def set_interpolation_coefficients(self):
        """
        computes the coefficients for the single polynomials of the spline.
        """

        left_boundary_slope = 0
        right_boundary_slope = 0

        if isinstance(self.boundary_condition, tuple):
            left_boundary_slope = self.boundary_condition[0]
            right_boundary_slope = self.boundary_condition[1]
        elif self.boundary_condition is None:
            pass
        else:
            msg = 'The given object {} of type {} is not a valid condition ' \
                  'for the border'.format(self.boundary_condition, type(self.boundary_condition))
            raise ValueError(msg)

        # getting the values such that we get a continuous second derivative
        # by solving a system of linear equations

        # setup the matrix
        n = len(self.x_list)
        mat = numpy.zeros((n, n))
        b = numpy.zeros((n, 1))
        x = self.x_list
        y = self.y_list

        if n > 2:
            for i in range(1, n - 1):
                mat[i, i - 1] = 1.0 / (x[i] - x[i - 1])
                mat[i, i + 1] = 1.0 / (x[i + 1] - x[i])
                mat[i, i] = 2 * (mat[i, i - 1] + mat[i, i + 1])

                b[i, 0] = 3 * ((y[i] - y[i - 1]) / (x[i] - x[i - 1]) ** 2
                               + (y[i + 1] - y[i]) / (x[i + 1] - x[i]) ** 2)
        elif n < 2:
            raise ValueError('too less points for interpolation')

        if self.boundary_condition is None:  # not a knot
            mat[0, 0] = 1.0 / (x[1] - x[0]) ** 2
            mat[0, 2] = -1.0 / (x[2] - x[1]) ** 2
            mat[0, 1] = mat[0, 0] + mat[0, 2]

            b[0, 0] = 2.0 * ((y[1] - y[0]) / (x[1] - x[0]) ** 3
                             - (y[2] - y[1]) / (x[2] - x[1]) ** 3)

            mat[n - 1, n - 3] = 1.0 / (x[n - 2] - x[n - 3]) ** 2
            mat[n - 1, n - 1] = -1.0 / (x[n - 1] - x[n - 2]) ** 2
            mat[n - 1, n - 2] = mat[n - 1, n - 3] + mat[n - 1, n - 1]

            b[n - 1, 0] = 2.0 * ((y[n - 2] - y[n - 3]) / (x[n - 2] - x[n - 3]) ** 3
                                 - (y[n - 1] - y[n - 2]) / (x[n - 1] - x[n - 2]) ** 3)
        else:
            mat[0, 0] = 2.0 / (x[1] - x[0])
            mat[0, 1] = 1.0 / (x[1] - x[0])

            b[0, 0] = 3 * (y[1] - y[0]) / (x[1] - x[0]) ** 2 - 0.5 * left_boundary_slope

            mat[n - 1, n - 2] = 1.0 / (x[n - 1] - x[n - 2])
            mat[n - 1, n - 1] = 2.0 / (x[n - 1] - x[n - 2])

            b[n - 1, 0] = 3 * (y[n - 1] - y[n - 2]) / (x[n - 1] - x[n - 2]) ** 2 + 0.5 * right_boundary_slope

        k = numpy.linalg.solve(mat, b)

        for i in range(1, n):
            c1 = k[i - 1, 0] * (x[i] - x[i - 1]) - (y[i] - y[i - 1])
            c2 = -k[i, 0] * (x[i] - x[i - 1]) + (y[i] - y[i - 1])
            self.interpolation_coefficients.append([c1, c2])
def set_interpolation_coefficients(self):
        """
        computes the coefficients for the single polynomials of the spline.
        """

        left_boundary_slope = 0
        right_boundary_slope = 0

        if isinstance(self.boundary_condition, tuple):
            left_boundary_slope = self.boundary_condition[0]
            right_boundary_slope = self.boundary_condition[1]
        elif self.boundary_condition is None:
            pass
        else:
            msg = 'The given object {} of type {} is not a valid condition ' \
                  'for the border'.format(self.boundary_condition, type(self.boundary_condition))
            raise ValueError(msg)

        # getting the values such that we get a continuous second derivative
        # by solving a system of linear equations

        # setup the matrix
        n = len(self.x_list)
        mat = numpy.zeros((n, n))
        b = numpy.zeros((n, 1))
        x = self.x_list
        y = self.y_list

        if n > 2:
            for i in range(1, n - 1):
                mat[i, i - 1] = 1.0 / (x[i] - x[i - 1])
                mat[i, i + 1] = 1.0 / (x[i + 1] - x[i])
                mat[i, i] = 2 * (mat[i, i - 1] + mat[i, i + 1])

                b[i, 0] = 3 * ((y[i] - y[i - 1]) / (x[i] - x[i - 1]) ** 2
                               + (y[i + 1] - y[i]) / (x[i + 1] - x[i]) ** 2)
        elif n < 2:
            raise ValueError('too less points for interpolation')

        if self.boundary_condition is None:  # not a knot
            mat[0, 0] = 1.0 / (x[1] - x[0]) ** 2
            mat[0, 2] = -1.0 / (x[2] - x[1]) ** 2
            mat[0, 1] = mat[0, 0] + mat[0, 2]

            b[0, 0] = 2.0 * ((y[1] - y[0]) / (x[1] - x[0]) ** 3
                             - (y[2] - y[1]) / (x[2] - x[1]) ** 3)

            mat[n - 1, n - 3] = 1.0 / (x[n - 2] - x[n - 3]) ** 2
            mat[n - 1, n - 1] = -1.0 / (x[n - 1] - x[n - 2]) ** 2
            mat[n - 1, n - 2] = mat[n - 1, n - 3] + mat[n - 1, n - 1]

