| 1 |
- {"ast":null,"code":"import \"core-js/modules/es.array.push.js\";\n/*\n* Licensed to the Apache Software Foundation (ASF) under one\n* or more contributor license agreements. See the NOTICE file\n* distributed with this work for additional information\n* regarding copyright ownership. The ASF licenses this file\n* to you under the Apache License, Version 2.0 (the\n* \"License\"); you may not use this file except in compliance\n* with the License. You may obtain a copy of the License at\n*\n* http://www.apache.org/licenses/LICENSE-2.0\n*\n* Unless required by applicable law or agreed to in writing,\n* software distributed under the License is distributed on an\n* \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n* KIND, either express or implied. See the License for the\n* specific language governing permissions and limitations\n* under the License.\n*/\n\n/**\n * AUTO-GENERATED FILE. DO NOT MODIFY.\n */\n\n/*\r\n* Licensed to the Apache Software Foundation (ASF) under one\r\n* or more contributor license agreements. See the NOTICE file\r\n* distributed with this work for additional information\r\n* regarding copyright ownership. The ASF licenses this file\r\n* to you under the Apache License, Version 2.0 (the\r\n* \"License\"); you may not use this file except in compliance\r\n* with the License. You may obtain a copy of the License at\r\n*\r\n* http://www.apache.org/licenses/LICENSE-2.0\r\n*\r\n* Unless required by applicable law or agreed to in writing,\r\n* software distributed under the License is distributed on an\r\n* \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\r\n* KIND, either express or implied. See the License for the\r\n* specific language governing permissions and limitations\r\n* under the License.\r\n*/\nimport { makeInner, getDataItemValue, queryReferringComponents, SINGLE_REFERRING } from '../../util/model.js';\nimport { createHashMap, each, isArray, isString, isObject, isTypedArray } from 'zrender/lib/core/util.js';\nimport { SOURCE_FORMAT_ORIGINAL, SOURCE_FORMAT_ARRAY_ROWS, SOURCE_FORMAT_OBJECT_ROWS, SERIES_LAYOUT_BY_ROW, SOURCE_FORMAT_KEYED_COLUMNS } from '../../util/types.js';\n// The result of `guessOrdinal`.\nexport var BE_ORDINAL = {\n Must: 1,\n Might: 2,\n Not: 3 // Other cases\n};\nvar innerGlobalModel = makeInner();\n/**\r\n * MUST be called before mergeOption of all series.\r\n */\nexport function resetSourceDefaulter(ecModel) {\n // `datasetMap` is used to make default encode.\n innerGlobalModel(ecModel).datasetMap = createHashMap();\n}\n/**\r\n * [The strategy of the arrengment of data dimensions for dataset]:\r\n * \"value way\": all axes are non-category axes. So series one by one take\r\n * several (the number is coordSysDims.length) dimensions from dataset.\r\n * The result of data arrengment of data dimensions like:\r\n * | ser0_x | ser0_y | ser1_x | ser1_y | ser2_x | ser2_y |\r\n * \"category way\": at least one axis is category axis. So the the first data\r\n * dimension is always mapped to the first category axis and shared by\r\n * all of the series. The other data dimensions are taken by series like\r\n * \"value way\" does.\r\n * The result of data arrengment of data dimensions like:\r\n * | ser_shared_x | ser0_y | ser1_y | ser2_y |\r\n *\r\n * @return encode Never be `null/undefined`.\r\n */\nexport function makeSeriesEncodeForAxisCoordSys(coordDimensions, seriesModel, source) {\n var encode = {};\n var datasetModel = querySeriesUpstreamDatasetModel(seriesModel);\n // Currently only make default when using dataset, util more reqirements occur.