semvec/pypi-packages huggingface dataset:
abzagent 0.3.0
The Fastest Way to build AI Agent Using Gemini.

# 🤖 AB Agent SDK

**AB Agent** is a lightweight, Pythonic SDK designed for building and running AI agents powered by Google's Gemini models. With a simple and intuitive API, developers can create intelligent agents, this is a begginer level package 
---

## 🚀 Features

- ✅ Gemini-powered AI agents
- ✅ Simple API: create, run, and extend agents easily
- ✅ Modular and extensible design
- ✅ Inspired by OpenAI's Agent SDK

---

## 📦 Installation

```bash
pip install abhello==0.1
```
imb 1.0.3
Python library for run inference of deep learning models in different backends
Requires Python: >=3.8

# InferenceMultiBackend

Python library for run inference of deep learning models in different backends

## Installation

For use triton inference client:
```pip install imb[triton]```

For use onnxruntime-gpu client:
```pip install imb[onnxgpu]```

For use onnxruntime client:
```pip install imb[onnxcpu]```

For support all implemented clients:
```pip install imb[all]```

## Usage

OnnxClient usage example
```
from imb.onnx import OnnxClient

onnx_client = OnnxClient(
    model_path='model.onnx',
    model_name='any name',
    providers=['CUDAExecutionProvider', 'CPUExecutionProvider'],
    max_batch_size=16,
    return_dict=True,
    fixed_batch=True,
    warmup=True
)

# if model has fixed input size (except batch size) then sample_inputs will be created
sample_inputs = onnx_client.sample_inputs
print('inputs shapes', [o.shape for o in sample_inputs])

outputs = onnx_client(*sample_inputs)
print('outputs shapes', [(o_name, o_value.shape) for o_name, o_value in outputs.items()])
```

TritonClient usage example
```
from imb.triton import TritonClient

triton_client = TritonClient(
    url='localhost:8000',
    model_name='arcface',
    max_batch_size=16,
    timeout=10,
    resend_count=10,
    fixed_batch=True,
    is_async=False,
    cuda_shm=False,
    max_shm_regions=2,
    scheme='http',
    return_dict=True,
    warmup=False
)

# if model has fixed input size (except batch size) then sample_inputs will be created
sample_inputs = triton_client.sample_inputs
print('inputs shapes', [o.shape for o in sample_inputs])

outputs = triton_client(*sample_inputs)
print('outputs shapes', [(o_name, o_value.shape) for o_name, o_value in outputs.items()])
```

## Notes

max_batch_size - maximum batch size for inference. If input data larger that max_batch_size, then input data will be splitted to several batches.

fixed_batch - if fixed batch is True, then each batch will have fixed size (padding the smallest batch to max_batch_size).

warmup - if True, model will run several calls on sample_inputs while initialization. 

return_dict - if True, __call__ return dict {'output_name1': output_value1, ...}, else [output_value1, ...]
processador-de-imagem-zoemateus324 0.0.2
My first application of image processing with python language
Requires Python: >=3.8

#   Image_procressing

Description.
The package image_processing is used to:
Processing:
    -Histrogram matching
    - Structural similarity
    -Resize image
Utils:
    -Read image
    -Save image
    -Plot image
    -Plot result
    -Plot Ristogram



## Installation
use the package manager [pip] (https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install image_processing
```

##usage
```python
from package name.module1_name import file1_name
file1_name.my_function()

##  Author
Zoe_santos

## License
[MIT](https://choosealicense.com/licenses/mit/)
python-hn 0.0.4
Add your description here
Requires Python: >=3.09

# HackerNews / Algolia Python Library

This is a simple library to interface with [HN Search API](https://hn.algolia.com/api) (provided by Algolia).

[Install](#install-instructions) | [Basic Usage](#usage) | [Development](#development) | [Roadmap](#roadmap)

<p align="right"></p>

> 👉 Note: As an example, I used this library to download ALL Hacker News posts and made it available as a [public dataset in Kaggle](https://www.kaggle.com/santiagobasulto/all-hacker-news-posts-stories-askshow-hn-polls).

