semvec/pypi-packages huggingface dataset:
cda-shuju-fenxishi-201710-201802 2023.2.15.0
CDA数据分析师 201710-201802
Requires Python: >=3.6

# CDA数据分析师 201710-201802

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201710-201802
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201710-201802
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201710-201802
cda-shuju-fenxishi-201710-201802 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiqiyiling-erlingyibalinger
cda-shuju-fenxishi-erlingyiqiyiling-erlingyibalinger <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201708-201710 2023.2.15.0
CDA数据分析师 201708-201710
Requires Python: >=3.6

# CDA数据分析师 201708-201710

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201708-201710
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201708-201710
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201708-201710
cda-shuju-fenxishi-201708-201710 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiqilingba-erlingyiqiyiling
cda-shuju-fenxishi-erlingyiqilingba-erlingyiqiyiling <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201704-201708 2023.2.15.0
CDA数据分析师 201704-201708
Requires Python: >=3.6

# CDA数据分析师 201704-201708

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201704-201708
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201704-201708
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201704-201708
cda-shuju-fenxishi-201704-201708 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiqilingsi-erlingyiqilingba
cda-shuju-fenxishi-erlingyiqilingsi-erlingyiqilingba <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201702-201704 2023.2.15.0
CDA数据分析师 201702-201704
Requires Python: >=3.6

# CDA数据分析师 201702-201704

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201702-201704
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201702-201704
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201702-201704
cda-shuju-fenxishi-201702-201704 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiqilinger-erlingyiqilingsi
cda-shuju-fenxishi-erlingyiqilinger-erlingyiqilingsi <port>
# 访问 http://localhost:{port} 查看文档
```
codestare-maze 0.1.2
Simple Maze Generator
Requires Python: >=3.9
Keywords: maze,dnd,rpg

codestar-maze
=============

Simple maze generators in python.

Usage
-----
Installing the package also installs the `maze-gen` entry point.

::

    usage: maze-gen [-h] [--file FILE] {growing_tree} ...

    positional arguments:
      {growing_tree}  uses the growing tree algorithm to generate the maze

    options:
      -h, --help      show this help message and exit
      --file FILE     file to redirect output

Currently only the `growing_tree` algorithm is supported. The appropriate
subcommand takes the following arguments:

::

    usage: maze-gen growing_tree [-h] [--choice {random,last,mixed}] [--t T] w h

    positional arguments:
      w                     Width of maze
      h                     Height of maze

    options:
      -h, --help            show this help message and exit
      --choice {random,last,mixed}
                            which method to choose next cell
      --t T                 Mixes between 'random' and 'last' choice. No effect for 'choice' other than 'mixed'


BSD 3-Clause License

Copyright (c) 2023, Maximilian Schmidt

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this
   list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice,
   this list of conditions and the following disclaimer in the documentation
   and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its
   contributors may be used to endorse or promote products derived from
   this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
cda-shuju-fenxishi-201611-201702 2023.2.15.0
CDA数据分析师 201611-201702
Requires Python: >=3.6

# CDA数据分析师 201611-201702

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201611-201702
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201611-201702
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201611-201702
cda-shuju-fenxishi-201611-201702 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiliuyiyi-erlingyiqilinger
cda-shuju-fenxishi-erlingyiliuyiyi-erlingyiqilinger <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201609-201611 2023.2.15.0
CDA数据分析师 201609-201611
Requires Python: >=3.6

# CDA数据分析师 201609-201611

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201609-201611
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201609-201611
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201609-201611
cda-shuju-fenxishi-201609-201611 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiliulingjiu-erlingyiliuyiyi
cda-shuju-fenxishi-erlingyiliulingjiu-erlingyiliuyiyi <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201606-201609 2023.2.15.0
CDA数据分析师 201606-201609
Requires Python: >=3.6

# CDA数据分析师 201606-201609

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201606-201609
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201606-201609