\n if (!datasetModel || !coordDimensions) {\n return encode;\n }\n var encodeItemName = [];\n var encodeSeriesName = [];\n var ecModel = seriesModel.ecModel;\n var datasetMap = innerGlobalModel(ecModel).datasetMap;\n var key = datasetModel.uid + '_' + source.seriesLayoutBy;\n var baseCategoryDimIndex;\n var categoryWayValueDimStart;\n coordDimensions = coordDimensions.slice();\n each(coordDimensions, function (coordDimInfoLoose, coordDimIdx) {\n var coordDimInfo = isObject(coordDimInfoLoose) ? coordDimInfoLoose : coordDimensions[coordDimIdx] = {\n name: coordDimInfoLoose\n };\n if (coordDimInfo.type === 'ordinal' && baseCategoryDimIndex == null) {\n baseCategoryDimIndex = coordDimIdx;\n categoryWayValueDimStart = getDataDimCountOnCoordDim(coordDimInfo);\n }\n encode[coordDimInfo.name] = [];\n });\n var datasetRecord = datasetMap.get(key) || datasetMap.set(key, {\n categoryWayDim: categoryWayValueDimStart,\n valueWayDim: 0\n });\n // TODO\n // Auto detect first time axis and do arrangement.\n each(coordDimensions, function (coordDimInfo, coordDimIdx) {\n var coordDimName = coordDimInfo.name;\n var count = getDataDimCountOnCoordDim(coordDimInfo);\n // In value way.\n if (baseCategoryDimIndex == null) {\n var start = datasetRecord.valueWayDim;\n pushDim(encode[coordDimName], start, count);\n pushDim(encodeSeriesName, start, count);\n datasetRecord.valueWayDim += count;\n // ??? TODO give a better default series name rule?\n // especially when encode x y specified.\n // consider: when multiple series share one dimension\n // category axis, series name should better use\n // the other dimension name. On the other hand, use\n // both dimensions name.\n }\n // In category way, the first category axis.\n else if (baseCategoryDimIndex === coordDimIdx) {\n pushDim(encode[coordDimName], 0, count);\n pushDim(encodeItemName, 0, count);\n }\n // In category way, the other axis.\n else {\n var start = datasetRecord.categoryWayDim;\n pushDim(encode[coordDimName], start, count);\n pushDim(encodeSeriesName, start, count);\n datasetRecord.categoryWayDim += count;\n }\n });\n function pushDim(dimIdxArr, idxFrom, idxCount) {\n for (var i = 0; i < idxCount; i++) {\n dimIdxArr.push(idxFrom + i);\n }\n }\n function getDataDimCountOnCoordDim(coordDimInfo) {\n var dimsDef = coordDimInfo.dimsDef;\n return dimsDef ? dimsDef.length : 1;\n }\n encodeItemName.length && (encode.itemName = encodeItemName);\n encodeSeriesName.length && (encode.seriesName = encodeSeriesName);\n return encode;\n}\n/**\r\n * Work for data like [{name: ..., value: ...}, ...].\r\n *\r\n * @return encode Never be `null/undefined`.\r\n */\nexport function makeSeriesEncodeForNameBased(seriesModel, source, dimCount) {\n var encode = {};\n var datasetModel = querySeriesUpstreamDatasetModel(seriesModel);\n // Currently only make default when using dataset, util more reqirements occur.\n if (!datasetModel) {\n return encode;\n }\n var sourceFormat = source.sourceFormat;\n var dimensionsDefine = source.dimensionsDefine;\n var potentialNameDimIndex;\n if (sourceFormat === SOURCE_FORMAT_OBJECT_ROWS || sourceFormat === SOURCE_FORMAT_KEYED_COLUMNS) {\n each(dimensionsDefine, function (dim, idx) {\n if ((isObject(dim) ? dim.name : dim) === 'name') {\n potentialNameDimIndex = idx;\n }\n });\n }\n var idxResult = function () {\n var idxRes0 = {};\n var idxRes1 = {};\n var guessRecords = [];\n // 5 is an experience value.\n for (var i = 0, len = Math.min(5, dimCount); i < len; i++) {\n var guessResult = doGuessOrdinal(source.data, sourceFormat, source.seriesLayoutBy, dimensionsDefine, source.startIndex, i);\n guessRecords.push(guessResult);\n var isPureNumber = guessResult === BE_ORDINAL.Not;\n // [Strategy of idxRes0]: find the first BE_ORDINAL.Not as the value dim,\n // and then find a name dim with the priority:\n // \"BE_ORDINAL.Might|BE_ORDINAL.Must\" > \"other dim\" > \"the value dim itself\".\n if (isPureNumber && idxRes0.v == null && i !