semvec/pypi-packages huggingface dataset:
high-pass-filters 0.0.1
high-pass filter package using Sobel and Prewitt
Requires Python: >=3.7

# high_pass_filters

Description. 
The package high_pass_filters is used to:

	- Prewitt filter
	- Sobel filter
    - Image get, plot

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install high_pass_filters

```bash
pip install high_pass_filters
```

## Usage

```python
from high_pass_filters.fsobel import sobel
from high_pass_filters.fprewitt import prewit
from high_pass_filters.image import get_image, plot_image

image = get_image(path)
plot_image(image)
plot_image(sobel(image))
plot_image(prewit(image))
```

## Author
Leonardo Costa

## License
[MIT](https://choosealicense.com/licenses/mit/)
HiveNAS 0.1.5
A Neural Architecture Search framework based on Artificial Bee Colony optimization
Requires Python: >=3.7
Keywords: nas neural architecture search optimization automl


# ![HiveNAS Logo](https://i.imgur.com/mDTdNim.jpg)

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ThunderStruct/HiveNAS/blob/main/colab/HiveNas.ipynb) [![Platform](https://img.shields.io/badge/python-v3.7-green)](https://github.com/ThunderStruct/HiveNAS) [![pypi](https://img.shields.io/badge/pypi%20package-0.1.5-lightgrey.svg)](https://pypi.org/project/HiveNAS/0.1.5/) [![License](https://img.shields.io/badge/license-MIT-orange)](https://github.com/ThunderStruct/HiveNAS/blob/master/LICENSE) [![Read the Docs](https://readthedocs.org/projects/hivenas/badge/?version=latest)](https://hivenas.readthedocs.io/en/latest/)

A feature-rich, Neural Architecture Search framework based on Artificial Bee Colony optimization

------------------------

## Getting Started

**HiveNAS** ([preprint](https://arxiv.org/abs/2211.10250)) is a modular NAS framework that can find and optimize a neural architecture with state-of-the-art performance.

### Installation

#### PyPi (recommended)

The Python package is hosted on the [Python Package Index (PyPI)](https://pypi.org/project/hivenas/).

The latest published version of HiveNAS can be installed using

```sh
pip install HiveNAS
```

#### Manual Installation
Simply clone the entire repo and extract the files in the `HiveNAS` folder, then import them into your project folder.

Or use one of the shorthand methods below
##### GIT
  - `cd` into your project directory
  - Use `sparse-checkout` to pull the library files only into your project directory
    ```sh
    git init HiveNAS
    cd HiveNAs
    git remote add -f origin https://github.com/ThunderStruct/HiveNAS.git
    git config core.sparseCheckout true
    echo "HiveNAS/*" >> .git/info/sparse-checkout
    git pull --depth=1 origin master
    ```
   - Import the newly pulled files into your project folder
##### SVN
  - `cd` into your project directory
  - `checkout` the library files
    ```sh
    svn checkout https://github.com/ThunderStruct/HiveNAS/trunk/HiveNAS
    ```
  - Import the newly checked out files into your project folder
  

### Documentation

Detailed examples and the full API docs are [hosted on Read the Docs](https://hivenas.readthedocs.io/en/latest/).

## License

This project is licensed under the MIT License - see the [LICENSE](https://github.com/ThunderStruct/HiveNAS/blob/master/LICENSE) file for details
tk-apis 0.3.1
Wrappers for some common SERP ingestion APIs we use at TK
Requires Python: >=3.6

# TK APIs

This is a collection of the wrapper functions used to call various third-party,
SERP-related data sources. The implementation returns things as 
[Pandas DataFrames](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html),
because this package was written by a data scientist for data science use.

## Code examples

The package contains modules for each of the APIs. 
You'll need to provide your own API keys; an easy way to do this is a `.env` file. 

My examples below assume you have defined a `.env` and use the `python-dotenv` package to 
load in this information:

```angular2html
from dotenv import load_dotenv
import os

load_dotenv()
```

Once the credentials are in, you can load the desired module(s) and execute queries accordingly:

### Moz

*Note: All values of `params` must be lists. Strings are not accepted.*
```angular2html
from tk_apis import moz_calls as moz

moz.get_url_data(url='https://www.terakeet.com',
                 api_key=os.environ['MOZ_AUTH'],
                 params='monthly_history_deltas': ['domain_authority'],
                 'daily_history_deltas': ['domain_authority'])
```

