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+<?php
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+$selected_csv =
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+
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+$branch = 'dev';
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+$season = 'NNBAR2021';
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+$url = "https://cmd.inp.nsk.su/~compton/gitlist/compton_tables/raw/".$branch."/tables/";
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+$url_total_info = "https://cmd.inp.nsk.su/~compton/gitlist/compton_tables/raw/".$branch."/tables/".$season.".csv";
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+$text = file_get_contents($url);
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+$arrays = explode("\n", $text);
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+
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+$cleanArrs = array_filter($arrays, function($value) {
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+ return end(explode('.', $value)) == "csv";
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+});
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+
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+function isSelected($a, $b){
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+ if ($a==$b){
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+ return "selected";
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+ }
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+ return "";
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+}
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+
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+$selected_csv = isset($_GET["csv_file"]) ? $_GET["csv_file"] : reset($cleanArrs);
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+$selected_season = substr($selected_csv, 0, -4);
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+
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+?>
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+
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+<html>
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+<head>
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+ <script src="plotly-latest.min.js"></script>
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+ <script src="fmin.min.js"></script>
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+ <!--<script src="https://cdn.plot.ly/plotly-latest.min.js"></script> -->
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+ <meta name="viewport" content="width=device-width, initial-scale=1">
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+ <link rel="stylesheet" href="main.css">
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+ <title>Compton interactive plots</title>
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+ <!-- <script src="chart.js"></script> -->
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+</head>
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+<body>
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+
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+<div style="display: flex; align-items: center; flex-wrap: wrap; justify-content: center; margin-top: min(20px,2%); margin-bottom: 40px;">
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+ <h2 style="margin: 0;"><a href="index.php" style="font-size: inherit;">Pictures</a> / Season</h2>
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+ <form name="form" action="" style="padding: 0 15px; margin: auto 0;" method="get">
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+ <select name="csv_file" class="select-css" style="padding: 0.3em 1.3em 0.3em 0.4em;" onchange="this.form.submit()">
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+ <?
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+ foreach($cleanArrs as $file){
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+ ?><option value="<?echo $file?>" <?echo isSelected($file, $selected_csv)?>><?echo substr($file, 0, -4)?></option>
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+ <?
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+ }
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+ ?>
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+ </select>
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+ </form>
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+ <h2 style="margin: 0;">summary</h2>
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+</div>
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+
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+<div style="display: flex; align-items: center; flex-wrap: wrap; justify-content: center;">
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+ <div style="max-width: 900px; width: 45%; min-width: min(100%, 600px); margin: 0 max(1%, 10px);">
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+ <h3>Mean energy measurements</h3>
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+ <div id="mean_energy_plot"></div>
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+ </div>
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+ <div style="max-width: 900px; width: 45%; min-width: min(100%, 600px); margin: 0 max(1%, 10px);">
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+ <h3>Spread measurements</h3>
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+ <div id="spread_plot"></div>
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+ </div>
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+ <div style="max-width: 900px; width: 45%; min-width: min(100%, 600px); margin: 0 max(1%, 10px);">
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+ <h3>Mean energy deviations</h3>
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+ <div id="energy_dev_plot"></div>
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+ </div>
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+ <div style="max-width: 900px; width: 45%; min-width: min(100%, 600px); margin: 0 max(1%, 10px);">
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+ <h3>Impact of mean energy deviations</h3>
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+ <div id="impact_energy_dev_plot"></div>
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+ </div>
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+</div>
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+<div style="margin-top: 40px; width: 100%;">
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+ <div style="width: min(100%, 600px); margin: 0 auto;">
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+ <h3 style="padding-right: 40px;">Methodology</h3>
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+ <p style="font-size: inherit;"><b>Mean energy measurements:</b> a difference between measured mean beam energy and target beam energy vs target beam energy.</p>
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+ <p style="font-size: inherit;"><b>Spread measurements:</b> a measured mean beam spread vs measured mean beam energy for the given energy points. A line shows the best fit of these points to a linear function (as a result of chi-square minimization)</p>
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+ <p style="font-size: inherit;"><b>Mean energy deviations:</b> a measured mean energy deviations (so-called mean_energy_sys_err) vs measured mean beam energy for the given energy points.</p>
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+ <p style="font-size: inherit;"><b>Impact of mean energy deviations:</b> a part of the mean energy deviation in total uncertainty (as d/√(d<sup>2</sup> + s<sup>2</sup>), where d is the mean energy deviation and s is the mean spread) for the given energy points.</p>
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+ </div>
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+</div>
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+
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+<script>
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+ function makeplot(){
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+ Plotly.d3.csv('<?echo $url.$selected_csv;?>', (allRows)=>{
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+ const {point_name, energy_point, mean_energy, mean_energy_stat_err, mean_energy_sys_err, mean_spread, mean_spread_stat_err} = parseResultsTable(allRows);
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+ makeMeanEnergyPlot(energy_point, mean_energy, mean_energy_stat_err, point_name, "mean_energy_plot");
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+ makeSpreadPlot(mean_energy, mean_spread, mean_spread_stat_err, point_name, "spread_plot");
