电机电流完善
This commit is contained in:
5
code/data_process/brake.py
Normal file
5
code/data_process/brake.py
Normal file
@ -0,0 +1,5 @@
|
||||
from common import clibs
|
||||
|
||||
def main():
|
||||
print("brake")
|
||||
|
||||
423
code/data_process/current.py
Normal file
423
code/data_process/current.py
Normal file
@ -0,0 +1,423 @@
|
||||
import threading
|
||||
import openpyxl
|
||||
import pandas
|
||||
import re
|
||||
import csv
|
||||
from common import clibs
|
||||
|
||||
|
||||
def initialization(path, w2t, insert_logdb):
|
||||
_, data_files = clibs.traversal_files(path, w2t)
|
||||
count = 0
|
||||
for data_file in data_files:
|
||||
filename = data_file.split("/")[-1]
|
||||
if filename == "configs.xlsx":
|
||||
count += 1
|
||||
elif filename == "T_电机电流.xlsx":
|
||||
...
|
||||
else:
|
||||
if not re.match("j[1-7].*\\.data", filename):
|
||||
msg = f"不合规 {data_file}\n"
|
||||
msg += "所有数据文件必须以 j[1-7]_ 开头，以 .data 结尾，比如j1_abcdef.data\n配置文件需要命名为\"configs.xlsx\"，结果文件需要命名为\"T_电机电流.xlsx\"\n"
|
||||
msg += "需要有配置文件\"configs.xlsx\"表格，以及数据处理文件\"T_电机电流.xlsx\"表格，请检查整改后重新运行\n"
|
||||
w2t(msg, "red", "FilenameIllegal")
|
||||
|
||||
if count != 1:
|
||||
msg = "需要有配置文件\"configs.xlsx\"表格，以及数据处理文件\"T_电机电流.xlsx\"表格，请检查整改后重新运行\n"
|
||||
w2t(msg, "red", "FilenameIllegal")
|
||||
|
||||
insert_logdb("INFO", "current", f"current: 获取必要文件：{data_files}")
|
||||
return data_files
|
||||
|
||||
|
||||
def current_max(data_files, rcs, trq, w2t, insert_logdb):
|
||||
insert_logdb("INFO", "current", "MAX: 正在处理最大电流值逻辑...")
|
||||
current = {1: [], 2: [], 3: [], 4: [], 5: [], 6: []}
|
||||
for data_file in data_files:
|
||||
if data_file.endswith(".data"):
|
||||
df = pandas.read_csv(data_file, sep="\t")
|
||||
else:
|
||||
continue
|
||||
|
||||
insert_logdb("INFO", "current", f"MAX: 正在处理 {data_file}")
|
||||
cols = len(df.columns)
|
||||
axis = int(data_file.split("/")[-1].split("_")[0].removeprefix("j"))
|
||||
rca = rcs[axis-1]
|
||||
insert_logdb("INFO", "current", f"MAX: 最大列数为 {cols}，{axis} 轴的额定电流为 {rca}")
|
||||
|
||||
col = df.columns.values[trq-1] # 获取 "device_servo_trq_feedback"
|
||||
c_max = df[col].abs().max()
|
||||
|
||||
scale = 1000
|
||||
_ = abs(c_max/scale*rca)
|
||||
current[axis].append(_)
|
||||
w2t(f"{data_file}: {_:.4f}\n")
|
||||
insert_logdb("INFO", "current", f"MAX: 获取到的列名为 {col}，最大电流为 {_}")
|
||||
|
||||
with open(data_file, "a+") as f_data:
|
||||
csv_writer = csv.writer(f_data, delimiter="\t")
|
||||
csv_writer.writerow([""] * (cols-1) + [_])
|
||||
|
||||
for axis, cur in current.items():
|
||||
if not cur:
|
||||
continue
|
||||
else:
|
||||
w2t(f"{axis}轴数据：")
|
||||
for value in cur:
|
||||
w2t(f"{value:.4f} ")
|
||||
w2t("\n")
|
||||
w2t("\n【MAX】数据处理完毕......")
|
||||
insert_logdb("INFO", "current", f"MAX: 获取最大电流值结束 current_max = {current}")
|
||||
return current
|
||||
|
||||
|
||||
def current_avg(data_files, rcs, trq, w2t, insert_logdb):
|
||||
insert_logdb("INFO", "current", "AVG: 正在处理平均电流值逻辑...")
