4Misc_Starth4Platformh@9VersionCheck xHH@Rg(HHdh xHH@Rg(HHdh x HH@Rg(HHdh ^Graph*GrWDashSettings#  ! -6Normal@ System Font Fixed Width<HHHH$$4 4 4 4 4 4 homeZd Macintosh HD:Users:amberlyxie:Desktop:dragnea.2:bodipy vlp:R Macintosh HDBD bodipy vlp cu dragnea.20/:Users:amberlyxie:Desktop:dragnea.2:bodipy vlp/ bodipy vlp Macintosh HD-Users/amberlyxie/Desktop/dragnea.2/bodipy vlp/RecentWindowstAdvanced Topics.ihfAnalysis.ihfCurve Fitting.ihfDemoLoader.ipfDialog Help.ihfErrors.ihfFitSetupPanelGetting Started.ihfGraph0:vlp3peaks,... vs WV;...Graph1:fitVlp1,...Graph2:fit_ecpeaks,...Graph3:vlp2peaks vs WV;...Graph4:vlp3peaks,... vs WV;ec vs WV;...Graph5:ecandbdp1,ecAndBdp2Table0:fit_vlp1peaks,fitVlp1,fit_bdpdmso,fit_ecpeaks,fitBdpDmso,fitEcPeaks... 4Misc_EndhTXOPState_Start hPeakFunctions2-64f4XOPState_EndhH {w @YS"N1V_FlagV_chisqvc7O?V_numNaNsV_numINFsV_npntsps@V_nterms@V_nheldV_startRowV_endRow`s@V_startColV_endColV_startLayerV_endLayerV_startChunkV_endChunkg_bxmp_kindmp_pnts @mp_noisemp_logmp_minxmp_maxxmpr_polmir_whatmir_annofp_pol?fp_minamptfp_poltfp_extenttfp_mwtfp_whatfpks_extentar_annoar_exafp_annoV_tol|=V_peakXV_peakPV_start@V_theEndr@V_startX@V_theEndX@afp_pksg_ptgng_atgnfpks_pktypepfr_orderpfr_bigraphleft8@graphtopK@graphright@graphbottom(@V_avgGz V_sdevf¾P?V_sem'Fm?V_rmsc@c?V_minlocRJ)Ty@V_min`C/V_maxlocs9q@V_max8?V_adev~У?V_skew b漵@V_kurtW:U9@V_minChunkLocV_maxChunkLocV_minLayerLocV_maxLayerLocV_maxRowLoc1@V_minRowLocU@V_maxColLocV_minColLocV_Sum02S_waveNames0D.5M2;S_pathamesMY USB:bodipy vlp:3.17.21:S_fileNamebdpvlp0d5m1.csvg_wleNameVLP1g_wxeNameWVg_beName_None_g_keepamep_wepame"peak data wave, including baselinep_wxpame X coordinates for peak data wavep_bpamebaselinep_wdpame data wavep_wxdameX coordinates for data wavep_bxameapproximate peak x width at 50%p_ptsame7(2;(4;(8;16;32;64;128;256;512;1024;2048;4096;8192;16384fbar_wrmefbar_fitefbar_wobasemir_pwhase2counts;integrate rectangular;integrate trapezoidalS_funcsaseZ line 2; poly 3 ; poly 4 ; poly 5 ; sin 4; dblexp 4; exp 3; lor 4; gauss 4;ar_wfitasear_owtasepfr_titleeS_Waveleenewvelee_New_;noneelee_None_;calcelee _Calculated_;rb_belee_None_fpks_blee_None_afp_blee_None_rb_owleefpks_weightspfr_sorthts* General text load from "ec.csv" Data length: 611, waves: WV, EC1 General text load from "ec1.csv" Data length: 611, waves: EC11 General text load from "vlp 1.csv" Data length: 611, waves: VLP11 General text load from "vlp 11.csv" Data length: 611, waves: VLP111 General text load from "vlp 2.csv" Data length: 611, waves: VLP21 General text load from "vlp 22.csv" Data length: 611, waves: VLP22 General text load from "vlp 3.csv" Data length: 611, waves: VLP31 General text load from "vlp 33.csv" Data length: 611, waves: VLP32 •make/N=(numpnts(EC1)) EC •EC=(EC1+EC11)/2 •make/N=(numpnts(VLP11)) VLP1 •VLP1=(VLP11+VLP111)/2 •make/N=(numpnts(VLP21)) VLP2 •VLP2=(VLP21+VLP22)/2 •make/N=(numpnts(VLP31)) VLP3 •VLP3=(VLP31+VLP32)/2 •Display EC,VLP1,VLP2,VLP3 vs WV as "VLP-BODIPY UV/Vis" •ModifyGraph rgb(VLP1)=(65535,65535,0),rgb(VLP2)=(0,65535,0),rgb(VLP3)=(1,16019,65535) •ModifyGraph rgb(VLP1)=(65535,43690,0) •ModifyGraph rgb(VLP1)=(65535,65532,16385) •Label left "Absorption";DelayUpdate •Label bottom "Wavelength (nm)" •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •RemoveFromGraph Res_EC •RemoveFromGraph Bkg_EC •RemoveFromGraph fit_EC Set Y data= EC, X data= WV, fit wave= fit_EC, residuals= res_EC Range set from graph: points= [610,0] ( equivalent to x= (240,850) ) FuncFit/M=2/H=EC_HoldStr fGaussFitBL coef :::EC[0,610] /X= :::WV /D=:::fit_EC 40 iterations with no convergence Curve fit with data subrange: EC[0,610] fit_EC[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,-0.17344,0.33771,0.8674,-1.8174,0.13002,278.19,14.234,0.21041,469.4,209.46} V_chisq= 0.0223143;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.0538,0.0872,0.128,0.222,0.00203,0.168,0.283,0.0621,8.78,19.8} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =-0.17344 ± 0.0538 K3 =0.33771 ± 0.0872 K4 =0.8674 ± 0.128 K5 =-1.8174 ± 0.222 K6 =0.13002 ± 0.00203 K7 =278.19 ± 0.168 K8 =14.234 ± 0.283 K9 =0.21041 ± 0.0621 K10 =469.4 ± 8.78 K11 =209.46 ± 19.8 •RemoveFromGraph Peak2 •RemoveFromGraph Peak1 •RemoveFromGraph fit_EC •RemoveFromGraph res_EC •RemoveFromGraph 'Peak 0' Reset using same data Range set from graph: points= [610,0] ( equivalent to x= (240,850) ) Reset using same data Range set from graph: points= [610,0] ( equivalent to x= (240,850) ) Reset using same data Range set from graph: points= [610,0] ( equivalent to x= (240,850) ) Reset using same data Range set from graph: points= [610,0] ( equivalent to x= (240,850) ) Reset using same data Range set from graph: points= [610,0] ( equivalent to x= (240,850) ) FuncFit/M=2/H=EC_HoldStr fGaussFitBL coef :::EC[0,610] /X= :::WV /D=:::fit_EC Fit converged properly Curve fit with data subrange: EC[0,610] fit_EC[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.0011729,0.022763,0.18928,-0.58727,0.13494,275.47,17.584} V_chisq= 0.0871356;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.000761,0.0044,0.00894,0.0317,0.00359,0.348,0.641} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.0011729 ± 0.000761 K3 =0.022763 ± 0.0044 K4 =0.18928 ± 0.00894 K5 =-0.58727 ± 0.0317 K6 =0.13494 ± 0.00359 K7 =275.47 ± 0.348 K8 =17.584 ± 0.641 FuncFit/M=2/H=EC_HoldStr fGaussFitBL coef :::EC[0,610] /X= :::WV /D=:::fit_EC Fit converged properly Curve fit with data subrange: EC[0,610] fit_EC[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.0012911,0.021958,0.18624,-0.57699,0.13514,275.29,17.914} V_chisq= 0.0870759;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.000763,0.00441,0.00908,0.0321,0.00359,0.352,0.654} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.0012911 ± 0.000763 K3 =0.021958 ± 0.00441 K4 =0.18624 ± 0.00908 K5 =-0.57699 ± 0.0321 K6 =0.13514 ± 0.00359 K7 =275.29 ± 0.352 K8 =17.914 ± 0.654 Reset using same data Range set from graph: points= [610,0] ( equivalent to x= (240,850) ) FuncFit/M=2/H=EC_HoldStr fGaussFitBL coef :::EC[0,610] /X= :::WV /D=:::fit_EC Fit converged properly Curve fit with data subrange: EC[0,610] fit_EC[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.0011729,0.022763,0.18928,-0.58727,0.13494,275.47,17.584} V_chisq= 0.0871356;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.000761,0.0044,0.00894,0.0317,0.00359,0.348,0.641} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.0011729 ± 0.000761 K3 =0.022763 ± 0.0044 K4 =0.18928 ± 0.00894 K5 =-0.58727 ± 0.0317 K6 =0.13494 ± 0.00359 K7 =275.47 ± 0.348 K8 =17.584 ± 0.641 •RemoveFromGraph Peak1 •CurveFit/X=1 exp VLP3 /X=WV /D FitProgressDialog allocating a dialogFitFunction instance Fit converged properly fit_VLP3= W_coef[0]+W_coef[1]*exp(-W_coef[2]*x) W_coef={-0.034443,0.77466,0.0037499} V_chisq= 3.35765;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0.0175,0.0857,0.000544} Coefficient values ± one standard deviation y0 =-0.034443 ± 0.0175 A =0.77466 ± 0.0857 invTau =0.0037499 ± 0.000544 •RemoveFromGraph fit_VLP3#1 •RemoveFromGraph res_EC •RemoveFromGraph fit_EC General text load from "vlp1.csv" Data length: 611, waves: VLP1_C General text load from "vlp2.csv" Data length: 611, waves: VLP2_C General text load from "vlp3.csv" Data length: 611, waves: VLP3_c •Rename VLP3_c,VLP3_C •KillWaves fit_EC,fit_VLP3,res_EC •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •AppendToGraph VLP3_C,VLP2_C,VLP1_C vs WV •RemoveFromGraph EC FuncFit/M=2/H=EC_HoldStr fGaussFitBL coef :::EC[0,610] /X= :::WV /D= Set Y data= VLP1, X data= WV, fit wave= fit_VLP1, residuals= res_VLP1 Range set from graph: points= [610,0] ( equivalent to x= (240,850) ) FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 40 iterations with no convergence Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.01993,-0.16931,0.62006,-0.75978,0.07316,280.52,16.107,-0.26462,303.11,0} coef[11]={105.75,0.11474,334.2,-65.166,0.063021,489.99,22.391,0.10157,508.09,9.9658} V_chisq= 0.0061932;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00183,0.0255,0.102,0.121,0.00277,0.256,0.513,0.104,24.4,26.4,0.138,0} W_sigma[13]={8.61,14.1,0.00219,1.07,1.02,0.00458,0.114,0.312} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.01993 ± 0.00183 K3 =-0.16931 ± 0.0255 K4 =0.62006 ± 0.102 K5 =-0.75978 ± 0.121 K6 =0.07316 ± 0.00277 K7 =280.52 ± 0.256 K8 =16.107 ± 0.513 K9 =-0.26462 ± 0.104 K10 =303.11 ± 24.4 K11 =105.75 ± 26.4 K12 =0.11474 ± 0.138 K13 =334.2 ± 8.61 K14 =-65.166 ± 14.1 K15 =0.063021 ± 0.00219 K16 =489.99 ± 1.07 K17 =22.391 ± 1.02 K18 =0.10157 ± 0.00458 K19 =508.09 ± 0.114 K20 =9.9658 ± 0.312 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020421,-0.1814,0.6792,-0.83689,0.076101,280.4,16.494,-0.33904,303.81,0} coef[11]={103.57,0.16887,330.31,-69.786,0.062047,489.82,22.009,0.10237,508.08,9.98} V_chisq= 0.00602122;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00179,0.025,0.0998,0.119,0.0027,0.255,0.495,0.227,24.9,27.2,0.263,0} W_sigma[13]={10.7,16.3,0.00223,1.13,1.09,0.00487,0.114,0.316} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020421 ± 0.00179 K3 =-0.1814 ± 0.025 K4 =0.6792 ± 0.0998 K5 =-0.83689 ± 0.119 K6 =0.076101 ± 0.0027 K7 =280.4 ± 0.255 K8 =16.494 ± 0.495 K9 =-0.33904 ± 0.227 K10 =303.81 ± 24.9 K11 =103.57 ± 27.2 K12 =0.16887 ± 0.263 K13 =330.31 ± 10.7 K14 =-69.786 ± 16.3 K15 =0.062047 ± 0.00223 K16 =489.82 ± 1.13 K17 =22.009 ± 1.09 K18 =0.10237 ± 0.00487 K19 =508.08 ± 0.114 K20 =9.98 ± 0.316 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020435,-0.1819,0.6819,-0.84056,0.076298,280.4,16.521,-0.3463,304.09,0} coef[11]={103.18,0.17528,330,-70.175,0.06196,489.79,21.968,0.10249,508.08,9.9849} V_chisq= 0.00601135;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00178,0.0249,0.0994,0.118,0.00269,0.255,0.494,0.247,25.1,27.5,0.283,0} W_sigma[13]={11,16.7,0.00224,1.14,1.1,0.00491,0.114,0.317} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020435 ± 0.00178 K3 =-0.1819 ± 0.0249 K4 =0.6819 ± 0.0994 K5 =-0.84056 ± 0.118 K6 =0.076298 ± 0.00269 K7 =280.4 ± 0.255 K8 =16.521 ± 0.494 K9 =-0.3463 ± 0.247 K10 =304.09 ± 25.1 K11 =103.18 ± 27.5 K12 =0.17528 ± 0.283 K13 =330 ± 11 K14 =-70.175 ± 16.7 K15 =0.06196 ± 0.00224 K16 =489.79 ± 1.14 K17 =21.968 ± 1.1 K18 =0.10249 ± 0.00491 K19 =508.08 ± 0.114 K20 =9.9849 ± 0.317 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020444,-0.18228,0.68393,-0.84333,0.076468,280.39,16.544,-0.3529,304.36,0} coef[11]={102.81,0.18125,329.72,-70.512,0.061872,489.76,21.922,0.10263,508.08,9.9917} V_chisq= 0.0060031;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00177,0.0247,0.099,0.118,0.00269,0.255,0.493,0.267,25.2,27.7,0.302,0} W_sigma[13]={11.2,17,0.00224,1.14,1.11,0.00495,0.115,0.318} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020444 ± 0.00177 K3 =-0.18228 ± 0.0247 K4 =0.68393 ± 0.099 K5 =-0.84333 ± 0.118 K6 =0.076468 ± 0.00269 K7 =280.39 ± 0.255 K8 =16.544 ± 0.493 K9 =-0.3529 ± 0.267 K10 =304.36 ± 25.2 K11 =102.81 ± 27.7 K12 =0.18125 ± 0.302 K13 =329.72 ± 11.2 K14 =-70.512 ± 17 K15 =0.061872 ± 0.00224 K16 =489.76 ± 1.14 K17 =21.922 ± 1.11 K18 =0.10263 ± 0.00495 K19 =508.08 ± 0.115 K20 =9.9917 ± 0.318 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020453,-0.18258,0.6855,-0.84545,0.076618,280.38,16.565,-0.35897,304.62,0} coef[11]={102.46,0.18685,329.47,-70.807,0.061775,489.72,21.869,0.10282,508.08,10.001} V_chisq= 0.00599613;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00176,0.0246,0.0986,0.118,0.00269,0.255,0.493,0.286,25.4,28,0.322,0} W_sigma[13]={11.5,17.3,0.00225,1.15,1.13,0.00501,0.115,0.319} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020453 ± 0.00176 K3 =-0.18258 ± 0.0246 K4 =0.6855 ± 0.0986 K5 =-0.84545 ± 0.118 K6 =0.076618 ± 0.00269 K7 =280.38 ± 0.255 K8 =16.565 ± 0.493 K9 =-0.35897 ± 0.286 K10 =304.62 ± 25.4 K11 =102.46 ± 28 K12 =0.18685 ± 0.322 K13 =329.47 ± 11.5 K14 =-70.807 ± 17.3 K15 =0.061775 ± 0.00225 K16 =489.72 ± 1.15 K17 =21.869 ± 1.13 K18 =0.10282 ± 0.00501 K19 =508.08 ± 0.115 K20 =10.001 ± 0.319 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020462,-0.18283,0.68675,-0.84709,0.076754,280.38,16.584,-0.3646,304.87,0} coef[11]={102.13,0.19212,329.26,-71.066,0.061662,489.66,21.805,0.10307,508.08,10.015} V_chisq= 0.00599029;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00175,0.0245,0.0982,0.117,0.00269,0.255,0.492,0.305,25.5,28.2,0.34,0} W_sigma[13]={11.7,17.6,0.00225,1.17,1.14,0.00507,0.116,0.32} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020462 ± 0.00175 K3 =-0.18283 ± 0.0245 K4 =0.68675 ± 0.0982 K5 =-0.84709 ± 0.117 K6 =0.076754 ± 0.00269 K7 =280.38 ± 0.255 K8 =16.584 ± 0.492 K9 =-0.3646 ± 0.305 K10 =304.87 ± 25.5 K11 =102.13 ± 28.2 K12 =0.19212 ± 0.34 K13 =329.26 ± 11.7 K14 =-71.066 ± 17.6 K15 =0.061662 ± 0.00225 K16 =489.66 ± 1.17 K17 =21.805 ± 1.14 K18 =0.10307 ± 0.00507 K19 =508.08 ± 0.116 K20 =10.015 ± 0.32 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020475,-0.18307,0.6878,-0.84838,0.076883,280.38,16.603,-0.36988,305.1,0} coef[11]={101.82,0.19708,329.06,-71.292,0.061527,489.59,21.726,0.10338,508.08,10.033} V_chisq= 0.00598555;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00174,0.0244,0.0979,0.117,0.00269,0.255,0.492,0.324,25.7,28.4,0.359,0} W_sigma[13]={11.9,17.9,0.00226,1.18,1.16,0.00515,0.117,0.321} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020475 ± 0.00174 K3 =-0.18307 ± 0.0244 K4 =0.6878 ± 0.0979 K5 =-0.84838 ± 0.117 K6 =0.076883 ± 0.00269 K7 =280.38 ± 0.255 K8 =16.603 ± 0.492 K9 =-0.36988 ± 0.324 K10 =305.1 ± 25.7 K11 =101.82 ± 28.4 K12 =0.19708 ± 0.359 K13 =329.06 ± 11.9 K14 =-71.292 ± 17.9 K15 =0.061527 ± 0.00226 K16 =489.59 ± 1.18 K17 =21.726 ± 1.16 K18 =0.10338 ± 0.00515 K19 =508.08 ± 0.117 K20 =10.033 ± 0.321 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020494,-0.18332,0.68874,-0.8494,0.077007,280.37,16.621,-0.37487,305.31,0} coef[11]={101.54,0.20177,328.89,-71.491,0.061364,489.5,21.632,0.10377,508.08,10.057} V_chisq= 0.00598205;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00173,0.0243,0.0975,0.117,0.00269,0.255,0.491,0.341,25.8,28.6,0.376,0} W_sigma[13]={12.1,18.1,0.00226,1.2,1.18,0.00524,0.118,0.323} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020494 ± 0.00173 K3 =-0.18332 ± 0.0243 K4 =0.68874 ± 0.0975 K5 =-0.8494 ± 0.117 K6 =0.077007 ± 0.00269 K7 =280.37 ± 0.255 K8 =16.621 ± 0.491 K9 =-0.37487 ± 0.341 K10 =305.31 ± 25.8 K11 =101.54 ± 28.6 K12 =0.20177 ± 0.376 K13 =328.89 ± 12.1 K14 =-71.491 ± 18.1 K15 =0.061364 ± 0.00226 K16 =489.5 ± 1.2 K17 =21.632 ± 1.18 K18 =0.10377 ± 0.00524 K19 =508.08 ± 0.118 K20 =10.057 ± 0.323 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.02043,-0.18303,0.68883,-0.85038,0.076942,280.38,16.602,-0.37505,305.32,0} coef[11]={101.58,0.20207,328.88,-71.591,0.061918,489.96,22.085,0.10178,508.08,9.9426} V_chisq= 0.00597923;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00173,0.0243,0.0977,0.117,0.00269,0.255,0.492,0.345,25.9,28.7,0.38,0} W_sigma[13]={12.2,18.3,0.00223,1.11,1.07,0.00475,0.113,0.313} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.02043 ± 0.00173 K3 =-0.18303 ± 0.0243 K4 =0.68883 ± 0.0977 K5 =-0.85038 ± 0.117 K6 =0.076942 ± 0.00269 K7 =280.38 ± 0.255 K8 =16.602 ± 0.492 K9 =-0.37505 ± 0.345 K10 =305.32 ± 25.9 K11 =101.58 ± 28.7 K12 =0.20207 ± 0.38 K13 =328.88 ± 12.2 K14 =-71.591 ± 18.3 K15 =0.061918 ± 0.00223 K16 =489.96 ± 1.11 K17 =22.085 ± 1.07 K18 =0.10178 ± 0.00475 K19 =508.08 ± 0.113 K20 =9.9426 ± 0.313 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020397,-0.18283,0.68905,-0.85154,0.076916,280.37,16.599,-0.37963,0} coef[10]={305.52,101.35,0.20668,328.7,-71.89,0.062085,490.02,22.129,0.10151,508.08,9.9225} V_chisq= 0.00597506;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00173,0.0243,0.0976,0.117,0.00269,0.255,0.492,0.367,26.2,29.1,0.402,0} W_sigma[13]={12.6,18.7,0.00222,1.09,1.05,0.00468,0.113,0.311} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020397 ± 0.00173 K3 =-0.18283 ± 0.0243 K4 =0.68905 ± 0.0976 K5 =-0.85154 ± 0.117 K6 =0.076916 ± 0.00269 K7 =280.37 ± 0.255 K8 =16.599 ± 0.492 K9 =-0.37963 ± 0.367 K10 =305.52 ± 26.2 K11 =101.35 ± 29.1 K12 =0.20668 ± 0.402 K13 =328.7 ± 12.6 K14 =-71.89 ± 18.7 K15 =0.062085 ± 0.00222 K16 =490.02 ± 1.09 K17 =22.129 ± 1.05 K18 =0.10151 ± 0.00468 K19 =508.08 ± 0.113 K20 =9.9225 ± 0.311 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020382,-0.18279,0.68934,-0.85222,0.076961,280.37,16.604,-0.38398,0} coef[10]={305.73,101.1,0.21102,328.55,-72.105,0.062166,490.1,22.198,0.10117,508.08,9.9036} V_chisq= 0.00597193;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00172,0.0242,0.0974,0.116,0.00269,0.255,0.491,0.387,26.5,29.4,0.422,0} W_sigma[13]={12.8,19,0.00221,1.08,1.03,0.0046,0.113,0.309} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020382 ± 0.00172 K3 =-0.18279 ± 0.0242 K4 =0.68934 ± 0.0974 K5 =-0.85222 ± 0.116 K6 =0.076961 ± 0.00269 K7 =280.37 ± 0.255 K8 =16.604 ± 0.491 K9 =-0.38398 ± 0.387 K10 =305.73 ± 26.5 K11 =101.1 ± 29.4 K12 =0.21102 ± 0.422 K13 =328.55 ± 12.8 K14 =-72.105 ± 19 K15 =0.062166 ± 0.00221 K16 =490.1 ± 1.08 K17 =22.198 ± 1.03 K18 =0.10117 ± 0.0046 K19 =508.08 ± 0.113 K20 =9.9036 ± 0.309 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020366,-0.18275,0.68962,-0.85292,0.076991,280.37,16.606,-0.38814,0} coef[10]={305.92,100.89,0.21517,328.41,-72.316,0.062289,490.21,22.301,0.10069,508.09,9.8774} V_chisq= 0.00597015;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00172,0.0242,0.0973,0.116,0.00269,0.255,0.491,0.407,26.7,29.7,0.442,0} W_sigma[13]={13.1,19.3,0.0022,1.05,1.01,0.00448,0.112,0.306} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020366 ± 0.00172 K3 =-0.18275 ± 0.0242 K4 =0.68962 ± 0.0973 K5 =-0.85292 ± 0.116 K6 =0.076991 ± 0.00269 K7 =280.37 ± 0.255 K8 =16.606 ± 0.491 K9 =-0.38814 ± 0.407 K10 =305.92 ± 26.7 K11 =100.89 ± 29.7 K12 =0.21517 ± 0.442 K13 =328.41 ± 13.1 K14 =-72.316 ± 19.3 K15 =0.062289 ± 0.0022 K16 =490.21 ± 1.05 K17 =22.301 ± 1.01 K18 =0.10069 ± 0.00448 K19 =508.09 ± 0.112 K20 =9.8774 ± 0.306 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020417,-0.18324,0.69064,-0.85321,0.077204,280.37,16.638,-0.39055,0} coef[10]={305.99,100.72,0.21717,328.36,-72.259,0.061839,489.96,22.059,0.1018,508.08,9.9416} V_chisq= 0.00596546;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.0017,0.024,0.0968,0.116,0.00269,0.255,0.491,0.41,26.5,29.6,0.445,13,0} W_sigma[14]={19.2,0.00223,1.11,1.07,0.00476,0.113,0.313} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020417 ± 0.0017 K3 =-0.18324 ± 0.024 K4 =0.69064 ± 0.0968 K5 =-0.85321 ± 0.116 K6 =0.077204 ± 0.00269 K7 =280.37 ± 0.255 K8 =16.638 ± 0.491 K9 =-0.39055 ± 0.41 K10 =305.99 ± 26.5 K11 =100.72 ± 29.6 K12 =0.21717 ± 0.445 K13 =328.36 ± 13 K14 =-72.259 ± 19.2 K15 =0.061839 ± 0.00223 K16 =489.96 ± 1.11 K17 =22.059 ± 1.07 K18 =0.1018 ± 0.00476 K19 =508.08 ± 0.113 K20 =9.9416 ± 0.313 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020381,-0.18297,0.6905,-0.85391,0.077146,280.36,16.63,-0.39438,306.16,0} coef[11]={100.54,0.22114,328.21,-72.512,0.062029,490.03,22.113,0.10149,508.08,9.9192} V_chisq= 0.00596257;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.0017,0.024,0.0968,0.116,0.00269,0.255,0.491,0.432,26.8,30,0.467,13.3,0} W_sigma[14]={19.6,0.00222,1.09,1.05,0.00469,0.113,0.311} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020381 ± 0.0017 K3 =-0.18297 ± 0.024 K4 =0.6905 ± 0.0968 K5 =-0.85391 ± 0.116 K6 =0.077146 ± 0.00269 K7 =280.36 ± 0.255 K8 =16.63 ± 0.491 K9 =-0.39438 ± 0.432 K10 =306.16 ± 26.8 K11 =100.54 ± 30 K12 =0.22114 ± 0.467 K13 =328.21 ± 13.3 K14 =-72.512 ± 19.6 K15 =0.062029 ± 0.00222 K16 =490.03 ± 1.09 K17 =22.113 ± 1.05 K18 =0.10149 ± 0.00469 K19 =508.08 ± 0.113 K20 =9.9192 ± 0.311 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020365,-0.18289,0.69054,-0.85426,0.077167,280.36,16.631,-0.39808,0} coef[10]={306.33,100.35,0.2249,328.09,-72.687,0.062132,490.12,22.196,0.1011,508.08,9.8977} V_chisq= 0.00596072;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.0017,0.024,0.0966,0.116,0.00269,0.255,0.491,0.452,27,30.2,0.486,13.6,0} W_sigma[14]={19.9,0.00221,1.07,1.03,0.00459,0.112,0.309} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020365 ± 0.0017 K3 =-0.18289 ± 0.024 K4 =0.69054 ± 0.0966 K5 =-0.85426 ± 0.116 K6 =0.077167 ± 0.00269 K7 =280.36 ± 0.255 K8 =16.631 ± 0.491 K9 =-0.39808 ± 0.452 K10 =306.33 ± 27 K11 =100.35 ± 30.2 K12 =0.2249 ± 0.486 K13 =328.09 ± 13.6 K14 =-72.687 ± 19.9 K15 =0.062132 ± 