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AtmosphericModel/AstroAtmosphere/dispersionModels.py
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#!/usr/bin/env Python3
import numpy as np
from scipy import integrate
import os
from .ciddorModel import Observatory
'''
@ Author: Joost van den Born
@ Contact: born@astron.nl
@ Description: This script offers a complete model of the atmospheric refraction
and dispersion. It uses the Ciddor1996 paper to calculate the
index of refraction for a wide range of conditions, with corresponding
error propagation if needed.
The actual refraction is calculated by any of the following models:
- Plane parallel atmosphere
- Cassini spherical homogeneous atmosphere
- Mathar's Barometric exponential model
- Other methods described in Corbard et al., 2019.
'''
class dispersion(Observatory):
'''
Class that sets the observer location.
These location attributes are then used to calculate the
appropriate atmospheric dispersion.
See also:
Observatory()
refraction()
...
Attributes
-----------
lat : float
Latitude of the observer in degrees
h : float
Altitude above mean sea level of the observer in meters
Other Observatory() attributes
Methods
-------
cassini(n1, n2, zenith, reduced_height=None)
Returns the atmospheric dispersion assuming Cassini's homegeneous
atmosphere model.
cassiniError(z, l1, l2, T, p, RH, xc, dl1=0, dl2=0, dT=0.2, dP=20,
dRH=0.02, dCO2=20, dz=0, lat=None, h=None)
Returns the uncertainty in the atmospheric dispersion, assuming
Cassini's homegeneous atmosphere model and the given parameters.
corbard(n1, n2, zenith, reduced_height=None)
Returns the atmospheric dispersion following the method described
in Corbard et al., 2019.
gammaCoefficients(n1, reduced_height=None)
Helper function for matharExponential(), that calculates the gamma
coefficients.
H_isotherm(T)
Sets the reduced height for the conditions at the observer, assuming
an isothermal atmosphere.
matharExponential(n1, n2, zenith, reduced_height=None)
Returns the atmospheric dispersion assuming Mathar's barometric
exponential model.
oriani(n1, n2, zenith, reduced_height=None)
Returns the atmospheric dispersion following the classic version
of Oriani's theorem.
planeParallel(n1, n2, zenith)
Returns the atmospheric dispersion assuming a plane parallel
atmosphere.
psi(x)
Submethod for corbard().
refractionIntegral(n1, n2, zenith, R0=None, useStandardAtmosphere=True, heightData=None, rhoData=None)
Returns the atmospheric dispersion following the refraction integral.
setReducedHeight(p, rho)
Sets the reduced atmospheric height.
set_H(p, rho)
Sets the reduced height for the conditions at the observer, assuming
an adiabatic atmosphere.
set_rc()
Sets the local radius of curvature of the Earth at the observer.
tan5(n1, n2, zenith, reduced_height=None)
Returns the atmospheric dispersion following Oriani's theorem,
including a fifth order term in the expansion.
Other Observatory() methods
'''
def __init__(self, lat, h):
Observatory.__init__(self)
self.h = h # height above sea level in meters
self.lat = lat # Latitude of the observer in degrees
self.rc = self.set_rc() * 1000 # Radius of curvature of the earth at the observer
def cassini(self, n1, n2, zenith, reduced_height=None):
'''
Refraction of spherical atmosphere, derived from geometry using Sine law
Parameters
----------
n1 : float
Refractive index at the shortest wavelength of interest
n2 : float
Refractive index at the longest wavelength of interest
zenith : float
Angle of observation in degrees.
zenith = 0 means the observation is directly overhead.
reduced_height : float (optional)
Reduced height of the atmosphere. Default value is calculated
from the object attributes.
Returns
-------
dR : float
Atmospheric dispersion in degrees
'''
if reduced_height:
H = reduced_height
else:
H = self.H
_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) )
_R2 = np.arcsin(n2 * self.rc * np.sin(np.deg2rad(zenith)) / (self.rc + H)) - np.arcsin(self.rc * np.sin(np.deg2rad(zenith)) / (self.rc + H) )
return np.degrees(_R1) - np.degrees(_R2)
def cassiniError(self, zenith, l1, l2, T, p, RH, xc, dl1=0, dl2=0, dT=0.2, dP=20,
dRH=0.02, dCO2=20, dz=0, lat=None, h=None):
'''
Calculates the uncertainty in the atmospheric dispersion,
following the Cassini model, by using fully propagated error analysis.
This involves almost endless use of the chain rule and evaluation
of both the moist air and dry air components.
Note: dry air does not contain any water, i.e. H=0, x_w=0, etc.
Not taken into account is the errror propagation in the scale height
of the atmosphere, from my results this should give a small or even
negligible difference.
