semvec/pypi-packages huggingface dataset:
rna-data 0.0.1
A library to manipulate data for our DMS prediction models.
Requires Python: >=3.10

[![Docs](https://github.com/rouskinlab/RNA_data/actions/workflows/sphinx.yml/badge.svg)](https://github.com/rouskinlab/RNA_data/actions/workflows/sphinx.yml)


# RNA_data

A repo to manipulate the data for our RNA structure prediction model.

See [the docs](https://rouskinlab.github.io/RNA_data/) for more information.
kaisia 0.0.4
GCP Big Query Migration helper
Requires Python: >=3.11

# Welcome to Kaisia
## easy way to compare GBQ tables schemas right in your terminal
### Usage


```
bash$ pip install kaisia
...
bash$ kaisia
usage: kaisia [-h] [-V] [-t1 TABLE1] [-t2 TABLE2] [-c CREDENTIALS]

options:
  -h, --help            show this help message and exit
  -V, --version         show program's version number and exit
  -t1 TABLE1, --table1 TABLE1
                        Table1 full GBQ id
  -t2 TABLE2, --table2 TABLE2
                        Table2 full GBQ id
  -c CREDENTIALS, --credentials CREDENTIALS
                        GCP Service Account key
```
pyutils-pkg 0.1.0
PyUtils Package
Requires Python: >=3.11,<4.0

## PyUtils
The PyUtils package is intended to be a collection of utility functions for Python engineers. It aims to provide reusable code snippets that simplify common tasks in Python programming.

## Features
- String Manipulation
- File Handling
- Data Conversion
- Date/Time Operations

## Tech Stacks
- Python

## Local Environment

Setup virtual environment and Poetry:
```shell
python3 -m venv .venv
source .venv/bin/activate
pip install poetry
poetry config virtualenvs.create false
```
Install project packages:
```shell
poetry install
```
Run tests:
```shell
pytest
```
Run tests coverage:
```shell
pytest --cov=pyutils tests/
```

## Documentation
Show Docs Locally:
  ```shell
  mkdocs serve
  ```
Deploy Docs to GitHub Pages:
  ```shell
  mkdocs gh-deploy
  ```

## Build
Build the project:
```shell
python3 -m build
```

## Distribution
Distribute the package to PyPI
```shell
python3 -m twine upload --repository pypi dist/*
```
pyutils-mustafaelghrib 0.1.0
PyUtils Package
Requires Python: >=3.11,<4.0

## PyUtils
The PyUtils package is intended to be a collection of utility functions for Python engineers. It aims to provide reusable code snippets that simplify common tasks in Python programming.

## Features
- String Manipulation
- File Handling
- Data Conversion
- Date/Time Operations

## Tech Stacks
- Python

## Local Environment

Setup virtual environment and Poetry:
```shell
python3 -m venv .venv
source .venv/bin/activate
pip install poetry
poetry config virtualenvs.create false
```
Install project packages:
```shell
poetry install
```
Run tests:
```shell
pytest
```
Run tests coverage:
```shell
pytest --cov=pyutils tests/
```

## Documentation
Show Docs Locally:
  ```shell
  mkdocs serve
  ```
Deploy Docs to GitHub Pages:
  ```shell
  mkdocs gh-deploy
  ```

## Build
Build the project:
```shell
python3 -m build
```
Pyne-script-utilities 1.0.0
A set of utility sub-modules that allow for code structure similar to pinescript in python
Requires Python: >=3.10

# Pyne Script

Pyne Script aims to offer a set of utility sub-modules that allow for code structure similar to Trading View Pine Script in Python.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install pyne_script.

```bash
pip install pyne_script
```

## Available sub-modules

- [series.py](https://github.com/80sVectorz/pyne_script/blob/main/docs/README_series.md)

## Contributing

Pull requests are welcome. For major changes, please contact me first
to discuss what you would like to change.