0.00221 K16 =490.12 ± 1.07 K17 =22.196 ± 1.03 K18 =0.1011 ± 0.00459 K19 =508.08 ± 0.112 K20 =9.8977 ± 0.309 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020402,-0.18325,0.69128,-0.85444,0.077334,280.36,16.656,-0.40021,0} coef[10]={306.4,100.21,0.22674,328.05,-72.651,0.06179,489.94,22.016,0.10193,508.08,9.9459} V_chisq= 0.00595765;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00169,0.0239,0.0962,0.115,0.00269,0.255,0.491,0.456,26.9,30.1,0.49,0} W_sigma[13]={13.5,19.9,0.00224,1.12,1.08,0.0048,0.113,0.314} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020402 ± 0.00169 K3 =-0.18325 ± 0.0239 K4 =0.69128 ± 0.0962 K5 =-0.85444 ± 0.115 K6 =0.077334 ± 0.00269 K7 =280.36 ± 0.255 K8 =16.656 ± 0.491 K9 =-0.40021 ± 0.456 K10 =306.4 ± 26.9 K11 =100.21 ± 30.1 K12 =0.22674 ± 0.49 K13 =328.05 ± 13.5 K14 =-72.651 ± 19.9 K15 =0.06179 ± 0.00224 K16 =489.94 ± 1.12 K17 =22.016 ± 1.08 K18 =0.10193 ± 0.0048 K19 =508.08 ± 0.113 K20 =9.9459 ± 0.314 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020374,-0.18302,0.69108,-0.85486,0.077286,280.36,16.65,-0.40367,306.55,0} coef[11]={100.05,0.23034,327.92,-72.861,0.061942,489.99,22.062,0.10167,508.08,9.9278} V_chisq= 0.00595525;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00169,0.0239,0.0962,0.115,0.00269,0.255,0.49,0.477,27.2,30.5,0.511,0} W_sigma[13]={13.8,20.2,0.00223,1.1,1.07,0.00474,0.113,0.312} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020374 ± 0.00169 K3 =-0.18302 ± 0.0239 K4 =0.69108 ± 0.0962 K5 =-0.85486 ± 0.115 K6 =0.077286 ± 0.00269 K7 =280.36 ± 0.255 K8 =16.65 ± 0.49 K9 =-0.40367 ± 0.477 K10 =306.55 ± 27.2 K11 =100.05 ± 30.5 K12 =0.23034 ± 0.511 K13 =327.92 ± 13.8 K14 =-72.861 ± 20.2 K15 =0.061942 ± 0.00223 K16 =489.99 ± 1.1 K17 =22.062 ± 1.07 K18 =0.10167 ± 0.00474 K19 =508.08 ± 0.113 K20 =9.9278 ± 0.312 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020359,-0.18294,0.69105,-0.85508,0.077303,280.36,16.65,-0.40703,306.7,0} coef[11]={99.879,0.23377,327.83,-73.01,0.06203,490.07,22.131,0.10134,508.08,9.9096} V_chisq= 0.00595354;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00168,0.0238,0.0961,0.115,0.00269,0.255,0.49,0.495,27.4,30.7,0.53,0} W_sigma[13]={14,20.5,0.00222,1.09,1.05,0.00466,0.113,0.31} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020359 ± 0.00168 K3 =-0.18294 ± 0.0238 K4 =0.69105 ± 0.0961 K5 =-0.85508 ± 0.115 K6 =0.077303 ± 0.00269 K7 =280.36 ± 0.255 K8 =16.65 ± 0.49 K9 =-0.40703 ± 0.495 K10 =306.7 ± 27.4 K11 =99.879 ± 30.7 K12 =0.23377 ± 0.53 K13 =327.83 ± 14 K14 =-73.01 ± 20.5 K15 =0.06203 ± 0.00222 K16 =490.07 ± 1.09 K17 =22.131 ± 1.05 K18 =0.10134 ± 0.00466 K19 =508.08 ± 0.113 K20 =9.9096 ± 0.31 FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020389,-0.18324,0.69165,-0.85523,0.077441,280.36,16.671,-0.40896,0} coef[10]={306.76,99.755,0.23547,327.79,-72.983,0.06175,489.92,21.986,0.10201,508.08,9.9486} V_chisq= 0.00595127;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00167,0.0237,0.0957,0.115,0.00269,0.255,0.49,0.5,27.2,30.6,0.534,14,0} W_sigma[14]={20.5,0.00224,1.12,1.09,0.00483,0.113,0.314} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020389 ± 0.00167 K3 =-0.18324 ± 0.0237 K4 =0.69165 ± 0.0957 K5 =-0.85523 ± 0.115 K6 =0.077441 ± 0.00269 K7 =280.36 ± 0.255 K8 =16.671 ± 0.49 K9 =-0.40896 ± 0.5 K10 =306.76 ± 27.2 K11 =99.755 ± 30.6 K12 =0.23547 ± 0.534 K13 =327.79 ± 14 K14 =-72.983 ± 20.5 K15 =0.06175 ± 0.00224 K16 =489.92 ± 1.12 K17 =21.986 ± 1.09 K18 =0.10201 ± 0.00483 K19 =508.08 ± 0.113 K20 =9.9486 ± 0.314 •Display VLP1 vs WV •InitializeMostEverything() •InitBaselineFit("VLP1","WV") •AddRegionToFit() •AddRegionToFit() •RemoveFromGraph Peak3 •RemoveFromGraph Peak1 •RemoveFromGraph Peak2 •RemoveFromGraph Peak4 •RemoveFromGraph Peak5 •Edit WV •AppendToTable VLP1 •make/N=(numpnts(VLP1)) VLP1Trial •VLP1Trial = VLP1- (1/(WV^4)) •appendtotable VLP1Trial •Display WV vs VLP1Trial •display VLP1Trial vs WV •appendtograph VLP1 vs WV •RemoveFromGraph VLP1Trial •CurveFit/X=1/TBOX=768 poly 4, EC /D fit_EC= poly(W_coef,x) W_coef={-0.021131,0.00058773,-3.0445e-06,4.2871e-09} V_chisq= 0.294997;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0.00355,5.04e-05,1.92e-07,2.07e-10} Coefficient values ± one standard deviation K0 =-0.021131 ± 0.00355 K1 =0.00058773 ± 5.04e-05 K2 =-3.0445e-06 ± 1.92e-07 K3 =4.2871e-09 ± 2.07e-10 •Make/D/N=2/O W_coef •W_coef[0] = {10,0} FitProgressDialog allocating a dialogFitFunction instance •Make/D/N=2/O W_coef •W_coef[0] = {1000000000,0} FitProgressDialog allocating a dialogFitFunction instance •Make/D/N=2/O W_coef •W_coef[0] = {5.5,0} FitProgressDialog allocating a dialogFitFunction instance •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •Rename fit_VLP1,VLP1BKG FitProgressDialog allocating a dialogFitFunction instance •RemoveFromGraph fit_VLP1 •KillWaves fit_VLP1 •VLP1Trial = VLP1-VLP1BKG •appendtotable VLP1BKG •RemoveFromGraph VLP1BKG •Make/D/N=2/O W_coef •W_coef[0] = {5.5,0} FitProgressDialog allocating a dialogFitFunction instance •Make/D/N=1/O W_coef •W_coef[0] = {5.7} FitProgressDialog allocating a dialogFitFunction instance FitProgressDialog allocating a dialogFitFunction instance •Make/D/N=1/O W_coef •W_coef[0] = {5.7} •FuncFit/X=1/TBOX=768 rayleigh W_coef VLP1 /X=WV /D FitProgressDialog allocating a dialogFitFunction instance Fit converged properly fit_VLP1= rayleigh(W_coef,x) W_coef={7.1367} V_chisq= 0.576873;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0.173} Coefficient values ± one standard deviation k =7.1367 ± 0.173 •Make/D/N=1/O W_coef •W_coef[0] = {5.7} •FuncFit/X=1/H="1"/TBOX=768 rayleigh W_coef VLP1 /X=WV /D FitProgressDialog allocating a dialogFitFunction instance 9 iterations with no decrease in chi square fit_VLP1= rayleigh(W_coef,x) W_coef={5.7} V_chisq= 0.642096;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0} Coefficient values ± one standard deviation k =5.7 ± 0 •VLP1Trial=VLP1-VLP1BKG •appendtotable VLP1BKG •RemoveFromTable VLP1BKG.d •print numpnts(VLP1) 611 •print numpnts(VLP1BKG) 200 •print numpnts(WV) 611 •Make/D/N=1/O W_coef •W_coef[0] = {5.7} •FuncFit/X=1/TBOX=768 rayleigh W_coef VLP1 /X=WV /D FitProgressDialog allocating a dialogFitFunction instance Fit converged properly fit_VLP1= rayleigh(W_coef,x) W_coef={7.1367} V_chisq= 0.576873;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0.173} Coefficient values ± one standard deviation k =7.1367 ± 0.173 •print numpnts(fit_VLP1) 200 •Make/D/N=1/O W_coef •W_coef[0] = {5.7} •FuncFit/L=611 /X=1/TBOX=768 rayleigh W_coef VLP1 /X=WV /D FitProgressDialog allocating a dialogFitFunction instance Fit converged properly fit_VLP1= rayleigh(W_coef,x) W_coef={7.1367} V_chisq= 0.576873;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0.173} Coefficient values ± one standard deviation k =7.1367 ± 0.173 •RemoveFromTable textWave0.d •Make/D/N=1/O W_coef •W_coef[0] = {5.7} •FuncFit/L=611 /X=1/H="1"/TBOX=768 rayleigh W_coef VLP1 /X=WV /D FitProgressDialog allocating a dialogFitFunction instance 9 iterations with no decrease in chi square fit_VLP1= rayleigh(W_coef,x) W_coef={5.7} V_chisq= 0.642096;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={4.89e-159} Coefficient values ± one standard deviation k =5.7 ± 4.89e-159 •VLP1Trial=VLP1-fit_VLP1 •appendtograph VLP1Trial •ModifyGraph rgb(VLP1)=(0,65535,0) •RemoveFromGraph VLP1Trial •AppendToGraph VLP1Trial vs WV •make /N=(numpnts(VLP1)) VLP1Trial2 •VLP1Trial2=VLP1-fit_VLP1 •appendtotable VLP1Trial2 •appendtotable fit_vlp1 •removefromgraph fit_vlp1 •AppendToGraph fit_VLP1 vs WV •RemoveFromGraph fit_VLP1 •KillWaves textWave0 •KillWaves fit_VLP1,VLP1Trial2 •RemoveFromGraph VLP1Trial •Make/D/N=1/O W_coef •W_coef[0] = {5.7} •FuncFit/L=611 /X=1/H="1"/TBOX=768 rayleigh W_coef VLP1 /X=WV /D FitProgressDialog allocating a dialogFitFunction instance 9 iterations with no decrease in chi square fit_VLP1= rayleigh(W_coef,x) W_coef={5.7} V_chisq= 0.642096;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={7.4e-157} Coefficient values ± one standard deviation k =5.7 ± 7.4e-157 •KillWaves VLP1Trial •make /N=(numpnts(VLP1)) VLP1Trial •VLP1Trial = VLP1-fit_VLP1 •AppendToGraph VLP1Trial vs WV •Display VLP1Trial,fit_VLP1 vs WV •Reverse'fit_VLP1' •RemoveFromGraph VLP1Trial •vlp1trial = vlp1-fit_vlp1 •Display VLP2,VLP3,VLP1,EC vs WV •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •ModifyGraph rgb(VLP2)=(0,0,65535),rgb(VLP3)=(1,65535,33232),rgb(VLP1)=(65535,65535,0) •Make/D/N=1/O W_coef •W_coef[0] = {5.7} •FuncFit/L=611 /X=1/TBOX=768 rayleigh W_coef VLP1 /X=WV /D FitProgressDialog allocating a dialogFitFunction instance Fit converged properly fit_VLP1= rayleigh(W_coef,x) W_coef={7.1367} V_chisq= 0.576873;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0.173} Coefficient values ± one standard deviation k1 =7.1367 ± 0.173 •Display VLP2 vs WV •Make/D/N=4/O W_coef •W_coef[0] = {5,0,50000,50000} •FuncFit/L=611 /X=1/TBOX=768 rayleigh2 W_coef VLP2 /X=WV /D FitProgressDialog allocating a dialogFitFunction instance Fit converged properly fit_VLP2= rayleigh2(W_coef,x) W_coef={1.4392e+05,4317.6,4099.3,-7.0028e+05} V_chisq= 1.70657;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={9.43e+04,815,1.12e+03,2.84e+05} Coefficient values ± one standard deviation k1 =1.4392e+05 ± 9.43e+04 k2 =4317.6 ± 815 k3 =4099.3 ± 1.12e+03 k4 =-7.0028e+05 ± 2.84e+05 •Make/D/N=1/O W_coef •W_coef[0] = {15} •FuncFit/L=611 /X=1/TBOX=768 polyneg10 W_coef VLP2 /X=WV /D FitProgressDialog allocating a dialogFitFunction instance Fit converged properly fit_VLP2= polyneg10(W_coef,x) W_coef={19.585} V_chisq= 2.7627;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={1.18} Coefficient values ± one standard deviation k1 =19.585 ± 1.18 •Make/D/N=1/O W_coef •W_coef[0] = {15} •FuncFit/L=611 /X=1/H="1"/TBOX=768 polyneg10 W_coef VLP2 /X=WV /D FitProgressDialog allocating a dialogFitFunction instance 9 iterations with no decrease in chi square fit_VLP2= polyneg10(W_coef,x) W_coef={15} V_chisq= 2.83139;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={1.01e-158} Coefficient values ± one standard deviation k1 =15 ± 1.01e-158 •make /N=(numpnts(VLP1)) VLP1Trial2 •VLP1Trial2 = VLP1-fit_VLP1 •AppendToGraph VLP1Trial2 vs WV •reverse 'fit_VLP1' •vlp1trial2 = VLP1-fit_vlp1 •RemoveFromGraph fit_VLP2 •ShowInfo •ModifyGraph rgb(VLP1Trial2)=(1,16019,65535) •KillWaves fit_EC •RemoveFromGraph fit_VLP2 •KillWaves wave0 •RemoveFromGraph fit_VLP2 •KillWaves fit_VLP2 •CurveFit/L=611 /X=1/TBOX=768 Power VLP2[pcsr(B),pcsr(A)] /X=WV /D FitProgressDialog allocating a dialogFitFunction instance Fit converged properly Curve fit with data subrange: VLP2[2,293] fit_VLP2= W_coef[0]+W_coef[1]*x^W_coef[2] W_coef={-0.0043188,3.3075e+06,-3.0261} V_chisq= 1.35215e-05;V_npnts= 292;V_numNaNs= 0;V_numINFs= 0; V_startRow= 2;V_endRow= 293; W_sigma={0.000215,1.49e+06,0.0729} Coefficient values ± one standard deviation y0 =-0.0043188 ± 0.000215 A =3.3075e+06 ± 1.49e+06 pow =-3.0261 ± 0.0729 •KillWaves res_VLP1,VLP1BKG,VLP111 General text load from "bodipy dmso.csv" Data length: 611, waves: BDPDMSO1 •AppendToGraph BDPDMSO1 vs WV •ModifyGraph rgb(BDPDMSO1)=(1,65535,33232) •RemoveFromGraph BDPDMSO1 •KillWaves BDPDMSO1 General text load from "bodipy dmso.csv" Data length: 611, waves: BDPDMSO1 •AppendToGraph BDPDMSO1 vs WV •ModifyGraph rgb(BDPDMSO1)=(1,65535,33232) •make /N=(numpnts(VLP2)) maskvlp2 •maskvlp2 = vlp2*0 •maskvlp2[557, 848]=1 •maskvlp2[406, 428] = 1 •CurveFit/L=611 /X=1/TBOX=768 Power VLP2[pcsr(B),pcsr(A)] /X=WV /M=maskvlp2 /D FitProgressDialog allocating a dialogFitFunction instance 40 iterations with no convergence Curve fit with data subrange: VLP2[1,594] fit_VLP2= W_coef[0]+W_coef[1]*x^W_coef[2] W_coef={0.0043539,1.4762e+07,-3.288} V_chisq= 0.0201213;V_npnts= 61;V_numNaNs= 0;V_numINFs= 0; V_startRow= 1;V_endRow= 594; W_sigma={0.0196,7.65e+07,0.948} Coefficient values ± one standard deviation y0 =0.0043539 ± 0.0196 A =1.4762e+07 ± 7.65e+07 pow =-3.288 ± 0.948 •RemoveFromGraph BDPDMSO1 •ModifyGraph rgb(VLP2)=(0,0,65535) •CurveFit/L=611 /X=1/TBOX=768 Power VLP2[pcsr(B),pcsr(A)] /X=WV /M=maskvlp2 /D FitProgressDialog allocating a dialogFitFunction instance 40 iterations with no convergence Curve fit with data subrange: VLP2[1,594] fit_VLP2= W_coef[0]+W_coef[1]*x^W_coef[2] W_coef={0.0043539,1.4762e+07,-3.288} V_chisq= 0.0201213;V_npnts= 61;V_numNaNs= 0;V_numINFs= 0; V_startRow= 1;V_endRow= 594; W_sigma={0.0196,7.65e+07,0.948} Coefficient values ± one standard deviation y0 =0.0043539 ± 0.0196 A =1.4762e+07 ± 7.65e+07 pow =-3.288 ± 0.948 •AppendToGraph maskvlp2 vs WV •reverse maskvlp2 •print numpnts(maskvlp2) 611 •maskvlp2 = maskvlp2*0 •maskvlp2[1, 279] = 1 •maskvlp2[413, 446] = 1 •CurveFit/L=611 /X=1/TBOX=768 Power VLP2[pcsr(B),pcsr(A)] /X=WV /M=maskvlp2 /D FitProgressDialog allocating a dialogFitFunction instance 40 iterations with no convergence Curve fit with data subrange: VLP2[2,593] fit_VLP2= W_coef[0]+W_coef[1]*x^W_coef[2] W_coef={-0.0062235,59073,-2.378} V_chisq= 0.000351456;V_npnts= 312;V_numNaNs= 0;V_numINFs= 0; V_startRow= 2;V_endRow= 593; W_sigma={0.00058,2.88e+04,0.0831} Coefficient values ± one standard deviation y0 =-0.0062235 ± 0.00058 A =59073 ± 2.88e+04 pow =-2.378 ± 0.0831 •RemoveFromGraph maskvlp2 •ModifyGraph rgb(fit_VLP2)=(1,65535,33232) •make /N = (numpnts(vlp2)) vlp2trial1 •vlp2trial1 = vlp2-fit_vlp2 •appendtograph vlp2trial1 vs wv •reverse vlp2trial1 •vlp2trial1 = vlp2trial1*0 •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •vlp2trial1 = vlp2-fit_VLP2 •ModifyGraph rgb(vlp2trial1)=(52428,1,20971) •ModifyGraph rgb(vlp2trial1)=(65535,65532,16385) •appendtograph fit_vlp2 vs wv •RemoveFromGraph fit_VLP2#1 •reverse fit_vlp2 •vlp2trial1 = vlp2-fit_vlp2 •ModifyGraph rgb(vlp2trial1)=(0,65535,65535) •RemoveFromGraph VLP1Trial2 •AppendToGraph BDPDMSO1 vs WV •KillWaves fit_VLP1,fit_VLP2,maskvlp2,VLP1Trial,VLP1Trial2,vlp2trial1 •ModifyGraph rgb(BDPDMSO1)=(36873,14755,58982) •Display VLP1 vs WV •ShowInfo •make /N=(numpnts(vlp1)) vlp1mask •vlp1mask = vlp1*0 •vlp1mask[0, 295]=1 •vlp1mask[421, 439]=1 •appendtograph vlp1mask vs wv •CurveFit/L=611 /X=1/TBOX=768 Power VLP1[pcsr(B),pcsr(A)] /X=WV /M=vlp1mask /D FitProgressDialog allocating a dialogFitFunction instance Fit converged properly Curve fit with data subrange: VLP1[0,594] fit_VLP1= W_coef[0]+W_coef[1]*x^W_coef[2] W_coef={-0.02088,107.52,-1.2733} V_chisq= 2.96044e-05;V_npnts= 315;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 594; W_sigma={0.000519,12.4,0.0208} Coefficient values ± one standard deviation y0 =-0.02088 ± 0.000519 A =107.52 ± 12.4 pow =-1.2733 ± 0.0208 •RemoveFromGraph vlp1mask •vlp1mask[529, 541]=1 •CurveFit/L=611 /X=1/TBOX=768 Power VLP1[pcsr(B),pcsr(A)] /X=WV /M=vlp1mask /D FitProgressDialog allocating a dialogFitFunction instance 40 iterations with no convergence Curve fit with data subrange: VLP1[1,592] fit_VLP1= W_coef[0]+W_coef[1]*x^W_coef[2] W_coef={-0.011865,2763.3,-1.8367} V_chisq= 0.000141808;V_npnts= 327;V_numNaNs= 0;V_numINFs= 0; V_startRow= 1;V_endRow= 592; W_sigma={0.000325,312,0.0202} Coefficient values ± one standard deviation y0 =-0.011865 ± 0.000325 A =2763.3 ± 312 pow =-1.8367 ± 0.0202 •vlp1mask=vlp1mask*0 •vlp1mask[418, 444]=1 •vlp1mask[0, 288]=1 •CurveFit/L=611 /X=1/TBOX=768 Power VLP1[pcsr(B),pcsr(A)] /X=WV /M=vlp1mask /D FitProgressDialog allocating a dialogFitFunction instance 40 iterations with no convergence Curve fit with data subrange: VLP1[6,593] fit_VLP1= W_coef[0]+W_coef[1]*x^W_coef[2] W_coef={-0.016513,334.28,-1.4755} V_chisq= 4.28452e-05;V_npnts= 310;V_numNaNs= 0;V_numINFs= 0; V_startRow= 6;V_endRow= 593; W_sigma={0.00048,47.4,0.0251} Coefficient values ± one standard deviation y0 =-0.016513 ± 0.00048 A =334.28 ± 47.4 pow =-1.4755 ± 0.0251 •make/N=(numpnts(vlp1)) vlp1peaks •vlp1peaks = vlp1-fit_vlp1 •appendtograph vlp1peaks vs wv •reverse fit_vlp1 •vlp1peaks = vlp1-fit_vlp1 •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •ModifyGraph rgb(VLP1)=(65535,65535,0),rgb(vlp1peaks)=(0,0,65535) •ModifyGraph rgb(VLP1)=(3,52428,1) •Display VLP2 vs WV •ShowInfo •make /N=(numpnts(vlp2)) vlp2mask •vlp2mask = vlp2*0 •vlp2[0, 286]=1 •vlp2 = (vlp21+vlp22)/2 •vlp2mask[0, 286]=1 •vlp2mask[423, 444]=1 •reverse vlp2mask •appendtograph vlp2mask vs wv •reverse vlp2mask •CurveFit/L=611 /X=1/TBOX=768 Power VLP2[pcsr(B),pcsr(A)] /X=WV /M=vlp2mask /D FitProgressDialog allocating a dialogFitFunction instance 40 iterations with no convergence Curve fit with data subrange: VLP2[1,595] fit_VLP2= W_coef[0]+W_coef[1]*x^W_coef[2] W_coef={-0.0067808,22888,-2.2249} V_chisq= 5.52396e-05;V_npnts= 308;V_numNaNs= 0;V_numINFs= 0; V_startRow= 1;V_endRow= 595; W_sigma={0.000254,4.5e+03,0.0336} Coefficient values ± one standard deviation y0 =-0.0067808 ± 0.000254 A =22888 ± 4.5e+03 pow =-2.2249 ± 0.0336 •make /N=(numpnts(vlp2)) vlp2peaks •reverse fit_vlp2 •vlp2peaks = vlp2-fit_vlp2 •appendtograph vlp2peaks vs wv •RemoveFromGraph vlp2mask •ModifyGraph rgb(vlp2peaks)=(1,16019,65535) •ModifyGraph rgb(fit_VLP2)=(3,52428,1) •Display VLP3 vs WV •make /N=(numpnts(vlp3)) vlp3mask •vlp3mask = vlp3*0 •ShowInfo •vlp3mask[0, 285]=1 •vlp3mask[420, 444]=1 •CurveFit/L=611 /X=1/TBOX=768 Power VLP3[pcsr(B),pcsr(A)] /X=WV /M=vlp3mask /D FitProgressDialog allocating a dialogFitFunction instance 40 iterations with no convergence Curve fit with data subrange: VLP3[2,588] fit_VLP3= W_coef[0]+W_coef[1]*x^W_coef[2] W_coef={-0.018129,17906,-2.046} V_chisq= 0.000370254;V_npnts= 309;V_numNaNs= 0;V_numINFs= 0; V_startRow= 2;V_endRow= 588; W_sigma={0.000768,4.01e+03,0.0385} Coefficient values ± one standard deviation y0 =-0.018129 ± 0.000768 A =17906 ± 4.01e+03 pow =-2.046 ± 0.0385 •reverse fit_vlp3 •make/N=(numpnts(vlp3)) vlp3peaks •vlp3peaks=vlp3-fit_vlp3 •appendtograph vlp3peaks vs wv •ModifyGraph rgb(vlp3peaks)=(0,0,65535) •ModifyGraph rgb(fit_VLP3)=(3,52428,1) •Display vlp2peaks,vlp3peaks,vlp1peaks vs WV •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •ReorderTraces vlp2peaks,{vlp3peaks} •ModifyGraph rgb(vlp2peaks)=(65535,43690,0),rgb(vlp1peaks)=(0,65535,0) •ShowInfo •SetAxis bottom 258,* •SetAxis bottom 258,650 •AppendToGraph EC vs WV •ModifyGraph rgb(EC)=(0,0,65535) General text load from "bodipy h2o.csv" Data length: 611, waves: BDPH2O1 General text load from "bodipy h2o1.csv" Data length: 611, waves: BDPH2O2 General text load from "bodipy dmso1.csv" Data length: 611, waves: BDPDMSO2 •make /N=(numpnts(BDPDMSO1)) BDPDMSO •BDPDMSO = (BDPDMSO1+BDPDMSO2)/2 •make /N=(numpnts(BDPH2O1)) BDPH2O •BDPH2O = (BDPH2O1+BDPH2O2)/2 •AppendToGraph BDPH2O,BDPDMSO vs WV •ModifyGraph rgb(BDPDMSO)=(52428,1,41942) •ModifyGraph rgb(BDPH2O)=(65535,16385,55749) •ModifyGraph rgb(BDPH2O)=(65535,32768,58981) •Display vlp1peaks,EC,BDPDMSO vs WV •SetAxis bottom 260,600 •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •ModifyGraph rgb(EC)=(3,52428,1),rgb(BDPDMSO)=(0,0,65535) •Display EC1 vs WV •ShowInfo •make /N = (numpnts(EC)) ecmask •ecmask = EC*0 •ecmask[1, 526] = 1 •appendtograph ecmask vs wv •CurveFit/L=611 /X=1/TBOX=768 Power EC[pcsr(B),pcsr(A)] /X=WV /M=ecmask /D FitProgressDialog allocating a dialogFitFunction instance 40 iterations with no convergence Curve fit with data subrange: EC[1,599] fit_EC= W_coef[0]+W_coef[1]*x^W_coef[2] W_coef={-0.0097307,41.311,-1.2354} V_chisq= 0.00124683;V_npnts= 526;V_numNaNs= 0;V_numINFs= 0; V_startRow= 1;V_endRow= 599; W_sigma={0.0013,19.8,0.0885} Coefficient values ± one standard deviation y0 =-0.0097307 ± 0.0013 A =41.311 ± 19.8 pow =-1.2354 ± 0.0885 •AppendToGraph fit_EC vs WV •RemoveFromGraph ecmask •reverse fit_EC •make /N=(numpnts(EC)) ecpeaks •ecpeaks = EC-fit_EC •appendtograph ecpeaks vs wv •RemoveFromGraph EC •AppendToGraph ecpeaks vs WV •ModifyGraph rgb(vlp1peaks)=(3,52428,1) •ModifyGraph rgb(fit_ecpeaks)=(4369,4369,4369) •KillWaves W_AreaNoBase,W_AreaRegion,W_AreaX1,W_AreaX2,W_AutoPeakInfo,W_BasePM,W_BaseRegion,W_coef,W_EstAmpsY,W_EstCentersP,W_EstCentersX,W_EstEdgesP,W_EstWidthsX,W_ParamConfidenceInterval,W_PeakPM,W_PM,W_sigma •RemoveFromGraph Res_BDPDMSO •ModifyGraph rgb(fit_BDPDMSO)=(51664,44236,58982) •Display Set6_summary •Display ecpeaks_BlSub •Display :WMPeakFits:Current:masterCoefManRecent •Display :WMPeakFits:Current:W_sigma •Display :Packages:MultiPeakFit2:MPF_SetFolder_6:fit_ecpeaks •KillWaves :Packages:MultiPeakFit2:MPF_SetFolder_1:'Baseline Coefs',:Packages:MultiPeakFit2:MPF_SetFolder_1:constraintsTextWave,:Packages:MultiPeakFit2:MPF_SetFolder_1:HoldStrings,:Packages:MultiPeakFit2:MPF_SetFolder_1:'Peak 