re-organize file storage structure, and add API related files, just framework

aio/code/data_process/brake.py

@@ -0,0 +1,298 @@
+# coding: utf-8
+from os import scandir
+from os.path import isfile, exists
+from sys import argv
+from openpyxl import load_workbook
+from time import time, sleep, strftime, localtime
+from threading import Thread
+from pandas import read_csv
+
+
+class GetThreadResult(Thread):
+    def __init__(self, func, args=()):
+        super(GetThreadResult, self).__init__()
+        self.func = func
+        self.args = args
+        self.result = 0
+
+    def run(self):
+        sleep(1)
+        self.result = self.func(*self.args)
+
+    def get_result(self):
+        Thread.join(self)  # 等待线程执行完毕
+        try:
+            return self.result
+        except Exception as Err:
+            return None
+
+
+def data_process(result_file, raw_data_dirs, av, rr, axis, vel, trq, w2t, estop):
+    # 功能：完成一个结果文件的数据处理
+    # 参数：结果文件，数据目录，以及预读取的参数
+    # 返回值：-
+    file_name = result_file.split('\\')[-1]
+    w2t(f"正在打开文件 {file_name} 需要 1min 左右", 1, 0, 'orange')
+
+    global stop
+    stop = 0
+    t_excel = GetThreadResult(load_workbook, args=(result_file, ))
+    t_wait = Thread(target=w2t_local, args=('.', 1, w2t))
+    t_excel.start()
+    t_wait.start()
+    t_excel.join()
+    wb_result = t_excel.get_result()
+    stop = 1
+    sleep(1.1)
+    w2t('')
+
+    prefix = result_file.split('\\')[-1].split('_')[0]
+    for raw_data_dir in raw_data_dirs:
+        if raw_data_dir.split('\\')[-1].split('_')[0] == prefix:
+            now_doing_msg(raw_data_dir, 'start', w2t)
+            _, data_files = traversal_files(raw_data_dir, w2t)
+            # 数据文件串行处理模式---------------------------------
+            # count = 1
+            # for data_file in data_files:
+            #     now_doing_msg(data_file, 'start', w2t)
+            #     single_file_process(data_file, wb_result, count, av, rr, axis, vel, trq, w2t, estop)
+            #     count += 1
+            #     now_doing_msg(data_file, 'done', w2t)
+            # ---------------------------------------------------
+            # 数据文件并行处理模式---------------------------------
+            threads = [Thread(target=single_file_process, args=(data_files[0], wb_result, 1, av, rr, axis, vel, trq, w2t, estop)),
+                       Thread(target=single_file_process, args=(data_files[1], wb_result, 2, av, rr, axis, vel, trq, w2t, estop)),
+                       Thread(target=single_file_process, args=(data_files[2], wb_result, 3, av, rr, axis, vel, trq, w2t, estop))]
+            [t.start() for t in threads]
+            [t.join() for t in threads]
+            # ---------------------------------------------------
+            now_doing_msg(raw_data_dir, 'done', w2t)
+
+    now_doing_msg(result_file, 'done', w2t)
+    w2t(f"正在保存文件 {file_name} 需要 1min 左右", 1, 0, 'orange')
+    stop = 0
+    t_excel = Thread(target=wb_result.save, args=(result_file, ))
+    t_wait = Thread(target=w2t_local, args=('.', 1, w2t))
+    t_excel.start()
+    t_wait.start()
+    t_excel.join()
+    stop = 1
+    sleep(1.1)
+    w2t('\n')
+
+
+def check_files(raw_data_dirs, result_files, w2t):
+    # 功能：检查数据文件以及结果文件的合规性
+    # 参数：数据文件夹，结果文件
+    # 返回值：-
+    if len(result_files) != 3:
+        msg = "结果文件数目错误，结果文件有且只有三个，请确认！"
+        for result_file in result_files:
+            w2t(result_file)
+        w2t(msg, 0, 2, 'red')
+
+    prefix = []
+    for result_file in result_files:
+        prefix.append(result_file.split('\\')[-1].split('_')[0])
+    if not sorted(prefix) == sorted(['reach33', 'reach66', 'reach100']):
+        wd = result_files[0].split('\\')
+        del wd[-1]
+        wd = '\\'.join(wd)
+        msg = f"""请关闭所有相关数据文件，并检查工作目录 {wd} 下，有且只允许有类似如下三个文件：
+              1. reach33_XXX制动性能测试.xlsx
+              2. reach66_XXX制动性能测试.xlsx
+              3. reach100_XX制动性能测试.xlsx"""
+        w2t(msg, 0, 3, 'red')
+
+    for raw_data_dir in raw_data_dirs:
+        components = raw_data_dir.split('\\')[-1].split('_')
+        sorted(components)
+        if components[0] not in ['reach33', 'reach66', 'reach100'] or \
+                components[1] not in ['load33', 'load66', 'load100'] or \
+                components[2] not in ['speed33', 'speed66', 'speed100']:
+            msg = f"""报错信息：数据目录 {raw_data_dir} 命名不合规，请参考如下形式：
+命名规则：reachAA_loadBB_speedCC
+规则解释：AA/BB/CC 指的是臂展/负载/速度的比例，例如reach66_load100_speed33：66%臂展，100%负载以及33%速度情况下的测试结果文件夹"""
+            w2t(msg, 0, 4, 'red')
+
+        _, raw_data_files = traversal_files(raw_data_dir, w2t)
+        if len(raw_data_files) != 3:
+            msg = f"数据目录 {raw_data_dir} 下数据文件个数错误，每个数据目录下有且只能有三个以 .data 为后缀的数据文件"
+            w2t(msg, 0, 5, 'red')
+        for raw_data_file in raw_data_files:
+            if not raw_data_file.split('\\')[-1].endswith('.data'):
+                msg = f"数据文件 {raw_data_file} 后缀错误，每个数据目录下有且只能有三个以 .data 为后缀的数据文件"
+                w2t(msg, 0, 6, 'red')
+
+    w2t("数据目录合规性检查结束，未发现问题......")
