{
"cells": [
{
"cell_type": "markdown",
"id": "86063af3",
"metadata": {},
"source": [
"# rf204a_extendedLikelihood\n",
"Extended maximum likelihood fit in multiple ranges.\n",
"\n",
"\n",
"\n",
"\n",
"**Author:** Harshal Shende, Stephan Hageboeck (C++ version) \n",
"This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Tuesday, March 19, 2024 at 07:15 PM."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e3f1e908",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:15:19.487856Z",
"iopub.status.busy": "2024-03-19T19:15:19.487503Z",
"iopub.status.idle": "2024-03-19T19:15:21.591974Z",
"shell.execute_reply": "2024-03-19T19:15:21.590568Z"
}
},
"outputs": [],
"source": [
"import ROOT"
]
},
{
"cell_type": "markdown",
"id": "f18f0237",
"metadata": {},
"source": [
"Setup component pdfs\n",
"---------------------"
]
},
{
"cell_type": "markdown",
"id": "e230f2dd",
"metadata": {},
"source": [
"Declare observable x"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b71230c6",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:15:21.600136Z",
"iopub.status.busy": "2024-03-19T19:15:21.599553Z",
"iopub.status.idle": "2024-03-19T19:15:21.946490Z",
"shell.execute_reply": "2024-03-19T19:15:21.945130Z"
}
},
"outputs": [],
"source": [
"x = ROOT.RooRealVar(\"x\", \"x\", 0, 11)"
]
},
{
"cell_type": "markdown",
"id": "7c182baf",
"metadata": {},
"source": [
"Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e17d8623",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:15:21.953883Z",
"iopub.status.busy": "2024-03-19T19:15:21.951957Z",
"iopub.status.idle": "2024-03-19T19:15:22.138961Z",
"shell.execute_reply": "2024-03-19T19:15:22.137341Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[#0] WARNING:InputArguments -- The parameter 'sigma1' with range [-inf, inf] of the RooGaussian 'sig1' exceeds the safe range of (0, inf). Advise to limit its range.\n",
"[#0] WARNING:InputArguments -- The parameter 'sigma2' with range [-inf, inf] of the RooGaussian 'sig2' exceeds the safe range of (0, inf). Advise to limit its range.\n"
]
}
],
"source": [
"mean = ROOT.RooRealVar(\"mean\", \"mean of gaussians\", 5)\n",
"sigma1 = ROOT.RooRealVar(\"sigma1\", \"width of gaussians\", 0.5)\n",
"sigma2 = ROOT.RooRealVar(\"sigma2\", \"width of gaussians\", 1)\n",
"\n",
"sig1 = ROOT.RooGaussian(\"sig1\", \"Signal component 1\", x, mean, sigma1)\n",
"sig2 = ROOT.RooGaussian(\"sig2\", \"Signal component 2\", x, mean, sigma2)"
]
},
{
"cell_type": "markdown",
"id": "1684f047",
"metadata": {},
"source": [
"Build Chebychev polynomial pdf"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "97f4f22c",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:15:22.143895Z",
"iopub.status.busy": "2024-03-19T19:15:22.143491Z",
"iopub.status.idle": "2024-03-19T19:15:22.494426Z",
"shell.execute_reply": "2024-03-19T19:15:22.492787Z"
}
},
"outputs": [],
"source": [
"a0 = ROOT.RooRealVar(\"a0\", \"a0\", 0.5, 0.0, 1.0)\n",
"a1 = ROOT.RooRealVar(\"a1\", \"a1\", 0.2, 0.0, 1.0)\n",
"bkg = ROOT.RooChebychev(\"bkg\", \"Background\", x, [a0, a1])"
]
},
{
"cell_type": "markdown",
"id": "7dadd1c6",
"metadata": {},
"source": [
"Sum the signal components into a composite signal pdf"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "211aad32",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:15:22.499948Z",
"iopub.status.busy": "2024-03-19T19:15:22.499550Z",
"iopub.status.idle": "2024-03-19T19:15:22.690838Z",
"shell.execute_reply": "2024-03-19T19:15:22.673828Z"
}
},
"outputs": [],
"source": [
"sig1frac = ROOT.RooRealVar(\"sig1frac\", \"fraction of component 1 in signal\", 0.8, 0.0, 1.0)\n",
"sig = ROOT.RooAddPdf(\"sig\", \"Signal\", [sig1, sig2], sig1frac)"
]
},
{
"cell_type": "markdown",
"id": "4a30cb68",
"metadata": {},
"source": [
"Extend the pdfs\n",
"-----------------------------"
]
},
{
"cell_type": "markdown",
"id": "05f568bb",
"metadata": {},
"source": [
"Define signal range in which events counts are to be defined"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a8b02eb3",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:15:22.696172Z",
"iopub.status.busy": "2024-03-19T19:15:22.695757Z",
"iopub.status.idle": "2024-03-19T19:15:22.896453Z",
"shell.execute_reply": "2024-03-19T19:15:22.874499Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[#1] INFO:Eval -- RooRealVar::setRange(x) new range named 'signalRange' created with bounds [4,6]\n"
]
}
],
"source": [
"x.setRange(\"signalRange\", 4, 6)"
]
},
{
"cell_type": "markdown",
"id": "0218a69e",
"metadata": {},
"source": [
"Associated nsig/nbkg as expected number of events with sig/bkg _in_the_range_ \"signalRange\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "614dbb06",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:15:22.912142Z",
"iopub.status.busy": "2024-03-19T19:15:22.911733Z",
"iopub.status.idle": "2024-03-19T19:15:23.047485Z",
"shell.execute_reply": "2024-03-19T19:15:23.030493Z"
}
},
"outputs": [],
"source": [
"nsig = ROOT.RooRealVar(\"nsig\", \"number of signal events in signalRange\", 500, 0.0, 10000)\n",
"nbkg = ROOT.RooRealVar(\"nbkg\", \"number of background events in signalRange\", 500, 0, 10000)"
]
},
{
"cell_type": "markdown",
"id": "845d4601",
"metadata": {},
"source": [
"Use AddPdf to extend the model. Giving as many coefficients as pdfs switches on extension."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3dfe5fd4",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:15:23.057966Z",
"iopub.status.busy": "2024-03-19T19:15:23.057571Z",
"iopub.status.idle": "2024-03-19T19:15:23.165361Z",
"shell.execute_reply": "2024-03-19T19:15:23.164122Z"
}
},
"outputs": [],
"source": [
"model = ROOT.RooAddPdf(\"model\", \"(g1+g2)+a\", [bkg, sig], [nbkg, nsig])"
]
},
{
"cell_type": "markdown",
"id": "6f87359e",
"metadata": {},
"source": [
"Sample data, fit model\n",
"-------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "d26b1539",
"metadata": {},
"source": [
"Generate 1000 events from model so that nsig,nbkg come out to numbers <<500 in fit"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0dcc8086",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:15:23.173088Z",
"iopub.status.busy": "2024-03-19T19:15:23.172668Z",
"iopub.status.idle": "2024-03-19T19:15:23.538576Z",
"shell.execute_reply": "2024-03-19T19:15:23.537184Z"
}
},
"outputs": [],
"source": [
"data = model.generate(x, 1000)\n",
"\n",
"canv = ROOT.TCanvas(\"Canvas\", \"Canvas\", 1500, 600)\n",
"canv.Divide(3, 1)"
]
},
{
"cell_type": "markdown",
"id": "e0976a74",
"metadata": {},
"source": [
"Fit full range\n",
" -------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "3ce155b2",
"metadata": {},
"source": [
"Perform unbinned ML fit to data, full range"
]
},
{
"cell_type": "markdown",
"id": "8a4f3c00",
"metadata": {},
"source": [
"IMPORTANT:\n",
"The model needs to be copied when fitting with different ranges because\n",
"the interpretation of the coefficients is tied to the fit range\n",
"that's used in the first fit"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7ed6861f",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:15:23.543610Z",
"iopub.status.busy": "2024-03-19T19:15:23.543197Z",
"iopub.status.idle": "2024-03-19T19:15:24.236703Z",
"shell.execute_reply": "2024-03-19T19:15:24.235685Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[#1] INFO:Minimization -- p.d.f. provides expected number of events, including extended term in likelihood.\n",
"[#1] INFO:Fitting -- RooAbsPdf::fitTo(model) fixing normalization set for coefficient determination to observables in data\n",
"[#1] INFO:Fitting -- using CPU computation library compiled with -mavx2\n",
"[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_model_modelData) Summation contains a RooNLLVar, using its error level\n",
"[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization\n",
"[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization\n",
"\n",
" RooFitResult: minimized FCN value: -3872.49, estimated distance to minimum: 4.30406e-05\n",
" covariance matrix quality: Full, accurate covariance matrix\n",
" Status : MINIMIZE=0 HESSE=0 \n",
"\n",
" Floating Parameter FinalValue +/- Error \n",
" -------------------- --------------------------\n",
" a0 4.2647e-01 +/- 7.59e-02\n",
" a1 1.7594e-01 +/- 1.10e-01\n",
" nbkg 5.1101e+02 +/- 3.60e+01\n",
" nsig 4.8899e+02 +/- 3.57e+01\n",
" sig1frac 8.6392e-01 +/- 1.08e-01\n",
"\n"
]
}
],
"source": [
"canv.cd(1)\n",
"\n",