== potentialNameDimIndex) {\n idxRes0.v = i;\n }\n if (idxRes0.n == null || idxRes0.n === idxRes0.v || !isPureNumber && guessRecords[idxRes0.n] === BE_ORDINAL.Not) {\n idxRes0.n = i;\n }\n if (fulfilled(idxRes0) && guessRecords[idxRes0.n] !== BE_ORDINAL.Not) {\n return idxRes0;\n }\n // [Strategy of idxRes1]: if idxRes0 not satisfied (that is, no BE_ORDINAL.Not),\n // find the first BE_ORDINAL.Might as the value dim,\n // and then find a name dim with the priority:\n // \"other dim\" > \"the value dim itself\".\n // That is for backward compat: number-like (e.g., `'3'`, `'55'`) can be\n // treated as number.\n if (!isPureNumber) {\n if (guessResult === BE_ORDINAL.Might && idxRes1.v == null && i !== potentialNameDimIndex) {\n idxRes1.v = i;\n }\n if (idxRes1.n == null || idxRes1.n === idxRes1.v) {\n idxRes1.n = i;\n }\n }\n }\n function fulfilled(idxResult) {\n return idxResult.v != null && idxResult.n != null;\n }\n return fulfilled(idxRes0) ? idxRes0 : fulfilled(idxRes1) ? idxRes1 : null;\n }();\n if (idxResult) {\n encode.value = [idxResult.v];\n // `potentialNameDimIndex` has highest priority.\n var nameDimIndex = potentialNameDimIndex != null ? potentialNameDimIndex : idxResult.n;\n // By default, label uses itemName in charts.\n // So we don't set encodeLabel here.\n encode.itemName = [nameDimIndex];\n encode.seriesName = [nameDimIndex];\n }\n return encode;\n}\n/**\r\n * @return If return null/undefined, indicate that should not use datasetModel.\r\n */\nexport function querySeriesUpstreamDatasetModel(seriesModel) {\n // Caution: consider the scenario:\n // A dataset is declared and a series is not expected to use the dataset,\n // and at the beginning `setOption({series: { noData })` (just prepare other\n // option but no data), then `setOption({series: {data: [...]}); In this case,\n // the user should set an empty array to avoid that dataset is used by default.\n var thisData = seriesModel.get('data', true);\n if (!thisData) {\n return queryReferringComponents(seriesModel.ecModel, 'dataset', {\n index: seriesModel.get('datasetIndex', true),\n id: seriesModel.get('datasetId', true)\n }, SINGLE_REFERRING).models[0];\n }\n}\n/**\r\n * @return Always return an array event empty.\r\n */\nexport function queryDatasetUpstreamDatasetModels(datasetModel) {\n // Only these attributes declared, we by default reference to `datasetIndex: 0`.\n // Otherwise, no reference.\n if (!datasetModel.get('transform', true) && !datasetModel.get('fromTransformResult', true)) {\n return [];\n }\n return queryReferringComponents(datasetModel.ecModel, 'dataset', {\n index: datasetModel.get('fromDatasetIndex', true),\n id: datasetModel.get('fromDatasetId', true)\n }, SINGLE_REFERRING).models;\n}\n/**\r\n * The rule should not be complex, otherwise user might not\r\n * be able to known where the data is wrong.\r\n * The code is ugly, but how to make it neat?\r\n */\nexport function guessOrdinal(source, dimIndex) {\n return doGuessOrdinal(source.data, source.sourceFormat, source.seriesLayoutBy, source.dimensionsDefine, source.startIndex, dimIndex);\n}\n// dimIndex may be overflow source data.\n// return {BE_ORDINAL}\nfunction doGuessOrdinal(data, sourceFormat, seriesLayoutBy, dimensionsDefine, startIndex, dimIndex) {\n var result;\n // Experience value.\n var maxLoop = 5;\n if (isTypedArray(data)) {\n return BE_ORDINAL.Not;\n }\n // When sourceType is 'objectRows' or 'keyedColumns', dimensionsDefine\n // always exists in source.