0 Coefs',:Packages:MultiPeakFit2:MPF_SetFolder_1:W_AutoPeakInfo •Display :Packages:MultiPeakFit2:MPF_SetFolder_6:fit_ecpeaks,:Packages:MultiPeakFit2:MPF_SetFolder_7:fit_BDPDMSO •Display vlp1peaks,ecpeaks,BDPDMSO •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •reverse vlp1peaks •reverse vlp1peaks •Display ecpeaks,BDPDMSO,vlp1peaks vs WV •SetAxis bottom 260,600 •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •ModifyGraph rgb(BDPDMSO)=(2,39321,1),rgb(vlp1peaks)=(0,0,65535) •Display :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_BDPDMSO •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •AppendToGraph :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp1peaks •ModifyGraph rgb(fit_BDPDMSO)=(3,52428,1),rgb(fit_vlp1peaks)=(0,0,65535) •displayhelpTopic "concatenate" •Edit :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_BDPDMSO,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp1peaks,WV •Rename fit_ecpeaks,'fit.ecpeaks' •Concatenate {:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_BDPDMSO,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks},W_Concatenate •Rename W_Concatenate,'fit_ec+fit_bdpdmso' •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp1peaks) 334 •print numpnts('fit_ec+fit_bdpdmso') 559 •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_BDPDMSO) 324 •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks) 235 •Display ecpeaks,ecmask,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •Display ecpeaks,fit_EC •AppendToGraph ecpeaks_BlSub •Display EC,ecpeaks •KillWaves Set6_Baseline,Set6_Peak_0,Set6_summary •print numpnts(ecpeaks) 611 •Display EC,ecpeaks vs WV ** a wave read gave error: Index out of range for wave "ListWave". •ShowInfo FuncFit/M=2/H=VLP1_HoldStr fGaussFitBL coef :::VLP1[0,610] /X= :::WV /D=:::fit_VLP1 Fit converged properly Curve fit with data subrange: VLP1[0,610] fit_VLP1[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,0.020323,-0.18248,0.68938,-0.85323,0.07743,280.35,16.67,-0.42598,307.62,0} coef[11]={98.8,0.25356,327.3,-73.738,0.061885,489.99,22.037,0.10172,508.08,9.9291} V_chisq= 0.00594776;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.00165,0.0235,0.0951,0.114,0.00269,0.255,0.49,0.617,28.5,32.3,0.652,0} W_sigma[13]={15.3,22.2,0.00223,1.11,1.07,0.00476,0.113,0.313} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =0.020323 ± 0.00165 K3 =-0.18248 ± 0.0235 K4 =0.68938 ± 0.0951 K5 =-0.85323 ± 0.114 K6 =0.07743 ± 0.00269 K7 =280.35 ± 0.255 K8 =16.67 ± 0.49 K9 =-0.42598 ± 0.617 K10 =307.62 ± 28.5 K11 =98.8 ± 32.3 K12 =0.25356 ± 0.652 K13 =327.3 ± 15.3 K14 =-73.738 ± 22.2 K15 =0.061885 ± 0.00223 K16 =489.99 ± 1.11 K17 =22.037 ± 1.07 K18 =0.10172 ± 0.00476 K19 =508.08 ± 0.113 K20 =9.9291 ± 0.313 Set Y data= vlp2peaks, X data= WV, fit wave= fit_vlp2peaks, residuals= res_vlp2peaks Range set to full extent of data (not graphed yet) Reset using same data Range set to full extent of data (not graphed yet) Reset using same data Range set to full extent of data (not graphed yet) Reset using same data Range set to full extent of data (not graphed yet) Reset using same data Range set to full extent of data (not graphed yet) Reset using same data Range set to full extent of data (not graphed yet) Reset using same data Range set to full extent of data (not graphed yet) •Display vlp3peaks,vlp2peaks,vlp1peaks,ecpeaks,BDPDMSO vs WV •KillWaves 'fit_ec+fit_bdpdmso',ecpeaks_BlSub,fit_ecpeaks •ShowInfo •make /N=(numpnts(ecpeaks)) fitecpeaksmask •fitecpeaksmask = ecpeaks*0 •fitecpeaksmask[544,598] = 1 •KillWaves res_vlp2peaks •ShowInfo •SetAxis bottom 250,600 •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •ModifyGraph rgb(vlp2peaks)=(65535,43690,0),rgb(vlp1peaks)=(32792,65535,1),rgb(ecpeaks)=(0,43690,65535),rgb(BDPDMSO)=(29524,1,58982) •KillWaves :Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Baseline Coefs',:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Bkg_BDPDMSO,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Bkg_vlp1peaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Bkg_vlp2peaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:blepswave,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:constraintsTextWave,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_BDPDMSO,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp1peaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp2peaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:HoldStrings,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:MPF2_CoefsBackup_0,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:MPF2_CoefsBackup_1,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:MPF2_CoefsBackup_2,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:MPF2_CoefsBackup_3,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:M_Covar,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 0 Coefs',:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 0 Coefseps',:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 1 Coefseps',:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 2 Coefseps',:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Res_BDPDMSO,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Res_vlp1peaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Res_vlp2peaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:W_AutoPeakInfo,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:W_sigma,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:W_sigma_0,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:W_sigma_1 •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks) 311 •Display :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •make /N=(numpnts(bdpdmso)) bdpdmsomask •bdpdmsomask = bdpdmso*0 •bdpdmsomask[324, 412]=1 •make /N = (numpnts(vlp1peaks)) vlp1peaksmask •vlp1peaksmask = vlp1peaks*0 •vlp1peaksmask[317, 403] = 1 •KillWaves vlp1peaksmask General text load from "bodipy dmso.csv" Data length: 611, waves: bdpdmso3 General text load from "bodipy dmso1.csv" Data length: 611, waves: bdpdmso4 •make /N = (numpnts(bdpdmso3)) bdpdmso5 •bdpdmso5 = (bdpdmso3+bdpdmso4)/2 •AppendToGraph bdpdmso5 vs WV •Edit BDPDMSO1 General text load from "bodipy dmso.csv" Data length: 611, waves: bdpdmso6 General text load from "bodipy dmso1.csv" Data length: 611, waves: bdpdmso7 •make /N=(numpnts(bdpdmso6)) bdpdmso8 •bdpdmso8 = (bdpdmso6+bdpdmso7)/2 •AppendToGraph bdpdmso8 vs WV •ModifyGraph rgb(bdpdmso8)=(16385,65535,65535) •RemoveFromGraph bdpdmso8 •KillWaves bdpdmso6,bdpdmso7,bdpdmso8 •ModifyGraph rgb(bdpdmso5)=(65535,32768,45875) •make /N=(numpnts(bdpdmso5)) bdpdmso5mask •bdpdmso5mask = bdpdmso5*0 •bdpdmso5mask[324, 418]=1 •AppendToGraph :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5 •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5) 595 •Edit :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •print numpnts(vlp1peaks) 611 •DeletePoints 612,9, :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5 •DeletePoints 612,26, :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5 •print numpnts(bdpdmso5) 611 •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5) 612 •DeletePoints 611,1, :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •DeletePoints 611,1, :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5 •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5) 611 •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks) 611 •Display :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks vs WV •reverse :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5 •reverse :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •ModifyGraph rgb(fit_bdpdmso5)=(1,16019,65535) •concatenate{:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks, :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5}, ec_and_bdp •matrixlls ec_and_bdp vlp1peaks •print m_B[0], m_B[1] 0.455187 0.203896 •AppendToGraph vlp1peaks vs WV •Make/N=611/D FinalSpectrum •FinalSpectrum = m_b[0]*:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks+m_b[1]*:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5 •Print m_B[0]/m_B[1] 2.23245 •Display FinalSpectrum vs WV •AppendToGraph vlp1peaks vs WV •ModifyGraph rgb(vlp1peaks)=(1,16019,65535) •RemoveFromGraph BDPDMSO •KillWaves BDPDMSO1,BDPDMSO2 •KillWaves M_A,M_B •Rename BDPDMSO,xtra •Rename bdpdmso3,bdpdmso1; Rename bdpdmso4,bdpdmso2; Rename bdpdmso5,bdpdmso; Rename EC1,ec1; Rename fit_EC,ecbkg; Rename fit_VLP1,vlp1bkg; Rename fit_VLP2,vlp2bkg; Rename fit_VLP3,vlp3bkg •KillWaves VLP1_C,VLP2_C,VLP3_C,fit_vlp2peaks •Rename EC11,ec2; Rename fitecpeaksmask,ecpeaksmask; Rename EC,ec •KillWaves VLP11,VLP21,VLP22,VLP31,VLP32 •TextBox/C/N=text0/F=0/H={10,3,10}/A=MC "m_b[0] .455187\rm_b[1] .203896" •Rename FinalSpectrum,vlp1LC •Legend/C/N=text1/F=0/H={10,3,10}/A=MC •matrixlls ec_and_bdp vlp2peaks •print m_B[0], m_B[1] 0.41584 0.420171 •Make/N=611/D FinalSpectrum1 •FinalSpectrum1 = m_b[0]*:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks+m_b[1]*:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5 •Print m_B[0]/m_B[1] 0.989692 •Display FinalSpectrum1,vlp2peaks vs WV •TextBox/C/N=text0/F=0/H={10,3,10}/A=MC "m_b[0] .41584\rm_b[1] .420171" •Rename FinalSpectrum1,vlp2LC •ModifyGraph rgb(vlp2peaks)=(1,16019,65535) •matrixlls ec_and_bdp vlp3peaks •print m_B[0], m_B[1] 0.826309 0.586956 •Make/N=611/D vlp3LC •vlp3LC = m_b[0]*:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks+m_b[1]*:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5 •Display vlp3LC,vlp3peaks vs WV •TextBox/C/N=text0/F=0/H={10,3,10}/A=MC "m_b[0] .826309\rm_b[1] .586956" •ModifyGraph rgb(vlp3peaks)=(1,16019,65535) •Edit :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •InsertPoints 0,300, :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •DeletePoints 299,35, :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •DeletePoints 299,35, :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •DeletePoints 299,35, :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •DeletePoints 299,35, :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •AppendToTable :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •print numpnts(fit_bdpdmso5) 611 •print numpnts(fit_ecpeaks) 