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转向从机型配置文件获取参数

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gitea
2025-01-14 14:14:33 +08:00
parent 137122947c
commit 0e67a2831c
25 changed files with 524877 additions and 203 deletions
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111
code/data_process/current.py
View File

@ -1,3 +1,4 @@
import json
import threading
import openpyxl
import pandas
@ -8,26 +9,26 @@ from common import clibs
def initialization(path, w2t, insert_logdb):
_, data_files = clibs.traversal_files(path, w2t)
count = 0
count, config_file = 0, None
for data_file in data_files:
filename = data_file.split("/")[-1]
if filename == "configs.xlsx":
if re.match(".*\\.cfg", filename):
config_file = filename
count += 1
elif filename == "T_电机电流.xlsx":
...
count += 1
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"
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"
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"
if count != 2:
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"
w2t(msg, "red", "FilenameIllegal")
insert_logdb("INFO", "current", f"current: 获取必要文件：{data_files}")
return data_files
return data_files, config_file
def current_max(data_files, rcs, trq, w2t, insert_logdb):
@ -71,7 +72,7 @@ def current_max(data_files, rcs, trq, w2t, insert_logdb):
return current
def current_avg(data_files, rcs, trq, w2t, insert_logdb):
def current_avg(data_files, rcs, trqh, 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:
@ -86,7 +87,7 @@ def current_avg(data_files, rcs, trq, w2t, insert_logdb):
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]
col = df.columns.values[trqh-1]
c_std = df[col].std()
c_avg = df[col].mean()
@ -113,7 +114,7 @@ def current_avg(data_files, rcs, trq, w2t, insert_logdb):
return current
def current_cycle(data_files, vel, trq, trqh, rrs, rcs, rpms, w2t, insert_logdb):
def current_cycle(data_files, vel, trq, trqh, sensor, rrs, rcs, params, w2t, insert_logdb):
result, hold, single, scenario, dur_time = None, [], [], [], 0
for data_file in data_files:
filename = data_file.split("/")[-1]
@ -137,6 +138,12 @@ def current_cycle(data_files, vel, trq, trqh, rrs, rcs, rpms, w2t, insert_logdb)
t_progress.start()
wb = t_excel.get_result()
ws = wb["统计"]
for idx in range(len(params)):
row = idx + 2
for col in range(2, 8):
ws.cell(row=row, column=col).value = params[idx][col-2]
if hold:
avg = current_avg(hold, rcs, trqh, w2t, insert_logdb)
for axis, cur_value in avg.items():
@ -144,9 +151,9 @@ def current_cycle(data_files, vel, trq, trqh, rrs, rcs, rpms, w2t, insert_logdb)
wb[sht_name]["O4"].value = float(cur_value[0])
if dur_time == 0:
p_single(wb, single, vel, rrs, w2t, insert_logdb)
p_single(wb, single, vel, trq, sensor, rrs, w2t, insert_logdb)
else:
p_scenario(wb, scenario, vel, rrs, dur_time, w2t, insert_logdb)
p_scenario(wb, scenario, vel, trq, sensor, rrs, dur_time, w2t)
clibs.stop = True
w2t(f"正在保存文件 {result}，需要 10s 左右......\n")
@ -227,12 +234,12 @@ def get_row_number(threshold, flag, df, row_s, row_e, axis, insert_logdb):
else:
count_1 = 0
places = {"start": "起点", "middle": "中间点", "end": "终点"}
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
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):
def p_single(wb, single, vel, trq, sensor, 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
@ -313,11 +320,11 @@ def p_single(wb, single, vel, rrs, w2t, insert_logdb):
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 = []
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, 0])
data.append(df_origin.iloc[row, 1])
data.append(df_origin.iloc[row, 2])
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=150000, max_col=4):
@ -328,14 +335,14 @@ def p_single(wb, single, vel, rrs, w2t, insert_logdb):
_ = f"{data[i]:.2f}"
cell.value = float(_)
i += 1
except Exception as Err:
except Exception:
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):
def p_scenario(wb, scenario, vel, trq, sensor, rrs, dur_time, w2t):
for data_file in scenario:
cycle = 0.001
axis = int(data_file.split("/")[-1].split("_")[0].removeprefix("j"))
@ -354,11 +361,11 @@ def p_scenario(wb, scenario, vel, rrs, dur_time, w2t, insert_logdb):
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 = []
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, 0])
data.append(df_origin.iloc[row, 1])
data.append(df_origin.iloc[row, 2])
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):
@ -369,34 +376,44 @@ def p_scenario(wb, scenario, vel, rrs, dur_time, w2t, insert_logdb):
_ = f"{data[i]:.2f}"
cell.value = float(_)
i += 1
except Exception as Err:
except Exception:
cell.value = None
if i % 3 == 0:
ws.cell((i//3)+2, 1).value = None
i += 1
def get_configs(configfile, w2t, insert_logdb):
def get_configs(config_file, w2t, insert_logdb):
try:
wb = openpyxl.load_workbook(configfile, read_only=True)
ws = wb["Target"]
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"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")
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
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)))
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"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
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():
@ -405,18 +422,20 @@ def main():
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 = initialization(path, w2t, insert_logdb)
rrs, avs, rcs, rpms = get_configs(path + "\\configs.xlsx", w2t, insert_logdb)
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, trq, w2t, insert_logdb)
current_avg(data_files, rcs, trqh, w2t, insert_logdb)
elif sub == "cycle":
current_cycle(data_files, vel, trq, trqh, rrs, rcs, rpms, w2t, insert_logdb)
current_cycle(data_files, vel, trq, trqh, sensor, rrs, rcs, params, w2t, insert_logdb)
if __name__ == '__main__':

