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# doc-cache created by Octave 4.2.1
# name: cache
# type: cell
# rows: 3
# columns: 9
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 19
example_optiminterp


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 54
 Example program of the optimal interpolation toolbox



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 54
 Example program of the optimal interpolation toolbox




# name: <cell-element>
# type: sq_string
# elements: 1
# length: 12
optiminterp1


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 789
 -- Loadable Function: [FI,VARI] = optiminterp1(X,F,VAR,LENX,M,XI)
     Performs a local 1D-optimal interpolation (objective analysis).

     Every elements in F corresponds to a data point (observation) at
     location X,Y with the error variance VAR.

     LENX is correlation length in x-direction.  M represents the number
     of influential points.

     XI is the data points where the field is interpolated.  FI is the
     interpolated field and VARI is its error variance.

     The background field of the optimal interpolation is zero.  For a
     different background field, the background field must be subtracted
     from the observation, the difference is mapped by OI onto the
     background grid and finally the background is added back to the
     interpolated field.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 63
Performs a local 1D-optimal interpolation (objective analysis).



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 12
optiminterp2


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 949
 -- Loadable Function: [FI,VARI] =
          optiminterp2(X,Y,F,VAR,LENX,LENY,M,XI,YI)
     Performs a local 2D-optimal interpolation (objective analysis).

     Every elements in F corresponds to a data point (observation) at
     location X,Y with the error variance VAR.

     LENX and LENY are correlation length in x-direction and y-direction
     respectively.  M represents the number of influential points.

     XI and YI are the data points where the field is interpolated.  FI
     is the interpolated field and VARI is its error variance.

     The background field of the optimal interpolation is zero.  For a
     different background field, the background field must be subtracted
     from the observation, the difference is mapped by OI onto the
     background grid and finally the background is added back to the
     interpolated field.  The error variance of the background field is
     assumed to have a error variance of one.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 63
Performs a local 2D-optimal interpolation (objective analysis).



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 12
optiminterp3


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 968
 -- Loadable Function: [FI,VARI] =
          optiminterp3(X,Y,Z,F,VAR,LENX,LENY,LENZ,M,XI,YI,ZI)
     Performs a local 3D-optimal interpolation (objective analysis).

     Every elements in F corresponds to a data point (observation) at
     location X, Y, Z with the error variance var

     LENX,LENY and LENZ are correlation length in x-,y- and z-direction
     respectively.  M represents the number of influential points.

     XI,YI and ZI are the data points where the field is interpolated.
     FI is the interpolated field and VARI is its error variance.

     The background field of the optimal interpolation is zero.  For a
     different background field, the background field must be subtracted
     from the observation, the difference is mapped by OI onto the
     background grid and finally the background is added back to the
     interpolated field.

     The error variance of the background field is assumed to have a
     error variance of one.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 63
Performs a local 3D-optimal interpolation (objective analysis).



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 12
optiminterp4


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1006
 -- Loadable Function: [FI,VARI] =
          optiminterp4(X,Y,Z,T,F,VAR,LENX,LENY,LENZ,LENT,M,XI,YI,ZI,TI)
     Performs a local 4D-optimal interpolation (objective analysis).

     Every elements in F corresponds to a data point (observation) at
     location X, Y, Z, T with the error variance var

     LENX,LENY,LENZ and LENT are correlation length in x-,y-,z-direction
     and time, respectively.  M represents the number of influential
     points.

     XI,YI,ZI and TI are the data points where the field is
     interpolated.  FI is the interpolated field and VARI is its error
     variance.

     The background field of the optimal interpolation is zero.  For a
     different background field, the background field must be subtracted
     from the observation, the difference is mapped by OI onto the
     background grid and finally the background is added back to the
     interpolated field.

     The error variance of the background field is assumed to have a
     error variance of one.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 63
Performs a local 4D-optimal interpolation (objective analysis).



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 12
optiminterpn


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 965
 -- Loadable Function: [FI,VARI] =
          optiminterpn(X,Y,...,F,VAR,LENX,LENY,...,M,XI,YI,...)
     Performs a local nD-optimal interpolation (objective analysis).

     Every elements in F corresponds to a data point (observation) at
     location X,Y,... with the error variance VAR.

     LENX,LENY,... are correlation length in x-direction y-direction,...
     respectively.  M represents the number of influential points.

     XI,YI,... are the data points where the field is interpolated.  FI
     is the interpolated field and VARI is its error variance.

     The background field of the optimal interpolation is zero.  For a
     different background field, the background field must be subtracted
     from the observation, the difference is mapped by OI onto the
     background grid and finally the background is added back to the
     interpolated field.  The error variance of the background field is
     assumed to have a error variance of one.


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 63
Performs a local nD-optimal interpolation (objective analysis).



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 16
test_optiminterp


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 198
 Tests 1D, 2D, 3D and 4D optimal interpolation.
 All tests should pass; any error indicates that either 
 there is a bug in the optimal interpolation package or 
 that it is incrorrectly installed.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 47
 Tests 1D, 2D, 3D and 4D optimal interpolation.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 20
test_optiminterp_err


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 103
 Tests 2D optimal interpolation and compares result 
 (analysis and error field) to global OI solution



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
 Tests 2D optimal interpolation and compares result 
 (analysis and error field)



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 21
test_optiminterp_mult


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 194
 Tests 1D, 2D and 3D optimal interpolation.
 All tests should pass; any error indicates that either 
 there is a bug in the optimal interpolation package or 
 that it is incrorrectly installed.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 43
 Tests 1D, 2D and 3D optimal interpolation.