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

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:])