mirror of
https://github.com/koalyptus/TableFilter.git
synced 2024-05-18 06:16:39 +02:00
332 lines
13 KiB
JavaScript
332 lines
13 KiB
JavaScript
import {Feature} from '../../feature';
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import Dom from '../../dom';
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import Str from '../../string';
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import {isArray, isFn, isUndef} from '../../types';
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export default class ColOps extends Feature {
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/**
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* Column calculations
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* @param {Object} tf TableFilter instance
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*/
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constructor(tf, opts) {
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super(tf, opts.name);
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//calls function before col operation
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this.onBeforeOperation = isFn(opts.on_before_operation) ?
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opts.on_before_operation : null;
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//calls function after col operation
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this.onAfterOperation = isFn(opts.on_after_operation) ?
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opts.on_after_operation : null;
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this.opts = opts;
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this.enable();
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}
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init() {
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if (this.initialized) {
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return;
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}
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// subscribe to events
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this.tf.emitter.on(['after-filtering'], () => this.calc());
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this.calc();
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this.initialized = true;
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}
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/**
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* Calculates columns' values
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* Configuration options are stored in 'opts' property
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* - 'id' contains ids of elements showing result (array)
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* - 'col' contains the columns' indexes (array)
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* - 'operation' contains operation type (array, values: 'sum', 'mean',
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* 'min', 'max', 'median', 'q1', 'q3')
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* - 'write_method' array defines which method to use for displaying the
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* result (innerHTML, setValue, createTextNode) - default: 'innerHTML'
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* - 'tot_row_index' defines in which row results are displayed
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* (integers array)
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*
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* - changes made by Nuovella:
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* (1) optimized the routine (now it will only process each column once),
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* (2) added calculations for the median, lower and upper quartile.
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*/
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calc() {
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var tf = this.tf;
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if (!tf.hasGrid()) {
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return;
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}
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if (this.onBeforeOperation) {
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this.onBeforeOperation.call(null, tf);
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}
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var opts = this.opts,
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labelId = opts.id,
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colIndex = opts.col,
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operation = opts.operation,
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outputType = opts.write_method,
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totRowIndex = opts.tot_row_index,
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excludeRow = opts.exclude_row,
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decimalPrecision = isUndef(opts.decimal_precision) ?
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2 : opts.decimal_precision;
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//nuovella: determine unique list of columns to operate on
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var ucolIndex = [],
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ucolMax = 0;
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ucolIndex[ucolMax] = colIndex[0];
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for (var ii = 1; ii < colIndex.length; ii++) {
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var saved = 0;
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//see if colIndex[ii] is already in the list of unique indexes
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for (var jj = 0; jj <= ucolMax; jj++) {
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if (ucolIndex[jj] === colIndex[ii]) {
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saved = 1;
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}
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}
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//if not saved then, save the index;
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if (saved === 0) {
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ucolMax++;
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ucolIndex[ucolMax] = colIndex[ii];
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}
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}
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if (isArray(labelId) && isArray(colIndex) && isArray(operation)) {
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var rows = tf.tbl.rows,
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colvalues = [];
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for (var ucol = 0; ucol <= ucolMax; ucol++) {
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//this retrieves col values
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//use ucolIndex because we only want to pass through this loop
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//once for each column get the values in this unique column
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colvalues.push(
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tf.getColValues(ucolIndex[ucol], false, true, excludeRow));
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//next: calculate all operations for this column
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var result,
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nbvalues = 0,
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temp,
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meanValue = 0,
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sumValue = 0,
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minValue = null,
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maxValue = null,
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q1Value = null,
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medValue = null,
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q3Value = null,
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meanFlag = 0,
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sumFlag = 0,
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minFlag = 0,
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maxFlag = 0,
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q1Flag = 0,
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medFlag = 0,
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q3Flag = 0,
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theList = [],
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opsThisCol = [],
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decThisCol = [],
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labThisCol = [],
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oTypeThisCol = [],
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mThisCol = -1;
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for (var k = 0; k < colIndex.length; k++) {
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if (colIndex[k] === ucolIndex[ucol]) {
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mThisCol++;
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opsThisCol[mThisCol] = Str.lower(operation[k]);
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decThisCol[mThisCol] = decimalPrecision[k];
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labThisCol[mThisCol] = labelId[k];
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oTypeThisCol = isArray(outputType) ?