Parameters
----------
zenith : float
Angle of observation in degrees.
zenith = 0 means the observation is directly overhead.
l1 : float
Wavelength of light in microns, at the shortest wavelength
of interest
l2 : float
Wavelength of light in microns, at the longest wavelength
of interest
T : float
Atmospheric temperature in Kelvin
p : float
Atmospheric pressure in Pascal
RH : float
Atmospheric relative humidity (0<=RH<=1)
xc : float
CO2 density in parts per million
dl1 : float
Uncertainty in the wavelength in microns, at the shortest
wavelength of interest
dl2 : float
Uncertainty in the wavelength in microns, at the longest
wavelength of interest
dT : float (default: 0.2)
Uncertainty in the atmospheric temperature in Kelvin
dP : float (default: 20)
Uncertainty in the atmospheric pressure in Pascal
dRH : float (default: 0.02)
Uncertainty in the atmospheric relative humidity
dCO2: float (default: 20)
Uncertainty in the atmospheric CO2 density in parts per million
dz : float (default: 0)
Uncertainty in the zenith angle, in degrees.
lat : float (default: None)
If entered, overrides the latitude attribute of the class.
h : float (default=None)
If entered, overrides the altitude attribute of the class.
Returns
-------
dRR : float
Uncertainty in the atmospheric dispersion in degrees.
'''
if lat == None:
lat = self.lat
else:
self.lat = lat
if h == None:
h = self.h
else:
self.h = h
# constants
_cf = 1.022
_w0 = 295.235 # um^-2
_w1 = 2.6422 # um^-2
_w2 = -0.032380 # um^-4
_w3 = 0.004028 # um^-6
_k0 = 238.0185 # um^-2
_k1 = 5792105 # um^-2
_k2 = 57.362 # um^-2
_k3 = 167917 # um^-2
_A = 1.2378847e-5 # K^-2
_B = -1.9121316e-2 # K^-2
_C = 33.93711047
_D = -6.3431645e3 # K^-2
_Mw = 0.018015 # kg/mol
_R = 8.314510 # J mol^-1 K^-1, gas constant
_a0 = 1.58123e-6 # K Pa^-1
_a1 = -2.9331e-8 # Pa^-1
_a2 = 1.1043e-10 # K^-1 Pa^-1
_b0 = 5.707e-6 # K Pa^-1
_b1 = -2.051e-8 # Pa^-1
_c0 = 1.9898e-4 # K Pa^-1
_c1 = -2.376e-6 # Pa^-1
_d = 1.83e-11 # K^2 Pa^-2
_e = -0.765e-8 # K^2 Pa^-2
_t = T - 273.15
_aa = 1.00062
_bb = 3.14e-8 # Pa^-1
_cc = 5.6e-7 # deg C^-2
_RH_a = 0
_RH_w = RH
# Calculated values
_rho_obs = self.rho(p, T, RH, xc)
self.rc = self.set_rc() * 1000
self.H = self.set_H(p, _rho_obs)
_alpha = self.rc * np.sin(np.deg2rad(zenith)) / (self.rc + self.H)
_p_a = p - RH*self.svp(T)
_p_w = RH*self.svp(T)
_x_w_a = self.xw(_p_a, T, _RH_a)
_x_w_w = self.f(_p_w, T)
_Z_a = self.Z(p=_p_a, T=T, RH=_RH_a)
_Z_w = self.Z(p=_p_w, T=T, RH=_RH_w, xw=_x_w_w)
_Ma = 1e-3 * (28.9635 + 12.011e-6 * (xc - 400) ) # Molar mass in kg/mol of dry air containing xc ppm of CO2
_p_sv = self.svp(T)
_rho_a = self.rho(p=p-RH*self.svp(T), T=T, RH=0, xc=xc)
_rho_axs = self.rho(p=101325, T=288.15, RH=0, xc=xc)
if RH == 0.0:
# nan is returned for _rho_w if we put in a (partial) pressure of 0
_rho_w = 0
else:
_rho_w = self.rho(p=RH*self.svp(T), T=T, RH=RH, xc=xc)
_rho_ws = self.rho(p=1333, T=293.15, RH=1333/self.svp(293.15), xc=xc)
_n_a1 = self.n_axs(l1, xc)
_n_w1 = self.n_ws(l1)
_n_a2 = self.n_axs(l2, xc)
_n_w2 = self.n_ws(l2)
_L1_a = (_n_a1**2 - 1) / (_n_a1**2 + 2) # Dry air component of L
_L1_w = (_n_w1**2 - 1) / (_n_w1**2 + 2)
_L1 = ( _rho_a / _rho_axs ) * _L1_a + ( _rho_w / _rho_ws ) * _L1_w # Overall L
