semvec/pypi-packages huggingface dataset:
unpackqa 0.2.1
QA bit unpacking for Landsat, MODIS, Sentinal, VIIRS, and more.
License: MIT
Requires Python: >=3.6.0


# unpackqa
Methods for unpacking, labelling and masking all common remote sensing QA bands which are stored in bit-wise values.  

- Works with single QA values, 1D arrays (eg. time series), or 2D arrays (eg. full scenes).  
- Get a QA mask in a single line of code: `unpackqa.unpack_to_array(qa,'LANDSAT_8_C2_L2_QAPixel','Cloud')`
- The same methods are used for all sensors, with specific product flags specified via arguments.  
- No file reading or writing, everything is handled as pre-loaded arrays.  
- Requires python 3.6+, with numpy and pyyaml as the only dependencies.  

## Installation

Install via pip:

```
pip install unpackqa
```

## Documentation
[https://sdtaylor.github.io/unpackqa](https://sdtaylor.github.io/unpackqa)
kernelthinning 0.0.9
Tools to compress distributions more effectively than i.i.d. sampling
License: MIT

# Kernel Thinning
This Python package implements **kernel thinning**, a tool for compressing distributions while maintaining 
better-than-Monte Carlo integration error uniformly across a reproducing kernel Hilbert space.

More details on kernel thinning can be found in the manuscript [Kernel Thinning](https://arxiv.org/pdf/2105.05842.pdf).

```
@article{dwivedi2021kernel,
  title={Kernel Thinning},
  author={Raaz Dwivedi and Lester Mackey},
  journal={arXiv preprint arXiv:2105.05842},
  year={2021}
}
```

## Installation
To install the `kernelthinning` package, use the following pip command:
```
pip install kernelthinning
```

## Getting started
The primary kernel thinning function is `thin` in the `kt` module:
```python
from kernelthinning import kt
coreset = kt.thin(X, m, split_kernel, swap_kernel, delta=0.5, seed=123, store_K=False)
    """Returns kernel thinning coreset of size floor(n/2^m) as row indices into X
    
    Args:
      X: Input sequence of sample points with shape (n, d)
      m: Number of halving rounds
      split_kernel: Kernel function used by KT-SPLIT (typically a square-root kernel, krt);
        split_kernel(y,X) returns array of kernel evaluations between y and each row of X
      swap_kernel: Kernel function used by KT-SWAP (typically the target kernel, k);
        swap_kernel(y,X) returns array of kernel evaluations between y and each row of X
      delta: Run KT-SPLIT with constant failure probabilities delta_i = delta/n
      seed: Random seed to set prior to generation; if None, no seed will be set
      store_K: If False, runs O(nd) space version which does not store kernel
        matrix; if True, stores n x n kernel matrix
    """
```
For example uses, please refer to the notebook `run_kt_experiment.ipynb`.

## Reproducing paper vignettes

1. The script `submit_jobs_run_kt.py` reproduces the vignette experiments of [Kernel Thinning](https://arxiv.org/pdf/2105.05842.pdf) on a Slurm cluster
by executing `run_kt_experiment.ipynb` with appropriate parameters. For the MCMC examples, it assumes that necessary data was downloaded and pre-processed following the steps listed in `PreProcess_MCMC_Data.ipynb`.
2. After all results have been generated, the notebook `plot_results.ipynb` can be used to reproduce the figures of [Kernel Thinning](https://arxiv.org/pdf/2105.05842.pdf).
nemesis-scan 1.0.4
A tool for scanning urls
License: MIT
Requires Python: >=3.8
Keywords: tool,url,scan,hacking

# Nemesis
![Nemesis](pics/Nemesis.png)
[![Code Grade](https://www.code-inspector.com/project/15087/status/svg)](https://frontend.code-inspector.com/public/project/15087/Nemesis/dashboard)
[![Code Quality](https://www.code-inspector.com/project/15087/score/svg)](https://frontend.code-inspector.com/public/project/15087/Nemesis/dashboard)

## Description
A simple tool for scanning urls for vulnerabilites and sensitive information. It has lot of pre defined regexes for dom xss possibility detection, secrets leakage, hidden parameters, extra links and much more. 

## Features
1. Supports scanning of both html and javascript urls
2. Pre defined regexes for dom xss (sinks & sources), web services, hidden parameters, endpoints and a lot more are already present. 
3. Shannon entropy helps to find additional suspicious data that can be missed by regexes but may generate false positive so disabled by default.

## Usage
```
usage: Nemesis.py [-h] [--- | -w WORDLIST | -u URL] [-o OUTPUT] [-e] [-t THREADS] [-b]

Nemesis

optional arguments:
  -h, --help            show this help message and exit
  ---, ---              Stdin
  -w WORDLIST, --wordlist WORDLIST
                        Absolute path of wordlist
  -u URL, --url URL     url to scan
  -o OUTPUT, --output OUTPUT
                        Output file
  -e, --enable-entropy  Enable entropy search
  -t THREADS, --threads THREADS
                        Number of threads
  -b, --banner          Print banner and exit

Enjoy bug hunting
```

## Example
1. Scan a single url
 - ```Nemesis -u google.com``` or ```Nemesis -u https://google.com/closurelibrary.js```
2. Scan from URLs
 - ```Nemesis -w hakrawler.txt```

## Limitations
* Output maybe repeated such as same links again and again
* Output to file saving is in work
* Additional logical errors and false positivies from faulty regex

## Support
If you wanted to support me freely for the tools I create, chekout [this out](https://github.com/machinexa2/machinexa2/blob/master/SUPPORT.md).
epippy 0.0.2
Expansion Planning Input Preprocessing in Python
License: MIT
Requires Python: >=3.7

<a href="https://www.montefiore.uliege.be/"><img src="https://www.montefiore.uliege.be/upload/docs/image/svg-xml/2019-04/montefiore_institute.svg" alt="University of Liège - Montefiore institute" width="230px"></a>

# EPIPPy
Expansion Planning Input Preprocessing in Python

EPIPPy provides a set of functions and scripts to preprocess data that can be then used in an expansion planning model.

## Current capabilities

(Retrieval), preprocessing and management of:
- <a href="https://github.com/montefesp/epippy/tree/master/epippy/generation">generation</a> data (hydro capacities and flows, RES existing capacities, capacity factors, potentials, ...)
- <a href="https://github.com/montefesp/epippy/tree/master/epippy/geographics">geographical</a> data (computation of countries and sub-regions shapes, manipulation of points, codes, ...)
- <a href="https://github.com/montefesp/epippy/tree/master/epippy/indicators/population">population</a> data
- <a href="https://github.com/montefesp/epippy/tree/master/epippy/indicators/emissions">emissions</a> (annual emission computation)
- <a href="https://github.com/montefesp/epippy/tree/master/epippy/load">load</a> (yearly and hourly load computation)
- <a href="https://github.com/montefesp/epippy/tree/master/epippy/technologies">technologies</a> (costs and parameters)
- <a href="https://github.com/montefesp/epippy/tree/master/epippy/topologies">topologies</a>

## Data

For EPIPPy to work properly, it has to have access to a database following a certain structure. This database can be downloaded <a href="ttps://zenodo.org/record/5519081">here</a>.

After downloading the database, the path to the folder containing it must be specified in epippy/\_\_init\_\_.py (to be updated).

## Dependencies
`To be complented`

## Installation

1. Clone git
2. Add epippy to your PYTHONPATH