+
+
+def now_doing_msg(docs, flag, w2t):
+    # 功能：输出正在处理的文件或目录
+    # 参数：文件或目录，start 或 done 标识
+    # 返回值：-
+    now = strftime('%Y-%m-%d %H:%M:%S', localtime(time()))
+    file_type = 'file' if isfile(docs) else 'dir'
+    if flag == 'start' and file_type == 'dir':
+        w2t(f"[{now}] 正在处理目录 {docs} 中的数据......")
+    elif flag == 'start' and file_type == 'file':
+        w2t(f"[{now}] 正在处理文件 {docs} 中的数据......")
+    elif flag == 'done' and file_type == 'dir':
+        w2t(f"[{now}] 目录 {docs} 数据文件已处理完毕")
+    elif flag == 'done' and file_type == 'file':
+        w2t(f"[{now}] 文件 {docs} 数据已处理完毕")
+
+
+def w2t_local(msg, wait, w2t):
+    while True:
+        global stop
+        if stop == 0 and wait != 0:
+            sleep(1)
+            w2t(msg, wait, 0, 'orange')
+        else:
+            break
+
+
+def single_file_process(data_file, wb_result, count, av, rr, axis, vel, trq, w2t, estop):
+    # 功能：完成单个数据文件的处理
+    # 参数：如上
+    # 返回值：-
+    df = read_csv(data_file, sep='\t')
+
+    conditions = sorted(data_file.split('\\')[-2].split('_'))  # ['loadxx', 'reachxx', 'speedxx']
+    result_sheet_name = find_result_sheet_name(conditions, count)
+    ws_result = wb_result[result_sheet_name]
+
+    row_start, row_end = find_row_start(data_file, df, conditions, av, rr, axis, vel, w2t, estop)
+    copy_data_to_result(df, ws_result, row_start, row_end, vel, trq, estop)
+
+
+def copy_data_to_result(df, ws_result, row_start, row_end, vel, trq, estop):
+    # 功能：将数据文件中有效数据拷贝至结果文件对应的 sheet
+    # 参数：如上
+    # 返回值：-
+    # 结果文件数据清零
+
+    data = []
+    for _row in range(row_start, row_end + 1):
+        data.append(df.iloc[_row, vel-1])
+        data.append(df.iloc[_row, trq-1])
+        data.append(df.iloc[_row, estop-1])
+
+    i = 0
+    row_max = 2000 if row_end-row_start < 2000 else row_end-row_start+20
+    for _row in range(2, row_max):
+        try:
+            ws_result.cell(row=_row, column=1).value = data[i]
+            ws_result.cell(row=_row, column=2).value = data[i+1]
+            ws_result.cell(row=_row, column=3).value = data[i+2]
+            i += 3
+        except:
+            ws_result.cell(row=_row, column=1).value = None
+            ws_result.cell(row=_row, column=2).value = None
+            ws_result.cell(row=_row, column=3).value = None
+
+
+def find_result_sheet_name(conditions, count):
+    # 功能：获取结果文件准确的sheet页名称
+    # 参数：臂展和速度的列表
+    # 返回值：结果文件对应的sheet name
+    # 33%负载_33%速度_1 - ['loadxx', 'reachxx', 'speedxx']
+    load = conditions[0].removeprefix('load')
+    speed = conditions[2].removeprefix('speed')
+    result_sheet_name = f"{load}%负载_{speed}%速度_{count}"
+
+    return result_sheet_name
+
+
+def find_row_start(data_file, df, conditions, av, rr, axis, vel, w2t, estop):
+    # 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
+    # 参数：如上
+    # 返回值：速度下降点位，最后的数据点位
+    ratio = float(conditions[2].removeprefix('speed'))/100
+    av_max = av * ratio
+    row_max = df.index[-1]
+    # threshold = 30 if axis == 2 and conditions[0].removeprefix('load') == '100' else 10
+    threshold = 0.95
+
+    for _row in range(row_max, -1, -1):
+        if df.iloc[_row, estop-1] != 0:
+            row_start = _row-20 if _row-20 > 0 else 0
+            break
+    else:
+        w2t(f"数据文件 {data_file} 采集的数据中没有 ESTOP 为非 0 的情况，需要确认", 0, 9, 'red')
+
+    for _row in range(row_start, row_max):
+        speed_row = (df.iloc[_row, vel-1] * 180) / 3.1415926 * rr * 60 / 360
+        if abs(speed_row) < 1:
+            row_end = _row+100 if _row+100 <= row_max else row_max
+            break
+    else:
+        w2t(f"数据文件 {data_file} 最后的速度未降为零 ", 0, 10, 'red')
+
+    av_estop = abs((df.iloc[row_start-10:row_start+10, vel-1].abs().mean() * 180) / 3.1415926)
+    if abs(av_estop/av_max) < threshold:
+        filename = data_file.split('\\')[-1]
+        w2t(f"[av_estop: {av_estop:.2f} | shouldbe: {av_max:.2f}] 数据文件 {filename} 触发 ESTOP 时未采集到指定百分比的最大速度，需要检查", 0, 0, '#8A2BE2')
+
+    return row_start, row_end
+
+
+def traversal_files(path, w2t):
+    # 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
+    # 参数：路径
+    # 返回值：路径下的文件夹列表 路径下的文件列表
+    if not exists(path):
+        msg = f'数据文件夹{path}不存在，请确认后重试......'
