semvec/pypi-packages huggingface dataset:
Topsis-Ridham-102216019 0.1.1
A Python package to perform TOPSIS analysis
Requires Python: >=3.6

# Topsis-Ridham-102216019

## Overview

`Topsis-Ridham-102216019` is a Python package that implements the **TOPSIS** (Technique for Order of Preference by Similarity to Ideal Solution) method for multi-criteria decision analysis (MCDA). This package allows you to evaluate and rank alternatives based on multiple criteria, considering both positive and negative impacts of each criterion.

The implementation supports reading input data from a CSV file, applying weights to the criteria, and calculating the TOPSIS score and rankings. The final results are saved to an output CSV file with the TOPSIS score and corresponding rank for each alternative.

## Features

- Normalize the decision matrix.
- Apply weights to the normalized data.
- Identify the ideal best and worst solutions based on the specified impacts.
- Calculate the distances to the ideal best and worst solutions.
- Compute the TOPSIS score for each alternative.
- Rank the alternatives based on the TOPSIS score.
- Save the results in a CSV file.

## Installation

You can install this package from PyPI using the following command:

```bash
pip Topsis-Ridham-102216019
```bash


## Usage
Please provide the filename for the CSV, including the .csv extension. After that, enter the weights vector with values separated by commas. Following the weights vector, input the impacts vector, where each element is denoted by a plus (+) or minus (-) sign. Lastly, specify the output file name along with the .csv extension.

```py -m topsis.__main__ [input_file_name.csv] [weight as string] [impact as string] [result_file_name.csv]```

## Example Usage
The below example is for the data have 5 columns.
```topsis-ridham input.csv "1,1,2,0.5,0.75" "+,+,-,-,-" output.csv ```

## Example Dataset

Fund Name | P1 | P2 | P3 | P4 | P5
------------ | ------------- | ------------ | ------------- | ------------ | ------------
M1 | 0.78 | 0.61 | 5.5 | 34.7 | 10.4
M2 | 0.88 | 0.77 | 5 | 58.4 | 16.26
M3 | 0.61 | 0.37 | 5.9 | 39.9 | 11.7
M4 | 0.76 | 0.58 | 4.2 | 57.7 | 15.81
M5 | 0.84 | 0.71 | 3.2 | 48 | 13.19
M6 | 0.76 | 0.58 | 4 | 68.8 | 18.54
M7 | 0.81 | 0.66 | 6.5 | 38.2 | 11.54
M8 | 0.81 | 0.66 | 3.2 | 32.8 | 9.37

## Output Dataset
Fund Name | P1 | P2 | P3 | P4 | P5 | TOPSIS Score | Rank
------------ | ------------- | ------------ | ------------- | ------------ | ------------ | ------------ | ------------
M1 | 0.78 | 0.61 | 5.5 | 34.7 | 10.4 | 0.45384759973942024 | 6
M2 | 0.88 | 0.77 | 5 | 58.4 | 16.26 | 0.5250616666395651 | 5
M3 | 0.61 | 0.37 | 5.9 | 39.9 | 11.7 | 0.286469615636936 | 8
M4 | 0.76 | 0.58 | 4.2 | 57.7 | 15.81 | 0.6022625270239917 | 3
M5 | 0.84 | 0.71 | 3.2 | 48 | 13.19 | 0.8452645767159974 | 2
M6 | 0.76 | 0.58 | 4 | 68.8 | 18.54 | 0.5852919015001125 | 4
M7 | 0.81 | 0.66 | 6.5 | 38.2 | 11.54 | 0.3427884605083087 | 7
M8 | 0.81 | 0.66 | 3.2 | 32.8 | 9.37 | 0.8900689209819633 | 1

<br>

## Important Points
1) There should be only numeric columns except the first column i.e. Fund Name.
2) Input file must contain atleast three columns.

<br>
subbrute 2.0.0
A lightweight subdomain brute-forcing tool with live updates via callback.
Requires Python: >=3.6

# SubBrute

SubBrute is a lightweight Python package for brute-forcing subdomains of a given domain using a wordlist.

