original_docstring: 
      _softThreshold solves a LASSO problem with isotropic total variation.
      Inputs:
              x           : input of the LASSO problem
              threshold   : a threshold value for soft-thresholding
      Output:
              x_threshold : optimized soft-thresholded result
      
docstring: _softThreshold solves a LASSO problem with isotropic total variation.
docstring_tokens: _softThreshold solves a LASSO problem with isotropic total variation .
code: def _softThreshold(x, threshold):
      '''
      _softThreshold solves a LASSO problem with isotropic total variation.
      Inputs:
              x           : input of the LASSO problem
              threshold   : a threshold value for soft-thresholding
      Output:
              x_threshold : optimized soft-thresholded result
      '''
      magnitude   = ((x.conj()*x).sum(axis=0))**0.5
      ratio       = np.maximum(magnitude-threshold, 0.0)/magnitude
      x_threshold = x*ratio[np.newaxis,:] 
  
      return x_threshold
code_tokens: def _softThreshold ( x , threshold ) : magnitude = ( ( x . conj ( ) * x ) . sum ( axis = 0 ) ) ** 0.5 ratio = np . maximum ( magnitude - threshold , 0.0 ) / magnitude x_threshold = x * ratio [ np . newaxis , : ] return x_threshold
short_docstring: _softThreshold solves a LASSO problem with isotropic total variation.
short_docstring_tokens: _softThreshold solves a LASSO problem with isotropic total variation .
comment: '''
      _softThreshold solves a LASSO problem with isotropic total variation.
      Inputs:
              x           : input of the LASSO problem
              threshold   : a threshold value for soft-thresholding
      Output:
              x_threshold : optimized soft-thresholded result
      '''