semvec/pypi-packages huggingface dataset:
hgmeans 3.0
HG-means clustering for minimum sum-of-squares formulation
Keywords: clustering optimization

# Clean files
make clean

# Copy /hgmeans folder with .cpp and .h files
make hgmeans

# Use Cython to create the interface with Python -- here the hgmeans.cpp file should be created
make build

# Create Python package -- here a tarball file should be created in /dist folder
make sdist

# To install the package locally, run:
python3 -m pip install dist/*
<!-- sudo pip install dist/* -->

# To publish the package in PyPi repository, run:
sudo twine upload dist/*

###

# Clean and compile
>> cd hgmeans
>> make clean
>> make hgmeans
>> cd ../

# Generate wheel file
>> python3 setup.py sdist bdist_wheel

# Install the package locally
>> sudo pip3 install dist/*

# Publish the package in PyPi repository
>> sudo twine upload dist/*
abalyzer-shivanprasad 0.0.1
Parses ABA and ACH banking numbers.
Requires Python: >=3.6

# abalyzer
Parses ABA (American Bankers Association) codes


### ABA Number Validation:
> Check digit\
> The ninth, check digit provides a checksum test using a position-weighted sum of each of the digits. High-speed check-sorting equipment will typically verify the checksum and if it fails, route the item to a reject pocket for manual examination, repair, and re-sorting. Mis-routings to an incorrect bank are thus greatly reduced.
>
> The following condition must hold: \
> (3(d1 + d4 + d7) + 7(d2 + d5 + d8) + (d3 + d6 + d9)) mod 10 = 0 [^1]


-----------
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[^1]: https://en.wikipedia.org/wiki/ABA_routing_transit_number
osmviews 0.0.5
ranking geo locations based on OpenStreetMap views
Requires Python: >=3.7

# OSMViews Python Client

Python client for [OSMViews](https://osmviews.toolforge.org), a world-wide
ranking of geographic locations based on OpenStreetMap tile logs. Please
see the [main repository](https://github.com/brawer/osmviews) for background.


## Usage

```python
# pip install osmviews
import osmviews
osmviews.download('/tmp/osmviews.tiff')
with osmviews.open('/tmp/osmviews.tiff') as o:
    print(f'Tokyo, Shibuya:      {o.rank( 35.658514, 139.701330):>9.2f}')
    print(f'Tokyo, Sumida:       {o.rank( 35.710719, 139.801547):>9.2f}')
    print(f'Zürich, Altstetten:  {o.rank( 47.391485,   8.488945):>9.2f}')
    print(f'Zürich, Witikon:     {o.rank( 47.358651,   8.590251):>9.2f}')
    print(f'Ushuaia, Costa Este: {o.rank(-54.794395, -68.251958):>9.2f}')
    print(f'Ushuaia, Las Reinas: {o.rank(-54.769225, -68.279174):>9.2f}')

Tokyo, Shibuya:      227437.98
Tokyo, Sumida:        60537.62
Zürich, Altstetten:   37883.31
Zürich, Witikon:      11711.94
Ushuaia, Costa Este:   2697.14
Ushuaia, Las Reinas:    257.89
```