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219 lines
6.8 KiB
Python
219 lines
6.8 KiB
Python
#!/usr/bin/env Python3
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import numpy as np
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from scipy import integrate
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from .ciddorModel import Observatory
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'''
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@ Author: Joost van den Born
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@ Contact: born@astron.nl
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@ Description: This script offers a complete model of the atmospheric refraction
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and dispersion. It uses the Ciddor1996 paper to calculate the
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index of refraction for a wide range of conditions, with corresponding
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error propagation if needed.
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The actual refraction is calculated by any of the following models:
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- Plane parallel atmosphere
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- Cassini spherical homogeneous atmosphere
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- Other methods described in Corbard et al., 2019.
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'''
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class refraction(Observatory):
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def __init__(self, lat, h):
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Observatory.__init__(self)
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self.h = h # height above sea level in meters
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self.lat = lat # Latitude of the observer in degrees
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self.rc = self.set_rc() * 1000 # Radius of curvature of the earth at the observer
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# self.H2 = self.H_isotherm(T) # Reduced height of isotherm atmosphere
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def setReducedHeight(self, P, rho):
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self.H = self.set_H(P, rho) # Reduced height of the atmosphere assuming ideal gas law
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def planeParallel(self, n, zenith):
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'''
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Refraction based on a flat atmosphere, easily derived from Snell's law.
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'''
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_R = np.arcsin(n * np.sin( np.deg2rad(zenith) )) - np.deg2rad(zenith)
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return np.degrees(_R)
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def cassini(self, n1, zenith, reduced_height=None):
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'''
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Refraction of spherical atmosphere, derived from geometry using Sine law
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'''
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if reduced_height:
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H = reduced_height
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else:
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H = self.H
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_R1 = np.arcsin(n1 * self.rc * np.sin(np.deg2rad(zenith)) / (self.rc + H)) - np.arcsin(self.rc * np.sin(np.deg2rad(zenith)) / (self.rc + H) )
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return np.degrees(_R1)
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def tan5(self, n1, zenith, reduced_height=None):
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'''
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Same as oriani's theorem, but including a higher order term.
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Found in Corbard et al., 2019.
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'''
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if reduced_height:
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H = reduced_height
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else:
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H = self.H
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_a = n1 - 1
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_b = H / self.rc
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_R = _a * (1 - _b) * np.tan( np.deg2rad(zenith) ) - _a * (_b - _a/2) * np.tan( np.deg2rad(zenith) )**3 + 3 * _a * (_b - _a/2)**2 * np.tan( np.deg2rad(zenith) )**5
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return np.degrees(_R)
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def oriani(self, n1, zenith, reduced_height=None):
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'''
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Classic refraction formula with two tan(z) terms. This does not assume any
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information about the structure of the atmosphere. Found with Oriani's theorem.
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'''
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if reduced_height:
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H = reduced_height
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else:
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H = self.H
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_a = n1 - 1
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_b = H / self.rc
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_R = _a * (1 - _b) * np.tan( np.deg2rad(zenith) ) - _a * (_b - _a/2) * np.tan( np.deg2rad(zenith) )**3
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return np.degrees(_R)
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def corbard(self, n1, zenith, reduced_height=None):
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'''
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Corbard et al., 2019, mention an additional formula based on the error function.
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Oriani's theorem can be derived from this equation by 'keeping only the three first
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terms of its asymptotic expansion'.
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'''
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if reduced_height:
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H = reduced_height
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else:
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H = self.H
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_a = n1 - 1
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_b = H / self.rc
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_R = _a * ( (2 - _a) / (np.sqrt( 2*_b - _a )) ) * np.sin( np.deg2rad(zenith) ) * self.psi( (np.cos(np.deg2rad(zenith))) / np.sqrt(2*_b - _a) )
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return np.degrees(_R)
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def refractionIntegral(self, n0, z0, R0=None, useStandardAtmosphere=True, heightData=None, rhoData=None):
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'''
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Using data that gives temperature, pressure and density as a function of height,
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the gladstone-dale relation is invoked to calculate the path of a light ray
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along the atmosphere. This is allows us to also include atmospheric activity in
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different atmosphere layers.
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Recommended use with the US Standard Atmosphere from 1976.
