semvec/pypi-packages huggingface dataset:
ReNaLabApp 0.0.1.dev14
Reality Navigation Lab App
Requires Python: >=3.9

# Reality Navigation App
RealityNavigation (ReNa) is a Python-based App for visualizing, recording, and processing (i.e., make prediction) on lab data streams in real-time.

Read the docs at [RealityNavigationDocs](https://realitynavigationdocs.readthedocs.io/en/latest/)

# Acknowledgement
We thank the members from [LIINC](https://liinc.bme.columbia.edu/) at Columbia University, [Advance Human-computer Interaction Lab](https://interaction.mystrikingly.com/), and [CWINS](http://www.cwins.wpi.edu/) at Worcester Polytechnic Institute for their feedback in developing the App. [ningkko](https://github.com/ningkko) for the great contribution to the original code base, and [AlexG1031](https://github.com/AlexG1031) for the help in the various versions. 


# to build windows app

pyinstaller main.py
then copy pylsl, ui, presets folders into the main folder
queueing-systems 0.1.2
Python package for queueing system parameter estimation using queueing theory

# queueing_systems

## Python Package for Queueing System Parameter Estimation using Queueing Theory

This Python package is designed to assist with estimating parameters in queueing systems using queueing theory. It provides a set of tools and algorithms to analyze and model queueing systems, allowing users to estimate various parameters such as arrival rates, service rates, queue lengths, and waiting times. With this package, users can leverage the power of queueing theory to optimize and improve the performance of their systems.
sqlitedave-package 2.0.1
A wrapper for simplified sqlite usage using sqlite3.
Requires Python: >=3.7

# SQLiteDave - A wrapper for simplified sqlite usage using sqlite3.

## usage - load_csv_to_table
'''
	mydb = sqlite_db()
	mydb.connect()
	print(mydb.queryone('SELECT CURRENT_DATE'))
	mydb.close()

'''

## usage - load_csv_to_table
'''
	mydb = sqlite_db()
	mydb.connect()
	print(mydb.dbstr())
	csvfilename = 'Station.tsv'
	tblname = 'Station'	
	mydb.load_csv_to_table(csvfilename,tblname,True,'\t')
	mydb.close()
'''

## usage - export_table_to_csv
'''
	mydb = sqlite_db()
	mydb.connect()
	print(mydb.dbstr())
	csvfilename = 'Station.tsv'
	tblname = 'Station'
	mydb.export_table_to_csv(csvfilename,tblname)
	mydb.close()

'''
vipickle 0.1.0
vipickle is a tiny python package for saving instances with unpickable attributes
Requires Python: <4.0,>=3.8

<div align="center">
    <img src="https://img.shields.io/pypi/v/vipickle" alt="vipickle package version" />
    <img src="https://img.shields.io/pypi/pyversions/vipickle" alt="Python supported versions" />
    <a href="https://github.com/h4c5/vipickle/actions/workflows/unit-tests.yml">
        <img src="https://github.com/h4c5/vipickle/actions/workflows/unit-tests.yml/badge.svg" alt="unit tests status" />
    </a>
    <a href="https://github.com/PyCQA/bandit">
        <img src="https://img.shields.io/badge/security-bandit-yellow.svg" alt="Security thanks to bandit package" />
    </a>
    <img src="https://img.shields.io/badge/formatting-black-black" alt="Black formatting" />
</div>

<div align="center">
    <h1>👑 VIPpickle</h1>
    <p ><b>Tiny python package for saving instances with unpickable attributes</b></p>
</div>

## Quickstart

Install `vipickle` with pip :

```bash
pip install vipickle
```

Then inherit from `VIPicklable` and define which attribute are not picklable and how they should be dumped and restored.

```python
import torch
from torchvision import models
from pathlib import Path

from vipickle import VIPicklable

class MyClass(VIPicklable):
    PICKLE_BLACKLIST = ["vision_model"]

    def __init__(self):
        self.vision_model = models.vgg16(weights='IMAGENET1K_V1')

    def _dump_vision_model_(self, save_dir: Path, overwrite:bool = True):
        model_weights_path = save_dir / "model_weights.pth"
        if overwrite or not model_weights_path.exists():
            torch.save(model.state_dict(), model_weights_path)

    def _restore_vision_model_(self, save_dir: Path):
        self.vision_model = models.vgg16()
        self.vision_model.load_state_dict(torch.load(save_dir / "model_weights.pth"))


# Create an instance
obj = Myclass()

# train could modify the model weights
obj.train()

# we save the instance to a folder, _dump_vision_model_ will dump the weights in the folder
obj.save("a/folder")
del obj

# we can then reload the object, _restore_vision_model_ will recreate the attribute vision_model and load the weights
obj = MyClass.load("a/folder")
obj.vision_model.eval()
```

