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Updated documentation, descriptions for v1.0 release.
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#!/usr/bin/env Python3
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import numpy as np
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from scipy.optimize import fsolve
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class ADC:
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def __init__(self, wedge=(1.6554, 6.28927, -1.48306)):
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self.p1 = prism('S-LAH71', 15, wedge[0], wedge[1])
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self.p2 = prism('S-FPL51', 15, wedge[1], wedge[2])
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self.p3 = prism('S-FPL51', 15, wedge[2], wedge[1])
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self.p4 = prism('S-LAH71', 15, wedge[1], wedge[0])
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self.theta = 90
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def setOrientation(self, theta):
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self.p1.setPrismOrientation(theta)
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self.p2.setPrismOrientation(theta)
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self.p3.setPrismOrientation(180-1*theta)
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self.p4.setPrismOrientation(180-1*theta)
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self.theta = theta
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def setFieldRotation(self, FR):
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self.p1.setPrismOrientation(self.theta-FR)
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self.p2.setPrismOrientation(self.theta-FR)
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self.p3.setPrismOrientation(180-1*self.theta-FR)
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self.p4.setPrismOrientation(180-1*self.theta-FR)
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def calculateDispersion(self, l1, l2, hx=0, hy=0, refraction=False):
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'''
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Calculates the dispersion for the ADC using the current position,
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for a normalized on sky field angle (hx, hy). Default field position
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is in the center of the FoV.
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'''
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_prismList = [self.p1, self.p2, self.p3, self.p4]
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_d1x_list, _d1y_list = [], []
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_pupilRatio = 0.07943/38.542
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hx = hx * 7.5e-3
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hy = hy * 7.5e-3
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ix = hx * 1/_pupilRatio
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iy = hy * 1/_pupilRatio
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for l in [l1,l2]:
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_deltaListX = np.zeros(len(_prismList))
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_deltaListY = np.zeros(len(_prismList))
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for p in range(4):
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_n_g = _prismList[p].indexOfRefraction(l)
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_b1x = _prismList[p].tilt1x
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_b1y = _prismList[p].tilt1y
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_b2x = _prismList[p].tilt2x
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_b2y = _prismList[p].tilt2y
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if p==0:
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_t_iz_n_x = ix # Zero before first prism
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_t_iz_n_y = iy # Zero before first prism
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# X direction
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_t_i1_n_x = _t_iz_n_x
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_d1x = deviationCalculation3(_t_i1_n_x, _b1x, _b2x, _n_g)
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_t_tz_n_x = _t_iz_n_x - _d1x
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_t_iz_n_x = _t_tz_n_x # Set output to new input (for the next prism)
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# Y direction
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_t_i1_n_y = _t_iz_n_y
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_d1y = deviationCalculation3(_t_i1_n_y, _b1y, _b2y, _n_g)
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_t_tz_n_y = _t_iz_n_y - _d1y
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_t_iz_n_y = _t_tz_n_y # Set output to new input (for the next prism)
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# Adding the deviations to the list with delta terms
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_deltaListX[p] = _d1x
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_deltaListY[p] = _d1y
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_d1x_list.append(_deltaListX.sum())
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_d1y_list.append(_deltaListY.sum())
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self.dispX = ( _d1x_list[1] - _d1x_list[0] ) * 3600 * 1000 * _pupilRatio
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self.dispY = ( _d1y_list[1] - _d1y_list[0] ) * 3600 * 1000 * _pupilRatio
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if refraction:
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rx = ( _d1x_list[0] ) * 3600 * 1000 * _pupilRatio
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ry = ( _d1y_list[0] ) * 3600 * 1000 * _pupilRatio
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return rx, ry
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return self.dispX, self.dispY
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def optimize(self, inputDispersion, l1, l2, x0=50):
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_solver = fsolve(optimizeFunction, x0=x0, args=(self, inputDispersion, l1, l2))
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return _solver[0]
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class prism:
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def __init__(self, glass, thickness, tilt1, tilt2=None):
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self.glass = glass
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self.thickness = thickness
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self.tilt1x = tilt1
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self.tilt1y = 0
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self.tilt2x = tilt2
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self.tilt2y = 0
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# Remember original tilts for reset:
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self.tilt1x_OG = tilt1
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self.tilt1y_OG = 0
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self.tilt2x_OG = tilt2
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self.tilt2y_OG = 0
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def indexOfRefraction(self, l):
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if self.glass == 'S-FPL51':
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a = [1.170105050E+00, 4.757107830E-02, 7.638324450E-01]
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b = [6.162039240E-03, 2.633728760E-02, 1.418826420E+02]
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elif self.glass == 'S-LAH71':
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a = [1.982800310E+00, 3.167584500E-01, 2.444726460E+00]
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b = [1.189874560E-02, 5.271560010E-02, 2.132206970E+02]
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return dispersionFormula(l, a, b)
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def setPrismOrientation(self, theta):
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self.tilt1x, self.tilt1y = tiltRotation(self.tilt1x_OG, self.tilt1y_OG, theta)
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self.tilt2x, self.tilt2y = tiltRotation(self.tilt2x_OG, self.tilt2y_OG, theta)
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def tiltRotation(tx,ty, theta):
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theta = np.deg2rad(theta-90)
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tx_new = tx * np.cos(theta) - ty * np.sin(theta)
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ty_new = tx * np.sin(theta) + ty * np.cos(theta)
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return tx_new, ty_new
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def dispersionFormula(l, a, b):
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n = np.sqrt( (a[0]* l**2)/(l**2 - b[0]) + (a[1] * l**2) / (l**2 - b[1]) + (a[2] * l**2) / (l**2 - b[2]) + 1)
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return n
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# def deviationCalculation(t_i1, b1, b2, n):
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# Returns deviation of the ray after the prism with apex angle alpha
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# Units: degrees
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# DO NOT USE!
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# alpha = b2 - b1
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# delta = t_i1 + np.arcsin( np.sin(np.deg2rad(alpha)) * np.sqrt(n**2-(np.sin(np.deg2rad(t_i1)))**2) - np.sin(np.deg2rad(t_i1)) * np.cos(np.deg2rad(alpha)) )*180/np.pi - alpha
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# return delta
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def deviationCalculation2(t_i1, b1, b2, n):
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# Returns deviation of the ray after the prism with apex angle alpha
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# only now using Snell's law only
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# Units: degrees
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t_i1 = np.deg2rad(t_i1)
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b1 = np.deg2rad(b1)
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b2 = np.deg2rad(b2)
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t_i = b1 - t_i1
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t_f = np.arcsin( 1 / n * np.sin( t_i ) )
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t_f_z = b1 - t_f
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t_i = b2 - t_f_z
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t_f = np.arcsin( n / 1 * np.sin( t_i ) )
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t_f_z = b2 - t_f
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delta = t_i1 - t_f_z
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delta = np.degrees(delta)
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return delta
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def deviationCalculation3(t_i1, b1, b2, n):
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# Using the formula from Hagen & Tkazcyk (2011)
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# Gives exactly the same results as deviationCaclulation2(), above
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t_i1 = np.deg2rad(t_i1)
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b1 = np.deg2rad(b1)
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b2 = np.deg2rad(b2)
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alpha = b2 - b1
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delta = t_i1 - b2 - np.arcsin( n * np.sin( np.arcsin(1/n * np.sin(t_i1 - b1) ) - alpha ) )
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return np.degrees(delta)
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def optimizeFunction(theta, adcObject, inputDispersion, l1, l2, FR=None):
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if FR:
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FRdeg = np.deg2rad(FR) # 30 degrees field rotation
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inputDispersionX = np.cos(FRdeg) * inputDispersion
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inputDispersionY = np.sin(FRdeg) * inputDispersion
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adcObject.setOrientation(theta)
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ADCdisp = adcObject.calculateDispersion(l1,l2)
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return np.sqrt((ADCdisp[1] - inputDispersionY)**2 + (ADCdisp[0] - inputDispersionX)**2)
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else:
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adcObject.setOrientation(theta)
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ADCdisp = adcObject.calculateDispersion(l1,l2)
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ADCdisp = ADCdisp[1]
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return ADCdisp - inputDispersion
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def angleFunc(x, a):
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'''
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Function to fit the ideal angle on
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'''
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return np.arccos(a * np.tan(np.deg2rad(x))) * 180/np.pi
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File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,156 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Atmospheric Dispersion Corrector model\n",
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"\n",
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"This notebook explains how you can use the ADC.py analytic ADC model for some quick simulations. For the model to be representative, the following assumptions must apply:\n",
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"* The ADC consists of two amici prisms, totaling 4 glass prisms in total\n",
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"* The ADC is located in the pupil plane of the instrument (or very close to it)\n",
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"* The rotation of the prisms is defined as follows: for $\\theta=0$ the ADC is in it's maximum dispersive configuration. For any other rotation angle, the top prism set rotates by $\\theta$ degrees and the bottom prism set also rotates by $\\theta$ degrees in opposite direction. The total relative angle is thus $2\\theta$. From this also follows that the minimum dispersion magnitude is reached at $\\theta=90$ degrees.\n",
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"\n",
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"The ADC.py model is really not made for very general use, so it will likely require some editing in the source code by the user to make it work for a different instrument than MICADO.\n",
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"\n",
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"\n",
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"We first start by importing the necessary files. Here we'll use the `AstroAtmosphere` package to calculate the atmospheric dispersion, but a manual input is also possible."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import ADC\n",
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"import AstroAtmosphere"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Then we calculate the atmospheric dispersion for a given set of conditions."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Atmospheric conditions\n",
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"T = 288.15 \t# k\n",
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"P = 101325 \t# Pa\n",
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"H = 0.3\n",
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"xc = 400 \t# ppm\n",
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"lat = -25 \t\t# degrees\n",
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"h = 2500 \t# m\n",
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"l1 = 1.49 \t\t# micron, start H-band\n",
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"l2 = 1.78 \t\t# micron, end H-band\n",
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"zenith = 30 \t# degrees\n",
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"\n",
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"# Initializing dispersion model\n",
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"at = AstroAtmosphere.Observatory()\n",
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"rho = at.rho(p=P, T=T, RH=H, xc=xc)\n",
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"disp = AstroAtmosphere.dispersion(lat, h)\n",
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"disp.setReducedHeight(P, rho)\n",
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"\n",
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"# Calculation of indices of refraction at l1 and l2\n",
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"n1 = at.n_tph(l=l1, T=T, p=P, RH=H, xc=xc)\n",
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"n2 = at.n_tph(l=l2, T=T, p=P, RH=H, xc=xc)\n",
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"\n",
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"# Calculation of atmospheric dispersion in milli arc sec\n",
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"dispersion = disp.cassini(n1, n2, zenith)*3.6e6"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now, we initialize the ADC class. We can calculate the dispersion in sky angles, i.e. in terms of the Field of View of the telescope, for a given rotation of the ADC prisms. In this case, if we set $\\theta=30$ degrees, we find that the ADC produces 58 milli arc seconds of dispersion in H-band."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The dispersion of the ADC for 30 degrees of rotation: 58.011 mas\n"
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]
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}
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],
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"source": [
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"# Initialize ADC object\n",
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"adc = ADC.ADC()\n",
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"\n",
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"# Calculate the dispersion for a given rotation\n",
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"theta = 30 # degrees\n",
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"adc.setOrientation(theta)\n",
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"adcDispersion = adc.calculateDispersion(l1,l2)[1]\n",
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"print(\n",
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" 'The dispersion of the ADC for {:.0f} degrees of rotation: {:.3f} mas'\\\n",
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" .format(theta, adcDispersion))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Of course, we're often much more interested in the optimum positioning of the ADC given an amount of atmospheric dispersion. This is done as follows."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The atmospheric dispersion: 24.771 mas\n",
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"The optimum ADC angle in this case: 68.1 degrees\n",
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"The residual dispersion: 0.000 mas\n"
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]
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}
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],
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"source": [
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"# Optimize for atmospheric dispersion, given wavelength range {l1,l2}\n",
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"adcOptimumAngle = adc.optimize(dispersion, l1, l2) \n",
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"adcDispersionOptimized = adc.calculateDispersion(l1,l2)[1]\n",
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"\n",
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"print('The atmospheric dispersion: {:.3f} mas'.format(dispersion) )\n",
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"print('The optimum ADC angle in this case: {:.1f} degrees'.format(adcOptimumAngle))\n",
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"print('The residual dispersion: {:.3f} mas'.format(dispersion - adcDispersionOptimized))\n"
|
||||
]
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||||
}
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||||
],
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||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
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||||
@@ -0,0 +1,87 @@
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#!/usr/bin/env Python3
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import numpy as np
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from AstroAtmosphere import *
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import matplotlib.pyplot as plt
|
||||
|
||||
'''
|
||||
@ Author: Joost van den Born
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||||
@ Contact: born@astron.nl
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||||
@ Description: This script uses the equations of the paper to calculate the
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residual dispersion due to the finite step size of the ADC.
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'''
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||||
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def angleFunc(x, a):
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'''
|
||||
Function to fit the ideal angle on
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||||
'''
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return np.arccos(a * np.tan(np.deg2rad(x))) * 180/np.pi
|
||||
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||||
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def angleFuncDeriv(x, a):
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return (a * (1+np.tan(np.deg2rad(x))**2)) / np.sqrt(1 - a**2 * np.tan(np.deg2rad(x))**2)
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def cassiniDispersionDeriv(n1, n2, zenith, alpha):
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a1 = n1 * alpha * np.cos(np.deg2rad(zenith)) / np.sqrt(1 - n1**2 * alpha**2 * np.sin(np.deg2rad(zenith))**2)
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||||
a2 = n2 * alpha * np.cos(np.deg2rad(zenith)) / np.sqrt(1 - n2**2 * alpha**2 * np.sin(np.deg2rad(zenith))**2)
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return a1 - a2
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||||
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||||
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||||
# Parameters at Cerro Armazones
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T = 279.65 # k
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P = 71200 # Pa
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||||
H = 0.22
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xc = 450 # ppm
|
||||
lat = -24.5983 # degrees
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h = 3064 # m
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l1 = 1.49 # micron
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l2 = 1.78 # micron
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zenith = 30 # degrees
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||||
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||||
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||||
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at = dispersion(lat, h)
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at.setReducedHeight(P, at.rho(P, T, H, xc))
|
||||
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||||
n1 = at.n_tph(l1, T, P, H, xc)
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n2 = at.n_tph(l2, T, P, H, xc)
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||||
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||||
alpha = at.rc / (at.rc+at.h)
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||||
# c_fH = 0.499413 # From Zemax
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c_fH = 0.46816061517198737 # Completely analytical cf
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# f_fH = 0.46462883308144837 # Best fit to analytical ADC model
|
||||
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||||
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||||
dispersion = at.cassini(n1, n2, zenith)*3.6e6
|
||||
dispersionDeriv = cassiniDispersionDeriv(n1, n2, zenith, alpha)*3.6e6
|
||||
optAngle = angleFunc(zenith, c_fH)
|
||||
optAngleDeriv = angleFuncDeriv(zenith, c_fH)
|
||||
maxDev = optAngleDeriv / dispersionDeriv
|
||||
print('Zenith = %s degrees' %zenith)
|
||||
print('Dispersion in mas: ', dispersion)
|
||||
print('Dispersion/degree zenith at z=%s: %s mas/degree z' %(zenith, dispersionDeriv))
|
||||
print('Optimum angle ADC: %s' %(optAngle))
|
||||
print('Optimum angle ADC/degree zenith at z=%s: %s' %(zenith, optAngleDeriv))
|
||||
print('Max deviation per mas dispersion: %s degrees' %(maxDev))
|
||||
print('At designed stepsize at most %s mas of residual dispersion' %(1/maxDev*(360/1262.5)))
|
||||
|
||||
|
||||
# Plotting as a function of zenith angle
|
||||
dataList = []
|
||||
for zenith in np.linspace(5,65,100):
|
||||
dispersion = at.cassini(n1, n2, zenith)*3.6e6
|
||||
dispersionDeriv = cassiniDispersionDeriv(n1, n2, zenith, alpha)*3.6e6
|
||||
optAngle = angleFunc(zenith, c_fH)
|
||||
optAngleDeriv = angleFuncDeriv(zenith, c_fH)
|
||||
maxDev = optAngleDeriv / dispersionDeriv
|
||||
dataList.append(1/maxDev*(360/1262.5))
|
||||
|
||||
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(111)
|
||||
ax.plot(np.linspace(5,65,100), dataList)
|
||||
ax.grid(True, alpha=0.2)
|
||||
ax.set_xlabel('Zenith (degrees)')
|
||||
ax.set_ylabel('Residual dispersion (mas)')
|
||||
plt.show()
|
||||
@@ -0,0 +1,21 @@
|
||||
NARR data provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their Web
|
||||
site at https://psl.noaa.gov/
|
||||
|
||||
The original "air.201806.nc" datafile was downloaded from
|
||||
https://psl.noaa.gov/data/gridded/data.narr.pressure.html
|
||||
|
||||
Specific download link (~650 MB):
|
||||
ftp://ftp.cdc.noaa.gov/Datasets/NARR/pressure/air.201806.nc [Last accessed on
|
||||
2020-06-17]
|
||||
|
||||
|
||||
The following files are a direct slice of this data:
|
||||
- export_dist.txt
|
||||
- export_heights.txt
|
||||
- export_press_atmosphere.txt
|
||||
- export_temp_atmosphere.txt
|
||||
|
||||
|
||||
This data file was not included here, because it has a very niche use for the
|
||||
AstroAtmosphere package and is a large file. The tradeoff seems to me to not very
|
||||
favourable, and therefore it is omitted.
|
||||
@@ -0,0 +1,97 @@
|
||||
NCEP North American Regional Reanalysis (NARR) gridded weather data.
|
||||
|
||||
Data from https://www.esrl.noaa.gov/psd/data/gridded/data.narr.pressure.html
|
||||
|
||||
|
||||
|
||||
###############
|
||||
# Header info #
|
