This is a Python Module For Encryption, Hashing And Other stuff

Related tags

Deep Learningpythonencryptionmodulepiphashing-libraryhashing-algorithmencryption-decryptionencryption-libraryfileencrtption
Overview

EnroCrypt

This is a Python Module For Encryption, Hashing And Other Basic Stuff You Need, With Secure Encryption And Strong Salted Hashing You Can Do Whatever You Want To
Downloads

Docs

Installation

EnroCrypt Is Avaliable On PyPi You Can Install It As Follows:
pip install -U enrocrypt

Manual Installation

If For Some Reason You Can't Install EnroCrypt From PyPi You Can Download It Manually Too:

  • Clone This Repo
  • Cut-Paste This Repo In Your Python Scripts Path
  • Enter In The Folder Where You See "setup.py" file
  • shift+right click in the Folder And Click On "Open Powershell window Here"
  • Type python setup.py install
    After Following All The Steps Mentioned Above (If You Don't Get An Error) EnroCrypt Is Installed, Now You Can Import It Right Away

Features

  • Strong Encryption
  • Strong Salted Hashing
  • File Encryption-Decryption
  • Some Basic Functions

Usage

    # For Encryption
    import enrocrypt
    value = enrocrypt.encryption.Encrypt(b'text')
    print(value)
    # For Decryption
    original_value = enrocrypt.encryption.DecryptList(value)
    print(original_value)

There Is Also a DecryptFunction, But In This Function You Have To Enter The Key And The Data Seperatly

(See Discussion For More Info)

You might also like...
TreeSubstitutionCipher - Encryption system based on trees and substitution

Tree Substitution Cipher Generation Algorithm: Generate random tree. Tree nodes

stepa 1 Jan 8, 2022
计算机视觉中用到的注意力模块和其他即插即用模块PyTorch Implementation Collection of Attention Module and Plug&Play Module

PyTorch实现多种计算机视觉中网络设计中用到的Attention机制，还收集了一些即插即用模块。由于能力有限精力有限，可能很多模块并没有包括进来，有任何的建议或者改进，可以提交issue或者进行PR。

PJDong 599 Dec 23, 2022
Implementation of Invariant Point Attention, used for coordinate refinement in the structure module of Alphafold2, as a standalone Pytorch module
Implementation of Invariant Point Attention, used for coordinate refinement in the structure module of Alphafold2, as a standalone Pytorch module

Invariant Point Attention - Pytorch Implementation of Invariant Point Attention as a standalone module, which was used in the structure module of Alph

Phil Wang 113 Jan 5, 2023
We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.
We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering Papers with code | Paper Nikola Zubić Pietro Lio University

Nikola Zubić 213 Dec 27, 2022
Snapchat-filters-app-opencv-python - Here we used opencv and other inbuilt python modules to create filter application like snapchat
Snapchat-filters-app-opencv-python - Here we used opencv and other inbuilt python modules to create filter application like snapchat

Snapchat like filter App using opencv python Backend : opencv and python Library

null 2 Jul 19, 2022
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

CatBoost 6.9k Jan 4, 2023
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

CatBoost 5.7k Feb 12, 2021
PyMove is a Python library to simplify queries and visualization of trajectories and other spatial-temporal data
PyMove is a Python library to simplify queries and visualization of trajectories and other spatial-temporal data

Use PyMove and go much further Information Package Status License Python Version Platforms Build Status PyPi version PyPi Downloads Conda version Cond

Insight Data Science Lab 64 Nov 15, 2022
BBB streaming without Xorg and Pulseaudio and Chromium and other nonsense (heavily WIP)
BBB streaming without Xorg and Pulseaudio and Chromium and other nonsense (heavily WIP)

BBB Streamer NG? Makes a conference like this... ...streamable like this! I also recorded a small video showing the basic features: https://www.youtub

Lukas Schauer 60 Oct 21, 2022
Comments
  • Current error.py is not friendly or scalable

    Current error.py is not friendly or scalable

    The current error.py file contains a class which contains several functions for raising an error. This is not a very good practice. Also, you are using the cryptography library to get a error. Instead just create different classes for each exception and create your own exception instead of importing from cryptography for starting this is fine but after that not. To learn more about it refer here And here and here. If you feel like I am being to rude or bossy sorry, I like this library and am just highlighting a few issues which when fixed would make the library great.

    bug 
    opened by pybash1 3
  • Add Passlib and Bcrypt For More Hashing Support

    Add Passlib and Bcrypt For More Hashing Support

    It will be good if we use passlib and bcrypt make some new functions as cryptography does not have good hashing functions and hence less people will use the modules for hashing if we don't use preferred modules.