"model1 = ROOT.RooAddPdf(model)\n",
"r = model1.fitTo(data, Save=True, PrintLevel=-1)\n",
"r.Print()\n",
"\n",
"frame = x.frame(Title=\"Full range fitted\")\n",
"data.plotOn(frame)\n",
"model1.plotOn(frame, VisualizeError=r)\n",
"model1.plotOn(frame)\n",
"model1.paramOn(frame)\n",
"frame.Draw()"
]
},
{
"cell_type": "markdown",
"id": "6ea98323",
"metadata": {},
"source": [
"Fit in two regions\n",
"-------------------------------------------"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ae86eabb",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:15:24.241111Z",
"iopub.status.busy": "2024-03-19T19:15:24.240767Z",
"iopub.status.idle": "2024-03-19T19:15:24.417689Z",
"shell.execute_reply": "2024-03-19T19:15:24.416721Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[#1] INFO:Eval -- RooRealVar::setRange(x) new range named 'left' created with bounds [0,4]\n",
"[#1] INFO:Eval -- RooRealVar::setRange(x) new range named 'right' created with bounds [6,10]\n",
"[#1] INFO:Minimization -- p.d.f. provides expected number of events, including extended term in likelihood.\n",
"[#1] INFO:Eval -- RooRealVar::setRange(x) new range named 'fit_nll_model_modelData_left' created with bounds [0,4]\n",
"[#1] INFO:Eval -- RooRealVar::setRange(x) new range named 'fit_nll_model_modelData_right' created with bounds [6,10]\n",
"[#1] INFO:Fitting -- RooAbsPdf::fitTo(model) fixing normalization set for coefficient determination to observables in data\n",
"[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_model_modelData) Summation contains a RooNLLVar, using its error level\n",
"[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization\n",
"[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization\n",
"\n",
" RooFitResult: minimized FCN value: -1134.15, estimated distance to minimum: 3.61209e-05\n",
" covariance matrix quality: Full, accurate covariance matrix\n",
" Status : MINIMIZE=0 HESSE=0 \n",
"\n",
" Floating Parameter FinalValue +/- Error \n",
" -------------------- --------------------------\n",
" a0 3.2415e-01 +/- 1.09e-01\n",
" a1 3.0373e-02 +/- 2.12e-01\n",
" nbkg 5.0182e+02 +/- 3.94e+01\n",
" nsig 4.1091e+02 +/- 2.63e+02\n",
" sig1frac 8.5838e-01 +/- 2.74e-01\n",
"\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f was fitted in a subrange and no explicit Range() and NormRange() was specified. Plotting / normalising in fit range. To override, do one of the following\n",
"\t- Clear the automatic fit range attribute: .removeStringAttribute(\"fitrange\");\n",
"\t- Explicitly specify the plotting range: Range(\"\").\n",
"\t- Explicitly specify where to compute the normalisation: NormRange(\"\").\n",
"\tThe default (full) range can be denoted with Range(\"\") / NormRange(\"\").\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_left'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_left'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_left'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_left'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_left'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_left'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_left'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_left'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_left'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_left'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_left'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f was fitted in a subrange and no explicit Range() and NormRange() was specified. Plotting / normalising in fit range. To override, do one of the following\n",
"\t- Clear the automatic fit range attribute: .removeStringAttribute(\"fitrange\");\n",
"\t- Explicitly specify the plotting range: Range(\"\").\n",
"\t- Explicitly specify where to compute the normalisation: NormRange(\"\").\n",
"\tThe default (full) range can be denoted with Range(\"\") / NormRange(\"\").\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData_left,fit_nll_model_modelData_right'\n"
]
}
],
"source": [
"canv.cd(2)\n",
"x.setRange(\"left\", 0.0, 4.0)\n",
"x.setRange(\"right\", 6.0, 10.0)\n",
"\n",
"model2 = ROOT.RooAddPdf(model)\n",
"r2 = model2.fitTo(data, Range=\"left,right\", Save=True, PrintLevel=-1)\n",
"r2.Print()\n",
"\n",
"frame2 = x.frame(Title=\"Fit in left/right sideband\")\n",
"data.plotOn(frame2)\n",
"model2.plotOn(frame2, VisualizeError=r2)\n",
"model2.plotOn(frame2)\n",
"model2.paramOn(frame2)\n",
"frame2.Draw()"
]
},
{
"cell_type": "markdown",
"id": "e3d0ddee",
"metadata": {},
"source": [
"Fit in one region\n",
"-------------------------------------------\n",
"Note how restricting the region to only the left tail increases\n",
"the fit uncertainty"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "76b5aa39",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:15:24.428958Z",
"iopub.status.busy": "2024-03-19T19:15:24.428317Z",
"iopub.status.idle": "2024-03-19T19:15:25.430949Z",
"shell.execute_reply": "2024-03-19T19:15:25.392636Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[#1] INFO:Eval -- RooRealVar::setRange(x) new range named 'leftToMiddle' created with bounds [0,5]\n",
"[#1] INFO:Minimization -- p.d.f. provides expected number of events, including extended term in likelihood.\n",
"[#1] INFO:Eval -- RooRealVar::setRange(x) new range named 'fit_nll_model_modelData' created with bounds [0,5]\n",
"[#1] INFO:Fitting -- RooAbsPdf::fitTo(model) fixing normalization set for coefficient determination to observables in data\n",
"[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_model_modelData) Summation contains a RooNLLVar, using its error level\n",
"[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization\n",
"[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization\n",
"\n",
" RooFitResult: minimized FCN value: -1620.17, estimated distance to minimum: 0.000658484\n",
" covariance matrix quality: Full, accurate covariance matrix\n",
" Status : MINIMIZE=0 HESSE=0 \n",
"\n",
" Floating Parameter FinalValue +/- Error \n",
" -------------------- --------------------------\n",
" a0 7.0685e-01 +/- 7.00e-01\n",
" a1 2.2397e-01 +/- 6.03e-01\n",
" nbkg 7.1175e+02 +/- 1.22e+03\n",
" nsig 4.4359e+02 +/- 1.28e+02\n",
" sig1frac 9.7243e-01 +/- 8.92e-01\n",
"\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f was fitted in a subrange and no explicit Range() and NormRange() was specified. Plotting / normalising in fit range. To override, do one of the following\n",
"\t- Clear the automatic fit range attribute: .removeStringAttribute(\"fitrange\");\n",
"\t- Explicitly specify the plotting range: Range(\"\").\n",
"\t- Explicitly specify where to compute the normalisation: NormRange(\"\").\n",
"\tThe default (full) range can be denoted with Range(\"\") / NormRange(\"\").\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f was fitted in a subrange and no explicit Range() and NormRange() was specified. Plotting / normalising in fit range. To override, do one of the following\n",
"\t- Clear the automatic fit range attribute: .removeStringAttribute(\"fitrange\");\n",
"\t- Explicitly specify the plotting range: Range(\"\").\n",
"\t- Explicitly specify where to compute the normalisation: NormRange(\"\").\n",
"\tThe default (full) range can be denoted with Range(\"\") / NormRange(\"\").\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) only plotting range 'fit_nll_model_modelData'\n",
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) p.d.f. curve is normalized using explicit choice of ranges 'fit_nll_model_modelData'\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : png file rf204a_extendedLikelihood.png has been created\n"
]
},
{
"data": {
