semvec/pypi-packages huggingface dataset:
mpg-heatindex 0.2.3
This package use calculate heat index
Requires Python: >=3.9

# HEAT INDEX (HI)

The Heat Index (HI) is an index that describes how hot the temperature actually feels to the human body when relative humidity is taken into account. It combines both the air temperature and humidity to provide a more accurate measure of perceived heat, which is often referred to as the apparent temperature or feels-like temperature.

The Heat Index is important for assessing the health risks associated with high temperatures, such as heat stroke, dehydration, and heat exhaustion.

Heat Index Formula
The Heat Index can be calculated using the empirical formula below, which is valid when the temperature is in Fahrenheit (Â°F) and the relative humidity is in percentage (%).

## Formula:

![Formula HEAT INDEX](https://cdck-file-uploads-global.s3.dualstack.us-west-2.amazonaws.com/flex020/uploads/sws/original/2X/1/107877b6f0aefcbb913cbcf20636d0fe8e1d1438.png)


## Features

- Calculates the Heat Index based on temperature and humidity.
- Retrieves current weather data from a public API.
- Displays weather information with periodic updates.

## Code Example:

```bash
from heat_package.index import main

# coordinates from user actual location
latitude = -8.55309646857836
longitude = 125.5523432617644
main(latitude, longitude)
pyfingridapi 0.1.1
This module is intented for easy access to fingrid API
Requires Python: >=3.8
Keywords: API, Fingrid, FingridApi, FingridOpenData


# Pyfingridapi

`pyfingridapi` is a Python client library for interacting with the Fingrid API. It provides convenient methods for accessing time-series data, dataset information, notifications, and the health status of the Fingrid API.

## Features

- Fetch real-time and historical time-series data.
- Retrieve detailed dataset metadata.
- Access active notifications and alerts.
- Check the health status of the Fingrid API services.
- Built using `httpx` for robust HTTP interactions and `pydantic` for data validation.

## Installation

Install the library using pip:

```bash
pip install pyfingriaApi
```

## Usage

### Initialization

Create an instance of the `FingridApi` client:

```python
import httpx
from pyfingridapi import FingridApi

# Initialize the API client
with httpx.Client() as client
    api_client = FingridApi(client=client, x_api_key="your_api_key")
```

### Fetching the Latest Data by Dataset

```python
latest_data = api_client.get_last_data_by_dataset(datasetId=12345)
print(latest_data)
```

### Fetching Multiple Time Series Data

```python
time_series_data = api_client.get_multiple_timeseries_data(
    datasetId=[123, 456],
    startTime="2024-01-01T00:00:00Z",
    endTime="2024-01-31T23:59:59Z"
)
print(time_series_data)
```

### Retrieving Dataset Information

```python
dataset_info = api_client.get_dataset(datasetId=12345)
print(dataset_info)
```

### Fetching Active Notifications

```python
notifications = api_client.get_active_notifications()
print(notifications)
```

### Checking API Health Status

```python
health_status = api_client.get_health_status()
print(health_status)
```

## Models

The library uses `pydantic` models to structure and validate the data retrieved from the API. Key models include:

- `DataRecord`: Represents a single data point in a time series.
- `TimeSeriesDataResponse`: Encapsulates time-series data and pagination information.
- `DataSet`: Contains detailed metadata about a dataset.
- `ActiveNotifications`: Represents active notifications from Fingrid.
- `HealthStatus`: Describes the health status of the Fingrid API.

## Logging

`pyfingridapi` uses Python's `logging` module to log errors and debug information. To configure logging:

```python
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("fingridapi")
```

## TODO

- Add support for API endpoints such as `GetDatasetFile`, `GetDatasetFileData`, and `GetDatasetShorts`.
- test this as module
- push this to pypi_test
- push this to pypi
- create documentation site

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! Feel free to submit issues or pull requests to enhance the library.
rvc-dv-clone 0.4.0
Retrieval-based Voice Conversion library
Requires Python: >=3.8

# RVC Convert

A Python package for Retrieval-based Voice Conversion, based on the [RVC-Project](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI).

Note: This is a fork of the [RVC-Project](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI) with the goal of making it a Python package for a custom use case. There is no need to install this package - normally you would just clone the repo and use it directly.

## Installation

Download `hubet_base.pth` from [here](https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pth) and put it in the models folder.

## Usage

```python

# test.py

from rvc_dv_clone.rvc_infer import rvc_convert


input_dir = ""
input = "test.wav"

output_dir = ""
output = "output.wav"

# show your trained models
model = [
    "models/f01/f01.pth",
]

import os
# Check if the input file exists
if not os.path.exists(f"{input_dir}/{input}"):
    raise FileNotFoundError(f"Input file {input_dir}/{input} not found")

wav=rvc_convert(
    model_path=model[0],
    f0_up_key=2,
    input_path=f"{input_dir}/{input}",
    output_dir_path=f"{output_dir}/{output}",
)

```

## License

This project is licensed under the MIT License - see the LICENSE file for details.