content: from SimpleCV.base import np, warnings
  from SimpleCV.ImageClass import Image
  
  class DFT:
      """
      **SUMMARY**
  
      The DFT class is the refactored class to crate DFT filters which can
      be used to filter images by applying Digital Fourier Transform. This
      is a factory class to create various DFT filters.
  
      **PARAMETERS**
  
      Any of the following parameters can be supplied to create 
      a simple DFT object.
  
      * *width*        - width of the filter
      * *height*       - height of the filter
      * *channels*     - number of channels of the filter
      * *size*         - size of the filter (width, height)
      * *_numpy*       - numpy array of the filter
      * *_image*       - SimpleCV.Image of the filter
      * *_dia*         - diameter of the filter 
                        (applicable for gaussian, butterworth, notch)
      * *_type*        - Type of the filter 
      * *_order*       - order of the butterworth filter 
      * *_freqpass*    - frequency of the filter (lowpass, highpass, bandpass)
      * *_xCutoffLow*  - Lower horizontal cut off frequency for lowpassfilter
      * *_yCutoffLow*  - Lower vertical cut off frequency for lowpassfilter
      * *_xCutoffHigh* - Upper horizontal cut off frequency for highpassfilter
      * *_yCutoffHigh* - Upper vertical cut off frequency for highassfilter
      
  
  
      **EXAMPLE**
  
      >>> gauss = DFT.createGaussianFilter(dia=40, size=(512,512))
      
      >>> dft = DFT()
      >>> butterworth = dft.createButterworthFilter(dia=300, order=2, size=(300, 300))
  
      """
      width = 0
      height = 0
      channels = 1
      _numpy = None
      _image = None
      _dia = 0
      _type = ""
      _order = 0
      _freqpass = ""
      _xCutoffLow = 0
      _yCutoffLow = 0
      _xCutoffHigh = 0
      _yCutoffHigh = 0
  
      def __init__(self, **kwargs):
          for key in kwargs:
              if key == 'width':
                  self.width = kwargs[key]
              elif key == 'height':
                  self.height = kwargs[key]
              elif key == 'channels':
                  self.channels = kwargs[key]
              elif key == 'size':
                  self.width, self.height = kwargs[key]
              elif key == 'numpyarray':
                  self._numpy = kwargs[key]
              elif key == 'image':
                  self._image = kwargs[key]
              elif key == 'dia':
                  self._dia = kwargs[key]
              elif key == 'type':
                  self._type = kwargs[key]
              elif key == 'order':
                  self._order = kwargs[key]
              elif key == 'frequency':
                  self._freqpass = kwargs[key]
              elif key == 'xCutoffLow':
                  self._xCutoffLow = kwargs[key]
              elif key == 'yCutoffLow':
                  self._yCutoffLow = kwargs[key]
              elif key == 'xCutoffHigh':
                  self._xCutoffHigh = kwargs[key]
              elif key == 'yCutoffHigh':
                  self._yCutoffHigh = kwargs[key]
  
      def __repr__(self):
          return "<SimpleCV.DFT Object: %s %s filter of size:(%d, %d) and channels: %d>" %(self._type, self._freqpass, self.width, self.height, self.channels)
  
      def __add__(self, flt):
          if not isinstance(flt, type(self)):
              warnings.warn("Provide SimpleCV.DFT object")
              return None
          if self.size() != flt.size():
              warnings.warn("Both SimpleCV.DFT object must have the same size")
              return None
          flt_numpy = self._numpy + flt._numpy
          flt_image = Image(flt_numpy)
          retVal = DFT(numpyarray=flt_numpy, image=flt_image, size=flt_image.size())
          return retVal
  
      def __invert__(self, flt):
          return self.invert()
  
      def _updateParams(self, flt):
          self.channels = flt.channels
          self._dia = flt._dia
          self._type = flt._type
          self._order = flt._order
          self._freqpass = flt._freqpass
          self._xCutoffLow = flt._xCutoffLow
          self._yCutoffLow = flt._yCutoffLow
          self._xCutoffHigh = flt._xCutoffHigh
          self._yCutoffHigh = flt._yCutoffHigh
  
      def invert(self):
          """
          **SUMMARY**
  
          Invert the filter. All values will be subtracted from 255.
  
