var id1 = variables.id1; var id2 = variables.id2; var name1 = variables.name1; var name2 = variables.name2; var currYear = variables.currYear; var cubeName = variables.cubeName; var countryISO = variables.countryISO; var monthNumberDict = variables.monthNumberDict; var hierarchy = variables.hierarchy; var id = variables.id; var abbreviate = formatters.abbreviate; var titleCase = formatters.titleCase; var timeScale = (countryISO === "fra") ? "Quarter" : "Month"; var periods = variables["period0"]; var periodsName = variables["period0"]; if (periods === "6 Months") { periods = 6 } else if (periods === "1 Year"){ periods = 12 } else if (periods === "3 Years"){ periods = 36 } else if (periods === "5 Years"){ periods = 60 } var dollar = formatters.dollar; var euro = formatters.euro; var sterling = formatters.sterling; var yen = formatters.yen; var cad = formatters.cad; var rands = formatters.rands; var coin = dollar if (countryISO === "deu") {coin = euro} else if (countryISO === "can") {coin = cad} else if (countryISO === "jpn") {coin = yen} else if (countryISO === "esp") {coin = euro} else if (countryISO === "fra") {coin = euro} else if (countryISO === "gbr") {coin = sterling} else if (countryISO === "zaf") {coin = rands} return { "data": [ ("/api/stats/prophet?" + hierarchy + "=" + id + "&cube=" + cubeName + "&drilldowns=" + timeScale + "&locale=&measures=Trade+Value&parents=true&sparse=false&periods=" + periods + "&Trade+Flow=1"), ("/api/stats/prophet?" + hierarchy + "=" + id + "&cube=" + cubeName + "&drilldowns=" + timeScale + "&locale=&measures=Trade+Value&parents=true&sparse=false&periods=" + periods + "&Trade+Flow=2") ], "dataFormat": function (ref) { var resp1 = ref[0]; var resp2 = ref[1]; var data = resp1.data; var prevData = [].concat(resp1.data).slice(0).filter(function (d) { return d["Trade Value"]; }); var data2 = resp2.data; var prevData2 = [].concat(resp2.data).slice(0).filter(function (d) { return d["Trade Value"]; }); data = data.map(function (d) { return Object.assign({}, d, { type: "Imports (Prediction)", value: (d["Trade Value Prediction"]), legendLabel: ("Imports from " + name1 + " (Predicted)"), shape: "Line", icon: "/images/icons/balance/import_val.png", color: "#710000"//"#823C74" }); }); prevData = prevData.map(function (h) { return (Object.assign({}, h, { type: "Imports (Observed)", value: (h["Trade Value"]), legendLabel: ("Imports from " + name1 + " (Observed)"), shape: "Line", icon: "/images/icons/balance/import_val.png", color: "#942217" })); }); data2 = data2.map(function (d) { return Object.assign({}, d, { type: "Exports (Prediction)", value: (d["Trade Value Prediction"]), legendLabel: ("Exports from " + name1 + " (Predicted)"), shape: "Line", icon: "/images/icons/balance/export_val.png", color: "#005A7E"//""#006571"//"#1BBB83" }); }); prevData2 = prevData2.map(function (h) { return (Object.assign({}, h, { type: "Exports (Observed)", value: (h["Trade Value"]), legendLabel: ("Exports from " + name1 + " (Observed)"), shape: "Line", icon: "/images/icons/balance/export_val.png", color: "#4495bc" //"#008D53"//"#00A58D" })); }); return data.concat(prevData).concat(data2.concat(prevData2)); }, "confidence": [ function (d) { return d.type.includes("Prediction") ? d["Trade Value Lower Bound"] : d["Trade Value"]; }, function (d) { return d.type.includes("Prediction") ? d["Trade Value Upper Bound"] : d["Trade Value"]; } ], "confidenceConfig": { fillOpacity: 0.2, fill: function (d) { return d.color; } }, "groupBy": ["type"], "total": undefined, "type": "Plot", "discrete": "x", "legendConfig": { "label": function (d) { return d.type; }, shapeConfig: { fontColor: function () { return "#ffffff"; }, height: function () { return 25; }, width: function () { return 25; }, backgroundImage: function (d) { //const tfId = d["Trade Flow ID"] === 1 ? "import" : "export"; return ("" + (d.icon)); }, labelConfig: { fontSize: function () { return 16; }, fontColor: function () { return "#ffffff"; } } }, //"shapeConfig": { // "fontColor": () => "#blue", // "labelConfig": { // fontColor: () => "#FFFFFF", // } //} }, "shape": function (d) { return d.shape; }, "shapeConfig": { sort: function (a, b) { return a.type.includes("Observed") ? 1 : -1; }, Circle: { fill: function (d) { return d.color; }, fillOpacity: 1 }, Line: { stroke: function (d) { return d.color; }, strokeDasharray: function (d) { return d.type.includes("Prediction") ? "10" : "0"; }, strokeWidth: function (d) { return d.type ? d.type.includes("Prediction") ? "2" : "2" : "2"; } }, }, "legendTooltip":{ "tbody":[], "title": function (d) { //const imgUrl = `/images/icons/balance/${(d.tradeflow === "2") ? "export_val.png" : "import_val.png"}`; return ("\n \n ") }, }, "tooltipConfig": { //"title": d => d["type"], "title": function (d) { //const imgUrl = `/images/icons/balance/${(d.tradeflow === "2") ? "export_val.png" : "import_val.png"}`; return ("\n \n ") }, "tbody": [ ["Month", function (d) { return monthNumberDict[d["Date"].slice(5,7)]; }], ["Year", function (d) { return ("" + (d["Date"].substr(0, 4))); }], //[`Date`, d => d[`Date`]], [function (d) { return d.type.includes("Imports") ? "Imports Value" : "Exports Value"; }, function (d) { return d.type.includes("Prediction") ? coin(d["Trade Value Prediction"]) : coin(d["Trade Value"]); }] ] }, "title": ("Trade Forecasts for " + periodsName), "x": "Date", "time": "Date", "timeline": false, "y": "value", "yConfig": { "tickFormat": function (d) { return coin(d); } }, }

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