if (file.exists(path_name)) { + load(path_name) + } else { + DCA_list = list() + TCI = array(NA, c(t, length(quantiles)), dimnames=list(as.character(tail(date,t)), quantiles)) + NET = array(NA, c(t, k, length(quantiles)), dimnames=list(as.character(tail(date,t)), NAMES, quantiles)) + for (j in 1:length(quantiles)) { + dca = ConnectednessApproach(Y, + model="QVAR", + connectedness="Time", + nlag=nlag, + nfore=nfore, + window.size=window.size, + VAR_config=list(QVAR=list(tau=quantiles[j])))
时间: 2024-02-14 15:21:24 浏览: 137
这段代码看起来像是在检查一个文件是否存在,如果存在就加载该文件,如果不存在则初始化一些变量并且使用 ConnectednessApproach 函数计算一些值。具体来说,这段代码的逻辑如下:
首先,它检查一个名为 path_name 的文件是否存在。
如果该文件存在,就使用 load 函数加载该文件。
如果该文件不存在,就初始化 DCA_list 和 TCI 两个变量,并且使用一个 for 循环计算 NET 变量的值。具体来说,for 循环的每一次迭代都会使用 ConnectednessApproach 函数计算一个值,计算所使用的参数包括 Y、model、connectedness、nlag、nfore、window.size 和一个名为 quantiles 的参数,其中 quantiles 是一个列表。在 for 循环的每一次迭代中,计算得到的结果都会存储在 NET 变量的对应位置中。
最后,该代码块返回一个值,但是由于代码块中没有明确的 return 语句,所以返回值可能是一个默认值或者是最后一个计算得到的值。
相关问题
import os from datetime import datetime import random import string def batch_rename_files(folder_path, random_string_length=0): """ 批量重命名文件,名字改为该文件的创建或修改日期,如果有重名的文件,可以添加一个随机字符串 :param folder_path: 文件夹路径 :param random_string_length: 随机字符串长度,默认为0 """ for filename in os.listdir(folder_path): # 获取文件的创建时间或修改时间 full_path = os.path.join(folder_path, filename) if os.path.isfile(full_path): timestamp = os.path.getmtime(full_path) else: continue # 格式化时间 date_time = datetime.fromtimestamp(timestamp) new_name = date_time.strftime("%Y-%m-%d %H-%M-%S") # 添加随机字符串 if random_string_length > 0: random_string = ''.join(random.choices(string.ascii_lowercase + string.digits, k=random_string_length)) new_name += '-' + random_string # 拼接新的文件名 file_extension = os.path.splitext(filename)[1] new_filename = new_name + file_extension new_full_path = os.path.join(folder_path, new_filename) # 重命名文件 os.rename(full_path, new_full_path) if __name__ == '__main__': folder_path = 'your_folder_path' batch_rename_files(folder_path, random_string_length=5)
好的,根据你的要求,将以上代码修改为将修改后的文件命名恢复原来的命名,可以按照以下方式修改:
```
import os
from datetime import datetime
import random
import string
def restore_original_names(folder_path):
"""
批量将文件名恢复为原来的名称,需要在修改文件名之前备份原来的名称
:param folder_path: 文件夹路径
"""
for filename in os.listdir(folder_path):
full_path = os.path.join(folder_path, filename)
if os.path.isfile(full_path):
# 获取备份的文件名
backup_filename = os.path.splitext(filename)[0] + ".bak"
backup_full_path = os.path.join(folder_path, backup_filename)
if os.path.exists(backup_full_path):
# 将备份的文件名恢复为原来的文件名
os.rename(backup_full_path, full_path)
def batch_rename_files(folder_path, random_string_length=0):
"""
批量重命名文件,名字改为该文件的创建或修改日期,如果有重名的文件,可以添加一个随机字符串
:param folder_path: 文件夹路径
:param random_string_length: 随机字符串长度,默认为0
"""
# 先将文件名备份
for filename in os.listdir(folder_path):
full_path = os.path.join(folder_path, filename)
if os.path.isfile(full_path):
# 获取文件的后缀名
file_extension = os.path.splitext(filename)[1]
# 备份原来的文件名
backup_filename = os.path.splitext(filename)[0] + ".bak"
backup_full_path = os.path.join(folder_path, backup_filename)
os.rename(full_path, backup_full_path)
# 批量重命名文件
for filename in os.listdir(folder_path):
full_path = os.path.join(folder_path, filename)
if os.path.isfile(full_path):
# 获取文件的创建时间或修改时间
timestamp = os.path.getmtime(full_path)
# 格式化时间
date_time = datetime.fromtimestamp(timestamp)
new_name = date_time.strftime("%Y-%m-%d %H-%M-%S")
# 添加随机字符串
if random_string_length > 0:
random_string = ''.join(random.choices(string.ascii_lowercase + string.digits, k=random_string_length))
new_name += '-' + random_string
