semvec/pypi-packages huggingface dataset:
searchmaude 2.0.0
This Query Tool is a Python-based GUI application specifically designed for querying and analyzing medical device adverse event report data from the US FDA MAUDE (Manufacturer and User Facility Device Experience) database.
Requires Python: >=3.7

# searchmaude

SearchMaude is a Python-based GUI application specifically designed for querying and analyzing medical device adverse event report data from the US FDA MAUDE (Manufacturer and User Facility Device Experience) database.
## Features

Exact Query: Search by exact match of device generic names
Related Query: Search for relevant keywords in report text for broader searches
Time Range Filtering: Support for custom query time ranges
Result Quantity Control: Configurable number of returned results (up to 1000)
Individual Case Query: View detailed individual adverse event reports

## Summary Statistical Analysis:

Event type statistics
Product problem statistics
Injury outcome statistics
Product list statistics
Manufacturer statistics

Individual Case Export: Export individual case data to Excel format
Summary Data Export: Export statistical summary data to Excel
Multiple Formats: Complete data and simplified data worksheets

## Installation

You can install EasyPV via PyPI:

pip install searchmaude
tileops 0.0.1.dev2
TileOPs kernels for efficient inference
Requires Python: >=3.8

# TileOPs (TOP)

**TileOPs (TOP)** is a high-performance machine learning operator collections built on top of [TileLang](https://github.com/tile-ai/tilelang). It offers efficient, modular, and composable implementations optimized for AI workloads.

Note: TileOPs is still under rapid development.

---

![DeepSeek-V3.2-Exp DeepSeek Sparse Attention (DSA) performance on H800 SXM](https://raw.githubusercontent.com/tile-ai/TileOPs/main/docs/figures/sparse_mla_perf.png)
*DeepSeek-V3.2-Exp DeepSeek Sparse Attention (DSA) performance on H800 SXM*

## 📦 Installation

### Requirements

- Python 3.8+
- PyTorch >= 2.1
- GLIBCXX_3.4.32
- [TileLang](https://github.com/tile-ai/tilelang)

### Method 1: Install with Pip

```bash
pip install tileops
```

### Method 2: Install from source (editable mode for development)

```bash
git clone https://github.com/tile-ai/TileOPs
cd TileOPs
pip install -e '.[dev]' -v # remove -e option if you don't want to install in editable mode, -v for verbose output
```

## 🚀 Quick Usage

### Sparse MLA

```python
import torch
from top import SparseMLAKernel

batch_size = 1
seq_len = 1024
seq_len_kv = 2048
q_start_index_s = 1024
n_heads = 128
head_dim = 512
tail_dim = 64
topk = 2048
kv_stride = 1
kv_group = 1
sm_scale = None

sparse_mla = SparseMLAKernel(
    batch=batch_size,
    seq_len=seq_len,
    seq_len_kv=seq_len_kv,
    q_start_index_s=q_start_index_s,
    heads=n_heads,
    dim=head_dim,
    tail_dim=tail_dim,
    topk=topk,
    kv_stride=kv_stride,
    kv_group=kv_group,
    sm_scale=sm_scale,
    is_casual=True,
    dtype=torch.bfloat16,
    device='cuda',
)

# Evaluate the Sparse MLA kernel performance
sparse_mla.check()
latency = sparse_mla.profile()
print(f"Latency: {latency:.4f} ms")
print(f'fwd tflops = ',
        (batch_size * seq_len * (head_dim + tail_dim + head_dim) * topk * 2 * n_heads) / (latency * 1e-3) / 1e12)
```

### MLA

```python
import torch
import top
from top import MLAKernel

device = "cuda"
dtype = torch.float16

batch = 128
heads = 64
kv_heads = 1
kv_ctx = 8192
dim = 512
pe_dim = 64

# Query input: [batch, heads, dim]
q = torch.randn(batch, heads, dim, device=device, dtype=dtype)

# Query positional encoding: [batch, heads, pe_dim]
q_pe = torch.randn(batch, heads, pe_dim, device=device, dtype=dtype)

# KV cache input: [batch, kv_ctx, kv_heads, dim]
kv = torch.randn(batch, kv_ctx, kv_heads, dim, device=device, dtype=dtype)

# KV positional encoding: [batch, kv_ctx, kv_heads, pe_dim]
k_pe = torch.randn(batch, kv_ctx, kv_heads, pe_dim, device=device, dtype=dtype)

# Use MLA kernel
block_N = 64
block_H = 64
num_split = 1

mla = MLAKernel(batch, heads, kv_heads, kv_ctx, dim, pe_dim, block_N, block_H, num_split)

out = mla(q, q_pe, kv, k_pe)
```

## Acknowledgments
cmlibs.exporter 0.10.1
Export CMLibs Zinc data to common mesh formats.
Requires Python: >=3.8
Keywords: Export, CMLibs, Zinc


===============
CMLibs Exporter
===============

CMLibs exporter classes.  This software can be found on PyPi and installed with the following command::

  pip install cmlibs.exporter

When using the thumbnail exporter there are additional requirements for hardware or software rendering.
To install the thumbnail exporter with support for hardware rendering install *cmlibs.exporter* with::

  pip install 'cmlibs.exporter[opengl_hardware]'

To install the thumbnail exporter with support for software rendering install *cmlibs.exporter* with::

  pip install 'cmlibs.exporter[opengl_software]'

To force the use of the software renderer even when hardware rendering is available, set an environment variable like so::

  CMLIBS_EXPORTER_RENDERER=osmesa

either in the environment the exporter is run in or before calling the export thumbnail method.

