Metadata-Version: 1.1
Name: maoud
Version: 0.1.dev0
Summary: Simulating generalized fading channels
Home-page: https://github.com/mirca/maoud
Author: mirca
Author-email: jvmirca@gmail.com
License: MIT
Description-Content-Type: UNKNOWN
Description: 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},}
        
Keywords: statistics probability
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