            b[n - 1, 0] = 2.0 * ((y[n - 2] - y[n - 3]) / (x[n - 2] - x[n - 3]) ** 3
                                 - (y[n - 1] - y[n - 2]) / (x[n - 1] - x[n - 2]) ** 3)
        else:
            mat[0, 0] = 2.0 / (x[1] - x[0])
            mat[0, 1] = 1.0 / (x[1] - x[0])

            b[0, 0] = 3 * (y[1] - y[0]) / (x[1] - x[0]) ** 2 - 0.5 * left_boundary_slope

            mat[n - 1, n - 2] = 1.0 / (x[n - 1] - x[n - 2])
            mat[n - 1, n - 1] = 2.0 / (x[n - 1] - x[n - 2])

            b[n - 1, 0] = 3 * (y[n - 1] - y[n - 2]) / (x[n - 1] - x[n - 2]) ** 2 + 0.5 * right_boundary_slope

        k = numpy.linalg.solve(mat, b)

        for i in range(1, n):
            c1 = k[i - 1, 0] * (x[i] - x[i - 1]) - (y[i] - y[i - 1])
            c2 = -k[i, 0] * (x[i] - x[i - 1]) + (y[i] - y[i - 1])
            self.interpolation_coefficients.append([c1, c2])
def set_interpolation_coefficients ( self ) : left_boundary_slope = 0 right_boundary_slope = 0 if isinstance ( self . boundary_condition , tuple ) : left_boundary_slope = self . boundary_condition [ 0 ] right_boundary_slope = self . boundary_condition [ 1 ] elif self . boundary_condition is None : pass else : msg = 'The given object {} of type {} is not a valid condition ' 'for the border' . format ( self . boundary_condition , type ( self . boundary_condition ) ) raise ValueError ( msg ) # getting the values such that we get a continuous second derivative # by solving a system of linear equations # setup the matrix n = len ( self . x_list ) mat = numpy . zeros ( ( n , n ) ) b = numpy . zeros ( ( n , 1 ) ) x = self . x_list y = self . y_list if n > 2 : for i in range ( 1 , n - 1 ) : mat [ i , i - 1 ] = 1.0 / ( x [ i ] - x [ i - 1 ] ) mat [ i , i + 1 ] = 1.0 / ( x [ i + 1 ] - x [ i ] ) mat [ i , i ] = 2 * ( mat [ i , i - 1 ] + mat [ i , i + 1 ] ) b [ i , 0 ] = 3 * ( ( y [ i ] - y [ i - 1 ] ) / ( x [ i ] - x [ i - 1 ] ) ** 2 + ( y [ i + 1 ] - y [ i ] ) / ( x [ i + 1 ] - x [ i ] ) ** 2 ) elif n < 2 : raise ValueError ( 'too less points for interpolation' ) if self . boundary_condition is None : # not a knot mat [ 0 , 0 ] = 1.0 / ( x [ 1 ] - x [ 0 ] ) ** 2 mat [ 0 , 2 ] = - 1.0 / ( x [ 2 ] - x [ 1 ] ) ** 2 mat [ 0 , 1 ] = mat [ 0 , 0 ] + mat [ 0 , 2 ] b [ 0 , 0 ] = 2.0 * ( ( y [ 1 ] - y [ 0 ] ) / ( x [ 1 ] - x [ 0 ] ) ** 3 - ( y [ 2 ] - y [ 1 ] ) / ( x [ 2 ] - x [ 1 ] ) ** 3 ) mat [ n - 1 , n - 3 ] = 1.0 / ( x [ n - 2 ] - x [ n - 3 ] ) ** 2 mat [ n - 1 , n - 1 ] = - 1.0 / ( x [ n - 1 ] - x [ n - 2 ] ) ** 2 mat [ n - 1 , n - 2 ] = mat [ n - 1 , n - 3 ] + mat [ n - 1 , n - 1 ] b [ n - 1 , 0 ] = 2.0 * ( ( y [ n - 2 ] - y [ n - 3 ] ) / ( x [ n - 2 ] - x [ n - 3 ] ) ** 3 - ( y [ n - 1 ] - y [ n - 2 ] ) / ( x [ n - 1 ] - x [ n - 2 ] ) ** 3 ) else : mat [ 0 , 0 ] = 2.0 / ( x [ 1 ] - x [ 0 ] ) mat [ 0 , 1 ] = 1.0 / ( x [ 1 ] - x [ 0 ] ) b [ 0 , 0 ] = 3 * ( y [ 1 ] - y [ 0 ] ) / ( x [ 1 ] - x [ 0 ] ) ** 2 - 0.5 * left_boundary_slope mat [ n - 1 , n - 2 ] = 1.0 / ( x [ n - 1 ] - x [ n - 2 ] ) mat [ n - 1 , n - 1 ] = 2.0 / ( x [ n - 1 ] - x [ n - 2 ] ) b [ n - 1 , 0 ] = 3 * ( y [ n - 1 ] - y [ n - 2 ] ) / ( x [ n - 1 ] - x [ n - 2 ] ) ** 2 + 0.5 * right_boundary_slope k = numpy . linalg . solve ( mat , b ) for i in range ( 1 , n ) : c1 = k [ i - 1 , 0 ] * ( x [ i ] - x [ i - 1 ] ) - ( y [ i ] - y [ i - 1 ] ) c2 = - k [ i , 0 ] * ( x [ i ] - x [ i - 1 ] ) + ( y [ i ] - y [ i - 1 ] ) self . interpolation_coefficients . append ( [ c1 , c2 ] )
computes the coefficients for the single polynomials of the spline.
computes the coefficients for the single polynomials of the spline .