semvec/pypi-packages huggingface dataset:
Project0 0.1.4
Offline vocal assistant and tts
License: MIT
Keywords: vocal assistant offline tts

=========
Project 0
=========

This project aim to create an offline vocal assistant capable of understand short sentence and start external module attached to it.
------------------------------------------------------------------------------------------------------------------------------------
Some of it
-  Plex API (to play music and other stuff)
-  Weather Forecast
-  Web Radio
-  Wikipedia search page
and more, to read it out loud. 

This software is main based on:
-------------------------------
Zhang, A. (2017). Speech Recognition (Version 3.8) (Software). Available from https://github.com/Uberi/speech_recognition#readme.

To use, simply do
-----------------
-  import Project0
-  print Project0.main.launch()

:Authors: Fenrir77

:Version:  0.1.0 of 2020/10/08
fpldata 0.0.4
Getting data out of FPL API made easier
License: MIT
Keywords: FPL

# FPLDATA 
## Get processed and cleaner data from the FPL API!

## Installation
Run the following code to install:
```python 
pip install fpldata
```

## Usage
### Get the names of team in EPL. Returns a list
```python
from fpldata import TeamData

t1 = TeamData()
print(t1.list_team_name())
```
### Get the EPL table. Returns a pandas DataFrame

```python
from fpldata import TeamData
t1 = TeamData()
print(t1.get_table())
```
### Compares two teams based on their Strengths scores. Input should be the short name of the team. Returns a pandas DataFrame
```python
from fpldata import TeamData
t1 = TeamData()
data = t1.team_compare('ARS','LIV')
print(data)
```

### Get player list with their id. Returns dictionary of players with ids as key and name as value
```python
from fpldata import PlayerData
p1 = PlayerData()
print(p1.player_list())
```

### Search player by name. Returns dictionary of player with ids as key and name as value
```python
from fpldata import PlayerData
p1 = PlayerData()
data = p1.search_player('David')
print(data)
```
compsyn 0.2.2
python package to explore the color of language
License: MIT
Keywords: Image Analysis,Computational Synaesthesia,Color Science,Computational Social ScienceCognitive Science

comp-syn
~~~~~~~~

comp-syn is a python package which provides a novel methodology to explore relationships between abstract concepts and color. We provide functionalities to create a dataset for word - image associations through Google Cloud and WordNet, as and demonstrate a variety of image and word related analyses. We introduce constructs such as *Colorgrams* as a tool for analysis, as well as the ability to create *color representational vectors* for images, and words as aggregations of images. Our work demonstrates a strong relationship between abstract semantic domains and colors, and supports claims for collective cognition and embodied cognition. The package allows you to explore these relationships yourself!

The theoretical frameword is described in:

Douglas Guilbeault , Ethan Nadler, Mark Chu, Ruggiero Lo Sardo, Aabir Abubaker Kar, and Bhargav Srinivasa Desikan. "Color Associations in Abstract Semantic Domains." *Forthcoming in Cognition* (2020).

The work arose through a collaboration between the contributors at the Santa Fe Institute's Complex System Summer School 2019. 


Documentation and Examples
~~~~~~~~~~~~~~~~~~~~~~~~~~

The
`notebooks <https://github.com/comp-syn/comp-syn/tree/master/notebooks>`__
directory showcases the usage of compsyn, with examples and basic usage.

We currently provide functionality to download and organise image data to be loaded as vector objects, as well as a preliminary analysis using them.


Installation
~~~~~~~~~~~~

Run ``pip install compsyn`` to download and install from PyPI.

Run ``python setup.py install`` for default installation.

Run ``pip install .`` to install from source.


Dependencies
~~~~~~~~~~~~

-  Python 2.7+, 3.4+
-  numpy, scipy, scikit-learn, matplotlib, PIL

Notes
~~~~~

To use the package one would need to download the JzAzBz array to convert the images to the appropriate vector form. It can be downloaded here (`link <https://drive.google.com/file/d/1wspjIBzzvO-ZQbiQs3jgN4UETMxTVD2c/view>`_). We provide sample images downloaded from Google Images to test the functionality.