_L2_a = (_n_a2**2 - 1) / (_n_a2**2 + 2) # Dry air component of L
_L2_w = (_n_w2**2 - 1) / (_n_w2**2 + 2)
_L2 = ( _rho_a / _rho_axs ) * _L2_a + ( _rho_w / _rho_ws ) * _L2_w # Overall L
_n1 = np.sqrt( (1 + 2 * _L1) / (1 - _L1) )
_n2 = np.sqrt( (1 + 2 * _L2) / (1 - _L2) )
# Partial derivatives for derivation to l1
_dn_wdl1 = _cf * 1e-8 * (-1 * _w1 * 2/l1**3 - _w2 * 4/l1**5 - _w3 * 6/l1**7)
_dLdn_w1 = _rho_w / _rho_ws * 6 * _n_w1 / (_n_w1**2 + 2)**2
_dn_adl1 = 1e-8 * (1 + 0.534e-6 * (xc - 450)) * ( - (2 * _k1 * l1 ) / ((_k0 * l1**2 - 1)**2) - (2 * _k3 * l1) / ((_k2 * l1**2 - 1)**2) )
_dLdn_a1 = _rho_a / _rho_axs * 6 * _n_a1 / (_n_a1**2 + 2)**2
_dndL1 = ( ( (1 + 2*_L1) / (1 - _L1)**2 ) + (2 / (1-_L1)) ) / (2 * np.sqrt( (1 + 2*_L1) / (1 - _L1) ) )
_dddn1 = _alpha / np.sqrt( 1 - _alpha**2 * _n1**2 )
_dddl1 = _dddn1 * _dndL1 * ( _dLdn_a1 * _dn_adl1 + _dLdn_w1 * _dn_wdl1)
# Partial derivatives for derivation to l2
_dn_wdl2 = _cf * 1e-8 * (-1 * _w1 * 2/l2**3 - _w2 * 4/l2**5 - _w3 * 6/l2**7)
_dLdn_w2 = _rho_w / _rho_ws * 6 * _n_w2 / (_n_w2**2 + 2)**2
_dn_adl2 = 1e-8 * (1 + 0.534e-6 * (xc - 450)) * ( - (2 * _k1 * l2 ) / ((_k0 * l2**2 - 1)**2) - (2 * _k3 * l2) / ((_k2 * l2**2 - 1)**2) )
_dLdn_a2 = _rho_a / _rho_axs * 6 * _n_a2 / (_n_a2**2 + 2)**2
_dndL2 = ( ( (1 + 2*_L2) / (1 - _L2)**2 ) + (2 / (1-_L2)) ) / (2 * np.sqrt( (1 + 2*_L2) / (1 - _L2) ) )
_dddn2 = _alpha / np.sqrt( 1 - _alpha**2 * _n2**2 )
_dddl2 = _dddn2 * _dndL2 * ( _dLdn_a2 * _dn_adl2 + _dLdn_w2 * _dn_wdl2)
# Partial derivatives for derivation to T
# Dry air parts:
_drho_adp_a = _Ma / (_Z_a * _R * T) * (1 - _x_w_a * (1 - _Mw / _Ma))
_dp_adp_sv = - RH
_dp_svdT = (2*_A*T + _B - _D/(T**2)) * np.exp(_A * T**2 + _B * T + _C + _D/T )
_drho_adTZ = - _p_a * _Ma / (_R * ( _Z_a * T)**2) * (1 - _x_w_a * (1 - _Mw / _Ma))
_dTZdT_a = _p_a / T * (_a2 * _t**2 + _a1 * _t + _a0 + (_b1*_t + _b0) * _x_w_a + (_c0 + _c1*_t) * _x_w_a**2 ) - _p_a * (_a1 + 2*_a2*_t + _b1*_x_w_a + _c1*_x_w_a**2) - 2*(_p_a/T)**2 * (_d + _e * _x_w_a**2) + _Z_a
_dTZdxw_a = 2*_e*_p_a**2 / T * _x_w_a - _p_a * (_b1*_t + _b0 + 2 * (_c0 + _c1*_t) * _x_w_a)
_dxw_adf = _RH_a * _p_sv / _p_a
_dfdT = 2*_cc*_t
_dxw_adp_sv = self.f(_p_a, T) * _RH_a / _p_a
_drho_adxw_a = 0 # as rho_a has not water content, i.e. rho = p*M/ZRT
# Moist air parts:
_drho_wdp_w = _Ma / (_Z_w * _R * T) * (1 - _x_w_w * (1 - _Mw / _Ma))
_dp_wdp_sv = RH
_dp_svdT = (2*_A*T + _B - _D/(T**2)) * np.exp(_A * T**2 + _B * T + _C + _D/T )
_drho_wdTZ = - _p_w * _Ma / (_R * (_Z_w * T)**2) * (1 - _x_w_w * (1 - _Mw / _Ma))
_dTZdT_w = _p_w / T * (_a2 * _t**2 + _a1 * _t + _a0 + (_b1*_t + _b0) * _x_w_w + (_c0 + _c1*_t) * _x_w_w**2 ) - _p_w * (_a1 + 2*_a2*_t + _b1*_x_w_w + _c1*_x_w_w**2) - 2*(_p_w/T)**2 * (_d + _e * _x_w_w**2) + _Z_w
_dTZdxw_w = 2*_e*_p_w**2 / T * _x_w_w - _p_w * (_b1*_t + _b0 + 2 * (_c0 + _c1*_t) * _x_w_w)
_dxw_wdf = 1 # _RH_w * _p_sv / _p_w
_dfdT = 2*_cc*_t
_dxw_wdp_sv = self.f(_p_w, T) / _p_sv # * _RH_w / _p_w
_drho_wdxw_w = _p_w * _Ma / (_Z_w * _R * T) * (_Mw / _Ma - 1)
_tA = _drho_adp_a * _dp_adp_sv * _dp_svdT + _drho_adTZ * (_dTZdT_a + _dTZdxw_a * (_dxw_adf * _dfdT + _dxw_adp_sv * _dp_svdT) ) + _drho_adxw_a * ( _dxw_adf * _dfdT + _dxw_adp_sv * _dp_svdT )