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outputType[k] : null;
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switch (opsThisCol[mThisCol]) {
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case 'mean':
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meanFlag = 1;
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break;
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case 'sum':
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sumFlag = 1;
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break;
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case 'min':
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minFlag = 1;
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break;
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case 'max':
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maxFlag = 1;
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break;
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case 'median':
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medFlag = 1;
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break;
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case 'q1':
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q1Flag = 1;
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break;
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case 'q3':
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q3Flag = 1;
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break;
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}
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}
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}
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for (var j = 0; j < colvalues[ucol].length; j++) {
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//sort the list for calculation of median and quartiles
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if ((q1Flag === 1) || (q3Flag === 1) || (medFlag === 1)) {
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if (j < colvalues[ucol].length - 1) {
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for (k = j + 1; k < colvalues[ucol].length; k++) {
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/* eslint-disable */
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if (eval(colvalues[ucol][k]) <
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eval(colvalues[ucol][j])) {
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/* eslint-enable */
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temp = colvalues[ucol][j];
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colvalues[ucol][j] = colvalues[ucol][k];
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colvalues[ucol][k] = temp;
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}
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}
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}
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}
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var cvalue = parseFloat(colvalues[ucol][j]);
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theList[j] = parseFloat(cvalue);
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if (!isNaN(cvalue)) {
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nbvalues++;
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if (sumFlag === 1 || meanFlag === 1) {
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sumValue += parseFloat(cvalue);
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}
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if (minFlag === 1) {
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if (minValue === null) {
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minValue = parseFloat(cvalue);
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} else {
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minValue = parseFloat(cvalue) < minValue ?
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parseFloat(cvalue) : minValue;
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}
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}
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if (maxFlag === 1) {
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if (maxValue === null) {
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maxValue = parseFloat(cvalue);
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} else {
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maxValue = parseFloat(cvalue) > maxValue ?
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parseFloat(cvalue) : maxValue;
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}
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}
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}
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}//for j
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if (meanFlag === 1) {
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meanValue = sumValue / nbvalues;
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}
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if (medFlag === 1) {
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var aux = 0;
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if (nbvalues % 2 === 1) {
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aux = Math.floor(nbvalues / 2);
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medValue = theList[aux];
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} else {
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medValue = (theList[nbvalues / 2] +
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theList[((nbvalues / 2) - 1)]) / 2;
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}
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}
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var posa;
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if (q1Flag === 1) {
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posa = 0.0;
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posa = Math.floor(nbvalues / 4);
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if (4 * posa === nbvalues) {
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q1Value = (theList[posa - 1] + theList[posa]) / 2;
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} else {
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q1Value = theList[posa];
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}
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}
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if (q3Flag === 1) {
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posa = 0.0;
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var posb = 0.0;
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posa = Math.floor(nbvalues / 4);
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if (4 * posa === nbvalues) {
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posb = 3 * posa;
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q3Value = (theList[posb] + theList[posb - 1]) / 2;
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} else {
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q3Value = theList[nbvalues - posa - 1];
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}
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}
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for (var i = 0; i <= mThisCol; i++) {
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switch (opsThisCol[i]) {
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case 'mean':
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result = meanValue;
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break;
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case 'sum':
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result = sumValue;
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break;
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case 'min':
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result = minValue;
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break;
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case 'max':
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result = maxValue;
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break;
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case 'median':
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result = medValue;
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break;
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case 'q1':
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result = q1Value;
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break;
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case 'q3':
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result = q3Value;
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break;
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}
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var precision = !isNaN(decThisCol[i]) ? decThisCol[i] : 2;
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//if outputType is defined
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if (oTypeThisCol && result) {
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result = result.toFixed(precision);
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if (Dom.id(labThisCol[i])) {
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switch (Str.lower(oTypeThisCol)) {
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case 'innerhtml':
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if (isNaN(result) || !isFinite(result) ||
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nbvalues === 0) {
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Dom.id(labThisCol[i]).innerHTML = '.';
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} else {
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Dom.id(labThisCol[i]).innerHTML =
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result;
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}
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break;
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case 'setvalue':
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Dom.id(labThisCol[i]).value = result;
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break;
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case 'createtextnode':
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var oldnode = Dom.id(labThisCol[i])
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.firstChild;
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var txtnode = Dom.text(result);
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Dom.id(labThisCol[i])
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.replaceChild(txtnode, oldnode);
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break;
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}//switch
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}
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} else {
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try {
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if (isNaN(result) || !isFinite(result) ||
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nbvalues === 0) {
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Dom.id(labThisCol[i]).innerHTML = '.';
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} else {
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Dom.id(labThisCol[i]).innerHTML =
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result.toFixed(precision);
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}
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} catch (e) { }//catch
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}//else
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}//for i
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// row(s) with result are always visible
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var totRow = totRowIndex && totRowIndex[ucol] ?
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rows[totRowIndex[ucol]] : null;
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if (totRow) {
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totRow.style.display = '';
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}
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}//for ucol
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}//if typeof
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if (this.onAfterOperation) {
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this.onAfterOperation.call(null, tf);
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}
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}
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destroy() {
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if (!this.initialized) {
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return;
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}
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// unsubscribe to events
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this.tf.emitter.off(['after-filtering'], () => this.calc());
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this.initialized = false;
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}
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}
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