semvec/pypi-packages huggingface dataset:
tlsfeatmark 0.1
a benchmark tool for Joy and Zeek

# TLSfeatmark

### What is Tlsfeatmark
```Tlsfeatmark``` is a benchmark tool for TLS analytics using **Joy** and **Zeek**. It generates
nice JSON output on several statistics for each pcap and all pcaps analyzed:
 - the number of TCP stream found
 - the number of TLS stream found
 - the number of certificates found
 - the elapsed time of analysis
 
### Sample output

```
===== Summary =====
{
    "cpu": "11th Gen Intel(R) Core(TM) i7-11700K @ 3.60GHz",
    "os": "Linux 4.18.0-348.2.1.el8_5.x86_64",
    "time": "2022-06-27 13:43:03",
    "joy": {
        "job": {
            "tool": "joy",
            "pcap_path": "/home/dev/tlsfeatmark/pcaps/small_pcaps",
            "pcap_num": 5,
            "tls_total": 323,
            "cert_total": 294,
            "elapsed_total": 0.32
        },
        "task": [
            {
                "name": "2021-01-13-Emotet-epoch-2-infection-traffic-with-Trickbot-gtag-mor13-2.pcap",
                "tls_num": 46,
                "cert_num": 78,
                "elapsed": 0.06
            },
            {
                "name": "2021-01-04-Emotet-infection-with-Trickbot-traffic.pcap",
                "tls_num": 10,
                "cert_num": 10,
                "elapsed": 0.04
            }
            ... # skip several other tasks
        ]
    },
    "zeek": {
        "job": {
            "tool": "zeek",
            "pcap_path": "/home/dev/tlsfeatmark/pcaps/small_pcaps",
            "pcap_num": 5,
            "tls_total": 323,
            "cert_total": 477,
            "elapsed_total": 1.06
        },
        "task": [
            {
                "name": "2021-01-13-Emotet-epoch-2-infection-traffic-with-Trickbot-gtag-mor13-2.pcap",
                "tls_num": 46,
                "cert_num": 84,
                "elapsed": 0.23
            },
            {
                "name": "2021-01-04-Emotet-infection-with-Trickbot-traffic.pcap",
                "tls_num": 10,
                "cert_num": 15,
                "elapsed": 0.17
            },
            ...        
            # skip several other tasks
        ]
    }
}

```


### Environment
```Tlsfeatmark``` relies on Joy and Zeek, and they work well on Linux and Mac OSX.

 - Linux: Centos8/Ubuntu20.04, tested
 - Mac: x86/M1, tested 
 - Windows: untested 

### How to install 

1. Install Joy

see Joy [official documentation](https://github.com/cisco/joy/wiki/Installation) for installation.

2. Install Zeek

see Zeek [official documentation](https://docs.zeek.org/en/master/install.html) for installation.

3. Install tlsfeatmark

```pip install tlsfeatmark```

### How to use
```Tlsfeatmark``` is easy to use once Joy and Zeek are installed.

1. Configure ```pcap_path``` in ```config.txt```

```pcap_path``` is the pcap file or dir containing pcaps to be analyzed.

```pcap_path``` supports absolute and relative path. For relative path (relative to ```main.py```), use ```./``` as prefix, for example, ```./pcaps/small_pcaps```.

2. Run ```main.py```

3. View results in ```output``` folder.

### License
```Tlsfeatmark``` is under MIT license, see LICENSE for more information.
kvv-StoredObjects 0.0.16
StoredObjects is a helper package with University objects for the Publications Activity Module
Requires Python: >=3.6

# StoredObjects

 This object is needed to save, process and transfer data about University objects for 
 the **Publications Activity Module**.

 Objects used in this module:
 - Publication - stores bibliographic data about a publication (title, published date, authors, citations, identifiers in databases)
 - Author - stores data about a person with publications (names in databases, affiliations, publications)
 - Department - stores information about department name and list of employees in it
 - University - stores list of departments
triplerecovery2 0.0.1
Triple Recovery paper implementation in python

# triple-recovery
A paper implementation in numpy python

# Requirements
- Python >= 3.7

# Installation
```bash
pip3 install triplerecovery
```
## Auto Installed
-  numpy >=1.22.4
-  opencv-python >=4.6.0.66

# Usage
## Embedding
```py
import cv2
import triplerecovery as trir

