Source code for xopto.pf.mpc

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import numpy as np

from .pfbase import PfBase


[docs]class MPc(PfBase): def __init__(self, n: float, b: float): ''' Modified power of cosines scattering phase function constructor. .. math:: Pc(\\cos(\\theta)) &= \\frac{(n + 1)}{2}^{(n + 1)} * (1 + \\cos(\\theta))^n MPc(\\cos(\\theta)) &= b Pc (\\cos(\\theta)) + \\frac{3}{2}(1 - b)\\cos(\\theta)^2 Parameters ---------- n: float Parameter of the power of cosine scattering phase function. b: float Contribution of the Power of cosine scattering component. Contribution of the Rayleigh scattering component is (1 - b). Examples -------- Modified Power of cosines scattering phase function. n = {0.1, 0.5, 1.0, 2.0, 5.0, 10.0} and b=0.5 >>> import numpy as np >>> from matplotlib import pyplot as pp >>> >>> cos_theta = np.linspace(-1.0, 1.0, 1000) >>> >>> pp.figure() >>> for n in [0.1, 0.5, 1.0, 2.0, 5.0, 10.0]: >>> pp.semilogy(cos_theta, MPc(n, 0.5)(cos_theta), label='n={}, b=0.5'.format(n)) >>> pp.legend() ''' super().__init__() self._n = float(n) self._b = min(max(float(b), 0.0), 1.0) self._k1 = self._b*(n + 1.0)/(2**(n + 1)) self._k2 = (1.0 - self._b)*3.0/2.0 self._gs = np.zeros((20 + 1,)) self._gs[1] = self._n/(self._n + 2.0) for moment in range(2, 20 + 1): self._gs[moment] = self._gs[moment - 1]*(self._n - moment + 1)/\ (self._n + moment + 1) self._gs *= self._b self._gs[0] = 1.0 self._gs[2] += (1.0 - self._b)*0.4 self._gs = np.abs(np.maximum(self._gs, 0.0)) def __call__(self, costheta: float or np.ndarray) -> float or np.ndarray: ''' Call method of the Gegenbauer kernel scattering phase function object. Parameters ---------- costheta: float or np.ndarray Scattering angle cosines at which the scattering phase function is evaluated. Returns ------- f: float or np.ndarray Scattering phase function at the specified scattering angle cosines. ''' return self._k1*(1.0 + costheta)**self._n + self._k2*costheta**2
[docs] def g(self, n: int) -> float: ''' Overloads the :py:meth:`PfBase.g` method of the base class with an analytical solution. ''' if n < self._gs.size: g = float(self._gs[n]) else: g = np.prod(np.arange(self._n, self._n - n + 2))/ \ np.prod(np.arange(self._n + 2, self._n + n + 2)) g = abs(max(float(g), 0.0)) return g
[docs] def gs(self, last: int) -> np.ndarray: ''' Overloads the :py:meth:`PfBase.gs` method of the base class with an analytical solution. ''' gs = np.zeros((last + 1,)) if last + 1 < self._gs.size: gs[:last + 1] = self._gs[:last + 1] else: gs[:self._gs.size] = self._gs for moment in range(self._gs.size, last): g = gs[moment - 1]*(self._n - moment + 1)/ \ (self._n + moment + 1) gs[moment] = abs(max(g, 0.0)) return gs
[docs] def fastg(self, n: int, **kwargs) -> float: ''' Overloads the :py:meth:`PfBase.fastg` method of the base class with an analytical solution. ''' return self.g(n)
[docs] def fastgs(self, last: int, **kwargs) -> np.ndarray: ''' Overloads the :py:meth:`PfBase.fastgs` method of the base class with an analytical solution. ''' return self.gs(last)
def __repr__(self): return 'MPc({}, {})'.format(self._n, self._b)