num_matches, label_subs = 0, collections.defaultdict(int)
时间: 2024-05-25 20:14:42 浏览: 16
这是一行 Python 代码,其中包含两个变量的初始化操作。
`num_matches` 被初始化为 0,这是一个整数类型的变量。它通常用于计数器,记录某个事件发生的次数。
`label_subs` 被初始化为一个默认值为 0 的字典。这个字典保存了一个标签(label)和对应的出现次数(substitutions)。在这个字典中,如果一个标签没有被显式地添加到字典中,它的值会默认为 0。这个字典通常用于统计分类问题中每个类别的样本数。
相关问题
优化代码 def fault_classification_wrapper(vin, main_path, data_path, log_path, done_path): start_time = time.time() isc_path = os.path.join(done_path, vin, 'isc_cal_result', f'{vin}_report.xlsx') if not os.path.exists(isc_path): print('No isc detection input!') else: isc_input = isc_produce_alarm(isc_path, vin) ica_path = os.path.join(done_path, vin, 'ica_cal_result', f'ica_detection_alarm_{vin}.csv') if not os.path.exists(ica_path): print('No ica detection input!') else: ica_input = ica_produce_alarm(ica_path) soh_path = os.path.join(done_path, vin, 'SOH_cal_result', f'{vin}_sohAno.csv') if not os.path.exists(soh_path): print('No soh detection input!') else: soh_input = soh_produce_alarm(soh_path, vin) alarm_df = pd.concat([isc_input, ica_input, soh_input]) alarm_df.reset_index(drop=True, inplace=True) alarm_df['alarm_cell'] = alarm_df['alarm_cell'].apply(lambda _: str(_)) print(vin) module = AutoAnalysisMain(alarm_df, main_path, data_path, done_path) module.analysis_process() flags = os.O_WRONLY | os.O_CREAT modes = stat.S_IWUSR | stat.S_IRUSR with os.fdopen(os.open(os.path.join(log_path, 'log.txt'), flags, modes), 'w') as txt_file: for k, v in module.output.items(): txt_file.write(k + ':' + str(v)) txt_file.write('\n') for x, y in module.output_sub.items(): txt_file.write(x + ':' + str(y)) txt_file.write('\n\n') fc_result_path = os.path.join(done_path, vin, 'fc_result') if not os.path.exists(fc_result_path): os.makedirs(fc_result_path) pd.DataFrame(module.output).to_csv( os.path.join(fc_result_path, 'main_structure.csv')) df2 = pd.DataFrame() for subs in module.output_sub.keys(): sub_s = pd.Series(module.output_sub[subs]) df2 = df2.append(sub_s, ignore_index=True) df2.to_csv(os.path.join(fc_result_path, 'sub_structure.csv')) end_time = time.time() print("time cost of fault classification:", float(end_time - start_time) * 1000.0, "ms") return
Here are some suggestions to optimize the code:
1. Use list comprehension to simplify the code:
```
alarm_df = pd.concat([isc_input, ica_input, soh_input]).reset_index(drop=True)
alarm_df['alarm_cell'] = alarm_df['alarm_cell'].apply(str)
```
2. Use context manager to simplify file operation:
```
with open(os.path.join(log_path, 'log.txt'), 'w') as txt_file:
for k, v in module.output.items():
txt_file.write(f"{k}:{v}\n")
for x, y in module.output_sub.items():
txt_file.write(f"{x}:{y}\n\n")
```
3. Use `Pathlib` to simplify path operation:
```
fc_result_path = Path(done_path) / vin / 'fc_result'
fc_result_path.mkdir(parents=True, exist_ok=True)
pd.DataFrame(module.output).to_csv(fc_result_path / 'main_structure.csv')
pd.DataFrame(module.output_sub).to_csv(fc_result_path / 'sub_structure.csv')
```
4. Use f-string to simplify string formatting:
```
print(f"time cost of fault classification: {(end_time - start_time) * 1000.0} ms")
```
Here's the optimized code:
```
def fault_classification_wrapper(vin, main_path, data_path, log_path, done_path):
start_time = time.time()
isc_path = Path(done_path) / vin / 'isc_cal_result' / f'{vin}_report.xlsx'
if not isc_path.exists():
print('No isc detection input!')
isc_input = pd.DataFrame()
else:
isc_input = isc_produce_alarm(isc_path, vin)
ica_path = Path(done_path) / vin / 'ica_cal_result' / f'ica_detection_alarm_{vin}.csv'
if not ica_path.exists():
print('No ica detection input!')
ica_input = pd.DataFrame()
else:
ica_input = ica_produce_alarm(ica_path)
soh_path = Path(done_path) / vin / 'SOH_cal_result' / f'{vin}_sohAno.csv'
if not soh_path.exists():
print('No soh detection input!')
soh_input = pd.DataFrame()
else:
soh_input = soh_produce_alarm(soh_path, vin)
alarm_df = pd.concat([isc_input, ica_input, soh_input]).reset_index(drop=True)
alarm_df['alarm_cell'] = alarm_df['alarm_cell'].apply(str)
print(vin)
module = AutoAnalysisMain(alarm_df, main_path, data_path, done_path)
module.analysis_process()
with open(Path(log_path) / 'log.txt', 'w') as txt_file:
for k, v in module.output.items():
txt_file.write(f"{k}:{v}\n")
for x, y in module.output_sub.items():
txt_file.write(f"{x}:{y}\n\n")
fc_result_path = Path(done_path) / vin / 'fc_result'
fc_result_path.mkdir(parents=True, exist_ok=True)
pd.DataFrame(module.output).to_csv(fc_result_path / 'main_structure.csv')
pd.DataFrame(module.output_sub).to_csv(fc_result_path / 'sub_structure.csv')
end_time = time.time()
print(f"time cost of fault classification: {(end_time - start_time) * 1000.0} ms")
return
```
from sympy import simplify import numpy as np import sympy as sp from sympy import symbols x, y, z = symbols('x y z') F = (x**2 + y**2 - 1)*(x**2 + z**2 - 1)*(y**2 + z**2 - 1) - 1 F_x = F.diff(x) F_y = F.diff(y) F_z = x**2 * F.diff(x) - y * F.diff(y) F_x = simplify(F_x) F_y = simplify(F_y) F_z = simplify(F_z) G_x = - F_x/F_z G_y = - F_y/F_z G_x = simplify(G_x) G_y = simplify(G_y) Fi = F_x.diff(x) Fj = F_x.diff(y) Fk = F_y.diff(y) N = G_x.cross(G_y) # normale N = simplify(N/N.norm()) E = simplify(G_x.norm()**2) F = simplify(G_x.dot(G_y)) G = simplify(G_y.norm()**2) I = simplify(N.dot(Fi)) J = simplify(N.dot(Fj)) K = simplify(N.dot(FK)) x = Symbol('x') factor(det(Matrix([[I - x*E, J - x*F], [J - x*F, K - x*G]])).subs({x:0, y:0}))跑不出来,
从代码上看,这是一个使用 SymPy 执行符号计算的程序。其中,定义了一些变量和表达式,对这些表达式求导,然后求取法向量、曲率等数据。最后一行代码是要计算一个行列式,并且把其中的 $x$ 和 $y$ 分别代入为 $0$,最后对行列式进行因式分解。但是,这段代码可能存在一些问题,例如变量 FK 应该是 Fk,因此会导致代码无法运行。如果你遇到了问题,可以检查一下代码是否有语法错误或变量名错误。
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