semvec/pypi-packages huggingface dataset:
ellipsefitting 1.0
Least squares fitting of Ellipses
License: MIT

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3723294.svg)](https://doi.org/10.5281/zenodo.3723294)
[![bdhammel](https://circleci.com/gh/bdhammel/least-squares-ellipse-fitting.svg?style=shield)](https://app.circleci.com/pipelines/github/bdhammel/least-squares-ellipse-fitting)


# Least Squares fitting of ellipses, python routine 

based on the  publication 
[Halir, R., Flusser, J.: 'Numerically Stable Direct Least Squares 
            Fitting of Ellipses'](./media/WSCG98.pdf)

## Install 

```bash
pip install lsq-ellipse
```

[https://pypi.org/project/lsq-ellipse/](https://pypi.org/project/lsq-ellipse/)

## Example execution

```python
import numpy as np
from ellipse import LsqEllipse
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse

if __name__ == '__main__':
    # avalible in the `example.py` script in this repo
    X1, X2 = example.make_test_ellipse()

    X = np.array(list(zip(X1, X2)))
    reg = LsqEllipse().fit(X)
    center, width, height, phi = reg.as_parameters()

    print(f'center: {center[0]:.3f}, {center[1]:.3f}')
    print(f'width: {width:.3f}')
    print(f'height: {height:.3f}')
    print(f'phi: {phi:.3f}')

    fig = plt.figure(figsize=(6, 6))
    ax = plt.subplot()
    ax.axis('equal')
    ax.plot(X1, X2, 'ro', zorder=1)
    ellipse = Ellipse(
        xy=center, width=2*width, height=2*height, angle=np.rad2deg(phi),
        edgecolor='b', fc='None', lw=2, label='Fit', zorder=2
    )
    ax.add_patch(ellipse)

    plt.xlabel('$X_1$')
    plt.ylabel('$X_2$')

    plt.legend()
    plt.show()
```

![ellipse fit](./media/ellipse_fit.png)


**Cite this work**
```
@software{ben_hammel_2020_3723294,
  author       = {Ben Hammel and Nick Sullivan-Molina},
  title        = {bdhammel/least-squares-ellipse-fitting: v2.0.0},
  month        = mar,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v2.0.0},
  doi          = {10.5281/zenodo.3723294},
  url          = {https://doi.org/10.5281/zenodo.3723294}
}
```
Ben Hammel, & Nick Sullivan-Molina. (2020, March 21). bdhammel/least-squares-ellipse-fitting: v2.0.0 (Version v2.0.0). Zenodo. http://doi.org/10.5281/zenodo.3723294
dorkscan 0.1.1
Automate sending dorked queries to search engine
License: MIT
Keywords: dorking,google dork,web parser,web scraping

# DorkScan

An atypical search engine scanner that scans search engines with dorked queries returning potentially vulnerable URLs.

## Installation

### PyPI

`pip install dorkscan`

### Source

> *If using a virtual environment pass the full directory path to where you cloned DorkScan*

```sh
$git clone https://gitlab.com/unkwn1/dorkscan.git
$cd dorkscan
$pip install -e dorkscan
```

## DorkScan Function

> Scrape a search engine result page for (potentially) vulnerably sites.

```python
def DorkScan(
    dorks: list[str],
    search_engine: str="BING",
    pages: int=1,
    console: Console=Console()
    ) -> list[ScanResults]
```

## Usage

To view an example of the function in action run:
 `python3 -m dorkscan`

```python
from dorkscan import DorkScan
results = DorkScan(
    search_engine: str="BING",
    pages: int=1,
    dorks: list = ["inurl:.php?", "inurl:admin.php"]
    )
print(results)
```

### Args

#### Required Args

```sh
dorks: list[str] -
    a list of string dork queries ie. "inurl:.php?=","inurl:admin.php"
search_engine: str -
    Bing / Ask / Wow.
```

#### Optional Args

```sh
pages: int -
    Number of search engine result pages to scan.
silent: bool -
    Disable stdout printing
console: Console -
    [rich](https://rich.readthedocs.io/en/stable/reference/console.html) console object for printing.
```

#### Return

```sh
`list[ScanResults]` -
    list item for each page. Each page's result dictionary contains `{"dork": str, "links": list}`
```