semvec/pypi-packages huggingface dataset:
odoo10-addon-partner-capital 10.0.1.0.0.99.dev37
Partners Capital
Requires Python: ~=2.7

.. image:: https://img.shields.io/badge/licence-AGPL--3-blue.svg
   :target: http://www.gnu.org/licenses/agpl-3.0-standalone.html
   :alt: License: AGPL-3

===============
Partner Capital
===============

This module extends the functionality of partners to support setting the
country of origin of capital, registered capital amount and turnover.

By capital country we mean country of origin of the capital of the company.

By registered capital amount we mean the amount of money registered in the
corresponding commercial registry, A.K.A. social capital or just capital of a
company.

By turnover we can define a range for a concrete amount.

Usage
=====

To use this module, you need to:

* Go to any partner's form.
* You will find the new fields in *Sales & Purchases > Capital*.

To manage turnover ranges, you need to:

* Go to *Sales > Configuration > Address Book > Turnover ranges*.

.. image:: https://odoo-community.org/website/image/ir.attachment/5784_f2813bd/datas
   :alt: Try me on Runbot
   :target: https://runbot.odoo-community.org/runbot/134/10.0

Known issues / Roadmap
======================

* Add automatic rules to calculate turnover range and company size.
* Allow customization of company sizes.

Bug Tracker
===========

Bugs are tracked on `GitHub Issues
<https://github.com/OCA/partner-contact/issues>`_. In case of trouble, please
check there if your issue has already been reported. If you spotted it first,
help us smashing it by providing a detailed and welcomed `feedback
<https://github.com/OCA/
partner-contact/issues/new?body=module:%20
partner_capital%0Aversion:%20
10.0%0A%0A**Steps%20to%20reproduce**%0A-%20...%0A%0A**Current%20behavior**%0A%0A**Expected%20behavior**>`_.

Credits
=======

Contributors
------------

* Antonio Espinosa <antonio.espinosa@tecnativa.com>
* Rafael Blasco <rafael.blasco@tecnativa.com>
* Jairo Llopis <jairo.llopis@tecnativa.com>
* David Vidal <david.vidal@tecnativa.com>

Maintainer
----------

.. image:: https://odoo-community.org/logo.png
   :alt: Odoo Community Association
   :target: https://odoo-community.org

This module is maintained by the OCA.

OCA, or the Odoo Community Association, is a nonprofit organization whose
mission is to support the collaborative development of Odoo features and
promote its widespread use.

To contribute to this module, please visit https://odoo-community.org.
BrainGB 1.0.4
Emory Brain Benchmark package
Requires Python: >=3.6

# BrainGB

BrainGB is a python package for testing Graph Neural Networks on Brain Networks. 

# Installation

To install BrainGB as a package, simply run
```shell
pip install BrainGB
```

Alternatively, you can also download the repository from Github. The main package is under the src folder. If you choose to go with this method, please check the [Specification of Dependencies](#Specification-of-Dependencies) section for dependency requirements. 

# Getting Started

To import the models detailed in the paper:
```pycon
from BrainGB import GAT, GCN, BrainNN, GCN
```

The BrainNN is required and will be served as the parent module of the GAT, GCN models. You may choose either GAT or GCN as the submodule. 

To initialize a GCN model
```pycon
sample: Data = Data()  # A torch geometric data

num_features = data.x.shape[1]
num_nodes = data.x.shape[0]
gcn_model = GCN(num_features, num_nodes)

model = BrainNN(args.pooling, gcn_model, MLP(2 * num_nodes))
```

To initialize a GAT model, simply replace the GCN with GAT. Both models are customizable. Please refer to the documentation for more details. 

# Specification of Dependencies

BrainGB depends on the following frameworks:

```
torch~=1.10.2
numpy~=1.22.2
nni~=2.4
PyYAML~=5.4.1
scikit-learn~=1.0.2
networkx~=2.6.2
scipy~=1.7.3
tensorly~=0.6.0
pandas~=1.4.1
libsvm~=3.23.0.4
matplotlib~=3.4.3
tqdm~=4.62.3
torch-geometric~=2.0.3
h5py~=3.6.0
```

To install the dependencies, run:
```shell
pip install -r requirements.txt
```

Notice that if you install the package through pip, the dependencies are automatically installed. 

# Running Example Scripts

The repository also comes with example scripts. To train our model on any of the datasets we tested, simply run:
```shell
python -m main.example_main --dataset_name=<dataset_name> [--model_name=<variant>]
```

The `dataset_name` is the name of the dataset you want to use. The following datasets are available and tested:

- HIV
- PPMI (Can be downloaded [here](https://www.ppmi-info.org/access-data-specimens/download-data))
- PNC

The following datasets are also available but not tested:

- ABIDE
- BP
- ABCD

Please place the dataset files in the `datasets` folder under the package examples folder. Create the folder if it does not exist.

The `model_name` is the name of the model you want to use. Two choices are available:

- GCN
- GAT

# Contribution

We welcome contributions to the package. Please feel free to open an issue or pull request.