semvec/pypi-packages huggingface dataset:
pytei-client 0.1.0
Minimal wrapper for Hugging Face's Text Embeddings Inference.
Requires Python: >=3.5

# PyTEI
PyTEI is a minimal python interface for Hugging Face's [Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference).

PyTEI supports in-memory and persistent caching for text embeddings.

## Installation
First, clone the git repository by running:

```shell
git clone https://github.com/daniel-gomm/PyTEI.git
```

Next, install this repository as python package using pip by running the following command from the [root directory](./) 
of this repository:

```shell
pip install .
```

Add the `-e`-flag in case you want to modify the code.

## Usage
Prerequisite for using PyTEI is a running [Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference)
instance, for example a local docker container running TEI. Such a docker contain can be spun-up by running:

```shell
docker run --gpus all -p 8080:80 \
  -v $PWD/data:/data \
  --pull always ghcr.io/huggingface/text-embeddings-inference:1.6 \
  --model-id Alibaba-NLP/gte-Qwen2-1.5B-instruct
```

### TEI Client

> For more details check out the [Documentation](https://daniel-gomm.github.io/PyTEI/).

Establish a connection to TEI through a [TEIClient](./src/pytei/client.py). The client gives you access to the 
text-embedding API of the TEI instance:

```python
from pytei import TEIClient

client = TEIClient(url="127.0.0.1:8080/embed")

text_embedding = client.embed("Lorem Ipsum")
```

The default configuration uses in-memory caching of embeddings. For persistent caching use the 
[DuckDBDataStore](./src/pytei/store.py) or implement your own caching solution by extending the 
[DataStore](./src/pytei/store.py) base-class.

```python
from pytei import TEIClient
from pytei.store import DuckDBEmbeddingStore

persistent_data_store = DuckDBEmbeddingStore(db_path="data/embedding_database.duckdb")
client = TEIClient(embedding_store=persistent_data_store, url="127.0.0.1:8080/embed")

text_embedding = client.embed("Lorem Ipsum")
```

For a more detailed description and the full description of the API check out the 
[Documentation](https://daniel-gomm.github.io/PyTEI/.)
scepy 0.1.65
Your package description here
Requires Python: >=3.7

![PyPI](https://img.shields.io/pypi/v/scepy)

# SCEPY

**SCEPY** (Single-Case Experiment Python) is a Python package designed for analyzing single-case experimental data. It provides a comprehensive set of tools to prepare, analyze, and visualize data specifically tailored for single-case research designs. SCEPY enables researchers to explore detailed individual-level patterns, offering insights often overlooked in traditional group-based analyses.

To learn more about SCEPY, including installation instructions, tutorials, and API documentation, visit the [official SCEPY documentation](https://ahsankhodami.github.io/scepy/intro.html).
django-expanded-test-cases 0.8.1
Expands the existing Django TestCase class with extra functionality.
Requires Python: >=3.6
Keywords: django, testcases, tests, unittest, unittesting

# Django Expanded Test Cases


[![PyPI](https://img.shields.io/pypi/v/django-expanded-test-cases?color=blue)](https://img.shields.io/pypi/v/django-expanded-test-cases?color=blue)
[![Python Versions](https://img.shields.io/badge/python-%3E%3D3.6-brightgreen)](https://img.shields.io/badge/python-%3E%3D3.6-brightgreen)
[![Django Versions](https://img.shields.io/badge/django-%3E%3D2.2-brightgreen)](https://img.shields.io/badge/django-%3E%3D2.2-brightgreen)
[![Run Tests](https://github.com/brodriguez8774/django-expanded-test-cases/actions/workflows/test.yaml/badge.svg)](https://github.com/brodriguez8774/django-expanded-test-cases/actions/workflows/test.yaml)
[![Coverage Status](https://coveralls.io/repos/github/brodriguez8774/django-expanded-test-cases/badge.svg?branch=main)](https://coveralls.io/github/brodriguez8774/django-expanded-test-cases?branch=main)
[![Documentation Status](https://readthedocs.org/projects/django-expanded-test-cases/badge/?version=latest)](https://django-expanded-test-cases.readthedocs.io/en/latest/?badge=latest)
[![GitHub](https://img.shields.io/github/license/brodriguez8774/django-expanded-test-cases)](https://img.shields.io/github/license/brodriguez8774/django-expanded-test-cases)
[![PyPI Downloads per Month](https://img.shields.io/pypi/dm/django-expanded-test-cases.svg)](https://pypi.python.org/pypi/django-expanded-test-cases)


## Description

Expands the existing [Django](https://docs.djangoproject.com/en/dev/)
[TestCase class](https://docs.djangoproject.com/en/dev/topics/testing/overview/) with extra functionality.