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+ makeMeanDevPlot(mean_energy, mean_energy_sys_err, point_name, "energy_dev_plot");
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+ makeImpactDevPlot(point_name, mean_spread, mean_energy_sys_err, "impact_energy_dev_plot");
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+ }
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+ );
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+ }
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+
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+ function parseResultsTable(allRows){
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+ // Extracts a row following the energy point in the total csv file (or null if energy point is not exists)
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+ let data = {};
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+ for(let key of Object.keys(allRows[0])){
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+ data[key] = [];
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+ }
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+ data['point_name'] = [];
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+
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+ let float_keys = ['energy_point', 'mean_energy', 'mean_energy_stat_err', 'mean_spread', 'mean_spread_stat_err', 'mean_energy_sys_err'];
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+
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+ for (var i=0; i<allRows.length; i++){
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+ for(let key of Object.keys(allRows[i])){
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+ let value = allRows[i][key];
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+ if (float_keys.includes(key)){
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+ value = parseFloat(value);
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+ }
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+ data[key].push(value);
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+ }
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+ data['point_name'].push(allRows[i]['energy_point']+'_'+allRows[i]['first_run']);
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+ }
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+ return data;
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+ }
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+
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+ function linear_coeffs(x, y, yerr, remove_outliers=false){
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+ // Returns a chi square minimizer for linear function
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+ function chi2min(X){
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+ let k = X[0], b = X[1];
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+ let chi2 = 0;
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+ for(let i=0; i<x.length; i++){
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+ if (remove_outliers&&(y[i]<0.06))
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+ continue;
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+ y0 = k*x[i] + b;
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+ chi2 += Math.pow((y[i] - y0)/yerr[i], 2);
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+ }
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+ return chi2;
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+ }
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+ return chi2min;
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+ }
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+
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+ function getPlotlyDefaults(){
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+ var layout = {
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+ margin: {
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+ l: 50,
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+ r: 50,
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+ b: 50,
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+ t: 50,
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+ pad: 4
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+ },
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+ font: {
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+ size: 18,
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+ },
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+ xaxis: {
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+ title: "Mean beam energy, MeV",
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+ automargin: true,
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+ },
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+ yaxis: {
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+ //domain: [0, 0.2],
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+ automargin: true,
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+ rangemode: 'positive',
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+ title: "Spread, MeV",
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+ hoverformat: '.2f',
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+ },
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+ showlegend: true,
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+ legend: {
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+ x: 0,
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+ xanchor: 'left',
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+ y: 1,
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+ },
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+ paper_bgcolor: 'rgba(0,0,0,0)',
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+ plot_bgcolor: 'rgba(0,0,0,0)',
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+ autosize: true,
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+ };
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+ var config = {
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+ toImageButtonOptions: {
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+ format: 'png', // one of png, svg, jpeg, webp
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+ filename: 'spread_plot_<?echo $selected_season;?>',
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+ scale: 4,
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+ },
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+ responsive: true,
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+ modeBarButtonsToRemove: ['select2d', 'lasso2d', 'zoomIn2d', 'zoomOut2d', 'resetScale2d', 'hoverClosestGl2d', 'hoverClosestPie', 'toggleHover', 'toggleSpikelines'],
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+ displayModeBar: true,
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+ };
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+ return {
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+ "layout" : layout, "config" : config,
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+ };
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+ }
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+
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+ function makeMeanEnergyPlot(energy_point, mean_energy, mean_energy_stat_err, texts, elementId){
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+ // Plots mean energy - target energy vs target energy
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+ var energy_diff = [];
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+ Plotly.d3.zip(mean_energy, energy_point).forEach(e => energy_diff.push(e[0] - e[1]));
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+ var trace1 = {
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+ x: energy_point,
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+ y: energy_diff,
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+ yaxis: 'y',
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+ mode: 'markers',
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+ text: Plotly.d3.zip(texts, mean_energy),
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+ text2: mean_energy,
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+ hovertemplate: "Point: %{text[0]}<br>Mean energy = %{text[1]:.3f} ± %{error_y.array:.3f} MeV" + "<extra></extra>",
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+ //hovermode: "x",
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+ error_y: {
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+ type: 'data',
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+ array: mean_energy_stat_err,
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+ color: '#260101',
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+ },