|
||||
current = {1: [], 2: [], 3: [], 4: [], 5: [], 6: []}
|
||||
for data_file in data_files:
|
||||
if data_file.endswith(".data"):
|
||||
df = pandas.read_csv(data_file, sep="\t")
|
||||
else:
|
||||
continue
|
||||
|
||||
insert_logdb("INFO", "current", f"AVG: 正在处理 {data_file}")
|
||||
cols = len(df.columns)
|
||||
axis = int(data_file.split("/")[-1].split("_")[0].removeprefix("j"))
|
||||
rca = rcs[axis-1]
|
||||
insert_logdb("INFO", "current", f"AVG: 最大列数为 {cols}，{axis} 轴的额定电流为 {rca}")
|
||||
|
||||
col = df.columns.values[trq-1]
|
||||
c_std = df[col].std()
|
||||
c_avg = df[col].mean()
|
||||
|
||||
scale = 1000
|
||||
_ = (abs(c_avg)+c_std*3)/scale*rca
|
||||
current[axis].append(_)
|
||||
w2t(f"{data_file}: {_:.4f}\n")
|
||||
|
||||
insert_logdb("INFO", "current", f"AVG: 获取到的列名为 {col}，平均电流为 {_}")
|
||||
with open(data_file, "a+") as f_data:
|
||||
csv_writer = csv.writer(f_data, delimiter="\t")
|
||||
csv_writer.writerow([""] * (cols-1) + [_])
|
||||
|
||||
for axis, cur in current.items():
|
||||
if not cur:
|
||||
continue
|
||||
else:
|
||||
w2t(f"{axis}轴数据：")
|
||||
for value in cur:
|
||||
w2t(f"{value:.4f} ")
|
||||
w2t("\n")
|
||||
w2t("\n【AVG】数据处理完毕......\n")
|
||||
insert_logdb("INFO", "current", f"AVG: 获取平均电流值结束 current_avg = {current}")
|
||||
return current
|
||||
|
||||
|
||||
def current_cycle(data_files, vel, trq, trqh, rrs, rcs, rpms, w2t, insert_logdb):
|
||||
result, hold, single, scenario, dur_time = None, [], [], [], 0
|
||||
for data_file in data_files:
|
||||
filename = data_file.split("/")[-1]
|
||||
if filename == "T_电机电流.xlsx":
|
||||
result = data_file
|
||||
elif re.match("j[1-7]_hold_.*\\.data", filename):
|
||||
hold.append(data_file)
|
||||
elif re.match("j[1-7]_s_.*\\.data", filename):
|
||||
scenario.append(data_file)
|
||||
dur_time = float(filename.split("_")[3])
|
||||
elif re.match("j[1-7]_.*\\.data", filename):
|
||||
single.append(data_file)
|
||||
|
||||
clibs.stop = True
|
||||
w2t(f"正在打开文件 {result}，需要 10s 左右......\n")
|
||||
t_excel = clibs.GetThreadResult(openpyxl.load_workbook, args=(result, ))
|
||||
t_excel.daemon = True
|
||||
t_excel.start()
|
||||
t_progress = threading.Thread(target=clibs.tl_prg, args=("Processing......", ))
|
||||
t_progress.daemon = True
|
||||
t_progress.start()
|
||||
wb = t_excel.get_result()
|
||||
|
||||
if hold:
|
||||
avg = current_avg(hold, rcs, trqh, w2t, insert_logdb)
|
||||
for axis, cur_value in avg.items():
|
||||
sht_name = f"J{axis}"
|
||||
wb[sht_name]["O4"].value = float(cur_value[0])
|
||||
|
||||
if dur_time == 0:
|
||||
p_single(wb, single, vel, rrs, w2t, insert_logdb)
|
||||
else:
|
||||
p_scenario(wb, scenario, vel, rrs, dur_time, w2t, insert_logdb)
|
||||
|
||||
clibs.stop = True
|
||||
w2t(f"正在保存文件 {result}，需要 10s 左右......\n")
|
||||
t_excel = threading.Thread(target=wb.save, args=(result, ))
|
||||
t_excel.daemon = True
|
||||
t_excel.start()
|
||||
t_excel.join()
|
||||
clibs.stop = False
|
||||
t_progress.join()
|
||||
|
||||
w2t("----------------------------------------------------------\n")
|
||||
w2t("全部处理完毕")
|
||||
|
||||
|
||||
def find_point(data_file, df, flag, row_s, row_e, threshold, step, end_point, skip_scale, axis, seq, w2t, insert_logdb):
|
||||
if flag == "lt":
|
||||
while row_e > end_point:
|
||||
speed_avg = df.iloc[row_s:row_e].abs().mean()
|
||||
if speed_avg < threshold:
|
||||
row_e -= step
|
||||
row_s -= step
|
||||
continue
|
||||
else:
|
||||
# one more time，如果连续两次 200 个点的平均值都大于 2，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）
|
||||
# 从实际数据看，这开逻辑很小概率能触发到
|
||||
speed_avg = df.iloc[row_s-end_point*skip_scale:row_e-end_point*skip_scale].abs().mean()
|
||||
if speed_avg < threshold:
|
||||
insert_logdb("WARNING", "current", f"【lt】{axis} 轴第 {seq} 次查找数据有异常，row_s = {row_s}, row_e = {row_e}！")
|
||||
row_e -= end_point*skip_scale
|
||||
row_s -= end_point*skip_scale
|
||||
continue
|
||||
else:
|
||||
return row_s, row_e
|
||||
else:
|
||||
w2t(f"{data_file} 数据有误，需要检查，无法找到第 {seq} 个有效点...", "red", "AnchorNotFound")
|
||||
elif flag == "gt":
|
||||
while row_e > end_point:
|
||||
speed_avg = df.iloc[row_s:row_e].abs().mean()
|
||||
# if axis == 1 and seq == 1:
|
||||
# insert_logdb("DEBUG", "current", f"【gt】{axis} 轴，speed_avg = {speed_avg}，row_s = {row_s}, row_e = {row_e}！")
|
||||
if speed_avg > threshold:
|
||||
row_e -= step
|
||||
row_s -= step
|
||||
continue
|
||||
else:
|
||||
# one more time，如果连续两次 200 个点的平均值都小于 2，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）
|
||||
# 从实际数据看，这开逻辑很小概率能触发到
|
||||
speed_avg = df.iloc[row_s-end_point*skip_scale:row_e-end_point*skip_scale].abs().mean()
|
||||
if speed_avg > threshold:
|
||||
insert_logdb("WARNING", "current", f"【gt】{axis} 轴第 {seq} 次查找数据有异常，row_s = {row_s}, row_e = {row_e}！")
|
||||
row_e -= end_point*skip_scale
|
||||
row_s -= end_point*skip_scale
|
||||
continue
|
||||
else:
|
||||
return row_s, row_e
|
||||
else:
|
||||
w2t(f"{data_file} 数据有误，需要检查，无法找到第 {seq} 个有效点...", "red", "AnchorNotFound")
|
||||
|
||||
|
||||
def get_row_number(threshold, flag, df, row_s, row_e, axis, insert_logdb):
|
||||
count_1, count_2 = 0, 0
|
||||
if flag == "start" or flag == "end":
|
||||
for number in df.iloc[row_s:row_e].abs():
|
||||
count_2 += 1
|
||||
if number > threshold:
|
||||
count_1 += 1
|
||||
if count_1 == 10:
|
||||
return row_s + count_2 - 10
|
||||
else:
|
||||
count_1 = 0
|
||||
elif flag == "middle":
|
||||
for number in df.iloc[row_s:row_e].abs():
|
||||
count_2 += 1
|
||||
if number < threshold: # 唯一的区别
|
||||
count_1 += 1
|
||||
if count_1 == 10:
|
||||
return row_s + count_2 - 10
|
||||
else:
|
||||
count_1 = 0
|
||||
|
||||
places = {"start": "起点", "middle": "中间点", "end": "终点"}
|
||||
insert_logdb("WARNING", "current", f"{axis} 轴获取{places[flag]}数据 {row_e} 可能有异常，需关注！")
|
||||
return row_e
|
||||
|
||||
|
||||
def p_single(wb, single, vel, rrs, w2t, insert_logdb):
|
||||
# 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:
|
||||
axis = int(data_file.split("/")[-1].split("_")[0].removeprefix("j"))
|
||||
sht_name = f"J{axis}"
|
||||
ws = wb[sht_name]
|
||||
pandas.set_option("display.precision", 2)
|
||||
df_origin = pandas.read_csv(data_file, sep="\t")
|
||||
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)
|
||||
|
||||
step = 50 # 步进值
|
||||
end_point = 200 # 有效数值的数目
|
||||
threshold = 2 # 200个点的平均阈值线
|
||||
skip_scale = 2
|
||||
row_start, row_middle, row_end = 0, 0, 0
|
||||
row_e = df.index[-1]
|
||||
row_s = row_e - end_point
|
||||
speed_avg = df.iloc[row_s:row_e].abs().mean()
|
||||
if speed_avg < 2:
|
||||
# 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
|
||||
row_s, row_e = find_point(data_file, df, "lt", row_s, row_e, threshold, step, end_point, skip_scale, axis, 0, w2t, insert_logdb)
