semvec/pypi-packages huggingface dataset:
simpleencrypt 1.0.6
SimpleEncrypt is a Cross-Language Encryption Library
Keywords: Encryption,Cross-Language Encryption

# About SimpleEncrypt

[SimpleEncrypt](https://github.com/shreyasnayak/SimpleEncrypt) is a Cross-Language Encryption Library that provides the ability to encrypt and decrypt data in C++ and Python 3

# Installing Dependency library

SimpleEncrypt Python package has a dependency on C++ shared library. Before running the simple encrypt python package, build and install C++ shared library.

## Build and install C++ shared library

- Install dependencies `sudo apt-get install libssl-dev openssl`
- Download or clone simple encrypt [C++ shared library](https://github.com/shreyasnayak/SimpleEncrypt/tree/main/CPP)
- Compile and install

```
git clone https://github.com/shreyasnayak/SimpleEncrypt
mkdir CPP/Build && cd CPP/Build
cmake ..
make
sudo make install
```

# Install python package [SimpleEncrypt](https://pypi.org/project/simpleencrypt/)

Installation

```
python3 -m pip install simpleencrypt
```

### Usage

```python
from simpleencrypt import aes256

AES_KEY = b'171A065A7675A09AECEC118DBC008A822A041FC2EBF2B3E4CF7A4C966E5D5897'
AES_IV = b'2B5442AD8739992F'
plainText = b'Hello World'

# encryption
encrypted = aes256.encrypt(plainText,AES_KEY,AES_IV)
print(encrypted)

# decryption
decrypted = aes256.decrypt(encrypted,AES_KEY,AES_IV)
print(decrypted)
```
plot-yar 0.4
Plotting_graphs

 # This is my package for plotting approximations

## Installation:

```bash
pip install plot_yar
```

## Description:

```python
plot_yar.plot_approx(X_data, Y_data, input_function, plot_name='plot_name', plot_title='plot_title', 
x_label='x_label', y_label='y_label', Y_absolute_sigma = 0, scientific_view = True, print_cross = True, 
save_as_csv = False, to_latex = False, save_fig=True): 
```

### Parameters:

 -  __X_data:__ array_like.  
X-axis data
 

 -  __Y_data:__ array_like.  
Y-axis data


 -  __input_function:__ string.  
Approximation function.    
You could use template functions from dict or your own funcitons: 
 ```python
input_function = 'linear' 
fun_examples = {'linear':'a0*x+a1', 'poly_2':'a0*x**2+a1*x+a2', 'poly_3':'a0*x**3+a1*x**2+a2*x+a3',
'exp':'e^(a0*x+a1)+a2', 'ln':'ln(a0*x+a1)+a2'}
```
 - __plot_name:__ string.  
Name of your plot (picture)


 - __plot_title:__ string.  
Your plot title


 - __y_label:__ string.  
Y-axis label

 - __x_label:__ string.  
X-axis label


 - __Y_absolute_sigma:__ float or array_like.  
Absolute Y-axis data error


 - __scientific_view:__ bool. (default True)  
If True numbers will be written as $6.7 E + 01$ instead $670$


 - __print_cross:__ bool.  (default True)  
Print crosses error or not?

 - __to_latex:__ bool.  (default False)  
If True save table of estimated coeffs to latex table, you could use this package:
```latex
 \usepackage{booktabs}
```

 - __save_as_csv:__ bool.  (default False)  
If True save table of estimated coeffs to csv table


 - __save_fig:__ bool.  (default True)  
If True create a folder "pictures" and save plot to .png


### Example:
 - in:
```python
from plot_yar import plot_approx
plot_approx([[1.1,2.5,3.2],[2,3,4]],[[2,3,5],[5,6,8]], 'linear')
```

 - out:

```bash
Coeffs table 0: 

  coeffs  coeffs_values  standard error  relative se, %
0    a_0      1.327E+00       2.811E-01       2.119E+01
1    a_1      3.265E-01       1.659E+00       5.080E+02

Standart_error_Y_0 = 4.629E-01
Coeffs table 1: 

  coeffs  coeffs_values  standard error  relative se, %
0    a_0      1.500E+00       8.333E-02       5.556E+00
1    a_1      1.833E+00       8.056E-01       4.394E+01

Standart_error_Y_1 = 2.357E-01
```

![name](plot_name.png)