          **RETURNS**
  
          Inverted Filter
  
          **EXAMPLE**
  
          >>> flt = DFT.createGaussianFilter()
          >>> invertflt = flt.invert()
          """
  
          flt = self._numpy
          flt = 255 - flt
          img = Image(flt)
          invertedfilter = DFT(numpyarray=flt, image=img,
                               size=self.size(), type=self._type)
          invertedfilter._updateParams(self)
          return invertedfilter
  
      @classmethod
      def createGaussianFilter(self, dia=400, size=(64, 64), highpass=False):
          """
          **SUMMARY**
  
          Creates a gaussian filter of given size.
  
          **PARAMETERS**
  
          * *dia*       -  int - diameter of Gaussian filter
                        - list - provide a list of three diameters to create
                                 a 3 channel filter
          * *size*      - size of the filter (width, height)
          * *highpass*: -  bool 
                           True: highpass filter 
                           False: lowpass filter
  
          **RETURNS**
  
          DFT filter.
  
          **EXAMPLE**
  
          >>> gauss = DFT.createGaussianfilter(200, (512, 512),
                                              highpass=True)
          >>> gauss = DFT.createGaussianfilter([100, 120, 140], (512, 512),
                                               highpass=False)
          >>> img = Image('lenna')
          >>> gauss.applyFilter(img).show()
          """
          if isinstance(dia, list):
              if len(dia) != 3 and len(dia) != 1:
                  warnings.warn("diameter list must be of size 1 or 3")
                  return None
              stackedfilter = DFT()
              for d in dia:
                  stackedfilter = stackedfilter._stackFilters(self.createGaussianFilter(d, size, highpass))
              image = Image(stackedfilter._numpy)
              retVal = DFT(numpyarray=stackedfilter._numpy, image=image,
                           dia=dia, channels = len(dia), size=size,
                           type="Gaussian", frequency=stackedfilter._freqpass)
              return retVal
  
          freqpass = "lowpass"
          sz_x, sz_y = size
          x0 = sz_x/2
          y0 = sz_y/2
          X, Y = np.meshgrid(np.arange(sz_x), np.arange(sz_y))
          D = np.sqrt((X-x0)**2+(Y-y0)**2)
          flt = 255*np.exp(-0.5*(D/dia)**2) 
          if highpass:
              flt = 255 - flt
              freqpass = "highpass"
          img = Image(flt)
          retVal = DFT(size=size, numpyarray=flt, image=img, dia=dia,
                       type="Gaussian", frequency=freqpass)
          return retVal
  
      @classmethod
      def createButterworthFilter(self, dia=400, size=(64, 64), order=2, highpass=False):
          """
          **SUMMARY**
  
          Creates a butterworth filter of given size and order.
  
          **PARAMETERS**
  
          * *dia*       - int - diameter of Gaussian filter
                        - list - provide a list of three diameters to create
                                 a 3 channel filter
          * *size*      - size of the filter (width, height)
          * *order*     - order of the filter
          * *highpass*: -  bool 
                           True: highpass filter 
                           False: lowpass filter
  
          **RETURNS**
  
          DFT filter.
  
