# 问题





预测模型执行代码过慢，想要快速预测。

每次都是将历史数据作为训练数据喂给模型，然后让模型输出预测数据。这个模型的训练过程太慢。

# 解决

（1）讲训练的模型存储起来，每次预测的时候直接用存储的模型进行预测，躲开训练环节；

（2）有新数据的时候，可以在不紧急的时候训练模型并存储起来；依照执行速度，可以设定更新频率，比如2个小时更新一次模型，1天更新一次模型。

# 实现

建立路径：

![在这里插入图片描述](/static/img/images/00331.png)

quikly_predict.py 内容
```python
import os
from fbprophet import Prophet
import pandas as pd
import matplotlib.pyplot as plt
import math
import pickle
class data_quikly_predict:
    pwd = os.path.dirname(os.path.abspath(__file__))
    data_dir = os.path.join(pwd, "data")
    def __init__(self):
        if not os.path.exists(self.data_dir):
            os.mkdir(self.data_dir)
        self.model_list = self.find_all_file(self.data_dir)
    def update_model_list(self):
        self.model_list = self.find_all_file(self.data_dir)
    def find_all_file(self, dirname):
        result = []
        for maindir, subdir, file_name_list in os.walk(dirname):
            for filename in file_name_list:
                apath = os.path.join(maindir, filename)
                result.append(apath)
        return result
    def create_new_model(self, df: pd.DataFrame, new_model_name: str):
        """
        :param df: 训练新模型所需要的数据
        :param new_model_name: 模型名称
        :return:
        """
        assert ("ds" in df.columns) and ("y" in df.columns)
        df['ds'] = df['ds'].astype('datetime64[ns]')
        model = Prophet(yearly_seasonality=True, weekly_seasonality=True)
        model.fit(df)  # 训练模型m
        model_file = os.path.join(self.data_dir, new_model_name)
        if model_file in self.model_list:
            print("模型名称已经被使用,但即将被置换\r\n")
        with open(model_file, 'wb') as f:
            pickle.dump(model, f)
        self.update_model_list()
    def predict(self, future_time: pd.DataFrame, model_name: str):
        """
        :param future_time:未来需要预测的时间点DataFrame，只有一行'ds'
        :param model_name:想要使用的模型名称
        :return:
        """
        model_file = os.path.join(self.data_dir, model_name)
        if model_file not in self.model_list:
            raise print("没有在data里找到这个模型文件，请确保用数据训练出了可使用的模型\r\n")
        with open(model_file, 'rb') as f:
            model = pickle.load(f)
            forecast = model.predict(future_time)  # 开始预测
            forecast = forecast[['ds', 'yhat']]
        return forecast
    def dummy_predict(self, periods, freq: str, model_name: str):
        """
        傻瓜化预测
        :param periods: 需要预测多少个数据结果
        :param freq: 从训练数据末尾调用频率
        :param model_name: 模型名称
        :return:
        """
        model_file = os.path.join(self.data_dir, model_name)
        if model_file not in self.model_list:
            raise print("没有在data里找到这个模型文件，请确保用数据训练出了可使用的模型\r\n")
        with open(model_file, 'rb') as f:
            model = pickle.load(f)
        future_time = model.make_future_dataframe(periods=periods, freq=freq, include_history=False)  # 预测的设置 还没预测
        forecast = model.predict(future_time)  # 开始预测
        forecast = forecast[['ds', 'yhat']]
        return forecast
    def quikly_plot_forecast(self, forecast: pd.DataFrame):
        plt.plot(list(forecast['ds']), list(forecast['yhat']), color='b')
        plt.show()
if __name__ == '__main__':
    dataqp = data_quikly_predict()
    timelist = list(pd.date_range(start='2021-01-01 00:00:00', end='2022-01-01 00:00:00', freq='H'))
    y = [math.sin(data.hour) for k, data in enumerate(timelist)]
    data_df = pd.DataFrame({'ds': timelist, 'y': y})
    dataqp.create_new_model(data_df, "camera1")
    future = pd.DataFrame({'ds': list(pd.date_range(start='2022-05-01 00:00:00',
                                                    end='2022-05-05 01:00:00',
                                                    freq='H'))})  # 预测的设置 还没预测
    forecast_res = dataqp.predict(future, model_name="camera1")
    dataqp.quikly_plot_forecast(forecast_res)
```

