drbobbeaty · September 25, 2012 13:57 · fatso83 · Oct 3, 2013
diff --git a/unpinned_merchants.html b/unpinned_merchants.html
 <html>
  <head>
    <title>Unpinned Merchants</title>
    <script type='text/javascript' src='https://www.google.com/jsapi'></script>
    <script type='text/javascript' src='zingchart/resources/jquery.min.js'></script>
    <script type='text/javascript'>
    google.load('visualization', '1', {packages:['table']});
    google.setOnLoadCallback(reload_executions);
    // set up the fixed locations and paths for this metric visualization.
    // we need to be able to pick the server (prod, uat, dev).
    var view_loc = '_design/pinning/_view/unpinned';
    var opts = '&reduce=false'
    // get the date a week ago formatted as YYYY-MM-DD
    var when = new Date();
    when.setDate(when.getDate() - 7);
    var weekAgo = when.getFullYear()+'-'
                  +('0'+(when.getMonth()+1)).substr(-2,2)+'-'
                  +('0'+when.getDate()).substr(-2,2);
    // these will be the data sets we can get from the selected database
    var divisions = new Array();
    var runtimes = new Object();
    // this is the Google DataTable we'll be creating each time
    var dtable = null;

    // the Google Table needs to have a few config parameters to make it
    // look like we want it to look.
    var table_config = {
      showRowNumber: true,
      width: '700px',
      height: '503px'
    };
    // we can use this format spec to format the sales value column once
    // we have loaded up the table.
    var sv_format = {
      prefix: '$',
      pattern: '#,###.00'
    };

    // This method looks at the selected data set and loads that into
    // a new table for the target div and redraws it.
    function render(tbl) {
      // save this data table for later
      dtable = tbl;
      // now create a Google Table and populate it with this data
      var dest = document.getElementById('table_div');
      var table = new google.visualization.Table(dest);
      table.draw(tbl, table_config);
    }

    // this downloads the current data table as a CSV file to the client
    function toCSV() {
      var data = dtable;
      var csvData = [];
      var tmpArr = [];
      var tmpStr = '';
      for (var i = 0; i < data.getNumberOfColumns(); i++) {
        // replace double-quotes with double-double quotes for CSV compatibility
        tmpStr = data.getColumnLabel(i).replace(/"/g, '""');
        tmpArr.push('"' + tmpStr + '"');
      }
      csvData.push(tmpArr);
      for (var i = 0; i < data.getNumberOfRows(); i++) {
        tmpArr = [];
        for (var j = 0; j < data.getNumberOfColumns(); j++) {
          switch(data.getColumnType(j)) {
            case 'string':
              // replace double-quotes with double-double quotes for CSV compat
              tmpStr = data.getValue(i, j).replace(/"/g, '""');
              tmpArr.push('"' + tmpStr + '"');
              break;
            case 'number':
              tmpArr.push(data.getValue(i, j));
              break;
            case 'boolean':
              tmpArr.push((data.getValue(i, j)) ? 'True' : 'False');
              break;
            case 'date':
              // decide what to do here, as there is no universal date format
              break;
            case 'datetime':
              // decide what to do here, as there is no universal date format
              break;
            case 'timeofday':
              // decide what to do here, as there is no universal date format
              break;
            default:
              // should never trigger
          }
        }
        csvData.push(tmpArr.join(','));
      }
      var output = csvData.join('\n');
      var uri = 'data:application/csv;charset=UTF-8,' + encodeURIComponent(output);
      window.open(uri);
    }

    // This function takes the data coming from CouchDB and formats it
    // into a series of nice DataTable objects for Google's tools.
    // There will be one set per run (execution_tag), and we'll organize
    // it that way for easy retrieval.
    function parse_series(data) {
      var table = new google.visualization.DataTable();
      table.addColumn('string', 'Merchant');
      table.addColumn('string', 'Category');

      for(var i in data.rows) {
        table.addRow([data.rows[i].value.name,
                      data.rows[i].value.taxonomy.category]);
      }
      return table;
    }

