semvec/pypi-packages huggingface dataset:
CopulaSimilarity 0.1.1
A package for locally sensitive Copula-Based Similarity Metric along with image quality metrics.
Requires Python: >=3.8

# Copula-based Similarity Metric (CSM)
A novel locally sensitive image similarity metric based on Gaussian Copula.


## 📖 Overview

Copula-based Similarity Metric (CSM) is a unique approach for measuring image similarity that leverages the properties of Gaussian copulas to provide a locally sensitive measure of similarity between images. Unlike traditional metrics, CSM is designed to capture both global and local image features, making it particularly effective for applications in medical imaging, remote sensing, and any domain requiring fine-grained image comparison.

## 🌟 Features

- **Locally Sensitive**: Captures detailed differences at a granular level.
- **Gaussian Copula-Based**: Utilizes statistical properties for robust similarity measurement.
- **Versatile Usage**: Suitable for various image types and applications.
- **Extensible**: Easily integrates with other image quality metrics like SSIM, FSIM, and ISSM.


## 🚀 Getting Started

### Installation

To install the CopulaSimilarity package, you can use pip:

```bash
pip install CopulaSimilarity
```

### Usage

you can import the package and estimate the similarity map as follow:

```
from CopulaSimilarity.CSM import CopulaBasedSimilarity as CSMSimilarity

copula_similarity = CSMSimilarity()

#load your images
image1 = cv2.imread('path_to_image1')
image2 = cv2.imread('path_to_image2')

#calculate the similarity map
csm_value = copula_similarity.compute_local_similarity(image, blurred_image)

# Optionally: you can show the similarity map using cv2 or matplotlib

#if you need a metric you can calculat the mean of the copula similarity map
csm_mean = np.mean(csm_value)
```

Other metrics can also be used, the implementation is based on (image-similarity-measures)[https://github.com/nekhtiari/image-similarity-measures/tree/master] package. you can either install it using `pip install image-similarity-measures` command, or you can also use our implementation.
To use other metrics such as SSIM FSIM and ISSM, it's very similar however they only return a value. Tutorial:

```
from similarity_metrics.fsim_quality import FSIMsimilarity
from similarity_metrics.issm_quality import ISSMsimilarity

fsim_similarity = FSIMsimilarity()
issm_similarity = ISSMsimilarity()

#load your images
image1 = cv2.imread('path_to_image1')
image2 = cv2.imread('path_to_image2')

ssim_value = fsim_similarity.fsim(image1, image2)
issm_value = issm_similarity.issm(image1, image2)
```

## 📚 Example Use Case

The example below shows a comparative study on MRI imagery, where the first frame is fixed, and similarity metrics (SSIM, FSIM, ISSM, and CSM) are calculated between that frame and subsequent ones. This highlights the differences captured by each metric, demonstrating the unique sensitivity and accuracy of CSM in various scenarios.

![csm_vs_other_metrics](images/analysis4.gif)

## Licence

The work can be used for research purposes.

<!--Please cite use if you use our implementation as following:-->
collada2obj 0.1.2
A library that can be used to easily convert a collada file (.dae) to a wavefront file (.obj).
Keywords: robotics, 3D Modelling, dae, obj


# collada2obj
[![codecov](https://codecov.io/gh/WrenchRobotics/collada2obj/graph/badge.svg?token=1UXBQPRTHV)](https://codecov.io/gh/WrenchRobotics/collada2obj)

This Python library can be used to easily convert collada (i.e., `.dae`) files
to wavefront (i.e., `.obj` ) files. This transformation between file formats for 3d
models is useful for a variety of applications, though this library was motivated
by the need for this transformation in some robotics applications.

At the moment, this library is a work in progress and is not yet fully functional.
However, the basic functionality is there and the library can be used to convert
simple collada files to obj files.

See the `examples` directory for example usages of the library.

