def cleanUselessLck():
for filename in os.list(lckdir):
filepath = os.path.join(lckdir, filename)
try:
if os.path.isfile(filepath):
os.unlink(filepath)
except Exception, e:
print e
def RegressionMain(file_path="/home/sap/IdeaProjects/XAI/April/weights",
trainStep=3,
predStep=3,
self=None):
self.file_path = file_path
#self.linear = linear
allFiles = os.list(file_path)
filteredFiles = [i for i in allFiles if i.startswith('clustered')]
def cleanUselessLck():
for filename in os.list(lckdir):
filepath = os.path.join(lckdir, filename)
try:
if os.path.isfile(filepath):
os.unlink(filepath)
except Exception, e:
print e
def fit(self):
'进行层位分割'
hor = ReadHor(self.horadress)
hor.read() # 读取层位信息
horData = hor.filedata
LineNameIn = self.action1(hor.LineName,
os.list(self.foldadress)) #得到共有的文件名
for linename in LineNameIn:
fileadress = self.foldadress + '/' + linename + '.txt' #循环设定需要读取扥文件名
Wave = ReadSeisWave(fileadress)
WaveHead = Wave.getHead()
WaveData = Wave.getNum()
CDP_TB = horData.loc[horData['LineName' == linename],
['CDP', self.Top, self.Bot]]
WaveHor = pd.DataFrame(self.action2(WaveData, WaveHead, CDP_TB),
columns=self.HorName)
WaveHor.to_excel(
'/' + '/'.join(self.foldadress.strip().split('/')[:-1]) +
self.HorName + '/' + linename + '.xlsx')
def main():
# parse the arguments
parser = argparse.ArgumentParser(description='Process some integers.')
# required parameters
parser.add_argument("func",
default='help',
type=str,
help="train/test/help")
parser.add_argument("--data_dir", default="data", type=str, required=False)
parser.add_argument("--task_name", default=None, type=str, required=False)
parser.add_argument("--tag", default=None, type=str, required=False)
parser.add_argument("--input_dir", default=None, type=str, required=False)
parser.add_argument("--output_dir", default=None, type=str, required=False)
parser.add_argument("--model_name",
default="bert-base-uncased",
type=str,
required=False)
args = parser.parse_args()
# do the func
if args.func == "help":
print("train to generate model, test to evaluate model")
else:
# gather parameters
tag = args.tag
if tag == None:
tag = args.tag = str(uuid.uuid1())
print("params: {}\ntag: {}".format(str(args), tag))
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = args.n_gpu = torch.cuda.device_count()
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger.warning("device: %s, n_gpu: %s", device, n_gpu)
set_seed(args)
args.task_name = args.task_name.lower()
# TODO task specific settings
num_labels = None
if args.func == "train":
pass # train on the task
# gather parameters
config = BertConfig.from_pretrained()
output_dir = args.output_dir = args.output_dir if args.output_dir else "model"
if os.path.exists(output_dir) and os.list(output_dir):
raise ValueError("Output dir exists")
config = BertConfig.from_pretrained(args.model_name,
num_labels=num_labels,
finetuning_task=args.task_name)
tokenizer = BertTokenizer.from_pretrained(args.model_name,
do_lower_case="uncased"
in args.model_name)
model = BertForSequenceClassification.from_pretrained(
args.model_name, from_tf=False, config=config)
elif args.func == "test":
pass # test on the task
else:
raise NotImplementedError
for i in range(len(x)):
if x[-(x + 1)] > 0:
break
t = x[-(x + 1)]
return t
def find_miles(x):
ori = find_nonzero(x, 0)
des = find_nonzero(x, 1)
distance = des - ori
return distance
'''didi car-hailing PHEV'''
didi_list = os.list("data/didi")
sum_didi = [] ###################
run1 = []
run2 = []
