semvec/pypi-packages huggingface dataset:
shuzi-julian 2024.3.3.0
数字巨链
Requires Python: >=3.6

# 数字巨链

## 下载

### Docker

```
docker pull apachecn0/shuzi-julian
docker run -tid -p <port>:80 apachecn0/shuzi-julian
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install shuzi-julian
shuzi-julian <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g shuzi-julian
shuzi-julian <port>
# 访问 http://localhost:{port} 查看文档
```
roipy 1.3.8
Plotting brain regions of interest (ROI) for demonstration purposes in Python
Requires Python: >=3.8

# roipy

Plotting brain regions of interest (ROI) for demonstration purposes in Python

## Description

Have you ever wanted to plot a region of the brain to include in a talk or presentation? What about in a lecture? Some might arbitrarily highlight the area of the cortex in which they _think_ a particular region might fall. Others might scour the internet for images. Well...you won't need to do that any more. With `roipy`, you can plot exact regions of the brain using the [**Destrieux Atlas**](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2937159/).

## Installation

This package is hosted on [PyPi](https://pypi.org/project/roipy/). You may use `pip` to install.

```bash
pip install roipy
```

## Usage

There are three types of brain plots included in `roipy`. A "univariate" plot, a "multivariate" plot and a "whole brain multivariate" plot. There are two methods for generating each plot:

1. Using the domain general class, `roipy.plotting.Plot()`
2. Executing each individual plotting method via its own specific function (e.g., `roipy.utils.plot_multivariate()`)

Below is an example of each methods implementation.

```python
# import
from roipy.plotting import Plot
from roipy.utils import plot_multivariate

# Domain general Plot()
P = Plot()
P.plot_multivariate(roi=[1,3,5,34], cmap='viridis')

#Individual function
plot_multivariate(roi=[1,3,5,34], shape='pial', view='lateral', hemi='left', cmap='viridis')

# Save the plot
import matplotlib.pyplot as plt
plt.savefig('brain.png')

```

The parameter `roi` is given a list of integers which correspond to the Destrieux surface atlas. 

You can also animate the plots with `roipy.animation.Anim()`. 

```python
# import
from roipy.animation import Anim
from roipy.plotting import Plot

# Plot() instance
P = Plot()

# Anim() instance
A = Anim()
a.animate(lambda frame: P.plot_multivariate(roi=[1,3,5,34], cmap='viridis'), fname='brain.gif', save=True)
```
>Admittedly, this is more complex as it requires use of the `lambda` function in Python. But, using `roipy.plotting.utils.plot_univariate`, you can devise your own animation style.

## Example brain

![brain](/brain.png)
shiyong-spring-cloud-yu-docker-shizhan-wei-fuwu 2024.3.3.0
使用Spring Cloud与Docker实战微服务
Requires Python: >=3.6

# 使用Spring Cloud与Docker实战微服务

## 下载

### Docker

```
docker pull apachecn0/shiyong-spring-cloud-yu-docker-shizhan-wei-fuwu
docker run -tid -p <port>:80 apachecn0/shiyong-spring-cloud-yu-docker-shizhan-wei-fuwu
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install shiyong-spring-cloud-yu-docker-shizhan-wei-fuwu
shiyong-spring-cloud-yu-docker-shizhan-wei-fuwu <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g shiyong-spring-cloud-yu-docker-shizhan-wei-fuwu
shiyong-spring-cloud-yu-docker-shizhan-wei-fuwu <port>
# 访问 http://localhost:{port} 查看文档
```
httpSocketGateway 0.3.0.1
A small socket gateway server. A simple socket client for sending json
Requires Python: >=3.8

# Gateway Server or Client

This is *client* and *server* code for a gateway between internal and external network. That's to provide a way to communicate between the inside and the outside of network environment.

NOT FOR PRODUCTION USE

## Usage

*use README.md from github*

### Gateway

Receive **http request** (with json and files) and **send json with http properties** to the WSClient (socket) and **wait response in json** from client to send it back to the http request (only json).

