def shortrepr(self):
return "Uniform / " + self.stepper.shortrepr()
registeredclass.Registration(
'Uniform',
StepDriver,
UniformDriver,
ordering=2,
params=[
parameter.RegisteredParameter('stepper',
TimeStepper,
tip='Time stepping method.'),
parameter.PositiveFloatParameter('stepsize',
1.0,
tip='Time step size.')
],
tip="Take fixed, uniform timesteps.",
discussion=xmlmenudump.loadFile(
'DISCUSSIONS/engine/reg/uniformdriver.xml'))
#=--=##=--=##=--=##=--=##=--=##=--=##=--=##=--=##=--=##=--=##=--=##=--=##=--=#
# "Basic" versions of the drivers don't require users to choose a
# stepping method. They just have to select Static, Uniform or
# Adaptive, and provide a tolerance and min step size for Adaptive.
class BasicStepDriver(registeredclass.RegisteredClass):
registry = []
def reset(self, continuing):
self.count = 0
def next(self):
t = self.time0 + self.delay + self.count*self.interval
self.count += 1
return t
registeredclass.Registration(
'Periodic',
Schedule,
Periodic,
ordering=0,
params=[
parameter.NonNegativeFloatParameter(
'delay', 0.0, tip='Time before the first output.'),
parameter.PositiveFloatParameter(
'interval', 0.0, tip='Time between outputs.')
],
tip="Produce evenly spaced periodic output.",
discussion=xmlmenudump.loadFile('DISCUSSIONS/engine/reg/periodicsched.xml'))
class Geometric(UnconditionalSchedule):
def __init__(self, timestep, factor):
self.timestep = timestep
self.factor = factor
self.nextstep = timestep
self.lasttime = None
UnconditionalSchedule.__init__(self)
def reset(self, continuing):
if not continuing:
self.nextstep = self.timestep
self.radius = radius
self.sigma = sigma
def __call__(self, image):
image.blur(self.radius, self.sigma)
registeredclass.Registration(
'Blur',
ImageModifier,
BlurImage,
ordering=2.00,
params=[
parameter.PositiveFloatParameter(
'radius',
1.0,
tip="Radius of the Gaussian kernel, in voxel units."
"The convolution kernel is zero beyond this radius."),
parameter.PositiveFloatParameter(
'sigma',
1.0,
tip="Standard deviation of the Gaussian, in voxel units.")
],
tip=
"Blur an image by convolving it with a Gaussian operator of the given radius and standard deviation (sigma).",
discussion=xmlmenudump.loadFile('DISCUSSIONS/image/reg/blurimage.xml'))
if config.dimension() == 2:
class ContrastImage(ImageModifier):
def __init__(self, sharpen):