semvec/pypi-packages huggingface dataset:
silabel 0.5
Silabel is a python package to syllabelize indonesian word
Requires Python: >=3.6
Keywords: silabel syllable indonesian indonesia jcop nlp bahasa

# Syllabelizer for Indonesian Language in Python
This is a rule-based syllabelizer for Indonesian Language. Please note that this project does not aim to replicate rules in Ejaan yang Disempurnakan (EYD).

## Installation :zap:
```
pip install silabel
```

## Example Usage :robot:
```python
from silabel import Syllabelizer

s = Syllabelizer()
s.syllabelize("menggunakan") # ['meng', 'gu', 'na', 'kan']
s.syllabelize("memperkirakan") # ['mem', 'per', 'ki', 'ra', 'kan']
```

## Quick Check :mag:
Here are some results to check if this package suits your interest
```
BSD                       : b-s-d
SMP                       : s-m-p
main                      : ma-in
april                     : ap-ril
swasta                    : swas-ta
instan                    : in-stan
dengan                    : de-ngan
pandai                    : pan-dai
makhluk                   : makh-luk
saudara                   : sau-da-ra
menyapu                   : me-nya-pu
etiopia                   : e-ti-o-pi-a
masyhur                   : masy-hur
biografi                  : bi-o-gra-fi
instrumen                 : in-stru-men
pengarang                 : pe-nga-rang
reboisasi                 : re-boi-sa-si
musyawarah                : mu-sya-wa-rah
dramatisasi               : dra-ma-ti-sa-si
memproklamasikan          : mem-pro-kla-ma-si-kan
berkesinambungan          : ber-ke-si-nam-bu-ngan
mempertanggungjawabkan    : mem-per-tang-gung-ja-wab-kan
```

## Known Issue :persevere:
There are words with vocal diphthong that would not be syllabelize, such as `re-boi-sa-si`, but it is good enough for my use case so I prefer to let it be.

## End notes :heart:
There is no reference used in this project. I simply create a set of rules based on indonesian vocal, consonant, and diphthong to syllabelize indonesian words.

I made this repo for educational purposes so it might need further tweaking to reach production level. <br>
Feel free to create an issue if you need help, and I hope I'll have the time to help you. Thank you.

:heart: from Indonesia
flowstock 0.0.1.dev0
Estimation of movements from snapshots
Requires Python: >=3.5

Estimate flows of people from snapshots using the method of [Akagi2018]_.

There are two parts to this package

- a fake data generator
- an estimator

A couple IPython notebooks are included as demonstrations.
One of these generates fake data and attempts to recover the underlying parameters.
It is most useful for demonstration and testing.

Another is designed to run on real world data.
It requires the user to supply additional data: counts of people in New Zealand SA2 regions and properties of those regions.
The later information is available from StatsNZ:

- `Centroid locations`_
- `Higher geographies`_

.. _`Centroid locations`: https://datafinder.stats.govt.nz/layer/93620-statistical-area-2-2018-centroid-true/
.. _`Higher geographies`: https://datafinder.stats.govt.nz/layer/95065-statistical-area-2-higher-geographies-2018-generalised/data/

We have assumed that the centroid-to-centroid distance separating regions is an appropriate metric.
In principle, the user can specify an arbitrary distance measure on the space, allowing distances to be set by actual driving time, non-local travel via airports, or even asymmetry during commutes.

There are still some rough edges and there may be more corner cases to be taken care of.

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

A conda environment specification is included.
This environment has been testing on Linux and OS X.

.. code-block:: bash

    conda env create -f environment.yml
    conda activate movement
    pip install -e .


.. [Akagi2018] Y. Akagi, T. Nishimura, T. Kurashima, and H. Toda, “A Fast and Accurate Method for Estimating People Flow from Spatiotemporal Population Data,” in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, Jul. 2018, pp. 3293–3300, doi: 10.24963/ijcai.2018/457.