_tB = _drho_wdp_w * _dp_wdp_sv * _dp_svdT + _drho_wdTZ * (_dTZdT_w + _dTZdxw_w * (_dxw_wdf * _dfdT + _dxw_wdp_sv * _dp_svdT) ) + _drho_wdxw_w * ( _dxw_wdf * _dfdT + _dxw_wdp_sv * _dp_svdT )
_dL1drho_a = _L1_a / _rho_axs
_dL2drho_a = _L2_a / _rho_axs
_dL1drho_w = _L1_w / _rho_ws
_dL2drho_w = _L2_w / _rho_ws
_dddT = _tA * (_dddn1 * _dndL1 * _dL1drho_a - _dddn2 * _dndL2 * _dL2drho_a) + _tB * (_dddn1 * _dndL1 * _dL1drho_w - _dddn2 * _dndL2 * _dL2drho_w)
# Partial derivatives for derivation to p
# Dry air parts:
_drho_adZ = - _p_a * _Ma / ( _Z_a**2 * _R * T) * (1 - _x_w_a * (1 - _Mw / _Ma))
_dZdp_a = - 1 / T * (_a0 + _a1 * _t + _a2 * _t**2 + (_b0 + _b1*_t)*_x_w_a + (_c0 + _c1*_t)*_x_w_a**2 ) + 2*_p_a/(T**2) * (_d + _e * _x_w_a**2)
_dfdp = _bb
_dxw_adp_a = - self.f(_p_a, T) * _RH_a * _p_sv / (_p_a)**2
# Moist air parts
_dZdp_w = - 1 / T * (_a0 + _a1 * _t + _a2 * _t**2 + (_b0 + _b1*_t)*_x_w_w + (_c0 + _c1*_t)*_x_w_w**2 ) + 2*_p_w/(T**2) * (_d + _e * _x_w_w**2)
# Not necessary, since dpwdp=0
_pA = _drho_adp_a + _drho_adZ * _dZdp_a + _drho_adxw_a * (_dxw_adf * _dfdp + _dxw_adp_a)
_dddP = _pA * (_dddn1 * _dndL1 * _dL1drho_a - _dddn2 * _dndL2 * _dL2drho_a)
# Partial derivatives for derivation to H
# Dry air parts:
_dp_adH = - _p_sv
_dZdxw_a = (_p_a / T)**2 * 2 * _e * _x_w_a - (_p_a / T) * ( (_b0 + _b1 * _t) + 2 * (_c0 + _c1 * _t) * _x_w_a )
_dxw_adH = self.f(_p_a, T) * _p_sv / _p_a
# Moist air parts:
_dp_wdH = _p_sv
_dZdxw_w = (_p_w / T)**2 * 2 * _e * _x_w_w - (_p_w / T) * ( (_b0 + _b1 * _t) + 2 * (_c0 + _c1 * _t) * _x_w_w )
_dxw_wdH = 0
_dxw_wdp_w = 0
_drho_wdZ = - _p_w * _Ma / ( _Z_w**2 * _R * T) * (1 - _x_w_w * (1 - _Mw / _Ma))
_hA = _drho_adp_a * _dp_adH + _drho_adZ * ( _dZdp_a * _dp_adH + _dZdxw_a * (_dxw_adH + _dxw_adp_a * _dp_adH + _dxw_adf * _dfdp * _dp_adH) ) + _drho_adxw_a * (_dxw_adH + _dxw_adp_a * _dp_adH + _dxw_adf * _dfdp * _dp_adH)
_hB = _drho_wdp_w * _dp_wdH + _drho_wdZ * ( _dZdp_w * _dp_wdH + _dZdxw_w * ( _bb * _p_sv ) ) + _drho_wdxw_w * ( _bb * _p_sv )
_dddRH = _hA * (_dddn1 * _dndL1 * _dL1drho_a - _dddn2 * _dndL2 * _dL2drho_a) + _hB * (_dddn1 * _dndL1 * _dL1drho_w - _dddn2 * _dndL2 * _dL2drho_w)
# Partial derivatives for derivation to xc
# Dry air parts:
_drho_adMa = _p_a * (1 - _x_w_a) / (_Z_a * _R * T)
_dL1drho_axs = - (_rho_a * _L1_a)/(_rho_axs**2)
_dL2drho_axs = - (_rho_a * _L2_a)/(_rho_axs**2)
_drho_axsdMa = _drho_adMa
_dL1dL_a = _rho_a / _rho_axs
_dL_adn_a1 = 6 * _n_a1 / (_n_a1**2 + 2)**2
_dL2dL_a = _dL1dL_a
_dL_adn_a2 = 6 * _n_a2 / (_n_a2**2 + 2)**2
# Moist air parts:
_drho_wdMa = _p_w * (1 - _x_w_w) / (_Z_w * _R * T)
_dL1drho_ws = - (_rho_w * _L1_w)/(_rho_ws**2)
_dL2drho_ws = - (_rho_w * _L2_w)/(_rho_ws**2)
_drho_wsdMa = _drho_wdMa
# Other parts:
_dMadx_c = 12.011e-9
_dn_adx_c1 = (self.n_as(l1) - 1) * 0.534e-6
_dn_adx_c2 = (self.n_as(l2) - 1) * 0.534e-6
_dL1dx_c = _dL1drho_a * _drho_adMa * _dMadx_c + _dL1drho_w * _drho_wdMa * _dMadx_c + _dL1drho_axs * _drho_axsdMa * _dMadx_c + _dL1drho_ws * _drho_wsdMa * _dMadx_c + _dL1dL_a * _dL_adn_a1 * _dn_adx_c1
_dL2dx_c = _dL2drho_a * _drho_adMa * _dMadx_c + _dL2drho_w * _drho_wdMa * _dMadx_c + _dL2drho_axs * _drho_axsdMa * _dMadx_c + _dL2drho_ws * _drho_wsdMa * _dMadx_c + _dL2dL_a * _dL_adn_a2 * _dn_adx_c2
_dddCO2 = _dddn1 * _dndL1 * _dL1dx_c - _dddn2 * _dndL2 * _dL2dx_c
