semvec/pypi-packages huggingface dataset:
numerical-derivative-checker 0.8
Simple numerical derivative checker with randomized input sampling.
License: MIT
Requires Python: >=3.7

# Numerical Derivative Checker
This package contains a numericcal derivative checker that can be used to verify first or second order derivatives by 
comparing them to the central finite difference approximation.

The following combinations of input and output types are currently supported

| Derivative order    |        |  Output type |        |
| -------------  |:------:|:----------------:|:------:|
|                |        | _scalar_           | _vector_ |
|                | _scalar_ | first / second     | first  |
| **Input type** | _vector_ | first / second     | first  |
|                | _matrix_ | first            | ---  |

# Sampling types
In ```helper_functions.py```, there are different sampling routines defined to randomize the function inputs. 
The following samplers are currently implemented:
- ScalarSampler
- VectorSampler
- MatrixSampler
- PosDefMatrixSampler

Each sampling routine has various parameters such as minimum/maximum values or dimensionality.

# Examples
 
Scalar input and output: f(x) = x**2
```
from numerical_derivative_checker import check_derivative, ScalarSampler
check_derivative(sampler=ScalarSampler(n_samples=200),
                 fun=lambda x: np.sin(x),
                 grad=lambda x: np.cos(x),
                 hess=lambda x: -np.sin(x))
```

Vector input and scalar output: f(x) = x^T A x
```
from numerical_derivative_checker import check_derivative, VectorSampler
A = np.random.rand(5, 5)
check_derivative(sampler=VectorSampler(n=5),
                 fun=lambda x: x @ A @ x,
                 grad=lambda x: (A + A.T) @ x)
```


Pos. def. matrix input and scalar output: f(X) = log(det(X))
```
from numerical_derivative_checker import check_derivative, PosDefMatrixSampler
check_derivative(sampler=PosDefMatrixSampler(n=3),
                 fun=lambda x: np.linalg.det(x),
                 grad=lambda x: np.linalg.det(x) * np.linalg.inv(x),
                 tol=1e-3)
```

see ```examples.py``` for more examples.
custom-actions 0.0.1
Sample Package to test things
License: MIT
Requires Python: >=3.7

<p align="center">
  <a href="https://github.com/actions/typescript-action/actions"><img alt="typescript-action status" src="https://github.com/actions/typescript-action/workflows/build-test/badge.svg"></a>
</p>

# Create a JavaScript Action using TypeScript

Use this template to bootstrap the creation of a TypeScript action.:rocket:

This template includes compilation support, tests, a validation workflow, publishing, and versioning guidance.  

If you are new, there's also a simpler introduction.  See the [Hello World JavaScript Action](https://github.com/actions/hello-world-javascript-action)

## Create an action from this template

Click the `Use this Template` and provide the new repo details for your action

## Code in Main

> First, you'll need to have a reasonably modern version of `node` handy. This won't work with versions older than 9, for instance.

Install the dependencies  
```bash
$ npm install
```

Build the typescript and package it for distribution
```bash
$ npm run build && npm run package
```

Run the tests :heavy_check_mark:  
```bash
$ npm test

 PASS  ./index.test.js
  ✓ throws invalid number (3ms)
  ✓ wait 500 ms (504ms)
  ✓ test runs (95ms)

...
```

## Change action.yml

The action.yml contains defines the inputs and output for your action.

Update the action.yml with your name, description, inputs and outputs for your action.

See the [documentation](https://help.github.com/en/articles/metadata-syntax-for-github-actions)

## Change the Code

Most toolkit and CI/CD operations involve async operations so the action is run in an async function.

```javascript
import * as core from '@actions/core';
...

async function run() {
  try { 
      ...
  } 
  catch (error) {
    core.setFailed(error.message);
  }
}

run()
```

See the [toolkit documentation](https://github.com/actions/toolkit/blob/master/README.md#packages) for the various packages.

## Publish to a distribution branch

Actions are run from GitHub repos so we will checkin the packed dist folder. 

Then run [ncc](https://github.com/zeit/ncc) and push the results:
```bash
$ npm run package
$ git add dist
$ git commit -a -m "prod dependencies"
$ git push origin releases/v1
```

Note: We recommend using the `--license` option for ncc, which will create a license file for all of the production node modules used in your project.

Your action is now published! :rocket: 

See the [versioning documentation](https://github.com/actions/toolkit/blob/master/docs/action-versioning.md)

## Validate

You can now validate the action by referencing `./` in a workflow in your repo (see [test.yml](.github/workflows/test.yml))

```yaml
uses: ./
with:
  milliseconds: 1000
```

See the [actions tab](https://github.com/actions/typescript-action/actions) for runs of this action! :rocket:

## Usage:

After testing you can [create a v1 tag](https://github.com/actions/toolkit/blob/master/docs/action-versioning.md) to reference the stable and latest V1 action
probfit 1.2.0
Distribution Fitting/Regression Library
License: MIT
Requires Python: !=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7

.. -*- mode: rst -*-

probfit
=======

.. image:: https://img.shields.io/pypi/v/probfit.svg
   :target: https://pypi.python.org/pypi/probfit

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1477853.svg
   :target: https://doi.org/10.5281/zenodo.1477853

.. image:: https://github.com/scikit-hep/probfit/actions/workflows/main.yml/badge.svg
   :target: https://github.com/scikit-hep/probfit/actions/workflows/main.yml

*probfit* is a set of functions that helps you construct a complex fit. It's
intended to be used with `iminuit <http://iminuit.readthedocs.org/>`_. The
tool includes Binned/Unbinned Likelihood estimators, 𝝌² regression,
Binned 𝝌² estimator and Simultaneous fit estimator.
Various functors for manipulating PDFs such as Normalization and
Convolution (with caching) and various built-in functions
normally used in B physics are also provided.