\n var dimName;\n var dimType;\n if (dimensionsDefine) {\n var dimDefItem = dimensionsDefine[dimIndex];\n if (isObject(dimDefItem)) {\n dimName = dimDefItem.name;\n dimType = dimDefItem.type;\n } else if (isString(dimDefItem)) {\n dimName = dimDefItem;\n }\n }\n if (dimType != null) {\n return dimType === 'ordinal' ? BE_ORDINAL.Must : BE_ORDINAL.Not;\n }\n if (sourceFormat === SOURCE_FORMAT_ARRAY_ROWS) {\n var dataArrayRows = data;\n if (seriesLayoutBy === SERIES_LAYOUT_BY_ROW) {\n var sample = dataArrayRows[dimIndex];\n for (var i = 0; i < (sample || []).length && i < maxLoop; i++) {\n if ((result = detectValue(sample[startIndex + i])) != null) {\n return result;\n }\n }\n } else {\n for (var i = 0; i < dataArrayRows.length && i < maxLoop; i++) {\n var row = dataArrayRows[startIndex + i];\n if (row && (result = detectValue(row[dimIndex])) != null) {\n return result;\n }\n }\n }\n } else if (sourceFormat === SOURCE_FORMAT_OBJECT_ROWS) {\n var dataObjectRows = data;\n if (!dimName) {\n return BE_ORDINAL.Not;\n }\n for (var i = 0; i < dataObjectRows.length && i < maxLoop; i++) {\n var item = dataObjectRows[i];\n if (item && (result = detectValue(item[dimName])) != null) {\n return result;\n }\n }\n } else if (sourceFormat === SOURCE_FORMAT_KEYED_COLUMNS) {\n var dataKeyedColumns = data;\n if (!dimName) {\n return BE_ORDINAL.Not;\n }\n var sample = dataKeyedColumns[dimName];\n if (!sample || isTypedArray(sample)) {\n return BE_ORDINAL.Not;\n }\n for (var i = 0; i < sample.length && i < maxLoop; i++) {\n if ((result = detectValue(sample[i])) != null) {\n return result;\n }\n }\n } else if (sourceFormat === SOURCE_FORMAT_ORIGINAL) {\n var dataOriginal = data;\n for (var i = 0; i < dataOriginal.length && i < maxLoop; i++) {\n var item = dataOriginal[i];\n var val = getDataItemValue(item);\n if (!isArray(val)) {\n return BE_ORDINAL.Not;\n }\n if ((result = detectValue(val[dimIndex])) != null) {\n return result;\n }\n }\n }\n function detectValue(val) {\n var beStr = isString(val);\n // Consider usage convenience, '1', '2' will be treated as \"number\".\n // `Number('')` (or any whitespace) is `0`.\n if (val != null && Number.isFinite(Number(val)) && val !== '') {\n return beStr ? BE_ORDINAL.Might : BE_ORDINAL.Not;\n } else if (beStr && val !== '-') {\n return BE_ORDINAL.Must;\n }\n }\n return BE_ORDINAL.Not;\n}","map":{"version":3,"names":["makeInner","getDataItemValue","queryReferringComponents","SINGLE_REFERRING","createHashMap","each","isArray","isString","isObject","isTypedArray","SOURCE_FORMAT_ORIGINAL","SOURCE_FORMAT_ARRAY_ROWS","SOURCE_FORMAT_OBJECT_ROWS","SERIES_LAYOUT_BY_ROW","SOURCE_FORMAT_KEYED_COLUMNS","BE_ORDINAL","Must","Might","Not","innerGlobalModel","resetSourceDefaulter","ecModel","datasetMap","makeSeriesEncodeForAxisCoordSys","coordDimensions","seriesModel","source","encode","datasetModel","querySeriesUpstreamDatasetModel","encodeItemName","encodeSeriesName","key","uid","seriesLayoutBy","baseCategoryDimIndex","categoryWayValueDimStart","slice","coordDimInfoLoose","coordDimIdx","coordDimInfo","name","type","getDataDimCountOnCoordDim","datasetRecord","get","set","categoryWayDim","valueWayDim","coordDimName","count","start","pushDim","dimIdxArr","idxFrom","idxCount","i","push","dimsDef","length","itemName","seriesName","makeSeriesEncodeForNameBased","dimCount","sourceFormat","dimensionsDefine","potentialNameDimIndex","dim","idx","idxResult","idxRes0","idxRes1","guessRecords","len","Math","min","guessResult","doGuessOrdinal","data","startIndex","isPureNumber","v","n","fulfilled","value","nameDimIndex","thisData","index","id","models","queryDatasetUpstreamDatasetModels","guessOrdinal","dimIndex","result","maxLoop","dimName","dimType","dimDefItem","dataArrayRows","sample","detectValue","row","dataObjectRows","item","dataKeyedColumns","dataOriginal","val","beStr","Number","isFinite"],"sources":["E:/git/2021项目/安科院大屏/node_modules/echarts/lib/data/helper/sourceHelper.js"],"sourcesContent":["\n/*\n* Licensed to the Apache Software