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@@ -1,171 +1,162 @@
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-<!-- https://www.createwithdata.com/chartjs-and-csv/ -->
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-<!-- https://towardsdatascience.com/4-ways-to-improve-your-plotly-graphs-517c75947f7e -->
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-
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<?php
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-$branch = 'dev';
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-
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-function getsmth($branch){
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- $url = "https://cmd.inp.nsk.su/~compton/gitlist/compton_tables/raw/".$branch."/tables/";
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- $text = file_get_contents($url);
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- $arrays = explode("\n", $text);
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- $clean_arrs = array_filter($arrays, function($value){
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- $temp_arr = explode('/', $value);
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- return preg_match("/[A-Z]+[0-9]+\//", $value);
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- });
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- foreach($clean_arrs as &$val){
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- $val = substr($val, 0, -1);
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- }
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- return $clean_arrs;
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-}
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-
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-$availableSeasons = getsmth($branch);
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-
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-$season = isset($_GET["season"])&&in_array($_GET["season"], $availableSeasons) ? $_GET["season"] : reset($availableSeasons);
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-$url = "https://cmd.inp.nsk.su/~compton/gitlist/compton_tables/raw/".$branch."/tables/".$season."/";
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-$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($_GET["csv_file"])&&in_array($_GET["csv_file"], $cleanArrs) ? $_GET["csv_file"] : reset($cleanArrs);
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+require __DIR__ . '/commonfoos.php';
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+
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+$ini_array = parse_ini_file("config.ini");
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+$token = $ini_array["apitoken"];
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+
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+$availableSeasons = available_seasons($host, $owner, $repo, "energy_points", $token);
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+
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+$season = isset($_GET["season"]) && in_array($_GET["season"], $availableSeasons) ? $_GET["season"] : end($availableSeasons);
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+$season_url = season_table_url($host, $owner, $repo, "", $token, $season);
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+
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+$energyPoints = energy_point_urls($host, $owner, $repo, "energy_points/{$season}", $token);
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+$selected_csv = isset($_GET["csv_file"]) && array_key_exists($_GET["csv_file"], $energyPoints) ? $_GET["csv_file"] : key($energyPoints);
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?>
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<html>
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+
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<head>
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<script src="plotly-latest.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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+
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<body>
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<div id="gd" style="height: max(80vh, 600px); padding-bottom: 20px;"></div>
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-
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+
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<form name="form" action="" method="get">
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<p style="text-align: center;">Select energy point:</p>
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- <div style="margin: 0 auto; display: flex; justify-content: center;">
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- <select name="season" class="select-css" style="margin: 0;" onchange="this.form.submit()">
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- <? foreach($availableSeasons as $s){ ?>
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- <option value="<?echo $s?>" <?echo isSelected($s, $season)?>><?echo $s?></option>
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- <? } ?>
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- </select>
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- <select name="csv_file" class="select-css" style="margin: 0;" onchange="this.form.submit()">
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- <? foreach($cleanArrs as $file){ ?>
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- <option value="<?echo $file?>" <?echo isSelected($file, $selected_csv)?>><?echo $file?></option>
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- <? } ?>
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- </select>
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- </div>
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+ <div style="margin: 0 auto; display: flex; justify-content: center;">
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+ <select name="season" class="select-css" style="margin: 0;" onchange="this.form.submit()">
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+ <? foreach ($availableSeasons as $s) { ?>
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+ <option value="<? echo $s ?>" <? echo isSelected($s, $season) ?>>
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+ <? echo $s ?>
