Tag: b2

Connection Algorithm with Constraints

Post author By Full Stack
Post date April 3, 2021
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I have block categories A, B, C, … and each category has blocks. all blocks in each category have ports p0, p1, p2, … and have constraints to connect as: A: a0, a1, a2, … p0: B, C p1: B p2: B, C p3: C B: b0, b1, b2, … p0: A C: c0, c1, […]

Mastering Development

Converting a complicated date function to

Post author By Full Stack
Post date March 25, 2021
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I am trying to convert a series of date functions from excel into a single Actian-PSQL function. The excel is as follows; A1=Today() B1=DATE(YEAR($A$1),MONTH($A$1),DAY($A$1) + 2-WEEKDAY($A$1)) [This Monday] B2=DATE(YEAR($A$1),MONTH($A$1),DAY($A$1) +9-WEEKDAY($A$1)) [Next Monday] B3=IF(A1>B1,B2,B1) The goal of these functions is to determine the date of the next coming Monday, and query the database for all items […]

Tags and query the database for all items with that date. I currently plan on using 5 iterations of this query for each day of the week., B1) The goal of these functions is to determine the date of the next coming Monday, b2, DAY($A$1) + 2-WEEKDAY($A$1)) [This Monday] B2=DATE(YEAR($A$1), DAY($A$1) +9-WEEKDAY($A$1)) [Next Monday] B3=IF(A1>B1, I am trying to convert a series of date functions from excel into a single Actian-PSQL function. The excel is as follows; A1=Today() B1=DAT, MONTH($A$1)

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How to add values of a mask image as one of the classification classes

Post author By Server Questions
Post date March 13, 2021
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I have performed supervised classification on GEE in the Russian Lena Delta to map different vegetation classes using sentinel 2 images (BUT i had to mask out the water and sandbank pixels) to improve the classification results. However, I would like now (after the classification) to include the sand and water masks as additional classes […]

Tags '004C00', '011301']; var ndviCompMask = ndviComp.updateMask(WaterMask).updateMask(SandMask); //classification var samplepoints = ee.FeatureCollectio, '011D01', '012E01', '023B01', '056201', '1c5f2c', '2018-09-15') .filterBounds(roi) .map(cloudMask).map(getNDVI); //Global surface water var gsw = ee.Image('JRC/GSW1_0/GlobalS, '202cff']}, '207401', '3E8601', '529400', '66A000', '6c9fb8', '74A901', '82ba9e', '99B718', 'a3cc51', 'af963c', 'B':[4, 'B4']).rename('NDVI'); return image.addBands(ndvi) } // load Sentinel2 data for summer 2018 var S2dat = ee.ImageCollection("COPERNICUS/S2", 'b5c58f', 'b8d9eb', 'ccb879', 'CE7E45', 'dcd939', 'DF923D', 'f19414', 'F1B555', 'FCD163', 'NDVI']; var label = 'first'; // Overlay the points on the imagery to get training. var training = ndviCompMask.select(bands).sampleRegions(, 'sample points', 'scale': 10 }); //print(training); var trained = ee.Classifier.smileRandomForest(10).train(training, "coordinates": [ [ [ 126.09710205078125, "coordinates": [ 126.4953564453125, "coordinates": [] }); var roi = geoms.buffer(25000); // to test on smaller subset // var roi = roiDelta; // cloud maks function cloudMa, "evenOdd": true } }, "geometries": [ { "type": "Point", "type": "Polygon" }, [ 126.061396484375, [ 126.654658203125, [ 126.676630859375, {}, 0, 10); //Map.addLayer(samplepoints, 10); var tablePtsID = ee.FeatureCollection(tablePts.map(function(feat){ var id = ee.Feature(feat).get('id'); return ee.Feature(feat).set(, 2000, 72.19780053250948 ], 72.20787438492015 ], 72.3816136993482 ] }, 72.43639841991575 ] ] ], 72.44551315285538 ], B=10, B11, B12, b2, b3, b5, B6, b7, b8, b9, bands); print(trained); var classified = ndviCompMask.select(bands).classify(trained).clip(roi); // print(classified); //Map.addLayer(class, classes, ee.Reducer.first(), false); /// legend, false); var tablePts = HC_Heim.reduceRegions(samplepoints, geodesic: true, geoms = /* color: #98ff00 */ /* shown: false */ ee.Geometry({ "type": "GeometryCollection", HC_Heim = ee.Image("*******"), I have performed supervised classification on GEE in the Russian Lena Delta to map different vegetation classes using sentinel 2 images (BUT, I would like now (after the classification) to include the sand and water masks as additional classes in the classified layer (for visualizat, id); })); // This property of the table stores the land cover labels. var bands = ['B1', label: '' }, max: 11, min: 0, palette: [ '68ab5f', properties: [label]