          **EXAMPLE**
  
          >>> flt = DFT.createButterworthfilter(100, (512, 512), order=3,
                                               highpass=True)
          >>> flt = DFT.createButterworthfilter([100, 120, 140], (512, 512),
                                               order=3, highpass=False)
          >>> img = Image('lenna')
          >>> flt.applyFilter(img).show()
          """
          if isinstance(dia, list):
              if len(dia) != 3 and len(dia) != 1:
                  warnings.warn("diameter list must be of size 1 or 3")
                  return None
              stackedfilter = DFT()
              for d in dia:
                  stackedfilter = stackedfilter._stackFilters(self.createButterworthFilter(d, size, order, highpass))
              image = Image(stackedfilter._numpy)
              retVal = DFT(numpyarray=stackedfilter._numpy, image=image,
                           dia=dia, channels = len(dia), size=size,
                           type=stackedfilter._type, order=order,
                           frequency=stackedfilter._freqpass)
              return retVal
          freqpass = "lowpass"
          sz_x, sz_y = size
          x0 = sz_x/2
          y0 = sz_y/2
          X, Y = np.meshgrid(np.arange(sz_x), np.arange(sz_y))
          D = np.sqrt((X-x0)**2+(Y-y0)**2)
          flt = 255/(1.0 + (D/dia)**(order*2))
          if highpass:
              frequency = "highpass"
              flt = 255 - flt
          img = Image(flt)
          retVal = DFT(size=size, numpyarray=flt, image=img, dia=dia,
                       type="Butterworth", frequency=freqpass)
          return retVal
  
      @classmethod    
      def createLowpassFilter(self, xCutoff, yCutoff=None, size=(64, 64)):
          """
          **SUMMARY**
  
          Creates a lowpass filter of given size and order.
  
          **PARAMETERS**
  
          * *xCutoff*       - int - horizontal cut off frequency
                            - list - provide a list of three cut off frequencies
                                     to create a 3 channel filter
          * *yCutoff*       - int - vertical cut off frequency
                            - list - provide a list of three cut off frequencies
                                     to create a 3 channel filter
          * *size*      - size of the filter (width, height)
  
          **RETURNS**
  
          DFT filter.
  
          **EXAMPLE**
  
          >>> flt = DFT.createLowpassFilter(xCutoff=75, size=(320, 280))
  
          >>> flt = DFT.createLowpassFilter(xCutoff=[75], size=(320, 280))
  
          >>> flt = DFT.createLowpassFilter(xCutoff=[75, 100, 120],
                                            size=(320, 280))
  
          >>> flt = DFT.createLowpassFilter(xCutoff=75, yCutoff=35,
                                            size=(320, 280))
  
          >>> flt = DFT.createLowpassFilter(xCutoff=[75], yCutoff=[35],
                                            size=(320, 280))
  
          >>> flt = DFT.createLowpassFilter(xCutoff=[75, 100, 125], yCutoff=35,
                                            size=(320, 280))
          >>> # yCutoff will be [35, 35, 35]
  
          >>> flt = DFT.createLowpassFilter(xCutoff=[75, 113, 124],
                                            yCutoff=[35, 45, 90],
                                            size=(320, 280))
          
          >>> img = Image('lenna')
          >>> flt.applyFilter(img).show()
          """
          if isinstance(xCutoff, list):
              if len(xCutoff) != 3 and len(xCutoff) != 1:
                  warnings.warn("xCutoff list must be of size 3 or 1")
                  return None
              if isinstance(yCutoff, list):
                  if len(yCutoff) != 3 and len(yCutoff) != 1:
                      warnings.warn("yCutoff list must be of size 3 or 1")
                      return None
                  if len(yCutoff) == 1:
                      yCutoff = [yCutoff[0]]*len(xCutoff)
              else:
                  yCutoff = [yCutoff]*len(xCutoff)
              stackedfilter = DFT()
              for xfreq, yfreq in zip(xCutoff, yCutoff):
                  stackedfilter = stackedfilter._stackFilters(self.createLowpassFilter(xfreq, yfreq, size))
              image = Image(stackedfilter._numpy)
              retVal = DFT(numpyarray=stackedfilter._numpy, image=image,
                           xCutoffLow=xCutoff, yCutoffLow=yCutoff,
                           channels=len(xCutoff), size=size,
                           type=stackedfilter._type, order=self._order,
                           frequency=stackedfilter._freqpass)
              return retVal
  
          w, h = size
          xCutoff = np.clip(int(xCutoff), 0, w/2)
          if yCutoff is None:
              yCutoff = xCutoff
          yCutoff = np.clip(int(yCutoff), 0, h/2)
          flt = np.zeros((w, h))
          flt[0:xCutoff, 0:yCutoff] = 255
          flt[0:xCutoff, h-yCutoff:h] = 255
          flt[w-xCutoff:w, 0:yCutoff] = 255
          flt[w-xCutoff:w, h-yCutoff:h] = 255
          img = Image(flt)
          lowpassFilter = DFT(size=size, numpyarray=flt, image=img,
                              type="Lowpass", xCutoffLow=xCutoff,
                              yCutoffLow=yCutoff, frequency="lowpass")
          return lowpassFilter
  