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+ showlegend: false,
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+ marker: {
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+ color: '#260101',
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+ },
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+ type: "scatter",
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+ };
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+ var traces = [trace1];
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+
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+ var {layout, config} = getPlotlyDefaults();
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+ layout.xaxis.title = "Target energy, MeV";
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+ layout.yaxis.title = "Mean energy - Target energy, MeV";
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+
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+ Plotly.newPlot(elementId, traces, layout, config);
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+
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+ }
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+
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+ function makeMeanDevPlot(mean_energy, mean_energy_sys_err, texts, elementId){
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+ // Plots mean energy deviations vs mean measured energy
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+ var trace1 = {
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+ x: mean_energy,
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+ y: mean_energy_sys_err,
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+ text: texts,
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+ mode: 'markers',
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+ marker: {
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+ size: 12,
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+ symbol: 'diamond',
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+ },
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+ hovertemplate: "Point: %{text}<br>x = %{x:.2f} MeV<br>y = %{y:.3f} MeV" + "<extra></extra>",
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+ showlegend: false,
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+ };
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+ var traces = [trace1];
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+
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+ var {layout, config} = getPlotlyDefaults()
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+
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+ layout.yaxis.title = "Mean energy deviation, MeV";
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+ Plotly.newPlot(elementId, traces, layout, config);
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+
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+ }
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+
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+ function makeSpreadPlot(mean_energy, mean_spread, mean_spread_stat_err, texts, elementId){
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+ // Plots spreads vs mean measured energy
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+ var chi2min = linear_coeffs(mean_energy, mean_spread, mean_spread_stat_err, true);
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+ var solution = fmin.nelderMead(chi2min, [0.001, 0]);
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+
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+ var trace1 = {
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+ x: mean_energy,
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+ y: mean_spread,
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+ yaxis: 'y',
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+ mode: 'markers',
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+ text: texts,
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+ hovertemplate: "Point: %{text}<br>x = %{x:.2f} MeV<br>y = %{y:.3f} ± %{error_y.array:.3f} MeV" + "<extra></extra>",
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+ hovermode: "x",
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+ error_y: {
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+ type: 'data',
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+ array: mean_spread_stat_err,
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+ color: '#260101',
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+ },
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+ showlegend: false,
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+ marker: {
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+ color: '#260101',
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+ },
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+ type: "scatter",
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+ };
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+ var trace2 = {
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+ x: [Plotly.d3.min(mean_energy), Plotly.d3.max(mean_energy)],
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+ y: [solution.x[0]*Plotly.d3.min(mean_energy) + solution.x[1], solution.x[0]*Plotly.d3.max(mean_energy) + solution.x[1]],
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+ mode: 'lines',
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+ name: 'Fit: y = ' + Math.round(parseFloat(solution.x[0])*1e5)/1e5 + 'x ' + (Math.sign(solution.x[1])>0 ? "+" : "") + Math.round(solution.x[1]*1e3)/1e3,
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+ hovermode: false,
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+ hoverinfo: 'none',
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+ };
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+ var traces = [trace1, trace2];
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+
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+ var {layout, config} = getPlotlyDefaults()
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+
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+ Plotly.newPlot(elementId, traces, layout, config);
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+
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+ }
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+
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+ function makeImpactDevPlot(point_name, mean_spread, mean_energy_sys_err, elementId){
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+ let arr = Plotly.d3.zip(mean_spread, mean_energy_sys_err);
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+ let impact_factor = [];
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+ arr.forEach(e => impact_factor.push(e[1]/Math.pow(e[0]**2+e[1]**2, 0.5)));
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+ var data = [{
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+ type: 'bar',
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+ x: impact_factor,
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+ y: point_name,
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+ orientation: 'h',
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+ showlegend: false,
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+ text: impact_factor.map(e => String(Math.round(e*1e2)) + '%'),
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+ textposition: 'auto',
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+ hovertemplate: "Point: %{y}<br>Impact of %{text} <extra></extra>",
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+ //hoverinfo: 'none',
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+ }];
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+
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+ var {layout, config} = getPlotlyDefaults();
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+ layout.xaxis.range = [0, 1];
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+ layout.yaxis.title = "";
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+ layout.xaxis.title = "Impact of mean energy deviations";
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+ Plotly.newPlot(elementId, data, layout, config);
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+ }
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+
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+ makeplot();
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+</script>
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+
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+</body>
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+</html>
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