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+ </option>
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+ <? } ?>
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+ </select>
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+ <select name="csv_file" class="select-css" style="margin: 0;" onchange="this.form.submit()">
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+ <? foreach ($energyPoints as $key => $file) { ?>
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+ <option value="<? echo $key ?>" <? echo isSelected($key, $selected_csv) ?>>
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+ <? echo $key ?>
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+ </option>
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+ <? } ?>
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+ </select>
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+ </div>
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</form>
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<script>
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- function makeplot(){
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- Plotly.d3.csv('<?echo $url_total_info;?>', (allRows)=>{
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- const {mean_energy, mean_spread, mean_energy_stat_err} = parseResultsTable(allRows, "<?echo $selected_csv;?>");
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- Plotly.d3.csv('<?echo $url.$selected_csv;?>', function(data){processData(data, mean_energy, mean_spread, mean_energy_stat_err)});
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- }
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- );
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+ function makeplot() {
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+ Plotly.d3.csv('<? echo $season_url; ?>', (allRows) => {
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+ const {
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+ mean_energy,
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+ mean_spread,
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+ mean_energy_stat_err
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+ } = parseResultsTable(allRows, "<? echo $selected_csv; ?>");
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+ Plotly.d3.csv('<? echo $energyPoints[$selected_csv]; ?>', function(data) {
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+ processData(data, mean_energy, mean_spread, mean_energy_stat_err)
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+ });
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+ });
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}
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-
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- function parseResultsTable(allRows, energy_point){
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+
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+ function parseResultsTable(allRows, energy_point) {
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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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data = energy_point.slice(0, -4).split('_');
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- for (var i=0; i<allRows.length; i++){
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- if (allRows[i].first_run == data[1] ){
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+ for (var i = 0; i < allRows.length; i++) {
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+ if (allRows[i].first_run == data[1]) {
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return allRows[i];
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}
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}
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return null;
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}
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-
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- function parseRow(row){
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+
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+ function parseRow(row) {
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// Parses a row from the detailed csv file
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- row['start_time'] = Date.parse(row['compton_start']);
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+ row['start_time'] = Date.parse(row['compton_start']);
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row['stop_time'] = Date.parse(row['compton_stop']);
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- row['center_time'] = new Date((row['start_time'] + row['stop_time'])/2);
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- row['timedelta'] = (row['stop_time'] - row['start_time'])/2/1000; // in seconds
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+ row['center_time'] = new Date((row['start_time'] + row['stop_time']) / 2);
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+ row['timedelta'] = (row['stop_time'] - row['start_time']) / 2 / 1000; // in seconds
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- text_energy = "E = " + parseFloat(row['e_mean']).toFixed(2) + "±" + parseFloat(row['e_std']).toFixed(2) + " MeV<br>";
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- text_spread = "spread = " + parseFloat(row['spread_mean']).toFixed(2) + "±" + parseFloat(row['spread_std']).toFixed(2) + " MeV<br>";
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- text_str = text_energy + text_spread + "<b>Compton</b><br>" + "<i>Start: </i>" +
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- row['compton_start'] + "<br><i>Stop: </i>" + row['compton_stop'] + "<br><br>";
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+ text_energy = "E = " + parseFloat(row['e_mean']).toFixed(2) + "±" + parseFloat(row['e_std']).toFixed(2) + " MeV<br>";
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+ text_spread = "spread = " + parseFloat(row['spread_mean']).toFixed(2) + "±" + parseFloat(row['spread_std']).toFixed(2) + " MeV<br>";
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+ text_str = text_energy + text_spread + "<b>Compton</b><br>" + "<i>Start: </i>" +
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+ row['compton_start'] + "<br><i>Stop: </i>" + row['compton_stop'] + "<br><br>";
