Mercurial > public > atmospheric_lidar_ghk / changeset
changeset
changed output_path commands should work now with LINUS
Sun, 04 Oct 2020 22:22:08 +0200
- author
- Volker Freudenthaler
- date
- Sun, 04 Oct 2020 22:22:08 +0200
- changeset 47
- 6444f3746640
- parent 46
- c1b90afc0109
- child 48
- a882c113284f
changed output_path commands should work now with LINUS
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/GHK_0.9.8f_Py3.7.py Sun Oct 04 22:22:08 2020 +0200 @@ -0,0 +1,2904 @@ +# -*- coding: utf-8 -*- +""" +Copyright 2016, 2019 Volker Freudenthaler + +Licensed under the EUPL, Version 1.1 only (the "Licence"). + +You may not use this work except in compliance with the Licence. +A copy of the licence is distributed with the code. Alternatively, you may obtain +a copy of the Licence at: + +https://joinup.ec.europa.eu/community/eupl/og_page/eupl + +Unless required by applicable law or agreed to in writing, software distributed +under the Licence is distributed on an "AS IS" basis, WITHOUT WARRANTIES OR CONDITIONS +OF ANY KIND, either express or implied. See the Licence for the specific language governing +permissions and limitations under the Licence. + +Equation reference: http://www.atmos-meas-tech-discuss.net/amt-2015-338/amt-2015-338.pdf +With equations code from Appendix C +Python 3.7, seaborn 0.9.0 + +Code description: + +From measured lidar signals we cannot directly determine the desired backscatter coefficient (F11) and the linear depolarization ratio (LDR) +because of the cross talk between the channles and systematic errors of a lidar system. +http://www.atmos-meas-tech-discuss.net/amt-2015-338/amt-2015-338.pdf provides an analytical model for the description of these errors, +with which the measured signals can be corrected. +This code simulates the lidar measurements with "assumed true" model parameters from an input file, and calculates the correction parameters (G,H, and K). +The "assumed true" system parameters are the ones we think are the right ones, but in reality these parameters probably deviate from the assumed truth due to +uncertainties. The uncertainties of the "assumed true" parameters can be described in the input file. Then this code calculates the lidar signals and the +gain ratio eta* with all possible combinations of "errors", which represents the distribution of "possibly real" signals, and "corrects" them with the "assumed true" +GHK parameters (GT0, GR0, HT0, HR0, and K0) to derive finally the distributions of "possibly real" linear depolarization ratios (LDRCorr), +which are plotted for five different input linear depolarization ratios (LDRtrue). The red bars in the plots represent the input values of LDRtrue. +A complication arises from the fact that the correction parameter K = eta*/eta (Eq. 83) can depend on the LDR during the calibration measurement, i.e. LDRcal or aCal +in the code (see e.g. Eqs. (103), (115), and (141); mind the mistake in Eq. (116)). Therefor values of K for LDRcal = 0.004, 0.2, and 0.45 are calculated for +"assumed true" system parameters and printed in the output file behind the GH parameters. The full impact of the LDRcal dependent K can be considered in the error +calculation by specifying a range of possible LDRcal values in the input file. For the real calibration measurements a calibration range with low or no aerosol +content should be chosen, and the default in the input file is a range of LDRcal between 0.004 and 0.014 (i.e. 0.009 +-0.005). + +Tip: In case you run the code with Spyder, all output text and plots can be displayed together in an IPython console, which can be saved as an html file. + +Ver. 0.9.7: includes the random error (signal noise) of the calibration and standard measurements +Changes: + Line 1687 Eta = (TaR * TiR) / (TaT * TiT) + Line 1691 K = Etax / Eta # K of the real system; but correction in Line 1721 with K0 / Etax + should work with nTCalT = nTCalR = 0 +Ver. 0.9.7b: + ToDo: include error due to TCalT und TCalR => determination of NCalT and NCalR etc. in error calculation line 1741ff + combined error loops iNI and INCal for signals +Ver. 0.9.7c: individual error loops for each of the six signals +Ver. 0.9.7c2: different calculation of the signal noise errors +Ver. 0.9.7c3: n.a.different calculation of the signal noise errors +Ver. 0.9.7c4: test to speed up the loops for error calculation by moving them just before the actual calculation: still some code errors +Ver. 0.9.8: + - correct calculation of Eta for cleaned anaylsers considering the combined transmission Eta = (TaT* TiT)(1 + cos2RotaT * DaT * DiT) and (TaR * TiR)(1 + cos2RotaR * DaR * DiR) according to the papers supplement Eqs. (S.10.10.1) ff + - calculation of the PLDR from LDR and BSR, BSR, and LDRm + - ND-filters can be added for the calibration measurements in the transmitted (TCalT) and the reflected path (TCalR) in order to include their uncertainties in the error calculation. +Ver. 0.9.8b: change from "TTa = TiT * TaT" to "TTa = TiT * TaT * ATPT" etc. (compare ver 0.9.8 with 0.9.8b) removes + - the strong Tp dependence of the errors + - the factor 2 in the GH parameters + - see c:\technik\Optik\Polarizers\DepCal\ApplOpt\GH-parameters-190114.odt +Ver. 0.9.8c: includes error of Etax +Ver. 0.9.8d: Eta0, K0 etc in error loop replaced by Eta0y, K0y etc. Changes in signal noise calculations +Ver. 0.9.8e: ambiguous laser spec. DOLP (no discrimination between left and right circular polarisation) replaced by Stokes parameters Qin, Uin +Ver. 0.9.8e2: Added plot of LDRsim, Etax, Etapx, Etamx; LDRCorr and aLDRcorr consistently named +Ver. 0.9.8e3: Change of OutputFile name; Change of Ir and It noise if (CalcFrom0deg) = False; (Different calculation of error contributions tested but not implemented) +Ver. 0.9.8e4: text changed for y=+-1 (see line 274 ff and line 1044 ff +Ver. 0.9.8e5: changed: LDRunCorr = LDRsim / Etax +Ver. 0.9.8e6: K(0.05) instead K(0.02) +Ver. 0.9.8f: Tip from Ioannis Binietoglou for LINUX compatibility: Using os.path.join should work in all operating systems. + # After line 1007: + output_path = os.path.join('output_files', OutputFile) + with open(output_path, 'w') as f: + # Line 1147 + file = open(output_path, 'r') +------------ + + ======================================================== +simulation: LDRsim = Ir / It with variable parameters (possible truths) + G,H,Eta,Etax,K + It = TaT * TiT * ATP1 * TiO * TiE * (GT + atrue * HT) + LDRsim = Ir / It +consistency test: is forward simulation and correction consistent? + LDRCorr = (LDRsim / Eta * (GT + HT) - (GR + HR)) / ((GR - HR) - LDRsim / Eta * (GT - HT)) => atrue? +assumed true: G0,H0,Eta0,Etax0,K0 => actual retrievals of LDRCorr + => correct possible truths with assumed true G0,H0,Eta0 + measure: It, Ir, EtaX + LDRunCorr = LDRsim / Etax + correct it with G0,H0,K0: + LDRCorr = (LDRsim / (Etax / K0) * (GT0 + HT0) - (GR0 + HR0)) / ((GR0 - HR0) - LDRsim0 / (Etax / K0) * (GT0 - HT0)) +""" +# Comment: The code might works with Python 2.7 with the help of following line, which enables Python2 to correctly interpret the Python 3 print statements. +from __future__ import print_function +# !/usr/bin/env python3 + +import os +import sys + +from scipy.stats import kurtosis +from scipy.stats import skew +# use: kurtosis(data, fisher=True,bias=False) => 0; skew(data,bias=False) => 0 +# Comment: the seaborn library makes nicer plots, but the code works also without it. +import numpy as np +import matplotlib.pyplot as plt + +try: + import seaborn as sns + + sns_loaded = True +except ImportError: + sns_loaded = False + +# from time import clock # python 2 +from timeit import default_timer as clock + +# from matplotlib.backends.backend_pdf import PdfPages +# pdffile = '{}.pdf'.format('path') +# pp = PdfPages(pdffile) +## pp.savefig can be called multiple times to save to multiple pages +# pp.savefig() +# pp.close() + +from contextlib import contextmanager + +@contextmanager +def redirect_stdout(new_target): + old_target, sys.stdout = sys.stdout, new_target # replace sys.stdout + try: + yield new_target # run some code with the replaced stdout + finally: + sys.stdout.flush() + sys.stdout = old_target # restore to the previous value + +''' +real_raw_input = vars(__builtins__).get('raw_input',input) +''' +try: + import __builtin__ + + input = getattr(__builtin__, 'raw_input') +except (ImportError, AttributeError): + pass + +from distutils.util import strtobool + + +def user_yes_no_query(question): + sys.stdout.write('%s [y/n]\n' % question) + while True: + try: + return strtobool(input().lower()) + except ValueError: + sys.stdout.write('Please respond with \'y\' or \'n\'.\n') + + +# if user_yes_no_query('want to exit?') == 1: sys.exit() + +abspath = os.path.abspath(__file__) +dname = os.path.dirname(abspath) +fname = os.path.basename(abspath) +os.chdir(dname) + +# PrintToOutputFile = True + +sqr05 = 0.5 ** 0.5 + +# ---- Initial definition of variables; the actual values will be read in with exec(open('./optic_input.py').read()) below +# Do you want to calculate the errors? If not, just the GHK-parameters are determined. +Error_Calc = True +LID = "internal" +EID = "internal" +# --- IL Laser IL and +-Uncertainty +Qin, dQin, nQin = 1., 0.0, 0 # second Stokes vector parameter; default 1 => linear polarization +Vin, dVin, nVin = 0., 0.0, 0 # fourth Stokes vector parameter +RotL, dRotL, nRotL = 0.0, 0.0, 1 # alpha; rotation of laser polarization in degrees; default 0 +# IL = 1e5 #photons in the laser beam, including detection efficiency of the telescope, atmodspheric and r^2 attenuation +# --- ME Emitter and +-Uncertainty +DiE, dDiE, nDiE = 0., 0.00, 1 # Diattenuation +TiE = 1. # Unpolarized transmittance +RetE, dRetE, nRetE = 0., 180.0, 0 # Retardance in degrees +RotE, dRotE, nRotE = 0., 0.0, 0 # beta: Rotation of optical element in degrees +# --- MO Receiver Optics including telescope +DiO, dDiO, nDiO = -0.055, 0.003, 1 +TiO = 0.9 +RetO, dRetO, nRetO = 0., 180.0, 2 +RotO, dRotO, nRotO = 0., 0.1, 1 # gamma +# --- PBS MT transmitting path defined with (TS,TP); and +-Uncertainty +TP, dTP, nTP = 0.98, 0.02, 1 +TS, dTS, nTS = 0.001, 0.001, 1 +TiT = 0.5 * (TP + TS) +DiT = (TP - TS) / (TP + TS) +# PolFilter +RetT, dRetT, nRetT = 0., 180., 0 +ERaT, dERaT, nERaT = 0.001, 0.001, 1 +RotaT, dRotaT, nRotaT = 0., 3., 1 +DaT = (1 - ERaT) / (1 + ERaT) +TaT = 0.5 * (1 + ERaT) +# --- PBS MR reflecting path defined with (RS,RP); and +-Uncertainty +RS_RP_depend_on_TS_TP = False +if (RS_RP_depend_on_TS_TP): + RP, dRP, nRP = 1 - TP, 0.0, 0 + RS, dRS, nRS = 1 - TS, 0.0, 0 +else: + RP, dRP, nRP = 0.05, 0.01, 1 + RS, dRS, nRS = 0.98, 0.01, 1 +TiR = 0.5 * (RP + RS) +DiR = (RP - RS) / (RP + RS) +# PolFilter +RetR, dRetR, nRetR = 0., 180., 0 +ERaR, dERaR, nERaR = 0.001, 0.001, 1 +RotaR, dRotaR, nRotaR = 90., 3., 1 +DaR = (1 - ERaR) / (1 + ERaR) +TaR = 0.5 * (1 + ERaR) + +# +++ Orientation of the PBS with respect to the reference plane (see Polarisation-orientation.png and Polarisation-orientation-2.png in /system_settings) +# Y = +1: PBS incidence plane is parallel to the reference plane and polarisation in reference plane is finally transmitted. +# Y = -1: PBS incidence plane is perpendicular to the reference plane and polarisation in reference plane is finally reflected. +Y = 1. + +# Calibrator = type defined by matrix values +LocC = 4 # location of calibrator: behind laser = 1; behind emitter = 2; before receiver = 3; before PBS = 4 + +# --- Additional attenuation (transmission of the ND-filter) during the calibration +TCalT, dTCalT, nTCalT = 1, 0., 0 # transmitting path; error calc not working yet +TCalR, dTCalR, nTCalR = 1, 0., 0 # reflecting path; error calc not working yet + +# *** signal noise error calculation +# --- number of photon counts in the signal summed up in the calibration range during the calibration measurements +NCalT = 1e6 # default 1e6, assumed the same in +45° and -45° signals +NCalR = 1e6 # default 1e6, assumed the same in +45° and -45° signals +NILfac = 1.0 # duration of standard (0°) measurement relative to calibration measurements +nNCal = 0 # error nNCal: one-sigma in steps to left and right for calibration signals +nNI = 0 # error nNI: one-sigma in steps to left and right for 0° signals +NI = 50000 #number of photon counts in the parallel 0°-signal +eFacT = 1.0 # rel. amplification of transmitted channel, approximate values are sufficient; def. = 1 +eFacR = 10.0 +IoutTp0, IoutTp, dIoutTp0 = 0.5, 0.5, 0.0 +IoutTm0, IoutTm, dIoutTm0 = 0.5, 0.5, 0.0 +IoutRp0, IoutRp, dIoutRp0 = 0.5, 0.5, 0.0 +IoutRm0, IoutRm, dIoutRm0 = 0.5, 0.5, 0.0 +It0, It, dIt0 = 1 , 1, 0 +Ir0, Ir, dTr0 = 1 , 1, 0 +CalcFrom0deg = True + +TypeC = 3 # linear polarizer calibrator +# example with extinction ratio 0.001 +DiC, dDiC, nDiC = 1.0, 0., 0 # ideal 1.0 +TiC = 0.5 # ideal 0.5 +RetC, dRetC, nRetC = 0.0, 0.0, 0 +RotC, dRotC, nRotC = 0.0, 0.1, 0 # constant calibrator offset epsilon +RotationErrorEpsilonForNormalMeasurements = False # is in general False for TypeC == 3 calibrator + +# Rotation error without calibrator: if False, then epsilon = 0 for normal measurements +RotationErrorEpsilonForNormalMeasurements = True +# BSR backscatter ratio +# BSR, dBSR, nBSR = 10, 0.05, 1 +BSR = np.zeros(5) +BSR = [1.1, 2, 5, 10., 50.] +# theoretical molecular LDR LDRm +LDRm, dLDRm, nLDRm = 0.004, 0.001, 1 +# LDRCal assumed atmospheric linear depolarization ratio during the calibration measurements (first guess) +LDRCal0, dLDRCal, nLDRCal = 0.25, 0.04, 1 +LDRCal = LDRCal0 +# measured LDRm will be corrected with calculated parameters +LDRmeas = 0.015 +# LDRtrue for simulation of measurement => LDRsim +LDRtrue = 0.004 +LDRtrue2 = 0.004 +LDRunCorr = 1. +# Initialize other values to 0 +ER, nER, dER = 0.001, 0, 0.001 +K = 0. +Km = 0. +Kp = 0. +LDRCorr = 0. +Eta = 0. +Ir = 0. +It = 0. +h = 1. + +Loc = ['', 'behind the laser', 'behind the emitter', 'before the receiver', 'before the PBS'] +Type = ['', 'Mechanical rotator', 'HWP rotator', 'Linear polarizer', 'QWP rotator', 'Circular polarizer', + 'real HWP +-22.5°'] + +bPlotEtax = False + +# end of initial definition of variables +# ******************************************************************************************************************************* +# --- Read actual lidar system parameters from optic_input.py (must be in the programs sub-directory 'system_settings') +# ******************************************************************************************************************************* + +# InputFile = 'optic_input_0.9.8e4-PollyXT_Lacros.py' +InputFile = 'optic_input_example_lidar_ver0.9.8e.py' +InputFile = 'calibrator-test-1-ver0.9.8e.py' +InputFile = 'optic_input_0.9.8e4-BRC-532.py' +InputFile = '' +InputFile = '' +InputFile = '' +InputFile = 'RALI-may2020-X.py' +InputFile = 'mulhacen_run_532xp-bias.py' + + +# ******************************************************************************************************************************* + +''' +print("From ", dname) +print("Running ", fname) +print("Reading input file ", InputFile, " for") +''' +input_path = os.path.join('.', 'system_settings', InputFile) +# this works with Python 2 and 3! +exec(open(input_path).read(), globals()) +# end of read actual system parameters + + +# --- Manual Parameter Change --- +# (use for quick parameter changes without changing the input file ) +# DiO = 0. +# LDRtrue = 0.45 +# LDRtrue2 = 0.004 +# Y = -1 +# LocC = 4 #location of calibrator: 1 = behind laser; 2 = behind emitter; 3 = before receiver; 4 = before PBS +# #TypeC = 6 Don't change the TypeC here +# RotationErrorEpsilonForNormalMeasurements = True +# LDRCal = 0.25 +# # --- Errors +Qin0, dQin, nQin = Qin, dQin, nQin +Vin0, dVin, nVin = Vin, dVin, nVin +RotL0, dRotL, nRotL = RotL, dRotL, nRotL + +DiE0, dDiE, nDiE = DiE, dDiE, nDiE +RetE0, dRetE, nRetE = RetE, dRetE, nRetE +RotE0, dRotE, nRotE = RotE, dRotE, nRotE + +DiO0, dDiO, nDiO = DiO, dDiO, nDiO +RetO0, dRetO, nRetO = RetO, dRetO, nRetO +RotO0, dRotO, nRotO = RotO, dRotO, nRotO + +DiC0, dDiC, nDiC = DiC, dDiC, nDiC +RetC0, dRetC, nRetC = RetC, dRetC, nRetC +RotC0, dRotC, nRotC = RotC, dRotC, nRotC + +TP0, dTP, nTP = TP, dTP, nTP +TS0, dTS, nTS = TS, dTS, nTS +RetT0, dRetT, nRetT = RetT, dRetT, nRetT + +ERaT0, dERaT, nERaT = ERaT, dERaT, nERaT +RotaT0, dRotaT, nRotaT = RotaT, dRotaT, nRotaT + +RP0, dRP, nRP = RP, dRP, nRP +RS0, dRS, nRS = RS, dRS, nRS +RetR0, dRetR, nRetR = RetR, dRetR, nRetR + +ERaR0, dERaR, nERaR = ERaR, dERaR, nERaR +RotaR0, dRotaR, nRotaR = RotaR, dRotaR, nRotaR + +LDRCal0, dLDRCal, nLDRCal = LDRCal, dLDRCal, nLDRCal + +# BSR0, dBSR, nBSR = BSR, dBSR, nBSR +LDRm0, dLDRm, nLDRm = LDRm, dLDRm, nLDRm +# ---------- End of manual parameter change + +RotL, RotE, RetE, DiE, RotO, RetO, DiO, RotC, RetC, DiC = RotL0, RotE0, RetE0, DiE0, RotO0, RetO0, DiO0, RotC0, RetC0, DiC0 +TP, TS, RP, RS, ERaT, RotaT, RetT, ERaR, RotaR, RetR = TP0, TS0, RP0, RS0, ERaT0, RotaT0, RetT0, ERaR0, RotaR0, RetR0 +LDRCal = LDRCal0 +DTa0, TTa0, DRa0, TRa0, LDRsimx, LDRCorr = 0., 0., 0., 0., 0., 0. +TCalT0, TCalR0 = TCalT, TCalR + +TiT = 0.5 * (TP + TS) +DiT = (TP - TS) / (TP + TS) +ZiT = (1. - DiT ** 2) ** 0.5 +TiR = 0.5 * (RP + RS) +DiR = (RP - RS) / (RP + RS) +ZiR = (1. - DiR ** 2) ** 0.5 + +C2aT = np.cos(np.deg2rad(2. * RotaT)) +C2aR = np.cos(np.deg2rad(2. * RotaR)) +ATPT = float(1. + C2aT * DaT * DiT) +ARPT = float(1. + C2aR * DaR * DiR) +TTa = TiT * TaT * ATPT # unpolarized transmission +TRa = TiR * TaR * ARPT # unpolarized transmission +Eta0 = TRa / TTa + +# --- alternative texts for output +dY = ['perpendicular', '', 'parallel'] +dY2 = ['reflected', '', 'transmitted'] +if ((abs(RotL) < 45 and Y == 1) or (abs(RotL) >= 45 and Y == -1)): + dY3 = "the parallel laser polarisation is detected in the transmitted channel." +else: + dY3 = "the parallel laser polarisation is detected in the reflected channel." + +# --- check input errors +if ((Qin ** 2 + Vin ** 2) ** 0.5) > 1: + print("Error: degree of polarisation of laser > 1. Check Qin and Vin! ") + sys.exit() + +# --- this subroutine is for the calculation of the PLDR from LDR, BSR, and LDRm ------------------- +def CalcPLDR(LDR, BSR, LDRm): + PLDR = (BSR * (1. + LDRm) * LDR - LDRm * (1. + LDR)) / (BSR * (1. + LDRm) - (1. + LDR)) + return (PLDR) +# --- this subroutine is for the calculation with certain fixed parameters ------------------------ +def Calc(TCalT, TCalR, NCalT, NCalR, Qin, Vin, RotL, RotE, RetE, DiE, RotO, RetO, DiO, + RotC, RetC, DiC, TP, TS, RP, RS, + ERaT, RotaT, RetT, ERaR, RotaR, RetR, LDRCal): + # ---- Do the calculations of bra-ket vectors + h = -1. if TypeC == 2 else 1 + # from input file: assumed LDRCal for calibration measurements + aCal = (1. - LDRCal) / (1. + LDRCal) + atrue = (1. - LDRtrue) / (1. + LDRtrue) + + # angles of emitter and laser and calibrator and receiver optics + # RotL = alpha, RotE = beta, RotO = gamma, RotC = epsilon + S2a = np.sin(2 * np.deg2rad(RotL)) + C2a = np.cos(2 * np.deg2rad(RotL)) + S2b = np.sin(2 * np.deg2rad(RotE)) + C2b = np.cos(2 * np.deg2rad(RotE)) + S2ab = np.sin(np.deg2rad(2 * RotL - 2 * RotE)) + C2ab = np.cos(np.deg2rad(2 * RotL - 2 * RotE)) + S2g = np.sin(np.deg2rad(2 * RotO)) + C2g = np.cos(np.deg2rad(2 * RotO)) + + # Laser with Degree of linear polarization DOLP + IinL = 1. + QinL = Qin + UinL = 0. + VinL = Vin + # VinL = (1. - DOLP ** 2) ** 0.5 + + # Stokes Input Vector rotation Eq. E.4 + A = C2a * QinL - S2a * UinL + B = S2a * QinL + C2a * UinL + # Stokes Input Vector rotation Eq. E.9 + C = C2ab * QinL - S2ab * UinL + D = S2ab * QinL + C2ab * UinL + + # emitter optics + CosE = np.cos(np.deg2rad(RetE)) + SinE = np.sin(np.deg2rad(RetE)) + ZiE = (1. - DiE ** 2) ** 0.5 + WiE = (1. - ZiE * CosE) + + # Stokes Input Vector after emitter optics equivalent to Eq. E.9 with already rotated input vector from Eq. E.4 + # b = beta + IinE = (IinL + DiE * C) + QinE = (C2b * DiE * IinL + A + S2b * (WiE * D - ZiE * SinE * VinL)) + UinE = (S2b * DiE * IinL + B - C2b * (WiE * D - ZiE * SinE * VinL)) + VinE = (-ZiE * SinE * D + ZiE * CosE * VinL) + + # Stokes Input Vector before receiver optics Eq. E.19 (after atmosphere F) + IinF = IinE + QinF = aCal * QinE + UinF = -aCal * UinE + VinF = (1. - 2. * aCal) * VinE + + # receiver optics + CosO = np.cos(np.deg2rad(RetO)) + SinO = np.sin(np.deg2rad(RetO)) + ZiO = (1. - DiO ** 2) ** 0.5 + WiO = (1. - ZiO * CosO) + + # calibrator + CosC = np.cos(np.deg2rad(RetC)) + SinC = np.sin(np.deg2rad(RetC)) + ZiC = (1. - DiC ** 2) ** 0.5 + WiC = (1. - ZiC * CosC) + + # Stokes Input Vector before the polarising beam splitter Eq. E.31 + A = C2g * QinE - S2g * UinE + B = S2g * QinE + C2g * UinE + + IinP = (IinE + DiO * aCal * A) + QinP = (C2g * DiO * IinE + aCal * QinE - S2g * (WiO * aCal * B + ZiO * SinO * (1. - 2. * aCal) * VinE)) + UinP = (S2g * DiO * IinE - aCal * UinE + C2g * (WiO * aCal * B + ZiO * SinO * (1. - 2. * aCal) * VinE)) + VinP = (ZiO * SinO * aCal * B + ZiO * CosO * (1. - 2. * aCal) * VinE) + + # ------------------------- + # F11 assuemd to be = 1 => measured: F11m = IinP / IinE with atrue + # F11sim = TiO*(IinE + DiO*atrue*A)/IinE + # ------------------------- + + # analyser + if (RS_RP_depend_on_TS_TP): + RS = 1. - TS + RP = 1. - TP + + TiT = 0.5 * (TP + TS) + DiT = (TP - TS) / (TP + TS) + ZiT = (1. - DiT ** 2) ** 0.5 + TiR = 0.5 * (RP + RS) + DiR = (RP - RS) / (RP + RS) + ZiR = (1. - DiR ** 2) ** 0.5 + CosT = np.cos(np.deg2rad(RetT)) + SinT = np.sin(np.deg2rad(RetT)) + CosR = np.cos(np.deg2rad(RetR)) + SinR = np.sin(np.deg2rad(RetR)) + + DaT = (1. - ERaT) / (1. + ERaT) + DaR = (1. - ERaR) / (1. + ERaR) + TaT = 0.5 * (1. + ERaT) + TaR = 0.5 * (1. + ERaR) + + S2aT = np.sin(np.deg2rad(h * 2 * RotaT)) + C2aT = np.cos(np.deg2rad(2 * RotaT)) + S2aR = np.sin(np.deg2rad(h * 2 * RotaR)) + C2aR = np.cos(np.deg2rad(2 * RotaR)) + + # Analyzer As before the PBS Eq. D.5; combined PBS and cleaning pol-filter + ATPT = (1. + C2aT * DaT * DiT) # unpolarized transmission correction + TTa = TiT * TaT * ATPT # unpolarized transmission + ATP1 = 1. + ATP2 = Y * (DiT + C2aT * DaT) / ATPT + ATP3 = Y * S2aT * DaT * ZiT * CosT / ATPT + ATP4 = S2aT * DaT * ZiT * SinT / ATPT + ATP = np.array([ATP1, ATP2, ATP3, ATP4]) + DTa = ATP2 * Y + + ARPT = (1 + C2aR * DaR * DiR) # unpolarized transmission correction + TRa = TiR * TaR * ARPT # unpolarized transmission + ARP1 = 1 + ARP2 = Y * (DiR + C2aR * DaR) / ARPT + ARP3 = Y * S2aR * DaR * ZiR * CosR / ARPT + ARP4 = S2aR * DaR * ZiR * SinR / ARPT + ARP = np.array([ARP1, ARP2, ARP3, ARP4]) + DRa = ARP2 * Y + + + # ---- Calculate signals and correction parameters for diffeent locations and calibrators + if LocC == 4: # Calibrator before the PBS + # print("Calibrator location not implemented yet") + + # S2ge = np.sin(np.deg2rad(2*RotO + h*2*RotC)) + # C2ge = np.cos(np.deg2rad(2*RotO + h*2*RotC)) + S2e = np.sin(np.deg2rad(h * 2 * RotC)) + C2e = np.cos(np.deg2rad(2 * RotC)) + # rotated AinP by epsilon Eq. C.3 + ATP2e = C2e * ATP2 + S2e * ATP3 + ATP3e = C2e * ATP3 - S2e * ATP2 + ARP2e = C2e * ARP2 + S2e * ARP3 + ARP3e = C2e * ARP3 - S2e * ARP2 + ATPe = np.array([ATP1, ATP2e, ATP3e, ATP4]) + ARPe = np.array([ARP1, ARP2e, ARP3e, ARP4]) + # Stokes Input Vector before the polarising beam splitter Eq. E.31 + A = C2g * QinE - S2g * UinE + B = S2g * QinE + C2g * UinE + # C = (WiO*aCal*B + ZiO*SinO*(1-2*aCal)*VinE) + Co = ZiO * SinO * VinE + Ca = (WiO * B - 2 * ZiO * SinO * VinE) + # C = Co + aCal*Ca + # IinP = (IinE + DiO*aCal*A) + # QinP = (C2g*DiO*IinE + aCal*QinE - S2g*C) + # UinP = (S2g*DiO*IinE - aCal*UinE + C2g*C) + # VinP = (ZiO*SinO*aCal*B + ZiO*CosO*(1-2*aCal)*VinE) + IinPo = IinE + QinPo = (C2g * DiO * IinE - S2g * Co) + UinPo = (S2g * DiO * IinE + C2g * Co) + VinPo = ZiO * CosO * VinE + + IinPa = DiO * A + QinPa = QinE - S2g * Ca + UinPa = -UinE + C2g * Ca + VinPa = ZiO * (SinO * B - 2 * CosO * VinE) + + IinP = IinPo + aCal * IinPa + QinP = QinPo + aCal * QinPa + UinP = UinPo + aCal * UinPa + VinP = VinPo + aCal * VinPa + # Stokes Input Vector before the polarising beam splitter rotated by epsilon Eq. C.3 + # QinPe = C2e*QinP + S2e*UinP + # UinPe = C2e*UinP - S2e*QinP + QinPoe = C2e * QinPo + S2e * UinPo + UinPoe = C2e * UinPo - S2e * QinPo + QinPae = C2e * QinPa + S2e * UinPa + UinPae = C2e * UinPa - S2e * QinPa + QinPe = C2e * QinP + S2e * UinP + UinPe = C2e * UinP - S2e * QinP + + # Calibration signals and Calibration correction K from measurements with LDRCal / aCal + if (TypeC == 2) or (TypeC == 1): # rotator calibration Eq. C.4 + # parameters for calibration with aCal + AT = ATP1 * IinP + h * ATP4 * VinP + BT = ATP3e * QinP - h * ATP2e * UinP + AR = ARP1 * IinP + h * ARP4 * VinP + BR = ARP3e * QinP - h * ARP2e * UinP + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): # calibrator taken out + IS1 = np.array([IinPo, QinPo, UinPo, VinPo]) + IS2 = np.array([IinPa, QinPa, UinPa, VinPa]) + GT = np.dot(ATP, IS1) + GR = np.dot(ARP, IS1) + HT = np.dot(ATP, IS2) + HR = np.dot(ARP, IS2) + else: + IS1 = np.array([IinPo, QinPo, UinPo, VinPo]) + IS2 = np.array([IinPa, QinPa, UinPa, VinPa]) + GT = np.dot(ATPe, IS1) + GR = np.dot(ARPe, IS1) + HT = np.dot(ATPe, IS2) + HR = np.dot(ARPe, IS2) + elif (TypeC == 3) or (TypeC == 4): # linear polariser calibration Eq. C.5 + # parameters for calibration with aCal + AT = ATP1 * IinP + ATP3e * UinPe + ZiC * CosC * (ATP2e * QinPe + ATP4 * VinP) + BT = DiC * (ATP1 * UinPe + ATP3e * IinP) - ZiC * SinC * (ATP2e * VinP - ATP4 * QinPe) + AR = ARP1 * IinP + ARP3e * UinPe + ZiC * CosC * (ARP2e * QinPe + ARP4 * VinP) + BR = DiC * (ARP1 * UinPe + ARP3e * IinP) - ZiC * SinC * (ARP2e * VinP - ARP4 * QinPe) + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): # calibrator taken out + IS1 = np.array([IinPo, QinPo, UinPo, VinPo]) + IS2 = np.array([IinPa, QinPa, UinPa, VinPa]) + GT = np.dot(ATP, IS1) + GR = np.dot(ARP, IS1) + HT = np.dot(ATP, IS2) + HR = np.dot(ARP, IS2) + else: + IS1e = np.array([IinPo + DiC * QinPoe, DiC * IinPo + QinPoe, ZiC * (CosC * UinPoe + SinC * VinPo), + -ZiC * (SinC * UinPoe - CosC * VinPo)]) + IS2e = np.array([IinPa + DiC * QinPae, DiC * IinPa + QinPae, ZiC * (CosC * UinPae + SinC * VinPa), + -ZiC * (SinC * UinPae - CosC * VinPa)]) + GT = np.dot(ATPe, IS1e) + GR = np.dot(ARPe, IS1e) + HT = np.dot(ATPe, IS2e) + HR = np.dot(ARPe, IS2e) + elif (TypeC == 6): # diattenuator calibration +-22.5° rotated_diattenuator_X22x5deg.odt + # parameters for calibration with aCal + AT = ATP1 * IinP + sqr05 * DiC * (ATP1 * QinPe + ATP2e * IinP) + (1. - 0.5 * WiC) * ( + ATP2e * QinPe + ATP3e * UinPe) + ZiC * (sqr05 * SinC * (ATP3e * VinP - ATP4 * UinPe) + ATP4 * CosC * VinP) + BT = sqr05 * DiC * (ATP1 * UinPe + ATP3e * IinP) + 0.5 * WiC * ( + ATP2e * UinPe + ATP3e * QinPe) - sqr05 * ZiC * SinC * (ATP2e * VinP - ATP4 * QinPe) + AR = ARP1 * IinP + sqr05 * DiC * (ARP1 * QinPe + ARP2e * IinP) + (1. - 0.5 * WiC) * ( + ARP2e * QinPe + ARP3e * UinPe) + ZiC * (sqr05 * SinC * (ARP3e * VinP - ARP4 * UinPe) + ARP4 * CosC * VinP) + BR = sqr05 * DiC * (ARP1 * UinPe + ARP3e * IinP) + 0.5 * WiC * ( + ARP2e * UinPe + ARP3e * QinPe) - sqr05 * ZiC * SinC * (ARP2e * VinP - ARP4 * QinPe) + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): # calibrator taken out + IS1 = np.array([IinPo, QinPo, UinPo, VinPo]) + IS2 = np.array([IinPa, QinPa, UinPa, VinPa]) + GT = np.dot(ATP, IS1) + GR = np.dot(ARP, IS1) + HT = np.dot(ATP, IS2) + HR = np.dot(ARP, IS2) + else: + IS1e = np.array([IinPo + DiC * QinPoe, DiC * IinPo + QinPoe, ZiC * (CosC * UinPoe + SinC * VinPo), + -ZiC * (SinC * UinPoe - CosC * VinPo)]) + IS2e = np.array([IinPa + DiC * QinPae, DiC * IinPa + QinPae, ZiC * (CosC * UinPae + SinC * VinPa), + -ZiC * (SinC * UinPae - CosC * VinPa)]) + GT = np.dot(ATPe, IS1e) + GR = np.dot(ARPe, IS1e) + HT = np.dot(ATPe, IS2e) + HR = np.dot(ARPe, IS2e) + else: + print("Calibrator not implemented yet") + sys.exit() + + elif LocC == 3: # C before receiver optics Eq.57 + + # S2ge = np.sin(np.deg2rad(2*RotO - 2*RotC)) + # C2ge = np.cos(np.deg2rad(2*RotO - 2*RotC)) + S2e = np.sin(np.deg2rad(2. * RotC)) + C2e = np.cos(np.deg2rad(2. * RotC)) + + # As with C before the receiver optics (rotated_diattenuator_X22x5deg.odt) + AF1 = np.array([1., C2g * DiO, S2g * DiO, 0.]) + AF2 = np.array([C2g * DiO, 1. - S2g ** 2 * WiO, S2g * C2g * WiO, -S2g * ZiO * SinO]) + AF3 = np.array([S2g * DiO, S2g * C2g * WiO, 1. - C2g ** 2 * WiO, C2g * ZiO * SinO]) + AF4 = np.array([0., S2g * SinO, -C2g * SinO, CosO]) + + ATF = (ATP1 * AF1 + ATP2 * AF2 + ATP3 * AF3 + ATP4 * AF4) + ARF = (ARP1 * AF1 + ARP2 * AF2 + ARP3 * AF3 + ARP4 * AF4) + ATF2 = ATF[1] + ATF3 = ATF[2] + ARF2 = ARF[1] + ARF3 = ARF[2] + + # rotated AinF by epsilon + ATF1 = ATF[0] + ATF4 = ATF[3] + ATF2e = C2e * ATF[1] + S2e * ATF[2] + ATF3e = C2e * ATF[2] - S2e * ATF[1] + ARF1 = ARF[0] + ARF4 = ARF[3] + ARF2e = C2e * ARF[1] + S2e * ARF[2] + ARF3e = C2e * ARF[2] - S2e * ARF[1] + + ATFe = np.array([ATF1, ATF2e, ATF3e, ATF4]) + ARFe = np.array([ARF1, ARF2e, ARF3e, ARF4]) + + QinEe = C2e * QinE + S2e * UinE + UinEe = C2e * UinE - S2e * QinE + + # Stokes Input Vector before receiver optics Eq. E.19 (after atmosphere F) + IinF = IinE + QinF = aCal * QinE + UinF = -aCal * UinE + VinF = (1. - 2. * aCal) * VinE + + IinFo = IinE + QinFo = 0. + UinFo = 0. + VinFo = VinE + + IinFa = 0. + QinFa = QinE + UinFa = -UinE + VinFa = -2. * VinE + + # Stokes Input Vector before receiver optics rotated by epsilon Eq. C.3 + QinFe = C2e * QinF + S2e * UinF + UinFe = C2e * UinF - S2e * QinF + QinFoe = C2e * QinFo + S2e * UinFo + UinFoe = C2e * UinFo - S2e * QinFo + QinFae = C2e * QinFa + S2e * UinFa + UinFae = C2e * UinFa - S2e * QinFa + + # Calibration signals and Calibration correction K from measurements with LDRCal / aCal + if (TypeC == 2) or (TypeC == 1): # rotator calibration Eq. C.4 + # parameters for calibration with aCal + AT = ATF1 * IinF + ATF4 * h * VinF + BT = ATF3e * QinF - ATF2e * h * UinF + AR = ARF1 * IinF + ARF4 * h * VinF + BR = ARF3e * QinF - ARF2e * h * UinF + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): + GT = ATF1 * IinE + ATF4 * VinE + GR = ARF1 * IinE + ARF4 * VinE + HT = ATF2 * QinE - ATF3 * UinE - ATF4 * 2 * VinE + HR = ARF2 * QinE - ARF3 * UinE - ARF4 * 2 * VinE + else: + GT = ATF1 * IinE + ATF4 * h * VinE + GR = ARF1 * IinE + ARF4 * h * VinE + HT = ATF2e * QinE - ATF3e * h * UinE - ATF4 * h * 2 * VinE + HR = ARF2e * QinE - ARF3e * h * UinE - ARF4 * h * 2 * VinE + elif (TypeC == 3) or (TypeC == 4): # linear polariser calibration Eq. C.5 + # p = +45°, m = -45° + IF1e = np.array([IinF, ZiC * CosC * QinFe, UinFe, ZiC * CosC * VinF]) + IF2e = np.array([DiC * UinFe, -ZiC * SinC * VinF, DiC * IinF, ZiC * SinC * QinFe]) + AT = np.dot(ATFe, IF1e) + AR = np.dot(ARFe, IF1e) + BT = np.dot(ATFe, IF2e) + BR = np.dot(ARFe, IF2e) + + # Correction parameters for normal measurements; they are independent of LDR --- the same as for TypeC = 6 + if (not RotationErrorEpsilonForNormalMeasurements): # calibrator taken out + IS1 = np.array([IinE, 0., 0., VinE]) + IS2 = np.array([0., QinE, -UinE, -2. * VinE]) + GT = np.dot(ATF, IS1) + GR = np.dot(ARF, IS1) + HT = np.dot(ATF, IS2) + HR = np.dot(ARF, IS2) + else: + IS1e = np.array([IinFo + DiC * QinFoe, DiC * IinFo + QinFoe, ZiC * (CosC * UinFoe + SinC * VinFo), + -ZiC * (SinC * UinFoe - CosC * VinFo)]) + IS2e = np.array([IinFa + DiC * QinFae, DiC * IinFa + QinFae, ZiC * (CosC * UinFae + SinC * VinFa), + -ZiC * (SinC * UinFae - CosC * VinFa)]) + GT = np.dot(ATFe, IS1e) + GR = np.dot(ARFe, IS1e) + HT = np.dot(ATFe, IS2e) + HR = np.dot(ARFe, IS2e) + + elif (TypeC == 6): # diattenuator calibration +-22.5° rotated_diattenuator_X22x5deg.odt + # parameters for calibration with aCal + IF1e = np.array([IinF + sqr05 * DiC * QinFe, sqr05 * DiC * IinF + (1. - 0.5 * WiC) * QinFe, + (1. - 0.5 * WiC) * UinFe + sqr05 * ZiC * SinC * VinF, + -sqr05 * ZiC * SinC * UinFe + ZiC * CosC * VinF]) + IF2e = np.array([sqr05 * DiC * UinFe, 0.5 * WiC * UinFe - sqr05 * ZiC * SinC * VinF, + sqr05 * DiC * IinF + 0.5 * WiC * QinFe, sqr05 * ZiC * SinC * QinFe]) + AT = np.dot(ATFe, IF1e) + AR = np.dot(ARFe, IF1e) + BT = np.dot(ATFe, IF2e) + BR = np.dot(ARFe, IF2e) + + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): # calibrator taken out + # IS1 = np.array([IinE,0,0,VinE]) + # IS2 = np.array([0,QinE,-UinE,-2*VinE]) + IS1 = np.array([IinFo, 0., 0., VinFo]) + IS2 = np.array([0., QinFa, UinFa, VinFa]) + GT = np.dot(ATF, IS1) + GR = np.dot(ARF, IS1) + HT = np.dot(ATF, IS2) + HR = np.dot(ARF, IS2) + else: + IS1e = np.array([IinFo + DiC * QinFoe, DiC * IinFo + QinFoe, ZiC * (CosC * UinFoe + SinC * VinFo), + -ZiC * (SinC * UinFoe - CosC * VinFo)]) + IS2e = np.array([IinFa + DiC * QinFae, DiC * IinFa + QinFae, ZiC * (CosC * UinFae + SinC * VinFa), + -ZiC * (SinC * UinFae - CosC * VinFa)]) + # IS1e = np.array([IinFo,0,0,VinFo]) + # IS2e = np.array([0,QinFae,UinFae,VinFa]) + GT = np.dot(ATFe, IS1e) + GR = np.dot(ARFe, IS1e) + HT = np.dot(ATFe, IS2e) + HR = np.dot(ARFe, IS2e) + + else: + print('Calibrator not implemented yet') + sys.exit() + + elif LocC == 2: # C behind emitter optics Eq.57 ------------------------------------------------------- + # print("Calibrator location not implemented yet") + S2e = np.sin(np.deg2rad(2. * RotC)) + C2e = np.cos(np.deg2rad(2. * RotC)) + + # AS with C before the receiver optics (see document rotated_diattenuator_X22x5deg.odt) + AF1 = np.array([1, C2g * DiO, S2g * DiO, 0.]) + AF2 = np.array([C2g * DiO, 1. - S2g ** 2 * WiO, S2g * C2g * WiO, -S2g * ZiO * SinO]) + AF3 = np.array([S2g * DiO, S2g * C2g * WiO, 1. - C2g ** 2 * WiO, C2g * ZiO * SinO]) + AF4 = np.array([0., S2g * SinO, -C2g * SinO, CosO]) + + ATF = (ATP1 * AF1 + ATP2 * AF2 + ATP3 * AF3 + ATP4 * AF4) + ARF = (ARP1 * AF1 + ARP2 * AF2 + ARP3 * AF3 + ARP4 * AF4) + ATF1 = ATF[0] + ATF2 = ATF[1] + ATF3 = ATF[2] + ATF4 = ATF[3] + ARF1 = ARF[0] + ARF2 = ARF[1] + ARF3 = ARF[2] + ARF4 = ARF[3] + + # AS with C behind the emitter + # terms without aCal + ATE1o, ARE1o = ATF1, ARF1 + ATE2o, ARE2o = 0., 0. + ATE3o, ARE3o = 0., 0. + ATE4o, ARE4o = ATF4, ARF4 + # terms with aCal + ATE1a, ARE1a = 0., 0. + ATE2a, ARE2a = ATF2, ARF2 + ATE3a, ARE3a = -ATF3, -ARF3 + ATE4a, ARE4a = -2. * ATF4, -2. * ARF4 + # rotated AinEa by epsilon + ATE2ae = C2e * ATF2 + S2e * ATF3 + ATE3ae = -S2e * ATF2 - C2e * ATF3 + ARE2ae = C2e * ARF2 + S2e * ARF3 + ARE3ae = -S2e * ARF2 - C2e * ARF3 + + ATE1 = ATE1o + ATE2e = aCal * ATE2ae + ATE3e = aCal * ATE3ae + ATE4 = (1 - 2 * aCal) * ATF4 + ARE1 = ARE1o + ARE2e = aCal * ARE2ae + ARE3e = aCal * ARE3ae + ARE4 = (1 - 2 * aCal) * ARF4 + + # rotated IinE + QinEe = C2e * QinE + S2e * UinE + UinEe = C2e * UinE - S2e * QinE + + # Calibration signals and Calibration correction K from measurements with LDRCal / aCal + if (TypeC == 2) or (TypeC == 1): # +++++++++ rotator calibration Eq. C.4 + AT = ATE1o * IinE + (ATE4o + aCal * ATE4a) * h * VinE + BT = aCal * (ATE3ae * QinEe - ATE2ae * h * UinEe) + AR = ARE1o * IinE + (ARE4o + aCal * ARE4a) * h * VinE + BR = aCal * (ARE3ae * QinEe - ARE2ae * h * UinEe) + + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): + # Stokes Input Vector before receiver optics Eq. E.19 (after atmosphere F) + GT = ATE1o * IinE + ATE4o * h * VinE + GR = ARE1o * IinE + ARE4o * h * VinE + HT = ATE2a * QinE + ATE3a * h * UinEe + ATE4a * h * VinE + HR = ARE2a * QinE + ARE3a * h * UinEe + ARE4a * h * VinE + else: + GT = ATE1o * IinE + ATE4o * h * VinE + GR = ARE1o * IinE + ARE4o * h * VinE + HT = ATE2ae * QinE + ATE3ae * h * UinEe + ATE4a * h * VinE + HR = ARE2ae * QinE + ARE3ae * h * UinEe + ARE4a * h * VinE + + elif (TypeC == 3) or (TypeC == 4): # +++++++++ linear polariser calibration Eq. C.5 + # p = +45°, m = -45° + AT = ATE1 * IinE + ZiC * CosC * (ATE2e * QinEe + ATE4 * VinE) + ATE3e * UinEe + BT = DiC * (ATE1 * UinEe + ATE3e * IinE) + ZiC * SinC * (ATE4 * QinEe - ATE2e * VinE) + AR = ARE1 * IinE + ZiC * CosC * (ARE2e * QinEe + ARE4 * VinE) + ARE3e * UinEe + BR = DiC * (ARE1 * UinEe + ARE3e * IinE) + ZiC * SinC * (ARE4 * QinEe - ARE2e * VinE) + + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): + # Stokes Input Vector before receiver optics Eq. E.19 (after atmosphere F) + GT = ATE1o * IinE + ATE4o * VinE + GR = ARE1o * IinE + ARE4o * VinE + HT = ATE2a * QinE + ATE3a * UinE + ATE4a * VinE + HR = ARE2a * QinE + ARE3a * UinE + ARE4a * VinE + else: + D = IinE + DiC * QinEe + A = DiC * IinE + QinEe + B = ZiC * (CosC * UinEe + SinC * VinE) + C = -ZiC * (SinC * UinEe - CosC * VinE) + GT = ATE1o * D + ATE4o * C + GR = ARE1o * D + ARE4o * C + HT = ATE2a * A + ATE3a * B + ATE4a * C + HR = ARE2a * A + ARE3a * B + ARE4a * C + + elif (TypeC == 6): # real HWP calibration +-22.5° rotated_diattenuator_X22x5deg.odt + # p = +22.5°, m = -22.5° + IE1e = np.array([IinE + sqr05 * DiC * QinEe, sqr05 * DiC * IinE + (1 - 0.5 * WiC) * QinEe, + (1 - 0.5 * WiC) * UinEe + sqr05 * ZiC * SinC * VinE, + -sqr05 * ZiC * SinC * UinEe + ZiC * CosC * VinE]) + IE2e = np.array([sqr05 * DiC * UinEe, 0.5 * WiC * UinEe - sqr05 * ZiC * SinC * VinE, + sqr05 * DiC * IinE + 0.5 * WiC * QinEe, sqr05 * ZiC * SinC * QinEe]) + ATEe = np.array([ATE1, ATE2e, ATE3e, ATE4]) + AREe = np.array([ARE1, ARE2e, ARE3e, ARE4]) + AT = np.dot(ATEe, IE1e) + AR = np.dot(AREe, IE1e) + BT = np.dot(ATEe, IE2e) + BR = np.dot(AREe, IE2e) + + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): # calibrator taken out + GT = ATE1o * IinE + ATE4o * VinE + GR = ARE1o * IinE + ARE4o * VinE + HT = ATE2a * QinE + ATE3a * UinE + ATE4a * VinE + HR = ARE2a * QinE + ARE3a * UinE + ARE4a * VinE + else: + D = IinE + DiC * QinEe + A = DiC * IinE + QinEe + B = ZiC * (CosC * UinEe + SinC * VinE) + C = -ZiC * (SinC * UinEe - CosC * VinE) + GT = ATE1o * D + ATE4o * C + GR = ARE1o * D + ARE4o * C + HT = ATE2a * A + ATE3a * B + ATE4a * C + HR = ARE2a * A + ARE3a * B + ARE4a * C + + else: + print('Calibrator not implemented yet') + sys.exit() + + else: + print("Calibrator location not implemented yet") + sys.exit() + + # Determination of the correction K of the calibration factor. + IoutTp = TTa * TiC * TiO * TiE * (AT + BT) + IoutTm = TTa * TiC * TiO * TiE * (AT - BT) + IoutRp = TRa * TiC * TiO * TiE * (AR + BR) + IoutRm = TRa * TiC * TiO * TiE * (AR - BR) + # --- Results and Corrections; electronic etaR and etaT are assumed to be 1 + Etapx = IoutRp / IoutTp + Etamx = IoutRm / IoutTm + Etax = (Etapx * Etamx) ** 0.5 + + Eta = (TRa / TTa) # = TRa / TTa; Eta = Eta*/K Eq. 84 => K = Eta* / Eta; equation corrected according to the papers supplement Eqs. (S.10.10.1) ff + K = Etax / Eta + + # For comparison with Volkers Libreoffice Müller Matrix spreadsheet + # Eta_test_p = (IoutRp/IoutTp) + # Eta_test_m = (IoutRm/IoutTm) + # Eta_test = (Eta_test_p*Eta_test_m)**0.5 + + # ----- random error calculation ---------- + # noise must be calculated with the photon counts of measured signals; + # relative standard deviation of calibration signals with LDRcal; assumed to be statisitcally independent + # normalised noise errors + if (CalcFrom0deg): + dIoutTp = (NCalT * IoutTp) ** -0.5 + dIoutTm = (NCalT * IoutTm) ** -0.5 + dIoutRp = (NCalR * IoutRp) ** -0.5 + dIoutRm = (NCalR * IoutRm) ** -0.5 + else: + dIoutTp = (NCalT ** -0.5) + dIoutTm = (NCalT ** -0.5) + dIoutRp = (NCalR ** -0.5) + dIoutRm = (NCalR ** -0.5) + # Forward simulated 0°-signals with LDRCal with atrue; from input file + + It = TTa * TiO * TiE * (GT + atrue * HT) + Ir = TRa * TiO * TiE * (GR + atrue * HR) + # relative standard deviation of standard signals with LDRmeas; assumed to be statisitcally independent + if (CalcFrom0deg): # this works! + dIt = ((It * NI * eFacT) ** -0.5) + dIr = ((Ir * NI * eFacR) ** -0.5) + ''' + dIt = ((NCalT * It / IoutTp * NILfac / TCalT) ** -0.5) + dIr = ((NCalR * Ir / IoutRp * NILfac / TCalR) ** -0.5) + ''' + else: # does this work? Why not as above? + dIt = ((NCalT * 2 * NILfac / TCalT ) ** -0.5) + dIr = ((NCalR * 2 * NILfac / TCalR) ** -0.5) + + # ----- Forward simulated LDRsim = 1/Eta*Ir/It # simulated LDR* with Y from input file + LDRsim = Ir / It # simulated uncorrected LDR with Y from input file + # Corrected LDRsimCorr from forward simulated LDRsim (atrue) + # LDRsimCorr = (1./Eta*LDRsim*(GT+HT)-(GR+HR))/((GR-HR)-1./Eta*LDRsim*(GT-HT)) + ''' + if ((Y == -1.) and (abs(RotL0) < 45)) or ((Y == +1.) and (abs(RotL0) > 45)): + LDRsimx = 1. / LDRsim / Etax + else: + LDRsimx = LDRsim / Etax + ''' + LDRsimx = LDRsim + + # The following is correct without doubt + # LDRCorr = (LDRsim/(Etax/K)*(GT+HT)-(GR+HR))/((GR-HR)-LDRsim/(Etax/K)*(GT-HT)) + + # The following is a test whether the equations for calibration Etax and normal signal (GHK, LDRsim) are consistent + LDRCorr = (LDRsim / (Etax / K) * (GT + HT) - (GR + HR)) / ((GR - HR) - LDRsim / (Etax / K) * (GT - HT)) + # here we could also use Eta instead of Etax / K => how to test whether Etax is correct? => comparison with MüllerMatrix simulation! + # Without any correction: only measured It, Ir, EtaX are used + LDRunCorr = LDRsim / Etax + # LDRunCorr = (LDRsim / Etax * (GT / abs(GT) + HT / abs(HT)) - (GR / abs(GR) + HR / abs(HR))) / ((GR / abs(GR) - HR / abs(HR)) - LDRsim / Etax * (GT / abs(GT) - HT / abs(HT))) + + #LDRCorr = LDRsimx # for test only + + F11sim = 1 / (TiO * TiE) * ((HR * Eta * It - HT * Ir) / (HR * GT - HT * GR)) # IL = 1, Etat = Etar = 1 ; AMT Eq.64; what is Etax/K? => see about 20 lines above: = Eta + + return (IoutTp, IoutTm, IoutRp, IoutRm, It, Ir, dIoutTp, dIoutTm, dIoutRp, dIoutRm, dIt, dIr, + GT, HT, GR, HR, K, Eta, LDRsimx, LDRCorr, DTa, DRa, TTa, TRa, F11sim, LDRunCorr) + + + +# ******************************************************************************************************************************* + +# --- CALC with assumed true parameters from the input file +LDRtrue = LDRtrue2 +IoutTp0, IoutTm0, IoutRp0, IoutRm0, It0, Ir0, dIoutTp0, dIoutTm0, dIoutRp0, dIoutRm0, dIt0, dIr0, \ +GT0, HT0, GR0, HR0, K0, Eta0, LDRsimx, LDRCorr, DTa0, DRa0, TTa0, TRa0, F11sim0, LDRunCorr = \ +Calc(TCalT, TCalR, NCalT, NCalR, Qin0, Vin0, RotL0, RotE0, RetE0, DiE0, + RotO0, RetO0, DiO0, RotC0, RetC0, DiC0, TP0, TS0, RP0, RS0, + ERaT0, RotaT0, RetT0, ERaR0, RotaR0, RetR0, LDRCal0) +Etax0 = K0 * Eta0 +Etapx0 = IoutRp0 / IoutTp0 +Etamx0 = IoutRm0 / IoutTm0 +# --- Print parameters to console and output file +OutputFile = 'output_' + InputFile[0:-3] + '_' + fname[0:-3] +'.dat' +output_path = os.path.join('output_files', OutputFile) +# with open('output_files\\' + OutputFile, 'w') as f: +with open(output_path, 'w') as f: + with redirect_stdout(f): + print("From ", dname) + print("Running ", fname) + print("Reading input file ", InputFile) # , " for Lidar system :", EID, ", ", LID) + print("for Lidar system: ", EID, ", ", LID) + # --- Print iput information********************************* + print(" --- Input parameters: value ±error / ±steps ----------------------") + print("{0:7}{1:17} {2:6.4f}±{3:7.4f}/{4:2d}".format("Laser: ", "Qin =", Qin0, dQin, nQin)) + print("{0:7}{1:17} {2:6.4f}±{3:7.4f}/{4:2d}".format("", "Vin =", Vin0, dVin, nVin)) + print("{0:7}{1:17} {2:6.4f}±{3:7.4f}/{4:2d}".format("", "Rotation alpha = ", RotL0, dRotL, nRotL)) + print("{0:7}{1:15} {2:8.4f} {3:17}".format("", "=> DOP", ((Qin ** 2 + Vin ** 2) ** 0.5), " (degree of polarisation)")) + + print("Optic: Diatt., Tunpol, Retard., Rotation (deg)") + print("{0:12} {1:7.4f} ±{2:7.4f} /{8:2d}, {3:7.4f}, {4:3.0f}±{5:3.0f}/{9:2d}, {6:7.4f}±{7:7.4f}/{10:2d}".format( + "Emitter ", DiE0, dDiE, TiE, RetE0, dRetE, RotE0, dRotE, nDiE, nRetE, nRotE)) + print("{0:12} {1:7.4f} ±{2:7.4f} /{8:2d}, {3:7.4f}, {4:3.0f}±{5:3.0f}/{9:2d}, {6:7.4f}±{7:7.4f}/{10:2d}".format( + "Receiver ", DiO0, dDiO, TiO, RetO0, dRetO, RotO0, dRotO, nDiO, nRetO, nRotO)) + print("{0:12} {1:9.6f}±{2:9.6f}/{8:2d}, {3:7.4f}, {4:3.0f}±{5:3.0f}/{9:2d}, {6:7.4f}±{7:7.4f}/{10:2d}".format( + "Calibrator ", DiC0, dDiC, TiC, RetC0, dRetC, RotC0, dRotC, nDiC, nRetC, nRotC)) + print("{0:12}".format(" Pol.-filter ------ ")) + print("{0:12}{1:7.4f}±{2:7.4f}/{3:2d}, {4:7.4f}±{5:7.4f}/{6:2d}".format( + "ERT, RotT :", ERaT0, dERaT, nERaT, RotaT0, dRotaT, nRotaT)) + print("{0:12}{1:7.4f}±{2:7.4f}/{3:2d}, {4:7.4f}±{5:7.4f}/{6:2d}".format( + "ERR, RotR :", ERaR0, dERaR, nERaR, RotaR0, dRotaR, nRotaR)) + print("{0:12}".format(" PBS ------ ")) + print("{0:12}{1:7.4f}±{2:7.4f}/{3:2d}, {4:7.4f}±{5:7.4f}/{6:2d}".format( + "TP,TS :", TP0, dTP, nTP, TS0, dTS, nTS)) + print("{0:12}{1:7.4f}±{2:7.4f}/{3:2d}, {4:7.4f}±{5:7.4f}/{6:2d}".format( + "RP,RS :", RP0, dRP, nRP, RS0, dRS, nRS)) + print("{0:12}{1:7.4f},{2:7.4f}, {3:7.4f},{4:7.4f}, {5:1.0f}".format( + "DT,TT,DR,TR,Y :", DiT, TiT, DiR, TiR, Y)) + print("{0:12}".format(" Combined PBS + Pol.-filter ------ ")) + print("{0:12}{1:7.4f},{2:7.4f}, {3:7.4f},{4:7.4f}".format( + "DT,TT,DR,TR :", DTa0, TTa0, DRa0, TRa0)) + print("{0:26}: {1:6.3f}± {2:5.3f}/{3:2d}".format( + "LDRCal during calibration in calibration range", LDRCal0, dLDRCal, nLDRCal)) + print("{0:12}".format(" --- Additional ND filter attenuation (transmission) during the calibration ---")) + print("{0:12}{1:7.4f}±{2:7.4f}/{3:2d}, {4:7.4f}±{5:7.4f}/{6:2d}".format( + "TCalT,TCalR :", TCalT0, dTCalT, nTCalT, TCalR0, dTCalR, nTCalR)) + print() + print(Type[TypeC],"calibrator is located", Loc[LocC]) + print("Rotation error epsilon is considered also for normal measurements = ", RotationErrorEpsilonForNormalMeasurements) + print("The PBS incidence plane is ", dY[int(Y + 1)], "to the reference plane" ) + print("The laser polarisation in the reference plane is finally", dY2[int(Y + 1)], "=>", dY3) + print("RS_RP_depend_on_TS_TP = ", RS_RP_depend_on_TS_TP) + # end of print actual system parameters + # ****************************************************************************** + + + print() + + K0List = np.zeros(7) + LDRsimxList = np.zeros(7) + LDRCalList = 0.0, 0.004, 0.05, 0.1, 0.2, 0.3, 0.45 + # The loop over LDRCalList is ony for checking whether and how much the LDR depends on the LDRCal during calibration and whether the corrections work. + # Still with assumed true parameters in input file + + ''' + facIt = NCalT / TCalT0 * NILfac + facIr = NCalR / TCalR0 * NILfac + ''' + facIt = NI * eFacT + facIr = NI * eFacR + if (bPlotEtax): + # check error signals + # dIs are relative stdevs + print("LDRCal, IoutTp, IoutTm, IoutRp, IoutRm, It, Ir, dIoutTp,dIoutTm,dIoutRp,dIoutRm,dIt, dIr") + + for i, LDRCal in enumerate(LDRCalList): + IoutTp, IoutTm, IoutRp, IoutRm, It, Ir, dIoutTp, dIoutTm, dIoutRp, dIoutRm, dIt, dIr, \ + GT0, HT0, GR0, HR0, K0, Eta0, LDRsimx, LDRCorr, DTa0, DRa0, TTa0, TRa0, F11sim0, LDRunCorr = \ + Calc(TCalT0, TCalR0, NCalT, NCalR, Qin0, Vin0, RotL0, RotE0, RetE0, DiE0, + RotO0, RetO0, DiO0, RotC0, RetC0, DiC0, TP0, TS0, RP0, RS0, + ERaT0, RotaT0, RetT0, ERaR0, RotaR0, RetR0, LDRCal) + K0List[i] = K0 + LDRsimxList[i] = LDRsimx + + if (bPlotEtax): + # check error signals + print( "{:0.2f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}".format(LDRCal, IoutTp * NCalT, IoutTm * NCalT, IoutRp * NCalR, IoutRm * NCalR, It * facIt, Ir * facIr, dIoutTp, dIoutTm, dIoutRp, dIoutRm, dIt, dIr)) + #print( "{:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}".format(IoutTp, IoutTm, IoutRp, IoutRm, It, Ir, dIoutTp, dIoutTm, dIoutRp, dIoutRm, dIt, dIr)) + # end check error signals + print('===========================================================================================================') + print("{0:8},{1:8},{2:8},{3:8},{4:9},{5:8},{6:9},{7:9},{8:9},{9:9},{10:9}".format( + " GR", " GT", " HR", " HT", " K(0.000)", " K(0.004)", " K(0.05)", " K(0.1)", " K(0.2)", " K(0.3)", " K(0.45)")) + print("{0:8.5f},{1:8.5f},{2:8.5f},{3:8.5f},{4:9.5f},{5:9.5f},{6:9.5f},{7:9.5f},{8:9.5f},{9:9.5f},{10:9.5f}".format( + GR0, GT0, HR0, HT0, K0List[0], K0List[1], K0List[2], K0List[3], K0List[4], K0List[5], K0List[6])) + print('===========================================================================================================') + print() + print("Errors from neglecting GHK