611 •Rename fit_bdpdmso5,bdpdmsopeaksfit; Rename fit_ecpeaks,ecpeaksfit •concatenate{ecpeaksfit, bdpdmsopeaksfit}, ecandbdp •matrixlls ecandbdp vlp1peaks •print m_B[0], m_B[1] -0.000933888 0.203896 •appendtograph ecandbdp vs wv •reverse ecandbdp •AppendToGraph ecpeaksfit vs WV •reverse ecpeaksfit •concatenate{ecpeaksfit, bdpdmsopeaksfit}, ecandbdp •RemoveFromGraph ecandbdp •RemoveFromGraph ecpeaksfit •ModifyGraph rgb(fit_ecpeaks)=(0,65535,0) •reverse :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •appendtograph ec_and_bdp vs wv •ModifyGraph rgb(ec_and_bdp)=(1,65535,33232) •Display ecpeaksfit,bdpdmsopeaksfit vs WV •concatenate{ecpeaksfit, bdpdmsopeaksfit}, ecandbdp •appendtograph ecandbdp vs wv •reverse ecandbdp •appendtograph ecandbdp vs wv •Display ecpeaksfit,bdpdmsopeaksfit vs WV •appendtograph ecandbdp vs wv •RemoveFromGraph ecandbdp •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •killwaves ecandbdp •concatenate{ecpeaksfit, bdpdmsopeaksfit}, ecandbdp •appendtograph ecandbdp •ModifyGraph rgb(ecandbdp)=(1,16019,65535) •RemoveFromGraph ecandbdp •matrixlls ecandbdp vlp1peaks •print m_B[0], m_B[1] 0.495335 0.203896 •KillWaves vlp1LC,vlp2LC,vlp3LC •Make/N=611/D vlp1LC •vlp1LC = m_b[0]*ecpeaksfit+m_b[1]*bdpdmsopeaksfit •Display vlp1peaks,vlp1LC vs WV •ShowInfo •AppendToGraph ecpeaksfit vs WV •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •ModifyGraph rgb(vlp1LC)=(65535,65535,0),rgb(ecpeaksfit)=(36873,14755,58982) •ShowInfo •Display :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •ShowInfo •Edit :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •Display :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks vs WV •reverse fit_ecpeaks •Display fit_ecpeaks vs WV •ShowInfo •AppendToGraph :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •ModifyGraph rgb(fit_ecpeaks)=(0,65535,65535) •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •Edit :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •Edit fit_ecpeaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •AppendToTable WV •Rename fit_ecpeaks,'fit_ecpeaks?' •reverse :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •ShowInfo/W=MultiPeak2Panel •make /N=(numpnts(wv)) wvrev •wvrev = wv •reverse wvrev •AppendToTable wvrev •KillWaves :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •Edit :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •KillWaves :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •Display bdpdmsopeaksfit vs WV •KillWaves bdpdmsopeaksfit •KillWaves :Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Baseline Coefs',:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Bkg_BDPDMSO,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Bkg_bdpdmso5,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Bkg_BDPH2O,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Bkg_ecpeaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:blepswave,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:constraintsTextWave,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_BDPDMSO,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso5,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_BDPH2O,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:HoldStrings,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:MPF2_CoefsBackup_0,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:MPF2_CoefsBackup_1,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:MPF2_CoefsBackup_2,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:MPF2_ResultsListTitles,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:MPF2_ResultsListWave,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:M_Covar,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 0',:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 0 Coefs',:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 0 Coefseps',:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 1 Coefseps',:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Res_BDPDMSO,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Res_bdpdmso5,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Res_BDPH2O,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Res_ecpeaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:W_AutoPeakInfo,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:W_sigma,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:W_sigma_0,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:W_sigma_1 •Display :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks vs WV[0,310] •Edit WV •Display 'fit_ecpeaks?',ecpeaksfit vs WV •KillWaves wvrev,'fit_ecpeaks?',ecpeaksfit •Display :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks vs WV[300,610] •reverse :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •ShowInfo •Display ecpeaks vs WV •ShowInfo •ShowInfo •KillWaves :Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Baseline Coefs',:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Bkg_ecpeaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:blepswave,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:constraintsTextWave,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:HoldStrings,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:MPF2_CoefsBackup_0,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:MPF2_CoefsBackup_1,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:M_Covar,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 0',:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 0 Coefs',:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 0 Coefseps',:Packages:MultiPeakFit2:MPF_SetFolder_NaN:Res_ecpeaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:W_AutoPeakInfo,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:W_sigma,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:W_sigma_0,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:W_sigma_1 •Display :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks vs WV[0,310] •ShowInfo •reverse :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •Display :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks vs WV[300,610] •ShowInfo •ShowInfo •print numpnts(ecpeaksmask) 611 •reverse ecpeaksmask •Display ecpeaksmask,ecpeaks ** a wave read gave error: Index out of range for wave "ListWave". •print numpnts(ecpeaks) 611 •Display ecpeaksmask,ecpeaks •ShowInfo •Display :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •ShowInfo •print numpnts(wv) 611 •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks) 311 •ShowInfo •Display VLP2 vs WV General text load from "bdpvlp0d0m.csv" Data length: 611, waves: 0D0M1 General text load from "bdpvlp0d0m1.csv" Data length: 611, waves: 0D0M2 General text load from "bdpvlp0d5m.csv" Data length: 611, waves: 0D.5M1 General text load from "bdpvlp0d5m1.csv" Data length: 611, waves: 0D.5M2 •Display '0D.5M2','0D.5M1','0D0M2','0D0M1' vs WV •make /N=(numpnts('0D0M1')) '0D0M' •'0D0M'=('0D0M1'+'0D0M2')/2 •make /N=(numpnts('0D.5M1')) '0D.5M' •'0D.5M'=('0D.5M1'+'0D.5M2')/2 •AppendToGraph '0D.5M','0D0M' vs WV •Legend/C/N=text0/J/F=0/H={10,3,10}/A=MC "\\s(VLP2) .5M_30D\r\\s('0D.5M') 5M_0D\r\\s('0D0M') 0D_0M" •ModifyGraph rgb('0D.5M')=(1,16019,65535),rgb('0D0M')=(3,52428,1) •AppendToGraph vlp2peaks vs WV •Display '0D.5M' vs WV •ShowInfo •make /N=(numpnts('0D.5M')) '0D.5Mmask' •'0D.5Mmask'='0D.5M'*0 •'0D.5Mmask'[1, 296]=1 •'0D.5Mmask'[431, 447]=1 •'0D.5Mmask'[596, 611]=1 •AppendToGraph '0D.5Mmask' vs WV •CurveFit/TBOX=768 Power '0D.5M' /X=WV /M='0D.5Mmask' /D FitProgressDialog allocating a dialogFitFunction instance 40 iterations with no convergence 'fit_0D.5M'= W_coef[0]+W_coef[1]*x^W_coef[2] W_coef={-8.7848e-05,1.1857e+10,-4.793} V_chisq= 0.00052999;V_npnts= 328;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0.000103,1.18e+10,0.18} Coefficient values ± one standard deviation y0 =-8.7848e-05 ± 0.000103 A =1.1857e+10 ± 1.18e+10 pow =-4.793 ± 0.18 •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •Display '0D0M' vs WV •make /N=(numpnts('0D0M')) '0D0Mmask' •'0D0Mmask'='0D0M'*0 •ShowInfo •'0D0Mmask'[0, 296]=1 •'0D0Mmask'[428, 444]=1 •'0D0Mmask'[611, 600]=1 •AppendToGraph '0D0Mmask' vs WV •CurveFit/TBOX=768 Power '0D0M' /X=WV /M='0D0Mmask' /D FitProgressDialog allocating a dialogFitFunction instance 40 iterations with no convergence fit_0D0M= W_coef[0]+W_coef[1]*x^W_coef[2] W_coef={-0.0088185,2.2104,-0.82559} V_chisq= 0.000949289;V_npnts= 315;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0.00392,3,0.267} Coefficient values ± one standard deviation y0 =-0.0088185 ± 0.00392 A =2.2104 ± 3 pow =-0.82559 ± 0.267 •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •make /N=(numpnts('0D0M')) '0D0Mpeaks' •'0D0Mpeaks'='0D0M'-fit_0D0M •Display '0D0Mpeaks' vs WV •RemoveFromGraph '0D0Mmask' •RemoveFromGraph '0D.5Mmask' •make /N=(numpnts('0D.5M')) '0D.5Mpeaks' •'0D.5Mpeaks'='0D.5M'-'fit_0D.5M' •AppendToGraph '0D0Mpeaks','0D.5Mpeaks' vs WV •SetAxis bottom *,600 •ModifyGraph rgb(vlp2peaks)=(65535,65535,0),rgb('0D0Mpeaks')=(65535,32768,32768),rgb('0D.5Mpeaks')=(51664,44236,58982) •RemoveFromGraph VLP2 •RemoveFromGraph '0D.5M','0D0M' •ModifyGraph rgb(vlp2peaks)=(3,52428,1),rgb('0D0Mpeaks')=(1,16019,65535),rgb('0D.5Mpeaks')=(65535,0,0) •ModifyGraph lblMargin(left)=5;DelayUpdate •Label left "Absorbance" •ModifyGraph lblMargin=5;DelayUpdate •Label bottom "Wavelength (nm)" •Legend/C/N=text0/J "\\s(vlp2peaks) 30D_.5M\r\\s('0D0Mpeaks') 0D_0M\r\\s('0D.5Mpeaks') 0D_.5M" •ReorderTraces '0D0Mpeaks',{'0D.5Mpeaks'} •Legend/C/N=text0/J "\\s(vlp2peaks) 30D_.5M\r\\s('0D.5Mpeaks') 0D_.5M\r\\s('0D0Mpeaks') 0D_0M\r" •SetAxis left 0,* •SetAxis left -0.05,0.35 •Edit :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •print wavemax(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks) 0.202631 •wavestats :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks V_npnts= 311; V_numNaNs= 0; V_numINFs= 0; V_avg= 0.0128638; V_Sum= 4.00065; V_sdev= 0.0387261; V_sem= 0.00219596; V_rms= 0.0407476; V_adev= 0.019986; V_skew= 3.6908; V_kurt= 12.8125; V_minloc= 0; V_maxloc= 568.677; V_min= 0.00145722; V_max= 0.202631; V_minRowLoc= 0; V_maxRowLoc= 289; V_startRow= 0; V_endRow= 310; •HideInfo •ShowInfo •wavestats ecpeaks V_npnts= 611; V_numNaNs= 0; V_numINFs= 