Distribution
============

This software uses regex to extract the version number information from the package. The version number for this package is stored in 'src/cmlibs/exporter/__init__.py'
hack4u-project-jonar3107 0.5.0
Una biblioteca para consultar cursos de hack4u

# Hack4u Academy Courses Library

UNa biblioteca Python para consultar cursos de la academia Hack4u

## Cursos disponibles:

- Introduccion a Linux [15 horas]
- Personalizacion de Linux [3 horas]
- Introduccion al Hacking [53 horas]

## Instalacion
Instala el paquete usando `pip3`:

```python3
pip3 install hack4u
```

## Uso basico

## Listar todos los cursos

```python
from hack4u import list_courses

from course in list_courses():
    print(course)
```
## Obtener un curso por nombre

```python
from hacl4u import get_course_by_name

course = get_course_by_name("Introduccion a Linux")
print(course)
```
## Calcular duracion total de los cursos

```python3
from hack4u.utils import total_duration

print(f"Duracion total: {total_duration()} horas")
```
django-admin-contexts 0.4.0
Contexts for Django Admin homepage
Requires Python: >=3.9

# django-admin-contexts

![Python Compatibility](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue.svg)
![Django Compatibility](https://img.shields.io/badge/django-4.2%20|%205.0%20|%205.1%20|%205.2-%2344B78B?labelColor=%23092E20)
[![PyPi Version](https://img.shields.io/pypi/v/django-admin-contexts.svg)](https://pypi.python.org/pypi/django-admin-contexts)
![CI badge](https://github.com/matagus/django-admin-contexts/actions/workflows/ci.yml/badge.svg)
[![codecov](https://codecov.io/gh/matagus/django-admin-contexts/graph/badge.svg?token=a64SxEDQk0)](https://codecov.io/gh/matagus/django-admin-contexts)
[![License](https://img.shields.io/badge/License-BSD_3--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)

Display only a set of apps and models in the Django Admin homepage, based on the chosen context. This is useful when
you have a lot of apps and models and you want to focus on a specific set of them.

![Demo: selecting a context to dsiplay only a subset of models](https://raw.githubusercontent.com/matagus/django-admin-contexts/main/screenshots/demo.gif)


Installation
============

Via `pip` command:

```bash
pip install django-admin-contexts
```

...or you can clone the repo and install it using `pip` too:

```bash
git clone git://github.com/matagus/django-admin-contexts.git
cd django-admin-contexts
pip install -e .
```

then add `django_admin_contexts` to your `settings.py` **before** `django.contrib.admin`:

```python
INSTALLED_APPS = (
    # ...
    "django_admin_contexts",
    # ...
    "django.contrib.admin",
    # ...
)
```

then run the migrations:

```bash
python manage.py migrate
```

Usage
=====

Browse to the Django Admin and create some contexts in the "Django Admin Contexts" section, associating them with one
or more models.

Then, you can see the contexts in the Django Admin homepage.


Contributing
============

Contributions are welcome! ❤️

Please read [Contributing.md](CONTRIBUTING.md) for detailed instructions on how to help.

Running Tests
-------------

`hatch run test:test` will run the tests in every Python + Django versions combination.

`hatch run test.py3.14-5.2:test` will run them for python 3.14 and Django 5.2. Please see possible combinations using
`hatch env show` ("test" matrix).


License
=======

`django-admin-contexts` is released under an BSD License - see the `LICENSE` file for more information.


Acknowledgements
================

Develop & built using [![Hatch project](https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg)](https://github.com/pypa/hatch) [![linting - Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) [![code style - black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
types-six 1.17.0.20251009
Typing stubs for six
Requires Python: >=3.9

## Typing stubs for six

This is a [type stub package](https://typing.python.org/en/latest/tutorials/external_libraries.html)
for the [`six`](https://github.com/benjaminp/six) package. It can be used by type checkers
to check code that uses `six`. This version of
`types-six` aims to provide accurate annotations for
`six==1.17.*`.

This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/six`](https://github.com/python/typeshed/tree/main/stubs/six)
directory.