      @classmethod
      def createHighpassFilter(self, xCutoff, yCutoff=None, size=(64, 64)):
          """
          **SUMMARY**
  
          Creates a highpass filter of given size and order.
  
          **PARAMETERS**
  
          * *xCutoff*       - int - horizontal cut off frequency
                            - list - provide a list of three cut off frequencies
                                     to create a 3 channel filter
          * *yCutoff*       - int - vertical cut off frequency
                            - list - provide a list of three cut off frequencies
                                     to create a 3 channel filter
          * *size*      - size of the filter (width, height)
  
          **RETURNS**
  
          DFT filter.
  
          **EXAMPLE**
  
          >>> flt = DFT.createHighpassFilter(xCutoff=75, size=(320, 280))
  
          >>> flt = DFT.createHighpassFilter(xCutoff=[75], size=(320, 280))
  
          >>> flt = DFT.createHighpassFilter(xCutoff=[75, 100, 120],
                                             size=(320, 280))
  
          >>> flt = DFT.createHighpassFilter(xCutoff=75, yCutoff=35, 
                                             size=(320, 280))
  
          >>> flt = DFT.createHighpassFilter(xCutoff=[75], yCutoff=[35],
                                             size=(320, 280))
  
          >>> flt = DFT.createHighpassFilter(xCutoff=[75, 100, 125], yCutoff=35,
                                             size=(320, 280))
          >>> # yCutoff will be [35, 35, 35]
  
          >>> flt = DFT.createHighpassFilter(xCutoff=[75, 113, 124],
                                             yCutoff=[35, 45, 90],
                                             size=(320, 280))
          
          >>> img = Image('lenna')
          >>> flt.applyFilter(img).show()
          """
          if isinstance(xCutoff, list):
              if len(xCutoff) != 3 and len(xCutoff) != 1:
                  warnings.warn("xCutoff list must be of size 3 or 1")
                  return None
              if isinstance(yCutoff, list):
                  if len(yCutoff) != 3 and len(yCutoff) != 1:
                      warnings.warn("yCutoff list must be of size 3 or 1")
                      return None
                  if len(yCutoff) == 1:
                      yCutoff = [yCutoff[0]]*len(xCutoff)
              else:
                  yCutoff = [yCutoff]*len(xCutoff)
              stackedfilter = DFT()
              for xfreq, yfreq in zip(xCutoff, yCutoff):
                  stackedfilter = stackedfilter._stackFilters(
                                  self.createHighpassFilter(xfreq, yfreq, size))
              image = Image(stackedfilter._numpy)
              retVal = DFT(numpyarray=stackedfilter._numpy, image=image,
                           xCutoffHigh=xCutoff, yCutoffHigh=yCutoff,
                           channels=len(xCutoff), size=size,
                           type=stackedfilter._type, order=self._order,
                           frequency=stackedfilter._freqpass)
              return retVal
  
          lowpass = self.createLowpassFilter(xCutoff, yCutoff, size)
          w, h = lowpass.size()
          flt = lowpass._numpy
          flt = 255 - flt
          img = Image(flt)
          highpassFilter = DFT(size=size, numpyarray=flt, image=img,
                               type="Highpass", xCutoffHigh=xCutoff,
                               yCutoffHigh=yCutoff, frequency="highpass")
          return highpassFilter
  
      @classmethod
      def createBandpassFilter(self, xCutoffLow, xCutoffHigh, yCutoffLow=None,
                               yCutoffHigh=None, size=(64, 64)):
          """
          **SUMMARY**
  
          Creates a banf filter of given size and order.
  