### WSClient

**modify `process_message` method** to handle, process and respond to the message *received* from the gateway.

You can also send json without modification to communicate with any other server (ws server) with the **simple** parameter.

## Example

### Client

#### Send a request

```python
# Exemple of use
from httpSocketGateway import WSClient
import time

class CustomWSClient(WSClient):

    # Override the process_message method
    def process_message(self, message, files_path):
        print("You receive", message)
        return {"success": "Hello World"}, []

client = CustomWSClient('localhost', 8080, reconnect=True, simple=True)
client.run()
time.sleep(1)
client.send({
        "Hello": "World"
})
time.sleep(30)
client.stop()
```

#### Receive a request

You should override the `process_message` method from `WSClient` class.

```python
from httpSocketGateway import WSClient
import time

class WebSocketClient(WebSocketBaseClient):
    def process_message(self, message, files_path):
        print(f"Received: {message}")
        return {"success": "Hello World"}, []

client = WebSocketClient("localhost", 8080, simple=True)
client.run()
time.sleep(1)
client.close()
```

**simple** parameter is used to send json without modification to match with the **gateway server**.

With **simple** parameter you can send json to any other server.


### Gateway

**TODO**
shiyong-ionic2-zhizuo-yidong-yingyong 2024.3.3.0
使用Ionic2制作移动应用
Requires Python: >=3.6

# 使用Ionic2制作移动应用

## 下载

### Docker

```
docker pull apachecn0/shiyong-ionic2-zhizuo-yidong-yingyong
docker run -tid -p <port>:80 apachecn0/shiyong-ionic2-zhizuo-yidong-yingyong
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install shiyong-ionic2-zhizuo-yidong-yingyong
shiyong-ionic2-zhizuo-yidong-yingyong <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g shiyong-ionic2-zhizuo-yidong-yingyong
shiyong-ionic2-zhizuo-yidong-yingyong <port>
# 访问 http://localhost:{port} 查看文档
```
shishang-zui-jiandan-de-intellij-idea-jiaocheng 2024.3.3.0
史上最简单的 IntelliJ IDEA 教程
Requires Python: >=3.6

# 史上最简单的 IntelliJ IDEA 教程

## 下载

### Docker

```
docker pull apachecn0/shishang-zui-jiandan-de-intellij-idea-jiaocheng
docker run -tid -p <port>:80 apachecn0/shishang-zui-jiandan-de-intellij-idea-jiaocheng
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install shishang-zui-jiandan-de-intellij-idea-jiaocheng
shishang-zui-jiandan-de-intellij-idea-jiaocheng <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g shishang-zui-jiandan-de-intellij-idea-jiaocheng
shishang-zui-jiandan-de-intellij-idea-jiaocheng <port>
# 访问 http://localhost:{port} 查看文档
```
shenruqianchu-java-web 2024.3.3.0
深入浅出Java Web
Requires Python: >=3.6

# 深入浅出Java Web

## 下载

### Docker

```
docker pull apachecn0/shenruqianchu-java-web
docker run -tid -p <port>:80 apachecn0/shenruqianchu-java-web
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install shenruqianchu-java-web
shenruqianchu-java-web <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g shenruqianchu-java-web
shenruqianchu-java-web <port>
# 访问 http://localhost:{port} 查看文档
```
easymerge 0.4
A simple Python library for merging dataframes accommodating variations in language, spelling, and typos.

## EasyMerge: DataFrame Merger

### Overview

The `merge` function in this Python library facilitates the iterative merging of multiple DataFrames based on a specified column. This functionality is particularly useful when dealing with diverse datasets that may contain variations in language, spelling, or typos.

