SciLineage
Lineage tracking for Python data pipelines.
SciLineage is a lightweight library for building data processing pipelines with automatic provenance tracking. It captures the full computational lineage of your results, enabling reproducibility and intelligent caching.
Features
- Automatic Lineage Tracking: Every computation captures its inputs and function, building a complete provenance graph
- Input Classification: Automatically distinguishes variable inputs from constants for accurate lineage
- Pluggable Caching: Register a backend via
configure_backend()to enable cache lookups via lineage hashes - Lightweight: Core dependency is only
scicanonicalhash - Type Safe: Full type hints throughout
Installation
pip install scilineage
Quick Start
Basic Usage
from scilineage import lineage_fcn
@lineage_fcn
def process(data, factor):
return data * factor
# Call returns a LineageFcnResult, not the raw result
result = process([1, 2, 3], 2)
# Access the computed value
print(result.data) # [2, 4, 6]
# Access lineage information
print(result.invoked.inputs) # {'arg_0': [1, 2, 3], 'arg_1': 2}
print(result.invoked.fcn.fcn.__name__) # 'process'
Multi-Output Functions
@lineage_fcn(unpack_output=True)
def split_data(data):
mid = len(data) // 2
return data[:mid], data[mid:]
first, second = split_data([1, 2, 3, 4])
print(first.data) # [1, 2]
print(second.data) # [3, 4]
Chaining Computations
@lineage_fcn
def normalize(data):
max_val = max(data)
return [x / max_val for x in data]
@lineage_fcn
def scale(data, factor):
return [x * factor for x in data]
raw = [10, 20, 30, 40]
normalized = normalize(raw)
scaled = scale(normalized, 100)
print(scaled.data) # [25.0, 50.0, 75.0, 100.0]
Extracting Lineage
from scilineage import extract_lineage, get_upstream_lineage
@lineage_fcn
def step1(x):
return x + 1
@lineage_fcn
def step2(x):
return x * 2
result = step2(step1(5))
lineage = extract_lineage(result)
print(lineage.function_name) # 'step2'
print(lineage.function_hash) # SHA-256 of function bytecode
chain = get_upstream_lineage(result)
for record in chain:
print(f"{record['function_name']}: inputs={record['inputs']}")
Manual Interventions
from scilineage import manual
# Step outside the pipeline for a manual correction
edited_data = [1, 2, 3]
# Re-enter the pipeline — the intervention is documented in lineage
corrected = manual(
edited_data,
label="outlier_removal",
reason="amplitude < 0.1 in trial 3 is sensor artifact",
)
API Reference
@lineage_fcn(unpack_output=False, unwrap=True, generates_file=False)
Decorator to convert a function into a LineageFcn.
unpack_output: Whether to unpack a tuple return into separateLineageFcnResultsunwrap: If True, automatically unwrapLineageFcnResultinputs to their raw datagenerates_file: If True, marks the function as producing files as side effects
LineageFcnResult
Wrapper around computed values that carries lineage.
.data: The actual computed value.invoked: TheLineageFcnInvocationthat produced this.hash: Unique hash based on computation lineage.output_num: Index for multi-output functions
LineageFcnInvocation
Represents a specific function invocation with captured inputs.
.fcn: The parentLineageFcn(function wrapper).inputs: Dict of captured input values.outputs: Tuple ofLineageFcnResultresults.compute_lineage_hash(): Generate lineage hash for cache key computation
LineageFcn
The decorated function wrapper.
.fcn: The original wrapped function.hash: SHA-256 hash of function bytecode.invocations: AllLineageFcnInvocations created from this
License
MIT License - see LICENSE for details.
1 maintainer
Metadata
1 maintainer
Release files for scilineage 0.1.36
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| File | Size | Uploaded | Provenance | |
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Total release size: 15.0 kB
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