+        w2t(msg, 0, 1, 'red')
+    else:
+        dirs = []
+        files = []
+        for item in scandir(path):
+            if item.is_dir():
+                dirs.append(item.path)
+            elif item.is_file():
+                files.append(item.path)
+
+        return dirs, files
+
+
+def main(path, av, rr, axis, vel, trq, estop, w2t):
+    # 功能：执行处理所有数据文件
+    # 参数：initialization函数的返回值
+    # 返回值：-
+    time_start = time()
+    raw_data_dirs, result_files = traversal_files(path, w2t)
+
+    try:
+        # threads = []
+        check_files(raw_data_dirs, result_files, w2t)
+
+        prefix = []
+        for raw_data_dir in raw_data_dirs:
+            prefix.append(raw_data_dir.split('\\')[-1].split("_")[0])
+
+        for result_file in result_files:
+            if result_file.split('\\')[-1].split('_')[0] not in set(prefix):
+                continue
+            else:
+                now_doing_msg(result_file, 'start', w2t)
+                data_process(result_file, raw_data_dirs, av, rr, axis, vel, trq, w2t, estop)
+            # threads.append(Thread(target=data_process, args=(result_file, raw_data_dirs, AV, RR, RC, AXIS)))
+        # [t.start() for t in threads]
+        # [t.join() for t in threads]
+    except Exception as Err:
+        msg = f"出现错误：{Err}\n程序运行错误，请检查配置文件是否准确设定，以及数据文件组织是否正确，也有可能是结果文件损坏，尝试重新复制一份，再运行！"
+        w2t(msg, 0, 11, 'red')
+
+    w2t("----------------------------------------------------------")
+    w2t("全部处理完毕")
+    time_end = time()
+    time_total = time_end - time_start
+    msg = f"数据处理时间：{time_total // 3600:02.0f} h {time_total % 3600 // 60:02.0f} m {time_total % 60:02.0f} s"
+    w2t(msg)
+
+
+if __name__ == "__main__":
+    stop = 0
+    main(path=argv[1], av=argv[2], rr=argv[3], axis=argv[4], vel=argv[5], trq=argv[6], estop=argv[7], w2t=argv[8])

aio/code/data_process/current.py

@@ -0,0 +1,375 @@
+from openpyxl import load_workbook
+from os import scandir
+from os.path import exists
+from sys import argv
+from pandas import read_csv, concat, set_option
+from re import match
+from threading import Thread
+from time import sleep
+from csv import reader
+
+
+class GetThreadResult(Thread):
+    def __init__(self, func, args=()):
+        super(GetThreadResult, self).__init__()
+        self.func = func
+        self.args = args
+        self.result = 0
+
+    def run(self):
+        sleep(1)
+        self.result = self.func(*self.args)
+
+    def get_result(self):
+        Thread.join(self)  # 等待线程执行完毕
+        try:
+            return self.result
+        except Exception as Err:
+            return None
+
+
+def w2t_local(msg, wait, w2t):
+    while True:
+        global stop
+        if stop == 0 and wait != 0:
+            sleep(1)
+            w2t(msg, wait, 0, 'orange')
+        else:
+            break
+
+
+def traversal_files(path, w2t):
+    # 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
+    # 参数：路径
+    # 返回值：路径下的文件夹列表 路径下的文件列表
+    if not exists(path):
+        msg = f'数据文件夹{path}不存在，请确认后重试......'
+        w2t(msg, 0, 8, 'red')
+    else:
+        dirs = []
+        files = []
+        for item in scandir(path):
+            if item.is_dir():
+                dirs.append(item.path)
+            elif item.is_file():
+                files.append(item.path)
+
+        return dirs, files
+
+
+def initialization(path, sub, w2t):
+    _, data_files = traversal_files(path, w2t)
+    count = 0
+
+    for data_file in data_files:
+        filename = data_file.split('\\')[-1]
+        if sub != 'cycle':
+            if not (match('j[1-7].*\\.data', filename) or match('j[1-7].*\\.csv', filename)):
+                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。"
+                w2t(msg, 0, 6, 'red')
+        else:
+            if filename.endswith('.xlsx'):
+                count += 1
+            elif not (match('j[1-7].*\\.data', filename) or match('j[1-7].*\\.csv', filename)):
+                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。"
+                w2t(msg, 0, 7, 'red')
+
+    if sub == 'cycle' and count != 1:
+        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')
+
+    return data_files
+
+
+def current_max(data_files, rcs, trqh, w2t):
+    current = {1: [], 2: [], 3: [], 4: [], 5: [], 6: [], 7: []}
+    for data_file in data_files:
+        if data_file.endswith('.data'):
+            df = read_csv(data_file, sep='\t')
+        elif data_file.endswith('.csv'):
+            df = read_csv(data_file, sep=',', encoding='gbk', header=8)
+
+        axis = int(data_file.split('\\')[-1].split('_')[0].removeprefix('j'))
+        rca = rcs[axis-1]
+
+        col = df.columns.values[trqh-1]
+        c_max = df[col].max()
+
+        scale = 1 if data_file.endswith('.csv') else 1000
+        _ = abs(c_max/scale*rca)
+        current[axis].append(_)
+        w2t(f"{data_file}: {_:.4f}")
+
+    for axis, cur in current.items():
+        if not cur:
+            continue
+        else:
+            w2t(f"{axis}轴数据：", 1, 0, 'purple')
+            for value in cur:
+                w2t(f"{value:.4f} ", 1, 0, 'purple')
+            w2t('')
+    w2t("\n【MAX】数据处理完毕......")
