semvec/pypi-packages huggingface dataset:
st-card 1.0.1
A custom streamlit component for displaying KPI metrics
Requires Python: >=3.6

# st_card
A custom streamlit component for displaying KPI metrics

## Installation

```sh
pip install st-card
```

## Getting started

```python
from st_card import st_card

st_card('Competed Orders', value=15000, delta=-250)
```

## Demo

[![Open in Streamlit][share_badge]][share_link] 

[share_badge]: https://static.streamlit.io/badges/streamlit_badge_black_white.svg
[share_link]: https://share.streamlit.io/akshanshkmr/st_card/main/st_card/demo/app.py

[github_badge]: https://badgen.net/badge/icon/GitHub?icon=github&color=black&label
[github_link]: https://github.com/akshanshkmr/st_card/

[pypi_badge]: https://badgen.net/pypi/v/st_card?icon=pypi&color=black&label
[pypi_link]: https://pypi.org/project/st-card/
aaa-features 0.2.1
Python code to calculate signal features. This code was extracted from https://github.com/malfante/AAA

## NOTE: This code is a extract from https://github.com/malfante/AAA. 
This code calculates the feature vector from a MSEED file to used it later for training and classification purposes. 


# Automatic Analysis Architecture 

Welcome to this automatic classification scheme! Please carefully read the following before asking questions :)

If used, the software should be credited as follow:    

**Automatic Analysis Architecture, M. MALFANTE, J. MARS, M. DALLA MURA**   
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1216028.svg)](https://doi.org/10.5281/zenodo.1216028)   

and the original paper for which the code was developped should be cited:   

**Malfante, M., Dalla Mura, M., Metaxian, J. P., Mars, J. I., Macedo, O., & Inza, A. (2018). Machine Learning for Volcano-Seismic Signals: Challenges and Perspectives. IEEE Signal Processing Magazine, 35(2), 20-30.**  

We thank you for the respect of the authors work.

## Set up and requierements needed to run the code
This code was developed under Python 3, and needs the following libraries. .

- `obspy>=1.1`
- `python_speech_features`
- `sympy`


Create and activate your working environment (in a terminal session):  

`conda create -n aaa_features python=3.9`  
`conda activate aaa_features`

# Clone the repository and install 

`git clone https://github.com/awacero/aaa_features.git` 

`cd aaa_features` 

`pip install .` 


# Install the package aaa_features from PYPI:

`pip install aaa_features`  


## Configuration files 
	 	
- the feature setting file, contained in `config_sample/features*.json` 

## MSEED data sample
- the folder data_sample contains a MSEED file from the station EC RETU SHZ 2012-06-28

## Run the code to get the features

`python call_aaa_features.py`

## More info	

If you still have questions, try running and exploring the code. 
The playground files are relatively easy to play with. 

If you still have question, fell free to ask ! 

**Contact:** marielle.malfante@gmail.com
dictionarysnek 0.0.2
A Dictionary API Wrapper for Python
Requires Python: >=3.8,<4.0

<h1>dictionarysnek</h1>

## Table of contents
* [General info](#general-info)
* [Dependencies](#dependencies)
* [Setup](#setup)
* [Usage](#usage)

## General info
A Dictionary API Wrapper for Python.
	
## Dependencies
Project is created with:
* Requests Version: 2.26.0
* Dictionary API: dictionaryapi.dev
	
## Setup
Python-pip:
```
$ pip install dictionarysnek
```
Poetry:
```
$poetry add dictionarysnek
```

## Usage
* Import the module: ```from dictionarysnek import dictionarysnek```
* Get the data: ```data = dictionarysnek.getdata("mundane")```
* Pass it to the desired function to get the output: ```synonyms = dictionarysnek.getsyn(data)```

## Functions
* Get Synonyms -> Returns an array of Synonyms -> ```dictionarysnek.getsyn(json: list)```
* Get Antonyms -> Returns an array of Antonyms -> ```dictionarysnek.getant(json: list)```
* Get Definition -> Returns the definition of the given word -> ```dictionarysnek.getdefi(json: list)```
* Get Word -> Returns the given word -> ```dictionarysnek.getword(json: list)```
* Get Phonetic -> Returns the phonetic of the given word -> ```dictionarysnek.getphonetic(json: list)```
* Get Part of Speech -> Returns the Part of Speech of the given word -> ```dictionarysnek.getpos(json: list)```
* Get Example -> Gives an usage of the given word -> ```dictionarysnek.getex(json: list)```
* Get Origin -> Gives information about the origin of the given word -> ```dictionarysnek.getorigin(json: list)```
tiedpyranges 0.1.3
Extends the pyranges module with operations on joined genomic intervals (e.g. exons of same transcript)

# tiedpyranges
Extends the pyranges module with operations on joined genomic intervals (e.g. exons of same transcript)

Install with:

``pip install tiedpyranges``

or

``conda install -c mmariotti tiedpyranges``


Then use within python:

``>>> from tiedpyranges import pyranges``

The ``pyranges`` module imported will have all functions from the original pyranges (see https://pyranges.readthedocs.io/).

Additionally, you have available tiedpyranges-specific functions documented in the documentation.tar file included in github.