By Andrew Collette
Achieve hands-on adventure with HDF5 for storing clinical information in Python. This functional consultant fast will get you in control 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 workouts, you are going to discover subject matters similar to clinical datasets, hierarchically equipped teams, user-defined metadata, and interoperable documents. Examples are appropriate for clients of either Python 2 and Python three. in case you are acquainted with the fundamentals of Python information research, this is often a great creation to HDF5.
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Additional info for Python and HDF5: Unlocking Scientific Data
Check for negative values and clip to 0 for ix in xrange(100): for iy in xrange(1000): val = dset[ix,iy] # Read one element if val < 0: dset[ix, iy] = 0 # Clip to 0 if needed or # Check for negative values and clip to 0 for ix in xrange(100): val = dset[ix,:] # Read one row val[ val < 0 ] = 0 # Clip negative values to 0 dset[ix,:] = val # Write row back out In the first case, we perform 100,000 slicing operations. In the second, we perform only 100. This may seem like a trivial example, but the first example creeps into real-world code frequently; using fast in-memory slices on NumPy arrays, it is actually reasonably quick on modern machines.
If you only use a subset of the data, the extra time spent reading from disk is wasted. Keep in mind that chunks bigger than 1 MiB by default will not participate in the fast, in-memory “chunk cache” and will instead be read from disk every time. Performance Example: Resizable Datasets In the last example of Chapter 3, we discussed some of the performance aspects of resizable datasets. It turns out that with one or two exceptions, HDF5 requires that resizable datasets use chunked storage. This makes sense if you think about how con‐ tiguous datasets are stored; expanding any but the last axis would require rewriting the entire dataset!
Reading with astype You may not always want to go through the whole rigamarole of creating a destination array and passing it to read_direct. astype context manager. astype('float64'): ... info | 25 Finally, here are some tips to keep in mind when using HDF5’s automatic type conver‐ sion. They apply both to reads with read_direct or astype and also to writing data from NumPy into existing datasets: 1. ” For example, you can convert integers to floats, and floats to other floats, but not strings to floats or integers.
Python and HDF5: Unlocking Scientific Data by Andrew Collette