semvec/pypi-packages huggingface dataset:
dranspose 0.2.0
A stream matrix transposition framework
Requires Python: >=3.10

# dranspose

dranspose is a framework to perform a distributed transpose from multiple streams of partial events into parallel workers with full events.

This is useful e.g. for assembling image frames which are captured by separate detectors.

* [Documentation](https://felix-engelmann.github.io/dranspose/)

## Installation

To install the package run

    pip install dranspose

## Usage

The `dranspose` cli wrapper allows to start different parts of the system or run them combined

## Testing

To get debug logs for the tests which are useful when debugging use

    pytest --log-cli-level=DEBUG
insipid-sphinx-theme 0.4.4
An insipid Sphinx theme
Requires Python: >=3.6
Keywords: mobile-friendly

An Insipid Sphinx_ Theme
========================

.. _Sphinx: https://www.sphinx-doc.org/

Quick Start
    #. Install the Python package ``insipid-sphinx-theme``
    #. Add ``html_theme = 'insipid'`` to your ``conf.py``
    #. Run Sphinx!

Online documentation (and example of use)
    https://insipid-sphinx-theme.readthedocs.io/

Source code repository (and issue tracker)
    https://github.com/mgeier/insipid-sphinx-theme/

License
    BSD-2-Clause (same as Sphinx itself),
    for more information take a look at the ``LICENSE`` file.

    SVG icons from `Font Awesome`__, `CC BY 4.0`__.

    __ https://fontawesome.com/
    __ https://creativecommons.org/licenses/by/4.0/
grogupy 0.4.0
Python package calculating relativistic magnetic interactions  for the generalised Heisenberg model.
Requires Python: >=3.9

# grogupy: Relativistic magnetic interactions from non-orthogonal basis sets

[![PyPI version](https://badge.fury.io/py/grogupy.svg)](https://badge.fury.io/py/grogupy)
[![DOI](https://zenodo.org/badge/950552693.svg)](https://doi.org/10.5281/zenodo.15449541)
![Static Badge](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12%20%7C%203.13%20%7C-blue)
![Static Badge](https://img.shields.io/badge/License-MIT-blue)
![Static Badge](https://img.shields.io/badge/Code%20style-Black-black)

grogupy is an open-source package created to easily extract magnetic interaction 
parameters from density functional theory (DFT) calculations. It can also handle 
very large systems, with hundreds of atoms in the unit cell using GPU 
acceleration. Because the underlying theory focuses on non-orthogonal, LCAO, 
basis sets, the most straightforward software, and our recommendation to use for 
the DFT part is [Siesta](https://siesta-project.org/siesta). In principle any 
plane wave based DFT software could be used with Wannierization, but the magnetic 
parameters are very sensitive to the atomic positions and Wannier orbitals might 
be off centered. grogupy can extract magnetic parameters from different levels 
of theoretical complexity, for example it can use as input collinear, 
non-collinear and spin-orbit Hamiltonians using 
[sisl](https://sisl.readthedocs.io/en/latest/index.html). More on the 
theoretical background can be found on [arXiv](https://arxiv.org/abs/2309.02558). 
grogupy was created by the [TRILMAX Consortium](https://trilmax.elte.hu).

## Features ##

- Flexible API to set up more complicated systems with complex magnetic entities
- Command line interface to extract and visualize the magnetic parameters
- Multiple output formats for atomistic spin dynamics softwares
- Interactive visualizations for system exploration

## Tutorials and examples ##

You can start with the 
[Quick start guides](https://grogupy.readthedocs.io/en/latest/quickstart/index.html) 
or learn more about the best practices in the 
[Tutorials](https://grogupy.readthedocs.io/en/latest/tutorials/index.html) section.

## Documentation ##

The documentation can be found 
[here](https://grogupy.readthedocs.io/), and the API reference is 
[here](https://grogupy.readthedocs.io/en/latest/API/index.html).

## Installation ##

grogupy can be installed using `pip`:

```bash
pip install grogupy
```

Or you can install optional dependecies, like visualization packages or packages 
for MPI or GPU acceleration:

```bash
pip install grogupy[viz,mpi,gpu]
```
llama-index-llms-mistralai 0.9.0
llama-index llms mistral ai integration
Requires Python: <4.0,>=3.9

# LlamaIndex Llms Integration: Mistral

## Installation

Install the required packages using the following commands:

```bash
%pip install llama-index-llms-mistralai
!pip install llama-index
```

## Basic Usage

### Initialize the MistralAI Model

To use the MistralAI model, create an instance and provide your API key:

```python
from llama_index.llms.mistralai import MistralAI

llm = MistralAI(api_key="<replace-with-your-key>")
```

### Generate Completions

To generate a text completion for a prompt, use the `complete` method:

```python
resp = llm.complete("Paul Graham is ")
print(resp)
```

### Chat with the Model

You can also chat with the model using a list of messages. Here’s an example:

```python
from llama_index.core.llms import ChatMessage

messages = [
    ChatMessage(role="system", content="You are CEO of MistralAI."),
    ChatMessage(role="user", content="Tell me the story about La plateforme"),
]
resp = MistralAI().chat(messages)
print(resp)
```

