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Refactored some variable names and updated descriptions
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@@ -39,39 +39,45 @@ class dispersion(Observatory):
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Methods
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-------
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setReducedHeight(p, rho)
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Sets the reduced atmospheric height.
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planeParallel(n1, n2, zenith)
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Returns the atmospheric dispersion assuming a plane parallel
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atmosphere
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cassini(n1, n2, zenith, reduced_height=None)
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Returns the atmospheric dispersion asusming Cassini's homegeneous
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atmosphere model
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tan5(n1, n2, zenith, reduced_height=None)
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Returns the atmospheric dispersion following Oriani's theorem,
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including a fifth order term in the expansion.
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oriani(n1, n2, zenith, reduced_height=None)
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Returns the atmospheric dispersion following the classic version
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of Oriani's theorem.
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corbard(n1, n2, zenith, reduced_height=None)
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Returns the atmospheric dispersion following the method described
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in Corbard et al., 2019.
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refractionIntegral(n1, n2, z0, R0=None, useStandardAtmosphere=True, heightData=None, rhoData=None)
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Returns the atmospheric dispersion following the refraction integral.
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psi(x)
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Submethod for corbard()
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set_rc()
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Sets the local radius of curvature of the Earth at the observer.
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set_H(p, rho)
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Sets the reduced height for the conditions at the observer, assuming
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an adiabatic atmosphere.
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H_isotherm(T)
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Sets the reduced height for the conditions at the observer, assuming
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an isothermal atmosphere.
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Returns the atmospheric dispersion assuming Cassini's homegeneous
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atmosphere model.
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cassiniError(z, l1, l2, T, p, RH, xc, dl1=0, dl2=0, dT=0.2, dP=20,
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dRH=0.02, dCO2=20, dz=0, lat=None, h=None)
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Returns the uncertainty in the atmospheric dispersion, assuming
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Cassini's homegeneous atmosphere model and the given parameters.
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Cassini's homegeneous atmosphere model and the given parameters.
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corbard(n1, n2, zenith, reduced_height=None)
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Returns the atmospheric dispersion following the method described
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in Corbard et al., 2019.
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gammaCoefficients(n1, reduced_height=None)
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Helper function for matharExponential(), that calculates the gamma
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coefficients.
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H_isotherm(T)
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Sets the reduced height for the conditions at the observer, assuming
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an isothermal atmosphere.
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matharExponential(n1, n2, zenith, reduced_height=None)
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Returns the atmospheric dispersion assuming Mathar's barometric
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exponential model.
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oriani(n1, n2, zenith, reduced_height=None)
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Returns the atmospheric dispersion following the classic version
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of Oriani's theorem.
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planeParallel(n1, n2, zenith)
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Returns the atmospheric dispersion assuming a plane parallel
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atmosphere.
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psi(x)
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Submethod for corbard().
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refractionIntegral(n1, n2, zenith, R0=None, useStandardAtmosphere=True, heightData=None, rhoData=None)
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Returns the atmospheric dispersion following the refraction integral.
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setReducedHeight(p, rho)
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Sets the reduced atmospheric height.
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set_H(p, rho)
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Sets the reduced height for the conditions at the observer, assuming
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an adiabatic atmosphere.
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set_rc()
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Sets the local radius of curvature of the Earth at the observer.
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tan5(n1, n2, zenith, reduced_height=None)
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Returns the atmospheric dispersion following Oriani's theorem,
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including a fifth order term in the expansion.
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Other Observatory() methods
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'''
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def __init__(self, lat, h):
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@@ -113,7 +119,7 @@ class dispersion(Observatory):
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return np.degrees(_R1) - np.degrees(_R2)
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def cassiniError(self, z, l1, l2, T, p, RH, xc, dl1=0, dl2=0, dT=0.2, dP=20,
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def cassiniError(self, zenith, l1, l2, T, p, RH, xc, dl1=0, dl2=0, dT=0.2, dP=20,
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dRH=0.02, dCO2=20, dz=0, lat=None, h=None):
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'''
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Calculates the uncertainty in the atmospheric dispersion,
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@@ -128,7 +134,7 @@ class dispersion(Observatory):
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Parameters
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----------
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z : float
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zenith : float
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Angle of observation in degrees.
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zenith = 0 means the observation is directly overhead.
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l1 : float
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@@ -222,7 +228,7 @@ class dispersion(Observatory):
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_rho_obs = self.rho(p, T, RH, xc)
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self.rc = self.set_rc() * 1000
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self.H = self.set_H(p, _rho_obs)
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_alpha = self.rc * np.sin(np.deg2rad(z)) / (self.rc + self.H)
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_alpha = self.rc * np.sin(np.deg2rad(zenith)) / (self.rc + self.H)
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_p_a = p - RH*self.svp(T)
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_p_w = RH*self.svp(T)
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_x_w_a = self.xw(_p_a, T, _RH_a)
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@@ -369,7 +375,7 @@ class dispersion(Observatory):
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# Partial derivatives for derivation to z
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_zenith = np.deg2rad(z)
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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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_H = self.H
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@@ -420,14 +426,14 @@ class dispersion(Observatory):
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return _rList[0] - _rList[1]
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def gammaCoefficients(self, n0, reduced_height=None):
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def gammaCoefficients(self, n1, reduced_height=None):
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'''
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Submethod for matharExponential(). Calculates the gamma
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coefficients.
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Parameters
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----------
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n0 : float
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n1 : float
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Refractive index at the wavelength of interest at the observer
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reduced_height : float (optional)
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Reduced height of the atmosphere. Default value is calculated
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@@ -444,7 +450,7 @@ class dispersion(Observatory):
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else:
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H = self.H
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_chi0 = n0**2 - 1
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_chi0 = n1**2 - 1
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_Khat = H / self.rc
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_g1 = (1 - 5/24 * _Khat * _chi0**2 - 3/4 * _chi0 - 3/8 * _Khat**2 * _chi0 \
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@@ -483,7 +489,7 @@ class dispersion(Observatory):
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* _chi0**2 - 180 * _Khat**3 * _chi0 - 9/5 * _Khat * _chi0**4) * 35/128
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g = np.array([_g1, _g3, _g5, _g7, _g9])
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g*= n0*_chi0/2
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g*= n1*_chi0/2
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return g
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@@ -659,7 +665,7 @@ class dispersion(Observatory):
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return np.exp(x**2) * _int[0]
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def refractionIntegral(self, n1, n2, z0, R0=None, useStandardAtmosphere=True,
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def refractionIntegral(self, n1, n2, zenith, R0=None, useStandardAtmosphere=True,
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heightData=None, rhoData=None):
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'''
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Using data that gives temperature, pressure and density as a
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@@ -678,7 +684,7 @@ class dispersion(Observatory):
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Refractive index at the shortest wavelength of interest
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n2 : float
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Refractive index at the longest wavelength of interest
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z0 : float
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zenith : float
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Angle of observation in degrees.
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zenith = 0 means the observation is directly overhead.
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R0 : float (optional)
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@@ -711,7 +717,7 @@ class dispersion(Observatory):
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_rList = []
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z0 = np.deg2rad(z0)
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zenith = np.deg2rad(zenith)
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# Integration of the refraction integral over the atmosphere layers.
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for _n in [n1, n2]:
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@@ -723,7 +729,7 @@ class dispersion(Observatory):
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_n_h = 1 + (_n - 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( (_n * R0) / (_n_h * _R) * np.sin(z0) )
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_z = np.arcsin( (_n * R0) / (_n_h * _R) * np.sin(zenith) )
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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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