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gfc1XX32lbtQ8RYWwvYvR31HHlrt09Baiv1+dmq2+ZvWX66XZwHJMKtirPvjGxPZnv7s2c2sZ6fp7tN+Fg68V5Y3va0ZhGLo/Xq/ZGoCF2z7Q7zRScojQLB4+m+qlG+NMHebqjTEKEMahsGy5ypw4jnuPoMYlo1K79M/SubVZJsZKcbYzKEWwA9PhRaqXTiljpD7V+2pn/QTfffed3gbjT3tuOr2dxm7Lr6TqMdjF498M9PvVS/i1cADHvAh2g2yPLKPbZZp+W1g/wUGRbs9cPP5aoX4YMP4cjsfhSLIDpexM1I+H9D/rawcdX13dI/+sijM5vV6BlvfbIagnZu+H77bGJVPfQp3dq8VYEcpBxZ1ZWtSbBVryKNVlmBtTyua1z8yjNb5x+ZOp/nupbMN3331nTxw+sNu53W71h5YZXHpBx1iz+mawzIUDNPIo2HX2Me1wYdhvpOuM/ZGxwJ31RueqScDcbDYbeUPFYpqmzl7dPp+zpjegEEKehGk/K8uyoijU/cYoA+q2PL0ziqK+hhj84YEQ4vvvv9f/bKlNS+QysaeU607tAPRhYto3AMDMeJTqWZbZwaVyvirVu7dQvW6maT+HH3/8sX62BrnTybKsfgc3Y1mW5Xmu75v0EX+iKGJ8MaCeR8Guk43Uq0gyDGvOYDqibUNEuj5/GVDGPFWIORust4erJgGYDpUpqr4rh6HKtMv62Dcy66I/WZaVfvmXQ/H12No8z+0+3ja1u9JrLurOPM83m812u+3Ytl/84hf6n7/97W/VoF+/+c1vjp5ty52H8aVFf2KaptRigGXyK9UVNQJi1dG5nupdXsh4+mazUV8neqz1wCD3uS0nbhy/U23bwEJ4GuxynmEYDleqcBDpxq+eI6IQA8xKFEUy09vUj+sfLf05y94HdCGjsL7EkGWZOl5Xb03+GcexfK7cezVemLPRf/7nf6rbcp76PUOzf2RQevydAYBf/Ep1JY5jmWl2ldxO9S6yx30k+UrvgNzztt9sWCmAwdNgL+1I0q+hI93+1XNEPRRiGleG75clB/zS2CXYTmf7Q6r2EMNRCaguqKn+zLSe7fJQPgxDdWcURfJONQd5xD/ZjtD2GrGbqr+70jkM0bAaBDswHb6kuk5e21i2XK+Sl6Y63Ogr2NM0ncKPyYDXvAt2edTt+/mYcgc0kQTrpxBTf0obhXDAGXXeo9Fb5Ij5lB6xRVFUf6CmqiSKbE/Ns6qKFOp4Xb2XSZF7UHsn2r5CoU9ZunuTLzHKPo9gBybCo1RXXV1UPhg5OXqqf/311z/++KM9fIC67um89RLscisyVp9RVkvT1Pdva8CgPAp2RU4/qd/hjo700h2Qqny5+5Ggr1F/xQCXN298RZcvB/cG2lbnrf76oMajNZdnqzoXps3HXJ9SzUc2YLvdxnGsjwOvGFemUE8sbZ7RmJopjyZHh6mZwHhrxrI13mnNMtEnMK4eUjqlSwQ7ekewH8qvVK8J5/pU70XjnNXFTeU1Nb755pua3FZqcnigy8oOqmOwG1eqluTK1Xff+jVxj34tYK78CnZ9+qq5DXFpp94jvepySEaSD72rUnpbWPpF7Nwg2ReCFX2QxkS2H+r9S35pfz/jIftZpZevrmptzUv0qHGeNcu58e3o+V5TcBn3KJ9gx0BY0e35leql0S3bU5/qbpQ24JtvvjEaX/qs2RRiOga7qPh+om8k+vz9WjiAG34Fe9GizjJEIaaNgyK9qqRVNT7j4I3va0byN+2+5tYGyb4QrOhD6Qe79i+QRtYM9LHVw13Pu/aFGDsQjQYb+49e3kjVi9ZshPrS1g89q9LfXiZF095LPrHjWzsOwY6BsKIP4leqV4VzY6of56DclnfqvdbVIXuxmEKM42D3a+EAznga7FWz6qsQc1CkS+0j3X6ndvsHXeAGj4eXZ2z8hWBFYyxse+6xzBeCFY1RsOG5xzIHMCh/Q+ZnYzcAAAAAAABgKYYtxMgrFw76EgAAlwh2AJgZgh0AHBu8R0z9BdIBAN4h2AFgZgh2AHDJ11OqhM/ng+EgrGiMhW3PPZb5QrCiMQo2PPeCILDvZC0A6Iu/wf5k7AZ0YoS7p+sAAKAQ7AAwJ8Q4ANh6OzUpiqLAkiRJX/MvZVwCatDXAoClIdgBYGZGCXYAgKGfHjHyB8w4jvWBvpIkSdNU3ujlVQAAzhDsADAzBDsATEQPp1TJ+C6dT5Zlm81moJ80/T0fDAdhRWMsS972CHYMihWNUSx8wxsl2Be+zAEMzd+Q6aHdsqaeZVn5Cwy2aPxd6DgIKxpjWfK2R7BjUKxojGLhG94owb7wZQ5gaP6GTA9jxERRlOd5aazTxREAfESwA8DMEOwAMB39FJBksgshwjBUd8p7ttutfhpqj/ytfuEgrGiMZeHbHsGO4bCiMQo2PPfBzjIHMCh/Q6afqyZlWbbdbmWmyzQXQsRxXBTFQAfrAIBBEewAMDMEOwBMhK8FJOFz9QsHYUVjLGx77rHMF4IVjVGw4bnHMgcwKH9Dpp8eMQAAAAAAAGhEIQYAAAAAAMCRJ2M3AAAAAMA8BUFg3OPpeQQA0CMKMQAAAAAGQdkFAGw9nJqUJEmPkwEARkewA8DMEOwAMB09FGKiKAqCIIqiLMvsR7MsS5LE7pQIAJgsgh0AZoZgB4Dp6OHUpCiKiqJIkmSz2ch7wjAUQuR5rv4cqFOisbeg6yMwKfJQL4qi0WfSiyzLptAMNwh2ALb2gVwz5URS/S9/+cvvfve7NlPOJvxHDHYAk7XYYBdjv5f+L7utd2iMomi49+DvNcNxEFb0QOTX3YGWbRRF6sCuzatkWbbZbMIw1H+mk3eqP41He1F/eK22Pb000KYZSZKkabrdbktnPuiSHwjBjn6xoocwkVQ3ptSTUGajeqj3VG+smMgNT4/0r7766q9//WvV3PR9kHj8XvR5Ct8iXXIT7HzYgS6mGexxHKsAGfpwvd9gF9aeqGYnpb/NQRTe8rrxaI8V3bvtdjtoAshf2MTDT22NL6TaI3+Lsxupz7DfptY3TDzeJ7VsRhzHcrLtdms8NPSSnwGWzEKwovs1nVRXAWhPqUf9QKne+Pb1A+CvvvpK3aifuOpdE+ltsHCA40wn2EunlEe5Dg7X+w32Q3dS9sF8jzwOR5J9IVjRx9k+MO4Ujw3x0kZy1QSZHoJGcKugNFree1PrHzVet74ZxrLV37J6O4Mu+RlgySwEK/oIXqS6fCiOY/tPGfgq51X+99vO+gm+++47/UWNP3VqwRrvWr4XIr09Fg5Qw99gl2FuHK7X/BjZpZ31E7QP9sJ6m7LmIv8s3Un1/huwrofBegFMSpZlQRBsHgRBoHer06u8A726vGF0Iyzt2hdFUX171ENjnXQqm203o7TXZfjAfqjxnQJAFY9S3ZhSNkydbx+GoXpolFT/93//d6H9ZKqGEvjLX/5iTFn1rvX3QqQDOJq/wS6rLfJMJTkf1dRRrrnWJdizLFOnPsl3ZOyk9BOy+jdcjWdoXjce7bGiD6U+3TVdDY0Cdo/sObepKKsG23fGcbzdbvstS6vfH4QQW43dKvt1Vasa3059J6DOb2KeWDILwYo+iC+pbvcZrGqVmrI+S1tqGelFUcgjdb3LumzGN9980/gqpQ0m0huxcIBSvgS7rSb37L6ERxsi2NW71k9QMp6lXmKgzvi6Hq6aBGA6VK1XBYocvOrQiz7Iy1ja9ydJ4uxnzDiO0weqVd1na7RfDhLc4/wBoEdzSnVJDfoYhmH3n0/dRLo+lGNf8wSwWP4Ge5ZlMsDt86eEEGrU3o6vPnSwq28WeZ6POKB4P5evzvM8juNBL6UBoA15cUrx0NeuS1qVdsZzdhXPLMtkSsqu7PJ2L1mpLxOu5lCFYAcmYjapriRJkiRJnud5nkdR1PGo2kGkq8pR6RcPjxDswER4GuzqokIyRoxXVFWY6R+ry8shqTbLvVK/L9FGD4UYuaRkQS7Pc/n7BvkOjMW4wtzRMxm3QiGTUV03LkmS434rwHEIdmA65pHqiiy+yCPgYU+/70z/arEtu3a1X8YKdv2ystJENkVgRN4Fu16StkeWUb+eetG7XH65kKN95Xk+Vpt7OzVJ/abBgTswItVjUJUw7AOglvMpTaXGn9FU7xXFGPuqI8eFGLnXsfeUC0k2gh0YnUeprg9/KG9n2jiOqjuGOgI2Jnbg66+//vHHH3/88UfjfjXKo05VYXz5atGS+2Cn7AIYPAp2STWvpgqj3otjBwV7I2P/Nazhhp/ZbrdyIJyBrr89aOMxHazog7S8jFzj6F9VHbDbDKyoT2mM2iVjwZ6JnMYeMavxjXTUOISw0X77oto1b2eWg/US7OgFK7o9v1LdCHP9iUb6DTEIZeMwk+qypt99911RFN98801NpBu7oapX9DrSpUGD3feFAwzBr2BXQWdHhHricJd5HiLY1dus2UkN/b6KovA4HEn2hWBFH6RlIg83DLs+c13969phV/VGhmhwFVHxO569Z7KfWLq7qnkKJJbMQrCi2/Mr1WumLH0jgx7j2kqXpLqyhp7PVYvdaDCR3oiFA9j8CvbSpspHq66xPdDPdaVKG1Aa7FUNlg+VrpRB38jPqpYsAB/J0x3VnypTXHarTpJED3d52HroTKIoMgLxuPnYgmql0xupPVbHSwDL5FeqG1MKrZxtp3ocx93fxRGRrvdX/+abb/785z/bk83pXCQAU+NRsNc3aaBxvgYKdiFElmVV79reSQ09NJjHVwzhcicLwYrGWNj23GOZLwQrGqNgw3OPZQ5gUP6GDD1iAAAAAAAAHBmwEOOgb1WbrkoAgL4Q7AAwM5yHBQDu9VmIkVd7kmkeRdFmswmCYNDBFIwBb4Z7IQBYJoIdAGbGfbADAAx9nlIlf7osiiLLss1ms91usyxL03SgI2l/zwfDQVjRGAvbniDYMQxWNEbBhie5DHaWOYBB+RsyPZ+aJIcaViV2iusA4DuCHQBmhmAHgHE9GWKmxnWhAAC+I9gBYGbcBLs92penP18DQI/67BEThuFms0mSJM9zec3tQa+8DQAYGsEOADPjONgLy3CvBQC+6Hmw3jAM0zSN41j2cszzXHZ9BAD4iGAHgJkh2AFgdL6ObSN8HpgHB2FFYyxse+6xzBeCFY1RsOG5xzIHMCh/Q6bPHjH2KaBVdwIAvECwA8DMEOwAMLp+ButVZ5Yap5jmed7L/AEAjhHsADAzBDsATESfhRgZ4nqyR1HUcvQv47J5+p9JkmRZxqX1AMAlgh0AZqZ7sAMAetHnKVVJkhx3SJ0kSZqm+j2qVbKfZBiGcp+ht9bf88FwEFY0xsK2Jwh2DIMVjVGw4UlHB7t6emklvfR+ljmAQfkbMpNot6zBZ1lm3C+P4/Vj9ziOVbj7u9BxEFY0xsK21wXBjhqsaIyCDa87vZIehqEK+aoKO8scwKD8DZk+B+tNkiQo0+a5pf0h0zQNw1D9qcc9AMABgh0AZuboYI+iKAzDoiiyLNtut3meywCXxXR5v/xGxGmnAFCvz0KMPLwuLI1PlDku9wFRFOkH5cb5q4wlBgAuEewAMDNdgl1VWKIoKopC5jkVdgA4VJ+FGFHWC7297Xa73W6FEJvNpuVTSsv5h/5mCwCoQbADwMwcEezyKXIIGHuUGSrsAHCQSRRiZFdGOWC73sWxzRPrHdEYAICOYAeAmTm6wh4EQZZlWZaladqyMk55HQBsfRZittvtZrORQ6Z3mQ8dGgFgIgh2AJiZLsEex7EsxLQfC4byOgDY+izEyJ7naZpuNpv2dW75k6l+T57n8h7jwN04ARXzttvtrq+v1+u1EGK9Xl9dXe12O2H9rjJ2M/2TZVn9sVfHq1pCCCEPUusncNSUzgh29IhgH8JsUr0mORtD1Y36ZkykkW0cF+ySvi1RYQcGMptgr+FFZg67Y2qsUjsghFBjhsVxrFolRxbYbrfGbfUsx+2EMx8/fvz1r399dnamNtTz8/Nf/epXHz9+lBOw9o+mvvSWPio/aPo2agQAACAASURBVMZnzT2ZA/VhJZtaOtxg8fDzWsdmGHNos0zUApSM5hmPxnHcsYVTRrDDQLAPZAapblRj9abWh+qhSpdS92yvaf/M2Iktd2RhGOrLRF9EfK6BI0w/2O1Irzn0rT8eHivYFbm0S+9U9IP2vto/iXA03oy+4PQ9t/GlhWSfsaurK/1gXTo/P3/9+rWcgLV/tOkne2Fln91g1c7S7Kt/j+0Zc2gzQz2UjRbqQacenXEthmCHgWAfiO+prgJBn0Y+VBqbXQ7ZS5dSx2zX7zHaPz/61xW54uyqulFhn/HSAIYz/WC3I92O6NLD9ekEu1S6qEuDXY+1XmK/53BUe9M4juM47ljfqkeyz9jp6WnpZ3u1WskJWPs15HVq7NuSETfGo3ay23MwnjXEPqAm0Yyj+apkPzoTjWbU/GlTAVj6p2y53kkkDEMvCjEEO3pBsB9t3qmu4sX+sypUj25haRs6ZrvRJOPtTNbRwV76taSorrDzuQZKzSDYa5pUc7g+kWAvrFqS/ZCxY5Lvwmh/l6JYn+GoSuPq24Xo3NeoBsk+V/f396La3d1dwdqvpbJDX24qSkpLvOpRI03scDSOtIyn9/sWSh+Sx4tVFXT9oe4biR3K9dMbtRW1MNXTj4vpcRHs6AXB3sW8U91oj16zNurX9sRHNKPlnbqabDdyvnhYnoMWrLtzGex8roFSMwj20rejGlB1uD6RYC8evjWU9oipWp79tr/PcBRaDUwvIPX4EsbLDTRnjI4fTrsoXXRqiemBbv+upSd7abm99LkDHbLrr1U1TelD9pHx0c0oHn5k2D70tVbqn6vaYFTT5YGvbLkXRRmCHX0h2I8241SvKmSUbgxdfnhUzdDn1j3b7fbXn1AwES6DfeKLAhjLDIJdqSlAG5lvGz3Y23xrqAn2Lu1/UrURHMe4TIZkXz4DqHd2drbf729ubvQ7Ly4uTk5OxmqSj4qHS0umaSqsT+J2u5V/ygslbDab4vFeQV5VIY5j9Sx5T/hwlQQ151JVg703DjBuT5DneRAERfVOazhGdm02G5XFNW8kiqI8z+VtOaa9mlguQyFEnuebzUathSkj2NELgr27hae6ys/u4dNXtqv5RFEkr6ChJpg4gh2YCE+DXSfnf9xVnEYP9kYq2I3uS+pVSl+9lUMrNzWENna6/kNBjy9hvNxAc8boPn78+Ktf/er8/FxtqBcXF7/85S///ve/ywlY+zXkEjPO0BbWSZvq0W1ZL+vSiNDnU/VainF2qNLm90bZUdBonvFEeaeDHjFVf9ZQ3V7sBasaXNP+SSHY0ReC/WgzTvWWPWL0d1H/WvVKn94l24vHpwBULeepcRnsE18UwFh8D3ZjDlUHtHJu9Yfrowd7TRvqT+zq2P4+e8Rst9vNZqOKRvJ3g6r9E1BKlnuV1Wq13+9Xq9XJycnt7e2zZ89qJi4eV4jRiyRJjqtwH117lr8r6n+qGfryS516C3IT1d+Ouh3HcZqm0//tlGBHdwT7pHiX6urnXPUD71jsbI+iSHV7VNmYpmnVd5uJINiBmXEf7IqMkUNffTrBXkP1gozj2HiDWZYZPY+O0GchJoqi7XYrW5nnuRwBwZcvTpgIecyt91gOgqBqlEd7YhyhND7kQWSe52maHp3spXNunJt8YhRF3qWH3KmUhrX9rqMoqukpOh0EO7oj2B3zKNXtoox8CVXIUAfrdrQ6U5Ptss36/V7EI8EOeGdqwW606qAAmUKwN1I/C9nx2Fv7j+tIMwVeNx719JVbuqIbJ1gy+dFu7OtoDCIrHvd1lBPL28bZNMLqj91vX0ejN2bVZe2MtukGOjWp8TQio6nGn6ULc/qnJjnGx3nGCPajzTvVS5tkDIXbV1SWble9ZLvR4KNHnZyf0m1m7EYB4/M92PWn16SoneETCXa7PcbTq5ZDj+3veYyYqnU8BHJ8xgTH6x20TPbSwDWSvero09Dvp94+8bU07GpCcKBCTMunVLW89H1N/2CdYEdfCPajzTvVS9sgH+o+eIHh6O2qpv12I6dfYR9iLde8loNXAbxjfwz1fJt+sFe9i9IJ9FScTrBLpd8aSlsotMtX99L+n1W9zBHkGGZpmgZBYJwPPJDgsaFfDpgTPaar+iSr7nbyNMgkSfRnDXQ+uewyrd8Tx/FYp48G1UqnN1qunzhqvy8vuoIT7IAvPE11ow1CGxhouFG0esz2LMuMC81OdrwDxX2wAzjOZIP9aNMJ9ir1kdhj+wc5B1sOiFA1tk1fOIF8xoyhBOwV3TgB3JDRNuUzPLtg09IR7OiIYPcCqb4oDoKdZQ6MjmCfpj57xCiyuC5/PfBiNEoAbciisj62orwx/W4d6I5gB+aHVF84gh2DolPrKAh2X/RfiJFjyAdBsNls5DDsvb8EgFHIj3Oe5zLiZQfIOI5J9tkj2IFZItWXjGBHL6rOAaEKMxaC3Rc9X75adm7kMnjALEVRVBRFkiSyuB49GLlZGBLBDswYqb5MBDt6JM8K8ff0kPkh2H3R52dGrmNn557xgZ8xe6QAe5il+gnYNtAdISMIdvTHCHbjURnjBDsGRcJILoOdZb4QxopW2c26x9D8DZnB2y37PQ4xZ38XOhpVDdmobjdOAHTH5lSFYMcRjg5ztgr0hW2pxkDBzjJfCAoxGIu/IdPDGDFBEOgXedJDPMsyeVoaAMAjBDsAzAzBDjcYHQZoo+fBerMsG+7a4AAA9wh2AJgZgh0AxtXnYL0AAAAAoNQM8wcAi0UhBgAAAMAgKLsAgK3nU5MAAAAAAAvEADFASxRiAAAAAAAAHOnhak/yzM8wDIUQctwveVv9OVCPRH8vVYVGXL4aU7DkzYlgR++4fDVGt/BtaZRgX/gyX47Pqf74ftY9huZvyPTQbv3qd6X0S+X1yN+FjkYUYjAFS96cCHb0jkIMRrfwbWmUYF/4Ml8OCjEYi78h42u7BWOwz1pzIUZLdo7XMRA2J/cI9hmjEIPRsS25xzJfCAoxGIu/IeP3VZM8XejoKHj4l9UPzA/BDgAAgHljsF4AAAAAAABHKMTAM0HFbQAAAAAApo9CDAAAAAAAgCMUYuA3OsUAAAAAo