# 如何使用

下面代码是正常过程，先执行训练，然后预测数据。
```python
import math
import pandas as pd
from fbprophet_persistent_prodict.quikly_predict import data_quikly_predict
dataqp = data_quikly_predict()
timelist = list(pd.date_range(start='2021-01-01 00:00:00', end='2022-01-01 00:00:00', freq='H'))
y = [math.sin(data.hour) for k, data in enumerate(timelist)]
data_df = pd.DataFrame({'ds': timelist, 'y': y})
dataqp.create_new_model(data_df, "camera6")
future = pd.DataFrame({'ds': list(pd.date_range(start='2022-05-01 00:00:00',
                                            end='2022-05-05 01:00:00',
                                            freq='H'))})  # 预测的设置 还没预测
forecast_res = dataqp.predict(future, model_name="camera6")
dataqp.quikly_plot_forecast(forecast_res)
```

先预测，后用训练更新模型：

```csharp
import math
import pandas as pd
from fbprophet_persistent_prodict.quikly_predict import data_quikly_predict
dataqp = data_quikly_predict()
future = pd.DataFrame({'ds': list(pd.date_range(start='2022-05-01 00:00:00',
                                            end='2022-05-05 01:00:00',
                                            freq='H'))})  # 预测的设置 还没预测
forecast_res = dataqp.predict(future, model_name="camera6")
timelist = list(pd.date_range(start='2021-01-01 00:00:00', end='2022-01-01 00:00:00', freq='H'))
y = [math.sin(data.hour) for k, data in enumerate(timelist)]
data_df = pd.DataFrame({'ds': timelist, 'y': y})
dataqp.create_new_model(data_df, "camera6")
```

python 时序预测模型 fbprophet 持久化存储 大数据快速预测

利用python解释器对文件内置的__file__属性获取：




```python
os.path.dirname(os.path.abspath(__file__))
```

os.path.abspath(__file__)是当前py文件在操作系统的绝对路径文件名，os.path.dirname获取文件所在路径。

python 获取当前py文件的绝对路径

# 安装Prophet





python: 3.7.9

pystan: 2.19.0.0

pandas

fbprophet: 0.6.0

anaconda方式：

conda install pystan=2.19.0.0

conda install -c conda-forge fbprophet=0.6.0

# Prophet介绍

https://www.cnblogs.com/bonelee/p/9577432.html

https://facebook.github.io/prophet/docs/quick_start.html

可以预测数据，也可以给出趋势。时间序列预测上，充满专家经验：周期趋势、离群点、突变点、突变。

# 时间跨度

make_future_dataframe中的fre是Offset aliases形式的，用的pandas时间跨度

https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases

比如1hour1min可以表示时间跨度是61分钟

# 预测未来1

利用函数make_future_dataframe生成未来时间DataFrame对象future ：

```python
from fbprophet import Prophet
import pandas as pd
import matplotlib.pyplot as plt
import math
timelist = list(pd.date_range(start='2021-01-01 00:00:00', end='2022-01-01 00:00:00', freq='H'))
y = [math.sin(data.hour) for k, data in enumerate(timelist)]
data_df = pd.DataFrame({'ds': timelist, 'y': y})
data_df['ds'] = data_df['ds'].astype('datetime64[ns]')
m = Prophet()
m.fit(data_df)  # 训练模型m
future = m.make_future_dataframe(periods=50, freq='H',include_history=False)  # 预测的设置 还没预测
forecast = m.predict(future)  # 开始预测
# plt.plot(data_df['ds'][-50:], data_df['y'][-50:], color='b')
# plt.plot(forecast['ds'], forecast['yhat'], color='r')
# plt.show()
plt.plot(list(data_df['ds'][-50:])+list(forecast['ds']),
         list(data_df['y'][-50:])+list(forecast['yhat']), color='b')
plt.show()
```

