普通工作指示程序运行进度：





```python
import time
from tqdm import tqdm
import random
# 使用tqdm提示训练进度
with tqdm(total=1000, desc='i am desc') as pbar:
    for step in range(1000):
        time.sleep(0.1)
        _loss = random.random()
        pbar.set_postfix({'loss': '%.4f' % float(_loss)})
        pbar.update(1)
```

- - -

ftplib 指示文件下载进度：
```python
with open(filename, 'wb') as fd:
    total = ftpclient.size(filename)
    with tqdm(total=total) as pbar:
        def callback_(data):
            l = len(data)
            pbar.update(l)
            fd.write(data)
        ftpclient.retrbinary('RETR {}'.format(filename), callback_)
```

tqdm python 指示进度的简单用法 ftplib 指示下载进度

[https://mirrors.tuna.tsinghua.edu.cn/help/pypi/](https://mirrors.tuna.tsinghua.edu.cn/help/pypi/)





```python
pip install pip -U
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
```

豆瓣的比较好用
```handlebars
pip config set global.index-url https://pypi.doubanio.com/simple
```

pip 配置 清华镜像源 豆瓣镜像源

[https://stackoverflow.com/questions/17980362/using-python-requests-to-send-json-boolean/17980622](https://stackoverflow.com/questions/17980362/using-python-requests-to-send-json-boolean/17980622)






You need to json encode it to get it to a string.

```python
import json 
payload = json.dumps({"on":True})
```

此时：

payload就是
```python
'{"on": true}'
```

python POST 传 flase true JSON boolean 布尔变量

继承FTP类-->写dirs方法-->使用dirs()获取返回的文件列表






示例：

```python
from ftplib import FTP
import os
class FTP_P(FTP):
    def dirs(self, *args):
        '''List a directory in long form.
        By default list current directory to stdout.
        Optional last argument is callback function; all
        non-empty arguments before it are concatenated to the
        LIST command.  (This *should* only be used for a pathname.)'''
        cmd = 'LIST'
        templist = []
        func = None
        if args[-1:] and type(args[-1]) != type(''):
            args, func = args[:-1], args[-1]
        for arg in args:
            if arg:
                cmd = cmd + (' ' + arg)
        self.retrlines(cmd, templist.append)
        return templist
def download_single_url_ftp(filename, met_full_path="met"):
    ftp = FTP_P()
    ftp.connect(r"140.90.78.251")  # 连接的ftp sever和端口
    ftp.login("anonymous")  # 连接的用户名，密码
    # print(ftp.getwelcome()) #打印出欢迎信息
    ftp.cwd(r"archives/gdas1")  # 设置FTP当前操作的路径
    print(ftp.dirs())  # 显示目录下所有目录信息
    print("文件大小", ftp.size(filename) / 1e6, 'M')  # 获取文件大小
    # ftp.retrbinary(f"RETR {filename}", open(os.path.join(met_full_path, filename), "wb").write) #接收服务器上文件并写入本地文件 给文件写入句柄
    ftp.quit()  # 退出ftp
download_single_url_ftp('gdas1.jan21.w1')
```



其他：

```python
ftp登陆连接
from ftplib import FTP            #加载ftp模块
ftp=FTP()                         #设置变量
ftp.set_debuglevel(2)             #打开调试级别2，显示详细信息
ftp.connect("IP","port")          #连接的ftp sever和端口
ftp.login("user","password")      #连接的用户名，密码
print ftp.getwelcome()            #打印出欢迎信息
ftp.cmd("xxx/xxx")                #进入远程目录
bufsize=1024                      #设置的缓冲区大小
filename="filename.txt"           #需要下载的文件
file_handle=open(filename,"wb").write #以写模式在本地打开文件
ftp.retrbinaly("RETR filename.txt",file_handle,bufsize) #接收服务器上文件并写入本地文件
ftp.set_debuglevel(0)             #关闭调试模式
ftp.quit()                        #退出ftp
ftp相关命令操作
ftp.cwd(pathname)                 #设置FTP当前操作的路径
ftp.dir()                         #显示目录下所有目录信息
ftp.nlst()                        #获取目录下的文件
ftp.mkd(pathname)                 #新建远程目录
ftp.pwd()                         #返回当前所在位置
ftp.rmd(dirname)                  #删除远程目录
ftp.delete(filename)              #删除远程文件
ftp.rename(fromname, toname)#将fromname修改名称为toname。
ftp.storbinaly("STOR filename.txt",file_handel,bufsize)  #上传目标文件
ftp.retrbinary("RETR filename.txt",file_handel,bufsize)  #下载FTP文件
```

python ftplib FTP 返回文件列表

用strftime的时候加个-符号




```python
print(datetime.datetime.now().strftime("%-Y  %-m  %-d   %-H:%-M:%-S"))
```

结果：

```python
2021  2  26   9:38:5
```

python datetime 前端不要带0 时间 小时不要0

Python





```python
select_this_hour_clickhouse_sql = "select * from default.zjk_forest_fire_risk_forecast_grid_data " \
                                          "where data_time='{}' and grid_id='{}'".format(now_datatime, name)
```

一个好看的sql写法 format写 字符串

python matplotlib





在绘图前 加一句

```python
plt.figure()
```

即可。

MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently

Python





```python
import numpy as np
import matplotlib.pyplot as plt
time_step = 0.02
period = 5.
time_vec = np.arange(0, 20, time_step)
sig = np.sin(2 * np.pi / period * time_vec) + \
       0.5 * np.random.randn(time_vec.size)
from scipy import fftpack
sample_freq = fftpack.fftfreq(sig.size, d=time_step)
sig_fft = fftpack.fft(sig)
pidxs = np.where(sample_freq > 0)
freqs = sample_freq[pidxs]
power = np.abs(sig_fft)[pidxs]
freq = freqs[power.argmax()]
print(np.allclose(freq, 1./period) ) # 检查是否找到了正确的频率
sig_fft[np.abs(sample_freq) > freq] = 0
main_sig = fftpack.ifft(sig_fft)
plt.figure()
plt.plot(time_vec, sig)
plt.plot(time_vec, main_sig, linewidth=3)
plt.title("signal")
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.show()
```

![在这里插入图片描述](/static/img/images/00752.png)

python scipy 信号去噪声

安装：




```python
pip install sklearn
```

一维数组聚类：
```python
from sklearn.cluster import KMeans
import numpy as np
x = np.random.random(1000).reshape(-1,1)
km = KMeans(n_clusters=5,max_iter=1000).fit(x)
print(km.cluster_centers_)
y = KMeans(n_clusters=5,max_iter=1000).fit_predict(x) #会得出每个sample属于哪一类
print(y)
```

二维数组聚类：

```python
from sklearn.cluster import KMeans
import numpy as np
x = np.random.random(1000).reshape(-1,2)
km = KMeans(n_clusters=5,max_iter=1000).fit(x)
print(km.cluster_centers_)
y = KMeans(n_clusters=5,max_iter=1000).fit_predict(x)#会得出每个sample属于哪一类
print(y)
```