Foundation (ASF) under one\n* or more contributor license agreements. See the NOTICE file\n* distributed with this work for additional information\n* regarding copyright ownership. The ASF licenses this file\n* to you under the Apache License, Version 2.0 (the\n* \"License\"); you may not use this file except in compliance\n* with the License. You may obtain a copy of the License at\n*\n* http://www.apache.org/licenses/LICENSE-2.0\n*\n* Unless required by applicable law or agreed to in writing,\n* software distributed under the License is distributed on an\n* \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n* KIND, either express or implied. See the License for the\n* specific language governing permissions and limitations\n* under the License.\n*/\n\n\n/**\n * AUTO-GENERATED FILE. DO NOT MODIFY.\n */\n\n/*\r\n* Licensed to the Apache Software Foundation (ASF) under one\r\n* or more contributor license agreements. See the NOTICE file\r\n* distributed with this work for additional information\r\n* regarding copyright ownership. The ASF licenses this file\r\n* to you under the Apache License, Version 2.0 (the\r\n* \"License\"); you may not use this file except in compliance\r\n* with the License. You may obtain a copy of the License at\r\n*\r\n* http://www.apache.org/licenses/LICENSE-2.0\r\n*\r\n* Unless required by applicable law or agreed to in writing,\r\n* software distributed under the License is distributed on an\r\n* \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\r\n* KIND, either express or implied. See the License for the\r\n* specific language governing permissions and limitations\r\n* under the License.\r\n*/\nimport { makeInner, getDataItemValue, queryReferringComponents, SINGLE_REFERRING } from '../../util/model.js';\nimport { createHashMap, each, isArray, isString, isObject, isTypedArray } from 'zrender/lib/core/util.js';\nimport { SOURCE_FORMAT_ORIGINAL, SOURCE_FORMAT_ARRAY_ROWS, SOURCE_FORMAT_OBJECT_ROWS, SERIES_LAYOUT_BY_ROW, SOURCE_FORMAT_KEYED_COLUMNS } from '../../util/types.js';\n// The result of `guessOrdinal`.\nexport var BE_ORDINAL = {\n Must: 1,\n Might: 2,\n Not: 3 // Other cases\n};\nvar innerGlobalModel = makeInner();\n/**\r\n * MUST be called before mergeOption of all series.\r\n */\nexport function resetSourceDefaulter(ecModel) {\n // `datasetMap` is used to make default encode.\n innerGlobalModel(ecModel).datasetMap = createHashMap();\n}\n/**\r\n * [The strategy of the arrengment of data dimensions for dataset]:\r\n * \"value way\": all axes are non-category axes. So series one by one take\r\n * several (the number is coordSysDims.length) dimensions from dataset.\r\n * The result of data arrengment of data dimensions like:\r\n * | ser0_x | ser0_y | ser1_x | ser1_y | ser2_x | ser2_y |\r\n * \"category way\": at least one axis is category axis. So the the first data\r\n * dimension is always mapped to the first category axis and shared by\r\n * all of the series. The other data dimensions are taken by series like\r\n * \"value way\" does.\r\n * The result of data arrengment of data dimensions like:\r\n * | ser_shared_x | ser0_y | ser1_y | ser2_y |\r\n *\r\n * @return encode Never be `null/undefined`.\r\n */\nexport function makeSeriesEncodeForAxisCoordSys(coordDimensions, seriesModel, source) {\n var encode = {};\n var datasetModel = querySeriesUpstreamDatasetModel(seriesModel);\n // Currently only make default when using dataset, util more reqirements occur.