7.1. More on Dataset nesting¶
You may have noticed how working in the subdataset felt as if you would be
working in an independent dataset – there was no information or influence at
all from the top-level DataLad-101
superdataset, and you build up a
completely stand-alone history:
$ git log --oneline
6e38da4 Provide project description
0259c02 [DATALAD RUNCMD] analyze iris data with classification analysis
5b94db4 add script for kNN classification and plotting
fbe9e32 [DATALAD] Added subdataset
291157d Apply YODA dataset setup
9da5830 [DATALAD] new dataset
In principle, this is no news to you. From section Dataset nesting and the YODA principles you already know that nesting allows for a modular re-use of any other DataLad dataset, and that this re-use is possible and simple precisely because all of the information is kept within a (sub)dataset.
What is new now, however, is that you applied changes to the dataset. While
you already explored the looks and feels of the longnow
subdataset in
previous sections, you now modified the contents of the midterm_project
subdataset.
How does this influence the superdataset, and how does this look like in the
superdataset’s history? You know from section Dataset nesting that the
superdataset only stores the state of the subdataset. Upon creation of the
dataset, the very first, initial state of the subdataset was thus recorded in
the superdataset. But now, after you finished your project, your subdataset
evolved. Let’s query the superdataset what it thinks about this.
# move into the superdataset
$ cd ../
$ datalad status
modified: midterm_project (dataset)
From the superdataset’s perspective, the subdataset appears as being “modified”. Note how it is not individual files that show up as “modified”, but indeed the complete subdataset as a single entity.
What this shows you is that the modifications of the subdataset you performed are not automatically recorded to the superdataset. This makes sense – after all it should be up to you to decide whether you want record something or not –, but it is worth repeating: If you modify a subdataset, you will need to save this in the superdataset in order to have a clean superdataset status.
This point in time in DataLad-101 is a convenient moment to dive a bit deeper into the functions of the datalad status command. If you are interested in this, checkout the dedicated Findoutmore.
More on datalad status
First of all, let’s start with a quick overview of the different content types and content states various datalad status commands in the course of DataLad-101 have shown up to this point:
You have seen the following content types:
file
, e.g.,notes.txt
: any file (or symlink that is a placeholder to an annexed file)directory
, e.g.,books
: any directory that does not qualify for thedataset
typesymlink
, e.g., the.jgp
that was manually unlocked in section Input and output: any symlink that is not used as a placeholder for an annexed filedataset
, e.g., themidterm_project
: any top-level dataset, or any subdataset that is properly registered in the superdataset
And you have seen the following content states: modified
and untracked
.
The section Miscellaneous file system operations will show you many instances of deleted
content
state as well.
But beyond understanding the report of datalad status, there is also additional functionality: datalad status can handle status reports for a whole hierarchy of datasets, and it can report on a subset of the content across any number of datasets in this hierarchy by providing selected paths. This is useful as soon as datasets become more complex and contain subdatasets with changing contents.
When performed without any arguments, datalad status will report
the state of the current dataset. However, you can specify a path to any
sub- or superdataset with the --dataset
option.
In order to demonstrate this a bit better, we will make sure that not only the
state of the subdataset within the superdataset is modified, but also that the
subdataset contains a modification. For this, let’s add an empty text file into
the midterm_project
subdataset:
$ touch midterm_project/an_empty_file
If you are in the root of DataLad-101
, but interested in the status
within the subdataset, simply provide a path (relative to your current location)
to the command:
$ datalad status midterm_project
untracked: midterm_project/an_empty_file (file)
Alternatively, to achieve the same, specify the superdataset as the --dataset
and provide a path to the subdataset with a trailing path separator like
this:
$ datalad status -d . midterm_project/
untracked: midterm_project/an_empty_file (file)
Note that both of these commands return only the untracked
file and not
not the modified
subdataset because we’re explicitly querying only the
subdataset for its status.
If you however, as done outside of this hidden section, you want to know about
the subdataset record in the superdataset without causing a status query for
the state within the subdataset itself, you can also provide an explicit
path to the dataset (without a trailing path separator). This can be used
to specify a specific subdataset in the case of a dataset with many subdatasets:
$ datalad status -d . midterm_project
modified: midterm_project (dataset)
But if you are interested in both the state within the subdataset, and the state of the subdataset within the superdataset, you can combine the two paths:
$ datalad status -d . midterm_project midterm_project/
modified: midterm_project (dataset)
untracked: midterm_project/an_empty_file (file)
Finally, if these subtle differences in the paths are not easy to memorize,
the -r/--recursive
option will also report you both status aspects:
$ datalad status --recursive
modified: midterm_project (dataset)
untracked: midterm_project/an_empty_file (file)
This still was not all of the available functionality of the
datalad status command. You could for example adjust whether and
how untracked dataset content should be reported with the --untracked
option, or get additional information from annexed content with the --annex
option. To get a complete overview on what you could do, check out the technical
documentation of datalad status here.
Before we leave this hidden section, lets undo the modification of the subdataset by removing the untracked file:
$ rm midterm_project/an_empty_file
$ datalad status --recursive
modified: midterm_project (dataset)
Let’s save the modification of the subdataset into the history of the
superdataset. For this, to avoid confusion, you can specify explicitly to
which dataset you want to save a modification. -d .
specifies the current
dataset, i.e., DataLad-101
, as the dataset to save to:
$ datalad save -d . -m "finished my midterm project" midterm_project
add(ok): midterm_project (file)
save(ok): . (dataset)
action summary:
add (ok: 1)
save (ok: 1)
More on how save can operate on nested datasets
In a superdataset with subdatasets, datalad save by default tries to figure out on its own which dataset’s history of all available datasets a save should be written to. However, it can reduce confusion or allow specific operations to be very explicit in the command call and tell DataLad where to save what kind of modifications to.
If you want to save the current state of the subdataset into the superdataset
(as necessary here), start a save
from the superdataset and have the
-d/--dataset
option point to its root:
# in the root of the superds
$ datalad save -d . -m "update subdataset"
If you are in the superdataset, and you want to save an unsaved modification
in a subdataset to the subdatasets history, let -d/--dataset
point to
the subdataset:
# in the superds
$ datalad save -d path/to/subds -m "modified XY"
The recursive option allows you to save any content underneath the specified directory, and recurse into any potential subdatasets:
$ datalad save . --recursive
Let’s check which subproject commit is now recorded in the superdataset:
$ git log -p -n 1
commit e295e506c5d6d49a0b22ab16f922d8d22d30c53c
Author: Elena Piscopia <elena@example.net>
Date: Wed Dec 14 16:59:24 2022 +0100
finished my midterm project
diff --git a/midterm_project b/midterm_project
index 291157d..6e38da4 160000
--- a/midterm_project
+++ b/midterm_project
@@ -1 +1 @@
-Subproject commit 291157d9c10d6a1f9f95ac6524a47d445ec0d508
+Subproject commit 6e38da4ff41cdb99f2a766fa2470605abe65c42b
As you can see in the log entry, the subproject commit changed from the
first commit hash in the subdataset history to the most recent one. With this
change, therefore, your superdataset tracks the most recent version of
the midterm_project
dataset, and your dataset’s status is clean again.