## Installation

To install `subbrute`, run the following command:

```bash
pip install subbrute
```

## Usage

### Python Script

```python
import subbrute

domain = "example.com"
wordlist = subbrute.load("list.txt")

def the_update(subdomain, ip, status):
    if status == "valid":
        print(f"Found Subdomain: {subdomain} IP: {ip}")
    else:
        print(f"Not Found: {subdomain}")

results = subbrute.scan(domain, wordlist, callback=the_update)

print("Scan complete!")
```

### Example Wordlist (`list.txt`)

Create a text file named `list.txt` with subdomain names like this:

```plaintext
www
api
mail
test
```

### Example Output

```plaintext
Found Subdomain: www.example.com IP: 93.184.216.34
Not Found: api.example.com
Not Found: mail.example.com
Found Subdomain: test.example.com IP: 93.184.216.35
Scan complete!
```


### Customizing the Function

You can customize the live update function to display the results, log them, or perform other actions as needed. Here's an example of a more advanced function:

```python
def advanced_update(subdomain, ip, status):
    if status == "valid":
        print(f"Subdomain found: {subdomain} -> {ip}")
    else:
        print(f"Subdomain not found: {subdomain}")
```

## Thanks

Thank you for using SubBrute! 

## License

This project is licensed under the MIT License.
MZAPI 0.3.6
米粥SDK

# 米粥API (MZAPI)
[![PyPI version](https://badge.fury.io/py/MZAPI.svg)](https://pypi.org/project/MZAPI/)
[![FOSSA Status](https://app.fossa.com/api/projects/git%2Bgithub.com%2Fxiaomizhoubaobei%2FMZAPI-python.svg?type=shield)](https://app.fossa.com/projects/git%2Bgithub.com%2Fxiaomizhoubaobei%2FMZAPI-python?ref=badge_shield)

## 简介

MZAPI是一个基于Python的SDK，旨在帮助开发者快速构建和接入各种API服务。通过这个SDK，用户可以轻松地完成各种API调用。

## 安装依赖

本项目依赖可以前往[依赖](https://github.com/xiaomizhoubaobei/MZAPI-python/blob/master/dependencies.md)查看

本库已经发布到了pypi可直接安装MZAPI，安装过程中会自动安装本库所需要的依赖：

```bash
pip install MZAPI
```

## 使用方法

详细的使用方法请查看 [XMZAPI 使用方法](https://github.com/xiaomizhoubaobei/MZAPI-python/blob/master/USAGE.md)。

## 联系作者

如果你在使用XMZAPI时遇到任何问题，或者有任何建议和反馈，欢迎通过以下方式联系作者：

- **姓名**：祁筱欣
- **开发者邮件**：[mzapi@x.mizhoubaobei.top](mailto:mzapi@x.mizhoubaobei.top)
- **项目网址**：[https://github.com/xiaomizhoubaobei/XMZAPI-python](https://github.com/xiaomizhoubaobei/MZAPI-python)

## 开源许可证

MZAPI采用 [MIT许可证](LICENSE)。

## 贡献

我们非常欢迎社区的贡献！以下是一些贡献方式：

- 提交问题报告和功能请求
- 提交代码改进和修复
- 改进文档和示例

如果你对项目感兴趣，欢迎成为贡献者！更多信息请查看[贡献指南](https://github.com/xiaomizhoubaobei/MZAPI-python/blob/master/CONTRIBUTING.md)。

## License
[![FOSSA Status](https://app.fossa.com/api/projects/git%2Bgithub.com%2Fxiaomizhoubaobei%2FMZAPI-python.svg?type=large)](https://app.fossa.com/projects/git%2Bgithub.com%2Fxiaomizhoubaobei%2FMZAPI-python?ref=badge_large)
whistlingduck 0.1.0
Add your description here
Requires Python: >=3.12

<div align="center">
  <picture>
    <source media="(prefers-color-scheme: light)" srcset="logo/whistlingduck-logo.png">
    <source media="(prefers-color-scheme: dark)" srcset="logo/whistlingduck-logo.png">
    <img alt="Whistling Duck logo" src="logo/whistlingduck-logo.png" height="200">
  </picture>
</div>
<br>

# Whistling Duck

Whistling Duck is a Python library written in DuckDB for data quality logging & checks, it is created to make data quality implementation easy. [Detailed Documentation](https://thewhistlingducklib.github.io/whistlingduck/Home/)

## Why Whistling Duck?

**📦 Easy to Install -** Install like a standard Python library and implement without any
additional setup or configuration

**🛠 Easy to Use -** Write checks or log data quality status in pure Python and integrate them with new or existing data pipelines

**🧩 Easy to learn -** Everything is a Python function call, all the functions have uniform signatures for a predictable,easy-to-learn API.

**📝 Easy to validate -** All operations return JSON outputs that contain input parameters, unique identifiers, operation types, and operation timestamps, ensuring consistent logging without guesswork

**🤝 Provides Multi Support -** Works with all data structures supported by DuckDB,Pandas and Polars DataFrames, and DuckDB tables with new or existing connections.

