semvec/pypi-packages huggingface dataset:
boto3-pydantic-fis 1.0.2
Pydantic models for AWS Fis
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, fis

# boto3-pydantic-fis

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Fis.
boto3-pydantic-firehose 1.0.2
Pydantic models for AWS Firehose
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, firehose

# boto3-pydantic-firehose

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Firehose.
varpy 2.0.1
Value at Risk tools
Requires Python: >=3.10
Keywords: value at risk, risk management, financial risk, risk metrics

# VaRpy - Value at Risk Models

A Python library for computing Value at Risk (VaR) and Conditional Value at Risk (CVaR) using various statistical distributions and GARCH models.

## Download

```bash
pip install varpy
```

## Recent Refactoring

The codebase has undergone a major refactoring to improve code organization, maintainability, and consistency. Here are the key changes:

### 1. Standardized Model Structure

Each VaR model (Normal, Student's t, EVT) follows the same pattern:

- Constructor takes only `theta` and `horizon` parameters
- `run` method accepts returns data as input
- Private helper methods for specific computations
- Consistent method naming and documentation

Example structure:
```python
class SomeVar(BaseVar):
    def __init__(self, theta: float, horizon: int):
        super().__init__(theta, horizon)

    def run(self, ret: NDArray[np.float64]) -> None:
        # 1. Get GARCH forecasts
        # 2. Process innovations
        # 3. Fit distribution
        # 4. Compute VaR/CVaR
        pass
```

### 3. Improved Type Hints

- Added comprehensive type hints using `numpy.typing`
- Consistent use of `NDArray[np.float64]` for numpy arrays
- Proper return type annotations for all methods

### 5. Enhanced Documentation

- Comprehensive docstrings for all classes and methods
- Clear parameter and return type documentation
- Consistent documentation style across all models

### 6. Code Organization

- Moved models to `varpy/var/models/` directory
- Consistent file naming (lowercase with underscores)
- Clear separation of concerns between models

## Available Models

### Normal Distribution
- Assumes returns follow a normal distribution
- Uses GARCH(1,1) with Gaussian innovations
- Suitable for well-behaved financial returns

### Student's t Distribution
- Assumes returns follow a Student's t distribution
- Uses GARCH(1,1) with Student's t innovations
- Better suited for heavy-tailed returns

### Extreme Value Theory (EVT)
- Uses Generalized Pareto Distribution for tail modeling
- Combines GARCH with EVT for better tail estimation
- Most suitable for extreme risk measurement

## Usage Example

```python
import numpy as np
from varpy.var.models import Normal, Student, EVT

# Initialize model
model = Normal(theta=0.05, horizon=1)

# Compute VaR and CVaR
returns = np.array([...])  # Your return data
model.run(returns)

# Get results
var = model.var
cvar = model.cvar
```

More exemples [here](exemples.ipynb)

## Dependencies

- numpy
- scipy
- arch (for GARCH modeling)

## Contributing

When adding new VaR models, please follow the established pattern:
1. Inherit from `BaseVar`
2. Implement the `run` method
3. Use private helper methods for specific computations
4. Add comprehensive type hints and documentation
5. Follow the modular design pattern
boto3-pydantic-finspace-data 1.0.2
Pydantic models for AWS Finspace_data
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, finspace_data

# boto3-pydantic-finspace_data

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Finspace_data.
boto3-pydantic-finspace 1.0.2
Pydantic models for AWS Finspace
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, finspace

# boto3-pydantic-finspace

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Finspace.
boto3-pydantic-evidently 1.0.2
Pydantic models for AWS Evidently
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, evidently

# boto3-pydantic-evidently

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Evidently.
boto3-pydantic-events 1.0.2
Pydantic models for AWS Events
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, events

# boto3-pydantic-events

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Events.
boto3-pydantic-es 1.0.2
Pydantic models for AWS Es
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, es

# boto3-pydantic-es

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Es.
boto3-pydantic-entityresolution 1.0.2
Pydantic models for AWS Entityresolution
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, entityresolution

# boto3-pydantic-entityresolution

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Entityresolution.
boto3-pydantic-emr-serverless 1.0.2
Pydantic models for AWS Emr_serverless
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, emr_serverless

# boto3-pydantic-emr_serverless

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Emr_serverless.
boto3-pydantic-emr-containers 1.0.2
Pydantic models for AWS Emr_containers
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, emr_containers

# boto3-pydantic-emr_containers

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Emr_containers.
numpyUtilsUpdated 0.0.5
A collection of numpy utilities for data analysis and manipulation.
Requires Python: >=3.0

# numpyUtilsUpdated

A comprehensive collection of utility functions designed to enhance your NumPy workflows. This module provides a range of tools to simplify common NumPy operations, improve code readability, and boost your productivity when working with numerical data in Python.

