semvec/pypi-packages huggingface dataset:
NKPy 0.1.26
Nanhe Korean Python
License: MIT

# NKPy

- [Korean(한국어)](#Korean)
- [English(영어)](#English)
- [Commands(명령어들)](https://github.com/NKPy/NKPy/blob/master/nkpy/vars.py)

## Korean
NKPy를 선택해 주셔서 감사합니다!
NKPy의 뜻은 난해한 한국어 파이썬의 약자(Nanhe Korean Python)입니다.

이것의 특징은 뭐, 설명드릴께 있나요. 한국어인데 뭐가 안좋겠어요?

그래도 굳이 설명 드리자면 이 언어는 정말 효율적이고 ~~쓸때 없으며~~ 보기 쉬운 언어라고 볼 수 있겠습니다.

그냥 한번 예문을 보도록 하겠습니다.

```
출력괄호열어작은따옴표안녕하세요!작은따옴표괄호닫아 주석안녕하세요! 출력
```

어때요? 정말 간단하죠?

NKPy를 쓰면 하루하루를 보람찬 하루로 바꿀 수 있게 될것입니다.

오타 발견시 이슈에 업로드 해주시기 바랍니다!

## English
Thanks for choose NKPy!
NKPy means "Nanhe Korean Python".

NKPy's special feature is, This is Korean so why it is not good?

But you want know what is the NKPy's special feature so this language is really efficient~~, useless~~ and easy to see.

Just look at the example!

```
출력괄호열어작은따옴표Hello!작은따옴표괄호닫아 주석Print Hello!
```

how is it? How it really simple?

If you use NKPy, you will be able to change your day into a rewarding day.

If you found a typo, open issue so we can fix it!
maoud 0.1.dev0
Simulating generalized fading channels
License: MIT
Keywords: statistics probability

Simulation of generalized fading channels in Python
===================================================

Let's say you have a complicated density function for which there is no implementation in ``Scipy``, e.g., Yacoub's Kappa-Mu.
Don't worry, **maoud** got you covered::

    import numpy as np
    import scipy.special as sps

    def kappa_mu_pdf(x, kappa, mu):
        return (2.0 * mu * np.power(1.0 + kappa, (mu + 1.0) / 2.0) * np.power(x, mu)
                * np.exp(-mu * (1.0 + kappa) * x * x - mu * kappa + 2 * x * mu
                * np.sqrt(kappa * (1.0 + kappa))) * sps.ive(mu - 1, 2 * mu * x
                * np.sqrt(kappa * (1.0 + kappa))) / (np.power(kappa, (mu - 1.0) / 2.0)))

Then you want to do the following in order to draw samples::

    from maoud.sampling import rejection_sampling

    n_samples = int(1e6)
    kappa = 0.75
    mu = 0.87
    low = 1e-6
    high = 3

    kappa_mu_samples, af = rejection_sampling(kappa_mu_pdf, low, high,
                                              n_samples, kappa, mu)

To verify that the samples are in accordance with Yacoub's Kappa-Mu density, let's plot the histogram of the samples::

    import matplotlib.pyplot as plt

    x = np.linspace(1e-6, 3, 1000)
    y = kappa_mu_pdf(x, kappa, mu)

    plt.plot(x, y)
    plt.hist(kappa_mu_samples, bins=50, normed=True)

.. image:: https://github.com/mirca/acceptance-rejection/raw/master/kappa_mu.png

SHAZAM!!

Citation
========

If you made use of the code available in this repository, please consider
citing the following work::

    @ARTICLE{7986939,
    author={J. V. M. Cardoso and W. J. L. Queiroz and H. Liu and M. S. Alencar},
    journal={Tsinghua Science and Technology},
    title={On the performance of the energy detector subject to impulsive noise in κ—μ, α—μ, and η—μ fading channels},
    year={2017},
    volume={22},
    number={4},
    pages={360-367},
    doi={10.23919/TST.2017.7986939},
    month={Aug},}