semvec/pypi-packages huggingface dataset:
standlib 0.0.8
A custom standard library module for Python
Requires Python: >=3.6

Standlib 🐍

This project is a set of pre-built modules, functions and classes that were made to make your life easier when programming in Python, it works like a Swiss army knife.

Version:

* This library is still only in beta version, it is worth mentioning that this means that it is in constant development.

Instalation:

* To install it is very simple, just install it via pip ```pip install pystdlib``` and then you can use it without any problems.


Purpose:
The Double class provides a custom implementation for double-precision floating-point numbers in Python, offering greater control over precision and formatting than the built-in float type.

Attributes:

precision (int): Specifies the number of decimal places to maintain.
Methods:

* init(value, precision=None): Creates a new Double object.
* value (int, float, or str): The initial value.
* precision (int, optional): The desired precision. Defaults to the class-wide default precision.
* repr(): Returns a string representation suitable for debugging.
* str(): Returns a formatted string representation.
* add, sub, mul, truediv, mod, pow, floordiv: Overloaded arithmetic operators for performing various mathematical operations on Double objects.
* set_default_precision(precision): Sets the default precision for all Double objects.
* get_default_precision(): Gets the current default precision.

Features:

Customizable precision: Control the number of decimal places to retain.
Arithmetic operations: Supports addition, subtraction, multiplication, division, modulo, exponentiation, and floor division.
String formatting: Provides flexible formatting options.
Default precision: Sets a global default precision for all Double objects.
Internal Implementation:

Stores values as strings to maintain exact precision.
Implements custom arithmetic operations for accurate calculations.
Handles edge cases like division by zero and large numbers.
Benefits:

Improved accuracy: Avoids floating-point errors common in standard float types.
Flexibility: Customize precision to suit specific use cases.
Control: Offers fine-grained control over number representation.
Use Cases:

Financial calculations: Where precise calculations are essential.
Scientific computing: For applications requiring high-precision numbers.
Custom data types: As a building block for more complex data structures.
processamento-imagem-pack 0.0.1
Image Processing Package
Requires Python: >=3.8

# processamento_imagem_pack

Description. 
The package processamento_imagem_pack is used to:
	Processing:
		- Histogam matching
		- Structural similarity
		- Resize image 
	Utils:
		- Read image
		- Save image
		- Plot image
		- Plot result
		- Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install processamento_imagem_pack
```

## Author
Lucas Henrique

## License
[MIT](https://choosealicense.com/licenses/mit/)
entegywrapper 9.0.4
Python SDK for the Entegy API
Requires Python: <4.0.0,>=3.10.0

# Python SDK for the Entegy API

## Installation