# Partial derivatives for derivation to z
_zenith = np.deg2rad(zenith)
_dz = np.deg2rad(dz)
_R = self.rc
_H = self.H
_dddz = (_R * np.cos(_zenith)) / (_R + _H) * ( _n1 / np.sqrt(1-(_n1**2 * _R**2 * np.sin(_zenith)**2)/(_R + _H)**2) - _n2 / np.sqrt(1-(_n2**2 * _R**2 * np.sin(_zenith)**2)/(_R + _H)**2) )
# Final error:
_dispersionError = np.sqrt( (_dddl1 * dl1)**2 + (_dddl2 * dl2)**2 + (_dddT * dT)**2 + (_dddP * dP)**2 + (_dddRH * dRH)**2 + (_dddCO2 * dCO2)**2 + (_dddz * _dz)**2)
return np.degrees(_dispersionError)
def independentCassiniError(self, n1, n2, zenith, dn1=0, dn2=0, dz=0,
lat=None, h=None):
'''
Calculates the uncertainty in the atmospheric dispersion,
following the Cassini model, without assumptions about the
refractivity model.
This involves a much shorter use the chain rule.
Not taken into account is the errror propagation in the scale height
of the atmosphere, from my results this should give a small or even
negligible difference.
Parameters
----------
n1 : float
Refractive index at the first wavelength of interest at the
observer
n2 : float
Refractive index at the second wavelength of interest at the
observer
zenith : float
Angle of observation in degrees.
zenith = 0 means the observation is directly overhead.
dn1 : float
Uncertainty in the provided value of the refractive index
at the first wavelength
dn2 : float
Uncertainty in the provided value of the refractive index
at the second wavelength
dz : float (default: 0)
Uncertainty in the zenith angle, in degrees.
lat : float (default: None)
If entered, overrides the latitude attribute of the class.
h : float (default=None)
If entered, overrides the altitude attribute of the class.
Returns
-------
dRR : float
Uncertainty in the atmospheric dispersion in degrees.
'''
if lat == None:
lat = self.lat
else:
self.lat = lat
if h == None:
h = self.h
else:
self.h = h
# Some precalculation and variable setting
_zenith = np.deg2rad(zenith)
_dz = np.deg2rad(dz)
_R = self.rc
_H = self.H
# Partial derivatives for derivation to n1, n2, and z
_dddn1 = - _R * np.sin(_zenith) / ( (_R + _H) * np.sqrt(1 - (n1 * _R * np.sin(_zenith) / (_R + _H))**2 ) )
_dddn2 = _R * np.sin(_zenith) / ( (_R + _H) * np.sqrt(1 - (n2 * _R * np.sin(_zenith) / (_R + _H))**2 ) )
_dddz = (_R * np.cos(_zenith)) / (_R + _H) * ( n1 / np.sqrt(1-(n1**2 * _R**2 * np.sin(_zenith)**2)/(_R + _H)**2) - n2 / np.sqrt(1-(n2**2 * _R**2 * np.sin(_zenith)**2)/(_R + _H)**2) )
# Combining the partial derivatives
_dispersionError = np.sqrt( (_dddn1 * dn1)**2 + (_dddn2 * dn2)**2 + (_dddz * dz)**2)
return np.degrees(_dispersionError)
def corbard(self, n1, n2, zenith, reduced_height=None):
'''
Corbard et al., 2019, mention an additional formula based on
the error function. Oriani's theorem can be derived from this
equation by 'keeping only the three first terms of its
asymptotic expansion'.
Parameters
----------
n1 : float
Refractive index at the shortest wavelength of interest
n2 : float
Refractive index at the longest wavelength of interest
zenith : float
Angle of observation in degrees.
zenith = 0 means the observation is directly overhead.
reduced_height : float (optional)
Reduced height of the atmosphere. Default value is calculated
from the object attributes.
