semvec/pypi-packages huggingface dataset:
mlb-sdk 1.1.1
Python wrapper for MLB APIs
Requires Python: >=3.0
Keywords: mlb major league baseball sdk mlb-sdk sports professional

# MLB SDK

This library is intended to make it easier to use the MLB APIs (Stats API, DSP, etc). It does not provide access to the underlying APIs, you need to come with your own credentials provided by the league.

## Copyright Notice
This package and its author are not affiliated with MLB or any MLB team. This API wrapper interfaces with MLB's Stats API. Use of MLB data is subject to the notice posted at http://gdx.mlb.com/components/copyright.txt.
topsis-manvendra-102103799 0.1
Topsis Implementation Package


## Overview

This is a Python package that provides an implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. TOPSIS is a multi-criteria decision-making method that helps in ranking a set of alternatives by evaluating them based on multiple criteria.

## Installation

You can install the package using pip:

pip install topsis-manvendra-102103799

## USAGE
from topsis-manvendra-102103799 import topsis

#Example data (replace this with your actual data)

data = {
    'Alternative1': [1, 2, 3, 4],
    'Alternative2': [4, 3, 2, 1],
    # Add more alternatives and their values
}

#Criteria weights (replace this with your actual weights)

weights = [0.25, 0.25, 0.25, 0.25]

#Criteria impacts ('+' or '-' for each criterion)

impacts = ['+', '+', '+', '-']

#Perform TOPSIS analysis

result = topsis(data, weights, impacts)

#Display the ranking

print("Ranking:", result)


## Parameters
data: A dictionary where keys are alternative names, and values are lists representing the performance values for each criterion.

weights: A list of weights corresponding to the importance of each criterion.

impacts: A list of impacts ('+' or '-') corresponding to the desired effect of each criterion.

## License
This package is distributed under the MIT License - see the LICENSE file for details.
fontparser 0.1.1
font file parser library in Python
Requires Python: >=3.10.2

# font-parser
一个用于解析PCF字体文件的Python库

## Use
```Python
from fontparse import PCF
font = PCF("./unifont-15.1.04-l.pcf") # your PCF file
font.printGlyph(font.encodeChar("a"), " ", "*")
```
lvdb 2.1.2
A package to locally shard vector data to conserve memory in memory-limited environments
Requires Python: >=3.10

# LVDB

This is a vector sharding package leveraging Python generators and built-in NumPy text loading to
conserve memory within an environment with resource constraints. It aims to reduce
memory overhead through saving and loading from shard files, in essence
trading runtime for memory.

Usage:
* The entry point for this package is the LVInstance class. This can be
imported from lvdb as LVInstance.
* An instance of this class should be initialized and data can be inserted into this instance.
* To use with GPU resources, set the device to gpu.
* To enable in-memory caching, set a time-to-live value.
* Implement an interface for either a shard or the entire store, implement the metaclass from lvdb.interfaces. This enables you to use customize external storage options like S3.