\n if (!datasetModel || !coordDimensions) {\n return encode;\n }\n var encodeItemName = [];\n var encodeSeriesName = [];\n var ecModel = seriesModel.ecModel;\n var datasetMap = innerGlobalModel(ecModel).datasetMap;\n var key = datasetModel.uid + '_' + source.seriesLayoutBy;\n var baseCategoryDimIndex;\n var categoryWayValueDimStart;\n coordDimensions = coordDimensions.slice();\n each(coordDimensions, function (coordDimInfoLoose, coordDimIdx) {\n var coordDimInfo = isObject(coordDimInfoLoose) ? coordDimInfoLoose : coordDimensions[coordDimIdx] = {\n name: coordDimInfoLoose\n };\n if (coordDimInfo.type === 'ordinal' && baseCategoryDimIndex == null) {\n baseCategoryDimIndex = coordDimIdx;\n categoryWayValueDimStart = getDataDimCountOnCoordDim(coordDimInfo);\n }\n encode[coordDimInfo.name] = [];\n });\n var datasetRecord = datasetMap.get(key) || datasetMap.set(key, {\n categoryWayDim: categoryWayValueDimStart,\n valueWayDim: 0\n });\n // TODO\n // Auto detect first time axis and do arrangement.\n each(coordDimensions, function (coordDimInfo, coordDimIdx) {\n var coordDimName = coordDimInfo.name;\n var count = getDataDimCountOnCoordDim(coordDimInfo);\n // In value way.\n if (baseCategoryDimIndex == null) {\n var start = datasetRecord.valueWayDim;\n pushDim(encode[coordDimName], start, count);\n pushDim(encodeSeriesName, start, count);\n datasetRecord.valueWayDim += count;\n // ??? TODO give a better default series name rule?\n // especially when encode x y specified.\n // consider: when multiple series share one dimension\n // category axis, series name should better use\n // the other dimension name. On the other hand, use\n // both dimensions name.\n }\n // In category way, the first category axis.\n else if (baseCategoryDimIndex === coordDimIdx) {\n pushDim(encode[coordDimName], 0, count);\n pushDim(encodeItemName, 0, count);\n }\n // In category way, the other axis.\n else {\n var start = datasetRecord.categoryWayDim;\n pushDim(encode[coordDimName], start, count);\n pushDim(encodeSeriesName, start, count);\n datasetRecord.categoryWayDim += count;\n }\n });\n function pushDim(dimIdxArr, idxFrom, idxCount) {\n for (var i = 0; i < idxCount; i++) {\n dimIdxArr.push(idxFrom + i);\n }\n }\n function getDataDimCountOnCoordDim(coordDimInfo) {\n var dimsDef = coordDimInfo.dimsDef;\n return dimsDef ? dimsDef.length : 1;\n }\n encodeItemName.length && (encode.itemName = encodeItemName);\n encodeSeriesName.length && (encode.seriesName = encodeSeriesName);\n return encode;\n}\n/**\r\n * Work for data like [{name: ..., value: ...}, ...].\r\n *\r\n * @return encode Never be `null/undefined`.\r\n */\nexport function makeSeriesEncodeForNameBased(seriesModel, source, dimCount) {\n var encode = {};\n var datasetModel = querySeriesUpstreamDatasetModel(seriesModel);\n // Currently only make default when using dataset, util more reqirements occur.\n if (!datasetModel) {\n return encode;\n }\n var sourceFormat = source.sourceFormat;\n var dimensionsDefine = source.dimensionsDefine;\n var potentialNameDimIndex;\n if (sourceFormat === SOURCE_FORMAT_OBJECT_ROWS || sourceFormat === SOURCE_FORMAT_KEYED_COLUMNS) {\n each(dimensionsDefine, function (dim, idx) {\n if ((isObject(dim) ? dim.name : dim) === 'name') {\n potentialNameDimIndex = idx;\n }\n });\n }\n var idxResult = function () {\n var idxRes0 = {};\n var idxRes1 = {};\n var guessRecords = [];\n // 5 is an experience value.\n for (var i = 0, len = Math.min(5, dimCount); i < len; i++) {\n var guessResult = doGuessOrdinal(source.data, sourceFormat, source.seriesLayoutBy, dimensionsDefine, source.startIndex, i);\n guessRecords.push(guessResult);\n var isPureNumber = guessResult === BE_ORDINAL.Not;\n // [Strategy of idxRes0]: find the first BE_ORDINAL.Not as the value dim,\n // and then find a name dim with the priority:\n // \"BE_ORDINAL.Might|BE_ORDINAL.Must\" > \"other dim\" > \"the value dim itself\".\n if (isPureNumber && idxRes0.v == null && i !