**🪶 Minimal Dependencies -** There are only three dependencies DuckDB , Polars & Pyarrow. Which makes the package lightweight and fast

**📚 Well Documented & IDE support -** Comes with detailed documentation, docstrings, examples, and Python type hints, providing excellent editor support for code completion.

## Installation

```
pip install whistlingduck
```

## Contributions

Looking forward to contributions and suggestions! Contributions are very much welcome - feel free to open pull requests, suggest improvements, or start discussions. We aim to keep dependencies minimal to maintain simplicity and reliability. Your input helps make this project better.

## Discussions
For any suggestions, questions, ideas, please use GitHub Discussions.

### Contact 
Email: whistlingducklib@gmail.com
Jadio 0.2
JynPopMod Python Module

AUDIO/MIC utils: from Jadio import *

Hereâ€™s a complete example-based guide for **all functions** in your code, written in a consistent style:

---

### **1. Text-to-Speech**
#### **`play_audio`**
Converts a given text into speech using the `pyttsx3` library.

**Example Usage:**
```python
play_audio("Hello! How can I help you?")
# The program will audibly say: "Hello! How can I help you?"
```

---

### **2. Record Audio**
#### **`record_audio`**
Listens for audio input via a microphone and converts it to text using the Google Speech Recognition API.

**Example Usage:**
```python
text = record_audio()
print(f"You said: {text}")
# You say: "I love coding!" -> Output: "You said: I love coding!"
```

**Error Handling:**
- If the input is not recognized: *"Sorry, I couldn't understand that."*
- If there's a network issue: *"Could not request results; check your network connection."*

---

### **3. Filter Profanity**
#### **`filter_swears_in_text`**
Cleans a given string by censoring offensive words using the `better_profanity` library.

**Example Usage:**
```python
filtered_text = filter_swears_in_text("This is a damn test.")
print(filtered_text)
# Output: "This is a test."
```

---

### **4. Speech-to-Text with Profanity Filtering**
#### **`speech_to_text_with_filter`**
Captures audio input, converts it to text, and censors any offensive words.

**Example Usage:**
```python
filtered_text = speech_to_text_with_filter()
print(f"Filtered text: {filtered_text}")
# You say: "This is a damn test." -> Output: "This is a test"
```

**Error Handling:**
- If the audio isn't recognized: *"Sorry, I couldn't understand what you said."*
- If there's a network issue: *"Error with the speech recognition service."*

---

### **5. Simple Speech-to-Text**
#### **`speech_to_text`**
Captures audio input and converts it to text without any additional processing.

**Example Usage:**
```python
text = speech_to_text()
print(f"You said: {text}")
# You say: "It's a sunny day!" -> Output: "You said: It's a sunny day!"
```

**Error Handling:**
- If the input isn't recognized: *"Could not understand audio."*
- If there's a network issue: *"Could not request results."*
Jamnsee 0.2
JynPopMod Python Module

CAMERA/WEBCAM-CAM utils: from JynPopMod.utils.camera_utils import *

### 1. **JynPopMod**
This function simply prints a message that provides information about "JynPopMod," linking to the relevant GitHub repository.

**Example Usage:**
```python
JynPopMod()  # Outputs the message with a GitHub link
```

---

### 2. **capture_photo**
This function captures a photo using your computer's webcam. It uses OpenCV to access the webcam and save a captured frame as an image file (in this case, as "captured_photo.jpg").

**Example Usage:**
```python
capture_photo()  # Captures a photo and saves it as "captured_photo.jpg"
```

---

### 3. **record_video**
This function records a video from your computer's webcam for a specified duration (default is 10 seconds). It writes the video to a file (`recorded_video.avi`) using OpenCV. The video recording can also be stopped manually by pressing the 'q' key.

**Example Usage:**
```python
record_video(15)  # Records a video for 15 seconds
```

---

### 4. **get_camera_resolution**
This function gets and prints the resolution of your webcam (width x height).

**Example Usage:**
```python
get_camera_resolution()  # Prints the current camera resolution
```

---

### 5. **camera_zoom**
This function zooms in on the camera feed by a specified factor. It enlarges the image by resizing the video frame from the webcam.

**Example Usage:**
```python
camera_zoom(3.0)  # Zooms in by a factor of 3 on the webcam feed
```

---

### 6. **capture_screenshot**
This function captures a screenshot of your screen and saves it to the specified output path (like a file on your computer).

