semvec/pypi-packages huggingface dataset:
Paid.ai-Client 0.4.8
A package to interact with the Paid API.
Requires Python: >=3.6

# AgentPaid Python SDK

Official Python SDK for the AgentPaid API.

## Installation

```bash
pip install Paid.ai-Client
```

## Usage

```python
from paid_client import PaidClient

# Initialize the client
client = PaidClient(
    api_key='YOUR_API_KEY',
    api_url='YOUR_API_URL'  # Optional, defaults to production URL
)

# Example: Record usage
client.record_usage(
    'agent_id',
    'customer_id',
    'event_name',
    {'key': 'value'}
)
# Signals are automatically flushed:
# - Every 30 seconds
# - When the buffer reaches 100 events
# To manually flush:
client.flush()
```

## API Documentation

### Usage Recording
- `record_usage(agent_id: str, external_user_id: str, signal_name: str, data: Any) -> None`
- `flush() -> None`

### Accounts
- `create_account(data: CreateAccountRequest) -> Account`
- `get_account(account_id: str) -> Account`
- `get_account_by_external_id(external_id: str) -> Account`
- `list_accounts() -> List[Account]`
- `update_account(account_id: str, data: UpdateAccountRequest) -> Account`
- `update_account_by_external_id(external_id: str, data: UpdateAccountRequest) -> Account`
- `delete_account(account_id: str) -> None`
- `delete_account_by_external_id(external_id: str) -> None`

### Orders
- `create_order(data: CreateOrderRequest) -> Order`
- `get_order(order_id: str) -> Order`
- `list_orders() -> List[Order]`
- `add_order_lines(order_id: str, lines: List[Dict[str, Any]]) -> List[OrderLine]`
- `activate_order(order_id: str) -> str`
- `delete_order(order_id: str) -> None`

### Agents
- `create_agent(agent: CreateAgentRequest) -> Agent`
- `get_agent(agent_id: str) -> Agent`
- `get_agent_by_external_id(external_id: str) -> Agent`
- `list_agents() -> List[Agent]`
- `update_agent(agent_id: str, data: UpdateAgentRequest) -> Agent`
- `update_agent_by_external_id(external_id: str, data: UpdateAgentRequest) -> Agent`
- `delete_agent(agent_id: str) -> None`
- `delete_agent_by_external_id(external_id: str) -> None`

### Contacts
- `create_contact(data: CreateContactRequest) -> Contact`
- `get_contact(contact_id: str) -> Contact`
- `get_contact_by_external_id(external_id: str) -> Contact`
- `list_contacts(account_id: Optional[str] = None) -> List[Contact]`
- `delete_contact(contact_id: str) -> str`
- `delete_contact_by_external_id(external_id: str) -> str`
ipcuykeh 0.1.0
Contoh package Python buatan sendiri
Requires Python: >=3.7

# ipchecker

Package Python sederhana untuk memeriksa IP address.

## Fitur

- Cek apakah IP valid
- Cek apakah IP private
- Cek apakah IP termasuk dalam subnet
- Ambil IP publik dari internet

## Instalasi

- pip install ipchecker

## Contoh Penggunaan

```python
from ipchecker import is_valid_ip, is_private_ip, is_in_subnet, get_public_ip

print(is_valid_ip("192.168.1.1"))
print(is_private_ip("8.8.8.8"))
print(is_in_subnet("192.168.1.10", "192.168.1.0/24"))
print(get_public_ip())
```

---

### ✅ `setup.py`

```python
from setuptools import setup, find_packages

setup(
    name='ipchecker',
    version='0.1.0',
    description='Simple IP address checker utility',
    author='Nama Kamu',
    author_email='email@kamu.com',
    packages=find_packages(),
    install_requires=[
        'requests'
    ],
    classifiers=[
        "Programming Language :: Python :: 3",
        "License :: OSI Approved :: MIT License",
    ],
)
```
IPCUY 0.1.1
Contoh package Python buatan sendiri
Requires Python: >=3.7

# ipchecker

Package Python sederhana untuk memeriksa IP address.

