我正在使用功能组图——散点图。图表上显示的图例（系列）会自动按照首次出现的值排序，因此它看起来像一个随机数（附图）。我想通过从小到大对值进行排序来自定义图例，但一无所获（案例：1 - 11）。有没有人有办法解决吗？
原始代码：
var target_image = ee.Image("users/noahdornier/clustering_indo_1000"), world = ee.Image("users/noahdornier/WORLD_1000M"), aoi = /* color: #d63000 */ /* shown: false */ ee.Geometry.MultiPolygon( [[[[102.75002721516972, 1.4680288033344118], [100.70657018391972, 2.8951792673714145], [100.33303502766972, 2.8951792673714145], [97.49856237141972, 5.393312517501195], [95.12551549641972, 6.0601299904573915], [94.90578893391972, 5.513615104828367], [95.83962682454472, 4.057567062888153], [96.68557409016972, 3.4875182905271256], [97.55349401204472, 2.313507800956766], [98.77297643391972, 0.699129134064055], [99.16848424641972, -0.09186910187547472], [100.99221471516972, -2.7056091356781256], [104.05740026204472, -5.772994218064749], [105.27688268391972, -7.072133582451994], [107.03469518391972, -7.791135467379643], [117.31789830891972, -9.236175756560042], [119.06472448079472, -8.910710527895949], [121.45974401204472, -9.095177102155404], [125.25002721516972, -8.411103906919744], 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0.4032852058161235], [97.04956515511151, 3.1263453887943085], [95.02808078011151, 3.2141015008427587], [96.23657687386151, 2.006881952438559], [97.99438937386151, -0.4096962540592795], [99.84009249886151, -3.615306120450092], [100.67505343636151, -3.7249440696109857]]], [[[115.59448703011151, 4.529422807622796], [114.34204562386151, 1.8092377906205146], [112.16675265511151, 1.7213888877333188], [110.51880343636151, 1.282087779087179], [109.42017062386151, 2.621611575144256], [108.12378390511151, 5.0986941574905105], [107.44263156136151, 4.485613417733092], [107.88208468636151, 3.4773237331185536], [107.57446749886151, 2.9508113440379873], [109.02466281136151, 1.919042912195218], [108.43140109261151, 0.5131449569469554], [108.49731906136151, -1.8595685043657795], [109.70581515511151, -2.0791651491500165], [109.79370578011151, -3.1327470370299695], [115.02319796761151, -4.4043790834229775], [115.63843234261151, -5.170733075792206], [116.45142062386151, -4.251009078762999], 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[124.82300265511151, 0.3593408675863514], [125.54810031136151, 1.282087779087179], [126.09741671761151, 3.4115248458765195], [127.56958468636151, 3.784323102607278]]], [[[141.1267135926115, -9.4197108929551], [141.1047409363615, -2.3645946322802285], [138.1384323426115, -1.0907861927655182], [134.8864792176115, -0.27786271874933127], [131.9201706238615, -0.058137235446128754], [131.3928268738615, 1.457818715679925], [130.5139206238615, 0.24947915374714633], [129.1296432801115, 0.7767998935047579], [129.1296432801115, 2.906923436614606], [127.54761203011151, 2.42404943787239], [126.16333468636151, 1.304054839767967], [125.96558078011151, 0.7328583956222846], [127.02026828011151, 1.3699548585860928], [127.04224093636151, 0.3813130647399897], [126.77856906136151, -0.8271514811527663], [127.37183078011151, -2.0352481611992785], [129.0637253113615, -1.881529451829153], [130.2941940613615, -2.4524076065961915], [131.9201706238615, -3.1327470370299695], [132.7331589051115, 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-9.744701766492971], [121.04370578011151, -10.91197781716022], [122.91138156136151, -11.278530407999517], [124.46505708754266, -10.254535925842475], [125.72388156136151, -9.202879619423301], [124.95483859261151, -8.768821017436395]]]]), image = ee.Image("users/insgis/noah/moef_image_v1"); Map.addLayer(target_image); Map.addLayer(world); function generatesScatter(feature, xProperty, yProperty, series, title, hAxis_title, xAxis_title, hMinMax, vMinMax) { var scatter_chart = ui.Chart.feature.groups(feature, xProperty, yProperty, series) .setChartType('ScatterChart') .setOptions({ title: title, pointSize: 2, dataOpacity: 0.4, hAxis: { viewWindow: { min: hMinMax[0], max: hMinMax[1], }, 'title': hAxis_title, titleTextStyle: { italic: false, bold: true }, }, vAxis: { viewWindow: { min: vMinMax[0], max: vMinMax[1], }, 'title': xAxis_title, titleTextStyle: { italic: false, bold: true } }, }); return scatter_chart } function stratifiedSampling(img_input, numPoints, classBand, classVal, pts, scale, region) { // Sampling var str_point = img_input.stratifiedSample({ numPoints: numPoints, classBand: classBand, // Class to be sampled classValues: classVal, // Points each class classPoints: [pts, pts, pts, pts, pts, pts, pts, pts, pts, pts, pts], scale: scale, // region: region, geometries: false }) return str_point } // Class references // Define a dictionary that