![在这里插入图片描述](/static/img/images/00332.png)

# 预测未来2

未来任意时间：

```python
# 1 取数据
# https://www.kesci.com/mw/dataset/5d64a35a8499bc002c07bb1b
from fbprophet import Prophet
import pandas as pd
import matplotlib.pyplot as plt
import math
timelist = list(pd.date_range(start='2021-01-01 00:00:00', end='2022-01-01 00:00:00', freq='H'))
y = [math.sin(data.hour) for k, data in enumerate(timelist)]
data_df = pd.DataFrame({'ds': timelist, 'y': y})
data_df['ds'] = data_df['ds'].astype('datetime64[ns]')
m = Prophet()
m.fit(data_df)  # 训练模型m
future = pd.DataFrame({'ds': list(pd.date_range(start='2022-05-01 00:00:00', end='2022-05-05 00:00:00', freq='H'))})  # 预测的设置 还没预测
forecast = m.predict(future)  # 开始预测
# plt.plot(data_df['ds'][-50:], data_df['y'][-50:], color='b')
plt.plot(forecast['ds'], forecast['yhat'], color='r')
plt.show()
# plt.plot(list(data_df['ds'][-50:])+list(forecast['ds']),
#          list(data_df['y'][-50:])+list(forecast['yhat']), color='b')
# plt.show()
```


![在这里插入图片描述](/static/img/images/00333.png)

# 预测模型持久化存储

时序预测模型 fbprophet

直接dict即可：





```python
>>> aa = [("kevin", 100,), ("dylan", 100,), ("daisy", 100,)]
>>> dict(aa)
{'kevin': 100, 'dylan': 100, 'daisy': 100}
```

引申

只要是字典结构的数据都能这么玩，比如：

```python
aa = {("kevin", 100,), ("dylan", 100,), ("daisy", 100,)}
aa = [["kevin", 100,], ["dylan", 100,], ["daisy", 100,]]
```

python 列表元组 转 字典

# 参考





python的re模块说明：

[https://docs.python.org/zh-cn/3/library/re.html](https://docs.python.org/zh-cn/3/library/re.html)

一些常见的正则表达式写法（邮箱、手机号、密码、身份证）：

[https://zhuanlan.zhihu.com/p/338826624](https://zhuanlan.zhihu.com/p/338826624)

# 使用

## re.findall

非常有用！

![在这里插入图片描述](/static/img/images/00334.png)

```powershell
in_str = """
Name: 10.10.90.86:9866 (hadoop001)
DFS Used: 149719834624 dgf
Name: 10.10.90.87:9866 (hadoop002)
DFS Used: 1497198231234624 dgfg
Name: 10.10.90.88:9866 (hadoop003)
DFS Used: 14971981323134624 gfdg
"""
import re
pattern1 = re.compile(r"Name: [0-9]+.[0-9]+.[0-9]+.[0-9]+")
dalllist1 = re.findall(pattern1, in_str)
dalllist1 = list(map(lambda x: x[6:], dalllist1))
pattern2 = re.compile(r"DFS Used: [0-9]+")
dalllist2 = re.findall(pattern2, in_str)
dalllist2 = list(map(lambda x: x[14:], dalllist2))
for i in zip(dalllist1, dalllist2):
    print(i)
```

## re.search

感觉基本没啥用。

![在这里插入图片描述](/static/img/images/00335.png)

```powershell
in_str = """
Name: 10.10.90.86:9866 (hadoop001)
DFS Used: 149719834624 dgf
Name: 10.10.90.87:9866 (hadoop002)
DFS Used: 1497198231234624 dgfg
Name: 10.10.90.88:9866 (hadoop003)
DFS Used: 14971981323134624 gfdg
"""
import re
pattern1 = re.compile(r"Name: [0-9]+.[0-9]+.[0-9]+.[0-9]+")
dalllist1 = re.search(pattern1, in_str)
print(dalllist1.group())
```