### Installation

You can install the library using pip:

```bash
pip install easymerge
```

### Usage
Consider the following example where we have multiple DataFrames containing information about countries:

Dataframe 1:

| Country | Capital       |
|-------------------|---------------|
| USA               | Washington DC|
| China             | Beijing       |
| India             | New Delhi     |
| FrAnce            | Paris         |
| Canada            | Ottawa        |

Dataframe 2:

| Country | Population (in Millions) |
|-------------------|---------------------------|
| Canada            | 38.25                     |
| United States     | 331.90                    |
| China             | 1410.78                   |
| Indía             | 1417.00                   |


Dataframe 3:

| Country | Continent     |
|-------------------|---------------|
| UsA               | North America|
| ChinA             | Asia          |
| indIa             | Asia          |
| france            | Europe        |
| CaNada            | North America|



We can merge these DataFrames using the merge function:

```python

from easymerge import merger
import pandas as pd

# Define the DataFrames
df1 = pd.DataFrame({
'Country': ['USA', 'China', 'India','FrAnce', 'Canada'],
'Capital': ['Washington DC', 'Beijing', 'New Delhi', 'Paris', 'Ottawa']
})

df2 = pd.DataFrame({
'Country': ['canada','United States', 'China', 'Indía'],=
'Population (in Millions)': [38.25, 331.9, 1410.78, 1417]
})

df3 = pd.DataFrame({
'Country': ['UsA', 'ChinA', 'indIa','france', 'CaNada'],
'Continent ': ['North America', 'Asia', 'Asia', 'Europe', 'North America']
})

# Perform iterative merging
merged_df = merger.merge(df1, df2, df3, column_name='Country')

print(merged_df)


```


This code will merge the provided DataFrames based on the "Country" column, accounting for variations in case and spelling. The resulting DataFrame will contain all the information merged based on the specified column.

Merged Dataframe:

| Country | Continent     | Population (in Millions) | Capital       |
|-------------------|---------------|--------------------------|---------------|
| USA               | North America| 331.90                   | Washington DC|
| China             | Asia          | 1410.78                  | Beijing       |
| India             | Asia          | 1417.00                  | New Delhi     |
| FrAnce            | Europe        | NaN                      | Paris         |
| Canada            | North America| 38.25                    | Ottawa        |
shenru-lijie-openstack-zidonghua-bushu 2024.3.3.0
深入理解OpenStack自动化部署
Requires Python: >=3.6

# 深入理解OpenStack自动化部署

## 下载

### Docker

```
docker pull apachecn0/shenru-lijie-openstack-zidonghua-bushu
docker run -tid -p <port>:80 apachecn0/shenru-lijie-openstack-zidonghua-bushu
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install shenru-lijie-openstack-zidonghua-bushu
shenru-lijie-openstack-zidonghua-bushu <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g shenru-lijie-openstack-zidonghua-bushu
shenru-lijie-openstack-zidonghua-bushu <port>
# 访问 http://localhost:{port} 查看文档
```
shengwu-xinxixue-zhaji-disanban 2024.3.3.0
生物信息学札记（第三版）
Requires Python: >=3.6

# 生物信息学札记（第三版）

## 下载

### Docker

```
docker pull apachecn0/shengwu-xinxixue-zhaji-disanban
docker run -tid -p <port>:80 apachecn0/shengwu-xinxixue-zhaji-disanban
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install shengwu-xinxixue-zhaji-disanban
shengwu-xinxixue-zhaji-disanban <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g shengwu-xinxixue-zhaji-disanban
shengwu-xinxixue-zhaji-disanban <port>
# 访问 http://localhost:{port} 查看文档
```
shengwu-xinxixue-jiangyi-jiegou-xinxi-bufen-gonghaipeng 2024.3.3.0
生物信息学讲义：结构信息部分（龚海鹏）
Requires Python: >=3.6

