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Original file line number | Diff line number | Diff line change |
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"""Ingest extracted features into the database.""" | ||
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import json | ||
import os.path as op | ||
from pathlib import Path | ||
import hashlib | ||
from dateutil.parser import parse as parse_date | ||
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from neurostore.database import db | ||
from neurostore.models import ( | ||
Pipeline, | ||
PipelineConfig, | ||
PipelineRun, | ||
PipelineRunResult, | ||
) | ||
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def ingest_feature(feature_directory): | ||
"""Ingest demographics data into the database.""" | ||
# read pipeline_info.json from the base feature directory | ||
with open(op.join(feature_directory, "pipeline_info.json")) as f: | ||
pipeline_info = json.load(f) | ||
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||
# search if there is an existing pipeline with the same name and version | ||
pipeline = ( | ||
db.session.query(Pipeline) | ||
.filter( | ||
Pipeline.name == pipeline_info["name"], | ||
Pipeline.version == pipeline_info["version"], | ||
) | ||
.first() | ||
) | ||
# create a pipeline if it does not exist | ||
if not pipeline: | ||
pipeline = Pipeline( | ||
name=pipeline_info["name"], | ||
version=pipeline_info["version"], | ||
description=pipeline_info.get("description"), | ||
study_dependent=( | ||
True if pipeline_info.get("type", False) == "dependent" else False | ||
), | ||
ace_compatible="ace" | ||
in pipeline_info.get("arguments", {}).get("input_sources", []), | ||
pubget_compatible="pubget" | ||
in pipeline_info.get("arguments", {}).get("input_sources", []), | ||
derived_from=pipeline_info.get("derived_from", None), | ||
) | ||
db.session.add(pipeline) | ||
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# search within the pipeline and see if there are any existing pipeline configs | ||
# that match the "arguements" field in the pipeline_info.json | ||
# create a hash of the config arguments | ||
config_hash = hashlib.sha256( | ||
json.dumps(pipeline_info["arguments"]).encode() | ||
).hexdigest() | ||
pipeline_config = ( | ||
db.session.query(PipelineConfig) | ||
.filter( | ||
PipelineConfig.pipeline_id == pipeline.id, | ||
PipelineConfig.config_hash == config_hash, | ||
) | ||
.first() | ||
) | ||
# create a pipeline config if it does not exist | ||
if not pipeline_config: | ||
pipeline_config = PipelineConfig( | ||
pipeline_id=pipeline.id, | ||
config=pipeline_info["arguments"], | ||
config_hash=config_hash, | ||
) | ||
db.session.add(pipeline_config) | ||
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# create a new pipeline run | ||
pipeline_run = PipelineRun( | ||
pipeline_id=pipeline.id, | ||
config_id=pipeline_config.id, | ||
) | ||
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# get a list of all the paper directories in the feature directory | ||
paper_dirs = [d for d in Path(feature_directory).iterdir() if d.is_dir()] | ||
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# for each subject directory, read the results.json file and the info.json file | ||
pipeline_run_results = [] | ||
for paper_dir in paper_dirs: | ||
with open(op.join(paper_dir, "results.json")) as f: | ||
results = json.load(f) | ||
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with open(op.join(paper_dir, "info.json")) as f: | ||
info = json.load(f) | ||
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# use the directory name as the base_study_id | ||
base_study_id = paper_dir.name | ||
# create a new result record | ||
pipeline_run_results.append( | ||
PipelineRunResult( | ||
base_study_id=base_study_id, | ||
data=results, | ||
date_executed=parse_date(info["date"]), | ||
file_inputs=info["inputs"], | ||
run=pipeline_run, | ||
) | ||
) | ||
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db.session.add(pipeline_run) | ||
db.session.add_all(pipeline_run_results) | ||
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db.session.commit() |
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