## re.match

用这个来匹配密码很好，用户设置密码的时候必须有数字和大小写之类的。

![在这里插入图片描述](/static/img/images/00336.png)

```powershell
import re
while True:
    pwd = input("input your password\r\n")
    if re.match(re.compile("^[a-z]{8,16}$"), pwd) == None:
        print("no pass")
    else:
        print("password is ok")
```

密码强度：[https://www.huaweicloud.com/articles/e43ea3c29dfbebc830af0e0b69b71ce3.html](https://www.huaweicloud.com/articles/e43ea3c29dfbebc830af0e0b69b71ce3.html)

## re.split

```powershell
import re
text = """Ross McFluff: 834.345.1254 155 Elm Street
...
... Ronald Heathmore: 892.345.3428 436 Finley Avenue
... Frank Burger: 925.541.7625 662 South Dogwood Way
...
...
... Heather Albrecht: 548.326.4584 919 Park Place"""
entries = re.split("\n+", text)
print([re.split(":? ", entry, 3) for entry in entries])
```


# 一些别的

> []

匹配方括号里的所有字符的集合。集合set的意味在里面。

比如`[34]`就是匹配3或者匹配4。

比如`[A-Za-z]` 就是匹配所有的大写字母或者小写字母。

比如`[0-9]` 就是匹配所有的数字。

  

> ^

如果放到 [  ]  里面，就表示这个集合里能匹配的东西都不是想匹配的。

比如`[^heo]`就表示匹配除了这三个字母集合外的所有字符集合。

如果放到 [  ]  外面，就表示匹配一个行开始。

比如`^[heo]`就表示匹配某一个新行，新行以三个字母的某一个字母开头。

要匹配 ^ 字符本身，请使用 `\^`。

> $

匹配行结尾。这符号很特殊。是一个零长度字符的匹配。

> .

匹配除换行符 \n 之外的任何单字符。要匹配 . ，请使用 `\.` 。

python 正则表达式 教程 使用 查找字符串 搜索是否含有 匹配密码强度 分割出合适数据

身在Docker容器里身不由己，wget能很好解决：





下载：
```powershell
wget https://repo.anaconda.com/miniconda/Miniconda3-py38_4.9.2-Linux-x86_64.sh
```

安装：

-b是静默安装，无需交互。
```powershell
sh Miniconda3-py38_4.9.2-Linux-x86_64.sh -b
```

所以记住这2个仓库：

[https://repo.anaconda.com/miniconda/](https://repo.anaconda.com/miniconda/)

[https://repo.anaconda.com/archive/](https://repo.anaconda.com/archive/)

Linux wget miniconda 快速安装miniconda环境

参考：[https://zhuanlan.zhihu.com/p/108418193](https://zhuanlan.zhihu.com/p/108418193)





![在这里插入图片描述](/static/img/images/00360.png)

什么时候需要定义类 而不是函数？

python安装opencv：





> pip install opencv-python

提取视频帧 视频转图片：
```python
import os
mp4file = 'input.mp4'
savefile = "./output"
if not os.path.exists(savefile):
    os.mkdir(savefile)
import cv2
cap = cv2.VideoCapture(mp4file)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
fps = cap.get(cv2.CAP_PROP_FPS)
size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
print(fps)
print(size)
i = 0
while (cap.isOpened()):
    i = i + 1
    ret, frame = cap.read()
    if ret == True:
        cv2.imwrite(savefile + '/' + str(i) + '.jpg', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    else:
        break
print(i)
cap.release()
cv2.destroyAllWindows()
```

图片转视频：

```python
import os
mp4file = 'out.mp4'
savefile = "./output"
if not os.path.exists(savefile):
    os.mkdir(savefile)
import cv2
fourcc = cv2.VideoWriter_fourcc(*'XVID')
fps = 30
size =(720, 1280)
videoWriter = cv2.VideoWriter(mp4file, fourcc, fps, size)  # 最后一个是保存图片的尺寸
for i in range(1, 1402):
    frame = cv2.imread(savefile + '/' + str(i) + '.jpg')
    videoWriter.write(frame)
videoWriter.release()
```


