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Please be aware that a double is twice as long as a float. So, if you
wanted to store 100 doubles and specified IEEE-NATIVE-FLOAT, you have
only written the first 50 doubles to disk. So, unless you have actually
calculated the number of elements in the data from the _size_ of the
array, you will have to recreate your data because half of it will be
missing - there is no way of fixing missing data.
Cheers,
Nathanael Hübbe
On 05/02/2013 05:47:50 PM, John K wrote:
Someone else just discovered a mess that I've made in our data, where
I was using double (64-bit IEEE floating point) for in-memory storage
for certain fields in compound data types/structs, but on disk was
using IEEE-NATIVE-FLOAT (i.e. 32-bit).
To be more specific, I had a C++ type double that was being mapped to
HDF type H5T_NATIVE_FLOAT rather than DOUBLE. The end result hasn't
been pretty - the files are usable only in the code that retained
that incorrect mapping.
Since repairing the type mapping to double->NATIVE_DOUBLE, the
previously written data files have invalid values for those fields. I
was kind of hoping the internal HDF5 type mapping would resolve the
translation correctly, but it hasn't.
I'm assuming at this point I'm going to have to rewrite these old
files with the bad mapping in them. Does anyone have any suggestions
as to how best accomplish this task? The original files contain:
1 dataset with the dodgy type mappings
1 dataset consisting of references to the above (essentially the same
data but from a different indexing)
several dimension scales for the above two datasets
a slew of datatypes
What would have to change are the datatypes and the one dataset with
the dodgy type mappings. Is there a quick and easy way to create a
new file with the change in types (I can back-fill the correct values
for the broken fields later) or do I just need to rewrite the whole
thing from scratch?
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