semvec/pypi-packages huggingface dataset:
nbconvert-a11y 2024.3.25
nbconvert templates using modern standards
Requires Python: >=3.8

# `nbconvert-a11y`

`nbconvert-a11y` contains templates for accessible notebook representations and accessibility tests for Jupyter notebook products.

```bash
pip install nbconvert-a11y
```

`nbconvert-a11y` can be used with the [`nbconvert` command line tool](https://nbconvert.readthedocs.io/en/latest/usage.html).
it provides the `a11y` exporter with several variants that can be used. the default theme uses a flexible table representation

```bash
jupyter nbconvert --to a11y Untitled.ipynb           # flexible table navigation
jupyter nbconvert --to a11y-table Untitled.ipynb     # a11y is an alias for a11y-table
jupyter nbconvert --to a11y-landmark Untitled.ipynb  # cells are section landmarks
jupyter nbconvert --to a11y-list Untitled.ipynb      # cells are list items
```

```python
from nbconvert_a11y.exporter import A11y, Table, Section, List
```

A an example of the canonical Lorenz differential differential equations can be viewed @ https://deathbeds.github.io/nbconvert-a11y/exports/html/lorenz-executed-a11y.html

## History

the `nbconvert-a11y` project is forked from initial development in the [`notebook-for-all`]() repository. 
this collaboration between [Space Telescope Science Institute](https://www.stsci.edu/), [Iota School](https://iotaschool.com/) and [Quansight Labs](https://www.quansight.com/labs)
brought input from blind and visual impaired notebook users as to what their most assistive experiences could be.

## License
Licensed under a [3-Clause BSD license](LICENSE).
witchy 0.0.2
this is a file tool for python

# Witchy

This is a python library for file processing which allow you to modify files in a simple way. Also, it provide some function that allow you to convert the format of the file or doing compression on it.

# Install
```
pip install witchy
```
> **Attention: make sure you have installed the `FFmpeg` and correctly set the path of your local environment**.

# Usage
## basic function

```python
from witchy import File # load the library

file = File("path/to/your/file") # create a File object

print(file) # show the information of the file

create_time = file("ctime") # get the attribute of the file

file["ctime"] = "2020-01-01 00:00:00" # modify the attribute of the file

file.append(b'\x00\x00\x00\x00') # append data to the file

file.save("path/your/file") # save the modified file
```

## convert function
```python
from witchy import File

file = File("yourpic.jpg")

file.convert("path/output.png","PNG") # convert jpg to png format
```
image-diversity 0.1.6
A package for computing entropy-based diversity measures on image sets
Requires Python: >=3.8

# MEASURING DIVERSITY OF IMAGE SETS

Assess a set of image's diversity by computing an approximation of the Entropy of the images' mappings into a pre-trained network latents.

<br>

## Installation

Install from [pypi](https://pypi.org/project/image-diversity/):

```
pip install image-diversity
```

<br>

## Usage (console)

To compute the Truncated CLIP Entropy (TCE) of a set of images in a directory, run
```
python3 -m image_diversity <path/to/dir>
```

<br>

To compute the Truncated Inception Entropy (TIE), change the div_type option to TIE 
```
python3 -m image_diversity <path/to/dir> --div_type TIE
```

### Arguments

```
python3 -m image_diversity <path/to/dir>
--div_type <str>        Encoding network: TCE (default) or TIE
--n_eigs <int>          Number of eigenvalues for truncation (default: 20)
--device <str>          Device to use. Automatic by default
--batch_size <int>      Batch size for data loading (default: 16)
```
<br>

## Usage (script)

Computing Truncated CLIP Entropy (TCE)
```
from image_diversity import ClipMetrics

clip_metrics = ClipMetrics()
tce = clip_metrics.tce("path/to/img/dir")
```
<br>

Computing Truncated Inception Entropy (TIE)
```
from image_diversity import InceptionMetrics

inception_metrics = InceptionMetrics()
tie = inception_metrics.tie("path/to/img/dir")
```
<br>
<br>
<br>

Inception implementation based on [pytorch-fid](https://github.com/mseitzer/pytorch-fid)

CLIP implementation based on [CLIP](https://github.com/openai/CLIP)