138 lines
4.3 KiB
Python
138 lines
4.3 KiB
Python
from openpyxl import load_workbook
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from os import scandir
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from os.path import exists
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from sys import argv
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from pandas import read_csv
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def traversal_files(path, w2t):
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# 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
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# 参数：路径
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# 返回值：路径下的文件夹列表 路径下的文件列表
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if not exists(path):
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msg = f'数据文件夹{path}不存在，请确认后重试......'
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w2t(msg, 0, 1)
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else:
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dirs = []
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files = []
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for item in scandir(path):
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if item.is_dir():
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dirs.append(item.path)
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elif item.is_file():
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files.append(item.path)
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return dirs, files
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def initialization(path, sub, w2t):
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_, data_files = traversal_files(path, w2t)
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count = 0
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for data_file in data_files:
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if sub != 'cycle':
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if not (data_file.endswith('.data') or data_file.endswith('.csv')):
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msg = f"所有文件必须以 .data 结尾，请检查后重新运行。"
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w2t(msg, 0, 2)
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else:
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if data_file.endswith('.xlsx'):
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count += 1
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elif not (data_file.endswith('.data') or data_file.endswith('.csv')):
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msg = f"所有文件必须以 .data 结尾，请检查后重新运行。"
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w2t(msg, 0, 3)
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if sub == 'cycle' and count != 1:
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w2t("未找到电机电流数据处理excel表格，确认后重新运行！", 0, 4)
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return data_files
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def current_max(data_files, rc, trq, w2t):
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current = {1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0}
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for data_file in data_files:
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if data_file.endswith('.data'):
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df = read_csv(data_file, sep='\t')
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elif data_file.endswith('.csv'):
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df = read_csv(data_file, sep=',', encoding='gbk', header=8)
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axis = int(data_file.split('\\')[-1].split('_')[0].removeprefix('j'))
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rca = rc[axis-1]
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col = df.columns.values[trq-1]
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c_max = df[col].max()
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scale = 1 if data_file.endswith('.csv') else 1000
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_ = abs(c_max/scale*rca)
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current[axis] = _
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w2t(f"{data_file}: {_:.4f}")
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w2t("【MAX】数据处理完毕......")
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return current
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def current_avg(data_files, rc, trq, w2t):
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current = {1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0}
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for data_file in data_files:
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if data_file.endswith('.data'):
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df = read_csv(data_file, sep='\t')
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elif data_file.endswith('.csv'):
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df = read_csv(data_file, sep=',', encoding='gbk', header=8)
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axis = int(data_file.split('\\')[-1].split('_')[0].removeprefix('j'))
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rca = rc[axis-1]
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col = df.columns.values[trq - 1]
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c_std = df[col].std()
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c_avg = df[col].mean()
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scale = 1 if data_file.endswith('.csv') else 1000
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_ = (abs(c_avg)+c_std)/scale*rca
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current[axis] = _
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w2t(f"{data_file}: {_:.4f}")
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w2t("【AVG】数据处理完毕......")
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return current
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def current_cycle(dur, data_files, rc, vel, trq, w2t):
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result = None
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hold = []
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single = []
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for data_file in data_files:
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if data_file.endswith('.xlsx'):
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result = data_file
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elif (data_file.endswith('.csv') or data_file.endswith('.data')) and data_file.startswith('hold', 3, 10):
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hold.append(data_file)
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else:
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single.append(data_file)
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if hold != []:
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avg = current_avg(hold, rc, trq, w2t)
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wb = load_workbook(result)
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for k, v in avg.items():
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try:
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shtname = f"J{k}"
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wb[shtname]["J4"].value = v
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except:
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pass
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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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rcs = rc[axis-1]
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pass
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# =======================================
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def main(path, sub, rc, vel, trq, dur, w2t):
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data_files = initialization(path, sub, w2t)
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if sub == 'max':
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current_max(data_files, rc, trq, w2t)
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elif sub == 'avg':
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current_avg(data_files, rc, trq, w2t)
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elif sub == 'cycle':
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current_cycle(dur, data_files, rc, vel, trq, w2t)
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else:
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pass
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if __name__ == '__main__':
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main(*argv[1:])
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