fix(contours): use xSanplot instead of absMedian (#1790)

* fix(contours): use xSanplot instead of absMedian

* chore: move calculateSanPlot to data folder

src/component/panels/SpectrumsPanel/base/setting/processSnapPlot.ts

@@ -1,19 +1,16 @@
-import { xNoiseSanPlot, xyReduce } from 'ml-spectra-processing';
+import { xyReduce } from 'ml-spectra-processing';
 
 import { Data1D } from '../../../../../data/types/data1d';
 import { Data2D } from '../../../../../data/types/data2d';
 
+import { calculateSanPlot } from '../../../../../data/utilities/calculateSanPlot';
+
 export function processSnapPlot<T extends '1D' | '2D'>(
   dimension: T,
   data: T extends '1D' ? Data1D : Data2D,
   yLogBase: number,
 ) {
-  const input =
-    dimension === '1D'
-      ? prepare1DData(data as Data1D)
-      : prepare2DData(data as Data2D);
-
-  const sanResult = xNoiseSanPlot(input);
+  const sanResult = calculateSanPlot(dimension, data);
   const sanPlot: any = {};
   const lines: any = {};
   for (let plotKey in sanResult.sanplot) {
@@ -28,29 +25,6 @@ export function processSnapPlot<T extends '1D' | '2D'>(
   return { sanPlot, lines };
 }
 
-function prepare1DData(data: Data1D) {
-  const length = data.re.length;
-  const jump = Math.floor(length / 307200) || 1;
-  const array = new Float64Array((length / jump) >> 0);
-  let index = 0;
-  for (let i = 0; i < array.length; i += jump) {
-    array[index++] = data.re[i];
-  }
-  return array;
-}
-
-function prepare2DData(data: Data2D) {
-  let cols = data.z[0].length;
-  let rows = data.z.length;
-  let jump = Math.floor((cols * rows) / 204800) || 1;
-  const array = new Float64Array(((cols * rows) / jump) >> 0);
-  let index = 0;
-  for (let i = 0; i < array.length; i += jump) {
-    array[index++] = data.z[(i / rows) >> 0][i % rows];
-  }
-  return array;
-}
-
 function getLine(value, data, options) {
   const { log10, abs } = Math;
   const { yLogBase } = options;

src/data/data2d/Processing2D.ts

@@ -1,5 +1,7 @@
 import { Conrec } from 'ml-conrec';
-import { matrixAbsoluteMedian } from 'ml-spectra-processing';
+
+import { Data2D } from '../types/data2d';
+import { calculateSanPlot } from '../utilities/calculateSanPlot';
 
 export const defaultContourOptions = {
   positive: {
@@ -41,7 +43,8 @@ export default class Processing2D {
 
     this.conrec = new Conrec(minMax.z, { xs, ys, swapAxes: false });
 
-    this.median = matrixAbsoluteMedian(minMax.z);
+    const sanResult = calculateSanPlot('2D', minMax as Data2D);
+    this.median = sanResult.positive;
 
     this.minMax = minMax;
   }

src/data/utilities/calculateSanPlot.ts

@@ -0,0 +1,43 @@
+import { xNoiseSanPlot } from 'ml-spectra-processing';
+
+import { Data1D } from '../types/data1d';
+import { Data2D } from '../types/data2d';
+
+export function calculateSanPlot<T extends '1D' | '2D'>(
+  dimension: T,
+  data: T extends '1D' ? Data1D : Data2D,
+) {
+  const input =
+    dimension === '1D'
+      ? prepare1DData(data as Data1D)
+      : prepare2DData(data as Data2D);
+
+  return xNoiseSanPlot(input);
+}
+
+function prepare1DData(data: Data1D) {
+  const length = data.re.length;
+  const jump = Math.floor(length / 307200) || 1;
+  const array = new Float64Array((length / jump) >> 0);
+  let index = 0;
+  for (let i = 0; i < array.length; i += jump) {
+    array[index++] = data.re[i];
+  }
+  return array;
+}
+
+function prepare2DData(data: Data2D) {
+  let cols = data.z[0].length;
+  let rows = data.z.length;
+  let jump = Math.floor((cols * rows) / 204800) || 1;
+  const array = new Float64Array(((cols * rows) / jump) >> 0);
+  let index = 0;
+  // console.log('jump', jump, cols * rows);
+  for (let r = 0; r < rows; r += 1) {
+    for (let c = 0; c < cols; c += jump) {
+      array[index++] = data.z[r][c];
+    }
+  }
+
+  return array;
+}