Add frontend assets and plugin bundles

Add the legacy frontend themes, scripts, and plugin assets required by the main SPOTA interfaces.

assets/plugins/flot/jquery.flot.categories.min.js

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+/* Flot plugin for plotting textual data or categories.
+
+Copyright (c) 2007-2013 IOLA and Ole Laursen.
+Licensed under the MIT license.
+
+Consider a dataset like [["February", 34], ["March", 20], ...]. This plugin
+allows you to plot such a dataset directly.
+
+To enable it, you must specify mode: "categories" on the axis with the textual
+labels, e.g.
+
+	$.plot("#placeholder", data, { xaxis: { mode: "categories" } });
+
+By default, the labels are ordered as they are met in the data series. If you
+need a different ordering, you can specify "categories" on the axis options
+and list the categories there:
+
+	xaxis: {
+		mode: "categories",
+		categories: ["February", "March", "April"]
+	}
+
+If you need to customize the distances between the categories, you can specify
+"categories" as an object mapping labels to values
+
+	xaxis: {
+		mode: "categories",
+		categories: { "February": 1, "March": 3, "April": 4 }
+	}
+
+If you don't specify all categories, the remaining categories will be numbered
+from the max value plus 1 (with a spacing of 1 between each).
+
+Internally, the plugin works by transforming the input data through an auto-
+generated mapping where the first category becomes 0, the second 1, etc.
+Hence, a point like ["February", 34] becomes [0, 34] internally in Flot (this
+is visible in hover and click events that return numbers rather than the
+category labels). The plugin also overrides the tick generator to spit out the
+categories as ticks instead of the values.
+
+If you need to map a value back to its label, the mapping is always accessible
+as "categories" on the axis object, e.g. plot.getAxes().xaxis.categories.
+
+*/(function(e){function n(e,t,n,r){var i=t.xaxis.options.mode=="categories",s=t.yaxis.options.mode=="categories";if(!i&&!s)return;var o=r.format;if(!o){var u=t;o=[],o.push({x:!0,number:!0,required:!0}),o.push({y:!0,number:!0,required:!0});if(u.bars.show||u.lines.show&&u.lines.fill){var a=!!(u.bars.show&&u.bars.zero||u.lines.show&&u.lines.zero);o.push({y:!0,number:!0,required:!1,defaultValue:0,autoscale:a}),u.bars.horizontal&&(delete o[o.length-1].y,o[o.length-1].x=!0)}r.format=o}for(var f=0;f<o.length;++f)o[f].x&&i&&(o[f].number=!1),o[f].y&&s&&(o[f].number=!1)}function r(e){var t=-1;for(var n in e)e[n]>t&&(t=e[n]);return t+1}function i(e){var t=[];for(var n in e.categories){var r=e.categories[n];r>=e.min&&r<=e.max&&t.push([r,n])}return t.sort(function(e,t){return e[0]-t[0]}),t}function s(t,n,r){if(t[n].options.mode!="categories")return;if(!t[n].categories){var s={},u=t[n].options.categories||{};if(e.isArray(u))for(var a=0;a<u.length;++a)s[u[a]]=a;else for(var f in u)s[f]=u[f];t[n].categories=s}t[n].options.ticks||(t[n].options.ticks=i),o(r,n,t[n].categories)}function o(e,t,n){var i=e.points,s=e.pointsize,o=e.format,u=t.charAt(0),a=r(n);for(var f=0;f<i.length;f+=s){if(i[f]==null)continue;for(var l=0;l<s;++l){var c=i[f+l];if(c==null||!o[l][u])continue;c in n||(n[c]=a,++a),i[f+l]=n[c]}}}function u(e,t,n){s(t,"xaxis",n),s(t,"yaxis",n)}function a(e){e.hooks.processRawData.push(n),e.hooks.processDatapoints.push(u)}var t={xaxis:{categories:null},yaxis:{categories:null}};e.plot.plugins.push({init:a,options:t,name:"categories",version:"1.0"})})(jQuery);