```bash
pip install entegywrapper

poetry add entegywrapper
```

## Usage

```python
from entegywrapper import EntegyAPI

api = EntegyAPI("api-key", "api-secret", "project-id", "project-region")
api = EntegyAPI(["api-key-1", "api-key-2", "api-key-3"], ["api-secret-1", "api-secret-2", "api-secret-3"], "project-id", "project-region")
```

## Ported Modules

-   API Objects
-   Content
    -   [Content Objects](https://situ.entegysuite.com.au/Docs/Api/content-objects)
    -   [Content Management](https://situ.entegysuite.com.au/Docs/Api/content-get)
    -   [Categories](https://situ.entegysuite.com.au/Docs/Api/category-available)
    -   [Documents](https://situ.entegysuite.com.au/Docs/Api/document-addfile)
    -   [MultiLinks](https://situ.entegysuite.com.au/Docs/Api/multilink-get)
-   Attendance Tracking
    -   [Attendance Tracking Objects](https://situ.entegysuite.com.au/Docs/Api/track-objects)
    -   [Track Management](https://situ.entegysuite.com.au/Docs/Api/track-addcheckin)
-   Notification
    -   [Notification - Send Bulk](https://situ.entegysuite.com.au/Docs/Api/notifications-send-bulk)
-   Plugins
    -   [External Authentication](https://situ.entegysuite.com.au/Docs/Api/plugins-authenticate-external)
-   Points & Achievement
    -   [Points & Achievement Objects](https://situ.entegysuite.com.au/Docs/Api/point-constants)
    -   [Point Management](https://situ.entegysuite.com.au/Docs/Api/point-award)
-   Profile
    -   [Profile Objects](https://situ.entegysuite.com.au/Docs/Api/profile-object)
    -   [Profile Management](https://situ.entegysuite.com.au/Docs/Api/profile-get)
    -   [Profile Types](https://situ.entegysuite.com.au/Docs/Api/profiletype-get)
    -   [Profile Custom Fields](https://situ.entegysuite.com.au/Docs/Api/profilecustomfield-get)
    -   [Profile Links](https://situ.entegysuite.com.au/Docs/Api/profilelink-selected)
    -   [Profile Payments](https://situ.entegysuite.com.au/Docs/Api/profile-payment-add)

## Modules to Port

-   Lead Capture
    -   [Lead Capture Objects](https://situ.entegysuite.com.au/Docs/Api/lead-capture-objects)
    -   [Lead Capture Management](https://situ.entegysuite.com.au/Docs/Api/capture-lead-add)
-   Points & Achievement
    -   [Achievement Management](https://situ.entegysuite.com.au/Docs/Api/achievement-all)
-   Project
    -   [Project Objects](https://situ.entegysuite.com.au/Docs/Api/project-objects)
    -   [Project Management](https://situ.entegysuite.com.au/Docs/Api/project-get)
    -   [Project API Keys](https://situ.entegysuite.com.au/Docs/Api/projectapikey-get)
-   Submission Forms
    -   [Submission Form - Get](https://situ.entegysuite.com.au/Docs/Api/submission-form-get)
CLTV 0.1.0
A customer Lifetime Value package for preprocessing data
Requires Python: >=3.6

# Customer Lifetime Value (CLTV) Analysis

## Overview

This package provides tools for Customer Lifetime Value (CLTV) analysis, including data processing, visualization, and predictive modeling. It utilizes the BetaGeoFitter and GammaGammaFitter from the `lifetimes` library to calculate and analyze customer lifetime value.

## Features

- **Data Processing and Visualization**: 
  - Clean and preprocess data
  - Calculate CLTV using BG/NBD and Gamma-Gamma models
  - Visualize CLTV and customer segments

- **Logistic Regression**:
  - Train a logistic regression model to classify high-value customers
  - Evaluate model performance using various metrics

- **Enhanced Visualizations**:
  - Generate correlation heatmaps
  - Cluster customers using K-Means and visualize clusters

## Requirements

To use this package, you need the following Python packages:

- `pandas`
- `numpy`
- `matplotlib`
- `seaborn`
- `lifetimes`
- `scikit-learn`

You can install these dependencies using:

```bash
pip install -r requirements.txt
```
## Usage
To import the package and run the main function, use the following format:
```bash
from CLTV.cltv import main
```
tinydb-helper 0.1.5
Tinydb CRUD wrapper for table
Requires Python: <4.0,>=3.9

# Tinydb Helper
A Tinydb crud wrapper that make crud operations a breeze

## Installation
You can install the package using pip:

```bash
pip install tinydb-helper
```

## Usage
This package will install Tinydb

```python
from tinydb_helper.helper import Table
from tinydb import TinyDB, Query
import time

def main() -> None:
    db = TinyDB("db.json")
    timestamp = str(int(time.time()))

    # Exemple creating project table with custom primary key using default primary key generator 
    project = Table(db=db, table="project", primary_key="proj_id")
     
    # Example creating person table with custom primary key generator
    person = Table(db=db, table="person", key_gen=lambda: timestamp)

    # Example inserting a few record in person and project tables
    for i in range(4):
        project.insert(dict(name=f"test-{i}", year=f"{2004 + i}"), id=str(i + 1))
        person.insert(dict(first=f"first-{i}", year=f"{2004 + i}", id=str(i + 1)))

    # Exemple get all object from table person
    person.all()

    # Exemple find person object matching primary key field equal to "0"
    person.find(id="0")

    # Exemple filter person table base on Tinydb Query conditions
    person.filter(Query()['first'] == 'first-0')

    # Exemple update person object matching primary key field equal to "0" 
    person.update(id="0", item=dict(test='pass'))

     # Exemple delete person object matching primary key field equal to "0"
    person.delete(id="0")

if __name__ == "__main__":
    main()
```
nysg-tools 0.8
Una colección de herramientas utiles para los laboratorios de la UBA FCEN

# Labo-Tools 

Una colección de funciones utiles para los laboratorios de Física de la UBA.

## Install