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USSA1976 data generated from code supplied by http://www.pdas.com/atmos.html
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'''
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if not R0:
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R0 = self.rc
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if useStandardAtmosphere:
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_h, _, _, _, _Temp, _Press, _Dens,_,_,_ = np.genfromtxt('1976USSA.txt', unpack=True, skip_header=6)
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else:
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_Dens = rhoData
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_h = heightData
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z0 = np.deg2rad(z0)
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_Dens = np.asarray(_Dens)
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_h = np.asarray(_h)
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_R = R0 + _h * 1000
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# Gladstone-Dale relation between refractive index and density
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_n_h = 1 + (n0 - 1) / _Dens[0] * _Dens
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# Calculation of the zenith angle for given height and refractive index, from the refractive invariant
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_z = np.arcsin( (n0 * R0) / (_n_h * _R) * np.sin(z0) )
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# Calculation of log space derivative of n and r
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_lnn = np.log(_n_h)
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_lnr = np.log(_R)
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_dlnn = np.asarray([_lnn[i+1] - _lnn[i] for i in range(len(_lnn)-1)])
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_dlnr = np.asarray([_lnr[i+1] - _lnr[i] for i in range(len(_lnr)-1)])
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_dz = np.asarray([(_z[i] - _z[i+1]) for i in range(len(_z)-1)])
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# Calculation of the integrand in eq. 3 in Auer & Standish
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_Integrand = -1*(_dlnn/_dlnr) / (1 + _dlnn/_dlnr) * _dz
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return np.degrees(np.sum(_Integrand))
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def psi(self, x):
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_f = lambda a: np.exp(-a**2)
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_int = integrate.quad(_f, x, np.inf)
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return np.exp(x**2) * _int[0]
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def set_rc(self):
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'''
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Returns the radius of the curvature at the observer in km,
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taking into account the latitude of the observer and the corresponding
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ellipsoidality of the earth.
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'''
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_A = 6378.137 # km
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_B = 6356.752 # km
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_phi = np.deg2rad(self.lat)
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_rc0 = (_A*_B)**2 / (_A**2 * np.cos(_phi)**2 + _B**2 * np.sin(_phi)**2)**(3/2)
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return _rc0 + self.h/1000
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def set_H(self, P, rho):
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'''
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Calculates the reduced height of the atmosphere, assuming ideal gas law.
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'''
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_phi = np.deg2rad(self.lat)
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_c1 = 5.2790414e-3
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_c2 = 2.32718e-5
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_c3 = 1.262e-7
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_c4 = 7e-10
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_g0 = 9.780327 # m/s^2
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_g0_local = _g0 * (1 + _c1 * np.sin(_phi)**2 + _c2 * np.sin(_phi)**4 + _c3 * np.sin(_phi)**6 + _c4 * np.sin(_phi)**8)
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_g = _g0_local - (3.0877e-6 - 4.3e-9 * np.sin(_phi)**2) * self.h + 7.2e-13 * self.h**2
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return P / (rho * _g)
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def H_isotherm(self, T):
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'''
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Calculates the reduced height of the atmosphere, assuming an isotherm distribution.
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'''
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_kb = 1.38064852e-23 # kg m2 s-2 K-1
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_m = 4.809651698e-26 # kg (weight per molecule of air, assuming dry air)
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_phi = np.deg2rad(self.lat)
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_c1 = 5.2790414e-3
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_c2 = 2.32718e-5
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_c3 = 1.262e-7
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_c4 = 7e-10
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_g0 = 9.780327 # m/s^2
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_g0_local = _g0 * (1 + _c1 * np.sin(_phi)**2 + _c2 * np.sin(_phi)**4 + _c3 * np.sin(_phi)**6 + _c4 * np.sin(_phi)**8)
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return (_kb*T) / (_m * _g0_local)
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def cassiniError(self, n, dn, zenith, dz=0, reduced_height=None):
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'''
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Calculates the uncertainty in the refraction angle in degrees.
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'''
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if reduced_height:
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_H = reduced_height
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else:
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_H = self.H
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_zenith = np.deg2rad(zenith)
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_dz = np.deg2rad(dz)
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_R = self.rc
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_dRdn = (_R * np.sin(_zenith)) / ((_R + _H) * np.sqrt(1 - ((n**2 * _R**2 * np.sin(_zenith)**2)/(_R + _H)**2)))
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_dRdz = (_R * np.cos(_zenith)) / (_R + _H) * ( n / np.sqrt(1-(n**2 * _R**2 * np.sin(_zenith)**2)/(_R + _H)**2) - 1 / np.sqrt(1-(_R**2 * np.sin(_zenith)**2)/(_R + _H)**2) )
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_dR = np.sqrt( (_dRdn * dn)**2 + (_dRdz * _dz)**2)
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return np.degrees(_dR)
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