+OwHGiPQgy80m4Uz6DC0K2bgSiKoiga6MrEXWRZVtOqKTS4voViGo0EPFWV6gR7o8mm+pQdneeNT1wmPrZAvwj2gdRk+BDxTiEGM1QUhbzwin0D9fI8z/N8rGRPkkQeokVRZNy52Ww2m43xUJZlcnr7ob788MMPjdPIZpS20G5kkiS9NxKYPSPMCfb2JpjqSs13cvnEfpt9RJ4bzavJ85pdFQrL2C0C/Db9YC/N9iiK5EO9Hwy3iXejGaV3qgzXW1h/nN+JHY6+8LrxqCfKjraLohAV/9U/kU2lvTAMwzDcbrejvLrKpTAM5T3b7Va/U96I49ie3nhij+05qNkHtR82Pq0z1pjPjberJkCNqaW6tN1uVSTaz4rjWD7Ub7PbRLqd2OopNXmuHpJLe4j9kb/4qC6EXNE1h+jo0TSDvSgKOzkVPUL7PRJuebhutLD0Tr39cvGWJn9f8e7xB4Rkn7HKw3EKMS1st1sVzfrtmsna3zlc6OvZpwJO3qn+VEfnxm2p9wP3NsluNMP4025/GIYUYmos7dO6KI353Hi7aoLZm1Oq6we1dsYaDzkuxBgBrpoqm1GT58ZDNTWmBWI5LISgEHOgOQW7etGqbC8ex/sohZjGXY/xQ698d1X7hX5a3stcRkGyz1j5ITiFmHZUmlRFnp6hRowax75GGdiuffRFvpA8rrUPZ1Xj9fiTjbEb32O4t3mzRm3FCGhjeaLR0j6ti9KYz423qyaYvTmluuwLU/WrafhgiPBsfKdGU4vHu5WaJhk7guGWqo9YDgsht3gKMe3NKdiNVtmvLpukGjZKIcbYv5S2XG+tqP260U/Le5nLKEj2GRMUYjoQFeSjKlxKe1AbSaTHuj2r3ttcWMfBKs3jONZvF9U9YnrpLvj9AyHE95r6ZxktLLTlqd4XRZl6S/u0LkpjPjferppg9uaU6kr9VwWj2V0cl+d2M1rmub0jWLhFfVSXTH6YKcS0N7Nglw2WvXLsV1f39BuPR8R7my4tRrFGtVnP/17a7/EHhGSfMVFfiBHaf4+DvvyJC9tUqqK8qnN1VbJXVTpKl6f6kdPQJmpVdtvN05th7ISq6jLdw/FrjZyn+rPmWfYPvHYPfH3xotTSPq2L0pjPjberJpi9maW68S5q3nL3tDwuzyX9WLxlntvfl7Coj+qSyY8KhZj2Zhbs6k+70iEn1ivavRRijov3xkKMXUw3von0mGkef0BI9hkTpYfgdhWGQkwZO+PsZFdHisZBZM0+oKg9773qILXNwag+WemORzzuaWlMfMQrtnRQ2m61sSfF4wN3Y8fJAXqNpX1aF6UxnxtvV00we3NKdcVZjxhjngdNrNrQMs/tHQFYDgsht3gKMe3NKdjln8ZL6H8aXcVHOTWptHkG++QpPfz1ikw/Le9lLqMg2WdMlB6C21UYrRZT98SFbSr1yV6U1S+Mzo1VReshTjpVNX7J2CVUvRe7SeqEVcdjxFQ9q7RzJgMHNGLhzFhjPjferppg9uaU6i1f2ngXvWj5TvUvKvad6p6a9quJ6f9YLOyjumTyw0Ahpr05Bbu6rT8q60eqZGM8cazD9ZpCjGqePVJPzWrq4onwmXENcLVoMD8lF6PHsbIsE0IkSZJlWZ7nQojNZmN/fMIwzPNcTqw/sWqepY9GURRFUWOTZDOq7rHnkGWZvDNJkiRJ5J0yENq8XI+SJEnTNI5j1QzVQuMeIUQURWmaOmubpwh24FDepfpkyUgXQoRhqL99+y3reR5FUZ7nakfgeDcEYJa8C3bj0ZqHhBBpmtrHySNSB5/b7VZfFFV7LvVNpJPutZyxeN141BPWz57meUlG75jqJxbL21TkR7uqdms8avxwp982zlYt/ZFQObqv4/Yx9Sy9PfaZSvrEVafIdieEaBxHwFhKxp9G+1sukyVb2qd1URrzufF21QSzN6dUV9z3iJHjCNRPU/Mea/LcOBGAzo86lsNClHaHYd3XmFOw6w8ZZ/e03CN0IVocrutvxF44KsPtVhkL01jaHXn8ASHZZ0xYR9t25aX07CT7icXyNpX6ZC8dV0VfVsLq+lg18RCqxogp3VvYD/V+0mnLyapaWLrD6/erxcws7dO6KI353Hi7aoLZm1OqK+4LMUXTZlO6xxHa14mqPC99iJq7tKiP6pJRiDnULIO9aBqExX7X3R30TkubV7MAq0pXvbT8Z1UvDEyd/qnhzKXWsiwzwl0/vdOgp49xbqcbSZIYR8ZxHKtOlcXjHZV9ftBxggf6bXWPzchovUN7FEXGo9vHPR4BoCO/Ut0xO8Nr8rymP7+ozXP7IX1XhcAydouAqSPYGx16uF7l0OSvWRGHCvqakXtB4HHjUU9fufJ2ILRqi1rt+getEEXZEwWbSn/kyfPGmfMzxpbjHst8xhrzufF21QQ4GqmOobHMF6Lquy/r3j2C3Rf0iIEnSgPeyw+dN5IkMerKcmBCOnQAgI9IdQCYGYLdX74WkITP1S80Mn72FEVhdH7RJv18Jz1ielfau285C5Mtxz2W+YzRI2YKSPXlvNmJYJkvBD1iRkSwe/pm6RED31R90DjjeABFUcRxHD7Qh2oHAHiHVAfQO47Bx0Wwe8rXApLwufqFRnU9Yox1bvSUoUcM+sOW4x7LfMboEYPRsdm4xzJfgppCDOseQ/M3ZJ6M3QCghfqApw4PAAAwPOPqhPqfSZJkWRZFUS9XMASAefO1gCR8rn6hkdkjRq+n2+tc7yxDjxj0hy3HPZb5jNEjBqNjs+lIXo1Fv+fx0ZoIwzDPc+N+lvns0SMGI/I3ZBgjBl75fOHqssynawwA+Ga3211fX6/XayHEer2+urra7XZjNwpAiSzLwjAsNPJ+2QWmKIosy+SddIoBgHoUYjB5do1TVmFKazEAAK+8evXq7u7u/v5eCHF/f7/f71++fDl2owCUK70mbpqmYRiqP8MwzLLMWZMAwEcUYuA/LzujAQCEEOL58+fv3r1Tf97c3Lx48WK85gColOd5lmVBEARBEEWRXm3RCzRRFMkTlAAAVSjEYPKMji96Rxi7U4yfpwgCwGLpVRjp5uZmlJYAaGO73W63WyHEZrNpM33QZOD2YlisP+A4fl81ychuT8fpQZWH9XvIag3oIAP4jWCfN7V+G7993d/fn56eDt8iAG3pgSy7xiRJ0jgcDDEOADa/e8QUj43dHPTMXK2la5gfUoB5Idjnrf36pQoDTBxjwYCx1YGj+V2IweJYZZeLiwsh6AUDAL46Pz837vkU7ACmJMsyY6TePM/lPUZFxhi7F3O12+1evXpVPw0noAFVKMRgQtpcxFQ/ZP+Xf/mX29tbOsUAwGQ1BvuHDx9Kgp1LWQMTI4fgVbUYeUaS+leO4yuEkP9y+eol+Pbbb58/f944Gf1bgVIUYjAVsqxuXMT02bOnxmSr1Wq1Wgkhnjx58r//+7//8z//M0JbAQAtlAb7y5cv9fLK7e1tabBzKWtgarbbbZ7nsoNDmqZyyF4hRBRFcRxvNpsgCDabTRzHpVe5xszc3NzYo60DaCnwt0IZBB43Hrbr6+u7uzsr0D+v4qL4vNKDIDg7O/s8cVGoQdvVRqFPzKaCI7DluMcyn5nSYD8/P1+tVm/evBGP17gZ7A8T39zc2GHOpoIjsNm4xzKfq/1+/2kkr/r1ywaAgfkbMr62W/i80FFqvV7Ln0wfK9T/i8e1lcdTFfrV8+R2QSEGHbHluMcyn5mKYBer1Ureb9RWquZDIQa9YLNxj2U+Y+v1+v7urmEiNgAMzN+Q4dQkTMJ+vy89WK+a2LyLYWIAYGJqgt1+qCTYNe13EAAAN87OzsZuAuAxXwtIwufqF0p16hEjSs5OokcMOmLLcY9lPjP0iMGksNm4xzKfq0+J3bhyg6AoCiPe2STQI39Dhh4xmIqzszP7IqY1DpoYAOBeabBfXFxU/Y7KpawBwAsHXQtJTcwVlABlcoUYe5T1JEmiKOIyeLN3eXlpXMRUP/62M9uYGG1kWRZFUY/XMsiyzIvPZpZl8oKajX744Ycffvih+3ygI9gXqzTY379/f3l5WTp96cSDt9JnpHojUh2AXwj2RvXB7o1iSsIwFEJst1t1j2ykvN9o7dQaj+4+fvz4+vVreRHT1WolRKH+u7q6kmOzn56evn79WgihT3xycvLVV1+piV+/fv3x40dhFeARx/FxH/zSZ6kY0T+zU6PecmNTf//73+tTfv311/pbbj+fLqa8JI9GsC+cEex//OMf//73v3/8+NFIdRnadrDL2yr51WzZVCRSnVSfOFFm7EahN6LxP3L7cAR7+2CXfxYPR5vOAqeXhTmhz8N2uzUWvVwlagIhRBzH+p9uGwh3xKfTRz//p/djl7+Xfvz4UU3861//+uzsTJ/+V7/6laAQY9lut2EYhmF46FPsIFOJqX8qp0Y1Uk/n0uj8/vvv1QTyYF2Ge818hmjw/LZVgh2KWrkfP358CO3Pqa6HdvEo2CuTf4T3MD2kOqk+cSyBeaMQMwSCvSrYVRVGpboQ4vvvvy+cF2J6me2EPg9yE9GXu1wfagJji+TDPGPCKMSU/aLy+vVrNfHDwfrnp8hDdn1uS7N9UHq/faf9qPoKXRpkdrXeeG7ji7Z8tAsV66V/6mSgf/311/JPFfT6E1UjZRYN0eb5basEOxS1cq+uruwxYvTQLh4Fe2Xyj/AeRkWqF6S6h1gC80YhpiOCvTgk2OVDqqRu5HzpxMO1uYeZdJ9FL8IwlIU643hdr97Zv6O6bCFcEmaPmBKr1UpNrILHeIo+t+WwE1l9juw41rsCGlXz7UM93k52o+oso1nd1ic2pjRS1Whn71lpzLamq6eMcpXs6qfUQlue/batqsEOXsUZgh06tXLlGUml9IlL6ck/wnsYCaluzJ9U9whLYN4oxByNYDfm3ybY5f2yC0zxONgN8h0d1Kvo0DZ3n8kkButNkiTP8yPGEAqaDNBYTMV+v7+/v9/v9zXTlF42dfY2m428oVI1TdPSsa+SJEnTVP2p3xZCRFEkB80yztsUj8deNbJbvbqcLM9zOY2cTP+kq0+omoP+XMfkj6V//vOf//CHP/zwww+//e1v1Z1q0SVJIoOlx+HTpOyBftv38SMJdpSS0V31qHyoJtjrnz5XpPoRSHVggtihKwT7cLIsk++o99TtOdu713I6kgU59afgh1Mc3SNGCHrEFGU9rtVHyagxy9uqWlx1HmlpZdq4U1XWjdNM9LnpE9jzLH1p9RZsjSe72r951v8Kqp9uKrRhHe0dW7/hqb8ptYiME3a8Q7DDplYuPWIOJd87qU6q+2hRH9UFOq5HDFtFQbA/OCjYVVx///33+kBgqo9MYyO76D3bx/8YhA8dh4x3VXC8vmDicSGm9DLVf/zjH9XE2gSfniIvfa3PbTn0pRTHsd6BsDTZ9Qm6J3vpGa1xHBuf7vpzVmveka5+ORyU7ProX/rtwtrhqZkMMezZbLZVgh02tXJfv35tp7oe2oUZ7J/pyT/CexiJvgRIdUGqe4UlMG8UYo5GsBtNqrlH0YsvVS9Rc2ZTj3qZ//gfA7nWFbni5Qo2KkzCqt65bitckR8foV0CST8il8frf//739XE2gSfnvLLX/5ysXFvn3RqF9GLipjrnuyl09iNCQc+dVOSr9j+pNPS0QTsZw3X+NlsqwQ7bGrlfvz40U51PbQLM9jLk3+E9zASUl0h1b3DEpg3CjFHI9iV9sFeFMX333+vxugtHSOmdOEM0ebuM3liL3fHoijST2BL0zRJEnlPkiSbzSbLMnnym7xnlEZiXLe3t2/fvl2tVvv9frVanZycCCGePXtWNsGne/7jP/6fEAs99TSKoqIo5CmL8jzSPM/Vh0ifTN6QH7EhWiJfPQxD+dL6ycDyfFR5AqdS1ZKqD37vgaD6sf/mN7+RN3744Yd+X2IhCHbUePr0qZ3qt7e3z58/1ydrTP7lINWPRqoDmCaC/Ti/+c1vVJ7/4Q9/qJrMj2PL7rWcfonHfaX0Ep1R2Zpg49EXuW3K/4z7jRvWbf2/Jdbd7VK36mRYNJ10qqbspcRu9H40zkq1+0aWvrT+0BHxZRTyjZeQnTVkA+RDbS50Omgn9rmOIECwo6hYudVh3vAj6nI2FVJdR6p7Zzkf1WWiR8xxCHbd0cEu/1Q9H6sWwhB6yXaPPwZ8hmdMfoKOKcQ8qsUsNO5LQzAuG/2rtC9iX8le1RIjZw8N64PY3T7tBstkV8foOhX04eOh5odoKiQW7IyVrlxRFeYUYjSlaUmqGy9Bqk8TC3beKMQcrSarCXa7wTXBrrfHzblUfZnE5auB3iz7SnjSdrs1DjHjOC7toZckidE3ofeW6DOX81f9G4vHh8JqJNceRVFkhHtp1gsh/u3f/s0I99///vd//etf5W3jooD9NpXrNAOoR6orpDqAeSDYlaODXdbW9QmMs6j61Xu2B70vSmeCwOPGo14QBOLzJagf3S9Xur72jdvi8+WuSyZGPRkl2+12oNNQ/cKW4x7LfMZKV25NmNu3qyZADVJdx2bjXulXFNbCPLT69tkU5jgCwa7zd3OiRwywXLKIq0JcleGJdQDwEamOCbI75I/dIsAnBPtcjX/VJKAMO2kX4jhO0zTPc/0Hq957PAIA3CDVAWBmCPa58rUnj/C5GxIa6f1Yu5ya9MUXXwghfvrpp9PT07Ozs8vLy6dPnw7ffJ9kD8TDJYepryuEjHss8xnrcmrS1dXVzc3N/f39z3/+8y+//PK//uu/9vs9wV6KVK9BwrjHMp8xTk1yhmCv4e/m5Gu7hc8LHY1UIcZYwwcWYoS+jzg/P//w4cPt7S2H7GiJkHGPZT5jxxVidrvds2fPzs7O3r17Jx/68ssv//a3v8nbBDsOQsK4xzKfsY6FmJohTtlm0J6/IcMYMViKm5ubFy9evH37duyGAADa+vbbb4UQqgojhFBVGEGwA4C31IBB+uBBjCKE5fC1gCR8rn6hUbceMUI/O8mY82q1ur+/H6DJmCFCxj2W+Ywd1yNmvV43hjbBjpZIGPdY5jPWy6lJXA4PHfm72fjdI6aXK3hjNvb7fZtpOF4Hpoxgh9IysQl2AADgF78LMVwMD7rVatVmmtPTUweNAXAcgh1Ky8Qm2AEAgF/8LsQAtvPz86qHLi4uzs7OXDYGANBFY2gT7ADgHh1WgY4oxGByOp6L8OHDh9L7Ly4u3r9/f3l52WnuAACHZGjrFfYvv/xS3SbYAQCAjyjEYG5ub2/V7S+++OKLL74QQqxWq5OTk9vb22fPno3XNADAYeR1qVerlTz59OTk5OTk5Oc//7kg2AFPBJaxWwQA4/N1kGHh8wjJqFd1ySTRerh1fRdfFGwqOBJbjnss8xk77qpJokXyAy2x2bjHMp+rtuU0rpqEgfm72dAjBgAAAAAAwBEKMZghP6uiAAAAAID5oxADAAAAAADgCIUYAAAAAAAARyjEAAAAAABa4cJXQHcUYgAAAAAAAByhEINpCaixAwAAALOwXq+vrq52u93YDQGm5cnYDQCGRWUHAAAAcOrTEXhxfx/s9/uXL1+O2xxgaugRg4niEtQAAACAfx7/Dnpzc/PixYtxWgJMVVB4+303sLo6+PteoKi1Wroyg+DTFqtu1N7+/Dy2DRxB35zgBsE+Y6UfqHZh3pD8QEtsNu6xzGepobu5+fDnv9sfxgMt+bvZ+N0jpnhs7OYAALoi2AFgTgLL2C2CS49W9/39/VjtAKaGMWIAAAAADIKS+rLohZfC7B1zenrqtjXAdFGIgWfUDynyBnt3APAdwQ4A8/bFF1/89NNP6/X67Oxs7LYAk+D3qUnw2nH9VDltAQCm6eizDwh2APBFXbgb3WEefPnllz/99JMQerGPmAAAIABJREFU4v7+fr/fCyG4mjVAIQajUQfc6gZnDQOAv+xUBwAs3D/90z/97W9/U3/e3NwIId6+fTtei4BJ8HWQYeHzCMnQVVznqOGqSdVzOGBuQD1Cxj2W+QxUrcTGAK+6n6smoS9sNu6xzOenVY8Ytc4rfmVdrVZy4F6CHR35u9nQIwbTRN8YAAAAYELkiUW9zIcrKGHhKMRgxqjmAAAA9C+KIuOeJEmiKEqSZITWwJXVatXXfLiCEhaOQgwAAACAtqIoyvM8yzJ1TxAEaZoKIdI0PWisbsze+fm5fed///d/X11dMWQvloxCDCaqzUU31DSHXqEDAODMQVlNsAMTl2VZnuf6PbIXTFEUWZbJwRroF7NEFYH94cMHuxbz008/7ff7ly9fDt4qYKooxGCKWl7ElCueAsD0HZTVBDswcZvNJo5j/Z40TcMwVH+GYah3lsHC3d7erlarL774wrj/5ubmxYsXY7QImAQKMViK4LGxmwMA6IpgBxyLoiiOY7vDiz5kjDxxyWWrMC2PS+hPnz598+bNP//zP9sTyktZA8v0ZOwGAI7YVz8FAHiNYAdcSpLEGBqmjcYiKZ9fjxxR8L6/vw+CoOYaSff39wzciwWiEIOp4LdMAACAacqyLE3TI4om1FkWofowXhZZTk9Pq2oxVGGwTH6fmkSfZACYGYIdACZIno4UPRBCbDYb+yLWQKmzszP7zouLC/ctASbC4968dEWeB7Ue1ReumrXauNL1CYIgUGepqiex2aA9thb3WOYzcNBKbDOxtpt4lPClt4EabCpdZFmmn5QkB+hNkkTVZdSjQRCo8XpZ5jNT+fOIesBY2w8bwG63e/bs2fn5uRoX5uLi4v379//xH/9BmKMLfzcbX9stfF7o0LUsxBg/jNur3p5AL8So2bLZoD22FvdY5jPQciU2pro9jT4ZhRgcgU2lR0EQbLdbVYLZbDbyT/22YJnPztGFGCFEEASvX7++ubnZ7/er1ers7Ozy8vL58+eEObrwd7Pxtd3C54UO3UE9Yg6dM4UYdMHW4h7LfAaGW4n0iEFHbCo90gsxQogkSdI0lbf1yyqxzGemvBCj31tbiKF7I3rn72bja7uFzwsdukELMUVRGLNls0F7bC3uscxngEIMJotNxT2W+cxQiMHU+LvZ+D1YLwAAAABgfK2/Du92u+vr6/V6LYRYrVa/+MUv5LWT1uv11dXVbrcbro3ARFCIAQAAAAA48urVq7u7O3lB63/84x//+Mc/9vu9EOL+/n6/3798+ZJaDGaPQgwAAAAA4HCV4/fWef78+bt379Sff/vb39Ttm5ubFy9evH37tnvTgCnz9ZQq4fP5YNAZI7kIxojBZLC1uMcynwHGiMFksam4xzKfmZKqS80AMaJyjJjGF1qtVrK/DFDP35ChRwwAAACAQQSWsVuEYbT7LixPQWozGYUYzNuTsRsAPOJnQRMAAAAlPP2xGgNZrVYtJ5PD9wJzRY8YAAAAAIAj5+fnNY9eXFycnZ05awwwCgoxAAAAAIADHXue2YcPH/RazJdffqluX1xcvH///vLysmPTgImjEAMAAAAAcOT29na1WsnTlE5OTk5OTn7+858LIVar1cnJye3t7bNnz8ZuIzAsXwcZFj6PkAxd6bWNhpiznDmbDdpja3GPZT4DXDUJk8Wm4h7LfGbMHjDq76qVXHHVJMIcffF3s/F7sF5j3HVP1wEAQCHYAQAAMG9+F2I4QEcp9UXu4QbbCeANgh02K9U/32aDAQAA3vG7EAOUMo7Lg2MHEgMATAHVFgAYHQfUQI8YrBcAAAAAcAgKM0AHFGIAAAAAAAAcoRADAAAAADgKJ48Ch2OMGAAAAACDCKyx+hj1CQAoxGB8+g7a30vBAwAULkMOQOLjDwA2CjGYFvbWADADMsyprQMAANgoxGBCOFwHAAAAJuJR98ajjtTlHCjKA4apDNabZVmSJFEUZVlmPCTvT5JkhGYBAI5FsAMA4LWiKGQN5bhKSvGg73YB3ptEISZJks1mI4/UN5tNFEXqoSAI0jQVQqRpao/1BRyELQhwhmAHAGC22HsD3UyiEJOm6Xa7zbIsy7I4jvM8l/fLH0uLosiyTFZS+fkUALxAsAMAMBsUXoB+jT+KXpZlm81Gb0YQBNvtNoqiIAjCMFR92uUPqupPhgCchyAIhPi0Hodbn9qP7mw2aIuQORrBvmT6SnSzQtls0BKbinss83kIgsA8TFeH1rWrV39QbQxVuwm2FhzB381m/B4xURSpZZdlmTwoV53Y9d7sURSp31QBAJNFsAMAACkIAnkmsroBYEJXTVKH49vttuVTGj/JnpbHAGAeCHYAABbO2HFTiwHEFHrEKHK8gDiO1fiOjYomAzcZAFCHYAcAAO0Fj43dHGAokyjE6EfnctTGlsfrAIBpItgBAMCh9Atm8+sLZmz8Qoy8xGnpQ/qAjkKINE3DMHTULAxst9tdX1+v1+uxGwKgfwT7Aumpvl6vr66udrvd2I0CML7AMnaLAGB8kyjECO3ypfKGHMoxSZI8z+Uhu/yXq5zOw263e/Xq1d3d3f39vXH/WE0C0COCfWmMVL+/v9/v9y9fvhy7XQDGxxmmM3RsMY1RewFlEld7khc6VX/KS5zK20mSpGkqb8dxrB+v+3upKlxfX9/d3b179+7hjk/r8fXrqzdv3gzxily+GkcgZLog2BfFSnUhhDg/P7+5ueHy1ZgONhX3WObzYF6+Wi+htL58ddWcuZQ1uvB3U/G13cLnhY71eq31hfm8ElerU6OPTF8oxOAIhIx7LHNPPU71RyjEYDrYVNxjmc+D2XdF/d20binEYGj+birjn5qEpdnv91XH6zUP9cfLDyoATFZ9dA+f6gAAAJ6hEAPXVqvV6elp2SNB9UMAgImqj25SHQAAwEAhBiM4Ozs7Pz8vvX+gV/SzwxoA+KE01S8uLkZpDAAAwMRRiMEILi8vP3z4YB+1X15ejtIeAEAXdqpfXFy8f/9+xCYBAAbBxY6APlCIwQiePn16e3u7Wq1Wq5V+/7NnzwZ6Rf3C2Ov1+urqiktlA0BfjFRfrVYnJye3t7eDvuhut7u+vl6v14JgBwAAXvF1kGHh8wjJ0Dm4ntFut3v16tX//b//R73Q+fn5hw8fbm9vnz59OsQrYh4IGfdY5jPg5rIXMtifP3+urplNsKMRCeMey3wegtI/WqxYrpqEofm7qdAjBvP37bffPn/+XL/n5ubmxYsXb9++HatJAIAuZLCrKowg2AEAgD98LSAJn6tf0DnoEbNer+/v77Wi/KeXXK1WXFcVNQgZ91jmM+DmJ82HYDcR7KhBwrgXBCUDirAWvEOPGEyWv5uK3z1igsfGbg6maL/fVx2U1zwEYCwEOxoR7IBHCsvYLcJhBtoTq728vsdn14/l8LWAJHyufkF5nLTOesQIuU+RP5wacc9GBYWQcY9lPgOj94jZ7/f6PWxRUEgY91jmM2DWRXrtEVP5omw5aMffTeXJ2A0ABnd2drbf729uHt15cXFxcnIiHo7R/f0MA8ACPQT7o2SXwf6nP/1JkOoAMAR6qwA98fvUJMzGoEfLl5eXHz58OD+/UPdcXFy8f//+8vJywFcFAAzmIdjP1T0EOwAA8AWFGMzf06dPb29vV6uVuufk5OT29vbZs2cjtgoAcDQV7DLbV6sVwQ4A7tDjEOjG44679DqeATU8S1G4WKH6y1kPsTnBxFbhHst8Btxf9qL0VdiWYGOrcI9lPgNHXzKp9VRlL8qWg3b83VToEQMAAACgWZZlSZJEUZRlmfGQvD9JkhGaBQC+oRADAAAAoEGSJJvNRpZgNptNFEXqoSAI0jQVQqRpyuWHAaARhRgAAAAADdI03W63WZZlWRbHcZ7n8n7ZC6YoiizL5DkC9IuZJypsQH98PaVK+Hw+GBTGiMGUsVW4xzKfAcaIwWSxVXSRZdlms9EXYBAE2+02iqIgCMIwVCcryZ4y8k+Wue/KB4gRjBGDqfB3U6FHDEZDx1UAAAAvRFGkvu1kWSarLersJP00pSiKVGcZzJOXX3uBaXkydgOAEQRBSacYAAAA1FN1lu1222b6xiFjPP01GwC6oEcMxsf+FwAAwAtyIJg4jtXAvfWKJsM3GQAmh0IMFoR9PQAAwHH0soscjrdNIQb+2u12YzcBmC0KMQAAAADqyGtXlz6kj9QrhEjTNAxDR83CYHa73atXr8ZuBTBbfhdigsfGbg4AoCuCHQAmSHaBUdelljfkGL1JkuR5Lmsx8l8uXz0D33777fPnz8duBTBbvl7tSfh8qSpIxsWkXV3l9NGLavezOcHEVuEey3wGuHw1JoutoiN5BWv1p7x2tbydJEmapvJ2HMeqEMMy99d6vb6/v390xKx+HGm9Srl8NYbm76bia7uFzwsdEoUYTBxbhXss8xmgEIPJYqtwj2Xuqf1+f3p6KoSgEIOJ83dT8fvUJHih8USD6+vr9XothFiv11dXVwwMBgATVx/su92OYAcAf61Wq9PT0/IqDIA+UIjB4NS1CasuUnh3d3d/fy+EuL+/3+/3L1++5JAdAKasPthfvXpFsAOA187Ozty/KHV8LAeFGIxD/wH13bt36vbNzc2LFy/evn3rsgEAgB49f/58lGAHAPTl8vKy/IHBzgKR12mijo+F8PWUKuHz+WDLZKyvx3UQsyiyWq1kBA/QjM+3H3W3ZHOCha3CPZa5X6oGZymdeMhgZ4wYtMJW4R7L3GvlR+uHrM+D1v319fXd3Z1exxdCnJ+fr1arN2/eHDInLIi/IeNru4XPC32Zqgsx5Yfsd3d3nwYJ67sZaqdAIQb12CrcY5n7xV5fn8d3LDNcsFOIQRtsFe6xzL3muBDz6TpNluHq+JgBf0OGU5MwRZ9GCAMAeGW1WtU8RLADCxRYxm4Rpmi/31dVW2oeAvz1ZOwGAKaLi4uTk5OxWwEAONL5+fnNzY1+D8EOLJanP1bDMVmsr+oRI4fvVdioMAP0iMH4zs/P1e2Li4v3799XDg8GAJi8Dx8+EOwAgIOcnZ3p+w7p4uLi7Oys8RqsgHcoxGB8q9VK9mZfrVYnJye3t7fPnj0bu1EAgCPd3t4S7ACAg1xeXlLHx3L4OraN8HlgnmWqGaxX3u9mhTJYL9pjq3CPZe6XxlFynQU7g/WiDbYK91jm/jLH8jl8sN4jVvxut3v79u3Nzc1+v1+tVmdnZ5eXl6qOz+YEm79bha/tFj4v9GWiEAPvsFW4xzL3C4UY+IWtwj2Wub86FmI6rnWCHS35u1VwahJGMJHx8ifSDAAAAGC6Djxm9vJrMeCW31dNMi6A52kxDACgEOwAAPiL3TbQht+FGA7QfVcUI3RLGeVFAbREsAMAMFHsooGe+F2Ige/kT98BdREA8J8Kc3mDmhoALA25D7REIQZjejx8L+UYAPAYlRcAAIA2KMQAAAAAGIT9SxtFWwCgEAOnHnbGU9kB038eADqayPjKdKsEpomjLO8RrsAAKMTAKbkzns7RMgcHANDRQ7AH4yaq0QzqMgDgGEfVQHsUYgAAAABg0cz6tV1bp9AC9OdnYzcAizOpHykn1RgAAABgFEVRyB6F6sZhTx+gScCM0SMGS/H4uqrsLADAe1wwGwAA+IhCDEbj+IDZOECnLwwA+I7KCwAA8BGnJmHprq+v1+u1EGK9Xl9dXe12u7FbBAA4HqkOAI5RFAcORSEGS3d3d3d/fy+EuL+/3+/3L1++5KgdAHwk05tUB4De0IUcGMbIF5vsYvQrZeIg2iVFP91jrD3HK1Q7NenRHub8/Hy1Wr1588ZZSzBZhIx7LHO/6OurdN05XqHX19d2epPqUEgY91jm3vl8xP7pb+2x6jXZ+zqewj4FXvB3q/C13cLnhb5MvhRihBCr1Ur+moqFI2TcY5n7ZWqFmPV6XZrepDokEsY9lrl35CorOUx2WIUR09inwAv+bhV+n5oUPDZ2czAT+/2eQ3ZgLAQ7jlMT3aQ6AACYFL8LMcVjYzcHM7FarU5PT8duBbBQBDuOUxPdpDowosAydosAYHxcvhpOTXLnW+hnJ11cXJycnIzYGgDAcc7Ozv70pz8Zd5LqwLgoqc8baxc4jt89YoB+XVxcvH///vLycuyGAAAOJtP7/Pxc3UOqA8DxpvgDKjATFGIwjtF/HdEbsFqt5L8nJye3t7fPnj0brVkAgGM9ffpUCLFarUh1AAAwZb4OMix8HiF5mT6NwV5xySQxxgrVG8PmBBtbhXssc79M7apJ+iuyLcHGVuEey9w7nwbxkWut6ZJJw61aeyyhoijYnGDzd6tgjBgAAAAAgBCy5DF2G8TD6EL+fs0G6nFqEgAAAABACEaGAZygRwwAAAAAYHzqpCSudI55m0qPmCzLkiSJoihJEuOhqvsBAFNGsAMA4KuRyiDFY0KI3W53fX29Xq+FEOv1+urqarfbjdM4oD+TKMQkSbLZbLIsE0KkaaqXP4MgSNPUvh++MKJz7OaUY8sCekewzxjHxAAAZ169enV3d3d/fy+EuL+/3+/3L1++ZL8D302iEJOmaRzHWZZlWSYLn/JnUvlvURTG/fDFbrd7HJ13Y7cIgCME+1xZwf7pmHjsdgEAhjH2ULnPnz9/9+6d+vPm5ubFixdv374dsUlAd5MYhtoYDTuKIiFElmVBEIRhKH9Q1e8vfRYm6Pr6+u7uTovOz+trGpevDrQmsTnBRMh0QbDPlRXsQghxfn5+c3PD5asxcWwV7rHMvRMEwejXrjbbU2a1WsnfA7Bw/obMJHrEGMsuz3N5aC4ejtHV7TzPHbYLXd3c3BgH6w84GQGYOYJ9rkqD/ebmZpTGAJi+wDJ2i+CN/X5f8xCFGHhtEoUYRf5YKlr3VLeTnaCfjvp81B9SK4u1BswPwT4nBDuAQxWWsVuEWlNaQavVquah09NTl40B+jWhQkwURZvNJgzD9gFtJztBPx31+ag/NPG1xpdA4GgE+8wQ7ACwCFPKxfPzc+Oei4uLs7OzURoD9GUqhZggCPI83263aqQAzMDZ2Zkdnd5R3yKm+XUCmCyCfZZKg/3i4mKUxhyNYAeALlzm5ocPH/T9zsXFxfv37y8vLx02AejfJAoxQRDI30v1gQOEEPqAjkKINE3DMHTcNnRxeXlpRCeAhSDY58oOdnlMPGKTAABDmUCl+vb2drVaydOUVqvVycnJ7e3ts2fPxm4X0MmTsRvw6WIZURQZP5lGUZQkyWazybJMPcpVTv3y9OnT29vbt2/frlarmtG2pmEC+xlgLgj2GTOCXR0TP3/+fOymAQBm6OnTp2/evHnz5k0QBAzQi9kY/2pPSZKkaWrcqX4y1R+N41g/Xvf3UlXL9Pgc/Emsu+DR5asbxiZje1sgVvrRCPaF0NfXRNZdcMjlqyfSZjjDGu8ueyAL6/pDSZLY97PMvRM8+l/5L5Uu1+gEdzSYFH+3Cl/bLXxe6Ms0zUJMURSqYRRiYGClu8cy98sEj48pxKAGa7wjWUaX55PmeS6E0BNACBGGoX0/y9wLu93u22+/vbm5ub+7E4JCDLzh71YxiTFisCh+flIAAAAWLU3TOI5ljxj5zUd2fpH/FkVh3A9f7Ha7V69e3d3dceIP4IyvBSThc/VrmfSOJxNZd/SIQT1Wunssc79M8IdKesSgBmu8I2MByrHYsyyTo7OrQcHU/fZTME3X19d3d3fv3r37fDRMjxh4wt+tgh4xGETw2NjNAQB0RbADC2d828nzXF0XT79AXhRF8gQl+OLm5ubdu3dtpyb/gT5QiMEgiqKQe2t1AwDgNYIdgCR7wYh2pyAFTQZvLmrt93vzjKSmdcKKA7qjEAPX1uu1/Pfq6mq327lvwG63u76+Vs1w3wAAmA0jUScS7FdXV+7bACxEFEWbzSYMw5YF2aLJ0A1GvdVqdXp62n76aa44qnvwDoUYuCaL7vf39/v9/uXLl44P2Y3RyPRfAAhtADiUkagTCfb9fi/vd9kMYAmCIMjzfLvdqhFhMANnZ2fn5+clD0yr2FKHPpvwDoUYuFBa47i5uXnx4sXbt29dtuTbb799/vz5AefBAgCqGYk6kWC/ubkRQjhuBjB7clDeoij0EWGEEPpIvUIIdYlr+OLy8vLDhw/ltZjHqHAAffF1kGHh8wjJy6FdveLR3fofq9XK5aXy1ut12ct92pBqNii2twVipbvHMp8+4wIWpdNMI9ibm8H2tjSs8S6yLNtsNnEcG1WYKIrkQ9vt1rgtWOb+2O12b9++/dObN0LUXTLJ/bo86KpJbG8L5O9K97Xdouz4z9/3MldlhZiSo/a7u7uDzk092n6/r3ghCjEowUp3j2CfPvW5qE5UISYR7M3N4DO+NKzxLpIkSdPUuFP1hdEfjeNYDeLLMvdIydE6hRhMnr8r3dd2C58X+nK0KcRM44dTCjEowUp3j2U+ffSIgb9Y4+6xzD1CIQY+8nelPxm7AVi0i4uLk5MTl694dna23+/l8AEAgI7Oz8+NRJ1OsJ+dnblsBgB451E93c9vs4CnGKwXTunDgF1cXLx///7y8tJlA+zRyC4uLlw2AL3gIoXARNiJOp1gd9wMdESwA+7pFxsauy2P7Ha76+vr9XothFiv11dXV1wIDzNDIQZOrVar1Wolb5ycnNze3j579sxlA54+fXp7e2s0Qz3KgZ8vuEghMBF2ok4n2B03Ax0R7MAkTOBgeLfbvXr16u7uTp5een9/v9/vX758OXa7gD75ekqV8Pl8sOWwx4iRa2wi6042IwgC/YzXqnZNpM3QDb1SWOnuscynr+p0/Ymsu4PGe55Im6EbdKWwxt1jmXvh0yHxpz+0Bx6vOmcr8vr6+u7u7t27d/qd8kxYxoiBwd+V7mu7hc8LfTkeKh2f75lgIebh9qc7KcR4hELM/LDMp2/6hZj2zZhIm6GjEDMzLHMvlBdixhupt2oIdtF0ChXb2wL5u9I5NQkAAADAIALL2C