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Piecewise Linear Interpolation In One Dimension
Syntax interp(new_grid, old_table)
interp(new_grid, old_table, ind)
See Also interp1 , interp2 , lagrange , cubespl

Description
Interpolates multiple variables onto a new grid of values. (The function interp1 can handle the complex case with only one variable.) The argument old_table is a real or double-precision matrix and each column corresponds to one of the variables on the old grid. The argument new_grid is a real or double-precision column vector that contains the new grid of values for the independent variable. The argument ind is an integer scalar between one and the column dimension of old_table that specifies the column corresponding to the independent variable in both table_old and the return value. If ind is not present, the first column is used for the independent variable. The return value has the same row dimension as new_grid and the same column dimension as old_table. The type of the return value is real, if both arguments are real, and double-precision otherwise.

Both the new and old grid of independent variables values (both new_grid and the independent variable column of old_table) must be strictly monotone increasing .

Example
The interp function uses a linear approximation between independent variable values in the old grid. For instance, suppose you recorded the following temperature data:
time temperature
1:00 65
2:00 68
3:00 72
4:00 73
5:00 73
To interpolate the temperature at half-hour intervals, enter
     old_table = { ...
               [1., 65.], ...
               [2., 68.], ...
               [3., 72.], ...
               [4., 73.], ...
               [5., 73.]  ...
     }
     new_grid  = {1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5, 5.}
     interp(new_grid, old_table)
 to which O-Matrix will respond
     {
     [ 1 , 65 ]
     [ 1.5 , 66.5 ]
     [ 2 , 68 ]
     [ 2.5 , 70 ]
     [ 3 , 72 ]
     [ 3.5 , 72.5 ]
     [ 4 , 73 ]
     [ 4.5 , 73 ]
     [ 5 , 73 ]
     }
 The first column in the matrix above is the time at half-hour intervals, and the second column is the interpolated values for temperature.

The interp function will extrapolate the linear approximation if an new grid value falls outside the range of old grid. If you continue the previous example by entering
     new_grid = {.5, 5.5}
     interp(new_grid, old_table)
 O-Matrix will respond
     [ 0.5 , 63.5 ]
     [ 5.5 , 73 ]
 This approximates the temperature at 12:30 and 5:30, both of which lie outside the range of the old grid (the first column of table).