Python Sth

EasyDict

from easydict import EasyDict as edict
mydict = edict({})
mydict.foo = 3
print(mydict.foo) # 3

目录

检查目录，并生成一级目录

path = './xx/'
if os.path.isdir(path) == False:
    os.mkdir(path)

多级目录生成

path = './xx/yy/zz/'
if not os.path.isdir(path):
    os.makedirs(path)

R & W in TXT

ref

np.loadtxt()用于从文本加载数据。 文本文件中的每一行必须含有相同的数据。

loadtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0)

• fname：文件名
• dtype：数据类型，默认float
• comments：注释
• delimiter：分隔符，默认是空格
• skiprows：跳过前几行，0是第一行，必须int
• usecols：要读取哪些列，0是第一列。例如，usecols = （1,4,5）将提取第2，第5和第6列。默认读取所有列。
• unpack如果为True，将分列读取。

with open('odom.txt', 'r') as f:
    data = f.readlines()  #txt中所有字符串读入data
 
    for line in data:
        odom = line.split()        #将单个数据分隔开存好
        numbers_float = map(float, odom) #转化为浮点数

TXT追加内容

my_open = open(file_name, 'a')
#打开fie_name路径下的my_infor.txt文件,采用追加模式
#若文件不存在,创建，若存在，追加
my_open.write('xx\n')
my_open.close()

文件操作

文件移动

import shutil
shutil.move(src,des)

Print

pprint

it is used for pretty-print data structures.

Random

np.random.rand()

numpy.random.rand(d0,d1,…,dn)

• rand函数根据给定维度生成[0,1)之间的数据，包含0，不包含1
• dn为每个维度
• 返回值为指定维度的数组

np.random.randn()

• randn函数返回具有标准正态分布的数据。
• dn为每个维度
• 返回值为指定维度的数组

np.random.randint()

numpy.random.randint(low, high, size)

• 返回开区间 [low, high)范围内的整数值
• 默认high是None,如果只有low，那范围就是[0,low)。如果有high，范围就是[low,high)
• size是输出数组的维度（形状），可以是列表，或者元组

np.random.choice()

numpy.random.choice(a, size)

• a可以是一个数或者一个array，如果是一个数则sample范围是【0，a);如果是array，则从中sample
• size为sample大小；如果size=(m,n,k)，则采样$mnk$个。

String

数字补0

n = 123
number = "%05d"%n ### number = 00123

Plt

图片保存去除周边空白ref

plt.figure(figsize=(15,8)) 
p = plt.subplot(111)
plt.savefig('image_name', ,bbox_inches='tight')

bbox_inches =’tight’ 只能用于保存图片，不能用于显示。

Time

import time
start_time = time.time()

Zip

name = [ "Manjeet", "Nikhil", "Shambhavi", "Astha" ] 
roll_no = [ 4, 1, 3, 2 ] 
marks = [ 40, 50, 60, 70 ] 
# using zip() to map values 
mapped = zip(name, roll_no, marks) 
# converting values to print as set 
mapped = set(mapped)

The zipped result is : {('Shambhavi', 3, 60), ('Astha', 2, 70), ('Manjeet', 4, 40), ('Nikhil', 1, 50)}

How to unzip?
Unzipping means converting the zipped values back to the individual self as they were. This is done with the help of “*” operator.

name = [ "Manjeet", "Nikhil", "Shambhavi", "Astha" ] 
roll_no = [ 4, 1, 3, 2 ] 
marks = [ 40, 50, 60, 70 ] 
# using zip() to map values 
mapped = zip(name, roll_no, marks) 
# converting values to print as list 
mapped = list(mapped) 
# printing resultant values  
print ("The zipped result is : ",end="") 
print (mapped) 
print("\n") 
# unzipping values 
namz, roll_noz, marksz = zip(*mapped) 
print ("The unzipped result: \n",end="") 
# printing initial lists 
print ("The name list is : ",end="") 
print (namz) 
print ("The roll_no list is : ",end="") 
print (roll_noz) 
print ("The marks list is : ",end="") 
print (marksz)

The zipped result is : [('Manjeet', 4, 40), ('Nikhil', 1, 50), 
('Shambhavi', 3, 60), ('Astha', 2, 70)]
The unzipped result: 
The name list is : ('Manjeet', 'Nikhil', 'Shambhavi', 'Astha')
The roll_no list is : (4, 1, 3, 2)
The marks list is : (40, 50, 60, 70)

Parser

import argparse
    parser = argparse.ArgumentParser(description="Process some parameters.")
    parser.add_argument('--trainFeature', '-tf', type=int, required=True, default=1, help='train feature')
    parser.add_argument('--trainInitial', '-ti', type=int, required=True, default=1,help='train initial')
    parser.add_argument('--learningRate', '-lr', type=float, required=True, default=0.00001,help='learning rate')
    parser.add_argument('--criticNum', '-cn', type=int, required=True, default=5,help='critic number')
    parser.add_argument('--clipRange', '-cr', type=float, required=True,default=0.01, help='clip range')

    args = parser.parse_args()
    opts = {k:v for k,v in args._get_kwagrs()}
    n_critic = args.criticNum
    train_initial = args.trainInitial
    train_feature = args.trainFeature
    lr = args.learningRate
    clip = args.clipRange

    dgan = StageTwo(n_critic,train_initial,train_feature,lr,clip)

python xx.py -trainFeature 1 -trainInitial 1 -learningRate 0.001 -criticNum 5 -clipRange 0.01

Matplotlib

5 Quick and Easy Data Visualizations in Python with Code

8 of the best articles on visualizing data

ElementTree

！！！！！所有的输入都必须是字符串！！！！！

def indent(elem, level=0):
  i = "\n" + level*"  "
  if len(elem):
    if not elem.text or not elem.text.strip():
      elem.text = i + "  "
    if not elem.tail or not elem.tail.strip():
      elem.tail = i
    for elem in elem:
      indent(elem, level+1)
    if not elem.tail or not elem.tail.strip():
      elem.tail = i
  else:
    if level and (not elem.tail or not elem.tail.strip()):
      elem.tail = i

def createXML():
    root =ET.Element('annotation')
    filename = ET.Element('filename')
    filename.text = 'xx'
    root.append(filename)

    folder = ET.Element('folder')
    folder.text = 'cad60/'
    root.append(folder)

    object = ET.Element('object')
    root.append(object)

    name = ET.SubElement(object,'name')
    name.text = 'person'
    action = ET.SubElement(object, 'action')
    action.text = '12'
    bndbox = ET.SubElement(object,'bndbox')
    xmax = ET.SubElement(bndbox,'xmax')
    xmax.text = '1'
    xmin = ET.SubElement(bndbox, 'xmin')
    xmin.text = '2'
    ymax = ET.SubElement(bndbox, 'ymax')
    ymax.text = '3'
    ymin = ET.SubElement(bndbox,'ymin')
    ymin.text = '4'

    size = ET.Element('size')
    root.append(size)
    depth = ET.SubElement(size,'depth')
    depth.text = '1'
    height = ET.SubElement(size, 'height')
    height.text = '2'
    width = ET.SubElement(size, 'width')
    width.text = '3'

    indent(root)

    tree = ET.ElementTree(root)
    tree.write('wq.xml','utf-8')

Naming style