semvec/pypi-packages huggingface dataset:
graynet 0.5.7
Individualized single-subject networks from T1 mri features such as cortical thickness and gray matter density. 

# graynet

[![DOI](http://joss.theoj.org/papers/10.21105/joss.00924/status.svg)](https://doi.org/10.21105/joss.00924)
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# News

 - Volumetric version of graynet is available now - [check here](https://raamana.github.io/graynet/volumetric.html)


# Overview

Individualized single-subject networks from T1-weighted magnetic resonance imaging (MRI) features such as:
  - vertex-wise features such as cortical thickness, gyrification and curavature
  - volumetric features such as gray matter density (T1w images) or metabolic uptake (PET) or another voxel-wise feature
  - Subcortical morphometric features
  - or any other similar feature distributed over a domain (cortex, or whole brain) to enable compuatation of inter-regional edge weights

Applicable for whenever network-level features are useful, among which common use cases are: 
 - Biomarker development.
 - Brain-behaviour relationships (e.g. for the diagnosis and prognosis of many brain disorders such as Alzheimer's, Parkinson's, Schizophrenia and the like).
 - Aging (changes in network properties over age and their relations to other variables).

Docs: https://raamana.github.io/graynet/

Quick illustration:

![graynet_flyer](docs/vis/graynet_flyer.jpg)

## Installation

`pip install -U graynet`

## Citation

If you found any parts of graynet to be useful in your research, I'd appreciate if you could cite the software paper in JOSS below, as well as the methods paper that motivated the tool development in that order:

 - Raamana et al., (2018). graynet: single-subject morphometric networks for neuroscience connectivity applications. Journal of Open Source Software, 3(30), 924, https://doi.org/10.21105/joss.00924
 - Raamana, P. R., & Strother, S. C. (2020), “Does size matter? Relationship between predictive power of single subject morphometric networks to spatial scale and edge weight”, Brain Structure and Function, 225(8), 2475-2493. DOI: 10.1007/s00429-020-02136-0
 - Raamana, P. R., Weiner, M. W., Wang, L., Beg, M. F., & Alzheimer's Disease Neuroimaging Initiative. (2015). Thickness network features for prognostic applications in dementia. Neurobiology of aging, 36, S91-S102. https://www.sciencedirect.com/science/article/pii/S0197458014005521

---

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onchaindata 0.0.8
Toolkit for getting onchain data
Requires Python: >=3.11

# Onchain data
This repo is archived and moved to https://github.com/newgnart/fa-dae2-gnart
A data engineering toolkit for extracting, transforming, and loading EVM blockchains data.

## Usage
### Setup python environment
```bash
uv sync
cp .env.example .env # Then fill the .env file
```

### Most basic usage: Extract data from Etherscan to local parquet files
Fill the `scripts/extraction/contracts.json` file with the contracts you want to extract data for.
```bash
uv run scripts/extraction/runner.py --logs --transactions --from-block 18000000 --to-block 19000000 # or just --logs or --transactions, if --from-block defaulted to the contract creation block, --to-block defaulted to the latest block
```
tradion_api_client 0.2.0
tradion api client
Requires Python: >=3.13

# Introduction

This package is written to be used as a tradion API Client in Python. With the correct API credentials, User can access tradion interactive & market data.

## Installation

install

```bash
pip install tradion_api_client
```

## Reference

-   This is a python client package for Tradion Api. Tradion is a product from [Tradion](https://weblive.rmoneyindia.net/). The original documentation for tradion client is [click here](https://weblive.rmoneyindia.net/apidocs)

## Package Structure

-   For better understanding the package, the user should have a clear understanding of the architecture of the package.

Here is the folder structure of the project:

```
└───tradion_api_client
    ├─── core
    │    ├─── __init__.py
    │    ├─── interactive.py
    │    └─── market_data.py
    ├─── utils
    │    ├─── __init__.py
    │    ├─── http_client.py
    │    └─── logger.py
    ├─── __init__.py
    ├─── config.py
    ├─── exceptions.py
```

## Features

-   **Authentication**: Login with password and OTP
-   **Order Management**: Place, modify, and cancel orders
-   **Portfolio**: View positions and holdings
-   **Funds**: Check available margins
-   **Market Data**: Real-time streaming via WebSocket
-   **Contract Master**: Download contract files and search for instrument tokens by trading symbol

## Usage

```python
from tradion_api_client import InteractiveClient
from tradion_api_client import MarketDataClient
```

### Contract Master

Download contract master files and search for instrument tokens:

```python
import asyncio
from tradion_api_client import InteractiveClient

async def main():
    client = InteractiveClient()
    
    # Download contract master file
    nse_file = await client.download_contract_master("NSE")
    print(f"Downloaded: {nse_file}")
    
    # Get token by trading symbol (use correct format for eg. -EQ suffix)
    token_info = await client.get_token_by_symbol("RELIANCE-EQ", "NSE")
    if token_info:
        print(f"Token: {token_info['token']}")
        print(f"Lot Size: {token_info['lot_size']}")
    
    # Get symbol of indices by using this method
    indices_token_info_nse = await client.get_token_by_symbol("NIFTY 50", "NSE", "INDICES.json")
    indices_token_info_bse = await client.get_token_by_symbol("SENSEX", "BSE", "INDICES.json")
    indices_token_info_mcx = await client.get_token_by_symbol("MCXAGRI", "MCX", "INDICES.json")
    
    await client.close()

asyncio.run(main())
```

For more details, see [Contract Master Documentation](docs/contract_master.md)