semvec/pypi-packages huggingface dataset:
braindynamics-starprotocol 1.0.3
Functional brain network analysis protocol developed by Varga et al., 2024
Requires Python: >=3.12

# BrainDynamics protocol

This protocol is based on our method: **Varga, L.** et al. Brain dynamics supported by a hierarchy of complex correlation patterns defining a robust functional architecture. *Cell Systems*, Volume **15**, Issue **8**, 770 - 786.e5 (2024). DOI: https://doi.org/10.1016/j.cels.2024.07.003

## Requirements

- Python (tested for 3.12 and later versions)
    - igraph (0.11.6 or later)
    - pandas (2.2.2 or later)
    - scipy (1.14.0 or later)
    - matplotlib (3.9.2 or later)
    - cliffs_delta (1.0.0 or later)

- C/C++ compiler (e.g. gcc on Linux, Xcode Developer Tools for MacOS or Visual C++ Build Tools on Windows)

## Setting up

### 1) Set up virtual python environment using python or conda.

For example:

```
python -m venv braindynamics_env
```

OR via Anaconda:

```
conda create --name braindynamics_env python=3.12
```

### 2) Install package from PyPi

```
 pip install braindynamics-starprotocol
```

### 3) Usage

For an example of the pipeline, check `examples/pipeline_example.ipynb` notebook, or run the `.py` file with the same name.

For more details, you can also refer to the [documentation available on ReadTheDocs](https://braindynamics-starprotocols.readthedocs.io)

Link to the PyPi project site for release history: https://pypi.org/project/braindynamics-starprotocol

### 4) Citation

Please cite our publication when using our code: **Varga, L.** et al. Protocol to study brain dynamics of mammals through the hierarchy of complex correlation patterns defining a robust functional architecture. _STAR Protocols_, Volume **6**, Issue **2**, 103693 (2025). DOI: https://doi.org/10.1016/j.xpro.2025.103693
talkytrend 4.0.1
A python package to retrieve  economic data such as Trend for any financial symbol.
Requires Python: >=3.10
Keywords: finance, crypto, bot, trend, economic


<br>
<table style="border: 1px solid transparent">
  
  <tr>
    <td>
<a href="http://talky.readthedocs.io"><img src="https://img.shields.io/badge/Wiki-%23000000.svg?style=for-the-badge&logo=wikipedia&logoColor=white"></a>
<a href="https://github.com/mraniki/tt/"><img src="https://img.shields.io/badge/github-%23000000.svg?style=for-the-badge&logo=github&logoColor=white"></a><br>
<a href="https://hub.docker.com/r/mraniki/tt"><img src="https://img.shields.io/docker/pulls/mraniki/tt?style=for-the-badge"></a><br>
       </td>
    <td align="center"><img width="200" alt="Logo" src="https://user-images.githubusercontent.com/8766259/226854338-e900f69e-d884-4a9a-90b1-b3dde7711b31.png"></td>
  </tr>
  <tr>
    <td>
      <a href="https://pypi.org/project/talkytrend/"><img src="https://img.shields.io/pypi/v/talkytrend?style=for-the-badge&logo=PyPI&logoColor=white"></a><br>
      <a href="https://pypi.org/project/talkytrend/"><img src="https://img.shields.io/pypi/dm/talkytrend?style=for-the-badge&logo=PyPI&logoColor=white"></a><br>
      <a href="https://github.com/mraniki/talkytrend/"><img src="https://img.shields.io/github/actions/workflow/status/mraniki/talkytrend/%F0%9F%91%B7Flow.yml?style=for-the-badge&logo=GitHub&logoColor=white"></a><br>
      <a href="https://talky.readthedocs.io/projects/talkytrend/"><img src="https://readthedocs.org/projects/talkytrend/badge/?version=latest&style=for-the-badge"></a><br>
      <a href="https://codebeat.co/projects/github-com-mraniki-talkytrend-main"><img src="https://codebeat.co/badges/24c90aab-02d7-4cd1-9ad8-5907e180c9e6"/></a> <br>
      <a href="https://codecov.io/gh/mraniki/talkytrend"><img src="https://codecov.io/gh/mraniki/talkytrend/branch/main/graph/badge.svg?token=WAHUEMAJN6"/></a><br>
    </td>
    <td align="left"> 
Retrieve asset trend and economic data.<br>
Trading view connectivity<br>
News connectivity<br>
       FOMC reminder<br>
    </td>
     
  </tr>
</table>


<h5>How to use it</h5>
<pre>
<code>
    talky = TalkyTrend()
    result = await talky.fetch_signal()
    #  BUY
    result = await talky.fetch_event()
    print(result)
    #  Title:  FDA advisers say new Alzheimer’s drug lecanemab slows cognitive decline
    # Description:  Panel’s opinion could pave way for full regulatory approval next month for treatment of disease that affects 6.5m Americans
    monitor = await talky.monitor() #ongoing monitoring
    # New signal for BTCUSD (4h): STRONG_SELL
    # Key event: {'title': 'OPEC-JMMC Meetings', 'country': 'ALL', 'date': '2023-06-04T06:15:00-04:00', 'impact': 'High', 'forecast': '', 'previous': ''}\
    # Key news: FDA advisers say new Alzheimer’s drug lecanemab slows cognitive decline
</code>
</pre>
</details>