# image can be rgb or grey
# grey must have two dimetions so add cv2.
# if you know the image is grey then use cv2.IMREAD_GRAYSCALE

imarr=cv2.imread("<your image path>")
embeded_image=trir.embed(imarr).imarr
```
## Recovery
```py
import cv2
import triplerecovery as trir

imarr=cv2.imread("<your embeded image path>")
recovered_image=trir.recover(imarr).imarr
# OR for changed interpolation
recovered_image=trir.recover(imarr, interpolation = cv2.INTER_CUBIC).imarr
```
triplerecovery 0.0.8
Triple Recovery paper implementation in python

# triple-recovery
A paper implementation in numpy python

# Requirements
- Python >= 3.7

# Installation
```bash
pip3 install triplerecovery
```
## Auto Installed
-  numpy >=1.22.4
-  opencv-python >=4.6.0.66

# Usage
## Embedding
```py
import cv2
import triplerecovery as trir

# image can be rgb or grey
# grey must have two dimetions so add cv2.
# if you know the image is grey then use cv2.IMREAD_GRAYSCALE

imarr=cv2.imread("<your image path>")
embeded_image=trir.embed(imarr).imarr
```
## Recovery
```py
import cv2
import triplerecovery as trir

imarr=cv2.imread("<your embeded image path>")
recovered_image=trir.recover(imarr).imarr
# OR for changed interpolation
recovered_image=trir.recover(imarr, interpolation = cv2.INTER_CUBIC).imarr
```
ruth-tts-converter-python 0.0.2
A Python CLI for Ruth NLP
Requires Python: >=3.7,<3.9

# To use this code:

**"Use it in the GPU inference"**
https://colab.research.google.com/drive/1aUaZiGLz22r4qUhAgaDcrKa_C5JxqkfX?usp=sharing
ruth-tts-converter 0.0.2
A Python CLI for Ruth NLP
Requires Python: >=3.7,<3.9

# To use this code:

**"Use it in the GPU inference"**
https://colab.research.google.com/drive/1aUaZiGLz22r4qUhAgaDcrKa_C5JxqkfX?usp=sharing
ruth-tts-transformer 0.0.7
A Python CLI for Ruth NLP
Requires Python: >=3.7,<3.9

# To use this code:

**"Use it in the GPU inference"**
https://colab.research.google.com/drive/1aUaZiGLz22r4qUhAgaDcrKa_C5JxqkfX?usp=sharing
mirrorlib 0.1.0
A generic specification for mirroring data
Requires Python: >=3.10,<4.0

# mirrorlib

mirrorlib is a flat, generic specification for tracking urls and files together. The strength of mirrorlib is its simplicity and ability to track any type of data. It is designed to make data verification and integrity easy and provide all the data upfront to manage your mirror. mirrorlib is not designed to be the *fastest* mirror specification, but to be as portable and simple as possible. This allows many developers to quickly develop on top of the code and have the flexibility to use the data as they please.

mirrorlib was inspired by [RedoxOS](https://web.archive.org/web/20220521191003/https://doc.redox-os.org/book/ch04-10-everything-is-a-url.html) and their philosophy of "everything is a URL". In mirrorlib everything is a URI that is tracked together in one place.

mirrorlib is not specific to any type of database and can be implemented using any language, you could even implement it as a JSON file if you wanted to. The reference implementation is currently implemented using python and sqlite. It's not required, but all data is usually stored in a single table called 'mirror'. A mirror contains only URIs and their associated metadata as text.

## Getting Started