corrections and/or calibration:") + print("{0:>10},{1:>10},{2:>10},{3:>10},{4:>10},{5:>10}".format( + "LDRtrue", "LDRunCorr", "1/LDRunCorr", "LDRsimx", "1/LDRsimx", "LDRCorr")) + + aF11sim0 = np.zeros(5) + LDRrange = np.zeros(5) + LDRsim0 = np.zeros(5) + LDRrange = [0.004, 0.02, 0.1, 0.3, 0.45] # list + LDRrange[0] = LDRtrue2 # value in the input file; default 0.004 + + # The loop over LDRtrueList is only for checking how much the uncorrected LDRsimx deviates from LDRtrue ... and whether the corrections work. + # LDRsimx = LDRsim = Ir / It or 1/LDRsim + # Still with assumed true parameters in input file + for i, LDRtrue in enumerate(LDRrange): + #for LDRtrue in LDRrange: + IoutTp, IoutTm, IoutRp, IoutRm, It, Ir, dIoutTp, dIoutTm, dIoutRp, dIoutRm, dIt, dIr, \ + GT0, HT0, GR0, HR0, K0, Eta0, LDRsimx, LDRCorr, DTa0, DRa0, TTa0, TRa0, F11sim0, LDRunCorr = \ + Calc(TCalT0, TCalR0, NCalT, NCalR, Qin0, Vin0, RotL0, RotE0, RetE0, DiE0, + RotO0, RetO0, DiO0, RotC0, RetC0, DiC0, TP0, TS0, RP0, RS0, + ERaT0, RotaT0, RetT0, ERaR0, RotaR0, RetR0, LDRCal0) + print("{0:10.5f},{1:10.5f},{2:10.5f},{3:10.5f},{4:10.5f},{5:10.5f}".format(LDRtrue, LDRunCorr, 1/LDRunCorr, LDRsimx, 1/LDRsimx, LDRCorr)) + aF11sim0[i] = F11sim0 + LDRsim0[i] = Ir / It + # the assumed true aF11sim0 results will be used below to calc the deviation from the real signals + print("LDRsimx = LDR of the nominal system directly from measured signals without calibration and GHK-corrections") + print("LDRunCorr = LDR of the nominal system directly from measured signals with calibration but without GHK-corrections; electronic amplifications = 1 assumed") + print("LDRCorr = LDR calibrated and GHK-corrected") + print() + print("Errors from signal noise:") + print("Signal counts: NI, NCalT, NCalR, NILfac, nNCal, nNI, stdev(NI)/NI = {0:10.0f},{1:10.0f},{2:10.0f},{3:3.0f},{4:2.0f},{5:2.0f},{6:8.5f}".format( + NI, NCalT, NCalR, NILfac, nNCal, nNI, 1.0 / NI ** 0.5)) + print() + print() + '''# das muß wieder weg + print("IoutTp, IoutTm, IoutRp, IoutRm, It , Ir , dIoutTp, dIoutTm, dIoutRp, dIoutRm, dIt, dIr") + LDRCal = 0.01 + for i, LDRtrue in enumerate(LDRrange): + IoutTp, IoutTm, IoutRp, IoutRm, It, Ir, dIoutTp, dIoutTm, dIoutRp, dIoutRm, dIt, dIr, \ + GT0, HT0, GR0, HR0, K0, Eta0, LDRsimx, LDRCorr, DTa0, DRa0, TTa0, TRa0, F11sim0, LDRunCorr = \ + Calc(TCalT0, TCalR0, NCalT, NCalR, DOLP0, RotL0, RotE0, RetE0, DiE0, + RotO0, RetO0, DiO0, RotC0, RetC0, DiC0, TP0, TS0, RP0, RS0, + ERaT0, RotaT0, RetT0, ERaR0, RotaR0, RetR0, LDRCal0) + print( "{:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}, {:0.4f}".format( + IoutTp * NCalT, IoutTm * NCalT, IoutRp * NCalR, IoutRm * NCalR, It * facIt, Ir * facIr, + dIoutTp, dIoutTm, dIoutRp, dIoutRm, dIt, dIr)) + aF11sim0[i] = F11sim0 + # the assumed true aF11sim0 results will be used below to calc the deviation from the real signals + # bis hierher weg + ''' + +# file = open('output_files\\' + OutputFile, 'r') +file = open(output_path, 'r') +print(file.read()) +file.close() + +# --- CALC again assumed truth with LDRCal0 and with assumed true parameters in input file to reset all 0-values +LDRtrue = LDRtrue2 +IoutTp0, IoutTm0, IoutRp0, IoutRm0, It0, Ir0, dIoutTp0, dIoutTm0, dIoutRp0, dIoutRm0, dIt0, dIr0, \ +GT0, HT0, GR0, HR0, K0, Eta0, LDRsimx, LDRCorr, DTa0, DRa0, TTa0, TRa0, F11sim0, LDRunCorr = \ +Calc(TCalT0, TCalR0, NCalT, NCalR, Qin0, Vin0, RotL0, RotE0, RetE0, DiE0, + RotO0, RetO0, DiO0, RotC0, RetC0, DiC0, TP0, TS0, RP0, RS0, + ERaT0, RotaT0, RetT0, ERaR0, RotaR0, RetR0, LDRCal0) +Etax0 = K0 * Eta0 +Etapx0 = IoutRp0 / IoutTp0 +Etamx0 = IoutRm0 / IoutTm0 +''' +if(PrintToOutputFile): + f = open('output_ver7.dat', 'w') + old_target = sys.stdout + sys.stdout = f + + print("something") + +if(PrintToOutputFile): + sys.stdout.flush() + f.close + sys.stdout = old_target +''' +if (Error_Calc): + # --- CALC again assumed truth with LDRCal0 and with assumed true parameters in input file to reset all 0-values + LDRtrue = LDRtrue2 + IoutTp0, IoutTm0, IoutRp0, IoutRm0, It0, Ir0, dIoutTp0, dIoutTm0, dIoutRp0, dIoutRm0, dIt0, dIr0, \ + GT0, HT0, GR0, HR0, K0, Eta0, LDRsimx, LDRCorr, DTa0, DRa0, TTa0, TRa0, F11sim0, LDRunCorr = \ + Calc(TCalT0, TCalR0, NCalT, NCalR, Qin0, Vin0, RotL0, RotE0, RetE0, DiE0, + RotO0, RetO0, DiO0, RotC0, RetC0, DiC0, TP0, TS0, RP0, RS0, + ERaT0, RotaT0, RetT0, ERaR0, RotaR0, RetR0, LDRCal0) + Etax0 = K0 * Eta0 + Etapx0 = IoutRp0 / IoutTp0 + Etamx0 = IoutRm0 / IoutTm0 + + # --- Start Error calculation with variable parameters ------------------------------------------------------------------ + # error nNCal: one-sigma in steps to left and right for calibration signals + # error nNI: one-sigma in steps to left and right for 0° signals + + iN = -1 + N = ((nTCalT * 2 + 1) * (nTCalR * 2 + 1) * + (nNCal * 2 + 1) ** 4 * (nNI * 2 + 1) ** 2 * + (nQin * 2 + 1) * (nVin * 2 + 1) * (nRotL * 2 + 1) * + (nRotE * 2 + 1) * (nRetE * 2 + 1) * (nDiE * 2 + 1) * + (nRotO * 2 + 1) * (nRetO * 2 + 1) * (nDiO * 2 + 1) * + (nRotC * 2 + 1) * (nRetC * 2 + 1) * (nDiC * 2 + 1) * + (nTP * 2 + 1) * (nTS * 2 + 1) * (nRP * 2 + 1) * (nRS * 2 + 1) * (nERaT * 2 + 1) * (nERaR * 2 + 1) * + (nRotaT * 2 + 1) * (nRotaR * 2 + 1) * (nRetT * 2 + 1) * (nRetR * 2 + 1) * (nLDRCal * 2 + 1)) + print("number of system variations N = ", N, " ", end="") + + if N > 1e6: + if user_yes_no_query('Warning: processing ' + str( + N) + ' samples will take very long. Do you want to proceed?') == 0: sys.exit() + if N > 5e6: + if user_yes_no_query('Warning: the memory required for ' + str(N) + ' samples might be ' + '{0:5.1f}'.format( + N / 4e6) + ' GB. Do you anyway want to proceed?') == 0: sys.exit() + + # if user_yes_no_query('Warning: processing' + str(N) + ' samples will take very long. Do you want to proceed?') == 0: sys.exit() + + # --- Arrays for plotting ------ + LDRmin = np.zeros(5) + LDRmax = np.zeros(5) + LDRstd = np.zeros(5) + LDRmean = np.zeros(5) + LDRmedian = np.zeros(5) + LDRskew = np.zeros(5) + LDRkurt = np.zeros(5) + LDRsimmin = np.zeros(5) + LDRsimmax = np.zeros(5) + LDRsimmean = np.zeros(5) + + F11min = np.zeros(5) + F11max = np.zeros(5) + Etaxmin = np.zeros(5) + Etaxmax = np.zeros(5) + + aQin = np.zeros(N) + aVin = np.zeros(N) + aERaT = np.zeros(N) + aERaR = np.zeros(N) + aRotaT = np.zeros(N) + aRotaR = np.zeros(N) + aRetT = np.zeros(N) + aRetR = np.zeros(N) + aTP = np.zeros(N) + aTS = np.zeros(N) + aRP = np.zeros(N) + aRS = np.zeros(N) + aDiE = np.zeros(N) + aDiO = np.zeros(N) + aDiC = np.zeros(N) + aRotC = np.zeros(N) + aRetC = np.zeros(N) + aRotL = np.zeros(N) + aRetE = np.zeros(N) + aRotE = np.zeros(N) + aRetO = np.zeros(N) + aRotO = np.zeros(N) + aLDRCal = np.zeros(N) + aNCalTp = np.zeros(N) + aNCalTm = np.zeros(N) + aNCalRp = np.zeros(N) + aNCalRm = np.zeros(N) + aNIt = np.zeros(N) + aNIr = np.zeros(N) + aTCalT = np.zeros(N) + aTCalR = np.zeros(N) + + # each np.zeros((LDRrange, N)) array has the same N-dependency + aLDRcorr = np.zeros((5, N)) + aLDRsim = np.zeros((5, N)) + aF11corr = np.zeros((5, N)) + aPLDR = np.zeros((5, N)) + aEtax = np.zeros((5, N)) + aEtapx = np.zeros((5, N)) + aEtamx = np.zeros((5, N)) + + # np.zeros((GHKs, N)) + aGHK = np.zeros((5, N)) + + atime = clock() + dtime = clock() + + # --- Calc Error signals + # ---- Do the calculations of bra-ket vectors + h = -1. if TypeC == 2 else 1 + + for iLDRCal in range(-nLDRCal, nLDRCal + 1): + # from input file: LDRCal for calibration measurements + LDRCal = LDRCal0 + if nLDRCal > 0: + LDRCal = LDRCal0 + iLDRCal * dLDRCal / nLDRCal + # provides the intensities of the calibration measurements at various LDRCal for signal noise errors + # IoutTp, IoutTm, IoutRp, IoutRm, dIoutTp, dIoutTm, dIoutRp, dIoutRm + + aCal = (1. - LDRCal) / (1. + LDRCal) + for iQin, iVin, iRotL, iRotE, iRetE, iDiE \ + in [(iQin, iVin, iRotL, iRotE, iRetE, iDiE) + for iQin in range(-nQin, nQin + 1) + for iVin in range(-nVin, nVin + 1) + for iRotL in range(-nRotL, nRotL + 1) + for iRotE in range(-nRotE, nRotE + 1) + for iRetE in range(-nRetE, nRetE + 1) + for iDiE in range(-nDiE, nDiE + 1)]: + + if nQin > 0: Qin = Qin0 + iQin * dQin / nQin + if nVin > 0: Vin = Vin0 + iVin * dVin / nVin + if nRotL > 0: RotL = RotL0 + iRotL * dRotL / nRotL + if nRotE > 0: RotE = RotE0 + iRotE * dRotE / nRotE + if nRetE > 0: RetE = RetE0 + iRetE * dRetE / nRetE + if nDiE > 0: DiE = DiE0 + iDiE * dDiE / nDiE + + if ((Qin ** 2 + Vin ** 2) ** 0.5) > 1.0: + print("Error: degree of polarisation of laser > 1. Check Qin and Vin! ") + sys.exit() + # angles of emitter and laser and calibrator and receiver optics + # RotL = alpha, RotE = beta, RotO = gamma, RotC = epsilon + S2a = np.sin(2 * np.deg2rad(RotL)) + C2a = np.cos(2 * np.deg2rad(RotL)) + S2b = np.sin(2 * np.deg2rad(RotE)) + C2b = np.cos(2 * np.deg2rad(RotE)) + S2ab = np.sin(np.deg2rad(2 * RotL - 2 * RotE)) + C2ab = np.cos(np.deg2rad(2 * RotL - 2 * RotE)) + + # Laser with Degree of linear polarization DOLP + IinL = 1. + QinL = Qin + UinL = 0. + VinL = Vin + # VinL = (1. - DOLP ** 2) ** 0.5 + + # Stokes Input Vector rotation Eq. E.4 + A = C2a * QinL - S2a * UinL + B = S2a * QinL + C2a * UinL + # Stokes Input Vector rotation Eq. E.9 + C = C2ab * QinL - S2ab * UinL + D = S2ab * QinL + C2ab * UinL + + # emitter optics + CosE = np.cos(np.deg2rad(RetE)) + SinE = np.sin(np.deg2rad(RetE)) + ZiE = (1. - DiE ** 2) ** 0.5 + WiE = (1. - ZiE * CosE) + + # Stokes Input Vector after emitter optics equivalent to Eq. E.9 with already rotated input vector from Eq. E.4 + # b = beta + IinE = (IinL + DiE * C) + QinE = (C2b * DiE * IinL + A + S2b * (WiE * D - ZiE * SinE * VinL)) + UinE = (S2b * DiE * IinL + B - C2b * (WiE * D - ZiE * SinE * VinL)) + VinE = (-ZiE * SinE * D + ZiE * CosE * VinL) + + # ------------------------- + # F11 assuemd to be = 1 => measured: F11m = IinP / IinE with atrue + # F11sim = (IinE + DiO*atrue*(C2g*QinE - S2g*UinE))/IinE + # ------------------------- + + for iRotO, iRetO, iDiO, iRotC, iRetC, iDiC, iTP, iTS, iRP, iRS, iERaT, iRotaT, iRetT, iERaR, iRotaR, iRetR \ + in [ + (iRotO, iRetO, iDiO, iRotC, iRetC, iDiC, iTP, iTS, iRP, iRS, iERaT, iRotaT, iRetT, iERaR, iRotaR, iRetR) + for iRotO in range(-nRotO, nRotO + 1) + for iRetO in range(-nRetO, nRetO + 1) + for iDiO in range(-nDiO, nDiO + 1) + for iRotC in range(-nRotC, nRotC + 1) + for iRetC in range(-nRetC, nRetC + 1) + for iDiC in range(-nDiC, nDiC + 1) + for iTP in range(-nTP, nTP + 1) + for iTS in range(-nTS, nTS + 1) + for iRP in range(-nRP, nRP + 1) + for iRS in range(-nRS, nRS + 1) + for iERaT in range(-nERaT, nERaT + 1) + for iRotaT in range(-nRotaT, nRotaT + 1) + for iRetT in range(-nRetT, nRetT + 1) + for iERaR in range(-nERaR, nERaR + 1) + for iRotaR in range(-nRotaR, nRotaR + 1) + for iRetR in range(-nRetR, nRetR + 1)]: + + if nRotO > 0: RotO = RotO0 + iRotO * dRotO / nRotO + if nRetO > 0: RetO = RetO0 + iRetO * dRetO / nRetO + if nDiO > 0: DiO = DiO0 + iDiO * dDiO / nDiO + if nRotC > 0: RotC = RotC0 + iRotC * dRotC / nRotC + if nRetC > 0: RetC = RetC0 + iRetC * dRetC / nRetC + if nDiC > 0: DiC = DiC0 + iDiC * dDiC / nDiC + if nTP > 0: TP = TP0 + iTP * dTP / nTP + if nTS > 0: TS = TS0 + iTS * dTS / nTS + if nRP > 0: RP = RP0 + iRP * dRP / nRP + if nRS > 0: RS = RS0 + iRS * dRS / nRS + if nERaT > 0: ERaT = ERaT0 + iERaT * dERaT / nERaT + if nRotaT > 0: RotaT = RotaT0 + iRotaT * dRotaT / nRotaT + if nRetT > 0: RetT = RetT0 + iRetT * dRetT / nRetT + if nERaR > 0: ERaR = ERaR0 + iERaR * dERaR / nERaR + if nRotaR > 0: RotaR = RotaR0 + iRotaR * dRotaR / nRotaR + if nRetR > 0: RetR = RetR0 + iRetR * dRetR / nRetR + + # print("{0:5.2f}, {1:5.2f}, {2:5.2f}, {3:10d}".format(RotL, RotE, RotO, iN)) + + # receiver optics + CosO = np.cos(np.deg2rad(RetO)) + SinO = np.sin(np.deg2rad(RetO)) + ZiO = (1. - DiO ** 2) ** 0.5 + WiO = (1. - ZiO * CosO) + S2g = np.sin(np.deg2rad(2 * RotO)) + C2g = np.cos(np.deg2rad(2 * RotO)) + # calibrator + CosC = np.cos(np.deg2rad(RetC)) + SinC = np.sin(np.deg2rad(RetC)) + ZiC = (1. - DiC ** 2) ** 0.5 + WiC = (1. - ZiC * CosC) + + # analyser + # For POLLY_XTs + if (RS_RP_depend_on_TS_TP): + RS = 1.0 - TS + RP = 1.0 - TP + TiT = 0.5 * (TP + TS) + DiT = (TP - TS) / (TP + TS) + ZiT = (1. - DiT ** 2.) ** 0.5 + TiR = 0.5 * (RP + RS) + DiR = (RP - RS) / (RP + RS) + ZiR = (1. - DiR ** 2.) ** 0.5 + CosT = np.cos(np.deg2rad(RetT)) + SinT = np.sin(np.deg2rad(RetT)) + CosR = np.cos(np.deg2rad(RetR)) + SinR = np.sin(np.deg2rad(RetR)) + + # cleaning pol-filter + DaT = (1.0 - ERaT) / (1.0 + ERaT) + DaR = (1.0 - ERaR) / (1.0 + ERaR) + TaT = 0.5 * (1.0 + ERaT) + TaR = 0.5 * (1.0 + ERaR) + + S2aT = np.sin(np.deg2rad(h * 2.0 * RotaT)) + C2aT = np.cos(np.deg2rad(2.0 * RotaT)) + S2aR = np.sin(np.deg2rad(h * 2.0 * RotaR)) + C2aR = np.cos(np.deg2rad(2.0 * RotaR)) + + # Analyzer As before the PBS Eq. D.5; combined PBS and cleaning pol-filter + ATPT = (1 + C2aT * DaT * DiT) # unpolarized transmission correction + TTa = TiT * TaT * ATPT # unpolarized transmission + ATP1 = 1.0 + ATP2 = Y * (DiT + C2aT * DaT) / ATPT + ATP3 = Y * S2aT * DaT * ZiT * CosT / ATPT + ATP4 = S2aT * DaT * ZiT * SinT / ATPT + ATP = np.array([ATP1, ATP2, ATP3, ATP4]) + DTa = ATP2 * Y + + ARPT = (1 + C2aR * DaR * DiR) # unpolarized transmission correction + TRa = TiR * TaR * ARPT # unpolarized transmission + ARP1 = 1 + ARP2 = Y * (DiR + C2aR * DaR) / ARPT + ARP3 = Y * S2aR * DaR * ZiR * CosR / ARPT + ARP4 = S2aR * DaR * ZiR * SinR / ARPT + ARP = np.array([ARP1, ARP2, ARP3, ARP4]) + DRa = ARP2 * Y + + # ---- Calculate signals and correction parameters for diffeent locations and calibrators + if LocC == 4: # Calibrator before the PBS + # print("Calibrator location not implemented yet") + + # S2ge = np.sin(np.deg2rad(2*RotO + h*2*RotC)) + # C2ge = np.cos(np.deg2rad(2*RotO + h*2*RotC)) + S2e = np.sin(np.deg2rad(h * 2 * RotC)) + C2e = np.cos(np.deg2rad(2 * RotC)) + # rotated AinP by epsilon Eq. C.3 + ATP2e = C2e * ATP2 + S2e * ATP3 + ATP3e = C2e * ATP3 - S2e * ATP2 + ARP2e = C2e * ARP2 + S2e * ARP3 + ARP3e = C2e * ARP3 - S2e * ARP2 + ATPe = np.array([ATP1, ATP2e, ATP3e, ATP4]) + ARPe = np.array([ARP1, ARP2e, ARP3e, ARP4]) + # Stokes Input Vector before the polarising beam splitter Eq. E.31 + A = C2g * QinE - S2g * UinE + B = S2g * QinE + C2g * UinE + # C = (WiO*aCal*B + ZiO*SinO*(1-2*aCal)*VinE) + Co = ZiO * SinO * VinE + Ca = (WiO * B - 2 * ZiO * SinO * VinE) + # C = Co + aCal*Ca + # IinP = (IinE + DiO*aCal*A) + # QinP = (C2g*DiO*IinE + aCal*QinE - S2g*C) + # UinP = (S2g*DiO*IinE - aCal*UinE + C2g*C) + # VinP = (ZiO*SinO*aCal*B + ZiO*CosO*(1-2*aCal)*VinE) + IinPo = IinE + QinPo = (C2g * DiO * IinE - S2g * Co) + UinPo = (S2g * DiO * IinE + C2g * Co) + VinPo = ZiO * CosO * VinE + + IinPa = DiO * A + QinPa = QinE - S2g * Ca + UinPa = -UinE + C2g * Ca + VinPa = ZiO * (SinO * B - 2 * CosO * VinE) + + IinP = IinPo + aCal * IinPa + QinP = QinPo + aCal * QinPa + UinP = UinPo + aCal * UinPa + VinP = VinPo + aCal * VinPa + # Stokes Input Vector before the polarising beam splitter rotated by epsilon Eq. C.3 + # QinPe = C2e*QinP + S2e*UinP + # UinPe = C2e*UinP - S2e*QinP + QinPoe = C2e * QinPo + S2e * UinPo + UinPoe = C2e * UinPo - S2e * QinPo + QinPae = C2e * QinPa + S2e * UinPa + UinPae = C2e * UinPa - S2e * QinPa + QinPe = C2e * QinP + S2e * UinP + UinPe = C2e * UinP - S2e * QinP + + # Calibration signals and Calibration correction K from measurements with LDRCal / aCal + if (TypeC == 2) or (TypeC == 1): # rotator calibration Eq. C.4 + # parameters for calibration with aCal + AT = ATP1 * IinP + h * ATP4 * VinP + BT = ATP3e * QinP - h * ATP2e * UinP + AR = ARP1 * IinP + h * ARP4 * VinP + BR = ARP3e * QinP - h * ARP2e * UinP + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): # calibrator taken out + IS1 = np.array([IinPo, QinPo, UinPo, VinPo]) + IS2 = np.array([IinPa, QinPa, UinPa, VinPa]) + GT = np.dot(ATP, IS1) + GR = np.dot(ARP, IS1) + HT = np.dot(ATP, IS2) + HR = np.dot(ARP, IS2) + else: + IS1 = np.array([IinPo, QinPo, UinPo, VinPo]) + IS2 = np.array([IinPa, QinPa, UinPa, VinPa]) + GT = np.dot(ATPe, IS1) + GR = np.dot(ARPe, IS1) + HT = np.dot(ATPe, IS2) + HR = np.dot(ARPe, IS2) + elif (TypeC == 3) or (TypeC == 4): # linear polariser calibration Eq. C.5 + # parameters for calibration with aCal + AT = ATP1 * IinP + ATP3e * UinPe + ZiC * CosC * (ATP2e * QinPe + ATP4 * VinP) + BT = DiC * (ATP1 * UinPe + ATP3e * IinP) - ZiC * SinC * (ATP2e * VinP - ATP4 * QinPe) + AR = ARP1 * IinP + ARP3e * UinPe + ZiC * CosC * (ARP2e * QinPe + ARP4 * VinP) + BR = DiC * (ARP1 * UinPe + ARP3e * IinP) - ZiC * SinC * (ARP2e * VinP - ARP4 * QinPe) + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): # calibrator taken out + IS1 = np.array([IinPo, QinPo, UinPo, VinPo]) + IS2 = np.array([IinPa, QinPa, UinPa, VinPa]) + GT = np.dot(ATP, IS1) + GR = np.dot(ARP, IS1) + HT = np.dot(ATP, IS2) + HR = np.dot(ARP, IS2) + else: + IS1e = np.array( + [IinPo + DiC * QinPoe, DiC * IinPo + QinPoe, ZiC * (CosC * UinPoe + SinC * VinPo), + -ZiC * (SinC * UinPoe - CosC * VinPo)]) + IS2e = np.array( + [IinPa + DiC * QinPae, DiC * IinPa + QinPae, ZiC * (CosC * UinPae + SinC * VinPa), + -ZiC * (SinC * UinPae - CosC * VinPa)]) + GT = np.dot(ATPe, IS1e) + GR = np.dot(ARPe, IS1e) + HT = np.dot(ATPe, IS2e) + HR = np.dot(ARPe, IS2e) + elif (TypeC == 6): # diattenuator calibration +-22.5° rotated_diattenuator_X22x5deg.odt + # parameters for calibration with aCal + AT = ATP1 * IinP + sqr05 * DiC * (ATP1 * QinPe + ATP2e * IinP) + (1 - 0.5 * WiC) * ( + ATP2e * QinPe + ATP3e * UinPe) + ZiC * ( + sqr05 * SinC * (ATP3e * VinP - ATP4 * UinPe) + ATP4 * CosC * VinP) + BT = sqr05 * DiC * (ATP1 * UinPe + ATP3e * IinP) + 0.5 * WiC * ( + ATP2e * UinPe + ATP3e * QinPe) - sqr05 * ZiC * SinC * (ATP2e * VinP - ATP4 * QinPe) + AR = ARP1 * IinP + sqr05 * DiC * (ARP1 * QinPe + ARP2e * IinP) + (1 - 0.5 * WiC) * ( + ARP2e * QinPe + ARP3e * UinPe) + ZiC * ( + sqr05 * SinC * (ARP3e * VinP - ARP4 * UinPe) + ARP4 * CosC * VinP) + BR = sqr05 * DiC * (ARP1 * UinPe + ARP3e * IinP) + 0.5 * WiC * ( + ARP2e * UinPe + ARP3e * QinPe) - sqr05 * ZiC * SinC * (ARP2e * VinP - ARP4 * QinPe) + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): # calibrator taken out + IS1 = np.array([IinPo, QinPo, UinPo, VinPo]) + IS2 = np.array([IinPa, QinPa, UinPa, VinPa]) + GT = np.dot(ATP, IS1) + GR = np.dot(ARP, IS1) + HT = np.dot(ATP, IS2) + HR = np.dot(ARP, IS2) + else: + IS1e = np.array( + [IinPo + DiC * QinPoe, DiC * IinPo + QinPoe, ZiC * (CosC * UinPoe + SinC * VinPo), + -ZiC * (SinC * UinPoe - CosC * VinPo)]) + IS2e = np.array( + [IinPa + DiC * QinPae, DiC * IinPa + QinPae, ZiC * (CosC * UinPae + SinC * VinPa), + -ZiC * (SinC * UinPae - CosC * VinPa)]) + GT = np.dot(ATPe, IS1e) + GR = np.dot(ARPe, IS1e) + HT = np.dot(ATPe, IS2e) + HR = np.dot(ARPe, IS2e) + else: + print("Calibrator not implemented yet") + sys.exit() + + elif LocC == 3: # C before receiver optics Eq.57 + + # S2ge = np.sin(np.deg2rad(2*RotO - 2*RotC)) + # C2ge = np.cos(np.deg2rad(2*RotO - 2*RotC)) + S2e = np.sin(np.deg2rad(2 * RotC)) + C2e = np.cos(np.deg2rad(2 * RotC)) + + # AS with C before the receiver optics (see document rotated_diattenuator_X22x5deg.odt) + AF1 = np.array([1, C2g * DiO, S2g * DiO, 0]) + AF2 = np.array([C2g * DiO, 1 - S2g ** 2 * WiO, S2g * C2g * WiO, -S2g * ZiO * SinO]) + AF3 = np.array([S2g * DiO, S2g * C2g * WiO, 1 - C2g ** 2 * WiO, C2g * ZiO * SinO]) + AF4 = np.array([0, S2g * SinO, -C2g * SinO, CosO]) + + ATF = (ATP1 * AF1 + ATP2 * AF2 + ATP3 * AF3 + ATP4 * AF4) + ARF = (ARP1 * AF1 + ARP2 * AF2 + ARP3 * AF3 + ARP4 * AF4) + ATF1 = ATF[0] + ATF2 = ATF[1] + ATF3 = ATF[2] + ATF4 = ATF[3] + ARF1 = ARF[0] + ARF2 = ARF[1] + ARF3 = ARF[2] + ARF4 = ARF[3] + + # rotated AinF by epsilon + ATF2e = C2e * ATF[1] + S2e * ATF[2] + ATF3e = C2e * ATF[2] - S2e * ATF[1] + ARF2e = C2e * ARF[1] + S2e * ARF[2] + ARF3e = C2e * ARF[2] - S2e * ARF[1] + + ATFe = np.array([ATF1, ATF2e, ATF3e, ATF4]) + ARFe = np.array([ARF1, ARF2e, ARF3e, ARF4]) + + QinEe = C2e * QinE + S2e * UinE + UinEe = C2e * UinE - S2e * QinE + + # Stokes Input Vector before receiver optics Eq. E.19 (after atmosphere F) + IinF = IinE + QinF = aCal * QinE + UinF = -aCal * UinE + VinF = (1. - 2. * aCal) * VinE + + IinFo = IinE + QinFo = 0. + UinFo = 0. + VinFo = VinE + + IinFa = 0. + QinFa = QinE + UinFa = -UinE + VinFa = -2. * VinE + + # Stokes Input Vector before receiver optics rotated by epsilon Eq. C.3 + QinFe = C2e * QinF + S2e * UinF + UinFe = C2e * UinF - S2e * QinF + QinFoe = C2e * QinFo + S2e * UinFo + UinFoe = C2e * UinFo - S2e * QinFo + QinFae = C2e * QinFa + S2e * UinFa + UinFae = C2e * UinFa - S2e * QinFa + + # Calibration signals and Calibration correction K from measurements with LDRCal / aCal + if (TypeC == 2) or (TypeC == 1): # rotator calibration Eq. C.4 + AT = ATF1 * IinF + ATF4 * h * VinF + BT = ATF3e * QinF - ATF2e * h * UinF + AR = ARF1 * IinF + ARF4 * h * VinF + BR = ARF3e * QinF - ARF2e * h * UinF + + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): + GT = ATF1 * IinE + ATF4 * VinE + GR = ARF1 * IinE + ARF4 * VinE + HT = ATF2 * QinE - ATF3 * UinE - ATF4 * 2 * VinE + HR = ARF2 * QinE - ARF3 * UinE - ARF4 * 2 * VinE + else: + GT = ATF1 * IinE + ATF4 * h * VinE + GR = ARF1 * IinE + ARF4 * h * VinE + HT = ATF2e * QinE - ATF3e * h * UinE - ATF4 * h * 2 * VinE + HR = ARF2e * QinE - ARF3e * h * UinE - ARF4 * h * 2 * VinE + + elif (TypeC == 3) or (TypeC == 4): # linear polariser calibration Eq. C.5 + # p = +45°, m = -45° + IF1e = np.array([IinF, ZiC * CosC * QinFe, UinFe, ZiC * CosC * VinF]) + IF2e = np.array([DiC * UinFe, -ZiC * SinC * VinF, DiC * IinF, ZiC * SinC * QinFe]) + + AT = np.dot(ATFe, IF1e) + AR = np.dot(ARFe, IF1e) + BT = np.dot(ATFe, IF2e) + BR = np.dot(ARFe, IF2e) + + # Correction parameters for normal measurements; they are independent of LDR --- the same as for TypeC = 6 + if (not RotationErrorEpsilonForNormalMeasurements): # calibrator taken out + IS1 = np.array([IinE, 0, 0, VinE]) + IS2 = np.array([0, QinE, -UinE, -2 * VinE]) + + GT = np.dot(ATF, IS1) + GR = np.dot(ARF, IS1) + HT = np.dot(ATF, IS2) + HR = np.dot(ARF, IS2) + else: + IS1e = np.array( + [IinFo + DiC * QinFoe, DiC * IinFo + QinFoe, ZiC * (CosC * UinFoe + SinC * VinFo), + -ZiC * (SinC * UinFoe - CosC * VinFo)]) + IS2e = np.array( + [IinFa + DiC * QinFae, DiC * IinFa + QinFae, ZiC * (CosC * UinFae + SinC * VinFa), + -ZiC * (SinC * UinFae - CosC * VinFa)]) + GT = np.dot(ATFe, IS1e) + GR = np.dot(ARFe, IS1e) + HT = np.dot(ATFe, IS2e) + HR = np.dot(ARFe, IS2e) + + elif (TypeC == 6): # diattenuator calibration +-22.5° rotated_diattenuator_X22x5deg.odt + # p = +22.5°, m = -22.5° + IF1e = np.array([IinF + sqr05 * DiC * QinFe, sqr05 * DiC * IinF + (1 - 0.5 * WiC) * QinFe, + (1 - 0.5 * WiC) * UinFe + sqr05 * ZiC * SinC * VinF, + -sqr05 * ZiC * SinC * UinFe + ZiC * CosC * VinF]) + IF2e = np.array([sqr05 * DiC * UinFe, 0.5 * WiC * UinFe - sqr05 * ZiC * SinC * VinF, + sqr05 * DiC * IinF + 0.5 * WiC * QinFe, sqr05 * ZiC * SinC * QinFe]) + + AT = np.dot(ATFe, IF1e) + AR = np.dot(ARFe, IF1e) + BT = np.dot(ATFe, IF2e) + BR = np.dot(ARFe, IF2e) + + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): # calibrator taken out + # IS1 = np.array([IinE,0,0,VinE]) + # IS2 = np.array([0,QinE,-UinE,-2*VinE]) + IS1 = np.array([IinFo, 0, 0, VinFo]) + IS2 = np.array([0, QinFa, UinFa, VinFa]) + GT = np.dot(ATF, IS1) + GR = np.dot(ARF, IS1) + HT = np.dot(ATF, IS2) + HR = np.dot(ARF, IS2) + else: + # IS1e = np.array([IinE,DiC*IinE,ZiC*SinC*VinE,ZiC*CosC*VinE]) + # IS2e = np.array([DiC*QinEe,QinEe,-ZiC*(CosC*UinEe+2*SinC*VinE),ZiC*(SinC*UinEe-2*CosC*VinE)]) + IS1e = np.array( + [IinFo + DiC * QinFoe, DiC * IinFo + QinFoe, ZiC * (CosC * UinFoe + SinC * VinFo), + -ZiC * (SinC * UinFoe - CosC * VinFo)]) + IS2e = np.array( + [IinFa + DiC * QinFae, DiC * IinFa + QinFae, ZiC * (CosC * UinFae + SinC * VinFa), + -ZiC * (SinC * UinFae - CosC * VinFa)]) + GT = np.dot(ATFe, IS1e) + GR = np.dot(ARFe, IS1e) + HT = np.dot(ATFe, IS2e) + HR = np.dot(ARFe, IS2e) + + + else: + print('Calibrator not implemented yet') + sys.exit() + + elif LocC == 2: # C behind emitter optics Eq.57 + # print("Calibrator location not implemented yet") + S2e = np.sin(np.deg2rad(2 * RotC)) + C2e = np.cos(np.deg2rad(2 * RotC)) + + # AS with C before the receiver optics (see document rotated_diattenuator_X22x5deg.odt) + AF1 = np.array([1, C2g * DiO, S2g * DiO, 0]) + AF2 = np.array([C2g * DiO, 1 - S2g ** 2 * WiO, S2g * C2g * WiO, -S2g * ZiO * SinO]) + AF3 = np.array([S2g * DiO, S2g * C2g * WiO, 1 - C2g ** 2 * WiO, C2g * ZiO * SinO]) + AF4 = np.array([0, S2g * SinO, -C2g * SinO, CosO]) + + ATF = (ATP1 * AF1 + ATP2 * AF2 + ATP3 * AF3 + ATP4 * AF4) + ARF = (ARP1 * AF1 + ARP2 * AF2 + ARP3 * AF3 + ARP4 * AF4) + ATF1 = ATF[0] + ATF2 = ATF[1] + ATF3 = ATF[2] + ATF4 = ATF[3] + ARF1 = ARF[0] + ARF2 = ARF[1] + ARF3 = ARF[2] + ARF4 = ARF[3] + + # AS with C behind the emitter -------------------------------------------- + # terms without aCal + ATE1o, ARE1o = ATF1, ARF1 + ATE2o, ARE2o = 0., 0. + ATE3o, ARE3o = 0., 0. + ATE4o, ARE4o = ATF4, ARF4 + # terms with aCal + ATE1a, ARE1a = 0., 0. + ATE2a, ARE2a = ATF2, ARF2 + ATE3a, ARE3a = -ATF3, -ARF3 + ATE4a, ARE4a = -2 * ATF4, -2 * ARF4 + # rotated AinEa by epsilon + ATE2ae = C2e * ATF2 + S2e * ATF3 + ATE3ae = -S2e * ATF2 - C2e * ATF3 + ARE2ae = C2e * ARF2 + S2e * ARF3 + ARE3ae = -S2e * ARF2 - C2e * ARF3 + + ATE1 = ATE1o + ATE2e = aCal * ATE2ae + ATE3e = aCal * ATE3ae + ATE4 = (1 - 2 * aCal) * ATF4 + ARE1 = ARE1o + ARE2e = aCal * ARE2ae + ARE3e = aCal * ARE3ae + ARE4 = (1. - 2. * aCal) * ARF4 + + # rotated IinE + QinEe = C2e * QinE + S2e * UinE + UinEe = C2e * UinE - S2e * QinE + + # --- Calibration signals and Calibration correction K from measurements with LDRCal / aCal + if (TypeC == 2) or (TypeC == 1): # +++++++++ rotator calibration Eq. C.4 + AT = ATE1o * IinE + (ATE4o + aCal * ATE4a) * h * VinE + BT = aCal * (ATE3ae * QinEe - ATE2ae * h * UinEe) + AR = ARE1o * IinE + (ARE4o + aCal * ARE4a) * h * VinE + BR = aCal * (ARE3ae * QinEe - ARE2ae * h * UinEe) + + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): + # Stokes Input Vector before receiver optics Eq. E.19 (after atmosphere F) + GT = ATE1o * IinE + ATE4o * h * VinE + GR = ARE1o * IinE + ARE4o * h * VinE + HT = ATE2a * QinE + ATE3a * h * UinEe + ATE4a * h * VinE + HR = ARE2a * QinE + ARE3a * h * UinEe + ARE4a * h * VinE + else: + GT = ATE1o * IinE + ATE4o * h * VinE + GR = ARE1o * IinE + ARE4o * h * VinE + HT = ATE2ae * QinE + ATE3ae * h * UinEe + ATE4a * h * VinE + HR = ARE2ae * QinE + ARE3ae * h * UinEe + ARE4a * h * VinE + + elif (TypeC == 3) or (TypeC == 4): # +++++++++ linear polariser calibration Eq. C.5 + # p = +45°, m = -45° + AT = ATE1 * IinE + ZiC * CosC * (ATE2e * QinEe + ATE4 * VinE) + ATE3e * UinEe + BT = DiC * (ATE1 * UinEe + ATE3e * IinE) + ZiC * SinC * (ATE4 * QinEe - ATE2e * VinE) + AR = ARE1 * IinE + ZiC * CosC * (ARE2e * QinEe + ARE4 * VinE) + ARE3e * UinEe + BR = DiC * (ARE1 * UinEe + ARE3e * IinE) + ZiC * SinC * (ARE4 * QinEe - ARE2e * VinE) + + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): + # Stokes Input Vector before receiver optics Eq. E.19 (after atmosphere F) + GT = ATE1o * IinE + ATE4o * VinE + GR = ARE1o * IinE + ARE4o * VinE + HT = ATE2a * QinE + ATE3a * UinE + ATE4a * VinE + HR = ARE2a * QinE + ARE3a * UinE + ARE4a * VinE + else: + D = IinE + DiC * QinEe + A = DiC * IinE + QinEe + B = ZiC * (CosC * UinEe + SinC * VinE) + C = -ZiC * (SinC * UinEe - CosC * VinE) + GT = ATE1o * D + ATE4o * C + GR = ARE1o * D + ARE4o * C + HT = ATE2a * A + ATE3a * B + ATE4a * C + HR = ARE2a * A + ARE3a * B + ARE4a * C + + elif (TypeC == 6): # real HWP calibration +-22.5° rotated_diattenuator_X22x5deg.odt + # p = +22.5°, m = -22.5° + IE1e = np.array([IinE + sqr05 * DiC * QinEe, sqr05 * DiC * IinE + (1 - 0.5 * WiC) * QinEe, + (1. - 0.5 * WiC) * UinEe + sqr05 * ZiC * SinC * VinE, + -sqr05 * ZiC * SinC * UinEe + ZiC * CosC * VinE]) + IE2e = np.array([sqr05 * DiC * UinEe, 0.5 * WiC * UinEe - sqr05 * ZiC * SinC * VinE, + sqr05 * DiC * IinE + 0.5 * WiC * QinEe, sqr05 * ZiC * SinC * QinEe]) + ATEe = np.array([ATE1, ATE2e, ATE3e, ATE4]) + AREe = np.array([ARE1, ARE2e, ARE3e, ARE4]) + AT = np.dot(ATEe, IE1e) + AR = np.dot(AREe, IE1e) + BT = np.dot(ATEe, IE2e) + BR = np.dot(AREe, IE2e) + + # Correction parameters for normal measurements; they are independent of LDR + if (not RotationErrorEpsilonForNormalMeasurements): # calibrator taken out + GT = ATE1o * IinE + ATE4o * VinE + GR = ARE1o * IinE + ARE4o * VinE + HT = ATE2a * QinE + ATE3a * UinE + ATE4a * VinE + HR = ARE2a * QinE + ARE3a * UinE + ARE4a * VinE + else: + D = IinE + DiC * QinEe + A = DiC * IinE + QinEe + B = ZiC * (CosC * UinEe + SinC * VinE) + C = -ZiC * (SinC * UinEe - CosC * VinE) + GT = ATE1o * D + ATE4o * C + GR = ARE1o * D + ARE4o * C + HT = ATE2a * A + ATE3a * B + ATE4a * C + HR = ARE2a * A + ARE3a * B + ARE4a * C + else: + print('Calibrator not implemented yet') + sys.exit() + + for iTCalT, iTCalR, iNCalTp, iNCalTm, iNCalRp, iNCalRm, iNIt, iNIr \ + in [ + (iTCalT, iTCalR, iNCalTp, iNCalTm, iNCalRp, iNCalRm, iNIt, iNIr) + for iTCalT in range(-nTCalT, nTCalT + 1) # Etax + for iTCalR in range(-nTCalR, nTCalR + 1) # Etax + for iNCalTp in range(-nNCal, nNCal + 1) # noise error of calibration signals => Etax + for iNCalTm in range(-nNCal, nNCal + 1) # noise error of calibration signals => Etax + for iNCalRp in range(-nNCal, nNCal + 1) # noise error of calibration signals => Etax + for iNCalRm in range(-nNCal, nNCal + 1) # noise error of calibration signals => Etax + for iNIt in range(-nNI, nNI + 1) + for iNIr in range(-nNI, nNI + 1)]: + + # Calibration signals with aCal => Determination of the correction K of the real calibration factor + IoutTp = TTa * TiC * TiO * TiE * (AT + BT) + IoutTm = TTa * TiC * TiO * TiE * (AT - BT) + IoutRp = TRa * TiC * TiO * TiE * (AR + BR) + IoutRm = TRa * TiC * TiO * TiE * (AR - BR) + + if nTCalT > 0: TCalT = TCalT0 + iTCalT * dTCalT / nTCalT + if nTCalR > 0: TCalR = TCalR0 + iTCalR * dTCalR / nTCalR + # signal noise errors + # ----- random error calculation ---------- + # noise must be calculated from/with the actually measured signals; influence of TCalT, TCalR errors on noise are not considered ? + # actually measured signal counts are in input file and don't change + # relative standard deviation of calibration signals with LDRcal; assumed to be statisitcally independent + # error nNCal: one-sigma in steps to left and right for calibration signals + if nNCal > 0: + if (CalcFrom0deg): + dIoutTp = (NCalT * IoutTp) ** -0.5 + dIoutTm = (NCalT * IoutTm) ** -0.5 + dIoutRp = (NCalR * IoutRp) ** -0.5 + dIoutRm = (NCalR * IoutRm) ** -0.5 + else: + dIoutTp = dIoutTp0 * (IoutTp / IoutTp0) + dIoutTm = dIoutTm0 * (IoutTm / IoutTm0) + dIoutRp = dIoutRp0 * (IoutRp / IoutRp0) + dIoutRm = dIoutRm0 * (IoutRm / IoutRm0) + # print(iTCalT, iTCalR, iNCalTp, iNCalTm, iNCalRp, iNCalRm, iNIt, iNIr, IoutTp, dIoutTp) + IoutTp = IoutTp * (1. + iNCalTp * dIoutTp / nNCal) + IoutTm = IoutTm * (1. + iNCalTm * dIoutTm / nNCal) + IoutRp = IoutRp * (1. + iNCalRp * dIoutRp / nNCal) + IoutRm = IoutRm * (1. + iNCalRm * dIoutRm / nNCal) + + IoutTp = IoutTp * TCalT / TCalT0 + IoutTm = IoutTm * TCalT / TCalT0 + IoutRp = IoutRp * TCalR / TCalR0 + IoutRm = IoutRm * TCalR / TCalR0 + # --- Results and Corrections; electronic etaR and etaT are assumed to be 1 for true and assumed true systems + # calibration factor + Eta = (TRa / TTa) # = TRa / TTa; Eta = Eta*/K Eq. 84; corrected according to the papers supplement Eqs. (S.10.10.1) ff + # possibly real calibration factor + Etapx = IoutRp / IoutTp + Etamx = IoutRm / IoutTm + Etax = (Etapx * Etamx) ** 0.5 + K = Etax / Eta + # print("{0:6.3f},{1:6.3f},{2:6.3f},{3:6.3f},{4:6.3f},{5:6.3f},{6:6.3f},{7:6.3f},{8:6.3f},{9:6.3f},{10:6.3f}".format(AT, BT, AR, BR, DiC, ZiC, RetO, TP, TS, Kp, Km)) + # print("{0:6.3f},{1:6.3f},{2:6.3f},{3:6.3f}".format(DiC, ZiC, Kp, Km)) + + # For comparison with Volkers Libreoffice Müller Matrix spreadsheet + # Eta_test_p = (IoutRp/IoutTp) + # Eta_test_m = (IoutRm/IoutTm) + # Eta_test = (Eta_test_p*Eta_test_m)**0.5 + ''' + for iIt, iIr \ + in [(iIt, iIr) + for iIt in range(-nNI, nNI + 1) + for iIr in range(-nNI, nNI + 1)]: + ''' + + iN = iN + 1 + if (iN == 10001): + ctime = clock() + print(" estimated time ", "{0:4.2f}".format(N / 10000 * (ctime - atime)), "sec ") # , end="") + print("\r elapsed time ", "{0:5.0f}".format((ctime - atime)), "sec ", end="\r") + ctime = clock() + if ((ctime - dtime) > 10): + print("\r elapsed time ", "{0:5.0f}".format((ctime - atime)), "sec ", end="\r") + dtime = ctime + + # *** loop for different real LDRs ********************************************************************** + iLDR = -1 + for LDRTrue in LDRrange: + iLDR = iLDR + 1 + atrue = (1. - LDRTrue) / (1. + LDRTrue) + # ----- Forward simulated signals and LDRsim with atrue; from input file; not considering TiC. + It = TTa * TiO * TiE * (GT + atrue * HT) # TaT*TiT*TiC*TiO*IinL*(GT+atrue*HT) + Ir = TRa * TiO * TiE * (GR + atrue * HR) # TaR*TiR*TiC*TiO*IinL*(GR+atrue*HR) + # # signal noise errors; standard deviation of signals; assumed to be statisitcally independent + # because the signals depend on LDRtrue, the errors dIt and dIr must be calculated for each LDRtrue + if (CalcFrom0deg): + ''' + dIt = ((NCalT * It / IoutTp * NILfac / TCalT) ** -0.5) + dIr = ((NCalR * Ir / IoutRp * NILfac / TCalR) ** -0.5) + ''' + dIt = ((It * NI * eFacT) ** -0.5) + dIr = ((Ir * NI * eFacR) ** -0.5) + else: + dIt = ((It * NI * eFacT) ** -0.5) + dIr = ((Ir * NI * eFacR) ** -0.5) + ''' + # does this work? Why not as above? + dIt = ((NCalT * 2. * NILfac / TCalT ) ** -0.5) + dIr = ((NCalR * 2. * NILfac / TCalR) ** -0.5) + ''' + # error nNI: one-sigma in steps to left and right for 0° signals + if nNI > 0: + It = It * (1. + iNIt * dIt / nNI) + Ir = Ir * (1. + iNIr * dIr / nNI) + + # LDRsim = 1/Eta*Ir/It # simulated LDR* with Y from input file + LDRsim = Ir / It # simulated uncorrected LDR with Y from input file + + # ----- Backward correction + # Corrected LDRCorr with assumed true G0,H0,K0,Eta0 from forward simulated (real) LDRsim(atrue) + LDRCorr = (LDRsim / (Etax / K0) * (GT0 + HT0) - (GR0 + HR0)) / ((GR0 - HR0) - LDRsim / (Etax / K0) * (GT0 - HT0)) + + # The following is a test whether the equations for calibration Etax and normal signal (GHK, LDRsim) are consistent + # LDRCorr = (LDRsim / Eta * (GT + HT) - (GR + HR)) / ((GR - HR) - LDRsim / Eta * (GT - HT)) + # Without any correction + LDRunCorr = LDRsim / Etax + # LDRunCorr = (LDRsim / Etax * (GT / abs(GT) + HT / abs(HT)) - (GR / abs(GR) + HR / abs(HR))) / ((GR / abs(GR) - HR / abs(HR)) - LDRsim / Etax * (GT / abs(GT) - HT / abs(HT))) + + + ''' + # -- F11corr from It and Ir and calibration EtaX + Text1 = "!!! EXPERIMENTAL !!! F11corr from It and Ir with calibration EtaX: x-axis: F11corr(LDRtrue) / F11corr(LDRtrue = 0.004) - 1" + F11corr = 1 / (TiO * TiE) * ( + (HR0 * Etax / K0 * It / TTa - HT0 * Ir / TRa) / (HR0 * GT0 - HT0 * GR0)) # IL = 1 Eq.(64); Etax/K0 = Eta0. + ''' + # Corrected F11corr with assumed true G0,H0,K0 from forward simulated (real) It and Ir (atrue) + Text1 = "!!! EXPERIMENTAL !!! F11corr from real It and Ir with real calibration EtaX: x-axis: F11corr(LDRtrue) / aF11sim0(LDRtrue) - 1" + F11corr = 1 / (TiO * TiE) * ( + (HR0 * Etax / K0 * It / TTa - HT0 * Ir / TRa) / (HR0 * GT0 - HT0 * GR0)) # IL = 1 Eq.(64); Etax/K0 = Eta0. + + # Text1 = "F11corr from It and Ir without corrections but with calibration EtaX: x-axis: F11corr(LDRtrue) devided by F11corr(LDRtrue = 0.004)" + # F11corr = 0.5/(TiO*TiE)*(Etax*It/TTa+Ir/TRa) # IL = 1 Eq.(64) + + # -- It from It only with atrue without corrections - for BERTHA (and PollyXTs) + # Text1 = " x-axis: IT(LDRtrue) / IT(LDRtrue = 0.004) - 1" + # F11corr = It/(TaT*TiT*TiO*TiE) #/(TaT*TiT*TiO*TiE*(GT0+atrue*HT0)) + # ! see below line 1673ff + + aF11corr[iLDR, iN] = F11corr + aLDRcorr[iLDR, iN] = LDRCorr # LDRCorr # LDRsim # for test only + aLDRsim[iLDR, iN] = LDRsim # LDRCorr # LDRsim # for test only + # aPLDR[iLDR, iN] = CalcPLDR(LDRCorr, BSR[iLDR], LDRm0) + aEtax[iLDR, iN] = Etax + aEtapx[iLDR, iN] = Etapx + aEtamx[iLDR, iN] = Etamx + + aGHK[0, iN] = GR + aGHK[1, iN] = GT + aGHK[2, iN] = HR + aGHK[3, iN] = HT + aGHK[4, iN] = K + + aLDRCal[iN] = iLDRCal + aQin[iN] = iQin + aVin[iN] = iVin + aERaT[iN] = iERaT + aERaR[iN] = iERaR + aRotaT[iN] = iRotaT + aRotaR[iN] = iRotaR + aRetT[iN] = iRetT + aRetR[iN] = iRetR + + aRotL[iN] = iRotL + aRotE[iN] = iRotE + aRetE[iN] = iRetE + aRotO[iN] = iRotO + aRetO[iN] = iRetO + aRotC[iN] = iRotC + aRetC[iN] = iRetC + aDiO[iN] = iDiO + aDiE[iN] = iDiE + aDiC[iN] = iDiC + aTP[iN] = iTP + aTS[iN] = iTS + aRP[iN] = iRP + aRS[iN] = iRS + aTCalT[iN] = iTCalT + aTCalR[iN] = iTCalR + + aNCalTp[iN] = iNCalTp # IoutTp, IoutTm, IoutRp, IoutRm => Etax + aNCalTm[iN] = iNCalTm # IoutTp, IoutTm, IoutRp, IoutRm => Etax + aNCalRp[iN] = iNCalRp # IoutTp, IoutTm, IoutRp, IoutRm => Etax + aNCalRm[iN] = iNCalRm # IoutTp, IoutTm, IoutRp, IoutRm => Etax + aNIt[iN] = iNIt # It, Tr + aNIr[iN] = iNIr # It, Tr + + # --- END loop + btime = clock() + # print("\r done in ", "{0:5.0f}".format(btime - atime), "sec. => producing plots now .... some more seconds ..."), # , end="\r"); + print(" done in ", "{0:5.0f}".format(btime - atime), "sec. => producing plots now .... some more seconds ...") + # --- Plot ----------------------------------------------------------------- + print("Errors from GHK correction uncertainties:") + if (sns_loaded): + sns.set_style("whitegrid") + sns.set_palette("bright6", 6) + # for older seaborn versions use: + # sns.set_palette("bright", 6) + + ''' + fig2 = plt.figure() + plt.plot(aLDRcorr[2,:],'b.') + plt.plot(aLDRcorr[3,:],'r.') + plt.plot(aLDRcorr[4,:],'g.') + #plt.plot(aLDRcorr[6,:],'c.') + plt.show + ''' + + # Plot LDR + def PlotSubHist(aVar, aX, X0, daX, iaX, naX): + # aVar is the name of the parameter and aX is the subset of aLDRcorr which is coloured in the plot + # example: PlotSubHist("DOLP", aDOLP, DOLP0, dDOLP, iDOLP, nDOLP) + fig, ax = plt.subplots(nrows=1, ncols=5, sharex=True, sharey=True, figsize=(25, 2)) + iLDR = -1 + for LDRTrue in LDRrange: + aXmean = np.zeros(2 * naX + 1) + iLDR = iLDR + 1 + LDRmin[iLDR] = np.amin(aLDRcorr[iLDR, :]) + LDRmax[iLDR] = np.amax(aLDRcorr[iLDR, :]) + if (LDRmax[iLDR] > 10): LDRmax[iLDR] = 10 + if (LDRmin[iLDR] < -10): LDRmin[iLDR] = -10 + Rmin = LDRmin[iLDR] * 0.995 # np.min(aLDRcorr[iLDR,:]) * 0.995 + Rmax = LDRmax[iLDR] * 1.005 # np.max(aLDRcorr[iLDR,:]) * 1.005 + + # Determine mean distance of all aXmean from each other for each iLDR + meanDist = 0.0 + for iaX in range(-naX, naX + 1): + # mean LDRCorr value for certain error (iaX) of parameter aVar + aXmean[iaX + naX] = np.mean(aLDRcorr[iLDR, aX == iaX]) + # relative to absolute spread of LDRCorrs + meanDist = (np.max(aXmean) - np.min(aXmean)) / (LDRmax[iLDR] - LDRmin[iLDR]) * 100 + + plt.subplot(1, 5, iLDR + 1) + (n, bins, patches) = plt.hist(aLDRcorr[iLDR, :], + bins=100, log=False, + range=[Rmin, Rmax], + alpha=0.5, density=False, color='0.5', histtype='stepfilled') + + for iaX in range(-naX, naX + 1): + # mean LDRCorr value for certain error (iaX) of parameter aVar + plt.hist(aLDRcorr[iLDR, aX == iaX], + range=[Rmin, Rmax], + bins=100, log=False, alpha=0.3, density=False, histtype='stepfilled', + label=str(round(X0 + iaX * daX / naX, 5))) + + if (iLDR == 2): + leg = plt.legend() + leg.get_frame().set_alpha(0.1) + + plt.tick_params(axis='both', labelsize=10) + plt.plot([LDRTrue, LDRTrue], [0, np.max(n)], 'r-', lw=2) + plt.gca().set_title("{0:3.0f}%".format(meanDist)) + plt.gca().set_xlabel('LDRtrue', color="red") + + # plt.ylabel('frequency', fontsize=10) + # plt.xlabel('LDRCorr', fontsize=10) + # fig.tight_layout() + fig.suptitle(LID + ' with ' + str(Type[TypeC]) + ' ' + str(Loc[LocC]) + ' - ' + aVar + ' error contribution', fontsize=14, y=1.10) + # plt.show() + # fig.savefig(LID + '_' + aVar + '.png', dpi=150, bbox_inches='tight', pad_inches=0) + # plt.close + return + + def PlotLDRsim(aVar, aX, X0, daX, iaX, naX): + # aVar is the name of the parameter and aX is the subset of aLDRsim which is coloured in the plot + # example: PlotSubHist("DOLP", aDOLP, DOLP0, dDOLP, iDOLP, nDOLP) + fig, ax = plt.subplots(nrows=1, ncols=5, sharex=True, sharey=True, figsize=(25, 2)) + iLDR = -1 + for LDRTrue in LDRrange: + aXmean = np.zeros(2 * naX + 1) + iLDR = iLDR + 1 + LDRsimmin[iLDR] = np.amin(aLDRsim[iLDR, :]) + LDRsimmax[iLDR] = np.amax(aLDRsim[iLDR, :]) + # print("LDRsimmin[iLDR], LDRsimmax[iLDR] = ", LDRsimmin[iLDR], LDRsimmax[iLDR]) + # if (LDRsimmax[iLDR] > 10): LDRsimmax[iLDR] = 10 + # if (LDRsimmin[iLDR] < -10): LDRsimmin[iLDR] = -10 + Rmin = LDRsimmin[iLDR] * 0.995 # np.min(aLDRsim[iLDR,:]) * 0.995 + Rmax = LDRsimmax[iLDR] * 1.005 # np.max(aLDRsim[iLDR,:]) * 1.005 + # print("Rmin, Rmax = ", Rmin, Rmax) + + # Determine mean distance of all aXmean from each other for each iLDR + meanDist = 0.0 + for iaX in range(-naX, naX + 1): + # mean LDRCorr value for certain error (iaX) of parameter aVar + aXmean[iaX + naX] = np.mean(aLDRsim[iLDR, aX == iaX]) + # relative to absolute spread of LDRCorrs + meanDist = (np.max(aXmean) - np.min(aXmean)) / (LDRsimmax[iLDR] - LDRsimmin[iLDR]) * 100 + + plt.subplot(1, 5, iLDR + 1) + (n, bins, patches) = plt.hist(aLDRsim[iLDR, :], + bins=100, log=False, + range=[Rmin, Rmax], + alpha=0.5, density=False, color='0.5', histtype='stepfilled') + + for iaX in range(-naX, naX + 1): + # mean LDRCorr value for certain error (iaX) of parameter aVar + plt.hist(aLDRsim[iLDR, aX == iaX], + range=[Rmin, Rmax], + bins=100, log=False, alpha=0.3, density=False, histtype='stepfilled', + label=str(round(X0 + iaX * daX / naX, 5))) + + if (iLDR == 2): + leg = plt.legend() + leg.get_frame().set_alpha(0.1) + + plt.tick_params(axis='both', labelsize=10) + plt.plot([LDRsim0[iLDR], LDRsim0[iLDR]], [0, np.max(n)], 'r-', lw=2) + plt.gca().set_title("{0:3.0f}%".format(meanDist)) + plt.gca().set_xlabel('LDRsim0', color="red") + + fig.suptitle('LDRsim - ' +LID + ' with ' + str(Type[TypeC]) + ' ' + str(Loc[LocC]) + ' - ' + aVar + ' error contribution', fontsize=14, y=1.10) + return + + + # Plot Etax + def PlotEtax(aVar, aX, X0, daX, iaX, naX): + # aVar is the name of the parameter and aX is the subset of aLDRcorr which is coloured in the plot + # example: PlotSubHist("DOLP", aDOLP, DOLP0, dDOLP, iDOLP, nDOLP) + fig, ax = plt.subplots(nrows=1, ncols=5, sharex=True, sharey=True, figsize=(25, 2)) + iLDR = -1 + for LDRTrue in LDRrange: + aXmean = np.zeros(2 * naX + 1) + iLDR = iLDR + 1 + Etaxmin = np.amin(aEtax[iLDR, :]) + Etaxmax = np.amax(aEtax[iLDR, :]) + Rmin = Etaxmin * 0.995 # np.min(aLDRcorr[iLDR,:]) * 0.995 + Rmax = Etaxmax * 1.005 # np.max(aLDRcorr[iLDR,:]) * 1.005 + + # Determine mean distance of all aXmean from each other for each iLDR + meanDist = 0.0 + for iaX in range(-naX, naX + 1): + # mean Etax value for certain error (iaX) of parameter aVar + aXmean[iaX + naX] = np.mean(aEtax[iLDR, aX == iaX]) + # relative to absolute spread of Etax + meanDist = (np.max(aXmean) - np.min(aXmean)) / (Etaxmax - Etaxmin) * 100 + + plt.subplot(1, 5, iLDR + 1) + (n, bins, patches) = plt.hist(aEtax[iLDR, :], + bins=50, log=False, + range=[Rmin, Rmax], + alpha=0.5, density=False, color='0.5', histtype='stepfilled') + for iaX in range(-naX, naX + 1): + plt.hist(aEtax[iLDR, aX == iaX], + range=[Rmin, Rmax], + bins=50, log=False, alpha=0.3, density=False, histtype='stepfilled', + label=str(round(X0 + iaX * daX / naX, 5))) + if (iLDR == 2): + leg = plt.legend() + leg.get_frame().set_alpha(0.1) + plt.tick_params(axis='both', labelsize=10) + plt.plot([Etax0, Etax0], [0, np.max(n)], 'r-', lw=2) + plt.gca().set_title("{0:3.0f}%".format(meanDist)) + plt.gca().set_xlabel('Etax0', color="red") + fig.suptitle('Etax - ' + LID + ' with ' + str(Type[TypeC]) + ' ' + str(Loc[LocC]) + ' - ' + aVar + ' error contribution', fontsize=14, y=1.10) + return + + def PlotEtapx(aVar, aX, X0, daX, iaX, naX): + # aVar is the name of the parameter and aX is the subset of aLDRcorr which is coloured in the plot + # example: PlotSubHist("DOLP", aDOLP, DOLP0, dDOLP, iDOLP, nDOLP) + fig, ax = plt.subplots(nrows=1, ncols=5, sharex=True, sharey=True, figsize=(25, 2)) + iLDR = -1 + for LDRTrue in LDRrange: + aXmean = np.zeros(2 * naX + 1) + iLDR = iLDR + 1 + Etapxmin = np.amin(aEtapx[iLDR, :]) + Etapxmax = np.amax(aEtapx[iLDR, :]) + Rmin = Etapxmin * 0.995 # np.min(aLDRcorr[iLDR,:]) * 0.995 + Rmax = Etapxmax * 1.005 # np.max(aLDRcorr[iLDR,:]) * 1.005 + + # Determine mean distance of all aXmean from each other for each iLDR + meanDist = 0.0 + for iaX in range(-naX, naX + 1): + # mean Etapx value for certain error (iaX) of parameter aVar + aXmean[iaX + naX] = np.mean(aEtapx[iLDR, aX == iaX]) + # relative to absolute spread of Etapx + meanDist = (np.max(aXmean) - np.min(aXmean)) / (Etapxmax - Etapxmin) * 100 + + plt.subplot(1, 5, iLDR + 1) + (n, bins, patches) = plt.hist(aEtapx[iLDR, :], + bins=50, log=False, + range=[Rmin, Rmax], + alpha=0.5, density=False, color='0.5', histtype='stepfilled') + for iaX in range(-naX, naX + 1): + plt.hist(aEtapx[iLDR, aX == iaX], + range=[Rmin, Rmax], + bins=50, log=False, alpha=0.3, density=False, histtype='stepfilled', + label=str(round(X0 + iaX * daX / naX, 5))) + if (iLDR == 2): + leg = plt.legend() + leg.get_frame().set_alpha(0.1) + plt.tick_params(axis='both', labelsize=10) + plt.plot([Etapx0, Etapx0], [0, np.max(n)], 'r-', lw=2) + plt.gca().set_title("{0:3.0f}%".format(meanDist)) + plt.gca().set_xlabel('Etapx0', color="red") + fig.suptitle('Etapx - ' + LID + ' with ' + str(Type[TypeC]) + ' ' + str(Loc[LocC]) + ' - ' + aVar + ' error contribution', fontsize=14, y=1.10) + return + + def PlotEtamx(aVar, aX, X0, daX, iaX, naX): + # aVar is the name of the parameter and aX is the subset of aLDRcorr which is coloured in the plot + # example: PlotSubHist("DOLP", aDOLP, DOLP0, dDOLP, iDOLP, nDOLP) + fig, ax = plt.subplots(nrows=1, ncols=5, sharex=True, sharey=True, figsize=(25, 2)) + iLDR = -1 + for LDRTrue in LDRrange: + aXmean = np.zeros(2 * naX + 1) + iLDR = iLDR + 1 + Etamxmin = np.amin(aEtamx[iLDR, :]) + Etamxmax = np.amax(aEtamx[iLDR, :]) + Rmin = Etamxmin * 0.995 # np.min(aLDRcorr[iLDR,:]) * 0.995 + Rmax = Etamxmax * 1.005 # np.max(aLDRcorr[iLDR,:]) * 1.005 + + # Determine mean distance of all aXmean from each other for each iLDR + meanDist = 0.0 + for iaX in range(-naX, naX + 1): + # mean Etamx value for certain error (iaX) of parameter aVar + aXmean[iaX + naX] = np.mean(aEtamx[iLDR, aX == iaX]) + # relative to absolute spread of Etamx + meanDist = (np.max(aXmean) - np.min(aXmean)) / (Etamxmax - Etamxmin) * 100 + + plt.subplot(1, 5, iLDR + 1) + (n, bins, patches) = plt.hist(aEtamx[iLDR, :], + bins=50, log=False, + range=[Rmin, Rmax], + alpha=0.5, density=False, color='0.5', histtype='stepfilled') + for iaX in range(-naX, naX + 1): + plt.hist(aEtamx[iLDR, aX == iaX], + range=[Rmin, Rmax], + bins=50, log=False, alpha=0.3, density=False, histtype='stepfilled', + label=str(round(X0 + iaX * daX / naX, 5))) + if (iLDR == 2): + leg = plt.legend() + leg.get_frame().set_alpha(0.1) + plt.tick_params(axis='both', labelsize=10) + plt.plot([Etamx0, Etamx0], [0, np.max(n)], 'r-', lw=2) + plt.gca().set_title("{0:3.0f}%".format(meanDist)) + plt.gca().set_xlabel('Etamx0', color="red") + fig.suptitle('Etamx - ' + LID + ' with ' + str(Type[TypeC]) + ' ' + str(Loc[LocC]) + ' - ' + aVar + ' error contribution', fontsize=14, y=1.10) + return + + # calc contribution of the error of aVar = aX to aY for each LDRtrue + def Contribution(aVar, aX, X0, daX, iaX, naX, aY, Ysum, widthSum): + # aVar is the name of the parameter and aX is the subset of aY which is coloured in the plot + # example: Contribution("DOLP", aDOLP, DOLP0, dDOLP, iDOLP, nDOLP, aLDRcorr, DOLPcontr) + iLDR = -1 + # Ysum, widthSum = np.zeros(5) + meanDist = np.zeros(5) # iLDR + widthDist = np.zeros(5) # iLDR + for LDRTrue in LDRrange: + aXmean = np.zeros(2 * naX + 1) + aXwidth = np.zeros(2 * naX + 1) + iLDR = iLDR + 1 + # total width of distribution + aYmin = np.amin(aY[iLDR, :]) + aYmax = np.amax(aY[iLDR, :]) + aYwidth = aYmax - aYmin + # Determine mean distance of all aXmean from each other for each iLDR + for iaX in range(-naX, naX + 1): + # mean LDRCorr value for all errors iaX of parameter aVar + aXmean[iaX + naX] = np.mean(aY[iLDR, aX == iaX]) + aXwidth[iaX + naX] = np.max(aY[iLDR, aX == iaX]) - np.min(aY[iLDR, aX == iaX]) + # relative to absolute spread of LDRCorrs + meanDist[iLDR] = (np.max(aXmean) - np.min(aXmean)) / aYwidth * 1000 + # meanDist[iLDR] = (aYwidth - aXwidth[naX]) / aYwidth * 1000 + widthDist[iLDR] = (np.max(aXwidth) - aXwidth[naX]) / aYwidth * 1000 + + print("{:12}{:5.0f} {:5.0f} {:5.0f} {:5.0f} {:5.0f} {:5.0f} {:5.0f} {:5.0f} {:5.0f} {:5.0f}"\ + .format(aVar,meanDist[0],meanDist[1],meanDist[2],meanDist[3],meanDist[4],widthDist[0],widthDist[1],widthDist[2],widthDist[3],widthDist[4])) + Ysum = Ysum + meanDist + widthSum = widthSum + widthDist + return(Ysum, widthSum) + + # print(.format(LDRrangeA[iLDR],)) + + # error contributions to a certain output aY; loop over all variables + def Contribution_aY(aYvar, aY): + Ysum = np.zeros(5) + widthSum = np.zeros(5) + # meanDist = np.zeros(5) # iLDR + LDRrangeA = np.array(LDRrange) + print() + print(aYvar + ": contribution to the total error (per mill)") + print(" of individual parameter errors of combined parameter errors") + print(" at LDRtrue {:5.3f} {:5.3f} {:5.3f} {:5.3f} {:5.3f} {:5.3f} {:5.3f} {:5.3f} {:5.3f} {:5.3f}"\ + .format(LDRrangeA[0],LDRrangeA[1],LDRrangeA[2],LDRrangeA[3],LDRrangeA[4],LDRrangeA[0],LDRrangeA[1],LDRrangeA[2],LDRrangeA[3],LDRrangeA[4])) + print() + if (nQin > 0): Ysum, widthSum = Contribution("Qin", aQin, Qin0, dQin, iQin, nQin, aY, Ysum, widthSum) + if (nVin > 0): Ysum, widthSum = Contribution("Vin", aVin, Vin0, dVin, iVin, nVin, aY, Ysum, widthSum) + if (nRotL > 0): Ysum, widthSum = Contribution("RotL", aRotL, RotL0, dRotL, iRotL, nRotL, aY, Ysum, widthSum) + if (nRetE > 0): Ysum, widthSum = Contribution("RetE", aRetE, RetE0, dRetE, iRetE, nRetE, aY, Ysum, widthSum) + if (nRotE > 0): Ysum, widthSum = Contribution("RotE", aRotE, RotE0, dRotE, iRotE, nRotE, aY, Ysum, widthSum) + if (nDiE > 0): Ysum, widthSum = Contribution("DiE", aDiE, DiE0, dDiE, iDiE, nDiE, aY, Ysum, widthSum) + if (nRetO > 0): Ysum, widthSum = Contribution("RetO", aRetO, RetO0, dRetO, iRetO, nRetO, aY, Ysum, widthSum) + if (nRotO > 0): Ysum, widthSum = Contribution("RotO", aRotO, RotO0, dRotO, iRotO, nRotO, aY, Ysum, widthSum) + if (nDiO > 0): Ysum, widthSum = Contribution("DiO", aDiO, DiO0, dDiO, iDiO, nDiO, aY, Ysum, widthSum) + if (nDiC > 0): Ysum, widthSum = Contribution("DiC", aDiC, DiC0, dDiC, iDiC, nDiC, aY, Ysum, widthSum) + if (nRotC > 0): Ysum, widthSum = Contribution("RotC", aRotC, RotC0, dRotC, iRotC, nRotC, aY, Ysum, widthSum) + if (nRetC > 0): Ysum, widthSum = Contribution("RetC", aRetC, RetC0, dRetC, iRetC, nRetC, aY, Ysum, widthSum) + if (nTP > 0): Ysum, widthSum = Contribution("TP", aTP, TP0, dTP, iTP, nTP, aY, Ysum, widthSum) + if (nTS > 0): Ysum, widthSum = Contribution("TS", aTS, TS0, dTS, iTS, nTS, aY, Ysum, widthSum) + if (nRP > 0): Ysum, widthSum = Contribution("RP", aRP, RP0, dRP, iRP, nRP, aY, Ysum, widthSum) + if (nRS > 0): Ysum, widthSum = Contribution("RS", aRS, RS0, dRS, iRS, nRS, aY, Ysum, widthSum) + if (nRetT > 0): Ysum, widthSum = Contribution("RetT", aRetT, RetT0, dRetT, iRetT, nRetT, aY, Ysum, widthSum) + if (nRetR > 0): Ysum, widthSum = Contribution("RetR", aRetR, RetR0, dRetR, iRetR, nRetR, aY, Ysum, widthSum) + if (nERaT > 0): Ysum, widthSum = Contribution("ERaT", aERaT, ERaT0, dERaT, iERaT, nERaT, aY, Ysum, widthSum) + if (nERaR > 0): Ysum, widthSum = Contribution("ERaR", aERaR, ERaR0, dERaR, iERaR, nERaR, aY, Ysum, widthSum) + if (nRotaT > 0): Ysum, widthSum = Contribution("RotaT", aRotaT, RotaT0, dRotaT, iRotaT, nRotaT, aY, Ysum, widthSum) + if (nRotaR > 0): Ysum, widthSum = Contribution("RotaR", aRotaR, RotaR0, dRotaR, iRotaR, nRotaR, aY, Ysum, widthSum) + if (nLDRCal > 0): Ysum, widthSum = Contribution("LDRCal", aLDRCal, LDRCal0, dLDRCal, iLDRCal, nLDRCal, aY, Ysum, widthSum) + if (nTCalT > 0): Ysum, widthSum = Contribution("TCalT", aTCalT, TCalT0, dTCalT, iTCalT, nTCalT, aY, Ysum, widthSum) + if (nTCalR > 0): Ysum, widthSum = Contribution("TCalR", aTCalR, TCalR0, dTCalR, iTCalR, nTCalR, aY, Ysum, widthSum) + if (nNCal > 0): Ysum, widthSum = Contribution("CalNoiseTp", aNCalTp, 0, 1, iNCalTp, nNCal, aY, Ysum, widthSum) + if (nNCal > 0): Ysum, widthSum = Contribution("CalNoiseTm", aNCalTm, 0, 1, iNCalTm, nNCal, aY, Ysum, widthSum) + if (nNCal > 0): Ysum, widthSum = Contribution("CalNoiseRp", aNCalRp, 0, 1, iNCalRp, nNCal, aY, Ysum, widthSum) + if (nNCal > 0): Ysum, widthSum = Contribution("CalNoiseRm", aNCalRm, 0, 1, iNCalRm, nNCal, aY, Ysum, widthSum) + if (nNI > 0): Ysum, widthSum = Contribution("SigNoiseIt", aNIt, 0, 1, iNIt, nNI, aY, Ysum, widthSum) + if (nNI > 0): Ysum, widthSum = Contribution("SigNoiseIr", aNIr, 0, 1, iNIr, nNI, aY, Ysum, widthSum) + print("{:12}{:5.0f} {:5.0f} {:5.0f} {:5.0f} {:5.0f} {:5.0f} {:5.0f} {:5.0f} {:5.0f} {:5.0f}"\ + .format("Sum ",Ysum[0],Ysum[1],Ysum[2],Ysum[3],Ysum[4],widthSum[0],widthSum[1],widthSum[2],widthSum[3],widthSum[4])) + + + # Plot LDR histograms + if (nQin > 0): PlotSubHist("Qin", aQin, Qin0, dQin, iQin, nQin) + if (nVin > 0): PlotSubHist("Vin", aVin, Vin0, dVin, iVin, nVin) + if (nRotL > 0): PlotSubHist("RotL", aRotL, RotL0, dRotL, iRotL, nRotL) + if (nRetE > 0): PlotSubHist("RetE", aRetE, RetE0, dRetE, iRetE, nRetE) + if (nRotE > 0): PlotSubHist("RotE", aRotE, RotE0, dRotE, iRotE, nRotE) + if (nDiE > 0): PlotSubHist("DiE", aDiE, DiE0, dDiE, iDiE, nDiE) + if (nRetO > 0): PlotSubHist("RetO", aRetO, RetO0, dRetO, iRetO, nRetO) + if (nRotO > 0): PlotSubHist("RotO", aRotO, RotO0, dRotO, iRotO, nRotO) + if (nDiO > 0): PlotSubHist("DiO", aDiO, DiO0, dDiO, iDiO, nDiO) + if (nDiC > 0): PlotSubHist("DiC", aDiC, DiC0, dDiC, iDiC, nDiC) + if (nRotC > 0): PlotSubHist("RotC", aRotC, RotC0, dRotC, iRotC, nRotC) + if (nRetC > 0): PlotSubHist("RetC", aRetC, RetC0, dRetC, iRetC, nRetC) + if (nTP > 0): PlotSubHist("TP", aTP, TP0, dTP, iTP, nTP) + if (nTS > 0): PlotSubHist("TS", aTS, TS0, dTS, iTS, nTS) + if (nRP > 0): PlotSubHist("RP", aRP, RP0, dRP, iRP, nRP) + if (nRS > 0): PlotSubHist("RS", aRS, RS0, dRS, iRS, nRS) + if (nRetT > 0): PlotSubHist("RetT", aRetT, RetT0, dRetT, iRetT, nRetT) + if (nRetR > 0): PlotSubHist("RetR", aRetR, RetR0, dRetR, iRetR, nRetR) + if (nERaT > 0): PlotSubHist("ERaT", aERaT, ERaT0, dERaT, iERaT, nERaT) + if (nERaR > 0): PlotSubHist("ERaR", aERaR, ERaR0, dERaR, iERaR, nERaR) + if (nRotaT > 0): PlotSubHist("RotaT", aRotaT, RotaT0, dRotaT, iRotaT, nRotaT) + if (nRotaR > 0): PlotSubHist("RotaR", aRotaR, RotaR0, dRotaR, iRotaR, nRotaR) + if (nLDRCal > 0): PlotSubHist("LDRCal", aLDRCal, LDRCal0, dLDRCal, iLDRCal, nLDRCal) + if (nTCalT > 0): PlotSubHist("TCalT", aTCalT, TCalT0, dTCalT, iTCalT, nTCalT) + if (nTCalR > 0): PlotSubHist("TCalR", aTCalR, TCalR0, dTCalR, iTCalR, nTCalR) + if (nNCal > 0): PlotSubHist("CalNoiseTp", aNCalTp, 0, 1, iNCalTp, nNCal) + if (nNCal > 0): PlotSubHist("CalNoiseTm", aNCalTm, 0, 1, iNCalTm, nNCal) + if (nNCal > 0): PlotSubHist("CalNoiseRp", aNCalRp, 0, 1, iNCalRp, nNCal) + if (nNCal > 0): PlotSubHist("CalNoiseRm", aNCalRm, 0, 1, iNCalRm, nNCal) + if (nNI > 0): PlotSubHist("SigNoiseIt", aNIt, 0, 1, iNIt, nNI) + if (nNI > 0): PlotSubHist("SigNoiseIr", aNIr, 0, 1, iNIr, nNI) + plt.show() + plt.close + + + + # --- Plot LDRmin, LDRmax + iLDR = -1 + for LDRTrue in LDRrange: + iLDR = iLDR + 1 + LDRmin[iLDR] = np.amin(aLDRcorr[iLDR, :]) + LDRmax[iLDR] = np.amax(aLDRcorr[iLDR, :]) + LDRstd[iLDR] = np.std(aLDRcorr[iLDR, :]) + LDRmean[iLDR] = np.mean(aLDRcorr[iLDR, :]) + LDRmedian[iLDR] = np.median(aLDRcorr[iLDR, :]) + LDRskew[iLDR] = skew(aLDRcorr[iLDR, :],bias=False) + LDRkurt[iLDR] = kurtosis(aLDRcorr[iLDR, :],fisher=True,bias=False) + + fig2 = plt.figure() + LDRrangeA = np.array(LDRrange) + if((np.amax(LDRmax - LDRrangeA)-np.amin(LDRmin - LDRrangeA)) < 0.001): + plt.ylim(-0.001,0.001) + plt.plot(LDRrangeA, LDRmax - LDRrangeA, linewidth=2.0, color='b') + plt.plot(LDRrangeA, LDRmin - LDRrangeA, linewidth=2.0, color='g') + + plt.xlabel('LDRtrue', fontsize=18) + plt.ylabel('LDRTrue-LDRmin, LDRTrue-LDRmax', fontsize=14) + plt.title(LID + ' ' + str(Type[TypeC]) + ' ' + str(Loc[LocC]), fontsize=18) + # plt.ylimit(-0.07, 0.07) + plt.show() + plt.close + + # --- Save LDRmin, LDRmax to file + # http://stackoverflow.com/questions/4675728/redirect-stdout-to-a-file-in-python + with open('output_files\\' + OutputFile, 'a') as f: + # with open('output_files\\' + LID + '-' + InputFile[0:-3] + '-LDR_min_max.dat', 'w') as f: + with redirect_stdout(f): + print("Lidar ID: " + LID) + print() + print("minimum and maximum values of the distributions of possibly measured LDR for different LDRtrue") + print("LDRtrue , LDRmin, LDRmax") + for i in range(len(LDRrangeA)): + print("{0:7.4f},{1:7.4f},{2:7.4f}".format(LDRrangeA[i], LDRmin[i], LDRmax[i])) + print() + # Print LDR statistics + print("LDRtrue , mean , median, max-mean, min-mean, std, excess_kurtosis, skewness") + iLDR = -1 + LDRrangeA = np.array(LDRrange) + for LDRTrue in LDRrange: + iLDR = iLDR + 1 + print("{0:8.5f},{1:8.5f},{2:8.5f}, {3:8.5f},{4:8.5f},{5:8.5f}, {6:8.5f},{7:8.5f}"\ + .format(LDRrangeA[iLDR], LDRmean[iLDR], LDRmedian[iLDR], LDRmax[iLDR]-LDRrangeA[iLDR], \ + LDRmin[iLDR]-LDRrangeA[iLDR], LDRstd[iLDR], LDRkurt[iLDR], LDRskew[iLDR])) + print() + # Calculate and print statistics for calibration factors + print("minimum and maximum values of the distributions of signal ratios and calibration factors for different LDRtrue") + iLDR = -1 + LDRrangeA = np.array(LDRrange) + print("LDRtrue , LDRsim, (max-min)/2, relerr") + for LDRTrue in LDRrange: + iLDR = iLDR + 1 + LDRsimmin[iLDR] = np.amin(aLDRsim[iLDR, :]) + LDRsimmax[iLDR] = np.amax(aLDRsim[iLDR, :]) + # LDRsimstd = np.std(aLDRsim[iLDR, :]) + LDRsimmean[iLDR] = np.mean(aLDRsim[iLDR, :]) + # LDRsimmedian = np.median(aLDRsim[iLDR, :]) + print("{0:8.5f}, {1:8.5f}, {2:8.5f}, {3:8.5f}".format(LDRrangeA[iLDR],LDRsimmean[iLDR],(LDRsimmax[iLDR]-LDRsimmin[iLDR])/2,(LDRsimmax[iLDR]-LDRsimmin[iLDR])/2/LDRsimmean[iLDR])) + iLDR = -1 + print("LDRtrue , Etax , (max-min)/2, relerr") + for LDRTrue in LDRrange: + iLDR = iLDR + 1 + Etaxmin = np.amin(aEtax[iLDR, :]) + Etaxmax = np.amax(aEtax[iLDR, :]) + # Etaxstd = np.std(aEtax[iLDR, :]) + Etaxmean = np.mean(aEtax[iLDR, :]) + # Etaxmedian = np.median(aEtax[iLDR, :]) + print("{0:8.5f}, {1:8.5f}, {2:8.5f}, {3:8.5f}".format(LDRrangeA[iLDR], Etaxmean, (Etaxmax-Etaxmin)/2, (Etaxmax-Etaxmin)/2/Etaxmean)) + iLDR = -1 + print("LDRtrue , Etapx , (max-min)/2, relerr") + for LDRTrue in LDRrange: + iLDR = iLDR + 1 + Etapxmin = np.amin(aEtapx[iLDR, :]) + Etapxmax = np.amax(aEtapx[iLDR, :]) + # Etapxstd = np.std(aEtapx[iLDR, :]) + Etapxmean = np.mean(aEtapx[iLDR, :]) + # Etapxmedian = np.median(aEtapx[iLDR, :]) + print("{0:8.5f}, {1:8.5f}, {2:8.5f}, {3:8.5f}".format(LDRrangeA[iLDR], Etapxmean, (Etapxmax-Etapxmin)/2, (Etapxmax-Etapxmin)/2/Etapxmean)) + iLDR = -1 + print("LDRtrue , Etamx , (max-min)/2, relerr") + for LDRTrue in LDRrange: + iLDR = iLDR + 1 + Etamxmin = np.amin(aEtamx[iLDR, :]) + Etamxmax = np.amax(aEtamx[iLDR, :]) + # Etamxstd = np.std(aEtamx[iLDR, :]) + Etamxmean = np.mean(aEtamx[iLDR, :]) + # Etamxmedian = np.median(aEtamx[iLDR, :]) + print("{0:8.5f}, {1:8.5f}, {2:8.5f}, {3:8.5f}".format(LDRrangeA[iLDR], Etamxmean, (Etamxmax-Etamxmin)/2, (Etamxmax-Etamxmin)/2/Etamxmean)) + + # Print LDR statistics + print("LDRtrue , mean , median, max-mean, min-mean, std, excess_kurtosis, skewness") + iLDR = -1 + LDRrangeA = np.array(LDRrange) + for LDRTrue in LDRrange: + iLDR = iLDR + 1 + print("{0:8.5f},{1:8.5f},{2:8.5f}, {3:8.5f},{4:8.5f},{5:8.5f}, {6:8.5f},{7:8.5f}".format(LDRrangeA[iLDR], LDRmean[iLDR], LDRmedian[iLDR], LDRmax[iLDR]-LDRrangeA[iLDR], LDRmin[iLDR]-LDRrangeA[iLDR], LDRstd[iLDR],LDRkurt[iLDR],LDRskew[iLDR])) + + + with open('output_files\\' + OutputFile, 'a') as f: + # with open('output_files\\' + LID + '-' + InputFile[0:-3] + '-LDR_min_max.dat', 'a') as f: + with redirect_stdout(f): + Contribution_aY("LDRCorr", aLDRcorr) + Contribution_aY("LDRsim", aLDRsim) + Contribution_aY("EtaX, D90", aEtax) + Contribution_aY("Etapx, +45°", aEtapx) + Contribution_aY("Etamx -45°", aEtamx) + + + # Plot other histograms + if (bPlotEtax): + + if (nQin > 0): PlotLDRsim("Qin", aQin, Qin0, dQin, iQin, nQin) + if (nVin > 0): PlotLDRsim("Vin", aVin, Vin0, dVin, iVin, nVin) + if (nRotL > 0): PlotLDRsim("RotL", aRotL, RotL0, dRotL, iRotL, nRotL) + if (nRetE > 0): PlotLDRsim("RetE", aRetE, RetE0, dRetE, iRetE, nRetE) + if (nRotE > 0): PlotLDRsim("RotE", aRotE, RotE0, dRotE, iRotE, nRotE) + if (nDiE > 0): PlotLDRsim("DiE", aDiE, DiE0, dDiE, iDiE, nDiE) + if (nRetO > 0): PlotLDRsim("RetO", aRetO, RetO0, dRetO, iRetO, nRetO) + if (nRotO > 0): PlotLDRsim("RotO", aRotO, RotO0, dRotO, iRotO, nRotO) + if (nDiO > 0): PlotLDRsim("DiO", aDiO, DiO0, dDiO, iDiO, nDiO) + if (nDiC > 0): PlotLDRsim("DiC", aDiC, DiC0, dDiC, iDiC, nDiC) + if (nRotC > 0): PlotLDRsim("RotC", aRotC, RotC0, dRotC, iRotC, nRotC) + if (nRetC > 0): PlotLDRsim("RetC", aRetC, RetC0, dRetC, iRetC, nRetC) + if (nTP > 0): PlotLDRsim("TP", aTP, TP0, dTP, iTP, nTP) + if (nTS > 0): PlotLDRsim("TS", aTS, TS0, dTS, iTS, nTS) + if (nRP > 0): PlotLDRsim("RP", aRP, RP0, dRP, iRP, nRP) + if (nRS > 0): PlotLDRsim("RS", aRS, RS0, dRS, iRS, nRS) + if (nRetT > 0): PlotLDRsim("RetT", aRetT, RetT0, dRetT, iRetT, nRetT) + if (nRetR > 0): PlotLDRsim("RetR", aRetR, RetR0, dRetR, iRetR, nRetR) + if (nERaT > 0): PlotLDRsim("ERaT", aERaT, ERaT0, dERaT, iERaT, nERaT) + if (nERaR > 0): PlotLDRsim("ERaR", aERaR, ERaR0, dERaR, iERaR, nERaR) + if (nRotaT > 0): PlotLDRsim("RotaT", aRotaT, RotaT0, dRotaT, iRotaT, nRotaT) + if (nRotaR > 0): PlotLDRsim("RotaR", aRotaR, RotaR0, dRotaR, iRotaR, nRotaR) + if (nLDRCal > 0): PlotLDRsim("LDRCal", aLDRCal, LDRCal0, dLDRCal, iLDRCal, nLDRCal) + if (nTCalT > 0): PlotLDRsim("TCalT", aTCalT, TCalT0, dTCalT, iTCalT, nTCalT) + if (nTCalR > 0): PlotLDRsim("TCalR", aTCalR, TCalR0, dTCalR, iTCalR, nTCalR) + if (nNCal > 0): PlotLDRsim("CalNoiseTp", aNCalTp, 0, 1, iNCalTp, nNCal) + if (nNCal > 0): PlotLDRsim("CalNoiseTm", aNCalTm, 0, 1, iNCalTm, nNCal) + if (nNCal > 0): PlotLDRsim("CalNoiseRp", aNCalRp, 0, 1, iNCalRp, nNCal) + if (nNCal > 0): PlotLDRsim("CalNoiseRm", aNCalRm, 0, 1, iNCalRm, nNCal) + if (nNI > 0): PlotLDRsim("SigNoiseIt", aNIt, 0, 1, iNIt, nNI) + if (nNI > 0): PlotLDRsim("SigNoiseIr", aNIr, 0, 1, iNIr, nNI) + plt.show() + plt.close + print("---------------------------------------...producing more plots...------------------------------------------------------------------") + + if (nQin > 0): PlotEtax("Qin", aQin, Qin0, dQin, iQin, nQin) + if (nVin > 0): PlotEtax("Vin", aVin, Vin0, dVin, iVin, nVin) + if (nRotL > 0): PlotEtax("RotL", aRotL, RotL0, dRotL, iRotL, nRotL) + if (nRetE > 0): PlotEtax("RetE", aRetE, RetE0, dRetE, iRetE, nRetE) + if (nRotE > 0): PlotEtax("RotE", aRotE, RotE0, dRotE, iRotE, nRotE) + if (nDiE > 0): PlotEtax("DiE", aDiE, DiE0, dDiE, iDiE, nDiE) + if (nRetO > 0): PlotEtax("RetO", aRetO, RetO0, dRetO, iRetO, nRetO) + if (nRotO > 0): PlotEtax("RotO", aRotO, RotO0, dRotO, iRotO, nRotO) + if (nDiO > 0): PlotEtax("DiO", aDiO, DiO0, dDiO, iDiO, nDiO) + if (nDiC > 0): PlotEtax("DiC", aDiC, DiC0, dDiC, iDiC, nDiC) + if (nRotC > 0): PlotEtax("RotC", aRotC, RotC0, dRotC, iRotC, nRotC) + if (nRetC > 0): PlotEtax("RetC", aRetC, RetC0, dRetC, iRetC, nRetC) + if (nTP > 0): PlotEtax("TP", aTP, TP0, dTP, iTP, nTP) + if (nTS > 0): PlotEtax("TS", aTS, TS0, dTS, iTS, nTS) + if (nRP > 0): PlotEtax("RP", aRP, RP0, dRP, iRP, nRP) + if (nRS > 0): PlotEtax("RS", aRS, RS0, dRS, iRS, nRS) + if (nRetT > 0): PlotEtax("RetT", aRetT, RetT0, dRetT, iRetT, nRetT) + if (nRetR > 0): PlotEtax("RetR", aRetR, RetR0, dRetR, iRetR, nRetR) + if (nERaT > 0): PlotEtax("ERaT", aERaT, ERaT0, dERaT, iERaT, nERaT) + if (nERaR > 0): PlotEtax("ERaR", aERaR, ERaR0, dERaR, iERaR, nERaR) + if (nRotaT > 0): PlotEtax("RotaT", aRotaT, RotaT0, dRotaT, iRotaT, nRotaT) + if (nRotaR > 0): PlotEtax("RotaR", aRotaR, RotaR0, dRotaR, iRotaR, nRotaR) + if (nLDRCal > 0): PlotEtax("LDRCal", aLDRCal, LDRCal0, dLDRCal, iLDRCal, nLDRCal) + if (nTCalT > 0): PlotEtax("TCalT", aTCalT, TCalT0, dTCalT, iTCalT, nTCalT) + if (nTCalR > 0): PlotEtax("TCalR", aTCalR, TCalR0, dTCalR, iTCalR, nTCalR) + if (nNCal > 0): PlotEtax("CalNoiseTp", aNCalTp, 0, 1, iNCalTp, nNCal) + if (nNCal > 0): PlotEtax("CalNoiseTm", aNCalTm, 0, 1, iNCalTm, nNCal) + if (nNCal > 0): PlotEtax("CalNoiseRp", aNCalRp, 0, 1, iNCalRp, nNCal) + if (nNCal > 0): PlotEtax("CalNoiseRm", aNCalRm, 0, 1, iNCalRm, nNCal) + if (nNI > 0): PlotEtax("SigNoiseIt", aNIt, 0, 1, iNIt, nNI) + if (nNI > 0): PlotEtax("SigNoiseIr", aNIr, 0, 1, iNIr, nNI) + plt.show() + plt.close + print("---------------------------------------...producing more plots...