    // This method simply hits the selected database (on the server)
    // for the proper CouchDB view, and then processes it into a series
    // of ZingCharts data sets that we then render the first one.
    function reload() {
      // hit CouchDB for the view we need to process
      var svr_opt = document.getElementById('server_opt');
      var div_opt = document.getElementById('division_opt');
      var run_opt = document.getElementById('run_opt');
      var et = run_opt.value + '-' + div_opt.value;
      var url = svr_opt.value + '/' + view_loc + '?' +
                'startkey=' + encodeURI(JSON.stringify([et])) +
                '&endkey=' +  encodeURI(JSON.stringify([et,{}])) + opts + '&callback=?';
      $.getJSON(url, function(data) {
        var series = parse_series(data);
        render(series);
      });
    }

    // When we change divisions we need to update the available run times
    // for the new division, and in order to do that, we have this method.
    function set_runs_for_division(division) {
      division = (typeof(division) !== 'undefined' ? division : document.getElementById('division_opt').value);
      runtimes[division].sort();
      runtimes[division].reverse();
      var run_opt = document.getElementById('run_opt');
      run_opt.options.length = 0;
      for (var i in runtimes[division]) {
        var tag = runtimes[division][i];
        run_opt.options[run_opt.options.length] = new Option(tag, tag);
      }
      // at this point, call back to the the data we need, and then render it
      reload();
    }

    // This function takes the list of executions currently loaded on the database
    // and parses their 'execution_tag's into divisions and times and places them
    // in the datastructre to make it much easier to manipulate.
    function parse_execution_tags(data) {
      divisions = new Array();
      runtimes = new Object();

      for(var i in data.rows) {
        // get the execution_tag and exclude the very early ones
        var exec_tag = data.rows[i].key;
        if (!/-\D+$/i.test(exec_tag) || (exec_tag.substring(0,10) < weekAgo)) {
          continue;
        }
        // now get the timestamp and division from the execution_tag
        var runtime = exec_tag.replace(/-\D+/g, '');
        var division = exec_tag.replace(/^.*\.\d\d\d-/g, '');
        if (typeof(runtimes[division]) == 'undefined') {
          runtimes[division] = new Array();
          divisions.push(division);
        }
        runtimes[division].push(runtime);
      }

      // sort the divisions and create the contents of the drop-down
      if (divisions.length > 0) {
        divisions.sort();
        var div_opt = document.getElementById('division_opt');
        div_opt.options.length = 0;
        for (var d in divisions) {
          div_opt.options[div_opt.options.length] = new Option(divisions[d], divisions[d]);
        }
      }

      // given the default division, load up the run times we just parsed
      set_runs_for_division(divisions[0]);
    }

    // When we change a database, we need to reload all the known run (executions)
    // that exist on that database. Then, we can populate the 'division' and 'run'
    // in a nested datastructure so that it's each to update the run times for a
    // given division.
    function reload_executions() {
      // hit CouchDB for the view of all executions it knowns about
      var svr_opt = document.getElementById('server_opt');
      var url = svr_opt.value + '/_design/general/_view/executions?descending=true&callback=?';
      $.getJSON(url, function(data) {
        parse_execution_tags(data);
      });
    }
 		</script>
  </head>
  <body>
    <p align="center">
      Database:
      <select id="server_opt" onchange="reload()">
        <option value='/db/production' selected="selected">Production</option>
        <option value='/db/uat'>UAT</option>
        <option value='/db/dev'>Dev on UAT</option>
      </select>
      Division:
      <select id="division_opt" onchange="set_runs_for_division(this.value)">
      </select>
      Run:
      <select id="run_opt" onchange="reload()">
      </select>
    </p>
    <div id='table_div' style="width:700px; margin-top:10px; margin-left:auto; margin-right:auto;"></div>
    <p align="center">
      <input type="button" id="toCSV" value="Click to download data as CSV" onclick="toCSV()" />
    </p>
  </body>
 </html>
	<html>
	<head>
	<title>Unpinned Merchants</title>
	<script type='text/javascript' src='https://www.google.com/jsapi'></script>
	<script type='text/javascript' src='zingchart/resources/jquery.min.js'></script>
	<script type='text/javascript'>
	google.load('visualization', '1', {packages:['table']});
	google.setOnLoadCallback(reload_executions);
	// set up the fixed locations and paths for this metric visualization.
	// we need to be able to pick the server (prod, uat, dev).
	var view_loc = '_design/pinning/_view/unpinned';
	var opts = '&reduce=false'
	// get the date a week ago formatted as YYYY-MM-DD
	var when = new Date();
	when.setDate(when.getDate() - 7);
	var weekAgo = when.getFullYear()+'-'
	+('0'+(when.getMonth()+1)).substr(-2,2)+'-'
	+('0'+when.getDate()).substr(-2,2);
	// these will be the data sets we can get from the selected database
	var divisions = new Array();
	var runtimes = new Object();
	// this is the Google DataTable we'll be creating each time
	var dtable = null;