||||
###############
|
||||
$ ncdump -h air.201806.nc
|
||||
netcdf air.201806 {
|
||||
dimensions:
|
||||
time = UNLIMITED ; // (240 currently)
|
||||
level = 29 ;
|
||||
y = 277 ;
|
||||
x = 349 ;
|
||||
variables:
|
||||
double time(time) ;
|
||||
time:axis = "T" ;
|
||||
time:coordinate_defines = "point" ;
|
||||
time:delta_t = "0000-00-00 03:00:00" ;
|
||||
time:long_name = "Time" ;
|
||||
time:standard_name = "time" ;
|
||||
time:units = "hours since 1800-1-1 00:00:0.0" ;
|
||||
time:actual_range = 1914576., 1915293. ;
|
||||
float level(level) ;
|
||||
level:GRIB_id = 100s ;
|
||||
level:GRIB_name = "hPa" ;
|
||||
level:actual_range = 1000.f, 100.f ;
|
||||
level:axis = "Z" ;
|
||||
level:coordinate_defines = "point" ;
|
||||
level:long_name = "Level" ;
|
||||
level:positive = "down" ;
|
||||
level:standard_name = "level" ;
|
||||
level:units = "millibar" ;
|
||||
float lat(y, x) ;
|
||||
lat:axis = "Y" ;
|
||||
lat:coordinate_defines = "point" ;
|
||||
lat:long_name = "Latitude" ;
|
||||
lat:standard_name = "latitude" ;
|
||||
lat:units = "degrees_north" ;
|
||||
float lon(y, x) ;
|
||||
lon:axis = "X" ;
|
||||
lon:coordinate_defines = "point" ;
|
||||
lon:long_name = "Longitude" ;
|
||||
lon:standard_name = "longitude" ;
|
||||
lon:units = "degrees_east" ;
|
||||
float y(y) ;
|
||||
y:long_name = "northward distance from southwest corner of domain in projection coordinates" ;
|
||||
y:standard_name = "projection_y_coordinate" ;
|
||||
y:units = "m" ;
|
||||
float x(x) ;
|
||||
x:long_name = "eastward distance from southwest corner of domain in projection coordinates" ;
|
||||
x:standard_name = "projection_x_coordinate" ;
|
||||
x:units = "m" ;
|
||||
int Lambert_Conformal ;
|
||||
Lambert_Conformal:false_easting = 5632642.22547 ;
|
||||
Lambert_Conformal:false_northing = 4612545.65137 ;
|
||||
Lambert_Conformal:grid_mapping_name = "lambert_conformal_conic" ;
|
||||
Lambert_Conformal:latitude_of_projection_origin = 50. ;
|
||||
Lambert_Conformal:longitude_of_central_meridian = -107. ;
|
||||
Lambert_Conformal:standard_parallel = 50., 50. ;
|
||||
float air(time, level, y, x) ;
|
||||
air:GRIB_id = 11s ;
|
||||
air:GRIB_name = "TMP" ;
|
||||
air:_FillValue = 9.96921e+36f ;
|
||||
air:coordinates = "lat lon" ;
|
||||
air:dataset = "NARR 3-hourly" ;
|
||||
air:grid_mapping = "Lambert_Conformal" ;
|
||||
air:level_desc = "Pressure Levels" ;
|
||||
air:long_name = "3-hourly Air Temperature on Pressure Levels" ;
|
||||
air:parent_stat = "Other" ;
|
||||
air:standard_name = "air_temperature" ;
|
||||
air:statistic = "Individual Obs" ;
|
||||
air:units = "degK" ;
|
||||
air:valid_range = 137.5f, 362.5f ;
|
||||
air:var_desc = "Air temperature" ;
|
||||
air:missing_value = -9.96921e+36f ;
|
||||
air:actual_range = 187.1279f, 320.1487f ;
|
||||
|
||||
// global attributes:
|
||||
:Conventions = "CF-1.2" ;
|
||||
:centerlat = 50. ;
|
||||
:centerlon = -107. ;
|
||||
:comments = "" ;
|
||||
:institution = "National Centers for Environmental Prediction" ;
|
||||
:latcorners = 1.000001f, 0.897945f, 46.3544f, 46.63433f ;
|
||||
:loncorners = -145.5f, -68.32005f, -2.569891f, 148.6418f ;
|
||||
:platform = "Model" ;
|
||||
:standardpar1 = 50. ;
|
||||
:standardpar2 = 50.000001 ;
|
||||
:title = "8x Daily NARR" ;
|
||||
:history = "created Mon Jul 18 21:01:07 MDT 2016 by NOAA/ESRL/PSD" ;
|
||||
:dataset_title = "NCEP North American Regional Reanalysis (NARR)" ;
|
||||
:references = "https://www.esrl.noaa.gov/psd/data/gridded/data.narr.html" ;
|
||||
:source = "http://www.emc.ncep.noaa.gov/mmb/rreanl/index.html" ;
|
||||
}
|
||||
@@ -0,0 +1 @@
|
||||
[0.0, 0.23179527183433432, 0.46359054366866864, 0.6953858155030029, 0.9271810873373373, 1.1589763591716715, 1.3907716310060059, 1.6225669028403402, 1.8543621746746746, 2.086157446509009, 2.317952718343343, 2.5497479901776776, 2.7815432620120117, 3.0133385338463463, 3.2451338056806804, 3.476929077515015, 3.708724349349349, 3.9405196211836833, 4.172314893018018, 4.404110164852352, 4.635905436686686, 4.867700708521021, 5.099495980355355, 5.33129125218969, 5.5630865240240235, 5.794881795858358, 6.026677067692693, 6.258472339527026, 6.490267611361361, 6.7220628831956954, 6.95385815503003, 7.185653426864364, 7.417448698698698, 7.649243970533033, 7.8810392423673665, 8.112834514201701, 8.344629786036036, 8.57642505787037, 8.808220329704705, 9.04001560153904, 9.271810873373372, 9.503606145207707, 9.735401417042041, 9.967196688876376, 10.19899196071071, 10.430787232545045, 10.66258250437938, 10.894377776213712, 11.126173048048047, 11.357968319882382, 11.589763591716716, 11.82155886355105, 12.053354135385385, 12.28514940721972, 12.516944679054053, 12.748739950888387, 12.980535222722722, 13.212330494557056, 13.444125766391391, 13.675921038225725, 13.90771631006006, 14.139511581894393, 14.371306853728727, 14.603102125563062, 14.834897397397397, 15.066692669231731, 15.298487941066066, 15.5302832129004, 15.762078484734733, 15.993873756569068, 16.225669028403402, 16.45746430023774, 16.68925957207207, 16.921054843906404, 17.15285011574074, 17.384645387575073, 17.61644065940941, 17.848235931243742, 18.08003120307808, 18.31182647491241, 18.543621746746744, 18.77541701858108, 19.007212290415413, 19.23900756224975, 19.470802834084083, 19.70259810591842, 19.93439337775275, 20.166188649587085, 20.39798392142142, 20.629779193255754, 20.86157446509009, 21.093369736924423, 21.32516500875876, 21.556960280593092, 21.788755552427425, 22.02055082426176, 22.252346096096094, 22.48414136793043, 22.715936639764763, 22.9477319115991, 23.179527183433432, 23.411322455267765, 23.6431177271021, 23.874912998936434, 24.10670827077077, 24.338503542605103, 24.57029881443944, 24.802094086273772, 25.033889358108105, 25.26568462994244, 25.497479901776774, 25.72927517361111, 25.961070445445444, 26.19286571727978, 26.424660989114113, 26.656456260948445, 26.888251532782782, 27.120046804617115, 27.35184207645145, 27.583637348285784, 27.81543262012012, 28.047227891954453, 28.279023163788786, 28.510818435623122, 28.742613707457455, 28.97440897929179, 29.206204251126124, 29.43799952296046, 29.669794794794793, 29.901590066629126, 30.133385338463462, 30.365180610297795, 30.59697588213213, 30.828771153966464, 31.0605664258008, 31.292361697635133, 31.524156969469466, 31.755952241303802, 31.987747513138135, 32.21954278497247, 32.451338056806804, 32.68313332864114, 32.91492860047548, 33.146723872309806, 33.37851914414414, 33.61031441597848, 33.84210968781281, 34.073904959647145, 34.30570023148148, 34.53749550331582, 34.76929077515015, 35.00108604698448, 35.23288131881882, 35.46467659065315, 35.696471862487485, 35.92826713432182, 36.16006240615616, 36.39185767799049, 36.62365294982482, 36.85544822165916, 37.08724349349349, 37.319038765327825, 37.55083403716216, 37.7826293089965, 38.01442458083083, 38.24621985266516, 38.4780151244995, 38.70981039633383, 38.941605668168165, 39.1734009400025, 39.40519621183684, 39.63699148367117, 39.8687867555055, 40.10058202733984, 40.33237729917417, 40.564172571008505, 40.79596784284284, 41.02776311467718, 41.25955838651151, 41.491353658345844, 41.72314893018018, 41.95494420201451, 42.186739473848846, 42.41853474568318, 42.65033001751752, 42.88212528935185, 43.113920561186184, 43.34571583302052, 43.57751110485485, 43.809306376689186, 44.04110164852352, 44.27289692035786, 44.50469219219219, 44.736487464026524, 44.96828273586086, 45.20007800769519, 45.431873279529526, 45.66366855136386, 45.8954638231982, 46.12725909503253, 46.359054366866864, 46.5908496387012, 46.82264491053553, 47.054440182369866, 47.2862354542042, 47.51803072603854, 47.74982599787287, 47.981621269707205, 48.21341654154154, 48.44521181337587, 48.67700708521021, 48.90880235704454, 49.14059762887888, 49.37239290071321, 49.604188172547545, 49.83598344438188, 50.06777871621621, 50.29957398805055, 50.53136925988488, 50.76316453171922, 50.99495980355355, 51.226755075387885, 51.45855034722222, 51.69034561905655, 51.92214089089089, 52.15393616272522, 52.38573143455956, 52.61752670639389, 52.849321978228225, 53.08111725006256, 53.31291252189689, 53.54470779373123, 53.776503065565564, 54.0082983373999, 54.24009360923423, 54.471888881068566, 54.7036841529029, 54.93547942473723, 55.16727469657157, 55.399069968405904, 55.63086524024024, 55.86266051207457, 56.094455783908906, 56.32625105574324, 56.55804632757757, 56.78984159941191, 57.021636871246244, 57.25343214308058, 57.48522741491491, 57.717022686749246, 57.94881795858358, 58.18061323041791, 58.41240850225225, 58.644203774086584, 58.87599904592092, 59.10779431775525, 59.339589589589586, 59.57138486142392, 59.80318013325825, 60Line truncated
|
||||
@@ -0,0 +1 @@
|
||||
[0.0, 0.21553884711779447, 0.43107769423558895, 0.6466165413533834, 0.8621553884711779, 1.0776942355889725, 1.2932330827067668, 1.5087719298245612, 1.7243107769423558, 1.9398496240601504, 2.155388471177945, 2.370927318295739, 2.5864661654135337, 2.8020050125313283, 3.0175438596491224, 3.233082706766917, 3.4486215538847116, 3.664160401002506, 3.8796992481203008, 4.095238095238095, 4.31077694235589, 4.526315789473684, 4.741854636591478, 4.957393483709273, 5.172932330827067, 5.3884711779448615, 5.6040100250626566, 5.819548872180451, 6.035087719298245, 6.25062656641604, 6.466165413533834, 6.681704260651629, 6.897243107769423, 7.112781954887217, 7.328320802005012, 7.5438596491228065, 7.7593984962406015, 7.974937343358396, 8.19047619047619, 8.406015037593985, 8.62155388471178, 8.837092731829573, 9.052631578947368, 9.268170426065163, 9.483709273182956, 9.699248120300751, 9.914786967418546, 10.13032581453634, 10.345864661654135, 10.56140350877193, 10.776942355889723, 10.992481203007518, 11.208020050125313, 11.423558897243106, 11.639097744360901, 11.854636591478696, 12.07017543859649, 12.285714285714285, 12.50125313283208, 12.716791979949875, 12.932330827067668, 13.147869674185463, 13.363408521303258, 13.578947368421051, 13.794486215538846, 14.010025062656641, 14.225563909774435, 14.44110275689223, 14.656641604010025, 14.872180451127818, 15.087719298245613, 15.303258145363408, 15.518796992481203, 15.734335839598996, 15.949874686716791, 16.165413533834585, 16.38095238095238, 16.596491228070175, 16.81203007518797, 17.027568922305765, 17.24310776942356, 17.45864661654135, 17.674185463659146, 17.88972431077694, 18.105263157894736, 18.32080200501253, 18.536340852130326, 18.751879699248118, 18.967418546365913, 19.182957393483708, 19.398496240601503, 19.614035087719298, 19.829573934837093, 20.045112781954884, 20.26065162907268, 20.476190476190474, 20.69172932330827, 20.907268170426065, 21.12280701754386, 21.338345864661655, 21.553884711779446, 21.76942355889724, 21.984962406015036, 22.20050125313283, 22.416040100250626, 22.63157894736842, 22.847117794486213, 23.062656641604008, 23.278195488721803, 23.493734335839598, 23.709273182957393, 23.924812030075188, 24.14035087719298, 24.355889724310774, 24.57142857142857, 24.786967418546364, 25.00250626566416, 25.218045112781954, 25.43358395989975, 25.64912280701754, 25.864661654135336, 26.08020050125313, 26.295739348370926, 26.51127819548872, 26.726817042606516, 26.942355889724308, 27.157894736842103, 27.373433583959898, 27.588972431077693, 27.804511278195488, 28.020050125313283, 28.235588972431078, 28.45112781954887, 28.666666666666664, 28.88220551378446, 29.097744360902254, 29.31328320802005, 29.528822055137844, 29.744360902255636, 29.95989974937343, 30.175438596491226, 30.39097744360902, 30.606516290726816, 30.82205513784461, 31.037593984962406, 31.253132832080198, 31.468671679197993, 31.684210526315788, 31.899749373433583, 32.11528822055138, 32.33082706766917, 32.54636591478697, 32.76190476190476, 32.97744360902256, 33.19298245614035, 33.40852130325814, 33.62406015037594, 33.83959899749373, 34.05513784461153, 34.27067669172932, 34.48621553884712, 34.70175438596491, 34.9172932330827, 35.1328320802005, 35.34837092731829, 35.56390977443609, 35.77944862155388, 35.994987468671674, 36.21052631578947, 36.426065162907264, 36.64160401002506, 36.857142857142854, 37.07268170426065, 37.288220551378444, 37.503759398496236, 37.719298245614034, 37.934837092731826, 38.150375939849624, 38.365914786967416, 38.581453634085214, 38.796992481203006, 39.0125313283208, 39.228070175438596, 39.44360902255639, 39.659147869674186, 39.87468671679198, 40.09022556390977, 40.30576441102757, 40.52130325814536, 40.73684210526316, 40.95238095238095, 41.16791979949875, 41.38345864661654, 41.59899749373433, 41.81453634085213, 42.03007518796992, 42.24561403508772, 42.46115288220551, 42.67669172932331, 42.8922305764411, 43.10776942355889, 43.32330827067669, 43.53884711779448, 43.75438596491228, 43.96992481203007, 44.185463659147864, 44.40100250626566, 44.616541353383454, 44.83208020050125, 45.047619047619044, 45.26315789473684, 45.478696741854634, 45.694235588972425, 45.909774436090224, 46.125313283208015, 46.340852130325814, 46.556390977443606, 46.771929824561404, 46.987468671679196, 47.20300751879699, 47.418546365914786, 47.63408521303258, 47.849624060150376, 48.06516290726817, 48.28070175438596, 48.49624060150376, 48.71177944862155, 48.92731829573935, 49.14285714285714, 49.35839598997494, 49.57393483709273, 49.78947368421052, 50.00501253132832, 50.22055137844611, 50.43609022556391, 50.6516290726817, 50.8671679197995, 51.08270676691729, 51.29824561403508, 51.51378446115288, 51.72932330827067, 51.94486215538847, 52.16040100250626, 52.37593984962406, 52.59147869674185, 52.807017543859644, 53.02255639097744, 53.238095238095234, 53.45363408521303, 53.669172932330824, 53.884711779448615, 54.100250626566414, 54.315789473684205, 54.531328320802004, 54.746867167919795, 54.962406015037594, 55.177944862155385, 55.39348370Line truncated
|
||||
@@ -0,0 +1 @@
|
||||
[[100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.00000000001, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 99999.99999999999, 99999.99999999999, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 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100000.00000000001, 100000.00000000001, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.00000000001, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.00000000001, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.00000000001, 100000.00000000001, 100000.00000000001, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.00000000001, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 99999.99999999999, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100000.0, 100Line truncated
|
||||
@@ -0,0 +1 @@
|
||||
[[284.050537109375, 284.05081584207153, 284.0510945747681, 284.0513733074647, 284.0516520401612, 284.0519307728577, 284.0522095055543, 284.05248823825076, 284.05276697094735, 284.05304570364393, 284.05332443634046, 284.053603169037, 284.0538819017336, 284.05416063443016, 284.0544393671267, 284.05471809982316, 284.05499683251975, 284.0552755652163, 284.0555542979128, 284.05583303060934, 284.0561117633059, 284.05639049600245, 284.05666922869904, 284.0569479613956, 284.05722669409215, 284.0575054267887, 284.0577841594852, 284.05806289218174, 284.05834162487827, 284.0586203575748, 284.0588990902713, 284.05917782296797, 284.0594565556645, 284.059735288361, 284.06001402105755, 284.06029275375414, 284.06057148645067, 284.06085021914726, 284.0611289518438, 284.0614076845403, 284.06168641723684, 284.06196514993337, 284.06224388262996, 284.0625226153265, 284.062801348023, 284.0630800807196, 284.06335881341613, 284.0636375461127, 284.06391627880924, 284.0641950115058, 284.06447374420236, 284.06475247689883, 284.0650312095954, 284.06530994229195, 284.06558867498853, 284.06586740768506, 284.0661461403816, 284.0664248730782, 284.0667036057747, 284.06698233847123, 284.0672610711678, 284.06753980386435, 284.0678185365609, 284.0680972692574, 284.06837600195394, 284.0686547346505, 284.06893346734705, 284.06921220004364, 284.06949093274017, 284.0697696654367, 284.0700483981333, 284.0703271308298, 284.0706058635264, 284.07088459622287, 284.0711633289194, 284.071442061616, 284.0717207943125, 284.0719995270091, 284.0722782597056, 284.07255699240216, 284.0728357250987, 284.07311445779527, 284.07339319049186, 284.0736719231884, 284.0739506558849, 284.07422938858144, 284.07450812127803, 284.0747868539745, 284.0750655866711, 284.0753443193677, 284.0756230520642, 284.07590178476073, 284.07618051745726, 284.0764592501538, 284.0767379828504, 284.0770167155469, 284.0772954482435, 284.07757418094, 284.07785291363655, 284.0781316463331, 284.07841037902966, 284.0786891117262, 284.0789678444228, 284.0792465771193, 284.07952530981584, 284.07980404251236, 284.08008277520895, 284.0803615079055, 284.080640240602, 284.0809189732986, 284.0811977059951, 284.08147643869165, 284.0817551713882, 284.08203390408477, 284.0823126367813, 284.0825913694779, 284.08287010217435, 284.08314883487094, 284.08342756756747, 284.08370630026405, 284.0839850329606, 284.0842637656571, 284.0845424983537, 284.0848212310502, 284.08509996374676, 284.08537869644334, 284.0856574291398, 284.0859361618364, 284.08621489453293, 284.0864936272295, 284.08677235992604, 284.0870510926226, 284.08732982531916, 284.0876085580157, 284.0878872907122, 284.0881660234088, 284.08844475610533, 284.08872348880186, 284.0890022214984, 284.089280954195, 284.0895596868915, 284.08983841958803, 284.0901171522846, 284.09039588498115, 284.0906746176777, 284.09095335037426, 284.0912320830708, 284.0915108157673, 284.09178954846385, 284.09206828116044, 284.09234701385697, 284.0926257465535, 284.0929044792501, 284.0931832119466, 284.09346194464314, 284.0937406773397, 284.09401941003625, 284.09429814273284, 284.0945768754293, 284.0948556081259, 284.0951343408224, 284.095413073519, 284.09569180621554, 284.09597053891207, 284.0962492716086, 284.0965280043052, 284.0968067370017, 284.0970854696983, 284.09736420239483, 284.106526646489, 284.1175328703515, 284.1285390942141, 284.13954531807667, 284.15055154193925, 284.1615577658018, 284.17256398966435, 284.1835702135269, 284.19457643738946, 284.205582661252, 284.21658888511456, 284.22759510897714, 284.23860133283966, 284.24960755670224, 284.2606137805648, 284.2716200044274, 284.2826262282899, 284.2936324521525, 284.3046386760151, 284.31564489987767, 284.3266511237402, 284.33765734760277, 284.3486635714653, 284.35966979532793, 284.37067601919046, 284.38168224305304, 284.39268846691556, 284.40369469077814, 284.4147009146407, 284.4257071385033, 284.4367133623658, 284.4477195862284, 284.458725810091, 284.4697320339535, 284.4807382578161, 284.49174448167867, 284.5027507055412, 284.51375692940377, 284.52476315326635, 284.53576937712893, 284.54677560099145, 284.55778182485403, 284.5687880487166, 284.5797942725792, 284.5908004964417, 284.6018067203043, 284.6128129441669, 284.6238191680294, 284.634825391892, 284.64583161575456, 284.6568378396171, 284.66784406347966, 284.67885028734224, 284.68985651120477, 284.70086273506735, 284.7118689589299, 284.7228751827925, 284.73388140665503, 284.7448876305176, 284.7558938543802, 284.76690007824277, 284.7779063021053, 284.7889125259679, 284.7999187498304, 284.810924973693, 284.82193119755556, 284.8329374214181, 284.84394364528066, 284.8549498691432, 284.86595609300576, 284.87696231686834, 284.8879685407309, 284.89897476459345, 284.909980988456, 284.9209872123186, 284.9319934361812, 284.9429996600437, 284.9540058839063, 284.96501210776887, 284.9760183316314, 284.987024555494, 284.99803077935655, 285.0090370032191, 285.0200432270817, 285.03104945094424, 285.04205567480676, 285.05306189866934, 285.0640681225319, 285.07507434639444, Line truncated
|
||||
@@ -0,0 +1,168 @@
|
||||
#!/usr/bin/env Python3
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.lines import Line2D
|
||||
from matplotlib import cm
|
||||
from matplotlib.colors import LogNorm
|
||||
from AstroAtmosphere import *
|
||||
from scipy import interpolate
|
||||
import json
|
||||
|
||||
|
||||
'''
|
||||
@ Author: Joost van den Born
|
||||
@ Contact: born@astron.nl
|
||||
@ Description: This script uses the NCEP NARR data to model a real structured atmosphere
|
||||
from data with a resolution of 2.5 km per data point. We interpolate this
|
||||
data to a higher resolution.
|
||||
Then a full integration over this atmosphere is done and compared to the
|
||||
values for the refraction and dispersion if we had only taken the atmospheric
|
||||
conditions directly at the observatory.
|
||||
'''
|
||||
|
||||
|
||||
# Load a variable from a json text file.
|
||||
def loadVariableFromTXT(filename):
|
||||
variableFile = open(filename, 'r')
|
||||
variable = json.load(variableFile)
|
||||
variableFile.close()
|
||||
return np.asarray(variable)
|
||||
|
||||
|
||||
plt.rc('font', family='serif')
|
||||
plt.rc('axes', labelsize='large')
|
||||
|
||||
# h, _, _, _, Temp, Press, Dens, vSound, _,_ = np.genfromtxt('si1py_modified.txt', unpack=True, skip_header=6)
|
||||
|
||||
dist = loadVariableFromTXT('export_dist.txt')
|
||||
heights = loadVariableFromTXT('export_heights.txt')
|
||||
press = loadVariableFromTXT('export_press_atmosphere.txt')
|
||||
temp = loadVariableFromTXT('export_temp_atmosphere.txt')
|
||||
print("Loading complete!")
|
||||
|
||||
# Conditions at Cerro Armazones
|
||||
T = temp[0,0] # k
|
||||
P = press[0,0]# Pa
|
||||
H = 0.0
|
||||
xc = 314 # ppm
|
||||
lat = -24.5983 # degrees
|
||||
h0 = 0 # m
|
||||
l1 = 1.49 # micron
|
||||
l2 = 1.78 # micron
|
||||
zenith = 60 # degrees
|
||||
|
||||
# Calculate the index of refraction
|
||||
at = Observatory()
|
||||
n1 = at.n_tph(l=l1, T=T, p=P, RH=H, xc=xc)
|
||||
n2 = at.n_tph(l=l2, T=T, p=P, RH=H, xc=xc)
|
||||
rho = at.rho(p=P, T=T, RH=H, xc=xc)
|
||||
|
||||
RHlist = np.asarray([H]*len(press[:,0]))
|
||||
xclist = np.asarray([xc]*len(press[:,0]))
|
||||
Dens = at.rho(p=press[:,0], T=temp[:,0], RH=RHlist, xc=xclist)
|
||||
|
||||
refr = refraction(lat, h0)
|
||||
refr.setReducedHeight(P, rho)
|
||||
disp = dispersion(lat, h0)
|
||||
disp.setReducedHeight(P, rho)
|
||||
|
||||
|
||||
# We must create a cube of weather data:
|
||||
# We assume a flat atmosphere for now, so that h does not need to be adjusted
|
||||
# x_max = 200 km, gives zenith_max = ~66.7 degrees
|
||||
# Every 20 km in horizontal direction a new perturbation and interpolation
|
||||
# for every 500 m.
|
||||
x_max = dist[-1] # km
|
||||
|
||||
Temp_cube_n = []
|
||||
Press_cube_n = []
|
||||
|
||||
for x in range(len(dist)):
|
||||
Temp_cube_n.append(temp[:,0])
|
||||
Press_cube_n.append(press[:,0])
|
||||
|
||||
Temp_cube_n = np.asarray(Temp_cube_n).T
|
||||
Press_cube_n = np.asarray(Press_cube_n).T
|
||||
|
||||
|
||||
|
||||
fig = plt.figure()
|
||||
ax1 = fig.add_subplot(211)
|
||||
im = ax1.pcolormesh(dist,heights, temp, cmap=cm.coolwarm)
|
||||
fig.colorbar(im, ax=ax1, label='Temperature (K)')
|
||||
ax1.set_aspect(1)
|
||||
ax1.set_ylabel('Height (km)')
|
||||
|
||||
ax2 = fig.add_subplot(212)
|
||||
im2 = ax2.pcolormesh(dist, heights, press, cmap=cm.coolwarm, norm=LogNorm())
|
||||
ax2.set_aspect(1)
|
||||
ax2.set_xlabel('Horizontal distance (km)')
|
||||
ax2.set_ylabel('Height (km)')
|
||||
fig.colorbar(im2, ax=ax2, label='Pressure (Pa)')
|
||||
plt.tight_layout()
|
||||
plt.show(block=False)
|
||||
|
||||
# Now for a given zenith angle we need to find the temp and press along the line of sight
|
||||
f_Temp_2d = interpolate.interp2d(dist, heights, temp, kind='linear')
|
||||
f_Press_2d = interpolate.interp2d(dist, heights, press, kind='linear')
|
||||
f_Temp_2d_n = interpolate.interp2d(dist, heights, Temp_cube_n, kind='linear')
|
||||
f_Press_2d_n = interpolate.interp2d(dist, heights, Press_cube_n, kind='linear')
|
||||
print('Interpolation complete!')
|
||||
|
||||
|
||||
|
||||
zenithRange = np.linspace(30,65,10)
|
||||
refrDiff, dispDiff = [], []
|
||||
|
||||
for zenith in zenithRange:
|
||||
z = np.deg2rad(zenith)
|
||||
y_los = heights
|
||||
x_los = np.linspace(0, heights.max()*np.tan(z), len(heights))
|
||||
|
||||
Temp_los, Press_los = [], []
|
||||
Temp_los_n, Press_los_n = [], []
|
||||
for i in range(len(heights)):
|
||||
Temp_los.append(f_Temp_2d(x_los[i], y_los[i])[0])
|
||||
Press_los.append(f_Press_2d(x_los[i], y_los[i])[0])
|
||||
Temp_los_n.append(f_Temp_2d_n(x_los[i], y_los[i])[0])
|
||||
Press_los_n.append(f_Press_2d_n(x_los[i], y_los[i])[0])
|
||||
|
||||
Temp_los = np.asarray(Temp_los)
|
||||
Press_los = np.asarray(Press_los)
|
||||
Temp_los_n = np.asarray(Temp_los_n)
|
||||
Press_los_n = np.asarray(Press_los_n)
|
||||
rho_los = at.rho(p=Press_los, T=Temp_los, RH=H, xc=xc)
|
||||
rho_los_n = at.rho(p=Press_los_n, T=Temp_los_n, RH=H, xc=xc)
|
||||
|
||||
refrInt = refr.refractionIntegral(n1, z, useStandardAtmosphere=False, heightData=heights, rhoData=rho_los_n)*3600*1000
|
||||
refrInt_pert = refr.refractionIntegral(n1, z, useStandardAtmosphere=False, heightData=heights, rhoData=rho_los)*3600*1000
|
||||
refrDiff.append(refrInt_pert - refrInt)
|
||||
|
||||
dispInt = disp.refractionIntegral(n1, n2, z, useStandardAtmosphere=False, heightData=heights, rhoData=rho_los_n)*3.6e6
|
||||
dispInt_pert = disp.refractionIntegral(n1, n2, z, useStandardAtmosphere=False, heightData=heights, rhoData=rho_los)*3.6e6
|
||||
dispDiff.append(dispInt_pert - dispInt)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
fig3 = plt.figure(figsize=(9,7))
|
||||
ax1 = fig3.add_subplot(211)
|
||||
ax2 = fig3.add_subplot(212)
|
||||
ax1.tick_params(axis ='both', direction='in', which='both', labelsize=16)
|
||||
ax2.tick_params(axis ='both', direction='in', which='both', labelsize=16)
|
||||
ax1.grid(True, alpha=0.3)#, which='major')
|
||||
ax2.grid(True, alpha=0.3)#, which='major')
|
||||
|
||||
|
||||
ax1.plot(zenithRange, np.log10(np.abs(refrDiff)), 'k-', linewidth=2)
|
||||
ax2.plot(zenithRange, np.log10(np.abs(dispDiff)), 'k-', linewidth=2)
|
||||
ax1.set_xlabel('Zenith (degrees)', fontsize=18)
|
||||
ax2.set_xlabel('Zenith (degrees)', fontsize=18)
|
||||
ax1.set_ylabel('$\log_{10}($|$R - R_{o}$|$)$ (mas)', fontsize=18)
|
||||
ax2.set_ylabel('$\log_{10}($|$\Delta R - \Delta R_{o}$|$)$ (mas)', fontsize=18)
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,251 @@
|
||||
#!/usr/bin/env Python3
|
||||
import numpy as np
|
||||
import sys
|
||||
from AstroAtmosphere import *
|
||||
import matplotlib.pyplot as plt
|
||||
from netCDF4 import Dataset
|
||||
from pdb import set_trace
|
||||
from matplotlib import cm
|
||||
from scipy.interpolate import interp1d, interp2d
|
||||
from matplotlib.colors import LogNorm
|
||||
import json
|
||||
|
||||
# Saves a variable, preferably a list or string to json text file.
|
||||
# Useful for long simulations, where you're making a plot
|
||||
def saveVariableToTXT(variable, filename):
|
||||
variableFile = open(filename, 'w')
|
||||
|
||||
if type(variable) == np.ndarray:
|
||||
json.dump(variable.tolist(), variableFile)
|
||||
elif type(variable) in [list, str, float]:
|
||||
json.dump(variable, variableFile)
|
||||
else:
|
||||
print('Not a list, array or string. Check variable type.')
|
||||
|
||||
variableFile.close()
|
||||
return
|
||||
|
||||
# Load a variable from a json text file.
|
||||
def loadVariableFromTXT(filename):
|
||||
variableFile = open(filename, 'r')
|
||||
variable = json.load(variableFile)
|
||||
variableFile.close()
|
||||
return variable
|
||||
|
||||
|
||||
def calculateDistanceResolution(lat, lon):
|
||||
# Calculate local radius of the earth
|
||||
_A = 6378.137 # km
|
||||
_B = 6356.752 # km
|
||||
_phi = np.deg2rad(lat)
|
||||
_rc0 = (_A*_B)**2 / (_A**2 * np.cos(_phi)**2 + _B**2 * np.sin(_phi)**2)**(3/2)
|
||||
|
||||
# Calculate circumference of the earth at constant latitude.
|
||||
latR = 2 * np.pi * np.cos(_phi) * _rc0
|
||||
distRes = latR/360 * lon
|
||||
return distRes
|
||||
|
||||
def H_isotherm(T, lat):
|
||||
'''
|
||||
Calculates the reduced height of the atmosphere, assuming an isotherm distribution.