184
code/data_process/iso.py
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@ -1,5 +1,183 @@
import pdfplumber
import openpyxl
import os
from common import clibs
def main():
print("iso")
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
pdf.close()
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
pdf.close()
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
pdf.close()
def main():
path = clibs.data_dp["_path"]
w2t = clibs.w2t
dirs, files = clibs.traversal_files(path, 1)
filename = f"{path}/iso-results.xlsx"
tmpfile = f"{path}/data.txt"
wb, ws = None, None
try:
wb = openpyxl.load_workbook(filename)
ws = wb.active
for i in range(3, 50):
ws.cell(row=i, column=7).value = None
except Exception as Err:
clibs.insert_logdb("ERROR", "iso", f"main: 无法打开文件 {filename}")
w2t(f"发生错误：{Err}", "red", "FileOpenError")
p_files = []
for file in files:
if file.endswith(".pdf") and file.split("/")[-1] == "ISO.pdf":
w2t(f"正在处理{file}......\n")
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}......\n")
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}......\n")
p_iso_1000(file, p_files, ws, tmpfile)
w2t(f"文件{file}已处理完毕。\n")
else:
pass
wb.save(filename)
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......", "red")
else:
os.remove(tmpfile)
w2t("------------------------------------------\n")
w2t("所有文件均已处理完毕！\n")
if __name__ == "__main__":
main()

