biodata-cache 0.33.2

Caching and synchronization for AIND metadata.

biodata-cache

biodata-cache is a set of one-line functions that handle the entire process of caching and retrieving data (and metadata) from AIND data assets.

In the background, the cache repackages data/metadata into dataframes and stores them on S3 in versioned folders (data-asset-cache/bdc-v{version}/), or in memory for testing. Each release writes to its own versioned folder, so older versions of the website remain accessible while new versions are deployed. A top-level data-asset-cache/cache_versions.json index lists all available version folders.

Important: this package is not at 1.0. It is changing fast and breaking changes are still occurring, although rarely. To reduce the chance of impact on your code the cache tables are versioned. This does mean that if you want the latest version of the tables you need to keep biodata-cache up-to-date, but it also means your code won't immediately break when I change the way the tables work.

Installation

Note that you must set the backend to S3 or biodata-cache will automatically re-cache the tables locally in memory. This can take a LONG time.

pip install biodata-cache
export BIODATA_CACHE_BACKEND='S3'

Usage

Set backend

export BIODATA_CACHE_BACKEND='S3'

Options are 'S3', 'MEMORY'.

Fetch data

from biodata_cache import unique_project_names

project_names = unique_project_names()

Cache tables

get_cache_registry returns the following information about all available cache tables. Paths are versioned — {version} is the installed biodata-cache package version (e.g. 0.27.3).

Table	Description	Location	Type	Partitioned	Columns
unique_project_names	Unique project names across all assets	s3://allen-data-views/data-asset-cache/bdc-v{version}/unique_project_names.pqt	metadata	False	project_name
unique_subject_ids	Unique subject_ids across all assets	s3://allen-data-views/data-asset-cache/bdc-v{version}/unique_subject_ids.pqt	metadata	False	subject_id
unique_genotypes	Unique genotypes across all assets where subject.subject_details.genotype is present	s3://allen-data-views/data-asset-cache/bdc-v{version}/unique_genotypes.pqt	metadata	False	genotype
asset_basics	Commonly used asset metadata, one row per data asset	s3://allen-data-views/data-asset-cache/bdc-v{version}/asset_basics.pqt	metadata	False	_id, _last_modified, modalities, project_name, data_level, subject_id, acquisition_start_time, acquisition_end_time, code_ocean, process_date, genotype, age, acquisition_type, location, name, experimenters, experimenters_normalized, instrument_id, instrument_id_normalized, investigators, investigators_normalized
source_data	Mapping from derived asset names to their source raw asset names	s3://allen-data-views/data-asset-cache/bdc-v{version}/source_data.pqt	metadata	False	name, source_data, pipeline_name, processing_time
quality_control	Quality control table with one row per QC metric, partitioned by subject_id	s3://allen-data-views/data-asset-cache/bdc-v{version}/qc/	asset	True (by subject_id)	name, stage, modality, value, status, asset_name
platform_qc	Tag-level QC statuses aggregated per platform, one row per asset/tag combination	s3://allen-data-views/data-asset-cache/bdc-v{version}/platform_qc/	platform	True (by platform)	asset_name, tag, status, timestamp, instrument_id_normalized, experimenters_normalized
platform_smartspim	SmartSPIM assets with processing status and neuroglancer links, one row per (asset, channel)	s3://allen-data-views/data-asset-cache/bdc-v{version}/platform_smartspim.pqt	metadata	False	name, raw_name, processed, institution, processing_end_time, stitched_link, raw_link, channel, segmentation_link, quantification_link, alignment_link
platform_exaspim	ExaSPIM assets with processing status and neuroglancer links, one row per asset	s3://allen-data-views/data-asset-cache/bdc-v{version}/platform_exaspim.pqt	metadata	False	name, raw_name, processed, raw_link, fused_link
platform_fib	Fiber photometry assets in long form, one row per asset/fiber/channel combination	s3://allen-data-views/data-asset-cache/bdc-v{version}/platform_fib.pqt	metadata	False	asset_name, fiber, patch_cord, channel, intended_measurement, targeted_structure
metadata_upgrade	Metadata upgrade status for each asset across versions	s3://allen-data-views/data-asset-cache/bdc-v{version}/metadata_upgrade.pqt	metadata	False	_id, name, project_name, data_level, v2_id, upgrader_version, last_modified, status, upgrade_datetime
foraging_sessions	Foraging behavior sessions with key performance metrics, one row per session	s3://allen-data-views/data-asset-cache/bdc-v{version}/foraging_sessions.pqt	metadata	False	subject_id, session_date, session, nwb_suffix, rig, trainer, trainer_normalized, task, curriculum_name, curriculum_version, current_stage_actual, foraging_eff, foraging_eff_random_seed, finished_trials, finished_rate, total_trials, bias_naive
behavior_curriculum	Behavior assets with curriculum name and stage, one row per behavior asset	s3://allen-data-views/data-asset-cache/bdc-v{version}/behavior_curriculum.pqt	asset	False	asset_name, curriculum_name, stage_name, stage_node_id
time_to_qc	Time from processing completion to QC completion for derived assets	s3://allen-data-views/data-asset-cache/bdc-v{version}/time_to_qc.pqt	metadata	False	name, process_end_time, qc_time

Hive-partitioned tables use key=value directory segments, enabling DuckDB queries like:

import duckdb
duckdb.query("""
    SELECT * FROM read_parquet(
        's3://allen-data-views/data-asset-cache/bdc-v0.27.3/qc/**',
        hive_partitioning=true,
        union_by_name=true
    )
""")

The cache_registry.json registry lives at s3://allen-data-views/data-asset-cache/bdc-v{version}/cache_registry.json. The top-level s3://allen-data-views/data-asset-cache/cache_versions.json lists all available version folders as a JSON array.

The raw_to_derived function is not a table stored in S3, instead it is used by passing an asset_name (or list of asset names) and a modality. The function returns the latest derived asset matching the requested pattern.

Custom cache table

The custom function allows you to store and retrieve your own user-defined DataFrames in the cache by name. This requires write authentication to the active backend.

from biodata_cache import custom
import pandas as pd

df = pd.DataFrame({"col": [1, 2, 3]})
custom("my_data", df)

retrieved_df = custom("my_data")

Update all cache tables

We run a nightly capsule on Code Ocean with this code to update all cache tables (not the custom ones).

from biodata_cache.sync import update_all_tables
update_all_tables()