## Fitur

- Cek apakah IP valid
- Cek apakah IP private
- Cek apakah IP termasuk dalam subnet
- Ambil IP publik dari internet

## Instalasi

- pip install ipchecker

## Contoh Penggunaan

```python
from ipchecker import is_valid_ip, is_private_ip, is_in_subnet, get_public_ip

print(is_valid_ip("192.168.1.1"))
print(is_private_ip("8.8.8.8"))
print(is_in_subnet("192.168.1.10", "192.168.1.0/24"))
print(get_public_ip())
```

---

### ✅ `setup.py`

```python
from setuptools import setup, find_packages

setup(
    name='ipchecker',
    version='0.1.0',
    description='Simple IP address checker utility',
    author='Nama Kamu',
    author_email='email@kamu.com',
    packages=find_packages(),
    install_requires=[
        'requests'
    ],
    classifiers=[
        "Programming Language :: Python :: 3",
        "License :: OSI Approved :: MIT License",
    ],
)
```
pygwml 0.0.1
Geographically Weighted Machine Learning in Python
Requires Python: >=3.9

# PyGWML (Geographically Weighted Machine Learning in Python)
Python 3 Based Implementation of Geographically Weighted Machine Learning Models such as:
 - GXGB (Geographically Weighted XGBoost).
 - GWMLP (Geographically Weighted Multi-layer Perceptrons).
 - GWRNN (Geographically Weighted RNN).
   
# Installation
You can directly install it with the command:
```python

$ pip install pygwml

```

# Potential issues and solutions
 - Pygwml requires the pacakge esda as a dependency for computing Moran's Index. We recommend users to install esda 2.5, and then Pygwml can be used smoothly with any additional action. If you use the latest version of esda 2.6, you will need to install matplotlib manually in order to import Pygwml successfully.
 - Libpysal is used specifically for the Incremental Spatial Autocorrelation (ISA) analysis to help find the optimal spatial bandwidth (ISA_op_bw function).  libpysal >= 4.4.0 is needed to be installed for this.

# Example 1: Implementation GXGB Model
Below shows an example on how to fit a GXGB model and use it to make predictions.
```python

from pygwml import GXGB
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score

#Instantiate GXGB model with chosen parameters
model = GXGB(band_width=32, kernel="fixed", n_estimators=100, learning_rate=0.1)

#Fit model on training data
model.fit(X_train, y_train, coords_train)

#Predict on test data
local_weight = 0.5  # blend local and global predictions (adjust as needed)
y_pred, y_pred_global, y_pred_local = model.predict(X_test, coords_test, local_weight=local_weight)

#Evaluate performance
print("R2 score (combined):", r2_score(y_test, y_pred))
print("R2 score (global only):", r2_score(y_test, y_pred_global))
print("R2 score (local only):", r2_score(y_test, y_pred_local))

#(Optional) #Get Feature Importances
local_feature_importance=model.get_local_feature_importance()
local_feature_importance=model.global_model.feature_importances_
globally_enhanced_local_feature_importances=model.get_globally_enhanced_local_feature_importances()
```

# Parameters
If you want to learn more about the major parameters in this package, please refer to the Description of Parameters(https://github.com/moin-t/PyGWML/Description_Parameters).


# Authors
 - Moin tariq - AI for Digital Earth Lab, Shandong University, Jinan, China - Email: sci.mointariq@gmail.com, moin.tariq@mail.sdu.edu.cn , moin.bsma1810@iiu.edu.pk
 - Muhammad Irfan Haider Khan - Key Laboratory of Artificial Intelligence, Optics and Electronics (iOPEN), NWPU, Xi’an, Shaanxi, China - Email: vice.haider@gmail.com, irfankhan@mail.nwpu.edu.cn
   
# Project URL:
https://github.com/moin-t/PyGWML
ncep-data-req 0.1.2.3
Download and process NCEP GFS 0.25° data via ASCII interface
Requires Python: >=3.7

# ncep-data-req

Download and preprocess NCEP GFS 0.25Â° atmospheric forecast data from NOAA using ASCII interface.

## Overview

This package provides utility functions to directly download and preprocess Global Forecast System (GFS) 0.25-degree resolution data from the NOAA NOMADS server. It supports extraction of pressure-level and surface-level variables for specific forecast hours, structured into usable `xarray.Dataset` objects.

