Small dependency resolution library for scientific datasets
Project description
depgraph is a tiny Python library for expressing networks of dependencies required to construct datasets. Networks are declared in terms of the relationships between source and target datasets (network graph edges). depgraph can then report descendants and parents for any particular node and instruct builds in a manner similar to make. When a DependencyGraph object returns a dataset that must be built, it provides a reason, such as:
the dataset is missing
the dataset is out of date and required by another dataset
the dataset is a target dataset
depgraph is intended to be a component for assembling dataset build tools. Important considerations for such a build tool are that it must:
permit reproducible analysis
be documenting
perform fast rebuilds to enable experimentation
depgraph contains the following classes:
depgraph.DependencyGraph
depgraph.Dataset
depgraph.DatasetGroup
depgraph.Reason
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, DependencyGraph
# 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")
graph = DependencyGraph()
# Declare relationships
graph.add_dataset(da0, (raw0, raw1))
graph.add_dataset(da1, (raw2,))
graph.add_dataset(db0, (da0, da1))
graph.add_dataset(db1, (da1, raw3))
graph.add_dataset(dc0, (db0, db1))
graph.add_dataset(dc1, (db1,))
graph.add_dataset(dc2, (db1,))
# Query buildsteps to build a product
while True:
targets = graph.buildable(DC1)
if len(targets) == 0:
break
for target, reason in targets:
# 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(target.name,
target.tool,
reason))
# Call a function or start a subprocess that will result in the
# target being built and saved to a file
my_build_func(target.tool, target.name)
# [...]
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