semvec/pypi-packages huggingface dataset:
tkTimePicker 2.0.2
This package provides you with easy to customize timepickers
License: MIT
Requires Python: >=3.6
Keywords: tkinter,timepicker,time,tktimepicker

# tkTimePicker

This is a simple and fully customizable timepicker widgets made for tkinter. You can make use of
three types of timepicker:

1. clock timepicker
2. old-spin timepicker
3. modern-spin timepicker

quick example

```python
import tkinter as tk
from tktimepicker import AnalogPicker, AnalogThemes
# note: you can also make use of mouse wheel or keyboard to scroll or enter the spin timepicker
root = tk.Tk()

time_picker = AnalogPicker(root)
time_picker.pack(expand=True, fill="both")

# theme = AnalogThemes(time_picker)
# theme.setDracula()

root.mainloop()
```

For more examples refer [Examples](https://github.com/PaulleDemon/tkTimePicker/tree/master/examples),
For documentation read [Documentation](https://github.com/PaulleDemon/tkTimePicker/blob/master/Documentation.md)

**Clock time picker**

Available themes:

<a id="themes"></a>
**NavyBlue**

![NavyBlue](https://github.com/PaulleDemon/tkTimePicker/blob/master/ReadMeImages/NavyBlue.png?raw=True)

**Dracula**

![Dracula](https://github.com/PaulleDemon/tkTimePicker/blob/master/ReadMeImages/DraculaDark.png?raw=True)

**purple**

![Purple](https://github.com/PaulleDemon/tkTimePicker/blob/master/ReadMeImages/Purple.png?raw=True)

**Old-spin timepicker**

![old-spin timepicker](https://github.com/PaulleDemon/tkTimePicker/blob/master/ReadMeImages/SpinTimeold.png?raw=True)

**Modern-spin timepicker**

![old-spin timepicker](https://github.com/PaulleDemon/tkTimePicker/blob/master/ReadMeImages/SpinTimeModern.png?raw=True)

> Note: you can also use mouse wheel to change the time when using modern timepicker, 
> or you can also make use of keypress to change time if you hover over the timepicker.


> Note: You can also have your own colors besides this. you can also make use of 24 hrs clock, read the
> documentation for more info.

**Other libraries you might be interested in**

* [tkvideoplayer](https://pypi.org/project/tkvideoplayer/) - Plays video files in tkinter.

* [tkstylesheet](https://pypi.org/project/tkstylesheet/) - Helps you style your tkinter application using stylesheets.

* [PyCollision](https://pypi.org/project/PyCollision/) - Helps you draw hitboxes for 2d games.
flowtorch 0.8
Normalizing Flows for PyTorch
License: MIT
Requires Python: >=3.7
Keywords: Deep Learning,Bayesian Inference,Statistical Modeling,Variational Inference,PyTorch

<p align="center"><img src="https://github.com/facebookincubator/flowtorch/raw/main/website/static/img/logo.svg" width="200rem" /></p>

[![](https://github.com/facebookincubator/flowtorch/workflows/Python%20package/badge.svg)](https://github.com/facebookincubator/flowtorch/actions?query=workflow%3A%22Python+package%22)

Copyright © Meta Platforms, Inc

This source code is licensed under the MIT license found in the
[LICENSE.txt](https://github.com/facebookincubator/flowtorch/blob/main/LICENSE.txt) file in the root directory of this source tree.

# Overview

FlowTorch is a PyTorch library for learning and sampling from complex probability distributions using a class of methods called [Normalizing Flows](https://arxiv.org/abs/1908.09257).

# Installing

An easy way to get started is to install from source:

    git clone https://github.com/facebookincubator/flowtorch.git
    cd flowtorch
    pip install -e .

# Further Information

We refer you to the [FlowTorch website](https://flowtorch.ai) for more information about installation, using the library, and becoming a contributor. Here is a handy guide:

* [What are normalizing flows?](https://flowtorch.ai/users)
* [How do I install FlowTorch?](https://flowtorch.ai/users/installation)
* [How do I construct and train a distribution?](https://flowtorch.ai/users/start)
* [How do I contribute new normalizing flow methods?](https://flowtorch.ai/dev)
* [Where can I report bugs?](https://github.com/facebookincubator/flowtorch/issues)
* [Where can I ask general questions and make feature requests?](https://github.com/facebookincubator/flowtorch/discussions)
* [What features are planned for the near future?](https://github.com/facebookincubator/flowtorch/projects)
t-normalizer 0.0.1
TextNormalizer perform fully supervised text normalization
License: MIT
Requires Python: >=3.7

[![PyPI - Python](https://img.shields.io/badge/python-v3.7+-blue.svg)](https://pypi.org/project/bertopic/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

# TextNormalizer
`TextNormalizer` is a strings normalizer that uses [SentenceTransformers](https://www.sbert.net/) as a backbone to obtain vector representations of sentences.
It is designed for repeated normalization of strings against a large corpus of strings. <br>
The main contribution of `TextNormalizer` is to gain time by eliminating the need to compute the normalized strings embeddings every time. 
## Setup
``` bash
pip install t-normalizer
```
## Usage
1. Create and instance of `TextNormalizer`, can be initialized with a `SentenceTransformer` model or a `SentenceTransformer` model path.
2. Obtain the vector representation of the normalized string with `.fit` method.
3. Transform the string with to the most similar normalized form using the `.transform` method.

``` python
from textnormalizer import TextNormalizer

normalizer = TextNormalizer()

normalized_text = ['senior software engineer', 'solutions architect', 'junior software developer']
to_normalize = ['experienced software engineer', 'software architect', 'entry level software engineer']

normalizer.fit(normalized_text)
transformed = normalizer.transform(to_normalize)
```
The model along with the normalized strings and their  vector representations can be saved and loaded with `.save` and `.load` methods.
## Serialization

``` python
# save
normalizer.save('path/to/model')

# load
model = TextNormalizer.load('path/to/model')
```
pyLoadModularJson 0.9.1
Load nested JSON files into a dictionary
License: MIT
Keywords: json,config,modular,nested

# pyModularJSON

Allows recursive loading of JSON files featuring comments.
Now uses json5 so it is a bit less picky on line terminations


Authored by Edwin Peters

## Install
`pip install pyLoadModularJson`

or

`setup.py install`

## Usage

Also check *tests* folder for a few examples

base.json:
```
// comments
{
    "param2a": 2,
    "nestedParam2a":{
	"a": "notaNumber",
	"c": "set by base"
    }
}
```

main.json:
```
// comments
{
    "configBase": ["base.json"], // parent config file name relative to this file
    "param1": 4,
    "nestedParam1":{
	"a":39,
	"b":["peee","e","new"],
	"c": "set by main"
    } 
}
```

In Python:
```
from pyLoadModularJson import loadModularJson

cfg = loadModularJson('base.json')

print(cfg)

```

Child files will overwrite attributes from base files.


See more examples in `tests`