchorNotFound")
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def get_row_number(threshold, flag, df, row_s, row_e, axis, insert_logdb):
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count_1, count_2 = 0, 0
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if flag == "start" or flag == "end":
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for number in df.iloc[row_s:row_e].abs():
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count_2 += 1
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if number > threshold:
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count_1 += 1
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if count_1 == 10:
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return row_s + count_2 - 10
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else:
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count_1 = 0
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elif flag == "middle":
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for number in df.iloc[row_s:row_e].abs():
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count_2 += 1
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if number < threshold: # 唯一的区别
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count_1 += 1
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if count_1 == 10:
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return row_s + count_2 - 10
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else:
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count_1 = 0
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places = {"start": "起点", "middle": "中间点", "end": "终点"} # 因为是终点数据，所以可能有异常
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insert_logdb("WARNING", "current", f"{axis} 轴获取{places[flag]}数据 {row_e} 可能有异常，需关注！")
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return row_e
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def p_single(wb, single, vel, trq, sensor, rrs, w2t, insert_logdb):
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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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axis = int(data_file.split("/")[-1].split("_")[0].removeprefix("j"))
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sht_name = f"J{axis}"
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ws = wb[sht_name]
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pandas.set_option("display.precision", 2)
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df_origin = pandas.read_csv(data_file, sep="\t")
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rr = rrs[axis-1]
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addition = 180 / 3.1415926 * 60 / 360 * rr
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col_names = list(df_origin.columns)
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df = df_origin[col_names[vel-1]].multiply(addition)
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step = 50 # 步进值
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end_point = 200 # 有效数值的数目
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threshold = 5 # 200个点的平均阈值线
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skip_scale = 2
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row_start, row_middle, row_end = 0, 0, 0
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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].abs().mean()
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if speed_avg < 2:
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# 第一次过滤：消除速度为零的数据，找到速度即将大于零的上升临界点
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row_s, row_e = find_point(data_file, df, "lt", row_s, row_e, threshold, step, end_point, skip_scale, axis, "pre-1", w2t, insert_logdb)
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row_e -= end_point*skip_scale
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row_s -= end_point*skip_scale
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# 第二次过滤：消除速度大于零的数据，找到速度即将趋近于零的下降临界点
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row_s, row_e = find_point(data_file, df, "gt", row_s, row_e, threshold, step, end_point, skip_scale, axis, "pre-2", w2t, insert_logdb)
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row_e -= end_point*skip_scale
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row_s -= end_point*skip_scale
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# 第三次过滤：消除速度为零的数据，找到速度即将大于零的上升临界点
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row_s, row_e = find_point(data_file, df, "lt", row_s, row_e, threshold, step, end_point, skip_scale, axis, "pre-3", w2t, insert_logdb)
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row_e -= end_point*skip_scale
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row_s -= end_point*skip_scale
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# 正式第一次采集：消除速度大于零的数据，找到速度即将趋近于零的下降临界点
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row_s, row_e = find_point(data_file, df, "gt", row_s, row_e, threshold, step, end_point, skip_scale, axis, 1, w2t, insert_logdb)