合成，一定得注意尺寸的大小。

```python
# coding=utf-8
import os
import cv2
from PIL import Image
def makevideo(path, fps):
    """ 将图片合成视频. path: 视频路径，fps: 帧率 """
    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
    path1 = r'D:\yifei111\PycharmProjects\python_test\jigan_res4'
    im = Image.open(r'D:\yifei111\PycharmProjects\python_test\jigan1\251.jpg')
    print(im.size)
    vw = cv2.VideoWriter(path, fourcc, fps, im.size)
    for i in os.listdir(path1):
        frame = cv2.imread(path1 + '\\' + i)
        frame = frame[200:649, :]
        frame = cv2.resize(frame, im.size)
        vw.write(frame)
if __name__ == '__main__':
    video_path = r'D:\yifei111\PycharmProjects\python_test\DST_test_new4.mp4'
    makevideo(video_path, 30)  # 图片转视频
```

python opencv 提取视频帧 视频转图片 图片转视频

借助pd.date_range快速实现





## 生成 Timestamp对象 列表

生成一系列Timestamp对象，Timestamp对象是对python datetime再封装得到的，一般情况直接用起来就好了。
```python
import pandas as pd
a = list(pd.date_range(start='2021-06-21 00:00:00', end='2021-06-22 00:00:00', freq='H'))
print(a)
```

得到：

[Timestamp('2021-06-21 00:00:00', freq='H'), Timestamp('2021-06-21 01:00:00', freq='H'), Timestamp('2021-06-21 02:00:00', freq='H'), Timestamp('2021-06-21 03:00:00', freq='H'), Timestamp('2021-06-21 04:00:00', freq='H'), Timestamp('2021-06-21 05:00:00', freq='H'), Timestamp('2021-06-21 06:00:00', freq='H'), Timestamp('2021-06-21 07:00:00', freq='H'), Timestamp('2021-06-21 08:00:00', freq='H'), Timestamp('2021-06-21 09:00:00', freq='H'), Timestamp('2021-06-21 10:00:00', freq='H'), Timestamp('2021-06-21 11:00:00', freq='H'), Timestamp('2021-06-21 12:00:00', freq='H'), Timestamp('2021-06-21 13:00:00', freq='H'), Timestamp('2021-06-21 14:00:00', freq='H'), Timestamp('2021-06-21 15:00:00', freq='H'), Timestamp('2021-06-21 16:00:00', freq='H'), Timestamp('2021-06-21 17:00:00', freq='H'), Timestamp('2021-06-21 18:00:00', freq='H'), Timestamp('2021-06-21 19:00:00', freq='H'), Timestamp('2021-06-21 20:00:00', freq='H'), Timestamp('2021-06-21 21:00:00', freq='H'), Timestamp('2021-06-21 22:00:00', freq='H'), Timestamp('2021-06-21 23:00:00', freq='H'), Timestamp('2021-06-22 00:00:00', freq='H')]

## 生成 str对象 列表

```python
import pandas as pd
a = list(pd.date_range(start='2021-06-21 00:00:00', end='2021-06-22 00:00:00', freq='H'))
a=list(map(str,a))
print(a)
```

得到：

['2021-06-21 00:00:00', '2021-06-21 01:00:00', '2021-06-21 02:00:00', '2021-06-21 03:00:00', '2021-06-21 04:00:00', '2021-06-21 05:00:00', '2021-06-21 06:00:00', '2021-06-21 07:00:00', '2021-06-21 08:00:00', '2021-06-21 09:00:00', '2021-06-21 10:00:00', '2021-06-21 11:00:00', '2021-06-21 12:00:00', '2021-06-21 13:00:00', '2021-06-21 14:00:00', '2021-06-21 15:00:00', '2021-06-21 16:00:00', '2021-06-21 17:00:00', '2021-06-21 18:00:00', '2021-06-21 19:00:00', '2021-06-21 20:00:00', '2021-06-21 21:00:00', '2021-06-21 22:00:00', '2021-06-21 23:00:00', '2021-06-22 00:00:00']

