semvec/pypi-packages huggingface dataset:
AutoImageExtractor 1.1
A python package used to automatically extract 100 different features from a group of images.
Requires Python: >=3.6

# AutoImageExtractor

This is a python module used to automatically extract features from a directory of images in a single line.

## Code

### from AutoImageExtractor import FeatureExtractor

### data=FeatureExtractor.extract(image_path,classname,technique_list)

## Parameters

Image path- provide the path of the directory of images

Classname- Mention the name of the class the images belong to, if any

technique_list- mention the list of techniques, available- GLCM, LBP, BRIEF. If not, default statistical parameters are calculated.
gage 0.1.2
Model evaluation and publishing toolkit
Requires Python: >=3.10

# Gage

Gage is a tool for running model evaluations and publishing results.
It's under active development by the team that developed Guild AI.

## Installing

Install the latest version of Gage using `pip`:

``` shell
pip install gage
```

## Contributing

We warmly encourage contributions to this project at any level. We are
committed to growing and supporting a rich, diverse set of contributors.
If you would like to contribute something, feel free to [open an
issue](https://github.com/gageml/gage/issues) to ask questions, make
suggestions, or otherwise let us know what you're thinking. Changes to
the project are accepted using GitHub pull requests.

Please read [Contributor Covenant Code of Conduct](CONTRIBUTING.md) to
familiarize yourself with the project code of conduct and contribution
policy. If you feel that something is missing from this document or
could be improved, please let us know by either opening an issue or by
emailing the project administrator at garrett@placeon.earth. Private
correspondence via email will be held in strict confidence.

### Setting up a Local Development Environment

1. Fork the source code repository at https://github.com/gageml/gage

2. Create a project-specific virtual environment. **Use Python 3.10 or
   later.**

   ``` shell
   cd gage
   virtualenv --python python3.10 .venv  # Any version >= 3.10 is okay
   ```

3. Activate the virtual environment and use `pip` to install the project
   as "editable".

   ``` shell
   source .venv/bin/activate  # Works on most POSIX shells - change as needed
   pip install -e '.[dev]'    # Installs gage and its dev requirements
   ```

4. Run tests using [Groktest](https://github.com/gar1t/groktest)
   (optional).

   ``` shell
   groktest .
   ```
across-burstcube 0.0.9
ACROSS API Python client for BurstCube
Requires Python: >=3.9
Keywords: astronomy, api, across

# ACROSS API Python BurstCubeTOO Client

# Introduction
This is the Python API for the Astrophysics Cross-Observatory Science Support
(ACROSS), a NASA initiative to provide cross-observatory support for
time-domain and multi-messenger (TDAMM) coordination and observation planning.

The client provides support for accessing the BurstCube TOO interface that
ACROSS provides.

# Installation

To install the client, enter the command `pip install across-burstcube`.

The `across_client` module should now be available for use.

# Usage

The relevant classes are:

`BurstCubeTOO`

Main interface for submitting, updating, fetching and deleting BurstCube TOO
requests.

`BurstCubeTriggerInfo`

Class that provides extended information on the triggering event, this is an
parameter for `BurstCubeTOO`.

`BurstCubeTOORequests`

Allows to fetch multiple `BurstCubeTOO` entries using various filter criteria.

Note that submission of BurstCubeTOO requests requires an API Token, which will
be supplied by ACROSS.
cch-labeler 0.0.0
A package for labeling data
Requires Python: >=3.10
Keywords: labeling

# Python Bindings for Security Labeler


## Usages

**From Python Interpreter**

```bash
Python 3.10.13 (main, Aug 24 2023, 12:59:26) [Clang 15.0.0 (clang-1500.0.40.1)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from labeler import labeler
INFO:2023-12-08 06:19:28,950 get_library_name:12 - Operating System: macos-14.1.2-arm64-arm-64bit
INFO:2023-12-08 06:19:28,950 get_library_name:13 - CPU Architecture: ('64bit', '')
>>> labeler.evaluate('{"number":1}', "when number > 0 then 'tag1' else 'tag2'")
'tag1'
```


<br>

**From Python Script**

```python
from labeler import labeler

content = '{"number":1}'
statement = "when number > 0 then 'tag1' else 'tag2'"

result: str = labeler.evaluate(content, statement)

print(f"result:{result}")
```


<br>

## How to Build (as the developer)

**Prerequisites**

- Shared libraries must have been built prior building the Python pacakge.

  - Libraries are expected to exist in the [labeler](../labeler/) directory with `libkoverse_labeler-` prefix.



<br>

**Make Targets**

Several make targets are provided.

- **clean** - delete files under `build/*`, `dist/*`, `src/*.egg-info/`, `inputs/*`, `src/labeler/libs/*.so`, `src/labeler/libs/*.dylib` directory but keep the `.gitkeep` file.

- **init** - install Python **build** package.

- **copy_test_data** - copy the test data from `../inputs` directory to the current directory.

- **copy_library** - copy library files from `../labeler` directory into `./src/labeler/libs/` directory.

- **install_extra_dependencies** - install extra depdencies needed for testing.

- **build** - build and package

- **tests** - run the test

- **test_upload** - upload package to Test PyPI.


