os.makedirs(os.path.join('..', 'data'), exist_ok=True) data_file = os.path.join('..', 'data', 'house_tiny.csv') Traceback (most recent call last): Cell In[73], line 2 os.makedirs(os.path.join('..', 'data'), exist_ok=True) File D:\Program\anaconda3\lib\os.py:225 in makedirs mkdir(name, mode) PermissionError: [WinError 5] 拒绝访问。: '..\\data'

在这种情况下，出现了权限错误。这意味着您没有足够的权限来创建文件夹 `data`。有几种方法可以解决这个问题：

1. 以管理员身份运行您的代码。右键单击您使用的Python IDE或命令提示符，然后选择“以管理员身份运行”。

2. 更改文件夹的权限。右键单击 `data` 文件夹，选择“属性”，然后进入“安全”选项卡。点击“编辑”按钮，然后添加您的用户帐户并为其分配适当的权限。

3. 将数据文件保存在不需要管理员权限的位置，例如您的用户文件夹。您可以修改代码中的路径，将 `data_file` 设置为 `data_file = os.path.join('~', 'house_tiny.csv')`。

请注意，第一种方法是最简单和最常见的解决方法，但如果您没有管理员权限，可能需要尝试其他方法。

相关问题

此代码import os import numpy as np from PIL import Image def process_image(image_path, save_path): # 读取nii文件 image_array = np.load(image_path).astype(np.float32) # 归一化到0-255之间 image_array = (image_array - np.min(image_array)) / (np.max(image_array) - np.min(image_array)) * 255 # 将数据类型转换为uint8 image_array = image_array.astype(np.uint8) # 将三维图像分成若干个二维图像 for i in range(image_array.shape[0]): image = Image.fromarray(image_array[i]) image.save(os.path.join(save_path, f"{i}.png")) def process_label(label_path, save_path): # 读取nii文件 label_array = np.load(label_path).astype(np.uint8) # 将标签转换为灰度图 label_array[label_array == 1] = 255 label_array[label_array == 2] = 128 # 将三维标签分成若干个二维标签 for i in range(label_array.shape[0]): label = Image.fromarray(label_array[i]) label.save(os.path.join(save_path, f"{i}.png")) # LiTS2017数据集路径 data_path = "C:\\Users\\Administrator\\Desktop\\LiTS2017" # 保存路径 save_path = "C:\\Users\\Administrator\\Desktop\\2D-LiTS2017" # 创建保存路径 os.makedirs(save_path, exist_ok=True) os.makedirs(os.path.join(save_path, "image"), exist_ok=True) os.makedirs(os.path.join(save_path, "mask"), exist_ok=True) # 处理Training Batch 1 image_path = os.path.join(data_path, "Training Batch 1", "volume-{}.npy") for i in range(131): process_image(image_path.format(i), os.path.join(save_path, "image")) # 处理Training Batch 2 label_path = os.path.join(data_path, "Training Batch 2", "segmentation-{}.npy") for i in range(131): process_label(label_path.format(i), os.path.join(save_path, "mask"))出现FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Administrator\\Desktop\\LiTS2017\\Training Batch 1\\volume-0.npy'，修复它，并给出完整代码

首先，这个错误提示表明程序找不到第一个npy文件。因此，需要检查路径是否正确。

同时，还需要确保所有的路径和文件名都是正确的。下面是修复后的完整代码：

import os
import numpy as np
from PIL import Image

def process_image(image_path, save_path):
    # 读取nii文件
    image_array = np.load(image_path).astype(np.float32)
    # 归一化到0-255之间
    image_array = (image_array - np.min(image_array)) / (np.max(image_array) - np.min(image_array)) * 255
    # 将数据类型转换为uint8
    image_array = image_array.astype(np.uint8)
    # 将三维图像分成若干个二维图像
    for i in range(image_array.shape[0]):
        image = Image.fromarray(image_array[i])
        image.save(os.path.join(save_path, f"{i}.png"))

def process_label(label_path, save_path):
    # 读取nii文件
    label_array = np.load(label_path).astype(np.uint8)
    # 将标签转换为灰度图
    label_array[label_array == 1] = 255
    label_array[label_array == 2] = 128
    # 将三维标签分成若干个二维标签
    for i in range(label_array.shape[0]):
        label = Image.fromarray(label_array[i])
        label.save(os.path.join(save_path, f"{i}.png"))

# LiTS2017数据集路径
data_path = "C:\\Users\\Administrator\\Desktop\\LiTS2017"
# 保存路径
save_path = "C:\\Users\\Administrator\\Desktop\\2D-LiTS2017"
# 创建保存路径
os.makedirs(save_path, exist_ok=True)
os.makedirs(os.path.join(save_path, "image"), exist_ok=True)
os.makedirs(os.path.join(save_path, "mask"), exist_ok=True)

# 处理Training Batch 1
image_path = os.path.join(data_path, "Training Batch 1", "volume-{}.npy")
for i in range(131):
    if os.path.exists(image_path.format(i)):
        process_image(image_path.format(i), os.path.join(save_path, "image"))
    else:
        print(f"{image_path.format(i)} not exists!")

# 处理Training Batch 2
label_path = os.path.join(data_path, "Training Batch 2", "segmentation-{}.npy")
for i in range(131):
    if os.path.exists(label_path.format(i)):
        process_label(label_path.format(i), os.path.join(save_path, "mask"))
    else:
        print(f"{label_path.format(i)} not exists!")

在这个修复后的代码中，我们添加了对文件是否存在的检查，并输出了相应的提示信息。现在我们可以运行代码进行处理，同时会得到相应的提示信息帮助我们快速定位错误。

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