Strict dependencies
-------------------

- `Python <http://docs.python-guide.org/en/latest/starting/installation/>`__ (2.7+, 3.5+)
- `NumPy <https://scipy.org/install.html>`__
- `iminuit <http://iminuit.readthedocs.org/>`_ (<2)

Optional dependencies
---------------------

- `matplotlib <http://matplotlib.org/>`_ for the plotting functions

Getting started
---------------

.. code-block:: python

    import numpy as np
    from iminuit import Minuit
    from probfit import UnbinnedLH, gaussian
    data = np.random.randn(10000)
    unbinned_likelihood = UnbinnedLH(gaussian, data)
    minuit = Minuit(unbinned_likelihood, mean=0.1, sigma=1.1)
    minuit.migrad()
    unbinned_likelihood.draw(minuit)

Documentation and Tutorial
--------------------------

* `Documentation <http://probfit.readthedocs.org/>`_
* The tutorial is an IPython notebook that you can view online
  `here <http://nbviewer.ipython.org/urls/raw.github.com/scikit-hep/probfit/master/tutorial/tutorial.ipynb>`_.
  To run it locally: `cd tutorial; ipython notebook --pylab=inline tutorial.ipynb`.
* Developing probfit: see the `development page <http://probfit.readthedocs.io/en/latest/development.html>`_

License
-------

The package is licensed under the `MIT <http://opensource.org/licenses/MIT>`_ license (open source).
SilverBotPackage 1.0.0
Package For SilverBot
License: MIT

# SilverBot
SilverBot Is A Unbranded Lobby Bot That Is Awesome For Trolling

# Info

For The Default Skin You Can [Join Our Discord](discord.gg/hmXa5cQCJH) Server And Do The Command ?item item name to get the ID of that cosmetic and put in the Settings.json where it goes

# Support

For Any More Support [Join Our Discord](discord.gg/hmXa5cQCJH)
AutoWebScraper 0.0.3
Automatically Scrape Websites.
License: MIT
Keywords: web scraper

AUTOWS is a library to automatically complete web-scraping related tasks such as:
-finding element(s) by class or ID
-finding elements by XPATH
-finding tables, paragraphs, headers, footers, headings, images, etc.
-finding images' source
-finding and returning images!

Change Log
==========

0.0.1 (3/10/2021)
-----------------

-First Release

=================

0.0.2 (3/10/2021)
-----------------

-I had to fix the "find_elements()"'s get_text parameter.

=================

0.0.3 (3/10/2021)
-----------------

-I had to fix the "get_images()" method. What other web scraping library offers such features?
stylizers 0.0.1
A Beautiful Library which will modify your Beautiful Name into an Extraordinary Styled Name.
License: MIT
Requires Python: >=3.0

# stylizers

### An Extraordinary Styling Library

This library is used to style your beautiful into an extraordinary styled name.

# Usage

To use this library the following sample code is given below

```
import stylizers

style_name = stylizers.stylo("Divyanshu")
print(style_name)
```

Here we've used the function `stylo()` which will styled your name.
It uses the default style. The parameter of this function is your name.

To customise your name the following sample code is given below:

```
import stylizers

custom_name = stylizers.styeriment("Divyanshu", "🐍", "🐯", "🏵️")
print(custom_name)
```

Here we've used the function `styeriment()`
The parameters of this function are:
* your_name
* emoji_inner: The Emoji which is used to style the background.
* emoji_center: The Emoji which is used to style your alphabet character.
* emoji_outer: The Emoji which is used to style the border.

**To experiment with the name style use `styeriment()` function and for default style use `stylo()` function.**

# Requirements

1. Python version should be 3.0+.
2. pip should be updated to the latest version. To upgrade pip use `pip install --upgrade pip`.

# Installation

To install this library in your PC you just have to follow the instructions given below:

1. First open your `command prompt` for Windows 10 users open your `PowerShell`.
2. It must that Python is installed in your PC and Path variable in the environment.
3. To install it write `pip install stylizers` in your command prompt. It takes few seconds in the installation process.

Bravo, we've installed this library in our PC.
Now, just go and play with it in your `Python IDE`.

> Python is a very beautiful, easy and powerful language that anyone could understand.
> Mastering in Python is much easier than other languages.

# GitHub Links

#### My Profile Link
[Divyanshu Singh](https://github.com/DivyanshuSingh96)

#### Repository Link
[stylizers](https://github.com/DivyanshuSingh96/stylizers)

Made with 🐍 by Divyanshu Singh
SilverBotSetup 1.0.0
Package For SilverBot
License: MIT

# SilverBot
SilverBot Is A Unbranded Lobby Bot That Is Awesome For Trolling

# Info

For The Default Skin You Can [Join Our Discord](discord.gg/hmXa5cQCJH) Server And Do The Command ?item item name to get the ID of that cosmetic and put in the Settings.json where it goes

# Support

For Any More Support [Join Our Discord](discord.gg/hmXa5cQCJH)