**Example Usage:**
```python
capture_screenshot("screenshot.png")  # Saves a screenshot as "screenshot.png"
```
reposlayer 0.1.4
Python repository manager
Requires Python: >=3.6
Keywords: python, repository, manager

# Reposlayer

Reposlayer is a Python repository manager that allows you to count lines of code, characters, and more in your projects.

## Installation

You can install Reposlayer using `pip`:

```bash
pip install Reposlayer
```

## Usage

```bash
Reposlayer count /path/to/repo --count-lines --count-chars
```

## Features

- Count lines of code in a file or directory
- Count characters in a file or directory
- Automatically ignore your .gitignore file
tcmaker 0.0.7
testcase maker for online judge
Requires Python: >=3.6
Keywords: testcase, online judge, OJ, hustoj

### 1. 설치 방법
```
pip install tcmaker
```

### 2. 데이터 형식 지정 예시
실행 폴더에 `format.txt`파일이 있어야 합니다. 이 파일에는 데이터의 형식을 지정합니다.

```format.txt
[Var]
n: 1~100
x: -1000~1000
m: 1~n
y: 0~1

[Format]
n m
drept(n,x,' ')
rept(m,y,'\n')
```
여기서 `rept(반복횟수,변수명,구분자)` 명령이 있습니다. `drept`명령은 사용법은 동일한데 데이터의 중복없이 반복하는 명령입니다. 값의 범위는 변수 정의에 따릅니다.

위 명령으로 생성되는 데이터의 예시
```
5 4
-12 123 2 -982
0
1
1
0
```

### 3. 실행 방법 예시
아래와 같은 파일을 만들어서 실행합니다.
```
import tcmaker

start_n = 1    # 데이터 시작 번호
end_n = 10     # 데이터 끝 번호

for i in range(start_n, end_n+1):
  format_file = 'format.txt'        # 데이터 형식 지정 파일명
  input_file = f'test{i:02d}.in'    # 만들 입력 파일명
  output_file = f'test{i:02d}.out'  # 만들 출력 파일명
  run_file = 'solve.py'             # 정답 코드 파일명
  terminal = 'PowerShell'           # 터미널 종류(PowerShell 또는 아무거나)

  tcm = tcmaker.tcmaker()
  tcm.generate(format_file, input_file, output_file, run_file, terminal)
```

### 4. 기타
아직 부족한 점이 많습니다. 변수명 정의 또는 `rept`명령에 변수와 수식 혼용 사용가능합니다. 차차 업데이트하도록 하겠습니다.
tfp_causalimpact_customized 0.2.20
A stable version of the tfcasualimpact package
Requires Python: >=3.10

# tfp_causalimpact_customized

#### A stable multi-chain rebuilt of [TFP CausalImpact](https://github.com/google/tfp-causalimpact)

## Features

- Added convergence tests to the Gibbs sampling process.

- Improved summary round to 3 digits
- Added support for Japanese fonts and characters in Matplotlib plots.
- Enhanced compatibility with Japanese data visualization requirements.
- Enhanced plotting capabilities for chain convergence visualizations.

- Enhancements Over tfcausalimpact [tfcausalimpact](https://github.com/WillianFuks/tfcausalimpact)
    - **Stability:** Resolved the issue of results changing from run to run, ensuring consistent outcomes.
      See [Result change from run to run in tfcausalimpact](https://stackoverflow.com/questions/69257795/result-change-from-run-to-run-in-tfcausalimpact).

## Getting Started

1. **Installation**
   ```bash
   uv add tfp_causalimpact_customized
   ```
2. **Plot options** (Currently only Matplotlib is supported)
   Important:y_formatter_unit must be a dictionary with the **keys** that are the same as legend_labels **y_labels**.

```python
plot_options = {
    'chart_width': 1000,
    'chart_height': 200,
    'x_label': 'Date',
    'y_labels': ['Observed1', 'Pointwise Effect1', 'Cumulative Effect1'],
    'title': 'Customized Matplotlib Plot',
    'title_font_size': 16,
    'axis_title_font_size': 14,
    'y_formatter': 'millions',
    'y_formatter_unit': {
        'Observed1': ' units',
        'Pointwise Effect1': ' effect',
        'Cumulative Effect1': ' total'
    },
    'legend_labels': {
        'mean': 'Average',
        'observed': 'Observed',
        'pointwise': 'Pointwise Effect',
        'cumulative': 'Cumulative Effect',
        'pre-period-start': 'Start of Pre-Period',
        'pre-period-end': 'End of Pre-Period',
        'post-period-start': 'Start of Post-Period',
        'post-period-end': 'End of Post-Period'
    }
}
   ```