|
||||
row_e -= end_point*skip_scale
|
||||
row_s -= end_point*skip_scale
|
||||
# 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
|
||||
row_s, row_e = find_point(data_file, df, "gt", row_s, row_e, threshold, step, end_point, skip_scale, axis, 0, w2t, insert_logdb)
|
||||
row_e -= end_point*skip_scale
|
||||
row_s -= end_point*skip_scale
|
||||
# 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
|
||||
row_s, row_e = find_point(data_file, df, "lt", row_s, row_e, threshold, step, end_point, skip_scale, axis, 0, w2t, insert_logdb)
|
||||
row_e -= end_point*skip_scale
|
||||
row_s -= end_point*skip_scale
|
||||
# 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
|
||||
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)
|
||||
row_end = get_row_number(threshold, "end", df, row_s, row_e, axis, insert_logdb)
|
||||
row_e -= end_point*skip_scale
|
||||
row_s -= end_point*skip_scale
|
||||
# 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
|
||||
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)
|
||||
row_middle = get_row_number(threshold, "middle", df, row_s, row_e, axis, insert_logdb)
|
||||
row_e -= end_point*skip_scale
|
||||
row_s -= end_point*skip_scale
|
||||
# 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
|
||||
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)
|
||||
row_start = get_row_number(threshold, "start", df, row_s, row_e, axis, insert_logdb)
|
||||
elif speed_avg > 2:
|
||||
# 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
|
||||
row_s, row_e = find_point(data_file, df, "gt", row_s, row_e, threshold, step, end_point, skip_scale, axis, 0, w2t, insert_logdb)
|
||||
row_e -= end_point*skip_scale
|
||||
row_s -= end_point*skip_scale
|
||||
# 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
|
||||
row_s, row_e = find_point(data_file, df, "lt", row_s, row_e, threshold, step, end_point, skip_scale, axis, 0, w2t, insert_logdb)
|
||||
row_e -= end_point*skip_scale
|
||||
row_s -= end_point*skip_scale
|
||||
# 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
|
||||
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)
|
||||
row_end = get_row_number(threshold, "end", df, row_s, row_e, axis, insert_logdb)
|
||||
row_e -= end_point*skip_scale
|
||||
row_s -= end_point*skip_scale
|
||||
# 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
|
||||
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)
|
||||
row_middle = get_row_number(threshold, "middle", df, row_s, row_e, axis, insert_logdb)
|
||||
row_e -= end_point*skip_scale
|
||||
row_s -= end_point*skip_scale
|
||||
# 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
|
||||
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)
|
||||
row_start = get_row_number(threshold, "start", df, row_s, row_e, axis, insert_logdb)
|
||||
|
||||
insert_logdb("INFO", "current", f"{axis} 轴起点：{row_start}")
|
||||
insert_logdb("INFO", "current", f"{axis} 轴中间点：{row_middle}")
|
||||
insert_logdb("INFO", "current", f"{axis} 轴终点：{row_end}")
|
||||
insert_logdb("INFO", "current", f"{axis} 轴数据非零段点数：{row_middle-row_start+1}")
|
||||
insert_logdb("INFO", "current", f"{axis} 轴数据为零段点数：{row_end-row_middle+1}")
|
||||
if abs(row_end+row_start-2*row_middle) > 1000:
|
||||
insert_logdb("WARNING", "current", f"{axis} 轴数据占空比异常！")
|
||||
|
||||
data = []
|