run3 = []
a = lambda x: sum(x == 1)
b = lambda x: sum(x == 2)
c = lambda x: sum(x == 3)
for didi_id in didi_list:
one_vehicle = pd.read_csv("/data/didi/" + didi_id)
one_vehicle['time'] = pd.to_datetime(one_vehicle['time']).apply(
lambda x: x.date()) # year,month,day
agg = one_vehicle.groupby('time').agg({"vehiclestatus": [a, b, c]})
agg = agg.apply(lambda x: x / sum(x), axis=1)
name = agg.columns
r1 = list(agg[name[0]])
# coding:utf-8
import re
import time
import datetime
import redis
import sys
import os
input = sys.argv[-1]
rds = redis.Redis(host='redis-logcount.yg.hunantv.com', port=8889)
log_list = []
if input == '1':
file = datetime.date.today()
log_list.append('/mfs/logs/eru/odan/web-macvlan/' + str(file) + '.log')
elif input == '2':
log_list = os.list("/mfs/logs/eru/odan/web-macvlan")
else:
filename = '/mnt/mfs/logs/eru/odan/web-macvlan/' + input
with open(filename) as f:
date = ''
for line in f:
t = re.search(r'^\[([\d-]+\s+[\d:]+)', line)
if t:
datetime = time.strptime(t.group(1), '%Y-%m-%d %H:%M:%S')
timestamp = '%s' % time.mktime(datetime)
if not date:
date = time.strftime('%Y-%m-%d', datetime)
rds.hincrby(date, timestamp, 1)
data = rds.hgetall(date)
for key in sorted(data.keys()):
print key, data[key]
import numpy as np
import pandas as pd
import keras
from keras.callbacks import Callback
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score
from sklearn.model_selection import train_test_split
# In[2]:
os.list('/home/zxt/test/iWildCam')
# ## 1. Loading the 32x32 dataset
# In[5]:
# Split data between train and test sets:
x_train = np.load('/home/zxt/test/iWildCam/X_train.py')
x_test = np.load('/home/zxt/test/iWildCam/X_test.npy')
y_train = np.load('/home/zxt/test/iWildCam/y_train.npy')
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
for i in range(len(x)):
if x[-(x + 1)] > 0:
break
t = x[-(x + 1)]
return t
def find_miles(x):
ori = find_nonzero(x, 0)
des = find_nonzero(x, 1)
distance = des - ori
return distance
'''didi car-hailing PHEV'''
didi_list = os.list("data/didi")
sum_didi = [] ###################
run1 = []
run2 = []
run3 = []
a = lambda x: sum(x == 1)
b = lambda x: sum(x == 2)
c = lambda x: sum(x == 3)
for didi_id in didi_list:
one_vehicle = pd.read_csv("/data/didi/" + didi_id)
one_vehicle['time'] = pd.to_datetime(one_vehicle['time']).apply(
lambda x: x.date()) #year,month,day
agg = one_vehicle.groupby('time').agg({"runmodel": [a, b, c]})
agg = agg.apply(lambda x: x / sum(x), axis=1)
name = agg.columns
r1 = list(agg[name[0]])
def list(cls):
"""Liste les sauvegardes disponnibles"""
'\n'.join(i.replace('.txt', '') for i in os.list(cls._save_dir))
def do_dir(self, args):
try:
# if using input from a file
if args[0] == '<':
try:
# gets the contents of the given directory
data = os.listdir(self.from_file(args[1]))
data = ' '.join(data)
try:
# if using input from file and overwrite
if args[2] == '>':
try:
# outputs contents of the directory to the given file
self.overwrite(args[3], data)
self.line()
except IndexError:
# if no filename is given
print("Error: No filename given")
self.line()
# if using input from file and append
elif args[2] == '>>':
try:
# outputs contents of the directory to the given file
self.append(args[3], data)
self.line()
except IndexError:
# If no filename is given
print("Error: No filename given")
self.line()
# if using input from file and no output
except IndexError:
# print contents of directory joined by 3 spaces
print(data)
self.line()
except FileNotFoundError:
