前端初始化
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/*
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing,
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* software distributed under the License is distributed on an
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* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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* KIND, either express or implied. See the License for the
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* specific language governing permissions and limitations
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* under the License.
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*/
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/**
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* AUTO-GENERATED FILE. DO NOT MODIFY.
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*/
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/*
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing,
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* software distributed under the License is distributed on an
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* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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* KIND, either express or implied. See the License for the
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* specific language governing permissions and limitations
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* under the License.
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*/
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// import * as echarts from '../../core/echarts.js';
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// import { createHashMap, each, HashMap, hasOwn, keys, map } from 'zrender/lib/core/util.js';
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// import SeriesModel from '../../model/Series.js';
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// import {
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// isCartesian2DDeclaredSeries, findAxisModels, isCartesian2DInjectedAsDataCoordSys
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// } from './cartesianAxisHelper.js';
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// import { getDataDimensionsOnAxis } from '../axisHelper.js';
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// import { AxisBaseModel } from '../AxisBaseModel.js';
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// import type Axis from '../Axis.js';
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// import GlobalModel from '../../model/Global.js';
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// import { Dictionary } from '../../util/types.js';
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// import {
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// AXIS_EXTENT_INFO_BUILD_FROM_DATA_ZOOM, ensureScaleRawExtentInfo, ScaleRawExtentInfo, ScaleRawExtentResult
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// } from '../scaleRawExtentInfo.js';
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// import { initExtentForUnion, unionExtentFromNumber } from '../../util/model.js';
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/**
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* @obsolete
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* PENDING:
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* - This file is not used anywhere currently.
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* - This is a similar behavior to `dataZoom`, but historically supported separately.
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* Can it be merged into `dataZoom`?
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* - The impl need to be fixed, @see #15050 , and,
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* - Remove side-effect.
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* - Need to fix the case:
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* series_a =>
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* x_m (category): dataExtent: [3,8]
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* y_i:
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* series_b =>
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* x_m (category): dataExtent: [4,6]
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* y_j:
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* series_c =>
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* x_m (category): dataExtent: [5,7]
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* y_j:
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* dataZoom control y_i, so series_a is excluded.
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* So x_m.condExtent = [4,6] U [5,7] = [4,7] , and use it to call ensureScaleRawExtentInfo.
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* (incorrect?, supposed to be [3,8]?)
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*
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* See test case `test/axis-filter-extent.html`.
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*
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* The responsibility of this processor:
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* Enable category axis to use the specified `min`/`max` to shrink the extent of the orthogonal axis in
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* Cartesian2D. That is, if some data item on a category axis is out of the range of `min`/`max`, the
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* extent of the orthogonal axis will exclude the data items.
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* A typical case is bar-racing, where bars are sorted dynamically and may only need to
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* displayed part of the whole bars.
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*
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* IMPL_MEMO:
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* - For each triple xAxis-yAxis-series, if either xAxis or yAxis is controlled by a dataZoom,
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* the triple should be ignored in this processor.
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* - Input:
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* - Cartesian series data ("series approximate extent" has been prepared).
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* - Axis original `ScaleRawExtentInfo`
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* (the content comes from ec option and "series approximate extent").
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* - Modify(result):
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* - `ScaleRawExtentInfo#min/max` of the determined "target axis".
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* - "series approximate extent".
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*/
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// The priority is just after dataZoom processor.
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// echarts.registerProcessor(echarts.PRIORITY.PROCESSOR.FILTER + 10, {
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// getTargetSeries: function (ecModel) {
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// const seriesModelMap = createHashMap<SeriesModel>();
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// ecModel.eachSeries(function (seriesModel: SeriesModel) {
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// isCartesian2DDeclaredSeries(seriesModel) && seriesModelMap.set(seriesModel.uid, seriesModel);
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// });
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// return seriesModelMap;
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// },
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// overallReset: function (ecModel, api) {
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// const seriesRecords = [] as SeriesRecord[];
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// const axisRecordMap = createHashMap<AxisRecord>();
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// prepareDataExtentOnAxis(ecModel, axisRecordMap, seriesRecords);
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// calculateFilteredExtent(axisRecordMap, seriesRecords);
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// shrinkAxisExtent(axisRecordMap);
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// }
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// });
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// type AxisRecord = {
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// rawExtentInfo?: ScaleRawExtentInfo;
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// rawExtentResult?: ScaleRawExtentResult;
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// tarExtent?: number[];
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// };
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// type SeriesRecord = {
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// seriesModel: SeriesModel;
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// xAxisModel: AxisBaseModel;
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// yAxisModel: AxisBaseModel;
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// };
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// function prepareDataExtentOnAxis(
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// ecModel: GlobalModel,
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// axisRecordMap: HashMap<AxisRecord>,
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// seriesRecords: SeriesRecord[]
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// ): void {
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// ecModel.eachSeries(function (seriesModel: SeriesModel) {
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// // If pie (or other similar series) use cartesian2d, the logic below is
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// // probably wrong, therefore skip it temporarily.
