semvec/pypi-packages huggingface dataset:
galsim-hub 0.0.1rc4
Support for Tensorflow Hub modules in GalSim

# GalSim Hub

[![PyPI](https://img.shields.io/pypi/v/galsim-hub)](https://pypi.org/project/galsim-hub/) [![PyPI - License](https://img.shields.io/pypi/l/galsim-hub)](https://github.com/McWilliamsCenter/galsim_hub/blob/master/LICENSE) 

GalSim Hub provides a collection of TensorFlow modules for GalSim.

## TL;DR

GalSim Hub makes it easy to incorporate galaxy light profiles modelled by deep neural networks directly with a GalSim script:

```py
import galsim
import galsim_hub
from astropy.table import Table

# Load a generative model from the online repository
model = galsim_hub.GenerativeGalaxyModel('hub:Lanusse2020')

# Defines the input conditions, for this model flux_radius, mag_auto, and redshift
catalog = Table([[5., 10. ,20.], [24., 24., 24.], [0.5, 0.5, 0.5] ],
             names=['flux_radius', 'mag_auto', 'zphot'])

# Sample light profiles for these parameters
profiles = model.sample(catalog)
...
```
You can read more, and try out a live demo here: [![colab link](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/McWilliamsCenter/galsim_hub/blob/master/notebooks/GalsimHubDemo.ipynb)

## Installation

To install:
```sh
 $ pip install --user galsim-hub
```
**Note**: GalSim-Hub currently assumes TensorFlow 1.15 to be installed, and will not try to install it as a dependency.


## Usage

This module can be used directly under the GalSim Yaml driver:
```sh
 $ galsim examples/demo14.yaml
```
or from Python:
```py
import galsim
import galsim_hub
from astropy.table import Table

# Load a generative model from the online repository
model = galsim_hub.GenerativeGalaxyModel('hub:Lanusse2020')

# Defines the input conditions, for this model flux_radius, mag_auto, and redshift
catalog = Table([[5., 10. ,20.], [24., 24., 24.], [0.5, 0.5, 0.5] ],
             names=['flux_radius', 'mag_auto', 'zphot'])

# Sample light profiles for these parameters
profiles = model.sample(catalog)
...
```

## Adding a model to the repository

This repository is intended to host a collection of deep generative models, any new contributions are welcome.
Submitting your model to the Hub means that it becomes trivially accessible to any GalSim user, increasing the chances that it will be used in practice :-)

To submit a model, please open a Pull Request adding a new folder under the `hub`directory. As the procedure is not currently completely documented, do not hesitate to reach out to the maintainers of the repository for guidance.
neosensory-python 0.0.1
A package to help developers interact with Neosensory products
Requires Python: >=3.6

Neosensory Python SDK
=====================

A Python package for interacting with Neosensory products. This is designed to work with `Bleak <https://github.com/hbldh/bleak>`_, a cross-platform Python Bluetooth Low Energy (BLE) client for Windows, MacOS, and Linux. Please see the Bleak project page for more specifics on supported platforms. **IMPORTANT: At the moment this is tested and only working on Windows.**

Requirements
============
This SDK relies on `Bleak <https://github.com/hbldh/bleak>`_ to take care of the underlying Bluetooth low energy shenanigans. It is included as a dependency and should get automatically installed if using pip or setup.py.

Installation
============

you can clone this repo and run

.. code-block:: bash

	python setup.py develop

from within the root directory.


Usage
=====
You may need to first pair your Neosensory Buzz in advance with your operating system (OS). To do so, find your OS's Bluetooth settings/panel and follow its instructions for pairing a new device. To put Buzz into pairing mode, hold down the (+) and (-) buttons until the LEDs flash blue.

See this repo's `examples <https://github.com/neosensory/neosensory-sdk-for-python/tree/master/examples>`_ directory to get up and running quickly. 

Documentation
=============
You can learn more about the available commands on this `project documentation page <https://neosensory.github.io/neosensory-sdk-for-python/neosensory_python.html#module-neosensory_python>`_. Neosensory platform-agnostic documentation can be obtained from the Neosensory `developer site <https://neosensory.com/developers/>`_.

