semvec/pypi-packages huggingface dataset:
tdk-cli 0.2.1
Command-line tool for TDK Dictionary, sozluk.gov.tr with rich output.
Requires Python: >=3.5

![ss](https://user-images.githubusercontent.com/16024979/162456060-f07675cb-a196-452d-8b49-b0cbc4cdc23e.png)

# tdk-cli

<div align="center">
<a href="https://github.com/agmmnn/tdk-cli">
<img alt="GitHub release (latest by date)" src="https://img.shields.io/github/v/release/agmmnn/tdk-cli"></a>
<a href="https://pypi.org/project/tdk-cli/">
<img alt="PyPI" src="https://img.shields.io/pypi/v/tdk-cli"></a>

Command-line tool for [TDK](https://en.wikipedia.org/wiki/Turkish_Language_Association) Dictionary, [sozluk.gov.tr](https://sozluk.gov.tr/) with [rich](https://github.com/Textualize/rich) output.

</div>

## Install:

```
pip install tdk-cli
```

## Usage:

```
tdk <word>
```

### Example Usage:

```
tdk kemal
```

![ss](https://user-images.githubusercontent.com/16024979/162456447-d0e1984e-4351-4b69-8bcd-360bcd3f7d30.png)

## Arguments

```
  -h, --help     show this help message and exit
  -p, --plain    returns plain text output
  -v, --version  show program's version number and exit
```

## Dependencies

- [rich](https://pypi.org/project/rich/)
MLVRAtests 0.1.0
Set of tools to measure and test for violations of the 1982 Voting Rights Amendment
Requires Python: >=3.6, <4

Machine Learning Voting Right Act Tests
====

Centernoid X-Symetry

Uses weighted centernoid and K-Mean Clustering to determin the compactness of a district by mesuring the distance from Geographic Center and Boundry 

```Python
weighted_centroind(df, District_ID, GEOID='GEOID20', Population='POP100', Lat='INTPTLAT20', Lon='INTPTLON20', dist_geo='geometry')
```

```Python 
kmean_xSymetry(df, district_id, pop, lat, lon, geoid, district_geometry)
```

Ecological Inference 

Uses Elecologic Ingernce Model from King 1999 using a default covariate as target vote share and lmbda as 0.5 to match what was writen in [King's Paper]('https://dash.harvard.edu/bitstream/handle/1/4125130/binom.pdf;sequence=2')

```Pyhton
ecological_inference(Total_Population_Col, Target_Population_Col, 
                        Target_Vote_Share_Percentage, covariate=None, lmbda=0.5)
```