fp2hdf - convert floating point data to HDF
fp2hdf -h[elp] fp2hdf infile [infile...] -o[utfile outfile] [-r[aster]
fp2hdf converts floating point data to HDF Scientific Data Set (SDS)
and/or 8-bit Raster Image Set (RIS8) format, storing the results in an
HDF file. The image data can be scaled about a mean value.
Input file(s) contain a single two-dimensional or three-dimensional
floating point array in either ASCII text, native floating point, or
HDF SDS format. If an HDF file is used for input, it must contain an
SDS. The SDS need only contain a dimension record and the data, but if
it also contains maximum and minimum values and/or scales for each
axis, these will be used. If the input format is ASCII text or native
floating point, see "Notes" below on how it must be organized.
Print a helpful summary of usage, and exit.
Data from one or more input files are stored as one or more data
sets and/or images in one HDF output file, outfile.
Store output as a raster image set in the output file -f[loat]
Store output as a scientific data set in the the output file.
This is the default if the "-r" option is not specified.
-e[xpand] horiz vert [depth]
Expand float data via pixel replication to produce the image(s).
horiz and vert give the horizontal and vertical resolution of
the image(s) to be produced; and optionally, depth gives the
number of images or depth planes (for 3D input data).
-i[nterp] horiz vert [depth]
Apply bilinear, or trilinear, interpolation to the float data to
produce the image(s). horiz, vert, and depth must be greater
than or equal to the dimensions of the original dataset.
Store the palette with the image. Get the palette from palfile;
which may be an HDF file containing a palette, or a file
containing a raw palette.
If a floating point mean value is given, the image will be
scaled about the mean. The new extremes (newmax and newmin), as
newmax = mean + max(abs(max-mean), abs(mean-min))
newmin = mean - max(abs(max-mean), abs(mean-min))
will be equidistant from the mean value. If no mean value is
given, then the mean will be: 0.5 * (max + min)
If the input file format is ASCII text or native floating point, it
must have the following input fields:
[plane1 plane2 plane3 ...]
row1 row2 row3 ...
col1 col2 col3 ...
data1 data2 data3 ...
format Format designator ("TEXT", "FP32" or "FP64").
Dimension of the depth axis ("1" for 2D input).
nrows Dimension of the vertical axis.
ncols Dimension of the horizontal axis.
Maximum data value.
Minimum data value.
plane1, plane2, plane3, ...
Scales for depth axis.
row1, row2, row3, ...
Scales for the vertical axis.
col1, col2, col3, ...
Scales for the horizontal axis.
data1, data2, data3, ...
The data ordered by rows, left to right and top to bottom; then
optionally, ordered by planes, front to back.
For FP32 and FP64 input format, format, nplanes, nrows, ncols,
and nplanes are native integers; where format is the integer
representation of the appropriate 4-character string (0x46503332
for "FP32" and 0x46503634 for "FP64"). The remaining input
fields are composed of native 32-bit floating point values for
FP32 input format, or native 64-bit floating point values for
FP64 input format.
Convert floating point data in "f1.txt" to SDS format, and store it as
an SDS in HDF file "o1":
fp2hdf f1.txt -o o1
Convert floating point data in "f2.hdf" to 8-bit raster format, and
store it as an RIS8 in HDF file "o2":
fp2hdf f2.hdf -o o2 -r
Convert floating point data in "f3.bin" to 8-bit raster format and SDS
format, and store both the RIS8 and the SDS in HDF file "o3":
fp2hdf f3.bin -o o3 -r -f
Convert floating point data in "f4" to a 500x600 raster image, and
store the RIS8 in HDF file "o4". Also store a palette from "palfile"
with the image:
fp2hdf f4 -o o4 -r -e 500 600 -p palfile
Convert floating point data in "f5" to 200 planes of 500x600 raster
images, and store the RIS8 in HDF file "o5". Also scale the image data
so that it is centered about a mean value of 10.0:
fp2hdf f5 -o o5 -r -i 500 600 200 -m 10.0
October 30, 1999