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 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 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
