semvec/pypi-packages huggingface dataset:
igwn-rucio-lfn2pfn 0.0.35
LFN2PFN algorithms for IGWN

# igwn-rucio-lfn2pfn

Algorithm for LFN to PFN conversion for rucio in the international gravitational wave network

## Plugging into rucio as an external module
As currently configured, this module should be made available to rucio by setting the following in the `rucio.cfg`:
```
[policy]
lfn2pfn_module=igwn_lfn2pfn.frames_lfn2pfn
lfn2pfn_algorithm=ligo_legacy
```
And installing via e.g.,:
```
pip install git+https://git.ligo.org/rucio/igwn-rucio-lfn2pfn.git
```
tasnem-har 1.0.1
Teacher Assisted Slim Neural Model For Human Activity Recognition.

# TASNeM-HAR

![PyPI - License](https://img.shields.io/pypi/l/tasnem-har?style=flat-square)
![PyPI](https://img.shields.io/pypi/v/tasnem-har?style=flat-square)
![GitHub Repo stars](https://img.shields.io/github/stars/chenrz925/TASNeM-HAR?style=flat-square)

Teacher Assisted Slim Neural Model For Human Activity Recognition.

## Install

```shell
pip install tasnem-har
```

## Build from source

```shell
git clone https://github.com/chenrz925/TASNeM-HAR.git
cd TASNeM-HAR
pip install .
```

## Execute example profile

```shell
wget https://raw.githubusercontent.com/chenrz925/TASNeM-HAR/main/examples/train_realworld.toml
```

Before running the downloaded profile, you can modify the root directory into your path.
You can download the RealWorld-HAR dataset by 

```shell
wget -c http://wifo5-14.informatik.uni-mannheim.de/sensor/dataset/realworld2016/realworld2016_dataset.zip
```

Decompress the zip file, and put "channel_25.npy", "channel_75.npy", "channel_max.npy", "channel_mean.npy",
"channel_min.npy", and "channel_std.npy" into the place of decompressed files. Finally, you will get:

```
.
├── channel_25.npy
├── channel_75.npy
├── channel_max.npy
├── channel_mean.npy
├── channel_min.npy
├── channel_std.npy
├── proband1
├── proband10
├── proband11
├── proband12
├── proband13
├── proband14
├── proband15
├── proband2
├── proband3
├── proband4
├── proband5
├── proband6
├── proband7
├── proband8
└── proband9

15 directories, 6 files
```

When you deploy the dataset, you can launch the example profile using `waterch-tasker`.

```shell
waterch-tasker launch -f train_realworld.toml
```

Your new HAR models are available in path ".tasker/storage/pickle/TASNeM-RealWorld-HAR", including a training stage and a validating stage.
cyanobyte 0.0.2
A package that generates library files for a peripheral given an intermediary layer (YAML files)
Requires Python: >=3.7

# Peripheral Codegen
This project is an example of how to describe peripherals with an intermediary layer (YAML files) which can be used to generate library files for a peripheral.

It can also generate reference documentation for a peripheral, useful for embedding into datasheets.

The tool works well for I2C devices, while SPI support is in alpha.

This is not an official Google product.

## Setup
Note: This project requires Python3. You can install using pip

`pip install cyanobyte`

## Run Codegen
`cyanobyte-codegen -t templates/doc.md -o ./build peripherals/MCP4725.yaml`

### Options
* `-t` - A template file. You can provide multiple template files.
* `-o` - The output directory where files will be generated.
* `-e` - The directory that emboss folder is stored.
* `-d` - Debug flag to print out additional information.
* `-c` - Clean the output directory before generating files.

One or multiple files can be passed as an argument.

### Clean
`rm -rf ./build`

## Run Validator
`cyanobyte-validator peripherals/MCP9808.yaml`

One or multiple files can be passed as an argument.

## Peripheral YAML file
The current spec is described in `docs/cyanobyte.md`. You can find all examples in the `peripherals/` directory.

## Test
### Lint
`python3 -m pylint --rcfile=test/pylintrc cyanobyte/*.py`  
`python3 -m pylint --rcfile=test/pylintrc test/sampleData/*.py`

### Unit test
`python3 -m unittest test.test_codegen`

## Templates
The [templates](templates) directory includes a set of canonical templates which can be used with this codegen tool.