## Additionnal dependencies

#### Dev dependencies

```bash
pip install vipickle[dev]
```

#### Unit tests dependencies

```bash
pip install vipickle[test]
```

#### Documentation dependencies

```bash
pip install vipickle[doc]
```
optibar 1.0.2
Make Advanced And Customizable ProgressBar
Keywords: python,progress,progressbar,fast,bar,python3

OptiBar 1.0.2

Make Advanced And Customizable ProgressBar
Fast, Optimized And Professonial
Hello, welcome, the optibar module is very fast and allows
you to customize to some extent to create an advanced bar,
this website will teach you how you can create an advanced bar
with this module, of course this module for now Made for
pip users only,

Bugs Fixed & Refreshed Again To Make It Is Quicker !
lytics-smm-demo 0.1.1
A Python package for interacting with the Lytics SMM API
Requires Python: >=3.6

# lytics-smm-demo

A Python package for interacting with the [Lytics.lol](https://lyticss.lol) SMM API.

## Installation

Install the package using pip:

```shell
pip install lytics-smm-demo
```
\* currently demo *

## Usage
To use the functions in this package, import the Lytics class from lytics, and create an instance with your API key.

```python
from lytics import Lytics

api_key = 'API_Key_Here'
lytics = Lytics(api_key)

```
you can then use any of the functions below using the 'lytics' object.

## Functions
### For a list of availible services: 
```python
services = lytics.services()
print(services)
```

### To check your balance:
```python
balance = lytics.balance()
print(balance)
```

### To create a new order, there is multiple options:
```python
# Default
order = lytics.order(service_id=1, link='http://example.com/username', quantity=100, runs=2, interval=5)
print(order)

# Custom Comments
order = lytics.order(service_id=1, link='http://example.com/username', comments="good pic\ngreat photo\n:)\n;)")
print(order)

# Mentions with Hashtags
order = lytics.order(service_id=1, link='http://example.com/username', quantity=100, usernames="test, testing", hashtags="#goodphoto")
print(order)

# Mentions User Followers
order = lytics.order(service_id=1, link='http://example.com/username', quantity=1000, username="test")
print(order)

# Package
order = lytics.order(service_id=1, link='http://example.com/username')
print(order)

# Subscriptions (Old posts only)
order = lytics.order(service_id=1, username='username', min=100, max=110, posts=0, delay=30, expiry='11/11/2022')
print(order)

# Subscriptions (Unlimited new posts and 5 old posts)
order = lytics.order(service_id=1, username='username', min=100, max=110, old_posts=5, delay=30, expiry='11/11/2022')
print(order)

# Poll
order = lytics.order(service_id=1, link='http://example.com/test', quantity=100, answer_number='7')
print(order)

# Invites from Groups
order = lytics.order(service_id=1, link='http://example.com/test', quantity=100, groups="group1\ngroup2")
print(order)
```

### To check the status of a order / multiple orders: 
```python
status = lytics.status(order_id=11234)
print(status)

# check multiple order status's
multi_status = lytics.multi_status(order_ids=[12342, 34634, 12301, 34453])
print(multi_status)

```

### To create a new refill / multiple refills:
```python
refill = lytics.refill(order_id=12345)
print(refill)

# create multiple refills
multi_refill = lytics.multi_refill(order_ids=[12342, 34634, 12983, 23443])
print(multi_refill)
```

### To check the status of a refill / multiple refills:
```python
status = lytics.refill_status(refill_id=23423)
print(status)

# check multiple refill status's
multi_status = lytics.multi_refill_status(refill_ids=[23423, 12344, 91818, 12311])
print(status)
```
ormstrorm 0.0.1.1
Small library for easy work with databases.
Keywords: python,orm,database,sql,sqlite3

﻿# Welcome to ORMStorm

ORMStorm is a small library for easy work with databases.

## The key features are:

 - **Simplicity:** The library is very simple, and it won't take long to learn it.
 - **Coding speed:** Integrating the library into your projects won't take long.
 - **Dynamic:** Unlike others, this library will allow you to very quickly create new tables and add databases to them.

## Installing

    pip install ormstorm
   
## Usage

Using standard tables

    from ormstorm import Table, Types, Column, create_session  
  
  

    class ExampleTable(Table):  
        __tablename__ = "example"  
      
        id = Column(Types.INTEGER, primary_key=True, autoincrement=True)  
        text = Column(Types.STRING)  
      
      
    LocalSession = create_session("example.sqlite3", [ExampleTable])  
      
    with LocalSession() as session:  
        session.insert(ExampleTable(text="Hello, world!"))

Using a dynamic table

    from ormstorm import DynamicTable, Types, Column, create_session  
      
      
    LocalSession = create_session("example.sqlite3", [])  
      
    with LocalSession() as session:  
        NewTable = DynamicTable(  
            "new_table", {"id": Column(Types.INTEGER, primary_key=True, autoincrement=True), "text": Column(Types.STRING)}  
        )  
      
        session.create(NewTable)  
        session.insert(NewTable(text="Easy use of dynamic tables!"))

## Note

This library is strictly not recommended for use in large projects because of the small functionality!