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// // TODO: support union extent in this case.
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// // e.g. make a fake seriesData by series.coord/series.center, and it can be
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// // performed by data processing (such as, filter), and applied here.
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// if (!isCartesian2DInjectedAsDataCoordSys(seriesModel)) {
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// return;
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// }
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// const axesModelMap = findAxisModels(seriesModel);
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// const xAxisModel = axesModelMap.xAxisModel;
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// const yAxisModel = axesModelMap.yAxisModel;
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// const xAxis = xAxisModel.axis;
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// const yAxis = yAxisModel.axis;
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// const xRawExtentInfo = ensureScaleRawExtentInfo(xAxis);
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// const yRawExtentInfo = ensureScaleRawExtentInfo(yAxis);
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// // If either axis controlled by other filter like "dataZoom",
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// // use the rule of dataZoom rather than adopting the rules here.
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// if (
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// (xRawExtentInfo && xRawExtentInfo.from === AXIS_EXTENT_INFO_BUILD_FROM_DATA_ZOOM)
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// || (yRawExtentInfo && yRawExtentInfo.from === AXIS_EXTENT_INFO_BUILD_FROM_DATA_ZOOM)
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// ) {
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// return;
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// }
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// seriesRecords.push({
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// seriesModel: seriesModel,
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// xAxisModel: xAxisModel,
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// yAxisModel: yAxisModel
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// });
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// });
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// }
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// function calculateFilteredExtent(
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// axisRecordMap: HashMap<AxisRecord>,
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// seriesRecords: SeriesRecord[]
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// ) {
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// each(seriesRecords, function (seriesRecord) {
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// const xAxisModel = seriesRecord.xAxisModel;
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// const yAxisModel = seriesRecord.yAxisModel;
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// const xAxis = xAxisModel.axis;
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// const yAxis = yAxisModel.axis;
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// const xAxisRecord = prepareAxisRecord(axisRecordMap, xAxisModel);
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// const yAxisRecord = prepareAxisRecord(axisRecordMap, yAxisModel);
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// xAxisRecord.rawExtentInfo = ensureScaleRawExtentInfo(xAxis);
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// yAxisRecord.rawExtentInfo = ensureScaleRawExtentInfo(yAxis);
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// xAxisRecord.rawExtentResult = xAxisRecord.rawExtentInfo.calculate();
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// yAxisRecord.rawExtentResult = yAxisRecord.rawExtentInfo.calculate();
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// const data = seriesRecord.seriesModel.getData();
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// // For duplication removal.
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// // key: series data dimension corresponding to the condition axis.
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// const condDimMap: Dictionary<boolean> = {};
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// // key: series data dimension corresponding to the target axis.
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// const tarDimMap: Dictionary<boolean> = {};
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// let condAxis: Axis;
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// let tarAxisRecord: AxisRecord;
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// function addCondition(axis: Axis, axisRecord: AxisRecord) {
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// // But for simplicity and safety and performance, we only adopt this
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// // feature on category axis at present.
