SciPy 0.6.0 API Documentation Generated by Endo, 2007-10-17

Function summary

• convolve(input, weights, output = None, mode = 'reflect', cval = 0.0, origin = 0)
• convolve1d(input, weights, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0, origin = 0)
• correlate(input, weights, output = None, mode = 'reflect', cval = 0.0, origin = 0)
• correlate1d(input, weights, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0, origin = 0)
• gaussian_filter(input, sigma, order = 0, output = None, mode = 'reflect', cval = 0.0)
• gaussian_filter1d(input, sigma, axis = -Const(1), order = 0, output = None, mode = 'reflect', cval = 0.0)
• gaussian_gradient_magnitude(input, sigma, output = None, mode = 'reflect', cval = 0.0)
• gaussian_laplace(input, sigma, output = None, mode = 'reflect', cval = 0.0)
• generic_filter(input, function, size = None, footprint = None, output = None, mode = 'reflect', cval = 0.0, origin = 0, extra_arguments = (), extra_keywords = { })
• generic_filter1d(input, function, filter_size, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0, origin = 0, extra_arguments = (), extra_keywords = { })
• generic_gradient_magnitude(input, derivative, output = None, mode = 'reflect', cval = 0.0, extra_arguments = (), extra_keywords = { })
• generic_laplace(input, derivative2, output = None, mode = 'reflect', cval = 0.0, extra_arguments = (), extra_keywords = { })
• laplace(input, output = None, mode = 'reflect', cval = 0.0)
• maximum_filter(input, size = None, footprint = None, output = None, mode = 'reflect', cval = 0.0, origin = 0)
• maximum_filter1d(input, size, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0, origin = 0)
• median_filter(input, size = None, footprint = None, output = None, mode = 'reflect', cval = 0.0, origin = 0)
• minimum_filter(input, size = None, footprint = None, output = None, mode = 'reflect', cval = 0.0, origin = 0)
• minimum_filter1d(input, size, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0, origin = 0)
• percentile_filter(input, percentile, size = None, footprint = None, output = None, mode = 'reflect', cval = 0.0, origin = 0)
• prewitt(input, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0)
• rank_filter(input, rank, size = None, footprint = None, output = None, mode = 'reflect', cval = 0.0, origin = 0)
• sobel(input, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0)
• uniform_filter(input, size = 3, output = None, mode = 'reflect', cval = 0.0, origin = 0)
• uniform_filter1d(input, size, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0, origin = 0)

Functions

• convolve(input, weights, output = None, mode = 'reflect', cval = 0.0, origin = 0)

  Multi-dimensional convolution.

  The array is convolved with the given kernel.

• convolve1d(input, weights, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0, origin = 0)

  Calculate a one-dimensional convolution along the given axis.

  The lines of the array along the given axis are convolved with the given weights. The weights parameter must be a one-dimensional sequence of numbers.

• correlate(input, weights, output = None, mode = 'reflect', cval = 0.0, origin = 0)

  Multi-dimensional correlation.

  The array is correlated with the given kernel.

• correlate1d(input, weights, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0, origin = 0)

  Calculate a one-dimensional correlation along the given axis.

  The lines of the array along the given axis are correlated with the given weights. The weights parameter must be a one-dimensional sequence of numbers.

• gaussian_filter(input, sigma, order = 0, output = None, mode = 'reflect', cval = 0.0)

  Multi-dimensional Gaussian filter.

  The standard-deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes. The order of the filter along each axis is given as a sequence of integers, or as a single number. An order of 0 corresponds to convolution with a Gaussian kernel. An order of 1, 2, or 3 corresponds to convolution with the first, second or third derivatives of a Gaussian. Higher order derivatives are not implemented.'

  Note: The multi-dimensional filter is implemented as a sequence of one-dimensional convolution filters. The intermediate arrays are stored in the same data type as the output. Therefore, for output types with a limited precision, the results may be imprecise because intermediate results may be stored with insufficient precision.

• gaussian_filter1d(input, sigma, axis = -Const(1), order = 0, output = None, mode = 'reflect', cval = 0.0)

  One-dimensional Gaussian filter.

  The standard-deviation of the Gaussian filter is given by sigma. An order of 0 corresponds to convolution with a Gaussian kernel. An order of 1, 2, or 3 corresponds to convolution with the first, second or third derivatives of a Gaussian. Higher order derivatives are not implemented.

• gaussian_gradient_magnitude(input, sigma, output = None, mode = 'reflect', cval = 0.0)

  Calculate a multidimensional gradient magnitude using gaussian derivatives.

  The standard-deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes..

• gaussian_laplace(input, sigma, output = None, mode = 'reflect', cval = 0.0)

  Calculate a multidimensional laplace filter using gaussian second derivatives.

  The standard-deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes..

• generic_filter(input, function, size = None, footprint = None, output = None, mode = 'reflect', cval = 0.0, origin = 0, extra_arguments = (), extra_keywords = { })

  Calculates a multi-dimensional filter using the given function.

  At each element the provided function is called. The input values within the filter footprint at that element are passed to the function as a 1D array of double values.