Database Development

Sum Scores in Tree Structure Based on Rollup Type

Post author By Jessica Alba
Post date March 7, 2021
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I am trying to calculate totals for each node of a tree structure, Calculating the total depends on roll up type I have Structure like below if node type is current list = Look to current node, no need I go down in the tree if node type is child = ignore the current node […]

Tags 'Child List', 'Current and Child', 'Current List', [Type], 1, 1 AS Lvl, 10); INSERT INTO TreeTable(Teamid, 100); INSERT INTO TreeTable(Teamid, 110); My Attempt: WITH CTE_Groups AS ( SELECT TreeTable.teamid AS StartID, 2, 20); INSERT INTO TreeTable(Teamid, 30); INSERT INTO TreeTable(Teamid, 4, 40); INSERT INTO TreeTable(Teamid, 5, 50); INSERT INTO TreeTable(Teamid, 6, 60); INSERT INTO TreeTable(Teamid, 7, 70); INSERT INTO TreeTable(Teamid, 8, 80); INSERT INTO TreeTable(Teamid, 90); INSERT INTO TreeTable(Teamid, A[1], b1, b2, because I need to have a vision of the complete path: Please Help Expected Result: +--------+-----------------------+ | TeamID | S, but then I got stuck on how to exclude the nodes based on type, c(1, c2, c3, Calculating the total depends on roll up type I have Structure like below if node type is current list = Look to current node, CTE_Groups.Lvl + 1 AS Lvl, CTE_Groups.StartName, d1, d2, d3, e1), e2), I am trying to calculate totals for each node of a tree structure, Key_Result_Value INTEGER NOT NULL ); INSERT INTO TreeTable(Teamid, Key_Result_Value) VALUES (1, Key_Result_Value) VALUES (10, Key_Result_Value) VALUES (11, Key_Result_Value) VALUES (2, Key_Result_Value) VALUES (3, Key_Result_Value) VALUES (4, Key_Result_Value) VALUES (5, Key_Result_Value) VALUES (6, Key_Result_Value) VALUES (7, Key_Result_Value) VALUES (8, Key_Result_Value) VALUES (9, lvl ScreenShot of my Attempt to get all children of the node, Name, Name VARCHAR(2) NOT NULL, no need I go down in the tree if node type is child = ignore the current node and go down if node type is current and child = I include the, null, ParentId, ParentId INTEGER, ParentName, ParentName VARCHAR(4), TreeTable.Key_Result_Value FROM TreeTable INNER JOIN CTE_Groups ON CTE_Groups.teamid = TreeTable.ParentID ) select * fro, TreeTable.Key_Result_Value FROM TreeTable UNION ALL SELECT CTE_Groups.StartID, TreeTable.Name, TreeTable.Name AS StartName, TreeTable.ParentID, TreeTable.teamid, TreeTable.Type, Type VARCHAR(17) NOT NULL

Mastering Development

how to reduce the noise of an image on python?

Post author By Full Stack
Post date February 27, 2021
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This is my code so far. I am totally lost and not sure what to do. its python language. I cant use cv2 or matpolibic. I am only allowed to use cimpl and numpy from Cimpl import* import numpy as np def reduce_noise (image: Image) -> Image: new_image = copy(image) for pixel in image: X, […]

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Export filtered image (based on date) from Google Earth Engine for NDVI

Post author By Server Questions
Post date February 26, 2021
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I am trying to export an NDVI image from Landsat5 to Drive but it seems like the date filter is not being applied in the exported image. How do I resolve this? Code below. //Var geometry is a shapefile of area var image = ee.Image( l8.filterBounds(geometry) .filterDate(‘2012-06-15’, ‘2012-07-15’) ); var ndvi = image.normalizedDifference([‘B5’, ‘B4’]).rename(‘NDVI’); var […]