## License

[GPL-3.0](https://choosealicense.com/licenses/gpl-3.0/)
gen3-util-plugin-nvidia 0.0.4
CEDAR / NVIDIA collaboration
Requires Python: >=3.9, <4
Keywords: bioinformatics

# nvidia-collaboration
OHSU CEDAR Cyclic IF and H&amp;E images

## Getting started

### Setup python environment

```commandline
source venv/bin/activate
python3 -m pip install -r requirements.txt
python3 -m pip install -r requirements-dev.txt  
python3 -m pip install -e .
```

## Run meta import with --plugin_path

The input directory:

```
tests/fixtures-new/
├── HandE
├── RegisteredImages
│   ├── 17633-6-Scene-001
│   ├── 18538-6-Scene-001
│   ├── 19142-6-Scene-001
│   ├── 24952-6-Scene-001
│   ├── 30411-6-Scene-001
│   ├── 31022-6-Scene-001
│   ├── 31480-6-Scene-001
│   ├── 33548-6-Scene-001
│   ├── 38592-6-Scene-001
│   ├── 48411-6-Scene-001
│   ├── 54774-4-Scene-001
│   ├── 57494-6-Scene-001
│   └── 57658-6-Scene-001
└── annotations

```

will produce:

```
gen3_util meta  import dir tests/fixtures/ tmp/output --project_id aced-nvidia --plugin_path gen3_util_plugin_nvidia --remove_path_prefix tests/fixtures/
summary:
  ResearchStudy:
    count: 1
  ResearchSubject:
    count: 13
  Patient:
    count: 13
  Specimen:
    count: 13
  DocumentReference:
    count: 738
    size: 74437
msg: OK


```

### expected results

Output files:

```
tmp/output
├── DocumentReference.ndjson
├── Patient.ndjson
├── ResearchStudy.ndjson
├── ResearchSubject.ndjson
└── Specimen.ndjson
```

Identifiers:

```
$ cat tmp/output/*.ndjson | jq -rc '. as $r | .identifier[]? | [$r.resourceType, .value]'
["Patient","P1"]
["Specimen","T1"]
```


## Run the tests

```
pytest  tests/
```
## Distribution

- PyPi

```
# update pypi

# pypi credentials - see https://twine.readthedocs.io/en/stable/#environment-variables

export TWINE_USERNAME=  #  the username to use for authentication to the repository.
export TWINE_PASSWORD=  # the password to use for authentication to the repository.

# this could be maintained as so: export $(cat .env | xargs)

rm -r dist/
python3  setup.py sdist bdist_wheel
twine upload dist/*
```
northstar-datazone 0.0.9
Datazone Client Package
Requires Python: >=3.9,<4.0

# northstar-package-datazone
Datazone Client Library

## Installation
You can install package via pip
````shell
pip install northstar-datazone
````

## Configuration & Login
Run the following command in your terminal
````shell
datazone auth configure
````
When prompted, enter the following information:
````shell
Host [app.datazone.co]: <DATAZONE_ADDRESS>
Username: <USERNAME>
Password: <PASSWORD>
Repeat for confirmation: <PASSWORD_CONFIRM>
````
Then you can run following command to login
````shell
datazone auth login
````
It will keep your tokens until refresh token expired. (Default 30min). Then you can re-run and login again.

## Create first repository
````shell
datazone repository create hello-world
cd hello-world
````

It creates a new folder in your current directory. Thıs folder has two file initially. `config.yaml` and `first_pipeline.py`.
After you modify your script you can deploy project via following command. (It takes about one minute).

```shell
datazone repository deploy
```

After the deployment is complete, you can check your repository status via following command.

```shell
datazone repository summary
```

If repository has deployed, you can run your first execution with following command. 
````shell
datazone execution run <pipeline_id>
````

## Commands
Also you can check all command with help sub command. `datazone auth --help` or `datazone repository summary --help`
```shell
# Auth
datazone auth configure
datazone auth login

# Repository
datazone repository list
datazone repository create
datazone repository deploy
datazone repository summary <file_name>
datazone repository clone <repository_id>
datazone repository pull

# Dataset
datazone dataset list
datazone dataset show <dataset_id> [<branch_name>] [<size>] [<transaction_id>]
datazone dataset transactions <dataset_id>

# View
datazone dataset view create
datazone dataset view list <dataset_id>
datazone dataset view delete <view_id>

# Source
datazone source create
datazone source list
datazone source update <source_id>
datazone source delete <source_id>

# Extract
datazone extract create
datazone extract list
datazone extract update <extract_id>
datazone extract delete <extract_id>
datazone extract execute <extract_id>

# Schedule
datazone schedule create
datazone schedule list
datazone schedule delete <source_id>

# Execution
datazone execution run [--extract-id] [--pipeline-id] [<execution_type>] [<transform_selection>]
datazone execution list [--extract-id] [--pipeline-id]
datazone execution log <execution_id>

# Pipeline
datazone pipeline create

# Common
datazone version
datazone info
```