--- /dev/null
+# Created by Octave 3.6.1, Fri Mar 30 22:42:15 2012 UTC <root@t61>
+# name: cache
+# type: cell
+# rows: 3
+# columns: 8
+# 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: 790
+ -- 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: 955
+ -- 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: 971
+ -- 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: 1008
+ -- 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: 971
+ -- 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: 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.
+
+
+
+# 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.
+
+
+
+
+
git://git.creatis.insa-lyon.fr / CreaPhase.git / blobdiff