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row_end = get_row_number(threshold, "end", df, row_s, row_e, axis, insert_logdb)
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row_e -= end_point*skip_scale
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row_s -= end_point*skip_scale
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# 正式第二次采集：消除速度为零的数据，找到速度即将大于零的上升临界点
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row_s, row_e = find_point(data_file, df, "lt", row_s, row_e, threshold, step, end_point, skip_scale, axis, 2, w2t, insert_logdb)
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row_middle = get_row_number(threshold, "middle", df, row_s, row_e, axis, insert_logdb)
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row_e -= end_point*skip_scale
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row_s -= end_point*skip_scale
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# 正式第三次采集：消除速度大于零的数据，找到速度即将趋近于零的下降临界点
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row_s, row_e = find_point(data_file, df, "gt", row_s, row_e, threshold, step, end_point, skip_scale, axis, 3, w2t, insert_logdb)
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row_start = get_row_number(threshold, "start", df, row_s, row_e, axis, insert_logdb)
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elif speed_avg > 2:
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# 第一次过滤：消除速度大于零的数据，找到速度即将趋近于零的下降临界点
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row_s, row_e = find_point(data_file, df, "gt", row_s, row_e, threshold, step, end_point, skip_scale, axis, "pre-1", w2t, insert_logdb)
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row_e -= end_point*skip_scale
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row_s -= end_point*skip_scale
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# 第二次过滤：消除速度为零的数据，找到速度即将大于零的上升临界点
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row_s, row_e = find_point(data_file, df, "lt", row_s, row_e, threshold, step, end_point, skip_scale, axis, "pre-2", w2t, insert_logdb)
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row_e -= end_point*skip_scale
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row_s -= end_point*skip_scale
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# 第一次正式采集：消除速度大于零的数据，找到速度即将趋近于零的下降临界点
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row_s, row_e = find_point(data_file, df, "gt", row_s, row_e, threshold, step, end_point, skip_scale, axis, 1, w2t, insert_logdb)
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row_end = get_row_number(threshold, "end", df, row_s, row_e, axis, insert_logdb)
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row_e -= end_point*skip_scale
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row_s -= end_point*skip_scale
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# 第二次正式采集：消除速度为零的数据，找到速度即将大于零的上升临界点
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row_s, row_e = find_point(data_file, df, "lt", row_s, row_e, threshold, step, end_point, skip_scale, axis, 2, w2t, insert_logdb)
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row_middle = get_row_number(threshold, "middle", df, row_s, row_e, axis, insert_logdb)
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row_e -= end_point*skip_scale
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row_s -= end_point*skip_scale
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# 第三次正式采集：消除速度大于零的数据，找到速度即将趋近于零的下降临界点
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row_s, row_e = find_point(data_file, df, "gt", row_s, row_e, threshold, step, end_point, skip_scale, axis, 3, w2t, insert_logdb)
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row_start = get_row_number(threshold, "start", df, row_s, row_e, axis, insert_logdb)
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insert_logdb("INFO", "current", f"{axis} 轴起点：{row_start}")
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insert_logdb("INFO", "current", f"{axis} 轴中间点：{row_middle}")
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insert_logdb("INFO", "current", f"{axis} 轴终点：{row_end}")
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insert_logdb("INFO", "current", f"{axis} 轴数据非零段点数：{row_middle-row_start+1}")
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insert_logdb("INFO", "current", f"{axis} 轴数据为零段点数：{row_end-row_middle+1}")
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if abs(row_end+row_start-2*row_middle) > 1000:
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insert_logdb("WARNING", "current", f"{axis} 轴数据占空比异常！")
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data, first_c, second_c, third_c = [], vel-1, trq-1, sensor-1
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for row in range(row_start, row_end+1):
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data.append(df_origin.iloc[row, first_c])
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data.append(df_origin.iloc[row, second_c])
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data.append(df_origin.iloc[row, third_c])
