semvec/pypi-packages huggingface dataset:
pymep 1.0.7
Python Math Expression Parser

# python.math.expression.parser.pymep

pymep can parse or evaluate math expressions.

This algorithm does not use a decision tree. It is a kind of Recursive Ascent Parser (https://en.wikipedia.org/wiki/Recursive_ascent_parser). In fact, it is LR parser (Left-Right Parser) without backtracking. This recursive algorithm is faster than decision trees

## pypi version

  https://pypi.org/project/pymep/

## Installation

pip install pymep

## Examples

Here you can see some examples:

  Real Numbers:    

	from pymep.realParser import parse
	from pymep.realParser import eval


	#Real Expresion parser
	fx="cos(10)"
	print(parse(fx))

	xi=5
	fx = "1 + x"
	print(eval(fx, xi))

	var = {"x":"1+1", "Z":1}
	eval(" 2*(-(((z*3)*sqrt(x^(2)))+3))",var)


  For Complex Numbers:

    from pymep.complexParser import parse
	from pymep.complexParser import eval
	from pymep.complex import Complex

	#Operation with complex numbers
	a = Complex(1,2)
	print(a.__radd__(10).__complex__())
	print(Complex.radd(10, a).__complex__())


	#Complex Expresion parser
	fx="cos(10+2j)"
	print(parse(fx).__complex__())

	xi=5
	fx = "1 +j+x"
	print(eval(fx, xi).__complex__())

	var={"x":"1+2j", "Y":complex(2,1)}
	f_x = "x-y"
	eval(f_x,var).__complex__()


 There is a full list of examples inside!!

Enjoy it!!

The java version of this library is: https://github.com/sbesada/java.math.expression.parser

NOTE: Regarding to the OS where you excute the tests, it is possible that some tests fail beacause of rounding issues. The mathematical library that was used in this project is "math". In the future, it is possible that the math library changes. 

PD: If you think that my work deserves a donation, you can do it: https://sbesada.github.io/
PyIF 0.1
An open source implementation to compute bi-variate Transfer Entropy.

# PyIF

An open source implementation to compute bi-variate Transfer Entropy.


Then `from PyIF import te_compute` to import the needed functions. Then all that is needed then is to call the `te_compute` function.

## Installation

To install from pip all that is needed is run the line `pip install PyIF`.

To install a the development release of Py-TE run the following command
```bash
pip install -e .
```

## Example

``` python
from PyIF import te_compute as te
import numpy as np
rand = np.random.RandomState(seed=23)

X_1000 = rand.randn(1000, 1).flatten()
Y_1000 = rand.randn(1000, 1).flatten()

TE = te.te_compute(X_1000, Y_1000, k=1, embedding=1, safetyCheck=True, GPU=False)

print(TE)
```