## Installation

To install this library, you can use `pip`:

```bash
pip install collada2obj
```

### Local Installation

If you want to install this library locally (recommended only if you want to tweak/play around with `collada2obj` itself), then you can clone this repository and
install it using `pip` in the following way: 

Change to the root directory of this repo
and then run the following command:
 
```bash
pip install -e .
```

## Usage

```python
from collada2obj import ColladaFileConverter

def main(input_filename: str = "../convert_base_mesh/base.dae", output_filename: str = "./out.obj"):
    # Setup
    print(
        "Create a converter for the Collada file \"{}\" and export to OBJ file \"{}\"."
        .format(input_filename, output_filename)
    )
    converter = ColladaFileConverter(dae_filename=input_filename, obj_filename=output_filename)
    converter.export_obj()

if __name__ == '__main__':
    main()
```

## Acknowledgements

This library was initially created by the user [@georgethrax](https://github.com/georgethrax) on GitHub.
It was later expanded and refactored by the Wrench Robotics team.
shishu 0.0.1
A small example package
Requires Python: >=3.6

# shishu
## 简介
shishu是一款基于Python的开源的数学计算软件。
## 安装
```
pip install shishu
```
## 功能
shishu目前支持的功能
- a=sqrt(3) ->一个实数
a*a -> 实数
allianceauth-wiki-js 0.1.1
Simple User and group management for Wiki JS as a service of AllianceAuth
Requires Python: >=3.8
Keywords: allianceauth, eveonline

# Wiki-JS service

Simple User and group management for [Wiki JS](https://wiki.js.org/) as a service of AllianceAuth

## Setup

0. install and setup your Wiki.js instance [from the Wiki Docs](https://docs.requarks.io/)
1. activate venv
2. `pip install -U allianceauth-wiki-js`
3. add `'wikijs',` to your `INSTALLED_APPS` in your projects `local.py`
4. generate a `Full Access` API key iun the wiki with maximum expiration be sure to copy it as the key wont be shown again.
5. add the settings ( outlined below ) to your `local.py`
6. run migrations and restart auth
7. setup permissions ( outlined below )

## Permissions

| Perm                          | Codename      | Admin        | Frontend                        |
| ----------------------------- | ------------- | ------------ | ------------------------------- |
| Can access the WikiJS service | access_wikijs | -            | Gives access to Wiki.js service |
| Can add wiki js               | -             | Admin add    | -                               |
| Can change wiki js            | -             | Admin Edit   | -                               |
| Can delete wiki js            | -             | Admin Delete | -                               |
| Can view wiki js              | -             | Admin View   | -                               |

## Settings

| Setting                         | default | Description                                                     |
| ------------------------------- | ------- | --------------------------------------------------------------- |
| WIKIJS_API_KEY                  | ""      | your global API key from the wiki admin section                 |
| WIKIJS_URL                      | ""      | You Wiki's base URL                                             |
| WIKIJS_AADISCORDBOT_INTEGRATION | True    | Enables an AADiscordbot cog with the ability to search the wiki |

If you have issues with auth not being able to access the wiki due to SSL/redirection or similar. ( Cloudlfair can cause issues)

| Setting        | default    | Description                |
| -------------- | ---------- | -------------------------- |
| WIKIJS_API_URL | WIKIJS_URL | URL Overide for API access |

add this setting to your local py with a direct link to the wiki

```python
# if auth is on the same box as wiki
WIKIJS_API_URL = "http://localhost:3000"

# if auth is on a different machine you could use the public ip adress of that machine.
WIKIJS_API_URL = "http://10.0.0.150:3000"
```

## FAQ

- I lost admin when i registered my admin user.
  - add a group called `Administrators` to your auth instance and give it to anyone who needs admin on the wiki.
- I cant lock down my wiki to registered members only.
  - i had to delete a row from the database manually to remove the guest roles permissions. Ask in the AA discord.
existenz-api-fetcher 0.1.4
Easily fetch swiss weather and hydrological data from the Existenz API
Requires Python: >=3.7

# :droplet: Existenz API Fetcher

This package is designed to automate the loading and use of sensor data from the MeteoSwiss Federal Office's
and the Federal Office for the Environment's (FOEN) networks of stations and is built on top of Christian Studer's ExistenzAPIs (https://api.existenz.ch/).

