semvec/pypi-packages huggingface dataset:
correlation-integral 0.0.5
Correlation Integral caluclations done fast with openmp
Requires Python: >=3.6

Correlation Integral
====================

Calculates the correlation integral. Does not take self-distance into account.
Memory complexity is :code:`len(data)*len(Rs)`. Calculations are done with single
precision floating point data, and the theoretical maximum data length is
:code:`3.037.000.499` points divided by the number of Rs that you want to calculate


Usage
-----

To import the library run:

.. code-block:: python

  import correlation_integral as ci

Three methods can be used

.. code-block:: python

  ci.euclidean
  ci.manhattan
  ci.chebyshev

All three methods take the following arguments:

- **data**: one-dimensional float32 numpy array with the data

- **dims**: integer specifying the dimension for which to calculate the correlation integral

- **r**   : one-dimensional float32 numpy array with the distances to calculate the correlation integral for

And they return the following output:

- **cd**: one-dimensional float32 numpy array which contains the correlation-integral for each **r** given in the input

The signature looks like this:

.. code-block:: python

  cd = ci.euclidean(data,dims,r)



Installation
------------

.. code-block:: bash

  pip install correlation-integral


Example
-------

.. code-block:: python

  import correlation_integral as ci
  import numpy as np
  import matplotlib.pyplot as plt

  # example Henon map - create & plot data
  a=1.4;
  b=0.3;

  x=np.zeros(shape=(10000+5000),dtype=np.float32)
  y=np.zeros(shape=(10000+5000),dtype=np.float32)
  x[0]=1
  y[0]=0
  for i in range(1,len(x)-1):
      x[i+1]=1-a*x[i]**2+y[i]
      y[i+1]=b*x[i]

  x=x[5000:]
  y=y[5000:]


  # correlation integral analysis done only with x-variable
  nrsteps=10;
  dims=np.arange(1,16) # calculate for d=1 uptil d=16
  nrdims=dims.size;
  rs=np.logspace(-2.5,0.5,nrsteps)
  Cds=np.zeros((nrsteps,nrdims));

  print("Start")
  for j in range(nrdims):
    Cds[:,j] = ci.chebyshev(x,dims[j],rs)
    print("x",end="")
  print("\nDONE!")

  # Plot the results
  for i in range(len(dims)):
    plt.plot(rs,Cds[:,i])
  plt.xscale("log")
  plt.yscale("log")
  plt.show()
cryptology 0.0.14
Decrypt/Encrypt text using various cipher techniques

# Cryptology
This is a package which decrypt/encrypts text using a few different methods.

## Installation
Run the following to install:

```python
pip install cryptology
```

## Usage
```python
from cryptology import atbash_decrypt

# Generate decryption
string=atbash_decrypt(cipher_text="GSRHRHMLGEVIBHVXIVGZGZOO")

print(string)
```

Other functions include:

```python
import cryptology as cp

# Generate decryption

# Atbash
str_atbash_decrypted=cp.atbash_decrypt(cipher_text="GSRHRHMLGEVIBHVXIVGZGZOO")

str_atbash_encrypted=cp.atbash_encrypt(plain_text="THISISNOTVERYSECRETATALL")

# Ceaser
str_ceaser_decrypted=cp.ceaser_decrypt(cipher_text="MTBVZNHPQDINIDTZGWJFPYMNX",
                                      shift=5)

str_ceaser_encrypted=cp.ceaser_encrypt(plain_text="HOWQUICKLYDIDYOUBREAKTHIS",
                                      shift=5)

# Vigenere
str_ceaser_decrypted=cp.vigenere_decrypt(cipher_text="YDXGJHCJVODXUGGZ",
                                         keyword="NIGHTTIME",
                                         permutation="thislepgywnomarkdbfcjquvxz")

str_ceaser_encrypted=cp.vigenere_encrypt(plain_text="VISABILITYISPOOR",
                                         keyword="NIGHTTIME",
                                         permutation="thislepgywnomarkdbfcjquvxz")

# Playfair
str_playfair_decrypted=cp.playfair_decrypt(cipher_text="GIVHYCHGSYPCFHWHGDHPUTSMYTLD",
                                        keyword="GLAMORGAN")

str_playfair_decrypted=cp.playfair_encrypt(plain_text="MEETMEATTREFFORESTSTATION",
                                        keyword="GLAMORGAN")

print(string)
```