## 生成 python datetime对象 列表

```python
import pandas as pd
a = list(pd.date_range(start='2021-06-21 00:00:00', end='2021-06-22 00:00:00', freq='H'))
a=list(map(lambda x:x.to_pydatetime(),a))
print(a)
```

得到：

```python
[datetime.datetime(2021, 6, 21, 0, 0), datetime.datetime(2021, 6, 21, 1, 0), datetime.datetime(2021, 6, 21, 2, 0), datetime.datetime(2021, 6, 21, 3, 0), datetime.datetime(2021, 6, 21, 4, 0), datetime.datetime(2021, 6, 21, 5, 0), datetime.datetime(2021, 6, 21, 6, 0), datetime.datetime(2021, 6, 21, 7, 0), datetime.datetime(2021, 6, 21, 8, 0), datetime.datetime(2021, 6, 21, 9, 0), datetime.datetime(2021, 6, 21, 10, 0), datetime.datetime(2021, 6, 21, 11, 0), datetime.datetime(2021, 6, 21, 12, 0), datetime.datetime(2021, 6, 21, 13, 0), datetime.datetime(2021, 6, 21, 14, 0), datetime.datetime(2021, 6, 21, 15, 0), datetime.datetime(2021, 6, 21, 16, 0), datetime.datetime(2021, 6, 21, 17, 0), datetime.datetime(2021, 6, 21, 18, 0), datetime.datetime(2021, 6, 21, 19, 0), datetime.datetime(2021, 6, 21, 20, 0), datetime.datetime(2021, 6, 21, 21, 0), datetime.datetime(2021, 6, 21, 22, 0), datetime.datetime(2021, 6, 21, 23, 0), datetime.datetime(2021, 6, 22, 0, 0)]
```

# 转换

 pd.Timestamp 转 datetime.datetime  ：
```python
pd.Timestamp("2021-06-06").to_pydatetime()
```

datetime.datetime  转 pd.Timestamp：

```python
pd.to_datetime(datetime.datetime.now())
```

python 生成一段日期列表 pd.date_range Timestamp 转 datetime datetime 转 Timestamp

（1）本质核心是多进程里执行os.system("wget filename")，开17个进程的进程池意味着同时在下载17个文件。





（2）将指令全部放列表needgfsfile里面。

```python
import datetime
import multiprocessing
import os
def downloadgfs(filename):
    os.system(filename)
start_hour = 12
yesterday = (datetime.datetime.now() - datetime.timedelta(days=1)).replace(hour=start_hour, microsecond=0)
ymdstr = f"{str(yesterday.year).zfill(4)}{str(yesterday.month).zfill(2)}{str(yesterday.day).zfill(2)}{str(start_hour).zfill(2)}"
if not os.path.exists(ymdstr):
    os.mkdir(ymdstr)
needgfsfile = [
    f"wget ftp://ftpprd.ncep.noaa.gov/pub/data/nccf/com/gfs/prod/gfs.{ymdstr[:-2]}/{ymdstr[-2:]}/atmos/gfs.t{ymdstr[-2:]}z.pgrb2.0p50.f{str(i * 24).zfill(3)} -P {ymdstr}/"
    for i in range(17)]
p = multiprocessing.Pool(processes=17)  # 进to程池
_ = [p.apply_async(func=downloadgfs, args=(i,)) for i in needgfsfile]
p.close()
p.join()
```

python wget 并行下载多个文件 同时下载多个文件 ftp文件 多进程

## 1 加解释器后加路径





![在这里插入图片描述](/static/img/images/00435.png)

## 2 配置

![在这里插入图片描述](/static/img/images/00436.png)

这样点一下后切到用户目录作为Root path

![在这里插入图片描述](/static/img/images/00437.png)

手动修改这里：

![在这里插入图片描述](/static/img/images/00438.png)

## 3 第一次需要手动上传

![在这里插入图片描述](/static/img/images/00439.png)

