RESHAPE - Reshapes an NDF, treating its arrays as vectors

Description:

This application reshapes an NDF  to create another NDF by copying array values. The array components in the input NDF are treated as vectors. Each output array is filled in order with values from the input vector, until it is full or the input vector is exhausted. Output data and variance pixels not filled are set to the bad value; unfilled quality pixels are set to zero. The filling is in Fortran order, namely the first dimension, followed by the second dimension,...to the highest dimension.

It is possible to form a vectorized NDF using parameter VECTORIZE without having to specify the shape.

Usage:

reshape in out shape=?

Parameters:

IN = NDF (Read)

The input NDF to be reshaped.

OUT = NDF (Read)

The NDF after reshaping.

SHAPE( ) = _INTEGER (Read)

The shape of the output NDF. For example, [50,30,20] would create 50 columns by 30 lines by 20 bands. It is only accessed when VECTORIZE=FALSE.

TITLE = LITERAL (Read)

Title for the output NDF structure. A null value (!) propagates the title from the base NDF to the output NDF. [!]

VECTORIZE = _LOGICAL (Read)

If TRUE, the output NDF is the vectorized form of the input NDF. If FALSE, parameter SHAPE is used to specify the new shape. [FALSE]

Examples:

reshape shear normal shape=[511,512]

This reshapes the NDF called shear to form NDF normal, whose shape is 511$\times$512 pixels. One example is where the original image has 512$\times$512 pixels but one pixel was omitted from each line during some data capture, causing the image to be sheared between lines.

reshape cube cube1d vectorize

This vectorizes the NDF called cube to form NDF cube1d. This could be used for a task that only permits one-dimensional data.

Related Applications

KAPPA: CHAIN, PASTE, RESHAPE.

Implementation Status:

• This routine correctly processes the DATA, QUALITY, VARIANCE, LABEL, TITLE, UNITS, and HISTORY, components of an NDF data structure and propagates all extensions. WCS, and AXIS  information is lost.

• All non-complex numeric data types can be handled.

• Any number of NDF dimensions is supported.