[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

maps/Sudan/Darfur_area_estimate

@@ -30,7 +30,7 @@ if (debug > 0){
   print("Area (ha)", roi.area().divide(10000))
 }
 
-// 2. Color Palettes 
+// 2. Color Palettes
 var palettes = require('users/gena/packages:palettes');
 var classVis = {palette: palettes.cmocean.Speed[7].slice(0,-2)}
 var classVis2 = {min: 0, max: 1.0, palette: ['yellow', 'green'], opacity: 0.8}
@@ -46,9 +46,9 @@ var cropPixels = {}
 var nonCropPixels = {}
 for (var key in roiDict){
   var args = {
-      reducer: ee.Reducer.sum().unweighted(), 
-      geometry: roiDict[key], 
-      scale: 10, 
+      reducer: ee.Reducer.sum().unweighted(),
+      geometry: roiDict[key],
+      scale: 10,
       maxPixels: 1e12,
   }
   cropPixels[key] = ee.Number(mask.reduceRegion(args).get("classification"))
@@ -72,25 +72,25 @@ for (var key in roiDict){
   var totalPixels = cropPixels[key].add(nonCropPixels[key])
   if (debug > 0){
     print("Total Pixels:", totalPixels)
-    print("Reference Crop Proportion", cropProportion)  
+    print("Reference Crop Proportion", cropProportion)
   }
   var totalArea = totalPixels.divide(10e7)
   var sampleBasedCropArea = cropProportion.multiply(totalArea)
   print(key + " Sample Based Crop Area Estimate (Mha) ", sampleBasedCropArea)
 
   // 6. Prepare values for equation
   var testSamples = referenceSamples.filter(ee.Filter.or(
-    ee.Filter.eq("subset", "testing"), 
+    ee.Filter.eq("subset", "testing"),
     ee.Filter.eq("subset", "validation")
   ))
   // Get map classification
   testSamples = mask.sampleRegions({collection: testSamples, scale: 10})
   var W_0 = nonCropPixels[key].divide(totalPixels)
   var W_1 = cropPixels[key].divide(totalPixels)
-  var n_0_ = testSamples.filter("classification == 0").size() 
-  var n_1_ = testSamples.filter("classification == 1").size() 
-  
-  var n_01 = testSamples.filter("classification == 0").filter("label == 1").size() 
+  var n_0_ = testSamples.filter("classification == 0").size()
+  var n_1_ = testSamples.filter("classification == 1").size()
+
+  var n_01 = testSamples.filter("classification == 0").filter("label == 1").size()
   var n_11 = testSamples.filter("classification == 1").filter("label == 1").size()
   var p_01 = W_0.multiply(n_01.divide(n_0_))
   var p_11 = W_1.multiply(n_11.divide(n_1_))
@@ -100,18 +100,13 @@ for (var key in roiDict){
   var numerator1 = (W_1.multiply(p_11)).subtract(p_11.pow(ee.Number(2)))
   var denominator0 = n_0_.subtract(ee.Number(1))
   var denominator1 = n_1_.subtract(ee.Number(1))
-  
+
   var sum0 = numerator0.divide(denominator0)
   var sum1 = numerator1.divide(denominator1)
-  
+
   var standardError = (sum0.add(sum1)).sqrt()
   var standardErrorArea = totalArea.multiply(standardError)
   var confidenceInterval = ee.Number(1.96).multiply(standardErrorArea)
   print(key + " 95% Confidence Interval", confidenceInterval)
-
-}
-
-
-
-
 
+}