	// the Google Table needs to have a few config parameters to make it
	// look like we want it to look.
	var table_config = {
	showRowNumber: true,
	width: '700px',
	height: '503px'
	};
	// we can use this format spec to format the sales value column once
	// we have loaded up the table.
	var sv_format = {
	prefix: '$',
	pattern: '#,###.00'
	};

	// This method looks at the selected data set and loads that into
	// a new table for the target div and redraws it.
	function render(tbl) {
	// save this data table for later
	dtable = tbl;
	// now create a Google Table and populate it with this data
	var dest = document.getElementById('table_div');
	var table = new google.visualization.Table(dest);
	table.draw(tbl, table_config);
	}

	// this downloads the current data table as a CSV file to the client
	function toCSV() {
	var data = dtable;
	var csvData = [];
	var tmpArr = [];
	var tmpStr = '';
	for (var i = 0; i < data.getNumberOfColumns(); i++) {
	// replace double-quotes with double-double quotes for CSV compatibility
	tmpStr = data.getColumnLabel(i).replace(/"/g, '""');
	tmpArr.push('"' + tmpStr + '"');
	}
	csvData.push(tmpArr);
	for (var i = 0; i < data.getNumberOfRows(); i++) {
	tmpArr = [];
	for (var j = 0; j < data.getNumberOfColumns(); j++) {
	switch(data.getColumnType(j)) {
	case 'string':
	// replace double-quotes with double-double quotes for CSV compat
	tmpStr = data.getValue(i, j).replace(/"/g, '""');
	tmpArr.push('"' + tmpStr + '"');
	break;
	case 'number':
	tmpArr.push(data.getValue(i, j));
	break;
	case 'boolean':
	tmpArr.push((data.getValue(i, j)) ? 'True' : 'False');
	break;
	case 'date':
	// decide what to do here, as there is no universal date format
	break;
	case 'datetime':
	// decide what to do here, as there is no universal date format
	break;
	case 'timeofday':
	// decide what to do here, as there is no universal date format
	break;
	default:
	// should never trigger
	}
	}
	csvData.push(tmpArr.join(','));
	}
	var output = csvData.join('\n');
	var uri = 'data:application/csv;charset=UTF-8,' + encodeURIComponent(output);
	window.open(uri);
	}

	// This function takes the data coming from CouchDB and formats it
	// into a series of nice DataTable objects for Google's tools.
	// There will be one set per run (execution_tag), and we'll organize
	// it that way for easy retrieval.
	function parse_series(data) {
	var table = new google.visualization.DataTable();
	table.addColumn('string', 'Merchant');
	table.addColumn('string', 'Category');

	for(var i in data.rows) {
	table.addRow([data.rows[i].value.name,
	data.rows[i].value.taxonomy.category]);
	}
	return table;
	}