This package was tested with the following type checkers:
* [mypy](https://github.com/python/mypy/) 1.18.2
* [pyright](https://github.com/microsoft/pyright) 1.1.406

It was generated from typeshed commit
[`2ed2765d8d4a90b043d6511ab801dd7dc8b8e992`](https://github.com/python/typeshed/commit/2ed2765d8d4a90b043d6511ab801dd7dc8b8e992).
types-Jetson.GPIO 2.1.12.20251009
Typing stubs for Jetson.GPIO
Requires Python: >=3.9

## Typing stubs for Jetson.GPIO

This is a [type stub package](https://typing.python.org/en/latest/tutorials/external_libraries.html)
for the [`Jetson.GPIO`](https://github.com/NVIDIA/jetson-gpio) package. It can be used by type checkers
to check code that uses `Jetson.GPIO`. This version of
`types-Jetson.GPIO` aims to provide accurate annotations for
`Jetson.GPIO==2.1.12`.

This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/Jetson.GPIO`](https://github.com/python/typeshed/tree/main/stubs/Jetson.GPIO)
directory.

This package was tested with the following type checkers:
* [mypy](https://github.com/python/mypy/) 1.18.2
* [pyright](https://github.com/microsoft/pyright) 1.1.406

It was generated from typeshed commit
[`2ed2765d8d4a90b043d6511ab801dd7dc8b8e992`](https://github.com/python/typeshed/commit/2ed2765d8d4a90b043d6511ab801dd7dc8b8e992).
types-grpcio 1.0.0.20251009
Typing stubs for grpcio
Requires Python: >=3.9

## Typing stubs for grpcio

This is a [type stub package](https://typing.python.org/en/latest/tutorials/external_libraries.html)
for the [`grpcio`](https://github.com/grpc/grpc) package. It can be used by type checkers
to check code that uses `grpcio`. This version of
`types-grpcio` aims to provide accurate annotations for
`grpcio==1.*`.

This stub package is marked as [partial](https://typing.python.org/en/latest/spec/distributing.html#partial-stub-packages).
If you find that annotations are missing, feel free to contribute and help complete them.


This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/grpcio`](https://github.com/python/typeshed/tree/main/stubs/grpcio)
directory.

This package was tested with the following type checkers:
* [mypy](https://github.com/python/mypy/) 1.18.2
* [pyright](https://github.com/microsoft/pyright) 1.1.406

It was generated from typeshed commit
[`2ed2765d8d4a90b043d6511ab801dd7dc8b8e992`](https://github.com/python/typeshed/commit/2ed2765d8d4a90b043d6511ab801dd7dc8b8e992).
django-sync-env 1.0.1
Backup, Sync and Restore Databases and Media
Requires Python: <4.0,>=3.10

# Django sync env

django-sync-env is a Django app to manage backing up and restoring django databases and media assets easily.

Detailed documentation is in the "docs" directory (it's a work in progress).


## Requirements:
- configurable backups and restore which can be run on demand, celery cron or in dev.
- support s3 bucket and file storage options first, add azure blob storage later
- interactive prompts for local dev
- command line options with --no-input for CICD and automations
- restore latest option for media and backups for X env
- backup before restore option


## Setup

1. Add "django_sync_env" to your INSTALLED_APPS setting like this:

```
   INSTALLED_APPS = [
   ...,
   "django_sync_env",
   ]
```

2. Configure the app via a settings file .typically `./settings/partial/sync_env.py` for base configuration,
   don't forget to import this file via your base.py settings file
   And override any required settings per environment via `./settings/partials/[env].py`
   see [example_partials](docs/example_partials.md)

There is a [s3 terraform example](docs/example_terraform_aws_s3_bucket.md) for provisioning
an aws s3 bucket, iam user, roles and policy to allow for remote backup/restore to/from a secure s3 bucket.

See [management commands](docs/management_commands.md) for more details for each of the commands available.

- `./manage.py sync_env_backup_db` [usage](docs/management_commands.md#managepy-syncenvbackupdb)
- `./manage.py sync_env_backup_media`
- `./manage.py sync_env_restore_db`
- `./manage.py sync_env_restore_media`
- `./manage.py sync_env_list_backups`

See [Tasks](docs/tasks.md) for more details for each of the tasks available.

## Development and Publishing
See [documentation](docs/development_and_release_notes.md). 

## Existing project migration

* To run db backup manually without storing anything on the local server you can run following command
```shell
export AWS_ACCESS_KEY_ID="<backup-user-key>"
export AWS_SECRET_ACCESS_KEY="<backup-user-secret>"

pg_dump --dbname="postgresql://<db-user>:<db-password>@<db-host>:5432/<db-name>" --format=custom | gzip --stdout | aws s3 cp - "s3://<project-name>-<env>-backups/<sensible-name-here>-<db-name>-$(date +'%d-%m-%Y-%H%M%S').psql.bin.gz"
```
* After that you can restore this database backup using `./manage.py sync_env_restore_db`

## TODO:
- remove command options which we wont use
- make sure all management commands work with good error logging and handling
- Update the sync-env-backup command to enable the option to specify particular databases
- Update the sync-env-backup command to exclude particular tables in particular databases