netCDF4 via h5py
Project description
A Python interface for the netCDF4 file-format that reads and writes HDF5 files API directly via h5py, without relying on the Unidata netCDF library.
This is an experimental project. It currently passes basic tests for reading and writing netCDF4 files with Python, but it has not been tested for compatibility with other netCDF4 interfaces.
Motivations
Why did I write h5netcdf? Well, here are a few reasons:
To prove it could be done (it seemed like an obvious thing to do) and that netCDF4 is not actually that complicated.
We’ve seen occasional reports of better performance with h5py than netCDF4-python that I wanted to be able to verify. For at least one workflow, h5netcdf was reported to be almost 4x faster than netCDF4-python. If you give it a try, I would love to hear how it works for you!
h5py seems to have thought through multi-threading pretty carefully, so this in particular seems like a case where things could make a difference. I’ve started to care about this because I recently hooked up a multi-threaded backend to xray.
It’s one less massive binary dependency (netCDF C). Anecdotally, HDF5 users seem to be unexcited about switching to netCDF – hopefully this will convince them that they are really the same thing!
Finally, side-stepping the netCDF C library (and Cython bindings to it) gives us an easier way to identify the source of performance issues and bugs.
Install
Ensure you have a recent version of h5py installed (I recommend using conda). At least version 2.1 is required (for dimension scales); versions 2.3 and newer have been verified to work. Then: pip install h5netcdf
Usage
h5netcdf has two APIs, a new API and a legacy API. Both interfaces currently reproduce most of the features of the netCDF interface, with the noteable exceptions of:
support for operations the rename or delete existing objects.
suport for creating unlimited dimensions.
We simply haven’t gotten around to implementing these features yet.
New API
The new API supports direct hierarchical access of variables and groups. Its design is an adaptation of h5py to the netCDF data model. For example:
import h5netcdf
import numpy as np
with h5netcdf.File('mydata.nc', 'w') as f:
# set dimensions with a dictionary
f.dimensions = {'x': 5}
# and update them with a dict-like interface
# f.dimensions['x'] = 5
# f.dimensions.update({'x': 5})
v = f.create_variable('hello', ('x',), float)
v[:] = np.ones(5)
# you don't need to create groups first
# you also don't need to create dimensions first if you supply data
# with the new variable
v = f.create_variable('/grouped/data', ('y',), data=np.arange(10))
# access and modify attributes with a dict-like interface
v.attrs['foo'] = 'bar'
# you can access variables and groups directly using a hierarchical
# keys like h5py
print(f['/grouped/data'])Warning: The design of the new API is *not yet finished*. I only recommended using it for experiments. Please share your feedback in this GitHub issue.
Legacy API
The legacy API is designed for compatibility with netCDF4-python. To use it, import h5netcdf.legacyapi:
import h5netcdf.legacyapi as netCDF4
# everything here would also work with this instead:
# import netCDF4
import numpy as np
with netCDF4.Dataset('mydata.nc', 'w') as ds:
ds.createDimension('x', 5)
v = ds.createVariable('hello', float, ('x',))
v[:] = np.ones(5)
g = ds.createGroup('grouped')
g.createDimension('y', 10)
g.createVariable('data', 'i8', ('y',))
v = g['data']
v[:] = np.arange(10)
v.foo = 'bar'
print(ds.groups['grouped'].variables['data'])The legacy API is designed to be easy to try-out for netCDF4-python users, but it is not an exact match. Here is an incomplete list of functionality we don’t include:
Utility functions chartostring, num2date, etc., that are not directly necessary for writing netCDF files.
We don’t support the endian argument to createVariable. The h5py API does not appear to offer this feature.
h5netcdf variables do not support automatic masking or scaling (e.g., of values matching the _FillValue attribute). We prefer to leave this functionality to client libraries (e.g., xray), which can implement their exact desired scaling behavior.
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