## Features

- Access GFS 0.25Â° forecast data from NOAA
- Support for both pressure-level and surface variables
- Handles multiple or single forecast hours
- Outputs structured `xarray.Dataset` objects for easy analysis

## Installation

You can install the package from PyPI (after publishing):

```bash
pip install ncep_data_req

from ncep_data_req import get_data_preprocess, get_data_preprocess_s

# Example: Download temperature on pressure levels for 6 forecast hours
ds = get_data_preprocess(
    '2025-05-23', utc=0, ft=6, var='tmpprs', pvar='yes',lon_range=(45,90),lat_range=(10,20)
)

# Example: Download temperature on surface level for 6 forecast hours
ds = get_data_preprocess(
    '2025-05-23', utc=0, ft=6, var='pratesfc', pvar='no',lon_range=(45,90),lat_range=(10,20)
)




# Example: Download  variable at single forecast hour
ds_surface = get_data_preprocess_s(
    '2025-05-23', utc=0, ft=6, var='tmpprs', pvar='yes',lon_range=(45,90),lat_range=(10,20)
)


# Example: Download  variable on surface level at single forecast hour
ds = get_data_preprocess(
    '2025-05-23', utc=0, ft=6, var='pratesfc', pvar='no',lon_range=(45,90),lat_range=(10,20)
)


Parameters
Longitude: 0.00000000000Â°E to 359.75000000000Â°E (1440 points, avg. res. 0.25Â°)
Latitude: -90.00000000000Â°N to 90.00000000000Â°N (721 points, avg. res. 0.25Â°)
Altitude: 1000.00000000000 to 20. (26 points, )

utc: Initialization hour (0, 6, 12, or 18 UTC)

ft: Forecast hour (0 to 384 depending on GFS run)

var: GFS variable name (e.g., tmpprs, rhprs, ugrdprs, etc.)

pvar: 'yes' if pressure-level variable, 'no' for surface

Output
Returns an xarray.Dataset containing:

Dimensions: time/levels, lat, lon

Coordinates: pressure levels, lat/lon grid

Data variables: selected GFS variable 
check this link ( https://nomads.ncep.noaa.gov/dods/gfs_0p25_1hr/)


contact:subhrjitrath17@gmail.com
regenerator 0.1.2
Simple class for reusable generators.
Requires Python: >=3.8

# regenerator
Create a reusable generator.

Depending on your process, it is likely faster to simply run the code to create your generator to create a "copy". However, if all you have is the generator itself, the `regenerator` package can create a copy. For large iterables, it is expected that copying the generator will offer speed-up. Furthermore, generators can be copied multiple times. The use-case therefore is in operations that need to be repeated, but creating a list out of the generator is too expensive.


## Example use

```
# Import
from regenerator import Regenerator

# Create a generator
generator = (x for x in range(10))

# Create a Regenerator from generator
regenerator = Regenerator(generator)

# Run through generator once
for x in regenerator:
    print(x)

# Run through the generator again
for x in regenerator:
    print(x)

...
```


## Installation

This package is available through pip,

```
pip install regenerator
```
humannum 1.2.1
Human Friendly Numbers
Requires Python: <4.0,>=3.10.0

[![PyPI Version](https://badge.fury.io/py/humannum.svg)](https://badge.fury.io/py/humannum)
[![Python Build](https://github.com/nbiotcloud/humannum/actions/workflows/main.yml/badge.svg)](https://github.com/nbiotcloud/humannum/actions/workflows/main.yml)
[![Documentation](https://readthedocs.org/projects/humannum/badge/?version=stable)](https://humannum.readthedocs.io/en/stable/?badge=stable)
[![Coverage Status](https://coveralls.io/repos/github/nbiotcloud/humannum/badge.svg?branch=main)](https://coveralls.io/github/nbiotcloud/humannum?branch=main)
[![python-versions](https://img.shields.io/pypi/pyversions/humannum.svg)](https://pypi.python.org/pypi/humannum)

# Human Friendly Numbers

* [Documentation](https://humannum.readthedocs.io/en/stable/)
* [PyPI](https://pypi.org/project/humannum/)
* [Sources](https://github.com/nbiotcloud/humannum)
* [Issues](https://github.com/nbiotcloud/humannum/issues)

## Installation

Installing it is pretty easy:

```bash
pip install humannum
```