I have some data like this
var data = [{date:'2013/01/01', claimNo:1},
{date:'2013/01/01', claimNo:2},
{date:'2013/01/02', claimNo:3}]
I want to plot a line chart in DC so that the days are on the X-Axis and the total # of claims are on the Y-Axis.
I have code like this
var ndx = crossfilter(data);
data.forEach(function (e) {
e.dd = dateFormat.parse(e.dd);
e.month = d3.time.month(e.dd);
});
var dateDim = ndx.dimension(function (d) {
return d.dd;
});
var datesClaimsGroup = dateDim.group();
var claimsLineChart = dc.lineChart("#claims-line-chart");
claimsLineChart
.width(200)
.height(40)
.renderArea(true)
.margins({ top: 0, left: -1, right: 2, bottom: 1 })
.group(datesClaimsGroup)
.dimension(dateDim)
.x(d3.time.scale().domain([data[0].dd, data[data.length - 1].dd]))
.title(function (d) {
return d.value;
});
The chart is plotted but the values in Y-Axis are the date occurance counts and not the claim counts. I know I am supposed to use a function to count the claims but I am not getting there.
For datesClaimsGroup you need to provide a reduce function to count the claims. Otherwise just .group() will default to an identity count reduce function as you observed.
var datesClaimsGroup = dateDim.group().reduceSum(...)
or
var datesClaimsGroup = dateDim.group().reduce(...)
Related
I have created a grid and I want to plot a time series for each of the squares of this evenly distributed grid.
But I keep getting the same error:
Line 20: ImageCollection.fromImages, argument 'images': Invalid type.
Expected type: List<Image<unknown bands>>.
Actual type: List<ImageCollection>.
Actual value: [<ImageCollection>, <ImageCollection>, <ImageCollection>, <ImageCollection>, <ImageCollection>, <ImageCollection>]
If anyone is kind to provide some help, Id really appreciate it.
This is what I got so far:
var grid = ee.FeatureCollection("projects/ee-moreirarmt/assets/grid")
Map.centerObject(grid)
var chirps_col = ee.ImageCollection("UCSB-CHG/CHIRPS/PENTAD")
.select("precipitation")
.filterDate("2020-01-01", "2021-01-01")
var seq = ee.List.sequence(1, ee.Number(grid.size()))
var vizparam = ({bands:"precipitation",min: 13.39358901977539, max: 27.54470443725586,
palette: ['001137', '0aab1e', 'e7eb05', 'ff4a2d', 'e90000']})
var split_by_each_grid = seq.map(function(x){
var fil = grid.filterMetadata('id', 'equals', x)
var split_chirps_col = chirps_col.filterBounds(fil)
return split_chirps_col.set('id', x)})
var splitted_chirps_col = ee.ImageCollection.fromImages(split_by_each_grid)
for(var a = 1; a < splitted_chirps_col.size().getInfo() + 1; a++){
Map.addLayer(splitted_chirps_col.median().filter(ee.Filter.eq('id',a)), vizparam, "Chirps_per_grid"+a)
}
for(var a = 1; a < final_col.size().getInfo() + 1; a++){
var chart = Chart.image.seriesByRegion(splitted_chirps_col.filter(ee.Filter.eq('id',a)), grid, ee.Reducer.mean(), "precipitation", 5500)
print(chart)
}
Here's the code:
https://code.earthengine.google.com/fbdf0b0e9e1f395eaa8435b805bdc809
Thanks in advance and stay safe.
