semvec/pypi-packages huggingface dataset:
reinhard-color-transfer 0.1.3
An implementation of Color transfer between images by E. Reinhard et. al.
Requires Python: >=3.11

## Reinhard Color Transfer 
[![PyPI version](https://badge.fury.io/py/reinhard-color-transfer.svg)](https://badge.fury.io/py/reinhard-color-transfer)

An implementation of ["Color transfer between images" by E. Reinhard et. al."](https://doi.org/10.1109/38.946629)

## Installation

You can install the package using `pip`:

```bash
pip install reinhard-color-transfer
```


## Usage

```python
from PIL import Image
from reinhard_color_transfer import transfer_color

source_image = Image.open("path/to/source/image")
color_image = Image.open("path/to/color/image")
alpha = 0.5 # Amount of color transfer
result_image: Image.Image = transfer_color(source_image, color_image, alpha)
```

Following is a visualization of result using sample images:

![Color Transfer Visualization](https://raw.githubusercontent.com/dariush-bahrami/reinhard_color_transfer/refs/heads/master/assets/Color-Transfer-Visualization.jpg)
msfunds 1.1
Fetches Funds data from Morningstar.
Requires Python: >=3.7

<h1>msfunds</h1> 

- Interacts with the Morningstar API to retrieve fund-related data.
- Includes a screener to filter and identify funds based on specific criteria.

<h2>Requirements & Installation :</h2>

- Requirements: `ua_generator`, `httpx`, `pandas`, `beautifulsoup4`, `nest_asyncio`, `importlib_resources`.  
- Install using: `pip install msfunds` (https://pypi.org/project/msfunds/)
 


<h2>Example:</h2>

```python
import msfunds

# Retrieve funds available in France, corresponding to the keyword "Technology" and display their one year return
funds_fra = msfunds.ScreenerFunction().screener(Country="France", Keyword="Technology", Fields=["GBRReturnM12"])
funds_fra = funds_fra.sort_values(by="GBRReturnM12")
funds_fra = funds_fra.head(5)
print(funds_fra)

# Selection of a fund based on one year return:
fund_id = funds_fra.iloc[0]['SecId']

# Fetching a specific fund data: 
fund_data = msfunds.Fund(fund_id)

# Display the fund ESG-related data: 
fund_esg = fund_data.ESG

# Display the fund Portfolio-related data: 
fund_portfolio = fund_data.Portfolio

# Display top holdings of the fund: 
fund_holdings = pd.DataFrame(fund_portfolio['HoldingData']['equityHoldingPage'])

```