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row['text_str'] = text_str + "<b>Runs: </b>" + row['run_first'] + " - " + row['run_last'] + "<br><br>";
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return row;
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}
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-
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- function processSpread(data, elementId, mean_value){
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- let x = [], y = [], std_y = []
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- for (var i=0; i<data.length; i++){
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- const {center_time, spread_mean, spread_std} = parseRow(data[i]);
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+
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+ function processSpread(data, elementId, mean_value) {
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+ let x = [],
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+ y = [],
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+ std_y = []
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+ for (var i = 0; i < data.length; i++) {
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+ const {
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+ center_time,
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+ spread_mean,
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+ spread_std
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+ } = parseRow(data[i]);
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x.push(center_time);
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y.push(spread_mean);
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std_y.push(spread_std);
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}
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makeSpreadPlot(elementId, x, y, std_y, mean_value);
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}
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-
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+
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function kde(x, y, w) {
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- const ts = (t) => t.getTime()/1000;
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+ const ts = (t) => t.getTime() / 1000;
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const toDateTime = (secs) => {
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let t = new Date(0);
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t.setSeconds(secs);
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return t;
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};
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const steps = 1000;
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- const dt = (ts(x[x.length - 1]) - ts(x[0]))/steps;
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+ const dt = (ts(x[x.length - 1]) - ts(x[0])) / steps;
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const kernel = (x, x0, w0, y0) => {
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- if (Math.abs(x-x0)>w0){
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+ if (Math.abs(x - x0) > w0) {
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return 0;
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}
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- return y0/w0;
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+ return y0 / w0;
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//return y0*3*(1-((x-x0)/w0/2)**2)/4;
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- };
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+ };
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const get_est = (timestamp) => {
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let val = 0
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- for (var i=0; i<x.length; i++){
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+ for (var i = 0; i < x.length; i++) {
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val += kernel(timestamp, ts(x[i]), w[i], y[i]);
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}
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return val;
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};
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//console.log(x, y);
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- const timestamp_arr = Plotly.d3.range(steps).map(function(i){return ts(x[0])+i*dt;});
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-
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+ const timestamp_arr = Plotly.d3.range(steps).map(function(i) {
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+ return ts(x[0]) + i * dt;
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+ });
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+
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let kdex = [];
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let kdey = [];
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- for (var j=0; j<timestamp_arr.length; j++){
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+ for (var j = 0; j < timestamp_arr.length; j++) {
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kdex.push(toDateTime(timestamp_arr[j]));
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kdey.push(get_est(timestamp_arr[j]));
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}
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//console.log(kdex, kdey);
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return [kdex, kdey]
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}
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-
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- function oldAverage(E, L){
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+
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+ function oldAverage(E, L) {
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//Averager by the old method with E and L only
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- if (E.length !== L.length){
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+ if (E.length !== L.length) {
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return null;
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}
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let EL = 0;