0; V_avg= 0.0162022; V_Sum= 9.89952; V_sdev= 0.0434347; V_sem= 0.00175718; V_rms= 0.0463249; V_adev= 0.0248526; V_skew= 3.01488; V_kurt= 7.80288; V_minloc= 146; V_maxloc= 610; V_min= -0.000928569; V_max= 0.219261; V_minRowLoc= 146; V_maxRowLoc= 610; V_startRow= 0; V_endRow= 610; •Display ecpeaksmask,ecpeaks vs WV •reverse ecpeaksmask •Display :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks) 311 •wavestats :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks V_npnts= 311; V_numNaNs= 0; V_numINFs= 0; V_avg= -0.0600095; V_Sum= -18.6629; V_sdev= 0.0637305; V_sem= 0.00361383; V_rms= 0.0874623; V_adev= 0.0387001; V_skew= 2.83874; V_kurt= 6.80599; V_minloc= 405.29; V_maxloc= 273.452; V_min= -0.0827524; V_max= 0.189233; V_minRowLoc= 84; V_maxRowLoc= 17; V_startRow= 0; V_endRow= 310; •ShowInfo •ShowInfo •Edit :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •Display bdpdmsomask,bdpdmso vs WV •AppendToGraph :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso •Display vlp1peaks,vlp1mask vs WV •ShowInfo •make /N=(numpnts(vlp1peaks)) vlp1peaksmask •vlp1peaksmask=vlp1peaks*0 •vlp1peaksmask[295, 426]=1 •vlp1peaksmask[539, 595]=1 Set Y data= vlp1peaks, X data= WV, fit wave= fit_vlp1peaks, residuals= res_vlp1peaks Range set from graph: points= [610,0] ( equivalent to x= (240,850.421) ) FuncFit/M=2/H=vlp1peaks_HoldStr fGaussFitBL coef :::vlp1peaks[0,610] /X= :::WV /D=:::fit_vlp1peaks Fit converged properly Curve fit with data subrange: vlp1peaks[0,610] fit_vlp1peaks[0,610]= fGaussFitBL(coef,WV[p]) coef={545,610,-0.005186,0.022542,0.12958,-0.36581,0.046789,277.86,12.566,0.0013902,0} coef[10]={606.81,7.2245,0.12449,500.25,24.785} V_chisq= 0.0564529;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0,0,0.000783,0.00365,0.00702,0.0235,0.00316,0.666,1.08,0.00399,16.8,24.4,0.00222,0.349,0.54} Coefficient values ± one standard deviation K0 =545 ± 0 K1 =610 ± 0 K2 =-0.005186 ± 0.000783 K3 =0.022542 ± 0.00365 K4 =0.12958 ± 0.00702 K5 =-0.36581 ± 0.0235 K6 =0.046789 ± 0.00316 K7 =277.86 ± 0.666 K8 =12.566 ± 1.08 K9 =0.0013902 ± 0.00399 K10 =606.81 ± 16.8 K11 =7.2245 ± 24.4 K12 =0.12449 ± 0.00222 K13 =500.25 ± 0.349 K14 =24.785 ± 0.54 FuncFit/M=2/H=vlp1peaks_HoldStr fGaussFit coef :::vlp1peaks[0,610] /X= :::WV /D=:::fit_vlp1peaks Fit converged properly Curve fit with data subrange: vlp1peaks[0,610] fit_vlp1peaks[0,610]= fGaussFit(coef,WV[p]) coef={0.0033055,0.075311,257.77,43.51,-0.0033202,1065.1,434.35,0.11989,500.89,21.986} V_chisq= 0.0559605;V_npnts= 611;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0.0049,0.00275,1.8,2.73,0.0278,4.32e+03,3.34e+03,0.00235,0.339,0.529} Coefficient values ± one standard deviation K0 =0.0033055 ± 0.0049 K1 =0.075311 ± 0.00275 K2 =257.77 ± 1.8 K3 =43.51 ± 2.73 K4 =-0.0033202 ± 0.0278 K5 =1065.1 ± 4.32e+03 K6 =434.35 ± 3.34e+03 K7 =0.11989 ± 0.00235 K8 =500.89 ± 0.339 K9 =21.986 ± 0.529 FuncFit/M=2/H=vlp1peaks_HoldStr fGaussFit coef :::vlp1peaks[0,610] /X= :::WV /D=:::fit_vlp1peaks **** Singular matrix error during curve fitting **** There may be no dependence on these parameters: coef[4] •AppendToGraph :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp1peaks •Display vlp2peaks,vlp3peaks vs WV •make /N=(numpnts(vlp2peaks)) vlp2peaksmask •vlp2peaksmask=vlp2peaks*0 •ShowInfo •vlp2peaks[534, 595]=1 •Display vlp2peaks,vlp2mask,vlp2bkg,VLP2 vs WV •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •vlp2peaks=VLP2-vlp2bkg •vlp2peaksmask[534, 595]=1 •vlp2peaksmask[298, 432]=1 •RemoveFromGraph vlp2peaks •make /N=(numpnts(vlp3peaks)) vlp3peaksmask •vlp3peaksmask=vlp3peaks*0 •vlp3peaksmask[532, 590]=1 •vlp3peaksmask[283, 434]=1 •AppendToGraph :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp3peaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp2peaks,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp1peaks •ModifyGraph rgb(fit_bdpdmso)=(32792,65535,1),rgb(fit_vlp1peaks)=(0,65535,65535),rgb(fit_vlp3peaks)=(65535,0,52428),rgb(fit_vlp2peaks)=(29524,1,58982) •RemoveFromGraph fit_vlp1peaks#1 •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •concatenate{:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks, :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso}, ecAndBdp •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp1peaks) 1093 •print numpnts(ecAndBdp) 2128 •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks) 311 •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso) 595 •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp1peaks) 1093 •Display ecandbdp •KillWaves M_A,M_B,res_vlp1peaks,SetNaN_Baseline,SetNaN_Peak_0,SetNaN_summary,W_AutoPeakInfo,W_coef,W_sigma •Display ecandbdp •KillWaves ecandbdp •concatenate {:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks, :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso}, ecAndBdp •print numpnts(ecAndBdp) 906 •matrixlls ecAndBdp :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp1peaks •print m_B[0], m_B[1] 0 0 •Display ecAndBdp •Edit :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp1peaks •Rename fit_vlp1peaks1,fitVlp1 •matrixlls ecAndBdp fitVlp1 •print m_B[0], m_B[1] 0 0 •Make/N=521/D FinalSpectrum •FinalSpectrum = m_b[0]*:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks+m_b[1]*:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso •Print m_B[0]/m_B[1] NaN •Display xtra •matrixlls ecAndBdp fitVlp1 •print m_B[0], m_B[1] 0 0 •print m_B[0], m_B[1] 0 0 •Display ecAndBdp,fitVlp1 •ModifyGraph rgb(fitVlp1)=(0,65535,65535) •matrixlls ecAndBdp, fitVlp1 •print m_B[0], m_B[1] 0 0 •MatrixLLS ecAndBdp, fitVlp1 •print m_B[0], m_B[1] 0 0 •print numpnts(ecAndBdp) 906 •Legend/C/N=text0/F=0/H={10,3,10}/A=MC •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp2peaks) 1078 •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp3peaks) 603 •print numpnts(:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp1peaks) 1093 •Redimension/N=906 fitVlp1 •matrixlls ecAndBdp fitVlp1 •print m_B[0], m_B[1] 0 0 •Display ecAndBdp,fitVlp1 •ModifyGraph rgb(fitVlp1)=(0,65535,65535) •Display ecAndBdp •Display fitVlp1 •AppendToTable :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •Rename fit_bdpdmso,fitBdpDmso •Rename fit_ecpeaks,fitEcPeaks •redimension /N=1000 fitBdpDmso •redimension /N=1000 fitEcPeaks •Display fitEcPeaks,fitBdpDmso •ModifyGraph rgb(fitBdpDmso)=(0,65535,65535) •Display fitEcPeaks •AppendToGraph :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_ecpeaks •ModifyGraph rgb(fit_ecpeaks)=(29524,1,58982) •RemoveFromGraph fitBdpDmso •DeletePoints 357,40, fitEcPeaks •DeletePoints 311,649, fitEcPeaks •DeletePoints 595,405, fitBdpDmso •Display :Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_bdpdmso •AppendToGraph fitBdpDmso •ModifyGraph rgb(fitBdpDmso)=(0,0,65535) •Display fitVlp1,:Packages:MultiPeakFit2:MPF_SetFolder_NaN:fit_vlp1peaks •ModifyGraph rgb(fit_vlp1peaks)=(0,0,65535) •RemoveFromGraph fit_vlp1peaks •concatenate {fitEcPeaks, fitBdpDmso}, ecAndBdp •killwaves ecAndBdp •concatenate {fitEcPeaks, fitBdpDmso}, ecAndBdp •Display ecAndBdp •print numpnts(ecAndBdp) 906 •print numpnts(fitEcPeaks) 311 •print numpnts(fitBdpDmso) 595 •AppendToGraph fitVlp1 •print numpnts(fitVlp1) 1093 •ecAndBdp=ecAndBdp*0 •ecAndBdp=fitEcPeaks+fitBdpDmso •RemoveFromGraph ecAndBdp •killwaves ecAndBdp •make /N=(906) ecAndBdp •AppendToGraph fitEcPeaks,fitBdpDmso •ModifyGraph rgb(fitVlp1)=(0,65535,65535),rgb(fitBdpDmso)=(0,65535,0) •ModifyGraph rgb(fitVlp1)=(0,43690,65535) •killwaves ecAndBdp •concatenate{fitEcPeaks, fitBdpDmso}, ecAndBdp •matrixlls ecAndBdp fitVlp1 •print m_B[0], m_B[1] 0 0 •AppendToGraph ecAndBdp •print numpnts(fitvlp1) 1093 •Redimension/N=1093 ecAndBdp •make /N=(numpnts(fitbdpdmso)) ecandbdp1 •ecandbdp1=fitbdpdmso1+fitecpeaks1 •AppendToGraph ecandbdp1 •RemoveFromGraph ecAndBdp •ModifyGraph rgb(ecandbdp1)=(36873,14755,58982) •matrixlls ecandbdp1 fitvlp1 •print m_B[0], m_B[1] 0 0 •Display ecandbdp1 •DisplayHelpTopic "Concatenate" •concatenate/NP {fitEcPeaks, fitBdpDmso}, ecAndBdp2 •AppendToGraph ecAndBdp2 •ModifyGraph rgb(ecAndBdp2)=(0,65535,65535) •AppendToTable ecAndBdp,ecandbdp1,ecAndBdp2 •print numpnts(wv) 611 •Redimension/N=611 fitVlp1,ecAndBdp •Display ecAndBdp,fitVlp1 vs WV •ModifyGraph rgb(fitVlp1)=(0,65535,65535) •reverse fitvlp1 •reverse ecandbdp •Redimension/N=1093 fitVlp1,ecAndBdp •reverse fitvlp1 •reverse ecandbdp !U ;X@TP܉^cWVc????@@@x@p@h@`@X@P@H@@@8@0@(@ @@@@@@@@@؉@Љ@ȉ@@@@@@@@@@x@p@h@`@X@P@H@@@8@0@(@ @@@@@@@@@؈@Ј@Ȉ@@@@@@@@@@x@p@h@`@X@P@H@@@8@0@(@ 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О?Jp?a$z?vz?/?1'L?)U#??CX?s|?;ͣ?W ?oF?㕄?_8¤?rA6?z !A?#O׀?/S]?j5?v+=A? }x?[0?Vz?}>[`A?6?aWھ?L?D-:?Q7:bv?=?pmf?TY"G&?[^??^d?J?|)AP?Cԑ˫??8:E?R ?18?81N?,! b?ػt?05 ?/#?dC[`?3Ш?`RK?ܞ?<+,?9MO¬?p6*Ŭ?8Ƭ?SZǬ?Ǭ?<Ǭ?L[6Ƭ?J(Ŭ?VŬ?T2Ƭ?.kȬ? +=fͬ?pԬ?<\߬?hH)?TCtߕ?_H{#!?aOF?|R >u?4c?}0?3eU?4Ju\®?D?xgޯ?rugI??N{*?fI̲?2M?bb?C?ڟ?[Vd?Af(? ?<1(?ޒkk?w#2QZ? 7?}?v ??z ??,~0?8-6T?HTT?D.4?d>Q?QCr??8P(??)?eHB?xZ?2 ?A7{? d ?sTb?O'+r?|XNY?5?[? v(?Curve fit with data subrange: VLP1[6,593] fit_VLP1= W_coef[0]+W_coef[1]*x^W_coef[2] W_coef={-0.016513,334.28,-1.4755} V_chisq= 4.28452e-05;V_npnts= 310;V_numNaNs= 0;V_numINFs= 0; V_startRow= 6;V_endRow= 593; W_sigma={0.00048,47.4,0.0251} Coefficient values ± one standard deviation y0 =-0.016513 ± 0.00048 A =334.28 ± 47.4 pow =-1.4755 ± 0.0251   gbܸAecvlp1peaksc????N*tVO;9̹[w98s8/+N߹Ј956 )E8,*9T%8 J9̹v۸97- v888w9m tjnffrs(f'~9|8lܹ|ķc@*/W7b9$ŸSq̹8)wT9xȽdž-z8W*ZT08\:Mȹa8%N58ʄl8(]bV8Gt8TH̹8ʸ9UٹѸ&n߶$9+97*8d3׫81:CΦTg8U5y8`LW:罴 MhNyp9!d9=]ظͭ67889"[=ω89*c 9[s$̸L5 I9gμZ㒹Ë7y 8Spvϸ&L,8vf]|uD+8[5(+ӟ8ָǹ湥LH9S93z P87SG8^-θ788ZN2Kwɹ6YQhb8RӨ+8L؛)Ƶ(6v4׹cOa ȸҹ808_L((58l_fg|q8x q,198#N /I:$67B8#8Bձy c3$98/9I)jƸ^.9׹=) #;8[㸞G9ƣ8#8E&9z9>8-R?9ӱ㷕브T!:-^N9=8b99988yN=9G9S9:~(8>@CY8 }͟78z9t8ycj9994Ɛ-988P9<6`9ۙ9e9'8kb9h9994[9O9m9 :d'99 :":99Mq9:P|:v-9$:ly:b:fv:,~:2o:ȣA:&D:h::_9#:ߛ:hs:9B:v :K :Ve::MBq:U::3ϔ: :#0:|4:ʇ:s8::;~{:4:f;+[;;!X;Tc;+;s;V;D˰;G;;;u >71>c>ͣ>Y>>b ><>==i= T=u=R=4=p=B=L==Z===ߔ=R==]2=۪==f=́={=Kr=Ei=)`=RV= L=6B=L6=~.=#=[z==:\=h:r|:)::k ;,;"9;>=;\;|w;;Ȗ;4;y;b;i6;;;+; ;|;p;:;Eq;n;;<2;<<~   _b=bcvlp2maskc?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????Xm|bincvlp2bkgc???@41yU&?`$(?w&$+?;zz-?+*/?1j/1?$jRg2?k:3?)rU4?`) 6?K*R7?PW"8?pdy9?yA;? g)~P?HB!