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1 files changed, 0 insertions, 185 deletions

diff --git a/vendor/github.com/lucasb-eyer/go-colorful/soft_palettegen.go b/vendor/github.com/lucasb-eyer/go-colorful/soft_palettegen.go
deleted file mode 100644
index 0154ac9..0000000
--- a/vendor/github.com/lucasb-eyer/go-colorful/soft_palettegen.go
+++ /dev/null
@@ -1,185 +0,0 @@
-// Largely inspired by the descriptions in http://lab.medialab.sciences-po.fr/iwanthue/
-// but written from scratch.
-
-package colorful
-
-import (
-	"fmt"
-	"math"
-	"math/rand"
-)
-
-// The algorithm works in L*a*b* color space and converts to RGB in the end.
-// L* in [0..1], a* and b* in [-1..1]
-type lab_t struct {
-	L, A, B float64
-}
-
-type SoftPaletteSettings struct {
-	// A function which can be used to restrict the allowed color-space.
-	CheckColor func(l, a, b float64) bool
-
-	// The higher, the better quality but the slower. Usually two figures.
-	Iterations int
-
-	// Use up to 160000 or 8000 samples of the L*a*b* space (and thus calls to CheckColor).
-	// Set this to true only if your CheckColor shapes the Lab space weirdly.
-	ManySamples bool
-}
-
-// Yeah, windows-stype Foo, FooEx, screw you golang...
-// Uses K-means to cluster the color-space and return the means of the clusters
-// as a new palette of distinctive colors. Falls back to K-medoid if the mean
-// happens to fall outside of the color-space, which can only happen if you
-// specify a CheckColor function.
-func SoftPaletteEx(colorsCount int, settings SoftPaletteSettings) ([]Color, error) {
-
-	// Checks whether it's a valid RGB and also fulfills the potentially provided constraint.
-	check := func(col lab_t) bool {
-		c := Lab(col.L, col.A, col.B)
-		return c.IsValid() && (settings.CheckColor == nil || settings.CheckColor(col.L, col.A, col.B))
-	}
-
-	// Sample the color space. These will be the points k-means is run on.
-	dl := 0.05
-	dab := 0.1
-	if settings.ManySamples {
-		dl = 0.01
-		dab = 0.05
-	}
-
-	samples := make([]lab_t, 0, int(1.0/dl*2.0/dab*2.0/dab))
-	for l := 0.0; l <= 1.0; l += dl {
-		for a := -1.0; a <= 1.0; a += dab {
-			for b := -1.0; b <= 1.0; b += dab {
-				if check(lab_t{l, a, b}) {
-					samples = append(samples, lab_t{l, a, b})
-				}
-			}
-		}
-	}
-
-	// That would cause some infinite loops down there...
-	if len(samples) < colorsCount {
-		return nil, fmt.Errorf("palettegen: more colors requested (%v) than samples available (%v). Your requested color count may be wrong, you might want to use many samples or your constraint function makes the valid color space too small.", colorsCount, len(samples))
-	} else if len(samples) == colorsCount {
-		return labs2cols(samples), nil // Oops?
-	}
-
-	// We take the initial means out of the samples, so they are in fact medoids.
-	// This helps us avoid infinite loops or arbitrary cutoffs with too restrictive constraints.
-	means := make([]lab_t, colorsCount)
-	for i := 0; i < colorsCount; i++ {
-		for means[i] = samples[rand.Intn(len(samples))]; in(means, i, means[i]); means[i] = samples[rand.Intn(len(samples))] {
-		}
-	}
-
-	clusters := make([]int, len(samples))
-	samples_used := make([]bool, len(samples))
-
-	// The actual k-means/medoid iterations
-	for i := 0; i < settings.Iterations; i++ {
-		// Reassing the samples to clusters, i.e. to their closest mean.
-		// By the way, also check if any sample is used as a medoid and if so, mark that.
-		for isample, sample := range samples {
-			samples_used[isample] = false
-			mindist := math.Inf(+1)
-			for imean, mean := range means {
-				dist := lab_dist(sample, mean)
-				if dist < mindist {
-					mindist = dist
-					clusters[isample] = imean
-				}
-
-				// Mark samples which are used as a medoid.
-				if lab_eq(sample, mean) {
-					samples_used[isample] = true
-				}
-			}
-		}
-
-		// Compute new means according to the samples.
-		for imean := range means {
-			// The new mean is the average of all samples belonging to it..
-			nsamples := 0
-			newmean := lab_t{0.0, 0.0, 0.0}
-			for isample, sample := range samples {
-				if clusters[isample] == imean {
-					nsamples++
-					newmean.L += sample.L
-					newmean.A += sample.A
-					newmean.B += sample.B
-				}
-			}
-			if nsamples > 0 {
-				newmean.L /= float64(nsamples)
-				newmean.A /= float64(nsamples)
-				newmean.B /= float64(nsamples)
-			} else {
-				// That mean doesn't have any samples? Get a new mean from the sample list!
-				var inewmean int
-				for inewmean = rand.Intn(len(samples_used)); samples_used[inewmean]; inewmean = rand.Intn(len(samples_used)) {
-				}
-				newmean = samples[inewmean]
-				samples_used[inewmean] = true
-			}
-
-			// But now we still need to check whether the new mean is an allowed color.
-			if nsamples > 0 && check(newmean) {
-				// It does, life's good (TM)
-				means[imean] = newmean
-			} else {
-				// New mean isn't an allowed color or doesn't have any samples!
-				// Switch to medoid mode and pick the closest (unused) sample.
-				// This should always find something thanks to len(samples) >= colorsCount
-				mindist := math.Inf(+1)
-				for isample, sample := range samples {
-					if !samples_used[isample] {
-						dist := lab_dist(sample, newmean)
-						if dist < mindist {
-							mindist = dist
-							newmean = sample
-						}
-					}
-				}
-			}
-		}
-	}
-	return labs2cols(means), nil
-}
-
-// A wrapper which uses common parameters.
-func SoftPalette(colorsCount int) ([]Color, error) {
-	return SoftPaletteEx(colorsCount, SoftPaletteSettings{nil, 50, false})
-}
-
-func in(haystack []lab_t, upto int, needle lab_t) bool {
-	for i := 0; i < upto && i < len(haystack); i++ {
-		if haystack[i] == needle {
-			return true
-		}
-	}
-	return false
-}
-
-const LAB_DELTA = 1e-6
-
-func lab_eq(lab1, lab2 lab_t) bool {
-	return math.Abs(lab1.L-lab2.L) < LAB_DELTA &&
-		math.Abs(lab1.A-lab2.A) < LAB_DELTA &&
-		math.Abs(lab1.B-lab2.B) < LAB_DELTA
-}
-
-// That's faster than using colorful's DistanceLab since we would have to
-// convert back and forth for that. Here is no conversion.
-func lab_dist(lab1, lab2 lab_t) float64 {
-	return math.Sqrt(sq(lab1.L-lab2.L) + sq(lab1.A-lab2.A) + sq(lab1.B-lab2.B))
-}
-
-func labs2cols(labs []lab_t) (cols []Color) {
-	cols = make([]Color, len(labs))
-	for k, v := range labs {
-		cols[k] = Lab(v.L, v.A, v.B)
-	}
-	return cols
-}