|
||||
'''
|
||||
_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(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 calculatePressure(Th, P0, h, lat):
|
||||
redH = H_isotherm(Th, lat)
|
||||
Ph = P0 * np.exp(-h/redH)
|
||||
return Ph
|
||||
|
||||
plt.rc('font', family='serif')
|
||||
plt.rc('axes', labelsize='large')
|
||||
|
||||
# Import the real data
|
||||
nc = Dataset('air.201806.nc', 'r')
|
||||
latitudes = np.asarray(nc.variables['lat'][:])
|
||||
longitudes = np.asarray(nc.variables['lon'][:])
|
||||
air_temp = nc.variables['air']
|
||||
air_press = np.asarray(nc.variables['level'])
|
||||
|
||||
|
||||
|
||||
|
||||
at = Observatory()
|
||||
|
||||
# Parameters at Cerro Armazones
|
||||
T = air_temp[0,:,0,0][0] # k
|
||||
P = air_press[0] # Pa
|
||||
H = 0
|
||||
xc = 450 # ppm
|
||||
lat = 24.44768
|
||||
h = 0 # m
|
||||
l1 = 1.49 # micron
|
||||
l2 = 1.78 # micron
|
||||
|
||||
rho = at.rho(p=P, T=T, RH=H, xc=xc)
|
||||
disp = dispersion(lat, h)
|
||||
disp.setReducedHeight(P, rho)
|
||||
|
||||
air_dens = at.rho(p=air_press, T=air_temp[0,:,0,0], RH=np.asarray([0]*len(air_press)), xc=np.asarray([0]*len(air_press)))
|
||||
|
||||
heights = disp.calcHeight(air_press, air_temp[0,:,0,0], RH=np.asarray([0]*len(air_press)), xc=np.asarray([0]*len(air_press)), P0=air_press[0]) / 1e3
|
||||
distance_lat = calculateDistanceResolution(latitudes[100], longitudes[100,:]) - calculateDistanceResolution(latitudes[100,0], longitudes[100,0])
|
||||
temp_selection_ground = air_temp[0,0,100,:]
|
||||
temp_selection_upper = air_temp[0,-1,100,:]
|
||||
print(distance_lat[1])
|
||||
|
||||
|
||||
#########################################################
|
||||
# Extrapolating using the U.S. Standard Atmosphere 1976.
|
||||
# The end point of the real data is used to shift the USSA data accordingly (linearly)
|
||||
h_ussa1976, _, _, _, air_temp_ussa1976, air_press_ussa1976,_,_,_,_ = np.genfromtxt('../US1976 Standard Atmosphere/si1py_modified.txt', unpack=True, skip_header=6)
|
||||
temp_ip = interp1d(h_ussa1976, air_temp_ussa1976)
|
||||
press_ip = interp1d(h_ussa1976, air_press_ussa1976)
|
||||
|
||||
ussa1976_start = temp_ip(heights[-1])
|
||||
diff_temp_list = temp_selection_upper - ussa1976_start
|
||||
start_i = np.argmax(h_ussa1976 > heights[-1])
|
||||
ussa1976_temp_array = np.tile(air_temp_ussa1976[start_i:], (len(temp_selection_upper),1))
|
||||
ussa1976_temp_array = np.asarray([ussa1976_temp_array[i] + diff_temp_list[i] for i in range(len(diff_temp_list))]).T
|
||||
|
||||
# Combining the (masked) arrays
|
||||
new_heights = np.append(heights, h_ussa1976[start_i:])
|
||||
tempData = np.append(air_temp[0,:,100,:], ussa1976_temp_array, axis=0)
|
||||
tempData_mask = np.append(air_temp[0,:,100,:].mask, np.zeros(ussa1976_temp_array.shape), axis=0)
|
||||
tempData = np.ma.masked_array(tempData, mask=tempData_mask)
|
||||
|
||||
# Now the same for the pressure data
|
||||
# Combining the (masked) arrays
|
||||
start_i = np.argmax(h_ussa1976 > heights[-1])
|
||||
new_heights = np.append(heights, h_ussa1976[start_i:])
|
||||
pressData = []
|
||||
for i in range(len(temp_selection_upper)):
|
||||
calc_press_data = calculatePressure(Th=ussa1976_temp_array[:,i], P0=air_press[0], h=h_ussa1976[start_i:]*1000, lat=lat)
|
||||
pressData_i = np.append(air_press*100, calc_press_data*100, axis=0)
|
||||
pressData.append(pressData_i)
|
||||
pressData = np.asarray(pressData).T
|
||||
#########################################################
|
||||
|
||||
# We'd now like to export a subset of the atmospheric data for a distance of ~200 km.
|
||||
# Let's use interpolation to create a uniform gridded dataset
|
||||
export_heights = np.linspace(0,new_heights.max(),400)
|
||||
export_dist = np.linspace(0,distance_lat[136]-distance_lat[130],1000)
|
||||
atmosphere_temp_func = interp2d(distance_lat[130:136]-distance_lat[130], new_heights, tempData[:,130:136], kind='linear')
|
||||
export_temp_atmosphere = atmosphere_temp_func(export_dist, export_heights)
|
||||
|
||||
atmosphere_press_func = interp2d(distance_lat[130:136]-distance_lat[130], new_heights, pressData[:,130:136], kind='linear')
|
||||
export_press_atmosphere = atmosphere_press_func(export_dist, export_heights)
|
||||
|
||||
# saveVariableToTXT(export_heights, 'export_heights.txt')
|
||||
# saveVariableToTXT(export_dist, 'export_dist.txt')
|
||||
# saveVariableToTXT(export_temp_atmosphere, 'export_temp_atmosphere.txt')
|
||||
# saveVariableToTXT(export_press_atmosphere, 'export_press_atmosphere.txt')
|
||||
|
||||
|
||||
# Plot the real data + extrapolated standard atmosphere.
|
||||
# fig = plt.figure()
|
||||
# ax = fig.add_subplot(211)
|
||||
# ax2 = fig.add_subplot(212)
|
||||
# im1 = ax.pcolormesh(export_dist, export_heights, export_temp_atmosphere, cmap=cm.coolwarm)
|
||||
# im2 = ax2.pcolormesh(export_dist, export_heights, export_press_atmosphere, cmap=cm.coolwarm, norm=LogNorm())
|
||||
# ax2.set_xlabel('Lateral distance (km)')
|
||||
# ax.set_ylabel('Height (km)')
|
||||
# ax2.set_ylabel('Height (km)')
|
||||
# cbar = fig.colorbar(im1, ax=ax, label='Temperature (K)')
|
||||
# lbls = cbar.get_ticks()
|
||||
# cbar.ax.invert_yaxis()
|
||||
# cbar.set_ticks(lbls[::-1])
|
||||
# cbar2 = fig.colorbar(im2, ax=ax2, label='Pressure (Pa)')
|
||||
# lbls = cbar2.get_ticks()
|
||||
# cbar2.ax.invert_yaxis()
|
||||
# cbar2.set_ticks(lbls[::-1])
|
||||
# plt.tight_layout()
|
||||
# plt.show(block=False)
|
||||
# set_trace()
|
||||
|
||||
|
||||
|
||||
|
||||
# # Plot the real data + extrapolated standard atmosphere.
|
||||
# fig = plt.figure()
|
||||
# ax = fig.add_subplot(211)
|
||||
# ax2 = fig.add_subplot(212)
|
||||
# im1 = ax.imshow(tempData[:,130:137], cmap=cm.coolwarm, interpolation= 'bicubic', aspect='auto', origin='lower')
|
||||
# # im1 = ax.pcolormesh(distance_lat[130:137], new_heights, tempData[:,130:137], cmap=cm.coolwarm)
|
||||
# im2 = ax2.pcolormesh(distance_lat, new_heights, pressData, cmap=cm.coolwarm, norm=LogNorm())
|
||||
# ax2.set_xlabel('Lateral distance (km)')
|
||||
# ax.set_ylabel('Height (km)')
|
||||
# ax2.set_ylabel('Height (km)')
|
||||
# cbar = fig.colorbar(im1, ax=ax, label='Temperature (K)')
|
||||
# lbls = cbar.get_ticks()
|
||||
# cbar.ax.invert_yaxis()
|
||||
# cbar.set_ticks(lbls[::-1])
|
||||
# cbar2 = fig.colorbar(im2, ax=ax2, label='Pressure (Pa)')
|
||||
# lbls = cbar2.get_ticks()
|
||||
# cbar2.ax.invert_yaxis()
|
||||
# cbar2.set_ticks(lbls[::-1])
|
||||
# plt.tight_layout()
|
||||
# plt.show(block=False)
|
||||
|
||||
|
||||
# Plot the real data and properties
|
||||
fig = plt.figure(figsize=(9,4))
|
||||
fig2 = plt.figure()
|
||||
ax1 = fig2.add_subplot(221)
|
||||
ax2 = fig2.add_subplot(222)
|
||||
ax3 = fig2.add_subplot(223)
|
||||
ax4 = fig.add_subplot(121)
|
||||
ax5 = fig2.add_subplot(224)
|
||||
ax6 = fig.add_subplot(122)
|
||||
|
||||
ax1.grid(alpha=0.3)
|
||||
ax2.grid(alpha=0.3)
|
||||
ax3.grid(alpha=0.3)
|
||||
ax5.grid(alpha=0.3)
|
||||
|
||||
ax1.plot(air_temp[0,:,0,0], heights)
|
||||
ax1.set_xlabel('Temperature (K)')
|
||||
ax1.set_ylabel('Height (km)')
|
||||
|
||||
ax2.plot(air_press, heights)
|
||||
ax2.set_xlabel('Pressure (Pa)')
|
||||
ax2.set_ylabel('Height (km)')
|
||||
|
||||
ax3.plot(air_dens, heights)
|
||||
ax3.set_xlabel('Density (g cm$^{-3}$)')
|
||||
ax3.set_ylabel('Height (km)')
|
||||
|
||||
im = ax4.imshow(np.rot90(air_temp[0,0,:,:].T), aspect=1, cmap='jet')
|
||||
ax4.set_yticks([])
|
||||
ax4.set_xticks([])
|
||||
# fig.colorbar(im, ax=ax4, label='Temperature (K)')
|
||||
|
||||
ax5.plot(distance_lat, temp_selection_ground, 'b-', label='ground')
|
||||
ax5.plot(distance_lat, temp_selection_upper, 'r-', label='upper atmosphere')
|
||||
ax5.legend()
|
||||
ax5.set_ylabel('Temperature (K)')
|
||||
ax5.set_xlabel('Distance (km)')
|
||||
|
||||
im2 = ax6.imshow(air_temp[0,:,100,:], aspect='auto', origin='lower', interpolation='bilinear', extent=[0,distance_lat[-1],0,heights[-1] ], cmap='jet')
|
||||
ax6.set_xlabel('Distance (km)', fontsize=18)
|
||||
ax6.set_ylabel('Height (km)', fontsize=18)
|
||||
cbar = fig.colorbar(im2, ax=ax6)#, label='Temperature (K)')
|
||||
cbar.ax.tick_params(labelsize=16)
|
||||
cbar.set_label('Temperature (K)', size=18)
|
||||
lbls = cbar.get_ticks()
|
||||
cbar.ax.invert_yaxis()
|
||||
cbar.set_ticks(lbls[::-1])
|
||||
ax6.tick_params(axis ='both', direction='in', labelsize=16)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
@@ -0,0 +1,232 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
""" Tables.py - Make tables of atmospheric properties (Python 3)
|
||||
|
||||
Adapted by
|
||||
Richard J. Kwan, Lightsaber Computing
|
||||
from original programs by
|
||||
Ralph L. Carmichael, Public Domain Aeronautical Software
|
||||
|
||||
Revision History
|
||||
Date Vers Person Statement of Changes
|
||||
2004 Oct 04 1.0 RJK Initial program
|
||||
2017 Jun 04 1.1 RLC All indents are with spaces; all prints in ( )
|
||||
New version for Python 3 that does integer div with //
|
||||
"""
|
||||
|
||||
import sys, math
|
||||
|
||||
version = "1.1 (2017 Jun 04)"
|
||||
greeting = "Tables - A Python program to compute atmosphere tables"
|
||||
author = "Ralph L. Carmichael, Public Domain Aeronautical Software"
|
||||
modifier = "Richard J. Kwan, Lightsaber Computing"
|
||||
farewell = "Four files added to your directory."
|
||||
finalmess = "Normal termination of tables."
|
||||
|
||||
# P H Y S I C A L C O N S T A N T S
|
||||
|
||||
FT2METERS = 0.3048 # mult. ft. to get meters (exact)
|
||||
KELVIN2RANKINE = 1.8 # mult deg K to get deg R
|
||||
PSF2NSM = 47.880258 # mult lb/sq.ft to get sq.m
|
||||
SCF2KCM = 515.379 # mult slugs/cu.ft to get kg/cu.m
|
||||
TZERO = 288.15 # sea-level temperature, kelvins
|
||||
PZERO = 101325.0 # sea-level pressure, N/sq.m
|
||||
RHOZERO = 1.225 # sea-level density, kg/cu.m
|
||||
AZERO = 340.294 # speed of sound at S.L. m/sec
|
||||
BETAVISC = 1.458E-6 # viscosity constant
|
||||
SUTH = 110.4 # Sutherland's constant, kelvins
|
||||
|
||||
def LongUSTable():
|
||||
Itxt = open('us1py.prt', 'w')
|
||||
Itxt.write(' alt sigma delta theta ')
|
||||
Itxt.write(' temp press dens a visc k.visc\n')
|
||||
Itxt.write(' Kft ')
|
||||
Itxt.write(' degR lb/sq.ft s/cu.ft fps s/ft-s sq.ft/s\n')
|
||||
|
||||
for i in range(-1, 57):
|
||||
altKm=5*i*FT2METERS
|
||||
(sigma, delta, theta) = Atmosphere(altKm)
|
||||
Itxt.write("%4i %9.3E %9.3E %6.4f " %
|
||||
(5*i, sigma, delta, theta))
|
||||
temp=(KELVIN2RANKINE*TZERO)*theta
|
||||
pressure=(PZERO/PSF2NSM)*delta
|
||||
density=(RHOZERO/SCF2KCM)*sigma
|
||||
asound=(AZERO/FT2METERS)*math.sqrt(theta)
|
||||
Itxt.write("%5.1f %8.3E %9.3E %6.1f" %
|
||||
(temp, pressure, density, asound))
|
||||
viscosity=(1.0/PSF2NSM)*MetricViscosity(theta)
|
||||
kinematicViscosity=viscosity/density
|
||||
Itxt.write("%6.3f %8.2E\n" %
|
||||
(1.0E6*viscosity, kinematicViscosity))
|
||||
|
||||
Itxt.close()
|
||||
|
||||
def ShortUSTable():
|
||||
Itxt = open('us2py.prt', 'w')
|
||||
Itxt.write(' alt sigma delta theta ')
|
||||
Itxt.write(' temp press dens a visc k.visc ratio\n')
|
||||
Itxt.write(' Kft ')
|
||||
Itxt.write(' degR psf s/cu.ft fps s/ft-sec sq.ft/s 1/ft\n')
|
||||
|
||||
for i in range(-1, 66):
|
||||
altKm=i*FT2METERS
|
||||
(sigma, delta, theta) = SimpleAtmosphere(altKm)
|
||||
Itxt.write("%4i %6.4f %6.4f %6.4f" %
|
||||
(i, sigma, delta, theta))
|
||||
temp=KELVIN2RANKINE*TZERO*theta
|
||||
pressure=PZERO*delta/47.88
|
||||
density=RHOZERO*sigma/515.379
|
||||
asound=(AZERO/FT2METERS)*math.sqrt(theta);
|
||||
Itxt.write("%6.1f %6.1f %9.7f %6.1f" %
|
||||
(temp, pressure, density, asound))
|
||||
viscosity=(1.0/PSF2NSM)*MetricViscosity(theta)
|
||||
kinematicViscosity=viscosity/density
|
||||
vratio=asound/kinematicViscosity
|
||||
Itxt.write("%6.3f %8.2E %4.2f\n"%
|
||||
(1.0E6*viscosity, kinematicViscosity, 1.0E-6*vratio))
|
||||
|
||||
Itxt.close()
|
||||
|
||||
def LongSITable():
|
||||
Itxt = open('si1py.prt', 'w')
|
||||
Itxt.write(' alt sigma delta theta ')
|
||||
Itxt.write(' temp press dens a visc k.visc\n')
|
||||
Itxt.write(' Km ')
|
||||
Itxt.write(' K N/sq.m kg/cu.m m/sec kg/m-s sq.m/s\n')
|
||||
|
||||
for i in range(-4,173):
|
||||
altKm=0.5*i
|
||||
(sigma, delta, theta) = Atmosphere(altKm)
|
||||
Itxt.write("%4.1f %8.4E %8.4E %6.4f" %
|
||||
(0.5*i, sigma,delta,theta))
|
||||
temp=TZERO*theta
|
||||
pressure=PZERO*delta
|
||||
density=RHOZERO*sigma
|
||||
asound=AZERO*math.sqrt(theta)
|
||||
Itxt.write("%6.1f %8.5E %8.3E %5.1f" %
|
||||
(temp,pressure,density,asound))
|
||||
viscosity=MetricViscosity(theta)
|
||||
kinematicViscosity=viscosity/density
|
||||
Itxt.write("%6.2f %8.2E\n" %
|
||||
(1.0E6*viscosity, kinematicViscosity))
|
||||
|
||||
Itxt.close()
|
||||
|
||||
def ShortSITable():
|
||||
Itxt = open('si2py.prt', 'w')
|
||||
Itxt.write(' alt sigma delta theta ')
|
||||
Itxt.write(' temp press dens a visc k.visc ratio\n')
|
||||
Itxt.write(' Km ')
|
||||
Itxt.write(' K N/sq.m kcm m/sec kg/m-s sq.m/s 1/m\n')
|
||||
|
||||
for i in range(-1, 41):
|
||||
altKm=0.5*i
|
||||
(sigma, delta, theta) = SimpleAtmosphere(altKm)
|
||||
Itxt.write("%4.1f %6.4f %6.4f %6.4f" %
|
||||
(altKm, sigma, delta, theta))
|
||||
temp=TZERO*theta
|
||||
pressure=PZERO*delta
|
||||
density=RHOZERO*sigma
|
||||
asound=AZERO*math.sqrt(theta)
|
||||
Itxt.write("%6.1f %6.0f %5.3f %5.1f" %
|
||||
(temp, pressure,density,asound))
|
||||
viscosity=MetricViscosity(theta)
|
||||
kinematicViscosity=viscosity/density
|
||||
vratio=asound/kinematicViscosity
|
||||
Itxt.write("%6.2f %8.2E %5.2f\n" %
|
||||
(1.0E6*viscosity, kinematicViscosity, 1.0E-6*vratio))
|
||||
|
||||
Itxt.close()
|
||||
|
||||
def Atmosphere(alt):
|
||||
""" Compute temperature, density, and pressure in standard atmosphere.
|
||||
Correct to 86 km. Only approximate thereafter.
|
||||
Input:
|
||||
alt geometric altitude, km.
|
||||
Return: (sigma, delta, theta)
|
||||
sigma density/sea-level standard density
|
||||
delta pressure/sea-level standard pressure
|
||||
theta temperature/sea-level std. temperature
|
||||
"""
|
||||
|
||||
REARTH = 6369.0 # radius of the Earth (km)
|
||||
GMR = 34.163195
|
||||
NTAB = 8 # length of tables
|
||||
|
||||
htab = [ 0.0, 11.0, 20.0, 32.0, 47.0,
|
||||
51.0, 71.0, 84.852 ]
|
||||
ttab = [ 288.15, 216.65, 216.65, 228.65, 270.65,
|
||||
270.65, 214.65, 186.946 ]
|
||||
ptab = [ 1.0, 2.2336110E-1, 5.4032950E-2, 8.5666784E-3, 1.0945601E-3,
|
||||
6.6063531E-4, 3.9046834E-5, 3.68501E-6 ]
|
||||
gtab = [ -6.5, 0.0, 1.0, 2.8, 0, -2.8, -2.0, 0.0 ]
|
||||
|
||||
h = alt*REARTH/(alt+REARTH) # geometric to geopotential altitude
|
||||
|
||||
i=0; j=len(htab)-1
|
||||
while (j > i+1):
|
||||
k = (i+j)//2 # this is floor division in Python 3
|
||||
if h < htab[k]:
|
||||
j = k
|
||||
else:
|
||||
i = k
|
||||
tgrad = gtab[i] # temp. gradient of local layer
|
||||
tbase = ttab[i] # base temp. of local layer
|
||||
deltah=h-htab[i] # height above local base
|
||||
tlocal=tbase+tgrad*deltah # local temperature
|
||||
theta = tlocal/ttab[0] # temperature ratio
|
||||
|
||||
if 0.0 == tgrad:
|
||||
delta=ptab[i]*math.exp(-GMR*deltah/tbase)
|
||||
else:
|
||||
delta=ptab[i]*math.pow(tbase/tlocal, GMR/tgrad)
|
||||
sigma = delta/theta
|
||||
return ( sigma, delta, theta )
|
||||
|
||||
def SimpleAtmosphere(alt):
|
||||
""" Compute temperature, density, and pressure in simplified
|
||||
standard atmosphere.
|
||||
|
||||
Correct to 20 km. Only approximate thereafter.
|
||||
|
||||
Input:
|
||||
alt geometric altitude, km.
|
||||
Return: (sigma, delta, theta)
|
||||
sigma density/sea-level standard density
|
||||
delta pressure/sea-level standard pressure
|
||||
theta temperature/sea-level std. temperature
|
||||
"""
|
||||
REARTH = 6369.0 # radius of the Earth (km)
|
||||
GMR = 34.163195 # gas constant
|
||||
|
||||
h = alt*REARTH/(alt+REARTH) # geometric to geopotential altitude
|
||||
|
||||
if h<11.0: # troposphere
|
||||
theta=(288.15-6.5*h)/288.15
|
||||
delta=math.pow(theta, GMR/6.5)
|
||||
else: # stratosphere
|
||||
theta=216.65/288.15
|
||||
delta=0.2233611*math.exp(-GMR*(h-11.0)/216.65)
|
||||
sigma = delta/theta
|
||||
return ( sigma, delta, theta )
|
||||
|
||||
def MetricViscosity(theta):
|
||||
t=theta*TZERO
|
||||
return BETAVISC*math.sqrt(t*t*t)/(t+SUTH)
|
||||
|
||||
def main():
|
||||
print ("Executing ", sys.argv[0])
|
||||
print (greeting)
|
||||
print (author)
|
||||
if modifier != "":
|
||||
print ("Modified by ", modifier)
|
||||
print (" version ", version)
|
||||
LongUSTable()
|
||||
ShortUSTable()
|
||||
LongSITable()
|
||||
ShortSITable()
|
||||
|
||||
print (farewell)
|
||||
print (finalmess)
|
||||
|
||||
main()
|
||||
@@ -0,0 +1,242 @@
|
||||
#!/usr/bin/env Python3
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.lines import Line2D
|
||||
from matplotlib import cm
|
||||
from matplotlib.colors import LogNorm
|
||||
import sys
|
||||
from AstroAtmosphere import *
|
||||
from scipy import interpolate
|
||||
|
||||
'''
|
||||
@ Author: Joost van den Born
|
||||
@ Contact: born@astron.nl
|
||||
@ Description: This script uses the US Standard Atmosphere and perturbs it
|
||||
at several points. Then full integration is used to calculate
|
||||
the atmospheric dispersion. This is then compared to when we
|
||||
would have only used the atmospheric conditions directly at
|
||||
the observatory. This is done to assess whether we should
|
||||
monitor the weather at every height along the line of sight of
|
||||
the telescope.
|
||||
'''
|
||||
|
||||
def perturbAtmosphere(h, Temp_perturbed, Press_perturbed):
|
||||
for _i in range(len(h)):
|
||||
if h[_i] >= 1.0 and h[_i] <= 2.0:
|
||||
Tnoise = np.random.normal(0,5,1)
|
||||
Temp_perturbed[_i] = Temp_perturbed[_i] + Tnoise
|
||||
Pnoise = np.random.normal(0,1000,1)
|
||||
Press_perturbed[_i] = Press_perturbed[_i] + Pnoise
|
||||
if h[_i] >= 7.0 and h[_i] <= 10.0:
|
||||
Tnoise = np.random.normal(0,5,1)
|
||||
Temp_perturbed[_i] = Temp_perturbed[_i] + Tnoise
|
||||
Pnoise = np.random.normal(0,500,1)
|
||||
Press_perturbed[_i] = Press_perturbed[_i] + Pnoise
|
||||
if h[_i] >= 25.0 and h[_i] <= 30.0:
|
||||
Tnoise = np.random.normal(0,10,1)
|
||||
Temp_perturbed[_i] = Temp_perturbed[_i] + Tnoise
|
||||
Pnoise = np.random.normal(0,250,1)
|
||||
Press_perturbed[_i] = Press_perturbed[_i] + Pnoise
|
||||
if h[_i] >= 50.0 and h[_i] <= 60.0:
|
||||
Tnoise = np.random.normal(0,15,1)
|
||||
Temp_perturbed[_i] = Temp_perturbed[_i] + Tnoise
|
||||
Pnoise = np.random.normal(0,4,1)
|
||||
Press_perturbed[_i] = Press_perturbed[_i] + Pnoise
|
||||
return Temp_perturbed, Press_perturbed
|
||||
|
||||
plt.rc('font', family='serif')
|
||||
plt.rc('axes', labelsize='large')
|
||||
|
||||
h, _, _, _, Temp, Press, Dens, vSound, _,_ = np.genfromtxt('si1py_modified.txt', unpack=True, skip_header=6)
|
||||
|
||||
# Conditions at Cerro Armazones
|
||||
T = 288.15 # k
|
||||
P = 101324 # Pa
|
||||
H = 0.0
|
||||
xc = 314 # ppm
|
||||
lat = -24.5983 # degrees
|
||||
h0 = 0 # m
|
||||
l1 = 1.49 # micron
|
||||
l2 = 1.78 # micron
|
||||
zenith = 60 # degrees
|
||||
|
||||
# Calculate the index of refraction
|
||||
at = Observatory()
|
||||
n1 = at.n_tph(l=l1, T=T, p=P, RH=H, xc=xc)
|
||||
n2 = at.n_tph(l=l2, T=T, p=P, RH=H, xc=xc)
|
||||
rho = at.rho(p=P, T=T, RH=H, xc=xc)
|
||||
|
||||
refr = refraction(lat, h0)
|
||||
refr.setReducedHeight(P, rho)
|
||||
disp = dispersion(lat, h0)
|
||||
disp.setReducedHeight(P, rho)