3CpO33+5oL3rm8JB8wKAoxcMfPYiUAAACOVFjGbhEmbbVanZ6eVj1a8xDgFwoxAAAAAIBKLutnZ2dn+oXwJK5z6h26wtWjEPP/2zt73ziS9P4/7SWk5MiZySlBTow7yLC9ChbSAQbJdSLK2aYH7MLAWeKLgQ01o4RkYunCDUQuaDiw/gDDmS/jKPHhbuHzBt7FOREEzeTUrJPT/mT1LyiyWVPvVf1W1f39YCANe6qqn6p66ts9NdX1AAAAAAAAAEDviePL8v7+/uvXr/m5mL29vVevXrVoEggAsfDMpLq3DaW8MU+HUYarkPfBjaTvlJv1kuYRKsFmoaYxVKeHcHGvrqiwLyJx1F6BNo8NeXx1eLNeCHsMeMW9Cigc3dowaPMkuNqsN4KQSYz5fP78+fOTk5PFYjEYDBaLxZIxGqeCv0UIYmvoSNVuSrnROw8XtTrr6kSM4SBojLq/BKJ/mwdtHicuky+R9F35qEmRVKS3CLcQNRUOGgNtngSKiZj2QiYJIBZe6mAiRgceTQL1ksrzgGmOXwAAAAAAAMpivmHHbTIAlYOJGAAAAAAAAAAAAICGWGnbgFLgce6kyDCfDgCwAmEHAAAAWiCRZewAdIO0J2Jwgw4AAB0Dwg4AAADUR69+8OhVZWMGHSGT9kQMAAAAAAAAAABH7PthtxovqVrq2/wbeIGOkMEeMQAAAAAAAAAAAAANgYkY0BxZlmWpRFG6ZD6fP3nyZDQaEdFoNJpMJvP5vG2jAACgZQo9h7ADAMxkEm1bBCSwSAGAxsFEDKiVK1nPL2nRGkeKu4T5fP7o0aPz8/O3b98S0du3bxeLxcOHD3HL3jFwgwiAL/kybZujRR7dEPaeAGGPh1yibYuACs0oQW8BUBOYiAFA5uIu4fj4+ObNm19//XXxwcnJya1bt54/f96ebaB6ivtC3CAC0DHk0Q1h7wkQdgD6A9Y5ghRJeL8cbPYTLUXXFD9BCR0VSd8JZvA/mLHDo9GI/WQqMBgM2PFIKtJb+PYv3xdyCejf5kGbx4nLWIuk75Rm8Ach7JHD3UJU0BHShR6d2zRo82hZEnbiVsREtlOv8vaM/3M2mz169IifYd/d3X39+vXp6en6+rquENAYFd6uK0Mvpdu5WBEDwAXCEF4sFsqbdfNHAAAAogXCDgAAqSOsd8M6x/7QsaWOqU4gUcqzX50n0RUxJBmMH04jBytiugfaPE6wIgY0BlbEdAy0ebS4rIiJoeesFx2rqhsKAQ1Q7e26XEi6nbvStgEAxMvOzs5isTg5OeEP7u3tra6utmUSAACAMkDYAQCAJ+m9rK3rHIfDYcMmAeAIHk0CtfDkyRP+Ib5Ed8za399//fr17u5ucWRvb+/Vq1f7+/stWgUAAM3Tma0QIewAAMBgwt62FaUYDAa6qRbDRwDEACZiQMWwW/Pz83P+YKKRQdfX109PTweDwWAwIKLBYLC6unp6enrjxo22TQMAgEbpTMhnCDsAADCYsBOlvSpmZ2eHn1tn7O3t7ezstGIPAI6k+kgVpfw8GGn2fO4GT548efr0KRFxD5Zmu7u7g8Hg8ngsfWfdI8acWHcQFNTt544Pneq2WBcOshI6PDaTIOkx1WHnybJsZ2eH3wqRiFIRdt1B38SA0ZiwmzsiWNjRuQ2DARUtV8Ke50sTMVf371H0nVXA5/P5w4cPb926VTxzytY58jPs8MMWwR4xOlK1m1Ju9IIOVEGG2zHrSsgpyh2zMBHTDPW1kpeyW3tQfo/+bZ4OtHkHqiAjfL8tSELYdQd9EwOeBoTd5RS+wk6YiGmc1AdUt2fYL97xEzH5UooY6usi4PP5/Pnz5ycnJ4vFYjAY7Ozs7O/v8+scU/fDWon5d1PlcfmeJNHOTdgpO9AH3ROFxWLBPY25NBFDROfn5+zTSCpumIght0hPkVQkcjARA9yBsEfIsrCLxC/suoO+iQEPJmKAI8pp3OR6oXuysCTs6U/EhCWcCa/gAAAgAElEQVQGPDHfrusKSf12Pe09YvJl2jYHYMcsAEBZIOyxwfZS0X0EYQcAmMkl2rYIcMKO7gCgJdKeiAERotwZq9gxK8sy9ttI8aYVdGbgYgQAAEoMWyFGLuwAAACUyMIuAFUHwXQm2GJ9YCIGVAyLAKqLDBrJTyKRmAEAAKlgCPkciaJGYgYAAKTC69evDZ+2LqdQ9XSZz+fKYItt2xUXmIgBFbO+vk7LS9kRGRRUC6bYAWgehHwGtSILe9sWAdB9Tk9PiUgZuxrTHqAMx8fHN2/e5IMtnpyc3Lp1qz2LYiTVvW2oE7sudaAKSrIs4wU8rSrqAidhT8dgqm0lNsXOi/vu7u7r16///d//PWD3L2zWGxsdaPMOVEHGa6e9CMFmvZXTgLCfnJzMZjP2646XGdisNyq6MaC6UQuBi1sdImXs6vhri816q6XaJuKi6IoE3K6TRthTv13HihgA1OBZ1DjBFDsAAHQMpbAT0fPnz9szCoCUyJaxHr/4VPg7yS+zZTE3UfxEaP9isdDNwhDR27dvI7S5FVKdQKJOTG0mUYVMFdRdeXA5S375Uc32VY0ugnXAD6fmVoqcCo1vbIpdQHZLrIiJnw60efxV0I1uw6jv0ooYfsWmr7AnreqUoLAPBoPFYsEfKSnscgmgblJUDJlUauG7+u/iTlcVuzr+2la7IiaVLtZR3v7mV8QE9yBWxIC+UOyPxW+UpTzYFSqbl026leI03jzFfn5+HqHNAMSGbnTHOeqjIvUmitN+g7AvFgsIOwDVwu/HRNTxDWKwq2Bb7OzsyDG5rl+/TugIDkzEAACSYTAYDIdD3aeGjwAAAMSJQdjNmg8A8EWIZdNtdIF7MAXQAPv7+0Kwxdu3b797944QQYkDEzEAgJRQTrHv7e21YgwAAIDyKIWdHW/eGAA6jLwf0xWdWQZziW5XQWw+1QDr6+t8sMVr16599913xafY3pGBiRhQGeJax/TR7R6FhY4tIk+x37lzh8VfRMRTAKqlV1rXq8rGhizsbAX7119/jY4AoEJOTk7UszAcnZmQUVb25OSE7QUO6mZ9ff3p06dsOdKPP/4ofIpeIEzEgKrQrXXs3v0TFjq2izDFvrKy8uHDh/fv39NlX1AXvQ6A5tFpXdt21QKEvV0KYV9dXSWia9eusRXsi8Wiw17XHzKJti2KjmbaR7EfU3e7wrz5lNeTWS6949WDYd2d6CASNlwX6MMjcgYwEQOqQbfWMcXlf+bdALHQsXX4KfZf/vKX3377bfERIp4CUBW9ChUPYW8dJuz/8A//QMu/nXbY6/pDLtG2RdHRzEbavdp0qcLNp1x6x6sHw7o7zt3WrbDfTXX0xyGVpBrtidIPM0ZJVcEaYGw5StlVysFgmNZk52UgtIs/i0oXlTVE2VQeT6iXZSoxvr4W0P0goOwL3yinecrx8NKlA22eShWsESJdYk+mgjV8tZewp9LFOmIWdl+vQ/jq+El9vDCaqUV9EYiL45PJZLFYcE+F5Jr/42U+nx8fH5+cnLx9+3Y4HO7s7Ozv76+vr7NP+RaQKktEtLe3t7q6+uzZM6HYSuJbe/VgWHe7RHcOo9bb9d3dXbkjjo+PzQIuHJ/NZkLXP3v2LOnb9bRXxCS6Rqt7VLj8LzYEt+pwTZPDsNYRfZE0EPYYWBpE0v1Nx8YXhD0SzK2NjgCgPEv7MSX43dXrMVJ586m9vb1Xr17t7+83ZzEgIiJlR/gWInc9Jb4dQdoTMVjoaKax7zP6NX5Zx5ZBVhtl09pBXj0Y3N2Jfu81rHXsmNf1DQi7mWYGbLdDxQutBmGPhG57HQAxsLTRniQM8V9xvR4jFXYVZLtQnZ6e3rhxozmLARERKTvCtxC56ynx7QhSXclDnVjr2EwVKjmLspBMu/zvat3348eP5eV/MVNUSng6KdOu6iTSL3Qkh/YvnyA4sTVjfc5TCZlqrSMRKb3OWkH5fQdEJjk60OapCLuuBIXW5XkhiLq1xJGzNLqJ6PIqFSbsLo0PYQ9jMpnIDW7wOl9hJzya1DgdUHXqkLAvH+T+yIt/osbwGKmwUFqobzN34w0Iu1cXV1Jyecw3245m6H5RYF2fqMikvSIGxIO8/K843oo99YGFjlEh9wV10esAaB6d1rVoUn1A2COBNXhPvA6AWEjkO6z5MdLz83Msp+0q5u0ImrSkWjARA6qhWP4nyHkqy//m8/mTJ09GoxERjUajyWSiS4mFjlEh9wV5ep3c9Uk/bgpAVXCqTlRiLXGLuI9uCHsksB03y69gh7DXzeHhofLg5uam8qM6SPH5u0qopOIptpf56cXRaFS0RiS+4WVGJYkbrnhjZpi3IyhffmvkyZK08YxmqlDJWZSFaA7yrzT6aDabPXjwYGdnpxgXu7u7fEVynxbwTVM+QXBia8b6nKcS+JKL97rT6Sood/329jZdLmqtwWpgogNtnoqwu48U0oy1yFEK+/b2NrEa5XkeqnvNKH9wYmvGVITdxet8hT0V742cs7MzIjo7O+MPsubd2NgQ2rnuNq+1/NlsNplM2Jf/4XA4Ho9ns1l9pysvC57CfvniVDFyxuOxvPqerYaWE3v5YX3K7ytf7qdweW+lvJNbK+hip84M/vi1a9dI4vr160RU68CsD6yIAXWR0KpA5dZfLdoDmkS561t75gAAqkG3p2N7FoHmQNfXxHQ63dzc3NraEo6zVTB5nk+n0zzPSbNkJi28AvSkR4pLYnr2zGwDROLkOjO++eYb/viPP/54+/ZtIe+7d++IKNGBmfAGWvVtKdQYzVShkrO4b6fE73GbSh/ptv7idx2+OsTVyKWC1jTlEwQntmasz3kqIVPt+KU7na6CusJzbNbbBh1o81SE3X2kZERk210vQrTCXhifBQp7M8ofnNiaMRVhVyq8ixnmlfBJeG+0TKfT6XRKREdHR2dnZ5ubm+x4lmUbGxvsIyJix9mfdStGfeU/efLk/Pycn9Ejot3d3cFg8PTp0zrOWF4WPIRd2qmXEtnNej6fP3/+/OTkZLFYDAaDnZ2d/f39mzdvmlujmbvxkp3ia4bLezOVOLm1glY7dWZ8++23f/mXfykc/5M/+ZMPHz4I56p1YNZHGrdTSlK5FzSQyv26rpBuTMQsFgv9E6cXxusqgYmYWk3yKjlgIsbY9XR+fj4ajeJ34I6RhGiYSUXYHUdK8cB9WhMxptF9abyuDpiIqdUk95LDJmLMwk4pfM9MgizLhImYg4ODYhXM4eHh0dGR+aJcoSU1lW8I0KPbMrYkjU3EZMT9wpgL/yeDVSIwEWOlEicvPxGj/0Xcg/oGZn3g0SRQJSnulWbe+ouRYr2AFfP+XlavAABEi17Yc8oo0TX5wIW0N27sKJmNtg1UYA7Qk9z3PSvJzcKA8kTi5FWdK8WBGddETAx7sJN0wWjsvO3Sz1ozdnZ25K2/lETSSl5mlE/cfK2bNEO36xuoihiEPZKR2zy9rTg5C3skTdSwqvsWUp6GzYCwR0Vuo20DFRh+pXP5AQ+A+InEyas6V4oDM6KJmOl0enR0VDxfysiy7OjoiIiOjo4au0Mqrgo1XR7ijKpYba2jvKpqUW79df/+ffln07p9wxEvM8onDqt1GT+v2wz+4D/90z99/PHHRfo7d+6wUKns0xjGZtJEIuwNjNw+CHtaKIX96uPsQt8jaaKGVd23kIJgP2/ADBdhJ6JIxmY/iWTe0xHlZO7e3t7Ozo5LRZqsrG5gagfssjn9ujYQkdQ7bV27y89Qu2hypsewIqY+v5Vt/sUvfqEca3fv3pWP37lzRy6TDcw6rK2VKCZisAd7ols9d4b19fXT09PBYMCWNA8Gg9XV1eKmDQQQiZ8rzfj888+/+OKL4uD79++JaGVlhf374cMHdoR9irEZDIS9A84T+/ckI0phb9uotInEz8sIOxF1Y2zGBr9TLxEdHR0Vcax5Ipn3dEQXoGd/f9+lIo1V1jHiTHE8aWGviqJTWKj7tjSt/Ay1sovNZ+Hfz2YzWl5CePlTdDV+W8zm8NM6ss3ff//9H/7wB3msffXVV/IYXFtbI2nZIxuYJa1tnigmYohoc3Pz4OBAOCjouKDyiaILqPn8+fMWraoEftpUOfBiZn19/enTp4UoPHv27MaNG3yCFCoREZH4udKMxWIh7M3+X//1X3//939PRL/85S+//fZbvoRujM22gLB3zHk6IOzRf/WLmkj8vKSwd3Jsts7h4eHLly+ZmLN/OzDDrvuVTrg/bB3dwPzyyy/VcdzleEk9JgZNK4O6i51ZX18novp+is6X0dn805/+9Gc/+5lsxieffCKPwRcvXgg2E1GEA9OJPCaI6OzsjP/z4OCg+JPd0POf1m1MHcUaHsar43S5Z0V0iZXHhYNEV690EXxMWSlSzS67FFhtApdOCTuLsoJejlSJn1sraLW5qodO3W0GMhSNsNdXeIrC7qIVxF5dE/bLlzZBWdEOLsRLwL3MsCqnC+X93MnrbDZD2GOAloU9vxRzBi/ycqd7eZ2LJRWW5nWWSqSgfGL/EaGWweTQSYRXAnd9CO5uXUYK0mchgbaPbRndzbBiraBw0NzmLqUJNntZGxWxrIgJw/DMW5y/10WyPXX9RNfyweDn0wAi8fM+b8OeNBD2SMiW/usmna5cxUTi5xD2SMjzvIhdzTg8PCy+XXRgOUwqeHsyf1/b71vcxWJh+Ch+fTDYT5fP18dG6m1eOWlPxFjnmdo2UCSS7akBqJVI/LzP27AnDYQdNASmYXyIxM8h7KB7lNnr3duTe79Nb4Eh1D3fqi6949WDcmIXa+Vc//u//2tIH6e+ObZ5f0h7IiZFDHuwt2JPVcT3K3X19KGOVRGJnyvNuHPnDh9Ng8HCqcRgM0iRSBy+VuKbAQNNE4mfQ9iTA3dPBsrvga0bmMqIMwJxrjOtFvPuZmZ9cOkdrx5UJiZbjE7dKXT225qkHQzO1l9NLvNcU+XQ8hOnGxsbGxsb/KfCn8WbOmpUU+PMZrPt7W15e+o3b97kDnUJqKxXRXSJdcZw7/lXXH7lBUmPIMrbxBApHqcsjggdZG2N4ASGTlGaYXYeueK6g/yRYMxVtlbQ0WblcGOhfJRbxAuJiagYmyAYikbYqypHJkVh15Vw1QVd2SAmlze+kfZH0AlLccS3j6xpAjpFaYbZMFIJo6Gy8incMdfXpYKONpcUdn5sggZgzlG8cv2Nimtpy7nc85ZBeRaXU1vTTCYT+Svo7u7ueDx2LFl3Afrtb38r3dUsqV/iuu63nYquBPnGj0fZOy7wPciboStQmbh4r3MS2X4h5pFLc5Fecg0VdGln5XHZXc2Fu9isyxI/cdlNy/frZ2dnxRH+fZFYyFu5MdUWWDCbzcbjcbHV8+PHj4UbAuupvWwrk3g2m00mE7ZUbDgcjsfj2WymPMjfrKc7HnLtOHediCmOK1vJekavBOV11pBR1cW2r23LJSj9vLzrGiqobHClGTrbhONEhJv18sQj7LVKUyrCrpMm+XgvJmIkVc+NIumo6oZCzAkCbkAdM+quSr4yK/t5JX6rq6DBXcOEnYjksQlqhX2vEl78p2FlliyhzBm9Tm1NE7wHtiDsyguQdFezNBGTOlY3cNFh882q4WEZvhBzD/KJrd2trJTBDPebbV1zESe57ol1JbscN9vsUpqQ2NrR0RKX3eS8B3ue8kSM9RQuwlH+LNbEs9nswYMH/BTs7u7u1tbWp59+Khzc3t7uyUSMUEFDD8pNt729rbxrD+7u8jqrzMhuzVVd7DcRY31vxVBB+TqktJlvcMfmWrpZTNmT4yEeYW+mQ2MWdqWqb29v/+53v5OPKycsEmVppC/Xi+QEqtq6iIzujO4JXJzHKrnKjLqrUth1RGeGFbOdJGG9kvraD1VvHrlbWTeUuc66OH+1eA1Y9zTm3UnPz8+rMok1OnVoOUxe0USM4b1L77ikKUr2Suxlhm9rKN9bE7iU7HLc3QzHxNYTRUuqdueYiCl9Fmti5UI45WPYunmKFNGMc++JGPeFpsHdXV5nlRmV24axNYTmjMEKrsNd7nU28w1uKI10r5Q9OVFqFfZmOjRmYdctb7537554XJqwSJolFRKqJicIFRndGd0TuDiPVXKVGXVXpbDriM4MK16jQ2ezo7Ar30PVm+dKT2wvrzKV7+vDa8B6palkRYz1dMz3qUPLYfL6J2LyFFbEhLWG8r1Lab5epzzuboZjYuuJoiVVu3NMxJQ+izWxz+bVHZ+IyZe/kLiIhVlndWe0mmQ97qWzyowucq/MGKzgOtzl3uU+Rs5ovy9M2ZMTRWjzarugmQ71GrBeCcon9lB1jeglypIKSRMx5CBTvl+WwrrbxXmskqvMaOxqp9O5mGHFa3ToDDYLu9nm1D05RS70xOflUqb7A+BV1cLxoG+a8Xis3G/18ePHyvTmiuuHWKdmYYTmctlCQVeO+b2ud4TEyjS7u7uyGcoCWQmGR0eVua5fvy4kdvcN5XtrAuG9+5POXu2v9+GLORcBQ5b4QdQkoCYsnLvxZq8vsP3PdR8FtGqTmC2M03iDzbqPMgRxAP0jfv1pBwctCBCZqDBclShiYTd8FKfNoCpcrtElIw3Fw/7+/uvXr4X9Vl+9erW/vy8nLh9iqRuwL7HFw7Z8a3z++edffPFFVU2k6x0hmZzm+++//5//+R/ZjM8++0xOTETKxAYzbt++/e7dOyFxk76hc8VvvvmmVjOEiYxKymwRTMQANT7h3JMfBk4417LYEVD5kc86oxYwWxin8Qab5Y+cpmCyIl3e9aiOoEd46I9wc9MPjTfgJTIRYrgqUcTCbvgoTptBtWTGS/bNmze//vrr4s+Tk5Nbt249f/68GdsqZH19/fT0dDAYFHuXrq6unp6e3rhxQ058fHxctuJ5dxRd2RqLxeL8/Lwq39D1jpBMTnP79u0/+7M/k83413/9VzkxESkT68y4du3ad999JyduclDoXPHLL7/szNhsgsbX4FSGYHzldWmgcXSnsJ7ay7bgxMqFcKo9YpaeS/I9Y2yQZgFevrywU5mAz+i+0DS4u5XHdfYX762ljcdjWSiEdZjKjNZTG85uLtl6UGcza3CntdCkfYEmqVXYm5GmmIVdt8r67t27S8eldexJq3qufDRJv1Y/QGTMZ3RP4OI8VslVZtRdlcKuIzozrPhezqxXUl/7U/fkFLmQlOpeuf5ncOu+KlcmcbgkMHiauTTfAWIuzTwFqbeTf2lrnRxeE7LMNwwtrHtvTaB87/XIvLlPZTMCcLHZsYJhWDdnUJqhSywc1GWJH6yI6Szz+fzJkyej0YiIRqPRZDLxXRWmXI+3trY2HA6VzzfmOWVZlmUZ0dWbrmKtqXWhqbWDvHowuLvljJ999hkt3/4q12HGQ5Zlz549E4/m+fHz57969sziheZf3ACIjJLCrltl/dVXXwnHZTqv6mQUdqbe1scHohV20lyVXEprnqL9T05O+OOGRzZAf8iIm9lZxvGxNfYViPRfg60JghOXsc1cu/Pzc99TJI3vI4osfbWdFWCb/JHXo6PmxDrcfcOF3BbISQkeKVXStYmYbJkGMsZJJQ+RKtfjvXjx4sWLF8VB4SoozPNVWaUY0NdVTss3HREdHx//+te/vnnzJvMuuYOePXt248YN3glv3Ljx9OlTlx7Udbe1QsqMLLSwdR1mMJmesAJZF7At0HQ3Z8sWuM2/5ESUdc+LUwTCzigv7LpV1p988okgWVdc/J7dRVWXamO4hK2vr9OyMPKqHr+wE9HR0VEqwl50ARN2Q5uDvrN80a/jsbXC2Vr3uvLPSHZJwn37uslHGr16yuvRUXNiX2MKfJ28YTOUx/mDvsZEhHnBTMwIxtPynWL5MhtoHN0prKe2JtDFKNUF2gwziXlQx57gMPuAY32VhfAHdR3EJ3YP22ktrYwZhtYwlGx971Wy+aDTYmb9k0ckPIKxlKsrbp0OtQp7Mx2airAb7FwaMpfPAiTNkiNplMG9EJ1bKjuIiIoOsgp7JfrsYoYyo7q5qljNbj5oOO5lkktiqHrzXEhKsy9Hw8ISKIPCCInDgjpZr1a6mDvF83pyRkHlujQEnLdQUDzSaO7B8sLoEgyLN+PatWtCYiEoEp9L95ypNR7T2travXv32DQK/94ccEpZQS8zXDZnULa5I2Xytkuqduc136+XKSTsdC7H3RP4BtoMM4mIejcRs/z9xKsQ/qA1RLTuU+XDpS4Bp4PNMLSGoWTre6+S5YOmOzDHORfD/MtVUV1x63QQ2tzFT9zLbKZDvfzZK0G1wq61M1eMjtRZciS9XDgWonNLawfp9JZPYC2tEjOUGdXNhYkYUA7W6O2+dIZZLZcPFpF6ipG1u7u7vb3NJ9alsc7FKIeVcPbt7W055s6bN2+UGQWVc6l1QihbY3Nzc2trSzh4//79oonYzIK5B8sLo9I2sxm3b99Wvhech4iUJSuPE9Gnn37qeJatrS0hsaFlvMzgK+7SpL6k69VpP5rUpTXnFdJcoE3utlJ/h9ldQp3OGiLaK2xncMDp5CJVXwYyyhUN7/i0EQ9/cwJiAsKupDlh73yr8361PPzLVN3aQa0LOx7RB70lk17B6ILFuKQpHzhG+XgpESlDLGW0VNXu3ai7bKEgR6E6Pj4mInMP1mSb2YzvvvuOrYtRBkXinUcXyMklHhNfsnAWXcApXR3dzdBFAQNZnuy4zLIl4/k/hY/CygwuJOx0LsfdE4xGI+X91mAwMN+HeZmUdVHfrT7gUmtlIfxBXQcRW+6hf+KR78GiQGtpZcywjghlydb37iVrb5gC7qR8vbR+EQACtQp7A6puOEtswq63k/sjF/5PFcGRRO12u5ZZ5cvaQVZhr0SfXcywXeYqEHNPr/MYHWXsJ+6yCJpBMeIihjdU6V0ud1zltdrlamUeg8IsDF2KWzPXweZxFxyve2ZdaeV1z2CGEquA6/TZ/RQGwpQ/WNh9Sder014RA3Ts7Owon9BTPjceRvd+NFXuBaUg57MEnkvXQfyf7j3oUlqwGa2gXfzi/ntWrno5nTvLi38BiIkGhH2JTowBpbAbalbm0qbsIJLWwAufNiPspNqeptu4XtNBA2SZ+IqVpbuMPBduOlyWqrW1Ku3K4VWzMJefx9vyzWDugsaaKMATDFl0WhcWYklJ5X4LfWZgIqab6GKUVhn0sXPLYYTH9rTJqjiXroP4NO496FJasBmNYZpjCZh8caCYcFl6dcObQRdpQNi7dzekE/alcb70I3j4uZQdRJehrxltCbtgRh9wvKaDJsgVW7AlR0Y0HAwMO9GMhkMxjQQLKn9R4DKWsxsSs1+t+JNKa2EwEBjmeEZCE3l1kDUXf3A4HAaEcBqNRkozdFoXENtIR+UBp6z6HNb4yYGJGJH5fP7kyRMmlKPRaDKZeIV8jgTro4kCvrXu7IAgIn1rXB0vvShG10F8Gl0PyubpEivrYjBjdXX197///W9+8xs+ca0jQlz5Ij/G7RJk2jb5kmvmXEB/6ICqU/3CTtRlcZdbY6ZpjeBbPpftG5Q9mGWZ3FOGy4S5W4WM169f/+d//mci+ou/+AvF5ayRQWG/qiY+NgFQo5qv4VfcCMeLT4XFOJbEAqonkkCBcm0gW5koKJIQM4iWJWswGPz85z9nMxS8fBWJxdn/5eM6M0gfjShsKk25QFKJMuAUC94kV9bXDB26q4CuGTsGJmJEHj16dH5+zpZgsd31Hj58mOKdwfr6+tOnT4uKPHv2zHCz7l3rzi2HKdC1xjfffHN1vIqpWWsHKRPozCMiZWlyYlZH5Vn++q//+q/+6q9YmiLxF198UcmIEO4t1JMvjnhOvoA+w3y1G6pONQu7bgh2YBApW+Pzzz//9G/+5iJFRZWUO8iaIMsyXU8pE5PKn5Vn+e///m8i+ru/+7t3794JiZu81XG6qqY/NgGIAszC2GBrA+XFhp999pnuBruAT/DDDz/88MMP/D2zLMUBZpBm4WRQXUkuTRc1aW9vb21tbTgcConZ5UOuLC1/mwgj5Btox8iTRTCe/zO4XqSaKN3d3R2Px6Vs1Z/O67h7Aq/Ek8nEWuul5s2FJzk6ha417t27t3Tc2AhKb3TpbmVi/r21s4SMyhl3pT/rEsuz4zrfkG02rODVbOJifGmKcmlPXfubO8VroIFKENrc2oNWlL/b1KfqeTrCLjZ1rh5u3UDZGle//ql0Scaqz16Ji/del2CdPyvNMCT2uo6Y3wdXULyqqiruboYusXBc9xGoj6UhhletL+nGSdkdjbtAE/hKwXg8LhYbPn78+M2bNzql4rXOvNOWkFhphvBeNoOIZrOZ8ri5NGXFhdJWV1fv3r27trYmvC/OwidmIZwMeF0ylPjeqOhI16tT3WSYpB2SM2njZeGJMpea6h5Cs+5zLudVnk4uX5fMbH/mszu0tTTrxt0sO9e8/GfiMu50PYrhs415vvS22AZSbC7LHvjWxPx733AYSruV/hz2BCZf2UwZ5KjC2EaShbqKKJtO2f7mTvEaaKASKhf24OgVxUnNp5MTGJxKOCIka1jYhfTi5o75xWmUpiaHvTVYBfVXN1nr2HGrsFsVycVFrYlJ1Ue+UTmUpVntNydwvbLkedHatQo7pe/MyZElFTUpVeRxlhP5jI7UCbjHExKUEUwlXsLoIrlylvIXINI3nbWCXoGclHh9qTGQrld3+dEkNtXEvzFj2FzaZXdrl9N5meRrf1hp5qqdn5+LXxWEYqu2s138tjHn65pdTcbWYNcFXlvxe/lzwM7q5+fnpHqkmajqp430QRZqDUAA4sRLcMpHr7CerkVVNxToK+xEilmYfPkU6eLU10xhBFWn2i9tvqpujdjimFiH2jdKoLUhF37JvwLCDoAfylkY4IP1oukuSi4qmi/jWHJYrgAcvxSU1Oq24otFRaoTSOTww6kymbVM5XHH304dT+f1u6jXFK/VJGVGl0vtHHAAACAASURBVF/YuLlV/uMrqU93MlLAc0Y8F/66/BW5uytihCqU3C0nF996Lcxx/OFUuWpAt5QgYCUdqJDKhb3kihjH07ms1vGSAi+TwoR9qQRST8T0TtjzXNa0ogH6sCLGxWbf33LdY42w/+sW9m64dEJgRUwtGMaVUcA7o+oCta6IscpvQdgikTLrVkpegAwlm2saXFkerIjp8oqYMJRbVZufCQygsUlNF3Z2dnQbdFvoYjgxXWvcvXtXsfG40HWZ3yqQAHTm6VxUuVm6a+Jc+QRy6QUv3Iv9T9nSlrpKm+WtatwHpnK46cZgPAMTVIIhPkLl50pY2DXLYTqDsjUUESKyTK55lkWk6oZAG16JAy/6RGT28+WIMAEXxLqF3dMcACLDfPeVawUcEcrIFkjUetFUJhAS13FrEUb57raGW/rjH/9YMgSe75eaDpInCzN+NptNJhMWT2s4HAqRxopk7mVub28LW1Xfv3//zZs3XlaFpbHWRZnA63TKU89mM2Wt5YzL36HtJaeIrjV++9vfyscv5yaklslzuQd1TaRsRl3b6swrXJSIeD9ZWVnhv2kUUeh451FPuIjzL/6v5RIE19VVUDkGiWhra8txYFbolp3x6oSoXNhZxjKq7nI6QwK5LkLitoSdeyOOXJd6pYWyNTY3N5XCohc9i3zxWMW8eG9V9ZwTdraxIr+H4p07d1ZWVpTOo3N+pcyy44Urrq2t3bt3j23ZyL+/aAHHC4f767KR6xb2zrh0QhBpftfBy2douN908S3P3sxmswcPHvBfbnd3d7e3t12uNfFDy+gOKhuBSSvLYr1oygmEqEPC3bjSDKUEuSQWMiprLR/nu1tXms4kQ2V90fWdy5caXV65AVMk7RUxhlC+wZyeng4Gg2Kr6tXV1dPTU1180Aqx1qW+EF/r6+vKWptzdfV3JV1rfPLJJ/LxN7MZkdQWGVFGb8/Py3uju3m8i/J+8v79eyL66KOPiOjatWvv/vhHyvPF27fPnj69sb6u/lklU73MqL+z0O7e3vaDB7/75psHf/u35ijaPHIFiejFixetDEzQPNUK+/r6OhG15TzKuhDn/K0Lu/SQSTeXOipb48WLF0phuVxcIReTvz0/f/b0KVURttNsm+yizE9++OEHIvrxxx/ZXMzKysqHDx+YzsvOo3N+Usns0dERcVshXAQrffuW8vyHxeI3//Ef7H3RAh4orymaSwyEHfQOt6GhZvmmi4jGqlhpx8fHN2/e/Prrr4sjJycnt27dev78eVWVaBHhm63uoLIRiKhoBOtFU0iwurq6urpaRB1S6rbSNq+K+CbOVaGIWHebC5SRK6uMo+QSAk93CsfLX5cpM4vTLkRkjTSWe06SBWf0yqVMY62LS4RL6+ms5ukSM18RfjIt31yRo6uUcNz0qwX3A4VLacV7l7ZVHie2HL3an1kcFrzoFEYn0OQT964SHw6gk14dObUKe3CHenkdj7IuvPO3KOyiqtPVWHYvOUV0yike0elk7ifsVn2z2pYXwi4hP7Opi3ttMMPpelHHRUT10lGVW3bSpSOnlI+l+6phdGhvwK5OSoPBgG959oYtc5PhE3cer0bw0mpfMQ9L7K5dupoGX4ysjqTDy8G8WiYsWYSkurcNEWVZNhwO3XeZdSwzLKOyBK80wXtEWbec9KqUdr8lWpqYZ4fLN1fkeO2pKe52KZAL/6tLc9wMstQP1mUyC9aX++XcfZevSnw4gE56deTUKuzBHerldTxW3W5R2BWSlbMFMX0Udq2qi+849KpODvLlu/Oi6iRqlLv8eot1G+uiDL5WlVt20qUjR+O9Qb1QX9dFvhDQWnGukc/Pz9kXZubti8XC8P25SNxtfBsheHtdZQLrcZfEjtrlWNOACppLtp7RSpm9pdMV9rQfTXKP4xg51piULYb4ylSX0Cy7OFy86TmLxeJil0edDlyuMlUsO1Xubqg66LdDbtgy1wL+1xfK2J08++stq2zpfq/cdeGW3aAPws4+akvYLwbI8hDJ84sPCCPokrdFCE/1XItC1evAFElU9av44u1by465Ps8KVYbDb/xKIOydQO7yIFxcN+zVOuYB4sxgMBC+98pHXD7qGP1pBHN1ytQ0oKE61rZ1kPZEjAFdx2d6zAV6ZfQt2eymw+HQ7Mqj0ag4S5lKqRNdrH5ZykfLa8DMZ+kD7OHGi7kJ81Wzqgt/VXcV+qv+1V/LXXxR2dJULtDCej9lGq8BAmKjM8LOnL8tYc9zhUixlBB2nsFgcDXj7Kbq2eXLOqsuz7DrDg4HA8OTCLINTX/ntM2wlPneTQ7CDlWPmR6G5lnCcXSUlNtLt2fhZuT4Nb/4xS/6HptGH/NosVgodSMsDJAul+54oVqCfAnHvcwwRHfidVJ+rzy1ULJcpi6wbBkH60+Qr7QnYljwAgFzCMbiQs5f1F1uOr0yhpUcEDWNJeDPUqZScoJs6T8xFxDY3d29WiricnFt8dcbacKFxZDm39sr64A28rc+inateA0Q0Ba+sXXTEnbinL8FYc9zUT8uVr1hRKjZ3du7EHavr0wlf59v+Iqgo+4vkJXYCFWPFbYZOZHPfES7blbezmbNvlAnIiLa29t79erVZ599Jm8A//333//hD38QYtO8evVqf3+/dhOjYX9///Xr17oAPXL6gH30dbvvf/PNN7rS8mWKotifRaQnLzPMNTXAn1o2iZVMUiS+V69effXVV/IZgx2sviAGEZL2RIzOIdqzqBRKJy7+VI6reiurWAuj+BsUXHUQ/+ROu3ermpuDvf39+9vbLhMuOgRvLBCC+ekEmi7HLwAyTWtdnZidv2Fhz0j63p7H8FU6agRh39vfj2cOohSWL5DZ7dt/Xqzwuf3nV+/39vc3t7a2Pv30aooqy/hrim5yX3eQvW+zKUA9sDg11ZRV+RRJi9M9VbC7t7e3vy+Hm/m3f/s3OTbQT3/605/97Ge9jk3jH6AnIM6ULjrVl19+GRa1KizcVX2hiHSR+JSBZYPP2O0gXyLmubGYYcYT0Xg8LmJr3b17l70fDofj8Xg2mynryB8kotlsNplM2BJxr4wBCQwZZ7NZUZfBYPD48eM3b97wia0JrGa4J1bv+u5ZqdSppAeZd0nrySt66darc6/CT8zO4+snfGWvX79+/fr1YgwWwfzYWfJl1y0Sr62t3bt3zzBgr7zRYVjxKEe0tQfNVQbNwC5MvMNUKOwuI7q88gvHZVkQEjch7HrlsdqvPJ405XuwEPYl76pc3ktcEeR6OTq/MO6YmCuFXTdIzcLu5cMCZmGHqkfI1aOXDvcqeDm9OIqQNLxXW2MDYQjkDo2gbENdMxbvKw8qVD7cVSXdraysruTgM1qb0VDrdL06Vbvzy0bnHYIt3yo6bHd3d3t7W9k3gksFZwxI4JuxWjMcE1OuvsPTlWA4njTle7BYVXglJLVdng1m8O9dfCPMqawlsNtxvjX45TPyuHMvWTiLckTjlj0JLkfJ1Z8VCjsRCVNyyowlE1gzeiV2SWBOfHn7rnh52d8ZyvegTmT4BNVqXbWJrRX09TShNQyV9SqZxyrsUPXYWNpuvLY7nx69VJyfn+ecV5u3eBcS9xlzI/g2I3sftr8+Ky3YjJI1dUSurKHk4DO6NKOu1pVUsxVStTu/bHTeIeQtJ9idty5v+YwBCXwzljFDqJRTabnmlj2oUqlTvgcnk4nBuwxao0R3FpfuFhK7ZzRX0FyywGQyMVdQGHfuJQtnUbb5eDx2L8QrGaiQwgmLPysUdiLi3UCXsWQCa0avxHIC1wFr0HMSr/0uJnWG8j1Yn7DLp/NKbMgY7KIubmne8IsfsF4l81iF3VqCVzJQCVgRU/5l3vSdtTPv1VgR44KhEQxSRrZoFd1bEWO