Returns
-------
dR : float
Atmospheric dispersion in degrees
'''
if reduced_height:
H = reduced_height
else:
H = self.H
_rList = []
for _n in [n1, n2]:
_a = _n - 1
_b = H / self.rc
_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) )
_rList.append(np.degrees(_R))
return _rList[0] - _rList[1]
def gammaCoefficients(self, n1, reduced_height=None):
'''
Submethod for matharExponential(). Calculates the gamma
coefficients.
Parameters
----------
n1 : float
Refractive index at the wavelength of interest at the observer
reduced_height : float (optional)
Reduced height of the atmosphere. Default value is calculated
from the object attributes.
Returns
-------
g : array
List of values, corresponding to the gamma coefficients.
g[0] corresponds to g_1, g[1] to g_3, g[2] to g_5, and so on.
'''
if reduced_height:
H = reduced_height
else:
H = self.H
_chi0 = n1**2 - 1
_Khat = H / self.rc
_g1 = (1 - 5/24 * _Khat * _chi0**2 - 3/4 * _chi0 - 3/8 * _Khat**2 * _chi0 \
- _Khat - 6 * _Khat**3 + 5/8 * _chi0**2 + 63/128 * _chi0**4 + 3/8 \
* _Khat * _chi0 + 35/256 * _Khat * _chi0**3 + 2 * _Khat**2 + 5/36 \
* _Khat**2 * _chi0**2 - 35/64 * _chi0**3 + 9/16 * _Khat**3 * _chi0 \
- 120 * _Khat**5 + 24 * _Khat**4 - 231 / 512 * _chi0**5 - 63 / 640 \
* _Khat * _chi0**4 - 35 / 512 * _Khat**2 * _chi0**3 - 5 / 36 * _Khat**3\
* _chi0**2 - 9 / 8 * _Khat**4 * _chi0 )
_g3 = (35/96 * _chi0**3 - 5/12 * _chi0**2 + 333 * _Khat**4 * _chi0 - 2400 \
* _Khat**5 + 3605/4608 * _Khat**2 * _chi0**3 + 63/256 * _Khat * _chi0**4\
- 819/16 * _Khat**3 * _chi0 - 35/18 * _Khat**2 * _chi0**2 - 35/96 \
* _Khat * _chi0**3 + 69/8 * _Khat**2 * _chi0 + 5/8 * _Khat * _chi0**2 \
- 3/2 * _Khat * _chi0 + 1355/216 * _Khat**3 * _chi0**2 + 10 * _Khat**2 \
+ 336 * _Khat**4 - 2 * _Khat + _chi0/2 - 21/64 * _chi0**4 - 54 * _Khat**3 \
+ 77/256 * _chi0**5 ) * 1/2
_g5 = (-96 * _Khat**3 + 8 * _Khat**2 - 7/24 * _chi0**3 + 65/4 * _Khat**2 \
* _chi0**2 + 35/48 * _Khat * _chi0**3 + 30 * _Khat**2 * _chi0 - 5/3 \
* _Khat * _chi0**2 - 3 * _Khat * _chi0 + 20091/8 * _Khat**4 * _chi0 \
- 32605/216 * _Khat**3 * _chi0**2 - 35/9 * _Khat**2 * _chi0**3 + 984 \
* _Khat**4 + _chi0**2/3 - 10200 * _Khat**5 - 77/320 * _chi0**5 + 21/80 \
* _chi0**4 - 1089/4 * _Khat**3 * _chi0 - 7/16 * _Khat * _chi0**4 ) * 3/8
_g7 = (-17520 * _Khat**5 - 9/40 * _chi0**4 - 8755/12 * _Khat**3 * _chi0**2 \
+ 1056 * _Khat**4 + _chi0**3/4 + 23517/4 * _Khat**4 * _chi0 - 11/3 \
* _Khat * _chi0**2 + 21 * _Khat**2 * _chi0 - 7/4 * _Khat * _chi0**3 \
+ 505/9 * _Khat**2 * _chi0**2 - 801/2 * _Khat**3 * _chi0 + 33/160 \
* _chi0**5 - 48 * _Khat**3 + 63/80 * _Khat * _chi0**4 + 2345/96 \
* _Khat**2 * _chi0**3) * 5/16
_g9 = (_chi0**4 / 5 + 384 * _Khat**4 - 13440 * _Khat**5 + 5562 * _Khat**4 \
* _chi0 + 3125/36 * _Khat**2 * _chi0**3 - 11/60 * _chi0**5 - 25/6 \
* _Khat * _chi0**3 - 26950/27 * _Khat**3 * _chi0**2 + 340/9 * _Khat**2 \
* _chi0**2 - 180 * _Khat**3 * _chi0 - 9/5 * _Khat * _chi0**4) * 35/128
g = np.array([_g1, _g3, _g5, _g7, _g9])
g*= n1*_chi0/2
return g
def H_isotherm(self, T):
'''
Calculates the reduced height of the atmosphere,
assuming an isotherm distribution.
