FAQ
===

HDF5
----

My results files are large, can I compress them?
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

There is an HDF5 tool that allows you to "repack" and compress your
datasets called ``h5repack``. A good ratio of compression to time is
using GZIP level 2 but read the documentation to customize for yourself:

.. code:: bash

   h5repack -f GZIP=2 original.wepy.h5 compressed.wepy.h5

There are other more involved mechanisms, but this should cut your size
significantly.

I get a 'valid identifiers' error when accessing HDF5 data.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

If you get something like this:

::

   ValueError: Type names and field names must be valid identifiers: "b'decision_id'"

When accessing your WepyHDF5 data its likely because you have a version
of ``h5py`` that is too new. Simply downgrade to before version 3.0.

You can check this by running:

.. code:: bash

   pip list | grep h5py

And install an ealier version:

.. code:: bash

   pip install 'h5py<3'

See I think I have the wrong versions of dependencies for more info on
fixing dependencies.

Clearing HDF5 Write Locks
~~~~~~~~~~~~~~~~~~~~~~~~~

Sometimes if processes don't shut down correctly there will be open
locks on the dataset. If you are absolutely sure that no processes are
still active you can manually clear these flags using the HDF5 toolkit:

.. code:: bash

   h5clear -s -m $file

Dependencies
------------

I think I have the wrong versions of dependencies
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

All known version constrained dependencies are recorded both in the
``setup.py`` file (this is what gets installed) as well as the
``requirements.in`` file. The ``requirements.in`` file allows for more
complex expressions of dependencies and may have notes as to why they
are constrained.

You can check ``info/changelog.org`` to find when and why a constraint
was added as well.