nmr-processing - npm Package Compare versions

Comparing version 0.8.0 to 0.9.0

CHANGELOG.md

 # Changelog
 
+## [0.9.0](https://www.github.com/cheminfo/nmr-processing/compare/v0.8.0...v0.9.0) (2021-02-09)
+
+
+### Features
+
+* change nH to integrationSum ([#33](https://www.github.com/cheminfo/nmr-processing/issues/33)) ([801960a](https://www.github.com/cheminfo/nmr-processing/commit/801960a8fa765d3b3bbc9ec555496a9b505827e7))
+
 ## [0.8.0](https://www.github.com/cheminfo/nmr-processing/compare/v0.7.0...v0.8.0) (2021-02-09)
@@ -4,0 +11,0 @@

package.json

 {
   "name": "nmr-processing",
-  "version": "0.8.0",
+  "version": "0.9.0",
   "description": "",
@@ -5,0 +5,0 @@ "main": "lib/index.js",

src/peaks/peaksToRanges.js

@@ -7,14 +7,2 @@ // import { Ranges } from 'spectra-data-ranges';
 
-const defaultOptions = {
-  nH: 100,
-  clean: 0.5,
-  thresholdFactor: 1,
-  compile: true,
-  integralType: 'sum',
-  optimize: true,
-  joinOverlapRanges: true,
-  frequencyCluster: 16,
-  keepPeaks: false,
-};
-
 /**
@@ -25,10 +13,10 @@ * This function clustering peaks and calculate the integral value for each range from the peak list returned from extractPeaks function.
  * @param {Object} [options={}] - options object with some parameter for GSD, detectSignal functions.
- * @param {Number} [options.integrationSum = 100] - Number of hydrogens or some number to normalize the integral data. If it's zero return the absolute integral value
- * @param {String} [options.integralType = 'sum'] - option to chose between approx area with peaks or the sum of the points of given range ('sum', 'peaks')
- * @param {Number} [options.frequencyCluster = 16] - distance limit to clustering peaks.
+ * @param {Number} [options.integrationSum=100] - Number of hydrogens or some number to normalize the integral data. If it's zero return the absolute integral value
+ * @param {String} [options.integralType='sum'] - option to chose between approx area with peaks or the sum of the points of given range ('sum', 'peaks')
+ * @param {Number} [options.frequencyCluster=16] - distance limit to clustering peaks.
  * @param {Number} [options.clean=0.4] - If exits it remove all the signals with integration < clean value
- * @param {Boolean} [options.compile = true] - If true, the Janalyzer function is run over signals to compile the patterns.
- * @param {Boolean} [options.keepPeaks = false] - If true each signal will contain an array of peaks.
- * @param {String} [options.nucleus = '1H'] - Nucleus
- * @param {String} [options.frequency = 400] - Observed frequency
+ * @param {Boolean} [options.compile=true] - If true, the Janalyzer function is run over signals to compile the patterns.
+ * @param {Boolean} [options.keepPeaks=false] - If true each signal will contain an array of peaks.
+ * @param {String} [options.nucleus='1H'] - Nucleus
+ * @param {String} [options.frequency=400] - Observed frequency
  * @returns {Array}
@@ -38,10 +26,28 @@ */
 export function peaksToRanges(data, peakList, options = {}) {
-  options = Object.assign({}, defaultOptions, options);
   let {
     integrationSum = 100,
-    joinOverlapRanges,
+    joinOverlapRanges = true,
     clean = 0.4,
-    compile,
+    compile = true,
+    integralType = 'sum',
+    frequency = 400,
+    frequencyCluster = 16,
+    keepPeaks = false,
+    nucleus = '1H',
   } = options;
-  let signals = detectSignals(data, peakList, options);
+
+  let signalOptions = {
+    integrationSum,
+    integralType,
+    frequencyCluster,
+    frequency,
+    nucleus,
+  };
+
+  if (data.x[0] > data.x[1]) {
+    data.x = data.x.reverse();
+    data.y = data.y.reverse();
+  }
+
+  let signals = detectSignals(data, peakList, signalOptions);
   if (clean) {
@@ -88,4 +94,4 @@ for (let i = 0; i < signals.length; i++) {
           }
-          options.nH = Math.abs(nHi);
-          let ranges = detectSignals(data, peaks1, options);
+          signalOptions.integrationSum = Math.abs(nHi);
+          let ranges = detectSignals(data, peaks1, signalOptions);
 