+    return current
+
+
+def current_avg(data_files, rcs, trqh, w2t):
+    current = {1: [], 2: [], 3: [], 4: [], 5: [], 6: [], 7: []}
+    for data_file in data_files:
+        if data_file.endswith('.data'):
+            df = read_csv(data_file, sep='\t')
+        elif data_file.endswith('.csv'):
+            df = read_csv(data_file, sep=',', encoding='gbk', header=8)
+
+        axis = int(data_file.split('\\')[-1].split('_')[0].removeprefix('j'))
+        rca = rcs[axis-1]
+
+        col = df.columns.values[trqh - 1]
+        c_std = df[col].std()
+        c_avg = df[col].mean()
+
+        scale = 1 if data_file.endswith('.csv') else 1000
+        _ = (abs(c_avg)+c_std)/scale*rca
+        current[axis].append(_)
+        w2t(f"{data_file}: {_:.4f}")
+
+    for axis, cur in current.items():
+        if not cur:
+            continue
+        else:
+            w2t(f"{axis}轴数据：", 1, 0, 'purple')
+            for value in cur:
+                w2t(f"{value:.4f} ", 1, 0, 'purple')
+            w2t('')
+    w2t("\n【AVG】数据处理完毕......")
+    return current
+
+
+def current_cycle(dur, data_files, rcs, vel, trq, trqh, rpm, w2t):
+    result = None
+    hold = []
+    single = []
+    for data_file in data_files:
+        filename = data_file.split('\\')[-1]
+        if data_file.endswith('.xlsx'):
+            result = data_file
+        elif match('j[1-7]_hold_.*\\.data', filename) or match('j[1-7]_hold_.*\\.csv', filename):
+            hold.append(data_file)
+        else:
+            single.append(data_file)
+
+    w2t(f"正在打开文件 {result}，需要 10s 左右", 1, 0, 'orange')
+    global stop
+    stop = 0
+    t_excel = GetThreadResult(load_workbook, args=(result, ))
+    t_wait = Thread(target=w2t_local, args=('.', 1, w2t))
+    t_excel.start()
+    t_wait.start()
+    t_excel.join()
+    wb = t_excel.get_result()
+    stop = 1
+    sleep(1.1)
+    w2t('')
+
+    if hold != []:
+        avg = current_avg(hold, rcs, trqh, w2t)
+        for axis, cur_value in avg.items():
+            try:
+                shtname = f"J{axis}"
+                wb[shtname]["J4"].value = float(cur_value)
+            except:
+                pass
+
+    if dur == 0:
+        p_single(wb, single, vel, trq, rpm, w2t)
+    else:
+        p_scenario(wb, single, vel, trq, rpm, dur, w2t)
+
+    w2t(f"正在保存文件 {result}，需要 10s 左右", 1, 0, 'orange')
+    stop = 0
+    t_excel = Thread(target=wb.save, args=(result, ))
+    t_wait = Thread(target=w2t_local, args=('.', 1, w2t))
+    t_excel.start()
+    t_wait.start()
+    t_excel.join()
+    stop = 1
+    sleep(1.1)
+    w2t('\n')
+    w2t("----------------------------------------------------------")
+    w2t("全部处理完毕")
+
+
+def find_point(data_file, pos, flag, df, _row_s, _row_e, w2t, exitcode, threshold, step, end_point):
+    if flag == 'lt':
+        while _row_e > end_point:
+            speed_avg = df.iloc[_row_s:_row_e, 0].abs().mean()
+            if speed_avg < threshold:
+                _row_e -= step
+                _row_s -= step
+                continue
+            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')
+    elif flag == 'gt':
+        while _row_e > end_point:
+            speed_avg = df.iloc[_row_s:_row_e, 0].abs().mean()
+            if speed_avg > threshold:
+                _row_e -= step
+                _row_s -= step
+                continue
+            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')
+
+
+def p_single(wb, single, vel, trq, rpm, w2t):
+    # 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
+    # 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
+    # 3. 记录第二个点的位置，并将其中的数据拷贝至对应位置
+    for data_file in single:
+        rpm = 1 if rpm == 0 else rpm
+        scale = 1000 if data_file.endswith('.csv') else 1
+        axis = int(data_file.split('\\')[-1].split('_')[0].removeprefix('j'))
+        shtname = f"J{axis}"
+        ws = wb[shtname]
+        addition = 1
+        set_option("display.precision", 2)
+        if data_file.endswith('.data'):
+            df = read_csv(data_file, sep='\t')
+            rr = float(wb['统计'].cell(row=2, column=axis+1).value)
+            addition = 180 / 3.1415926 * 60 / 360 * rr
+        elif data_file.endswith('.csv'):
+            df = read_csv(data_file, sep=',', encoding='gbk', header=8)
+            csv_reader = reader(open(data_file))
+            i = 0
+            cycle = 0.001
+            for row in csv_reader:
+                i += 1
+                if i == 3:
+                    cycle = float(row[0].split(':')[1].split('ms')[0]) / 1000
+                    break
+            ws["H11"] = cycle
+
+        col_names = list(df.columns)
+        df_1 = df[col_names[vel-1]].multiply(rpm*addition)
+        df_2 = df[col_names[trq-1]].multiply(scale)