          **PARAMETERS**
  
          * *xCutoffLow*    - int - horizontal lower cut off frequency
                            - list - provide a list of three cut off frequencies
          * *xCutoffHigh*   - int - horizontal higher cut off frequency
                            - list - provide a list of three cut off frequencies
          * *yCutoffLow*    - int - vertical lower cut off frequency
                            - list - provide a list of three cut off frequencies
          * *yCutoffHigh*   - int - verical higher cut off frequency
                            - list - provide a list of three cut off frequencies
                                     to create a 3 channel filter
          * *size*      - size of the filter (width, height)
  
          **RETURNS**
  
          DFT filter.
  
          **EXAMPLE**
  
          >>> flt = DFT.createBandpassFilter(xCutoffLow=75,
                                             xCutoffHigh=190, size=(320, 280))
  
          >>> flt = DFT.createBandpassFilter(xCutoffLow=[75],
                                             xCutoffHigh=[190], size=(320, 280))
  
          >>> flt = DFT.createBandpassFilter(xCutoffLow=[75, 120, 132],
                                             xCutoffHigh=[190, 210, 234],
                                             size=(320, 280))
  
          >>> flt = DFT.createBandpassFilter(xCutoffLow=75, xCutoffHigh=190,
                                             yCutoffLow=60, yCutoffHigh=210,
                                             size=(320, 280))
  
          >>> flt = DFT.createBandpassFilter(xCutoffLow=[75], xCutoffHigh=[190],
                                             yCutoffLow=[60], yCutoffHigh=[210],
                                             size=(320, 280))
  
          >>> flt = DFT.createBandpassFilter(xCutoffLow=[75, 120, 132],
                                             xCutoffHigh=[190, 210, 234], 
                                             yCutoffLow=[70, 110, 112], 
                                             yCutoffHigh=[180, 220, 220], 
                                             size=(320, 280))
          
          >>> img = Image('lenna')
          >>> flt.applyFilter(img).show()
          """
          lowpass = self.createLowpassFilter(xCutoffLow, yCutoffLow, size)
          highpass = self.createHighpassFilter(xCutoffHigh, yCutoffHigh, size)
          lowpassnumpy = lowpass._numpy
          highpassnumpy = highpass._numpy
          bandpassnumpy = lowpassnumpy + highpassnumpy
          bandpassnumpy = np.clip(bandpassnumpy, 0, 255)
          img = Image(bandpassnumpy)
          bandpassFilter = DFT(size=size, image=img,
                               numpyarray=bandpassnumpy, type="bandpass",
                               xCutoffLow=xCutoffLow, yCutoffLow=yCutoffLow,
                               xCutoffHigh=xCutoffHigh, yCutoffHigh=yCutoffHigh,
                               frequency="bandpass", channels=lowpass.channels)
          return bandpassFilter
  
      @classmethod
      def createNotchFilter(self, dia1, dia2=None, cen=None, size=(64, 64), type="lowpass"):
          """
          **SUMMARY**
  
          Creates a disk shaped notch filter of given diameter at given center.
  
          **PARAMETERS**
  
          * *dia1*       -  int - diameter of the disk shaped notch
                         - list - provide a list of three diameters to create
                                 a 3 channel filter
          * *dia2*       -  int - outer diameter of the disk shaped notch
                                  used for bandpass filter
                         - list - provide a list of three diameters to create
                                 a 3 channel filter
          * *cen*        - tuple (x, y) center of the disk shaped notch
                           if not provided, it will be at the center of the 
                           filter
          * *size*       - size of the filter (width, height)
          * *type*:      - lowpass or highpass filter
  