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// const rawExtentResult = axisRecord.rawExtentResult;
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// if (axis.type === 'category'
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// && (rawExtentResult.dataMinMax[0] < rawExtentResult.resultMinMax[0]
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// || rawExtentResult.resultMinMax[1] < rawExtentResult.dataMinMax[1]
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// )
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// ) {
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// each(getDataDimensionsOnAxis(data, axis.dim), function (dataDim) {
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// if (!hasOwn(condDimMap, dataDim)) {
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// condDimMap[dataDim] = true;
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// condAxis = axis;
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// }
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// });
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// }
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// }
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// function addTarget(axis: Axis, axisRecord: AxisRecord) {
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// const rawExtentResult = axisRecord.rawExtentResult;
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// const fixMinMax = rawExtentResult.fixMinMax;
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// if (axis.type !== 'category'
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// && (!fixMinMax[0] || !fixMinMax[1])
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// ) {
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// each(getDataDimensionsOnAxis(data, axis.dim), function (dataDim) {
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// if (!hasOwn(condDimMap, dataDim) && !hasOwn(tarDimMap, dataDim)) {
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// tarDimMap[dataDim] = true;
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// tarAxisRecord = axisRecord;
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// }
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// });
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// }
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// }
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// addCondition(xAxis, xAxisRecord);
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// addCondition(yAxis, yAxisRecord);
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// addTarget(xAxis, xAxisRecord);
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// addTarget(yAxis, yAxisRecord);
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// const condDims = keys(condDimMap);
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// const tarDims = keys(tarDimMap);
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// const tarDimExtents = map(tarDims, function () {
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// return initExtentForUnion();
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// });
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// const condDimsLen = condDims.length;
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// const tarDimsLen = tarDims.length;
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// if (!condDimsLen || !tarDimsLen) {
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// return;
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// }
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// const singleCondDim = condDimsLen === 1 ? condDims[0] : null;
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// const singleTarDim = tarDimsLen === 1 ? tarDims[0] : null;
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// const dataLen = data.count();
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// // Time consuming, because this is a "block task".
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// // Simple optimization for the vast majority of cases.
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// if (singleCondDim && singleTarDim) {
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// for (let dataIdx = 0; dataIdx < dataLen; dataIdx++) {
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// const condVal = data.get(singleCondDim, dataIdx) as number;
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// if (condAxis.scale.contain(condVal)) {
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// unionExtentFromNumber(tarDimExtents[0], data.get(singleTarDim, dataIdx) as number);
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// }
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// }
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// }
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// else {
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// for (let dataIdx = 0; dataIdx < dataLen; dataIdx++) {
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// for (let j = 0; j < condDimsLen; j++) {
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// const condVal = data.get(condDims[j], dataIdx) as number;
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// if (condAxis.scale.contain(condVal)) {
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// for (let k = 0; k < tarDimsLen; k++) {
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// unionExtentFromNumber(tarDimExtents[k], data.get(tarDims[k], dataIdx) as number);
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// }
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// // Any one dim is in range means satisfied.
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// break;
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// }
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// }
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// }
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// }
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// each(tarDimExtents, function (tarDimExtent, i) {
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// // FIXME: if there has been approximateExtent set?
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// data.setApproximateExtent(tarDimExtent as [number, number], tarDims[i]);
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// const tarAxisExtent = tarAxisRecord.tarExtent = tarAxisRecord.tarExtent || initExtentForUnion();
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// unionExtentFromNumber(tarAxisExtent, tarDimExtent[0]);
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// unionExtentFromNumber(tarAxisExtent, tarDimExtent[1]);
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// });
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// });
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// }
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// function shrinkAxisExtent(axisRecordMap: HashMap<AxisRecord>) {
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// axisRecordMap.each(function (axisRecord) {
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// const tarAxisExtent = axisRecord.tarExtent;
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// if (tarAxisExtent) {
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// const rawExtentResult = axisRecord.rawExtentResult;
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// const fixMinMax = rawExtentResult.fixMinMax;
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// // const rawExtentInfo = axisRecord.rawExtentInfo;
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// // Shrink the original extent.
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// if (!fixMinMax[0] && tarAxisExtent[0] > rawExtentResult.resultMinMax[0]) {
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// // rawExtentInfo.modifyDataMinMax('min', tarAxisExtent[0]);
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// }
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// if (!fixMinMax[1] && tarAxisExtent[1] < rawExtentResult.resultMinMax[1]) {
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// // rawExtentInfo.modifyDataMinMax('max', tarAxisExtent[1]);
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// }
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// }
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// });
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// }
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// function prepareAxisRecord(
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// axisRecordMap: HashMap<AxisRecord>,
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// axisModel: AxisBaseModel
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// ): AxisRecord {
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// return axisRecordMap.get(axisModel.uid)
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// || axisRecordMap.set(axisModel.uid, {});
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// }
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