semvec/pypi-packages huggingface dataset:
coco_types 0.0.7
Package for handling COCO datasets types.
License: MIT
Requires Python: >=3.9
Keywords: COCO,COCO dataset

# COCO Types

Note: This package loads the data as is and does not create dictionaries mapping ids to lists of images/annotations/categories.

## Installation

The package is available on pypi [here](https://pypi.org/project/coco-types/), you can install it with:
```
pip install coco-types
```

## Loading COCO data

You can load COCO dataset labels into Pydantic objects by using the `Dataset` and `DatasetKP` classes.

For an object detection dataset:
```python
import coco_types

with open("path/to/json", encoding="utf-8") as data_file:
    dataset = coco_types.Dataset.parse_raw(data_file.read())
```

For a keypoint detection dataset:
```python
import coco_types

with open("path/to/json", encoding="utf-8") as data_file:
    dataset = coco_types.DatasetKP.parse_raw(data_file.read())
```


## Usage example:
```python
import coco_types

with open("path/to/json", encoding="utf-8") as data_file:
    dataset = coco_types.Dataset.parse_raw(data_file.read())

img = dataset.images[0]
print(f"Image's filename {img.file_name}")
print(f"Image's id {img.id}")
print(f"Image's height {img.height}")
print(f"Image's width {img.width}")

img_annotations = [annotation for annotation in dataset.annotations
                   if annotation.image_id == img.id]
ann = img_annotations[0]
print(f"Annotation's id: {ann.id}")
print(f"Annotation's image id: {ann.image_id}")
print(f"Annotation's category id: {ann.category_id}")
print(f"Annotation's iscrowd: {ann.iscrowd}")
print(f"Annotation's bbox: {ann.bbox}")
print(f"Annotation's area {ann.area}")

for cat in dataset.categories:
    if cat.id == ann.category_id:
        break

print(f"Category's name {cat.name}")
print(f"Category's supercategory {cat.supercategory}")
```

### Keypoints
If using a dataset with keypoints (`coco_types.DatasetKP`), then annotations will have two additional attributes: `keypoints` and `num_keypoints`.\
In the same way, categories will have  two additional attributes: `keypoints` and `skeleton`.


## TypedDict versions
A `TypedDict` version of the pydantic models can be accessed using `coco_types.dicts.*` (for example `coco_types.dicts.Dataset`). This can be useful if you have data that is slightly malformed / follows a slightly different format but is still usable.
teneva-ht-jax 0.1.2
Compact implementation of basic operations in the Hierarchical Tucker (HT) format for approximation and sampling from multidimensional arrays and multivariate functions
License: MIT
Requires Python: >=3.8
Keywords: low-rank representation tensor hierarchical tucker approximation

# teneva_ht_jax


## Description

This python package, named **teneva_ht_jax** (**ten**sor **eva**luation with **H**ierarchical **T**ucker and **jax**), provides a very compact implementation of basic operations in the Hierarchical Tucker (HT) format, including approximation and sampling from multidimensional arrays and multivariate functions. The program code is organized within a functional paradigm and it is very easy to learn and use.


## Installation

> Current version "0.1.2".

The package can be installed via pip: `pip install teneva_ht_jax` (it requires the [Python](https://www.python.org) programming language of the version >= 3.8). It can be also downloaded from the repository [teneva_ht_jax](https://github.com/AndreiChertkov/teneva_ht_jax) and installed by `python setup.py install` command from the root folder of the project.

> Required python packages [numpy](https://numpy.org) (1.22+), [scipy](https://www.scipy.org) (1.8+), [jax](https://github.com/google/jax) (3.3+; cpu version) and [optax](https://github.com/deepmind/optax) (0.1.5+) will be automatically installed during the installation of the main software product. However, it is recommended that you manually install them first.


## Documentation and examples

- See detailed [online documentation](https://teneva-ht-jax.readthedocs.io) for a description and various numerical examples for each function.
- See the jupyter notebooks in the `./demo` folder with brief description and demonstration of the capabilities of each function from the `teneva_ht_jax` package. Note that all examples from this folder are also presented in the online documentation.


## Authors

- [Andrei Chertkov](https://github.com/AndreiChertkov)
- [Gleb Ryzhakov](https://github.com/G-Ryzhakov)
- [Ivan Oseledets](https://github.com/oseledets)
- **Will be extended soon ;)**

> ✭ 🚂 The stars that you give to **teneva_ht_jax**, motivate us to develop faster and add new interesting features to the code 😃
smartonnx 0.1.6
Tool to convert a ONNX model to Cairo smart contract.
License: MIT
Requires Python: >=3.7.11,<3.9
Keywords: packaging,dependency,poetry

# smartonnx

Tool to convert a ONNX model to Cairo smart contract

## Installation 

Make sure you have the correct Python version (>= 3.7.11 , < 3.9). We recommend [Pyenv](https://github.com/pyenv/pyenv) or similar Python version management tools. 