@@ -138,3 +144,3 @@ for (let j = 0; j < ranges.length; j++) {
     };
-    if (options.keepPeaks) {
+    if (keepPeaks) {
       ranges[i].signal[0].peak = signal.peaks;
@@ -158,9 +164,11 @@ }
  * @param {object} data - spectra data
- * @param {object} peakList - nmr signals
- * @param {object} options
- * @param {...number} options.nH - Number of hydrogens or some number to normalize the integral data, If it's zero return the absolute integral value
- * @param {String} options.integralType - option to chose between approx area with peaks or the sum of the points of given range
- * @param {...number} options.frequencyCluster - distance limit to clustering the peaks.
+ * @param {array} peakList - nmr signals
+ * @param {object} [options = {}]
+ * @param {number} [options.integrationSum='100'] - Number of hydrogens or some number to normalize the integration data, If it's zero return the absolute integral value
+ * @param {string} [options.integralType='sum'] - option to chose between approx area with peaks or the sum of the points of given range
+ * @param {number} [options.frequencyCluster=16] - distance limit to clustering the peaks.
  * range = frequencyCluster / observeFrequency -> Peaks withing this range are considered to belongs to the same signal1D
- * @return {Array} nmr signals
+ * @param {string} [options.nucleus='1H'] - - Nucleus
+ * @param {String} [options.frequency = 400] - Observed frequency
+ * @return {array} nmr signals
  * @private
@@ -170,3 +178,3 @@ */
   let {
-    nH = 100,
+    integrationSum = 100,
     integralType = 'sum',
@@ -255,4 +263,4 @@ frequencyCluster = 16,
 
-  if (nH > 0) {
-    let integralFactor = nH / spectrumIntegral;
+  if (integrationSum > 0) {
+    let integralFactor = integrationSum / spectrumIntegral;
     for (let i = 0; i < signals.length; i++) {
@@ -259,0 +267,0 @@ let integral = signals[i].integralData;

src/xy/xyAutoPeaksPicking.js

@@ -13,2 +13,3 @@ import { gsd, joinBroadPeaks, optimizePeaks } from 'ml-gsd';
  * @param {number} [options.broadWidth = 0.25] - Threshold to determine if some peak is candidate to clustering into range.
+ * @param {number} [options.thresholdFactor=3] - the factor that multiplies the noise level to set up a threshold to select peaks with respect to the intensity.
  * @param {number} [options.noiseLevel = median(data.y) * (options.thresholdFactor || 3)] - Noise threshold in spectrum y units. Default is three/thresholdFactor times the absolute median of data.y.
@@ -21,3 +22,2 @@ * @param {number} [options.factorWidth = 4] - factor to determine the width at the moment to group the peaks in signals in 'GSD.optimizePeaks' function.
  * @param {object} [options.optimization.options = {}] - options for the specific kind of algorithm.
- * @param {Boolean} [options.compile = true] - If true, the Janalyzer function is run over signals to compile the patterns.
  * @param {Boolean} [options.smoothY = true] - Select the peak intensities from a smoothed version of the independent variables?
@@ -24,0 +24,0 @@ * @param {Boolean} [options.optimize = true] - if it's true adjust an train of gaussian or lorentzian shapes to spectrum.

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Improved metrics

Total package byte prevSize

259496

increased by0.37%

Lines of code

10497

increased by0.11%

No dependency changes