||||
for row in range(row_start, row_end+1):
|
||||
data.append(df_origin.iloc[row, 0])
|
||||
data.append(df_origin.iloc[row, 1])
|
||||
data.append(df_origin.iloc[row, 2])
|
||||
|
||||
i = 0
|
||||
for row in ws.iter_rows(min_row=2, min_col=2, max_row=150000, 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 as Err:
|
||||
if i % 3 == 0:
|
||||
ws.cell((i//3)+2, 1).value = None
|
||||
cell.value = None
|
||||
i += 1
|
||||
|
||||
|
||||
def p_scenario(wb, scenario, vel, rrs, dur_time, w2t, insert_logdb):
|
||||
for data_file in scenario:
|
||||
cycle = 0.001
|
||||
axis = int(data_file.split("/")[-1].split("_")[0].removeprefix("j"))
|
||||
sht_name = f"J{axis}"
|
||||
ws = wb[sht_name]
|
||||
pandas.set_option("display.precision", 2)
|
||||
df_origin = pandas.read_csv(data_file, sep="\t")
|
||||
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 = []
|
||||
for row in range(row_start, row_end+1):
|
||||
data.append(df_origin.iloc[row, 0])
|
||||
data.append(df_origin.iloc[row, 1])
|
||||
data.append(df_origin.iloc[row, 2])
|
||||
|
||||
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 as Err:
|
||||
cell.value = None
|
||||
if i % 3 == 0:
|
||||
ws.cell((i//3)+2, 1).value = None
|
||||
i += 1
|
||||
|
||||
|
||||
def get_configs(configfile, w2t, insert_logdb):
|
||||
try:
|
||||
wb = openpyxl.load_workbook(configfile, read_only=True)
|
||||
ws = wb["Target"]
|
||||
except Exception as Err:
|
||||
insert_logdb("ERROR", "current", f"无法打开 {configfile}，获取配置文件参数错误 {Err}")
|
||||
w2t(f"无法打开 {configfile}", color="red", desc="OpenFileError")
|
||||
|
||||
# 最大角速度，额定电流，减速比，额定转速
|
||||
rrs, avs, rcs, rpms = [], [], [], []
|
||||
for i in range(2, 8):
|
||||
rrs.append(abs(float(ws.cell(row=2, column=i).value)))
|
||||
avs.append(abs(float(ws.cell(row=3, column=i).value)))
|
||||
rpms.append(abs(float(ws.cell(row=4, column=i).value)))
|
||||
rcs.append(abs(float(ws.cell(row=6, column=i).value)))
|
||||
|
||||
insert_logdb("INFO", "current", f"current: 获取减速比：{rrs}")
|
||||
insert_logdb("INFO", "current", f"current: 获取角速度：{avs}")
|
||||
insert_logdb("INFO", "current", f"current: 获取额定电流：{rcs}")
|
||||
insert_logdb("INFO", "current", f"current: 获取额定转速：{rpms}")
|
||||
return rrs, avs, rcs, rpms
|
||||
|
||||
|
||||
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"])
|
||||
w2t = clibs.w2t
|
||||
insert_logdb = clibs.insert_logdb
|
||||
insert_logdb("INFO", "current", "current: 参数初始化成功")
|
||||
|
||||
data_files = initialization(path, w2t, insert_logdb)
|
||||
rrs, avs, rcs, rpms = get_configs(path + "\\configs.xlsx", w2t, insert_logdb)
|
||||
if sub == "max":
|
||||
current_max(data_files, rcs, trq, w2t, insert_logdb)
|
||||
elif sub == "avg":
|
||||
current_avg(data_files, rcs, trq, w2t, insert_logdb)
|
||||
elif sub == "cycle":
|
||||
current_cycle(data_files, vel, trq, trqh, rrs, rcs, rpms, w2t, insert_logdb)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
5
code/data_process/iso.py
Normal file
5
code/data_process/iso.py
Normal file
@ -0,0 +1,5 @@
|
||||
|
||||
from common import clibs
|
||||
def main():
|
||||
print("iso")
|
||||
|
||||
5
code/data_process/wavelogger.py
Normal file
5
code/data_process/wavelogger.py
Normal file
@ -0,0 +1,5 @@
|
||||
|
||||
from common import clibs
|
||||
def main():
|
||||
print("wavelogger")
|
||||
|
||||
Reference in New Issue
Block a user