I used the .combine command to convert two image collections into a two-band image collection (in the last line) to use in a function in the next step. This command is executed but writes 0 elements in the console. Where does this problem come from?
code link: https://code.earthengine.google.com/ed0992093ff830d926c7dd14403477c6
Code:
var ndvi = function(img){
var bands = img.select(['B2','B3','B4','B8']).multiply(0.0001)
.clip(geometry);
var index = bands.normalizedDifference(['B8','B4']).rename('NDVI_S2');
return index
.copyProperties(img,['system:time_start','system:time_end','system:index']);
};
var S2 = ee.ImageCollection('COPERNICUS/S2_SR')
.filterBounds(geometry)
.filterDate('2018-10-24','2019-06-30')
//.filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE',20))
.map(ndvi);
print(S2);
var START = '2018-10-24';
var END = '2019-06-30';
var DATES = [ '2018-12-19', '2018-12-29', '2019-01-23', '2019-02-12', '2019-03-04',
'2019-03-19', '2019-04-08', '2019-04-13', '2019-05-13', '2019-05-18', '2019-05-23',
'2019-05-28', '2019-06-02', '2019-06-07', '2019-06-12', '2019-06-17', '2019-06-22',
'2019-06-27'];
var addTime = function(x) {
return x.set('Date', ee.Date(x.get('system:time_start')).format("YYYY-MM-dd"))};
var Sentinel = ee.ImageCollection(S2)
.filter(ee.Filter.date(START, END))
.map(addTime)
.filter(ee.Filter.inList('Date',ee.List(DATES)));
print(Sentinel);
var PMODIS =
ee.Image('MODIS/006/MCD43A4/2018_12_19').select('Nadir_Reflectance_Band4');
var MODProjection = PMODIS.projection();
print('MODIS projection:', MODProjection);
var Viz = {min: 0, max: 1, palette: ['be6c44','ca3a3a','e4ae0c','565c04','819536']};
var S2_resampled = Sentinel.map(function(img){
var S2Mean = img
// Force the next reprojection to aggregate instead of resampling.
.reduceResolution({
reducer: ee.Reducer.mean(),
maxPixels: 2146
})
// Request the data at the scale and projection of the Sentinel image.
.reproject({
crs: MODProjection
});
return S2Mean
.copyProperties(img,['system:time_start','system:time_end','system:index']);
});
Map.addLayer(S2_resampled)
var M_ndvi = function(img){
var bands =
img.select(['Nadir_Reflectance_Band1','Nadir_Reflectance_Band5']).multiply(0.0001)
.clip(geometry);
var index=bands
.normalizedDifference(['Nadir_Reflectance_Band1','Nadir_Reflectance_Band5'])
.rename(
'NDVI_MOD');
return index
.copyProperties(img,['system:time_start','system:time_end','system:index']);
};
var MOD = ee.ImageCollection('MODIS/006/MCD43A4')
.filterBounds(geometry)
.filterDate('2018-10-24','2019-06-30')
.map(M_ndvi);
var MODIS = ee.ImageCollection(MOD)
.filter(ee.Filter.date(START, END))
.map(addTime)
.filter(ee.Filter.inList('Date',ee.List(DATES)));
print(MODIS);
var S2_and_MOD = S2_resampled.combine(MODIS, false);
print(S2_and_MOD);
var Diff = S2_and_MOD.map(function(img){
var clip = img.clip(geometry);
var Diffe = clip.expression('NDVI_S2 - NDVI_MOD',
{'NDVI_S2':clip.select('NDVI_S2') ,
'NDVI_MOD':clip.select('NDVI_MOD')}).rename('Diff');
return Diffe
.copyProperties(img,['system:time_start','system:time_end']); });
print(Diff);
ee.Image.combine() uses the system:ID property to join the 2 images. See the documentation here. Since your images do not match, the resulting collection has no images.
A solution that should fit your needs utilizes the ee.Join.inner() to take advantage of the Date property that you have created to join the 2 image collections. A similar question was answered here.