## Features

* **Array Manipulation:** Functions for reshaping, flattening, transposing, and joining NumPy arrays with ease.
* **Data Inspection:** Tools for quickly checking array properties like shape, data type, minimum/maximum values, and the presence of NaN or infinite values.
* **Indexing and Selection:** Utility functions to simplify complex indexing and selection tasks.
* **Mathematical Operations:** Convenient wrappers for common mathematical operations and aggregations.
* **Saving and Loading:** Streamlined functions for saving and loading NumPy arrays to and from files.
* **Type Checking:** Functions to easily verify the data type of NumPy arrays.

## Installation

```bash
pip install numpyUtilsUpdated
boto3-pydantic-emr 1.0.2
Pydantic models for AWS Emr
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, emr

# boto3-pydantic-emr

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Emr.
sense-energy 0.13.8
API for the Sense Energy Monitor
Keywords: sense, energy, api

# sense_api
## Sense Energy Monitor API Interface
The Sense API provides access to the unofficial API for the Sense Energy Monitor.  Through the API,
one can retrieve both realtime and trend data including individual devices.

Systematic access to the Sense monitor data. Exploratory work on pulling data from Sense
to be used in other tools - HomeASsistant, Smartthings, ActiveTiles, etc. 

Python version based on the work done here in Powershell:
https://gist.github.com/mbrownnycnyc/db3209a1045746f5e287ea6b6631e19c

## Local Device Emulation
The SenseLink class emulates the energy monitoring functionality of TP-Link Kasa HS110 Smart Plugs
 and allows you to report "custom" power usage to your Sense Home Energy Monitor.  This requires 
enabling "TP-Link HS110/HS300 Smart Plug" in the Sense app.

Based off the work of https://github.com/cbpowell/SenseLink

### Contributors

Feel free to fork and PR! 

https://github.com/kbickar

### Todo

- Add POST/PUT where/if applicable
- CLI
- Improved error handling


### Install

```
pip install sense_energy
```

### Web API Example Usage:
```python
    from sense_energy import Senseable
    sense = Senseable()
    sense.authenticate(username, password)
    sense.update_realtime()
    sense.update_trend_data()
    print ("Active:", sense.active_power, "W")
    print ("Active Solar:", sense.active_solar_power, "W")
    print ("Daily:", sense.daily_usage, "KWh")
    print ("Daily Solar:", sense.daily_production, "KWh")
    print ("Active Devices:",", ".join(sense.active_devices))
```

There are plenty of methods for you to call so modify however you see fit

If using the API to log data, you should only create one instance of Senseable and 
then reuse that to get the updated stats.  Creating the instance authenticates 
with the Sense API which should only be once every 15-20 minutes at most.  
Calling the `update_trend_data()` function will update all the trend stats 
and `get_realtime()` will retrieve the latest real time stats.

The get_realtime() is by default rate limited to one call per 30 seconds. This can
be modified by setting the Senseable object attribute `rate_limit` to a different value.

### Local emulation Example Usage:
```python
	async def test():
		import time
		def test_devices():
			devices = [PlugInstance("lamp1", start_time=time()-20, alias="Lamp", power=10), 
					   PlugInstance("fan1", start_time=time()-300, alias="Fan", power=140)]
			for d in devices:
				yield d
		sl = SenseLink(test_devices)
		await sl.start()
		try:
			await asyncio.sleep(180)  # Serve for 3 minutes
		finally:
			await sl.stop()

	if __name__ == "__main__":
		asyncio.run(test())
```
boto3-pydantic-elbv2 1.0.2
Pydantic models for AWS Elbv2
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, elbv2

# boto3-pydantic-elbv2

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Elbv2.
boto3-pydantic-elb 1.0.2
Pydantic models for AWS Elb
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, elb

# boto3-pydantic-elb

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Elb.
boto3-pydantic-elastictranscoder 1.0.2
Pydantic models for AWS Elastictranscoder
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, elastictranscoder

# boto3-pydantic-elastictranscoder

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Elastictranscoder.
boto3-pydantic-elasticbeanstalk 1.0.2
Pydantic models for AWS Elasticbeanstalk
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, elasticbeanstalk

# boto3-pydantic-elasticbeanstalk

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Elasticbeanstalk.