feat(meanFilter): filter a serie2D by it's median (#36)

* feat(meanFilter): filter a serie2D by it's median

* feat(meanFilter): allows to rename the filtered serie

package.json

@@ -55,6 +55,7 @@
     "chemcalc": "^3.3.3",
     "jcampconverter": "^2.3.0",
     "ml-array-max": "^1.0.0",
+    "ml-array-mean": "^1.0.0",
     "ml-gsd": "^2.0.1",
     "ml-regression-polynomial": "^1.0.2",
     "mzmjs": "^0.2.0",

src/Chromatogram.js

@@ -10,6 +10,7 @@ import {merge} from './util/merge';
 import {getKovatsRescale} from './getKovatsRescale';
 import {getClosestTime} from './util/getClosestTime';
 import {applyLockMass} from './ms/applyLockMass';
+import {meanFilter} from './ms/meanFilter';
 import {toJSON} from './to/json';
 import {getClosestData} from './util/getClosestData';
 
@@ -261,6 +262,22 @@ export class Chromatogram {
         const json = JSON.parse(JSON.stringify(this));
         return fromJSON(json);
     }
+
+    /**
+     * Filter the given serie2D based on it's median value
+     * @param {string} serieName
+     * @param {object} [options]
+     * @param {string} [options.serieName = 'msMedian'] - Name of the new serie
+     * @param {number} [options.factor = 2] - The values under the median times this factor are removed
+     */
+    meanFilter(serieName, options = {}) {
+        var serie = meanFilter(this, serieName, options);
+        if (options.serieName) {
+            this.series[options.serieName] = serie;
+        } else {
+            this.series.msMedian = serie;
+        }
+    }
 }
 
 Chromatogram.prototype.applyLockMass = applyLockMass;

src/__tests__/ms/meanFilter.js

@@ -0,0 +1,62 @@
+import {Chromatogram} from '../..';
+import {meanFilter} from '../../ms/meanFilter';
+
+test('simple case', () => {
+    let chromatogram = new Chromatogram(
+        [1, 2], {
+            ms: [
+                [[100, 200, 300], [10, 20, 300]],
+                [[600], [274]]
+            ]
+        }
+    );
+
+    let ms = meanFilter(chromatogram, 'ms');
+    expect(ms.data).toEqual([
+        [[300], [300]],
+        [[], []]
+    ]);
+});
+
+describe('inplace', () => {
+    it('default behavior', () => {
+        let chromatogram = new Chromatogram(
+            [1, 2], {
+                ms: [
+                    [[100, 200, 300], [10, 20, 300]],
+                    [[600], [274]]
+                ]
+            }
+        );
+        chromatogram.meanFilter('ms');
+        expect(chromatogram.series.msMedian.data).toEqual([
+            [[300], [300]],
+            [[], []]
+        ]);
+        expect(chromatogram.series.ms.data).toEqual([
+            [[100, 200, 300], [10, 20, 300]],
+            [[600], [274]]
+        ]);
+    });
+
+    it('input name', () => {
+        let chromatogram = new Chromatogram(
+            [1, 2], {
+                ms: [
+                    [[100, 200, 300], [10, 20, 300]],
+                    [[600], [274]]
+                ]
+            }
+        );
+        chromatogram.meanFilter('ms', {serieName: 'filtered'});
+        expect(chromatogram.series.filtered.data).toEqual([
+            [[300], [300]],
+            [[], []]
+        ]);
+        expect(chromatogram.series.ms.data).toEqual([
+            [[100, 200, 300], [10, 20, 300]],
+            [[600], [274]]
+        ]);
+    });
+});
+

src/ms/meanFilter.js

@@ -0,0 +1,28 @@
+import arrayMean from 'ml-array-mean';
+import {serieFromArray} from '../serieFromArray';
+
+export function meanFilter(chromatogram, serieName, options = {}) {
+    const {
+        factor = 2
+    } = options;
+
+    var serie = chromatogram.getSerie(serieName);
+    var filtered = [];
+    for (var i = 0; i < serie.data.length; i++) {
+        filtered.push(applyFilter(serie.data[i], factor));
+    }
+
+    return serieFromArray(filtered);
+}
+
+function applyFilter(serie, factor) {
+    const meanIntensity = factor * arrayMean(serie[1]);
+    var filtered = [[], []];
+    for (var i = 0; i < serie[0].length; i++) {
+        if (serie[1][i] > meanIntensity) {
+            filtered[0].push(serie[0][i]);
+            filtered[1].push(serie[1][i]);
+        }
+    }
+    return filtered;
+}

yarn.lock

@@ -4352,6 +4352,10 @@ ml-array-max@^1.0.0:
   version "1.0.0"
   resolved "https://registry.yarnpkg.com/ml-array-max/-/ml-array-max-1.0.0.tgz#c2ee7d984dddb8cb77e6a1e29714a6f271a17a48"
 
+ml-array-mean@^1.0.0:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/ml-array-mean/-/ml-array-mean-1.0.0.tgz#0f51113f1fbaba7262c843a945234228fe87b157"
+
 ml-array-utils@^0.3.0:
   version "0.3.0"
   resolved "https://registry.yarnpkg.com/ml-array-utils/-/ml-array-utils-0.3.0.tgz#c0cc5afdc1cb051f5a83b1ce71641c243672f649"
@@ -5483,10 +5487,6 @@ rollup@^0.42.0:
   dependencies:
     source-map-support "^0.4.0"
 
-round-to@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/round-to/-/round-to-2.0.0.tgz#bcef4f2bcafd9480902c2142150b28c897f03e37"
-
 run-async@^2.0.0, run-async@^2.2.0:
   version "2.3.0"
   resolved "https://registry.yarnpkg.com/run-async/-/run-async-2.3.0.tgz#0371ab4ae0bdd720d4166d7dfda64ff7a445a6c0"