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i = 0
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for row in ws.iter_rows(min_row=2, min_col=2, max_row=150000, max_col=4):
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for cell in row:
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try:
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if i % 3 == 0:
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ws.cell((i//3)+2, 1).value = float(((i//3)+1)/1000)
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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 Exception:
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if i % 3 == 0:
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ws.cell((i//3)+2, 1).value = None
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cell.value = None
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i += 1
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def p_scenario(wb, scenario, vel, trq, sensor, rrs, dur_time, w2t):
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for data_file in scenario:
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cycle = 0.001
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axis = int(data_file.split("/")[-1].split("_")[0].removeprefix("j"))
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sht_name = f"J{axis}"
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ws = wb[sht_name]
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pandas.set_option("display.precision", 2)
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df_origin = pandas.read_csv(data_file, sep="\t")
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rr = rrs[axis-1]
|
||
addition = 180 / 3.1415926 * 60 / 360 * rr
|
||
|
||
col_names = list(df_origin.columns)
|
||
df = df_origin[col_names[vel-1]].multiply(addition)
|
||
|
||
row_start = 3000
|
||
row_end = row_start + int(dur_time/cycle)
|
||
if row_end > df.index[-1]:
|
||
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...", "red", "DataOverLimit")
|
||
|
||
data, first_c, second_c, third_c = [], vel-1, trq-1, sensor-1
|
||
for row in range(row_start, row_end+1):
|
||
data.append(df_origin.iloc[row, first_c])
|
||
data.append(df_origin.iloc[row, second_c])
|
||
data.append(df_origin.iloc[row, third_c])
|
||
|
||
i = 0
|
||
for row in ws.iter_rows(min_row=2, min_col=2, max_row=250000, max_col=4):
|
||
for cell in row:
|
||
try:
|
||
if i % 3 == 0:
|
||
ws.cell((i//3)+2, 1).value = float(((i//3)+1)/1000)
|
||
_ = f"{data[i]:.2f}"
|
||
cell.value = float(_)
|
||
i += 1
|
||
except Exception:
|
||
cell.value = None
|
||
if i % 3 == 0:
|
||
ws.cell((i//3)+2, 1).value = None
|
||
i += 1
|
||
|
||
|
||
def get_configs(config_file, w2t, insert_logdb):
|
||
try:
|
||
with open(config_file, mode="r", encoding="utf-8") as f_config:
|
||
configs = json.load(f_config)
|
||
except Exception as Err:
|
||
insert_logdb("ERROR", "current", f"get_config: 无法打开 {config_file}，获取配置文件参数错误 {Err}")
|
||
w2t(f"无法打开 {config_file}", color="red", desc="OpenFileError")
|
||
|
||
# 最大角速度，额定电流，减速比，额定转速
|
||
version = configs["VERSION"]
|
||
rcs = [abs(_) for _ in configs["MOTOR"]["RATED_TORQUE"]] # 电机额定电流，rc for rated current
|
||
m_max_rcs = [] # 电机最大电流
|
||
m_hold_rcs = [] # 电机堵转电流
|
||
m_rts = [] # 电机额定转矩rt for rated torque
|
||
m_max_rts = [] # 电机峰值转矩
|
||
m_r_rpms = [] # 电机额定转速
|
||
m_max_rpms = [] # 电机最大转速
|
||
m_tcs = [] # 电机转矩常数，tc for torque constant
|
||
rrs = [abs(_) for _ in configs["TRANSMISSION"]["REDUCTION_RATIO_NUMERATOR"]] # 减速比，rr for reduction ratio
|
||
r_max_sst = [] # 减速器最大启停转矩，sst for start and stop torque
|
||
r_max_t = [] # 减速器瞬时最大转矩
|
||
sc = [] # 采样周期，sc for sample cycle
|
||
r_rts = [] # 减速器额定转矩
|
||
r_r_rpms = [] # 减速器额定转速
|
||
r_life_cycle = [] # 减速器L10寿命
|
||
r_avg_t = [] # 减速器平均负载转矩允许最大值
|
||
|
||
insert_logdb("INFO", "current", f"get_configs: 机型文件版本 {config_file}_{version}")
|
||
insert_logdb("INFO", "current", f"get_configs: 减速比 {rrs}")
|
||
insert_logdb("INFO", "current", f"get_configs: 额定电流 {rcs}")
|
||
return rcs, m_max_rcs, m_hold_rcs, m_rts, m_max_rts, m_r_rpms, m_max_rpms, m_tcs, rrs, r_max_sst, r_max_t, sc, r_rts, r_r_rpms, r_life_cycle, r_avg_t
|
||
|
||
|
||
def main():
|
||
sub = clibs.data_dp["_sub"]
|
||
path = clibs.data_dp["_path"]
|
||
vel = int(clibs.data_dp["_vel"])
|
||
trq = int(clibs.data_dp["_trq"])
|
||
trqh = int(clibs.data_dp["_trqh"])
|
||
sensor = int(clibs.data_dp["_sensor"])
|
||
w2t = clibs.w2t
|
||
insert_logdb = clibs.insert_logdb
|
||
insert_logdb("INFO", "current", "current: 参数初始化成功")
|
||
|
||
data_files, config_file = initialization(path, w2t, insert_logdb)
|
||
params = get_configs(f"{path}/{config_file}", w2t, insert_logdb)
|
||
rcs, rrs = params[0], params[8]
|
||
if sub == "max":
|
||
current_max(data_files, rcs, trq, w2t, insert_logdb)
|
||
elif sub == "avg":
|
||
current_avg(data_files, rcs, trqh, w2t, insert_logdb)
|
||
elif sub == "cycle":
|
||
current_cycle(data_files, vel, trq, trqh, sensor, rrs, rcs, params, w2t, insert_logdb)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|