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diff --git a/pkg/tests/testthat/test.computeFilaments.R b/pkg/tests/testthat/test.computeFilaments.R
index 9de6274..e8f8752 100644
--- a/pkg/tests/testthat/test.computeFilaments.R
+++ b/pkg/tests/testthat/test.computeFilaments.R
@@ -1,36 +1,103 @@
-#TODO: toy dataset, check that indices returned are correct + colors
+context("Check that computeFilaments behaves as expected")
 
-context("Check that getParamsDirs behaves as expected")
+getDataTest = function(n, shift)
+{
+	x = seq(0,10,0.1)
+	L = length(x)
+	s1 = cos(x)
+	s2 = sin(x)
+	s3 = c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] )
+	#sum((s1-s2)^2) == 97.59381
+	#sum((s1-s3)^2) == 57.03051
+	#sum((s2-s3)^2) == 40.5633
+	s = list( s1, s2, s3 )
+	series = list()
+	for (i in seq_len(n))
+	{
+		index = (i%%3) + 1
+		level = mean(s[[index]])
+		serie = s[[index]] - level + rnorm(L,sd=0.05)
+		# 10 series with NAs for index 2
+		if (index == 2 && i >= 60 && i<= 90)
+			serie[sample(seq_len(L),1)] = NA
+		series[[i]] = list("level"=level,"serie"=serie) #no need for more
+	}
+	if (shift)
+	{
+		# Simulate shift at origin when predict_at > 0
+		series[2:(n+1)] = series[1:n]
+		series[[1]] = list("level"=0, "serie"=s[[1]][1:(L%/%2)])
+	}
+	new("Data", data=series)
+}
 
-test_that("on input of sufficient size, beta is estimated accurately enough", {
-	n = 100000
-	d = 2
-	K = 2
-	Pr = c(0.5, 0.5)
+test_that("output is as expected on simulated series",
+{
+	data = getDataTest(150, FALSE)
 
-	betas_ref = array( c(1,0,0,1 , 1,-2,3,1), dim=c(2,2,2) )
-	for (i in 1:(dim(betas_ref)[3]))
+	# index 142 : serie type 2
+	f = computeFilaments(data, 142, limit=60, plot=FALSE)
+	# Expected output: 22 series of type 3 (closer), then 50-2-10 series of type 2
+	expect_identical(length(f$indices), 60)
+	expect_identical(length(f$colors), 60)
+	for (i in 1:22)
 	{
-		beta_ref = betas_ref[,,i]
-		#all parameters are supposed to be of norm 1: thus, normalize beta_ref
-		norm2 = sqrt(colSums(beta_ref^2))
-		beta_ref = beta_ref / norm2[col(beta_ref)]
+		expect_identical((f$indices[i] %% 3) + 1, 3)
+		expect_match(f2$colors[i], f$colors[1])
+	}
+	for (i in 23:60)
+	{
+		expect_identical((f$indices[i] %% 3) + 1, 2)
+		expect_match(f2$colors[i], f$colors[23])
+	}
+	expect_match(colors[1], "...")
+	expect_match(colors[23], "...")
+})
 
-		io = generateSampleIO(n, d, K, Pr, beta_ref)
-		beta = getParamsDirs(io$X, io$Y, K)
-		betas = .labelSwitchingAlign(
-			array( c(beta_ref,beta), dim=c(d,K,2) ), compare_to="first", ls_mode="exact")
+test_that("output is as expected on simulated series",
+{
+	data = getDataTest(150, TRUE)
 
-		#Some traces: 0 is not well estimated, but others are OK
-		cat("\n\nReference parameter matrix:\n")
-		print(beta_ref)
-		cat("Estimated parameter matrix:\n")
-		print(betas[,,2])
-		cat("Difference norm (Matrix norm ||.||_1, max. abs. sum on a column)\n")
-		diff_norm = norm(beta_ref - betas[,,2])
-		cat(diff_norm,"\n")
+	# index 143 : serie type 3
+	f = computeFilaments(data, 143, limit=70, plot=FALSE)
+	# Expected output: 22 series of type 2 (closer) then 50-2 series of type 3
+	expect_identical(length(f$indices), 70)
+	expect_identical(length(f$colors), 70)
+	for (i in 1:22)
+	{
+		# -1 because of the initial shift
+		expect_identical(( (f$indices[i]-1) %% 3 ) + 1, 2)
+		expect_match(f$colors[i], f$colors[1])
+	}
+	for (i in 23:70)
+	{
+		expect_identical(( (f$indices[i]-1) %% 3 ) + 1, 3)
+		expect_match(f$colors[i], f$colors[23])
+	}
+	expect_match(colors[1], "...")
+	expect_match(colors[23], "...")
+})
 
-		#NOTE: 0.5 is loose threshold, but values around 0.3 are expected...
-		expect_that( diff_norm, is_less_than(0.5) )
+test_that("output is as expected on simulated series",
+{
+	data = getDataTest(150, TRUE)
+
+	# index 144 : serie type 1
+	f = computeFilaments(data, 144, limit=50, plot=FALSE)
+	# Expected output: 2 series of type 3 (closer), then 50-2 series of type 1
+	expect_identical(length(f$indices), 50)
+	expect_identical(length(f$colors), 50)
+	for (i in 1:2)
+	{
+		# -1 because of the initial shift
+		expect_identical(( (f$indices[i]-1) %% 3 ) + 1, 3)
+		expect_match(f$colors[i], f$colors[1])
+	}
+	for (i in 3:50)
+	{
+		expect_identical(( (f$indices[i]-1) %% 3 ) + 1, 1)
+		expect_match(f$colors[i], f$colors[3])
 	}
+	expect_match(colors[1], "...")
+	expect_match(colors[3], "...")
 })