+        df = concat([df_1, df_2], axis=1)
+
+        _step = 5 if data_file.endswith('.csv') else 50
+        _end_point = 30 if data_file.endswith('.csv') else 200
+        _adjust = 0 if data_file.endswith('.csv') else 150
+        _row_e = df.index[-1]
+        _row_s = _row_e - _end_point
+        speed_avg = df.iloc[_row_s:_row_e, 0].abs().mean()
+        if speed_avg < 2:
+            # 过滤尾部为零无效数据
+            _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)
+            # 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
+            row_end = _row_e - _adjust
+            _row_e -= _end_point
+            _row_s -= _end_point
+            _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)
+            # 速度已经快要降为零了，继续寻找下一个速度上升点
+            _row_e -= _end_point
+            _row_s -= _end_point
+            _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)
+        elif speed_avg > 2:
+            # 过滤尾部非零无效数据
+            _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)
+            # 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
+            row_end = _row_e - _adjust
+            _row_e -= _end_point
+            _row_s -= _end_point
+            _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)
+            # 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
+            _row_e -= _end_point
+            _row_s -= _end_point
+            _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)
+
+        row_start = _row_s + _adjust
+        data = []
+        for row in range(row_start, row_end):
+            data.append(df.iloc[row, 0])
+            data.append(df.iloc[row, 1])
+
+        i = 0
+        for row in ws.iter_rows(min_row=2, min_col=2, max_row=70000, max_col=3):
+            for cell in row:
+                try:
+                    _ = f"{data[i]:.2f}"
+                    cell.value = float(_)
+                    i += 1
+                except:
+                    cell.value = None
+
+
+def p_scenario(wb, single, vel, trq, rpm, dur, w2t):
+    for data_file in single:
+        cycle = 0.001
+        rpm = 1 if rpm == 0 else rpm
+        scale = 1000 if data_file.endswith('.csv') else 1
+        axis = int(data_file.split('\\')[-1].split('_')[0].removeprefix('j'))
+        shtname = f"J{axis}"
+        ws = wb[shtname]
+        addition = 1
+        set_option("display.precision", 2)
+        if data_file.endswith('.data'):
+            df = read_csv(data_file, sep='\t')
+            rr = float(wb['统计'].cell(row=2, column=axis+1).value)
+            addition = 180 / 3.1415926 * 60 / 360 * rr
+        elif data_file.endswith('.csv'):
+            df = read_csv(data_file, sep=',', encoding='gbk', header=8)
+            csv_reader = reader(open(data_file))
+            i = 0
+            for row in csv_reader:
+                i += 1
+                if i == 3:
+                    cycle = float(row[0].split(':')[1].split('ms')[0]) / 1000
+                    break
+
+        ws["H11"] = cycle
+
+        col_names = list(df.columns)
+        df_1 = df[col_names[vel-1]].multiply(rpm*addition)
+        df_2 = df[col_names[trq-1]].multiply(scale)
+        df = concat([df_1, df_2], axis=1)
+
+        row_start = 300
+        row_end = row_start + int(dur/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...", 0, 9, 'red')
+
+        data = []
+        for row in range(row_start, row_end):
+            data.append(df.iloc[row, 0])
+            data.append(df.iloc[row, 1])
+
+        i = 0
+        for row in ws.iter_rows(min_row=2, min_col=2, max_row=70000, max_col=3):
+            for cell in row:
+                try:
+                    _ = f"{data[i]:.2f}"
+                    cell.value = float(_)
+                    i += 1
+                except:
+                    cell.value = None
+
+
+# =======================================
+
+
+def main(path, sub, rcs, vel, trq, trqh, dur, rpm, w2t):
+    data_files = initialization(path, sub, w2t)
+    if sub == 'max':
+        current_max(data_files, rcs, trqh, w2t)
+    elif sub == 'avg':
+        current_avg(data_files, rcs, trqh, w2t)
+    elif sub == 'cycle':
+        current_cycle(dur, data_files, rcs, vel, trq, trqh, rpm, w2t)
+    else:
+        pass
+
+
+if __name__ == '__main__':
+    stop = 0
+    main(*argv[1:])

aio/code/data_process/iso.py

@@ -0,0 +1,198 @@
+# _*_ encoding：utf-8 _*_
+import pdfplumber
+from openpyxl import load_workbook
+from os import scandir, remove
+from os.path import exists
+from sys import argv
+
+
+def traversal_files(path, w2t):
+    # 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
+    # 参数：路径
+    # 返回值：路径下的文件夹列表 路径下的文件列表
+    if not exists(path):
+        msg = f'数据文件夹{path}不存在，请确认后重试......'