	// This method simply hits the selected database (on the server)
	// for the proper CouchDB view, and then processes it into a series
	// of ZingCharts data sets that we then render the first one.
	function reload() {
	// hit CouchDB for the view we need to process
	var svr_opt = document.getElementById('server_opt');
	var div_opt = document.getElementById('division_opt');
	var run_opt = document.getElementById('run_opt');
	var et = run_opt.value + '-' + div_opt.value;
	var url = svr_opt.value + '/' + view_loc + '?' +
	'startkey=' + encodeURI(JSON.stringify([et])) +
	'&endkey=' + encodeURI(JSON.stringify([et,{}])) + opts + '&callback=?';
	$.getJSON(url, function(data) {
	var series = parse_series(data);
	render(series);
	});
	}

	// When we change divisions we need to update the available run times
	// for the new division, and in order to do that, we have this method.
	function set_runs_for_division(division) {
	division = (typeof(division) !== 'undefined' ? division : document.getElementById('division_opt').value);
	runtimes[division].sort();
	runtimes[division].reverse();
	var run_opt = document.getElementById('run_opt');
	run_opt.options.length = 0;
	for (var i in runtimes[division]) {
	var tag = runtimes[division][i];
	run_opt.options[run_opt.options.length] = new Option(tag, tag);
	}
	// at this point, call back to the the data we need, and then render it
	reload();
	}

	// This function takes the list of executions currently loaded on the database
	// and parses their 'execution_tag's into divisions and times and places them
	// in the datastructre to make it much easier to manipulate.
	function parse_execution_tags(data) {
	divisions = new Array();
	runtimes = new Object();

	for(var i in data.rows) {
	// get the execution_tag and exclude the very early ones
	var exec_tag = data.rows[i].key;
	if (!/-\D+$/i.test(exec_tag) \|\| (exec_tag.substring(0,10) < weekAgo)) {
	continue;
	}
	// now get the timestamp and division from the execution_tag
	var runtime = exec_tag.replace(/-\D+/g, '');
	var division = exec_tag.replace(/^.*\.\d\d\d-/g, '');
	if (typeof(runtimes[division]) == 'undefined') {
	runtimes[division] = new Array();
	divisions.push(division);
	}
	runtimes[division].push(runtime);
	}

	// sort the divisions and create the contents of the drop-down
	if (divisions.length > 0) {
	divisions.sort();
	var div_opt = document.getElementById('division_opt');
	div_opt.options.length = 0;
	for (var d in divisions) {
	div_opt.options[div_opt.options.length] = new Option(divisions[d], divisions[d]);
	}
	}

	// given the default division, load up the run times we just parsed
	set_runs_for_division(divisions[0]);
	}

	// When we change a database, we need to reload all the known run (executions)
	// that exist on that database. Then, we can populate the 'division' and 'run'
	// in a nested datastructure so that it's each to update the run times for a
	// given division.
	function reload_executions() {
	// hit CouchDB for the view of all executions it knowns about
	var svr_opt = document.getElementById('server_opt');
	var url = svr_opt.value + '/_design/general/_view/executions?descending=true&callback=?';
	$.getJSON(url, function(data) {
	parse_execution_tags(data);
	});
	}
	</script>
	</head>
	<body>
	<p align="center">
	Database:
	<select id="server_opt" onchange="reload()">
	<option value='/db/production' selected="selected">Production</option>
	<option value='/db/uat'>UAT</option>
	<option value='/db/dev'>Dev on UAT</option>
	</select>
	Division:
	<select id="division_opt" onchange="set_runs_for_division(this.value)">
	</select>
	Run:
	<select id="run_opt" onchange="reload()">
	</select>
	</p>
	<div id='table_div' style="width:700px; margin-top:10px; margin-left:auto; margin-right:auto;"></div>
	<p align="center">
	<input type="button" id="toCSV" value="Click to download data as CSV" onclick="toCSV()" />
	</p>
	</body>
	</html>