```
pip install mirrorlib
```

## Documentation



## Golden rules

- Everything is a URI
- Use text as much as possible
- Keep data simple

## Contributing

## TODO

The specification and reference implementation is still being developed and any suggestions and issues are welcome.

The longterm goal is to implement the reference specification in Rust and use Python bindings instead. For the time being

## History

mirrorlib was originally created to be used in another archiving project, youmirror. Youmirror is a tool developed in early 2022 for downloading and archiving media, originally Youtube videos. However, I'm always looking for ways that components of larger projects can be broken down into modules that may be helpful more broadly. I am hoping that mirrorlib will be used to archive all kinds of data and inspire even better projects in the future!
title-checker 0.9
A Python script for checking the titles of each youtube links in a text file

# Youtube Links Title Checker 🔎

A Python script for checking the titles of each links in a text file and logging it in another file :)

_it may not be useful for you but for me it was a good help_ 💡

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

## 🔹Features

- Using [Selenium](https://selenium-python.readthedocs.io/) for scraping the titles of the links
- Using [typer](https://typer.tiangolo.com/) and [questionary](https://questionary.readthedocs.io/en/stable/) for CLI
- You can choose between `headless` and `normal` mode for browser behaviour
- Creates a new file with the date for name to log the results
- You can easily give it any file that contains several links that are unknown to you

## 🔹Installation

```bash
pip install --upgrade title_checker
```

## 🔹Usage

- **CLI:**

    ```bash
     title_checker start
    ```

- **In your python script:**

    ```python
    from title_checker.title_checker import checker
    checker('PATH_TO_YOUR_URL_LIST')
    ```

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

### Any contribution is welcomed! 🤞
rpc-rabbitmq 0.0.4
RPC Server/Client implementation for RabbitMQ
Requires Python: >=3.7

# RabbitMQ RPC Implementation

A RPC Server and Client implementation working with RabbitMQ.

## RabbitRpc

Base class that both the server and client implementations utilize. Handles basic connection logic, reconnection, starting and stopping of the ioloop and standup of channels.

## RabbitRpcServer

Inherits from `RabbitRpc` and provides a way to respond to incoming requests. Expects to be passed in an array of methods (inherited from `RpcBaseMethod`) that handles the requests.

## RabbitRpcClient

Inherits from `RabbitRpc` and provides the framework to send requests and wait for responses. Handles multiple requests at a time and relaying back to the caller. Depends on using Requests inherited from `RpcBaseRequest`

## RpcBaseMethod

Works with `RabbitRpcServer` and handes the logic needed to respond to a given request. New methods inherit from this to provide needed functionality. Methods inheriting from this, need to set the name of the queue, typically tied to method name, that they listen to.

```python
from json import dumps
from RpcBaseMethod import RpcBaseMethod
class HelloWorld(RpcBaseMethod):

    def __init__(self):
        super().__init__()
        self.queue = 'hello_world'

    def method(self, channel, pika_method, props, body):
        self._logger.info('Response Received')
        self.response = dumps({'msg': 'Hello World'})
        super().method(channel, pika_method, props, body)
```

The above is an example method. It is listening for requests on the `hello_world` queue and responding back with hello world

#### Example

Below is an example script for running a server to respond to `HelloWorld`
```python
from RabbitRpc import RabbitRpcServer
from HelloWorld import HelloWorld

methods = [HelloWorld()]

server = RabbitRpcServer('testrpc', methods, 'localhost')

print('Starting Server...')

try:
   server.start()
except:
   server.close()
```

An example client script is Below

```python
from RabbitRpc import RabbitRpcClient
import threading
from RequestHelloWorld import RequestHelloWorld
import time

def main(client):
    request = RequestHelloWorld(client)
    while not client.ready:
        time.sleep(0.2)
        print('waiting to start...')
    print('Making Request...')
    request.make_request()

if __name__ == '__main__':

    client = RabbitRpcClient('testrpc', 'localhost')

    print('Starting Client...')
    try:
        cl_thread = threading.Thread(target=main, args=[client])
        cl_thread.start()
        client.start()
    except (RuntimeError, RuntimeWarning, KeyboardInterrupt, Exception) as e:
        print(e)
        client.close()
```
This defines a process to run to send requests and starts up the client main io loop. It sends a request for `HelloWorld` and prints the response once received. 

In order to run these examples, be sure to replace `localhost` with the url of the broker being used.
rpc-rabbit 0.0.3
RPC Server/Client implementation for RabbitMQ
Requires Python: >=3.7

# RabbitMQ RPC Implementation

A RPC Server and Client implementation working with RabbitMQ.

## RabbitRpc

Base class that both the server and client implementations utilize. Handles basic connection logic, reconnection, starting and stopping of the ioloop and standup of channels.

## RabbitRpcServer

Inherits from `RabbitRpc` and provides a way to respond to incoming requests. Expects to be passed in an array of methods (inherited from `RpcBaseMethod`) that handles the requests.

## RabbitRpcClient

Inherits from `RabbitRpc` and provides the framework to send requests and wait for responses. Handles multiple requests at a time and relaying back to the caller. Depends on using Requests inherited from `RpcBaseRequest`

## RpcBaseMethod

Works with `RabbitRpcServer` and handes the logic needed to respond to a given request. New methods inherit from this to provide needed functionality. Methods inheriting from this, need to set the name of the queue, typically tied to method name, that they listen to.