Different TestCase classes are provided, each providing separate sets of functionality.


For full documentation, see [ReadTheDocs](https://django-expanded-test-cases.readthedocs.io/en/latest/).
For changes in the latest version, see [ReadTheDocs - Version History](https://django-expanded-test-cases.readthedocs.io/en/latest/version_history.html).


## Example ETC Debug Output on UnitTest Error
A main functionality that **ExpandedTestCases** provides is verbose response debug info on UnitTest errors.<br>
Below is an example of such output, when a test fails while checking the `<h1>` tag for a very simple page.

![ETC Error Debug Output Screenshot](https://user-images.githubusercontent.com/14208531/231304818-fb0dbe31-ead9-4de8-8efe-a3fc858cbddd.png)


## Installation

Install with

    pip install django-expanded-test-cases

For full color output, also install

    pip install colorama

## Package Development & Running Project Tests

As standard for Python development, this project has its own testing to ensure it functions as desired.
After installing dependencies, everything required to run tests are provided in one of two files:
* To run tests via standard django `python manage.py test`, execute the `<project_root>/runtests.py` file.
* To run tests via `pytest`, execute the `<project_root>/runpytests.py` file.
ma1522-linear-algebra_yeeshin504 0.1.0
Python scripts for NUS MA1522 Linear Algebra for Computing
Requires Python: >=3.10

# linear-algebra

## About

This project builds on SymPy's Matrix class and is designed for students taking NUS MA1522 Linear Algebra for Computing. It has implementations of most of the algorithms taught during the course (as of Sem 1 AY24/25). Documentation of the code is still a work in progress. 

### Key Features

1. Import matrices from LaTeX directly. LaTeX expression can be obtained from Canvas by right-clicking on the equations.
2. Step-by-step workings for most algorithms (including LU Factorisation and SVD)

## Installation and Usage

### Installation

#### Prerequisites

This project is best supported in a Jupyter Notebook environment with Python 3.12+. You can download Python from [here](https://www.python.org/downloads/).

#### Clone the Repository

```bash
git clone https://github.com/YeeShin504/linear-algebra.git
cd linear-algebra
```

#### Install Dependencies

It is recommended to use a virtual environment for managing dependencies.

1. Create a virtual environment:
    ```bash
    python -m venv venv
    ```

2. Activate the virtual environment:
    - On Windows:
      ```bash
      venv\Scripts\activate
      ```
    - On macOS/Linux:
      ```bash
      source venv/bin/activate
      ```

3. Install the required dependencies:
    ```bash
    pip install -r requirements.txt
    ```

### Usage

Create a Jupyter Notebook `test.ipynb`. Within the notebook, run the following code.
```python
from symbolic import *

# Create Matrix objects
A = Matrix([[1, 2, 3],
            [4, 5, 5],
            [7, 8, 9]])

b = Matrix([[1], 
            [2], 
            [3]])

# Join matrices along the columns via `row_join`. 
augmented_matrix = A.aug_line().row_join(b)

# `aug_line` adds a visual line that can be seen using `display`
display(augmented_matrix)

# Solution to the matrix equation Ax = b can be found using `solve`.
A.solve(rhs=b)

# Alternatively, the full steps with LU Factorisation can be found using `ref` with the appropriate options.
augmented_matrix.ref(matrices=2, verbosity=2)
```

Documentation of more functions can be found using the following commands.
```python
sympy_commands()
```

More usage examples can be found under the examples folder. Proper documentation would be coming soon for all functions found in `symbolic.py`.

### FAQ

## Development

### Work in Progress

- [ ] Include better documentations for how to use the functions.
- [ ] Add conditions to generate specific matrices (either randomly or symbolically).
- [ ] Better `ref` algorithm to determine boundary conditions for a given matrix.
- [ ] Offline Optical Character Recognition (OCR) support to key in entries of matrices quickly and accurately during exam conditions.

### Credits

I would like to thank [@DenseLance](https://github.com/DenseLance) for his contributions.

### License

This project is licensed under the MIT License - see the [LICENSE](LICENSE.txt) file for details.