### Using Random Seed

To set a random seed for reproducibility, initialize the model with the `random_seed` parameter:

```python
resp = MistralAI(random_seed=42).chat(messages)
print(resp)
```

## Streaming Responses

### Stream Completions

You can stream responses using the `stream_complete` method:

```python
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
    print(r.delta, end="")
```

### Stream Chat Responses

To stream chat messages, use the following code:

```python
messages = [
    ChatMessage(role="system", content="You are CEO of MistralAI."),
    ChatMessage(role="user", content="Tell me the story about La plateforme"),
]
resp = llm.stream_chat(messages)
for r in resp:
    print(r.delta, end="")
```

## Configure Model

To use a specific model configuration, initialize the model with the desired model name:

```python
llm = MistralAI(model="mistral-medium")
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
    print(r.delta, end="")
```

## Function Calling

You can call functions from the model by defining tools. Here’s an example:

```python
from llama_index.llms.mistralai import MistralAI
from llama_index.core.tools import FunctionTool


def multiply(a: int, b: int) -> int:
    """Multiply two integers and return the result."""
    return a * b


def mystery(a: int, b: int) -> int:
    """Mystery function on two integers."""
    return a * b + a + b


mystery_tool = FunctionTool.from_defaults(fn=mystery)
multiply_tool = FunctionTool.from_defaults(fn=multiply)

llm = MistralAI(model="mistral-large-latest")
response = llm.predict_and_call(
    [mystery_tool, multiply_tool],
    user_msg="What happens if I run the mystery function on 5 and 7",
)
print(str(response))
```

### LLM Implementation example

https://docs.llamaindex.ai/en/stable/examples/llm/mistralai/
hashdb-cli 0.1.3
Access the open analysis hashdb via cli
Requires Python: ~=3.10

# HashDB CLI
For information about hashdb take a look at https://hashdb.openanalysis.net. This is a small python CLI which allows querying for hashes/algorithms from the commandline.

## Installation

```
pip install hashdb-cli
```

## Usage

```
Usage: hashdb [OPTIONS] COMMAND [ARGS]...

Options:
  --install-completion [bash|zsh|fish|powershell|pwsh]
                                  Install completion for the specified shell.
  --show-completion [bash|zsh|fish|powershell|pwsh]
                                  Show completion for the specified shell, to
                                  copy it or customize the installation.
  --help                          Show this message and exit.

Commands:
  add         Add a new string to hashdb
  algorithms  Load and dump available algorithms.
  get         Get original strings for a given algorithm and a hash.
  hunt        Check if given hashes are available via different hash...
  resolve     Try to hunt for a single hash and grab the string afterwards.
  string      Get information about a string which is already available...
```

### List algorithms

```
hashdb algorithms (--description)
```

### Get string
Hashdb requires the hash to be an unsigned integer, however, hex strings can be used in combination with `-h/--hex` parameter.

```
hashdb get <algo_name> <h1> <h2> <h3> ... <hn> (--hex)
```

### Hunt
Pass multiple hashes to the API in order to find a fitting algorithm.

```
hashdb hunt <h1> <h2> <h3> ... <hn> (--hex)
```

### Resolve
Combination of hunt and get.

```
hashdb resolve <h1> ... <hn> (--hex)
```

### String
Get information about a string in the database

```
hashdb string <string>
```
bkdetect 0.2.1
Инструмент поиска источников текста и совпадающих фрагментов.
Requires Python: >=3.9

## bk_detect

Инструмент для поиска документов-источников и совпадающих фрагментов по заданному тексту.

![Project Preview](img/img.png)

### Основные возможности
- Индексация файлов `.txt`, `.csv`, `.docx`, `.html` с разбиением на строки или параграфы.
- Нормализация текста: удаление HTML, фильтрация стоп-слов, стемминг для русского и английского языков.
- Поиск схожих документов на базе HashingVectorizer из scikit-learn.
- Вывод оценок схожести и конкретных фрагментов, пересекающихся с запросом.

### Установка
```bash
pip install -r requirements.txt
```
или
```bash
pip install .
```
После установки доступна команда `bk-detect`.

### Быстрый старт
```bash
bk-detect sample_docs query/query.txt
```

### Опции CLI

| Опция | Описание |
|-------|----------|
| `--language {ru,en}` | Язык текста (по умолчанию `ru`). |
| `--chunk-size <int>` | Размер порции документов при индексации. |
| `--top-k <int>` | Сколько документов выводить в верхнем списке. |
| `--max-positions <int>` | Максимум фрагментов на один файл. |
| `--snippet-len <int>` | Длина выводимого фрагмента. |
| `--no-stemming` | Отключить стемминг. |
| `--keep-stopwords` | Оставить стоп-слова. |

### Архитектура модулей
- `documents.py` — модель документа и метаданных.
- `loaders.py` — чтение файлов и выделение текстовых фрагментов.
- `text_pipeline.py` — очистка, токенизация и стемминг текста.
- `indexing.py` — построение индекса и расчёт схожести.
- `source_finder.py` — фасад для поиска документов и совпадающих позиций.
- `bkDetetct.py` — CLI-интерфейс.