+        w2t(msg, 0, 1, 'red')
+    else:
+        dirs = files = []
+        for item in scandir(path):
+            if item.is_dir():
+                dirs.append(item.path)
+            elif item.is_file():
+                files.append(item.path)
+
+        return dirs, files
+
+
+def p_iso(file, p_files, ws, tmpfile):
+    p_files.append(file)
+
+    pdf = pdfplumber.open(file)
+    with open(tmpfile, mode='w', encoding='utf-8') as fb:
+        for page in pdf.pages:
+            fb.write(page.extract_text())
+
+    with open(tmpfile, mode='r', encoding='utf-8') as fb:
+        lines = fb.readlines()
+        lines = [line for line in lines if not line.startswith('Page ')]
+        for line in lines:
+            if line.strip() == "Pose Accuracy and Repeatability":
+                index = lines.index(line)
+                ws.cell(row=3, column=7).value = float(lines[index+4].split()[1])
+                ws.cell(row=4, column=7).value = float(lines[index+5].split()[1])
+                ws.cell(row=5, column=7).value = float(lines[index+6].split()[1])
+                ws.cell(row=6, column=7).value = float(lines[index+7].split()[1])
+                ws.cell(row=7, column=7).value = float(lines[index+8].split()[1])
+
+                ws.cell(row=8, column=7).value = float(lines[index+4].split()[2])
+                ws.cell(row=9, column=7).value = float(lines[index+5].split()[2])
+                ws.cell(row=10, column=7).value = float(lines[index+6].split()[2])
+                ws.cell(row=11, column=7).value = float(lines[index+7].split()[2])
+                ws.cell(row=12, column=7).value = float(lines[index+8].split()[2])
+            elif line.strip() == 'Pose Accuracy Variation':
+                index = lines.index(line)
+                ws.cell(row=13, column=7).value = float(lines[index+4].split()[1])
+                ws.cell(row=14, column=7).value = float(lines[index+5].split()[1])
+                ws.cell(row=15, column=7).value = float(lines[index+6].split()[1])
+            elif line.strip() == 'Distance Accuracy':
+                index = lines.index(line)
+                ws.cell(row=16, column=7).value = float(lines[index + 4].split()[1])
+                ws.cell(row=17, column=7).value = float(lines[index + 4].split()[2])
+            elif line.strip() == 'Stabilisation Time and Overshoot':
+                index = lines.index(line)
+                ws.cell(row=18, column=7).value = float(lines[index + 7].split()[3])
+                ws.cell(row=19, column=7).value = float(lines[index + 7].split()[2])
+            elif line.strip() == 'Velocity Accuracy and Repeatability':
+                index = lines.index(line)
+                ws.cell(row=20, column=7).value = float(lines[index + 4].split()[1])
+                ws.cell(row=21, column=7).value = float(lines[index + 4].split()[2])
+                ws.cell(row=22, column=7).value = float(lines[index + 4].split()[3])
+            elif line.strip()[:31] == 'Path Accuracy and Repeatability':
+                index = lines.index(line)
+                ws.cell(row=29, column=7).value = float(lines[index + 4].split()[1])
+                ws.cell(row=30, column=7).value = float(lines[index + 4].split()[2])
+            elif line.strip() == 'Corner Overshoot and Roundoff':
+                index = lines.index(line)
+                ws.cell(row=35, column=7).value = float(lines[index + 4].split()[1])
+                ws.cell(row=36, column=7).value = float(lines[index + 4].split()[2])
+            elif line.strip() == 'Robot Weaving':
+                index = lines.index(line)
+                ws.cell(row=41, column=7).value = float(lines[index + 4].split()[2])
+                ws.cell(row=42, column=7).value = float(lines[index + 4].split()[3])
+                ws.cell(row=43, column=7).value = float(lines[index + 4].split()[4])
+            else:
+                pass
+
+
+def p_iso_100(file, p_files, ws, tmpfile):
+    p_files.append(file)
+
+    pdf = pdfplumber.open(file)
+    with open(tmpfile, mode='w', encoding='utf-8') as fb:
+        for page in pdf.pages:
+            fb.write(page.extract_text())
+
+    with open(tmpfile, mode='r', encoding='utf-8') as fb:
+        lines = fb.readlines()
+        lines = [line for line in lines if not line.startswith('Page ')]
+        for line in lines:
+            if line.strip() == 'Velocity Accuracy and Repeatability':
+                index = lines.index(line)
+                ws.cell(row=26, column=7).value = float(lines[index + 4].split()[1])
+                ws.cell(row=27, column=7).value = float(lines[index + 4].split()[2])