# 生物信息学讲义：结构信息部分（龚海鹏）

## 下载

### Docker

```
docker pull apachecn0/shengwu-xinxixue-jiangyi-jiegou-xinxi-bufen-gonghaipeng
docker run -tid -p <port>:80 apachecn0/shengwu-xinxixue-jiangyi-jiegou-xinxi-bufen-gonghaipeng
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install shengwu-xinxixue-jiangyi-jiegou-xinxi-bufen-gonghaipeng
shengwu-xinxixue-jiangyi-jiegou-xinxi-bufen-gonghaipeng <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g shengwu-xinxixue-jiangyi-jiegou-xinxi-bufen-gonghaipeng
shengwu-xinxixue-jiangyi-jiegou-xinxi-bufen-gonghaipeng <port>
# 访问 http://localhost:{port} 查看文档
```
shendu-xuexi-python-jiaocheng-shiyong-keras-python-theano-he-tensorflow-kaifa-shendu-xuexi-moxing 2024.3.3.0
深度学习：Python 教程：使用 Keras、Python、Theano 和 TensorFlow 开发深度学习模型
Requires Python: >=3.6

# 深度学习：Python 教程：使用 Keras、Python、Theano 和 TensorFlow 开发深度学习模型

## 下载

### Docker

```
docker pull apachecn0/shendu-xuexi-python-jiaocheng-shiyong-keras-python-theano-he-tensorflow-kaifa-shendu-xuexi-moxing
docker run -tid -p <port>:80 apachecn0/shendu-xuexi-python-jiaocheng-shiyong-keras-python-theano-he-tensorflow-kaifa-shendu-xuexi-moxing
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install shendu-xuexi-python-jiaocheng-shiyong-keras-python-theano-he-tensorflow-kaifa-shendu-xuexi-moxing
shendu-xuexi-python-jiaocheng-shiyong-keras-python-theano-he-tensorflow-kaifa-shendu-xuexi-moxing <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g shendu-xuexi-python-jiaocheng-shiyong-keras-python-theano-he-tensorflow-kaifa-shendu-xuexi-moxing
shendu-xuexi-python-jiaocheng-shiyong-keras-python-theano-he-tensorflow-kaifa-shendu-xuexi-moxing <port>
# 访问 http://localhost:{port} 查看文档
```
redis-kaifa-yunwei-shijian-zhinan 2024.3.3.0
Redis开发运维实践指南
Requires Python: >=3.6

# Redis开发运维实践指南

## 下载

### Docker

```
docker pull apachecn0/redis-kaifa-yunwei-shijian-zhinan
docker run -tid -p <port>:80 apachecn0/redis-kaifa-yunwei-shijian-zhinan
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install redis-kaifa-yunwei-shijian-zhinan
redis-kaifa-yunwei-shijian-zhinan <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g redis-kaifa-yunwei-shijian-zhinan
redis-kaifa-yunwei-shijian-zhinan <port>
# 访问 http://localhost:{port} 查看文档
```
real-time-rendering-3rd-tilian-zongjie 2024.3.3.0
《Real-Time Rendering 3rd》 提炼总结
Requires Python: >=3.6

# 《Real-Time Rendering 3rd》 提炼总结

## 下载

### Docker

```
docker pull apachecn0/real-time-rendering-3rd-tilian-zongjie
docker run -tid -p <port>:80 apachecn0/real-time-rendering-3rd-tilian-zongjie
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install real-time-rendering-3rd-tilian-zongjie
real-time-rendering-3rd-tilian-zongjie <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g real-time-rendering-3rd-tilian-zongjie
real-time-rendering-3rd-tilian-zongjie <port>
# 访问 http://localhost:{port} 查看文档
```
readings-in-database-systems-5th-edition 2024.3.3.0
Readings in Database Systems, 5th Edition
Requires Python: >=3.6

# Readings in Database Systems, 5th Edition

## 下载

### Docker

```
docker pull apachecn0/readings-in-database-systems-5th-edition
docker run -tid -p <port>:80 apachecn0/readings-in-database-systems-5th-edition
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install readings-in-database-systems-5th-edition
readings-in-database-systems-5th-edition <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g readings-in-database-systems-5th-edition
readings-in-database-systems-5th-edition <port>
# 访问 http://localhost:{port} 查看文档
```