<h5>Documentation</h5>
<a href="https://talky.readthedocs.io/projects/talkytrend/en/latest/"><img src="https://img.shields.io/badge/Documentation-000000?style=for-the-badge&logo=readthedocs&logoColor=white"></a><br>
findmyorder 4.0.1
A python package to identify and parse order for trade execution.
Requires Python: >=3.10
Keywords: trading, order, trade, buy, sell


<table style="border: 1px solid transparent">
  <tr>
    <td>
<a href="http://talky.readthedocs.io"><img src="https://img.shields.io/badge/Wiki-%23000000.svg?style=for-the-badge&logo=wikipedia&logoColor=white"></a>
<a href="https://github.com/mraniki/tt/"><img src="https://img.shields.io/badge/github-%23000000.svg?style=for-the-badge&logo=github&logoColor=white"></a>
<br>
<a href="https://hub.docker.com/r/mraniki/tt"><img src="https://img.shields.io/docker/pulls/mraniki/tt?style=for-the-badge"></a>
<br>
    </td>
    <td align="center"><img width="200" alt="Logo" src="https://user-images.githubusercontent.com/8766259/233823991-cceaa05a-ff15-4796-a6bb-bcb3ee0d8859.jpg"></td>
  </tr>
  <tr>
    <td>
      <a href="https://pypi.org/project/findmyorder/"><img src="https://img.shields.io/pypi/v/findmyorder?style=for-the-badge&logo=PyPI&logoColor=white"></a><br>
      <a href="https://pypi.org/project/findmyorder/"><img src="https://img.shields.io/pypi/dm/findmyorder?style=for-the-badge&logo=PyPI&logoColor=white"></a><br>
      <a href="https://github.com/mraniki/findmyorder"><img src="https://img.shields.io/github/actions/workflow/status/mraniki/findmyorder/%F0%9F%91%B7Flow.yml?style=for-the-badge&logo=GitHub&logoColor=white"></a><br>
   <a href="https://talky.readthedocs.io/projects/findmyorder/index.html"><img src="https://readthedocs.org/projects/findmyorder/badge/?version=latest&style=for-the-badge"></a><br>
   <a href="https://codebeat.co/projects/github-com-mraniki-findmyorder-main"><img src="https://codebeat.co/badges/9b113098-d22d-498d-9c61-eb1e96c1311a"/></a><br>
   <a href="https://codecov.io/gh/mraniki/findmyorder"><img src="https://codecov.io/gh/mraniki/findmyorder/branch/main/graph/badge.svg?token=4838MSZNCC"/> </a><br>
    </td>
     <td align="left"> 
        Find My order,<br>
       a parsing package to find trading order.
     </td>
  </tr>
</table>

<h5>How to use it</h5>
<pre>
<code>
      from findmyorder import FindMyOrder
         fmo = FindMyOrder()
         msg_order = "buy EURUSD sl=1000 tp=1000 q=1 comment=FOMC"
         order = await fmo.get_order(msg_order)
         #{'action': 'BUY', 'instrument': 'EURUSD', 'stop_loss': '1000', 'take_profit': '1000', 'quantity': '2', 'order_type': None, 'leverage_type': None, 'comment': None, 'timestamp': datetime.datetime(2023, 5, 3, 12, 10, 28, 731282, tzinfo=datetime.timezone.utc)}
</code>
</pre>


<h5>Documentation</h5>
<a href="https://talky.readthedocs.io/projects/findmyorder/en/latest/"><img src="https://img.shields.io/badge/Documentation-000000?style=for-the-badge&logo=readthedocs&logoColor=white"></a><br>
litegrad 0.0.1
A minimal implementation of PyTorch's autograd engine
Requires Python: >=3.9

<div align="center">

# litegrad
[![Python](https://img.shields.io/badge/Python-3776ab?logo=python&logoColor=white)](https://www.python.org/)
[![Code Quality](https://github.com/miguelbper/litegrad/actions/workflows/code-quality.yaml/badge.svg)](https://github.com/miguelbper/litegrad/actions/workflows/code-quality.yaml)
[![Unit Tests](https://github.com/miguelbper/litegrad/actions/workflows/tests.yaml/badge.svg)](https://github.com/miguelbper/litegrad/actions/workflows/tests.yaml)
[![codecov](https://codecov.io/gh/miguelbper/litegrad/graph/badge.svg)](https://codecov.io/gh/miguelbper/litegrad)
[![License](https://img.shields.io/badge/License-MIT-green.svg?labelColor=gray)](LICENSE)

A minimal implementation of PyTorch's autograd engine

</div>

---

## Description

*Package currently under construction.*