semvec/pypi-packages huggingface dataset:
ru-soundex 1.1.1
Soundex algorithm for russian, english and finnish languages
License: MIT

# Soundex for Russian
Russian, English and Finnish Phonetic algorithm based on Soundex.

Package has both implemented phoneme transformation into letter-number sequence and distance engine for comparison of Soundex sequences (based on Levenstein distance).

### Quick start
1. Install this package via ```pip```

```python
pip install ru-soundex
```

2. Import Soundex algorithm.

Package supports a lot of opportunities, it's possible to cut a result sequence (like in the original Soundex version) or also code vowels.

```python
from ru_soundex.soundex import RussianSoundex

soundex = RussianSoundex(delete_first_letter=True)
soundex.transform('ёлочка')
...

J070530

soundex = Soundex(delete_first_letter=True, code_vowels=True)
soundex.transform('ёлочка')
...

JA7A53A
```

> A structure of the library is scalable, `RussianSoundex` class inherits basic class `Soundex` (original for English language). In order to extend our algorithm, you need just inherit own class from `Soundex` and override methods.

3. Import Soundex distance for usage of string comparision

```python
from ru_soundex.distance import SoundexDistance

soundex = RussianSoundex(delete_first_letter=True)
soundex_distance = SoundexDistance(soundex)
soundex_distance.distance('ёлочка', 'йолочка')
...

0
```
pyampd 0.0.1
Peak detection using AMPD and ASS-AMPD algorithms
License: MIT

[![Build Status](https://travis-ci.com/ig248/pyampd.svg?branch=master)](https://travis-ci.com/ig248/pyampd)
[![Coverage Status](https://codecov.io/gh/ig248/pyampd/branch/master/graph/badge.svg)](https://codecov.io/gh/ig248/pyampdgit)

# AMPD algorithm in Python
Implements a function `find_peaks` based on the Automatic Multi-scale
Peak Detection algorithm proposed by Felix Scholkmann et al. in
"An Efficient Algorithm for Automatic Peak Detection in
Noisy Periodic and Quasi-Periodic Signals", Algorithms 2012,
 5, 588-603

![Peak finding](https://raw.githubusercontent.com/ig248/pyampd/master/ass_ampd.png)

## Usage
Install from PyPI:

```
pip install pyampd
```

Or install from source:

```
pip install git+https://github.com/ig248/pyampd
```

Import function:

```python
from pyampd.ampd import find_peaks
```

See `notebooks/ampd.ipynb` for usage examples.

### Specifying maximum scale
To improve run-time on large time-series, it is possible to specify the maximum scale to consider:
```python
peaks = find_peaks(x, scale=100)
```
will only consider windows up to +-100 point either side of peak candidates.

### Adaptive Scale Selection
If the characteristic scale of the signal changes over time, a new algorithm called
Adaptive Scale Selection can track the changes in optimal scales and detect peaks accordingly:
```python
peaks = find_peaks_adaptive(x, window=200)
```
will select the optimal scale at each point using a 200-point running window.


### Original implementation
`find_peaks` is not identical to the algorithm proposed in the original paper (especially near start and end of time series).
 A performance-optimized version of the original implementation is provided in `find_peaks_original`.


## Tests
Run
```bash
pytest
```

## Other implementations
- R: https://cran.r-project.org/web/packages/ampd/index.html
- MATLAB: https://github.com/mathouse/AMPD-algorithm
- Python: https://github.com/LucaCerina/ampdLib

## Improvements
This Python implementation provides significant speed-ups in two areas:
1. Efficient tracking of local minima without using random numbers
2. Introduction of maximum window size, reducing algorithm run-time from
quadratic to linear in the number of samples.
3. Better handling of peaks near start/end of the series
4. Addition of new Adaptive Scale Selection 

## ToDo
- It may be possible to avoid repeated comparisons, and reduce worst-case
runtime from `O(n^2)` to `O(n log(n))`.
- `find_peaks_adaptive` could benefit from specifying both `window` and `max_scale`

## References
Original paper: https://doi.org/10.1109/ICRERA.2016.7884365
scodb 1.0
Conector escrito em python para API do SCODB.
License: MIT
Keywords: Demolay,SCODB

Scodb
-----

Para utilizadar o conector, você pode seguir o exemplo::

    >>> from pprint import pprint
    >>> import scodb
    >>> api = scodb.ScodbConnector(url, user, password)
    >>> data = api.member('103238')
    >>> pprint(data)

Métodos disponíveis:
- member: Busca um membro com base na CID.
- authenticate: Dado um usuário e senha, valida estes dados no SISDM.
- chapter: Busca informações de um Capítulo com base em seu número.
- convent: Busca informações de um Convento com base em seu número.
- council: Informando o tipo de entidade e seu número, retorna uma coleção com as informações dos membros do Conselho Consultivo.