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201606-201609
cda-shuju-fenxishi-201606-201609 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiliulingliu-erlingyiliulingjiu
cda-shuju-fenxishi-erlingyiliulingliu-erlingyiliulingjiu <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201604-201606 2023.2.15.0
CDA数据分析师 201604-201606
Requires Python: >=3.6

# CDA数据分析师 201604-201606

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201604-201606
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201604-201606
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201604-201606
cda-shuju-fenxishi-201604-201606 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiliulingsi-erlingyiliulingliu
cda-shuju-fenxishi-erlingyiliulingsi-erlingyiliulingliu <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201602-201604 2023.2.15.0
CDA数据分析师 201602-201604
Requires Python: >=3.6

# CDA数据分析师 201602-201604

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201602-201604
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201602-201604
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201602-201604
cda-shuju-fenxishi-201602-201604 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiliulinger-erlingyiliulingsi
cda-shuju-fenxishi-erlingyiliulinger-erlingyiliulingsi <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201512-201602 2023.2.15.0
CDA数据分析师 201512-201602
Requires Python: >=3.6

# CDA数据分析师 201512-201602

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201512-201602
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201512-201602
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201512-201602
cda-shuju-fenxishi-201512-201602 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiwuyier-erlingyiliulinger
cda-shuju-fenxishi-erlingyiwuyier-erlingyiliulinger <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201511-201512 2023.2.15.0
CDA数据分析师 201511-201512
Requires Python: >=3.6

# CDA数据分析师 201511-201512

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201511-201512
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201511-201512
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201511-201512
cda-shuju-fenxishi-201511-201512 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiwuyiyi-erlingyiwuyier
cda-shuju-fenxishi-erlingyiwuyiyi-erlingyiwuyier <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201509-201511 2023.2.15.0
CDA数据分析师 201509-201511
Requires Python: >=3.6

# CDA数据分析师 201509-201511

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201509-201511
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201509-201511
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201509-201511
cda-shuju-fenxishi-201509-201511 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiwulingjiu-erlingyiwuyiyi
cda-shuju-fenxishi-erlingyiwulingjiu-erlingyiwuyiyi <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201507-201509 2023.2.15.0
CDA数据分析师 201507-201509
Requires Python: >=3.6

# CDA数据分析师 201507-201509

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201507-201509
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201507-201509
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201507-201509
cda-shuju-fenxishi-201507-201509 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiwulingqi-erlingyiwulingjiu
cda-shuju-fenxishi-erlingyiwulingqi-erlingyiwulingjiu <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201505-201507 2023.2.15.0
CDA数据分析师 201505-201507
Requires Python: >=3.6

# CDA数据分析师 201505-201507

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201505-201507
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201505-201507
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201505-201507
cda-shuju-fenxishi-201505-201507 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiwulingwu-erlingyiwulingqi
cda-shuju-fenxishi-erlingyiwulingwu-erlingyiwulingqi <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201503-201505 2023.2.15.0
CDA数据分析师 201503-201505
Requires Python: >=3.6

# CDA数据分析师 201503-201505

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201503-201505
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201503-201505
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201503-201505
cda-shuju-fenxishi-201503-201505 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiwulingsan-erlingyiwulingwu
cda-shuju-fenxishi-erlingyiwulingsan-erlingyiwulingwu <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201501-201503 2023.2.15.0
CDA数据分析师 201501-201503
Requires Python: >=3.6

# CDA数据分析师 201501-201503

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201501-201503
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201501-201503
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201501-201503
cda-shuju-fenxishi-201501-201503 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyiwulingyi-erlingyiwulingsan
cda-shuju-fenxishi-erlingyiwulingyi-erlingyiwulingsan <port>
# 访问 http://localhost:{port} 查看文档
```
doc-classifier 0.0.9
A package to classify scientific documents by field of study