Pycharm Deployment ssh 解释器 文件同步设置

[https://stackoverflow.com/questions/19237512/rbf-interpolation-linalgerror-singular-matrix](https://stackoverflow.com/questions/19237512/rbf-interpolation-linalgerror-singular-matrix)

RBF interpolation: LinAlgError: singular matrix 热力图

[https://pytorch.org/get-started/previous-versions/](https://pytorch.org/get-started/previous-versions/)





```handlebars
# CUDA 10.1
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch
```

pytorch 安装

北京外国语大学开源软件镜像站





```handlebars
channels:
  - defaults
show_channel_urls: true
channel_alias: https://mirrors.bfsu.edu.cn/anaconda
default_channels:
  - https://mirrors.bfsu.edu.cn/anaconda/pkgs/main
  - https://mirrors.bfsu.edu.cn/anaconda/pkgs/free
  - https://mirrors.bfsu.edu.cn/anaconda/pkgs/r
  - https://mirrors.bfsu.edu.cn/anaconda/pkgs/pro
  - https://mirrors.bfsu.edu.cn/anaconda/pkgs/msys2
custom_channels:
  conda-forge: https://mirrors.bfsu.edu.cn/anaconda/cloud
  msys2: https://mirrors.bfsu.edu.cn/anaconda/cloud
  bioconda: https://mirrors.bfsu.edu.cn/anaconda/cloud
  menpo: https://mirrors.bfsu.edu.cn/anaconda/cloud
  pytorch: https://mirrors.bfsu.edu.cn/anaconda/cloud
  simpleitk: https://mirrors.bfsu.edu.cn/anaconda/cloud
```

上海交通大学开源软件镜像站

```handlebars
channels:
  - defaults
show_channel_urls: true
channel_alias: https://anaconda.mirrors.sjtug.sjtu.edu.cn/
default_channels:
  - https://anaconda.mirrors.sjtug.sjtu.edu.cn/pkgs/main
  - https://anaconda.mirrors.sjtug.sjtu.edu.cn/pkgs/free
  - https://anaconda.mirrors.sjtug.sjtu.edu.cn/pkgs/mro
  - https://anaconda.mirrors.sjtug.sjtu.edu.cn/pkgs/msys2
  - https://anaconda.mirrors.sjtug.sjtu.edu.cn/pkgs/pro
  - https://anaconda.mirrors.sjtug.sjtu.edu.cn/pkgs/r
custom_channels:
  conda-forge: https://anaconda.mirrors.sjtug.sjtu.edu.cn/conda-forge
  soumith: https://anaconda.mirrors.sjtug.sjtu.edu.cn/cloud/soumith
  bioconda: https://anaconda.mirrors.sjtug.sjtu.edu.cn/cloud/bioconda
  menpo: https://anaconda.mirrors.sjtug.sjtu.edu.cn/cloud/menpo
  viscid-hub: https://anaconda.mirrors.sjtug.sjtu.edu.cn/cloud/viscid-hub
  atztogo: https://anaconda.mirrors.sjtug.sjtu.edu.cn/cloud/atztogo
```

清华大学开源软件镜像站

```handlebars
channels:
  - defaults
show_channel_urls: true
default_channels:
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
custom_channels:
  conda-forge: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  msys2: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  bioconda: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  menpo: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  simpleitk: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
```