```python
from json import dumps
from RpcBaseMethod import RpcBaseMethod
class HelloWorld(RpcBaseMethod):

    def __init__(self):
        super().__init__()
        self.queue = 'hello_world'

    def method(self, channel, pika_method, props, body):
        self._logger.info('Response Received')
        self.response = dumps({'msg': 'Hello World'})
        super().method(channel, pika_method, props, body)
```

The above is an example method. It is listening for requests on the `hello_world` queue and responding back with hello world

#### Example

Below is an example script for running a server to respond to `HelloWorld`
```python
from RabbitRpc import RabbitRpcServer
from HelloWorld import HelloWorld

methods = [HelloWorld()]

server = RabbitRpcServer('testrpc', methods, 'localhost')

print('Starting Server...')

try:
   server.start()
except:
   server.close()
```

An example client script is Below

```python
from RabbitRpc import RabbitRpcClient
import threading
from RequestHelloWorld import RequestHelloWorld
import time

def main(client):
    request = RequestHelloWorld(client)
    while not client.ready:
        time.sleep(0.2)
        print('waiting to start...')
    print('Making Request...')
    request.make_request()

if __name__ == '__main__':

    client = RabbitRpcClient('testrpc', 'localhost')

    print('Starting Client...')
    try:
        cl_thread = threading.Thread(target=main, args=[client])
        cl_thread.start()
        client.start()
    except (RuntimeError, RuntimeWarning, KeyboardInterrupt, Exception) as e:
        print(e)
        client.close()
```
This defines a process to run to send requests and starts up the client main io loop. It sends a request for `HelloWorld` and prints the response once received. 

In order to run these examples, be sure to replace `localhost` with the url of the broker being used.
sportstat 2.0.0
Package for scraping soccer data from a variety of sources.
Requires Python: >=3.6
Keywords: soccer,football,Premier League,Serie A,La Liga,Bundesliga,Ligue 1,web scraping,data

This is sportstat, a Python package that I hope will give more people access to soccer data. Gone are the days of downloading spreadsheets one-by-one, copy-pasting, or entering data into spreadsheets by hand. I try to make sportstat as easy-to-use as possible so that anybody with a bit of Python experience can use it.

To install sportstat, run ```pip install sportstat``` from the command line.

Data can be scraped from the following sources:
* [FBRef](https://fbref.com/en/)
    * Match and seasonal data for players and teams
* [Understat](https://understat.com/)
    * Match and seasonal data for players and teams
* [FiveThirtyEight](https://projects.fivethirtyeight.com/soccer-predictions/)
    * Several FiveThirtyEight metrics on a match level (SPI, win probabilities, xG, NSxG, and more)
* [ClubELO](http://clubelo.com/)
    * ELO scores for teams on a specific date
* [Capology](https://www.capology.com/)
    * Player salary data
* [Transfermarkt](https://www.transfermarkt.us/)
    * Market value, transfer history, market value history, and more

For documentation, head over to the [GitHub's wiki page](https://github.com/oseymour/sportstat/wiki)

For usage examples, look at Examples.ipynb in the code directory or some of my example analyses in the analytics_examples folder.

I'd love to hear your feedback, bugs you find, or new features you want! You can reach me via email at osmour043@gmail.com or my Twitter handle is [@owen_seymour](https://twitter.com/owen_seymour).
InstallIt 1.5.0
Insert project description here
Requires Python: >=3.6

.. image:: https://img.shields.io/pypi/status/InstallIt
    :alt: PyPI - Status

.. image:: https://img.shields.io/pypi/wheel/InstallIt
    :alt: PyPI - Wheel

.. image:: https://img.shields.io/pypi/pyversions/InstallIt
    :alt: PyPI - Python Version

.. image:: https://img.shields.io/github/v/release/hendrikdutoit/InstallIt