Data is obtained by querying a two-year, daily time series from the InfluxDB database and is returned as a `pandas` dataframe.
Functions to compute potential evapotranspiration using the `pyet` module have been implemented.
Take a look at the Jupyter Notebooks tutorials in the examples directory to learn how to use the various functionalities of this package.

## Requirements
```
influxdb_client
pandas
pyet
folium
```

## :gear: Installation
To install the package from the PypI index:

`pip install existenz_api_fetcher`

To install in developer mode:

`pip install -e .`
v-program5 0.1.5
A Python package for training and predicting with a neural network using backpropagation.
Requires Python: >=3.6

# v_program5

v_program5 is a Python package for demonstrating basic package structure and execution. This package includes functionality for loading a dataset, training a Gaussian Naive Bayes model, and evaluating its performance.

## Installation

To install the package, use pip:

```sh
pip install v_program5
md-preview022 1.0.0
A simple Markdown preview tool for the terminal.
Requires Python: >=3.6

# mdm

`mdm` is a simple Markdown preview tool for the terminal. It allows you to quickly view the content of Markdown files directly in your terminal with formatted output.

## Development Setup

1. **Clone the Repository**

   ```bash
   git clone https://github.com/akaashvaa/preview_md_file.git
   cd mdm
   ```

2. **Install the Package Locally**

   Install the package in editable mode. This allows you to make changes to the code and see those changes without reinstalling the package.

   ```bash
   pip install -e .
   ```

3. **Install Dependencies**

   Ensure you have the required dependencies. Install them using:

   ```bash
   pip install -r requirements.txt
   ```

4. **Run and Test**

   Use the `mdm` command to test your changes. For example:

   ```bash
   mdm path/to/your/README.md
   ```

## License

This project is licensed under the MIT License. See the LICENSE file for details.
tamil-sentence-extractor 0.2
A simple Python module for extracting Tamil sentences from text.
Requires Python: >=3.6

# Tamil Sentence Extractor

Tamil Sentence Extractor is a simple Python module designed to extract Tamil sentences from a mixed-language text. This tool is particularly useful when you need to isolate Tamil text from a larger body of content that may include multiple languages.

## Installation

You can install Tamil Sentence Extractor using pip:

```bash
pip install tamil_sentence_extractor
```

## Usage

Here's an example of how to use the `extract_tamil_sentences` function:

```python
from tamil_sentence_extractor import extract_tamil_sentences

# Sample input text containing mixed languages
input_text = "யாதும் ஊரே யாவரும் கேளிர், means The world is one family in English. जीवन में மாற்றம் ஒன்றே மாறாதது यानी परिवर्तन ही स्थिर है। Telugu లో ఈ పదం అంటే మార్పు మాత్రమే స్థిరంగా ఉంటుంది Malayalam യാതും ഊരെ എല്ലാവരും സുഹൃത്താണ് എന്നതിനെക്കുറിച്ച് നമ്മൾ കണക്കാക്കിയാലോ"

# Extract Tamil sentences
tamil_sentences = extract_tamil_sentences(input_text)

# Print the extracted Tamil sentences
print(tamil_sentences)
```

### Example Output

```bash
யாதும் ஊரே யாவரும் கேளிர், மாற்றம் ஒன்றே மாறாதது
```

## Features

- **Extract Tamil Sentences**: Isolates Tamil sentences from a text that includes multiple languages.
- **Simple Integration**: Easy to integrate into existing Python projects.

## Requirements

- Python 3.6 or higher

## Contributing

If you have suggestions for improvements, feel free to fork the repository and submit a pull request. Contributions are always welcome!

## Author

- **Sathishkumar K**  
  Email: sathish30062002@gmail.com