Small dependency resolution library for scientific datasets
Project description
Travis status
depgraph is a tiny Python library for expressing networks of dependencies required to construct datasets. Networks are declared in terms of the relationships (graph edges) between source and target datasets (graph nodes). Target datasets can then report sets of precursor datasets in the correct order. This makes it simple to throw together build script and construct dependencies in parallel.
Traditionally, each Dataset is designed to correspond to a file. A DatasetGroup class handles cases where multiple files can be considered a single file (e.g. a binary data file and its XML metadata).
When a Dataset requires a different dataset to be built to satisfy its dependencies, it provides a reason, such as:
the dependency is missing
the dependency is out of date
depgraph is intended to be a reusable component for constructing scientific dataset build tools. Important considerations for such a build tool are that it must:
permit reproducible analysis
be documenting so that a workflow can be easily reported
perform fast rebuilds to enable experimentation
Beyond the Python standard library, depgraph has no dependencies of its own, so it is easy to include in projects running on a laptop, on a large cluster, or in the cloud. depgraph supports modern Python implementations (Python 2, Python 3, PyPy).
Example
Declare a set of dependencies resembling the graph below:
R0 R1 R2 R3 [raw data] \ / | | DA0 DA1 / \ / \ / DB0 DB1 \ / | \ \ / | \ DC0 DC1 DC2 [products]
from depgraph import Dataset, buildmanager
# Define Datasets
# use an optional keyword `tool` to provide a key instructing our build tool
# how to assemble this product
R0 = Dataset("data/raw0", tool="read_csv")
R1 = Dataset("data/raw1", tool="read_csv")
R2 = Dataset("data/raw2", tool="database_query")
R3 = Dataset("data/raw3", tool="read_hdf")
DA0 = Dataset("step1/da0", tool="merge_fish_counts")
DA1 = Dataset("step1/da1", tool="process_filter")
DB0 = Dataset("step2/db0", tool="join_counts")
DB1 = Dataset("step2/db1", tool="join_by_date")
DC0 = Dataset("results/dc0", tool="merge_model_obs")
DC1 = Dataset("results/dc1", tool="compute_uncertainty")
DC2 = Dataset("results/dc2", tool="make_plots")
# Declare relationships
DA0.dependson(R0, R1)
DA1.dependson(R2)
DB0.dependson(DA0, DA1)
DB1.dependson(DA1, R3)
DC0.dependson(DB0, DB1)
DC1.dependson(DB1)
DC2.dependson(DB1)
# Define a function that builds individual dependencies
# The *buildmanager* decorator transforms it into a loop that builds all
# dependencies below a target
@buildmanager
def batchbuilder(dependency):
# [....]
return exitcode
batchbuilder(DC1)
# Alternatively, implement the build loop manually:
def build(dependency):
# This may have the same logic as `batchbuilder` above, but we
# will call it directly rather than wrapping it in @buildmanager
# [....]
return exitcode
for group in buildall(DC1):
for dep, reason in group:
# Each target is a dataset with a 'name' attribute and whatever
# additional keyword arguments where defined with it.
# The 'reason' is a depgraph.Reason object that codifies why a
# particular target is necessary (e.g. it's out of date, it's missing,
# and required by a subsequent target, etc.)
print("Building {0} with {1} because {2}".format(dep.name, dep.tool,
reason))
# Call a function or start a subprocess that will result in the
# target being built and saved to a file
return_val = built(dep)
# Optionally, perform logging, clean-up, or error handling operations
# [....]
Changes
Master
Performance improvements
buildall generator function, which is more efficient than repeatedly calling Dataset.buildnext()
0.3
Cyclic graph detection
Graphviz export
0.2
Rewrite, dropping DependencyGraph and making Dataset the primary class
0.1
First version, copied from depchain module of asputil package
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.