    :alt: GitHub release (latest by date)

.. image:: https://img.shields.io/github/license/hendrikdutoit/InstallIt
    :alt: License

.. image:: https://img.shields.io/github/issues-raw/hendrikdutoit/InstallIt
    :alt: GitHub issues

.. image:: https://img.shields.io/pypi/dm/InstallIt
    :alt: PyPI - Downloads

.. image:: https://img.shields.io/github/search/hendrikdutoit/InstallIt/GitHub
    :alt: GitHub Searches

.. image:: https://img.shields.io/codecov/c/gh/hendrikdutoit/InstallIt
    :alt: CodeCov
    :target: https://app.codecov.io/gh/hendrikdutoit/InstallIt

.. image:: https://img.shields.io/github/workflow/status/hendrikdutoit/InstallIt/Pre-Commit
    :alt: GitHub Actions - Pre-Commit
    :target: https://github.com/hendrikdutoit/InstallIt/actions/workflows/pre-commit.yaml

.. image:: https://img.shields.io/github/workflow/status/hendrikdutoit/InstallIt/CI
    :alt: GitHub Actions - CI
    :target: https://github.com/hendrikdutoit/InstallIt/actions/workflows/ci.yaml

.. image:: https://img.shields.io/pypi/v/InstallIt
    :alt: PyPi

Bright Edge eServices installation utilities

    Utilities for installing a Linux machine.

=======
Testing
=======

This project uses ``pytest`` to run tests and also to test docstring examples.

Install the test dependencies.

.. code-block:: bash

    $ pip install -r requirements_test.txt

Run the tests.

.. code-block:: bash

    $ pytest tests
    === XXX passed in SSS seconds ===

==========
Developing
==========

This project uses ``black`` to format code and ``flake8`` for linting. We also support ``pre-commit`` to ensure these have been run. To configure your local environment please install these development dependencies and set up the commit hooks.

.. code-block:: bash

    $ pip install black flake8 pre-commit
    $ pre-commit install

=========
Releasing
=========

Releases are published automatically when a tag is pushed to GitHub.

.. code-block:: bash

    # Set next version number
    export RELEASE = x.x.x
    
    # Create tags
    git commit --allow -empty -m "Release $RELEASE"
    git tag -a $RELEASE -m "Version $RELEASE"
    
    # Push
    git push upstream --tags
idom-router 0.0.1
A URL router for IDOM
Requires Python: >=3.7
Keywords: idom,components

# idom-router

A URL router for IDOM

# Installation

Use `pip` to install this package:

```bash
pip install idom-router
```

For a developer installation from source be sure to install [NPM](https://www.npmjs.com/) before running:

```bash
git clone https://github.com/idom-team/idom-router
cd idom-router
pip install -e . -r requirements.txt
```

# Running the Tests

To run the tests you'll need to install [Chrome](https://www.google.com/chrome/). Then you
can download the [ChromeDriver](https://chromedriver.chromium.org/downloads) and add it to
your `PATH`. Once that's done, simply `pip` install the requirements:

```bash
pip install -r requirements.txt
```

And run the tests with `pytest`:

```bash
pytest tests
```

You can run the tests in headless mode (i.e. without opening the browser):

```bash
pytest tests
```

You'll need to run in headless mode to execute the suite in continuous integration systems
like GitHub Actions.

# Releasing This Package

To release a new version of idom-router on PyPI:

1. Install [`twine`](https://twine.readthedocs.io/en/latest/) with `pip install twine`
2. Update the `version = "x.y.z"` variable in `idom-router/__init__.py`
3. `git` add the changes to `__init__.py` and create a `git tag -a x.y.z -m 'comment'`
4. Build the Python package with `python setup.py sdist bdist_wheel`
5. Check the build artifacts `twine check --strict dist/*`
6. Upload the build artifacts to [PyPI](https://pypi.org/) `twine upload dist/*`

To release a new version of `idom-router` on [NPM](https://www.npmjs.com/):

1. Update `js/package.json` with new npm package version
2. Clean out prior builds `git clean -fdx`
3. Install and publish `npm install && npm publish`