Download Python and HDF5: Unlocking Scientific Data by Andrew Collette PDF

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By Andrew Collette

Achieve hands-on adventure with HDF5 for storing clinical information in Python. This sensible advisor fast will get you on top of things at the information, most sensible practices, and pitfalls of utilizing HDF5 to archive and percentage numerical datasets ranging in dimension from gigabytes to terabytes. via real-world examples and useful routines, you are going to discover issues similar to clinical datasets, hierarchically geared up teams, user-defined metadata, and interoperable documents. Examples are appropriate for clients of either Python 2 and Python three. if you are accustomed to the fundamentals of Python facts research, this is often an amazing advent to HDF5.

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Additional info for Python and HDF5: Unlocking Scientific Data

Example text

Only one axis at a time can be sliced with a list. 2. Repeated elements are not allowed. 3. Indices in the list must be given in increasing order. Automatic Broadcasting In a couple of examples so far, we’ve made slicing assignments in which the number of elements on the left- and right-hand sides were not equal. For example, in the Boolean array example: >>> dset[data<0] = 0 This kind of expression is handled by broadcasting, similar to the built-in NumPy broadcasting that handles such things where arrays are involved.

Create_dataset("Images", (100, 480, 640), dtype='uint8') A contiguous dataset would store the image data on disk, one 640-element “scanline” after another. shape (480, 640) Figure 4-1(A) shows how this works. Notice that data is stored in “blocks” of 640 bytes that correspond to the last axis in the dataset. When we read in the first image, 480 of these blocks are read from disk, all in one big block. This leads us to the first rule (really, the only one) for dealing with data on disk, locali‐ ty: reads are generally faster when the data being accessed is all stored together.

The data in your dataset is flattened to disk using the same rules that NumPy (and C, incidentally) uses. If you think about it, this means that certain operations are much faster than others. Consider as an example a dataset containing one hundred 640×480 grayscale images. create_dataset("Images", (100, 480, 640), dtype='uint8') A contiguous dataset would store the image data on disk, one 640-element “scanline” after another. shape (480, 640) Figure 4-1(A) shows how this works. Notice that data is stored in “blocks” of 640 bytes that correspond to the last axis in the dataset.

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