# if the directory doesn't exist
print(f"Error: '{args[1]}' no such file")
self.line()
except IndexError:
# if no file given for input
print("Error: No file given")
self.line()
# if using overwrite with no directory listed
elif args[0] == '>':
# gets the contents of the current directory
data = os.listdir(self.cwd)
data = ' '.join(data)
try:
# outputs contents of current dir to file
self.overwrite(args[1], data)
self.line()
except IndexError:
# if no filename given
print("Error: No filename given")
self.line()
# if using append with no directory listed
elif args[0] == '>>':
# gets the contents of the current directory
data = os.listdir(self.cwd)
data = ' '.join(data)
try:
# outputs content is current dir to file
self.append(args[1], data)
self.line()
except IndexError:
# if no filename given
print("Error: No filename given")
# if using overwrite with a given directory
elif args[1] == '>':
try:
# gets contents of given directory
data = os.listdir(args[0])
data = ' '.join(data)
try:
# outputs content of given dir to file
self.overwrite(args[2], data)
self.line()
except IndexError:
# if no filename given
print('Error: No filename given')
except FileNotFoundError:
# if the directory doesn't exist
print(f"Error: '{args[0]}' no such directory")
self.line()
# if using append with a given directory
elif args[1] == '>>':
try:
# gets contents of given directory
data = os.list(args[0])
data = ' '.join(data)
try:
# outputs contents of given dir to file
self.append(args[2], data)
self.line()
except IndexError:
# if no filename given
print("Error: No filename given")
self.line()
except FileNotFoundError:
# if the directory doesn't exist
print(f"Error: '{args[0]}' no such directory")
self.line()
except IndexError:
try:
# If using dir from a given directory without output
data = os.listdir(args[0])
data = ' '.join(data)
# prints contents of the given directory separated by 3 spaces
print(data)
self.line()
except IndexError:
# if using dir with no arguments
data = os.listdir(os.getcwd())
# prints the contents of the current directory separated by 3 spaces
print(' '.join(data))
self.line()
except FileNotFoundError:
# if the given file doesn't exist
print('Error: No such directory')
self.line()
def list_files(directory):
return os.list(directory)
import HTSeq
import numpy
import pylab as plt
import os
CH = [str(x) for x in range(1,20)] + ['X', 'Y']
gtffile = HTSeq.GFF_Reader( "Mus_musculus.GRCm38.82.gtf" )
tsspos = set()
for feature in gtffile:
if feature.type == "exon" and feature.attr["exon_number"] == "1":
if feature.iv.chrom in CH:
tsspos.add( feature.iv.start_d_as_pos )
Fs = os.list('.')
for F in Fs:
if F[-4:] == '.bam':
bamfile = HTSeq.BAM_Reader( "Tspan8_negative_MHCII_high_rep1_HQ.bam" )
bamfile = HTSeq.BAM_Reader(F)
halfwinwidth = 1000
coverage = HTSeq.GenomicArray( "auto", stranded=False, typecode="i" )
for almnt in bamfile:
if almnt.aligned:
#### method 1
if almnt.inferred_insert_size > 0:
#iv = HTSeq.GenomicInterval( almnt.iv.chrom, almnt.iv.start, almnt.iv.start + almnt.inferred_insert_size, "." )
#coverage[ almnt.iv ] += 1
image_bl=cv2.blur(image, ksize=(k,k))
cv2.imshow(str(k), image_bl)
cv2.waitKey(0)
return
def resize(fname, width, height):
image = cv2.imread(fname)
cv2.imshow('Original Image', image)
cv2.waitKey(0)
org_h, org_w=image.shape[0:2]
if org_w>= org_h:
new_image=cv2.resize(image, (width, height))
else:
new_image=cv2.resize(image, (height, width))
return fname, new_image
listOfFiles=os.list('.')