Parameters
----------
T : float
Atmospheric temperature in Kelvin
Returns
-------
H : float
Reduced height of the atmosphere in meters
'''
_kb = 1.38064852e-23 # kg m2 s-2 K-1
_m = 4.809651698e-26 # kg (weight per molecule of air, assuming dry air)
_phi = np.deg2rad(self.lat)
_c1 = 5.2790414e-3
_c2 = 2.32718e-5
_c3 = 1.262e-7
_c4 = 7e-10
_g0 = 9.780327 # m/s^2
_g0_local = _g0 * (1 + _c1 * np.sin(_phi)**2 + _c2 * np.sin(_phi)**4 + _c3 * np.sin(_phi)**6 + _c4 * np.sin(_phi)**8)
return (_kb*T) / (_m * _g0_local)
def matharExponential(self, n1, n2, zenith, reduced_height=None):
'''
Model on atmospheric refraction described by Richard Mathar in
"A Barometric Exponential Model of the Atmosphere’s Refractive Index: ZenithAngles and Second Order Aberration in the Entrance Pupil" (2020)
ArXiv id: 2004.11808 (https://arxiv.org/abs/2004.11808)
The code in this function is adapted from eqs 3, 16, 17 and 18 in this
work. It is an adaptation of the RefrExpo.cxx C++ file written by Mathar.
This model assumes the refractive index, n, is related to the susceptibility,
chi, following: n(r) = sqrt(1+chi(r)) (eq. 9).
R. Mathar then assumes that the susceptibility is proportional to the
air density and reduced height, following eq. 10:
chi(r) = chi0 * exp(-(r-rho)/K).
The refraction is described using a Taylor expansion; R in terms of
the odd powers of the tangent of the observed zenith angle (eq. 3), not
unlike Oriani's theorem. This requires coefficients g_i.
Parameters
----------
n1 : float
Refractive index at the wavelength of interest at the observer
n2 : float
Refractive index at the longest wavelength of interest at the
observer
zenith : float
Angle of observation in degrees.
zenith = 0 means the observation is directly overhead.
reduced_height : float (optional)
Reduced height of the atmosphere. Default value is calculated
from the object attributes.
Returns
-------
dR : float
Atmospheric refraction in degrees
'''
if reduced_height:
_H = reduced_height
else:
_H = self.H
_zenith = np.deg2rad(zenith)
_rc = self.rc # rho
# Atmospheric refraction for the first wavelength
_gamma1 = self.gammaCoefficients(n1, reduced_height=_H)
_R1 = sum([_gamma1[i]*np.tan(_zenith)**(2*i+1) for i in range(len(_gamma1))])
# Atmospheric refraction for the second wavelength
_gamma2 = self.gammaCoefficients(n2, reduced_height=_H)
_R2 = sum([_gamma2[i]*np.tan(_zenith)**(2*i+1) for i in range(len(_gamma2))])
# The atmospheric dispersion in radians
_dR = _R1 - _R2
return np.degrees(_dR)
def oriani(self, n1, n2, zenith, reduced_height=None):
'''
Classic refraction formula with two tan(z) terms.
This does not assume any information about the structure
of the atmosphere. Found with Oriani's theorem.
Parameters
----------
n1 : float
Refractive index at the shortest wavelength of interest
n2 : float
Refractive index at the longest wavelength of interest
zenith : float
Angle of observation in degrees.
zenith = 0 means the observation is directly overhead.
reduced_height : float (optional)
Reduced height of the atmosphere. Default value is calculated
from the object attributes.
Returns
-------
dR : float
Atmospheric dispersion in degrees
'''
if reduced_height:
H = reduced_height
else:
H = self.H
_rList = []
for _n in [n1, n2]:
_a = _n - 1
_b = H / self.rc
_R = _a * (1 - _b) * np.tan( np.deg2rad(zenith) ) - _a * (_b - _a/2) * np.tan( np.deg2rad(zenith) )**3
_rList.append(np.degrees(_R))
return _rList[0] - _rList[1]
def planeParallel(self, n1, n2, zenith):
'''
Refraction based on a flat atmosphere, easily derived from Snell's law.
Parameters
----------
n1 : float
Refractive index at the shortest wavelength of interest
n2 : float
Refractive index at the longest wavelength of interest
zenith : float
Angle of observation in degrees.
zenith = 0 means the observation is directly overhead.
Returns
-------
dR : float
Atmospheric dispersion in degrees
'''
_R1 = np.arcsin(n1 * np.sin( np.deg2rad(zenith) )) - np.deg2rad(zenith)
_R2 = np.arcsin(n2 * np.sin( np.deg2rad(zenith) )) - np.deg2rad(zenith)
return np.degrees(_R1) - np.degrees(_R2)
def psi(self, x):
'''
Submethod for corbard(). Described explicitly in Corbard et al.,2019.
Parameters
----------
x : float
Input of the function
Returns
-------
y : float
Output of the function
'''
_f = lambda a: np.exp(-a**2)
_int = integrate.quad(_f, x, np.inf)
return np.exp(x**2) * _int[0]
def refractionIntegral(self, n1, n2, zenith, R0=None, useStandardAtmosphere=True,
heightData=None, rhoData=None):
'''
Using data that gives temperature, pressure and density as a
function of height, the gladstone-dale relation is invoked to
calculate the path of a light ray along the atmosphere.
This is allows us to also include atmospheric activity in
different atmosphere layers.
Recommended use with the US Standard Atmosphere from 1976.