## Install instructions

```bash
$ pip install python-hn
```

## Usage

```python
from hn import search_by_date

# Search everything (stories, comments, etc) containing the keyword 'python'
search_by_date('python')

# Search everything (stories, comments, etc) from author 'pg' and keyword 'lisp'
search_by_date('lisp', author='pg', created_at__lt='2018-01-01')

# Search only stories
search_by_date('lisp', author='pg', stories=True, created_at__lt='2018-01-01')

# Search stories *or* comments
search_by_date(q='lisp', author='pg', stories=True, comments=True, created_at__lt='2018-01-01')
```

##### Tags

Tags are part of HN Search API provided by Algolia. You can read more in [their docs](https://hn.algolia.com/api). They can form complex queries, for example:

```python
# All the comments in the story `6902129`
tags = PostType('comment') & StoryID('6902129')
```

The available tags are:
* `PostType`: with options `story`, `comment`, `poll`, `pollopt`, `show_hn`, `ask_hn`, `front_page`.
* `Author`: receives the username as param (`Author('pg')`).
* `StoryID`: receives the story id (`StoryID('6902129')`)


##### Filters

Filters can be applied to restrict the search by:

* Creation Date: `created_at`
* Points: `points`
* Number of comments: `num_comments`

They can accept `>, <, >=, <=` operators with a syntax similar to Django's.
* `lt` (`<`): Lower than. Example `ponts__lt=100`
* `lte` (`<=`): Lower than or equals to. Example `ponts__lte=100`
* `gt` (`>`): Greater than. Example `created_at__gt='2018'` (created after 2018-01-01).
* `gte` (`>=`): Greater than or equals to. Example `num_comments__gte=50`.

Examples (See [Algolia docs](https://hn.algolia.com/api) for more info):

```python
# Created after October 1st, 2018
search_by_date(created_at__gt='2018-10')

# Created after October 1st, 2017 and before January 1st 2018
search_by_date(created_at__gt='2018-10', created_at__lt='2018')

# Stories with *exactly* 1000 points
search_by_date(tags=PostType('story'), points=1000)

# Comments with more than 50 points
search_by_date(tags=PostType('comment'), points__gt=50)

# Stories with 100 comments or more
search_by_date(tags=PostType('story'), num_comments__gt=100)
```

##### Search

_[TODO]_

## Development

I'm in the process of updating this project and migrating to `uv`. You should be able to just do:

```bash
$ uv run py.test
```
PyOghma 0.0.55
A Python API for OghmaNano


# PyOghma

A python API for OghmaNano allowing for the programmatic discription and exceution of drift-diffusion simulations.

## Installation
Prior to installing PyOghma it is critical that OghmaNano is installed. OghmaNano can be found to download at: [oghma-nano.com](https://www.oghma-nano.com)

PyOghma may be installed using pip

```bash
    python -m pip install PyOghma
```
    
## Usage/Examples

In order to use PyOgham a soruce simulation generated by OghamNano must be generated, and the sim.json within sim.Oghma extracted. Once compleated modifications upon this simulation may be made programatically as in the example below by PyOghma.

```python
import PyOghma as po

Oghma = po.OghmaNano()
Results = po.Results()


source_simulation = "\exapmle\pm6y6\"

Oghma.set_source_simulation(source_simulation)

experiment_name = 'NewExperiment'

Oghma.set_experiment_name(experiment_name)

mobility = 1e-5
trap_desnsity = 1e-18
trapping_crosssection = 1e-20
recombination_crosssection = 1e-20
urbach_energy = 40e-3
temperature = 300
intensity = 0.5


experiment_name = 'NewExperiment' + str(1)
Oghma.clone('NewExperiment0')

Oghma.Optical.Light.set_light_Intensity(intensity)
Oghma.Optical.Light.update()

Oghma.Thermal.set_temperature(temperature)
Oghma.Thermal.update()

Oghma.Epitaxy.load_existing()
Oghma.Epitaxy.pm6y6.dos.mobility('both', mobility)
Oghma.Epitaxy.pm6y6.dos.trap_density('both', trap_desnsity)
Oghma.Epitaxy.pm6y6.dos.trapping_rate('both', 'free to trap',trapping_crosssection)
Oghma.Epitaxy.pm6y6.dos.trapping_rate('both', 'trap to free',recombination_crosssection)
Oghma.Epitaxy.pm6y6.dos.urbach_energy('both', urbach_energy)
Oghma.Epitaxy.update()

Oghma.add_job(experiment_name)
Oghma.run_jobs()

```


## Authors

- [@CaiWilliams](https://www.github.com/CaiWilliams)