2v6ozOjZjJ1fEpBp2m+giZnh2GTncsFW+XMdsOTp6cMaABL4Zw84ScGrlvrwX5FdPznqVnDrle3A0Gukmd4vEhjSMwWBQJFCexcVOZeJqXdRasrWmjDCbrWfRNaOBTnp15BSxe/g/lYQJO+8Guow1SW7YgPU+y9IHcloiWpJ095I7Q/ketAq7i9YpFclLn4NvG6wV9C3ZXFNGQMk8VmGHqkfIZDJZLBYnJyeENneGtVSWZQ8ePBB2yuDZ29tbXV199uwZLXv1VZs7JO4t1kawNqNXrt///vcff/yxb2nBZvDU19313TYE1Dpdr057s96CxWJh+NRwPxScsWModnBk5IpbduDIYrEwuBD7yJzGpZxUcK9FmcoaztKNZuwVdQh7h90g45VcF0knJ6LwLboBOQi7o491wxXNg5QHwt5D2GbkbVsRPdx2129ms+KweRZGF5JGtwE8wiN4EdaMulzBQYX62Zu9qnVHJmKCl4pVu8YsRdivz/pbdszChDMYDAwuxD4yp3EpJxXca1GmsoazdKMZe0Udwt4lN7iab2FLGs1hjDGrXhFWYXf0sW64onmQ8kDYewiL3tK2FfFxGWusiLCpjDKjm4Uxh6SpL2JOrwhrRl2u4KBC/ezNftXa6QGmKGHWsxf7Uw6mywKFKvPy74MzBiTwzVjeDPn40gOobpEXwiqVOuV7cDweW71LmYZP/PjxY/NZXOxUJq7WRa0ls7A1BoSW8bKZP4uyzXXNaKCTXh05rL/4PysUdiLi3UCXsWQCa0avxFdH5Jd7JJ0q7O8M5XvQKuxmVTcokovWWRNX66IuJVsrG1Yyj1XYHX21ky4dOWq3UGpa915ERHR+fu6+BakhmTWx3PKGjqi106MloBHC2kqXK7jlfTPW1926kus4Y619FAMdWRHDfhI8OT7m5e/OnTtsJn40GmXLCNmVK6CIyJzr4tRSmtFoNJlM5vO5MsFoNJLTyMzn8ydPnrDEYWYIpS39cEr6tes5twYmy7zstCbuJOYW0K2v40uQ0wiJ41mMV7K7WUX4mvLBNfgBW5QccLperWnsA0JvFn5iFUbZDYhof3/fmtFFtK3Kr4T3Z4UZeS4schEfM1Ic0sDPqBOR5+CFsJcUdjkBr3WxKZJcWd8SDJUVhF15Ogh799HMUHSJ4WgkLnW5XO3CFsqZ8nKrutjXM+t6aj5xgdlCr8RdpT+NUF9NdSX3p20rpCMTMUp+/5//+f7//T/K87fn5/I1gL+JFVZAHR8f//rXv+aLMrhULgWqZI+IP3z4UMjLIi+ySfEije7+49GjR/wMOh94VWdGfjkjWFSweN1YXydyuIPnpmDY9cNs53w+5+20Viohiq9Gwlc14biuBYr0uvV1/LmENNevX79+/ToZF+PpzPOqi3vi4v2NGzeePn2qq6yV9fV1Vi9W09XV1dXV1bW1NSJaWVn58OHD+/fvWcnPnj1jteZPVxw0V6Rfaxp7AN+bvJ+QTRj5jER0fHxMRDdv3iSmk3phN4t2kUan/EoBZHMrT//xH9n1SL4qiQ6dqV4OzGbzi8mX5YGsHLwQduXB8sIuJOC1TqdIDas6GX2DNYK5WB5dZWVhl08HYQeJIiw+3NnZUS7aEmKuywmUhetKK201ACAm1AtlUoBZX/crX17vpHyv/PlIDsTrHnlRmZiIxuOx/6pI17XrRV3c7XSJE9xtzEFMBXSOpExjTWA96JLYSwGslQ2uoNLh79y58/HHH5f0Lq+WCU4GKoR1NP8n/94ckdqQUXki83vl6ZYcvpKXo0TbnjwytwaE3Ytqhd2awCWjV2Iv4VJW1mVYWU+tHEH1Cbtjrb0aB1QCURO367W+5BrJ1ZzNZtvb28Kirfv3779586bIpUzgVZqXA8PbKyGsGb0EvD4zUqfzwp5qtCe6iJjobHyqtfTB7ffSJXLhf78IkS5xgruNS3Tqgiw0LqmuEPNBl8S6jEqslQ2uoPUXXR4v7/JqmeBkoEIyKXy11U+U6mT1xqUEVdagphI5csqr1moIO0+1wu4ljNaDZc6ixMs3vE7dsLBD1aMlyzJKqM2zbDAY7Ozs7O/vK9cj838KvjSfz58/f35ycrJYLPhChFyDwYAlEIKOmUszJ/ayEzgS1oy6XMGd0s/e9Kp1usKeqt2kvsY3VZfG2qyOu/n8Ysk6EZ2fnw+Hw8J9F4uF4anUgMRVmx4Xvi2Q9ESMl2+4mOToSIbTuaTERExyKIQ9hi6odWLFQF78Q+Q8aiDsZahc2GOeiKlE2JUJmhd2qHq0RD4RI1hWiYdYC/E6C5wWADPpjpFU7SbfFTE9h5t8WTosfefHD6deYEUMAytiQFXUcsve1jSKO7n4l26k1KTVEHYerIghrIgB1RHbRIzZFEzEAJAc6Y6RxDfrzR1efYOre55TntN4PNmV9vfy3R5M+Qy5V+JO0qvd1GqtrFyyciuBXnkXICL1nrW+r7awXZ729vaJsjy/vFhlmftVSzkeSbONDoTdCwg7aRzJFwh7r5hOp4eHh4eHh9PptG1bllj6TuAzPwgAALWS+ESMCy6TNe6vWKuQX+zYeHkfn2WFsS4RlMmWWBkhEuEklZGnWXCWLMu8fg+sluLsvBnKg+64VDa7RD6L+eyyI62trQ2Hwz57V3+JYQ5Fidtl4s2bGRPkKymWY5pm2d7+fqHDAWNTKb90GSTeJTGEXQeEnYh+9atfWYXdemoIe3+YTqdbW1vT6ZS9OTw8bNOay0lt/s7d6q4lh1IdZ6nEJABAtKQ6EeMbR/PxePxmNqMsmzx5MhyNKMvWBoN7P//5YDikLBuORuPJZDafC9pdTGrM5vOLjJStrQ3u3fv5YDAkyobD0Xg8mc3mRNl4PGEHV1fX7t69JyeYzeZFmsFg+Pjx+M2b2Ww2n0yeDIcjPnGe01Li4fDxeCzYdmVSlg1Ho8lkwtrkyZMno9GIiEaXBw2BNt0TZ1nmnrg/4STlFnj8+HGxQ768TG4+n8vN6HIiZUZDafkywsHZbDaZTHzNsFZWSZFdd5whO9KLFy9evHhRrXcFtz9oANY7NZ7AYQ6F121BtFdX1+7euydfMsaTCTvIhPrNbHbjxg3B08bj8Ww2m81m4/FYGfDYMDZJ47dK+SUiL62GsCuBsDsKu1nVqRFhh6pHwtbW1sbGBpuIOTg4ODo6avLsoparvNHqrtYETpZUepZKTAIAxIv5Whsns9nswYMHfC12d3e3t7dnsxlLwNereD+bzWh5te3t27eFEpQNQkQPHjxwzCgnlkvmTVImLirCJ6bl+y0549bWllBBQ2lya5gTu9vZW6wtoPON4O7+9NNPzaUpcenN8pX1KkH3vvzprM1orji8ujGWe0ez5M/9VYWwExF/TTELu5en6RxemZGIrCOdbBcgCHsw5YXdq7sh7F5ngarHAxGdnZ0p/ySi8gGk5RcAADDSFfYk7Z5MJvLjxLu7u+PxmCVQXv7ZT4sG2C2vfDqyPSzNZ1QmFkrmTTJXhE/Ml6DMeOfOHaVtytKUrWFI7G5nb7G2gM43grtbftJeKE2JS2+Wr6xXCbr35U9nbUZzxeHVjbHUO5p7bqWfKIeV0MU6p7JuS8FfU8zC7uVpOodXZiQi60g3twaEvQzWFrC2uVd3Q9i9zgJVj4SzszOhYYno4OCgeI+ZFwBAfaQr7EluMhy2yb8hDgKP3CCOj2WyjIbE7ibx4QOKxJlDVAslytK8Tu2VuLdYW0DnG/V1t5JKQqKU727lINWVHHw6l2Y0VBxe3Ri+IsOPFJfyw4Sdv6aYS/byNJ3Dlxd2L5GBsLtQXtgr724l/RR2qHoksH1h+IbNsow9qUSOKq3vFPQWAMBMusKe3h4xi8VCd30N+0hASLZYLBwNe/v2rTmxXHKYte510aX3OnUlrQoMvlF3dzt+2u3e7G3Fk6CMGDqeIkzY2dkTEnavxBgR5YGwt0g/a50Qm5ubV39YF7wswz+bCgAAXSW9iZjBYDAcDnUfjUYj5QbjhlwCRQlFRkfDhsOhObFggLkiBmvd66JL73XqYDtBQZZl1g6V98Znb8p3t+OnjfWmUMGaIgK4NyPcuHWsvaPzE3d9DhN2dvaEhN0rMUZESazCLtyQQNirPctwOGy91qBaMPkCAOgV6U3EENHOzo4canFvb29nZ0d48krIZS52b2/v8ePHcka63AvAkNGcWEjAm6SriNlUZUblHjG60pQHDYnD7AQM5lTj8djQjLqnB0nf3fJWAsHO02RvGh6VrO8sFEHFgQFz75j9xKrPYcJO0nbmcsm6XBUKO6n2iDGU5n5qjIiSWIXdrHUQ9vJnab3WgMEWv7AHkYSDVrD4BQDQXwzXzmiZzWYsXEXB3t7e/fv3lQEXiYjFdFxbWyOia9euFbn4GBlyCSwX+1FlZWWFvzfiM965c2dlZYWIhsPheDwmou3tbf7OQEjAwhXxFeETy2YQUWFGUYKc8c6dOx999JFQQWVputZQJvays6LuTRVrC7g0o3vGzc3Nra2tqkpzyehV2WCUJVdyuoCKw6sbw7d3eGE06HOwsF+/fp2IBoNBIdpmYeddxVqXQoetws7MEOw0XyZcEru3OYaAtQUg7MEllz8dVD0eNjY2irbl3+eazXoBAKAq0hX2JFfErK+vn56e0uVK7MFgsLq6enp6euPGDWX6R48enZ+f//DDD0T0448/stmHn/zkJ6urq2w+Qi5hPp+zXOwx4/fv3xMRu+1eXV0tMq6srHz48IF9WmwlcHR0NBgMmG1ygocPHwoVKRIrzSCiwgy+BD4jO8v//d//8RVcW1tTNouyNXSJHe0ELgQ3ozLjixcvXrx4UVVpfejN3lY8CYreYX+ae0cQRp0+Bwj7T37yEyK6du3au3fviGixWBgkV1B+ZhW5eRpvhizsq6urvBm8ndbLhDmxss0xIsoAYW+RftY6TqbT6cbGBnt27OXLl7lmJRQWvwAAwBVtzwSFQ5fhKqzJlItUlaElC3QxEWl5jt9asrUQoTqyGXL5cnhRsxlerWE1SdfgSftSJXi1QHBzeXVKw2ZUQrUV9DpLA+cFVpgimdPohFHQZ2VGsyY7llxeRc0xd13M8GoNF5PcE/cKrxYIbq5qda/5jGElV3s6x9Lg0s2DNgcA1Eq6IpNqtCe6DFVVbWjJAkNcySKxS8k6q+QzepkxWA4vajZDOItXYkc7Dcf7g1cLlA/bWV9ptWYMK7ny0zkWCK9uHqZR5mZ31OcAYa9E+Xl0/mwuwcWMAq/EBpMg7Eog7JXQgLBD1aMFbQ4AqJV0RSbJR5MK5C36hU8NUyHBgQ/ZR+aglVmWmSOeuoQSyLLMaqF77Eyv1vAKeVBffIRU6FULNBMOoyi5V20LCjIO+SOrPiuxCrs5ATPGqvy6T/lCzBa6XID4Ar0SQ9jd6VULQNgBAACAhkl7IiZfXowkf5rneUB0Q3PgQ/aROVBonufWBNapOxfj3eOVerWGsG7Kaqd74k7Sqxaor7LKknvVtqDA0OkGHSMpmDSPVdjNFwVmjFXYdZ8yXHTY5QLEF+iVGMLuTq9aAMIOAAAANEzaEzEu6AKCmqMb6mIiCkescRMdAyvqfhFyDM3oXsGw1vCiS79udaku1K3qdKkuwBdHfQ7IqFRCF8mtVth1ZniVpkwcTJdGHOoSJ12qC+gqHfPSLlUHdYmTLtWlDro/EbO/v//69WshuiE77pvr1atXQjJlGr5ka4IA4+US3CsY1hoAABAPjvockJEpYYDkVivsOjPCKgUAAAAAAGKj+xMxyuiGRGSObqiLiSgks8ZNLBlY0TE0o3sFw1oDAADiwVGfAzKur6+z476SW62w68yotjUAAKAZMom2LQIAgPZJdZNhkhY7uVTELP26ErLL8EyGjNnyds2ybZl+P2fHisglKM8i26Yz2ytxGTvTpY66BPitIaNXaXx1gs2IhOCuCW4x0Ax8BzmqulWfwzLKeVsRdlJpu1elIOwCXRJ2672HoxkxAFXvKl1q8y7VhbpVHdQlTpqpS7otttK2AaUIaPTgfvLK6Js4zIGUWWq10z0x0FGtBzbjz12itxVPiEiE3XeqWk5cibBnDvG8zSVUmBjogLC3SD9rDQAAIHW6/2gSAAAAAAAAAAAAQCRgIsZCwIOszTz72oxhqEsDZwkzrEvVibYuIAl67qWoi2+WAFCXBrIEAFXvKj330mjrEnaiaKuDujSQJYBeCTsmYgAAAAAAAAAAAAAaIvY9YqbT6XQ6JaLNzc3Nzc3gcorZNa/n7V1yWdOEnbpyM6o1CQAAgqlE2PnfTLx2Y6lEtBsQdqg6AAAAAEBXiXpFzHQ63draYrfsW1tbh4eHwUXlec5uTIs3jrmKLLpc+TIBCRyNL2lGtSYBAEAYVQk7r2AB29OWFO0GhB2qDgAAAADQVaKO9pRl2cbGBvvh9PDw8OjoiLc2ICAFsiBLN7I0diJkiVkhEwXCjizIgiwJZQElibZne56lsRMhC7JEmCUSol4RQ0TFj6XsDbt3BwAAkC4QdgAAAAAA0GfinYgpdhCQDwIAAEgRCDsAAAAAAACxb9YrINyvRxusC1mQpe4sjZ2o51lAA0DYkQVZkKWxLKB5onWGnmdp7ETIgix1Z0mUxCZi+N9RE30YDAAAAA+EHQAAugpUHQAAlMT7aBIAAAAAAAAAAABAx4h3Iob9RiosWRd2FgAAAJAQEHYAAAAAAADinYghoo2Nja2tLfae3anjfh0AAJIGwg4AAAAAAHpO7GG3Nzc3X758yd5HbioAAAAXIOwAAAAAAKDPxD4RAwAAAAAAAAAAANAZon40CQAAAAAAAAAAAKBLJDkRM51ODw8PDw8PhR0fU4TVZXNz8/DwsG1bKqMbOz50qV8KN0t6yCj7okvd1HMg7JEDYY8KqDqIH6h6/EDYowLC3jvy1Dg7OyOijY2NjY0NIjo4OGjbonAODg74uqTYHTKsLmdnZ20bUgrWHawuGxsbbZtTCsHNEq0OG/iCX/Hd1I3h01sg7JEDYY8KqDqIH6h6/EDYowLC3kPSawveNZnLtmpOKYQrU+oXqvxy+KUu60wE2XuloKQFQT2ibQAABXlJREFU71cpVufs7KzQbt5yYfh3YPj0GQh7zEDYYwOqDuIHqh45EPbYgLD3kPRkUejd5NyUR7gs8WqSKGx0Jd0peeJOJZP6kDk7Ozs4OJD9Svi5oAPDp8+k7qU8EPY4Sd1+ntTHC1S9D6TupTzdU/Ucwh4fqQ8ZCHsAie0Rwx6ZEx5oTPc5unw5ZNXLly+TflZzc3Pz4OAg9cf/Ch87vKRlg0qzsbGxtbU1nU6n0yl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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"canv.cd(3)\n",
"x.setRange(\"leftToMiddle\", 0.0, 5.0)\n",
"\n",
"model3 = ROOT.RooAddPdf(model)\n",
"r3 = model3.fitTo(data, Range=\"leftToMiddle\", Save=True, PrintLevel=-1)\n",
"r3.Print()\n",
"\n",
"frame3 = x.frame(Title=\"Fit from left to middle\")\n",
"data.plotOn(frame3)\n",
"model3.plotOn(frame3, VisualizeError=r3)\n",
"model3.plotOn(frame3)\n",
"model3.paramOn(frame3)\n",
"frame3.Draw()\n",
"\n",
"canv.Draw()\n",
"\n",
"canv.SaveAs(\"rf204a_extendedLikelihood.png\")"
]
},
{
"cell_type": "markdown",
"id": "780c07da",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "c92638dc",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2024-03-19T19:15:25.439356Z",
"iopub.status.busy": "2024-03-19T19:15:25.438934Z",
"iopub.status.idle": "2024-03-19T19:15:25.939745Z",
"shell.execute_reply": "2024-03-19T19:15:25.933610Z"
}
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ROOT import gROOT \n",
"gROOT.GetListOfCanvases().Draw()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}