# 拼接新的文件名
new_filename = new_name + file_extension
new_full_path = os.path.join(folder_path, new_filename)
# 重命名文件
os.rename(full_path, new_full_path)
# 将文件名恢复为原来的名称
restore_original_names(folder_path)
if __name__ == '__main__':
folder_path = 'your_folder_path'
batch_rename_files(folder_path, random_string_length=5)
```
这个修改后的代码首先会备份原来的文件名,然后批量重命名所有的文件,并最后将所有文件的名字恢复为原来的名称。
将下面代码简洁化:def split_dataset(img_path, target_folder_path, output_path): filename = [] total_imgs = os.listdir(img_path) #for root, dirs, files in os.walk(img_path): for img in total_imgs: filename.append(img) np.random.shuffle(filename) train = filename[:int(len(filename) * 0.9)] test = filename[int(len(filename) * 0.9):] out_images = os.path.join(output_path, 'imgs') if not os.path.exists(out_images): os.makedirs(out_images) out_images_train = os.path.join(out_images, 'training') if not os.path.exists(out_images_train): os.makedirs(out_images_train) out_images_test = os.path.join(out_images, 'test') if not os.path.exists(out_images_test): os.makedirs(out_images_test) out_annotations = os.path.join(output_path, 'annotations') if not os.path.exists(out_annotations): os.makedirs(out_annotations) out_annotations_train = os.path.join(out_annotations, 'training') if not os.path.exists(out_annotations_train): os.makedirs(out_annotations_train) out_annotations_test = os.path.join(out_annotations, 'test') if not os.path.exists(out_annotations_test): os.makedirs(out_annotations_test) for i in train: print(os.path.join(img_path, i)) print(os.path.join(out_images_train, i)) shutil.copyfile(os.path.join(img_path, i), os.path.join(out_images_train, i)) annotations_name = "gt_" + i[:-3] + 'txt' shutil.copyfile(os.path.join(target_folder_path, annotations_name), os.path.join(out_annotations_train, annotations_name)) for i in test: shutil.copyfile(os.path.join(img_path, i), os.path.join(out_images_test, i)) annotations_name = "gt_" + i[:-3] + 'txt' shutil.copyfile(os.path.join(target_folder_path, annotations_name), os.path.join(out_annotations_test, annotations_name))
def split_dataset(img_path, target_folder_path, output_path):
filename = os.listdir(img_path)
np.random.shuffle(filename)
train = filename[:int(len(filename) * 0.9)]
test = filename[int(len(filename) * 0.9):]
out_images = os.path.join(output_path, 'imgs')
os.makedirs(out_images, exist_ok=True)
out_images_train = os.path.join(out_images, 'training')
os.makedirs(out_images_train, exist_ok=True)
out_images_test = os.path.join(out_images, 'test')
os.makedirs(out_images_test, exist_ok=True)
out_annotations = os.path.join(output_path, 'annotations')
os.makedirs(out_annotations, exist_ok=True)
out_annotations_train = os.path.join(out_annotations, 'training')
os.makedirs(out_annotations_train, exist_ok=True)
out_annotations_test = os.path.join(out_annotations, 'test')
os.makedirs(out_annotations_test, exist_ok=True)
for i in train:
img_src = os.path.join(img_path, i)
img_dst = os.path.join(out_images_train, i)
shutil.copyfile(img_src, img_dst)
annotations_name = "gt_" + i[:-3] + 'txt'
annotations_src = os.path.join(target_folder_path, annotations_name)
annotations_dst = os.path.join(out_annotations_train, annotations_name)
shutil.copyfile(annotations_src, annotations_dst)
for i in test:
img_src = os.path.join(img_path, i)
img_dst = os.path.join(out_images_test, i)
shutil.copyfile(img_src, img_dst)
annotations_name = "gt_" + i[:-3] + 'txt'
annotations_src = os.path.join(target_folder_path, annotations_name)
annotations_dst = os.path.join(out_annotations_test, annotations_name)
shutil.copyfile(annotations_src, annotations_dst)