pattern="*.jpg"
n=len(sys.argv)
if n==3:
width =int(sys.rgv[1])
height =int(sys.rgv[2])
else:
width =1280
height =960
if not os.path,exists('new_folder'):
os.makedirs('new_folder')
for filename in listOfFiles:
if fnmatch.fnmatch(filename, pattern):
filename, new_image=resize(filename,width,height)
cv2.imwrite("new_folder" + filename,new_image)
#cv2.imshow('resized image', new_image)
import pandas as pd
from pandas import ExcelWriter
from pandas import ExcelFile
from os import listdir as list
file_list= (list("C:\\Users\\dia5cob\\Desktop\\NAP\\NAP-Forms"))
df = pd.DataFrame(file_list)
writer = ExcelWriter("C:\\Users\\dia5cob\\Desktop\\NAP\\NAP_Forms_list.xlsx")
df.to_excel(writer, 'Sheet1', index=False)
writer.save()
writer.close()
a = open('name').read()
print(a)
#定位读写
#seek第二个参数0表示文件的开头，1表示当前的位置，2表示末尾,第一个参数+向右-向左调
f = open(filename)
f.seek(2,0) #从文件的开头跳两个字节 使用seek可以重新读取 ，
f.tell() #当前的位置
##文件的常用操作
import os
os.rename('oldname.txt','filename.txt') #重命名
os.remove('name.txt') #删除文件
os.mkdir('file') #创建文件夹
os.rmdir('file') #删除文件夹
os.getwd() #返回绝对路径
os.chdir('') #改变默认路径
os.list("./") #获取当前路径下的目录列表
##批量重命名
import os
folder_name = "file" #输入文件夹中的名称
file_names =os.listdir(folder_name) #列出文件夹中所有文件的名字
for name in file_names:
print(name)
old_file_name = folder_name+"/"+name
new_file_name = folder_name+"/"+"[OK]-"+name #新名字
os.rename(old_file_name,new_file_name)
#pickle储存Python的原生对象
import pickle
D = [2,3,4]
F = open('name.pkl','wb')
pickle.dump(D,F)
F.close()
print "hello" print "1+2+3" import os dir() os.list()
for i in os.listdir(test):
print (dir_path+"\\"+i)
if ".txt" == i[-4:]:
os.remove(dir_path+"\\"+i)
# 取出某个目录内，1小时内新建的所有文件名。
#算法：遍历这个目录，取到所有的文件
#每个文件用stat取到创建时间
#用创建时间和当前时间去比对，是否小于3600
#放到一个列表里面
import os
import time
result=[]
current_timestamp=time.time()获取当前的时间戳
for i in os.list('e:\\test'):
if os.path.isfile(i):
if current_timestamp-os.stat('e:\\test'+i).st_ctime<=3600:
result.append('e:\\test'+'\\'+i)
#小练习，把所有的txt文件干掉。
#新建一个空的子目录xxx，放在某个层级下，,把它删掉
#encoding=utf-8
import os
import os.path
dir_count=0
file_count=0
import fnmatch
import os
for file in os.list('.'):
if fnmatch.fnmatch(file,'*.xml'):
print file
import os
from tqdm import tqdm
from random import shuffle
list_images = os.list('./dataset')
if not os.path.exists('./data'):
os.mkdir('./data')
for x in list_images:
label = x.split('_')[0]
print(label)
if not os.path.exists(os.path.join('./data',label)):
os.mkdir(os.path.join('./data',label))
os.rename(os.path.join('./dataset',x), os.path.join('./data',label,x))
base_dir = ''
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
#Directory with our cat picture
train_cats_dir = os.path.join(train_dir, 'cats')
# Directory with our dogs picture
train_dog_dir = os.path.join(train_dir, 'dogs')
#Directory with our cat picture
validation_cats_dir = os.path.join(validation_dir, 'cats')
# Directory with our dogs picture
validation_dog_dir = os.path.join(validation_dir, 'dogs')
train_cat_fnames = os.list(train_cats_dir)
print(train_cat_fnames[:10])
train_dog_fnames = os.listdir(train_dogs_dir)
train_dog_fnames.sort()
print(train_dog_fnames[:10])
print('total training cat images', len(os.listdir(train_cats_dir)))
print('total training cat images', len(os.listdir(train_dogs_dir)))
print('total training cat images', len(os.listdir(validation_cats_dir)))
print('total training cat images', len(os.listdir(validation_cats_dir)))
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
#Parameters for our graph