SciDB
Database operations layer for SciStack. Provides abstractions for defining typed variables, configuring the database, and saving/loading data by metadata.
Named Filters (value-based, reusable)
Filter objects are first-class values — assign them to a variable and reuse
them in any where= clause, or compose them with & / | / ~:
# Define once (this IS a ColumnFilter — no special wrapper needed)
clean_gr = GAITRiteLoadedCycle["StepLengths_GR"] != 0
# Use anywhere
for_each(mean_change_from_reference,
inputs={"baseline": Fixed(GAITRiteLoadedCycle["StepLengths_GR"], session="BL"),
"value": GAITRiteLoadedCycle["StepLengths_GR"]},
outputs=[DeltaStepLength],
where=clean_gr & (UAStartFoot() == "A"))
# Compose named filters
clean_and_unilateral = clean_gr & (UAStartFoot() == "U")
for_each(..., where=clean_and_unilateral)
MATLAB equivalent:
clean_gr = GAITRiteLoadedCycle("StepLengths_GR") ~= 0;
scidb.for_each(@meanChangeFromReference, ..., where=clean_gr & (UAStartFoot() == "A"));
Permanent Schema-Level Exclusions
For data that should be excluded from every analysis (e.g., a failed recording session), use the exclusion registry instead of per-call filters.
# Mark a specific trial as excluded (persisted in the database)
scidb.exclude_schema(subject=1, trial=2,
reason="equipment malfunction during recording")
# Exclude an entire subject (trial omitted = wildcard)
scidb.exclude_schema(subject=3, reason="participant withdrew")
# Inspect currently-excluded combinations
exclusions_df = scidb.list_exclusions()
# Re-include (logged; the original exclusion row is preserved)
scidb.include_schema(subject=1, trial=2,
reason="re-reviewed video, recording was valid")
MATLAB equivalent:
scidb.exclude_schema("equipment malfunction", 'subject', 1, 'trial', 2)
scidb.include_schema("re-reviewed, recording was valid", 'subject', 1, 'trial', 2)
tbl = scidb.list_exclusions()
Exclusions are applied automatically by for_each before the iteration loop.
The full exclusion table is hashed and stored in version_keys
(__schema_overrides_hash) so that adding or removing an exclusion
invalidates cached results.
from scidb import configure_database, BaseVariable
import numpy as np
db = configure_database("experiment.duckdb", ["subject", "session"])
class RawSignal(BaseVariable):
schema_version = 1
RawSignal.save(np.array([1, 2, 3]), subject=1, session="A")
(raw,) = RawSignal.load(subject=1, session="A") # generator; unpack for single result
all_versions = list(RawSignal.load(subject=1, session="A", version="all"))
Logging
Every scidb operation logs through the shared scistacklog facade. configure_database() points the file sink at scidb.log next to the database file and writes a run-context header (package versions, Python version, pid), so each log file is self-describing.
Two sinks with independent levels, both defaulting to INFO:
- console (stderr): the pipeline narrative — for_each banner, periodic progress, run summary with failure reasons,
[timing]summaries. - file (
scidb.log): the same narrative, with date + millisecond timestamps and the originating layer ([scidb],[scifor],[matlab], …) on every line; one record per line.
For debugging, raise a sink to DEBUG to capture the full execution trace (named internal steps with durations, per-iteration [run]/[skip] lines, per-phase [timing] tables, column/dtype dumps):
from scidb import Log
Log.set_level("DEBUG", sink="file") # full detail in scidb.log only
Log.set_level("DEBUG") # both sinks
or set the SCIDB_LOG_LEVEL environment variable, or pass -v to the scidb CLI.
1 maintainer
Metadata
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Release files for scistack-db 0.1.30
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