USSA1976 data generated from code supplied by http://www.pdas.com/atmos.html
The method described in eq. 3 in Auer & Standish (2000) is used for the
calculation.
Parameters
----------
n1 : float
Refractive index at the shortest wavelength of interest
n2 : float
Refractive index at the longest wavelength of interest
zenith : float
Angle of observation in degrees.
zenith = 0 means the observation is directly overhead.
R0 : float (optional)
Local radius of curvature of the Earth in km.
useStandardAtmosphere : bool
Flag to use the included USSA1976 data
heightData : 1D Array
If "useStandardAtmosphere" is False, the function will ask
for alternative atmospheric data to integrate over. This
gives the altitudinal points
rhoData : 1D Array
If useStandardAtmosphere is False, the function will ask
for alternative atmospheric data to integrate over. This
gives the atmospheric density at the points defined by
"heightData".
Returns
-------
dR : float
Atmospheric dispersion in degrees
'''
if not R0:
R0 = self.rc
if useStandardAtmosphere:
_datafileLocation = os.path.join(os.path.dirname(__file__), 'data/1976USSA.txt')
_h, _, _, _, _Temp, _Press, _Dens,_,_,_ = np.genfromtxt(_datafileLocation, unpack=True, skip_header=6)
else:
_Dens = rhoData
_h = heightData
_rList = []
zenith = np.deg2rad(zenith)
# Integration of the refraction integral over the atmosphere layers.
for _n in [n1, n2]:
_Dens = np.asarray(_Dens)
_h = np.asarray(_h)
_R = R0 + _h * 1000
# Gladstone-Dale relation between refractive index and density
_n_h = 1 + (_n - 1) / _Dens[0] * _Dens
# Calculation of the zenith angle for given height and refractive index, from the refractive invariant
_z = np.arcsin( (_n * R0) / (_n_h * _R) * np.sin(zenith) )
# Calculation of log space derivative of n and r
_lnn = np.log(_n_h)
_lnr = np.log(_R)
_dlnn = np.asarray([_lnn[i+1] - _lnn[i] for i in range(len(_lnn)-1)])
_dlnr = np.asarray([_lnr[i+1] - _lnr[i] for i in range(len(_lnr)-1)])
_dz = np.asarray([(_z[i] - _z[i+1]) for i in range(len(_z)-1)])
# Calculation of the integrand in eq. 3 in Auer & Standish
_Integrand = -1*(_dlnn/_dlnr) / (1 + _dlnn/_dlnr) * _dz
_rList.append(np.degrees(np.sum(_Integrand)))
return _rList[0] - _rList[1]
def setReducedHeight(self, p, rho):
'''
Sets the reduced atmospheric height.
Parameters
----------
p : float
Atmospheric pressure in Pascal
rho : float
Density of the atmospheric air at the observer
'''
self.H = self.set_H(p, rho) # Reduced height of the atmosphere assuming ideal gas law
def set_H(self, p, rho):
'''
Calculates the reduced height of the atmosphere, assuming ideal gas law.
Parameters
----------
p : float
Atmospheric pressure in Pascal
rho : float
Density of the atmospheric air at the observer
Returns
-------
H : float
Reduced height of the atmosphere in meters
'''
_phi = np.deg2rad(self.lat)
_c1 = 5.2790414e-3
_c2 = 2.32718e-5
_c3 = 1.262e-7
_c4 = 7e-10
_g0 = 9.780327 # m/s^2
_g0_local = _g0 * (1 + _c1 * np.sin(_phi)**2 + _c2 * np.sin(_phi)**4 + _c3 * np.sin(_phi)**6 + _c4 * np.sin(_phi)**8)
_g = _g0_local - (3.0877e-6 - 4.3e-9 * np.sin(_phi)**2) * self.h + 7.2e-13 * self.h**2
return p / (rho * _g)
def set_rc(self):
'''
Returns the radius of the curvature at the observer in km,
taking into account the latitude of the observer and the corresponding
ellipsoidality of the earth.
Returns
-------
rc : float
Local radius of curvature of the Earth in kilometers.
'''
_A = 6378.137 # km
_B = 6356.752 # km
_phi = np.deg2rad(self.lat)
_rc0 = (_A*_B)**2 / (_A**2 * np.cos(_phi)**2 + _B**2 * np.sin(_phi)**2)**(3/2)
return _rc0 + self.h/1000
def tan5(self, n1, n2, zenith, reduced_height=None):
'''
Same as oriani's theorem, but including a higher order term.
Found in Corbard et al., 2019.
Parameters
----------
n1 : float
Refractive index at the shortest wavelength of interest
n2 : float
Refractive index at the longest wavelength of interest
zenith : float
Angle of observation in degrees.
zenith = 0 means the observation is directly overhead.
reduced_height : float (optional)
Reduced height of the atmosphere. Default value is calculated
from the object attributes.
Returns
-------
dR : float
Atmospheric dispersion in degrees
'''
if reduced_height:
H = reduced_height
else:
H = self.H
_rList = []
for _n in [n1, n2]:
_a = _n - 1
_b = H / self.rc
_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
_rList.append(np.degrees(_R))
return _rList[0] - _rList[1]
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