Using inner join, I was able to accomplish what appeared to be your goal of finding the difference in NDVI between the S2 and MODIS collections. The full working script can be found here: https://code.earthengine.google.com/dc45df1b7cf83723d53e9f7917975e2d
Code:
// Function - Calculate S2 NDVI
var ndvi = function(img){
var bands = img.select(['B2','B3','B4','B8']).multiply(0.0001)
.clip(geometry);
var index = bands.normalizedDifference(['B8','B4']).rename('NDVI_S2');
return index
.copyProperties(img,['system:time_start','system:time_end','system:index']);
};
// Get S2 NDVI images
var S2 = ee.ImageCollection('COPERNICUS/S2_SR')
.filterBounds(geometry)
.filterDate('2018-10-24','2019-06-30')
//.filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE',20))
.map(ndvi);
print('S2 NDVI ImageCollection',S2);
// Set Date Parameters
var START = '2018-10-24';
var END = '2019-06-30';
// Create Date List
var DATES = [ '2018-12-19', '2018-12-29', '2019-01-23', '2019-02-12', '2019-03-04',
'2019-03-19', '2019-04-08', '2019-04-13', '2019-05-13', '2019-05-18', '2019-05-23',
'2019-05-28', '2019-06-02', '2019-06-07', '2019-06-12', '2019-06-17', '2019-06-22',
'2019-06-27'];
// Function - Add 'Date' field to image
var addTime = function(x) {
return x.set('Date', ee.Date(x.get('system:time_start')).format("YYYY-MM-dd"))};
// Run addTime on S2 ImageCollection
var Sentinel = ee.ImageCollection(S2)
.filter(ee.Filter.date(START, END))
.map(addTime)
.filter(ee.Filter.inList('Date',ee.List(DATES)));
print('Date Added S2', Sentinel);
// Get MODIS Projection
var PMODIS = ee.Image('MODIS/006/MCD43A4/2018_12_19').select('Nadir_Reflectance_Band4');
var MODProjection = PMODIS.projection();
print('MODIS projection:', MODProjection);
// Set Visualization Parameters
var Viz = {min: 0, max: 1, palette: ['be6c44','ca3a3a','e4ae0c','565c04','819536']};
// Reproject S2 images to MODIS projection
var S2_resampled = Sentinel.map(function(img){
var S2Mean = img
// Force the next reprojection to aggregate instead of resampling.
.reduceResolution({
reducer: ee.Reducer.mean(),
maxPixels: 2146
})
// Request the data at the scale and projection of the Sentinel image.
.reproject({
crs: MODProjection
});
return S2Mean
.copyProperties(img,['system:time_start','system:time_end','system:index']);
});
print('S2_resampled',S2_resampled);
Map.addLayer(S2_resampled);
// Function - Calculate MODIS NDVI
var M_ndvi = function(img){
var bands = img.select(['Nadir_Reflectance_Band1','Nadir_Reflectance_Band5']).multiply(0.0001)
.clip(geometry);
var index = bands.normalizedDifference(['Nadir_Reflectance_Band1','Nadir_Reflectance_Band5']).rename('NDVI_MOD');
return index
.copyProperties(img,['system:time_start','system:time_end','system:index']);
};
// Get MODIS NDVI Images
var MOD = ee.ImageCollection('MODIS/006/MCD43A4')
.filterBounds(geometry)
.filterDate('2018-10-24','2019-06-30')
.map(M_ndvi);
// Run addTime on MODIS ImageCollection
var MODIS = ee.ImageCollection(MOD)
.filter(ee.Filter.date(START, END))
.map(addTime)
.filter(ee.Filter.inList('Date',ee.List(DATES)));
print('MODIS',MODIS);
// Combine MODIS and S2 Image Collections using Date
// Specify the join type
var join_type = ee.Join.inner();
// Set the join parameter
var filter = ee.Filter.equals({
leftField: 'Date',
rightField: 'Date'
});
// Execute the join
var inner_join = ee.ImageCollection(join_type.apply(MODIS,S2_resampled,filter));
// Flatten joined images into a single image with 2 bands
var S2_and_MOD = inner_join.map(function(feature) {
return ee.Image.cat(feature.get('primary'), feature.get('secondary'));
});
print('Combined S2 and MODIS Collection:',S2_and_MOD);
// Calculate the difference between S2 and MODIS NDVI values
var Diff = S2_and_MOD.map(function(img){
var clip = img.clip(geometry);
var Diffe = clip.expression('NDVI_S2 - NDVI_MOD',
{'NDVI_S2':clip.select('NDVI_S2') , 'NDVI_MOD':clip.select('NDVI_MOD')}).rename('Diff');
return Diffe
.copyProperties(img,['system:time_start','system:time_end']); });
print('NDVI Difference Collection',Diff);
How to get the difference image of this month and the previous month in Google Earth Engine with a smart way?
