semvec/pypi-packages huggingface dataset:
gethue 0.26
Open source SQL Query Assistant for Databases/Warehouses
Requires Python: >=3.6

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<kbd><img src="https://raw.githubusercontent.com/gethue/compose/master/docs/images/compose_button.png"/></kbd>

Compose
-------

[Query Editor component](https://docs.gethue.com/developer/components/scratchpad/)

Compose is the open source module powering the [Hue SQL Editor](http://gethue.com). It comes as a Web service API for querying any [Databases & Data Warehouses](https://docs.gethue.com/administrator/configuration/connectors/) or building your own [Cloud SQL Editor](https://docs.gethue.com/developer/components/).


<img src="https://cdn.gethue.com/uploads/2021/05/sql-scratchpad-v0.5.png" width="450">


# Start

Hello World query

    curl -u hue:hue -X POST http://localhost:8005/editor/v1/query/sqlite --data 'statement=SELECT 100, 200'

Docker

    docker run -it -p 8005:8005 gethue/compose:latest

Pypi

    pip install gethue

    compose-admin migrate
    compose-admin createsuperuser
    compose-admin start

    compose auth
    compose query
    compose storage list s3a://demo-gethue

# Dev

One time

    git clone https://github.com/gethue/compose.git
    cd compose
    ./install.sh  # If you want a Python virtual-env
    pre-commit install

Start API

    source python_env/bin/activate
    python compose/manage.py runserver 0.0.0.0:8005

Config

    compose/conf/local_settings.py

Checks

    pre-commit run --all-files
    python compose/manage.py test

Hue

    npm run webpack-npm

# API

Live

* https://api.gethue.com/api/schema/swagger-ui/
* https://api.gethue.com/api/schema/redoc/

Query

    curl -u hue:hue -X POST http://localhost:8005/api/editor/execute/sqlite --data 'statement=SELECT 100, 200'

    curl -u hue:hue -X POST http://localhost:8005/api/editor/execute/sqlite --data 'statement=SELECT 100, 200'
    curl -u hue:hue -X POST http://localhost:8005/api/editor/check_status --data 'qid=abc'
    curl -u hue:hue -X POST http://localhost:8005/api/editor/fetch_result_data --data 'qid=abc'
tap-terminusdb 0.1.1
Singer.io tap for extracting data from TerminusDB

# tap-terminusdb

This is a [Singer](https://singer.io) tap that produces JSON-formatted data
following the [Singer
spec](https://github.com/singer-io/getting-started/blob/master/SPEC.md).

This tap:

- Pulls raw data from [TerminusDB](https://terminusdb.com/)
- Extract the specific streams in config.json
- Outputs the schema for each streams
- Incrementally pulls data based on the input state

## To install

`tap-terminusdb` can be install via pip with Python >= 3.7:

`python3 -m pip install -U tap-terminusdb`

## To use

You can start a project in a directory using conjunction with TerminusDB easily by:

`terminusdb startproject`

This will create the config.json that stores information about the endpoint and database that you are connecting to. In addition you can add the `streams` settings with:

`terminusdb config streams=[MyClass1, MyClass2]`


Then you can data from TerminusDB into a Singer.io target. For details about how to use a Singer.io target you can [see here](https://github.com/singer-io/getting-started/blob/master/docs/RUNNING_AND_DEVELOPING.md#developing-a-target).

For example, if you are extracting data from TerminusDB to [google spreadsheet](https://github.com/singer-io/target-gsheet):

```
tap-terminusdb -c config.json | target-gsheet -c gsheet-config.json
```
demo-fa 0.1.1
Solar Filaments data augmentation demo package

# Torch Compatible Augmentation Engine For Solar Filaments v0.0.2

### An ML-Ready Filament Augmentation Toolkit with Labeled Magnetic Helicity Sign

##### *ABSTRACT*
A halo Coronal Mass Ejection can have a devastating impact on Earth by causing damage to satellites and electrical transmission line facilities and disrupting radio transmissions. To predict the orientation of the magnetic field (and therefore the occurrence of a geomagnetic storm) associated with an occurring CME, filaments' sign of magnetic helicity can be used. This would allow us to predict a geomagnetic storm.

With the deluge of image data produced by ground-based and space-borne observatories and the unprecedented success of computer vision algorithms in detecting and classifying objects (events) on images, identification of filaments' chirality appears to be a well-fitted problem in this domain. To be more specific, Deep Learning algorithms with a Convolutional Neural Network (CNN) backbone are made to attack this very type of problem. The only challenge is that these supervised algorithms are data-hungry; their large number of model parameters demand millions of labeled instances to learn. Datasets of filaments with manually identified chirality, however, are costly to be built. This scarcity exists primarily because of the tedious task of data annotation, especially that identification of filaments' chirality requires domain expertise. In response, we created a pipeline for the augmentation of filaments based on the existing and labeled instances. This Python toolkit provides a resource of unlimited augmented (new) filaments with labeled magnetic helicity signs. Using an existing dataset of H-alpha based manually-labeled filaments as input seeds, collected from August 2000 to 2016 from the big bear solar observatory (BBSO) full-disk solar images, we augment new filament instances by passing labeled filaments through a pipeline of chirality-preserving transformation functions. This augmentation engine is fully compatible with PyTorch, a popular library for deep learning and generates the data based on users requirement.