|
||||
|
||||
|
||||
|
||||
# We must create a cube of weather data:
|
||||
# We assume a flat atmosphere for now, so that h does not need to be adjusted
|
||||
# x_max = 200 km, gives zenith_max = ~66.7 degrees
|
||||
# Every 20 km in horizontal direction a new perturbation and interpolation
|
||||
# for every 500 m.
|
||||
|
||||
x_max = 200 # km
|
||||
|
||||
Temp_cube = []
|
||||
Press_cube = []
|
||||
Temp_cube_n = []
|
||||
Press_cube_n = []
|
||||
Temp_cube.append(Temp)
|
||||
Press_cube.append(Press)
|
||||
Temp_cube_n.append(Temp)
|
||||
Press_cube_n.append(Press)
|
||||
|
||||
|
||||
|
||||
for x in range(int(x_max/(2))):
|
||||
# Adding noise
|
||||
# 1-2 km: 2 degree noise, 100 Pa noise
|
||||
# 7-10 km: 3 degree noise, 150 Pa noise
|
||||
# 25-30 km: 5 degree noise, 250 Pa noise
|
||||
Temp_perturbed = Temp.copy()
|
||||
Press_perturbed = Press.copy()
|
||||
|
||||
Temp_perturbed, Press_perturbed = perturbAtmosphere(h, Temp_perturbed, Press_perturbed)
|
||||
Temp_cube.append(Temp_perturbed)
|
||||
Press_cube.append(Press_perturbed)
|
||||
Temp_cube_n.append(Temp)
|
||||
Press_cube_n.append(Press)
|
||||
|
||||
|
||||
f_Temp_n = interpolate.interp1d(np.linspace(0,x_max, int(x_max/2)+1), Temp_cube_n, axis=0)
|
||||
f_Press_n = interpolate.interp1d(np.linspace(0,x_max, int(x_max/2)+1), Press_cube_n, axis=0)
|
||||
f_Temp = interpolate.interp1d(np.linspace(0,x_max, int(x_max/2)+1), Temp_cube, axis=0)
|
||||
f_Press = interpolate.interp1d(np.linspace(0,x_max, int(x_max/2)+1), Press_cube, axis=0)
|
||||
|
||||
Temp_interpolated_n = f_Temp_n(np.linspace(0,x_max, int(x_max/0.5)+1) )
|
||||
Press_interpolated_n = f_Press_n(np.linspace(0,x_max, int(x_max/0.5)+1) )
|
||||
Temp_interpolated = f_Temp(np.linspace(0,x_max, int(x_max/0.5)+1) )
|
||||
Press_interpolated = f_Press(np.linspace(0,x_max, int(x_max/0.5)+1) )
|
||||
|
||||
x, y = np.mgrid[0:200:(int(x_max/0.5)+1)*1j, 0:np.max(h):len(h)*1j]
|
||||
|
||||
fig = plt.figure()
|
||||
ax1 = fig.add_subplot(211)
|
||||
im = ax1.pcolor(x,y, Temp_interpolated, cmap=cm.coolwarm)
|
||||
fig.colorbar(im, ax=ax1, label='Temperature (K)')
|
||||
ax1.set_aspect(1)
|
||||
ax1.set_ylabel('Height (km)')
|
||||
|
||||
ax2 = fig.add_subplot(212)
|
||||
im2 = ax2.pcolor(x,y, Press_interpolated, cmap=cm.coolwarm, norm=LogNorm())
|
||||
ax2.set_aspect(1)
|
||||
ax2.set_xlabel('Horizontal distance (km)')
|
||||
ax2.set_ylabel('Height (km)')
|
||||
fig.colorbar(im2, ax=ax2, label='Pressure (Pa)')
|
||||
plt.tight_layout()
|
||||
plt.savefig('2d_atmosphere.png', bbox_inches='tight')
|
||||
plt.savefig('2d_atmosphere.pdf', bbox_inches='tight')
|
||||
plt.show(block=False)
|
||||
|
||||
|
||||
# Now for a given zenith angle we need to find the temp and press along the line of sight
|
||||
x = np.linspace(0,200,(int(x_max/0.5)+1))
|
||||
y = h
|
||||
|
||||
f_Temp_2d_n = interpolate.interp2d(y,x,Temp_interpolated_n, kind='linear')
|
||||
f_Press_2d_n = interpolate.interp2d(y,x,Press_interpolated_n, kind='linear')
|
||||
f_Temp_2d = interpolate.interp2d(y,x,Temp_interpolated, kind='linear')
|
||||
f_Press_2d = interpolate.interp2d(y,x,Press_interpolated, kind='linear')
|
||||
|
||||
fig2 = plt.figure()
|
||||
ax = fig2.add_subplot(121)
|
||||
ax.grid(True, alpha=0.3)
|
||||
ax.plot(Temp, h, 'k-')
|
||||
|
||||
ax2 = fig2.add_subplot(122)
|
||||
ax2.grid(True, alpha=0.3)
|
||||
ax2.plot(Press, h, 'k-')
|
||||
|
||||
|
||||
zenithRange = np.linspace(30,65,100)
|
||||
refrDiff, dispDiff = [], []
|
||||
|
||||
for zenith in zenithRange:
|
||||
z = np.deg2rad(zenith)
|
||||
y_los = np.linspace(0, 86, len(h))
|
||||
x_los = np.linspace(0, 86*np.tan(z), len(h))
|
||||
|
||||
Temp_los, Press_los = [], []
|
||||
Temp_los_n, Press_los_n = [], []
|
||||
for i in range(len(h)):
|
||||
Temp_los.append(f_Temp_2d(y_los[i], x_los[i])[0])
|
||||
Press_los.append(f_Press_2d(y_los[i], x_los[i])[0])
|
||||
Temp_los_n.append(f_Temp_2d_n(y_los[i], x_los[i])[0])
|
||||
Press_los_n.append(f_Press_2d_n(y_los[i], x_los[i])[0])
|
||||
|
||||
Temp_los = np.asarray(Temp_los)
|
||||
Press_los = np.asarray(Press_los)
|
||||
Temp_los_n = np.asarray(Temp_los_n)
|
||||
Press_los_n = np.asarray(Press_los_n)
|
||||
rho_los = at.rho(p=Press_los, T=Temp_los, RH=H, xc=xc)
|
||||
rho_los_n = at.rho(p=Press_los_n, T=Temp_los_n, RH=H, xc=xc)
|
||||
|
||||
ax.plot(Temp_los, h, label='z=%i' %zenith)
|
||||
ax2.plot(Press_los, h, label='z=%i' %zenith)
|
||||
|
||||
refrInt = refr.refractionIntegral(n1, z, useStandardAtmosphere=False, heightData=h, rhoData=rho_los_n)*3600*1000
|
||||
refrInt_pert = refr.refractionIntegral(n1, z, useStandardAtmosphere=False, heightData=h, rhoData=rho_los)*3600*1000
|
||||
refrDiff.append(refrInt_pert - refrInt)
|
||||
|
||||
dispInt = disp.refractionIntegral(n1, n2, z, useStandardAtmosphere=False, heightData=h, rhoData=rho_los_n)*3.6e6
|
||||
dispInt_pert = disp.refractionIntegral(n1, n2, z, useStandardAtmosphere=False, heightData=h, rhoData=rho_los)*3.6e6
|
||||
dispDiff.append(dispInt_pert - dispInt)
|
||||
|
||||
ax.legend()
|
||||
ax2.legend()
|
||||
|
||||
|
||||
|
||||
# fig3 = plt.figure()
|
||||
# ax1 = fig3.add_subplot(211)
|
||||
# ax2 = fig3.add_subplot(212)
|
||||
# ax1.tick_params(axis ='both', direction='in')
|
||||
# ax2.tick_params(axis ='both', direction='in')
|
||||
# ax1.grid(True, alpha=0.3)
|
||||
# ax2.grid(True, alpha=0.3)
|
||||
# ax1.semilogy(zenithRange, np.abs(refrDiff), 'k-')
|
||||
# ax2.semilogy(zenithRange, np.abs(dispDiff), 'k-')
|
||||
# ax1.set_xlabel('Zenith (degrees)')
|
||||
# ax2.set_xlabel('Zenith (degrees)')
|
||||
# ax1.set_ylabel('|$R_{p} - R_{SA}$| (mas)')
|
||||
# ax2.set_ylabel('|$\Delta R_{p} - \Delta R_{SA}$| (mas)')
|
||||
# plt.tight_layout()
|
||||
# plt.savefig('differential_USSA_for_different_z.png', bbox_inches='tight')
|
||||
# plt.savefig('differential_USSA_for_different_z.pdf', bbox_inches='tight')
|
||||
# plt.show()
|
||||
|
||||
fig3 = plt.figure(figsize=(9,7))
|
||||
ax1 = fig3.add_subplot(211)
|
||||
ax2 = fig3.add_subplot(212)
|
||||
ax1.tick_params(axis ='both', direction='in', which='both', labelsize=16)
|
||||
ax2.tick_params(axis ='both', direction='in', which='both', labelsize=16)
|
||||
ax1.grid(True, alpha=0.3)
|
||||
ax2.grid(True, alpha=0.3)
|
||||
|
||||
|
||||
ax1.plot(zenithRange, np.log10(np.abs(refrDiff)), 'k-', linewidth=2)
|
||||
ax2.plot(zenithRange, np.log10(np.abs(dispDiff)), 'k-', linewidth=2)
|
||||
ax1.set_xlabel('Zenith (degrees)', fontsize=18)
|
||||
ax2.set_xlabel('Zenith (degrees)', fontsize=18)
|
||||
ax1.set_ylabel('$\log_{10}($|$R - R_{o}$|$)$ (mas)', fontsize=18)
|
||||
ax2.set_ylabel('$\log_{10}($|$\Delta R - \Delta R_{o}$|$)$ (mas)', fontsize=18)
|
||||
plt.tight_layout()
|
||||
# plt.savefig('differential_USSA_for_different_z.png', bbox_inches='tight')
|
||||
# plt.savefig('differential_USSA_for_different_z.pdf', bbox_inches='tight')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
# Comparison of different air columns.
|
||||
|
||||
|
||||
@@ -0,0 +1,91 @@
|
||||
#!/usr/bin/env Python3
|
||||
import numpy as np
|
||||
from pdb import set_trace
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.lines import Line2D
|
||||
import sys
|
||||
from AstroAtmosphere import *
|
||||
|
||||
'''
|
||||
@ Author: Joost van den Born
|
||||
@ Contact: born@astron.nl
|
||||
@ Description: We plot the total refraction as a function of height.
|
||||
We find that most refraction happens in the lower atmosphere
|
||||
i.e. <10 km.
|
||||
'''
|
||||
|
||||
def refractionIntegral2(n, R, z0, n0, R0):
|
||||
z0 = np.deg2rad(z0)
|
||||
return (n0 * R0 * np.sin(z0))/(n*np.sqrt((n*R)**2 - (n0 * R0 * np.sin(z0))**2 ))
|
||||
|
||||
def n_for_heights(n0, z0, 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
|
||||
'''
|
||||
if useStandardAtmosphere:
|
||||
_h, _, _, _, _Temp, _Press, _Dens,_,_,_ = np.genfromtxt('si1py_modified.txt', unpack=True, skip_header=6)
|
||||
else:
|
||||
_Dens = rhoData
|
||||
_h = heightData
|
||||
z0 = np.deg2rad(z0)
|
||||
_Dens = np.asarray(_Dens)
|
||||
_h = np.asarray(_h)
|
||||
_R = R0 + _h * 1000
|
||||
|
||||
# Gladstone-Dale relation between refractive index and density
|
||||
_n_h = 1 + (n0 - 1) / _Dens[0] * _Dens
|
||||
_z_h = np.arcsin( (n0 * R0) / (_n_h * _R) * np.sin(z0) )
|
||||
|
||||
return _h, _n_h, np.degrees(_z_h)
|
||||
|
||||
|
||||
h, _, _, _, Temp, Press, Dens, vSound, _,_ = np.genfromtxt('si1py_modified.txt', unpack=True, skip_header=6)
|
||||
|
||||
|
||||
# Conditions
|
||||
T = 288.15 # k
|
||||
P = 101324 # Pa
|
||||
H = 0.0
|
||||
xc = 314 # ppm
|
||||
lat = -24.5983 # degrees
|
||||
h0 = 0 # m
|
||||
l1 = 1.49 # micron
|
||||
l2 = 1.78 # micron
|
||||
zenith = 60 # degrees
|
||||
|
||||
# Calculate the index of refraction
|
||||
at = Observatory()
|
||||
n1 = at.n_tph(l=l1, T=T, p=P, RH=H, xc=xc)
|
||||
n2 = at.n_tph(l=l2, T=T, p=P, RH=H, xc=xc)
|
||||
rho = at.rho(p=P, T=T, RH=H, xc=xc)
|
||||
|
||||
refr = refraction(lat, h0)
|
||||
refr.setReducedHeight(P, rho)
|
||||
disp = dispersion(lat, h0)
|
||||
disp.setReducedHeight(P, rho)
|
||||
|
||||
|
||||
fig = plt.figure(figsize=(5,4))
|
||||
ax = fig.add_subplot(111)
|
||||
ax.grid(True, alpha=0.3)
|
||||
ax.set_xlabel('Height (km)')
|
||||
ax.set_ylabel('Normalized cumulative refraction')
|
||||
ax.set_xlim([0, h[-1]])
|
||||
|
||||
for z in [30]:
|
||||
refList = []
|
||||
dispList = []
|
||||
for i in range(len(h)):
|
||||
refList.append(refr.refractionIntegral(n1, z, useStandardAtmosphere=False, heightData=h[:i+1], rhoData=Dens[:i+1])*3600)
|
||||
dispList.append(disp.refractionIntegral(n1, n2, z, useStandardAtmosphere=False, heightData=h[:i+1], rhoData=Dens[:i+1])*3.6e6)
|
||||
|
||||
ax.plot(h, np.asarray(refList)/refList[-1], 'k-', label='$\lambda=1.49$ $\mu$m, z=%i$^{\\circ}$' %z)
|
||||
ax.set_ylim([0,1.1])
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
@@ -0,0 +1,179 @@
|
||||
alt sigma delta theta temp press dens a visc k.visc
|
||||
Km K N/sq.m kg/cu.m m/sec kg/m-s sq.m/s
|
||||
-2.0 1.2067E+00 1.2611E+00 1.0451 301.2 1.27783E+05 1.478E+00 347.9 18.51 1.25E-05
|
||||
-1.5 1.1522E+00 1.1912E+00 1.0338 297.9 1.20696E+05 1.411E+00 346.0 18.36 1.30E-05
|
||||
-1.0 1.0996E+00 1.1244E+00 1.0226 294.7 1.13931E+05 1.347E+00 344.1 18.21 1.35E-05
|
||||
-0.5 1.0489E+00 1.0607E+00 1.0113 291.4 1.07478E+05 1.285E+00 342.2 18.05 1.40E-05
|
||||
0.0 1.0000E+00 1.0000E+00 1.0000 288.1 1.01325E+05 1.225E+00 340.3 17.89 1.46E-05
|
||||
0.5 9.5288E-01 9.4213E-01 0.9887 284.9 9.54613E+04 1.167E+00 338.4 17.74 1.52E-05
|
||||
1.0 9.0748E-01 8.8701E-01 0.9774 281.7 8.98763E+04 1.112E+00 336.4 17.58 1.58E-05
|
||||
1.5 8.6376E-01 8.3454E-01 0.9662 278.4 8.45597E+04 1.058E+00 334.5 17.42 1.65E-05
|
||||
2.0 8.2168E-01 7.8462E-01 0.9549 275.2 7.95014E+04 1.007E+00 332.5 17.26 1.71E-05
|
||||
2.5 7.8119E-01 7.3715E-01 0.9436 271.9 7.46917E+04 9.570E-01 330.6 17.10 1.79E-05
|
||||
3.0 7.4225E-01 6.9204E-01 0.9324 268.7 7.01211E+04 9.093E-01 328.6 16.94 1.86E-05
|
||||
3.5 7.0482E-01 6.4920E-01 0.9211 265.4 6.57804E+04 8.634E-01 326.6 16.78 1.94E-05
|
||||
4.0 6.6885E-01 6.0854E-01 0.9098 262.2 6.16604E+04 8.193E-01 324.6 16.61 2.03E-05
|
||||
4.5 6.3432E-01 5.6997E-01 0.8986 258.9 5.77525E+04 7.770E-01 322.6 16.45 2.12E-05
|
||||
5.0 6.0117E-01 5.3341E-01 0.8873 255.7 5.40482E+04 7.364E-01 320.5 16.28 2.21E-05
|
||||
5.5 5.6936E-01 4.9878E-01 0.8760 252.4 5.05393E+04 6.975E-01 318.5 16.12 2.31E-05
|
||||
6.0 5.3887E-01 4.6600E-01 0.8648 249.2 4.72176E+04 6.601E-01 316.5 15.95 2.42E-05
|
||||
6.5 5.0964E-01 4.3499E-01 0.8535 245.9 4.40754E+04 6.243E-01 314.4 15.78 2.53E-05
|
||||
7.0 4.8165E-01 4.0568E-01 0.8423 242.7 4.11052E+04 5.900E-01 312.3 15.61 2.65E-05
|
||||
7.5 4.5485E-01 3.7799E-01 0.8310 239.5 3.82996E+04 5.572E-01 310.2 15.44 2.77E-05
|
||||
8.0 4.2921E-01 3.5185E-01 0.8198 236.2 3.56515E+04 5.258E-01 308.1 15.27 2.90E-05
|
||||
8.5 4.0470E-01 3.2721E-01 0.8085 233.0 3.31541E+04 4.958E-01 306.0 15.10 3.05E-05
|
||||
9.0 3.8128E-01 3.0398E-01 0.7973 229.7 3.08006E+04 4.671E-01 303.8 14.93 3.20E-05
|
||||
9.5 3.5891E-01 2.8211E-01 0.7860 226.5 2.85846E+04 4.397E-01 301.7 14.75 3.36E-05
|
||||
10.0 3.3756E-01 2.6153E-01 0.7748 223.3 2.64998E+04 4.135E-01 299.5 14.58 3.53E-05
|
||||
10.5 3.1720E-01 2.4219E-01 0.7635 220.0 2.45401E+04 3.886E-01 297.4 14.40 3.71E-05
|
||||
11.0 2.9780E-01 2.2403E-01 0.7523 216.8 2.26998E+04 3.648E-01 295.2 14.22 3.90E-05
|
||||
11.5 2.7545E-01 2.0710E-01 0.7519 216.7 2.09847E+04 3.374E-01 295.1 14.22 4.21E-05
|
||||
12.0 2.5464E-01 1.9146E-01 0.7519 216.7 1.93993E+04 3.119E-01 295.1 14.22 4.56E-05
|
||||
12.5 2.3541E-01 1.7699E-01 0.7519 216.7 1.79340E+04 2.884E-01 295.1 14.22 4.93E-05
|
||||
13.0 2.1763E-01 1.6363E-01 0.7519 216.7 1.65795E+04 2.666E-01 295.1 14.22 5.33E-05
|
||||
13.5 2.0119E-01 1.5127E-01 0.7519 216.7 1.53275E+04 2.465E-01 295.1 14.22 5.77E-05
|
||||
14.0 1.8600E-01 1.3985E-01 0.7519 216.7 1.41702E+04 2.279E-01 295.1 14.22 6.24E-05
|
||||
14.5 1.7196E-01 1.2929E-01 0.7519 216.7 1.31005E+04 2.107E-01 295.1 14.22 6.75E-05
|
||||
15.0 1.5898E-01 1.1953E-01 0.7519 216.7 1.21117E+04 1.948E-01 295.1 14.22 7.30E-05
|
||||
15.5 1.4698E-01 1.1051E-01 0.7519 216.7 1.11976E+04 1.801E-01 295.1 14.22 7.90E-05
|
||||
16.0 1.3589E-01 1.0217E-01 0.7519 216.7 1.03527E+04 1.665E-01 295.1 14.22 8.54E-05
|
||||
16.5 1.2564E-01 9.4465E-02 0.7519 216.7 9.57164E+03 1.539E-01 295.1 14.22 9.24E-05
|
||||
17.0 1.1616E-01 8.7339E-02 0.7519 216.7 8.84961E+03 1.423E-01 295.1 14.22 9.99E-05
|
||||
17.5 1.0740E-01 8.0752E-02 0.7519 216.7 8.18215E+03 1.316E-01 295.1 14.22 1.08E-04
|
||||
18.0 9.9302E-02 7.4662E-02 0.7519 216.7 7.56512E+03 1.216E-01 295.1 14.22 1.17E-04
|
||||
18.5 9.1815E-02 6.9032E-02 0.7519 216.7 6.99471E+03 1.125E-01 295.1 14.22 1.26E-04
|
||||
19.0 8.4893E-02 6.3828E-02 0.7519 216.7 6.46738E+03 1.040E-01 295.1 14.22 1.37E-04
|
||||
19.5 7.8494E-02 5.9017E-02 0.7519 216.7 5.97989E+03 9.616E-02 295.1 14.22 1.48E-04
|
||||
20.0 7.2578E-02 5.4569E-02 0.7519 216.7 5.52921E+03 8.891E-02 295.1 14.22 1.60E-04
|
||||
20.5 6.6979E-02 5.0460E-02 0.7534 217.1 5.11291E+03 8.205E-02 295.4 14.24 1.74E-04
|
||||
21.0 6.1807E-02 4.6670E-02 0.7551 217.6 4.72884E+03 7.571E-02 295.7 14.27 1.88E-04
|
||||
21.5 5.7045E-02 4.3173E-02 0.7568 218.1 4.37446E+03 6.988E-02 296.0 14.29 2.05E-04
|
||||
22.0 5.2660E-02 3.9945E-02 0.7585 218.6 4.04741E+03 6.451E-02 296.4 14.32 2.22E-04
|
||||
22.5 4.8622E-02 3.6965E-02 0.7603 219.1 3.74551E+03 5.956E-02 296.7 14.35 2.41E-04
|
||||
23.0 4.4902E-02 3.4214E-02 0.7620 219.6 3.46678E+03 5.500E-02 297.0 14.38 2.61E-04
|
||||
23.5 4.1474E-02 3.1674E-02 0.7637 220.1 3.20939E+03 5.081E-02 297.4 14.40 2.83E-04
|
||||
24.0 3.8316E-02 2.9328E-02 0.7654 220.6 2.97167E+03 4.694E-02 297.7 14.43 3.07E-04
|
||||
24.5 3.5404E-02 2.7161E-02 0.7672 221.1 2.75206E+03 4.337E-02 298.1 14.46 3.33E-04
|
||||
25.0 3.2721E-02 2.5158E-02 0.7689 221.6 2.54915E+03 4.008E-02 298.4 14.48 3.61E-04
|
||||
25.5 3.0246E-02 2.3308E-02 0.7706 222.0 2.36163E+03 3.705E-02 298.7 14.51 3.92E-04
|
||||
26.0 2.7964E-02 2.1597E-02 0.7723 222.5 2.18831E+03 3.426E-02 299.1 14.54 4.24E-04
|
||||
26.5 2.5858E-02 2.0016E-02 0.7740 223.0 2.02807E+03 3.168E-02 299.4 14.57 4.60E-04
|
||||
27.0 2.3916E-02 1.8553E-02 0.7758 223.5 1.87991E+03 2.930E-02 299.7 14.59 4.98E-04
|
||||
27.5 2.2124E-02 1.7201E-02 0.7775 224.0 1.74289E+03 2.710E-02 300.1 14.62 5.39E-04
|
||||
28.0 2.0470E-02 1.5950E-02 0.7792 224.5 1.61614E+03 2.508E-02 300.4 14.65 5.84E-04
|
||||
28.5 1.8943E-02 1.4793E-02 0.7809 225.0 1.49888E+03 2.320E-02 300.7 14.67 6.32E-04
|
||||
29.0 1.7533E-02 1.3722E-02 0.7826 225.5 1.39037E+03 2.148E-02 301.0 14.70 6.84E-04
|
||||
29.5 1.6231E-02 1.2731E-02 0.7844 226.0 1.28994E+03 1.988E-02 301.4 14.73 7.41E-04
|
||||
30.0 1.5028E-02 1.1813E-02 0.7861 226.5 1.19698E+03 1.841E-02 301.7 14.75 8.01E-04
|
||||
30.5 1.3917E-02 1.0964E-02 0.7878 227.0 1.11092E+03 1.705E-02 302.0 14.78 8.67E-04
|
||||
31.0 1.2891E-02 1.0177E-02 0.7895 227.5 1.03122E+03 1.579E-02 302.4 14.81 9.38E-04
|
||||
31.5 1.1942E-02 9.4488E-03 0.7912 228.0 9.57402E+02 1.463E-02 302.7 14.83 1.01E-03
|
||||
32.0 1.1065E-02 8.7740E-03 0.7930 228.5 8.89024E+02 1.355E-02 303.0 14.86 1.10E-03
|
||||
32.5 1.0228E-02 8.1493E-03 0.7968 229.6 8.25726E+02 1.253E-02 303.8 14.92 1.19E-03
|
||||
33.0 9.4469E-03 7.5724E-03 0.8016 231.0 7.67272E+02 1.157E-02 304.7 14.99 1.30E-03
|
||||
33.5 8.7297E-03 7.0395E-03 0.8064 232.4 7.13279E+02 1.069E-02 305.6 15.07 1.41E-03
|
||||
34.0 8.0709E-03 6.5470E-03 0.8112 233.7 6.63380E+02 9.887E-03 306.5 15.14 1.53E-03
|
||||
34.5 7.4654E-03 6.0917E-03 0.8160 235.1 6.17243E+02 9.145E-03 307.4 15.21 1.66E-03
|
||||
35.0 6.9085E-03 5.6705E-03 0.8208 236.5 5.74564E+02 8.463E-03 308.3 15.29 1.81E-03
|
||||
35.5 6.3961E-03 5.2807E-03 0.8256 237.9 5.35066E+02 7.835E-03 309.2 15.36 1.96E-03
|
||||
36.0 5.9245E-03 4.9198E-03 0.8304 239.3 4.98495E+02 7.257E-03 310.1 15.43 2.13E-03
|
||||
36.5 5.4901E-03 4.5854E-03 0.8352 240.7 4.64618E+02 6.725E-03 311.0 15.51 2.31E-03
|
||||
37.0 5.0899E-03 4.2756E-03 0.8400 242.1 4.33223E+02 6.235E-03 311.9 15.58 2.50E-03
|
||||
37.5 4.7209E-03 3.9883E-03 0.8448 243.4 4.04115E+02 5.783E-03 312.8 15.65 2.71E-03
|
||||
38.0 4.3806E-03 3.7218E-03 0.8496 244.8 3.77116E+02 5.366E-03 313.7 15.72 2.93E-03
|
||||
38.5 4.0666E-03 3.4746E-03 0.8544 246.2 3.52061E+02 4.982E-03 314.6 15.79 3.17E-03
|
||||
39.0 3.7767E-03 3.2450E-03 0.8592 247.6 3.28801E+02 4.626E-03 315.4 15.87 3.43E-03
|
||||
39.5 3.5089E-03 3.0318E-03 0.8640 249.0 3.07198E+02 4.298E-03 316.3 15.94 3.71E-03
|
||||