# Overview
A machine learning model to classify scientific documents (articles and thesis) by field of study.
#
Available languages : Arabic, French, English.
#
Training set : 117976, Test set : 50558, Accuracy : 87%.
#
Available labels : 'Sciences and technology', 'Matter sciences', 'Mathematics and computer science', 'Natural and life sciences', 'Earth and universe sciences', 'Economics, marketing and management', 'Law and political sciences', 'Literature and foreign languages', 'social and human sciences', 'Sport and physical activities', 'Health sciences', 'Architecture and urban planning'.
# Use
from doc_classifier import classify
# 
summary = 'This article analyzes the basic classification of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. It combines analysis on common algorithms in machine learning, such as decision tree algorithm, random forest algorithm, artificial neural network algorithm, SVM algorithm, Boosting and Bagging algorithm, BP algorithm. Through the development of theoretical systems, further improvement of autonomous learning capabilities, the integration of multiple digital technologies, and the promotion of personalized custom services, the purpose is to improve people's awareness of machine learning and accelerate the speed of popularization of machine learning.'
#
label = classify(summary)
#
print(label)
cda-shuju-fenxishi-201411-201501 2023.2.15.0
CDA数据分析师 201411-201501
Requires Python: >=3.6

# CDA数据分析师 201411-201501

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201411-201501
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201411-201501
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201411-201501
cda-shuju-fenxishi-201411-201501 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyisiyiyi-erlingyiwulingyi
cda-shuju-fenxishi-erlingyisiyiyi-erlingyiwulingyi <port>
# 访问 http://localhost:{port} 查看文档
```
ayanatest 1.0.0
Streaming video data via networks
Keywords: python,video,stream,video stream,camera stream,sockets


# ayanatest

Under construction! Not ready for use yet! Currently experimenting and planning!

Developed by Florian Dedov from NeuralNine (c) 2020

## Examples of How To Use (Buggy Alpha Version)

Creating A Server

```python
from ayanatest import StreamingServer

server = StreamingServer('127.0.0.1', 9999)
server.start_server()

# Other Code

# When You Are Done
server.stop_server()
```

Creating A Client
```python
from ayanatest import CameraClient
from ayanatest import VideoClient
from ayanatest import ScreenShareClient

# Choose One
client1 = CameraClient('127.0.0.1', 9999)
client2 = VideoClient('127.0.0.1', 9999, 'video.mp4')
client3 = ScreenShareClient('127.0.0.1', 9999)

client1.start_stream()
client2.start_stream()
client3.start_stream()
```

Check out: https://www.youtube.com/c/NeuralNine
cda-shuju-fenxishi-201408-201411 2023.2.15.0
CDA数据分析师 201408-201411
Requires Python: >=3.6

# CDA数据分析师 201408-201411

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201408-201411
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201408-201411
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201408-201411
cda-shuju-fenxishi-201408-201411 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyisilingba-erlingyisiyiyi
cda-shuju-fenxishi-erlingyisilingba-erlingyisiyiyi <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201405-201408 2023.2.15.0
CDA数据分析师 201405-201408
Requires Python: >=3.6

# CDA数据分析师 201405-201408

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201405-201408
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201405-201408
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201405-201408
cda-shuju-fenxishi-201405-201408 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyisilingwu-erlingyisilingba
cda-shuju-fenxishi-erlingyisilingwu-erlingyisilingba <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201312-201405 2023.2.15.0
CDA数据分析师 201312-201405
Requires Python: >=3.6

# CDA数据分析师 201312-201405

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201312-201405
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201312-201405
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201312-201405
cda-shuju-fenxishi-201312-201405 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyisanyier-erlingyisilingwu
cda-shuju-fenxishi-erlingyisanyier-erlingyisilingwu <port>
# 访问 http://localhost:{port} 查看文档
```
cda-shuju-fenxishi-201307-201312 2023.2.15.0
CDA数据分析师 201307-201312
Requires Python: >=3.6

# CDA数据分析师 201307-201312

## 下载

### Docker

```
docker pull apachecn0/cda-shuju-fenxishi-201307-201312
docker run -tid -p <port>:80 apachecn0/cda-shuju-fenxishi-201307-201312
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install cda-shuju-fenxishi-201307-201312
cda-shuju-fenxishi-201307-201312 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g cda-shuju-fenxishi-erlingyisanlingqi-erlingyisanyier
cda-shuju-fenxishi-erlingyisanlingqi-erlingyisanyier <port>
# 访问 http://localhost:{port} 查看文档
```
ai-youdao-202210-202302 2023.2.15.0
AI有道 202210-202302
Requires Python: >=3.6

# AI有道 202210-202302

## 下载

### Docker

```
docker pull apachecn0/ai-youdao-202210-202302
docker run -tid -p <port>:80 apachecn0/ai-youdao-202210-202302
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install ai-youdao-202210-202302
ai-youdao-202210-202302 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g ai-youdao-erlingereryiling-erlingersanlinger
ai-youdao-erlingereryiling-erlingersanlinger <port>
# 访问 http://localhost:{port} 查看文档
```
ai-youdao-202206-202210 2023.2.15.0
AI有道 202206-202210
Requires Python: >=3.6

# AI有道 202206-202210

## 下载

### Docker

```
docker pull apachecn0/ai-youdao-202206-202210
docker run -tid -p <port>:80 apachecn0/ai-youdao-202206-202210
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install ai-youdao-202206-202210
ai-youdao-202206-202210 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g ai-youdao-erlingererlingliu-erlingereryiling
ai-youdao-erlingererlingliu-erlingereryiling <port>
# 访问 http://localhost:{port} 查看文档
```
ai-youdao-202202-202206 2023.2.15.0
AI有道 202202-202206
Requires Python: >=3.6

# AI有道 202202-202206

## 下载

### Docker

```
docker pull apachecn0/ai-youdao-202202-202206
docker run -tid -p <port>:80 apachecn0/ai-youdao-202202-202206
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install ai-youdao-202202-202206
ai-youdao-202202-202206 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g ai-youdao-erlingererlinger-erlingererlingliu
ai-youdao-erlingererlinger-erlingererlingliu <port>
# 访问 http://localhost:{port} 查看文档
```
ai-youdao-202110-202202 2023.2.15.0
AI有道 202110-202202
Requires Python: >=3.6

