A Python interface for the netCDF4 file-format that reads and writes local or remote HDF5 files directly via h5py or h5pyd, without relying on the Unidata netCDF library.
- It has one less binary dependency (netCDF C). If you already have h5py installed, reading netCDF4 with h5netcdf may be much easier than installing netCDF4-Python.
- We've seen occasional reports of better performance with h5py than netCDF4-python, though in many cases performance is identical. For one workflow, h5netcdf was reported to be almost 4x faster than netCDF4-python.
- Anecdotally, HDF5 users seem to be unexcited about switching to netCDF -- hopefully this will convince them that netCDF4 is actually quite sane!
- 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 in the netCDF libraries/specification.
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, though some tests only pass on h5py 2.6. Then:
pip install h5netcdf
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 notable exception of support for operations the rename or delete existing objects. We simply haven't gotten around to implementing this yet. Patches would be very welcome.
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'])
# add an unlimited dimension
f.dimensions['z'] = None
# explicitly resize a dimension and all variables using it
f.resize_dimension('z', 3)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
endianargument tocreateVariableyet (see GitHub issue). - h5netcdf variables do not support automatic masking or scaling (e.g., of values matching
the
_FillValueattribute). We prefer to leave this functionality to client libraries (e.g., xarray), which can implement their exact desired scaling behavior. - No support yet for automatic resizing of unlimited dimensions with array
indexing. This would be a welcome pull request. For now, dimensions can be
manually resized with
Group.resize_dimension(dimension, size).
h5py implements some features that do not (yet) result in valid netCDF files:
- Data types:
- Booleans
- Complex values
- Non-string variable length types
- Enum types
- Reference types
- Arbitrary filters:
- Scale-offset filters
By default [*], h5netcdf will not allow writing files using any of these features, as files with such features are not readable by other netCDF tools.
However, these are still valid HDF5 files. If you don't care about netCDF
compatibility, you can use these features by setting invalid_netcdf=True
when creating a file:
# avoid the .nc extension for non-netcdf files
f = h5netcdf.File('mydata.h5', invalid_netcdf=True)
...
# works with the legacy API, too, though compression options are not exposed
ds = h5netcdf.legacyapi.Dataset('mydata.h5', invalid_netcdf=True)
...| [*] | h5netcdf we will raise h5netcdf.CompatibilityError. |
h5py 3.0 introduced new behavior for handling variable length string.
Instead of being automatically decoded with UTF-8 into NumPy arrays of str,
they are required as arrays of bytes.
The legacy API preserves the old behavior of h5py (which matches netCDF4), and automatically decodes strings.
The new API also currently preserves the old behavior of h5py, but issues a
warning that it will change in the future to match h5py. Explicitly set
decode_vlen_strings=False in the h5netcdf.File constructor to opt-in to
the new behavior early, or set decode_vlen_strings=True to opt-in to
automatic decoding.
By default [†] h5netcdf raises a ValueError if variables with no dimension
scale associated with one of their axes are accessed.
You can set phony_dims='sort' when opening a file to let h5netcdf invent
phony dimensions according to netCDF behaviour.
# mimic netCDF-behaviour for non-netcdf files
f = h5netcdf.File('mydata.h5', mode='r', phony_dims='sort')
...Note, that this iterates once over the whole group-hierarchy. This has affects
on performance in case you rely on lazyness of group access.
You can set phony_dims='access' instead to defer phony dimension creation
to group access time. The created phony dimension naming will differ from
netCDF behaviour.
f = h5netcdf.File('mydata.h5', mode='r', phony_dims='access')
...| [†] | Keyword default setting phony_dims=None for backwards compatibility. |