def loopTest():
loopCount = 10000
print 'dimensions', 'ms', 'max value', 'min value'
#histo=[0 for i in xrange(100)]
print 'der'
for i in xrange(2, 5):
s = SimplexNoise(0, i)
maxV = 0.0
minV = 0.0
t = time()
for a in xrange(loopCount):
v = s.simplexNoise([random.random() * 100 for x in xrange(i)],
True)
d = v[1]
v = d[0]
maxV = max(maxV, v)
minV = min(minV, v)
#print i,(time()-t)/loopCount*1000,maxV,minV
print 'derivative check'
stepSize = .01
stepCount = 200
v = [
s.simplexNoise([x * stepSize] * i, True) for x in xrange(stepCount)
]
estimate = v[0][0]
for val in v[:-1]:
der = val[1]
estimate += sum(der)
estimate *= stepSize
true = v[-1][0]
print estimate - true, estimate, true
print 'reg'
if False:
for i in xrange(2, 5):
s = SimplexNoise(0, i)
maxV = 0.0
minV = 0.0
t = time()
for a in xrange(loopCount):
v = s.simplexNoise(
([random.random() * 100 for x in xrange(i)]))
maxV = max(maxV, v)
minV = min(minV, v)
print i, (time() - t) / loopCount * 1000, maxV, minV
def loopTest():
loopCount=10000
print 'dimensions','ms','max value','min value'
#histo=[0 for i in xrange(100)]
print 'der'
for i in xrange(2,5):
s=SimplexNoise(0,i)
maxV=0.0
minV=0.0
t=time()
for a in xrange(loopCount):
v=s.simplexNoise([random.random()*100 for x in xrange(i)],True)
d=v[1]
v=d[0]
maxV=max(maxV,v)
minV=min(minV,v)
#print i,(time()-t)/loopCount*1000,maxV,minV
print 'derivative check'
stepSize=.01
stepCount=200
v=[s.simplexNoise([x*stepSize]*i,True) for x in xrange(stepCount)]
estimate=v[0][0]
for val in v[:-1]:
der=val[1]
estimate+=sum(der)
estimate*=stepSize
true=v[-1][0]
print estimate-true,estimate,true
print 'reg'
if False:
for i in xrange(2,5):
s=SimplexNoise(0,i)
maxV=0.0
minV=0.0
t=time()
for a in xrange(loopCount):
v=s.simplexNoise(([random.random()*100 for x in xrange(i)]))
maxV=max(maxV,v)
minV=min(minV,v)
print i,(time()-t)/loopCount*1000,maxV,minV
def verifyDerivatives(dimensions, verbose=True):
if verbose: print 'verifyDerivatives:'
passedList = []
for d in dimensions:
s = SimplexNoise(0, d)
stepSize = .01
stepCount = 256
v = [
s.simplexNoise([x * stepSize] * d, True) for x in xrange(stepCount)
]
estimate = v[0][0]
changeSum = 0
for i in xrange(len(v) - 1):
val = v[i]
der = val[1]
estimate += sum(der)
changeSum += abs(val[0] - v[1 + 1][0])
estimate *= stepSize
true = v[-1][0]
error = abs(estimate - true)
error /= changeSum
error *= 100
passed = error < 0.1
if verbose:
print str(d) + ' dimensional:'
print ' error percent: %5.8f' % error
print ' ' + ('passed' if passed else 'FAILED')
passedList.append(passed)
return all(passedList)
def verifyDerivatives(dimensions,verbose=True):
if verbose: print 'verifyDerivatives:'
passedList=[]
for d in dimensions:
s=SimplexNoise(0,d)
stepSize=.01
stepCount=256
v=[s.simplexNoise([x*stepSize]*d,True) for x in xrange(stepCount)]
estimate=v[0][0]
changeSum=0
for i in xrange(len(v)-1):
val=v[i]
der=val[1]
estimate+=sum(der)
changeSum+=abs(val[0]-v[1+1][0])
estimate*=stepSize
true=v[-1][0]
error=abs(estimate-true)
error/=changeSum
error*=100
passed=error<0.1
if verbose:
print str(d)+' dimensional:'
print ' error percent: %5.8f' % error
print ' '+('passed' if passed else 'FAILED')
passedList.append(passed)
return all(passedList)
def analyze(dimensions,verbose=True):
if verbose: print 'analyze:'
for d in dimensions:
if verbose:
print str(d)+' dimensional:'
count=50000
s=SimplexNoise(0,d)
data=[s.simplexNoise([random.random()*1000.0 for a in xrange(d)],True) for x in xrange(count)]
distributionCheck([v[0] for v in data],[v[1] for v in data],verbose)
def analyze(dimensions, verbose=True):
if verbose: print 'analyze:'
for d in dimensions:
if verbose:
print str(d) + ' dimensional:'
count = 50000
s = SimplexNoise(0, d)
data = [
s.simplexNoise([random.random() * 1000.0 for a in xrange(d)], True)
for x in xrange(count)
]
distributionCheck([v[0] for v in data], [v[1] for v in data], verbose)
def noise2D(sizeX,sizeY,scale):
s=SimplexNoise(0,2)
noiseTex=Texture("NoiseTex")
noiseTex.setup2dTexture(sizeX,sizeY, Texture.TUnsignedByte, Texture.FRgb)
p=noiseTex.modifyRamImage()
print 'making tex '+str(sizeX)+' by '+str(sizeY)
for y in range(sizeY):
for x in range(sizeX):
index = (sizeX * y + x)*noiseTex.getNumComponents()*noiseTex.getComponentWidth()
v,der=s.simplexNoise([x/scale,y/scale],True)
p.setElement(index, min(255,max(0,der[1]*8*128+128)))#Blue
p.setElement(index+1, min(255,max(0,der[0]*8*128+128)))#Green
p.setElement(index+2, min(255,max(0,v*128+128)))#Red
return noiseTex
def noise1D(sizeX, scale):
s = SimplexNoise(0, 1)
noiseTex = Texture("NoiseTex")
noiseTex.setup2dTexture(sizeX, sizeX, Texture.TUnsignedByte, Texture.FRgb)
p = noiseTex.modifyRamImage()
print 'making tex ' + str(sizeX) + ' by ' + str(sizeX)
for y in range(sizeX):
v, der = s.simplexNoise([y / scale], True)
for x in range(sizeX):
index = (sizeX * y + x) * noiseTex.getNumComponents(
) * noiseTex.getComponentWidth()
p.setElement(index, min(255, max(0, 0))) #Blue
p.setElement(index + 1, min(255, max(0, 0))) #Green
p.setElement(index + 2, min(255, max(0, v * 128 + 128))) #Red
return noiseTex
def noise3D(sizeX,sizeY,sizeZ,scale):
s=SimplexNoise(0,3)
noiseTex=Texture("NoiseTex")
noiseTex.setup3dTexture(sizeX,sizeY,sizeZ, Texture.TUnsignedByte, Texture.FRgba)
p=noiseTex.modifyRamImage()
for z in range(sizeZ):
offset = sizeX * sizeY * z
print 'making slice '+str(z)+' of '+str(sizeZ)
for y in range(sizeY):
for x in range(sizeX):
index = (offset + sizeX * y + x)*noiseTex.getNumComponents()*noiseTex.getComponentWidth()
v=s.simplexNoise([x/scale,y/scale,z/scale],True)
der=v[1]
v=v[0]
p.setElement(index, min(255,max(0,der[1]*8*128+128)))#Blue
p.setElement(index+1, min(255,max(0,der[0]*8*128+128)))#Green
p.setElement(index+2, min(255,max(0,v*128+128)))#Red
p.setElement(index+3, 255)#Alpha
return noiseTex
def noise3D(sizeX, sizeY, sizeZ, scale):
s = SimplexNoise(0, 3)
noiseTex = Texture("NoiseTex")
noiseTex.setup3dTexture(sizeX, sizeY, sizeZ, Texture.TUnsignedByte,
Texture.FRgba)
p = noiseTex.modifyRamImage()
for z in range(sizeZ):
offset = sizeX * sizeY * z
print 'making slice ' + str(z) + ' of ' + str(sizeZ)
for y in range(sizeY):
for x in range(sizeX):
index = (offset + sizeX * y + x) * noiseTex.getNumComponents(
) * noiseTex.getComponentWidth()
v = s.simplexNoise([x / scale, y / scale, z / scale], True)
der = v[1]
v = v[0]
p.setElement(index, min(255,
max(0, der[1] * 8 * 128 + 128))) #Blue
p.setElement(index + 1,
min(255, max(0, der[0] * 8 * 128 + 128))) #Green
p.setElement(index + 2, min(255, max(0, v * 128 + 128))) #Red
p.setElement(index + 3, 255) #Alpha
return noiseTex