  Either a size or a footprint with the filter must be provided. An output array can optionally be provided. The origin parameter controls the placement of the filter. The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to 'constant'. The extra_arguments and extra_keywords arguments can be used to pass extra arguments and keywords that are passed to the function at each call.

• generic_filter1d(input, function, filter_size, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0, origin = 0, extra_arguments = (), extra_keywords = { })

  Calculate a one-dimensional filter along the given axis.

  The function iterates over the lines of the array, calling the given function at each line. The arguments of the line are the input line, and the output line. The input and output lines are 1D double arrays. The input line is extended appropiately according to the filter size and origin. The output line must be modified in-place with the result. The origin parameter controls the placement of the filter. The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to 'constant'. The extra_arguments and extra_keywords arguments can be used to pass extra arguments and keywords that are passed to the function at each call.

• generic_gradient_magnitude(input, derivative, output = None, mode = 'reflect', cval = 0.0, extra_arguments = (), extra_keywords = { })

  Calculate a gradient magnitude using the provdide function for
  the gradient.
  
  The derivative parameter must be a callable with the following
  signature:
  
  derivative(input, axis, output, mode, cval,
             *extra_arguments, **extra_keywords)
  
  The extra_arguments and extra_keywords arguments can be used to pass
  extra arguments and keywords that are passed to derivative2 at each
  call.
  

• generic_laplace(input, derivative2, output = None, mode = 'reflect', cval = 0.0, extra_arguments = (), extra_keywords = { })

  Calculate a multidimensional laplace filter using the provided
  second derivative function.
  
  The derivative2 parameter must be a callable with the following
  signature:
  
  derivative2(input, axis, output, mode, cval,
              *extra_arguments, **extra_keywords)
  
  The extra_arguments and extra_keywords arguments can be used to pass
  extra arguments and keywords that are passed to derivative2 at each
  call.
  

• laplace(input, output = None, mode = 'reflect', cval = 0.0)

  Calculate a multidimensional laplace filter using an estimation for the second derivative based on differences.

• maximum_filter(input, size = None, footprint = None, output = None, mode = 'reflect', cval = 0.0, origin = 0)

  Calculates a multi-dimensional maximum filter.

  Either a size or a footprint with the filter must be provided. An output array can optionally be provided. The origin parameter controls the placement of the filter. The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to 'constant'.

• maximum_filter1d(input, size, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0, origin = 0)

  Calculate a one-dimensional maximum filter along the given axis.

  The lines of the array along the given axis are filtered with a maximum filter of given size.

• median_filter(input, size = None, footprint = None, output = None, mode = 'reflect', cval = 0.0, origin = 0)

  Calculates a multi-dimensional median filter.

  Either a size or a footprint with the filter must be provided. An output array can optionally be provided. The origin parameter controls the placement of the filter. The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to 'constant'.

• minimum_filter(input, size = None, footprint = None, output = None, mode = 'reflect', cval = 0.0, origin = 0)

  Calculates a multi-dimensional minimum filter.

  Either a size or a footprint with the filter must be provided. An output array can optionally be provided. The origin parameter controls the placement of the filter. The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to 'constant'.

• minimum_filter1d(input, size, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0, origin = 0)

  Calculate a one-dimensional minimum filter along the given axis.

  The lines of the array along the given axis are filtered with a minimum filter of given size.

• percentile_filter(input, percentile, size = None, footprint = None, output = None, mode = 'reflect', cval = 0.0, origin = 0)

  Calculates a multi-dimensional percentile filter.

  The percentile parameter may be less then zero, i.e., percentile = -20 equals percentile = 80. Either a size or a footprint with the filter must be provided. An output array can optionally be provided. The origin parameter controls the placement of the filter. The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to 'constant'.

• prewitt(input, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0)

  Calculate a Prewitt filter.

• rank_filter(input, rank, size = None, footprint = None, output = None, mode = 'reflect', cval = 0.0, origin = 0)

  Calculates a multi-dimensional rank filter.

  The rank parameter may be less then zero, i.e., rank = -1 indicates the larges element. Either a size or a footprint with the filter must be provided. An output array can optionally be provided. The origin parameter controls the placement of the filter. The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to 'constant'.

• sobel(input, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0)

  Calculate a Sobel filter.

• uniform_filter(input, size = 3, output = None, mode = 'reflect', cval = 0.0, origin = 0)

  Multi-dimensional uniform filter.

  The sizes of the uniform filter are given for each axis as a sequence, or as a single number, in which case the size is equal for all axes.

  The multi-dimensional filter is implemented as a sequence of one-dimensional uniform filters. The intermediate arrays are stored in the same data type as the output. Therefore, for output types with a limited precision, the results may be imprecise because intermediate results may be stored with insufficient precision.

• uniform_filter1d(input, size, axis = -Const(1), output = None, mode = 'reflect', cval = 0.0, origin = 0)

  Calculate a one-dimensional uniform filter along the given axis.

  The lines of the array along the given axis are filtered with a uniform filter of given size.

Imported Names