    Feature-Request 
    opened by Morgan-Phoenix 0
  • Dependencies

    Dependencies

    When Anyone Installs EnroCrypt The Dependencies Are Not Getting Installed Because install_requires is not present in setup.py

    **- The Issue Is Present On All OS

    • Using Python 3.9.5
    • Module-Version Latest(v1.0.4)**

    This Bug Can Be Solved If install_requires is present in setup.py

    bug 
    opened by Morgan-Phoenix 0
  • Fix some common programming practices

    Fix some common programming practices

    I think variable names should be meaningful. So, I changed final to listString. Also, since we are specifying data to be str in the arguments I don't think we need to convert it to a str again. And also, renamed to basic.py, basicfuncs.py just didn't sound right.

    opened by pybash1 0
Releases(v1.1.5)
Owner
GitHub Repository https://morgan-phoenix.github.io/EnroCrypt/
Neural Articulated Radiance Field

Neural Articulated Radiance Field NARF Neural Articulated Radiance Field Atsuhiro Noguchi, Xiao Sun, Stephen Lin, Tatsuya Harada ICCV 2021 [Paper] [Co

Atsuhiro Noguchi 144 Jan 03, 2023
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions

Natural Posterior Network This repository provides the official implementation o

Oliver Borchert 54 Dec 06, 2022
Split your patch similarly to `git add -p` but supporting multiple buckets

split-patch.py This is git add -p on steroids for patches. Given a my.patch you can run ./split-patch.py my.patch You can choose in which bucket to p

102 Oct 06, 2022
Code for Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games

Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games How to run our algorithm? Create the new environment using: conda

MARL @ SJTU 8 Dec 27, 2022
Improving Deep Network Debuggability via Sparse Decision Layers

Improving Deep Network Debuggability via Sparse Decision Layers This repository contains the code for our paper: Leveraging Sparse Linear Layers for D

Madry Lab 35 Nov 14, 2022
A deep learning tabular classification architecture inspired by TabTransformer with integrated gated multilayer perceptron.

The GatedTabTransformer. A deep learning tabular classification architecture inspired by TabTransformer with integrated gated multilayer perceptron. C

Radi Cho 60 Dec 15, 2022
Parametric Contrastive Learning (ICCV2021)

Parametric-Contrastive-Learning This repository contains the implementation code for ICCV2021 paper: Parametric Contrastive Learning (https://arxiv.or

DV Lab 156 Dec 21, 2022
A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

Tom 50 Dec 16, 2022
A Broader Picture of Random-walk Based Graph Embedding

Random-walk Embedding Framework This repository is a reference implementation of the random-walk embedding framework as described in the paper: A Broa

Zexi Huang 23 Dec 13, 2022
An open-source project for applying deep learning to medical scenarios

Auto Vaidya An open source solution for creating end-end web app for employing the power of deep learning in various clinical scenarios like implant d

Smaranjit Ghose 18 May 29, 2022
Must-read Papers on Physics-Informed Neural Networks.

PINNpapers Contributed by IDRL lab. Introduction Physics-Informed Neural Network (PINN) has achieved great success in scientific computing since 2017.

IDRL 330 Jan 07, 2023
Least Square Calibration for Peer Reviews

Least Square Calibration for Peer Reviews Requirements gurobipy - for solving convex programs GPy - for Bayesian baseline numpy pandas To generate p

Sigma <a href=[email protected]"> 1 Nov 01, 2021
A library for researching neural networks compression and acceleration methods.

A library for researching neural networks compression and acceleration methods.

Intel Labs 100 Dec 29, 2022
Python KNN model: Predicting a probability of getting a work visa. Tableau: Non-immigrant visas over the years.

The value of international students to the United States. Probability of getting a non-immigrant visa. Project timeline: Jan 2021 - April 2021 Project

Zinaida Dvoskina 2 Nov 21, 2021
Colossal-AI: A Unified Deep Learning System for Large-Scale Parallel Training

ColossalAI An integrated large-scale model training system with efficient parallelization techniques Installation PyPI pip install colossalai Install

HPC-AI Tech 7.1k Jan 03, 2023
This repository is an unoffical PyTorch implementation of Medical segmentation in 3D and 2D.

Pytorch Medical Segmentation Read Chinese Introduction：Here！ Recent Updates 2021.1.8 The train and test codes are released. 2021.2.6 A bug in dice was

EasyCV-Ellis 618 Dec 27, 2022
Global Filter Networks for Image Classification

Global Filter Networks for Image Classification Created by Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, Jie Zhou This repository contains PyTorch

Yongming Rao 273 Dec 26, 2022
Supervised domain-agnostic prediction framework for probabilistic modelling

A supervised domain-agnostic framework that allows for probabilistic modelling, namely the prediction of probability distributions for individual data

The Alan Turing Institute 112 Oct 23, 2022
A python-image-classification web application project, written in Python and served through the Flask Microframework. This Project implements the VGG16 covolutional neural network, through Keras and Tensorflow wrappers, to make predictions on uploaded images.

Image Classification in Python Implementing image classification in Flask using Keras. The VGG16 is a convolution neural network model architecture th

Gerald Maduabuchi 19 Dec 12, 2022
A Number Recognition algorithm

Paddle-VisualAttention Results_Compared SVHN Dataset Methods Steps GPU Batch Size Learning Rate Patience Decay Step Decay Rate Training Speed (FPS) Ac

1 Nov 12, 2021