Installation and Usage¶
There are a number of possible ways to install
From the simplest to the most involved these are:
As an end user¶
Quite simply: You do not need to install
It does not add any functionality to end users.
As a developer to run tests with
You may want to install
dials_data so that you can run regression tests locally.
Or you might say that continuous integration should take care of this.
Both are valid opinions.
However you do not need to install
dials_data from source. You can simply run:
pip install -U dials_data
or, in a conda environment:
conda install -c conda-forge dials_data
This will install or update an existing installation of
You can then run your tests as usual using:
although, depending on the configuration of the code under test, you probably need to run it as:
to actually enable those tests depending on files from
As a developer to write tests with
dials_data as above.
If your test is written in pytest and you use the fixture provided by
dials_data then you can use regression datasets in your test by
dials_data fixture to your test, ie:
def test_accessing_a_dataset(dials_data): location = dials_data("x4wide", pathlib=True)
dials_data in the test is a
dials_data.download.DataFetcher instance, which can be called with
the name of the dataset you want to access (here:
x4wide). If the
files are not present on the machine then they will be downloaded.
If either the download fails or
--regression is not specified then
the test is skipped.
The return value (
location) is a
pathlib.Path object pointing
to the directory containing the requested dataset.
To get a python
py.path.local object instead you can call:
def test_accessing_a_dataset(dials_data): location = dials_data("x4wide", pathlib=False)
However, please note that the
py.path support is deprecated, and
will be removed at some point in the future. Currently, if you do not
pathlib= argument a
py.path object is returned.
You can see a list of all available datasets by running:
or by going through the dataset definition files in the repository.
If you want the tests on your project to be runnable even when
dials_data is not installed in the environment you could add a
dummy fixture to your
conftest.py, for example:
import pytest try: import dials_data as _ # noqa: F401 except ModuleNotFoundError: @pytest.fixture(scope="session") def dials_data(): pytest.skip("Test requires python package dials_data")
As a developer who wants to add files to
Follow the steps in Contributing to install
dials_data into a
You can install
pip as above unless you want to
immediately use your dataset definition in tests without waiting for your
pull request to be accepted. In this case you can follow the instructions
in the next step.
As a developer who wants to extend
Have a look at the Contributing page.
Install your own fork of
dials_data by running:
pip install -e path/to/fork
in a cctbx/DIALS environment use
libtbx.pip respectively, followed by
a round of
If you made substantial changes or updated your source copy you may also have to run:
python setup.py develop
This will update your python package index and install/update any
dials_data dependencies if necessary.
To switch back from using your checked out version to the ‘official’
dials_data you can uninstall it with:
pip uninstall dials_data
and then reinstall it following the instructions at the top of this page.
Where are the regression datasets stored?¶
In order of evaluation:
If the environment variable
DIALS_DATAis set and exists or can be created then use that location.
If the file path
/dls/science/groups/scisoft/DIALS/dials_dataexists and is readable then use this location. This is a shared directory specific to Diamond Light Source.
If the environment variable
LIBTBX_BUILDis set and the directory
dials_dataexists or can be created underneath that location then use that.
~/.cache/dials_dataif it exists or can be created.
dials_datawill fail with a RuntimeError.