```sh
python -m pip install nysg_tools
```

## Usage

```py
!pip install nysg_tools
import nysg_tools as ny
```

## Licensing
The code in this project is licensed under MIT license.
polystar 0.4.5
Polygon and polyhedron operations
Requires Python: >=3.8

# ![polystar]
A C++ library for polygon and polyhedron operations.
Wrapped for use in python using [pybind11](https://github.com/pybind/pybind11).

[polystar]: https://raw.githubusercontent.com/g5t/polystar/master/polystar.svg

# Dependencies
## TetGen
A modified version of [TetGen](http://tetgen.org) is used to create
refined tetrahedral meshes in bounding polyhedra.

The modified version is included as part of this repository.

# Installation
Install via `pip`, e.g.,
```cmd
python -m pip install polystar
```

This repository can be installed locally via
```cmd
python -m pip install .
# or 
python setup.py install
```

Alternatively, the python module, C++ library, and [catch2](https://github.com/catchorg/Catch2) based tests can be built directly using `cmake`.
mongol-register-parser 0.1.4
mongol register parser
Requires Python: >=3.6

# v0.1.4

## Суулгах:

https://pypi.org/project/mongol-register-parser/

https://github.com/ganbaaelmer/mongol_register_parser

эсвэл

```

pip install mongol-register-parser

```

## Ашиглах заавар:

### -option1: .csv файлаас регистерийн тугаарын мэдээллийг задлах:

```

from mongol_register_parser import mongol_register_parser


fileName ='your_file_name.csv'

register_number_column = "register_number_column_name"

df, birthYearColumn, birthMonthColumn, birthDayColumn, birthColumn, ageColumn, genderExtractedColumn, birthPlaceColumn = mongol_register_parser.mongol_register_parser(your_csv_file_name, register_number_column_name)

```

#### үр дүн:

df дотор df, birthYearColumn, birthMonthColumn, birthDayColumn, birthColumn, ageColumn, genderExtractedColumn, birthPlaceColumn гэсэн баганууд үүснэ.

df, birthYearColumn, birthMonthColumn, birthDayColumn, birthColumn, ageColumn, genderExtractedColumn, birthPlaceColumn гэсэн list утгууд буцаана

age_added.csv гэсэн файл дискэн дээр хадгалагдана.



### -option2: 1ш регистрийн тугаарын мэдээллийг задлах:

```

birthplace, birthYear,birthMonth,birthDay, birthdate, age, gender = mongol_register_parser.single_register_parser(text)

```


#### үр дүн:

birthplace, birthYear,birthMonth,birthDay, birthdate, age, gender утгууд буцаана.

## Тайлбар:

Монгол регистрийн дугаарын эхний 2 орон буюу үсэг нь регистрийн дугаар авч байгаа иргэний байнга 
оршин суудаг аймаг /дүүрэг/, сум /хороо/-ны код, дараагийн 6 орон нь төрсөн он, сар, өдөр 9 дэх орон нь хүйс, 
сүүлийн нэг орон нь хяналтын код байна.

Регистрийн эхний 2 тоон орон нь тухайн иргэний төрсөн оны сүүлийн 2 тоог тэмдэглэдэг. 
Түүний дараагийн 2 орон төрсөн сарыг илэрхийлэх бөгөөд 2000 он болон түүнээс хойш төрсөн хүмүүсийн
хувьд төрсөн сар дээр нь 20-ийг нэмж тэмдэглэдэг. Харин дараагийн 2 орон төрсөн өдрийг илэрхийлнэ.

Регистрийн дугаарын сүүлээсээ 2 дахь тоо нь хүйсийг заах бөгөөд хэрвээ тус тоо сондгой бол хүйс нь эрэгтэй,
үгүй бол эмэгтэй гэж үзнэ. Харин хамгийн сүүлийн орон бол, тус регистрийн дугаарыг үнэн оруулсан эсэхийг шалгахад
ашиглагддаг тоо болно. Хэрхэн уг тоог ашиглан шалгаж болох талаар нээлттэй эх сурвалжуудад баттай мэдээлэл байхгүй ч,
энэхүү нийтлэлийн коммент хэсэгт уг тоо регистрийн дугаарт орж байгаа үсэг болон тоонуудын нийлбэрийг тодорхой нэг
тоонд хуваасны үлдэгдэлтэй тэнцэх ёстой гэх мэдээлэл байна.

## Анхаарах зүйлс:

-Уг parser нь 1930-с 1940 онд хүртэлх төрсөн хүмүүсийг Регистрийн дугаарыг бододгүй.

-Хэрэв буруу Регистрийн дугаар орж ирвэл 1000 - 1200 оны 01 сарын 01 гэж хадгалдаг тул анхаарна уу.
finicapi 0.1.31
Finic.ai is a platform for deploying integrations and workflow automations in Python. This is the Python client for Finic

# `Finic`

Finic.ai is a platform for deploying integrations and workflow automations in Python. This is the Python client for Finic

## Contributing

To run locally, run:

```
python3 setup.py sdist bdist_wheel
pip install /path/to/folder/dist/finicapi-0.1.0.tar.gz
```