+                ws.cell(row=28, column=7).value = float(lines[index + 4].split()[3])
+            elif line.strip()[:31] == 'Path Accuracy and Repeatability':
+                index = lines.index(line)
+                ws.cell(row=33, column=7).value = float(lines[index + 4].split()[1])
+                ws.cell(row=34, column=7).value = float(lines[index + 4].split()[2])
+            elif line.strip() == 'Corner Overshoot and Roundoff':
+                index = lines.index(line)
+                ws.cell(row=39, column=7).value = float(lines[index + 4].split()[1])
+                ws.cell(row=40, column=7).value = float(lines[index + 4].split()[2])
+            elif line.strip() == 'Robot Weaving':
+                index = lines.index(line)
+                ws.cell(row=47, column=7).value = float(lines[index + 4].split()[2])
+                ws.cell(row=48, column=7).value = float(lines[index + 4].split()[3])
+                ws.cell(row=49, column=7).value = float(lines[index + 4].split()[4])
+            else:
+                pass
+
+
+def p_iso_1000(file, p_files, ws, tmpfile):
+    p_files.append(file)
+
+    pdf = pdfplumber.open(file)
+    with open(tmpfile, mode='w', encoding='utf-8') as fb:
+        for page in pdf.pages:
+            fb.write(page.extract_text())
+
+    with open(tmpfile, mode='r', encoding='utf-8') as fb:
+        lines = fb.readlines()
+        lines = [line for line in lines if not line.startswith('Page ')]
+        for line in lines:
+            if line.strip() == 'Velocity Accuracy and Repeatability':
+                index = lines.index(line)
+                ws.cell(row=23, column=7).value = float(lines[index + 4].split()[1])
+                ws.cell(row=24, column=7).value = float(lines[index + 4].split()[2])
+                ws.cell(row=25, column=7).value = float(lines[index + 4].split()[3])
+            elif line.strip()[:31] == 'Path Accuracy and Repeatability':
+                index = lines.index(line)
+                ws.cell(row=31, column=7).value = float(lines[index + 4].split()[1])
+                ws.cell(row=32, column=7).value = float(lines[index + 4].split()[2])
+            elif line.strip() == 'Corner Overshoot and Roundoff':
+                index = lines.index(line)
+                ws.cell(row=37, column=7).value = float(lines[index + 4].split()[1])
+                ws.cell(row=38, column=7).value = float(lines[index + 4].split()[2])
+            elif line.strip() == 'Robot Weaving':
+                index = lines.index(line)
+                ws.cell(row=44, column=7).value = float(lines[index + 4].split()[2])
+                ws.cell(row=45, column=7).value = float(lines[index + 4].split()[3])
+                ws.cell(row=46, column=7).value = float(lines[index + 4].split()[4])
+            else:
+                pass
+
+
+def main(path, w2t):
+    dirs, files = traversal_files(path, 1)
+
+    try:
+        wb = load_workbook(path + "/iso-results.xlsx")
+        ws = wb.active
+        for i in range(3, 50):
+            ws.cell(row=i, column=7).value = None
+        tmpfile = f"{path}\\data.txt"
+    except Exception as Err:
+        w2t(f"发生错误：{Err}", 0, 2, 'red')
+
+    p_files = []
+    for file in files:
+        if file.endswith('.pdf') and file.split('\\')[-1] == 'ISO.pdf':
+            w2t(f"正在处理{file}......")
+            p_iso(file, p_files, ws, tmpfile)
+            w2t(f"文件{file}已处理完毕。\n")
+
+        elif file.endswith('.pdf') and file.split('\\')[-1] == 'ISO-V100.pdf':
+            w2t(f"正在处理{file}......")
+            p_iso_100(file, p_files, ws, tmpfile)
+            w2t(f"文件{file}已处理完毕。\n")
+
+        elif file.endswith('.pdf') and file.split('\\')[-1] == 'ISO-V1000.pdf':
+            w2t(f"正在处理{file}......")
+            p_iso_1000(file, p_files, ws, tmpfile)
+            w2t(f"文件{file}已处理完毕。\n")
+
+        else:
+            pass
+    wb.save(path + '/iso-results.xlsx')
+    wb.close()
+
+    if len(p_files) == 0:
+        w2t(f"目录 {path} 下没有需要处理的文件，需要确认......", 0, 3, 'red')
+    else:
+        remove(tmpfile)
+        w2t("------------------------------------------")
+        w2t("所有文件均已处理完毕！")
+
+
+if __name__ == '__main__':
+    main(path=argv[1], w2t=argv[2])

aio/code/data_process/wavelogger.py

@@ -0,0 +1,186 @@
+import os
+import random
+
+from pandas import read_csv
+from csv import reader
+from sys import argv
+from os.path import exists
+from os import scandir, remove
+from openpyxl import Workbook
+from random import randint
+
+def traversal_files(path, w2t):
+    # 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
+    # 参数：路径
+    # 返回值：路径下的文件夹列表 路径下的文件列表
+    if not exists(path):
+        msg = f'数据文件夹{path}不存在，请确认后重试......'