40.0 3.2616E-03 2.8337E-03 0.8688 250.4 2.87125E+02 3.995E-03 317.2 16.01 4.01E-03
|
||||
40.5 3.0329E-03 2.6496E-03 0.8736 251.7 2.68467E+02 3.715E-03 318.1 16.08 4.33E-03
|
||||
41.0 2.8214E-03 2.4783E-03 0.8784 253.1 2.51116E+02 3.456E-03 318.9 16.15 4.67E-03
|
||||
41.5 2.6257E-03 2.3190E-03 0.8832 254.5 2.34974E+02 3.216E-03 319.8 16.22 5.04E-03
|
||||
42.0 2.4445E-03 2.1708E-03 0.8880 255.9 2.19952E+02 2.995E-03 320.7 16.29 5.44E-03
|
||||
42.5 2.2768E-03 2.0327E-03 0.8928 257.3 2.05965E+02 2.789E-03 321.5 16.36 5.87E-03
|
||||
43.0 2.1214E-03 1.9041E-03 0.8976 258.6 1.92937E+02 2.599E-03 322.4 16.43 6.32E-03
|
||||
43.5 1.9774E-03 1.7843E-03 0.9024 260.0 1.80799E+02 2.422E-03 323.3 16.50 6.81E-03
|
||||
44.0 1.8438E-03 1.6727E-03 0.9072 261.4 1.69484E+02 2.259E-03 324.1 16.57 7.34E-03
|
||||
44.5 1.7199E-03 1.5685E-03 0.9120 262.8 1.58932E+02 2.107E-03 325.0 16.64 7.90E-03
|
||||
45.0 1.6050E-03 1.4714E-03 0.9168 264.2 1.49090E+02 1.966E-03 325.8 16.71 8.50E-03
|
||||
45.5 1.4983E-03 1.3808E-03 0.9216 265.5 1.39905E+02 1.835E-03 326.7 16.78 9.14E-03
|
||||
46.0 1.3992E-03 1.2961E-03 0.9263 266.9 1.31330E+02 1.714E-03 327.5 16.85 9.83E-03
|
||||
46.5 1.3071E-03 1.2171E-03 0.9311 268.3 1.23322E+02 1.601E-03 328.4 16.92 1.06E-02
|
||||
47.0 1.2215E-03 1.1433E-03 0.9359 269.7 1.15841E+02 1.496E-03 329.2 16.99 1.14E-02
|
||||
47.5 1.1437E-03 1.0743E-03 0.9393 270.6 1.08849E+02 1.401E-03 329.8 17.04 1.22E-02
|
||||
48.0 1.0748E-03 1.0095E-03 0.9393 270.6 1.02287E+02 1.317E-03 329.8 17.04 1.29E-02
|
||||
48.5 1.0100E-03 9.4865E-04 0.9393 270.6 9.61215E+01 1.237E-03 329.8 17.04 1.38E-02
|
||||
49.0 9.4912E-04 8.9147E-04 0.9393 270.6 9.03286E+01 1.163E-03 329.8 17.04 1.47E-02
|
||||
49.5 8.9193E-04 8.3776E-04 0.9393 270.6 8.48858E+01 1.093E-03 329.8 17.04 1.56E-02
|
||||
50.0 8.3819E-04 7.8728E-04 0.9393 270.6 7.97716E+01 1.027E-03 329.8 17.04 1.66E-02
|
||||
50.5 7.8770E-04 7.3986E-04 0.9393 270.6 7.49663E+01 9.649E-04 329.8 17.04 1.77E-02
|
||||
51.0 7.4026E-04 6.9530E-04 0.9393 270.6 7.04511E+01 9.068E-04 329.8 17.04 1.88E-02
|
||||
51.5 6.9630E-04 6.5342E-04 0.9384 270.4 6.62082E+01 8.530E-04 329.7 17.02 2.00E-02
|
||||
52.0 6.5758E-04 6.1395E-04 0.9336 269.0 6.22084E+01 8.055E-04 328.8 16.96 2.10E-02
|
||||
52.5 6.2085E-04 5.7668E-04 0.9289 267.7 5.84321E+01 7.605E-04 328.0 16.89 2.22E-02
|
||||
53.0 5.8599E-04 5.4150E-04 0.9241 266.3 5.48678E+01 7.178E-04 327.1 16.82 2.34E-02
|
||||
53.5 5.5293E-04 5.0831E-04 0.9193 264.9 5.15047E+01 6.773E-04 326.3 16.75 2.47E-02
|
||||
54.0 5.2158E-04 4.7700E-04 0.9145 263.5 4.83322E+01 6.389E-04 325.4 16.68 2.61E-02
|
||||
54.5 4.9187E-04 4.4748E-04 0.9098 262.1 4.53405E+01 6.025E-04 324.6 16.61 2.76E-02
|
||||
55.0 4.6370E-04 4.1964E-04 0.9050 260.8 4.25201E+01 5.680E-04 323.7 16.54 2.91E-02
|
||||
55.5 4.3702E-04 3.9341E-04 0.9002 259.4 3.98620E+01 5.354E-04 322.9 16.47 3.08E-02
|
||||
56.0 4.1175E-04 3.6869E-04 0.8954 258.0 3.73576E+01 5.044E-04 322.0 16.40 3.25E-02
|
||||
56.5 3.8782E-04 3.4541E-04 0.8907 256.6 3.49988E+01 4.751E-04 321.2 16.33 3.44E-02
|
||||
57.0 3.6516E-04 3.2349E-04 0.8859 255.3 3.27778E+01 4.473E-04 320.3 16.26 3.64E-02
|
||||
57.5 3.4373E-04 3.0286E-04 0.8811 253.9 3.06871E+01 4.211E-04 319.4 16.19 3.85E-02
|
||||
58.0 3.2344E-04 2.8344E-04 0.8763 252.5 2.87198E+01 3.962E-04 318.6 16.12 4.07E-02
|
||||
58.5 3.0426E-04 2.6518E-04 0.8716 251.1 2.68692E+01 3.727E-04 317.7 16.05 4.31E-02
|
||||
59.0 2.8612E-04 2.4800E-04 0.8668 249.8 2.51289E+01 3.505E-04 316.8 15.98 4.56E-02
|
||||
59.5 2.6897E-04 2.3186E-04 0.8620 248.4 2.34929E+01 3.295E-04 315.9 15.91 4.83E-02
|
||||
60.0 2.5276E-04 2.1668E-04 0.8573 247.0 2.19554E+01 3.096E-04 315.1 15.84 5.11E-02
|
||||
60.5 2.3746E-04 2.0243E-04 0.8525 245.6 2.05110E+01 2.909E-04 314.2 15.77 5.42E-02
|
||||
61.0 2.2300E-04 1.8904E-04 0.8477 244.3 1.91546E+01 2.732E-04 313.3 15.69 5.75E-02
|
||||
61.5 2.0935E-04 1.7647E-04 0.8430 242.9 1.78811E+01 2.565E-04 312.4 15.62 6.09E-02
|
||||
62.0 1.9647E-04 1.6468E-04 0.8382 241.5 1.66860E+01 2.407E-04 311.5 15.55 6.46E-02
|
||||
62.5 1.8431E-04 1.5361E-04 0.8334 240.2 1.55648E+01 2.258E-04 310.7 15.48 6.86E-02
|
||||
63.0 1.7285E-04 1.4323E-04 0.8287 238.8 1.45132E+01 2.117E-04 309.8 15.41 7.28E-02
|
||||
63.5 1.6204E-04 1.3351E-04 0.8239 237.4 1.35275E+01 1.985E-04 308.9 15.33 7.72E-02
|
||||
64.0 1.5185E-04 1.2439E-04 0.8191 236.0 1.26036E+01 1.860E-04 308.0 15.26 8.20E-02
|
||||
64.5 1.4225E-04 1.1585E-04 0.8144 234.7 1.17382E+01 1.743E-04 307.1 15.19 8.72E-02
|
||||
65.0 1.3321E-04 1.0785E-04 0.8096 233.3 1.09277E+01 1.632E-04 306.2 15.12 9.26E-02
|
||||
65.5 1.2470E-04 1.0036E-04 0.8048 231.9 1.01690E+01 1.528E-04 305.3 15.04 9.85E-02
|
||||
66.0 1.1668E-04 9.3354E-05 0.8001 230.5 9.45909E+00 1.429E-04 304.4 14.97 1.05E-01
|
||||
66.5 1.0914E-04 8.6800E-05 0.7953 229.2 8.79502E+00 1.337E-04 303.5 14.90 1.11E-01
|
||||
67.0 1.0204E-04 8.0672E-05 0.7906 227.8 8.17408E+00 1.250E-04 302.6 14.82 1.19E-01
|
||||
67.5 9.5371E-05 7.4944E-05 0.7858 226.4 7.59372E+00 1.168E-04 301.7 14.75 1.26E-01
|
||||
68.0 8.9101E-05 6.9593E-05 0.7811 225.1 7.05149E+00 1.091E-04 300.7 14.67 1.34E-01
|
||||
68.5 8.3209E-05 6.4595E-05 0.7763 223.7 6.54509E+00 1.019E-04 299.8 14.60 1.43E-01
|
||||
69.0 7.7675E-05 5.9929E-05 0.7715 222.3 6.07234E+00 9.515E-05 298.9 14.53 1.53E-01
|
||||
69.5 7.2478E-05 5.5576E-05 0.7668 221.0 5.63120E+00 8.879E-05 298.0 14.45 1.63E-01
|
||||
70.0 6.7601E-05 5.1515E-05 0.7620 219.6 5.21972E+00 8.281E-05 297.1 14.38 1.74E-01
|
||||
70.5 6.3026E-05 4.7728E-05 0.7573 218.2 4.83607E+00 7.721E-05 296.1 14.30 1.85E-01
|
||||
71.0 5.8734E-05 4.4199E-05 0.7525 216.8 4.47851E+00 7.195E-05 295.2 14.23 1.98E-01
|
||||
71.5 5.4712E-05 4.0912E-05 0.7478 215.5 4.14543E+00 6.702E-05 294.3 14.15 2.11E-01
|
||||
72.0 5.0905E-05 3.7852E-05 0.7436 214.3 3.83531E+00 6.236E-05 293.4 14.08 2.26E-01
|
||||
72.5 4.7296E-05 3.5007E-05 0.7402 213.3 3.54709E+00 5.794E-05 292.8 14.03 2.42E-01
|
||||
73.0 4.3927E-05 3.2365E-05 0.7368 212.3 3.27939E+00 5.381E-05 292.1 13.98 2.60E-01
|
||||
73.5 4.0786E-05 2.9912E-05 0.7334 211.3 3.03083E+00 4.996E-05 291.4 13.92 2.79E-01
|
||||
74.0 3.7856E-05 2.7635E-05 0.7300 210.3 2.80012E+00 4.637E-05 290.7 13.87 2.99E-01
|
||||
74.5 3.5125E-05 2.5522E-05 0.7266 209.4 2.58605E+00 4.303E-05 290.1 13.81 3.21E-01
|
||||
75.0 3.2580E-05 2.3563E-05 0.7232 208.4 2.38749E+00 3.991E-05 289.4 13.76 3.45E-01
|
||||
75.5 3.0209E-05 2.1746E-05 0.7198 207.4 2.20338E+00 3.701E-05 288.7 13.70 3.70E-01
|
||||
76.0 2.8001E-05 2.0061E-05 0.7164 206.4 2.03272E+00 3.430E-05 288.0 13.65 3.98E-01
|
||||
76.5 2.5946E-05 1.8501E-05 0.7131 205.5 1.87458E+00 3.178E-05 287.4 13.59 4.28E-01
|
||||
77.0 2.4033E-05 1.7055E-05 0.7097 204.5 1.72810E+00 2.944E-05 286.7 13.54 4.60E-01
|
||||
77.5 2.2253E-05 1.5716E-05 0.7063 203.5 1.59247E+00 2.726E-05 286.0 13.48 4.95E-01
|
||||
78.0 2.0597E-05 1.4477E-05 0.7029 202.5 1.46692E+00 2.523E-05 285.3 13.43 5.32E-01
|
||||
78.5 1.9058E-05 1.3331E-05 0.6995 201.6 1.35076E+00 2.335E-05 284.6 13.37 5.73E-01
|
||||
79.0 1.7627E-05 1.2270E-05 0.6961 200.6 1.24331E+00 2.159E-05 283.9 13.32 6.17E-01
|
||||
79.5 1.6298E-05 1.1290E-05 0.6927 199.6 1.14396E+00 1.996E-05 283.2 13.26 6.64E-01
|
||||
80.0 1.5063E-05 1.0384E-05 0.6893 198.6 1.05213E+00 1.845E-05 282.5 13.21 7.16E-01
|
||||
80.5 1.3917E-05 9.5464E-06 0.6860 197.7 9.67291E-01 1.705E-05 281.8 13.15 7.71E-01
|
||||
81.0 1.2853E-05 8.7731E-06 0.6826 196.7 8.88933E-01 1.574E-05 281.1 13.10 8.32E-01
|
||||
81.5 1.1866E-05 8.0591E-06 0.6792 195.7 8.16590E-01 1.454E-05 280.4 13.04 8.97E-01
|
||||
82.0 1.0950E-05 7.4002E-06 0.6758 194.7 7.49827E-01 1.341E-05 279.7 12.98 9.68E-01
|
||||
82.5 1.0101E-05 6.7924E-06 0.6724 193.8 6.88237E-01 1.237E-05 279.0 12.93 1.04E+00
|
||||
83.0 9.3145E-06 6.2318E-06 0.6690 192.8 6.31441E-01 1.141E-05 278.3 12.87 1.13E+00
|
||||
83.5 8.5856E-06 5.7151E-06 0.6657 191.8 5.79087E-01 1.052E-05 277.6 12.82 1.22E+00
|
||||
84.0 7.9106E-06 5.2391E-06 0.6623 190.8 5.30848E-01 9.690E-06 276.9 12.76 1.32E+00
|
||||
84.5 7.2857E-06 4.8006E-06 0.6589 189.9 4.86416E-01 8.925E-06 276.2 12.70 1.42E+00
|
||||
85.0 6.7074E-06 4.3968E-06 0.6555 188.9 4.45509E-01 8.217E-06 275.5 12.65 1.54E+00
|
||||
85.5 6.1724E-06 4.0253E-06 0.6521 187.9 4.07863E-01 7.561E-06 274.8 12.59 1.67E+00
|
||||
86.0 5.6777E-06 3.6835E-06 0.6488 186.9 3.73232E-01 6.955E-06 274.1 12.53 1.80E+00
|
||||
@@ -0,0 +1,137 @@
|
||||
#!/usr/bin/env Python3
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from AstroAtmosphere import *
|
||||
from ADC import *
|
||||
import sys
|
||||
import scipy.optimize as opt
|
||||
|
||||
|
||||
|
||||
'''
|
||||
@ Author: Joost van den Born
|
||||
@ Contact: born@astron.nl
|
||||
@ Description: This script finds the filter constant using
|
||||
- Direct application of equation 18 and 19
|
||||
- The minimization algorithm implemented in the ADC class
|
||||
and compares these two.
|
||||
'''
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
class filter:
|
||||
def __init__(self, name, l1, l2):
|
||||
self.name = name
|
||||
self.lmin = np.min([l1,l2])
|
||||
self.lmax = np.max([l1,l2])
|
||||
self.bandwidth = self.lmax - self.lmin
|
||||
self.optimumAngle = None
|
||||
self.residualDispersion = None
|
||||
self.optimumAngleForAllZ = None
|
||||
self.cf = None
|
||||
|
||||
def analytic_optimum_position_ADC(self, z, l1, l2, dispersion_object, adc_object):
|
||||
disp = dispersion_object
|
||||
adc = adc_object
|
||||
|
||||
_R = disp.rc / (disp.rc + disp.H)
|
||||
n1 = at.n_tph(l=self.lmin, T=T, p=P, RH=H, xc=xc)
|
||||
n2 = at.n_tph(l=self.lmax, T=T, p=P, RH=H, xc=xc)
|
||||
dn_atm = n2 - n1
|
||||
_dispersion = np.deg2rad(disp.cassini(n1,n2,z))
|
||||
|
||||
_A1 = np.deg2rad(adc.p1.tilt2x_OG - adc.p1.tilt1x_OG)
|
||||
_A2 = np.deg2rad(adc.p2.tilt2x_OG - adc.p2.tilt1x_OG)
|
||||
|
||||
_A3 = -1 * np.deg2rad(adc.p3.tilt2x_OG - adc.p3.tilt1x_OG) # -1 because doublet gets flipped after setting rotation
|
||||
_A4 = -1 * np.deg2rad(adc.p4.tilt2x_OG - adc.p4.tilt1x_OG) # -1 because doublet gets flipped after setting rotation
|
||||
|
||||
_dn_p1 = adc.p1.indexOfRefraction(l1) - adc.p1.indexOfRefraction(l2)
|
||||
_dn_p2 = adc.p2.indexOfRefraction(l1) - adc.p2.indexOfRefraction(l2)
|
||||
|
||||
_pupilRatio = 0.07943/38.542
|
||||
_Psi = (_dn_p1 * (_A1 + _A4) + _dn_p2 * (_A2 + _A3)) * _pupilRatio
|
||||
|
||||
_cf = -1/_pupilRatio * (dn_atm)/((_dn_p1 * (_A1 + _A4) + _dn_p2 * (_A2 + _A3)))
|
||||
_thetaOpt = np.arccos(1/_Psi * _dispersion)
|
||||
return np.degrees(_thetaOpt), _cf
|
||||
|
||||
def ADCposAnalytical(z, Cf):
|
||||
return np.arccos(Cf*np.tan(np.deg2rad(z)))*180/np.pi
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
Iband = filter('I-band',0.8,0.934)
|
||||
Jband = filter('J-band',1.15,1.345)
|
||||
Hband = filter('H-band',1.49,1.78)
|
||||
Kband = filter('K-band',1.97,2.32)
|
||||
|
||||
|
||||
##########################################################
|
||||
# Atmospheric model
|
||||
# Parameters at Cerro Armazones
|
||||
T = 279.65 # k
|
||||
P = 71200 # Pa
|
||||
H = 0.22
|
||||
xc = 450 # ppm
|
||||
lat = -24.5983 # degrees
|
||||
h = 3064 # m
|
||||
|
||||
# Initializing dispersion model
|
||||
at = Observatory()
|
||||
|
||||
# Density of the atmosphere (following CIPM-81/91 equations)
|
||||
rho = at.rho(p=P, T=T, RH=H, xc=xc)
|
||||
|
||||
# Initializing dispersion object
|
||||
disp = dispersion(lat, h)
|
||||
disp.setReducedHeight(P, rho)
|
||||
|
||||
##########################################################
|
||||
|
||||
adc = ADC()
|
||||
|
||||
|
||||
# Unlock this when the above works
|
||||
# zRange = range(2,62)
|
||||
zRange = np.arange(5,65.1,0.1)
|
||||
optimumAngleFilterList = []
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(111)
|
||||
|
||||
|
||||
for f in [Hband, Iband, Jband, Kband]:
|
||||
# For new filters or if the zenith values are different, the new values are calculated
|
||||
print ('Starting %s...' %f.name )
|
||||
# Calculating indices of refraction for l1 and l2
|
||||
n1 = at.n_tph(l=f.lmin, T=T, p=P, RH=H, xc=xc)
|
||||
n2 = at.n_tph(l=f.lmax, T=T, p=P, RH=H, xc=xc)
|
||||
dispersion_obs = disp.cassini(n1, n2, zRange)*3.6e6
|
||||
|
||||
adc.setOrientation(0)
|
||||
theta_opt = [adc.optimize(disp, f.lmin, f.lmax) for disp in dispersion_obs]
|
||||
f.optimumAngleForAllZ = theta_opt
|
||||
# Plotting the data
|
||||
ax.plot(zRange, f.optimumAngleForAllZ, label=f.name)
|
||||
|
||||
for f in [Hband, Iband, Jband, Kband]:
|
||||
popt, pcov = opt.curve_fit(ADCposAnalytical, zRange[:-50], f.optimumAngleForAllZ[:-50], p0=0.5)
|
||||
f.cf = popt[0]
|
||||
ax.plot(zRange, ADCposAnalytical(zRange, f.cf), 'k--', label='best fit %s' %f.name)
|
||||
theta_opt_analytical, cf_analytical = f.analytic_optimum_position_ADC(zRange, f.lmin, f.lmax, disp, adc)
|
||||
ax.plot(zRange, ADCposAnalytical(zRange, cf_analytical), 'b--', label='analytical cf %s' %f.name)
|
||||
print('{} - Best fit: {} - Analytical: {}'.format(f.name, f.cf, cf_analytical))
|
||||
|
||||
# Plot properties
|
||||
ax.set_title('Optimum rotation angle of the first ADC prism', fontsize=9)
|
||||
ax.set_ylabel('Rotation angle (deg)', fontsize=9)
|
||||
ax.set_xlabel('Zenith angle (deg)', fontsize=9)
|
||||
ax.grid(True, alpha=0.5)
|
||||
ax.legend(fontsize=9)
|
||||
|
||||
plt.show()
|
||||
|
||||
@@ -0,0 +1,299 @@
|
||||
#!/usr/bin/env Python
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from mpl_toolkits.axes_grid1 import host_subplot
|
||||
from AstroAtmosphere import *
|
||||
import pandas as pd
|
||||
|
||||
'''
|
||||
@ Author: Joost van den Born
|
||||
@ Contact: born@astron.nl
|
||||
@ Description: Modeling the atmospheric refraction using multiple
|
||||
models of the atmosphere and then comparing them.
|
||||
|
||||
Models included:
|
||||
- Full integration method
|
||||
- Plane-parallel atmosphere
|
||||
- Cassini's model
|
||||
- Tan5 model (A*tan(z) + B*tan^3(z) + C*tan^5(z))
|
||||
- Oriani's theorem (A*tan(z) + B*tan^3(z))
|
||||
- Erf model (from Corbard et al., 2019)
|
||||
- SLALIB
|
||||
|
||||
One of the figures and all three tables are used in the paper.
|
||||
'''
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
plt.rc('font', family='serif')
|
||||
plt.rc('axes', labelsize='large')
|
||||
|
||||
# Conditions at Cerro Armazones
|
||||
# T = 279.65 # k
|
||||
# P = 71200 # Pa
|
||||
# H = 0.22
|
||||
# xc = 450 # ppm
|
||||
# Conditions for the standard atmosphere
|
||||
T = 288.15 # k
|
||||
P = 101325 # Pa
|
||||
H = 0.0
|
||||
xc = 314 # ppm
|
||||
lat = -24.5983 # degrees
|
||||
h = 3064 # m
|
||||
l1 = 1.49 # micron
|
||||
l2 = 1.78 # micron
|
||||
zenith = 30 # degrees
|
||||
|
||||
# Calculate the index of refraction
|
||||
at = Observatory()
|
||||
n1 = at.n_tph(l=l1, T=T, p=P, RH=H, xc=xc)
|
||||
n2 = at.n_tph(l=l2, T=T, p=P, RH=H, xc=xc)
|
||||
rho = at.rho(p=P, T=T, RH=H, xc=xc)
|
||||
|
||||
refractionAnalysis = True
|
||||
dispersionAnalysis = True
|
||||
tableCompare = True
|
||||
errorTable = True
|
||||
if dispersionAnalysis:
|
||||
# Initiate the refraction class
|
||||
ref = dispersion(lat, h)
|
||||
ref.setReducedHeight(P, rho)
|
||||
|
||||
# Compare the refraction models to check if the output is okay
|
||||
print ('Plane - parallel atmosphere dispersion: %s mas' %(ref.planeParallel(n1, n2, zenith) * 3.6e6))
|
||||
print ('Cassini dispersion: %s mas' %(ref.cassini(n1, n2, zenith) * 3.6e6))
|
||||
print ('Tan5 dispersion: %s mas' %(ref.tan5(n1, n2, zenith) * 3.6e6))
|
||||
print ('Oriani dispersion: %s mas' %(ref.oriani(n1, n2, zenith) * 3.6e6))
|
||||
print ('Corbard dispersion: %s mas' %(ref.corbard(n1, n2, zenith)*3.6e6))
|
||||
print ('Full integration: %s mas' %(ref.refractionIntegral(n1, n2, zenith)*3.6e6))
|
||||
print('-'*35)
|
||||
|
||||
|
||||
# Now we calculate the dispersion over a wide range of zenith angles
|
||||
# for ideal gas atmosphere
|
||||
zenithRange = np.linspace(5, 85, 1000)
|
||||
ppList = ref.planeParallel(n1, n2, zenithRange) * 3.6e6
|
||||
cassiniList = ref.cassini(n1, n2, zenithRange) * 3.6e6
|
||||
tan5List = ref.tan5(n1, n2, zenithRange) * 3.6e6
|
||||
orianiList = ref.oriani(n1, n2, zenithRange) * 3.6e6
|
||||
corbardList = [ref.corbard(n1, n2, z) * 3.6e6 for z in zenithRange]
|
||||
fullIntegrationList = [ref.refractionIntegral(n1, n2, z) * 3.6e6 for z in zenithRange]
|
||||
|
||||
# for isothermal atmosphere
|
||||
cassiniList_isotherm = ref.cassini(n1, n2, zenithRange, reduced_height=ref.H_isotherm(T)) * 3.6e6
|
||||
tan5List_isotherm = ref.tan5(n1, n2, zenithRange, reduced_height=ref.H_isotherm(T)) * 3.6e6
|
||||
orianiList_isotherm = ref.oriani(n1, n2, zenithRange, reduced_height=ref.H_isotherm(T)) * 3.6e6
|
||||
corbardList_isotherm = [ref.corbard(n1, n2, z, reduced_height=ref.H_isotherm(T)) * 3.6e6 for z in zenithRange]
|
||||
|
||||
# check when the variation between the different models pass different values
|
||||
dispersionWidth = []
|
||||
widthList = []
|
||||
for z in range(len(zenithRange)):
|
||||
compList = [cassiniList[z], tan5List[z], orianiList[z], corbardList[z], cassiniList_isotherm[z], tan5List_isotherm[z], orianiList_isotherm[z], corbardList_isotherm[z], fullIntegrationList[z]]
|
||||
width = np.max(compList) - np.min(compList)
|
||||
widthList.append(width)
|
||||
dispersionWidth.append((zenithRange[z], width))
|
||||
|
||||
masBorder = np.argmax(np.asarray(widthList)>1)
|
||||
p1masBorder = np.argmax(np.asarray(widthList)>0.1)
|
||||
p01masBorder = np.argmax(np.asarray(widthList)>0.01)
|
||||
p001masBorder = np.argmax(np.asarray(widthList)>0.001)
|
||||
|
||||
plotAbsolute = 1
|
||||
if plotAbsolute:
|
||||
# Plotting it all, with vertical lines where the variation between the models exceed 1, 0.1, 0.01 and 0.001 mas.
|
||||
fig = plt.figure(figsize=(9,5))
|
||||
ax = fig.add_subplot(111)
|
||||
ax.set_xlim([30,85])
|
||||
ax.set_xlabel('Zenith (degrees)')
|
||||
ax.set_ylabel('Dispersion (mas)')
|
||||
ax.grid(True, alpha=0.3)
|
||||
ax.axvline(dispersionWidth[masBorder][0], linestyle='--', color='k', linewidth=0.5)
|
||||
ax.text(dispersionWidth[masBorder][0]-0.5, 402, '1 mas', ha='center', va='bottom', rotation='vertical')
|
||||
ax.axvline(dispersionWidth[p1masBorder][0], linestyle='--', color='k', linewidth=0.5)
|
||||
ax.text(dispersionWidth[p1masBorder][0]-0.5, 402, '0.1 mas', ha='center', va='bottom', rotation='vertical')
|
||||
ax.axvline(dispersionWidth[p01masBorder][0], linestyle='--', color='k', linewidth=0.5)
|
||||
ax.text(dispersionWidth[p01masBorder][0]-0.5, 402, '10 $\mu$as', ha='center', va='bottom', rotation='vertical')
|
||||
ax.axvline(dispersionWidth[p001masBorder][0], linestyle='--', color='k', linewidth=0.5)
|
||||
ax.text(dispersionWidth[p001masBorder][0]-0.5, 402, '1 $\mu$as', ha='center', va='bottom', rotation='vertical')
|
||||
|
||||
ax.plot(zenithRange, ppList, 'k-', label='Plane-parallel', alpha=0.2)
|
||||
ax.plot(zenithRange, cassiniList, 'g-', label='Cassini')
|
||||
ax.plot(zenithRange, tan5List, 'b-', label='$Atan(z) - Btan^3(z) + Ctan^5(z)$')
|
||||
ax.plot(zenithRange, orianiList, 'y-', label='$Atan(z) - Btan^3(z)$')
|
||||
ax.plot(zenithRange, corbardList, 'r-', label='Error function')
|
||||
ax.plot(zenithRange, fullIntegrationList, 'c-', label='Full integration')
|
||||
|
||||
ax.plot(zenithRange, cassiniList_isotherm, 'g--', label='Cassini (isotherm)')
|
||||
ax.plot(zenithRange, tan5List_isotherm, 'b--', label='$Atan(z) - Btan^3(z) + Ctan^5(z)$ (isotherm)')
|
||||
ax.plot(zenithRange, orianiList_isotherm, 'y--', label='$Atan(z) - Btan^3(z)$ (isotherm)')
|
||||
ax.plot(zenithRange, corbardList_isotherm, 'r--', label='Error function (isotherm)')
|
||||
|
||||
|
||||
ax.tick_params(axis ='both', direction='in')
|
||||
ax.legend()
|
||||
plt.tight_layout()
|
||||
# plt.show()
|
||||
|
||||
plotRelative = 1
|
||||
if plotRelative:
|
||||
# RELATIVE TO FULL INTEGRATION
|
||||
fig = plt.figure(figsize=(9,6))
|
||||
ax = fig.add_subplot(111)
|
||||
ax.set_xlim([30,85])
|
||||
|
||||
ax.set_xlabel('Zenith (degrees)', fontsize=18)
|
||||
ax.set_ylabel(r'$\log_{10}(|(\Delta R - \Delta R_{int})|$ mas$^{-1})$', fontsize=18)
|
||||
|
||||
ax.axhline(np.log10(0.4), linestyle='-', linewidth=1, color='red')
|
||||
ax.text(30.5, np.log10(0.4)+0.05, 'Residual dispersion requirement', fontsize=12)
|
||||
|
||||
ax.grid(True, which='major', alpha=0.3)
|
||||
ax.grid(True, which='minor', alpha=0.2)
|
||||
|
||||
ax.plot(zenithRange, np.log10(np.abs(ppList-fullIntegrationList)), color='k', linestyle='-', linewidth=2, label='Plane-parallel')
|
||||
ax.plot(zenithRange, np.log10(np.abs(cassiniList-fullIntegrationList)), color='g', linestyle=(0, (1, 1)), linewidth=2, label='Cassini')
|
||||
ax.plot(zenithRange, np.log10(np.abs(orianiList-fullIntegrationList)), color='y', linestyle='-.', linewidth=2, label='Oriani')
|
||||
ax.plot(zenithRange, np.log10(np.abs(tan5List-fullIntegrationList)), color='b', linestyle=(0, (5, 1)), linewidth=2, label='Oriani incl. fifth order')
|
||||
ax.plot(zenithRange, np.log10(np.abs(np.asarray(corbardList)-fullIntegrationList)), color='r', linestyle=(0, (3, 1, 1, 1, 1, 1)), linewidth=2, label='Error function')
|
||||
ax.plot(zenithRange, np.log10(np.abs(cassiniList-fullIntegrationList)), color='g', linestyle=(0, (1, 1)), linewidth=2)
|
||||
|
||||
ax.set_ylim(top=2.6, bottom=-7.2)
|
||||
ax.tick_params(axis ='both', direction='in', labelsize=16)
|
||||
ax.legend(fontsize=14, labelspacing=0.3, columnspacing=0.5, handletextpad=0.3, ncol=1, loc='lower right')
|
||||
plt.tight_layout()
|
||||
# plt.savefig('./figures/dispersionModels_with_SLALIB_minimal2.pdf', transparent=True, bbox_inches='tight')
|
||||
# plt.savefig('./figures/dispersionModels_with_SLALIB_minimal2.png', transparent=True, bbox_inches='tight')
|
||||
|
||||
|
||||
|
||||
|
||||
if refractionAnalysis:
|
||||
# Initiate the refraction class
|
||||
ref = refraction(lat, h)
|
||||
ref.setReducedHeight(P, rho)
|
||||
|
||||
|
||||
# Compare the refraction models to check if the output is okay
|
||||
print ('Plane - parallel atmosphere refraction: %s arcsec' %(ref.planeParallel(n1, zenith) * 3.6e3))
|
||||
print ('Cassini refraction: %s arcsec' %(ref.cassini(n1, zenith) * 3.6e3))
|
||||
print ('Tan5 refraction: %s arcsec' %(ref.tan5(n1, zenith) * 3.6e3))
|
||||
print ('Oriani refraction: %s arcsec' %(ref.oriani(n1, zenith) * 3.6e3))
|
||||
print ('Corbard refraction: %s arcsec' %(ref.corbard(n1, zenith)*3.6e3))
|
||||
print ('Full integration: %s mas' %(ref.refractionIntegral(n1, zenith)*3.6e3))
|
||||
print('-'*35)
|
||||
|
||||
|
||||
# Now we calculate the dispersion over a wide range of zenith angles
|
||||
# for ideal gas atmosphere
|
||||
zenithRange = np.linspace(0, 88, 1000)
|
||||
ppList = ref.planeParallel(n1, zenithRange) * 3.6e3
|
||||
cassiniList = ref.cassini(n1, zenithRange) * 3.6e3
|
||||
tan5List = ref.tan5(n1, zenithRange) * 3.6e3
|
||||
orianiList = ref.oriani(n1, zenithRange) * 3.6e3
|
||||
corbardList = [ref.corbard(n1, z) * 3.6e3 for z in zenithRange]
|
||||
fullIntegrationList = [ref.refractionIntegral(n1, z) * 3.6e3 for z in zenithRange]
|
||||
|
||||
# for isothermal atmosphere
|
||||
cassiniList_isotherm = ref.cassini(n1, zenithRange, reduced_height=ref.H_isotherm(T)) * 3.6e3
|
||||
tan5List_isotherm = ref.tan5(n1, zenithRange, reduced_height=ref.H_isotherm(T)) * 3.6e3
|
||||
orianiList_isotherm = ref.oriani(n1, zenithRange, reduced_height=ref.H_isotherm(T)) * 3.6e3
|
||||
corbardList_isotherm = [ref.corbard(n1, z, reduced_height=ref.H_isotherm(T)) * 3.6e3 for z in zenithRange]
|
||||
|
||||
# Plotting it all, with vertical lines where the variation between the models exceed 1, 0.1, 0.01 and 0.001 arcsec.
|
||||
fig = plt.figure(figsize=(9,5))
|
||||
ax = fig.add_subplot(111)
|
||||
ax.set_xlim([80,88])
|
||||
ax.set_xlabel('Zenith (degrees)')
|
||||
ax.set_ylabel('Refraction (arcsec)')
|
||||
ax.grid(True, alpha=0.3)
|
||||
|
||||
ax.plot(zenithRange, ppList, 'k-', label='Plane-parallel', alpha=0.2)
|
||||
ax.plot(zenithRange, cassiniList, 'g-', label='Cassini')
|
||||
ax.plot(zenithRange, tan5List, 'b-', label='$Atan(z) - Btan^3(z) + Ctan^5(z)$')
|
||||
ax.plot(zenithRange, orianiList, 'y-', label='$Atan(z) - Btan^3(z)$')
|
||||
ax.plot(zenithRange, corbardList, 'r-', label='Error function')
|
||||
ax.plot(zenithRange, fullIntegrationList, 'c-', label='Full integration')
|
||||
|
||||
|
||||
ax.tick_params(axis ='both', direction='in')
|
||||
ax.legend()
|
||||
plt.tight_layout()
|
||||
|
||||
|
||||
|
||||
if tableCompare:
|
||||
refTableData = []
|
||||
dispTableData = []
|
||||
ref = refraction(lat, h)
|
||||
ref.setReducedHeight(P, rho)
|
||||
disp = dispersion(lat, h)
|
||||
disp.setReducedHeight(P, rho)
|
||||
zenithRange = [10, 30, 50, 60, 70, 80, 85]
|
||||
headerList = np.array(['z', 'Plane-parallel', 'Cassini', 'tan3', 'tan5', 'Error function', 'Full integration'])
|
||||
refTableData.append(headerList)
|
||||
dispTableData.append(headerList)
|
||||
|
||||
for z in zenithRange:
|
||||
ppValue = round(ref.planeParallel(n1, z) * 3.6e3, 3)
|
||||
cassiniValue = round(ref.cassini(n1, z) * 3.6e3, 3)
|
||||
tan3Value = round(ref.oriani(n1, z) * 3.6e3, 3)
|
||||
tan5Value = round(ref.tan5(n1, z) * 3.6e3, 3)
|
||||
erfValue = round(ref.corbard(n1, z) * 3.6e3, 3)
|
||||
fullIntValue = round(ref.refractionIntegral(n1, z) * 3.6e3, 3)
|
||||
refTableData.append([z, ppValue, cassiniValue, tan3Value, tan5Value, erfValue, fullIntValue])
|
||||
|
||||
ppValue = round(disp.planeParallel(n1, n2, z) * 3.6e6, 3)
|
||||
cassiniValue = round(disp.cassini(n1, n2, z) * 3.6e6, 3)
|
||||
tan3Value = round(disp.oriani(n1, n2, z) * 3.6e6, 3)
|
||||
tan5Value = round(disp.tan5(n1, n2, z) * 3.6e6, 3)
|
||||
erfValue = round(disp.corbard(n1, n2, z) * 3.6e6, 3)
|
||||
fullIntValue = round(disp.refractionIntegral(n1, n2, z) * 3.6e6, 3)
|
||||
dispTableData.append([z, ppValue, cassiniValue, tan3Value, tan5Value, erfValue, fullIntValue])
|
||||
|
||||
dfr = pd.DataFrame(np.asarray(refTableData))
|
||||
dfd = pd.DataFrame(np.asarray(dispTableData))
|
||||
print('-'*35,'\nREFRACTION COMPARISON:\n')
|
||||
print(dfr.to_string(index=False))
|
||||
print('-'*35)
|
||||
print('-'*35,'\nDISPERSION COMPARISON:\n')
|
||||
print(dfd.to_string(index=False))
|
||||
print('-'*35)
|
||||
|
||||
# print(dfr.to_latex(index=False))
|
||||
# print(dfd.to_latex(index=False))
|
||||
|
||||
|
||||
if errorTable:
|
||||
ref = dispersion(lat, h)
|
||||
ref.setReducedHeight(P, rho)
|
||||
dispTableData = []
|
||||
headerList = np.array(['s_P', 's_T', 's_H', 's_disp', 's_disp(dl=0)'])
|
||||
dispTableData.append(headerList)
|
||||
|
||||
# dPlist = [2, 20, 200, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 20, 200]
|
||||
# dTlist = [0, 0, 0, 0.02, 0.2, 2, 0, 0, 0, 0, 0, 0, 0.02, 0.2, 2 ]
|
||||
# dRHlist= [0, 0, 0, 0, 0, 0, 0.002, 0.02, 0.2, 0, 0, 0, 0.002, 0.02, 0.2]
|
||||
# dCO2list=[0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 20, 200, 2, 20, 200]
|
||||
dPlist = [0, 10, 10, 20, 100]
|
||||
dTlist = [0, 0.1, 0.2, 0.2, 1.0]
|
||||
dRHlist= [0, 0.01, 0.01, 0.02, 0.05]
|
||||
dCO2list=[0, 20, 20, 20, 20]
|
||||
|
||||
for i in range(len(dPlist)):
|
||||
# nerr = at.dn_tph(l1, T, P, H, xc, dl=0, dT=dTlist[i], dP=dPlist[i], dRH=dRHlist[i], dCO2=dCO2list[i])
|
||||
disp_error = ref.cassiniError(zenith, l1, l2, T, P, H, xc, dl1=0.001, dl2=0.001, dT=dTlist[i], dP=dPlist[i], dRH=dRHlist[i],
|
||||
dCO2=dCO2list[i], dz=0) * 3.6e9
|
||||
|
||||
disp_error_dl0 = ref.cassiniError(zenith, l1, l2, T, P, H, xc, dl1=0, dl2=0, dT=dTlist[i], dP=dPlist[i], dRH=dRHlist[i],
|
||||
dCO2=dCO2list[i], dz=0) * 3.6e9
|
||||
|
||||
dispTableData.append([dPlist[i], dTlist[i], dRHlist[i], disp_error, disp_error_dl0])
|
||||
|
||||
dfd = pd.DataFrame(np.asarray(dispTableData))
|
||||
|
||||
print('-'*35,'\nUNCERTAINTY ANALYSIS:\n')
|
||||
print(dfd.to_string(index=False))
|
||||
print('-'*35)
|
||||
|
||||
plt.show()
|
||||
@@ -0,0 +1,247 @@
|
||||
#!/usr/bin/env Python
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from mpl_toolkits.axes_grid1 import host_subplot
|
||||
from AstroAtmosphere import *
|
||||
from pyslalib import slalib
|
||||
|
||||
|
||||
'''
|
||||
@ Author: Joost van den Born
|
||||
@ Contact: born@astron.nl
|
||||
@ Description: This script was used to compare the impact of using the old
|
||||
B&S equations, and the particular implementation of SLALIB.
|
||||
The rest of the script is almost a one-to-one copy of other
|
||||
script that compared the atmospheric models.
|
||||
|
||||
Modeling the atmospheric refraction using multiple models of
|
||||
the atmosphere and then comparing them.
|
||||
|
||||
Models included:
|
||||
- Full integration method
|
||||
- Plane-parallel atmosphere
|
||||
- Cassini's model
|
||||
- Tan5 model (A*tan(z) + B*tan^3(z) + C*tan^5(z))
|
||||
- Oriani's theorem (A*tan(z) + B*tan^3(z))
|
||||
- Erf model (from Corbard et al., 2019)
|
||||
- SLALIB
|
||||
'''
|
||||
class atmosphereSLALIB:
|
||||
def __init__(self, T=279.65, P=712, H=0.22):
|
||||
self.name = 'SLALIB atmosphere'
|
||||
self.T = T
|
||||
self.P = P
|
||||
self.H = H
|
||||
self.h = 3064
|
||||
self.lat = -24.5983
|
||||
self.TLR = 0.0065
|
||||
self.prec = 1e-8
|
||||
|
||||
def atmModel(self, A, B, zenith):
|
||||
zenith = np.radians(zenith)
|
||||
return A * np.tan(zenith) + B * (np.tan(zenith)**3)
|
||||
|
||||
def dispersion(self, l1, l2, z):
|
||||
A1, B1 = slalib.sla_refco(self.h, self.T, self.P, self.H, l1, np.deg2rad(self.lat), self.TLR, self.prec)
|
||||
A2, B2 = slalib.sla_atmdsp(self.T, self.P, self.H, l1, A1, B1, l2)
|
||||
D1 = np.degrees(self.atmModel(A1, B1, z)) * 3600
|
||||
D2 = np.degrees(self.atmModel(A2, B2, z)) * 3600
|
||||
dispersionValue = ((D1 - D2) * 1000)
|
||||
return dispersionValue
|
||||
|
||||
def refractivity(self, l=None):
|
||||
'''
|
||||
SLALIB uses the same atmospheric model as ZEMAX
|
||||
based on the works of Hohenkerk & Sinclair and Seidelmann.
|
||||
They take the B&S refraction formula as a basis.
|
||||
'''
|
||||
zObs = np.deg2rad(45.06563)
|
||||
h = 0
|
||||
T = self.T
|
||||
P = self.P
|
||||
H = self.H
|
||||
lat = 24
|
||||
tlr = 0.0065
|
||||
eps = 1e-10
|
||||
if not l:
|
||||
l = self.lrange
|
||||
R = []
|
||||
if type(l) == float:
|
||||
l = [l]
|
||||
for wl in l:
|
||||
R.append(slalib.sla_refro(zObs, h, T, P, H, wl, lat, tlr, eps)+1)
|
||||
return np.asarray(R)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
plt.rc('font', family='serif')
|
||||
# plt.rc('xtick', labelsize='x-small')
|
||||
# plt.rc('ytick', labelsize='x-small')
|
||||
plt.rc('axes', labelsize='large')
|
||||
|
||||
# Standard atmospheric conditions
|
||||
# T = 279.65 # k
|
||||
# P = 71200 # Pa
|
||||
# H = 0.22
|
||||
# xc = 450 # ppm
|
||||
T = 288.15 # k
|
||||
P = 101325 # Pa
|
||||
H = 0.0
|
||||
xc = 314 # ppm
|
||||
lat = -24.5983 # degrees
|
||||
h = 3064 # m
|
||||
l1 = 1.49 # micron
|
||||
l2 = 1.78 # micron
|
||||
zenith = 30 # degrees
|
||||
|
||||
# Calculate the index of refraction
|
||||
at = Observatory()
|
||||
n1 = at.n_tph(l=l1, T=T, p=P, RH=H, xc=xc)
|
||||
n2 = at.n_tph(l=l2, T=T, p=P, RH=H, xc=xc)
|
||||
rho = at.rho(p=P, T=T, RH=H, xc=xc)
|
||||
|
||||
#######################################################
|
||||
# DIRECT COMPARISON
|
||||
ref = dispersion(lat, h)
|
||||
ref.setReducedHeight(P, rho)
|
||||
print('Ciddor refractivity: ', n1, n2)
|
||||
print ('Cassini dispersion: %s mas' %(ref.cassini(n1, n2, zenith) * 3.6e6))
|
||||
n1c = n1
|
||||
n2c = n2
|
||||
|
||||
refraction = refraction(lat, h)
|
||||
refraction.setReducedHeight(P, rho)
|
||||
refractionValueCiddor = refraction.cassini(n1c, zenith) * 3.6e6 # in mas
|
||||
|
||||
# SLALIB
|
||||
atSLALIB = atmosphereSLALIB(T, P/100, H)
|
||||
n1 = atSLALIB.refractivity(l1)
|
||||
n2 = atSLALIB.refractivity(l2)
|
||||
|
||||
print('B&S refractivity: ', n1, n2)
|
||||
print ('SLALIB model: %s mas' %(atSLALIB.dispersion(l1, l2, zenith) ) )
|
||||
|
||||
# SLALIB refraction:
|
||||
A1, B1 = slalib.sla_refco(atSLALIB.h, atSLALIB.T, atSLALIB.P, atSLALIB.H, l1, np.deg2rad(atSLALIB.lat), atSLALIB.TLR, atSLALIB.prec)
|
||||
refractionValueSLALIB = np.degrees(atSLALIB.atmModel(A1, B1, zenith)) * 3.6e6 # in mas
|
||||
|
||||
print('Differential dispersion = {} mas'.format(atSLALIB.dispersion(l1, l2, zenith) - ref.cassini(n1c, n2c, zenith) * 3.6e6) )
|
||||
print('Differential refraction = {} mas'.format( refractionValueSLALIB - refractionValueCiddor ) )
|
||||
|
||||
#######################################################
|
||||
|
||||
refractionAnalysis = True
|
||||
dispersionAnalysis = True
|
||||
tableCompare = True
|
||||
if dispersionAnalysis:
|
||||
# Initiate the refraction class
|
||||
ref = dispersion(lat, h)
|
||||
ref.setReducedHeight(P, rho)
|
||||
|
||||
# Compare the refraction models to check if the output is okay
|
||||
print ('Plane - parallel atmosphere dispersion: %s mas' %(ref.planeParallel(n1, n2, zenith) * 3.6e6))
|
||||
print ('Cassini dispersion: %s mas' %(ref.cassini(n1, n2, zenith) * 3.6e6))
|
||||
print ('Tan5 dispersion: %s mas' %(ref.tan5(n1, n2, zenith) * 3.6e6))
|
||||
print ('Oriani dispersion: %s mas' %(ref.oriani(n1, n2, zenith) * 3.6e6))
|
||||
print ('Corbard dispersion: %s mas' %(ref.corbard(n1, n2, zenith)*3.6e6))
|
||||
print ('Full integration: %s mas' %(ref.refractionIntegral(n1, n2, zenith)*3.6e6))
|
||||
print ('SLALIB model: %s mas' %(atSLALIB.dispersion(l1, l2, zenith) ) )
|
||||
|
||||
# Now we calculate the dispersion over a wide range of zenith angles
|
||||
# for ideal gas atmosphere
|
||||
zenithRange = np.linspace(30, 85, 1000)
|
||||
ppList = ref.planeParallel(n1, n2, zenithRange) * 3.6e6
|
||||
cassiniList = ref.cassini(n1, n2, zenithRange) * 3.6e6
|
||||
tan5List = ref.tan5(n1, n2, zenithRange) * 3.6e6
|
||||
orianiList = ref.oriani(n1, n2, zenithRange) * 3.6e6
|
||||
corbardList = [ref.corbard(n1, n2, z)[0] * 3.6e6 for z in zenithRange]
|
||||
fullIntegrationList = np.asarray([ref.refractionIntegral(n1, n2, z) * 3.6e6 for z in zenithRange])
|
||||
slalibList = atSLALIB.dispersion(l1, l2, zenithRange)
|
||||
|
||||
# for isothermal atmosphere
|
||||
cassiniList_isotherm = ref.cassini(n1, n2, zenithRange, reduced_height=ref.H_isotherm(T)) * 3.6e6
|
||||
tan5List_isotherm = ref.tan5(n1, n2, zenithRange, reduced_height=ref.H_isotherm(T)) * 3.6e6
|
||||
orianiList_isotherm = ref.oriani(n1, n2, zenithRange, reduced_height=ref.H_isotherm(T)) * 3.6e6
|
||||
corbardList_isotherm = [ref.corbard(n1, n2, z, reduced_height=ref.H_isotherm(T))[0] * 3.6e6 for z in zenithRange]
|
||||
|
||||
# check when the variation between the different models pass different values
|
||||
dispersionWidth = []
|
||||
widthList = []
|
||||
for z in range(len(zenithRange)):
|
||||
compList = [cassiniList[z], tan5List[z], orianiList[z], corbardList[z], cassiniList_isotherm[z], tan5List_isotherm[z], orianiList_isotherm[z], corbardList_isotherm[z], slalibList[z]]#, fullIntegrationList[z]]
|
||||
width = np.max(compList) - np.min(compList)
|
||||
widthList.append(width)
|
||||
dispersionWidth.append((zenithRange[z], width))
|
||||
|
||||
masBorder = np.argmax(np.asarray(widthList)>1)
|
||||
p1masBorder = np.argmax(np.asarray(widthList)>0.1)
|
||||
p01masBorder = np.argmax(np.asarray(widthList)>0.01)
|
||||
p001masBorder = np.argmax(np.asarray(widthList)>0.001)
|
||||
|
||||
plotAbsolute = 1
|
||||
if plotAbsolute:
|
||||
# Plotting it all, with vertical lines where the variation between the models exceed 1, 0.1, 0.01 and 0.001 mas.
|
||||
fig = plt.figure(figsize=(9,5))
|
||||
ax = fig.add_subplot(111)
|
||||
ax.set_xlim([30,85])
|
||||
ax.set_xlabel('Zenith (degrees)')
|
||||
ax.set_ylabel('Dispersion (mas)')
|
||||
ax.grid(True, alpha=0.3)
|
||||
ax.axvline(dispersionWidth[masBorder][0], linestyle='--', color='k', linewidth=0.5)
|
||||
ax.text(dispersionWidth[masBorder][0]-0.5, 402, '1 mas', ha='center', va='bottom', rotation='vertical')
|
||||
ax.axvline(dispersionWidth[p1masBorder][0], linestyle='--', color='k', linewidth=0.5)
|
||||
ax.text(dispersionWidth[p1masBorder][0]-0.5, 402, '0.1 mas', ha='center', va='bottom', rotation='vertical')
|
||||
ax.axvline(dispersionWidth[p01masBorder][0], linestyle='--', color='k', linewidth=0.5)
|
||||
ax.text(dispersionWidth[p01masBorder][0]-0.5, 402, '10 $\mu$as', ha='center', va='bottom', rotation='vertical')
|
||||
ax.axvline(dispersionWidth[p001masBorder][0], linestyle='--', color='k', linewidth=0.5)
|
||||
ax.text(dispersionWidth[p001masBorder][0]-0.5, 402, '1 $\mu$as', ha='center', va='bottom', rotation='vertical')
|
||||
|
||||
ax.plot(zenithRange, ppList, 'k-', label='Plane-parallel', alpha=0.2)
|
||||
ax.plot(zenithRange, cassiniList, 'g-', label='Cassini')
|
||||
ax.plot(zenithRange, tan5List, 'b-', label='$Atan(z) - Btan^3(z) + Ctan^5(z)$')
|
||||
ax.plot(zenithRange, orianiList, 'y-', label='$Atan(z) - Btan^3(z)$')
|
||||
ax.plot(zenithRange, corbardList, 'r-', label='Error function')
|
||||
ax.plot(zenithRange, fullIntegrationList, 'c-', label='Full integration')
|
||||
|
||||
ax.plot(zenithRange, cassiniList_isotherm, 'g--', label='Cassini (isotherm)')
|
||||
ax.plot(zenithRange, tan5List_isotherm, 'b--', label='$Atan(z) - Btan^3(z) + Ctan^5(z)$ (isotherm)')
|
||||
ax.plot(zenithRange, orianiList_isotherm, 'y--', label='$Atan(z) - Btan^3(z)$ (isotherm)')
|
||||
ax.plot(zenithRange, corbardList_isotherm, 'r--', label='Error function (isotherm)')
|
||||
|
||||
|
||||
ax.tick_params(axis ='both', direction='in')
|
||||
ax.legend()
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
plotRelative = 1
|
||||
if plotRelative:
|
||||
# RELATIVE TO FULL INTEGRATION
|
||||
fig = plt.figure(figsize=(9,6))
|
||||
ax = fig.add_subplot(111)
|
||||
ax.set_xlim([30,85])
|
||||
|
||||
ax.set_xlabel('Zenith (degrees)', fontsize=18)
|
||||
ax.set_ylabel(r'$\log_{10}(|(\Delta R - \Delta R_{int})|$ mas$^{-1})$', fontsize=18)
|
||||
|
||||
ax.axhline(np.log10(0.4), linestyle='-', linewidth=1, color='red')
|
||||
ax.text(30.5, np.log10(0.4)+0.05, 'Residual dispersion requirement', fontsize=12)
|
||||
|
||||
ax.grid(True, which='major', alpha=0.3)
|
||||
ax.grid(True, which='minor', alpha=0.2)
|
||||
|
||||
ax.plot(zenithRange, np.log10(np.abs(ppList-fullIntegrationList)), color='k', linestyle='-', linewidth=2, label='Plane-parallel')
|
||||
ax.plot(zenithRange, np.log10(np.abs(cassiniList-fullIntegrationList)), color='g', linestyle=(0, (1, 1)), linewidth=2, label='Cassini')
|
||||
ax.plot(zenithRange, np.log10(np.abs(orianiList-fullIntegrationList)), color='y', linestyle='-.', linewidth=2, label='Oriani')
|
||||
ax.plot(zenithRange, np.log10(np.abs(tan5List-fullIntegrationList)), color='b', linestyle=(0, (5, 1)), linewidth=2, label='Oriani incl. fifth order')
|
||||
ax.plot(zenithRange, np.log10(np.abs(np.asarray(corbardList)-fullIntegrationList)), color='r', linestyle=(0, (3, 1, 1, 1, 1, 1)), linewidth=2, label='Error function')
|
||||
ax.plot(zenithRange, np.log10(np.abs(np.asarray(slalibList)-fullIntegrationList)), color='m', linewidth=2, label='SLALIB')
|
||||
ax.plot(zenithRange, np.log10(np.abs(cassiniList-fullIntegrationList)), color='g', linestyle=(0, (1, 1)), linewidth=2)
|
||||
|
||||
ax.set_ylim(top=2.6, bottom=-7.2)
|
||||
ax.tick_params(axis ='both', direction='in', labelsize=16)
|
||||
ax.legend(fontsize=14, labelspacing=0.3, columnspacing=0.5, handletextpad=0.3, ncol=1, loc='lower right')
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
|
||||
@@ -0,0 +1,177 @@
|
||||
#!/usr/bin/env Python3
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from mpl_toolkits.axes_grid1 import host_subplot
|
||||
from matplotlib.colors import LogNorm
|
||||
from tabulate import tabulate
|
||||
import AstroAtmosphere
|
||||
|
||||
|
||||
'''
|
||||
@ Author: Joost van den Born
|
||||
@ Contact: born@astron.nl
|
||||
@ Description: A comparison of various atmospheric refraction models.
|
||||
Also these are used to calculate the difference in
|
||||
atmospheric dispersion, given a set of conditions.
|
||||
This is a reproduction of Figure 1 of Spàno (2014).