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let sL = 0;
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- for (let i = 0; i<E.length; i++){
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- EL += parseFloat(E[i])*parseFloat(L[i]);
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+ for (let i = 0; i < E.length; i++) {
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+ EL += parseFloat(E[i]) * parseFloat(L[i]);
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sL += parseFloat(L[i]);
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}
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- return EL/sL;
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+ return EL / sL;
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}
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-
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+
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function processData(allRows, mean_energy, mean_spread, mean_energy_stat_err) {
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// Processes all data rows
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var dict = {};
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@@ -177,44 +168,44 @@ $selected_csv = isset($_GET["csv_file"])&&in_array($_GET["csv_file"], $cleanArrs
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dict['compton'] = [];
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dict['lum'] = [];
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dict['twidth'] = [];
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-
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- for (var i=0; i<allRows.length; i++){
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+
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+ for (var i = 0; i < allRows.length; i++) {
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const row = parseRow(allRows[i]);
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-
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- dict['x'].push( row['center_time'] );
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- dict['e_mean'].push( row['e_mean'] );
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- dict['e_std'].push( row['e_std'] );
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- dict['spread_mean'].push( row['spread_mean'] );
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- dict['spread_std'].push( row['spread_std'] );
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- dict['compton'].push( row['text_str'] );
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- dict['lum'].push( row['luminosity'] );
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- dict['twidth'].push( row['timedelta'] );
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+
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+ dict['x'].push(row['center_time']);
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+ dict['e_mean'].push(row['e_mean']);
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+ dict['e_std'].push(row['e_std']);
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+ dict['spread_mean'].push(row['spread_mean']);
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+ dict['spread_std'].push(row['spread_std']);
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+ dict['compton'].push(row['text_str']);
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+ dict['lum'].push(row['luminosity']);
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+ dict['twidth'].push(row['timedelta']);
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}
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-
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+
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const [a, b] = kde(dict['x'], dict['lum'], dict['twidth']);
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dict['kdex'] = a;
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dict['kdey'] = b;
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//console.log(dict['kdex'], dict['kdey']);
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//oldAverage(y, dict['lum']);
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-
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+
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dict['mean_energy_total'] = mean_energy;
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dict['old_mean_energy_total'] = oldAverage(dict['e_mean'], dict['lum']);
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dict['mean_spread_total'] = mean_spread;
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- dict['mean_energy_stat_err'] = mean_energy_stat_err;
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-
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+ dict['mean_energy_stat_err'] = mean_energy_stat_err;
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+
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makePlotly(dict, "gd");
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}
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-
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- function makePlotly(dict, elementId){
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+
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+ function makePlotly(dict, elementId) {
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const getYRange = (y, std_y) => {
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const ys = [...y].sort();
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const std_ys = [...std_y].sort();
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- let idx = Math.floor(ys.length/2);
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+ let idx = Math.floor(ys.length / 2);
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const y0 = parseFloat(ys[idx]);
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const std0 = parseFloat(std_ys[idx]);
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- return [y0-6*std0, y0+6*std0];
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+ return [y0 - 6 * std0, y0 + 6 * std0];
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};
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-
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+
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var trace1 = {
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x: dict['x'],
