Reproducibility is the hallmark of the scientific method.

Modern science has become so complex that many science projects rely on multiple software packages to work in unison, resulting networks of data products along the analyses. Versioning and managing these data products are essential in making modern data- and computation-intensive science reproducible.

Motivated by the Event Horizon Telescope (EHT)'s observational data calibration pipelines and theory data analyses tools, hallmark is a lightweight tool designed to version control and manage data products in a complex workflow. It provides a simple abstraction and a uniform Application Programming Interface (API) on top of different backend technologies such as Git Large File Storage (LFS), Globus, iRODS, etc. By using hallmark with other tools such as yukon and banyan in Project Laniakea, researchers can utilize computing infrastructures in a global scale to accelerate their science.

When performing large scale parameter surveys and constructing simulation libraries, it is common to encode parameter values in the file paths. Example include Ma+0.94_i70/sed_Rh160.h5. hallmark provides a monkey-patched pandas DataFrame, called ParaFrame, to decode file paths back to proper parameters, and put the result into a pandas DataFrame. ParaFrame uses python parse to parse the file paths. Because parse is the opposite of format, this means format string used to generate the surveys and libraries in the first place can be reused. In addition, ParaFrame has a nice interface to perform filter, which makes parameter selection much easier than pure pandas. Examples of using ParaFrame can be found in the Jupyter Notebook demos/ParaFrame.ipynb.

From a user point of view, the frontends of hallmark provide simple and consistent methods to access data products.

Suppose that we need to access a repository containing data in EHT's first science release, we start by registering the repository locally. In bash, this is

bash$ alias hm='hallmark'
bash$ hm mount https://eventhorizontelescope.org/data/2017April_SR1 SR1
Hallmark: mounting remote repository "https://eventhorizontelescope.org/data/2017April_SR1" on local file system "SR1"...  DONE.
bash$ ls
Desktop   Documents ...       SR1

In python, this is

>>> import hallmark as hm
>>> sr1 = hm.mount("https://https://eventhorizontelescope.org/data/2017April_SR1")

The mount points now acts like standard directory that one can easily inspect by standard Unix utilities and python functions. E.g.,

bash$ find SR1
SR1
SR1/EHTC_FirstM87Results_Apr2019_txt.tgz
...
SR1/INVENTORY.txt
SR1/LICENSE.txt
SR1/README.md
SR1/run.sh
bash$ cat SR1/README.md
# First M87 EHT Results: Calibrated Data
...

or in python

>>> sr1
{'EHTC_FirstM87Results_Apr2019_txt.tgz': <data object>, ..., 'README.md': <data object>, 'run.sh': <data object>}
>>> f = sr1['README.md']
>>> str(f)
# First M87 EHT Results: Calibrated Data
...