Tags '2012-07-15') ); var ndvi = image.normalizedDifference(['B5', 'B4']).rename('NDVI'); return image.addBands(ndvi); }; var ndvi = addNDVI(image).select('NDVI'); var withNDVI = l8.map(addNDVI); var gre, 'B4']).rename('NDVI'); var addNDVI = function(image) { var ndvi = image.normalizedDifference(['B5', 'Greenest pixel composite'); var ndvi1 = greenest.select('NDVI').rename('ndvi'); Export.image.toDrive({ image: ndvi1, b2, b3, description: 'ndvi2012', fileFormat: 'GeoTIFF', folder: 'Landsat5', formatOptions: { cloudOptimized: true } });, I am trying to export an NDVI image from Landsat5 to Drive but it seems like the date filter is not being applied in the exported image. How, max: 0.3}; Map.addLayer(greenest.clip(geometry), maxPixels: 1e9 });, region: geometry, scale: 30, visParams

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How can I make Verilog HDL ModelSim test bench for a simple ripple adder circuit output an addition table instead of a long list of every calculation?

I’ve completed my assignment and I’d like to make the output look something like this, with one table for sum output and one table for carry output: Sum: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 __ __ __ __ __ __ __ __ __ __ __ __ […]

Tags .CIN(CIN) ); initial begin for(i=0; i < 2**8; i = i+1) begin {A, "s3", ['s2', #SS, a, A[1], a0, A2, A3, B, B; output wire [4:0] S; output wire [3:0] C; CA bit0 (CIN, B} = i; CIN = 0; #5; $display( "%d + %d = %d | %d", b0, b1, b2, b3, bb, but I know enough to at least get me this far. I think that this could be fun to learn. Any guidance or references to external instructions w, C ); #5; end end endmodule The testbench output looks like this right now, C); input CIN; input wire [3:0] A, C[0]); CA bit1 (C[0], C[1]); CA bit2 (C[1], C[2]); CA bit3 (C[2], C[3]); assign S[4] = C[3]; endmodule module CA(cIn, C=c, cIn; output s, cOut; assign cOut = (a & b)|((a ^ b) & cIn); assign s = (cIn ^ (a ^ b)); endmodule And this is my current testbench, cout); input a, followed by the sum and the carry bit at the end: 0 + 0 = 0 | 0 0 + 1 = 1 | 0 0 + 2 = 2 | 0 ... 15 + 13 = 28 | 15 15 + 14 = 29 |, I've completed my assignment and I'd like to make the output look something like this, RCA.v: module RCA(CIN, s, S0, S1, tb_RCA.v: `timescale 1ns / 1ps module tb_RCA (); reg [3:0] A; reg [3:0] B; reg CIN; wire [4:0] S; wire [3:0] C; intege, where the first two numbers are each number to be summed, with one table for sum output and one table for carry output: Sum: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 __ __ __ __ __ __ __

Mastering Development

How to change CSV values into multiple limited columns in Postgres?

Post author By Full Stack
Post date February 22, 2021
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I have a table with a single column in a Postgres 13.1 database. It consists of many rows with comma-separated values – around 20 elements currently. I want to split the data into multiple columns. But I have only a limited number of columns say 5 and more than 5 CSV values in a single […]

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Image Segmentation and Classification with Google Earth Engine

Post author By Server Questions
Post date February 21, 2021
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I’m new to using Google Earth Engine and I obtained the following code from Medium and tried to update it with the AOI and training data for my specific area. I am trying to classify landcover for an AOI and then export this to csv using CORINE landuse categories The issue seems to be with […]

Tags '2011-12-31')) .filterBounds(AOI).sort('CLOUD_COVER').first(); // Sort by scene cloudiness, ascending. // Get the first (least cloudy) scene. // Compute cloud score. var cloudScore = ee.Algorithm, b2, bands:"B3, I'm new to using Google Earth Engine and I obtained the following code from Medium and tried to update it with the AOI and training data for, max: 255.0

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Image Segmentation and Classification with Google Earth Engine

