radarsys Files · apps/abs/utils/gaussfit.py

Task #714: Modulo Web ABS...

Task #714: Modulo Web ABS git-svn-id: http://jro-dev.igp.gob.pe/svn/jro_hard/radarsys/trunk/webapp@204 aa17d016-51d5-4e8b-934c-7b2bbb1bbe71

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      from numpy import *

      from scipy import optimize

      def gaussian(height, center_x, center_y, width_x, width_y):

          """Returns a gaussian function with the given parameters"""

          width_x = float(width_x)

          width_y = float(width_y)

          return lambda x,y: height*exp(

                      -(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)

      def moments(data):

          """Returns (height, x, y, width_x, width_y)

          the gaussian parameters of a 2D distribution by calculating its

          moments """

          total = data.sum()

          X, Y = indices(data.shape)

          x = (X*data).sum()/total

          y = (Y*data).sum()/total

          col = data[:, int(y)]

          width_x = sqrt(abs((arange(col.size)-y)**2*col).sum()/col.sum())

          row = data[int(x), :]

          width_y = sqrt(abs((arange(row.size)-x)**2*row).sum()/row.sum())

          height = data.max()

          return height, x, y, width_x, width_y

      def fitgaussian(data):

          """Returns (height, x, y, width_x, width_y)

          the gaussian parameters of a 2D distribution found by a fit"""

          params = moments(data)

          errorfunction = lambda p: ravel(gaussian(*p)(*indices(data.shape)) -

                                       data)

          p, success = optimize.leastsq(errorfunction, params)

          return p

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				from numpy import *
				from scipy import optimize

				def gaussian(height, center_x, center_y, width_x, width_y):
				"""Returns a gaussian function with the given parameters"""
				width_x = float(width_x)
				width_y = float(width_y)
				return lambda x,y: height*exp(
				-(((center_x-x)/width_x)2+((center_y-y)/width_y)2)/2)

				def moments(data):
				"""Returns (height, x, y, width_x, width_y)
				the gaussian parameters of a 2D distribution by calculating its
				moments """
				total = data.sum()
				X, Y = indices(data.shape)
				x = (X*data).sum()/total
				y = (Y*data).sum()/total
				col = data[:, int(y)]
				width_x = sqrt(abs((arange(col.size)-y)*2col).sum()/col.sum())
				row = data[int(x), :]
				width_y = sqrt(abs((arange(row.size)-x)*2row).sum()/row.sum())
				height = data.max()
				return height, x, y, width_x, width_y

				def fitgaussian(data):
				"""Returns (height, x, y, width_x, width_y)
				the gaussian parameters of a 2D distribution found by a fit"""
				params = moments(data)
				errorfunction = lambda p: ravel(gaussian(p)(indices(data.shape)) -
				data)
				p, success = optimize.leastsq(errorfunction, params)
				return p