semvec/pypi-packages huggingface dataset:
elle-beam2d 1.0.3
Differentiable 2D beam model.
Requires Python: >=3.5
Keywords: finite-element-analysis,beam,structural-analysis

---
title:  Elle - Beam2d
description: Differentiable 2D beam element.
---

# Elle - Beam2d

[![Build Status][travis-image]][travis-link]
[![PyPI Version][pypi-v-image]][pypi-v-link]
[![Commits since latest release][gh-image]][gh-link]

* Free software: BSD 3-Clause License

## Installation

Elements are packaged and versioned individually and can be installed as follows:

    pip install elle.beam2d

You can also install the in-development version with:

    pip install https://github.com/claudioperez/elle-beam2d/archive/master.zip

## Overview

### Geometry


## Development

To run all the tests run:

    tox


[pypi-v-image]: https://img.shields.io/pypi/v/elle.svg
[pypi-v-link]: https://pypi.org/project/elle-beam2d/

[travis-image]: https://api.travis-ci.org/claudioperez/elle.svg?branch=master
[travis-link]: https://travis-ci.org/claudioperez/elle-beam2d

[gh-link]: https://github.com/claudioperez/elle-beam2d/compare/v0.0.0...master
[gh-image]: https://img.shields.io/github/commits-since/claudioperez/elle-beam2d/v0.0.0.svg


# Changelog

## 1.0.0 (2020-07-30)

- First release on PyPI.
popularpackage-angelica-12 0.0.7
An university project package
Requires Python: >=3.6

# PopUlarity

# Introduction 
This is a small program created by a student of the Libera UniversitÃ  di Bolzano. 

# What does the program do? 
The program, as first step, provides some information about a given word, these are: definition, right pronunciation (IPA notation) first in the British accent and then in the American one (also the audio file is avalaible if required) so as to make a comparison between the two varieties. As last step the popularity of the given word is provided, i.e. you can check where a word is most commonly used and to which country's lexicon it belongs to. 

# How does the program work? 
1. First of all the user has to give an audio as input, i.e. he has to say the desired word using his computer's mic. 
2. Then the program will recognise the given word and this will be shown first as an audio and then also in the written form. 
3. If the recognised word is exact the user can go on with the program, otherwise he will be asked to repeat the word (an error can occur if the given word doesn't exist in the dictionary). 
4. A definition will be provided and if the first one is not the one the user was looking for, other definitions can be showed (an error occurs when all the definitions are provided). 
5. Later the right pronunciation in the British accent will appear and the user can opt for the audio or not. 
6. Same step for the American pronunciation.
7. As final step the user is asked to choose between a pie or a bar chart for the results to be shown, and in the end the popularity of the word will be given. 

Please note: only english words are allowed; only the two British and American varieties are avalaible; when some error occurs (as already written above) it may be because of the non-existance of the word or even because some features doesn't exist, in this case the user is kindly asked to try with another word from the beginning. 

# How does the user can employ the program? 
Please run the code below: the user has to say something and then to press the stop bottom when finished. 

     import popularpackage

Please note: remember always to listen to the showed audio, otherwise the program won't work. 

Please run the code below. 

     from scipy.io.wavfile import write as wav_write
     wav_write("vocal.wav", sr, audio) 

# Final notes 
It is important to add that the final values in the last part of the program are calculated on a scale from 0 to 100, where 100 is the location with the most popularity as a fraction of total searches in that location.
A higher value means a higher proportion of all queries, not a higher absolute query count. So a tiny country where 80% of the queries are for "tiger" will get twice the score of a giant country where only 40% of the queries are for "tiger". 

# Credits 
- Direct access to the microphone inside Google Colab notebook: 
  https://ricardodeazambuja.com/deep_learning/2019/03/09/audio_and_video_google_colab/
- Information about the given word: 
  Oxford Dictionary API 
- Popularity of the word: 
  https://pypi.org/project/pytrends/