------------------------------------------------------------------") + + if (nQin > 0): PlotEtapx("Qin", aQin, Qin0, dQin, iQin, nQin) + if (nVin > 0): PlotEtapx("Vin", aVin, Vin0, dVin, iVin, nVin) + if (nRotL > 0): PlotEtapx("RotL", aRotL, RotL0, dRotL, iRotL, nRotL) + if (nRetE > 0): PlotEtapx("RetE", aRetE, RetE0, dRetE, iRetE, nRetE) + if (nRotE > 0): PlotEtapx("RotE", aRotE, RotE0, dRotE, iRotE, nRotE) + if (nDiE > 0): PlotEtapx("DiE", aDiE, DiE0, dDiE, iDiE, nDiE) + if (nRetO > 0): PlotEtapx("RetO", aRetO, RetO0, dRetO, iRetO, nRetO) + if (nRotO > 0): PlotEtapx("RotO", aRotO, RotO0, dRotO, iRotO, nRotO) + if (nDiO > 0): PlotEtapx("DiO", aDiO, DiO0, dDiO, iDiO, nDiO) + if (nDiC > 0): PlotEtapx("DiC", aDiC, DiC0, dDiC, iDiC, nDiC) + if (nRotC > 0): PlotEtapx("RotC", aRotC, RotC0, dRotC, iRotC, nRotC) + if (nRetC > 0): PlotEtapx("RetC", aRetC, RetC0, dRetC, iRetC, nRetC) + if (nTP > 0): PlotEtapx("TP", aTP, TP0, dTP, iTP, nTP) + if (nTS > 0): PlotEtapx("TS", aTS, TS0, dTS, iTS, nTS) + if (nRP > 0): PlotEtapx("RP", aRP, RP0, dRP, iRP, nRP) + if (nRS > 0): PlotEtapx("RS", aRS, RS0, dRS, iRS, nRS) + if (nRetT > 0): PlotEtapx("RetT", aRetT, RetT0, dRetT, iRetT, nRetT) + if (nRetR > 0): PlotEtapx("RetR", aRetR, RetR0, dRetR, iRetR, nRetR) + if (nERaT > 0): PlotEtapx("ERaT", aERaT, ERaT0, dERaT, iERaT, nERaT) + if (nERaR > 0): PlotEtapx("ERaR", aERaR, ERaR0, dERaR, iERaR, nERaR) + if (nRotaT > 0): PlotEtapx("RotaT", aRotaT, RotaT0, dRotaT, iRotaT, nRotaT) + if (nRotaR > 0): PlotEtapx("RotaR", aRotaR, RotaR0, dRotaR, iRotaR, nRotaR) + if (nLDRCal > 0): PlotEtapx("LDRCal", aLDRCal, LDRCal0, dLDRCal, iLDRCal, nLDRCal) + if (nTCalT > 0): PlotEtapx("TCalT", aTCalT, TCalT0, dTCalT, iTCalT, nTCalT) + if (nTCalR > 0): PlotEtapx("TCalR", aTCalR, TCalR0, dTCalR, iTCalR, nTCalR) + if (nNCal > 0): PlotEtapx("CalNoiseTp", aNCalTp, 0, 1, iNCalTp, nNCal) + if (nNCal > 0): PlotEtapx("CalNoiseTm", aNCalTm, 0, 1, iNCalTm, nNCal) + if (nNCal > 0): PlotEtapx("CalNoiseRp", aNCalRp, 0, 1, iNCalRp, nNCal) + if (nNCal > 0): PlotEtapx("CalNoiseRm", aNCalRm, 0, 1, iNCalRm, nNCal) + if (nNI > 0): PlotEtapx("SigNoiseIt", aNIt, 0, 1, iNIt, nNI) + if (nNI > 0): PlotEtapx("SigNoiseIr", aNIr, 0, 1, iNIr, nNI) + plt.show() + plt.close + print("---------------------------------------...producing more plots...------------------------------------------------------------------") + + if (nQin > 0): PlotEtamx("Qin", aQin, Qin0, dQin, iQin, nQin) + if (nVin > 0): PlotEtamx("Vin", aVin, Vin0, dVin, iVin, nVin) + if (nRotL > 0): PlotEtamx("RotL", aRotL, RotL0, dRotL, iRotL, nRotL) + if (nRetE > 0): PlotEtamx("RetE", aRetE, RetE0, dRetE, iRetE, nRetE) + if (nRotE > 0): PlotEtamx("RotE", aRotE, RotE0, dRotE, iRotE, nRotE) + if (nDiE > 0): PlotEtamx("DiE", aDiE, DiE0, dDiE, iDiE, nDiE) + if (nRetO > 0): PlotEtamx("RetO", aRetO, RetO0, dRetO, iRetO, nRetO) + if (nRotO > 0): PlotEtamx("RotO", aRotO, RotO0, dRotO, iRotO, nRotO) + if (nDiO > 0): PlotEtamx("DiO", aDiO, DiO0, dDiO, iDiO, nDiO) + if (nDiC > 0): PlotEtamx("DiC", aDiC, DiC0, dDiC, iDiC, nDiC) + if (nRotC > 0): PlotEtamx("RotC", aRotC, RotC0, dRotC, iRotC, nRotC) + if (nRetC > 0): PlotEtamx("RetC", aRetC, RetC0, dRetC, iRetC, nRetC) + if (nTP > 0): PlotEtamx("TP", aTP, TP0, dTP, iTP, nTP) + if (nTS > 0): PlotEtamx("TS", aTS, TS0, dTS, iTS, nTS) + if (nRP > 0): PlotEtamx("RP", aRP, RP0, dRP, iRP, nRP) + if (nRS > 0): PlotEtamx("RS", aRS, RS0, dRS, iRS, nRS) + if (nRetT > 0): PlotEtamx("RetT", aRetT, RetT0, dRetT, iRetT, nRetT) + if (nRetR > 0): PlotEtamx("RetR", aRetR, RetR0, dRetR, iRetR, nRetR) + if (nERaT > 0): PlotEtamx("ERaT", aERaT, ERaT0, dERaT, iERaT, nERaT) + if (nERaR > 0): PlotEtamx("ERaR", aERaR, ERaR0, dERaR, iERaR, nERaR) + if (nRotaT > 0): PlotEtamx("RotaT", aRotaT, RotaT0, dRotaT, iRotaT, nRotaT) + if (nRotaR > 0): PlotEtamx("RotaR", aRotaR, RotaR0, dRotaR, iRotaR, nRotaR) + if (nLDRCal > 0): PlotEtamx("LDRCal", aLDRCal, LDRCal0, dLDRCal, iLDRCal, nLDRCal) + if (nTCalT > 0): PlotEtamx("TCalT", aTCalT, TCalT0, dTCalT, iTCalT, nTCalT) + if (nTCalR > 0): PlotEtamx("TCalR", aTCalR, TCalR0, dTCalR, iTCalR, nTCalR) + if (nNCal > 0): PlotEtamx("CalNoiseTp", aNCalTp, 0, 1, iNCalTp, nNCal) + if (nNCal > 0): PlotEtamx("CalNoiseTm", aNCalTm, 0, 1, iNCalTm, nNCal) + if (nNCal > 0): PlotEtamx("CalNoiseRp", aNCalRp, 0, 1, iNCalRp, nNCal) + if (nNCal > 0): PlotEtamx("CalNoiseRm", aNCalRm, 0, 1, iNCalRm, nNCal) + if (nNI > 0): PlotEtamx("SigNoiseIt", aNIt, 0, 1, iNIt, nNI) + if (nNI > 0): PlotEtamx("SigNoiseIr", aNIr, 0, 1, iNIr, nNI) + plt.show() + plt.close + + # Print Etax statistics + Etaxmin = np.amin(aEtax[1, :]) + Etaxmax = np.amax(aEtax[1, :]) + Etaxstd = np.std(aEtax[1, :]) + Etaxmean = np.mean(aEtax[1, :]) + Etaxmedian = np.median(aEtax[1, :]) + print("Etax , max-mean, min-mean, median, mean ± std, eta") + print("{0:8.5f} ±({1:8.5f},{2:8.5f}),{3:8.5f},{4:8.5f}±{5:8.5f},{6:8.5f}".format(Etax0, Etaxmax-Etax0, Etaxmin-Etax0, Etaxmedian, Etaxmean, Etaxstd, Etax0 / K0)) + print() + + # Calculate and print statistics for calibration factors + iLDR = -1 + LDRrangeA = np.array(LDRrange) + print("LDR...., LDRsim, (max-min)/2, relerr") + for LDRTrue in LDRrange: + iLDR = iLDR + 1 + LDRsimmin[iLDR] = np.amin(aLDRsim[iLDR, :]) + LDRsimmax[iLDR] = np.amax(aLDRsim[iLDR, :]) + # LDRsimstd = np.std(aLDRsim[iLDR, :]) + LDRsimmean[iLDR] = np.mean(aLDRsim[iLDR, :]) + # LDRsimmedian = np.median(aLDRsim[iLDR, :]) + print("{0:8.5f}, {1:8.5f}, {2:8.5f}, {3:8.5f}".format(LDRrangeA[iLDR], LDRsimmean[iLDR], (LDRsimmax[iLDR]-LDRsimmin[iLDR])/2, (LDRsimmax[iLDR]-LDRsimmin[iLDR])/2/LDRsimmean[iLDR])) + iLDR = -1 + print("LDR...., Etax , (max-min)/2, relerr") + for LDRTrue in LDRrange: + iLDR = iLDR + 1 + Etaxmin = np.amin(aEtax[iLDR, :]) + Etaxmax = np.amax(aEtax[iLDR, :]) + # Etaxstd = np.std(aEtax[iLDR, :]) + Etaxmean = np.mean(aEtax[iLDR, :]) + # Etaxmedian = np.median(aEtax[iLDR, :]) + print("{0:8.5f}, {1:8.5f}, {2:8.5f}, {3:8.5f}".format(LDRrangeA[iLDR], Etaxmean, (Etaxmax-Etaxmin)/2, (Etaxmax-Etaxmin)/2/Etaxmean)) + iLDR = -1 + print("LDR...., Etapx , (max-min)/2, relerr") + for LDRTrue in LDRrange: + iLDR = iLDR + 1 + Etapxmin = np.amin(aEtapx[iLDR, :]) + Etapxmax = np.amax(aEtapx[iLDR, :]) + # Etapxstd = np.std(aEtapx[iLDR, :]) + Etapxmean = np.mean(aEtapx[iLDR, :]) + # Etapxmedian = np.median(aEtapx[iLDR, :]) + print("{0:8.5f}, {1:8.5f}, {2:8.5f}, {3:8.5f}".format(LDRrangeA[iLDR], Etapxmean, (Etapxmax-Etapxmin)/2, (Etapxmax-Etapxmin)/2/Etapxmean)) + iLDR = -1 + print("LDR...., Etamx , (max-min)/2, relerr") + for LDRTrue in LDRrange: + iLDR = iLDR + 1 + Etamxmin = np.amin(aEtamx[iLDR, :]) + Etamxmax = np.amax(aEtamx[iLDR, :]) + # Etamxstd = np.std(aEtamx[iLDR, :]) + Etamxmean = np.mean(aEtamx[iLDR, :]) + # Etamxmedian = np.median(aEtamx[iLDR, :]) + print("{0:8.5f}, {1:8.5f}, {2:8.5f}, {3:8.5f}".format(LDRrangeA[iLDR], Etamxmean, (Etamxmax-Etamxmin)/2, (Etamxmax-Etamxmin)/2/Etamxmean)) + + f.close() + + +''' + # --- Plot F11 histograms + print() + print(" ############################################################################## ") + print(Text1) + print() + + iLDR = 5 + for LDRTrue in LDRrange: + iLDR = iLDR - 1 + #aF11corr[iLDR,:] = aF11corr[iLDR,:] / aF11corr[0,:] - 1.0 + aF11corr[iLDR,:] = aF11corr[iLDR,:] / aF11sim0[iLDR] - 1.0 + # Plot F11 + def PlotSubHistF11(aVar, aX, X0, daX, iaX, naX): + fig, ax = plt.subplots(nrows=1, ncols=5, sharex=True, sharey=True, figsize=(25, 2)) + iLDR = -1 + for LDRTrue in LDRrange: + iLDR = iLDR + 1 + + #F11min[iLDR] = np.min(aF11corr[iLDR,:]) + #F11max[iLDR] = np.max(aF11corr[iLDR,:]) + #Rmin = F11min[iLDR] * 0.995 # np.min(aLDRcorr[iLDR,:]) * 0.995 + #Rmax = F11max[iLDR] * 1.005 # np.max(aLDRcorr[iLDR,:]) * 1.005 + + #Rmin = 0.8 + #Rmax = 1.2 + + #plt.subplot(5,2,iLDR+1) + plt.subplot(1,5,iLDR+1) + (n, bins, patches) = plt.hist(aF11corr[iLDR,:], + bins=100, log=False, + alpha=0.5, density=False, color = '0.5', histtype='stepfilled') + + for iaX in range(-naX,naX+1): + plt.hist(aF11corr[iLDR,aX == iaX], + bins=100, log=False, alpha=0.3, density=False, histtype='stepfilled', label = str(round(X0 + iaX*daX/naX,5))) + + if (iLDR == 2): plt.legend() + + plt.tick_params(axis='both', labelsize=9) + #plt.plot([LDRTrue, LDRTrue], [0, np.max(n)], 'r-', lw=2) + + #plt.title(LID + ' ' + aVar, fontsize=18) + #plt.ylabel('frequency', fontsize=10) + #plt.xlabel('LDRCorr', fontsize=10) + #fig.tight_layout() + fig.suptitle(LID + ' ' + str(Type[TypeC]) + ' ' + str(Loc[LocC]) + ' - ' + aVar, fontsize=14, y=1.05) + #plt.show() + #fig.savefig(LID + '_' + aVar + '.png', dpi=150, bbox_inches='tight', pad_inches=0) + #plt.close + return + + if (nQin > 0): PlotSubHistF11("Qin", aQin, Qin0, dQin, iQin, nQin) + if (nVin > 0): PlotSubHistF11("Vin", aVin, Vin0, dVin, iVin, nVin) + if (nRotL > 0): PlotSubHistF11("RotL", aRotL, RotL0, dRotL, iRotL, nRotL) + if (nRetE > 0): PlotSubHistF11("RetE", aRetE, RetE0, dRetE, iRetE, nRetE) + if (nRotE > 0): PlotSubHistF11("RotE", aRotE, RotE0, dRotE, iRotE, nRotE) + if (nDiE > 0): PlotSubHistF11("DiE", aDiE, DiE0, dDiE, iDiE, nDiE) + if (nRetO > 0): PlotSubHistF11("RetO", aRetO, RetO0, dRetO, iRetO, nRetO) + if (nRotO > 0): PlotSubHistF11("RotO", aRotO, RotO0, dRotO, iRotO, nRotO) + if (nDiO > 0): PlotSubHistF11("DiO", aDiO, DiO0, dDiO, iDiO, nDiO) + if (nDiC > 0): PlotSubHistF11("DiC", aDiC, DiC0, dDiC, iDiC, nDiC) + if (nRotC > 0): PlotSubHistF11("RotC", aRotC, RotC0, dRotC, iRotC, nRotC) + if (nRetC > 0): PlotSubHistF11("RetC", aRetC, RetC0, dRetC, iRetC, nRetC) + if (nTP > 0): PlotSubHistF11("TP", aTP, TP0, dTP, iTP, nTP) + if (nTS > 0): PlotSubHistF11("TS", aTS, TS0, dTS, iTS, nTS) + if (nRP > 0): PlotSubHistF11("RP", aRP, RP0, dRP, iRP, nRP) + if (nRS > 0): PlotSubHistF11("RS", aRS, RS0, dRS, iRS, nRS) + if (nRetT > 0): PlotSubHistF11("RetT", aRetT, RetT0, dRetT, iRetT, nRetT) + if (nRetR > 0): PlotSubHistF11("RetR", aRetR, RetR0, dRetR, iRetR, nRetR) + if (nERaT > 0): PlotSubHistF11("ERaT", aERaT, ERaT0, dERaT, iERaT, nERaT) + if (nERaR > 0): PlotSubHistF11("ERaR", aERaR, ERaR0, dERaR, iERaR, nERaR) + if (nRotaT > 0): PlotSubHistF11("RotaT", aRotaT, RotaT0, dRotaT, iRotaT, nRotaT) + if (nRotaR > 0): PlotSubHistF11("RotaR", aRotaR, RotaR0, dRotaR, iRotaR, nRotaR) + if (nLDRCal > 0): PlotSubHistF11("LDRCal", aLDRCal, LDRCal0, dLDRCal, iLDRCal, nLDRCal) + if (nTCalT > 0): PlotSubHistF11("TCalT", aTCalT, TCalT0, dTCalT, iTCalT, nTCalT) + if (nTCalR > 0): PlotSubHistF11("TCalR", aTCalR, TCalR0, dTCalR, iTCalR, nTCalR) + if (nNCal > 0): PlotSubHistF11("CalNoise", aNCal, 0, 1/nNCal, iNCal, nNCal) + if (nNI > 0): PlotSubHistF11("SigNoise", aNI, 0, 1/nNI, iNI, nNI) + + + plt.show() + plt.close + + ''' +''' + # only histogram + #print("******************* " + aVar + " *******************") + fig, ax = plt.subplots(nrows=5, ncols=2, sharex=True, sharey=True, figsize=(10, 10)) + iLDR = -1 + for LDRTrue in LDRrange: + iLDR = iLDR + 1 + LDRmin[iLDR] = np.min(aLDRcorr[iLDR,:]) + LDRmax[iLDR] = np.max(aLDRcorr[iLDR,:]) + Rmin = np.min(aLDRcorr[iLDR,:]) * 0.999 + Rmax = np.max(aLDRcorr[iLDR,:]) * 1.001 + plt.subplot(5,2,iLDR+1) + (n, bins, patches) = plt.hist(aLDRcorr[iLDR,:], + range=[Rmin, Rmax], + bins=200, log=False, alpha=0.2, density=False, color = '0.5', histtype='stepfilled') + plt.tick_params(axis='both', labelsize=9) + plt.plot([LDRTrue, LDRTrue], [0, np.max(n)], 'r-', lw=2) + plt.show() + plt.close + # --- End of Plot F11 histograms + ''' + + +''' + # --- Plot K over LDRCal + fig3 = plt.figure() + plt.plot(LDRCal0+aLDRCal*dLDRCal/nLDRCal,aGHK[4,:], linewidth=2.0, color='b') + + plt.xlabel('LDRCal', fontsize=18) + plt.ylabel('K', fontsize=14) + plt.title(LID, fontsize=18) + plt.show() + plt.close + ''' + +# Additional plot routines ======> +''' +#****************************************************************************** +# 1. Plot LDRCorrected - LDR(measured Icross/Iparallel) +LDRa = np.arange(1.,100.)*0.005 +LDRCorra = np.arange(1.,100.) +if Y == - 1.: LDRa = 1./LDRa +LDRCorra = (1./Eta*LDRa*(GT+HT)-(GR+HR))/((GR-HR)-1./Eta*LDRa*(GT-HT)) +if Y == - 1.: LDRa = 1./LDRa +# +#fig = plt.figure() +plt.plot(LDRa,LDRCorra-LDRa) +plt.plot([0.,0.5],[0.,0.5]) +plt.suptitle('LDRCorrected - LDR(measured Icross/Iparallel)', fontsize=16) +plt.xlabel('LDR', fontsize=18) +plt.ylabel('LDRCorr - LDR', fontsize=16) +#plt.savefig('test.png') +# +''' +''' +#****************************************************************************** +# 2. Plot LDRsim (simulated measurements without corrections = Icross/Iparallel) over LDRtrue +LDRa = np.arange(1.,100.)*0.005 +LDRsima = np.arange(1.,100.) + +atruea = (1.-LDRa)/(1+LDRa) +Ita = TiT*TiO*IinL*(GT+atruea*HT) +Ira = TiR*TiO*IinL*(GR+atruea*HR) +LDRsima = Ira/Ita # simulated uncorrected LDR with Y from input file +if Y == -1.: LDRsima = 1./LDRsima +# +#fig = plt.figure() +plt.plot(LDRa,LDRsima) +plt.plot([0.,0.5],[0.,0.5]) +plt.suptitle('LDRsim (simulated measurements without corrections = Icross/Iparallel) over LDRtrue', fontsize=10) +plt.xlabel('LDRtrue', fontsize=18) +plt.ylabel('LDRsim', fontsize=16) +#plt.savefig('test.png') +# +''' \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README_2_05.06.2020_141855.md Sun Oct 04 22:22:08 2020 +0200 @@ -0,0 +1,34 @@ +Calculation of polarisation correction factors for atmospheric lidar system, developed by Volker Freudenthaler (LMU, Munich, Germany). + +# Theory +The theoretical basis of the script is described in detail in : + +Freudenthaler, V.: +About the effects of polarising optics on lidar signals and the Δ90 calibration, +Atmos. Meas. Tech., 9, 4181-4255, doi:10.5194/amt-9-4181-2016, 2016 +http://www.atmos-meas-tech.net/9/4181/2016/ + +Additional information can be found in: + +Bravo-Aranda, J. A., Belegante, L., Freudenthaler, V., Alados-Arboledas, L., Nicolae, D., Granados-Muñoz, M. J., +Guerrero-Rascado, J. L., Amodeo, A., D'Amico, G., Engelmann, R., Pappalardo, G., Kokkalis, P., Mamouri, R., +Papayannis, A., Navas-Guzmán, F., Olmo, F. J., Wandinger, U., Amato, F., and +Haeffelin, M.: +Assessment of lidar depolarization uncertainty by means of a polarimetric lidar simulator, +Atmos. Meas. Tech., 9, 4935-4953, doi:10.5194/amt-9-4935-2016, 2016. +http://www.atmos-meas-tech.net/9/4935/2016/ + +# Use +To run the script you need to: + +1. Read the information in the script header. + +1. Create a file describing your system settings and parameters. You can find an example file in the "system_settings" + folder. Give a descriptive name and save it in the "system_settings" folder. + +2. Edit the "GHK_0.9.8e5_Py3.7.py" or the current script file with an ASCII editor and set the variable InputFile below line 281 to the filename you chose in step 1. + +3. Run the script "GHK_0.9.8e5_Py3.7.py" (or the current version). + +Note: _0.9.8e5 in the script filename is the script version and _Py3.7 indicates that it is tested with Python 3.7. +