The [peripherals](peripherals) directory includes a set of peripheral description files that have been created along with
the project. It is not an exhaustive list.

## Development setup
`pip install -r requirements.txt --user`


For more advanced development, also install the dev list.
`pip install -r requirements-dev.txt --user`

## Contributors
Contributions are welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for more information.

When a pull request is submitted, a continuous integration task is run. The CI task must
be completed successfully before a patch is merged. You can see the specific rules run in
[cloudbuild.yaml](cloudbuild.yaml).

## License
See [LICENSE](LICENSE)
sixgill-innovation-datasets 0.1.2a2
Library for interacting with Sixgill's Innovation datasets.
Requires Python: >=3.6

# Innovation Datasets

This project was created to help organize the Innovation Team's research datasets.  It is split into two parts:
1. Dataset Versioning and Management
    * Datasets are stored in the `datasets/` folder, and versioned using DVC.
    * Currently, we only use Google Cloud Storage for file storage.
    * All datasets can be downloaded using `dvc get ...` or `dvc import ...` without installing the `innovation_datasets` package.
2. Python Code for Using Datasets
    * We provide Python code for downloading and handling the datasets.
    * Currently, all datasets are subclasses of `torch.utils.data.Dataset`, which makes them easy to use with PyTorch.
    * **Python code is versioned alongside the datasets, so they are always synchronized together.**

## Getting Started

### Requirements

* Python >= 3.6
* PyTorch ≥ 1.5 and torchvision that matches the PyTorch installation. Install them together at [pytorch.org](https://pytorch.org) to make sure of this

### Installation

Install using `pip`:

```bash
$ pip install sixgill-innovation-datasets
```

Or install a non-release branch/tag:

```bash
$ pip install https://github.com/sixgill/innovation-datasets/tree/<branch-name>
```

Or install from source:
```bash
git clone https://github.com/sixgill/innovation-datasets && cd innovation-datasets
git checkout <branch-name>
python setup.py install
```

### Basic Usage

The source code is organized into modules, which contain one or more datasets from a similar source.  For example:

```
innovation_datasets         (top-level)
    - youtube_vos           (module)
        - vos_2019          (module)
            - Images        (dataset)
            - FramePairs    (dataset)
            - Videos        (dataset)
            ...
        ...
```

In this case, all datasets in the `innovation_datasets.youtube_vos` module were sourced from the [YouTubeVOS website](https://youtube-vos.org).  To use a dataset, import and instantiate it in a Python script:

```python
from innovation_datasets.youtube_vos import vos_2019

dataset = vos_2019.Images(root="./data")
```

The raw data files will automatically be downloaded and stored in the `./data` folder.  Currently, all datasets are subclasses of `torch.utils.data.Dataset`.  This makes it easy to use them with PyTorch and related libraries.  

**TODO:** Discuss making datasets more generic and removing the PyTorch dependency.  This would make `innovation_datasets` easier to use with other libraries, and *significantly* reduce the total size of library dependencies.
tasnem-har-WaterCH 1.0.0
Teacher Assisted Slim Neural Model For Human Activity Recognition.

# TASNeM-HAR

![PyPI - License](https://img.shields.io/pypi/l/tasnem-har?style=flat-square)
![PyPI](https://img.shields.io/pypi/v/tasnem-har?style=flat-square)
![GitHub Repo stars](https://img.shields.io/github/stars/chenrz925/TASNeM-HAR?style=flat-square)

Teacher Assisted Slim Neural Model For Human Activity Recognition.

## Install

```shell
pip install tasnem-har
```

## Build from source

```shell
git clone https://github.com/chenrz925/TASNeM-HAR.git
cd TASNeM-HAR
pip install .
```

## Execute example profile

```shell
wget https://raw.githubusercontent.com/chenrz925/TASNeM-HAR/main/examples/train_realworld.toml
```

Before running the downloaded profile, you can modify the root directory into your path.

```
waterch-tasker launch -f train_realworld.toml
```

Your new HAR models are available in path ".tasker/storage/pickle/TASNeM-RealWorld-HAR", including a training stage and a validating stage.