parameters: (self, x, y)
return_statement: return ((np.dot(self.b, x) - np.dot(self.a[1:], y))/self.a[0]).reshape((1,-1))
docstring: y[n] = (b[0]*x[n] + b[1]*x[n-1] + ... + b[M]*x[n-M]
                        - a[1]*y[n-1] - ... - a[N]*y[n-N])/a[0]
docstring_summary: y[n] = (b[0]*x[n] + b[1]*x[n-1] + ... + b[M]*x[n-M]
                        - a[1]*y[n-1] - ... - a[N]*y[n-N])/a[0]
docstring_tokens: y [ n ] = ( b [ 0 ] * x [ n ] + b [ 1 ] * x [ n - 1 ] + ... + b [ M ] * x [ n - M ] - a [ 1 ] * y [ n - 1 ] - ... - a [ N ] * y [ n - N ] ) / a [ 0 ]
function: def transform(self, x, y):
          # Difference equation. Filter in time domain
          # Compares raw and filtered data and produces a difference array
          # b = coefficient array for raw data
          # a = coefficient array for filtered data
          # (b*x - a*y)/a[0]
          """
          y[n] = (b[0]*x[n] + b[1]*x[n-1] + ... + b[M]*x[n-M]
                        - a[1]*y[n-1] - ... - a[N]*y[n-N])/a[0]
          """
          return ((np.dot(self.b, x) - np.dot(self.a[1:], y))/self.a[0]).reshape((1,-1))
function_tokens: def transform ( self , x , y ) : # Difference equation. Filter in time domain # Compares raw and filtered data and produces a difference array # b = coefficient array for raw data # a = coefficient array for filtered data # (b*x - a*y)/a[0] return ( ( np . dot ( self . b , x ) - np . dot ( self . a [ 1 : ] , y ) ) / self . a [ 0 ] ) . reshape ( ( 1 , - 1 ) )