+        w2t(msg, 0, 1, 'red')
+    else:
+        dirs = []
+        files = []
+        for item in scandir(path):
+            if item.is_dir():
+                dirs.append(item.path)
+            elif item.is_file():
+                files.append(item.path)
+
+        return dirs, files
+
+
+def find_point(bof, step, pos, data_file, flag, df, row, w2t):
+    # bof: backward or forward
+    # pos: used for debug
+    # flag: greater than or lower than
+    if flag == 'gt':
+        while 0 < row < df.index[-1]-100:
+            _value = df.iloc[row, 2]
+            if _value > 2:
+                if bof == 'backward':
+                    row -= step
+                elif bof == 'forward':
+                    row += step
+                continue
+            else:
+                if bof == 'backward':
+                    row_target = row - step
+                elif bof == 'forward':
+                    row_target = row + step
+                break
+        else:
+            if bof == 'backward':
+                w2t(f"[{pos}] 在 {data_file} 中，无法正确识别数据，需要确认...", 0, 2, 'red')
+            elif bof == 'forward':
+                row_target = row + 100
+    elif flag == 'lt':
+        while 0 < row < df.index[-1]-100:
+            _value = df.iloc[row, 2]
+            if _value < 2:
+                if bof == 'backward':
+                    row -= step
+                elif bof == 'forward':
+                    row += step
+                continue
+            else:
+                if bof == 'backward':
+                    row_target = row - step
+                elif bof == 'forward':
+                    row_target = row + step
+                break
+        else:
+            if bof == 'backward':
+                w2t(f"[{pos}] 在 {data_file} 中，无法正确识别数据，需要确认...", 0, 3, 'red')
+            elif bof == 'forward':
+                row_target = row + 100
+
+    return row_target
+
+
+def get_cycle_info(data_file, df, row, step, w2t):
+    # end -> middle: low
+    # middle -> start: high
+    # 1. 从最后读取数据，无论是大于1还是小于1，都舍弃，找到相反的值的起始点
+    # 2. 从起始点，继续往前寻找，找到与之数值相反的中间点
+    # 3. 从中间点，继续往前寻找，找到与之数值相反的结束点，至此，得到了高低数值的时间区间以及一轮的周期时间
+    if df.iloc[row, 2] < 2:
+        row = find_point('backward', step, 'a1', data_file, 'lt', df, row, w2t)
+
+    _row = find_point('backward', step, 'a2', data_file, 'gt', df, row, w2t)
+    _row = find_point('backward', step, 'a3', data_file, 'lt', df, _row, w2t)
+    row_end = find_point('backward', step, 'a4', data_file, 'gt', df, _row, w2t)
+    row_middle = find_point('backward', step, 'a5', data_file, 'lt', df, row_end, w2t)
+    row_start = find_point('backward', step, 'a6', data_file, 'gt', df, row_middle, w2t)
+
+    return row_end-row_middle, row_middle-row_start, row_end-row_start
+
+
+def initialization(path, w2t):
+    _, data_files = traversal_files(path, w2t)
+
+    for data_file in data_files:
+        if not data_file.lower().endswith('.csv'):
+            w2t(f"{data_file} 文件后缀错误，只允许 .csv 文件，需要确认！", 0, 1, 'red')
+
+    return data_files
+
+
+def preparation(data_file, wb, w2t):
+    shtname = data_file.split('\\')[-1].split('.')[0]
+    ws = wb.create_sheet(shtname)
+    csv_reader = reader(open(data_file))
+    i = 0
+    begin = 70
+    for row in csv_reader:
+        i += 1
+        if i == 1:
+            begin = int(row[1])
+            break
+    df = read_csv(data_file, sep=',', encoding='gbk', skip_blank_lines=False, header=begin - 1, on_bad_lines='warn')
+    low, high, cycle = get_cycle_info(data_file, df, df.index[-1]-110, 5, w2t)
+
+    return ws, df, low, high, cycle
+
+
+def single_file_proc(ws, data_file, df, low, high, cycle, w2t):
+    _row = _row_lt = _row_gt = count = 1
+    _step = 5
+    _data = {}
+    row_max = df.index[-1]-100
+    print(data_file)
+    while _row < row_max:
+        if count not in _data.keys():
+            _data[count] = []
+
+        _value = df.iloc[_row, 2]
+        if _value < 2:
+            _row_lt = find_point('forward', _step, 'c'+str(_row), data_file, 'lt', df, _row, w2t)
+            _start = int(_row_gt + (_row_lt - _row_gt - 50) / 2)
+            _end = _start + 50
+            value = df.iloc[_start:_end, 2].mean() + df.iloc[_start:_end, 2].std()
+            _data[count].append(value)
+        else:
+            _row_gt = find_point('forward', _step, 'c'+str(_row), data_file, 'gt', df, _row, w2t)
+            if _row_gt - _row_lt > cycle * 2:
+                count += 1
+
+        _row = max(_row_gt, _row_lt)
+
+    for i in range(2, 10):
+        ws.cell(row=1, column=i).value = f"第{i-1}次测试"
+        ws.cell(row=i, column=1).value = f"第{i-1}次精度变化"
+
+    print(_data)
+    for i in sorted(_data.keys()):
+        _row = 2
+        _column = i + 1
+        for value in _data[i]:
+            ws.cell(row=_row, column=_column).value = float(value)
+            _row += 1
+
+
+def execution(data_files, w2t):
+    wb = Workbook()
+    for data_file in data_files:
+        ws, df, low, high, cycle = preparation(data_file, wb, w2t)
+        print(f"low = {low}")
+        print(f"high = {high}")
+        print(f"cycle = {cycle}")
+        single_file_proc(ws, data_file, df, low, high, cycle, w2t)
+
+    wd = data_files[0].split('\\')
+    del wd[-1]
+    wd = '\\'.join(wd)
+    filename = wd + '\\result.xlsx'
+    wb.save(filename)
+    wb.close()
+    w2t('----------------------------------------')
+    w2t('所有文件均已处理完毕')
+
+
+def main(path, w2t):
+    data_files = initialization(path, w2t)
+    execution(data_files, w2t)
+
+
+if __name__ == '__main__':
+    main(path=argv[1], w2t=argv[2])