== potentialNameDimIndex) {\n idxRes0.v = i;\n }\n if (idxRes0.n == null || idxRes0.n === idxRes0.v || !isPureNumber && guessRecords[idxRes0.n] === BE_ORDINAL.Not) {\n idxRes0.n = i;\n }\n if (fulfilled(idxRes0) && guessRecords[idxRes0.n] !== BE_ORDINAL.Not) {\n return idxRes0;\n }\n // [Strategy of idxRes1]: if idxRes0 not satisfied (that is, no BE_ORDINAL.Not),\n // find the first BE_ORDINAL.Might as the value dim,\n // and then find a name dim with the priority:\n // \"other dim\" > \"the value dim itself\".\n // That is for backward compat: number-like (e.g., `'3'`, `'55'`) can be\n // treated as number.\n if (!isPureNumber) {\n if (guessResult === BE_ORDINAL.Might && idxRes1.v == null && i !== potentialNameDimIndex) {\n idxRes1.v = i;\n }\n if (idxRes1.n == null || idxRes1.n === idxRes1.v) {\n idxRes1.n = i;\n }\n }\n }\n function fulfilled(idxResult) {\n return idxResult.v != null && idxResult.n != null;\n }\n return fulfilled(idxRes0) ? idxRes0 : fulfilled(idxRes1) ? idxRes1 : null;\n }();\n if (idxResult) {\n encode.value = [idxResult.v];\n // `potentialNameDimIndex` has highest priority.\n var nameDimIndex = potentialNameDimIndex != null ? potentialNameDimIndex : idxResult.n;\n // By default, label uses itemName in charts.\n // So we don't set encodeLabel here.\n encode.itemName = [nameDimIndex];\n encode.seriesName = [nameDimIndex];\n }\n return encode;\n}\n/**\r\n * @return If return null/undefined, indicate that should not use datasetModel.\r\n */\nexport function querySeriesUpstreamDatasetModel(seriesModel) {\n // Caution: consider the scenario:\n // A dataset is declared and a series is not expected to use the dataset,\n // and at the beginning `setOption({series: { noData })` (just prepare other\n // option but no data), then `setOption({series: {data: [...]}); In this case,\n // the user should set an empty array to avoid that dataset is used by default.\n var thisData = seriesModel.get('data', true);\n if (!thisData) {\n return queryReferringComponents(seriesModel.ecModel, 'dataset', {\n index: seriesModel.get('datasetIndex', true),\n id: seriesModel.get('datasetId', true)\n }, SINGLE_REFERRING).models[0];\n }\n}\n/**\r\n * @return Always return an array event empty.\r\n */\nexport function queryDatasetUpstreamDatasetModels(datasetModel) {\n // Only these attributes declared, we by default reference to `datasetIndex: 0`.\n // Otherwise, no reference.\n if (!datasetModel.get('transform', true) && !datasetModel.get('fromTransformResult', true)) {\n return [];\n }\n return queryReferringComponents(datasetModel.ecModel, 'dataset', {\n index: datasetModel.get('fromDatasetIndex', true),\n id: datasetModel.get('fromDatasetId', true)\n }, SINGLE_REFERRING).models;\n}\n/**\r\n * The rule should not be complex, otherwise user might not\r\n * be able to known where the data is wrong.\r\n * The code is ugly, but how to make it neat?\r\n */\nexport function guessOrdinal(source, dimIndex) {\n return doGuessOrdinal(source.data, source.sourceFormat, source.seriesLayoutBy, source.dimensionsDefine, source.startIndex, dimIndex);\n}\n// dimIndex may be overflow source data.\n// return {BE_ORDINAL}\nfunction doGuessOrdinal(data, sourceFormat, seriesLayoutBy, dimensionsDefine, startIndex, dimIndex) {\n var result;\n // Experience value.\n var maxLoop = 5;\n if (isTypedArray(data)) {\n return BE_ORDINAL.Not;\n }\n // When sourceType is 'objectRows' or 'keyedColumns', dimensionsDefine\n // always exists in source.\n var dimName;\n var dimType;\n if (dimensionsDefine) {\n var dimDefItem = dimensionsDefine[dimIndex];\n if (isObject(dimDefItem)) {\n dimName = dimDefItem.name;\n dimType = dimDefItem.type;\n } else if (isString(dimDefItem)) {\n dimName = dimDefItem;\n }\n }\n if (dimType != null) {\n return dimType === 'ordinal' ? 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