<br>

**Building the package**


```bash
make init
make build
```

<br>

## How to Run Tests

```bash
make tests
```

<br>

## How to Publish to TestPyPI

```bash
make test_upload
```
<br>


## How to Install the package (as a user)

```bash
pip install cffi

pip install -i https://test.pypi.org/simple/ labeler

# or in one go
pip install -i https://test.pypi.org/simple/ labeler --extra-index-url https://pypi.org/simple/ cffi

```

<br>

## Editable Install

This assumes the package has been built locally already.

```bash
pip install -e .
```

<br>

**Install extra dependencies**

```bash
pip install -e ".[dev]"
```

<br>
hypersync-lancedb-pipe 0.1.1
A serverless embedded streaming OLAP data pipeline
Requires Python: >=3.8

# hypersync-lancedb-pipe

A serverless embedded streaming OLAP data pipeline that leverages historical blockchain data from [Hypersync](https://github.com/enviodev/hypersync-client-python) and mutable columnar storage format [lance](https://lancedb.github.io/lance/).

Since Lance is designed to be mutable, it is possible to create an embedded streaming pipeline using the same data source. The main advantage of this streaming approach is that it doesn't require any parquet glob file management. This reduces the complexity of streaming to the same as batch processing. The other main benefit is that LanceDB has tight integration with both
[polars](https://lancedb.github.io/lancedb/python/polars_arrow/#from-polars-dataframe) and [duckdb](https://lancedb.github.io/lancedb/python/duckdb/). LanceDB accepts polars dataframes as
data inputs, which allows for a more flexible ETL pipeline, allowing polars to be used as a preprocessing tool. 

Since LanceDB leverages the [Apache Arrow Standard](https://arrow.apache.org/overview/), there is a lot of flexibility to query from ths database - such as querying larger than memory
datasets with polars lazyframes and a dataframe API, or using an embedded OLAP engine like duckdb for faster speed and SQL API.

### Getting Started
1. This repository uses rye to manage dependencies and the virtual environment. To install, refer to this link for instructions [here](https://rye-up.com/guide/installation/). 
2. Once rye is installed, run `rye sync` to install dependencies and setup the virtual environment, which has a default name of `.venv`. 
3. Activate the virtual environment with the command `source .venv/bin/activate`.

### Running the Pipeline
There are some script examples in the `scripts` folder. These examples demonstrate the versatility of the lancedb writer.

* Run `historical_sync.py` file to backfill data from a historical block number. Assumes there is no existing table.
* Run `head_sync.py` to sync the database to the head of the chain. Assumes existing table exists.
* Run `backfill_sync.py` to perform a backfill sync from the earliest block number. Assumes existing table exists.
pychoreographer 0.0.8
Python library for automating file ops, system monitoring

# PyChoreographer

A customizable Python library for automating file operations, system monitoring, and routine tasks

## Installation

```bash
$ pip install pychoreographer
```
## Features

<!-- features-begin -->

* **Environment Checker**
    * Script to validate if a project's required dependencies are installed within the current virtual environment.
  
* **Basic Directory Cleanup**

    * Delete Old Files: Automates the process of deleting files in a directory that are older than a specified number of days, helping in cleaning up temporary folders and reducing clutter.
    
    * Remove Empty Folders: Scans and removes any empty directories within a specified path, keeping your file system organized and tidy.

*  **Targeted File Organizing**
  
    * Sort Files by Extension: Automatically organizes files within a directory into subfolders based on their file extensions, such as images, documents, and more, for improved file management.

<!-- features-end -->

## Usage

`pychoreographer -help` can be used to get current features
as follows:

```bash
pychoreographer -help
```

## Contributing

Interested in contributing? Check out the contributing guidelines. 
Please note that this project is released with a Code of Conduct. 
By contributing to this project, you agree to abide by its terms.

## License

`pychoreographer` was created by Rajaa Lebchiri. It is licensed under the terms
of the MIT license.
sectec 1.2
Easy to use Webscraper module for python

# Sectec 1.0 - Python WebScraper Module

Sectec is a user-friendly and easy to use python module for webscrapping. With Sectec you can easily scrap the content from any website you want

## Usage

Using Sectec is simple just import `sectec` and use it

## Syntax

All syntaxes of the Sectec is here:

```python
import sectec as st

website_url = 'https://www.youtube.com'

# Fetch and print the title of the website
title = st.getTitle(website_url)
print("Title:", title)

# Fetch and print the entire HTML content of the website
html_content = st.getHTML(website_url)
print("HTML Content:", html_content)

# Fetch and print all the text from the website
text_content = st.getText(website_url)
print("Text Content:", text_content)
```

# Founder

Sapit was created by "ALI LODHI" He is from Pakistan, Ali Lodhi is well experienced python programmer who loves writing python code. The Idea of Sapit came when he was making a Desktop Assistant for his personal use and he had to write a lot of code so that's why to ease of use he created sapit.

# Versions

Sapit 1.0

## Sapit 1.1

In Sapit 1.1 we added a new function `saveHTML` function. Now you can save the scrapped HTML code in HTML file

```python
import sectec as st

st.saveHTML ('https://www.instagram.com', filename='instagram.html')
```

Sapit 1.2 

- Minor updates
- Updated Description
- Bug Fixes