semvec/pypi-packages huggingface dataset:
UFANET 0.0.1
UFA-Net method proposed in paper Unsupervised Cross-Domain Functional MRI Adaptation for Automated Major Depressive Disorder Identification
License: MIT
Requires Python: >=3.6

# UFANet

#### This is a code implemention of the UFA-Net method proposed in the manuscipt "Unsupervised Cross-Domain Functional MRI Adaptation for Automated Major Depressive Disorder Identification".

### 0. Dependencies
`torch==1.10.0`<br>
`torchvision==0.2.1`<br>
`numpy==1.21.2`<br>
`scikit_learn==1.1.3`<br>

### 1. Data Construction
We construct a demo consisting of 10 source examples and 10 target examples.

Run: `synthesize_data.py`<br>

The shape of the constructed data and label:<br>
> src_data.npy `(SrcNum, 1, T, NodeNum, 1)`<br>
> src_lbl.npy `(SrcNum, )`<br>
> tgt_data.npy `(TgtNum, 1, T, NodeNum, 1)`<br>
> tgt_lbl.npy `(TgtNum, )`<br>
> adj_matrix.npy `(NodeNum, NodeNum)`<br>

where<br>
`SrcNum` is the number of subjects in the source domain<br>
`TgtNum` is the number of subjects in the target domain<br>
`T` is the number of time points of a fMRI scan (here is 200)<br>
`NodeNum` is the number of brain nodes/ROIs (here is 116, corresponding to AAL116 atlas)<br>

### 2. Model Training and Validation
This is a two-step optimization method.

#### 2.1. Using $L_{C}$ to initialize the network parameter (not involve domain adaptation)<br>
Run: `main_pretrain.py`<br>
The pretrained model is saved in: `checkpoints_pretrain`

#### 2.2. Using $L_{C}$ and $L_{MMD}$ to train the whole network<br>
Run: `main_UDA.py`<br>
The classification results are saved in: `checkpoints`
ghgql 1.0.6
Thin wrapper for interacting with the Github GraphQL API
License: MIT
Requires Python: >=3.8,<4
Keywords: Python,Github,GraphQL,API,Wrapper

# ghgql

Thin wrapper for interacting with the [Github GraphQL API](https://docs.github.com/en/graphql).

## Status

[![Documentation Status](https://readthedocs.org/projects/ghgql/badge/?version=latest)](https://ghgql.readthedocs.io/en/latest/?badge=latest)
[![CodeQL](https://github.com/kwk/ghgql/actions/workflows/codeql.yml/badge.svg)](https://github.com/kwk/ghgql/actions/workflows/codeql.yml)
[![ci-cd](https://github.com/kwk/ghgql/actions/workflows/ci-cd.yml/badge.svg)](https://github.com/kwk/ghgql/actions/workflows/ci-cd.yml)
[![codecov](https://codecov.io/gh/kwk/ghgql/branch/main/graph/badge.svg?token=ASSPTOL3JU)](https://codecov.io/gh/kwk/ghgql)
[![release](https://img.shields.io/github/release/kwk/ghgql.svg)](https://github.com/kwk/ghgql/releases)

## Documentation

Please find the documentation and usage examples here: https://ghgql.readthedocs.io/en/latest/

## Installation

```bash
$ pip install ghgql
```

## Usage

For a more in-depth example, take a look at [the example in the documentation](https://ghgql.readthedocs.io/en/latest/example.html). Here's a basic example.

```python
import os
import fnc
import ghgql

query = """
query ($searchQuery: String!) {
  search(query: $searchQuery, type: ISSUE, first: 1) {
    edges {
      node {
        ... on Issue {
          id
          number
          title
          url
        }
      }
    }
  }
}
"""

with ghgql.GithubGraphQL(token=os.getenv("GITHUB_TOKEN")) as ghapi:
    result = ghapi.query(query=query, variables={"searchQuery": "llvm/llvm-project"})
    print(fnc.get("search.edges", result))
```

Should output something like this:

```yaml
[{'node': {'id': 'I_kwDOHicqdc5RG-tC',
   'number': 16,
   'title': 'llvm/llvm-project',
   'url': 'https://github.com/KhushP786/open-sauced-goals/issues/16'}}]
```

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

### Commit message conventions and semantic versioniong (semver)

We use semantic versioning and [these commit message conventions](https://www.conventionalcommits.org/en/v1.0.0/)
can be used to automatically bump the version number and generate the changelog.

## License

`ghgql` was created by Konrad Kleine. It is licensed under the terms of the MIT license.

## Credits

This project was created with the help of [this python packaging documentation](https://py-pkgs.org/01-introduction).

`ghgql` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
yasta 0.1.3
A modern task runner.
License: MIT
Requires Python: >=3.10,<4.0

# Yasta

![Yasta logo](https://i.imgur.com/MGw6TNI_d.webp?maxwidth=760)

Yasta is a modern task runner written in Python, it's also what we call microbus drivers in Egypt! 🚐

Yasta makes running and managing your tasks a breeze! 🌬️

# How to install

```
pip install yasta
```

# How to use

Yasta consists of 5 commands

1. init (initializes a pyproject.toml file with a test command)
2. add (adds a task to the list of tasks)
3. delete (deletes a task from the list of tasks)
4. show (shows the tables of tasks)
5. run (runs a task)

You can know more about the commands and flags by running

```
yasta --help
```

# Example

![Yasta init, adding and running tasks](https://i.imgur.com/Y55f9AN_d.webp?maxwidth=1520)

![Yasta force running commands, ignoring failed tasks](https://i.imgur.com/FsOrx3X_d.webp?maxwidth=1520)