|
||||
'''
|
||||
|
||||
|
||||
class atmosphericModel:
|
||||
|
||||
def __init__(self, T, P, H, CO2, l1, l2):
|
||||
self.T = T
|
||||
self.P = P
|
||||
self.H = H
|
||||
self.CO2 = CO2
|
||||
self.lrange = np.linspace(l1, l2, 100)
|
||||
|
||||
|
||||
def BarrellAndSears(self, l=None):
|
||||
if not l:
|
||||
l = self.lrange
|
||||
return (AstroAtmosphere.BarrellAndSears(l, T=self.T, p=self.P)-1)
|
||||
|
||||
|
||||
def Ciddor(self, l=None):
|
||||
if not l:
|
||||
l = self.lrange
|
||||
return (AstroAtmosphere.Ciddor(l, CO2=self.CO2)-1)
|
||||
|
||||
|
||||
def Edlen1953(self, l=None):
|
||||
if not l:
|
||||
l = self.lrange
|
||||
return(AstroAtmosphere.Edlen1953(l)-1)
|
||||
|
||||
|
||||
def Edlen1966(self, l=None):
|
||||
if not l:
|
||||
l = self.lrange
|
||||
return(AstroAtmosphere.Edlen1966(l)-1)
|
||||
|
||||
|
||||
def Owens(self, l=None):
|
||||
if not l:
|
||||
l = self.lrange
|
||||
return (AstroAtmosphere.Owens(l, T=self.T, p=self.P, H=self.H)-1)
|
||||
|
||||
|
||||
def BonschPotulski(self, l=None):
|
||||
if not l:
|
||||
l = self.lrange
|
||||
return (AstroAtmosphere.BonschPotulski(l, T=self.T, p=self.P, CO2=self.CO2)-1)
|
||||
|
||||
def BirchDowns(self, l=None):
|
||||
if not l:
|
||||
l = self.lrange
|
||||
return (AstroAtmosphere.BirchDowns(l)-1)
|
||||
|
||||
def PeckReeder(self, l=None):
|
||||
if not l:
|
||||
l = self.lrange
|
||||
return (AstroAtmosphere.PeckReeder(l)-1)
|
||||
|
||||
def slalib(self, l=None):
|
||||
if not l:
|
||||
l = self.lrange
|
||||
return(AstroAtmosphere.slalib(l, T=self.T, p=self.P, H=self.H)-1)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# Calculation of the refractivity of the different models
|
||||
# multiplication by 1e8 to make the differentials more
|
||||
# pronounced.
|
||||
at = atmosphericModel(T=288.15, P=101325, H=0, CO2=300, l1=0.3, l2=2.5)
|
||||
BSdata = 1e8 * at.BarrellAndSears()
|
||||
CiddorData = 1e8 * at.Ciddor()
|
||||
Edlen1953Data = 1e8 * at.Edlen1953()
|
||||
Edlen1966Data = 1e8 * at.Edlen1966()
|
||||
OwensData = 1e8 * at.Owens()
|
||||
BonschPotulskiData = 1e8 * at.BonschPotulski()
|
||||
BirchDownsData = 1e8 * at.BirchDowns()
|
||||
PeckReederData = 1e8 * at.PeckReeder()
|
||||
slalibData = 1e8 * at.slalib()
|
||||
|
||||
# Plotting the results
|
||||
fig = plt.figure(figsize=(9,5))
|
||||
ax2 = fig.add_subplot(111)
|
||||
ax2.grid(True, alpha=0.3)
|
||||
ax2.plot(at.lrange, BSdata - CiddorData, 'b-', label='Barrell & Sears (1939)')
|
||||
ax2.plot(at.lrange, CiddorData - CiddorData, 'k-', label='Ciddor (1996)')
|
||||
ax2.plot(at.lrange, Edlen1953Data - CiddorData, 'r:', label='Edlen (1953)')
|
||||
ax2.plot(at.lrange, Edlen1966Data - CiddorData, 'g--', label='Edlen (1966)')
|
||||
ax2.plot(at.lrange, OwensData - CiddorData, 'm--', label='Owens (1967)')
|
||||
ax2.plot(at.lrange, BonschPotulskiData - CiddorData, 'g-.', label='Bonsch & Potulski (1998)')
|
||||
ax2.plot(at.lrange, BirchDownsData - CiddorData, 'r-.', label='Birch & Downs (1993, 1994)')
|
||||
ax2.plot(at.lrange, PeckReederData - CiddorData, 'b--', label='Peck & Reeder (1972)')
|
||||
ax2.plot(at.lrange, slalibData - CiddorData, 'y-', label='ZEMAX / SLALIB')
|
||||
box = ax2.get_position()
|
||||
ax2.set_position([box.x0, box.y0, box.width, box.height])
|
||||
ax2.legend(loc='center left', bbox_to_anchor=(1, 0.5))
|
||||
ax2.set_xlabel('Wavelength ($\mu m$)')
|
||||
ax2.set_ylabel('$\Delta$n $\\times$ $10^{8}$')
|
||||
ax2.set_ylim([-3,3])
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# Let's compare the impact on the atmospheric dispersion.
|
||||
# For the dispersion we must compare the delta for two lambda values
|
||||
methodList = [ at.BarrellAndSears,
|
||||
at.Ciddor,
|
||||
at.Edlen1953,
|
||||
at.Edlen1966,
|
||||
at.Owens,
|
||||
at.BonschPotulski,
|
||||
at.BirchDowns,
|
||||
at.PeckReeder,
|
||||
at.slalib
|
||||
]
|
||||
|
||||
|
||||
filters = [ [0.800, 0.934], # I-band
|
||||
[1.150, 1.345], # J-band
|
||||
[1.490, 1.780], # H-band
|
||||
[1.970, 2.320] # K-band
|
||||
]
|
||||
filterDispersion = []
|
||||
|
||||
# Iterate over the four MICADO broad band filters
|
||||
for f in filters:
|
||||
# Wavelengths of this particular filter
|
||||
l1 = f[0]
|
||||
l2 = f[1]
|
||||
|
||||
dispersionList = []
|
||||
for m in methodList:
|
||||
d1 = m(l=l1)
|
||||
d2 = m(l=l2)
|
||||
deltan = d1 - d2
|
||||
|
||||
# We assume, for sake of simplicity, a plane parallel atmosphere
|
||||
# and calculate the dispersion in milli arcseconds.
|
||||
dispersion = deltan * np.tan(np.deg2rad(45)) * 206265 * 1000
|
||||
dispersionList.append(dispersion)
|
||||
|
||||
# We compare the atmospheric dispersion relative to the atmospheric
|
||||
# dispersion as calculated using the Ciddor model.
|
||||
dispersionList = np.asarray(dispersionList) - dispersionList[1]
|
||||
filterDispersion.append(dispersionList)
|
||||
|
||||
# Print the results in a table.
|
||||
methodStringList = ['Barrell & Sears (1939)',
|
||||
'Ciddor (1996)',
|
||||
'Edlen (1953)',
|
||||
'Edlen (1966)',
|
||||
'Bonsch & Potulski (1998)',
|
||||
'Birch & Downs (1993, 1994)',
|
||||
'Peck & Reeder (1972)',
|
||||
'slalib']
|
||||
tableData = []
|
||||
for i in range(len(methodStringList)):
|
||||
tableData.append([methodStringList[i], filterDispersion[0][i], filterDispersion[1][i], filterDispersion[2][i], filterDispersion[3][i]])
|
||||
print(tabulate(tableData, headers=['Model', 'I-band\n(0.8 - 0.934)', 'J-band\n(1.15 - 1.345)', 'H-band\n(1.49 - 1.78)', 'K-band\n(1.97 - 2.32)']))
|
||||
|
||||
@@ -0,0 +1,70 @@
|
||||
#!/usr/bin/env Python3
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import AstroAtmosphere as atm
|
||||
from ADC import *
|
||||
|
||||
'''
|
||||
@author: Joost van den Born
|
||||
@Contact: born@astron.nl
|
||||
@Description: Caclulates the differential dispersion between the ADC and
|
||||
the atmosphere following the ciddor and cassini dispersion
|
||||
model.
|
||||
This script generates figure 4 from the paper.
|
||||
'''
|
||||
|
||||
# Initializing ADC object and defining the filter bands
|
||||
adc = ADC()
|
||||
bandName = ['I-band', 'J-band', 'H-band', 'K-band']
|
||||
bandList = [(0.8, 0.934), (1.15, 1.345), (1.49, 1.78), (1.97, 2.32)]
|
||||
|
||||
# Some empty lists for later use
|
||||
wavelengthList = []
|
||||
atDispersionList = []
|
||||
adcDispersionList = []
|
||||
|
||||
for i in range(len(bandList)):
|
||||
# We want to find the dispersion of the atmosphere and
|
||||
# and of the ADC, at z=30 deg.
|
||||
lMin = bandList[i][0]
|
||||
lMax = bandList[i][1]
|
||||
z = 30 # degrees
|
||||
lRange = np.linspace(lMin, lMax, 100)
|
||||
wavelengthList.append(lRange)
|
||||
|
||||
# Set ADC position optimized for H-band, z=30 deg.
|
||||
atDispersion = atm.quick_dispersion(lMin, lMax, z, conditions='CERRO_ARMAZONES') * 3.6e6
|
||||
adcOptimumAngle = adc.optimize(atDispersion, lMin, lMax)
|
||||
adc.setOrientation(adcOptimumAngle)
|
||||
|
||||
# Now iterate over the band to see how the differential dispersion changes
|
||||
atDispersionValues = []
|
||||
adcDispersionValues = []
|
||||
for l in lRange:
|
||||
atDispersionValues.append(atm.quick_dispersion(np.min(lRange), l, z, conditions='CERRO_ARMAZONES')* 3.6e6 )
|
||||
adcDispersionValues.append(adc.calculateDispersion(np.min(lRange),l)[1])
|
||||
atDispersionList.append(atDispersionValues)
|
||||
adcDispersionList.append(adcDispersionValues)
|
||||
|
||||
# Calculating the differential dispersion
|
||||
atDispersionList = np.asarray(atDispersionList)
|
||||
adcDispersionList = np.asarray(adcDispersionList)
|
||||
deltaDispersion = atDispersionList - adcDispersionList
|
||||
|
||||
|
||||
|
||||
# Plot the results
|
||||
plt.rc('font', family='serif')
|
||||
plt.rc('axes', labelsize='large')
|
||||
fig = plt.figure(figsize=(9,6))
|
||||
|
||||
ax = fig.add_subplot(111)
|
||||
linestyleList = [':', '-.', '--', '-']
|
||||
for i in range(len(bandList)):
|
||||
ax.plot(wavelengthList[i], deltaDispersion[i], label=bandName[i], linestyle=linestyleList[i], linewidth=2, color='k')
|
||||
ax.set_ylabel('Differential $\Delta$R (mas)', fontsize=18)
|
||||
ax.set_xlabel('$\lambda$ ($\mu$m)', fontsize=18)
|
||||
ax.grid(True, alpha=0.3)
|
||||
ax.legend(fontsize=16, loc='lower right')
|
||||
ax.tick_params(axis ='both', direction='in', labelsize=16)
|
||||
plt.show()
|
||||
@@ -0,0 +1,324 @@
|
||||
#!/usr/bin/env Python
|
||||
from __future__ import division
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import AstroAtmosphere as atm
|
||||
import sys
|
||||
from ADC import *
|
||||
|
||||
'''
|
||||
@author: Joost van den Born
|
||||
@Description: Calculates differential refraction (dispersion) between stars with a different
|
||||
effective temperature. Similar to Gubler & Tytler 1998
|
||||
'''
|
||||
|
||||
def blackbody(l, T):
|
||||
'''
|
||||
Calculates the intensity output of a star with effective temperature T at wavelength l
|
||||
'''
|
||||
h = 6.626070150e-34 # J*s
|
||||
k = 1.38064852e-23 # J/K
|
||||
c = 299792458 # m/s
|
||||
return 2 * np.pi * h * c**2 / l**5 * 1 / (np.exp( h * c / (l * k * T)) - 1)
|
||||
|
||||
def effectiveWavelength(T, lmin, lmax):
|
||||
'''
|
||||
Calculates effective wavelength of a star from the effective temperature.
|
||||
'''
|
||||
lmin = lmin/1e6
|
||||
lmax = lmax/1e6
|
||||
_l = np.linspace(lmin, lmax, 1000)
|
||||
_I = blackbody(_l, T)
|
||||
return np.sum(_I*_l)/np.sum(_I)*1e6
|
||||
|
||||
if __name__ == '__main__':
|
||||
# Parameters at Cerro Armazones
|
||||
T = 279.65 # k
|
||||
P = 71200 # Pa
|
||||
H = 0.22
|
||||
xc = 450 # ppm
|
||||
lat = -24.5983 # degrees
|
||||
h = 3064 # m
|
||||
l1 = 1.49 # micron
|
||||
l2 = 1.78 # micron
|
||||
zenith = 60 # degrees
|
||||
offset = 0#.285/2 # degrees, ADC offset to simulate finite step size
|
||||
|
||||
S2temps = [2500, 4000, 5500, 7000, 8500]
|
||||
S1temps = np.linspace(2500, 1e4, 100)
|
||||
|
||||
|
||||
########################################################################################
|
||||
########################################################################################
|
||||
# 1. We look at the differential refraction through the atmosphere, without an ADC
|
||||
# at = atm.dispersion(lat, h)
|
||||
at = atm.refraction(lat, h)
|
||||
at.setReducedHeight(P, at.rho(P, T, H, xc) )
|
||||
|
||||
|
||||
# Calculating the differential atmospheric refraction for stars of different temperatures
|
||||
data = []
|
||||
for T2 in S2temps:
|
||||
# Properties of star 2
|
||||
l_eff_2 = effectiveWavelength(T2, l1, l2)
|
||||
n2 = at.n_tph(l_eff_2, T, P, H, xc)
|
||||
R2 = at.cassini(n2, zenith)
|
||||
|
||||
# Calculating the differences with star 1
|
||||
dataEntry = []
|
||||
for T1 in S1temps:
|
||||
l_eff_1 = effectiveWavelength(T1, l1, l2)
|
||||
n1 = at.n_tph(l_eff_1, T, P, H, xc)
|
||||
R1 = at.cassini(n1, zenith)
|
||||
diffRef = (R1 - R2) * 3.6e9
|
||||
dataEntry.append(diffRef)
|
||||
data.append(dataEntry)
|
||||
|
||||
# Plotting the results
|
||||
plt.rc('font', family='serif')
|
||||
plt.rc('axes', labelsize='large')
|
||||
fig = plt.figure(figsize=(9,6))
|
||||
ax = fig.add_subplot(111)
|
||||
ax2 = ax.twiny()
|
||||
ax.grid(True, alpha=0.3)
|
||||
linestylesList = [':', '-.', '--', '-', (0, (3, 1, 1, 1, 1, 1))]
|
||||
for i in range(len(data)):
|
||||
ax.plot(S1temps, data[i], label=str(S2temps[i]) + ' K', linestyle=linestylesList[i], color='k', linewidth=2)
|
||||
|
||||
S1temp_ticks = ax.get_xticks()
|
||||
S1temp_tick_loc = ax.get_xticks()
|
||||
|
||||
l_eff_ticks = []
|
||||
for T1 in S1temp_ticks:
|
||||
l_eff_ticks.append(round( (effectiveWavelength(T1, l1, l2)*1000-1620),1))
|
||||
|
||||
ax2.set_xticklabels(l_eff_ticks)
|
||||
ax2.set_xlim(np.min(S1temps), np.max(S1temps))
|
||||
ax2.set_xlabel('$\lambda_{eff} - 1620$ (nm) ', fontsize=18)
|
||||
ax2.tick_params(axis ='both', direction='in', labelsize=16)
|
||||
|
||||
ax.set_xlabel('Temperature of Star 1 (K)', fontsize=18)
|
||||
ax.set_ylabel('$\Delta$R ($\mu$as)', fontsize=18)
|
||||
ax.legend(fontsize=16, labelspacing=0.3)
|
||||
ax.set_xlim(np.min(S1temps), np.max(S1temps))
|
||||
ax.tick_params(axis ='both', direction='in', labelsize=16)
|
||||
# plt.show()
|
||||
|
||||
########################################################################################
|
||||
########################################################################################
|
||||
|
||||
|
||||
|
||||
########################################################################################
|
||||
########################################################################################
|
||||
# 2. We look now at the differential refraction through the atmosphere, including an ADC
|
||||
at = atm.refraction(lat, h)
|
||||
at.setReducedHeight(P, at.rho(P, T, H, xc) )
|
||||
|
||||
adc = ADC()
|
||||
|
||||
# Conditions at the band cut-on and cut-off wavelength
|
||||
n_l1 = at.n_tph(l1, T, P, H, xc)
|
||||
n_l2 = at.n_tph(l2, T, P, H, xc)
|
||||
|
||||
|
||||
atd = atm.dispersion(lat, h)
|
||||
atd.setReducedHeight(P, at.rho(P, T, H, xc) )
|
||||
# Optimize ADC for broad band edges at zenith distance.
|
||||
atm_disp_full_band = atd.cassini(n_l1, n_l2, zenith)
|
||||
print(atm_disp_full_band*3.6e6)
|
||||
adcOptimumAngle = adc.optimize(atm_disp_full_band*3.6e6, l1, l2)
|
||||
adc.setOrientation(adcOptimumAngle+offset)
|
||||
|
||||
|
||||
# Calculating the differential atmospheric refraction for stars of different temperatures
|
||||
data = []
|
||||
for T2 in S2temps:
|
||||
# Properties of star 2
|
||||
l_eff_2 = effectiveWavelength(T2, l1, l2)
|
||||
n2 = at.n_tph(l_eff_2, T, P, H, xc)
|
||||
R2_atm = at.cassini(n2, zenith)
|
||||
R2_adc = adc.calculateDispersion(l_eff_2, l1, refraction=True)
|
||||
|
||||
# Calculating the differences with star 1
|
||||
dataEntry = []
|
||||
for T1 in S1temps:
|
||||
l_eff_1 = effectiveWavelength(T1, l1, l2)
|
||||
n1 = at.n_tph(l_eff_1, T, P, H, xc)
|
||||
R1_atm = at.cassini(n1, zenith)
|
||||
R1_adc = adc.calculateDispersion(l_eff_1, l1, refraction=True)
|
||||
diffRef = ( (R1_adc[1] - R2_adc[1]) + ((R1_atm - R2_atm) * 3.6e6 ) ) * 1e3
|
||||
dataEntry.append(diffRef)
|
||||
data.append(dataEntry)
|
||||
|
||||
# Plotting the results
|
||||
plt.rc('font', family='serif')
|
||||
plt.rc('axes', labelsize='large')
|
||||
fig = plt.figure(figsize=(9,6))
|
||||
ax = fig.add_subplot(111)
|
||||
ax2 = ax.twiny()
|
||||
ax.grid(True, alpha=0.3)
|
||||
linestylesList = [':', '-.', '--', '-', (0, (3, 1, 1, 1, 1, 1))]
|
||||
for i in range(len(data)):
|
||||
ax.plot(S1temps, data[i], label=str(S2temps[i]) + ' K', linestyle=linestylesList[i], color='k', linewidth=2)
|
||||
|
||||
S1temp_ticks = ax.get_xticks()
|
||||
S1temp_tick_loc = ax.get_xticks()
|
||||
|
||||
l_eff_ticks = []
|
||||
for T1 in S1temp_ticks:
|
||||
l_eff_ticks.append(round( (effectiveWavelength(T1, l1, l2)*1000-1620),1))
|
||||
|
||||
ax2.set_xticklabels(l_eff_ticks)
|
||||
ax2.set_xlim(np.min(S1temps), np.max(S1temps))
|
||||
ax2.set_xlabel('$\lambda_{eff} - 1620$ (nm) ', fontsize=18)
|
||||
ax2.tick_params(axis ='both', direction='in', labelsize=16)
|
||||
|
||||
ax.set_xlabel('Temperature of Star 1 (K)', fontsize=18)
|
||||
ax.set_ylabel('$\Delta$R ($\mu$as)', fontsize=18)
|
||||
ax.legend(fontsize=16, labelspacing=0.3, loc='lower right')
|
||||
ax.set_xlim(np.min(S1temps), np.max(S1temps))
|
||||
ax.tick_params(axis ='both', direction='in', labelsize=16)
|
||||
plt.show()
|
||||
|
||||
########################################################################################
|
||||
########################################################################################
|
||||
|
||||
|
||||
|
||||
########################################################################################
|
||||
########################################################################################
|
||||
# 3. Now we look at the differential dispersion, without an ADC
|
||||
at = atm.dispersion(lat, h)
|
||||
at.setReducedHeight(P, at.rho(P, T, H, xc) )
|
||||
|
||||
# Conditions at the band cut-on and cut-off wavelength
|
||||
n_l1 = at.n_tph(l1, T, P, H, xc)
|
||||
n_l2 = at.n_tph(l2, T, P, H, xc)
|
||||
|
||||
# Optimize ADC for broad band edges at zenith distance.
|
||||
atm_disp_full_band = at.cassini(n_l1, n_l2, zenith)
|
||||
print(atm_disp_full_band*3.6e6)
|
||||
|
||||
|
||||
# Check that we can use dispersion? If not, how to calculate refraction...?
|
||||
# We plot below the difference in atmospheric dispersion of the two stars, incl. an ADC.
|
||||
data = []
|
||||
for T2 in S2temps:
|
||||
l_eff_2 = effectiveWavelength(T2, l1, l2)
|
||||
n2 = at.n_tph(l_eff_2, T, P, H, xc)
|
||||
dataEntry = []
|
||||
for T1 in S1temps:
|
||||
l_eff_1 = effectiveWavelength(T1, l1, l2)
|
||||
n1 = at.n_tph(l_eff_1, T, P, H, xc)
|
||||
atm_disp = at.cassini(n1, n2, zenith) * 3.6e9
|
||||
dataEntry.append(atm_disp)
|
||||
data.append(dataEntry)
|
||||
|
||||
plt.rc('font', family='serif')
|
||||
plt.rc('axes', labelsize='large')
|
||||
fig2 = plt.figure(figsize=(9,6))
|
||||
ax = fig2.add_subplot(111)
|
||||
ax2 = ax.twiny()
|
||||
ax.grid(True, alpha=0.3)
|
||||
linestylesList = [':', '-.', '--', '-', (0, (3, 1, 1, 1, 1, 1))]
|
||||
for i in range(len(data)):
|
||||
ax.plot(S1temps, data[i], label=str(S2temps[i]) + ' K', linestyle=linestylesList[i], color='k', linewidth=2)
|
||||
|
||||
S1temp_ticks = ax.get_xticks()
|
||||
S1temp_tick_loc = ax.get_xticks()
|
||||
|
||||
l_eff_ticks = []
|
||||
for T1 in S1temp_ticks:
|
||||
l_eff_ticks.append(round( (effectiveWavelength(T1, l1, l2)*1000-1620),1))
|
||||
|
||||
# ax2.plot(S1temps, data[0], alpha=0)
|
||||
ax2.set_xticklabels(l_eff_ticks)
|
||||
ax2.set_xlim(np.min(S1temps), np.max(S1temps))
|
||||
ax2.set_xlabel('$\lambda_{eff} - 1620$ (nm) ', fontsize=18)
|
||||
ax2.tick_params(axis ='both', direction='in', labelsize=16)
|
||||
|
||||
ax.set_xlabel('Temperature of Star 1 (K)', fontsize=18)
|
||||
ax.set_ylabel('$\Delta(\Delta$R) ($\mu$as), excl. ADC', fontsize=18)
|
||||
ax.legend(fontsize=16, labelspacing=0.3, loc='lower right')
|
||||
ax.set_xlim(np.min(S1temps), np.max(S1temps))
|
||||
ax.tick_params(axis ='both', direction='in', labelsize=16)
|
||||
# plt.show()
|
||||
|
||||
########################################################################################
|
||||
########################################################################################
|
||||
########################################################################################
|
||||
|
||||
|
||||
|
||||
|
||||
########################################################################################
|
||||
########################################################################################
|
||||
# 4. Now we add the ADC and look again at the differential refraction, but on the detector
|
||||
at = atm.dispersion(lat, h)
|
||||
at.setReducedHeight(P, at.rho(P, T, H, xc) )
|
||||
adc = ADC()
|
||||
|
||||
# Conditions at the band cut-on and cut-off wavelength
|
||||
n_l1 = at.n_tph(l1, T, P, H, xc)
|
||||
n_l2 = at.n_tph(l2, T, P, H, xc)
|
||||
|
||||
# Optimize ADC for broad band edges at zenith distance.
|
||||
atm_disp_full_band = at.cassini(n_l1, n_l2, zenith)
|
||||
print(atm_disp_full_band*3.6e6)
|
||||
adcOptimumAngle = adc.optimize(atm_disp_full_band*3.6e6, l1, l2)
|
||||
adc.setOrientation(adcOptimumAngle+offset)
|
||||
|
||||
|
||||
# Check that we can use dispersion? If not, how to calculate refraction...?
|
||||
# We plot below the difference in atmospheric dispersion of the two stars, incl. an ADC.
|
||||
data = []
|
||||
for T2 in S2temps:
|
||||
l_eff_2 = effectiveWavelength(T2, l1, l2)
|
||||
n2 = at.n_tph(l_eff_2, T, P, H, xc)
|
||||
atm_disp2 = at.cassini(n_l1, n2, zenith) * 3.6e6
|
||||
adc_disp2 = adc.calculateDispersion(l1, l_eff_2)[1]
|
||||
|
||||
dataEntry = []
|
||||
for T1 in S1temps:
|
||||
l_eff_1 = effectiveWavelength(T1, l1, l2)
|
||||
n1 = at.n_tph(l_eff_1, T, P, H, xc)
|
||||
atm_disp1 = at.cassini(n_l1, n1, zenith) * 3.6e6
|
||||
adc_disp1 = adc.calculateDispersion(l1,l_eff_1)[1]
|
||||
diffDisp = ((adc_disp1 - adc_disp2) - (atm_disp1 - atm_disp2)) * 1e3
|
||||
dataEntry.append(diffDisp)
|
||||
data.append(dataEntry)
|
||||
|
||||
plt.rc('font', family='serif')
|
||||
plt.rc('axes', labelsize='large')
|
||||
fig2 = plt.figure(figsize=(9,6))
|
||||
ax = fig2.add_subplot(111)
|
||||
ax2 = ax.twiny()
|
||||
ax.grid(True, alpha=0.3)
|
||||
linestylesList = [':', '-.', '--', '-', (0, (3, 1, 1, 1, 1, 1))]
|
||||
for i in range(len(data)):
|
||||
ax.plot(S1temps, data[i], label=str(S2temps[i]) + ' K', linestyle=linestylesList[i], color='k', linewidth=2)
|
||||
|
||||
S1temp_ticks = ax.get_xticks()
|
||||
S1temp_tick_loc = ax.get_xticks()
|
||||
|
||||
l_eff_ticks = []
|
||||
for T in S1temp_ticks:
|
||||
l_eff_ticks.append(round( (effectiveWavelength(T, l1, l2)*1000-1620),1))
|
||||
|
||||
# ax2.plot(S1temps, data[0], alpha=0)
|
||||
ax2.set_xticklabels(l_eff_ticks)
|
||||
ax2.set_xlim(np.min(S1temps), np.max(S1temps))
|
||||
ax2.set_xlabel('$\lambda_{eff} - 1620$ (nm) ', fontsize=18)
|
||||
ax2.tick_params(axis ='both', direction='in', labelsize=16)
|
||||
|
||||
ax.set_xlabel('Temperature of Star 1 (K)', fontsize=18)
|
||||
ax.set_ylabel('$\Delta(\Delta$R) ($\mu$as), incl. ADC', fontsize=18)
|
||||
ax.legend(fontsize=16, labelspacing=0.3, loc='lower right')
|
||||
ax.set_xlim(np.min(S1temps), np.max(S1temps))
|
||||
ax.tick_params(axis ='both', direction='in', labelsize=16)
|
||||
plt.show()
|
||||
|
||||
########################################################################################
|
||||
########################################################################################
|
||||
########################################################################################
|
||||
@@ -0,0 +1,353 @@
|
||||
#!/usr/bin/env Python
|
||||
from __future__ import division
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from mpl_toolkits.axes_grid1 import host_subplot
|
||||
from AstroAtmosphere import *
|
||||
from tabulate import tabulate
|
||||
|
||||
|
||||
|
||||
'''
|
||||
@ Author: Joost van den Born
|
||||
@ Contact: born@astron.nl
|
||||
@ Description: An exploration of the parameter space for the Ciddor-Cassini model.
