## print("Hello World!");

## 
## // variable

## var season = "rain";

## 
## print("I like ", season);

## 
## // To comment use '//'

## // print("Hello Mars!")


## 
## // Store a number in a variable.

## var number = 10;

## print('The answer is:', number);

## 
## // Use square brackets [] to make a list.

## var numbers = [0, 1, 1, 2, 3, 5];

## print('List of numbers:', numbers);

## 
## // Make a list of strings.

## var strings = ['a', 'b', 'c', 'd', 'e'];

## print('List of strings:', strings);

## 
## // Use curly brackets {} to make a dictionary of key:value pairs.

## var object = {

##   season: 'longrain',

##   year: 2019,

##   crops: ['maize', 'rice']

## };

## 
## print('Dictionary:', object);

## 
## // Access dictionary items using square brackets.

## print('Print year:', object['year']);

## 
## // Access dictionary items using dot notation.

## print('Print stuff:', object.crops);

## 

## 
## // Basic structure

## var myFunction = function(parameter) {

##   // do something

##   statement;

##   return statement;

## };

## 

## 
## // Basic structure

## var addnumber = function(number) {

##   var newvalue =  number + 10

##   return newvalue;

## };

## 
## print(addnumber(20))


## 
## // Make a sequence the hard way.

## var eeList = ee.List([1, 2, 3, 4, 5]);

## 
## // Make a sequence the easy way!

## var sequence = ee.List.sequence(1, 5);

## print('Sequence:', sequence);


## 
## var sum1 = sequence.map(addnumber);

## print("EE server computed objects ", sum1)

## 
## var sum2  = numbers.map(addnumber)

## print("EE client computed objects ", sum2)


## var array1 = ee.Array([1,2,3]) //1-D Array

## print(array1.length());

## var array2 = ee.Array([[1,2,3],[4,5,6]])

## //2-D Array; use length() to return the lengths of each axis

## print(array2.length());

## var first = array2D.slice({axis: 0,start: 0,end: 1,step: 1});

## print(first);


## var image = ee.Image(1);

## print(image);


## var point = ee.Geometry.Point([1.5, 1.5])

## print(point)

## var pointFeature = ee.Feature(point,

## {name: 'mypoint', location: 'no-idea'})

## print(pointFeature)


## var collection = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR');

## 
## // Construct start and end dates:

## var start = ee.Date('2018-01-01');

## var end = ee.Date('2018-12-31');

## 
## //Filter the Landsat 8 collection using the point and the dates

## var filteredCollection = collection.filterBounds(point).filterDate(start, end);

## 

## // Inspect number of tiles returned after the search; we will use the one with more tiles

## print(filteredCollection.size());

## 
## // Also Inspect one file

## var image = filteredCollection.first();

## print(image);

## 
## // print the band names

## print(image.bandNames());

## 
## // Center the map on the image.

## Map.centerObject(point, 9);

## 
## // Image display

## Map.addLayer(image, {}, "Landsat surface reflectance");

## 
## // We specify visualization parameters in a JavaScript dictionary for better plots

## var visParams = {

##   bands: ['B4', 'B3', 'B2'],

##   min: 0,

##   max: 3000,

## };

## 
## Map.addLayer(image, visParams, "Landsat surface reflectance");


## 
## var image = filteredCollection

##               // Sort the collection by a metadata property.

##               .sort('CLOUD_COVER')

##               // Get the first image out of this collection.

##               .first();

## 
## print('Least cloudy Landsat scene of the year:', image2);

## 
## Map.addLayer(image, visParams, "Landsat least cloudy image");


## // Method 1: Using available function

## var ndvi = image.normalizedDifference(['B5', 'B4']);

## 
## // Display the results

## var ndviParams = {min: -1, max: 1, palette: ['blue', 'white', 'green']};

## Map.addLayer(ndvi, ndviParams, 'NDVI');

## 
## 
## // Method 2: Using band arithmetic

## var nir = image.select('B5');

## var red = image.select('B4');

## var ndvi2 = nir.subtract(red).divide(nir.add(red)).rename('NDVI');

## 
## // Method 3: Using image expressions

## var ndvi3 = image.expression(

##     '(NIR - RED) / (NIR + RED)', {

##       'NIR': image.select('B5'),

##       'RED': image.select('B4')

## });

## 

## // Create binary images from thresholds on NDVI.

## // This threshold is excpected to detect green areas

## var veg = ndvi.gt(0.4);

## 
## //Mask areas with the binary image

## var green = veg.updateMask(veg);

## 
## // Define visualization parameters for the spectral indices.

## var ndviViz = {min: -1, max: 1, palette: ['FF0000', '00FF00']};

## 
## Map.addLayer(green, ndviViz, 'vegetations');


## /**

##  * Function to mask clouds based on the pixel_qa band of Landsat 8 SR data.

##  * @param {ee.Image} image input Landsat 8 SR image

##  * @return {ee.Image} cloudmasked Landsat 8 image

##  */

## function maskL8sr(image) {

##   // Bits 3 and 5 are cloud shadow and cloud, respectively.

##   var cloudShadowBitMask = (1 << 3);

##   var cloudsBitMask = (1 << 5);

##   // Get the pixel QA band.

##   var qa = image.select('pixel_qa');

##   // Both flags should be set to zero, indicating clear conditions.

##   var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0)

##                  .and(qa.bitwiseAnd(cloudsBitMask).eq(0));

##   return image.updateMask(mask);

## }


## 
## var image = filteredCollection.first();

## 
## var image_masked = maskL8sr(image);

## 
## Map.addLayer(image, visParams, "Landsat tile with cloud");

## Map.addLayer(image_masked, visParams, "Landsat tile cloud masked");

## 

## 
## var maskedCollection = filteredCollection.map(maskL8sr);

## 
## Map.addLayer(filteredCollection.median(), visParams, "Landsat composite with cloud");

## Map.addLayer(maskedCollection.median(), visParams, "Landsat composite cloud masked");


## // here we compute the following composites

## // general structure: ImageCollection.reduce(ee.Reducer.Name(parameter));

## 
## // Mean

## var mean = maskedCollection.reduce(ee.Reducer.mean());

## 
## //alternate: var mean = maskedCollection.mean();

## 
## // Median

## var med = maskedCollection.reduce(ee.Reducer.median());

## 
## //alternate: var med = maskedCollection.median();

## 

## // Standard Deviation

## var sd = maskedCollection.reduce(ee.Reducer.stdDev());


## 
## var CRS = 'EPSG:4326'; // Only if you want export in specific reference system

## 
## mean = mean.select(["B2_mean","B3_mean","B4_mean","B5_mean"])

## 
## Export.image.toDrive({

##   image: mean,

##   description: 'exporting-composite-to-drive',

##   fileNamePrefix: 'ibadan_landsat_composite',

##   folder: 'GEE_export', // Name of the Google Drive folder

##   scale: 30,

##   maxPixels: 1e13,

##   crs: CRS

## });

## 

## 
## // Simple Cloud Score

## var maskCloudsTOA = function(image, th) {

##   var scored = ee.Algorithms.Landsat.simpleCloudScore(image);

## 

##   //Specify cloud threshold (0-100)- lower number masks out more clouds

##   var mask = scored.select(['cloud']).lte(th);

## 

##   //Make sure no band is just under zero

##   var allBandsGT = image.reduce(ee.Reducer.min()).gt(-0.001);

##   return image.updateMask(mask.and(allBandsGT));

## };

## 
## var toa_dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA');

##