def updateNoise(self):
"""Updates the noise sample. Does not change any of the noise parameters
but choses a new random sample given the previously set parameters.
"""
if not (self.noiseType in [
'binary', 'Binary', 'normal', 'Normal', 'uniform', 'Uniform'
]):
Ph = numpy.random.uniform(0, 2 * numpy.pi, int(self._size**2))
Ph = numpy.reshape(Ph, (int(self._size), int(self._size)))
In = self.noiseTex * exp(1j * Ph)
Im = numpy.real(ifft2(In))
Im = ifftshift(Im)
gsd = filters.getRMScontrast(Im)
factor = (gsd * self.noiseClip)
numpy.clip(Im, -factor, factor, Im)
self.tex = Im / factor
elif self.noiseType in ['normal', 'Normal']:
self.tex = numpy.random.randn(int(
self._sideLength[1]), int(
self._sideLength[0])) / self.noiseClip
elif self.noiseType in ['uniform', 'Uniform']:
self.tex = 2.0 * numpy.random.rand(int(self._sideLength[1]),
int(self._sideLength[0])) - 1.0
else:
numpy.random.shuffle(
self.noiseTex) # pick random noise sample by shuffleing values
self.tex = numpy.reshape(
self.noiseTex,
(int(self._sideLength[1]), int(self._sideLength[0])))
def updateNoise(self):
"""Updates the noise sample. Does not change any of the noise parameters but choses a new random sample given the previously set parameters.
"""
if not(self.noiseType in ['binary','Binary','normal','Normal','uniform','Uniform']):
Ph=numpy.random.uniform(0,2*numpy.pi,int(self._size**2))
Ph=numpy.reshape(Ph,(int(self._size),int(self._size)))
In=self.noiseTex*exp(1j*Ph)
self.tex=numpy.real(ifft2(In))
self.tex=ifftshift(self.tex)
gsd=filters.getRMScontrast(self.tex)
factor=(gsd*self.noiseClip)
numpy.clip(self.tex,-factor,factor,self.tex)
self.tex=self.tex/factor
else:
numpy.random.shuffle(self.noiseTex) # pick random noise sample by shuffleing values
self.tex=numpy.reshape(self.noiseTex,(int(self._sideLength[1]),int(self._sideLength[0]))) #reshape to sqaure
def updateNoise(self):
"""Updates the noise sample. Does not change any of the noise parameters
but choses a new random sample given the previously set parameters.
"""
if not(self.noiseType in ['binary','Binary','normal','Normal','uniform','Uniform']):
if (self.noiseType in ['image', 'Image']) and (self.imageComponent in ['amplitude','Amplitude']):
self.noiseTex = numpy.random.uniform(0,1,int(self._size**2))
self.noiseTex = numpy.reshape(self.noiseTex,(int(self._size),int(self._size)))
if self.filter in ['Butterworth','butterworth']:
self.noiseTex = fftshift(self._filter(self.noiseTex))
elif self.filter in ['Gabor','gabor']:
self.noiseTex = fftshift(self._gabor(self.noiseTex))
elif self.filter in ['Isotropic','isotropic']:
self.noiseTex = fftshift(self._isotropic(self.noiseTex))
self.noiseTex[0][0] = 0
In = self.noiseTex * exp(1j*self.noisePh)
Im = numpy.real(ifft2(In))
else:
Ph = numpy.random.uniform(0,2*numpy.pi,int(self._size**2))
Ph = numpy.reshape(Ph,(int(self._size),int(self._size)))
In = self.noiseTex * exp(1j*Ph)
Im = numpy.real(ifft2(In))
Im = ifftshift(Im)
gsd = filters.getRMScontrast(Im)
factor = gsd*self.noiseClip
numpy.clip(Im, -factor, factor, Im)
self.tex = Im / factor
elif self.noiseType in ['normal','Normal']:
self.noiseTex = numpy.random.randn(int(self._sideLength[1]),int(self._sideLength[0])) / self.noiseClip
elif self.noiseType in ['uniform','Uniform']:
self.noiseTex = 2.0 * numpy.random.rand(int(self._sideLength[1]),int(self._sideLength[0])) - 1.0
else:
numpy.random.shuffle(self.noiseTex) # pick random noise sample by shuffleing values
self.noiseTex = numpy.reshape(self.noiseTex,(int(self._sideLength[1]),int(self._sideLength[0])))
if self.noiseType in ['binary','Binary','normal','Normal','uniform','Uniform']:
if self.filter in ['butterworth', 'Butterworth', 'Gabor','gabor','Isotropic','isotropic']:
if self.units == 'pix':
if self._size[0] == self._size[1]:
baseImage = numpy.array(
Image.fromarray(self.noiseTex).resize(
(int(self._size[0]), int(self._size[1])),
Image.NEAREST
)
)
else:
msg = ('NoiseStim can only apply filters to square noise images')
raise ValueError(msg)
else:
baseImage = numpy.array(
Image.fromarray(self.noiseTex).resize(
(int(self._size), int(self._size)),
Image.NEAREST
)
)
baseImage = numpy.array(baseImage).astype(
numpy.float32) * 0.0078431372549019607 - 1.0
FT = fft2(baseImage)
spectrum = numpy.absolute(fftshift(FT))
angle = numpy.angle(FT)
if self.filter in ['butterworth','Butterworth']:
spectrum = fftshift(self._filter(spectrum))
elif self.filter in ['isotropic','Isotropic']:
spectrum = fftshift(self._isotropic(spectrum))
elif self.filter in ['gabor','Gabor']:
spectrum = fftshift(self._gabor(spectrum))
spectrum[0][0] = 0 # set DC to zero
FT = spectrum * exp(1j*angle)
Im = numpy.real(ifft2(FT))
gsd = filters.getRMScontrast(Im)
factor = gsd*self.noiseClip
numpy.clip(Im, -factor, factor, Im)
self.tex = Im / factor
else:
if not(self.noiseType in ['image','Image']):
self.tex = self.noiseTex
def updateNoise(self):
"""Updates the noise sample. Does not change any of the noise parameters
but choses a new random sample given the previously set parameters.
