filters.py :  » Game-2D-3D » PsychoPy » PsychoPy-0.96.02 » psychopy » Python Open Source

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Python Open Source » Game 2D 3D » PsychoPy 
PsychoPy » PsychoPy 0.96.02 » psychopy » filters.py
"""
Various useful functions for creating filters:
  - ``makeGrating()`` #for various sin.sqr waves etc
  - ``makeMask()`` #create a matrix to use as a mask
  - ``maskGrating()`` #make and apply a mask to a matrix
  - ``makeRadialMatrix()`` #make a matrix where intensity depends on radius
  - ``makeGauss()`` #make a gaussian envelope
  
"""

import numpy    
import Image
from psychopy import log

def makeGrating(res,
    ori=0.0,    #in degrees
    cycles=1.0,
    phase=0.0,    #in degrees
    gratType="sin",
    contr=1.0):
  """
  A function returning an array containing a luminance grating of the specified params
  """
  
  ori *= (numpy.pi/180)
  phase *= (numpy.pi/180)
  cycle1D = numpy.arange(0.0,cycles*2.0*numpy.pi,cycles*2.0*numpy.pi/res),
  xrange, yrange = numpy.mgrid[0.0 : cycles*2.0*numpy.pi : cycles*2.0*numpy.pi/res,
                 0.0 : cycles*2.0*numpy.pi : cycles*2.0*numpy.pi/res]
  if gratType is "none":
    res=2
    intensity = numpy.ones((res,res),Float)
  elif gratType is "sin":
    intensity= contr*(numpy.sin( xrange*numpy.sin(ori)+yrange*numpy.cos(ori) + phase))
  elif gratType is "ramp":
    intensity= contr*( xrange*numpy.cos(ori)+yrange*numpy.sin(ori) )/(2*numpy.pi)
  elif gratType is "sqr":#square wave (symmetric duty cycle)
    intensity = numpy.where(onePeriodX>pi, 1, -1)
  elif gratType is "sinXsin":
    intensity = numpy.sin(onePeriodX)*numpy.sin(onePeriodY)
  else:#might be a filename of an image
    try:
      im = Image.open(gratType)
    except:
      log.error( "couldn't find tex...",gratType)
      return
  return intensity
  
def maskMatrix(matrix, shape='circle', radius=1.0, center=[0.0,0.0]):
  """Make and apply a mask to an input matrix (e.g. a grating)
  
  **Arguments:**

            - **matrix** :  a square numpy array to which the mask should be applied
            - **shape** :  shape of the mask, curently: 'circle','gauss','ramp' (linear gradient from center)
            - **radius** :  scale factor to be applied to the mask (circle with radius of [1,1] will extend just to the edge of the matrix). Radius can asymmetric, e.g. [1.0,2.0] will be wider than it is tall.
      - **center** :  the centre of the mask in the matrix ([1,1] is top-right corner, [-1,-1] is bottom-left)
  """
  alphaMask = makeMask(matrix.shape[0],shape,radius, center=[0.0,0.0])
  return matrix*alphaMask

def makeMask(matrixSize, shape='circle', radius=1.0, center=[0.0,0.0]):
  """
  Returns a matrix to be used as an alpha mask (circle,gauss,ramp)
  
  **Arguments:**

            - **matrixSize** :  number of elements in each dimension of the matrix
            - **shape** :  shape of the mask, curently: 'circle','gauss','ramp' (linear gradient from center)
            - **radius** :  scale factor to be applied to the mask (circle with radius of [1,1] will extend just to the edge of the matrix). Radius can asymmetric, e.g. [1.0,2.0] will be wider than it is tall.
      - **center** :  the centre of the mask in the matrix ([1,1] is top-right corner, [-1,-1] is bottom-left)
    """
  rad = makeRadialMatrix(matrixSize, center, radius)
  if shape=='ramp':
    outArray=1-rad
  elif shape=='circle':
    #outArray=numpy.ones(matrixSize,'f')
    outArray=numpy.where(numpy.greater(rad,1.0),0.0,1.0)
  elif shape=='gauss':
    outArray=makeGauss(rad,mean=0.0,sd=0.33333)
  else:
    raise 'err', 'unknown shape'
  return outArray

def makeRadialMatrix(matrixSize, center=[0.0,0.0], radius=1.0):
  """Generate a square matrix where each element val is
  its distance from the centre of the matrix
  
  **Arguments:**

            - **matrixSize** :  number of elements in each dimension of the matrix
            - **radius** :  scale factor to be applied to the mask (circle with radius of [1,1] will extend just to the edge of the matrix). Radius can asymmetric, e.g. [1.0,2.0] will be wider than it is tall.
      - **center** :  the centre of the mask in the matrix ([1,1] is top-right corner, [-1,-1] is bottom-left)
  """
  if type(radius) in [int, float]: radius = [radius,radius]
  
  yy, xx = numpy.mgrid[0:matrixSize, 0:matrixSize]#NB need to add one step length because
  xx = ((1.0- 2.0/matrixSize*xx)+center[0])/radius[0]
  yy = ((1.0- 2.0/matrixSize*yy)+center[1])/radius[1]
  rad = numpy.sqrt(numpy.power(xx,2) + numpy.power(yy,2))
  return rad

def makeGauss(x, mean=0.0, sd=1.0, gain=1.0, base=0.0):
  """
  Return the gaussian distribution for a given set of x-vals
    
  **Arguments:**

            - **mean** :  then centre of the distribution
            - **sd** :  the width of the distribution
            - **gain** :  the height of the distribution
      - **base** :  an offset added to the result
      
  """
  simpleGauss = numpy.exp( (-numpy.power(mean-x,2)/(2*sd**2)) )
  return base + gain*( simpleGauss )
  
def getRMScontrast(matrix):
  """Returns the RMS contrast (the sample standard deviation) of a array"""
  matrix = matrix.flat
  RMScontrast = (sum((matrix-numpy.mean(matrix))**2)/len(matrix))**0.5
  return RMScontrast
  
def conv2d(smaller, larger):
  """convolve a pair of 2d numpy matrices
  Uses fourier transform method, so faster if larger matrix
  has dimensions of size 2^n
  
  Actually right now the matrices must be the same size (will sort out
  padding issues another day!)
  """
  smallerFFT = numpy.fft2(smaller)
  largerFFT = numpy.fft2(larger)
  
  invFFT = numpy.ifft(smallerFFT*largerFFT)
  return invFFT.real
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