          **RETURNS**
          DFT notch filter
  
          **EXAMPLE**
  
          >>> notch = DFT.createNotchFilter(dia1=200, cen=(200, 200),
                                            size=(512, 512), type="highpass")
          >>> notch = DFT.createNotchFilter(dia1=200, dia2=300, cen=(200, 200),
                                            size=(512, 512))
          >>> img = Image('lenna')
          >>> notch.applyFilter(img).show()
          """
          if isinstance(dia1, list):
              if len(dia1) != 3 and len(dia1) != 1:
                  warnings.warn("diameter list must be of size 1 or 3")
                  return None
  
              if isinstance(dia2, list):
                  if len(dia2) != 3 and len(dia2) != 1:
                      warnings.warn("diameter list must be of size 3 or 1")
                      return None
                  if len(dia2) == 1:
                      dia2 = [dia2[0]]*len(dia1)
              else:
                  dia2 = [dia2]*len(dia1)
  
              if isinstance(cen, list):
                  if len(cen) != 3 and len(cen) != 1:
                      warnings.warn("center list must be of size 3 or 1")
                      return None
                  if len(cen) == 1:
                      cen = [cen[0]]*len(dia1)
              else:
                  cen = [cen]*len(dia1)
  
              stackedfilter = DFT()
              for d1, d2, c in zip(dia1, dia2, cen):
                  stackedfilter = stackedfilter._stackFilters(self.createNotchFilter(d1, d2, c, size, type))
              image = Image(stackedfilter._numpy)
              retVal = DFT(numpyarray=stackedfilter._numpy, image=image,
                           dia=dia1+dia2, channels = len(dia1), size=size,
                           type=stackedfilter._type,
                           frequency=stackedfilter._freqpass)
              return retVal
  
          w, h = size
          if cen is None:
              cen = (w/2, h/2)
          a, b = cen
          y, x = np.ogrid[-a:w-a, -b:h-b]
          r = dia1/2
          mask = x*x + y*y <= r*r
          flt = np.ones((w, h))
          flt[mask] = 255
          if type == "highpass":
              flt = 255-flt
          if dia2 is not None:
              a, b = cen
              y, x = np.ogrid[-a:w-a, -b:h-b]
              r = dia2/2
              mask = x*x + y*y <= r*r
              flt1 = np.ones((w, h))
              flt1[mask] = 255
              flt1 = 255 - flt1
              flt = flt + flt1
              np.clip(flt, 0, 255)
              type = "bandpass"
          img = Image(flt)
          notchfilter = DFT(size=size, numpyarray=flt, image=img, dia=dia1,
                            type="Notch", frequency=type)
          return notchfilter
  
      def applyFilter(self, image, grayscale=False):
          """
          **SUMMARY**
  
          Apply the DFT filter to given image.
  
          **PARAMETERS**
  
          * *image*     - SimpleCV.Image image
          * *grayscale* - if this value is True we perfrom the operation on the 
                          DFT of the gray version of the image and the result is
                          gray image. If grayscale is true we perform the 
                          operation on each channel and the recombine them to
                          create the result.
  
          **RETURNS**
  
          Filtered Image.
  
          **EXAMPLE**
  
          >>> notch = DFT.createNotchFilter(dia1=200, cen=(200, 200),
                                            size=(512, 512), type="highpass")
          >>> img = Image('lenna')
          >>> notch.applyFilter(img).show()
          """
  
          if self.width == 0 or self.height == 0:
              warnings.warn("Empty Filter. Returning the image.")
              return image
          w, h = image.size()
          if grayscale:
              image = image.toGray()
          fltImg = self._image
          if fltImg.size() != image.size():
              fltImg = fltImg.resize(w, h)
          filteredImage = image.applyDFTFilter(fltImg)
          return filteredImage
  
      def getImage(self):
          """
          **SUMMARY**
  
          Get the SimpleCV Image of the filter
  
          **RETURNS**
  
          Image of the filter.
  
          **EXAMPLE**
  
          >>> notch = DFT.createNotchFilter(dia1=200, cen=(200, 200),
                                            size=(512, 512), type="highpass")
          >>> notch.getImage().show()
          """
          if isinstance(self._image, type(None)):
              if isinstance(self._numpy, type(None)):
                  warnings.warn("Filter doesn't contain any image")
              self._image = Image(self._numpy)
          return self._image
  
      def getNumpy(self):
          """
          **SUMMARY**
  
          Get the numpy array of the filter
  
          **RETURNS**
  
          numpy array of the filter.
  