This project uses [Poetry](https://github.com/python-poetry/poetry) for project management. Install Poetry using the [installation guide](https://python-poetry.org/docs/#installing-with-the-official-installer)

It is preffered to install the dependencies within a virtual environment for the project

Check the current virtual environment (and the alternatives) with 

```Shell
$ poetry env list
```

Select the correct one with 

```Shell
$ poetry env use [name]
```

If the virtual environment is correct, install the dependencies with 

```Shell
$ poetry install
```
and update them if needed with 

```Shell
$ poetry update 
```
mynamejuiy 0.0.1
Who is Juiy?
License: MIT
Keywords: Juiy,Juiy

แพ็คเกจ เพื่อเทสสำหรับการเรียนของ จุ้ย”
=======================================

PyPi: https://pypi.org/project/mynamejuiy/

สวัสดีจ้าาาา เทสเทสเทสเทส ### วิธีติดตั้ง

เปิด CMD / Terminal

.. code:: python

   pip install mynamejuiy

วิธีใช้งานแพ็คเพจนี้
~~~~~~~~~~~~~~~~~~~~

-  เปิด IDLE ขึ้นมาแล้วพิมพ์…

.. code:: python

   from mynamejuiy import juiy

   juiy = Juiy()
   juiy.show_name()
   juiy.show_IG()
   juiy.about()
   juiy.show_Art()
   juiy.show_page()
shgo 1.0.0
Simplicial homology global optimisation
License: MIT
Keywords: optimization

.. image:: https://travis-ci.org/Stefan-Endres/shgo.svg?branch=master
    :target: https://travis-ci.org/Stefan-Endres/shgo
.. image:: https://coveralls.io/repos/github/Stefan-Endres/shgo/badge.png?branch=master
    :target: https://coveralls.io/github/Stefan-Endres/shgo?branch=master

Repository: https://github.com/Stefan-Endres/shgo

Description
-----------

Finds the global minimum of a function using simplicial homology global
optimisation (shgo_). Appropriate for solving general purpose NLP and blackbox
optimisation problems to global optimality (low dimensional problems).
The general form of an optimisation problem is given by:

.. _shgo: https://stefan-endres.github.io/shgo/

::

    minimize f(x) subject to

    g_i(x) >= 0,  i = 1,...,m
    h_j(x)  = 0,  j = 1,...,p

where x is a vector of one or more variables. ``f(x)`` is the objective
function ``R^n -> R``, ``g_i(x)`` are the inequality constraints.
``h_j(x)`` are the equality constrains.


Installation
------------
Stable:

.. code::

    $ pip install shgo
    
Latest:

.. code::

    $ git clone https://github.com/Stefan-Endres/shgo
    $ cd shgo
    $ python setup.py install
    $ python setup.py test

Documentation
-------------
The project website https://stefan-endres.github.io/shgo/ contains more detailed examples, notes and performance profiles.

Quick example
-------------

Consider the problem of minimizing the Rosenbrock function. This function is implemented in ``rosen`` in ``scipy.optimize``

.. code:: python

    >>> from scipy.optimize import rosen
    >>> from shgo import shgo
    >>> bounds = [(0,2), (0, 2), (0, 2), (0, 2), (0, 2)]
    >>> result = shgo(rosen, bounds)
    >>> result.x, result.fun
    (array([ 1.,  1.,  1.,  1.,  1.]), 2.9203923741900809e-18)

Note that bounds determine the dimensionality of the objective function and is therefore a required input, however you can specify empty bounds using ``None`` or objects like numpy.inf which will be converted to large float numbers.
scrubadubdub 0.2.1
A Python package to scrub PII
License: MIT
Requires Python: >=3.9,<3.11

<h1 align="center">🫧 scrub 🫧</h1>

**scrub** is a lightweight, extensible Python package designed to remove Personally Identifiable Information (PII) from any text input. It leverages advanced Machine Learning algorithms to detect PII and provides multiple levels of scrubbing to ensure optimal anonymization of sensitive information. safeguarding user privacy.

## Installation

Install the latest version of scrub from [PyPI](https://pypi.org/project/scrubadubdub/):

```bash
pip install scrubadubdub
```

## Key Features

- Advanced PII detection using Machine Learning algorithms.
- Support for a wide range of PII types (names, email addresses, phone numbers, etc.).
- Customizable detection threshold and levels of sanitization.
- Extensible architecture to allow for the addition of new PII types and algorithms.

## Basic Usage

```python
from scrubadubdub import Scrub

scrubber = Scrub()
input_text = "My name is John Doe and my email address is john.doe@email.com, and my phone number is 123-456-7890"
scrubbed_text = scrubber.scrub(input_text)
print(scrubbed_text)
```

```txt
My name is [REDACTED-NAME] and my email address is [REDACTED-EMAIL], and my phone number is [REDACTED-PHONE]
```

<!-- ## Advanced Usage

With scrub, you can customize the detection threshold, sanitization levels, and even integrate additional scrubbing functionalities based on your needs.

```python
# Set custom threshold and level
scrubber.set_threshold(0.9)
scrubber.set_sanitization_level(scrub.SanitizationLevels.MEDIUM)

# Enable additional sanitization features
scrubber.enable_email_sanitization()

# Add custom PII detection function
def custom_detection(text):
    ...

scrubber.add_detection_function(custom_detection)
``` -->

<!-- ## Documentation

More details on setting up and using scrub can be found in the [Documentation](https://scrub.readthedocs.io).

## Contributing

We love contributions! If you'd like to contribute to scrub, please read our [Contributing Guidelines](./CONTRIBUTING.md) for more information on how to get started. -->

## License

scrub is licensed under the MIT License. See [LICENSE](./LICENSE) for more details.