|
||||
The results here are given in table 3 and Appendix A of the paper.
|
||||
|
||||
'''
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
plt.rc('font', family='serif')
|
||||
plt.rc('axes', labelsize='large')
|
||||
|
||||
at = Observatory()
|
||||
|
||||
pdrFigs = False
|
||||
errorSpace = False
|
||||
generate_images = True
|
||||
|
||||
|
||||
T = 279.65 # k
|
||||
P = 71200 # Pa
|
||||
H = 0.22
|
||||
xc = 450 # ppm
|
||||
lat = -24.5983 # degrees
|
||||
h = 3064 # m
|
||||
l1 = 1.49 # micron
|
||||
l2 = 1.78 # micron
|
||||
zenith = 30 # degrees
|
||||
|
||||
rho = at.rho(p=P, T=T, RH=H, xc=xc)
|
||||
disp = dispersion(lat, h)
|
||||
disp.setReducedHeight(P, rho)
|
||||
|
||||
# Change dl, dT, dP, dRH, dCO2 to the values of interest.
|
||||
n1 = at.n_tph(l=1.49, T=279.65, p=71200, RH=0.22, xc=450)
|
||||
n2 = at.n_tph(l=1.78, T=279.65, p=71200, RH=0.22, xc=450)
|
||||
R1ca = at.dn_tph(l=1.49, T=279.65, p=71200, RH=0.22, xc=450,
|
||||
dl=0.001, dT=0.4, dP=10, dRH=0.01, dCO2=20)
|
||||
R2ca = at.dn_tph(l=1.78, T=279.65, p=71200, RH=0.22, xc=450,
|
||||
dl=0.001, dT=0.4, dP=10, dRH=0.01, dCO2=20)
|
||||
|
||||
|
||||
# print (n1, at.dn_tph(l=1.49, T=288.0, p=101325, RH=0.0, xc=450, dl=0.0, dT=0.5, dP=0.0, dRH=0.0, dCO2=0) )
|
||||
# print(disp.cassiniError( zenith, l1, l2, T, P, H, xc, dl1=0.001, dl2=0.001, dT=0.4, dP=10, dRH=0.01, dCO2=20 )*3.6e9)
|
||||
|
||||
zenith_true = 30 # degrees
|
||||
dispValue = disp.cassini(n1, n2, zenith_true)
|
||||
defaultDispCA = disp.cassini(n1, n2, zenith_true)*3.6e6
|
||||
errorDispValue = disp.cassiniError(zenith, l1, l2, T, P, H, xc, dl1=0.001, dl2=0.001, dT=0.4, dP=10, dRH=0.01, dCO2=20 )
|
||||
print ('Dispersion between 1.49 and 1.78 micron at z = %i deg: %.3f +/- %.3f mas' %(zenith_true, dispValue*3.6e6, errorDispValue*3.6e6))
|
||||
|
||||
|
||||
R1s = at.n_tph(l=1.49, T=288.15, p=101325, RH=0.0, xc=450)
|
||||
R2s = at.n_tph(l=1.78, T=288.15, p=101325, RH=0.0, xc=450)
|
||||
|
||||
# Varying the parameters in the non-errror formula:
|
||||
if pdrFigs:
|
||||
Trange = np.linspace(262, 298, 1000)
|
||||
Prange = np.linspace(69000, 73000, 1000)
|
||||
Hrange = np.linspace(0.1, 0.3, 1000)
|
||||
|
||||
fig = plt.figure()
|
||||
|
||||
# Variation in T
|
||||
dispListS, dispListCA = [], []
|
||||
for t in Trange:
|
||||
R1ca = at.n_tph(l=1.49, T=t, p=71200, RH=0.22, xc=450)
|
||||
R2ca = at.n_tph(l=1.78, T=t, p=71200, RH=0.22, xc=450)
|
||||
dispValueS = disp.cassini(R1s, R2s, zenith_true) * 3.6e6
|
||||
dispValueCA = disp.cassini(R1ca, R2ca, zenith_true) * 3.6e6
|
||||
dispListS.append(dispValueS)
|
||||
dispListCA.append(dispValueCA)
|
||||
ax1 = fig.add_subplot(131)
|
||||
ax1.grid(True, alpha=0.3)
|
||||
ax1.plot(Trange, np.abs(dispListCA-defaultDispCA), 'r-', label='Cerro Armazones (p=712 mbar, T=6.5C, H=22%)')
|
||||
ax1.set_xlabel('Temperature (K)')
|
||||
ax1.set_ylabel('Dispersion (mas)')
|
||||
ax1.tick_params(axis ='both', direction='in')
|
||||
|
||||
# Variation in P
|
||||
dispListS, dispListCA = [], []
|
||||
for p in Prange:
|
||||
R1ca = at.n_tph(l=1.49, T=279.65, p=p, RH=0.22, xc=450)
|
||||
R2ca = at.n_tph(l=1.78, T=279.65, p=p, RH=0.22, xc=450)
|
||||
dispValueS = disp.cassini(R1s, R2s, zenith_true) * 3.6e6
|
||||
dispValueCA = disp.cassini(R1ca, R2ca, zenith_true) * 3.6e6
|
||||
dispListS.append(dispValueS)
|
||||
dispListCA.append(dispValueCA)
|
||||
ax2 = fig.add_subplot(132)
|
||||
ax2.grid(True, alpha=0.3)
|
||||
ax2.plot(Prange/100, np.abs(dispListCA-defaultDispCA), 'r-', label='Cerro Armazones (p=712 mbar, T=6.5C, H=22%)')
|
||||
ax2.set_xlabel('Pressure (mbar)')
|
||||
ax2.set_ylabel('Dispersion (mas)')
|
||||
ax2.tick_params(axis ='both', direction='in')
|
||||
|
||||
# Variation in T
|
||||
dispListS, dispListCA = [], []
|
||||
for h in Hrange:
|
||||
R1ca = at.n_tph(l=1.49, T=279.65, p=71200, RH=h, xc=450)
|
||||
R2ca = at.n_tph(l=1.78, T=279.65, p=71200, RH=h, xc=450)
|
||||
dispValueS = disp.cassini(R1s, R2s, zenith_true) * 3.6e6
|
||||
dispValueCA = disp.cassini(R1ca, R2ca, zenith_true) * 3.6e6
|
||||
dispListS.append(dispValueS)
|
||||
dispListCA.append(dispValueCA)
|
||||
ax3 = fig.add_subplot(133)
|
||||
ax3.grid(True, alpha=0.3)
|
||||
ax3.plot(Hrange*100, np.abs(dispListCA-defaultDispCA), 'r-', label='Cerro Armazones (p=712 mbar, T=6.5C, H=22%)')
|
||||
ax3.set_xlabel('Humidity (%)')
|
||||
ax3.set_ylabel('Dispersion (mas)')
|
||||
ax3.tick_params(axis ='both', direction='in')
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
|
||||
# Plotting variations in parameters with corresponding errors.
|
||||
if errorSpace:
|
||||
Trange = np.linspace(-50, 50, 100)
|
||||
Prange = np.linspace(-50, 50, 10)
|
||||
Hrange = np.linspace(-0.025, 0.025, 10)
|
||||
|
||||
fig = plt.figure()
|
||||
# Variation in T
|
||||
errorRange = [0.1, 0.2, 0.3, 1.0]
|
||||
errorRange = errorRange[::-1]
|
||||
|
||||
ax1 = fig.add_subplot(111)
|
||||
ax1.grid(True, alpha=0.3)
|
||||
|
||||
for e in range(len(errorRange)):
|
||||
dispList = []
|
||||
errorList = []
|
||||
for t in Trange:
|
||||
R1n = at.n_tph(l=1.49, T=279.65+t, p=71200, RH=0.22, xc=450)
|
||||
R2n = at.n_tph(l=1.78, T=279.65+t, p=71200, RH=0.22, xc=450)
|
||||
dispValue = disp.cassini(R1n, R2n, zenith_true) * 3.6e6
|
||||
errorValue = disp.cassiniError(zenith_true, l1, l2, T, P, H, xc, dl1=0.0, dl2=0.0, dT=errorRange[e], dP=0, dRH=0, dCO2=0 ) * 3.6e6
|
||||
dispList.append(dispValue)
|
||||
errorList.append(errorValue)
|
||||
dispList = np.asarray(dispList)
|
||||
errorList = np.asarray(errorList)
|
||||
ax1.fill_between(Trange+279.65, dispList-errorList, dispList+errorList, label='$\delta p$ = %s $^{\circ}$C' %(errorRange[e]))
|
||||
|
||||
ax1.plot(Trange+279.65, dispList, 'k-')
|
||||
ax1.set_xlabel('Temperature (K)')
|
||||
ax1.set_ylabel('Dispersion (mas)')
|
||||
ax1.set_xlim([np.min(Trange)+279.65,np.max(Trange)+279.65])
|
||||
ax1.set_ylim([19,32])
|
||||
handles, labels = ax1.get_legend_handles_labels()
|
||||
ax1.legend(handles[::-1], labels[::-1], title='Error in measurements')
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
|
||||
# Varying the parameters in the uncertainty formula:
|
||||
if generate_images:
|
||||
################################################
|
||||
# Listing some values and the results in a table
|
||||
pList = [101325, 101325, 71200, 71200, 71200]
|
||||
TList = [288.15, 288.15, 279.65, 279.65, 279.65]
|
||||
HList = [0, 0, 0.22, 0.22, 0.6]
|
||||
CO2List = [450] * len(pList)
|
||||
lList = [1.35] * len(pList)
|
||||
dpList = [0, 20, 20, 10, 20]
|
||||
dTlist = [0, 0.2, 0.2, 0.1, 0.2]
|
||||
dRHlist = [0, 0.02, 0.02, 0.01, 0.02]
|
||||
dCO2list = [0, 20, 20, 10, 20]
|
||||
dlList = [0, 0.001, 0.001, 0.001, 0.001]
|
||||
|
||||
refractionList, sigmaList = [], []
|
||||
for i in range(len(pList)):
|
||||
n = at.n_tph(l=lList[i], T=TList[i], p=pList[i], RH=HList[i], xc=CO2List[i])
|
||||
s = at.dn_tph(l=lList[i], T=TList[i], p=pList[i], RH=HList[i], xc=CO2List[i],
|
||||
dl=dlList[i], dT=dTlist[i], dP=dpList[i], dRH=dRHlist[i], dCO2=dCO2list[i])
|
||||
refractionList.append((n-1)*1e8)
|
||||
sigmaList.append(s*1e8)
|
||||
|
||||
tableData = []
|
||||
for i in range(len(pList)):
|
||||
tableData.append([pList[i], TList[i], HList[i], CO2List[i], lList[i], dpList[i], dTlist[i], dRHlist[i], dCO2list[i], dlList[i], refractionList[i], sigmaList[i]])
|
||||
print('REFRACTION: ')
|
||||
print (tabulate(tableData, headers = ['p', 'T', 'H', 'x_c', 'l', 'dp', 'dT', 'dH', 'dx_c', 'dl', 'n', 's']))
|
||||
|
||||
#################################################
|
||||
print ('\n\n\n')
|
||||
|
||||
################################################
|
||||
# Listing some values and the results in a table
|
||||
pList = [101325, 101325, 101325, 101325, 101325]
|
||||
TList = [288.15, 288.15, 288.15, 288.15, 288.15]
|
||||
HList = [0.0, 0.0, 0.0, 0.0, 0.0]
|
||||
CO2List = [450] * len(pList)
|
||||
# lList = [1.35] * len(pList)
|
||||
dpList = [0, 10, 10, 20, 100]
|
||||
dTlist = [0, 0.1, 0.2, 0.2, 1.0]
|
||||
dRHlist = [0, 0.01, 0.01, 0.02, 0.05]
|
||||
dCO2list = [0, 20, 20, 20, 20]
|
||||
# dlList = [0, 0.001, 0.001, 0.001, 0.001] # Uncomment this and comment the next
|
||||
# line to check the impact with uncertainty in wavelength
|
||||
dlList = [0, 0, 0, 0, 0]
|
||||
dzList = [0] * len(pList)
|
||||
|
||||
dispersionList, errorList = [], []
|
||||
for i in range(len(pList)):
|
||||
zenith_true = 30 # degrees
|
||||
n1 = at.n_tph(l=1.49, T=TList[i], p=pList[i], RH=HList[i], xc=CO2List[i])
|
||||
n2 = at.n_tph(l=1.78, T=TList[i], p=pList[i], RH=HList[i], xc=CO2List[i])
|
||||
dn1 = at.dn_tph(l=1.49, T=TList[i], p=pList[i], RH=HList[i], xc=CO2List[i],
|
||||
dl=dlList[i], dT=dTlist[i], dP=dpList[i], dRH=dRHlist[i], dCO2=dCO2list[i])
|
||||
dn2 = at.dn_tph(l=1.78, T=TList[i], p=pList[i], RH=HList[i], xc=CO2List[i],
|
||||
dl=dlList[i], dT=dTlist[i], dP=dpList[i], dRH=dRHlist[i], dCO2=dCO2list[i])
|
||||
dispValue = disp.cassini(n1, n2, zenith_true) * 3.6e6
|
||||
errorDispValue = disp.cassiniError( zenith_true, 1.49, 1.78, TList[i], pList[i], HList[i], CO2List[i],
|
||||
dl1=dlList[i], dl2=dlList[i], dT=dTlist[i], dP=dpList[i], dRH=dRHlist[i], dCO2=dCO2list[i], dz=dzList[i] ) * 3.6e9
|
||||
dispersionList.append(dispValue)
|
||||
errorList.append(errorDispValue)
|
||||
|
||||
tableData = []
|
||||
for i in range(len(pList)):
|
||||
tableData.append([pList[i], TList[i], HList[i], CO2List[i], dpList[i], dTlist[i], dRHlist[i], dCO2list[i], dlList[i], dispersionList[i], errorList[i]])
|
||||
print('DISPERSION: ')
|
||||
print (tabulate(tableData, headers = ['p', 'T', 'H', 'x_c', 'dp', 'dT', 'dH', 'dx_c', 'dl', 'dispersion', 'error']))
|
||||
|
||||
# Variation of the uncertainty in P
|
||||
pRange = np.linspace(0, 100, 1000)
|
||||
errorList = []
|
||||
for p in pRange:
|
||||
errorDispValue = disp.cassiniError( zenith_true, 1.49, 1.78, T=279.65, p=71200, RH=0.22, xc=450,
|
||||
dl1=0.001, dl2=0.001, dT=0.2, dP=p, dRH=0.01, dCO2=20, dz=0 ) * 3.6e9
|
||||
errorList.append(errorDispValue)
|
||||
|
||||
fig = plt.figure(figsize=(4,3))
|
||||
ax = fig.add_subplot(111)
|
||||
ax.grid(True, alpha=0.3)
|
||||
ax.plot(pRange, errorList, 'k-')
|
||||
ax.set_xlabel('$\sigma_p$ (Pa)')
|
||||
ax.set_ylabel('$\sigma_{\Delta R}$ ($\mu$as)')
|
||||
ax.set_xlim([np.min(pRange), np.max(pRange)])
|
||||
ax.set_ylim([np.min(errorList)-0.05*(np.max(errorList) - np.min(errorList)), np.max(errorList)])
|
||||
ax.tick_params(axis ='both', direction='in')
|
||||
plt.tight_layout()
|
||||
|
||||
|
||||
# Variation of the uncertainty in T
|
||||
TRange = np.linspace(0.0, 1, 1000)
|
||||
errorList = []
|
||||
for T in TRange:
|
||||
errorDispValue = disp.cassiniError( zenith_true, 1.49, 1.78, T=279.65, p=71200, RH=0.22, xc=450,
|
||||
dl1=0.001, dl2=0.001, dT=T, dP=10, dRH=0.01, dCO2=20, dz=0 ) * 3.6e9
|
||||
errorList.append(errorDispValue)
|
||||
|
||||
fig = plt.figure(figsize=(4,3))
|
||||
ax = fig.add_subplot(111)
|
||||
ax.grid(True, alpha=0.3)
|
||||
ax.plot(TRange, errorList, 'k-')
|
||||
ax.set_xlabel('$\sigma_T$ (K)')
|
||||
ax.set_ylabel('$\sigma_{\Delta R}$ ($\mu$as)')
|
||||
ax.set_xlim([np.min(TRange), np.max(TRange)])
|
||||
ax.set_ylim([np.min(errorList)-0.05*(np.max(errorList) - np.min(errorList)), np.max(errorList)])
|
||||
ax.tick_params(axis ='both', direction='in')
|
||||
plt.tight_layout()
|
||||
|
||||
# Variation of the uncertainty in H
|
||||
HRange = np.linspace(0.000, 0.05, 1000)
|
||||
errorList = []
|
||||
for H in HRange:
|
||||
errorDispValue = disp.cassiniError( zenith_true, 1.49, 1.78, T=279.65, p=71200, RH=0.22, xc=450,
|
||||
dl1=0.001, dl2=0.001, dT=0.2, dP=10, dRH=H, dCO2=20, dz=0 ) * 3.6e9
|
||||
errorList.append(errorDispValue)
|
||||
|
||||
fig = plt.figure(figsize=(4,3))
|
||||
ax = fig.add_subplot(111)
|
||||
ax.grid(True, alpha=0.3)
|
||||
ax.plot(HRange, errorList, 'k-')
|
||||
ax.set_xlabel('$\sigma_H$')
|
||||
ax.set_ylabel('$\sigma_{\Delta R}$ ($\mu$as)')
|
||||
ax.set_xlim([np.min(HRange), np.max(HRange)])
|
||||
ax.set_ylim([np.min(errorList)-0.05*(np.max(errorList) - np.min(errorList)), np.max(errorList)])
|
||||
ax.tick_params(axis ='both', direction='in')
|
||||
plt.tight_layout()
|
||||
|
||||
|
||||
# Variation of the uncertainty in xc
|
||||
CO2Range = np.linspace(0, 200, 1000)
|
||||
errorList = []
|
||||
for CO2 in CO2Range:
|
||||
errorDispValue = disp.cassiniError( zenith_true, 1.49, 1.78, T=279.65, p=71200, RH=0.22, xc=450,
|
||||
dl1=0.001, dl2=0.001, dT=0.2, dP=10, dRH=0.01, dCO2=CO2, dz=0 ) * 3.6e9
|
||||
errorList.append(errorDispValue)
|
||||
|
||||
fig = plt.figure(figsize=(4,3))
|
||||
ax = fig.add_subplot(111)
|
||||
ax.grid(True, alpha=0.3)
|
||||
ax.plot(CO2Range, errorList, 'k-')
|
||||
ax.set_xlabel('$\sigma_{x_c}$ (ppm)')
|
||||
ax.set_ylabel('$\sigma_{\Delta R}$ ($\mu$as)')
|
||||
ax.set_xlim([np.min(CO2Range), np.max(CO2Range)])
|
||||
ax.set_ylim([np.min(errorList)-0.05*(np.max(errorList) - np.min(errorList)), np.max(errorList)])
|
||||
ax.tick_params(axis ='both', direction='in')
|
||||
plt.tight_layout()
|
||||
|
||||
# Variation of the uncertainty in wavelength
|
||||
lRange = np.linspace(0.0, 0.001, 1000)
|
||||
errorList = []
|
||||
for l in lRange:
|
||||
errorDispValue = disp.cassiniError( zenith_true, 1.49, 1.78, T=279.65, p=71200, RH=0.22, xc=450,
|
||||
dl1=l, dl2=l, dT=0.2, dP=10, dRH=0.01, dCO2=20, dz=0 ) * 3.6e9
|
||||
errorList.append(errorDispValue)
|
||||
|
||||
fig = plt.figure(figsize=(4,3))
|
||||
ax = fig.add_subplot(111)
|
||||
ax.grid(True, alpha=0.3)
|
||||
ax.plot(lRange*1000, errorList, 'k-')
|
||||
ax.set_xlabel('$\sigma_{\lambda}$ (nm)')
|
||||
ax.set_ylabel('$\sigma_{\Delta R}$ ($\mu$as)')
|
||||
ax.set_xlim([np.min(lRange)*1000, np.max(lRange)*1000])
|
||||
ax.set_ylim([np.min(errorList)-0.05*(np.max(errorList) - np.min(errorList)), np.max(errorList)])
|
||||
ax.tick_params(axis ='both', direction='in')
|
||||
plt.tight_layout()
|
||||
|
||||
|
||||
# Variation of the uncertainty in zenith
|
||||
zRange = np.linspace(0, 60/3600, 1000)
|
||||
errorList = []
|
||||
for z in zRange:
|
||||
errorDispValue = disp.cassiniError( zenith_true, 1.49, 1.78, T=279.65, p=71200, RH=0.22, xc=450,
|
||||
dl1=0.001, dl2=0.001, dT=0.2, dP=10, dRH=0.01, dCO2=20, dz=z ) * 3.6e9
|
||||
errorList.append(errorDispValue)
|
||||
|
||||
fig = plt.figure(figsize=(4,3))
|
||||
ax = fig.add_subplot(111)
|
||||
ax.grid(True, alpha=0.3)
|
||||
ax.plot(zRange*3600, errorList, 'k-')
|
||||
ax.set_xlim([np.min(zRange)*3600, np.max(zRange)*3600])
|
||||
ax.set_ylim([np.min(errorList)-0.05*(np.max(errorList) - np.min(errorList)), np.max(errorList)])
|
||||
ax.set_xlabel('$\sigma_z$ (arcseconds)')
|
||||
ax.set_ylabel('$\sigma_{\Delta R}$ ($\mu$as)')
|
||||
ax.tick_params(axis ='both', direction='in')
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
@@ -0,0 +1,85 @@
|
||||
import numpy as np
|
||||
from AstroAtmosphere import *
|
||||
|
||||
'''
|
||||
Date: 02 May 2019
|
||||
Author: Joost van den Born
|
||||
Description: This script compares the uncertainty propagation results to a Monte Carlo method.
|
||||
'''
|
||||
|
||||
|
||||
# Parameters at Cerro Armazones
|
||||
T = 288.15 # k
|
||||
P = 101325 # Pa
|
||||
H = 0.0
|
||||
xc = 450 # ppm
|
||||
lat = -24.5983 # degrees
|
||||
h = 3064 # m
|
||||
l1 = 1.49 # micron
|
||||
l2 = 1.78 # micron
|
||||
zenith = 45 # degrees
|
||||
|
||||
dl1 = 0
|
||||
dl2 = 0
|
||||
dT = 0
|
||||
dP = 2
|
||||
dRH = 0
|
||||
dCO2= 0
|
||||
dz = 1/3600 # degrees
|
||||
|
||||
# Initializing dispersion model
|
||||
at = Observatory()
|
||||
|
||||
dPlist = [2, 20, 200, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 20, 200]
|
||||
dTlist = [0, 0, 0, 0.02, 0.2, 2, 0, 0, 0, 0, 0, 0, 0.02, 0.2, 2 ]
|
||||
dRHlist= [0, 0, 0, 0, 0, 0, 0.002, 0.02, 0.2, 0, 0, 0, 0.002, 0.02, 0.2]
|
||||
dCO2list=[0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 20, 200, 2, 20, 200]
|
||||
|
||||
for i in range(len(dPlist)):
|
||||
nerr = at.dn_tph(l1, T, P, H, xc, dl=0, dT=dTlist[i], dP=dPlist[i], dRH=dRHlist[i], dCO2=dCO2list[i])
|
||||
print(nerr, dPlist[i], dTlist[i], dRHlist[i], dCO2list[i])
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
disp = dispersion(lat, h)
|
||||
rho_obs = at.rho(P, T, H, xc)
|
||||
disp.setReducedHeight(P, rho_obs)
|
||||
zenith_true = 10
|
||||
|
||||
# We do a Monte Carlo error analysis on the dispersion.
|
||||
print('MONTE-CARLO UNCERTAINTY ANALYSIS')
|
||||
print('-'*35)
|
||||
|
||||
for j in range(len(dPlist)):
|
||||
sigmaP = dPlist[j] # mbar
|
||||
sigmaT = dTlist[j] # K
|
||||
sigmaH = dRHlist[j]
|
||||
sigmaCO2 = dCO2list[j] # ppm
|
||||
|
||||
N = 10000
|
||||
l1sample = np.random.normal(l1, 0, N)
|
||||
l2sample = np.random.normal(l2, 0, N)
|
||||
Tsample = np.random.normal(T, sigmaT, N)
|
||||
Psample = np.random.normal(P, sigmaP, N)
|
||||
Hsample = np.random.normal(H, sigmaH, N)
|
||||
CO2sample = np.random.normal(xc, sigmaCO2, N)
|
||||
dzsample = np.random.normal(zenith_true, dz, N)
|
||||
|
||||
results = []
|
||||
nList = []
|
||||
|
||||
for i in range(N):
|
||||
# Calculate the refraction at l1 and l2
|
||||
R1ca2 = at.n_tph(l=l1sample[i], T=Tsample[i], p=Psample[i], RH=Hsample[i], xc=CO2sample[i])
|
||||
R2ca2 = at.n_tph(l=l2sample[i], T=Tsample[i], p=Psample[i], RH=Hsample[i], xc=CO2sample[i])
|
||||
dispersionValue = disp.cassini(R1ca2, R2ca2, zenith_true)
|
||||
results.append(dispersionValue)
|
||||
nList.append(R1ca2)
|
||||
|
||||
cassErrMC = np.std(results)*3.6e9
|
||||
nerrMC = np.std(nList)
|
||||
print(nerrMC, dPlist[j], dTlist[j], dRHlist[j], dCO2list[j])
|
||||
|
||||
Reference in new issue
Block a user