# AI有道 202110-202202

## 下载

### Docker

```
docker pull apachecn0/ai-youdao-202110-202202
docker run -tid -p <port>:80 apachecn0/ai-youdao-202110-202202
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install ai-youdao-202110-202202
ai-youdao-202110-202202 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g ai-youdao-erlingeryiyiling-erlingererlinger
ai-youdao-erlingeryiyiling-erlingererlinger <port>
# 访问 http://localhost:{port} 查看文档
```
ai-youdao-202107-202110 2023.2.15.0
AI有道 202107-202110
Requires Python: >=3.6

# AI有道 202107-202110

## 下载

### Docker

```
docker pull apachecn0/ai-youdao-202107-202110
docker run -tid -p <port>:80 apachecn0/ai-youdao-202107-202110
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install ai-youdao-202107-202110
ai-youdao-202107-202110 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g ai-youdao-erlingeryilingqi-erlingeryiyiling
ai-youdao-erlingeryilingqi-erlingeryiyiling <port>
# 访问 http://localhost:{port} 查看文档
```
TimeExtral-advanced 0.1.0
Time but better

# TimeExtral_advanced

### Purpose
The purpose of this module is to provide users with convenient commands and more precise time measurements, as well as the ability to set specific values for variables such as x and y. Some examples of these commands include waitms, waitns, sleep, calctime, set-x, and set-y.


## How To Install

You can install the module TimeExtral_advanced by typing `pip install TimeExtral_advanced` or if you're using pip3: `pip3 install yourmodulenamehere`

## Examples
```py
import TimeExtral_advanced
import requests

print("The Word 'Hi' will appear in 5 seconds.")
TimeExtral_advanced.waitms(5000)
print("Hi!")
TimeExtral_advanced.sleep(5)
HTML = requests.post("https://youtube.com/").text
print("The html code of youtube.com at this moment is:\n\n\n" + HTML)
```
archivista 0.4.6
Transformador de contenidos para Pelican
Requires Python: >=3.6

# Archivista

Transformador de contenidos a la estructura y metadatos que requiere **Pelican;**
así puede mantenerse un depósito _sencillo_ en la nube con directorios y archivos
para generar los directorios y archivos en _content_.

Ofrece la ventaja de determinar los metadatos a falta de éstos; por ejemplo,
si no se tiene un _Title_ se toma el nombre del archivo MD. También destaca por
crear índices _automáticos_ cuando se colocan documentos PDF o páginas MD en directorios.

Además identifica archivos _descargables_ como PDF, XLSX, DOCX, etc. El propósito de esto
es que el conjunto de páginas, imágenes y archivos web (JPG, PNG, GIF, etc.)
se suban a un servicio de hospedaje gratuito (por ejemplo GitHub) y lo más pesado
a un _almacén frío_ como **Google Storage**.

## Historial de versiones

- 0.4.6 Metadatos JavaScripts Stylesheets
- 0.4.5 Mejor configuración y breadcrumb se puede deshabilitar
- 0.4.4 Marca error si la rama no está configurada
- 0.4.3 Metadato "Preview" para imágenes previas de artículos
- 0.4.2 Cuando hay un elemento se omite el breadcrumb
- 0.4.1 Breadcrumb
- 0.3.8 Los subdirectorios con fecha la pueden tener en cualquier parte
- 0.3.7 Los subdirectorios con fecha se ordenan del más reciente al más pasado
- 0.3.6 Al crear los mensajes son cuantitativos
- 0.3.5 Orden ascendente en subdirectorios e índices
- 0.3.4 Descarta metadatos desconocidos en los archivos md
- 0.3.3 Ya no necesita el archivo CSV, ahora puede obtener metadatos de los archivos md; también algunas configuraciones son opcionales
- 0.3.2 Comando con dos órdenes (mostrar y crear) y puede eliminar lo previo en content
- 0.3.1 Configuración en un archivo externo settings.ini
- 0.2.0 Obtener los metadatos desde un archivo CSV
- 0.1.0 Primeros experimentos

## Configuración

Debe crear un archivo `settings.ini` con las opciones

- almacen_frio: La ruta al depósito Google Storage donde van a estar los archivos descargables
- descargables_extensiones:
- eliminar_content_rama:
- fecha_por_defecto:
- imagenes_extensiones:
- indice_maximo_elementos_como_encabezado:
- nextcloud_ruta: