semvec/pypi-packages huggingface dataset:
cossif 0.0.3
CosSIF: Cosine Similarity-based Image Filtering
Requires Python: >=3.10.9

# CosSIF: Cosine Similarity-based Image Filtering

Suppose we are presented with two categories of skin lesions: benign and malignant. Our objective is to sift out benign images that bear resemblance to malignant ones, and similarly, malignant images that bear resemblance to benign ones.

```python
from cossif import cossif
```

```python
csf = cossif.CosSIF()
```

```python
# Filter benign images
csf.calculate_and_filter(
    target_path=cls_benign,              # Path to the folder that contains benign images.
    secondary_path=cls_malignant,        # Path to the folder that contains malignant images.
    filtered_path=cls_benign_filtered,   # Path to the folder that will contain filtered benign images.
    removed_path=cls_benign_removed,     # Path to the folder that will contain removed benign images.
    record_save_path=save_path,          # Save path of the similarity calculation record.
    record_keep=False,                   # Chose whether to keep the similarity calculation record or not.
    file_name='t_benign_x_s_malignant',  # Name of the similarity calculation record file.
    filter_type='dissimilar',            # Type of filtering. Can be either "similar" or "dissimilar".
    filter_range=0.85,                   # The filtering ratio. A value of 0.85 means that 15% of the images will be removed.
)
```

<p align="left">
<img src="https://github.com/mominul-ssv/cossif-package/raw/main/plots/github/benign.png" alt="image_description" style="padding: 5px" width="100%">
</p>
<p align="left">
<img src="https://github.com/mominul-ssv/cossif-package/raw/main/plots/github/benign_filtered.png" alt="image_description" style="padding: 5px" width="100%">
</p>
<p align="left">
<img src="https://github.com/mominul-ssv/cossif-package/raw/main/plots/github/benign_removed.png" alt="image_description" style="padding: 5px" width="100%">
</p>

```python
# Filter malignant images
csf.calculate_and_filter(
    target_path = cls_malignant,
    secondary_path = cls_benign,
    filtered_path=cls_malignant_filtered,
    removed_path=cls_malignant_removed,
    record_save_path = save_path,
    record_keep=False,
    file_name = 't_malignant_x_s_benign',
    filter_type = 'similar',
    filter_range = 0.85,
)
```

<p align="left">
<img src="https://github.com/mominul-ssv/cossif-package/raw/main/plots/github/malignant.png" alt="image_description" style="padding: 5px" width="100%">
</p>
<p align="left">
<img src="https://github.com/mominul-ssv/cossif-package/raw/main/plots/github/malignant_filtered.png" alt="image_description" style="padding: 5px" width="100%">
</p>
<p align="left">
<img src="https://github.com/mominul-ssv/cossif-package/raw/main/plots/github/malignant_removed.png" alt="image_description" style="padding: 5px" width="100%">
</p>
Vectorgrad 0.0.1
A lightweight autograd engine that supports tensor operations and gradients with a small, customizable neural network library on top
Requires Python: >=3.6

## VectorGrad

Welcome to VectorGrad, a spin-off of the original [micrograd](https://github.com/karpathy/micrograd).

This is an automatic differentiation library that supports tensor operations and calculus. Here's why I built it:
1) It's much more lightweight and readable compared to bigger libraries such as TensorFlow so users can get a better idea
of how everything works

2) Its orders of magnitude more computationally efficient than its predecessor as it leverages numpy statically typed arrays
and operations to bundle parameters and leverage parallelism and SIMD calculations

3) It includes a dynamic and customizable neural network library that can build neural networks of arbitrary size and complexity,
and also allows users to choose the activation function at each layer to allow for more robust model architecture
py-valuelock 0.1.0
A Python package for locking based on values.

# py-valuelock

![PyPI](https://img.shields.io/pypi/v/py-valuelock)
![License](https://img.shields.io/pypi/l/py-valuelock)
![Python Versions](https://img.shields.io/pypi/pyversions/py-valuelock)

A Python package for locking based on values.

## Installation

You can install py-valuelock using pip:

```bash
pip install py-valuelock
```

## Usage

```python
from py_valuelock import get_lock

# Your code here
```

## Features

* Locking based on values.
* Thread-safe.

## Documentation

Detailed documentation can be found on the [GitHub repository](https://github.com/killbus/py-valuelock).
textbase-cli 0.1.1
A python library to help you create chatbots easily with some more configurations
Requires Python: >=3.8.1,<4.0.0

# Textbase Command Line Interface (CLI) Documentation
The Textbase CLI is a powerful tool that enables you to interact with the Textbase service using command-line arguments. This documentation provides a comprehensive guide on how to use the CLI to perform various operations, such as deploying bots, checking bot health, listing bots, and deleting bots.

## Installation
To install the Textbase CLI, you need to have Python and pip installed on your system. 

Install the required package using pip: 
```bash
pip install textbase-cli
```

## Usage

The Textbase CLI provides the following commands for interacting with the Textbase service:

### Local test command
The test function is a command-line interface (CLI) command that is used to run a Google Cloud Function locally for testing purposes. It uses the click library to define the command and its options.

Relative path to the `main.py` is supposed to be passed in the `--path` flag
#### Example:
```bash
textbase-cli test --path path/to/zip/folder
```

```bash
POST
body:
{
  "data": {
    "messages": [
      {
        "role": "user",
        "content": [{
          "data_type": "STRING",
          "value": "yoooo"
        }]
      },
      {
        "role": "user",
        "content": [{
          "data_type": "STRING",
          "value": "What is the best anime"
        }]
      },
      {
        "role": "user",
        "content": [{
          "data_type": "AUDIO",
          "value": "Who is Bill Gates?"
        }]
      }
    ],
    "state": {}
  }
}

response:
{
  "response": {
    "data": {
      "messages": [
        {
          "type": "string",
          "value": "Hello! How can I assist you today?"
        },
        {
          "type": "audio",
          "value": ""
        }
      ],
      "state": {
        "counter": 0
      }
    },
    "errors": [
      {
        "message": ""
      }
    ]
  },
  "status_code": 200
}
```
### Deploy Command

The deploy command allows you to deploy a bot using a zip folder. You need to provide the path to the zip folder, the bot name, and your Textbase API key.

```bash
textbase-cli deploy --path /path/to/zip-folder --bot_name YourBotName --api_key YourAPIKey
```

### Health Command

The health command allows you to check the health status of a bot, whether it is still deploying, failed or successfully deployed. You need to provide the bot ID and your Textbase API key.

```bash
textbase-cli health --bot_id YourBotId --api_key YourAPIKey
```

### List Command
The list command retrieves the list of bots associated with your Textbase account. You need to provide your Textbase API key.

```bash
textbase-cli list --api_key YourAPIKey
```

### Delete Command
The delete command allows you to delete a bot. You need to provide the bot ID and your Textbase API key.

```bash
textbase-cli delete --bot_id YourBotId --api_key YourAPIKey
```

## Contributing

Feel free to contribute to the Textbase CLI by opening issues or submitting pull requests. We welcome your feedback and contributions!
aibloks 0.9.1
Client library for the Ai Bloks REST API
Requires Python: >=3.6, <4
Keywords: ai,data,development

# Ai Bloks Python API Library

[![PyPI version](https://img.shields.io/pypi/v/anthropic.svg)](https://pypi.org/project/anthropic/)

The Ai Bloks Python library provides access to the [Ai Bloks REST API](https://app.aibloks.com/api/docs/) from any Python 3.10+ application. 

## Installation

```sh
pip install aibloks
```

## Usage

```python
from aibloks import AiBloksClient, AiBloksException, Library

username         = os.environ.get("USERNAME")
api_key          = os.environ.get("API_KEY")
endpoint_url     = os.environ.get("ENDPOINT_URL")
s3_access_key    = os.environ.get("S3_ACCESS_KEY")
s3_access_secret = os.environ.get("S3_ACCESS_SECRET")

try:
    # Create an instance of the AiBloksClient
    aib_client = AiBloksClient(username, api_key, endpoint_url)
 
    # Create a new library
    my_library = aib_client.create_library("my_library")

    # Import files from an AWS S3 Bucket into the library
    my_library.import_documents(service="aws_s3", 
                                bucket_name="aibloks-sample-docs", 
                                access_key=s3_access_key, 
                                access_secret=s3_access_secret )

except AiBloksException as e:
    print(e)
```

## Handling errors

When the library is unable to connect to the API (e.g., due to network connection problems or a timeout), a exception of type AiBloksException is raised. 

## Requirements

Python 3.10 or higher.
simple-sqs-client 0.9.6
Very simple SQS Client
Requires Python: >=3.6
Keywords: simple_sqs_client

# Very Sipmle SQS Client

## Requirements
- Python 3+
## Installing
`pip install simple-sqs-client`

## Usage
### SQSClientBuilder

You can use SQSClientBuilder which is more flexible approach and allows you to fill the connection parameters in different moment of code execution if necessary.
Just keep the variable holding the builder in the scope
``` 
with SQSClientBuilder() \
            .with_region(AWS_SQS_REGION) \
            .with_queue_url(AWS_SQS_QUEUE_REACT_URL) \
            .with_aws_credentials(
        aws_access_key_id=AWS_SQS_ACCESS_KEY_ID,
        aws_secret_access_key=AWS_SQS_SECRET_ACCESS_KEY
    ).build() as sqs_client:
    event = {message: "hello world"}
    sqs_client.send_message(body=json_util.dumps(event))
```

### Direct
You can also create Client using its constructor
```
  SQSClient(region_name, aws_access_key_id, aws_secret_access_key, queue_url)
```
data-trans-functions 1.0.0
Implementation of three functions used in data transformation: matrix transpose, time series windowing and cross-correlation.
Requires Python: >=3.10

# Data Trans Functions

## Description

The `data-trans-functions` package provides utility functions to perform operations on 2D matrices 
and 1D arrays. With features like 2D matrix transposition, 1D windowing, and 2D convolution, it 
simplifies common operations while ensuring type safety.

## Features

- **transpose2d**: Transposes a 2D matrix, effectively switching its axes.
- **window1d**: Generates a series of 1D sliding windows from an input array.
- **convolution2d**: Computes the 2D convolution of an input matrix using a given kernel.

## Installation

To install the package via `pip`, run:
pip install data-trans-functions

## Usage

Here are brief examples for each function:

```python
from data_trans_functions import transpose2d, window1d, convolution2d
import numpy as np

# Using transpose2d:
matrix = [[1, 2, 3], [4, 5, 6]]
print(transpose2d(matrix))  # Returns: [[1, 4], [2, 5], [3, 6]]

# Using window1d:
arr = [1, 2, 3, 4]
print(window1d(arr, 2, shift=2))  # Returns: [[1, 2], [3, 4]]

# Using convolution2d:
input_matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
kernel = np.array([[1, 0], [0, -1]])
print(convolution2d(input_matrix, kernel))  # Returns: [[3, 2], [3, 2]]
```

## Recommendations

It's recommended to use these functions in combination with a type checker like mypy for safer usage.
OverDose 0.0.2
Overdose of drugs in the US
Requires Python: >=3.7

# How to install overdose
$ pip install overdose

# How to run overdose
$ overdose

## Graph

<img src='https://github.com/i-inose/OverDose/blob/main/result.png?raw=true' height=480, width=640>


"Cocaine (T40.5)," "Heroin (T40.1)," and "Opioids (T40.0-T40.4,T40.6)" are up since 2019.
"Opioids (T40.0-T40.4,T40.6)" are particularly prolific.
django-rest-auth-e2e 0.9.6
Create a set of REST API endpoints for Authentication and Registration
Keywords: django rest auth registration rest-framework django-registration api

===========
Deprecated
===========
Please use https://github.com/iMerica/dj-rest-auth as this project is no longer maintained. Thanks!


Welcome to django-rest-auth
===========================

.. image:: https://travis-ci.org/Tivix/django-rest-auth.svg
    :target: https://travis-ci.org/Tivix/django-rest-auth


.. image:: https://coveralls.io/repos/Tivix/django-rest-auth/badge.svg
    :target: https://coveralls.io/r/Tivix/django-rest-auth?branch=master


.. image:: https://readthedocs.org/projects/django-rest-auth/badge/?version=latest
    :target: https://readthedocs.org/projects/django-rest-auth/?badge=latest


Django-rest-auth provides a set of REST API endpoints for Authentication and Registration


Documentation
-------------
http://django-rest-auth.readthedocs.org/en/latest/


Source code
-----------
https://github.com/Tivix/django-rest-auth


Stack Overflow
-----------
http://stackoverflow.com/questions/tagged/django-rest-auth