### SemRush
```angular2html
from tk_apis import semrush_calls as sr

sr.get_related_keywords(keyword='babylonian empire', 
                        api_key=os.environ['SEMRUSH_KEY'])
sr.get_top_related_keyword(keyword='babylonian empire', 
                           api_key=os.environ['SEMRUSH_KEY'])

sr.get_keyword_data(keyword='babylonian empire', 
                    api_key=os.environ['SEMRUSH_KEY'])
sr.get_search_volume(keyword='sumerians', 
                     api_key=os.environ['SEMRUSH_KEY'])

sr.get_organic_results(keyword='babylonian empire',
                       api_key=os.environ['SEMRUSH_KEY'],
                       n=10)

sr.get_url_organic_kws(u='https://www.nordstrom.com',
                       api_key=os.environ['SEMRUSH_KEY'])

```

### SerpApi
```angular2html
from tk_apis import serpapi_calls as sa

serp = sa.get_serp_result(keyword='arnold schwarzenegger', 
                          api_key=os.environ['SERPAPI_KEY'])

sa.extract_organic_df(result_set=serp)
sa.extract_knowledge_graph(result_set=serp)
sa.extract_top_stories(result_set=serp)
sa.extract_related_searches(result_set=serp)
sa.extract_related_questions(result_set=serp)
```
jupyter-xprahtml5-proxy 0.3.5
Xpra for JupyterLab
Requires Python: >=3.6
Keywords: jupyter,xpra,jupyterhub,jupyter-server-proxy

![build](https://github.com/FZJ-JSC/jupyter-xprahtml5-proxy/workflows/build/badge.svg)

# jupyter-xprahtml5-proxy
Integrate Xpra in your Jupyter environment for an fast, feature-rich and easy to use remote desktop in the browser.

## Requirements
- Python 3.6+
- Jupyter Notebook 6.0+
- JupyterLab >= 3.x
- jupyter-server-proxy >= 3.1.0

This package executes the `xpra` command. This command assumes the `xpra` command is available in the environment's $PATH.

## Security
[Xpra](https://xpra.org/) supports password and encryption.  
Both are NOT required, if you can trust _everyone_, who can log in to the host where your JupyterLab is running.
(incl. [jupyter-server-proxy](https://github.com/jupyterhub/jupyter-server-proxy), [jupyter-xprahtml5-proxy](https://github.com/FZJ-JSC/jupyter-xprahtml5-proxy) and [Xpra-HTML5](https://xpra.org/))
  
**BUT (!!!)**  
if you are on a multiuser system, where you CANNOT trust everyone,  
you must(!) ensure that your Xpra session is secured by at least a password.

Currently this requires 
- that [jupyter-server-proxy](https://github.com/jupyterhub/jupyter-server-proxy) includes this [pull request](https://github.com/jupyterhub/jupyter-server-proxy/pull/226) and 
- you use jupyter-xprahtml5-proxy with a version number >= 0.3.0 (not on pypi)

### Xpra
[Xpra](https://xpra.org/) is an open-source multi-platform persistent *remote display* solution for forwarding applications and desktop screens. It allows you to run X11 programs, usually on a remote host, and direct their display to your local machine.  
Best of it. It is fast, fast, fast and comes with a build-in html5 client to allow remote.

### Jupyter-Server-Proxy
[Jupyter-Server-Proxy](https://jupyter-server-proxy.readthedocs.io) lets you run arbitrary external processes (such as Xpra-HTML5) alongside your notebook, and provide authenticated web access to them.

## Install 

#### Create and Activate Environment
```
virtualenv -p python3 venv
source venv/bin/activate
```

#### Install jupyter-xprahtml5-proxy
```
pip install git+https://github.com/FZJ-JSC/jupyter-xprahtml5-proxy.git
```

#### Enable jupyter-xprahtml5-proxy Extensions
For Jupyter Classic, activate the jupyter-server-proxy extension:
```
jupyter serverextension enable --sys-prefix jupyter_server_proxy
```

For Jupyter Lab, install the @jupyterlab/server-proxy extension:
```
jupyter labextension install @jupyterlab/server-proxy
jupyter lab build
```

#### Start Jupyter Classic or Jupyter Lab
Click on the Xpra icon from the Jupyter Lab Launcher or the Xpra item from the New dropdown in Jupyter Classic.  
Connect to your database as instructed in the Quickstart section.

## Configuration
This package calls `xpra` with a bunch of settings. Please read the [Xpra manual](https://xpra.org/manual.html) if you want to know the details.  
You have to modify `setup_xprahtml5()` in `jupyter_xprahtml5_proxy/__init__.py` for change.

## Credits
- Xpra
- jupyter-server-proxy

## License
BSD 3-Clause