"""
if not(self.noiseType in ['binary','Binary','normal','Normal','uniform','Uniform']):
if (self.noiseType in ['image', 'Image']) and (self.imageComponent in ['amplitude','Amplitude']):
self.noiseTex = numpy.random.uniform(0,1,int(self._size**2))
self.noiseTex = numpy.reshape(self.noiseTex,(int(self._size),int(self._size)))
if self.filter in ['Butterworth','butterworth']:
self.noiseTex = fftshift(self._filter(self.noiseTex))
elif self.filter in ['Gabor','gabor']:
self.noiseTex = fftshift(self._gabor(self.noiseTex))
elif self.filter in ['Isotropic','isotropic']:
self.noiseTex = fftshift(self._isotropic(self.noiseTex))
self.noiseTex[0][0] = 0
In = self.noiseTex * exp(1j*self.noisePh)
Im = numpy.real(ifft2(In))
else:
Ph = numpy.random.uniform(0,2*numpy.pi,int(self._size**2))
Ph = numpy.reshape(Ph,(int(self._size),int(self._size)))
In = self.noiseTex * exp(1j*Ph)
Im = numpy.real(ifft2(In))
Im = ifftshift(Im)
gsd = filters.getRMScontrast(Im)
factor = gsd*self.noiseClip
numpy.clip(Im, -factor, factor, Im)
self.tex = Im / factor
elif self.noiseType in ['normal','Normal']:
self.noiseTex = numpy.random.randn(int(self._sideLength[1]),int(self._sideLength[0])) / self.noiseClip
elif self.noiseType in ['uniform','Uniform']:
self.noiseTex = 2.0 * numpy.random.rand(int(self._sideLength[1]),int(self._sideLength[0])) - 1.0
else:
numpy.random.shuffle(self.noiseTex) # pick random noise sample by shuffleing values
self.noiseTex = numpy.reshape(self.noiseTex,(int(self._sideLength[1]),int(self._sideLength[0])))
if self.noiseType in ['binary','Binary','normal','Normal','uniform','Uniform']:
if self.filter in ['butterworth', 'Butterworth', 'Gabor','gabor','Isotropic','isotropic']:
if self.units == 'pix':
if self._size[0] == self._size[1]:
baseImage = imresize(self.noiseTex, (int(self._size[0]),int(self._size[1])), interp='nearest')
else:
msg = ('NoiseStim can only apply filters to square noise images')
raise ValueError(msg)
else:
baseImage = imresize(self.noiseTex, (int(self._size),int(self._size)), interp='nearest')
baseImage = numpy.array(baseImage).astype(
numpy.float32) * 0.0078431372549019607 - 1.0
FT = fft2(baseImage)
spectrum = numpy.absolute(fftshift(FT))
angle = numpy.angle(FT)
if self.filter in ['butterworth','Butterworth']:
spectrum = fftshift(self._filter(spectrum))
elif self.filter in ['isotropic','Isotropic']:
spectrum = fftshift(self._isotropic(spectrum))
elif self.filter in ['gabor','Gabor']:
spectrum = fftshift(self._gabor(spectrum))
spectrum[0][0] = 0 # set DC to zero
FT = spectrum * exp(1j*angle)
Im = numpy.real(ifft2(FT))
gsd = filters.getRMScontrast(Im)
factor = gsd*self.noiseClip
numpy.clip(Im, -factor, factor, Im)
self.tex = Im / factor
else:
if not(self.noiseType in ['image','Image']):
self.tex = self.noiseTex