study area 25E-75E，5S-35N。
// study area 25E-75E，5S-35N。
var geometry =
ee.Geometry.Polygon(
[[[25, 35],
[25, 5],
[75, 5],
[75, 35]]], null, false);
var regions = ee.FeatureCollection([
ee.Feature(geometry)
]);
// imgCol
var now = ee.Date(Date.now());
var NDVICollection=ee.ImageCollection('MODIS/006/MOD13Q1')
.filterDate('2010-01-01',now)
.filterBounds(regions)
.select('NDVI');
var col = NDVICollection.map(function(img){
return img.multiply(0.0001)
.copyProperties(img,['system:time_start','system:time_end']);
});
// grouped by month
var months = ee.List([11,12,1,2]);
var byMonth = ee.ImageCollection.fromImages(
months.map(function (m) {
return col.filterDate('2019-11-01',now).filter(ee.Filter.calendarRange(m, m, 'month'))
.select('NDVI').mean()
.set('month', m);
}));
mask
var meanNDVI = byMonth.reduce(ee.Reducer.mean());
var mask = meanNDVI.gt(0.1);
Create difference image
**var img12 = byMonth.filter(ee.Filter.eq('month', ee.Number(12))).first().updateMask(mask);
var img11 = byMonth.filter(ee.Filter.eq('month', ee.Number(11))).first().updateMask(mask);
var img1 = byMonth.filter(ee.Filter.eq('month', ee.Number(1))).first().updateMask(mask);
var img2 = byMonth.filter(ee.Filter.eq('month', ee.Number(2))).first().updateMask(mask);
var ndviChange_12 = img12.subtract(img11).set('name','ndviChange_12');
var ndviChange_1 = img1.subtract(img12).set('name','ndviChange_1');
var ndviChange_2 = img12.subtract(img1).set('name','ndviChange_2');
var ndviChange = ee.ImageCollection([ndviChange_12,ndviChange_1,ndviChange_2]);**
I want make it much more smart, what should I do? make a function or something else?
Show the image
Map.centerObject(regions);
Map.addLayer(byMonth);
Map.addLayer(ndviChange);
I think the solution is to use iterate on your byMonth collection . Check this example in the documentations.
https://developers.google.com/earth-engine/ic_iterating
Trying to pull a list of ratings from a collection of Reviews and then average them to come up with an aggregated average rating for a Plate. When I look at the data output from the ratings variable I get nothing but "undefined undefined undefined".
averageRating: function() {
var reviews = Reviews.findOne({plateId: this._id});
var ratings = _.pluck(reviews, 'rating');
var sum = ratings.reduce(function(pv, cv){return pv + cv;}, 0);
var avg = sum / ratings.length;
//Testing output
var test = "";
var x;
for (x in reviews) {
text += reviews[x] + ',';
}
return test;
}
Sorry if this is a super newbie question, but I've been at this for hours and cannot figure it out.