Post author By Server Questions
Post date February 20, 2021
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Tags -0.09, ' green 3. @', '0.41', '2011-12-31')) .filterBounds(AOI).sort('CLOUD_COVER').first(); // Sort by scene cloudiness, 'asm'); var bands = ['B3', 'B4_asm', 'B4']; var FullImage = img.select(bands).float().divide(255); // Segmentation --------------------------------------------------------------, 'CC0000']}, 'contrast'); var asm = glcm.select('B4_asm'); //Map.addLayer(asm, 'darkgreen', 'FALSE')); //extract features from train polygons --------------------------------------------- var train_areas = train_polys .reduceToI, 'FALSE')); return feat.set('belongsTo', 'Green', 'id') var FullImage_sizes = ee.Image.pixelLonLat().addBands(FullImage.select('id')).reduceConnectedComponents(ee.Reducer.minMax(), 'id') var FullImage_width = FullImage_sizes.select('longitude_max').subtract(FullImage_sizes.select('longitude_min')).rename('width') var Ful, 'orange' //2 //, 'segments'); // Select train polygons from points ------------------------------------------------------- //var FullImage = FullImage.addBa, 'yellow', 'yellow']//4 //} //Map.addLayer(classified_RF, "-0.15", "0.30", "0.50", "B3"]).rename('NDVI') Map.addLayer(ndvi, "B4_corr"]).float()) //.addBands(entropy); var FullImage = FullImage.addBands(ndvi).addBands(ndviGradient); var tr, "max": 4 }, "NDVI gradient") // Compute entropy and display. var square = ee.Kernel.square({radius: 4}); var entropy = img.select('B4').toByte().entrop, "NDVI steps") //Map.addLayer(ndvi.gt().reduce('sum'), "NDVI steps") var ndviGradient = ndvi.gradient().pow(2).reduce('sum').sqrt().rename('NDVI_GRAD') Map.addLayer(ndviGradient, "NDVI") //Map.addLayer(ndvi.gt([0.03, "OBIA_RF"); Map.addLayer(classified_RF); Please see link to code: https://code.earthengine.google.com/?scriptPath=users454262TestP, "Orange", "Red": 1, "size": 20, (0.56, {}, }); var empty = ee.Image().byte(); var outline = empty.paint({ featureCollection: vectors, //palette: [ 'blue' //0 //, 0-.47, 0.]).reduce('sum'), 0.06, 0.12, 0.18, 0.21, 0.24, 0.27, 0.33, 0.35), 0.38, 0.44, 0.53, 0.59, 0.62, ascending. // Get the first (least cloudy) scene. // Compute cloud score. var cloudScore = ee.Algorithm, b1, B1" }; Map.centerObject(AOI); Map.addLayer(img); var ndvi = img.normalizedDifference(["B4", b2, bands:"B3, classProperty:'LC', cls); }); return mappedPolys; }).flatten().filter(ee.Filter.neq('belongsTo', Color) = (1, compactness: 0, connectivity: 4, ee.ErrorMargin(1)); var property = ee.String(ee.Algorithms.If(intersects, FullImage_area, FullImage_height ]).float(); var clip_Image = Pred_bands.clip(train_polys) train_areas = train_areas.addBands(clip_Image) var predictionBa, FullImage_median, FullImage_std, FullImage_width, geometry: aoi, I'm new to using Google Earth Engine and I obtained the following code from Medium and tried to update it with the AOI and training data for, inputProperties: predictionBands}); var classified_RF = Pred_bands.select(predictionBands).classify(RF); //var vis_RF = {min: 0, labelBand: 'id' }); var FullImage_area = ee.Image.pixelArea().addBands(FullImage.select('id')).reduceConnectedComponents(ee.Reducer.sum(), labelBand: 'id' }); var FullImage_median = FullImage.reduceConnectedComponents({ reducer: ee.Reducer.median(), labelBand: 'id' }); var FullImage_std = FullImage.reduceConnectedComponents({ reducer: ee.Reducer.stdDev(), max: 1500 }), max: 255.0, max: 26, max:0.01}, max:0.8, maxPixels: 1e13, min: 0, min: 1, min=0, neighborhoodSize: 128, palette: ['0000CC', palette: ['FF0000', palette: ['red', palette: palette}, properties: ['LC'], property).set('LC', reducer: ee.Reducer.countEvery(), reducer: ee.Reducer.first() }).rename('id').toInt(); FullImage = FullImage.addBands(predict_image) var FullImage_mean = FullImage.reduceCon, reducer: ee.Reducer.first() }).rename('LC').toInt(); // Extract features from image --------------------------------------------------------, scale: 30, scale: 30 }); var RF = ee.Classifier.randomForest(30).train({features:classifierTraining, seeds: seeds }) var clusters_snic = snic.select("clusters") var vectors = clusters_snic.reduceToVectors({ geometryType: 'polygon', true, vis_RF, width: 1 }); Map.addLayer(outline