By Andrew Collette
ISBN-10: 1449367836
ISBN-13: 9781449367831
Achieve hands-on event with HDF5 for storing medical information in Python. This functional advisor quick will get you up to the mark at the information, top practices, and pitfalls of utilizing HDF5 to archive and proportion numerical datasets ranging in dimension from gigabytes to terabytes. via real-world examples and functional routines, you will discover subject matters similar to clinical datasets, hierarchically geared up teams, user-defined metadata, and interoperable records. Examples are appropriate for clients of either Python 2 and Python three. in case you are conversant in the fundamentals of Python info research, this is often an awesome creation to HDF5.
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Additional resources for Python and HDF5: Unlocking Scientific Data
Sample text
It could be 10, or 100, or 1000. shape[0]+1, 1000) ) dset1[-1,:] = arr Here, every time a new 1000-element array is added, the dataset is simply expanded by a single entry. But if the number of resize calls is equal to the number of insertions, this doesn’t scale well, particularly if traces will be added thousands of times. resize((ntraces,1000)) In the real world, it takes a little more work than this to get the best performance. We’ve gone about as far as we can go without discussing how the data is actually stored by HDF5.
Keeping data together helps for lots of reasons, not the least of which is taking advantage of caching performed by the operating system and HDF5 itself. info Figure 4-1. Contiguous storage on disk, accessing (A) an entire image all at once, and (B) a 64x64 image tile. Gray regions are the data being retrieved. It’s easy to see that applications reading a whole image, or a series of whole images, will be efficient at reading the data. The advantage of contiguous storage is that the layout on disk corresponds directly to the shape of the dataset: stepping through the last index always means moving through the data in order on disk.
Array([42]) Note, by the way, how using Ellipsis provides an array with one element, whereas integer indexing provides the element itself. ] array(42) Note how using Ellipsis has again returned an array, in this case a scalar array. info How can we get the value itself, without it being wrapped in a NumPy array? It turns out there’s another way to slice into NumPy arrays (and Dataset objects). You can index with a somewhat bizarre-looking empty tuple: >>> dset[()] 42 So keep these in your toolkit: 1.