There are some HDF-EOS2 files in which some objects are not created by HDF-EOS2 APIs and one can access those objects via HDF-EOS2 APIs. In other words, those objects are "invisiable" by HDF-EOS2 APIs. When using HDF-EOS2 APIs to access these files, those "invisiable" objects will simply be ignored. We can these files hybrid HDF-EOS2/HDF4 files or simply hybrid files. This page explains how to read those non-HDF-EOS2 objects from hybrid files.
The HDF-EOS2 library is built on the HDF4 library. For this reason, a valid HDF-EOS2 file is a valid HDF4 file, and any part of an HDF-EOS2 file can be read by the HDF4 library.
Furthermore, a valid HDF-EOS2 file can be modified by any HDF4 application. For example, some HDF4 objects can be added to an existing HDF-EOS2 file. If these added objects are located where the HDF-EOS2 library accesses, those objects become invisible to HDF-EOS2 APIs. This is how a hybrid file is generated.
One common "invisiable" object found in an hybrid file is an attribute of a data field or a geo-location field. HDF-EOS2 library only supports adding attributes to a grid object or a swath object. We could see attributes attached to fields in several data products. Again, these attributes are invisible to HDF-EOS2 APIs. Those who are writing converters may need to handle this case; otherwise, the generated file will lose some attributes.
To access those attributes, one needs to rely on HDF4. Since HDF4 APIs are not similar to HDF-EOS2 APIs, one needs to learn how to use HDF4 APIs. Although HDF4 provides various interfaces, only a few APIs are actually necessary to read attributes. We will show an example on how to read attributes from an AE_Ocean file. You can download the file here. We will access one attribute attached to data field named High_res_cloud in swath named Swath1.
As we mentioned earlier, an HDF-EOS2 data field or geo-location field is implemented by one HDF4 object. As our best knowledge, an HDF-EOS2 field is mapped to one HDF4 Vdata if the data field is one-dimensional array. If the dimension rank is greater than one, the field is mapped to an HDF4 SDS. Supposed that there is a two-dimensional HDF-EOS2 data field named High_res_cloud, This field is actually an HDF4 SDS named High_res_cloud.
In this document, we will cover only when the rank is greater than one. To access HDF4 SDS, one needs to use the HDF4 SD interfaces. The HDF4 has its own SD interfaces to open and close a file. After opening a file, one can access an HDF4 SDS that corresponds to an HDF-EOS2 data field. Then, one can access attributes attached to that HDF4 SDS. For more information about HDF4, check here. The following will simply explain how to access an HDF-EOS2 data field attribute with HDF4 interfaces.
The first thing to do is to open the hybrid file using
SWopenin that it opens a file.
Next step is to obtain the identifier of the HDF4 SDS that corresponds to the data field named High_res_cloud. As we mentioned earlier, there will be one HDF4 SDS with the same name.
SDselectis simply the return value of
SDstart. The second parameter is the index of the data field, which is an integer value. If one does not know the index but does know the name of the data field, one can get the index by calling
SDnametoindexwill return the index of the data field from data field's name given by the second parameter.
Note that it is possible that multiple data fields share the same name. If multiple data fields
share the same name,
SDnametoindex only returns an index of one of them. To understand how
one can obtain the index of the particular data field of which name is shared with others is out of
the scope of this page. One can read HDF4 documents.
Fortunately we don't find multiple data fields share the same name under the same grid or swath objects for
any HDF-EOS2 files. So our example will safely assume that each data field has a unique name under the same grid
object or the swath object.
Now that we got the identifier of the SDS, we can access attributes attached to this SDS.
Here, we will access the first attribute of High_res_cloud. To get information about
the first attribute,
SDattrinfo is used.
SDselect. The second parameter specifies the first attribute of the SDS specified by the first parameter. If one wants to access n-th attribute, this value should be n - 1. These two parameters are inputs to this function, and this function will give the other three arguments as outputs. The outputs of this API are the attribute name, the attribute data type and the number of elements of the attribute.
From the number of elements and the data type, we can calculate the size of buffer to retrieve the attribute. Assuming that the data type is 32-bit floating point, one can write the following code:
SDattrinfogives the number of elements, not the number of bytes. That's why the above code multiplies
attrcountby the size of the data type.
SDreadattr will read actual data from the attribute and fill the buffer.
SDattrinfo. The third parameter specifies the buffer that this function fills. As usual, passing insufficient buffer will result in buffer-overrun.
The last step is to terminate access to the SD interface identifier returned by
Previously, the contents of the first attribute of High_res_cloud were stored at a variable
attrdata. One can perform desired operation on this variable. Let's print every element
to see its contents.
Some HDF-EOS2 files even have HDF4 SDS and HDF4 Vdata that are invisible to HDF-EOS2. One file from MAC03S0 is an example. This file contains several HDF4 SDS and one Vdata that are invisible to HDF-EOS2.
To access these HDF4 objects, one needs to rely on HDF4 as we did for attributes attached to data fields and geo-location fields. We will not explain this case because accessing those objects has nothing to do with using HDF-EOS APIs. If you consider writing converters, you may need to handle this case not to lose any objects in the source file which is a hybrid file.