semvec/pypi-packages huggingface dataset:
skoots 0.0.5
SKeletOn ObjecT Segmentation (SKOOTS)
Requires Python: >=3.10

![](resources/skooting_in_progress_v2.png)

# SKOOTS

<img src="resources/overview.png" align="center" width="150px"/> 
SKeletOn ObjecT Segmentation - 2D/3D instance segmentation of arbitrary biomedical images.

## <img src="resources/installation.png" align="center" width="150px"/>

<!-- start installation -->
To install, make sure pyTorch 2.1 is installed with CUDA support.

```bash
pip install skoots
```

Documentation: skoots.readthedocs.io

BioRxiv: https://www.biorxiv.org/content/10.1101/2023.05.05.539611v1
edwh-nostr-messagebus 0.3.0
EducationWarehouse NOSTR Messagebus
Requires Python: >=3.11
Keywords: education,edwh,nostr

# .

[![PyPI - Version](https://img.shields.io/pypi/v/-.svg)](https://pypi.org/project/-)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/-.svg)](https://pypi.org/project/-)

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install -
```

## License

`-` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
render-engine-lunr 2024.1.1b3
Plugin and theme for Lunr.js

# lunr

Plugin and theme for Lunr.js

## How to use this theme

1. Install the theme

   ```python
   pip install render_engine_lunr
   ```

2. Import the theme into your project

   ```python
   from render_engine import Site
   from render_engine_lunr import Lunr

   app = Site()
   app.register_theme(Lunr)
   ```
lbvh 0.0.13
A small bounding volume hierarchy implementation
Requires Python: >=3.8

# LBVH

An implementation of the LBVH algorithm for performing fast bounding-box intersections.
NVIDIA has a great series of blog posts that describes, in detail, the algorithm for LBVH construction/traversal:

1. https://developer.nvidia.com/blog/thinking-parallel-part-i-collision-detection-gpu/
2. https://developer.nvidia.com/blog/thinking-parallel-part-ii-tree-traversal-gpu/
3. https://developer.nvidia.com/blog/thinking-parallel-part-iii-tree-construction-gpu/

This repo's implementation is CPU-only for now, but it does include python and Mathematica bindings.

## Python

Precompiled binaries are available for many versions of python on mac, linux and windows. 
To install python bindings for use with `jax` or `numpy`, do

```sh
pip3 install lbvh
```

The python bindings expect arrays of bounding boxes to be specified in one of the following formats:

```py
# list of 2D bounding boxes, as arrays with shape (*, 4)
[[min_x, min_y, max_x, max_y], ... ]

# list of 2D bounding boxes, as arrays with shape (*, 2, 2)
[[[min_x, min_y], [max_x, max_y]], ... ]

# list of 3D bounding boxes, as arrays with shape (*, 6)
[[min_x, min_y, min_z, max_x, max_y, max_z], ... ]

# list of 3D bounding boxes, as arrays with shape (*, 2, 3)
[[[min_x, min_y, min_z], [max_x, max_y, max_z]], ... ]
```

See https://github.com/samuelpmish/LBVH/tree/main/python for some basic examples of bounding box queries.


## C++

To build from source:

### 1. clone the repo 
```sh
git clone git@github.com:samuelpmish/LBVH.git
```

### 2. configure CMake 
```sh
cd LBVH
cmake . -Bbuild -DCMAKE_BUILD_TYPE=Release 
```
This project has a few (off by default) configure arguments that control which targets get built, including
- `LBVH_ENABLE_PYTHON_BINDINGS`
- `LBVH_ENABLE_MATHEMATICA_BINDINGS`
- `LBVH_ENABLE_TESTS`

### 3. build
```sh
cmake --build build
```

----

To use LBVH in another CMake project, you can use `FetchContent` to automatically build it along with the parent CMake project

```cmake

...

# fetch and build LBVH
include(FetchContent)
FetchContent_Declare(
  LBVH
  GIT_REPOSITORY https://github.com/samuelpmish/LBVH.git
  GIT_TAG main
)
set(LBVH_ENABLE_PYTHON_BINDINGS OFF CACHE INTERNAL "")
set(LBVH_ENABLE_MATHEMATICA_BINDINGS OFF CACHE INTERNAL "")
set(LBVH_ENABLE_TESTING OFF CACHE INTERNAL "")
FetchContent_MakeAvailable(LBVH)

...

# link it against any targets in the parent project
target_link_libraries(my_project PUBLIC LBVH)
```
sokudoku 0.0.4
PyPI test
Requires Python: >=3.7

# sokudoku
This is a programmed version of what is called Spritz, in which letters flow at the speed of reading.
This reduces visual fatigue as the user no longer needs to follow the text with their eyes when reading.
The program displays either the text of the image you have entered or the text you have typed word by word.
A space is required for each word as you type.

# Packages required to run sokudoku
pip install pytesseract

brew install mecab

brew install mecab-ipadic

pip install unidic-lite  

pip install mecab-python3

# Result of running sokudoku
The result is displayed in the console.
The size of the text will be the same as the user's environment.

https://github.com/Hikaru163/sokudoku/assets/87521143/ecc87af0-c6e3-4ae3-8b95-509f9bca7b42
xapi-ir 0.0.1
Using x-api.ir using this library
Requires Python: >=3.7

Using x-api.ir using this library

## Install
 bash
pip install xapi


<hr>

## Example
 python
from xapi_r import Connect

con = Connect("username_xapi", "pssword_xapi")

print(con.login())

<hr>

## Do you have a problem?
### <a href='https://t.me/amir_ershadi_2'>click</a>
stactools-naip 0.5.0
STAC item creation tools for NAIP dataset.
Requires Python: >=3.8
Keywords: stactools,pystac,catalog,STAC

# stactools-naip

stactools-naip is a [stactools](https://github.com/stac-utils/stactools) package to generate STAC objects
for NAIP images.

## How to use

To install, run:

```shell
pip install stactools-naip
pip install stactools[s3]
```

To create a STAC Item:

```shell
aws s3 cp --request-payer requester s3://naip-analytic/va/2018/60cm/fgdc/37077/m_3707763_se_18_060_20180825.txt .
export AWS_REQUEST_PAYER='requester'
stac naip create-item --fgdc m_3707763_se_18_060_20180825.txt \
  VA 2018 s3://naip-analytic/va/2018/60cm/rgbir_cog/37077/m_3707763_se_18_060_20180825.tif \
  json-dest/
```

## Development

```shell
pip install -e .[dev]
```


Install pre-commit hooks with:

```shell
pre-commit install
```

Run these before commit with:

```shell
pre-commit run --all-files
```
xapi-r 0.0.1
Using x-api.ir using this library
Requires Python: >=3.7

Using x-api.ir using this library

## Install
 bash
pip install xapi


<hr>

## Example
 python
from xapi import Connect

con = Connect("username_xapi", "pssword_xapi")

print(con.login())

<hr>

## Do you have a problem?
### <a href='https://t.me/amir_ershadi_2'>click</a>