149
code/data_process/wavelogger.py
View File

@ -1,5 +1,148 @@
import pandas
import csv
import openpyxl
from common import clibs
def main():
print("wavelogger")
def find_point(bof, step, margin, threshold, pos, data_file, flag, df, row, w2t):
# bof: backward or forward
# pos: used for debug
# flag: greater than or lower than
row_target = None
row_origin = df.index[-1] - margin + 1
if flag == "gt":
while 0 < row < row_origin:
value = float(df.iloc[row, 2])
if value > threshold:
row = row - step if bof == "backward" else row + step
continue
else:
row_target = row - step if bof == "backward" else row + step
break
else:
if bof == "backward":
clibs.insert_logdb("ERROR", "wavelogger", f"find_point-gt: [{pos}] 在 {data_file} 中，无法正确识别数据，需要确认...")
w2t(f"[{pos}] 在 {data_file} 中，无法正确识别数据，需要确认...", "red", "DataError")
elif bof == "forward":
row_target = row + margin # to end while loop in function `single_file_proc`
elif flag == "lt":
while 0 < row < row_origin:
value = float(df.iloc[row, 2])
if value < threshold:
row = row - step if bof == "backward" else row + step
continue
else:
row_target = row - step if bof == "backward" else row + step
break
else:
if bof == "backward":
clibs.insert_logdb("ERROR", "wavelogger", f"find_point-lt: [{pos}] 在 {data_file} 中，无法正确识别数据，需要确认...")
w2t(f"[{pos}] 在 {data_file} 中，无法正确识别数据，需要确认...", "red", "DataError")
elif bof == "forward":
row_target = row + margin # to end while loop in function `single_file_proc`
return row_target
def get_cycle_info(data_file, step, margin, threshold, 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
csv_reader = csv.reader(open(data_file))
begin = int(next(csv_reader)[1])
df = pandas.read_csv(data_file, sep=",", encoding="gbk", skip_blank_lines=False, header=begin - 1, on_bad_lines="skip")
row = df.index[-1] - margin
if float(df.iloc[row, 2]) < threshold:
row = find_point("backward", step, margin, threshold, "a1", data_file, "lt", df, row, w2t)
_row = find_point("backward", step, margin, threshold, "a2", data_file, "gt", df, row, w2t)
_row = find_point("backward", step, margin, threshold, "a3", data_file, "lt", df, _row, w2t)
row_end = find_point("backward", step, margin, threshold, "a4", data_file, "gt", df, _row, w2t)
row_middle = find_point("backward", step, margin, threshold, "a5", data_file, "lt", df, row_end, w2t)
row_start = find_point("backward", step, margin, threshold, "a6", data_file, "gt", df, row_middle, w2t)
# print(f"row_end = {row_end}")
# print(f"row_middle = {row_middle}")
# print(f"row_start = {row_start}")
return row_end-row_middle, row_middle-row_start, row_end-row_start, df
def initialization(path, w2t):
_, data_files = clibs.traversal_files(path, w2t)
for data_file in data_files:
if not data_file.lower().endswith(".csv"):
clibs.insert_logdb("ERROR", "wavelogger", f"init: {data_file} 文件后缀错误，只允许 .csv 文件，需要确认！")
w2t(f"{data_file} 文件后缀错误，只允许 .csv 文件，需要确认！", "red", "FileTypeError")
return data_files
def preparation(data_file, step, margin, threshold, wb, w2t):
shtname = data_file.split("/")[-1].split(".")[0]
ws = wb.create_sheet(shtname)
low, high, cycle, df = get_cycle_info(data_file, step, margin, threshold, w2t)
return ws, df, low, high, cycle
def single_file_proc(ws, data_file, step, threshold, margin, data_length, df, cycle, w2t):
row, row_lt, row_gt, count, count_i, data = 1, 1, 1, 1, 1, {}
row_max = df.index[-1] - margin
while row < row_max:
if count not in data.keys():
data[count] = []
value = float(df.iloc[row, 2])
if value < threshold:
row_lt = find_point("forward", step, margin, threshold, "c"+str(row), data_file, "lt", df, row, w2t)
start = int(row_gt + (row_lt - row_gt - data_length) / 2)
end = start + data_length
value = df.iloc[start:end, 2].mean() + 3 * df.iloc[start:end, 2].std()
if value > 1:
msg = f"{data_file} 文件第 {count} 轮 第 {count_i} 个数据可能有问题，需人工手动确认，确认有问题可删除，无问题则保留"
clibs.insert_logdb("WARNING", "wavelogger", msg)
w2t(msg, "orange")
data[count].append(value)
count_i += 1
else:
row_gt = find_point("forward", step, margin, threshold, "c"+str(row), data_file, "gt", df, row, w2t)
if row_gt - row_lt > cycle * 2:
count += 1
count_i = 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"
for i in sorted(data.keys()):
row, column = 2, i + 1
for value in data[i]:
ws.cell(row=row, column=column).value = float(value)
row += 1
def execution(data_files, w2t):
wb = openpyxl.Workbook()
step, margin, data_length, threshold = 5, 50, 50, 5
for data_file in data_files:
ws, df, low, high, cycle = preparation(data_file, step, margin, threshold, wb, w2t)
single_file_proc(ws, data_file, step, threshold, margin, data_length, df, cycle, w2t)
wd = "/".join(data_files[0].split("/")[:-1])
filename = wd + "/result.xlsx"
wb.save(filename)
wb.close()
w2t("----------------------------------------\n")
w2t("所有文件均已处理完毕\n")
def main():
path = clibs.data_dp["_path"]
w2t = clibs.w2t
data_files = initialization(path, w2t)
execution(data_files, w2t)
if __name__ == "__main__":
main()