sklearn scikit-learn k-means 聚类算法 一维数组聚类 python 二维数组聚类

例子：





里面的坐标是WGC0084的。

```python
from geopandas import GeoDataFrame
from shapely.geometry import Polygon
import matplotlib.pyplot as plt
record_area = {'新地类': {0: '裸岩', 1: '乔木林'},
               '面积': {0: 2.46, 1: 0.12},
               'Shape_Length': {0: 0.010351519392398598, 1: 0.0023489068258685593},
               'Shape_Area': {0: 2.634822326382532e-06, 1: 1.246329599286182e-07},
               'geometry': {0: Polygon([(115.30992347400002, 41.03045785900008), (115.3098257480001, 41.03042899800005),
                                (115.30969286300001, 41.030391199000064), (115.30960737200007, 41.030363582000064),
                                (115.30965327800004, 41.03029873600008), (115.30983235100007, 41.03028598000003),
                                (115.31004008700006, 41.030290766000064), (115.31030859000009, 41.03026627100007),
                                (115.31056930500006, 41.03019715200003), (115.31077333600001, 41.03014305800008),
                                (115.31089930700011, 41.03007015400004), (115.31105807100005, 41.03001293300008),
                                (115.31135016400003, 41.029988169000035), (115.3116654150001, 41.029762223000034),
                                (115.3118061460001, 41.029661358000055), (115.31249887400008, 41.029374731000075),
                                (115.31273030900002, 41.029320978000044), (115.3130813470001, 41.029239447000066),
                                (115.3131580270001, 41.02922081600008), (115.313297379, 41.029186956000046),
                                (115.31355647200007, 41.02892670500006), (115.31357707600012, 41.02886651400007),
                                (115.31291644200007, 41.02877125500004), (115.31159314900003, 41.028738934000046),
                                (115.31138943700012, 41.02892181000004), (115.31061582600012, 41.02947425800005),
                                (115.30987346500001, 41.029959062000046), (115.30967965600007, 41.030048538000074),
                                (115.30934632200001, 41.03028793000004), (115.30921034800008, 41.03033235700008),
                                (115.3091987890001, 41.03033314600003), (115.30982119300006, 41.03049919700004),
                                (115.30992347400002, 41.03045785900008)]),
                            1: Polygon([(115.30751786600001, 41.03123731200003), (115.30736804700007, 41.03119306200006),
                                (115.30724995300011, 41.03118725400003), (115.30725101300004, 41.03124084600006),
                                (115.30736995600012, 41.03128952700007), (115.30771220700001, 41.03141429200008),
                                (115.30786805900004, 41.03147224600008), (115.30819713300002, 41.03159461300004),
                                (115.30831718700006, 41.03146102800008), (115.3081085990001, 41.03141336600004),
                                (115.30803719100004, 41.03138201500008), (115.3077433850001, 41.031301418000055),
                                (115.30762438200009, 41.03126877200003), (115.30751786600001, 41.03123731200003)])
                            }
               }
g = GeoDataFrame(record_area, crs="EPSG:4326")
print("地图")
print(g)
g.plot()
plt.show()
print("面积直接算 算的图形面积  不是地理面积：")
print(g.area)
g.to_crs(epsg=3310, inplace=True)
print("计算地图面积：")
print(g.area)
```

geopandas 计算面积 crs=“EPSG:4326“转成 3110后计算面积 还是比较准确的 不知道为什么

shapely介绍：





[https://blog.csdn.net/wiborgite/article/details/85167397](https://blog.csdn.net/wiborgite/article/details/85167397)

geopandas介绍：

[https://developer.ibm.com/zh/technologies/analytics/tutorials/working-with-geospatial-vector-data-in-python/](https://developer.ibm.com/zh/technologies/analytics/tutorials/working-with-geospatial-vector-data-in-python/)

官方资料：[https://geopandas.org/aggregation_with_dissolve.html](https://geopandas.org/aggregation_with_dissolve.html)

![在这里插入图片描述](/static/img/images/00753.png)

![在这里插入图片描述](/static/img/images/00754.png)

有一个geopandas的GeoDataFrame对象后：

```python
g['all']=1
qq=g.dissolve(by="all",aggfunc="sum")
qq.plot()
plt.show()
f=qq.loc[1,"geometry"] #拿出图形
fg={"bianjie":list(f.exterior.coords)}  #外环边界
print(list(f.interiors))  #内环
```

获取地图块边界 融合dissolve geopandas

**将python字典写入json文件：





这句就好用了。dumps可以把字典转成字符串，然后文件方法write写入这个字符串。**
```python
with open('jsonFile.json', 'w', encoding="utf-8") as f:
    f.write(json.dumps(zidian, ensure_ascii=False, indent=1))
```