@?8B!Y@?KE3UA?¦A?@ўB?hDC?#C?pWI"D?8do9E?/E?p1g:F?4G?ϝMG?XH?AY06I?P:*tI?t"J?nJ=K?oK?PtL?H(JM?5>M?@zN?ط^l^O?޶fP?4n#8bP?ְBP?L<Q?K+mqQ?,7Q?TPm (R?4R?P%R?d<z:`?"W>Ep`?(`?͍`?a?:9OyHa?>a?za?Q: a?`$b?[b?@Sb?bdb?ꥹ4c?RԇR-!d?Z(Zd?*tڔd?ܠd?~D{ e?oH]De?C`e?:e?&.],e?.S\1f?\9nf??Sf?6hUf?*kо#g?5"`g?Z.Wx{?Vn{?q3{?H|?~J B|?{ʊeu|?5Ժ`|?|?8&}?wC}?9x}?3bݬ}? 7e@}?!v~?aoL~?hS/~?S$7~?#~?/n!%?\?kw2?=?n7?z3??"I"m9?ͮ~U?Jp?MHe?њQތ?3!O?Kq ܗ?"?u|:_,?$V(T?i}??e#И?y?%S $?SsO?(L}y?m[?oZ%eЙ?,܌'?4JM>(?ˑѪT?m?u}?Lۚ?C ?:7?bYof?$M?f聇ě?U?K|$?ORmT?q<%? dC>?Qo?e簚? frK?>~?堛?ճ.?>B?AmDM??TV!?%x?kW"?)%Y?? *.[ǟ?@"?oح?kTA8?ϝT?Z4(?yqM?:s?5Ɋ혤?.??Nh ?|\4?gƭ[?>?e@0?Vɧԥ?A%? z'?_Q?˖{{? U?!BiѦ?^U?F"(?6_U?]>́?`(?c?ܧ?ք ?78?at%g?a2?I,ƨ??A?I'?Ki0Y?2ߊ?PPV ?Q?r"?ОbJV?A8?QMi?B*3?0d*??Ic`?x.퇗?^:Ϋ?K)?-h??nx?nO2?PuVy?srY'?,}b?҅<?Mרۭ?{?A`W?ɪ?Ԯ?:_?. 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Wb NAw[wc0D0Mc????/9.on9-590899 9G[t*99m.+99)9@\e:*:O:IfC:+:_x:9RJ:9(8&8b+9:9XMZ6ns:2q#999(993R:>zB:[8 :$99B98 :p%:9-!9m9pg:3:eQ:b޹92:K+c:.99:A:Ft:1-9_0 :xm:IgS:q9 2:9k:9A4:^v: ::f:h:195(:[O:/:2:@:Z:k:>\:QEe:(:YO:(::1:^:S: ::z::CO:vڲ:W:j:W:E:;:b:Z:{:::6(:1:w:E,:+:Za:"j::E::y:>:b:BG:Da:ћ::G::Z/::::/:0!:ݭ:?::R:Nۨ:x:{C:>9zR:Ɨ:i[::9:}:m:::9θ:U::}[:::^:L:gu: :W:tĮ::l:te::(::::H/:8;h:v:Q::F:::D::0:;::̿:Z:J;k:3:Ԥ:e::W:|*::wd:JR:Λ:Ь:;R: :::;c::z:A::Ɗ::.:C;a:y:߿:X.:2:V:7:{H: ;w ::1 :*]::;@:w:::O;6:ۋ:RD:;I;[::E:o ;:C:*\;l;B;.;.;Q;];(;i;=>=!Õ=z5=D=̧==ؠ=~=v=j= ?`=1\=tR=QL=~F=@==;=4=p0=,=*=w%=!==Q=S=E=c=F =J==v*<24<<<<<<»<<lr;;;);;;ݖ; b;Ɓ;M΄;|;A~;P;;{{;?ے;3;;G;@߲;;yΰ;[;ռ;;Ƶ;;d;m;X;P;c;r;i<;;h<9<4M5<3<\?<@y7<=9]r/9.:ԟ9s9o9?Z':O8R:$i9E9K_9XD7*K99:r98J2:[9u99=49'B:9W:9p&Ӳ=:z*:A9489G8+9)98{E`:7u8:~s6kZ98Q9@x8ց9P9yy9>98&:Ϸ9<1:= 89B19X9J:9`a:95:fD7-:>%9h :D\991:8R99SM:c9M9~9"r9):ۿ9R:9g:v~96@99u9+28:Z9j)9~9a8P9 98B:Ad:9{67ۮ9%8:9xXd1:Q9:@k6Ѳ*:~U~9a: 89:{+9 :.G9f~9R8:Y"9qe9fE5:d⸙6:\:1:eM9OE979~9O: ]v9kx6:yٌ98n9F9O988Pv998Q&9[_9|9P\:SI9_8ML9ˆ9El9s9g9Q8V9rH8T0998n9H3M9o}8>69;-9 9ͧ9N9O0T9 F9F9֐9"d9e9d:)&9E:77V:|/9'9f9A,:Ug9r::q9/;9:h::ig:J:X{:>:::k;;%;G;+6;ot;[t;?;ě;4;w,>TU%>.>5>oY8>#8>25>_0>S +>$>8>u>u> >G>>P===@=B=x=^==E=<=T=3=a=|== = =1=v=2=N=wB=ct=i= _=S=zG=@=Am2=*=j/ ==8==u=U$;Lέ;W;°;`T;;l;L;c;4;;`;Q;ԡ;;&;';D;; ;;g6;W;V;b;.f;;s;;Gl;'=9=s:=?5=3=0=:-=^,=&=&=< ==![=`=x=]=B2= =, =!=8;=!=r=B=,=n== = ===P%=/c/=8<=PL=W6a=}x= M qwQ?ZP?ܧfnP?Pw=O?iN?>;H-N?4'd(M?ĻBL?$rmK?mJ?` I?0{I?bF[H?^LEG?dF?>vLF?: E?uE?x}lD?v)MC?=5DC?}WsB?"'|.B?֝CA?؊Lz(A?y@@?U1@?hw??vVE>?³=?,XRz>g0S>lpr>)>'fit_0D.5M'= W_coef[0]+W_coef[1]*x^W_coef[2] W_coef={-8.7848e-05,1.1857e+10,-4.793} V_chisq= 0.00052999;V_npnts= 328;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 610; W_sigma={0.000103,1.18e+10,0.18} Coefficient values ± one standard deviation y0 =-8.7848e-05 ± 0.000103 A =1.1857e+10 ± 1.18e+10 pow =-4.793 ± 0.18  0 w]wc0D0Mmaskc???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????ˀ6wܐwfit_0D0MK HɅ@???n@M ?V#}?25? 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MultiPeakFit2!   MPF_SetFolder_NaN!9 E $ {w @YS"N1MPF2ConstraintsShowingnegativePeaksdisplayPeaksFullWidth?panelPositionMPF2_UserCursorsMPF2OptionsShowing?XPointRangeBeginXPointRangeEnd@XPointRangeReversed?AutoFindNoiseLevelD6?AutoFindSmoothFactor@AutoFindTrimFraction?MPF2_FitCurvePointslK؂@MPF2_FitDatem!AMPF2_FitPoints`j@MPF2_FitChiSq q-Qm?graphlefty@graphtopb@graphright @graphbottom@v@DoEqualWidthsContraintDoPairedLocationConstraintPairedLocationDistanceResultGraph_IncludeData?ResultGraph_IncludeFitCurve?ResultGraph_IncludePeaks?ResultGraph_IncludeResidual?ResultGraph_IncludeBackgroundResultGraph_PeaksOnDataAxisResultGraph_PeaksAddLinesResultGraph_PeaksAddTagsResultGraph_TagPkNumResultGraph_TagRealLocResultGraph_TagFWHM?ResultGraph_TagPkArea?ResultGraph_TagHeightYWvNameaph_TagHeightroot:vlp3peaksXWvNameaph_TagHeightroot:WVGraphNameh_TagHeightMultipeakFit_SetNaNMPF2WeightWaveNamehtMPF2MaskWaveNameehtroot:vlp3peaksmaskFuncListStringeehtT{Constant_BLFunc, root:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Baseline Coefs', EPSW=root:Packages:MultiPeakFit2:MPF_SetFolder_NaN:blepswave, STRC=BLStruct}{MPFXGaussPeak,root:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 0 Coefs', EPSW=root:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 0 Coefseps'}{MPFXGaussPeak,root:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 1 Coefs', EPSW=root:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 1 Coefseps'}{MPFXGaussPeak,root:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 2 Coefs', EPSW=root:Packages:MultiPeakFit2:MPF_SetFolder_NaN:'Peak 2 Coefseps'}SavedFunctionTypeshtConstant;Gauss;Gauss;Gauss;interPeakConstraintsMPF2_Results_DataWavesTitleb\f01\K(65535,1,1)Y Wave:\f00\K(0,0,0) root:ecpeaks \f01\K(65535,1,1)X Wave:\f00\K(0,0,0) root:WVMPF2_Results_DateTitleitle?Multi-peak fit completed 1:55 PM 3/11/21 Multi-peak Fit Set NaNMPF2_ReportNameeTitleitleMultipeakSetNaNReportMPF2_TDReportNameitleitleMultipeakSetNaN_TDHY ym yBaseline Coefs????w^iq?!AeY y yHoldStrings????0I|k yk yW_AutoPeakInfo????0͌CC4iCA!Aذ5AV0= >>@@@W_>*!>,>6>h>>C>pE>CE>A>ҋ;>42>(>8>u >t=-V=m===Bp=%F=ܴ =n=G<?W ?<"?%?@@?@ˏE?;:?K5:?@(8?>?`$?O;? ?.?h'?1?@0?IQ?8`@o*?l4? ?[`ξ.?@fE?&/?Z?+?@9?ǎ!?Y:$n3? $ ?`6s!tq `I,U>2? 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Ꝅ?IӇ@E6&z "%aŠ)\w@o3#aqo_+wPwrO z[KCjP¥6A?6p?$y?/Ȅ?xZ?wН?5?UJ?+Dvy?N!?? d?u?E%?tK8?ȸ?vGm?-C #?B*w1_?By&S?P  YxxBkg_ecpeaksb<_=????n@qHSm ym yblepswave????ư>XUom yPeak 0 Coefseps????ư>ư>ư>5jHXom yMPF2_CoefsBackup_0????ݙL>f?hXom yMPF2_CoefsBackup_1????q@^]3@ Ƽ?`m ym ydM_Covar ????Go3>aI>n'1VODLeIj5l?n |>=! d>إ~!>aI>f1@6?ëMw~<޶:?OGt2.GNkBҾ#NF?ƺjUF?;sC>n'1ëMw~of_?Ky(^~/@z?ަZ?|[^"?c``j&_ef`VO<޶:?Ky(Vt>> .?a^}]>hKTݾPrm⾍ nDLOGt2^~/@z?>PH?s_-Ì?oʈ0?dF(]'NoCy},x:eIj5.GNަZ? .?s_-Ì?q:i??&?e^[y{d/x^6*&l?kBҾ|[^"?a^}]>oʈ0??&? Q9w>n^ca5>;#L]ʾn |>#NF?c`hKTݾdF(]e^[y{n^ca5>Mb p?ZQz?Sfq>=! d>ƺjUF?`j&_ePrm'NoCd/x;#ZQz?ѱ+ɍ? i"?إ~!>;sC>f` ny},x:^6*&L]ʾSfq> i"? >$m ym y W_sigma ????@X9yD?MGU?00i* ? t5R? 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VﻖԻ!vrS`\6:1Zk;f;,<2< b{9xܡxdPeak3dQl????|@Wn9e9 X9F9o-9 9m88aP887X76T54 3%1.+QC(#,AoỰŻV,3K 9Q:2f;Ȱ;u;(ng >`>[>C>j>i>j>C>[>`>g >>=@=f=@Z=됾=~===w=JX=;=Mz=+=g #include Function rayleigh(w,x) : FitFunc Wave w Variable x //CurveFitDialog/ These comments were created by the Curve Fitting dialog. Altering them will //CurveFitDialog/ make the function less convenient to work with in the Curve Fitting dialog. //CurveFitDialog/ Equation: //CurveFitDialog/ f(x) = (k1/(x^4))*(10^8) //CurveFitDialog/ End of Equation //CurveFitDialog/ Independent Variables 1 //CurveFitDialog/ x //CurveFitDialog/ Coefficients 1 //CurveFitDialog/ w[0] = k1 return (w[0]/(x^4))*(10^8) End Function rayleigh2(w,x) : FitFunc Wave w Variable x //CurveFitDialog/ These comments were created by the Curve Fitting dialog. Altering them will //CurveFitDialog/ make the function less convenient to work with in the Curve Fitting dialog. //CurveFitDialog/ Equation: //CurveFitDialog/ f(x) = (10^8)*((k1/((x+k2)^4))+(k3/(x^5))+(k4/(x^6))) //CurveFitDialog/ End of Equation //CurveFitDialog/ Independent Variables 1 //CurveFitDialog/ x //CurveFitDialog/ Coefficients 4 //CurveFitDialog/ w[0] = k1 //CurveFitDialog/ w[1] = k2 //CurveFitDialog/ w[2] = k3 //CurveFitDialog/ w[3] = k4 return (10^8)*((w[0]/((x+w[1])^4))+(w[2]/(x^5))+(w[3]/(x^6))) End Function polyneg6(w,x) : FitFunc Wave w Variable x //CurveFitDialog/ These comments were created by the Curve Fitting dialog. Altering them will //CurveFitDialog/ make the function less convenient to work with in the Curve Fitting dialog. //CurveFitDialog/ Equation: //CurveFitDialog/ f(x) = (k1/(x^6))*(10^11) //CurveFitDialog/ End of Equation //CurveFitDialog/ Independent Variables 1 //CurveFitDialog/ x //CurveFitDialog/ Coefficients 1 //CurveFitDialog/ w[0] = k1 return (w[0]/(x^6))*(10^11) End Function polyneg8(w,x) : FitFunc Wave w Variable x //CurveFitDialog/ These comments were created by the Curve Fitting dialog. Altering them will //CurveFitDialog/ make the function less convenient to work with in the Curve Fitting dialog. //CurveFitDialog/ Equation: //CurveFitDialog/ f(x) = (k1/(x^8))*(10^17) //CurveFitDialog/ End of Equation //CurveFitDialog/ Independent Variables 1 //CurveFitDialog/ x //CurveFitDialog/ Coefficients 1 //CurveFitDialog/ w[0] = k1 return (w[0]/(x^8))*(10^17) End Function polyneg10(w,x) : FitFunc Wave w Variable x //CurveFitDialog/ These comments were created by the Curve Fitting dialog. Altering them will //CurveFitDialog/ make the function less convenient to work with in the Curve Fitting dialog. //CurveFitDialog/ Equation: //CurveFitDialog/ f(x) = (k1/(x^10))*(10^22) //CurveFitDialog/ End of Equation //CurveFitDialog/ Independent Variables 1 //CurveFitDialog/ x //CurveFitDialog/ Coefficients 1 //CurveFitDialog/ w[0] = k1 return (w[0]/(x^10))*(10^22) End Window vlp1bkgsub() : Graph PauseUpdate; Silent 1 // building window... Display /W=(35,45,867,615) VLP1 vs WV AppendToGraph fit_VLP1 AppendToGraph vlp1peaks vs WV ModifyGraph rgb(VLP1)=(3,52428,1),rgb(vlp1peaks)=(0,0,65535) Cursor/P A VLP1 593;Cursor/P B VLP1 6 ShowInfo TextBox/C/N=CF_VLP1/X=4.89/Y=23.91 "Coefficient values ± one standard deviation\r\ty0 \t=-0.016513 ± 0.00048\r\tA \t=334.28 ± 47.4\r\tpow\t=-1.4755 ± 0.0251" Legend/C/N=text0/J/F=0/H={10,3,10}/A=MC/X=35.75/Y=2.77 "\\s(VLP1) VLP1\r\\s(fit_VLP1) fit_VLP1\r\\s(vlp1peaks) vlp1peaks" EndMacro Window vlp3bkgsub() : Graph PauseUpdate; Silent 1 // building window... Display /W=(35,45,912,593) VLP3 vs WV AppendToGraph fit_VLP3 AppendToGraph vlp3peaks vs WV ModifyGraph rgb(fit_VLP3)=(3,52428,1),rgb(vlp3peaks)=(0,0,65535) Cursor/P A VLP3 588;Cursor/P B VLP3 2 ShowInfo TextBox/C/N=CF_VLP3 "Coefficient values ± one standard deviation\r\ty0 \t=-0.018129 ± 0.000768\r\tA \t=17906 ± 4.01e+03\r\tpow\t=-2.046 ± 0.0385" EndMacro Window vlp2bkgsub() : Graph PauseUpdate; Silent 1 // building window... Display /W=(61,48,946,615) VLP2 vs WV AppendToGraph fit_VLP2 AppendToGraph vlp2peaks vs WV ModifyGraph rgb(fit_VLP2)=(3,52428,1),rgb(vlp2peaks)=(1,16019,65535) Cursor/P/A=0 A VLP2 595;Cursor/P B VLP2 1 ShowInfo TextBox/C/N=CF_VLP2/X=4.68/Y=23.06 "Coefficient values ± one standard deviation\r\ty0 \t=-0.0067808 ± 0.000254\r\tA \t=22888 ± 4.5e+03\r\tpow\t=-2.2249 ± 0.0336" EndMacro