semvec/pypi-packages huggingface dataset:
ll2location 0.0.3
ll2location

# ll2location

## **Installation**
``pip install ll2location``

## **Usage example: Find from all country**
```
from latlong.gadm import Gadm
from latlong.latlong import LatLong

#init all gadm package in all country, it may take a long time, maybe a few hours
gadm = Gadm()  
gadm.get_shp_fully()

#init lat long
lat = 10.773213
long = 106.671818

#from lat long to location
latlng = LatLong()
code, country_info, detail_info = latlng.to_location(10.773213, 106.671818)
print(detail_info)
```
The output could be:
```
{'GID_3': 'VNM.25.10.4_1',
  'GID_0': 'VNM',
  'COUNTRY': 'Vietnam',
  'GID_1': 'VNM.25_1',
  'NAME_1': 'Hồ Chí Minh',
  'NL_NAME_1': 'NA',
  'GID_2': 'VNM.25.10_1',
  'NAME_2': 'Quận 10',
  'NL_NAME_2': 'NA',
  'NAME_3': 'Phường 12',
  'VARNAME_3': 'Ward 12',
  'NL_NAME_3': 'NA',
  'TYPE_3': 'Phường',
  'ENGTYPE_3': 'Ward',
  'CC_3': 'NA',
  'HASC_3': 'NA'}
```

## **Usage example: Find from specific country code**

```
from latlong.latlong import LatLong
from latlong.gadm import Gadm, GetGadm

#show country code infomation
get_gadm = GetGadm()
codes_detail = get_gadm.get_codes_detail()
print(codes_detail)

#init gadm package in specific country
code = "VNM"
gadm = Gadm()
gadm.get_shp_by_code(code)

#init lat long
lat = 10.773213
long = 106.671818

#from lat long to location in specific country
latlng = LatLong()
detail_info = latlng.to_location_by_country_code(code, lat, long)

print(detail_info)
```
The output could be:
```
{'GID_3': 'VNM.25.10.4_1',
 'GID_0': 'VNM',
 'COUNTRY': 'Vietnam',
 'GID_1': 'VNM.25_1',
 'NAME_1': 'Hồ Chí Minh',
 'NL_NAME_1': 'NA',
 'GID_2': 'VNM.25.10_1',
 'NAME_2': 'Quận 10',
 'NL_NAME_2': 'NA',
 'NAME_3': 'Phường 12',
 'VARNAME_3': 'Ward 12',
 'NL_NAME_3': 'NA',
 'TYPE_3': 'Phường',
 'ENGTYPE_3': 'Ward',
 'CC_3': 'NA',
 'HASC_3': 'NA'}
```

## **LICENSE**
From https://gadm.org/license.html.

GADM license
The data are freely available for academic use and other non-commercial use. Redistribution or commercial use is not allowed without prior permission.

Using the data to create maps for publishing of academic research articles is allowed. Thus you can use the maps you made with GADM data for figures in articles published by PLoS, Springer Nature, Elsevier, MDPI, etc. You are allowed (but not required) to publish these articles (and the maps they contain) under an open license such as CC-BY as is the case with PLoS journals and may be the case with other open access articles. Data for the following countries is covered by a a different license Austria: Creative Commons Attribution-ShareAlike 2.0 (source: Government of Ausria)
sheet2csv 1.0.7
A package to export Google Sheet to CSV via API
Requires Python: >=3.6

# sheet2csv

[![PyPI version](https://badge.fury.io/py/sheet2csv.svg)](https://badge.fury.io/py/sheet2csv)
[![Test](https://github.com/sledilnik/sheet2csv/actions/workflows/test.yml/badge.svg)](https://github.com/sledilnik/sheet2csv/actions/workflows/test.yml)

Python package to transform Google Sheet to CSV.

## Install

```sh
pip install sheet2csv
```

## Example

```python
import os

import sheet2csv

SHEET_ID = "PUT_ID_OF_SHEET_HERE"
RANGE_STATS = "SheetName!A3:ZZ"
GOOGLE_API_KEY = os.environ["GOOGLE_API_KEY"]

# First row of range will be become header row of CSV
sheet2csv.sheet2csv(
    id=SHEET_ID, range=RANGE_STATS, api_key=GOOGLE_API_KEY, filename="export.csv",
)
```

## Development

```sh
git clone https://github.com/sledilnik/sheet2csv
cd sheet2csv
pipenv install
pipenv run pip install -e .
pipenv run test
```
rsvidx 0.3.0
Vector indexer
Keywords: ml,vector,semantic search,vector indexer,similarity search

# RSvIDX

## Vector indexing system

### Vector indexing

**RSvIDX can be used for general purpose vector indexing and similarity search. Applications include:**

- Semantic search
- Document deduplication
- Recommendation
- Classification
- Image search
- Reverse image search
- Audio similarity (like Shazam)
- Q&A


### Features
- fast and efficient math library
- optimised persistant operations (you now no longer need to explicitly save the index as this is scheduled automatically)
- better in-memory performance compaired to RSvIDX_v3
- no dependencies (new build size is 98% smaller)
- Usable in C with bindings in Python3
- No intermideate C bindings


### Example:

Try out https://apps.apple.com/gb/app/headlines-today/id1363257410 on the App Store for a semantic search example.


# Installation
(For Python3)

```
pip3 install rsvidx
```


# Usage
(For Python3)

***Create an index***
You can finetune number of buckets to your use case. Less buckets results in higher accuracy but more items to brute force search. Less buckets results in lower accuracy but fewer items to brute force search.
```python3 
import rsvidx
file_name = "test.index"
number_of_buckets = 8
dimensions = 4
index = rsvidx.Similarity(file_name, number_of_buckets, dimensions)
```



***Add item to index***
```python3
add_vector = [1,2,3,4]
add_uid = "1234"
index.add(add_vector, add_uid)
```



***Get items from index***

This will get items that are similar to the get vector
```python3
get_vector = [1,2,3,4]
results = index.get(get_vector)
```



***Remove items from index***
```python3 
remove_uid = "1234"
index.remove(remove_uid)
```