conda 镜像站

Python





```handlebars
import cmath
import json
import requests
from geopandas import GeoDataFrame,GeoSeries
from shapely.geometry import shape, Polygon, MultiPoint,Point
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def get_zjk_df():
    """
    张家口
    :return:
    """
    url = "https://geo.datav.aliyun.com/areas_v2/bound/130700.json"
    r = requests.get(url)
    b = shape(r.json()['features'][0]['geometry'])
    geodf = GeoDataFrame([{'geometry': b}], crs="EPSG:4326")
    return geodf
a="""
115.503 40.419
115.63025 40.22925
115.72225 40.08925
115.78275 39.96275
115.824 39.851
115.862 39.74475
115.899 39.63675
115.9335 39.5265
115.95975 39.4155
115.975 39.306
115.974 39.20675
115.959 39.12975
115.93325 39.07725
115.89825 39.04325
115.86125 39.028
115.827 39.03175
115.79725 39.04425
115.781 39.0655
115.7795 39.09725
115.78725 39.13525
115.80775 39.1865
115.851 39.24625
115.9285 39.312
116.04525 39.3845
116.19325 39.46125
116.353 39.54275
116.5025 39.6265
116.64025 39.70825
116.77675 39.78975
116.92275 39.86725
117.0855 39.945
117.2595 40.0265
117.44625 40.115
117.6475 40.2025
117.856 40.2845
118.06225 40.355
118.263 40.4185
118.449 40.48875
118.63075 40.5655
118.8145 40.6455
119.0 40.72825
119.1935 40.814
119.3765 40.912
119.56275 41.02025
119.75525 41.13875
119.9655 41.26175
120.18925 41.3805
120.42075 41.491
120.66425 41.592"""
a=a.splitlines()
a=list(filter(lambda x:len(x)>5,a))
lon=list(map(lambda x:float(x.split()[0]),a))
lat=list(map(lambda x:float(x.split()[1]),a))
geodf = get_zjk_df()
ax=geodf.plot(color='white', edgecolor='black')
fblock=[]
for i in zip(lon,lat):
    fblock.append(Point(i))
fblock_df = GeoSeries(fblock)
fblock_df.plot(ax=ax, marker='o', color='red', markersize=5)
# 显示与保存
plt.title("zjk")
plt.xlabel('lon')
plt.ylabel('lat')
plt.grid()
plt.savefig("./nihao.svg")
plt.show()
```

geopandas python 画地图和路径点

装载自：[https://www.debugger.wiki/article/html/1577444402436939](https://www.debugger.wiki/article/html/1577444402436939)





![在这里插入图片描述](/static/img/images/00479.png)

```handlebars
import numpy as np
import pandas as pd
def detect_outliers(data, threshold=3):
    """离群点检测"""
    mean_d = np.mean(data)
    std_d = np.std(data)
    outliers = []
    for y in data:
        z_score = (y - mean_d) / std_d
        if np.abs(z_score) > threshold:
            outliers.append(y)
    return outliers
```

![在这里插入图片描述](/static/img/images/00480.png)

```handlebars
def detect_outliers(sr):
    q1 = sr.quantile(0.25)
    q3 = sr.quantile(0.75)
    iqr = q3-q1 #Interquartile range
    fence_low  = q1-1.5*iqr
    fence_high = q3+1.5*iqr
    outliers = sr.loc[(sr < fence_low) | (sr > fence_high)]
    return outliers
```

python 数据 分析 数据处理 离群点检测

df里都是数字的：




```handlebars
df[df<0]=np.nan
```

不然：
```handlebars
import numpy as np
import pandas as pd
import datetime
df = pd.DataFrame({'a': [0, -1, 2],
                   'b': [-3, 2, 1],
                   'c': ['foo', 'goo', 'bar'],
                   'd': [datetime.datetime.now(), datetime.datetime.now(), datetime.datetime.now()]})
df[df._get_numeric_data() < 0] = np.nan
print(df)
```

结果：

![在这里插入图片描述](/static/img/images/00481.png)

python pandas 表里所有负数赋为空

读取




```handlebars
stationdf = pd.read_excel('./jzk_point_city/zjk_point_excel.xlsx', engine='openpyxl', index_col=0)
```

写入
```handlebars
from pandas import ExcelWriter
with ExcelWriter('./zjk_point_excel.xlsx') as writer:
    df.to_excel(writer)
```

python pandas openpyxl excel xlsx文件

Python





```handlebars
from datetime import datetime
import time
dt = datetime(2015, 4, 19, 12, 20)
stp = dt.timestamp()
print(stp) # 打印时间戳
stp = time.time()
dt = datetime.fromtimestamp(stp)
print(dt)  # date_time
```

python datetime 转 timestamp 转 datetime

修改环境变量





vim ~/.bashrc

在最后添加：

export PATH=~/anaconda3/bin:$PATH



重启环境变量：

source ~/.bashrc