          **EXAMPLE**
  
          >>> notch = DFT.createNotchFilter(dia1=200, cen=(200, 200),
                                            size=(512, 512), type="highpass")
          >>> notch.getNumpy()
          """
          if isinstance(self._numpy, type(None)):
              if isinstance(self._image, type(None)):
                  warnings.warn("Filter doesn't contain any image")
              self._numpy = self._image.getNumpy()
          return self._numpy
  
      def getOrder(self):
          """
          **SUMMARY**
  
          Get order of the butterworth filter
  
          **RETURNS**
  
          order of the butterworth filter
  
          **EXAMPLE**
  
          >>> flt = DFT.createButterworthFilter(order=4)
          >>> print flt.getOrder()
          """
          return self._order
  
      def size(self):
          """
          **SUMMARY**
  
          Get size of the filter
  
          **RETURNS**
  
          tuple of (width, height)
  
          **EXAMPLE**
  
          >>> flt = DFT.createGaussianFilter(size=(380, 240))
          >>> print flt.size()
          """
          return (self.width, self.height)
  
      def getDia(self):
          """
          **SUMMARY**
  
          Get diameter of the filter
  
          **RETURNS**
  
          diameter of the filter
  
          **EXAMPLE**
  
          >>> flt = DFT.createGaussianFilter(dia=200, size=(380, 240))
          >>> print flt.getDia()
          """
          return self._dia
  
      def getType(self):
          """
          **SUMMARY**
  
          Get type of the filter
  
          **RETURNS**
  
          type of the filter
  
          **EXAMPLE**
  
          >>> flt = DFT.createGaussianFilter(dia=200, size=(380, 240))
          >>> print flt.getType() # Gaussian
          """
          return self._type
  
      def stackFilters(self, flt1, flt2):
          """
          **SUMMARY**
  
          Stack three signle channel filters of the same size to create
          a 3 channel filter.
  
          **PARAMETERS**
  
          * *flt1* - second filter to be stacked
          * *flt2* - thrid filter to be stacked
  
          **RETURNS**
  
          DFT filter
  
          **EXAMPLE**
  
          >>> flt1 = DFT.createGaussianFilter(dia=200, size=(380, 240))
          >>> flt2 = DFT.createGaussianFilter(dia=100, size=(380, 240))
          >>> flt2 = DFT.createGaussianFilter(dia=70, size=(380, 240))
          >>> flt = flt1.stackFilters(flt2, flt3) # 3 channel filter
          """
          if not(self.channels == 1 and flt1.channels == 1 and flt2.channels == 1):
              warnings.warn("Filters must have only 1 channel")
              return None
          if not (self.size() == flt1.size() and self.size() == flt2.size()):
              warnings.warn("All the filters must be of same size")
              return None
          numpyflt = self._numpy
          numpyflt1 = flt1._numpy
          numpyflt2 = flt2._numpy
          flt = np.dstack((numpyflt, numpyflt1, numpyflt2))
          img = Image(flt)
          stackedfilter = DFT(size=self.size(), numpyarray=flt, image=img, channels=3)
          return stackedfilter
  
      def _stackFilters(self, flt1):
          """
          **SUMMARY**
  
          stack two filters of same size. channels don't matter.
  
          **PARAMETERS**
  
          * *flt1* - second filter to be stacked
  
          **RETURNS**
  
          DFT filter
  
          """
          if isinstance(self._numpy, type(None)):
              return flt1
          if not self.size() == flt1.size():
              warnings.warn("All the filters must be of same size")
              return None
          numpyflt = self._numpy
          numpyflt1 = flt1._numpy
          flt = np.dstack((numpyflt, numpyflt1))
          stackedfilter = DFT(size=self.size(), numpyarray=flt,
                              channels=self.channels+flt1.channels,
                              type=self._type, frequency=self._freqpass)
          return stackedfilter
  