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John -- Thanks for looking at the libs. You commented: > My only comment is that when i was thinking about this same problem a few > years > back, I realized that that amount of compression you can get it very data > type > dependent. In particular, if you require your data be stored as floating > point, > many datasets wont compress at all, because the low order bits of the mantissa > are essentially random. Unless there is some repeating pattern like a > constant > field or missing data. (I was working with gridded model output) > > It might be worth collecting compression ratios from your library on various > data files and types, so that people get a sense of what to expect for the > different cases. You are right that the amount of compression will be data dependent and not necessarily reflect the information content. I would be willing to compile any compression ratios that people want to send me. You can use the nczip program to make a compressed netcdf file of any normal netcdf files you have around. This doesnt' require installing the znetcdf library or changing your programs. Send me the compression ratios and a description of the type of data (or a cdl). One way to try to improve the compression ratio without changing the netcdf structure is to normalize floating point numbers to a reasonable precision. I.e. new_val = (round(val * 1000.0)/1000.0) This would be the same as using a scale and offset in the netcdf but wouldn't require the reading software to understand the particular scheme used. --Bill Noon Northeast Regional Climate Center Cornell University
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