maps the original values to new numeric values var classDict = ee.Dictionary({ "Bush / Shrub": 1, "Swamp Shrub": 2, "Plantation Forest": 3, "Primary Swamp Forest": 4, "Secondary Swamp Forest": 5, "Primary Mangrove Forest": 6, "Dryland Agriculture": 7, "Secondary Mangrove Forest": 8, "Primary Dry Land Forest": 9, "Secondary Dry Land Forest": 10, "Shrub-Mixed Dryland Farm": 11 }); // List of class var classList = classDict.keys() // List of class values // Using for remapping raster value from 0 to 10 --> 1 to 11 var valueList = classDict.values().sort() // SEA var SEA_select = world.select('VV_mean', 'VV_stdDev', 'VH_mean', 'VH_stdDev'); // K-Means Clustering // Create training dataset. var training = SEA_select.sample({ region: aoi, scale: 1000, numPixels: 10000 }); // Start unsupervised clusterering algorithm and train it. var kmeans = ee.Clusterer.wekaKMeans(11).train(training); // Cluster the input using the trained clusterer. var result = SEA_select.cluster(kmeans); // Remapping class raster var cluster_image = result.select("cluster") .remap([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], valueList) .rename("cluster") // Add SEA Band to image input var cluster_concat = cluster_image.addBands(world) // Cluster - Str sampling var cluster_sample = stratifiedSampling( cluster_concat, 1100, "cluster", valueList, 100, 1000, aoi ) // Cluster - mean chart var cluster_mean_chart = generatesScatter( cluster_sample, "VV_mean", "VH_mean", "cluster", "VV Mean .vs VH Mean - Cluster Results", "VV Mean", "VH Mean", [-22, -1.5], [-30, -5] ) print("Cluster Mean:",cluster_mean_chart)
解决方案：
很多时候我们会出现上面的问题，如果我们时需要按照图例的大小进行排序的话，我们就会遇到上面的问题，要更改默认行为，您必须跳过一些步骤才能以正确的排序顺序创建 Google Charts API 数据表。
修改后的代码：
// We create a list of rows for the data table // Each row would contain 12 values, 1 X-axis valuie and 11 Y-axis series values // Only 1 of the Y-axis series will be populated var rows = valueList.map(function(cluster) { var features = cluster_sample.filter(ee.Filter.eq('cluster', cluster)) return features.toList(100).map(function(f) { var f = ee.Feature(f); var cluster = f.get('cluster'); var vv = f.get('VV_mean'); var vh = f.get('VH_mean'); var empty = ee.List([{}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}]) var row = empty.set(ee.Number(cluster).subtract(1), {v: vh}); var fullrow = ee.List([{v: vv}]).cat(row) return {c: fullrow} }) }).flatten() // Rows is server-side object created using EE API // Convert it to cliend-side javascript object rows.evaluate(function(rowsResult) { print(rowsResult) var dataTable = { cols: [ {id: 'vv', label: 'vv', type: 'number'}, {id: 'vh1', label: '1', type: 'number'}, {id: 'vh2', label: '2', type: 'number'}, {id: 'vh3', label: '3', type: 'number'}, {id: 'vh4', label: '4', type: 'number'}, {id: 'vh5', label: '5', type: 'number'}, {id: 'vh6', label: '6', type: 'number'}, {id: 'vh7', label: '7', type: 'number'}, {id: 'vh8', label: '8', type: 'number'}, {id: 'vh9', label: '9', type: 'number'}, {id: 'vh10', label: '10', type: 'number'}, {id: 'vh11', label: '11', type: 'number'}, ], rows: rowsResult }; var chart = ui.Chart(dataTable, 'ScatterChart') .setOptions({ title: 'VV Mean .vs VH Mean - Cluster Results', pointSize: 2, dataOpacity: 0.4, hAxis: { viewWindow: { min: -22, max: -1.5, }, 'title': 'VV Mean', titleTextStyle: { italic: false, bold: true }, }, vAxis: { viewWindow: { min: -30, max: -5, }, 'title': 'VH Mean', titleTextStyle: { italic: false, bold: true } }, }); ; print(chart) })
修改后的图里结果：
这里面用到的函数：
ui.Chart(dataTable, chartType, options, view, downloadable)
A chart widget.
Arguments:
dataTable (List<List<Object>>|Object|String, optional):
A 2-D array of data or a Google Visualization DataTable literal. See: http://developers.google.com/chart/interactive/docs/reference#DataTable
chartType (String, optional):
The chart type; e.g 'ScatterChart',
'LineChart', and 'ColumnChart'. For the complete list of charts, see: https://developers.google.com/chart/interactive/docs/gallery
options (Object, optional):
An object defining chart style options such as:
- title (string) The title of the chart.
- colors (Array) An array of colors used to draw the chart. Its format should follow the Google Visualization API's options: https://developers.google.com/chart/interactive/docs/customizing_charts
view (Object, optional):
Sets a DataView initializer object, which acts as a filter over the underlying data. See: https://developers.google.com/chart/interactive/docs/reference#DataView
downloadable (Boolean, optional):
Whether the chart can be downloaded as CSV, SVG, and PNG. Defaults to true.