License
=======

Please note that while this project has an Apache 2.0 license, usage of the Neosensory API to interface with Neosensory products is still subject to the Neosensory developer terms of service located `here <https://neosensory.com/legal/dev-terms-service>`_
ltl-parser 0.1.0
Parse the DOT file generated from SPOT API and creates a Graph with node-edge information.

## Graph Parser for LTL formulation
LTL Graph parser takes a **.dot** file as an input generated from the SPOT library, parses it and creates a Graph with node-edge information.

### API Reference
class **LTLGraphCreation** contain following APIs.
  - **parsing_buffer()**
    - This method takes the **.dot** file as string and creates the graph object. Internally a callback method is invoked to store the node and edge information in a dictionary object. 

  - **graph_print()**
    - Print the full graph information with edges and nodes.

  - **get_action(src, dst)**
    - Returns a list of actions taken from a source vertex to the destination vertex in the LTL formaulation.

  - **check_dead_nodes()**
    - Print all the dead nodes (if present any) in the formulated graph. 

  - **is_dead(node)**
    - Check whether a vertex/node is in a dead state, i.e., no other vertex/state can be reachable from this vertex/node.

  - **recurse_traversal(cur_node, path, actions, p_len=20)**
    - It prints the path of length p_len (default: 20) and the corresponding actions taken along the edges from a starting vertex. While traversing the graph, it avoids the dead vertex and randomly choose the next valid vertex with an action.

  - **self.graph**
    - This class variable holds all the node and edge information of the LTL graph generated from the SPOT library.
gnomecast 1.9.11
A native Linux GUI for Chromecasting local files.

.. figure:: https://raw.githubusercontent.com/keredson/gnomecast/master/screenshot.png
   :alt: alt text

   alt text

GnomeCast
=========

This is a native Linux GUI for casting local files to ChromeCast
devices. It supports:

-  Both audio and video files (anything ``ffmpeg`` can read)
-  Realtime transcoding (only when needed)
-  Subtitles (embedded and external SRT files)
-  Fast scrubbing (waiting 20s for buffering to skip 30s ahead is
   wrong!)

Install
-------

Please run:

::

    $ pip3 install gnomecast

If installing in a ``mkvirtualenv`` built virtual environment, make sure
you include the ``--system-site-packages`` parameter to get the GTK
bindings.

Run
---

::

    $ gnomecast

If you see:

.. code:: python

    AttributeError: module 'html5lib.treebuilders' has no attribute '_base'

This is a known bug in ``html5lib`` (used by ``pycaptions``). Run this
to fix it:

::

    # pip3 install --upgrade html5lib==1.0b8

*Please report bugs!*

Thanks To...
------------

-  https://github.com/balloob/pychromecast
-  https://github.com/pbs/pycaption
-  https://www.ffmpeg.org/

And everyone who made this project #2 on GitHub's trending list! That's
so awesome!!!

.. figure:: https://raw.githubusercontent.com/keredson/gnomecast/master/trending.png
   :alt: alt text

   alt text

Transcoding
-----------

Chromecasts only support a handful of media formats. See:
https://developers.google.com/cast/docs/media

So some amount of transcoding is necessary if your video files don't
conform. But we're smart about it. If you have an ``.mkv`` file with
``h264`` video and ``AAC`` audio, we use ``ffmpeg`` to simply rewrite
the container (to ``.mp4``) without touching the underlying streams,
which my XPS 13 can at around 100x realtime (it's fully IO bound).

Now if you have that same ``.mkv`` file with and ``A3C`` audio stream
(which Chromecast doesn't support) we'll rewrite the container, copy the
``h264`` stream as is and only transcode the audio (at about 20x).

If neither your file's audio or video streams are supported, then it'll
do a full transcode (at around 5x).

We write the entire transcoded file to your ``/tmp`` directory in order
to make scrubbing fast and glitch-free, a good trade-off IMO. Hopefully
you're not running your drive at less than one video's worth of free
space!

Subtitles
---------

Chromecast only supports a handful of subtitle formats, ``.srt`` not
included. But it does support
`WebVTT <https://w3c.github.io/webvtt/>`__. So we extract whatever
subtitles are in your video, convert them to WebVTT, and then reattach
them to the video through Chomecast's API.