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y: dict['e_mean'],
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@@ -268,32 +259,38 @@ $selected_csv = isset($_GET["csv_file"])&&in_array($_GET["csv_file"], $cleanArrs
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type: "scatter",
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};
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var traces = [trace1, trace2, trace3];
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-
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+
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var updatemenus = [];
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- console.log(dict);
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- if (dict['mean_energy_total']){
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+ console.log(dict);
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+ if (dict['mean_energy_total']) {
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updatemenus = [{
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- buttons: [
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- {
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- args:[{'shapes[0].visible': true, 'shapes[1].visible': false, 'title': '<E> = ' + parseFloat(dict['mean_energy_total']).toFixed(3) + '±' + parseFloat(dict['mean_energy_stat_err']).toFixed(3) + ' MeV, ' + '<Spread> = ' + parseFloat(dict['mean_spread_total']).toFixed(3) + ' MeV',}],
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+ buttons: [{
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+ args: [{
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+ 'shapes[0].visible': true,
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+ 'shapes[1].visible': false,
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+ 'title': '<E> = ' + parseFloat(dict['mean_energy_total']).toFixed(3) + '±' + parseFloat(dict['mean_energy_stat_err']).toFixed(3) + ' MeV, ' + '<Spread> = ' + parseFloat(dict['mean_spread_total']).toFixed(3) + ' MeV',
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+ }],
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label: 'Current average method',
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method: 'relayout'
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}, {
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- args:[{'shapes[0].visible': false, 'shapes[1].visible': true, 'title': '<E> = ' + parseFloat(dict['old_mean_energy_total']).toFixed(3) + ' MeV, ' + '<Spread> = ' + parseFloat(dict['mean_spread_total']).toFixed(3) + ' MeV',}],
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+ args: [{
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+ 'shapes[0].visible': false,
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+ 'shapes[1].visible': true,
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+ 'title': '<E> = ' + parseFloat(dict['old_mean_energy_total']).toFixed(3) + ' MeV, ' + '<Spread> = ' + parseFloat(dict['mean_spread_total']).toFixed(3) + ' MeV',
|
|
|
+ }],
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|
|
label: 'Former average method',
|
|
|
method: 'relayout'
|
|
|
- },
|
|
|
- ],
|
|
|
- direction: 'center',
|
|
|
- showactive: 'true',
|
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|
- type: 'dropdown',
|
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|
- y: 1.1,
|
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|
- xanchor: 'left',
|
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|
- yanchor: 'top',
|
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|
- active: 0,
|
|
|
- }];
|
|
|
+ }, ],
|
|
|
+ direction: 'center',
|
|
|
+ showactive: 'true',
|
|
|
+ type: 'dropdown',
|
|
|
+ y: 1.1,
|
|
|
+ xanchor: 'left',
|
|
|
+ yanchor: 'top',
|
|
|
+ active: 0,
|
|
|
+ }];
|
|
|
}
|
|
|
-
|
|
|
+
|
|
|
var layout = {
|
|
|
title: '<E> = ' + parseFloat(dict['mean_energy_total']).toFixed(3) + '±' + parseFloat(dict['mean_energy_stat_err']).toFixed(3) + ' MeV, ' + '<Spread> = ' + parseFloat(dict['mean_spread_total']).toFixed(3) + ' MeV',
|
|
|
updatemenus: updatemenus,
|
|
|
@@ -333,55 +330,59 @@ $selected_csv = isset($_GET["csv_file"])&&in_array($_GET["csv_file"], $cleanArrs
|
|
|
plot_bgcolor: 'rgba(0,0,0,0)',
|
|
|
autosize: true,
|
|
|
};
|
|
|
-
|
|
|
- if (dict['mean_energy_total']){
|
|
|
+
|
|
|
+ if (dict['mean_energy_total']) {
|
|
|
layout['shapes'] = [{
|
|
|
- type: 'line',
|
|
|
- yref: 'y3',
|
|
|
- xref: 'paper',
|
|
|
- x0: 0,
|
|
|
- x1: 1,
|
|
|
- y0: dict['mean_energy_total'],
|
|
|
- y1: dict['mean_energy_total'],
|
|
|
- line: {
|
|
|
- color: '#590A0A',
|
|
|
+ type: 'line',
|
|
|
+ yref: 'y3',
|
|
|
+ xref: 'paper',
|
|
|
+ x0: 0,
|
|
|
+ x1: 1,
|
|
|
+ y0: dict['mean_energy_total'],
|
|
|
+ y1: dict['mean_energy_total'],
|
|
|
+ line: {
|
|
|
+ color: '#590A0A',
|
|
|
+ },
|
|
|
},
|
|
|
- },
|
|
|
- {
|
|
|
- type: 'line',
|
|
|
- yref: 'y3',
|
|
|
- xref: 'paper',
|
|
|
- x0: 0,
|
|
|
- x1: 1,
|
|
|
- y0: dict['old_mean_energy_total'],
|
|
|
- y1: dict['old_mean_energy_total'],
|
|
|
- line: {
|
|
|
- color: '#590A0A',
|
|
|
- },
|
|
|
- visible: false,
|
|
|
- },
|
|
|
- {
|
|
|
- type: 'line',
|
|
|
- yref: 'y2',
|
|
|
- xref: 'paper',
|
|
|
- x0: 0,
|
|
|
- x1: 1,
|
|
|
- y0: dict['mean_spread_total'],
|
|
|
- y1: dict['mean_spread_total'],
|
|
|
- line: {
|
|
|
- color: '#590A0A',
|
|
|
+ {
|
|
|
+ type: 'line',
|
|
|
+ yref: 'y3',
|
|
|
+ xref: 'paper',
|
|
|
+ x0: 0,
|
|
|
+ x1: 1,
|
|
|
+ y0: dict['old_mean_energy_total'],
|
|
|
+ y1: dict['old_mean_energy_total'],
|
|
|
+ line: {
|
|
|
+ color: '#590A0A',
|
|
|
+ },
|
|
|
+ visible: false,
|
|
|
},
|
|
|
- visible: true,
|
|
|
- }];
|
|
|
+ {
|
|
|
+ type: 'line',
|
|
|
+ yref: 'y2',
|
|
|
+ xref: 'paper',
|
|
|
+ x0: 0,
|
|
|
+ x1: 1,
|
|
|
+ y0: dict['mean_spread_total'],
|
|
|
+ y1: dict['mean_spread_total'],
|
|
|
+ line: {
|
|
|
+ color: '#590A0A',
|
|
|
+ },
|
|
|
+ visible: true,
|
|
|
+ }
|
|
|
+ ];
|
|
|
}
|
|
|
-
|
|
|
- Plotly.newPlot('gd', traces, layout, {modeBarButtonsToRemove: ['toImage'], responsive: true,});
|
|
|
-
|
|
|
+
|
|
|
+ Plotly.newPlot('gd', traces, layout, {
|
|
|
+ modeBarButtonsToRemove: ['toImage'],
|
|
|
+ responsive: true,
|
|
|
+ });
|
|
|
+
|
|
|
}
|
|
|
-
|
|
|
+
|
|
|
makeplot();
|
|
|
-
|
|
|
</script>
|
|
|
|
|
|
</body>
|
|
|
-</html>
|
|
|
+
|
|
|
+</html>
|