**读取json文件 到python变量（可能是字典或者列表）：**

```python
with open('jsonFile.json', 'r', encoding="utf-8") as f:
    zidian=json.load(f)
```


 load、dump是针对文件的。

 

 loads、dumps是针对字符串的。

json python 读写json 读取和写入json文件 load dump

linux上别人装的环境不知道位置怎么办？





第一步 命令行激活环境

conda activate 环境

第二步 命令行输入python进入写python界面

第三步 随便import一个模块

```python
import os
```

第四步 输出模块的目录

```python
print(os.__file__)
```

第五步 对照着找即可

anaconda 安装位置 linux

Python





```python
l1 = ['b','c','d','b','c','a','a']
l2 = []
[l2.append(i) for i in l1 if not i in l2]
print l2
```

python 列表去重 保持顺序

用这个：





g.to_csv("nihao.csv",encoding="utf_8_sig")

pandas to_csv 中文乱码 解决

http://esri.github.io/geometry-api-java/doc/RelationalOperators.html





![在这里插入图片描述](/static/img/images/00756.png)

![在这里插入图片描述](/static/img/images/00757.png)

![在这里插入图片描述](/static/img/images/00758.png)

![在这里插入图片描述](/static/img/images/00759.png)

![在这里插入图片描述](/static/img/images/00760.png)

![在这里插入图片描述](/static/img/images/00761.png)


![在这里插入图片描述](/static/img/images/00762.png)

![在这里插入图片描述](/static/img/images/00763.png)

![在这里插入图片描述](/static/img/images/00764.png)

python shaply 几何图形关系 Contains Crosses Disjoint Equals Intersects Overlaps Touches Within

极坐标多边形 https://blog.csdn.net/qq_30638831/article/details/79938841





极坐标转直角坐标 https://blog.csdn.net/qq_32425195/article/details/105233463

1。直角坐标转极坐标

import cmath

cn = complex(3,4)

cmath.polar(cn)  #返回长度和弧度


2.极坐标转直角坐标

cn1 = cmath.rect(2, cmath.pi)

cn1.real，cn1.imag





```python
import geopandas
from geopandas import GeoSeries
from shapely.geometry import Polygon
import matplotlib.pyplot as plt
def draw_a_poly(quyu):
    """
    用geopandas画一个多边形
    :param quyu: 二维list
    :return: 无
    """
    p = Polygon(quyu)
    g = GeoSeries(p)
    g.plot()
    plt.show()
import cmath
def get_poly(rad, num_p):
    """
    返回规则num_p边行的各个点
    :param rad: 几何中心到顶点的距离
    :param num_p: 有多少边
    :return: 二维list  N*2
    """
    r = [rad for i in range(num_p)]
    theta = [cmath.pi / num_p * 2 * i for i in range(num_p)]
    ploy = [[cmath.rect(*i).real, cmath.rect(*i).imag] for i in zip(r, theta)]
    return ploy
draw_a_poly(get_poly(2, 16))
```

![在这里插入图片描述](/static/img/images/00765.png)

python 极坐标 多边形点 画多边形 规则多边形 取圆上的若干点

python的logging 不是进程安全的。





第三方模块loguru可以支持进程安全。

注意：不要实例化logger，只需要from loguru import logger即可！

安装：

```python
pip install loguru
```

使用：
```python
from loguru import logger
import datetime
if __name__ == '__main__':
    log_file_name = './log_record/log.log'  # linux路径 我给的相对路径
    logger.add(log_file_name,  # 可以带有路径 没路径的logger会自己创建
               rotation='100 KB',  # 按文件大小切割日志
               enqueue=True,  # 这使得进程安全
               colorize=True,  # 彩色显示 只要系统支持
               level='WARNING')  # 这个级别以上的才会被写入文件，包含这个级别的
    ziduan = {i: 123432 for i in range(10)}
    for i in range(1000):
        logger.debug(ziduan)
        logger.info(ziduan)
        logger.warning(ziduan)
        logger.error(ziduan)
        logger.critical(ziduan)
        logger.critical(datetime.datetime)
```

教程：

[https://huangkai1008.github.io/post/python/loguru/](https://huangkai1008.github.io/post/python/loguru/)

[https://loguru.readthedocs.io/en/stable/api/logger.html](https://loguru.readthedocs.io/en/stable/api/logger.html)

github：

[https://github.com/Delgan/loguru#asynchronous-thread-safe-multiprocess-safe](https://github.com/Delgan/loguru#asynchronous-thread-safe-multiprocess-safe)

效果：

![在这里插入图片描述](/static/img/images/00768.png)

为了让loguru不输出数据到控制台，可以remove掉之前add的东西。

```python
from loguru import logger
import datetime
if __name__ == '__main__':
    log_file_name = './log_record/log__{time}.log'  # linux路径 我给的相对路径
    logger.remove(handler_id=None)  # 清除logger之前的句柄
    logger.add(sink=log_file_name,  # 可以带有路径 没路径的logger会自己创建
               rotation='10 KB',  # 按文件大小切割日志
               retention='30 days',  # 只保留30天的日志
               encoding='utf-8',  # 编码
               enqueue=True,  # 这使得进程安全
               colorize=False,  # 彩色显示 只要系统支持
               format='<green>{time:YYYY-MM-DD HH:mm:ss.SSS}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>',
               level='DEBUG')  # 这个级别以上的才会被写入文件，包含这个级别的
    ziduan = {i: 123432 for i in range(10)}
    for i in range(1000):
        logger.debug(ziduan)
        logger.info(ziduan)
        logger.warning(ziduan)
        logger.error(ziduan)
        logger.critical(ziduan)
        logger.critical(datetime.datetime)
```

python logging loguru 日志切割 进程安全 日志切分 一天一输出

Python





```python
file_path="./log_record/"
if not os.path.exists(file_path):
    os.makedirs(file_path)
file_name=file_path+'log.log'
if not os.path.exists(file_name):
    os.system(r"touch {}".format(file_name))  # 调用系统命令行来创建文件
```

python 检测文件目录是否存在 不存在就创建 检测文件是否存在 不存在就创建 linux

Python





```python
import datetime
print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
```

