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I am trying to create a code that could be helpful for making quarterly composites from 2013 to 2016 in order to calculate NDVI and EVI. I have figured out this code:

var start = ee.Date('2013-01-01');
var end = ee.Date('2019-01-01');
var numbQuarters = end.difference(start, 'month').divide(3).ceil();
//NDVI
var ndvi = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
  .filterBounds(table)
  .filterMetadata('CLOUD_COVER','less_than',30)
  .map(function(image) {
    return image.select().addBands(image.normalizedDifference(['B5', 'B4']));
  });
//EVI?
// make a composite image for every quarter
var quarterlyImages = ee.ImageCollection.fromImages(
            ee.List.sequence(1, numbQuarters).map(function(quarter){
              var startTemp = start.advance(ee.Number(quarter).subtract(1).multiply(3), 'month');
              var endTemp = start.advance(ee.Number(quarter).multiply(3), 'month');
              //var image = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")
                                //.filterDate(startTemp, endTemp)
                                //.filterBounds(table)
                                //.filterMetadata('CLOUD_COVER','less_than',30)
                                // define a way to composite an image
                                //.mosaic(); // .mean(); .mossaic(); .max(); etc
              var subset = ndvi.filterDate(startTemp,endTemp);
              
              //return image.set('system:time_start', startTemp.millis(),
                               //'system:time_end', endTemp.millis());
              return subset.mean().set('system:time_start',startTemp);
            }));
            
//var median = quarterlyImages.median();
//print(quarterlyImages,median);
//Map.addLayer(median,{ min:-1, max: 1, palette: ['blue', 'white', 'green']},'median');
//Lay anh trong imageCollection
var sort = quarterlyImages.toList(100);
var clip1 = ee.Image(sort.get(1)).clip(table);//1st image 2nd quarter of 2013
var clip2 = ee.Image(sort.get(2)).clip(table);//2nd image 3rd quarter of 2013
var clip3 = ee.Image(sort.get(3)).clip(table);//3rd image 4th quarter of 2013


// print some stuff
///print('image collection', quarterlyImages)
//print('startDates', quarterlyImages.aggregate_array('system:time_start'))
//print('sendDates', quarterlyImages.aggregate_array('system:time_end'))
Map.addLayer(clip1,{ min:-1, max: 1, palette: ['blue', 'white', 'green']}, 'Phu Quoc 01');
Map.addLayer(clip2,{ min:-1, max: 1, palette: ['blue', 'white', 'green']}, 'Phu Quoc 02');
Map.addLayer(clip3,{ min:-1, max: 1, palette: ['blue', 'white', 'green']}, 'Phu Quoc 03');
Map.centerObject(table, 7);

however when I apply this code above to extract images, some of them have too many clouds so that I could not see the image clearly. I am thinking of using a cloud masking function to solve this problem but I assume that I did not make it correctly and therefore it did not work well. Here is the code:

var cloud_pixel_qa =  image.select('pixel_qa').bitwiseAnd(32).neq(0);//2^5
var cloud_shadow_pixel_qa =  image.select('pixel_qa').bitwiseAnd(8).neq(0);//2^3
var maskedClouds = ((cloud_pixel_qa).or(cloud_shadow_pixel_qa));
return image.updateMask((maskedClouds.add(1).unmask(0)).eq(1));
var L8_coll_mask=L8_coll.map(masking_cloud)
Map.addLayer(L8_coll_mask.median().clip(LĐ_T),vizParams,'false color composite cloud mask');

Does anyone have any suggestions?

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