I figured out the issue. As listed above var reviews gets set to a cursor which apparently .pluck does not work on. By first converting the cursor to an array of objects I was then able to use .pluck. So updated code looks like this:
averageRating: function() {
var reviewsCursor = Reviews.find({plateId: this._id});
//Converts cursor to an array of objects
var reviews = reviewsCursor.fetch();
var ratings = _.pluck(reviews, 'rating');
var sum = ratings.reduce(function(pv, cv){return pv + cv;}, 0);
var avg = (sum / ratings.length).toPrecision(2);
return avg;
}
I have a crossfilter with the following data structure being inputted.
project | subproject | cost
data = [
["PrA", "SubPr1", 100],
["PrA", "SubPr2", 150],
["PrA", "SubPr3", 100],
["PrB", "SubPr4", 300],
["PrB", "SubPr5", 500],
["PrC", "SubPr6", 450]]
I can create a barchart that has the summed cost per project:
var ndx = crossfilter(data)
var projDim = ndx.dimension(function(d){return d.project;});
var projGroup = costDim.group().reduceSum(function(d){return d.budget;});
What I want to do is create a dc.js histogram by project cost...so {450: 2, 300: 1}, etc. As far as I can tell, crossfilter can have only attributes of each row be input for a dimension. Is there a way around this?
Accepting the challenge!
It is true, crossfilter does not support this kind of double-reduction, but if you are willing to accept a slight loss of efficiency, you can create "fake dimensions" and "fake groups" with the desired behavior. Luckily, dc.js doesn't use very much of the crossfilter API, so you don't have to implement too many methods.
The first part of the trick is to duplicate the dimension and group so that the new dimension and old dimension will each observe filtering on the other.
The second part is to create the fake groups and dimensions, which walk the bins of the copied group and rebin and refilter based on the values instead of the keys.
A start of a general solution is below. For some charts it is also necessary to implement group.top(), and it is usually okay to just forward that to group.all().
function values_dimension(dim, group) {
return {
filter: function(v) {
if(v !== null)
throw new Error("don't know how to do this!");
return dim.filter(null);
},
filterFunction: function(f) {
var f2 = [];
group.all().forEach(function(kv) {
if(f(kv.value))
f2.push(kv.key);
});
dim.filterFunction(function(k) {
return f2.indexOf(k) >= 0;
});
return this;
}
};
}
function values_group(group) {
return {
all: function() {
var byv = [];
group.all().forEach(function(kv) {
if(kv.value === 0)
return;
byv[kv.value] = (byv[kv.value] || 0) + 1;
});
var all2 = [];
byv.forEach(function(d, i) {
all2.push({key: i, value: d});
});
return all2;
}
};
}
// duplicate the dimension & group so each will observe filtering on the other
var projDim2 = ndx.dimension(function(d){return d.project;});
var projGroup2 = projDim2.group().reduceSum(function(d){return d.budget;});
var countBudgetDim = values_dimension(projDim2, projGroup2),
countBudgetGroup = values_group(projGroup2);
jsfiddle here: http://jsfiddle.net/gordonwoodhull/55zf7L1L/
JSFillde Link
Denormalize + Map-reduce. Note the data already include the cost per project as the 4th column ( and this can be pre-calculated easily). It's a hack, but hopefully an easy one in order to get DC.js and crossfilter works without too much change.
var data = [
["PrA", "SubPr1", 100, 450],
["PrA", "SubPr2", 150, 450],
["PrA", "SubPr3", 200, 450],
["PrB", "SubPr4", 300, 800],
["PrB", "SubPr5", 500, 800],
["PrC", "SubPr6", 450, 450]
];
var newdata = data.map(function (d) {
return {
project: d[0],
subproject: d[1],
budget: d[2],
cost: d[3]
};
})
var ndx = crossfilter(newdata),
costDim = ndx.dimension(function (d) {
return d.cost;
}),
visitedProj = {},
costGroup = costDim.group().reduce(function (p, v) {
if (visitedProj[v.project]) return p;
console.info(v.project);
visitedProj[v.project] = true;
return p + 1;
}, null, function () {
return 0;
});
dc.rowChart("#costChart")
.renderLabel(true)
.dimension(costDim)
.group(costGroup)
.xAxis().ticks(2);
dc.renderAll();
Map-Reduce can be very powerful and the API can be accessed from here. JSFillde Link