<table>

 <thead>

  <tr>

   <td><strong>Directive</strong></td>

   <td><strong>Meaning</strong></td>

   <td><strong>Example</strong></td>

  </tr>

 </thead>

 <tbody>

  <tr>

   <td><code>%a</code></td>

   <td>Abbreviated weekday name.</td>

   <td>Sun, Mon, ...</td>

  </tr>

  <tr>

   <td><code>%A</code></td>

   <td>Full weekday name.</td>

   <td>Sunday, Monday, ...</td>

  </tr>

  <tr>

   <td><code>%w</code></td>

   <td>Weekday as a decimal number.</td>

   <td>0, 1, ..., 6</td>

  </tr>

  <tr>

   <td><code>%d</code></td>

   <td>Day of the month as a zero-padded decimal.</td>

   <td>01, 02, ..., 31</td>

  </tr>

  <tr>

   <td><code>%-d</code></td>

   <td>Day of the month as a decimal number.</td>

   <td>1, 2, ..., 30</td>

  </tr>

  <tr>

   <td><code>%b</code></td>

   <td>Abbreviated month name.</td>

   <td>Jan, Feb, ..., Dec</td>

  </tr>

  <tr>

   <td><code>%B</code></td>

   <td>Full month name.</td>

   <td>January, February, ...</td>

  </tr>

  <tr>

   <td><code>%m</code></td>

   <td>Month as a zero-padded decimal number.</td>

   <td>01, 02, ..., 12</td>

  </tr>

  <tr>

   <td><code>%-m</code></td>

   <td>Month as a decimal number.</td>

   <td>1, 2, ..., 12</td>

  </tr>

  <tr>

   <td><code>%y</code></td>

   <td>Year without century as a zero-padded decimal number.</td>

   <td>00, 01, ..., 99</td>

  </tr>

  <tr>

   <td><code>%-y</code></td>

   <td>Year without century as a decimal number.</td>

   <td>0, 1, ..., 99</td>

  </tr>

  <tr>

   <td><code>%Y</code></td>

   <td>Year with century as a decimal number.</td>

   <td>2013, 2019 etc.</td>

  </tr>

  <tr>

   <td><code>%H</code></td>

   <td>Hour (24-hour clock) as a zero-padded decimal number.</td>

   <td>00, 01, ..., 23</td>

  </tr>

  <tr>

   <td><code>%-H</code></td>

   <td>Hour (24-hour clock) as a decimal number.</td>

   <td>0, 1, ..., 23</td>

  </tr>

  <tr>

   <td><code>%I</code></td>

   <td>Hour (12-hour clock) as a zero-padded decimal number.</td>

   <td>01, 02, ..., 12</td>

  </tr>

  <tr>

   <td><code>%-I</code></td>

   <td>Hour (12-hour clock) as a decimal number.</td>

   <td>1, 2, ... 12</td>

  </tr>

  <tr>

   <td><code>%p</code></td>

   <td>Locale&rsquo;s AM or PM.</td>

   <td>AM, PM</td>

  </tr>

  <tr>

   <td><code>%M</code></td>

   <td>Minute as a zero-padded decimal number.</td>

   <td>00, 01, ..., 59</td>

  </tr>

  <tr>

   <td><code>%-M</code></td>

   <td>Minute as a decimal number.</td>

   <td>0, 1, ..., 59</td>

  </tr>

  <tr>

   <td><code>%S</code></td>

   <td>Second as a zero-padded decimal number.</td>

   <td>00, 01, ..., 59</td>

  </tr>

  <tr>

   <td><code>%-S</code></td>

   <td>Second as a decimal number.</td>

   <td>0, 1, ..., 59</td>

  </tr>

  <tr>

   <td><code>%f</code></td>

   <td>Microsecond as a decimal number, zero-padded on the left.</td>

   <td>000000 - 999999</td>

  </tr>

  <tr>

   <td><code>%z</code></td>

   <td>UTC offset in the form +HHMM or -HHMM.</td>

   <td>&nbsp;</td>

  </tr>

  <tr>

   <td><code>%Z</code></td>

   <td>Time zone name.</td>

   <td>&nbsp;</td>

  </tr>

  <tr>

   <td><code>%j</code></td>

   <td>Day of the year as a zero-padded decimal number.</td>

   <td>001, 002, ..., 366</td>

  </tr>

  <tr>

   <td><code>%-j</code></td>

   <td>Day of the year as a decimal number.</td>

   <td>1, 2, ..., 366</td>

  </tr>

  <tr>

   <td><code>%U</code></td>

   <td>Week number of the year (Sunday as the first day of the week). All days in a new year preceding the first Sunday are considered to be in week 0.</td>

   <td>00, 01, ..., 53</td>

  </tr>

  <tr>

   <td><code>%W</code></td>

   <td>Week number of the year (Monday as the first day of the week). All days in a new year preceding the first Monday are considered to be in week 0.</td>

   <td>00, 01, ..., 53</td>

  </tr>

  <tr>

   <td><code>%c</code></td>

   <td>Locale&rsquo;s appropriate date and time representation.</td>

   <td>Mon Sep 30 07:06:05 2013</td>

  </tr>

  <tr>

   <td><code>%x</code></td>

   <td>Locale&rsquo;s appropriate date representation.</td>

   <td>09/30/13</td>

  </tr>

  <tr>

   <td><code>%X</code></td>

   <td>Locale&rsquo;s appropriate time representation.</td>

   <td>07:06:05</td>

  </tr>

  <tr>

   <td><code>%%</code></td>

   <td>A literal &#39;%&#39; character.</td>

   <td>%</td>

  </tr>

 </tbody>

</table>